FPL_form_v_fixture_share

FPL_form_v_fixture_share.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for FPL form vs fixtures analysis. \n",
    "Data can be downloaded at https://github.com/jcheong0428/jcheong0428.github.io/tree/master/assets/post20181226/FPL_form_v_fixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T22:34:03.842431Z",
     "start_time": "2018-12-31T22:34:03.800537Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd, matplotlib.pyplot as plt, numpy as np, seaborn as sns, glob, os\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import Treatment\n",
    "\n",
    "def find_minute_score(x):\n",
    "    '''\n",
    "    Adds points based on min played\n",
    "    '''\n",
    "    points = 0\n",
    "    if x>0:\n",
    "        points+=1\n",
    "    if x>=60:\n",
    "        points+=1\n",
    "    return points\n",
    "\n",
    "def bootstrap(vals,n=5000,pct=.95):\n",
    "    '''\n",
    "    returns deviance from mean to draw on barplot\n",
    "    '''\n",
    "    means = []\n",
    "    for ix in range(0,5000):\n",
    "        means.append(np.mean(np.random.choice(vals,len(vals),replace=True)))\n",
    "    return np.sort(means)[int(n*(1-((1-pct)/2)))]-np.mean(means)\n",
    "\n",
    "\n",
    "def calculate_gw_team_point(x): \n",
    "    '''\n",
    "    calculate gameweek score earned \n",
    "    '''\n",
    "    points = 0\n",
    "    if (x.team_h_score > x.team_a_score) and x.was_home:\n",
    "        points = 3\n",
    "    elif (x.team_h_score < x.team_a_score) and not x.was_home:\n",
    "        points = 3\n",
    "    elif x.team_h_score == x.team_a_score:\n",
    "        points = 1\n",
    "    else: \n",
    "        points = 0\n",
    "    return points\n",
    "\n",
    "def find_previous_ranking(row): \n",
    "    '''\n",
    "    Finds out opponent ranking by looking at opponent team's ranking from previous week. \n",
    "    Returns nan for first game week. \n",
    "    '''\n",
    "    prev_team_ranking, opponent_ranking = np.nan,np.nan\n",
    "    if row['round']>1:\n",
    "        prev_round=row['round']-1\n",
    "        this_team = row['team']\n",
    "        opponent = row['opponent_team']\n",
    "        try: \n",
    "            opponent_ranking = df_nona.query('round==@prev_round and team==@opponent')['team_ranking'].mode()[0]\n",
    "            prev_team_ranking = df_nona.query('round==@prev_round and team==@this_team')['team_ranking'].mode()[0]\n",
    "        except:\n",
    "            try: \n",
    "                # go before round for missing gw\n",
    "                prev_round = prev_round-1\n",
    "                opponent_ranking = df_nona.query('round==@prev_round and team==@opponent')['team_ranking'].mode()[0]\n",
    "                prev_team_ranking = df_nona.query('round==@prev_round and team==@this_team')['team_ranking'].mode()[0]\n",
    "            except:\n",
    "                print('error',prev_round,opponent)\n",
    "    return prev_team_ranking, opponent_ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This section looks at how the data was aggregated from the FPL repo. You can skip to the next section for the analyses.\n",
    "Original data is from https://github.com/vaastav/Fantasy-Premier-League"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:34:35.568052Z",
     "start_time": "2019-01-01T16:34:35.546511Z"
    }
   },
   "outputs": [],
   "source": [
    "season = '2016-17'\n",
    "\n",
    "# import players_raw to figure out teams\n",
    "players_raw = pd.read_csv(f'../Fantasy-Premier-League/data/{season}/players_raw.csv')\n",
    "id_to_team = players_raw[['id','team']]\n",
    "\n",
    "# season\n",
    "if season == '2016-17':\n",
    "    team_id_name = {1:\"ARS\", 2:\"BOU\", 3:\"BUR\", 4:\"CHE\", 5:\"CRY\", 6:\"EVE\", 7:\"HUL\", 8:\"LEI\", 9:\"LIV\", 10:\"MCI\", \n",
    "                    11:\"MUN\", 12:\"MID\", 13:\"SOU\", 14:\"STO\", 15:'SUD',16:\"SWA\", 17:\"TOT\", 18:\"WAT\", 19:\"WBA\", 20:\"WHU\"}\n",
    "elif season =='2017-18':\n",
    "    team_id_name = {1:\"ARS\", 2:\"BOU\", 3:\"BRI\", 4:\"BUR\", 5:\"CHE\", 6:\"CRY\", 7:\"EVE\", 8:\"HUD\", 9:\"LEI\", 10:\"LIV\", \n",
    "     11:\"MCI\", 12:\"MUN\", 13:\"NEW\", 14:\"SOU\", 15:\"STO\", 16:\"SWA\", 17:\"TOT\", 18:\"WAT\", 19:\"WBA\", 20:\"WHU\"}\n",
    "elif season == '2018-19':\n",
    "# 2018-2019\n",
    "    team_id_name = {1:\"ARS\", 2:\"BOU\", 3:\"BRI\", 4:\"BUR\", 5:\"CAR\", 6:\"CHE\", 7:\"CRY\", 8:\"EVE\", 9:\"FUL\", 10:\"HUD\", \n",
    "     11:\"LEI\", 12:\"LIV\", 13:\"MCI\", 14:\"MUN\", 15:\"NEW\", 16:\"SOU\", 17:\"TOT\", 18:\"WAT\", 19:\"WHU\", 20:\"WOL\"}\n",
    "else:\n",
    "    print('NEED TEAM LIST')\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:35:06.113280Z",
     "start_time": "2019-01-01T16:34:37.677813Z"
    }
   },
   "outputs": [],
   "source": [
    "player_dir = f'../Fantasy-Premier-League/data/{season}/players/'\n",
    "players = glob.glob(os.path.join(player_dir,'*'))\n",
    "df = pd.DataFrame()\n",
    "for player in players:\n",
    "    try:\n",
    "        _df = pd.read_csv(os.path.join(player,'gw.csv'))\n",
    "        pid = _df['element'][0]\n",
    "        team_id = id_to_team.query('id==@pid')['team'].values[0]\n",
    "        \n",
    "        _df['player_name'] = os.path.split(player)[-1]\n",
    "        _df['team'] = team_id\n",
    "        _df['team_name'] = team_id_name[team_id]\n",
    "        for form_n in [2,4,6]:\n",
    "            _df[f'form{form_n}']=_df['total_points'].rolling(window=form_n).mean()\n",
    "            for rowix, row in _df.iterrows():\n",
    "                if np.isnan(row[f'form{form_n}']):\n",
    "                    _df.loc[rowix,f'form{form_n}'] = _df.loc[_df.index[0]:rowix,'total_points'].mean()\n",
    "            _df[f'form{form_n}'] = np.append(np.nan,_df[f'form{form_n}'].values)[:-1]\n",
    "        # create column for previous form. \n",
    "        df = pd.concat([df,_df],axis=0)\n",
    "    except:\n",
    "        print(f'Error with {player}')\n",
    "        pass\n",
    "df_nona = df.sort_values(['player_name','round']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:38:37.779663Z",
     "start_time": "2019-01-01T16:35:06.115027Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Calculate points earned each gw\n",
    "df_nona['gw_team_point'] = df_nona.apply(lambda x: calculate_gw_team_point(x),axis=1)\n",
    "\n",
    "df_nona['gw_team_cum_point'] = 0\n",
    "for player in df_nona.player_name.unique():\n",
    "    gw_team_cum_point = df_nona.query('player_name==@player').cumsum()['gw_team_point']\n",
    "    df_nona.loc[gw_team_cum_point.index,'gw_team_cum_point'] = gw_team_cum_point\n",
    "    \n",
    "# for each round, do groupby 'team', and find mode value\n",
    "gw_rankings_dict={}\n",
    "for gw in range(1,df_nona['round'].max()+1):\n",
    "    gw_df = df_nona.query('round==@gw')\n",
    "    # ascending=True means Top of table would be ranked 20, as a difficulty ranking, and worst team as 1. \n",
    "    # ascending=False means rank on table, (e.g. MCI/LIV =1st)\n",
    "    gw_rankings = pd.DataFrame(gw_df.groupby('team')['gw_team_cum_point'].apply(lambda x: x.mode()).rank(ascending=True))\n",
    "    gw_rankings.index = gw_rankings.index.droplevel(1)\n",
    "    gw_rankings.columns = ['epl_ranking']\n",
    "    gw_rankings = gw_rankings.sort_values('epl_ranking',ascending=False)\n",
    "#     Even style\n",
    "#     gw_rankings.iloc[:5] = 5\n",
    "#     gw_rankings.iloc[5:10] = 4\n",
    "#     gw_rankings.iloc[10:15] = 3\n",
    "#     gw_rankings.iloc[15:] = 2\n",
    "#     FPL style\n",
    "    gw_rankings.iloc[:2] = 5\n",
    "    gw_rankings.iloc[2:6] = 4\n",
    "    gw_rankings.iloc[6:12] = 3\n",
    "    gw_rankings.iloc[12:] = 2\n",
    "    gw_rankings_dict[gw] = gw_rankings.to_dict()['epl_ranking']\n",
