08-Orient-Naphthalene.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Refine orientation of naphthalene molecule\n",
    "\n",
    "Simulate PDF with separate inter and intramolecular displacement parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## initialization\n",
    "\n",
    "We start with some necessary imports from diffpy packages and define a convenience function for plotting the PDF fit.\n",
    "It is safe to skip to the [next section](#global-constants).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use `print()` function as in Python 3\n",
    "from __future__ import print_function\n",
    "\n",
    "# notebook plotting setup\n",
    "%matplotlib notebook\n",
    "import matplotlib\n",
    "# make figures smaller\n",
    "matplotlib.rc('figure', figsize=(5, 3.75))\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "# numerical math and optimization\n",
    "import numpy as np\n",
    "from scipy.optimize import leastsq\n",
    "\n",
    "# srfit PDF fitting objects\n",
    "from diffpy.srfit.pdf import PDFContribution\n",
    "from diffpy.srfit.fitbase import FitRecipe, FitResults, Calculator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## global constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the initial CIF structure\n",
    "g_cif = \"naphthalene.cif\"\n",
    "# offset of difference curve in PDF plot\n",
    "g_diff_baseline = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define several helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myLoadCrystal(filename):\n",
    "    \"Load pyobjcryst.Crystal object from a CIF file.\"\n",
    "    from pyobjcryst.crystal import CreateCrystalFromCIF\n",
    "    with open(filename, 'rb') as fp:\n",
    "        rv = CreateCrystalFromCIF(fp, oneScatteringPowerPerElement=True)\n",
    "    return rv\n",
    "\n",
    "\n",
    "def getCrystalWithMolecule(ciffilename):\n",
    "    \"\"\"\n",
    "    Load the CIF file and group all scatterers as one Molecule.\n",
    "\n",
    "    This assumes the asymmetric unit is a single whole Molecule,\n",
    "    i.e., there are no other molecules or independent atoms in\n",
    "    the crystal and the molecule does not need to be\n",
    "    symmetry-expanded.\n",
    "    \"\"\"\n",
    "    from pyobjcryst.molecule import Molecule\n",
    "    # center of the molecule in fractional coordinates\n",
    "    xyzmol = np.array([0.0, 0.0, 0.0])\n",
    "    crst = myLoadCrystal(ciffilename)\n",
    "    mol = Molecule(crst, \"mol\")\n",
    "    for i in reversed(range(crst.GetNbScatterer())):\n",
    "        a = crst.GetScatterer(i)\n",
    "        xyzm = np.array([a.X, a.Y, a.Z]) - xyzmol\n",
    "        xc, yc, zc = crst.FractionalToOrthonormalCoords(*xyzm)\n",
    "        mol.AddAtom(xc, yc, zc, a.GetScatteringPower(), a.GetName())\n",
    "        crst.RemoveScatterer(a)\n",
    "    crst.AddScatterer(mol)\n",
    "    mol.X, mol.Y, mol.Z = xyzmol\n",
    "    return crst\n",
    "\n",
    "\n",
    "class quatzero(Calculator):\n",
    "    \"\"\"Return the zero-th component of normalized quaternion.\n",
    "    \"\"\"\n",
    "\n",
    "    def __call__(self, q1, q2, q3):\n",
    "        ssq = q1**2 + q2**2 + q3**2\n",
    "        q0 = np.sqrt(1.0 - ssq) if ssq < 1 else 0.0\n",
    "        return q0\n",
    "\n",
    "# end of quatzero\n",
    "\n",
    "\n",
    "def plotrecipe(axis, rec, what='obs, calc, diff'):\n",
    "    choice = set(what.replace(',', ' ').split())\n",
    "    r = rec.pcnt.r.value\n",
    "    gobs = rec.pcnt.y.value\n",
    "    gcalc = rec.pcnt.evaluate()\n",
    "    gdiff = gobs - gcalc\n",
    "    yall = [('obs', gobs),\n",
    "            ('calc', gcalc),\n",
    "            ('diff', gdiff + g_diff_baseline)]\n",
    "    pargs = sum([(r, y) for n, y in yall if n in choice], ())\n",
