Batsmen Analysis of Mumbai and Chennai Leg of IPL 2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "initial-attribute",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\anaconda3\\envs\\DataAnalysisEnv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# import cufflinks as cf\n",
    "# from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "# init_notebook_mode(connected=True)\n",
    "# cf.go_offline()\n",
    "%matplotlib inline\n",
    "\n",
    "deliveries = pd.read_csv(\"IPL Cricsheet CS new/all_matches.csv\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "hired-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'season', 'start_date', 'venue', 'innings', 'ball',\n",
       "       'batting_team', 'bowling_team', 'striker', 'non_striker', 'bowler',\n",
       "       'runs_off_bat', 'extras', 'wides', 'noballs', 'byes', 'legbyes',\n",
       "       'penalty', 'wicket_type', 'player_dismissed', 'other_wicket_type',\n",
       "       'other_player_dismissed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "infinite-proportion",
   "metadata": {},
   "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>match_id</th>\n",
       "      <th>season</th>\n",
       "      <th>start_date</th>\n",
       "      <th>venue</th>\n",
       "      <th>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>striker</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>...</th>\n",
       "      <th>extras</th>\n",
       "      <th>wides</th>\n",
       "      <th>noballs</th>\n",
       "      <th>byes</th>\n",
       "      <th>legbyes</th>\n",
       "      <th>penalty</th>\n",
       "      <th>wicket_type</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>other_wicket_type</th>\n",
       "      <th>other_player_dismissed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193617</th>\n",
       "      <td>1254058</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193618</th>\n",
       "      <td>1254058</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193619</th>\n",
       "      <td>1254058</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193620</th>\n",
       "      <td>1254058</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193621</th>\n",
       "      <td>1254058</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-09</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk, Chennai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198725</th>\n",
       "      <td>1254078</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>Narendra Modi Stadium, Ahmedabad</td>\n",
       "      <td>2</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Punjab Kings</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198726</th>\n",
       "      <td>1254078</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>Narendra Modi Stadium, Ahmedabad</td>\n",
       "      <td>2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Punjab Kings</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198727</th>\n",
       "      <td>1254078</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>Narendra Modi Stadium, Ahmedabad</td>\n",
       "      <td>2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Punjab Kings</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198728</th>\n",
       "      <td>1254078</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>Narendra Modi Stadium, Ahmedabad</td>\n",
       "      <td>2</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Punjab Kings</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198729</th>\n",
       "      <td>1254078</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>Narendra Modi Stadium, Ahmedabad</td>\n",
       "      <td>2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Punjab Kings</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5113 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id season  start_date                                     venue  \\\n",
       "193617   1254058   2021  2021-04-09  MA Chidambaram Stadium, Chepauk, Chennai   \n",
       "193618   1254058   2021  2021-04-09  MA Chidambaram Stadium, Chepauk, Chennai   \n",
       "193619   1254058   2021  2021-04-09  MA Chidambaram Stadium, Chepauk, Chennai   \n",
       "193620   1254058   2021  2021-04-09  MA Chidambaram Stadium, Chepauk, Chennai   \n",
       "193621   1254058   2021  2021-04-09  MA Chidambaram Stadium, Chepauk, Chennai   \n",
       "...          ...    ...         ...                                       ...   \n",
       "198725   1254078   2021  2021-04-26          Narendra Modi Stadium, Ahmedabad   \n",
       "198726   1254078   2021  2021-04-26          Narendra Modi Stadium, Ahmedabad   \n",
       "198727   1254078   2021  2021-04-26          Narendra Modi Stadium, Ahmedabad   \n",
       "198728   1254078   2021  2021-04-26          Narendra Modi Stadium, Ahmedabad   \n",
       "198729   1254078   2021  2021-04-26          Narendra Modi Stadium, Ahmedabad   \n",
       "\n",
       "        innings  ball           batting_team                 bowling_team  \\\n",
       "193617        1   0.1         Mumbai Indians  Royal Challengers Bangalore   \n",
       "193618        1   0.2         Mumbai Indians  Royal Challengers Bangalore   \n",
       "193619        1   0.3         Mumbai Indians  Royal Challengers Bangalore   \n",
       "193620        1   0.4         Mumbai Indians  Royal Challengers Bangalore   \n",
       "193621        1   0.5         Mumbai Indians  Royal Challengers Bangalore   \n",
       "...         ...   ...                    ...                          ...   \n",
       "198725        2  15.6  Kolkata Knight Riders                 Punjab Kings   \n",
       "198726        2  16.1  Kolkata Knight Riders                 Punjab Kings   \n",
       "198727        2  16.2  Kolkata Knight Riders                 Punjab Kings   \n",
       "198728        2  16.3  Kolkata Knight Riders                 Punjab Kings   \n",
       "198729        2  16.4  Kolkata Knight Riders                 Punjab Kings   \n",
       "\n",
       "           striker non_striker  ... extras  wides  noballs  byes  legbyes  \\\n",
       "193617   RG Sharma     CA Lynn  ...      0    NaN      NaN   NaN      NaN   \n",
       "193618   RG Sharma     CA Lynn  ...      0    NaN      NaN   NaN      NaN   \n",
       "193619   RG Sharma     CA Lynn  ...      0    NaN      NaN   NaN      NaN   \n",
       "193620   RG Sharma     CA Lynn  ...      0    NaN      NaN   NaN      NaN   \n",
       "193621   RG Sharma     CA Lynn  ...      0    NaN      NaN   NaN      NaN   \n",
       "...            ...         ...  ...    ...    ...      ...   ...      ...   \n",
       "198725  EJG Morgan  KD Karthik  ...      0    NaN      NaN   NaN      NaN   \n",
       "198726  EJG Morgan  KD Karthik  ...      0    NaN      NaN   NaN      NaN   \n",
       "198727  KD Karthik  EJG Morgan  ...      0    NaN      NaN   NaN      NaN   \n",
       "198728  KD Karthik  EJG Morgan  ...      0    NaN      NaN   NaN      NaN   \n",
       "198729  KD Karthik  EJG Morgan  ...      0    NaN      NaN   NaN      NaN   \n",
       "\n",
       "        penalty  wicket_type  player_dismissed other_wicket_type  \\\n",
       "193617      NaN          NaN               NaN               NaN   \n",
       "193618      NaN          NaN               NaN               NaN   \n",
       "193619      NaN          NaN               NaN               NaN   \n",
       "193620      NaN          NaN               NaN               NaN   \n",
       "193621      NaN          NaN               NaN               NaN   \n",
       "...         ...          ...               ...               ...   \n",
       "198725      NaN          NaN               NaN               NaN   \n",
       "198726      NaN          NaN               NaN               NaN   \n",
       "198727      NaN          NaN               NaN               NaN   \n",
       "198728      NaN          NaN               NaN               NaN   \n",
       "198729      NaN          NaN               NaN               NaN   \n",
       "\n",
       "       other_player_dismissed  \n",
       "193617                    NaN  \n",
       "193618                    NaN  \n",
       "193619                    NaN  \n",
       "193620                    NaN  \n",
       "193621                    NaN  \n",
       "...                       ...  \n",
       "198725                    NaN  \n",
       "198726                    NaN  \n",
       "198727                    NaN  \n",
       "198728                    NaN  \n",
       "198729                    NaN  \n",
       "\n",
       "[5113 rows x 22 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries_2021 = deliveries[(deliveries['season'] == 2021) | (deliveries['season'] == '2021')]\n",
    "deliveries_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "quick-shadow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MA Chidambaram Stadium, Chepauk, Chennai    2461\n",
       "Wankhede Stadium, Mumbai                    2427\n",
       "Narendra Modi Stadium, Ahmedabad             225\n",
       "Name: venue, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries_2021.venue.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "chinese-chess",
   "metadata": {},
   "outputs": [],
   "source": [
    "Chepauk_2021 = deliveries_2021[deliveries_2021['venue'] == 'MA Chidambaram Stadium, Chepauk, Chennai']\n",
    "Wankhede_2021 = deliveries_2021[deliveries_2021['venue'] == 'Wankhede Stadium, Mumbai']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "major-stuff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "unlikely-fraud",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "extreme-extreme",
   "metadata": {},
   "outputs": [],
   "source": [
    "Chepauk_2021['Over Number'] = (Chepauk_2021['ball'] // 1) + 1\n",
    "Chepauk_2021['Ball Number'] = round(((Chepauk_2021['ball']) % 1) * 10)\n",
    "Chepauk_2021 = Chepauk_2021.astype({\"Over Number\": int, \"Ball Number\": int})\n",
    "Chepauk_2021 = Chepauk_2021.head(2449)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "urban-astrology",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "atomic-antenna",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-135-81869608ef92>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Wankhede_2021['Over Number'] = (Wankhede_2021['ball'] // 1) + 1\n",
      "<ipython-input-135-81869608ef92>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Wankhede_2021['Ball Number'] = round(((Wankhede_2021['ball']) % 1) * 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>match_id</th>\n",
       "      <th>season</th>\n",
       "      <th>start_date</th>\n",
       "      <th>venue</th>\n",
       "      <th>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>striker</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>...</th>\n",
       "      <th>noballs</th>\n",
       "      <th>byes</th>\n",
       "      <th>legbyes</th>\n",
       "      <th>penalty</th>\n",
       "      <th>wicket_type</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>other_wicket_type</th>\n",
       "      <th>other_player_dismissed</th>\n",
       "      <th>Over Number</th>\n",
       "      <th>Ball Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193867</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193868</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193869</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193870</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193871</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198240</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198241</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198242</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198243</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.5</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198244</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2427 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id season  start_date                     venue  innings  ball  \\\n",
       "193867   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.1   \n",
       "193868   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.2   \n",
       "193869   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.3   \n",
       "193870   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.4   \n",
       "193871   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.5   \n",
       "...          ...    ...         ...                       ...      ...   ...   \n",
       "198240   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.2   \n",
       "198241   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.3   \n",
       "198242   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.4   \n",
       "198243   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.5   \n",
       "198244   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.6   \n",
       "\n",
       "                       batting_team         bowling_team         striker  \\\n",
       "193867          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193868          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193869          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193870          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193871          Chennai Super Kings       Delhi Capitals    F du Plessis   \n",
       "...                             ...                  ...             ...   \n",
       "198240  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198241  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198242  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198243  Royal Challengers Bangalore  Chennai Super Kings       YS Chahal   \n",
       "198244  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "\n",
       "           non_striker  ... noballs  byes  legbyes  penalty  wicket_type  \\\n",
       "193867    F du Plessis  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "193868    F du Plessis  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "193869    F du Plessis  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "193870    F du Plessis  ...     NaN   NaN      1.0      NaN          NaN   \n",
       "193871      RD Gaikwad  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "...                ...  ...     ...   ...      ...      ...          ...   \n",
       "198240       YS Chahal  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "198241       YS Chahal  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "198242       YS Chahal  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "198243  Mohammed Siraj  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "198244       YS Chahal  ...     NaN   NaN      NaN      NaN          NaN   \n",
       "\n",
       "        player_dismissed  other_wicket_type  other_player_dismissed  \\\n",
       "193867               NaN                NaN                     NaN   \n",
       "193868               NaN                NaN                     NaN   \n",
       "193869               NaN                NaN                     NaN   \n",
       "193870               NaN                NaN                     NaN   \n",
       "193871               NaN                NaN                     NaN   \n",
       "...                  ...                ...                     ...   \n",
       "198240               NaN                NaN                     NaN   \n",
       "198241               NaN                NaN                     NaN   \n",
       "198242               NaN                NaN                     NaN   \n",
       "198243               NaN                NaN                     NaN   \n",
       "198244               NaN                NaN                     NaN   \n",
       "\n",
       "       Over Number Ball Number  \n",
       "193867           1           1  \n",
       "193868           1           2  \n",
       "193869           1           3  \n",
       "193870           1           4  \n",
       "193871           1           5  \n",
       "...            ...         ...  \n",
       "198240          20           2  \n",
       "198241          20           3  \n",
       "198242          20           4  \n",
       "198243          20           5  \n",
       "198244          20           6  \n",
       "\n",
       "[2427 rows x 24 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wankhede_2021['Over Number'] = (Wankhede_2021['ball'] // 1) + 1\n",
    "Wankhede_2021['Ball Number'] = round(((Wankhede_2021['ball']) % 1) * 10)\n",
    "Wankhede_2021 = Wankhede_2021.astype({\"Over Number\": int, \"Ball Number\": int})\n",
    "Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "continuing-barrel",
   "metadata": {},
   "outputs": [],
   "source": [
    "Chepauk_2021['isDot'] = Chepauk_2021['runs_off_bat'].apply(lambda x: 1 if x==0 else 0)\n",
    "Chepauk_2021['isOne'] = Chepauk_2021['runs_off_bat'].apply(lambda x: 1 if x==1 else 0)\n",
    "Chepauk_2021['isFour'] = Chepauk_2021['runs_off_bat'].apply(lambda x: 1 if x==4 else 0)\n",
    "Chepauk_2021['isSix'] = Chepauk_2021['runs_off_bat'].apply(lambda x: 1 if x==6 else 0)\n",
    "Chepauk_2021['isBoundary'] = Chepauk_2021['runs_off_bat'].apply(lambda x:1 if (x==4 or x==6) else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "popular-hours",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wankhede_2021['isDot'] = Wankhede_2021['runs_off_bat'].apply(lambda x: 1 if x==0 else 0)\n",
    "Wankhede_2021['isOne'] = Wankhede_2021['runs_off_bat'].apply(lambda x: 1 if x==1 else 0)\n",
    "Wankhede_2021['isFour'] = Wankhede_2021['runs_off_bat'].apply(lambda x: 1 if x==4 else 0)\n",
    "Wankhede_2021['isSix'] = Wankhede_2021['runs_off_bat'].apply(lambda x: 1 if x==6 else 0)\n",
    "Wankhede_2021['isBoundary'] = Wankhede_2021['runs_off_bat'].apply(lambda x:1 if (x==4 or x==6) else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "incorporated-employee",
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = pd.DataFrame(Chepauk_2021.groupby('striker').sum()['runs_off_bat'])\n",
    "balls = pd.DataFrame(Chepauk_2021.groupby('striker').count()['ball'])\n",
    "wides = pd.DataFrame(Chepauk_2021.groupby('striker').count()['wides'])\n",
    "dots = pd.DataFrame(Chepauk_2021.groupby('striker').sum()['isDot'])\n",
    "fours = pd.DataFrame(Chepauk_2021.groupby('striker').sum()['isFour'])\n",
    "sixes = pd.DataFrame(Chepauk_2021.groupby('striker').sum()['isSix'])\n",
    "boundaries = pd.DataFrame(Chepauk_2021.groupby('striker').sum()['isBoundary'])\n",
    "# dismissals = pd.DataFrame(Chepauk_2021['player_dismissed'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "terminal-compensation",
   "metadata": {},
   "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>match_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   match_id\n",
       "striker                    \n",
       "AB de Villiers            3\n",
       "AD Russell                3\n",
       "Abdul Samad               3\n",
       "Abhishek Sharma           2\n",
       "Arshdeep Singh            1\n",
       "...                     ...\n",
       "V Kohli                   3\n",
       "V Shankar                 4\n",
       "Virat Singh               2\n",
       "WP Saha                   2\n",
       "Washington Sundar         2\n",
       "\n",
       "[69 rows x 1 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "innings = pd.DataFrame(Chepauk_2021.groupby('striker').agg({'match_id': 'nunique'}))\n",
    "innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "accessory-travel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Runs</th>\n",
       "      <th>Balls</th>\n",
       "      <th>Dots</th>\n",
       "      <th>Fours</th>\n",
       "      <th>Sixes</th>\n",
       "      <th>Boundaries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>125</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries\n",
       "striker                                                       \n",
       "AB de Villiers      125     66    16     13      5          18\n",
       "AD Russell           45     40    22      5      2           7\n",
       "Abdul Samad          26     18     7      1      2           3\n",
       "Abhishek Sharma       7     10     6      0      0           0\n",
       "Arshdeep Singh        1      2     1      0      0           0\n",
       "...                 ...    ...   ...    ...    ...         ...\n",
       "V Kohli              71     64    26      9      0           9\n",
       "V Shankar            50     44    17      0      3           3\n",
