RT Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import vtk\n",
    "from vtk.util import numpy_support as nps\n",
    "import sys\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "\n",
    "matplotlib.rcParams.update(matplotlib.rcParamsDefault)\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = 'AdvectionDiffusion/right/asi2/p1/RT1/'\n",
    "f_name = 'int_r.dat'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SPINE_COLOR = 'gray'\n",
    "def latexify(fig_width=None, fig_height=None, columns=1):\n",
    "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
    "    Call this before plotting a figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig_width : float, optional, inches\n",
    "    fig_height : float,  optional, inches\n",
    "    columns : {1, 2}\n",
    "    \"\"\"\n",
    "\n",
    "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
    "\n",
    "    # Width and max height in inches for IEEE journals taken from\n",
    "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
    "\n",
    "    assert(columns in [1,2])\n",
    "\n",
    "    if fig_width is None:\n",
    "        fig_width = 3.39 if columns==1 else 6.9 # width in inches\n",
    "\n",
    "    if fig_height is None:\n",
    "        golden_mean = (np.sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
    "        fig_height = fig_width*golden_mean # height in inches\n",
    "\n",
    "    MAX_HEIGHT_INCHES = 8.0\n",
    "    if fig_height > MAX_HEIGHT_INCHES:\n",
    "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
    "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
    "        fig_height = MAX_HEIGHT_INCHES\n",
    "\n",
    "    params = {'backend': 'ps',\n",
    "              'text.latex.preamble': ['\\usepackage{gensymb}'],\n",
    "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
    "              'axes.titlesize': 8,\n",
    "              #'text.fontsize': 8, # was 10\n",
    "              'legend.fontsize': 8, # was 10\n",
    "              'xtick.labelsize': 8,\n",
    "              'ytick.labelsize': 8,\n",
    "              'text.usetex': True,\n",
    "              'figure.figsize': [fig_width,fig_height],\n",
    "              'font.family': 'serif'\n",
    "    }\n",
    "\n",
    "    matplotlib.rcParams.update(params)\n",
    "\n",
    "\n",
    "def format_axes(ax):\n",
    "\n",
    "    for spine in ['top', 'right']:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    for spine in ['left', 'bottom']:\n",
    "        ax.spines[spine].set_color(SPINE_COLOR)\n",
    "        ax.spines[spine].set_linewidth(0.5)\n",
    "\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "\n",
    "    for axis in [ax.xaxis, ax.yaxis]:\n",
    "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "latexify()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the int_r.dat file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys = [], []\n",
    "with open(base_path + f_name) as f: \n",
    "    for line in f: \n",
    "        a, b = line.split()\n",
    "        xs.append(int(a))\n",
    "        ys.append(float(b))\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'AdvectionDiffusion/right/asi2/p1/RT1/int_r.dat')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function post_execute at 0x11a65a2a8> (for post_execute):\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "latex was not able to process the following string:\n'AdvectionDiffusion/right/asi2/p1/RT1/int_r.dat'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/Users/alex/.matplotlib/tex.cache/84317547d2619c33fa30a7ccee212a42.tex\nLaTeX2e <2018-12-01>\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2018/09/03 v1.4i Standard LaTeX document class\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/size10.clo))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.def\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.dfu)))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/was/gensymb.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n)\nNo file 84317547d2619c33fa30a7ccee212a42.aux.\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 ... AdvectionDiffusion/right/asi2/p1/RT1/int_\n                                                  r.dat}\nNo pages of output.\nTranscript written on 84317547d2619c33fa30a7ccee212a42.log.\n\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m                     \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/_pylab_helpers.pyc\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2053\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2054\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2055\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2056\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2057\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    707\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[0;32m--> 236\u001b[0;31m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[1;32m    237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[1;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m--> 365\u001b[0;31m                      texfile], tex)\n\u001b[0m\u001b[1;32m    366\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dvi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(self, command, tex)\u001b[0m\n\u001b[1;32m    342\u001b[0m                     \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m                     \u001b[0mtex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unicode_escape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m                     exc=exc.output.decode('utf-8')))\n\u001b[0m\u001b[1;32m    345\u001b[0m         \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\n'AdvectionDiffusion/right/asi2/p1/RT1/int_r.dat'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/Users/alex/.matplotlib/tex.cache/84317547d2619c33fa30a7ccee212a42.tex\nLaTeX2e <2018-12-01>\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2018/09/03 v1.4i Standard LaTeX document class\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/size10.clo))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.def\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.dfu)))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/was/gensymb.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n)\nNo file 84317547d2619c33fa30a7ccee212a42.aux.\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 ... AdvectionDiffusion/right/asi2/p1/RT1/int_\n                                                  r.dat}\nNo pages of output.\nTranscript written on 84317547d2619c33fa30a7ccee212a42.log.\n\n\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "latex was not able to process the following string:\n'AdvectionDiffusion/right/asi2/p1/RT1/int_r.dat'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/Users/alex/.matplotlib/tex.cache/84317547d2619c33fa30a7ccee212a42.tex\nLaTeX2e <2018-12-01>\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2018/09/03 v1.4i Standard LaTeX document class\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/size10.clo))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.def\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.dfu)))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/was/gensymb.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n) (./84317547d2619c33fa30a7ccee212a42.aux)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 ... AdvectionDiffusion/right/asi2/p1/RT1/int_\n                                                  r.dat}\nNo pages of output.\nTranscript written on 84317547d2619c33fa30a7ccee212a42.log.\n\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    247\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'jpg'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'jpeg'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mjpg_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'jpg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mretina_figure\u001b[0;34m(fig, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;34m\"\"\"format a figure as a pixel-doubled (retina) PNG\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m     \u001b[0mpngdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'retina'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0;31m# Make sure that retina_figure acts just like print_figure and returns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;31m# None when the figure is empty.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    707\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[0;32m--> 236\u001b[0;31m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[1;32m    237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[1;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m--> 365\u001b[0;31m                      texfile], tex)\n\u001b[0m\u001b[1;32m    366\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dvi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/alex/miniconda2/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(self, command, tex)\u001b[0m\n\u001b[1;32m    342\u001b[0m                     \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m                     \u001b[0mtex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unicode_escape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m                     exc=exc.output.decode('utf-8')))\n\u001b[0m\u001b[1;32m    345\u001b[0m         \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\n'AdvectionDiffusion/right/asi2/p1/RT1/int_r.dat'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/Users/alex/.matplotlib/tex.cache/84317547d2619c33fa30a7ccee212a42.tex\nLaTeX2e <2018-12-01>\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2018/09/03 v1.4i Standard LaTeX document class\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/size10.clo))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.def\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1enc.dfu)))\n(/usr/local/texlive/2019/texmf-dist/tex/latex/was/gensymb.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/local/texlive/2019/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/local/texlive/2019/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n) (./84317547d2619c33fa30a7ccee212a42.aux)\n(/usr/local/texlive/2019/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 ... AdvectionDiffusion/right/asi2/p1/RT1/int_\n                                                  r.dat}\nNo pages of output.\nTranscript written on 84317547d2619c33fa30a7ccee212a42.log.\n\n\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.plot(xs, ys)\n",
    "plt.xlabel('index')\n",
    "plt.ylabel('RT')\n",
    "plt.title(base_path + f_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 0.000811\n",
    "cycle_time = dt*1000\n",
    "right_residence_times = {\n",
    "    'ASI2': {'prox':[.2214,  0.36337, 0.51839, 0.73986, 1.0446],\n",
    "             'med' :[0.29585, 0.42878,  0.57607 , 0.80149, 1.1435],\n",
    "             'dist':[.59844, 0.71051, 0.95027, 1.2456, 2.2204]},\n",
    "    'ASI4': {'prox':[.49766, 0.79491, 1.1573, 1.5185, 2.3435]},\n",
    "    'ASI6': {'prox':[.52155, 0.99527, 1.2981, 1.6884, 2.3579]}\n",
    "}\n",
    "\n",
    "right_volumes = {\n",
    "    'ASI2': {'prox':[0.37912, 0.48379, 0.68129, 0.88389, 1.1165],\n",
    "             'med': [0.2626, 0.40586, 0.58183, 0.80693, 0.94628], \n",
    "             'dist': [.19253, 0.30718, .45923, .65179, .89421]}, \n",
    "    'ASI4': {'prox':[0.90235, 1.3218, 1.9063, 2.5823, 2.8892]},\n",
    "    'ASI6': {'prox':[1.4014, 1.4651, 2.109, 2.9514, 4.0107]},\n",
    "}\n",
    "\n",
    "baseline_residence_times = {\n",
    "    'ASI2': 0.06204,\n",
    "    'ASI4': 0.086032,\n",
    "    'ASI6': 0.11363,\n",
    "}\n",
    "baseline_volumes = {\n",
    "    'ASI2': 0.24673,\n",
    "    'ASI4': 0.56148,\n",
    "    'ASI6': 0.25788\n",
    "}\n",
    "\n",
    "z_scores = ['p1', 'p2', 'p3', 'p4', 'p5']\n",
    "\n",
    "plotted_z_scores = {\n",
    "    'p1': 6,\n",
    "    'p2': 8,\n",
    "    'p3': 10,\n",
    "    'p4': 12, \n",
    "    'p5': 14,\n",
    "}\n",
    "plotted_shapes = ['ASI2', 'ASI4', 'ASI6']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RT vs Shape Index, Prox RCA, stratified by Z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 244.08x150.85 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 168,
       "width": 334
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.811\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()#figsize=(7,7))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "format_axes(ax)\n",
    "cur_pos = 'prox'\n",
    "\n",
    "for z, size in enumerate(z_scores):\n",
    "    res_times = np.zeros((len(plotted_shapes), len(z_scores)))\n",
    "    for a, shape in enumerate(plotted_shapes): \n",
    "        res_times[a, z] = right_residence_times[shape][cur_pos][z]\n",
    "    ax.plot([2, 4, 6], res_times[:,z]/cycle_time, label = 'Z-score = ' + str(plotted_z_scores[size]), marker='o')\n",
    "        \n",
    "        \n",
    "ax.plot([2, 4, 6], [baseline_residence_times[shape] for shape in plotted_shapes], label='baseline', linestyle='-.')\n",
    "ax.plot([2, 4, 6], [2, 2, 2,], label='decision boundary', linestyle='-.', color='k')\n",
    "\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "ax.set_xlabel('Aneurysm Shape Index')\n",
    "plt.xticks([2, 4, 6])\n",