    "    rv = axis.plot(*pargs)\n",
    "    if 'calc' in choice and 'diff' in choice:\n",
    "        rv[-1].set_color(rv[-2].get_color())\n",
    "    return rv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define PDF model composed of inter and intra-molecular components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcnt = PDFContribution('pcnt')\n",
    "pcnt.loadData('naphthalene.gr')\n",
    "pcnt.qdamp = 0.06\n",
    "pcnt.setCalculationRange(1.1, 25)\n",
    "# intra-molecular contribution from one molecule:\n",
    "crst = getCrystalWithMolecule(g_cif)\n",
    "mol = crst.GetScatterer(\"mol\")\n",
    "pcnt.addStructure('mol', mol, periodic=False)\n",
    "# inter-molecular contributions (wide)\n",
    "wcrst = getCrystalWithMolecule(g_cif)\n",
    "wmol = wcrst.GetScatterer('mol')\n",
    "pcnt.addStructure('wcrst', wcrst, periodic=True)\n",
    "pcnt.addStructure('wmol', wmol, periodic=False)\n",
    "pcnt.setEquation('scale * (mol + wcrst - wmol)')\n",
    "\n",
    "# speed up simulation by using parallel jobs.\n",
    "pcnt.wcrst.parallel(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define FitRecipe for this PDF model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define FitRecipe for this organic crystal\n",
    "ocfit = FitRecipe()\n",
    "ocfit.clearFitHooks()\n",
    "ocfit.addContribution(pcnt)\n",
    "\n",
    "# expose the overall data scale\n",
    "ocfit.addVar(ocfit.pcnt.scale, name='scale', value=0.1)\n",
    "ocfit.addVar(ocfit.pcnt.qdamp, name='qdamp')\n",
    "\n",
    "# expose unit cell parameters\n",
    "pwcrst = ocfit.pcnt.wcrst.phase\n",
    "ocfit.addVar(pwcrst.a)\n",
    "ocfit.addVar(pwcrst.b)\n",
    "ocfit.addVar(pwcrst.c)\n",
    "# objcryst uses radians for cell angle beta, but let us refine\n",
    "# in degrees.\n",
    "ocfit.newVar('beta', value=np.degrees(pwcrst.beta.value))\n",
    "ocfit.constrain(pwcrst.beta, 'radians(beta)')\n",
    "\n",
    "# objcryst links all atoms of the same type to one scattering power\n",
    "# with common Biso.  It is thus sufficient to constrain just one of\n",
    "# those.\n",
    "\n",
    "# first let's ensure there are only 2 independent scattering powers\n",
    "# for \"C\" and \"H\" in the CIF file.\n",
    "assert 2 == crst.GetScatteringPowerRegistry().GetNb(), \\\n",
    "    \"unexpected number of atom species\"\n",
    "\n",
    "# expose isotropic displacement parameters for carbon and hydrogen\n",
    "# in the molecule.  CIF file has sites labeled \"C1\", \"H1\" so we can\n",
    "# refer to them by those names.\n",
    "pmol = ocfit.pcnt.mol.phase\n",
    "ocfit.addVar(pmol.C1.Biso, name='bisoC', value=1.0)\n",
    "ocfit.restrain('bisoC', lb=0.02, sig=1e-4)\n",
    "# hydrogen gives negligible contribution, so we keep them constant\n",
    "ocfit.addVar(pmol.H1.Biso, name='bisoH', value=1, fixed=True)\n",
    "\n",
    "# for intra-molecular component we use the same binter value for all atoms:\n",
    "ocfit.addVar(pwcrst.mol.C1.Biso, name='binter', value=10)\n",
    "ocfit.constrain(pwcrst.mol.H1.Biso, 'binter')\n",
    "\n",
    "# expose rotation quaternions for the molecule\n",
    "pwmol = ocfit.pcnt.wcrst.phase.mol\n",
    "ocfit.addVar(pwmol.q1, name='wq1')\n",
    "ocfit.addVar(pwmol.q2, name='wq2')\n",
    "ocfit.addVar(pwmol.q3, name='wq3')\n",
    "\n",
    "# constrain q0 so we have normalized quaternion.\n",
    "# first define a function \"fixq0\" that calculates q0 from [q1, q2, q3]\n",
    "ocfit.registerCalculator(quatzero('fixq0'), argnames=[])\n",
    "ocfit.constrain(pwmol.q0, 'fixq0(wq1, wq2, wq3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## fix all variables and show the initial residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== INITIAL ==\n",
      "\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       10.43700850\n",