       "Virat Singh          15     26    14      1      0           1\n",
       "WP Saha               8     15    12      0      1           1\n",
       "Washington Sundar    18     27    16      1      0           1\n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021 = runs.merge(balls, on='striker')\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(wides, on='striker')\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(dots, on='striker')\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(fours, on='striker')\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(sixes, on='striker')\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(boundaries, on='striker')\n",
    "batsmen_in_Chepauk_2021['ball'] = batsmen_in_Chepauk_2021['ball'] - batsmen_in_Chepauk_2021['wides']\n",
    "batsmen_in_Chepauk_2021.drop('wides', axis=1, inplace=True)\n",
    "batsmen_in_Chepauk_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries']\n",
    "\n",
    "batsmen_in_Chepauk_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "amazing-actor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_dismissed</th>\n",
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       "      <th></th>\n",
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       "      <td>0</td>\n",
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       "      <th>Mohammed Siraj</th>\n",
       "      <td>0</td>\n",
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       "      <th>Harbhajan Singh</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Suchith</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>TA Boult</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>KS Williamson</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Mujeeb Ur Rahman</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>CV Varun</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lalit Yadav</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  player_dismissed\n",
       "striker                           \n",
       "HV Patel                         0\n",
       "MP Stoinis                       0\n",
       "Mohammed Siraj                   0\n",
       "Harbhajan Singh                  0\n",
       "J Suchith                        0\n",
       "TA Boult                         0\n",
       "Arshdeep Singh                   0\n",
       "KS Williamson                    0\n",
       "Mujeeb Ur Rahman                 0\n",
       "CV Varun                         0\n",
       "Lalit Yadav                      0"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dismissals = pd.DataFrame(Chepauk_2021['player_dismissed'].value_counts())\n",
    "dismissals.index.names = ['striker']\n",
    "zero_dismissals = (set(Chepauk_2021.striker.unique()) - set(dismissals.index))\n",
    "zero_dismissals\n",
    "zero_dismissals_dict = dict()\n",
    "zero_dismissals_dict['striker'] = list(zero_dismissals)\n",
    "zero_dismissals_dict['player_dismissed'] = 0\n",
    "zero_dismissals_dict\n",
    "zero_dismissals_dict = pd.DataFrame(zero_dismissals_dict)\n",
    "zero_dismissals_dict\n",
    "zero_dismissals_dict.set_index('striker', inplace=True)\n",
    "zero_dismissals_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "decimal-martin",
   "metadata": {},
   "outputs": [],
   "source": [
    "dismissals = dismissals.append(zero_dismissals_dict)\n",
    "dismissals.drop('N Pooran', axis = 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "pharmaceutical-amber",
   "metadata": {},
   "outputs": [],
   "source": [
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(dismissals, on='striker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "opened-allowance",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AB de Villiers</th>\n",
       "      <td>125</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals\n",
       "striker                                                                   \n",
       "AB de Villiers      125     66    16     13      5          18           2\n",
       "AD Russell           45     40    22      5      2           7           3\n",
       "Abdul Samad          26     18     7      1      2           3           2\n",
       "Abhishek Sharma       7     10     6      0      0           0           2\n",
       "Arshdeep Singh        1      2     1      0      0           0           0\n",
       "...                 ...    ...   ...    ...    ...         ...         ...\n",
       "V Kohli              71     64    26      9      0           9           3\n",
       "V Shankar            50     44    17      0      3           3           4\n",
       "Virat Singh          15     26    14      1      0           1           2\n",
       "WP Saha               8     15    12      0      1           1           2\n",
       "Washington Sundar    18     27    16      1      0           1           2\n",
       "\n",
       "[69 rows x 7 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries', 'Dismissals']\n",
    "batsmen_in_Chepauk_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "studied-rachel",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Abdul Samad</th>\n",
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       "      <th>Abhishek Sharma</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals  \\\n",
       "striker                                                                      \n",
       "AB de Villiers      125     66    16     13      5          18           2   \n",
       "AD Russell           45     40    22      5      2           7           3   \n",
       "Abdul Samad          26     18     7      1      2           3           2   \n",
       "Abhishek Sharma       7     10     6      0      0           0           2   \n",
       "Arshdeep Singh        1      2     1      0      0           0           0   \n",
       "...                 ...    ...   ...    ...    ...         ...         ...   \n",
       "V Kohli              71     64    26      9      0           9           3   \n",
       "V Shankar            50     44    17      0      3           3           4   \n",
       "Virat Singh          15     26    14      1      0           1           2   \n",
       "WP Saha               8     15    12      0      1           1           2   \n",
       "Washington Sundar    18     27    16      1      0           1           2   \n",
       "\n",
       "                   Innings  \n",
       "striker                     \n",
       "AB de Villiers           3  \n",
       "AD Russell               3  \n",
       "Abdul Samad              3  \n",
       "Abhishek Sharma          2  \n",
       "Arshdeep Singh           1  \n",
       "...                    ...  \n",
       "V Kohli                  3  \n",
       "V Shankar                4  \n",
       "Virat Singh              2  \n",
       "WP Saha                  2  \n",
       "Washington Sundar        2  \n",
       "\n",
       "[69 rows x 8 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.merge(innings, on='striker')\n",
    "batsmen_in_Chepauk_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries', 'Dismissals', 'Innings']\n",
    "batsmen_in_Chepauk_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sapphire-static",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "deadly-scout",
   "metadata": {},
   "source": [
    "Metrics : Dot Ball %, Balls per Boundary, Strike Rate, Runs Per Innings, Balls per Dismissal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "honest-keyboard",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Runs</th>\n",
       "      <th>Balls</th>\n",
       "      <th>Dots</th>\n",
       "      <th>Fours</th>\n",
       "      <th>Sixes</th>\n",
       "      <th>Boundaries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TK Curran</th>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBK Jaiswal</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YS Chahal</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries\n",
       "striker                                                       \n",
       "AB de Villiers        4      9     5      0      0           0\n",
       "AD Russell           63     29    15      3      7          10\n",
       "AM Rahane             8      8     5      1      0           1\n",
       "AT Rayudu            64     41    17      2      6           8\n",
       "BA Stokes             0      3     3      0      0           0\n",
       "...                 ...    ...   ...    ...    ...         ...\n",
       "TK Curran            21     16     4      2      0           2\n",
       "V Kohli              80     54    13      7      3          10\n",
       "Washington Sundar     7     11     7      1      0           1\n",
       "YBK Jaiswal          22     17    10      5      0           5\n",
       "YS Chahal             8     21    14      0      0           0\n",
       "\n",
       "[67 rows x 6 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs = pd.DataFrame(Wankhede_2021.groupby('striker').sum()['runs_off_bat'])\n",
    "balls = pd.DataFrame(Wankhede_2021.groupby('striker').count()['ball'])\n",
    "wides = pd.DataFrame(Wankhede_2021.groupby('striker').count()['wides'])\n",
    "dots = pd.DataFrame(Wankhede_2021.groupby('striker').sum()['isDot'])\n",
    "fours = pd.DataFrame(Wankhede_2021.groupby('striker').sum()['isFour'])\n",
    "sixes = pd.DataFrame(Wankhede_2021.groupby('striker').sum()['isSix'])\n",
    "boundaries = pd.DataFrame(Wankhede_2021.groupby('striker').sum()['isBoundary'])\n",
    "innings = pd.DataFrame(Wankhede_2021.groupby('striker').agg({'match_id': 'nunique'}))\n",
    "\n",
    "\n",
    "batsmen_in_Wankhede_2021 = runs.merge(balls, on='striker')\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(wides, on='striker')\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(dots, on='striker')\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(fours, on='striker')\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(sixes, on='striker')\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(boundaries, on='striker')\n",
    "batsmen_in_Wankhede_2021['ball'] = batsmen_in_Wankhede_2021['ball'] - batsmen_in_Wankhede_2021['wides']\n",
    "batsmen_in_Wankhede_2021.drop('wides', axis=1, inplace=True)\n",
    "batsmen_in_Wankhede_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries']\n",
    "\n",
    "batsmen_in_Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "dependent-white",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Runs</th>\n",
       "      <th>Balls</th>\n",
       "      <th>Dots</th>\n",
       "      <th>Fours</th>\n",
       "      <th>Sixes</th>\n",
       "      <th>Boundaries</th>\n",
       "      <th>Dismissals</th>\n",
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       "    <tr>\n",
       "      <th>striker</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
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       "      <th>AM Rahane</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TK Curran</th>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBK Jaiswal</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YS Chahal</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals\n",
       "striker                                                                   \n",
       "AB de Villiers        4      9     5      0      0           0           1\n",
       "AD Russell           63     29    15      3      7          10           2\n",
       "AM Rahane             8      8     5      1      0           1           1\n",
       "AT Rayudu            64     41    17      2      6           8           4\n",
       "BA Stokes             0      3     3      0      0           0           1\n",
       "...                 ...    ...   ...    ...    ...         ...         ...\n",
       "TK Curran            21     16     4      2      0           2           1\n",
       "V Kohli              80     54    13      7      3          10           1\n",
       "Washington Sundar     7     11     7      1      0           1           1\n",
       "YBK Jaiswal          22     17    10      5      0           5           1\n",
       "YS Chahal             8     21    14      0      0           0           0\n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dismissals = pd.DataFrame(Wankhede_2021['player_dismissed'].value_counts())\n",
    "dismissals.index.names = ['striker']\n",
    "zero_dismissals = (set(Wankhede_2021.striker.unique()) - set(dismissals.index))\n",
    "zero_dismissals\n",
    "zero_dismissals_dict = dict()\n",
    "zero_dismissals_dict['striker'] = list(zero_dismissals)\n",
    "zero_dismissals_dict['player_dismissed'] = 0\n",
    "zero_dismissals_dict\n",
    "zero_dismissals_dict = pd.DataFrame(zero_dismissals_dict)\n",
    "zero_dismissals_dict\n",
    "zero_dismissals_dict.set_index('striker', inplace=True)\n",
    "zero_dismissals_dict\n",
    "\n",
    "dismissals = dismissals.append(zero_dismissals_dict)\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(dismissals, on='striker')\n",
    "batsmen_in_Wankhede_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries', 'Dismissals']\n",
    "batsmen_in_Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "ignored-adams",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Dots</th>\n",
       "      <th>Fours</th>\n",
       "      <th>Sixes</th>\n",
       "      <th>Boundaries</th>\n",
       "      <th>Dismissals</th>\n",
       "      <th>Innings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TK Curran</th>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBK Jaiswal</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YS Chahal</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals  \\\n",
       "striker                                                                      \n",
       "AB de Villiers        4      9     5      0      0           0           1   \n",
       "AD Russell           63     29    15      3      7          10           2   \n",
       "AM Rahane             8      8     5      1      0           1           1   \n",
       "AT Rayudu            64     41    17      2      6           8           4   \n",
       "BA Stokes             0      3     3      0      0           0           1   \n",
       "...                 ...    ...   ...    ...    ...         ...         ...   \n",
       "TK Curran            21     16     4      2      0           2           1   \n",
       "V Kohli              80     54    13      7      3          10           1   \n",
       "Washington Sundar     7     11     7      1      0           1           1   \n",
       "YBK Jaiswal          22     17    10      5      0           5           1   \n",
       "YS Chahal             8     21    14      0      0           0           0   \n",
       "\n",
       "                   Innings  \n",
       "striker                     \n",
       "AB de Villiers           1  \n",
       "AD Russell               2  \n",
       "AM Rahane                1  \n",
       "AT Rayudu                4  \n",
       "BA Stokes                1  \n",
       "...                    ...  \n",
       "TK Curran                1  \n",
       "V Kohli                  2  \n",
       "Washington Sundar        1  \n",
       "YBK Jaiswal              1  \n",
       "YS Chahal                1  \n",
       "\n",
       "[67 rows x 8 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.merge(innings, on='striker')\n",
    "batsmen_in_Wankhede_2021.columns = ['Runs', 'Balls', 'Dots', 'Fours', 'Sixes', 'Boundaries', 'Dismissals', 'Innings']\n",
    "batsmen_in_Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "national-hybrid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Runs</th>\n",
       "      <th>Balls</th>\n",
       "      <th>Dots</th>\n",
       "      <th>Fours</th>\n",
       "      <th>Sixes</th>\n",
       "      <th>Boundaries</th>\n",
       "      <th>Dismissals</th>\n",
       "      <th>Innings</th>\n",
       "      <th>Dot %</th>\n",
       "      <th>Strike Rate</th>\n",
       "      <th>Runs Per Innings</th>\n",
       "      <th>Balls Per Boundary</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>125</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>24.24</td>\n",
       "      <td>189.39</td>\n",
       "      <td>41.67</td>\n",
       "      <td>3.67</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>55.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>15.00</td>\n",
       "      <td>5.71</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>38.89</td>\n",
       "      <td>144.44</td>\n",
       "      <td>8.67</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>60.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>10.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>40.62</td>\n",
       "      <td>110.94</td>\n",
       "      <td>23.67</td>\n",
       "      <td>7.11</td>\n",
       "      <td>21.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>50</td>\n",
       "      <td>44</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>38.64</td>\n",
       "      <td>113.64</td>\n",
       "      <td>12.50</td>\n",
       "      <td>14.67</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>57.69</td>\n",
       "      <td>7.50</td>\n",
       "      <td>26.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>80.00</td>\n",
       "      <td>53.33</td>\n",
       "      <td>4.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>59.26</td>\n",
       "      <td>66.67</td>\n",
       "      <td>9.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals  \\\n",
       "striker                                                                      \n",
       "AB de Villiers      125     66    16     13      5          18           2   \n",
       "AD Russell           45     40    22      5      2           7           3   \n",
       "Abdul Samad          26     18     7      1      2           3           2   \n",
       "Abhishek Sharma       7     10     6      0      0           0           2   \n",
       "Arshdeep Singh        1      2     1      0      0           0           0   \n",
       "...                 ...    ...   ...    ...    ...         ...         ...   \n",
       "V Kohli              71     64    26      9      0           9           3   \n",
       "V Shankar            50     44    17      0      3           3           4   \n",
       "Virat Singh          15     26    14      1      0           1           2   \n",
       "WP Saha               8     15    12      0      1           1           2   \n",
       "Washington Sundar    18     27    16      1      0           1           2   \n",
       "\n",
       "                   Innings  Dot %  Strike Rate  Runs Per Innings  \\\n",
       "striker                                                            \n",
       "AB de Villiers           3  24.24       189.39             41.67   \n",
       "AD Russell               3  55.00       112.50             15.00   \n",
       "Abdul Samad              3  38.89       144.44              8.67   \n",
       "Abhishek Sharma          2  60.00        70.00              3.50   \n",
       "Arshdeep Singh           1  50.00        50.00              1.00   \n",
       "...                    ...    ...          ...               ...   \n",
       "V Kohli                  3  40.62       110.94             23.67   \n",
       "V Shankar                4  38.64       113.64             12.50   \n",
       "Virat Singh              2  53.85        57.69              7.50   \n",
       "WP Saha                  2  80.00        53.33              4.00   \n",
       "Washington Sundar        2  59.26        66.67              9.00   \n",
       "\n",
       "                   Balls Per Boundary  Balls Per Dismissal  \n",
       "striker                                                     \n",
       "AB de Villiers                   3.67                33.00  \n",
       "AD Russell                       5.71                13.33  \n",
       "Abdul Samad                      6.00                 9.00  \n",
       "Abhishek Sharma                 10.00                 5.00  \n",
       "Arshdeep Singh                   2.00                 2.00  \n",
       "...                               ...                  ...  \n",
       "V Kohli                          7.11                21.33  \n",
       "V Shankar                       14.67                11.00  \n",
       "Virat Singh                     26.00                13.00  \n",
       "WP Saha                         15.00                 7.50  \n",
