{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected Goals Model\n",
    "\n",
    "Load in pickled preprocessed data, separate into train and test split and train Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:17:37.364283Z",
     "start_time": "2021-01-03T11:17:34.480145Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import FCPython\n",
    "\n",
    "import pickle\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import Metrica_Functions_TLMAnalytics as mfun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess StatsBomb Event Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:18:25.439347Z",
     "start_time": "2021-01-03T11:18:24.009193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[16, 43, 11, 2]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get competition ids for relevant competitions\n",
    "comps = pd.read_json('open-data-master/data/competitions.json')\n",
    "male_comps = comps[comps['competition_gender'] == 'male']\n",
    "male_comps_id = list(male_comps['competition_id'].unique())\n",
    "male_comps_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:18:30.120116Z",
     "start_time": "2021-01-03T11:18:29.265432Z"
    }
   },
   "outputs": [],
   "source": [
    "# For those competitions, identify the match ids\n",
    "male_df_list = []\n",
    "for comp in male_comps_id:\n",
    "    json_files = [x for x in os.listdir('open-data-master/data/matches/' + str(comp)) if 'json' in x]\n",
    "    for event_json in json_files:\n",
    "        male_df = pd.read_json('open-data-master/data/matches/' + str(comp) + '/' + event_json)\n",
    "        male_df_list.append(male_df)\n",
    "\n",
    "male_df = pd.concat(male_df_list)\n",
    "male_matches = list(male_df['match_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:21:42.967968Z",
     "start_time": "2021-01-03T11:18:41.920269Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# For those matches, load the events and append to single dataframe\n",
    "json_files = [int(x.split('.')[0]) for x in os.listdir('open-data-master/data/events') if 'json' in x]\n",
    "male_matches = [x for x in json_files if x  in male_matches]\n",
    "df_list = []\n",
    "count = 1\n",
    "for event_json in male_matches:\n",
    "    print('Loading: {}. {} / {}'.format(event_json, count, len(male_matches)))\n",
    "    df = pd.read_json('open-data-master/data/events/' + str(event_json) + '.json')\n",
    "    df['match_id'] = event_json\n",
    "    df_list.append(df)\n",
    "    count +=1\n",
    "    \n",
    "df = pd.concat(df_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Shot Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:21:51.925176Z",
     "start_time": "2021-01-03T11:21:42.970933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>index</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>minute</th>\n",
       "      <th>second</th>\n",
       "      <th>type</th>\n",
       "      <th>possession</th>\n",
       "      <th>possession_team</th>\n",
       "      <th>play_pattern</th>\n",
       "      <th>...</th>\n",
       "      <th>match_id</th>\n",
       "      <th>clearance</th>\n",
       "      <th>off_camera</th>\n",
       "      <th>miscontrol</th>\n",
       "      <th>50_50</th>\n",
       "      <th>out</th>\n",
       "      <th>injury_stoppage</th>\n",
       "      <th>half_start</th>\n",
       "      <th>player_off</th>\n",
       "      <th>half_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65f16e50-7c5d-4293-b2fc-d20887a772f9</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-03 00:02:29.094</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>{'id': 16, 'name': 'Shot'}</td>\n",
       "      <td>6</td>\n",
       "      <td>{'id': 217, 'name': 'Barcelona'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>...</td>\n",
       "      <td>15946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b0f73423-3990-45ae-9dda-3512c2d1aff3</td>\n",
       "      <td>283</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-03 00:05:39.239</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>{'id': 16, 'name': 'Shot'}</td>\n",
       "      <td>11</td>\n",
       "      <td>{'id': 217, 'name': 'Barcelona'}</td>\n",
       "      <td>{'id': 1, 'name': 'Regular Play'}</td>\n",
       "      <td>...</td>\n",
       "      <td>15946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13b1ddab-d22e-43d9-bfe4-12632fea1a27</td>\n",
       "      <td>755</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-03 00:15:28.625</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>{'id': 16, 'name': 'Shot'}</td>\n",
       "      <td>26</td>\n",
       "      <td>{'id': 217, 'name': 'Barcelona'}</td>\n",
       "      <td>{'id': 8, 'name': 'From Keeper'}</td>\n",
       "      <td>...</td>\n",
       "      <td>15946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id  index  period  \\\n",
       "0  65f16e50-7c5d-4293-b2fc-d20887a772f9    148       1   \n",
       "1  b0f73423-3990-45ae-9dda-3512c2d1aff3    283       1   \n",
       "2  13b1ddab-d22e-43d9-bfe4-12632fea1a27    755       1   \n",
       "\n",
       "                timestamp  minute  second                        type  \\\n",
       "0 2021-01-03 00:02:29.094       2      29  {'id': 16, 'name': 'Shot'}   \n",
       "1 2021-01-03 00:05:39.239       5      39  {'id': 16, 'name': 'Shot'}   \n",
       "2 2021-01-03 00:15:28.625      15      28  {'id': 16, 'name': 'Shot'}   \n",
       "\n",
       "   possession                   possession_team  \\\n",
       "0           6  {'id': 217, 'name': 'Barcelona'}   \n",
       "1          11  {'id': 217, 'name': 'Barcelona'}   \n",
       "2          26  {'id': 217, 'name': 'Barcelona'}   \n",
       "\n",
       "                        play_pattern  ... match_id  clearance off_camera  \\\n",
       "0  {'id': 1, 'name': 'Regular Play'}  ...    15946        NaN        NaN   \n",
       "1  {'id': 1, 'name': 'Regular Play'}  ...    15946        NaN        NaN   \n",
       "2   {'id': 8, 'name': 'From Keeper'}  ...    15946        NaN        NaN   \n",
       "\n",
       "  miscontrol 50_50 out injury_stoppage half_start player_off half_end  \n",
       "0        NaN   NaN NaN             NaN        NaN        NaN      NaN  \n",
       "1        NaN   NaN NaN             NaN        NaN        NaN      NaN  \n",
       "2        NaN   NaN NaN             NaN        NaN        NaN      NaN  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots = df[~df['shot'].isnull()]\n",
    "shots.reset_index(drop = True, inplace = True)\n",
    "shots.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Expected Goal Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:30:41.273039Z",
     "start_time": "2021-01-03T11:29:02.994836Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ciaran\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:36: RuntimeWarning: invalid value encountered in arccos\n"