      "Contributions  10.43700850\n",
      "Restraints     0.00000000\n",
      "Chi2           10.43700850\n",
      "Reduced Chi2   0.00436512\n",
      "Rw             0.56522530\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "scale  1.00000000e-01 +/- 2.04728043e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "a       8.25600000e+00\n",
      "b       5.98300000e+00\n",
      "beta    1.22729000e+02\n",
      "binter  1.00000000e+01\n",
      "bisoC   1.00000000e+00\n",
      "bisoH   1.00000000e+00\n",
      "c       8.67700000e+00\n",
      "qdamp   6.00000000e-02\n",
      "wq1     0.00000000e+00\n",
      "wq2     0.00000000e+00\n",
      "wq3     0.00000000e+00\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "No correlations greater than 25%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ocfit.fix('all')\n",
    "ocfit.free('scale')\n",
    "ocfit.scalarResidual()\n",
    "print(\"== INITIAL ==\\n\")\n",
    "print(FitResults(ocfit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## refine cell parameters and displacement factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== INTERMEDIATE ==\n",
      "\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       1.13505177\n",
      "Contributions  1.13505177\n",
      "Restraints     0.00000000\n",
      "Chi2           1.13505177\n",
      "Reduced Chi2   0.00047571\n",
      "Rw             0.18639807\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "a       8.25930643e+00 +/- 1.45578417e-01\n",
      "b       5.97087370e+00 +/- 1.88982267e-01\n",
      "binter  4.00755355e+00 +/- 3.00569970e+00\n",
      "bisoC   1.00690959e-01 +/- 1.60461948e-01\n",
      "c       8.62336219e+00 +/- 2.99909625e-01\n",
      "scale   6.07769639e-02 +/- 2.11014539e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "beta   1.22729000e+02\n",
      "bisoH  1.00000000e+00\n",
      "qdamp  6.00000000e-02\n",
      "wq1    0.00000000e+00\n",
      "wq2    0.00000000e+00\n",
      "wq3    0.00000000e+00\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(scale, binter)   0.7746\n",
      "corr(scale, bisoC)    0.7198\n",
      "corr(bisoC, binter)   0.5281\n",
      "corr(b, c)            -0.3548\n",
      "corr(a, b)            -0.3245\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ocfit.free('a', 'b', 'c')\n",
    "ocfit.free('bisoC', 'binter')\n",
    "leastsq(ocfit.residual, ocfit.values)\n",
    "print(\"== INTERMEDIATE ==\\n\")\n",
    "print(FitResults(ocfit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## refine the orientation quaternion for the molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== REFINED ==\n",
      "\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       1.11814904\n",
      "Contributions  1.11814904\n",
      "Restraints     0.00000000\n",
      "Chi2           1.11814904\n",
      "Reduced Chi2   0.00046922\n",
      "Rw             0.18500498\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "a       8.25653601e+00 +/- 1.49678218e-01\n",
      "b       5.97224986e+00 +/- 2.00729528e-01\n",
      "binter  4.02720809e+00 +/- 3.05757739e+00\n",
      "bisoC   1.01797986e-01 +/- 1.61042229e-01\n",
      "c       8.62940749e+00 +/- 3.25811597e-01\n",
      "scale   6.09873155e-02 +/- 2.13044460e-02\n",
      "wq1     -1.47429823e-03 +/- 7.68744088e-02\n",
      "wq2     -6.29903556e-03 +/- 5.53275468e-02\n",
      "wq3     8.01815295e-03 +/- 7.31804534e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "beta   1.22729000e+02\n",
      "bisoH  1.00000000e+00\n",
      "qdamp  6.00000000e-02\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(scale, binter)   0.7761\n",
      "corr(scale, bisoC)    0.7220\n",
      "corr(bisoC, binter)   0.5306\n",
      "corr(wq2, wq3)        -0.5003\n",
      "corr(b, c)            -0.3845\n",
      "corr(a, b)            -0.3468\n",
