cycle_O2_DIC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import axes\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define path to data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LP01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_lp01_01 = '/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.JRA.L.LP01-cycle1/OUT/yearly/'\n",
    "path_lp01_02 = '/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.JRA.L.LP01-cycle2/OUT/yearly/'\n",
    "path_lp01_03 = '/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.JRA.L.LP01-cycle3/OUT/yearly/'\n",
    "path_lp01_04 = '/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.JRA.L.LP01-cycle4/OUT/yearly/'\n",
    "path_lp01_05 = '/scratch/usr/shkifmmp/ORCA05/hind_1/cycles/05/' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "O2_lp01_01 = xr.open_mfdataset(paths = sorted(glob(path_lp01_01 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2\n",
    "O2_lp01_02 = xr.open_mfdataset(paths = sorted(glob(path_lp01_02 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2\n",
    "O2_lp01_03 = xr.open_mfdataset(paths = sorted(glob(path_lp01_03 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2\n",
    "O2_lp01_04 = xr.open_mfdataset(paths = sorted(glob(path_lp01_04 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2\n",
    "O2_lp01_05 = xr.open_mfdataset(paths = sorted(glob(path_lp01_05 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIC_lp01_01 = xr.open_mfdataset(paths = sorted(glob(path_lp01_01 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC\n",
    "DIC_lp01_02 = xr.open_mfdataset(paths = sorted(glob(path_lp01_02 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC\n",
    "DIC_lp01_03 = xr.open_mfdataset(paths = sorted(glob(path_lp01_03 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC\n",
    "DIC_lp01_04 = xr.open_mfdataset(paths = sorted(glob(path_lp01_04 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC\n",
    "DIC_lp01_05 = xr.open_mfdataset(paths = sorted(glob(path_lp01_05 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DICP_lp01_01 = xr.open_mfdataset(paths = sorted(glob(path_lp01_01 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP\n",
    "DICP_lp01_02 = xr.open_mfdataset(paths = sorted(glob(path_lp01_02 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP\n",
    "DICP_lp01_03 = xr.open_mfdataset(paths = sorted(glob(path_lp01_03 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP\n",
    "DICP_lp01_04 = xr.open_mfdataset(paths = sorted(glob(path_lp01_04 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP\n",
    "DICP_lp01_05 = xr.open_mfdataset(paths = sorted(glob(path_lp01_05 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mDICP_lp01_01\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mNA\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Filter elements from this object according to a condition.\n",
       "\n",
       "This operation follows the normal broadcasting and alignment rules that\n",
       "xarray uses for binary arithmetic.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "cond : DataArray or Dataset\n",
       "    Locations at which to preserve this object's values. dtype must be `bool`.\n",
       "other : scalar, DataArray or Dataset, optional\n",
       "    Value to use for locations in this object where ``cond`` is False.\n",
       "    By default, these locations filled with NA.\n",
       "drop : bool, optional\n",
       "    If True, coordinate labels that only correspond to False values of\n",
       "    the condition are dropped from the result. Mutually exclusive with\n",
       "    ``other``.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataArray or Dataset\n",
       "    Same xarray type as caller, with dtype float64.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "\n",
       ">>> import numpy as np\n",
       ">>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=(\"x\", \"y\"))\n",
       ">>> a\n",
       "<xarray.DataArray (x: 5, y: 5)>\n",
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])\n",
       "Dimensions without coordinates: x, y\n",
       "\n",
       ">>> a.where(a.x + a.y < 4)\n",
       "<xarray.DataArray (x: 5, y: 5)>\n",
       "array([[ 0.,  1.,  2.,  3., nan],\n",
       "       [ 5.,  6.,  7., nan, nan],\n",
       "       [10., 11., nan, nan, nan],\n",
       "       [15., nan, nan, nan, nan],\n",
       "       [nan, nan, nan, nan, nan]])\n",
       "Dimensions without coordinates: x, y\n",
       "\n",
       ">>> a.where(a.x + a.y < 5, -1)\n",
       "<xarray.DataArray (x: 5, y: 5)>\n",
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8, -1],\n",
       "       [10, 11, 12, -1, -1],\n",
       "       [15, 16, -1, -1, -1],\n",
       "       [20, -1, -1, -1, -1]])\n",
       "Dimensions without coordinates: x, y\n",
       "\n",
       ">>> a.where(a.x + a.y < 4, drop=True)\n",
       "<xarray.DataArray (x: 4, y: 4)>\n",
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 5.,  6.,  7., nan],\n",
       "       [10., 11., nan, nan],\n",
       "       [15., nan, nan, nan]])\n",
       "Dimensions without coordinates: x, y\n",
       "\n",
       ">>> a.where(lambda x: x.x + x.y < 4, drop=True)\n",
       "<xarray.DataArray (x: 4, y: 4)>\n",
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 5.,  6.,  7., nan],\n",
       "       [10., 11., nan, nan],\n",
       "       [15., nan, nan, nan]])\n",
       "Dimensions without coordinates: x, y\n",
       "\n",
       "See also\n",
       "--------\n",
       "numpy.where : corresponding numpy function\n",
       "where : equivalent function\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/envs/py3_std/lib/python3.7/site-packages/xarray/core/common.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DICP_lp01_01.where?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LP09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_lp09_01 = '/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1/OUT/yearly/'\n",
    "path_lp09_05 = '/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle5/OUT/yearly/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "O2_lp09_01 = xr.open_mfdataset(paths = sorted(glob(path_lp09_01 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2\n",
    "O2_lp09_05 = xr.open_mfdataset(paths = sorted(glob(path_lp09_05 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).O2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIC_lp09_01 = xr.open_mfdataset(paths = sorted(glob(path_lp09_01 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC\n",
    "DIC_lp09_05 = xr.open_mfdataset(paths = sorted(glob(path_lp09_05 + 'OR*_DIC.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "DICP_lp09_01 = xr.open_mfdataset(paths = sorted(glob(path_lp09_01 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP\n",
    "DICP_lp09_05 = xr.open_mfdataset(paths = sorted(glob(path_lp09_05 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"deptht\":46, \"y\":100, \"x\":100}).DICP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "maskdir = '/scratch/usr/shklvn09/NUDATA/ORCA/05-data/meshmask/mesh_mask.nc'\n",
    "tmask = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).tmask.squeeze()\n",
    "e1t = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e1t.squeeze()\n",
    "e2t = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e2t.squeeze()\n",
