notebooks/main_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make main table and heatmap for comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_context('poster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get all outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../reports/tables/cambridge_main_results.csv',\n",
       " '../reports/tables/facesfamous_main_results.csv',\n",
       " '../reports/tables/penandpaper_main_results.csv',\n",
       " '../reports/tables/emofaces_main_results.csv',\n",
       " '../reports/tables/faceattract_main_results.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_list = [\n",
    "    '../reports/tables/%s' % x for x in os.listdir('../reports/tables/')\n",
    "    if x.endswith('.csv') and 'summary' not in x\n",
    "]\n",
    "table_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### collect all outputs and merge into one table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>% correct</th>\n",
       "      <th>df</th>\n",
       "      <th>p</th>\n",
       "      <th>percentile</th>\n",
       "      <th>t</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Face evaluation</th>\n",
       "      <th>Attractiveness</th>\n",
       "      <td>76.875000</td>\n",
       "      <td>45</td>\n",
       "      <td>0.720</td>\n",
       "      <td>64.379177</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.368613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emotion recognition</th>\n",
       "      <td>88.095238</td>\n",
       "      <td>16</td>\n",
       "      <td>0.294</td>\n",
       "      <td>87.491950</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.149958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eyes test</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>49</td>\n",
       "      <td>0.425</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.813489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likability</th>\n",
       "      <td>43.750000</td>\n",
       "      <td>45</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.039227</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.358192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Face recognition</th>\n",
       "      <th>CFMT 1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>0.561</td>\n",
       "      <td>72.955529</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.611469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 2</th>\n",
       "      <td>36.666667</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.020554</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.532865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 3</th>\n",
       "      <td>33.333333</td>\n",
       "      <td>26</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.978091</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.334648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous context</th>\n",
       "      <td>58.260870</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.810858</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.403991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous familiarity</th>\n",
       "      <td>31.304348</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.016127</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.596492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous naming</th>\n",
       "      <td>13.913043</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.836081</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.392772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Visuo-spatial</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>173</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.523086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-B</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>173</td>\n",
       "      <td>0.046</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.015520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPS2-Visual</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>128</td>\n",
       "      <td>0.321</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       % correct   df      p  percentile  \\\n",
       "                 Unnamed: 1                                                \n",
       "Face evaluation  Attractiveness        76.875000   45  0.720   64.379177   \n",
       "                 Emotion recognition   88.095238   16  0.294   87.491950   \n",
       "                 Eyes test             72.000000   49  0.425   21.000000   \n",
       "                 Likability            43.750000   45  0.002    0.039227   \n",
       "Face recognition CFMT 1               100.000000   26  0.561   72.955529   \n",
       "                 CFMT 2                36.666667   26  0.002    0.020554   \n",
       "                 CFMT 3                33.333333   26  0.033    0.978091   \n",
       "                 Famous context        58.260870   26  0.029    0.810858   \n",
       "                 Famous familiarity    31.304348   26  0.002    0.016127   \n",
       "                 Famous naming         13.913043   26  0.029    0.836081   \n",
       "Visuo-spatial    BRLD-A                33.000000  173  0.013    1.000000   \n",
       "                 BRLD-B                38.000000  173  0.046    2.000000   \n",
       "                 LPS2-Visual           48.000000  128  0.321   16.000000   \n",
       "\n",
       "                                         t         z  \n",
       "                 Unnamed: 1                           \n",
       "Face evaluation  Attractiveness       0.36  0.368613  \n",
       "                 Emotion recognition  1.08  1.149958  \n",
       "                 Eyes test           -0.80 -0.813489  \n",
       "                 Likability          -3.29 -3.358192  \n",
       "Face recognition CFMT 1               0.59  0.611469  \n",
       "                 CFMT 2              -3.40 -3.532865  \n",
       "                 CFMT 3              -2.25 -2.334648  \n",
       "                 Famous context      -2.32 -2.403991  \n",
       "                 Famous familiarity  -3.47 -3.596492  \n",
       "                 Famous naming       -2.31 -2.392772  \n",
       "Visuo-spatial    BRLD-A              -2.52 -2.523086  \n",
       "                 BRLD-B              -2.01 -2.015520  \n",
       "                 LPS2-Visual         -1.00 -1.000508  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_df = pd.DataFrame()\n",
    "for csv in table_list:\n",
    "    this_df = pd.read_csv(csv, index_col=[0, 1])\n",
    "    big_df = pd.concat([big_df, this_df])\n",
    "big_df.sort_index(inplace=True)\n",
    "big_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### custom order for publication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>% correct</th>\n",
       "      <th>df</th>\n",
       "      <th>p</th>\n",
       "      <th>percentile</th>\n",
       "      <th>t</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Face recognition</th>\n",
       "      <th>CFMT 1</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>0.561</td>\n",
       "      <td>72.955529</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.611469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 2</th>\n",
       "      <td>36.666667</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.020554</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-3.532865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 3</th>\n",
       "      <td>33.333333</td>\n",
       "      <td>26</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.978091</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-2.334648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous familiarity</th>\n",
       "      <td>31.304348</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.016127</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-3.596492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous context</th>\n",
       "      <td>58.260870</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.810858</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>-2.403991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous naming</th>\n",
       "      <td>13.913043</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.836081</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>-2.392772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Face evaluation</th>\n",
       "      <th>Emotion recognition</th>\n",
       "      <td>88.095238</td>\n",
       "      <td>16</td>\n",
       "      <td>0.294</td>\n",
       "      <td>87.491950</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.149958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eyes test</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>49</td>\n",
       "      <td>0.425</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.813489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attractiveness</th>\n",
       "      <td>76.875000</td>\n",
       "      <td>45</td>\n",
       "      <td>0.720</td>\n",
       "      <td>64.379177</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.368613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likability</th>\n",
       "      <td>43.750000</td>\n",
       "      <td>45</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.039227</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>-3.358192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Visuo-spatial</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>173</td>\n",
       "      <td>0.013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.523086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-B</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>173</td>\n",
       "      <td>0.046</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-2.015520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPS2-Visual</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>128</td>\n",
       "      <td>0.321</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       % correct   df      p  percentile  \\\n",
       "                 Unnamed: 1                                                \n",
       "Face recognition CFMT 1               100.000000   26  0.561   72.955529   \n",
       "                 CFMT 2                36.666667   26  0.002    0.020554   \n",
       "                 CFMT 3                33.333333   26  0.033    0.978091   \n",
       "                 Famous familiarity    31.304348   26  0.002    0.016127   \n",
       "                 Famous context        58.260870   26  0.029    0.810858   \n",
       "                 Famous naming         13.913043   26  0.029    0.836081   \n",
       "Face evaluation  Emotion recognition   88.095238   16  0.294   87.491950   \n",
       "                 Eyes test             72.000000   49  0.425   21.000000   \n",
       "                 Attractiveness        76.875000   45  0.720   64.379177   \n",
       "                 Likability            43.750000   45  0.002    0.039227   \n",
       "Visuo-spatial    BRLD-A                33.000000  173  0.013    1.000000   \n",
       "                 BRLD-B                38.000000  173  0.046    2.000000   \n",
       "                 LPS2-Visual           48.000000  128  0.321   16.000000   \n",
       "\n",
       "                                         t         z  \n",
       "                 Unnamed: 1                           \n",
       "Face recognition CFMT 1               0.59  0.611469  \n",
       "                 CFMT 2              -3.40 -3.532865  \n",
       "                 CFMT 3              -2.25 -2.334648  \n",
       "                 Famous familiarity  -3.47 -3.596492  \n",
       "                 Famous context      -2.32 -2.403991  \n",
       "                 Famous naming       -2.31 -2.392772  \n",
       "Face evaluation  Emotion recognition  1.08  1.149958  \n",
       "                 Eyes test           -0.80 -0.813489  \n",
       "                 Attractiveness       0.36  0.368613  \n",
       "                 Likability          -3.29 -3.358192  \n",
       "Visuo-spatial    BRLD-A              -2.52 -2.523086  \n",
       "                 BRLD-B              -2.01 -2.015520  \n",
       "                 LPS2-Visual         -1.00 -1.000508  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordered_df = big_df.iloc[[4, 5, 6, -5, -6, -4, 1, 2, 0, 3, -3, -2, -1]]\n",
    "ordered_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set decimal places and column order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>% correct</th>\n",
       "      <th>percentile</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>df</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Face recognition</th>\n",
       "      <th>CFMT 1</th>\n",
       "      <td>100</td>\n",
       "      <td>73</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.59</td>\n",
       "      <td>26</td>\n",
       "      <td>0.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 2</th>\n",
       "      <td>37</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>-3.53</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 3</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.33</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>26</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous familiarity</th>\n",
       "      <td>31</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>26</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous context</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.40</td>\n",
       "      <td>-2.32</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous naming</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-2.31</td>\n",
       "      <td>26</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Face evaluation</th>\n",
       "      <th>Emotion recognition</th>\n",
       "      <td>88</td>\n",
       "      <td>87</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.08</td>\n",
       "      <td>16</td>\n",
       "      <td>0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eyes test</th>\n",
       "      <td>72</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>49</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attractiveness</th>\n",
       "      <td>77</td>\n",
       "      <td>64</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>45</td>\n",
       "      <td>0.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likability</th>\n",
       "      <td>44</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>-3.36</td>\n",
       "      <td>-3.29</td>\n",
       "      <td>45</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Visuo-spatial</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>-2.52</td>\n",
       "      <td>173</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-B</th>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>173</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPS2-Visual</th>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>128</td>\n",
       "      <td>0.321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      % correct percentile     z     t   df  \\\n",
       "                 Unnamed: 1                                                   \n",