      "corr(c, wq1)          0.3020\n",
      "corr(a, wq1)          0.2773\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ocfit.free('wq1', 'wq2', 'wq3')\n",
    "leastsq(ocfit.residual, ocfit.values)\n",
    "print(\"== REFINED ==\\n\")\n",
    "print(FitResults(ocfit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotate molecule away from the minimum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== MISORIENTED ==\n",
      "\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       3.70104995\n",
      "Contributions  3.70104995\n",
      "Restraints     0.00000000\n",
      "Chi2           3.70104995\n",
      "Reduced Chi2   0.00154856\n",
      "Rw             0.33658600\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "bisoC  7.72032683e-02 +/- 1.36123113e-01\n",
      "scale  5.65946216e-02 +/- 1.30118999e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "a       8.25653601e+00\n",
      "b       5.97224986e+00\n",
      "beta    1.22729000e+02\n",
      "binter  4.02720809e+00\n",
      "bisoH   1.00000000e+00\n",
      "c       8.62940749e+00\n",
      "qdamp   6.00000000e-02\n",
      "wq1     1.00000000e-01\n",
      "wq2     -6.29903556e-03\n",
      "wq3     8.01815287e-03\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(scale, bisoC)   0.5890\n",
      "\n"
     ]
    },
    {
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       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rotate molecule away from the minimum\n",
    "ocfit.wq1 << 0.1\n",
    "ocfit.fix('a', 'b', 'c', 'binter', 'wq1', 'wq2', 'wq3')\n",
    "leastsq(ocfit.residual, ocfit.values)\n",
    "print(\"== MISORIENTED ==\\n\")\n",
    "print(FitResults(ocfit))\n",
    "fig1, ax1 = subplots()\n",
    "plotrecipe(ax1, ocfit)\n",
    "ax1.set_title('misoriented molecule');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## refine molecule orientation to return to the correct structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "== ORIENTED BACK ==\n",
      "\n",
      "Some quantities invalid due to missing profile uncertainty\n",
      "Overall (Chi2 and Reduced Chi2 invalid)\n",
      "------------------------------------------------------------------------------\n",
      "Residual       1.11814904\n",
      "Contributions  1.11814904\n",
      "Restraints     0.00000000\n",
      "Chi2           1.11814904\n",
      "Reduced Chi2   0.00046843\n",
      "Rw             0.18500498\n",
      "\n",
      "Variables (Uncertainties invalid)\n",
      "------------------------------------------------------------------------------\n",
      "bisoC  1.01797938e-01 +/- 1.36123612e-01\n",
      "scale  6.09873033e-02 +/- 1.33204977e-02\n",
      "wq1    -1.47305152e-03 +/- 6.96312206e-02\n",
      "wq2    -6.29963699e-03 +/- 5.38988952e-02\n",
      "wq3    8.01854029e-03 +/- 7.06878617e-02\n",
      "\n",
      "Fixed Variables\n",
      "------------------------------------------------------------------------------\n",
      "a       8.25653601e+00\n",
      "b       5.97224986e+00\n",
      "beta    1.22729000e+02\n",
      "binter  4.02720809e+00\n",
      "bisoH   1.00000000e+00\n",
      "c       8.62940749e+00\n",
      "qdamp   6.00000000e-02\n",
      "\n",
      "Variable Correlations greater than 25% (Correlations invalid)\n",
      "------------------------------------------------------------------------------\n",
      "corr(scale, bisoC)   0.5771\n",
      "corr(wq2, wq3)       -0.5143\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ocfit.free('wq1', 'wq2', 'wq3')\n",
    "leastsq(ocfit.residual, ocfit.values)\n",
    "print(\"== ORIENTED BACK ==\\n\")\n",
    "print(FitResults(ocfit))\n",
    "fig2, ax2 = subplots()\n",
    "plotrecipe(ax2, ocfit);\n",
    "ax2.set_title('molecule oriented back');"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}