    "e3t = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e3t.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmask, e3t = tmask.rename({'z':'deptht'}), e3t.rename({'z':'deptht'}) #renaming it for mask and tracers to have same dimension name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing global integral"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LP01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "O2_int_01 = (O2_lp01_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht')) # applying mask to e1t, e2t, e3t gives the same results as computed here \n",
    "O2_int_02 = (O2_lp01_02.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "O2_int_03 = (O2_lp01_03.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "O2_int_04 = (O2_lp01_04.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "O2_int_05 = (O2_lp01_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "O2_int_conc = np.hstack((np.array(O2_int_01.values).squeeze(), \n",
    "                       np.array(O2_int_02.values).squeeze(), \n",
    "                       np.array(O2_int_03.values).squeeze(),\n",
    "                       np.array(O2_int_04.values).squeeze(),\n",
    "                       np.array(O2_int_05.values).squeeze())) #concatenating all the cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIC_int_01 = (DIC_lp01_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DIC_int_02 = (DIC_lp01_02.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DIC_int_03 = (DIC_lp01_03.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DIC_int_04 = (DIC_lp01_04.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DIC_int_05 = (DIC_lp01_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIC_int_conc = np.hstack((np.array(DIC_int_01.values), \n",
    "                       np.array(DIC_int_02.values), \n",
    "                       np.array(DIC_int_03.values),\n",
    "                       np.array(DIC_int_04.values),\n",
    "                       np.array(DIC_int_05.values))) #concatenating all the cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "DICP_int_01 = (DICP_lp01_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DICP_int_02 = (DICP_lp01_02.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DICP_int_03 = (DICP_lp01_03.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DICP_int_04 = (DICP_lp01_04.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DICP_int_05 = (DICP_lp01_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "DICP_int_conc = np.hstack((np.array(DICP_int_01.values), \n",
    "                       np.array(DICP_int_02.values), \n",
    "                       np.array(DICP_int_03.values),\n",
    "                       np.array(DICP_int_04.values),\n",
    "                       np.array(DICP_int_05.values))) #concatenating all the cycles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LP09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "O2_int_lp09_1 = (O2_lp09_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "O2_int_lp09_5 = (O2_lp09_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIC_int_lp09_1 = (DIC_lp09_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DIC_int_lp09_5 = (DIC_lp09_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "DICP_int_lp09_1 = (DICP_lp09_01.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))\n",
    "DICP_int_lp09_5 = (DICP_lp09_05.where(tmask) * e1t * e2t * e3t).sum(('x','y','deptht'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "305"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DICP_int_conc.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(O2_int_conc, color='black', linewidth=2, label='LP01')\n",
    "ax.plot(O2_int_lp09_1, color='red', linewidth=2, label='LP09')\n",
    "ax.plot(np.arange(244, 305),O2_int_lp09_5, color='red', linewidth=2)\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"mmol\", fontsize = 16)\n",
    "ax.set_title(\"Global ocean O2 content - ORCA05\", fontsize = 18)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "axes.Axes.tick_params(ax, axis='both' ,labelsize=14)\n",
    "ax.grid()\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=12)\n",
    "\n",
    "\n",
    "locs, labels = plt.xticks()\n",
    "plt.xticks(np.arange(0, 305, step=61), ('cycle01', 'cycle02', 'cycle03', 'cycle04', 'cycle05'), rotation=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "savepath = '/home/shkifmmp/_WORK/notebooks/shared-notebooks/figures/'\n",
    "plt.savefig(savepath + 'O2_ORCA05_lp01_lp09.png', format='png', facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAHwCAYAAAC7T84CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACGS0lEQVR4nOzdeZxN9R/H8ddnbGNN9qUkypJK1ha/agopWoSoKEqUJUVJyi6VilIhS6JQ0qLyI6mfSUJlKSpEdrKvwzBm5vv749zRGDOYcce5M/N+Ph73ce8953vPeZ85o+5nvt/zPeacQ0RERERERM5emN8BREREREREMgsVWCIiIiIiIkGiAktERERERCRIVGCJiIiIiIgEiQosERERERGRIFGBJSIiIiIiEiQqsEQkTcysjZk5M4vw4/On2fZ4M9M9KEREROScU4ElIgCYWbiZdTSz/5nZTjM7Zmb7zOwXMxtsZpX8zihnz8z6BQrbhEdM4HwvNLPXzezKFD43PtC+SDLrzjezPoHflX2BbW42s0/NrImZWfof2dkJ/Fwan+N9RgT2W/Ac7KuYmb1iZn+Y2SEzO2hmSwPnrUAK2VySR5SZLTGzrmaWPYX9VDCzEWa2MrCfaDP7y8xGm1mtFD6Tzcy2BvbR6zTHUdHMppnZ3sD2fzCzm1NomzT/8eM4k5+ZiEhaJfsfSBHJWsysHDAdqAx8D7wO/APkA64CHgaeNrMyzrktfuWUoOoDrAOyAefjnee2wBNmNhTo7s7gTvRmVhv4AigGfAlMAg4ApYGGwKdAJ2BE8A8hqPoCE4Bp53CfEYH9jgf2pddOzOxa4CugAN75eRPvvN8E9AMeMrMGzrm/kvn4h8AMwIASwIPAULz/VrRPsp+2wEjgSOBzvwKxQAWgKdDOzKo45/5Mso/bgJLA34Esg5L73TOz8sD8wDZfAfYD7YBZZnabc+7bZPL/AIxOsuxYMu1ERIJGBZZIFmdmuYH/AuWBJs65z5NpEw50BTTsLvOY6ZxblHiBmXUDpgJPATuBwafagJmVwPviHg7c6Jybl6TJQDNrgFfAiQ8C5+gLvP/f13HO/ZJo9Qgza4hXVH5pZtWcc9FJNrHEOTcx0fZGACuBR8zseefczsDyeniFzJ9AA+fc1iQ5egKPpxCzLbAW6BbIGgHMSabdS0BBoIZz7tfAdt8H/gCGm1mlZAqztYnzi4icCxoiKCKPAJWAV5MrrgCcc0eccy8l/dKUHDMrYmbDzWxTYKjYpsD7wil8JHtgmNQGMztqZsvM7N5ktnuLmU0xs7WBYUf7zOwbM7sxVUebfObGZvZjYAhUVOD1XSm0rWZmU81seyDvJjP7MPDX9cTt6gXy7TOzI4HjeuxsjsvMIs1svZmVCuwzYZjULDOrcLY/B+fcHuAevB6onmaW9zQf6Y7Xc9UjmeIqYZuznHMfnW7f5mlnZj8lOg/LzWxAknZn9Ptl/17jd7OZPW1mfwfO119m1jpRu7L27/V6rRMPJUuyvTM9n+sD56mSmf3XvKF4+83sk0Cxk9BuPF7vFcC6RPvtd7qfVSp1B4oCPZMUVwA452YAbwAV8QqdU3LOHQIW4vVoJf6dHxxY1iK5/04452Kdc68n7b0ys2JAI7zewxnAjuRyBH4X7wQiE4qrwHajgLF4vWQpDUHMaWb5TndsIiLBogJLRJoFnsee7YbM7Dy8ITwdgFnAk8DXgffzzCx/Mh8bDNyLN7SoD5AT+NDM2iRp1wYoBLyP95fw1/GGKX1nZtefReaOwOeBbb8ADAy8nmZmSYdA3Y735bIu3hfCx4ExQBng8kTt2gPf4A2xHIT3l/m/gZFm9upZHldeYC4QBzwHDMf7i/8XZpYtDT+CEwSKrM+B84D/nKZ5UyAG72dxtj7A6wFxeD+z7sD/+Pf3M62/Xy8CDwCjgGeAeGC8mdUJrN8ZWA/ecLIHEj0S9pua8wne8MhIYGPgOCYDTfDOcYJReD9n8HqHE/b5WXI/nLNwJudoTKK2ZyKhsNoDYGYXA9WBeckM/zud1ni9ax8452LxhjA2CZzrxK4EcgELktnGwsBzcgVWM+AwcNDMdpjZW8lsW0QkuJxzeuihRxZ+ALuB/ckszwYUSfLInWh9G7wvwxGJlg0KLOuYZFudAssHJvP5DcB5iZafF1i2J8n+8iaTsTiwC5iRZPl47z9vpz3284EoYA1QINHyAnhfoA8CBQPL8uB9Gd8BlE5mW2GB55J416BMTqbNMLzCqHwajysy8DN7Jsny7oHlDc7gmPsF2tY8RZtugTaPJ/2ZAkUC7/MH3i8Lwu9g88C2Pkj4OSb9uZ7F79dSIGei5aWBo8CHSbbhgPHJZEvt+Vwf2FbzJG2HB5ZXSuZclD3bn2EKP9czPkd4vZa7Er2PCHy2D96//aLAFYmO4+dEbe8ILHszDRn/xOuVSnh/ZWBbHZK0a5rc8sC6ywLrXkyy/CfgaaAx3rVjHyX8PIB86fEz10MPPfRwzqkHS0QogPflKqnKeAVF4ken02zr7kC7pBeVj8IrGO5O5jMjnXP7E94EXr+DV/xEJFp+KOG1meULDAmLw/sSdfVpcqWkPl6P0JvOueM/g8Drt/B6LOoFFjfA+6I5xCUz0YdzLj7wshneX9rfDQxnO/7Au14pDK8HLK3HFY83SUFi/ws8X3pGR316CT+Lk2aXSyRhXXK/O6nVMvD8dKKfI3DCzxXS9vs1wjkXk2h7W4C/OPOfVarOZ8BW59zHSZYlnKNLznC/wZBwjvafspXnAN4fN5Lqz79/WFgGdMTrZbszmf2k6nfBzK7D++/M8d4159wyvMkxHk7SPE/g+WgymzqSpE3Ctq52zr3mnJvmnHvfOXcv8DxeofhEarKKiKSGJrkQkQMk/0V6HV4BAlAVeO0MtnUxsMh5Q32Oc87FmtkqvGFESa1IZlnCMKNyCQsC1zgNwit0CiZpn9bJNy4OPP+RzLrfk2RI+EK+9DTbrBx4Tm5GswTFE16k4bi2OueOJFm2O/Cc0nVuqXUmX5gT1iU3LC+1LgX+cc5tP027tPx+rU1m2W7gojPMlqrzeZp9wlmco8B1REmvJdqTuIBMIuEcncmQuAIkX4iNxpv4JAdeYdIDuIB/i5rE+0nt70