interactive-pitch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d12d3dd0",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:04.575251Z",
     "iopub.status.busy": "2022-03-21T17:12:04.574390Z",
     "iopub.status.idle": "2022-03-21T17:12:05.900130Z",
     "shell.execute_reply": "2022-03-21T17:12:05.900561Z",
     "shell.execute_reply.started": "2022-03-21T15:59:44.180040Z"
    },
    "papermill": {
     "duration": 1.374918,
     "end_time": "2022-03-21T17:12:05.900906",
     "exception": false,
     "start_time": "2022-03-21T17:12:04.525988",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Arc\n",
    "import bz2\n",
    "from glob import glob\n",
    "import imageio\n",
    "import requests\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35309ce3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:05.970813Z",
     "iopub.status.busy": "2022-03-21T17:12:05.970266Z",
     "iopub.status.idle": "2022-03-21T17:12:05.974712Z",
     "shell.execute_reply": "2022-03-21T17:12:05.975206Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.674196Z"
    },
    "papermill": {
     "duration": 0.040781,
     "end_time": "2022-03-21T17:12:05.975366",
     "exception": false,
     "start_time": "2022-03-21T17:12:05.934585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(10.5,6.8)})\n",
    "length = 105\n",
    "width = 68"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a423c47c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.046231Z",
     "iopub.status.busy": "2022-03-21T17:12:06.045584Z",
     "iopub.status.idle": "2022-03-21T17:12:06.063096Z",
     "shell.execute_reply": "2022-03-21T17:12:06.063599Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.683352Z"
    },
    "papermill": {
     "duration": 0.053406,
     "end_time": "2022-03-21T17:12:06.063768",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.010362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_pitch(length, width, linecolor, bounds = 15):\n",
    "\n",
    "    \"\"\"\n",
    "    mainly stolen from fc python\n",
    "    param length: an int the length of the field\n",
    "    param width: an int the height of the field\n",
    "    param linecolor: the color of the lines\n",
    "\"\"\"\n",
    "    #Create figure\n",
    "    fig=plt.figure()\n",
    "    #fig.set_size_inches(7, 5)\n",
    "    ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "    #Pitch Outline & Centre Line\n",
    "    plt.plot([0,0],[0,width], color=linecolor)\n",
    "    plt.plot([0,length],[width,width], color=linecolor)\n",
    "    plt.plot([length,length],[width,0], color=linecolor)\n",
    "    plt.plot([length,0],[0,0], color=linecolor)\n",
    "    plt.plot([length/2,length/2],[0,width], color=linecolor)\n",
    "    plt.fill_between(x = [-bounds, length+bounds],\n",
    "                     y1 = [width+bounds, width + bounds],\n",
    "                     y2 = [-bounds, -bounds], color='green')\n",
    "\n",
    "    #Left Penalty Area\n",
    "    plt.plot([16.5 ,16.5],[(width/2 +16.5),(width/2-16.5)],color=linecolor)\n",
    "    plt.plot([0,16.5],[(width/2 +16.5),(width/2 +16.5)],color=linecolor)\n",
    "    plt.plot([16.5,0],[(width/2 -16.5),(width/2 -16.5)],color=linecolor)\n",
    "\n",
    "    #Right Penalty Area\n",
    "    plt.plot([(length-16.5),length],[(width/2 +16.5),(width/2 +16.5)],color=linecolor)\n",
    "    plt.plot([(length-16.5), (length-16.5)],[(width/2 +16.5),(width/2-16.5)],color=linecolor)\n",
    "    plt.plot([(length-16.5),length],[(width/2 -16.5),(width/2 -16.5)],color=linecolor)\n",
    "\n",
    "    #Left 5-meters Box\n",
    "    plt.plot([0,5.5],[(width/2+7.32/2+5.5),(width/2+7.32/2+5.5)],color=linecolor)\n",
    "    plt.plot([5.5,5.5],[(width/2+7.32/2+5.5),(width/2-7.32/2-5.5)],color=linecolor)\n",
    "    plt.plot([5.5,0.5],[(width/2-7.32/2-5.5),(width/2-7.32/2-5.5)],color=linecolor)\n",
    "\n",
    "    #Right 5-meters Box\n",
    "    plt.plot([length,length-5.5],[(width/2+7.32/2+5.5),(width/2+7.32/2+5.5)],color=linecolor)\n",
    "    plt.plot([length-5.5,length-5.5],[(width/2+7.32/2+5.5),width/2-7.32/2-5.5],color=linecolor)\n",
    "    plt.plot([length-5.5,length],[width/2-7.32/2-5.5,width/2-7.32/2-5.5],color=linecolor)\n",
    "\n",
    "    #Prepare Circles\n",
    "    centreCircle = plt.Circle((length/2,width/2),9.15,color=linecolor,fill=False)\n",
    "    centreSpot = plt.Circle((length/2,width/2),0.8,color=linecolor)\n",
    "    leftPenSpot = plt.Circle((11,width/2),0.8,color=linecolor)\n",
    "    rightPenSpot = plt.Circle((length-11,width/2),0.8,color=linecolor)\n",
    "\n",
    "    #Draw Circles\n",
    "    ax.add_patch(centreCircle)\n",
    "    ax.add_patch(centreSpot)\n",
    "    ax.add_patch(leftPenSpot)\n",
    "    ax.add_patch(rightPenSpot)\n",
    "\n",
    "    #Prepare Arcs\n",
    "    leftArc = Arc((11,width/2),height=18.3,width=18.3,angle=0,theta1=308,theta2=52,color=linecolor)\n",
    "    rightArc = Arc((length-11,width/2),height=18.3,width=18.3,angle=0,theta1=128,theta2=232,color=linecolor)\n",
    "\n",
    "    #Draw Arcs\n",
    "    ax.add_patch(leftArc)\n",
    "    ax.add_patch(rightArc)\n",
    "            #Axis titles\n",
    "    #Tidy Axes\n",
    "    plt.axis('off')\n",
    "    \n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1fc3a05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.135258Z",
     "iopub.status.busy": "2022-03-21T17:12:06.134662Z",
     "iopub.status.idle": "2022-03-21T17:12:06.172843Z",
     "shell.execute_reply": "2022-03-21T17:12:06.173332Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.712669Z"
    },
    "papermill": {
     "duration": 0.075837,
     "end_time": "2022-03-21T17:12:06.173496",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.097659",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "match_results = pd.read_csv(\"../input/euro-cup-2020/eurocup_2020_results.csv\")\n",
    "#original dataset has whitespaces, so annoying\n",
    "match_results[['team_name_home', 'team_name_away']] = match_results[['team_name_home', 'team_name_away']].applymap(lambda x: str.strip(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d71e9922",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.241357Z",
     "iopub.status.busy": "2022-03-21T17:12:06.240689Z",
     "iopub.status.idle": "2022-03-21T17:12:06.262490Z",
     "shell.execute_reply": "2022-03-21T17:12:06.262929Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.766985Z"
    },
    "papermill": {
     "duration": 0.056979,
     "end_time": "2022-03-21T17:12:06.263108",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.206129",
     "status": "completed"
    },
    "tags": []
   },
   "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>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>pens</th>\n",
       "      <th>pens_home_score</th>\n",
       "      <th>pens_away_score</th>\n",
       "      <th>team_name_home</th>\n",
       "      <th>team_name_away</th>\n",
       "      <th>team_home_score</th>\n",
       "      <th>team_away_score</th>\n",
       "      <th>possession_home</th>\n",
       "      <th>possession_away</th>\n",
       "      <th>total_shots_home</th>\n",
       "      <th>total_shots_away</th>\n",
       "      <th>shots_on_target_home</th>\n",
       "      <th>shots_on_target_away</th>\n",
       "      <th>duels_won_home</th>\n",
       "      <th>duels_won_away</th>\n",
       "      <th>events_list</th>\n",
       "      <th>lineup_home</th>\n",
       "      <th>lineup_away</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Final</td>\n",
       "      <td>11.07.2021</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>England</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66%</td>\n",
       "      <td>34%</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>53%</td>\n",
       "      <td>47%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 2' \", ...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Kane', 'Player_Number': '9'}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>07.07.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>59%</td>\n",
       "      <td>41%</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>50%</td>\n",
       "      <td>50%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 30' \",...</td>\n",
       "      <td>[{'Player_Name': 'Kane', 'Player_Number': '9'}...</td>\n",
       "      <td>[{'Player_Name': 'Krogh Damsgaard', 'Player_Nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>06.07.2021</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29%</td>\n",
       "      <td>71%</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>49%</td>\n",
       "      <td>51%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 51' \",...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Torres', 'Player_Number': '1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           stage          date   pens pens_home_score pens_away_score  \\\n",
       "0         Final     11.07.2021   True               3               2   \n",
       "1   Semi-finals    07.07.2021   False           False           False   \n",
       "2   Semi-finals     06.07.2021   True               4               2   \n",
       "\n",
       "  team_name_home team_name_away  team_home_score  team_away_score  \\\n",
       "0          Italy        England                1                1   \n",
       "1        England        Denmark                2                1   \n",
       "2          Italy          Spain                1                1   \n",
       "\n",
       "  possession_home possession_away  total_shots_home  total_shots_away  \\\n",
       "0             66%            34%                 19                 6   \n",
       "1             59%            41%                 20                 6   \n",
       "2             29%            71%                  7                16   \n",
       "\n",
       "   shots_on_target_home  shots_on_target_away duels_won_home duels_won_away  \\\n",
       "0                     6                     2            53%           47%    \n",
       "1                    10                     3            50%           50%    \n",
       "2                     4                     5            49%           51%    \n",
       "\n",
       "                                         events_list  \\\n",
       "0  [{'event_team': 'away', 'event_time': \" 2' \", ...   \n",
       "1  [{'event_team': 'away', 'event_time': \" 30' \",...   \n",
       "2  [{'event_team': 'away', 'event_time': \" 51' \",...   \n",
       "\n",
       "                                         lineup_home  \\\n",
       "0  [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "1  [{'Player_Name': 'Kane', 'Player_Number': '9'}...   \n",
       "2  [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "\n",
       "                                         lineup_away  \n",
       "0  [{'Player_Name': 'Kane', 'Player_Number': '9'}...  \n",
       "1  [{'Player_Name': 'Krogh Damsgaard', 'Player_Nu...  \n",
       "2  [{'Player_Name': 'Torres', 'Player_Number': '1...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_results.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e47d7638",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.340810Z",
     "iopub.status.busy": "2022-03-21T17:12:06.340198Z",
     "iopub.status.idle": "2022-03-21T17:12:06.346185Z",
     "shell.execute_reply": "2022-03-21T17:12:06.345682Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.799395Z"
    },
    "papermill": {
     "duration": 0.04899,
     "end_time": "2022-03-21T17:12:06.346328",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.297338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "italy_wales_matches = (match_results['team_name_home'].isin([\"Italy\", \"Wales\"])) | (match_results['team_name_away'].isin([\"Italy\", \"Wales\"]))\n",
    "iw_matches = match_results[italy_wales_matches]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d74db376",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.435294Z",
     "iopub.status.busy": "2022-03-21T17:12:06.434657Z",
     "iopub.status.idle": "2022-03-21T17:12:06.438391Z",
     "shell.execute_reply": "2022-03-21T17:12:06.438822Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.812995Z"
    },
    "papermill": {
     "duration": 0.058979,
     "end_time": "2022-03-21T17:12:06.439029",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.380050",
     "status": "completed"
    },
    "tags": []
   },
   "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>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>pens</th>\n",
       "      <th>pens_home_score</th>\n",
       "      <th>pens_away_score</th>\n",
       "      <th>team_name_home</th>\n",
       "      <th>team_name_away</th>\n",
       "      <th>team_home_score</th>\n",
       "      <th>team_away_score</th>\n",
       "      <th>possession_home</th>\n",
       "      <th>possession_away</th>\n",
       "      <th>total_shots_home</th>\n",
       "      <th>total_shots_away</th>\n",
       "      <th>shots_on_target_home</th>\n",
       "      <th>shots_on_target_away</th>\n",
       "      <th>duels_won_home</th>\n",
       "      <th>duels_won_away</th>\n",
       "      <th>events_list</th>\n",
       "      <th>lineup_home</th>\n",
       "      <th>lineup_away</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Final</td>\n",
       "      <td>11.07.2021</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>England</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66%</td>\n",
       "      <td>34%</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>53%</td>\n",
       "      <td>47%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 2' \", ...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Kane', 'Player_Number': '9'}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>06.07.2021</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29%</td>\n",
       "      <td>71%</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>49%</td>\n",
       "      <td>51%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 51' \",...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Torres', 'Player_Number': '1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Quarter-finals</td>\n",
       "      <td>02.07.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>46%</td>\n",
       "      <td>54%</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>58%</td>\n",
       "      <td>42%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 13' \",...</td>\n",
       "      <td>[{'Player_Name': 'Lukaku', 'Player_Number': '9...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Round of 16</td>\n",