       "Face recognition CFMT 1                     100         73  0.61  0.59   26   \n",
       "                 CFMT 2                      37         <1 -3.53 -3.40   26   \n",
       "                 CFMT 3                      33          1 -2.33 -2.25   26   \n",
       "                 Famous familiarity          31         <1 -3.60 -3.47   26   \n",
       "                 Famous context              58          1 -2.40 -2.32   26   \n",
       "                 Famous naming               14          1 -2.39 -2.31   26   \n",
       "Face evaluation  Emotion recognition         88         87  1.15  1.08   16   \n",
       "                 Eyes test                   72         21 -0.81 -0.80   49   \n",
       "                 Attractiveness              77         64  0.37  0.36   45   \n",
       "                 Likability                  44         <1 -3.36 -3.29   45   \n",
       "Visuo-spatial    BRLD-A                      33          1 -2.52 -2.52  173   \n",
       "                 BRLD-B                      38          2 -2.02 -2.01  173   \n",
       "                 LPS2-Visual                 48         16 -1.00 -1.00  128   \n",
       "\n",
       "                                          p  \n",
       "                 Unnamed: 1                  \n",
       "Face recognition CFMT 1               0.561  \n",
       "                 CFMT 2               0.002  \n",
       "                 CFMT 3               0.033  \n",
       "                 Famous familiarity   0.002  \n",
       "                 Famous context       0.029  \n",
       "                 Famous naming        0.029  \n",
       "Face evaluation  Emotion recognition  0.294  \n",
       "                 Eyes test            0.425  \n",
       "                 Attractiveness       0.720  \n",
       "                 Likability           0.002  \n",
       "Visuo-spatial    BRLD-A               0.013  \n",
       "                 BRLD-B               0.046  \n",
       "                 LPS2-Visual          0.321  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = ordered_df[['% correct', 'percentile', 'z', 't', 'df', 'p']].copy()\n",
    "clean_df.loc[:, '% correct'] = clean_df.loc[:, '% correct'].round(0).astype(int)\n",
    "clean_df.loc[:, 'percentile'] = clean_df.loc[:, 'percentile'].round(0).astype(int)\n",
    "clean_df.loc[:, 'percentile'] = [[str(x), '<1'][x < 1] for x in clean_df.loc[:, 'percentile']]\n",
    "clean_df.loc[:, 'z'] = clean_df.loc[:, 'z'].round(2)\n",
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean_df.to_csv('../reports/tables/summary_table.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export for use in paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'\\\\begin{tabular}{llrlrrrr}\\n\\\\toprule\\n                &                &  \\\\% correct & percentile &     z &     t &   df &      p \\\\\\\\\\n{} & Unnamed: 1 &            &            &       &       &      &        \\\\\\\\\\n\\\\midrule\\nFace recognition & CFMT 1 &        100 &         73 &  0.61 &  0.59 &   26 &  0.561 \\\\\\\\\\n                & CFMT 2 &         37 &         <1 & -3.53 & -3.40 &   26 &  0.002 \\\\\\\\\\n                & CFMT 3 &         33 &          1 & -2.33 & -2.25 &   26 &  0.033 \\\\\\\\\\n                & Famous familiarity &         31 &         <1 & -3.60 & -3.47 &   26 &  0.002 \\\\\\\\\\n                & Famous context &         58 &          1 & -2.40 & -2.32 &   26 &  0.029 \\\\\\\\\\n                & Famous naming &         14 &          1 & -2.39 & -2.31 &   26 &  0.029 \\\\\\\\\\nFace evaluation & Emotion recognition &         88 &         87 &  1.15 &  1.08 &   16 &  0.294 \\\\\\\\\\n                & Eyes test &         72 &         21 & -0.81 & -0.80 &   49 &  0.425 \\\\\\\\\\n                & Attractiveness &         77 &         64 &  0.37 &  0.36 &   45 &  0.720 \\\\\\\\\\n                & Likability &         44 &         <1 & -3.36 & -3.29 &   45 &  0.002 \\\\\\\\\\nVisuo-spatial & BRLD-A &         33 &          1 & -2.52 & -2.52 &  173 &  0.013 \\\\\\\\\\n                & BRLD-B &         38 &          2 & -2.02 & -2.01 &  173 &  0.046 \\\\\\\\\\n                & LPS2-Visual &         48 &         16 & -1.00 & -1.00 &  128 &  0.321 \\\\\\\\\\n\\\\bottomrule\\n\\\\end{tabular}\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.to_latex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make a heatmap with z-score differences between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get differences for each pairing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Face recognition</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Face evaluation</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Visuo-spatial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>CFMT 1</th>\n",
       "      <th>CFMT 2</th>\n",
       "      <th>CFMT 3</th>\n",
       "      <th>Famous familiarity</th>\n",
       "      <th>Famous context</th>\n",
       "      <th>Famous naming</th>\n",
       "      <th>Emotion recognition</th>\n",
       "      <th>Eyes test</th>\n",
       "      <th>Attractiveness</th>\n",
       "      <th>Likability</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <th>BRLD-B</th>\n",
       "      <th>LPS2-Visual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Face recognition</th>\n",
       "      <th>CFMT 1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.144333</td>\n",
       "      <td>-2.946117</td>\n",
       "      <td>-4.207961</td>\n",
       "      <td>-3.015460</td>\n",
       "      <td>-3.004241</td>\n",
       "      <td>0.538490</td>\n",
       "      <td>-1.424958</td>\n",
       "      <td>-0.242856</td>\n",
       "      <td>-3.969661</td>\n",
       "      <td>-3.134555</td>\n",
       "      <td>-2.626988</td>\n",
       "      <td>-1.611977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 2</th>\n",
       "      <td>4.144333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.198217</td>\n",
       "      <td>-0.063627</td>\n",
       "      <td>1.128873</td>\n",
       "      <td>1.140092</td>\n",
       "      <td>4.682823</td>\n",
       "      <td>2.719375</td>\n",
       "      <td>3.901477</td>\n",
       "      <td>0.174672</td>\n",
       "      <td>1.009778</td>\n",
       "      <td>1.517345</td>\n",
       "      <td>2.532357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 3</th>\n",
       "      <td>2.946117</td>\n",
       "      <td>-1.198217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.261844</td>\n",
       "      <td>-0.069343</td>\n",
       "      <td>-0.058124</td>\n",
       "      <td>3.484606</td>\n",
       "      <td>1.521159</td>\n",
       "      <td>2.703261</td>\n",
       "      <td>-1.023544</td>\n",
       "      <td>-0.188438</td>\n",
       "      <td>0.319128</td>\n",
       "      <td>1.334140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous familiarity</th>\n",
       "      <td>4.207961</td>\n",
       "      <td>0.063627</td>\n",
       "      <td>1.261844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.192501</td>\n",
       "      <td>1.203720</td>\n",
       "      <td>4.746450</td>\n",
       "      <td>2.783003</td>\n",
       "      <td>3.965105</td>\n",
       "      <td>0.238300</td>\n",
       "      <td>1.073406</td>\n",
       "      <td>1.580972</td>\n",
       "      <td>2.595984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous context</th>\n",
       "      <td>3.015460</td>\n",
       "      <td>-1.128873</td>\n",
       "      <td>0.069343</td>\n",
       "      <td>-1.192501</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011219</td>\n",
       "      <td>3.553950</td>\n",
       "      <td>1.590502</td>\n",
       "      <td>2.772604</td>\n",
       "      <td>-0.954201</td>\n",
       "      <td>-0.119095</td>\n",
       "      <td>0.388472</td>\n",
       "      <td>1.403483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous naming</th>\n",
       "      <td>3.004241</td>\n",
       "      <td>-1.140092</td>\n",
       "      <td>0.058124</td>\n",
       "      <td>-1.203720</td>\n",
       "      <td>-0.011219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.542731</td>\n",
       "      <td>1.579283</td>\n",
       "      <td>2.761385</td>\n",
       "      <td>-0.965420</td>\n",
       "      <td>-0.130314</td>\n",
       "      <td>0.377253</td>\n",
       "      <td>1.392264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Face evaluation</th>\n",
       "      <th>Emotion recognition</th>\n",
       "      <td>-0.538490</td>\n",
       "      <td>-4.682823</td>\n",
       "      <td>-3.484606</td>\n",
       "      <td>-4.746450</td>\n",
       "      <td>-3.553950</td>\n",
       "      <td>-3.542731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.963448</td>\n",
       "      <td>-0.781346</td>\n",
       "      <td>-4.508150</td>\n",
       "      <td>-3.673045</td>\n",
       "      <td>-3.165478</td>\n",
       "      <td>-2.150466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eyes test</th>\n",
       "      <td>1.424958</td>\n",
       "      <td>-2.719375</td>\n",
       "      <td>-1.521159</td>\n",
       "      <td>-2.783003</td>\n",
       "      <td>-1.590502</td>\n",
       "      <td>-1.579283</td>\n",
       "      <td>1.963448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.182102</td>\n",
       "      <td>-2.544703</td>\n",
       "      <td>-1.709597</td>\n",
       "      <td>-1.202030</td>\n",
       "      <td>-0.187019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attractiveness</th>\n",
       "      <td>0.242856</td>\n",
       "      <td>-3.901477</td>\n",
       "      <td>-2.703261</td>\n",
       "      <td>-3.965105</td>\n",
       "      <td>-2.772604</td>\n",
       "      <td>-2.761385</td>\n",
       "      <td>0.781346</td>\n",
       "      <td>-1.182102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.726805</td>\n",
       "      <td>-2.891699</td>\n",
       "      <td>-2.384132</td>\n",
       "      <td>-1.369121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likability</th>\n",
       "      <td>3.969661</td>\n",
       "      <td>-0.174672</td>\n",
       "      <td>1.023544</td>\n",
       "      <td>-0.238300</td>\n",
       "      <td>0.954201</td>\n",
       "      <td>0.965420</td>\n",
       "      <td>4.508150</td>\n",
       "      <td>2.544703</td>\n",
       "      <td>3.726805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.835106</td>\n",
       "      <td>1.342672</td>\n",
       "      <td>2.357684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Visuo-spatial</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <td>3.134555</td>\n",
       "      <td>-1.009778</td>\n",
       "      <td>0.188438</td>\n",
       "      <td>-1.073406</td>\n",
       "      <td>0.119095</td>\n",
       "      <td>0.130314</td>\n",
       "      <td>3.673045</td>\n",
       "      <td>1.709597</td>\n",
       "      <td>2.891699</td>\n",
       "      <td>-0.835106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.507566</td>\n",
       "      <td>1.522578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-B</th>\n",
       "      <td>2.626988</td>\n",
       "      <td>-1.517345</td>\n",
       "      <td>-0.319128</td>\n",
       "      <td>-1.580972</td>\n",
       "      <td>-0.388472</td>\n",
       "      <td>-0.377253</td>\n",
       "      <td>3.165478</td>\n",
       "      <td>1.202030</td>\n",
       "      <td>2.384132</td>\n",
       "      <td>-1.342672</td>\n",
       "      <td>-0.507566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.015012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPS2-Visual</th>\n",
       "      <td>1.611977</td>\n",
       "      <td>-2.532357</td>\n",
       "      <td>-1.334140</td>\n",
       "      <td>-2.595984</td>\n",
       "      <td>-1.403483</td>\n",
       "      <td>-1.392264</td>\n",
       "      <td>2.150466</td>\n",
       "      <td>0.187019</td>\n",
       "      <td>1.369121</td>\n",
       "      <td>-2.357684</td>\n",
       "      <td>-1.522578</td>\n",
       "      <td>-1.015012</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Face recognition                      \\\n",
       "Unnamed: 1                                     CFMT 1    CFMT 2    CFMT 3   \n",
       "Face recognition CFMT 1                      0.000000 -4.144333 -2.946117   \n",
       "                 CFMT 2                      4.144333  0.000000  1.198217   \n",
       "                 CFMT 3                      2.946117 -1.198217  0.000000   \n",
       "                 Famous familiarity          4.207961  0.063627  1.261844   \n",
       "                 Famous context              3.015460 -1.128873  0.069343   \n",
       "                 Famous naming               3.004241 -1.140092  0.058124   \n",
       "Face evaluation  Emotion recognition        -0.538490 -4.682823 -3.484606   \n",
       "                 Eyes test                   1.424958 -2.719375 -1.521159   \n",
       "                 Attractiveness              0.242856 -3.901477 -2.703261   \n",
       "                 Likability                  3.969661 -0.174672  1.023544   \n",
       "Visuo-spatial    BRLD-A                      3.134555 -1.009778  0.188438   \n",
       "                 BRLD-B                      2.626988 -1.517345 -0.319128   \n",
       "                 LPS2-Visual                 1.611977 -2.532357 -1.334140   \n",
       "\n",
       "                                                                        \\\n",
       "Unnamed: 1                           Famous familiarity Famous context   \n",
       "Face recognition CFMT 1                       -4.207961      -3.015460   \n",
       "                 CFMT 2                       -0.063627       1.128873   \n",
       "                 CFMT 3                       -1.261844      -0.069343   \n",
       "                 Famous familiarity            0.000000       1.192501   \n",
       "                 Famous context               -1.192501       0.000000   \n",
       "                 Famous naming                -1.203720      -0.011219   \n",
       "Face evaluation  Emotion recognition          -4.746450      -3.553950   \n",
       "                 Eyes test                    -2.783003      -1.590502   \n",
       "                 Attractiveness               -3.965105      -2.772604   \n",
       "                 Likability                   -0.238300       0.954201   \n",
       "Visuo-spatial    BRLD-A                       -1.073406       0.119095   \n",
       "                 BRLD-B                       -1.580972      -0.388472   \n",
       "                 LPS2-Visual                  -2.595984      -1.403483   \n",
       "\n",
       "                                                       Face evaluation  \\\n",
       "Unnamed: 1                           Famous naming Emotion recognition   \n",
       "Face recognition CFMT 1                  -3.004241            0.538490   \n",
       "                 CFMT 2                   1.140092            4.682823   \n",
       "                 CFMT 3                  -0.058124            3.484606   \n",
       "                 Famous familiarity       1.203720            4.746450   \n",