       "Washington Sundar               27.00                13.50  \n",
       "\n",
       "[69 rows x 13 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021['Dot %'] = batsmen_in_Chepauk_2021.apply(lambda x: (x['Dots']/x['Balls']*100), axis=1)\n",
    "batsmen_in_Chepauk_2021['Strike Rate'] = batsmen_in_Chepauk_2021.apply(lambda x: (x['Runs']/x['Balls']*100), axis=1)\n",
    "batsmen_in_Chepauk_2021['Runs Per Innings'] = batsmen_in_Chepauk_2021.apply(lambda x: (x['Runs']/x['Innings']), axis=1)\n",
    "batsmen_in_Chepauk_2021['Balls Per Boundary'] = batsmen_in_Chepauk_2021.apply(lambda x: (x['Balls']/x['Boundaries']) if (x['Boundaries'] > 0) else (x['Balls']), axis=1)\n",
    "batsmen_in_Chepauk_2021['Balls Per Dismissal'] = batsmen_in_Chepauk_2021.apply(lambda x: (x['Balls']/x['Dismissals']) if(x['Dismissals'] > 0) else (x['Balls']), axis=1)\n",
    "batsmen_in_Chepauk_2021 = batsmen_in_Chepauk_2021.round(decimals=2)\n",
    "batsmen_in_Chepauk_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "orange-spectrum",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Balls Per Boundary</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>51.72</td>\n",
       "      <td>217.24</td>\n",
       "      <td>31.5</td>\n",
       "      <td>2.90</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62.50</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>41.46</td>\n",
       "      <td>156.10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.12</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TK Curran</th>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>131.25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>80</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24.07</td>\n",
       "      <td>148.15</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.40</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.64</td>\n",
       "      <td>63.64</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBK Jaiswal</th>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.82</td>\n",
       "      <td>129.41</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YS Chahal</th>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66.67</td>\n",
       "      <td>38.10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Runs  Balls  Dots  Fours  Sixes  Boundaries  Dismissals  \\\n",
       "striker                                                                      \n",
       "AB de Villiers        4      9     5      0      0           0           1   \n",
       "AD Russell           63     29    15      3      7          10           2   \n",
       "AM Rahane             8      8     5      1      0           1           1   \n",
       "AT Rayudu            64     41    17      2      6           8           4   \n",
       "BA Stokes             0      3     3      0      0           0           1   \n",
       "...                 ...    ...   ...    ...    ...         ...         ...   \n",
       "TK Curran            21     16     4      2      0           2           1   \n",
       "V Kohli              80     54    13      7      3          10           1   \n",
       "Washington Sundar     7     11     7      1      0           1           1   \n",
       "YBK Jaiswal          22     17    10      5      0           5           1   \n",
       "YS Chahal             8     21    14      0      0           0           0   \n",
       "\n",
       "                   Innings   Dot %  Strike Rate  Runs Per Innings  \\\n",
       "striker                                                             \n",
       "AB de Villiers           1   55.56        44.44               4.0   \n",
       "AD Russell               2   51.72       217.24              31.5   \n",
       "AM Rahane                1   62.50       100.00               8.0   \n",
       "AT Rayudu                4   41.46       156.10              16.0   \n",
       "BA Stokes                1  100.00         0.00               0.0   \n",
       "...                    ...     ...          ...               ...   \n",
       "TK Curran                1   25.00       131.25              21.0   \n",
       "V Kohli                  2   24.07       148.15              40.0   \n",
       "Washington Sundar        1   63.64        63.64               7.0   \n",
       "YBK Jaiswal              1   58.82       129.41              22.0   \n",
       "YS Chahal                1   66.67        38.10               8.0   \n",
       "\n",
       "                   Balls Per Boundary  Balls Per Dismissal  \n",
       "striker                                                     \n",
       "AB de Villiers                   9.00                 9.00  \n",
       "AD Russell                       2.90                14.50  \n",
       "AM Rahane                        8.00                 8.00  \n",
       "AT Rayudu                        5.12                10.25  \n",
       "BA Stokes                        3.00                 3.00  \n",
       "...                               ...                  ...  \n",
       "TK Curran                        8.00                16.00  \n",
       "V Kohli                          5.40                54.00  \n",
       "Washington Sundar               11.00                11.00  \n",
       "YBK Jaiswal                      3.40                17.00  \n",
       "YS Chahal                       21.00                21.00  \n",
       "\n",
       "[67 rows x 13 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Wankhede_2021['Dot %'] = batsmen_in_Wankhede_2021.apply(lambda x: (x['Dots']/x['Balls']*100), axis=1)\n",
    "batsmen_in_Wankhede_2021['Strike Rate'] = batsmen_in_Wankhede_2021.apply(lambda x: (x['Runs']/x['Balls']*100), axis=1)\n",
    "batsmen_in_Wankhede_2021['Runs Per Innings'] = batsmen_in_Wankhede_2021.apply(lambda x: (x['Runs']/x['Innings']), axis=1)\n",
    "batsmen_in_Wankhede_2021['Balls Per Boundary'] = batsmen_in_Wankhede_2021.apply(lambda x: (x['Balls']/x['Boundaries']) if (x['Boundaries'] > 0) else (x['Balls']), axis=1)\n",
    "batsmen_in_Wankhede_2021['Balls Per Dismissal'] = batsmen_in_Wankhede_2021.apply(lambda x: (x['Balls']/x['Dismissals']) if(x['Dismissals'] > 0) else (x['Balls']), axis=1)\n",
    "batsmen_in_Wankhede_2021 = batsmen_in_Wankhede_2021.round(decimals=2)\n",
    "batsmen_in_Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "written-keyboard",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'       Dot %, SR, RPI, BPB, BPD\\nDot %    1   0.33  0.5   2  0.33           \\nSR       3     1   0.25  3  0.5\\nRPI      2     4    1    3   5\\nBPB    0.5   0.33  0.33  1  0.33\\nBPD      3     2   0.2   3   1\\n'"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Saaty Scale (Chennai Leg)\n",
    "\n",
    "'''       Dot %, SR, RPI, BPB, BPD\n",
    "Dot %    1   0.33  0.5   2  0.33           \n",
    "SR       3     1   0.25  3  0.5\n",
    "RPI      2     4    1    3   5\n",
    "BPB    0.5   0.33  0.33  1  0.33\n",
    "BPD      3     2   0.2   3   1\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "blind-europe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Dot %': [1,3,2,0.5,3], 'SR': [0.33, 1, 4, 0.33, 2], 'RPI': [0.5, 0.25, 1, 0.33, 0.2], 'BPB': [2,3,3,1,3], 'BPD': [0.33, 0.5, 5, 0.33, 1]}\n",
    "Chennai_leg_saaty_scale = pd.DataFrame(data=data)\n",
    "Chennai_leg_saaty_scale.rename(index={0:'Dot %', 1:'SR', 2:'RPI', 3:'BPB', 4:'BPD'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "marine-italian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Dot %</th>\n",
       "      <th>SR</th>\n",
       "      <th>RPI</th>\n",
       "      <th>BPB</th>\n",
       "      <th>BPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dot %    SR   RPI  BPB   BPD\n",
       "Dot %    1.0  0.33  0.50    2  0.33\n",
       "SR       3.0  1.00  0.25    3  0.50\n",
       "RPI      2.0  4.00  1.00    3  5.00\n",
       "BPB      0.5  0.33  0.33    1  0.33\n",
       "BPD      3.0  2.00  0.20    3  1.00"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chennai_leg_saaty_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "great-genre",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Dot %</th>\n",
       "      <th>SR</th>\n",
       "      <th>RPI</th>\n",
       "      <th>BPB</th>\n",
       "      <th>BPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.043081</td>\n",
       "      <td>0.219298</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.046089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.109649</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.069832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.522193</td>\n",
       "      <td>0.438596</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.698324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.043081</td>\n",
       "      <td>0.144737</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.046089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.261097</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.139665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Dot %        SR       RPI       BPB       BPD\n",
       "Dot %  0.105263  0.043081  0.219298  0.166667  0.046089\n",
       "SR     0.315789  0.130548  0.109649  0.250000  0.069832\n",
       "RPI    0.210526  0.522193  0.438596  0.250000  0.698324\n",
       "BPB    0.052632  0.043081  0.144737  0.083333  0.046089\n",
       "BPD    0.315789  0.261097  0.087719  0.250000  0.139665"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chennai_leg_saaty_scale = Chennai_leg_saaty_scale.apply(lambda x: (x/x.sum()))\n",
    "Chennai_leg_saaty_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "authentic-oasis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>0.116080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>0.175164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.423928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>0.073974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>0.210854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        weights\n",
       "Dot %  0.116080\n",
       "SR     0.175164\n",
       "RPI    0.423928\n",
       "BPB    0.073974\n",
       "BPD    0.210854"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = []\n",
    "for i in range(len(Chennai_leg_saaty_scale)):\n",
    "    weights.append(Chennai_leg_saaty_scale.iloc[i].mean())\n",
    "weights\n",
    "\n",
    "chennai_weights = pd.DataFrame(data=weights, columns=['weights'])\n",
    "chennai_weights.index = ['Dot %', 'SR', 'RPI', 'BPB', 'BPD']\n",
    "chennai_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "appropriate-infrared",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dot %</th>\n",
       "      <th>SR</th>\n",
       "      <th>RPI</th>\n",
       "      <th>BPB</th>\n",
       "      <th>BPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dot %    SR   RPI   BPB  BPD\n",
       "Dot %    1.0  0.20  0.20  0.20    2\n",
       "SR       5.0  1.00  0.50  1.00    4\n",
       "RPI      5.0  2.00  1.00  0.25    3\n",
       "BPB      5.0  1.00  4.00  1.00    4\n",
       "BPD      0.5  0.25  0.33  0.25    1"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Dot %': [1,5,5,5,0.5], 'SR': [0.2, 1, 2, 1, 0.25], 'RPI': [0.2, 0.5, 1, 4, 0.33], 'BPB': [0.2,1,0.25,1,0.25], 'BPD': [2, 4, 3, 4, 1]}\n",
    "Mumbai_leg_saaty_scale = pd.DataFrame(data=data)\n",
    "Mumbai_leg_saaty_scale.rename(index={0:'Dot %', 1:'SR', 2:'RPI', 3:'BPB', 4:'BPD'}, inplace=True)\n",
    "Mumbai_leg_saaty_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "stone-citation",
   "metadata": {},
   "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>Dot %</th>\n",
       "      <th>SR</th>\n",
       "      <th>RPI</th>\n",
       "      <th>BPB</th>\n",
       "      <th>BPD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.044944</td>\n",
       "      <td>0.033167</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.224719</td>\n",
       "      <td>0.082919</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.449438</td>\n",
       "      <td>0.165837</td>\n",
       "      <td>0.092593</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.224719</td>\n",
       "      <td>0.663350</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.056180</td>\n",
       "      <td>0.054726</td>\n",
       "      <td>0.092593</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Dot %        SR       RPI       BPB       BPD\n",
       "Dot %  0.060606  0.044944  0.033167  0.074074  0.142857\n",
       "SR     0.303030  0.224719  0.082919  0.370370  0.285714\n",
       "RPI    0.303030  0.449438  0.165837  0.092593  0.214286\n",
       "BPB    0.303030  0.224719  0.663350  0.370370  0.285714\n",
       "BPD    0.030303  0.056180  0.054726  0.092593  0.071429"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mumbai_leg_saaty_scale = Mumbai_leg_saaty_scale.apply(lambda x: (x/x.sum()))\n",
    "Mumbai_leg_saaty_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "broken-distinction",
   "metadata": {},
   "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>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dot %</th>\n",
       "      <td>0.071130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR</th>\n",
       "      <td>0.253351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.245037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPB</th>\n",
       "      <td>0.369437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BPD</th>\n",
       "      <td>0.061046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        weights\n",
       "Dot %  0.071130\n",
       "SR     0.253351\n",
       "RPI    0.245037\n",
       "BPB    0.369437\n",
       "BPD    0.061046"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = []\n",
    "for i in range(len(Mumbai_leg_saaty_scale)):\n",
    "    weights.append(Mumbai_leg_saaty_scale.iloc[i].mean())\n",
    "weights\n",
    "\n",
    "mumbai_weights = pd.DataFrame(data=weights, columns=['weights'])\n",
    "mumbai_weights.index = ['Dot %', 'SR', 'RPI', 'BPB', 'BPD']\n",
    "mumbai_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "attached-harrison",
   "metadata": {},
   "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>Dot %</th>\n",
       "      <th>Strike Rate</th>\n",
       "      <th>Balls Per Boundary</th>\n",
       "      <th>Runs Per Innings</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>24.24</td>\n",
       "      <td>189.39</td>\n",
       "      <td>3.67</td>\n",
       "      <td>41.67</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>55.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>5.71</td>\n",
       "      <td>15.00</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>38.89</td>\n",
       "      <td>144.44</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.67</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>60.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>40.62</td>\n",
       "      <td>110.94</td>\n",
       "      <td>7.11</td>\n",
       "      <td>23.67</td>\n",
       "      <td>21.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>38.64</td>\n",
       "      <td>113.64</td>\n",
       "      <td>14.67</td>\n",
       "      <td>12.50</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>53.85</td>\n",
       "      <td>57.69</td>\n",
       "      <td>26.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>80.00</td>\n",
       "      <td>53.33</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>59.26</td>\n",
       "      <td>66.67</td>\n",
       "      <td>27.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>13.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dot %  Strike Rate  Balls Per Boundary  Runs Per Innings  \\\n",
       "striker                                                                       \n",
       "AB de Villiers     24.24       189.39                3.67             41.67   \n",
       "AD Russell         55.00       112.50                5.71             15.00   \n",
       "Abdul Samad        38.89       144.44                6.00              8.67   \n",
       "Abhishek Sharma    60.00        70.00               10.00              3.50   \n",
       "Arshdeep Singh     50.00        50.00                2.00              1.00   \n",
       "...                  ...          ...                 ...               ...   \n",
       "V Kohli            40.62       110.94                7.11             23.67   \n",
       "V Shankar          38.64       113.64               14.67             12.50   \n",
       "Virat Singh        53.85        57.69               26.00              7.50   \n",
       "WP Saha            80.00        53.33               15.00              4.00   \n",
       "Washington Sundar  59.26        66.67               27.00              9.00   \n",
       "\n",
       "                   Balls Per Dismissal  \n",
       "striker                                 \n",
       "AB de Villiers                   33.00  \n",
       "AD Russell                       13.33  \n",
       "Abdul Samad                       9.00  \n",
       "Abhishek Sharma                   5.00  \n",
       "Arshdeep Singh                    2.00  \n",
       "...                                ...  \n",
       "V Kohli                          21.33  \n",
       "V Shankar                        11.00  \n",
       "Virat Singh                      13.00  \n",
       "WP Saha                           7.50  \n",
       "Washington Sundar                13.50  \n",
       "\n",
       "[69 rows x 5 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials = batsmen_in_Chepauk_2021[['Dot %', 'Strike Rate', 'Balls Per Boundary', 'Runs Per Innings', 'Balls Per Dismissal']]\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "previous-surge",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-277-b0fb76ed2882>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['Dot %'].apply(lambda x: x**2)\n",
      "<ipython-input-277-b0fb76ed2882>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['Strike Rate'].apply(lambda x: x**2)\n",
      "<ipython-input-277-b0fb76ed2882>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['Runs Per Innings'].apply(lambda x: x**2)\n",
      "<ipython-input-277-b0fb76ed2882>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['Balls Per Boundary'].apply(lambda x: x**2)\n",
      "<ipython-input-277-b0fb76ed2882>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['Balls Per Dismissal'].apply(lambda x: x**2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(398.1707529440102,\n",
       " 944.6513448886843,\n",
       " 176.251154038775,\n",
       " 79.65573237375952,\n",
       " 183.9539271121984)"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['Dot %'].apply(lambda x: x**2)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['Strike Rate'].apply(lambda x: x**2)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['Runs Per Innings'].apply(lambda x: x**2)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['Balls Per Boundary'].apply(lambda x: x**2)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['Balls Per Dismissal'].apply(lambda x: x**2)\n",
    "\n",
    "sq_dot, sq_sr, sq_rpi, sq_bpb, sq_bpd = np.sqrt(batsmen_in_Chepauk_2021_essentials[['sq_Dot', 'sq_SR', 'sq_RPI', 'sq_BPB', 'sq_BPD']].sum())\n",
    "sq_dot, sq_sr, sq_rpi, sq_bpb, sq_bpd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "offshore-parish",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-278-dd225aa1ce2c>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['Dot %'].apply(lambda x: x/sq_dot)\n",
      "<ipython-input-278-dd225aa1ce2c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['Strike Rate'].apply(lambda x: x/sq_sr)\n",
      "<ipython-input-278-dd225aa1ce2c>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['Runs Per Innings'].apply(lambda x: x/sq_rpi)\n",
      "<ipython-input-278-dd225aa1ce2c>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['Balls Per Boundary'].apply(lambda x: x/sq_bpb)\n",
      "<ipython-input-278-dd225aa1ce2c>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['Balls Per Dismissal'].apply(lambda x: x/sq_bpd)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dot %</th>\n",
       "      <th>Strike Rate</th>\n",
       "      <th>Balls Per Boundary</th>\n",
       "      <th>Runs Per Innings</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "      <th>sq_Dot</th>\n",
       "      <th>sq_SR</th>\n",
       "      <th>sq_RPI</th>\n",
       "      <th>sq_BPB</th>\n",
       "      <th>sq_BPD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>AB de Villiers</th>\n",
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       "      <td>3.67</td>\n",