    "ax.set_ylabel('RT [cycles]')\n",
    "plt.title('Residence Time vs ASI, Proximal RCA')\n",
    "plt.savefig(\"../figures/RT_shape.eps\", bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print cycle_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w96cpmLVwIuSRshC2kBASSBRwEkIIGTScc1w7Z0HxwQrYrS1u19LmjcOCn6UiLoF2GiJkuKGAkxBCyKC4W3sPpw5c6XauuXZiHB5eORWJaeoQtSy0LtTUK/727R2VvdUhU0bLnY/+w5iG+5PULb3nJGTooICTEEJIULU2tePMkSpc+GsNeEfXPkbRo+SY/4+p0GcmIiJi5G0tdMJ0U/nOF1cTL9TY3Cag/tuJK7g/Kb7x5R9PqV2sH28PVfsICSQKOAkhhAQF7+C4VPo9vi6sRLO9vTOdMWDGIxPwg6d0UIyKDGELQ+f/njJrtxy9lNzTPqIXamxxL31QNjX/CX3VP2dOtgazLUuWLNEdP348Qa/XN82aNaspPj7eAQAnT55UmUym2FOnTl3KzMzs1+kyw11+fv44AFCr1c76+nrZli1bboW6TeFqSAScjLH1ADQAdELSBs652cf9iwBsA/ARgHoAqQDUnPPVwW4rIYQQ4KbZhlMfXcHtavcOusQ0NR5+eiq0SSN3VfkJ002lr2BTxDmw5VNTyqTRMW3B7Om02Wyy7du3V+fl5VnENIvFItPpdGOysrLqKNj0Lj09ffru3burMzMzm4TXa2ZWVpadXi/vwj7gZIxtA/AW57xe8ryMMTZZTOuBGq6g0wzgIAWbhBASfPdsrfj6UCUuf33TLT0uIRoLfj4FUzLGDnh7laHunS+uJvYWbIo4B/79i6uJi/Xj+3yUoL/i4+Od0mATAJ599tlkAPjggw+qg1XvUJafnz9u0qRJrWJwqdVqna+99loNBZs9C/uAE8ByuHoqzwrP34LrSKWVAN7zkW8F5/ysj+uEEEICxOnowPkvvoPhaBXaW5yd6TJ5BGb/ZBLmPJ6MyGja5uhCTb3Cc85mb87X2OIu1NQrgrWQaOXKlW6ruAoKChKOHz+esHfvXrNWq3X2lG8k27p1a9LevXvdRlo9g3bibigEnKPhGko/CwCc83rhr+PUYFS2efPmsmCUSwghw1W10YriAxWov+XeuaN7YAweWj4FKm1MiFo2eFJ++2lGMMt/6t+/Svf33qrfPdGnz7FVq1Z1BpwWi0W2bt265KysrDppui/5+fnj1Gq1EwDq6+tlZrNZ8eGHH1ZLr5vNZoVOp2uxWq3yxx9/vCE7O7tzikBubu4EjUbjAACz2axYs2bNnczMzKbCwkLlxo0bkwBg69atNQcOHBhdVVUVVVJSUiG2de3atUliuTabTS6tN1gsFkvnX05i26XtDnb9Q1XYB5yc8wTpc8aYOI/zTC9ZnxbmcgLAPAAv9jIETwghpA/qbzfhq4NXUXXBvWMnYXwsHl45FRP1AT3HnAyCvg6lFxQUJFitVrl0sUxOTk6y59diIJibmzth48aNSdnZ2ZcAYMGCBWkvvviiRRrcTpw4cUZRUdEVISitee6551Lr6urkO3furFm7dm2SeN+jjz46NS8v76aYNz8/f1xOTk5yT0HnkiVLdDabza9udjGo9aa4uDgWAE6cOKES6zKZTFHz58/Xm83mi9Qr7F3YB5xebAPwGef8oI977gI4wTn/DOhcRHQNQIKPPACATZs29fhX6ubNm/2cdUMIIcNXW4sDZcerce6z6+hwdP23GKWQYd6TkzHzsSTIZBEhbCHpD3Eo/dChQ1ekQZPJZIrS6/Vt3vKkpaW1Pv/88zoAEHsud+7cWSPm279/v9ZoNF4U73/55ZfvPPPMM3UAUFhYqCwtLVV5BndPPvlk3QsvvJBcUlJSodVqnXa7XSYGlWKAV1BQkGAymWKlgWpOTk7d/Pnz9QC8BpzHjh3rcbFxX4ivzeLFixvENL1e3xYfH+949dVXx+/atetGIOoZboZUwMkYewmAjnPuc+jCc+4m5/wzxpiaMfYS59zXvE9CCCE94Jyj4swtlHxyFfdskviDAdMfvA/zs1MRq4oKXQNDyN9h7As19Yq+DI+L/vzrh4zB3AxeOpQuHe62WCyyo0ePqlasWDHGZDLFiulZWVl1x44dM2dmZjbt3bvXvGfPHu2+ffvG2O32zhXvR48eVQGuYEzMJ3zdBgAGgyFWqVR26w3UaDSO8vLyUeJzb/dUVlZGAcCOHTu00vTXXnutZkAvhB+mTZvWCgAJCQkOabpKpXJWVVVFB7v+oWrIBJxCL+Xi3oJN4d71AN7zGEIXt0cihBDSR3eu23HqwBV8f9Xmlj5usgoPPz0V41JUIWrZ0HJ/krrl/qT4xr4sHJqVFN8Y7JOHnnrqKR3QfSj98OHDKo1G4zQajZe85SssLFQuXbq0QexpLCwsVC5btmxqXl6eZcqUKa2AK2j1NsycmpraZrfbuw1xW61W+YwZM+75am9qamob0LeFOoEaUtdqtc6kpKTWuro6txiqoaFBlpKS0upve0aaITHmwRibA9eq8xWStPU93CtuhzTX45IaQGXQGkkIIcNQc2Mb/rrvMg68dcYt2IxRRWHhP03Hz3+TQcFmH7384ym1/u4MxRjw6x9PqQ1mewoKChJKS0tV77//fqU0MLRYLLIdO3aM9+zJkzIYDLGvvvrqePF5dna2PSkpqVX8Wq/XN0mvA655nIBrsZJer28qKCjonO5msVhkR44cSXj77bd9DkuLecWN1z3L9ubYsWPmkpKSCn8evuoGgDfeeOPGnj17OntXTSZTlM1mk7/88st3ess7UoV9D6ewSGgbgG1CL6carkVAZ4TrcwC8Igajwir2t8X5m8I9ywGYaTidEEL80+HsgPFULf7+ZzNam7rijYgIhvsXTsS8n6YgKibsP0LC0mL9eHv+T6dX97b5O2NA/hP6qmAfb7lu3bpkpVLpLCoqUhUVFalsNpu8qqoqqry8fJTdbvfaOylSq9VOq9UqFwM/q9Uqf+eddzp7SY1G46WcnJzknJycZJ1O1wK45nFKr+fm5k44ffp0rLjae//+/ebMzMym4uLi2PXr10+w2+2ynJycZM9V4GJecaW41WqVv/nmm+4bwAbJqlWr6qxWq0z8vsxms+Lrr7829TTXlQCM+7v7bIgwxurgCjI9ZXDOzwrB5B4AkyWbw6sBvCK5Vw3X6UQDWqW+efNmvmnTpoEUQQghYe/Gt3U4deAKrDfcRzUn6Ucjc2UaEsaP6iHnsNBj32NZWZlBoVBMT09P9zq83FcnTDeV//7F1cTzXobXZyXFN/6azlInYcZoNE5vaWm5lJGR4TmK3Kuw//PUc1skL9cPAjjokVYPYEMw20UIIcON/W4LSj65iqtlt93SVVoFMldORcpMzYg/JSiQFuvH2xfrx397oaZe8bdv76jsrQ6ZMlrufPQfxjQEe84mIYMt7ANOQgghweVoc+KbE9dx9ng1HO0dnenyqAjM/WkKZi2cCHkknRIULK6FRBRgkuGNAk5CCBmhOOe4ds6C4oMVsFvd4520eeOw4GepiEtQhKh1hJDhhAJOQggZge5+fw/FB67gu0vupxdqkuLwyNNTkZjmbeo8IYT0DwWchBAygrQ2O3DmyDVc/LIGHR1di0ajR8kx/x9Toc9MREQEzdMkhAQWBZyEEDIC8A6OS6Xf4+vCSjTb2zvTGQNmPDIBP3hKB8WoyBC2kBAynFHASQghw9xNsw2nPrqC29XuO+wkpqnx8NNToU3y+9AbQgjpFwo4CSFkmLpna8XXhypx+Wv3vbDjEqKx4OdTMCVjLG1zRAgZFBRwEkLIMON0dODCFzU4c/Qa2lu6DomRySMw+yeTMOfxZERG0zZHhJDBQwEnIYQMI9VGK4oPVKD+VpNb+uRZWjy0PA3xY2JC1DJCyEhGASchhAwDtjtNKP74KqouWNzSE8bHInNlGibpNSFqGSGEUMBJCCFDWluLA2ePV+Obz66jw9G1zVGUQoZ5T07GzMeSIJNFhLCFpFe13yhQcUKFVrsM0Uon0hY3IHE2nTxEhhUKOAkhZAjinKPCcAsln1TiXn2r27XpC+7D/OxUxKqiQtQ64pfLnypxcnsiar9x3ybgy61A4uxGPPKbWkx7wt5DbkKGFAo4CSFkiLnznR2nPrqC76/a3NLHTVbh4ZVTMW6yKkQtI34r/Q8t/pKfDN7h/XrtN3H46JdT8ZOtVXhwjTWYTVmyZInu+PHjCXq9vmnWrFlN8fHxDgA4efKkymQyxZ46depSZmZmU2/ljDQ7duzQ1tfXy9RqtbOysjL6mWeeqaPXqWcUcBJCyBDR0tiOv//ZDOOpG+Bdo+eIUUVhwbJU/MMPx4PRKUHh7/KnSp/Bpoh3AH/ZmIKE5LZg9nTabDbZ9u3bq/Py8jonAFssFplOpxuTlZVFQZQX+fn543Jycur0en2bmJaenj7daDReCmW7whlN7CGEkDDX4ezAxb/W4I+vlaL8ZFewGRHB8MCiiXhm83xMe/A+CjaHipPbE3sNNkW8Azi5IzGYzYmPj3dKg00AePbZZ5MB4IMPPqgOZt1D1aFDh0aPHTvWKU2bNGlSa3FxcWyo2hTuqIeTEELC2I0rdTj1UQWsNxrd0ifpRyNzZRoSxo8KUctIv9R+o+g2Z7PXPGfjUPuNIlgLiVauXFknfV5QUJBw/PjxhL1795q1Wq2zp3wj2aRJk1pnz549ff/+/ebMzMwmi8Uiu379ejT1BveMAk5CCAlD9rstKPnTVVw13HZLV2kVyFyRhpT7tXRKUDh5PT4jqOW/96N0v+993VbWl6JXrVrVGXBaLBbZunXrkrOysuqk6b7k5+ePU6vVTgCor6+Xmc1mxYcfflgtvW42mxU6na7FarXKH3/88Ybs7OzOKQK5ubkTNBqNAwDMZrNizZo1dzIzM5sKCwuVGzduTAKArVu31hw4cGB0VVVVVElJSYXY1rVr1yaJ5dpsNrm03mD64IMPqh999NGpDz/88PSsrKw6APjb3/52ZTDqHqoo4CSEkDDiaHfi3InrKDtWDUd717CrPCoCGUtS8MCiiZBH0ilBJDj6OpReUFCQYLVa5Vu2bLklpuXk5CR7fi0Ggrm5uRM2btyYlJ2dfQkAFixYkPbiiy9apMHtxIkTZxQVFV0RgtKa5557LrWurk6+c+fOmrVr1yaJ9z366KNT8/Lybop5hXmVyT0FnUuWLNHZbDa/fnnEoLYnWq3WuWrVqjsFBQVjjh8/nqBUKp3FxcWx0kCauKOAkxBCwgDnHNfOW/DVwQo0WNxHTtPmjcOCn6UiLkERotaRkUAcSj906NAV6VC6yWSKki6OkUpLS2t9/vnndQAg9lzu3LmzRsy3f/9+rdFovCje//LLL9955pln6gCgsLBQWVpaqvIM7p588sm6F154IbmkpKRCq9U67Xa7TAwqxWCyoKAgwWQyxUoD1ZycnLr58+frAXgNOI8dO2bu50vTTU5OTvKcOXPuGY3GSyaTKWrFihWpy5Ytm3ro0KErFHR6RwEnIYSE2N3v76H4wBV8d8l9BFOTFIdHnp6KxDR1iFpG/ObvMHbtN4o+DY+LXvqrMZibwUuH0qUBk8VikR09elS1YsWKMSaTqXNBTFZWVt2xY8fMmZmZTXv37jXv2bNHu2/fvjF2u71zxfvRo0dVACANVoWv2wDAYDDEKpXKbnNENRqNo7y8vHNysrd7KisrowDX1kTS9Ndee61mQC+EH0wmU9T58+djxeBXr9e3GY3GSzk5OckHDhwYTQGndxRwEkJIiLQ2O3DmyDVc/LIGHR1d+xxFj5Jj/j+mQp+ZiAhaeT68JM5uQeLsxj4tHEqc0xjsk4eeeuopHdB9KP3w4cMqjUbj7Gm7n8LCQuXSpUsbxJ7GwsJC5bJly6bm5eVZpkyZ0gq4glZvi49SU1Pb7HZ7tyFuq9UqnzFjxj1f7U1NTW0DAM/V9b4Eakj97t278kmTJrV6pq9Zs+bOH/7whzH+tmekoW2RCCFkkPEODtNXtdj3WinOf/5dZ7DJGDDz0Qn45RsPYsYjEyjYHK4e+U0tmJ8fvywCeCSvNpjNKSgoSCgtLVW9//77ldLA0GKxyHbs2DE+ISHB0VNeg8EQ++qrr44Xn2dnZ9uTkpJaxa/1en2T9DrgmscJuBYr6fX6poKCggRpnUeOHEl4++23b/hqs5g3Pz9/nLeyvTl27Ji5pKSkwp+Hr7ozMzObrl+/Hm0ymdyO8tq3b1/CmjVr7vhSB/EUAAAgAElEQVTKO5JRDychhAyim9dsOPU/V3C72n3ULTFNjYefToM2SRmilpFBM+0JO36ypbrXzd9ZBPCTrVXBPt5y3bp1yUql0llUVKQqKipS2Ww2eVVVVVR5efkou93utXdSpFarnVarVS4GflarVf7OO+909pKKQ805OTnJOp2uBXDN45Rez83NnXD69OlYjUbjMJvNCnGroeLi4tj169dPsNvtspycnGRx9bpnXnGVu9Vqlb/55ps3g/Mqufvb3/52Ze3atUnx8fEOsW46acg3xqXHVRCfNm/ezDdt2hTqZhBChqB7tlZ8XViJy6Xun4dxCdFY8PMpmJIxlrY5Cg89/hDKysoMCoVienp6emBOk7n8qRIndySi9mz34fXEOY14JI/OUidhxWg0Tm9pabmUkZExt695qYeTEEKCyOnowIUva3Dm02tob+nqKJLJIzD7J5Mw5/FkREbTNkcj0rQn7Jj2xLeo/UaBihMqtNpliFY6kba4IdhzNgkZbBRwEkJIkFw3WnHqQAXqb7mPsk2epcVDy9MQPyYmRC0jYcW1kIgCTDKsDYmAkzG2HoAGgE5I2sA597mfliSPFUCqkKc+qA0lhBAAtjvNKP64AlUX3BfQJoyPRebKNEzSa0LUMkIICY2wDzgZY9sAvCUGi8LzMsbY5J4CSMbYSwBSOeerhec6AGVwBZ6EEBIU7a1OlB2vwrkT38Hp6FoMEqWQYd6TkzHzsSTIZLQ5CCFk5An7gBPAcgAfATgrPH8LwHoAKwG810OebQAWik8452bGGBhjyznnB4PZWELIyMM5x1XDbXz1yVXcq3ffnm/agvvwYHYqYlVRPeQmhJDhb0B/ajPGivr5+AtjrMjPakajaygdkl5Nr72VQm+mGoBn72c9gMW9VbZ58+aynh5+tten119/HULwO+DH0qVLfZb/+uuvd7u+dOnSgNXfW/mHDx/udj0xMTFg9fdWfm2t+9Z1tbW1Aau7t/ITExO7te3w4cMBq7u38nt7b9B7L3DvPUuNHYf+7Szm/igdq7Zl4te7F6L+ngVjU1RYvmEuFj43HfWNFnrvDcJ7z9v7ghASHgbaw8k45z/pV0bG/uLPfZzzBOlz5gooAeBMD1nE63c90u9KrhFCyIC0NLbj7382w3jqBjx3l8tcnoZHlj4ARhu3E0IIgIEHnCdCkHcbgM98DI33dOhwPfwIODdt2pTR07XNmzfTpqWEEFRdtOCPX5aitanrABZpaJk2bxwFm4QQIhHwjd8ZY+8C4AA2AHgawG4AlQBWc86/GGDZLwnl9BgUMsYWwRXMJkgXFTHGTgAA57zXYfWe0MbvhIxsN67U4dRHFbDeaHRLn6gfjYdXpiFh/KgQtYwE0OBt/E7IEBNuG79Xcs63A53B5yLO+ReMsbcA9DvgFALJxb6CTYE4lD4a7vM4RwMw9Ld+QsjIZb/bgtI/XUWF4bZbukqrQOaKNKTcrwVj1KNJCCE9CUbAWQcAjLGFAOolvZqecyr9xhibA2AF53yFJG095/xtz3s552cZY/XoPrSuxsCmABBCRhhHuxPnTnyHsuNVcLR1bXMkj4pARlYKHlg8EfJIOiWIEEJ6E8xtkRbDvUexX2P3wiKhbQC2Cb2cagDzICwaEoLRV6TBKFxbJ60WHp0LjWhLJEKIPzjnuHbegq8OVqDB4n4ATNq8cVjws1TEJShC1Doy3BgtRkXxjWJVY3ujLC4yzpk5IbMhXZtOJw+RYSUYAWcCY+x3cO2VuZwxFg/gFfQz4IRrw3Y1gEUe6eLQug7AIsaYWpyzyTl/mzG2jbk2iRdPGur33E1CyMhx9/t7KP64At+Z3AdlNBPi8Mgv0pCYltBDTkL65svrXyp3X9idaLQa46Tp/37u35GuSW9cff/q2scmPWYPVfsICaSAB5yc8+2MsZ8DyOCcfyMMrVvRfV9Mf8vz+b+70GvZreeSc76hP/URQkam1mYHznx6DRe/qEFHR9ffx9Gj5Jj/lA76zERE0ClBJEDeN72v3XFmRzLvoS/GaDXGrfty3dTfzPtN1bP6Z63BbMuSJUt0x48fT9Dr9U2zZs1qio+PdwDAyZMnVSaTKfbUqVOXMjMzm4LZhqHIZDJFHT16VPXGG28kNTQ0nPO8np+fP85qtco1Go3DbDYrdu7cWaPVap2haGs4CMqQOuf8E8nXnwP4PBj1EELIQPEOjstff4/SQ5Votrd3pjMGpD8yAT9cqoMiLjKELSTDzZfXv1T6CjZFHBzbz2xPSYpLagtmT6fNZpNt3769Oi8vzyKmWSwWmU6nG5OVlVVHwWZ3hYWFSoPBEKtWq512u73bRO4dO3ZozWaz4sMPP6wGXMHp7Nmzp3/33Xflg9/a8BCUP9cZY+8yxqyMsReE528xxlTBqIsQQvrr5jUbDr5dhi/ev+wWbCamqbFy4zw8+r/+gYJNEnC7L+xO7C3YFHFwvHfhve7HOwVQfHy8UxpsAsCzzz6bDAAffPBBdTDrHqqys7PtW7ZsuTVlypRWb9ffeOONpDVr1twRn+v1+jYAKCgoGLFzcgIecDLGdsG17+ZcCPuZcc5fgWseJyGEhFxTQxs+/28TPtlWhttVDZ3pcQnR+MkL6cj+/2ZDm6QMYQvJcGW0GBWeczZ7U24tjzNajEFbpbZy5co66fOCgoKE48ePJ/z+97+vHslDwP1lMpmi7Ha7bPTo0Q5pukqlcp44cWLEdr4FY0jdJtmHU/onXL+3RSKEkEBwOjpw8a81OHPkGtpauj5HZfIIzP7JJMx5PBmR0bTNEem7mf89s7c9ogfkF5/+It3fey/+08WyvpS9atWqzoDTYrHI1q1bl5yVlVUnTfclPz9/nFqtdgJAfX29TDqULF43m80KnU7XYrVa5Y8//nhDdnZ25xSB3NzcCRqNxgEAZrNZsWbNmjuZmZlNhYWFyo0bNyYBwNatW2sOHDgwuqqqKqqkpKRCbOvatWuTxHJtNptcWm+oXLlyJRoAxo4d6xasx8fHO6qqqqJC06rQC0bAKe2Wl+6EPDoIdRFCiF+um6woPlCBupvu09Emz9LioeVpiB8TE6KWERI++jqUXlBQkGC1WuVbtmy5Jabl5OQke34tBoK5ubkTNm7cmJSdnX0JABYsWJD24osvWqTB7cSJE2cUFRVdEYLSmueeey61rq5OvnPnzpq1a9cmifc9+uijU/Py8m6KefPz88fl5OQk9xR0LlmyRGez2fz6i1IMavujrq7Oa2wVHx/vvH79enR/yx3qghFwTmGMLeOcH4KwFRJjLA8+jgsjhJBgsd1pxlcHK3DtvNsUNajHxeLhlWmYlK4JUcsICS/iUPqhQ4euSIfSTSZTlDgH0VNaWlrr888/rwMAsedy586dNWK+/fv3a41G40Xx/pdffvnOM888Uwe4Ft6UlpaqPIO7J598su6FF15ILikpqdBqtU673S4Tg0oxmCwoKEgwmUyx0kA1Jyenbv78+XoAXgPOY8eOmfv50vRJQkKCw1u6zWaTiTsAjETBCDg3APicMXYQABhjuwGc5ZzPC0JdhBDiVXurE2XHq3DuxHdwOrpOCYpUyPCDJydj5o+SIJPTNkckMPwdxjZajIq+DI+L/ueJ/zEGczN46VC6dLjbYrHIjh49qlqxYsUYk8kUK6ZnZWXVHTt2zJyZmdm0d+9e8549e7T79u0bY7fbO1e8Hz16VAV0LZiRfN0GAAaDIVapVHabI6rRaBzl5eWjxOfe7qmsrIwCXKvBpemvvfZazYBeiAAQg/Xbt2/LpIG7zWaTz5o1a8Su+A/GPpw2AHMZY8sBTAZg5px/whhL4ZxXBbo+QgiR4pzjquE2Sv50FY117gtIpy24D/P/UYdR8SN2VIuEWLo2vSVdk97Yl4VDMzQzGoN98tBTTz2lA7oPpR8+fFil0WicRqPxkrd8hYWFyqVLlzaIPY2FhYXKZcuWTc3Ly7OIK7gtFovM2+Kj1NTUNm9bClmtVvmMGTPu+WpvampqGwB4rq73ZbCG1DMzM5uUSqXz7t27cgjBNQA0NDTIFi9e3OAj67AWtD/vOecHOefbJXtybgtWXYQQAgCWGjsK/883+Mv/NboFm2NTVPj5hgwsfG46BZsk5Fbfv7qW+TnLjIHhpftfqg1mewoKChJKS0tV77//fqU0MLRYLLIdO3aM72mIGHD1Ur766qvjxefZ2dn2pKSkVvFrvV7fJL0OuOZxAq7FSnq9vkm6VZDFYpEdOXIk4e23377hq81i3vz8/HHeyvbm2LFj5pKSkgp/Hr7q9sfatWu//8Mf/jBGfG4ymaLEdg+07KFqwD2cjLEO9H5sJfPjHkII6ZeWxnb8/bAZxpM3wCX/08QoI/HgsimYNn88WARNIyfh4bFJj9nz5uVV97b5OwPDb+b9pirYx1uuW7cuWalUOouKilRFRUUqm80mr6qqiiovLx9lt9u99k6K1Gq102q1ysXAz2q1yt95553OXlKj0XgpJycnOScnJ1mn07UArnmc0uu5ubkTTp8+HSueyLN//35zZmZmU3Fxcez69esn2O12WU5OTrK4et0zr7jK3Wq1yt98882bwXmV3BUXF8fu27cv4eTJkyrA1XuakpLSumvXrhsAsGXLllvStpnNZkVRUdGVwWhbuGKcDywOZIz9BcBq+N72SAPgd5zzlQOqLMQ2b97MN23aFOpmEEIEHR0cplM38PWfzWi919UJExHBMPPHSZj3xGRExwTlQDUyfPX4l0lZWZlBoVBMT09P9zq83FdfXv9S+d6F9xLLreXdhtdnaGY0vnT/S3SWOgkrRqNxektLy6WMjIy5fc0biP+JN3DOr/Vyj40xRmebE0ICpraiDic/qoC1ptEtfaJ+NDJXpGH0faN6yElIeHhs0mP2xyY99q3RYlQU3yhWNbY3yuIi45yZEzIbgj1nk5DBNuCAk3P+jZ/39RaUEkJIr+x3W1D6p6uoMNx2S1dpFXhoeRomz9KCMRo+J0NHuja9hQJMMtwFfKyJMbYQwO8AvMg5Pyek/Q7Au7RKnRDSX452J86d+A5lx6vgaOva5kgeFYGMrBQ8sHgi5JF0ShAhhISjYExuWgzgcwD1YgLn/LfC5u87glAfIWQY45yj6oIFxR9XoMHi3gmUNncsHvzZFChHB+2YaUIIIQEQlKMtOecUWBJCBqzu5j0UH6jAdZP7mkTNhDg88os0JKYl9JCTEEJIOAlGwJnax3RCCHHT1uzAmU+v4cIXNejo6NpJI3qUHPOf0kGfmYgIGZ0SRAghQ0UwAk4zY2wXXNsgVTPGVABeCUI9hJBhhndwXP76JkoLK9Hc0HV0M2NA+iMT8MOlOijiIkPYQkIIIf0RjKMttwvnp19jjIldE3SWOiHEp1vXGnDyoyu4XeV+8ltimhoPP50GbZIyRC0jhBAyUEHZEZlzvpoxtg3AQrjOUv88GPUQQoa+poY2lBZW4nLJ927po9TReOjnUzBl7lja5ogQQoa4YGyL9DPO+Z8452YA5kCXTwgZHpzODlz8sgZnjlxDW0vXyXkRcobZiychIysFkdG0zRHpXWtFBe6Vfo2Oe42IGBWHUQ/OR3RaWqibRQiRCEYP5zbG2Fnac5MQ0pPvTHdx6sAV1N1sckufPEuLh5ZPQfyY2BC1jAwl90pLYfmPP6DJYOh2LXbuXGj/ZQ1GPfhgCFpGCPEUjIDTBmA5Y0wD4CNx83dCCLHdacZXBytw7bzFLV09LhYPr0zDpHRNiFpGhpr6gwfx/WubgI4Or9ebDAZc/+cXcN+bb0D9858Pcuv6prm8XHHv1CmVs7FRJouLc456+OGGmBkz6OQhMqwEY9FQ54HujLGfM8aeBvAXzvmXga6LEDI0tLc6cbaoGt/85Tqcjq4AIVIhww+enIyZP0qCTE7bHBH/3Cst9RlsdurowPevvobIxMSw7Om0f/650rLr3cSW8vI4afqd3++EYsaMRm3ur2qVCxfaQ9U+QgIpKIuGRJzzTxhjowF8whirBLCbc/6fwayTEBI+OOe4WnYbJZ9cRWNdq9u1aQvuw/x/1GFUfHSIWkeGKst//KH3YFPU0QHLH3aFXcBp/a//1t7eti0ZnHu93lJeHlfz65enjvvthqrR//RP1mC2ZcmSJbrjx48n6PX6plmzZjXFx8c7AODkyZMqk8kUe+rUqUuZmZlNvZUz0phMpqijR4+q3njjjaSGhoZuo7n5+fnjrFarvKqqKhoA/u3f/q1Gr9e3dS9pZAjGoqEXABwA8L8BvAjgLoANnPM9jLHJdK46ISODpcaOUx9VoLai3i19bLISD/9iKsZPjg9Ry8hQ1lpR4XXOpi9NZ86gtaIibBYS2T//XOkr2OzEOW79bltKZFJSWzB7Om02m2z79u3VeXl5nXNdLBaLTKfTjcnKyqqjYLO7wsJCpcFgiFWr1U673d5tdWNubu6EN99886ZWq3WKz+fPn683m80XxbSRJhhjWG8DqAMwG8BKznka53wPAHDOr3HOfwtgub+FMcZ0jLGXGGN1ft6/iDFWxhhbL+TbJuwLSggZBC332vG3/d/iwNYzbsFmjDISP35uGpZvmEvBJum3xpKSfuW7V/p1gFvSf5Zd7yb2GmyKOIfl3d2JwWxPfHy8UxpsAsCzzz6bDAAffPBBdTDrHqqys7PtW7ZsuTVlypRWb9ePHDmScPny5c7hmzfffPOm3W6X/dd//deIPY83KCcNAVjBOb/m7aLQw+nXpw1jbBGAOQDqAaj70AY1gG1CWw5yzlf3IS8hpB86OjhMxbX4+v9VovWeozM9IoJh5o+TMO+JyYiOCeosHjIM8bY2NJcb0WQwoMlwBk1f/71f5XTcawxwy/qnubxc4TlnszctFy/GNZeXK4K1kGjlypVuHToFBQUJx48fT9i7d695pPbGDZTNZpNXVFREi73D4utYWVk5YucQBeN//7d6CjYFVgAf+VMQ5/wzAJ8JgWdfrOCcn+1jHkJIP9VW1OPkR1dgrXH/UJ84PQGZK6di9H2jQtQyMtR0NDej+fx5NJ0xoMlgQPP58+AtA4+zIkb1Kcbrs0vTpmcEs/yq5SvS/b13+uVLZX0pe9WqVZ0Bp8Vika1bty45KyurTpruS35+/ji1Wu0EgPr6epnZbFZ8+OGH1dLrZrNZodPpWqxWq/zxxx9vyM7O7pwikJubO0Gj0TgAwGw2K9asWXMnMzOzqbCwULlx48YkANi6dWvNgQMHRldVVUWVlJRUiG1du3ZtkliuzWaTS+sNJc85nSaTKQoAfvCDH4zY6QnBWKX+SS/Xtwe6zkDavHlzn35RCRnJGutaUPKnSlScueWWrtIq8NDyNEyepaVTgohPzoYGNJ09i+ayMjSdMaC5vBxwOHrP2EejHpwf8DKHo74OpRcUFCRYrVb5li1bOv8TyMnJSfb8WgwEc3NzJ2zcuDEpOzv7EgAsWLAg7cUXX7RIg9uJEyfOKCoquiIEpTXPPfdcal1dnXznzp01a9euTRLve/TRR6fm5eXdFPPm5+ePy8nJSe4p6FyyZInOZrP5dZqEGNQGyr/+678mPfjggw3+BvHDUTAWDS0E8DsAL4p7cIZgodDTkl7ReUJb6n1lIIR0sdY2ouZyHdpbHIhUyJE0LQGaxK4eIke7E+c++w5lx6rgaOtaLSyPikBGVgoeWDwR8kg6JYh057Ba0WQoE4bIDWi9fBm9zWeMnDgRsXPnuh7z5qL2f29Ecx8WDsXOmxc2C4bCmTiUfujQoSvSoXSTyRTV0+rqtLS01ueff14HAGLP5c6dO2vEfPv379cajcaL4v0vv/zynWeeeaYOcC28KS0tVXkGd08++WTdCy+8kFxSUlKh1WqddrtdJgZqYjBZUFCQYDKZYqUBXE5OTt38+fP1ALwGnMeOHQvJ6Yc7duzQXr9+PdpoNF4KRf3hIhhD6osBfA7XvEsAAOf8t4yxPAA7glCfp7sATgjD8eI80GsA/Jqou2nTph6HRTZv3uznLG9ChqbvLt+F4dOqbivLASAxTY25P02Go60DxR9XoMHiPsw5Ze5YLPjZFChHKwaruWQIaP/+e1dwKQyRt5l7/8yPmpIqBJjzEDs3A5Hjx7tdH/Mva3D9n1/wb2ukiAho1+T2t/l+83cYu7m8XNGX4XFRysGPjcHcDF46lC4d7rZYLLKjR4+qVqxYMcZkMnUeAZaVlVV37Ngxc2ZmZtPevXvNe/bs0e7bt2+M3W7vXPF+9OhRFQBIg1Xh6zYAMBgMsUqlstscUY1G4ygvL++ch+PtnsrKyijAFcxJ01977bWaAb0QAVZYWKj8/PPPVSM92ASCE3BaOOeDEVh65Tl3k3P+GWNMzRh7iXP+XqjaRUi4M31Vi7/+8XKPnU21FfX48++7B6KaCXF4+Ok0TJg6YhdfEgHnHG1VVa65lwYDmgxlaL9xw3emiAgopk/v7L2MyciAPMH3e2nUgw/ivjc29775e0QE7nvzjbDagzNmxowWxYwZjX1ZOKSYObMx2CcPPfXUUzqg+1D64cOHVRqNxtlTwFRYWKhcunRp51BxYWGhctmyZVPz8vIs4gpui8Ui87b4KDU1tc3blkJWq1U+Y8aMe77am5qa2gYAnqvrfRnsIfXi4uLYAwcOjJb2rObn54+TTj8YSYIRcKb2MT2gGGPrAbznMYReP1j1EzIUfXf5rs9g05voWDl++JQO6Q8nIkJGpwSNRLyjw7UvptB72WQwwGnp5fM/MhIxM2d2BZizZ0MW1/cFPerlyxE5YQIsf9iFpjNnul2PnTcP2jW5YRVsirS5v6qt+fXLU/36hWMM2l+trg1mewoKChJKS0tVnkPpFotFtmPHjvFbt27tsdfQYDDEFhUVqXbt2nUDcG0XlJSU1Cp+rdfrm1599dXx4nXANY9z165dN1atWlW3Y8eOpoKCggQxYLVYLLIjR44k7N+/32dXuJjXM4ATy/aWZzCH1E0mU9T69esnrF+//mZhYaGyrq5Ofvr06VhaNBRYZsbYLgC/45xXM8ZUAF4JQj3dMMbE7ZDOAvhMckkNoHIw2kDIUGT4tKpPwWZsfBT+16s/hCIuMniNImGHt7ejxWQSgssyNJWVoaOhwWceFhOD2NkPICYjA7Fz5yFm1v2IUARm2sWoBx/EqAcfRGtFBe6Vfo2Oe42IGBWHUQ/OD+s5m8qFC+1jN2yo7nXzd8Yw7rcbqoJ9vOW6deuSlUqls6ioSFVUVKSy2WzyqqqqqPLy8lF2u91r76RIrVY7rVarPD8/fxzg6p185513OntJjUbjpZycnOScnJxknU7XArjmcUqv5+bmTjh9+nSsRqNxmM1mxf79+82ZmZlNxcXFsevXr59gt9tlOTk5yeLqdc+84ip3q9Uqf/PNN28G51VyV1xcHLtv376EkydPqgBX72lKSkqrGOzOnz9fb7fbZcuWLVNJ8z3zzDMjdmid8b58yvhbqGuj9RcBiIWf5ZzPG0B5i+Cal8k80ucAeIVzvkKSto1zvkHyfDmAbZzzAfdwbt68mW/atGmgxRASdB0dHO0tDrS1ONHW4kB7ixNtzR7PWxxoa3ag4W4LzGfv9F6oh1+89gO3hURk+OloaUHzhQtdQ+TfnANvbvaZJ0KpRGxGBmLnuRb5KPR6sMgh9YdJj9sqlJWVGRQKxfT09PSABA32zz9XWt7dndhy8WK3XyTFzJmN2l+tprPUSVgxGo3TW1paLmVkZMzta96g7MLMOV/NGNsGYCEAM+f88/6UIwSUTwNYJDz/WChPDCh1ABYxxtSSIfS3hLpFagBB3R+NkEDgnMPR3iEJDoUAsdmB9lb3NGnA6HreFUy2tTjhaA3+Xs01l+so4BxmnI330PzNN53D4y0XLoC3t/vMI9NqXcPjQpAZnZYGJqMdCvyhXLjQrly48Nvm8nLFvVOnVM7GRpksLs456uGHG4I9Z5OQwTaggJMx9oC49ZEnzrkZrpN++pxXUsZZuIbHe7p+EMBBj7R6ABu85yAk8DqcHX71JHYLDJs97mlxgncMnY0Q2lsCv1ciGVyOurrO/S+bDAa0XLrU68rvyMRE19xLYZuiqJQU2mt1gGJmzGihAJMMdwPt4dwG4PEQ5CVkQDjnaG91SnoOheCv2Ym2Vmkw6B4gevYktjc74Gj3Y2uWwcaAqGgZomLkiFTIEaWQCQ85ImPkXV8rZLhdbUdl2e0+VxGpoGMqh5r2W7c6ey+bDQa0VlztNU+UTtc1RJ6RgcgJEwahpYSQ4WagnxjzGGNFcM156UvXDAPQ5/H/oay3jbSJf5yODo+eQ8+eRDEYdO859OxhbG9x9GmRzGCRRUZ0BoNRMXJECkGjNEB0XRP+9ZIWqZAhMkoGFuFfr5O1trFfAWfSNNoGKZxxztH+3Xeu3ssy10br7dev+87EGKKnTevaZD1jDuRare88hBDihwEFnJzz0YFqyHDV60baT6Rg4rTh/TLyDldvYlfPoNCT6LaoRQgWhR7Hbj2JwnWnI/x6ExmD0JMoBoFi4OceBHZecwskJWkKGWQh2F5IkxiHxDS11/doTxLT1PQHU5jhHR1oq6x022TdcbuXPyTkcsSkp3cNkc+ZA5lK5TsPIYT0A42JBZE/G2kf/v05/OiX06B/KHFwG9cLzrmrN7HZifZWac+hZFjZbRGL955EsacxHMmjIjp7EqMUMklQKD6X9iTKuoamYyQ9izFyyCMjhvwctrlPpODw78/5uy0g5j6REvQ2Ed+4w4GWy9+iyXBGGCIvg7Pe9x8NLDoaMQ88IPRgZiBm1ixExMb6zEMIIYFAAWeQ+LuRNufAX/94GUqNIiA9nX3ZDqet1TUH0W2OoiS47HCG35hzRARDZEw/AkO3NBkio2W0WbnExGmj8aNfTuv1PcsY8KNfThv2vfLhqKOtDS0XL3b2XjZ/8w067vk8jAURo0YhJmOOcETkXChmpCMiKmqQWkwIIV0o4AySvkk5M90AACAASURBVGykzTlw5sg1aBLjet8ORwgUQ7kdTn9ERsskQ8ddw85RCplrEYtkrmKk2OsYLXMfqo6RQSYf+r2J4Ur/UCKUGsWInwISLjqamtB87lznEHnz+fPgbW0+88gSEtyOiFRMm0ZbFBFCwgIFnEFgrW3s03w4APj+qg0F64uD1KL+iZAz3/MOe1iw0tn7KKTJo2WI8HMBCwmtidNGY+K00bTILQScNhuays66AswyA1qMJsDhe+sp+bhxiJ03rzPIjNLp6A8yQkhYooAzCGou14Wu8j5shxPldShaSIuWQxZJQ84jlSYxjgLMIHPcueNaPS4MkbdeuYLehkWikpMRM28uYjNcAWbkhAkUYBJChgQKOIOgvxtiy6MiMCo+2ut2OFExMkRGB3Y7HELI4Gm/caNzD8ymMwa0VVX1mid66lS3IfLIsWOD31BCCAkCCjiDoL8bYs/PTsWsH08McGsIIYONc462a9c6ey+bDAY4vv/edyaZDAq9vjPAjJ0zBzK1enAaTELqdnWDotpoVbU3O2WRMTJncrqmYWyyik4eIsPKgANOxtiP4TrTXA2gnnP+nwNu1RDX3w2xaSNtQoYm7nSi9coVtwDTefeuzzwsKgox99/vGiKfOxexDzyAiFGjBqnFw8vVuqv4+82/o7GtEXFRcfjh+B9iSsKUUDerV9fO31EajlYl3q62u81fOf3naxibrGyc+9OU2smzxthD1T5CAikQPZzvAXgXwB7OuS0A5Q15tJE2IcMbb2tDs9HYuf9l09mz6LD7jgtYbCxiZ8929V7OnQvFzJmIiI4epBYPT19//zXePf8uym6VdbuWMS4Dv5r1K8y/b34IWta7c59d1371ydXkns7ou11tjzv67sWpmcvTqmYtnGgNZluWLFmiO378eIJer2+aNWtWU3x8vAMATp48qTKZTLGnTp26lJmZ2RTMNgxFJpMp6ujRo6o33ngjqaGh4Zyve3NycpLXrFlzZyS/joEIOM9yzncEoJxhhTbSJmT46GhuRvP5C13nkJ87B97ie8RTFh/vOr1HeCimTwOT0yymQPlTxZ+wuXQzOrj308fKbpVh9YnVeP3B17Esbdkgt863a+fvKH0Fm504UHywIkWlVbQFs6fTZrPJtm/fXp2Xl2cR0ywWi0yn043JysqqG8lBUk8KCwuVBoMhVq1WO+12u8+9xwoLC5X79+/Xrly50vewxzAXiP/9zP7cxBh7i3P+SgDqGxJoI21Chi6n3Y7ms2fRZHCdQd5cXg60t/vMIx8zpuuIyLlzET1lClgE7fQQDF9//7XPYFPUwTvweunruC/uvrDq6TQcrUrsNdgUcdf9k2eN+TZY7YmPj3dKg00AePbZZ5MB4IMPPqgOVr1DWXZ2tj07O9teWFio7O3eoqIilVKpDM9NsgdRIAJOf39tRtzsd9pIm5ChwXH3btcKcoMBrZe/BTp8BzOREyd2HhEZO3cuIidNoi2KBsm759/tNdgUdfAO7D6/O2wCztvVDQrPOZu957HH3a5uUARrIdHKlSvd9vIrKChIOH78eMLevXvNWq12xAdKA5Gfnz/uzTffvLlv374xoW5LqAUi4FzBGFvux306ALkBqG9IoY20CQk/7d9/39l72WQwoK2ystc8UVNShQBzHmLnZiBy/PhBaCnxdLXuqtc5m74Ybhlwte5qUBcS/cevvsgIWuEAPn7LkO7vvf/y7o/79AKtWrWqM+C0WCyydevWJWdlZdVJ033Jz88fp1arnQBQX18vM5vNig8//LBaet1sNit0Ol2L1WqVP/744w3Z2dmdUwRyc3MnaDQaBwCYzWaFONexsLBQuXHjxiQA2Lp1a82BAwdGV1VVRZWUlFSIbV27dm2SWK7NZpNL6w214uLi2NTU1DYK2l0CEXDWo/dhdQZgRHfj0UbahIQG5xzt1dWd+182lZWhvabGd6aICCimTesaIs/IgHz0iP4vLKQ456hprIHRYsTHVz7uVxl/v/n3IbFyPdT6OpReUFCQYLVa5Vu2bLklpuXk5CR7fi0Ggrm5uRM2btyYlJ2dfQkAFixYkPbiiy9apMHtxIkTZxQVFV0RgtKa5557LrWurk6+c+fOmrVr1yaJ9z366KNT8/Lybop58/Pzx+Xk5CT3FHQuWbJEZ7PZ/DrrVQxqB2Lfvn0Ju3btujHQcoaLQAScn3HOf9vbTYyxdwNQFyGE+MQ7OtBacRVNhjNdWxTdsfjOFBmJmJkzuzZZnz0bsjj6AzEUOOe4ee8mjFaj62Fx/dvQ1jCgchvbGgPUwuFLHEo/dOjQFWmvnMlkitLr9W3e8qSlpbU+//zzOgAQey537txZI+bbv3+/1mg0XhTvf/nll+8888wzdYBrMU1paanKM7h78skn61544YXkkpKSCq1W67Tb7TIxqBSDyYKCggSTyRQrDVRzcnLq5s+frwfgNeA8duyYX2tOAkEcSh+s+oaCQASc8X7e1/uYFSGE9BF3ONBy6VLXHphlZeiw+d6hjcXEIOaBWZ1D5DGz7keEQjFILSZSd5rudAsu77YEfjFvXFRw/4Dwdxj7dnWDoi/D46IVr8w1BnMzeOlQunS422KxyI4ePapasWLFGJPJFCumZ2Vl1R07dsycmZnZtHfvXvOePXu0+/btG2O32ztXvB89elQFANJgVfi6DQAMBkOst8U0Go3GUV5e3rkprbd7KisrowBgx44dWmn6a6+91svwRfDRULp3gQg4df7cxDnfHoC6CCEjXEdrK1ouXOgaIj93DrzJ964tEUolYjMyuvbA1OvBIiMHqcVEdLflLkxWE8ot5TBajTBZTLjdfNuvvKooFdI16UiMS8QnFZ/0ue4fjv9hn/MEw9hkVcvYZGVjXxYOjU1WNgb75KGnnnpKB3QfSj98+LBKo9E4jUbjJW/5CgsLlUuXLm0QexoLCwuVy5Ytm5qXl2eZMmVKK+AKWr0FX6mpqW3ethSyWq3yGTNm3PPV3tTU1DYA8Fxd78tgDalbLBbZ6dOnY0+fPt0ZoNvtdtnu3bvHFBUVqUbqMHsgAs5UxthHAHYDMHDOu417MMZ+zDn/IgB1EUJGGGfjPTSfO9c5RN5y/gJ4L1sUyTSazv0vY+fNRXRaGpjMr88ZEiC2VhtMVpMrsLSaYLQYUXuv1q+8cZFx0Gv0SNekQ691/ZsUl9S5C0BVQ1WfFg7NHTc3rOZvzv1pSu3Rdy9O9WuPF+a6P5jtKSgoSCgtLVV5DqVbLBbZjh07xm/durXHXkODwRArDaKys7PtSUlJreLXer2+6dVXXx0vDbJyc3Mn7Nq168aqVavqduzY0VRQUJAgBqwWi0V25MiRhP379/sc/hbz5ufnj5POHxXL9pZnsIbUxS2TpGnvvvvu+NWrV9/xTB9JAhFwZsDVyzkawFwA3gLLupG2DychpH8cdXWuPTCFBT4tJhPg9D0yFZmY6LYHZlRKCm1RNIjutd+DyWrqDCyNViOu26/7lTdGHoPpo6e7AkxtOtI16UhWJSOC9byH6a9m/QqrT6z2a2ukCBaB1bNW+/29DIbJs8bYH/r5lOpeN39nQObytKpgH2+5bt26ZKVS6SwqKlIVFRWpbDabvKqqKqq8vHyU3W732jspUqvVTqvVKs/Pzx8HuHon33nnnc5eUqPReCknJyc5JycnWafTtQCueZzS67m5uRNOnz4dq9FoHGazWbF//35zZmZmU3Fxcez69esn2O12mbeTesS84ip3q9UqH6x5k8XFxbH79u1LOHnypApw9Z6mpKS0ega7BQUFCQcOHEgAgP+/vXsPj7I+8AX+/c1MJpPJbZIMBAQEcqGS0LIloYQSxR5ha23dja60u7i66moj7hFOtylsj6kUq8eC2AueLVKt1LrotvpYju7y6HppbbmuhC5bCJZcAEElkCGXyT2Z93f+eN938s4tmUnmTSbJ9/M8PDPzXn9Jhsw3v+u2bdtmHD161GkMyFOJkNEshRPNhYT4MwCN4Wo4tf0TPnBu2bJFbt68ebyLQTSp9DddQnfNUX8TeW/d8C1Z9vnz/bWXzpISJM2aNQYlJQDoHujGn678KaDP5Zm2M5BRVNfZLXZck31NQLjMy8yD1RJ77fNwKw0BatgcwUpDEf9SqampOepwOBYWFxeHbV6OVaS11AFwLXVKSCdPnlzY09NzqqSkpDTWc+OyzpoQ4k0Aq7Tnq6SUvxFCzANwG4DVUGs+j8bjXkQ0cUkp0X/hglZ7qYbM/nPD1IQJgeRrrlH7YGoTrdvc7qHPobjo8/XhdMtpnGw+iRMetd9lQ2tDVDWLNmFDYVYhit3FWJSzCMXuYuS78pFkiU/f2VsLb8VVaVdh1/FdONoU+vFSmluKysWVCTPhezjzF0/zzl887U+XzrU7zp30ZPR3+6xJKVbf3OKcdrP7bBKNtVEHTiHEfQByANwPoADANm3bMcNhx7T9RDQB9NbVofPQYSidHbCkpiF1eRmSCwtjvo6UEn0NDYMDfI4exUDTMK1JNhtSiosHm8iXLIE1I2OEXwlFq1/pR0Nrg39Az8nmk6hrrcOAMjDsuRZhQb4rXw2WOcUodhejMKsQydZkU8tcNrMMZTPLUN9SjyMXj6CjrwNp9jQsm7EsofpsDkcdSMSASZNbPGo4V0kp/VWr2qpDLwNYLaV8Jw7XJ6Ix0nnoEJr/+SfoOhpaY+QsLYX7Hx5A6vLlEc+XPh96PvgA3f5lImvgaxl6sRKRnIyUxYsH58BcvBgWp3PIc2h0fIoPjW2N/mBZ66nFB1c+QJ8SdqrFAAIC8zPn+4NlcU4xPpX9KaTYUsag5OEVZBVMqIBJNBXFI3CeCXr9FoCvxitsCiHyoDbXb5VSZkV5zkaota4eAPkANkkpQxczJyK/1ldewScPb464hnjX0aP48O/vxczvPQLXX/0VAEDp60PPiRP+2svuY8egdA45mwksqalIKVmiLRFZCseiYljs9rh/PaRSpIJz7ecCwuWpK6fQPdAd1flXp18dEC4X5ixEalLq8CcSERnEpQ+nkZSyTQjxfvB2IcQ8KeXZWK4lhFgFYAnU5TNdUZ7zdQD5UspK7XUegBqowZOIwug8dGjIsOmnKPik+jvoPPKfGLh4Ed3Hj0P29g55itXl8s9/mVJaCsc113CKIpP4l4DU5rg84TmBWk8tOvuH/iNAd1XqVf5gWewuxsLshchMjnZtDyKiyOIROMMNTQy3bROAdTFdWMq3AbytBc9obQVwg+EajUIICCFuk1K+Esv9iaaK5n/+yfBhUycl2l97LeJuW24unEuXwlmqDvKx5+dziiITSCnR1NUU0OcyliUgpzunq8FSC5dFOUXIdnC9eCIyRzwCZ6UWCI1rkeUJIVYbXmdDnaszpsAZK6020wW1RtSoFepo+WED55YtW6KfTTgG8RqEQVOb9Pkge3uh9PZC9vZC9vT4nys9PZC9fZC9PVB6tG29hm29vZA9vYHP+3ox4LmC7mPHhr95BElzr/YvEelcWoqkWbMYME0wmiUgsx3ZAc3iRTlFmO6cbnKJiYgGxatJPbgf5x+CXrchyiUwR0m/R/Bv4StjdP8Qox2EQYlJKooa+AzhTw18+nM1zMmeHv/zIQNhTw+UPi0E9vRA6VO3+a+vbcMwK+yMpcyKCkz7xjeQlMvgEm/6EpB6sDzpOYlLXbEtAelvGs8pxozUGfwjgIjGVTwC56+klMNOeSSEeDoO9xpOpH6erYgycG7evLkk0r4tW7bENEv+SAZhUGyklJBaOPPX+umBr7cnIPwpPT3+Wj098A0X/iIFwuGWVpwK7HOvZtiMg/a+9sBwGcMSkKlJqSjKKcKinEVhl4AkIkoU8QicW+N83GhEGonuQmitp6liGoTxnYeRdNVVE7qmU0oJ2d9vaObVa/LCNOlGCH9Kr7bN2GTcq12rpyco/A3WAE4pQkAkJ8OSnAzhcAw+T06GcCTDkqxtcyRD2PVtyRDJjsDnyXZYtPM7DxxA6y9/FXNRLKkhi6PQMDr7O3HKcyqgWdzMJSCnjEungMb3gF4vkJwO5K0Epi8c71IRkcGoA6eUMrg5fVTHjZIeKrMRGD6zMcYrHcU0CENR0PyTnXELnLK/P6iPX4TwF1QbONjHzxD+ggNhb1BtoiH8IU7LpE4UQgt6Qwc+R9B+NfDFEgiN4VIkJcW99ip5/vwRBc7U5Ym7gksiiPcSkPMz58NmifvEIhNb42+B97YB5w6E7pu7Ali5Eci7fowLRUThTKrfXlLKY0KIcFMouaDODzomeuvqwvbZHErX++/j0o9+BGtGZpjawN7AJl1Dv76APoTaI3w+k76yxCSSkoau7Rs2ENqHCHzafocDwq6d53Co97RMjpql5MJCOEtLY3rPOpcu5aA3A+MSkHqfy4bWBvjk8P8XjUtAFucUY5F7UVyXgJy0jv0CeH0DEGmZzXMHgBduAW7eASy5Y2zLRkQhJnTgFEIsAfBtKeUaw+bHAVRq//SR6xjLKZE6Dx0e0Xmep3fFuSRjzGYz1MbZhwx8wpEMix74wtT2WRzaOcbAZ6zts2uB0G7nnI5x4P6HB/Dh398bXa28xQL3A6ZOOJHQ9CUg9XB5ovlEzEtAFucMri8+FktATjqNvx06bOqkAry+HnDNYU0n0ThL6MCpBcqvQV1pCEKIlwE0Sik3aYfkAVglhHDpKwlJKbcJIbYKIbZicKWh1aFXN4/S2TGWtwtlsQQ05cazv19I4HM4YLHb1WvaEvrtRENIXb4cMx/ZMny/Y4sFM7/3yITubxwLn+LDmbYz/mA50ZeAnDTe2zZ82NRJBXjvCQbOCPbu3Zv+0EMPzT5//nxye3v7f431/ffv3+/cuHHjrEOHDmVIKf3TElZXV+c+//zz086fP39irMtE5kjohCClPAYg4gSBWq1lSM2lIZCOi5EOpkhZ8lmkfPoz4fv7BfQXHKYGMIlNcRQ71223IWnWLDT/ZCe63g9ZLAzOpUvhfmDdpA2b8VwCsiinCAuzFyLNzoFVcXfpVPg+m0M5t189jwOJQlRUVHgBXLjzzjvHZTW+8vLyrmefffZccXHxp43bS0tLuxobG73jUSYyR0IHzolqpIMpZm7Zwn5xNK5Sly9H6vLlk36hguAlIE961IDZ0R9d64S+BGRRTpF/InUuAWkiKYH2jwFPHXD0uZFdo/E9Bs4I3G73uHb8nz59esj9KyoqvFoYpkmCgdMEHIRBE11yYeGkeT/qS0AaB/Sc9JxEW29bVOdPT5mOIneRv88ll4A0UV8n4KkHmusGH5tPA54GIMr14CPqZXYhGk8MnCbhIAyi8dHc3YyTzSdxwnOCS0AmIkUB2j9Sayub67VAqT1vv2DefZPTzbu25rfPP3NVzb7/NzMe15pT/Om2rz78eH2k65fc9JefXP939wWsEHDh1Enn7IXFXSO95/bt290A0Nraam1sbHRUV1d/UlRU1AeofS337NmTlZ+f33vs2LHUvLy8nkcffbRJP7e6ujrX5XL5jOe/+OKL5wCgubnZun79+tl5eXk9Ho/H1tbWZtP3hVNbW2u/99575xr7de7fv99ZWVk59/z588n79u07/cYbb6QDwLvvvpvx7LPPntPLGeu9aOwwcJqEgzCIzNfS0xIwzyWXgEwgvR1qLaVHC5XNdWqw9DQA/SPIRA4X4F4ApE4D/vTvsZ+ftzL2c6YQr9drLSsr6yovL+8C1NBXVlZWdPjw4dqioqK+jRs3zpo3b15fVVXVRwCai4uLF7pcLl9VVVXz7t27szwej80YQNeuXTtXf75y5coFVVVVF+++++4WQA2na9eunRspCBYVFfUF9+ssLy/v2rVr17lrr7124eHDh536vdauXet49NFHZ+rXivVeNHYYOE001QdhEMVT8BKQtZ5afNTxUVTn6ktA6sGy2M0lIONCUdRayebTag2lp05rBq8DvNEtzxlAWIHs+UBOIeDW/unPnTmA/vPafVNsA4fmlrP/ZhT0sAmooW/RokWdepjbtm1bwH+26667rv2dd97JqKqqai4sLOy955578gDgi1/8YntFRYV3x44dFwBg9+7dWbW1tU49AALA2rVrW8rKyooARAyB4fp1ZmdnDwDATTfd1K5vy8vL63n33XczRnMvGhsMnCabKoMwiOIpYAlILVyea4/u8yLFloJrsq8JqL3kEpCj1OsN06+yXq2tjHIUf4CUbLW20l1gCJcLgKx5gDWKWTZWblQndY9maiRhAVZ+K/YyjsD1f3ffx8HN3GN5/dE0p4czb968vuPHjzsBNYxWV1fn7tmzJysnJ2fg+PHjzszMTJ++77nnnmt85pln3Hv27Jnm9XqtTzzxxLmqqqrmhoYGOzDYXK97+OGHR9x/Qm8+D2bGvSh+GDjHyGQahEEUT1wCMkEoPqDtvKGm8vRgyPR+Evv1LDYgO08LlAVqoPTXVo5y0FXe9cDNP4Z8bQMEFEg5WPkJwP9awgJx8w7OwTlCZ8+etS9evLgLAObMmbNow4YNF/Wm7HXr1tnOnj2bDAAvvPCC6+abb27Xaxb37t2bfssttyyoqqpqzs/P7wOAqqqqZrPLO5b3otjxtzIRhahvqceRi0fQ0deBNHsals1YhoKsglFfN55LQBbnFKPAVYCkaGrEaFBPe2DTtz5gx1MP+Hpjv57TrTV9a6FSbwbPmhtdbeUI/dJ3PV7v+yf8T9uvUWY5FbBPCOCwshD/d+AW/IXvenzVtFJMLvv373ca+3CeOHEi9bXXXmvcv3+/88KFC8l33XWXv6lar/msra21v/zyy1kHDx5M3blz50eAOqXR7NmzewHg7rvvbtm+fXtXdXV1rrGP57p162bpx1+6dClkubhw23TNzc3WcFM5RXMvGj8MnETkd/iTw3j6+NOoaaoJ2VeSW4L7F9+PspnRzTMbvATkSc9JnG45HfMSkPq/BdkLuARktBQf0PqhIVAawmVH0/DnB7MkqbWVwf0qcwpGX1s5Agfqm/HtV/8IRS7C/r5FKBQXsMJyAmnoRgdScEBZhDo5GwBw8NX/xqysFKwocA9z1antiSeeOHf48GHn4cOHnfoo88OHD9e63W5feXl51/3333/xjjvumFtSUtLpcrl8zz777Lk1a9bkP/XUU9Ouu+46b0NDQ3J1dXUuAHg8HttTTz3l7wNz8uTJU+vWrZu1bt26WTk5OQMej8f2ve997yKgBtZvfvObswHgS1/6Ut6TTz55AQCM21544YVzH3zwQfJjjz02AwDuuOOOuU8++eSFI0eOpD7//PPT2trabHqoHOpeNL6ElMM3W5Fqy5YtcvPmzeNdDCJTvFr3KrYc2gJliH5xFmHBd5d/F7cU3hKw3bgEpN40HssSkPMy5/nnueQSkDHobjXMW1k3OHjnSuPIaitTp2lN3wWD/SpzCgDXXMCaOPUTX911CP95JrqprgBg2fxs/LIy6sGZEUeS1dTUHHU4HAuLi4tPRTqGaDI7efLkwp6enlMlJSWlsZ6bOL9BiGjcHP7k8LBhE1CXf/zuwe/CIiywWWz+9cVjWQJyTvqcgEnUuQTkMHwDQOu5oOmFtJDZGd0UUAGsdiA73zBgZ8FgbWWKK/7lj7PTTd6YwiYAHDlzBaebvFiQa/5cnEQUHgMnEeHp408PGzZ1ChRUH6iO6lguARmD7pagidDrBkeCK/2xXy8tN8z0QlptpSVi97iE0t3nQ8PlDtRd8qKuqQN1lzpQc65l+BPDOFDfzMBJNI4YOMeIWYMwaOqRUqJf6Uevrxe9vl70+frQ4+tBn6/P/9q4b7htl7svh+2zGSt9CUjjXJdcAjKIbwBoOWsIlIb+lV0jGFhrTdaavwuCwmUB4Jg4wb6zdwANlztwukkNl/VauDzf0oV49frq6Bm+7zARmYeB02TxHIRBiUFKiQFlICTABT8fSfjrVUK3hTs+EeRl5mH13NVY5F7EJSCDdV0J7VfpqQOunBlZbWX6zMF+lf5m8AIgc86Eqa0EAG9PP+ovdWi1lV7Uac8/ah3BXJ4xSnPw445oPPF/oImGG4RR01SDyrcqww7CoMiklBiQA0MGsuDAZqwBjDb8DbUvmjkiJ7ub5t+EysWV412M8ePrV2sr/ROhG9YG746tjyEAwOZQQ2XA9ELaa0dG3ItvprauftRf9qo1lnqt5aUOfNLWE9N1LAKYl5OKgulpKMxNQ+H0dCRZBf7hxT/EXCaOUicaXwycJolpEMah72Jm2swJVdM5oAxEHeAi7Y856Blq/6LtbzhZJVmSkGxNht1qR7I1OeB58GPEbRb1+bFLx7DvzL6YyzBlBvp0ekL7VTafVsNmFFM8hUi/KmgidO15xmzAMrFWQ2rp7EPdpQ6cblIDpd7X8pI3tlp4m0VgnjsVhdPTUDg9DQW56Sicnob57lQ4kkJrcJ8/dC7mUersv0k0vhg4TRLTIAypYNfxXTEFTp/ii7pJNurm3CFCYPC2aCbqnsxswjYY3myGUGeJIujFYVs8l2ksyS0ZUeBcNmNZ3Mow7gb6gJYz4eet7B7BIBVbSmC/SuO8lckTK6hLKeHp7BtsBjfUWDZ3DD/tlVGSVSDPnYaC3DQtXKajMDcN83JSYbdF/57ecEMh7vjZEShRNDRYBLD+Bq7yRjTeGDhNUN9SH/MgjKNNR1H5H5WwW+2BAU/pRe9AaOAbkFO7A7xFWEJCmP7cYXXEL+hZQrfZrfZJtXRiQVYBSnJLYnrPluaWTrxBb1ICnc2GQHl6cHqhlrPASP6IypgdNL2Q9jxj1oSrrZRS4rK3V+tX6cXpSx3a4B0vWrpi63dqt1mQPy3NX2NZmJuGgunpmJvjRJJ19N+XFQVuPH7rp7XJ3yMfZxHA92/9DJvTiRLA5PnUTCBHLh4Z0XkHPzkY55KYR0DAYXOE1OpFCm4Om8PfhBtt4Btq/2QKfIng/sX3o/Ktyqhq5S3Ckth9Nwd61cE5xn6Vcf2RxQAAIABJREFU+uCdnrbYr5eUCuTkB/ar1B/tqfEvv8mklLjY3uOfZqiuyet/bI9xJLcjyaL2r5yerj2moTA3HVdnO2G1RJw/PS6+tvRqzM5yYsc7dTgSpnl92fxsrL+hkGGTKEHwU9sEHX0dpt9DQERdaxcpzI0m/NmEDUKY+4FCY6dsZhk2L98c9UpD497fWEqg83LoROjNp9VJ0kfSxzdzTmjzt3sBkHGVujj3BKMoEh+3daNOq6k8rQXL+ksd6OiNLVg67Va1b6XWBK43h8/KSjE9WA5lRYEbKwrcON3kxYH6ZnT0DCDNYcOKAjf7bBIlGAZOE4x0MMVf5v8lbrj6hqjCn83CwEfxdWvhrbgq7SrsOr4LR5uOhuwvzS1F5eLKsQ2b/T3qMo3B/Sqb64HeEdRW2tOCphfS/mXnA3Zn/Ms/BhRF4kJLt3+aIX0AT/2lDnT1xdZNIC3ZFhAo9b6WV2WmwDKOwXI4C3LTGTCJEhwDpwlGOpjiruK7Jl6/OJpUyrp7UPZJE+o//gRHUpLRYbEgTVGwrLsXBUoTsCC2aW2iIiXQ0RR+wE7rhyOorRSAa05ov0p3oTqf5QT9Q82nSHx4pSugCbzuUgcaLnegpz+271GGw4YFuen+vpV6P8sZGQ7+IUtEpmDgNMGUGYRBk8uxXwCvbwCkggIABf1BA0XOHQBeuAW4eQew5I7Yr9/fA1xpCJwIXW8O722P/Xr29NBlG90LgOw8ICkl9usliH6fgnOeLtQblnM83eRFY3Mn+gZiC5YuZxIWGJvBtemGpqUnM1gS0Zhi4DTJpBqEQZNf42/9YXNIUgFeX6/WIOZdH2a/BLyfBDZ964N3Ws8DMU+YL4CsuaH9Kt2F6lrhEzg09Q0oOOvpDFp1x4szzZ3o98X2fXKn2f2DdxbkDva1zEm1M1hSzKqrq3Off/75addee633xRdfPBfPa54/f/6EGceP1N69e9Mfeuih2efPn09ub2//LzPvNVJm/DzGAwOnSSbcIAya2t7bFn3TtVSA334fcOYE9avUaitHMmguOTPMeuCFWm2lI/brJZCefh/ONHdqg3cG+1me9XTBF81EkgbT05P9K+7ojwXT05Cdajep9DQVPfroo02NjY1x/Y9XWlra1djY6DXr+JGqqKjwArhw55135pt9r5Ey4+cxHhg4TZSQgzCIgl06pTaXx+LDQ8DT5bGdIyyAa64WKBcMDt5xLwBSp03o2koA6O7zoeFyR8CKO3WXOnDO0xnVBOVGMzMdATWWhblpKJiWjkxnkjmFJwqSmZk50NbWFreMUFFR4dXCnSnHj4bb7U74lUzi/fMYDxOi8EKIjQByAHgA5APYJKVsHeL4VQC2AvglgFbtHJeUcszbrctmlqFsZhnqW+px5OIRdPR1IM2ehmUzlrHPJkVPSnXt7v4uYKAH6O+O/Djsvm61P6X+2HY+vmV1ZAYu26gP3smeD9iS43uvcdDZO4CGyx3+QFl/SV0z/HxLF2SMwXKWKyWkf2XB9DSkOxgsiWhySfjAKYT4OoB8PSwKIfIA1EANkUNxQQ2djQBeGY+waVTQ34+CNi/Q6wWSJZAT28odlGCkBHx9hjDXFRji/I89URyjPfrDZIRjEm39+JRsYM7nAvtV5hQCqe4JX1sJAN6efq22Uqu11JrDL7R0x3QdIYA5WU5tjfA0/yCe/GlpSE1O+F/BFIN//Md/vOqHP/zhzHhc6wtf+ELbu+++Wx/p+t/4xjc++cEPfvCxcf/vf/9757XXXts1kvtVV1fnejweW05OzgAAnD17NjkrK8tf89fc3Gxdv3797Ly8vB6Px2Nra2uzBfcnrK6uzm1sbHTox3zxi19sr6io8NbW1trvvffeuYcOHcqQUtYYj3e5XD4AaG1ttTY2NjpefPHFc5GOX7du3Sy9fI2NjY4HHnjgcnl5edf+/fudlZWVc8+fP5+8b9++02+88UY6ALz77rsZzz777LmioqKo1mDdvn2721iW6urqT4LPHa4MtbW1Tr3M27dvd//4xz+eMWfOnN6DBw/WxVLO4X4eALB//37nnj17svLz83uPHTuWmpeX1/Poo482AYN9UwHgscceu/CrX/0q++zZs/aDBw/W7d69O2v79u0zzp8/n/zwww9fqKqqagaA4uLihe3t7dZHHnnko7vvvnsEa/oObSL8ttsK4Ab9hZSyUQgBIcRtUspXhjhvjZTymPnFG0bjb9X+ceGaLOeuAFZuDD/4gmIjpbrCTNgwFyHghQS9SDWDEY6JeQDMJLNyE1B2/3iXYtTauvsDRoTrfS0/bottCiiLAObmpBpW3FGbxPOnpSHFbjWp9ESj96UvfSlv3rx5vTt37vxI31ZcXLwwKyvLH15Xrly5oKqq6qIeRKqrq3PXrl07Vw+da9eunQsA+ut169bNeuihh2ZXVFScKioq6nv22WfPFRcXf1q/3u7du7M8Ho9ND0jGa4Q7/vOf/3zhfffd12wMQnPmzFn05ptvni4vL+/atWvXuWuvvXbh4cOHnfo1165d63j00UdnRjPQxuv1WsvKyrrKy8u7AKC2ttZeVlZWdPjw4Vo9CEZbBn1fVVVVc0NDQ/Lx48edABBtOaP5eQDAxo0bZ82bN6+vqqrqIwDNxcXFC10ul6+qqqrZ2De1paXFtmPHjgvr16+fDQB33313y7JlyzqLi4s/fdddd/m/lltuueXK2rVrW6IN6LFK6MCp1Wa6oDaLG7UCWA1gqMA5Ilu2bIltEfShGKaZCWu008wkMikjh7eQMBhtE/AwTciTPQBabIAtRR1Eoz8mpYRui/UY7yfAK3fHXp68lfH/Gk3U2tWH002D/Sv1vpZN7b0xXcdqEZiX4/QP3CmYnoYFuemY706FI4nBkiaW/fv3O994442sy5cvB4zQXrx4sT/c7N69O6u2ttZpDFpr165tKSsrKwJwrra21v7SSy+5T548+Ud9/4MPPnj59ttv9x8/ffr0gNq5wsLC3nvuuScPAPSa0B07dlwId/zevXvTDx06lHHw4ME64zW+8pWvtNx7771zDx48WJednT0AADfddJN/jrW8vLyed999NyPa74UeNgE19C5atKhTD4KxlMFIr6HUDVfOaH4eum3btn1kfH3ddde1v/POOxl6jaXb7fZ5vV6r/nMzBu+ioqK+5cuXt3/nO9+ZoQdbj8djMytsAgkeOAHkaY/BC+VeMeyL5GtaX04AWArgvqH6fcZdvKaZiZdwATDWvn4hNYNDHTMVAmCSFuYcYcJcimFf8GM0x6SEBkWrif9d3382toFDc8uB6QuHP24ceDp6cbpJ7VupTjWk1lo2d8QWLJOsAvPdqYPrhOeqwXJeTirsNotJpaeJ7Ac/+MHHwc3cY3n9kTSnHz582AkMPXCmoaHBDgw2OesefvjhCwCwb9++DEANMfo+7XnE8FJeXt713HPPNT7zzDPuPXv2TPN6vdYnnnjinB6WjI4ePepMT08PKV9OTs7AiRMnUo3b4hmY5s2b16fXTsZShmhEKmc0Pw9deXl5V3V1de6ePXuycnJyBo4fP+7MzMwMOC9cmXUbN268eOedd+bv3Lnzo927d2cZ/0AwQ6IHTleE7a0YOnBeAfCWlPJtwD+I6AyArOFuuHnz5pJI+7Zs2RJ9gop1mpl3HgGc7hj7+g1Rexh8zIAJK8QkGqvdENCiCXHhjnEOXVtoM4REMwPgWFu5Ua1tj+Y9KyzAym+ZX6YhSClxuaM3YI1wva/llc7YPm/sVgvypqX6B+3oA3jm5jiRZGWwpMmtrKysC1D7aEYKOfn5+X2A2kQcbn9BQUHvcNcItnfv3vSbb765Xa9927t3b/ott9yyINw98vPz+7xeb0jzgcfjsS1atKgzmvuNxNmzZ+16zeJIy+DxeGL6oIjm56GbM2fOog0bNlzUm+bXrVtnO3v2bLJ+/nD3qqio8GZmZg5s377d3dDQkGxGv02jRP/EjFQj6UJoradfcN9NKeXbQgiXEOLrUsqfxrOAYY1kmpmPaoCnV5hTnvHiD4CjbPJN0oJgpBCpP1rYnDliedcDN/94+Fp5YVG7gORdPybFklKiqb0XddpIcGNfy7bu2AbeJdsshv6V6f6m8DlZKbAxWNIUVV5e3rV8+fL2H/3oR25jf8rf//736YsWLeoC1D5/27dv76qurs41HrNu3bpZO3fu/KiiosJbVFTUZWyeNe4HgEuXLgX8gj569KjzzTffzND3V1RUeGfPnu1vhjAer99/9+7dWXooam5utv7bv/1b1ksvvdRovG4soTfY/v37ncY+nCdOnEh97bXXGqMtg15rWVtba9ef/+53vwvbpB+pnNH8PPSyXrhwIdnYB1Ovja2trbUfOXIktbCwcNhmnQ0bNlx85JFHZv/iF79oiOZ7NBpCxjqPxxgSQiyBNiJdStlo2F4D4GikkefaNEo/NTahCyFatG2bRlqeLVu2yM2bNw9/4OGngTdGfBvzWJPj2+Q7ZI2ggwFwImr8LfDeE8C5/aH75parNZt518f9tlJKfNzWo44EN6y8U9/UAW9vSLeoIaUkWf19KwsN64TPznLCapn4o+fJdBHfJDU1NUcdDsfC4uLiU2NZoLGwbt26WQCQn5/f29raaq2pqUk9cOBAxvr16z8x1KDNAtRmZI/HY/ve97530Ria9EE/eXl5PdrrlqKior7a2lr7N7/5zdlvvPFG1o033tjywgsvnPv5z3+e1dDQkKz3cQwe1R58vNvt9hnvHzxC/LHHHpuhH//kk09eOHLkSOrDDz88q62tzXb77bdfNgbhYPv373fqTdnA8KPUw5VB3799+3b3sWPHUpcsWdLZ2tpqBYAdO3bM/MpXvtLywAMPXI62nNH+PM6ePZtcUlLS6XK5fDfddFP7mjVr8q+77rr222+/vWXjxo2zDh06lPE3f/M3zcHlNCouLl548uTJqN7TJ0+eXNjT03OqpKSkNJrjjRI6cAL+oHiDsdZSCNEAdS7OkEFDQggXgBYAq/UmdW27BFA5mhrOqAPne08Av3k09hukTgNcV8fYLBxljaDNAVhYi0NDO1DfjB+/U4eWs/+NFZYTSEM3OpCCA8oiZM37DDbcUIgVBe7hLxSBokh81Nrtr7Gs02ot6y91oLMvtkqJtGRbyIjwgulpmOVKgYXBkkZuSgZOmppqa2vt+/bty4jUXSLYaAJnojepA8DjACq1f/rIdehhU6sF/baUco22vVUIsS0obN4GoHFMmtMBIDl9ZOddWzUpppmhiemX73+Ib7/6R21VnNmo880OPODMFdzxsyP4/q2fwVeXzhnyWj5F4vyVLv8yjvqI8PpLHejpj20+0XSHDQsMk6LrfS1nZjq4TjgRUYz0OT537tz50VNPPTVtqNrfeEr4wCml3CaE2CqE2IrBlYZWGw7JA7BKCOEyNKE/rh2vcwGIOBgo7kY6XcwEm2aGJo8D9c2GsBmZIoF/evW/MSsrBSsK3BjwKTh3pctfU3la61/ZcLkDfQOxBUuXMwkLpqejQF95R5t2aHp6MoMlEVGc3Hjjjd6f/OQn06qrq3MffPDBy2N134QPnAAwVL9LrabzlaBtrQDGrxPl9IXqpO6TZJoZmvx+/E5d1Ot9KxJ48KU/YFpaMs40d6LPF1uwzEm1+5vAjX0t3Wl2BksiIpOVl5d3lZeXDzsZfrxNiMA5IU2waWZo8vIpEj39PnT3+9Ddpz529anPe/p9qL/Ugf88E3HSh7CudPYNO/3QtPRkLDD0rdSbxHPSJv566kREFBsGTrPkXZ+Q08xQYpFSondACQiCPXog9AfEAXT3KdrrgYDjuvsGjw09T33sjbFpO1YzMhwBNZZ6sHQ57abel4iIxs5oB5kzcJppyZ3qqPNxmGaG4qPfp4QNd/5Ap4fAPh+6+5WAQBgQAsMGSfVfgk8UEdHffG4Ovn3TQmQ4ksa7KERjpR+A9Pl8FqvVau5fckQJRlEUC9QlBEe0mhMDp8kOKIvw475qtPSGmWam7zPYoBRikk33Pmb0pmJ/IIwY7ga0RwVd/QPoiaJWUH8+EG3HxgSXkmRFit0a+Kg9b2rvwQcXvTFfc0FuOsMmTTWNiqIUdHR0ODMzMzvGuzBEY8nr9aYqitINdeXGmDFwmiie08xMNMam4q7+SDV8alNxV99AQGAcujZx7JqKx4rdagkNg3YrnHYrHFow9D+3W+HUHh3a9qHPsyHZZhlyXsrTTV78+Q9/F3O5RzMfJ9EE9bbP5yu9ePHiDCllU3p6eqfFYlE42I0mKyklFEWxeL3e1Kamplyfz9cE4O1hTwyDgdMkI51mZqwYm4q7+oJq94Kairv6fegxDjbpH+K8SdBUbGQRgNNu8we6kBBo2J6SZAiBYYKgPxAm2eCwW+C02+CwWcZ9WcUFuen43PzsmAYOLZufjQW5I5xvlmji+lefz7e8u7t75fnz57MtFsssDDFRPNEkIRVF6fb5fE0+n+89AP86koswcJok1mlmdrxT5w+cQzUVhxtAMtWbigNq/4ICYUqSGgr1WsHg5uTgWkGn3RawP8kqpsRUPRtuKMQdPzsS1XvWIoD1NxSaXyiiBFNSUtJdU1Ozwefz/bXP51sFYD4Ajo6jya4PajP62wD+taSkpHskF2HgNMHpJm/M08wcOXMFn/7um+gdUGKeMDtR2W2WiLV/YZuDg0JhSAi0W5BiCISOJMuUCINjYUWBG4/f+ulha+UtAvj+rZ9hczpNWdqH7W7tHxFFiYHTBAfqo1qSNIS3ZyDOJYnMahFw6rV/wYEwuCnYEAgDQqDdErZWMMVuTYimYorN15ZejdlZTux4pw5HwvzBtGx+NtaPci11IiKamhg4TdAxyuAoBAy1eMP0CQzTlzCkVjDJFnKe3cYwSKFWFLixosCN001eHKhvRkfPANIcNqwocLPPJhERjRgDpwnSHCP7tn7ri5/C35fPR7KNTcU0vhbkpjNgEhFR3DBwmmCkTY6ri3LhSLLGuTRERERE44vtqibQp5mJBaeZISIiosmKgdMkG24oxBBzbQfgNDNEREQ0mTFwmkSfZma40MlpZoiIiGiyYx9OE3GaGSIiIiIGTtNxmhkiIiKa6hg4xwinmSEiIqKpin04iYiIiMhUrOEkIqKwpJSQUoFUFCiK+igsFiTZkwOO6/a2o7erC1LxISUjE47UtID9lz88i76uroDrKIrP8NoXsE8qChSfL/C19nj1os9g2tz5Adf/r//YB6/nMkq+XAFnRqbp3xciih0DJxFNeoriQ193d0h4CQ024UNO4HMf5i5eErAaWE9HB87811FIRYHdmYqC0mUB97/84VmcPX7McC1fwHUVn2/IexrDWYZ7Glb+7T0B12889j6Ovv4qFEVB3pKl+Nxf3haw/9ArL+GDA+9ByshBzv/cuF8qId/LRV/4c3zx/vUB2/a/9Av89ztvAABW3fsAFq++KWD/mzt/hKbG+th/cGHc8PcPhATOE795C02NdSi69gsMnEQJioGTaJxIKf0f6oMf+jIwkOjHBB1nT3EiLStwcYG2SxfR0dICKRW4ps9AWnZOwP4Lp06gp6MjpFZJShkQOgLLE/xcPWf+Z0sxs+BTAdd///VX0dZ0EVJR8LmK25A5fUbA/td/+H0ovoHow44SGHyMwaxi48PInZ8fcP0dd94G38AApKLgf734a1gsg6t2XT53Fv/yTxvi8WMDAHzjpf8HIQav3958Cfue2g4AmHb1vJDAebH+NH73L8/F5d7Trp4HBAXOztYWnK/9IwDANWNmyDmdrS248vGFuNxfKqEhVFgHvxdKuP2W+PXekoovZJtFu364exNRYmDgnOIGm8xk4Ae/NAYQaaiRkf5j0t3ukKa1i/WntWMlrlpwTcAHTW9XFy6cOqGFKEMTmjFUGUPIMMd97pY1AaGi44oHR//t15BSItWVFVLLc+HUCfzxnTeDrh/69SqKAoQNgYPhx5U7E3/xzf8dcP0/Hfo9fvvCzyAVBZ9afi2+8Hf3Bez/zc9/iuNv7fN/LZByxD+34pWrcOMD/ytg2/uv/xrH/+PfAQD/4+5KfPbGmwP2/+5fduOT+j+N+J5GKekZIYGz7vAB//WLr78hJHDWv38Iii80LIyEr78vdNtAv//6UlEAw3vDEsfAAwCKTwl47xnf52YHruGuHzYQjub+QsBisUBYLLBYrLDaQj82nBkZyJiWC4vFArsjJWT/tKvnaedbDI9W/+vA5+p+i9Ua8Fp/Pu3q+SHX/8yqG5FX8jnWbhIlMAbOMXb41V+i9eLHgTVKEUKPMWCFhEBFovQvbsXCFSsDrv/ak/8HFxvr1Fqgb30HuXkFAfv/7z1fw0Bf32CQCtNkFq2/fuQJzPrUwoBtLz28EYpvAACw4V9+DZvhg669+RL2bntkxPcLVvoXfxXwod/d4UXNv+8FAOTMvjokcLY2XUTt738Tl3uHC04DfX3o8DQDAHo6vKHnKAp8AwNxuX+4n5sxVMkwYTa+tUxDh5qIoShOgXO46yuKAqthn8Vmgz3FGRJqhEUNNRZr+NAT/Fw/J5gjNQ2f+vx1sFitSM8JnVfXPWcuSr5cEXpf/bpWa8R76kFPf50c1D8SAOYtXoI133kMwmJBqisrZP+yW76KP/vzm4b5Gq3a9yHoeyGGXzLt82tux+fX3B5x/59Xro+4Lx4WfWG1qdcnotFj4BxjZ/5wFB+fPhWXa3W3tYZs62xtgbf5MgCgv683ZP9AXx98/f1xuX+k0KO3eAXvj3ctk9q0lhT2+uECVzzvP1zgCrc/3P2F0D/gxWAAEEGBQAh/QBEWtbbJmekKuVbm9FzMLPwUhMUaNnTMuqYIKRkZ2vUMocL/2hBARKTQpZZjRlDtJgCUfKUC17SuhMVigSs3tFn3yw9+y/99ihjo/MErfO2Wvj3c1/8PP3spICgZ5cyagwd//quQc+IlPceNr2zYGHH/jPxCzMg3b/natKzskC4WsewnIjIbA+cYS8imNb3JzBhsDM1oIjgYaAHFZksKuVRufiGUgYGw97GnODH/s6X+QGOsQQkMNIOvIQIDx2C5rCE1Tc5MF67723tgsVjgSAud8/SqBQtx4wPfCPu1CIs19GsOuLdeJisgBJKSk0OuX7C0DPf983PqKN5kR8j+6/72bly79q7B64noao+iVfLlCpR8uSLi/utuvztu9wpnwbIVQ+4vXPZ5U+8f7ntORESJQYSrCUo0QoiNAHIAeADkA9gkpQyt3hvlOcPZsmWL3Lx582gugcY/vI+utraIgU5EDFihtU7pOe6Qmp6OKx4oPh+ExYKUjEzYkgJDYX9PD2ARgTVpcQw9REQTHH8hEpkg4Ws4hRBfB5AvpazUXucBqIEaIuN2zljJ++xSU68fPDI5WJKDtUBEREQ0tibCSkNbAezSX0gpGwFACHFbxDNGdg4RERERmSChazi1mkkXgOCm8FYAqwG8Eo9zjLZs2VIz0vISERERUaiEDpwA8rTHK0Hbrxj2xeOcqG3ZsmW0lyAiosQlN2/ezH6cRHGW6IEzdO4TVSsih8eRnOO3efPmkijKNSJ67amZ9yCKF75faSLh+5UosSV6H85Io8pdCK3BHM05RERERGSSRA+cekAMnrE4G0BjHM8hIiIiIpMkdOCUUh6DWmMZ3EzuAvBWvM4hIiIiIvMkdODUPA6gUn+hjUKHlPIV7fUSIcTLsZxDRERERGMn0QcNQUq5TQixVQixFYOrBq02HJIHYJUQwqWvJBTFOUREREQ0RibE0pZERERENHFNhCZ1IiIiIprAGDiJiIiIyFQMnERERERkKgZOIiIiIjIVAycRERERmYqBk4iIiIhMxcBJRERERKZi4CQiIiIiUzFwElHcCCE2CiEahBC7xrssNLmEe2/p28azXEQUHQZOIoobKeU2AG+Pdzlo8onw3joWZtuQtCWPiWiMJfxa6kQ04bQCcI13IWhSCnhvSSnfRux/4KyKa4mIKCqs4SQioilBCLERwJLxLgfRVMQazhgJIVYB2AogD8B9AJZqu/IAbJJSNhqOAYBNANYAyJNSrjZcZysAj/YyH8AuKeUxIcRtAL4N9ZfiJinlNiHE1wHsgvqX/CYp5TFTv0iaVIQQSwA8A/U9egMGa3hWA6iE+l7LA5AD9X26xnCuC+p7uUHb75JSVgZdf6O2T38/5wG4YtbXQ1PHUO8tIUQe1N+Lq6SUIuicVu2lC0C+lLJS+926VDtG//28S0rZaPoXQkQQUsrxLsOEowXKtwCU6OFP+1CvgfrLTQ+dL0MNpW8D2Kp/UAsh3oL6i+4VwzUbAKzWf/kJIWoAvC2l3KQFTkgpfzpmXyRNKob3Z6X+PtIGX6wCsMbwPm4BcJ/+3tTeh48bXm+E9gGuvX4ZQKOUcpPhXjUAjgYHU6JYRPPe0kJngx449VAZdM4uw/FLANQYAyoRjQ3WcI7MFQAw1jRqtZPHoNZoVmrHuAyhUv+FtwrqX+SrAy+JV6D+ta5vvwHAGSGER7v+NpO+Fpoa9BofY3+3BqjvRWONeSOAbMD/4b3E+IcR1PdpDYBK7cP7NgBZQfc6Gs+C09QTw3sruCa9EcDLQggAeEvr47kJRDTuGDjjqxFAqeF1a5hjlkTY7jGeK6VsFUKsgVaTGs9C0tQVpvlwqObEPADQa9gN9A/wUu2a4d7PRKMxoveW9of/Gqh/4H9d6xJSCYCtQ0TjjIEzvvIwfO1OI8KP4M0Jc+4a7d/LQogSfrDTGGsEhuzKcRRQ+3nyvUlxNqL3ltaC9LahC4je/Snse1gIsZGtR0Rjg6PUR0Fr9tGfr4I2cGioc7RfhPrgIP1cF9TmI2O/o5ehDhB6BWoz5jvxLT1NRdp7zSg7zGEuIOC9ujHoGlu1/fociME1oKsiXJcoKjG8t4LfZ0ugDrrUr/M2Amvx/c+1/wv8Q4lojLCGc3RKtaAJqKMfS7Sm8CW9qbl5AAAD6UlEQVRQR/a6tIEZu4L6e5YIIbYKIZZCbUrPhzZwQ/twr0TgL1IPgCXhBhsRDUd7P+ofws8IITZB/WD+GoA8IcRWbXDaVm07hBCNUspXDO9VfVaFHACP69eWUq427G+AGlaPAVjF2iMajeHeW1D/EN8K+P9Avw9qgMwx/JGUA63/vHbNViHEJsNqRezfSTRGOEp9BDjSkYiIiCh6bFInIiIiIlMxcMbI0FwOIcSuMH3iiIiIiMiATepEREREZCrWcBIRERGRqRg4iYiIiMhUDJxEREREZCoGTiIiIiIyFQMnEREREZmKgZOIiIiITMXASURERESmYuAkIiIiIlMxcBIRERGRqRg4iYiIiMhUDJxEFEAIsVEI0WDW8URENPUwcBJRsGMA3g7eKITYGsvxREREOiGlHO8yENEEIISokVKWjHc5iIho4mENJxENSwixEcCS8S4HERFNTLbxLgARRUcIsQrAVgB5AO4DsFTblQdgk5SyMej4rQA82st8ALuklMe0fRsBtGr7XADypZSVQog8ALsArJJSCu3Y2/R7GZrVd0kpG8MdP9z9hRBLADyjlfsGAKu0Y1YDqAz+OoiIaOJjkzrRBKKFzrcAlBjC4xIANVBDY6O27S2oAe8Vw7kNUEPdEgBLpZSbDPt2SSkrted5ABqMAVK/R3CoHOL4iPfXgqpe5kop5U/1MgCAXg4iIpo82KRONLFcAQA9bBqeHwOwCfCH0lXGsKd5BWptZCOAjUKIrdqx0M813iPWMumiuD8wWLtqHGzUALXWk4iIJhkGTqLJoRFAqfZ8CQYDnZEHQKkWUNdox70shJAAvhrHsgx5f+MGNp8TEU0NDJxEk0MegKPa80ao/TKD5QA4qtVAvi2lXC2lzILazL4rzPERaX1AIxny/rHch4iIJgcGTqIJSOsDqT9fBW3gEABoTdnHtME++jEuALdpxywB8G19n5TybaghUZcd5pb+/dq1jDWYAcdHcX8EbSciokmOo9SJJqZSQ//LpVAHEflDoJSyROujuRRqU3Y+gDXaKPFSADmGWsocAMYBQ1u15y8DuE9K2SqlbBVCbNIH9mCwv2ik44e6vzHwPiOE0ENwJYBsIcRW44AmIiKa+DhKnWgCGWq0OBERUaJikzoRERERmYqBk2iC0Go39ebrXez/SEREEwWb1ImIiIjIVKzhJCIiIiJTMXASERERkakYOImIiIjIVAycRERERGQqBk4iIiIiMhUDJxERERGZioGTiIiIiEzFwElEREREpmLgJCIiIiJTMXASERERkakYOImIiIjIVAycRERERGQqBk4iIiIiMhUDJxERERGZ6v8DuCZ40FhCaJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 244.08x150.85 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 168,