     ]
    }
   ],
   "source": [
    "left_post_x, left_post_y = (120, 36)\n",
    "right_post_x, right_post_y = (120, 44)\n",
    "\n",
    "shots_model=pd.DataFrame(columns=['goal','x','y'])\n",
    "for index, shot in shots.iterrows():\n",
    "    \n",
    "    # Keep only open play\n",
    "    open_play = 0\n",
    "    if shot['shot']['type']['name'] == 'Open Play':\n",
    "        open_play = 1\n",
    "\n",
    "    # Keep non-headed shots\n",
    "    header = 0\n",
    "    if shot['shot']['body_part']['name'] == 'Head':\n",
    "        header = 1\n",
    "    if (open_play == 1) & (header == 0):\n",
    "        # Goal\n",
    "        if shot['shot']['outcome']['name'] == 'Goal':\n",
    "            shots_model.loc[index, 'goal'] = 1\n",
    "        else:\n",
    "            shots_model.loc[index, 'goal'] = 0\n",
    "\n",
    "        # X, Y locations\n",
    "        shot_location_x, shot_location_y = shot['location']\n",
    "        shots_model.loc[index, 'x'] = shot_location_x\n",
    "        shots_model.loc[index, 'y'] = shot_location_y\n",
    "        shots_model.loc[index,'c']=abs(shot_location_y-40)\n",
    "\n",
    "        # Distance to centre of goal and angle\n",
    "        shots_model.loc[index,'distance']=np.sqrt((120-shot_location_x)**2 + (40-shot_location_y)**2)\n",
    "        \n",
    "        # Angle to goal (radians)\n",
    "        a = np.sqrt((shot_location_x - right_post_x)**2 + (shot_location_y - right_post_y)**2)\n",
    "        b = np.sqrt((left_post_x - right_post_x)**2 + (left_post_y - right_post_y)**2)\n",
    "        c = np.sqrt((left_post_x - shot_location_x)**2 + (left_post_y - shot_location_y)**2)\n",
    "        angle_ac = np.arccos((a**2 + c**2 - b**2)/(2*a*c))\n",
    "        if angle_ac<0:\n",
    "            angle_ac=np.pi+angle_ac\n",
    "        shots_model.loc[index,'angle'] =angle_ac\n",
    "\n",
    "        # Play pattern\n",
    "        shots_model.loc[index, 'play_pattern'] = shot['play_pattern']['name']\n",
    "\n",
    "        # Body part\n",
    "        shots_model.loc[index, 'body_part'] = shot['shot']['body_part']['name']\n",
    "\n",
    "        # First time\n",
    "        shots_model.loc[index, 'first_time'] = 0\n",
    "        if 'first_time' in shot['shot'].keys():\n",
    "            shots_model.loc[index, 'first_time'] = 1\n",
    "\n",
    "        # Technique\n",
    "        shots_model.loc[index, 'technique'] = shot['shot']['technique']['name']\n",
    "\n",
    "        # Pressure\n",
    "        shots_model.loc[index, 'under_pressure'] = 0\n",
    "        if shot['under_pressure'] == True:\n",
    "            shots_model.loc[index, 'under_pressure'] = 1\n",
    "\n",
    "        # Freeze Frame - defender location\n",
    "        # Number of defenders between shot location and goal\n",
    "        # Distance to nearest defender between shot location and goal\n",
    "        # Distance to nearest defender\n",
    "        # TBD: position of nearest defender\n",
    "        # TBD: angle of goal left after removing blocked defenders (assume ~1m width)\n",
    "        \n",
    "        freeze_frame_def = []\n",
    "        for player in shot['shot']['freeze_frame']:\n",
    "            if player['teammate'] == False:\n",
    "                freeze_frame_def.append(player)\n",
    "        \n",
    "        distance_nearest_defender = None\n",
    "        distance_nearest_blocking_defender = None\n",
    "        blocking_defender = []\n",
    "        for defender in freeze_frame_def:\n",
    "            # defender specific\n",
    "            defender_x, defender_y = defender['location']\n",
    "            distance_defender = np.sqrt((shot_location_x - defender_x)**2 + (shot_location_y - defender_y)**2)\n",
    "            if distance_nearest_defender == None:\n",
    "                distance_nearest_defender = distance_defender\n",
    "            elif distance_defender < distance_nearest_defender:\n",
    "                distance_nearest_defender = distance_defender\n",
    "            distance_defender = None\n",
    "\n",
    "            blocking = mfun.is_inside(shot_location_x, shot_location_y\n",
    "                                , left_post_x, left_post_y\n",
    "                                , right_post_x, right_post_y\n",
    "                                , defender_x, defender_y)\n",
    "            if blocking == True:\n",
    "                # If defender is blocking part of the goal..\n",
    "                blocking_defender.append(defender)\n",
    "\n",
    "                # Measure distance of blocking defender\n",
    "                distance_blocking_defender = np.sqrt((shot_location_x - defender_x)**2 + (shot_location_y - defender_y)**2)\n",
    "                \n",
    "                if distance_nearest_blocking_defender == None:\n",
    "                    distance_nearest_blocking_defender = distance_blocking_defender\n",
    "                elif distance_blocking_defender < distance_nearest_blocking_defender:\n",
    "                    distance_nearest_blocking_defender = distance_blocking_defender\n",
    "                distance_blocking_defender = None\n",
    "        \n",
    "        shots_model.loc[index, 'distance_nearest_defender'] = distance_nearest_defender\n",
    "        shots_model.loc[index, 'distance_nearest_blocking_defender'] = distance_nearest_blocking_defender\n",
    "        shots_model.loc[index, 'number_blocking_defenders'] = len(blocking_defender)\n",
    "\n",
    "        \n",
    "        # Key Pass info\n",
    "        # TBD - get info from previous pass\n",
    "        # Eg. cross / through ball / where it was etc\n",
    "        \n",
    "        # StatsBomb xG\n",
    "        shots_model.loc[index, 'statsbomb_xg'] = shot['shot']['statsbomb_xg']\n",
    "\n",
    "# # Make locations numeric\n",
    "# shots_model['x'] = pd.to_numeric(shots_model['x'])\n",
    "# shots_model['y'] = pd.to_numeric(shots_model['y'])\n",
    "\n",
    "# # Try squared distances\n",
    "# shots_model['d2'] = shots_model['distance']**2\n",
    "# shots_model['x2'] = shots_model['x']**2\n",
    "# shots_model['c2'] = shots_model['c']**2\n",
    "# # Try angle * x location\n",
    "# shots_model['ax'] = shots_model['angle']*shots_model['x']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Preprocessed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:30:41.336836Z",
     "start_time": "2021-01-03T11:30:41.275002Z"
    }
   },
   "outputs": [],
   "source": [
    "shots_model.to_pickle(\"./shots_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expected Goals Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Shot Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:30:41.524375Z",