    "\n",
    "# calculate this week ranking\n",
    "df_nona['team_ranking']  = df_nona.apply(lambda x: gw_rankings_dict[x['round']][x['team']],axis=1)\n",
    "\n",
    "df_nona['opponent_ranking'] = np.nan\n",
    "for (rowix, row) in df_nona.iterrows(): \n",
    "    prev_team_ranking, opponent_ranking = find_previous_ranking(row)\n",
    "    df_nona.loc[rowix, 'opponent_ranking'] = opponent_ranking\n",
    "    df_nona.loc[rowix, 'prev_team_ranking'] = prev_team_ranking\n",
    "df_nona['team_ranking_diff'] = df_nona['prev_team_ranking']-df_nona['opponent_ranking'] # if I'm better, positive coefficients. \n",
    "df_nona['season'] = season\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:38:42.182394Z",
     "start_time": "2019-01-01T16:38:37.781621Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# figure out player position. \n",
    "df_nona['position'] = None\n",
    "df_nona['minute_points']=df_nona['minutes'].apply(find_minute_score)\n",
    "for player_name in df_nona.player_name.unique():\n",
    "    _tempdf = df_nona.query('player_name==@player_name')\n",
    "    if _tempdf['minutes'].median()>=60:\n",
    "        if np.any(_tempdf['saves']>0):\n",
    "            position = 'GK'\n",
    "        else:\n",
    "            clf = LinearRegression()\n",
    "            X = _tempdf[['clean_sheets','assists','goals_scored','bonus','goals_conceded',\n",
    "                             'minute_points','yellow_cards','red_cards','own_goals','penalties_missed']]\n",
    "            clf.fit(X,_tempdf['total_points'].as_matrix())\n",
    "            if clf.coef_[0]>2:\n",
    "                position = \"DF\"\n",
    "            if clf.coef_[0]>.5 and clf.coef_[0]<1.5:\n",
    "                position = 'MF'\n",
    "            if clf.coef_[0]<.5:\n",
    "                position = 'FW'\n",
    "        df_nona.loc[df_nona.player_name==player_name,'position']=position\n",
    "df_nona.to_csv(f\"player_{season}.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:38:48.143856Z",
     "start_time": "2019-01-01T16:38:42.184149Z"
    }
   },
   "outputs": [],
   "source": [
    "fixture_df = pd.DataFrame()\n",
    "for fixture in df_nona.fixture.unique():\n",
    "    _fixture_df = df_nona.query('fixture==@fixture')\n",
    "    team1, team2 = _fixture_df.groupby('team').count().sort_values('assists',ascending=False).iloc[:2].index.values\n",
    "#     team1, team2 = _fixture_df.team.unique()\n",
    "    df1 = pd.DataFrame(_fixture_df.query('team==@team1')[['round','team','opponent_team','prev_team_ranking','team_ranking_diff','opponent_ranking','was_home','team_h_score','team_a_score']].iloc[0]).T.reset_index()\n",
    "    df2 = pd.DataFrame(_fixture_df.query('team==@team2')[['round','team','opponent_team','prev_team_ranking','team_ranking_diff','opponent_ranking','was_home','team_h_score','team_a_score']].iloc[0]).T.reset_index()\n",
    "    fixture_df = pd.concat([fixture_df,df1,df2],ignore_index=True)\n",
    "fixture_df['clean_sheets'] = fixture_df['was_home']*(fixture_df['team_a_score']==0) | ~fixture_df['was_home']*(fixture_df['team_h_score']==0)\n",
    "fixture_df['goals_scored'] = fixture_df['was_home']*(fixture_df['team_h_score']>0) | ~fixture_df['was_home']*(fixture_df['team_a_score']>0)\n",
    "fixture_df['season'] = season\n",
    "fixture_df['team_name']=fixture_df['team'].apply(lambda x: team_id_name[x])\n",
    "fixture_df.to_csv(f'fixture_{season}.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data combine 16-17 and 17-18 season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:53:19.373038Z",
     "start_time": "2019-01-01T16:53:19.358180Z"
    }
   },
   "outputs": [],
   "source": [
    "fixture_df = pd.DataFrame()\n",
    "for season in ['2016-17', '2017-18']:\n",
    "    fixture_df = pd.concat([fixture_df, pd.read_csv(f'fixture_{season}.csv')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:55:11.118268Z",
     "start_time": "2019-01-01T16:55:10.136455Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SNydPnkRaWhreffddhIeHY/Xq1cVO2FSWvv32W3z55ZewsbHBmTNn8Ndff8HV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      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "sns.set_style('white')\n",
    "y = 'clean_sheets'\n",
    "f,ax=plt.subplots(1,2,figsize=(13,4))\n",
    "g=sns.factorplot(x='opponent_ranking', y=y, hue='was_home',data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[0])\n",
    "ax[0].set(ylim=[0,1.],xlabel='Fixture Difficulty',ylabel='Clean Sheet Probability')\n",
    "plt.close(g.fig)\n",
    "sns.despine()\n",
    "g=sns.factorplot(x='team_ranking_diff', y=y, hue='was_home', data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[1])\n",
    "ax[1].set(ylim=[0,1.],xlabel='Fixture Difficulty Difference',ylabel='Clean Sheet Probability')\n",
    "plt.close(g.fig)\n",
    "plt.legend([])\n",
    "sns.despine()\n",
    "plt.suptitle('Clean Sheet Probability by Fixture Difficulty',y=1.1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:55:18.098475Z",
     "start_time": "2019-01-01T16:55:16.440836Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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T2BIRtra2Sp1jzpw5uHDhAvbs2QOZTIY6depg5syZSsdQEtbW1rC0tFQoi4yMhEwmQ+PG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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixture_df['goals_scored_numeric']=np.nan\n",
    "for rowix, row in fixture_df.iterrows():\n",
    "    if row.was_home:\n",
    "        fixture_df.loc[rowix,'goals_scored_numeric'] = row.team_h_score\n",
    "    else:\n",
    "        fixture_df.loc[rowix,'goals_scored_numeric'] = row.team_a_score  \n",
    "        \n",
    "y = 'goals_scored_numeric'\n",
    "f,ax=plt.subplots(1,2,figsize=(13,4))\n",
    "g=sns.factorplot(x='opponent_ranking', y=y, hue='was_home',data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[0])\n",
    "ax[0].set(ylim=[0,3.],xlabel='Fixture Difficulty',ylabel='Expected Goals')\n",
    "plt.close(g.fig)\n",
    "sns.despine()\n",
    "g=sns.factorplot(x='team_ranking_diff', y=y, hue='was_home', data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[1])\n",
    "ax[1].set(ylim=[0,3.],xlabel='Fixture Difficulty Difference',ylabel='Expected Goals')\n",
    "plt.close(g.fig)\n",
    "plt.legend([])\n",
    "sns.despine()\n",
    "plt.suptitle('Expected Goals by Fixture Difficulty',y=1.1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:55:20.735750Z",
     "start_time": "2019-01-01T16:55:19.746488Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Rr18/xedG/t81a9ZAJBJh7ty5SgP7ODo6Ijw8HIIgKP72aIv8c6CuSbW27NixA7m5ufjw\nww+V7nVra2uMHTsWjRo1UjSN1SV1919FvvUmoqqHo60SGShVTZHk8vLyin1hkHN3d0e7du1w4cIF\nAEXNXlV9aQ4ODsb333+P8+fPAyj64vNyU0eZTIaUlBRcvXoVjx49AvDfYBPl4e7ujt9//x1XrlzB\nkSNHcObMGVy7dg0FBQW4ePEiLl68iN69eyu+qMtkMly8eBGWlpbw9/cvtr8xY8YomtLGxMQgLy8P\nAQEBqFu3brF127dvDwsLC1y+fBkFBQUwNTVVLJM345MrLCzEhQsXYGJighYtWqg8DxsbG1y/fh3Z\n2dmK0R1Lkp6eXux3ZmFhARsbGwQHB6Nv375o1aqVym3l/fzkrl69Wu5z7datm8r1ASjuBZFIhBkz\nZiitIwgCUlNTce3aNSQmJgJQfS9osn9daNCgAVq3bo0TJ04gJiZG8aW+rE1WAaBr167F+hg7OzvD\n1dUV8fHxSElJgb29vaK57MWLF3H37l1Fv9WdO3fC2Ni4TE0GS5uq48XfoTo9e/bEvn37cOzYMUWz\n2zlz5mi07at4+fOTmpqKxMRE2NvbqxxoplWrVjAyMkJMTIxW46iowcLk97GqqTPeeeedCptSQ950\nOioqCjk5OahRowZkMhl27dqFWrVqKQbhIiLDwuSRyEA1aNAA//77Lx4+fFhsWd26dREbG6tU9vPP\nPysGwgCK+jsBRaNaqtu/qalpsf2fOXMG27Ztw/Xr15GcnKxIEORfpgUt9OXx8vJSTEfx/PlznDt3\nDlu2bMGRI0fw22+/wdfXF7169UJmZiYKCgpgb2+veKOhjvw81J2viYkJGjZsiMTERGRmZqJ+/fqK\nZS/Pn5eZmakYHMjDw6PU42qSPPr5+WH9+vWlrvcyCwuLYsn/q5yrqhF769WrB1NTU8U9I/fPP/8g\nMjISV65cQVJSkuKalHQvlGX/2hYaGooTJ05g165d8PPzQ1ZWFo4ePQpXV1c0b95c4/2oOgegaBTO\n+Ph4pKWlKdbp1asX5s+fj927d+OTTz5BYmIirly5gjZt2qBBgwYaH7MsU3WUZPr06ejYsSMKCgow\nZMiQYg8edOHlz4+81URKSkqJx8/IyCj2cONVPHnyRGU82ib//L08WmtFMzc3R3BwMLZs2YKoqCiE\nhITg9OnTePjwIfr27Vup04sQUeVh8khkoJo0aaKYOP7lpoPaIpPJlCZbnzZtGrZs2QITExM0a9YM