       "      <td>26.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>52%</td>\n",
       "      <td>48%</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>50%</td>\n",
       "      <td>50%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 2' \", ...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Arnautovic', 'Player_Number'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Round of 16</td>\n",
       "      <td>26.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Wales</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>47%</td>\n",
       "      <td>53%</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>39%</td>\n",
       "      <td>61%</td>\n",
       "      <td>[{'event_team': 'home', 'event_time': \" 26' \",...</td>\n",
       "      <td>[{'Player_Name': 'Moore', 'Player_Number': '13...</td>\n",
       "      <td>[{'Player_Name': 'Dolberg', 'Player_Number': '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Group stage: Matchday 3</td>\n",
       "      <td>20.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Wales</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70%</td>\n",
       "      <td>30%</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>56%</td>\n",
       "      <td>44%</td>\n",
       "      <td>[{'event_team': 'home', 'event_time': \" 39' \",...</td>\n",
       "      <td>[{'Player_Name': 'Chiesa', 'Player_Number': '1...</td>\n",
       "      <td>[{'Player_Name': 'James', 'Player_Number': '20...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Group stage: Matchday 2</td>\n",
       "      <td>16.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>49%</td>\n",
       "      <td>51%</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43%</td>\n",
       "      <td>57%</td>\n",
       "      <td>[{'event_team': 'home', 'event_time': \" 19' \",...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "      <td>[{'Player_Name': 'Embolo', 'Player_Number': '7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Group stage: Matchday 2</td>\n",
       "      <td>16.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Wales</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>64%</td>\n",
       "      <td>36%</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>40%</td>\n",
       "      <td>60%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 42' \",...</td>\n",
       "      <td>[{'Player_Name': 'Yilmaz', 'Player_Number': '1...</td>\n",
       "      <td>[{'Player_Name': 'Moore', 'Player_Number': '13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Group stage: Matchday 1</td>\n",
       "      <td>12.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Wales</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35%</td>\n",
       "      <td>65%</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>46%</td>\n",
       "      <td>55%</td>\n",
       "      <td>[{'event_team': 'away', 'event_time': \" 30' \",...</td>\n",
       "      <td>[{'Player_Name': 'Moore', 'Player_Number': '13...</td>\n",
       "      <td>[{'Player_Name': 'Embolo', 'Player_Number': '7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Group stage: Matchday 1</td>\n",
       "      <td>11.06.2021</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Italy</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>36%</td>\n",
       "      <td>64%</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>51%</td>\n",
       "      <td>49%</td>\n",
       "      <td>[{'event_team': 'home', 'event_time': \" 45' \",...</td>\n",
       "      <td>[{'Player_Name': 'Yilmaz', 'Player_Number': '1...</td>\n",
       "      <td>[{'Player_Name': 'Insigne', 'Player_Number': '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        stage          date   pens pens_home_score  \\\n",
       "0                      Final     11.07.2021   True               3   \n",
       "2                Semi-finals     06.07.2021   True               4   \n",
       "5             Quarter-finals    02.07.2021   False           False   \n",
       "13               Round of 16    26.06.2021   False           False   \n",
       "14               Round of 16    26.06.2021   False           False   \n",
       "25   Group stage: Matchday 3    20.06.2021   False           False   \n",
       "36   Group stage: Matchday 2    16.06.2021   False           False   \n",
       "37   Group stage: Matchday 2    16.06.2021   False           False   \n",
       "49   Group stage: Matchday 1    12.06.2021   False           False   \n",
       "50   Group stage: Matchday 1    11.06.2021   False           False   \n",
       "\n",
       "   pens_away_score team_name_home team_name_away  team_home_score  \\\n",
       "0                2          Italy        England                1   \n",
       "2                2          Italy          Spain                1   \n",
       "5            False        Belgium          Italy                1   \n",
       "13           False          Italy        Austria                2   \n",
       "14           False          Wales        Denmark                0   \n",
       "25           False          Italy          Wales                1   \n",
       "36           False          Italy    Switzerland                3   \n",
       "37           False         Turkey          Wales                0   \n",
       "49           False          Wales    Switzerland                1   \n",
       "50           False         Turkey          Italy                0   \n",
       "\n",
       "    team_away_score possession_home possession_away  total_shots_home  \\\n",
       "0                 1             66%            34%                 19   \n",
       "2                 1             29%            71%                  7   \n",
       "5                 2             46%            54%                 10   \n",
       "13                1             52%            48%                 27   \n",
       "14                4             47%            53%                 11   \n",
       "25                0             70%            30%                 23   \n",
       "36                0             49%            51%                 13   \n",
       "37                2             64%            36%                 18   \n",
       "49                1             35%            65%                  9   \n",
       "50                3             36%            64%                  3   \n",
       "\n",
       "    total_shots_away  shots_on_target_home  shots_on_target_away  \\\n",
       "0                  6                     6                     2   \n",
       "2                 16                     4                     5   \n",
       "5                 14                     4                     3   \n",
       "13                16                     6                     3   \n",
       "14                16                     1                     8   \n",
       "25                 3                     6                     1   \n",
       "36                 6                     3                     1   \n",
       "37                16                     6                     7   \n",
       "49                18                     2                     4   \n",
       "50                24                     0                     8   \n",
       "\n",
       "   duels_won_home duels_won_away  \\\n",
       "0             53%           47%    \n",
       "2             49%           51%    \n",
       "5             58%           42%    \n",
       "13            50%           50%    \n",
       "14            39%           61%    \n",
       "25            56%           44%    \n",
       "36            43%           57%    \n",
       "37            40%           60%    \n",
       "49            46%           55%    \n",
       "50            51%           49%    \n",
       "\n",
       "                                          events_list  \\\n",
       "0   [{'event_team': 'away', 'event_time': \" 2' \", ...   \n",
       "2   [{'event_team': 'away', 'event_time': \" 51' \",...   \n",
       "5   [{'event_team': 'away', 'event_time': \" 13' \",...   \n",
       "13  [{'event_team': 'away', 'event_time': \" 2' \", ...   \n",
       "14  [{'event_team': 'home', 'event_time': \" 26' \",...   \n",
       "25  [{'event_team': 'home', 'event_time': \" 39' \",...   \n",
       "36  [{'event_team': 'home', 'event_time': \" 19' \",...   \n",
       "37  [{'event_team': 'away', 'event_time': \" 42' \",...   \n",
       "49  [{'event_team': 'away', 'event_time': \" 30' \",...   \n",
       "50  [{'event_team': 'home', 'event_time': \" 45' \",...   \n",
       "\n",
       "                                          lineup_home  \\\n",
       "0   [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "2   [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "5   [{'Player_Name': 'Lukaku', 'Player_Number': '9...   \n",
       "13  [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "14  [{'Player_Name': 'Moore', 'Player_Number': '13...   \n",
       "25  [{'Player_Name': 'Chiesa', 'Player_Number': '1...   \n",
       "36  [{'Player_Name': 'Insigne', 'Player_Number': '...   \n",
       "37  [{'Player_Name': 'Yilmaz', 'Player_Number': '1...   \n",
       "49  [{'Player_Name': 'Moore', 'Player_Number': '13...   \n",
       "50  [{'Player_Name': 'Yilmaz', 'Player_Number': '1...   \n",
       "\n",
       "                                          lineup_away  \n",
       "0   [{'Player_Name': 'Kane', 'Player_Number': '9'}...  \n",
       "2   [{'Player_Name': 'Torres', 'Player_Number': '1...  \n",
       "5   [{'Player_Name': 'Insigne', 'Player_Number': '...  \n",
       "13  [{'Player_Name': 'Arnautovic', 'Player_Number'...  \n",
       "14  [{'Player_Name': 'Dolberg', 'Player_Number': '...  \n",
       "25  [{'Player_Name': 'James', 'Player_Number': '20...  \n",
       "36  [{'Player_Name': 'Embolo', 'Player_Number': '7...  \n",
       "37  [{'Player_Name': 'Moore', 'Player_Number': '13...  \n",
       "49  [{'Player_Name': 'Embolo', 'Player_Number': '7...  \n",
       "50  [{'Player_Name': 'Insigne', 'Player_Number': '...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iw_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60d9a750",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.512659Z",
     "iopub.status.busy": "2022-03-21T17:12:06.511987Z",
     "iopub.status.idle": "2022-03-21T17:12:06.526099Z",
     "shell.execute_reply": "2022-03-21T17:12:06.526588Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.851826Z"
    },
    "papermill": {
     "duration": 0.052841,
     "end_time": "2022-03-21T17:12:06.526757",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.473916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('../input/socceranalytics/matches.json') as f:\n",
    "    data = json.load(f)\n",
    "    matches = pd.json_normalize(data, sep = \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d70be980",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.599544Z",
     "iopub.status.busy": "2022-03-21T17:12:06.597181Z",
     "iopub.status.idle": "2022-03-21T17:12:06.602615Z",
     "shell.execute_reply": "2022-03-21T17:12:06.602180Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.877710Z"
    },
    "papermill": {
     "duration": 0.041841,
     "end_time": "2022-03-21T17:12:06.602743",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.560902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'match_date', 'kick_off', 'home_score', 'away_score',\n",
       "       'match_status', 'match_status_360', 'last_updated', 'last_updated_360',\n",
       "       'match_week', 'competition_competition_id', 'competition_country_name',\n",
       "       'competition_competition_name', 'season_season_id',\n",
       "       'season_season_name', 'home_team_home_team_id',\n",
       "       'home_team_home_team_name', 'home_team_home_team_gender',\n",
       "       'home_team_home_team_group', 'home_team_country_id',\n",
       "       'home_team_country_name', 'home_team_managers',\n",
       "       'away_team_away_team_id', 'away_team_away_team_name',\n",
       "       'away_team_away_team_gender', 'away_team_away_team_group',\n",
       "       'away_team_country_id', 'away_team_country_name', 'away_team_managers',\n",
       "       'metadata_data_version', 'metadata_shot_fidelity_version',\n",
       "       'metadata_xy_fidelity_version', 'competition_stage_id',\n",
       "       'competition_stage_name', 'stadium_id', 'stadium_name',\n",
       "       'stadium_country_id', 'stadium_country_name', 'referee_id',\n",
       "       'referee_name', 'referee_country_id', 'referee_country_name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5dfa902e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.676509Z",
     "iopub.status.busy": "2022-03-21T17:12:06.675782Z",
     "iopub.status.idle": "2022-03-21T17:12:06.683086Z",
     "shell.execute_reply": "2022-03-21T17:12:06.682471Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.889842Z"
    },
    "papermill": {
     "duration": 0.044882,
     "end_time": "2022-03-21T17:12:06.683220",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.638338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = (matches[\"home_team_home_team_name\"] == \"Italy\") | (matches[\"away_team_away_team_name\"] == \"Italy\")\n",
    "italy_matches = matches[mask]\n",
    "match_id = italy_matches[italy_matches[\"away_team_away_team_name\"] == \"Wales\"].match_id.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b3f2b92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.757701Z",
     "iopub.status.busy": "2022-03-21T17:12:06.757116Z",
     "iopub.status.idle": "2022-03-21T17:12:06.760078Z",
     "shell.execute_reply": "2022-03-21T17:12:06.760493Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.901595Z"
    },
    "papermill": {
     "duration": 0.042327,
     "end_time": "2022-03-21T17:12:06.760653",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.718326",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3788766"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4833282a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:06.832527Z",
     "iopub.status.busy": "2022-03-21T17:12:06.831858Z",
     "iopub.status.idle": "2022-03-21T17:12:07.263262Z",
     "shell.execute_reply": "2022-03-21T17:12:07.262693Z",
     "shell.execute_reply.started": "2022-03-21T15:59:45.913785Z"
    },
    "papermill": {
     "duration": 0.468299,
     "end_time": "2022-03-21T17:12:07.263417",
     "exception": false,