       "      <td>41.67</td>\n",
       "      <td>33.00</td>\n",
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       "      <td>0.200487</td>\n",
       "      <td>0.236424</td>\n",
       "      <td>0.046073</td>\n",
       "      <td>0.179393</td>\n",
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       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>55.00</td>\n",
       "      <td>112.50</td>\n",
       "      <td>5.71</td>\n",
       "      <td>15.00</td>\n",
       "      <td>13.33</td>\n",
       "      <td>0.138132</td>\n",
       "      <td>0.119092</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.071683</td>\n",
       "      <td>0.072464</td>\n",
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       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>38.89</td>\n",
       "      <td>144.44</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.67</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.097672</td>\n",
       "      <td>0.152903</td>\n",
       "      <td>0.049191</td>\n",
       "      <td>0.075324</td>\n",
       "      <td>0.048925</td>\n",
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       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>60.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.074101</td>\n",
       "      <td>0.019858</td>\n",
       "      <td>0.125540</td>\n",
       "      <td>0.027181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.125574</td>\n",
       "      <td>0.052930</td>\n",
       "      <td>0.005674</td>\n",
       "      <td>0.025108</td>\n",
       "      <td>0.010872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>40.62</td>\n",
       "      <td>110.94</td>\n",
       "      <td>7.11</td>\n",
       "      <td>23.67</td>\n",
       "      <td>21.33</td>\n",
       "      <td>0.102017</td>\n",
       "      <td>0.117440</td>\n",
       "      <td>0.134297</td>\n",
       "      <td>0.089259</td>\n",
       "      <td>0.115953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>38.64</td>\n",
       "      <td>113.64</td>\n",
       "      <td>14.67</td>\n",
       "      <td>12.50</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.097044</td>\n",
       "      <td>0.120298</td>\n",
       "      <td>0.070922</td>\n",
       "      <td>0.184168</td>\n",
       "      <td>0.059798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>53.85</td>\n",
       "      <td>57.69</td>\n",
       "      <td>26.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.135243</td>\n",
       "      <td>0.061070</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.326405</td>\n",
       "      <td>0.070670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>80.00</td>\n",
       "      <td>53.33</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.200919</td>\n",
       "      <td>0.056455</td>\n",
       "      <td>0.022695</td>\n",
       "      <td>0.188310</td>\n",
       "      <td>0.040771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>59.26</td>\n",
       "      <td>66.67</td>\n",
       "      <td>27.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>13.50</td>\n",
       "      <td>0.148831</td>\n",
       "      <td>0.070576</td>\n",
       "      <td>0.051063</td>\n",
       "      <td>0.338959</td>\n",
       "      <td>0.073388</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dot %  Strike Rate  Balls Per Boundary  Runs Per Innings  \\\n",
       "striker                                                                       \n",
       "AB de Villiers     24.24       189.39                3.67             41.67   \n",
       "AD Russell         55.00       112.50                5.71             15.00   \n",
       "Abdul Samad        38.89       144.44                6.00              8.67   \n",
       "Abhishek Sharma    60.00        70.00               10.00              3.50   \n",
       "Arshdeep Singh     50.00        50.00                2.00              1.00   \n",
       "...                  ...          ...                 ...               ...   \n",
       "V Kohli            40.62       110.94                7.11             23.67   \n",
       "V Shankar          38.64       113.64               14.67             12.50   \n",
       "Virat Singh        53.85        57.69               26.00              7.50   \n",
       "WP Saha            80.00        53.33               15.00              4.00   \n",
       "Washington Sundar  59.26        66.67               27.00              9.00   \n",
       "\n",
       "                   Balls Per Dismissal    sq_Dot     sq_SR    sq_RPI  \\\n",
       "striker                                                                \n",
       "AB de Villiers                   33.00  0.060878  0.200487  0.236424   \n",
       "AD Russell                       13.33  0.138132  0.119092  0.085106   \n",
       "Abdul Samad                       9.00  0.097672  0.152903  0.049191   \n",
       "Abhishek Sharma                   5.00  0.150689  0.074101  0.019858   \n",
       "Arshdeep Singh                    2.00  0.125574  0.052930  0.005674   \n",
       "...                                ...       ...       ...       ...   \n",
       "V Kohli                          21.33  0.102017  0.117440  0.134297   \n",
       "V Shankar                        11.00  0.097044  0.120298  0.070922   \n",
       "Virat Singh                      13.00  0.135243  0.061070  0.042553   \n",
       "WP Saha                           7.50  0.200919  0.056455  0.022695   \n",
       "Washington Sundar                13.50  0.148831  0.070576  0.051063   \n",
       "\n",
       "                     sq_BPB    sq_BPD  \n",
       "striker                                \n",
       "AB de Villiers     0.046073  0.179393  \n",
       "AD Russell         0.071683  0.072464  \n",
       "Abdul Samad        0.075324  0.048925  \n",
       "Abhishek Sharma    0.125540  0.027181  \n",
       "Arshdeep Singh     0.025108  0.010872  \n",
       "...                     ...       ...  \n",
       "V Kohli            0.089259  0.115953  \n",
       "V Shankar          0.184168  0.059798  \n",
       "Virat Singh        0.326405  0.070670  \n",
       "WP Saha            0.188310  0.040771  \n",
       "Washington Sundar  0.338959  0.073388  \n",
       "\n",
       "[69 rows x 10 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['Dot %'].apply(lambda x: x/sq_dot)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['Strike Rate'].apply(lambda x: x/sq_sr)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['Runs Per Innings'].apply(lambda x: x/sq_rpi)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['Balls Per Boundary'].apply(lambda x: x/sq_bpb)\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['Balls Per Dismissal'].apply(lambda x: x/sq_bpd)\n",
    "\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "ruled-investigation",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-279-a3fd39360953>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: x*chennai_weights.iloc[0])\n",
      "<ipython-input-279-a3fd39360953>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: x*chennai_weights.iloc[1])\n",
      "<ipython-input-279-a3fd39360953>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['sq_RPI'].apply(lambda x: x*chennai_weights.iloc[2])\n",
      "<ipython-input-279-a3fd39360953>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['sq_BPB'].apply(lambda x: x*chennai_weights.iloc[3])\n",
      "<ipython-input-279-a3fd39360953>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['sq_BPD'].apply(lambda x: x*chennai_weights.iloc[4])\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dot %</th>\n",
       "      <th>Strike Rate</th>\n",
       "      <th>Balls Per Boundary</th>\n",
       "      <th>Runs Per Innings</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "      <th>sq_Dot</th>\n",
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       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>AB de Villiers</th>\n",
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       "      <td>41.67</td>\n",
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       "      <td>0.003408</td>\n",
       "      <td>0.037826</td>\n",
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       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>55.00</td>\n",
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       "      <td>5.71</td>\n",
       "      <td>15.00</td>\n",
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       "      <td>0.036079</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.015279</td>\n",
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       "      <th>Abdul Samad</th>\n",
       "      <td>38.89</td>\n",
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       "      <td>8.67</td>\n",
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       "      <td>0.010316</td>\n",
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       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>60.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.00</td>\n",
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       "      <td>0.005731</td>\n",
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       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.014577</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.002292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>40.62</td>\n",
       "      <td>110.94</td>\n",
       "      <td>7.11</td>\n",
       "      <td>23.67</td>\n",
       "      <td>21.33</td>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.020571</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>0.024449</td>\n",
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       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>38.64</td>\n",
       "      <td>113.64</td>\n",
       "      <td>14.67</td>\n",
       "      <td>12.50</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.011265</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.013624</td>\n",
       "      <td>0.012609</td>\n",
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       "      <th>Virat Singh</th>\n",
       "      <td>53.85</td>\n",
       "      <td>57.69</td>\n",
       "      <td>26.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.015699</td>\n",
       "      <td>0.010697</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.014901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>80.00</td>\n",
       "      <td>53.33</td>\n",
       "      <td>15.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>0.008597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>59.26</td>\n",
       "      <td>66.67</td>\n",
       "      <td>27.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>13.50</td>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.015474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dot %  Strike Rate  Balls Per Boundary  Runs Per Innings  \\\n",
       "striker                                                                       \n",
       "AB de Villiers     24.24       189.39                3.67             41.67   \n",
       "AD Russell         55.00       112.50                5.71             15.00   \n",
       "Abdul Samad        38.89       144.44                6.00              8.67   \n",
       "Abhishek Sharma    60.00        70.00               10.00              3.50   \n",
       "Arshdeep Singh     50.00        50.00                2.00              1.00   \n",
       "...                  ...          ...                 ...               ...   \n",
       "V Kohli            40.62       110.94                7.11             23.67   \n",
       "V Shankar          38.64       113.64               14.67             12.50   \n",
       "Virat Singh        53.85        57.69               26.00              7.50   \n",
       "WP Saha            80.00        53.33               15.00              4.00   \n",
       "Washington Sundar  59.26        66.67               27.00              9.00   \n",
       "\n",
       "                   Balls Per Dismissal    sq_Dot     sq_SR    sq_RPI  \\\n",
       "striker                                                                \n",
       "AB de Villiers                   33.00  0.007067  0.035118  0.100227   \n",
       "AD Russell                       13.33  0.016034  0.020861  0.036079   \n",
       "Abdul Samad                       9.00  0.011338  0.026783  0.020854   \n",
       "Abhishek Sharma                   5.00  0.017492  0.012980  0.008418   \n",
       "Arshdeep Singh                    2.00  0.014577  0.009271  0.002405   \n",
       "...                                ...       ...       ...       ...   \n",
       "V Kohli                          21.33  0.011842  0.020571  0.056932   \n",
       "V Shankar                        11.00  0.011265  0.021072  0.030066   \n",
       "Virat Singh                      13.00  0.015699  0.010697  0.018039   \n",
       "WP Saha                           7.50  0.023323  0.009889  0.009621   \n",
       "Washington Sundar                13.50  0.017276  0.012362  0.021647   \n",
       "\n",
       "                     sq_BPB    sq_BPD  \n",
       "striker                                \n",
       "AB de Villiers     0.003408  0.037826  \n",
       "AD Russell         0.005303  0.015279  \n",
       "Abdul Samad        0.005572  0.010316  \n",
       "Abhishek Sharma    0.009287  0.005731  \n",
       "Arshdeep Singh     0.001857  0.002292  \n",
       "...                     ...       ...  \n",
       "V Kohli            0.006603  0.024449  \n",
       "V Shankar          0.013624  0.012609  \n",
       "Virat Singh        0.024146  0.014901  \n",
       "WP Saha            0.013930  0.008597  \n",
       "Washington Sundar  0.025074  0.015474  \n",
       "\n",
       "[69 rows x 10 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: x*chennai_weights.iloc[0])\n",
    "batsmen_in_Chepauk_2021_essentials['sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: x*chennai_weights.iloc[1])\n",
    "batsmen_in_Chepauk_2021_essentials['sq_RPI'] = batsmen_in_Chepauk_2021_essentials['sq_RPI'].apply(lambda x: x*chennai_weights.iloc[2])\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPB'] = batsmen_in_Chepauk_2021_essentials['sq_BPB'].apply(lambda x: x*chennai_weights.iloc[3])\n",
    "batsmen_in_Chepauk_2021_essentials['sq_BPD'] = batsmen_in_Chepauk_2021_essentials['sq_BPD'].apply(lambda x: x*chennai_weights.iloc[4])\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "generous-chicago",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\anaconda3\\envs\\DataAnalysisEnv\\lib\\site-packages\\pandas\\core\\frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sq_Dot</th>\n",
       "      <th>sq_SR</th>\n",
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       "      <th>sq_BPB</th>\n",
       "      <th>sq_BPD</th>\n",
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       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>0.007067</td>\n",
       "      <td>0.035118</td>\n",
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       "      <td>0.003408</td>\n",
       "      <td>0.037826</td>\n",
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       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>0.016034</td>\n",
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       "      <td>0.005572</td>\n",
       "      <td>0.010316</td>\n",
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       "      <td>0.005731</td>\n",
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       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>0.014577</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.002292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>V Kohli</th>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.020571</td>\n",
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       "      <th>V Shankar</th>\n",
       "      <td>0.011265</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.013624</td>\n",
       "      <td>0.012609</td>\n",
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       "      <td>0.010697</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.014901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>0.008597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.015474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sq_Dot     sq_SR    sq_RPI    sq_BPB    sq_BPD\n",
       "striker                                                            \n",
       "AB de Villiers     0.007067  0.035118  0.100227  0.003408  0.037826\n",
       "AD Russell         0.016034  0.020861  0.036079  0.005303  0.015279\n",
       "Abdul Samad        0.011338  0.026783  0.020854  0.005572  0.010316\n",
       "Abhishek Sharma    0.017492  0.012980  0.008418  0.009287  0.005731\n",
       "Arshdeep Singh     0.014577  0.009271  0.002405  0.001857  0.002292\n",
       "...                     ...       ...       ...       ...       ...\n",
       "V Kohli            0.011842  0.020571  0.056932  0.006603  0.024449\n",
       "V Shankar          0.011265  0.021072  0.030066  0.013624  0.012609\n",
       "Virat Singh        0.015699  0.010697  0.018039  0.024146  0.014901\n",
       "WP Saha            0.023323  0.009889  0.009621  0.013930  0.008597\n",
       "Washington Sundar  0.017276  0.012362  0.021647  0.025074  0.015474\n",
       "\n",
       "[69 rows x 5 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials.drop(['Dot %', 'Strike Rate', 'Balls Per Boundary', 'Runs Per Innings', 'Balls Per Dismissal'], axis=1, inplace=True)\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "latter-manhattan",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-282-c86ac6c6d527>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['pos_sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: (x-max_dot)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['pos_sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: (x-max_sr)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['pos_sq_RPI'] = batsmen_in_Chepauk_2021_essentials['sq_RPI'].apply(lambda x: (x-max_rpi)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['pos_sq_BPB'] = batsmen_in_Chepauk_2021_essentials['sq_BPB'].apply(lambda x: (x-max_bpb)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['pos_sq_BPD'] = batsmen_in_Chepauk_2021_essentials['sq_BPD'].apply(lambda x: (x-max_bpd)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['neg_sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: (x-min_dot)**2)\n",
      "<ipython-input-282-c86ac6c6d527>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Chepauk_2021_essentials['neg_sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: (x-min_sr)**2)\n"
     ]
    }
   ],
   "source": [
    "max_dot = batsmen_in_Chepauk_2021_essentials['sq_Dot'].min()\n",
    "max_sr = batsmen_in_Chepauk_2021_essentials['sq_SR'].max()\n",
    "max_rpi = batsmen_in_Chepauk_2021_essentials['sq_RPI'].max()\n",
    "max_bpb = batsmen_in_Chepauk_2021_essentials['sq_BPB'].min()\n",
    "max_bpd = batsmen_in_Chepauk_2021_essentials['sq_BPD'].max()\n",
    "\n",
    "min_dot = batsmen_in_Chepauk_2021_essentials['sq_Dot'].max()\n",
    "min_sr = batsmen_in_Chepauk_2021_essentials['sq_SR'].min()\n",
    "min_rpi = batsmen_in_Chepauk_2021_essentials['sq_RPI'].min()\n",
    "min_bpb = batsmen_in_Chepauk_2021_essentials['sq_BPB'].max()\n",
    "min_bpd = batsmen_in_Chepauk_2021_essentials['sq_BPD'].min()\n",
    "\n",
    "batsmen_in_Chepauk_2021_essentials['pos_sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: (x-max_dot)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['pos_sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: (x-max_sr)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['pos_sq_RPI'] = batsmen_in_Chepauk_2021_essentials['sq_RPI'].apply(lambda x: (x-max_rpi)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['pos_sq_BPB'] = batsmen_in_Chepauk_2021_essentials['sq_BPB'].apply(lambda x: (x-max_bpb)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['pos_sq_BPD'] = batsmen_in_Chepauk_2021_essentials['sq_BPD'].apply(lambda x: (x-max_bpd)**2)\n",
    "\n",
    "batsmen_in_Chepauk_2021_essentials['neg_sq_Dot'] = batsmen_in_Chepauk_2021_essentials['sq_Dot'].apply(lambda x: (x-min_dot)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['neg_sq_SR'] = batsmen_in_Chepauk_2021_essentials['sq_SR'].apply(lambda x: (x-min_sr)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['neg_sq_RPI'] = batsmen_in_Chepauk_2021_essentials['sq_RPI'].apply(lambda x: (x-min_rpi)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['neg_sq_BPB'] = batsmen_in_Chepauk_2021_essentials['sq_BPB'].apply(lambda x: (x-min_bpb)**2)\n",