     "start_time": "2021-01-03T11:30:41.337833Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goal</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>c</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "      <th>play_pattern</th>\n",
       "      <th>body_part</th>\n",
       "      <th>first_time</th>\n",
       "      <th>technique</th>\n",
       "      <th>under_pressure</th>\n",
       "      <th>distance_nearest_defender</th>\n",
       "      <th>distance_nearest_blocking_defender</th>\n",
       "      <th>number_blocking_defenders</th>\n",
       "      <th>statsbomb_xg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>111.7</td>\n",
       "      <td>51.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>14.345034</td>\n",
       "      <td>0.336567</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Right Foot</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Half Volley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.303840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.075164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>27</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.317821</td>\n",
       "      <td>0.248710</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Volley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.062892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>34.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>28.535066</td>\n",
       "      <td>0.273578</td>\n",
       "      <td>From Keeper</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.884441</td>\n",
       "      <td>5.124451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>107</td>\n",
       "      <td>25</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.849433</td>\n",
       "      <td>0.268489</td>\n",
       "      <td>From Corner</td>\n",
       "      <td>Right Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.244996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12.6</td>\n",
       "      <td>17.331186</td>\n",
       "      <td>0.323048</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Half Volley</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.039608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  goal      x     y     c   distance     angle  play_pattern   body_part  \\\n",
       "0    0  111.7  51.7  11.7  14.345034  0.336567  Regular Play  Right Foot   \n",
       "1    0    114    27  13.0  14.317821  0.248710  Regular Play   Left Foot   \n",
       "2    0     92  34.5   5.5  28.535066  0.273578   From Keeper   Left Foot   \n",
       "4    0    107    25  15.0  19.849433  0.268489   From Corner  Right Foot   \n",
       "5    0  108.1  27.4  12.6  17.331186  0.323048  Regular Play   Left Foot   \n",
       "\n",
       "   first_time    technique  under_pressure  distance_nearest_defender  \\\n",
       "0         1.0  Half Volley             0.0                   1.303840   \n",
       "1         1.0       Volley             0.0                   3.700000   \n",
       "2         0.0       Normal             0.0                   2.884441   \n",
       "4         0.0       Normal             0.0                   3.244996   \n",
       "5         1.0  Half Volley             1.0                   2.039608   \n",
       "\n",
       "   distance_nearest_blocking_defender  number_blocking_defenders  statsbomb_xg  \n",
       "0                                 NaN                        0.0      0.075164  \n",
       "1                                 NaN                        0.0      0.062892  \n",
       "2                            5.124451                        1.0      0.020535  \n",
       "4                                 NaN                        0.0      0.035420  \n",
       "5                                 NaN                        0.0      0.089920  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load shot model\n",
    "shots_model = pd.read_pickle(\"./shots_model.pkl\")\n",
    "shots_model.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate into Train/Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:02.677856Z",
     "start_time": "2021-01-03T11:31:02.655917Z"
    }
   },
   "outputs": [],
   "source": [
    "# Separate into train, test data for modelling\n",
    "X_train, X_test, y_train, y_test = train_test_split(shots_model.loc[:, shots_model.columns != 'goal']\n",
    "                                                    , shots_model.loc[:, shots_model.columns == 'goal']\n",
    "                                                    , test_size=0.2, random_state=42)\n",
    "train = pd.concat([y_train, X_train], axis=1)\n",
    "test = pd.concat([y_test, X_test], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:04.578548Z",
     "start_time": "2021-01-03T11:31:04.503376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Generalized Linear Model Regression Results                    \n",
      "==================================================================================\n",
      "Dep. Variable:     ['goal[0]', 'goal[1]']   No. Observations:                 9295\n",
      "Model:                                GLM   Df Residuals:                     9290\n",
      "Model Family:                    Binomial   Df Model:                            4\n",
      "Link Function:                      logit   Scale:                          1.0000\n",
      "Method:                              IRLS   Log-Likelihood:                -2901.7\n",
      "Date:                    Sun, 03 Jan 2021   Deviance:                       5803.5\n",
      "Time:                            11:31:04   Pearson chi2:                 9.45e+03\n",
      "No. Iterations:                         6                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "=============================================================================================\n",
      "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "Intercept                     1.0519      0.205      5.122      0.000       0.649       1.454\n",
      "distance                      0.1080      0.008     12.989      0.000       0.092       0.124\n",
      "angle                        -1.6109      0.181     -8.917      0.000      -1.965      -1.257\n",
      "distance_nearest_defender    -0.1242      0.021     -5.858      0.000      -0.166      -0.083\n",
      "number_blocking_defenders     0.3260      0.054      6.053      0.000       0.220       0.432\n",
      "=============================================================================================\n"
     ]
    }
   ],
   "source": [
    "# A GLM for fitting goal probability\n",
    "model_variables = ['distance'\n",
    "                   , 'angle'\n",
    "                   , 'distance_nearest_defender'\n",
    "                   , 'number_blocking_defenders'\n",
    "                   ]\n",
    "\n",
    "model=''\n",
    "for v in model_variables[:-1]:\n",
    "    model = model  + v + ' + '\n",
    "model = model + model_variables[-1]\n",