       "                 Famous context           0.011219            3.553950   \n",
       "                 Famous naming            0.000000            3.542731   \n",
       "Face evaluation  Emotion recognition     -3.542731            0.000000   \n",
       "                 Eyes test               -1.579283            1.963448   \n",
       "                 Attractiveness          -2.761385            0.781346   \n",
       "                 Likability               0.965420            4.508150   \n",
       "Visuo-spatial    BRLD-A                   0.130314            3.673045   \n",
       "                 BRLD-B                  -0.377253            3.165478   \n",
       "                 LPS2-Visual             -1.392264            2.150466   \n",
       "\n",
       "                                                                          \\\n",
       "Unnamed: 1                           Eyes test Attractiveness Likability   \n",
       "Face recognition CFMT 1              -1.424958      -0.242856  -3.969661   \n",
       "                 CFMT 2               2.719375       3.901477   0.174672   \n",
       "                 CFMT 3               1.521159       2.703261  -1.023544   \n",
       "                 Famous familiarity   2.783003       3.965105   0.238300   \n",
       "                 Famous context       1.590502       2.772604  -0.954201   \n",
       "                 Famous naming        1.579283       2.761385  -0.965420   \n",
       "Face evaluation  Emotion recognition -1.963448      -0.781346  -4.508150   \n",
       "                 Eyes test            0.000000       1.182102  -2.544703   \n",
       "                 Attractiveness      -1.182102       0.000000  -3.726805   \n",
       "                 Likability           2.544703       3.726805   0.000000   \n",
       "Visuo-spatial    BRLD-A               1.709597       2.891699  -0.835106   \n",
       "                 BRLD-B               1.202030       2.384132  -1.342672   \n",
       "                 LPS2-Visual          0.187019       1.369121  -2.357684   \n",
       "\n",
       "                                     Visuo-spatial                        \n",
       "Unnamed: 1                                  BRLD-A    BRLD-B LPS2-Visual  \n",
       "Face recognition CFMT 1                  -3.134555 -2.626988   -1.611977  \n",
       "                 CFMT 2                   1.009778  1.517345    2.532357  \n",
       "                 CFMT 3                  -0.188438  0.319128    1.334140  \n",
       "                 Famous familiarity       1.073406  1.580972    2.595984  \n",
       "                 Famous context          -0.119095  0.388472    1.403483  \n",
       "                 Famous naming           -0.130314  0.377253    1.392264  \n",
       "Face evaluation  Emotion recognition     -3.673045 -3.165478   -2.150466  \n",
       "                 Eyes test               -1.709597 -1.202030   -0.187019  \n",
       "                 Attractiveness          -2.891699 -2.384132   -1.369121  \n",
       "                 Likability               0.835106  1.342672    2.357684  \n",
       "Visuo-spatial    BRLD-A                   0.000000  0.507566    1.522578  \n",
       "                 BRLD-B                  -0.507566  0.000000    1.015012  \n",
       "                 LPS2-Visual             -1.522578 -1.015012    0.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_df = ordered_df.loc[:, ['z']]\n",
    "\n",
    "diff_df = pd.DataFrame()\n",
    "for i in z_df.index:\n",
    "    this = z_df.loc[i, 'z']\n",
    "    this_diff = z_df - this\n",
    "    this_diff = this_diff.T\n",
    "    this_diff.index = [[i[0]], [i[1]]]\n",
    "    diff_df = pd.concat([diff_df, this_diff])\n",
    "\n",
    "diff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diff_df = diff_df.T.iloc[1:, :-1]\n",
    "diff_df.index = diff_df.index.get_level_values(1)\n",
    "diff_df.columns = diff_df.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CFMT 1</th>\n",
       "      <th>CFMT 2</th>\n",
       "      <th>CFMT 3</th>\n",
       "      <th>Famous familiarity</th>\n",
       "      <th>Famous context</th>\n",
       "      <th>Famous naming</th>\n",
       "      <th>Emotion recognition</th>\n",
       "      <th>Eyes test</th>\n",
       "      <th>Attractiveness</th>\n",
       "      <th>Likability</th>\n",
       "      <th>BRLD-A</th>\n",
       "      <th>BRLD-B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CFMT 2</th>\n",
       "      <td>-4.144333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.198217</td>\n",
       "      <td>0.063627</td>\n",
       "      <td>-1.128873</td>\n",
       "      <td>-1.140092</td>\n",
       "      <td>-4.682823</td>\n",
       "      <td>-2.719375</td>\n",
       "      <td>-3.901477</td>\n",
       "      <td>-0.174672</td>\n",
       "      <td>-1.009778</td>\n",
       "      <td>-1.517345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFMT 3</th>\n",
       "      <td>-2.946117</td>\n",
       "      <td>1.198217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.261844</td>\n",
       "      <td>0.069343</td>\n",
       "      <td>0.058124</td>\n",
       "      <td>-3.484606</td>\n",
       "      <td>-1.521159</td>\n",
       "      <td>-2.703261</td>\n",
       "      <td>1.023544</td>\n",
       "      <td>0.188438</td>\n",
       "      <td>-0.319128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous familiarity</th>\n",
       "      <td>-4.207961</td>\n",
       "      <td>-0.063627</td>\n",
       "      <td>-1.261844</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.192501</td>\n",
       "      <td>-1.203720</td>\n",
       "      <td>-4.746450</td>\n",
       "      <td>-2.783003</td>\n",
       "      <td>-3.965105</td>\n",
       "      <td>-0.238300</td>\n",
       "      <td>-1.073406</td>\n",
       "      <td>-1.580972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous context</th>\n",
       "      <td>-3.015460</td>\n",
       "      <td>1.128873</td>\n",
       "      <td>-0.069343</td>\n",
       "      <td>1.192501</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011219</td>\n",
       "      <td>-3.553950</td>\n",
       "      <td>-1.590502</td>\n",
       "      <td>-2.772604</td>\n",
       "      <td>0.954201</td>\n",
       "      <td>0.119095</td>\n",
       "      <td>-0.388472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Famous naming</th>\n",
       "      <td>-3.004241</td>\n",
       "      <td>1.140092</td>\n",
       "      <td>-0.058124</td>\n",
       "      <td>1.203720</td>\n",
       "      <td>0.011219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.542731</td>\n",
       "      <td>-1.579283</td>\n",
       "      <td>-2.761385</td>\n",
       "      <td>0.965420</td>\n",
       "      <td>0.130314</td>\n",
       "      <td>-0.377253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emotion recognition</th>\n",
       "      <td>0.538490</td>\n",
       "      <td>4.682823</td>\n",
       "      <td>3.484606</td>\n",
       "      <td>4.746450</td>\n",
       "      <td>3.553950</td>\n",
       "      <td>3.542731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.963448</td>\n",
       "      <td>0.781346</td>\n",
       "      <td>4.508150</td>\n",
       "      <td>3.673045</td>\n",
       "      <td>3.165478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eyes test</th>\n",
       "      <td>-1.424958</td>\n",
       "      <td>2.719375</td>\n",
       "      <td>1.521159</td>\n",
       "      <td>2.783003</td>\n",
       "      <td>1.590502</td>\n",
       "      <td>1.579283</td>\n",
       "      <td>-1.963448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.182102</td>\n",
       "      <td>2.544703</td>\n",
       "      <td>1.709597</td>\n",
       "      <td>1.202030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Attractiveness</th>\n",
       "      <td>-0.242856</td>\n",
       "      <td>3.901477</td>\n",
       "      <td>2.703261</td>\n",
       "      <td>3.965105</td>\n",
       "      <td>2.772604</td>\n",
       "      <td>2.761385</td>\n",
       "      <td>-0.781346</td>\n",
       "      <td>1.182102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.726805</td>\n",
       "      <td>2.891699</td>\n",
       "      <td>2.384132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Likability</th>\n",
       "      <td>-3.969661</td>\n",
       "      <td>0.174672</td>\n",
       "      <td>-1.023544</td>\n",
       "      <td>0.238300</td>\n",
       "      <td>-0.954201</td>\n",
       "      <td>-0.965420</td>\n",
       "      <td>-4.508150</td>\n",
       "      <td>-2.544703</td>\n",
       "      <td>-3.726805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.835106</td>\n",
       "      <td>-1.342672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-A</th>\n",
       "      <td>-3.134555</td>\n",
       "      <td>1.009778</td>\n",
       "      <td>-0.188438</td>\n",
       "      <td>1.073406</td>\n",
       "      <td>-0.119095</td>\n",
       "      <td>-0.130314</td>\n",
       "      <td>-3.673045</td>\n",
       "      <td>-1.709597</td>\n",
       "      <td>-2.891699</td>\n",
       "      <td>0.835106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.507566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRLD-B</th>\n",
       "      <td>-2.626988</td>\n",
       "      <td>1.517345</td>\n",
       "      <td>0.319128</td>\n",
       "      <td>1.580972</td>\n",
       "      <td>0.388472</td>\n",
       "      <td>0.377253</td>\n",
       "      <td>-3.165478</td>\n",
       "      <td>-1.202030</td>\n",
       "      <td>-2.384132</td>\n",
       "      <td>1.342672</td>\n",
       "      <td>0.507566</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LPS2-Visual</th>\n",
       "      <td>-1.611977</td>\n",
       "      <td>2.532357</td>\n",
       "      <td>1.334140</td>\n",
       "      <td>2.595984</td>\n",
       "      <td>1.403483</td>\n",
       "      <td>1.392264</td>\n",
       "      <td>-2.150466</td>\n",
       "      <td>-0.187019</td>\n",
       "      <td>-1.369121</td>\n",
       "      <td>2.357684</td>\n",
       "      <td>1.522578</td>\n",
       "      <td>1.015012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       CFMT 1    CFMT 2    CFMT 3  Famous familiarity  \\\n",
       "Unnamed: 1                                                              \n",
       "CFMT 2              -4.144333  0.000000 -1.198217            0.063627   \n",
       "CFMT 3              -2.946117  1.198217  0.000000            1.261844   \n",
       "Famous familiarity  -4.207961 -0.063627 -1.261844            0.000000   \n",
       "Famous context      -3.015460  1.128873 -0.069343            1.192501   \n",
       "Famous naming       -3.004241  1.140092 -0.058124            1.203720   \n",
       "Emotion recognition  0.538490  4.682823  3.484606            4.746450   \n",
       "Eyes test           -1.424958  2.719375  1.521159            2.783003   \n",
       "Attractiveness      -0.242856  3.901477  2.703261            3.965105   \n",
       "Likability          -3.969661  0.174672 -1.023544            0.238300   \n",
       "BRLD-A              -3.134555  1.009778 -0.188438            1.073406   \n",
       "BRLD-B              -2.626988  1.517345  0.319128            1.580972   \n",
       "LPS2-Visual         -1.611977  2.532357  1.334140            2.595984   \n",
       "\n",
       "                     Famous context  Famous naming  Emotion recognition  \\\n",
       "Unnamed: 1                                                                \n",
       "CFMT 2                    -1.128873      -1.140092            -4.682823   \n",
       "CFMT 3                     0.069343       0.058124            -3.484606   \n",
       "Famous familiarity        -1.192501      -1.203720            -4.746450   \n",
       "Famous context             0.000000      -0.011219            -3.553950   \n",
       "Famous naming              0.011219       0.000000            -3.542731   \n",
       "Emotion recognition        3.553950       3.542731             0.000000   \n",
       "Eyes test                  1.590502       1.579283            -1.963448   \n",
       "Attractiveness             2.772604       2.761385            -0.781346   \n",
       "Likability                -0.954201      -0.965420            -4.508150   \n",
       "BRLD-A                    -0.119095      -0.130314            -3.673045   \n",
       "BRLD-B                     0.388472       0.377253            -3.165478   \n",
       "LPS2-Visual                1.403483       1.392264            -2.150466   \n",
       "\n",
       "                     Eyes test  Attractiveness  Likability    BRLD-A    BRLD-B  \n",
       "Unnamed: 1                                                                      \n",
       "CFMT 2               -2.719375       -3.901477   -0.174672 -1.009778 -1.517345  \n",
       "CFMT 3               -1.521159       -2.703261    1.023544  0.188438 -0.319128  \n",
       "Famous familiarity   -2.783003       -3.965105   -0.238300 -1.073406 -1.580972  \n",
       "Famous context       -1.590502       -2.772604    0.954201  0.119095 -0.388472  \n",
       "Famous naming        -1.579283       -2.761385    0.965420  0.130314 -0.377253  \n",
       "Emotion recognition   1.963448        0.781346    4.508150  3.673045  3.165478  \n",
       "Eyes test             0.000000       -1.182102    2.544703  1.709597  1.202030  \n",
       "Attractiveness        1.182102        0.000000    3.726805  2.891699  2.384132  \n",
       "Likability           -2.544703       -3.726805    0.000000 -0.835106 -1.342672  \n",
       "BRLD-A               -1.709597       -2.891699    0.835106  0.000000 -0.507566  \n",
       "BRLD-B               -1.202030       -2.384132    1.342672  0.507566  0.000000  \n",
       "LPS2-Visual          -0.187019       -1.369121    2.357684  1.522578  1.015012  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "separate into significant (z>+/-2) and non-significant differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sig = diff_df[abs(diff_df) > 2.]\n",
    "nsig = diff_df[abs(diff_df) <= 2.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### make the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12), dpi=300)\n",
    "\n",
    "mask = np.tri(sig.shape[0], k=-1).T\n",
    "my_max = abs(sig).max().max()\n",
    "\n",
    "sns.heatmap(\n",
    "    sig,\n",
    "    square=True,\n",
    "    ax=ax,\n",
    "    vmin=-my_max,\n",
    "    vmax=my_max,\n",
    "    annot=True,\n",
    "    fmt='.1f',\n",
    "    annot_kws={'fontsize': 16},\n",
    "    cbar_kws={\n",
    "        'shrink':0.7,\n",
    "        'label': 'z-score difference (row task minus column task)',\n",
    "        'orientation': 'horizontal',\n",
    "        'pad': 0.22\n",
    "    },\n",
    "    linewidths=0.5,\n",
    "    mask=mask)\n",
    "\n",
    "sns.heatmap(\n",
    "    nsig,\n",
    "    square=True,\n",
    "    ax=ax,\n",
    "    alpha=0.5,\n",
    "    vmin=-my_max,\n",
    "    vmax=my_max,\n",
    "    #annot=True,fmt='.1f',annot_kws={'fontsize':10,'alpha':0.5},\n",
    "    cbar=False,\n",
    "    linewidths=0.5,\n",
