world-university-rankings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The dataset is available at - https://www.kaggle.com/mylesoneill/world-university-rankings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load all the libraries and include matplotlib line to make all the plots static within the IPython notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = 12, 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data using the read_csv function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cwurData = pd.read_csv(\"data/cwurData.csv\")\n",
    "shanghaiData = pd.read_csv(\"data/shanghaiData.csv\")\n",
    "timesData = pd.read_csv(\"data/timesData.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if CU Boulder is present in the institution column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"University of Colorado Boulder\" in cwurData['institution'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>world_rank</th>\n",
       "      <th>year</th>\n",
       "      <th>survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2012</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>41</td>\n",
       "      <td>2013</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>63</td>\n",
       "      <td>2014</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>54</td>\n",
       "      <td>2015</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      world_rank  year survey\n",
       "45            46  2012   CWUR\n",
       "140           41  2013   CWUR\n",
       "262           63  2014   CWUR\n",
       "1253          54  2015   CWUR"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwur_cu_ranking = cwurData[cwurData.institution.isin([\"University of Colorado Boulder\"])][['world_rank', 'year']]\n",
    "cwur_cu_ranking['survey'] = \"CWUR\"\n",
    "cwur_cu_ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat the same for Times and Shanghai data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"University of Colorado Boulder\" in timesData['university_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "times_cu_ranking = timesData[timesData.university_name.isin([\"University of Colorado Boulder\"])][['world_rank', 'year']]\n",
    "times_cu_ranking['survey'] = \"Times\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"University of Colorado at Boulder\" in shanghaiData['university_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "shanghai_cu_ranking = shangaiData[shanghaiData.university_name.isin([\"University of Colorado at Boulder\"])][['world_rank', 'year']]\n",
    "shanghai_cu_ranking['survey'] = \"Shanghai\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine the three dataframes into a single dataframe using the append function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>world_rank</th>\n",
       "      <th>year</th>\n",
       "      <th>survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2012</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>41</td>\n",
       "      <td>2013</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>63</td>\n",
       "      <td>2014</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>54</td>\n",
       "      <td>2015</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>2011</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>77</td>\n",
       "      <td>2012</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>91</td>\n",
       "      <td>2013</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>97</td>\n",
       "      <td>2014</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>97</td>\n",
       "      <td>2015</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>=127</td>\n",
       "      <td>2016</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     world_rank  year survey\n",
       "45           46  2012   CWUR\n",
       "140          41  2013   CWUR\n",
       "262          63  2014   CWUR\n",
       "1253         54  2015   CWUR\n",
       "66           67  2011  Times\n",
       "277          77  2012  Times\n",
       "692          91  2013  Times\n",
       "1098         97  2014  Times\n",
       "1498         97  2015  Times\n",
       "1930       =127  2016  Times"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_university_rankings = cwur_cu_ranking.append(times_cu_ranking).append(shanghai_cu_ranking)\n",
    "all_university_rankings.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strip out non-numeric values from the world_rank column using regex, apply and lambda\n",
    "\n",
    "* Apply is used to call a function on a series element or a dataframe\n",
    "* Lambda is just a cool way of saying functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>world_rank</th>\n",
       "      <th>year</th>\n",
       "      <th>survey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>2012</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>41</td>\n",
       "      <td>2013</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>63</td>\n",
       "      <td>2014</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>54</td>\n",
       "      <td>2015</td>\n",
       "      <td>CWUR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>2011</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>77</td>\n",
       "      <td>2012</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>91</td>\n",
       "      <td>2013</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>97</td>\n",
       "      <td>2014</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>97</td>\n",
       "      <td>2015</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>127</td>\n",
       "      <td>2016</td>\n",
       "      <td>Times</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>2005</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>34</td>\n",
       "      <td>2006</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>34</td>\n",
       "      <td>2007</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>34</td>\n",
       "      <td>2008</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>34</td>\n",
       "      <td>2009</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>32</td>\n",
       "      <td>2010</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>33</td>\n",
       "      <td>2012</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>33</td>\n",
       "      <td>2013</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>34</td>\n",
       "      <td>2014</td>\n",
       "      <td>Shanghai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     world_rank  year    survey\n",
       "45           46  2012      CWUR\n",
       "140          41  2013      CWUR\n",
       "262          63  2014      CWUR\n",