    "\n",
    "#Fit the model\n",
    "xG_model = smf.glm(formula=\"goal ~ \" + model, data=train, \n",
    "                           family=sm.families.Binomial()).fit()\n",
    "print(xG_model.summary())        \n",
    "xG_model_params=xG_model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate xG on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:17.119822Z",
     "start_time": "2021-01-03T11:31:17.111841Z"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate xG for GLM using each shot as input (row of shots_model)\n",
    "def calculate_xG(sh):\n",
    "    # For the model 'b', get the intercept\n",
    "    bsum=xG_model_params[0]\n",
    "    # For as many variables as put in the model, \n",
    "    # multiply the coefficient by the value of that shot.\n",
    "    for i,v in enumerate(model_variables):\n",
    "        # bsum = intercept + (coefficient * variable value)\n",
    "        bsum=bsum+xG_model_params[i+1]*sh[v]\n",
    "    # Calculate probability of goal as 1 / 1 + exp(model output)\n",
    "    xG = 1/(1+np.exp(bsum)) \n",
    "    return xG   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:17.727181Z",
     "start_time": "2021-01-03T11:31:17.368141Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>goal</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>c</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "      <th>play_pattern</th>\n",
       "      <th>body_part</th>\n",
       "      <th>first_time</th>\n",
       "      <th>technique</th>\n",
       "      <th>under_pressure</th>\n",
       "      <th>distance_nearest_defender</th>\n",
       "      <th>distance_nearest_blocking_defender</th>\n",
       "      <th>number_blocking_defenders</th>\n",
       "      <th>statsbomb_xg</th>\n",
       "      <th>xG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3580</th>\n",
       "      <td>0</td>\n",
       "      <td>102.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>23.847222</td>\n",
       "      <td>0.248088</td>\n",
       "      <td>From Corner</td>\n",
       "      <td>Right Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.894868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025732</td>\n",
       "      <td>0.060465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13598</th>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>26</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.866069</td>\n",
       "      <td>0.192701</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.440307</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052736</td>\n",
       "      <td>0.113889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4691</th>\n",
       "      <td>0</td>\n",
       "      <td>102.1</td>\n",
       "      <td>36.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>18.278403</td>\n",
       "      <td>0.422998</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Half Volley</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.664332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.047882</td>\n",
       "      <td>0.105522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>1</td>\n",
       "      <td>112.5</td>\n",
       "      <td>38.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.736924</td>\n",
       "      <td>0.939567</td>\n",
       "      <td>From Goal Kick</td>\n",
       "      <td>Right Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.204159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354846</td>\n",
       "      <td>0.444201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>0</td>\n",
       "      <td>117.4</td>\n",
       "      <td>30.7</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.656604</td>\n",
       "      <td>0.263018</td>\n",
       "      <td>Regular Play</td>\n",
       "      <td>Left Foot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.605551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520617</td>\n",
       "      <td>0.227407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      goal      x     y     c   distance     angle    play_pattern  \\\n",
       "3580     0  102.5  23.8  16.2  23.847222  0.248088     From Corner   \n",
       "13598    0    115    26  14.0  14.866069  0.192701    Regular Play   \n",
       "4691     0  102.1  36.3   3.7  18.278403  0.422998    Regular Play   \n",
       "8187     1  112.5  38.1   1.9   7.736924  0.939567  From Goal Kick   \n",
       "675      0  117.4  30.7   9.3   9.656604  0.263018    Regular Play   \n",
       "\n",
       "        body_part  first_time    technique  under_pressure  \\\n",
       "3580   Right Foot         0.0       Normal             0.0   \n",
       "13598   Left Foot         1.0       Normal             0.0   \n",
       "4691    Left Foot         0.0  Half Volley             0.0   \n",
       "8187   Right Foot         0.0       Normal             0.0   \n",
       "675     Left Foot         0.0       Normal             0.0   \n",
       "\n",
       "       distance_nearest_defender  distance_nearest_blocking_defender  \\\n",
       "3580                    3.894868                                 NaN   \n",
       "13598                   5.000000                           10.440307   \n",
       "4691                    1.664332                                 NaN   \n",
       "8187                    1.204159                                 NaN   \n",
       "675                     3.605551                                 NaN   \n",
       "\n",
       "       number_blocking_defenders  statsbomb_xg        xG  \n",
       "3580                         0.0      0.025732  0.060465  \n",
       "13598                        1.0      0.052736  0.113889  \n",
       "4691                         0.0      0.047882  0.105522  \n",
       "8187                         0.0      0.354846  0.444201  \n",
       "675                          0.0      0.520617  0.227407  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add an xG to my dataframe\n",
    "train_xG=train.apply(calculate_xG, axis=1)\n",
    "train['xG'] = train_xG\n",
    "\n",
    "test_xG=test.apply(calculate_xG, axis=1)\n",
    "test['xG'] = test_xG\n",
    "test.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check (x, y) Probabilities by # Defenders and Distance to Nearest Defenders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:25.195303Z",