    "    mask=mask)\n",
    "\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "plt.suptitle('z-score comparison between tasks', fontsize=24, y=1.01)\n",
    "plt.tight_layout()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig('../reports/figures/overview_heatmap.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3UOfURMT/UB/w/XVg71ECvt0ekiStvAz6liRJkiRpkYmIJRHxX5TlXh/VY1NrUjJUZ0S8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\nemKSF6zR8EclueVKKwGOHoO7JHnXVGa1PCelBwyXvXCENfWg9N0vVsP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W5m+d0P1r86M2sTafbW3ufr29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hyoppaWbP/8Z1f2zf3hf+fh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\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++RlKTEjwiZdWVCsxQZrUv0CHDynSwELfz/x3euZnaGRJ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\nmak+8Y3bavTRkui+iAEAAABEQ/j2ZgcAIAKMMRd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S3p7R8c9msAT15zHxdy/on7apwOeaQ2767eh500prkiJMtmjs9l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\nAzBa9ib5oyR/Xpblsdzq5FtJXlaWZW+jC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GsqSwcAmuiW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EnA4sSXMZuYv9cjbwxYg4vHC8IeD3wJtpHOzd6Pjl/v5f0vWO\nx5vY32xSm3PsTczMzMzMrAkTOTNhI75Zb0XrV5Q9C3x8vAeMiOMl/YOUNWUBagO1PybpgYj46njr\nMKsiaW5gD2qDUP4MvCMinh7rGBExQ9Iw8CNql6zPPQF8YrztjIi7JO1J7U3VBUlZin423mObtSPL\n7HM5cLmk+UkrN2xHCgTPAxWHS//CyJhoEVLG/N0lzQR+TLp5OrPbbbfJpZlJM6VArHy/DbrWKLPu\nOgKYh9Fji8vocMA3QER8Jgvgfj8j5+rpwOeBLzXY7z+SdiWtSFJs05LAOsB1nWynTVprV5TdRwpA\nMTOz/lm1ouzMJvfdvPT6pcnzYwV8Z24qPM/HOqs0WbfZuEXEQ8CxwLGSViGtYLYDsFK2SXFMW3UN\nYwXgQOBASTeQVjA7KyIe6WrDzZokaW3SOHtb0moMzWT1LgfSPgacBfyw0epPZmWStgR+QrqWAc1l\nki++ngb8P0nTIuLrWdku1AZ8l/vsi6SkDHMBi5WOXVxpKt9vBeBwYJ9mPpfZZDat3w0wMzMzMxsQ\nw5PwYVb2f4Xn+QWW0yLivnYOGhFXABsBj2ZF5cDvr0javZ06zCpsSAo6LZoFfKCZgO9cRJwCXEB1\n5ohh4KR2bwBFxEXAb6mdQLR+O8c165SIeDoiTouILUiBtzsBFwLPkPpt/oDRY428fGXgYOCfkn4t\nacds2XAzsyklC0B5D6PHFE8Ce3Yxe/a+pOz1MHJu/ng2oaeuiLiMlFG/PD7ZpOMttMnqlYXn+dj4\n+FbG2mZm1hUrVpTd2OS+m1EdtHV5MztHxMNAecWyJZqs26wjIuKfEfHliHgl8HbgJNJ16eK1i1zV\nNYw3kwLI75F0vqStJTmhpvWcpAUl7S3pT6SJt7sA81M/sLscAJu//hUpO/iyEbGPA76tFZJWA85g\nZPJ6VVbusa5n5OfYr0raNMvyfXDpWMXn55ASjywUEctExBKkJDkbk4LPZxeOW65jN0krt/o5zSYb\nB32bmZmZmbXnF4y+UDhU52E2GSxXUXZpJw6cXUh8J5AHxxazww4Bx0varBN1mWU2LDzPLxr+OCL+\nPY5jfZaRC4llF4/jeFV+XFG2VoeObdYxEfF4FgD+XlKGlY1IWWtvYWTM0ygA/O3ADOBuScdkAZBm\nZlPFBxl9rhwmZVm7q1sVZlk5z2T079KFgK2b2P2MirI1OtEuGwiLUntzv1NjY7Ne8zU8GySLUXt+\nvmesnSS9mrQiCNR+F5oK+s48WXrdcKKZWTdFxO8iYm9gGdIKZheQkkI0CgDP35sb2Iq0guU9kr6b\nZVs26ypJb5R0EnA3cBywJiP9supaW1Ww913AYcAqEbFRRJweEc/27lPYADmV9Le8PMGg/PppUp8t\nTrIpZ6SfRpqIswUpK3fxvaFs300iYtuI+GWxz2ZJSa6KiO2BDYA7S/vm5gD2bucDm00GDvo2MzMz\nM2tDlvFyc9KF83IW2KJ6weD9fJiVVWXe+WOnDh4RfwLeBTxRemsYmBM4W9LrO1WfTXlVAUk/Hc+B\nIuJfwJVUnzuvH88xKxRvoOYXKleqs63ZRLEkqZ+uzOgL9VA/A3hetigp++xtks6V5CBCa8RjWBsU\n61eU/aQH9Z5XUbZhRVnZ70qvh4DXtN8cGxBV5+Tbe94Ks854E2lcmz+cHdAms/kqyppZheFdhefF\na9xPkTLMNmue0uty5m+znouIWRHx04h4PykAfB/SqnvQXPbvJYCPAtdJ+rukL0havjett6lA0gKS\n9pB0I/AHYDdSZuN6Wb3LZcXVVTcDpmcZ72f2ov02mCRtT0pMU87Infe3O4FPAStHxEIRsXyWlXtJ\nYGfgL9QGfi9HCiQvyoPGN4mIK8dqV0RcS0oyVVxdOP93CNi+hY9pNil5CRIzMzMzszZFxKWSXkfK\ngrYJoy+ukD2fBXwH8Ex6m8iWoHbiwgOdrCAibpC0JSmDePEm0DDpIuZFkt4SEf/rZL02JS1HbX++\nqY3jXUA6xxc93cEMKXeQsokXL4K+rEPHNusYSa8kZar9IFA1UaecXaisWJ7fPH0vsKWkE4DPOfOQ\nlUXEMv1ug1mHrErt+fHmHtR7W0XZa5vcr3zzdMlONcomvfJkXvA1D5ukurniglkfVAV4L0BtBu6y\n8gp8+fXtX0fEC81ULGku0vW94njn8Wb2NeuViHgU+B7wPUnTgR2BbYHVCpsNl/6FkesdImVQ/qqk\n35BWMzsvIp7qYrNtQGVJcPYCPsToIG+o7X/lflk5MT4iWlmdwayRjxWe530w//dkYL+IqJncFREP\nAadKOh04mpT4o9hvF6Y2kPywiGg6CVVE/FPSJ0grqJbvy79c0poR8edmj2c22Tjo28zMzMysAyLi\nYUnvBk4kzcAv/8Cck7SM2nv71ESzZrxYUVYvaG/cIuI3krYjZRwsrkA1DLwcuFjS2yNirJtRZo0s\nXFH2YBvH+31FWVM3PZsREcOSHiUtw5ybu1PHN2tHlr1qe9JN0DzQu+rGUvlvRr7NfcDSFdsUL+7P\nCXwc2FDSuxx4YxOVpMUi4pF+t8MmrfLKOo/1aKLLfYXn+W/VpcbaKSJekPQUKVAst2CH22aT139J\nkweKf9uXBO7tT3PMzCzzCLUrUi5Bg6BvSfMB76D6OuBlLdT9uoqyx1rY36ynIuI/wFdJAdwiTXB/\nP7BmadNiRmUYmcT+juxxnKTzgB9GxG+63W6b3LJz7vakYO83ZcVjBXuXA72fIiUp2Qr/RrMukLQi\n8FZG3+/O/z0tIvYa6xgRMRv4hKRXAO+j9lyae4KUOK1VZ5LO4StUHPetgIO+bWBNG3sTMzMzMzNr\nRkS8GBF7AMdS+wN4iJTB8tA+NtFsLA9TG8TXlUx+EfEz0uz+vL5ivWsA50jyb1ZrR1UAVTsXwO+s\nKFtYUicDsxcqvXY2LOsbSYtJ2lPSr4GZwNdJy3nmNzZh9JLHxZtPQ6T+ezLwtohYFnglcBBwe+kY\nxeMMkYLHrpG0NGYFE2HpbEmbAX/tdztsUpuj9HqByq06b9GKsrma3Lc8pnIyIcvdUlGmnrfCbBKQ\nNI+kZSRVnY/NOu2eirLVKsqKNgLmzZ6Xrw1e1ELdGxWe59fF/9XC/mZ9E8lhEbEWsDwpuc/ZwEPU\nXsMuXscYIo3rdwR+Jek2SZ+UVJWQwqYwSWtIOha4m3TN7E2M9KHi9bXyNbPiNlcAOwFLR8ROVK/u\nYNYJ6xeeFwOqnwY+0eKx9gVqMoIz0q8vjYiW+3JEvEia/FCVnKRqIprZwPANdDMzMzOzDouI/UgX\nbMrLrQ0BB0raqN6+Zn32cEXZWDeFxi0iTgSOoHqSxCbASd2q26aEB6m92De9jePVy0y1ahvHfImk\n5agNvmonM7lZyyTNK2kbSReSAgVOAN5OuoZYFehdlJ/DLyctSbtsROwVEdcBRMTMiPhaRKwKvA04\ng9oVJvJjrgic2tEPZ4PgZknv60fF2XfjOOBiYJl+tMEGxqOl13NmGa+6bcWKsmZX1fGkNKvnioqy\nTXreCrMJStKqkr4r6Z+k4Jj/AQ9JelzSFZJ2lTTvGIcxG48/VJSNdX7euvC8GHB4a5YJeUxZ8obd\nqf2teFMz+5tNMPeTVjW5E3ig9F69yfB5+auBo4C7JB3pSe1TW3Y9YWdJvwNuBvYBFqE22LuonFjh\nL8BngOUjYtOIOG08AbJmLVqn9DrvrxdGRPnaRkMRcQ9wIdVJoACuHVcLk6vrlL+yjWOaTXgO+jYz\nMzMz6459SEFP5Rn504BTnOXBJqiqzDsbd7PCiPg8cD7Vgd+7Sjqkm/XbQKvKbNVOf55GCkLNsx4/\nT+qnb2njmEXvLDzPvwf3dujYZnVJmiZpE0mnAPeRlsXcApib6mxDRfn7AXwBWCEiNouIsyKiKts+\nABFxXUR8mJTR6D+l4+V1bCTpneV9bUpbjLQSyIm9DJCS9GbSjdm9qc4cZNaKe6ntR+/pQb1bFJ7n\n59q7x9pJ0uLAPKXiehPhbOr5NSmIFUZ+w31EUrnPmE1qkt4s6WBJl0iav4ntp0k6nLSc/EeBlRkZ\nNw+RVqDaEPg+8A9J23Sv9TZFXVd4np+fd6iXaV7Sy4HtqQ46PK+Fej8NrFJRfkMLxzDrG0kLSNpO\n0nmk7N5XAJ9iZCWTcqB3WTkAfEFgf+B2Sft3s+028UhaTdK3Sb+7fki6hjzWdTYK29xLmjzw+oh4\nfUQclQXOmvVK1d90gGvGebzLG7x36ziPCdUr8g0BvZhgb9Y3Dvo2MzMzM+uCiJgNfBC4veLtZUnZ\njc0mmt8WnucXp3f+n/6yAAAgAElEQVSS1O1l5z8M3Eh14PeXJH28y/XbYKq6yflRSXOO52ARMSsi\ndo6IDSNilYiYF3g58LMOtBVSMEDZdRVlZh0hae3s5tP/gEtI5+KFaD7Q+1HgRGCdiFgtIg6PiDED\nCIsi4i+kCQ95AGG5rh1bOZ5NGXsAN0pavZuVZAFbXyGNj16FA76tM/5Yej0E7Cmpa/1L0tyk82n5\nHNvMTdU3FJ7nfxvu6lDTbJLLltL+LqPPj0uRgv7MJj1JO0m6Ffg98GVSpuR6wS/5PtOAn5C+B3NS\nO64uZ4N9BXBmtqKIWadcS21m4iWAH2V99CWS5iCtWDlfVlQek5zZTIWStgUOo3a88RiNg7zM+qqw\n4tl5pOzep5My3y/I6Ak7MPoaSfHaSXEbStsOka61HJmt8rBIFz+O9ZmkuSV9WNI1wC3Ax4FFaZzV\nm8L7TwOnAZuSsnp/Jrt2ZtYPK1LdX6vuezfj7w3ea+c6Q3m11LzNS7RxTLMJz0HfZmZmZmZdEhFP\nANsBswrF+YW+3bOsgWYTSdUSaksAX+xmpVk22PeQlsyEkYsy+fflGEl7drMNNpB+VVG2AulmfUdE\nxL0R0XY2bkk7kjIely+iVn0Gs3GT9GpJh0j6B2lSwceBpWk+09Bs4BfAtsCyEfHRiKhaOrxpETET\nOIba1VGGgHXbObYNrCFgNeAPkj7WjQokvYr0HTmIFLAF1d8Ls1YVM2LlfWpN4BNdrPNzwEoV5b9u\nYt/3VpTd1F5zbMAcw8iKUfnf74Mlvb1/TTJrj6RFJF0O/Ah4DaOD+cZaJv4g4P3Z83JwYFE5AHxv\nSd9rs+lmAETEC6Rg7WKg6hCwJfBbSVtmvw03J40HNitsU/z3uoiIRnVJepmkE0iBssVJ9vkxfhIR\nz3fsw5l1QJ0Vz7YmTX6ouj5SPp8Pk7KA7wAsTFq94QfA49QGgBfP9RsCv5K0YDc/n/XV3cApwFtp\nPqnCbNLkmB2BpSNip4i4IkssZdZPi9cpH2+AdqP9yoHbrXi6TrnPtTbQHPRtZmZmZtZFEXEzKWC2\nfHNnGvCd3rfIrL6IuAP4A7U3hT4r6X1drvs+YHNS5thcflF0GnCCpIO72QYbLBFxE3BboSjvzwdK\n2q0/raqVTQA6ntoL/48Cv+l9i2zQSFpG0v6SbiRlVPkSKVillSVlbwU+CywXEVtExDkdvnF/fp1y\nL8NpVfL+Oi/wHUkXSqp3I6plkj4K/Ik0GSf/jph1yvnAM4XX+fjkG5Le1enKJG1FCkAs9+OngEvH\n2HcpUiBLed8bO9ZAm/Sy8cA2wHNZ0TAp6O+irP+ZTSqS5iUFXm1EdUbOupm+Ja0IHFjavnh9pZ5i\ngoidx9FssyrfJP29z+X9bG3gAtJvw5+TJtrWW3HkqKpCSYtL+kSWGfkuYE/StbtyP38GOHy8H8Cs\n0zqw4tntpPP8ihGxaUScGRFPR8TVEbEHsEx2zN9SPdlniDTh05N8Bld+baJeVu9i9vjrgf1J19o2\ni4jTI6Je8KpZP8xXp/yRcR6v0X5PNXivoWyyW5W5x3tMs8nAQd9mZmZmZt13NClwJJdf6Flb0vsr\ntjfrp6NLr/Og67MkfaqbFUfE34F3MzIzv5xl6MuSzpXkZdmsWd9k5CZLcRLBSZKOk7RY31oGSPog\ncBWwQKE4b+dxWRZ8s3GR9HZJVwJ3AkcCb6C1QO+HgWOBN0fEGhFxZDZBpxvurFPurEZWpTw5bQvg\nL5LWb+eg2QSJS4DvAvNTP+D7xXbqsaktIh5ndOZNSP1sbuB8Sbt0qq7sWGcBcxSK837944ioe1NV\n0hBwMilzYdHzpBUfzF4SEX8iZTbOJ4QNk/rO+ZKukvQBZ7S0SeQbQL4yX9U4oFGm7wOAubLnxXF3\n/no28BAjY5iivOxbkhZpvdlmo0XE/0grnVWtqFR+FK+/5dv9OiIuqHP4RUnXD7cmjWHK4+b89Vci\n4j8d+Dhm49bGimf5+4+RgrTXjYhVI+Ib2ferRkQ8FxFnRMT/ka5xFwMci9+17SSt3bEPaRNR8W99\nMfv79aSkCitFxLoR8e0uXmsza9e8dcqfHOfx6t7riIjn6r3XouK4Z466W5kNgDnH3sTMzMzMzNoR\nEbMl7U26qFj8wTkEHAqc15eGmVU7F/gqIzcy8wvScwLflLQTKUvPJRHxaPUhxi8ifp9lFf8Z6cZR\n8YbTEGmJ+bd1ul4bWKeRbryvSu0kgr2B7SXNAH4C/CEiepLNVdJ6pGzLxexxRU8B3+5FW2ygbURa\nOrioXh/PxycvkLJdzQAuiohZ3WlajWfqlN/do/ptcvgvsCKj+3F+Tn85cKWkw4EvR0RLgdmSPgCc\nQMrKVQ7QKj7/C/CR8X4As8yXSJmR80lfeT+bB/hBNinsoGzVkpZJegtwCLAx9ccZX22w/xtIk4Xe\nQe134aJu/AawyS8iLpG0EXA2sCwj5+f1s8dsSX8nZci8B3icFCTesfF3RBzaqWPZ1CTpVcDHqB4H\n5K9XqLPvPKTsrsVrGPk+s0irAJ4QEU9Jmg/YFvh/pKywxToWJWX9/EqbH8eMiDha0kqkINdy3ywq\nj3n/R1rto56ZpMCteeocD+DUiDiy5UabdYCkZYDtSf14ray4PAGiniHSRN8rSNdGLhxPMGI2NtoY\n+B2jr3HnPkJacdMG3y2kyeW/iAhf57LJpGpiGKSxbcsi4gVJnWiXmQFDw8NendLMzMzMrBcknQZ8\niNoLfB9okDnFrOeyTCPXUjsTvphZcxi4lRSQd0VEVC752kYb3gWcz8gSbMULTGUvXXiKCM/et1Ek\nrU66iTJP6a3yMttPkQKZPtSFNswNbJA9tgZeVWhDVTas3SPiR51uh00tkg4GDmbsm5mQAllnAKdH\nxANdbloNScuTAnrLbT0jInbsdXtsYpK0EHA86cZ9vaDsYVLmrO0j4r9NHHNh4DjSGL38dyE3RApM\nPAz4RoNlY82aJmlP4ERqb6AWn98CXADcSFo56t6ImF06ztzAdNIy8WsB72PsccYeEfHDijZtDRyV\nHa+8f/787RFxXauf1waXpHLgyvykLN/FvlPUtZuS/i1o7ZJ0PGlycFU/PZU0QezGqsllkrYinbPL\nk42HgX0i4qSKfV4O/JKR8zbZPncDy/dqUrINPklfJGX9nouxfx/+HdgyIv41xjH/RBp/lMcakLIi\nf9R92Hop+734AdLvxXeQVvprJdAb0rXuU0iTFu7tULuOAD5N7djo1ohYvRN12MQhaTbVY4H7gd8W\nHjd06hwp6R5gqeyl75FYx9Trz+30r24cs5vHNZvInOnbzMzMzKx3vkzK6FYeh3+CdGPIbEKIiD9I\nOgj4OtXZNPPH6sBrgf90oQ2XZDdNzyMFDlQFxJiNKSL+Kmk30k2b/CJfVV9akPpLFrZrFVL25PLE\nhfLF/WFSkKsDvq2b8j74AHAGMCMi/tzH9kAKFoDR34+nSas+mAEQEU8AO0r6OSnoajGqxynrAH+W\ntHdEnFXveJLWJ012WJ7G2b2vB3aLiFs79mFsyouIk7KM2ntS3Y8BXgesUdxP0tPAE6SJkQtR+9ty\nrMCW71QFfGfeAaxUsX/+nTjVAd9WIc9SXJ44UzWRpri0fac5sNDaImlOUvbtcp99Enh/RFwxxiG2\nKTwvfieiKuA7e+PubHWHGxl9Pl+WdE6+uukPYNZARHxN0kXA14DNSMGwZfeTMtEeFRHPNnHYW6n9\nHXcXsHdE/KLNJps1RdJcwBakQO/NGUn40Eywd77Nw8BZpGsjN3ahmT8hBX0X2zNEWsXKBldxLDAE\nLE1awfS9WdnD2Xn5AuDyJs+7ZmZmL3HQt5mZmZlZj0TEvyW9jpFlvHO+OWkTTkQcLmka6YZQORAl\nl1+4/F+X2nC5pA2Ai0jZKvxdsXGJiDMlPQOcSe2SqsUJBd0KfJ1Zqi9XDiy8GNi9S22wqS0/X88i\n9bMZpGVlJ0rG4odIWRXvyh53RsQj/W2STVQRcbak35Em82xA9Tl9YeB0SZsAH4+Ip/MNsszI3wD2\nYyTgpeoYzwBfAo5xlkLrkn1IS8fvQ/V4uypAdgFqf08WVfXV/BjfjIjPNdj31Q2OcTvwqQb7mtWb\nnFteLaobPCHYOuHtjJ5Qlv9W23msgO8sYHxzavv4MFBvog0AEXGLpLMZvZIJwIY46Ns6KJvou4Wk\nJUn9fWVSgOzDpGsh15dXFBnD37N/h0lZa08Azo2IWZ1rtVm1bFW/T5JWuVkkK24lq/cLwKWk35Q/\n63K//Xed8qrJFzYY8jFE1SpiuSWAnbLHM5IuIyVm+HlEPN+TVpqZ2aTmoG8zMzMzsx6KiNv63Qaz\nZkXE1yXdCXyb2myaReUlvTvZhhslvQk4m5S500FXNi4R8VNJ6wInA2+kui91Jeg7Ip6RdC8pqwvU\nXvwfIi0X/pGqpcLN2jQE/IkU6H1GRDzU3+bUyjLHOnusNS0i7gI2knQAcBhpQk+ueG7dGXirpO0j\n4k+S1gROA1ajcXbvq4HdI6LeDXqztmWTCT4m6a/AEaRg7nqTLcdrCLgH2Dcizh9j21dX1DlEWtXn\nnRHxcAfaY4PPv9dssnpr4Xk+LvhNRDSzMt96pKDD8gplkFYvG8tPSEHfRW9sYj+zlkXEA3Rmxcnz\nSb8zr42IxzpwPLNWvB/4SKlsrEBvgFtI10ZOj4j7u9CuKk/WKb+3R/Vbb61L6pvbMDIhYbj0by7v\nl/MDW2ePxySdA5wWEdd0ua1mZjaJefaYmZmZmZmZ1RURpwGvImXseYHqjINdyfRdaMNdpJuoXy20\nYZjuZouzARQRNwNvAT4G/IPa/tytTN8wOrNPMbDwHmDbiNjZAd/WYfcD3wJeFxFvjIjvTsSAb7N2\nRMRRpPP6rdSOT/LAq1cD10k6FbgeeC21AVn5tk+QlqTf0AHf1isRcQKwJnAOo5cBb0fex08AVh0r\n4DvLUju9sG++/7HA6yOiq+N9m/SG+vww64Q1Ksp+3OS+7y48L57H/xYRMyu2L7uxYv9VmqzbrC8i\n4taIuNgB39Znja4P5+OEh4DvAG+MiDUj4ugeBnwDvKyibJg0acIGTERcHxF7AcsCHwaupP5vvGL/\nzfvroqRVIK+WNFPSVyW9pieNNzOzScWZvs3MzMzMzKyhLKvfxyR9ibRs5jakLFj50vJdDwKJiBeA\ngyX9BDickZuqDvq2lmTLFZ8AnCDpncAHgHcAyzZ5Q368/s3o7HF/A34EnBQR9bL+mI1bFkRoNvAi\n4i/ZqiDfAPYrvFUM7J4b+BAjN1qrMmz9AtjLwa3WD9kkg20lrQrsQxpvL1XYZKwxbzGI4F7gh6Qx\nxh1NNmFl4FngPmAmcDFwQUT8t8n9beratd8NMOuQFSrKrm9y382oPU8PA5c3s3NE3CtpFqPv2y9V\nb3szM6srHxPPIv2+mwFcnF1X7pfVs3/LQb+X9boh1jsR8SxwBnCGpOWAXUgrkb0y26Rq3JDL+8qK\nwIHAgZJuIq0SeVaPJy2YmdkE5aBvMzMzMzMza0pEPAL8IHsgaQVgVVLG5F614VbgPZLWAvYlLXu4\naK/qt8ESEVeSMq4gaZExNm/XVaQlXf8C/D7LOm42sCTNQ1op4tXAfyLipj43yQZYRDwH7C/pItKE\nmuUYyZY1XHpeNAQ8CHwyIs7oXYvNqkXE34H9JO0PrEOamLYO6Vw6nTSBoehZUh++GbgO+C3wu1YD\nWyLidmChthpvU1JEnNLvNph1yBLUjhPuHGunwnWRqrFGU0HfmceBxQuvF6i3oZmZ1ciDZG8mBXqf\nEREP9q85owyRJijflT3uBO6KiAf62irrmWwV08OAwyT9H2nS5AeABbNNmgkAfyPwBuBISVeSAsB/\nGhHPdK3hZmY2oTno28zMzMzMzMYlyxzYbPbATtf9J+AjknYH3gysBizdj7bYYOj2ksRZQIyDYqxn\nJB0NFCczDEfEbl2uc0XgJECkoNv85tTJwN7drNsMICKukrQ68C3gI9QGfufysvNJ2b0f6nVbzRqJ\niBdJAdy/zcskDZGCABck9eFHsgxyZmbWvvkryppZkWmzwvPiWOM54Dct1F+e1DOrhX3NzKaqIeB+\n4HRgRkTc0uf21IiIy2ltEpANsIi4BrhG0sdJqzvtDKxH9SR1CmVD2WNOYNPs8ZSk86kew5iZ2YBz\n0LeZmZmZmZlNWhExm7TkcrPLLpuZTRXbMbIsfH7zqKtB36SMsxszEmibW7PL9ZoVTQOeB2Yz0veL\n/bGY+Xtt0uSxS3vcRrOWRcQwKQCxmSBEMzNrzfMVZfMBT4+x32al1/nY43fNTsyRNAcj2T5zjzez\nr1mvSJoL2BJ4FynxwuKk3393kVYbOSsi/tW/FtoU8xxpAu8M4JJswqTZpBERT5P67wxJK5Gyf+8I\nrJhtUjVpvfga0thhxzrbmJnZgHPQt5mZmZmZmZmZ2eAaGnuTjilmSy5mI1q+h22wKUzSjsCRwMuy\nonKwd7FsmJSR/mJJPwb2j4hHe9JQMzMzm2geBlZmdMDUYjQI+s6CYN9JdZDVZS3UvRq1wVoO+rau\nkDQn8DZgHeCoiHihiX3eDRwLrFAozsfUa5ACwQ+VdAHw2Yj4d2dbbTZaRHyj320w65SImAl8Gfiy\npA1Jq5ZtzUgG7/I4oyoAfBRJewLnRMQjHW6umZlNEA76NjMzMzMzMzMzG1zlLMfdNE+d8sV6VL9N\nUZJWBk4CNqC2vxeDvYepDf4eAnYCNpH00Yi4sMvNNTMzs4nn/oqy1wD/a7DPeqQsm/l4uxiEdXEL\nda9XeJ4fZ2YL+5uNSdIqwAGkFaEWzorPB/4xxn77A98kraZTlPf74tj7vaQx9e4RcXYn2m02UUla\nEHg18KgnOlinRMQvgV9KWoh0vt4FWDd7u2qSWfH6RnEscgLwHUm/AE4Dfh4Rs7rVbpsSXrqGJqnd\n1RWGyv924JjF45lNCQ76NjMzMzMzMzMzs05Yu065r0FaV0gaAj4FHALMR/WSxvlNn1OBNwGrMjr4\nO99mWeB8SWcD+0bEg11supmZmU0sNwKbl8o2AK5qsM+WhefFccXMiLi1hbp3ryj7Uwv7mzUk6UvA\nl4C5GL3qzStpEPQt6X3AUYXty6rG3QsCZ0iaLyJOaafdZvVI+hSpr70kIg7tcp3LAl8BRAr2Xjp7\n6zhgv27WbVNPRDwBnAycLOlVpOzfOwIvzzapl+27+HxuYKvs8aikc4DTI+KarjXcpopuBFc7YNus\nRb7hYmZmZmZmZmZmZm2RtALwnTpvP9bLttjUIGl14AekQO5i8ErREClz5x4R8XNJ8wCHA/uWtilm\nx9oG2FDSvs5QaGbWOVnAyhtJ2ZOXA5ZgZMLO09njUVKG45nALRFxe39aa1PQ7wvP8wzGO0o6LCKe\nLW8saVFS8FU56GqYlD25KZJ2Bdakdgzzx2aPYdaIpBOBPRg9Xs6fr9JgvwWBEwv7QP0xN6XyacBJ\nkm6NiBvG026zMXwGWKpU1tWgb1K/3oPa1dTW7HK9NsVFxD+AL0j6IrAxKQB8S0ZW26sKAC+ftxcj\n9d89JP0XOB04LSKim223gVVvHNCMesHd7Ryz0XHNBpaDvs3MzMzMzMzMzCawLFD1bGChFnZbvOI4\nv+xYo0bMDbwMWBmYg9HL2+f//qsL9doUJWku4CDgs4xkKyze0Cw+vxDYMyIeAIiI54BPSvo58CNS\n0GHxxlLeb5cEzpS0HbBPRNzX1Q9lVpJlsV8ImJd0H2cOunATMyLu6PQxzYokvY4UYLIl6Zzb6v73\nAb8EZkTElR1unlnR1cDjjB5vLwccDexTsf23gEUZPebNndVMhZLeQZo0WQ5yeQa4tJljmDUiaU9g\nT2pXucm9ssHuHyP9zqsK+K4ak5TH1HMBp0haPSJmt9JusyYV+2G7wYLNeKCiviFgeg/qNiM7l14G\nXJZNPvsQsAtpIjxUn+urAsKnAwcCB0q6ibQq2lkRcX93Wm4DyJm+zSaAoeHhXox/zMzMzMzMzMzM\nbLwk7U9aWruVi3nlC+bduhBY76Z/HgDzrYj4TJfqtilE0jrA94FVaZzd+0ngkxHxwwbHWgQ4Htie\n+kHjw6Sss5+MiFM78RnMqkhahRQUuzmwOinIqts3PYcjwomBrCskvR74BikbIbTXn/Pz8u3AZyLi\nonbaZlaPpO+TsmeWxwIXkDLI3kbKjPxl4IOlbfI+/peIeP0Y9cwJHJAdZ76K+s6IiB078JFsCpO0\nGGny7SKF4nJ/vTgi3lNn/38CK9XZ7xfAscAdwPKk8fSHGX2uz/fZo9GY3Gw8JN3DSKbvIdK4do4u\n1zkf8BS1QbRPR8SC3azbrBFJrwV2BXYAls6Kq1YioVRGYdsXgSuAQyPi+i411SYZSbPpzaSabujJ\n3wazfvIFPTMzMzMzMzMzs4nv26QMPqtnr8cTPNWtAMJGN5OGgRldqtemEEnHkDIOTqNxdu9rgZ0j\nYmaj40XEY8AOki4kBX8vTm3fHSItgzxD0rbAXhHxv858IjOQNB34OrBNodgZrmzSkjQN+CrwGUZn\nqO/EEuACLpT0U2CXiHiijWOaVTkS2JGR++f5WOC92aOoKoBqmDRmryFpftIkiPVIQVlL1jnGi8A3\nx9d8s1H2oTYbff78X6SJlJdV7ShpXdJKTlWrOJ0UEcXs97eSss6eSpogsUDhvSFgP8BB3zYIXlen\nfK6etsKsJCL+Bnxa0udIk4h3AbZgpG9WXbMrXkMZIo19NgOuzx5mOV+fMJugHPRtZmZmZmZmZmY2\nwUXEbEn7ANdkRc0ET/Uq03e9OoaB47MbUGbt2o/RASe5vOx54GDgiIhouq9HxNmSriFNTtiY+sHk\n7wL+JukzEXFyG5/DDABJ2wA/Aual98vT+8atdZykBYFzSefSTgR758qBKlsDv5e0eUT8twPHNwMg\nIm6TdDTwWWqDXcvKY5Jh4BbqT3ZcnhQQWzxeVRDW0RHxl/F/CrOX7Mbo/pn3u68Bh0TECw323bbw\nvNhPHwQ+VbVDRFwlaRfS34FigPkaklaPiL+2/AnMJohsjHNEnbef7GVbzOqJiBeBnwM/l/Qy0goM\nOwNrZpsUg72h9pqHWdmu/W6AmdXnoG8zMzMzMzMzM7NJICJ+J2kG6aL7RF5eM79ZdBrwyX42xAZS\nOchqCPgb8OGI+PN4DhgR9wCbSvo4cDgpCLccWA6wMHAi4KBva4ukdwGnkzIhw8Q+p5uNSdJ8wC+A\nt1F/NYb8dbPqZSUcAlYFrpD01oh4cLztNqtwIPAqUmbvYsBsWbmfPw5s12Di2T+BWaR78/W2uRI4\naBxtNhtF0prAStSeiw+LiIObOMTWVJ+DT42IZ+rtFBHnS/oVsEFp/00AB31bJUnzAMcA87Sw2yIV\nx+lGRvm5gZcBbyKtAFU1IajhClNm/ZCNj48BjpH0euAjwPbAEtkm5QBwsxoRcUq/22Bm9Tno28zM\nzMzMzMzMbPL4LOkm/GLj2LfbmXueIt3wvAk4PSKu6HJ9NnUVb7Z/CzgwIp5v96ARcaykK0gTFt5I\n