    "batsmen_in_Chepauk_2021_essentials['neg_sq_BPD'] = batsmen_in_Chepauk_2021_essentials['sq_BPD'].apply(lambda x: (x-min_bpd)**2)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "molecular-chamber",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>0.007067</td>\n",
       "      <td>0.035118</td>\n",
       "      <td>0.100227</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.037826</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>6.148250e-06</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.010045</td>\n",
       "      <td>4.694167e-04</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>0.014105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>0.016034</td>\n",
       "      <td>0.020861</td>\n",
       "      <td>0.036079</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.015279</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.011033</td>\n",
       "      <td>1.913246e-05</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>3.909132e-04</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.016030</td>\n",
       "      <td>0.002849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>0.011338</td>\n",
       "      <td>0.026783</td>\n",
       "      <td>0.020854</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.010316</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>2.156101e-05</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>3.803362e-04</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.019774</td>\n",
       "      <td>0.002375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>0.017492</td>\n",
       "      <td>0.012980</td>\n",
       "      <td>0.008418</td>\n",
       "      <td>0.009287</td>\n",
       "      <td>0.005731</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.017609</td>\n",
       "      <td>6.985766e-05</td>\n",
       "      <td>0.005551</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>2.492452e-04</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.024453</td>\n",
       "      <td>0.000967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>0.014577</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.019241</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.006075</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.026685</td>\n",
       "      <td>0.001222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.020571</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>0.024449</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.007087</td>\n",
       "      <td>3.219671e-05</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>3.411918e-04</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.010887</td>\n",
       "      <td>0.005279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>0.011265</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.013624</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.012332</td>\n",
       "      <td>1.611633e-04</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>1.311158e-04</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.017686</td>\n",
       "      <td>0.002373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>0.015699</td>\n",
       "      <td>0.010697</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.004269</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.021263</td>\n",
       "      <td>0.001167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.017291</td>\n",
       "      <td>1.690383e-04</td>\n",
       "      <td>0.005132</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>1.241914e-04</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.024250</td>\n",
       "      <td>0.000612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.015474</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.014273</td>\n",
       "      <td>5.830096e-04</td>\n",
       "      <td>0.004194</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.020303</td>\n",
       "      <td>0.001293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sq_Dot     sq_SR    sq_RPI    sq_BPB    sq_BPD  \\\n",
       "striker                                                               \n",
       "AB de Villiers     0.007067  0.035118  0.100227  0.003408  0.037826   \n",
       "AD Russell         0.016034  0.020861  0.036079  0.005303  0.015279   \n",
       "Abdul Samad        0.011338  0.026783  0.020854  0.005572  0.010316   \n",
       "Abhishek Sharma    0.017492  0.012980  0.008418  0.009287  0.005731   \n",
       "Arshdeep Singh     0.014577  0.009271  0.002405  0.001857  0.002292   \n",
       "...                     ...       ...       ...       ...       ...   \n",
       "V Kohli            0.011842  0.020571  0.056932  0.006603  0.024449   \n",
       "V Shankar          0.011265  0.021072  0.030066  0.013624  0.012609   \n",
       "Virat Singh        0.015699  0.010697  0.018039  0.024146  0.014901   \n",
       "WP Saha            0.023323  0.009889  0.009621  0.013930  0.008597   \n",
       "Washington Sundar  0.017276  0.012362  0.021647  0.025074  0.015474   \n",
       "\n",
       "                   pos_sq_Dot  pos_sq_SR  pos_sq_RPI    pos_sq_BPB  \\\n",
       "striker                                                              \n",
       "AB de Villiers       0.000050   0.000066    0.001672  6.148250e-06   \n",
       "AD Russell           0.000257   0.000502    0.011033  1.913246e-05   \n",
       "Abdul Samad          0.000129   0.000272    0.014463  2.156101e-05   \n",
       "Abhishek Sharma      0.000306   0.000917    0.017609  6.985766e-05   \n",
       "Arshdeep Singh       0.000212   0.001156    0.019241  8.624403e-07   \n",
       "...                       ...        ...         ...           ...   \n",
       "V Kohli              0.000140   0.000515    0.007087  3.219671e-05   \n",
       "V Shankar            0.000127   0.000493    0.012332  1.611633e-04   \n",
       "Virat Singh          0.000246   0.001061    0.015148  5.390252e-04   \n",
       "WP Saha              0.000544   0.001114    0.017291  1.690383e-04   \n",
       "Washington Sundar    0.000298   0.000955    0.014273  5.830096e-04   \n",
       "\n",
       "                   pos_sq_BPD  neg_sq_Dot  neg_sq_SR  neg_sq_RPI  \\\n",
       "striker                                                            \n",
       "AB de Villiers       0.001799    0.001011   0.001233    0.010045   \n",
       "AD Russell           0.004219    0.000521   0.000435    0.001302   \n",
       "Abdul Samad          0.004889    0.000758   0.000717    0.000435   \n",
       "Abhishek Sharma      0.005551    0.000457   0.000168    0.000071   \n",
       "Arshdeep Singh       0.006075    0.000590   0.000086    0.000006   \n",
       "...                       ...         ...        ...         ...   \n",
       "V Kohli              0.003112    0.000731   0.000423    0.003241   \n",
       "V Shankar            0.004574    0.000762   0.000444    0.000904   \n",
       "Virat Singh          0.004269    0.000537   0.000114    0.000325   \n",
       "WP Saha              0.005132    0.000242   0.000098    0.000093   \n",
       "Washington Sundar    0.004194    0.000466   0.000153    0.000469   \n",
       "\n",
       "                     neg_sq_BPB  neg_sq_BPD  positive  negative  \n",
       "striker                                                          \n",
       "AB de Villiers     4.694167e-04    0.001345  0.003593  0.014105  \n",
       "AD Russell         3.909132e-04    0.000200  0.016030  0.002849  \n",
       "Abdul Samad        3.803362e-04    0.000084  0.019774  0.002375  \n",
       "Abhishek Sharma    2.492452e-04    0.000021  0.024453  0.000967  \n",
       "Arshdeep Singh     5.390252e-04    0.000001  0.026685  0.001222  \n",
       "...                         ...         ...       ...       ...  \n",
       "V Kohli            3.411918e-04    0.000543  0.010887  0.005279  \n",
       "V Shankar          1.311158e-04    0.000131  0.017686  0.002373  \n",
       "Virat Singh        8.624403e-07    0.000189  0.021263  0.001167  \n",
       "WP Saha            1.241914e-04    0.000056  0.024250  0.000612  \n",
       "Washington Sundar  0.000000e+00    0.000205  0.020303  0.001293  \n",
       "\n",
       "[69 rows x 17 columns]"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['positive'] = batsmen_in_Chepauk_2021_essentials['pos_sq_BPB'] + batsmen_in_Chepauk_2021_essentials['pos_sq_BPD'] + batsmen_in_Chepauk_2021_essentials['pos_sq_Dot'] + batsmen_in_Chepauk_2021_essentials['pos_sq_RPI'] + batsmen_in_Chepauk_2021_essentials['pos_sq_SR']\n",
    "batsmen_in_Chepauk_2021_essentials['negative'] = batsmen_in_Chepauk_2021_essentials['neg_sq_BPB'] + batsmen_in_Chepauk_2021_essentials['neg_sq_BPD'] + batsmen_in_Chepauk_2021_essentials['neg_sq_Dot'] + batsmen_in_Chepauk_2021_essentials['neg_sq_RPI'] + batsmen_in_Chepauk_2021_essentials['neg_sq_SR']\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "surrounded-modeling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sq_Dot</th>\n",
       "      <th>sq_SR</th>\n",
       "      <th>sq_RPI</th>\n",
       "      <th>sq_BPB</th>\n",
       "      <th>sq_BPD</th>\n",
       "      <th>pos_sq_Dot</th>\n",
       "      <th>pos_sq_SR</th>\n",
       "      <th>pos_sq_RPI</th>\n",
       "      <th>pos_sq_BPB</th>\n",
       "      <th>pos_sq_BPD</th>\n",
       "      <th>neg_sq_Dot</th>\n",
       "      <th>neg_sq_SR</th>\n",
       "      <th>neg_sq_RPI</th>\n",
       "      <th>neg_sq_BPB</th>\n",
       "      <th>neg_sq_BPD</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>0.007067</td>\n",
       "      <td>0.035118</td>\n",
       "      <td>0.100227</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.037826</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>6.148250e-06</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.010045</td>\n",
       "      <td>4.694167e-04</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.059942</td>\n",
       "      <td>0.118764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>0.016034</td>\n",
       "      <td>0.020861</td>\n",
       "      <td>0.036079</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.015279</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.011033</td>\n",
       "      <td>1.913246e-05</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>3.909132e-04</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.126611</td>\n",
       "      <td>0.053376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>0.011338</td>\n",
       "      <td>0.026783</td>\n",
       "      <td>0.020854</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.010316</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>2.156101e-05</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>3.803362e-04</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.140619</td>\n",
       "      <td>0.048730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>0.017492</td>\n",
       "      <td>0.012980</td>\n",
       "      <td>0.008418</td>\n",
       "      <td>0.009287</td>\n",
       "      <td>0.005731</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.017609</td>\n",
       "      <td>6.985766e-05</td>\n",
       "      <td>0.005551</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>2.492452e-04</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.156374</td>\n",
       "      <td>0.031091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>0.014577</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.019241</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.006075</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.163355</td>\n",
       "      <td>0.034961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.020571</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>0.024449</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.007087</td>\n",
       "      <td>3.219671e-05</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>3.411918e-04</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.104339</td>\n",
       "      <td>0.072658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>0.011265</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.013624</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.012332</td>\n",
       "      <td>1.611633e-04</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>1.311158e-04</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.132990</td>\n",
       "      <td>0.048708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>0.015699</td>\n",
       "      <td>0.010697</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.004269</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.145817</td>\n",
       "      <td>0.034158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.017291</td>\n",
       "      <td>1.690383e-04</td>\n",
       "      <td>0.005132</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>1.241914e-04</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.024734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.015474</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.014273</td>\n",
       "      <td>5.830096e-04</td>\n",
       "      <td>0.004194</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.142490</td>\n",
       "      <td>0.035959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sq_Dot     sq_SR    sq_RPI    sq_BPB    sq_BPD  \\\n",
       "striker                                                               \n",
       "AB de Villiers     0.007067  0.035118  0.100227  0.003408  0.037826   \n",
       "AD Russell         0.016034  0.020861  0.036079  0.005303  0.015279   \n",
       "Abdul Samad        0.011338  0.026783  0.020854  0.005572  0.010316   \n",
       "Abhishek Sharma    0.017492  0.012980  0.008418  0.009287  0.005731   \n",
       "Arshdeep Singh     0.014577  0.009271  0.002405  0.001857  0.002292   \n",
       "...                     ...       ...       ...       ...       ...   \n",
       "V Kohli            0.011842  0.020571  0.056932  0.006603  0.024449   \n",
       "V Shankar          0.011265  0.021072  0.030066  0.013624  0.012609   \n",
       "Virat Singh        0.015699  0.010697  0.018039  0.024146  0.014901   \n",
       "WP Saha            0.023323  0.009889  0.009621  0.013930  0.008597   \n",
       "Washington Sundar  0.017276  0.012362  0.021647  0.025074  0.015474   \n",
       "\n",
       "                   pos_sq_Dot  pos_sq_SR  pos_sq_RPI    pos_sq_BPB  \\\n",
       "striker                                                              \n",
       "AB de Villiers       0.000050   0.000066    0.001672  6.148250e-06   \n",
       "AD Russell           0.000257   0.000502    0.011033  1.913246e-05   \n",
       "Abdul Samad          0.000129   0.000272    0.014463  2.156101e-05   \n",
       "Abhishek Sharma      0.000306   0.000917    0.017609  6.985766e-05   \n",
       "Arshdeep Singh       0.000212   0.001156    0.019241  8.624403e-07   \n",
       "...                       ...        ...         ...           ...   \n",
       "V Kohli              0.000140   0.000515    0.007087  3.219671e-05   \n",
       "V Shankar            0.000127   0.000493    0.012332  1.611633e-04   \n",
       "Virat Singh          0.000246   0.001061    0.015148  5.390252e-04   \n",
       "WP Saha              0.000544   0.001114    0.017291  1.690383e-04   \n",
       "Washington Sundar    0.000298   0.000955    0.014273  5.830096e-04   \n",
       "\n",
       "                   pos_sq_BPD  neg_sq_Dot  neg_sq_SR  neg_sq_RPI  \\\n",
       "striker                                                            \n",
       "AB de Villiers       0.001799    0.001011   0.001233    0.010045   \n",
       "AD Russell           0.004219    0.000521   0.000435    0.001302   \n",
       "Abdul Samad          0.004889    0.000758   0.000717    0.000435   \n",
       "Abhishek Sharma      0.005551    0.000457   0.000168    0.000071   \n",
       "Arshdeep Singh       0.006075    0.000590   0.000086    0.000006   \n",
       "...                       ...         ...        ...         ...   \n",
       "V Kohli              0.003112    0.000731   0.000423    0.003241   \n",
       "V Shankar            0.004574    0.000762   0.000444    0.000904   \n",
       "Virat Singh          0.004269    0.000537   0.000114    0.000325   \n",
       "WP Saha              0.005132    0.000242   0.000098    0.000093   \n",
       "Washington Sundar    0.004194    0.000466   0.000153    0.000469   \n",
       "\n",
       "                     neg_sq_BPB  neg_sq_BPD  positive  negative  \n",
       "striker                                                          \n",
       "AB de Villiers     4.694167e-04    0.001345  0.059942  0.118764  \n",
       "AD Russell         3.909132e-04    0.000200  0.126611  0.053376  \n",
       "Abdul Samad        3.803362e-04    0.000084  0.140619  0.048730  \n",
       "Abhishek Sharma    2.492452e-04    0.000021  0.156374  0.031091  \n",
       "Arshdeep Singh     5.390252e-04    0.000001  0.163355  0.034961  \n",
       "...                         ...         ...       ...       ...  \n",
       "V Kohli            3.411918e-04    0.000543  0.104339  0.072658  \n",
       "V Shankar          1.311158e-04    0.000131  0.132990  0.048708  \n",
       "Virat Singh        8.624403e-07    0.000189  0.145817  0.034158  \n",
       "WP Saha            1.241914e-04    0.000056  0.155725  0.024734  \n",
       "Washington Sundar  0.000000e+00    0.000205  0.142490  0.035959  \n",
       "\n",
       "[69 rows x 17 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['positive'] = batsmen_in_Chepauk_2021_essentials['positive'].apply(lambda x: np.sqrt(x))\n",
    "batsmen_in_Chepauk_2021_essentials['negative'] = batsmen_in_Chepauk_2021_essentials['negative'].apply(lambda x: np.sqrt(x))\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "useful-vienna",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sq_Dot</th>\n",
       "      <th>sq_SR</th>\n",
       "      <th>sq_RPI</th>\n",
       "      <th>sq_BPB</th>\n",
       "      <th>sq_BPD</th>\n",
       "      <th>pos_sq_Dot</th>\n",
       "      <th>pos_sq_SR</th>\n",
       "      <th>pos_sq_RPI</th>\n",
       "      <th>pos_sq_BPB</th>\n",
       "      <th>pos_sq_BPD</th>\n",
       "      <th>neg_sq_Dot</th>\n",
       "      <th>neg_sq_SR</th>\n",
       "      <th>neg_sq_RPI</th>\n",
       "      <th>neg_sq_BPB</th>\n",
       "      <th>neg_sq_BPD</th>\n",
       "      <th>positive</th>\n",
       "      <th>negative</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>0.007067</td>\n",
       "      <td>0.035118</td>\n",
       "      <td>0.100227</td>\n",
       "      <td>0.003408</td>\n",
       "      <td>0.037826</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.001672</td>\n",
       "      <td>6.148250e-06</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.010045</td>\n",
       "      <td>4.694167e-04</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.059942</td>\n",
       "      <td>0.118764</td>\n",
       "      <td>0.664577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>0.016034</td>\n",
       "      <td>0.020861</td>\n",
       "      <td>0.036079</td>\n",
       "      <td>0.005303</td>\n",
       "      <td>0.015279</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.011033</td>\n",
       "      <td>1.913246e-05</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>3.909132e-04</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.126611</td>\n",
       "      <td>0.053376</td>\n",
       "      <td>0.296553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>0.011338</td>\n",
       "      <td>0.026783</td>\n",
       "      <td>0.020854</td>\n",
       "      <td>0.005572</td>\n",
       "      <td>0.010316</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>2.156101e-05</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000435</td>\n",
       "      <td>3.803362e-04</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>0.140619</td>\n",
       "      <td>0.048730</td>\n",
       "      <td>0.257357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abhishek Sharma</th>\n",
       "      <td>0.017492</td>\n",
       "      <td>0.012980</td>\n",
       "      <td>0.008418</td>\n",
       "      <td>0.009287</td>\n",
       "      <td>0.005731</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.017609</td>\n",
       "      <td>6.985766e-05</td>\n",
       "      <td>0.005551</td>\n",
       "      <td>0.000457</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>2.492452e-04</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.156374</td>\n",