       "width": 334
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()#figsize=(7,7))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "format_axes(ax)\n",
    "cur_shape = 'ASI2'\n",
    "positions =  ['prox', 'med', 'dist']\n",
    "\n",
    "for z, size in enumerate(z_scores):\n",
    "    res_times = np.zeros((len(plotted_shapes), len(z_scores)))\n",
    "    for a, pos in enumerate(positions): \n",
    "        res_times[a, z] = right_residence_times[cur_shape][pos][z]\n",
    "    ax.plot(positions, res_times[:,z]/cycle_time, label = 'Z-score = ' + str(plotted_z_scores[size]), marker='o')\n",
    "        \n",
    "        \n",
    "        \n",
    "ax.plot(positions, [baseline_residence_times[shape] for shape in plotted_shapes], label='baseline', linestyle='-.')\n",
    "ax.plot(positions, [2, 2, 2], label='decision boundary', linestyle='-.', color='k')\n",
    "\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) \n",
    "#plt.legend()\n",
    "plt.xlabel('position')\n",
    "plt.ylabel('RT [cycles]')\n",
    "ax.set_ylim([0, 3])\n",
    "plt.title('Residence Time vs Position, RCA, ASI=2')\n",
    "plt.savefig(\"../figures/RT_pos.eps\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volume plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37912, 0.48379, 0.68129, 0.88389, 1.1165, 0.2626, 0.40586, 0.58183, 0.80693, 1.0183, 0.19253, 0.30718, 0.45923, 0.65179, 0.89421, 0.90235, 1.3218, 1.9063, 2.5823, 2.8892, 1.4014, 1.4651, 2.109, 2.9514, 4.0107]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Residence Time vs Aneurysm Volume, Aggregated')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LyYoPJU7C9byWqOGH6Dbzu+y5w4maf4hKjT3y/TZt+7OJ1oVi/z7BzL7h7nkhrVN5MpTfuvvCqufnELWr44nzyEFUjjlppAgU/wEc4e5XF0+kAtYRxMVzPJFM6sOs2NpT1GQWrbODROD+ubzrWVpfMY/UFGIf/DrDO/avaBW7l9hXLyMu2AcSwSfEBbsI3t9sZh+s1/ph0b20OH8sp3Ki7f2BXdLtB4G93P0vVa+fRlxLiqDwHWb2JXf3OttftMI9CBzq7pdl65pEVMgUregnEcftfcRxe1W27FQiMCwC+fea2aeH0CIO8b19Kr3nekTvjcvqLZwCvjxgbpQM5WNUFvZ/QrTwPnN8pfW9l+jiNoEotJ1vZru00Do/XGakv/uBt+a9RsxsMnHuLHoWfJ74Lv9N/H7zsmXXJH7rYjqm9xPngApmtjtx3S8qZb8FnOjuD1ctdwBRMF2HONedzfAnWdqNcgvvMqLw/gx3v8PM5hG9ZSDKHQ17NJnZYVROUH468KG8BdLMXkQcty+hXOYZlvVVOYAIpk4g9u9+4hjK9/GNieB8cnrocqJcWVERkso+ZxIB4trABWa2bXXLq5ntTJRrCr8iWmir33M28fu8rMlnOIZyF9LlaV2zqxey6N55FuV9/XVmtnnWIns9MZQDM3sn5XL0ibV6baXz2QWUKy1uAI6sHmJkMezk+8R5eRJwrpm9xN2rg8MXEpVrxXFzHXG+/Ue2zNpED7EDiaE+XaMWuoyZTTCzjc1sv9SF4KeUv6MHKM9pUu1wyhfyhcBO1cEcgLvfQxSairThk6g6mVp0VyxOPEuBY6qDubSu3xPjTQp7phaBdhxHnIghLvS75sFcep/H3f0YWsggZtF188B0dzGwi7v/vLoLmbsvcvd3E0ERxHf8pVRwa+RmYO/qQrG7L3P3/yJquQrV3YqK8QR5i8sh7v7N6j7RqfD0WsrjJzfJPhdmZsR3B9Hqsq+7n15jPUvd/dPAe7KHT00FqNHsQeK3rehGkU5uJ2UPFYXCvasvHOk3zLtZ7lj9+6cT3ynZQ7Pc/YvV3e3cfbm7f4PKlrKPDKGWfXZ2+/XpmKwnL1ieXbWv593lPla93wKkC+DBlFuZn0XtbnZtSZUcedbA2TXe+z6iRrXQbha4RcQ54+r8QXcvufsZVM77VOt4fBZRu1w4zt0/4VXjiNL6zia+l6KAfVQqAA2nQeJY/5m7P+bud7v7N7Lf9SeUzxnNWnffTHls40VVAXz++3+2OpgD8Bh7/A5i7BTEsfa66uWqlIDX5sFcWtdSypN8F+saBF5XfR1z98VUXnfWIrqKdcxjYvt8PEyz1s09iS7bEPvgz2otZNHd+2PZQ2e6+1urK0vcfcDdv04ch4UdW9iO4TZInEsrhgB4dPE+LnuonzhO9suDubTsI1R2w1zfYgz4M9J5+BuUyz6fdff3VQdzaX2/IArXxTG7v8XY9+GUdyu/sNZ2Unn+O8Ciu3hNqYLwM9lDP3D3FbqTeoxd3YWowK+r2+ur4xh3/5a7P+TuD7j7T7xyGM5niAANorJkn1qt2ulcsxPl88oLqVERR1QaFNfry4ADaxxXC4lKq3k09/7s9hdqBXNpnQuIVvS8DLBLC+uv53jiM0KUL19dHcyl9/07cd75XXpobWr3TvgE5Z4AfycqcSu6rbr7Q8RwqUYVxR0ZawHdb82sVO+P2EnuIro25Cf3vwM7V0fjmfxk+imPdLk1pQLf8UQgALBfqnUoTM5u97Fin//cxcROdRhRu9puLWn+GU/1xok7PkT5pF1P/j18tbrAX8NJlOeJ2ZIYl9HI51Pho578wv78Gs/nn3euu9csCACkbS8yZT1BuaUFovBTnMx+5O7XNNgmiJaK4gS5LuW5ckarr3j9RBfVNZ/fSSewWq7Nbq/Biv3Nj6bczfJKdz+PBtz9QqLmEaJltbq7dKt+R7SoQbQI1RprWbQ075c9VN39Iz+W6wbxWYH6PUSXqobjzVp0AFHYhujac0Gd5WZnt19lMT6yVWekwng9+fH1XFtxvru3Uq4ZvYUYq1OXu8+nXBnWR2VFyXC43N1vrPekx1iOPJlAo4CgZnfLpNX9ZjnRhev9xH74k3rLJhe6+x9qPeEx72p+/p/r7n+qs+w/icH9hec0ed9W5HPSvaFJpddh2e1zGlwTDieOX4jrzPvrLAeARyKIvKX0Q42WHwHn19v/UiE0H0t0oa/Y86dY9m9EIpxC9e+3N+XcAfdRWUlXa323EeOFCl0fG1RPaqk+MHuo3sTy5xHXcYgeSe+osxxEOWTjdPsxovdNTalipdnn7fb6qt1LdA+sKQWv+fCe93mDlucUaH4ye6gioEsVADukuyXg3dWV2dm6lhHX4brjwlMrmRMB1SLga/WWTet8EPhr9tDa9ZZtJPWsyz/bCdWViVXv+zTlXmAAh6fW8WJ944mkgoWP1VufR4+G99B+mb2hsRbQtetmIkjZMp20VpCalfOa4qZRt0dLXV4LsGv23AOUm8qfBfzaIhlIrfUsd/dT3X2Ou8/zNganp6bhPLnIuU22eTHR1abe+iZQmRyhle9hKZUBQLM5/lbIalQlzxI4pcbzecG87gkw80HiRDzF3fMTXD4ovZXPWSL6hxdG+1yGjQKO6sqMmmNOk+pAr/o3a+t7Tn6d3e7oe06/Vx6c1SuYv4ny+IHf12iByy86XzazQ+qNU3P389z9O+4+t1GFUBvyWuufuvsTdZb7FZW/QzutdO0cj1AZqEDl73upt5YMZsi/7xBc3XyRFVoCqj9zUSCame7+hxW7Guf7zX+b2bvqBTjufpFHL4OLG1Q4FppVFOT7XaPjFir3mVrn2nb9nHLl3hrUaW1Mhb+8S9/pDdaZt3Se41myogbywGTr1CV6ZTFcv19+XF7RYrkiPy53aaG3Tbe8hXKlx/3UuU6k8kteqXW01R9nne9fP69uSavhMqJnVj3dXl+1ed64y/NulIfV3FnjOlXLJZQbHYrsvoX881xT3QJVLVUg1G2lc/fF7v4Gd9/G3ddMZeBm8sqLdsZ+515GORh8iibdcAE8kqHcm+5OpHLc3U6U50FcSlxbG61rAa1dU1o21sbQXUhllsp+4mS2LWDZ438gune1suNXZ687KbX2NZPXKlQPaP065Sb6nYAbzGwBcaBfQbRW1E3H3aL8Pe9193vrLll2PfUTo2xNZW3yB9Lg0GbyoLLWwN7C4uom/VrLZLerU+9PJ8aQFJpmUqv1fhapaPPWv0Nb7GKSZ4Rs9DlHg0bp7atr/u6uuVR4uur+M4WAVCDYPntuv1QJ0cyM7PZQvufZROt3H7CXma1VoyvP26qWr1Ykligywv0I+J6Z/ZZoSbzcG6Sc75SZbURld79a2wZEDaqZ/ZhyrfBhZvbRJi3hhWbnx8VV96uvN/m5cycz+y7N5QXsLcxsfKPa5i6rO2VC5jJin9+ICGBfz4rjEvPWuR+lmt/cWcS+N5U4p34H+IqZXU3sN1cAN7YYAOcaTktB5bHb6LiFymN3yIV3d3/KzM6hvB8eSuVciYWDKV9nbqjVPSrz0uz271vclOuJcVhFIXFbymMkR9pw/X75cbl1i8flGtnttYlrbbNt6Ia84upHTc4Fsymfs2cAe1E7AMyvOzVbtHPuXjKz+ZRb4Xq9vmrNzkv577lGi78nxHFQHGtbUz7ft/V5kt9THrfZltTyNYNI5rItMYb6VdkinTZM5d/LIPAtM6u3bC5P2rQ15YqWvLzx1xYrQq5naF1GK4y1gO4r7v67Wk+Y2RuI2r61iB/6CjN7ozefX6V6nM7RHWxXdZPx54hWv3wMzIy07qOBkpn9magVO69e62ETeR/yulMxVFnQ4Lnq76FmBs8mGjWdVxcOa8lrqaovUtV95lv9zNWqP+cKY4Na0FEXgZVIO2mCm863WMc0Krsb70L7J741zayvg4Iv7v4PM7uWuHCsRrTGfa94PrXMFxeVJ6lR+HT3281sFlFALy6MU4jWh9el9dxNFCouAC6r13WlTW+n8qLz+SaVTOtkt6cTtd61EgpVa3ZMVtcaNzomX0HtqR0a6SPO191IINOKWmNzKng5TXoxPvRQVgzo8hbfFb5nd/+PmR1IdFktanwnEQXQvdL9B8xsLrHfXNRi4aGl+QCTTo/bofgB5YBunzqVKIdVLV9Tan1ZM3uoUdfgZ6QKjv9Q7oZYK6vpSBmu3y+/xm1F5XCDVq1NjwO61D08P2fsa2aNziHV5593UTug2yC73Wo5oVGLWrfXV63ZeSn/PZ9NZ8mk8jJLzz5PGle9H9H74kVE/oKNqbyeVeu0Qin/XiYx9O+l22XqtqnLZeLuPyf6OhdpmTcGLqvX3THTjTS9ee0W7j7o7ocSXQQvZ8VWjz6iufjTwK1m9stUwGzHWtntVmrjoXIS3mpd/x6qDLWgWx1E1euC1kyvP+fKbqCTAKkD3fiex7HivHXtmJ3dru52eSjlC8kv0tiHFbj7T4nW2dOofeHdiBhjcDEx+WknFQTVDq+6/0oiyUO9v+pqyVa7XXZ8TFpMO9BuEqdahvNYavWcMTu7vWdKzgGAmc2kPAj/z+7+f7VW4DFp7hZE0phaY5vXJVobLgAWWEyo28zKkrGxpjQ+rGhxKypRnmExV1RRy/8klePdqq1BZfmm2fjvXN41c4UusyNouH6/0XKNqx4HtzmNz3PVc4ztZ5GZuVoexLdaNmq0f3V7fdWanZe6/Xv25POY2duJAOd8omfLrkRDRh7MPUQMD2ql91wz3f5eul2mbttYa6FryN3/apFG+yKisDYduNTMtmkwriU/mG5095fWWa6T7bkQuNAixfUeROKTXSgXCAr7A1ua2fYNElBUy7MEtXrRajT3U/49LHL3NesuOTKqT3qr09nBVL2e6fUK8zIk1d/zlu7eSpe3bvopke1tdaJL4HOzJCB5gFdvID4QrX3ERKjvJQoVexAXq+2pnJB+E2LOtjd6ZI9rm5ntRMzFOBTbm9lLm3RnG5LUxW455WvQ7imIGU6tzHfWNnd3M7uOSBwwnugmWGT8bJQMpXo99xKTqR9PzAu1J7HfzKQyGN6AmFplko/uKVEgkqN8I90+lKxVnGidKypRfuaNJxR+nEjEUCzfzji/vJDWyri7TvVk/+uCJyjPCfk2d28UOI+I1A1vVtMFGxtHZPysnt5mKeUeFa2WjRot1+31tSu/ln7T3RsmB2pBHqx05fNYzEX8heyhEtGV9EYiYcrfiJwWd6QuqVcy9Otc/r382t2bZQlupttl6rYpoKvi7peY2f9SznC1HnGx3KtOy0Q+lu35ZtbfZIBqJ9v0MFErcS6AxSS9ryVq9osWxOcTSTxaneg1H0Dfapayuql+qfweppvZOl0Y59dN1YHuBjQJ6FK3nfFV3ZmqP9OmxDxr0l0PE132ilr2TWltDFPXuPtjZnYB0RLSRxTMv2Rm21AeE3kvLWalTOM7rk5/xRyPuxFdHN9MfNZ+IoHKrzo8j+S11n9x95bmM0qtSHdTDjDfRe/neXuY8pi4TWmeZKWZ6vNzswJzLydBnk05E9ybiPEZ/UTKbYiL/49rvG4FaT/4Q/o7JXVL2jmtdxbl8V6fN7NWk3+srH5EzCc5kahEeZ6X537NC/ANp9Fx9wEzW0S52+VzaSF9evpu83GajTI/V1uZ9r+heJDyePOhFpp7ZV/K5ZESsInXmCO4FjP7HeWM2kea2SlVY+/updza0mrZaMMGz3V7fe3Kyyzd+D3zfAtD/jyp6+yp2UMXAe/xxhmUu9Fg0O3vpdtl6rapy2Vtn6CcZh6iRr3WXBxQma1yKpUDRusyszeY2f5mtnUq2OXPTTWzl5nZZrVe6+7/cvdvpff6XfZUO1nf8u1e16rmoqmjUeHwJirHzOxeb8Gcme1hZgea2bYpcUlPeMyJkh/ArRR0XwEsNbN/m9kl2XryLEytfs6ZZnawmW2fEqtIA2ksWd4drdXveauUTfKVZrZB81c0NTu7XaQkfkv22A/rjXszs/FmtpmZvarW8x5zPP7K3Q+h8vzyfMoTrrcsZVTMu6m1XLOeMovl2RYPMbNed5vKz0Gt/r4vMLNZZvaqGt3Mq7ukNaslrTW1Sbfkc9LtZDGn4vaqcNpyAAAgAElEQVSUg4Vf1RgfBsRE12b2fDPbrdbz7v5kyob6TiqnQFmLUZ5wyWM6lJ+nu0UlCmb2MqC4Rt1J5XWvnj9nt3esu1SlV1BZ0d3O+PSVaf8bik6Oyw3M7HAz28XMZljvs1zmyVCubTWYS2ZntzdixXHw12e3m02IXXh5g+e6vb525b/nTqm7e0Nm1m9m7zSzvSzkmSQ7+TyNysVHUD7m/k3MaVc3mDOzcUQFTaHTfS3/XqxO99ta73+oxVzVL7bK7MP5+rasLtfX0c4E8k0poKvBYyLvd1AZoHzOzJ5bY/FbqKzFazqHSCqInEvMWXQTWfctM/syMY7vj1Q2QdfazqepnOupeh6vRq91KiewPLzJNq9G5Txu1etbTGXmyHoBcL7OKUTCgJ8RF9+PNHvNEOWpnN9Sd6my/YhjZCMqTxp5etuj03dTV6qZ/z7xm/+Bcpciaezy7PbbLOYcauYrRCAzj9pZ8tp1JeWECjtYpDHPj4Oa3S3NbHuia4oTCZaaVVZUzyHW8rGcOZhy17LBGutsJu8COIXeT6yc/7771zm/Vvs0MaXE1VSlmfaYcD5vTW+2vr2bPN+x1A276DY7jiwRTlKzu6XFnKRPEEHLFS1UtP2SygQYnew3K5t8TroiYD0of77Fcbx5i+9brcYUEjXkc1d6C1NB5IpunoUR2/+GKD8udzSzVoaRfIjYp39LXMt71vsrnYPz+T/b7RL6UyrHdFWPGc6nGHp9s/3GzHagcQtPt9fXrvw4mExlMFzPG4gyy6VEd8d8e/LPs2PqMVaXRdblRo0Nm2e3/9hCgqfdqRyvVq8lvFkPl3lUnjtbKbtvR0x59Wtiepm80m0e5Rwcq9Hk+mlmaxITr3eNAro63P06KgveU6ico6ZYrkR5fAREzfYB9dabaq6+Qblr02IqC555euW9zezFTTY1j/CbTeRdLf98J5jZlnWXjH7m1Rkeq+UTA+9kZsc2Wf5Uyhn2Bmgts95QfCe7vb+Z7VlvQTNbn8qJi/N5yfLPuQlNAm/gWCLlbqHR3ElS9h3KiTemAac1qvk1szdRWaM85O/ZK+ek6yda74ua9T81GNd3E+XxN6sRE4c3kh/HA1RWtrQqv1D/1mO+y3ZcRGXGyHbmpOvEmVR+R2c2qhwxs52pzPxb6/fNB8sfUuP5Yl0voMlE010wO7t9AOV5MO8hpjdYQZqbKP/djm/yHi+icjxdu9eAldHlQNHisl0qMBZdVQdoMA1HlR9QHu+zFk0q0sxsHyoLYae1+D7AM70K8jm5Gu1/W9D746tTP6WykvqMRkGImW1FZWH47BpTcXTT2yiXn54mtrdlqUvy+dlDe5pZ3lr6S8rH4FTg8/XWlVq7mlXQdnt9bUnTb83NHvpco4qiVHH6P9lD1+XXuTQXW9FK1w98vd51OT3+bRp3P87Hnm1p9ecHLIYGfK/q4Xrz0D3daBmP+QDzCtkPWiStqvfeq1FZ9ltIdh5PDUH5OeMzKZit53+pnOpryBTQNfYJyhcWiFTKtaLur1OeI6YPOM/MjrUYuPsMM9uQaJHKJ2Y8NXUzKfyScirTZxFJUVao3UhN4h8kUpQXqnf0Zr5FuVXtWUSNcD5/FWY2wcxOIb6LZn4CzM/u/6+ZnWpmFZkGzWxNM/sqlYXc09z99ja3vy0p6UI+p9AFtX5PM3sRcaAW/bRvIrtouPu1VF5EPmhmp1skr8nXM8nMPk6MCSlcMgLJH0Yld7+TOLYKBxO/WcVJ0sxWM7N3URl030hrk8e3Ij/pv7fO4xVSa1FeAfRpMzvBYoxOhdSdLK/M+GG746As5ujLu3a2ncggjSPJX7dNqi3uiZTA6eTsod2AuVY132A61x1CnBuLa9YCahd88slc32lmx1QXNsxsX6I2dSgZUFvxG8rjKvahPO5yTpPpKb6W3X63mX22VvdXM9ucyvPQ3Da7nq2UUiXK7OyhT1BOBHZJqxUVqRtxPjbnCDM7x6omC0/71zFExtBiX7mOynNPq/L9771mdkS+/5lZn5m9nmhh7mpChG5J5668t8xLgKvMbIXuvBZZeS+jXKnwEDHt0grM7HEzK6W/82st06K84uqSel2Xm8jPt31k002l82BekfI+M/t6dVCbehRcSpNuh91eX4c+Qrk1ajpwbToPVkitsVdQrrQcBD5cY30fpNwavT/wE6saSmLRzfxcyhVZ9Vyb3d6UmHOz4jppMXzhQOK6Xt0iWK9rY540qV7XxlMoD8WZQGS2n1XjmvFColUuvx5+tEbFxUnAP9PtdYCrU2+dfF1TzOx7VJbdu0JJURpw9yXpRJ8HAV81s7l5wg93X5x2tiuIH3E1It30f5vZNUQr3POInSHfUS8Avlj1nsvN7J3pPccTLUBXmtltwO3EzrcWkeksbzE73d3zrhKtfL4BMzssbfeGxPiOy8zsJiKImURk1SzS1F5DZaGxen3LUyvJNWm7+4CPEyewa4ixZ88h+lPnXYPmU05C02uziMLcZkT3gx+a2anEBfwJ4oTySsoFxweBQ2ocuEcSJ73tsvuHps/5b+K3eRmVKX7voHIeJWnuI0QrRDH/1gFEuunfEyfOtYnJRvMg7z7goC7N6Ya7/93M5hHjcIqaxqdpntTiFKLyZnPiWPgi8JF0fC0kzhMvppzYCKKG/2MdbGZeyHmSOLd04kziYl14F3Fs9MoXiXFfRcXKrsDfzOwPxPEyDdiGOJ8UHgXe6O610nV/FXg3UWjpI4Lq4y3m7RxHTDZdBAe/J8Y9vbqbH6jg5TnpPkZlDfHsJi/9JlF5UdQWfwx4j5ndQHT/7SOmmng55QDkP/S+xXE4nQmcSHy+vBtkw2QoNXyWmEet6Lr5VuBAizkm7yKuQztSeS39J/Bm72zC+i+l7S2mTTiDOOb/Qhzv21IuLP+OKA/0rNKkU+5+tkXyp+K6vB1wYzqObiPKBlsT19HCk8T31rN5Ic3s5ZQrRqCDiisAd7/azO6kPC7zCDM7seju5+4/MbPdies6xLF1mEVClYeJ8lwxR+lyooxXVOiu0DrZ7fV18HlvMrMjiUrI8cT+fpGZ/Z1I6jZA/JYvo3J4yYfd/Zoa6/u9mZ1IXOMgzlevS5/nvrT+XSi3QP2H8vjh6s/zA+AEyolEjgXeYmZ/IsqM6xHHTZ5AZBHlTKwVFTSZ2yknY/mOme1NfLenF5/J3e82s4OBC4ny4BSiYvhzqYyxlNhHqsfWfsPdq+cXLWKGQ4n5DacRx/of0vXstrTNu1JOiHQtHU64Xota6Jpw97lU1vSvQxQaqpe7mbjAzqta9g1EJL4L5WBugDjxH1xrLEBqwXkDlck3tiAKh0em54oL0DKiRqyjjHTu/jfignJj9vA2RODxJsoBybeobIavt767ie8hr6mcSvR5P5zoEpcHc2cTKcuHZSLbFIjPJA7gwvOIC/4RxMFVHBc3A69291trrOcxoiB4BuW+2s8iEugcQdTI58HcxcCOK1nmz5VeCqRfSxT8i771qxFZyg4nxiXlwdzvgVem1r1uml11/9feZIqQ1Mq2K1HBUVgHeA2x7YdSGcxdCbzGI119y1IXlbyi4Nfe4VQaHvOi/SV76GCLvv49kc5/s4hWmCJAG0dUqrydCODzYO5m4FXunm9jvr7/EMF/3oqzKTFm9k2Ug7kLif2q1fmCOjW76v78dM6tKwUS+xA1woVpxDXkMOL72p5yweuPwG6pa9UqIbU0FmMki895P5XfSSvrKRFB3ImUf+uJxDF4BNGVMw/mzgVe2iTDXqP3u4f47fJpjixtw0GUg7kLiOv5U6yk3P14onKkOJcU89/OIj5LHszdSZy7et37JK+4eozK63i78h4WzwYOrHr+aKK1vLi+TyN+syOI3gSrEd/NQUBeRqj3m3Z7fW1JAcieVE5m/ULi3HgolRVEi4DD3f3LDdb3GeCjlK/Lk4h9vyj/TErb/m4qu5hXfB53fzwtnx9z61EuM+5DOZh7ijiW82zOr6jT5fNLlFsRJxNdoA+jajyfu/+WqNTJk7BtRFwvDkvPFcHck8BH3L3uEAp3n09cv/Lu169In+UA4ncfJDLSd3VKEAV0rTmOyuDqUIv+9hXcfYG770QU6k8jDspFRK3AI0S/4y8AW7j7fzVqQXD3XxMnzPcTY1sWEgWep4nCyjxih9jc3T/uQ5gqwSNz48uInfdSolWqeJ9fAHu5e9MBo9n6HnL31xM78deIYPEhyjVPNxLdpbZ197fXqWnvGXd/2N33J2rEvkfUnDxKfOZ7iYvEocB27l43y5m7P+HuRxI1hl8gft8HiM/5OPH7nw7s4u77pS5A0iZ3X+7uHyYK5p8marXuIy4kS4iWnB8SF4Cd3P2fdVY1FOdROW9Nw7nnCu5+HxF87kcU7v9KHAMDRM3sLUQr0p7u/po0hqpde1CZJnmFmsM25d2RJtGDriE5dy+5+2eJwu7HiIL8PcTvu5RoMTmPuMBu53Um487Wdz1x7jye2FceTOu6i0jAtL+771/V1b0nUjfyvBt6S+OE3f1Rj3mRdiWuJTdRvpYsIsbKnUkUCF/hwz9H43Cobo07q5NWs7R/nULUtH+S6O54H3G+X0IU5L4JbO/ub+m0MiR7v3nEuerDxHX6IaIg+i+ii+y+7v5Gj0RiKzV3/y4xufOHiLLB3USh9kmiTPIL4vzwYnf/fZ3V1NJKUpsKqRveW7OHfu7uQ6mQOYvKxBkVYxrdfdDdjyMCnW8Tx9xjxHXgNqKCe3N3/yWVPa9qzo/Y7fV1IgUvmxFlvfOIoONx4lj4D9Fb6wTghe7e9Brn7l8gyj9fJI6jRcS+8XeiEWDLtA81/Dzu/leiJf0EogL0YeIa+QTlsWqfStt1StrO4nq8PjUSDLn7RUTl3hWUy58PVW1LsexNREPGG4j94nbiOl285tr0/pu6eysNG7cSvW/eQ7TELyKuQQuJ6/NO7n5q3RV0qK9Uavu4EhERkRaY2d+IVpongA1GQ0FepBfMbCeiwP59dz+62fKjhZn9g3JPgte7+68aLT/c6xtpFhOBFy1jx7p7J+NTpQmNoRMREemBNCDe0t2fKpiTMa4IUhaO6FY0YGavICYu/ydwo7vf2GT5jahM1HFzL9c30lKCkHcQrXu3NmuZTUnx8qEFK9XnWZUooBMREemNvBXiO3WXElnFpbG+RYKblbnFaSoxTgvgATPbyBtPw3Ai5eFLt9foNt/t9Y20PsqJu5ab2QZNcgMcSzlj+GJ6