       "1253         54  2015      CWUR\n",
       "66           67  2011     Times\n",
       "277          77  2012     Times\n",
       "692          91  2013     Times\n",
       "1098         97  2014     Times\n",
       "1498         97  2015     Times\n",
       "1930        127  2016     Times\n",
       "34           35  2005  Shanghai\n",
       "533          34  2006  Shanghai\n",
       "1033         34  2007  Shanghai\n",
       "1543         34  2008  Shanghai\n",
       "2046         34  2009  Shanghai\n",
       "2546         32  2010  Shanghai\n",
       "3045         32  2011  Shanghai\n",
       "3546         33  2012  Shanghai\n",
       "3830         33  2013  Shanghai\n",
       "3930         34  2014  Shanghai"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_university_rankings['world_rank'] = all_university_rankings['world_rank'].apply(lambda x : re.sub(\"[^0-9]\", \"\", str(x)))\n",
    "all_university_rankings.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_university_rankings['world_rank'] = all_university_rankings['world_rank'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create a groupby variable that groups the **all_university_ranking** dataframe by **survey**\n",
    "* Unpack the subplot tuple into a **_figure_** variable and a **_subplot_** variable\n",
    "* Create a point plot for each group with **year** on the **x_axis** and **world_rank** on the **y_axis** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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US79ixXJWrFjmjbF582MD7hMX15gLL7wEsCepfvzx+yXGn5aWxhNPPILT6cTh\ncHDrrXeU2L4sHA4H9977IA6HA5fLxSuvjCA/XxNjRUSk+kufOxtXXh6NzhqAI0jV5CpDtY7csqyy\nr3YUgDGmBxAHrLMsy7OaUEf3Y/HefIq93uFI31+qt2uuuYHLL78agB07tnHDDVfyv/+9wsqVf5Cb\na//5OZ1ONm7cwBtvvMbVV1/K1q1bcDgc3Hjj/3HJJZf7PW7Hjp2Ii2uMy+Xihx/GANC0aXNatmwd\nMBbPOPq///6LlSvtITFlWUzq1luHc9JJ9p/0J598wEMP/Ztly5aQk2PH73K52L59G6NHf8l1113O\nxo3rcTgc3HbbcP7xj5PL8CmVrkOHTgwaNBSArVu38MUXn1TKcUVERKqKq7CQtOnTcEREEBukanKV\npVaXrTTGxADvY4+Jf9hnU5z7MS3ArvuLtZNabPjwezjppA6MGvUWu3fvYuzY7xg79jsAIiMjycvL\nK1Lp5bjjjufuu++jZ8+kgMd0OByccUYSkyZN8E5ALV6usriuXbsTHh5Bfn6et4e7LOUq69evz+uv\nv8Ubb7zGL79MZN68ucybNxeAqKgo8vLyikz6bdQojrvvvo8BA84p9djlqZxz2213MGfODNLT0/ny\ny08ZMGBgiV9gREREgunAH8sp2J9CbN8zCW3QINjhHJFam9AbY44BfgI6AS9ZlvWjz2bPZNe8ALvn\nFmsntVz//gPp0+dMZs+eycKF81i7dg2pqakcOJBJ/fr1adLkGE444UT69DmTpKQ+3rH3JUlK6sOk\nSRO8VWJ69Cg5oY+MjKRr1+4sXmyPb4+La0yHDp3KFH+DBtE8+uh/uOqq6/j110msWrWCbdu2cuBA\nJlFRUcTFNeaEE06kR49enHXW2d4KP4F4Yi6pwk1xsbGNuOeee3j66acpKCjglVdeYOTI98q8v4iI\nyNGUNs09Gbb/gCBHcuRqZUJvjOkATARaAS+4J9X68oypjwhwiKhi7aQOCA8Pp3//s+lfSTVok5L6\nMmfO4nLt89prbx7Re7Zunchttw0/omMAFU7Er7zySgYMGHzE7y8iIlKVcrZuIdv6m/odOhJ57HHB\nDueI1bqE3hhzLvAtEAncYlnWR36aecqZxAc4TJNi7fyKi6tPWFjpPbVSfgkJNW+VNrHp2tVcunY1\nk65bzaVrFzzrRs8CoNUlQ2lcgetQ3a5drUro3YtBjQcygMGWZc0J0HSV+/GkANs9r/9R0vulpqoD\nvyokJDRZi1AfAAAgAElEQVQkOTkz2GFIBeja1Vy6djWTrlvNpWsXPAWZGSTPmk34MU0paNGu3Neh\nOl67al3lpjyMMT2xV4hNAZJKSOYBZgL5QD9jjL917893P2otexEREZFaJH3WTFwFBTW+VKWvWnEW\nxpgGwGjs8znXsqwSl8y0LGs/8DX20JoHih2rNzAYsICfqyRgERERETnqXAUFpM2cTkhUFDG9Aler\nq2mq7ZAbY0w/wHd2n6cm/OXGmM7u5y7gduAmoCX2EJmzjTGBZjX+bFnWGvfzB4BewHPGmNOAxdiT\naK8FDgDXuxelEhEREZFaIHPZEgrT0mjU/2xC69ULdjiVptom9NjJ9cXYSbuHCzjR/eNw/34/h8a8\nnwx0CXA8F7AXWANgWdY+9zCdJ4ChwHnY9efHAc+U1ssvIiIiIjVL2tQp4HDQ6Kz+wQ6lUlXbhN6y\nrM+Az8rY/Eb3T3nfIwW4x/0jIiIiIrVU9saN5GzcQIPOJxPRtFmww6lUtWIMvYiIiIhISQ4tJFU5\n681UJ0roRURERKRWK0hLI3PJIiKaH0v9Dh2DHU6lU0IvIiIiIrVa2qwZUFhIo/4DcDgcwQ6n0imh\nFxEREZFay5mfT/rMGYTUr09Mz17BDqdKKKEXERERkVrrwOJFFGZmENu7DyGRkcEOp0oooRcRERGR\nWsnlcpE6zV2q8szaVarSlxJ6EREREamVctavJ3fLZqK7dCO8SUKww6kySuhFREREpFZK9ZaqHBDk\nSKqWEnoRERERqXXy96dwYNkSIo5vQb0TTgx2OFVKCb2IiIiI1DppM6aD00lcLS1V6UsJvYiIiIjU\nKs68PNJnzyQkOpqGp/cMdjhVTgm9iIiIiNQqmQvm4zx4kEZ9+hESERHscKqcEnoRERERqTW8pSpD\nQojtd1awwzkqlNCLVIJLL72A3r1P5c47bw12KEG1fft2evc+ld69T+Xjj98PWhzDh99C796nctll\nQ4IWg4iIBEf232vJ27Gdht1PIbxx42CHc1QooRepRLV90k15BPOzqF+/AdHRDWnQIDpoMYiISHAc\nKlV5dpAjOXrCgh2ASHXw999rmTZtMqtXr2THjm1kZh6gsLCAyMgoGjeOJzExkVNP7cE555ynJLEG\nePnl14MdgoiIBEF+cjIH/1hOZOtEotq2C3Y4R40SeqnTsrIO8tJLzzF9+lTvaw6Hg/DwcCIjo8jN\nzWHHjm3s2LGNuXNn8+GHo3j00SdJSuobxKhFRETEn7QZ08DlqhOlKn0poZc6y+l08sgjD7Bs2WIA\nkpL6csklw+jQoSP16zcAoKCggPXrLaZM+ZWxY78jMzODJ554mJEj36NTp87BDF9ERER8OHNySJ8z\ni9CYGKJPOS3Y4RxVSujlMFsytvH9up/YfmAnAC2ij+WS9hfQKqZFkCOrXAsWzPMm84MHD+Whhx4/\nrE1YWBgnntiBE0/sQKdOnXnyyUcoKCjgrbf+x6hRHx/tkEVERCSAjPnzcGZn03jAQELCw4MdzlGl\nhF6K+GXTNCZtmowLl/e1DembeWXJWwxKHMh5if2DGF3lWr58KWAPsRk69JJS25955gDOOGMS+fkF\nJCa2IS8vj4gAtW23b9/G119/zqJFC9i/P4Xw8HBatmzFwIHnc/HFlxEaGup3v9zcXCZOHM/cubPY\nuHEDmZkZADRqFEeHDh0ZNGgIPXsm+d33o4/e49NPP6R+/fr89tsscnJy+OGHMUyd+hu7d+8iJyeb\nJk0SOO20HlxzzY00a9bM73FcLhe//jqJSZMmsGnTBrKzc2jatCm9e/fjyiuvIS6uMXfc8X+sXPkH\nnTt34e23Pyjxc1u2bAnffvsVa9euIT09nQYNGnDCCSdx6aVXcMYZ/s8FYN++ZMaN+57Fixeyffs2\nDh48QEREBMcc05STT+7KJZdcTtsA4yOHD7+FFSuW06xZc8aMmVBifCIiUvO5nE7Spk2B0FAa9Tsz\n2OEcdUroxeuXTdOYuOk3v9tcuLzbaktSf/DgAe9zp7OwTPu89FLpky2XLVvCo4/ez8GDBwkPj8Dl\ncpGdnc3atX+xdu1frFixnOeee+mw/Xbv3sU999zBjh3bAPuLRlhYGC6Xi337kpk1awazZs1g0KAh\nPPzwEwHf3+WCzMxM7r33Dtau/YuQkBAiIiIpKChg166djB//AzNnTufDDz+nWbPmRfYtLCzkiSce\nZs6cmUVi2LVrJ6NHf8HUqb/x6qtvkpubCxDwi4kdh4vRo7/knXfewOFwEBkZicvlIiMjg8WLF7J4\n8ULuuecBLrlk2GH7Llq0gMcee5CcnGxvHBEREeTl5bF16xa2bt3Czz//xP33P8LgwUNLuBp1Z/yk\niEhdlrXmT/J276Jhj56ExTYKdjhHncpWCmAPs5m0aXKp7SZtmsyWjG1HIaKqd/zxLQE78fzqq89w\nOp1HfMz09HSeeOIhOnXqzCeffM306b8zY8Z83nnnQ1q1ag3ArFnTWbJk0WH7Pvfcf7zJ/JVXXsPY\nsROZPn0e06fP49NPv6Znz14ATJo0gUmTAvc6u1xOXnzxGdLS0nnhhVeZNu13pkyZzfjxv3HRRZe6\n40zjgw/ePWzfr7/+wpvMt29/Au+++zHTp89j6tS5PPvsizidTp5++nHy8/NK/SyWL1/Ku+++yeWX\nX80PP0xiypQ5TJ06l6eeGkFUVD0ARo0aSVbWwSL7ZWRk8OSTj5CTk01UVBQPPfQ4v/46g6lT5zJ1\n6lz++9+RtGzZisLCQl599QW2bdtaaiwiIlK7pblLVcYNGBjkSIJDCb0AMHbdT0WG2QTiwsXYdT8d\nhYiq3qBBFxAbGwvA7Nkzufnm65g0aQIHDhwoZc/ANm5czwknnMQrr7xBu3btva936tS5SK/6okUL\niuy3detmVqxYjsPhoFu3U/nXv+4mIeEYwO6dbtOmHc8//wpxcfYCGRMn/hgwhpycHJYuXcLIke+R\nlNSXsDD7RlxcXBz//veDHHecPRdiwYLfi+xXUFDA6NFfANCgQTSvvTaSjh07AfZcgn79+vPmm++y\nc+d2Nm7cUOpn8ccfy7jpplsYPvwemjRJACA8PJz+/c/muutuBOwhRitW/FFkv+nTp3Dw4AEcDgdX\nX309gwcP9U5SDg8P59RTe/DUUyMA+47Czz/Xjr9HERGpmLzduzm4aiVRbdsR1Tox2OEEhYbc1AA/\nrJ/I8r2rqvQ99ueklrnthvTNPDHvhSqLJTTEQecmnbi43eAqew+A2NhGPPXUCJ588hEyMzNYt+5v\nXnzxWV5++XnatTN06vQPOnbszD/+0ZnmzY8t0zEdDge3336n31JZHTv+g/DwCPLz80hJSS6yrWXL\n1kyaNJW0tDSioqL8Hjs8PJxOnTozZ85MNm3aWGIcF110qd8x8g6Hg65du7tr7Wdy4MABoqPtuvqr\nVq3wjtk/55zziIuLO2z/li1bc/HFw/j6689LfH+wx/1fc80Nfrd163YqYN8dKf5ZDB48lN69+5Ke\nnu79UlNc+/aGBg2iOXjwAJs3l/xZiIhI7ZbmLj0dV4cWkipOCb3Uaaecchoff/wVn3/+Eb/99jN5\neXk4nU4say2WtZYffhgDwHHHHU+vXr258MJLadGiZcDjxcTE0r79CX63ORwOYmNj2bcvmf379/vd\nNyYmtsR4GzSwe6qzsrJKbHfaaT0CbmvU6NDYwuzsLG9Cv379Ou/rXbp0C7j/gAEDy5TQd+nSzXt3\noDjPnRHgsM8iLCyM+PgmxMc3KfH40dF2Ql/aZyEiIrVXYVYW6b/PJSwujuhu3YMdTtAooa8BLm43\nuMp7q/+79B02pG8uU9