     "start_time": "2021-01-03T11:31:18.264550Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AGyJne7ybsMzMCqqRXUgRMROYWTbvnLLX59aSpwNITv2+g7y/b19R5dnvfbgj2gMQ6yeK30TkAGJmVlBFD60OIGZmBVX02pkDiJlZQRU7fDiAmJkVkp8H0kf1uw7z/rY9A80A7GgvetNNr5AKvx8cQMzMCqq14A9FdwAxMysgUfyKpQOImVkR+ZG2fUOf7vMoZNmLWKbe1svf/LzHQdF/kVLuF0kUfZsdQMzMCqrg8cMBxMysqFwDMTOzmrkT3czM6iNoKXgEGXABpM90mBeinEUoQ19V777r4R+MPjwosfy7W/TmnUYo+iYOuABiZtY3eCS6mZnVQYAK/kCQghfPzGyAErS0KPfUbXbSZElPS5on6WtdpPmYpCclzZV0fXd5ugZiZlZQjWrCktQKTAcOBdqA2ZJmRMSTJWnGAl8H3hsRSyVt1V2+/TqAuMO86kqbsM7S1feRzyZLj7ZL590vPViGrM+mgG3xA2G0egM3ZxIwLyLmJ/nqBmAq8GRJms8A0yNiKUBE/K27TN2EZWZWQCK5jDfvBAyXNKdkOrUku5HAgpLXbem8UrsAu0i6V9L9kiZ3V8Z+XQMxM+uzar+Z4uKImNh1bhXKq3CDgLHAgcAo4A+S9oiIV7paoWsgZmYFpfShUnmmbrQBo0tejwIWZaT5n4hYHRHPAk+TBJQuOYCYmRWUlH/qxmxgrKQxkoYAxwIzytL8EjgoWa+GkzRpza+WqZuwmqFHO5B7uXO6L3eG16vebW5oB2+eMjRwfX2kY70/aeS9sCKiXdI0YBbQClwVEXMlnQfMiYgZ6XuHSXoSWAOcGRFLquXrAGJmVkQSyjG+I6+ImAnMLJt3TsnfAZyRTrk4gJiZFVTRK3kOIGZmBSSgtYE1kJ7QrwJIIQcO9niZGpR/T5eziJ9NI+U5Vez1vpOs9fVgv0gBTpf72+DCope9XwUQM7N+o/ZxIL3OAcTMrKBcAzEzs7oUPH44gJiZFVEyDqTYEaTPBhB3mK9vVkXsfC/gZ5q307knHxXb0EF85Xn1/8GGffZRuCr+A6X6bAAxM+vf/EhbMzOrl8eBmJlZXVwDMTOzmgnUWuxOEAeQ9VHEu+o2skx159WD+6WR25fr7K7e9WXk3ciO9obl1cuj1aHwZ9XFUfyRhA4gZmYFJNHQu/H2BAcQM7Oicg3ErOdFBEtfeYW2hYt4fdlyVq9eTUQwaNAghg4dwtYjRrDdttswePDgZhfVLDfXQBqgEIMGi9jfAfWXq7f7Nxo4cLFt4SLufeBB/vjgHJ56+v9YsHARCxYuZPCgwYzabls22WRjBg8aTEuLaG9fw8pVK/nri3/jby8tZqsRwxk9cjvGbL89kybuzXvfNYl37LH7+geWijPFvNtbtlze/VTv3X+L2C/Sy2fZfeqOvUUtV6pPBBAb2FasWMGtt/+GW389i3vue4DlK97gvfvuw3v23YfDD3k/o0dux+iRI9l0002q5rN69Wr++sKLPN+2kGeee477Zj/ET665jueeX8A+e03g/Qfsz7Ef/TA7jdmxV7bLrCqpoeNAJE0GLiZ5pO2VEXFh2fsnAT8AFqazLo2IK6vl6QBihdTe3s6dv/8D19/438z49SwmvXMvjj7qSL515hnssvNOdZ0xDh48mO1Hj2L70aPY7937cuLxxwKw9JVXuO/BOcy8407ec+gR7DRmR447+iN8/CNT2WrEiAZvmVl+jaoZSWoFpgOHAm3AbEkzIuLJsqQ/j4hpefMt9kXGNuC89trr/OCS6YwZP5Fvf+/77D1hPE/NvpdZt9zIZ048gV3H7tzw5oYtNt+cIw47hEt/cAFtTz3Kt776FR586GF2fed7OP7kz/LIY080dH1mubUo/1TdJGBeRMyPiFXADcDU9S7e+mZg1givvfY6533/X9hpwiQeeexxbr3hWu6/83a+/LlT2WbrrXqtHIMHD+bwQw/mmssv4y9PPMxe7xjPhz7+CT70sU/w0COP9lo5zNLb8eafqhsJLCh53ZbOK/dRSY9JuknS6O4yLWQAiYhOUxMKUDk1dgVlUw+XK9dy5WXqasqRdw3T2jVruOI/r2Hs3vvyzPz53HfHr7juin9nwp67d1HuBk7dlG3TTTbmzC9+nmf+9ACTDz6Iqcd9iuM//VleeOHF+rY3+8OpfZ9n7fe86j6uc5arHj3+fctThKiYikAt+SdguKQ5JdOppVllZF++kbcCO0bEeOC3wNXdla+QAcQGhqf/PI/3H/kRfnLNddxxyy+4+j8uZee3j2l2sSoMHTqUaaeezP/N+SM7bD+ad+x3ED+55vrC/MhYPyWh1pbcE7A4IiaWTJeX5NYGlNYoRgGLSlcXEUsiYmX68grgnd0V0QHEet3q1av57j//K/tNnsJHpnyIe2fdxvg9xjW7WN0aNmwYF3z7bGbd/HN+/NOrOXjKR/nzM/ObXSzrzxrXhDUbGCtpjKQhwLHAjM6r0rYlL6cAT3WXqa/Csl710uLFfOykzzBkyBDm3DWLHbbvtpm1cCbsuQf33TGTS358JftNPpIrL/kRRx7xgWYXy/qjBl3GGxHtkqYBs0gu470qIuZKOg+YExEzgC9JmgK0Ay8DJ3WXrwOI9ZpHHn+CD3/iJI47+iOcf/ZZtLa2NrtIdWttbeX0z3+Wd+8zkWNOOoVH587l7H88vbgD0qzPSSoWjTueImImMLNs3jklf38d+HoteTqA9LheHrmde7kc6fLklXN9N97yP0w78+tc+oML+NiHpzS2DHXLyLvGL+y79tmbB347k6NPPIVHH3+Cn06/hI033qj+ImWuP2sf5BjB3qOj1aGyXA6eDVfwW5m4D8R63BVXX8uZ3/oOv7nl5yXBo//YbtttuOvWm9lo2DCOOOZ4li1b3uwiWb9QQ/9Hk2q+DiDWo6669nrOv+iH3DnjJibsuUezi9Njhg4dylXTL2aXsTtx5LGfZPlyBxFbf5JyT83gAGI95mc33Mg537uI3/7PLwp5eW6jtbS0cPm//jM7bj+aqcefyIoVK5pdJOvLRCNHoveI5geQqBzE0/tlaNQgpjoHW63PQKq6B6vlKEOuwXHZ08233sbXv/Nd7rjl5+yy89vz5bV2beWUqxxrc0w5t6eu9a/b7pYWceW//ZBtt96Koz91Mu3tq9ftl7r3cZ7PNMexkfe4qvt4rPP4b2gZGqfpv0u4BmID0KOPz+W007/KrTf8jN123aXZxel1ra2t/PSyi1kba/nqt89vdnGsLyt4DcRXYfWCvd97AC8tXsLOb397Fymac3bTfRHqK9dzC9oYseWWfOXsc9enRDXIU87e/4K1t7dz9fU3MvvhRxiU+5LljHIW4kKc7gsxb/58Rgzfkofv/X0vlGcASEeiF5kDSC94afGSAXVlzo6jRzW7CIUwaNAgxu++W7OL0WsG0jHeawo+rsgBpBd01Dzu+vWM7ATr076aa9kcabLyaVTeefOqez/0YA0k7xc4V7ocabLyaVTeufOqb7mDDu9/l2g3VUcneoE5gPQl9f6g1/3j3UcCT76M8iUr/6HMXH/Gl7o8XWYgyLH+ejc3M+86y2mFUfQ7GziAmJkVUvM6x/NyADEzKyrXQMzMrGYdTyQsMAcQM7OicgApmIaOKK0zr4ZekdSoMvRw53t5up7evnpFg66UamRneGY/fr098nXexbfuu/bmvAghjwF3AYCgxeNAzMysHgUPkg4gZmZFJFwDMTOzergJy966Uytdt/s3sj8g98C+HH0Svb5cxmL1JcopR4dD7gF6ZfMymx7yzsuRJNfg+zwLrkd/R5PuUNssne7I21ub3sAmLEmTgYtJnol+ZURc2EW6o4FfAPtExJxqeRY7vJmZDVQdl/E24ImEklqB6cDhwDjgOEnjMtJtAnwJeCBPER1AzMyKqnGPtJ0EzIuI+RGxCrgBmJqR7nzgIuDNPMVzADEzK6S0DyTvBMMlzSmZTi3JbCSwoOR1Wzpv3dqkvYDREXFb3hK6D8TMrKhq6wNZHBETu8opY95bPTmSWoAfASfVssL+HUCKMGiwp+UaoFfvwL46l4u1ObLK2fmeZ8Bj3fLcOj2r8zgrq/KZOSv35dnn3by6lyvCuIIcHfm5sql3cGMf0dhbmbQBo0tejwIWlbzeBNgDuDu9A/A2wAxJU6p1pPfvAGJm1pc1LoDMBsZKGgMsBI4Fju94MyJeBYavW63uBv6xu6uwHEDMzApICDVoHEhEtEuaBswiuYz3qoiYK+k8YE5EdPG0u+ocQMzMiqqBTXIRMROYWTbvnC7SHpgnTwcQM7Mi8q1MBpi8nfY9+WjautXZqZ3ZYd6Dy+UoZ3t7O3+c8yeWLV/B3nuOY5utRuQfGV6eLvPOu1lf6vJ0GduXp2M978j3elXc0Tbn+nJ1WGfccaE/dWr3Ot/KxKxX3fSrWZx21jmsbm9HEitXruLDhx/CT/7lAjbccINmF8+sNgUPwA4g1m/87t77OfHLZ/HGm50H0f7y9jt5ffmXufXqHzepZGZ16ANPJCx2/cisBt+44IcVwQPgzZUr+d099/G/855pQqnM1kPjbmXSI1wDyVTQQYNZ6n2yYK4BgTkGCWalWZvVv1G+XEaarOVy3v137dq1zHn08Yzl1/ndH/7I343ZYd2MPP0iWW3QFYMGqewXyWy7rrNfJM/x2NAnGfa2Bj61sF9xH4hZr5DSa+bXrOni/RYGD/Lhbn1MIQP+OsUOb2Y5SeLQ/d+NuvjCrV27hiPe/75eLpXZemjg7dx7igOI9RsXfu10hmVcaTVsww359Mc/yshttm5CqczqVfPdeHudA4j1G+N325W7fv5TJo7fnaFDhrDRsA3ZbNNNOHvaqVxy3jeaXTyz2hW8BtK/GoX77CM2e7jc9T5iNk+HfJ4Oc4C1a6q/7mK5KM8/c7l1ZXrnbmN54Oaf8eLiJSxbvoLtt9uGwYMHw5rVRPmiLa0VWVXceygyzrEylqs4FcvsL8/RIZ/rTr8Z6Rr6A9KHO7X70x16PRLdrDm2Hr4lWw/fstnFMFsPvgrLzMzqVfDakwOImVlROYCYmVnNRBc37iwOB5D1kafTvjRNLZ38eUeG5+oMz5N/1ojyOu+qm9XRXTYv1rRXpsma17665rwzZXZ8V86LQYM7z2it/IrkOifM+t5HxpLl+zhzuRwdw5lpsvLKmNdd3l2p9fivNf8BT9BS7H3lAGJmVlQFr4EUu3RmZgNZA8eBSJos6WlJ8yR9LeP90yQ9LukRSfdIGtddng4gZmZFpMaNRJfUCkwHDgfGAcdlBIjrI2LPiJgAXAT8sLsiOoCYmRVV42ogk4B5ETE/IlYBNwBTSxNExGslLzciR4+Z+0AGhHpHuue5nXu+TvSKTvPVKyuXW1X5LA9WryrLZ3Vlmi7uwNtJa8ao89bBlekGD+n8ekjlvbVyjdPO+kJndaJXpMvbaVoxFD3nctanZF380bXhkuaUvL48Ii5P/x4JLCh5rw3YtzwDSV8AzgCGAO/vboUOIGZmRdTRhJXf4oiY2FVuGfMqzoUiYjowXdLxwDeBE6ut0E1YZmZF1bgmrDZgdMnrUcCiKulvAI7qLlMHEDOzolJL/qm62cBYSWMkDQGOBWZ0WpU0tuTlB4E/d5epm7CA3n+EbQ+ur96BhLkfaZvjbrxZA/vaO/dlsHJFZdYr36hcrnxeVj9Je8YAxHIZTyOMjP4Nhm7Y6aWy+niyzvbK52V9obPm5fkcso6XXAMJ+/IdevvwHYEbRY0bSBgR7ZKmAbOAVuCqiJgr6TxgTkTMAKZJOgRYDSylm+YrcAAxMyuuBg4kjIiZwMyyeeeU/P3lWvN0ADEzK6qC3/bFAcTMrJBU+FuZOICYmRWR8M0UrY/I7Lut7268FY+hhcoBgVkd5stfq5y37NXOr9+o7HwvzztT+QBBgA2HVc7beLNOLzO7cjMGIEbZPLVkPeo3z/7MWmHGPBsY3IRlZmZ1cROW9Sd7HzaVl5a8zM5jdkhmZJ5VZ13a23le5vNAsi7/LU+XdduSPJcuZ53JZdzepOL5Hxm3klDGM0IqRgxnXsZbWYZ5z/6FEVu+jYd/e1tlehvYpOxjtEAcQKwmLy15mWUrMpqRrC7Jvix2M4U1kZuwrD/pqHncddO1yYwyaPBCAAAGtElEQVSMmxtG+VMEAd5c3jnNioz+jgL2gbDRphVJNKxyHhts1DlN+ZMNATL6Tg46+oTKdGaAr8KyfixtNspqPspqiir/kX9jeWWa11+pnPfyS51fv/JyZUnezBidXkYbZIw63/xtlfPKtyfrbqiDM/IqH9UeWV+t3r7jgfVpvgrLzMzq5hqImZnVxX0gZmZWu5qfB9LrHECs8bIu4y3vbF+VMZBw6ZLKrBYu6DzjxRcrs351WbdFat1s48qZW1f2w6i8ySCroz3rqYhZ22y2PoRrIGZmVif3gZiZWe1yPWmwqRxAzMyKSHgkupmZ1cMDCa2vyKopV60+q+s0eQ76lSsrZsWSv1XMW/vMs51er3hqUUWaV16tzKvc5psNrZg3bLfKUe0tG5Y90nbLrbrNO0lYfi+szB1aZXl1m8QGoAY2YUmaDFxM8kjbKyPiwrL3zwBOAdqBl4BPR8RfquVZ7PBmZjaQqSX/VC0bqRWYDhwOjAOOkzSuLNmfgIkRMR64Cbiou+I5gJiZFZGU3Mok71TdJGBeRMyPiFXADcDU0gQRcVdEdFTL7wdGdZepm7DMzIqqcX0gI4HSQVVtwL5V0p8M/Lq7TB1AzMyKqrY+kOGS5pS8vjwiLu/IKSN95t09JX0SmAgc0N0KHUCaovyzbOBdWrMOuKw75laky+oMr3LwdtxiIXI8lAkqb6eeVeVeurRi1pKHn+/0+snnK2/5vrQ94+FUZbZ4obJM496oXG7EyG07z8gqZ9at4XM8iCrzthQd2avaRQk5fkR6fLyAe/d7X81XYS2OiIldvNcGjC55PQqouCJF0iHA2cABEdHt1SkOIGZmBaXGnRjMBsZKGgMsBI4Fji9b117Aj4HJEVF5SWQGBxAzsyISDesDiYh2SdOAWSSX8V4VEXMlnQfMiYgZwA+AjYFfpIHr+YiYUi1fBxAzs0Jq7DPRI2ImMLNs3jklfx9Sa54OIANCVrt62evI2fbeMS+rjb8jSUafS2y0eecZW2xdueCqykfT3vds5ycQPvBaZbPsq2synoBYZrOML+Krayr7QKaUlyGrnOXbAmhQWb9IVj9QlX22bl/n7QNxn8SA4JHoZmZWM9/O3czM6uN7YZmZWb0KXgNRZI0R6K2VS22bbbrpyAnj92xaGdZPvn33yGNPADBh/B7rmXUjP6uo+rKrmY888SQAE/ZIb6OTdfxkzuv8xL5YnXGJ+eLkysFnlr4OwE5bbMKSRZ3HhrzWXvnkv/Yc+2VQRp/BpoMqz+623G6Lt/5+ZunrsMEwdhrdeWyIBlfemDHXzRQz5lXszzz9VV3PrEPe9XUv/3Fe7B/FPB557HE23ngj2hYu6rGNmThul3jwustyp2/d+9CHqowD6RHNDiB/AkYA85pWiN6xc/p/f9jO3tiWCen/j/TgOvLojXL42OibdgZeioi9emoFE3ffNR68voYAMuGQXg8gTW3C6smdb32XpLsBIuJAl8MGNPeBmJlZXQreB+IAYmZWSL4Ky8zM6uUaiJmZ1UzKvqNBgRS7dGZmA1gD78bbIxxAzMyKyn0gZmZWM98Ly6wuOwMbd4zDaKIJwLIml8EGLF+FZVaPl5pdgNQyilMWG4hcAzGrje9QYJZqcQ3EzMxqJRW+BlLs8GZmNpCpJf/UXVbSZElPS5on6WsZ779P0sOS2iUdnad4DiBmZkXVUQvJM1XNRq3AdOBwYBxwnKRxZcmeB04Crs9bPDdhmZkVUkOvwpoEzIuI+QCSbgCmAk92JIiI59L3Kh+60wXXQMzMiqq2GshwSXNKplNLchoJLCh53ZbOWy+ugZiZFVZNneiLqzxQKiuj9X6aoAOImVkRNXYkehswuuT1KGDR+mbqJiwzs6JSDVN1s4GxksZIGgIcC8xY3+I5gJiZFVZjIkhEtAPTgFnAU8CNETFX0nmSpgBI2kdSG3AM8GNJc7srnZuwzMwKqbEDCSNiJjCzbN45JX/PJmnays0BxMysqAo+Et0BxMyssBxAzMysHq6BmJlZzeTngZiZWb1cAzEzs/o4gJiZWR3kGoiZmdXFAcTMzGqX7x4lzeQAYmZWVK6BmJlZzRp7N94e4QBiZlZYDiBmZlYP10DMzKwuxY4fDiBmZsXkW5mYmVk93IluZmb1cwAxM7N6uAZiZma1a+wjbXuCA4iZWWEVO4AoIppdBjMzKyPpdmB4DYssjojJPVWeLA4gZmZWl2JfZGxmZoXlAGJmZnVxADEzs7o4gJiZWV0cQMzMrC4OIGZmVhcHEDMzq4sDiJmZ1cUBxMzM6vL/Acxs1GbJe2OWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kzFKGpgbaobAOltC+lPDM2Suo3g8/A6P8z9tQBSIiUlLlq0Z7UwUiIlJSZZhOpRZVICIiJVXu6kMViIhIKWkurHbR1MBwgXkXWc522ee2lfGn38yR6E0NtEP6c27yCP2Sd+cUzkxdWCIikk9nyYeiqwIRESkho/yNLlUgIiJlpEfallC79P3nLWeh+1eC/WkXqV963v0tMHaSN05S6ADEJsZFyn52LYBiICIikkvJ6w9VICIiZaUWiIiINExBdBERycego+Q1yMCuQMo6q24pBgS2uOztHFQP/agLGxBY4Ey4eQPkWfcl08mtiYH1QTDYsOy7M7ArEBGRtqWR6CIikoMBVvIHgqgCEREpI4OODrVAREQkB3Vh9acyjjLv9xHlA7ycZZB7kHnO4HvwJJKlEEVuL1SEvCPDNYtvVmUv+sCqQEREBghDt/GKiEgemkxRRETyUgxERERyKXn90cYViALmlZWLyb/ZwfB2HnmekneEd/Elqb39nKPAixzB3urR6tC208BrLiwREcnHDNM4EBERyUMtEBERaZgBnWqBFGCgzaqbab0CYwtFbi9X3g2kS6+Yc728mviDLfJJsU0tQ8YBiO0aF2mjwYZF3oVlZhOBS4BO4Cp3/1YgzQeAc4g+gCfd/YO18myPCkREZLApcByImXUClwEHAdOBx8xssrs/XZVmK+BLwF7uPtvM1quXb8nnehQRGbzMLPOrjt2BZ939eXdfCtwMTEqkORm4zN1nA7j76/UyVQUiIlJSZtlfwCgzm1r1+mRVVmOAaVV/T4+XVdsa2NrM/mBmD8ddXjWpC0tEpISicSAN9WHNdPcJNbJLSgaDhgBbAfsBY4Hfm9kO7j6nrw0OwgpEAfPgNgsNhjdz4GKz5ZytNrU/BQaig0kSC7PmnWlQXcYAdt4BemWcxbeMrNAHSk0HxlX9PRaYEUjzsLsvA14ws2eIKpTH+spUXVgiIqWUPf6RoaXyGLCVmW1mZqsBxwGTE2l+AewPYGajiLq0nq+V6SBsgYiItImCxoG4e4+ZnQbcRXQb7zXu/pSZnQtMdffJ8XsHm9nTwHLgi+7+Zq18VYGIiJRVgeNA3H0KMCWx7Oyq/zvw+fiViSoQEZEyMrDOckcZylmBFBZg7edAbd4A9qrklUqXc712Cb73u7zB8KxB7VyFyhY/DsbCcwbfswTW+32Ed4Gj1Us5Or38T5QqZwUiIjLImaHZeEVEJCe1QESaz92ZPWcu02e8wrz581nW04O7M6RzCN3dq7H+6FFstMH6dHV1tbqoIpmpBdKOyjqoLpiuqJhEYL3cAxdzrpex6NNnvMIfHp3KHx97nL//77NMmzGDaS+/QlfXEMZuuCFrjBhOV1cXHR1GT89ylixdyiuvvc7rM99kvVHrMm6jDdls43Hsvst49tp9Au/cftu+KxbLOUAvc198hkF1RcZJUsVclQGPBQ2eLGX8oSRKfhxUgUjpLVy4iNvvvpfb77qXBx+dyoKFC9lrt1159+4TOOSA/Ri30UaMG7Mha66xRs18li1bxiuvvc5LL8/guRf/j4em/omrb/wJL06bzm7jd+I9++zFce87gi023aSf9kykBrPCxoE0iyoQKaWenh7u+/0fuOnWXzL5rnvYfefxHHPEoXz1C59l6y02z/WchK6uLjYeO4aNx45h7z1244RjjwFg9py5PDT1T0y5737efejRbLHpxhx/9CSOnXQ46603quhdE8msyOeBNIMqECmVt+bN44fX3cT3rvwRYzZYn+OPnsQFZ5/JBuvVfTRBbmuPXItDD9yfQw/cn4vP/Sr3/u5Bbrr1l5x9wXc45ID9OP20Uxi/4/ZN275In9QCEanvrXnz+O4Pr+H7V/2Yg/fdh9tvuJrxO/T/Sburq4tDDtifQw7Y/+3K7PAPfZzxO2zH1874PLu+c8d+L5MMUtF0vK0uRU3lGObo3vuVP6PEq8DtJ9P0GTzOUIbc2wu8isorlCb4WlH/tcLrv+L8VixfzpXX3cRWu+/Hc8+/yEN33sqNP/gu47ffto+8lxf3qrMfa44Yzhc//Umee/S3THzPvkz6yEl88JOf4dVXX8uwfxmOk6/Idsz7+3PPnFcqUeCVIa/c2wuumK0MmbLKs/1iWUf2VyuUowKRQemZZ5/jPUcfz9U3/oR7fno91176HbbcbNNWFyulu7ub0z5xAv/70P1sMm4s79zvEK6+8Sd4aaeklwHBDOvsyPxqBVUg0u+WLVvGNy6+lL2PeD9HHzaRP9x5Kzttv22ri1XXsGFD+eZZp3PXLdfxw+tu5ICjj+efz7/Q6mLJQNbgIwn7myoQ6VdvzHyTgz/wYX730CNMvXsynz35Y3R2dra6WA0Zv8P2PDTl5xzx3gPZ+/BjuP2ue1tdJBmoOiz7qxXFa8lWZVB64m9PsfvEI3nXhF2Z8j8/ZpNxY1tdpNw6Ozv53Ckn8cvrruRTZ5zF1y/6vrq0pFBRw6KwB0o1xeC7C6u/f+RFjuYOjt5OLmv2enUXBNe75Zd3cNqXzuHSb57DB448LFpvxfL0ulnKUJRQ1qlR0qEVV6bZc5d38siU2zjmpE/z5N+e4kff+zYjhg/vlab29pILQ+sFlqVOGKETSM7H5WY5LpD+bJp9Euvv7ZVByW/jVQtEmu7KG27mi1/7Jnf/5Nq48hhYNtpgfe6/7SaGDxvGoR/8GPMXLGh1kWRAaCD+oRiIDETX3HQL5130fe772Q2M32G7Vhenabq7u7nmkgvZeovNOeLDJ7FgwcJWF0kGgLJ3YakCkaa57pbbOPvCi7n3p9eX8vbconV0dHDFf53PpuPGMumjJ7Nw4aJWF0namaEgevMUOGAolXXWQUw5t59noFjWQWaFrhcqWnJgX+DlK7jtjl/xpW9cyD0/uZatN98023p5BwQu76n/yj3gMLx/fb06DK666Hw2XG80x3z8FHqWLq2zXvL4Zvissn7OzV4v25e9/u8kd95ZFXSeaMGgQrVAZNB58qm/c8rpZ3H7dVew7dZbtro4/a6zs5MfXXIhK1Y4p593QauLI+2s5C2QwXcXVgvsss8BvDHzTbbcfLN4SZFXMF7zz9z5rEKyF6dPZ/S66/CFc87PW5iq7fXf1R5QYDDS6Onp4dpbbuWxJ55kSGcfP7VMm8tZpuBqRZ5oeuf17AsvMHrddfnTg78pcBuDWDwSvcxUgfSDN2a+OajuzNl07JhWF6EUhgwZwk7bbtPqYvSb+fMHz3e835T8VmVVIP2g0vK4/86fRwsKG7sRWLfQ9UKrrWg871C6VRn7kidN1h9ilvEVobxyr5e4wgwO58gwxiNTmQpcL5Bu/8PfF1hPcqsE0UtsYFcghXZ/5DyReVXgrhndMUWdmLNWPFkqkGSarNvLXfHkVGhFkDwxh7oespwMslZ0ib+zHpYir2hTn01ged5H4ebZfp/ba196oJSIiOSgR9qKiEheaoGIiEjD2uCJhKpARETKShVIEZo8FiBvYDZ3QLegO6eC6XLe0bUiEPgO5ZVMFwyYZwmiZ0gD6SnSmxxETwUts9w5FUqXmmW3j/WSi0Iz73ZkGAuQeVbdnOOG8o4pyRvobmqAPHhgCsq7SJbts2+hNqlAREQGIbVARESkYYZaICIikoe6sPpXYf3jRcZEPP3/Qkdl54yn5I1l5FzPk+uFnkYYLGciXTBWk1Pgx+kdieezh7oQkmkAS+YVjIFk+PxCcZLQLueOiyS3V79I2fMPfL8zdcEUGJMYaIMLS172gVWBiIgMFLqNV0REclMFIiIijVMMRERE8lILJI8mDhzs90GDOWWeHTfDwLBMwfcMAfNQukAw3Jf3BPJKpFseCKKHAuvJZcFyZvhsQpPSBQf2ddb+G6AzvSwZfLfQA6RCF5PJ3QlO4hsoe+ozzRoNL3Dq+7xSgfyM28u7XrbMA8tafPJWDERERHIreQVS7g42EZFByjCsoyPzq25+ZhPN7Bkze9bMzqyR7hgzczObUC9PVSAiImVllv1VMxvrBC4DDgG2A443s+0C6dYAPgs8kqV4qkBERMqoMpVJ1ldtuwPPuvvz7r4UuBmYFEh3HnAhsDhLERUDWRWNzoTrgZG6fa0X3mD9RXlHlGcJmEMqqB0MmIeWLVtSMx8AetLreZbge6KcPT3L+eNf/878hYvYZZst2WDdtcMB80Aw3JJB8yGBn8jyQGC9q7t3kdIpwiHZZLGCA+1Do+FD6TIkSo2Gzxh8b+geEg/+d+Umy92vXx4N38Y7ysymVv19hbtfEf9/DDCt6r3pwB69tma2MzDO3e8ws//MskFVIDKg3Hrfg5z6rctY1tODmbFk2TKO2vddXPXVzzF09e76GYiUSWOV7Ux37ytuUfNKwcw6gIuBExvZoLqwZMD4zdQnOfFrFzPrrXnMW7iItxYsZMnSZfzydw9z7JfOb3XxRBpTuY23gBgIUYtjXNXfY4EZVX+vAewAPGBmLwJ7ApPrBdJVgciA8ZXLrmXRkiWp5YuXLOX+qU/yjxenBdYSKbHiKpDHgK3MbDMzWw04DphcedPd57r7KHff1N03BR4GjnT3qeHsIu3bhVXowL5+HiSY2nzOQYPRwpx5ZZhVN8sgwWVL0+v1BJYl0vnyZXXTBMvQxwDEFStWMPXv/0y/V+X+hx5jmw3WWbkgw4BAulZLpbHOrnTmyWM8JL1eprhI6JIu9JTC1LKMs/+mFmX87vd73KKJM/S2TQymuKlM3L3HzE4D7gI6gWvc/SkzOxeY6u6Ta+cQ1r4ViEgVM6PDjOV9XFiYGV1DAsFvkTIrsLJz9ynAlMSys/tIu1+WPNWFJQOCmXHQhJ36/L0tX7GCQ/bcuX8LJbIqio2BNIUqEBkwzv+34xnWnb7Tatjq3Xz8sPcwZvS6LSiVSF5W5DiQplAFIgPGTltswn3fPZtd37E53V1DGL56N2uNGMaXP3wUl/z7x1tdPJHGlbwFMrBjIGWYeTc0kDBT4DLjYK5MgxkzDBLM8hhaSA8SzBAwB/CliYGtyb+z5hUYbFhd9gnjRvPIRWfy2uy3mL94MRuPXoeuIUNg4bz0eqFBgsmgeU862O+rrZ5alunnG3qEbmKAowVn3s2wLNOMvZAqafAxu80YXFhD3sfQDrTH1yZVRqKX2MCuQGTQWn/tNVmfNVtdDJFVoAdKiYhIXiVvUakCEREpK1UgIiLSMCM8CWiJlKQCafFI8CzyjgLPnXfOdXOOag8HzEOjxRNThWQJmAMsWVT77z6XJfIKBdFDM/smhR5NGwqidycC5N1D02kCxzPLHLfBq8nEMg+ksdBJJMvnHhydHipYnbyjQmRYMbB+lnh8aa+yM32qTWThRzGXSEkqEBERSVELREREcilt6yyiCkREpIxMt/GKiEheaoGUTQkC9lmC5lmDolnWy7IslKaPqdN7rRaagj0URF+8oPffC+en0yxaWH9ZcPr4QLA/aUhgCvbAVO0MHVY/79DI/lQwPH3laMHH4yaOcWeBn1/w3JNcL5Ak66j2lk/xXu6TayFCN3+UyCCsQERE2oC6sEREJDd1YYmISC66jVeaK8NsvFmWhQbjhZYlB/JlGWwI6VjG/MDsuPPmphb5wkTsZFFgsOGyDDGQrkAMZGh6kKAlBy6G4kChH3UyxhKKuYQGQXYm8g8d88Cjd/N/7tI2TAMJRUQkL7VAREQkF8VARESkcaYWiIiI5GAoBiLOymBmX0HNvI+vXQWpgWgZHnsLeDLIGxpoFxxImAh+L0gH0X3O7PR6cxOB9QULUkl8SWBwYYJ1BwYNDh+ezmut3nlZ6D780ADE7u5Emu5UktSxAyzDo4Wb+rmHBuOtymNupVjqwhIRkVzUhSUDyYTjT+WNOW+xxbiNogXLA7emhm59TaYLtVxCt+Mmb30NPbcktCwp1JIILUtONxK6/Td0i25nYr3QrbfJNMBz015h9DojefzWa9LpZXAz6+MW7vJQBSINeWPOXOYvDHRRSS7zFy2E2eoKkj6oC0sGki3GjQHgN1d8O1qwMDAgcEF6QCBz5/T+e86bqSQ+K72sv2MgrLVW7/XWWTedZmRg2VojE3mvlU4zbI3UogNOOSOdTgTQXVhFyRpELDLYKLVnga0Ee7OMVofU42p93lvpNLNmpRaRcSZ7AAAGQklEQVQtf613pbJsdroCWbGgfgXSMTxdgXStna5AOhPdbx7owrLkjL0APSMShQocl2CAvPKvvruFyDJrcClmFs5Ad2GJiEhuaoGIiEguZWwZVVEFIiJSSnoeSHtQ/3NY3quf0G28yScJBoLhy2emBxIuenFmr7/nzErPxrtwYSDmkjBsWPqrPnJuOq+hnb1/sJ0jRqTSBJ+KGNrnLCqHuORXmv2uXeIUzWSUfp9VgYiIlJViICIi0jhTC0RERHIwNBJdRETy0EDC9pBloFF/C7ZcAwtTRc3Y5E3uc+iLGlhm8XrWEV0ZeUdozqfAshWJgi5OT4ey9LX0CPZXXu490v3VuenH5S7IMBfW8LfS+7LBonTwfeyI3rPoDt1gdDqz5L5Aep8Dx8VCx6ryeVWOdfBzyPKZ5v3cs63W70reddNvSn4cVIGIiJSVWiAiItIwM01lIiIiOZW8BVLu0omIDGZm2V91s7KJZvaMmT1rZmcG3v+8mT1tZn8xs/vMbJN6eaoF0nRG1XDj+N8MjxXN/LjQvAHWxLJQgDf04KTKF7XyWNfVVk+n6R6aXpacwTYQaF86Kz06/aU5vYPtLyxOjwKfu7x+EH2tzvS10tLAzRLrJcowNHRDQGg23uQ+h45L6FG4leNZOda1Au11l+VJsyrrlbt7pf0VdxeWmXUClwEHAdOBx8xssrs/XZXsz8AEd19oZqcCFwLH1spXLRARkZIys8yvOnYHnnX35919KXAzMKk6gbvf7+4L4z8fBsbWy1QtEBGRMjIabYGMMrOpVX9f4e5XxP8fA0yrem86sEeNvD4B/KreBlWBiIiUUsPPRJ/p7hP6ziwl2E9uZh8GJgD71tvgwKpAyjggsFCB70BqYFggTXBK6N7Hxkh/Ud0DX4/KFdGQaMCddadnofURgce5Ll/WO5sN0o+vXbo0Hcv4y4LeMZCXlqQH/y3L8Dl3BY7LvEDsZKdEGWyDDdKZjVovvSyxzxaKAw3pTi+rlKszOtYWOmGEPr/kstDnHuzWGOBxi5IPvGtYcXdhTQfGVf09FpiR2pzZgcBXgH3dPT1qN0ExEBGRMqpM517MXViPAVuZ2WZmthpwHDC51+bMdgZ+CBzp7q9nKeLAaoGIiAwYxd2F5e49ZnYacBfQCVzj7k+Z2bnAVHefDHwbGAH8NA7Kv+TuR9bKVxWIiEhZFdgl5+5TgCmJZWdX/f/ARvM0b2GcwMymr7XmmmPG77RjnZT9XMbg5vKX4Ym/PgXA+B23j7PKklfG7aWSZV0vmS6wXqCcTzz9DADjt9kqWhCYyNBXBJ7O19M7BsKi9JiPZa9GcZEXlkRjPTbrXo3XFvReb3GgTJ5hny3Q97964Me5/vCVY19eWLKUzmFD2WL0yN6Jhg5PbyAxZiY4cWIglvHEP/4JwPjt3hGvmDFukXeCxUzno3wTM6a+55nlLWeRGtvgE3/5KyNGDGf6yzOaVtIJ223tj954eeb0nbsc9HiNIHpTtLoC+TMwGni2ZYXoH1vG/w6E/eyPfRkf//tEE7eRRX+UQ9+N9rQl8Ia779ysDUzY/h3+6E0NVCDjD+z3CqSlXVjNPPjSvszsAQB330/lkEGt5HNhKQYiIlJWJb8tWRWIiEgp6YmEIiKSl1ogIiLSMLO3Zygoq3KXTkRkEMswy25LqQIRESkrxUBERKRhlbmwSkwViJTRlsCIyjiMFhoPzG9xGWTQ0l1YInm80eoCxOZTnrLIYKQWiEhjNEOBSCz4LJ/yUAUiIlJG2Z7z0VKqQEREykoxEBERyUUtEBERaZzuwhIRkbzUAhERkXxUgYiISKM0El1ERHIrd/2hCkREpLzKXYOoAhERKSUNJBQRkbxUgYiISD6qQEREJA+1QEREpGGmkegiIpKXWiAiIpKPKhAREcnB1AIREZFcVIGIiEjjDHVhiYhIPmqBiIhIwzQbr4iI5KcKRERE8lALREREcil3/aEKRESknDSViYiI5KEguoiI5KcKRERE8lALREREGqdH2oqISG7lrkDM3VtdBhERSTCzXwOjGlhlprtPbFZ5QlSBiIhILuW+yVhEREpLFYiIiOSiCkRERHJRBSIiIrmoAhERkVxUgYiISC6qQEREJBdVICIikosqEBERyeX/A8HI3Ylb2W4LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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q6FvHoscqk37sMW9mp8SmxUpaPHJx2W/j62hiP5CWGF4FiIjIMGE03BN90KkAEREpqZKXHypARETKSk1YIiJSSNnDN0O3ABnuAfMyBMcHPe/S3OB7IK1mdkps2ui4oTy1MLBe9qtyyij/YIVDtwARERnmyl7UqQARESkpK3ltSQWIiEhJlbv4UAEiIlJKGgurWYbKqLptCTpHbDuUA/lD1WAfgmBTR8HgdNHe3C0f2XeEjeJrpiYsEREpprPkXdFVgIiIlJBRzopRNRUgIiJl1MQpbVulnAVIGTsJljWO0NJ8laAj4WB3SmzWL7blk/oVHY234EpFOyA2dTbFkTeKr2IgIiJSSMnLDxUgIiJlpRqIiIg0TEF0EREpxqCj5CVIOQqQlgVKR2DAPDoP6oAYzkILA7UxmjoYb0RQe0h3QBz+o/iWvPwoSQEiIiIZ6okuIiIFGGAlnxBEBYiISBkZdHSoBiIiIgWoCWtQlTFo3sS0m7m/pgXkW9xjvlXpwHoEaiPSCuaziReDJmY9n3ZsYD23YWCzYRZYH2QlLz+GWwEiIjI8GHqMV0REitBgiiIiUpRiICIiUkjJy4+hXIAMcq/lUgawA/uM3a7o5xns4HsrFQ7IN7GHdy74XjBLIcGLT8Fh4INJZXcQGcAe9B7eTRpdYJCnFtBYWCIiUowZpn4gIiJShGogIiLSMAM6VQMpmTKOaLs++8tuO+ixk2bGfeKSGlTB329MLKNoh7bIDnRNC9+sR+e/Zo3s29T9DS96CktERBo3BPqBlHysRxGRkcvMol8Rac0yswfM7EEzO6XGOm82s/vM7F4zu3ygNFUDEREpqWbVQMysEzgfOBBYANxhZnPd/b6qdXYEPgm82t2fNbMtBkpXNRARkRJK+oE0rQayN/Cguz/s7muAK4EjM+scD5zv7s8CuPtTAyU6RGogLR5lt1kd39oRiI5Jq3AgPyZoP3DSwRUHfbrcgKhRYSO3ixqsNiawXnBO28J5Kra74D6jR9CN6NgXk9ZwH7HXGp5QaqKZzav6+0J3vzD992RgftV7C4B9MtvvBGBmfwQ6gdPd/dp6OxwiBYiIyEjT8JS2T7v7jJqJ5WVL0i5gR2A/YApwi5lNc/fnau1QTVgiImXVYfGv+hYAU6v+ngIsDKzzS3fvcfdHgAdICpTa2Wvw44iIyGAxi3/Vdwewo5ltZ2ajgLcCczPr/AJ4XbJbm0jSpPVwvUTVhCUiUkYG1tmce3x37zWzk4DrSOIbc9z9XjM7E5jn7nPT9w4ys/uAtcDJ7v5MvXRLWoCUIMDarP3FBqdjtotJq6nbBTMWkXZosxb2YC8qNkCe2y6wTlRbdUSgOyrQXmtZxCpRgfXIDXN5b+KUtkUNq8B6c3sSuvs1wDWZZadV/duBj6SvKCUtQERERjYzNBqviIgUVPKxTFSAyLDg7jz73BIWPPFvli5bTk9vL+5OV1cno0eNYsvNJzJpyy3o7u5ud1ZFoqkGUjatnC1vsOMdofWa2SHQ+yLyFZn3Js6AuGDhv/njvLv407y/cP8/H2L+wieYv/DfdHd3MWXrrdho7Bi6u7vpMKN37VpWr1nDE08u4qlnFrPFZhOYOmkrtps6hb332J1X77UHu+/y0nUFS+6OL/IHnN0uGA+IiZ2Egqahdv2BVwkqvF1EXKRoTKm0MxmWQMk/48grQGTIWbFyJVffcBNXX38jf7jjLpavWMGr99qTV83Yk4Nf91qmTtqKqZO2ZvxG4+qm09PTwxNPLeKxx5/goX89xq133s0lV/yERxc8zl67v4z9930lbz3yULZ/0TaD9MlE6rCo/h1tpQJESqm3t5ff/uFWLv/Z1cy94Ub2nv4yjj50Fp/58Ins9OLtCs2T0N3dzTaTJ7HN5Ensu/fLOe5NbwTg2eeWcOudd3PNjb/jVUe8he233YZj3ngYbzniELaYOLHZH00kmuYDEWnA80uX8d3LruQbF/+AyVtvyTFHHsY5n/oYW22xecv2uekmG3PIATM55ICZfO2MU/nNLX/i8p9fzWlfOo+DX/daPn7i8UyftkvL9i9Sk2ogIgN7fukyvn7xpXzzkh9y0Mx9ufr732X6tJ0HvW9Pd3c3B+8/k4P3n5kWZv/LYcf9N9N32ZkzTv4fXr7btEHNj4xgyXC87c5FXSUpQApcJMowNW2zRpQtGvgOphUKfAczEZF2RBB9PYP9fX19XHLlVXz63POYtd++3PrLK9hhu22TdfrWNu88x/4Qq9YbP3ZDTn7vO/ng7GO56IqfcOTsE3jtPjP46umfDNSIsiPFxnY2bGUHxIhezEU7G0Jkp8ui56YMU+FGjBrcYg2OxjvoSp49Gc4eeOgR9n/zcVxyxU+54Yo5fP/rX1xXeJTI6NGjOGn2sfy/W65l26mT2f3AI7nkiqvwwR75QEYWM6yzI/rVDipAZND19PRw9je+w75vfBv/cchB/PEXl7Pbzi9pd7YGNGbDDfnCKR/hussv4buXXckBbz6Ofz78aLuzJcNZ8wZTbAkVIDKoFj2zmIOOeRe///MdzLvmKj74rrfT2dnZ7mw1ZPquO3Pr3P/l8IP2Z983vo2rb7ix3VmS4ap5w7m3Jntt2auMSHffez97H3o0r5yxB9f88EK2nTK53VkqrLOzkw8fP5tfzrmAEz95Bp/7+gVq0pKmSioWTZvStiVKEkQfQBlH2Q1vGJdWM3tzZwPdMQFzgL5sgDxmncB6MesAP557DSd95my+ddanePNhs5J1+nozmw183Jt5kY760Q0Q+H7F9GncNvcKjj7hw9xz7/1c+tWzGTd2LMGAa0fgfi0bbA92RI9IK7QOoXPTwsB68HAWDLSXcsTeNij5Y7yqgUjLXXT5Tzj57C9z/WUXpYXH8DJpyy246cffZ+yYDTnk7e9l2fLl7c6SDAsNxD8UA5HhaM6VP+Wsr3+b314xh+m7vrTd2WmZ0aNHMecrZ7PT9ttx+OwTWb5iRbuzJMNA2ZuwVIBIy/zgql9y2pe/yW+unMMOI2B8qY6ODi485wxeNGUyR77r/axYubLdWZKhzCh9EH1oxEBitXuk3VbPIhjTSTBqBN3AekW3C8VAvI+fXfsbPvmFr/Kbyy9ip22n4H1rB047u06tfMXkMytwhxbcKttzqyPwhFggLUvz2QFcfM5nmf3RT3P08R9k7pxv0dVV+ZmF2t4z++uLWAfy4Y3gHWjo/jC7YeQ9ZDPjIjEzGQZFdOxraXxj8B+SKPtYWKqBSNPdc98DnHDqWVw955vsvMOL252dQdfZ2cmlXzmbPu/j45//aruzI0OZaiCy58yDWPTMM+zw4u3SJRHzKATFDOvRzLSK1egeXbCQzSdsykfP+nJj+499gqylsvN6RKxTY5Xe3rV8/6pfcMc9f6OrqzNyu9gLQVwemrRS1HF48OFH2Hyzzbjr9zfEpSn1pT3Ry0wFyCBY9MwzLFs+coKqL5o8qd1ZKIWurk5223mndmdj0Iyk7/igKXkTlgqQQVCpedw096p0yfCPgeSTivjMgxwDCa9XMAaSm5EwcOcY6geSXS+Uz1BahbeLWafYQJCvO+LowDpSWCWIXmLDvACJbP6IGmG2iQqPaBtaLVsQxBY8xQqC7DIPbRcqCLLL1oYKi4IFXVFFL8yBoVc8W9AECh4LBtGzI8WGCp78oriVIjoShkbxbeZovDFif2+5/UVOhTuElT2IPswLEBGRoap9wfFYKkBERMpKNRAREWmYoQJEREQKUgFSQFOnq22W+Gla89t51b9D64WeSCo6VWxkMDwb/I58AioXNF/bk98uFCDPph/aX8x2ITGB9di5QbPB79BcJX2BZRHbeWd3IFvZIHrk97poYD2bfuyAvcF4dY28DvTbzF0TW3yRjJnStpQj9Fr4yb0SKWcBIiIiJSjE6lMBIiJSRoZqICIiUoSasMonesTcImnVSNur/h2bp9hZCnO92iPiHZCPLYTiHWt7c8vILusNxEBCcYvMeuG0I2Igre5IGBHLsM7Az6YrE98IxUkC588zaQXTjvnIoetMTFt/7Ci7UXGDTKyvkbQGTLtgOkNdyT/jyCtARESGAj3GKyIihakAERGRxikGIiIiRakGMhAfOGAWHeRuVufCFgXaPRBkrLG76FF1swHygqPxRgXMIR8071kT2C4fWPfebPB9dWC7gh0QiwZcQ0O1x3QI7BqdTz6br0CnwRjBmHMosO5W/28IPzyRu6ENRt8Di9Zj1N7cdhGB/MKdCyNG6I3tNBjTAbGVFAMREZHCVICIiEijDMMUAxERkUJKXgMpd/EmIjJSVYYyiX0NlJzZLDN7wMweNLNT6qx3tJm5mc0YKM3hXQNp+Yi9BafCje3BHrMspid61Ki6EQFzyAXNvTcQRA8