m2RrTNMYOhERkS4zs/0ojz+c5+5/GMntERkpZjaBGG/3auCcNGZpZXUtMbYNIrHZHDNbr3ohM1vdzL5CZaXNl6qX68H6RlRKvlTMkzqemKZrRvVyaaqB/6Jy6pBvDlcCvLFIY+hERESGKAVwRxGD6It5AfuIgfUz3f1PI7h5IiPKzH5EJB87fojJTHrOzN5H5TyXy4iWpX8TCW42IrJl5/NDngu8tVbm8m6vb6SZ2f5EgF5klxwkksL9i0g0tB6RPX3t7GXXAHt4zHMoPaCATkREZIjMbEcqJ8mFyOZ3rLvXmgRdZMwws76VMTipx8zeT0wJNbnJosuJDJUnNspc3u31jbQ05/B3qAzaaikRGWs/sLIH8qOdAjoREZEhMrO1ifTamxM18DcA/+Pul47oholIR8xsfSLF/x5EGvq1iHkyHwGcSIl/Zpo3cdjXN9LMbBrwNiLpy1ZEl9JiPr2/A1cBs73GXL7SfQroRERERERERiklRRERERERERmlFNCJiIiIiIiMUpqHbowxs0cpD8pdDtw3gpsjIiIiIquu9SnHG0vcfdpIbsyqSgHd2DOZGIRL+v+8EdwWERERERkbmmX5lA6py6WIiIiIiMgopRa6sWc5qYVu3LhxrL/++iO8OSIiIiKyKrrvvvsYGHhmSr3lI7ktqzIFdGPPfaRuluuvvz5XXnnlCG+OiIiIiKyKdtttN+6+++7irvI29Ii6XIqIiIiIiIxSCuhERERERERGKQV0IiIiIiIio5QCOhERERERkVFKAZ2IiIiIiMgopYBORERERERklFJAJyIiIiIiMkopoBMRERERERmlFNCJiIiIiKyCSqUSpVKp6WMyuo0f6Q0QEREREZHuGRgcZFx/P4898TSXzl/AHQsXsfSp5UyaOJ7NnjudvXaYwdTJE55ZTkY3BXQiIiIiIquIwcESDy56kjkX38q8m+9l+cBgxfPX/fVezpnr7Lj1Bsza90WsO30S/f19I7S10g0K6ERERESqFF3S+vr6Gj4msjIZHCzxj3se5VOnzWfxkmV1l1s+MMhVN9zNDbc/wMlHz2STDacpqBvFFNCJiIiIJOqqJqPVwOAgDy56smkwl1u8ZBknnjafrxy3M+tMn8Q4BXWjkgI6EREREdRVTUa3cf39zLn41paDucLiJcuYc8ltnHDodj3aMuk1VS2JiIjImFd0VfvQV6/iqhvuXiGYKxRd1T701av45z2PMjiobIEy8kqlEouXLGPezfd29Pp5N93D4iXLlP1ylFJAJyIiImPawOAgDyxa2lFXtQcWLWVAQZ2sBC6dv6BuRUQzywcGmXvdgq5ujwwfBXQiIiIypg21q5rGHclI6+vr446Fi4a0jjsWLlLCn1FKAZ2IiIiMWeqqJquKpU8tH9HXy8hRQCciIiJjmrqqyapg0sSh5Toc6utl5CigExERkTFLXdVkVVAqldh04+lDWsemG09XS/MopYBORERExjR1VZNVwd4zZzB+XGdF+/Hj+tlrhxnd3SAZNgroREREZExTVzUZ7fr6+pg6eQI7br1BR6/fcZsNmTp5glqaRykFdCIiIjJmqauarCoGBgeZte+LmDp5QluvmzZlArP22ULTb4xiCuhERERkTFNXNVkVjOvvZ93pkzj56JktB3XTpkzgpKNmsu70SZp+YxRTQCciIiJjlrqqyaqkv7+PTTacxleO25mdt31O3YqK8eP62Xnb5/DlY3dmkw2n0a9gblRTp28REREZ04quajfc/kBbk4vnXdXUuiEri/7+PtaZPokTDt2Oow/YirnXLeCOhYtY+tRyJk0cz6YbT2evHWYwdfIEBgZLCuZWAQroREREZEzLu6qdeNr8loK6vKuaCsSysikqGNZYfTUO2m3TihbkfLynKiJWDepyKSIiImOeuqrJqqivr2+F7sC1HpPRTS10IiIiIqirmoiMTgroRERERBJ1VROR0UYBnYiIiEiVWl3S1E1NRFZGGkMnIiIiIiIySimgExERERERGaUU0IlIU6VSqWLsSL3HRERERGR4aQydiNQ1MDjIuP5+HnviaS6dX5ntbbPn5tneYjkRERERGV4K6ESkpsHBEg8uepI5F9/KvJvvZfnAYMXz1/31Xs6Z6+y49QbM2vdFmlxXREREZAQooBORFQwOlvjHPY/yqdPms3jJsrrLLR8Y5Kob7uaG2x/g5KNnapJdERERkWGmPlIiUmFgcJAHFi1tGszlFi9ZxomnzeeBRUsZGNS4OhEREZHhooBORCqM6+9nzsW3thzMFRYvWcacS27TZLsiIiIiw0gBnYg8o1QqsXjJMubdfG9Hr5930z0sXrJM2S9FREREhokCOhGpcOn8BSskQGnV8oFB5l63oKvbIyIiIiL1KaATkWf09fVxx8JFQ1rHHQsX0denbpciIiIiw0EBnYhUWPrU8hF9vYiIiIi0TgGdiFSYNHFos5kM9fUiIiIi0joFdCLyjFKpxKYbTx/SOjbdeLqSooiIiIgMEwV0IlJh75kzGD+us1PD+HH97LXDjO5ukIiIiIjUpYBORJ7R19fH1MkT2HHrDTp6/Y7bbMjUyROUFEVERERkmCigE5EKA4ODzNr3RUydPKGt102bMoFZ+2zBwKC6W4qIiIgMFwV0IlJhXH8/606fxMlHz2w5qJs2ZQInHTWTdadPYly/WudEREREhosCOhFZQX9/H5tsOI2vHLczO2/7nLpj6saP62fnbZ/Dl4/dmU02nEa/gjkRERGRYaX84iJSU39/H+tMn8QJh27H0QdsxdzrFnDHwkUsfWo5kyaOZ9ONp7PXDjOYOnkCA4MlBXMiIiIiI0ABnYjUVXSfXGP11Thot00rkp3kUxOom6WIiIjIyFBAJyJN1cpaqUyWIiIiIiNPY+hERERERERGKQV0IiIiIiIio5QCOhERERERkVFKAZ2IiIiIiMgopYBORERERERklFJAJyIiIiIiMkopoBMRERERERmlFNCJiIiIiIiMUgroRERERERERikFdCIiIiIiIqOUAjoREREREZFRSgGdiIiIiIjIKKWATkREREREZJRSQCciIiIiIjJKKaATEREREREZpRTQiYiIiIiIjFLjO3mRmR3W7Q1pl7ufPdLbICIiIiIiMpI6CuiA2UCpi9vRCQV0IiIiIiIypnUa0BX6urIV7RvpYFJERERERGTEDTWguxe4vRsb0qLNgA2GuhIzeyXwDmBHYCNgIvAwcAPwC+Asd39qqO+T3qsfeAswC9gOmA48ANwG/BD4obsv78Z7iYiIiIjI2DLUgO6X7v6ermxJC8zsO8AxQ3j9FOD7RIBVbX1gn/T3UTN7o7vf0Ol7pfebTgSIO1c9tWH6ew3wbjM7yN0XDuW9RERERERk7BkzWS7NbDzwc8rBXAm4DDgF+G9gDvBYem4T4LdmtsUQ3+/XlIO5R4EfpPc6Pd0H2B64KAWbIiIiIiIiLRtqC91ocgywe7p9P/B6d/9DvoCZrQucD7wamAacAczs8P2OI7p0AtwC7OXud2fv9UngQuBlwFbAJ4GPdPheIiIiIiIyBnXaQvc14OvAlV3cllZckb13u47Pbh9SHcwBuPsDwP7AfemhHcys7YDOzCZQDs4GgYPzYC69133Aaym3Cr7PzNZp971ERERERGTs6qiFzt0/2O0NafF9zyda0NpiZlsR3SgBbnL3uoGouz9qZmdRDsh2A+a3+Zb7AUVwdqG731rnve43s+8BJwCrAwcCp7X5XiIiIiIiMkaNaJdLM5tGdDmcCvzN3W/r0Vttnd3+UwvL/yO73UlWzT2z25c0WfZiIqADOAAFdCIiIiIi0qKeBnRm9hKiC+OZ1VkczexY4GRgSvbYtcC767VoDcG5wNXEFAWPNVkWIgNl4fEO3m/b7PZ1TZa9Prv9ig7eS0RERERExqieBHRpDNmZlDNK/glYmD1/JPC/NV76KuBqM9vN3W/u1vaked4W5tvQxBuy250El5tmt//ZaEF3X2JmDwFrA2uZ2Zru/kgH7ykiIiIiImNMr1roTgPemm6XKI9fw8wmAp/NnusDHgLWJJK0rEVkl3xZj7atITN7M+UumsuILpHtvH4i8VkAHnP3xS287B4ioANYD6gb0JnZgna2p4bnDPH1IiIiIiKykuh6QGdmWwOzKAdrfwH+li3yOmDd9PyTwP7ufoWZbQJcAGwDvNTMDnD3X3R7+5ps+ybAt7KHvu3uD7a5mmnZ7SdafM3S7PbUJss+r73NERERERGRVVUvJhY/hAjkAL4LbO/uV2TP590ZZxfPufs/gYOINP8QCUKGjZk9m2iNK1rK/gGc1MGqJma3n2zxNXlAN7HuUiIiIiIiIpledLncJf1/BDjB3UvFE2bWR2UGyIopCNz9TjP7AzGZ97AlCDGz9YHLgM3TQ0uAg9x9UQerG8hul+ouVakvuz1Yd6nwr/Y2ZwXPAcYNcR0iIiIiIrIS6EVA91wikLne3au7HG5LuQVsKXBtjdffQQR06/Vg21ZgZs8ngrkXpIeeBA509xs6XGWeFfNZLb4mX+6pRgu6+4x2NyiXxuCp26aIiIiIyCqgF10u10r/76nx3GvS/xIwz92frrFM0Vo1udsbVs3MZhLTChTB3BPEmL7LhrDaPKBbvcXXTMpuPzqE9xYRERERkTGkFwFd0cJUayzYHtntK+u8/rnpfyvzxXXMzA5O27BueugRYA93/81Q1uvug8B/0t2pZtZKYLpRdvu+oby/iIiIiIiMHb0I6O4iWtm2yB80s9WBnbKHVmgFM7NpRLfMEk3mbxsKMzsB+Anlro53ATu5+++79Ba3ZLdnNNmWKZRbNe9z954GsiIiIiIisuroRUBXBEUvMbPts8ffSbnV7u46Y9ROBNZIt6/rwbZhZicBX6TctfNGYKa7dzKBeD3/l93evu5SIU/+8pcuboOIiIiIiKziepEU5RzgKCJgutTMvkyMETs+PV8Cfpy/wMy2Ss/PqlpPV5nZ8UTQWLgCeEMPWsUuAj6Qbu8LnNlg2X2z20MZuyciIiIiImNM11vo3P0q4EIioJsGnAx8DJiQFnkI+J9ieTN7DdFKNiu9pgSc7+7zu7ldZvYq4AvZQ78C9utRF8crgQfS7QPTZOu1tmkD4Oh092mqAl0REREREZFGetHlEmJy8UuIAC3/ewg4wN0fypa9k8p52K4EDu/mxpjZBGAO5fnXbgLe7O4NpwjolLsvBz6T7vYDvzCzzaq2aX0i8J2SHvquu/8HERERERGRFvWiyyXuvgTYL7W+7UlMQfBX4Bx3X1y1+L+IzJh3AF8DzsgnI++SI6ice20+8D4za+W1f3X3S/MHzGw28PZ09yx3P7zG674FvBl4JbAJcKOZnQ84kcnzYGB6WtaBT7SyMSIiIiIiIoWeBHQFd7+CGKfWaJmSmW3s7g/2cFMOr7r/rjZeexZwadOlqrj7gJntC/wc2JUYRzirxqI3Aa9VdksREREREWlXRwGdmT0zV5y7PzLUjWg1mDOzNUlZMN39rjbeYqtOtmuo3P3R1Ep5MBHMbQesTUw+fjMxdcIZ7r5sJLZPRERERERGt05b6BYQyUu+B7yna1vT3GeJJCIl2th2d5/SfKnWpS6Wh7e4bAk4N/2JiIiIiIh0TU+7XPZIX/NFREREREREVn29ynIpIiIiIiIiPTbUFroNzezVXdmSFt9vGN9LRERERERkpTbUgP3Wou8AACAASURBVO516U9ERERERESG2VADuuEez9bt+elERERERERGrU4DurtQcCUiIiIiIjKiOgro3H1Gl7dDRERERERE2qQslyIiIiIiIqOUAjoREREREZFRSgGdiIiIiIjIKKWATkREREREZJRSQCciIiIiIjJKKaATEREREREZpRTQiYiIiIiIjFIK6EREREREREYpBXQiIiIiIiKjlAI6ERERERGRUUoBnYiIiIiIyCilgE5ERERERGSUGvaAzsz6zGyN4X5fERERERGRVc34Xr+BmW0AHAXsCmwNTMvfOz1/PvB94Gx3H+z1NomIiIiIiKwKehbQmVkfcDJwAjAhPdyX/peyRWcAM4EdgGPN7CB3v7NX2yUiIiIiIrKq6EmXSzPrB34OfByYSARyfXUWn5Hd3ga4xsw26cV2iYiIiIiIrEp6NYbuc8D+2f1Lgfel/9VuBuYSAV8JWB+Y06PtEhERERERWWV0PaAzs+cDx6W7TwH7uvu+7v5t4F/Vy7v7Le6+D/BG4Mn08Ewz27fb2yYiIiIiIrIq6UUL3TuB1YjWtuPdvVar3Arc/efAB7OH3tKDbRMREREREVll9CKg2z39fwD4bpuv/QHwb6L75Su6uVEiIiIiIiKrml4EdDOI1rl57l5qsmwFdx8Abkh3N+zydomIiIiIiKxSehHQFfPMPdzh6x9K/3s+R56IiIiIiMho1ouArgjInt3h65+f/ncaEIqIiIiIiIwJvQjo/k6MgdvRzCY0WzhnZhsBryS6bN7Rg20TERERERFZZfQioCuyWq4JfKDVF5lZH/Btyl0tL+/ydomIiIiIiKxSehHQ/QBYkm5/xsze3OwFZjYNOBd4XXpoGXBmD7ZNRERERERkldH1xCPufr+ZnQT8DzEf3TlmdgzRcrd5sZyZbQlsRkxzcAiwRnqqBPyvu9/d7W0TERERERFZlfQkk6S7f8nMDDiSCNB2Tn+FPuCmqvvFFAcXA5/oxXaJiIiIiIisSnrR5RIAdz8KOI7oftmX/iACt1J2v/g/AHwROKDd+etERERERkKpVKJUKjV9TESkV3o615u7f93MzgbeCewNvAKYnC0yQLTUXQ58193/1cvtEREREemGgcFBxvX389gTT3Pp/AXcsXARS59azqSJ49nsudPZa4cZTJ084ZnlRER6peeTd7v7IuBL6Q8zm0xMPv5Eek5ERERk1BgcLPHgoieZc/GtzLv5XpYPDFY8f91f7+Wcuc6OW2/ArH1fxLrTJ9Hf31dnbSIiQ9PzgK6auy+hnAVTREREZNQYHCzxj3se5VOnzWfxkmV1l1s+MMhVN9zNDbc/wMlHz2STDacpqBORnlAfABEREZEWDAwO8sCipU2DudziJcs48bT5PLBoKQODGlcnIt03LC10ZrYmMIVyApSWuPtdvdkiERERkfaM6+9nzsW3thzMFRYvWcacS27jhEO369GWichY1rOAzsxeC7wfeCWwegerKDECXUJFREREqpVKJR574mnm3XxvR6+fd9M9HH3AVqyx+mr09anrpYh0T0+6XJrZ/wC/JCYNn0x52oJW/qi6LSIiIjLiLp2/YIUEKK1aPjDI3OsWdHV7RESgBy1gZrY3cMIQVqFATkRERFYqfX193LFwaMm571i4SK1zItJ1vejS+O70vwQsAD4MXAs85O7Le/B+IiIiIj239KmhFWOG+noRkVp6EdBtn/4vA3Z393/24D1EREREhtWkiUMrNg319SIitfRiDN1aROvcFQrmREREZFVQKpXYdOPpQ1rHphtPp1TS1AUi0l29COgeSP/v78G6RUREREbE3jNnMH5cZ0Wn8eP62WuHGd3dIBERehPQ3UQkNtmkB+sWERERGXZ9fX1MnTyBHbfeoKPX77jNhkydPEFJUUSk63oR0M1O/3c0s+f3YP0iIiIiw25gcJBZ+76IqZMntPW6aVMmMGufLRgYVHdLEem+rgd07v5T4DfAasBPzGzNbr+HiIiIyHAb19/PutMncfLRM1sO6qZNmcBJR81k3emTGNev1jkR6b5epVs6BLiIyHh5q5l9D7gKuBt4stWVuPtdvdk8ERERkfb19/exyYbT+MpxOzPnktuYd9M9NScbHz+unx232ZBZ+2zButMn0a9gTkR6pCcBnbs/ZGafB84H1gM+mf7aUaJ3AaeIiIhIR/r7+1hn+iROOHQ7jj5gK+Zet4A7Fi5i6VPLmTRxPJtuPJ29dpjB1MkTGBgsKZgTkZ7qScBkZqcCH0138w7jrZzRSi0uJyIiIjIiiu6Ta6y+GgfttmlFspN8agJ1sxSRXut6QGdmrwE+lu52EpzpzCciIiKjQq2slcpkKSLDqRctdO9O/0vAP4FPA1cC97v7QA/eT0REREREZEzqRUC3bfr/OLCTu9/Xg/cQEREREREZ83oxD90GROvcFQrmREREREREeqcXAd0j6f+jPVi3iIiIiIiIJL0I6G4jEpts2oN1i4iIiIiISNKLgO6c9H8HM9usB+sXERERERERehPQnQ38X1r3eWY2vQfvISIiIiIiMuZ1Pculuz9tZm8CfgNsBdxhZv8LXA7cCTzi7oPdfl8REREREZGxphcTi5+Xbt4FbAysDZyS/oplWllVyd17Ma2CiIiIiIjIKqEXAdNBxLQFZP8hEqWIiIiIiIhIl/SqBUzBm4iIiIiISI/1IqDbpAfrFBERkVGiVIoOOn19fQ0fExGRoetFUpR/dXudvWZmzwJuJYLRXd39d11Y50nAiW285Ah3nz3U9xURERkpA4ODjOvv57EnnubS+Qu4Y+Eilj61nEkTx7PZc6ez1w4zmDp5wjPLiYjI0CnpSPga3W9ZfEmX1yciIrLSGhws8eCiJ5lz8a3Mu/lelg9UJrS+7q/3cs5cZ8etN2DWvi9i3emT6O9Xa52IyFCN+YDOzD4JHN2DVb80/X8Y+FwLy/+xB9sgIiLSc4ODJf5xz6N86rT5LF6yrO5yywcGueqGu7nh9gc4+eiZbLLhNAV1IiJDNGYDOjNbDfgy8P4erHstYsoGgOvd/Uvdfg8REZGVwcDgIA8uerJpMJdbvGQZJ542n68ctzPrTJ/EOAV1IiId6yigM7OB7G7FfHFVzw1Fz+ahM7PnAecAr+zF+im3zgH8uUfvISIiMuLG9fcz5+JbWw7mCouXLGPOJbdxwqHb9WjLRETGhk5HJPdl/6ur1aqfG8pfV5nZGmb2WeBvlIO5+4E/dfmt8oDuL11et4iIyEqhVCqxeMky5t18b0evn3fTPSxesuyZDJgiItK+oaSYahRwrax9J44EPgY8K93/LbA9cEuX3ydPiKKATkREVlmXzl+wQgKUVi0fGGTudQu6uj0iImNNp10aG2WEHA3z0N0LfBI4w91LZtbt9T+TEMXdF6RpEWYCmwHjgH8DV7v7om6/sYiIyHDp6+vjjoVDu5TdsXCR5qYTERmCjgK6RnPNreTz0P0b+CDwfXdf0os3MLNJQBEh3m72/+zdf5zUV33o/9fMLkuWH7sbBSMIEaKbY6iQCq0FUdegloS2ltpcbUOpaTX0VmsTU2q/rTUxufdaWw03tb2tYqtV+o22tdb+kIBaG1op+OsiaUo80CYoAZJC4kLC792Z+8dnJjvALjs7M5/d+fF6Ph7zmM985pzP5w3ZsPOec877hP8JvB24/IKmAyGETcBvxhifSCMWSZLSdurMwIT2l6RWV5dVLkMIGeCKGOPjtbxujPGztbzeCBaSjMIBLC08htMO/ALwuhDCDTHGsqZ9hhD2VxnfnCr7S5L0rM7J1X2UqLa/JLW6mv8rGkL4SuHw72KM91bQfwPwS8AB4CW1jG2cvOyC198BPgB8CThKsp3BG0nW8l1eeP2FEMKSGOOTZVz/hTWMVZKkiuXzeXrn9rDzocqKogD0zu0hn8877VKSKpTG12KvAfIkiUwlpgOdNO5IUmlBlC8Cb7xgeud/Ah8MIfwN8FXgCpIk7W7gHeMWpSRJNXD9snl8+ouxosIo7W1ZVi6dV/ugJKmFVFPlsuZCCFlgUeFlo35Vt57kz/CTwM+MtFYvxvgfwK+UnHprCGFaGdf/bpWPWu0TKElqcZlMhq6pHSxfNKui/suvnU3X1A5H5ySpCpVuLL4IuG2UZteFED4+hst2kCRCP1B4faiS2CZaIYH7t8JjNJ8D/gt4HjAZeDWweZTrz6smvsIaPKdtSpJqYjCXY+2qBezae2RMm4t3T+tg7Q3XMJjL05Y1oZOkSlU65fLfgJcCS0Z4P0NSov/qCq+fBz5fYd+GEWPMhRC+BdxQOHXVRMYjSdJYtWWzzOzp5O51y7hj446ykrruaR3cdcsyZvZ0kjWZk6SqVDTlMsaYJ1nvlSdJ3kofRReeH8vjG8BdlcTWgL5fctw1YVFIklShbDbD/NndbLitj77Fc2hvG/7jRXtblr7Fc7jn1j7mz+42mZOkGqi4KEqM8RshhF/k4o3E7yRJ9L4FfKHMy+WAc0A/sBfYFmNs6LVeIYRpMcZnymg6veTYjcYlSQ0pm80wo6eT9WuWsG71Qrbu3M++A/2cOjNA5+R2euf2sHLpPLqmdjCYy5vMSVKNVFXlMsb4qQvPhRDuLBx+M8bYKqNsAIQQ5gA7SNbEnQ0hdMcYRyv7tbDk+OHUgpMkKWXFtXDTp0zixhW95xU7yefzF7WTJFUvjSqX3ys8ytlTrdkcItlbrgOYBrzqUo1DCC8H5hVePgPsTDM4SZLGQyaTuahy5XDnJEnVq/k+dNVWYWxkhSInfwvcVDj1nhDCPxfWHJ4nhNAOlG68/mcxxlPjEackSZKk5lBX+9A1iQ8wtNfb64EPF5K3Z4UQeoDPAssKp47QOkVgJEmSJNVIzUfoml0I4QGgr/Dyrhjj+0rfjzH+WwjhN4APFU79CvBjIYTPA0dJtib4KeA5hfdPAjfGGI+mHLokSZKkJmNCl4IY4z0hhFPABpINw+cD7xqm6SPAW2KMXx3P+CRJkiQ1B6dcpiTG+EfAi4D3A98k2ZLgHHAQ+DLwy8BLTeYkSZIkVcoROiDGeDNwc5ltXzOG6x4E3lN4SJIkSVJNOUInSZIkSQ3KhE6SJEmSGpQJnSRJkiQ1KBM6SZIkSWpQJnSSJEmS1KBM6CRJkiSpQaW+bUEIYTLwZuA6YBHwXGBajHFG4f3nAh8GPhZjfCDteCRJkiSpWaSa0IUQ3gr8L2BmyekMkC95fRXws8DPhBA2A2+JMT6VZlySJEmS1AxSm3IZQvgDYCNJMpcpeVxoXsnxKmB7YdROkiRJknQJqSR0IYTbgXcUXmaAh4EPAduHaX4A2MNQsnc18Ik04pIkSZKkZlLzhC6EcAVwd+FlHvjlGOMPxBjfDTx0YfsY405gIfBrhfYZ4MdCCK+qdWySJEmS1EzSGKG7BZhCkpzdHWP86GgdYoz5GOP/Bu4oOf1zKcQmSZIkSU0jjYRuZeH5OPC7Y+y7AThSOH5FzSKSJEmSpCaURkLXSzI69y8xxjNj6RhjPA18k2Ta5ZUpxCZJkiRJTSONhO7ywvMTFfZ/vPB8WQ1ikSRJkqSmlUZC1194vvySrUY2p/D8/RrEIklS1fL5PPl8ftRzkiSNtzQ2Ft9PsvfcK0IImRhj2b/tQgiXA68imbL5aAqxSZJUtsFcjrZslqdPnmPLjv3sO9DPqTMDdE5u5+ore1i5dB5dUzuebSdJ0nhLI6H7IvDDwBXAzYxtT7nfI5lqmQf+qeaRSZJUplwuz9H+02zavIftDx5mYDB33vs7HzrMfVsjyxfNYu2qBczs6SSbzYxwNUmS0pHG14mfAM4Vju8NIYxarTKE0BZC+BDw1sKpHPDJFGKTJGlUuVyeRw4d4/Z7t7Ft18GLkrmigcEc23Yd5PZ7t/HooWPkck7BlCSNr5ondDHGR4APk1SqnAY8EEL4eAjhTcALiu1CCNNCCItDCO8GIvCuwlt54M9ijLHWsUmSNJrBXI4j/ae4c+MOjp84W1af4yfOcsfGHRzpP8WgSZ0kaRylNeH/N4AtJEldG/AW4NPAjxXezwDHgG8AvwNcVdL3W8A7U4pLkqRLastm2bR5T9nJXNHxE2fZdP/DtDntUpI0jlJJ6GKMOeANwL2FU5nCA5IRuHzJuUxJm78CVhT2o5MkaVzl83mOnzjL9gcPV9R/++5DHD9x1uqXkqRxk1pJrhjjQIzxduAa4I+BR7g4iQM4AvwF8KoY45tjjM+kFZMkSaPZsmP/iGvmRjMwmGPrzv01jUeSpEtJo8rleWKMe4F3AIQQngs8H+gGTgJHY4yPpR2DJEnlyGQy7DvQP3rDS9h3oJ9MxmmXkqTxkXpCVyrG+CTw5HjeU2oUxSlapR8EhzsnKV2nzgxMaH9JksYi1YQuhJAF3ggsjDHeOUKbL5BMwfwk8NkY42CaMUn1xo2LpfrSObm6X43V9pckaSxS+60TQlgGbALmk0yvHDahAxYBs4GVwLtDCDe5ZYFahRsXS/Uln8/TO7eHnQ9VVhQFoHduD/l83pF1SdK4SOXr/hDCa4B/JEnmMsCUEMLsYdpNAmYxVCjlZcC2EMLVacQl1RM3Lpbq0/XL5tHeVtmvx/a2LCuXzqttQJIkXULNE7oQwjSSkbnLSJK00yR70A23oc8A8Erg/cD3C+eeB3ymMF1TakpuXCzVp0wmQ9fUDpYvmlVR/+XXzqZraoejc5KkcZNG0nQz8AKSveb+A3hZjPHnYoxHL2wYY8zHGHfGGH8b+AHgocJb1wL/LYXYpLrgxsVS/RrM5Vi7agFdUzvG1K97Wgdrb7jGL1wkSeMqjYTuDYXnPPCmctfDxRgfB34GKM47+9kUYpMmnBsXS/WtLZtlZk8nd69bVnZS1z2tg7tuWcbMnk6/cJEkjas0ErqFJMnczhjjt8fSMca4B/hXkqmaP5RCbFJdcONiqb5lsxnmz+5mw2199C2eM+Kauva2LH2L53DPrX3Mn91t0SJJ0rhLo8rlcwrP+yrsv49kXd2M2oQj1Rc3LpYaQzabYUZPJ+vXLGHd6oVs3Xn+tiK9c0u3FcmbzEmSJkQaCd2ZwnWnV9h/SuH5ZG3CkeqPGxdLjaE4fXL6lEncuKL3vC9SSqc9O81SkjRR0phy+SjJlMnlIYRKfsMtLTwfql1IUn1x42KpsWQymYtGxYc7J0nSeEsjofvnwvPzgP8+lo4hhJuAF5KswfvnUZpLDam4cXE1ihsXS5IkqbWlkdB9quR4QwhhdTmdQgivAz5acurPaxqVVEfcuFiSJEm1UPN5WzHGb4QQ/h74CWAy8NchhK0kCdo3SKZSniRZKzcLWEKyXcEbSKZq5oG/jzH+a61jk+pB6cbF23YdHHP/4sbFkiRJUloLcW4h2X7gqsLrlYXHpRQXIuwl2ZxcalrFjYt37T0yps3FSzcutgiDJEmS0phySYzxv4DlwJdJErVyHgBbgFfGGKur6S7VOTculiRJUi2kktABxBifiDH+KLAC+AgjV608Cvw18LoY46oY45NpxSTVEzculiRJUrVSr30eY3wAeAB4ewihG7gCeC7JOronY4yPpR2DVK/cuFgqT7Gq63D7wLl1gCSplY3rZlYxxmPAsfG8p1Tv3LhYGtlgLkdbNsvTJ8+xZcf5X3hcfWXpFx5JO0mSWo27E0t1YrhRBkce1MpyuTxH+0+zafMetj94mIHB3Hnv73zoMPdtjSxfNIu1qxYws6fTUWxJUssxoZMk1Z1cLs