/aBFs3GTtCjwQ1LAdzmzWrmfOeOaTRYzgLdTG0BSDIqaRZqkcwNwK/Ao8BjwDLAQKYBrUWB1YC1g\nNUbuY5bPx0PAKsAFktZrZYUHs0ay1XW2B45jdKbksmKfvB/YMiJua3DcFyX9gzRhoWwIuBR4XyfG\nM2bA/1WU/QM4dKwds4DxFaheYeesJuo+hRT0XbROE/vZFBURz0l6iDTpppW/50Ol5zt3tGH16yoa\nBn7fxXrN2hYRNwP7SToA2BLYBdiUxhPRzMxsgnPQt5mZmZmZmZmZ2SQREQ9JErBAg82GgD8wksEn\nv1m/coebMxt4EXgeeNxBKtZDQ8AdwC4RcXUnDxwRIWkd4MvAFxgdmGvWCZ8GlqZ6UkExoOReUiDh\nU6TzrNmEJGk34IPU79N/Bb5HyhD7eAvHXQTYgZSZ8A2MzvSd//tW4FPAUW1/ELNMNqbdQ9L5wBHA\na+tsOgv4MWny2QNNHPrvpMkMxe/Hk8DnI+L49lptNsobCs/zc+YPIuLFJvbdovC8OAa+OyJubGL/\n35VeDwFqYj+b2g4j/c1fgfFPeuzmZMmqsXrutC7Wa9YxETELOA84T9LSwE6kyRKr9bVhZmY2LkPD\nw75ebWZmZmZmZmZmNkgk3QMslb0cAoYjYo4+NsmsLZJmMxLkdyqwbyvBg+Osc23STfxVCnX7u2Tj\nJmku4B5GVmsoB8eeS8oC/tuIeKL3LTRrTZa5/p/UrkAyRMrgfWBEfLsD9exKCuxepKKep4CVsmXs\nzTpO0huB9UgTKOcBHgb+DFwaEY+0cJxDgIOyl3cCJwInu+9ap0n6JbA+oyfKrBsRf2hi32uAt1E7\nkWdGROzWZP0vMBIcOwQ8FBFLtvIZbOqR9B7gQpqfcFsOwO524FPV6k/nRsS2Xa7XrKskvZk0yXJb\n4JiIGHNVCDMz6z9n+jYzMzMzMzMzMzOzyeBhYO+IOK8XlUXEH7Il7o8C9u5FnTbwNgEWZyRgJA/G\nehrYLiIu6lfDzMZpX0b3aUh9+kFgo4i4pROVRMSPJF0FXA2sWHp7flK27wM7UZdZWUT8EfhjBw51\nCWkFh9906rthVkdxRZHcP8baKVthYZ2KfQEub6H+J4CFC68XamFfm6Ii4ueSfg68h4m50lI52/fV\nwK79aYpZ50TEDcANkj4JLNPv9piZWXOm9bsBZmZmZmZmZmZm1hVDdHeJY7NeuhRYo1cB37mIeCYi\nPgpsDtzby7ptIG1Uep1nDNzDAd822UgaAvakNuD7CWCTTge1RsQdpO9Q8VycT5zYXZLvedqEFhG/\nj4jjHPBtPbBARdljTey3MVC1os0wcGUL9c9uYVuzon1JK4UMNfEoa2afdh9PAb8B9gI2joinO/jZ\nzfoqIp6LiP/2ux1mZtYcZ/o2MzMzMzMzMzMbPJ8A5ut3I8w6JSI273P9l0paAzihn+2wSW/1wvM8\n4PuaiDizT9oRJokAACAASURBVO0xa8e6wCsYnflyGDgoIm7uRoURMVPSfsA5jA42XwLYkNaCEs3M\nBlVVluS5gOfG2G+z0us8sPbPEfFQC/UvXHr9eAv72hQWEXdIWht4WYPNhoDzgEULr4dJ44BOmg28\nCDxP6sMPtfg9MOs6ST+sKB6OiN163hizNrk/m7XGQd9mZmZmZmZmZmYDJiLO7ncbzAZNdpN/m363\nwya15akNxDq5Hw0x64B3FJ7ngYEBfLeblUbEeZJ+A6zH6O/Txjjo28wM4BFgRUafIxcGHhhjv82o\nHacMA5c3W7GkFUnZwovHcdC3NS0i/jbWNpKer9jv191pkdmEtgu1q+4MAw6StcloF9yfzZrmpc7M\nzMzMzMzMzMzMzMy6b8GKsmt73gqzzlir9HoYODMiqjLMdtppTbTHzGyqeriibJVGO0h6PfDy7OVQ\n6e1ftFD3uoXnebDWvS3sb2ZmrRui9txtNlm5P5s1wZm+zczMzMzMzMzMzMzMuu+5irK7e94Ks85Y\nqaLskh7VXaxnmBQU8Ooe1W0GgKTpwGuBVwBLAfMD8wBDEfGpiu1fBTwWEff3sp02Jf0F2LBU9hbg\nugb7vKfwvDh55xFam6C2Y0XZzS3sb9YsBwSajcjP2/5e2CBwfzZrgoO+zczMzMzMzMzMzGxgSFoM\nWJPqQCwi4v0V+7wZGI6IG3vYVJt6HgFWZnQw1VzArP40x6wtizG6LwPc2aO67wZeZPSKxov2qG6b\noiQtAGxDCo5dH1ikweY1Qd/AXsAnJP0aODEizu14I82S35deDwE7AMdUbSxpDlKw9nBpn2HgomZX\ncJC0DrAptX8bbmpmf7MWHEn1CjpmU1V+zjYbBO7PZk1w0LeZmZmZmZmZmZmZTWqSVgZ2JgVivY7q\njECNbhztAOwraSZwInBsRDzbjbbalHY78MZS2dLAzD60xaxdc1eUPdSLiiNiWNLjjA70XqAXddvU\nI2kp4PPAHqRJZNA482C9scZ0YA5gA2ADSTcDB0TE1Z1pqdlLfgW8QOpv+WoIb5C0e0R8v2L7jwOr\nFLYt9uGzmqlQ0rLAqaTJOMX9h4HLW/0AZo1ExFH9boOZmZlZPzno28zMzMysDZJ+WFE8HBG79bwx\nZm1yf7ZB4v5sg0TSi3XeGo6Iutf3GuzXaw3badYOSasDhwJbZUXjXf51erbvysDhwP6SvhARP267\nkWYjbgC2L5Wti4O+bXJ6GFiO0cF9S5GycHeVpDmpzeztiTrWcZL2JI0LFmb0GKNeYHejcch0Ri9X\nvxZwpaSvAV9pNpuy2Vgi4gFJlwHvJvW5PJj7OEnzAsdHxGxJQ8DepKzJef8r9sP7gCvGqi/L8H0q\n8EpG9/Fh4FcRcVf7n8rMzMzMzHK+2WJmZmZm1p5dqF760kGFNhntgvuzDY5dcH+2wTHeINbx7mc2\n4WXL0H8V+DQpi2GnA7GWBX4kaSNg74h4pq0GmyUXA98qlW0JnNGHtpi168GKsun0IOgbWIHabLQ9\nyTJuU4OkuYEfkibq5OOHRkHZzSxDP73wPN92GvAlYDVJ2zjw2zroWFLQN4z0z7mAbwMHSfoXsBJp\nsk7+fvnf70VE5URiSYsA6wG7ksYy0xq0w8zMzMzMOqje4NvMzMzMzFozhAOrbHC4P9sgcX+2QTFc\neox3v14/zDpO0mLAlcDnSIlN8sCUev2umb8D0wvPixkRPwxclmVFNGtLRPwDuIbRQVXvk7RyXxtm\nNj5Vwd3v6VHdxXry79O/e1S3DThJ8wAXMhLw3Wh80dTvTUkLAYuX9oXC3wLgpPG32my0iLgMuIjq\ngO4lgbcAS1e8n3sQOLrq2JLWIK328FPSajvTGP09yY91UURc2MnPZWZmZmZmDvo2MzMzM+sUBzbZ\nIHF/tkHi/mxmNkAkLUoK+F6PzgViLQYsXNoXRgJg3gacK8mTiKwTDim9nhP4Xj8aYtamawrP8/Pl\ndpLm6kHdO1WU/akH9drU8ANg0+x5eYyRjy2eB24CzgGeqdiubBHgEWrHGMXnH5FU1bfNxmtXIBgd\n0F2c3Fg1js7L94yIx+sc918V25fdCew9vmabmZmZmVkjc/a7AWZmZmZmA6KZZVzNJgv3Zxsk7s9m\nZgNC0jRScNVa1A9SAbgDuJEUkLIPsMAYh54HuA14Tfa6nKVwCHgXsD/wrfF/AjOIiF9KOo2URT7v\nXxtKOjIiPt3f1pm15KqKshWAzwD/r1uVStqNkb8DRVd3q06bOiTtAXyI6jHGLNI4ZAZwbUQ8m+1z\nD9BwRZCIuEvSK4AdgIOB5agNvB0CjpJ0cUQ81KnPZFNXRDwkaRPgEmA1Rvfrch/P++BsYN9GGboj\n4mlJ/wVWrDjOECnge4OIuKfNj2BTgKR6K3UMR8Qrx7FfrzVsp5mZmVk3OOjbzMzMzMzMzMxsYruD\n8U1eGO9+ZhPV54GNqA4ueYiULXlGRPwzfyPLmNkw6Dsi7gVWk7QR8HXgTYwOKs+DYA6RdG5E3NGB\nz2JT217AaxkJXB0C9pe0MnBARMzsZ+PMmhER/5Z0K7BqVpT35S9K+nVE/LbTdUpaA/gmtX8HngAu\n63R9NrVIWgI4gupxxvXAzhFx+3iPnwWJ/0DS6cDhwL6MDvwGWBz4JHDQeOsxK4qIOyW9AfgqqW/l\nqzEUJznmbgf2joirmzj034HpFcc5F/hYRDzQRrNtapnO6NUPcmNdy6i3X6/5mouZmZn1nIO+zczM\nzMzMzMzMJrCImN7L/cwmIkkrkAKgijfV8xv8F5CWoG8rK2ZEXAWsLWl/UjDWHKVN5gc+RQqYMRu3\niHhG0jtJQar5JIMhYCtgC0lXABcDvwduj4gn+9ZYs8aOIGU9Lk6SmQ+4WNK7IuK6TlUk6bXA5cCi\njA4yHCZN+Hm+U3XZlPVpYBFq+9eVwJZ5Zu92Zcf5RDZp4gRqJ5l9RNJXIuLFTtRnlp0fPyfpGFIm\n+/WAlUmr3TwM/Bn4GXBJRMxu8rC3klbCyb8nVwBHR8SlHW6+TR1Vv/Na3a/X+h1wbmZmZlOUg77N\nzMzMzMzMzMzMbKL7PCkwpRyIdRqwS0R07GZ/RBwt6R/A+YwEfueBWDtK+nynAr9s6pG0SeHl10jZ\n5V/DSB+bE9gse+T7PA08DjxP5wJbvBS9dcLpwKHA8oWyYWBh4NeSjgAOiYhZ461A0hDwGeArwLzU\nfgeeIWX/Nhs3SXMCu1E7zrgFeE9EPNfpOiPie9kKD59hdL9ehrSyyeWdrtOmtoi4Bzgqe7TreuA8\n4DekYPF/jrG9mZmZmZl1iIO+zczMzMzMzMzMzGzCkjQvsAO1gVjXRMTO3agzIi6S9HngSEYHYi0K\nbApc2I16bUq4lOrA7SFG9/GiBbJHJ3kpemtbRLwoaW9SZvqiYdI9yC+QshbPAH4cEbc1e+wsGHZH\nYFdSUHnxO0Lh9aER8b9xfwizZH3gZYzuY8PAXt0I+C74MrAtoydOALwVB33bBBYR5wLn9rsdZmZm\nZmZTkYO+zczMzMzMzMzMBpCkZYDdgVMi4s5+t8esDRsDCzE6EOt5YK8u13sMKevna0rl6+Cgb2tf\n1XLweRBrtwOyvRS9dUxEXCrpW8ABjGSsHy48Xwb4HPA5SY8ANwF/Ax4FHgOeAuYHFiFNrFkVWAtY\nMqsi76/l78UwcFVEHNGdT2ZTzPqF53kfviwiru9mpRHxnKSTgMMY3cff0s16zcwmmPGOTT2mNTMz\nsynJQd9mZmZmZmZmZmaD6XDgw8DBkq4CfgD8NCJm9bdZZi1br/A8D8S6KCKim5VGxGxJ3yMFfxcD\nsdbuZr02ZZQzFlc9r9rWbCL6ArA6aSWEcqZkGOnXiwMbZY9Git+DepnxbwY+2HJLzaqtUVF2To/q\nvogU9A0jkyVW6lHdZmZ9FRHTermf2YDy70UbJO7PZk3wQMjMzMzMzMzMzGzASJoO7JC9nIOUKfks\n4H+Slu5Xu8zGabWKsl4FYl1WeJ4HYi3fo7pt6hge42E2oUXEC8BWwE+pP3GhmP17rEej/j8EXAu8\nMyIe6+gHsalsekVZV7N8F/wVmF0qW6xHdZuZmdnkVhxDm0127s9mTXKmbzMzMzMzMzMzs8GzKynh\nQzHACuCGiLivb60yG58VqA38u7kXFUdESHqBNHki50Asa5dvYNrAiYjngfdJOgA4FJiP6qDt8U5k\nGAJeBI4EvuyVS6zDFqa2b97di4qzlUUeAxYtFC/Si7rNiiQtALwCWAqYH5gHGIqIn1VsO09EPNfj\nJpqZ2Wi79rsBZh3k/mzWAgd9m5mZmZmZmZmZDZ53VpQNAzN63A6zTli4oqwngViZxxkd6F3VHrOm\neCl6G3QRcZSks0mB39sDc2dvjSfYO58gMRu4ADg4Iv7WfivNasxRUfZMD+ufr/TaqzxY10laGdgC\n2ABYi+rVbIapjik5SNJWwE+AGRFxV9caamZmlSLilH63waxT3J/NWuOgbzMzMzMzMzMzs8EjqoNF\nftvrhph1yYs9rGvBPtZtZjbpRMSdwK6SPkcK/N4KWJeUNbZZLwLXAxcBp2fHNOuWpyrKlga63u8k\nLQzMy+ix+5PdrtemLklbAgcAby8Ut7oKyXTgtcAhwBclnQh8LSIe7EgjzczMzMysLgd9m5mZmZmZ\nmZmZDZ6F6pTf19NWmHXGExVlSwH/7XbFkhYnZaktBmI93u16zcwGQUTcD3wb+LakuYDVgdeQssku\nBcxPCgSfRcqq/ABwF3AbcEtE9DLTsk1t/6N20uRr6UHQN/CWwvM88NZZk63jJK0OnAysnRUVA72r\nJgw3CgSfXthmHmA/4P+zd9/RrlXl3se/+xzg0LsIKEj1kWoBxIYUBUFQQFBBRSzotWDFgveqiNdr\nuWLBfhEU0FdUUERFEBEQVEARC0UeLCAgRYoU6XD2+8dKzEp2snf23un5fsbIOMlM5pozYzx7nZXk\nt+baPyJempk/nedUJUmSJE3D0LckSZLUBRHx137PoU2TmblhvyehwWY9a5RYzxojN9D88twrA7f2\neC7SfF0HbEp9GGUrehD6BrYr3a8GX1xtVpJmKTMfBH5buUmD5kpgp4a25wKn92DsvRoeT1bmI3VM\nRBwEfA5YktoxbbOgN5XnWz1XtV7DayYoTuY5PSLekZlHzn22kiRJkqZj6FuSJEnqnInSv+v1cR6z\nMdMX+Bpf1rNGifWscXQ2cCBTa2l74Lu9n440L1cAuzS07U5vannvhseTlflIkqTR8QvgdZX7kxSf\nHV8WEe/PzNu7NWhErEXzY/YLuzWmxk9EfAB4HzOHvdvd3lLAWg3N1W0uBD4ZEXdm5lfnM44kSZKk\n5gx9S5IkSd0xDGG96S7RKZVZzxol1rPGxaeBA6ivpwngXRFxcmYOw9+CVHUexSXjoRbE2i8i3peZ\n13dr0IjYGNifqf93XNCtMSVJUl+cATwMLCi1rQQcBryti+N+AliWqccaP+7imBojEfFW4P2Vh80+\nA5Y/L94MrEr930Ez6zA1QF5dHbx6rH5URFyWmb+ay7wladxExBLA44FtgM2AdSu31SmOFZahuFrD\n/cB9wD+Bm4DrgT8ClwMXZubVvZ671Mh6lrrP0LckSZIkSZI0YjLz9xHx3xRBlfKP79sAX4qI1xn8\n1hD5CcWPQItKbUsDHwde2o0BI2IB8HmKH6Ea/1Z+1I0xJUlSf2TmzRHxY+C51B87vzkiLsjMb3V6\nzIh4L7Af9aFZgMsy87JOj6fxExHbURwvtwp7XwN8HfgpcFFm3hURNwBrTLfdzPxLRDwFeBPFCZLV\nwHc5+L0Q+HJEPCkzH+7QW9IIiohhr4/JzDR3pTmJiEcCewF7AjsCSzW8pNnCIMtWbqsCGzTZ5rUU\nJ7OdBPzUfbB6xXqWemumszQlSZIkSZIkDaHMPBw4nPpV2CaAg4DvR8Rm/ZqbNBuZeSdwClNreb+I\neHuXhv0C8Gymrl54QWZe1aUxJUlS/3ys4XH1eOO4iHh1pwaJiIUR8b8Ux+mNYdxJiiv2SPMSEUsC\nX6YIX5dNALcDrwI2yMz3ZubZmXnXbLafmb/KzAOAJwG/oT74XbU58Jo5vgWNj4kRuEmzEhHbRsQ3\nKE6++QLwHIqT3JvV1uQ0t2b1uC7wauA04NqIODwi1uzNO9M4sp6l/vCMM0mSJKk7/LJPo8R61iix\nnjVWMvPwiLgQOBpYm9qX6M8FnhsRPwN+BlxAcdnM2/s2WWl6HwZeVHpcreWPR8Qqmfm+TgwSEStS\nBGT2pfmqiJ/sxDgafhGxbqvnMvOaufTrtenmKUnjJjPPi4jvUaxQWF61eCngqIh4DvCBzLx8rmNE\nxO4UV+LZqrR9SvcTOH7Ob0KqeRXwWOqPZ6ure+/YqZMYM/MPEfF04CsUV+BpHO/1wJc6MZZG2rBe\nhczvGDUrEbEBxXcKz6s0lWtoLn8HrfpUt7sm8F7gHRHxBeCDsz3JR2rFepb6y9C3JEmS1DnlH4Q+\n2Oe5SPNlPWuUWM8aSxGxaeXuNRQB1sMoVlspr8C2feVW7XMX8M/K7Q468+PrZGY+qwPb0RjLzEsi\n4hiKlerL+/UJ4D8jYmeKINbpc9l+RCwLvBJ4J7AOzYNYF2bmd+b1RjRKrqb5PnKS6X97adWv12aa\np8ZIRHylxVOTmdlyheNp+vXatPOUZuGNwNOARzD1eGMf4AUR8RvgTIqA9s1MvXR9dZXlVSguVb9l\nZZvPBTasvKR8nFH1MPDazHyos29JY+ptTA1g30wHA99VmflgRBwIrAbsSv2KnZtHxFaZ+ZtOjilJ\nwyYi3kZxMvtS1K96XNaqvR2NKymX25cB3g7sHxGvzcwfzWH70r9Zz1L/+YWeJEmS1AWZeXi/5yB1\nivWsUWI9a8xcSvMv1hvDrGUrVm6PadF3tpoFWqS5egewI7ABU4NYTwZOjYh/AGdRC2It07iRiNiY\nqUGs7Slqv9WPUvfi5ek11VxX93NVQA2aV9D8R/pJistpz6Zfr7UzT6ktmXlDRLwQ+DHFZemr9V2+\n5Pw2wNYNXSca7t/XZPONqx9ONPz7n5n58/m+Bykinkj9Kt/VGntnpwPfVZm5OCJeDVwBLN/w9PaA\noW9JYykilgGOozh5rNn3DeXvzRrb2zFd33JgdoLiCoDfj4iPZuZ729y+9G/WszQ4DH1LkiRJkiRJ\no6vVl+ozrbYywfxDif0OgWnEZOadEbEncC5FaLsxMDUBPBLYr6FrYxDriiabn25losUUK29eNq83\noFHUaiWr2fbrNUPnasUTGTT2MvO8iNgb+C6wdOmpcshkpppv9nyrQAzAxzLziNnOVWrh2aX71Vr8\nXWYe381BKydNHE+xYn65vrft5rgaetfQ/2NjqSsqVxQ7FXgmzRdgaAyxVt0N/Bn4C8VV+P5VaXuI\n4thkEbA6sBawLrB+Q/9mxxzVtgXAeyJiXeDAzPTvT22xnqXBYuhbkiRJkiRJGl0zBRIbHzdbUUUa\nGJl5eUTsDJxO8aPQXFYQmimIVX7dYuDgzPzGbOcqSUNoLquxNfbrNQPn6rjM/HFEbAecDKxDfY23\nOmZghtdUX1cNqkxQhF3elpmfn9+MpTpbNTyeBL7Vo7G/TRH6rpoANu3R2BpCmblev+cgdUNELAH8\nkOJqB9A8IFt9fCfFVUZOA87MzOtmOdZyFFcxexawG/DU0hjVY47Gxy8F7scrmqkN1rM0eAx9S5Ik\nSZIkSePDMLeGXmb+NiK2Ab5DEWpprOuZTnZo5+9ggmIFopdn5g/mNFGNC1dGlqQRlJkXR8RmwEeB\n11L8rt5O4Lvc1uwYpfrcRcBrMvP3HZiuVLZBk7af9GjsX5fuV+t9lR6NLUmD5OPADkwfjr0J+BTw\nxcy8a64DZebdwPmV24ciYkPgdZXbcg1jloOyr4qIizPzi3MdW2PDepYGjKFvSZIkSZIkaXQZKtRI\nysxrImJb4O3AeyjCJO2e1FC+DG2z56AIlL8lM6+f10Q1ys5lbifSzLWf1G2ewCA1yMx/AQdHxCeA\ndwD7AyuXXlK+vPx0yn8n5wGfzsyTOzZRqd6qTK3Lv/di4My8LyLuBFYoNRv6ljRWKlcLeQvTB2SP\npfjOYc7h2FYy8y/AOyPiY8ARwMupD8eW738sIr6bmTd1eh4aDdazNJgMfUuSJEmSJEmjaf1+T0Dq\npsxcDBwREUcB/wG8Atik4WUzhbHKIax7KMLeR2bmxR2cqkZQZu7Qy35Sl+3Y437SUMnMq4A3RsTb\ngJ2AnYFtgC2AlabpOglcDfwWOAf4YWZe3c25SsCiJm239XwWNWZSJI2bI0v3yyedTwAPAq/IzBO6\nPYnMvAV4RUScAnwNWIb6oCwUKycfTrGKstSM9SwNoInJSReUkCRJkuYqIhZT/6FyApjMzIV9nZg0\nB9azRon1LEnjKSIeRy2ItSXwGJqHsR4GbgSupBbEOisz7+nNTCVJ0iiIiJWAtShWNl4ELAbupQjZ\nXp+ZD/ZxehpDEZHARpWH1e9E1m5n1cuIuAFYo9x3Nt+jRMTSFCdSlgNhd2Smq31LGgsRsQtwOlPD\nqBPA/cBemfnjPszrqcCZwNINT00ADwFROclN+jfrWRpcnlUpSZIkSZIkSRoJmXkFcEW5rRI+aQxi\n3VFZKVySJGnOMvMO4I5+z0MquQXYmPqr3awPzBj67oBNm7Td2INxJWlQvKFJWzUse0g/ArIAmXl+\nRBwMHMPUAO9CYD/gI/2Ymwaa9SwNqAX9noAkSZIkSZIkSd2Smfdl5s2ZeV1mXp+Z/zTwLUmSpBH1\n5yZtu/Zo7OeX7lcDWH/t0diS1FcRsQywC7XwaTmM+pPM/EK/5gaQmV8Fzqc+IEvl8Yv7MikNLOtZ\nGmyGviVJkiRJkiRJkiRJ6rGIWC8idomIgyPiMxGxb7/npKH3q9L9akDrpRGxsJuDVq6u80rqg1cA\nF3VzXEkaINsDS1fuTzQ8974ez6WVD5ful8OyW0TE+n2YjwaX9SwNsCX6PQFJkiRJkiRJkgZNRKwF\nBPDY0u2HmXlUXycmSWPMfbO6ISIuBB5RaprMzA27PObjgIuBRQ1PrQqc1M2xNfLObNK2AXAQ8H9d\nHPcQYB2mhr7P6uKYGnIR8fJWz2Xm8XPp12vTzVNjZ6vS/epJNwCXZeav+zCfZk4DbgQeydT99dbA\nVT2fkQaV9SwNMEPfkiRJkiRJkqSBFRE3AGuUmiYzs6vfbUfEZsAfmjx1H2CwUNLYc9+sEbMuRVik\nqjE00g03UVs9sWyLHoytEZaZGREXA0+sNFWDWkdExC8z85JOjxkRzwEOY+rfzk3AuZ0eTyPlWFrv\nc6cLU0/Xr9cMfavqcU3aJoEf93oirWTm4oj4CXAAU/+GNgdO7P2sNKCsZ2mALej3BCRJkiRJkiSN\npohYLiLeXFmVU5qPiYZbt13bZMwJYLMejC1Jw8J9s0bJJL0NEN7ZMG517DV7OAeNrk9Tv1+eBJYD\nTo2ILTs5UETsDnyb+gUHJypjHpWZgxLM1WCb6zFFs+OCXt6ksnVbtHf8ZJt5urBFuyeeqcx6lgaY\nK31LkiRJneGX1xol1rNGifUs9UhELAGsAqwNbAPsBOwKrAScDtzQv9lpBFT35736Yf3uhnGrY6/R\n5LXSvFVOjtkWCGDlym1RF4eczMxXd3H7Gg/um6W5W6lF+8o9nYVG1TeAd1F/Uswk8GjgFxHxbuDL\nmfngXAeIiNWA9wFvohbyLvsn8Jm5bl9jp/H/9rn06zVD32r0SJrX5J96PZEZ/LFJ2wTwmF5PRAPN\nepYGmKFvSZIkaf78ck+jxHrWKLGepQYR8Rhgb+DJFOHsFYFlKL4nXEB7fzcTldcuUbktKm2j2Ws9\n+ULDaPUW7a0CWtKsRcRywCuBN1CEvXulum829K1h475Zo2SPFu0P93QWGkmZuTgiDgLOo/5zWnXF\n788C746IbwJnAgncPNN2I2IN4GkU9fsSis+CjZ/5qo8Pzczb5v9uJGlorNii/R89ncXMrm94PEmx\n7241f40n61kaYIa+JUmSpPlZv98TkDrIetYosZ6lkojYBPgcsEPDU54cITW3X4v2Oa+GKJVFxNOA\nEyhW3HRfLLXHfbOGWuXKOKsDzwI+TfOTI2/t6aQ0sjLzVxHxdoqAd7nWqmGodYB3VG5ldSHuiPgl\nxQr0qwKPaHgdTK3jSeDEzDx6vu9BY2Oux8IeQ2vQLN+i/ZaezmJmrU7yWaGns9Cgs56lAWboW5Ik\nSZqHzPxbv+cgdYr1rFFiPUs1EfF64FPAkjT/UbSbK3H7I6zqVMJOa8+y28Im21mHztfXUtSCWP9F\n87+NQVvRSEMoIp4HfIeitvtxRQT3zarjvlmjJCIWAZcx+xOBy7U7ERHdXnG7PF41hDsJXNHlcTVG\nMvPzEbE6cBhTg9/Qep89Ufp32xavK2+jfP8s4MC5zllj5/Ae95O6ackW7ff3dBYzu7tFuyFZlVnP\n0gAz9C1JkiRJkiSNqIjYG/h8qakXAe9ehxc1XBYD3wMeP8t+Ew33r+7UhNoYrxzEuqzL42rERcRj\nga9T/D4zSfPAlNRr7ps1MjLz/oh4M/DDeW6q2yfIVGu40ZldHldjJjMPj4ibgCOZmg9pduzRWJcz\nHaOUj2VOAg7MzEELhGlAZeacwttz7Sd1WauQ7AM9ncXMyvMp7+OX7sNcNLisZ2mALej3BCRJkiRJ\nkiR1XkSsCHyp8rAaLJwpvDLRcGtH42vL/e8AjgZuaHNbGnGZuRh4Q+VhY71Nd2s0m75zubUKtpw+\nl/ctlXyYYsWpxhU2y4+7fZPquG/WqMnMH1GcyAC14+CZbo3a7TfXW3mcqruBr8z1fUutZOaXgKcD\nl9L6WKDVvn2y4TXN+t0NHJyZL8rMe+czV0kaYtUMXt2+snKsPTAy05ON1Q7rWRpgrvQtSZIkSZIk\njaY3AY+g9WW3m2kMhs8UDmwMrUxQXObzncBFwG9d5U2NMvOCiDgGOIj2VzZurMVe/ahTHucW4Nge\njasRgeafGAAAIABJREFUFBEbA3vTer/8V4rw6uXATcBdFKtW+SOmus59s0bQm4GdgWX7PZFpNP5/\n8ObMvLmP89EIy8yLIuKJwMuAdwGblp5udfJDo8bPk9UTFT6cmTd1aq6SJEmSWjP0LUmSJEmSJI2m\n/6D5KoKTwInACcDFwK2ZeU9EPI4iaFgNfk9S/Hj/PoCIWBpYHlgb2AzYAXghsHLDOEsBm2Tm57rz\ntjQiDqUIv67a74lMoxxqeRh4RWb+q4/z0fDblfqgd3V/ex/wVuDoQVs1S2PHfbNGRmZeFxH/DXyU\nwTt5pvGEiX8Bb83MY/swF42RynHG8cDxEbENsA/FyRFb0H525DbgHOCHwEnugyVJkqTempicHLTP\nuJIkSdLwiIiX93sO85WZx/d7DhoM1rNGifWscRcRWwO/Yuoq3PcBe2fmj1v0S2Cj0uv/mpkbNXtt\n5fUrAB+gCCuWx5kEDsjMb8z1PWj0RcSrgS8zdYX5QXMN8NrMPKPfE9Fwi4gTKcJV5dDqYuCFmXly\n3yYmlbhv1iiJiCWAs4B1ZnjpOtTqvXose02Hp7OY4kSFB4A7gVuBqyhOwvx+Zt7W4fGktkXEUhQr\nfz8GWAtYAVhEUbf3UgS9rwOuzMxr+zVPSRpkEbGY+oUUJoDJzFzY14k1MUxzVX8MU40M01ylTnGl\nb0mSJGl+jmXwVguaLUOFqjoW61mj41isZ423ZzQ8rn7pfWirwHfFT4GNqf39rB8Rm2bm5c1enJl3\nAYdExM+Ab1Gs8l39cv1zEfGzzPz7PN6HRlhmHhMR2zNzEOtp1L7LrtbyuR2eznRBrJ9n5kMdHk/j\nqVzr1Vo+1cC3Bon7Zo2SSo08c6bXRcQNwBoNfdfv1rykQZOZDwC/q9wkSZIkDTBD35IkSVJnDPLq\nV9MZ9kCkusN61iixnjWuntCk7TrgszP0+yXwuoa2nYGmoe+qzPx+RLwCOIFa/a4EHAHsP9NkNb4y\nc8YrM7QIYu3YtUlJ3bMaU/+P/0I/JiJNx32zJEkaNRGxLsVJbasDy1C7GtpdwA3ANZUT2yVJkgaa\noW9JkiSpM4YxnDesQUh1n/WsUWI9a1w1W032+Myc6W/iwiZtzwCOnGnAzPxWROwOvIzaat8viohP\nZOZF7U1bkkbakk3aLuv5LCRJkqQRFxFbAnsBz6I4MX75NvpcC1xEcRW0H2TmdV2dpCRJ0hwY+pYk\nSZI6o1VAbxjDhpL1rFFiPWtcPZKpdf6LNvr9GbgXWJpacPvJsxj3P4EXUR9sfAew3yy2IUmj6nZg\nXer3zzf1aS6SpJoJPPlWkoZeRCwLvAR4DbB16al29/HV1cD3Bj4bEWcBR2TmGR2dqCRJ0jws6PcE\nJEmSpBE3McA3abb6XbPWszqp3zVrPavblm3SdulMnSorgV9OfS0+OiIe0c6glVWwTqa2uvgEsFdE\nrNROf6kF95EaFdmkbdWez0LqDPfNGiVbA+uXbhv0dzqSpNmKiH2AK4H/o9ivl49VJmdxq/ZZQLFK\n+GkR8bOI2KSX70eSJKkVV/qWJEmSOq/6xeAkcEnlJg0r61mjxHrWOFmqSdvtbfa9HNiqoW0L4Kw2\n+58MvLj0eElgd+AbbfaX6mTmmv2eg9Qh5wMvbGjbBFf71hBy36xRUjlxUZI0hCJiFeA4iu8dyiej\nzfUqf+V+1e1tB1wcEQdn5jFz3K4kSVJHGPqWJEmSOqMaJIT6lS23oAg6fRv4VmZe0Z/pSbNiPWuU\nWM8aV/c3abu7zb5/bdK2Ke2Hvn/VpG07DH1L0snAJxra9gDO6f1UJEnSOIqIJYENgTWBFYGlKXIj\nC+nC1Rsy8/hOb1OqiohHAT8FNqb2vd90pqvxZn2rbRPAIuCoiFg/M98727lqfEVEs+/ZpKFkPUuD\nYWJycq4nt0mSJEmKiMWVu40H1q1WlLgE+BZwYmb+uZtzk2bLetYosZ417iLiEoqgNtR++Fw5M+9q\no+/LgOOpXx3/S5n5xlmM/wC10MAkcGFmPm1Wb0KSRlBEfA94PrV97J3Ahpl5a18nJkmSRlZELAUc\nAOwJ7AQs06uxM3Nhr8bSeImI5SiupLN5palZ+Kn8PeBdwJ8ojr/vBO4Blqc4+WEl4LHU/22UA9+N\n9w/LzA/N/11olFS+jy4vQDIs/v39n/tsVVnP0mBzpW9JkiRpfp4BvAjYF1i70jZJ80sAAmxJsbrs\nhyLidxQBw29n5tXdn6o0I+tZo8R61ri7AdiM+ppfjeJHzpm0Wul7Nu4EVqH2Rft6s+wvSaPqPcCu\nFFccAVgB+Bywf99mJEmSRlZE7Ad8BFi30tTL8JYrEKqbPkUR+G4Vzr4f+CHFd3y/ycyrpttYRCwA\nAngK8DJgB6auHl79juPwiLgkM0/pyDvRqBvEfeGwBXk1OKxnaQC40rckSZLUIRFRDRjuA6xVaZ5u\nhdny878Gvk0RMLyua5OU2mQ9a5RYzxpHEXE08CrqV+veIzNPa6PvmsD11P9YektmrjGL8W+nCDJW\n+9+bmcu1/w4kaXRFxLuAj1K/jz4SOCQz/dFGkiR1RER8Bngjra961k2usqmuiYitgF+1eHoS+ALw\n/sy8fR5jrAd8EtiL