nfl/nfl_baseline_2.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# NFL Baseline 2 Model"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Importing necessary libraries."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "from collections import defaultdict\n",
      "\n",
      "import cPickle as pickle\n",
      "import math\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import pylab\n",
      "import seaborn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = seaborn.color_palette()\n",
      "\n",
      "from matplotlib import rc\n",
      "\n",
      "rc(\"figure\", facecolor=\"white\")\n",
      "rc(\"axes\", facecolor=\"white\")\n",
      "rc(\"axes\", edgecolor=\"grey\")\n",
      "\n",
      "\n",
      "rc(\"grid\", alpha=0.9)\n",
      "rc(\"grid\", linewidth=0.2)\n",
      "rc(\"grid\", linestyle=\":\")\n",
      "\n",
      "\n",
      "rc('font',**{'family':'serif','serif':['Palatino']})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def side_by_side(*objs, **kwds):\n",
      "    from pandas.core.common import adjoin\n",
      "    space = kwds.get('space', 4)\n",
      "    reprs = [repr(obj).split('\\n') for obj in objs]\n",
      "    print adjoin(space, *reprs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Creating a dictionary containing team name keyed by its abbreviation.\n",
      "team_dict = {'ARI': 'Arizona Cardinals',\n",
      "             'ATL': 'Atlanta Falcons',\n",
      "             'BAL': 'Baltimore Ravens',\n",
      "             'BUF': 'Buffalo Bills',\n",
      "             'CAR': 'Carolina Panthers',\n",
      "             'CHI': 'Chicago Bears',\n",
      "             'CIN': 'Cincinnati Bengals',\n",
      "             'CLE': 'Cleveland Browns',\n",
      "             'DAL': 'Dallas Cowboys',\n",
      "             'DEN': 'Denver Broncos',\n",
      "             'DET': 'Detroit Lions',\n",
      "             'GB' : 'Green Bay Packers',\n",
      "             'HOU': 'Houston Texans',\n",
      "             'IND': 'Indianapolis Colts',\n",
      "             'JAC': 'Jacksonville Jaguars',\n",
      "             'KC' : 'Kansas City Chiefs',\n",
      "             'MIA': 'Miami Dolphins',\n",
      "             'MIN': 'Minnesota Vikings',\n",
      "             'NE' : 'New England Patriots',\n",
      "             'NO' : 'New Orleans Saints',\n",
      "             'NYG': 'New York Giants',\n",
      "             'NYJ': 'New York Jets',\n",
      "             'OAK': 'Oakland Raiders',\n",
      "             'PHI': 'Philadelphia Eagles',\n",
      "             'PIT': 'Pittsburgh Steelers',\n",
      "             'SD' : 'San Diego Chargers',\n",
      "             'SEA': 'Seattle Seahawks',\n",
      "             'SF' : 'San Francisco 49ers',\n",
      "             'STL': 'St. Louis Rams',\n",
      "             'TB' : 'Tampa Bay Buccaneers',\n",
      "             'TEN': 'Tennessee Titans',\n",
      "             'WAS': 'Washington Redskins'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_team_name(abbr):\n",
      "    return team_dict[abbr]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainDF = pd.read_csv('trainNFL.csv', index_col=0)\n",
      "testDF = pd.read_csv('testNFL.csv', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainDF = trainDF[['game_id','year','week','home_team','away_team','home_score','away_score','visitor_point_spread','over_or_under','winner']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainDF = trainDF.sort(column='game_id').reset_index(drop=True)\n",
      "testDF = testDF.sort(column='game_id').reset_index(drop=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Python/2.7/site-packages/pandas-0.13.1_213_gc174c3d-py2.7-macosx-10.9-intel.egg/pandas/core/frame.py:2542: FutureWarning: column is deprecated, use columns\n",
        "  warnings.warn(\"column is deprecated, use columns\", FutureWarning)\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainDF.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>game_id</th>\n",
        "      <th>year</th>\n",
        "      <th>week</th>\n",
        "      <th>home_team</th>\n",
        "      <th>away_team</th>\n",
        "      <th>home_score</th>\n",
        "      <th>away_score</th>\n",
        "      <th>visitor_point_spread</th>\n",
        "      <th>over_or_under</th>\n",
        "      <th>winner</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 1</td>\n",
        "      <td> ATL</td>\n",
        "      <td>  SF</td>\n",
        "      <td> 36</td>\n",
        "      <td> 28</td>\n",
        "      <td>  7.0</td>\n",
        "      <td> 42.5</td>\n",
        "      <td> ATL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 1</td>\n",
        "      <td> CLE</td>\n",
        "      <td> JAC</td>\n",
        "      <td>  7</td>\n",
        "      <td> 27</td>\n",
        "      <td>-10.0</td>\n",
        "      <td> 38.0</td>\n",
        "      <td> JAC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 1</td>\n",
        "      <td> DAL</td>\n",
        "      <td> PHI</td>\n",
        "      <td> 14</td>\n",
        "      <td> 41</td>\n",
        "      <td>  6.0</td>\n",
        "      <td> 40.0</td>\n",
        "      <td> PHI</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 1</td>\n",
        "      <td>  GB</td>\n",
        "      <td> NYJ</td>\n",
        "      <td> 16</td>\n",
        "      <td> 20</td>\n",
        "      <td>  2.5</td>\n",
        "      <td> 36.0</td>\n",
        "      <td> NYJ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 9</td>\n",
        "      <td> 2000</td>\n",
        "      <td> 1</td>\n",
        "      <td>  NO</td>\n",
        "      <td> DET</td>\n",
        "      <td> 10</td>\n",
        "      <td> 14</td>\n",
        "      <td>  1.0</td>\n",
        "      <td> 39.5</td>\n",
        "      <td> DET</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   game_id  year  week home_team away_team  home_score  away_score  \\\n",
        "0        1  2000     1       ATL        SF          36          28   \n",
        "1        2  2000     1       CLE       JAC           7          27   \n",
        "2        3  2000     1       DAL       PHI          14          41   \n",
        "3        4  2000     1        GB       NYJ          16          20   \n",
        "4        9  2000     1        NO       DET          10          14   \n",
        "\n",
        "   visitor_point_spread  over_or_under winner  \n",
        "0                   7.0           42.5    ATL  \n",
        "1                 -10.0           38.0    JAC  \n",
        "2                   6.0           40.0    PHI  \n",
        "3                   2.5           36.0    NYJ  \n",
        "4                   1.0           39.5    DET  \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "### Train Data Operations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trdf = trainDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_historic_games(team, gameId, trdf):\n",
      "    df = trdf[trdf.game_id < gameId]\n",
      "    hdf = df[df.home_team == team]\n",
      "    adf = df[df.away_team == team]\n",
      "    newdf = pd.concat([hdf, adf])\n",
      "    newdf = newdf.sort_index()\n",
      "    try:\n",
      "        return pd.concat([hdf, adf])\n",
      "    except:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_points(team, df):\n",
      "    pFor = []\n",
      "    pAgainst = []\n",
      "    for r, v in df.iterrows():\n",
      "        hTeam = v.home_team\n",
      "        aTeam = v.away_team\n",
      "        hScore = v.home_score\n",
      "        aScore = v.away_score\n",
      "        if (team == hTeam):\n",
      "            pFor.append(hScore)\n",
      "            pAgainst.append(aScore)\n",
      "        elif (team == aTeam):\n",
      "            pFor.append(aScore)\n",
      "            pAgainst.append(hScore)\n",
      "    return (sum(pFor)/len(df), sum(pAgainst)/len(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in xrange(1, 50):\n",
      "    lst = []\n",
      "    for r, v in trdf.iterrows():\n",
      "        gameId = v.game_id\n",
      "        home = v.home_team\n",
      "        away = v.away_team\n",
      "        homehGames = get_historic_games(home, gameId, trdf)\n",
      "        awayhGames = get_historic_games(away, gameId, trdf)\n",
      "        if (isinstance(homehGames, pd.DataFrame) and isinstance(awayhGames, pd.DataFrame)):\n",
      "            if (len(homehGames.index) >= i and len(awayhGames.index) >= i):\n",
      "                homehGames = homehGames[-i:]\n",
      "                awayhGames = awayhGames[-i:]\n",
      "                hFor, hAgainst = get_points(home, homehGames)\n",
      "                aFor, aAgainst = get_points(away, awayhGames)\n",
      "            else:\n",
      "                hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "        else:\n",
      "            hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "        v = v.append(pd.Series([hFor, hAgainst, aFor, aAgainst], index=['hFor', 'hAgainst', 'aFor', 'aAgainst'], name=r))\n",
      "        lst.append(pd.DataFrame(v).T)\n",
      "    trdfn = pd.concat(lst)\n",
      "    csv_name = \"baseline2/data_iter_\"+str(i)+\".csv\"\n",
      "    trdfn.to_csv(csv_name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_diff(hFor, hAgainst, aFor, aAgainst):\n",
      "    if sum([hFor, hAgainst, aFor, aAgainst]) > 0:\n",
      "        return (hFor + aAgainst)/2  - (aFor + hAgainst)/2 + 3\n",
      "    else:\n",
      "        return 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_predicted_winner(diff_xy, home_team, away_team):\n",
      "    if diff_xy >= 0:\n",
      "        return home_team\n",
      "    else:\n",
      "        return away_team"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def is_prediction_correct(winner, predicted_winner):\n",
      "    if winner == predicted_winner:\n",
      "        return True\n",
      "    else:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "for i in xrange(1, 50):\n",
      "    csv_name = \"baseline2/data_iter_\"+str(i)+\".csv\"\n",
      "    trdfn = pd.read_csv(csv_name, index_col=0)\n",
      "    years = set(trdfn.year)\n",
      "    lst = []\n",
      "    for year in years:\n",
      "        trdfny = trdfn[trdfn.year == year]\n",
      "        trdfny['diff_xy'] = trdfny.apply(lambda x: get_diff(x['hFor'], x['hAgainst'], x['aFor'], x['aAgainst']), axis=1)\n",
      "        trdfny['predicted_winner'] = trdfny.apply(lambda x: get_predicted_winner(x['diff_xy'], x['home_team'], x['away_team']), axis=1)\n",
      "        trdfny['correct_prediction'] = trdfny.apply(lambda x: is_prediction_correct(x['winner'], x['predicted_winner']), axis=1)\n",
      "        lst.append(float(sum(trdfny.correct_prediction))*100/len(trdfny.index))\n",
      "    dfs.append(pd.DataFrame(lst, columns=[i], index=years).T)\n",
      "ans = pd.concat(dfs)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = ans.T.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = ans.T.mean()\n",
      "pd.DataFrame(tmp, columns=[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 55.911578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 56.487615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 57.393433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 58.688076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 58.873848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 58.654954</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 58.964574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 59.229551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 59.362039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 60.037442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 59.680300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 59.542051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 59.949597</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 59.998560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 61.019585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 60.439228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 59.805588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 60.033122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 59.226671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 59.269873</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> 59.314516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> 59.135945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> 58.912730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> 59.091302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> 58.912730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 58.689516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> 58.957373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> 58.823445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> 58.689516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> 58.510945</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> 58.466302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> 58.421659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> 57.841302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td> 58.153802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> 58.243088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> 58.243088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> 58.019873</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> 58.198445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> 57.707373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> 57.528802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> 57.618088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> 57.618088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> 57.662730</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> 57.618088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td> 57.618088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> 57.841302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> 58.064516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> 58.153802</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> 57.930588</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>49 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "            1\n",
        "1   55.911578\n",
        "2   56.487615\n",
        "3   57.393433\n",
        "4   58.688076\n",
        "5   58.873848\n",
        "6   58.654954\n",
        "7   58.964574\n",
        "8   59.229551\n",
        "9   59.362039\n",
        "10  60.037442\n",
        "11  59.680300\n",
        "12  59.542051\n",
        "13  59.949597\n",
        "14  59.998560\n",
        "15  61.019585\n",
        "16  60.439228\n",
        "17  59.805588\n",
        "18  60.033122\n",
        "19  59.226671\n",
        "20  59.269873\n",
        "21  59.314516\n",
        "22  59.135945\n",
        "23  58.912730\n",
        "24  59.091302\n",
        "25  58.912730\n",
        "26  58.689516\n",
        "27  58.957373\n",
        "28  58.823445\n",
        "29  58.689516\n",
        "30  58.510945\n",
        "31  58.466302\n",
        "32  58.421659\n",
        "33  57.841302\n",
        "34  58.153802\n",
        "35  58.243088\n",
        "36  58.243088\n",
        "37  58.019873\n",
        "38  58.198445\n",
        "39  57.707373\n",
        "40  57.528802\n",
        "41  57.618088\n",
        "42  57.618088\n",
        "43  57.662730\n",
        "44  57.618088\n",
        "45  57.618088\n",
        "46  57.841302\n",
        "47  58.064516\n",
        "48  58.153802\n",
        "49  57.930588\n",
        "\n",
        "[49 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp.plot(label=\"Baseline 2\")\n",