JbvBn9lppZ4sJzFtDDzK4MFFzgDfMDr9hNKjxJm1N5Fe/at0Z4/+5ERIJOBdY5YmbjgNuBHc65y0/TthvexAOxeH85fNg5tyGw7mvgGryx7renb2rJIn4HbjCzi51z6xIWBnpWvgUws9iUPhwEyRURJ9w3KfDFci5eb9MbwHK84XvxQE8g2fvgnIHU3J8poe3pirmEdg/iTXWfnLWQ5uOKO4N9n62Ee2GtSqmBc+6gmW0AKplZbnfy7HOpYaTfDJUp/bzO9Gd1xufzDPaZmv0m52n+nRgjwU14Q0dPEjhHG4GKZpbHOZdsARIobvKnsJ3V7t/pz2ea2TxgHl4vc8JkNAl/jKh2hseR8LvfHK9wS+mPFg/jXWcHkDBxRulk2iUsO+0tJJxzx8xsK15vtIhIulCBde6MB97mxIucU7IU7/qIw2bWAe9+Hy0C617FGwbxaHqElCzpE+AGvKL++bPc1lq8L3PZE/cymHdT0gok/5f9y/Dun5RYQq9BQvu6QCm8Pza8l7ihmb1wFnn/DjxXAb5LJlfiDAnFRjVg9im2uTrwvMslf1+exNLruNLMzArhDbXbj/dF+lQ+498JGpIO20uNVcBdZlb8NL1Yafn9OlupOZ+pldqi8n1OPie/neYzn+EVKQ/iFUXJeSRR21Nyzs03sw+AB83sTefcfOfcOjNbCtQxb6r0lafbDl5xlQ9vopbVyazvArQys2cCPXTL8YYHXptM22sCz4uSWXcC8245cQH/TowhIhJ0ugbrHHHOzSUw41ICMytvZl+b2WLz7kZfKdB2TqK/NC7E+59Bwna+w/sLt0iwjMW7r013M0vuGhY487+6T8O7GP6RJMvbBZYnNw18h8SzegVeP4Z349XvA4sTegSS9mzdQtqvvwKvUDoEPJ54BrrA68fxJsBIKKa+wbvO5ykzK5l0Q2aWkO1jvC+C/c27x1jSdueZWcIwp/Q6rjQJFFdT8YaLDUqpxyORV/B62V8x72a2yW3zFktm2v0kJiVsz8xO+P9Sop8rpO3360xF4c3mmFRqzmda9kkK+z2Jc26tc+7bJI+9p/nYq3jDE18ys5OGUJp3n7JueNelvXuGuQfi/e4mnkK/R+D5o8TT0SfaTzYze9LMEv5w0Rbv/4mvOuc+SfoIZCkM3BU49ii8a94izKxqou3mw/t9WA38nGh5SkMxB+L9cfmrMzxWEZFUUw+Wv0YDjznnVpvZ1cAITh4S1BaYec6TSZbhnIs2s0bAdOAzM4vEKya24X3RroTXgxoHbDrN5l7Bu4/S8MCXuaV4PT5t8XopXknmM7uAnwLDaA14CG/a80cSfcGfF8gzxMzKApuBq/B6TpbjXRuSas65fWb2DN7MaD8F7k0E3gx0lwCPJkzAEehRbovX4/e7mY3Fm32wKN71U0OBL5xzmwM9z2OBFYG/9m/g31nYGuP1jq1Pr+M6Q7cF/qgThjehSDW8nqv8eF96k5t+/ATOuW2Bqeu/wJsmfRrekMcDeD1zt+JN9d7hNNuZamZT8HpZLjWzL4G9eL1SDfh3Cvy0/H6dqYVAPTPrgTe9unPOfZTK85mWfQIMNrNJeNc1/e6c+/0Un0kV59xWM2uMd44WBPazEG+W0Ai8STw2AneeQUGdsM01ZvYR0NLMrnfO/eCcmx2Yzn4ksMrMPsSbrCIW799SU7zp3S8P/N5dhzdrY0rDj7/Euz6rLV7RD96w2brAN2b2Ot7vWTu8IYKNnHOJewR7mdk1eDcs3ojXW9YQb0jlT3iT2IiIpA+/pzHMSg+gLN7/PMH7j3003v+AEh4rkrRvhfc/wlxJlkcA0/0+Hj0y1wPIjTdL4By8oucYXi/SIrwvrhWTtG9DkmnaA8uL4v2xYHNgG5vxCpgiKXy+Ht5MZRvxegp+B+5PJt+VePc82ovXixsJXE8yU7Int+w0x3433v2VDgUe84HGKbStjdeTsiuQdyPefY7KJWlXB69HZQfefYi2Bn62TwHhaTyuSGB9MpnKBn6W/c7gWPsF2iY8YgLH8jPePbiuTOFz40k0TXuSdYXwrg1ahDe0MCZw3j/B++J+JucgLPD7t4TAfYvwZq3re5a/XxHJ7OuknyPeRBvf4H1pd8n87M/0fK4n0bTjiZZHBLbbJsnyZ/CGNh4703OYxn/fJfAmqlkR+PlG4f1/py+JbpOQTN6nU9heZbw/usxJsrwiXpH1V2A/R/CK31FAtUCbVwPbvuM0mWcF9nFhkv1+gfffpsN4f6Sol8xn7wp8fksgw6HA8T6X+HzpoYceeqTHw5xLr+uKJanAX6inO+cuN7MCwCrn3ElDjQJt6+H9he1G59yOJOsi8P6np0kuRERERERCiK7B8onz7rOzzszuAe86g4Rx5WZWDe+vfXcmLa5ERERERCR0qQfrHAmMR4/Amxp2O96wjP/hDaUoiTdV7UfOuQFm9i3e2P6EKYE3OufuDGznB7xrYvLhXbjc1jk36xweioiIiIiIpEAFloiIiIiISJBoiKCIiIiIiEiQaJr2c6BIkSKubNmy53y/hw4dIm/evOd8v5L+dG4zN53fzEvnNnPT+ZVgWbx48S7nXFGfM+TMnj37GLzbbWTzM0sIiTezbbGxsf2rV6+e4iU6KrDOgbJly7Jo0WlvMB90kZGRREREnPP9SvrTuc3cdH4zL53bzE3nV4LFzDb4nSEsLKxDgQIF6lx00UX7wsLCdE0REB8fb9HR0eetX7/+7SVLlnROqcjSEEERERERETlBtmzZHipVqtQhFVf/CgsLc3nz5o0uW7ZsTPbs2fum2O5chhIRERERkdDnnDsvZ86cx/zOEYpy5859xDlXIqX1KrBERERERCQpMzO/M4SkQK9einWUCiwREREREZEgUYElIiIiIiISJCqwREREREQkwyhduvQV06ZNy5942fTp0/OHhYXVyJMnT7W8efNWK1u27OXDhg0rnLB+/vz5uatUqVI5d+7c1apUqVJ5/vz5uRPW/fLLL+H/+c9/Lj3//POrmlmNs82nAktERERERDK8okWLHjt8+PDSgwcPLh04cODmp556quzixYvDjxw5Ys2aNbukefPmu/fs2fPrfffdt7tZs2aXHDlyxABy5szpmjZtuvett94KyvT4KrBERERERCTTCAsL44EHHtiXP3/+2GXLluWeMWNG/tjYWOvdu/eO3Llzu169eu1wzjF9+vT8AFWrVj3atWvXXVdddVV0MPavGw2LiIiIiEiKgjFs7kw45xYHYztxcXFMmjSp4MGDB7NVr1798LRp086rVKlSdFjYv31LlSpVil6+fHnuZs2aHQjGPhNTgSUiIiIiIhnezp07c+TPn/+qsLAwSpYsGTNixIh1VatWPTpx4sRsBQoUiEvcNn/+/HEHDx7Mlh45VGCJiIiIiEiKgtWzlN6KFi16bPv27cuSLs+XL1/cwYMHT7g0KioqKlv+/PnjkrYNBl2DJSIiIiIimdYVV1xxZOXKlXni4+OPL1u5cmXuK664IijXXCWlAktERERERDKUmJgYO3z48PFHbGxsim0bNmx4MFu2bG7QoEHFoqOj7cUXXywKcPvttx8EiI+P5/Dhw3b06FEDOHz4sEVHR1tas2mIoIiIiIiIZCgtWrS4NPH76tWrR6XUNjw83E2dOnVN+/btyw4aNOiCcuXKRU+dOnVNeHi4A1i9enXOSpUqXZHQPm/evNVLlSoVs2XLluVpyaYCS0REREREMoy0FD516tSJ/uOPP1Ykt65ixYoxwbzOTEMERUREREREgkQ9WCIiIiIiSbz11lscPXqUxx57jHz58vkdRzIQFVgiIiIiIokcOHCAvn37snfvXmrVqsWNN97odyTJQDREUEREREQkkREjRrB3716uv/56FVeSaiqwREREREQCDh06xJAhQwDo1auXz2kkI1KBJSIiIiISMHr0aHbt2kXt2rWpX7++33EkA1KBJSIiIiICHDlyhFdffRWA3r17Y5bme81KFqYCS0REREQEePfdd/nnn3+46qqraNSokd9xJINSgSUiIiIiWV5MTAyDBw8GvGuv1HslaaUCS0RERESyvPfff59NmzZRpUoV7r77br/jyCmULl36imnTpuVPvGz69On5w8LCauTJk6da3rx5q5UtW/byYcOGFU5YP3/+/NxVqlSpnDt37mpVqlSpPH/+/NwJ66Kjo61t27YXFitW7MoCBQpc1apVqzJHjx5Nc4WtAktEREREsrT4+HheeeUVAJ577jnCwvQVOSMqWrToscOHDy89ePDg0oEDB25+6qmnyi5evDj8yJEj1qxZs0uaN2++e8+ePb/ed999u5s1a3bJkSNHDKBXr14lfv311zzLly//Y+XKlb8vX748z7PPPlsyrTn02yMiIiIiWdrMmTNZvXo1ZcqUoXnz5n7HkbMUFhbGAw88sC9//vyxy5Ytyz1jxoz8sbGx1rt37x25c+d2vXr12uGcY/r06fkBZs2aVbBTp047ihcvHleqVKnYDh067Pjwww+LpHX/2YN3KCIiIiIiGc+wYcMA6Ny5M9mz6+vxScxqnJP9OLc4GJuJi4tj0qRJBQ8ePJitevXqh6dNm3ZepUqVohP3TFaqVCl6+fLluZs1a3bAOUd8fHyiGI7t27fn2L17d7bChQvHpXb/+g0SERERkSzrjz/+YPbs2eTJk4dHHnnE7zhyFnbu3Jkjf/78V4WFhVGyZMmYESNGrKtaterRiRMnZitQoMAJhVL+/PnjDh48mA2gbt26+0eMGFG8YcOGB2NjYxk5cmQxgKioqDAVWCIiIiIiqfDmm28C0Lp1a84//3yf04SoIPUspbeiRYse2759+7Kky/Plyxd38ODBEy6NioqKypY/f/44gBdffPGfjh07Zr/qqqsuy5kzp3vggQd2rlixIk/p0qWPpSWHrsESERERkSxpz549fPDBBwB06dLF5zSSXq644oojK1euzJN4GODKlStzX3HFFdEA+fLlc++///7GHTt2LNu8efPywoULx1apUuVwWoeLqsASERERkSxpzJgxREdH06BBAypVquR3HEmFmJgYO3z48PFHbGxsim0bNmx4MFu2bG7QoEHFoqOj7cUXXywKcPvttx8EWLduXY7169fniI+P57vvvsv72muvlerTp8/WtGbTEEERERERyXJiY2MZPnw4AE888YTPaSS1WrRocWni99WrV49KqW14eLibOnXqmvbt25cdNGjQBeXKlYueOnXqmvDwcAewcuXKXI888sjFe/bsyV68ePFjffv23dykSZMDac0WMgWWmXUCHgXKBhb9AbzgnPvvKT5zBfA2UBvYA4wCBjrnXGB9E+AxoBoQDvwJDHLOfZloG+2AB4EqeD16S4Hezrl5idr0A/om2f1251yJNB6uiIiIiPjo888/Z9OmTVSsWJEGDRr4HUdSYcuWLctT+5k6depE//HHHyuSW3fbbbdFpWWbKQmlIYKbgR5AdaAm8D9gmpldmVxjMysAzAa2A7WALkB3oFuiZjcGttMIr8iaAXxuZtcnahMBTAHqAlcDq4BZZnZCVRxYXjLR44o0HqeIiIiI+Mg5x9ChQwHv2ivdWFiCKWR6sJxzXyRZ9LyZdQCuBU6aDQRoCeQBWjvnooHfzawy0M3MhjpP0v7e/mbWCGgM/BDYb8vEDQL7bAzcCqxOtCrWObctTQcnIiIiIiFj7ty5LFy4kMKFC9O6dWu/40gmEzIFVmJmlg24B8gHzE+h2bXAD4HiKsEsYCDeMMN1KXwuP7D3FLvPiTecMGmbcma2BYgBfgKec86tPcUxtAfaAxQvXpzIyMhT7DJ9REVF+bJfSX86t5mbzm/mpXObuen8ZhzPPPMMAHfccQe//PKLz2kkswmpAitwTdUCvAInCrjbOZfSeMgSeMMKE9ueaN1JBVbgOq8LgA9OEeOFwL6/TLTsJ6ANsBIoBvQC5ptZFefc7uQ24pwbDYwGqFmzpouIiDjFLtNHZGQkfuxX0p/Obeam85t56dxmbjq/GcPSpUv55ZdfyJs3L6+99hqFCxf2O5JkMiFVYOFd53QVUBBoCkwwswjn3O8ptHdJ3lsKyzGzpsCrwL3OuQ3JbczMnsCbaKOec+74zCHOuZlJ2i0E1gKtgaGnPiQRERERCRWDBw8GoH379iquTi0+Pj7ewsLCTvpendXFx8cbEJfS+pC6os85F+OcW+OcW+Sc6wn8CnRNofk2vJ6qxIoFnrcnXhgorj4AHkw8g2CSNk/g9V41dM79fJqcUXizHCadCENEREREQtSaNWuYOnUqOXLkoFu3bqf/QNb2+86dO88LFBOCNznK0aNHc2zYsKEgMC+ldqHWg5VUGJArhXULgMFmFu6cOxJYVh/YCqxPaGRmzYEJeJNhfJLchsysGzAAr7hK8YeVqH04UAmYc4bHISIiIiI+e+2114iPj6dNmzZccMEFfscJabGxsY9s27Zt7LZt2y4nxDplfBRvZvvj4uLejI+PH5lSo5ApsMzsZeC/wCa8iSjux5tCvVFg/UtAbedc3cBHJuPdm2q8mb0AVACeBfonug/WvXg9V08Dc80soccrxjm3J9CmOzAIaAX8lahNtHNuf6DNa8BXwEa8XrLeQF68wk1EREREQtw///zDe++9h5nRvXt3v+OEvBo1auwA7vQ7R0YUMgUW3nC/iYHn/XhTs9/mnJsVWF8SKJ/Q2Dm338zqA8OBRXiz/g3hxGuiHsM7xjcCjwTf4xVvAJ2AHHj3wkpsAt7EFuBNjPEhUATYCSwErknpWi4RERERCS3Dhg0jJiaGJk2aUKlSJb/jSCYWMgWWc65NatcHZhi84RSfiTiD/ZY9gzb3nq6NiIiIiISmffv2MWLECAB69OjhcxrJ7DSeUkREREQytZEjR3Lw4EFuvvlmateu7XccyeRUYImIiIhIphUdHc0bb7wBwLPPPutvGMkSVGCJiIiISKb13nvvsWPHDqpXr069evX8jiNZgAosEREREcmUDh06xAsvvABAz549MdMtnST9qcASERERkUxp2LBh/PPPP9SsWZMmTZr4HUeyCBVYIiIiIpLp7N69m8GDBwPw8ssvExamr71ybug3TUREREQynVdffZUDBw5Qv3596tat63ccyUJUYImIiIhIprJ7927efvttAAYNGuRzGslqVGCJiIiISKYybNgwDh06RIMGDahVq5bfcSSLUYElIiIiIpnG/v37efPNNwHo3bu3z2kkK1KBJSIiIiKZxrBhw9i/fz8RERHUqVPH7ziSBanAEhEREZFMYc+ePQwZMgSAvn37+pxGsioVWCIiIiKSKbzyyiscOHCAevXqERER4XccyaJUYImIiIhIhrdt27bj115p5kDxkwosEREREcnwXnzxRaKjo7nzzjupXbu233EkC1OBJSIiIiIZ2saNGxk1ahRmxsCBA/2OI1mcCiwRERERydAGDBhATEwM9957L1deeaXfcSSLU4ElIiIiIhnW6tWrGT9+PNmyZaN///5+xxFRgSUiIiIiGVffvn2