8jh45x58Ydl6wEOzCYY9uug+zae4S71y1znakkqeWkltCFENqBNcCPk2xfMK1wv3J/0+ZjjC9KKTxJUp0azOU42n961GSu1PETZ7lj4w423NbHDCvBSpJaSCoJXQjhRcBm4MUVXqK4wbgkqcW0ZbNs2rxnTHs0QpLUbbr/YdavWZJSZJIk1Z+aryAPIUwiSeZ6KX8PuuH2pJMktZh8Ps/xE2fZ/uDhivpv332I4yfOnldQSJKkZpbGCN3NJMlcniQ5206yz9yjwHEgN2JPSVLL27Jj/0UFUMo1MJhj68793Liit7ZBSZJUp9JI6G4sOf5gjPE3UriHJKkJZTIZ9h3or+oa+w70WyFWktQy0ti0Z2Hh+QngPSlcX5LUxE6dGZjQ/pIkNZI0ErrnkEy3/GqM0d+qkqQx6Zxc3eSRavtLktRI0kjoniw8P5PCtSVJTSyfz9M7t6eqa/TO7bEoiiSpZaSR0H2HpBjK1SlcW5LU5K5fNo/2tsp+PbW3ZVm5dF5tA5IkqY6lkdB9tvD8IyGEq1K4viSpSWUyGbqmdrB80ayK+i+/djZdUzssiiJJahlpJHSfAPYBbcCfhBAmp3APSVKTGszlWLtqAV1TO8bUr3taB2tvuIbBnNMtJUmto+YJXYzxNPAm4AjQB+wMIbwphDCj1veSJDWftmyWmT2d3L1uWdlJXfe0Du66ZRkzezppyzo6J0lqHTUvBRZC2FA4/Brw48Ai4NOF946TbC4+WMal8jHGF9U6PklS/ctmM8yf3c2G2/rYdP/DbN99aNjNxtvbsiy/djZrb7iGmT2dZE3mJEktJo3azreRbFtAyXPxN2w30FXGNTIlfSVJLSibzTCjp5P1a5awbvVCtu7cz74D/Zw6M0Dn5HZ65/awcuk8uqZ2MJjLm8xJklpSWpv1XOq3qr9xJUllKU6fnD5lEjeu6D2v2Enp1gROs5Qktao0ErrrUrimJKmFDVe10kqWkiSlkNDFGLfV+pqSVFQclRlupMYP+JIkqdWkNeVSkmpqMJejLZvl6ZPn2LLj/LVUV19ZupYqaSdJktQKTOgk1b1cLs/R/tNs2ryH7Q8evqja4c6HDnPf1sjyRbNYu2qB1Q4lSVLLGJeELoTwapK1dYuA5wLTYow/VHivG3g38IkY43+MRzySGkcul+eRQ8e4c+MOjp84O2K7gcEc23YdZNfeI9y9bhnzZ3eb1EmSpKaX6rykEMLrQwi7gX8C7gBWA68GXlbS7CrgN4E9IYQ/CCGUt4uspKY3mMtxpP/UqMlcqeMnznLHxh0c6T/FYM7dTyRJUnNLLaELIawHNgMvJdmqoPi40LzCczvwduAfQwiXpRWXpImXz+fPKzk/0rm2bJZNm/eUncwVHT9xlk33P2wpe0mS1PRSmXIZQrgJ+D2SzcEzwFPAV4CrSaZdlnoaOArMKLR9BfBHwC+mEdsI8V4G7AHmA9fFGB+o0XU7gLcCbyb5c08FngC+DfxZjPFztbiP1CjGUtgkm8nw9MlzbH/wcEX32r77EOtWL2T6lElWv5QkSU2r5gldCKEL+HDJqd8F7ooxng4h/DEXJHQxxi+HEOYD7wfeSZLUvSWE8Psxxt21jm8Ev0+SzNVMCOFK4B+AhRe8Nbfw+IkQwv3ATTHG/lreW6pHlRQ22b3vyEXtyjUwmGPrzv3cuKK3FuFLkiTVpTSmXL4NeA7J6NxHYoy/GWM8fakOMcYTMcZbOT8R/IUUYrtICOG9wLoaX7ML+DJDydwTwP8B3gv8/8CpwvkbgL8KIVhtVE2tWNjk9nu3sW3XwRGTtGJhk9vv3cajh46x9KXPJ1x5ecX33Xeg39E5SZLU1NJI6G4oPJ8iKXYyFu8DjheOX1WrgIYTQpgUQvgwcHcKl38/UBwWeAB4SYzxV2KM/zPG+HPADwKPFN5/HXBLCjFIdaGawiZPHjvNu25aTKU52akzA5V1lCRJahBpJHQLSEbnvhpjPD5a41KFqYdfJ5l2eVUKsQEQQnghSaL1zhSufQVDI37PAP/twimVMca9wE8CxWGK33aUTs2qmsImf77lO7xg5jSu7Z1Z0b07Jzfe/1blFoyRJEmCdIqiPLfw/FiF/Yv9ptQglvOEEKaTjBq+CyhW0nwCOAD8UI1ucxMwqXD8pzHGo8M1ijE+FEL4HHAjMBtYAXyxRjFIdSGfz1dd2ORtb3gpq14xn2/vPTLm/r1ze8jn8w0x7XIsBWPasqnuOCNJkhpIGgndM8DlwLQK+xe/ij9Wm3DO81bOnwb6T8DNJNMua5XQ/WjJ8f2jtN1MktBBskefCZ2azpYd+6sqbPKlr3+XN17XS9fUjjGN8rW3ZVm5dF5F9x1vlRSMcdN0SZIE6Uy5fIxkyuTLx9oxhNBJsnYuTzJqlpbDJMVbXhtj/F6Nr7245Phro7Qtff9HahyHNOEymQz7DlRXxHXfgX7ashme0zW27SmXXzubrqkddT86V2nBmJybpkuSJNJJ6P6x8PzCEMIbxtj3PUBX4Xhb7UJ61mMk0y17Y4x/GmOs6SeiQnXL5xVefr+M7QhKk8kX1zIWqV5UW5ik2H/KZeVPKOie1sHaG65hsM6TnmoKxhzpP1X3fz5JkpS+NBK6T5GMsAF8JIRQ1iZQIYRf5fzpkPfVOrAY42djjPfGGE/U+toFs0qOR11DGGN8hmRjdYCuwgbnUlOptjBJsX+2zJG27mkd3HXLMmb2dNJW59MSqykYs+n+h+v+zydJktJX8zV0McbdIYRNwM8DVwBfDyH8b2ALQ6NvAIQQnkNStv8dwCtJpmrmgb+LMX6z1rGNg+6S45Nl9jkFTC8cdwGX3LMvhLB/zFGdb06V/aWy5fN5euf2sPOhyoqiwFBhk3fdtJhPbX6Y7bsPDTstsb0ty/JrZ7P2hmsaYo1ZLQrGrFu9kOlTJtX9tFJJkpSetGp6/zLwUpL1ZF3AnYXHs0IITwI9JaeKn0geBX4xpbjSNrnk+JKJWYlTJceTR2w15IXlhyNNvOuXzePTX4wVFUYpLWzy3O5O1q9ZwrrVC9m68/wqkL1zS6tA5us+mSuqtmDM1p37uXFFWZMgJElSk0oloYsxngohrAA+AfxUyVt5hqZjXl5yrvjpawfJvm3fTyOucTBYclzu4pbST56VfbJTTRX3+yod9RjunEaXyWTomtrB8kWz2Lbr4Jj7FwubALQV/uqnT5nEjSt6h/3vAzTMNMRaFYzxgkF46wAAIABJREFUZ1KSpNaW2q67hU3FfzqE8HrgVuA6oHOYpnmSzcT/EPhMjHFwmDaN4pmS43LXw5W2O1NG+++WH86w5gBtVV6jKbkPWDoGcznWrlrArr1HxrRWrLSwSWmSNlwC06hJTa0KxkiSpNaVWkJXFGP8EvClEMIkYBHwfJK1ZieBo8DuGOPTl7hEIylN6MrdGL00yT0+WuMY47yxBHShwho8p21ewH3A0tOWzTKzp5O71y3jjjKrOZYWNmnmv+daFYyRJEmta9w+DcQYzwHfGq/7TZDHGZpCOnu0xiGEaQwVRHkqxji2UneqieI+YKOVji/uA7Zr7xHuXreM+bO7mzrZqKVsNsP82d1suK2PTfc3T2GTatSyYEyjjlBKkqTq+fVuDcUYTxZGwOYDM0IIU0fZIqF0pGxvmrFpeIO5HEf7T1e0D9iG2/qY0QCl8etFNpthRk/zFTapRq0KxkiSpNZlQld7/0aS0AG8HPinS7RdWnL8f1OLSCOqdh+w9WuWpBRZcyomv81S2KQatSwYI0mSWldFCV0I4eO1DmQY+RjjW8fhPrX2BeANheNVXDqhW1Vy/MXUItKw3Ads4jRTYZNq1LpgjCRJaj2VjtDdTPll+avRiAnd50gqdk4CfimEsCHGeFHGEEJYBKwuvDwC3D9+IarIfcA0kSwYI0mSqlVN7fVMmY9y2g/XpiHFGI+SJHSQFDz5+xDCrNI2IYSrgc8z9Pf/AQuijD/3AVM9KC0Y07d4Du1tw/+z3N6WpW/xHO65tc+CPJIk6VmVjtDdVUabnwOuIknOniFJYL4O7AeeBiYDVwBLgDeRbGeQB/4F+GCFcaUuhPAA0Fd4eVeM8X3DNLsT+AngxSR/vj0hhL8EvgcE4EaGtiv4Z+DDKYasS3AfMNUDC8ZIkqRKVZTQxRgvmdCFEH4TeBFJgvanwO2X2GtuUwhhPfA+4LeAV5HsTXdrJbHVgxjj0yGE60jW0y0CeoB1wzT9CvDGGKNZwQRxHzDVCwvGSJKkSlQz5XJYIYQfBO4mSeY2xhhvGW3j8BjjQIzxt4H/RTKi9yshhNfWOrbxFGN8jGR07pdIErcjwADJZupbgZuA18UYj01YkC2uuA9YNYr7gEm1kslkLprGO9w5SZIkSGfbgncCbcAx4NfG2PdukpGsGcDbgX+sbWjDizHeTFLopZy2rxnDdQeAjYWH6pD7gEmSJKmR1XyEDriOZHTugRjjybF0jDGeAx4gGaV7ee1Dk4aU7gNWieI+YI6cSJIkaaKkkdDNLjw/VWH/U4XnmTWIRbqk4j5gY92guXQfMEmSJGmipJHQFUflKt2c69rCs2vLlLrSfcDKTepK9wGzQIUkSZImUhoJ3UMkUyaXhRBeOpaOIYTXkSR0eeCbKcQmXcR9wCRJktSo0iiK8nnglSTJ4mdCCNfFGI+M1imEsAD485JT96UQmzQs9wGTJElSI0ojodsI/DrwPGAB8FAI4X8CfxljfOLCxiGE+SQVJtcDl5GMzn0bEzqNM/cBkyRJUqOpeUIXY3wmhPDzwD8Urj8TuBe4N4RwGDhEUvhkKjCHoeInxU/JB4E3xBitNqEJMVzVSitZSpIkqR6lsYaOGOOXgNXAk4VTmcJjNslm268EXkYyild8D+AbJJttH0wjLkmSJElqJqkkdAAxxvuBlwD/C9hb8laG85M4gP8AbgWWxRhjWjFJkiRJUjNJYw3ds2KMTwHvBd4bQngRsAi4AngOcAZ4DNgVY9w78lUkSZIkScNJNaErFWP8T+A/x+t+kiRJktTsUptyKUmSJElKlwmdJEmSJDWoiqZchhC+UvIyH2N87QjvVeO860qSJEmSzlfpGrrXkGwAnik8D/deNYa7rqQqFTdIH27TdPfakyRJajzVFEW51Kc/PxlKdWQwl6Mtm+Xpk+fYsmM/+w70c+rMAJ2T27n6yh5WLp1H19SOZ9tJkiSpMVSa0P1Che9JGme5XJ6j/afZtHkP2x88zMBg7rz3dz50mPu2RpYvmsXaVQuY2dNJNut3MpfiSKckSaoXFSV0McZPVvKepPGVy+V55NAx7ty4g+Mnzo7YbmAwx7ZdB9m19wh3r1vG/NndJnXDcKRTkiTVm3Hbh07S+BrM5Tjaf3rUZK7U8RNnuWPjDjbc1seMnk7aTOqe5UinJEmqR36FLDWptmyWTZv3lJ3MFR0/cZZN9z9sMleiONJ5+73b2Lbr4EXJXFFxpPP2e7fx6KFj5HLWdpIkSelKPaELIXSEEC4aCQwhXB5CuDuE8C8hhN0hhL8IIVyXdjxSK8jn8xw/cZbtDx6uqP/23Yc4fuLss+vCWtlgLseR/lMVjXQe6T/FoEmdJElKUWoJXQhhYQjhb4DvAz98wXtzgK8D7wFeAbwUuBH4cgjhI2nFJLWSLTv2jziSNJqBwRxbd+6vaTyNypFOSZJUz1JJ6AojbV8D3gBcBlx1QZM/BF5Esr3BhY9bQgj/I424pFaRyWTYd6C/qmvsO9Df8hUbHemUJEn1ruYJXQhhMrCJJJErJmlXlrz/EpJEL194PAC8E/gkQ5uVrw8hvLDWsUmt5NSZgQnt3ywc6ZQkSfUsjSqXa4DZJMnZk8Bbgc0l77+p5PjbwOtjjIMAIYQHgXuADuAm4HdSiE9qCZ2Tq/vfu9r+zcCRTkmSVO/SmHK5suT4DTHGvy8mbAU/XnL8Jxe8dy/weOH4hhRik1pCPp+nd25PVdfondvjVEEc6ZQkSfUtjYRuCcno3K4Y487SN0IIzy28X/QPpe/HGPPADpJpl/NTiE1qGdcvm0d7W2X/i7e3ZVm5dB7ARUldPp9vqUTPkU5JklTP0vikMaPwvG+Y91aQJGt54D9ijAeGafNU4XlmCrFJLSGTydA1tYPli2axbdfBMfdffu1suqZ2cOLUOb6w/VH2Hejn1JkBOie3c/WVPaxcOo+uqR0M5nK0ZZt3O8viSOfOhyorigJDI51Ou5QkSWlII6HrLDw/M8x7ry05fmCE/sWE8FytApJa0WAux9pVC9i198iYSu53T+tg7Q0v4aljp3jb+7/MuYHzC4LsfOgw922NLF80i7WrFjCzp5NsE5fmv37ZPD79xVhRYZTSkU5JkqQ0pPHVenGE7Yph3vvRkuMvj9D/6sLzkzWLSGpBbdksM3s6uXvdMrqmdpTVp3taB++7ZRmXT7+M9//ZNy5K5ooGBnNs23WQ2+/dxqOHjpFr0s2zS0c6K1Ec6XR0TpIkpSWNhO5BkmmVrwghPPspMoRwLTCv8HIA+NKFHUMIrwGuIZmS+WAKsUktJZvNMH92Nxtu66Nv8ZwR19S1t2XpWzyHe259NVdeMZ0PfPIbxO99f9TrHz9xljs27uBI/ykGmzSpK450lpsUFyUjndc07d+LJEmqD2lMudwMvB64HPjjEMJ/J9mT7g8K7+eBB2KM59UCDyFcA/xpyaktKcSmBlcsxlE64jHcOQ3JZjPM6Olk/ZolrFu9kK0795+3Jq537tCauKeOneK3/mh7Wclc0fETZ9l0/8OsX7Nk9MYNqHSk846NO8qavto9rYO7blnW9NNRJUnSxEsjofsUcCfQDdwMvJlkxO6ykjYfLh6EEHqBDwDXl7Q5Avx5CrGpQRWLbzx98hxbduxv2SIdlWorJBXTp0zixhW9wybEJ06dG3bNXDm27z7EutULmT5lUlMm1qUjnZvuf5jtuw8Nu6auvS3L8mtns/aGa0zmJEnSuKh5Qhdj/H4I4Wbgr4E2YMoFTe6LMX6h5HUX8FMkI3cZkumYvxhjPF7r2NSYcrk8R/tPs2nzHrY/ePiiD9KtVqSjGsMlW5lMhnw+zxe2P1pRMgfJmrqtO/dz44reakOsW2MZ6RzM5f0ZlCRJ4yKVDZJijH8XQugDfg94ReH0f5GMzP3OBc33Fp4zwH7gbTHGr6QRlxpPLpfnkUPHuHOUqW7FIh279h7h7nXLmD+72w/UY5DJZNh3oH/0hpew70B/U47OlSpnpLO0nSRJUtpS2/E2xvivwCtDCJ3A1Bjj0RHaPR1C2AD8K/C3McbBtGJSYxnM5Tjaf3rUZK5UsUjHhtv6mNHT6QfrMTh1ZmBC+zeSkUY6JUmSxltqCV1RjPEUcGqUNuvTjkONpy2bZdPmPWPaQw2av0hHWjonV/fPQbX9JUmSNHZWj1BdyufzHD9xlu0PHq6o//bdhzh+4ux50+A0snw+T+/cnqqu0Tu3x79vSZKkcTYuX6mHEK4ErgMWAc8FpsUYbyy8NxW4CfjLGOOx8YhHjWHLjv3DVhIsRysU6ai165fN49NfjBX9nbe3ZVm5dF7tg5IkSdIlpTpCF0J4aQjh74BHgY8DtwE/T1LVsugq4KPA/hDCr6UZjxqHRTrGVyaToWtqB8sXzaqo//JrZ9M1tcO/b0mSpHGWWkIXQngzsBP4MZIKlsXHheYVnruB3wsh/GUIwU+FskjHOBvM5Vi7agFdUzvG1K97Wgdrb7iGwZzTLSVJksZbKgldCOF1wCagkySJGwS+STJSd6E24BxD+9D9NBdvbaAWZJGO8dWWzTKzp5O71y0rO6nrntbBXbcsY6YVRSVJkiZEzRO6EMJkkumVxU/TnwGujDG+HPjihe1jjJ8HXgz8feFUBrg9hPCiWsemxmGRjomRzWaYP7ubDbf10bd4Du1tw/8T0d6WpW/xHO65tc89/yRJkiZQGkMYbwHmkIy4fS7GeNNoHWKMB4DVIYRPA28mGbV7K/BbKcSnBmGRjomRzWaY0dPJ+jVLWLd6IVt37mffgX5OnRmgc3I7vXN7WLl0Hl1TOxjM5U3mJEmSJlAaCd1PFJ7PAe8cY9/bgZ8EJpNUxVSLKi3SsW3XwTH3LxbpUGWK0yenT5nEjSt6zyt2Ujrq6TRLSZKkiZXGGrprSUbndsQYHx9LxxjjYZJCKhnAevMtziIdEy+TyVxUuXK4c5IkSZoYaSR0MwvPwxVAKcd3C8/TaxCLGphFOiRJkqRLSyOhO114rnS+W1fh+ZkaxKIGZ5EOSZIkaWRprKE7RLKn3MvG2jGE0Aa8imTK5qEax6UGZZEOSZIkaXhpJHTbgGuAl4QQXhlj/OoY+r4DmEGS0I2ln5qcRTokSZKki6Ux5fLTJcd/GkKYUU6nEMIbgA+WnPqrmkalpmCRDkmSJGlIzRO6GOO/APeTVKp8MfDNEMLPhxCeVzh3nhDCy0MInwI+B0wiGZ3bHmP8Sq1jkyRJkqRmksaUS0g2F/8aMB+YC3ziwgYhhG8BVzFUBKWY7B0Ffi6luDRBitMih5sq6eiaJEmSVJlUEroY49EQQh/wWeDlJW/lCw+AH2QoiSue+0/gjTHG76URl8bfYC5HWzbL0yfPsWXH+cVMrr6ytJhJ0k6SJElS+dIaoSPG+FgI4ZXALwC3AgsYZsplwRPA/wE+HGN8Oq2YNL5yuTxH+0+zafMetj94mIHB3Hnv73zoMPdtjSxfNIu1qxYws6fTCpWSJEnSGKSW0AHEGAeAjwEfCyHMAX4EeD7JtgYnSaZX7oox/nuacWj85XJ5Hjl0jDs37uD4ibMjthsYzLFt10F27T3C3euWuYecJEmSNAapJnSlYoyPAY+N1/00cQZzOY72nx41mSt1/MRZ7ti4gw239TGjp7Npth9w7aAkSZLSNG4J3ViFEFbGGLdOdBwau7Zslk2b95SdzBUdP3GWTfc/zPo1S1KKbPy4dlCSJEnjoe4SuhDCc4DfB24C2iY4HI1RPp/n6ZPn2P7g4Yr6b999iHWrFzJ9yqSGHcFy7aAkSZLGS00SuhDCVOA6oJdkL7lDwLYY44ExXudngXuBmQxVvlSD2bJj/0VJTLkGBnNs3bmfG1f01jaoceLaQUmSJI2nqhK6EMI04H8AtwCdw7z/eeCdMcZDo1xnDvAR4AZGroSpBpDJZNh3oL+qa+w70N+Qo3OuHZQkSdJ4q3jxTiEJ+xbwq8AULk7EMsBq4FshhKsvcZ23A//OUDJXHJk7UmlsmlinzgxMaP+JUu3aQZM5SZIkjVU11Rj+imSKZWkSlil5FF0B/F0IYVJp5xDCzBDCFuAPgOmF0/lC3z8n2bdODahzcnUzeavtPxHy+TzHT5ytau3g8RNnn62AKUmSJJWjooQuhLCKZE+54qfPfwPeCDwXmAosIZlCmS88eoFfKum/CNgNvJ6hhDADfA+4Psb48zHGpyqJTRMrn8/TO7enqmv0zu1pyMSmFmsHJUmSpLGodCjkDSXH/wKsjDGeLjm3C3h7COErwF+SJGxvBf4whPBDwFbgcoYSwjzwYeA9McaTFcakOnH9snl8+ouxouSmvS3LyqXzah9Uylp57aAkSZImTqVTLl9WcrzugmTuWTHGz5JMzcwAi0IILwL+mqFkLgM8DCyPMb7LZK7xZTIZuqZ2sHzRrIr6L792Nl1TOxoysWnVtYOSJEmaOJUmdHNIErKHYoxxlLafLDneBMxlaGRuA7A4xvi1CuNQHRrM5Vi7agFdUzvG1K97Wgdrb7iGwVzjTbeE1lw7qPLl8/mLphIPd06SJGksKv0EWVwktaeMtg+WHP9I4fk48LMxxi0V3l91rC2bZWZPJ3evW8YdZZbw757WwV23LGvYTbaLawd3PlRZURQYWjtYOjpZ/LA/2jnVr8FcjrZslqdPnmPLjv3sO9DPqTMDdE5u5+ore1i5dB5dUzuebSdJkjQWlSZ0kwvPx8po+0TJcQZ4EnhNjPHfK7x31UII80m2W/hRYB6QAx4jWdv3kRjjd2pwj18APj6GLnfFGN9X7X3rRTabYf7sbjbc1sem+x9m++5Dw66pa2/Lsvza2ay94ZqGTeaKarl20CSgOeRyeY72n2bT5j1sf/DwRT8bOx86zH1bI8sXzWLtqgUN//+AJEkaf5UmdFmSaZOjfnKNMZ4LIRRf5oG3TXAytwbYSLJ3XqmXFB6/HEL49Rjjh6u81Q9W2b/hZbMZZvR0sn7NEtatXsjWnecnJr1zSxOTfEN/kC1dO7ht18Ex9y+uHQSTgGaRy+V55NCxUTeaHxjMsW3XQXbtPcLd65Yxf3a3/z0lSVLZxnvRTowx/u043/NZIYQfAz7F0NrBrwFfKhyvAF4BdAC/H0I4FmP85MVXKVuxcEwO+P8YWjc4kn+t4l51q7hZ9vQpk7hxRe+wUwdL2zWy4trBXXuPjGlz8dK1gxmo2yTA6Z/lG8zlONp/etT/jqWOnzjLHRt3sOG2Pmb0dDbF/xOSJCl9453Q/d9xvt+zQgjTgD9hKJl7V4zx3pIm7w0h3AJ8lGRq6B+GEDbHGI9UcK8McG3h5d4Y4werCL0pDPeBv9mSgGrXDubJc6QOkwCnf45dWzbLps17xpTYQ/Lfc9P9D7N+zZKUIpMkSc1mvBO6Z8b5fqXWAc8vHH/mgmQOgBjjx0IIvcCvA9MKz++u4F5XAV2F429V0F8Nqrq1g5m6SwKc/jl2+Xyep0+eY/uDlRXI2b77EOtWL2T6lElN96WHJEmqvfFO6MZeLaJ23lJy/LuXaPcBkoIpk4GbQgi/EWMca13x0n36JmxUUhOjkrWD9ZgEuAasclt27K+oOA4kf59bd+7nxhW9tQ1KkiQ1pZbY+CqE8HxgUeHl4Rjjt0dqG2N8KoTwNeDVwAuAHwa+PsZblhZEMaFrQZWsHaynJMA1YJXLZDLsO9Bf1TX2Heh3dE6SJJWlJRI6zh8xK2cT82JCB8neeWNN6Ir3ywO7QgjtJInhDwCXAYeBr8YYnxihv5pEuWsH6y0JcA1YdU6dGZjQ/pIkqXVUm9DNDiG8evRmlbWPMf5zBTENp3TY4tEy2n+v5PjFFdyvOEJ3AHgbcDsw+4I2+RDC3wLrY4z/WcE91GTqJQmox+mfjaZzcnX/tFbbX5IktY5qPzX8ROFRjswY2+ep3QjirJLjx8pof6jk+Iqx3CiE8DyGkrcrgQ+N0DQDrAb6Qgiry01eQwj7xxLPMOZU2V8pqackoJ6mfzaafD5P79wedj5UWUIM0Du3h3w+37IJsSRJKl8taoxnynjkGdqHbbS2XHBcC90lxyfLaH+q5LhrxFbDe9kFrw8C7wTmkRRauRL474XzAJcDfxtCKHck8IVVPtrG+OfROCgmAdUoJgHVqrfpn43o+mXzaG+r7J/X9rYsK5fOq21AkiSpaVWT0I0l6bowYRutba1NLjk+XUb70oRu8oithldaEGU38LIY4x/GGL8bYzwbYzwQY/wosBh4uNCuB/j9Md5HTaaekoB6mf7ZiDKZDF1TO1i+aNbojYex/NrZdE3taOmEWJIkla/SOVrzaxpF+gZLjssZwij9JDXWeWf3An9LshfdgyNtTB5j/K8Qws8D3yicWhVC6I0x7hvl+t8dYzwXmoOjdHWnNAnYtuvg6B0uUEwCaqWepn82osFcjrWrFrBr75ExFZbpntbB2huuYTCXb9kqoZIkaWwq+tQVY6w2qRhvpRuaX1ZG+9I2Z8ZyoxjjGeA7hcdobb8ZQvgm8EOFU68HLpnQxRjnjSWeCxXW4L2wmmsoHfWSBLgGrHpt2Swzezq5e90y7ihz64fuaR3cdcsyN2eXJEljUos1dI2gNKGbUkb7zpLjYzWO5ULfKDm+KuV7qY6VJgHljraVJgG1HNGpp+mfjSqbzTB/djcbbuujb/GcEf8+29uy9C2ewz239rkpuyRJGrNWmRdVOtRw4fYBw3lByfHjNY7lQt8vOR5rARaNUbFoyHAbfdfDaFJpErDp/ofZvvvQsNUm29uyLL92NmtvuKbmIzr1Nv2zkWWzGWb0dLJ+zRLWrV7I1p372Xegn1NnBuic3E7v3B5WLp1H19QOBnN5kzlJkjRmrZLQ/XvJ8bwy2l9Zcry3khuGEKbFGJ8ZvSXTS46rKy2oEQ3mcrRlszx98hxbdpz/ofrqK0s/VCftJlI9JAH1Mv2zGRT/HqZPmcSNK3qH/TKhtJ0kSdJYtEpC9xBJMZQM8PIy2i8tOf6/5d4khNAORJJRwMtCCFfEGP9rlG4LS44fHrGVKpbL5Tnaf5pNm/ew/cHDF4147XzoMPdtjSxfNIu1qxbUxRqmiU4CXANWe8ONANfDqLAkSWpsLbGGLsb4FLCz8PLKEMIPjNQ2hPBchpK+J4FvjeE+AyRFVIpFVW64VPsQwguA5YWXeeAr5d5L5cnl8jxy6Bi337uNbbsOjrhZ9sBgjm27DnL7vdt49NAxcrnq93OrhUwmc9GH/uHOpcE1YJIkSfWvVUboAP4CWFY4fh/w30Zo95tAcQHQn8cYx7ptwd8A1xSO3x1C+HSMcaThjQ3ApMLxFxqwemhdG8zlONp/mjvLHGECOH7iLHds3MGG2/qYUeNCI42oHqZ/SpIkaWStlND9CfBrwFzgxhDC/wDuiDE+OxQTQlgH3F54eQr4UAX3+TDwKyQFThYA94UQ3hJjPFFyn8tI9qt7U+HU6UJsqqG2bJZNm/eMaQ0YJEndpvsfZv2aJSlF1lgmevqnJEmSRtYyCV2M8UQI4e0km35ngd8GfjKE8PfAAPBahqY/AtwWY3zswuuEEP4MeEvh5SdjjDdfcJ8nQghvAz5TuM9PA68MIXwWOEhSQXM1Q5U0B4G3xBgrKr6i4eXzeZ4+eY7tD1a2l9r23YdYt3oh06dMcp1TgWvAJEmS6k9LrKErijH+A/BzQHG0bCHwW8AdDCVzA8DtMcaNVdznr0imdBb3sLsCeAfw/sJzMZn7L+DGGONfVnovjWzLjv0jrpkbzcBgjq0799c0HkmSJKnWWiqhA4gxfppkjdsHSbYzeIakkMl/Ah8DXhZj/N81uM/nSDYK/y3gqyQFVs6R7Gv3VWA98JIY4+ervZculslk2Hegul0g9h3odwRKkiRJda1lplyWijEeAN5deIy1783AzWW2fQr4ncJD4+zUmYEJ7S9JkiSlreVG6NQ6OidX931Ftf0lSZKktJnQqSnl83l65/ZUdY3euT3nVXGUJEmS6o0JnZrW9cvmjbgZ9mja27KsXDoP4KKkLp/Pm+hJkiSpLjinTE0pk8nQNbWD5YtmsW3XwTH3X37tbLqmdnDi1Dm+sP3R8zbTvvrK0s20c7Rl/V5EkiRJE8OETk1rMJdj7aoF7Np7ZEybi3dP62DtDS/hqWOneNv7v8y5gfO3Ptj50GHu2xpZvmgWa1ctYGZPJ1k31ZYkSdIEcGhBTastm2VmTyd3r1tG19SOsvp0T+vgfbcs4/Lpl/H+P/vGRclc0cBgjm27DnL7vdt49NAxcjmnYEqSJGn8mdCpqWWzGebP7mbDbX30LZ4z4pq69rYsfYvncM+tr+bKK6bzgU9+g/i97496/eMnznLHxh0c6T/FoEmdJEmSxplTLtX0stkMM3o6Wb9mCetWL2Trzv3nrYnrnTu0Ju6pY6f4rT/aXlYyV3T8xFk23f8w69csSfFPIUmSJF3MhE4toa2wxm36lEncuKKXTGZozVuxYuWJU+eGXTNXju27D7Fu9UKmT5l03rUlSZKkNDnlUi0lk8lclHAVX39h+6MVJXOQrKnbunN/teFJkiRJY2JCJ5EkdfsO9Fd1jX0H+h2dkyRJ0rgyoZMKTp0ZmND+kiRJ0liZ0EkFnZOrW1JabX9JkiRprEzoJJLCKL1ze6q6Ru/cnmcLrEiSJEnjwYROKrh+2bwR96kbTXtblpVL59U2IEmSJGkUJnSqO/l8/qKRruHO1VImk6FragfLF82qqP/ya2fTNbXDoiiSJEkaVy76Ud0YzOVoy2Z5+uQ5tuw4f/Pvq68c2vy72C6N+69dtYBde49w/MTZsvt1T+tg7Q3XMJjLP7vfnSRJkjQeTOhUF3K5PEf7T7Np8x62P3iYgcHz94Pb+dBh7tsaWb5oFmtXLWBmTyfZGidPbdksM3s6uXvdMu7YuKOspK57Wgd33bIslXgkSZKk0TjlUhMul8vzyKFj3H7vNrbtOnhRMlc0MJhj267J8eqNAAAfsklEQVSD3H7vNh49dIxcrvZTMLPZDPNnd7Phtj76Fs8ZcU1de1uWvsVzuOfWPubP7jaZkyRJ0oRwhE4TajCX42j/ae4sc0QM4PiJs9yxcQcbbutjRk9nzac5ZrMZZvR0sn7NEtatXsjWnedP/+ydWzr9M28yJ0mSpAljQqcJ1ZbNsmnznjGtWYMkqdt0/8OsX7MkpbiSJG36lEncuKL3vGInpcVZXDMnSZKkieSUS02YfD7P8RNn2f7g4Yr6b999iOMnzqZe/fLCypXDnZMkSZImggmdJtSWHftHXDM3moHBHFt37q9pPJIkSVIjMaHThMlkMuw70F/VNfYd6He0TJIkSS3LhE4T6tSZgQntL0mSJDUyEzpNqM7J1dXlqba/JEmS1MhM6DRh8vk8vXN7qrpG79yeVIuiSJIkSfXMhE4T6vpl80bcvHs07W1ZVi6dV9uAJEmSpAZiQqcJk8lk6JrawfJFsyrqv/za2XRN7bhkUZR8Pn/RCN5w5yRJkqRG5AIkTajBXI61qxawa++RMW0u3j2tg7U3XMNgLj/s5t6DuRxt2SxPnzzHlh372Xegn1NnBuic3M7VV/awcuk8uqZ2PNtOkiRJakQmdJpQbdksM3s6uXvdMu7YuKOspK57Wgd33bKMmT2dZIdJ5nK5PEf7T7Np8x62P3j4on3udj50mPu2RpYvmsXaVQtGvI4kSZJU7xya0ITLZjPMn93Nhtv66Fs8Z8Q1de1tWfoWz+GeW/uYP7t7xGTukUPHuP3ebWzbdXDETcsHBnNs23WQ2+/dxqOHjpHLOQVTkiRJjccROtWFbDbDjJ5O1q9ZwrrVC9m68/xpkr1zS6dJ5odN5gZzOY72n+bOMkf6AI6fOMsdG3ew4bY+ZvR0Djt9U5IkSapXJnSqG8VkavqUSdy4ove8YielRUxGSrraslk2bd4zprV4kCR1m+5/mPVrllQQtSRJkjRxnHKpupPJZC6qXDncuVL5fJ7jJ86y/cHDFd1z++5DHD9x1uqXkiRJaigmdGoaW3bsH3HN3GgGBnNs3bm/pvFIkiRJaTOhU1PIZDLsO9Bf1TX2Hei/5CigJEmSVG9M6NQ0Tp0ZmND+kiRJ0ngzoVPT6JxcXY2favtLkiRJ482ETk0hn8/TO7enqmv0zu2xKIokSZIaigmdmsb1y+aNuCn5aNrbsqxcOq+2AUmSJEkpM6FTU8hkMnRN7WD5olkV9V9+7Wy6pnZYFEWSJEkNxYROTWMwl2PtqgV0Te0YU7/uaR2sveEaBnNOt5QkSVJjMaFT02jLZpnZ08nd65aVndR1T+vgrluWMbOnk7aso3OSJElqLCZ0airZbIb5s7vZcFsffYvnjLimrr0tS9/iOdxzax/zZ3eTNZmTJElSA7JOu5pONpthRk8n69csYd3qhWzduZ99B/o5dWaAzsnt9M7tYeXSeXRN7WAwlzeZkyRJUsMyoVNTKk6fnD5lEjeu6D2v2Enp1gROs5QkSVIjM6FTUxuuaqWVLCVJktQsXEMnSZIkSQ3KhE6SJEmSGpQJnSRJkiQ1KBM6SZIkSWpQJnSSJEmS1KBM6CRJkiSpQZnQSZIkSVKDMqGTJEmSpAblxuKt5/nFg8cff5wVK1ZMZCySJElqUo8//njpy+eP1E7VMaFrPc/+Nx8cHOTgwYMTGYskSZJag3lHSpxy2XraJjoASZIkSbVhptx68kCm5PV3JyoQtaQ5DH2pMAg8NoGxqLX4s6eJ4s+eJtJE//w9n6F848Q437tlmNC1nu8BLywcfzfGOG8CY1GLCSHsZ+jn7zF//jRe/NnTRPFnTxPJn7/W4JRLSZIkSWpQJnSSJEmS1KBM6CRJkiSpQZnQSZIkSVKDMqGTJEmSpAZlQidJkiRJDcqETpIkSZIalAmdJEmSJDUoEzpJkiRJalAmdJIkSZLUoEzoJEmSJKlBmdBJkiRJUoMyoZMkSZKkBmVCJ0mSJEkNyoROkiRJkhpUJp/PT3QMkiRJkqQKOEInSZIkSQ3KhE6SJEmSGpQJnSRJkiQ1KBM6SZIkSWpQJnSSJEmS1KBM6CRJkiSpQbVPdAAaPyGE+cCvAj8KzANywGPAVuAjMcbvTFx0aiUhhMuAPcB84LoY4wMTG5GaVQjhFcAvAsuBFwCTgaeAXcDngU/GGM9MXIRqZiGEVcDNwFLgecAzwPeAfwA+EWN8dOKiU6sJIfww8K8kn//vijG+b2IjUq24D12LCCGsATYCU0Zochb49Rjjh8cvKrWqEMJHgXWFlyZ0qrkQwjTgY8DPjNL0UeCnY4y70o9KrSKEMB24D/jxSzQ7Dfx2jPGe8YlKrSyE0EnyRVYonDKhayKO0LWAEMKPAZ9iaIrt14AvFY5XAK8AOoDfDyEcizF+cvyjVKsIIbyXoWROqrkQQjvwN8DrCqfyJP/mfQ04Q/KBZjUwnWSU+J9CCMtijA9PQLhqMiGENpIRuFcXTuWAzcBuoJPkd+5S4DLgQyGEfIxxw0TEqpbyQYaSOTUZE7omV/iW+k8YSubeFWO8t6TJe0MItwAfBTLAH4YQNscYj4xzqGpyIYRJwD3AOyc6FjW9X2IomXsC+MkY49dKG4QQZgKfJfnQ3Q18HFg2nkGqab2DoWTuKHBDjPGbpQ1CCGtJfubagQ+EEP4qxnhgfMNUqwgh/Cjw9omOQ+mxKErzWwc8v3D8mQuSOQBijB8DPlR4OQ349XGKTS0ihPBC4AFM5jQ+fq3k+KYLkzmAwpdWbwAeL5xaGkIwoVMt3F5yfPOFyRxAjHET8MeFl5NI1nlKNRdCuBz4BMmX9mcnOBylxISu+b2l5Ph3L9HuAyRTkQBuCiFk0gtJrSKEMD2E8H7gOyTTjCAZMbnoA45UCyGEhSTTKAF2xxi/MlLbGOMxoHSK+Yo0Y1PzCyG8BHhh4eWBGOMXLtF8a8nxS9OLSi3uj4HZwEmSaZdqQiZ0TSyE8HxgUeHl4Rjjt0dqG2N8imR9CSSV4H445fDUGt4K/CbJWhGAfwJeDvz7hEWkZreo5LicLw4eKTmeVeNY1GIK1aK7Sabvrhml+WUlx+dSC0otK4RwE/DmwstfB/5jAsNRilxD19xeVnJ80ZSjYXyNoXn/PwJ8veYRqVUdBt4LfDzGmA/BddlKzV8A/0zyxdTTZbSfXXL8TCoRqaXEGI8DO8to+qaS43J+R0tlCyHMAf5P4eWXSEbq3jJyDzUyE7rm1ltyXM5eN98rOX5xjWNRa3oMeBfwsRjjiYkORs0vxjgAHCg8yvFTJcd7ah+RdL4QQg/wHoYSukPAn05cRGo2hWUznwB6gGPAL/planMzoWtupdOHHiuj/aGS4ytqHItaUIzxsxMdgzSSEMKbGZqieZaktLxUcyGE1wKvJfmydBUwtfDWYWBljNHRYdXSrzJU6fedMcZyPgOqgZnQNbfukuOTZbQ/VXLcVeNYJKluhBDmMzQdCeCPYoxHJyoeNb23Aj97wbnvAtcX1t1JNRFCuIak0B3A3xQqqqrJWRSluU0uOT79/9q783C5ijKP498gEEgQUVBkMYEw4QVZBARN2EQRHBYhLMOOSQCBcVgUxB1JRHmA8KADOM+ISG4ChB1BllFkCRMWkQECJJKXsElQUHaQQCSQ+aOqOdUnfXq5t2/37du/z/Pch+pz6pyuvn1uOO+pqrfqqJ8GdEMLa4mIdDAz+xihN27VuOlJYHL7WiRdYESFbSOBh8zs9LgYuUifxPVeLyIk3HmBsCandAEFdIPbu0l5SR3106UK3mtyW0RE2i5m/70F2CBuehPY191fbV+rpAscB3wYWJGQsOz8uH154NuU9xaL9NYPgU/H8lFxvU3pAhpyObilY/JXKKxVuc6iwloiIh3IzEYBNwPrxU1vA3u7+4Pta5V0A3d/IHk5GzjKzO4HfhG3HWVmF7n7Xa1vnQwGZjaGsEwQwHR3/3U72yOtpR66wS0N6IbVUX/FpPxak9siItI2ZjaWkEq+FMwtBPZw95vb1yrpZu5+PuWLi09oU1Okw5nZcMJQyw8QkuAd194WSauph25wey4pr1lYK7NWUn6+yW0REWkLM9sPmEY2CuEVYHd3v7t9rRIB4ArgS7G8ebWKIlV8j2y5qfuArxYsUbBlUt7azL4Zywvc/fJ+bJ/0MwV0g9vcpLxOHfXTSduPNbcpIiKtF29YziSbI/wMsIu7a8056Tcxycmy7l5r+kKaTl7ZpaW30gfye1G+vmaRneIPwB2AAroOpiGXg9scsmQon6mj/pik/EBhLRGRDmBmk4EpZMHcbGCsgjnpL2Z2hJk9TpifeWIdh3wkKb/UP60SkcFOPXSDmLu/bGZ/AMYCI8xsI3efW6muma1KFvS9BNzfomaKiDSdmZ1IyPhWciuwl7u/0aYmSXd4k2ye5jjgtBr1d07K+v+u9Iq7T6COOZhmNgGYGl9OdvdJ/dYoaSn10A1+aRf6pCr1vktInwxwsbtr2QIR6Uhmth1wRrLpN8BuCuakBW4iW/d1KzPbqaiimW0GHJJsmtGfDRORwUsB3eB3AbAglvc1s1PNLF1vDjM7EjghvnwLOKuF7RMRaRozW54s2xvAQ8D+dcxlEukzd38NOCfZdJGZbZGvF1PM3wQsFzddoyQ9ItJbGnI5yLn7m2b2NeA6QgD/A2BPM7seWAzsCGyTHPJ1d3926TOJiHSEicDI5PU9wDEFGd/y5rj7b/ulVdJNJgHbE+alrw780cxuJGQfXC5u34lsbucc4LDWN1NEBgsFdF3A3W8ws0OAXwLDgU3iT2ox8K24Lo6ISKeakHt9dAPHTgMU0EmfuPtbZrYLYa7SOEJv8R7xJ+/XwMTYsyci0isactkl3P1SYENCxre5hEXHFwFPEAK9zd39p+1roYhIU+QfVom0nLu/6u57EUbBXAI8TZhb9yYwH7gQ2M7d91YwJyJ9NWTJkiW1a4mIiIiIiMiAox46ERERERGRDqWATkREREREpEMpoBMREREREelQCuhEREREREQ6lAI6ERERERGRDqWATkREREREpEMpoBMREREREelQCuhEREREREQ6lAI6ERERERGRDqWATkREREREpEMpoBMREREREelQCuhEREREREQ6lAI6ERERERGRDqWATkREREREpEMt2+4GiIgMRGZ2IDAj2fRbd9+lXe0RGYjMbBJwSnw5zd0ntK81IiLdST10IiKVTcy93tnM1m1LS0REREQKKKATEckxsxHAjvHlO/G/ywBHtqdFIiIiIpUpoBMRWdp4sn8ff5FsP8zMlmtDe0REREQqUkAnIpIwsyHAhGTT+cCjsfwxYO9Wt0lERESkiAI6EZFynwNGxfLzwBzgimT/0S1vkYiIiEgBBXQiIuXSZCjXuvsSyrNd7mBm1uI2iYiIiFSkZQtERCIz+yCwb7LpagB3f8zM7gU+G7cfBZxQ41w9hLl4AJu7+2wzWx84AtgFWBsYCvwVmAlc6O53N9DW3WNbtwY+Tvj3/O/AfcCvgcvd/d062zfZ3Sc18HmWqp9LX78XcD3wTUIimbWAF4D7Y9suJHuguKu7/0+1947nnxLPB9Dj7hNz+9cmDJX9IrARsArwD8Lv5F7gRuCqot9Jrv1fdvcbzGwdwne9OzAi7lsA3AD83N0XJMd/NNbdB1gXWB54BrgZOMvdn6n1GQvatR3wv/Hle8AId/9LHcfdDOwUX/7Y3U+uUGcIsBvwb8BYYA3CdfQCMJvwOae7+9u9aXt8jx767zpr2/dkZtsDBxJ69NcEViD83h4iXPvT+vJ7ExFphHroREQy+wPDYvkvwG3JvmlJebyZrdDIic3sG8DDwEnAxoSAY0VgPeBw4C4zu8DMPlDjPKPM7B7CTeN4YDTwwXiukYQg7xJgjplt3kgbm+xC4HTC8NWhhAB2T+BF4Pak3sG1TmRmyxBunkum5vYfBcwHTiXcYK9GCExWAdYHDgUuA/5kZpvW03gzOwyYC3yH8H2tHH82Ar4NPGhmY2PdLwKPxPffDPgQ4fsw4FjgYTPbtp73zXP3WcDj8eUywAF1tP3jwBeSTdMr1NmEEGBfD3yFcB2tRAhMPgF8mZAQ6PH48GBAavX3ZGYfNbMbgTsIw683jOcpXeO7Af8NzDeznQpPJCLSRAroREQyaa/PRe7+XvL6UmBRLH8E2K+B8x4HnE246XuV0HPQA9wKLE7qHU7W+7AUM9uM0Ns0Jtk8G7gYuCjuK7V5A+BOM9uhgXY2y3hCkJD3IvA7wmcvGWdmw2uc7wuEXj6AJ4BZpR1mth/hBroUYD8HXAf8CrgGmJecZ31gZgx4qjkkHj8MeI3QuzcVuCepsypwqZl9gRAUrQ68FN9zOqGnpuRDwOWNPgRIpAHZQXXUPwgoPRi4293npzvNbAzhs6QB/+PA5fG9/ggsidvXAq4zs4E4d7Sl35OZfQL4A7Brsnke4d+GaYSe1NIyJ2sDN5nZgYiI9DMNuRQRAeK8uK2TTWmPHO7+qpldS+jFg/B0fqmejwITCYHWqcCZ7r4wed91gWuBUs/RiWZ2hru/mWvfKoQhoKvFTf8HHOHuD+XqjQYuALYn3OheYWabuftf62xrM4yL/z0PmAK8AmwJrOPui83sGuDnhJ6U4YSeuxmVThQdmpSnxXmNpZ67Kcm+04Af5odVmtkehB66FYEPA98Fjq/yfqXv+Bzg++7+j+RcByZtHQn8nvBwdApwiru/ldSdSOiphDAsbx9C72mjpgGTgSHAFma2gbvPq1L/kNyx74vB7I2E3zuExD+Hu/tNuXobE4KjLQmf7zwzm+fuM3vR/v7Ssu8pLldyJVnCpMcJf3935OqtCZxLyIa7LPArM5vr7g/37aOKiBRTD52ISJD2zt1bcMPck5TH1jt8LzrZ3SelwRyAuz9F6O0r9awNo3y4XMlJZDeTDwI75IO5eL75hLlTpV6sjwLfa6CdzfJLdz/W3Z9x9zfc/XZ3nxrbuJBwc1xSOOzSzIYR5uRB+B2lAcoGZHOmnnT371eaI+fuvyEMySvZs472T3f349MgIZ7rUsqH4i5DmP/4rTRIiHWnEoKnkt4Ou3yG8mGqhb10ZrYhWc/b25RnaAU4mdDDDKG3eNt8MBffcw6wA+Fag9Djd06cdzeQtOp7mkA2h3YB4fd2R75SfHCyL+EBAoSHCKfV/3FERBqngE5Eul6ct1bWC1RQ9WbC3LqSo+p8i9eAnxbtdHcnLI9QMirdb2ZDgX9PNp2Y78HLne+fhACw5DAzW7HOtjbLlBr7e5LyzjFZRSXjCHMEAW7LJa1Ih2ouZ2bVRp1cAnyfMBdv/xqByRJC4FPkttzrajfsdybltavUqyW9JqsNu0x7565z91dLL+I1cHiy/2R3f6LoRPEaOyzZtAkh6cxA0crv6etJ+RR3/1vRiWIP8olA6eHCbjFxi4hIv1BAJyIC/0oYagVhntxllSrFOXUXJZsOqWP+F4R5TG/VqPNUUl4pt+8zhKGCAAsJCRmqcvd7CVn3IPQSjKlSvdmez8/bynP3OwmJTCAMTSuak5gG2lNz++YD/4zlTwBXmtm/FLzfS+5+mrtf5u73loZtFnisRrbD9Gb+2WpBEWG+Vkn+e23EVcAbsbyemX02XyEGqWmwl38wsT1hHieEuV5FDy7e5+6zCXMzSwZSoo+WfE9x7twnk001s7LGnroHk02fr3WMiEhvaQ6diEj5cMvr3P2VKnV7yIbvrUy4gf5ljfNXDW6i15Ny/t/m9OZ9CfBfdS6Fl/ZCbUr5sL3+NLfOetOAH8fywYR5de8zs9XJAojXCUsevC/Oa5xOWAoCQm/eODN7lDBn6hZgpru/QWOq3fhDeSKbWksIvJOUez1c0d0XmtlVZNfqQZQHWhCGCq4Ty88RepRTaRKURxr4vdxJdg1uUecxrdCq7ykfPE82s2oPBEpWTcqNDM8WEWmIAjoR6WpmthohRXvJVmZ2Z1H9aDHZv59HUzuge73Gfsjm0MHSN5RpVsbh1D/UM7Vq7SpN83Kd9aYDPyKMFhlrZqPc/clk/4Fk2RovK+jlPJ4wRDWdd7hh/DkOWJws83CFu/+5jnZVC+jzWrnWWA9ZQLe/mZ2QmzOYzkW8uMJ8wnRYayPr4i1IykVDY9uhVd9TPivqkb04Ryv//kSky2jIpYh0u4MJiwuXrAtsU+MnfRi2hZltVeM9Chf4rtOH+ng8ZPPQWmFh7SoQF3xO5znl54ZVG25ZOsdCwryu8YR09fmek2WB7YAzgSfNbKqZfZjqFtfY3y6zgFLAuzqwY2mHmS1PWCC8pNJwypWT8j8q7C+SztesZ4hxq7Tqe+q0vz8R6TLqoRORbjexdpWajgbua8J5iqQB0rXuvldhzf5TdcHzPughS7RxMHEIZszWWBreN8/d/1B0gjgfbjow3czWAL5ECHZ2oDzBxTKEbIUbmdm2MXlMx3D3JXGI6aS46SCyYZW7kmWvvN/dKw17TYO4RubzpcFIYTKeJumv66wv0r+/2e6+eWFNEZE2UEAnIl3LzLYAPpVs2trd7ymqnzv2YrIhbgfE4W+vNbuN0YtJeXSTzpn2ZNVzE92MXopKriEMSV0Z2MDMNo4p89MkKT31nszdn4v1ewDMbAPCkNojgVLClK0IC59f0Lemt8U0wuLzQ4C9zezIGJgenKtTyQtJeURBnUpGJuXnGzgOBs511hfp398oM1smJkgSERkQNORSRLpZ2jv3VL3BXNSTlIdRPjyw2dJseZ+MvVA1mdmhZrarmX3SzFbI7U6Hq9UzjG5U7SqNi/Pi0rXSSouSl+Y1vkt5ZtEyZvYRMxtrZhWXBHD3ee4+Bfg08GiyqyOzDrr702RZTj8I7GhmKwG7xW3vAJcWHH5/Ut7EzOodBrhNUn60sFZlA+I666P0729lQtbZmsxsLzPbw8w2jd+RiEi/UEAnIl0pru2Wztm6pMFT3Ep5YoneJCqp1yyy9PxDgP+odUBMaz+dsGDyXOBzuSpphsOqvTVmNoLytO3N1pOUx5nZmmTDLX8XU8BXatfVhHTzdxPW/Srk7q8D6QLaKxfV7QA9SXlPQvBbWmfwBnd/cakjgrvJkoMsRxh+WpWZfQrYMtl0SyMNZWBdZ701l/KeyWNqHRCXOrgcuA54iPIeVBGRplJAJyLdak+yOUcAMxo5OJm3VbKxmW3bjIZVeK/XKO+l+qaZFfYSxAQZ5yabniYEoKnHkvIuRYlC4tpmZ9OHlPu1uPtdZEs7fBr4WvJ+FZOhRHcn5YPq6LlMU+7Pa6iRA8tVZPPh9gD2TvYVri0XlylI9//IzNYrqh8XIk8zuC4AfttgWwfMddZb8W89XVLjIDMbV1Q/fpZzCUEzhCHFVxTVFxHpKwV0ItKt0uGWD7h7o0PJIPSUpHOEju5Ti6qbTLb48VDg92Z2cLx5fJ+ZjSb0yqWZN7/t7vmMgDeSLZUwDLg6LuGQnmstQvCwT3M+QlU9Sfmk+N+Xgd9UOWYqWQ/Qx4Ab4rzIMmY21MymkA2zfJfOnD8HgLu/CVwdX64BlJLkvEh5L2QlPyabE7YKMMvMdslXinMPbyW7jt4DJlS4jmoZaNdZb51Dtu7dEOAKMzvezMpyEcTe5RmEB0YlP6mxtqWISJ8oKYqIdJ14A7lzsqnR4ZYAuPsTcc267eKmfc3seHd/qdpxvXyvBWZ2AGEI1zDCkMGLgdPjOmtvA+sRFkFOk0/81N2X6h2I57uAbE2tzwN/NrOZhOQZIwkLVS8LLALOo8awxj6aDpxKeNBYWkZiRrVMlO7+spkdSxYMbgHcb2YPEtL7v0pI778NkPYMneLu3tzmt1wPYakGyL7vGe7+TuXqgbs/a2b7EdblG04ICG8ys/mEOXaLACPMEys99H0POMndb6twyqoG4HXWK+7+upntTQhyVyP0vv0M+IGZzSL0wo0ExgDpfNVrgCktbq6IdBn10IlINxpP+c3qZX04VzokcCjNWQahIne/hXDzOyfZvDZh/bFDga3Jbu7fItyEn1DllMcS5vmUDCOkvx9PSPm/LPA3QqKSmX3+AFW4+7MsPSy02nDL0nHTgCMoT8m/OaG353Bgd7Jg7g3g6+7+kz43uP3uAJ7KbSscbply99sJ18ojyebRwAGE734M2d/Hc8CX3P3sPrR1wFxnfeHuDxN6LO9KNq9G6CEtfZZSMPcucBawXxyyKSLSbxTQiUg3mpCUZxYl3ajTlZSvzXVkfhhkM7n7g4SlFvYh9GrNJ/QOLCYMpZsF/BAY7e5n1TjXP939AMI6cBcT5totIgztvA/4LrCRuzc6b6q3epLyI+7+QD0HufuvgPWB7xCSdjxH6LFcRJj3dRuh12e0u/9nMxvcLhXmcM6p9/cVj3+YcB3tS/junyAExe8AfyUMlfwqsF58kNCXtg6066zX3P1pd98W2Ak4H/gToSd4MfAK8EfgDGBDdz/J3d9tW2NFpGsMWbJED45ERKT9zOwYsmQu33D3n7WzPSIiIp1APXQiIjJQfCX+dxGhJ0dERERqUEAnIiJtZ2abkWVUvLLKWmoiIiKSUEAnIiJtFdcm+0WyqS8JOERERLqKli0QEZGWM7ObCJkNhwM7ki3yfkFM/CIiIiJ1UEAnIiLtsAaQX9D6PqDaMgsiIiKSoyGXIiLSDtcDfyckQJlPWFT88+7+RltbJSIi0mG0bIGIiIiIiEiHUg+diIiIiIhIh1JAJyIiIiIi0qEU0ImIiIiIiHQoBXQiIiIiIiIdSgGdiIiIiIhIh1JAJyIiIiIi0qEU0ImIiIiIiHQoBXQiIiIiIiIdSgGdiIiIiIhIh1JAJyIiIiIi0qEU0ImIiIiIiHQoBXQiIiIiIiIdSgGdiIiIiIhIh/p//hN0i5Ig/ZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 299,
       "width": 442
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RTs = []\n",
    "vols = []\n",
    "shapes = ['ASI2', 'ASI4', 'ASI6']\n",
    "positions = ['prox', 'med', 'dist']\n",
    "\n",
    "for shape in shapes: \n",
    "    for position in positions: \n",
    "        if position in right_volumes[shape].keys():\n",
    "            vols += right_volumes[shape][position]\n",
    "            RTs += right_residence_times[shape][position]\n",
    "        \n",
    "\n",
    "# for longform, data_dict in zip([RTs, vols], [right_residence_times, right_volumes]):\n",
    "#     for position in positions: \n",
    "#         for shape in plotted_shapes: \n",
    "#             if position in data_dict.keys(): longform += data_dict[shape]\n",
    "        \n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "print vols\n",
    "sns.scatterplot(vols, RTs)\n",
    "plt.xlabel('Aneurysm volume')\n",
    "plt.ylabel('Residence time [s]')\n",
    "plt.title('Residence Time vs Aneurysm Volume, Aggregated')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 609,
       "width": 604
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "format_axes(ax)\n",
    "\n",
    "for shape in shapes: \n",
    "    for position in right_volumes[shape].keys(): \n",
    "        ax.scatter(right_volumes[shape][position], right_residence_times[shape][position], \n",
    "                   label=' '.join([shape, position]))\n",
    "        \n",
    "plt.xlabel('Aneurysm volume')\n",
    "plt.ylabel('Residence time [s]')\n",
    "plt.legend()\n",
    "plt.title('Residence Time vs Aneurysm Volume, Aggregated')\n",
    "plt.savefig(\"../figures/RT_vol.eps\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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