      "plt.grid(b=True, which='major', color='gray', linestyle=':')\n",
      "plt.xlabel('Number of games considered')\n",
      "plt.ylabel('Prediction accurcay')\n",
      "plt.ylim([0, 65])\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Library/Python/2.7/site-packages/matplotlib-1.4.x-py2.7-macosx-10.9-intel.egg/matplotlib/font_manager.py:1240: UserWarning: findfont: Font family [u'serif'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SJAnmquprLLiiUnq1mfUN7eV3uS1jz7yFGSsseHPnf3Hw+0tQK70w58EBGD+8F2fJRG2E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\nZI7FnIiISOZYzImIiGSOxZyIiEjm/h/ldopSVZTNpAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1088c4590>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "### Test Data Operations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tsdf = testDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_train_games(team, gameId, trdf):\n",
      "    df = trdf[trdf.game_id < gameId]\n",
      "    hdf = df[df.home_team == team]\n",
      "    adf = df[df.away_team == team]\n",
      "    newdf = pd.concat([hdf, adf])\n",
      "    newdf = newdf.sort_index()\n",
      "    try:\n",
      "        return pd.concat([hdf, adf])\n",
      "    except:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_points(team, df):\n",
      "    pFor = []\n",
      "    pAgainst = []\n",
      "    for r, v in df.iterrows():\n",
      "        hTeam = v.home_team\n",
      "        aTeam = v.away_team\n",
      "        hScore = v.home_score\n",
      "        aScore = v.away_score\n",
      "        if (team == hTeam):\n",
      "            pFor.append(hScore)\n",
      "            pAgainst.append(aScore)\n",
      "        elif (team == aTeam):\n",
      "            pFor.append(aScore)\n",
      "            pAgainst.append(hScore)\n",
      "    return (sum(pFor)/len(df), sum(pAgainst)/len(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 15\n",
      "\n",
      "lst = []\n",
      "for r, v in tsdf.iterrows():\n",
      "    gameId = v.game_id\n",
      "    home = v.home_team\n",
      "    away = v.away_team\n",
      "    homehGames = get_train_games(home, gameId, trdf)\n",
      "    awayhGames = get_train_games(away, gameId, trdf)\n",
      "    if (isinstance(homehGames, pd.DataFrame) and isinstance(awayhGames, pd.DataFrame)):\n",
      "        if (len(homehGames.index) >= i and len(awayhGames.index) >= i):\n",
      "            homehGames = homehGames[-i:]\n",
      "            awayhGames = awayhGames[-i:]\n",
      "            hFor, hAgainst = get_points(home, homehGames)\n",
      "            aFor, aAgainst = get_points(away, awayhGames)\n",
      "        else:\n",
      "            hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "    else:\n",
      "        hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "    v = v.append(pd.Series([hFor, hAgainst, aFor, aAgainst], index=['hFor', 'hAgainst', 'aFor', 'aAgainst'], name=r))\n",
      "    lst.append(pd.DataFrame(v).T)\n",
      "trdfn = pd.concat(lst)\n",
      "csv_name = \"baseline2/test_data_iter_\"+str(i)+\".csv\"\n",
      "trdfn.to_csv(csv_name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "\n",
      "csv_name = \"baseline2/test_data_iter_\"+str(i)+\".csv\"\n",
      "tsdfn = pd.read_csv(csv_name, index_col=0)\n",
      "\n",
      "years = set(tsdfn.year)\n",
      "for year in years:\n",
      "    tsdfny = tsdfn[tsdfn.year == year]\n",
      "    tsdfny['diff_xy'] = tsdfny.apply(lambda x: get_diff(x['hFor'], x['hAgainst'], x['aFor'], x['aAgainst']), axis=1)\n",
      "    tsdfny['predicted_winner'] = tsdfny.apply(lambda x: get_predicted_winner(x['diff_xy'], x['home_team'], x['away_team']), axis=1)\n",
      "    tsdfny['correct_prediction'] = tsdfny.apply(lambda x: is_prediction_correct(x['winner'], x['predicted_winner']), axis=1)\n",
      "    accuracy = float(sum(tsdfny.correct_prediction))*100/len(tsdfny.index)\n",
      "    dfs.append(pd.DataFrame([accuracy], index=[year], columns=[\"accuracy\"]))\n",
      "ans = pd.concat(dfs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>accuracy</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> 52.884615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 64.423077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 63.551402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 60.747664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 60.747664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 65.420561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 59.813084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 64.485981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 58.878505</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 68.224299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 55.140187</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 61.682243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 61.682243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td> 62.616822</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>14 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "       accuracy\n",
        "2000  52.884615\n",
        "2001  64.423077\n",
        "2002  63.551402\n",
        "2003  60.747664\n",
        "2004  60.747664\n",
        "2005  65.420561\n",
        "2006  59.813084\n",
        "2007  64.485981\n",
        "2008  58.878505\n",
        "2009  68.224299\n",
        "2010  55.140187\n",
        "2011  61.682243\n",
        "2012  61.682243\n",
        "2013  62.616822\n",
        "\n",
        "[14 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "float(mean(ans))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "61.44988189380713"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "### Train Data Operations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trdf_alpha = trainDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_historic_games(team, gameId, trdf):\n",
      "    df = trdf[trdf.game_id < gameId]\n",
      "    hdf = df[df.home_team == team]\n",
      "    adf = df[df.away_team == team]\n",
      "    newdf = pd.concat([hdf, adf])\n",
      "    newdf = newdf.sort_index()\n",
      "    try:\n",
      "        return pd.concat([hdf, adf])\n",
      "    except:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_points(team, df, alpha):\n",
      "    pFor = []\n",
      "    pAgainst = []\n",
      "    for r, v in df.iterrows():\n",
      "        hTeam = v.home_team\n",
      "        aTeam = v.away_team\n",
      "        hScore = v.home_score\n",
      "        aScore = v.away_score\n",
      "        if (team == hTeam):\n",
      "            pFor.append(hScore)\n",
      "            pAgainst.append(aScore)\n",
      "        elif (team == aTeam):\n",
      "            pFor.append(aScore)\n",
      "            pAgainst.append(hScore)\n",
      "    numGames = len(pFor)\n",
      "    weights = []\n",
      "    for i in xrange(1, numGames+1):\n",
      "        weights.append(alpha**i)\n",
      "    return (int(round(np.dot(pFor, weights)/float(sum(weights)))), int(round(np.dot(pAgainst, weights)/float(sum(weights)))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for alpha in np.arange(1, 1.01, 0.1):\n",
      "    for i in xrange(1, 50):\n",
      "        lst = []\n",
      "        for r, v in trdf_alpha.iterrows():\n",
      "            gameId = v.game_id\n",
      "            home = v.home_team\n",
      "            away = v.away_team\n",
      "            homehGames = get_historic_games(home, gameId, trdf_alpha)\n",
      "            awayhGames = get_historic_games(away, gameId, trdf_alpha)\n",
      "            if (isinstance(homehGames, pd.DataFrame) and isinstance(awayhGames, pd.DataFrame)):\n",
      "                if (len(homehGames.index) >= i and len(awayhGames.index) >= i):\n",
      "                    homehGames = homehGames[-i:]\n",
      "                    awayhGames = awayhGames[-i:]\n",
      "                    hFor, hAgainst = get_points(home, homehGames, alpha)\n",
      "                    aFor, aAgainst = get_points(away, awayhGames, alpha)\n",
      "                else:\n",
      "                    hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "            else:\n",
      "                hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "            v = v.append(pd.Series([hFor, hAgainst, aFor, aAgainst], index=['hFor', 'hAgainst', 'aFor', 'aAgainst'], name=r))\n",
      "            lst.append(pd.DataFrame(v).T)\n",
      "        trdf_alpha_n = pd.concat(lst)\n",
      "        csv_name = \"baseline2/alpha\" + str(alpha) + \"/train_data_iter_\"+str(i)+\".csv\"\n",
      "        trdf_alpha_n.to_csv(csv_name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_diff(hFor, hAgainst, aFor, aAgainst):\n",
      "    if sum([hFor, hAgainst, aFor, aAgainst]) > 0:\n",
      "        return (hFor + aAgainst)/2  - (aFor + hAgainst)/2 + 3\n",
      "    else:\n",
      "        return 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_predicted_winner(diff_xy, home_team, away_team):\n",
      "    if diff_xy >= 0:\n",
      "        return home_team\n",
      "    else:\n",
      "        return away_team"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def is_prediction_correct(winner, predicted_winner):\n",
      "    if winner == predicted_winner:\n",
      "        return True\n",
      "    else:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adfs = []\n",
      "for alpha in np.arange(0.5, 1.01, 0.1):\n",
      "    dfs = []\n",
      "    for i in xrange(1, 50):\n",
      "        csv_name = \"baseline2/alpha\" + str(alpha) + \"/train_data_iter_\"+str(i)+\".csv\"\n",
      "        trdfn = pd.read_csv(csv_name, index_col=0)\n",
      "        years = set(trdfn.year)\n",
      "        lst = []\n",
      "        for year in years:\n",
      "            trdfny = trdfn[trdfn.year == year]\n",
      "            trdfny['diff_xy'] = trdfny.apply(lambda x: get_diff(x['hFor'], x['hAgainst'], x['aFor'], x['aAgainst']), axis=1)\n",
      "            trdfny['predicted_winner'] = trdfny.apply(lambda x: get_predicted_winner(x['diff_xy'], x['home_team'], x['away_team']), axis=1)\n",
      "            trdfny['correct_prediction'] = trdfny.apply(lambda x: is_prediction_correct(x['winner'], x['predicted_winner']), axis=1)\n",
      "            lst.append(float(sum(trdfny.correct_prediction))*100/len(trdfny.index))\n",
      "        dfs.append(pd.DataFrame(lst, columns=[i], index=years).T)\n",
      "    tmp = pd.concat(dfs)\n",
      "    tmp = tmp.T.mean()\n",
      "    adfs.append(pd.DataFrame(tmp, columns=[alpha]).T)\n",
      "ans = pd.concat(adfs).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ans.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "      <th>13</th>\n",
        "      <th>14</th>\n",
        "      <th>15</th>\n",
        "      <th>16</th>\n",
        "      <th>17</th>\n",
        "      <th>18</th>\n",
        "      <th>19</th>\n",
        "      <th>20</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0.5</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.445853</td>\n",
        "      <td> 56.189516</td>\n",
        "      <td> 58.660714</td>\n",
        "      <td> 57.353111</td>\n",
        "      <td> 55.387385</td>\n",
        "      <td> 56.984447</td>\n",
        "      <td> 57.260945</td>\n",
        "      <td> 56.810196</td>\n",
        "      <td> 57.896025</td>\n",
        "      <td> 57.936348</td>\n",
        "      <td> 55.697005</td>\n",
        "      <td> 58.198445</td>\n",
        "      <td> 56.419931</td>\n",
        "      <td> 57.932028</td>\n",
        "      <td> 57.347350</td>\n",
        "      <td> 56.722350</td>\n",
        "      <td> 55.432028</td>\n",
        "      <td> 54.717742</td>\n",
        "      <td> 56.368088</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.6</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.267281</td>\n",
        "      <td> 56.275922</td>\n",
        "      <td> 58.464862</td>\n",
        "      <td> 57.534562</td>\n",
        "      <td> 56.281682</td>\n",
        "      <td> 57.926267</td>\n",
        "      <td> 57.841302</td>\n",
        "      <td> 57.165899</td>\n",
        "      <td> 58.794643</td>\n",
        "      <td> 58.610311</td>\n",
        "      <td> 57.399194</td>\n",
        "      <td> 58.109159</td>\n",
        "      <td> 57.226382</td>\n",
        "      <td> 58.424539</td>\n",
        "      <td> 57.883065</td>\n",
        "      <td> 56.723790</td>\n",
        "      <td> 56.680588</td>\n",
        "      <td> 55.119528</td>\n",
        "      <td> 56.189516</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.7</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.182316</td>\n",
        "      <td> 57.214862</td>\n",
        "      <td> 59.095622</td>\n",
        "      <td> 57.756336</td>\n",
        "      <td> 57.226382</td>\n",
        "      <td> 58.202765</td>\n",
        "      <td> 57.930588</td>\n",
        "      <td> 57.747696</td>\n",
        "      <td> 59.863191</td>\n",
        "      <td> 59.235311</td>\n",
        "      <td> 58.028514</td>\n",
        "      <td> 58.379896</td>\n",
        "      <td> 58.882488</td>\n",
        "      <td> 59.144585</td>\n",
        "      <td> 58.286290</td>\n",
        "      <td> 57.884505</td>\n",
        "      <td> 57.348790</td>\n",
        "      <td> 56.682028</td>\n",
        "      <td> 56.546659</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.8</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.408410</td>\n",
        "      <td> 56.900922</td>\n",
        "      <td> 59.183468</td>\n",
        "      <td> 58.472062</td>\n",
        "      <td> 57.682892</td>\n",
        "      <td> 58.510945</td>\n",
        "      <td> 58.283410</td>\n",
        "      <td> 58.063076</td>\n",
        "      <td> 60.262097</td>\n",
        "      <td> 59.500288</td>\n",
        "      <td> 58.873848</td>\n",
        "      <td> 59.408122</td>\n",
        "      <td> 59.194988</td>\n",
        "      <td> 59.459965</td>\n",
        "      <td> 58.423099</td>\n",
        "      <td> 58.555588</td>\n",
        "      <td> 57.707373</td>\n",
        "      <td> 57.887385</td>\n",
        "      <td> 57.752016</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0.9</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.358007</td>\n",
        "      <td> 57.802419</td>\n",
        "      <td> 59.000576</td>\n",
        "      <td> 58.922811</td>\n",
        "      <td> 58.034274</td>\n",
        "      <td> 59.146025</td>\n",
        "      <td> 58.696717</td>\n",
        "      <td> 59.279954</td>\n",
        "      <td> 60.437788</td>\n",
        "      <td> 59.229551</td>\n",
        "      <td> 59.052419</td>\n",
        "      <td> 60.260657</td>\n",
        "      <td> 59.955357</td>\n",
        "      <td> 60.574597</td>\n",
        "      <td> 59.411002</td>\n",
        "      <td> 59.848790</td>\n",
        "      <td> 59.180588</td>\n",
        "      <td> 59.182028</td>\n",
        "      <td> 58.957373</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1.0</th>\n",
        "      <td> 55.911578</td>\n",
        "      <td> 56.538018</td>\n",
        "      <td> 57.173099</td>\n",
        "      <td> 58.417339</td>\n",
        "      <td> 58.742800</td>\n",
        "      <td> 58.568548</td>\n",
        "      <td> 58.738479</td>\n",
        "      <td> 59.236751</td>\n",
        "      <td> 59.274194</td>\n",
        "      <td> 60.030242</td>\n",
        "      <td> 59.369240</td>\n",
        "      <td> 59.766705</td>\n",
        "      <td> 60.125288</td>\n",
        "      <td> 60.220334</td>\n",
        "      <td> 60.708525</td>\n",
        "      <td> 60.303859</td>\n",
        "      <td> 59.940956</td>\n",
        "      <td> 59.675979</td>\n",