\nPXv2xBtvvIEWLVpg8eLFiiaxZfXkyRPEx8ejTp06xZplWlpaKp7Wz5kzB6tXr8aePXvQq1evEt++\nvkyTpFY+X9rLX6pefsskP27NmjURGBhY4j51/QVNVdL8KueqbmoMQRCU1l26dCkWLFgAkUiEJk2a\nIDg4GI0bN4aPjw927typNKplefavC/I3Mfv378e0adNw8OBB5Obmlrn5XknThwBFyblcjx49sGDB\nAkXyKH+7XFlNBs+ePat44HPo0CF8/vnnigFiXkVJn0V1n5/69eujZcuWJe5XKpVqJXnMzs5GcnIy\nABQbcEvbyvJ3qSTamL8xNDQUW7Zswe7duxESElLp9x8RVT4mj0QGKiQkBCtXrkRUVBTCw8OV+rVp\nQt5XUl3fm/v376OwsFDRH0n+9s/FxQWrVq0qNnl7aX2uSvLXX39h/PjxCA4OxsKFC9WuFxoaitWr\nVyu9QZC/sRIEodiX1Dt37iAmJgbe3t6lnm9BQQEePHgAU1PTUkevrVOnDkxNTSEIglbeBmnbq5xr\nWlpasbLU1FRIpVJFE8Pk5GQsWrQINjY2WL16tWKkXLkX59J7mSb71xVzc3N07doVmzdvxrlz53Do\n0CGIRCK1U3ioI39z9jL59X7xjVODBg0QEBCA6OhoJCYm4tChQ6hZs6ZikvmK9OjRI3z33XcwNTXF\n22+/jSNHjuCnn37C1KlTNdpe/vlSldQ8e/ZM4zjkb7rr1KlTYZ+f6OhoyGQyNGrUSGU/cW2qV68e\nEhISkJqaWuztcn5+PrZt24bGjRvD399fcU1VJZyv8jdVTt50+tSpU3j69CmOHj0KZ2dnvPnmm6+8\nbyKqnjhgDpGBatKkCdq3b4/MzEx8++23Jb5tKiwsLDZFgbyv3rFjx5TmHZM7cOAAgKLBcwDg8uXL\nAIqS1pcTx4yMDMX0GuV5Wt68eXOIRCIcPXq0xAF35H3pGjduDKDorVmzZs3w/PlzRR/OF/3555+Y\nMmUKzp49i2bNmsHCwgIXLlxQOU+d/Dq0aNGixDkz5cf19PTE8+fPVfbTS01NRVBQED7++GOV11bX\nXuVcT548WWz9gwcPAoCin+DVq1chk8nQrl27Yoljfn6+4pqouhc02b8uyQcR2b17N06fPq2Y3qQs\nTp06VawsNjYWd+7cgYuLi1Iz4BePGRERgdjYWAQFBcHCwqKcZ1B+M2bMQGZmJoYNG4Yff/wR9evX\nx8aNG4t9dtTd//I3lC/PFZmTk1OsmXxJHBwcFM2m5W8DX/TPP/+gc+fO+OqrrzTeZ0mkUilWrFgB\nAHj//fe1ss+S+Pj4AABOnDhRbNmlS5cwY8YMxQMWeZN2VZ9T+d/c0pT290o+9+vChQuRmZlZ5ocl\nRKRfmDwSGbDvvvsODRo0wK5du/Dxxx8jLi6u2Drnzp1Dv379FF/Q5QOoODo64p133sHDhw8xbdo0\npSTn0qVL+Pnnn2FsbIywsDAA/71NOXHihNK6jx49wujRoxX93cqTLDk5OaFbt26KEQovXrxYbJ3L\nly9j1qxZMDY2xsCBAxXl8vhmzpyp9AXs9u3bWLdunWLuOktLS/Tu3Rt5eXn46quvkJWVpVg3Pj5e\nMUJq//79NYp50KBBAIrmpUxISFCU5+TkYMqUKUhMTMRrr71WKf2KXuVcf/31V6VE4MaNG/j5559h\nYmKiWF9+L5w/f15p31lZWZgwYQIePHgAAIo5D8u6/1dlbm6OvLw8lfdi8+bN4ebmhh07diAnJwe9\nevUq8/6joqIUA+0ARX1g5SOZyidsf5G8ueymTZsAoFzHfFX79+/HgQMH4OTkhE8//RTW1taYNGkS\nZDIZJk+erPS7kt+zL79NlA9882LfXKlUilmzZiEnJ6dM8QwcOBAFBQX46quvlPq6Pn78GFOnTsWd\nO3eKPaQqj8zMTEycOBHXrl1D48aNlUYm1pU+ffrA1NQUq1atwpUrVxTlT58+xdy5cwEA3bt3B/Df\nNd2zZ49SUi7vm6sJeTNqdW9/e/ToAWNjY2zatAkikYhNVokMHJutEhmwunXrYvv27ZgwYQKio6MR\nHR2Nxo0bw8nJCYIg4NatW4qmdI6Ojpg8ebJSH72ZM2di0KBB+OOPP3Dq1Cl4e3vj8ePHijdHkyZN\nUgxcExgYCAcHB1y6dAmdO3eGp6cnnj17hgsXLkAqlaJRo0aIj49XOYCPJmbNmoXMzEycOHEC/fr1\ng4uLCxo1agQTExMkJCTg33//hampKWbOnKmYzgMo+iJ+4sQJ7N69G506dYKfnx9yc3MRExODgoIC\n/PDDD4qmY+PHj8e1a9dw4sQJdOjQAb6+vsjJyVH0A/vwww8RFBSkUbxdu3bF2bNnsXnzZrz77rvw\n8vJC7dq1cenSJWRkZKBx48aYPHlyua6FNpT3XF1cXNC7d29FX7QzZ86goKAAkydPVrxlbN68Oby9\nvfH333+jU6dO8PHxQX5+Pi5evIjs7Gw0btwYt2/fVozAW9b9vypnZ2fcunULYWFhcHNzU3xhl+vZ\nsyfmz58PS0tLdO7cucz7b968OSZMmIDIyEjY2tri3LlzyMzMROfOnVUmwGZmZormsvb29vD19S33\nuZVHZmYmZs6cCaBoahl5stG9e3f8/vvvOHnyJBYuXIiJEycCKPodAUVTzdy+fRs9e/ZEx44dMWjQ\nIBw4cADbtm3D5cuX4eLigqtXr+LJkyfo0KEDDh8+rHFMQ4cOxfnz53Hs2DF06dIFXl5eMDc3VzyQ\n8PPzwyeffKLx/s6fP4/x48crfs7Pz8fDhw/xzz//IC8vDy4uLli6dGmFPMxxdnbG5MmTMWPGDISF\nhcHX1xeWlpa4ePEiMjMzERoaqmi23LJlSzRt2hTXr19H165d4evri0ePHuHSpUt49913FdNqlMTB\nwQHGxsa4cOEChg0bhrfeekvp2slHGo6Ojoavr69WknIiqr745pHIwNna2mLNmjVYu3YtQkNDIRKJ\ncO7cOZw5cwZGRkbo3r07Fi5ciAMHDhQb3MXW1hbbtm3D8OHDUaNGDRw5cgRxcXHo0KEDNmzYgCFD\nhijWrVmzJtavX493330XgiDgyJEjuHXrFgICArB+/XpMmjQJAHD06NFynUeNGjWwatUqLF68GMHB\nwSgoKMCZM2fw119/oaCgAP3798fOnTvRu3fvYtvOnTsXM2bMgIuLC06fPo1Lly7B29sby5cvV3rL\nY2lpifXr12PcuHGwtbXFX3/9hWvXrsHf3x9Lly5VnIOmpk+fjp9++gne3t64efMmTp06BRsbG4wc\nORKbN29GnTp1ynUttKG857p48WL07dsX165dQ0xMDDw8PLB06VKle8HY2BhLly7FgAEDYGlpib/+\n+gtXr16Fl5cX/ve//2Hx4sUAVN8Lmuz/Vc2cORNNmjTBrVu3cOLECUUfWTl5f69OnTppNBLuyz7+\n+GNMnjwZ6enpOHr0KF577TWEh4dj4cKFapsQypsy9ujRo9Rmhto2a9YsPHr0CN26dSs2R+s333wD\nc3NzrFu3TvGWTJ4ompmZITo6GteuXQNQ1H9u7dq1aNWqFe7evYtTp06hSZMm2Lp1K9zd3csUk7Gx\nMf73v//hm2++gZubGy5fvozz58/DyckJ4eHhWLlyZakDE70oOTkZu3btUvw7fPgwEhIS4OXlhfDw\ncOzcuVPnfWpf1L9/f6xZswatWrXCP//8g+joaNSvXx+TJ0/G7NmzFesZGxtj9erV6N+/P0xNTXH8\n+HFkZ2dj9uzZivlPS2NjY4Pp06fDzs4OZ86cUdmsWn7/8a0jEYkEbYyLT0REZCBmz56NiIgIrFu3\nrtTRPrXls88+w5EjR3Dw4EE4OTlVyDGJ5Hr06IGkpCRER0eXeXA1ItIvfPNIRERUCnmf3IsXL2L7\n9u1wdXWFv79/hRzz0KFDOHr0KAICApg4UoXJzc2FIAhYv349bt68iZCQECaORMQ+j0RERKVZsGAB\nNm3apBgYZty4cTpvPvrVV1/h+PHjyMvLg7GxMb788kudHo/oRf3798ft27eRl5eHmjVrlqkPKRHp\nL755JCIiKkXTpk1hZGSEBg0a4JtvvqmQeRY9PDwAFI0mvGDBAnh6eur8mERyXl5eEAQBYrEYy5Yt\nK/OUNESkn9jnkYiIiIiIiErFN49ERERERERUKiaPREREREREVComj0RERERERFQqJo9ERERERERU\nKiaPREREREREVComj0RERERERFQqJo9ERERERERUKiaPREREREREVComj0RERERERFQqJo9ERERE\nRERUKiaPREREREREVComj0RERERERFQqJo9ERERERERUKiaPREREREREVComj0RERERERFQqJo9E\nRERERERUKiaPREREREREVComj0RERERERFQqJo9ERERERERUKiaPREREREREVComj0RERERERFQq\nJo9ERERERERUKiaPREREREREVComj0RERERERFQqJo9ERERERERUKiaPREREREREVComj0RERERE\nRFQqJo9ERERERERUKiaPREREREREVComj0RERERERFSqKpM83rhxA82aNcODBw9KXC87OxvTp09H\nQEAAfHx88PHHHyMxMbFigiQiIqokrCeJiKiyVYnkMS4uDiNGjIBUKi113TFjxmD//v0YP3485syZ\ng9TUVAwePBjPnj2rgEiJiIgqHutJIiKqCio1eZRKpdi4cSPee+895OXllbr++fPncfz4ccyZMwe9\nevVC586dsXbtWjx79gybNm2qgIiJiIgqDutJIiKqSio1ebxw4QLmzZuHoUOHYvz48aWuf/LkSdSs\nWRMBAQGKsrp168LX1xd//fWXLkMlIiKqcKwniYioKqnU5NHNzQ1RUVEYNWoUjI2NS10/Pj4ezs7O\nxdZ1cnJCQkKCrsIkIiKqFKwniYioKjGpzIPXq1evTOtnZWXBysqqWHnNmjWRlZVVrhjat28PADh6\n9Gi5ticiItIV1pNERFSVVGoG7XNQAAAgAElEQVTyWFaCIKhdZmRUvpeoHECAiIj0BetJIiLSpSox\n2qqmrKyskJ2dXaw8Oztb5ZNWIiIiQ8J6koiIdKlaJY+urq5ITk4u9mT1zp07cHV1raSoiIiIqgbW\nk0REpEvVKnls06YNnj59ilOnTinKMjIycP78ebRu3boSIyMiIqp8rCeJiEiXqnTymJGRgb///lvR\nyd/X1xd+fn4YO3Ystm3bhkOHDuGDDz6AtbU1+vXrV8nREhERVSzWk0REhiEmJgb9+vVDv379EBMT\nU2lxVOnk8dixY+jbty/++ecfRdkvv/yCwMBA/Pjjj5g0aRIaNmyItWvXonbt2pUYKRERUcVjPUlE\npP8EQcCiRYuQlpaGtLQ0LF68uMQB0nRJJGh45Li4OLi5uek6ngr31ltvAQDOnz9fyZEQEVF1xnqS\niIh0oaCgAF26dFEq279/P0xNTSs8Fo2n6ujWrRuaNGmCkJAQdO3aFa+//rou4yIiIqpWDLmeFAQB\njx49Qm5uLmQymdr1jIyMYGFhgXr16kEkElVghERkSGJiYvDTTz8BAMaOHQtfX99Kjkh/aNxs9Ztv\nvoG1tTXmz5+PDh06YMCAAdi0aRMeP36sy/iIiIiqBUOtJwVBQEpKCh49eoSCgoIS1y0oKMCjR4+Q\nkpJSaU2uiEi/VaUmnvpI42arcmlpadi7dy/27t2LK1euwMTEBK1atUL37t3RqVMnWFpa6ipWnWBz\nHCIi0iZDqycfPnyIR48eoUGDBqhbt26p+8vIyEBqairq1auH+vXrazVWIqKq1MRTW6rSOZU5eXzR\n3bt3ceTIERw7dgynT5+Gubk5OnTogNDQUAQEBGgzTp1h8khERLpiCPVkcnIyCgoK0KhRI433GR8f\nD1NTUzg6OmolRiIiuaqUaGlLVTqnco+2mpubi6tXr+Lq1au4fv06BEFAw4YNcePGDXz00UcIDQ1F\nYmKiFkMlIiKqPgylnpTJZDA2Ni7TNsbGxiX2jSQioqpJ4wFzACAvLw9Hjx7Fvn378NdffyEnJwf1\n69dHz5490b17d3h4eAAAzp07h5EjR2L8+PHYvn27TgInIiKqalhPEhGRPtM4eRwzZgyOHTuGnJwc\nWFtbo2vXrujevTtatmxZbMQ0Pz8/tG7dGidOnNB6wERERFUR60kiorLjyKjVi8bJ45EjR9CuXTuE\nhISgXbt2MDMzK3H99u3bo1OnTq8cIBERUXXAepKIqGxeHBkVABYvXoyIiAhO5VOFaZw8btmyBc7O\nzqhRo4bK5U+fPkVsbKziaUHPnj21EyEREVE1wHqSiKhspFIp7t+/r/j53r17kEql1XpwG32n8YA5\nvXr1wuHDh9UuP3DgAIYPH66VoIiIiKob1pNERKTv1L55TElJwR9//KH4WRAEHDx4UOXIcIIg4PDh\nwzA3N9dJkERERFUN60ki0gfsc0hloTZ5tLOzw/Hjx3H16lUAgEgkwsGDB3Hw4EGV6xsZGWHMmDG6\niZKIiKiKYT1ZxMjICAUFBWXaprCwkM3SiKoA9jmkslKbPIpEIqxZswZPnjyBIAjo2LEjJk+ejA4d\nOhRb19jYGHXq1IGFhYVOgyUiIqoqWE8WsbCwQFZWFjIyMlC3bt1S18/IyEBeXh6sra0rIDoiKgn7\nHOqGPr/NLXHAHCsrK1hZWQEAIiIi4ObmBhsbmwoJjIiIqKpjPQnUq1cPeXl5SE1NRWZmJoyNjdWu\nW1hYqEgc69WrV4FREhFVDH1/m6s2ebx37x7q1q2reErq4OCAvLw83Lt3r8Qd2tnZaTdCIiKiKoj1\nZBGRSAR7e3s8evQIubm5kMlkatc1NTVVJI768kWKiCqeLPcp8h/EqlxWIJUWK8u9cx6FJurfmZk1\nlMDIopZWYtP3t7lqr2KHDh3w448/IiQkBAAQGBio0R/6GzduaC86IiKiKor15H9EIhHq169f2WEQ\nkYHIfxCLtIihKpdJZQBgq1T2cMNwmJQwx4Tt4NWwcNGfpqW6pDZ5HDlyJCQSidLPfEpIRERUhPUk\nEREZGrXJ46hRo5R+/vzzz3UeDFU8fe7QW1XxmhPpB9aTRERkaEp4gUv67sUOvWlpaVi8eDEEQajs\nsPQarzkRERERVVdq3zw2adKkzM1vRCIRrl+//spBUcXQ9w69VRGvOZH+YD1JRETaUpUHAXqR2iP2\n7NmTfTeIiIjUYD1JZHjY9YR0pboMAqQ2efzhhx+0fjAiIiJ9wXqSyLDo+/x9uvAsJx+x9zLULpeq\neKN2IT4VJiW8UfN2sVW7jHRP/W+GiIiIiIgAVM+uJyU1hQR03xwy9l4GRiw9WEKAhajzUtHI5YcA\nI2O1mxybEYaqe8X1n9o7w93dXWn+Kk36drAvBxERGQrWk0RU1ZXUFBKoWs0hqXoosc+jk5OT0s98\nLU9ERFSE9SQRERkatcnj999/r/Qz+3YQERH9h/UkEREZmjLP8yiTyXDlyhXs3bsXBw4cwI0bN14p\ngN27d6Nbt27w8vJCcHAwduzYUeL6GRkZCA8PR5s2beDn54cRI0YgMTHxlWIgIiLSFtaTRESkr8o0\nYM6OHTswb948pKenKyY2F4lEsLe3x9dff4127dqV6eB79+7F+PHjMWTIELRp0wZRUVGYOHEiLCws\n0KVLl2LrC4KAkSNHIikpCV999RXq1KmDxYsXY/Dgwdi1axdq165dpuMTERFpE+tJov9wWgsi/aNx\n8rhr1y5MmjQJjRo1wrBhw+Dk5ARBEJCQkIBNmzZh5MiRWL58OVq3bq3xwRcsWIDg4GCEh4cDAN5+\n+208efIEixYtUlkpJiYm4uLFi5gzZw569uwJAHBzc0PHjh1x5MgR9OrVS+Nj64PSRtAqTXlG2CqJ\nriYjrUp4zYlIHdaTRP/htBa6wYS8dMYioL5FIR7mFo3YamshhTFvO63R+BvrihUr4OXlhQ0bNsDM\nzExpWf/+/REWFoaFCxdqXCkmJycjKSkJY8eOVSoPCgrCvn37kJycDEdHR6VleXl5AICaNWsqyuRP\nUTMzMzU9Fb1R2ghapSnPCFslMYTRt3jNiUgd1pNE/6mO01pUdUzINSMSAYPeeIZ1t6wBAAPfyAIv\nkfZo/JU1MTERPXr0KFYhAoClpSX69OmD2FjN38jEx8cDAFxdXZXKnZ2dAQAJCQnFtmnSpAn8/f3x\n66+/Ii4uDhkZGZg1axYsLS3RsWNHjY9NRESkbawniUiX1CXkVJxn3XzMa5mOeS3T4Vk3v7LD0Ssa\nv3l0cnJSWVHJZWZmomHDhhof+NmzZwAAKysrpXL509KsrCyV23377bcYNmwYunbtCgAwMzPDr7/+\nWuzpKxERUUViPUlERPpO4zeP48aNw9atW7F582bIZDKlZVFRUYiIiMDo0aM1PrB8IAG1gRkVDy0u\nLg59+/bFa6+9hl9//RWrVq1C+/bt8cUXX+D8+fMaH5uIiEjbWE8SEZG+U/vmsUOHDirLp0+fjgUL\nFsDR0REikQj3799Heno6atWqhcjISMWTztJYWxe1Q87OzlYqlz9JlS9/0dq1awEAq1evVvThCAgI\nQP/+/fHdd9/h999/1+jYRESkW4YwqAPrSSL9UtqgeGUd9I6D2pE+Ups82tnZaVTm6uparD+GJuTb\nJCUlQSKRKMrv3LmjtPxF9+7dg5ubm9JQ4yKRCC1atEBERESZYyAiIu0zlEEdWE8S6ZfSBsUr66B3\n1WFQO45MSmWlNnlcv369Tg/s7OwMBwcH7N+/H506dVKUHzx4EC4uLmor4D/++ANPnz5FrVr/Pcm5\nfPky7O3tdRqvPjKEPxiG8PaDqKoxlFEWWU8SUXVX6SOTioxQaGYF4/yiFhWF5taAqJxD0FOFKN/k\ncmpcv34dTZs21Xj9kSNHIjw8HLVr18Y777yDw4cPY9++fViwYAEAICMjA0lJSWjcuDGsrKzwwQcf\n4M8//8TQoUMxfPhwWFhYYOfOnTh37pxiG9Jcpf/B0DFDeftBRNUH60kiqmrkI5NWCpEIOU4tYZl0\nGgCQ4+gPvfoyqoc0Th7z8/OxePFiREdH4/nz50qDARQWFiI7OxtZWVm4ceOGxgcPDQ1Ffn4+Vq9e\njW3btsHR0RFz5sxR9Ac5duwYwsPDERERAX9/fzg4OGDTpk2YN28ewsPDIRKJIBaLsWbNmjJNukz/\nqdQ/GDpmKG8/iKhqYD1JRFR20lr2eOrRp7LDIA1pnDwuWrQIq1atQsOGDVGrVi3cunULb731Fh4+\nfIiUlBRYWFhgypQpZQ4gLCwMYWFhKpeFhoYiNDRUqczNzQ1Lliwp83GIyHCx+TJVBNaTRESk7zRu\nVLx//374+fnhyJEjWLFiBQBg2rRpOHDgAJYtW8Y3OkQakPczldPHfqZVzYvNl9PS0rB48eJSp0Ag\nKg/Wk0REpAtV6fujxsljamoqOnfuDCMjIzRo0AA2Nja4dOkSAKBdu3bo1asXtm7dqrNAifSBvJ+p\njXkhbMwL9a6faVWkrvkykbaxniQiIl2oSt8fNW62amFhofTE1MnJCbdu3VL87OXlhQMHDmg3Oj3D\npnME6Hc/UyJDxnqSDAnnRCSqWFXl+6PGyaO7uzv++usv9O3bFwDQqFEjxRNVoOiJK0exVI8jfxIR\n6TfWk2RIDHFORCIAeJaTj9h7GWqXq2rddCE+FSZqHpwAgLeLrdplVY3GyWP//v0xZswY9O/fH8uX\nL0e3bt3w22+/ITw8HI0aNcLatWvh7e2ty1irNY78SUSk31hPEuk3Xc+Pre2kpDolJNVJ7L0MjFh6\nUP0KskLUealo5PJDgJGx2k2OzQhDdckINE4eg4ODkZWVhTVr1qBGjRpo3bo1BgwYgI0bNwIA7Ozs\nEB4errNAiYiIqjLWk0T6TdfzY2s7KalOCQlVHxonjwDw3nvv4b333lP8/PXXX+Ojjz7CkydP4Obm\nBjMzM60HSEREVF2wniTSb1Wl3xlRZSlT8ggAMpkM165dw927d2FsbAwnJye4u7vrIjYiIqJqh/Uk\nlRUH1COi6qJMyeOOHTswb948pKenK+ZJE4lEsLe3x9dff4127drpJEgiIqLqgPWk7ulbosUB9Yio\nOtE4edy1axcmTZqERo0aYdiwYXBycoIgCEhISMCmTZswcuRILF++HK1bt9ZlvERERFUS60nd08dE\niwPqEVF1onHyuGLFCnh5eWHDhg3F+mz0798fYWFhWLhwIStFIiIySKwndY+JFhFR5dI4eUxMTMTE\niRNVdva3tLREnz59MH/+fK0GR4attCGrS1OeeXZKwiGviagkrCeJiEjfafwt2snJCQkJCWqXZ2Zm\nomHDhloJigjQYMjq0pRjnp2ScMhrIioJ60kiItJ3GieP48aNw+jRo9G4cWO8//77MDIyUiyLiopC\nREQEZsyYoZMgqwK+BSMiopIYej1JRET6T23m0qFDB5Xl06dPx4IFC+Do6AiRSIT79+8jPT0dtWrV\nQmRkJLp27aqzYCsT34IREdGLWE8SqWcsAupbFOJhbtH3HFsLKYwrcFwjfRuVl6iqUJs82tnZaVTm\n6uoKV1dX7UZFRERUxbGeJH2hi0RLJAIGvfEM625ZAwAGvpGFihoUVx9H5SWqKtQmj+vXr6/IOIiI\nVJLlPkX+g9hyb1+gosl47p3zKCxnk3EAMGsogZFFrXJvX9XxmmuG9STpA10mWp518zGvZfor76es\n9HZUXpERCs2sYJyfBQAoNLcGREalbESkXWWuyTMzM3Hq1CmkpKTA1NQUdnZ2aN26NaysrHQRHxEZ\nuPwHsUiLGFru7aUyAFDuI/xww3CYvEJ9azt4NSxc9LcJFK/5q2E9SdWJ3iZa+kgkQo5TS1gmnQYA\n5Dj6o8Je5xL9vzIlj5GRkZg7dy5yc3MhCIKi3NzcHBMmTMCAAQO0HiAREZWMfXuqDtaTRKRL0lr2\neOrRp7LDIAOmcfIYFRWFGTNmoGnTphg2bBgaNWoEQRAQHx+PNWvWYNasWbCzs0P79u11GS8REb2A\nfXuqDtaTRESk7zROHlesWIGmTZti8+bNShMgu7u7o3Pnzujbty9WrlzJSpGIqAKxyVnVwXqSiIj0\nncY9UG7evIkePXooVYhypqam6NGjB27cuKHV4IiIiKoL1pNERKTvNE4ezczMkJOTo3Z5dnY2jI3L\nN2chkU78/6hkchyVjIh0ifUkERHpO42/Sfv6+mLjxo2KfjUvSk1NRWRkJFq0aKHV4Iheyf+PSiYz\nqwmZWU2OSkZEOsV6koiI9J3GfR5Hjx6NsLAwBAcHo2fPnnBxcQEAxMfH488//0RhYSFGjx6tqzir\nP87NUyk4KhkRVRTWk0REpO80Th4lEgnWrVuHWbNmYePGjUrLPDw8MHXqVLi7u5c5gN27d2PJkiVI\nTk6Gvb09RowYgZ49e6pdXyaTYdmyZdi+fTsePnwIZ2dnfPLJJ+jWrVuZj12hODcPEZFeYz1JRET6\nTuPk8d9//4Wnpye2bt2K9PR0pKSkQBAE2Nvbo169euU6+N69ezF+/HgMGTIEbdq0QVRUFCZOnAgL\nCwt06dJF5TbfffcdtmzZgrFjx6JJkybYs2cPxo0bBysrK7Rr165ccVQUvgUjItJfrCeJiEjfaZw8\nfvDBB+jVqxfGjx8PGxsb2NjYvPLBFyxYgODgYISHhwMA3n77bTx58gSLFi1SWSkmJSVh48aNmDFj\nBt577z0AQKtWrZCYmIjo6GhWikRVACesJ0PFepKIiPSdxp3unj9/DgcHB60dODk5GUlJSejcubNS\neVBQEOLj45GcnFxsm6ioKFhYWBRrrrNhwwZMnTpVa7ERUfm8OGF9WloaFi9eDEEQKjssogrBetIw\nxMTEoF+/fujXrx9iYmIqOxwiogql8ZvHIUOGYM2aNWjWrBk8PT1f+cDx8fEAAFdXV6VyZ2dnAEBC\nQgIcHR2VlsXGxsLV1RWnTp3C/Pnzcfv2bTg4OODLL79E165dXzkmIno1nLCeDBnrSf334gMyAFi8\neDEiIiIgKmUMA1nuU+Q/iFW5rEAqLVaWe+c8Ck3Uf0UzayiBkUWtMkSuf57l5CP2XobKZVIV1/RC\nfCpMSrim3i62WouNSJ9pnDxeu3YNaWlpeP/992FhYYE6derAyEj5xaVIJEJUVJRG+3v27BkAwMrK\nSqm8Zs2aAICsrKxi22RkZOD+/fuYPHkyRo8eDQcHB2zbtg1jxoxB3bp10bJlS01Ph4iISKtYT+q/\n8j4gy38Qi7SIoar3KQMA5cTl4YbhMCmhbZjt4NWwcDHsLgGx9zIwYulB1QtlhajzUtHI5YcAI/Xz\nrB6bEQY+5iQqncbJY15eHjw8PLR24NKasr1c4QJAQUEBMjIysHTpUrRv3x4A0LJlS8THx+OXX35h\npUhERJWG9WRx7ANNRKRfNE4e169fr9UDW1tbAwCys7OVyuVPUuXLX1SzZk0YGxsjICBAUWZkZITW\nrVtj+/btWo2PiPSDsQiob1GIh7lFT5xtLaQw5iw5pAOsJ5WVt4knaR+beBKRtpSaPKalpeHKlSuQ\nSqVo1qxZsf4V5SXvw5GUlASJRKIov3PnjtLyFzk7O0Mmk0EqlcLMzExRXlBQwMqIiFQSiYBBbzzD\nultFX7QHvpHFKVZJq1hPqsY+0FUHm3gSkbaobVEvCAJmzZqF9u3b4/PPP8eYMWPQuXNnfPHFF8We\ngpaHs7MzHBwcsH//fqXygwcPwsXFBXZ2dsW2efvttyEIAvbt26cok0qliI6ORosWLV45JiLST551\n8zGvZTrmtUyHZ938yg6H9ATrSSIiMjRq3zyuW7cOGzZsgLe3N7p06QKRSITTp0/j4MGDsLCwwI8/\n/vjKBx85ciTCw8NRu3ZtvPPOOzh8+DD27duHBQsWACjq+J+UlITGjRvDysoKrVq1Qrt27TBr1iw8\nf/4cLi4uiIyMREpKCubPn//K8RAR0aszlKbCrCeJiMjQqE0ed+zYgbZt22LZsmWKpi5DhgzBzJkz\nsWXLFkyfPh01atR4pYOHhoYiPz8fq1evxrZt2+Do6Ig5c+YohhM/duwYwsPDERERAX9/fwBFfSYW\nLVqE5cuX48mTJ2jatClWr16t1UEKiIio/AylqTDrSSIiMjRqk8c7d+6gX79+xfpIhIaGYuPGjYiL\ni9NKRRQWFoawsDCVy0JDQxEaGqpUZmFhgYkTJ2LixImvfGwiItINeVNhfcZ6koiIDI3aPo+5ubkq\nn5i+/vrrAIqP/kZERGRIWE8SEVExIiMUmv03P2+huTUgKmHi1mqmxAFzVI3MJi+TyWS6i4qIiKiK\nYz1JRETFiETIcWoJmVlNyMxqIsfRH/rUd0PjeR6JiIiIiIioZNJa9njq0aeyw9CJEpPH+Ph4xMTE\nKJU9e/YMABAbG6tyAllfX18thkdEpN9KmrxbE+WZ4LsknPy7bFhPEhGRISnx28XSpUuxdOlSlcvm\nzJmjsvzGjRuvHhURkYEocfJuTZRjgu+ScPLvsmE9SVQF/X+fM+P8LAD61+eMqDKpTR5HjRpVkXEQ\nERFVK6wniaqo/+9zZpl0GgD0rs8ZUWVi8khERFQOrCeJqi597nNGVJk4YA4RKbD/HRFVJlnuU+Q/\niFW7vEDF35jcO+dRWMLfGLOGEhhZ1NJKfEREho7JIxEpsP8dEVWm/AexSIsYqna5VAYAyg+VHm4Y\nDpMSurPZDl4NCxcOUkREpA3sPUxERERkqPR8QnMi0i7+dSAiIiIyVHo+oTkRaRebrRIRERFVEmMR\nUN+iEA9zi5r321pIYVzBuRsHlyEiTZU5eZRKpbh69Sru378PPz8/WFhYoLCwELVr19ZFfERERNUK\n60kqC5EIGPTGM6y7ZQ0AGPhGFl/8EVGVVabkcd++fZg9ezbS09MBAKtXr0Z+fj5Gjx6NUaNGYdiw\nYToJkoiIqDpgPVm9lTbidHlGlNZk1GjPuvmY1zJdsyCJiCqRxsnjiRMnMG7cOLz55psYNmwYfvjh\nBwCAg4MDxGIx5s+fj/r166NHjx46C5aIiKiqYj1Z/ZU64nQ5RpTmqNFEpE80HjDn119/hYeHByIi\nIpQqPjc3N0RGRsLHxwfr1q3TSZBERERVHetJIiLSdxonjzdu3EC3bt1gZFR8ExMTE3Tv3h0JCQla\nDY6IiKi6YD1JRET6TuPk0dTUVGVbf7nMzEyYmrJhBhERGSbWk0REpO80Th79/Pywfft25OXlFVuW\nlpaGyMhItGjRQqvBERERVResJ4mISN9pPGDOmDFjEBYWhnfffRdt27aFSCTC4cOHcezYMfzxxx/I\nz8/HF198octYiYiIqizWk0REpO80fvPYuHFjbNy4Eba2tli/fj0EQcCGDRuwbt06ODk5Ye3atXB3\nd9dlrERERFUW60kiItJ3Gr95/PfffyEWi7F+/XpkZmYiKSkJMpkM9vb2qF+/vi5jJKLqQmSEQjMr\nGOdnAQAKza0BkcbPqIiqNdaTRESk7zROHj/44AP06tUL48ePR506dVCnzsszHRGRwROJkOPUEpZJ\npwEAOY7+gEhUyUERVQzWk0REpO80Th6fP38OBwcHXcZSZclkMiA/B319GsLUpGp8EU69dxdGOSLk\neH5Q2aEoSLNEML5zR3s7fP4cA31fR2GhgNSsfNx4kI37T4sPREFVi7SWPZ569KnsMIgqnCHXk0RE\nZBg0Th6HDBmCNWvWoFmzZvD09NRlTFXOvXv3YCrNRqCHPUyMNb5kOmVibARY1kENSYfKDkXByMJK\nq/urU9MC7Zs5QSaTITc3Fx1y8rDzSirO3Xmi1eMQEWmDIdeTRERkGDTOhK5du4a0tDS8//77sLCw\nQJ06dYpNhCwSiRAVFVWmAHbv3o0lS5YgOTkZ9vb2GDFiBHr27KnRtvfv30f37t3x0Ucf4bPPPivT\ncTWVnZ2NZ8+ewcamHp5IjSGqIk3wHO3rQlSQg4L0xMoORcHU5nUYmdfU2v6ycwtw52FRomguk8Hy\n+RP08AKSH+fyDSQRVTmGWk8SEZHh0Dh5zMvLg4eHh1YPvnfvXowfPx5DhgxBmzZtEBUVhYkTJ8LC\nwgJdunQpcVtBEDB58mRkZWVpNaaXPX36FEZGRqhd5zU8TX+m02OReiIjI5ha1katGllwb1CTySMR\nVTmGWk8SEZHh0Dh5XL9+vdYPvmDBAgQHByM8PBwA8Pbbb+PJkydYtGhRqZViZGQk4uPjtR7Ty6RS\nKUxNTYs9PaaKJzIygoWFBWytzSo7FKKqgyPcVhmGVk8+y8lH7L0MtculUmmxsgvxqTAxUf3Vw9vF\nVmuxERGRbpS5A19mZiZOnTqFlJQUmJqaws7ODq1bt4aVVdn6uyUnJyMpKQljx45VKg8KCsK+ffuQ\nnJwMR0dHtdvOmzcPixYtwscff1zWUygTmUwGY2NjnR6DNGdkZAQT46rRdJioSuAIt1WOodSTsfcy\nMGLpQfUryArx8nizI5cfAoxU16nHZoTBVHvhERGRDpQpeYyMjMTcuXORm5sLQRAU5ebm5pgwYQIG\nDBig8b7kT0NdXV2Vyp2dnQEACQkJKitFmUyGSZMmITg4GG3bti1L+EREeokj3FYdrCeJiEifaZw8\nRkVFYcaMGWjatCmGDRuGRo0aQRAExMfHY82aNZg1axbs7OzQvn17jfb37FlR/8GXn8TWrFk04Iq6\nPhrr1q3D3bt3sXTpUk1DJyIi0jnWk0REpO80Th5XrFiBpk2bYvPmzTAz+6/Pmbu7Ozp37oy+ffti\n5cqVGleKLz6RVUVVH8O4uDgsXLgQixcvhrW1taahExER6RzrSSIi0ncaj6pw8+ZN9OjRQ6lClDM1\nNUWPHj1w48YNjQ8sr9Sys7OVyuVPUl+u9AoLCxEeHo4uXbogICAAUqlU0RlfJpOp7JhPRWL+vgav\n9qFIeZBW2aEQEekt1pNERKTvNE4ezczMkJOTo3Z5dnZ2mQaWkffhSEpKUiq/c+eO0nK5+/fv4/Ll\ny9ixYweaNWum+AcAPwpsn2AAACAASURBVP/8s+L/iYiIKgPrSSIi0ncaN1v19fXFxo0bERoaCltb\n5eG0U1NTERkZiRYtWmh8YGdnZzg4OGD//v3o1KmTovzgwYNwcXGBnZ2d0vq2trbYvn17sf306dMH\n/fr1Q+/evTU+NhERkbaxnjQAnBqHiAycxsnjl19+ib59+yI4OBg9e/aEi4sLgKLR4P78808UFhZi\n9OjRZTr4yJEjER4ejtq1a+Odd97B4cOHsW/fPixYsAAAkJGRgaSkJDRu3BhWVlbw9PRUuR9bW1u1\nyyrLuE+Hwr2ZJ4aNGgMAOHsyGj98MwlfTZuF1m2L+rusWfozEuNuY+hno7F+5RLc/Ocq8nJzYFPP\nFsE9QtHjvX4AgLzcXKz8dQHOnzmF7KwsODg548svRqFz+7fLFFP06QvY8ud+JKXch5Pd6xgzYhDa\ntnoLQFFzp8jf92LrrgO4/+AhXm9YH4P6hOD9d4MAFDV9HT7uW8z7djwWrtiAB6mP4NVMjNmTvsDa\nzTuw6+AxmJqZY/CQIfj0008Vx/ztt9+wcuVKpKSkwN7eHmFhYRg0aBDnzSQivcN60gBwahwiMnAa\nJ49isRjr1q3DrFmzsHHjRqVlHh4emDp1Ktzd3ct08NDQUOTn52P16tXYtm0bHB0dMWfOHHTt2hUA\ncOzYMYSHhyMiIgL+/v5l2ndl820ZgBPHDyt+vnzxHEQiEa7+fVGRPF44ewpBIb0wfeKXaN7CF98v\nWgpjY2NE7duFtct+gZdPC7g2FiNy7Qokxsdh6nfzYGVljUN7/8S4ceOwf9cO2FpoHtPGP/Zg2thP\nUN+mLhYuj8D4GfNx7PfVsKxRA/OWrMWug8cR/sUweDRpjBNnL2HOL6uQl5+PQX1CAACFMhlWbPgN\nP0z5ElJpIUaGz0afYWPRq2sHbFzyI/advIyFCxciMDAQEokEW7ZswU8//YRp06bBy8sL169fx8yZ\nM5GamooJEyZo9XoTEVU21pOGgVPjEJEhK9M8j15eXti6dSvS09ORkpICQRBgb2+PevXqlTuAsLAw\nhIWFqVwWGhqK0NDQErePjY0t97F1ybd1G2xZvxqP0lJRz7YBLl+IgV/rt3Ht74sAgPv37iIlOQn+\nAW1RkJ+P4B6hqFHDEgAQNuQj/LFlI+4kxMO1sRgP7qWghqUlGr5uh5pW1uj3wccICmyLWrWsgfx0\njWOaMHIofL09AAAjBr+PIyfOIS7xLlyd7LFl5wF89dkH6NaxaE4wZwc7pDxIxarI3zGwd3fFPkYO\n7YdmksYAAP83PXHl+i2MHTEYIpEIwz96E0uWrcC///4LiUSC//3vf/j000/RrVs3AICjoyOysrIw\nffp0jB49Gubm5q9+oYmIqhDWk0REpM/K1Hbw2rVrGDOmqBmml5cXmjdvjlWrVuGLL75AXFycTgKs\nrtzekMCmXn38fSEGD1MfIPX+PfTuNxh3kxLxOCMdF86ehqvbG7Bt0BDB7/ZC9OFDWLpwLr6dMBof\n9y/6IiCTFQIAeoUNRGLcbQzp3Q3hoz/F9sh1cHR0LPMw7C4O//WPqWVdNG9YXl4+EpJSIJVK4eOp\n/ET8rebNkPH4CTL+r737jmvq+v8H/goBBMGiIIJMxQqogKLgQESlbsFa966K8+seVbDVgrYVrXWh\nddaJRdQ6q7gFq9Qq1vFpsdSqFRzgYAkqAXJ/f/AjNYYRNIEQXs/Hg8cDTs65531uQt733JmWISuz\ns7aU/W5oYADruhYQ/f9TdgwMCg6DSiQSpKamIjk5GcuXL4e7u7vsZ+HChcjJycGDBw/KFDsRUWXA\nPElERNpM6SOPcXFxGD16NAwMDJCWlgYzMzMAgLm5OQ4fPox+/fohIiICzs7Oagu2svFo3RY3rl4G\nADR0boyGzo1gVtscf1z/HVcvxaKlVzukpT7H3CnjYFKzFlq28UYzj5b40KkRxg7+RLYc58Yu2BRx\nADeuXsbN3+Nw7mQU9u3ajo3fh8GjoYXS8eiIFfcVCBCKfZaYVFpQrqv7390BdXXlPzI6xVzrIZVK\nAQBBQUHw8vJSeL1u3brKBU1EVEkwTxIRkbZT+sjjqlWrUL9+fZw8eRIffvihrHz06NE4duwYbG1t\n8d1336klyMrKs01b3Lx2Ff+7FgdX94I77Lm6t8DlXy/gj5vX0NKrHc6fPYWsF5lYvGo9+g8bidbe\n7ZH94gUAoHBOF7F9M279cQMtvdphzOQZWLttN2xtbXHy9FmVxOlgbwNdXV1c+5/888d+/188apvW\nlB2lLAszMzOYmpoiKSkJ9vb2sp8///wTK1euVEncRESahHmSiIi0ndKTx1u3bmHgwIGoWbOmwmsm\nJiYYMGAAbt68qdLgKjs3dw9IcnLw6y8xcHP3kJXFxpxFrVqmcGjoiNrmdZDz+jViY87iaUoyrsf9\nhu++XgAAyM2VAABSHj/C+pXf4ua1q3iSkoxLv0Tj0aNHcG/mppI4jY2qo79/Z6zduhvHzvyCxIeP\nsftAFCIPHcenAz+WnZZaFiKRCGPHjsXOnTsRHh6OxMREnDp1CsHBwTAwMCjyIdpERJUZ8yQREWk7\npU9b1dXVRVpaWrGvZ2VlyU5VpAJ6+vpo2sIT1+J+g1PjghvVNG3uAalUCs823gAAL5+OuPN3Arau\nD8PLl9moY1EXnXr443LsL/gn4S/AHxg3ZRa2bViDlaEheJGZiToWlpg5axZ6+fVE7vN/VRLrZ5NG\no6bJB1i5cSeep6XDzrougqaORT+/zqU3Lsbo0aNRrVo17Ny5E6GhoahduzYGDBiAqVOnqiRmIiJN\nwjxJRETaTunJY6tWrRAeHo5evXrB1tZW7rWUlBSEh4ejZcuWKg+wsgsMWSz3t2ltcxw4fVH2t0gk\nwoixEzFi7ES5eh/3++/OetWNjPB/M+fKve5kbQrkvlIqBs9mLrh5br9cmbVlHbkyXbEYEz8diImf\nDlR6GV8FTlGo9/Zd/YYOHYqhQ4cqFScRUWXGPKl+YhFgbpCPp68LrsWvY5AHMR+zSERUbpSePE6b\nNg39+/dHr1694OPjI3v4cVJSEmJiYiASiTBz5kx1xUlERKTRmCfVTyQChjd8ge1/F9xtfFjDLLzD\nlRVERPSOlJ48Ojg4YP/+/VixYgXOnz+PEydOACh4PEPbtm0xc+ZMNGjQQG2BUtHa+g1DXn7xp0GZ\nmZrg2K515RgREVHVxDxZPlxNJVjWWvlnHBMRkeooPXkEAHt7e6xcuRKCICAtLQ1SqRS1atWCWCwu\nvTGpxe4NyyCg6EdtAOB7Q0RUjpgniYhIm5Vp8lhIJBLB1NQUGRkZyMvLY1KsQLbWlhUdAhERvYV5\nkoiItFGJj+rIzc3F7t27ERQUJFceFxeHnj17onXr1nB3d8eYMWOQmJio1kCJiIg0DfMkERFVJcVO\nHiUSCT799FMEBwfj559/Rl5eHgDg33//RUBAAO7evYt27dph5MiRuHfvHgYNGoRnz56VW+BEREQV\niXmSiIiqmmInj9u3b8e1a9fw2Wef4fLly9DVLTjDNSwsDDk5OejZsyc2btyIOXPm4KeffoJYLMb6\n9evLLXAiIqKKxDxJRERVTbGTx6ioKHTt2hUBAQEwNDQEULCX9ezZsxCJRAgICJDVrVmzJvr06YPo\n6Gi1B0xERKQJmCeJiKiqKXbyeP/+fXh4eMiVXb9+Ha9evYK5uTkaNWok95qdnR2ePHminiiJiIg0\nDPMkERFVNcXebVUqlSrcHe7XX38FAHh5eSnUf/HihWzPa1VgUdMIBnrvdLNaldARiQA9A4g/sER+\nZnKFxUFEVFUxTxIRUVVT7OzHzs4Ot27dkis7ffo0RCIROnTooFD/woULsLOzU3mAmspATxdSQUDC\no9QKi8HJyhRGegbIf8f2X4SG4fCJc8W+vnHZl2jdommpyxk9fT7EYjG2bdv2jpEQEVU+zJNERFTV\nFDt57NmzJ9auXQsfHx+0bdsWkZGRuH37NmrXrg1fX1+5uocPH8bFixcxbdo0tQesSRIepWL8+pMV\n1v+GCV3Q3KbGey3DwtwMy4JnF/laA3vb91o2EZE2Y54kIqKqptjJ48iRI/HLL79g8uTJEIlEEAQB\nenp6+Prrr6Gvrw8AOHXqFMLDw3H58mXUr18fI0eOLK+4SUX09fTQtLFTRYdBRFTpME8SEVFVU+zk\nUV9fH9u2bcOxY8dw/fp1GBkZoVevXvjwww9ldf744w/8/vvv6NWrFwIDA2FgYFAuQVP5efnqNTbs\n2IMzv/yGx0+eyiabMyeMgGODekW2uXjxIlatWoXbt29DV1cXnp6emDVrFho0aCCrc+rUKaxbtw63\nb9+GiYkJ/P39MWPGDNkGFxGRpmOeJCKiqqbEO76IxWL4+/vD39+/yNcnTJiAadOmQUen2Ju2UiWQ\nl6941aRYRwcikQifL16F638kYNrYobCpa4n7Dx/h+627MferFdi/ZSVEIpFcu6SkJPzf//0f+vbt\ni5kzZyIjIwMrVqzA+PHjcerUKYhEIhw5cgSzZ89G7969MX36dCQmJmL58uV48OABwsLCymvYRETv\njXmSiIiqkve6XSjvGlf5JT1KRvNO/RXKv5gxHh9364hXr3MQNG0MurQvuHOgR7MmyM5+hWXrtiEt\nIxOmNU3k2t28eROvX7/G+PHjYWFhAQCoW7cuzpw5g+zsbBgZGWHZsmXo2LEjlixZImtnaWmJSZMm\n4erVq2jRooUaR0xEVH6YJ4mISJtU3LMmSCNYmJth5aK5CuV1LcxRTV8f65cuAACkPH2O+w8e4X7S\nI8RcigMA5ObmKbRr2rQpqlWrhn79+qFbt27w8fFBq1at4ObmBgC4c+cOkpOTMWnSJOTl/de+Xbt2\n0NPTQ2xsLCePRETaQKSDfH1jiCVZAID8ajUAEY/AEhFVZpw8VnH6enpo4vRhsa9fvHwNS9duwb3E\nhzCqbgjHBvVQ3bDgmh0BgkJ9GxsbhIeHY+PGjdi3bx927NiBDz74AEOGDMH06dORnp4OAJg/fz7m\nz5+v0J4P0CYi0hIiEV7ZtUb1xIJnX76ybQW8dakDERFVLpw8UrGSHiZj+vwl+KhdK6xd/Dms61pA\nJBJh98EoXLx8rdh2bm5uWLNmDSQSCa5evYrIyEisX78ejRs3Rv369QEAQUFBRR5hrFWrltrGQ0RE\n5SvvA2tkuvSr6DCIiEhFKvz8kZ9//hk9e/aEm5sbunfvjoMHD5ZY/+nTp/jiiy/QsWNHuLu7o0+f\nPoiKiiqnaKuW+L/vIEciwZihfWFjZSm7Oc6F/z9xFKSKRx537twJX19fSCQS6Ovro02bNli0aBEA\n4PHjx2jQoAFMTU3x8OFDuLq6yn5q1aqFZcuW4c6dO+U3QCKiSoB5koiINEWFHnk8duwYZs+ejU8/\n/RTe3t44ffo05s6dCwMDA3Tr1k2hvkQiwZgxY/DixQtMnToVderUwYkTJzB9+nTk5+fDz8+vXON3\nsjLFhgldyrXPt/sHctW2/EaODtAVi7Fiww4M7++PHEkuDkadxS+XrgIAXr3OUWjTunVrLF26FJMm\nTcKwYcMgFouxe/duVKtWDR07doRYLMb06dMREhICHR0d+Pj4ID09HWFhYXjx4gUaN26stvEQEVU2\nlT1PEhGRdil28jhixIgyL0wkEmH79u1K11+xYgW6d++OoKAgAAU3TcnIyMCqVauKTIrnz5/HX3/9\nhb1798puwNK2bVs8evQImzZtKtek+Do3DwZ6umhkbVZufb6pejVdQJAi/+ULtfVhZ10XofNnYN22\nSEyZtxgmHxjDtZEjflixEAEzFuD3m/FwsLeRa9OwYUNs2LABYWFhmDlzJvLz8+Hi4oItW7bA3t4e\nADBw4EAYGxtj8+bN+PHHH2FsbAxPT0/MnDkT5ubmahsPEZEqMU8SEVFVU+zk8cGDB2rtOCkpCYmJ\niZg5c6ZcedeuXREVFYWkpCTY2trKvWZkZISBAwfC1dVVrtzBwQFXr15Va7xvS0nPLtf+3uZkbQpR\n7mvkZya/8zK+CpxSap0u7b1kj+l4042zP8l+37JykdxrXl5e8PJSbPOmnj17omfPnkpGSkSkeZgn\niYioqil28nj27Fm1dnz37l0AkN1ApVDh0al79+4pJMU2bdqgTZs2cmW5ubmIiYlBw4YN1RgtERGR\nPOZJIiKqalR6w5zU1FSl6754UXC6pbGxsVy5kZERACArK0up5Xz77bf4999/MW7cOKX7JiIiqgjM\nk0REVJmV6YY5ERER+OWXX/Dy5UtIpVJZeX5+PrKzs/HPP//gjz/+UGpZgqB4p8436eiUPK8VBAHf\nfvsttm/fjoCAAHTq1EmpfomIiNSFeZKIiLSZ0pPHTZs24bvvvoO+vj6MjY2RlpYGS0tLpKen49Wr\nVzAwMMDw4cOV7rhGjRoAgOxs+WsHC/ekFr5eFIlEgsDAQBw9ehQBAQGYM2eO0v0SERGpA/MkERFp\nO6VPW92/fz8aNWqE2NhYREZGQhAE7NixA3FxcViwYAFycnLQtGlTpTsuvIYjMTFRrvz+/ftyr78t\nKysLo0aNQlRUFObNm8eESEREGoF5koiItJ3Sk8eHDx/i448/hrGxMWxtbWFiYoK4uDiIxWIMGTIE\nPXr0KNPtx+3t7WFjY4Pjx4/LlZ88eRL16tWDlZWVQpv8/HxMnDgRN27cwIoVK/Dpp58q3R8REZE6\nMU8SEZG2U/q0VV1dXdlF+kBBUktISJD93apVK6xYsaJMnU+aNAlBQUEwMTFBhw4dcObMGURFRcmW\nk5qaisTERHz44YcwNjbG7t27cfnyZQwcOBCWlpa4fv26bFkikahMe3SJiIhUiXmSiIi0ndKTxwYN\nGuDatWvo378/gILTZd686D8jIwMSiaRMnffp0wcSiQRbtmzB3r17YWtriyVLlqBHjx4AgOjoaAQF\nBWHHjh1o1aoVTpw4AQCIjIxEZGSk3LLEYjHi4+PL1D8REZGqME8SEZG2U3ry2KdPH4SEhEAikWDh\nwoXw9fXFtGnTsGbNGjg4OGD79u1wdnYucwCDBg3CoEGDiu2zT58+sr937NhR5uUTERGVB+ZJIiLS\ndkpPHgcPHozk5GTs2rULurq66NKlCzp06IA1a9YAKHgO1ezZs9UWqKaxqGkEA70yPelEpXREIkDP\nAOIPLJGfmVxhcRARUQHmSSIi0nZlmv3MmDEDU6ZMga5uQbP169cjLi4O6enpcHd3h