     "start_time": "2022-03-21T17:12:06.795118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import bz2\n",
    "zipfile = bz2.BZ2File(f\"../input/socceranalytics/three-sixty/three-sixty/{match_id}.json.bz2\")\n",
    "file = zipfile.read()\n",
    "open(f\"{match_id}.json\", 'wb').write(file)\n",
    "with open(f\"{match_id}.json\") as f:\n",
    "    threesixty = json.load(f)\n",
    "    match_events360 = pd.json_normalize(threesixty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "162120fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:07.338249Z",
     "iopub.status.busy": "2022-03-21T17:12:07.337654Z",
     "iopub.status.idle": "2022-03-21T17:12:07.728744Z",
     "shell.execute_reply": "2022-03-21T17:12:07.727688Z",
     "shell.execute_reply.started": "2022-03-21T15:59:46.633397Z"
    },
    "papermill": {
     "duration": 0.430865,
     "end_time": "2022-03-21T17:12:07.728883",
     "exception": false,
     "start_time": "2022-03-21T17:12:07.298018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "zipfile = bz2.BZ2File(f\"../input/socceranalytics/events/events/{match_id}.json.bz2\")\n",
    "file = zipfile.read()\n",
    "open(f\"{match_id}.json\", 'wb').write(file)\n",
    "with open(f\"{match_id}.json\") as f:\n",
    "    events = json.load(f)\n",
    "    match_events = pd.json_normalize(events, sep = \"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "924fa336",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:07.806860Z",
     "iopub.status.busy": "2022-03-21T17:12:07.806252Z",
     "iopub.status.idle": "2022-03-21T17:12:07.809283Z",
     "shell.execute_reply": "2022-03-21T17:12:07.809728Z",
     "shell.execute_reply.started": "2022-03-21T15:59:47.307945Z"
    },
    "papermill": {
     "duration": 0.046402,
     "end_time": "2022-03-21T17:12:07.809888",
     "exception": false,
     "start_time": "2022-03-21T17:12:07.763486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "shots = match_events[match_events['type_name'] == 'Shot']\n",
    "passes = match_events[match_events['type_name'] == 'Pass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b1f2abc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:07.883226Z",
     "iopub.status.busy": "2022-03-21T17:12:07.882534Z",
     "iopub.status.idle": "2022-03-21T17:12:07.885353Z",
     "shell.execute_reply": "2022-03-21T17:12:07.885868Z",
     "shell.execute_reply.started": "2022-03-21T15:59:47.322370Z"
    },
    "papermill": {
     "duration": 0.040249,
     "end_time": "2022-03-21T17:12:07.886063",
     "exception": false,
     "start_time": "2022-03-21T17:12:07.845814",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# max_x = 0\n",
    "# max_y = 0\n",
    "# for frame in match_events360.freeze_frame:\n",
    "#     for player in frame:\n",
    "#         if max_x < player['location'][0]:\n",
    "#             max_x = player['location'][0]\n",
    "#         if max_y < player['location'][1]:\n",
    "#             max_y = player['location'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1cbb330c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:07.958516Z",
     "iopub.status.busy": "2022-03-21T17:12:07.957928Z",
     "iopub.status.idle": "2022-03-21T17:12:07.961661Z",
     "shell.execute_reply": "2022-03-21T17:12:07.962217Z",
     "shell.execute_reply.started": "2022-03-21T15:59:47.329662Z"
    },
    "papermill": {
     "duration": 0.041291,
     "end_time": "2022-03-21T17:12:07.962376",
     "exception": false,
     "start_time": "2022-03-21T17:12:07.921085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "shots = shots.reset_index(drop = True)\n",
    "x, y= shots['location'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f160c2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:08.035086Z",
     "iopub.status.busy": "2022-03-21T17:12:08.034459Z",
     "iopub.status.idle": "2022-03-21T17:12:08.468786Z",
     "shell.execute_reply": "2022-03-21T17:12:08.469266Z",
     "shell.execute_reply.started": "2022-03-21T15:59:47.344155Z"
    },
    "papermill": {
     "duration": 0.472051,
     "end_time": "2022-03-21T17:12:08.469435",
     "exception": false,
     "start_time": "2022-03-21T17:12:07.997384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x489.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = create_pitch(120, 80,'white')\n",
    "for shot in shots['location']:\n",
    "    x, y = shot\n",
    "    y = 80 - y\n",
    "    plt.scatter(x,y, marker = 'o')\n",
    "    #shotCircle = plt.Circle((x,y) , 2, color=\"red\")     \n",
    "    #ax.add_patch(shotCircle)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e7cadcb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:08.570694Z",
     "iopub.status.busy": "2022-03-21T17:12:08.561727Z",
     "iopub.status.idle": "2022-03-21T17:12:17.690049Z",
     "shell.execute_reply": "2022-03-21T17:12:17.690490Z",
     "shell.execute_reply.started": "2022-03-21T15:59:47.873420Z"
    },
    "papermill": {
     "duration": 9.186026,
     "end_time": "2022-03-21T17:12:17.690652",
     "exception": false,
     "start_time": "2022-03-21T17:12:08.504626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x489.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = create_pitch(120, 80,'white')\n",
    "for pass_ball in passes['location']:\n",
    "    x, y = pass_ball\n",
    "    y = 80 - y\n",
    "    plt.scatter(x,y, marker = 'o')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f245d70e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:17.768699Z",
     "iopub.status.busy": "2022-03-21T17:12:17.768095Z",
     "iopub.status.idle": "2022-03-21T17:12:18.117060Z",
     "shell.execute_reply": "2022-03-21T17:12:18.117508Z",
     "shell.execute_reply.started": "2022-03-21T15:59:58.907172Z"
    },
    "papermill": {
     "duration": 0.389684,
     "end_time": "2022-03-21T17:12:18.117674",
     "exception": false,
     "start_time": "2022-03-21T17:12:17.727990",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 756x489.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = create_pitch(120, 80,'white')\n",
    "for d in match_events360['freeze_frame'][4]:\n",
    "    x, y = d['location']\n",
    "    y = 80 - y\n",
    "    plt.scatter(x, y, color = 'red', marker = 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2be5eefa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:18.202229Z",
     "iopub.status.busy": "2022-03-21T17:12:18.201351Z",
     "iopub.status.idle": "2022-03-21T17:12:41.885896Z",
     "shell.execute_reply": "2022-03-21T17:12:41.885376Z",
     "shell.execute_reply.started": "2022-03-21T15:59:59.308891Z"
    },
    "papermill": {
     "duration": 23.728905,
     "end_time": "2022-03-21T17:12:41.886043",
     "exception": false,
     "start_time": "2022-03-21T17:12:18.157138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "game = pd.read_parquet(\"../input/socceranalytics/italywales.pq\")\n",
    "position_cols = [col for col in game if \"_x\" in col or \"_y\" in col or \"_type\" in col]\n",
    "x_pos = [col for col in game if \"_x\" in col]\n",
    "y_pos = [col for col in game if \"_y\" in col]\n",
    "type_cols = [col for col in game if \"_type\" in col]\n",
    "id_cols = [col for col in game if \"_id\" in col]\n",
    "game[x_pos] = game[x_pos].astype(float) / 100.0\n",
    "game[y_pos] = game[y_pos].astype(float) / 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3875cffc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:41.965525Z",
     "iopub.status.busy": "2022-03-21T17:12:41.964882Z",
     "iopub.status.idle": "2022-03-21T17:12:49.838488Z",
     "shell.execute_reply": "2022-03-21T17:12:49.837844Z",
     "shell.execute_reply.started": "2022-03-21T16:00:24.979835Z"
    },
    "papermill": {
     "duration": 7.914307,
     "end_time": "2022-03-21T17:12:49.838635",
     "exception": false,
     "start_time": "2022-03-21T17:12:41.924328",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "baseURL = \"https://www.uefa.com/uefaeuro-2020/teams/\"\n",
    "\n",
    "class Player:\n",
    "    def __init__(self, attr):\n",
    "        self.id             = attr[0]\n",
    "        self.name           = attr[1]\n",
    "        self.number         = attr[2]\n",
    "        self.position       = attr[3]\n",
    "        self.age            = attr[4]\n",
    "        self.gamesPlayed    = attr[5]\n",
    "        self.goals          = attr[6]\n",
    "\n",
    "def parseSite(teamID):\n",
    "    teamURL = baseURL + str(teamID) + \"/squad/\"\n",
    "\n",
    "    # Get the HTML from the site\n",
    "    page = requests.get(teamURL)\n",
    "\n",
    "    # Check for bad status code\n",
    "    if not page.status_code == 200:\n",
    "        print(\"Error: Could not get the page\")\n",
    "        return\n",
    "\n",
    "    # Parse the HTML\n",
    "    html = page.text\n",
    "    soup = BeautifulSoup(html, 'html.parser')\n",
    "    \n",
    "    # The HTML has multiple divs with class 'squad--team-wrap' that contain all \n",
    "    # the player infos split by postition\n",
    "    squad = soup.find_all('div', class_='squad--team-wrap')\n",
    "\n",
    "    # Extract the player infos from the HTML\n",
    "    players = []\n",
    "\n",
    "    for wrap in squad[1:5]:\n",
    "        postition = wrap.h5.text[:-1]\n",
    "\n",
    "        for player in wrap.find('tbody').find_all('tr'):\n",
    "            infos = player.find_all('td')\n",
    "\n",
    "            players.append(Player([\n",
    "                infos[0].find('a', class_='player-name')[\"href\"].split(\"/\")[-2].split(\"-\")[0], \n",
    "                infos[0].find('a', class_='player-name')[\"title\"], \n",
    "                infos[0].text.strip().split(\" \")[-1].strip(\"()\"), \n",
    "                postition, \n",
    "                infos[1].text.strip(), \n",
    "                infos[2].text.strip().replace(\"-\", \"0\"), \n",
    "                infos[3].text.strip().replace(\"-\", \"0\")\n",
    "            ]))\n",
    "\n",
    "    return players\n",
    "\n",
    "italy = parseSite(66) # Italy\n",
    "wales = parseSite(144) # Wales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb01490c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:49.920174Z",
     "iopub.status.busy": "2022-03-21T17:12:49.919527Z",
     "iopub.status.idle": "2022-03-21T17:12:49.921211Z",
     "shell.execute_reply": "2022-03-21T17:12:49.921761Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.695283Z"
    },
    "papermill": {
     "duration": 0.04511,
     "end_time": "2022-03-21T17:12:49.921919",
     "exception": false,
     "start_time": "2022-03-21T17:12:49.876809",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "player_map = dict()\n",
    "for player in italy:\n",
    "    player_map[player.id] = player.name\n",
    "for player in wales:\n",
    "    player_map[player.id] = player.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d29deaa0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:50.000683Z",
     "iopub.status.busy": "2022-03-21T17:12:50.000085Z",
     "iopub.status.idle": "2022-03-21T17:12:50.016883Z",
     "shell.execute_reply": "2022-03-21T17:12:50.017451Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.704767Z"
    },
    "papermill": {
     "duration": 0.05782,
     "end_time": "2022-03-21T17:12:50.017637",
     "exception": false,
     "start_time": "2022-03-21T17:12:49.959817",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open(f\"../input/socceranalytics/lineups/lineups/{match_id}.json\") as f:\n",
    "    lineup = json.load(f)\n",
    "    lineups = pd.json_normalize(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3297f09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:50.099229Z",
     "iopub.status.busy": "2022-03-21T17:12:50.098626Z",
     "iopub.status.idle": "2022-03-21T17:12:50.102481Z",
     "shell.execute_reply": "2022-03-21T17:12:50.101861Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.791369Z"
    },
    "papermill": {
     "duration": 0.044438,
     "end_time": "2022-03-21T17:12:50.102626",
     "exception": false,
     "start_time": "2022-03-21T17:12:50.058188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wales_lineup = lineup[0]\n",
    "italy_lineup = lineup[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6749ac6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:50.183720Z",
     "iopub.status.busy": "2022-03-21T17:12:50.183101Z",
     "iopub.status.idle": "2022-03-21T17:12:50.255143Z",
     "shell.execute_reply": "2022-03-21T17:12:50.255618Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.798809Z"
    },
    "papermill": {
     "duration": 0.11518,
     "end_time": "2022-03-21T17:12:50.255780",
     "exception": false,
     "start_time": "2022-03-21T17:12:50.140600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "random_point = game.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e0fbdb05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:50.334650Z",
     "iopub.status.busy": "2022-03-21T17:12:50.334089Z",
     "iopub.status.idle": "2022-03-21T17:12:50.359486Z",
     "shell.execute_reply": "2022-03-21T17:12:50.359976Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.883475Z"
    },
    "papermill": {
     "duration": 0.06602,
     "end_time": "2022-03-21T17:12:50.360145",
     "exception": false,
     "start_time": "2022-03-21T17:12:50.294125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49864    51.68\n",
      "Name: ball_x, dtype: float64\n",
      "49864    44.58\n",
      "Name: player1_x, dtype: float64\n",
      "49864    41.13\n",
      "Name: player2_x, dtype: float64\n",
      "49864    8.19\n",
      "Name: player3_x, dtype: float64\n",
      "49864    54.38\n",
      "Name: player4_x, dtype: float64\n",
      "49864    34.51\n",
      "Name: player5_x, dtype: float64\n",
      "49864    12.34\n",
      "Name: player6_x, dtype: float64\n",
      "49864    28.76\n",
      "Name: player7_x, dtype: float64\n",
      "49864    27.45\n",
      "Name: player8_x, dtype: float64\n",
      "49864    10.13\n",
      "Name: player9_x, dtype: float64\n",
      "49864    20.57\n",
      "Name: player10_x, dtype: float64\n",
      "49864    16.84\n",
      "Name: player11_x, dtype: float64\n",
      "49864    1.29\n",
      "Name: player12_x, dtype: float64\n",
      "49864    30.62\n",
      "Name: player13_x, dtype: float64\n",
      "49864    8.77\n",
      "Name: player14_x, dtype: float64\n",
      "49864    43.27\n",
      "Name: player15_x, dtype: float64\n",
      "49864    33.28\n",
      "Name: player16_x, dtype: float64\n",
      "49864    30.01\n",
      "Name: player17_x, dtype: float64\n",
      "49864    4.74\n",