        "      <td> 59.583813</td>\n",
        "      <td> 59.314516</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 49 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "            1          2          3          4          5          6   \\\n",
        "0.5  55.911578  56.445853  56.189516  58.660714  57.353111  55.387385   \n",
        "0.6  55.911578  56.267281  56.275922  58.464862  57.534562  56.281682   \n",
        "0.7  55.911578  56.182316  57.214862  59.095622  57.756336  57.226382   \n",
        "0.8  55.911578  56.408410  56.900922  59.183468  58.472062  57.682892   \n",
        "0.9  55.911578  56.358007  57.802419  59.000576  58.922811  58.034274   \n",
        "1.0  55.911578  56.538018  57.173099  58.417339  58.742800  58.568548   \n",
        "\n",
        "            7          8          9          10         11         12  \\\n",
        "0.5  56.984447  57.260945  56.810196  57.896025  57.936348  55.697005   \n",
        "0.6  57.926267  57.841302  57.165899  58.794643  58.610311  57.399194   \n",
        "0.7  58.202765  57.930588  57.747696  59.863191  59.235311  58.028514   \n",
        "0.8  58.510945  58.283410  58.063076  60.262097  59.500288  58.873848   \n",
        "0.9  59.146025  58.696717  59.279954  60.437788  59.229551  59.052419   \n",
        "1.0  58.738479  59.236751  59.274194  60.030242  59.369240  59.766705   \n",
        "\n",
        "            13         14         15         16         17         18  \\\n",
        "0.5  58.198445  56.419931  57.932028  57.347350  56.722350  55.432028   \n",
        "0.6  58.109159  57.226382  58.424539  57.883065  56.723790  56.680588   \n",
        "0.7  58.379896  58.882488  59.144585  58.286290  57.884505  57.348790   \n",
        "0.8  59.408122  59.194988  59.459965  58.423099  58.555588  57.707373   \n",
        "0.9  60.260657  59.955357  60.574597  59.411002  59.848790  59.180588   \n",
        "1.0  60.125288  60.220334  60.708525  60.303859  59.940956  59.675979   \n",
        "\n",
        "            19         20      \n",
        "0.5  54.717742  56.368088 ...  \n",
        "0.6  55.119528  56.189516 ...  \n",
        "0.7  56.682028  56.546659 ...  \n",
        "0.8  57.887385  57.752016 ...  \n",
        "0.9  59.182028  58.957373 ...  \n",
        "1.0  59.583813  59.314516 ...  \n",
        "\n",
        "[6 rows x 49 columns]"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ans.plot()\n",
      "plt.grid(b=True, which='major', color='gray', linestyle=':')\n",
      "plt.xlabel('Number of games considered')\n",
      "plt.ylabel('Prediction accurcay')\n",
      "plt.ylim([0, 63])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3Ps71DgGIehy0H3/aot9Fu999SqmmMjnJ9NNb2Ld1kGNWFFujTDiUI96QJhAu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\nTkREpHAs5kRERArHYk5ERKRw/x+qZNa1OydTMQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1165dd290>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ans.plot()\n",
      "plt.grid(b=True, which='major', color='gray', linestyle=':')\n",
      "plt.xlabel('Number of games considered')\n",
      "plt.ylabel('Prediction accurcay')\n",
      "plt.ylim([52, 62])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NuTwHrSLEkuRw/tp6dmSu9zuG5MvPl/6FGwm1eFgkhUfnZklBdJmVQx/XjXpN\np7UWldqAznDu9r3b5WPvrkrUGiVOh5fnntrH7p3lhMZRVc3rCfCn3xzkVEkLGTmx3P2ttWTkxNLa\n1IvTMXSyGUBS1hUs2PDQuG6vXygxawMAHQ17J/zafouTYvjOyjw0SolnSurYXT/yF4+xhGSZKquT\nV8408/CHp/n1sRp8F1GNThCEy4/ozC9zx1ta6SOKZQk6tGdz3LbuPrRqCXXIhz5l5M7cNLcAY1wO\nmaFmrtWU8d1V+URpVBz8qBa/b/g11V63FZ/HhtGSMyhfvndXFR63nys3zeUr963FHKPnw3cq+MOv\n9mG3jTxZrM/p5blffUJtZRcFRUnc8Y3V6KM05M1LAKCmYvhOUlIo0eonV5ghJnE+Gp2FnpYjBPyu\nSZ0DoCDWxAOrCjBqVPzxVCPPn2zgQHMP9b2uMSvIeYMhStpt/O5EPQ/sKuWJfRXsrGmn1enhcKuN\n/z5YhUusaxcE4SzlI4888silbsRYnE4nGs3QtczCyPzBEB0uLzvKG7GForhjUQ4WvRZZlnn39dOY\ntTLJbSUkf/YzhGJj8fl8w8ZYbY7GWnuEOEMX+YUb8fuhurwTk1lHWubQmeq9naexdZwkPn0Vxpis\n8M+sLl594RjmaB3bbluCJc7A4uXpWLtdVJ/p5PjhRuITjcQnDr61ZOtx8Ycn99HR5qB4RQbbbl+C\nSqUEQKNTceSTetRqJYWLUoa042JIkgRyiN6uM6jUURe1Vj5ap2ZxYjQlbVbO9Dg52m7jw8Yu3qpu\n56PGLko77NTa+uh0eXH5g1T0OHitspU/nmxgf4uVRrsbnVLBylQLN+SncPuCTLrdPko77ZzstLMk\nOQbd2Zh82jmdzhE/x8LUEXGOvMn0ebNiNrvIzwzP7Q9Sb3fR5fLS5fad918fvV7/2aPiiFe5yIkJ\n57n7HF4C/hAGbXh5mi45edT4WpYtRXHgBZDAWnecVeuL2b+7mn17qlm2JgvFBTPanQPFYs7lyz94\nu5xgIMTV181FpQ53PPooDTfftYyj++vZuf0UL/32ECvX53DtlkJUaiXtrXb++PR+nHYva6/OY+Pm\neYNG+onJJkxmHdXlncghGUkxtbPm49JW0lL9Dh2Nn5CUdQWSYvIdZrJRx4+vnE+NtY+2Pg9tfV7a\n+zy0OT2c6XZwptsx5DUpRh3FidEsToohJyZqUIW9rxdnY1Ar2d3QxRP7KvjuyjwSooYWufm0EX8n\npoeIc+R40c8AAAAgAElEQVSJ2uyfIi5/kB/vLaPbPXhmuEKCWJ2GeXEmNO56tJ4mri5cOdAR9ufL\ndT57+L+j5MwhPEqNn7eKruAhWk6/TWHGfIpXZnD4k3pOH29lwdLBa80d1hoUSi1RxvBoua2llxNH\nmkhKNbNwafqQcy9bk016dix/ee4IB/fWUl/TzZqrcnn71ZN43H423VDE6itzh21X7rwESg420trc\nS2rG8MVrJkul1hOXuozOxn3YOk5iSV58UefTqZQUJZgpShi8lM4bCNLu8tLu9NDa50WvUrAoMZok\nw8irBRSSxG3zMzBqVLxe1cYT+8r5zoo80s2jr6MXBGHqPPTQQ+zZs4e4uDh27Ngx7DE/+9nP2L17\nN3q9nscff5zc3KF/y6aK6MwnoKvdQUujjYXL0qd8/fREvVHVSrfbx/KUGObHm4mP0hKv12DRaVAq\nJFyOFsr27SQqJoO89HM7hPXPZNc4u1CZTKiMY8/0Tlu7mY5X9uJPglMf/5T586/hyD6ZTz6oYv6S\n1IFY+L0OvK5OzHEFAyPZXa+XgQwbNxeOOHpOSjFz73c2sPNvpzi6v4HtLxxDoZDYdtsSFi5LH/Y1\nAHnzEik52EjVmY4p78wBEjPX09m4j9aa9/C6rUC4UE24YM3ZojVyCBnQRcVjSS6e8OdCq1KSaY4i\nc4IdsSRJfL4gFePAuvZK/n55LnmxY3+jt3l8lHU7yLcYiRcjekGYlJtuuok777yTBx98cNjn9+zZ\nw5kzZ9ixYwfHjx/noYce4uWXX45Ye2ZFZz5TarN/8HY5ZSdaiY6NImtOXESvFV4WJaM3Dh05tzo9\n7KrrIE6v4auLstEMU7yltfo9AFJzNw3qYPpH5uqeVnRnJ7+NVWtZpTegLTPhqmhFcVUqXQ1vsPHq\nWA4ezqKmopPcueFZ6/3bjvbfYq+p6KS6vJOc/Hhy5yaM+n7VGhVbvriYnPwEDnxYwxWbCsiblzjs\nsSGfj776BnLyM5EkqD7TwRWfKRj1/JOhMyQSHV9Ib1cZzZVvjHm8tf0E2QtuQakaOqqOVD3rjdmJ\nGNQqfneijv86WMn/WTqHhYnRg46RZZlmh4eSDhsl7b3U94Y/A1nmKP553dxL/sV0qszEmuGXIxHn\nsOXLl9PU1DTi87t27WLbtm0ALF68GLvdTldXF/Hx8WOeezJ93qzozGfKh8ba1QfAkU/qI9qZy7JM\n5ZFnCAY8zF/3ABpdzKDnXi5rIijDlwrTh+3IXY4WbB2lRJkzMMfPHfScrTv8HnReG7qU8K3j8cQ3\nurAIxytnSP/813Fq6+hpPcb61T3UnOgla87dqNRR5+XLc5BDMu+9fhqAa7cUjrvDmF+cyvzi1BGf\n99sdlP3b4zjKy5n74AOkZVpoarDhcfvR6ad+G9LshbfitNUBIHH2PUjSuUeSBLJMa+372DpOUra/\njdzFd6E3DZ6UF8nP8Oq0WAxqJb86WsP/HKnmK4uyWZ5iobLHyfEOG8fbe+k6m45RSlAYZ8ITDFJr\nc3Gio3fE+vqzzUz5O3G5m4lx/nNZE0fabFN6zmXJMXyxcOQ7g2Pp6Ogg+bw0ZnJyMm1tbePqzEXO\nPMJ6OsMTlcpOtNDnnI/BGJlblJ6+dvzecOWwhtN/JXfJVwc6wxMd4VnMhXEmliRFD/v6kUblEL7N\nLkmg8/eNmS8/n3l+EbzyV1xlteTceTvxaSsp/eQFYqNrKP3wp2QWbsFhrUZSqDCYMzhZ0kxbs50F\nS9JISZ+azsLb2cmpR/4N99lvw527PyR3+Taa6q3UVnZSuGjkLwGTpVJHDdoXfSSm2Dyaq96ivW4P\nZQd+QVbRTcSlLpvy9oxkYWI0312Zzy8OV/Ps8TpeONWI++zyN71KwYoUC4uTolmYYCZKraLZ4ebR\nj8p4rbKVRYnRl83oXBBmkgv3kIjk75nozMfJ4/bj88tIcohgUEHJwUbWXZMXkWs5uqsAUKh09HaV\nYW0rITZlCf5giJfKmlBIcEvR8Hl7t6N1xFE5hG+zG7SgIDTqGvMLmebNA4WC3lPh0bYpNo+k/L9j\n/7t/oSC/kbqTLwHhW+whWcEHb51BqVRw9fXjr28+mr76Bk4/+q/4untI/fxWbMdKsB49Rs7Nd7EH\nqDoTmc58vCSFkvSCLRiis6g79TJ1J1+kz1ZP+rwbUCim59csL9bIP60u4P8drkIiPGIvToymIM6I\n6oI9lNNMepalxHC41XZZjc6FT6cvFqZf1Cg6EhITE2lraxt4PNwWqVNpVhSNmQl1gPtzzUmOWpQK\nOLq/Hjk08s5dF8PeUwlAXvHdKBRqGs5sx+9z8l5dB50uL1dnJZBm0g/72paa/lH5Z4Z09gF/EIfd\ng1EZXrbWPzIfT61lVZQeQ04Ozsoqgt5w1bU5BUm4/AvZ/dEyoqLDnXZ0fCGHP67D1uNm+bpsLHEX\nP8O699QpSh/6Eb7uHrK/ejc593yF+PXrkP1+NE1n0EepqT7TMepOatPFkrSQwlXfRm9MobNpH+UH\nn8Trtk5bPet0s56fXL2An1y9gNvmZ1CUYB7SkffbkpeCBLxW2TojYnexRM3w6SHiPD4bN24c2ISl\npKQEs9k8rlvsIGqzR1R3q43M9BYWbGph1cJS9Oo6Ksvqpvw6ciiIw1qDRh+LKTaP1PzrCfpdlJ16\nkzeq2jBqVGzNH75IitvRiq39xNlR+dARsc3qBhn0wbN587Od+XhrLUfPL0QOBHBWhu8cSJLE2qvz\ncLt1VDcsZ8GGh4hOWsNH71Wi1anYcG3+ZMMwoHvfAU49/K+EPB7yv/tt0m68AYC4dWsB6Pl4H7lz\nE7H3euhsnxl/YHSGBOatup/YlGW47I2U7f9vgp7mafscKyRpXLfz0kx6lqdYaLC7Od7ROw0tiyxR\nM3x6iDiHfe973+PWW2+ltraWK6+8kldeeWXQ9qdXXnklc+fOZevWrTz22GM8/vjj4z63yJlHUHdD\nO5kZrWjNMtpoK5ZkK47mM5y2p2COzccUl4/JMgeF8uKqIrkczYQCHsxJ4clpiZnrsLaVsKNNiVcO\n8aXCdAzq4f/ZRhuVA1jPTn7TuqwotFrUlondWjXPL6LltdexnzpN9IL5ABQuSsESF8Xxw01c9dm5\n7P+wBrfLz8bNhUQZLi4WrW/tpObp36DQaJj3zz/AsqR44Lmo9DQMOdnYSo6Tc/WXOHmsmeozHSQm\nj74JzHRRKDVkL7gFoyWbxrLtVB19lrT860nOufpSN22QLXnJHG61sqOylcUidy4I4/bzn/98zGMe\neOABHnjggWlozSwZmc8E1rYOzKY+fK0BvC810dQ6l67uGDzOTtrrP6Tq6LOUvP8vlB/6FT2txyZ9\nHfvZfLkpLpyPlyQFofTPUyHnkKDoZU3K8OvC3Y62s6Py9GFH5cC5rU9tbeiSkyb8h9tcFF6vbj9d\nNvAzhUJizVW5BAMh3nu9jIMf1WCO1rFyw+RLoMqyTMMLL1Lzq6dRm4ws+LdHB3Xk/eLXr0MOBIjp\nrQOgurxj0teMBEmSSEhfzdxV96PRRdNc+ebAzPiZIvUyG50LwqfVrOjMZ0J+xh9oR5JA49Ujd/mI\n1eRz4PAieoO3k7/sXpKyr0JvTMZpraa29AW8rp5JXcdxNl9uig135iFZ5q+14U54rXSQlso3h31d\na827wPAz2Pv1F4zR9nUPmsk+3hyY2mxGn5GO/Uw5ocC5TT4Wr8jAYNRw4kgTgUCIq66bh1o9ufKn\ncjBI9VO/pvGlP6NLTmLhE49hyh9+omH8+vCtdtehfSSnmqmv7sHnnXmbjxjM6STnfQGAhrLtyPLM\n2vFsS17yZZE7F7nc6SHiHHkiZx5Ban141BKfEx4hpnib0OpUHDvQgiEmj/SCzRSu+Q6ZRTcBYO04\nMeFrhIJ+nLY69MYU1Jrwe/64qZt6u4sVKTHkmFR0Ne3H0VMz6HVuRxvWMUblcMHWp+fNZJ9IDix6\nfhEhj4e+mtqBn6nVSlZuCBeKSUwxsWj55GeVNm9/jfad72KYk8PCn/w7+pSRN1HRJSdjzMvFdvwE\nOTnRBIMh6mu6J33tSEpILSQ2ZRluRzNdTQcudXMG6R+dN87y0bnI5U4PEefIE/uZR4gsy5hiHIRC\nkLzyMyBJeKorWbw8A6fdS8Wpc8sPLIkLQVJgay+d8HWctnrkUGDgFrvLH+TV8hY0SgU3z0snu+iL\ngET96T8TCvoHXtd6NleeMkKuvJ+t24VaCeqQF13y5JZImOeH11zbzy5R67dyfTZLVmZywy3FKCa5\n6Ymvx0rjy6+gMpuZ/+OH0ViG7sp2obh1ayEUIs4b/jeoPjP5fcMjLb3gcyiUWpqr3ibg67vUzRlk\na/7lMToXhE8r0ZmPg8Nqw2zuw2XXo422oE9Pw1FRydJV4RHo4U/qB45VaQyYLHPo623A55lYRaL+\nW+zm2PAs8NerWnH4AnwuN4lYvQZDTCaJWevxurpoqQ7fVj9/VB4dXzjiuWVZxtrjwqgJITF4g5X3\nqj/iSMv4vnyYi8525uflzQG0OjVbb1l8UTXS6597npDHQ9YdX0ZtGt9Etv5b7apTB9BoVVSdmVl5\n8/OptWZScz9D0O+iuertS92cQVKMelacHZ2XtM/M0bk3OLPSE4Iwk8yKzvxS52faqk6gUMj4HOFb\nH6aCAkIeDwa/nazcOGoru+juPNdGS9IiAKwTHJ07eqpAUmC05NDqdPN+XQfxeg2bcs6NolNzr0Oj\nj6W9fg8ue9PZUbk85qjc3efD5w0QJbsBBgrGtDk7efrwCzzx0ZP8595f0+Ma/QuINj4ObVIi9tNl\nyKGp++PqqKik4/3dGHKySbp247hfp0tMxDS3AMepk2RlR9PT1Tcwa38m6c8zJmauR2dIpKvpAC77\nyHWdL4Ut543OQzNodB6SZV441cjf7yzhr+XNBEeo7yByudNDxDnyRM48Qno7KgBQyrEAmOaGR86O\n8gqWrckC4Mi+eoIhmWeO1fKflQYqQ9lY28bfmQf9bvp6GzFEZ9DhkXm2pH6g/rr6vPrrSpWGrKKb\nQQ5Rc/z5cY3K4dzkN523F0mpRJsQ3vik3hbuUIwaAwebS/juW4+ys3IPoVEmaUXPLyLgdOJqaBz3\n+xuNHApR88yzAOTcew+ScmKT5+LXr4NQiESsAFSXz7xb7f15RkmhJGPejYA84ybDpRj1rEi10OSY\nOaPzQCjEb0rq+KA+/G/6VnU7/3WoErvXP+RYkcudHiLOkSdy5hESCLQjy2COzgTCI3MId+aFC1Mw\nGDUcO9jI86UNHGy10uMJsCu0hhe651DXNb7bvg5rDV5ZxceBYh79qIx6u4tVqRaKh6m/bo7LJy5t\nJV53NyCTMmf0UTmcm/ymsXegTUgY6DDrbc0A3L/qK3xj+e0oJIlnj77Iv+z6GY29LcOea2CJ2gV5\n88nq/PAjnBWVxK1bS/T8+RN+fdzaNQAYao8CzOhb7RD+94tJWkRfbz09rUcvdXMG6a8Kt2Mco3OH\n18/RNhsuf2RWEHgDQX5xuJpDrVbyLAYeu2o+xUnRlHc7+fHeM1T0iNGhIPQbszP/2te+xvvvv/+p\nnRQTCvpR6ezYHUZU8YnsP9lKVGYGCp0OZ2UlSpWC4lWZdCTp2NvcTYZZz8Pr51FoCtBKIo8fbOSl\n0024/cGRryHLfFjXyp+CW9hviyJWr+H+ZXP42uLsETvp9IItaHQxGGKyiE4YfVQO53Xmjs5Bk98a\nesOdeXZMOhtz1/Nf1z/M2oxlVHTX8P13HuPF0tfwBQePggYmwV2QN5+MoNtN/e+fR6HRkP2VOyd1\nDm18HOaiQoJlx4mN1VNb2UUwMHNGvMPJKNiCpFDTVPEGwYDnUjdnQIpRd97ofGjKpc3pYWdNO0/s\nK+cfd5Xy1NEafrKvAqvHN6XtcPoC/OxgJae7HCxMMPOdlfnER2m5b+kcbp6XhsPn52cHKthZ0/6p\n/dskCOdTPvLII4+MdoBer+e5557jF7/4BV6vlzlz5qDXD18XPFKcTicazcVVE5v0tXvr6Wk9RGtb\nAketiTz3biVXLc8gWFGGs6qa1Bu2UKOAY5IfrT/EQ1cUkWjQsSzJiFz/Kp1SCqetXj5p7sasVZNm\n0g3qoKusTp46UstBa7iq2w35qXy9OIdUk37U0bZCqSY+bTXxqctRKMa+LX3iUBNtzb3k9JwgcfE8\nYpeHd/R6sfQ1ZBm25FyDVqtFp9axOmMpubFZlHVWcaSllH0NR8iMSSPREN72VWU00rbzHbydXaR+\nfutFVQ1rfOnPWI8cJf3mLxC3etWkzxPyerAeOYqUt4A2m0x2fjyW2IuvCz9VnE4nPp9v4HOsVOsB\nmd7OMkKhINHDbIpzqaQa9eyu76TV6WVDRjw11j7er+/kT6ca2VHVxukuB1aPn1yLgZwYAxU9To62\n2ViQYMaoufiiklaPj58dqKTR7mZ1aiz3LskZ2OpXkiTyLEbmxhk52WnnWHsvjQ43CxKi8bpdg2Lc\nT5ZlGuxu9jR08tLpJt6obkOjUJAZHSUq3k3ChZ9lYepNps8bszPPzc1l27ZtrF+/ngMHDvDjH/+Y\n8vJyUlJSSExMvJj2jtul/ND0tB7F0VNFQ00y1SELPXYP+RkxJIccOMrO0Dp3IX9qdaAMycQd6mTp\n/GSMZh1KlQ6p5wi5nkOk5VxBWY+Lw202ynucZJmjCMkyfzzZyEtlTfR6/eRLdXwxvokrF21AOc6l\nXQqFEmkcHTnAgY9qsPW4yes+TOKGtZjnzcUT8PL88VfJj89h09wrBx2fYkpi45x1eIN+StpOsbt2\nH/FRseRYMpAkCUdFFc6KShKuumLcM88v5Glro+Ln/xeNxcLcB76LQjX5jkCbEE/L33ag0GhpkhMw\nmrTMKUiY9PmmmkajGfI5Npgz6Gk7hr27AkvSooHaApeaSaOivc9DWbeDXXWd7G7ootrahy8kszDR\nzHVzkrlrYSbXZCeyLDkGpULiWHsvh1utzIszEaOb/L7ybU4PPztQSYfLy8bsBO5YkDns70OcXsuq\ntFjqe12c6nJwpM3G/ORYEkzhCon+YIiybgfv1rbz/KlG3qntoKLHiSsQJBiSOdbey8lOO1nRURfV\n3k+j4T7LwtSaTHzH7Mz7Wa1WSktLqaurIzc3l//93/+lvb2dtWvXTviis0lLxTv4vFYaytMp92vw\n+IJYTDqWzommrLyWV7MWolQo+GJyPE1Hw2t0C+aHZ4oHAx6c3WdYkJbBNfMW0e32carLwUeNXexu\n6KLe7iLTrOe2zAA5vW+RlrECo2XyZVBHs3tnOcqQn8zOYyRffx1R6WnUWZvYVbOX5amLWJIyNFet\nUqooTpnPkuT5vF/7CU6fi6tzwvlpv82G9cgxDDk5GOdMrs1Vv3wSV30Dufd9A2Nu7kW9P6VeT2/p\nSfxnTtCUWIzHE2D52uxRX+P1+LHbPOijLs0fJkmhRKuPw9p2DE9fB7Epy2bMSDHVqOeTpm40SgUr\nUy3ckJ/KHQsyWZMWR2Z0FFpV+EukJEkUxJowa1QcabNxsLWH3BgD8VHaCV+zvtfFzw9WYvP6ubEg\nhW0FqShGiYdOpWRVaixBWeZ4Ry/7mroJhGT2NHTy3MkGPm7qpq7XhUKSWJZsYUteMncuzOTKrARs\nXv/A76LTFyDXYhw00VQQZpsxh0Jvv/02L7zwAp2dndxxxx28+eabGAwGfD4fW7dunbYi8peCHArS\n11uPwxmFXqPD5ghv/XmmvgfXmvm8d/2XCCBx35IcFiVEc+LNCkqPNvOZrUVodWpiEhfSVL4Da/sJ\nCtJX861luZR29PLi6SbcgSC3FqWzLj2OhtN/phswxV38LmPDCQZD2G1u4rXhvGb/GvP+meyZ0aPv\nA54Xl012dDpV3bX4g37USvWg4jFJ114z4TbZTpTSve8ApsJ5xG9YP+HXDyd+/TrsJ0+RZArR3GLH\n0evBFK0bcpzd5ubAR7Uc3V+PzxfkG/945SXboCU6oRBz/DzsXWewtZdiSV50SdpxoWSjjp9duwi1\nQhq1Q+13VVYCBo2KZ0vq+O9DVfxdcQ5Lksdfc6C828Evj1TjDYS4fX4GV2WN766KUiHxhblp5MYY\n+N/j9bxeFS4eFK/XsD49huKkaPIsxkGje51Kyb3FOaxPj+OPpxp5v76TI21WvlSYzooUy4z5QiUI\nEzFmZ/7qq69y7733sn79+kEfco1Gw/e///2INq6f0+m8JEshXI5mZAL09CSgN2rBEZ5o09jt5H/K\n2/HqDWw4+D6LP7cUSZJYujqTD94q58SRZlasy0artxBlzsDRU03A14dKY2BhYjTzE8yEZFApJGRZ\nxtFdhVKlJ8p0rlPd+beTOHq9XP+FBRiMEx/lnK/X6kaWw2VcgYEJcPVnJ78laGPHjPG8hDxqbY1U\n99QzLyGPqMxMlAYD9tMTn9EuB4PU/uZ/QZKY8/V7puyPZ/za1dQ8/Rss1jqayaG6vJPilRkDz7e3\n2Nm3u5qTx5oJhWTUGiVySObk0Sau+dzYkwgvRv+60QtjLEkSGXNv4HR3JY0VOzDHz0Opmhm3MLUT\nHKmuSLFgUCl58mgNTx2t4e6FWazLiBvxeE8gyKlOOyXtvRxusyLL8HdLclieMnblvwstTorhH5fK\nVFhdFCbHkmrUjfm5Kow38/D6QnbWtPNGdRvPlNSxt7Gb2xdkkGQY+iVQCBvpsyyMT9WTv0Kp05Fz\nz1dGPGYyfd6Yv62//vWv2bBhw7C/GBs3jr+4x8W4VB8ahzVcA73HGo3abEDSuFDHdxG9KIFuj581\nHbXkHtuHtyO8FGrJykwUCokjn9QNzLC1JC0EOYSt89TAeRWShOrsSMHn7sbnsWKKzUWSwv8cvVY3\nBz6s5fTxFp75+Yc0N1gv6n30z2TX9vWgtlhQasNfDhpszUhIFCTnjhnjwoRwidnTneEqdZJCgblo\nHp62drzdE6uH3vbOu7jqG0jceA3GvIu7vX4+dXQ0MYsWYqwvAcK7qMmyTE1FJ398ej+//tkeThxp\nIjbBwNYvLeY7/9+1qDVKTpW0RHxG9Ghrc3WGBJKyr8TvsdFU/rcJVw6cSYoSzHxvVT5RaiW/K61n\nZ037oOetHh+76zv5v4eq+O57J/jVsVr2t/Rg1qj49vLcSXXk/TLjLVybn0baGJNHz6dWKtiSn8Kj\nG4pYkGCmrNvBIx+V8XqVKGs7ErHOfPJcjU2073wXd/Pwy377RWQ/c7vdzjPPPENZWRleb/g2syRJ\n/OEPf5jwxWYbpzW8mUiPNZroDCOqjI8xpxejVmlIVajYZFFTDzjKK9ElJWE065i7IJmyE620NPaS\nlhmDJWkhzZVvYm0vJT5t5ZBr2HvObnkae+4W+6mSs8vF8uKpr+7id7/8hOu2zWfp6qxJjWJtPeGK\naGp7B/r08C12WZZp6G0hyRiPTjX2yH/e2c78TGfVwM/MRUVYDx3BfqqMhCvGd6vc73DQ8McXUer1\nZN1520TfCgAhOUS7s4sU09AJmHHr1mItOY5BI1N1poNnfv4hbS12ALJy41hzVS758xKRzn6Zmjs/\nmZPHmmlt6r2oUrQXKzlnIz2tx+hqPkhX80GiTGlEJ84nJqEIvSl1Vt36nRNj4PurC/ivQ1W8cqYZ\nq8eHUaOipL2X+l7XwHHpJj3FSdEUJ8WQaR5/BxwJiQYt316ey9E2Gy+ebuJvFa2YNWquyIy/ZG0S\nLj8du94HIPHqK8c4cuLGHJn/8Ic/xOFwUFdXx3XXXYfb7WblyqGd0uVGlkM4rbV4+1R4vFpcah1R\n8TmoVen4HF1ITS7M/cVjKioGXrd4Rfi27onD4epo2qh49KY0HN2VBPzuIddxnN2/3Bx3bpvPk8ea\nUSglbr5rGbfduxqNVskbr5Ty2kvH8Y+yXn0kA7ul+ewD+XKruxenr4/MmLRxnSNGZybVlER5Vw2h\ns2VcowfWm4//Vnvjiy8TcDjIuOWLaGIm13m+X/MJ//Dmw3zScHjIc3FrVqFQKon3tuL1BGhvtVO0\nOJWv/cMG7r5vLQVFSQMdOcD84nBq4+Sx5km1ZaooVRrmrf42GfNuxBSXj9vZRmv1O5Tt/29OfvQY\nDWXbsXdXIIcm/u9/KaSa9PxgTQFJBi276jr5W0UrTXYXhXEmbi1K5ydXz+fhDYV8viCVrBmyREyS\nJJalWHho7Vyi1EpeOt1Ii2Po76wgTEYoEKDjgz2oTEZiV019HzpmZ3769GkefvhhDAYDt99+O7/+\n9a/58MMPp7who7kUdYDdzjaCATe9XeE19VZfAI0+FVn24/V8SEW9laicbCSlEkf5uc48d24CBqOG\nk8eaBwqXWJIWIstBejsHd3qyHMLRU4VaG402Kjzhp7PdQVuznby5iUQZNOTOTeDe715BakY0xw81\n8ttf7B3onMdruK1P63vDk9+yotPGXWt5XkIe7oCHurMT5wy5c1BoteOuBOesrqH1zbfRpaaQsuVz\nE3oP5ytpDacs/nhiO/4LCtqoTSZiiheRUfcRV16Rxv0PXcPNdy0jLXP4Lw658xLQ6lScLmlBHqHm\n91QYT4zVGiOJmesoWPZ3LL7qYXIW3U5s8hKCAQ+djR9TeeQZju9+hObKt2ZUGdiRxOm1PLi6gOvn\nJPH14mx+fu0ivrcqn43ZicTpL24eyHCmqmZ4rF7D3Qsz8YVknimpwy82eBlE1GafHOuRY/htNhKu\nuAKFevTlkBGpzW6xhGd3ZmRk0NPTQ0xMDHa7fcIXuhiXIj/jPJsv77Ja0CkCtDkdKJUWgsEOQrpu\nnIFe2h0BDDnZ9NXUEvKHOxWlUsGCpWm4XX4qy8L5QkvSQgCs7YP3OHc72wj4+zDF5g2MTPpHiAuW\nnhsxx8RG8ZVvrWPJqkzamu08818fTqhkqa3HhVIBmqB7YPJbgy2cs8mKSR93DqwoIZwKKDubN1eo\nVJjmFuBqaMRvd4z6Wp/VStljT4AsM+fvvj7mh3kkITk0cP3Ovm7erf5oyDHx69ehDbqZ463BEmcY\n9Sebxl0AACAASURBVHwqlZLChSnYez001vVMqk3jMdE8o1KtJza5mJxFt7H4qkfIX/YNEjPXo1Bq\naKt9n9rSPxEKRaaM6lQyadV8YV4aq1JjiVJffEGZ0UxlLndpsoUrMuJpcrh55Uzk7to