Ji4ujTZs2XHrppX7HEVGBJSIiIiIZ0/Lly/noo4/ImTMnffr08TuOCKACS0REREQyqN69e+Oc49FHH6VMmTJ+xxEBVGCJiIiISAb0008/8cUXX5A7d26ee+45v+OIHKcCS0REREQynF69egHQpUsXSpQo4XMakX+pwBIRERGRDCUyMpJvv/2WAgUK8Mwzz/gdR+QEKrBEREREJMNwzvH8888D8PTTT1OoUCGfE4mcSAWWiIiIiGQYM2fOZP78+RQpUoQnn3zS7zgiJ1GBJSIiIiIZQnx8/PFrr5599lny58/vcyKRk6nAEhEREZEM4bPPPmPp0qWUKlWKjh07+h1HJFkqsEREREQk5MXFxR2/mXCvXr3InTu3z4lEkqcCS0RERERC3uTJk1mxYgVly5albdu2fscRSZEKLBEREREJaceOHaNfv34A9O3bl5w5c/obSOQUVGCJiIiISEibMGECa9eupUKFCrRq1crvOCKnpAJLRERERELW0aNHGTBgAAD9+vUje/bsPicSOTUVWCIiIiISssaOHcumTZu4/PLLadGihd9xRE5LBZaIiIiIhKTo6GgGDRoEQP/+/QkL01dXCX36LRURERGRkDRy5Ej++ecfqlWrxt133+13HJEzogJLREREREJOVFQUL7/8MgADBw7EzHxOJHJmVGCJiIiISMgZNGgQO3fu5JprrqFhw4Z+xxE5YyqwRERERCSk/PXXXwwZMgSAYcOGqfdKMhQVWCIiIiISMpxzdOnShWPHjtG2bVtq167tdySRVAmZAsvMOpnZMjM7EHgsMLNGp/nMFWb2vZlFm9kWM+tjif7EYWZNzOwbM9tpZgfN7CczuzOZ7TQ1sz/N7Gjg+aSrKM2so5mtM7MjZrbYzK4PzpGLiIiISIJPPvmEWbNmUbBgQV566SW/44ikWsgUWMBmoAdQHagJ/A+YZmZXJtfYzAoAs4HtQC2gC9Ad6Jao2Y2B7TQCqgEzgM8TF0dmdi0wBZgEXBV4nmpmVydq0wIYBrwY2M58YKaZlTnbgxYRERERz969e3n88ccBeOmllyhatKjPiURSL2Ruhe2c+yLJoufNrANwLbAsmY+0BPIArZ1z0cDvZlYZ6GZmQ53niSSf6R/oFWsM/BBY9iQwxzk3KPB+kJndFFh+X2BZN2C8c25M4P3jZnYr0AHomfqjFREREZGkevTowfbt26lTpw7t27f3O45ImoRSD9ZxZpbNzO4F8uH1FiXnWuCHQHGVYBZQCih7is3nB/Ym2c43SdrMAq4LZMkJ1EimzTcJbURERETk7ERGRjJmzBhy5MjB6NGjdVNhybBCpgcLvGuqgAVAOBAF3O2cW55C8xJ4wwoT255o3bpktt8JuAD4IMl2tidpuj2wHKAIkC2FNvVOcSztgfYAxYsXJzIyMqWm6SYqKsqX/Ur607nN3HR+My+d28xN5zftjhw5Qtu2bQFo2bIlO3bsYMeOHT6nEkmbkCqwgFV410EVBJoCE8wswjn3ewrtXZL3lsJyzKwp8Cpwr3NuwxlsJ+myM2nzb2PnRgOjAWrWrOkiIiJSappuIiMj8WO/kv50bjM3nd/MS+c2c9P5Tbvu3buzdetWLr/8ckaNGkXOnDn9jiSSZiFVYDnnYoA1gbeLzKwW0BVom0zzbfzby5SgWOD5hN6mQHH1AfCgc+7LM9xOwjZ2AXGnaSMiIiIiafDLL78wdOhQwsLCePfdd1VcSYYX6oNbw4BcKaxbAFxvZuGJltUHtgLrExaYWXNgItDGOfdJCtupn2RZfQLXfgWKvsWnaiMiIiIiqRcTE0Pbtm2Jj4+na9euuueVZAohU2CZ2ctmdr2ZlQ3c3+olIAJv2nTM7CUz+y7RRyYDh4HxZna5mTUBngWGOudc4DP3Bj7/LDDXzEoEHoUSbWcYcLOZ9TSzSmbWE7gJeCNRm6FAGzN7xMwqm9kwvMk03gn+T0JEREQkaxg8eDDLly+nfPnyDBgwwO84IkERSkMES+D1NJUA9uNNzX6bc25WYH1JoHxCY+fcfjOrDwwHFuHNDDgErxhK8BjeMb7BiQXT93jFG865+YFC7AWgP/A30MI591OifU0xs8JAr0CO34GGyVzLJSIiIiJnYMuWLbzwwgsAjBkzhjx58vicSCQ4QqbAcs61Se36wAyDN5ziMxFnuO9PgOSGDyZuMwIYcSbbExEREZFTGzp0KDExMdxzzz3cdNNNfscRCZqQGSIoIiIiIlnD7t27GTVqFAA9e/b0OY1IcKnAEhEREZFz6u233+bQoUM0aNCAatWq+R1HJKhUYImIiIjIObN3717efPNNQL1XkjmpwBIRERGRc6Z///7s2bOHiIgIbrghxUvpRTIsFVgiIiIick6sWLGCt99+m7CwMN544w3MzO9IIkGnAktERERE0p1zjq5duxIXF0e7du2oWrWq35FE0oUKLBERERFJdzNmzGDWrFmcd955DBw40O84IulGBZaIiIiIpKuYmBi6du0KQN++fSlatKjPiUTSjwosEREREUlXb7/9NqtXr6ZixYp06tTJ7zgi6UoFloiIiIikmx07dtC/f38Ahg4dSs6cOX1OJJK+VGCJiIiISLrp3bs3Bw4c4LbbbqNhw4Z+xxFJdyqwRERERCRd/Prrr4wZM4bs2bMzdOhQv+OInBMqsEREREQk6JxzPPHEEzjn6Ny5M5UqVfI7ksg5oQJLRERERILu008/Ze7cuRQuXJg+ffr4HUfknFGBJSIiIiJBFR0dzdNPPw3ACy+8wPnnn+9zIpFzRwWWiIiIiATVkCFD2LBhA1deeSXt2rXzO47IOaUCS0RERESCZvfu3QwePBiA119/nWzZsvmcSOTcUoElIiIiIkHz2muvERUVRYMGDbj55pv9jiNyzqnAEhEREZGg2LFjB2+99RbA8ZsLi2Q1KrBEREREJCheffVVDh06RKNGjbj66qv9jiPiCxVYIiIiInLWtm3bxvDhwwH1XknWpgJLRERERM7a4MGDiY6O5q677qJGjRp+xxHxjQosERERETkrW7duZeTIkQD069fP3zAiPlOBJSIiIiJn5cUXX+To0aM0bdqUq666yu84Ir5SgSUiIiIiafbnn3/yzjvvEBYWpt4rEVRgiYiIiEgaOed48skniYuLo3379lx++eV+RxLxnQosEREREUmTr776itmzZ1OwYEEGDhzodxyRkKACS0RERERS7ejRo3Tr1g3wpmUvUqSIz4lEQoMKLBERERFJtWHDhvH3339z2WWX0aFDB7/jiIQMFVgiIiIikir//PPP8SGBb7zxBjly5PA5kUjoUIElIiIiIqny1FNPERUVxZ133kn9+vX9jiMSUlRgiYiIiMgZ+/jjj/nwww/JkycPQ4cO9TuOSMhRgSUiIiIiZ2Tr1q089thjALz22muUL1/e50QioUcFloiIiIiclnOORx99lL1793LrrbceL7RE5EQqsERERETktKZMmcL06dM577zzGDt2LGbmdySRkKQCS0REREROaffu3XTp0gWAV199ldKlS/ucSCR0qcASERERkVPq0aMHO3fu5MYbb6Rt27Z+xxEJaSqwRERERCRFS5YsYdy4cWTPnp1Ro0YRFqavjyKnon8hIiIiIpIs5xxPPvkkzjm6dOlCxYoV/Y4kEvJUYImIiIhIsj7++GN++OEHihQpQu/evf2OI5IhqMASERERkZNs27aNzp07A/DCCy9QsGBBfwOJZBAhU2CZWSczW2ZmBwKPBWbW6DSfucLMvjezaDPbYmZ9LNGcoWZW0swmm9lKM4szs/HJbCPSzFwyjz8StemXzPptQf0BiIiIiIQI5xzt2rVj165d1K1bl3bt2vkdSSTDyO53gEQ2Az2A1XiFX2tgmpnVcM4tS9rYzAoAs4G5QC2gIjAeOAQMCTTLBewCXgbap7DfJkDORO9zAcuBj5O0WwVEJHofd2aHJSIiIpKxjBs37vg9r9577z1NbCGSCiFTYDnnvkiy6Hkz6wBcC5xUYAEtgTxAa+dcNPC7mVUGupnZUOdZD3QBMLNmKex3T+L3ZtYSyAuMS9I01jmnXisRERHJ1NauXcuTTz4JwPDhw7nwwgv9DSSSwYTknyPMLJuZ3QvkA+an0Oxa4IdAcZVgFlAKKHsWu28HzHTObUqyvFxgGOI6M/vIzMqdxT5EREREQk5cXBytW7cmKiqKe+65h/vvv9/vSCIZTsj0YIF3TRWwAAgHooC7nXPLU2heAm9YYWLbE61bl4b9VwBuBBonWfUT0AZYCRQDegHzzayKc253CttqT2BYYvHixYmMjExtnLMWFRXly34l/encZm46v5mXzm3mlhnO78cff8y8efMoXLgwLVu25Pvvv/c7kkiGE1IFFt51TlcBBYGmwAQzi3DO/Z5Ce5fkvaWw/Ey1A/4B/nvCTpybecJOzBYCa/GuExuabDDnRgOjAWrWrOkiIiLSGCntIiMj8WO/kv50bjM3nd/MS+c2c8vo53fNmjWMHz8egAkTJtCo0SnnGhORFIRUgeWciwHWBN4uMrNaQFegbTLNt+H1VCVWLPC8nVQys5x4BdMY51zsaXJGBWYZvDS1+xEREREJNQmzBkZHR9OqVSsVVyJnISSvwUokDG9Wv+QsAK43s/BEy+oDW4H1adhXY6AI8O7pGgb2WQmvt0tEREQkQ/vggw+IjIykaNGivP76637HEcnQQqbAMrOXzex6MysbuL/VS3jTok8KrH/JzL5L9JHJwGFgvJldbmZNgGeBoc45l2i7V5nZVUABoFDg/WXJRGgPfOecW5tMttfM7EYzu9jMrgY+wZtpcEIwjl1ERETELzExMfTt2xeAV199lSJFivicSCRjC6UhgiWAiYHn/XhTs9/mnJsVWF8SKJ/Q2Dm338zqA8OBRcBevPtfJb0mammS93cAG0g002BgRsCbgXtTyHYB8CFeD9dOYCFwjXNuQ6qOUERERCTEjBs3jvXr11O5cmVatWrldxyRDC9kCiznXJvUrg/MMHjDaT5np1ofaLOWU/TmOedSKrxEREREMqwjR47wwgsvANC/f3+yZcvmc6IQsmIFFCoExYv7nUQymJAZIigiIiIi59aQIUPYsmULVatWpWnTpn7HCR3OwUMPQfnykMGn3pdzTwWWiIiISBa0YsUKBgwYAHiFVliYvhYeN20a/PQT5M0LNWv6nUYyGP1LEhEREcli4uLiaNu2LTExMTzyyCPUrVvX70ihIzYWevb0XvfpA/ny+ZtHMhwVWCIiIiJZzPDhw1mwYAGlSpXi1Vdf9TtOaHnvPVi1yhse2K6d32kkA1KBJSIiIpKFrFu3jp6BHpqRI0dSsGBBfwOFksOHoV8/7/WgQZAzp69xJGNSgSUiIiKSRTjnaN++PYcPH6ZFixbceeedfkcKLW+9BVu3QvXqcM89fqeRDEoFloiIiEgW8d577/Htt99SuHBh3nzzTb/jhJa9e+Hll73XL78MmvRD0ki/OSIiIiJZwNatW+nWrRsAw4YNo1ixYj4nCjGDB8O+fVC3LtSv73caycBUYImIiIhkcs45OnXqxP79+2nUqBH333+/35FCy5498Pbb3uuXXvI3i2R4Z1VgmZn+9CEiIiIS4t577z2mTZtG/vz5GTlyJGbmd6TQMmIEHDoEt9wCtWr5nUYyuLPtwVoUlBQiIiIiki6WLl1Kp06dAHjrrbe48MILfU4UYg4fhmHDvNc9evibRTKF7KdrYGanml4mPIhZRERERCSI9u3bR7NmzThy5Ajt2rWjdevWfkcKPe+9B7t2Qc2acNNNfqeRTOC0BRbwOfA9kFxfcv7gxhERERGRYIiPj+fBBx9k7dq1VK9eXbMGJic2Fl57zXvdowdo6KQEwZkUWKuBts65dUlXmNmm4EcSERERkbP1yiuv8NVXX1GwYEE++eQTwsM18OgkH38M69dDhQpw991+p5FM4kyuwZoAFElh3TtBzCIiIiIiQTBnzhyef/55ACZOnMjFF1/sc6IQ5Ny/973q3h2yZfM3j2Qap+3Bcs6lOFelc25QcOOIiIiIyNnYsmUL9957L/Hx8Tz//PM0atTI70ihaeZMWL4cSpaEBx7wO41kIqmeRdDMSqdHEBERERE5O0eOHKF58+bs2LGDunXr0r9/f78jhSbnYFCgn6BbN8iVy988kqmkZZr2pUFPISIiIiJn5dixY7Ro0YL58+dTunRpJk+eTDYNe0ve7Nkwfz4ULgyPPup3Gslk0lJgaXoVERERkRASFxdHmzZt+PLLLzn//POZMWMGxYoV8ztWaHIO+vb1XnfvDvk1KbYEV1oKLBf0FCIiIiKSJs45OnTowOTJk8mXLx9ff/01V155pd+xQtfXX8PChVCkCARuwCwSTGkpsEREREQkBDjnePrppxkzZgzh4eFMnz6d2rVr+x0rdDkHffp4r595BvLl8zePZEoqsEREREQyqAEDBjB06FBy5MjBZ599xo033uh3pND2xRewaBGUKKHeK0k3KrBEREREMqChQ4fSr18/wsLC+PDDD7ntttv8jhTa4uKgd2/v9fPPQ548/uaRTCstBdamoKcQERERkTM2a9YsnnrqKQDGjRtH06ZNfU6UAUyZAr//DmXKQLt2fqeRTCzVBZZzrkZ6BBERERGR0/vnn394IHBj3P79+9O6dWufE2UAsbHQr5/3uk8f3fdK0pWGCIqIiIhkEHFxcbRs2ZKdO3dSt25dnn/+eb8jZQwffACrV8Mll8CDD/qdRjI5FVgiIiIiGcSgQYOYM2cOxYoVY+LEibqR8JmIiYH+/b3X/fpBjhy+xpHML3taPmRm/zvF6nhgP7AYeNc5tz0t+xARERGRf33//ff0798fM2PSpEmUKFHC70gZw9ixsGEDXHYZ3Huv32kkC0hTgQUYUAEoCawDtgPFgYuBfwLvGwJdzexG59yfQcgqIiIikiUtXLiQZs2aER8fz/PPP0+9evX8jpQx7Nr178yBAwaAevzkHEjrEMGhwBGghnOuvHPuOudceaBWYHl/4FJgJzAoKElFREREsqBp06Zx0003sWvXLho2bEi/hMka5PSeew727IG6daFJE7/TSBaR1gLrBaCfc25p4oXOucV4xdULzrnNwKvADWcXUURERCRrmjx5Ms2aNePIkSM88sgjTJs2jezZ0zoAKYv55RdveGCOHPD222DmdyLJItJaYFUAdqWwbidwSeD130DeNO5DREREJMv64IMPaNWqFXFxcfTq1YvRo0eTQxM0nBnn4KmnvOeuXaFSJb8TSRaS1gJrPfBICuvaB9YDFAF2p3EfIiIiIlnS//73Px566CGcc7zwwgsMHDgQUw/MmZsxA374AQoX9oYJipxDae1jHgBMNLNlwKfADqAY0BS4HLg/0K4e8NPZhhQRERHJKv766y+aNWtGXFwczzzzjO51lVrx8dCzp/f6uefgvPP8zSNZTpoKLOfch2a2C+96q+eAHMAxYBFwi3Pu20DTbkBcMIKKiIiIZHarV6/mlltuYe/evdx111