      "Name: player18_x, dtype: float64\n",
      "49864    34.14\n",
      "Name: player19_x, dtype: float64\n",
      "49864    11.73\n",
      "Name: player20_x, dtype: float64\n",
      "49864    33.57\n",
      "Name: player21_x, dtype: float64\n",
      "49864   -25.76\n",
      "Name: player22_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player23_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player24_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player25_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player26_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player27_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player28_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player29_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player30_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player31_x, dtype: float64\n",
      "49864   NaN\n",
      "Name: player32_x, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for col in x_pos:\n",
    "    print(random_point[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0a6eea1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:50.458139Z",
     "iopub.status.busy": "2022-03-21T17:12:50.456501Z",
     "iopub.status.idle": "2022-03-21T17:12:51.264091Z",
     "shell.execute_reply": "2022-03-21T17:12:51.264531Z",
     "shell.execute_reply.started": "2022-03-21T16:00:31.917187Z"
    },
    "papermill": {
     "duration": 0.866378,
     "end_time": "2022-03-21T17:12:51.264687",
     "exception": false,
     "start_time": "2022-03-21T17:12:50.398309",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAGACAYAAAAK6xyKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACPlklEQVR4nOzdeVxU1f/H8dcwMKADinui4r6CG264Z2amaJJpaohLaj/3tbSwXCgwl1zLNFTcl0xTwV0zzR2xVFxyAQXFXEi2URhmmN8f83XKBAUFLsjn+Xj4gLn33HPfdwT9cDj3XJXJZDIhhBBCCCGEUIyV0gGEEEIIIYTI76QoF0IIIYQQQmFSlAshhBBCCKEwKcqFEEIIIYRQmBTlQgghhBBCKEyKciGEEEIIIRQmRbkQQgghhBAKk6JcCCGEEEIIhUlRLoQQQgghhMKkKBdCCCGEEEJhUpQLIYQQQgihMCnKhRBCCCGEUJi10gGEEEIIIbJSSkoKUVFRPHqUpHQUISwKFLCjXLly2NjYpLlfZTKZTDmcSQghhBAi24SHh2NtbYu9fWFUKpXScYTAZDKRkBCH0ZhMpUqV0mwj01eEEEII8Up59ChJCnKRq6hUKhwcCj/ztzdSlAshhBDilSMFuchtnvc1KXPKhRBCCCGykaenB7a2tmg0GvR6PXXr1mf8+E+xtk57bjHAkCGD8PLypkWLVvj6TqZmzZp0794zB1OLnCZFuRBCCCFENvP3n0HlylUwGo0MHjyAAwd+oV279krHErmIFOVCCCGEEDlEr9eTnKynUKFChIScYPHihej1eoxGI/36DZBCPR+TolwIIYQQryyNxhqNJnvKHb3egF5vyFBbH5/xaDQabt26SePG7jRp0pT4+HgWL16GWq0mJiaGfv28aNKkKYUKFcqWvCJ3k6JcCCGEECKbPZ6+kpyczGeffcL69Wto1qwFfn5TiYqKRK1WEx8fT2TkdVxd6ygdVyhAinIhhBBCvLIyM5qdE2xtbWnevCVHjvzG4cOHaNmyNV9/PQuVSkX37p4kJ+uVjigUIksiCiGEEELkkNTUVH7/PRRnZ2cSEhIoXdoJlUrFiRPHuXkzSul4QkG5ZqS80LRCJOgTlI4hhBBCiDzMQePAsXeOKx3jKY/nlKekpFC5chU+/PAjLl68wMyZ0wgIWETNmi5UqVJV6ZhCQSqTyWRSOgSAaqos8i+EEEKIlxfW7TxOTuWVjiHEU6Kjb+DiUivNfTJ9RQghhBBCCIVJUS6EEEIIIYTCpCgXQgghhBBCYVKUCyGEEEIIoTApyoUQQgghhFCYFOVCCCGEEEIoTIpyIYQQQgghFJZrHh4khBBCCPEqMhhSWLZsCXv37kaj0aBWq2nQoBHDho1g166dHDnyG9OmzcxQX35+vnh4dKJePbcMnz8yMpLvv1/ApUsXcXBwIDU1lWbNWvB//zcUtVr9opdFQMAi+vUbgI2NzQv3If4hRbkQQgghRDb68sspJCcns3z5GrRaLQaDgeDgrej1KZnqx2g0MnHipEwdc//+PQYPHsDQoSMshb9Op2PlykBSUvSo1QUy1d+/LV36A15efTJdlBsMBqytpQT9L3lHhBBCCCGySWRkJAcPHmDbtl1otVoArK2t8fR8z9JGp9MxceIEwsOvYW/vwNdfz6RYseIEB29j164daLVaoqIimTLlK+bMmYWXlzctWrRiy5ZNrFu3Bo1GQ2pqKn5+06lQoeIT5//ppx9p0KAhnTq9Y9mm1WoZMmS45bW7uxu//HKYggULPvXa3d2NwYOHcfDgAeLi4hg+fDRvvNGWmTOnATBoUD+srKxYuDAAKysVc+fO5tq1KyQnJ9OgQSNGjRqLWq1myJBBVKtWjbCwcxQqVJg5cxZk23ueV0lRLoQQQohXlkZjjUaTPeWOXm9Arzc8s83ly5coV86ZQoUKpdvm4sXzrF69gVKlXsPf/0t+/HG9pWg+f/4cq1atp2zZck8dt2DBPDZs2ETx4iXQ6/WkphqfavPnn5do0sQ9k1f2JK1WS2Dgas6c+YPPP5/AG2+05ZNPPmPTpo0EBCy3FPN+fr64ubkxceIkUlNTmTx5IkFBW/H07ArArVu3WLx4mYySp0PeFSGEEEIIBdWpU5dSpV4DwNW1NidPHrfsq1u3XpoFOUDDho3w9Z1MixataN68BWXKlH3uuVauDGTv3t3ExcXx1VdfU6dO3ece065de0u2e/fukZycjK2t7VPtDh8+yIULYaxduxqApKQkSpYsadnfvn0HKcifQd4ZIYQQQryyMjKanZ2qVatBVFQk8fHx6Y6WazT/FLhWVlYYjf+MeBcoUDDdvr/+ehYXLpwnNDSEYcM+Yvz4iTRr1vw/56/OhQsXLK/79OlPnz796dfPi5QU85x2tVqNyZQKQHJycrr5Ht8U+u98/2YymZgxY3a6PxwUKPDi89fzA1kSUQghhBAimzg7O9OyZWumT/dDp9MB5qJ269afefjw4Qv3azAYuHXrJi4urvTp05/GjZty+fKlp9p169aDkJAT7NwZbNlmNBotBTlA2bLluHDhPAB79uzMcIaCBbUkJiZaXrds2ZqVKwMtRXts7AOio29l+tryKxkpF0IIIYTIRpMm+bJkyWL69fPCxsbGsiShRvPiSwmmpqby5ZeTSUxMRKVSUapUKYYNG/FUuxIlSrBo0RIWLlzA4sXf4+joiI2NDa1bt6FGjRoAjBo1lunT/dBq7Wnb9q0MZ/jgg94MH/5/2NrasnBhAKNHf8y3387D27snKpUKGxsbRo/+GCenMi98nfmJymQymZQOAaCaqlI6ghBCCCFeAWHdzuPkVF7pGEI8JTr6Bi4utdLcJ9NXhBBCCCGEUJgU5UIIIYQQQihMinIhhBBCCCEUJkW5EEIIIYQQCpOiXAghhBBCCIVJUS6EEEIIIYTCpCgXQgghhMhGnp4eXLt2NUv6CghYRIcObfH27kmPHl2ZNMmHR48eZbqfIUMGcfjwoSzJJLKGFOVCCCGEEHlIhw6dWLVqPatXb+DOnb/YuHGD0pFEFpAnegohhBBC5ICoqEimT/fjwYMHqNXWDBkyjKZNmwMQFnaOhQsXoNOZH1v/0UdDaN685TP7s7GxoXbtuty5cxuAhw8f8s0307l48QIAHTp44O3dD4CIiHC+/HIKjx49pHLlKuj1yRnK5e7uxuDBwzh48ABxcXEMHz6aN95om6XvizCTolwIIYQQryyNxhqNJnvKHb3egF5vyHD7yZM/x9OzK++840lERDiDBw9k/fpNWFtbM2OGP7Nnz6d48RLcv3+P/v29Wbt2Iw4ODun2p9Pp+P33UIYMGQ7AsmUBmEwm1qz5kYcPdQwc2I/KlavSrFlzpkz5nPff74WHR2fCws7y0UcfPjdXkSJFANBqtQQGrubMmT/4/PMJUpRnEynKhRBCCCGymclk4sqVP+nU6R0AKlasRNWq1QgLO4tarSY6+hZjxoywtFepVNy8GUXNmk8/kn3nzmBOnjxOVFQU7u5NadCgEQAhIScYM+YTVCoVWq097dq9TUjICerWrUt4+DU6dPAAwNW1DpUrVwHMhX16uVq2bA1Au3bt/3dcbe7du0dycjK2trbZ9E7lX1KUCyGEEOKVldnRbCWYTCaqVKnKokVLM9S+Q4dOjBw5hpiY+/zf/33Ipk0b6dbt/WzLp9GYC3C1Wg2A0WjMtnPlZ3KjpxBCCCFENlOpVFStWp0dO4IA8xzvq1cv4+pah9q16xIVFUVoaIil/YUL5zGZTM/ss1ix4owZ8wmBgUtISkqiUaMmBAVtwWQyodPp2LdvN40bN0Grtady5Srs3r0TgPPnwyyrwWi12nRziZwlI+VCCCGEENnIaDSi0WiYOvUrpk/3Y926NajV1kye/KVl3vbMmXNYsGAuc+bMIiUlhTJlyjJr1lxUKtUz+27evCUVKlRgy5ZNfPjhIGbNmo6Xl3nU/O23PSw3bE6e/CVffjmFVauWU7lylSemxTwrl8g5KtPzfgzLIaqpz/6iE0IIIYTIiLBu53FyKq90DADu379Hjx7vsX37Huzs7JSOIxQWHX0DF5en7xMAGSkXQgghhMgWGzasY/PmjYwYMVoKcvFcUpQLIYQQQmSDHj160aNHL6VjiDxCbvQUQgiR5/Q6CxFzwDjF/LHXWaUTCSHEy5GRciGEEHlKr7MQEATaFPPrCnHm1wDrZMEIIUQeJSPlQggh8hT//f8U5I9pU8zbhRAir5KiXAghRJ7iHJe57UIIkRdIUS6EECJPiSycue1CiOyTnJzMhg1rSUp6pHSUPE+KciGEEHmKT1vQ2Ty5TWdj3i5EbuTp6UGPHl3p3bsHPXp0ZevWnzN03NmzZ/jgg+706dPriad9ZkZo6Cn69fN6antg4BK8vXvi7d2TNm2a07VrZ8vrO3f+SrMvd3c3Hj58aHmdnJzMtGlfUrOmC3Z2BQC4d+8eQ4d+9EJZ8zu50VMIIUSe8vhmTv/95ikrkYXNBbnc5ClyM3//GVSuXIVr167St+8HNGvWghIlSjzzmJ07t9OxYyd69+6b5Xn69x9I//4DARgyZBBeXt60aNEqU33Y2toyZcpXT2wrUaIECxf+kGU58xMpyoUQQuQ56+pIES7ypsqVq+DgUIh79+5SokQJdu/eyYYN6zAYzHcvjxgxmkaNmrB69Qr279+Dra0du3fvJCBgOT/88D2//x5KSkoKjo6OTJw4mdKlnQA4evQwgYFL0euTsbGxYfTocbi6Zv6bJCoqkunT/Xjw4AFqtTVDhgyjadPmT7W7cOE8s2fP4NGjRxQoUICxY8dTq5YL0dHR9O/fm927f3m5NyofkqJcCCGEEK8sjcYajSZ7yh293oBeb8jUMWfO/IGjoyNVq1YDwN29KW+99TYqlYobN64zfPhggoJ20bt3X8LDw6lZsybdu/cEoE+ffowcOQaArVt/5rvv5vPVV19z82YUy5YFMG/ed2i19oSHX2PMmBFs3boj09c0efLneHp25Z13PImICGfw4IGsX7+JIkWKWNqkpKTw2Wef8Pnnk2nUqAknT57gs88+4aeftmb6fOIfUpQLIYQQQmQzH5/xmEwmbt6Mws9vOjY25hsjbt68yeLFPty7dxdra2v+/juGmJj7FCtW/Kk+jh07wk8//cijR48wGo2W7cePH+PWrZsMHjzQss1oNBATE5OpjDqdjitX/qRTp3cAqFixElWrViMs7CwtW7a2tLtx4zo2NtY0atQEgMaNm2BjY82NG9cpWFCbqXOKf0hRLoQQQohX1ouMZmeHx3PK9+/fy1dfTaVOnXoUK1aMSZN8GDlyDK1btyE1NZXXX29GcrL+qeNv345m7tzZBAauwsmpDGfPnmHSJJ//7TXh7t6MyZO/fOq469cjsvnKRFaR1VeEEEIIIXJI27btaNLEnZUrlwGQkJCAk1MZAIKCtqLXP12Qg3kU28bGmqJFi5GamsrPP/9k2de4cVOOHz9KePg1y7YLF85nOptWq6Vq1ers2GF+RG5ERDhXr15+am56+fIVSEkxWFaEOXXqJAaDgfLlK2T6nOIfMlIuhBBCCJGDhgwZTr9+vfH27seYMeMYP34sDg6FaNq0KYULO6Z5TJUqVXnjjXb06tUNR0dHmjVrzu+/nwbA2dmZKVO+ws9vKsnJyaSkpFCnTj1q1XLJdLapU79i+nQ/1q1bg1ptzeTJXz4xnxzAxsaGadNmPnGjp7//TMuUHPFiVCaTyaR0CADVVJXSEYQQQgjxCgjrdh4np/JKxxDiKdHRN3BxqZXmPpm+IoQQQgghhMJk+koOW+SxiFblW3FHd0fpKELkKaW0pQDke0eITCqlLcWhG4cYvH2w0lGEEM8gI+U5rFX5VpR2KK10DCHynNIOpeV7R4gXUNqhNK3KZ+5JjUKInCcj5Tnsju4Od3R3aLOijdJRhMhTDvQ9ACDfO0Jk0uPvHSFE7iYj5UIIIYQQQihMinIhhBBCCCEUJkW5EEIIIUQ28vT0oEePrvTu3YNu3d7hk0/GcPbsGaVjcfLkCXr06Gp5nZiYQPPmjfjppw2WbWvWrGTKlM9f6jwBAYuYP3/OS/WRH0hRLoQQQgiRzfz9Z7B69QZ++mkbHTt2YuzYEYSFnVM0U506dbl9O5qYmBgAzpz5gxo1anL6dKilzenTp3Bza5jhPg0GQ5bnzC/kRk8hhBBCiBzUpk1bLlw4z9q1q/D3n0FIyAkWL16IXq/HaDTSr98A2rVrD8CQIYOoVasW586d4/79e7Rt245hw0Y+c9+FC+f56qsprF270XLO3r17MH68D3Xq1LVss7Ozo2bNWpw+fYp27dpz+nQo3bv3ZOnSHwAwGo2cOfMHY8eOZ/funWzYsA6DIQWAESNG06hRE8D8m4B27doTGhpC5cpVGDVqLH5+voSHX6No0WKUKlWKokWL5ch7m5dJUS6EEEKIV5ZGY41Gkz3ljl5vQK9/sZFhFxdXfvvtIADVq9dk8eJlqNVqYmJi6NfPiyZNmlKoUCEA/vrrLxYtWsLDhzree68LnTt74uzsnO6+WrVcKFCgAKdPh+Lm1oA//jiNlZXVEwX5Yw0aNOL06VDatWvP77+fplcvL/bs2UV4+DWSk5Oxt7enTJmy2Nvb89Zbb6NSqbhx4zrDhw8mKGiXpR+dTseyZasAmDdvNlqtlg0bNhMb+4C+fb1o27bdC71P+YkU5UIIIYQQOcxkMlk+j419gJ/fVKKiIlGr1cTHxxMZeR1X1zoAtG3bDisrK+ztHahQoQK3bkVZivL09r3/fi82bfoRN7cG/PTTj7z33vtp5nBza8DMmV+j0+l49OghxYuXoH59N06fPkVycrJl6srNmzdZvNiHe/fuYm1tzd9/xxATc59ixYoD0LGjh6XP0NBTjBs3HgBHxyK8/rosZZsRUpQLIYQQ4pX1MqPZ2enixQtUrlwFgBkz/GnZsjVffz0LlUpF9+6eJCfrLW01Go3lcysrNUaj8bn72rZ9k++/X8Cff14iNPQUn38+Oc0ctWvX5a+/bnPgwH7LSHr9+m6sXbua5OQk2rRpC8CkST6MHDmG1q3bkJqayuuvN3siY4ECBV/2Lcn35EZPIYQQQogcdOjQr2zevJFevXoDkJCQQOnSTqhUKk6cOM7Nm1EvfQ5raxs6derCJ5+MoX37DtjZFUizna2tLbVqubBixTLLqHiNGjW5ePECZ878gZtbI0tGJ6cyAAQFbUWv16fZH0DDho0IDt4GQFxcLAcPygOsMkJGyoUQQgghspmPz3hsbGxISnpEhQqVmD17Aa6utQEYOnQkM2dOIyBgETVrulClStUsOec777zL0qU/0LVr92e2c3NryNKlP1C/fgPAXNCXLVuOqKhInJycABgzZhzjx4/FwaEQTZs2pXBhx3T7+/DDgXz11VR69OhK0aLFqFfPLUuu51WnMv17UpOCVFNVSkfIEfKocCFejJLfO71cwb8tOBeGyDjw2Q/rwnI8hhAvJD/+vxPW7TxOTuWVjqG4nTu3s3fvbmbPnq90FPE/0dE3cHGpleY+GSkXQohn6OUKAZ1B+79pmxUcza9BCnMhRO41atRQbt26ycyZc5WOIjJIinIhhHgG/7b/FOSPaTXm7VKUCyFyq3nzFiodQWSS3OgphBDP4Fw4c9uFEEKIFyFFuRBCPENkXOa2ixfT6yxEzAHjFPPHXmeVTiSEEDlLinIhhHgGn/2g+8/KXzq9ebvIGr3OQkAQVIgz/6dUIc78WgpzIUR+IkW5EEI8w7owGBQE12Mh1WT+OChI5pNnJf/9oE15cps2xbxdCCHyC7nRUwghnmNdmBTh2ck5nalA6W0XQohXkRTlQgghFBVZ2DxlJa3tQmSFGksqcffh3Szvt2TBklwaGP7cdp6eHtja2qLRmJdyatCgIaNHf5zh8wQHb+PIkd+YNm1mpjOOGTOCcePGU7ZsuUwf+1+hoacYO3Ykzs7OAKSmptKv3wDatWv/0n1nlYCARTx69IiRI8dkab9DhgzCy8ubFi1aPbXPz88XD49OL/2QJCnKhRBCKKaXK2gvg6k4qCIBH2Ad6GzAp63S6TJPHjSVO2VHQZ7Zfv39Z1C5cpVsyZGW1NRUVCoVc+YsyNJ+K1asyPLlawCIiAjnww+9adu2HVZWGZ8RbTAYsLZ++RI0q/p5WRMnTsqSfpS/EiGEEPnSfx/MRAUwBcD9gjDqAayro2S6zJMHTYnM2r49iE2bNmI0GrC3t2f8eB/Kl69ASkoK33wznVOnQnB0dKRatRpPHLdy5XJ+/XU/BoOREiVK4OPzBcWKFScgYBEREeEkJiZy585fBAQsp0+fXnzzzTwqV67C3bt3mT17BlFRkQC89dbb9O37ITExMcyY4c/Nm1EAeHn1oWPHTs/Nn5iYgL29vaUgv3HjOnPmzCIuLpaUlBR69vyATp26AODu7saAAR9x9Ohh3N2bcefOHWxtNURG3uDOnTvUrl2HSZN8UalU7N69kw0b1mEwmG82GTFiNI0aNQHMv3Vo1649oaEhVK5chVGjxuLn50t4+DWKFi1GqVKlKFq0GAApKSksWvQtv/9+Gr1eT5UqVRk/3oeCBQuyZcsm1q1bg0ajITU1FT+/6VSoUJGIiHDmzJlFTMx9AD74wBsPD/M38u+/h7Jy5XLu379H27btGDZsJPDsUfTMkKJcCCGEItJ6MJNKC7pZsG6eMplehjxoSjyLj894y/SVYcNGYmdnx/79e1m0aAkajYajR4/w1VdTCQgI5OefNxEdHc369T9hMBgYPHggpUs7AbBz53Zu3YpiyZIVWFlZsWnTRubNm4Ovrx8A58+HsWLFGhwdizyVYcqUz2nWrDlffz0LgNjYBwDMnj2DSpUqM336N9y/f49+/byoXr1GmiP7EREReHv3RK/X89dft5k8+UvAPGo9aZIPU6f6UaFCRXQ6Hf3798bVtQ4VKlQEwNbWjsDA1QD4+k7m2rVrLFjwPVZWVvTp04uTJ0/QpIk77u5Neeutt1GpVNy4cZ3hwwcTFLTLkkGn07Fs2SoA5s2bjVarZcOGzcTGPqBvXy/atm0HwKpVK9BqHSxtv/12HitWLGPIkOEsWDCPDRs2Ubx4CfR6PampRgwGA+PHj2Xw4GGWPuLiYi3n/euvv1i0aAkPH+p4770udO7saZnKkxWkKBdCCKGIV+3BTK/a9Yis9d/pKwsWzOXKlcsMGNAHAJPJREJCAgChoSF07NgJa2sbrK1tePvtjpw58wcAhw8f4uLFC/Tt+wEARqMRrdbe0m+zZs3TLMgfPnzIuXNnmD//nyd9Pm4XEnKSUaPGAlC8eAmaNm1BaOipNIvy/05fGTr0I1xd66DTJXL9+nW++OIzS1u9Xs/16xGWotzD48nR99atX8fW1haA6tVrcOvWTQBu3rzJ4sU+3Lt3F2tra/7+O4aYmPsUK1YcgI4dPSx9hIaeYty48Zbref31NpZ9hw8fRKfTceDAPkueqlWrAdCwYSN8fSfTokUrmjdvQZkyZQkPv4bRaLQU5ACFCztaPn88Tcfe3oEKFSpw61aUFOVCCCHyvsg48xSPtLbnReldT8w98wORnOPMN6/6tM17U3NEdjDRuXMXPvpoSOaOMpno338AnTt7prm/QIGCWZAtYypWrETp0k6cO3eGihUr4ejoyKpV69Nt/99sj39zAGBlpcZoNAAwaZIPI0eOoXXrNqSmpvL6681ITtan2096TCYTn3zyKQ0bNn5q39dfz+LChfOEhoYwbNhHjB8/kddee+2Z/T2d15ihHBkl65QLIYRQxKv2YKa0ric5CRzGyYORxNNatGjFjh3B3L17BzCPeF+6dAEwj+Lu2rUdg8FAUlISe/b8M3WjZctWbNq0kfj4eMA8+nvlyuXnnq9gwYLUrl2X9evXWLY9nr7SqFFjtm7dDEBMzH2OHTtMw4aNntvnvXv3iIq6Qblyzjg7l8fOzo6dO4Mt+69fj0CnS3xuP/+VkJCAk1MZAIKCtqLX69Nt27BhI4KDtwHmqSYHDx6w7GvZsjXr1q0mKSkJME97iYgIx2AwcOvWTVxcXOnTpz+NGzfl8uVLODuXR61Ws3//Xksf/56+kt1kpFwIIYQiHs+zflVWK0nrerQfQ4nVT7Z7/GAkGS3P3+rXb8DgwcP4+OMxpKYaSUlJoW3bdtSoUQtPz/e4evUKPXu+h6OjIzVr1uLvv/8GoEOHTsTGxjJkyEDAPBrctWt3y7SMZ5ky5StmzfqaDz7ojpWVFW+91YE+ffoxdux4pk/3w8vrfQCGDh1JpUqV0+zj8Zxyk8mEwWDgo4+GUK1adQBmzpzL3LmzWL16JampqRQtWhQ/v+mZfm/GjBnH+PFjcXAoRNOmTZ+YQvJfH344kK++mkqPHl0pWrTYE8sS9unTj4CAxXz4oTcqlQqVSsWAAR9RpkxZvvxyMomJiahUKkqVKsWwYSOwtrZmxozZfPPNdJYtC0ClUuHl5U2HDs+/6TUrqEwmkylHzvQcqqkqpSPkiAN9zT/BtVnR5jkthRD/Jt87Ii8yTkn7V9KpgHpKzmTIj987Yd3O4+RU3vJa6XXKhXgsOvoGLi610twnI+VCCCFENpEHI+UOUjiLvEDmlAshRDbpddZ8g59xivmjzCPOf3zamh+E9G959cFIQojsJSPlQgiRDXqdNd/QpzU/+8Jygx/IXOL85PHftf9+WX1FCPFsUpQLIUQ28N//T0H+mNzglz+tqyN/50KI55PpK0IIkQ2c01lrO73tQggh8jcpyoUQIhukdyOf3OAnchO570GI3EOKciGEyAZyg5/I7R7f9yAPNsp+np4eXLt2Ncv6++GH79m7d/dz2wUELGL+/DlZdl6RvWROuRBCZAO5wU/kdnLfQ+5lMBiwtk6/RPvooyE5mEbkFCnKhRAim8gNfiI3k/secl5UVCTTp/vx4MED1GprhgwZRtOmzQFwd3djwICPOHr0MO7uzXj33W74+n5BTEwMZcqUxWQy4e7elO7de+LrO5maNWvSvXtPAgIWERl5g8TERKKjb1GmTFn8/adjZ1cAgHv37jJmzIin9j18+JBvvpnOxYsXAOjQwQNv735KvTUCKcqFEEKIfCm/PNhIo7FGo8meckevN6DXGzLcfvLkz/H07Mo773gSERHO4MEDWb9+E0WKFAHA1taOwMDVAHz66ce4uTXiww8Hcvt2NF5ePXB3b5pmvxcvXiAwcDX29vaMGjWMXbt24unZ9Zn7li0LwGQysWbNjzx8qGPgwH5UrlyVZs2av+S7Il6UzCkXQggh8iG57yFnmUwmrlz5k06d3gGgYsVKVK1ajbCwfybxe3h0snx++vQpS9vSpZ1o1Khxun27uzfFwcEBlUqFi4srt27dfO6+kJATvPPOu6hUKrRae9q1e5uQkBNZes0ic2SkXAiRJ/VyBf+24FwYIuPAZz+sC1M6lRB5R3657yGzo9lKKlCg4Asdp9HYWj63srLCaDRmaJ/IXWSkXAiR5/RyhYDOUMERrFTmjwGdzduFEBm3rg5UHAPqKeaPr1pBnpuoVCqqVq3Ojh3mR/tGRIRz9eplXF3TftPr129gaXvnzl+cOhWSpXkaNWpCUNAWTCYTOp2Offt207hxkyw9h8gcGSkXQuQ5/m1Bq3lym1Zj3i6j5UKI3MZoNKLRaJg69SumT/dj3bo1qNXWTJ78pWU++X+NHfsJU6dOYvfunTg5OVGrlgv29vZZlunDDwcxa9Z0vLzeB+Dttz0sN50KZahMJpNJ6RAAqqkqpSPkiAN9DwDQZkUbhZMIkbf8+3vHOMk8Qv5fqSZQ++ZwMCFyufz4/05Yt/M4OZVXOgYA9+/fo0eP99i+fQ92dnYZPi4pKQlra2usra25f/8e/ft78+23iyhfvkL2hRXZLjr6Bi4utdLcJyPlQog8JzLOPGUlre1CCJFbbNiwjs2bNzJixOhMFeRgXj7R13cSJpMJg8HAgAEfSUH+ipOiXAiR5/jsN88h//cUFp3evF0IIXKLHj160aNHrxc6tmrVaqxatT6LE4ncTG70FELkOevCYFAQXI81T1m5Hmt+rdR88l5nIWIOGKeYP8pjyoUQQmSWjJQLIfKkdWG546bOXmchIOifx5VXiDO/BlnJQgghRMbJSLkQQrwE//3/FOSPaVPM24UQQoiMkqJcCCFegnM6N5emt10IIYRIixTlQgjxEiILZ267EEIIkRaZUy6EEC/Bp+2Tc8oBdDbm7f/Wy9X8cCPnwualG33254458UKI7GcwpLBs2RL27t2NRqNBrVbToEEjhg0bgbW1jdLxnuDrO5mQkBM4OjqSlJREkyZNGTv2E6ysnj2O6+npwTffzKNy5Sovdf7Ll/8kMvIGb7751kv1kxdJUS6EEC/h8c2c/vvNU1YiC5sL8n/f5NnL9cklHCs4ml+DFOZC5AdffjmF5ORkli9fg1arxWAwEBy8Fb0+RdGi3GAwYG39dCnYp08/unfviU6no2/fXuzfX4927drnSJ7Ll//kyJHfpCgXQgiReevqPHulFf+2T66pDubX/m2lKBfiVRcZGcnBgwfYtm0XWq0WAGtrazw93wPAaDTy3XfzOX78KADu7s0YNmwkarUaX9/J2NpqiIy8wZ07d6hduw6TJvmiUqmeuS8mJoYZM/y5eTMKAC+vPnTs2Akwj2i3a9ee0NAQKleuwsSJk9PNrtVqqV69Jnfu/AXwzH4Bdu3awcmTx9HpEunR4wO6d+8JwI0b15kzZxZxcbGkpKTQs+cHdOrU5X/X68aAAR9x9OhhXFxq89tvB9HpEvH27km9em4MGzYCX9/JhIdfw9ramvLlK+DnNz3L/n5yEynKhRAimzmnM788ve1CiKyj0Vij0WRPuaPXG9DrDc9sc/nyJcqVc6ZQoUJp7t+yZTOXL//JihVrARgzZjhbtmzmvfe6A3Dt2jUWLPgeKysr+vTpxcmTJ2jSxP2Z+2bPnkGlSpWZPv0b7t+/R79+XlSvXsMytUSn07Fs2arnXt/ff//N1atXGDjw/wCe2++DB3+zYsVaYmJi6Nu3F/XquVGxYiUmTfJh6lQ/KlSoiE6no3//3ri61qFChYoA2NraERi4GoAaNWpy5MhvTJs2E4Bff/0FnS6R9es3ARAfH//c3HmV3OgphBDZLDKdlVjS2y6EyD9CQk7g4dEZGxsbbGxs8PB4h5CQE5b9rVu/jq2tLTY2NlSvXoNbt24+d19