09Lpo6J3Y\nl/NLTeTMJ6dj1y4AEsex+icitdlzcnKwWq1cccUVbN68ma1bt7Jhw4YJX2i2cZzNl3f0JmCOUtAZ\nCn9TUhCe7KWI6eBMXQ/GgnzkQIC+mtqB1y5efvZW+5HwCFZnSERnTMbeXTGodKej++yWp2dvscuy\nzMmjzag1SgqKBu83rlIr2fqlxWz54iL8/iAv/OYAH75bMa7RpLXbhVEVGLT1af9M9rGWpZ2vP29e\n1nVe3rz/VnvZyKVdQz4fZx7/Kb6uLrLuuA3LkuJxX/NCzfY2HL4+lqTMR6/S8ZfTb+G6IH0Ru3Il\nkkpF196Px3XO+UvCMThVMvqklEtFUigxx+WRMe/zFK39RwwxWVjbSqg+9juCgaktoyqc8/+z997x\ncZVX/v/73ulVozJFvVuSZVlyb2DTew2EEAgBNpQkS75L2OwmIZtCGj+yyaYtIY0UAiGhBFOCTbON\nMa5yVbF676MyqtPv/f1xVa0uLGNYf14vv/wazX2e+8ydO/c853zO+ZxbliYQa9azo97N8UVoPDMY\nCPHjAxU8tr+CtoGzR9L3HE4/Aj09dB86jClt4bocs2FWY/7jH/+YyMhIbrnlFn75y1/y6KOP8o1v\nfGNRFnO2QJZlBnpqECQtgz4jEZEG/DrFcy5wKkZWFdlBWX0PlqzJvLkzzooj1kJFaTtDg8rDNtKZ\nhyyF6HWPGb1Tk99aGj30dA2RletCq5vai1m5Ppm7H9hEhM3Aru3lPPuHg6PnmAreoQA+bxCjPNw1\nbTjM3uBpRqfS4jDPPcHHbowi2hhJmbtqNNM3Yly9+VSQZZmqX/2G/vIK7Fs2E3/TjXM+31QY8crX\nxhdwXfal9PsHeK387QnHqM0mbCsKGKpvmECBTIfUjBiMJi2lx1uQzvKQqlpjZMmq+5Ta9K5yKg//\nllDwo+XZfVSgU4ncV5CKWhT4U1E9ntOsP/9WXQfekEQgLPG7Y7XnwvkfY7h37QZJWpAmx1wxqzGv\nqKhgcFDJhl69ejUZGRlUVlYu2oKmwpnmZ/xDbkKBAUK9ekDAHGVGbTYiyxJr4uJJj0xGtHZTWt82\nzpiPXRNBEMhfnYgUlikZDpuP9ThXQu2yFGagpxad0Y5Wr3RGKz4yOcQ+FeISbdz74PmkLbFTdbKD\n3/7PuzTVT12+5hlufarzelCZTGgsFkLhEM19rSRFxCEK4pw5MEEQyInJoM8/QEu/QiGYl2QiqNXT\nGvPml17GvXMX5sxMMh74wgdOdCodzqbPcWRyddbFROitvFr+Dh7fROon7rprAKj42S8Ie2dOYhJV\nIjnLYxkcCFBXPb8yu7nidPKMokpLRsFdRMWuYLC3nvJDTxDwzd1zHOpvobHsZVqq3mSor/ljU4K1\nGFxugtXAJ7PjGQiE+MPx+tPW530wEOKdug4sWjXr4iJp6PPyUsXZGRk6Fec48/lBlmXa396BoNEQ\ns3luUe1F4cy/+tWvTtCJ1Wg0fPWrX533iT4IzjQ/MxJiH2gbbu4QZUFtsCBJXSRE2FmbUIAgyLQE\napBs0agtlkke4LKV8QgCHB8NtTvRmxz0dpYTDgUY7G1ACvtHQ+ySJFN8rAWDUUN6lp3+yir6ysqn\nXaPRrOO2e9dxwRVZ9Pf6+NP/vs/+3TWTHszjW5+OhNhb+tsJyxKJw5ns8+HAck7hzVU6HeaMdAZq\nagkNTTSa3YcKqX/qabTRUeQ8/FVErZbqJg/hBSaaybLMSXclEXorsWYHerWOT+ZehT/k58WS1ycc\na1ueR9wN1+FraaXmd3+Yde7FDrWfbp5REFWkLLsVe9ImfANtlB98HN9Q57THy7JMf3cVlYd/z8l9\nP6WjYQ+tNW9xcv/PKHrvhzScfIm+zvKPBA8/HRaLy70w2U6+I4KTXf2T6uYXirfqOvCFJK5Ic3LH\nsiScJh1v1XZQ7D47+sjPhHOc+fzQX16Bt6mJ6PVr59zHYlE4c6/Xi2ZcspJWqx311D+uGEl+62pS\nvMgutYggiITC7TiM0axJyAdAtLVT1diLZUkm/vYOAp6xH6LFqicty05Lg4fO9n4EQcDmzEOWgvR1\nlk0KsddVdTLY72dpfhwiMie//yhFX/8v2ofrEqeCKApsvnQJn7l/AwaTljdfLuH5Pxfi845leI8Y\nc72vd0z5bbiHeXLE3MrSxmNEPOak+xTeXJLoLx/bfAzWN1D+458iajTkPPw1tFGRlNR08eBP3+Wt\nA/XzPi9A+2AnPd5ecuxjCYMXpZ2Hy2zn7er3aOufmBSY/JnbMKWn0/HODty798w4d1JqNBarnpMn\nWs/69qkjEASRxKzriUu/jICvh/KDjzPUNzFZS5bCdLcdp+zAL6go/A19XeWYI9NIL7ibtOWfISp2\nBVLIj7txL5VHfs/xXY9Qc+JpuluPTllK+X8RgiBw5/JkbDoNL1e0UOv5YM+/8V75lmQ7umF5WZUg\n8Ifj9fT5g7NPcg4fGXS8rTzDnZcsrsjarMZcp9Nx9OjR0ddHjhz5WHfMkWWZ/p4aVGojPR5F0rFt\nuLZXTS9atZZ4i4tIbTRiRCcldR2YlygGeaBionc+kgg34p1HOoaz2jtO0N9dBQhYohQFtPEh9v7K\nKoIeD0gSVb94nJZXX5txzamZMdz30GaS06MpK2rjdz/dTWuToiI2EmY3hPoxxE5MfkueY435eMRb\nXVh05onGfKkihToSag/29XHyB48i+Xxk/tsDoypvda1KKLxigYp2pR1KNGAkqx5ALaq4Ne96wrLE\n34pfnXC8qNGQ9ZUHEfV6qp/4Db726b0qURRYWhCLzxukptK9oPV9GBAEgdj0S0nKuZFQYJDywl/T\n312NFA7Q0bCX4vd/RO2Jpxnqa8bmyCN77ZfIWvMFbI6lRLrySc27jfwLvs2S1ffjSDoftcZIT9tx\naov+yvFd36GrZXIt//9FWLRq/iU/BUmG3x2rw7sAvYcRvFk75pWPSOYmRxi5KTuO/tMczj+HDxdh\nnw/3e3vQ2WOIyFu2qOea1Zg/+OCDfO5zn+P+++/nvvvu45577uGhhx6a0+QXXXQR1157LTfccAM3\n33wzAFVVVdx///1cf/31fP7zn6e6unrWec4kPxPw9RD0eTBoHPg0FgRk3GEl9BipVf4XBIE1CfkI\nqjBHmkvHePNTQu1Zy1zo9GqKCpuQJRmDJQ6dIZpe90kGPfUYrfGoNUZCwTAni1qx2vQkpUTRc0h5\ngCZ95jY0kTZqf/9HGv723IzcpsWq547713PeJZn0dA3xh1+8T+HeOro7FS/CEBwYTX5rHM1kV4z5\nfDgwQRDIjkmnc6gb96DCL1tzskEQ6CspRQoGKXvsx/jbO0j81CeJOW/T6Ni27j7UsTXUdizMWI6E\n9scbc4D1iStIj0xmb0MhNd0TvX5DXBzp999DeGiIip/8DCk0fRg5t0C5HiVHT3+ofbF5RnviRlKX\n34YcDlJ55PcU7f4hjWUvEfT3EZOwntxN/0F6wWcx2ZImjRVEFZaoDBKzr2PZeV9j6YaHiMu4AlFU\n01y5/SMjVLPY1zgnxsIV6U7cQ37+WtK4oDkGAiF21HdgHfbKx+PiFAfL7FZKOvt4u3bupadnGuc4\n87mja+8+JJ8Px0UXIqiUToOejlJ63WUzjlsUznzVqlW8+OKLbN68mS1btvDSSy9x4YUXzvkEf/nL\nX9i6dSsvvPACAI8//jjXX389L7/8Mtdccw2PP/74rHOcSX5mJMSu8unxqi0Y9QJ9goQk9RFvtY4e\ntzl1FQCNvirMGUroeXwSHIBGo2Lp8jj6en3UVnUOh9qXI4UDyHIYS5QyrvJkB35fiGUr4hFEge7C\nwwgaDXHXXk3eoz9A53TQ+OzfqX3yT8jS9CFgUSVy0ZXZfPqetWh1Kl5/sYjaqk70agmVHBrXLa2Z\nKIMNs06pwZ4vBzbGmyveudpkwpSaQn9lFdW/+g19xSVEb1hP4q23TBhX0n8ETWIFzcKxBSVdnXRX\nYtIaSYiInfi5BZHb8m8A4K8nXp40zn7hBcRsPo/+8goa//bctPPHJ9mIiDRQVty2IKW9mXAmeMYo\nVwEZK+5GEERkWcKVejF55z9M8tKb0Jvm1oVMEAQMllhi0y4mJn4tQX8vPe1Fi7ru04UzcY2vy4wj\n1WZkf0s3+5vnnyz51ohXnu6c1MhGFATuXp6MVavmH+UtE6RvTwe6vAFCMzw/5opznPnc0T4cYndc\nrNjMoH+AmuNP0Vr7zozjTjtnLkkSn/70p0lNTeX222/n9ttvJzk5eV4nOPWhbbFY8Hg8SJKEx+PB\nOs5Ang0YSX4Lt4fxq41onSZkUeHLEyLGar8zolPQyAYkSxttXglDQjwDlVXI4YlGYPmaBGCs5nxE\nQAbAOmzMxwvF+Do6GKqrx7Y8D5VejyHWRd6j38eQmEDrq69R9ctfTTrHqcjMcXLfQ1tISI4EGUyC\noqmvd7no9w/Q7fUsKMQ+gpypdNpzlyIHg3Ts2IkpNZXMB7+EII7dXpIs0SYrYXjJ2krnPB9UnUPd\ndAx2kROTgShMvm3znNnku3I40X6SE20Ta94FQSD98/ehczhoeuEf9BYVT3kOQRDILYgn4A9RdfLs\n9YxmgjUmi7zzH2b5lv8iPvMKNLqFt3a1J20CBDoaJgvz/F+FWhS4tyAVvVrk6eJGaubBnw8Mc+VW\nrZrNSVNvrqw6DXfnpxCWZX57tBZfaOGbSkmWqewe4IWyJv7r3RK+trOYnxyoxP8B5jyHucPb0kJf\nSSkRy/PQD5c0dzbtQ5bDRLkWrrUxHWY05qIo4nK5qK9fWMKSIAjceeed3HDDDTz3nOIR/ed//idP\nPfUUa9as4Zlnnjnr+qEP9NQgqvX01faDIBB2Kg/DULgNl2XsBygKIimmTARNgHfLirAsWULY62Wo\naWICUlJKFLYoIydPtBLwhzBaE9DqIxFENebIVPy+IBWl7didZpyxVnoKjwAQuWbV6By66Gjyfvh9\nzJkZdOzYSfl//2RUcW46REQauPNfN3LpdUvJClQiaDRooyJp6FVCyEkLSH4bQYotAb1aNyVvrrHZ\nyPnGV1HpJ7aQLGovI6QeQJZB1Pk4UDNzmOlUjGwccuzT93y/bblSw/7XE1uRTpE7VZtMZH3lyyAI\nVPz059Mq1i07ywVkZoPfF+LV58s5vO+Dq5bpjTFExGQz2NvAoKfhNKzu4wG7Ucfn8lMIhCV+caiK\n5jnqt79V244/LHFlumvG9rLL7FYuS3XQMeTn2XmG8/2hMEfbPPzpRD1feaeIH+2v4I2aDjy+IHFm\nPVU9g/z6aO1p8dDPYSLqPIP87mgt39ldynd2l/K9wjq23nwvf9t4Nd/ZXcq3d5fyswoNz4WvYr93\nMt31QTEnOddbbrmFf//3f+exxx7jscce40c/+tGcJn/22Wd55ZVX+MlPfsKvf/1rCgsLefjhh7nj\njjs4cOAAt95665wEaNpPSVw6lU84Xa+D/j78Q50YzIl0dygP+0GLkuwXDrfhMMVMOH5j8koADrUc\nm5AEN35+QRTIznMQDCi8uCAIxGbdQubKzyGqtJQVtREOScOlbAI9hQpfHrV6zJgPDAygsVrI/e53\nsC7LpWvfAU5+/1HCPt+Mn0elEtmwJR1zaxl6lxNBFGnwTE5+a29vnzDHbNfLO+QlKyaN5v42en19\nDAwMELV6FXHXXcPSb3+DoMEwafyblYqef6glffSazfV8AwMDY/Xl9oxpj0+NTGRT0mpqehrY33hk\n0vuWrCUk3XYrga5uqv73V6NRo/HzOeOsRMYYqShtI+APzXl9s70ezzMu1v0bCob5+x8PUXSkme1b\ni2lu8Hzg+R3JSl1s+zjvfLHW/0Ffn3qdF/N8BU4bdy5PZjAY5qcHKnAP+Wc8XvHK3UTo1KyM0k96\n/9TXN2bFkWQ1sLe5m901LVMeL8sy3d4ARxo72FHXwf8WVvPlt0/wqyM1vN/UhSjA5sQY7smN46eX\nLOeb5+WQZ7dS7O7jyeN1o0l28/38831efJxfy7JMYUM7PzlQyQ/2lnOwtYcubwCPP0hfGLxmC/0q\nDR5/EI/Xx6CsY0gwMxiaef5F4cyTk5P5zGc+Q0pKCiaTCaPRiNFonNPkDofSojI9PZ1LL72UEydO\ncPjwYW666SbUajU333wzhYWzZ8uaTDPra58ujPQvN5gT6OtTPN9OQUIKB5AkD85T1NIuyi5ADqtp\nD9ViGTbm/eWTBXWWFihc9YnCkZpz1yhfXjSSxb4inrDPh+dEMfqkJHT2yWE4tdHA0m99A+vKAjzH\njlPyre8SmuVLD/b3Ex4amkLGdcyYm0ymaTkaSZLp7vNR0dBDYByPPOIhl3UqCYyiVkvq5+7GnJY2\naY4eXy+HW4uQBq3EhvORQ2pqBsvmxZuf7KhEr9aRGpmILMv4AlMnsn0q7zpUgsizRa8QmiJxK+ET\nN2DKyab7wEHatr8x6X1BEMha5iAUlKgoOT01xbD4PKMUlnjx6SPUVXXiSrAiy/Dqc8c+cJmdJSoD\nrdFBT/uJeQnTfBg401zupoRoPpWTQG8gzE8PVuLxTR8te3PYK78izYV2Bq98BGpR5N6CVHQqkecr\nOzjZ2cehlm5eqWzlLydb+d6ek3zpzeN8dWcxTxQ18WxpE8c7erEbdVySGMXXN2bxo4vyuCMvidxo\nMxqViFoUuH9lGqlWA4WtHp4pblxQ7spMz4v/KwhJMvubu/junjJ+U9xMWVc/OdEWvrwmgx9uTOdb\nkSE+/aef8OX2En6wMYOfXrKcz0e8x93ql3hktYtP5ybOOP+i9DP/0pe+NO9JQalPD4fDmM1muru7\neffdd/nGN77BunXr2LFjB1dddRXvvPMOGzdunHWuUz/YYr0eSX4zaxwMiUZCOpEBWSYUdCOiwqa3\nTjjeoNVhDsUzqKunyQiiTkd/RQUZp8yfkGQnMSWS2qpOenu8REQOn6/fT22lm/jkSCKjTXQdOIgc\nDBKzbg2/OfQMZZ1V5LuWsjJ2GUsNmahValQ6Hbnf+DpVv3gc97u7Of7vXyX9C/dBQf6Un8/X2gaM\nabI3eJpRiSriLGP8v8FgpK1rkJbOQdq6BmntHP7XNUhb19CoEb/pwgzuuiYXs9lMdsxwvXlHJesS\nVsx4ffe1HUWSJUIdiazIdrG92YE/poXq7noyolNm/X7Caonm/jbyXUtRiSpO1PTzo6d38t17N5C/\nxD7heJfZzqXpm9letYt3qvdweeaWCe8LKhU5//EQx/7tIer+8GesS5diTp4Y8lqxNoX9u+ooPtbM\nspXxZ+z+W+hrk9HEK88dp7y4jZSMaG67Zx1vvFzC4X317Hmnki2XZy14fkEQcKVspqH0BdyN+4jP\nvOJD/7xn0+tLUh0MBkO8VtXGzw5V8h/rlkw6Xtbq2VFXRYROzeakmEnGfLr5XWY9n85N5E8n6vmf\ng1UTjtGIAg6TDpdJj8usx2XSkWYz4zBN7kY3fn6dSuTBdZn8+EAluxs7MWlUfCI7ftrjz70+5Xka\nCrPfPcRbtbV0+4IIwNrYSC5Pc5IUMebklo0mvl2E2WxmwFPHUF8TNkcuUTGJ084/1eu5YlZj/thj\njyEIArIsT5Di/M///M8Zx3V2dvLAAw8AYLPZuOuuuzj//PNxuVw88cQT/PrXvyYzM5MvfvGLC1r4\nYqC/pxZB1CB3hfBpzAQilB9GWGrFqomcUoo005rFMX89b1ceZktGOn2lJwkNeVEbJ4aal69OpLGu\nh6IjTZx3seLVlh5rQZYhb4XyY+o5dBiAiFUr2F38K4JSiOa+Nl6v2IFerWO5M4eVccsoiM0l88Ev\nobPH0PSPrZR8+7vYL9hM6r/chSYiYsJ5fW2KMTfEOpFkicbeFhIsLtQq5av3+UN84Uc76PRM5v2M\nejUJDjOuaCN7T7RS2zImmZoRnYJaVE9oujIVQlKYd2r2oBV1eLtiiYsxYa1PZpAW9jceISM6Zcbx\nMJY1P5J4d6S8A0mSeWpbKT/O3Dzpe7kp90p21e3jhZJ/siVlHXrNxLCmLjqajC/9K2U/fIyKn/yU\n/B8/hjhOO8HutOCMtVJV1oHPG0RvmL7DWzAQYu+uGnq7h1i5IVlJOjyDkGWZN18t4fihRuISbXzq\n7rWoNSouuSaHytJ23nunkpzlsThiF55oGh27gubK13E37SM27WJE1cI63n1ccV1mLIPBMDvr3fyi\nsJqH1magU6tG3x/hym9YEjcnr3w8NsZH0ecP0u0N4DTpcZkVAx5l0CIuUBrZqFHz4JoMfrS/gm01\n7Rg1Kq5Id806TpZlSjr7KGz1kGozsiE+et6f56OM9xo7eaGsmaFgGK0ocFGynUtSHdiNEzdQwd5e\nug8VYkxOGtXY6KhXRKscSYvXpGxWY240Gkcflh6Ph7fffpvNmzfPOnFiYiIvvzy5TCgzM5P/+Z//\nmdciBwYGFj2sEwoM4htowxKVga+hDa/GTMA2zJfTQowhaspxm1ILOFr6Fic6irkmawl9JaUMVFVh\nW5434bjcgji2by3mRGETmy5SFMyKjjYjCLC0IA5ZkuguPIwmwkp/rJXgiRDnJa/lgpT1HGkp4khr\nMQebj3GwWeGaU22JrChYxnkrHqbzD8/i3rWbnsNHSLnrThwXXzj6nfnalFCx3uWiY6ATfzgwKuMK\nUFzTRafHS0aClbVLY4mNMeGKMREbbcJq0o7Oc9s3t9HePZa5q1VpyIhKpryrhqGgF6Nm4uZlBIdb\nTtDj7SVDn0+RpMYeaSTVkk5R+CB7G49we/6Ns+q1jxjzkfrymmaFC65o8HCkvINV2RM7zEXorVyR\neQFbT77BsbZS1ieunDRn9Lq1uK64jLbtb9K6bTvx11838ftaEceO18soK2qjYO3UIbGqsg5ef7Fo\nVJjn2KFGktKi2HRRBhnZjgmfa4QDO9338XtvV3Jgdy12p5nb7lmLTq/8pHV6DVfdvJy/PXmQV/5+\nnH/50ibEBT54RZUWe8J62mp30N16lJiEtXMeK8sSA546TNbERd8ELNY1ng2CIHDr0gS8wTD7W7r5\n1ZEaHliVjkYl0h8IsWOYK9+cNPemRuPnvnIOhna+sOo0fHltBo/tq+DF8haMmunXF5JkDrV280ZN\nO839Sp7O+01dvFzRysUpdi5IsmPSLm572w8bJzp6+UtRAwaNiusyY7kg2Y5lms/sfvc95FAI5yUX\nIwgCAZ+Hno4iDJZYzJGTacipsBCbN+8w+7333svXvva1eZ3kg+JM/DgHPEpJmjkyjcF9FfjUdoJR\nOpBlwmE3sdaps6jz0+KQ9kfjsXUgD++6BioqJxlzvUFDVq6L0uMttDR6MJq0NNf3kLYkBrNFp6i+\n9fTguOhCGvtaAUiLTGK5K4flrhzu4hZa+ztGDXupu5JaTyN7zXZ++tgPaN/2BvXPPEvVLx+nY+cu\nMr74eQzxcWNh9lgXFb2TZVyPVSgCLndds4z8zOlrkZ3RRupa+pAkGVFUjFSOPZOyzmoqOmsoiM2d\nctybVUriW1QoG+jFHmkgxRXJsToHnapWansaSYuaObPzpLsSjagmPSqZUFiirrWfSIuOnn4/z75Z\nzsosx6QNwcrYPLaefIOSjoopjTlA0u234X5vD03Pv4jz4otRm8dyM3ILFGNecqx5kjHv7/Xxxssl\nlB5vQRAFNlyQTtqSGPbvrqG6zE1DzUEcsRY2XphBbkEcKpW4KPfwoT217NpeTkSkgdvvX4/RPNFD\nWLLUSd7KeIqONLN/dy0bL0xf8LnsiRtoq9tFR8N7RMevmXPDnObK12mvexeTLYWMFf+CeppN3+nA\nh8njisOSr95QmOMdvTx5vI77VqTyVs3CvfLFRrRBx5fXZvKj/RU8XawYqjWxY5ElXyjMe42dvF3b\nQbcviCjA2jilz3tJZx+76jvZWtHKtup2zkuM5tJUB9GGyWH+jzpaB3z8/lgtalHgy2sySLFNn8Ol\nNFV5B0Gtxr5FsQfuxr0gSziSzp/z72ZROPNTERkZSdtw6PbjhIHh+nJLZCptTbvx6ZLxGdWIQS8g\nkRzlnHKczaLDFEzARydlZi9WJivBjWD56gRKj7dworAJs1W56ZetUOrQewqVEHvkmlUcnkZuNdbi\n4Oqsi7k662K8QR+/LXyG9xsKOdpRyurrriF6wzqqf/N7eg4VcvT/fZmET96Et7kZRBGd3U59+ZFJ\n8x6r6ECrUbE0derIwwhcUUaqGj109/mIsSkP5Bx7Bi+dhFJ35ZTGvLW/g6L2MnLsGXhrDUAvMTYD\nCQ4L4SNO1NGt7G86MqMxHwwMUe9pJseegUaloball1BYYm2ui77BAPuKWjla7mZltmPCuIyoZHQq\nLaUd07dA1VgtJNz0CeqfepqmF/9Byp13jL4XGW0iLslGTWUngwN+TGYdkiRT+H4dO7aVEfCHSEiO\n5Oqbl+OMU0LY6VkO2lp62bujmpLjLWz961F2bitj/ZY0VqxNmrat7UJQdLiJbS8VY7LouOPzG7BG\nTG0kL78+l+oKN7u2l5G1zEm0fWEGT6u3EenMo6ftOAM91aMJnDOhq/UI7XXvIohqBj11VBx6gsxV\n96DRnV3aEqcLalHgvhWp/PxQFYfbPDx5vI7j7b1E6DQL8srPBGLNeh5ck8GPD1Tw5LE6DGoViVYD\n79R18G59J0OhMFqVyMUpdi5JcRAzHFLOirZwVbqL3Q2dvF3XwTt1bnbWu1kzzB8nWsf448FAiLZB\nH22DftoGfLQN+mgf9OPxBUmPNFHgjCDfEYFNf/bJhA8FQzx+uBpvSOKe/JQZDTnAQFU1Q/UNRG9Y\nj8pswd3eQ1vdPmT07HtfRWfH+3S7B1iy1MW1n8qfca75Yk6c+QiCwSCHDx/m/PMXL+7/YWGwqQkn\neAAAIABJREFUrwkQMFoT6WnzEFiiBUEgONQDKoizOqYdmxOZw1GOsae7ghuio+kvr0CWpAmiKQAZ\nWXZMZi3FR5sxmrSo1CLZeUoIrfvQYQS1GltBPg2H/wxAUkTctOc0aPTcmHMF7zcUsq1yJ6vj89HZ\n7eR842t07z9AzW+fpPHZvwOgczgQNRoaRjLZh415d5+P+rZ+VmY50Izj+KaCM0r5cbZ3D40a8yUx\naQiCMEE8ZjzeGvbKL03fzHNHB9BrVZgNGpKcFiSPHRVq9jce4dN510+7Yy3rrEZGHs2er25SMqrT\n4yPISo5iX1Erz75Zxoos+4Q51Co1WTHpnGg/Sa+vjwj91AYk9pqraP3nNlpfe53Yq65EZx976OYW\nxNHS4OHkiVbiEm3884UTtDb1ojdouPrm5axcl4QgTly3Ky6CT3xmJRddlc2+XdUcPdjAG1tL2P1m\nBeu3pLFhSzpqzczXejaUl7Sx9W/H0OnV3H7fOqJipn/AGM06rrwxjxf/cphXnzvOnV/YOGnNc4Uj\n6Tx62o7TXr9nVmM+2NtIfcnziGo92WsfwN24F3fjXsoOPs6SVfeiM56dxu2DQqsSeWBVOj85UMnB\nFqUHwY1ZZ59XPh7JEUYeWJXOzw5V8avD1cgooXWLVs31S2K5IMmOeYqQsl6t4rI0Jxel2DnU2sMb\n1e0caOnhQEsPS6LMSLJM+6Cf/ikqTwxqFWatimJ3H8XuPp6mkZQIIwXOCAqcNuLM+g/cLvmDQpJl\nfnesjvZBP5enOVkXP7PDA3B46/uUui4i7E/jha+/TnxsC8tzfVRVJ1FRpdCqtigj0Y7TH0WaF2eu\n1Wr59re/TX7+6d1RzIbF5sxlWWKorxm9yYE05GcgIOCPUHaJQ942MDOpLG08licncLg8giqqMS/P\npXvnbjr3vI/9lN61okpk2coEDuyuwTsUJGd5LHqDBn9XN4PV1UTkL0dtNNLQ20yEzjKtARpBki2e\nZY4sitrLaextITEiDkEQiN6wnojledQ//Vfatr0xmoTR4GnGrDUROdw//fhwQ5GcZOus19gZrRiM\n9u5BctOiATBqDKTYEqjqricQDqIdx4kGQgF21e3HqjOzLqGAX3newh5pQBAEEhxmkFUY/PG0UU+9\np5mUyIQpzzuixz6S/FbTohjztPgI0uIjWJfr4kBJG0cr3KzMmrjhWurI5ET7SU66q6YNtat0OpJu\nu5WqXz5Ow7N/J/P//evoe7n5cbz1Sim73ijHOxhQkhVXxXPptbmYLTOHE21RRq78RB6bL1vCoT11\nHNxTw85t5Rw/1MSVn8gjPWtu8qqnoq6qkxefOoxKJfDpe9bhiouYdczS/FiKjzgpL2nn8P56Vm9M\nWdC5TRHJGK2J9LpL8Q91TmuQg/4+qo/9GVkKk57/WQxmJ4nZN6DWmGiteYuyg4+TuepejJbpN6sL\nwYfFmZ8Kg0bFv61RDHpIljk/8ezfuGRFW/j8ilSeOFJLlEHLZakONiZMneB26nVWiyIb4qNZHxdF\nsbuPN2raKe8eQABijDpSIozDGfd6nCYdsWY9Fq0aQRBwD/k53t7LsQ4Pld0D1PUOsbWilRiDlgKn\njTyHlUSLAfPw8WcS/yhvptjdxzK7lU9kzX6vers97G0xEzJHoQtAbLyVnMxjyAjkrb+Ci250ERVj\nRD2L4wRniDP/MLDYP07foBsp7MdoTcDb3IJPbcY/nPwWkloQYVQwZipkpUQR3u9ENPfiPj8b9Xt7\nqf/LX4nesB5RMzHpJ3+NYsxBqS0H6DmshNij1qzGG/TRMdhFnjN7Tmu/csmFFHeUs61iJ/etuX30\n72qTifT77yXhEzegMhrxhfy0D3Sy1JE5+qMY4cvX5SXMeo1HPPO2rokyrDkxGdT2NFLVVcdSx1he\nwb7GIwwEBrkh53JCIYH+oSCZiQofp9epcUQZ8bntkFDP/qYj0xvzjkpUgsiSGCVxpLrJgyhA8nB2\n9qcvy+JASRt/e7OcFUsmeue5DqUBTnFH+bTGHMBx4RZaXnmVjp27iLv+WkzDpWpWm4GktCgaarqJ\ntpu46qblpGbO78FsMuu44IosNlyQxq7t5RzcU8szv91PbkEcl12XiyVCP/skQHODh707qzhZ1Ioo\nCtx691qSZqFGRiAIAlfdtJy66p28/dpJMnOcRETOn7sWBAFn8vnUFv2Vjoa9JGZfN+kYSQpRfewp\ngv5e4jOvIsKeMzo2LuMy1FoTjWVbqTj0BOkr7sYyx4SgueDDNuLjYdFp+Nb5OYQk+az2yscj32nj\nfy7JQ69WzZgpP911FgSBPEcEeY4I+vxBDGoVmlk+u92o45JUx2iJX3FHH8c6PBS7+3i7roO36xRZ\nZZNGNboZGNkYuMw67EYdavH0X9/9zd28UdOB06Tj3oKUOVUOFD63g5CoJTde5hNfvoL+7koqD/cS\nFbuC1LzplSunwqLVmX/ve9/DZrMB0N3dzSOPPMLPf/7zeZ/sbMVQnyLmYrIm4K1oYlBrJRChxaZS\n0aPtRYsRnXp6Pic11opqIBao4KCvjpuuvpKWl1+l9fVtk7KkXXERxCXa8PQMkZmjeJIjJWmRq1fR\nOE+51VWxeThM0eyuP8Bty28YbZ4yghHxmbquOmTk0XllWeZYhZsIs5Zk1+wcpit6LMw+HjmOTF6v\n3MlJd+UEY/5m9W4EBC5JP3+07M0+zoAkOS0UltuISNKwv/EIn1p27aSdty/oo6angbSoZPRqhbOu\nbeklwWlBPxz2S0+wjXrnxyvdFCwZ887To1KGefPJQj7jIahUJH/2M5z83g+pf+ppln7z4dH3rr0l\nn8babpatjJ/Tjno66PQaLr9hGctXJ/L6iycoOdZC5ckOLroym9WbUkaTCsdDlmWqyjrYu7Oa+mql\nqUdsQgQXXZUzb8/eEqHnsutyefW54/zzhRN8+p61C/J0bM48NBVWOlsOEZdxGSr12GZElmUaTr7E\nYG89ka4CnCkXTBrvSNqEWmOktvhvVB7+HWn5d2CzL533Oj4KEAUBrerDDRXPF0bN6cnrsOrmX7lg\n0qhZFx/FuvgoQpJEedcAJ7v6aRvw0T7oo653kOpTtPBFAWw6LbPtl4waNVuSYlgfFzXrBqPOM8hT\nRfUY1AplMpdrEvb7KSl2gy6ODTesRxCEM1KONh6zbmnq6+tHDTlAVFQUdXV1i7mmM44RY26MUDzz\n7hgnskokWq1C0PmwamwzjlepRDIdCUheE8daS3HeeB0qk4mm516cUqHttnvXcf9DW1BrVIT9fjzH\njmNISMAQ6xrjtWfgy8dDFEWuyLyAQDjIjtr3pz2u3tM0Yd7G9n66+3zkZ9qnNCSnwm4zIgiTjXl2\njBLCLxtXb17X00hlVy0Fsbk4TNG4exRjPsK1AyQ6LSCpSY9YQkt/++gmZjwqumoJy9IoX97WNYjX\nHyYtfmJo+dZLFVGUv75RPkHRSi2qyLan09TXSq+vj5kQuWol1mW59BQepre4ZPTv0XYzBWuTPpAh\nH4/YhAj+5UvncfXNeYiiwPatxTz58/dGpVcBwiGJ44WN/ObH7/Ls7w9SX91FepadOz6/gXsePH/B\nIfqCtYmkLYmhqqyDouHGP/OFKKqxJ25ACvkm9Tp3N75PV/NBjJZ4UnI/Oe1mISp2BRkFdwMC1cf+\nfK5n+jlMgloUybVbuTk7ngdWp/O9Lbn87+Ur+N7mpfzrqjRuzo5nU0I0aTYTIBOSZv7X1DfEU0UN\nfH1XMduq2xgKTq0g2esP8viRGkKSzL0FqbjMc4ucNWzfgVvrJFIfJi7NgW/QTW/nSUwRyZgiZlZ7\nO12YdcsxMDBAV1cX0dEKT9rZ2Ulv75mVdVxsznyorxkQMFriGGpqxhOjNJFXh3wIgkyMIXrWObKT\noyircBAw1FI61ETiJ2+i7k9P0fj8i6TefeeEY42mMS+/t6gYKRAgau1qQGlPCmNJanPBhakb+XvR\nq2yvfJerl1yMSpxseOpHM+SVcPZIiL0g0z4nrlGjFomOMNDeNXFnHKG3Em9xUd5ZQ1gKoxJVo+Vo\nl2UoegTuEc/cNt4zV87lVKVxkhL2Nx2d9JlLR/uXK3x5dfNY8tt4ZCTaWLvUxcHSNk5UdpK/ZMzY\nLbUv4XjbSUrdlWxIXMV0EASBlDvv4MR/fI26P/2F5f/96Gnl6MZfY0EUWLUhhexlsbz1WiknCpt4\n8hfvsXpDMpHRJg7srqGv14cgCuStjGfDhelz4sZngyAIXPPJfJ7471288XIJaVmOWbn/qRCTsJ7W\nmnfoaHgfe+JGBEGkr6uKxvJXUWvNpK+4C1E1c2ZyhD2bJavvo+rIH6gr/juhoBdn8gfzYM4Wzvzj\njg/rOqtFQQmxz9HAjkePL6Bk6Dd08o/yFv5Z1caWpBguTnEQZVDu1WBY4onDNXh8QW7KiiPPMbff\nnBwOc+TNY8j6ZRRsSgWgo1FxrBzJ5817rbAwmzerZ3755Zfzla98he3bt7Nt2za+8pWvcMUVVyxo\ngQvF4ie/NaE3OxFVWrzNLQzEKF+id2g4tGmd3RPKTo4k3KOUrx1qPk7s1Veic9hpfe11fO3Tt9Mc\nLUkbbqzS0NuiJIlZY6cdcypMWiNbUtbTOdRNYcuJKY9p8DQjIIz2Aj82nPxWsMQxZ01rZ5SRrj4f\nwVNaKGbbM/CF/NR5mhgKenmv4RB2YxQrXEq5mtujePPjw+yJw93ohH4nGpVmQmOUEZx0VyEgkDXs\n/Vc3Kd7rqZ45KNw5wLNvTfTOR3jzkvbpS9RGYFmSSfSmDQxUVtK1d9+sx88HU11jk0XHDZ9ewWe/\nuIEYh5nCvfW89WopXm+Qdeen8qWvX8SNt688LYZ8BLYoIxddlY13KMg7r5UuaA6N1kxU7Ar8Q530\ndpbhH+qi5sRfEBBIz/8sWv3MkawRmG0pZK35AhqdlabyV+hqnXwPzAfn+myfGXwUr3OkXsvN2Qk8\ndmEeN2fHo1ereLO2g4d3FfOH43U093t5pqSRas8ga+OU8rq5ovP9vTTJdkAmf1M64aCXruZCNLoI\nIh15s46fCqe9nznAl7/8ZVatWsXvf/97nnzySVavXs1DDz20oAWejfANupGkICZrPFIwSH97F16b\nAV1YpmdAMcLT1ZiPR1ZyFPJgBGrJoBhUtZqk229DDoVoeObZKcfIskz3ocOozWas2VkK59jbjMts\nn5GjnwpXLLkAgG0VO6c8T0NvCy6zHb1aRygsUVzdSbzdPMHAzgZXtBFZho6eidKvI5nmJ92V7K47\ngD/k5+L08xCHE1NGwux221jtaYJDMeat7X4KXEtp6mulqbd19P1AOEhVVy3JtnhMWmVcTfNIJvtk\nY5GRaGN1jpOSmi5OVHWO/j0tKhmdWkeJe3ZjDpB8x+0IKhX1f3kGKTR1KO50IyU9hvsf2sKVn8jj\nkmuW8uA3L+HyG5Zhi5pbQ6P5Yu2mVOxOM0VHmuntmV9f+RE4khSPo712F1XH/kQ4OERizo2YI1Pn\nNY/BEsuS1Z9HEDU0lb9KOORb0HrO4RzmAqNGxeVpTh69IJe78pJxmPTsa+7mO++d5P2mLpKsBu7M\nS55zVE6WZSr+sZ1eg5PkpAisEQY6WwqRwn4lajVFlHSxMKsx12q1PPDAA7zwwgu88MILPPDAA2i1\nZ19x/0Ix1Kf0CzZaE/G1tdNpcyHpVMTIIp6AUieaEj27l2yz6HBFmwh7Yuj3D9DU14p983mY0tNw\nv7ubgarqyeeuqyfQ2Ylt5QoElYoeby+DgaEF9RpPsMaS78qh1F1JXc/EHsg93l4GAoMk2hS+vLy+\nB68/TMGS+XGvzqjh8rRTM9qHjXmpu4q3qt9DJaq4KG3T6PsjCXAxtrHwmMmgITpCT2N7P+sTlEzz\n/U1HR9+v7q4jKIVG+XJZlqlp6cUZZcQ8jVb6qHf+5ph3rhZVZMek09zXhmcW3hzAEBuL8/JL8bW2\n0f7GW7Mef7qgUous2ZTCxgvTMRgX9/cliAIbL8xAkmT2v1uzoDmMljjMkekMeGrxDbRhT9yIPWHd\ngubSm+zEpl5IKDBAS/WZu+bn8H8XGpXIpsRovnN+Dg+sSmdJlBmnSce/rkqfV/WB5+gxajwKVVWw\nMQ1Zluho2IMgqhf8e1goZl319773PXp6ekZf9/T08IMf/GBRF3UqFtLbda4YHEl+sybgbW6mNU7x\nLBIMOobCiifoMs/N6GUnR+H3KCHRss4qBFEk5a7PAlD3p6cmtRvsPjTcu3zNMF/eq6zlVOW3ueLK\nzAsB2Fa5a8LfR+cd3iSM8uXDxnyu/XPHhGMm8uZ2UzQxxiiOthTR2NvCuviC0Q5zoHDmNotukjBN\notNCZ6+PpdHZqEX1hFD7qB77cIZ8d5+P3oHAlCH2ESxJihz1zouqx7zzkVD7bFnto+v61C2Iej2N\nf3+O0NDkBjQLwUJ7FC8Wlq2Ixxqh58iBBoYGAwuaY4TjtkSmk5g1uUxtXnOlXIDWEEVHwx68Awtr\nPXu2XeOPKz5O11kUBPKdEfzH+iV8f0vuKH8+VzS9uJU2SzpqtUB2Xiy97lIC3m6iY1eh1i68dfei\n9DMvLCwkMnJMrzcyMpKDBw/O+0QfBIub/NYEgojREoe3qRm3UzF4KVYDIfUAgixiM8xNfjI7ORJp\nQAkBl3UqHo9teR6Rq1bQW1RMz+GJnGBP4WEQRSJXFgDQ4JlfWdqpKIjNxWW2s6f+IH3+sZthZN6x\n5LcORFEgL12pmZ4PZw6Ta81B4c3DstI7eyTxDZR+6J0e74TktxEkDfPmXT1h8l05NPQ209KnSAWP\nisUMt1odr/w2E8Z75yMYadBS0lE+5ZhTobVFEH/j9QR7+2jZOrlZ0EJwtvGMKrXI+i1pBANhDu2p\nXdAcEfalZK66l/QVd3/gcKKo0igbAlmisWzrgvpsn23X+OOKc9dZQX9lFY2V7Xi1VrLz4tDqRNrq\ndgFjNNSpkGSJ7+/6Bb/Y/8cZ514Uzry/v3/CLqG/v/+MZ7MvFmQpzFBfCwaTE1GlwdvcgsdhRwhJ\nROm1CDoveqyIwtzCLlkpUcg+E2r0lI+TOE2+87MgitT/+S/IYSV5LODppb+iEuvSHNTDX9x8y9JO\nhSgoZWpBKcQ71XtG/14/TsZ10BukotHDkkQbphlae06F6WrNYSzjPN7qGg2NA/QO+gmGpCm5+ZEk\nuIa2iaH2kBSmrLOGeKsLq145ZjSTPWHm5KolSZGsynZQXN1F0TB3PlKnPlfPHCD++mvR2Gw0v/wq\ngXGRqY8TVq5PRm/QcHBPLQH//PMDBEHAGr0Elfr0NNeIsC/FGpNNf3cVnvai0zLnOZzDYqH5xZdo\nsyiiR3mr4ulsOsCgpx6bYxkGy9Sd7gqbT3Ci/eSC29fOhFmt1Lp163jkkUcoKirixIkTPPLII2zY\nsOG0L+TDgG/QjSwFMVoVj7W7w43XYkLXG6A/7ENQB2etMR+P1FgrOq0alTcK91A3XUOKETAlJ+G4\n6AKGGhrp2KEkqPUcPgyyTNTqsXKpBk8zOpUWxwzSsbPhgtQN6NU63qh6l5AUHptXrcNhiqaouhNJ\nkieIq8wVkRY9GrU4KcwOsDIuD5fZzidzr5mQPDJVjfkIRox5Y3s/q+OXoxJV7G88Ql1PI/6Qf8Km\nYKTt6Uxh9hGc6p2P8ub9bXi8c9uIqgwGEm+9Bcnno/Fvz81pzEcNWp2aNeel4B0Kcuxg4+wDFhmC\nIJCYdR2CoKKx4lWk8MLC/+dwDosNb3ML7v2H6LBlYDJrSUzS0FTxT1RqPUk5N045RpIlni/5J4Ig\ncGPO6a8Im9WYP/zww6jVau677z7uv/9+NBoNDz/88GzDTisWi58ZHE1+S0CWZeqCihEyDARp7ld4\n5ehp+phPBZVKJCPBxkCnYqTKO8eS3pJuuxVRq6X+mb8R9vnGVN+G+fKQFKapv43EiLg5RwKmglFj\n4ILUDXR7PRxsOkYoHKK5r5UkayyiIHL8FL4c5s6BiaKAI9I4pWceZbDxi6u/y8akibXco+pvtsmZ\n2aOeeXs/Jq2R5c4c6jxN7KjdC4x5+6BkstssOqKss9eYZiVHsTLbQVF1JyU1Snnh0hHe3D1379x5\n6cXo4+Joe/PtGcsL54KzlWdce14qao3IvnerCYelD3s56E12nCmbCfo8tNbumNfYs/Uaf9xw7jpD\n80sv02WII4CGZSviaCx/CSnsJyHr2mm7Ah5qPk69p4lNSWuIt87co35ROHOLxcKjjz7Kvn372Ldv\nH48++igWi2XeJ/ogWCx+RhGLUWRcg719tEYqBs4uizT3KUZvpm5pUyHJZUHqH+HNx4y5LjqauOuv\nJdjTQ9OLL9Fz9Bj6WBeGeCWk3trfTlgKLzjEPh5XZF4AwLbKnbT0txOWJZKG+fKjFW4MOhVZyWN5\nEPPhwFzRRvqHggx6g3M63j2FlOsIrCYtNouOxvZ+ANYnrADgnRqFIsgeNuZ9gwE6erxz8spHcMvF\nivF+YYdivEfrzWdoiXoqRLWaxE/eBJJE6+vb5jxuKpytPKPJrGPF2iR6e7yUHpuswvdhwJV6MRpd\nBO21u/APdc4+YBhn6zX+uOH/+nUOdPfQsXMXHQ5FRyM9w0NfZxmW6Eyi49ZMOUaSJV4oVrzym5de\nOes5FoUzB6ipqeH1119n69ato/8+DhjqawRBxGCJxdvcRLsrGSSZOIOOjkHlITKXGvPxiLebkQYj\nUAkqyt0Ty9HiP3EDmggrTc+/iOTzEbl69WhI+tT2pHPBvqKWUc9zPOIsTlbELqO8s5qdtYr4SVJE\nHO4eL83uAXLTYlAvsPnD+Faoc8FYjfnU9eyJDgsdPUP4/CHWxOejEkRkWcYxnCEPUDuN8ttMyE2L\nJiclisKT7dS39pEWmYRerZuXMQeIOX8TGpuN9rfeJuw9PZntZxvWb0lHEAXe31m1oMSz0w2VWkdC\n1jXIcpjG8lc/7OWcw1mMQE8PbW+8eUbv25ZXXyMgibi1LlzxGgbcbyOqtCQvvXna+vRDzcep723m\nvKQ1xM3ilS8Usz7R//znP3PXXXfx7W9/m6effpqvf/3rbN++fVEWcyYhS2GG+lswmF2IKg29TS10\nxTjR9geJjjSO1pinO+bnKcfbzSCL2FRO6nqb8AbHRDDURiOJn7oFhm+8qDVjIelRGdc5ZLLLsszT\n20/ywz8d4tu/2zelYR0rU1M4+mRbPMcrlVDxinnWl4/HaK35FLz5VBhVf5vOmDvNyDI0uQcw60ws\nG+4WN54vH5NxnXv+AsDNFylzvLizEpWoIseeQUt/Oz1z5M0BRI0G15WXEx4comPnu/M6/0cFkdFG\ncvPj6Gjtp6rsg9EJpwuRznzMken0ukvpdZ88bfPKskQoMLd79xzOfjQ9/yLVv/oNfSULUzOcL0KD\ng7Rtf5Muew5hCfKX1RIODhGfeSW6aShZSZZ4ftgrvyn3qkVb25yM+SuvvEJcXBwvvPACf/zjHzGZ\nFl4/txAsBj/jHWxHlkKjyW817d3IKhXa3gC2KCODwzXmCRHzC7PHxSjXRhe0I8sylV0Ty36cl1+K\nISEeTYQV69Kc0b83jHRLm8Uzl2WZJ18p4e9vVWAxavAHwvzv88cm7UyXu7KJsziRhsvFkiLiOTrM\nl+efYsznw4E5Z8honwruHi9qlUiEeeqM55HytKbhUPvmZEVoYUVs7ugxY8pv85M1XZ3jJNFpYffR\nZjq6h1hqH+HN5+edu664DEGtpuXVfyJLC+OVz3aeceOFimTu+zuqZjnyzEAQBJKyrwdBpLH8FSRp\n9mz72a6xLMvUFf+dE7u/j9/78axQOBM4m+7l/grlfh2qbzgj52vb/ibhoSG64gtwOjpRU4PJloI9\nceO0Yw42HaOht5nzk9YSZ5lbpHdROHOr1YrNZsNsNiNJEuvXr6esrGzeJ/ogWAx+ZnzbU4DKYU9T\n5/ETYTMQFAcQw3r0mvmJ+juijKhEgVDvZN4cFB4279Hvk/+T/57Q67zR04xNb8Wqm/6zhiWZx184\nzsu7q0l0WvjlVy5kVbaDYxVudhROzEYeKVMDiDZEYtQYOV7pJsqqHzWgI5gXZz5DrflUGKkxn64z\nW6JrLAkO4LzkNfz35d+Y0BSlutmDUa8eDfHPFaIocNOFGYQlma27q8fx5nNPggPQ2mzYN5+Pr6WF\nniNHZx8wBc52ntEVH0F6tp2Gmm6a6s8OQ2ewxOJI3Ih/qJP2ut2zHj/bNe5qKaS79QiyFMLTca70\nbaE4W+5lKRhkcLiD51DjwroAzut8gYASYrfE0DkUIj+vGkFUD3cInNqUSrLECyWvIwgCn8hVuPK/\nvlE2msszHRaFMzeZTAQCAQoKCvjmN7/JE088gdM5Px75bMTQOOW3IXcnpa5kxLCErieAoBNA60XP\n/BP91CoRV7SR7jYlrFzeOVnGVWO1orOPlZ8NBby4h7pnDLGHwhI//esR3thfT1p8BI9+cRPREQa+\neFM+eq2K379cTE//RF3rC1LWE22IZJkzi/q2PnoHAhQssX+gbmDO6JEw++zGPBgK09Pvn1H/fXx5\nGigeWbItYXSNPn+IZvcAafERc2rVeio2r0ggJkLPmwfqidY6Maj1cxaPGY/Ya68GoPXVf8577EcF\nmy5UEg7PFu8cIDb9MtQaE201bxPweWYfMA18g24ay7YiqnSAgKejZNYx53B2Y6ixETmoJOJ6mxbf\nmHfsepdgj4eB/ItZml2DRu0nNu1S9Kbpo7eneuWN7f08+2b5BIXK04VZjfm3vvUtgsEgd999N7Is\n09bWxje/+c3TvpAzjcHeJgRBhcESy94jpQxYI7F3D6EKSXQG+hFEeV415uMRZzfT3wdxZpfSk1sK\nz3j8bCH2QDDM//fnQ7x7tImclCh+8IVNo2FrR5SRz161lAFvkN++NNHb0Gv0/Pyq7/CFNXeMSrjm\nZy6cLwcwGzSYDJo5ceadHmVzMVWN+QhsZh0Wo2bUmJ+KutY+ZHn+IfYRaNQiN1yQgT84+j3XAAAg\nAElEQVQQZvveerLtGbT2d9DtnZ9hMKelYl2Wi+fYcYYazkxI70wjOT2auCQb5cVtuKf5PhYLsjR1\nApNaYyB+ydVIUpCm8tcWNLckhag98QxSOEDy0psxRSQx0FN7jjv/iGN8v4vF9sylYJCm5/8BajW9\nBonE+Hb0pjhcKVumHzOurnzEK391j6IMevm65NO+xlmNeVZWFiaTiZiYGH74wx/yyCOPkJo6v85I\nHxSnm5+RpTDegVb0ZheCoGJXvwSyTKQ7iN6goWm4W1qMcfY+5lMh3q6ESOKMCfhD/tHktukwk/Kb\nzx/ie384wIGSNvIzY/jufRsmNRq5alMq2cmR7Dnewv7i1gnvadVaRFGcpMc+HvPlwJxRRtq7hmbN\nIJ0t+Q2GhUKcFlo7Bye1VoWxtqfzyWQ/FZetS8Zs0PDqnlqWRCnc8HzU4EYQN+ydt7z2+rzHnk08\n43QQBGHUO9+3c3JEaTEgSTI7tpXx2H9tp7y4bcpjouNWYYpIoqf9OJ1N00tJT3eNWyq3MdTfTHTc\nGqJiC7A5cgH5tCbWne0IBQY58e73aa78YCWWcPbcywOVSgRJGxND0OMh2L94G9C2bW/g7+hAd9EV\nJCcWI8sCqXm3zChjfKDpKI29LZyfrHjlA0MBdhQ24og0sG7ZzM27FoUzPxtwuvkZ70AbshTCZE2g\nzN1Hu9FKSmsd/k4fkdFGmjyKMY+1zF8lDRTPHMAiKyUIZZ0zhy3HjPlEz3zQG+Tbv9vHsQo3a5e6\n+Nbn1qPXqSeNV4kCX7qlALVK5IkXT0yqAQ8EwxTXdJHkskwpujJfDswVbSQQkujp9894XOcMNebj\nkei0IMnQ7J7sKVXP0PZ0rjDo1Fx9Xir9QwH62pWwfuk8S9RAaYijczpw73yXYN/8HhxnC884G7KW\nuYi2mzhxpIk+z+KW4g30+3n6N/vZ83YlAX+I154/PmXTF0EQSV76yf+fvPeObuO+0r8/g14JsIC9\nV0lUl6xqyZZs2bEtJ26J4zQncRyn7CabTd0ku9l4N8lJNmU3Wad7UxzXuMeW3C1LVu+iKLF3EiQA\noheizvvHECApgmIRKTu/9zmH5xDEYDAYDuZ+773P81zkSh3d5/7KYOfkMb+Q/hx7HM0Mde9FrbNQ\nsuh9AKPBHNz2s/P8id69GB44RjTswdaz/5LHzL5brmV/WzsylYrsjRsACPVdPGmaK2L+AL1PPIlc\nryNUGkWnDaMyrkOXMXVbdLyu/PYlEoP9lcPdhCNxbtpciXyaluGC6cz/X0PSLEZnKmZXo8Q2Xy+G\niUUTmLN0c9aYJ1FkkfrKspAkVWh2XHzMZI+7H0EQKDaNrdY8/jDf/vV+znU62bKyiH/5+BWolFOv\nAkvzM7hzRy1O7wh/eGFiP7Cp20kkOvuRp1NhqlGoFyLdHPN0SPXNBycHyI4BDyqFjJLcS7t53Hxl\nJSqlnH0HfaN989kHc0Eup+CmG0lEIgy98v/mqE6ZTGDj1VUk4iKH9s5tPOpM0N0xzG9/+hZdbQ7q\n6vPYsqOGgD/CK8+l72VrjfnUXfE5lGoT/a276Gt5YdrKUDTso+vsYwiCnMrlH0p5yGv0uWj0uXgc\nLe+4Zezw0AC2gY4F1UmLYgJ73yEAEvEwTuvcSJxJBDy9M1IXLCQSkQjB7h70FeXoy6WSdbB3YSyJ\n+55+hpjPh+WOa9CpWwkEdSxec/NFX3O47yS9XitbytZRYMwlHk/wwv5ONCo5160vXZDj/P9lME/a\nuLrl+TSFEuRZeygoki4IU6YWd1hi89bkzm16WbLM7hqWY9Jk0OSY2oxDFEV6PAMUGvJQycfK5//1\nl2O09XnYsa6UL394zYxMXm7fVkNZvpGXD3WnhozAuJGnl9gvT2KqUagX4mLub+Mx3tZ1PKKxBN1W\nH2UFGcjnaHKThMmgZse6UuyuMLmqYqx+G87g7AlVedduR6bRYN21m0Tsnb2hLRSWry3GkKHmxKFu\nQsH5DXaiKHLgzXb+/KuDBPwRrt25hA984gqu2lFLYYmJM8f7aD2ffgSq1pDHonWfR62zMNT1Ft3n\n/oo4BR9FFBN0nX2cWMRPUc0NKQlqEiZLPWIiind49u2W+UI07KP1+P/Se/ZXNOz9Hj3nnsbjaJ73\nQOlzthMOOsjIrgVBhr3v4JwXD277OZoO/5z+ltm3muYTga5uxHgcQ3UVuhLpfxvsmf++edgxjPVv\nL6Jalovb2IYoQpirUKqnHi6UzMplgow7RrPyQ42D2F0htq8twaCb3ZjVmWLGd8je3l7a2tpSP5cT\n892fCXol8tveIemCXtpwhFiW1K/OzNIRSHgQEzJKc+Y28CQrQ4NaJcdqD7IopwpXyIM96Ey77XDI\nRTAaosQ81i+PxuKcbR+mqtjEP7x/5bQlmSSUChlfuHMVMgF+8ddThKPSje5Uix2FXGBpVfrPM5ee\nOcDgNIz2iw1ZGY+kVK7XNjGY9w75iMUTcya/XYhbrqpCJhMYHpCOf7Z6cwCFXk/etduJDDsZPnh4\nxq97t/QZZwKFQs6GrZVEwnF+97N9PP6Ho7z2wnlOHemlt8s55wA/EoryxB+P8doL59DrVXzsMxvZ\ntK0KQRCQyWXcfOdKZHKBF/96hvBIertglTaTunWfQ5dRzHD/UTrOPEQiLm07/hzbet7GO9xMRnYt\nuaNz18fDnLsEALf9nWO1dzU+jVIRZdhpIh6LYO87SNuJ33P6zX+n4/RDDA8cJxadmQT0Ykhm5QVV\nOzBb6gn5rAQ8sydxiqLIYMcbo/s8+I4SCJPkN0N1FdrRYL4QjPbuRx9FtlqPbKuBRCLG6bN1LF61\n6qKvOdQ7lpXnj7Zq/7ZPqnLdvKVyRu87l3vF5AbsBdi1axc/+tGPiMViqFRjK4o33pjdEIRLwXz2\nZxKJGCGflZi+jCNWFyaXnXqDEk9AWg2bs3VEu/3IYzrkc5zRLAgChTl6Bhx+dmRXcrjvJE32NnL1\nkwl1PWmc33qH/MQTIrUlmbOWY9WWZvLerVU8+1Y7j77cxO3ba2jrc7OkIhttmn47zP78pkahTldm\nd4cwaJVTvm8S2SYNWrViEqM9OSntUshv45GfrWfLiiL2trjRmCW9+ZVl62a9n4KdN2J9cTfWv72A\nZcvmGb3mcvUYxXgc6+6XUedkk7V+3ZxliGs2ltPb5aKrzZGWmKYzqMi2GMixGMjONZCdqycn10Bm\nlg5ZmirKYL+Hv/7pGK7hIGVV2dz+kdUYLuBv5BVkcOU1Nex9pYXXXjjPTXcsT3tsSpWB2rX30X7q\nT7htjbSeeJDqVR9PneOgt4/+ll0oVAbKl34wrQZYbypFoTLgsZ1DXJKYUie8UHANNeB1nMXpyuDQ\n0eXc9uFVlJVHcNsbcdsacQ2dwTV0BgQZBnMFBZXbpcx6loiGvbhtZ9EaCtCbyrCUbMBta8DRdxCD\neXaMar+7k4CnG5lCQyI2gq1nP4XV103aTtJjv0jeddeiXKA5HslgHi3K4eXeg+RmZ18So73D2c2e\nrkMwrmChtQ1TZWhCUZfJCGqOHFxCWGmgNX6eoCOfQmMeBvVEAzVJVy5l5bePerC39blp7Bhm9aJc\ninNndj7mcr+YNpj/5je/4de//jWLFi2a9c7fjRjxDyKKcRoSi0iIsPT0YczrV3Fk9IalNMpAEUET\nnfsYUpBIcJ0DXgp0JYCkN99avn7SdklZWtk4WVrngET6Ki9MP31nOnz4+kUcbLDyzFvtyGQCopie\nxT5X5GZO7wIniiIOd5D87OndAgVBoDTPSHu/m1g8kWopzHSG+Wxw+/Zq3jrZi5BQzklvDqAtKCBz\n7RpcR4/ha27BWDf7m+xCIOJy0fzjn+E9K2Wb5lUrqbzvXrQFs/eCVmsU3PmJKxBFkYA/gsPmY9gW\nYNjux2HzM2zz09flpLdzYsVJJhPIytGTbdFLQd5iIBKO8fqL54nFEmy+pppt19elDfgAW66poemM\nleMHu6lfWUh5dfrvoVyhoXrVJ+lseAS37SwtR39N5ap7CPhiDDQ/gijGKV/6QZTq9DdPQZBhttTj\n6JdmUBsyL59CJxYN0nP+aUDOmcZaQKC3y83S1cswZlVRXHszI4Eh3DYpsPtd7bSf6mPplm+gVM3u\nJu/oPwJiAkvJRgRBwJhVjVqXg3PwNMV170WhnLkR01DnHgCqln+EjoZHsPXuJ6/8auSKiWVj2563\n6P7zX0hEIpTedeesjnem8Le1IVOrecl7lpc79vLl3Exi59uIBYModLMzlwJ4tOF5Tg+OWcIWK2R8\nQKlGXqSlzxPmcHMtGp+RrrojnDg61sLMUBsoNOZJPxn5jMRG6PNauap8w6Ss/L0zzMrnimmDuVqt\npqqqakEP4nIi4O0nLCo56c9AHw1T2XqWgW0309/TR/3KQmwR6eZkmqPGPIlk31weNqOWqyY5wSUx\nlpmPldm7rF4AKgrmlpFq1Ar+4f0r+NffHOSvr0s9wfkM5iqlnKwMzUV75oFQlFA4Pi35LYmSPCPN\nPS6sjkCqh97R70EmEygrmNuiJh0qCk2sXpTHWY+ZQZmd4aCLbF3m9C+8AIXv3Ynr6DEGXniRundB\nMPecbaT5xz8l6nKTtX4diXAY98lTnPzHf6L4jtsovv3WCY6DM4UgCBiMagxGNeUXtGlisThOR5Bh\nm59huxTgHTY/w/YADpsfGsd63xqtkts/toa6+osvLOQKGTffuYL/+/nbvPDXM9z35a0oVelvUzK5\nksoVH6Xn3FM4+o9w/LWf4HRqKch3kFmwCVNO3UXfy5wrBXO3rfGyBvO+5r8Ri/gJRFcTCEjfj96u\nsUWRIAhoDfloDfkUVF6DrfttepufY7Dj9RQjfyaQiG+HkcnVZBWsGt23DEvxBvpaXmB44Dh5aVoQ\n6RDyDeJxnEdvLicjp47ckk1YO15juP8IuWVXTtjWfeoMAL6muS2Wp0N8ZIRgbx8Zi+roHiUzB7L1\nqJEY7cbamovvIA36PFbMmgy+tfUfCQ0eJ9C7HzGRQGyB3A33oTnUQUGFkTtu+RgDviEGvIMM+Ibo\n9w3RPNwx4f4+Pit3+UbYe7Kf4lwDq2rnpo6aKaYN5tdeey3f+c53uO222zCbxwJcdXX1RV41v/D7\n/fNWpgx6ezknVhNOCCxvPI4iM5u3D9lQquTsuHkJu1qkPmi2Zm4a8ySSjPZBR4jq7HLO2VoJRILo\nVRODW49nALVCjWVcCT6ZmZcVzL1EtbI2l2uvKOW1oz3oNQpqLpLdJvszsznHeVk6mrudEzLp8UiS\n33LMM7PDHe8EV5JnJJEQ6RzwUJxrQH0RFv9ccMe2Gs48cxh5pp1ztla2lM++1G5athRdWSnD+w8S\nvvtjqHMufr3M5RzPBKIo0v/Mc3Q/9DAAZZ/4GM/lu6jKKmPNtdfQ+eAf6H30cexv7aXqvnsxr1wx\nb++tUMjJzTeSmz/xOhVFkaA/MhrY/fh9YZatLiYzO/3CLtjXT8dvfkfV5+5DW1BAUWkm67dWcuit\nDva83MKOm5dMeQyCICOuvJruPhtlxV0U5AfweAy8vlfF1e5O1m4un7JVZcyqRiZX4bY3UlR70yU5\nI84UHkczwwPH0BqLOH+mDBgmryCDoQEv4ZEYas3kW3JOyQaGevZh7z1IbumVqGfof+FxNBEdcZNT\nvAG5Yux7mF24lv62l1L7m8nnHuyS5ID55dvw+/3oslchdL3FUPdbUtY/2pIUEwm8ZyXJn6+lFTEe\nR5DP7/c30NkFiQT6qkq6PZJRltMkpwCpbz7bYB6MhhgOuVidW0e06zUC9kYIQ+TFAZb+03d4bJdU\ntd15y0oKcs0syZ24/2g8ypDfQb9vkAHvEAXG3FRW/tKBLmLxBDdvqZxVy3QuMW/aRtFjjz3GoUOH\n+NrXvsanP/3p1M/lxHzeAL2eAc4malELUH3yAL1lVxLwR7jymhoyzFr6RzXmhRmXlskmteb9dj91\nOVWIiLQMT5T6xOIx+r1WSk2FyEZ7dqIo0jngJT9bh04z+0xqPO55bz0FOXq2ri6+KBt8LrrR/Gwd\nCXGM5HYhUrK0zJll5qX5E21drcMBQuH4vJHfxmNpVTbFOqlfeKRnbgQoQRAovPkmxHicwd3TTxFc\nCG1uzB+g6fs/pPtPD6Eym1n2vfvxblrCW92HebjhOXTrV7H6gf+hYOeNjAwO0fid+2n+8U+JOBfW\ne10QBPRGNWVV2azeUMbWHbVTBnKAgWefx3OmgaFXX0/9bdt76sjK0XPorXb6e9KrDsSEyL7XWnn4\nt4c411RGQrkZXUYxGQXvA+S89OxZHvyffVO+XiZXkpFdRzjoYCSw8NPi4rERes49CYKM8vr3Yx8K\nkGHSUL04V5ocOIUnvkymoKj6BkQxTn/bzCdW2nul8ceW4g0T/q5Q6cnMW044aMfvmt4cKBxy4Rw8\nhUafh8myCIPBgDkzl5yidURG3DgHT6W2Dfb0EPVIlcV4MLggzmz+UQJ2ojQ/NZWyTyuRMufyfgPe\nIXJkMrbGHXjsjWhkFkYe7iarYjXdQT2D/V6WriqiYIqESClXUmwqYH3xKm5d8h42lKwGJCLzrgNd\n6LVKtq8pmdUxLYjO/I033kj78/eIRCJGg1dFAB2rwm6iaGnxZZCZrWPj1VI/I6kxL826tJmzyTL7\ngD3AohypTdF0wXzzAd8QcTExgfzm8oXxBiJUFF56EDPoVPzmG9fwudvnLxtLYrpRqClZ2jRM9iSK\nR3XkSXlaR9/sZ5jPFIIg8MEtVyDGFJwemPvQoJytW1BkZDD48ivEwxc30Jlv+Ds6OPXPX8F55Cim\n5ctY8bMfk7FkMfu6JYe0cCzMm50HUOj1VN57Dyt+/EMMNTU49u3nxOe/gPXFXYjxi9sMxxLxBZ8T\nnYhEcBw4AIyVZwGUKgU7378cUYS/PX6KeGzitLpQMMKj/3eEN3c3YczQ8PHPb+aKbbeweMMXWXvl\nSj739W0sX1OMtc/Dgz/fx66nGhgJTWbIpwxkLoNXe3/rbiIjbvLLtyFT5uLzjGDJN1JSIflRXMg/\nGI/M/OXoMopxDZ4i4JleTx0OOfE6mtGbStOam1hKNgJg7z007b5s3XtBTJBfcfUEomBe+VYQZAx2\n7UldJ+7TUqY8YJESEV/z1KX2qWSF0yFJfnPmjFUbWpXSfWMuWvM+r5UdOjXqRJi8sqsZeaoHIQJF\nd32QN3c3IZfL2HbD7Dlj+0714/aHuW59WVqzr/nGjCicbW1tPPzwwzz88MO0t18em8eFQMBn5VS8\nDhkidScO0GJZjyjC9bcsRaGQSkGuUY15de7F7famg1GnwqhT0W/3U5tdiYAwaehKOhvXFPltnvrE\nC1U6HNOapyfB2V2jVq7TaMyTyM3UoVLK6RuSytHtKSb7/JHfxmPj0iJUYQsjgpfPPPdN/mPP//D7\n44+yq+UNTlrPMui3T+upL1eryX/PdcR8fuxvTT/Vaz4giiKDr7zGma99k/CQjeIP3EH9v/8rKrOJ\nSDzKwd7jmDUZqORKdrfuITE6stVQVcnyH36Pys98GgSBjt8+SMt//3zKYD3os3HPs1/h2fMvL+jn\ncR49TjwgXSuBjg6iXm/qufLqHNZsLMM26OPt18f04AO9bn770720nbdRWWvh0/+8leKyibwHg1HN\nLR9axcc+u5Eci4FjB7p44Idv0nC8b8JnNlkWgyDDbVtYNzifqwN77wE0+lwKKq/BPmqQZMk3UlIu\nHXvPRYK5IMgoqpHshPtbd027yHL0HQZELMUb0z6vN5WhNeTjsjUQDU/tZhiLBHD0HUapMZOVP1GS\npdZmkZW/ghH/IF6HtCj2nJEWZEfqpfvDVH1ze+9BTr7xbawdr896wehva0eu09GjlBIGtUKNXQih\nMGUQ6p29C5zV1UOJQoZMn4e8Xc7IgJW863bQ2BXB7QyxdnPZRStL6SCKIs/v60AmwM7Nl4ePMW0w\nf/bZZ7nrrrs4cOAA+/fv56677uK55567HMeWwnzpc0/1DeDCxLKMGLYONy5tATWLc6ldMub0Fkh4\nEKMqinJmT4q6EEUWPYPDAdRyNaWmQlqdXcTiY4YQSc/20glM9lHy2xyZ7HPBXDTQSXnaVKNQx3rm\nMwvmMplASZ6BPpuPeEJMMdkrFiAzT77flYVbiHuyiMRiNAw18UrbXv548q/8YO8DfOHFf+MjT32R\nL+36Lr89+nBqLvyFyH/P9QhyOf1PPYP3/NRZ/nzozKNeL63//XPaH/gVcrWaxf/6Tco+fFeqJ3li\noIFgNMTW8g1sKVuPPTDMsYGxbFeQyym44XpW//LnGOtqcex9G9vr6atsT57bRSg6wqvt+xY0O7fv\neQuQqhyIYiqzS+LanYvJMGnY93orQ1Yvxw508Ydf7MfjDrH1ulo+dO96dKNDh9Kd4/LqHO778lVs\nv3ER4VCUZx45ySO/P0w8Lv0/FUodxsxKgt5eIiOeBfmMiXiU7sa/AgJl9e9HJlemBtlY8oxodSos\n+Ub6e1yp40qHjOxqMrLr8Dnb8A5PnfEmEjEc/UeQK7Rk5qevygmCQE7xRhATEuN9Cth695NIRMkr\n25rqi48/z3nlVwMw2PkmiVgMV8NZXEY53QUqoio53qbJXg4+Zzs9Tc8iJmIMtL1EX8vfEKf4fl2I\nWDBIqH8AQ1UlPd4BqpRy7szKRQmQn8PI0NCsq2RxTyeCIGDOXkLvY08g02iw3HIr+15tQa1RsOWa\n2RPqznU6ae/zsGFZAbmzHN0MC+TN/stf/pJHHnmEBx54YMLvlxPz1WvcMygF0vVhD61Za5AJItff\nsjT1fCKRkOaYR/XzQroqtBiIJ0SGXEHqLFVE41E63WNloNS0tPFM9lQwX5gglg5z6eeOldmnysxD\nyATITuMFPxVK8oxEYgmGnAE6+j3kZekmDZWZT2ypWUakeR2blR/jz7f9jB9d903+aeOnuHPpzWwt\nW0+luYThkIvXOt6mz2NNuw91dhb5N97AyOAQDd/4Fg3/8m2cR48hJibenC6lZy4mEgy9+honPveP\n2PfsxVBTzYqf/RdZa9dM2G5vl0Te3Fq2jhtrtwGwu3Wyj7nKbKb2K19CrtfR8dsHCV7gaW312VLl\nekfQSetw55yOezpEvV5cx0+gryin6H2SPab71KkJ26g1Sm56/3IScZE//u9+dj3VgEot50OfWs/V\n19dNIBVNdY7lChlXXlPDZ7+2jZKKLNqb7HS3D6eeN1ukUrvHfm7Sa+cDA+2vEg46yC3djMFcDjAW\nzEe5IiXlmUQjcYYGvFPtBoCi2hsBgf6WXVMGQLetkVjET3bhWmTyqb8/2YWrkclVOPoOp91XPBbB\n1rMfuUJLTtEYSXT8edYZC8nIWYTf3Ym98QCEI/Tmq5DLFdgtakYGBiZUW8IhJx2nH0JAoGL5h9Ho\n87B176Pr7OMzKrsHOjpBFNFXV5Lj7eAOg5aCqIslKgWhbAOIIqH+gWn3Mx6m0Wospx1EPR6Kbn0f\nR0/YCQWjbN5enVoszgbP75OqsO/dMjcl2IL0zGOxGOXl5anHZWVlxP4ObSw73QF6wlpKhEF6DgwR\nVhpYszKHrJwxHbQj6AJBRCPMj9FB4Sijfaq+eY+nn0ytCaN67B/XafWgVctTWu53K7JMGhRyYcqe\nucMTIsuknZUNa9IJ7lSLHW8gsiDkt/GoLjEjlwk0dTnRKDWUZ5awqXQNt9ffyD9s+Djf2/F17lom\nSYG63FMTayru+ThLv38/mWtW4z13nvP/+QNOffGfsb2x55ItXwNdXTT8y7dp+99fkYjGKP/kx1n+\nw++jyZ0oc/GG/Zy0nqXMXEypuYgSUyHL8hbRaGuhyzX52DW5uVR//rMkwmFafvwzEtGxfvJTjVIZ\nd1vFJgD29xy7pM8wFRxvH0CMx7FctRV9ZQUKoxH3qTOTKgE1i/NYtqaI8EiMorJMPv3PV1G9aPYy\nn8xsHZu3Syqcno6xkrZpAfvmAW8fQ91vodJmUVh9Q+rvqTJ7nvTdL51B3xyk4JlVsJqQ34rTeiLt\nNo4k8a1kQ9rnk5ArNGQVrCIy4sLrmJzpDw8cIR4Nklu6OeVrnw75yey8R6qyBMpzWZpbS0+WtNDy\nNUvZeTwWof3kH4lFA5QsvoWs/JXUXfFZ9KZSnNYTtJ/6U8rNbyr429pBJ8dfNEC9PEYAKemqVylx\nmaS+dGgWJLhgyE2+EMeTkDP49MsozWaM23ZweG8HRpOG9VtmXyK3OYMcarBSWWRiyej/9XJg2jvt\n4sWL+f73v09TUxNNTU384Ac/YPHixZfj2OYVL7VLmdVarY3zLiOaeJDtd6ydsE2HQ9pmrnPML8QY\nCU5itAOpvrk/EmA46JpAfotE4/TZ/JQXmGbt/Ha5IZcJWDJ1aTPzeDzBsGdkxuS3JJLytD3HpS9j\nVfHCBnONSkFFkYm2Pg+RaPqsoMwsWUV2XySYC4KAqb6eJf/2LVb+z0+xXL2VYF8/rf/zC47f93kG\nnn+BeGh2E8jioRCdf/gTp770VXxNzWRv2sjqB35O0ftuTiv1OdhznLiYYGvZmDHRjbXbAdjVmr6U\nnrN5E3k7riXQ2Un3n/8CSKTMfT1HKDEVcu+auzCq9BzsPZ7qvc8n7HveApmMnK1bEGQyTMuXEXE4\nCPVP7nvufP8KPnjPOj7+uU2YZsjDSIeS8kwQpEEvSai1mWiNhficbZc8UWw8EokY3WefADFB2ZI7\nJpir2Af9ZJg1qEcVK0kS3MX65kkUVV+PIFMw0PbypOA3ErDhc7VjzKxCo59+wZPsqSeZ70mIiThD\nXW8hyJRYSi/ucmjIrERvKiWm9kKmkvrN11JgyMOaIwVXX1MzoijS1fg4Ib8VS/HGFMNeodJTs+bT\nGLNr8DjO03r8d8SiU39X3AONqO8sIiI6aYrE6Mhajs5cSYlSjsM4ymifha1rT+8hZIIAAwkSIyOU\nfPD97N3TTSyaoHx5AbsOdfPAk6f55i/3c/d3X+Lu777ML544xZHGwZRd9oV4cSJzSsQAACAASURB\nVH8nCVEyibkccsckpg3m3/3ud3G5XNx9993cfffduFwu7r///stxbClcaq/RFhjh5JCXHJwobH4S\ngpzVOT7UmonORZ2jwTznEjXmSRSNk6fl6LLI1mamhq70pimx9w75SCTEOTu/zRVz7efmZ+nw+COE\nwhOzT6c3TCIhzjqYJzPz86MGGgtFfhuPxeVZxOIJ2vvS90vLR4P5xTLz8dCXl1H7pS+y5jcPULDz\nRmI+H50P/oGjn7qPpgd+hXX3y7jPNBBxutL2okVRZPjgIU58/osMPPs8mlwLS/7tWyz6+lcuqmXf\n230YQRC4suyK1N9WFdSTb7Cwv/so3pH0JKeKT30CbXERA8+/gOv4CZ5u3I0oiry//iYUcgXri1fh\nHvFyzj6/A0lCViu+5hbMy5ehzpYCWeYqqb87ntWehFIpp3ZJHnLF1LesmVzHWp2KvIIM+rtdxGJj\nN2OzpR5RjONJk6HOBaKYYKB1NyG/lZyidWRkj/VdR0JRfN6RVIkdwJylw5ChprfTOS1HQaXNJLdk\nM5ERN7be/ROeS/qw55SkJ75dCF1GEbqMEjyOJsKhMWmcc/AUkRE3OUXrJrnOXXieBUEgp0iq4sQ3\nZLGl/ioKjLkMZitBAG9TM4Odr+MeOoMhs5LiRe+dsD+5Qk31qk+SmbcCv7uTlmO/Ihqe2G4QxQTW\njjcYqRoGjZxgdj3PBUYozSrDUiQlZXK9lFiEZsFo945688uOO4gq1fzwhMCpI72EEPm/fe38/rmz\nvHSwi7MdDhRyGbF4glcOd/Mf/3eYD//bbr73h8O8dqQbj1/q04+EY7x8uBuzQc3WVXMb1AUL5M2e\nk5PDf//3f8/pgOYLl9ozf7XThgislJ2nr1eDOWhl2c7Jrl2pOeaXqDFPoiB7rMwuCAJ1OZUc6D3O\noN9Ojztp4zo2zSlFfptHx7OZYK7nNy9bD9gZcgYnsO/t7tkx2VP7y9KhVMiIjsqQFrrMDrC4LIu/\n7evgfJeTxWlKYjqVllx9Nl1uiQU905W2JjeXynvvoeTOD2DdtRvrC7sYfuU1hsdtI9dq0RYVoi0q\nQltUiKYgH/tb+3AdO46gUFD8gTsovuM25BeZ0AQS87x1uJMV+YvJ1I6dM5kg4z01V/PHk3/l1fZ9\n3F5/46TXyjUaar/8Jc589Rs0/ex/OL5DQ2leCeuKVwKwqXQtr3W8zYHe4yzNu7ij2mxg3yOx/y1X\nbU39LWlo4z51isKdk491Osz0Oi6rzGZowMtArydV3jbn1mPteBWPrZGsKUhjM0XA00vP+acJevtQ\naswU1e6c8PxYiX0smAuCQGlFFudOW3E7g2ROY4OcX7kdR/8RBjveIKdoHQqljkQ8ynD/MRQqQ0py\nNxNYSjbS3fgEjv7DFFW/B1EUGeraA4JMkp9dgHTnubuln4Qrgq5cjyoRpcCYR0QlI5qbSSDcR6Tt\nZVQaM5UrPopMNjnsyGQKKpZ/CPl5LY6+QzQf+SU1a+5FrcsmFg3S1fAYHsd5xEAcdUcGZ7ZJ95ZS\nUxGZpnw6Gv9KsTqOXK+bsdY8Hg0hCwzhiMQx9Hlo0ZegcEcQAHNlNh+uzaHIYqA410ChRTKviidE\nmrudHGkc5NDZsR+ZAIvKs8jK0BAIRfngjjqUirnzrubVm/348eOsWbOGPXv2pL2BXXXVVbN+s3cC\n3nCU/X3DmOVRKullv3cVix2vkblqsmfwfGnMk9CoFeSYNPQ7pFXWIks1B3qP02RvSy9Ls44yuC8j\n+e1SkJKnDQcmBHPHLDXmScjlMoosBrqsXsxGNVmzIM/NFXWjsqCm7qnLm+XmEo70n8I14iFLO7tq\ngTLDSOkHP0DRre8j2NVNqH+AUH//6M8Aga7ulG42CdPyZVTedy+64pmt7PeOktW2lE32/t9WsYnH\nz/6NV9r28r5F16GQT/7KGyorKP/4R+n8/R/YcSBCxTdvSJkYLbHUYNZkcLj3BJ9cfSeKOQ4fGg9R\nFLHv2YtMrSZ749gxqy0WtEWFeBoaSUSjc7KfnQlKK7M48nYnPR3DqWCuNRai0mRKASMRTzG3Z4NY\nNMTAqLMaiGQVrKa4dicK5cTvwXgm+3iUlEvBvKfTOW0wVyh15Fdsp7/1RQY736C4dieuwdPEYyHy\nK7anDZhTISt/BX3Nf8PRd4TCyh14h1sI+QfJyl+FWjuznm/7ob2Uej0ot1sY6t5HYZFUGfBXGjEs\nlSEICqpWfvyi3vKCIKN08W0oVQasHa/RdOQBSupuHtXnu9Aq83E9cQjLTbfQ7elHEARKTIXIFSp8\n6iyyw8PEF+Uycqp3RteP234OARGHfQQDMJxbR0ZCuj7u/tymtHFPLhNYUpHNkopsPr6znj6bjyON\ngxxuHOR8lxNRBIVc4MZN5TM6b/OJKf/jzzzzDGvWrOHBBx9M+/zfSzB/vctONCFSl2hBlAuYrL3k\nlWSjMk8OmO6wCzEhUGGZPw/dQouBM20OwtH4hL55v3cQmSCjKGNs4ZBkspfmL8ykofnGVFrz2bq/\njUdJnpEuq3dBzGLSwWLWkm3S0NTlnDLzLjMXcaT/FF2uvlkH8yTkajXGutpJQ1nEeJyw3T4a5AdQ\n5+WSte6KGVcARFFkX/cR1HIV64omZ5RapYZtFZvY1fIGh/pOTDklTty6hs5dD1MxEKHkRC+USUx5\nmUzGxpI17G59k7NDTawsmHnGNxV8zS2MDA5iuWorcu3EQGdeuQLri7vxtbRgqr/090qHZADv6XDC\nNdLfBEHAnFuPredtfK72WU0oE0UR1+ApepufJxbxo9HnUrr4VoxZ6S2vk5m52HSChud/j7G2hvK7\nPzrBPGbF2ukdw3JLN2Pr3Y+tZz+5JZux9x0EBHKKJy/qLgaZXEV24RpsPW/jtjWmSvd5FVfP6PXO\noBtFWx9RTxTteypx9B2mvnwbBrmCzKVyBJWAOVqX1rzmQgiCQGH19ciVOvqan6ez4REACiqvJX7K\njyt0AH11Fd3W4xQYcmnq8PDE6y1cvb4SwsMkKlSIx+OMWAfRlV78HLptkgxS1hZEBCL6EhgRuXbn\nkhl//4pzjRTnGrltWw0ef5hj54fINGrIvAyJyIWYsgH1n//5nwA89NBDaX8uJ+baMx+JxdnTbUcW\nF6mXN+L16qmwnyRz9cq02wcSHsSIlvys+bPdTPbNrY4ApaZCtAoNTY52erwDFBrzUI5KR5I2rgXZ\n+ku2cZ0t5twzT2rNLwzmycx8DkSlJAnucpTYQbp5LCrPwuULTymzK8+Ubgpd7tm7SyUx1TkW5HI0\n+flkrllN4Xt3kj3LsaUtwx0M+e2sK16JRpn+BnJDzdUICLzY8saU/dinzr/EqxsyIMNAz18enVAt\n2Fwq9STni9We1JZbrp5cwh0rtU/um0+HmV7HhgwN2RY9PZ1OEonxBjKzZ7WPBGy0Hv8tnQ2PEI+N\nUFj9HhZv/NKkQC7G43jPN9H1p4do33MUAN8zj+I924j1BcmNL78wA6VKPi2jPQmZXElR1fWIiRgd\nDQ8T8PSQkVOXNpsWRZFzpwdwT3GN54wS0vpaX8Tv6iAjZxE6Y2HabS88z280vo7FGUUsKSSvfCuJ\neBhH30FuM+rRqARix1xEz8/OQjivbAsVyz6ELqOE6lX3UFh9PYE2yQ47WpRDMBqi1FzEK4e7OdPm\nwBEsIpBIIMuJg2x6W9d4LIzH0YwjnsDSHqLXWENsRGTx8oJJBkQzhcmg5porSlk9B6XFhVgQnfld\nd901o78tJOba093b4yAYi2MctKOSxwk4EigTERJVk635QtERYsIIQlhHhl6VZm9zQ1Ke1m/3I5fJ\nqcmuYMA3RCg6MqHE7vSO4AtGLjv5DeaugU5pzYfTZ+YzNYwZj7WLczHqVGxYemkOfLPB4nLp5tfU\nlf4mWp5itM/eXSoJrVaHRjv/csN9XVOX2JPIM1hYU7iMdmd3Ws14v3eQ/T1Hyc0rYfE//xNiLEbz\nj39KLCj9H2uyK8jRZXGk/xSRaaRD0yERjeJ4ez9KsxnzisnzyjOWLkWQy3GfPD3rfc/kOk4kRCLR\nOKWV2UTCMYYGxoiPxswK5AotblvjtCS0RDzKQNvLnDvwU3zONkw5i6nf9FUKKq9JlbgTkQjDh4/Q\n+vMHOPqJT9HwjW/R//Sz+EQdWlmURf94H9mbNpKIRAj19yOTyyguy8Q+5CcUjMzoM2cVrkZryCfg\n7gaY0vHtxKFunvzzcR598MiEBUwSWkMehsxKIiHpO5CUm6XD+PMcT8RpOrQHmQjFa9djGR3qYm1/\nhQIhTmskhnguii+Necy0n61gFYs3fAGTRbpf+9vbUZpM9Mul+02ZqYjWPskp0jGk5HwkhlwuIivR\nEZqG0e5xNCEmYvR4I2hDMboyJb+R7Te+O0Z9L4jOPHSBpCYcDmOzLfxQgktFLJHg1U4bxEUWj0gZ\nVfbQECMyJf+220pLz8SVYrJfrhEy5lVOUDhOngZQlzM20zat89tlJr9dCow6JVq1YpLW3O4OolHJ\n52T4UlOSySP/cQO1pZfuwDdTLCpL9s3TZw85uiz0Su0lZeY/fOgYX/rZW/PqphaLxzgwat+6bBpy\nWtJEZlfLZJnaU+ckBvsdS28ia9Uqim59HyPWQTp/93tAql5sKl1DKDrCKeulabFdJ04S8/mxbL0y\nrcROodNirKvF395O1De1zehc8cTrLXzkOy+RM/o96x6nNxdkckyWxUTDHoK+yQu3WCTA8MBx2k/9\nmdN7vou14zUUKgOVKz5G1apPoNZNzIjPf/+HNH3/h5LLniAj77prqfza1wkrdBTWFpJ3zXZMS6WJ\ncP4OaZFVMrqw7O2aWSY73uZVqTGnAt942Ad9vPxcY+r300fTX8dJv3a9qRRD5sxmb5+wniVz9F6a\nvWoVcoUGdYZk+xpKaHkhMIKsooSwzXZJA36iHg9hmx1DdVWKb5SnzcfqkO497T1BupGIovI6w7Qe\n7e4hqcQe6g0SlmuJKDMoqcgi2zK/w5AuJ6YM5r/73e/YsGEDra2tbNiwIfWzZs0aNm3adDmPcU44\nMuDCHY6is3pZVN4JyBHah5HXLMYbjPEvv9zPkcbB1PZ9HmmBkqGYXznU+IErIJHgkkjryf53Qn4D\n6SaflyVpzccHKYc7hCVTe1k1lpeCyiIzSoUsJYm7EIIgUGYuZtBnZyQ6ex1yIiFyqsVGl9WLwz1/\nOuYT1rP4IwGuLL0C+TSErfrcOkpNRRzqO8lwcOymmszKy8zFXDHacy/98F0YqquwvbGH8z/4ET2P\nPs6qfhkWZ5RD7YdndYyOoJOf7v9dSoppf3O0xL5tas6NeeUKSCTwnJl/v/Q9x3sJhWNE1dL56ukY\nnvB8yg1utNQeDjoY6nqL5qO/4vRb99N19jHctgaUaiMFlddSv/mrZOYtm3Sti/E43sZzqPNyWf6j\nH3DFH35H9ec/S7RQ+v4nyW/6SiloBtqlEnLJ+H7+DJGRU0dJ3fsor79zwiAUgFg0zlN/OU4smuA9\nty5FoZSx5+VmopHJZkaZucsorLqOsiV3zPi7+2rbXoqHIggqFcZaiWfQ3VNGW0cJTT2riAChYklS\nebGhK9Mh2fYx1FSnKmSJ4Bi3qHPAi1KXx3A8gaxCT8A6dWaeiEfxOM4TlWsxdwaxGcoQBIH6Fenb\nCn8vmJIA98EPfpAbbriB+++/n+985zupm7XBYJgw1/xyYLazXROiyEvtQ5AQ2aA+i0YdRR8tZ8Tf\nSulVq/l8YRW/fe4033/8FW7dVsmaJRaO9Uo3juwZzgqeKfKydMhkAv2jmXlNVjkyQUZCTFA6TpbW\nZb38nuxJXMqs7fxsHV1WLx5/BLNRTSgcwxeMUlNy+TLrS4VSIaOmxExTl5NQOIY2zYSjcnMx5+yt\n9HgGqM2ZWdaShM0VJBSWNM3t/e45cQnSIWm5uqV8esKTIAjcWLuNXx/9Cy+3vcWHlt8CjLm9vb/+\nphSDXaZUUvuVL3H22/+O89BhnIekAP4hgJde4cifjqErKkJbVISupBjLVVtQpLl2RFHkV0ceomGo\nCaVcwWeXfgDn0WNoS4rRV0ztrGVeuYKeRx7Dffo0OZtnppeG6a/jweEA/S4Xsgwvdn+YDLOGno6J\nxMeMnFoEmQJ732FcQw2MBIaSZxC9qRRzbj0myxI0+tyLBryQ1UoiEsFUv2QC6TFJfkvOgNeXl4Eg\npDLz4rJMBAF6p1hYpoMgCOSWXZn2uddeOI/N6mPNxjLWXVmBzzvC/tfbOLyvkysv8BwXZHIKqnZM\n+37J8xwgRGvnWa7yxDGtWoZMqUQURZrPufC4KlCqZbAcHPla8pH05tkbL+5KN+V7JoN5dRU99hfR\nKbUMDUkxqSBbj3U4gF6WTWOkm61aNRGNZ8pZ6t7hZhLxCIPKTIqHopzKk77Pi5bPj4ppPjCXeeZT\nBnOj0YjRaOQHP/gBRqMRlUrqI0ciEZxOJ1lZl8+mbrYfqsHmwRoYwex2U1vQQ1hupOnZtykH/tP2\nN/x+ObLFoAJetB3kxXFdgyLj/DHZARRyGflZOgZG5WkapYZFOVUM+u1YxpXlOge8aNWKd8TG9VJ0\n/ONHoZqN6pQsbS798ncSi8qyONfppKXHxYqayT4DYyS4vlkH827rmAFGW597XvgA/kiA4wMNlGQU\npHr60+HK0it4+PQzvN7+NrcvuRFH0Mn+nmOUj8vKk9AWFLD2d78iMjxMqH+AYF8/Z868jaurg9Jw\nFM+ZBjxnpFKldddu6v/931Bbcibs483OgzQMScNnjvSd4hZnHmIsRu7VV100EBqqq5Dr9bhPnp6V\ntn+66/jQuT7Ui44g0/lptpWzuDKbhhP9OGz+VKYsV2jIyK7DY28kHgthsizBbKnHZFmMUj1zlUmg\nowsA3TgrbADHBZ7scq0WTUEBgc4uRFFErVGQV5jBQK+bWCyemuY4FzQ3DnLk7U4seQaue69Uzt+8\nrZoTB7vZ/0YbqzeUoZsDPyh5np87/SrFQ1Jv37R8GQD2IT+eUc5MNJxAGzDRnRcnXyabcoLaTJAM\n5qryEgY6h1iUU0V7r1TNvOnKCn7/3FniQT0tkRhbtWpkVVpGbDa0BZO/a67REnuv1cvSuAq/2oIx\nR0eG6d1zz1qQnvlnPvMZ4uPmHkejUe67775Zv9HlxIttUvl8i/E4oijwmH2Q4sEowRwDKxat5+qK\njVxXtZWrSragcdUR7a9C7agn0racqpzZDZGfCQotBjz+CP5RUstXNt/HD3Z8I3WTikTj9Nv9lBdk\nvOttXC/EhfK0MVnau+eLMRMsSpLgptCbl83SCW48ugbHgvlUTnOzxaHeE8QSMbaUr59xsFMpVFxb\ntQVfJMC+7iM82fgiIiJ31N+Udh+CTIbaYsG8cgWFO2+k/vP/wDPXZHLg3g1seOwvrPjpf1Fw042E\n+vo5841vTbDRdIbc/PnUk2gVkjQuHI/Q/Zo0TnW8UUw6CHI55uXLCNtsjAwOXnTbmSIhJnih52lk\nOmlR3eVvp7RSqsJdWGovq7+DmjX3snLbd6le9QlyitfNKpCD5KkPoK8on/B322hmnpM7tj9DZQXx\nQIDwKBeptCKbeCyBtXfu14rPM8Lzj51CrpBx20fWoFRJeZtGq2TLjlrCIzH2vTZ3V79oPMqbnQeo\ntEsGT+bRYN56Tqpk1NVLkygzfQX0h53oK8rxt7VPmAEwG/jb2lFlZzEkH0EURUpHyW8mg4otKyXu\nkcuhwJsQiQTlyIu1eLsmLx4SiRhu+zmUGjOR5kHshlIQBBYtu3yE24XCtME8GAyiHacF1ev1BIPp\n5Q3vBrQ5/XR6glhGhinS2jkSjqO3xVHERaqv3M4XN97D59Z9jE+tvYvPb/oQv/jIZ6lWrMPdUULc\nWTgnbfR0SPXNR8kaBrV+glNXzztk4zofyMu+IJjP0TDmncaipHnMFMSj4ox85IKMbtfsSXBdA15k\n5iHU+f20j7JvLxV7uw4jMNG+dSa4vvoq5IKMp87t4kDP8bRZOUj++o++0szBhoFUi63EVEiJqZCT\n1kbCcmlGesW9n6TsYx8h4nDQ8I1v42uV7Ip/f/wxgtEQH15xK7csvh6jP47Q3kfG0vpJGXw6pCRq\nc2C1p8MTDS/iVfQgD0stQndigJIK6X/e3T5xAadUGcjIrkUmn7uqJdDZBUwO5vYhH6ZMLWrNWFFU\nXym1HAIpEtz0880vhkRC5JlHThIKRtlx8xLyLrivrN1UhjlLy7H9XVNK1abDkf5TeMN+KuwiCoMh\n9Tlbzg0hCHDtzUtAAJM/j0GfDWNdLWIslvqMs0F42EnE6cRQXZXql+dq87E5g9TkGDh9oIvcTC29\nXdL2gxHp3LrtDZP25RtuJREbQZVZReHgCDa9dNzrNpTN+rjebZg2mMfjcVpbx1ZwLS0tl31q2mw0\nd883S0SbTfqTuKNyTtp97DwjVRayrlg7aXuTQc1/fnYzm5cXolHJKcuf/4BaNE6elg5dA++s89ul\nzNrOz5o413yuVq7vNDKNGgqy9TR1OdNKd5RyJcUZBfR4BmY9dKRr0IO6ohFZyVlcIR9O76WR4Gx+\nB02Odpbk1pCjm127K0tnZkPJaoaDLkRE3r90Z9qs/ESzjUdebuL7fzzKd39/iMFhaSG6uXQtsUSM\no/1SkBUEgeLbb6Xq858hFghw9tvf4cBrT3Gs/zRLLDVcW3UlBcZcNtuk60G/Ob1pzYUwp3zaZx7M\np7qOj/Sd4unzu0iENVyZcSuqhAFR7wS1DJ1eRU/H8LzPbA90dqLKyUFpHMvAQ8EIfm94kvNbMpin\nGO0znKA2FQ682UZXm4PaJXlcsbl80vMKhZxtNywiHk/w5u6mWe/f7/ezu3kPGf44SneAjKX1CHI5\nwUCEvi4nxWWZZFsMFJaYkbt1xKMiVEjZs7dp9u8XaE/2y6vHBh6FjJgAWY+Hfa+2Uq3X4PcJ6BQ6\nTieiiHGRINZJ/1fXqFGMT5GJZVjAqSsgoZaTnXNxx73LjQXRmX/qU5/iE5/4BPfffz/f/e53+eQn\nP8m99947o51v376dm2++mVtuuYU77rgj9fennnqK2267jZ07d/Jf//Vf0+5npv2DAV+I8y4/OYlh\n8kQ7bzncfHRPCOwuiu+4jYz6JWlfp1bK+cbdV/DIf9yA2Tj72bXToTBnbOBKOqRkae9QZn4ps7Zz\nU2V26WZ/KRrzdxp15Zn4Q9Ep/09lmcWE4xEG/TOXZkZjcaz+QVBGQBCRZwzTdonZeZL4tvUi2vKL\nITlNrdxczNrCyVpvgENnpfJ2RWEGx5tsfP5Hb/D4a81cUSjJjg70TjSQyb9uB3Vf/WfEWIz4Lx+l\nri/GfVd8BJkgQxRFatoDxORwrmCG3vZ5eWjy8/E0nEWMTz/nGtJfxz3ufn5x+I/IURBpWc3GReXk\nqUsRFFFOdrdTWpmF1zOS6vPOByJuN1GXO01WLl1XlgscHg0XZOYZZi2mTC29XU7ENAvLi6G/x8We\nl5oxZKh5750rpmzBLF1ZREGxiYYT/Vhn2fpxx320ODvYGBz1tR8tsbc32RBFqFkildgray0gCui9\n2fgKpYrIXPTmvtY2gJQsTUCgvyFCDQKCICCXy5C5QoCAUZ5FMz4S3SESmiihcRJDMRHHbWtEqc7A\n1t6PW1MCgoysBZ7OOBcsSM/8jjvu4Cc/+QlqtRqtVstPf/rTCYF5Ojz00EM8++yzPPnkkwAcOnSI\nF154gccff5wXXniBe+65Z9YHPRWePCuVQNcoGmlyxdm+243cG6T8E3dT9tEPT9tbvBRj/Iuh8AJ5\n2oXosnoRBBakKrDQ0KgUmI3qVJk9RYB7F5FJZoqZmsfMpm/eO+RHMDpSj2Um+yX1zUVRZG/3YZRy\nJetLVs1pHzXZFXz1ys/w5c2fTvudiCdEjjQOYjKo+NmXruYrH16DXqvkL7ub+N6vGynQFXJm8Dze\n8MRFT86mjTS/fy1xmcD1+5wIB04BUr9TZnfRWaRhr+3UjI/TvGoF8WAQX8vceru+sJ8fvf0rwrEw\nOvsVKGOZLK3Kpi5bslU+ZW2mbLRv3n1B3/xSMGWJ/YIZ5kkoTSZU2VkTStClFVmEglEcUyws0yE8\nEuXpv5wgIYrc+qHV6AxTJyaCTOCam6RR1q+/eG7G7wHwavs+ABY7JR8J0wopmLeM9suTwbyqViKS\nGjw5DKrCKDPNqXGos0EyM9dXVdLt7qfcVY+twUEcuOWjq1m6qpBwIIIJEMIG4oIIDqnUPjxwPLUf\nn6udeDSIOXcpI40t2AzlACxZ/vctSUti2mAOsH79er7+9a/zta99jXXrZlYmS+LCf9yjjz7Kpz/9\naZSjJvjzxYp3hiKcdfow4yEnMkjJc30owzGq//FzFN3y3ul3sIDINmlQKeVpMz7JxtVDfrY+rSTq\n7wH5WTrsrhDxeAK7O4TZqEalXJiF0UIiGcyn0pvPJZh3Wb3ITFIw1yo0yE0O2vrmbp7R7uzG6rNx\nReFydMq5L5iuKFpBniH9dMCWbhduf5h1S/KRywSuWl3Mr75+DTs3VzDg8NPTZCQuJniz7ciE150Y\naGC30MHhWxejNBpp/+Vv6Hvy6ZR9a3RNHV3uPnpm6KRnXjH7UnsS8UScnx34PbbAMO+p3IGt08Sy\nqmxUSjlrS6QKXZe3c4wE1z63knY6XKxfDpMzc5BK7RGnk4hbWujNpdS+6+kGXMNBNm+rpqJmel5C\nZa2FyloLHS0O2pvtM3qPcCzCW12HyFRnIG/rQ5WVhbaoiEQ8QXuzHVOmFoNZw7NvtZGRo0ehlGHw\n5mD128lYVCd9Rodj+jcahSiK+NvaUefm4lXE0XUUom8tJSaAVa9k6bIC1m2R1CUFggy/S1rARBNG\nxJE4zoGTiAmpspNksWfmLUfeYWNYV0hITLBq2btHknYpmDKY/+hHPwLgC1/4wqSfL37xizPauSAI\n3H333dxyyy088cQTAHR3d3Ps2DFuvfVWPvKRj9DYOL2j1Ez6Bw8dbkMUbXesQwAAIABJREFUBFbK\nziO8YkUVFaj76pfJu/aaGR3rQkImEyjM0WN1+CctboY9I/iC0XesxA6X1jMHSZ4WT4jY3SHJMObv\nsMQOUJqfgVYtn5bR3j2LYN4x4ERmdGFR57Iqrx5BFaHV3j3nY9zbLWm+t85AWz5XHG60AkyQ0Om1\nSu67bTk/+eJWitWSPvnhQ2/w4v5OgiNRgtEQvz32CHKZnA/t/AzLfvA9VDk5dD/0MIO7X0aRkcHS\nq28Y/QxHJr9pGpiWLQWZbMbBfPx1/OdTT3HW1szaohUURKUKxppFUsa4tKQEcUSLKzGAJd+AWqOg\np3MhMvOJWvp0o0+TSG4b6JhoHjPTYH7mWC8Nx/spLDVz9XtmPqr22p1j2flMSvqvd7xNMBpiu7qW\nmNeLaflSBEGgt8vFSChK9aJcfvLICR58vpHn3+6gtCoL9YiBgSEHxjrpuLznZy5RizgcRD1edFVV\nvPjUGXIHalAYRM6JCcpGF98FxSZKK7MwihC0SvMJvGYN8VY/sVgAr7MVUUzgtp1FodSjUlmIBbIQ\nBTkhjZLsd2EVcV575mvXSmSxq6++Ou3PTPDoo4/y/PPP85Of/IRf//rXHDt2jHg8Tm9vL48++iif\n/OQn+eEPfzjrg77wgw453Zz3B9ARJK+tG8VQmCX/+k1yNm1Mu/078bjIYiAUjuPyhSc8nzSLKS8w\nXfT1C/kYJvZoZvv6TKNUUWjpcRGNJcg0KC+6/bv1sVwmUFuaSe+QPyUjHP+8UW0gW5tJh7M37evT\nPT7vaEOQJVhTvJS1JVKm6ZP34/aFZ3188UScAz3HMKj0LM9fMuvXz/TxwYYB1Co5K0bLpOOfrynJ\n5Hsf30quqgj0w/z6b0e481u7uOfBn+MMuSkRV3HoqIdzPgUF3/gW2uJixHgcy5bNrClZiU6pZV/X\nYRJiYsr3Tz5WGPQYa2rwtbQSCwSm3T7ZM999/g12t75JSUYB/7j+4xw9Jy1O1owOwIiEQ6ijuSRk\nEXp9AxSUZDBsD+AfJSZe6vnztbdL+vG83AnPJ5nskQtcBP1+P4akE1xHJ36/n9w84+giwznt+zWe\n7uHFpxpQqRXc/pHVhELBi24//nF+kYlFy/MY7Pdy9mT/RbePxCI8e/5l1HI1V4SkHrhp+TL8fn+q\nxN4bDHN09PfDjVZqRhdQ7t4oxkVSMHeePZt2/+ke+9vaSSDjeKKG7tM+QjovuZvUhIHqEnNq+/Wj\n2XlOUEqKhgwi8WZpP0M9R/C7uohF/JjzltJx8AB2vcRet5Sa3zX3n/GP57Vnvn27RJK57bbbJv3c\neuutM9p5bq50MVdVVbFjxw7OnDlDXl4eN910ExqNhu3bt9PR0UE4HJ5mTxfHn/Y3EpfJWRJvRX24\nj+pv/UtK2vJuQeEUjPbOFJP9769fnkTuaCZ+drTvmG26/OP/5gtjevP0pfCyzGI8YS+eEW/a5y+E\nNdwFwJrielbkS1mQzOSgvX/2JLiztma8YT/rClZMOVe8z+bjF0+e5dXDc8v+e4d8DDiCrK7LRT1F\nq0QmE9i59EoQYPW6ONV1UeKZXSSCBs4fyeKhl1r/P/bOOzyu8szb9zlTpRn1MupdliUXuci9Yopt\nCGBDIAUIhCSEbAhZ+AK7lCRsdhdCEsiyZEPCJiGBJGwIMS2UUAy2cZG7VSxbxZKsOuplpNFo2vfH\n0Ywkq8saW2Pe+7p0XZ6ZMzOvHx2d57xP+T38228O8K1fHeNnAWs5lrIK54YtaFUaViYuob2vk5NN\nkyuECl08IO1aODlp19KWM7xUtAODNpAH1n0LtaSl6EwbsREGb+0KgEmbBMCRmpMkJCtOYSqtYIf2\nVvH3v54YEWlz2mzY6hsITElGkgcvr95K9jHGG59b0S7JEokp4bS39tLTPfb1sfh4Pa/9qQCXy80N\nty6ZcA76aKzZlIpKJfPRm4UU/tt/0vTW26P2g3945lM6+rq4ImUNfSeV3bW3v7zEjKySeedEPTHh\nASzMiKTGbME4cN1zNQegS0lEUqvpGShomwztpys4EXc5la0yqsh+KucewGZVPjMjYVCJNGt+DHqj\nlgiXGo0jgCp9H26zDalfjaXtFK31SsFmWPQCmo+foDUwAYdkJTNjZhU/LyZjJmnvvfde778lSfKe\ntJ6CmWeeeWbcD7ZarTidToxGI21tbezatYtHH32UwMBAdu3axfr16ykoKCApKQmdbvwK8nPvUoY+\nbu/u5awko6WfhPwiFv/74xhTkif9/gv1OH7IwJUF6YP5LE8le8qQASuzYb1TeZwcr/TFFlUozjwu\nOmTc42fz46FFcHnZphGvp4QmcLS+kOqOOhYOFCyO9Xndvf30682o3DLZkRlo1VpiA+Kpd9dTctbs\nDftOdn2eEaQb0leOeN1md/LXD0v528flOJwuTpS1sm5xPHqtekr///yBeQUr5sWMe/zKhMW8cOwV\nbMZqHDorUo/Ej66+m4DNkdQ2WahrtlDXZKGqoYt/NGjRFLeRmZXA+uQV7Dyzl91VB5lvmjvhekIX\n5VLzf6/QcfwEEStXjHt8a287P937a1xuN/et+joxxigKK1qw2pxcnqdsLPrMZmyVVWQaE6mxw/GG\nU3xz7jI+/fAM1RWt5OTGTWgva4+bf7xehMvlZv7ieFIyBv+ee8/WgNs9LF9uNBq9wjRRpqBRP99t\nMKA2GumprPS+npgaTvmpJtqabZhihx8PcHhfFe/sKESrVfPFry0jZeC6MtXzPzpET4ahk9NdQRQ1\n95F09BU69u4j/Vt3ETJvHkaj0bsr16t1bMu5ipKfvYE+NgZdVBQ9LT20mC10SqDXqfn+11ZSerad\ngvIWTtV3Ige4MHRGYLZ1YkxPw1JegdNmQzVw3R9rfdbeft4rVtEWGE9mVgSH4j5CZ1PT3KLcZGYO\n7Mw9x+etTuHT90