289NJLfkfKeCZNguXL4cILoWNHv9NIFpTmqySdc7OB2WYWhjcUcJdzLh7AzPI556Kcc0eClFNEREQkU/vtt99o0KAB27dv55prrmHixImEhaX1ao4sKjoaEnr8BgyA8HB/80iWlKZ/tWb2ZsJr51y8c25H4uIKmBWkfCIiIiKZ3vz584mIiGD79u3Uq1eP2bNnky9fPr9jZTxvvAGbNkHVqvDAA36nkSwqrX8WecjMTrpiMFBcfQ1ceFapRERERLKIb7/9lvr167Nv3z6aNGnC9OnTVVylxY4dkDCkcsgQ3VRYfJPWAuseoK+ZPZSwwMzyADOBi4GbgpBNREREJFP79ddfufvuuzl8+DBt2rRhypQp5MqVy+9YGdPzz8PBg9CokXdjYRGfpHWSi6/NrB0wxsx2At/hFVflgRudc38HMaOIiIhIprNlyxZuv/12oqKiuO+++3j33Xd1zVVa/fwzvPuud1PhIUP8TiNZ3NlMcvG+mZUAPgZ+B8oAEc651cEKJyIiIpIZRUVFcccdd7Blyxb+85//MG7cOBVXaRUXB506eTcV7tYNKlb0O5FkcWf8L9nMwpI+gNeAsUBZoD7wV6J1qWJmncxsmZkdCDwWmFmj03zmCjP73syizWyLmfWxRHfhM7OSZjbZzFaaWZyZjU9mG23MzCXzCE/SrqOZrTOzI2a22MyuT+0xioiIiMTFxXHfffexdOlSLrnkEj7//HPCNdtd2o0fD4sWQenS0KuX32lEUtWDFQu4FNYZ8Gui9y6V2wbYDPQAVuMVfq2BaWZWwzm37KQdmhUAZgNzgVpARWA8cAhI6BvOBewCXgban2Lfh/GGN/57AImmmDezFsAwoCMwL/A808wuc85tTOVxioiISBblnKNTp05Mnz6dQoUKMWPGDIoUKeJ3rIzr0CHo3dt7/coroMlBJASkpggaQMoF1llzzn2RZNHzZtYBuBY4qcACWgJ5gNbOuWjgdzOrDHQzs6HOsx7oAmBmzU69e7ftFOu7AeOdc2MC7x83s1uBDkDP0x2biIiIiHOOp556ilGjRpErVy4+//xzLr30Ur9jZWxDhsA//0CtWnDvvX6nEQFSUWA55/qlY44TmFk2vJkK8wHzU2h2LfBDoLhKMAsYiDdkcV0qdpnbzDYA2fB64no755YGsuQEauANh0zsG+C6UxxDewK9ZsWLFycyMjIVcYIjKirKl/1K+tO5zdx0fjMvndvM7VTn1znHqFGjmDJlCtmzZ6dfv37Ex8fr9+Es5Nyzh6tfeolswNKWLdk/d67fkUSAs5jkIj2Y2RXAAiAciALuds4tT6F5CbxhhYltT7TuTAusVcDDwG9AfuAJ4EczqxqYsKMIXuG1PcnntgP1Utqoc240MBqgZs2aLiIi4gzjBE9kZCR+7FfSn85t5qbzm3np3GZuKZ3fuLg4HnvssePF1ZQpU2jSpMm5D5jZPPQQHDkCd91FtSee8DuNyHFBL7DMrBUQ5px7Pw0fXwVcBRQEmgITzCzCOfd7Cu2TDlm0FJanyDm3AK+o8zZgNh+vF+txAsMLT7GvdBsyKSIiIhnfxo0beeihh/jf//5HeHg4n376KQ0bNvQ7Vsa3cKE3uUXOnPBa0kFGIv5Kjx6s9/AmqUh1geWciwHWBN4uMrNaQFegbTLNt+H1VCVWLPCctLcpNRnizGwRkDAoehcQl8K+0rwfERERydw++eQT2rZty4EDByhSpAiffvopN9xwg9+xMr74eHj8ce/1U0/BJZf4m0ckifS44UJd4OYgbSsMbybA5CwArk8ynXp9YCuwPq07DEzzfiXwDxwv+hYHtp1YfVK+PkxERESyqNjYWLp3784999zDgQMHuPPOO/njjz9UXAVL4mnZn3vO7zQiJwl6D5ZzLk1XGJrZy8B/gU1410LdD0QAjQLrXwJqO+fqBj4yGegLjDezF4AKwLNAf+ecS7TdqwIvCwDxgfcxzrk/A+v7AgvxpocvgDcs8Eq8GQITDAU+MLOfgR+Bx4BSwDtpOVYRERHJnHbs2MG9997LnDlzyJ49O6+99hpdunQh0W065WwcOAA9AxM4Dx6sadklJKWpwDKz851ze4OcpQQwMfC8H29q9tucc7MC60uS6F5Vzrn9ZlYfGA4sAvbi3f9qaJLtLk3y/g5gA95Mg+Bd7zU60X6XAjc4535OtK8pZlYY6BXI8TvQ0Dm3Ie2HKyIiIpnJn3/+yQMPPMDmzZspXrw4U6dO5frrr/c7VubywguwYwdcdx3cf7/faUSSddoCy8yq4t3A1wGtgFeA+ma2Hbg9uZsAp4Vzrk1q1wdmGDxlf7tz7pR/MnLOdcW7zut0+UYAI07XTkRERLIW5xzDhw+na9euxMbGct111zF16lRKlSrld7TMZfVqeOMN7/WwYaBeQQlRZ3IN1ptAf2AYMAP4zDmXC+/mu6+mYzYRERGRkBYVFcX999/P448/TmxsLF26dGHOnDkqrtLDU0/BsWPQpg3UrOl3GpEUnUmBVcA5N805NwHI5pwbB+Cc+4R/Z+0TERERyTKcc8yYMYPatWvz0UcfkTdvXnr37s2wYcPImTOn3/Eyn1mz4KuvIH9+eOklv9OInFJqr8H6Psn79JiFUERERCQkxcbGMnXqVF5++WWWLfOukqhcuTKffvop27fr7i3p4tgxePJJ73Xv3lAi6Z1zRELLmRRYu80sv3PuoHOuVcJCMysBHE2/aCIiIiKh46effqJVq1asWePdsrNkyZJ069aNDh06kDdvXhVY6WXUKFi50rvfVZcufqcROa3TFljOuXoprDoCtAhuHBEREZHQM3r0aB5//HFiYmK45JJLeOaZZ3jwwQfJlSul23VKUOzfD/37e69feQX085YMIM33wXLO7QP2BS2JiIiISIg5cuQIjz/+OGPHjgWgc+fODBkyRNdZnSuDB8OuXVCnDjRu7HcakTNyVjcaNrMLgQuB8KTrnHP/O5tti4iIiPhp1apVtGrVikWLFhEeHs6oUaN48MEH/Y6VdaxfD6+/7r0eMkTTskuGkdYbDZcDJgG1ExYFnl3gtQOynXU6ERERkXMsLi6O119/nd69e3PkyBHKli3LZ599RrVq1fyOlnU4B+3awZEjcN99cPXVficSOWNp7cEaC5QBngRWAjHBCiQiIiLilxUrVvDwww+zcOFCANq0acPQoUM5//zzfU6WxYwbB99+C4UL/3tzYZEMIq0FVi2gjXPu02CGEREREfFDbGwsQ4YMoW/fvhw9epTSpUszevRoGjZs6He0rGfjRujWzXv91ltQTLddlYwlrQXWZtRrJSIiIhncnj17+PDDDxk9evTx+1o9/PDDDBkyhIIFC/obLiuKi4NWreDAAbjrLrj3Xr8TiaRaWgusF4EeZvY/59yhYAYSERERSW9xcXG88847PPfccxw4cACACy64gLFjx9KgQQOf02VhL70EP/zg3Ux4zBhNbCEZUpoKLOfcB2ZWCVhvZguBvSc3ca3POp2IiIhIkM2ZM4fu3buzePFiAG6++WYeeeQRGjduTO7cuX1Ol4XNmwf9+nmv338fihb1NY5IWqV1FsE2QE8gDqjOycMF3dnFEhEREQmu3377jWeffZavv/4agNKlS/PWW2/RuHFjTD0l/tqxA1q08IYIdu8O9ev7nUgkzdI6RLA/8DnQNnDDYREREZGQtH79enr37s2kSZNwzlGgQAF69OjBE088Qd68ef2OJ/Hx3nVXW7fCf/4Dgwb5nUjkrKS1wCoMjFBxJSIiIqFq165dDBo0iBEjRhATE0OOHDno1KkTzz//PEWKFPE7niQYMgRmz4YiReCjjyBHDr8TiZyVtBZY84DKwHdBzCIiIiJy1mJjY3njjTcYOHAgBw4cwMxo1aoVAwYM4OKLL/Y7niS2dCk8/7z3evx4KF3a1zgiwZDWAusJ4GMz2wt8zcmTXOCciz+bYCIiIiKptWzZMh5++OHjE1jceuutvPTSS1x11VX+BpOTHTsGLVt6z506QaNGficSCYq0FlgrAs/vp7DencW2RURERFIlJiaGQYMG8eKLLxIbG8uFF17IqFGjuO222/yOJimZMAFWrIBLLoFXXvE7jUjQpLUIGoBmChQREZEQsHnzZpo0acIvv/wCQMeOHXn55ZfJnz+/z8kkRTExMHCg93rAAMiTx988IkGU1vtg9QtyDhEREZFUmzVrFg8++CA7duygbNmyTJgwgRtuuMHvWHI648bBxo1w2WXQvLnfaUSCKizYGzSzVmb2QLC3KyIiIpLg119/5bbbbuPWW29lx44d1KtXj0WLFqm4ygiiouCFF7zX/ftDtmz+5hEJsqAXWMB7wPh02K6IiIhkYXFxccyaNYtGjRpRrVo1vv76awoUKMDgwYOZOXMmhQsX9juinIkBA2DLFqheHZo08TuNSNClx0QUdQHdDl1ERESCYt++fYwYMYIRI0awZcsWAPLkycOjjz5Kz549KVq0qM8J5Yz9/ju8/jqYwTvvQFh6/K1fxF9BL7Ccc3ODvU0RERHJmj799FMefvhhDhw4AED58uVp3bo1jz32mAqrjCY+Hh57DGJjoWNHqFXL70Qi6UJTqYuIiEhImjVrFvfddx/Hjh0jIiKC5557jnr16mGmgTIZ0ltvwY8/QokSMGiQ32lE0o0KLBEREQk5//vf/2jSpAnHjh3jqaee4tVXX1VhlZH99Rf07Om9HjUKChb0NY5Iejrjga9mFm9mcWf4iE3P0CIiIpI5Oed47bXXqF+/PocPH6ZNmzYqrjK6+Hh46CGIjoYHH4Q77/Q7kUi6Sk0Plm4uLCIiIulmxYoVdOzYkcjISAB69uzJwIEDVVxldKNHw/z5ULIkvPGG32lE0t0ZF1i6ubCIiIikh/j4eIYMGcLzzz/PsWPHKFKkCGPGjKFx48Z+R5Oz9c8/8Oyz3us334Tzz/c3j8g5oGuwRERExDe7d++mdevW/Pe//wWgffv2vPTSSxQqVMjnZBIUTz4J+/dDo0bQtKnfaUTOiTQXWGZWEngKuBEoBOwGIoGhzrltQUknIiIimdb8+fNp0aIFmzdvplChQkyYMIHbb7/d71gSLF98AR9/DHnywPDh3r2vRLKANN3dzcwqAL8CXYAo4GfgEPAE8KuZXRqsgCIiIpL5TJ48mRtuuIHNmzdz7bXXsnTpUhVXmcm+fd69rgBefBEuusjXOCLnUlpvnz0YOABUcM7d5Jy7zzl3E1AB2B9YLyIiInKSyZMn88ADDxAXF0fXrl35/vvvKVOmjN+xJJh69ICtW+Gaa6BzZ7/TiJxTaS2wbgJ6O+fWJ17onNsA9AusFxERETlBQnEVHx9Pv379GDp0KDly5PA7lgTTr7/CmDGQPTuMHQvZsvmdSOScSmuBlRM4mMK6g4H1IiIiIsclLa769u3rdyQJNufg6ae9506doEoVvxOJnHNpLbB+BR43sxM+b96NKjoG1ouIiIgQGxtLv379VFxlBTNmwHffQcGC0KeP32lEfJHWWQQHANOBFWY2BfgHKAHcA1wKNApOPBEREcnI1qxZw0MPPcS8efMwM1588UV69uzpdyxJD7Gx0L2797pPH9BU+5JFpanAcs59bWa3Ay8AzwMGOGAxcLtz7pvgRRQREZGM5sCBA4waNYq+ffsSHR1NyZIlmThxIjfffLPf0SS9jBsHK1bAxRf/O4OgSBaU5vtgOee+Br42szzA+cBe59zhoCUTERGRDGfz5s3079+fyZMnc/iw97WgVatWvPHGGxQuXNjndJJuoqIgYdjnSy9Brlz+5hHxUVqvwQLAzC4ErgIqAteY2c0JjzRsq5OZLTOzA4HHAjM75VBDM7vCzL43s2gz22JmfQLXgSWsL2lmk81spZnFmdn4ZLbRzsx+MLM9ZrbPzOaY2X+StOlnZi7JQzdTFhERCYiLi2PAgAFUqFCBsWPHcvjwYSIiIpgxYwYffPCBiqvMbsgQ2LYNatWC5s39TiPiqzT1YJlZOWASUDthUeDZ8e9wwdTOybkZ6AGsxiv8WgPTzKyGc25ZMhkKALOBuUAtvCJvPN4Nj4cEmuUCdgEvA+1T2G8EMAX4ETgMdAVmmdlVzrnVidqtCrRNEJfK4xMREcmUDhw4wH333ceMGTMAuOeeexg4cCAVK1b0OZmcE6tXe71WAK+9Bv/+rVskS0rrEMGxQBngSWAlEHO2QZxzXyRZ9LyZdQCuBU4qsICWQB6gtXMuGvjdzCoD3cxsqPOsB7oAmFmzFPbbMvH7wD4bA7fiFXsJYp1z6rUSERFJZN68ebRr146VK1dSuHBhpkyZQt26df2OJeeKc9C+PRw9Cq1bww03+J1IxHdpLbBqAW2cc58GM0wCM8uGNyNhPmB+Cs2uBX4IFFcJZgEDgbLAujTuPicQDuxNsrycmW3BKyZ/Ap5zzq1N4z5EREQytF27dtGjRw/GjRsHQOXKlfnqq68oX768z8nknBo3DiIjoWhRb5igiKS5wNpMEHqtkjKzK4AFeAVOFHC3c255Cs1LBHIktj3RurQWWC8E9v1lomU/AW3weuuKAb2A+WZWxTm3O7mNmFl7AsMSixcvTmRkZBrjpF1UVJQv+5X0p3Obuen8Zl6Z5dzOnj2bt99+mwMHDpA9e3buu+8+WrZsyaZNm9i0aZPf8XyTWc7vmcq5axe1n3iC7MCf7duzY3lKX9lEspa0FlgvAj3M7H/OuUNBzLMKb9KMgkBTYIKZRTjnfk+hvUvy3lJYfkbM7AngUaCec+7A8Z04NzNJu4XAWrzrxIYmG8y50cBogJo1a7qIiIi0RDorkZGR+LFfSX86t5mbzm/mldHPbXR0NJ07dz7ea3XTTTcxYsQIKlWq5HOy0JDRz2+qOAeNG8OhQ9CoEZcNHMhluvZKBEj7fbA+MLNKwPpAsZF0OJ1zzrVOw3ZjgDWBt4vMrBbepBNtk2m+Da+nKrFigeftpFKguHoBuM059/NpckaZ2R94N1UWERHJ9KKjo7n55ptZuHAh4eHhvP322zz88MOYvlRnTR99BF9+CQUKwDvvaGILkUTSOotgG6An3kx61Tl5uGCaepCSEYY3E2ByFgCDzSzcOXcksKw+sBVYn5qdmFk3YADQ0Dk37wzahwOVgDmp2Y+IiEhG5Jyjbdu2LFy4kDJlyvDll19StWpVv2OJX5Yvh3btvNevvgoXXOBvHpEQk9Yhgv2Bz4G2zrl9wQhiZi8D/wU2AfmB+/GmRW8UWP8SUNs5lzA10WSgLzDezF4AKgDPAv2dcy7Rdq8KvCwAxAfexzjn/gys7w4MAloBf5lZQq9YtHNuf6DNa8BXwEa8XrLeQF5gQjCOXUREJFQ55xg0aBAffvgh+fLl47///S+XX36537HEL7t2wZ13ekMD77//30JLRI5La4FVGBgRrOIqoAQwMfC8H29q9tucc7MC60sCx6cmcs7tN7P6wHBgEd4wxSGcfE3U0iTv7wA24M00CNAJyIF3L6zEJuBNbAFwAfAhUATYCSwErnHObUjlMYqIiGQYR48epXPnzowdOxaAiRMnqrjKyuLjvaJq/XrvhsJjx2pooEgy0lpgzQMqA98FK4hzrk1q1wdmGDzlDRecc6f8l++cK3sG2e49XRsREZHMZOvWrTRt2vT4NVfvvvsud911l9+xxE+vvAKzZ0ORIvD555A7t9+JREJSWgusJ4CPzWwv8DUnT3KBcy7+bIKJiIiIPxYsWECTJk3Ytm0bZcqU4fPPP6d69ep+xxI/LVgAvXp5r99/H0qX9jePSAhLa4G1IvD8fgrr3VlsW0RERHwyduxYOnbsyLFjx4iIiODjjz+maNGifscSPx05Ag89BHFx8NRTcNttficSCWlpLYIGELyZAkVERMRnBw4coGvXrsfvcdWlSxdee+01cuTI4XMy8d2gQbBqFVSu7L0WkVNK632w+gU5h4iIiPjku+++4+GHH2bjxo3kypWLd955hzZt2vgdS0LB8uXw8sve6zFjIFdKd88RkQRhafmQmT1gZlXMLFsy6/QvT0REJAM4dOgQnTp1ol69emzcuJGaNWuyZMkSFVfiOXgQmjeH2Fjo0AHq1PE7kUiGkNYhghPwhggeNbM/8KZCXwr8CtQ0sxbOuf8EJ6KIiIgE28qVK2nSpAkrVqwgR44c9OnThx49emhIoHicgzZtYOVKqFLFm0FQRM5IWgusQkC1wKM6UAd4GEiYEv3A2UcTERGRYIuLi2PcuHF069aNqKgoLrvsMiZPnkzVqlX9jiah5KWX4LPPoEABb0r2fPn8TiSSYaT1Gqx9wJzAAwAzKwp0AboC9wUjnIiIiASHc46vv/6aZ555ht9//x2AFi1aMHbsWPLpy7Mk9tVX3pTsZjBxIlx6qd+JRDKUNF2DlRzn3E7nXG+84YMqsEREREKAc4558+Zxyy230LBhQ37//XfKli3L5MmT+fDDD1VcyYlWrICWLb0hgi+8AHfc4XcikQwnPe5V9Q0p3x9LREREzpGFCxfSuXNnFi9eDEDBggXp1asXnTt3Jpdmg5OkDh2CZs28yS1atICePf1OJJIhpanAMrNewBJgqXPunySriwGHzjaYiIiIpN24cePo0KEDMTExFClShEcffZSuXbtSuHBhv6NJqHr8cfjzT6hUCcaO9YYIikiqpbUH6xkgH+DMbAeBYguIAx4BegQnnoiIiKTGsWPHeOqpp3jrrbcA6Ny5M6+88gq5c+f2OZmEtE8+gffeg/Bw+PhjTWohchbSWmCdB1TAm0GwRuC5I1AwsH6ombUCFgOLnHOfnWVOEREROY01a9bQvn175syZQ44cORg5ciRt27b1O5aEuuhoeOop7/WQIXDFFf7mEcng0jqLoANWBR4fJiw3s/J4xVZC4dUOrzfrpBsSi4iISHCsXLmS3r178+mnn+Kco0SJEnz22Wdce+21fkeTjOD112HjRrjySnj0Ub/TiGR4QZ3kwjn3N/A3MDVhmZmVCeY+RERExHPkyBH69OnD66+/TmxsLDly5KBVq1YMHDiQ0qVL+x1PMoJt27x7XgEMHQrZ9DdxkbOVHrMInsA5tzG99yEiIpLV7Ny5k7vuuosFCxZgZrRr145+/fpRqlQpv6NJRuEctG0LUVHedOx16/qdSCRTSPcCS0RERIJr3rx5tG7dmrVr11KmTBk++eQTatWq5XcsyWiGD4cZM+D8873XIhIUQbvRsIiIiKSvnTt38vDDD3P99dezdu1aatasycKFC1VcSer99hs8/bT3eswYuPBCf/OIZCIqsEREREKcc453332XSpUq8d5775EzZ0769OnD3LlzKVmypN/xJKPZuxeaNIGjR6FdO2ja1O9EIpmKhgiKiIiEsNjYWDp06MDYsWMBqFevHsOHD6dChQo+J5MMKT4eHnwQ1q6FatVg2DC/E4lkOiqwREREQlRUVBTNmzdn5syZhIeHM2bMGFq2bImZ+R1NMqpXX4Xp073rrj79FHQDapGgU4ElIiISgrZt20ajRo1YsmQJhQsX5quvvtJ9reTszJ8Pzz/vvf7gA7j4Yn/ziGRSugZLREQkxCxevJhrrrmGJUuWUL58eRYsWKDiSs7Orl1w770QF+dNbtGokd+JRDItFVgiIiIhwjnHiBEjuO6669iwYQNXX3018+fP59JLL/U7mmRkR47AXXfBpk1w9dUwaJDfiUQyNRVYIiIiIWDTpk3ceuutdOrUiZiYGDp27EhkZCTFihXzO5pkZPHx0Lq1Nzzwwgvhs88gZ06/U4lkaroGS0RExEcrV65k6NChfPDBBxw5coRChQoxcuRImjdv7nc0yeicgyeegI8/hgIF4L//hVKl/E4lkumpwBIREfHBnj176Nu3LyNGjCA+Ph6AZs2a8dZbb1GiRAmf00mm0K8fvP2212P1+edwxRV+JxLJEjREUERE5ByKi4tj5MiRVKhQgbfffhuAdu3asWLFCqZOnariSoJjxAgYMADCwmDKFLj5Zr8TiWQZ6sESERE5R3777TeefPJJfvvtNwAiIiIYNmwYV155pc/JJFOZNg06d/Zejx4NjRv7mUYky1GBJSIiks42bdpE9+7dmTJlCgBlypRhyJAhNG3aVDcNluD66CNo08a7/mrAAGjb1u9EIlmOCiwREZF0cPjwYb7//ntmzpzJ2LFjiY6OJleuXDz33HN0796d3Llz+x1RMhPnoH9/7wHw+OPQq5e/mUSyKBVYIiIiQfTDDz/w9ttvM336dA4fPnx8efPmzWnSpAktWrTwMZ1kSkePwsMPw+TJ3jVXQ4dCly6g3lERX6jAEhERCYJdu3bRvXt3xo8ff3xZzZo1adCgAXfddRe1atUiMjLSt3ySSe3cCU2awLx5kC+fN6FFw4Z+pxLJ0lRgiYiInAXnHBMmTODpp59m9+7d5MqVi2eeeYa2bdty0UUX+R1PMrM//oA77oB167z7W/33v3DVVX6nEsnyVGCJiIikgXOO7777jl69evHTTz8BULduXUaOHMmll17qczrJ9Fatguuvh717oWZNb+bA0qX9TiUi6D5YIiIiqTZv3jxuuukm6tevz08//USxYsWYOHEis2fPVnEl6W/3brj9dq+4atQIvv9exZVICFEPloiIyBn65Zdf6N27N7NmzQLg/PPP55lnnqFz587ky5fP53SSJRw9Ck2bwpo13nDAjz6CPHn8TiUiiajAEhERSUF8fDzTp0/ngw8+YNGiRaxfvx6A/Pnz061bN7p27cp5553nb0jJOuLjoXVrr8eqZEn46itvYgsRCSkqsERERJLYunUrkydPZsyYMfz111/Hl+fLl49OnTrRvXt3Chcu7GNCyXKOHIEnn/RmCcyfH2bMgAsu8DuViCRDBZaIiGR58fHx/Pjjj0ybNo05c+bw66+/4pwDoEyZMjzxxBPccsstVKpUiezZ9b9OOcfmzYO2beGvvyBHDm9CC80WKBKy9H8JERHJkuLj45k/fz4ff/wxn376KVu3bj2+LmfOnDRq1IgHHniAO+64Q0WV+CM+HgYPhl69vNeVK8PYsXDddX4nE5FTCJlZBM2sk5ktM7MDgccCM2t0ms9cYWbfm1m0mW0xsz5m/9623MxKmtlkM1tpZnFmNj6F7TQ1sz/N7Gjg+e5k2nQ0s3VmdsTMFpvZ9Wd90CIick7Fx8czb948nnjiCS688EKuv/563nrrLbZu3UrZsmXp3r073333HXv37uWzzz7j7rvvVnEl/jh6FJo1g+ee84qrHj1g6VIVVyIZQCj9X2Mz0ANYjVf4tQammVkN59yypI3NrAAwG5gL1AIqAuOBQ8CQQLNcwC7gZaB9cjs1s2uBKUBf4DOgCTDVzOo4534KtGkBDAM6AvMCzzPN7DLn3MazPnIREUlXe/bsYfTo0YwYMYJNmzYdX37RRRfRvHlz7rnnHmrWrEmiv9GJ+OfoUbjnHm8Si/PPh4kToWFDv1OJyBkKmQLLOfdFkkXPm1kH4FrgpAILaAnkAVo756KB382sMtDNzIY6z3qgC4CZNUth108Cc5xzgwLvB5nZTYHl9wWWdQPGO+fGBN4/bma3Ah2Anqk7UhERCbaVK1fy888/s3nzZqKjozEz9u7dy+bNm1mxYgWrV68mPj4e8Iqqe+65h+bNm6uoktCzfTu0bAnffQeFCnnPut5KJEMJmQIrMTPLBtwD5APmp9DsWuCHQHGVYBYwECgLrDvD3V0LvJVk2SygcyBLTqAG8FqSNt8A6qcXEfFRXFwcgwYNon///scLqORky5aNBg0a0LVrV2655RYVVRKaZs3ypmHfvh2KFoXZs6FqVb9TiUgqhVSBZWZXAAuAcCAKuNs5tzyF5iXwhhUmtj3RujMtsEok+lzi7ZQIvC4CZEuhTb2UNmpm7QkMSyxevDiRkZFnGCd4oqKifNmvpD+d28xN5/f04uPj+emnn3j//fdZuXIlZsb1119PqVKlyJ07N8458uXLR5EiRbjgggsoU6YMOXPmBOD777/3LbfObeaW1vMbduQI5UeNovS0aQDsrVaNFc89R8zevaDfF5EMJ6QKLGAVcBVQEGgKTDCzCOfc7ym0d0neWwrLTye57SRddiZt/m3s3GhgNEDNmjVdREREKiOdvcjISPzYr6Q/ndvMTec3ZcuXL2fSpEl88skn/P333wCUKFGCDz74gHr1UvybV8jQuc3c0nR+9+6FBg3gl1+8Kdj79+f8Z57humzZ0iWjiKS/kCqwnHMxwJrA20VmVgvoCrRNpvk2/u1lSlAs8Jy0t+lUUtpOwjZ2AXGnaSMiIunEOcf333/P4MGD+frrr48vL126NN26daNdu3bkz5/fx4QiabRnD9SvD0uWwMUXw2ef6XorkUwgpAqsZIThzQSYnAXAYDMLd84dCSyrD2wF1qdiHwsCn3s10bL6BK79cs7FmNniwLKpSdp8mor9iIhIKn377bf06tWLn376CYA8efLw4IMP0qJFC66//nqy6a/8klH984/Xc7V8OZQvD3PmwIUX+p1KRIIgZAosM3sZ+C+wCcgP3A9EAI0C618Cajvn6gY+MhlvavXxZvYCUAF4FujvnHOJtntV4GUBID7wPsY592dg+TBgrpn1BD4H7gZuAv6TKN5Q4AMz+xn4EXgMKAW8E6TDFxGRRI4ePUqPHj0YNmwYAIULF+bxxx+nc+fOFC5c2Od0Imdp7Vq45Rb4+2+oWNGbKbB0ab9TiUiQhEyBhTcEb2LgeT/e1Oy3OedmBdaXBMonNHbO7Tez+sBwYBGwF+/+V0OTbHdpkvd3ABvwZhrEOTffzO4FXgD6A38DLRLugRVoM8XMCgO9Ajl+Bxo65zac5TGLiEgSK1as4L777uO3334je/bs9O3bl65du5I3b16/o4mcvfnzoXFj2LkTatSAmTO9GQNFJNMImQLLOdcmtesDMwzecJrPnXYuXufcJ8Anp2kzAhhxum2JiEja7N27l1GjRjFgwACio6MpX748kydPpnbt2n5HEzl7zsF770GHDhAT4/VgTZ0KBQr4nUxEgixkCiwREcla4uPj2bt3L0uXLmXSpEl8/PHHHD58GIAHHniA4cOHa/IKyRz27YPHH4eJE733HTvCsGGQXV/DRDIj/csWEZFzZtmyZYwfP57vv/+e3377jbi4uBPW169fn27dunHrrbf6lFAkiPbuhaFD4c034cAByJMHRozwbiYsIpmWCiwREUlXu3btYu7cuUyaNInPPvvshHXnnXceF154IU2aNKFly5ZUqFDBp5QiQXTsGIwaBf36we7d3rKbboLhw6FyZV+jiUj6U4ElIiJB5ZxjwYIFfPTRR0RGRrJ8+fLj68LDw3nkkUdo3Lgx11xzjSaukMznm2/gySdhxQrv/Y03wqBBUKeOr7FE5NxRgSUiIkHzww8/8Nhjj/Hnn38eXxYeHs61117LzTffTNu2bSlZsqSPCUXSyapVXNGzJyxc6L0vXx5eew3uugvstPNtiUgmogJLRETOmnOOd999l44dO3Ls2DGKFy/Ogw8+yO23307t2rUJDw/3O6JI+nn9dXjmGQrHxkL+/NCrFzzxBOTK5XcyEfGBCiwREUmznTt3MnHiRN57773jQwG7du3K4MGDyZEjh8/pRM6RK6+EuDi2NmpEqXffheLF/U4kIj5SgSUiIqkSGxvLzJkzee+99/jqq6+IjY0FoHDhwrzyyis8/PDDPicUOcfq1oU1a/hr40ZKqbgSyfJUYImIyCnFxMSwZMkSfvzxR3788Ud++OEHdu3aBUBYWBiNGjXioYce4o477iBnzpw+pxXxSblysHGj3ylEJASowBIRkRPs37+fdevWsXr1ar744gu++OILoqKiTmhTsWJFHn74YR544AFNWiEiIpKICiwRkSzq6NGjTJ8+nUmTJrF27VpiYmLYtm0be/fuPalt5cqVqVOnDnXq1OG6667j0ksvxTQzmoiIyElUYImIZBFxcXHs3LmT3bt388knnzB8+HB27tx5UrvcuXNz8cUXc/HFF3Pttddy7733Ur58eR8Si4iIZDwqsEREMrGDBw8ycuRIvvrqK5YuXcqhQ4dOWH/llVfSpk0bbrzxRnLlykWRIkUoVqyYeqdERETSSAWWiEgmc+TIEebNm8c333zDuHHj2L179/F1RYoUoVChQlSpUoUuXbpw4403qpgSEREJIhVYIiKZQHx8PB999BHjx4/nhx9+4MiRI8fXXXvttXTv3p3//Oc/FC1a1MeUIiIimZ8KLBGRDG7OnDk8/fTTLFmy5PiyatWqUa9ePW6//Xauv/569VKJiIicIyqwREQyqMWLF9OvXz+mT58OQKlSpejbty933323eqpERER8ogJLRCQD2bt3L59++injxo1jwYIFAOTLl49nn32Wrl27kidPHp8TioiIZG0qsEREQlRcXByLFy/m66+/Zt68eaxfv55169YRGxsLwHnnnUfbtm3p0aMHxYoV8zmtiIiIgAosEZGQsm3bNr7++mtGjRrF7NmzT5gBECAsLIx69epx77330qJFC/Lly+dTUhEREUmOCiwRkXNs3bp1fPXVV2zfvp2oqCiyZctGXFwcc+fO5ddffz2hbdmyZbn11lupX78+FStW5KKLLlJRJSIiEsJUYImInAObNm1i6tSpTJkyhZ9//jnFdrlz5+bKK6+kZcuWNGjQgEsvvVQzAIqIiGQgKrBERNLB9u3biYyMZM6cOfzvf/9j9erVx9flzZuX22+/nSpVqpAvXz7i4+OJi4ujevXq/Oc//2HhwoVERET4F15ERETSTAWWiEgaRUVFsXLlSn755RcWLFjAvn37yJUrF3/++Sd//vnnCW3z5ctHgwYNaNGiBY0aNdJsfyIiIpmUCiwRkWQ459i8eTMrVqxgw4YNbN++nQ0bNrBy5Uq2bNnCgQMHTpqAIrE8efJQp04dbr75Zm666SZq1KhB9uz6T66IiEhmp//bi0iWMHfuXCZNmgR4vUnlypWjbNmyhIeHkyNHDnLkyMGOHTv49ttvWbhwIStXriQqKuqU28yRIwcVKlSgatWq1KlTh9KlSxMdHc0FF1xA7dq1yZkz57k4NBEREQkhKrBEJNNyzrFo0SKGDBnClClTUv35IkWKcNlll1G+fHlKlChBqVKlqFy5MmXKlKFgwYKcf/756pUSERGRE+ibgYhkKjt37mTatGksWLCAyMhI1q1bB3iz8z355JOUKVOG/fv3s2bNGjZu3MixY8c4duwYsbGx5M6dmxtvvJEbb7yRyy+/nCJFivh8NCIiIpLRqMASkQzv2LFjzJgxg/HjxzN9+nRiY2OPrytRogTNmzfnqaeeokyZMj6mFBERkaxABZaIZFjOOcaNG8dzzz3Hjh07AMiWLRsNGzbklltu4ZprrqFmzZpky5bN56QiIiKSVajAEpEMaeXKlTz66KPMnTsXgMsuu4yHHnqIli1bUrJkSZ/TiYiISFalAktEMpStW7fyzjvvMHjwYGJiYihatCivv/46999/P2bmdzwRERHJ4lRgiUhIiomJYfXq1Rw4cICYmBgWLlzIzJkzmTt3Ls45AB5++GFeffVVChUq5HNaEREREY8KLBEJGdHR0Xz44YeMGjWKJUuWnDBZRYKcOXPSsGFDunbtyg033OBDShEREZGUqcASEV845/jjjz/48ccfWbp0KUuWLGHZsmUcPXoUADOjXLlyFC1alGzZslGpUiUaNGjALbfcQsGCBf0NLyIiIpICFVgick7Fx8fz1ltvMXLkSFatWnXS+lq1atG5c2eaNm1K3rx5fUgoIiIiknYqsETknNmzZw8PPPAAM2bMAKBw4cLceuut1KhRg+rVq3PVVVdx3nnn+ZxSREREJO1UYIlIuoqNjeW7775j0qRJfP7550RFRVGoUCFGjRpF48aNyZ5d/xkSERGRzEPfbEQk6Jxz/Pzzz0yaNIkpU6YcvwkwwE033cR7773HRRdd5GNCERERkfShAktEztqxY8f4+eefmT9/Pj/++CPz589n586dx9dXqFCBli1bcv/993PJJZf4mFREREQkfYVMgWVmnYBHgbKBRX8ALzjn/nuKz1wBvA3UBvYAo4CBLuEmOV6bG4GhQBVgK/CKc+6dROsjgRuT2fyfzrkqgTb9gL5J1m93zpU48yMUyTyioqL466+/WLVqFT/88AMff/wxu3fvPqFNyZIladGiBS1btqRGjRq6CbCIiIhkCSFTYAGbgR7AaiAMaA1MM7MazrllSRubWQFgNjAXqAVUBMYDh4AhgTYXAzOAcUAr4D/ACDPb6Zz7NLCpJkDORJvOBSwHPk6yy1VARKL3cWk8TpEMZf/+/XzwwQd89dVX7Nq1i+3bt7Nly5aT2lWsWJGIiAiuu+466tSpQ7ly5VRUiYiISJYTMgWWc+6LJIueN7MOwLXASQUW0BLIA7R2zkUDv5tZZaCbmQ0N9GI9Bmx1zj0e+MwKM7saeBr4NLDfPYk3amYtgbx4RVlisc65bWk/QpGMZceOHbz66quMHDmSQ4cOnbAuR44cXHrppVSsWJHLL7+cJk2aULVqVRVUIiIikuWFTIGVmJllA+4B8gHzU2h2LfBDoLhKMAsYiDfMcF2gzTdJPjcLaG1mOZxzx5LZbjtgpnNuU5Ll5cxsCxAD/AQ855xbe+ZHJRLaVqxYwTfffMPcuXNZs2YNq1atOn7T35tuuolHHnmEChUqUKhQIcqUKaPZ/0RERESSEVLfkALXVC0AwoEo4G7n3PIUmpfAG1aY2PZE69YFnr9Npk12oAjwT5L9V8C7Hqtxks/8BLQBVgLFgF7AfDOr4pzbTTLMrD3QHqB48eJERkamcBjpJyoqypf9SvoL5rnduHEjo0aNYv78k/+Wcd1119G6dWsqVKhwfL9RUVFs3LgxKPuW5Onfbualc5u56fyKCIRYgYV3ndNVQEGgKTDBzCKcc7+n0N4leW/JLD+TNgna4RVdJ0ys4ZybecIGzBYCa/GuExuabDDnRgOjAWrWrOkiIiKSPYD0FBkZiR/7lfR3tud2//79fP7553z44Yd8++23xMfHkzdvXu6++25uvvlmrrjiCsqVK0ehQoWCF1rOmP7tZl46t5mbzq+IQIgVWM65GGBN4O0iM6sFdAXaJtN8G14PVWLFAs/bT9MmFjih58nMcuIVTGOcc7GnyRllZn8Al56qnUioWbNmDc899xxffvnl8eF/2bNn56GHHmLgwIGULFnS54QiIiIiGVtIFVjJCMOb1S85C4DBZhbunDsSWFYfbyr29YnaNE7yufrAomSuv2qMN2zw3dOFMrNwoBIw53RtRULBsWPHGDJkCP379+fIkSOYGTfddBP33XcfTZs2VU+ViIiISJCETIFlZi/jDc3bBOQH7sebFr1RYP1LQG3nXN3ARybj3ZtqvJm9AFQAngX6J7oP1jtAZzN7A+8eWXXwrqW6L5kI7YHvkpu4wsxeA74CNuL1gPXGm2lwwtkcs8i58PPPP9OuXTuWLfMm43zggQd48cUXueCCC3xOJiIiIpL5hEyBhTeUb2LgeT/e1Oy3OedmBdaXBMonNHbO7Tez+sBwYBGwF+/+V0MTtVlnZg2B14EOeL1bXRLdAwsAMysH3Azcm0K2C4AP8Xq4dgILgWuccxvO5oBF0tOyZcvo378/n332GQDlypXjnXfeoX79+j4nExEREcm8QqbAcs61Se36wAyDN5zmc98D1U/TZi3ecMSU1qdUeImElC1btjBv3jzGjBnDd999B0B4eDhPPPEEffr0IU+ePD4nFBEREcncQqbAEpG0+fPPP5k0aRJTpkzh77//Pr48b968PPLII/To0UOTV4iIiIicIyqwRDKgY8eOMWXKFEaMGMHcuXOPLy9QoABXX301t912Gw899BAFCxb0L6SIiIhIFqQCSyQDWblyJRMmTGDUqFHs3bsXgHz58nHfffdx//33c/3115MtWzafU4qIiIhkXSqwREKQc445c+bwzTffsGLFCtavX8/OnTv5559/jre5/PLL6dixI61atSJ//vw+phURERGRBCqwRELIkSNHmDx5Mm+88QbLly8/aX3+/Plp3rw5VatWpXPnzpiZDylFREREJCUqsERCwIoVK5gwYQLjxo1j586dAJQoUYI2bdpQrVo1ypcvT7FixShRogQ5cuQgMjJSxZWIiIhICFKBJeKTvXv38tFHHzF+/Hh+/vnn48urVatG165dad68Obly5fIxoYiIiIiklgoskXNk69atzJkzh02bNrF48WK+/PJLYmJigH+H/rVp04Y6deqod0pEREQkg1KBJZJOnHOsXLmSWbNm8eWXXxIZGYlz7vh6M6N+/fq0adOGxo0b6ybAIiIiIpmACiyRIImLi2P58uXMnDmT2bNns2TJEvbv3398fc6cObnllluoVKkSF110EY0bN+aCCy7wMbGIiIiIBJsKLJGzsHr1aqZPn87MmTNZuHAhBw8ePGF9iRIlqFu3Lrfeeit33HEH5513nk9JRURERORcUIElkkqrVq1i3LhxfPHFF6xateqEdWXKlKFevXo0bNiQ6667jpIlS/qUUkRERET8oAJLJAnnHNu3b2fPnj1ER0ezefNm1qxZw5o1a1i+fDk//vjj8bYFCxbktttu4/bbbyciIoJSpUr5mFxERERE/KYCKxPavWULvw0fzp4CBSAiwu84GcbatWvp1KkTP/zwA4cOHUqxXe7cubn//vtp1aoVderUIUeOHOcwpYiIiIiEMhVYmdDvd93FzYsX83nlyvDss37HCXmxsbFMmDCBrl27Hr+GqlChQhQrVozw8HBKlizJJZdcQvny5bnkkkuoU6cOBQsW9De0iIiIiIQkFViZUMFmzWDxYsr9/bffUULavn37mDJlCq+99hpr1qwBoGnTprz11lu6dkpERERE0iTM7wASfJUfe4yjwBUxMWz+9Ve/44Sc1atX88ADD1C8eHEee+wx1qxZQ7ly5Zg4cSJTp05VcSUiIiIiaaYCKxPKWbAgK4sWJQxYOXy433FCxqZNm2jfvj2VK1dm4sSJHDt2jLp16zJx4kRWrVpFy5YtMTO/Y4qIiIhIBqYCK5M6esMNALhvvvE5if+OHTtGr169uPTSSxkzZgwAjzzyCOvWrePbb7+lZcuWZM+u0bIiIiIicvZUYGVSF7VvD8BlmzZx9MgRn9P4Z/Pmzdx0000MGjSIo0ePcu+99/Lnn38yZswYLrroIr/jiYiIiEgmowIrkyperx47s2WjtHMsmTTJ7zi++Oabb6hWrRo//vgjpUqVYu7cuXz44YdUqFDB72giIiIikkmpwMqswsJYdeGFAGwaPdrnMOfW1q1b6dKlC7feeiu7du2ifv36LF26lOuvv97vaCIiIiKSyanAysRib78dgMq//MLuXbt8TpP+lixZQtu2bbn44ot56623AOjfvz8zZ86kWLFiPqcTERERkaxABVYmZnfcwf4cObjCOb7q29fvOEG3du1ahgwZQsOGDbnggguoUaMG48aN49ixYzRr1owlS5bQp08fsmXL5ndUEREREckiNHVaJuZy5mTPHXdw3mefkX3CBGKHDcsUs+X9+eef9OjRg+nTp5+wvGDBgrRp04YOHTroOisRERER8UXG/7Ytp3TRgAHw2WfcdegQ0yZOpFmbNn5HSpNDhw7x5Zdf8uGHHzJjxgzi4uLIkycPd955J3feeSc1a9akfPnyhIWpU1ZERERE/KMCK5MLq1KFbeXLU+Lvv/m1Rw/uatmSHDly+B3rjP3yyy+88cYbTJs2jcOHDwOQLVs2OnToQN++fSlevLjPCUVERERE/qU/92cBRXr3BuDBHTsY8847Pqc5M+vXr6dRo0bUrl2byZMnc/jwYa655hrefPNNtmzZwogRI1RciYiIiEjIUYGVBWRv2ZJDxYpRAVjy/PMcOHDA70in9PPPP3P11VczY8YM8ubNyzPPPMPff//NggULePzxx1VYiYiIiEjIUoGVFWTPTp5+/QDofPAgvZ5/3t88KTh8+DAvv/wyERER7Nixg3r16rFu3ToGDx5MuXLl/I4nIiIiInJaKrCyCHvoIY4VKcJVwLq33+b777/3O9Jxe/bs4eWXX6Z8+fL07NmT6OhoHn74YWbMmEHRokX9jiciIiIicsZUYGUV4eHkCPRcDQYeadOGgwcP+hpp1apVdOzYkQsvvJCePXuybds2atSowezZs3n33Xcz1GQcIiIiIiKgAitr6dABV64clwH11q/n1ltvZd++feds9845fv/9dwYOHEj16tWpVKkSI0eO5PDhwzRo0ICvv/6aX375hXr16p2zTCIiIiIiwaRp2rOSXLmwV16BZs0YGBZG+fnzqVu3Lp988gkXX3xxuu32n3/+4YMPPmDcuHGsWrXq+PJ8+fJx77338uSTT1KlSpV027+IiIiIyLmiAiuradIE/vMfisybx+R8+bh9yRKqVKnCc889xwMPPMBFF12Ups0651i+fDnr1q1j3759/PXXX/z222/8+uuvbNmy5Xi7IkWKcNddd3H33XdTt25dwsPDg3VkmcfevTB/PixcCFu2wHnnQbFiUKECVKzoPUREREQkJKnAymrMYMwYqF2bRgcPMumqq2j566/07t2b3r17c9FFF1G6dGkuuugirr32WmrXrk3ZsmXJnj07f//9N/v27SNfvnzky5ePvHnzsnLlSqZPn8706dPZvHlzsrvMkycP9erVo3379jRo0IDs2bPor118PERFQf783nk4ehT+/hv++gtWrfKely6FX38F51LezooV5yyyiIiIiKROFv2mm8VVqgTvvw933839y5Zxea9e9PvjD7777js2bNjAhg0bmD9/Ph9++GGqNluyZElq1KhBwYIFueiii6hatSpVq1alfPnyZMuWLZ0OJoiWLoXXXoPt26FoUbj0Uqhe3es5KlkSChb0CqNdu2DZMti82Wu7e7fX6xQb6z327fPe58gBOXN673fuhE2bICYG8uWDQoW8z8fHn5wjZ06oXRuuuw4uuQQOHvR6slatgjVroFw52LbtHP9wRERERORMqMDKqho3hv79oW9frnzxRT4bO5bYjz/m77//Zvv27axatYp58+axfPlyNmzYQExMDJdccgmFChXi8OHDREVFcfDgQYoVK0ajRo24/fbbqVatGmFhGWTelJgYWLcONm6EJUvg22+9x6mEhUHevF7Bk1bh4V4vVlSUt73y5b0hf5de6j1XrgxXXw25c6d9HyIiIiLiGxVYWVnv3t5z377w8MNk/+EHKr7yChUrVuSGG26gXbt2/uYLtr//hhkzYNYsiIyEQ4dOXB8eDh07Qv36Xo/TH3/A4sWwYQP8849XFB086BVZV17p9SQVLw5Fini9WzlzekVTwYJw/vleb9bRo941VEWKwAUXeL1Xe/d6vV4XXgi5cvnwgxARERGR9KICKyszgz59vOFqTz0F770H06bBY495hcYFF/id8Ow5B7Nne8f5008nrrvoIihb1hsyecMNXmF1qhsbHzvmFVjnnQdnM+Tx/PO9h4iIiIhkOhlkPJekq86dYflyqFvX61156SVv6FrPnl6vTUbknDfk74YboEEDr7g67zxo3twrJLdsgfXrvZ6sd96B++8/dXEF3jVVhQqdXXElIiIiIplayPRgmVkn4FGgbGDRH8ALzrn/nuIzVwBvA7WBPcAoYKBz/07BZmY3AkOBKsBW4BXn3DuJ1rcB3ktm87mdc0cStesIdAdKBrI96Zz7IdUHGqoqVPB6ehYuhNdfh6lT4eWXYcIEePppaN/eG94WCuLivAkpFi3yepTi4rxJKHLnhtWr4bffvIJq40avfaFC8MwzXiGZN6+/2UVEREQkUwuZAgvYDPQAVuP1rLUGpplZDefcsqSNzawAMBuYC9QCKgLjgUPAkECbi4EZwDigFfAfYISZ7XTOfZpoc4eB8om3n6S4agEMAzoC8wLPM83sMufcxrM+8lBhBtde6z1++gkefxx++cUbPtirF9SoARER0KrVubkX04oV3v2gfvsNduzwrlvatcu7Jmrv3tN/vkgR6NYNOnWCAgXSP6+IiIiIZHkhU2A5575Isuh5M+sAXAucVGABLYE8QGvnXDTwu5lVBrqZ2dBAL9ZjwFbn3OOBz6wws6uBp4HEBZZzzp1q3utuwHjn3JjA+8fN7FagA9AzFYeZcVx9tVdk/fe/Xk/Wjz/CvHne44UXvCnEH38c7r47+BM1/PUXdO3qTUiRkosu8oq9IkW8wjBhEopLLoHLLvOmOa9cWcP5REREROScCpkCKzEzywbcA+QD5qfQ7Frgh0BxlWAWMBBvmOG6QJtvknxuFtDazHI4544FluU2sw1ANuBXoLdzbmkgS06gBvBaku18A1yX6oPLSMzg9tu9x65dXsH12Wfw8cdez9L8wKnJnh3y5PGGEF56qXfN0/XXwxVXeNc9pSQ+3rsX1F9/efd3+usvb5uLFnnD/vLnh1tvhVq1oFQpKFzYK6iKF/cm4DA7Nz8HEREREZEzZIkuV/Jd4JqqBUA4EAW0TOkaLDP7BtjsnHs40bIywAbgOufcAjP7C5jonBuQqM0NwPdAKefcP2Z2LVAB+A3IDzwBNASqOudWm1kpYAtwo3NubqLt9AnkS3asnJm1B9oDFC9evMZHH32Uth/KWYiKiiJfOlw3lS06muKzZ1Nq2jTyrl+PneJ3KOb88zlatCgWF0eOvXsJi40FwI4dIywmhrC4uJM+48LC2NagAWsfeYRjhQoFPX9mkF7nVkKDzm/mpXObuen8SrDcdNNNi51zNf3OIWkTaj1Yq4CrgIJAU2CCmUU4535PoX3Sb/aWzPJTtnHOLcAr6ryVZvPxerEeB7qcZjspVhbOudHAaICaNWu6iIiIlJqmm8jISNJtv7fdBkOHerP1HTsGhw/DgQPeNVuzZnn3j/rzT3Lu3UvOU10vVby4dz3XJZd495WqVQu75hpKFihAyfRJnimk67kV3+n8Zl46t5mbzq+IQIgVWM65GGBN4O0iM6sFdAXaJtN8G1AiybJigeftp2kTC+xOIUOcmS0CLg0s2gXEpbCd7WR1Zt4NdnPm9G6wW6YMNG3qrYuLg+3bvdn8cub0iqnwcK8oy5nTe50zp6/xRURERESCKaQKrGSEASnNoLAAGGxm4Ylm/KuPNxX7+kRtGif5XH1gUaLrr05gZgZciTdkEOdcjJktDnxuapLtfHryFuS4bNm8a6dKlfI7iYiIiIjIOREyNxo2s5fN7HozK2tmV5jZS0AEMCmw/iUz+y7RRybjTa8+3swuN7MmwLNAwgyCAO8AF5jZG2ZW2cweAdqQaMIKM+trZg3MrJyZXQW8i1dgvZNoX0OBNmb2SGA7w4BSSdqIiIiIiEgWF0o9WCWAiYHn/XhTs9/mnJsVWF+SRPeqcs7tN7P6wHBgEbAX7/5XQxO1WWdmDYHX8aZU3wp0SXIPrIJ410ol7HcpcINz7udE25liZoWBXoEcvwMNnXMbgnb0IiIiIiKS4YVMgeWca5Pa9c655cANp/nc90D1U6zvined1+nyjQBGnK6diIiIiIhkXSEzRFBERERERCSjU4ElIiIiIiISJCqwREREREREgkQFloiIiIiISJCowBIREREREQkSFVgiIiIiIiJBogJLREREREQkSFRgiYiIiIiIBIkKLBERERERkSBRgSUiIiIiIhIkKrBERERERESCRAWWiIiIiIhIkKjAEhERERERCRJzzvmdIdMzs53ABh92XQTY5cN+Jf3p3GZuOr+Zl85t5qbzK8FykXOuqN8hJG1UYGViZrbIOVfT7xwSfDq3mZvOb+alc5u56fyKCGiIoIiIiIiISNCowBIREREREQkSFViZ22i/A0i60bnN3HR+My+d28xN51dEdA2WiIiIiIhIsKgHS0REREREJEhUYImIiIiIiASJCiwREREREZEgUYElkkWZmfmdQdKPzm/mZfb/9u491q6qwOP499cHhcpDkZeCI4rSqUGBwRd2RuzgC3xQZkAGdCQICogBrbwUmGjiozAao6QUlBkVJyNIiIVJxMkYjE4VnSKghhAQjTBUYylUkHd7788/1trt4Xpvb3t7Xnf390ma3rP3vufunV/2OmetvR7KZ3eLNfnmHo6YvlJIx7gkzRz0OURvNNk6M9y0UvJtP9ujAJJm1//zRbwFOu7d0bppp7o9+UZMM6lgxbhsjwBIOlzSX9WfU8hPY01+HdmeIOl4SS8f7JlFN3Xke4qkcyW9ddDnFN0laZGklcC7Bn0usfXGKZvPkfQkcF7dnsaSiGkmFaz4C5JmSHqfpFXA/wCHQQr56c62Jc2StFjSauAcYDFwq6Tj8tRy+qv5nifpAeBM4HXAd+q2Zw349KILJM0FPgq8AFggaed6b6cBbJqq+c2W9ClJjwKnAB+wfcGgzy0ipiYVrG2YqnF2vQY4FvgC8BPgHyQ9r68nF1tlvGzr6w8Bi4DFtg8GDqcsjPkJYJc+n2ZM0SbyPQlYCJwBHGh7EbAEOBkYHfs+MXw2US43GRv4I/DfwMHUBrCYHia4d2cAZwMfB862Pc/2VRlrFzF95ebdRkmSK0mzxuxeA/w78HngQuBNwKv6fY4xNRNlW59Abgd8B7ha0gzbjwBLgRcDcwZzxrElJsn3Z8BFwHUdT5x/C9wGrOv7ycYWmaRcbjL+W2Au5Qn0DOAtkp5Vfyef6UNsE/fuKPAjYAWwi6TtJF0CLJV0lqTdBnXOETE1KYy3UbWAP1TSDcBXJJ1Zu54A/Nr2tfVz4CbgV8BxTSGfrijDbYJsd6y7v2B7ie31HQOpDwbuAp5MtsNvknv3dtsrbY9ImiPp08AXgRHgdEk7D+zEY1ITZLsDPGPmwBcAd9teDfwAOAB4oaTtbY+mkjW8Jsi36br7M+BmSm+C+4CXUHoVfAJYLunvBnDKETFFKYi3UZKOA24AVgNrgUuAyyU9v/mQlrRdPXwJcCRwEGQs1rCbINvLarbrJM2svVSa+/8w4De21ybb4bcZ9+7MOp7uQuAYSrejuynj7S5LJWt4TZDtFU229bAFwKP1588Be1CeSj8u6bkdx8WQmSDfZZL2sf0Y8H3gKuADwD/aPgGYD2wP/HNHQ0pEDDnl+9S2R9IcSkH+Y9tn121HU76Q3WL7VEkzmxmN6v57KBNenG/74UGcd0xuS7OV9BzgF8D7bX93UOcdm2dL8q1PLR9rKs2STqZMfHGG7RUDuoSYwGZmuzNlzORFwIuAT1K6b/8B+KTtL9euv6lkDZlJ8r3V9vtrvntRGrzWN10K65PotwJH2b5/UNcQEZsvT7C2AeN0+9qx/ruvY9uNwNeAk2pr6Uh9itX0E/8ccDQwX9Luko4Yb4xA9NdWZDu77jsC+BOl8tzMIHlguhkNh63Id6btR8eM9XgaeBnwQK/POyY3hWz3rmMmFwJ3AtdSxu0cD6yiPMkilavhsIX5nljv3Uds3217fd3fzOz6FKVC/WAPTzkiuihfolqs6Qo2TrevpuvfDs2XL9tPUmal+hV17Q3KE871df/llNmrvkL5gDgX2LPHlxAT6EK2zQf4O4DrgDmSzgceprSo7trL849N60K+G75k15bwvSmV6WX1uBiQKWZ7D2WWOYBLgQuAA2yfbfta4F7gnZLm9f4KYlO29t7trJjVe/fFlImmLrH9RM8vICK6IhWslpC0x9httkdqC/YhKouO7idptu3fA/cDfw90zk50P3A9cJikOR3dyPaXdAWlQvUI8F7bC22v6vmFRa+ydf0y9hbgjZQW8JOAU20fa3tNzy8sgJ7m+3pJJ0r6PHB7PX5ZnnD0zxZku90k2S4HFtbjPmX7Ytv/r41r110MfMT2XT2+pOjQw3v3MJW1KJulUh4EvtmHS4qILkkFqwUk/Qtws6Sdxmx/maQfUFrITqZ0A7u87l5C+WL96qbFzPbjlL78TwDPre8xsx53KPBG2wtqiynKwrQ916Nsd6/H7Q08m9Jl7FiXtVf+s75/yoY+6HG+T9Tf3R84zvabbd/Z40uKaguzXVZ3T5btM6brbhrBbN9i+0c9vJwYo8f37pPAacA84J9sL7J9b48vKSK6KF+i2mE5ZerehWO2vw/4HbCf7UOBt1P68p9m+4fAjymzFc3v+J09KB/ia2DDB/iVtl9h+yYVszr2RW8tp/vZPgDgMgX//rbfaft7Y7LNU47+WE7v8l0JHG37HTXrNIr013J6VC7HUFhO7+7dnwJH2j4y927E9JQK1jRVvwzPALD9C8o0vR+RtH3dvy+lED/P9sOSTgO+Qelq0HxIX0BpMfsPlUkrFlHG5Fxq++nmbzU/qwycd8cA3OiBPmT7VPN3bN+jMrFFsu2TfuVb3//B+vdm1tdpFOmhfpbL0X99vnfX5N6NmL4yTfs0Uwt3dYyPmuGy9s0C4H+Bw21/X9JBlG4nP6eMs1kPLAW+6o5p1iUdSFmLY1dgP+BK4KLOgj76I9m2W/Jtr2Tbbsk3IrZUKljTlKS9KGva/Am4wvZDkn4IrLV9lKT5wNWUbgcfA67ueBL1WuA5tm+sr2dTxuPca29YM2e8WZCiD5JtuyXf9kq27ZZ8I2JzpYvgkFNHv+vaXWAHlUUHfw28EngM2Kce8lngbZIOcRnM/hPK7HB3dhTye1G6MBzRvK/tdbZ/a5c1c1LI90eybbfk217Jtt2Sb0RsrTzBmiYkzXJZE+P1lC4IH7X93aarQsdxtwF32H6PpFdQ1jR6G3ANMAIcA6wEzrV9e98vJP5Csm235Nteybbdkm9ETFWeYA05SXtKuh44oG46hTLT0ArYONubNk6r/a/AMZL+2mUQ7gmUrgp/BOZSZhV7cwr5wUu27ZZ82yvZtlvyjYitlSdYQ2Jsi1jH9l2AtcAC2zdL+i9gtPb3nm173ZjjZwF3UNbgOGu8vt2SBMxwZiXqi2Tbbsm3vZJtuyXfiOiVPMEaINXpseEZLWKvlfTsjsP2AW4B9q2vvwccrrLi+7qO99pd0jyXaba/DhxPWUSW+v5NId9Mx51CvoeSbbsl3/ZKtu2WfCOiH1LBGpDasjVqe0TSXElvl3QZcAVwcceh91MK+bX19QrKmhpfUl1BXtIc4L1sHEB7CfBC22sZIwV87yXbdku+7ZVs2y35RkS/pII1ILYtaSdJlwIPAacD9wGLgfdI+pikPV3WzlhFmbkI4Dbgw8C7gJWSlgF31t//ZX3v9bYfV1Z+H4hk227Jt72Sbbsl34jol1mDPoFt3DnAwcCRlKldd7S9WtJiyiDZeSorwa8GRmFDl4ZvS3oAeA1lEO6nbf/b2DdPq9lAJdt2S77tlWzbLflGRM+lgjUgkvYFTgSW2r6pdl14vO6+ktJidhNwK/BySh9wVAfY2l5BndGo4z1npnAfvGTbbsm3vZJtuyXfiOiXdBEcnL0pg2FvhA1dF1R/HrH9f5SFCd8A7EUp7BkzwFb1/2bAbgr54ZBs2y35tleybbfkGxF9kQrW4NxGeYL4N2N3SNqt/ngNZS2NVcDTkmZ3HmeXGYpSwA+dZNtuybe9km27Jd+I6ItUsAbnKeBbwPlNwV5b03YFzpC0T21Ru4tS4L/U9jptXNgwhleybbfk217Jtt2Sb0T0RcZgDYjLNLEXAj8HbpB0A2VK2NMpK8Z/s+PwEeCR9PWeHpJtuyXf9kq27ZZ8I6JfVJ92x4BIWgC8G5gP7AlcZXtJx/43UfqLn2V76WDOMqYi2bZb8m2vZNtuyTciei0VrCEhaTfbazpez6A8YfwgMNf2ZwZ2crFVkm27Jd/2SrbtlnwjoldSwRoydWaiUSeY1km27ZZ82yvZtlvyjYhuSwUrIiIiIiKiSzIzTkRERERERJekghUREREREdElqWBFRERERER0SSpYERERERERXZIKVkRERERERJekghUREREREdElqWBFRERERER0yZ8B1SHeh/Gpzi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "ax = plt.gca()\n",
    "\n",
    "plt.plot(DIC_int_conc, color='black', linewidth=2, label='LP01')\n",
    "ax.plot(DIC_int_lp09_1, color='red', linewidth=2, label='LP09')\n",
    "ax.plot(np.arange(244, 305),DIC_int_lp09_5, color='red', linewidth=2)\n",
    "\n",
    "ax.set_ylabel(\"$\\mu$mol.m$^{3}$.kg$^{-1}$\", fontsize = 16)\n",
    "ax.set_title(\"Global ocean DIC content - ORCA05\", fontsize = 18)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "ax.grid()\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=12)\n",
    "\n",
    "locs, labels = plt.xticks()\n",
    "plt.xticks(np.arange(0, 305, step=61), ('cycle01', 'cycle02', 'cycle03', 'cycle04', 'cycle05'), rotation=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "savepath = '/home/shkifmmp/_WORK/notebooks/shared-notebooks/figures/'\n",
    "plt.savefig(savepath + 'DIC_ORCA05_lp01_lp09.png', format='png', facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DICP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "ax = plt.gca()\n",
    "\n",
    "plt.plot(DICP_int_conc, color='black', linewidth=2, label='LP01')\n",
    "ax.plot(DICP_int_lp09_1, color='red', linewidth=2, label='LP09')\n",
    "ax.plot(np.arange(244, 305),DICP_int_lp09_5, color='red', linewidth=2)\n",
    "\n",
    "ax.set_ylabel(\"$\\mu$mol.m$^{3}$.kg$^{-1}$\", fontsize = 16)\n",
    "ax.set_title(\"Global ocean DIC content (Pre industrial) - ORCA05\", fontsize = 18)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "ax.grid()\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=12)\n",
    "\n",
    "locs, labels = plt.xticks()\n",
    "plt.xticks(np.arange(0, 305, step=61), ('cycle01', 'cycle02', 'cycle03', 'cycle04', 'cycle05'), rotation=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "savepath = '/home/shkifmmp/_WORK/notebooks/shared-notebooks/figures/'\n",
    "plt.savefig(savepath + 'DICP_ORCA05_lp01_lp09.png', format='png', facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py3_std]",
   "language": "python",
   "name": "conda-env-py3_std-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}