5t8j3gxskpm3zXUMjZZpVkYexM94jf8vuM9WHetZGmyGviVJkqQOi4gJYDtq\nAcNHVp6aLmA4Wfr3AorVJ07MzBu7OFVpRtazRon1rHESEW8GPk19oPCTmfnONvvfBSxbeVjtv15m\nXttG36WAe0tNE8CDmbmo/XcgzU5ELEOxX18RWJriKpcL6cJqP5l5bqe3qfETEV8FDqR+P/1z4N2Z\neUE/56bxExEvb/VcZh4/l369Nt08JWkcRcS7KVb4hv4EtAxcqWsi4nvA85l6Rb/bgOdl5vkdHOuF\nwHFA+TuN6vH7pzPzkE6NpeFWCskOI/fZqmM9S4PN0LckSZLURZWA4fYUAcMXANUVMtsJGC4GfkER\nMDwpM2/u4lSlGVnPGiXWs0ZdRGwLnE/9at1/BzbKzPvb6H8R8CTqw4ivyMyvtdH3yRQnScx5pXCp\nHRHxLGBP4LnA+j0adjIzl+jRWBphlWORLwKvpX71rEngEuCnFFccuQa4A/gXHf7BNTOv6eT2NLym\n+0F/uh/KBykI4A/6klQTERtTHE8sWWqufq4rP+42A1fquIhYi+IYeUGpeQK4FdgpMy/pwpjPBk6h\nOMG4PObdwNqZeVenx9TwqRwbDzP32fo361kabH45LUmSJHVR5dLc5wDnRMTBwA7UAoarV142SfMv\n3BdSrEi7HXBkRPyMImD4XS8ZqH6wnjVKrGeNgV9T/OC5aqltbeADwHva6P87itB32QuBGUPfwL5N\n2v7ZRj+pLRHxNOAIYNtKUy8CK1JHVY5FXhcRV1LUczX4PQFsCWzR5SlM4m9Emmqul+7u9354IILn\nkjRA3g8sxdQTy6g8fpAiFH4l8A+K4OoDPZ6jNFfPpfhurlzTk8DB3Qh8A2TmmRHxLuCz1B93LEvx\nHchXuzGuhs4r+z0BqYOsZ2mAudK3JEmS1AcRsQDYiSJguDewWuWpdlaYfQg4iyJgeHJm3tHFqUoz\nsp41SqxnjZKIOAo4iKk/hH4YOCwzW67YEhGvBI5p0neHzDxvmn7rUoQHlm/od2pmPn/u70YqRMQh\nwMeohWOhd2E/Lw+rjomIhcChwCHASg1Pu/KmeqrFit0z7vMGZKVv982SVBIRqwA3UL/KNxT7y9so\nAuFfc2ViDauIOBZ4OfVXJjsvM3fowdgXAE9uGPubmfnSbo8tSZJUZehbkiRJ6rPKj/3PoggY7kVt\nRc52AoYPAmcA3wZO8ct69Zv1rFFiPWvYRcQWwO+p/zGy+u+lwEeAkzLzoSZ9mwUFJoC/A7tl5qVN\n+qxJUfebMzUs/l+Z+dHOvDONq4g4kPoV1BqvxtDtL7sNFqojImIl4FTgqTQPeFvL6ilD35I0OiLi\nAOA4ph4rX0VxEu+1fZmY1CEtgtevzsxjezD2q4CjS2MDXJ6Zm3d7bEmSpCpD35IkSdIAiYglgGdT\nCxiuXHmqnYDh/cDpFCvM/iAz7+niVKUZWc8aJdazhlVEnAjsw9TgN5X7dwEfzMxPNul7EvACpoYF\n7gG+AnwPuJZiRfxdgTdTW622MWS+dWb+tpPvTeMlItYG/giswNSTCsq6vUKywULNS+WksrOBZ1Sa\nyvtlSm3dZEhWdSrh7WbaCX0PAutZkioi4svAq6k/Zn4YeLKfyTQKIuLPwPqVh9XPhJGZf+7B2GsA\nN1L/93VnZq7cupckSVJnLdHvCUiSJEmqqay0eTpwekS8FtiZImC4J/UBw8bwFcDSldftCdwbET+i\nCBiempn39WD6Uh3rWaPEetYQexuwI7AK9SFsKvdXqDzXzIcoTnKYKL1+ElgWeGPlVlYOk5fHOt9w\ngTrgP2kd+K7W3m3AlcA/gLuBB3o5QalNh1AEvhuD3b0+gUEqO7zH/SRJ3fO40v3qMfM3/UymEbJU\nk7brejFwZv4jIu4DFpWal+3F2JIkSVWu9C1JkiQNgYhYEtiFImD4fGqraLazwuzdwA8oAoanZ6bh\nF/WV9axRYj1rGETEsyhqbVHDU9UAwKsy87gWfY8GXkXzwHijxjBu9d/nZOaZ83kPGm8RsRzFamqN\nP6ZPUAS7PwN8JTOv6PXcpNmIiGUprpBQPWGsfLJM+fGdwDUUV2Po2vFBZu7YrW1LkqT+iIgrgQ0r\nD6ufyfbIzNP6NyupcyLiUmCTysNqja+Rmbf2aPxbqT+e/1dmrtiLsSVJksDQtyRJkjR0ImIp4DnU\nAoYrVJ5qJ2B4F3AKRcDwjMrKtVLfWM8aJdazBllEbA+cAKzJ1HD2MzPzFy36LQ+cCzyBqbXcTDkY\nDvCpzHzHXOctAUTEvsC3mXo1hVuAXTPz4r5MTJqlhlpuPJHmb8CngVMy8+q+TFCSJA29iLgWWLvy\nsHq8sVZm/qN/s5I6JyJOAZ5H/TH1tpl5UQ/GXpbiBM3yFdGuyMxNuz22JElSlaFvSZIkaYhVAoa7\nAS+k+KJzNgHD24GTM/Ogrk5SapP1rFFiPWsQRcQqwAeBg6it+j0JPCozb5ym3yOBsyhW0mpVw83a\njwNenZmL5zl1jbmI+BzwBqaesOAq8hoqEfFF4D+YWsvfpLjqwv39mpskSRoNEXEFsHHlYfVYY0k/\nl2lURMShwIepD31/JDPf24OxnwX8pGHsEzLzZd0eW+qkiNiyWXtm/qHXc5Hmy3rWODL0LUmSJI2I\niFgE7ArsC+wBrFR6unFVxH+3Z+bCHkxPmhXrWaPEetagiYhHAC8B9gE2y8zV2uizCPgY8CamruZd\nVa3hO4EPZOanOzNjjbuIOBvYnvof1k/NzOf3dWLSLEXEecDTqdUywB+AJ2amP9ZIkqR5a3K8MQms\nlpm393ViUodExGOBK6g/kfJ6IDLz7i6P/f+A/an/+3pZZp7QzXGlTouIxUz9bm8yM5fox3yk+bCe\nNY4W9HsCkiRJkjojM+/PzFMy8wBgDWB34BjgH0xdSdZAgQaa9axRYj1r0GTmzZl5ZGY+E1izzT73\nZ+Zbgc2AzwJ/rjw1Ubk9CJwP/CewoYFvddjaTN0/frUfE5Hm6RHU1/Ik8AkD35IkqYP+2KTtUT2f\nhdQlmXkl8GPqv1NbC/hEN8eNiKcAL6b+eP4fwHe6Oa7URRNNbtKwsp41Vgx9S5IkSSMoMx8ErgH+\nBvy99JQfdDV0rGeNEutZg6ZSk7N5/RWZ+ZbMDGA54NHAqpm5dGY+PTM/mpm3dmWyGmfLNmm7sOez\nkOZvuSZt5/R6EpIkaaSd16Rth15PQuqyd1KcfA61VbdfExGHdGOwiHg08E1qGavqKt/vz8wHujGm\n1AOTuAiJRof1rLHiMvaSJEnSCKmsNvECYG9gg4anDRNqqFjPGiXWs0ZRZt5HcQllqdvubdJ2U89n\nIc1fs8vN39jzWUiSpFF2GkUYtpwFeTHw+f5MR+q8zLw0It4FfIpayG8C+N+IWJ0ijD2rk9xbiYjN\nge8D61ILE04CP8zML3diDKmPqicwSKPAetbYMPQtSZIkDbGImKBYqWUfYC+KyxhC8wChH3Q10Kxn\njRLrWZI66lZgI+r3l8sCd/VnOtKc3Qg8lvpaXpLaKoWSJEnzkpm3RMRJwP7UgrBPj4gdMvOcvk5O\n6qDMPLKyAvch1Ae/3wXsERFvzcyfznX7EbEC8FbgPcDSpe0D/AJ46TymL0mSNGeGviVJkqQhExEL\ngWcB+wJ7AqtXnioHCVsFCCdKz/8cOLEbc5TaZT1rlFjPktQ1CWzb0LYWhr41fP4APLOhbQPg0j7M\nReqqSghrc2BNYEWKsNQSwEK6cKWbzPxgp7cpSUPs/RRXG1uKWlD1mIh4Umbe0deZSSUR0XhsPFs/\nAALYg/rg92bAGRFxNfB1ipD2bzLz1mnmshSwHsVnz50orta3ArWVY6v/ngAcVLn6mSRJUs8Z+pYk\nSZKGQEQsCexCESR8PrBy5anZBAkXUwsSficzb+jCVKUZWc8aJdazJPXEL4GXN7Q9A7iyD3OR5uMM\n4OCGtmdj6FsjIiJWB95CcQLkZj0e3tC3Bk5ErEHxeXFTYFXgPuA64PzM/EU/56bRlpl/iYj3Ah+n\n9p3EesB3I2KvzPTkSQ2Kc+jcFfDKCypMVG7rA++tviAibgNuB+4E7qU4KW0Ziu/zHkn993nl7VU9\nDGwJ/CoiACYz8/Edmr8kSVJbDH1LkiRJAyoilgZ2A/ahWKlihcpTsw0SnkctSHhjF6Yqzch61iix\nniWp535Esd8s72f3Ab7Sn+lIc3YGcDPF1UCqYZTXRMSRmdmpsIvUcxExAbwbOJTaipi95N+Puqqy\n+usTM/PCNl//aIqw7T4Uq9s3e821wBHAFzPz4U7NVarKzE9ExGbAK6gdd+wAXFIJhH8rMx/s3wyl\nOp08dqiuyN1s26tVblD7u2il2TYWUpzY1iwQLkmS1BMTk5Meg0iSJEmDIiKWpQgQ7ksRKFy28tR8\ngoQ3dWGq0oysZ40S61mS+isifgDsTv1ltbfKzN/1dWLSLEXEocCHqa/lN2fm5/s6MWmOImIhcBLF\nVW/aOTbutAmKVTabBmuluYqIBcALKa42shOwJLBeZl43Q79nACdTrOw9U4hxErgEeGFm/mnek5Ya\nVE7KOZLiSiPlgOskcAdwJnABxRV0bqBY/fgBOrgPz8xrOrUtjZ6IWExn6q3V/nam7+pm+7ry6z0G\n0dAq/e1NYD1ryFnPGkeu9C1JkiT1WUSsCDyPIki4C8UlBWH2QcKfUQQJv2uQUP1iPWuUWM8aFBHR\nauW/ycxs+f3eNP16bdp5Sm06jOKkm+r+dQL4SkQ8JTMf6N+0pFn7JMWKmxtT+zHy4xFxWWae08d5\nSXN1BLBn5X7jipiuPKWhFBHbA0cBG1WaqvW8IdAy9B0Rm1NcoWT5StNMfwMTwJbAhRGxkyezqVMi\n4sOlh/8CrgAeR60mJ4CVKVaj36eLU5nETIp6Y7bHHO2+3mMZSZI0cDzAliRJkvogIlYB9qL4Uv1Z\nwFKVp2YTJHwYOJdakPAfXZiqNCPrWaPEetaAmutljjt5eWSprzLz4oj4FHAItaDs44FjI+KAzByU\nkxykaWXmAxHxYuAXwDIU9bw0cFpEvAf4TGYu7uccpXZFxFbAm2ke9i4HC6WhEREHAl8GFjK1fjei\nOKm3Wb8J4GsUge92T4Cotq8MnB4RT8jMG+c4dansUJrXnftoDSLrUJIkaRYMfUuSJEk9EhGPAPam\nCBLuQO14fLZBwvKKsTd3fqbSzKxnjRLrWUOisQbb/VG036tS+eOtOulQYAuKqy9Ug98vBjaKiLdn\n5s/7OTmpXZn5+4jYB/guteD3IuATwFsi4hjgNOD3mflQ/2Yqzej91F9CG+qDhDdRnOBwJfAP4G7A\nqzNoYEXEdhSB7yVoHozdcJru+1GckNbYp1m4ttxWvb8GcDSwx1zmLrXQ7PNYuTa7+XnRz4Jqx3H9\nnoAkSdKwMfQtSZIkdVFErAW8ANgXeAawoPLUbIOEZ1MECU/OzFu6MFVpRtazRon1LEnDJzMfjog9\ngW8Dz6MWMtwa+FlEXAGcClxAETC8AbgzMx/s05SlljLzxxGxI0U9r0Otnh8DHF65PRQRVwE3ArcA\n91MEZjsR0JrMzFd3YDsaUxGxNrA7tXqs1vAE8BfgLZn5oz5NT5q1iFgCOIpa4Bumroq80TSbOLh0\nv3wyxARwPfB14BqKff4+lW1NNrx2t4jYMTPP7sBbkqD/JwFL08rMV/Z7DpIkScPG0LckSZLUYRFR\n/fFmX+Ap1MKBswkSPkR9kPDWLkxVmpH1rFFiPUvS8IqI15YengZsDDyO+kDVJpW2xr6dnMpkZvq9\nuuYsIh5u0twYKqwedywJPJai3jupOp6hb83HHhQnTjaGY38H7JSZd/RrYtIc7Q8E9Z8Jq8cZi4Gf\nUByDTBERGwFPZWqIexI4H9i9/DcREe+lWCn/fUz9DPpOis+cUie42rYkSZI0YvxyWpIkSeqAiNiA\nIkS4D8VKg1WzDRKeRS1IeFun5ym1w3rWKLGeNSKuYW4rtM21nzSIvkTzep6gPnBosEWDrlWNVtvL\n4e+Z+kj99PQmbfcD+xr41pB6Q8Pj6r73HOCgzPzrNH1fVLpf3oc/AOzX+DeRmQ8Dh1VOTKsGv6tB\n8Z0jYu3MvH7W70CqORc/C0qSJEkjydC3JEmSNA8R8V8UYcItK02NP8a3EyT8KUWQ8HsGCdVP1rNG\nifWsUZKZ6/WynzTgmoVfy2HZXo8tzcV0oe5mJ6Z1uratZXVC+TIK1RNwvpqZV/VpPtKcVa4KtS1T\nV67/IbBPZj40wyb2aXhc7f+dzLxumn4fpAiMP7bUtgDYFfhKe7OXpsrMHfo9B0mSJEndYehbkiRJ\nmp//prYST9VMQcIHqQ8S/rN705NmxXrWKLGeJWk0uWKhRpF1rWG0OlNr98R+TETqgB2btN0GvHKm\nwHdEPBp4IrXPn+W/i69P1zczH46Io4GPN/TbDkPfkiRJkqQmDH1LkiRJnVH+YabZqmkPAmdSCxLe\n3pNZSXNjPWuUWM+SJGnQuNK2RsGiJm1/6PkspM7YpnS/Gtw+ts0rPu1Rul/+/PkvipOKZ9LsNY9r\no58kSZIkaQwZ+pYkSZI6o3xJ+erKPvcDPwFOAk7JzDv6NDdptqxnjRLrWZJGwzW4GrJGQ7PVZKVh\n9K8mbZ5AqWG1YZO209vsu1vD42po/KzMfLCN/pdRO8apfmZdr82xJUl9FBFLArS5v5ckSeoIQ9+S\nJElS51V/3LkXeELl9qGI6OukWpjMzMf0exIaaNazRon1LElDKjPX6/ccpE7IzJ/1ew5Sh9wABPUn\n5KwMtLMysjRoHtWk7dKZOlXCfjvR/MS0M9oZODMfjIi7geVKzSu201eS1B8R8VjgRcAbgB2AK/s6\nIUmSNFYMfUuSJEmdNVH6d6XKbZAv3e1qiZqO9axRYj1LkiRJnfNHipBT2WMw9K3htDxTP4O1U8vb\nUYS1qyt0l7fRVui74j7qQ99LzqKvJKkiIhYAGwFrU5xAswxFLmoB7X0POFF57RKV26LKNpYDVqls\nd+vKv5IkSX1h6FuSJEnqnvKXiIMY3hvksKMGj/WsUWI9S5IkSfNzNvD6hrZnAb/tw1yk+WoWsm7n\ns+KuLV7/t8z8yyzGb1zZ+1+z6Ct1XEQsRRF2ncjMO/s9H2kmEbEd8CrgeRTh7G4a9O8VJUnSiDP0\nLUmSJHWPX/hplFjPGiXWsyRJkjQ/PwbuBpaltsrxy4Aj+jkpaY7uAB7V0LY8cPsM/Xaj/vNldbXv\nn7Q7cESsBizVsB1DtuqJiNgS2BF4IrAZxd/BGtRCrZM0yZRExOsrr/9WZp7Xm9lKU0XEqsBngP0r\nTb1YSKG6v3bRBkmS1BeGviVJkqTOMECoUWI9a5RYzxp6EbFuv+cwX5l5Tb/nIEmSOicz74qIrwGv\no3bMvUVE7JOZ3+nj1KS5uI1aYLvqMUwT+o6IdShCr9WTHsp9T5vF2NuU7lcDhLfOor80KxHxSOAN\nwIHAOqWnZhNg3YpiVeXXR8TlwHsz85TOzVKaWUSsDJwLbEL9iQpSX0TEgRT7VmnoWc/SYDP0LUmS\nJM2fKzpolFjPGiXWs0bF1Qz3D5dNV4eTJElD73DgJcAK1IKvX4yI8zPz+r7OTJqdPwPbNbQ9Hvj9\nNH32KN0vH6vfD5wxi7H3aXg8CVwyi/5SWyJiaeADwJuApWn+nUnjyvWtrFd6zWbAdyPiHOClmXnj\nfOcqtekoYFOKum3nO5PGmp6uT6v6b1zlexJ4sI2xNR7WA3ZgeL7D87tzTWc9rGdpYPljiyRJkjQ/\n6/d7AlIHWc8aJdazRo1fXEuzFBELgLWB1YBlKP6O7qncbs/Mm/s4PamvIuL5wC8z85Z+z0XDKzNv\niog3Al+jFrhaHTg9Ip6bmdf1dYJS+84HXtnQ9gLg+Gn67NfwuLra99mZeU87g0bE2sD+TA3TXNxO\nf6ldEfF44CRgAzqzIvJ6TA2I7wj8rnLFh1/MY9vSjCLimcC+NA9hlx+XTTa0Txfsbqzvct9/UJwU\ndDbw9cz8+6wmr3Hh93gaJdazNGAMfUuSJEnzkJl/6/ccpE6xnjVKrGeNoGFZVaWRPwqoZyqXqn8u\n8GyKS85vCCyY5vX3UKykfwnwM+Cnmfnn7s9U6r2IWAnYBtiJYlXZjYBNAEPfmpfM/H8RsTHwfmoh\nqc2BSyPiCOBoV33VEDi3dL8a7NsjIp6SmRc0vjgidqZYGbz62vKx+rfbGTAilgC+CizL1GP9c6f2\nkOYmInYFTqSotcZ6bWba11ROrFynobn6t7AG8KOI2DEzPXlB3fSO0v1yzVbv/57iBJpbgbuBdSlO\n7invt08HflV5vDSwPMVJw5sBG1e21xgAfwDYMzN/1fF3pFEz6N/j+X2dZsN6lgaMoW9JkiRJkiRw\niU8OAAAgAElEQVRJ0tCKiKcC7wJ2BxZWmtv5wWc5ih/0NwVeXNnW+cDnM/OELkxVaioilgZ2BZ5M\nETRZkWJ1+iUoTlxop54nKq9donJbVNnGcsAqFEGW8msH/UdbDZHM/EBE3Al8jNrJNisChwOHRcRv\ngQuAK4EbgDspQlMdq8PMNCSrOcvMKyPiImBragG/BcDJlVXrf1t9bURsAXydWv2W6/he4LszjRcR\nK1KskL8zU1elvSwzfzePtyP9W0TsAJxMcVzQGF6FuYWkHgUsSfOVkAFWAE6LiCdm5vVz2L40rYhY\nheLYubwfrtbgz4A3ZOYfG/osQ3GFhvIx8UOZeXiLMdYGDgIOoajp6t/PIuA7EfEkrxylIefnQY0S\n61ljx9C3JEmSJEmSNBxctUQqqazs/QVgr0pT+W+k3R98Jhr6PQ14akS8FXi9KxSqmyJidYqQ7Isp\nAtqd4v8X6omI+EZD01UUq8iXQ6wLKYK0W3VxKpP4m6fm72iKWq2aBB4JXBARpwJXUNT3ntQCrxMN\n/56QmXe1GqASVDwAOLSy7UaTlXlI8xYR61Ks8F0NfJdVa/aXwE+BXwN/Bc4GVp9h07cA7wFeC6xP\nLQxbDn6vDnwOeMF834fUxM4U/+83ru59AfDszHy4sUNm3hsR5wC7UavX50TE8pn5ryavvx74YEQc\nTXEFh6eVxnsUcAzw/E6+KY0UP49plFjP0gDyCxBJkiRJkiRpsJ2LK5ZIdSJiR4of31el9gPUXP5O\nmgVgJoBtgF9GxMGZafhKHRcRzwOOBVamez+itvqb8Edbdcp+NK+zaviqHMay7jTojgHeDGxSapuk\nCHjvWbnB1KB31YPAR5ttOCI2ogjfbkZxIkTjsUt1W5dTnNAmdcL/AavR/Hj3eOB/MvNP5SciYvFM\nG83Me4GPRcQRwOuBDwIrMfVEiD0jYrfMPG2+b0RqsHWTtsXAy5sFvkt+ThH6rloS2AH4YasOmXl9\nROwE/BjYnlp97x4RL8vMr89y7hptvwOO6/ckpA6xnqUBNjE56e9FkiRJkiRJkqThEBEvAr5G8SM9\ntBf2bicY3hjeqj4+LDM/NNt5Sq1ExDOAs6gtzNPrH2qqtb1JZl7Z47E1QirhwGr4qZVu1/cEMJmZ\nC7s8jsZARDyJYqXj5RueanY1kcbw9wcy879bbHdVitWRG7dR3v79wA6ZeeHcZi/VRMRzgNOYemz7\nALBfZn6vRb8bgDVKr59x/xoR6wPfA7Zg6t/HGZm5W6u+0lxExPcoVtku74N/kJl7zdBve4p9fLnf\nZzLzbW2MuTJwGbBmpWkCuAHYODPvmeNbkSRJmpMF/Z6AJEmSJEmSJEntiIhdgK9TBL7Ll5Ivmyjd\n7gSuAf4AXAj8EbgeuKfhdVAfUqk+ngAOj4hXdeHtaAxFxCKK1TWrl6RvrLtmJpharzNp7FO+LQbO\npD6AKM3H5DQ3aWhk5sXAXsC9DU81q+ny/RNaBb4r270NuLnJNqAWxN3HwLc66NCGxxMUq9Hv0yrw\nPVeZeRXwbODPpebqcfTOEfGYTo4nAWs3aftBG/1+16Rt23YGzMzbgbdTfyy+JvCadvpLkiR1kqFv\nSZIkSZIkSdLAi4h1gG9QC8tC/ercE8BNwMcpAluPysxVMnP9zHxCZj41MzfPzHUycwVgfeAFwBHA\njUwNe5fvfzYiNu7uO9SYeA2wHq1PMmh2axYQnO5W3V45XHg/8L/AS4B1M/M5lRCiNF8z1WO3b1JH\nZebZwBMpThabrs4mgIeADwAvb2PTlzfZ1gRwNbBLZv5oDtOVpoiIdYFnMvV4+ROZeWo3xszMm4ED\naH7MsnM3xtRYW5mptfbrmTpl5h3AtZWH1WPvJ0REu1cL+TZQvUpOtf+b2uwrSZLUMUvM/BJJkiRJ\nrUTExU2aJzNzq55PRpon61mjxHqWpJH0eWBVpq6yOUHx4/0hwMmZ+XA7G8vMvwF/A74XEYcCu1OE\nYqO03WpodhngK8B2HXknGmcH0/qkhV8BJwAXA7cCdwOPA06jviaPrNwmgKWB5SlWPNwM2AHYCVhI\n/d/KUsDdmfnN7rwtjanj+j0BqRsy808R8XRgH+BAiv//V6w8PUmxovH3gc9n5tVtbvaPwPbUgt+3\nA0cB/52Zd3do6hLAc5h60tjfgcO7OWhmXhgRpwB7N4y9LXB0N8fW2Fm6Sdv1bfa9FFiHWo0uAjap\ntE8rMycj4hsUJ/tU+68fEVtn5kVtji9JkjRvhr4lSZKk+XkC9T++N1uFTRoW1rNGifUsSSMkInYA\n9qB+X14NTX0deGNm3jXX7WfmYuAHEXEG8BHgrUz9/+NpEbFrZp4+13E03iJiM+Cx1GqqWmMPA6/P\nzGaBqL9FxLXAo0uv3zkz397ktd8HPhIR61GsYP8CaicuTACHRcT5mXlm596VxllmvrLfc5C6JTMn\ngZMqNyJiNYpw4D8z8945bPJy4BbgPOBU4IQ5bkeayVNL96vHst/KzPt7MPZxFKHv8vhP7MG40u1t\nvu5PwG4NbZvRRui74gyK0HfZLoChb0mS1DOGviVJkiRJkiRJg+6dDY+rAZZjMvO1nRqkEoZ5e0Tc\nARzG1BOG3g0Y+tZcbd/wuFrH/9si8F11JvBKavW4aUSsn5lXNXtxZdXZfSPiYIoVwan0XQAcGxGb\nZ2a7wRhJEpCZt85zE0dl5uc6Mhlpehs3aft+j8b+eel+9aSz1Xs0tsbHPU3aFrTZ969N2jadxdh/\naNK27Sz6SwMpIh4BPC4zz+v3XKT5sp41Dgx9S5IkSZ3RsdVjI+L5zdozs1dfzkvWs0aJ9SxJQ67y\nY80u1Pbp1aDs2Z0MfJdl5uERsTHwEupXSt4uItbOzHYvHy6VPaFJ263A4TP0+yVF6LtsZ+Co6Tpl\n5uciYkngE9T+ftYC/gd444yzlSR1TGY+0O85aGyswdTvQpqeKNZpmXlbRNwLLF1qXqUXY2us3MHU\nK/qtQHFcPZO/NGnbpN2BM/OeiLgbWJbaZ8THtttfGkQRcRDwUeAzFFckkYaW9axxYehbkiRJ6ozG\nLxnn43tNtjWJx+/qHetZo8R6lqThtwuwkPp98APA67s87tsoLv29cqltAtgL+EKXx9ZoWq90v3qM\n8rU2goAXNGl7JjOEvgEy81MRsQvwHGrBlNdExGcyM9uatSRJGibLNmm7qYfjN4a+l+nh2BoP1wLb\nNLRtQHuh78aVvieYRei74l7q/87WnGV/aSBERFB8pnxGv+cizZf1rHHjj5KSJEnS4Jro9wSkDrKe\nNUqsZ/VURLy/1XOZ+cG59Ou16eYpteFppfvVoOwpmfmnbg6amTdHxPHAW6gPnG+LoW/NTbNAyLlt\n9EuKEx2WpBbcfvIsxn0bcCm1Y5iFwCFAV1bKlyRJfXV/k7YVgNu6PXBELGTqyt73dXtcjZ1mnwOf\nAPy6jb5XUftsVz2ujohYlJnN/naaWbHh8XJt9pMGQkQsBfwX8C5gKTq7aIrUU9azxpWhb0mSJGlw\nlS9fLw0761mjxHpWr32A1l9WTxemnq5frxn61nw0u1z2iT0a+0SK0HfVBPD4Ho2t0bMcU/fLv5+p\nU2Y+FBFXAFuW+m8YEStl5h1t9L8iIk4DdqcWbtk/It6SmffO6h1IkpqqBE4WAROZeWe/56OxdjtT\nA09r04PQN8VVTRrHbmf1ZWk2ftOkbS/gyzN1zMz7IuJaYJ1S80Jga+AXM/WPiEdRhArLNe6JDWpb\nRDyD4kTyzYBVKY4dbqG4IsMFwE8z859dHH8H4EvAxvjdtubJepb6x9C3JEmSNLg8G1mjxHrWKLGe\n1S+NX163W4f9/tLbvxfN16OYQ1C2Q8rjVMOyXr5bc7WoSVu7AazLKULfZVsC57XZ/ySK0HfVssCu\nwMlt9pckARGxJbAj8ESKgMujgDWoHXNP0uQ3+Ih4feX138rMdvfd0lz8FXhSQ9uOFFf96LbdSver\n351c3YNxNV7OY+pq3c+JiCdl5sVt9L8UWJf6z5i70UboG9i+SVvXAo0aDRGxCsWVll7JzN8nLI6I\nU4APZuYfOjyHTwAHUn/MAv3/3lBDxHqWBsOCfk9AkiRJkiRJUlsmS7e59uv1TeqEZpfL/nsvBs7M\nu4G7GppX6sXYGkkPNGlrrK9W/tKkbdNZjH1+k7YdZtFfksZWRDwyIg6PiKuB3wKfBA4AtqIIuyyg\nCJhUb81sBbwBOCciLomIPbs9b42t3zVpe2GPxj6gSVuz+Uhzlpk3ARdSH/RbAHwrItZqYxMXle5X\nQ+OviIhmnzsbvaF0vzr+TW3005iKiP8AruL/s3ffUbJUVfvHv30vXHIOAooCIg8KiIBIFklKkBxE\nETG8gMgLCv58EUQUVMwJFDHCBRQQJChRBYkiGQTULUFykJzTvXd+f5wuprq6ZqZnOk7381mrF92n\nu+qcZu2pW121zz5wMLAktecLZY+pwPbAjZJ+IGlqC8bwYeBfDCfI+pqdTYjj2ax3uNK3mZmZmZmZ\nmZmZmfWyl0vaOnltu9iXqwbZRL1Y0jY38HwD295V0vbWcfR9OzCT2mJA7xjH9mY1qklVe3ap+1nA\nDNK/D88DTwGPAvcB90TEq10al/UZSXMCXwb2A+ak/Bwgn2Qy2jnCMrnPrAScIekSYLeIeLjZsZrl\nXJJ7niW0ridp04j4c7s6lbQLsCb1iVeubG/tcBywdu71EPBmUmLhfhFx2ijbXggcVmhbEvg2tUnd\nNSQdAKxLbYwPAdeOY9w2ICTNBvwa2In6SsRjyT6/H/BOSVtFxNMTGMObgZ8Am5SMYbxjsgHmeDbr\nPU76NjMzMzMzMzMz630TTTJ1cqr1g8eA5am9ebMUEO3uWNICwFyFvp9rd7/Wtx4hJWrn42khGkv6\nLqv03XDSd0QMSXqWVKk+SwBbrtHtzUosRUqG7bUb6zMkBXAlcA5wfkTM6vKYbBKStCpwOulY2YpE\nkmWoTxDfCLhJ0o4RcWUT+zbLuwq4H3h99XX27/6xktaMiCdb3aGkZYGjqP8beRG4oNX9mQEnAUcA\ni+fahqqvT5F0BCkx/PSIqJk8GRFXSbqDlCSebVcB9pY0L3BQRDyUfV7S3KQk8c/lPpuP9Sta+cVs\n8pNUAc4AtqI+XkY6p8i35xNZ1wFOl7R5RMxssP8pwOeBLzA8aa2YHFscwz34eG0lHM9mvclJ32Zm\nZmZmZmZmZr3tYx3ezqzXPFbStiYdSPqu9pPJbibd34F+rT/dV9K2Io3FVD5ZJUs2WanJ8SzU5PZm\nmV6aZDY7sDLp72Mv4H5JB0XEKd0dlk0mkjYHTiOtxlBMbikz6meqySpLF5qzY/niwHmSNoqIGyY8\naLOq6kSvnwJfoTYulwV+J2mbiGjZJEZJbwDOJ8VyPglrCDg1Il5oVV9mmYh4UdLnSYnd+UTs7LmA\nrwNvAvYt2cXPgG8Vtq0AuwG7SLqGdO6+CLAetf8e5P+ungbObvHXs8nv88D7qU94LcZPWWJssb0C\nbAx8BzhgrI4lrQicAKxBfUJu2esZwPeBL0dE2cpUZo5nsx7kpG8zMzMzMzMzM7MeFhHTO7mdWQ+6\nlXSDKW9nUnW3dtu58HoI+FcH+rX+dEtJ2/rAnxvY9gHgZWBarm1JSYtHxH/H2ljSVGD+QvO0ss+a\nTUCvVfuuMJwEsDTwa0kfBHZrZaKj9SdJ7wHOBOagPmkFJjbJ4fWkCQnFBJjs9XzA+ZJWi4gHJ7B/\ns6KjgU+TElZhONFqQ+BvknaNiFub7UTSdsAvSBPJin8rM0lJtWZtERHTJW0B7EJt/GXxDnDHCJsf\nBezD8CoM+cTvaaRE70wxYTH/3194YoPlSVqKVBl+tMRUSJMKHgKmkqrOL5j73BD1cbevpJ9GxIjX\nIyR9BDiGtFrZWNWQK8A1wF4R8fdxfEUbII5ns941pdsDMDMzMzMzMzMzMzMbxcW559kNoi0lrdrO\nTiW9Cfgw9QksV7WzX+tr1+aeZ7H8wepyyaOKiCHg39QnG767wb5Xpv6e0DMNbmvWqMoIj07vb6jw\nqJAmD10uaYEmxmN9TtIbSRW+s4TvvCz2/kqqoLwN6dj6aMlnix4DDgbupj7pJbMo8KMJDt2sRkQ8\nAxzIcIzlE1XfBtwk6RRJW0iaezz7lrSQpA9Luhb4HbAwtbGc9XV0RHRiZR4bbB8FzmXk84PSpO+I\neIVUATw7fpdVC6+UtOU//x/giGa/gPWdA0nnEVAfPy+RziGWi4g3RcTaEbFmRCwMrAP8Nref4nnC\nVFL1+jqSKpKOJlW+L65SUqy+XAGeBfYD1nGCrI3B8WzWo5z0bWZmZmZmZmZmZma97ApSheO8qcBP\nqtWL2+XHpIpCRee2sU/rb38l3ZDMWx7Yv8Htby5p26HBbbcpaXuywW3NyswgTRzIP6C+0mb+Bv14\nHsXt88barihLCHg7cJYk3x+1kfyUVBm5LOH7BGDFiFg/Ir4UEedExD+AWWPtNCJejIhvAiuQjvlP\nV98qVo3dtlq11qxpEXESMJ36ysSQ8kR2Bs4BnpR0l6SrSQncNSQdKemYapL4P0gTHaYDa1CbAJYZ\nIq1uckhbvphZTkS8BGwLHAq8Qv25wJ2jbHsB6ZhcrFybPS+ex+SfPwvs4hVErMTO1E4OgBQz/wFW\nrZ5D3F3cKCKujohdgS2ApwrbvzaJUdIS