       "      <td>0.031091</td>\n",
       "      <td>0.165849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arshdeep Singh</th>\n",
       "      <td>0.014577</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.002405</td>\n",
       "      <td>0.001857</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.019241</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.006075</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.163355</td>\n",
       "      <td>0.034961</td>\n",
       "      <td>0.176288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>0.011842</td>\n",
       "      <td>0.020571</td>\n",
       "      <td>0.056932</td>\n",
       "      <td>0.006603</td>\n",
       "      <td>0.024449</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.007087</td>\n",
       "      <td>3.219671e-05</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.000423</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>3.411918e-04</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.104339</td>\n",
       "      <td>0.072658</td>\n",
       "      <td>0.410505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>0.011265</td>\n",
       "      <td>0.021072</td>\n",
       "      <td>0.030066</td>\n",
       "      <td>0.013624</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000493</td>\n",
       "      <td>0.012332</td>\n",
       "      <td>1.611633e-04</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000904</td>\n",
       "      <td>1.311158e-04</td>\n",
       "      <td>0.000131</td>\n",
       "      <td>0.132990</td>\n",
       "      <td>0.048708</td>\n",
       "      <td>0.268073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virat Singh</th>\n",
       "      <td>0.015699</td>\n",
       "      <td>0.010697</td>\n",
       "      <td>0.018039</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.001061</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>5.390252e-04</td>\n",
       "      <td>0.004269</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>8.624403e-07</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.145817</td>\n",
       "      <td>0.034158</td>\n",
       "      <td>0.189795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.009621</td>\n",
       "      <td>0.013930</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.017291</td>\n",
       "      <td>1.690383e-04</td>\n",
       "      <td>0.005132</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>1.241914e-04</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.155725</td>\n",
       "      <td>0.024734</td>\n",
       "      <td>0.137062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>0.017276</td>\n",
       "      <td>0.012362</td>\n",
       "      <td>0.021647</td>\n",
       "      <td>0.025074</td>\n",
       "      <td>0.015474</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.014273</td>\n",
       "      <td>5.830096e-04</td>\n",
       "      <td>0.004194</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.142490</td>\n",
       "      <td>0.035959</td>\n",
       "      <td>0.201506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sq_Dot     sq_SR    sq_RPI    sq_BPB    sq_BPD  \\\n",
       "striker                                                               \n",
       "AB de Villiers     0.007067  0.035118  0.100227  0.003408  0.037826   \n",
       "AD Russell         0.016034  0.020861  0.036079  0.005303  0.015279   \n",
       "Abdul Samad        0.011338  0.026783  0.020854  0.005572  0.010316   \n",
       "Abhishek Sharma    0.017492  0.012980  0.008418  0.009287  0.005731   \n",
       "Arshdeep Singh     0.014577  0.009271  0.002405  0.001857  0.002292   \n",
       "...                     ...       ...       ...       ...       ...   \n",
       "V Kohli            0.011842  0.020571  0.056932  0.006603  0.024449   \n",
       "V Shankar          0.011265  0.021072  0.030066  0.013624  0.012609   \n",
       "Virat Singh        0.015699  0.010697  0.018039  0.024146  0.014901   \n",
       "WP Saha            0.023323  0.009889  0.009621  0.013930  0.008597   \n",
       "Washington Sundar  0.017276  0.012362  0.021647  0.025074  0.015474   \n",
       "\n",
       "                   pos_sq_Dot  pos_sq_SR  pos_sq_RPI    pos_sq_BPB  \\\n",
       "striker                                                              \n",
       "AB de Villiers       0.000050   0.000066    0.001672  6.148250e-06   \n",
       "AD Russell           0.000257   0.000502    0.011033  1.913246e-05   \n",
       "Abdul Samad          0.000129   0.000272    0.014463  2.156101e-05   \n",
       "Abhishek Sharma      0.000306   0.000917    0.017609  6.985766e-05   \n",
       "Arshdeep Singh       0.000212   0.001156    0.019241  8.624403e-07   \n",
       "...                       ...        ...         ...           ...   \n",
       "V Kohli              0.000140   0.000515    0.007087  3.219671e-05   \n",
       "V Shankar            0.000127   0.000493    0.012332  1.611633e-04   \n",
       "Virat Singh          0.000246   0.001061    0.015148  5.390252e-04   \n",
       "WP Saha              0.000544   0.001114    0.017291  1.690383e-04   \n",
       "Washington Sundar    0.000298   0.000955    0.014273  5.830096e-04   \n",
       "\n",
       "                   pos_sq_BPD  neg_sq_Dot  neg_sq_SR  neg_sq_RPI  \\\n",
       "striker                                                            \n",
       "AB de Villiers       0.001799    0.001011   0.001233    0.010045   \n",
       "AD Russell           0.004219    0.000521   0.000435    0.001302   \n",
       "Abdul Samad          0.004889    0.000758   0.000717    0.000435   \n",
       "Abhishek Sharma      0.005551    0.000457   0.000168    0.000071   \n",
       "Arshdeep Singh       0.006075    0.000590   0.000086    0.000006   \n",
       "...                       ...         ...        ...         ...   \n",
       "V Kohli              0.003112    0.000731   0.000423    0.003241   \n",
       "V Shankar            0.004574    0.000762   0.000444    0.000904   \n",
       "Virat Singh          0.004269    0.000537   0.000114    0.000325   \n",
       "WP Saha              0.005132    0.000242   0.000098    0.000093   \n",
       "Washington Sundar    0.004194    0.000466   0.000153    0.000469   \n",
       "\n",
       "                     neg_sq_BPB  neg_sq_BPD  positive  negative     score  \n",
       "striker                                                                    \n",
       "AB de Villiers     4.694167e-04    0.001345  0.059942  0.118764  0.664577  \n",
       "AD Russell         3.909132e-04    0.000200  0.126611  0.053376  0.296553  \n",
       "Abdul Samad        3.803362e-04    0.000084  0.140619  0.048730  0.257357  \n",
       "Abhishek Sharma    2.492452e-04    0.000021  0.156374  0.031091  0.165849  \n",
       "Arshdeep Singh     5.390252e-04    0.000001  0.163355  0.034961  0.176288  \n",
       "...                         ...         ...       ...       ...       ...  \n",
       "V Kohli            3.411918e-04    0.000543  0.104339  0.072658  0.410505  \n",
       "V Shankar          1.311158e-04    0.000131  0.132990  0.048708  0.268073  \n",
       "Virat Singh        8.624403e-07    0.000189  0.145817  0.034158  0.189795  \n",
       "WP Saha            1.241914e-04    0.000056  0.155725  0.024734  0.137062  \n",
       "Washington Sundar  0.000000e+00    0.000205  0.142490  0.035959  0.201506  \n",
       "\n",
       "[69 rows x 18 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Chepauk_2021_essentials['score'] = batsmen_in_Chepauk_2021_essentials['negative']/(batsmen_in_Chepauk_2021_essentials['negative'] + batsmen_in_Chepauk_2021_essentials['positive'])\n",
    "batsmen_in_Chepauk_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "wicked-accountability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GJ Maxwell</th>\n",
       "      <td>0.793034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Rana</th>\n",
       "      <td>0.749776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS Williamson</th>\n",
       "      <td>0.728776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA Lynn</th>\n",
       "      <td>0.719258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JM Bairstow</th>\n",
       "      <td>0.666724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DT Christian</th>\n",
       "      <td>0.157534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>0.137062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Nadeem</th>\n",
       "      <td>0.132793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mohammed Siraj</th>\n",
       "      <td>0.132793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Jansen</th>\n",
       "      <td>0.114956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   score\n",
       "striker                 \n",
       "GJ Maxwell      0.793034\n",
       "N Rana          0.749776\n",
       "KS Williamson   0.728776\n",
       "CA Lynn         0.719258\n",
       "JM Bairstow     0.666724\n",
       "...                  ...\n",
       "DT Christian    0.157534\n",
       "WP Saha         0.137062\n",
       "S Nadeem        0.132793\n",
       "Mohammed Siraj  0.132793\n",
       "M Jansen        0.114956\n",
       "\n",
       "[69 rows x 1 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_batsmen_in_Chepauk_2021 = pd.DataFrame(batsmen_in_Chepauk_2021_essentials['score'].sort_values(ascending=False))\n",
    "best_batsmen_in_Chepauk_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "previous-trick",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Dot %</th>\n",
       "      <th>Strike Rate</th>\n",
       "      <th>Balls Per Boundary</th>\n",
       "      <th>Runs Per Innings</th>\n",
       "      <th>Balls Per Dismissal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>55.56</td>\n",
       "      <td>44.44</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>51.72</td>\n",
       "      <td>217.24</td>\n",
       "      <td>2.90</td>\n",
       "      <td>31.5</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>62.50</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>41.46</td>\n",
       "      <td>156.10</td>\n",
       "      <td>5.12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TK Curran</th>\n",
       "      <td>25.00</td>\n",
       "      <td>131.25</td>\n",
       "      <td>8.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>24.07</td>\n",
       "      <td>148.15</td>\n",
       "      <td>5.40</td>\n",
       "      <td>40.0</td>\n",
       "      <td>54.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>63.64</td>\n",
       "      <td>63.64</td>\n",
       "      <td>11.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YBK Jaiswal</th>\n",
       "      <td>58.82</td>\n",
       "      <td>129.41</td>\n",
       "      <td>3.40</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YS Chahal</th>\n",
       "      <td>66.67</td>\n",
       "      <td>38.10</td>\n",
       "      <td>21.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Dot %  Strike Rate  Balls Per Boundary  Runs Per Innings  \\\n",
       "striker                                                                        \n",
       "AB de Villiers      55.56        44.44                9.00               4.0   \n",
       "AD Russell          51.72       217.24                2.90              31.5   \n",
       "AM Rahane           62.50       100.00                8.00               8.0   \n",
       "AT Rayudu           41.46       156.10                5.12              16.0   \n",
       "BA Stokes          100.00         0.00                3.00               0.0   \n",
       "...                   ...          ...                 ...               ...   \n",
       "TK Curran           25.00       131.25                8.00              21.0   \n",
       "V Kohli             24.07       148.15                5.40              40.0   \n",
       "Washington Sundar   63.64        63.64               11.00               7.0   \n",
       "YBK Jaiswal         58.82       129.41                3.40              22.0   \n",
       "YS Chahal           66.67        38.10               21.00               8.0   \n",
       "\n",
       "                   Balls Per Dismissal  \n",
       "striker                                 \n",
       "AB de Villiers                    9.00  \n",
       "AD Russell                       14.50  \n",
       "AM Rahane                         8.00  \n",
       "AT Rayudu                        10.25  \n",
       "BA Stokes                         3.00  \n",
       "...                                ...  \n",
       "TK Curran                        16.00  \n",
       "V Kohli                          54.00  \n",
       "Washington Sundar                11.00  \n",
       "YBK Jaiswal                      17.00  \n",
       "YS Chahal                        21.00  \n",
       "\n",
       "[67 rows x 5 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Wankhede_2021_essentials = batsmen_in_Wankhede_2021[['Dot %', 'Strike Rate', 'Balls Per Boundary', 'Runs Per Innings', 'Balls Per Dismissal']]\n",
    "batsmen_in_Wankhede_2021_essentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "fancy-solid",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-297-5478110980ec>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['Dot %'].apply(lambda x: x**2)\n",
      "<ipython-input-297-5478110980ec>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['Strike Rate'].apply(lambda x: x**2)\n",
      "<ipython-input-297-5478110980ec>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['Runs Per Innings'].apply(lambda x: x**2)\n",
      "<ipython-input-297-5478110980ec>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['Balls Per Boundary'].apply(lambda x: x**2)\n",
      "<ipython-input-297-5478110980ec>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['Balls Per Dismissal'].apply(lambda x: x**2)\n",
      "<ipython-input-297-5478110980ec>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['Dot %'].apply(lambda x: x/sq_dot)\n",
      "<ipython-input-297-5478110980ec>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['Strike Rate'].apply(lambda x: x/sq_sr)\n",
      "<ipython-input-297-5478110980ec>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['Runs Per Innings'].apply(lambda x: x/sq_rpi)\n",
      "<ipython-input-297-5478110980ec>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['Balls Per Boundary'].apply(lambda x: x/sq_bpb)\n",
      "<ipython-input-297-5478110980ec>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['Balls Per Dismissal'].apply(lambda x: x/sq_bpd)\n",
      "<ipython-input-297-5478110980ec>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: x*chennai_weights.iloc[0])\n",
      "<ipython-input-297-5478110980ec>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: x*chennai_weights.iloc[1])\n",
      "<ipython-input-297-5478110980ec>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['sq_RPI'].apply(lambda x: x*chennai_weights.iloc[2])\n",
      "<ipython-input-297-5478110980ec>:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['sq_BPB'].apply(lambda x: x*chennai_weights.iloc[3])\n",
      "<ipython-input-297-5478110980ec>:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['sq_BPD'].apply(lambda x: x*chennai_weights.iloc[4])\n",
      "C:\\Users\\Asus\\anaconda3\\envs\\DataAnalysisEnv\\lib\\site-packages\\pandas\\core\\frame.py:4308: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n",
      "<ipython-input-297-5478110980ec>:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['pos_sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: (x-max_dot)**2)\n",
      "<ipython-input-297-5478110980ec>:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['pos_sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: (x-max_sr)**2)\n",
      "<ipython-input-297-5478110980ec>:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['pos_sq_RPI'] = batsmen_in_Wankhede_2021_essentials['sq_RPI'].apply(lambda x: (x-max_rpi)**2)\n",
      "<ipython-input-297-5478110980ec>:43: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['pos_sq_BPB'] = batsmen_in_Wankhede_2021_essentials['sq_BPB'].apply(lambda x: (x-max_bpb)**2)\n",
      "<ipython-input-297-5478110980ec>:44: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['pos_sq_BPD'] = batsmen_in_Wankhede_2021_essentials['sq_BPD'].apply(lambda x: (x-max_bpd)**2)\n",
      "<ipython-input-297-5478110980ec>:46: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['neg_sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: (x-min_dot)**2)\n",
      "<ipython-input-297-5478110980ec>:47: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  batsmen_in_Wankhede_2021_essentials['neg_sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: (x-min_sr)**2)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D Padikkal</th>\n",
       "      <td>0.935306</td>\n",
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       "      <th>S Dhawan</th>\n",
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       "      <th>...</th>\n",
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       "      <th>BA Stokes</th>\n",
       "      <td>0.114087</td>\n",
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       "      <th>Mustafizur Rahman</th>\n",
       "      <td>0.109568</td>\n",
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       "      <th>HV Patel</th>\n",
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      "text/plain": [
       "                      score\n",
       "striker                    \n",
       "D Padikkal         0.935306\n",
       "S Dhawan           0.794420\n",
       "KL Rahul           0.708660\n",
       "F du Plessis       0.654974\n",
       "V Kohli            0.634895\n",
       "...                     ...\n",
       "KL Nagarkoti       0.118852\n",
       "Shubman Gill       0.117500\n",
       "BA Stokes          0.114087\n",
       "Mustafizur Rahman  0.109568\n",
       "HV Patel           0.093836\n",
       "\n",
       "[67 rows x 1 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['Dot %'].apply(lambda x: x**2)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['Strike Rate'].apply(lambda x: x**2)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['Runs Per Innings'].apply(lambda x: x**2)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['Balls Per Boundary'].apply(lambda x: x**2)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['Balls Per Dismissal'].apply(lambda x: x**2)\n",
    "\n",
    "sq_dot, sq_sr, sq_rpi, sq_bpb, sq_bpd = np.sqrt(batsmen_in_Wankhede_2021_essentials[['sq_Dot', 'sq_SR', 'sq_RPI', 'sq_BPB', 'sq_BPD']].sum())\n",
    "sq_dot, sq_sr, sq_rpi, sq_bpb, sq_bpd\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['Dot %'].apply(lambda x: x/sq_dot)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['Strike Rate'].apply(lambda x: x/sq_sr)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['Runs Per Innings'].apply(lambda x: x/sq_rpi)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['Balls Per Boundary'].apply(lambda x: x/sq_bpb)\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['Balls Per Dismissal'].apply(lambda x: x/sq_bpd)\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: x*chennai_weights.iloc[0])\n",
    "batsmen_in_Wankhede_2021_essentials['sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: x*chennai_weights.iloc[1])\n",
    "batsmen_in_Wankhede_2021_essentials['sq_RPI'] = batsmen_in_Wankhede_2021_essentials['sq_RPI'].apply(lambda x: x*chennai_weights.iloc[2])\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPB'] = batsmen_in_Wankhede_2021_essentials['sq_BPB'].apply(lambda x: x*chennai_weights.iloc[3])\n",
    "batsmen_in_Wankhede_2021_essentials['sq_BPD'] = batsmen_in_Wankhede_2021_essentials['sq_BPD'].apply(lambda x: x*chennai_weights.iloc[4])\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials.drop(['Dot %', 'Strike Rate', 'Balls Per Boundary', 'Runs Per Innings', 'Balls Per Dismissal'], axis=1, inplace=True)\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "max_dot = batsmen_in_Wankhede_2021_essentials['sq_Dot'].min()\n",
    "max_sr = batsmen_in_Wankhede_2021_essentials['sq_SR'].max()\n",
    "max_rpi = batsmen_in_Wankhede_2021_essentials['sq_RPI'].max()\n",
    "max_bpb = batsmen_in_Wankhede_2021_essentials['sq_BPB'].min()\n",
    "max_bpd = batsmen_in_Wankhede_2021_essentials['sq_BPD'].max()\n",
    "\n",
    "min_dot = batsmen_in_Wankhede_2021_essentials['sq_Dot'].max()\n",
    "min_sr = batsmen_in_Wankhede_2021_essentials['sq_SR'].min()\n",