5mZmVqC1EQG\nerowxCtIkhNKr6wGOQD0LZ2go2eA/Hdo/0VoGA6fOFdiHY+mTbBl5aJ3io+IqCpiniQiIm1W5kNn\nIpEIN27cwMOHD6Gvrw9LS8sq++BhSXICnuwYXWH91xmxBfp1G79T2/HD+6N/ry6yv79euRG6YjHm\nTgmQlRlXr/7eMRIRVTXMk0REpK3KNHk8d+4cQkJCkJKSAkEQABQkyTp16uDLL7+Er6+vWoIk1bO1\ntoSttaXsb+Pq1SEWi9G0sVMFRkVEVLkxTxIRkTZTevIYFxeHKVOmwMzMDDNmzECDBg0gCALu3r2L\nH3/8EVOnTsWOHTvQvHlzdcZL5Wz09Pmoa2GO7JcvEXvlOlo2d8OnA3ohYMYCbFv9NZq7NpLVHTF6\nLHT19LFt2zYAgFQqxcaNG7Fv3z4kJyfDxsYGAQEBsjsREhFpE+ZJIiLSdkpPHsPCwmBtbY19+/ah\nRo0acq8NGTIEffv2xbp167Bp0yaVB0kVK+rML/Dv2gGrv54HkUikdLvg4GDs378fEydORNOmTXHx\n4kXMnz8fr1+/xvDhw9UYMRFR+WOeJCIibaf05PHmzZuYNGmSQkIECu4g169fPyZELVWtmj6+mD4O\nenp6AIAr1/8opQVw79497NmzB3PmzMHo0QXXhXp7eyM/Px+rVq1Cv379YGhoqNa4iYjKE/MkERFp\nOx1VLUgkEiE3N1dViyMN4mBvI5s4KuvSpUsQBAEdO3ZEXl6e7MfX1xcvXrzAzZs31RQtEZFmYp4k\nIqLKTukjj02bNsW+ffswZMgQVH/rLpxZWVnYu3cvXF1dVR4gVbzq73CEMD09HQDQrVu3Il9/8uTJ\ne8VERKRpmCeJiEjbKT15nDx5MkaMGAE/Pz8MGzYM9erVAwDZjQBSUlIQEhKirjhJg4hQcN2jNF8q\nV/7y5Ut8YKIPALLTtsLDw2FgYKCwDBsbGzVHSURUvpgniYhI2yk9efTw8EBYWBgWLlyIpUuXym6c\nIggCzM3NsXz5crRu3VptgZLmMDIqOBKZ/PSZrCzzRRbu3L0Hd3d3AAWfFwDIyMiAp6enrN7Jkyex\nd+9ehISEoFatWuUYNRGRejFPEhGRtivTcx4/+ugjdOjQAX/++ScePHgAALC2tkaTJk2gq1umRWkF\nfUsn1BmxpUL7rwiODvawrFMb32/bjeqGBhBBhE27foKh4X9HGJ2dneHn54d58+YhKSkJjRo1wu3b\nt7FixQo0adIEVlZWFRI7EZE6MU8SEZE2K3MmE4vFcHNzg5ubmzriqTRe5+YBeoaAZbMK6b96NV1A\nkCL/ZXq59y0Wi7E85DMsXbMFcxYuh5mpCYb180fik0wkPngkqxcaGor169cjPDwcKSkpqF27Nvr1\n64epU6eWe8xEROWFeZKIiLRVqZPH+/fvy57VZ2BggMzMTHzyyScK9YKCgtCpUye1BKmJUtKzK7R/\nJ2tTiHJfIz8zWSXL27JyUZnKXZwbYseaxXJlemb1oFPN6L+/9fQwZcoUTJkyRSUxEhFpIuZJIiKq\nKkp8VMeuXbvg5+eHjRs34saNGwCA/Px8PHz4EMbGxrCysoKVlRVSU1MREhKCnJyccgmaiIhIEzBP\nEhFRVVLs5PH69etYtGgRPD09ceLECbRq1Uru9cDAQOzcuRM7d+5ESEgInj59igMHDqg9YCIiIk3A\nPElERFVNsZPHbdu2wcbGBuvXr4ednV2JC+nVqxecnJxw6tQplQdIRESkiZgniYioqil28nj16lX0\n7t0b+vr6Si2oc+fOuHXrlsoCIyIi0mTMk0REVNUUO3lMT08v8nEKhoaGGDVqFKytreXKLS0tkZWV\npfoIiYiINBDzJBERVTXF3m3VzMwM6emKj4EwMDDA3LlzFcqfPn2K2rVrqzY6DaCjo4Pc3NyKDoNQ\n8KDtvPw85OYJFR0KERHzJBERVTnFHnn88MMPER0drfSCTp8+DRcXF1XEpFEMDAyQk5ODjPS0ig6l\nShMEAfmvspCV/Qp/P63Yx6QQEQHMk0REVPUUe+SxT58+mDlzJg4dOoSPP/64xIVEREQgPj5eKx/+\nXrt2beTk5ODp0yfIFUTQFZf6aMxykZSfBUjzIH2tOUdFdV4/hkisp7Ll5eZLIXklQX5+PnJzc5H5\n8jUu/5uG/z3iaV9EVPGYJ4mIqKopdibUvXt37N+/H0FBQbh06RLGjx+PevXqydVJSkrCtm3bEBER\ngS5duqB9+/bqjrfciUQiWFtbIzkzB6eu/QMDvRIfjVlu/Fs0gM6rdLy6HVPRocgYNmwPcQ1zlS0v\nPfs1frn1AJI8AS9y8vDn4ywkpr1W2fKJiN4H8yQREVU1xU4eRSIRVq5ciQULFuDAgQM4ePAgzM3N\nYWlpCUEQ8OzZMyQnJ0MQBPTo0QOLFi0qz7jLlUgkAqoZYd/1lIoORWaMf3vopTzHk/9tq+hQZOq4\n+8DA3l5ly3t6JxnhV66obHlERKrEPElERFVNiedgGhsbY/ny5Rg6dCiOHDmCK1eu4M6dO5BKpahT\npw569+6NXr16oU2bNuUVLxERkcZgniQioqpEqQv4WrRogRYtWqglgJ9//hnr1q1DUlISrK2tMX78\nePTu3bvY+tnZ2Vi2bBlOnjyJly9fwsPDA59//rnCqUJERETlhXmSiIiqggq9gO/YsWOYPXs2vL29\nsXbtWrRs2RJz587F8ePHi20zY8YMHD9+HLNnz8aSJUuQkpKCESNG4MWLF+UYORERkfoxTxIRkSap\n0FuHrlixAt27d0dQUBAAoF27dsjIyMCqVavQrVs3hfpxcXGIiYnBpk2b4OPjAwDw8PDARx99hIiI\nCIwbN65c4yciIlIn5kkiItIkFXbkMSkpCYmJiejSpYtcedeuXXH37l0kJSUptLl48SKMjIzQtm1b\nWZmpqSk8PT1x/vx5tcdMRERUXpgniYhI04gEQRAqouOYmBiMGzcOhw4dgrOzs6w8Pj4en3zyidxe\n00LTpk1DYmIiDhw4IFf+1VdfISoqChcvXixzHM7OzhAEATVq1CixniAA0opZVUUS64gACIBUWtGh\n/EdHB4BIZYvjOlcC13nFUOF65zpXkpLrvEaNGjh37pz64ykHmp4nVf3ZVdtnrwz/r+r4f1TLuMr4\nHVQp3iuOqXKMCajy/1OVfUzvmycr7LTVwmsvjI2N5cqNjIwAAFlZig+Cz8rKUqhf2Kao+srQ0dGB\nVIk3SiQCxCLVbaSrhgjQEVd0EGrDdV7+uM7LH9c5FUfT86R6PrsV+9lT3/+jNo6LY1I1jqlMS66w\ncWnjmMqiwiaPpR3w1NFRPKO2pDZF1VdGfHz8O7UjIiJSJ+ZJIiLSNBV2zWPh6S/Z2dly5YV7Ros6\njdTY2FihfuEyitrTSkREVFkxTxIRkaapsMlj/fr1AQCJiYly5ffv35d7/e02SUlJCntW79+/X2R9\nIiKiyop5koiINE2FTR7t7e1hY2Oj8KyqkydPol69erCyslJo4+3tjczMTMTGxsrKUlNTERcXBy8v\nL7XHTEREVF6YJ4mISNOIg4ODgyuq8xo1amDdunVIS0uDSCTCli1bcPDgQXz55Zdo2LAhUlNTkZCQ\nAGNjY+jr68Pa2hqXL1/Gjz/+iJo1a+LRo0eYN28eBEHAN998AwMDg4oaChERkcoxTxIRkSapsEd1\nFNq9eze2bNmCx48fw9bWFuPGjUPv3r0BAPv370dQUBB27NiBVq1aAQAyMjIQGhqK06dPQyqVokWL\nFggMDISDg0NFAQOA1AAAHIVJREFUDoOIiEgtmCeJiEhTVPjkkYiIiIiIiDRfhV3zSERERERERJUH\nJ49ERERERERUKk4eiYiIiIiIqFScPBIREREREVGpOHkkIiIiIiKiUnHySERERERERKXi5FEDSaVS\nREREwN/fH+7u7ujUqRMWL16MrKysYtvk5eVh5cqVaN++PZo2bYohQ4bg5s2b5Rh15ScIArZt24au\nXbvCzc0NvXr1wpEjR0psk52djZCQELRt2xbu7u4YO3Ys/v333/IJWMtMnjwZnTt3LrEO1/f7y8vL\ng5ubG5ycnOR+3N3di23D9U6qdO/ePUyYMAGenp5o1aoV5s6di2fPnpXa7sKFC+jbty+aNm0KX19f\nbNmypRyiVc79+/cxbdo0eHt7o0WLFhg8eDAuXbpUarv//e9/GD58ONzd3eHt7Y3ly5cjNze3HCIu\nm6lTp+Lzzz9Xqq6mvk8///wzevbsCTc3N3Tv3h0HDx4ssX5l+967desWmjRpguTk5BLrafq4tHEb\nWOu2LwXSOBs2bBAaNWokLFu2TLh48aIQHh4utGzZUhg9enSxbYKDg4WmTZsKO3fuFM6cOSMMGzZM\ncHd3FxITE8sx8spt3bp1QqNGjYTvv/9eiI2NFUJDQwVHR0fh6NGjxbYZO3as0Lp1a2H//v3CiRMn\nBH9/f6Fdu3ZCZmZmOUZe+R08eFBwdHQUOnXqVGI9ru/39/fffwuOjo7CgQMHhGvXrsl+bty4UWwb\nrndSlefPnwvt2rUT+vbtK5w5c0aIiooSunTpIvj5+Ql5eXnFtrt69arQpEkTYfbs2UJMTIywfPly\nwcnJSdi8eXM5Rl+0tLQ0oV27doKfn59w9OhRISYmRpg0aZLg7OwsXLlypdh2//77r9C8eXMhICBA\niI6OFn744QfBxcVFCAkJKcfoSyaVSoUlS5YIjo6Owrx580qtr6nv09GjRwUnJyfhm2++Ec6fPy8s\nWLBAcHR0FKKiooptU5m+9/755x+hXbt2gqOjo/D48eMS62r6uLRxG1jbti85edQwUqlU8PT0FIKD\ng+XKjx49Kjg6Ogrx8fEKbZKSkoRGjRoJP/74o6wsJydH6NChg7BgwQK1x6wNJBKJ4OnpKSxcuFCu\nfNiwYcLgwYOLbHPlyhXB0dFRiImJkZU9f/5caNasmbBhwwa1xqtNkpOTBU9PT8HHx6fEySPXt2oc\nPnxYcHZ2Fl6+fKlUfa53UqXNmzcLjRs3Fp4+fSoru3TpkuDo6CjExsYW2+7TTz8V+vfvL1e2dOlS\nwcPDQ8jJyVFbvMrYunWr0KRJEyE5OVlWlpeXJ/Ts2VOYOHFise3mzZsntG/fXi7+Xbt2CY0aNZJb\nVkX5559/hFGjRgmurq6Cm5ubUpNHTX2fOnXqJEyfPl2ubNq0aUK3bt2KrF9Zvvdyc3OF8PBwwd3d\nXWjZsmWpk0dNH5c2bgNr4/YlT1vVMNnZ2ejVqxf8/Pzkyh0cHAAAiYmJCm0uXbqE/Px8dO3aVVam\nr6+PDh064Pz58+oNWEuIxWLs3LkT48aNkyvX09NDTk5OkW0uXrwIIyMjtG3bVlZmamoKT09Prvcy\n+OKLL9C2bVu0adOmxHpc36px69Yt2NnZwdDQUKn6XO+kSn369EFERARq164tK9PT0wOAYr9rc3Jy\nEBcXhy5dusiVd+3aFZmZmfj999/VF7ASLC0tMXLkSFhYWMjKxGIx7O3tkZSUVGy7ixcvomPHjtDX\n15eVdevWDfn5+bhw4YJaY1ZGcHAwsrKyEBkZCTMzs1Lra+r7lJSUhMTExCLjunv3bpHvUWX53rt6\n9SqWLVuG0aNHY/bs2aXW1/RxaeM2sDZuX3LyqGGMjY3xxRdfoEWLFnLlp0+fBgB8+OGHCm3u3r0L\nExMTmJqaypXb29vj0aNHeP36tfoC1hI6OjpwcnKChYUFBEHAs2fPsHHjRsTGxmLgwIFFtrl79y7s\n7e0hFovlyu3s7HDv3r3yCLvS27t3L/7880/Mnz+/1Lpc36qRkJAAfX19BAQEwN3dHZ6enliwYEGx\n15NwvZMq1apVC25ubgAKJhvXr1/HwoULYWdnV+wOpKSkJOTm5qJ+/fpy5fb29gBQ4Z/Dbt26KWy4\nZ2Rk4MqVK0XmbAB49eoVHj9+rDAmU1NTGBsbV/iYAGD+/PnYs2cPGjVqpFR9TX2f7t69CwBliquy\nfO81aNAAp0+fxuTJkxViLYqmj0sbt4G1cftSt6IDoNLduHEDGzduRKdOndCgQQOF17OysmBsbKxQ\nbmRkBKBgT46BgYHa49QWJ0+exNSpUwEAHTp0QK9evYqsV9J6L+nCbirw8OFDLF68GIsXL1b40i8K\n17dq/PXXX8jKykL//v0xYcIE/PHHHwgLC8O9e/ewY8cOiEQiufpc76Qu/fr1w99//w0DAwOsXbsW\n1apVK7LeixcvAEDhc1iY4zTtcyiVSjF//nxkZ2cjICCgyDrFjQnQnP8tR0fHMtXX1PfpXeKqLN97\nbx7BV0ZlGdebtGkbWFu2Lzl51HBXr17FhAkTYGNjg6+++qrIOoIglLiMtzcGqWSNGzdGeHg4EhIS\nsGrVKowbNw7bt29XWI8lrXcdHR7UL4kgCJg3bx7at28vd6pJaW2Kw/WtvBUrVsDExAROTk4AAE9P\nT5iZmeGzzz5DbGys3GkyANc7vTupVAqpVCpXpqv732bHvHnzkJ+fj/DwcEyYMAEbN26El5eXwnJK\ny3Hl+TksbUy5ubkIDAzEiRMnsGDBAri4uBS5nMo0JmVp0pje9C5xaev3XmUbl7ZtA2vL9iUnjxrs\n2LFjCAwMRL169bB582bUqlWryHrGxsbIzs5WKC/cO1HU3gsqnq2tLWxtbeHp6QljY2PMnTsX165d\nQ/PmzeXqGRsb48GDBwrts7Ozuc5LsWvXLiQkJODIkSPIy8sD8N+XZV5eHsRiscKXKde3arRs2VKh\nrEOHDgAKjkq+PXnkeqd3tXbtWqxZs0auLCEhQfZ74WmqrVu3Rs+ePbF58+YiJ481atQAAIU8V5jj\nCl8vDyWNKTMzE5MnT8aVK1cwf/58DB06tNjlFP7vFJe7NWVMZaFJ79Ob3iUubf3eq0zj0sZtYG3Z\nvuTkUUNt3boVS5YsQcuWLbF27doSv3QdHByQnp6OjIwMmJiYyMrv378PGxsbuYvxqWjp6emIjo5G\nmzZt5G560LhxYwDAkydPFNrUr18fv/76KwRBkJvo3L9/X+HaCpJ34sQJpKWlwdvbW+G1Jk2aYPHi\nxejTp49cOdf3+3v+/DnOnj2L1q1bw9bWVlZeeE1IUcmZ653e1YABA2Q7JgrFxcUhMzMTvr6+sjJd\nXV04OTnJrk17m52dHcRiscLNMgr/Ls/PYVFjAoCUlBSMHj0aiYmJWL58Obp3717icoyMjGBhYYH7\n9+/LlT9//hzZ2dkaMaay0qT36U2F/SYmJsrOuAAgW/dFxaWt33uVZVzatA2sjduXFX/skxTs3bsX\noaGh6N69OzZv3lzq3rrCPbUnTpyQlUkkEkRHRxe5F5cUSaVSBAYGIjIyUq784sWLAIq+9sPb2xuZ\nmZmIjY2VlaWmpiIuLo7rvRQhISHYt2+f3E/Hjh1haWkp+/1tXN/vTyQSYcGCBQgPD5crP3bsGMRi\nscJNCgCud3p3FhYWcHV1lfuJiorCZ599hszMTFm9rKwsXLt2rdhr7KpVqwYPDw+cPHlS7nSuEydO\noEaNGsWeGqoORY0pOzsbo0aNQnJyMrZu3VrqxLFQ27Ztce7cOUgkElnZiRMnIBaLizxDQF2KGtO7\n0KT36U329vawsbHB8ePH5cpPnjyJevXqwcrKSqGNtn7vVYZxads2sDZuX4qDg4ODKzoI+s/z588x\nZswYWFhYYNasWXj+/DmSk5NlP/r6+sjPz0d8fDz09fVhaGiIDz74AA8fPsQPP/wAQ0NDpKWlYeHC\nhUhKSsLSpUtRs2bNih6WxjM0NERqaip27NgBXV1dSCQSHDp0CGvWrEGfPn3Qt29fpKamIiEhAcbG\nxtDX14e1tTUuX76MH3/8ETVr1sSjR48wb948CIKAb775RmMu0NZEtWrVgoWFhdzPhQsX8OTJE8ye\nPVv2fnB9q5ahoSHS09Oxa9cu2XVOhw4dwurVqzFkyBD4+/tzvZNa1a9fH3v27EFsbCzMzMxw+/Zt\nfPnll0hJScG3334ru3lWfHw8MjIyZH9bWlpi/fr1uHPnDgwNDXHw4EFs2rQJU6ZMQatWrSpySFix\nYgXOnj2L8ePHo169enI5Oy0tDebm5gCAf/75B8nJybK/69evjy1btiAuLg4mJiaIjo7Gt99+i/79\n+8Pf378ih6Rg+/btsLW1xUcffSRXXlnepxo1amDdunVIS0uDSCTCli1bcPDgQXz55Zdo2LChVnzv\n3bp1C2fOnMGoUaNkpzZWtnFp4zawVm5flsfDJEl5Bw4cEBwdHYv9OXjwoOyByj/99JOsXU5OjvD1\n118Lbdq0EZo2bSoMGTJEuH79egWOpPKRSCTCxo0bhS5dugguLi5Cp06dhI0bNwr5+fmCIAjCTz/9\nJDg6OgqXLl2StUlPTxcCAwMFDw8PoXnz5sLYsWOFO3fuVNQQKrW5c+cKnTp1kv3N9a0ehZ/zrl27\nCi4uLsJHH30kbNiwgZ9zKjd//fWXMHbsWMHT01Nwd3cXxo8fLyQkJMjV6dixozBs2DC5spMnTwp+\nfn5CkyZNBF9fX+GHH34oz7CL5evrW2zOfvM7bdiwYULHjh3l2l65ckXo37+/4OLiIrRr10747rvv\nBIlEUt5DKFXHjh2FefPmFVleWd6niIgIoXPnzoKLi4vQvXt34cCBA7LXtOF7r3AMjx8/ViirLOPS\n1m1gbdu+FAlCKbcpIiIiIiIioiqP1zwSERERERFRqTh5JCIiIiIiolJx8khERERERESl4uSRiIiI\niIiISsXJIxEREREREZWKk0ciIiIiIiIqFSePVGWEhYXBycmpxJ9bt24BAIYPHw5fX98y95GVlYXU\n1FRVh/5O9u/frzC+Jk2aoHXr1ggICEB0dLRCm8J19ODBA1lZbGwsunfvDhcXFwwZMqTIssK+fvvt\nN7WN4+1lJyUlqbwvIiJVY+5h7inK2+vI2dkZ7u7u8Pf3x9q1a/H69Wu5+g8ePICTkxPCwsJkZRkZ\nGZg4cSKaNWsGT09PxMfHF1lGpEq6FR0AUXmbMGECHBwcinzNyspKVufVq1dlWu4ff/yBiRMnYtmy\nZWjVqtV7x6kqAwcORIsWLQAAEokET548wdGjRzF+/HjMmjUL48aNk9Xt3Lkz7OzsYGpqCgCQSqWY\nNWsWxGIxgoKCYGlpWWSZo6Mjli5digYNGpTLmAICAmBubo7Q0NBy6Y+I6H0x9zD3vM3BwQETJkwA\nAAiCgMzMTMTFxWH16tU4c+YMwsPDUb16dQCAqakpli5dCicnJ1n79evX4+zZsxg5ciQcHBxQr149\nhIWFKZQRqRInj1TleHl5lZpg27ZtW+bl/v3333jy5Mm7hqU2zZo1w8cffyxXNmbMGIwdOxbLly9H\ny5Yt0axZMwCAs7MznJ2dZfWePn2K1NRUjBo1CkOHDgUApKSkKJQBgK2tbTmMpsCFCxfwySeflFt/\nRETvi7mHuedttWvXVlhHI0aMQGRkJBYsWIClS5ciODgYAFC9enWFugkJCahZsyaCgoJKLCNSJZ62\nSlQFVatWDd988w10dHSwefPmYuvl5uYCAIyMjEosIyIiKg1zj3IGDhwILy8v/PTTTyWejpybm6uw\nPooqI1IlTh6JivDmdScJCQlwcXHBgAEDIAiCrM769evh5OSE3bt3IywsTLaXb8SIEbK2gYGBcqeY\nFHq7PDAwEN26dcOuXbvg6ekJT09PnD9/HgCQnJyMOXPmoHXr1nB1dUXv3r1x+PDh9x6jjY0Nmjdv\njtjYWOTn5wOQv+4kLCwMH330EQBgzZo1cHJywvDhwxXKfvvttyKvDZFIJAgLC0OXLl3g5uaGrl27\nYuPGjbK+iruepKRrWAqv+QCAAwcOwMnJCb/++it8fHzQr18/hfrnz5+Hk5NTkdfYEBFpGuYe5p5C\nfn5+kEgkuHTpklwMYWFhst8vX76Mhw8fwsnJSfbevl325vh69+4NV1dXtG7dGoGBgXJHrAuXuW3b\nNgwePBguLi4YOXJkmdsfPHgQK1asgI+PD1xdXdG/f3/ZGN506NAh9O3bF82aNYOPjw8WLFigMFEu\nrU+qGDxtlaqcFy9eFLknr0aNGtDT01Mod3Jywvjx47FmzRpERkZi0KBB+Pvvv7FmzRr4+Phg0KBB\n+Ouvv/D06VNERkZiwoQJcHV1LXNcjx8/xrp16zB58mQ8efIEzZo1Q0pKCvr37w9BEDB8+HCYmJjg\nzJkz+Oyzz/DkyROMGTPmndZBoYYNG+LKlStISkpSuC6ic+fOqFGjBhYvXozOnTujc+fOqFu3LuLj\n4+XKGjRogIcPHyose9KkSTh//jz8/f0xatQo3Lx5E9999x2eP3/+zqfTFF7zMWfOHHh4eGDAgAFo\n2LAhevToga1bt+LBgwewsbGR1T969Chq1qz5TqeCERGpEnPPf5h7StewYUMABTsRevToUWQ869ev\nR1paGoKCgmBjY4M2bdrIldnZ2QEomHCHhYWha9euGDBgAFJSUhAeHo7Lly9j3759smtNAWDVqlXw\n9fWFv78/qlWr9k7tDQ0NMXr0aOTm5mLLli0YP348oqOjUatWLQDApk2bsGzZMrRo0QIzZ87E8+fP\nsX37dty6dQsRERHQ1dUtU59Uvjh5pCpn0qRJRZbv2LGj2OtRJkyYgFOnTmH58uXo1KkTgoKCYGRk\nhK+//hpAwfUazZo1Q2RkpFLXtRTl9evXWLx4sVyS+OabbyCRSHDkyBHUqVMHADB06FDMnj0bq1at\nwieffAIzM7My91Xogw8+AACkp6crvObs7AxjY2MsXrwYTk5OsmstrKysFMreFhMTg/Pnz2PGjBmy\nmwEMHjwYubm52LVrV7HvQWkKr/mYM2cObG1tZf37+/tj69atiIqKwtixYwEU7H0+ffo0/Pz8itww\nIyIqT8w9/2HuKZ2JiQkAIC0trdh49u3bh5ycHFk8LVq0UChLSkrC2rVrMW7cOMyaNUu2jJ49e6JP\nnz5Yv3495s2bJyuvW7culi1bBpFI9E7tBUHAvn37ZDf6sba2xowZM3Dq1CkMGDAAGRkZCAsLQ7t2\n7bBhwwaIxWIABUekv/jiC1y8eBEODg5l6pPKFyePVOXMnTtX7sL8QkWVFdLT08M333yDgQMHYtCg\nQUhKSsKKFStkSVVVPDw8ZL9LpVKcPn0arVq1gq6urtwe6y5duuDnn3/GxYsX0atXr3fuLy8vDwBk\nSUJVoqOjoaOjg2HDhsmVz507FxMnTlT59RhNmjSBg4ODXAKPiYlBVlYW/Pz8VNoXEdG7YO75D3NP\n6VS1jk6dOgWpVApfX1+597J27dpo1KgRoqOj5SZiHh4ecn2WtX379u1lE0fgv8/306dPARQ8biUn\nJwdDhw6VTRwBoFevXmjcuDEcHBwQERFRpj6pfHHySFVOkyZN3mnvrIuLC4YPH46tW7fCx8dH4TQS\nVXhzT25aWhpevHiB06dP4/Tp00XWf/z48Xv1V7jXV9Wnfzx8+BBmZmYwNjaWKzc3N4e5ublK+yrk\n5+eH1atXIykpCba2tjh69Cjq1q0rt1FERFRRmHv+w9xTusIjju+7jhITEwEAgwYNKvL1t4+Ovt3f\n+7bX19cHULBTAoDsVGN7e3u5etWqVUOTJk3eqU8qX5w8EilJKpXi2rVrAIDr168jJSUFFhYW77Ss\nwgv33/bmXrjCOl27di32C/R9b1F+69YtGBsby12roQrFjU+dbf39/bF69WpERUVh2LBhiI6OxuDB\ng1W+Z5uIqDwx9yhPm3LPrVu3AJR8ZFoZhZO2devWwcDAoNT6b34W3qW9jk7J9+IsXF5J66esfVL5\n4uSRSEnbt2/H9evXMXPmTKxduxbBwcFYt25diW0Kv0QlEols7xsAPHv2rNT+TE1NYWhoiLy8PHh5\necm99ujRI8THx8PQ0PAdRlIgMTER8fHx8PPzU/kEy8rKCrGxscjOzpY7TejPP//Eli1bMHHiRLl1\n8yZl1k1R7Ozs4ObmhrNnz8LBwQGvXr2Cv7//uw+CiEgDMPcoT5tyz/Hjx1GtWrX3vuGbtbU1gIJr\nGRs1aiT3WkxMjMJRWlW3f1vdunUBFHwO6tevLyuXSCT47LPP4O/vr/I+SbX4qA4iJSQmJmLVqlXw\n9vbG+PHjMWHCBJw9exZHjhyR1SlMSIV7zADITpMp3IMIFNz+vHAvckl0dXXh4+ODmJgY/PXXX3Kv\nhYaGYtKkSUVeSK+M3NxchISEQCQSYfTo0e+0jJK0b98eUqkUe/fulSuPiIhAVFQUateuXeS6ycvL\nw8mTJ0tdvo6Ojtx6LuTv74+bN2/i8OHDcHBwQOPGjd9zJEREFYe5p2y0JfccPHgQcXFxGDx48HtP\nlDp27AgA2LBhg9wjX27duoWJEydi+/btam3/Ni8vL+jp6WHPnj1yyzt+/DiOHz+ulj5JtXjkkagU\ngiDg888/hyAICA4OBgCMGTMGhw8fxldffQUvLy+YmZnJzvOPiIjAs2fP4O/vj+7du2PDhg2YMWMG\nRo4ciZycHOzatQsWFhb4999/S+179uzZ+O233zB06FAMHToUVlZWiI6Oxrlz5zBw4EDZrbxLcv36\nddlpKHl5eXj8+DGioqLwzz//ICgoSC0TLF9fX3h7eyM0NBS3b9+Gq6srrl27hoMHD2LSpEmoWbMm\nWrZsCXNzc3z//ffIycmBmZkZDh06hJcvX5a6fFNTU1y+fBl79uyBt7c3rKysAAA9evRAaGgoTpw4\ngSlTpqh8XERE5YW5p+wqW+559uwZDh06BKDg/c7MzMSVK1dw6tQpuLu7Y8aMGe+2It7g6OiI4cOH\nY+fOnUhPT0enTp2Qnp6O8PBwGBkZYdq0aWpt/zYzMzNMmjQJK1euxOjRo9GpUyckJycjPDwcrVq1\ngq+vL3R0dFTaJ6kWJ49Epdi9ezcuX76M6dOny67z0NfXx5dffomRI0ciJCQEq1evRps2bdC9e3ec\nO3cOly5dQpcuXeDs7IyVK1di7dq1WLp0KerWrYuxY8fi9evXWLp0aal929nZYc+ePVi9ejX27NmD\nly9fwtbWFkFBQRg+fLhS8UdGRiIyMhJAwbUMtWrVgqurK+bNm6e25x/q6Ojg+++/x9q1a3HkyBEc\nPnwYdnZ2WLBgAQYPHgyg4IL3zZs3IzQ0FJs3b0b16tXh5+eHLl26KNwp722zZ8/Gd999h0WLFmHR\nokXo3bs3gII7sbVp0wYXLlzgXVaJqFJj7im7ypZ77t69izlz5sj+rl69OhwcHDBr1iyMGDFC9pzF\n9/X555/DwcEBu3fvxpIlS1CjRg14eHhg2rRpaNCggdrbv23ixIkwNzfHjh07EBoaCnNzcwwYMABT\npkyRHUlXdZ+kOiLhzePBRESV3JgxY5CRkaFw2hIREZG6MPdQVcFrHolIa9y/fx+//vor+vTpU9Gh\nEBFRFcHcQ1UJjzwSUaV3/vx5HDhwAFeuXAEAnDx5Uu4hxURERKrG3ENVEY88ElGlZ2hoiF9++QVG\nRkZYvXo1kzcREakdcw9VRTzySERERERERKXikUciIiIiIiIqFSePREREREREVCpOHomIiIiIiKhU\nnDwSERERERFRqTh5JCIiIiIiolL9PyykV/XU9wmtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'goals_scored'\n",
    "f,ax=plt.subplots(1,2,figsize=(13,4))\n",
    "g=sns.factorplot(x='opponent_ranking', y=y, hue='was_home',data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[0])\n",
    "ax[0].set(ylim=[0,1.],xlabel='Fixture Difficulty',ylabel='Goal Score Probability')\n",
    "plt.close(g.fig)\n",
    "sns.despine()\n",
    "g=sns.factorplot(x='team_ranking_diff', y=y, hue='was_home', data=fixture_df,ci=95,kind='bar',aspect=1.5,ax=ax[1])\n",
    "ax[1].set(ylim=[0,1.],xlabel='Fixture Difficulty Difference',ylabel='Goal Score Probability')\n",
    "plt.close(g.fig)\n",
    "plt.legend([])\n",
    "sns.despine()\n",
    "plt.suptitle('Goal Score Probability by Fixture Difficulty',y=1.1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T23:16:54.041020Z",
     "start_time": "2018-12-31T23:16:54.022353Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold, RepeatedKFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    import itertools\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),size=18,\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:27:45.196905Z",
     "start_time": "2019-01-01T16:27:44.339891Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "kf = RepeatedKFold(n_splits=5, n_repeats = 20)\n",
    "X = fixture_df.dropna()[['prev_team_ranking','opponent_ranking','was_home']].as_matrix()\n",
    "y = fixture_df.dropna()[['clean_sheets']].as_matrix().ravel()\n",
    "\n",
    "scores, cnf_matrices = [],[]\n",
    "for train_index, test_index in kf.split(X):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    clf = LogisticRegression(solver='lbfgs',class_weight='balanced')\n",
    "    clf.fit(X_train,y_train)\n",
    "    score = clf.score(X_test,y_test)\n",
    "    scores.append(score)\n",
    "    cnf_matrix = confusion_matrix(y_test, clf.predict(X_test))\n",
    "    cnf_matrices.append(cnf_matrix)\n",
    "\n",