IyEnefdc8El+8eAmaNm1BaOgpy3EdO3o8M9PKlcvx8nqfd955G3f3plSsWClD/Xbu7AlAsWLFaNasJadPhxIVFcn169f54ovP8PbuyeDBA9Dr9Vy/HmE5zsPjn9H2/6patRrXr19n5sxp7N+/F40md83Bz0oyUi6EENnMZ/+Tc8oBdHrzdiFE9srIaHZ2qlatBlFRkcTHx6c7Wv4sGs0//3BYWakxGg0Z2vcsBQoUfOb+x3PKb9y4zqBB/WnSpCnNmrXIZHIzk8mEo6Mjq1atf6E8ZcqUZe3ajZw6dZJjx47w/fffsmbNj9ja2r5QntxMRsqF+I9erhAxCoyTzB97uSqdSOR168JgUBBcj4VUk/njoCCZTy5EfuDs7EzLlq2ZPt0PnU4HmOeRb936Mw8fPqRRoybs2BGMwZCCwZDCjh3BNG7s/lLnbNSoMVu3bgYgJuY+x44dpmHDRpnup3z5Cnz00WAWLfoOk8n03H63bzc/zvjBgwccO3aYBg0a4uxcHjs7O3buDLa0u349Ap0uMc1zarVaEhP/2Xf37h3Uaitat27D6NHjiI198MpOYZGRciH+RVbJENllXZh8DQmRX02a5MuSJYvp188LGxsbUlNTadasBRqNDZ6eXbl5M4o+fT4AoEmTpnTp8u5LnW/s2PFMn+6Hl9f7AAwdOpJKlSq/UF+enu/x44/rOXTo1+f26+joSN++H6DTJdKnT3+qVKkKwMyZc5k7dxarV68kNTWVokWLpnuzZqNGjVm7dhW9e/egfv0GNG3anIUL5wOQmppKnz4fUqJEiRe6ltxOZTKZTEqHAFBNVSkdIUdcGHqB0g6l+eOvP5SOophS2lIcunGIwdsHKx3lKRGjzIX4f12PhYrzcjqN+LcDfQ8A0GZFG4WTiLxkkcciWpVvxR3dHaWjKKbea/W4nXCbWgtrKR0lx4R1O4+TU3mlYwjxlOjoG7i4pP29KNNXRI4r7VCaVuVbKR0jTbJKhhCvllblW1HaobTSMYQQ4rlk+koOu6O7wx3dnXw92vd4xDM3ioxLe6RcVskQIm+Sf3Nz97+5Qoh/yEi5EP/is9+8Ksa/ySoZQgghhMhuUpQL8S+ySoYQQgghlCDTV4T4D1klQwghhBA5TUbKhRBCCCGEUJgU5UIIIYQQ2cjT04Nr165mSV8BAYtwd3fjyJHfLNsePnxImzbN6dfPK0vO8SxDhgzi8OFDAPj6TmbjxvSf1CkyR4pyIYQQQog8pHr1GuzY8c8TMn/5ZS/Ozi+2LrvRaHzitcFgeKls4sXJnHIhhBB5Vi9X8G9rfpZAZJx5pSS5J0TkVlFRkUyf7seDBw9Qq60ZMmQYTZs2ByAs7BwLFy6wPH7+o4+G0Lx5yzT7cXNryNGjh4mPj6dQoUJs3x6Mh8c77NgRZGmzY0cwa9asBKBs2XJMmDCRokWLEhy8jV27dqDVaomKimTKlK/o06cXAwZ8xNGjh3F3b0bv3n2YO3c2165dITk5mQYNGjFq1FjUanU2v0P5mxTlQggh8oxeZ8F/PzjHQcwAKDQBbO3M+yo4QkBn8+dSmIvHNBprNJrsKXf0egN6fcZHlidP/hxPz668844nERHhDB48kPXrN2Ftbc2MGf7Mnj2f4sVLcP/+Pfr392bt2o04ODg81Y9KpeLNN9uxd+9u3N2bkpT0iMqVq1j2X7t2lYUL57N8+RqKFy/B4sUL+eab6ZZH258/f45Vq9ZTtmw5yzG2tnYEBq4GwM/PFzc3NyZOnERqaiqTJ08kKGgrnp5dX/StEhkgRbkQQog8oddZCAgCbYr5dYnPAbsn22g15pFzKcpFbmMymbhy5U86dXoHgIoVK1G1ajXCws6iVquJjr7FmDEjLO1VKhU3b0ZRs2baj2Tv2LEzkydP5O+/79OhQ6cn9oWGnqJp0xYUL14CgHfffY/evXta9tetW++JghzAw+OfPg4fPsiFC2GsXWsu0pOSkihZsuRLXL3ICCnKhRBC5An++/8pyAFwTrudc+EciSPyiMyOZivBZDJRpUpVFi1amuFjypQpi42Nhq1bf2bNmh+5ejXjN5IWKFDwmdtMJhMzZsymTJmyGe5TvDy50VMIIUSe4Bz3nw2RabeL/G87IXIBlUpF1arVLfO+IyLCuXr1Mq6udahduy5RUVGEhoZY2l+4cB6TyfTMPocOHc6wYSMpXNjxie0NGjTk2LHDxMTcB2Dr1s00btwkw1lbtmzNypWBlptAY2MfEB19K8PHixcjI+VCCCHyhMjCUOHfBbcPEABo/9mk05tv9hQiNzEajWg0GqZO/Yrp0/1Yt24NarU1kyd/SZEiRQCYOXMOCxbMZc6cWaSkpFCmTFlmzZqLSqVKt9/atetSu3bdp7ZXrlyFoUNHMnLkUMA8qj5hwsQM5x09+mO+/XYe3t49UalU2NjYMHr0xzg5lcnklYvMUJme92NYDlFNTf+L7lVyoO8BANqsaKNwEuXIeyBeRG75uvn3jYaRhcGnLayro2ikfOO/c8oBknpDwjdQrETaq6/klq8bJeXH9yCs23mcnF5sicCsdv/+PXr0eI/t2/dgZ2f3/APEKy06+gYuLmnfJyAj5UIIkUH/LQorxJlfgxTmOeHxe/zED0UPYd33yuYSIj0bNqxj8+aNjBgxWgpy8VxSlAshRAY9daMh5tf++6Uozynr6sh7LfKOHj160aNHL6VjiDxCbvQUQogMeupGw+dsz696nYWIOWCcYv7Y66zSiYQQIveTolwIITIoMp2l9tLbnh89nuJTIc78H8zjKT5SmAshxLNJUS6EEBnk0xZ0Nk9u09mYtwuzZ03xEUIIkT4pyoUQIoPW1YFBneF6YUjF/HFQZ5nj/G8yxUcIIV6M3OgphBCZIDcaPttTa4n/a7sQQoj0SVEuhBD5QC9X8G9rfgR9Wut5ZxWftk+vJS5TfER+ZzCkEBi4lL17d6NWq1Gr1ZQr58xHHw2hYsVKWXae0NBTGAwpNGnSFIDo6Gj69+/N7t2/PPfYgIBFbN68keLFS2AymbCzs2PChIlUrVrtmccFB2/jyJHfmDZtZpZcQ34mRbkQQrzierlCQGfQasyvKziaX0PWF+ZpriUuD1gS+dyXX04hKSmJpUtX4uDggMlk4ujRw9y4cT1TRbnBYMDaOv3S7fTpUzx69MhSlGdWhw6dGDlyDAA//fQj3303n7lzv32hvkTmSVEuhBCvOP+2/xTkj2k15u3ZMVouU3yE+EdkZCQHDx5g27ZdODg4AKBSqWjevKWlTUjICRYvXoher8doNNKv3wDatWsPwJAhg6hWrRphYecoVKgwc+YsYOXK5fz6634MBiMlSpTAx+cLHjx4wM8/b8JkSiUk5ATt2rXnzTfNfXz//bccO3aEpKQkfHwmUa9e/efm1ukSKVSoEGD+YWDcuJHExcWRnJxMrVoufPrp59jY2Dx13PbtQWzatBGj0YC9vT3jx/tQvnyFl30b8wUpynNYKW0pSjuUtjz2OCv7PXTjEIO3D87SfoXISa/Zv0btkrVxLelK7ZK1qVG8Bg62DtiqbSlXuBwqVFwdcZVkYzJJhiTCH4QTdjeMc3fPEXY3jKt/XyXVlKr0ZeQ6zunM505vu1DOIo9FtCrfiju6O1nWZ73X6nE74XaW9ZfXaDTWaDTZU+7o9Qb0esMz21y+fIly5ZwtBW5aqlevyeLFy1Cr1cTExNCvnxdNmjS1HHPr1i0WL16GtbU1O3du59atKJYsWYGVlRWbNm1k3rw5+Pr68e677/Ho0SPLaHd0dDRxcbHUrl2HIUOGs2vXDr77bj4BAYFp5ti5M5iQkBPEx8djMBj4/vsAANRqNb6+/hQu7IjJZMLXdxJBQVvp2rXbE8f/8cdp9u/fy6JFS9BoNBw9eoSvvpqa7vnEk6Qof0WUdihNq/KtlI4hRKY0KdOE913ex620G7VL1ibVlGopsI/dPMbS35cSlxxHsiGZFe+uABN4/+yNrbUtBW0KUrlIZWqXqk2fOn2oXao2pbSluHT/EufuniPochDbL28n2Zis9GUqLjLOPGUlre0id2lVvhWlHUpnaVEucpeIiHAmTfIhKSmJpk2bM3bsJ8TGPsDPbypRUZGo1Wri4+OJjLyOq6v5V07t23ewTFs5fPgQFy9eoG/fDwAwGo1otfbpnq9gwYK0aGGuD1xdazN//px02/57+sqOHcF8/vmnrFy5jtTUVNasWcWxY0dITU0lPj4eOzu7p47/7bdDXLlymQED+gBgMplISEh4gXcpf5KiPIfd0d3hju4ObVa0ydJ+s3rkXYjsUrlIZbzqeNG7dm9STamsDVvLV4e+4tzdc9zV3U33uGSDubi+9uCaZdup6FNsOL/B8lpro8WlpAtupd0Y2nAoAZ0D2HRxE6vPrua3G79hwpR9F5aL+ex/ck45gE5v3i5yl+z4PyK///+QkdHs7FStWg2ioiJJSEjAwcGBihUrsWrVejZuXM/FixcBmDHDn5YtW/P117NQqVR07+5JcrLe0keBAgUsn5tMJvr3H0Dnzp4ZOr+NzT/f+FZWaoxGY4aOe+ONN/H1ncSDBw84fvwIZ878zqJFS9FqtSxfvpTIyMg0jjLRuXMXPvpoSIbOIZ4k65QLIbKdg8aBoY2GcvTDoxz58AhFCxTlg80fUOO7Gvge9GV/xP5nFuQZpUvRcfLWSRadWsSbq96kzvd1uBxzmQUdFhAxKgK/N/wY3aQkEaPAOAkiRplvgnzVrQuDQUFwPRZSTeaPg4KyZz65EOJJzs7OtGr1OtOmfUli4j+jxo8eJVk+T0hIoHRpJ1QqFSdOHOfmzah0+2vZshWbNm0kPj4eAL1ez5UrlwHQau1JTEzMktynT5+icGFHChcuTEJCIo6ORdBqtSQmJrBnz640j2nRohU7dgRz9675Nz1Go5FLly5kSZ78QEbKhRDZxkplRb96/fiqzVccunEI30O+7AvfhyE1Z0atbiXcYtbRWcw6OovaJWvj19aFTlUTUKnM+7NzFZLcZl3Yq3+NQuRWX3wxlWXLAujf3xtra2scHApRokQJvL37ATB06EhmzpxGQMAiatZ0oUqVqun21aFDJ2JjYxkyZCBgHjnv2rU7VatW4/XX2zBhQjDe3j2fuNEzox7PKTeZTFhbW+PvPx0rKys6dvTg0KFf6dGjK0WKFKFu3fokJz89NbB+/QYMHjyMjz8eQ2qqkZSUFNq2bUeNGrUylSO/UplMplzx+1zVVJXSEXLE418jZtf0lazuNzvkpazixTUv15x5b88jyZDEyF0jOX379Ev1lxVfNxGj0p5bfT0WKs574W5FLpbX/r3Jjrx57T3ICmHdzuPkVF7pGEI8JTr6Bi4uaf+QIiPlQhEqVFiprGSljFdQ2UJlmf7mdFo6t2T8vvGsD1uvdCQLWYUk/1GpVOSSsSchhHgmmVMuFFHaoTT7vPdRrlA5paOILNS/Xn/++L8/uPr3VWp8VyNXFeSQ/mojqallWPLOEmzVtjkbSGQbjVrDzHYzcS2RD24aEEK8EqQoF4qITohmT/geTg46SbNyzZSOI16StZU1896ex4TmE2gR2ILJv07mYcpDpWM9xWe/edWRf9PpYVDQXRw0Dhzsd5DS9qWVCSeyTPGCxTnQ9wBVilbh0v1LSscRQogMkaJcKObrw1/Tb0s/fu7xMz1deyodR7ygQraF2OW1iypFq9BkSZNcXQSltwrJ8jMp9PipB1v/3MrJQSep91o9hZOKF1WjeA2ODzjOLxG/0HVDV1JSU5SOJIQQGSJzyoWidl/bTduVbQnqFYRzYWdmHJmhdCSRCcULFmeX1y5O3DrBiJ0j8sQ9As9ahWTa4WlcjrnM7t67ee/H9zgceThnw4mX0qxcMza/v5kJ+yaw4swKpeMIIUSmyEi5UFzY3TCaLm3KwPoDGdZomNJxRAaVti/Nb/1/Y8fVHQzbMSxPFOQZseniJj7Y9AGb399M+8qZW05MKKduqbpsfn8zfbb0kYJcCJEnSVEucoW/Ev+i3ap2TGg+Aa/aXkrHEc9hbWXNT+//xNpza5l0YJLScbLc/oj9dFnfhZXvrqRK0SpKxxHPUbVoVXZ4mX843HNtj9JxhHiKp6cHHh5vPfE0zeDgbbi7u7FxY87fED969HA2b/7piW3m9c47c/p0aLadNzh4G5GRNyyvL1/+k337nvye9fbuSVJS0n8PzRekKBe5xo24G7Rf3Z5Zb82iTYX8s55uXjSj3QzuP7zPV4e+UjpKtjl28xhfHPiCTe9vooB1gecfIBRRxK4Ih/sPRaOuxo/dN+Wbp7SKvKd48eKcOHHM8nr79iCqV6+R6X4Mhpd/+Frnzl3Yvn3bE9tOnz6FlZWK+vXdMtRHamrqU8uNPi/b9u1BTxXl+/fvfaLNqlXrsbOzy1CGV43MKRe5ysX7F/H+2ZtV766i/uL63Ht4T+lI4j/eq/keXap3ocEPDTDxaq///EPoD7Qo14KFHgvpv7W/0nFEGnb37kuRAuOxUZtv6MxPT2kVeYuHxzts3x5Es2YtuHXrJklJj6hc+Z8nd4aEnGDx4oXo9XqMRiP9+g2gXTvzFLohQwZRrVo1wsLOUahQYebMWcCKFcvYs2cXKpWKAgUKsHjxMqysrFi5cjm7dm0HoGbNWowbN4GCBQs+kaVVq9eZMWMaERHhVKxYCTCPYnt4vINKpWLlyuX8+ut+DAYjJUqUwMfnC4oVK05AwCIiIsJJTEzkzp2/8PX1Z+TIoXh4dObUqRA8PbtSrly5NK8jOHgrly5dYPbsmSxevJA+ffoTELAInS4Rb++e1Kvnxrhx43F3d+OXXw4/lTk/kKJc5Dr7wvex8uxKlnsup9PaTq984ZeXVC1alYUeC+mwpgOxSbFKx8kRg7cP5sTAEwyoP4Clvy9VOo74l6GNhlKn1HpLQf6YVgP+bbO+KO91Fvz3g3McRBYGn7awrk7WnkNkPY3GGo0me8odvd6AXp+xkWs3twZs2vQj8fHx7NgRRIcOnbh06aJlf/XqNVm8eBlqtZqYmBj69fOiSZOmFCpUCIBbt26xePEyrK2t2b49iN9+O8gPPwSi1WqJi4vFysqKo0ePsGvXdgICAilYUIuv7ySWLQtg+PBRT2SxsbGhffsOBAdvY8SI0eh0Og4e/JX1639i587t3LoVxZIlK7CysmLTpo3MmzcHX18/AM6fD2PFijU4OhYhOjqauLhYatasxciRYwCIj49P8zo6derC9u3BeHl506JFKwCSk5M5cuQ3pk2b+dJ/F68Cmb4icqVJByZRxK4IQxoNUTqK+J8C1gXY9P4mPv/lc07fPq10nBzzMOUh7/34HtPaTpOlEnORasWqMaX1FGzUf6W5P6uf0trrLAQEQYU483+cFeLMr3udzdrziFeXSqXizTfbsXfvbvbu3UP79m8/sT829gE+PuP54IPujB49jPj4eCIjr1v2t2/fAWtr8w8XR478Rteu3dFqtQAULuwImEfb33yzPVqtPSqVii5duhISciLNPJ07d2HXrh0YjUb27dtDnTp1KVmyFIcPHyIk5CR9+36At3dPNm36kdu3oy3HNWvWHEfHIpbXtra2vPnmWxm+DpE+GSkXuZIh1cCAbQM42O8gG89vlGksucCwxsO48vcVAk4HKB0lx12OucyEfROY234ur694Xek4Apj39jz8D/szqol5ysp/pff01hflvx+0/1nyXJti3i6j5blbZkazs1vHjp0ZMKAP9eq5WQrpx2bM8Kdly9Z8/fUsVCoV3bt7kpz8z9POChTI2ntbqlatRvHixTl27AjBwdvo2fMDwHzDZ//+A+jc2TPN4woUeHJaiZ1dAVQqVYavQ6RPRspFrnXx/kXWnFvDpNav3uoeeY2t2pYx7mOY8usUpaMoZuWZlZQtVJbm5ZorHSXfa1+5PRUcK/DtyW/TfUqrz/6sPadzOkV+etuFSEuZMmUZPHgYH3446Kl9CQkJlC7thEql4sSJ49y8GZVuP82bt2Tz5o3odDoA4uJiAWjUqAn79+9Bp9NhMpnYtm0LjRu7p9tP585dWLJkMVFRN2jV6nUAWrZsxaZNG4mPjwdAr9dz5crlDF/js65Dq9WSmJiY7uv8Topykav5/eZHL9delC9cXuko+Vr/+v05FX2Kc3fPKR1FMUaTkelHpvNZi8+UjpLv+bf1x2e/D4ZUQ7pPac3q+eSR6UyHSW+7EOnx9HyPatWqP7V96NCRzJ8/B2/vnuzfv5cqVaqmcbRZx46daNGiFQMH9sXbuyeffDKW1NRUmjVrTvv2HRk0qB9eXu8D0L//wHT7eeutDkREhPPWWx2wsbEBoEOHTrRv34EhQwbi5fU+/fp5cebMHxm+vmddh6dnV5YtC8DbuycnT56gUaPGJCU9onfvHnzzjTw8UGX673o2ClFNVT2/0SvgQN8DALRZkbVL/mVXv9khs1lntpuJIdXAZ/ulGFKCtZU1l4dfptemXpy4lfbcxJyQG77GNWoN4SPD8VjrwZk7ZxTLkZ+1cG5BQOcAan5XM0Pts+rr5vGc8n9PYdHZwKDOWTt9JTu+znPD905OC+t2HicnGcwRuU909A1cXGqluU9GykWu90PoD/Sv1x+NWqN0lHypp2tPImIjFC3Icwu9Uc83x76R0XIFDW4wmEWnFuX4edfVMRfg1wtDKuaPWV2QCyHyNynKRa535e8rnLt7jq41uyodJV+a0HwC0w5PUzpGrvFD6A+0qdiGSkUqKR0l3ylesDge1TxYeWalIudfVwcqjgH1FPNHKciFEFlJinKRJwT+EUhPl55Kx8h3yhUqR0ltSfaF71M6Sq6hS9ERfDmYtyq/9fzGIkt1qd6FXVd38SDpgdJRhBAiy0lRLvKEfeH7aF2hNWqVWuko+Upz5+YcjTqqdIxc52jUUVmFRQFvVnqT3dd2Kx1DCCGyhRTlIk+4q7vLjdgbNCrTSOko+Uqzcs04EnVE6Ri5zpGoI1KU5zAVKtpWbMv+8Cxe61AIIXIJKcpFnrE/Yj9tKuSf1QNyg+blmnMkUory//rz/p8Usi1EafvSSkfJN1xKuhCbFEtUfPprNwshRF4mRbnIM87fO0+1YtWUjpFv2GvsqV6sOqG3Q5WOkuuYMJmnsDjLaHlOqV6sOmF3s3jxcSGEyEWslQ4gcq+qRatSplAZLsdcJjohWuk4hD8Ip2/dvkrHyDcal2nM73/9jt6o/OORrVRWOGgcsLKyQmujRZeiUzoSR6KO0KxcM3668JPSUfKFSkUqER4brnQMAAraFKRB6QYkGZIIvR1KqilV6Ugil/P09MDW1haN5p+lfadPn42Tk5OCqURuI0W5eIpzYWe29txKtWLV0Bv12Fnb8UvEL/T4qQeJeuUehxv+IFyWoctBpe1Lcz32utIxaFuxLeu7rcfRzpG7d+9SdHFRdFE6KAS0BRRalu5G3A3qvVZPmZPnQ5WKVMoVT5T9rMVnfNHqC5KNyViprDCkGui9uTc7r+5UOprI5fz9Z1C5cpVsP4/RaEStlkUR8iIpysVTdvfeTZUiVbBWW1PQpiAAb1R8g8AugXTf2F2xXLcTbvOa/WuKnT+/KWhTkEeGR4pmcHJwYmvPrWg1Wu7cucPVy1eJivzfnOI4IOh/DRUozB+mPLR8f4jsV0pbij0JexTN0K1WN3xa+lDApgAFbApYtv/0/k+4LHTJFT/EirzF3d2N//u/oRw69CtxcXF89tnnhISc5PjxoxgMBvz8plOxonkwavv2IDZt2ojRaMDe3p7x430oX74CwcHb2LVrB1qtlqioSKZM+YpDh35l797daDQaVCoV3333Aw4ODhw7doTvv/8Wo9FIkSJFmDBhIuXKORMaeoq5c2fh4uLKuXNnUalUfPnlNMu5Rc6Qolw8od5r9SjjUAZr9ZNfGnbWdrxT/R3srO1IMiQpki0lNQVrK2tUqDBhUiRDflLApgCPUpQtynu59kJtZR7xiYiIIDX1P9MEUoD9SFGeD2jUGsWnUo12H429xv6p7dZW1vSp2wffg74KpBLPo9FYo9FkT7mj1xvQ6w0ZauvjM94yfUWtVrN8+RoAHBwcCAxczf79exk/fixffjmNoUNHsGrVcpYvX8rUqX788cdp9u/fy6JFS9BoNBw9eoSvvppKQEAgAOfPn2PVqvWULVuOuLg41q9fQ3DwHuzs7NDpdNja2vL3338zdeoXfP/9EipWrMS2bVuYPPlzli0zP4wrPDyczz+fwqeffk5g4BICA5fi6+uXDe+aSI8U5eIJxlQjKpUq3f0mk/LFsBTk+UeqKdXyNZeUlM4Pg3E5GOhfVKhyxfeDyDnGVGOa200mU7r7hHgsvekrb75pfhBZ9eo1UKlUtGjRCoAaNWry66+/APDbb4e4cuUyAwb0AcxfcwkJCZY+6tatR9my5QCwt7enbNly+PpOonFjd1q0aIlWq+X8+XNUqVLNMvrdqdM7zJw5DZ3OfI9O+fLlqV69BgCurrU5fPhQdrwN4hmkKBdPOHf3HOEPwqlZvCY2ahvL9kcpj9h0cRPJxmTFstlY2ZBiTFHs/PnNw5SHT/yKXglrzq3Bt4159NHOzi7twrxwDof6n4I2BXmY8lCZk+dDeqMeW2tbRTPMOjoLt9JuT42WG1INrDizQqFU4nkyM5qtBI3G/HVtZaXGxuafG0GtrNQYjY9/2DPRuXMXPvpoSJp9FCjwz2/t1Go1S5as4OzZM4SGnqRfPy/mzPk2AznSO7fIKbIkonhK+9XtOX7zOI9SHhGbFEuSIYmtf27lo6CPFM1VplAZbifeVjRDfvIo5REFrZWdnnFXd5f2q9sTGRdJ+QrlsbL6zz9ZNphv9lRAAZsCis+5z09uJ97GyUHZlSqCLgfx2b7PiE+OJz4pnkR9Irfib+Gx1oOb8TcVzSZebS1atGLHjmDu3r0DmG/mvHTpQpptdTodsbEPcHNrwKBBQ6hUqQrh4ddwda3D1auXuX49AoAdO4KoVq06Wq02x65DPJuMlIun/JX4F62Wt6K0fWnKFCrDtb+v8SDpgdKxqOhYkfAHuWNJtPzgZvxNKhapqHQMjkYdpfzc8pwYeAJNSY15ZDwO80cFV1+pVKQStxJuKXPyfCj8QTgVHZX/evw25FsWhS6idsnaPDI84tL9S0pHEnnEv+eUm19PyvCx9es3YPDgYXz88RhSU42kpKTQtm07atSo9VRbnS6RTz/9hOTkJEwmE9Wr1+D119/A1taWyZO/ZNKkiRiNBooUKcKUKV9lybWJrCFFuUjX7cTbuWpkulKRSkQ8iFA6Rr4REh1C3VJ1sVXbKjpt6bGHKQ/BDhijdBKz5uWas+T0EqVj5BsRsRG0cG6hdAzAPF3l979+VzqGyEO2bNme5vbjx09bPndycmL37l8srxs0aGi5GRTg7bc78vbbHZ/qo1Ond+jU6R3L65IlS1lu3vyvpk2b07Tp0w89+++5/vta5AyZviLyjNqlanMpRkalcsrDlIdcvH+Rhk4NlY6S66hQ0bRsU45EHVE6Sr5x6f4lapesrXQMIYTINlKUizyjbcW2/BLxy/MbiixzJOqIPEo+DbVK1CLmUQx3dXeVjpJvXLh3AXuNPRUcKygdRQghsoUU5SJPeM3+NZwcnDh9+/TzG4sscyTyCM3LSVH+X82dm3MkUkbJc9q+8H20rajQnb1CCJHNpCgXecJbld/i1+u/kmpKfX5jkWWORJmLchXpr12fHzUv11ymrihgX8Q+2ldur3QMIYTIFlKUizzhw3ofsvbcWqVj5DvRCdFExkXSoWoHpaPkGoVsC+FR1YPd13YrHSXf2XppK+0qt6N4weJKRxFCiCwnRbnI9WoWr0nVYlXZ+udWpaPkS18f+RqfFj5Kx8g1hjYayo4rO4iMi1Q6Sr7zIOkBWy5toV+9fkpHEUKILCdFucj1/q/h/7H096UYUnPvE9leZT9d+ImS2pK0dG6pdBTFFbAuwKgmo/j6yNdKR8m3Fp1axP81+D+ZUiXyFE9PD65du6p0jGcKDt7GZ599kunj3N3d8PJ6H2/vnvTo0ZXvv3/+00Nzkp+fL3/8kTfuR5N1ykWu9pr9a/Su3Zu6i+oqHSXfSjWlmkfLW/rQYU3+nsbyYf0PORZ1jAv30n6Snsh+J26d4P7D+/Rw7cH6sPVKxxEiTzIYDFhbZ10JGBCwnIIFC5KU9IiePbvRsmVrXF1zxxKmEydm/CFNSpOiXORqk1tPZvmZ5fLkRIWtOrOKKa2n4FbaLd+ugGNjZcMnzT6h+8buSkfJ9z7b/xlLOi9h88XN6I16peMI8cKOHj1MYOBS9PpkbGxsGD16HK6u5scUr1y5nF27zA8dqlmzFuPGTaBgwYIEBCwiMvIGiYmJREffokyZsvj7T8fOrgAhISdYvHgher0eo9FIv34DaNfOfHP0kCGDqFatGmFh5yhUqDAzZszmm2+mc+pUCI6OjlSrVsOSy2g08t138zl+/CgA7u7NGDZsJGq1+pnXk5SUhNFoxMHBAYCYmBhmzPDn5s0oALy8+tCxYyfA/NuDjh07cfLkce7fv4+Xlzfdu/f83/nc+OWXwxQsWPCp1+fOnWHBgnk8fKgDYMSI0TRp0pSIiHDmzJlFTMx9AD74wBsPj84MGTIILy9vWrRo9ZJ/W9lPinKRa9UtVZd3a7xLze9qKh0l30tJTWHWsVn4vu5Lp3WdlI6jiAFuA7jy9xVCokOUjpLv/Xr9V8LuhjG26Vi+PixTicSzaTTWaDTZU+7o9Qb0+hebWnnzZhTLlgUwb953aLX2hIdfY8yYEWzduoOjR4+wa9d2AgICKVhQi6/vJJYtC2D48FEAXLx4gcDA1djb2zNq1DB27dqJp2dXqlevyeLFy1Cr1cTExNCvnxdNmjSlUKFCANy6dYvFi5dhbW3Njz+uJzo6mvXrf8JgMDB48EBKl3YCYMuWzVy+/CcrVpgXWBgzZjhbtmzmvffSHpQYNKif5Zrefbcb5ctXAGD27BlUqlSZ6dO/4f79e/Tr50X16jWoXLkKYC7ilyxZQXR0NF5e3fHweMdSiKclLi6OCRM+5uuvZ1GnTl2MRiM6nQ6DwcD48WMZPHgYbdu2+1/b2Bf6e1GSzCkXuZJGrWFZl2VM/GUiD5IeKB1HYJ7LW8q+FKOajFI6So6rXbI2vq/7MmpX/rv23GrM7jGMdR9LrRK1lI4ixAs5fvwYt27dZPDggXh792Ty5IkYjQZiYmIICTnBm2+2R6u1R6VS0aVLV0JCTliOdXdvioODAyqVChcXV27duglAbOwDfHzG88EH3Rk9ehjx8fFERl63HNe+fQfLtJXQ0BA6duyEtbUNdnYFePvtjpZ2ISEn8PDojI2NDTY2Nnh4vPPE+f8rIGA5a9b8SHDwHk6fDuXgwQP/6+ck7777HgDFi5egadMWhIaeshz3eBTfyckJB4dC3L1755nvWVjYWSpWrEidOuYprWq1mkKFChEZeQOj0WgpyAEKF3Z8Zl+5kYyUi1xp+pvTuR57naW/L1U6ivgfvVFPtx+7cWLgCU7eOsmxm8eUjpQjCtkW4qf3f2LUrlEylzwXiYiNYMK+CWzotoFGAY1IMiQpHUnkUi8zmp29TLi7N2Py5C8zfaRGY2v53MrKCqPRCMCMGf60bNmar7+ehUqlont3T5KT/5niVaBAgZeP/QwODg40btyEkyeP07p1m+e212g0ls//fR1qtRrT/55LkpycnD1hcyEZKRe5TqdqnfCs4cmAbQOUjiL+40bcDQZsG8CGbhsoUbCE0nFyxLJ3lrEvfB/rwtYpHUX8R+AfgZy9c5Y57ecoHUWITGvcuCnHjx8lPPyaZduFC+cBaNSoCfv370Gn02Eymdi2bQuNG7s/t8+EhARKl3ZCpVJx4sRxy1zutDRs2Ihdu7ZjMBhISkpiz55dln2NGjVhx45gDIYUDIYUduwIztD5U1JSOHfuDM7O5f/XT2O2bt0MQEzMfY4dO0zDho2e20/ZsuUs78WePTst211d6xAREcG5c2cA89z3+Ph4nJ3Lo1ar2b9/r6VtXpy+IiPlIlep91o9lr6zlHc3vEtsUqzScUQatl/Zzqqzq1jTdQ1vr3n7lX7K6mj30TgXduaDzR8oHUWkY3DwYEIGhTCi8QgWnFygdBwh0jVixJAnbpRcs+ZHpkz5Cj+/qSQnJ5OSkkKdOvWoVcuFZs2ac/XqFctc7Ro1atG//8DnnmPo0JHMnDmNgIBF1KzpQpUqVdNt6+n5HlevXqFnz/dwdHSkZs1a/P333//b15WbN6Po08f8b1+TJk3p0uXddPsaNKgfVlZW6PV66td3o2tX89zzsWPHM326H15e71vyVapU+bnXMWrUWKZP90Ortadt27cs2wsXLszXX89i3rzZPHr0CCsrK0aMGEPjxk0sN64uWxaASqXCy8ubDh3y1j1QKpPJZFI6BIBqav5Yc/ZAX/M8qzYrnv9rndzQb3ZIL2vVolX5td+vDN8xnJ8v/axENJFBapWaPd57OBV9ign7JuTIOXP6a/yNim+wtutamixpwo24GzlyTvFiyhcuz2/9f8PnFx9Wn139xL689G8jZE/evPYeZIWwbudxciqvdAwhnhIdfQMXl7TvhZHpKyJXcC7szB7vPXz+y+dSkOcBRpORnj/1pH3l9sxpP+eVe5BL52qdWffeOt7/6X0pyPOAG3E3eGv1W8x4cwaeNTyVjiOEEC9EinKhuIZODTn64VFmHp1J4B+BSscRGXTv4T1aL29NQ6eGBHYJxNrq1ZgN16duHxZ3WkzHNR05dOOQ0nFEBl26f4lO6zqxsONChjUapnQcIYTINCnKhaK61uzKjg92MGT7EBaGLFQ6jsikuOQ43lr1FkUKFGGv916KFyyudKQXZqWywr+tP1NaT+GNlW8QejtU6Ugik07fPk3zZc0Z2mgoc9+ei5VK/osTQuQd8i+WUIQKFX5v+DHv7Xm0X92eoMtBSkcSL+iR4RHvbniXI1FHCBkUQr3X6ikdKdMK2xZmW89tuJdxp1FAIy7dv6R0JPGCImIjaLa0GbWK12KX1y40as3zDxJCiFxAinKhiNIOpalTqg4NfmjA73/9rnQc8ZJSTal8/svnTNg3gT299zCn/RwK2xZWOtZzWams+LD+h1wcdpErf1/hrdVvEfMoRulY4iXFJcfRYU0HDkcdplqxakrHEUKIDJGiXCgiOiGazus6c1d3V+koIgv9eP5Hai2sRQGbAlwafolBboNy7RSCZuWacXLgSfrX60+ndZ0Ys3sMhtTc+IAR8SKMJiO+B305f/e80lGEECJDcuf/lkKIPOv+w/sMDh5MhzUd6F2nN6cGnaKlc0ulY1mUcSjDmq5rWP/eemYdm0XLwJacvn1a6VgZ1ssVIkaBcZL5Yy9XpRPlbiZyxaq/QgjxXFKUCyGyxR9//UHr5a2Zdngaq95dxfYPttO9VnfsrO0UydOsXDMWeizkj8F/cPXvq9T4rgbrw9YrkuVF9XKFgM5QwRGsVOaPAZ2lMBcit/P09ODatatZ2ufEiRN4++03MBhSLNuio6Np3/4Ny2t3dzcePnyYpecV2UeKciFEttp4YSM1v6vJ+rD1DHQbSPTYaJa+s5Q2Fdpk+9SWasWq4dvGl2sjrxHQOYDIuEjqLarH5F8n8zAl7/1H5d8WtP+5b1GrMW8XQuQfcXFxhIScoGzZchw6JEu3vipejYWFhRC52iPDI1adXcWqs6sobV+anq49mfXWLEpqS7IubB1Ho45y7s45ImIjSDWlvvB5SmpLUrtkbeqXrk/3Wt0pV6gca8PW8t6P7/HHX39k3QUpxDmde2fT2y6EyF2ioiKZPt2PBw8eoFZbM2TIMJo2bQ5AWNg5Fi5cgE6XCMBHHw2hefO0p/7t3r2TZs1a0KRJU4KDt/LGG8//yfzGjevMmTOLuLhYUlJS6NnzAzp16gKYR9QHDx7GwYMHiIuLY/jw0RnqU2QtKcoV4l3Hmw/rf5hl/dV7rR63E25nWX9CZJfbibeZc3wOc47PoVaJWrzv8j4f1vuQ2qVqU6JgCS7cu0DY3TDC7oVx4d4F4pLiSDYmo7XRYsJEvdfqYau2pYBNAaoUrULtkrVxLelK7ZK1UVupOXfnHGfvnmXiLxP5JeKXlyryc5vIOPOUlbS2i1dDKW0pSjuU5kDfA1nWZ37//0GjsUajyZ5yR683oNdn/AbxyZM/x9OzK++840lERDiDBw9k/fpNWFtbM2OGP7Nnz6d48RLcv3+P/v29Wbt2Iw4ODk/1Exy8lZEjx+DqWps5c2Zy7949SpQoke55DQYDkyb5MHWqHxUqVESn09G/f29cXetQoUJFALRaLYGBqzlz5g8+/3yCFOUKkKL8FXE74bY8fVDkORfuXWDKr1Msrx00DriUdLEU2e0rt8deY4+t2pYaxWsAENglkCRDEkmGJCIeRBB2L4zgy8GE3Q3jduKrXXj47DfPIf/3FBad3rxdvBoO3ThEq/KtlI4hsoHJZOLKlT/p1OkdACpWrETVqtUICzuLWq0mOvoWY8aMsLRXqVTcvBlFzZq1nujnzz8vER8fT4MGjVCpVLz+elt27Aiib9/0B/qioiK5fv06X3zxmWWbXq/n+vUIS1Herl17AFxda3Pv3j2Sk5OxtbXNsusXzydFuUIe/ypfCPGPBH0Cx28e5/jN40/tezxy2GZFm5yOlWusCzN/9G9rnrISGWcuyB9vF3nf4O2Ds7zPrBx1z4syO5qtBJPJRJUqVVm0aOlz2wYFbSUxMYF33+0EQEpKCgULFnxmUW4ymXB0dGTVqvRvbtdozAW4Wq0GwGg0ZuYSRBaQolwIIfKQdWFShAuRF6lUKqpWrc6OHUF06tSFiIhwrl69jKtrHdRqNVFRUYSGhtCgQSMALlw4T82atVCpVJY+9Ho9e/bsIjBwNeXKOVu2v//+u/zxx2lKlnwtzXM7O5fHzs6OnTuD6dDBXMxfvx5BiRIl0Grts/GqRWZIUS6EEEIIkY2MRiMajYapU79i+nQ/1q1bg1ptzeTJX1KkSBEAZs6cw4IFc5kzZxYpKSmUKVOWWbPmPlGUHzx4gLJlyz1RkAO0b9+BoKCtDBjwf2me39rampkz5zJ37ixWr15JamoqRYsWxc9vevZdtMg0KcqFEEIIIbLJ/fv3ePjwISVKlMTOzo5vv12cZrtatVz4/vuAZ/bVrl17y9zvfxsw4CPL57t3/2L5/Pjxfx6M5uzszOzZ89Ps99/t0notcoYU5UIIIYQQ2WDDhnVs3ryRESNGY2enzIPTRN4hRbkQQgghRDbo0aMXPXr0UjqGyCPkiZ5CCCGEEEIoTIpyIYQQQgghFCZFuRBCCCGEEAqTolwIIYQQQgiFSVEuhBBCCJGNPD096NGjK71796Bbt3f45JMxnD175qX79fPz5Y8/nr984ZAhgzh8+FCa+9zd3Xj48OFLZxEvT1ZfEUIIIYTIZv7+M6hcuQoABw7sZ+zYEcyd+x2urrVfuM+JEydlVTyRC0hRLoQQQgiRg9q0acuFC+dZu3YV/v4zCAk5weLFC9Hr9RiNRvr1G2B5SNCQIYOoVasW586d4/79e7Rt245hw0Za9nl5edOiRSt0ukTmzp3NtWtXSE5OpkGDRowaNRa1Wp3hXPPnz+H330NJSUnB0dGRiRMnU7q0E9HR0fTv35suXd7l+PGjJCcnM3XqV2zevInz589ha2vHzJmzKVasOAArVy7n11/3YzAYKVGiBD4+X1CsWHEOHfqVxYsXYmVlhdFoZNy4CTRo0DDr3+A8SqavCCGEEOKVpdFYY29vly1/NJoXH9t0cXElPPwaANWr12Tx4mWsXLmO+fO/Z/78OcTHx1va/vXXXyxatISVK9eybdsWIiMjn+pv7tzZuLm5sWzZKlatWs+DB38TFLQ1U5n69OlHYOBqVq/eQLt2b/Pdd/88ATQuLpa6deuxcuU6OnfuwvDhg+nW7X3WrPmRGjVqsnHjBgB27tzOrVtRLFmygpUr19KsWQvmzZsDwA8/fM+nn05k1ar1rFq1nho1amT6fXuVyUi5EEIIIUQOM5lMls9jYx/g5zeVqKhI1Go18fHxREZex9W1DgBt27bDysoKe3sHKlSowK1bUTg7Oz/R3+HDB7lwIYy1a1cDkJSURMmSJTOV6dixI/z00488evQIo9H4xL6CBQvSvHlLAKpXr0HJkqWoVq06ADVq1OTkyeP/y3GIixcv0LfvBwAYjUa0WnsAGjZsxNy539CmTVuaNm1umc4jzKQoF0IIIcQrS683oNcblI7xlIsXL1iK0hkz/GnZsjVffz0LlUpF9+6eJCfrLW01Go3lcysr9VMFM5iL/BkzZlOmTNkXynP7djRz584mMHAVTk5lOHv2DJMm+Vj229g8meHJTFaWTCaTif79B9C5s+dT5xg9+mOuXr1CaGgIPj7j6dWrN56eXV8o76tIpq8IIYQQQuSgQ4d+ZfPmjfTq1RuAhIQESpd2QqVSceLEcW7ejMp0ny1btmblykBLcRwb+4Do6FsZPl6n02FjY03RosVITU3l559/ynQGc45WbNq00TL9Rq/Xc+XKZQBu3LhOlSpV6dHjA95+uyMXL55/oXO8qmSkXAghhBAim/n4jMfGxoakpEdUqFCJ2bMXWFZeGTp0JDNnTiMgYBE1a7pQpUrVTPc/evTHfPvtPLy9e6JSqbCxsWH06I9xciqT7jEGg8HStkqVqrzxRjt69eqGo6MjzZo15/ffn7/c4n916NCJ2NhYhgwZCJhHzrt27U7VqtVYuHCBZYqOvb2DrB7zHyrTvyc1KUg1VaV0hBxxoO8BANqsaKNwEuXIeyBehHzdiBchXzf58z0I63YeJ6fySsfI9S5cOM/EiRP4+edgpaPkG9HRN3BxqZXmPhkpF0IIIYTIZ779dh6HDx9i7NhPlI4i/keKciGEEEKIfGb48FEMHz5K6RjiX+RGTyGEEEIIIRQmRbkQQgghhBAKk6JcCCGEEEIIhcmc8hxWSluK0g6lLXfD50f1XqvH7YTbSscQQuQD8m+u/JsrRF4hI+Uix91OuM2hG4eUjiGEyAcO3TgkBakQIk+QkfIcdkd3hzu6O/lqvVghhFDK4O2DlY6guPz8W4LcwtPTA1tbW8uj6Rs0aMjo0R9n+Pjg4G0cOfIb06bNzPS5x4wZwbhx4ylbtlymj01LZOQNvvtuPleuXKZQoULY2Gjo3bsPrVu3YciQQXh5edOiRaunjvPz88XDoxP16rllSY5XkRTlQgghhBDZzN9/BpUrV8mx86WmpqJSqZgzZ0GW9Xn//j0GDx7IiBGjmD79GwBiYu5z4sTx5x4rT+98PinKhRBCCCEUsH17EJs2bcRoNGBvb8/48T6UL1+BlJQUvvlmOqdOheDo6Ei1ajWeOG7lyuX8+ut+DAYjJUqUwMfnC4oVK05AwCIiIsJJTEzkzp2/CAhYTp8+vfjmm3lUrlyFu3fvMnv2DKKiIgF466236dv3Q2JiYpgxw5+bN6MA8PLqQ8eOnZ7K+9NPP9KgQUM6dPhnX7FixZ9o+/vvoaxcuZz79+/Rtm07hg0bCfDEKLpOl8jcubO5du0KycnJNGjQiFGjxqJWq1myZDF79+5Go9GgUqn47rsfcHBwYNKkiURGXiclJYWyZcsxceJkChUqlOV/J0qSolwIIYQQryyNxhqNJnvKHb3egF5vyFBbH5/xlukrw4aNxM7Ojv3797Jo0RI0Gg1Hjx7hq6+mEhAQyM8/byI6Opr163/CYDAwePBASpd2AmDnzu3cuhXFkiUrsLKyYtOmjcybNwdfXz8Azp8PY8WKNTg6Fnkqw5Qpn9OsWXO+/noWALGxDwCYPXsGlSpVZvr0b7h//x79+nlRvXqNp0b2//zzEk2auD/zOv/66y8WLVrCw4c63nuvC507e+Ls7PxEm7lzZ+Pm5sbEiZNITU1l8uSJBAVtpU2btqxfv4bg4D3Y2dmh0+mwtbUFYOzYjy3XtGjRd6xatdxS8L8qpCgXQgiRI3qdBf/94BwHkYXBpy2sq6N0KiFyxn+nryxYMJcrVy4zYEAfAEwmEwkJCQCEhobQsWMnrK1tsLa24e23O3LmzB8AHD58iIsXL9C37wcAGI1GtFp7S7/NmjVPsyB/+PAh586dYf78hZZtj9uFhJxk1KixABQvXoKmTVsQGnrqhabbtG3bDisrK+ztHahQoQK3bkU9VZQfPnyQCxfCWLt2NQBJSUmULFkSe3t7ypYth6/vJBo3dqdFi5ZotVoAduzYzu7dOzAYDDx69OipPl8FUpQLIYTIdr3OQkAQaFPMryvEmV+DFOYie2VmNDtnmejcuQsffTQkc0eZTPTvP4DOnT3T3F+gQMEsyJa26tVrcOHC+We2efzbAAArKzVGo/GpNiaTiRkzZlOmTNmn9i1ZsoKzZ88QGnqSfv28mDPnW3S6RDZv3khAwHKKFCnC7t072bJl88tfUC4jSyIKIYTIdv77/ynIH9OmmLcLkR+1aNGKHTuCuXv3DmAe8b506QIADRs2Yteu7RgMBpKSktizZ5fluJYtW7Fp00bi4+MB0Ov1XLly+bnnK1iwILVr12X9+jWWbY+nrzRq1JitW81FbkzMfY4dO0zDho2e6uO9994nJOQku3fvtGz7+++/2bEjOFPX3rJla1auDLQU7LGxD4iOvoVOpyM29gFubg0YNGgIlSpVITz8GgkJCdjb21O4cGH0ej1BQVszdb68QkbKhRBCZDvnuMxtF+JVV79+AwYPHsbHH48hNdVISkoKbdu2o0aNWnh6vsfVq1fo2fM9HB0dqVmzFn///TcAHTp0IjY2liFDBgLmUeeuXbtTtWq1555zypSvmDXraz74oDtWVla89VYH+vTpx9ix45k+3Q8vr/cBGDp0JJUqVX7q+BIlSvD99wF89918Fi9eSIECBShQoAB9+vTP1LWPHv0x3347D2/vnqhUKmxsbBg9+mOsra359NNPSE5OwmQyUb16DV5//Q3Uait27drB++97UriwI/XquT13xD4vUplMJpPSIQBUU1VKR8gRj9eLlXXKhcgc+d7J2yLmmKes/Nf1wlBxTM7nyU/y4/dOWLfzODmVVzqGEE+Jjr6Bi0utNPfJ9BUhhBDZzqct6Gye3KazMW8XQgghRbkQQogcsK4ODOpsHhlPxfxxUGe5yVMIIR6TOeVCCCFyxLo6UoQLIUR6ZKRcCCGEEEIIhUlRLoQQQgghhMKkKBdCCCGEEEJhUpQLIYQQQmQjT08PevToird3T8uf6OhoAAICFpGS8s+TtXx9J7Nx4/psz7Rp00bc3d34889L2X6uZwkO3sZnn32iaIbcQm70FEIIIYTIZv7+M6hcucpT25cu/QEvrz7Y2NikcVT2CQ7eSsOGjQgK2kr16jVy9NwibVKUCyGEEEIoYObMaQAMGtQPKysrFi4MACA8/BrDhn3EnTt3qF27DpMm+aJSqdi9eycbNqzDYDCPrI8YMZpGjZoA5tH4jh07cfLkce7fv4+Xlzfdu/dM87zXrl3lwYO/8fObwYcf9mbkyDFoNBpLP2+/3ZFTp05y7949hg4dwYMHf7Nnzy7i4+OYOHEy9es3IDo6mv79e1vOCfDJJ59Sr54bBoOBceNGEhcXR3JyMrVqufDpp59jY2NDSkoK33wznVOnQnB0dKRatX9+IAgIWMSjR48YOXJMmq9fdVKUCyGEEOKVpdFYo9FkT7mj1xvQ6w0ZauvjM95S+KrVapYvX8Mnn3zGpk0bCQhYTsGCBS1tr127xoIF32NlZUWfPr04efIETZq44+7elLfeehuVSsWNG9cZPnwwQUG7LMclJSWxZMkKoqOj8fLqjofHO0/0+1hQ0BY6duyMk5MTVatW5+DBA7Rr196yPyVFz5IlK7hw4TxDhw5i+PBRLFu2in379vD999/xww/LAIiLi6Vq1WqMGjWW0NBTfPGFD5s2bcPGxgZfX38KF3bEZDLh6zuJoKCtdO3ajZ9/3kR0dDTr1/+EwWBg8OCBlC7t9ELv/6tGinIhhBBCiGyW3vSVtLRu/Tq2trYAVK9eg1u3bgJw8+ZNFi/24d69u1hbW/P33zHExNynWLHiAJbC2snJCQeHQty9e4cKFSo+0bfBkMLu3bsICAgEwMOjM8HBW58oyt98s73l3ElJSbz55lsA1KhRk5s3oyztbGxsePvtjgA0aNAQW1tbbty4TqVKlVmzZhXHjh0hNTWV+Ph47OzsAAgNDaFjx05YW9tgbW0+/syZPzL+Rr7CpCgXQgghxCsrM6PZucXjEXUAKys1RqM5/6RJPowcOYbWrduQmprK6683IzlZn85xVhiNxqf6PnToEDpdIsOHDwbAZDIRE3OfO3f+olSp157oR61W/++17b+yPN3nf+3Zs5MzZ35n0aKlaLVali9fSmRk5HOPU6utSU1NtbzW6/XPaP3qkdVXhBBCCCEUUrCglsTExAy1TUhIwMmpDABBQVtfqGgNDt7KuHET2LJlO1u2bGfr1h106vQO27cHZbqvlJQUdu/eCcAff5wmOTmZChUqkpCQiKNjEbRaLYmJCezZ888Um4YNG7Fr13YMBgNJSUlP7CtXrhx//nmR1NRUdDodR478lulMeZmMlAshhBBCZLN/zyk3v55EzZq1+OCD3gwf/n/Y2tpabvRMz5gx4xg/fiwODoVo2rQphQs7ZirDvXv3OH36FFOn+j2xvX37Dnz11VT69x+Yqf4KF3bkypXLrF69AoAvv/THxsaGjh09OHToV3r06EqRIkWoW7c+ycnJAHh6vsfVq1fo2fM9HB0dqVmzFn///TcAr7/+Bvv27aFnz/coVeo1atSomak8eZ3KZDKZlA4BoJqqUjpCjjjQ9wAAbVa0UTiJEHmLfO8I8WLy4/dOWLfzODmVVzrGK+3x6iu7d/+idJQ8JTr6Bi4utdLcJ9NXhBBCCCGEUJgU5UIIIYQQIlOcnJxklDyLSVEuhBBCCCGEwqQoF0IIIYQQQmFSlAshhBBCCKEwKcqFEEIIIYRQmBTlQgghhBBCKEyKciGEEEKIbOTp6YGHx1tPPKI+OHgb7u5ubNy4/rnHBwdvIzLyhuX15ct/sm/fnizPOWTIILp27Yy3d0969OjKhg1rs7T/ixcvMGnSxCzt81UiRbkQQgghRDYrXrw4J04cs7zevj2I6tVrZOjY7duDnirK9+/fm+UZAcaO/YRVq9Yzf/5CfvhhEVeuXM6yvmvWrIWvr9/zG+ZT1koHEEIIIYR41Xl4vMP27UE0a9aCW7dukpT0iMqVq1r2h4ScYPHihej1eoxGI/36DaBdu/YEB2/l0qULzJ49k8WLF9KnT38CAhah0yXi7d2TevXcGDduPGFh51i4cAE6XSIAH300hObNWwKwY0cwa9asBKBs2XJMmDCRokWLPjNvqVKvUb58eSIjb+Dk5MTcubO5du0KycnJNGjQiFGjxqJWq1myZDF79+5Go9GgUqn47rsfsLGxxtd3MuHh17C2tqZ8+Qr4+U0nNPQUCxbMYfnyNZYngnp6duXYsSMkJSXh4zOJevXqYzAYGDduJHFxcSQnJ1Orlguffvo5NjY22fS3kztIUS6EEEKIV5ZGY41Gkz3ljl5vQK83ZKitm1sDNm36kfj4eHbsCKJDh05cunTRsr969ZosXrwMtVpNTEwM/fp50aRJUzp16sL27cF4eXnTokUrAJKTkzly5DemTZsJQEJCAjNm+DN79nyKFy/B/fv36N/fm7VrN3L37h0WLpzP8uVrKF68BIsXL+Sbb6bj5zf9mXkjIsK5fv06VapUZe7c2bi5uTFx4iRSU1OZPHkiQUFbadOmLevXryE4eA92dnbodDpsbW05fPgQOl0i69dvAiA+Pj7Nc8TFxVK7dh2GDBnOrl07+O67+QQEBKJWq/H19adwYUdMJhO+vpMICtpK167dMvRe51VSlAshhBBCZDOVSsWbb7Zj797d7N27h4CAwCeK8tjYB/j5TSUqKhK1Wk18fDyRkddxda3z3L7PnTtDdPQtxowZ8cT5bt6M4ty5szRt2oLixUsA8O6779G7d890+3o8Iq/RaPj004mUL1+Bw4cPcuFCGGvXrgYgKSmJkiVLYm9vT9my5fD1nUTjxu60aNESrVZL1arVuH79OjNnTsPNrSHNm7dI81wFCxa0/KDh6lqb+fPnAJCamsqaNas4duwIqampxMfHY2dn99z3Ia+TolwIIYQQr6zMjGZnt44dOzNgQB/q1XOjcGHHJ/bNmOFPy5at+frrWahUKrp39yQ5WZ+hfk0mE1WqVGXRoqVP7Tt37mymMo4d+4mlUP53/zNmzKZMmbJPtV+yZAVnz54hNPQk/fp5MWfOt1StWo21azdy6tRJjh07wvfff8uaNT8+dayNjcbyuZWV2nIj7J49Ozlz5ncWLVqKVqtl+fKlREZGZuo68iK50VMIIYQQIgeUKVOWwYOH8eGHg57al5CQQOnSTqhUKk6cOM7Nm1GWfVqtlsTExHRf165dl6ioKEJDQyzbLlw4j8lkokGDhhw7dpiYmPsAbN26mcaNm2Qqd8uWrVm5MtBSNMfGPiA6+hY6nY7Y2Ae4uTVg0KAhVKpUhfDwa9y9ewe12orWrdswevQ4YmMfpDuFJS0JCYk4Ohb533UmsGfPrkzlzatkpFwIIYQQIod4er6X5vahQ0cyc+Y0AgIWUbOmC1WqVP3XMV2ZP38Oa9asZMSIMTRq1Ji1a1fRu3cP6tdvwLhx45k5cw4LFsxlzpxZpKSkUKZMWWbNmkvlylUYOnQkI0cOBcw/GEyYkLllCUeP/phvv52Ht3dPVCoVNjY2jB79MdbW1nz66SckJydhMpmoXr0Gr7/+BqGhp1i4cD5gnorSp8+HlChR4okVZJ6lY0cPDh36lR49ulKkSBHq1q1PcnJypjLnRSqTyWRSOgSAaqpK6Qg54kDfAwC0WdFG4SRC5C3yvSPEi8mP3zth3c7j5FRe6RhCPCU6+gYuLrXS3CfTV4QQQgghhFCYFOVCCCGEEEIoTIpyIYQQQgghFCZFuRBCCCGEEAqTolwIIYQQQgiFSVEuhBBCCCGEwmSdciGEEEK80goVKoBanfXjkEZjKvHxjzLUNj4+ns6d29OlS1fGjv3Esj0gYBGPHj1i5MgxWZ7vZYWGnmLBgjksX77mpfoJCFjE5s0bKV68BCaTCTs7OyZMmEjVqtVeqL+DBw9QvHgJXFxcXyrX5s0/kZycRK9evV+qn6wiRbkQQgghXmlqtRUPHuiyvN8iRbQZbrtnz05cXGqzd+8uRowYjY2NTZbnyUlGoxG1Wp3h9h06dLL84PHTTz/y3XfzmTv32xc698GDv1KzZs2XLsq7du32UsdnNSnKhRBCCCGyWVDQVoYPH8WKFYEcOvQrbdu2S7PdypXL+fXX/RgMRkqUKIGPzxcUK1acQ4d+ZfHihVhZWWE0Ghk3bgINGjRkyZLF7N27G41Gg0ql4rvvfsDBwYFJkyYSGXmdlJQUypYtx8SJkylUqBChoaeYO3cWLi6unDt3FpVKxZdfTqNixUoALFr0Hfv27cbBoRBubg0suUJDTzF79gxq1KjJ5ct/8n//N5SiRYsxe/YMHj16RIECBRg7djy1ark8973Q6RIpVKgQAAaDgXHjRhIXF0dycjK1arnw6aefY2Njw9mzZ5g1azomUyoGg4H+/QdSqFAhDh8+SEjICbZt20KvXr3p2LETK1cuZ9eu7QDUrFmLceMmULBgQQICFhEZeYPExESio29RpkxZ/P2nY2dXINf9lkKKciGEEEKIbHTlymXi4uJo2LAxMTExBAVtTbMo37lzO7duRbFkyQqsrKzYtGkj8+bNwdfXjx9++J5PP51I7dp1MRqNJCU9Ii4ujvXr1xAcvAc7Ozt0Oh22trYAjB37MY6ORQBzob1q1XKGDRsJQHh4OJ9/PoVPP/2cwMAlBAYuxdfXj99+O8hvvx1k5cr12NraMn782CfyRUSEWzKkpKTQrVsXPv98Mo0aNeHkyRN89tkn/PTT1jR/C7BzZzAhISeIj4/HYDDw/fcBAKjVanx9/Slc2BGTyYSv7ySCgrbStWs3Vq1aTu/efXjrrbcxmUwkJibi4OBAixatqVmzJt279wTg6NEj7Nq1nYCAQAoW1OLrO4llywIYPnwUABcvXiAwcDX29vaMGjWMXbt24unZNYv+drOOFOVCCCGEENkoKGgrHTt2QqVS8frrbzB79gzu3r1LyZIln2h3+PAhLl68QN++HwDmKSJarT0ADRs2Yu7cb2jTpi1NmzancuUqGI1GypYth6/vJBo3dqdFi5ZoteYpNTt2bGf37h0YDAYePXqEs7Oz5Tzly5enevUaALi61ubw4UOAeTT8zTffomDBggC8844ngYFLLMeVK+dM7dp1Abhx4zo2NtY0atQEgMaNm2BjY82NG9epUqXqU+/Bv6ev7NgRzOeff8rKletITU1lzZpVHDt2hNTUVOLj47GzswOgQYOGBAYu4ebNmzRu3ARX19ppvr8hISd48832lveqS5euzJkz07Lf3b0pDg4OALi4uHLr1s1n/XUpRopyIYQQQohskpKSwp49O7Gx0bBjRzBgnrKxffs2+vcf+ERbk8lE//4D6NzZ86l+Ro/+mKtXrxAaGoKPz3h69eqNp2dXlixZwdmzZwgNPUm/fl7MmfMtOl0imzdvJCBgOUWKFGH37p1s2bLZ0pdGo7F8bmWlxmg0ZuhaChQo8ALvwNPeeONNfH0n8eDBA44fP8KZM7+zaNFStFoty5cvJTIyEoCePb1o0aIVISEnmD17Bo0buzN48LBMn0+jsbV8/nj6T24kSyIKIYQQQmSTQ4d+xdm5AkFBu9iyZTtbtmxn3rzv2L496Km2LVu2YtOmjcTHxwOg1+u5cuUygGUEukePD3j77Y5cvHgenU5HbOwD3NwaMGjQECpVqkJ4+DUSEhKwt7encOHC6PV6goK2Zihrw4aN2L9/L48ePcJoNBIcvC3dtuXLVyAlxUBoaAgAp06dxGAwUL58heee5/TpUxQu7EjhwoVJSEjE0bEIWq2WxMQE9uzZZWkXGXmDsmXL8e673Xj//V5cuHAe4H9tEy3tGjVqwv79e9DpdJhMJrZt20Ljxu4ZuubcREbKhRBCCCGySVDQVtq37/DEttq162IymTh9OvSJ7R06dCI2NpYhQ8wj6CaTia5du1O1ajUWLlxAVFQkarUae3sHJk6chE6XyKeffkJychImk4nq1Wvw+utvoFZbsWvXDt5/35PChR2pV8/NUtA+S4sWrTh37ize3j0sN3reu3c3zbY2NjZMmzbziRs9/f1npruqzOM55SaTCWtra/z9p2NlZUXHjh4cOvQrPXp0pUiRItStW5/k5GQAfvxxHaGhp7CxscHGxoZx4yb8733y4MsvJ/PLL/ssN3pevXqFQYP6AVCjRq2nfguRF6hMJpNJ6RAAqqkqpSPkiAN9DwDQZkUbhZMIkbfI944QLyY/fu+EdTuPk1N5y+vcsE65EADR0TdwcamV5j4ZKRdCCCHEK00KZ5EXyJxyIYQQQgghFCZFuRBCCCGEEAqT6Ss5rJS2FKUdSlvm+AkhMqbea/UA5HtHiEyq91o9bifcVjqGEOI5ZKQ8hx26cUj+cRTiBdxOuC3fO0K8gNsJtzl045DSMYQQzyEj5Tls8PbBSkcQQgghhBC5jIyUCyGEEEIIoTAZKRdCCCHEK61w4QJYWWX9OGRqaipxcRlbbjE+Pp7OndvTpUtXxo795IXOFxCwiEePHjFy5JgXOj4jPD09sLW1RaPRoNfrqVu3PuPHf4q1ddoPBXrM13cyNWvWpHv3ns9sFxy8jTp16uLsbF5H/vLlP4mMvMGbb75laePt3ZOAgOXY2dm9/AXlITJSLoQQQohXWnYU5Jntd8+enbi41Gbv3l2kpKRkaY7U1FSy8lmQ/v4zWLVqPWvXbiQi4hoHDvySZX1v3x5EZOQNy+vLl/9k//69T7RZtWp9vivIQUbKhRBCCCGyXVDQVoYPH8WKFYEcOvQrbdu2A8yj35GRN0hMTCQ6+hZlypTF3386dnYFSExMwM/Pl/DwaxQtWoxSpUpRtGgxy3EREeEkJiZy585fBAQsJyzsLIGBS9Hrk7GxsWH06HG4utYhJuY+X3zhg06XiF6vp1mzFowYMfq5mfV6PcnJegoVKgRASkoKixZ9y++/n0av11OlSlXGj/ehYMGCTxz38OFDvvlmOhcvXgCgQwcPvL37ERy8lUuXLjB79kwWL15Inz79CQhYhE6XiLd3T+rVc2PcuPG4u7vxyy+Hn+r3VSdFuRBCCCFENrpy5TJxcXE0bNiYmJgYgoK2WopygIsXLxAYuBp7e3tGjRrGrl078fTsytKlAWi1WjZs2Exs7AP69vV64rjz58NYsWINjo5FuHkzimXLApg37zu0WnvCw68xZswItm7dgb29A7NmzaVgwYIYDCmMGjWMY8eO0LRp8zTz+viMR6PRcOvWTRo3dqdJk6YArFq1Aq3WgWXLVgHw7bfzWLFiGUOGDH/i+GXLAjCZTKxZ8yMPH+oYOLAflStXpVOnLmzfHoyXlzctWrQCIDk5mSNHfmPatJlZ+p7nRVKUCyGEEEJko6CgrXTs2AmVSsXrr7/B7NkzuHv3LiVLlgTA3b0pDg4OALi4uHLr1k0AQkNPMW7ceAAcHYvw+uttnui3WbPmODoWAeD48WPcunWTwYMHWvYbjQZiYmIoWLAgCxbM5dy5M5hMJv7+O4YrVy6nW5T7+8+gcuUqJCcn89lnn7B+/Rp69vTi8OGD6HQ6DhzYB5hH0qtWrfbU8SEhJxgz5hNUKhVarT3t2r1NSMgJmjVL+3zCTIpyIYQQQohskpKSwp49O7Gx0bBjRzAABoOB7du30b+/uYDWaGwt7a2srDAajRnqu0CBf0/vMOHu3ozJk798qt2yZQEkJMSzdOlKbG1tmTbtS5KTk5/bv62tLc2bt+TIkd/o2dMLk8nEJ598SsOGjTOUT2SO3OgphBBCCJFNDh36FWfnCgQF7WLLlu1s2bKdefO+Y/v2oOce27BhI4KDtwEQFxfLwYPpP9G4ceOmHD9+lPDwa5ZtFy6cByAhIYHixYtja2vL3bt3OXToYIayp6am8vvvoTg7OwPQsmVr1q1bTVJSEgA6nY6IiPCnjmvUqAlBQVswmUzodDr27dtN48ZNANBqtSQmJlra/vd1fiYj5UIIIYQQ2SQoaCvt23d4Ylvt2nUxmUycPh36zGM//HAgX301lR49ulK0aDHq1XNLt62zszNTpnyFn99UkpOTSUlJoU6detSq5cL77/dk4sQJfPBBd0qWLEmjRs8e6X48pzwlJYXKlavw4YcfAdCnTz8CAhbz4YfeqFQqVCoVAwZ8RMWKlf6TexCzZk3Hy+t9AN5+28MyVcbTsyvz589hzZqVjBgxhkaNGrN27Sp69+5B/foNLNN18iOVKSvX0HkJqqkqpSMIIYQQ4hUQ1u08Tk7lLa9zwzrlQgBER9/AxaVWmvtkpFwIIYQQrzQpnEVeIHPKhRBCCCGEUJgU5UIIIYQQQihMinIhhBBCCCEUJkW5EEIIIYQQCpOiXAghhBBCCIXJ6itCCCGEeKXlhiUR4+Pj6dy5PV26dGXs2E+yPEtmuLu7UblyFVQqFSaTif/7v2G0atVa0UxCRsqFEEII8YrLjoI8s/3u2bMTF5fa7N27i5SUlEyfy2AwZPqYZwkIWM7q1RsYNmwUU6ZMzPL+RebJSLkQQgghRDYLCtrK8OGjWLEikEOHfqVt23YAhIScYPHihej1eoxGI/36DaBdu/YADBkyiGrVqhEWdo5ChQoza9ZcvvtuPsePHwXA3b0Zw4aNRK1W4+s7GVtbDZGRN7hz5w61a9dh0iRfVKpnP5zRza0BDx8+JCEhgSJFirBmzSr27duN0WhEo9EwfrwP1apV/9/53Pi//xvKoUO/EhcXx2effU5IyEmOHz+KwWDAz286FStW4saN63z55WSSkpJITU3Fw6MzXl59SElJYdGib/n999Po9XqqVKnK+PE+6HQ6+vXzYvPmIGxtbQH4+OPRtGvX/qmnob7KZKRcCCGEECIbXblymbi4OBo2bEynTu8QFLTVsq969ZosXryMlSvXMX/+98yfP4f4+HjL/lu3brF48TLmzFnAli2buXz5T1asWMuKFWu5fPkSW7ZstrS9du0as2cvYN26n7h06SInT554braDBw/QsGEjihQpAkDHjp0IDFzNypXr+OijoUyf7vdEewcHBwIDVzNs2EjGjx9LnTp1WblyHR06eLB8+VIANm3aSMuWrVm9egNr126kc2dPAFatWoFW68CyZatYvXoDxYuXYMWKZZQoUYL69d3Yt28PANHR0Vy6dIE3/r+9e4+Kus7/OP4cZkBRERXBFQPDWyJqIiriDbJVt/CCmiKgrvfUkl305G+10KU0S8vKS1Jolq7ZxfWGly7btrbVrimoezIrEZNBC4VMEGa4zPD7g5ok7yiN0utxjufM9/v9zOfz/o788eLD5/uZvr+v2gd+m9JMuYiIiEg1Sk3dxv33D8RgMBAR0ZelSxdz+vRpfHx8+OGHsyxcmITZnIXRaCQ/P5+srG9o374jAAMG3IfJVBHX9u3bS2TkIFxdXQGIjBzMnj0fMnz4CADCwyMcM8133dWWkyezL1vT5MnjKCoq4vvv81ixItlx/ssvv+C1114hPz8fg8GA2ZxV6X2//31/R/8Gg4FevfoA0LZtIP/61z8B6NSpMytXvoDVaiUkpAshIV0B+PjjPRQWFvLhh/8AoKSkhNat2wAwcmQMzz//LJGRg9iyZRMDBw5x3OdvhUK5iIiISDUpLS3lvfd24+rqxq5dO4CK9eE7d25n/PhJLF78JL17h/PUU89gMBgYMSKK4uISx/vd3d2veSw3NzfHaxcXIzbb5deJp6S8Sp06ddiwYR2JiXN4880tuLi4MHfubFatWk3btoGcOXOGQYMG/GKMWo7+XV1/OZ4NgL5976VDh47s3fsf1q17ldTUbSQlLaS8vJxHHvkLXbp0u6iejh3vxm63cejQQXbuTGXt2vXXfN81hZaviIiIiFSTjz76F/7+d5Ka+g5bt+5k69advPDCSnbuTAWgoKCApk19MRgM7N37X7KzzZftq2vXUHbt2kFZWSllZaXs2rWDbt2631B9sbFjaNTIiy1bNlFSUozNZqNJk98B8Pe/v1WlPs3mLLy8vBg4cDATJ07hiy8OA9C7dzgbN/4Nq9UKQGFhIcePZzreN2LEKBIT59ChQ0dHDb8lmikXERERqSapqdsuelixQ4e7KS8vJz09jenT41myZBEpKckEBgbRqlXry/YVFTWM7GwzY8fGAhAaGsaQIUNvqD6DwcCMGX8mMXEOUVHDmTx5KuPHj8bT07PKa7o/+OB93n1394/LTwwkJFRsATl27DhSUl5iwoQxGAwGDAYDEydOISCgBQD9+g3gmWeeYvjwkTd0T7crQ3l5ebmziwAwJF356WARERGRa/H5A4fx9W3uOL4V9imXqzt48ACLFz/Jhg1vXXXXmNvVqVMnCApqd8lrmikXERGRGk3B+da3cGESn32295q2caypFMpFRERExKkefXS+s0twOj3oKSIiIiLiZArlIiIiIiJOplAuIiIiIuJkCuUiIiIiIk6mUC4iIiIi4mQK5SIiIlKjeXq607Bh3Zv+z9PT/ZrGj4qKJDZ2BHa7vdK5Y8cyquuWAbBaLfTuHUpeXp7j3LhxccydO9txfOTIFwweXPHlRgkJMxzfKDpt2mQ+/vgjAB5/fD5vv/0GAJs3b2Ljxr9Va92/VQrlIiIiUqNVxxcHXW+/RUVF7N69s1rquJzatd1p1y6I9PT9ABQWnsdqtVb6ZSA9fT+dO4cA8Nxzy7njDr8r9jls2APExIyuvqJ/w7RPuYiIiEg1mzTpQdaseZn+/f/w49fP/yw39wzPPruYnJzvKC4upl+/AYwbNxGA48czee65Z8jLywUgNnYMkZGDMJuzePrphZw9exaj0cS0aQ8RFtbzonE7d+5Cenoa/foN4NChgwQHd+b06dNkZh6jRYuWpKfvJyKiL1Axe//ssy/QsmWry95HSkoyFouF+PgEMjKOsmTJIiwWKyUlxURFDWPUqDigYnbdzc0VszmLkyeziYjoS69efUhJSSYnJ4eYmDiio2Ox2+0888zTpKXtw9XVFXf3OqSkrL0pn/ntRqFcREREpJoFBrajbdtANm9+m+jo2ErXkpLmMWHCJIKDQygtLeXhhx8kMDCIkJAuzJ49k6lTH+Lee/sBcO7cDwDMn/8YUVHDGDw4iuPHM5k6dRJvvPF3GjZsWKnvkJAuLFnyFABpafsJDg7h9Okc0tP307z5nRw6dJCZM2dTFU2b+rJ8eTJubm4UFRUxYcIYQkPDCAhoAUBmZiYrViRjt9sYOnQg58+fZ9Wq1eTm5hIdPZRBg6Iwm7NIS9vHxo2bcHFxIT8/v0q11AQK5SIiIiK/ggcfnM5DD01h0KAoxzmLxUJ6eho//HDWca6oqIhvvjmOt7c3NpvNEcgBPD0bUFhYyNGjXzFw4GAAAgJa0Lp1Gz7//H/07h1eacz27Tvy7benyMvL48CBdGJi4sjJyWHDhnUEBXWgXr16NGt2R5Xux2q1snjxk2RkHMVgMJCbe4aMjKOOUB4eHoGbmxsA/v7N6dGjFy4uLvj4+ODhUZ/Tp3No1qwZNlsZCxcm0aVLV3r27FOlWmoChXIRERGRX0Hz5ncSFtar0oOSdrsdgwHWrl2PyVR5WUtm5rEbHrN27doEBbXnk0/+jcVSROPG3jRo0JCvvvryx/XkXarcd3LyCry8GpOYmITJZCI+fjrFxcWO6z8FcgAXF+Mvjl2w2WzUq+fB669vIj19P/v27WXlymW89trreHk1rnJdtys96CkiIiLyK5k8+UE2bXqLoqIiAOrWrUunTsGsW/eqo01Oznfk5eXi798co9HIBx+877h27twP1K1bl9at72LXrlSgYt15RsbXtG/f8ZJjdu4cwvr1r9Kx490AmEwmmjW7g61bN99QKC8oKKBJkyaYTCaOHcvg0KED193H2bNnsVqtdO/eg+nT46lbtx4nT56sck23M4VyERERkV+Jj08T7rsvkvz8c45zSUkLOX48k7i4kcTFjeSxx/5CQUEBJpOJxYuXsmXLJuLiRjJ6dDSffvrxj+9ZwDvv7CIubiTz5z/G/PlPXLSe/CedO3fFbM4iODjEcS44uDNmc9YNhfLx4yexbdsW4uJGsnr1S3TqFHzdfeTkfMeMGVMZPTqa0aOjCQvrSfv2Hapc0+3MUF5eXu7sIgAMSQZnlyAiIiI1wOcPHMbXt7nj2NPTvVq2RbTb7Zw7Z7np/UrNderUCYKC2l3ymtaUi4iISI2m4Cy3Ay1fERERERFxMoVyEREREREnUygXEREREXEyhXIRERERESdTKBcRERERcTLtviIiIiI1Wv367hiNN38e0mazk59/9Z1dyspKWbt2De+//y5GoxGj0Yifnz9TpkwjIKAFmzdvorjYSkzM6CrX8vjj8wkMDGTEiFFXbPfyy6sICGhBv34DrtjuSjVNmzaZnJzvqFu3LhZLEU2bNmPUqFh69uxd5fpFoVxERERqOKPRhbNnC296vw0b1r2mdk888VesVitr1qzDw8OD8vJyPv30Y06c+IaAgBYMG/bATa/tcqZMmXZN7a5W08yZj9CrVx8A0tL2k5g4h0ce+Qv33HPvDdd4I8rKyjCZbs94e8tU7eHmQUFJgbPLEBERkduYh5uHs0uoJCsriz17PmT79nfw8KiozWAwVJpVTklJxmKxEB+fQEbGUZYsWYTFYqWkpJioqGGMGhUHVMyG16rlRlbWCXJycujQoSPz5j2OwVD5Cxh/OWt+4fGFr8+fL2DBgiSOH8/E29sbb28fGjZsRHx8QqWariYkpAuTJj3IunVrueeee7HZbKxcuYz//vdTALp378FDD8VjNBqveA9Xuvbuu7t5882NlJWVAjBjxp/p2jUUgKioSPr1G0Ba2j5atmzF3XcH88kn/2bRoiUA7Nix3XG8Y8d23nuv4v8iI+Mo3t7ezJr1fyxb9hzZ2WYCA4NISlrgqMfNzRWzOYuTJ7OJiOhLr159SElJJicnh5iYOKKjYwFYtuw5DhxIo7S0lAYNGvDoo/Np2tT3un5WbplQnj8n39kliIiISA1w+PAXzi7B4euvv8TPz5/69etfU/umTX1ZvjwZNzc3ioqKmDBhDKGhYQQEtADg2LFjLF++ChcXF8aOjeGzz/YSGtq9SrWtWZOCh0d93nxzM+fOnWPcuLgqz3QHBbXnhReWArB162a+/vorXnvtdQASEh5m69bNDB8+4qr3cLlr3buH0b//HzAYDJw48Q0PPzyV1NR3HOMXFhbyyivrgYoQfiVHjhxmw4a38PFpwqxZ8cybN5cXX0zB3d2dP/4xln37PqNbt4rAn5mZyYoVydjtNoYOHcj58+dZtWo1ubm5REcPZdCgKOrUqcPYseMcv8Bs27aFlSuXsWDBU9f1Gd4yoVxERESkpjt+PJN58+ZitVoJC+vJzJmPVLputVpZvPhJMjKOYjAYyM09Q0bGUUcoDw+PoFatWgDcdVdbTp7MrnItaWn7mTVrNgCenp6Eh0dUua/y8nLH63379hIZOQhXV1cAIiMHs2fPh45QfqV7uNy17OxsXnppLmfOnMZkMvH993nk5eXi5dUYgPvvj7zmWjt2vBsfnyYAtGnTlqZNfR1/xWjdug3Z2WZHKA8Pj8DNzQ0Af//m9OjRCxcXF3x8fPDwqM/p0znceWcA//nPJ2za9BYWiwWbzXadn14FhXIRERGRatKmTVvM5iwKCgrw8PAgIKAF69e/wdtvv8GRI0cuap+cvAIvr8YkJiZhMpmIj59OcXGx4/pPARHAxcWIzVZ2UR9GoxG7/eeQXFJSfFGbm+3IkcO0bNnymtpe6R4ud23evLnExycQHn4PdrudiIgeFBeXONq6u9dxvK64f7vjuKTk53YVY9S6YAyXXxxfuZ7Kxy7YbDa+/fYUzz+/lLVr1+Pr24z//e8Q8+bNvabP4kLaElFERESkmvj7+9OnTwSLFj3B+fM/PztnsVgv2b6goIAmTZpgMpk4diyDQ4cOXPeYfn5+HDlyGIDc3DOkpe2/ZLvOnUPYvXuHY9yPPtpz3WMBHDyYzurVLzN69DgAunYNZdeuHZSVlVJWVsquXTvo1q1qS2x+UlBQgK9vMwBSU7ddFLQv5Ofnx7FjRykpKaG0tJR//vMfNzT21RQWFuLqaqJRIy/sdjtbtmyqUj+aKRcRERGpRomJSbzySgrjx4/BZDLh4VEfb29vxowZd1Hb8eMnkZSUyPbtW/H3b06nTsHXPd6QIUOZM2c2o0YNx9+/OUFB7S/ZbuLEKSxY8Feio4fh5dWYwMB21KtX75rGWLp0CS+99CIWi4Xf/a4pc+Y85tiNJSpqGNnZZsaOrXgIMjQ0jCFDhl73fVwoIWEWs2fPxMOjPmFhYXh6Nrhs2/btO9K1ayixsQ/QuLE3rVq1IS8v94bGv5JWrVrTt28/YmIeoEGDBvTo0ZMDB9Kvux9D+YWLgERERERuc4cPf4Gvb3PHsbP3Kb9VlZWVYrPZqVWrFoWF55kyZQJ/+tMsx3pquflOnTpBUFC7S17TTLmIiIjUaLdzcK5O+fkFJCQ8jN1up6SkhP79/6BA7kQK5SIiIiK/QY0aNXJsWyjOpwc9RUREREScTKFcREREahw9Mie3mqv9TCqUi4iISI3i7l6bgoJzCuZyyygvL6eg4Bzu7rUv20a7r4iIiEiNUlpaitlsvuxe4CLO4O5eGz8/P8c3nf6SQrmIiIiIiJNp+YqIiIiIiJMplIuIiIiIOJlCuYiIiIiIkymUi4iIiIg4mUK5iIiIiIiTKZSLiIiIiDiZQrmIiIiIiJMplIuIiIiIOJlCuYiIiIiIkymUi4iIiIg42f8DjS689Kie8g0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 756x489.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length, width = 105, 68\n",
    "fig, ax = create_pitch(length, width,'white')\n",
    "x, y = random_point[\"ball_x\"].iloc[0] / 100, random_point[\"ball_y\"].iloc[0] /100\n",
    "x = length/2.0 + x\n",
    "y = width/2.0 + y\n",
    "plt.scatter(x, y, marker = 'o', color = 'black')\n",
    "p = 1\n",
    "names = [\"Ball\"]\n",
    "for i in range(3, len(position_cols), 3):\n",
    "    t, x, y = random_point[position_cols[i]].iloc[0], random_point[position_cols[i + 1]].iloc[0], random_point[position_cols[i + 2]].iloc[0]\n",
    "    if np.isnan(x) or np.isnan(y):\n",
    "        continue\n",
    "    t = int(t)\n",
    "    x = length/2.0 + x\n",
    "    y = width/2.0 + y\n",
    "    try:\n",
    "        name = player_map[random_point[f'player{p}_id'].iloc[0]]\n",
    "        names.append(name)\n",
    "    except:\n",
    "        print('faile')\n",
    "        continue\n",
    "    y = 80 - y\n",
    "    color = 'yellow' if t == 0 else 'red'\n",
    "    plt.scatter(x,y, marker = 'o', color = color)\n",
    "    p += 1\n",
    "plt.legend(names, loc = 'upper right', bbox_to_anchor = (1.25, 1))\n",
    "plt.savefig('figure.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2291fb93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.350978Z",
     "iopub.status.busy": "2022-03-21T17:12:51.350272Z",
     "iopub.status.idle": "2022-03-21T17:12:51.353473Z",
     "shell.execute_reply": "2022-03-21T17:12:51.353927Z",
     "shell.execute_reply.started": "2022-03-21T16:00:32.567854Z"
    },
    "papermill": {
     "duration": 0.048987,
     "end_time": "2022-03-21T17:12:51.354115",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.305128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(166313, 167)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3a62d973",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.444053Z",
     "iopub.status.busy": "2022-03-21T17:12:51.441189Z",
     "iopub.status.idle": "2022-03-21T17:12:51.445969Z",
     "shell.execute_reply": "2022-03-21T17:12:51.446415Z",
     "shell.execute_reply.started": "2022-03-21T16:11:21.535141Z"
    },
    "papermill": {
     "duration": 0.052004,
     "end_time": "2022-03-21T17:12:51.446580",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.394576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "short_play = game.iloc[8000:18010].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9c8145b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.531212Z",
     "iopub.status.busy": "2022-03-21T17:12:51.530615Z",
     "iopub.status.idle": "2022-03-21T17:12:51.553080Z",
     "shell.execute_reply": "2022-03-21T17:12:51.553604Z",
     "shell.execute_reply.started": "2022-03-21T16:00:32.593282Z"
    },
    "papermill": {
     "duration": 0.066084,
     "end_time": "2022-03-21T17:12:51.553763",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.487679",
     "status": "completed"
    },
    "tags": []
   },
   "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>time</th>\n",
       "      <th>ball_type</th>\n",
       "      <th>ball_id</th>\n",
       "      <th>ball_x</th>\n",
       "      <th>ball_y</th>\n",
       "      <th>ball_z</th>\n",
       "      <th>ball_sampling</th>\n",
       "      <th>player1_type</th>\n",
       "      <th>player1_id</th>\n",
       "      <th>player1_x</th>\n",
       "      <th>...</th>\n",
       "      <th>player31_type</th>\n",
       "      <th>player31_id</th>\n",
       "      <th>player31_x</th>\n",
       "      <th>player31_y</th>\n",
       "      <th>player31_sampling</th>\n",
       "      <th>player32_type</th>\n",
       "      <th>player32_id</th>\n",
       "      <th>player32_x</th>\n",
       "      <th>player32_y</th>\n",
       "      <th>player32_sampling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-20T16:05:14.094Z</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.57</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>102221</td>\n",
       "      <td>30.45</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-20T16:05:14.134Z</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.62</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>102221</td>\n",
       "      <td>30.47</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-20T16:05:14.174Z</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.53</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>102221</td>\n",
       "      <td>30.49</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-20T16:05:14.214Z</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.65</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>102221</td>\n",
       "      <td>30.52</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-20T16:05:14.254Z</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>36.62</td>\n",
       "      <td>1.01</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>102221</td>\n",
       "      <td>30.54</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 167 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time ball_type ball_id  ball_x  ball_y ball_z  \\\n",
       "0  2021-06-20T16:05:14.094Z         7       0   36.57   -0.70     18   \n",
       "1  2021-06-20T16:05:14.134Z         7       0   36.62   -1.41     12   \n",
       "2  2021-06-20T16:05:14.174Z         7       0   36.53   -1.27     16   \n",
       "3  2021-06-20T16:05:14.214Z         7       0   36.65   -0.62      0   \n",
       "4  2021-06-20T16:05:14.254Z         7       0   36.62    1.01     12   \n",
       "\n",
       "  ball_sampling player1_type player1_id  player1_x  ...  player31_type  \\\n",
       "0             0            1     102221      30.45  ...           None   \n",
       "1             0            1     102221      30.47  ...           None   \n",
       "2             0            1     102221      30.49  ...           None   \n",
       "3             0            1     102221      30.52  ...           None   \n",
       "4             0            1     102221      30.54  ...           None   \n",
       "\n",
       "  player31_id player31_x player31_y  player31_sampling  player32_type  \\\n",
       "0        None        NaN        NaN               None           None   \n",
       "1        None        NaN        NaN               None           None   \n",
       "2        None        NaN        NaN               None           None   \n",
       "3        None        NaN        NaN               None           None   \n",
       "4        None        NaN        NaN               None           None   \n",
       "\n",
       "  player32_id player32_x player32_y  player32_sampling  \n",
       "0        None        NaN        NaN               None  \n",
       "1        None        NaN        NaN               None  \n",
       "2        None        NaN        NaN               None  \n",
       "3        None        NaN        NaN               None  \n",
       "4        None        NaN        NaN               None  \n",
       "\n",
       "[5 rows x 167 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_play.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "aaff8a2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.638145Z",
     "iopub.status.busy": "2022-03-21T17:12:51.637593Z",
     "iopub.status.idle": "2022-03-21T17:12:51.640871Z",
     "shell.execute_reply": "2022-03-21T17:12:51.641334Z",
     "shell.execute_reply.started": "2022-03-21T16:00:32.641661Z"
    },
    "papermill": {
     "duration": 0.046886,
     "end_time": "2022-03-21T17:12:51.641493",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.594607",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.mkdir('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7f5d1cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.732340Z",
     "iopub.status.busy": "2022-03-21T17:12:51.731351Z",
     "iopub.status.idle": "2022-03-21T17:12:51.733421Z",
     "shell.execute_reply": "2022-03-21T17:12:51.733789Z",
     "shell.execute_reply.started": "2022-03-21T16:11:23.797080Z"
    },