u2sa25t/u/qiyWhISGJCdnVtnxi2vWrBkPPvgYd955LwsXzmPp0iX8+edK\nNm7cgNPpxOVysWPHdr77bjRjxnzDkCEXc/fd9xHupyRWaUmoZxJpYaH/Sbh79uxm0qQJrF69kpSU\nfaSmppKXl+vd7pmMWhKHw8ExxzQNuN03yS4sPDRvYPfuXd79mzc/LuD+bdq0IywsjIKCghLjKOmz\n8J1MG+izWLNmNZMn/8r69RapqftJS0st0taTyLtcpQ8VExGR2ilj3lxcuTnEnj8IR4BOpLqg7p65\nFHFJ+wt4ZclbpY6jd+DgkvYXHKWojq569erRr19/+vWzq/jk5OSwZs1qli9fyuzZM9m4cT0ul4vx\n48eyY8c2Xn/97cOOERpa8WkpEyaM4/XXXy6SKBcfulPW5DU0tPz/aWdnH+rprl+/fgnHDqVhwxhS\nUw+/y1C0XcU+C89k14kTx3tf8zeESYm8iEjdZpeqnIojLIxGffoFO5ygUkIvALSKacGgxIEBy1Z6\nDEocWOsWmAokKiqKbt1OoVu3U/jnP29l/vy5PPPMkxw4kMmSJYuYN29uiXXUy2PJkkW88soI7/te\nffX19OnTj4SEpjRs2NDbbsSIp/nll4mV8p7F+SbIpS2XXZWraX/yyQfeZL5Zs+bccMPNdO3anUaN\n4op80bj00gvYs2d31QUiIiLV2sFVK8lP3ktMUm9Cff6trItU5Ua8zkvsz+DEc3D4qd3twMHgxHNq\nTQ36iujZM4m77rrX+/vSpYeXnqwoz5h0h8PBs8++xA033EybNu2KJPMAeXmll4usqMjIQ5NxPfXf\n/XE6nWRmVs2QqNzcXL7//hsAGjaM4d13P2bQoCEce+xxh901yM/Pr5IYRESkZkib6i5VWYcnw3qo\nh16KOC+xPx3iDWPX/cS2AzsBaBF9LJe0v6DW9cxnZR1k3bp1JCYmljoZ1aNDh04++wdOesvr77//\nAqBp0+b06HFGwHabNpVeLrKiEhLs0pIul4s9e/YEnNy7efPGKkumd+zYxsGDdl36pKQ+NGnifxx+\nWloa+/enVEkMIiJS/eXu3EHWX39Sz5xAZAnFKuoKJfRymFYxLap00mt18OOP3/Paa/ZqrddeeyO3\n3FK28925c4f3efPmzUtoWT6eJNa3+ktxq1atKFP994pq3bqN9/mff64iKamP33ZTp5a+AFlFeT4H\nsMuKBjJ+/Ngqi0FERKo/z0JSjeroQlLFaciN1ElJSf28q5WOGTOaFSuWl7pPdnY2H3/8HgAhISH0\n63dWpcWTkGBXpdm+fRs5OTmHbU9J2cfzzz9dJOE/eLDiC2D507Vrd2/lnl9+meg3jq1bNzNmzOhK\nfV9fns8BwLL+9ttm1aoVfPnlpzRsGANwRAuBiYhIzVN48CAZ8+cRFh9PdJeuwQ6nWlBCL3VSkyZN\nuO++h3A4HOTk5HD33bfz0kvPs2zZkiIJYlZWFlu2bGbs2O+44YYrWbv2LxwOB9dddxMtW7autHh6\n9bIn1x48eIDnn3+KlJR9gJ2sTpo0gZtvvo6YmBiuv/5mwB4W89tvv1Ta+4Nd2ea88+zyqCkp+3jo\noX97F7AqKChg1qzp3HnnbXTs+A+aNau8uxO+mjVrRps2bQFYtmwxX3zxqfeLxe7du/n44/f597/v\nYMiQi+ne3V6cauPG9axbZ1VJPCIiUv2kz5mFKy+PRmcNwBGiVBY05EbqsHPPHUSDBg147bWXSEnZ\nx8SJPzJx4o+AvSqrw+E4bBJqw4Yx3HzzbVx88WWVGsv119/MnDmz2Lt3DzNnTmPmzGneVWUB2rZt\nz4gRr5KdncVbb72O0+nk9ddf5p133uC224Zz6aVXeI9V1nKO/trdfvtd/PnnajZsWMeyZUu47rrL\nCQ8Pp7CwEKfTSZs2bXn88We47bYbK+fE/bjnnge47747yc/P5/333+b9998mPDzcO25/wIBzuP32\nO/ntt5+ZOXMahYWF3HTT1URFRTFq1Ce0bduuymITEZHgchUWkjZ9Go6ICGIDDA2ti5TQS53Wu3c/\nevToxaxZ01m0aAEbNqxnz55dZGVl4XQ6iYmJIT6+CW3btufUU0+nX7+zqF+/wWHHcTgcpZZ6LKld\nXFwcH3zwGZ9++iHz588jJSWZ0NBQ2rQ5ifPOG8TgwUO9VWgefvgJPvvsI5KT9xIf34TjjmvhPbbv\nY0kxBGoXHR3Ne+99zOjRXzJjxjR27dpJYWEBrVsncs45g7jookupV68eTqe9IJXvAlH+3qMsn0dx\nXbt25913P+aLLz5m5coVZGSkExVVjy5dunHhhZfSx11r+NxzB7Fx4wamTv2NgwcP0KJFK29VoLJe\nDxERqVkO/LGcgv0pxPY9k9AGh/97XFfpX7wjsHdvhla2qQJHe6VYKb+BA/uSnZ1Fv379efbZF72v\n69rVXLp2NZOuW82la1cx215+gWzrb1o98zyRxwZe1bwqBevaHXNMTMC8XQOPRKSI3Nwcdu8OvGDT\n1q1bvKvKtm6deLTCEhGROi5n6xayrb+p36Fj0JL56kpDbkTE6+67b2fZsiVERdXju+/GExcXd1ib\n776zq9w4HA569Oh1tEMUEZE6Km3aVAAaDdBCUsWph15EvPr3t+v55uRkc889t7N06WLvZNS9e/cw\ncuTr3hrwp5xyOh07dgp4LBERkcpSkJlB5sL5hB/TlAadOgc7nGpHPfQi4jVkyEWsX28xbtz3bNy4\ngXvusRfcioiIKFLx58QTO/Dkk88GK0wREalj0mfNxFVQoFKVASihF5Ei7r33Ifr168+ECeP4668/\n2bcvGZcL4uOb0L694cwzBzBw4HmEhel/HyIiUvVcBQWkzZxOSFQUMe51W6Qo/YssIofp1u0UunU7\nJdhhiIiIkLlsCYVpaTTqfzah9eoFO5xqSfcsRERERKTaSps2FRwOGp3VP9ihVFtK6EVERESkWsrZ\ntJGcDetp8I/ORDRtFuxwqi0l9CIiIiJSLaVOnQJAo/4qVVkSJfQiIiIiUu0UpKWRuWQREc2PpX6H\njsEOp1pTQi8iIiIi1U7arBlQWEij/gNwOBzBDqdaU0IvIiIiItWKMz+f9JkzCKlfn5ieWpW8NEro\nRURERKRaObB4EYWZGcT27kNIZGSww6n2lNCLiIiISLXhcrlInTbFLlV5pkpVloUSehERERGpNnLW\nryd3y2aiu3QjvElCsMOpEZTQi4iIiEi1kTrNU6pyQJAjqTmU0IuIiIhItZC/P4UDy5YQcXwL6p1w\nYrDDqTGU0IuIiIhItZA+cwY4ncQNOFulKstBCb2IiIiIBJ0zL4+02TMJiY6m4Wk9gh1OjaKEXkRE\nRESCLnPBfJwHDtCoTz9CIiKCHU6NooRe5AgMH34LvXufymWXDQl2KCIiIjWWt1RlSAix/c4Kdjg1\njhJ6kSNQv34DoqMb0qBBdLBDERERqbGy/15L3o7tNOx+CuGNGwc7nBonLNgBiATDzz//xAsvPHNE\nxzjvvMG8/PLrlRSRiIhI3XWoVOXZQY6kZlJCL3VSeHg4DRpE+51BX1hYSHZ2lrtdBJEBlpyOioqq\n0hhFRETqgvzkZA7+sZzI1olEtW0X7HBqJCX0Uiedffa5nH32uX63LVmyiH//+w4Arr32Bm688f+O\nZmgiIiJ1StqMaeByEdd/gEpVVpASejlMzqaN7P12NLnbtgIQ1bIVCcOuICqxTZAjOzpcLlewQxAR\nEakTnDk5pM+ZRWhMDNGnnBbscGosJfRSRMrECaSMHwc+SW32OoutI54lfuhFxA9WNRdfw4ffwooV\ny2nWrDljxkzwvr5z5w4uv/xCAJ5++gXOOmsAkyf/wrhx37N162by8vJo2rQZZ545gKuvvt47fGfh\nwvmMGTOadessMjLSadIkgZ49e3HTTbfSqFGjgHFs3bqFceO+Z9myxezatYuCgnxiYxvRrl17+vXr\nz7nnDiI0NNTvvgUFBfz228/MnDmN9evXkZ6ehtPpJCYmlsTEtvTp05dBg4ZqiJGIiFS6jPnzcGZn\n03jAQELCw4MdTo2lhF68UiZOIOXHH/xvdLm825TU+1P0FqHnlqHD4cDlcvLGG6/x/fffEBISQkRE\nBLm5uWzZsplPP/2QFSuW8+abo/jqq88YNeotHA4HkZFRFBYWsnv3LneivoSPPvrS73j+7777mrff\nfgOn0wlASEgIoaGhpKTsY9++ZBYsmMeYMd/w6qtv0KRJQpF9MzIyuO++4axd+5c33tDQUEJDQ0lL\nS2XZssUsW7aYMWO+5Y033qFp02ZV8eGJiEgd5HI6SZs2BUJDadTvzGCHU6OpbKUA9jCblPHjSm2X\nMn4cOZs2HoWIageXy8Xs2TOYOHE8jzzyJFOnzmXKlDl89dX3dOjQCYDly5cyatRbvP/+O9x00y38\n/PN0pkyZzbhxP5OU1AeALVs2M2nShMOO/8svExk58nWcTiennno6o0Z9wtSpc5k27Xe+/fZHrrnm\nBsLDw9mwYR333XcnBQUFRfZ///23vcn8NdfcwNixE5kxYz7Tpv3O2LET+ec/byU0NJQdO7YxYsSR\nVQUSERHxlfXXGvJ276LhaacTFhv4LrSUTgm9AJD83TdFhtkE5HLZbaXMpk+fymOP/Yfzz7+AcPft\nxBYtWnLvvQ9523z11WcMG3YVN974f0RH2zXt4+Ob8OCDjxMSYv9nunjxwiLHzcrKYuRIu2zmqaee\nzquvvknHjp0IDw/H4XDQvPmx3HrrHTz44GMAbNy4gQkTin5pmz17JgAnn9yVW2+9g4SEY7zbEhKO\n4YYbbub66/9JWFgYu3fvJCMjvRI/GRERqcvSpk4GIE6lKo+YhtzUAMljviFzyeIqfY+ClJQyt81e\nZ7HxofuqLJYtoSHU79qdhMuuqLL3OJpatmxFv379D3vdmBMID48gPz+PkJAQrrrq2sPaxMXFcfzx\nLdi6dQu7du0ssm3atMlkZmbgcDi45ZZ/eRP/4s49dxAff/w+u3btZPLkX7j44su82w4cyASgfv36\nAeO/7rqbVOlHREQqVd6e3RxctZKotu2Iap0Y7HBqPPXQi1Sxzp27+n3d4XAQGxsLwLHHHkdcnP+V\n8WJi7Dae2vgey5cv9R7HmBNLjKFLl24AWNbf3rH2AG3a2PV+Fy6cz9ix3x42JAcIOJlWRESkotKm\nTQXUO19Z1ENfAyRcdkWV91Zve2kE2eusMrWt197Q4qFHqyyWhISGJCdnVtnxj7bGJSxh7UmWGzeO\nL7WN01l0SNT27dvcrzs5//yzSowhLy8fgPz8PPbu3UuzZvbk1ltv/Rf33383TqeT//3vVT766H16\n9uxFly7dOOWU02je/NhSzk5ERKR8CrOySP99LmFxcUR36x7scGqFGpHQG2N6AJ8B7YGnLct6OkC7\nbsAjQB+gEbAL+AV41rKsnX7aNwceBQYBxwKZwFxghGVZVTvGpZpJGHYFW0c8W/o4eoeDhGG1YyjM\n0RIWVvp/ZhXpBc/MzPA+z8rKKqGlzVN5x7en/9RTe/C//73DO++8ydq1a8jMzGDy5F+YPPkXAFq1\nSmTgwHO5+OJh3rH9IiIiRyJj3lxcuTnEnj8IRxn+jZTSVetP0RgTCTwN3Ad4xgn4zTiNMecA44F8\n4EtgC9AZ+D/gAmPMGZZlbfVp3wL4HTgeGAd8ADQHrgPON8ZcaFnWL1VxXtVRVGIb4odeFLhspVv8\n0IvqzAJT1Z1nzHxcXGMmTPitwsfp2rU7H3zwGatXr2Tu3NksWjSfDRvW43Q62bJlEx988C7ffTea\nESNeoXPnLpUVvoiI1EF2qcqpOMLCaNSnX7DDqTWqdUKP3VveFXgJO5H3O87Dnfh/7P61r2VZy3y2\nzQDeA94CfAuo/w87mb/bsqyRPu3fB5YAHxtj2lqWVXrXZy3hqS9ffGEpABwOLSxVzTRsGAPYE1ud\nTmfASbFl1alTZzp16sxttw0nIyOdxYsXMWnSeBYvXkh6ehqPP/4Qo0ePpUED9dSLiEjFHFy1kvzk\nvcQk9Sa0YcNgh1NrVPdJsaFAH8uyHgMOn613yBDs3vVxvsm824fANmCQu1ceY0wzYCiwEzvR97Is\nazUwBmgKXFwZJ1GTxA8eQstHn6Bee4MjMhJHZCT12htaPvakkvlqprW7KkBBQQGbN2+q1GPHxMTS\nv//Z/Pe/b3HVVdcBkJq6n5kzp1fq+4iISN2SNnUKoMmwla26J/RnWJY1rwztPDMCpxTfYFmWC5iO\nvZRnP/fLfbHPfbp7e3FT3Y91ctmyqMQ2tHjoUdq//R7t336PFg89qpJS1dDJJ9vVc1wuFzNmTC2x\n7axZM/jrrz8Pe33Pnt2sWrWixH3PP/8C7/PU1P0ViFRERARyd+4g668/qWdOILJFy2CHU6tU64Te\nsqycMjbt6H5cH2C75/UOFWwvUu2cddYAoqPt25VjxnzDzp07/LZbv34dTz/9GLfccgOjRh26IfXV\nV59x6aUXMHz4LWzcuCHg+2zb5p16oqo3IiJSYWnT7H7XRgMGBjmS2qdaJ/TlEOd+TAuwfX+xduVt\nL1LtREZGcddd9wJw8OAB7rzzVmbPnkl+vl2iMi0tjbFjv+Ouu24jPz+fhg1juMyn/Om55w6iXr36\nOJ1O7r77diZOHE9qaiou9/yJAwcOMHnyr4wYYReVio9vwhln9D7KZykiIrVB4cGDZMyfR1h8PNFd\n/K/PIhVX3SfFllV97EmzeQG25/q0830sa3uRaum88waTkZHOO++8yd69e3jssQcACA8P9yb2YNe5\nf/75l4mPb+J9LT6+Cc8++yJPPvkwaWmpvPTSc4BdQjMkJKTI/nFxjXn++VeoV6/eUTozERGpTdLn\nzMKVl0ejswbgOMIiDnK42pLQZ2GPkY8IsD3Kp53vY1nbSx3iqdfueSytbUntSjtGafuXpc3ll19N\nUlJfxo79lmXLlrJnz25ycrKJjY2ldes2nHFGby688GLq129w2L6nn96Tr776nvHjf2Dp0sVs377N\nW9++cePGtG7dhp49e3HBBRequo2IiFSIq7CQtOnTcEREEJvUJ9jh1Eq1JaHf534MtNxmk2Ltytve\nr7i4+oSFlX9BICldQkLwSlmdd15/zjtvbZnafvPN135fT0g4gbVrSz/GzJkzKvweRd/vJLp0earU\ndvz+IhIAACAASURBVP73bchJJ91foX0DHU9qJl27mknXreaqK9cuZf4CCvan0OzcgTRr3SzY4VSK\n6nbtaktCvwq7cs1JgL8M6ST34x8+7X1fL629X6mp6sCvCgkJDUlOzgx2GFIBunY1l65dzaTrVnPV\npWu37YcJAESe0bdWnHN1vHa1ZRDTZPfjucU3GGMigP7Y4+U9RbRnYq8o288YE+7neOe7H3+t3DBF\nRERE6o6crVvItv6mfoeORB57XLDDqbVqS0L/C3apyfONMcXLcNyPPYTmK8uy9gO4H792v/6Ab2P3\n/oMBC/i5iuMWERERqbXSptnrpDQaoIWkqlK1HXJjjOkHDPd5yVMT/nJjTGf3cxdwu2VZ+4wxN2D3\n1P9mjPkC2AqcBlwArKVY4u7+vRfwnDHmNGAx0Aq4FjgAXG9ZlrOyz0tERESkLijIzCBz4XzCj2lK\ng06dS99BKqzaJvTYyfXF2Em7hws40f3jcP9+H4BlWfOMMT2AJ4ELgUbANuC/wHOWZRWpOe/+EtAT\neAIYCpyHXX9+HPCMZVllmxUpIiIiIodJnzUTV0GBSlUeBdU2obcs6zPgs3LusxoYVo72KcA97h8R\nERERqQSuggLSZk4nJCqKmF5JwQ6n1qu2Cb2IiIhIbZKzaSN7vx1N7ratrHc4iGzRkoRhVxCV2CbY\noR0x33MDCIuLozAtjUb9zyZUixJWOSX0IiIiIlUsZeIEUsaPA5c9ktgFZK+z2DriWeKHXkT84CHB\nDfAIFD83gPzdu+0nZVikUY6cBjSJiIiIVKGUiRNI+fGHIgmvl8tFyo8/kDJxwtEPrBKUeG5A2tTJ\nNfbcahIl9CIiIiJVJGfTRrv3uhQp48eRs2njUYio8tTmc6tpNORGREREpIokf/dNwN7rIlwu9n7z\nNcff/2DVB1VJ9n7zdZnPLfm7b2jx0KNVH1QdpYReREREpIrkbN1S9rYb1rP+9luqMJrgKc/nIOWn\nhF5ERESkOggJof5JHUpvV01k/bUGnFqDszpQQi8iIiJSBfL27CEkMorC3Nwyta/Xth3H//v+Ko6q\n8mx7aQTZ66wytY1q2aqKo6nbyjUp1hjTtCJvYow5vyL7iYiIiNQ0OZs3sfPdt9j8+MMUZqSXbSeH\ng4RhV1RtYJUsYdgVZStLWQPPraYpbw/9r8aYvpZlZZR1B2PMEOBbQKsKiIiISK3kcrnI+nM1+3/9\nmey1fwEQ2bIVjc89n7zdu0iZ8GOJ+8cPvajGLTAVldiG+KEX2WUrS1ATz62mKW9CfzIwyRhztmVZ\nOaU1NsZcBPw/e3ceH2V19///NZN9hRCSkECAsBzZRNw3QAsuuKC4VHBpa+1ia+2ite1tf/3a3urd\ne2lrN7XazdZWResCiooiuICIdQNFwRMkrEkgQCD7OvP7YyYQQrYJM7lmrryfj8c8LjLXmZnP9biS\n4T1nznXOwj68joiIiEjU87e2Uv3uv6lc+gKN27cDkDppMllzLiR14iQ8bT3YXu8Riy8B4PHE9MJS\nbXW78dhiSahBuww4E3jaGHOJtbalq4bGmCuBR4OvsaLvJYqIiIhEF19jIwdWvUHlspdo2bMHPB4y\nTjmVrPMvIHnU6CPaZ198CWmTp1DxxEIatm3F4/GQVDiSnPlXkzy6qP8PIIw6HhsExsy74dhiRaiB\n/jzgDWAO8A/g6s4aGWMWBPfHAcuAS4+iRhEREZGo0Fpdzf5Xl1O54hV8NTV4EhIY9LlZZJ03h8Sc\n3G4fm1w05uBc7Dk5GVRUVPdHyf2i/bFJ/wsp0FtrPzbGXAS8Asw3xhyw1n6jfRtjzLXA3wiE+aXA\nZdba3l3eLSIiIhKFmvdUUPnySxxY9Qb+pia8qWkMufgSBs8+h/iMTKfLkwEu5LHt1to1wbHxS4Cv\nG2MqrbW3Axhjvgj8lcDsOc8DV1hrm8JZsIiIiEh/ady+jX1LX6D6nX+Dz0f8kGyyzjufQdNn4k1O\ndro8EaCPF6taa5cFe+IfB35ojNkL7AP+SCDMPwt83lrbHLZKRURERPqB3++nfuMG9i19gbqP1wOQ\nOHwEQ+ZcSMbJp+CJ11wfEl36/BtprX3SGHMjgRD/f4Af8ADPAPO7u2BWREREJNr4fT5q3n+PfUtf\noHFLCQAp5hiGXHARqVOOPTRjjUiUOaqPmNbaPxtjsoD/JRDmnwSutta2hqM4ERERkUjzNTdRtfpN\nKl9aSvPuXeDxkH7CiWTNuZCUMWOdLk+kR10GemPMTwn0uvfEA2wDsoCNwP9njDmikbX2zj7WKCIi\nIhJ2rbW17H9tBftfWUZrdRWe+HgGzTyLrPMuIHHYMKfLE+m17nrof9qH5/tJF/f7AQV6ERERcVzz\nvn3sX/YS+994HX9jA96UFLIuuIis2ecSP3iw0+WJhKy7QL8tjK/Tm55+ERERkYhpLN1J5dIXqHp7\nDbS2Ejd4MFmXXMqgmWcTl5LidHkifdZloLfWju7HOkREREQior7Ysu/F56n9cB0AicPyyZpzIRmn\nnoY3IcHh6kSOnuZdEhEREdfx+3zUrlvLvqUv0PDZJgCSx45jyAUXkTb1ODxer8MVioSPAr2IiIi4\nhq+5meq336Jy6Ys0lZcBkHbcNIbMuYiU8eMdrk4kMhToRUREJOa11tdz4I3XqFz2Eq3790NcHJln\nTCfr/AtIGj7c6fJEIqrPgd4YMxE4GcgFUghMX9klTVspIiIiPWko2czuxx+jcXtgbo7kkaPIuWoB\nyUVjOm3fsn8/lcuXceC1Ffjq6/EkJZN13hwGn3MeCUOG9GfpIo4JOdAbY04AHgBOpIcQ346mrRQR\nEZFu7V3yLHsXPwP+Q5Pj1Rdbtv38LrIvvYzsiy85eH9TeTmVL79I1eo38be0EJeRSfZlFzL47FnE\npaU5Ub6IY0IK9MaYCcCrQEa7u/cAdT08VNNWioiISJf2LnmWvYue7nyn339wX+qkyVQufYGaD94H\nv5+E3Dyyzp9D5hln4k1I7MeKRaJHqD30dxAI8/XAD4F/WmsPhL0qERERGTAaSjYHeuZ7sHfR0weD\nfdLoIobMuZD0E07UjDUy4IUa6GcFt7dYa/8Y7mJERERk4Kl4YuFhw2y6401NpeCmb5NyzAQ8nt6O\n/BVxt1ADfRaB4TPPRqAWERERGYAatm3tdVt/ayupEyZGsBqR2BPqd1Slwa0v3IWIiIiIiEjoQg30\nzxGY2WZGBGoRERGRASh55KiItBUZKEIN9P8DVAB3G2OyIlCPiIiIDDA5Vy2A3oyH93gCbUXkMCEF\nemttKXAOkAi8b4z5sjGmwBijq1JERESkT5KLxpA27fge22VfelmXC0yJDGShzkPvI3BRbFuA/0vw\nZ4wx3T7WWhvXh/pERETE5Wo+eI/atR/gSUjA39Jy5Iw3Hs8RC0uJyCEhrxTLkavDqndeRERE+qRu\n4wbKHvwDnsRERnz/h3g8HiqeWHhw5pvkkaPImX81yaOLHK5UJHqFGuhv6OPraKVYEREROUzDli2U\n3vtb/H4/w2/6NiljxgJQ+KMfO1yZSGwJKdBba/8WoTpERERkAGkqK2Xnb36Fr7GR/Bu/SdrkKU6X\nJBKztFayiIiI9KvmvXvZ8etf0lpTTe4XvkTGSac4XZJITFOgFxERkX7TUl3Fjl//gpZ9+xh6+ZUM\nnnm20yWJxDwFehEREekXrfX17PzNPTSXl5N1/hyyLrjI6ZJEXKEvs9xgjEkFrgZmA+OBQT09l7VW\nE8eKiIgMUL7mJkrv+x2NW7eQOX0GQ6+cj6c3i0mJSI9CDvTGmFOBfwEjwl+OiIiIuI2/tZWyB/9A\n/cYNpB9/InlfuF5hXiSMQl1YahiwBMgGNgGLgNuCu/8HSAWmAGcBccAG4IlwFSsiIiKxxe/3s+vv\nD1G79gNSJkxk2NdvxBOntSZFwinUMfTfIxDmPwGmWWt/GLzfD/yXtfZ71tpzgNHA88BEYJC19j/D\nVK+IiIjECL/fz55/PU7V6lUkjS5i+M3fwZuQ6HRZIq4TaqC/ILj9hbW2rsO+g4tHWWt3AvOAFcB3\njTGX9r1EERERiUWVLz5P5ctLSRyWz4jv3oo3OcXpkkRcKdRAP5pAcF/Tyb7DPnJba1uBuwEP8I2+\nFCciIiKxaf/rr7Ln6SeJHzKE4bfeRlxGhtMlibhWqIG+7aP1rnb31Qe3WZ20/yi4nRbi64iIiEiM\nqn7n3+z+58PEZWQw4tYfkDAk2+mSRFwt1EC/N7gd2u6+cgK98JM7aZ8T3HYW9kVERMRlatd/RNmf\nH8SblMTw732fxGH5Tpck4nqhBvr1BMJ7+7/Od4LbL3bS/qvBbXmIryMiIiIxpv6zTZTe/3s8Hg8F\n3/4eyaNGO12SyIAQ6jz0SwksJnUSsDJ438PAVcCVxph/EJimMh6YC1wfbLPkqCsVERGRqNW4cwc7\nf/tr/C0tFNz0bVKPmeB0SSIDRqg99IuD26va7rDWvkBgoSmAawnMTf8Uh8L8duDOvpcoIiIi0ay5\nooId9/wSX10tw67/CunTjne6JJEBJaRAb63dRGA8/JwOu64F7gB2EhiSA1AH/BM4zVq7+yjrFBER\nkSjUcmA/O+75Ba0H9pNz1dVknnGm0yWJDDihDrnBWnugk/taCExRebcxZgiQBFQE7xcREREXaq2r\nZedvfkVzxW6GXDSXrPPOd7okkQEp5EDfE2vtvo73GWOyrLWV4X4tERERcYavsZGdv/sNjdu3M+is\nz5E973KnSxIZsMIe6NszxgwCvg98BxgcydcSERGR/uFvaaHsgfto2FRMxsmnkHvtF/B4PD0/UEQi\nIiKB3hiTAXwPuAUFeREREdfw+3yUP/Rnaj/6kNQpxzLsK1/H4w11jg0RCaceA70xJh64EvgcUAC0\nAluBxdbaFR3aJgLfBm4HhrTb9Uq4ChYRERFn+P1+dj/2CNVvryF57DgKvnkznviIftkvIr3Q7V+h\nMWYSgakqx3ay+2ZjzDLgcmttnTHmJOBRYFy7Ni8D/2mtfStcBYuIiIgz9j67iAOvLidx+AiGf+cW\nvElJTpckInQT6I0xmcALwMgumniA84D7jTEPEOiFTw3uW0ogyL8dxlpFRETEIZWvLGPfc4tJGJrD\niFtuIy4tzemSRCSoux76r3MozD8D/B9gCQT5Y4AfApcSmIN+OoEwvwH4hrV25RHPJiIiIjGp6q3V\nVCx8hLhBgxh+6w+IH6zL40SiSXeB/uLg9hlr7RUd9r0FXGaMeQq4DBgDvAPMstbWhr9MERERcULN\nurWUP/RnvKmpjPjebSTm5jpdkoh00N1l6ZMBP3BPN23a9vmBnyjMi4iIuEed/ZSyB+7DEx/P8G/f\nQlJhodMliUgnugv0bd+nbeymjW3373eOvhwRERGJBg3btlL6+9/g9/kouOlmUsaPd7okEelCd4E+\nLrit76bNwX3W2v1hqUhEREQc1bSrnJ2//hW+hgaGfeVrpE2Z6nRJItINrQQhIiIiBzXv28eOe35B\na3UVudd8gcxTTnO6JBHpQW9WgxhpjKnrYt/BOauMMV1NbwmAtXZbKIWJiIhI/2qtqWHnb35Jy969\nZF96GYM/N8vpkkSkF3oK9B7g4148jwco6Wafn0NDeERERCTK+Boa2Pm7e2gqLWXwOecy5OJLnC5J\nRHqpNz30nl4+V3ftevscIiIi0s98zc2U3vd7GjZvJuP0M8i56mo8Hv3XLRIrugv0d4bxdfxhfK4u\nGWPSgVuAK4BxBI6vHHgd+KW19qNOHnMCcDswk8DMPmXAi8Bd1trS/qhbRETEKX6fj/I/P0jdho9J\nO24aw750Ax6vLrETiSVdBnpr7c/6sY6jZozJAFYTmD9/HXAfUAmcQGA12/nGmLnW2mXtHnM+sBho\nBv4JbAWmAl8D5hpjztDYfxERcSu/38+uf/yNmvfeJcUcQ/6NN+GJ782X9yISTdz0V/stAmF+qbX2\nwvY7jDFfAh4CfgFMC96XBPw12OQsa+377dq/CjwI3AtoEKGIiLjSnqf+RdXKN0gaOYqCm7+LNzHR\n6ZJEpA/c9J3aMcHts53sa7tvXLv7