+e0kTQPOAj5F+cSF4uszgLdFxI+rE4vNRuN4NutRvqhh\nZmZmZmZmZmZmZj2reiP/JOpvxq8F/LqRKsnjJelHwJbU3+S/NiJGWiLcbFQR8Sq1lQizm5XfkfSJ\nBnbxt9zzbNudJK0y2kaSFqa2kmEWz3c1PnqzWhFxc0QsGBELkSoUn1H4SD7enidVevscsDHwFmBx\nUtW4uYElqm0bkarJnURKDihL/h4CrgcOB74KfJs0SedE4I/AvdQngOcTHt8NHNTUl7e+JOl9wPuo\nTfqrAK8CO0TERyPi9mb6iIiZEfEjUrLsrdQmEWYanQhk1oi9gQupr1CcT5iaHVgGWBOYjdpjaIV0\nzNyblPi1IinHpCxJNvv8/cC2EVGctGnWFhExKyKOBN4M/BB4vPrWEKMkfVe3PYZULfxZRl9NJH9e\ncz+wQUTc0MSwrQ9JWhlYutBcIcXkBo1cS4iIPwJbkc4/iqYAH8j1NwdwNrA1I1dDzl7fRzo27xQR\nDzb0hWygOZ7NepuTvs3MzMzMzMzMzMys132T2kqaWaLKzsDZkl7Xik4kLSDpRGAfyqvLfrsV/dhA\n+3HueXYjcyrwM0nnSlp7lG3PpL6i7GzAbyQtWbaBpLmB00kJtkV/bXjUZqM7kZQwVazedh+wF7Bk\nROwaEd+NiEsi4s6IeCwiXo2IlyLiv9W2SyPiBxHxEWBJYFeGE2Pz+10NeCgiDouIgyJiv4jYIyI2\nj4hlgbcC32N4lYjiuL4o6Q1t/P9hk9PnC6+zhO8dI+KsVnYUEf8BNgXyyTJZfG4m6U2t7M8GV0S8\nQqqCfCr1Ca1DhQcln8naihU284nj+c/dDmwcEfe06CuYNSwiHoyIA4ClgE2AgxuZfBARJwCrkP5O\nZjHyaiLPAUeSqsre0pYvYZPdaoXX2XFz//EkpkbE30jXQMqOyTvlnv+C4QlrI1VDHiJNhnhbRPyh\n0TGY4Xg262mVoSFXtzczMzMzmyhJs6hfinUoIia8zHw79mnWCMez9RPHs5lZ/5H0S+BjlByLgSeB\nLwMnRsTTE9j3PMAHq/tYMrdfcs+vioj1m/oSZoCki0gVjctiGeDfwDciYnrJthcA7819luq2DwPf\nIS2FfB+wCLA5aan7ZXOfy/e1YURc0crvZoNH0hGkOIPaBMBfAgdGxLNN7n924DDgkFxzBZgJbBMR\n54+y7fKkyRIrUX9M/0FEfLaZsVn/kPRG0jL1mSxOvhkRh5Rv9dq2DzE8sWZcvxElrQVcSW1V5SFg\n74j4RePfwGxskj5NWiFhHsonN2bKksNHk33+dGCviHhqYiM06z5Ji5DOtd9OOp9+AXgEuA64pLpy\nj1kpSd8GPkt9Zfg3RcS4kvMkLQA8AMyVa64ALwLzkSbAn1zoi8Lr24BPRMQ14/smZo5ns143W7cH\nYGZmZmZmZmZmZmbWgE8D7waWoz5ZdmFStZ9vSvoDKYHqeuC2siRwSfOTKsGuDqwDbEdKgBlpqfoX\ngP9p8fexwbUfcC0wJ/UViAFWAFYcYdtvkRJRoDZRfAlSJfpiNfp8TOf/bm51wrc1S9KapGTsYkL1\nkRHxxVb0UU2u+qKk/5KO81ksTwWmS1opIh4dYds7JG1Iqmr/lmpz9nfwcUkHRcSMVozTJr33UXtM\nhZSYcng7O42IqyWdDWxf6HstUrVDs5aJiB9KOpM0yfHDDOeKFM97G03kys4xbgYOjYhzmx6kWZdF\nxOOkxMOTuz0Wm5SWzj3PzitOGW+CLEBEPC3pQurPEeYE3gV8t6Sv7PkMUmXlIzxRwZrgeDbrYVO6\nPQAzMzMzMzMzMzMzs7FExHPArkC2RHc+gTV7PhepQtAPgMuBJyTNkPSUpAckPSlpBqky+F+BHwG7\nAfMW9pWpkJb4/kRE/KvNX9EGRET8A9iz0FxMNrxzhG0vJlXzLlbtzi9/nH/k38s7sqkvYZZ8m+F7\njVm8ndSqhO+8iDga+Am1sbwIKXlxtO2eAHYveWt+YMNWjc8mvXVyz7NYPjUiXh7h861UXNWhAqzW\ngX5tAEXEvRHxceBNwOeBaxj5HGK0x+PACcAmEbGaE77NzIA0Ebfo2ib2d+kI7ccCS1G/YlSFtGrU\nOhHxRSfIWpMcz2Y9zJW+zczMzMzMzMzMBoSkdwGbkKoHCliw+pjWxm6HIsLXIa0lIuJ6SdsDZwJz\nUJskW1wGltzr+auPkZRVKsoSvj8ZEb+d2IjNykXEbyTNIlVynZv6GLxjlM33ISUEvpH6xO8y+Run\nQ8DJEXHqBIduBoCkVUmrL+Tj7lHgM23s9mBgR2AxhmP/Y5K+WE3uLhUR10o6D9iqMN5NgIvaOF6b\nPN5S0vb7DvWdX3Uhi+tFO9S3DaiIeIi0esi3JC0ArAG8nZQMviQwH+lcexbwIvAEcD8p+epG0mo6\n4670adZvJB0QEd/v9jisZyxC/W+ym5vY3y255/nrHKvk2vLXQX4F7BcRLzbRp1nG8WzWw3yzxczM\nzMzMzMzMrM9J2hY4FFg911xMjDWbFCLiQkkbAacBb2DiS9KPpgI8BezuyoXWLhFxiqRrSZXptyq8\nPWLSd0Q8ImlzUqWsxamvTp+XTwavAH8G9mpm3GZVO+aeZzfofxkRT7arw4h4RtKvgQMYjus5SMuE\n/3KMzU+l/u9spdaO0Cax4rEU4D+d6DginpD0Iml5+8xCnejbDCAingYurj7MJgVJi0fEf7vY/wrA\nccDagJO+LTNHSduIExMbcF9JW/Z7L/8b7xXSZPXjm+jLrMjxbNbDpoz9ETMzMzMzMzMzM5uMJE2V\n9BPgDFLCd35J7qEOPMzaIiKuJlUj/AmpAmGrJjFk+zkVeJsTvq3dIuLOiNgaeAfwQ+Be4OWIuH+M\n7QJYGTib4eM6lB+Hs6r1RwFbRsQLrf4eNpA2KGnrxKoIZ5S0vaeB7a7JPc+qKasVA7K+MHdJ2yMd\n7L9YwXCuDvZtZjYZXSRpkU53Kqki6XOkivfrdLp/63llSbJPN7G/kSY25H/nPQ281wmy1gaOZ7Me\n5qRvMzMzMzMzMzOz/nUMsDflid5mk1pEPBUR+5KS9o4iVRzKT2xoRP7zz5Eqxb4jIj4YEQ+3ftRm\n5SLi7xFxQEQsA7ylwW0ei4jtgS2APwAvURvTFeAh4GfAKhHxmYiY0Y7x20AS9ecT/+hAv3cWXleA\nFRvY7nbS5Ie8hVsyIusHL5e0zdeJjiVNpb6y90ud6NvMbBJbiZT43bF/yyW9FbgK+AaenGPlZi82\nRMSrTeyvOCkMahNkXwa2jojLmujDbCSOZ7MeNlu3B2BmZmZmZmZmZmatJ2lXYE9qE7IqOOHb+kxE\n3AV8RtKBwPqkiq9rkJIA30D5DflXgAeBf5GqtP0FuCwiXunEmM1GExEPjPPzFwIXVhMHlwcWAV4A\nHomIh9owRDOoT1J9qskkgEY9nnueVexedKyNImJI0nPUJvJ2JKnXJoWnqD9PXormlrBv1DIlfT9e\n/lGzxkg6DzgOONvnt9bHVgH+JGmTiHiqXZ1ImgJ8HvgiMI3hY3arVpuy/tHSmIiIV6XShWmyGPxS\nRFzRyj7NchzPZj3MSd9mZmZmZmZmZmZ9pnpT8su5puxC/VDhNcDzwLOkJFgnhNukFRGzgMuqj9dI\nmheYm7Q07avAixHRzJK0Zj0pImYC0e1x2MAonjPMJ6kSEe0+l1ikpK3RlY2LSelO1rLMXcDqhbaN\ngFs70PcWuedZ0svdHejX+tvmwPuApyWdAkyPiKu7PCazdliNlPi9aTt+40lamTSBYnXqr6uYdVo+\n9u4Fvt+tgZi1gOPZrAlO+jYzMzMza53sol9F0sxW7auF+ywzFBH+XWBlHM/WTxzPNog2BlZguPpU\nPtn7GdKF9HOBf0TEC10ZoVmHRMRzwHPdHoeZWZ95Cnhd7vVUYFlS8mw7LZd7np2TN3qMn7/w2hOA\nLHMTsFOhbWfg6A70vXtJ200d6NcGw4LA3sDekv4NHA+cGBEPdnVUZq21OmnVm80i4tlW7LC6gs4X\ngEOA2am/ruLEb+uWLP6O69AqO2bt5Hg2m6BGZ76bmZmZmdn4VJp4tGOfE+nPLON4tn7ieLZBsVHu\neX7Z4ZuA5SPi8Ii4zgnfZmZmNkEPUH/OukMH+t2u8HqIVBluVJJeR0raynPSt2UuyT3Pzp3Xk7Rp\nOzuVtAuwJvXJg5e3s18bKFk8VwABRwL3SLpA0q6S5ujq6Myal8X4msAF1VWemiLpHcB1wJeAaYye\n8H1ls/2ZTdA53R6AWQs5ns3GyUnfZmZmZmbtMdTEox37HE8/ZkWOZ+snjmcbFGuWtD0JbFyXanEA\nACAASURBVBkRj3V6MGadIGkNSUuOc5s5JIWk4yTtKWmxdo3PzKzPXJt7niVc7S1pWrs6lDQfqSpy\n8Zz51gY2z58bZQlb97RoaDb5XQXcn3udxfSxkhZqR4eSlgWOoj6eXwQuaEefNrDy1xsqpJUZNgN+\nDTws6VhJ63ZxfGbNyP5NrwBrA+dJmnsiO5I0m6SvAFcDb8/tOz+RPnv+LPCpiHh3c8M3m7C7uz0A\nsxa6u9sDMJtsnPRtZmZmZtYevV5J1mw8HM/WTxzPNijyiavZjcqfRcTDXRqPWVtIeoek4yU9BlwD\nbDnOXSwLvAX4CHAscJ+kUyWt1+Khmpn1m0tK2pYjVcVsl68Crytp/0sD2+5U0nZ9c8OxfhERQ8BP\nqf9Ntizwu1ZUjs2T9AbgfGDxXHN2zn6qV+OxFim7xpBPYK0ACwB7ApdXJ0IeXI1Ps153PLUJ39l/\n1wPOlTTXeHYmaQ3gBuAQ0sogxYre2fMKcBbwtog4tonxmzXrmW4PwKyFHM9m4+SkbzMzMzOz1mh1\nldd2P8xG0+34dDxbK3U7Ph3P1i0LUx9Tp3VjIGbtIOkNks4jJeztTop5gLeOc1fL5Z5XSMt37wRc\nVq3+vXD5ZmZmA+/3wNO510Ok4+hBkv6n1Z1J2gfYj/rzmyeAP4+x7XLAB0q2vaZlA7R+cDSQXxEn\ni+kNgb9JWrkVnUjaDriJNOmsGJMzgW+1oh8beFsBJ5Mqx5dNMs9fh8jefwtpcs3dkv4k6UPjTZw1\n65SI+Djwc8oTv98N/EHSnGPtR9I0SV8nrfiwUm4/2b4yFeBhYKeI2CEiHmzh1zEbt4iY0e0xmLWK\n49ls/Gbr9gDMzMzMzPqAK7NaP3E8Wz9xPNsge7mk7T8dH4VZG0jaEfgFMD/1x/pmkr6zxKtsnx8B\n3itp84i4ZdwDNRsHSYsCmwMrA0uQ4ntO0n2cqbT+vGYoIjZp8T5tgETES5J+AXyW4ePnEKng1LHV\nSrFfbfYGvqTZgEOrj3yCbH4lk1dG2X5O4ERgjsL2zwMXNDM26y8R8YykA4ETqE8gfBtwk6TTgenA\npeOpxi1pIVIS7qeB1Rk+pufPPYaAoyMiWvB1bMBFxPnA+ZLmAXYEdgM2Jp1TQO3xsHhsrVQ/uzHw\nnKTfAtMj4oq2D9xsHCJib0kvA/9L/XF7I+BsSVuPdJ4gaS3gOEDUH5ez56+dbwAHRcTTmJmZmXWZ\nk77NzMzMzJrzsW4PwKyFHM/WTxzPNuj+CyxP7Q3LskRws0lF0q6kZKzs2nYxSWXFce5yuZK2fALW\nksClkraMiL+Nc99mY5L0PlIy69p0bnXW4nL1ZhP1FWAPYJHq6yy2pgBfBLaXdDjw+/Emf0uaA9gO\n+AK1lTfzHgO+OcL2swHbkKrWivrk2jPGk7RrgyEiTpK0CSmuiwmEU4Cdq48Zkh4AHmV4tZHXSDoS\nWLD63tuBFaittlyM5SHgFuCQFn8lG3AR8Tzp3PkESUsAHyIlgK9W/UhZLMJwrM4HfBz4uKS7SJMe\nToiIe9s6cLMGRcT+kl4BDqT+uL0pcJak7fKJ39VzjK+RJuJMofYco/g8gL0i4vIOfB0zMzOzhlSG\nhnxdz8zMzMzMzMzMrJ9I+gmwN7U3PZePCFf7tklL0trA5aQKhSPdlJ8FzBsRLzW4z58AezFyxc3s\n+WPAqhHxUDPfwSwjaTFS9eHNqk2dWqHktX8XImLqWB82G4ukbYEzSt7KH1cfB84FrgNuBB4CngGe\nBaaRkgrnA5YBViUlI25B7YoOxUSuIWCbiDi3ZEwfBY4hVfcuO76/CqwWEf8Y/ze2fidpGnA28D7q\nE2LLjtVDJe3FSWkjvZe9fx/w7oi4Z3yjNZsYSSuSVrX5IPCmavNIiSP5GB6qPi4jVUj+nSfQWC+o\nTrb5POXnC+cBO0TEq5LWA35FmiQ/0kSc7Fzhm6RVS0ZcUcQsT9JDwOLVly35zSVpFiVx7d9y1m6O\nZ7Pe1qmqEWZmZmZmZmZmZtY5fyppW6/jozBrkWo1tuMoT/jOKmf+jVQxuWERsQ8pqXAz4BekJMSy\narKLkhJ0zZomaXHgr6S4y+J3qEMPs5aKiLOBLzFyYmuFdAz9CHAUafLOHaRVSV4EngbuB/4JnA98\nA/gAsAD1yVj5GP5cWcJ31TuBOUu2z/7WfuSEbxtJNblvW+BUyuO6eEwtSwSvMPLxvVL43O3Axk74\ntk6KiH9FxCERsSywIek8+ClqK9JnivE7pbrN8cDDkn4lacNOjd2sTEQcAhxOfcJ3BdgSOF3SD4BL\nGU74Hum4/Ddg9Yg4zAnfZmZm1ouc9G1mZmZmZmZmZtZ/zgeeLLR9oBsDMWuRTwKiNuEvu0F/KbBm\nRKwbEd9otMp3JiKej4iLImIv4M2k6rDZjf98AvhGknZq5kuYVZ1EijVwMrb1gYj4Kqm65liJguN5\nlE1WqAAzgX0j4nujDGmFQv95VzHOCUI2eCLilYj4IHAA8AIjr8Yw0jF8tMk2+b+H04G1IuLOpgdt\nNkERcXn1PHhJYEfgTOAVRk8Az96bF9gDuFjSXZK+JGnZjg3eLCciDgcOofY3XBav7wf2YzhHqux3\n5XPVz6wXEbd1YsxmZmZmE+GkbzMzMzMzMzMzsz4TES8CR1OobiVp3a4OzGziPkV94h/A4RGxUURc\n34pOIuKJiPhfUvJKtuxsvs+DW9GPDS5JOwKbUlshtlhdsN0Ps5aLiG8BWwEPUx5nzValrwB/B9aP\niJ+MMZwVSvZRAa4FtqyeJ5mNKSJ+CKxEqmg8k5GPo42uqpBtfzOwdUTsEhFPtXLMZhNVnexwZkTs\nCCxBmnR5efXtRib1LAMcBtwu6RJJH5U0T0cGb1YVEd8APkttzObjtGyVhgrwB+BtEfHjiPCETDMz\nM+tplaEhn6+YmZmZmZmZmZn1G0lzAbeSbr5DupF5L6ki8qPdGpfZeElaG/grtTfoh4DvRcTn2tjv\nwcDXSvpdOyKubVe/1t8kXQ+sRu2y81SfvwCcA1wB/Bv4L/A8qdpmy0XEPe3Yrw02SQuSJsh8CsiS\n/SZ6MzJLyHqGdDz+XkTMHKP/OUh/S/ntnwUOB3441vZmI5G0JLA7sAPwTsZfXO0x4DxgekT8pcXD\nM2sbSW8EdgM+RJoEASMf1yuF95+PiPnbODyzUpL2BY4a42MV4BFg/4g4rf2jsn4n6SFg8erLCjAU\nEVOb3Gc2GT1f1KHp/ZqNxfFs1tuc9G1mZmZmZmZmZtanJK0BXAbMWW2qAP8CdomIW7s2MLNxkPRZ\n4NvUJsf+B1BEzGhjvxXgNkC5foeAQyPi6+3q1/qXpFVI1V2LNzkBTgP2jYjHujQ8s5aStAhp1YTd\nSBMd8sZKFszcABwL/CYiXij5fFm/ywNnkCZN/Ac4F7jQ1b2tlSQtAKwBvB14E7AkMB8wB2mlkBeB\nJ4D7SZN4bgRuc/VYm+wkrUpK/t6Z4cnFmbIVFpzIZV0jaU/SeUSmJskQOAE4wCsuWKuUJckCzU60\nXYaSJNkW7LfMDyJirMkSNiAcz2a9bbZuD8DMzMzMzMzMzMzaIyKul7QLcDowjXQhfUXgGkknAMdG\nxE3dHKNZA9bKPc9uCP24nQnfABExJOko4Bhqk1g2BJz0bROxRe55/gbnbyJi9+4Myaw9IuJx4HvA\n9yS9nnTsXBtYAVgOWBiYl/R38CQpQfYx4CbgKuDKiLh3Av3eQUrENWubiHgauLj6MBsYEXEzaQLb\nQdUJxtsBWzN83M2vYmLWVRHxc0mvAL+kPDYXAZ7r+MBskFSonyAz0f0U/9uK/eYNAQu2eJ/WXxzP\nZj3ESd9mZmZmZn1A0h7AOdWbqmaTmuPZ+onj2XpBRJwraQvgVGBR0oXvOYE9gT0lPQlcB9wLPE26\n6dnSG/URcUQr92cD5/UlbX/sUN8X5J5nSbrLdKhv6z/vKml7BNi70wMx66SIeAD4TfVhZmaTnKQ5\ngMVICVULFN524rf1jIiYXk38PgGYUm3OftdtBZws6QMRMatbY7S+1+zxsLgSTqv220gfZkWOZ7Me\n4aRvMzMzM7MOkrQBsHFEHN7Cfb4NOA6YIeky4LfAiV662NrN8Wz9xPFsA+By4PvAkaQL6dlNTkiV\nNjdrc/9O+rZmLEz9DaC7O9FxRNwt6VVqr6Uv2om+rS+9Ofc8S4g6JiJe6NJ4zMwMkDTFCX9mo6sm\nem8JfICULDt34SPZ70snfFtPiYiTq7/pfs3w77rsmsgOwAmSdo8Ix66ZmZlNCk76NjMzMzNrM0nT\ngP2BTwLLAkOSfhgRT7Woi42r/50N2Kj6+Iqkb+MEAmsxx7P1E8ezDYrqstsnACsyfGMzS/zOtLMK\nim+cWrPmKWl7tYP9PwsslHs9fwf7tv6yIPXHxHO7MRAzM6txiKQNgenAGf6tZpZImgK8F9gN2AaY\nt/pW8fdj8fwme/+29o3OrHERcbqkl4HTgNmrzdn1kQ8CrwAf79LwrD+16lpYJ66p+bqdjcXxbNZj\nnPRtZmZmZtZGkj4EfA14I7UXw98N/L5F3WRJhfmKnYsB3wT+T9K+EXFai/qyAeZ4tn7ieLZBIWkT\n4CxSFbZ8rOefFxPAW8lLalorPFvStgRwT7s7ria6LFhofrnd/VrfKjsm/rvjozAzs6KpwCak33DH\nSDqDtErTRd0dlll3SFqHlOi9M8Or3BR/QxZl7z8BnAIcHxHXtW2Q1hckzexCt8VYrgB7SNpjAvsa\nigjnXVmer4NZP3E8m/Uon3yYmZmZmbWBpHmAE4FtqV3aMnu+IS1IKpRUqe4ru9BerNi5KHCKpPcC\n+0fEi832aYPH8Wz9xPFsg0TSm0hVrOZhOLHbS27bZPQYwxXqMyvSgaRvYGlgSqHvxzrQr/WnsgkM\nL3V8FGZdUj1HfhPwBmARYC7S8f2F6uMp4D8R4eOsdUuFVMl4d2B3SQ8AJ5ESwP/Z1ZGZtZmkt5IS\nvT8ILFNtHivRO/vMTOBC4Hjg9xHxSntGaX2o2wmF2e/Mbo/D+sOnSee3k9VN3R6A9RTHs1kPc9K3\nmZmZmVmLSXozcDbwVuqTU6i2bdii7lYjLTVfTCbMV+yskJYmfJekjSPi8Rb1bQPA8Wz9xPFsA+hb\npArF+ZgrxqTZZHAbsEGhbXtSYkm7bZ17nv0N3dmBfq0/3QOsRO2xeDHg4e4Mx6z9JK0BbANsCqxK\nA4kDkp4HbgUuBS4CLooIT1izTsifN0OaoHAQcJCkG4DpwMn+7Wb9QtIbSEneuwGrVJsbTfQG+Acp\n0fvEiHikHWO0gdDpf+OL10LKrhFOZD824CLit90eg1mrOJ7NepuTvs3MzMzMWkjSUsDFpIqAUJ9k\nlVWNWFXSfBFRVultPFYBZpGqD2b9jdTnKsCF1cTCZ5rs1waA49n6iePZBo2kZYCdKE/4rgAzgL8C\nN5OSEJ8FXI3NetX1uefZsXNnSYdExBPt6rRakfZjJW9d264+re/dAmxZaBNO+rY+JGlX4P9Iid6Z\nRpOj5gXWqj7+D7hf0rHADyPihZYO1Kxc2UTJNYDVge9KugA4gVTR+NVOD86sGZIWAnYGPgSsT4rx\n8SR6PwmcAhwfET4vtsnIE8nMzMxsUnPSt5mZmZlZi0iaDziPlFBYrAxUfH0jaSnjppIKI2K6pD+Q\nKma9n3TBfg5qkwnJPV8NOF/SRl5m00bjeLZ+4ni2AbUDtYneWazNAL4LfMcVCm0SOZc0kSafjLIg\n8A1grzb2uzfp+FxMCriojX1af/sTqWJs3ntJ1YzN+oKklUhVX1evNjWSSFgmv93SwFeBT0raLyJ+\n39Qgzeo9yvD5cjFO878ZK8DspN947weeknQqqcrxVR0aq9m4SZoT2JaU6P0+UhxD48neM4E/ko7v\nZ/u6hZmZmZlZ91SGhjyJzczMzMysFSSdRLpwPlJFzZeBY4FjIyLaNIbFSMkpB5ISYYpjyf57dER8\nph1jsP7geLZ+4ni2QSTpFGAXamNtFrCDE6VsMpL0F2BD6o+fn4mIo9vQ34akZPO5cs0VUkXm10eE\nL6zbuEmaCtwNLFVtqgCPAMtExMvdGpdZq0jaHfgZMI36SZbNyu/v6xFxaIv2awaApKWBD1cfb602\nj1XxOP+ZO0nVv0+KiLvbMUaz8ZA0BdgM2I2U8D1v9a3xVPX+BzCdNLHBK5NYy0m6m0leeTsilu32\nGMzMzGywOOnbzMzMzKwFJG1Eqvg3UkLhdOALEfFgh8azBPBjYHvqKxVlSV/rRcTVnRiPTS6OZ+sn\njmcbVJJuAN5BbZxNj4iPd3VgZhMkaRvgLGpjOvvv4cDXImJmi/raCfgVKTGm+O/HYRHxtVb0Y4NJ\n0v+RqtTnY9hxZZOepAOBb1O+ok7xZmSFcmU3LYvbZ69/ERF7T2y0ZqOTtDqwO7Ar8Lpqc6MJ4EPA\nlaTfmqdFRFOrSJlNhKSjSSuOLVZtGk+i91PAKcBxEXFte0ZoZmZmZmYT5aRvMzMzM7MmVaum3AaI\n+huRzwB7RsRpXRrbZ4DvFpqzC/h/i4h1Ozwk63GOZ+snjmcbZJLuBJapvsySozaNiL90bVBmTZJ0\nPWkyA9Qnft8MHApcEBGzJrj/dwKHAVtRnmT4ILBiRDw3oS9gBkiaRjo/Wa7aVAFmABtHxBVdG5hZ\nE6oVvqdXX4402TLzIvBvUlLh09XX8wELkFbEWQGYPff5kfY3BHwpIr7asi9iVlD9TfleUvXv7YC5\nq2+NlTSbvf8ScDapAviFXinEOkXSLIbPlWH0SsoVYCbwJ+B44KyIeKWtAzQzMzMzswlz0reZmZmZ\nWZMkbQX8gfqkkGeB93a7WqukTwA/p7wq4mYRcXEXh2c9xvFs/cTxbINM0j3AG6ovs7haPCIe796o\nzJoj6R3A1cBs1ab8sZPq8/8CvwOuA24Cbo+I50v2NScp6fbtpETy7YHlC/ul8Hq7iPhDC7+SDShJ\nq5GqwM5RbcompO0REWd3bWBmEyBpJeAaYC7qk7Kz4/P1pBUUrgRuG21lBkmzAysB6wJ7AGtW3yqr\nFj4TWNeVaK0TJM0D7ATsBmwMTKm+NVKFegrvPwL8GjgxIv7ernGaQU3S90iyGP0nadLOiRHxUNsH\nZmZmZmZmTXPSt5mZmZlZkySdBWxD/c3NLSPiwq4NLEfSD4H9qB/j6RHxga4NzHqO49n6iePZBpmk\nvwMrUzuZYNpoSVZmk4GkTwPfZ+TEQqhPcJkFPAe8QEpKnBeYWvjMWEvefz0iDp3gsM3qSNoCOAOY\nVm3KYvkS4CekqvWuKm89T9IVpATt/DkH1efXA/tExHVN7H8V4ChgQ8oTv28FVnUFZeskSUuRkr93\nI00gg7Grf+c/cwsp0fY3EfFIWwZpA22EpO8sFp8CTgGOj4hrOjowMzNrOUnzAdsC6wFvBRYhTZZ/\nGrgTuIH0+/K2rg3SrEGOZ7PGOOnbzMzMzKwJkhYlLfOeJY1kNziPj4hPdG1gBdUlxK8jVcvKVICX\ngUUi4oWuDMx6iuPZ+onj2QadpDNJF8jzCViLRcQTXR2YWQtI+hLwJRpLrmpUWWXv7PkPI+KACezT\nbFSS1gV+CyxZbcrH3ixS9c1/Aw+RKoG/wshxP24RcUSr9mWDSdK2wJnUr1wD8DXgy62acCZpf+B7\n1B7jsz4/FBGntqIfs/GqTkz4CLAr8Ppqc6MJ4DOBP5MSwM+KiJfbNU4bLIWk7wrpvOJPwPE41szM\n+oKkBYGvkFbHmSf31kiT2m8EvhsRJ3dgeGbj4ng2Gx8nfZuZmZmZNUHS1sDZ1F5EnwEsExEPdm1g\nJSTtCJxGfVXEbSPinK4NzHqG49n6iePZBp2kQ4EjqE3C2jQi/tLVgZm1iKQ9gB+RbgS14yJ3BXgV\n+FxEHNWG/duAklQ8D5kbmJ/a84C8tt3EiYhixXuzcZF0GbA+9eexh7djUoGkjwM/zzVlfy83RMQ7\nW92f2XhIqgAbA7sD2wPzVd8qO46XJa88S/pdeGJEXNaucdpgqCZ9A/yLNKngxF67FmJmNogkvYW0\nSo6AhUgTex8hVS++NCJebHA/m5Mm8ixG+cT34opo+fYbgI9FxK3jHb9ZnuPZrHtm6/YAzMzMzMwm\nubVyz7Obm3/o0YvoZwB3AG8utK8HOKnQwPFs/cXxbIPubFLSd972gJO+rS9ExHRJVwDfJMV2vkJy\nM7KbSJcDn/JysdYGS1B7w7KY7F2sOj+RyvWNcEUga4qkpUnnq8WE79PbVUU+In5Vrar86Wpf2d/S\napKWj4g72tGvWSMiYgi4CLhI0j7AVsBOwJbAvNWPDRX+C8PH+fmBjwMfl3QPw4m6d7V77NaXfkpa\n6ezqbg/EzGzQSZoN+ASwD7DKKB99QdJvga+Pdl5bnQT/M2D2atNIv+2K7dnvyzWAayV9LCJOaeAr\nmL3G8WzWG6Z0ewBmZmZmZpNcWSWpnkzQq958+h31SQNv78JwrDc5nq2fOJ5toEXELcBV1ZdZQtRH\nJS3evVGZtVZE3BkROwGrAz8BnmD4pk+jybL5z75KmoizSURs6IRva7N8wmpePnaH2vQwa4XNqI/f\n50gJ2e30RaBsIue2be7XrGER8WJEnB4Ru5IqFm4P/BK4n/rzlPzxOWtfBjgMuF3SxZJ2lTSts9/C\nJrOI2McJ32Zm3SdpIyCAY0jXeovXLPKPeYCPArdJOkxSXU6fpPcAvyAlyOZ/3412/SPbf/6cYw7g\nJEm7NPUFbaA4ns16hyt9m5mZmZk1Z6mStqtK2nrFX4CDqs+zm0nLdm841mMcz9ZPHM9mKVHkTwxf\nMJ8HOBbYoWsjMmuDiLgZ2FfSp4F3ARuQVnxYnnQsnadksxnAw8DtwM3AZcDFEfFMRwZtNsxJ2DZZ\nrZN7nq/y/XA7O42I5yRNBw6m9u9nzXb2azZREfEyaRWeswEkrQ18C1i/+pFi8jeF9g2rj8ck/RT4\nUUT8t93jNjPrBEnvHum9iLhsItt12mjjtMEm6VDgy9QWZB3r91+FlAD7JWBNSTtVzyWQNB9wMjCV\n2uTY0ZJly97L2qYAx0m6MSJub/yb2SByPJv1Fid9m5mZmZk1ZyHqf9Te2Y2BNOiGkrbXdXwU1qsc\nz9ZPHM828CLiIknHk6qqZJMJtpV0LPCpiJjVxeGZtVxEzAD+Wn28RtKcpMTvuYCZwIvA09WVFsy6\noZEq9Ga9bvmStt91qO/TSUnfmQqjLy1u1lWSVgHeD2wBrM1wckv+34MsySVTTGhZDPgC8FlJRwNf\njYjn2jluM7MOuITypMEhRs9nGmm7ThtrnDagJB1B+ne7+O97PlG1TP7f/y1JSbFZ8YaDSNeLiwmy\n2fOXSNeY7ycl2i4NrES6FlJcVSTbbi7gx8B7x/kVbYA4ns16j08+zMzMzMyas1Dh9QsR8WpXRtKY\nx4FZ1N5UKqt8aIPJ8Wz9xPFslnyKlAS1BsMXw/cE3inp/yLi4m4OzqwTIuIl0s0is17wsW4PwKxF\nlqD+5v4/O9R3vp/s/GaxDvVt1pBqovcuwM7AW6rNZZN+in9HxeSZYmLNXMDngN0k7R4Rl7RkwGZm\n3TXRSZGeTGk9R9L2wKGMXJWYQluxPXudFW/4f6RE1v0Ln8s+8xipkvIJEfF8YSzzAR8irQa4JPWJ\nshVgE0nrRcSV4/6y1vccz2a9yUnfZmZmZmbNKZ5T93QySUQMSXqK2mRIXxi1jOPZ+onj2YyU7Cpp\nM+B8YC2GL4CvDvxJ0l3ABcD1wL+Ah0kX11/u8YkSZmaTUkRM7/YYzFqkbILig53ouHp+8zQwf655\ngU70bTYaScsCu5GSUVRtLluWvij7zL+Bs0jVwDdg9Orfryedz+8VEcc1P3ozs64aKXFwPNt1mq/b\nWR1Jc5MSWvOKFYwB7gVuAZ4C5gZWAN5W+Gx2De9LpErH8xb2M0S6nrd1RDxcNp6IeBb4qaTTSFWW\nN8vtN++TgJNkrYbj2ax3OenbzMzMzKw5TwOL5l5PhpuMcxReeylYyzierZ84nm3gSfpI7uWvSBVQ\n3khtFZQ3k6qBl23fqqEMRYSvQ5qZmfWXF0ra5gJe7lD/cxVez+pQv2Y1JC0GfICU6L1WtXm0SocU\nPvM08Fvg+Ii4KrffNwAfBj5KSpzJ7yv771Tg55IeiYjzmvga1qOqk3TLDEXEmyewXaeNOk4zsz60\nP8Mr4hSrIVdI/+Z/IyJuKm4oaWngIGCfXPMQabLlV6k/p7gf2CIiHhtrUBHxhKQdgKuAlahPxN1G\n0mwRMWPMb2iDxPFs1qN8s8XMzMzMrDnFpMKpkuaNiJ5M1JM0O+kHdf7H9DNdGo71Hsez9RPHsxkc\nz8iVBCdaRcvMzMwM4FFSImr+nGIpUnW3tpK0MDANnztbl0iaB9ielOi9KSn5GsqXui+qkCYp/BGY\nDpwZEXWTJSLifuAbwDckbQocDqzDcDLLUPUxBfiFpLdExPNNfjXrPctQXsFyrOrGI23Xad2swmyT\nx0TjtNvxbVbmE9RP0qoAM4CPRsRvRtowIu4D/lfSJaQqxlNy+ylWVx4C/l8jCbK5/T8v6VPAZSX7\nnBdYD7i00f3ZQHA8m/UoJ32bmZmZmTXnIWB5ai9grwhc153hjGnF3PPsoqhvjFrG8Wz9xPFsNqzs\nRmg+IaWdN+J9E9bMzKw/PVrStgHwjw70vUHueXaucV8H+rUBJmkqsAUp0XsbhqvNj6eq9z9Jid4n\nRcSDjfYdEX+WdDFwLPA/1Ce2vA7YAzim0X3apDPRSbvdTLr2b