F1nsyQfPQdtlAO+QD8hFT4fauXcuf7n+IZStXs+cO27DVphtHT02bC353BX4igTx4Xyaw3p0/V4XDwoEe87mpTkNB9IZ6j1dvFxkwz+UhsI+mBsizeSrjCLrN1LzHeM2sEzgfOBBYANxhZnPd/b7MehsBHwRui0l3eBcgMuL89A938r7zf0RP71rMYHVPL0e9cg8u/tBxbDh6VLuzJ9KY5hWIewMPuvvDSbJ2JXAkcF9mvbOAc4GPxSSqJiwZNm685x/M/tqlLF66nKUrV/H8ilWs7unll3++m7d84bvtzp5IYyqP8ca+YKKZzat6/XdVapOB+VV/L0iXrdud2R7AVHf/VWwWVQORYeNT3/85K1fnm91Wrenhpnv+wT/mP8FLp27dhpyJFNRYDeRpd68Vt6g7bKaZdQBfA2Y3ssORV4BExRGa2JGwX9qeXxSbp1rr5WYWjIh3QD62EBHvgEDMY82q/HbBZZkYSChOEtouGxep0dmwr6+Pef/8V/69Kjfdfg8v3XTcugWhGEg25jFqg/w63YFYTczoxoE8RV0eAhcRzyyzYEqhzpOZZcF4R2ROY2dPjEp/4N3lDuCwineENHUokwXA1Kq/pwALq/7eCJgG3JzG17YC5prZEe4+r1aiI68AkWHJzOgwY22Ni5OZ0d0VKDBEyqx5heQdwI5mth3wOPBW4G2VN919CTBx3W7tZuBj9QoPUAxEhgkz48CX7Vjzjn5tXx8H77HzoOZJZL00HgOpyd17gZOA64D7gR+7+71mdqaZHVE0i6qByLDx+WMO4ZZ/PMLy1f2bx8aM7ua4mXszecLGbcqZSBHNHY3X3a8BrsksO63GuvvFpKkaiAwbu227Nb/9zHt5+YunMLqrk7GjR7HxmA049agDOO+dR7U7eyKNa1INpFWGcA2k1Z0EY7IQG3z32u/X3S63MGJZKNAeCDxng9HBdSIC66HA9+qV+WWrVg68zurACL3Z/YU6G1blfcbmG3HbJ97Bk88vY9nqHraZMJ7uzk5Y9nx+u1Anwe5MX5HRgWD/6A3zy2Km2g1NoZv94YcC+6EHBzoyy4J3qgUfGAl2Lgyt1sgUzdVpDfbFLhd9H+T9F1TpiV5iQ7gAEalty/Hj2LLdmRBZL5pQSkREiir5o8oqQEREykoFiIiINMwIx81KpJwFSDOmj226ZqYdkVZsYD2zLNjrPBQgzy4L9ETPTUML+VF1sz3MIR8wB1i5ov7fACuX5/OwOhOk7wkE9kNB5qxQcLo7P+2sjc70PO8dm9+uL6YndeCHH5hC1zPLrCPw+UJT72bOn/fl17FQHqJG480vWo9+9BFpNfEuu+gIvTHb9VtnMB7isfzIASVTzgJERERUAxERkYIUAxERkYaZHuMVEZGiVAMZgpoydHtgUWjKz1rbBZOPCKzHBt+zQfTYnugxw7mHepmvWNb/7+VLc6v4smW5ZazMpLUmcircrFCv81H5GQp9w/69zC001W+o13k22BkzVDxAVyaQ3xdYJ3Ruip737LLYgHnsEO+57ZsUwB6pQg9QlIgKEBGRMlITloiIFFbympgKEBGRstJjvG20PlPFtl3B0XhD7fOhzoWZ9TzUzh6a5jYbbwhNTbsqEBfJxEB8aT4GwvOBEXMzcZG+Ffm0vWfgjoTWnW9L7hgTmK523Lh+f3rgu2Ghu8JsLCM7qi+EYzWZY+yB7SzmnAZHAy44enMZBOMi2XUC25X8jr0hpo6EIiJSlGpEu9cSAAAHhklEQVQgIiJSSMlrVCpARERKyVQDERGRAgzFQIa1ZgXf1yedmG2DHQm9/t+1tst2LgwF0QOdC31FZvTdQMDcFz+bW9bzbP8Retc+n++k2Ld64I6EHaPzX/XO8fmpabvX9P98wZlcAx0CbYNMWj2BAH2oY2b2GIfOQ+y5KbJO7HaFB95VJ8H1UvJjpQJERKSs1IQlw8mME09n0ZJlbD9pi2RBaKiP0LLsPB7BuUZCc5JkHjfuzT+yG3rUNiv06K11BebQ6Mr8YANzhgSHJMmu1xlYJ7DsoScWsfkm45k350v59WVkMwsPiVMiKkCkIYuWLGXZqsAkUlLIspWBPjMiFWrCkuFk+0lbAnDjlz6eLFi6JL/S88/lFvniZ/ovWLw4sE4JYiCb9p+B0CZsmk9swoTcIpuwWf8F4zfJb7fRxrlF+5/ylfx6IoCewpLBVzhwOnBv9XRh8r9KYDc0nWxoxNxs7/TAyLtrnsoH1tc82b+AWros38y1ZvXAPdFHjc43BWwUKIyyvdpHjwo0YY0Zk1+W/cyh41JvKtzKsQ4e8wilHE1B1ouewhIRkcJUAxERkUIUAxERkcZpPpAmUftutKJ3LKGqctHqc2hk30yMYO3SFblVegIxkCef6h+neG5pPr6yKjTacMYGgR/iJivyI99ulTl+XRvnA+2dmwRiPKHP3IjKsS56zEt+p1oujcyu2EZG6c/rEClARERGIMVARESkcaYaiIiIFGCoJ7qIiBShjoQSI1RNjX1uIKaKG1on20Ep1GEptF1lWWU8qNAdUsfAd019K/MdAp9bkh8i5ckl/TsgPt2T73W+MiKIvmEgiL46sN0GG/TP++hAPoOfLvuZQ8clNIZWReW4hs5D7Lkpsk4zt5PmK/m5UAEiIlJWqoGIiEjDzDSUiYiIFKQaiIiIFKIYSDMEJxYd9Fy0X+g4ZJaF7lhCwyFkgr7WmR911rtHB3aXptWd9uIene+pzYZjA8sy6wWq5kufz/fwfiIzxey/AkO3r1g78HdhTGd+fz2BEWwnZPKwZagJIftZIP+ZQ8elO9/zfd3xTI516DwEH0rIntPgnWrE92VEGirHoLlPYZnZLOA8kudALnb3L2be/wjwHqAXWAS8y93/VS/NctePRERGMDOLfg2QTidwPnAwsAtwjJntklntL8AMd98NuAo4d6D8qQARESkjI6mBxL7q2xt40N0fdvc1wJXAkdUruPtN7l4ZpO7PwJSBEh0iTVgiIiNNw3OiTzSzeVV/X+juF6b/ngzMr3pvAbBPnbTeDfx6oB2qAFkf2WpjU2eFa7BjX+XPQLzDg23omVPfFZpBL9BmX0m/0sYfGoU2sMzW9o9ldD8bmL6295HcsvtX9N/u6Z582msj4mGdPfljF4qd7DS2/7LuKVvk1rEtt8rvYJPM1Ldjx+XXCcVFOjIxpVBnw+y5gnwMKxTnivi+tDweUPIgcOk1FgN52t1n1EopsCz4wzGz/wJmADMH2qEKEBGRMmrucO4LgKlVf08BFuZ2afZ64FPATHfPDw2RoQJERKSUmvoU1h3Ajma2HfA48Fbgbf32ZrYH8F1glrs/FZOoChARkbJqUg3E3XvN7CTgOpLHeOe4+71mdiYwz93nAl8CxgE/SZ/qeszdj6ibPW9qu31jzGzBxuPHT56+27TmJJj7KM38bIG0opJ37v77fQBMn7ZLje0i8xlzrkLrBJf11f8bCH037nngQQB233G7ZEEoBtKb76tBb2ZQwhX5GQmXP5nMSDg/3X5qV1du8MSe4GkY+LhYoAm4O/DbnNi97p5qfm8v3WNGsf2mmXjGmDH5Dbsy/TeCsYx8LOqefz4KwO4v2T7JZzBuETFbZFS8IyD6AhVKv/+f677nuzaYVDPjJAPnsxnu/uvfGTduLAseX9iyIM+MXXby2390QfT6nXseeGedGEhLtLsA+QuwOfBg2zIxOHZI/z8cPudgfJbp6f/vbuE+YgxGPvTdGJp2ABa5+x6t2sGMXV/it1/eQAEy/fWDXoC0tQmrlQdfhi4zuxnA3fdTPmRE01hYIiJSSMkfg1YBIiJSSpqRUEREilINREREGmYGneW+RJc7dyIiI9hAo+y2mwoQEZGyUgxEREQa1tyxsFpCBYiU0Q7AuEo/jDaaDixrcx5kxNJTWCJFLGp3BlLLKE9eZCRSDUSkMRqhQCQVmuelRFSAiIiUkZlqICIiUpBiICIiUohqICIi0jg9hSUiIkWpBiIiIsWoABERkUapJ7qIiBRW7vJDBYiISHmVuwRRASIiUkrqSCgiIkWpABERkWJUgIiISBGqgYiISMNMPdFFRKQo1UBERKQYFSAiIlKAqQYiIiKFqAAREZHGGWrCEhGRYlQDERGRhmk0XhERKU4FiIiIFKEaiIiIFFLu8kMFiIhIOWkoExERKUJBdBERKU4FiIiIFKEaiIiINE5T2oqISGHlLkDM3dudBxERyTCza4GJDWzytLvPalV+QlSAiIhIIeV+yFhEREpLBYiIiBSiAkRERApRASIiIoWoABERkUJUgIiISCEqQEREpBAVICIiUogKEBERKeT/AzT5f67WE8fHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "left_post_x, left_post_y = (65, (36/80)*65)\n",
    "right_post_x, right_post_y = (65, (44/80)*65)\n",
    "\n",
    "for dist_defender in range(1, 10, 2):\n",
    "    for num_blocking_defenders in range(5):\n",
    "        xG_2d = np.zeros((65,65))\n",
    "        # For each x, y grid calculate xG\n",
    "        for x in range(65):\n",
    "            for y in range(65):\n",
    "                shot={}\n",
    "\n",
    "                # Distance to centre of goal and angle\n",
    "                shot['distance']=np.sqrt((65-x)**2 + (65/2-y)**2)\n",
    "\n",
    "                # Angle to goal (radians)\n",
    "                a = np.sqrt((x - right_post_x)**2 + (y - right_post_y)**2)\n",
    "                b = np.sqrt((left_post_x - right_post_x)**2 + (left_post_y - right_post_y)**2)\n",
    "                c = np.sqrt((left_post_x - x)**2 + (left_post_y - y)**2)\n",
    "                angle_ac = np.arccos((a**2 + c**2 - b**2)/(2*a*c))\n",
    "                if angle_ac<0:\n",
    "                    angle_ac=np.pi+angle_ac\n",
    "                shot['angle'] =angle_ac\n",
    "\n",
    "                shot['distance_nearest_defender'] = dist_defender\n",
    "                shot['number_blocking_defenders'] = num_blocking_defenders\n",
    "\n",
    "                xG_2d[64-x,64-y] =  calculate_xG(shot)\n",
    "\n",
    "        (fig,ax) = FCPython.createGoalMouth()\n",
    "        pos=ax.imshow(xG_2d, aspect = 'auto', origin=\"lower\", extent=(0, 65, 0, 65), cmap=plt.cm.Reds)\n",
    "        fig.colorbar(pos, ax=ax)\n",
    "        ax.set_title('Expected Goals - # Blocking : '+str(num_blocking_defenders) + ' & Dist: ' + str(dist_defender))\n",
    "        plt.xlim((-1,66))\n",
    "        plt.ylim((-3,35))\n",
    "        plt.tight_layout()\n",
    "        plt.gca().set_aspect('equal', adjustable='box')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### StatsBomb Comparison\n",
    "\n",
    "There is a positive correlation between the logistic regression model and StatsBomb's own model, which is reassuring. It's not nearly as sophisticated, but seems to generally work as intended after looking at the previous shot location probabilities depending on number of blocking defenders and distance to nearest defenders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T11:31:25.309991Z",
     "start_time": "2021-01-03T11:31:25.197266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(test['xG'], test['statsbomb_xg'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}