LgHygWfah/mgPwPbgYuMMVoWT0REXGff0heoXPoCCXnDGP69\n7xOXmup0SSLSR24K9OuC24md7BvboQ1A21xcyzq0xVrrB1YQuJj37DDVJyIiEhUOrHydPU8+QXxW\nFiNuvY34zEynSxKRo+CmITcPAlcD3zTG7AeeBuqAScDPgRrgB+3aTw5uN3XxfG33Twp/qSIiIs6o\nfu9ddj38N7xpaQy/5TYSsoc6XZKIHCXX9NBba+sJzFTzO+AnwAfAp8AzQD1wqrV2dbuHZAW3+7t4\nyn0d2omIiMS0ug2fUP6nB/AkJjL8u98nqWC40yWJSBi4JtAbYxKBPwK3AkuBLwPzgf8GxgAvGWOO\nb/eQVALTaTZ18ZSN7dqJiIjEtIaSzey893cADL/5u6SMGeNwRSISLm4acnMb8AXgQWvtN9vd/y9j\nzJPAO8DjxphJ1toWAsNxPEBXl/QnB7d1Xb1gVlYq8fFaADcScnIynC5B+kjnLnbp3MWm3py3uu07\n2Py7X+NvbmLCD79P9umn9kNl0hP9zcWuaDt3bgr03yDQ4/7rjjustR8YY94GTgdOAtYAe4K7s7t4\nvrZBhXu62E9lZZdZX45CTk4GFRXVTpchfaBzF7t07mJTb85b8949bP+f/6Klupq8L30Z37jJOtdR\nQH9zsSsaz51rhtwQmIISYFcX+9uC+cjgdn1w29msOO3vX3uUdYmIiDiipaqKHff8gpbKSoZeeRWD\nZpzldEkiEgFuCvTlwa3pYv/YDu1eCm7ndGwYHI8/m8D4+hXhKlBERKS/tNbVsfM3v6J51y6y5lzI\nkDkX9vwgEYlJbgr0iwmMif9/xpjDhhIZY+YSmH6yHHgrePeLBKamvNAYM6PDc91GYMjNI9bafYiI\niMQQX1MTpff+lsZtW8mcMZOhV3ze6ZJEJIK6HENvjPkSgTHpYWGtfThcz9WFO4DPARcDa40xiwnM\nPX8scCWBWWu+aq1tDtbTaoy5HniZwAw4/wC2AacAc4GNHD5vvYiISNTzt7ZS9uD91NtPST/xJPK+\ncD0ej8fpskQkgrq7KPYhAoE+HO8CfiCigd5au88YcwrwXeAK4DsEZrApAx4FfmGt/bjDY1YbY04j\n8GFgHjAY2A7cA9xtre1qjnoREZGo4/f52PW3v1K7bi2pEycz7Ks34vG66ct4EelMT7PchOsjfb90\nDVhrawmsCvvzEB6zHrgqYkWJiIiEUUPJZnY//hiN27exyeMhqXAkOVctIGl0ERVPLKTqrTdJLhpD\nwbe+jTchwelyRaQfdBnorbWdfqQ3xpwKPAbkAPcRWIn1UwLDW9KBCQR6u28mMOPMNdbat8NbtoiI\nyMCzd8mz7F38DPgDI2L9QH2xZdvP7yJlwkTqN3xCYkEBw797K97k5O6fTERcI6R56I0xYwiswloP\nnGCtLe7QZD+BOd7XGGP+CrwGvGiMOdla+1kY6hURERmQ9i55lr2Lnu58p99P/YZP8KakMPyWHxCX\nnt6/xYmIo0IdWPcfwCDgh52E+cNYay3wQwLj0m/vW3kiIiLSULI50DPfA19DA637K/uhIhGJJqEG\n+vMJfMO3rJftXwluzwnxdURERCSo4omFB4fZdMvvD7QVkQEl1ECfF+Lj2i6Gzeu2lYiIiHSpYdvW\niLQVEXcINdDvJRDSL+pl+7Z2e0J8HRERERER6YVQA/3Lwe1/G2OO766hMeYE4H+CP/Z2iI6IiIh0\nkDxyVETaiog7hDTLDXAXgTnbs4G3jDH/BBYRWFW1FkgDJhKYtvI6IAGoA+4OV8EiIiIDTc5VC9j2\n87t6Hkfv8ZBz1YL+KUpEokZIgd5au9kYMxd4GsgEbgje2r/DtF9E6gBwubV289EWKiIiMlA1lZeB\n1wutrd22y770MpKLxvRTVSISLUJeD9pauwKYBPyRwGJSEAjxbTeAauBBYJK19tUw1CkiIjLg+Jqb\n2fWPv1P+lz/hTUwk/ZTTwNPJ4useD9nzLif74kv6v0gRcVyoQ24AsNaWAt8wxtwMHAuMJjDcphYo\nAT6y1nbfjSAiIiJdat67h9I/3EfjlhISRxRS8M1vkZg3jIZzz6PiiYU0bNuKx+MhqXAkOfOvJnl0\nkdMli4hD+hTo21hrW4APgjcREREJg9r1H1L2pwfx1daSefqZ5F73RbxJSQAkF42h8Ec/BiAnJ4OK\nimonSxWRKHBUgV5ERETCx+/zsfe5xexb8iyeuDhyv3A9g2aehaezYTYiIkEK9CIiIlGgtbqasj8/\nSN3H64kfOpSCb9xM8ujRTpclIjGgy0BvjHmVw2evOSrW2lnhei4RERE3qd+8mbIH7qVl3z7Sjp3K\nsK98nbj0dKfLEpEY0V0P/VlhfJ2wfTAQERFxC7/fz4HXVrB74aPg85E973KGXHgxHm/Ik9CJyADW\nXaB/OIyvo0AvIiLSjq+xkV0P/43qt98iLj2DYV+7kbTJU5wuS0RiUJeB3lp7fT/WISIiMmA0lZdR\nev+9NJXuJHnMWPK/cRMJQ7KdLktEYpQuihUREelH1e++Q/lDf8Hf2MDgWeeQc9UCPPH671hE+i6k\ndxBjzAcEhs/cbq19KTIliYiIuI+/pYWKp/7F/mUv4UlMZNjXvkHmqac5XZaIuECoXQKTgTjg4wjU\nIiIi4krNlZWUPXg/DZuKSRyWT/5NN5NUMNzpskTEJUIN9OXAcKAhArWIiIi4Tt3GDZQ9+Adaq6tI\nP+kUhl3/ZbzJKU6XJSIuEmqgfw24DjgVeD7s1YiIiLiE3+ejcukL7HnmKfB6yVlwLYNnn6NVX0Uk\n7EKd6PZXQBNwlzEmNQL1iIiIxLzWulpK7/89e55+krhBgyj8wX+Qdc65CvMiEhEhBXpr7TrgWmAc\n8LYx5jIFexERkUMatm1l210/o3btB6RMmMioO+4kZdx4p8sSERcLdZabEgKz3PgIXCD7FOAzxlQA\n9d091lo7pq9FioiIxIIDq1ay+5GH8Tc3M+TCi8med7lWfRWRiAt1DP2oTu7zAnlhqEVERCQm+Zqa\n2P3oP6la9Qbe1FTyb7yJ9GnHO12WiAwQoQb6O/v4Ov4+Pk5ERCSqNVXspuwP99G4bStJI0eR/81v\nkZiT63RZIjKAhBTorbU/i1AdIiIiMadm3VrK//JHfHV1ZM6YSe411+FNSHS6LBEZYLTWtIiISIj8\nPh97Fz3NvheW4ElIIO/6rzBo+gynyxKRAUqBXkREJAQtVVWU/+kB6jZ8QkJODvnfvJnkkZ1dYiYi\n0j/6HOiNMXHA+cAMoBDIAKqBrcAq4CVrrS8cRYqIiESD+k3FlD14Py2VlaRNO55hN3yVuNQ0p8sS\nkQGuT4HeGDMP+B0woptm240x37HWLu5TZSIiIlHC7/ezf/krVPxrIfh8DL3i82Sdf4GmpBSRqBBy\noDfG3EwgzLdpAIoJ9M6nAwZIJtBr/3Qw1N8XhlpFRET6na+hnvK/PUTNu/8mLiOT/Bu/SeqEiU6X\nJSJyUKgLS00Cfh380QL/ASyx1ra0axMPXAT8L4Fw/2tjzKvW2k/CU7KIiEj/aCzdSdn999JUXkby\nuPHk33gTCVlZTpclInKYUHvobwXigPXADGvtgY4NguF+sTHmVWAlcCxwC/C1o6xVRESk31S9vYZd\nDz+Ev7GRweeeT84Vn8cTr7kkRCT6hDr4b1Zwe3tnYb49a20VcHvwx8+FWpiIiIgT/C0t7H70H5T/\n6QHAQ/43vkXu/KsV5kUkaoX67pRPYNXXNb1s/05wWxDi64iIiPS75n17KXvgfho2f0ZiwXAKbrqZ\nxGH5TpclItKtUAN9E5AIpAF7e9E+pd3jREREolbtJx9T/scHaK2pJuPU08j74pfxJiU5XZaISI9C\nDfSbgOOBtmkre3JpcPtZiK8jIiLSL/w+H/teWMLexc+A10vutV9g0Nmz8Hg8TpcmItIroY6hb5tT\n/k5jzJndNTTGnA7c1eFxIiIiUaO1pobS3/+GvYueJj4ri8If/ZjBn5utMC8iMSXUHvrfAt8E8oDX\njDGPAU8BnwA1BOahnwRcAVxNYEaccuA34SpYREQkHBq2bKH0D7+nZe9eUidPIf+rNxKXkeF0WSIi\nIQsp0FtrDxhjLgCWArnAdcGbv12z9t0au4ALgjPeiIiI9IuGks3sfvwxGrdvAyB55ChyrlpActEY\n/H4/B954nYrH/om/tZUhcy8le+6lWvVVRGJWyHNwWWvXGmOOBe4AvghkcHiIBzgAPAzcZa3dc9RV\nioiI9NLeJc8GxsP7D/U11Rdbtv38LoZcNJeWfXupWv0m3rQ0Cr52I2lTpjpYrYjI0evTpLrW2grg\n28aYWwgsHDWKwHCbGqAE+Mha6wtblSIiIr2wd8mz7F30dOc7/X72LXkWgKTRRRR881skZA/tx+pE\nRCKjy0BvjHkFeIPAaq9rrLX1HdsEV4X9IHgTERFxTEPJ5kDPfC/kzL9aYV5EXKO7HvpZHFoZttkY\n8z6BcL8SWGWtrYx0cSIiIr1V8cTCw4bZdGfv00+S+qMfR7giEZH+0V2g3wKMDv47ATg1eLsN8Blj\nNnAo4K+01u6IXJkiIiLda9i2NSJtRUSiXZeB3lo7xhgzHJje7nYsgbnrvcDk4O0bgN8Ys43DA/7G\nCNcuIiIiIjLgdXtRrLV2J/B48IYxJgM4g0MB/xQghcAsN6OCt+sIBPy9wCoOBfx3I3QMIiIiJI8c\nRX2x7XVbERG3CHUe+mrgpeANY0wCcAIwg0DAPxPIJhDwhwLzgjc/gUWmREREIiLnqgVs+/ldPY+j\n93jIuWpB/xQlItIP+jRtZRtrbTPwdvD2SwBjzDHATAJDcY4/2gJFRER6I7loDNmXXtb1tJVB2Zde\nRnLRmH6qSkQk8o4q0LcJ9tSfyqGhOGcCg9o16bjwlIiISNhlX3wJwBELSwHg8ZB96WUH24iIX06m\n6AAAIABJREFUuEWfAr0xZjCB0D6dwHCbE4DkDs32AauBN4M3ERGRiMu++BLSJk+h4omFB2ezSR45\nipz5V5M8usjh6kREwq9Xgd4YU8jh4+QnE5jppr1NBC6CbQvwn1prezchsIiISBglF42hUPPMi8gA\n0d1Ksd/k0BCawg6728bOt4X31dbaikgVKSIiIiIineuuh/6+dv/ey6HhM6uBd621DZEsTERERERE\netabITeNwOsE5pNfBXxgrfVFtCoREREREemV7gL9EgLj5bOAy4M3P1BnjGkbbrMKWBOcn15ERERE\nRPpZl4HeWnuJMcYDTOLQbDbTgZHArOANoNUYs55DF8SustbuiGjVIiIiIjGmpKyKx5YXs31XDR4P\nFOams2D2eIryM50uTWJcyPPDG2NGcCjcTwemdHgeP7CDdgEf+MiNM97s3l3lumOKBjk5GVRU6Euf\nWKRzF7t07mKTzlvseO7NEhatKulseQTmTS9i7pmaUjVWOPV3l5ub2WVuD3ke+mDv+2PBW9uc9Gdw\nqBf/JAKz4lwdvAFUAYNDfS0RERGRWPfcmyU8s7Kk031+Pwf3KdRLXx31SrHW2v3AC8Fb26qx84D/\nAI4PNss42tcRERERiTUlZVUsWtV5mG9v0aoSpozJ1vAb6ZOjDvQAxpgJHBqCMx0o4vBhOCEP7RER\nERGJdQuXFx8xzKYzfn+g7e3XnRj5osR1Qg70xph44EQOhfczgKF0HtrrCSxAtfIoahQRERGJSdt2\n1USkrUh7PQZ6Y0w6cDqHLoQ9GUil8wC/j0MXwq4E3rfWNoWtWhEREREROUyXgd4Y81sCAf5YII7O\nA/x2DoX3ldbajyNRpIiIiEgsGpmXTvGOA71uK9IX3fXQf7uT+zYQDO8EAvy2iFQlIiIi4gILZo/n\n7off7dU4+lknDI98QeJK3QX6VuA9DgX4Vdbaff1SlYiIiIgLFOVnMm96UZfTVrb3l+c3cqCmiXNO\nLsTr0Xwi0nvdBfpB1tq6fqskTIwx2cBPgUuAYQTG9b8B3G2tXd+h7QnA7cBMAvPklwEvAndZa0v7\ns24RERFxp7lnFvHOxt3sqKg9Yl/bwlKFuRn87cUNLFyxibWb9vCViyaRPSjZgWolFrnq419wFdvV\nQC6Bha8sMBmYD9QBZ1lr1wbbng8sBpqBfwJbganAVUA5cEZPQ4q0UmxkaOXD2KVzF7t07mKTzlts\n+HRbJf/76AcUZKeRlhLPtl01eDxQmJvOgtnjD849X1XbxN+XbuSD4j2kJMVz3bmG0ybn4VFvfVRx\nxUqxUe4vQA4wy1q7uu1OY8xLwO8JrFy71hiTBPw1uPssa+377dq+CjwI3Eugl19ERESkT1p9Ph5Z\nVgzADRdNZExBILx3Fgoz0xK5+fJjWfVhGY8uL+ZPSz7hg017+OL5x5CektDvtUvscE2gN8ZMA84F\n7msf5gGstf8A/tHurkuAfODx9mE+6M/AT4CLjDGF1trtESxbREREXOy1D0rZUVHD9Kn5B8N8dzwe\nDzOOK+CYUVn8ZcknvLtxN8U79vOVCycyZUx2P1QsscjrdAFhNC+4/ReAMcZrjMkLzqPf0azgdlnH\nHdZaP7CCwHCksyNQp4iIiAwA1XVNLFq5mZSkOK44a2xIj80dnMKPrjmBK88eS01dM/c8sY5/vvwp\njc2tEapWYpmbAv3xgB/Yaoz5HbCfwEWuB4wxq40x09u1nRzcburiudrunxSRSkVERMT1nnljM7UN\nLVw6fQyD0hJDfrzX6+HC00bx/750EsOHprHi/Z387KF3KCmrikC1EsvcFOgLCfSq/xU4C/gxsAD4\nO3AasNwYc3awbVZwu7+L59rXoZ2IiIhIr20tr+b1taUUDE076vnlR+ZlcMf1J3HeyYXs2lfHfz38\nHs+uKqHV5wtTtRLrXDOGHsgIblOAE6y1bd9JPWGM+Rj4BYELXacAqQR685u6eK7G4DY1QrWKiIiI\nS/n9fh5ZZvED15wznvi4o+8/TYiPY8Hs8Rw3bih/ef4TFq0qYd1ne/na3EkMG6K4MtC5qYe+Jbj9\nebsw3+Z3QA0w0RgzhsAUlh6gq++/2iZ+jbl5+EVERMRZaz7exaadBzjxmBwmjR4S1ueeOCqLO284\nhdMnD6OkrIqf/fXfvPr+Dvy9WYpWXMtNPfRtw2R2ddxhrW02xhQD04CRwJ7grq4uFx8a3O7pYj8A\nWVmpxMfH9aFU6UlOTkbPjSQq6dzFLp272KTzFl3qGpp56o3PSEyI46Yrp5HTTe/50Zy7H99wKqvW\n7eT+J9fxj5ctn2zbz3fmH8+QTC1G1R+i7e/OTYH+E+B0YBTwTif7U4LbBuAjAuPsJwKvdtJ2YnC7\ntrsXrKxUB34kaKGU2KVzF7t07mKTzlv0eeLVTeyramTe9CI8ra1dnp9wnLtjCjL52ZdP4aEXNvDe\nxt3c9L/L+dKcCZw0Ifeonle6F41/d24acvNScHthxx3GmMFAEdAKfAy8HNw1p5O2icBsAuPrV0Sk\nUhEREXGdsr21LHtnO0MHJTPn1JH98ppZGUncctVxXHeeobnFx/2L1vOn5z6hrqGl5weLa7gp0D8L\nbAGuNcbM6LDvLiAJWGytrQZeJDA15YWdtL2NwJCbR6y1+xARERHpgd/v57FXimn1+Zk/azyJCf03\nJNfj8TDrhBH89MsnU5SfwVsfl/PTv77Nxq2V/VaDOMvjdAHhZIw5k0Dvuwd4CCgFziEwvGYbcIa1\ntjTY9oxgWy+BVWS3AacAc4GNwPSeAv3u3VW6AiUCovGrLOkdnbvYpXMXm3TeoscHxRX8/qmPmDw6\ni1vnT8Pj6T5iRerctbT6WLJ6C0tWb8Xv93PeKYVcPnMMCbrmL2yc+rvLzc3s8pfKTT30WGvfBE4E\nFgNXAHcQGFP/O+CktjAfbLuawPz0SwisMnsHgQWn7iEQ/NU7LyIiIj1qbmll4fJi4rwerjnX9Bjm\nIyk+zsu8GWP48RdOJDcrhZf+vZ07//4u23bpg5+buaqHvr+phz4y1OMUu3TuYpfOXWzSeYsOz71Z\nwjMrSzj/lELmzxrfq8f0x7lrbGrlidc28er7O4nzerhs5hjmnDISr1fx72ioh15ERETERfZVNfD8\nW1vJTEvkkjOLnC7nMEmJcXzhvGP43uePIz0lgSdf+4z/ffR9KvbXO12ahJkCvYiIiEgfPb5iE00t\nPj5/9lhSkqJzNvCpY7O566uncuIxORTvOMAdf/03Kz8s1WJULqJALyIiItIHG7ZW8s7G3YwtyOT0\nKcOcLqdb6SkJ3DRvCl+9eCJeDzz0wkbuffojquqanC5NwiA6P0qKiIiIRLFWn49HX7F4gGvONXgd\nvBC2tzweD2dMyeeYwiz+8vwnfFC8h892vs31F05k2rihTpcnR0E99CIiIiIhevX9neysqGXGcfkU\n5Wc6XU5Isgclc9vVxzN/1jjqGlv43ZMf8rcXN9LQpMWoYpUCvYiIiEgIquqaWLSyhNSkeC4/a6zT\n5fSJ1+Ph/FNGcsf1J1OYm84b60r52V/fYdOOA06XJn2gQC8iIiISgqdf/4y6xhbmzSgiMzXR6XKO\nyoicdH7yxZO44LSRVOyv578feY+n3/iMllaf06VJCBToRURERHqppKyKlevKGJ6TxudOGO50OWGR\nEO/l82eP40fXnkB2ZjJLVm/lvx5+j517ap0uTXpJgV5ERESkF3x+P48us/iBa88xxHndFaNM4WD+\n84ZTmD41n627qrnzb++w7N3t+DS9ZdRz12+iiIiISIS8tb6cz0qrOHlCLhNGZTldTkSkJMVzw4UT\nufnyY0lKiOOxV4r51cK17KtqcLo06YYCvYiIiEgP6hpa+Ndrn5EY72X+rHFOlxNxJ5gc7vrqqRw3\nNpsNWyu54y//Zs0n5U6XJV1QoBcRERHpwbNvllBV28RFp49iSGay0+X0i0FpiXznyqlcf8EEWn1+\n/vjsJzyweD019c1OlyYdaGEpERERkW6U7qll+Xs7yBmczJxTRzpdTr/yeDzMPK6ACSMH86cln/Dv\nDbsp3nGAGy6cyOSiIU6XJ0EK9CIiIiJd8Pv9PPaKpdXnZ8Hs8STExzldkiNys1L5j2tP4MU121i8\nqoRfPb6W2SeO4Mqzx5KUEEdJWRWPLS9m+64aAEbmpbNg9viYW3QrVinQi4iIiHThfbuHj7dUMmXM\nEKaNG+p0OY6K83q5+IzRHDsmmz8+9zHL39vBJ1v2cczIwby+tpT2k+EU7zjA3Q+/y7zpRcw9s8i5\nogcIjaEXERER6URTcyuPrygmzuvh6tnj8Xg8TpcUFUYNy+Cn15/MOSeNoGxvHa99cHiYb+P3wzMr\nS3juzZL+L3KAUaAXERER6cTSt7ex50AD555cSH52mtPlRJXEhDhOnzyM3nzGWbSqhJKyqsgXNYAp\n0IuIiIh0sOdAPc+v2cqg9ETmnjHa6XKi0sLlxZ32zHfk9wfaSuQo0IuIiIh08PiKTTS3+Ljq7HGk\nJOmSw85sC14AG+62EjoFehEREZF2Ptmyj/c+rWDc8EGcNjnP6XJEeqRALyIiIhLU0urj0VeK8QDX\nnmt0IWw3RualR6SthE6BXkRERCRoxfs7Kd1Ty1nTChg1LMPpcqLagtnje3VRrMcTaCuRo0AvIiIi\nAhyobWLxqs2kJcdz2cwxTpcT9YryM5k3vec55udNL9ICUxGmQC8iIiICPPXaZ9Q3tjJvxhgyUhOd\nLicmzD2ziMtmFHXaU+/xwGUztLBUf9Bl2yIiIjLgbS6tYtVHZYzISefs4wucLiemzD2ziCljslm4\nvPjgbDYj89JZMHu8eub7iQK9iIiIDGg+v59Hln0KwLXnjifOqwEMoSrKz+T26050uowBS7+xIiIi\nMqC9+WEZJWXVnDopj2NGZjldjkjIFOhFRERkwKpraObJ1z8jMcHL588e63Q5In2iQC8iIiID1uJV\nW6iua2buGaMZkpnsdDkifaJALyIiIgPSzooalr+3g9zBKZx38kinyxHpMwV6ERERGXD8fj+PvlKM\nz+9nwTnjSYhXJJLYpd9eERERGXDe+7SCDVsrmTo2m2njhjpdjshRUaAXERGRAaWxuZXHVxQTH+fh\n6tnjnS5H5Kgp0IuIiMiA8uKareytauS8k0eSNyTV6XJEjpoCvYiIiAwYFfvreWHNNganJ3LxGaOc\nLkckLBToRUREZMBYuLyYllYfV31uHMmJ8U6XIxIWCvQiIiIyIKwv2csHxXswIwZx6qQ8p8sRCRsF\nehEREXG9llYfj71SjMcD15xr8Hg8TpckEjYK9CIiIuJ6r7y7g7K9dZw9bTgj8zKcLkckrBToRURE\nxNX21zTy7JslpCXHc9nMMU6XIxJ2CvQiIiLiak+99hkNTa1cftZY0lMSnC5HJOwU6EVERMS1Nu08\nwJvryxmZm85ZxxU4XY5IRCjQi4iIiCv5fH4eWWaBwIWwXq8uhBV3UqAXERERV1r5YSlby6s5bXIe\npnCw0+WIRIwCvYiIiLhObUMzT72+maTEOD5/9jinyxGJKAV6ERERcZ1FK0uoqW/mkjNGk5WR5HQ5\nIhGlQC8iIiKusmN3Da++v5O8rBTOOanQ6XJEIi7e6QJERESk90rKqnhseTHbd9Xg8UBhbjoLZo+n\nKD/T6dKigt8fuBDW5/dz9TmGhHj1XYr7KdCLiIjEiOfeLGHRqhL8/kP3Fe84wN0Pv8u86UXMPbPI\nueKixDsbd/Pp9v1MGzeUqWOznS5HpF8o0IuIiMSA594s4ZmVJZ3u8/s5uG8gh/rGplYeX7GJ+DgP\n82frQlgZOPQ9lIiISJQrKati0arOw3x7i1aVUFJW1Q8VRafn12yhsrqR808ZSV5WqtPliPQbBXoR\nEZEot3B58WHDbLri9wfaDkS7K+tY+vY2sjKSuPj00U6XI9KvFOhFRESi3LZdNRFp6yYLl2+ipdXP\n/FnjSEqMc7ockX6lQC8iIuIiPr8ff2+6813ko817WbtpD8cUDubkCblOlyPS7xToRUREotzIvPRe\nt21u8fGTP7/N0re3caC2KYJVRYeWVh+PvlKMxwPXnGvweDxOlyTS7xToRUREotyC2ePpTU71eGDS\n6Cwq9tfzxKubuO2+N/n9Ux+ydtMeWn2+yBfqgGXvbGfXvjpmHT+Cwtzef/ARcRNNWykiIhLlivIz\nmTe9qMtpK9u0zUVfU9/Mmo/LWflhGR8U7+GD4j0MTk/kzGPzmX5sPnlD3DEDTGV1I8+u3kJ6SgLz\nZg7c6TpFFOhFRERiQNv88h0XloJAz3z7haXSUxI456RCZp84gq27qln5YRlrPt7F829t5fm3tmIK\nBzNjaj4nTcglKSF2LyB98rVNNDa1Mn/OONKSE5wuR8QxCvQiIiIxYu6ZRUwZk83C5cVs21WDxwOF\nueksmD2eovzMI9p7PB5GD8tk9LBM5n9uHO/ZClauK2Xjtv3Y7ft59BXLqRPzmHFcAaOHZcTU+PPi\nHft56+NdjMrLYObUAqfLEXGUAr2IiEgMKcrP5PbrTgQgJyeDiorqXj0uMSGO0ycP4/TJw9i9v55V\nH5bx5kdlvLa2lNfWljIiJ43pUws4fXIeGamJkTyEo+bz+XnkZQvAtecZvN7Y+SAiEgkK9CIiIgNM\n7uAULp85hnnTi1hfso+VH5aytngPC5cX8+Rrm5g2PoeZU/OZNHpIVIblN9aVsm13DWdMGca44YOc\nLkfEcQr0IiIiA5TX62Hq2Gymjs2mqq6JNesDF9K+u3E3727czZDMJM6cks/0qfnkDE5xulwAauqb\nefqNzSQnxnHl2WOdLkckKijQi4iICJmpiZx3ykjOPbmQzWVVrFxXxr837OK51Vt4bvUWJo7KYsZx\n+ZxockiId+5C2mdWbqamvpmrPjeOwelJjtUhEk0U6EVEROQgj8fD2IJBjC0YxNWzx/Pup7tZua6U\nDVsr2bC1ktSkeE6bnMeMqQWMGpbRr7Vt21XNax/sZNiQVM45aUS/vrZINFOgFxERkU4lJcZx5rH5\nnHlsPuX76lj5YSmrPypnxfs7WfH+TkbmpTNjagGnTc6L+LSRfr+fR5dZ/H645tzxxMdpbUyRNgr0\nIiIi0qNhQ1L5/NnjuHzmGD78bC+rPixj3aa9PLLM8viKTZx0TA7Tp+YzYVQW3ghMf/n2hl3YHQc4\nfvxQphRlh/35RWKZAr2IiIj0WpzXy/Hjczh+fA4HahpZvb6cNz4sY80nu1jzyS6GDkpm+tTAirRD\nMpPD8poNTS08sWIT8XFe5s8eH5bnFHETBXoRERHpk0HpSVxw2ijmnDqSTTsPBC6k3biLRStLWLyy\nhMljhjBjagHTxg0lIb7vQ2SWrN7K/pom5p4xmtwomW1HJJoo0IuIiMhR8Xg8jB8xmPEjBnP1OeN5\nZ2PgQtr1m/exfvM+0lMSOH3yMGYcl8+InPRun6ukrIrHlhezfVcNAPnZqWzfXc2QzCQuPH1UfxyO\nSMxRoBcREZGwSUmKZ+ZxBcw8roCde2pZ9WEpq9eXs+zd7Sx7dztF+ZnMOC6fUyfmkZJ0eAx57s0S\nFq0qwe8/dN+W8sBKuOOGDyIpwbnpMkWimasDvTEmA1gHjAb+01r7nx32nwDcDswEBgNlwIvAXdba\n0v6tVkRExF2GD01j/qzxXHHWWNZt2sPKD8v4aPNeSsqqWPhKMSdNyGXG1HxM4WCWrN7CMytLunyu\nf2/YzfChJcw9s6gfj0AkNrg60AO/IRDmAfztdxhjzgcWA83AP4GtwFTga8BcY8wZ1tpt/VeqiIiI\nO8XHeTnxmFxOPCaXyupG3vyojFUflrF6fTmr15czJCOJfdWNPT7PolUlTBmTTVF+Zj9ULRI7XBvo\njTGXAF8G3gNO7LAvCfhr8MezrLXvt9v3KvAgcC9wSf9UKyIiMjBkZSRx8RmjufD0Udht+1n5YSlr\nPt7Vq8f6/bBweTG3X3diz41FBhBXrspgjMkB/gS8DzzQSZNLgHzgmfZhPujPwHbgImNMYUQLFRER\nGaC8Hg8TRmXxtbmTSUjofRzZFrxYVkQOcWWgJxDmM4AvAK2d7J8V3C7ruMNa6wdWAB7g7AjVJyIi\nIkEewr8QlchA4rpAb4z5MoEe+B9bazd00WxycLupi/1t908KZ20iIiJypJF53U9l2de2IgOFqwK9\nMWY08FvgVWvtb7ppmhXc7u9i/74O7URERCRCFswej6cXnfQeT6CtiBzONYHeGOMF/k5giM31PTRP\nJTDrTVMX+xvbtRMREZEIKsrPZN70nqejnDe9SDPciHTCTbPc3ArMAK631m7voW0dgTHyiV3sT27X\nrktZWanEx2uRi0jIyclwugTpI5272KVzF5vcct5umDeVtLQkHn1pIz7/4fu8Hrjm/AnMP/cYZ4qL\nELecu4Eo2s6dKwK9MWYKcDfwpLX24S6atf8yb09wm91F26Ed2nWqsrLbvC99lJOTQUVFtdNlSB/o\n3MUunbvY5LbzNmtaAUV56SxcXnxwNpuReeksmD2eovxMVx2r287dQBKN584VgR64gkBv+5XGGF8X\nbX5qjPkpgWE5HwFnAROBVztpOzG4XRvuQkVERKRrRfmZmmdeJERuCfSrgV/RYTXYoCnAnGCb1cA7\nQD1wc/D++9s3NsYkArMJjK9fEbmSRURERESOnisCvbV2GZ3MKQ9gjLmeQHB/2Vp7Z/C+OAJTU15o\njJlhrV3Z7iG3ERhy85C1dl/H5xMRERERiSauCPShsta2BoP+y8BLxph/ANuAU4C5wEbgB85VKCIi\nIiLSO66ZtrIbfjoZimOtXQ2cBiwB5gF3EFhw6h7gDPXOi4iIiEgscH0PvbX27wQuhO1s33rgqv6t\nSEREREQkfAZCD72IiIiIiGsp0IuIiIiIxDAFehERERGRGKZALyIiIiISwxToRURERERimAK9iIiI\niEgMU6AXEREREYlhCvQiIiIiIjHM9QtLucnWqu08WfwcO2pKAShML+CK8XMZlVnocGUiIiIi4hQF\n+hjxYslyni95GT/+g/d9dmALv3j3Xi4qOo8LimY7WJ2IiIiIOEWBPga8WLKcJSUvdbrPj//gPoV6\nERERkYFHY+ij3Naq7Txf8nKP7Z4veZmtVdv7oSIRERERiSbqoY9yTxU/d9gwm6748fNU8XPceuJN\n/VBV+LW/PsDj8TAiLd9V1we4+foHN587N583Eel/er+USPE4XUAs2727quekfZRuef0nNLU29aqt\nBw9TcyaTnpBKWkIa6QlppCWkBreHfk6JT8bjiZ5T39n1ARA4HjdcH+Dm49OxuUdOTgYVFdVOlyEh\n0nmLHW5+T3HzsXXGqb+73NzMLsObeuhdxI+fdRXre2zn9XjbBf0jA//BbWIaafFppCemkhwXmQ8B\nbr8+wM3Hp2OLzWMTkf7n5vcUNx9bLFGgj3KF6QV8dmBLr9qOGTSaG4/9EjXNtdQ011LbXEdt8N+H\n/dwU2FY1VlNWu6tXz+31eElvF/oDHwBSD/858fD9yXFJ3X4ICOX6gEnZJua+tnPz8enYYvPYRKT/\nufk9xc3HFmsU6KPcFePn8ot37+1xHL0HD1eOnxsI1YlpvX7+Vl8rdS31weB/6ANAbVMdNS3BbXMt\ntcEPBPsbD1BaW96r547zxB0c/nOw5z8xjfT4VNIS01i54y1XXx/g5usfdGyxeWziDm4ehw3uG4vt\n5vcUNx9brImegdQxqD/G0EP3X2e1ubjo/H77Oqvjh4DAB4BA4D/0bUD7Dwh11LfUH/XrZiSkh6H6\n/lPdXBNS+1g6Ph1bQKI3gV+ddRdeT+xPGKax2LHB7WOVY/n4mlubqW2po6ap9rCOsCeLn6PV39rr\n53Ht+2VcIr8+6+4IVtN/NIZe+qTtDSxa3uTivHFkJKaTkdj7N51WXyu1LXWB0N8UeKN76ONHaenl\nm5wHD6kJqX0t2RE1zbW96rmA2Ds+HVtAk6+Z77/+/8hLzSE3NYe8tFzyUnPIS80lL3UoiXGJEa5W\nBhK3j1WOpuNr9rUcDOSHAnoXw1iDnVm9ncCiO25+v5TIUqCPERcUzWZStuGp4ufYHoNfQ8Z548hM\nzCAzMQOCI4JGbS8M4fqAUTH3Vd09793v2uPTsQWkJ6QxKCmT8rqKg3+X7WUlDQ4E/INBP3AbnDQo\nqmaakujn9rHKkTy+Fl/LwW+QD/v2uKmrgF5LYy/DeaI3gbSENPJShh68lqz9MNP0hFRe3LK819er\nufn9sjC9ILLFDHAK9DFkVGZhTP2h9ySU6wOuGD+3n6oKHzcfn44tcGw3HXcDozIL8fl9VDYcYHdd\nBeV1u4PbCnbXVbCxspiNlcWHPTYpLjHQo3/wlnuwlz8xLiGShycxyu1jlUM5vsftIq6b8PnOJ4AI\nhvT2Ab6htbFXNSR440lPSCcnZWiH2d4C130dmhgiLXh9WGqvvoUbmpKt98sYPLZYoy6io9BfY+jd\nLNquDwg3Nx+fjq13x9bQ0sDuuj2U1+1mV11F4Fa7m4r6PTT7Wg5r68FDVvLgI4J+XloOgxIzw96r\n7+aLK91yYWVTaxM1zbXcueaXNPuae/UYr8fLibnTIlxZeL23ey0+vy9szxfvjT9sKua2IN6+5zwt\n8fD9kRwip/fL2Dy2rkTjGHoF+qOgQB8esXwRVG+4+fh0bH3n8/vY17A/GPJ3Hwz6u+sqONB05H8U\nyXFJ7Xr1c8lLC/w7N2UoCX3o1de5639NrU2HTx7QVEtNS11g23x4z3Jb73LHD30S+MAyveDUdlMo\npx0xhXKiNyHqhrVF6+9lOLj52DqjQO8yCvThs7Vq+8HrA9zWUwiHHx/Ebm9hZ9x87pw6b/Ut9YFe\n/drDh+/srqs44kJyDx6GJGcdDPgHe/VTc8lMTO801Li5R62/jq2ptfmI6X4PX+/jyIsme9vDnhyX\ndFhYTUtIw1Zu4kBTVa8ePypjBF879otHc3j97k8fPczW6h29ajt20OiYG1LURu+X7qDMmfamAAAR\nYElEQVRA7zIK9JGh6fNil85dZAV69Sspr203fCfYu1/ddOT0cclxyUcE/VZfK3/75LFejXn9wUk3\nx9R/xlurtvd6PG/7Y2tubQ4G8rZe8nZT8h4W0A+1aeplOE+KS+ywIN+Rvcnt1+tIS0gjwXvk5W19\nPbZY4fbj64zeL2OXAr3LKNBHht7kYpfOnXPqmusPH74TvFXU7QlpDuz2clOGMmd07PTSL92ynN31\ne3rVNjEukbT4VGpb6no93WBiMJx3XDDviMAeHJudFp/ap+FQXXHzNyvg/uPrSO+XsSsaA71muRER\ncYHUhBSKBo2kaNDIw+5v9bWyt6HyYNBf/NmLvb74cHf9Hh7e8HgkynVcU2sTafGp5KXmdAjkqZ0H\n9DCH876ItjVJws3txycSSQr0IiIuFueNIzd1KLmpQzkWeL5kWa97pOM9ccw/5vLIFhhGj3/6dK8X\nq0uMS+TuM38c4YrCr+OaJG4bhx3ra66IOEWBXkRkAClML+j1QjCjMgs5o+DkyBYURmvK3hkQi9y0\nX5PEjcM23Lbmikh/8DpdgIiI9J8rxs/F04vLp2JxIRg3H5uISHcU6EVEBpBRmYVcVHRej+0uKjov\n5oY4uPnYRES6oyE3IiIDjJsvPnTzsYmIdEWBXkRkAHLzxZW6sFJEBhrNQ38UNA99ZLjxIq+BQucu\nduncxSadt9ilcxe7onEeeo2hFxERERGJYQr0IiIiIiIxTIFeRERERCSGKdCLiIiIiMQwBXoRERER\nkRimQC8iIiIiEsMU6EVEREREYpgCvYiIiIhIDFOgFxERERGJYQr0IiIiIiIxTIFeRERERCSGKdCL\niIiIiMQwBXoRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERER\nERERERERERGRo+VxugCJXcYYL/B14MvAJCABKAVeBu621u7o0P4E4HZgJjAYKANeBO6y1pZ28vz5\nwI+Bi4ACoBpYBfzcWvtOFzUdC9wBnAVkBF9jMXCntbbyKA/ZFaLtvBljioAfAucBw4FWoAR4Afg/\na+2eoz9qd4j0uQs+Jhm4C7gV8Fhru1xR3BiTBHwXuAYYT+DcrQPut9Y+1vcjdZ8oPHeD///27j1a\nqrKM4/gXRUVKLTFC0UTUh0VSmi7TNBNweYnykpdMEyPSpd3MTLPUspLSzNSSEF14o3CpeCEVry2N\nyrt5KbF8slSK5S1Rl6aBxOmP5x3ZzNkzZ2bOmTP7zPw+a531Mnu/+51385w588w77343cCKwHzAK\nWAE8CcwFznL3/zZ6ru2kaHHLOfZDwH3AYGCCuy+o6wTbWBFj18wcRQm9NCS9UOYBnwQWAVcD/wF2\nB3YEXgR2cPenU/09iV/at4BfAc8AHwQ+DTwH7OTuizLtbwLcBWwMXAc8CGwIHA6sDezn7jeX9Wl3\n4EbgZeByYAkwERgPPAFs5+5v9Ol/xABTtLiZ2TbAAuIP2y3A/URSuCewE7A49Sf3j2knaXbs0jEf\nBi4DRhNvfl3uvnqF/qwO3Eq8xh4A5gNrpfY3Jz7AndI3Zz+wFTB2I4gP2aOJZPB24vV5ILApcC+w\ni7v/r0/+AwaoosUtp39DgD8CY4EuIqH/XeNn3D6KGLtm5yiDGz1QOt5U4oXyAPAxd1+atp9qZpcS\nCdwpwBFpFO/itH9Xd3+o1IiZ3QlcAEwH9sm0fy6RFH7N3c/L1L+QSBIvNrPNS7/8ZrYu8QL5J/AR\nd38xHTLNzC4iRov3Aq7to/MfqAoVN+AMIpk/yd3PyLRzmpldAnyOGL0/ttdnPvA1NXZmti9wDfHG\nsgPw9jEVfIl4M7rG3Q/KtHM6kWR828zmufuDjZ1uWyla7M4ikpBfuPtXM+18l4jdjsAhRGLTyYoW\nt3I/AsYADwMfqvPYdleo2PVHjlLz1zoiZbYHXiVG4ZaW7bsglTunch9ilPa67AslmUX8gn8ije6W\nRo/2Jb4am56t7O6PEV8JvxfYP7NrKjAMODHzQikd8wV3H+HunZ7MQ/HiNoYYWZqX09cbUrlFTWfW\n/poWu2RTYAawrbs/UkN/jiZit8oovLu/DpxJfAN8dA3tdIKixW5r4HXg1OxGd38T+GVZfzpZ0eL2\nNjObQEx3mw78qZ5jO0TRYtf0HEUj9NIQdz8KOKrC7tdTWfrqaWIqb89pp8vM7iBGYscTbya7Eh82\n73D3rpz2fwN8FpjAyhGk/YD/AtcDmNmaxItnSc6LuWMVMG6PEn8YxwJ/Las/OlOn4zU5dgAX1jpv\n2syGEzH7l7s/kVPlN6kcX0t77a5IsUvtfKDK7vL+dKyixa0kjfZeCjgx5/v8ettodwWMXdNzFI3Q\nSzN8IpV3pHKrVD5ZoX5p+/sbrA+wTdq+iZldT8yVWwy8amZzyz5ZS75WxO0k4AVgpplNNbMxZjbW\nzI4CTgb+BpxdY/87WW9jR51vTuN6aH8RMRd1VJrnK5X1d+zq7Y/ka2XczgNGAJPTtypSn1bEruk5\nihJ66VPpCu6TiV/W09Lmd6fylQqHLSmrV1f9NFqxLnFRyu+JaQClK9vvBQ4A7k6jipKjFXEDcPfH\niT90jxBfbf4FWEiMOM0HdnT3l2o+kQ7UR7GrV9X20zc0rxDTbt7V4HO0vRbFrlp/phCrTT3s7lf0\ndfvtopVxM7P9gcnEVBJdn1KnVsSuv3IUTbmRPpOu+L6BWOniU+6+OO0aSvwCL6tw6NJMvWxZa/11\nUjkGONvdj8/0aTax2so+xFJRX6npZDpIC+OGmY0kVhbYmkjiFxB/9CYSFy1tbGb7u/sSpJs+jF29\neop1XzxHW2th7Cr15wjiNfgcq17nIhmtjJuZvZeY//0AK5NRqVELY9cvOYpG6KVPpFGD3wLvBA5y\n9/mZ3W8QI3VrVjh8SKZetqy1/vJUrgC+n62YRgpPTw/3rnwGnanFcYNYWWBb4DB3/7K7X+Xuc9z9\nC8DxxHrA59R4Oh2lj2NXr55i3RfP0bZaHLvyvgxKKxNdSFz8N97dn+mLtttNAeI2i0gqD3f3Fb1o\np+O0OHb9kqMooZdeM7OTiDVeXybWwf11WZXSjYGGVWhig7J69dYvfU221N1fy6n/eCpHVmivI7U6\nbmY2mlgT+Fl3vzKn/vnEqMmBlc6hUzUhdvWq2n5ao/5dxBuYvl3JKEDssn0ZSiy9dyJwDzHFzXvb\nbjtqddzM7Ehi7vcJFS5EB91bKFerY0c/5ShK6KVXzGwaMI1YiWR7d78/p9qfUzm2QjOl7aWln+qq\nn64Q/zswxMw2yKm/dirfqtBexylC3IhlwiAuiu3G3ZcRqxEM0fUPKzUpdvUqtT+mwv4tiCmdT6Q4\nCoWJXakvaxErbuxHrI893t1zX4udriBxOzSV081sRfaHmJ4IcGfa9rkGn6PtFCF2/ZWjKKGXhpnZ\nscQqJXcRdxWsdDfP21K5V04bawK7EXPXSlec/5b4xR5vZmvktDcplbdktt1KjE5M6l6dbVOptXop\nVNyeTeUoM+t2PU/6w7dOeo5e3RK7XTQxdnVJ1zQ8BAy3uF16ubzXaEcrSuxSO4OAK4hrVX7u7oe5\nuwY8chQoblcSNwTL+1lYVufPeQ10mgLFDvohR1FCLw0xs22Im8c8CUxKN5Op5OZSPTPbpWzf8cTX\nWXNKFz6m8vK0/YSy592FuPubAzdlds0A/gd818w2zNR/B/C99HB2HafYlooUN3f/B/FGtB7wjZzn\n/3Yqb1Ky0dzYNah0J+BpFrdZL/VzOBHPpcDMXrTfNgoYuy8TN4G7yt11F+YKihQ3d5/p7t/M+yHu\n7gtwftpW7x1n206RYpc0PUfRfCtpiJndRHyavQa4r0rVC9z9NTPbifgUvBpxY4ZFwIeJi0D+Cnw0\n+2JJo7N3E1/dX09c1b8psVzXMmAPd1/lec3sOGJ0YnF6juXAZ1IbtwEfr3DDo45RtLilVQduJ0bi\nbwf+QPxdGp9+FgM7u/uiXp76gNcPsZsBZKc2lVY6yd698A53n5E55lpiysbDRLzfQdw8bARwjLv/\nooFTbTtFil2aN/8MMV/4TCrPC37F3WfVeIptqUhx66GflxLTbsa7++9qOLW2V8TYNTtHUUIvDTGz\np4D3Uf13qAvYrJSMmdk4YlmmXYkL5v4JzAOmuXu39V/NbBjwHWIkaSPi4ro7gR+4e/ldRUvHTAKO\nA7YjlqZy4oXzM3dfnndMJyli3MxsU+CbxPrXGxMXUj4F3Aj8ROvQh2bHLtN+nkGp7cvcfWrmmMHA\nV4EpwJbEqPwfgbPcXdNtkiLFzsxGAf9I26r152l3H11lf9srUtx66OclREI/QQl9KGrslKOIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIFFm1\nW+KKiEgHMbMFwC7Ai8BYd1/SQ30DHiVuYT7b3ac0vZMiItLNaq3ugIiIFMaRwFLgPcA51Sqa2SDg\nQiKZfwH4etN7JyIiuZTQi4gIAO7uwGnp4WQz26NK9SOAjwFdwLHu/nKz+yciIvk05UZERN5mZoOB\nB4EPAk8D49z9jbI6GwKPA+sB89197/7up4iIrKQRehEReZu7Lyem3qwARgE/zKl2HpHMvwZ8sd86\nJyIiuTRCLyIi3ZjZ2cCxRGK/k7vfn7bvC1yXqh3j7tMzx+wJTAU+AgwH3gQWAlcCM939rQrPtQWw\nL7AXMA4Ylo59GrgZONfdn6tw7BTgYgB3X83MNgNOAPYERgJvuvu7G/pPEBEZIJTQi4hIN2Y2FHiM\nGKV/DNgWGEpMtdkIuMfdd0511wZmAwekw7syTZXeZx4BPu7uz5c9z3pAdv593rEvAZ909/ty+jmF\nSOi7iDn984F1M+286u7r13LOIiIDlabciIhIN2ne/NHp4TjgW8CPiWR+GTEtp2QOkcy/SVxUuw0x\nyr4Z8CVgSdp2tZnlve/cB3wD2BUYk459P/B54gPEMGBu+pBRySDgCuDfwCHE6PxI4PA6TltEZEDS\nCL2IiFRkZrOBw4gkfg3ifeMH7v69tP9A4CpiucsJ7n5vThtbAQ8AQ4CD3X1uHc8/lBjd3wI40t0v\nKts/hTTlBnge2NrdX6jjFEVEBjyN0IuISDVfJ0a91ySS+cdZ9ULZY1I5Ky+ZB3D3hcDl6eGh9Tx5\n+qZgXnq4Ww/Vz1QyLyKdaHCrOyAiIsXl7i+Z2enAT9Omk0sXt6bR8x3T9gVm9s4qTT2Wyu3ydprZ\nJGJ6zPbACGDtnGpbVmm/i7iAVkSk4yihFxGRnpQuWu1i1QtYR7PyfeSqGtsann2Q1r2fAxyU2dxF\n/sWx6/XQ9lM19kFEpK1oyo2IiDQqm2B31fADMQ8/61usTOavBvYh5ssPA9YhVqw5I+2vOgjl7ksb\nOQkRkYFOI/QiItKo11PZBUx09wUNtHFUKue4++S8Cj2sbiMi0vE0Qi8iIo16hkjmBwGb13uwma1P\nLC3ZRfUpO+Ma6p2ISIdQQi8iIg1x91eA+9PDTzfQxFqpHESFb4zNbCSxPr2IiFSghF5ERHrj7FTu\nYWZHVqtoZkPM7H2ZTS8Cb6R/751Tf3VgJrB6X3RURKRdaQ69iIg0zN3npptLHQRcYGYTgYuAhcSd\nY9cn7vq6B3AwcCZpCUx3X25m1xI3rppiZkuAWUSi/wHgFGAi8BdgbH+el4jIQKKEXkREalXp7uKT\ngdeAqUTSfnCFel3EHWWzTiSm1GwCHJd+svXPBV4FTm2syyIi7U9TbkREpCddZeUq3H2Zux8B7ECM\nsD9BJPhvEaPtdwNnAR919+llxz5L3ExqBrAIWAY8D9wK7O/u2QS/Wt9ERERERERERERERERERERE\nRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERk\nAPk/k3yUiDRk0x4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f513c9490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = all_university_rankings.groupby('survey')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.margins(0.05)\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.year, group.world_rank, marker='o', linestyle='-', ms=12, label=name)\n",
    "    \n",
    "ax.legend(loc=2,prop={'size':30})\n",
    "\n",
    "plt.title(\"CU Boulder Ranking\", fontsize=26)\n",
    "plt.xticks(fontsize=22)\n",
    "plt.yticks(fontsize=22)    \n",
    "plt.ylabel(\"World Rank\", fontsize=26)  \n",
    "plt.xlabel(\"Year\", fontsize=26)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see which countries have the highest educational ratings\n",
    "\n",
    "* Create a groupby variable that groups scores based on the country\n",
    "* After that, use the **agg()** function to calculate different statistics (mean score and university count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.672857</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>45.825517</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>45.139583</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>47.011000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>44.781111</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country       mean  count\n",
       "0  Argentina  44.672857      7\n",
       "1  Australia  45.825517     58\n",
       "2    Austria  45.139583     24\n",
       "3    Belgium  47.011000     20\n",
       "4     Brazil  44.781111     36"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwur_agg = cwurData.groupby(['country'])['score'].agg(['mean','count']).reset_index()\n",
    "cwur_agg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort the universities based on the descending order of the mean score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Israel</td>\n",
       "      <td>52.654091</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>USA</td>\n",
       "      <td>51.839860</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>51.208846</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>50.160000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>49.474653</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>47.958276</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>47.890000</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>47.774167</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Russia</td>\n",
       "      <td>47.381111</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Canada</td>\n",
       "      <td>47.359306</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Japan</td>\n",
       "      <td>47.229560</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>47.011000</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Germany</td>\n",
       "      <td>46.413130</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>46.399000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>France</td>\n",
       "      <td>46.367339</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Norway</td>\n",
       "      <td>46.330000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>46.295000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>46.198333</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>45.825517</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Finland</td>\n",
       "      <td>45.531500</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country       mean  count\n",
       "26          Israel  52.654091     22\n",
       "54             USA  51.839860    573\n",
       "50     Switzerland  51.208846     26\n",
       "43       Singapore  50.160000      5\n",
       "57  United Kingdom  49.474653    144\n",
       "33     Netherlands  47.958276     29\n",
       "49          Sweden  47.890000     24\n",
       "13         Denmark  47.774167     12\n",
       "40          Russia  47.381111      9\n",
       "6           Canada  47.359306     72\n",
       "28           Japan  47.229560    159\n",
       "3          Belgium  47.011000     20\n",
       "18         Germany  46.413130    115\n",
       "46    South Africa  46.399000     10\n",
       "17          France  46.367339    109\n",
       "35          Norway  46.330000     12\n",
       "20       Hong Kong  46.295000     12\n",
       "47     South Korea  46.198333     72\n",
       "1        Australia  45.825517     58\n",
       "16         Finland  45.531500     20"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_frame = cwur_agg.sort('mean', ascending=False)\n",
    "sorted_frame.head(20)"
   ]
  }
 ],
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