    "papermill": {
     "duration": 0.051631,
     "end_time": "2022-03-21T17:12:51.733958",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.682327",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def save_frames(df, length, width):\n",
    "    for index, frame in df.iterrows():\n",
    "        fig, ax = create_pitch(length, width,'white')\n",
    "        x, y = df.loc[index, \"ball_x\"] / 100, df.loc[index, \"ball_y\"] /100\n",
    "        x = length/2.0 + x\n",
    "        y = width/2.0 + y\n",
    "        plt.scatter(x, y, marker = 'o', color = 'black')\n",
    "        for i in range(3, len(position_cols), 3):\n",
    "            t, x, y = df.loc[index, position_cols[i]], df.loc[index, position_cols[i + 1]], df.loc[index, position_cols[i + 2]]\n",
    "            if np.isnan(x) or np.isnan(y):\n",
    "                continue\n",
    "            t = int(t)\n",
    "            x = length/2.0 + x\n",
    "            y = width/2.0 + y\n",
    "\n",
    "            y = 80 - y\n",
    "            color = 'yellow' if t == 0 else 'red'\n",
    "            plt.scatter(x,y, marker = 'o', color = color)\n",
    "        plt.ioff()\n",
    "        plt.savefig(f'images/{index}.png')\n",
    "        plt.close(fig)\n",
    "    #     if index % 100 == 0:\n",
    "    #         plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ec3b946",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T17:12:51.819466Z",
     "iopub.status.busy": "2022-03-21T17:12:51.818811Z",
     "iopub.status.idle": "2022-03-21T18:10:30.169135Z",
     "shell.execute_reply": "2022-03-21T18:10:30.169883Z"
    },
    "papermill": {
     "duration": 3458.395858,
     "end_time": "2022-03-21T18:10:30.170366",
     "exception": false,
     "start_time": "2022-03-21T17:12:51.774508",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_frames(short_play, length, width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "468480d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.261628Z",
     "iopub.status.busy": "2022-03-21T18:10:30.261054Z",
     "iopub.status.idle": "2022-03-21T18:10:30.265873Z",
     "shell.execute_reply": "2022-03-21T18:10:30.266405Z",
     "shell.execute_reply.started": "2022-03-21T16:00:39.600623Z"
    },
    "papermill": {
     "duration": 0.050284,
     "end_time": "2022-03-21T18:10:30.266567",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.216283",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#This function creates a gif like you've seen in my presentation\n",
    "def create_gif(images_path, gif_path):\n",
    "    images = []\n",
    "    filenames = sorted(glob(images_path))\n",
    "    #I know it's a double sort but this isn't A&D!\n",
    "    filenames.sort(key=lambda x: int(''.join(filter(str.isdigit, x))))\n",
    "    for index, filename in enumerate(filenames):\n",
    "        img = imageio.imread(filename)\n",
    "        images.append(img)\n",
    "    imageio.mimsave(gif_path, images, fps=10) # Save gif    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67c80e8c",
   "metadata": {
    "papermill": {
     "duration": 0.041819,
     "end_time": "2022-03-21T18:10:30.350815",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.308996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "05e3c0cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.437141Z",
     "iopub.status.busy": "2022-03-21T18:10:30.436493Z",
     "iopub.status.idle": "2022-03-21T18:10:30.440392Z",
     "shell.execute_reply": "2022-03-21T18:10:30.440857Z"
    },
    "papermill": {
     "duration": 0.04878,
     "end_time": "2022-03-21T18:10:30.441040",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.392260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sad\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    create_gif('images', 'frames10x.gif')\n",
    "except:\n",
    "    print('sad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2d86a589",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.527212Z",
     "iopub.status.busy": "2022-03-21T18:10:30.526615Z",
     "iopub.status.idle": "2022-03-21T18:10:30.531929Z",
     "shell.execute_reply": "2022-03-21T18:10:30.532490Z",
     "shell.execute_reply.started": "2022-03-21T16:00:39.610559Z"
    },
    "papermill": {
     "duration": 0.049927,
     "end_time": "2022-03-21T18:10:30.532649",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.482722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from math import pi\n",
    "# from bokeh.models.glyphs import Circle, Patches, Wedge\n",
    "# from bokeh.plotting import figure\n",
    "# from bokeh.models import Range1d, PanTool, ResetTool, HoverTool, ColumnDataSource, LabelSet\n",
    "# from bokeh.io import output_notebook, output_file, show\n",
    "\n",
    "# def draw_pitch(width = 700, height = 500,\n",
    "#                 measure = 'metres',\n",
    "#                 fill_color = 'green', fill_alpha = 0.5,\n",
    "#                 line_color = 'white', line_alpha = 1,\n",
    "#                 hspan = [0, 105], vspan = [0, 68],\n",
    "#                 tooltips = [],\n",
    "#                 source = [],\n",
    "#                 arcs = True):\n",
    "#     '''\n",
    "#     -----\n",
    "#     Draws and returns a pitch on a Bokeh figure object with width 105m and height 68m\n",
    "#     p = drawpitch()\n",
    "#     -----\n",
    "#     If you are using StatsBomb Data with a 120x80yard pitch, use:\n",
    "#     measure = 'SB'\n",
    "#     -----\n",
    "#     If you are using Opta Data, use:\n",
    "#     measure = 'Opta'\n",
    "#     -----\n",
    "#     If you are using any other pitch size, set measure to yards or metres\n",
    "#     for correct pitch markings and\n",
    "#     hspan = [left, right] // eg. for SBData this is: hspan = [0, 120]\n",
    "#     vspan = [bottom, top] //\n",
    "#     to adjust the plot to your needs.\n",
    "#     -----\n",
    "#     set arcs = False to not draw the penaltybox arcs\n",
    "#     '''\n",
    "\n",
    "#     # measures:\n",
    "#     # goalcenter to post, fiveyard-box-length, fiveyard-width,\n",
    "#     # box-width, penalty-spot x-distance, circle-radius\n",
    "\n",
    "\n",
    "#     measures = [3.66, 5.5, 9.16, 16.5, 40.32, 11, 9.15]\n",
    "\n",
    "#     hmid = hspan[1] / 2.0\n",
    "#     vmid = vspan[1] / 2.0\n",
    "\n",
    "#     p = figure(width = width,\n",
    "#         height = height,\n",
    "#         x_range = Range1d(hspan[0], hspan[1]),\n",
    "#         y_range = Range1d(vspan[0], vspan[1]),\n",
    "#         tooltips = tooltips)\n",
    "\n",
    "#     boxes = p.quad(top = [vspan[1], vmid+measures[2], vmid+measures[4]/2, vmid+measures[4]/2, vmid+measures[2]],\n",
    "#            bottom = [vspan[0], vmid-measures[2], vmid-measures[4]/2, vmid-measures[4]/2, vmid-measures[2]],\n",
    "#            left = [hspan[0], hspan[1]-measures[1], hspan[1]-measures[3], hspan[0]+measures[3], hspan[0]+measures[1]],\n",
    "#            right = [hspan[1], hspan[1], hspan[1], hspan[0], hspan[0]],\n",
    "#            color = fill_color,\n",
    "#            alpha = [fill_alpha,0,0,0,0], line_width = 2,\n",
    "#            line_alpha = line_alpha,\n",
    "#            line_color = line_color)\n",
    "#     boxes.selection_glyph = boxes.glyph\n",
    "#     boxes.nonselection_glyph = boxes.glyph\n",
    "\n",
    "#     #middle circle\n",
    "#     p.circle(x=[hmid], y=[vmid], radius = measures[6],\n",
    "#             color = line_color,\n",
    "#             line_width = 2,\n",
    "#             fill_alpha = 0,\n",
    "#             fill_color = 'grey',\n",
    "#             line_color= line_color)\n",
    "\n",
    "#     if arcs == True:\n",
    "#         p.arc(x=[hspan[0]+measures[5], hspan[1]-measures[5]], y=[vmid, vmid],\n",
    "#             radius = measures[6],\n",
    "#             start_angle = [(2*pi-np.arccos((measures[3]-measures[5])/measures[6])), pi - np.arccos((measures[3]-measures[5])/measures[6])],\n",
    "#             end_angle = [np.arccos((measures[3]-measures[5])/measures[6]), pi + np.arccos((measures[3]-measures[5])/measures[6])],\n",
    "#             color = line_color,\n",
    "#             line_width = 2)\n",
    "\n",
    "#     p.circle([hmid, hspan[1]-measures[5], hspan[0]+measures[5]], [vmid, vmid, vmid], size=5, color=line_color, alpha=1)\n",
    "#     #midfield line\n",
    "#     p.line([hmid, hmid], [vspan[0], vspan[1]], line_width = 2, color = line_color)\n",
    "#     #goal lines\n",
    "#     p.line((hspan[1],hspan[1]),(vmid+measures[0],vmid-measures[0]), line_width = 6, color = 'white')\n",
    "#     p.line((hspan[0],hspan[0]),(vmid+measures[0],vmid-measures[0]), line_width = 6, color = 'white')\n",
    "#     p.grid.visible = False\n",
    "#     p.xaxis.visible = False\n",
    "#     p.yaxis.visible = False\n",
    "\n",
    "#     return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "962a1ad2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.619043Z",
     "iopub.status.busy": "2022-03-21T18:10:30.618457Z",
     "iopub.status.idle": "2022-03-21T18:10:30.621229Z",
     "shell.execute_reply": "2022-03-21T18:10:30.621735Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.418412Z"
    },
    "papermill": {
     "duration": 0.047517,
     "end_time": "2022-03-21T18:10:30.621882",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.574365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "48b3cadf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.708901Z",
     "iopub.status.busy": "2022-03-21T18:10:30.708363Z",
     "iopub.status.idle": "2022-03-21T18:10:30.710903Z",
     "shell.execute_reply": "2022-03-21T18:10:30.711450Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.435074Z"
    },
    "papermill": {
     "duration": 0.047234,
     "end_time": "2022-03-21T18:10:30.711596",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.664362",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # new_index = random_point[x_pos].T.index.to_list()\n",
    "# new_index = [index.strip(\"_x\") for index in new_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b2475ee8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.798914Z",
     "iopub.status.busy": "2022-03-21T18:10:30.798326Z",
     "iopub.status.idle": "2022-03-21T18:10:30.800860Z",
     "shell.execute_reply": "2022-03-21T18:10:30.801390Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.447998Z"
    },
    "papermill": {
     "duration": 0.047757,
     "end_time": "2022-03-21T18:10:30.801556",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.753799",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# positions = random_point[x_pos].T\n",
    "# positions.index = new_index\n",
    "# positions.columns = [\"x\"]\n",
    "# positions[\"y\"] = random_point[y_pos].T.to_numpy()\n",
    "# positions[\"type\"] = random_point[type_cols].T.to_numpy()\n",
    "# positions[\"id\"] = random_point[id_cols].T.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "65685123",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.890679Z",
     "iopub.status.busy": "2022-03-21T18:10:30.890072Z",
     "iopub.status.idle": "2022-03-21T18:10:30.891472Z",
     "shell.execute_reply": "2022-03-21T18:10:30.892000Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.473937Z"
    },
    "papermill": {
     "duration": 0.048915,
     "end_time": "2022-03-21T18:10:30.892156",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.843241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ball_pos = positions.iloc[0]\n",
    "# positions = positions.iloc[1:]\n",
    "# positions[\"name\"] = positions[\"id\"].map(player_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b03f210",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:30.979512Z",
     "iopub.status.busy": "2022-03-21T18:10:30.978653Z",
     "iopub.status.idle": "2022-03-21T18:10:30.981316Z",
     "shell.execute_reply": "2022-03-21T18:10:30.981839Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.493180Z"
    },
    "papermill": {
     "duration": 0.048315,
     "end_time": "2022-03-21T18:10:30.982017",
     "exception": false,
     "start_time": "2022-03-21T18:10:30.933702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# positions['x'] = length / 2.0 + positions['x']\n",
    "# positions['y'] = width / 2.0 + positions['y']\n",
    "# positions['type'] = positions['type'].apply(lambda x: -1 if x is None else int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "76982a8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:31.070395Z",
     "iopub.status.busy": "2022-03-21T18:10:31.069777Z",
     "iopub.status.idle": "2022-03-21T18:10:31.072472Z",
     "shell.execute_reply": "2022-03-21T18:10:31.073040Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.509930Z"
    },
    "papermill": {
     "duration": 0.047577,
     "end_time": "2022-03-21T18:10:31.073203",
     "exception": false,
     "start_time": "2022-03-21T18:10:31.025626",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# home = positions[positions['type'] == 0]\n",
    "# away = positions[positions['type'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a6e591cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T18:10:31.162775Z",
     "iopub.status.busy": "2022-03-21T18:10:31.161723Z",
     "iopub.status.idle": "2022-03-21T18:10:31.163596Z",
     "shell.execute_reply": "2022-03-21T18:10:31.164041Z",
     "shell.execute_reply.started": "2022-03-21T16:00:40.526057Z"
    },
    "papermill": {
     "duration": 0.048528,
     "end_time": "2022-03-21T18:10:31.164199",
     "exception": false,
     "start_time": "2022-03-21T18:10:31.115671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TOOLTIPS = [\n",
    "#     (\"name\", \"@name\"),\n",
    "#     (\"(x,y)\", \"($x, $y)\"),\n",
    "# ]\n",
    "# Italy = ColumnDataSource(home)\n",
    "# Wales = ColumnDataSource(away)\n",
    "# p = draw_pitch(tooltips = TOOLTIPS)\n",
    "# p.circle(x=\"x\", y=\"y\", size=8, fill_alpha=0.5, color=\"red\",source=Italy)\n",
    "# p.circle(x=\"x\", y=\"y\", size=8, fill_alpha=0.5, color=\"yellow\",source=Wales)\n",
    "# output_file(\"interactive_pitch.html\")\n",
    "# show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "968cc911",
   "metadata": {
    "papermill": {
     "duration": 0.041402,
     "end_time": "2022-03-21T18:10:31.247269",
     "exception": false,
     "start_time": "2022-03-21T18:10:31.205867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3516.468256,
   "end_time": "2022-03-21T18:10:32.204821",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-03-21T17:11:55.736565",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}