0FrxPQOb2fWNpJWI62ol/93Ovvv\nwaMlyOZFxOmSVgYOoz7hNnM/cPp4xxgRV0i6FlizZJ/vwkmyVuV4NuttTvo2MzMzM2vOzdTebIT0\nQ7JXkwrXKrweAv7TjYFYT3I8Wz9xPJsNc3U1MzMza7WbgO0KbTsDP+1A3zsXXg/RmWRzG0CS1icl\neu8MLFxtHk+i95PAKcD0iLhmouOIiFmS9gHeTvptW+x3O5z0bWaTTER8rJPbmbVZ6d6HWwAAIABJ\nREFU/lp0/t/pB4EfjHNf3wA+BSxSfV1Muj0vIiZ6ve9cUpJs0coT3J/1J8ezWQ+bMvZHzMzMzMxs\nFDeVtG3c8VE0buuStr93fBTWqxzP1k8cz2ZmZmZm7XNR7nl2w34jSe9pZ6eSVgF2oT7h9cp29muD\nRdLKko6U9B9ShcC9SUkqFYaTU0ZaMacCzALOI8XqkhGxbzMJ35mImAkcVWjO/v5WaXb/ZmZm1pR3\nFl5n5wxnVf8Nb1hEvAScysirJlw9/uG95rIR2pdtYp/WfxzPZj3Mlb7NzMzMzJqTv6mY3WR5v6RF\nI+KxLo2plKRFgc2pvyHlpELLOJ6tnziezeBeXOXbzMzM2uNq4Flg3lxbBfixpLUi4rlWdyhpGqmS\n+GzUnuMMAee0uj8bLJKWBj4I7MZwZcBGKnrnP3crMB04KSIeafkgkytGaF94hHab/EZKkGrXdmZm\nNjEjJZlONKH1MmDfEd6LCe4T4PbC6+za+RJN7NP6j+PZrIc56dvMzMzMrAkREZL+CayYa54d2As4\nsjujGtEBpLEVb4z+tTvDsV7jeLZ+4ng2g4hYpttjMDMzs/4UETMk/Rw4kOGKxxXS+fc5kjavVnRr\nCUlTSNXh1mb4vDmrNndxRDzQqr5s8Ej6X9Iy9Vkl70wjid6PAycDx0fEDe0ZYY2HR2h/ugN9W4dF\nxIRWbp/odmZm1pSlKT93uGOC+ysms+aNdD7QiKdGaPcEMstzPJv1MJ/sm5mZmZk17wyGb/RkNzkP\nrlYI6gmSlgI+Tf2N0b9FxINdG5j1Isez9RPHs5mZmZlZ+3wXeDn3Ojvn3gC4XNLbWtGJpGWAi4Bt\nKE88+FYr+rGBtgjpvnn+92NZrGVJ4TOBPwA7AktFxP4dSvgGmLOkbQi4u0P9m5mZWbn5R2if6Oof\noyXCPj7BfQK8OEL73E3s0/qP49mshznp28zMzMyseb8k3ezJmxv4qaSuL6NZHcN0yn/gntbh4Vjv\nczxbP3E8m5mZmZm1SUQ8BBzFcKJsNoGxAqwBXC/pO5I0kf1LWkbSV4FbgHdTW4E56+vciPjzBL+C\nWdFYyd43k6rbvz4ito2IMyPi1U4OkFR1sTg2gL90eBxmZgNF0hklj991e1zWU0ZKMn1ygvsbbRWP\nCa+oExGzRnhr9onu0/qS49msh83W7QGYmZmZmU12EXG3pFOBD1G7pPH7gB8C+3dxeABfAzah/qbV\nc8BJnR+O9TLHs/UTx7OZmZmZWdt9AdgYWJ3h8+3sv3MABwAHSLoWuBK4HriNtAz308DzpISCBYAF\ngbdW97UOsC7DybZQf978GLBPm76XWRZ3jwK/AY6PiJu7OJ7MWtX/Pg7cD9xX/e/0ro3IzGwwbEft\nuUiF8olCNrimUXs+nBmpEvFYXhnpjYgY8b1xyo/VhWMtz/Fs1sOc9G1mZmZm1hpHAh9g+Edk9kN4\nX0kzgM9GRMcvAEr6FvBZyi9Gfjcimlkyy/qX49n6iePZzMzMzKxNImKGpF2Bq4GFqE8MyBJn3wWs\nOc7dj5TsXSFVg9slIh4Y96DNRlcBXgXOISVSnxcRM7o7pBqnASe1MDnGrG0kzdZjfz9mrdD11QNt\n0pnQqiARMXOCC+aYtZPj2awHOOnbzMzMzKwFIuIfkr4P/D/qK1t9GlhR0scj4uFOjEfSksDPgC0p\nrzjxMPCdTozFJh/Hs/UTx7OZmZmZWXtFxJ2SNgP+xHDidyaf/D3eJKmyyZkV4AVSwvel4x2r2Rhu\nJCV6/6ZXJ+JGxLPdHoP1Nklrk6pzviYiLmtzn/MB7wcErFB9vAX4EWlFCLN+UpzYZjaqiJjV7TGY\ntYrj2aw3OOnbzMzMzKx1vghsCyxPfWLh+4CQ9BXgmIh4oR0DkDQ/aWnjg0hLIxcTCivADGC3do3B\n+obj2fqJ49nMzMzMrI0i4kZJ7yJVIV6N8hVtmlUB7iIlfN/Qgv2ZZf4I/C4ibu32QMxa4Exg8dzr\nIdqfF7I48Gvqk2Hf0eZ+zbqhVec1ZmZmZhPipG8zMzMzsxaJiJerSxpfAsxLfWLhfMA3gUMl/Ro4\nBfhrRMxspl9JU4H3ADsCu1X7zi+BXCn899CIuKSZPq3/OZ6tnziezbpH0vrAgcDnIuLObo/HzMzM\n2ici7qpWmP0MqbLr/NW3mk2Myqp7Hw0cEREvNrk/sxoRcVW3x2DWYp2uQPzQCH2rw+MwMzMzM+t7\nTvo2MzMzM2uhamWrHYBzgdmpTeaj+nx+4JPVxzOSLgOuBW4A7gTuHekGpqSFgTcCy5AqZ60JrE2q\nGpvtn0J/+Zur34mIbzX3LW1QOJ6tnziezepJyiY9zEW6TjiFxpIDKtXPzlZ9zFHdxzzAQsBSpL+B\njYAlqtt8vpVjNzMzs94UEa8C35Z0LPAJYA9g1cLHRksCL56L3A6cAPw8Iv7bsoGamfW3YsXtdnt1\nhL4X6VD/ZmZmZmYDw0nfZmZmZmYtFhEXSdqFtKTl3NTezCxecF8AeH/18RpJz5OqWL1ESk6cq7qv\n2Uu6zF+8L944Hcq9/4WI+Pq4vowNPMez9RPHsw06SfMD21cf7yItwd3OJIDiZAczMzMbEBHxLPAD\n4AeS3khaAWcNYEVgadJ5yNykyWOvAi8CjwL3A/8CbgT+EhF3dHzwZmY2XsuM0D5vJwdhZmZmZjYI\nnPRtZmZmZtYGEfF7SesCZ5Muehcru+aTn8qSreal8YviY1XIegTYMyLOaXB/ZjUcz9ZPHM82qCT9\nD/B1YOFqUycqvuUnN5iZmdmAioh7SdW6T+j2WMzMrC0OHKH9hY6OwszMzMxsADjp28zMzMysTSLi\nFklrAN8BPlptLiYTFhMMJyrbV/H1ycD+EfF4C/qwAeZ4tn7ieLZBIqlCqm7/AUavPm9mZmZmZh0k\n6YaS5qGIWKPjg7GeJGkOYBfGN5l2rpL9fKRlgxo2DVgU2BjYhOFJv/nfmg+0oV8zMzMzs4HmpG8z\nMzMzszaKiCeBT0j6KWlZ47Wrb41VTXakRKyRkrWKlWpPB46IiFsnMm6zMo5n6yeOZxsghwG7Vp83\nkug9kZgvckK5mZmZNUXS1IiY2e1x2OQj6eKS5qGI2KTjgxnbO6hNlC0mzNqAi4iXJW0N7DjOTSuF\n58e1blRj9pe9HgJubHO/ZmZmZmYDx0nfZmZmZmYdEBHXAOtKWgvYn3Shflr17fFUky1+Ln9B/S5S\nJc8TI+KOJoZrNirHs/UTx7P1M0nLAQdTXm2eQltmiPoEAQrv5/872vYvApcBJwB3NjxwMzMz6wuS\nFiJVgL0pIho+F5A0D/CIpGuBvwKnRcRNbRqm9Z/3MPb5r9lkcgCwOTAP46v4nTfR7RpV/B2ZObPN\n/ZqZmZmZDRwnfZuZmZmZdVBEXA3sJmlfYDPgfdX/Lj2B3T0DXAlcClwcEde1bKBmDXA8Wz9xPFuf\n+hxpEkP+Bnyx+vzdwOPA88BCwNuprTL4b+Dh6us5gXmBpYAFc/0Uk2peJv0NXRERs1r8ncxaTtIc\npPh/KSKe6vZ4zMwmO0nbAZ8B1iedG+zL+CaALQfMDby7+vi8pOuBH0TEb1o8XOtfLUv2lvRESfNQ\nRCzSiv3jpHQbRUQ8IOlw4Ns0HiuNruDUKsXfm0PAzcDv2tyvmVnPk7QBLZ580459mjXC8WzWG5z0\nbWZmZmbWBdVkktOqDyQtAKwICHg9KaFqXlIFl5dJiVjPkZKu7gBuj4j7Oj9ys3qOZ+snjmfrF5Km\nAbtRX5m7AjwCHA6c8v/Zu/PoWOo67+PvvveybxcRBAVkna+AuCEqzogszoigqOCCjyKLgBuOC4yP\n4ygq8jgqKi7joDigMgruoOLgAi6oKIq4oIxfFVDZURAQuGz35vmjukylupJ0kl6Szvt1Tp9U/1K/\npc+pVDrpT32rGnCNiAcA1zDxn+zfzcyjGsbfjKKK4hHAnkwMEawOnEBR2dPQt+aliNgBeBnwFGDr\nSvsdwEXAGcCZmXnXcFYoSQtPRDwK+DiwY7upDN3uMMOhtqlsl+9LHg38d0QcAbw4M387l7VqUehl\nyHU5nZWMezm+1cg1nfcChzJ+fp1v6hcCXw88JzM9riUtVq3K12/3cKxejVkdT5qOx7M0zxj6liRJ\nkuaBzLyVIlxy0bDXIs2Vx7NGicezFrA9KC5QqH/4fg2wW2ZeXe+QmTdExI+AxzEeajkwIl6amStr\n+14HnAmcGRFPA04FNqr0+3vgrcC/9vh1SRNExK7AvhTH7YGZeec0+y8B/h14NbCUzg+F1qW4YGEv\n4PiIOCYzP9PzhUvSiImIVwHvoPjssV7xdS6h7+qFay2K9ziXRMQzMvP82a1Wi0Q/gtTV41EamMxc\nGREvowhFzeb4G9QxexdwFvAvmXntgOaUpPmuH+dg34toWDyepXnA0LckSZIkSZI0eh5be16GXg5v\nCnxXfIciPFta3n7+/ck6ZOaXI+IxwAUUFfHL4PexEXF2ZnrRhHouIl4IvI7iTgyl7YBfTNFnCfBp\n4AA6A4lV5fceRHFhwxMz8+VzXrTUZxGxBrAhcFf1Tg5Sv0XEsRSB76Zza4u5hb5L1bDtOsA5EXFQ\nZn5xhmNL0oKUmd+NiCOBLabZ9Vhg7fZ2+Xfg8T1ezipgJXAPcBtwE3AlcJl3ypGkDnO5CG2yMOxc\nL2wzZKvZ8niW5gFD35IkSZIkSdLo2amyXf7j+5LM/MY0/S5saHsSU4S+ATLz9xGxH/AjYHWKf9Yv\nBT4APKarFUtdiIgNgM8CezPxQ50xYFumCH0DbwQObO9bDyRWn9e/95KIWJaZL57D0qW+iIgdgJcB\nTwG2rrTfQXGnkjOAMw1gqV/av//fycRza/28+sCIWDczb+9y2FuAPwEbt583naPXAD4REbtk5m9m\ntXhp5vpRPVzqWmaeNt0+EfESxkPfZb+39G1RkqTpWBlZo8TjWZoHlgx7AZIkSZIkSZJ6bvPa8zGK\n4N90Lm5oe3w3E2bmpcAJTPxH/S4R8cxu+kvTiYg1ga8zHviuh7e3m6Lvg4HX0xnohqnDW2Xl+iMi\n4pBZLFvqSkTsGhFviohzI2LtLvZfEhHvAH5OEfrehuJYLR/rAnsB/wX8NiKe07/Va7FqX4jz4YZv\nlefO+4BPAc8Hur7wIDPfmJkPALYH3gD8ns4gwBhFxe9PRYRFriRJkjRfjC2whzSVYR+fHs9SA0Pf\nkiRJkiRJ0ui5P53/6P7RdJ0y8zrg5vbTMrD16BnMexLFrb1LLeCYGfSXpvJ2YNf2dtMHOdtO0fcY\nYLX2dhmKrVakXUVx7JbHfVXZ9p52wFHqmYh4YURcBvwQOA74J6a4gKHdZwnwaeBYiju6Vi+CqD7K\nY/1BwJkR8cE+vQwtXq8AHkjnubMFnA48ODP/T2Z+KjPvm+ngmXl5Zr4NeAjwWooQed3DgUNnOrYk\nLQJWzZSkwWst0IfUZNjHpcezNAmvfJckSZIkSZJGz5oNbb/rsu8vgd0ZD8Quj4itM/PK6Tpm5p0R\ncSZwNOMBsN0i4sGZ+Ycu55c6RMT2wMuZGNSuV+3ecpK+awAvqOxfHeNe4N+AkzPzjohYC3gu8DZg\n09ocy4FXA2+e48uRygrJn2W8cn1pjOIChl9M0f2NwIF0VrKq/1zUv/eSiFiWmS+ew9IlACJiKfBi\nJp5by+reR2bmx3s1V2beC7wrIi4Bvsz4+5xyztdGxKmZaWU3SYIfABsOexGStAhtPewFSD3k8SzN\nY4a+JUmSJEmSpNHT9H+/W7vsmxSh76qdgGlD323nUIS+q/YD/rPL/lKTVwNL6QxuQ1FN9mTg4kn6\n7kMR2C7DgdWvr8jMU8odM3MF8LGI+DrwTWD7ynwt4IiIeIvBQs1FRKwJfJ3iTgr1qvMwRaXviHgw\n8Hqaw95THZfVY/jCXgZytWjtTlFFvn4xzr/26/jKzG9GxNHAqUw83rcFngBc0I95JWkhycwDhr0G\nSVqMLHagUeLxLM1vS4a9AEmSJEmSJEk9t6Kh7d4u+17e0LbjDOa+pKHt72fQX5ogIpZRVN+uh1zv\nAPbJzEMz86LMXDnJEM+pbJfBV4CsBr6rMvNa4NkUFWurNgOeOMOXINW9Hdi1vd0U1N52ir7HAKu1\nt8tbF1dDt6uAm5h4rJfKtve0K41Lc7FbZbs81n6Zme/u56SZ+VHgx3Qe33v3c15JUiEinte+CE2S\nJEnSEBj6liRJkiRJkkbPX+kMQ63XZd8rGtp26HbizPwzcHelqTWT/lKDf2DiLerLkOshmfmNqTq2\nA+P70hmsHQNOm6pvZl4KfIbOn6W9uliz1CgitgdeTmd1byrPt5yk7xrAC5hY8b4Mct8HvBZYnpmb\nUJzzDwduaJhjOUX1fGkuHlN7Psbg7urxwYa2Jwxobkla0CJi64h4ZUR8OSL+aYZ9HwB8ErgiIq6O\niJMiYiYXCEuSJEmao6bbvEqSJEmSJEla2K4BHlFr2wy4pYu+9UrfLWZW6RuK0PlGjIcRt5hhf6nq\n8ZXtMvB9QWae1UXf3YENGD8Wq+Hvz3fR/9PA82ttu3TRT5rMq4GlTAxul04HTgYunqTvPhSB7erx\nXH59RbVyfWauAD4WEV8HvglsX5mvBRwREW/JzKZK41I3Nm1o+9aA5q5e8FMe05sPaG5JWpAi4nHA\n64CnMn5B2PeAr89gmG3aX1vAA4F/Bv45Is4Hjs7M3/RouVokImJLYKthr0OSJGkhMfQtSZIkSZIk\njZ7fN7Q9FPjfLvpeWdkug1Q7RkRrBuHAtWvPu60yLjXZuaHt9C777lfZLo9ngF9l5pUN+9dVw7dl\n/+26nFuaoF15/rlMDHq3gNuBA6erXA88p7JdPZ6zGviuysxrI+LZFMdy9TOhzYAnAt/u+gVIE21I\n510UrhrExJl5XUTcA6xWad54EHNL0kLTvlPIv1MEtFuMv38YY+Z3ZCpD3/U7ljwJ+HlEvDUz3zaH\n5WrxOQw4btiLmIH6HXQkSZIGbsmwFyBJkiRJkiSp5y5paNuvoa1DZt4C/LnWvDawUzf9I2J9OkPf\nq7rpK01iy4a2i7rsuw+docQxuqxomJnXA/fWmjfpcm6p7h8ogrKlskr3IdMFvtuB8X1pPp5Pm6pv\nZl4KfIbOkMpeXaxZmsyaQ57/jtrzdYayCkmaxyJiPYq7MLySIhtSvvco30/MNvRdqo61BvDWiPhI\nRBiM1Uy0FshDkiRpXjD0LUmSJEmSJI2eCyvbZTXYAyJi0y77X0bnh5pP6rLv4xrabumyr9RkI2ZR\nTbZ9q/AyyFI/nmdyG/vbas8NFmq2Hl/ZLkNXF2TmWV303R3YoNK36vNd9P90Q9suXfSTJlM/N0JR\nQb7v2hdBLK813zWIuSVpoYiINYGvUvx9Vg970257yAyHrYe+S+XYLeBw4GMzHFcaWwAPSZKkecHQ\ntyRJkiRJkjRiMjOB39Wa1wFObQelpvPT2vMW8MIupz+k1g/gpi77Sk3qleMBbu+i3z6V7eqH9HcD\nF8xg/tVrz+uVv6Vu7dzQdnqXfat3ayhDVQC/yswru+h/cUP/7bqcW2pyI50XIDxsQHNv0zD3DQOa\nW5IWiuOB3WgOrP7tHBoR95vBmJ8A3sX4Rcb1c3H5HuMFEXH4jFYrDb+St5W+JUnSgmDoW5IkSZIk\nSRpNZzD+wWT54fs+wNciYvNp+n6rsl0GBB4eEUdN1Ski9gCey8RQwRjw8y7XLDW5p6FtrS767VN7\nXlY4vDAzu6oIGxFLgXVrzU3VbaVubNnQdlGXffehM7A1RpdV6zPzejovWNiky7mlJr9oaDtgQHM/\no7JdntvrF7tJ0qIVEY8CXsP4e4dW5WsL+ArFe4v7Z+bN3Y6bmedn5msz8wnAFsDbgBW13cq/Pd8X\nEVvM/lVoERp2JW+rfkuSpAXB0LckSZIkSZI0mj5EUdG4VH74vgfwm4j4UEQ8dpK+5wLVD//Lvh+Y\nrGJbROwDnEVzBazvz2zp0gQ303lcbThVh4hYDXgSzR/If20Gc+/YMLehb83WRnQek1dN1ykitgR2\naD+tH49dhb7b6sfuOjPoK9U1VY8/oH289k37/H4EnT9L3V5AIUmLwTFMzIKU5+mrgL0z82mZ+fXM\nnPUdbDLz2sx8A8X75UvpfI+yNnDsbMfXojPsKt5W/pYkSQtGN7dylSRJkiRJkrTAZOb1EXES8DrG\nP+Qvv64JHAkcGREnZ+bRtb73RsQngVfU+q4GfCQiXgWcTREa2IiiStwTKvtV3Qd8qS8vUovFjQ1t\nDwGumaLP7hQVuqvHb+krM5h798p2Oc6VM+gvVa3d0HZ7F/2qVeurx/LdwAUzmH/12vNZB70kinPp\nvUz8rHEt4H3AM/s47/8FtqPz/cZMLuiRpJEVERsDBzLxPNkCrgD2yMyrezlfZv4xIv4e+AGwU7u5\nfA/+ooh4c2b+pZdzaqS8F/jYsBchSZK0kBj6liRJkiRJkkbXW4H9gIfSGX4tt6+dpO/bgEPpDM62\n2uPtVNu/Hqwtn5+VmVOFc6XpXAzsW2vbEzh/ij77V7arx+WVmXnZDOY+oqHtpzPoL1Xd09C2FnDn\nNP32qT0vz68XZuZd3UwcEUspzudVVq3XrGXmLRHxdYr3GWOMv0fYPyLelZk9r+4aEQcBb6Iz8H1l\nZv6g1/NJ0gK1J8WFXtW/++4DntvrwHcpM++IiGdRVPyuXwz0ZOBT/ZhXC19m3grcOux1SJIkLSRL\npt9FkiRJkiRJ0kKUmSuApwN/oLkKN8DvJul7A3A8nbctroYHqo+mse8A3jDjhUsT/bCyXYYKD46I\nNZt2jojlwME0X4TwhW4njYjDgIfTeWz/pNsxpJqb6TynbjhVh4hYDXgSzefYmVQ23rFhbkPfmqsT\nK9vVi8NeHRGnRsT6vZgkIpZExLHA6cDShjlP6sU8kjQiHl/ZLs+Tn8vMvr6HzczfUJyn6+839urn\nvJIkSdJiY+hbkiRJkiRJGmGZ+XtgN+AbdH4AD3D5FH3fDXyC5grh9UcZ/q7u87LMbAyVSzPwbTrD\nqZszecjvPcDy9nb9mO+qymBEPBF4P51B2xXAV7sZQ2pwY0PbQ6bpszvjFbrrx/NXZjD37pXt8hx9\n5Qz6Sx0y8wLgm0wMfJdfDwUui4iXRcSUFzdMJiLWaFeOvRh4B0X12Op5eQz4LXDKbF+DJI2ghza0\nnTGguc+sPW8BjxrQ3JIkSdKisGz6XSRJkiRJkiQtZO2q3U+OiAOBfwMeUfn2b6fpfjjF7cAPZTzg\n3aR++/CjMvMTs12zVMrMuyLicxTHYvUig6MiYmOKivS/BrYDjgOeTWf4EODSzLxkqrkiYhlwTHuc\ntei82OGszLy9d69Oi8zFwL61tj2B86fos39lu3r+vTIzL5vB3Ec0tP10Bv2lyRwJ/BxYh85z7wOB\nDwDvjojzKX4Gfkbx3uNW4K/AnRTn23WB9YFtgIdRvFfZB1iP8fN4/Q4O9wKHZ+a9/Xt5krTg3K+h\n7ccDmvvCynb5u2CTAc0tSZIkLQqGviVJkiRJkqRFIjM/D3w+Ih4OHAg8MjPrFZTrfe4DDo+ILwHv\nA7aofLsaqC1dCLw6MwcVLNDi8C7gYMb/p10ee89sP6qqlemp7P++poEjYm3gHykqIT8f2HiSMVYC\nJ85u+RIAP6xsl8fwwRFxQmbeVd85IpZTHPf1oOsY8IVuJ42Iw4CH03lM/6TbMaTJZOaVEXEERRXZ\nesVv2ttrAE9pP2ai+h6j6efglZl5IZKkqg3p/J1/0yAmbl+seRvFBTuljQYxtyRJkrRYGPqWJEmS\nJEmS5qmIWNYOXfdUZv6coirnTPqcHRHnAE+lCG09jOID/DuBGyiqd345M384+SjS7GTmryPiJOC1\nNFfxrqoHDseAS4GPTTL8FsBZtfGawoUnZeYvZv8qJL4N1INQmwMnAS9t2P89wHI6j2mAT3UzYUQ8\nEXg/neGvFcBXuxlDmk5mfiYi1gVOofNYrYa/Z6rp7iLl+Mdm5odmMaYkjbpVDW0bMKDgN7B0mueS\nJEmS5sDQtyRJkiRJkjR//XtEvIAioP2z9tcvZ+btw1hMO4B+dvshDdrrge0pKntXA9119QqztwEH\nZWbTvgC/A+6l+H/5ZPucB7xxFmuW/qZd/fJzwOEUx1oZ5j4qIjYGjgd+DWwHHAc8m+aLHC7NzEum\nmisilgHHtMdZi4k/E2PAWcP6XaLRlJmnRcSVwCeAzWi+48JctYBbgMMy84s9GE+SRtEtdF6A80AG\nEPpuXwC0Tm1u329IkiRJPWToW5IkSZIkSZq/tgMeAPxj+wHwd/jBuRahzFwVEc8DPgi8iMkDhNVw\n643A/pn56ynGXRkRvwV2aPh2i6Ia8gGZec+sFy+NexdwMOOfz5Rh7me2H1X1wFa5//uaBo6ItSl+\nV+wOPB/YeJIxVgInzm750uQy81sR8VDg3yiq16/V/tZcA98tiuP2DOC1mXnDHMfTIhQRpy2EMaUe\n+APwyFrbXhR3vum3XSvb5cVqfxzAvJIkSdKiYehbkiRJkiRJmr+2ZWKAdSVw5XSdIuIkilt4l8Yy\n80W9X540WO3g9ZER8QXgncBOk+x6L3A68PrM/FMXQ/8vsCMTf95uB16Xmf85t1VL4zLz1+1z9Gtp\nruJdVf1++fxS4GOTDL8FcFZtvGrYthzrpMz8xexfhTS5zPwLcGxEnAi8kOIChIfVdpsqBF7/Wbie\nIuz9ocz8Xc8WqsWiVfl6SA/H6tWY1fGkXvkp8Ixa27OZ5KKxHntO7fkYcNkA5pUkSZIWDUPfkiRJ\nkiRJ0vy1Ue35HZm5qot+BwGbtLfLkJ+hb42MzDwXODcidqGoarwNsAZwM/Bz4Kvt4GG3LgMOpPh5\nuQr4EPCRzPxzTxcuFV4PbE9R2bsa6K6rBr5bwG3AQZk5WWD2dxQXPCybZDyA84A3zmLN0oy0q3Gf\nCJwYEQ8EngA8luIuJltTVKJfh6Ia+EpgBXATcA3wW4pz+QWZ+bPBr14jqh+AZRzLAAAgAElEQVTh\nagPbmo++W9kuLwTbLSKelplf7tekEbENxYUQ9fcg3+vXnJIkSdJiZOhbkiRJkiRJmr/Wqz2/d4b9\nDaJopGXmT4Cf9GCoc4EbKQKGl/ZgPGlSmbkqIp4HfJDigpzJAtrVwPeNwP6Z+espxl0ZEb8Fdmj4\ndgv4KnBAu2K+NDCZeS3w6fZDGpapKsxPZ7L31HMZc6pxpbm4gOJ9w8bt52Xw+wMR8aP2RTk9FRHL\ngI8AazLx52IV8KVezydJkiQtZkuGvQBJkiRJkiRJk1qr9nyDiFg6g/5zDaJIi0Jm/jAzP2jgW4OS\nmfdk5pHAfhSV5luTPO4DTgV2zswfdTH0/zIxRNgC7gCOzsx9M/Ou3r0KSZrXxmqPXo7VizHr40o9\n0b4z1H8z8f3AGLAlcF5E3L+X87X/Pv00sCcTL1gbA77RvvBHkiRJUo9Y6VuSJEmSJEmav+5gYrXv\npcBjgB8MZzmSpF7KzHOBcyNiF2B3YBtgDeBm4OfAVzPzLzMY8jLgQIqw1VXAh4CPZOafe7pwSZr/\nrKKtxexdwMsp3lOUxoCdgEsj4hWZ+bm5ThIRj6ao8P2wSXZ521znkCRJkjSRoW9JkiRJkiRp/rqR\niaFvgDdQVIaVJI2IzPwJ8JMeDHUuxe+OC6xcL2mRugArZ2uRy8wbIuKdwHEUPw9l5e0x4AHApyPi\nEuCjwGdmcnFYRKwD7A88H3gyxd3ly/GpbJ+Rmd/rzSuSJEmSVGqNjfk3ryRJkiRJkjQfRcTZFB+o\n1z9AP4+ietuFmXlHQ7/rgE2qfTJzaf9XLEmSJEkatohYBlwEPJKJwW8Yr4RfPr8K+BnwW+BW4K/A\nncBawLrA+hR3I3kYsC1F0LtpnLLtcuDRmXlrT1+UJEmSJEPfkiRJkiRJ0nwVES8D/oPxD+lL1X/q\n3QzcAayqtG1Z2b/8cP8P/VvplN6bme8f0tySJGmeiIitgJ2AB1FcnLY2sAbQyszXNOy/PXBrZt44\nyHVK0qiIiM0pqt8/uOHbrdrzboIj0/VpATcAT8zM33S1SEmSJEkzsmzYC5AkSZIkSZI0qc8AbwfW\nYWJ1tuqH7Ru1H1NpAVv1YX3TGQOWD2FeSZI0ZBGxDvAc4GnAHsAGU+zeEfoGXgy8MiK+A3woMz/X\n80VK0gjLzKsjYi/gHGBHJoa0mwLb05kqGN4CrgCeauBbkiRJ6h9D35IkSZIkSdI8lZl/joi3Au+g\n+IC9flvuqcymclsvdRMakLoSEacNew1tY5n5omEvQpLms4jYBHgdcCRFNW+Y+n3BZO9RtgKWAnsC\ne0bEz4BjMvPbvVmpJI2+zPx9ROwKvBc4gsn/npzt34vl+f1TwMsz8y+zHEeSJElSFwx9S5IkSZIk\nSfNYZp4YEY8Ansd48FtabA5l+Md+GZAx9C1Jk4iIoyguVlufiUHvyc7hU4XBt6r0awGPBM6LiP8H\nvDkzh/17QZIWhMxcAbw4Iv4DOB54KsVFNTC799jVc/d3Kc7J35rbKiVJkiR1w9C3JEmSJEmSNP+9\nAEjg9cDq7bb5HnSa7+vTwmQFeUmahyJideA0iovUynP1VO8FurlzyVaV7XLfJcAbgB0j4jkGvyWp\ne5l5KfDMiHgAxfl6X2A3YJ0ZDDMG/AI4F/hkZv6y5wuVJEmSNClD35IkSZIkSdI81w40HR8RpwCH\nAE8A/g7YmOID+m7+z2dYVqNgmOE+f4Y0ZxFx2rDX0DaWmVatV09ExBrA2cA/MXWYu+vzaESsB9yP\niZW+yzuetIADgFOAI2e3aklavDLzBuC9wHsjYimwE7AdsDXjf2OuBawEVgA3AdcAvwV+kZm3DmPd\nkiRJkqA1NuYF8JIkSZIkSdIoiYjrgE3aT1sU4b6lU3SR5rWIWMVgAt/TBRL9WdKcDPBYnoq/F9RT\nEfEJ4P/QfGyX59W7gV8BlwNPBdasfL/jeIyIzYGfAxvSGfyubh+Wmaf35pVIkiRJkiTNb1b6liRJ\nkiRJkiQtBP2utF1WkC3nGqMIHP68z/NqcbJyvEZCRBxJc+C7BdwLfBb4GPC9zLyr3ec6xkPfjTLz\n6oh4EPB84E3A5oxX+a5W/H53RHwlM2/q1WuSJEmSJEmarwx9S5IkSZIkSZLmuz17ONYyYA1gPWAz\nYFtgF2BXYCkTw4Q7AP+Rmaf1cH4Jhlvt28C5eiIiNgLeSXPg+yLgkMz8zWzHb4fET42ITwLvAF7B\nxOA3wP2AVwFvnO08kiRJkiRJC4Whb0mSJEmSJEnSvJaZ3+n3HO3w4kuBYykC4WMU4fCPRMS6mfn+\nfq9B6iGD3RqEY4EN6LxLwnnA/mVl77lqj/PKiLgMOLkyXxkAPzwi3pyZK3sxnyRJkiRJ0ny1ZNgL\nkCRJkiRJktRzrcpDUhcy86bMPAF4FPDrdnMZKHxPRDx9aIvTqGn1+QHjFevL7Z8BH688Tu/j69Mi\nEBHLgBfRGfi+FHharwLfVZn5YeBEOt/fbArs3ev5JEmSJEmS5hsrfUuSJEmSJEmj59HA0mEvQlqI\nMvOKiNgbuBjYjCLEuAT4aEQ8JDNvHOoCtdDt2cOxllFUo1+P4ljdFtgF2JXid0AZ/G4BOwD/kZmn\n9XB+LW57APdnPPRNe/vFmXl3H+c9DngusEWt/fHA1/s4ryRpBiLiH4DXAP+SmZcPez2SJEnSqDD0\nLUmSJEmSJI2YzLx62GuQFrLMvD4ijgS+wnigcQPgrcCLh7YwLXiZ+Z1+zxERGwEvBY6lCISPUYTD\nPxIR62bm+/u9Bi0Ke1S2yyrfX8vMi/o5aWbeHRGnACcwMXD+2H7OK0mjJCJaFO8R1qLIjCyhu7tE\ntdr7LmP84rO1gHWADYEHUlx8tifFXRgAXtfLtUuSJEmLnaFvSZIkSZIkSZJqMvPciPge8A+MV0s+\nOCL+LTP/PNzVSZPLzJuAEyLiDODLwEMYP4bfExF/yMwvDnONGgk7N7R9dkBzn0MR+obxY3vrAc0t\nSQtORKwPPLP9eAywCd2FvGerHHtsyr0kSZIkzdiSYS9AkiRJkiRJkqR56tTa8zWAA4axEGmmMvMK\nYG/gunbTGMXnQh+NiE2GtjCNiq0a2vpa5bvil8CqWtuGA5pbkhaUiDgCuBw4DXgaRQXus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twI+A7wHnZeaKHpcpSZJmh/J3wEH9Pvb4OGtvMqD1JUmSpDnD0LckSZIkSZKk6TDYLUk9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\n0xqqdvmmMtYvTUPfktQ/ra7f+wGvAXY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Rc0lep+qBgU5LJJjw+1K7BXhf1+hTuU9oKv4plQ+o6ZfQefR6f4C7CFpDf706OOmAJ3+9y8ofZ2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\nw9aUuPj9290KLMxsR1xUn9rvSepnWBhOf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd8bd67890>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
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    "function",
    "builtin_function_or_method",
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}