    "min_rpi = batsmen_in_Wankhede_2021_essentials['sq_RPI'].min()\n",
    "min_bpb = batsmen_in_Wankhede_2021_essentials['sq_BPB'].max()\n",
    "min_bpd = batsmen_in_Wankhede_2021_essentials['sq_BPD'].min()\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['pos_sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: (x-max_dot)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['pos_sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: (x-max_sr)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['pos_sq_RPI'] = batsmen_in_Wankhede_2021_essentials['sq_RPI'].apply(lambda x: (x-max_rpi)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['pos_sq_BPB'] = batsmen_in_Wankhede_2021_essentials['sq_BPB'].apply(lambda x: (x-max_bpb)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['pos_sq_BPD'] = batsmen_in_Wankhede_2021_essentials['sq_BPD'].apply(lambda x: (x-max_bpd)**2)\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['neg_sq_Dot'] = batsmen_in_Wankhede_2021_essentials['sq_Dot'].apply(lambda x: (x-min_dot)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['neg_sq_SR'] = batsmen_in_Wankhede_2021_essentials['sq_SR'].apply(lambda x: (x-min_sr)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['neg_sq_RPI'] = batsmen_in_Wankhede_2021_essentials['sq_RPI'].apply(lambda x: (x-min_rpi)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['neg_sq_BPB'] = batsmen_in_Wankhede_2021_essentials['sq_BPB'].apply(lambda x: (x-min_bpb)**2)\n",
    "batsmen_in_Wankhede_2021_essentials['neg_sq_BPD'] = batsmen_in_Wankhede_2021_essentials['sq_BPD'].apply(lambda x: (x-min_bpd)**2)\n",
    "\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['positive'] = batsmen_in_Wankhede_2021_essentials['pos_sq_BPB'] + batsmen_in_Wankhede_2021_essentials['pos_sq_BPD'] + batsmen_in_Wankhede_2021_essentials['pos_sq_Dot'] + batsmen_in_Wankhede_2021_essentials['pos_sq_RPI'] + batsmen_in_Wankhede_2021_essentials['pos_sq_SR']\n",
    "batsmen_in_Wankhede_2021_essentials['negative'] = batsmen_in_Wankhede_2021_essentials['neg_sq_BPB'] + batsmen_in_Wankhede_2021_essentials['neg_sq_BPD'] + batsmen_in_Wankhede_2021_essentials['neg_sq_Dot'] + batsmen_in_Wankhede_2021_essentials['neg_sq_RPI'] + batsmen_in_Wankhede_2021_essentials['neg_sq_SR']\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['positive'] = batsmen_in_Wankhede_2021_essentials['positive'].apply(lambda x: np.sqrt(x))\n",
    "batsmen_in_Wankhede_2021_essentials['negative'] = batsmen_in_Wankhede_2021_essentials['negative'].apply(lambda x: np.sqrt(x))\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "batsmen_in_Wankhede_2021_essentials['score'] = batsmen_in_Wankhede_2021_essentials['negative']/(batsmen_in_Wankhede_2021_essentials['negative'] + batsmen_in_Wankhede_2021_essentials['positive'])\n",
    "batsmen_in_Wankhede_2021_essentials\n",
    "\n",
    "best_batsmen_in_Wankhede_2021 = pd.DataFrame(batsmen_in_Wankhede_2021_essentials['score'].sort_values(ascending=False))\n",
    "best_batsmen_in_Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "defined-tyler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.set(font_scale=1.4)\n",
    "# sns.set_style('darkgrid')\n",
    "graph = sns.barplot(x=best_batsmen_in_Chepauk_2021.head(30).index, y='score', palette='magma', data=best_batsmen_in_Chepauk_2021.head(30))\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., title='Average', shadow=True)\n",
    "plt.title(\"Best batsmen in the Chennai leg of IPL 2021\")\n",
    "plt.xticks(rotation=80);\n",
    "\n",
    "for p in graph.patches:\n",
    "        graph.annotate('{:.2f}'.format(p.get_height()), (p.get_x()+0.4, p.get_height()),\n",
    "                    ha='center', va='bottom',\n",
    "                    color= 'black', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "related-integrity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTlRqaqrCw8PNViezP57p74oXLy5JZueQZrdl69mzp9zd3bVmzRpNmjRJGRkZKlu2rEJDQ+/5Qj5/H1uSvLy8TMEoNTVVH374odatWyej0agyZcqodu3acnR0vOvn8OZ1jtmrTbf7+5V5b3/RnX1u8YABA3KtGxMTY/b/v1/Ix97e/p4/Pzi3MaRbV9C9V66urmb/t7OzM80n+9jGjx+v8ePH57jv34/tdqVKlTILqX+XlZWlCxcumL0B8vfHOfu4bp/P1atXVbVq1VzHjImJUenSpSXl/TH6p+/H4sWLNWfOHF25ckXFixdXtWrV5OLiopSUlDse2z+5evWqNm/enOtxZAf5q1evqlixYjlu/6dzXu/l9/h+Pfnkkxo8eLCkWz8vLi4u8vPzk7Oz8wOPne3w4cN64403lJaWpoULF6p69eqm2zw8PHJdDc7e9eHh4WHWvnbtWo0ePVoBAQGaN2+e2RtKAPAwIBQDgIU1btxYe/bs0S+//KJChQqZtplKt0LxuHHjFB4erkOHDikkJMR0W5EiRRQXF5djvOwL0uT2Aj2bnZ2dOnXqpE6dOunGjRvatWuX5s6dqzfffFPbt2+/pyvRZoff28XFxZlWzMaPH68NGzYoLCxMjRs3NoW67CvJ5sccb5d95eSJEyeaVt1vd7fH7WGXfWxvv/12ro9vbm8gZAsODtbixYsVGxub62MbHh6ukJAQjRkzxrSl/Z94eHioTJkyZudz3658+fJ5GievvvvuO3300UcaNmyYOnbsaNoWPGTIkBwXWrsXHh4eatiwoV5//fU79vHx8dHJkydztF+5cuWuYz/I73FeFSlSxCykWtr27dv15ptvqnjx4lq6dGmO72v58uWVnJysmJgYs3PWz5w5I4PBYHZO8/Tp0zVr1iw1a9ZMn3766V1/ZgGgoLB9GgAsrEmTJjpx4oR27dql+vXrm63elCtXTn5+flq9erXS09PNgk79+vV1+PBhnTt3zmy8b7/9Vp6enncNHF26dNG4ceMk3QpS7dq1U//+/ZWenm5aTbz9M0Hv5sCBA2aB4+DBg7pw4YIaNmwo6dZHTtWvX1+tWrUyBeLDhw8rPj7ebIXv7/XyMsf7UbNmTRkMBv3111+qXr266cvd3V0TJ07MNdgUhLw+/rerUKGCihcvrujoaLNj8/Pz0+TJk3Xw4ME73vfVV1+Vk5OTxo0bl+OCXEajUVOmTJGrq2uOqwPfTcOGDfXXX3+paNGiZvMJDw/XrFmz7usY72bfvn1ydXXV66+/bgrECQkJ2rdv332tymdr0KCBoqKiFBQUZHYcS5cuNV0srkGDBrp06ZIOHTpkul9KSop27Nhx17Ef5Pf4YRAeHq5BgwbJ399fq1atynW+zZo1k52dnTZt2mRqy8jI0I8//qgGDRqYTnWYO3euZs2apZdffllz5swhEAN4aLFSDAAWVr9+fdnZ2enHH3/McfVj6dYK3po1a1StWjWzbYS9evXSt99+q549e2rAgAHy9PTUunXrtGfPHo0fP950Uas71VywYIGKFCmievXq6cqVK5o5c6bKly9vuthS4cKFtX//fu3evVu1a9fOsW33dn379tUbb7yha9euKSwsTBUrVtSLL74o6VYI/eGHH7Rs2TL5+/vr+PHjmjNnjuzs7MzOzf17vbzM8X4UK1ZMffv21cyZM3Xjxg01btzYNHZKSkqOq98WlOxV3++//16NGjXK030cHBz01ltvadSoUZJuXbApKSlJ8+bN09mzZ+96/qifn5/Gjh2rUaNGqUuXLnrllVdUunRpRUdHa9myZTp16pRmzpx5TyuXL774or788kv16tVL/fr1k5+fn/bs2aN58+bpxRdfvOvP1P2oWbOmVqxYofHjx6tly5aKjY3VwoULdfny5QcKWAMHDtQrr7yi3r1769VXX5WLi4vWrFmjzZs3a9KkSZKk559/XnPnztXAgQM1dOhQFS5cWF988YWuXr1qOj86Nw/ye2wNsbGxuX7s2BNPPJHj/Oj09HS98847cnR01BtvvKFTp06ZfUyVl5eXypcvL19fX3Xu3FmTJ09WUlKSAgICtGrVKkVFRZkuGnfmzBl9+umnqlChgl588UXThfGylStXLtdTNQCgIBCKAcDCChUqpNq1a2vPnj2mi2zdrlmzZvrqq69yXO3Y29tbK1as0NSpUzVp0iSlpqYqMDDQ9Nm+dzN06FC5uLho/fr1mj9/vlxdXdW0aVMNGzbMdMXqnj176o8//tAbb7yhhQsX3vGqt0899ZTKly+vkSNHKi0tTS1atNC7775rOvdzxIgRMhqNmjFjhoxGo/z8/NS/f3+dPn1aW7ZsUXp6uhwdHXPUy8sc79fgwYNVokQJffnll1qyZIkKFy6shg0b6s0333xoPvf0ueee07p16/Tee+9p8ODBateuXZ7u99JLL8nd3V3z5s3TunXr5Orqqpo1a2r8+PG5bhe/3QsvvKBy5cpp8eLF+uyzz3T58mUVL15cNWvW1OTJk+/5qr+urq5atmyZwsLC9Omnn+rGjRsqVaqUBg0apL59+97TWHnxwgsvKDo6WmvWrNGqVavk4+Oj5s2bq3v37nrvvfd0/Pjx+7pycUBAgJYvX65p06Zp5MiRysrKkr+/v6ZNm2ZaOXd0dNSCBQv04Ycfaty4cXJ0dFSHDh3k5eVl+uij3DzI77E1nD9/PtcLW7Vq1SpHKD569Kiio6MlSf/5z39y3Kd9+/aaMmWKJOn999+Xh4eHvvzyS928eVMBAQGaO3euatasKUmm54JTp07p1VdfzTHW+PHj1alTpwc+PgCwBLuse726CAAAwGMuIiJCJ0+eVNu2bc0u7NaxY0f5+fmZfUQWAODRxkoxAADA36Smpurtt9/Wb7/9ptatWysjI0MbNmzQsWPH9O677xb09AAAFsRKMQAAQC42b96s+fPn6+TJk8rKylLVqlX1n//85x+vtA4AeLQQigEAAAAANouPZAIAAAAA2CxCMQAAAADAZhGKAQAAAAA2i6tP/39XryYqM5PTqwEAAADgcWJvb6dixdzueDuh+P/LzMwiFAMAAACAjWH7NAAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAIACcfjwH2rT5imVK+erFi2aKjx8b679li5dpIYNa6lChdJq3bq5fvvtF9NtM2d+qtKlvVSuXEnT1+23/xNCMQAAAAAg36WkpCgk5BV16RKiyMho9e3bX716hchoNJr127Vrhz76aKzmz1+iqKho9e7dTyEhryg+/ook6ciRQxo5cozOnLlk+mrUqEme50EoBgAAAADku927d8je3l69er0uJycndevWXZ6entq8eaNZv4sXL2jAgCGqXr2G7O3t1aXLq3JwsFdExHFJt0JxtWrV73sehGIAAAAAQL6LjDyhgIBAszZ//0qKiDhm1ta5c1cNGjTU9P89e35TQkKCAgKClJSUpJMnozRv3ueqWrWimjatp+XLl97TPBzv+wgAAAAAALhPSUlJcnFxNWtzcXFRcnLyHe8TEXFcvXuHaMSIUfLy8tLZs2fUoEEj9ezZRwsXLtP+/eEKCXlFPj4+atWqdZ7mwUoxAAAAACDfubi4KiXFPAAnJyfLzc0t1/7bt/+k559vrT59+mnw4LckSWXLltP69Rv19NNtZDAY1KhRE3Xq9Io2bPghz/MgFAMAAAAA8l1AQICioqLM2k6ejFRAQFCOvitWLFOfPj00ceJUvfXWcFP7oUMHNX36J2Z9U1NTVaiQc57nQSgGAAAAAOS74ODmMhpTNX/+bKWlpWn58qWKi4tVixatzPrt2PE/jRjxlpYvX62OHTuZ3ebm5qYpUz7Wd9+tU2Zmpnbs+J+++WaNXnmlW57nYZeVlZVlkSN6xF25kqDMTB4KAAAAAMgvf/55RMOGDdWxY0dVvnwFTZr0ierVa6DQ0KGSpClTpqlTpw7aufNnFSrkYnbfhQuXqGXLZ7R580Z99NEHOnv2tEqWLKWRI0erffsXTP3s7e3k5eV+xzkQiv8/QjEAAAAAPH7+KRSzfRoAAAAAYLMIxQAAAAAAm5Xvn1OcmZmpmTNnavXq1bpx44bq1q2rMWPGqGzZsrn2P3funCZPnqzw8HBlZGSoRo0aGjFihCpVqmTqExwcrLi4OLP7tW/fXlOmTLHqsQAAAAAA7o+np4scHKwXSTMy0hUff+fPPM6W76F41qxZWrFihT7++GP5+Pho6tSp6tOnj3744Qc5O5tfNjshIUE9e/aUv7+/Fi5cKAcHB82aNUs9evTQ999/Ly8vL8XHxysuLk6LFi1SxYoVTfctVKhQfh8aAAAAACCPHBwclRCzxWrju/s8k6d++bp92mg0auHChRo4cKCaN2+uoKAghYWF6fLly9q4cWOO/j///LNiYmL0ySefqHLlygoICNDkyZOVnJysn376SZIUEREhOzs71apVS97e3qYvDw+P/Dw0AAAAAMAjKF9D8bFjx5SUlKRGjRqZ2tzd3VWlShWFh4fn6F+nTh3NnTs3R8DNysrStWvXJN0KxaVLl5aLi0uO+wMAAAAAcDf5un06JiZGkuTj42PWXqJECV26dClH/5IlS6pkyZJmbYsXL1ZqaqqaN28uSTpx4oScnZ01YMAAHTp0SF5eXurYsaO6d+8ue3uuIwYAAAAAuLN8DcXJybdOcjYYDGbtBoNBRqPxH++/ceNGTZs2TT179lRgYKAkKTIyUtevX1f79u01ePBg7du3T1OmTNHVq1c1dOjQPM/tbp9bBQAAAAB49Hh7//NptfkairMvfmU0Gs2CsdFolKur613vu2TJEk2YMEEvvPCChg8fbmr/8ssvlZaWJjc3N0lSUFCQEhIS9Nlnn2nQoEFycHDI09yuXElQZmbWvR4SAAAAAOA+5CWwPqi4uJuyt7e76yJovu4vzt4KHRsba9YeGxubY0t1tszMTI0bN04ffvihXn/9dX300Udm26INBoMpEGcLDAxUSkqK4uPjLXwEAAAAAIDHSb6G4qCgILm7u2vv3r2mtoSEBB09elQNGjTI9T7//e9/tXz5co0ePVpvv/227OzsTLcZjUYFBwdrwYIFZvc5dOiQihYtKm9vb+scCAAAAADgsZCv26cNBoNCQkIUFham4sWLy8/PT1OnTpWPj49at26tjIwMxcfHy8PDQ4UKFdKPP/6olStXqn///mrdurXi4uJMY7m6usrNzU0tW7bU7Nmz5efnp8qVK2v37t2aP3++RowYkZ+HBgAAAAB4BNllZWXl64m0GRkZCgsL09q1a5WcnKy6detqzJgxKlOmjM6fP69WrVppwoQJ6tixowYOHKgtW3L/MOf+/fvrzTfflNFo1Oeff65vv/1WMTEx8vPz02uvvaauXbve07w4pxgAAAAA8o+3t4cSYnLPe5bg7vNMns4pzvdQ/LAiFAMAAABA/nlYQjEf5AsAAAAANu7w4T/Ups1TKlfOVy1aNFV4+N5c+y1dukgNG9ZShQql1bp1c/322y85+sTGxqpKlQr68ceN1p62RRCKAQAAAMCGpaSkKCTkFXXpEqLIyGj17dtfvXqFyGg0mvXbtWuHPvporObPX6KoqGj17t1PISGvKD7+ilm/N9/8zyP1SUCEYgAAAACwYbt375C9vb169XpdTk5O6tatuzw9PbV5s/lK78WLFzRgwBBVr15D9vb26tLlVTk42Csi4ripz6JFC+Tq6qbSpf3y+zDuW75efRoAAAAA8HCJjDyhgIBAszZ//0qKiDim9u07mNo6dza/mPGePb8pISFBAQFBkqRTp6L0+ecztGnTNj399JPWn7iFsFIMAAAAAA+R/D6/NykpSS4urmZtLi4uSk5OvuN9IiKOq3fvEI0YMUpeXl5KT0/XgAF99eGHE1WsmGcej/ThQCgGAAAAgIdEQZzf6+LiqpQU8wCcnJwsNze3XPtv3/6Tnn++tfr06afBg9+SJE2dOlFVq9bQ00+3uddDLnCEYgAAAAB4SBTE+b0BAQGKiooyazt5MtK0Lfp2K1YsU58+PTRx4lS99dZwU/v69Wu1bt0aVaxYRhUrltH589Hq16+3pk//5H4ehnxFKAYAAACAXFhiG/P69WvVtGk9lStXUs2aNdCGDd/ftebdzu+9XefOXTVo0FDT/+90fu+kSf8cSoODm8toTNX8+bOVlpam5cuXKi4uVi1atDLrt2PH/zRixFtavny1OnbsZHbbL7/s08mT5xUVFa2oqGj5+ZXR3LkLTSvJDzNCMQAAAIA8KYiQWFB1LbGN+eTJSA0ZMkBhYbN05swljR8/Uf369dSVK1fuULVgzu91dnbWihVrtHbt1woIKKv58+doyZKv5ObmptDQoQoNHSpJmjEjTEajUV26vKRy5UqavrZt2/KPNR5mhGIAAADgAdhKUCyokFhQdS2xjdnfv5KOHIlSgwYNlZCQoJiYv+Tu7i6DwemOdQvq/N6qVatpw4atOn36orZt26V69RpIkqZMmaYpU6ZJklavXq+//rqmM2cumX21bPlMjvH27Tui1q3b5rl+QSIUAwAA4LFgS6uJBVG3oEJiQdW11DZmd3d3nT17RhUr+mnQoP56993R8vAofMe6tn5+b0EgFAMAAOCRZ2uriQVRt6BCYkHVtcQ25mylS/spOjpOX3/9rcaMGaWdO3++4xi2fn5vQXAs6AkAAAAAD+r2kChJ3bp115w5s7R580a1b9/B1O/2kChJXbq8qjFjRioi4rgaN26qI0ei5O7ubpHVxNvrdu7c1azPnQJbw4a1lJWVpUmTwu47KFqrrjVC4m+//aLu3buoQgV/NWvW/KGqez/bmPv166U33hiYI3w6Ot6KXc2aNdfzz/9LGzd+f8e62ef3Dhs2VB9++IHKl69gdn6vdGtL8+3n995u4cIluW5n/ieexVzk4Gi9eJiRnq74q3f+nhUkQjEAAAAeeQUVTm0pKBZUSCyougEBAVqwYK5Z28mTkTlWZaVb25hHjRqhKVOmmd2+detmff75LK1Z862pLS3NqMKFi+RaM1v2+b1/l31ur3Tr/N682rfvyD/2cXB01M2Ta/M85r3y8O9otbEfFNunAQAA8MgrqK2ulrgoUjZHR0c5OTmZBbaHqa4lznXdunWzXnrpX2Z9/ykkFlRdS2xjrl69lv7444BWrVqhzMxMbd26WVu3btFLL3W+Y13kP0IxAAAAHnkFFU5tKSgWVEgsqLqW+JgiHx8fLVu2UvPmzVbFimU0ceJHWrx4uSpVCrhjXeQ/tk8DAADgkVdQW11vD2yvvdZHq1d/ddfAtmrVOjVq1MTsttsD28svv6Jt27Zo69Yt2rTp3YeqrqXOdV22bKXef/9dvfNOqPz9K/5jSCyoupJltjE3atREW7bcebfB33kWKyQHxzufx/6gMtLTFH81xWrjP4rssrKysgp6Eg+DK1cSlJnJQwEAAPAoSk1NVf36NTR48JumkDh+/Bj9/vths9XiHTv+p5CQzrmGxJiYGDVtWk8ffTTJFBL//e8+2rRp213D059/HtGwYUN17NhRlS9fQZMmfaJ69RqYBbZOnTpo586fVaiQi9l9swPbb7/9ovfff1cnT0bJ37+ixowZp+DgJ+96zAVVF9bl7e2hGweXWG38wrV6KC7uZq51rX1O8d/rent7KCFmi9Vquvs8o7i4m7K3t5OXl/sd+xGK/z9CMQAAwKONkIjHAaHYcgjF94hQDAAAANydZ1FnOTgZrDJ2RppR8ddSc6lZSA5OVtxOnJam+Gs5txMX1DZmQrHl5DUUc04xAAAAgDxxcDLoysoPrDK21yujJeUMxQ5OTrr6w2Sr1JSkYs8Nk5QznDo4OunajllWq1v0yf/kWhf5j1AMAAAAizp8+A+Fhg5RRMRxlS/vr8mTw1SvXoMc/ZYuXaSZM6cpLi5OFStW1AcfTDCd57t+/VpNmvSRLly4oDJlyujdd0erXbvn71jTs4izHAzWWcGUpAyjUfHXc1nFtLG6wOOIUAwAAACLSUlJUUjIKxo6NFQhIa9p9eqv1KtXiPbtOyLDbSFu164d+uijsVq1ar2qVq2mVatWKCTkFe3de1BXr8ZryJABWrVqvRo0aKiff96uV1/tpD/+iDD7POHbORgMuvDBm1Y7rtKjw5TrKqbBoL8+CbVaXd+3ptyxbtz8kVar6/36R7nWBR5HfE4xAAAALGb37h2yt7dXr16vy8nJSd26dZenp6c2b95o1u/ixQsaMGCIqlevIXt7e3Xp8qocHOwVEXFc/v6VdORIlBo0aKiEhATFxPwld3d3GQzWO78TgO1ipRgAAAAWExl5QgEBgWZt/v6VFBFxTO3bdzC1de7c1azPnj2/KSEhQQEBQZIkd3d3nT17Rg0b1lJWVpYmTQqTh0dh6x8AAJtDKAYAAIDFJCUlycXF1azNxcVFycnJd7xPRMRx9e4dohEjRpltjy5d2k/R0XH67bdf1L17F1Wo4K9mzZpbbe4AbBPbpwEAAGAxLi6uSkkxD8DJyclyc3PLtf/27T/p+edbq0+ffho8+C2z2xwdHeXk5KRmzZrr+ef/pY0bv7favAHYLkIxAAAALCYgIEBRUVFmbSdPRpq2Rd9uxYpl6tOnhyZOnKq33hpuat+6dbNeeulfZn3T0owqXLiIdSYNwKYRigEAAGAxwcHNZTSmav782UpLS9Py5UsVFxerFi1amfXbseN/GjHiLS1fvlodO3Yyu6169Vr6448DWrVqhTIzM7V162Zt3bpFL73UOT8PBYCNIBQDAADAYpydnbVixRqtXfu1AgLKav78OVqy5Cu5ubkpNHSoQkOHSpJmzAiT0WhUly4vqVy5kqavbdu2yMfHR8uWrdS8ebNVsWIZTZz4kRYvXq5KlQIK9uAAPJa40BYAAAAsqmrVatqwYWuO9ilTppn+vXr1+ruO0ahRE23Z8rOlpwYAObBSDAAAAACwWYRiAAAAAIDNYvs0AAAALMqzsLMcnA1WGTsj1aj4G6lWGRuAbSIUAwAAPMYOH/5DoaFDFBFxXOXL+2vy5DDVq9cgR7+lSxdp5sxpiouLU8WKFfXBBxPUqFETSdJvv/2q//53pCIjI+Xp6amBA4fqtdd637Gmg7NBZ0cMtcrxlJ04TRKhGIDlsH0aAADgMZWSkqKQkFfUpUuIIiOj1bdvf/XqFSKj0WjWb9euHfroo7GaP3+JoqKi1bt3P4WEvKL4+Cu6du2qevR4Ra+/3l+Rkee0YMESffjhf/Xzz9sL6KgAwLIIxQAAAI+p3bt3yN7eXr16vS4nJyd169Zdnp6e2rx5o1m/ixcvaMCAIapevYbs7e3VpcurcnCwV0TEcUVHR+vpp9vo5Zdfkb29vWrUqKWmTZ/U77/vKaCjAgDLYvs0AADAYyoy8oQCAgLN2vz9Kyki4pjat+9gauvcuatZnz17flNCQoICAoLk5eWlzz6bZ7rt2rWr2rPnlxz3AYBHFSvFAAAAj6mkpCS5uLiatbm4uCg5OfmO94mIOK7evUM0YsQoeXl5md1248Z1hYS8oho1aqlNm7ZWmTMA5DdCMQAAwGPKxcVVKSnmATg5OVlubm659t++/Sc9/3xr9enTT4MHv2V229mzZ/Tcc8+oWLFi+uKLL2Vvz8tIAI8Hns0AAADyweHDf6hNm6dUrpyvWrRoqvDwvbn2W7p0kRo2rKUKFUqrdevm+u23X3L02b8/XNWrB/xjzYCAAEVFRZm1nTwZqYCAoBx9V6xYpj59emjixKl6663hZrcdOnRQbdu21FNPtdLixSvk4uLyj7UB4FFBKAYAALAyS1wFWpKysrK0fPlSde78otLSjLmVMhMc3FxGY6rmz5+ttLQ0LV++VHFxsWrRopVZvx07/qcRI97S8uWr1bFjJ7PbYmNj1aVLR/XvP0jjxk1ghRjAY4dnNQAAYFMKYsXWEleBlqRp06Zo7tzPNXRoaJ6O1dnZWStWrNHatV8rIKCs5s+foyVLvpKbm5tCQ4cqNHSoJGnGjDAZjUZ16fKSypUrafratm2Lli9fosuXL+uTTyaZ3fbRRx/kaQ4A8LDj6tMAAMBmZK/YDh0aqpCQ17R69Vfq1StE+/YdkcFgMPXLXrFdtWq9qlatplWrVigk5BXt3XtQnp5eysrK0ooVyzR69Eg5Ojr8Y11LXAVakrp1666hQ0P1yy+78nzMVatW04YNW3O0T5kyzfTv1avX3/H+LVs+k+cQDgCPIlaKAQBAgbClFVtLXQXax8dXdnZ2eaoJAMgbQjEAAMh3BXWO7d1WbG/XuXNXDRo01PT/3FZst2/frdq16+TpeC15FWgAgGURigEAQL6ztRVbS10F+l4VK+wsb28Pq30VK+z8QPMDgIcB5xQDAIB8V1Dn2N7Pim2/fr30xhsDH2jF9varQL/2Wh+tXv3VXa8CvWrVOjVq1OS+62VzdDYocuCbDzzOnVSaGSYp1WrjA0B+YKUYAADkO1tbsbXEVaABANbBSjEAAMh3trZiKz34VaBv17RpMx0/fsYi8wIAW8dKMQAAyHes2AIAHhasFAMAgHzHii0A4GFBKAYAAPkue8V22LCh+vDDD1S+fAWzFVvpVki9fcX2dgsXLlHLls8UwMzvT7HCznJ0Nlht/PRUo67e4IJXAHA/CMUAAKBA2NKKraOzQQd7vm218WstmiquAg0A94dQDAAAbEYxD2c5FrLiim2KUVdvEk4B4FFCKAYAADbDsZBBu7s82MW67qbpV5MkQjEAPFIIxQAAoEAU9XCWk5VWbdNSjLpGOAUA5AGhGAAA6PDhPxQaOkQREcdVvry/Jk8OU716DXL0W7p0kWbOnKa4uDhVrFhRH3wwwXRV6LyOkc2pkEE/vviOVY6n9Tcfs2ILAMgTPqcYAAAbl5KSopCQV9SlS4giI6PVt29/9eoVIqPRaNZv164d+uijsZo/f4mioqLVu3c/hYS8ovj4K3keAwCAhw2hGAAAG7d79w7Z29urV6/X5eTkpG7dusvT01ObN28063fx4gUNGDBE1avXkL29vbp0eVUODvaKiDie5zEAAHjYEIoBALBxkZEnFBAQaNbm719JERHHzNo6d+6qQYOGmv6/Z89vSkhIUEBAUJ7HAADgYUMoBgDgIXL48B9q0+YplSvnqxYtmio8fG+u/ZYuXaSGDWupQoXSat26uX777Zd7HiNbUlKSXFxczdpcXFyUnJx8x/tERBxX794hGjFilLy8vO5rDAAAHgaEYgAAHhIFdW6vi4urUlLMw2tycrLc3Nxy7b99+096/vnW6tOnnwYPfuu+xgAA4GFBKAYAIBcFsWJbUOf2BgQEKCoqyqzt5MlIBQQE5ei7YsUy9enTQxMnTtVbb/3f5/3eyxgAADxMCMUAAPxNQa3YFtS5vcHBzWU0pmr+/NlKS0vT8uVLFRcXqxYtWpn127Hjfxox4i0tX75aHTt2uq8xAAB42BCKAQD4m4JasS2oc3udnZ21YsUarV37tQICymr+/DlasuQrubm5KTR0qEJDh0qSZswIk9FoVJcuL6lcuZKmr23bttx1DAAAHmaOBT0BAAAeNndbbW3fvoOprXPnrmZ9bl+xXb16RZ7GuN39nNvbr18vvfHGwAc+t7dq1WrasGFrjvYpU6aZ/r169fr7GgMAgIcZK8UAAPxNQa3Ycm4vAAD5j1AMAMDfFNTVmDm3FwCA/EcoBgDkWUFckbkgahbUim1Bndtb1MNZ3t4eVvsq6uF818cbAICClO/nFGdmZmrmzJlavXq1bty4obp162rMmDEqW7Zsrv3PnTunyZMnKzw8XBkZGapRo4ZGjBihSpUqmfps3LhRM2bMUHR0tMqVK6dhw4bpySefzK9DAgCbkH015aFDQxUS8ppWr/5KvXqFaN++IzIYDKZ+2VdkXrVqvapWraZVq1YoJOQV7d17UK6ubnkaoyBrSuarra+91kerV3911xXbVavWqVGjJvc1xt8VxLm9ToUMWvvcu3nuf686/jBBuplqtfEBAHgQ+b5SPGvWLK1YsULjx4/XypUr5eDgoD59+ig1Necfy4SEBPXs2VMpKSlauHChli1bJjc3N/Xo0UNXrlyRJP36668aNmyYunbtqnXr1ql58+YaMGCATpw4kd+HBgCPtYK4InNBXQWaqzEDAGA78nWl2Gg0auHChQoNDVXz5s0lSWFhYQoODtbGjRv1wgsvmPX/+eefFRMTo/Xr18vDw0OSNHnyZDVo0EA//fSTOnfurHnz5qlVq1bq3r27JCk0NFT79+/XokWL9NFHH+Xn4QHAY60grshcUFeBlrgaMwAAtiJfQ/GxY8eUlJSkRo0amdrc3d1VpUoVhYeH5wjFderU0dy5c02BOFtWVpauXbumzMxMHThwQMOGDTO7vUGDBtqwYYPVjgMAbFFBXJG5oK4CXVCKejjLqVDuW7otIS3FqGtsYwYAwEy+huKYmBhJko+Pj1l7iRIldOnSpRz9S5YsqZIlS5q1LV68WKmpqWrevLlu3LihpKQk+fr65mk8AMD9K4jP0C3Iz+0tCE6FDFr09Eirjd9z60ec2wsAwN/kayjOflf+7xc2MRgMMhqN/3j/jRs3atq0aerZs6cCAwP1119/3XW8rKws2dnZ5WluXl7ueeoHALaqfv1aWrRonry9/2/3zpkzJ9WrVw+zNkn64osvNGTIEM2ZM0ddu3a9rzEKqqYkpaemydHZ6Z8ekvtm7fHv5k7H/DjWtaVjtbW6tnSstlbXlo6Vug9PzXwNxYUKFZJ069zi24Os0WiUq6vrne4mSVqyZIkmTJigF154QcOH3/rIC2dnZ9P9b5c9Xl4DsSRduZKgzMysPPcHAFtTvXp9JSenaMKEyaarKf/111+qU6eJ4uJumvrt2PE/DRgwwHRF5ttvy+sYBVlTuvUHdGZz663YDvz5o1xr58eLBVuqa0vHamt1belYba2uLR0rdfOvpr293V0XQfP16tPZW6FjY2PN2mNjY3Nsqc6WmZmpcePG6cMPP9Trr7+ujz76SPb2t6ZdtGhRubq63tN4AID7UxBXZOYq0AAAwNrydaU4KChI7u7u2rt3rypUqCDp1scuHT16VN26dcv1Pv/973+1evVqjR49Wq+++qrZbXZ2dqpTp4727t2rLl26mNr37NmjBg0aWO9AAKCAHT78h0JDhygi4rjKl/fX5Mlhqlfvzs97s2fP1N69e7Rw4VJT244d/9N///ueTp8+paCgyho//mPVrVv/rnUL4orMXAUaAABYU76uFBsMBoWEhCgsLExbt27V8ePH9eabb8rHx0etW7dWRkaG4uLilJKSIkn68ccftXLlSvXr10+tW7dWXFyc6SsxMVGS1KtXL23atEkLFizQyZMnNXXqVP3555967bXX8vPQACDfpKSkKCTkFXXpEqLIyGj17dtfvXqF5HpthsTERI0d+77GjBll1n7u3Fn16NFFvXq9rsjIc3rrrWHq1u1l0wURAQAAbEW+hmJJGjx4sDp16qTRo0era9euysrK0vz582UwGHTp0iUFBwebPk7p22+/lSTNnj1bwcHBZl9z586VJAUHB2vChAlauXKlXnjhBe3atUuzZ8+Wv79/fh8aABt0+PAfatPmKZUr56sWLZoqPHzvXfvPnj1TvXt3N2vbseN/atkyWOXLl1Lbtq20b9/vdx1j9+4dsre3V69er8vJyUndunWXp6enNm/emKNvz57ddOrUSfXo0dus/aeftqhy5Srq3r2nHB0d9cwzz6pOnXr67rtv8njk+aeIu7O8vT2s8lXE3bmgDw8AABSwfN0+LUkODg4KDQ1VaGhojtv8/PwUERFh+v/MmTPzNGaHDh3UoUMHi80RAPIie8V26NBQhYS8ptWrv1KvXiHat+9IjqviJyYmasqUj/XZZ9P13HP/MrVnr9iOG/exunYN0fbtW9Wt28vasWPvHa+NEBl5QgEBgWZt/v6VFBFxTO3bmz8XzpgxW76+JTVp0ke6cuWyqT0zMyPHZ/fa29vr1KmTdzzewu7Ocnax3mfopiYbdSMh58cFGVwM+rjJu1ap+c4vE6RcagIAANuR76EYAB4Xt6/YSlK3bt01Z84sbd68MUc47dmzm1xd3dSjR2+zcHr7iq0ksxXb11/vn2vdpKSkHIHWxcXF9LF3t/P1LZmjTZJatGilcePG6Ntvv1Hbts9r587/adeuHXfsL0nOLga9X++dO97+oMaFf0xABQAA+S7ft08DwOPibiu2fzdjxmwtXrxc3t7eZu33s2Lr4uKqlBTzAJycnHxPV1OuUKGi5s1bpE8+mazq1StpzZrVeumlzipcuEiexwAAAHgcEIoB4D5ZasV2//5wffvtN0pLS9O2bVu0a9cOpabeecU0ICBAUVFRZm0nT0YqICAoz3NPSLip0qXL6H//+0XHj5/RrFlzdezYn6pevUaexwAAAHgcEIoB4D4V1IptcHBzGY2pmj9/ttLS0rR8+VLFxcWqRYtWea4bHx+vdu2e1qFDB2U0GrVw4TydP39ebdq0y/MYAAAAjwNCMQDcp4JasXV2dtaKFWu0du3XCggoq/nz52jJkq/k5uam0NChCg0d+o91n3iirCZPDlPv3t0VFFRe3377jVavXn9PgR4AAOBxwIW2AOA+3b5i+9prfbR69Vf3vWL77bcbFRRURcuWLc7Tim3VqtW0YcPWHO1TpkzLtf/w4SNztHXq1EWdOnXJ81wBAAAeR6wUA8B9YsUWAADg0cdKMQA8gIJYsS2ozwsGAAB4HBGKAeAR4+xi0NCaI6w2/rQ/JvJ5wQAAwGawfRoAAAAAYLNYKQaA+8Q2ZgAAgEcfoRgA7pOzi0H9qg632vhz/5zENmYAAAArY/s0AAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUA3gsHD78h9q0eUrlyvmqRYumCg/fe9f+s2fPVO/e3c3a9u7do2eeaa4KFUqrceM6WrNmlTWnDAAAgIcAoRjAIy8lJUUhIa+oS5cQRUZGq2/f/urVK0RGozFH38TERI0d+77GjBll1p6RkaGePbtq8OA3derUBYWFzdSgQf117tzZ/DoMAAAAFABCMYBH3u7dO2Rvb69evV6Xk5OTunXrLk9PT23evDFH3549u+nUqZPq0aO3Wfv169d0+fJlpaenKysrS3Z29jIYDHJwcMivwwAAAEABIBQDeORFRp5QQECgWZu/fyVFRBzL0XfGjNlavHi5vL29zdo9Pb3Uq9fr+ve/e6tUKU/9619tNGHCFJUu7WfVuQMAAKBgEYqBx1hBnWeb33WTkpLk4uJq1ubi4qLk5OQcfX19S+Y6RmZmplxcXLVgwRKdPRujZctW6r333tGRI4fvOncAAAA82gjFwGOqoM6zLYi6Li6uSkkxD8DJyclyc3O74zz/7ocfvtX+/eFq3/4FGQwGPfPMs3rmmdZatWpFnscAAADAo4dQDDymCuo824KoGxAQoKioKLO2kycjFRAQdMd5/t358+dlNKaatTk6OsnJySnPYwAAAODRQygGHlMFdZ5tQdQNDm4uozFV8+fPVlpampYvX6q4uFi1aNHqjvP8u+bNW+jIkcNasWKZsrKy9Msvu7Rhw/dq375DnscAAADAo4dQDDymCuo824Ko6+zsrBUr1mjt2q8VEFBW8+fP0ZIlX8nNzU2hoUMVGjr0jvPNVqVKVS1YsFRz534uf38/vfPO25oxY7Zq1arzj/cFAADAo8uxoCcAwDoseZ7tf/87XpLMzrOtVq36Q1W3atVq2rBha472KVOm5dp/+PCROdratGmrNm3a5nmeAAAAePSxUgw8pgrqPFvO7wUAAMCjhFAMPKYK6jzbgqrr4e4sb28Pq3x5uDvnee4AAAB4tLB9GnhMZZ9nO2zYUH344QcqX76C2Xm20p23FmfLPs/244/Ha9SoEfLz8/vH82wLqm4hF4NCAkLvOu79WnZiim4mpP5zRwAAADxyCMXAY6ygzrPl/F4AAAA8Ktg+DQAAAACwWYRiAAAAAIDNYvs08JjycHNWIVeD1cZPSTLqZmLO82w93J1VyMWKdZONnN8LAAAAiyEUA4+pQq4GtS7zH6uN/2P0rFxDcSEXgzpWeNNqddeeCiMUAwAAwGLYPg0AAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYyAeHD/+hNm2eUrlyvmrRoqnCw/fetf/s2TPVu3d30/+//nqlypUrafbl41NEb7892NpTBwAAAB5rhGLAylJSUhQS8oq6dAlRZGS0+vbtr169QmQ0GnP0TUxM1Nix72vMmFFm7S+//IrOnLlk+lqyZIVKlPDR22+PyK/DAAAAAB5LhGLAynbv3iF7e3v16vW6nJyc1K1bd3l6emrz5o05+vbs2U2nTp1Ujx697zheQkKCBg3qr4kTP1GpUqWtOXUAAADgsUcoBqwsMvKEAgICzdr8/SspIuJYjr4zZszW4sXL5e3tfcfxZs6cpsqVq6hdu+ctPlcAAADA1vA5xYCVJSUlycXF1azNxcVFycnJOfr6+pa861gJCQlasGCuVqz42qJzBAAAAGwVK8WAlbm4uColxTwAJycny83N7Z7H2rjxe/n5lVG9eg0sNT0AAADAphGKASsLCAhQVFSUWdvJk5EKCAi657F+/HGTOnR40VJTAwAAAGweoRiwsuDg5jIaUzV//mylpaVp+fKliouLVYsWre55rH37fmeVGAAAALAgQjFgZc7OzlqxYo3Wrv1aAQFlNX/+HC1Z8pXc3NwUGjpUoaFD8zRORkaGLl68IB8fX+tOGAAAALAhXGgLyAdVq1bThg1bc7RPmTIt1/7Dh4/M0ebg4KC//rpm4ZkBAAAAto2VYgAAAACAzSIUAwAAAABsFtunAStzdzPIxdXZauMnJ6