    "f,axes = plt.subplots(1,2, figsize=(10,5))\n",
    "sns.stripplot(scores,orient='v',color='k',ax=axes[0])\n",
    "sns.barplot(scores,orient='v',ax=axes[0])\n",
    "average_score = np.round(np.mean(scores),2)\n",
    "axes[0].set(title=f'Clean Sheet Prediction\\nAverage Accuracy {average_score}', ylabel='Accuracy',ylim=[.5,.8])\n",
    "plot_confusion_matrix(np.array(cnf_matrices).sum(axis=0),classes=clf.classes_,normalize=True,cmap=plt.cm.RdBu,title='Clean Sheet Prediction\\nConfusion Matrix')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# player df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T23:25:42.709348Z",
     "start_time": "2018-12-31T23:25:42.676861Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def plot_prediction_results(X,y, cols2use=cols2use,y_col=y_col):\n",
    "    kf = RepeatedKFold(n_splits=5, n_repeats = 20)\n",
    "    scores, cnf_matrices, coefs = [], [],[]\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "        scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "        clf = LogisticRegression(solver='lbfgs',class_weight='balanced')\n",
    "        clf.fit(scaler.transform(X_train),y_train)\n",
    "        score = clf.score(scaler.transform(X_test),y_test)\n",
    "        scores.append(score)\n",
    "        cnf_matrix = confusion_matrix(y_test, clf.predict(scaler.transform(X_test)))\n",
    "        cnf_matrices.append(cnf_matrix)\n",
    "        coefs.append(clf.coef_[0])\n",
    "    f,axes = plt.subplots(1,3, figsize=(20,5))\n",
    "    coefs_df = pd.DataFrame(np.array(coefs), columns = cols2use)\n",
    "    coefs_df = coefs_df.melt(var_name='Variable', value_name='Beta')\n",
    "    coefs_df = coefs_df.replace({'team_ranking_diff':'Fixture Difficulty\\nDifference','form6': \"Form\"})\n",
    "    sns.barplot(x ='Variable',y='Beta',data=coefs_df, ax = axes[0])\n",
    "    axes[0].set(title=f\"Coefficient Weights\")\n",
    "\n",
    "    sns.stripplot(scores,orient='v',color='k',ax=axes[1])\n",
    "    sns.barplot(scores,orient='v',ax=axes[1])\n",
    "    average_score = np.round(np.mean(scores),2)\n",
    "    axes[1].set(title=f\"Prediction Average Accuracy {average_score}\", ylabel='Accuracy',ylim=[.5,1])\n",
    "\n",
    "    plot_confusion_matrix(np.array(cnf_matrices).sum(axis=0),classes=clf.classes_,normalize=True,cmap=plt.cm.RdBu,title=f\" Prediction Confusion Matrix\")\n",
    "    plt.suptitle(f\"{y_col}\",y=1.02)\n",
    "    plt.tight_layout()\n",
    "    sns.despine()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T21:53:12.970644Z",
     "start_time": "2018-12-31T21:53:12.554458Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "player_df = pd.DataFrame()\n",
    "for season in ['2016-17', '2017-18']:\n",
    "    player_df = pd.concat([player_df, pd.read_csv(f'player_{season}.csv')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T23:22:19.552754Z",
     "start_time": "2018-12-31T23:22:19.549546Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_context('talk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T06:42:51.703616Z",
     "start_time": "2019-01-01T06:42:50.482379Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.58026883 0.41973117]\n",
      " [0.41673729 0.58326271]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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wwY2PMfar8Sy/ZD/22WZdttlgZdbc/URGjh7XYp+tM9n6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\nyjnezMzMzMzMzMxap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      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queried_df = player_df.query('round!=1 and minutes>=60 and (position==\"DF\" or position==\"GK\")')\n",
    "cols2use = ['team_ranking_diff','form6']\n",
    "y_col = 'clean_sheets'\n",
    "X = queried_df.dropna()[cols2use].as_matrix()\n",
    "y = queried_df.dropna()[[y_col]].as_matrix().ravel()\n",
    "plot_prediction_results(X,y, cols2use=cols2use,y_col=y_col)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-31T23:14:17.261798Z",
     "start_time": "2018-12-31T23:14:15.870013Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x12e2aadd8>"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5rq1rOcbxqKr6D/0B8iuTvAe4trFwrTFztSkHW5VLy2gH8NP99HF0t804JclXgW9U1T17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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 405.305x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 405.305x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queried_df = player_df.query('round!=1 and minutes>=60 and (position==\"MF\" or position==\"FW\")')\n",
    "queried_df['attacking_points'] = queried_df[['assists','goals_scored']].sum(axis=1)>0\n",
    "sns.factorplot(x='team_ranking_diff',y='attacking_points',hue='position',data = queried_df,kind='bar',ci=95)\n",
    "sns.factorplot(x='team_ranking_diff',y='total_points',hue='position',data = queried_df,kind='bar',ci=95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:29:07.029143Z",
     "start_time": "2019-01-01T16:29:05.618566Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.64489367 0.35510633]\n",
      " [0.44653342 0.55346658]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ACAsLQ0JCgsrHtrCwYEKYiIiIiD5prCFMREREREREREREVE6whjARERERERERERFROcGE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seS/ct32JLjr30AKZSMHCtrfj3LvVV8aRRQhhyrjROX0vgx9hJ55YMrebvR7h4/NkdfsAeETySk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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('talk')\n",
    "queried_df = player_df.query('round!=1 and minutes>=60 and (position==\"MF\" or position==\"FW\")')\n",
    "queried_df['attacking_points'] = queried_df[['assists','goals_scored']].sum(axis=1)>0\n",
    "cols2use = ['team_ranking_diff','form6']\n",
    "y_col = 'attacking_points'\n",
    "X = queried_df.dropna()[cols2use].as_matrix()\n",
    "y = queried_df.dropna()[[y_col]].as_matrix().ravel()\n",
    "plot_prediction_results(X,y, cols2use=cols2use,y_col=y_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:30:09.217052Z",
     "start_time": "2019-01-01T16:30:07.617497Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/Users/jinhyuncheong/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.84078882 0.15921118]\n",
      " [0.20387258 0.79612742]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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jsX37dkgkEvHJpr1796J///4wMDBQ2OfFixe4c+cOfv31VyQnJ6v0d09EHxeO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\nkS19AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queried_df = player_df.query('round!=1 and minutes>=60 and (position==\"MF\" or position==\"FW\")')\n",
    "queried_df['attacking_points'] = queried_df[['assists','goals_scored']].sum(axis=1)>0\n",
    "cols2use = ['team_ranking_diff','form6','ict_index']\n",
    "y_col = 'attacking_points'\n",
    "X = queried_df.dropna()[cols2use].as_matrix()\n",
    "y = queried_df.dropna()[[y_col]].as_matrix().ravel()\n",
    "plot_prediction_results(X,y, cols2use=cols2use,y_col=y_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-01T16:30:54.882228Z",
     "start_time": "2019-01-01T16:30:54.615492Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x13509ddd8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1342306d8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x133736198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1335e37f0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jk4RKpdKq5+3tLV544QVN2ZQpU4RCoRDffPONpmzXrl1CoVCIiRMnaspUKpXw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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "queried_df.dropna()[cols2use].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py36]",
   "language": "python",
   "name": "conda-env-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "nav_menu": {
    "height": "104px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}