uJaEynLP4kV6lUSLUuXGkuWusPoVIHEBSeQVNVLy69mhZtL1lJ4bPq+jPa48ky\n5s7cE05PSEigrKyMtLQ0UlNTKSsrIz5+4iHzLS0t3HLLLVx//fXcd9993HHHHaxdu5abbroJh8PB\nli1beOSRR7j//vsn/Kzx8gd/evMNbLKONEslG751/whHPlvwOPO6cyraqxo6CdSrvUpqF4OZyJmD\nsqsD/+sxH0rWwLS2iYvgJhaPKTpbhxzYTYQqnv6+fiwWC0vi5iNJbgoap9Zva3faOVR7nPCAULIi\n04e9duSUmXt+upO/fFhKqFHLshwTPX0OPj0+9ZGtB4oakCVYljN+UVBoQAjzouZwpv0sZkszn5tz\nOTmmdFLjQli3KJ4vXpnF/7tlKU//8wa0GhUFFUrR09yodCIDw8mvPYbNMfGYT2NmBqrAwAnz5t02\nC//x8TN09nXxlUU3snAgCnKkRAn5Ls4I4+z/vcLRb3+XU0/8hPmv/C/Lj9poNJcTEx+MWi1PariJ\n2+3mvdeKvFoEh/cNj4CMmS83j50vB2WTZEhLpa++wTt61iNqc7ZyeJTI7Xaz+4NS3vlbIQaDltv/\nabXXkU8Ft9uN+aOdHP32vZiO/x21205N/ErCr9yMtbaOood/QNkzz2Lv7OSDij109HWxJXMjfacr\ncVqthCxU2hoLBsLz7W4X37tlKUkxwSzLiUGWIL/YTEiiGrVTS2lFLUFzs3A7nVjKJ96df/Leadpc\nBuLtdWy7Yyn1vY0khcZTUdNJdFgAIedU669al4oLCG6KwY4btSkS27HBNtLQ6HnYuyy02pUb9lYk\nb6h+tjGd6/GYO/MbbrgBgC984Qu88MILmEzKLuKWW27hO9/5zoQfnJiYyBtvvDHieZVKxY9+9KMp\nLXK8O5XF6el0V1Vw1bx5GCdxk3GxiB0QJagfEma32Z3UNVmYmxJ+USeMnY82O0BkiB5ZlrwXOH8t\ngAMwBmpJNAVRVtOO0+lCpRp+v+t15u0TF8EdOquEhjPCMr02XpWykLcr/kFd35kpretEYwk9disb\nU1d7B6K0dlr539eL2FtQjyxLbN+YwZeuyqK7t58jJR/wzr4qrlg++Zvbtq4+Ss+2My8tguBJaHav\nTsqjqOk0McYobp5/7ajHaNQyOSnhHC9rpqPbRmiQjnXJy3mt5D0O159gTdL4CnayWk3Ignm05R+i\nz2xGP3AdGkqfvY8ndv8PdRYzWzMvY2vmZd7Xjp5uIt3WiPzLJ6lpaEATFkbk6lU0fvwJq051klfW\nSVHHs8TGLqamtgtrbz8BgWP/308VNlJZ1kJ6VhTdXX2cKmzA0m3DOKBP0VOphMlHtqWN3mM+FENa\nKp0FhfRWVRGck018YiiyShpWBOd2uXn/zWLy91QSEhbArd9cOa2xnb1nz1Lx3PN0nSxB1uvJ/MoX\nUBnn8vF7pdQmr2XlkxupeO55mnZ+QuvBQ5xcGIg+Xce1WVfQ8cZ7AITmLsDucPHxrgpUwKbL0lk+\nENEJMerITo3gZGUrSy8Po720k8qyVhZlZQFv0V1ympB588ZcX4/FxrH8s+jtFlYn9tJgMeNyu4jW\nmzhusbF64Uj51YBALbooA3IzBLebcET34z5uJtiQi7WnjlDTAlqPF9JiSESSLIQFR2KYxpjmC4FP\n+swbGxuHhcF1Oh2NM6SXPBOsWbmch754EwsWjC5+MVsINmgxBmiG5cxrGrtxuYeH2P0RlUoe5sD9\n2ZmDEmq32pxUN46cpx1tjESv1k2qor20XemPXpE03/tcRngKKpcOm95Mp2XytSL7apR+2TVJeTid\nLl7fVcG3nvyIvQX1ZKeE81/3beDOa+d5h/XkZcdQVtNBec3kc/MHixtxu2HFvMnpVK9NXsZV6eu5\nf/U30KnHdoDzB/KSxQOh5nUpSnvr7qrJVbWP16Jmd9r56d5fUd5WxfqUFdy+eHB8p7m6ngVH/85N\nNe9jM5uJvfYalvzyv0m762ss/NVzfJQ8B6tepvejT1EXfApuKNs3ds+1vd/B+28WI6sktmyfz9JV\nKcp424Nnvcf0VlWDLBOYNHzGw+DOfOyLtGc3bxkYh6rRqolNCKWxrpN+m0P5vf/fMfL3VBIVE8RX\nv7Nmyo7c2ddH1R9e4vg/f4+ukyWEr1zBkl88Q/z261m5MYOQsADy95zBFhZH7lNPkvr1r+Kw97N6\nXzN3fGJDrm/xDtgJmT+PX//tBJLVjhSg5pZrhjvnlfNjcLvBodHjxk1LdZ+3CK779Pg1Ewc/rcTh\ncJHUUUTwnAzvDHONQ9lJD82XD2XuYqVnPKIxhe4wpXYnXLcQU8oGQiKyOJ1fhlPWYDE0k5U89WjG\nbGZCZ75hwwbuvvtuXnrpJV566SXuvvtuNmwYexaxYHQkSSI+ykhjaw9Op1LJW3mRZVxnkpgBjXa1\nSh4R/vI35iaPHWqXJZnk0ATqus30jxMmdrvdtDprcNu15KUOzrCXZZkYbTKyro/88skVgdkc/Ryu\nO0G0IQJXTzD3/dcufvtmEWqVzHduXsSPv712xDm0dXUKAO8dqJrUd8Bgvnzl/Mn13erVOr6e9yXv\nRLmxWJiuVMUXDoTaE4JjSQtL4kTjyUkVEnqlXc/RaXe6nDyz/3cUmk+TF7eQby27TVGcczppePsd\nyh54gBxLFfaYJHKfepK0r9+JOlA5T40hRs7EreD310Zw6pp5ePSajv75HYq+/xido7TM7t1ZQWe7\nlZXr04mIMrJwaTwarYoj+6txudy4XS56KqsIiI/z5oM9NDcqlezacbQkzlWCA0Wn3eVyU1XRyisv\nHKLwSB0JyWHc8e3VU57y1Xb4CMfu+S51O15HGxFB9qMPkf3Qg16tfI1Gxebr5+FyKjUByDIRW6/i\nle3xVCQHoqtp5vj9D9BZfBJDagofFLeRf/AsMhIrVyaPiC56bgrLqu30BXbR36xCMgaji4qkaxzx\nGFufnfyPS9E4+4izVBC6aJFX28HWpfz+MscIjy9eGEen5MBgCadWrRzrNveRMOdzSLKK8jqlk6A+\nuI85szTEPl0mdOaPPvoo11xzDfn5+Rw8eJDPfe5zfP/7378Qa/NyPvnc2URclAGH003TgHRk5UWc\nYT6U882Zw2DePCo0wO8mv53LxBPU4nG5XdR0NYz5GWc76nCq+tDbYtBpNMNsPC9KKfw6WDu5Cu1j\nDUX0OWysiF/CD58/QGV9F1csS+K5f7mcq1Ykj2rvxVnRRIcFsOtoLT3WiSdV9fbZOVHWTEpsMDHT\nqIgej4zEUHRaldeZg9In73K7vEV946GPjUUXHUVHQaFX2tXldvHrQ3/iYN1x5kXP4Z9Xfx1rr5Wm\ngkJOPPAQZ57/LQ6Xm/eiVpL22GPe1q+hJEeYcPYb2B1hYe0TDyBL0B2RRmdBIcU/+BG21sHe8/bW\nHvZ+XE5QsJ71Vyp99jq9hgVL4ulst1Jxuok+cxNOq3VEiN3a24+le+xKdg8B8XHIWq03VA+KEhzA\n3146QllJE+lzo7j1myvHTQWMRl9TEyX/+WP62ztI+PwNLP7Ff406qyJrfgxpcyKpON1MabGZDyr2\n0Cj3ov3G58l57Pvoo6PB5cKakMbzrxUSNaCamTNK2Ds20kByTBAFpW04Iy1IbonqM60Ezc3C0dU1\n6kQ8h6WH9/7z9/Q7JJJtleT++w8wJCd5hYaa6pWbobF25ommINrVyjH1ncpNeW+NciPQ09BIkxyF\nytWDBYnMgfqY2ciM5sw9aLVabrvtNm677bZpLWomON+c7mxhcHqahdhIA1X1s0PGdSbs6yng8+fi\nNw/xUUaCAjXjyLoOzDZvryE9fPSc9L4qJRQZq08Bhtt4Y+YS3m94k8ruycmU7jurhNjDnGlYrNVs\n25DO166bP+57VLLE5pUpvPRuCZ8cqeGatePPYD92uhm7w8WKSe7Kp4JGLZOdEs7x0sG8+eqkPF48\n/jf2VB30asWPhSRJhC7Kxfz+hxz40m0gSThcDrJdTuZJElpVF0d+dQcArj5l5xW5YT0/NicgBwWR\nOMbfV5IpiMLqcKz6WhqsZmITQ2molYi79Tbq//gS7YePELP5KgDef6MYp8PFldflDNtdL12VwtED\nZzmyr5rwHCXidm7xW9M4YjHD/p8qFYEpyfRUnMFltyNrNCQO3Fja+53MXxzP9V9chEo9KeHOYbTl\nHwSXi9S7vkbs1i1jr0GS2LJtPr/62S7ee72I4pyPCFDruWbO5QTpjCx+9udUfrKfH+3qBDeYNGrU\neom4MZzrivmxvPJhKVK4E85CaUkji7OyaNmzl+6S08PmjVvOVFLy5FOc0q1BpXFyzaN3EBIfjdvt\nprqjFpMhkjOFPcRGGDCOcTOjkiXiU8Lpq65F1x6BTR3gdeYnPynAodIhac8i9Qdd9E3UePgkZ15Z\nWcmXvvQlb995cXExzz777NRXJ/D2udY1W7wyrrERBvR+KuM6FI8z98cBK+ciyxJZyeE0tvbSPsqE\nsxSvEtzY1eLH6pUwbU7UnBGvpcdEgzWEbslMn338CWpWex9HGgqJD4qhtEzZla7NnZyW9JUrklDJ\nEu/ur5pQD/uAR/VtkvnyqeJpxyw6o+zOQ/XB5MZkU9FeTV3XxDU4MVs2E5SVRUBCPLZwA80G6AnV\nY0xIJCA+zvsTsnAB8//j33Buv5UWh5olc01jFpcmRAfh6lac5cnmUpLSInC53NgG5F7bDik3UWUl\nZk4Xm0lOj2DeouG2j00IIS4plLISM42nqoDB4rcTjSfZXZVPy0C+PHqCnTkooXa306m0uAGGIB0r\n1qey7opMtn958bQcOUDrAaU+IWLFxMqBkaYgVqxPo7PdiqYqkq1zNhKkU65ddknFM4Uu2vrc3LIx\nHZvVTka2CWmMaJynxdGqcuGSnZSfNg/Jmw8qwZk//IjCf3mY6p5AbBojS9emExKvtBO293XS3d+D\nKTAGi9U+ZojdQ3ZqBM1BrUhumYbYxVgHnPnpk4pkbXO0mbigGJ/N4rhYTHhm/OAHP+ALX/gCQQOj\n/ObOncu7777r84VdisQPGbjS0tGHxWr3yxnmo5GRGIosS2OGv/wN73zzUULtSSFxSJI0Zntav9PO\nWYsiopIzSoeFJEmES4kguThcO/6Qi8N1BdiddlYkLOFgsZnIED2ZiZMLD4YF6Vm1IJbqxu4xW+0A\nHE4Xh04qn53uowlSHmdeWD4Yal83MPVtz4BE7XgY09NY+JPHafzWNfxyo4qPbsxg1bPPsvS/nmbR\n0z/1/sz/98cIWTCfo6eUliSP6ttoJJmCcHUrtixuKiU5XSnUa+xwE5CQQOeJAmw9Vv7xejGSrOxY\nR7sxyFuVjNsNxeVK26khNQWXy8WzB17gF/m/53ip0oY1UZgdhs42H+x22Hz9fC7bOndMhzkR9q5u\nuk6WEJQ1B2345M6dFZel4NTaiGpIZ13UGu/zH+ZXU1nfxVUrkonSKJuQOTlj2zgjIZSIED1NZpme\noFY6W2w4w2OQtVq6Tp3GabNR9t//Q/mzvwSNhsbMy5BliVUbB9svPSF2vUtZ+0TtZHNTwmnT2HCq\n7NToUrB1dmPr6KTGokPjtNJg6iQ7NmlSdvAnJnTmzc3NbNu2zXsSq1SqKc9zPl8ulZy5pz2trtlC\nVcPsKX6biZx5fJSR33//Kq5ekzrxwX7AoHjMSCU4rVpLXJCJ6o66YbKkHk63VODCiasrguSBToVz\nbTwnVNmx760cf4qYZ9RouCuNHqud1QvjplST4CmEe3d/1ZjHFJ9ppcdqZ8X8WJ+1SA7mzQfz0Mvi\nc9GrdeypOjiqHc9lT9VBfnf0L4Tog3l043eJCBzumIba+MgpM2qVxMKMsSuWE01G3P0BaFxBlDSX\nE58UCpIyQS182VJc/f3s/ls+bS09LFuTgmmMG+95i+LQB2io7A1GFRaGNjSUkpZy71S50iplZxgZ\nPXHo1DCQ27cMKYI7X9oPHwGXi/Dl47cBDmVX7T7qE0qQXSr2vqesxeVy89anZ1CrJLavT6TspBlZ\nJSmjTsdAliWWz4vB2q3DMjBwqKqiHWNmBr1nayh48CGaPtqJIT2NoG8/TFuXg/mL4wkJG9Td8ETA\n7N2K/SZy5llJYTj7A2mLqqEfLeagVIre2Y9d1qGT6nDbA8me5ZXsPplnrtFosFoHZ/2ePHkStfrC\nhoUvlZx5gE5NRIie+mbL4AzzWdCWdj7zzIcSFqxH5efFbx4yE8OQZWlc8Riro4/mnpEDOk40lgCg\n7jURPVBDcK6N85Ln4naoOdU6dlWvpb+H440nSQ5NoLRMKWJbPcVxjQvSI4mPMvLp8foxW+EOFHkG\nq/huepSn37zG3O3VpdeptaxIWExzbxunWypGfV+/o5+j9YX8+tCf+J+DfyBQE8Aj679DbNDI3aDH\nxh3dNsprO8lJjSBQP3YfsTFQS1iQDrrD6bVbMfc3YooNpq66naAlS+hTBXKooJNAo5aNm8ceXqLR\nqpm/MBqbrKcrQam8z685BsC1WVegtRpx6PvocY0+AnkohuQkkOVhFe3nS9tBJcQevnJyEy/7HDbe\nPPU+/aY24pNDOFXYSMXpJo6ebqKuuYf1ixMICTDQUNtJcloEunFsDMrAHrfVgCVYceZnSpsJypoD\nLhe9VdXEbLmKhT/+Tw4dVV5ffdlwUSRPG2hbkxZJgvT48TdAhgANMYEm2kxVgJuakByKDir27A6p\nx201Tjq6dbHwSc78zjvv5Ktf/SptbW088sgj3HnnnXzzm9+c1gIFEBdppLnDSulZZceXMgt25oKR\nBOjUpMQGU17bgd3hHPG6twhulH7zgsaTuF0SiYaR7ToeMhPCcXVF0OPqosHSNOoxB2tP4HQ5WZmw\nhP2FDYQG6byV9pNFkiS2rk7B4XTx0aGRaQG3201+cSMGvZr501ARmwoLMobnzWFw+tvQnvMum4VP\nKvfzs09/zddef4Af7/klH535lGCdkX9d921SwhLG/Z6jpxV7LskaO/zrIdEURE+L8jdY3FRGUmo4\nDoeLHkMMFTErcbhlLr96LvoJxEXmxig3ZFUqpdMhv/YYQVoD16dfjcauo1fXxc/2/pp+5/idBbJW\nS2BiAj1V1d7K/fPBabPRfvQ4+rg4CmnmaH3RhGv4oHwPnbZutmZdxjU35iJJ8N5rRby1W7nhunZd\nGmUDynpzckaK+JzLgvRI9HIQNn0vbp2dM2XNRG3cgDEzg8x//g7p3/omtXUWaqraycwxEX3OBqe6\ns07RdjjrID7KOO4Nmoec+ET6NTYIbqNbH0GNFIPG2UdNQhcqe5A35XkpMeEWe/v27SQmJrJz504A\nfvGLX3gnqgmmTlyUgcKKFo6dbsKgV3t3boLZR3ZKOGfqOqmo62Ru8nAn6nEoVe21rEhY7H2+s6+L\nqo5aXN3hpMaO7XhjIgJR95hwh5s53lBMXNDIi+L+gRB7JGl09Zxi66qUaUU+NuUl8uLbJ3nvQBXb\nNqQPC9Ofqeukud3K+sXxqFXTK66aLEPz5mtzlVqCeVFzCAsIYX/NEWKDojlSX8CplgpvtCI2KJpl\n8bnkxeUyJyIVWZ54jd58efbEjibJFERBtfJ7Km4u5br0HA7treLTjytoDEgiuK+ZzOiJR4PqOxoI\ntTbSSAxHS0/R3tfJZamraW9WJphFxRg50nqY3xx+mW8tv23cdIYhNZXe6rNYGxoJTDg/VcvOgkJc\nNhvSwkye2vs8ADq1jtyYbPLiFrIkbgHBukHH1uew8capfxCg0fO5OZdj1BnIW53Cob1V1Dd3My8t\ngoyEUP7yvqKRkDkJZ65RyyybG0O+LQBLcAtSs4ZudQi5PxucmLlvp1JXsOacXbndaae+q5HEoERK\n+pxkzJtcTU5OShS7iw3Um84Q1xWBS1YT3VfLsSAZk2Ty+/bZ0RjXmTscDm6++WZ27NhxUR34dEfC\nzUY8d4T9DheZSWEXVcbVw9ARkoJB5qaE8/beSk5VtY9w5mPNNi80KxW6rq5IUnIGdxjn2liSJFKM\nGVRSwJG6ohHtWZ19XRSaT5MRnkLJaSUsvWaKIXYPQYFa1i6KZ+c/dK5qAAAgAElEQVThGgrKm1k0\nZ3DHOigU45sq9qGM1m8uyzLrkpfz5qkP+OOJHUhIzIlIJS8+l2XxC4kLnnzo32Kx4HK5OXq6iYgQ\nPcmTKDhLMAWBXU+wJoyS5jL+aYHiLMpLlBuCrOYDdBwJxZiaMu7n9FRWktBZRUdADJ/uOQ1BsDJx\nMc2VSiX7FbnL6eis5JOq/aSEJYzbjmdIS6X5k130nKk8b2fuqWI/GKakSqOlTFT6Tg7WHudg7XEk\nSWJuZDp5cbnkxS/kUN0JumwWbsy5GqNOqfHZuCWLw/lniXO42bA0gY72TipKm4mIMhAeOTlNghXz\nYziQb6AjyExQcywVp5uJGQiXNzV2U3rSTEJKGElpw6eY1XU14nS7CET5+8ucZIHt3JQw3EcMtIWZ\nyXJ00K0ORRekqCFmRI0f2ZkNTMfnjXubq1ar0ev1NDWNHga8UFxKTmZoeGe2yLjOVM78UsOjBDda\nv3moPphQffCIMHvBQL7c2TlY/Aaj2zgrPk4ZG9pcNkJNLr/2GC63i1WJS9lfWE9QoJb56dMf13j1\nQCHcO/uqhn9PUSNqlTRu1fdMoVZ58uYW2rsHW/KuzbqCLRkbuXvZbTx//Y/59yse4Prsq6bkyEGx\ncWOHg+7efpZkRU/qRjlpoPc7xB2L1d5Hi0txUgC5i2MJtrfTdmhiYZueyipM/Y0EGrR0lLkxyAYW\nRM+leaDHPDYujO+t/SYh+mBePP4373ky6v8jfWC2eeX55c3dTifthw6jCg7iE3cVrt4gqvPTuCv7\nO/x86w/58sJtzAlP5VRzBS+d+BvffeeH/KngNQI0eq4ZcrPhBGrcLlRIdJS30mK24bC7JrUr97B0\nrglsBm8R3JnSZu9r+z727MozRrzPU/zm6lV+T5MdjBIfZUTtCAEJcvTVpLYdxxyhFB3nJs3+Il2f\n5Myzs7O5/fbbefrpp/nTn/7k/RFMD89cc7j4ym+C8TGFBxIWpKOkqm3UIrWU0ARaetuw2JTCJrfb\nzQnzSVQuHe7e4Alv1tLjQ3B2ROFwOzjZPFxAxiMUEyml0dZlY+X8mBFDX6bCnKQw0uJCyC9upLVT\n2aWZ23o5U9/JwsyoSeUhZwJv3nxIVXuIPpg7l36BTWmrCdGf39+EZ0raZELsoOTMAbAoN0pFTaUs\nWJpAeKSBK7YtIHhuFt2nS7F3jS0767Lb6a2pxZicSMrCEGSHhnmuJahVaq8me2S0kYjAML635i5k\nSebn+39Do6V51M/z9KmfbxFcd2kZ9s5OOjJNuGRwNCYDEs+/VkSM0cS27M38+xUP8Ovrf8zdy24j\nb6C74PM513h35QDv51fT4HQREKKn6Fg9uwZC7JPJl3v/TwEa4oNNODX96MNlzla2Ye930NneS9HR\nOqJMxlE/r3pAk72zWYcsQdoExW8eJEkiPki5GXRmGsjRNnIqyoHLpmdBqu8KPS8mE14denp6yM3N\npbm5maKiIu+PYHqYwg3efM1s2ZkLRkeSJLJTw2nr6qOqYeTFPNk7DlXZndd1NdJu7QRLFBEhAQRN\nILmZkRiKq1NxbscbBrXA23o7KGkuJzsqg+JTSs51qlXso/1ftqxWBoN8MDAYJN8rFHPhLm4eZz40\n1D6THDndhCxL5GaO3S41lBCjlqBADV1mZSd0sqmU9VfO4Z6HNmEw6gjLWwpuN+1Hj435GdbaOtwO\nB4a0FCwxDbhxo61Vbg6azRZCwwO9qnFZkel8femX6Onv5ad7nsM6imiQ2mBAH2PCcqZyQrGf8WjL\nV0Ls+0O7Ubl0OFtjWTEvhjP1nfxjiGZ/qD6YTWmreXDt3fzhhp9z7dwrvK85nS7+vrcSnVbFti8o\nlfp11e3o9GoSU6dWjLkoWdkR9xl7cDpcVJ9p48DuM7hcblZtzBi1j96TxqqrkUiKCUavnXwn1dwY\npZf8SJKR7F/8DEuADbU9mIgpatr7C+M6846ODm655RYeeeQRnnjiiWE/F5JLpc8clGIQU3jgrJBx\n9TATfeaXKhuXKFXr/zhQPeI1TxGc54JzolERgLG2hA4LscPoNo6NMKCzRyG51BxrHHTm+2uO4MbN\nqoSl7Cusx6BXT9o5jceGxfEE6FT8Y38VTqeL/CIlX778AjrzjIRQ9FoVRT5w5g1N7ZSebSc7JRzj\nJEdbSpJEoimIpiY3JkMUJS3lOF2DVeThy5YC0D6gBjcanhnmgcnJHO08Rm9oKx31/VSWt9Aziib7\nprTVbMncSE1XA8/m/37UHntDaiqOri76WyeesT4abreb1vyDuLUaSiNcOJsTSYwO4dufzyVAp+aP\n75bQ1TPxPPkDxY00t1vZlJdIZlY0i5Ypfw/J6eFTjhRtnK+09zVqlPPu5PF6jh44S3CIngVLRq8N\nONtRR5g+DFufPGVBqry0VNxuqO1s5GS98vcbrpvd/eUeZrTP/J133mHdunV85StfYcOGDRw7Nvad\nqa+51PK5t23J5s5r588aGVeRMx+b5TkmIkL07Dxcg9XmGPbaue1pBeZTADg7I0k550ZtNBvLskRa\nXBiOznAaupswD4Rd99UcQZIkouU0mtutLJsXg2aaMp5DCdRr2Lg0kZbOPj4+UkvRmVbmJIVe0J2K\nWiWTkxoxIm8+E5TW9uB2T64lbSiJpiBcbkgOSsVq7xtWBxGQmIguOor2Y8dwORyjvt+T2+6OMtJo\naSYyW/m7fv915QZttLGnX1n0eeZHZ3G47gSvFr8z4vXRlOCmgrW2jr76BurjA3FrVFjrE5ibHE5Y\nsJ4vb55Ld6+dl94dO2/v4a09yvdfO6Dtf/nnspm/OJ71V47ddz8WqVHRSC41zbo6VGqZ44dqsPc7\nWbkhbVSZ2o6+Ljpt3YTIigOebL7cQ3ZyNG5bIJ2OVo6fVX5HSaG+L/ScCWY0Z/7MM8/wy1/+kiNH\njvDDH/6QHTt2nNfiBIOsWxzPtg3pEx8ouOioVDKbVyRjtTnYfWx4sVusMRqtSkNVRy12p52TTaWE\nqCPArh+xMx+L9IQQnB2eUPtJmiwtlLVWMj86i4JTSr519YLzC7EPZeuqFACef70Ql8s96dnlM4mn\nkK+ofKTgznRxuxV1MhjUA58sniK4UBQ7FzcNjqaVJImwvKU4e3rpLjk16vt7KqtAkjghKzdjq5dl\nExyixzyQmhlNxlUtq7hv9deJMkSw4+S7XslSDx5nPl0lOE+IvSjGRXJAFtj1Xo2Cz61NJdEUxD8O\nVI077768toPiM60syYr21hYYjDpuuHUJcdMYHypJEiGacAi0EBSpKLzpAzQsWTn6sCJvp0if8rc0\nkSb7uQTo1AS4Q3HJNorMyu90Xtzo33UpMKYzV6vVrFu3DlmWufbaa0WeXPCZ5aqVypjRd/YNH1gi\nyzJJIfHUdjVQ3FSGzdmP0ak4hMnWQ2QkDMmbNxazr0YJ565KXMq+wgb0WhVLZrDSPDUuhLnJYd4o\ngy9V38bCF3nz/YUNnK5uZ/XC2EnfSHlI8BTB9Sg3GSebhs+Z94wKbTs8MtTudrvpqapCH2Nif3Mh\nGpWGpXHzWTzEQY01LS1IZ+RrS76Iy+3ihWOvDDu3PCNbp7szb80/iFuSqIzTEditjGz1dGeoVTLf\n3LYAtxt+/VoBLtfoeXnvrnzd+BP3pkJKeByS7KJdUkL8y9akjDnj/USDkraytAagkqVp1RjFGJSi\nunqbInizLD1zOsv2C8Z05i6Xi/LycsrLy6moqKCvr8/7uLy8/EKuUeRzfYzImY9PREgAy3NMnKnr\npOycnUxKaAJOl5N3yz4GwN4ejixLJJ4TWh3LxunxIbj7Awlwh1LUVMqe6oOoJJlYdToNLT3kZZvQ\naWZ2upNHrz020jBYzX0ByUgIJUCnmjFn7nC6+MPbJ5Flic9vSJny+z0785ZmN7HGaEqah+fNQ+bP\nQ9bpFI3zc+hvacXRbUFOiKWuq5HFMfPQa/QsXpGoFHRJI8PsbrebGnM3r+4s45XXOoiUUyhuKmV/\nzeDna8PD0ISGTquivb+tHUtpGbXRapLi0qmr0mAI0JAQPfi7zp0TxZrcOE5Vt/PxkZHKgO3dfew+\nVkd8lGFE2uJ8rhfpUcrN7klbPZu3z2PtFaM71/puM++Wf0JkYBiNVQaSY4PRTuPvID1yoKdcbUdy\n6ogJ8Y9BUDM6z9xms3HXXXcNe27oY48i3IVA5HN9i7DvxGxdncqBokbe3VfFnKRBXWdPEdyxhiJU\nsoqmmgDiowwjxiuOZeP46CB0WhWSJRpbUCk1nfUsiZ3PsRLlpuF8q9hHY21uPJ+eqGf1At8NVhkP\ntUomOzWCo6eaaO/qIyxYf16f935+NfUtPWxdnUJmytSjGBEhegJ0amqaulmYM4ePznxKZXsNGREp\ngCKxGrpoIW35h7A2NAybwe3Jl5tDlX2RRw0wOCSAdZdn0ttjQ6NV43S5OVXVRn5xI/lFDdS3DOq0\ny/VJGBbV8tLxHSyJnY9eo9jDkJZKx9Fj2Lu60QRP/qar7eAhAM7E69iQuI5n321iydzoEapnd147\nj0Mnzfz+7ydZOT8Ww5Ciwff2VeFwurh2XfqI953P9cKjdGhTd6OKMKAZxUG73W5+f/QVnC4nW5Ku\n4X8/aZlyiN3DkpQ0dg7IpBjlqVXfX0xmNGe+c+fOcX8Egs8SizKjiIkIZPfxOiy9g1XAnvY0gLSQ\nFKzWqXUpqGSJtLgQOhoG37M6KY99BQ1o1TJ5k+yXngpajYoffG0lVyy/ePlD73zzivPLm1ttDl5+\n/zR6rYovXTX1oizwVLQbqW+2MDdSES452Tw81B42oIB57u68p0qpki7SdKCSVSyNW+B9bfXlGYTN\nieTnLx/lth++x7/+z6e89kk5bV19rFoQy31fWswd1+Tg6gskU7eUVms7O0re877f6CmCm6J4TPOB\nAwC0ZkRh6FeKNLNH0fSPDgvk5isy6bDYePn9wdnidoeTd/ZXYdCr2ZSXOKXvngjPgBxZ3+tVHzyX\nI/UFHG88yUJTNtpepcp9us58QfzgOR5rmPm/pdmEb8WYBYJLBFmW2LIyhX67k51DwpJJIfFIKDuX\nGK1y4Zhqbi89IQRnZxgaWYNGVhOrSaPG3M2SudEEzJKOh5lmwUAR3PmG2l//pJyObhvbN2YQFjT9\nHX6iKQiH002ESnEexU3DRXzCli4BoO2cFjVPGPyktoNcUzaB2sHOgCf+cIjHf3+InYdr0KgltqxK\n4YdfX8mffrSVh+9Yzqa8JDavTEatkmk6HUdEYBhvnf6Qhm5lKzlY0T55Z+7otdJZUEhzqJo1iy+n\nrFpRPctOHn1Xun1DBrERBt769AzVjUrB3p7j9XR027hyRfKMn38eZ64xWMkvahiRr+939PP7Y39F\nJcl8dcnNVNQp659qW5qHAG0AGpeyy50TPbM3JrMNv3DmIp/rW0TOfHJcsTwJtUrmvf2DhXABGj0x\nRqUHXNOn3PmP5szHs3F6fCi4VawK3czdy27j6Emlt9gXIfbZQvpA3rygfPrOvL27jx2flBNq1LFt\nQ/p5nceevHl3p0xsUDSnmsuHCbroIsIxpKfRVXwSR2+v9/meyipcgTosAfKwgTvN7VYOl5hJTwjh\nqe+u54Xvb+bbn88lL9s0LPdrDNSyfJ6J2kYrmxO34nQ5+f1AMdx0KtrbjxxFcrqoSgrgivR1nKpu\nR5YgM2l0Z6jVqPj6tvm4XG6ef60Qt9vNm3sqkCX43NrRC9/Ox84GbSAhuiB0xj7au2288Pdiyms7\nvH9Pb57+gKaeVq6es4n44BjKa9rRqGWSzkOTIy1C+TtanDxzhXy+xifzzGcDIqfrW0Sf+eQIMepY\nszCOGrOFojOD4eHL0lazOHYeXc3KznC0aurxbOzpn3W1xbEuZTn7ChpQqySW5VyaspMwmDeva7bQ\n1jW9fvP/e/80ff1OvnhVFoF6zXmdx56K9rPmblYlLsHq6OMnnz43bFxoeN5S3A4HHcdPAMouuK+x\nkbZwLSpZxbL4XO+x+wvrAdi8Ipk5SWHjTunyCBM1V4WywJTFsYZijtQXojeZUAUETGlnXr7rfQBC\nl+cRoAqk7Gw7ybHB48r1Ls+JIS/bREF5C795o4iK2k5WzI/FFB446vHne72IDYrGJnWj00m8vquC\n+36+izv/4wN+/rdP2VH8HqH6EG6cdzV2h5Oqhi5S44LPS2fhsowVJIcmkB7hP21pPtFmFwgEg3gq\nwd8bMrBkW/ZmHlp/D9WNFgJ0KqLDRr8IjkVitBGtWqairpOGlh7O1HeyaE70pFXM/JWF3rz51Hfn\n9c0W/nGgmrhIA5vH6FOeCp6deU2jhZvmfY7l8Ysobirlv/b/1lvZHrbMkzc/CkBvtZIvP2t0MC86\na5ie+b7CBiRpctPo8rJNBAVq2HO8jq/k3oRKkvnDsb9idzsxpKZgra/HabNN+DkuhwPbiRK6AmUu\nW7uNyvpO+h2uERP/RuMb2+ajVsm8OdCOdt0MtqOdS2yQCTdufnzfEv7lK3lsXJpAn83Bp00f4nA7\n6DidyrMvF/HqznIcTve0Q+weNqWt5qebHyFQc2nKuHoQzlwgmAI5qeEkxQSxr1DJK3qwO1zUNVlI\nigme8qxklUomNS6Es41d7BoQplm9wD+Uqs6HwX7zqRfBvfhOCU6Xm69cnTMjc9ijwgLRalTUmLtR\nySruXXUnC0yKQttzh17C5XZhTE9DExpK++EjuF0u7465OUw9LMTe3tXHycpWclIjJlWpr1HLrF0U\nT3u3jVazhq2Zl2HuaeGtUx9gSE8Dl4veqpFywudSnr8bTb+TzjkxJIcleKf9zR2l+O1c4iKNbN+o\nCFmlxYUwL236E/omwpM37+hvZW1uPP/vy0t58J+SUYWbCZViCbKn8umJev78D0WkZ7rFb581/MKZ\ni3yubxE588kjSRJbV6XgcLr58NBZ7/O1Td04Xe4xi98msnFaQggOp5s3dlUgyxIrLsB88YtNenyI\n0m8+xbz56eo29hbUk5UUxuqFg3Y6n/NYJUskRBu9v0etSsP31txNRngKu6vy+cOxV0GSCMtbgr2z\nE0t5BT1VVQC0hGlYljAkxF7UgNvNsLVNxGUDofaPj9Tw+fnXEKIP5rWS93DFK/UYlkmIx5TsVGRh\n09Yr40tPVbcDymzvyXDz5XPYsiqFu7YvGLdl8XyvF572tPqBQj+H08GLx19FkiQevvKr/ObhK/nF\n9y7j1q1z2ZSXyKoZVED0F0TOXDAtRM58aly2NBGdVsV7+6u81bieqWpjtaVNZGNPKNFitbMgPYJg\nw/gT1y4FVAM67VPJm7vdbl74u6IMdsfncoY5nfM9jxOjg+h3uGhuVwrcAjR6Hlr/bRKDY3m37GNe\nLX6b8Dxl8ErbocN0VlTgkMGUlkXokNGt+wqUfPlUZHjnpoQRG2Fgf1EDkkvDrQu30++080GfUlVv\nKasY9/2dfd3oTp6lXyuzZN01AJRUtRFi1BIbYRj3vR70OjXf/nzuhLvymciZA96q/XfKPqauu5Er\n09eREpaIJEkkxwbzhSuyuO9LS4b1v39WEDlzgeACYAjQsH5RPOa2Xo6VKhek6gFnPt2xtulD5jSv\nuYSr2M/F028+2d35oRIzxWdaWZZjYn76zE7ASoxRLqBnB2aQgyK5+sjGe4kyRPDX4rfJN3QgqdW0\nHTyEtfosbSFqVqTkeY/vtNgorGglKzmMyNDJ52glSWLj0gRs/U72FzawLmU5WRFp7OorBY2Gpo92\ncuRb36HyhT/QdbIEt9M57P279rxOUK8T5qWj1mpp6bDS0mFlbnL4RREGGg+TMQoJiYZuM23WDl4t\nfpsgrYEvzr/uYi/NrxHOXCCYBp5CuHcHCuG8O/NpOvOkmGDUKnnSRVOXClPRaXe63IpsqwS3X5Mz\n42vxFMHVDnHmAOEBoXx/43cJ1Qfz+5I3cKUn0FtVjeRw0hKmZnn8Iu+x+cWNuFzuaQ3H2bhEESD6\n+EgNsiRz59Iv4pZlPr0ynrAVy+lvbaX+9TcpfOhR9t/+VT75j0d55U9P8/gHP6dytyLkNeeyLQCc\nqp58vvxCo1VpiAwMo6G7iT+deI0+h40vLbx+WAGhYOr4hSKFxWIRYWAf4snPCBtPnszEMDISQzl0\nspGWDivVDV2EB+vHDI9PZGONWua6dWm43O7zljf1J5S8uXpSFe07D53lbGM3Vy5PGjWdcb7nsUe7\n/Ow5zhwgxhjFoxvu5Yc7n2K3sZUNnhfiTYQHDhZo7fWE2KeQL/cQF2UkKzmMgrJmWjutpIYlckX6\nWj6o2ENvppr+3Bzk8hoSqy2k1VkxHCoh/hCYVOCSZdwqFaZlKwA4VTWQL0+eXL58KszE9SI2yESB\nuYQ91QdJC0tiU+qamVreJcF0fJ5fOHPhZHyLsO/02LoqhWdfOc6OT8pp6ewbd472ZGz81WvnzeTy\n/AKVSmZeWgSHS8wUVrSM2Y7ncrn50z9OoVXLfHnz3FGPOd/zODbSgFolUTOKMwdICo3nofX38Ezn\nU3BEOSYxZ3BXbrHaKShrJj0hhJhJ5qnP5bKliZyubmf3sTq2b8zgiwuu42DtcUqay9GoNMRlJWJd\nFkO7IZqAdhdBpfU4C07TV1NLxOpVqAOV0P6pqjZUsjTlGeCTYSauF7FB0RSYlXnqdy75ArIsgsRD\nmY6N/cKZCwSzkfWL4vndm0W8vVdpUZpuiP2zzoJ0xZk//Mu9Ex77+U2ZU8pFTwW1SiYuykiN2YLb\n7R411zwnMo1vbr2H6o8fI6zTQe7Sjd7XDhY34nBOL8TuYW1uHP/7eiEfH6lh+8YMgnRGntr6A2wO\nGxGBYcjSOU5v4Ottra2oBxxAv91JRV0HqfEh6LWz8xIfH6wIIm1MWcWcSP9RZpvNzM7ftEDgB+h1\nai7LS+TvnyrOPCX2wo8TvRS4akUyXT392OzOcY8L0Km56fI5Pl1LYnQQZxu7ae3sG/OmYWFMNq5v\nfYPOurPEmpK8z3uq2NfkTt+Zhxh15GWbyC9upKqhi5TYYIJ1RtCNv1PTRQxWoJfXduBwukcdrjJb\nWJ+8gj6HjSvT113spVwy+IUzFzlz3yJy5tNny6oUrzMfb1qasPHYGAO13PG5808xzISNE4fIuo4X\nAVi06ophj3v77Bw93URyTBDxUef3O75saSL5xY18cqRmWnbxZb4cZsbOgdoAtmVvnqklXXJMx+f5\nRaJCXAB9i+gznz7JMcHkZkZiCNB4HcFoCBv7npmwsVfWdYy8+VgcLjFjd7hmZDjOshwTBr2aT47W\n4jxnqthk8HUluziXfY/ImQsEF4GHbl9Ob59j2DQsgX+SYFIuolN15vsKGoCZ0QjQalSsyY3n/fxq\nispbyJ0TNen3ut1uTlW1ER6sJ8pHtQWC2Ylf7MwFgtmMIUBDVJi4cF4KxEcZkaWpOfO+fgeHT5mJ\njzKQFDMzdROXLR3oOT9aM6X3mdt6ae+2kZ0y+8RiBL7FL5y50A33LUKb3fcIG/uembCxVqMiJsJA\njbnbO2N7Io6easLW72T1wrgZc6A5qRFEhQWwr6Cevn7HpN83VT326SDOZd8jtNkF00LkwHyPsLHv\nmSkbJ8cG091rZ+fhye2KPSH2mciXe5BliY1LErDanOQXNU76fVOZlDZdxLnse4Q2u0AgEJwnt2ye\niyFAw3+/cpwDRQ3jHmt3ODl4spHo8MBh+vozwWVLlUlqnxytnfR7TlW3oVHLM74WwexHOHOBQCAY\nQnJsMI99fSUatcyTLx7mRFnzmMceK23GanOwZgZD7B4STUFkJIRw9HQT7d0TT5XrszmorO8iIyEU\njVoUY37W8AtnLvIzvkXkwHyPsLHvmUkbz00J55E7lgNu/vOFfErPto963N4T09dinwyXLU3E5XKz\n51jdhMeW1XTgcrl9PlxFnMu+R+TMBdNC5MB8j7Cx75lpGy/OiuZ7t+Zh63fy2P/u52xj17DXHU4X\nB4sbiQjRMyfRNwVn6xcnIMsSr3xUOuGY2BJPvtxHYjEexLnse0TOXCAQCGaQNQvjuOemRXT32vn+\nr/djbuv1vlZQ3oLFamf1wjhk2TdtYKFBOu7evgBLr51Hf7WXv35UimsMIZnZPPZU4HuEMxcIBIJx\nuHJFMndeO4+2rj6+/+t9tHcp+WuPFvvqBb6dP791dSqP/9MaQoP0vPhOCf/xQj6W3v5hxyhiMe1E\nhwcS/hkaoSsYxC+cucjP+BaRA/M9wsa+x5c23r4xg5suz6ShpYcfPL+fTouNA0UNhAbpyE6NmPgD\nzpOc1AieuX8juZmRHDpp5rs/30V5TYf39fqWHrp7+8lO9v2uXJzLvmc69lU99thjj838UmYWrVZ7\nsZdwSaPVaoWNfYywse/xtY0XZkTSYbFxuMTMpyfqaOuycdnSRFbMi/HZdw5Fr1OzYUkibtwcLG7k\no0M1hBp1pCeEcLDYTH5xI1tWJTMnybc5c3Eu+57p2FdoswsEAsEkkCSJu7cvpMdqZ/dAdbmvqtjH\nQiVL3Lolm7nJ4Tz95yP8z6snOFnZiieLLvLln12EMxcIBIJJIssS931pCU6nm7pmC/PTIy/KOvKy\nTfzXfRv58YuH+PiIIiqj06pIjR17DK/g0kZyT1aA+CIi5pn7FjFr2/cIG/ueC21jt9t90YeZ2B1O\nfvtmMW/vrWRJVjT/dtcqn3+nOJd9z3R8nl84c4FAIBCMTVlNOxEhAaKS/TOMcOYCgUAgEPg5Ps2Z\nb9q0CYPBgEqlQq1W8+qrr3pf+93vfsdPfvITDhw4QGhoqC+XIRAIBALBJY3PC+BeeumlEc66oaGB\nvXv3Ehc3uZGBImfuW0QOzPcIG/seYeMLg7Cz75mOz/O5aMxoUfwnnniCBx54YNKfIU4a3yK0ln2P\nsLHvETa+MAg7+55Zp80uSRK3334727Zt45VXXgHgww8/JCYmhrlz5076c85VwxGPxWPxWDwWj8Xj\nz8LjyeLTMPvLL79MdHQ0FRUVfOMb3yAtLY3nn3+e3/3udySEu+8AABIqSURBVN5jRP2dQCAQCATn\nxwWrZn/iiScICQnhj3/8I3q90j5hNpsxmUz89a9/JSJibH1jkTP3LSIH5nuEjX2PsPGFQdjZ98yq\nPnOr1YrT6cRoNNLW1saXv/xlHn30UdauXes9ZtOmTezYsUNUswsEAoFAcB74LMze0tLCPffcA0Bo\naCh33HHHMEcOXHT1JIFAIBAILgWEaIxAIBAIBH6OmGcuwGIR84l9jbCx7xE2vjAIO/ue6dhX7MwF\nAoFAIPBz/GJnLhAIBAKBYGyEMxcIBAKBwM/xC2cu8jO+ReTAfI+wse8RNr4wCDv7HpEzFwgEAoHg\nM4hf7MwFAoFAIBCMjXDmAoFAIBD4OX7hzEV+xreIHJjvETb2PcLGFwZhZ98jcuYCgUAgEHwG8Yud\nuUAgEAgEgrERzlwgEAgEAj/HL5y5yM/4FpED8z3Cxr5H2PjCIOzse0TOXCAQCASCzyB+sTMXCAQC\ngUAwNsKZCwQCgUDg5/iFMxf5Gd8icmC+R9jY9wgbXxiEnX2PyJkLBAKBQPAZxC925gKBQCAQCMZG\nOHOBQCAQCPwcv3DmIj/jW0QOzPcIG/seYeMLg7Cz7xE5c4FAIBAIPoP4xc5cIBAIBALB2AhnLhAI\nBAKBn+MXzlzkZ3yLyIH5HmFj3yNsfGEQdvY9ImcuEAgEAsFnEL/YmQsEAoFAIBgb4cwFAoFAIPBz\n/MKZi/yMbxE5MN8jbOx7hI0vDMLOvkfkzAUCgUAg+AziFztzgUAgEAgEYyOcuUAgEAgEfo5fOHOR\nn/EtIgfme4SNfY+w8YVB2Nn3iJy5QCAQCASfQfxiZy4QCAQCgWBshDMXCAQCgcDP8QtnLvIzvkXk\nwHyPsLHvETa+MAg7+x6RMxcIBAKB4DOIX+zMBQKBQCAQjI1w5gKBQCAQ+Dl+4cxFfsa3iByY7xE2\n9j3CxhcGYWffI3LmAoFAIBB8BvGLnblAIBAIBIKxEc5cIBAIBAI/xy+cucjP+BaRA/M9wsa+R9j4\nwiDs7HtEzlwgEAgEgs8gfrEzFwgEAoFAMDbCmQsEAoFA4Of4hTMX+RnfInJgvkfY2PcIG18YhJ19\nj8iZCwQCgUDwGcQvduYCgUAgEAjGRjhzgUAgEAj8HLWvv2DTpk0YDAZUKhVqtZpXX32VJ598kk8+\n+QS9Xk9eXh733nsvQUFBY36GxWLBaDT6eqmfWTz5GWFj3yFs7HuEjS8Mws6+Zzo+z+c5802bNrFj\nxw5CQ0O9z+3du5dVq1YB8IMf/IDQ0FC+973v+XIZAoFAIBBcslyQMPu59wtr1qxBlmVkWWbt2rU0\nNjZeiGUIBAKBQHBJ4nNnLkkSt99+O9u2beOVV14Z8fpf//pXLr/8cl8vQyAQCASCSxafO/OXX36Z\nN998k6eeeopf/epXHD582Pvac889h8FgYOvWreN+htlsHvb43B488fj8HpvN5mHPXez1XIqPh/bm\nzob1XIqPz7XzxV7PpfpYXC8uzPViqlzQPvMnnngCk8nEnXfeyY4dO3jllVf4wx/+gE6nu1BLEAgE\nAoHgksOnO3Or1eq9w2hra2PXrl3MmTOH3bt389vf/pbnnntOOHKBQCAQCM4Tn+7Ma2pquOeeewAI\nDQ1l69atfPGLX+Sqq67CbrcTEhICwKJFi3jsscd8tQyBQCAQCC5phJyrQCAQCAR+jlCAEwgEAoHA\nzxHOXCAQCAQCP0c4c4FAIBAI/Byfa7OfD4cOHeLxxx/H4XBw8803c9ttt13sJfk9Dz30ELt27SIi\nIoK33noLUPoaH3zwQWpqakhKSuInP/kJBoPhIq/Uf2loaODBBx+kra2N8PBwtm/fzg033CDsPMPY\nbDZuvfVW+vv70el0XH311dxxxx3Czj7A6XRy4403EhMTw69+9Sth4xlmtBkmU7XxrN2ZO51OHn74\nYZ599ll27NjBq6++SkVFxcVelt9z44038pvf/GbYc7/85S9ZvHgxb731Frm5uTz33HMXaXWXBmq1\nmocffpi3336bZ555hqeeeoqKigph5xlGp9Px4osv8sYbb/DHP/6RV199laqqKmFnH/Diiy+Snp7u\nfSxsPPO89NJLvP7667z66qvA1G08a515QUEBSUlJJCQkoNFouOaaa/joo48u9rL8nry8PIKDg4c9\nt3PnTrZv3w7A9u3b+fDDDy/G0i4ZoqKiyM7OBiA8PJwFCxZgNpuFnX1AQEAAAD09PTidTrRarbDz\nDNPY2MiuXbu46aabvM8JG8885zaWTdXGs9aZm81mYmNjvY9NJtMIWVfBzNDa2kpkZCQAkZGRtLa2\nXuQVXTpUV1dTVlZGbm6usLMPcLlcXHfddaxZs4ZbbrmFuLg4YecZ5vHHH+fBBx9ElgfdhbDxzDLa\nDJOp2njW5swlSbrYS/hMIkmSsP0M0dPTw3333cdDDz00Itcl7DwzyLLMm2++SW1tLXfddRdLliwZ\n9rqw8/nx8ccfExERQU5ODvn5+aMeI2x8/rz88stER0dTUVHBN77xDdLS0oa9Phkbz1pnbjKZaGho\n8D5ubGzEZDJdxBVdukRERNDc3ExUVBRNTU2Eh4df7CX5PXa7nXvvvZfrrruOK664AhB29iUJCQls\n2LCBgwcPCjvPIMeOHWPnzp3s2rWL/v5+LBYLDzzwgLDxDBMdHQ1Aeno6V155JQUFBVO28awNs8+f\nP5/q6mpqa2vp7+/nnXfeEaNSfcSmTZt47bXXAHj99de9zkcwPdxuN4888ggZGRnccccd3ueFnWeW\ntrY2urq6AGhvb2f37t1kZWUJO88g999/P7t27WLnzp08/fTTrFy5kp/+9KfCxjPIWDNMpmrjWS3n\nevDgQR5//HGcTic33XQTX/nKVy72kvye+++/n4MHD9LR0UFERAT33nsvmzdvFm0mM8jhw4e59dZb\nycrK8obG7r//fpYsWSLsPIOcPn2af/3Xf8XpdBIVFcWWLVu46aabRNuUjzh48CC/+93vRGvaDDPW\nDJOp2nhWO3OBQCAQCAQTM2vD7AKBQCAQCCaHcOYCgUAgEPg5wpkLBAKBQODnCGcuEAgEAoGfI5y5\nQCAQCAR+jnDmAoFAIBD4OcKZCy5ZNm3axLXXXjtsgMGmTZsoLy+fse+ora1l5cqVM/Z5k+Xpp5/m\nhhtu4P7777/g3+0v/Pd//zfvvPPOqK89++yzPPnkkz79/h07dnDvvff69DsEAg+zVs5VIJgJent7\neeONN9i2bdvFXsq4uFyuYYMsxqOrq4s///nP5Ofno1KpfLwy/2U8RzodLXGHw4FaLS6ZgtmJ2JkL\nLmnuuecefvGLX+BwOEa8du4ufejjTZs28fzzz3PzzTdz+eWXs3PnTv7yl79www03cOutt1JcXDzs\ns5555hm2bNnC1VdfzeHDh73P79q1i+3bt3P11Vdz4403cuLECQDy8/O5/vrr+eEPf8i2bdvYs2fP\niPX9//buPaTJLo4D+Ne1TVS0AkEhhLTESogoc5aal3C5ecu8TI1p0VVzpg3UFISSCruMKAWTCAsT\n7cJGgqWSSGWp858iIl1OrBS7oNamtnn5vX9ID02n7/tG8WLv+fy153LO8/vtjOfsnOeBo9FoIBaL\nERoaiszMTAwNDcFoNCIpKQnj4+OIi4tDZWXlnHKdnZ2QSCSQSCRQqVQWeZWUlCA+Ph5hYWHYs2cP\nBgYGAMzMMIhEIpSVlSEqKgqxsbF4+/YtTpw4gcjISCiVSgwPD3PXqKiogFQqxY4dO3D48GF8/vwZ\nAKDVapGamoro6GhERUWho6PDarvcuXMH4eHhiIiIQHx8PIaGhubNGZgZ5R44cABKpRJSqRT5+fkY\nGRkBALx58wb79u1DdHQ0IiMjudF4fn4+bt68CQAwGAzIyspCSEgI5HK5xQqMZrMZJSUlkEqlEIvF\nyM3NxdjYGFdHcXExUlJSkJiYCABQq9WIiopCeHg45HI5ent7uXqKiooQFBQEmUz2S2eAGOZvEcP8\noUJCQqi7u5uysrLo+vXr3D6dTjfns7Vjx48fp4mJCXr06BFt2LCBLl++TERE1dXVpFQqiYjo3bt3\n5OXlRRUVFUREVFdXR4GBgWQ2m6mvr4/Cw8NpeHiYiIja29tp27ZtRETU1tZGXl5e1NjYaDX2rq4u\n8vHxIZ1ORyaTiYqKiig7O5uIiN6/f08ikchqOZPJRAEBAVRfX09ERFVVVeTl5cXlNTQ0xJ175coV\nysnJscjj1q1bRER0/vx52rhxI7W2thIR0bFjx7hjGo2GsrKyyGQyERFRaWkp933I5XJ6/PgxERFN\nT0+TwWCYE2NbWxv5+/tTd3c3ERGNjY2RyWRaMOe7d++St7c36XQ6mpqaIoVCwcWTn59PNTU1XP1f\nv37l9ldVVRER0ZkzZ0ihUNDY2Bjp9Xry8/OjkpISIiIqKyuj06dP0+TkJE1NTVFhYSGpVCoiIsrL\ny6Pw8HD68OEDERFptVqSyWQ0NjZGRERqtZqSkpKIiOjGjRskk8loeHiYBgcHSSKRUFZWltV2Yphf\njc0ZMX80GxsbZGdnIzU1FfHx8f+q7K5du8Dn8yESiTA+Ps6VF4lEKC8v587j8XhISkoCAEilUhQX\nF0Ov16OzsxMjIyMWi60QETfadHZ2RlhYmNVrt7e3QyQSYfXq1QCA5ORkpKWlcXXMR6/XY2JiAhKJ\nBAAQFxdn8Wy4vb0djY2N0Ov1MJlMFmVtbW25xxG+vr64f/8+tm7dym13dHQgISEBzc3NePnyJTdS\nnZqagqOjIwBgy5YtUKlU6OrqQkREBFxdXefE2NLSArFYDE9PTwCAnZ3d3+YMAD4+Ptwxf39/aLVa\nJCQkQCQS4erVq/j06RMiIiLg7u4+55odHR3IzMyEnZ0d3N3dERAQwB1rbm6G0Wjklvg0m81Yu3Yt\ngJnfT1hYGLeqVXNzMwYGBpCcnMy1hcFg4OKPiIjAsmXLAABisRh6vX6elmKYX4t15swfz93dHUFB\nQbh27ZrFfltbW65Dm5qa4lYu+u77TVkoFAIAli5dym3P7gh/7GB/XHvYy8vL6lQ4gAWX9LWxsbGo\nc6EO/J/q7+9HXl4e1Go1PDw80NLSgpMnT3LH7ezsIBAIAMzk+D1fABAIBBY5x8XFISMjY8410tPT\nERkZiYaGBqSlpUGpVEIsFi+Y23z7Z5/j5ORkEc+3b98AADt37kRAQAAaGhqQk5OD2NhYiz8B1syu\nOyMjA9HR0VbPnd1OgYGBOHXqlNX4F7oGw/xO7Jk587+gUChQXV2N0dFRbt/mzZvx7NkzAEBdXR23\nnOa/NT09jdu3b4OI8ODBA9ja2sLDwwP+/v54/vw52trauBv7ixcv/lGdIpEIWq0WPT09MJvNqK2t\ntRhNzsfDwwMCgQANDQ0gIqjVapjNZgAAn8+Hvb09XFxcYDQaUVNT81P5hoaG4t69exbPil+/fg1g\nZmbAzc0N+/fvh1gsxtOnT+eUDw4ORlNTE7q7uwEAo6OjMJvNP51zb28vnJ2dsXv3bshkMjx58gSA\nZWcqEomg0WgwPj6Ovr4+tLa2WuRTW1uLwcFBAIDRaERPT8+8uT98+BCvXr0CMPMn8Pv7E35+fqiv\nr8eXL1/w8eNHNDU1/dSLdgzzM9jInPlfcHFxQUxMjMUoWS6X4+zZs9BoNNi+fTtWrFgxb/nZN+Uf\nt5ctWwaj0QiJRAIejweVSgU+n4+VK1eitLQU586dg8FgAJ/Px6ZNm7B+/Xqrdf7I09MTBQUFSE9P\nx+TkJNatW2cxip6vrFAohEqlQlFRES5evIjg4GA4OTnB0dERLi4u2Lt3L+Li4uDg4ACpVAqdTme1\nzh9nF2Zvx8TEYHh4GIcOHYJAIACPx0NKSgrWrFkDlUqFvr4+CIVCuLm5QalUzonR19cX2dnZUCgU\n4PP5cHBwQHl5+YI5LxRPVVUV2tvbIRQK4erqiqNHj87J58iRIygoKIBUKoWbm5vF442DBw/CZDIh\nJSUF9vb2WLJkCRQKBVatWjUndh8fHxQWFiI3NxeTk5Pg8XiQSCTw9vZGYmIiurq6EB0dDVdXVwQH\nB6O/v99qOzHMr8aWQGWYP8zo6Ci37rFGo8GlS5fQ3Nz8H0fFMMzvxEbmDPOHaWxsRGVlJQwGA5Yv\nX44LFy781yExDPObsZE5wzAMwyxy7AU4hmEYhlnkWGfOMAzDMIsc68wZhmEYZpFjnTnDMAzDLHKs\nM2cYhmGYRY515gzDMAyzyP0FoxbAmlNU8pIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d027b50>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting just the max values for all alpha values\n",
      "ans.max().plot()\n",
      "plt.grid(b=True, which='major', color='gray', linestyle=':')\n",
      "plt.xlabel('Alpha values')\n",
      "plt.ylabel('Prediction accurcay')\n",
      "plt.ylim([0, 65])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116225590>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "### Test Data Operations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tsdf_alpha = trainDF"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_train_games(team, gameId, trdf):\n",
      "    df = trdf[trdf.game_id < gameId]\n",
      "    hdf = df[df.home_team == team]\n",
      "    adf = df[df.away_team == team]\n",
      "    newdf = pd.concat([hdf, adf])\n",
      "    newdf = newdf.sort_index()\n",
      "    try:\n",
      "        return pd.concat([hdf, adf])\n",
      "    except:\n",
      "        return False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_points(team, df, alpha):\n",
      "    pFor = []\n",
      "    pAgainst = []\n",
      "    for r, v in df.iterrows():\n",
      "        hTeam = v.home_team\n",
      "        aTeam = v.away_team\n",
      "        hScore = v.home_score\n",
      "        aScore = v.away_score\n",
      "        if (team == hTeam):\n",
      "            pFor.append(hScore)\n",
      "            pAgainst.append(aScore)\n",
      "        elif (team == aTeam):\n",
      "            pFor.append(aScore)\n",
      "            pAgainst.append(hScore)\n",
      "    numGames = len(pFor)\n",
      "    weights = []\n",
      "    for i in xrange(1, numGames+1):\n",
      "        weights.append(alpha**i)\n",
      "    return (int(round(np.dot(pFor, weights)/float(sum(weights)))), int(round(np.dot(pAgainst, weights)/float(sum(weights)))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 15\n",
      "alpha = 1.0\n",
      "\n",
      "lst = []\n",
      "for r, v in tsdf_alpha.iterrows():\n",
      "    gameId = v.game_id\n",
      "    home = v.home_team\n",
      "    away = v.away_team\n",
      "    homehGames = get_historic_games(home, gameId, tsdf_alpha)\n",
      "    awayhGames = get_historic_games(away, gameId, tsdf_alpha)\n",
      "    if (isinstance(homehGames, pd.DataFrame) and isinstance(awayhGames, pd.DataFrame)):\n",
      "        if (len(homehGames.index) >= i and len(awayhGames.index) >= i):\n",
      "            homehGames = homehGames[-i:]\n",
      "            awayhGames = awayhGames[-i:]\n",
      "            hFor, hAgainst = get_points(home, homehGames, alpha)\n",
      "            aFor, aAgainst = get_points(away, awayhGames, alpha)\n",
      "        else:\n",
      "            hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "    else:\n",
      "        hFor, hAgainst, aFor, aAgainst = 0, 0, 0, 0\n",
      "    v = v.append(pd.Series([hFor, hAgainst, aFor, aAgainst], index=['hFor', 'hAgainst', 'aFor', 'aAgainst'], name=r))\n",
      "    lst.append(pd.DataFrame(v).T)\n",
      "tsdf_alpha_n = pd.concat(lst)\n",
      "csv_name = \"baseline2/alpha\" + str(alpha) + \"/train_data_iter_\"+str(i)+\".csv\"\n",
      "tsdf_alpha_n.to_csv(csv_name)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfs = []\n",
      "\n",
      "csv_name = \"baseline2/alpha\" + str(alpha) + \"/train_data_iter_\"+str(i)+\".csv\"\n",
      "tsdfn = pd.read_csv(csv_name, index_col=0)\n",
      "\n",
      "years = set(tsdfn.year)\n",
      "for year in years:\n",
      "    tsdfny = tsdfn[tsdfn.year == year]\n",
      "    tsdfny['diff_xy'] = tsdfny.apply(lambda x: get_diff(x['hFor'], x['hAgainst'], x['aFor'], x['aAgainst']), axis=1)\n",
      "    tsdfny['predicted_winner'] = tsdfny.apply(lambda x: get_predicted_winner(x['diff_xy'], x['home_team'], x['away_team']), axis=1)\n",
      "    tsdfny['correct_prediction'] = tsdfny.apply(lambda x: is_prediction_correct(x['winner'], x['predicted_winner']), axis=1)\n",
      "    accuracy = float(sum(tsdfny.correct_prediction))*100/len(tsdfny.index)\n",
      "    dfs.append(pd.DataFrame([accuracy], index=[year], columns=[\"accuracy\"]))\n",
      "ans = pd.concat(dfs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ans"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>accuracy</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> 58.709677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 58.709677</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 61.875000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 64.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 60.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 61.875000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 58.125000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 62.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 58.125000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 65.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 63.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 59.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 59.375000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013</th>\n",
        "      <td> 58.125000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>14 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "       accuracy\n",
        "2000  58.709677\n",
        "2001  58.709677\n",
        "2002  61.875000\n",
        "2003  64.375000\n",
        "2004  60.000000\n",
        "2005  61.875000\n",
        "2006  58.125000\n",
        "2007  62.500000\n",
        "2008  58.125000\n",
        "2009  65.000000\n",
        "2010  63.750000\n",
        "2011  59.375000\n",
        "2012  59.375000\n",
        "2013  58.125000\n",
        "\n",
        "[14 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "float(mean(ans))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "60.70852534562213"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}