UqIdFotfEBAACAxxmhGLAyF1dnNfHrbbXxfzm/kFAMAAAA3Ce2T8OmHD78h9q0eUrlyvmqRYumCg/fe9f+s2fPVO/e3c3aLl68oFdf7aQKFUqrZs0gzZv3uTWnDAAAAMCKCMWwGSkpKQoJeUVduoQoMjJaffv2V69eITIac66yJiYmauzY9zVmzCiz9qysLL32WjdVqhSoiIgzWrnyG02ePEF79+7Jr8MAAAAAYEGEYtiM3bt3yN7eXr16vS4nJyd169Zdnp6e2rx5Y46+PXt206lTJ9Wjh/m25337ftdff13S+++PlZOTk4KCKmvDhp9UsWLF/DoMAAAAABZEKIbNiIw8oYCAQLM2f/9Kiog4lqPvjBmztXjxcnl7e5u1Hzr0hwIDK2vs2PdVtWpFNWpUW+Hhe+Xp6WXVuQMAAACwDi60BZuRlJQkFxdXszYXFxclJyfn6OvrWzLXMa5du6rdu3eoWbMndeDAUR08eEBdunRUuXLl1ahRE6vMGwAAAID1sFIMm+Hi4qqUFPMAnJycLDc3tzyPYTA4q1ixYhoy5G0ZDAY1aNBQzz//L23c+IOlpwsAAAAgHxCKYTMCAgIUFRVl1nbyZKQCAoLyPEbFipWUlJSk9PR0U1tGRoaysrIsNk8AAAAA+YdQDJsRHNxcRmOq5s+frbS0NC1fvlRxcbFq0aJVnsdo3ryFihQpqnHjxig9PV179+7Rhg3f61//esF6EwcAAABgNYRi2AxnZ2etWLFGa9d+rYCAspo/f46WLPlKbm5uCg0dqtDQof84houLi7755gcdP35UVapU0Btv9NFHH01SvXoNrH8AAAAAACyOC23BplStWk0bNmzN0T5lyrRc+w8fPjJHW4UK/lq58htLTw0AAABAAWClGAAAAABgs1gphs1wdzPIxdXZauMnJ6UqIdFotfEBAAAAWB6hGDbDxdVZNfxettr4h85/TSgGAAAAHjFsnwYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbFa+h+LMzExNnz5dzZo1U82aNdW7d2+dPXs2T/fr27evwsLCctwWHByswMBAs6/Q0FBrTB8AAAAA8BjJ988pnjVrllasWKGPP/5YPj4+mjp1qvr06aMffvhBzs7Oud7HaDTq/fff144dO1SlShWz2+Lj4xUXF6dFixapYsWKpvZChQpZ9TgAAAAAAI++fF0pNhqNWrhwoQYOHKjmzZsrKChIYWFhunz5sjZu3Jjrffbv36+OHTtq3759Kly4cI7bIyIiZGdnp1q1asnb29v05eHhYe3DAQAAAAA84vI1FB87dkxJSUlq1KiRqc3d3V1VqlRReHh4rvfZuXOnWrZsqXXr1uUadCMiIlS6dGm5uLhYbd4AAAAAgMdTvm6fjomJkST5+PiYtZcoUUKXLl3K9T5Dhgy565gnTpyQs7OzBgwYoEOHDsnLy0sdO3ZU9+7dZW/PdcQAAAAAAHeWr6E4OTlZkmQwGMzaDQaDjEbjfY0ZGRmp69evq3379ho8eLD27dunKVOm6OrVqxo6dGiex/Hycr+v+sDtvL0LZts+dR/PmtR9fGtS9/GtSd3HtyZ1H9+a1H286+alZr6G4uyLXxmNRrNgbDQa5erqel9jfvnll0pLS5Obm5skKSgoSAkJCfrss880aNAgOTg45GmcK1cSlJmZdV9zwKMhP34J4+JuUvcxrWtLx2prdW3pWG2tri0dq63VtaVjtbW6tnSs1M2/mvb2dnddBM3X/cUlS5aUJMXGxpq1x8bG5thSnVcGg8EUiLMFBgYqJSVF8fHx9zdRAAAAAIBNyNdQHBQUJHd3d+3du9fUlpCQoKNHj6pBgwb3PJ7RaFRwcLAWLFhg1n7o0CEVLVpU3t7eDzxnAAAAAMDjK1+3TxsMBoWEhCgsLEzFixeXn5+fpk6dKh8fH7Vu3VoZGRmKj4+Xh4dHnj5n2GAwqGXLlpo9e7b8/PxUuXJl7d69W/Pnz9eIESPy4YgAAAAAAI+yfA3FkjR48GBlZGRo9OjRSk5OVt26dTV//nwZDAadP39erVq10oQJE9SxY8c8jffee+/Jy8tLkyZNUkxMjPz8/PTuu++qa9euVj4SAAAAAMCjLt9DsYODg0JDQxUaGprjNj8/P0VERNzxvtu2bcvRZjAYNGTIkH/86CYAAAAAAP6OD/IFAAAAANgsQjEAAAAAwGbdVyi+dOmSDh48qKSkJKWkpFh6TrABhw//oTZtnlK5cr5q0aKpwsP33rX/7Nkz1bt391xvi42NVZUqFfTjjxutMVUAAAAAj7F7CsX/+9//1LZtW7Vs2VLdunXT6dOnNXToUI0ePVqZmZnWmiMeMykpKQoJeUVduoQoMjJaffv2V69eITIajTn6JiYmauzY9zVmzKg7jvfmm//hM6kBAAAA3Jc8h+Kff/5ZAwYMUKlSpTR69GhlZWVJkho2bKg1a9Zo/vz5VpskHi+7d++Qvb29evV6XU5OTurWrbs8PT21eXPOld6ePbvp1KmT6tGjd65jLVq0QK6ubipd2s/a0wYAAADwGMpzKJ4+fbratGmjBQsWqHPnzqZQ3KtXL73++utau3at1SaJx0tk5AkFBASatfn7V1JExLEcfWfMmK3Fi5fL29s7x22nTkXp889naNKkT6w2VwAAAACPtzyH4sjISHXo0CHX2xo1aqRLly5ZbFJ4vCUlJcnFxdWszcXFRcnJyTn6+vqWzHWM9PR0DRjQVx9+OFHFinlaZZ4AAAAAHn95DsWFCxfWxYsXc73t/Pnz8vDwsNik8HhzcXFVSop5AE5OTpabm1uex5g6daKqVq2hp59uY+npAQAAALAheQ7FrVq10owZMxQeHm5qs7Oz04ULFzR37ly1bNnSKhPE4ycgIEBRUVFmbSdPRiogICjPY6xfv1br1q1RxYplVLFiGZ0/H61+/Xpr+nS2UgMAAADIuzyH4rffflu+vr7q3r27mjVrJkkaOnSo2rZtK4PBoLfeestqk7QVlviYovXr16pp03oqV66kmjVroA0bvn/o6gYHN5fRmKr582crLS1Ny5cvVVxcrFq0aPWPc832yy/7dPLkeUVFRSsqKlp+fmU0d+5CDR7MzyEAAACAvLun7dMrV67UBx98oMaNG6tJkyaqXLmyRowYoa+//lpFixa14jQff5b4mKKTJyM1ZMgAhYXN0pkzlzR+/ET169dTV65ceajqOjs7a8WKNVq79msFBJTV/PlztGTJV3Jzc1No6FCFhg7NwyMGAAAAAA/OMa8d33rrLXXp0kWdOnVSp06drDknm3T7xxRJUrdu3TVnzixt3rxR7dubX+CsZ89ucnV1U48evXXlymVTu79/JR05EiV3d3clJCQoJuYvubu7y2BweujqVq1aTRs2bM3RPmXKtFz7Dx8+8o5jSdK+fUfuejsAAAAA5CbPK8Xbtm3LdfUQlmGpjylyd3fX2bNnVLGinwYN6q933x0tD4/CD11dAAAAAHgY5DkU16tXTz/99JMyMzOtOR+bZYmPKcpWurSfoqPj9PXX32rMmFHaufPnh64uAAAAADwM8rx9ulKlSlq2bJm2bdsmf39/FS9e3Ox2Ozs7TZw40eITtBWW+JiibI6Ot76tzZo11/PP/0sbN36vZs2aP1R13d2c5OJa6J5r5FVyUooSEtOsNj4AAACAx0OeQ/HmzZtN22bPnDmjM2fOmN1uZ2dn0YnZmoCAAC1YMNes7eTJSHXsmPfzt7du3azPP5+lNWu+NbWlpRlVuHCRh66ui2sh+ZfM+9Wm79XJSz8RigEAAAD8ozyH4m3btllzHjbv9o8peu21Plq9+qt7/pii6tVr6Y8/DmjVqhV6+eVXtG3bFm3dukWbNr370NUFAAAAgIdBns8pznb16lVt375d69ev186dO3Xjxg1rzMvmWOJjinx8fLRs2UrNmzdbFSuW0cSJH2nx4uWqVCngoasLAAAAAA+DPK8US9Jnn32mOXPmKDU19f8GcHRUv379NHjwYItPztZY4mOKGjVqoi1b7u0CVwVVFwAAAAAKWp5D8erVqzV9+nS9+OKLevHFF1W8eHHFxsZq/fr1+vzzz1WqVCm9/PLL1pwrAAAAAAAWledQvHjxYnXu3FkffPCBqa1ChQpq1KiRnJyctGzZMkIxAAAAAOCRkudQfO7cOQ0fPjzX21q1aqX169dbbFK2yM3NUa6uLlYbPykpWYmJ6bnUdZKrFT8aKSkpRYlcBRoAAADAQyrPodjHx0fR0dG53nb+/Hl5eHhYbFK2yNXVRYXdy1lt/BsJZ5SYeDOXuoXk41XLanVjrhwkFAMAAAB4aOX56tOtWrXSzJkzFR4ebtYeHh6umTNnqmXLlhafHAAAAAAA1pTnleKBAwdq9+7d6t69u3x8fFS8eHFdvnxZMTEx8vf311tvvWXNeQIAAAAAYHF5DsXu7u76+uuvtWbNGv3++++6fv26ypQpo/r166tjx44qVMh656UCAAAAAGAN9/Q5xZJUqVIldevWTZJ06dIl7dmzx+KTAgAAAAAgP+T5nOLo6Gg9//zzGjFihKnt5MmTeuedd9SpUyfFxcVZZYIAAAAAAFhLnkPx5MmTZTAYNHfuXFNbcHCwNm/erKysLE2ZMsUqEwQAAAAAwFryHIr37t2roUOHqmLFimbtZcuW1YABA7Rr1y6LTw4AAAAAAGvKcyhOT09XZmZmrrc5OTkpKSnJYpMCAAAAACA/5DkU16pVSwsWLFBKSopZu9Fo1KJFi1SrVi1Lzw0AAAAAAKvK89WnhwwZoldffVWtWrVSs2bN5OXlpfj4eO3cuVM3b97U0qVLrTlPAAAAAAAsLs+huHr16lq1apVmz56tXbt26dq1a/Lw8FD9+vX1xhtvqHLlytacJwAAAAAAFpfn7dOS5OHhoUGDBmnXrl367bff1K5dOxmNRkVFRVlrfgAAAAAAWE2eQ/Evv/yiZ599Vl9//bUkaezYsVq5cqUuXLig4cOHa926ddaaIwAAAAAAVpHnUDxr1izVrVtX/fr1U2JiojZv3qw+ffrou+++02uvvaZFixZZcZoAAAAAAFhenkPx0aNH1atXLxUrVkx79uxRWlqa2rZtK0l68skndfr0aatNEgAAAAAAa8hzKHZycpKdnZ0kadeuXfLy8lJQUJAk6fr163Jzc7PODAvI4cN/qE2bp1SunK9atGiq8PC9d+0/e/ZM9e7dPUf7/v3hql49wFrTBAAAAAA8gDyH4uyrTx88eFA//PCDWrZsKUm6fPmy5s+fr+rVq1ttkvktJSVFISGvqEuXEEVGRqtv3/7q1StERqMxR9/ExESNHfu+xowZZdaelZWl5cuXqnPnF5WWlvN+AAAAAICCl+dQPGzYMO3bt09dunSRk5OT+vXrJ0lq3769Lly4oKFDh1prjvlu9+4dsre3V69er8vJyUndunWXp6enNm/emKNvz57ddOrUSfXo0dusfdq0KZo793MNHRqaX9MGAAAAANyjPIfioKAgbdmyRatWrdKWLVvk5+cnSfrvf/+r77777rH6nOLIyBMKCAg0a/P3r6SIiGM5+s6YMVuLFy+Xt7e3WXu3bt21fftu1a5dx6pzBQAAAADcP8d76ezu7q4aNWqYtbVp08aiE3oYJCUlycXF1azNxcVFycnJOfr6+pbMdQwfH1+rzA0AAAAAYDl5Xim2JS4urkpJMQ/AycnJj93FxAAAAADA1hGKcxEQEKCoqCiztpMnIxUQEFRAMwIAAAAAWAOhOBfBwc1lNKZq/vzZSktL0/LlSxUXF6sWLVoV9NQAAAAAABZEKM6Fs7OzVqxYo7Vrv1ZAQFnNnz9HS5Z8JTc3N4WGDlVo6NCCniIAAAAAwALu6UJbtqRq1WrasGFrjvYpU6bl2n/48JG5tjdt2kzHj5+x4MwAAAAAAJbCSjEAAAAAwGYRigEAAAAANovt03/j5uYoV1cXq42flJSsxMR0q40PAAAAAMg7QvHfuLq6yGAoZrXxjcarSky8abXxAQAAAAB5x/ZpAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM3K91CcmZmp6dOnq1mzZqpZs6Z69+6ts2fP5ul+ffv2VVhYWI7bNm7cqHbt2ql69epq3769duzYYY2pAwAAAAAeM/keimfNmqUVK1Zo/PjxWrlypRwcHNSnTx+lpqbe8T5Go1HvvvturmH3119/1bBhw9S1a1etW7dOzZs314ABA3TixAlrHgYAAAAA4DGQr6HYaDRq4cKFGjhwoJo3b66goCCFhYXp8uXL2rhxY6732b9/vzp27Kh9+/apcOHCOW6fN2+eWrVqpe7du8vf31+hoaGqUaOGFi1aZOWjAQAAAAA86vI1FB87dkxJSUlq1KiRqc3d3V1VqlRReHh4rvfZuXOnWrZsqXXr1snDw8PstszMTB04cEANGzY0a2/QoMEdxwMAAAAAIJtjfhaLiYmRJPn4+Ji1lyhRQpcuXcr1PkOGDLnjeDdu3FBSUpJ8fX3zPB4AAAAAANnyNRQnJydLkgwGg1m7wWCQ0Wi85/FSUlLuOl5WVpbs7OzyNJaXl/s9179f3t4e/9yJuo9kXVs6Vlura0vHamt1belYba2uLR2rrdW1pWO1tbq2dKzUfXhq5msoLlSokKRb5xbfHmSNRqNcXV3veTxnZ2fT/W+XPV5eA7EkXbmSoMzMrHz5RsXF3czRRl3r17WlY6Xu41GTuvlT15aO1dbq2tKx2lpdWzpWW6trS8dK3fyraW9vd9dF0Hw9p7hkyZKSpNjYWLP22NjYHFuq86Jo0aJydXW12HgAAAAAANuSr6E4KChI7u7u2rt3r6ktISFBR48eVYMGDe55PDs7O9WpU8dsPEnas2fPfY0HAAAAALAt+bp92mAwKCQkRGFhYSpevLj8/Pw0depU+fj4qHXr1srIyFB8fLw8PDxMW63/Sa9evdSvXz9VrVpVTz31lNatW6c///xT48ePt/LRAAAAAAAedfm6UixJgwcPVqdOnTR69Gh17dpVWVlZmj9/vgwGgy5duqTg4GBt2LAhz+MFBwdrwoQJWrlypV544QXt2rVLs2fPlr+/vxWPAgAAAADwOMjXlWJJcnBwUGhoqEJDQ3Pc5ufnp4iIiDved9u2bbm2d+jQQR06dLDYHAEAAAAAtiHfV4oBAAAAAHhYEIoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLPyPRRnZmZq+vTpatasmWrWrKnevXvr7Nmzd+x/9epVvf3222rQoIHq16+v999/X4mJiWZ9goODFRgYaPYVGhpq7UMBAAAAADziHPO74KxZs7RixQp9/PHH8vHx0dSpU9WnTx/98MMPcnZ2ztF/8ODBSklJ0RdffKGEhASNHDlSo0eP1tSpUyVJ8fHxiouL06JFi1SxYkXT/QoVKpRvxwQAAAAAeDTl60qx0WjUwoULNXDgQDVv3lxBQUEKCwvT5cuXtXHjxhz99+/fr71792rChAmqWrWqGjZsqPHjx+uHH37QxYsXJUkRERGys7NTrVq15O3tbfry8PDIz0MDAAAAADyC8jUUHzt2TElJSWrUqJGpzd3dXVWqVFF4eHiO/uHh4fLy8jJbAa5bt67s7OxM/SMiIlS6dGm5uLhY/wAAAAAAAI+VfA3FMTExkiQfHx+z9hIlSujSpUs5+sfGxsrX19eszWAwqFixYvrrr78kSSdOnJCzs7MGDBig4OBgdejQQYsXL1ZmZqaVjgIAAAAA8LjI13OKk5OTJd0KtrczGAwyGo259v973+z+qampkqTIyEhdv35d7du31+DBg7Vv3z5NmTJFV69e1dChQ/M8Ny8v93s4kgfj7V0wW7up+3jWpO7jW5O6j29N6j6+Nan7+Nak7uNbk7qPd9281MzXUJx98Suj0WgWdo1Go1xdXXPtn1tYvr3/l19+qbS0NLm5uUmSgoKClJCQoM8++0yDBg2Sg4NDnuZ25UqCMjOz8uUbFRd3M0cbda1f15aOlbqPR03q5k9dWzpWW6trS8dqa3Vt6Vhtra4tHSt186+mvb3dXRdB83X7dMmSJSXd2hZ9u9jY2BxbqiXJ19c3R1+j0airV6+atlUbDAZTIM4WGBiolJQUxcfHW3L6AAAAAIDHTL6G4qCgILm7u2vv3r2mtoSEBB09elQNGjTI0b9+/fqKi4vTqVOnTG3ZF9iqV6+ejEajgoODtWDBArP7HTp0SEWLFpW3t7eVjgQAAAAA8DjI1+3TBoNBISEhCgsLU/HixeXn56epU6fKx8dHrVu3VkZGhuLj4+Xh4aFChQqpZs2aqlOnjt5++22NHTtWKSkpGj16tDp06GBaWW7ZsqVmz54tPz8/Va5cWbt379b8+fM1YsSI/Dw0AAAAAMAjKF9DsSQNHjxYGRkZGj16tJKTk1W3bl3Nnz9fBoNB58+fV6tWrTRhwgR17NhRdnZ2mjlzpsaOHavXXntNBoNBbdq00ciRI03jvffee/Ly8tKkSZMUExMjPz8/vfvuu+ratWt+HxoAAAAA4BGT76HYwcFBoaGhCg0NzXGbn5+fIiIizNq8vLw0ffr0O45nMBg0ZMgQDRkyxOJzBQAAAAA83vL1nGIAAAAAAB4mhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2CxCMQAAAADAZhGKAQAAAAA2i1AMAAAAALBZhGIAAAAAgM0iFAMAAAAAbBahGAAAAABgswjFAAAAAACbRSgGAAAAANgsQjEAAAAAwGYRigEAAAAANotQDAAAAACwWYRiAAAAAIDNIhQDAAAAAGwWoRgAAAAAYLMIxQAAAAAAm0UoBgAAAADYLEIxAAAAAMBmEYoBAAAAADaLUAwAAAAAsFmEYgAAAACAzSIUAwAAAABsFqEYAAAAAGCzCMUAAAAAAJtFKAYAAAAA2Kx8D8WZmZmaPn26mjVrppo1a6p37946e/bsHftfvXpVb7/9tho0aKD69evr/fffV2JiolmfjRs3ql27dqpevbrat2+vHTt2WPswAAAAAACPgXwPxbNmzdKKFSs0fvx4rVy5Ug4ODurTp49SU1Nz7T948GCdO3dOX3zxhWbOnKlffvlFo0ePNt3+66+/atiwYeratavWrVun5s2ba8CAATpx4kR+HRIAAAAA4BGVr6HYaDRq4cKFGjhwoJo3b66goCCFhYXp8uXL2rhxY47++/fv1969ezVhwgRVrVpVDRs21Pjx4/XDDz/o4sWLkqR58+apVatW6t69u/z9/RUaGqoaNWpo0aJF+XloAAAAAIBHUL6G4mPHjikpKUmNGjUytbm7u6tKlSoKDw/P0T88PFxeXl6qWLGiqa1u3bqys7NTeHi4MjMzdeDAATVs2NDsfg0aNMh1PAAAAAAAbueYn8ViYmIkST4+PmbtJUqU0KVLl3L0j42Nla+vr1mbwWBQsWLF9Ndff+nGjRtKSkrK0edO492Nvb2d6d9ly5a5p/veq9tr3e6JJ/wKpG6ZMqUKpG5pP59c261Zt5Sfd77XlCRfP68Cqevj51kgdb1LFyuQusWtWPdONb1KFcyxehZQ3aIlC6ZuEd+i+V7Tw4o171bX3adg6rqWKJi6hbzz//fW2Yo171bXULxg6jp6Fkxdh2LW+xt0x5pFCuhYCxdMXXv3ogVT17VI/td0KWy1mnet6+xRIHXtDG4FU9fRNd/r2tkXsnrNOx2vaQ5ZWVlZVp3FbdavX6/hw4fr8OHDMhgMpvbhw4fr0qVLWrp0qVn/UaNG6eTJk/rqq6/M2p966im99NJL6tSpk5o3b64FCxYoODjYdPvXX3+tUaNG6fjx47Kzu/sDAAAAAACwXfm6fbpQoVvvAhiNRrN2o9EoV9ec70oUKlQoR9/b+zs7O991PAIxAAAAAOBu8jUUlyxZUtKtbdG3i42NzbGlWpJ8fX1z9DUajbp69ap8fX1VtGhRubq65nk8AAAAAABul6+hOCgoSO7u7tq7d6+pLSEhQUePHlWDBg1y9K9fv77i4uJ06tQpU1v2BbTq1asnOzs71alTx2w8SdqzZ0+u4wEAAAAAcLt8vdCWwWBQSEiIwsLCVLx4cfn5+Wnq1Kny8fFR69atlZGRofj4eHl4eKhQoUKqWbOm6tSpo7fffltjx45VSkqKRo8erQ4dOphWgnv16qV+/fqpatWqeuqpp7Ru3Tr9+eefGj9+fH4eGgAAAADgEZSvF9qSpIyMDIWFhWnt2rVKTk5W3bp1NWbMGJUpU0bnz59Xq1atNGHCBHXs2FGSdOXKFY0dO1Y7d+6UwWBQmzZtNHLkSNP5ydKtC3jNmjVLly5dUsWKFTVs2DA1adIkPw8LAAAAAPAIyvdQDAAAAADAwyJfzykGAAAAAOBhQigGAAAAANgsQjEAALgnnHkF4GGWmZlZ0FPAI4ZQDNyDzMxMZWRkKCsrq0BeFPJC1LKuXbumhISEgp4GHhNxcXFKTU0t0DlkP0fExMTo5s2bVqtjZ2dntbHxf9/H6OhoxcXFFfBs8k9SUpKuXbtGoMEDycjIkL39rYjD66aCYenHPSMjw6Lj5YZQbCFGo7HAnsRTUlLyvabRaMz3mgUp+5fR3t5eDg4OsrOzK5AXhY/LC9GjR49qyZIl2rVrl44dO6aYmJh8DRPZ3885c+Zo27Zt+Va3oBXUc1Rqamq+vzA5f/68YmJilJ6ebvVa2cf28ccf6+zZs1avdzfZP9vjxo3TF198YdEXEtljbdq0SevXr38oXmwW1N/e1NRU3bx5U0lJSVYZP/uxnjBhgtauXWuVGnkRHx+vK1eu5NvrjG+//VbDhg3T9OnTtXz5cm3dulV//vmn/vrrLyUmJubLHKwt+3u7cuVK/frrr2bt1vydyh47IyNDN27ceGjeeMjIyNCFCxcUGxurxMTEB3rOyv45dXBw0LvvvivJ/HVTfh/z339m4+PjrTqHmJgYszfR0tLSdOPGDavVu921a9ck/d9jfPvjnpWVpczMTGVmZt73z/iWLVv04YcfavXq1fr5559Nrx0t+dyUr59T/DjJysqSnZ2dLl++rO3btysiIkKurq4qVaqUfHx85OPjoyJFiqhEiRJycnKySu2YmBht3rxZf/75p1xcXOTr66vSpUurdOnS8vb2VsmSJeXoaJ1vcdu2bTVjxgxVqVJFGzZskI+Pj8qWLatixYrJwcEh1zlL9x/qzp8/r99++00ZGRkqU6aMfHx8VLRoUTk7OysxMdH0udWWlP04x8fHa82aNVq1apUkKSAgQDVq1FCNGjUUGBgoT09PU19rOnnypH766Sd5eHgoKChIpUuXVtGiReXo6Kj4+HgVL17covVu3Lih8+fPy2g0qmjRoipcuLDc3Nzk5ORkegf2fm3cuFHz5s1TqVKlVLRoUZUqVUrFixdX6dKlVaJECZUsWVIlSpSQu7u7PD09H7je32WPt2vXLlWrVk2STE/W2W94WPv7+ccff+jmzZsqXry4fHx8VKxYMavVMhqNOnTokPbt2ydJ8vb2lo+Pj0qUKKGiRYuqWLFiVnmuOHfunLZs2aLo6GgVLVrU9P318fFR4cKF5evra7HvbWZmpuzt7RUVFaVly5bpzz//lKurq7y9vVWiRAn5+fmpePHiqlmzpsWfL+zs7JSYmKiff/5ZAwcONJtT9u359YZW9vNvTEyM2rZtm+vz8YNavHixatWqpQ4dOlh87Lu5/W/fkiVLdODAAVWsWFEVK1aUn5+f/Pz8dP36dRUvXlzly5e3av25c+fqyy+/lIuLi3x8fOTv768qVaqoUqVKqlatmkqVKvVAtbK/b5mZmSpdurSpfkZGhunnydLPi9k17OzsFB4ervnz5+v06dMKDAxU6dKl5efnpyeeeEIXL15UkyZNVKZMGYvXT0lJUUxMjE6fPq3U1FQ5OTnJ0dFRpUqV0hNPPCFfX1/5+fnJ1dVVtWvXlpeXl8VqX7lyRd99951OnTql4sWLq1y5cipXrpzKlClj0efn7O/tzz//rH379qlz587q37+/3NzcLFbj77K/r8ePH9e6det07tw5ubu7q2TJkipZsqTp+bFixYpWe92Ym9WrV+uzzz5TWlqafHx8VL58efn7+6ts2bLy9vZW3bp18/xznpKSoq+++krnzp2Tvb29vvnmG3Xq1Em+vr4qXry4DAaD7O3tlZycrHXr1qlr165WO67MzEx9++23+vDDDzVnzhxVq1ZN06dPV1RUlNq2bWvR587sv32RkZEaO3asPD09NX36dG3dulXz58+Xu7u7hg4danqtYy3du3dXp06d1KNHDy1YsECpqalq0KCBKlWqpCJFijzw38B9+/Zp3bp1KlSokBwdHVWkSBFT1ildurR8fHxUsmRJFS1aVGXLljX76N684iOZ7lP2D2H2u7hubm6ys7NTSkqKHBwcVLx4cdnZ2WncuHEW/0HMyMiQg4ODRo0apa1bt6pkyZIyGAxKTExUZmam3NzclJKSopEjR1rl85ozMzO1ZMkSvfLKK3JxcVGTJk0UHx8vJycneXt7q0KFCqpSpYqCgoJUqVIllS5dWq6urvdd7+bNmxo2bJh27twpV1dXJSQkyNnZWaVLl1ZQUJCSkpL0+eefW/AIb8l+nKdNm6ZVq1apfv368vb2VmRkpM6dO6dr164pOTlZb7zxhoYMGWLx+tL//SE7e/asRo0apWPHjsnBwUE3btyQg4OD6UWKm5ubpk2bZrF6cXFxmjJlitavXy97e3s5OzurRIkSpj9YNWrUUJs2bR6ozqlTp3TmzBnt3LlT33zzjTIzM1W4cGG5u7srJSXF9OQ2YMAA1ahR44GPLTdDhw5VkSJFNGbMGKu8wLxd9mN75coVffLJJ9q8ebNKlCghNzc3FS5cWCVKlFDx4sVVqVIl/etf/7JIzeyf4fXr12vatGm6cuWKChcuLEkyGAzy9vaWwWBQly5d9Nxzz1mk5u0GDx6sXbt2ydvbW6mpqaYXuV5eXnJ2dtasWbMs9qL29ufFvXv3qkaNGnJ0dNTFixd1/fp1ZWVlKS4uTuPHj9fTTz9tkZq3u3Tpkt577z01b95c3bt3L/BdHTNnztS2bdvUr18/VatWTUWLFpWLi4tFQvLChQt1/fp1vfbaa/L09LTAbPMm+3v8wQcfaNu2bapXr542b95suq1w4cLKysrSmDFj1K5dO4u/WZmeni5HR0eFhYXp22+/1UsvvaRy5copIiJCERERio6O1unTp9WpUyeNGzfONN8HMWzYMJ0+fVqjR4+22vPg7bIfs1dffVXJyclq1qyZ5syZo6JFi+rmzZtydnZWkSJFNGfOHAUEBFj1DeG1a9fqk08+Ua1ateTs7KzLly/rzz//VGZmpry8vDRjxgwFBQVZpFZqaqr69eunAwcOqEqVKkpLSzOtQBUqVEhubm5asGCBRRc5MjIytGLFCi1cuFDJycnq0aOHOnXqZPE3uG/Xs2dPRUVFqVKlSkpLS9P169eVnp4ug8GgtLQ0ffHFF1ZZZLhd9s/MuXPn9Nprr6latWpq2rSpjhw5oqNHjyo6Olo3b96Uh4eHfv/993sae8OGDZo5c6ZpG36FChXk5uam4sWLy9fXVyVLltS+ffv022+/ac+ePVY7tiNHjmjw4MF6+eWX1b17d61cuVJTp05VvXr1dPToUX322Wdq2LChRWpmP88MHDhQCQkJGjFihOzs7DRkyBBVqFBB58+fV/ny5fXhhx/Kw8PDIjVz87///U9VqlRRiRIl9NZbb2nXrl1KSkpSenq6PDw8VLZsWVWvXl3+/v7q2LHjfeWCjIwMnT17VpGRkfrzzz91+vRpRUdH68qVK8rMzJS7u7syMzPv+7mBleL7lP0Ceu3atRo+fLg6deok6daKZlRUlI4fP67w8HCrvGDIrr1161aNHz9ezzzzjIxGo86dO6czZ87ozJkzOnTokEqWLGnx2tn1e/bsKenWE8CaNWt07do1nT59WidOnNCJEye0ZcsWLV26VMnJyXJzc1OtWrX0zjvvqFKlSnmuk/3Gw759+3Tw4EGtWLFCRqNRI0aM0NixY7Vw4UL98MMPeumll6xynNk2bdqk//znP+rcubOysrKUkJCg69ev6+rVqzpz5oxq1apltdqZmZlycHDQtm3bFBcXp1WrVmnv3r1au3atBg8erMmTJ2vnzp0aNGiQReplZGTI0dFRP/74o3799Vd9/PHHql27tiIjI3X06FFFRERo7dq1ioiIUJs2be77RZ+dnZ38/f3l7++vQ4cOqV27dvrPf/6j4sWL6/Tp0zpw4IA+/fRTFSlSxGpP4rGxsYqOjtamTZu0detW1a1bV5UqVVKFChVUrlw5PfHEExatnZ6eLicnJ23dulW//fab+vXrJ29vb506dUrnzp3T+fPntWfPHjVo0ED/+te/TD//DyL752fp0qVq1qyZ3nvvPTk5Oen48eM6ceKEIiMjtWfPHqu8IRATE6OdO3fqs88+U+PGjZWVlaXo6GidOHFCR48eVWRkpEVXeW5/Xvzwww/Ngm9cXJyio6N19OhRi79Jefu79MePH9fu3bv1ww8/qFq1aipTpoz8/Pzk6+ursmXLWvUFye2io6M1c+ZMSVJoaKj8/f0VEBCgChUq6IknnlCpUqVUu3bt+x5/5cqVOnv2rE6fPq2nnnpKlSpVUvHixeXh4SFnZ2eL747Klv093rhxo95880117txZ58+f1xtvvKFSpUrptddeU/369VW3bl2r1M9+ftyxY4d69eqlHj16SJLatWun9PR0JSUl6fLly6bv84P+XsXGxurQoUO6fPmyunTpouLFiysgIEDVqlVTjRo1VLVqVavsekhKStIff/yh1atXq3LlytqyZYtWrlypy5cvq3v37nr22WdNK/GWDsTZbzxkZmZq0aJFGjFihNq3b2+6/fr16+rdu7eeeOIJi6xUZ4eYw4cP69SpU1q4cKEqVqyo8+fP6+LFi7pw4YLOnTsno9Fo8Z9rBwcHhYSE6OWXX9bixYu1bds2RURE6LXXXlPVqlVlMBgsWi8mJkZ//PGHli9frsqVK0uS/vrrL509e9b0JrW1A7FkvhvB3d1dkyZNkouLi1mf+Ph4Xbx40ax/XsZs166d2rVrp3feeUexsbFq3LixIiMjFRMTozNnzighIUEeHh4aOnSoVY4t+2/ur7/+qlKlSmnAgAE6cOCANm3apEGDBmnAgAF6//339f3331ssFGc/z/z++++aN2+eKleurHHjxqlIkSL6+OOPdfPmTfXo0UMXL15UYGCgRWrm5qmnnjL9e+TIkUpNTdWVK1d0/vx5nTp1SidPntTBgwe1atUqtWvX7p5DcVZWlhwcHFShQgVVqFDBbGEmISFB586d05EjR3T48OH7zj+E4gdw+fJlubm5qVWrVqa27C1ct/9wWJqdnZ1SU1Pl7+8vb29vSbdWfbK3keUnOzs7lSxZUj4+PqpcubLatWsn6dZ5DNlPakeOHNGqVau0dOlSffDBB3keO3sTQ3h4uOrUqaMaNWpow4YNqlq1qho0aKAiRYrIzc3N9IaEpWWHvewtPdl/ED09PeXp6any5curTp06VqmdLfsx2Ldvn1q2bCl/f3/t3LlTlStXVqNGjfTOO+9o9erVevbZZy1ad9++fWrXrp1eeOEFSVLZsmXNQkb2O+gP+qLv2rVrmjNnjn755RfTG0iVK1dW5cqVlZqaqujoaKts0ZNuPbatW7fWU089pbNnz5pOhdi0aZMSEhJUv359TZkyxWL1sn9+9u7dq2bNmqlfv35mt6enpys6Otpi9W6vmZiYqH/961+mF1nZj7E1ZL84OXXqlMqWLatGjRpJuvVc8cQTT+iJJ56wykpt9vNidui7nbe3t7y9va3y+5r9O1CsWDF17txZCQkJOnXqlPbv369ff/1Vjo6OSkhI0BtvvGG156rbZWVlqUyZMjp06JDOnDmjffv26Y8//tDRo0e1fft2JSQkqEaNGlq1atV9vfGSmJio5s2b68qVKzp48KC2bdumzMxMFS1aVCVLllTVqlXv6Xn+XtjZ2Sk5OVkpKSl69tlnlZGRIU9PT6WkpKhSpUoKDQ3V77//bnousXRgc3Z2liTVrVs3xws6g8Egg8GgokWLms33Qbi5uenjjz9WSkqKoqOjdfLkSUVFRWnjxo2aPXu2KleurG+++eaBatwu+3c3KipKXl5eqly5ss6fP68nnnhChw8fVpMmTfT6668rMjLSam98ZP/dPXnypC5duqS2bdtKkimUFilSRP3799fq1astst04+5hPnz6tSpUqqV69epKkokWLmr2BlpaW9sC17qRQoUL697//rSpVqmj8+PHq2rWrhg4dqv79+1tk/OxjjI6ONr3xm83X11e+vr4WC2h5kf17YTQaFRQUZArEmZmZplMDsl9n5XUnQvbfnOzTrsaOHWv6fc0WFxens2fPysXF5Z4WaO7H+fPnVbZsWUnSjz/+KAcHB7Vo0UKSlJyc/EC7J/8u+40sZ2dn2dnZ6ebNm/r+++/Vv39/eXh4yNHRUZcvXzadhpEfihcvrvj4eJUvX95sh0tSUpKuXr16XwuGt/8c3LhxQ+vXr1dUVJR8fX3Vp08f0y7Vzp073/e8CcUPwNHRUa1atdL333+f79vlnJ2d9eKLL2r27NmaPn26xd9R/CfZT1RXr17Vtm3bdODAAbm7u5tWRsqWLSsXFxfVrl1btWrVUmRkpOldv7zKfjxvP182PT1dbm5uunDhgqpXr6709HTt2bPHaqu1ycnJCgoK0hdffGGVrej/JPsFa2JioulJJCsrS46Ojrp+/bqaNGmiKVOmKDIyUv7+/g9UKzY2VoULF5ajo6Nq166t2NhYJScny8XFJccfpuxzNR70Z/7y5csqWbKkTpw4oUaNGpkusOHg4KCaNWtq1qxZGj169APVuBMfHx/9+9//lnTrj/HVq1cVExOjixcv6uTJkxYN41FRUSpUqJD8/PxMW6b/ztHR0ew8yAd5wyE5OVmbNm1SmTJlVKRIETVu3Fh79uxR7dq1rXKOaW5KlSqloKAgbdy40fRmmbUZjUbVrFlTc+bMUWhoqIoVKyZ3d/d8qV29enVVr17d9P9r166Z3iE/ePCgAgIC8mUe2b+TBoNBAQEBCggIMDt3Lj4+3nRBlPvh5uamkSNHmv6fvWPm2LFjOnDggNUvwhgbG6vSpUvr5MmTqlGjhtzd3XX58mVJ0hNPPKFPPvnE4oHt6tWrevLJJ1W9enVVrlxZmZmZmjNnjmrXri1/f3+rndvr5uam2rVrKysrS40bN5b0fxfOiY2NtdopH4mJiSpatKiOHj1qOtUh+2fGyclJERERkmSR7eF/l/3zm5aWJk9PTy1btkw9e/Y0e41z+fJlnTp1yqL1ihcvLhcXF0VGRuYamCz1M5X9RtS5c+d06dIl3bx5U/v379cff/whg8GgChUq6OrVq4qPj7dIvdsVLVpUvr6++vrrr9WtW7cCO8Uj+/VElSpV9O2332revHl66aWX5O7unuNxvpc59u3bV4mJifL29lZgYKDKli2rihUrKjAwUOXLlze9OWpN2b8PtWrV0tq1azVx4kQtXbpUb7/9tipXrqyoqChFRUWpd+/eFq2blZWlVq1aadq0aabTObt06aK0tDQtX75cJUuWtPrfwuzv66VLl7RixQrt3btXbm5upvN+s68JcPubMvc7/syZM7Vr1y5dv35dNWrUUIsWLTRu3Dh16NCBUJzfsrf3bNmyRV9++aWkWxeUyb64RvHixeXu7q6iRYtaPKxmP6GePXtW+/fv1//+9z81bNhQ5cuXV5kyZRQYGCh/f3/Vrl1bJUqUsGjtv8/DwcFBc+fO1ddff60yZcrowoULcnd3V0ZGhlxcXBQTE6PRo0frhRdeUJcuXe75xVL2H/yWLVvq448/1v79++Xt7a1z586ZAmJUVJRefvllix9f9i/fmTNn9NNPP+nSpUtq0qSJ6UVRlSpVVLFiRZUvX96qf1iyH4O2bdtq9uzZaty4sXx9fbVz504VLVpUWVlZunDhgkV2CLRq1Uru7u6qUKGCvLy8tH37dpUoUULPPvusvLy8rHKcJUuWVJ06dTRp0iR99NFHpnNADhw4oAULFpjetbeGmzdv6siRI0pMTJSXl5dKlSqlwMBAValSRa1atbLo8Q4bNsx0oalChQpp1apVKlKkiBo2bCgvLy8VLlzYoi/k//zzT7377rtydnaWp6enHB0dFR0draSkJNWqVct0YS8PDw+5urpa9Hkqe4vp8uXLtW7dOv3000/6888/VblyZdMFT9zc3Cx6ca/s58WdO3dq2bJlkqSzZ8+qcuXK8vPzU6lSpeTp6Sl/f3/5+flZpObfZWVlaePGjTp06JCuX7+uESNGqHTp0qpWrZrFzhHPq/j4eC1fvlyHDh1ShQoVVLFiRVWqVEmVK1dWsWLFTCsw9xuqsreWZl+kp3bt2qpdu7a6detm4SPJqXTp0qpcubLGjh2rWbNmycPDQ8ePH5ck/fbbb6btn5YMbJmZmQoJCVFsbKzCw8MVHx+vpKQktW/fXiVKlFClSpVUvXp1VatWTdWqVXvgv73Zf38uXryoLVu26MKFC/Ly8lLp0qVVqlQp+fr6ysPD44Ev5vV32c95DRo0kJeXl6ZNm6Zx48YpLS1Np0+flnTr4lBVqlQxzdMasrKyVKVKFT377LP67LPPdP78eTVr1kwlSpTQxo0btXnz5gd64fv3WnZ2djpx4oT27t2rgQMH6tlnnzU9V2Q/d1jqOTL7MZs9e7Y2btxoCoe1atUyXXTyww8/tOipd9mv17799lv9+OOP2r59u/bt26eAgADTebYPcnGie5X9vDNkyBBdunRJ+/fv13fffWd6E69ixYoqUaKEgoKC8vw3Ij09XQMHDlRcXJxpy3tERIRWrVolo9EoFxcXeXt7q3z58qpUqZL69Olj1euIvPjiizp48KB+/PFHhYSEqFu3btq+fbsmT56sUqVKmd7kspTsXZPvv/++kpOTNXPmTBmNRo0ZM0aHDh1Snz59LFovN9l/+1evXq1vvvlG1atXl4uLi2lbc1ZWlulaFH379r3n8bN/jteuXauzZ89q7dq1+vbbb7V3714FBQWpTJkyWr58uZo3b37fpwFwoa0H8Ouvv+r777/XxYsXdfToUV2/fl329vby8fGRs7Oz3n33XTVv3tyiNbMD+bvvvqt9+/apbdu2cnNzM73rGB8fr6ioKL3xxhsaMGCARWvfLvsPSXBwsIYMGaJOnTqpffv2evHFF+Xi4qKpU6cqODhYo0aNeuB35hITEzV69GgFBgbq1VdfVatWrUwrwxEREVq9erXVLkwRHR2tnTt3KiUlRadPn9bFixcVGxurhIQEXbp0SZ07d7baVsHbXblyRRMnTlTz5s1Vt25dPf3003ruuedM88m+2Mz9Sk9P188//6zo6GgdP35cp0+f1rVr1xQdHS0HBwf5+PioQoUKpm23ltyuffDgQY0dO1bHjh1TkSJFVLRoUSUmJqpy5coaMmSI1a6YGBYWpgULFigrK0tOTk4qVKiQfH19Vbt2bcXExGjUqFEW2W6UkpKizz//XNHR0Tpz5oyuXLmimJgYubu7m945LVWqlOnFrqVWVW/cuKETJ07o4MGDOnbsmE6ePKmTJ08qLS1NhQoVUqlSpeTl5aXnnntOXbp0sUjN261bt0779+83nbucmJgoR0dH+fj4yNXVVdOmTXvg3Q1/Fx8fr7179+qvv/7SsWPHFBkZqStXrigrK0s3b97UW2+9pe7du1u0pnTrIj3Lli3TzJkzVaJECV25ckXffPONJk+erPLly+s///lPvuzmyX5eHjhwoA4ePKjq1atr+/bt8vb2VlJSkmmHybp16+75IiTZbzxs3rxZX3zxha5evaqMjAwVL15cJUqUUGBgoG7cuKHOnTtb/Pv6d9HR0dq+fbvatm2rX3/9VWPGjFGxYsWUnp6uvn37qnv37hZfxUxPT1dycrLp4kSXLl1SdHS0oqKidPLkSZ0/f17nzp1T/fr1tXTpUovUHzp0qHbu3KnixYvr4sWLKlKkiNLS0lS2bFldvnxZc+bMsdo20EuXLunAgQN6+umn9eWXX2rq1KnKyMhQxYoVNWbMGNWrV88i1z24m/j4eH399dfatGmTzpw5o6SkJJUoUUIDBgxQhw4dcpyH+iDGjx+v8+fPKy4uTklJSbKzs5PBYFCRIkXk5OSkSZMmWTSofv311ypfvrzq1q2r1NRUUz0pb+fQ3o/t27ebLk4UFRVl+nig7MWMmTNnWuzCZXlx7do1Xbx4UceOHdORI0d0/PhxnT17VtevX1dGRoYOHDhwX9/j1NRUJSUlKT4+3rT768KFC7pw4YLOnj0rOzs7ffXVV1Y4opyyd9tJt67FcPjwYb311lsW+Vn6/fff9cQTT8jHx0cpKSk53tC4dOmS1q9fryeeeCJfdmtlP+e99NJLat++venaQxkZGYqJ+X/svWWYVWX//v2ZPd3d3cEw1BADQ4OEIEgqCKggIiglSEkKEoKkdEm3dIN0Dkx3d3f3/wXHWveMcd8ysza/5zkOz3fMDPva61pXfPM8M0lMTCQkJIT27ds3qbpT+PwRI0YwePBgJkyYwIQJE/D29uabb74hLi6Ob775hm+++abJNuq/TrGEKC0tJTIyksDAQB49esTXX3/dLDKTv4JwCb333nvMnDlTXOhlZWUUFhaSm5tLcnKySPIiT5SWltK5c2d+//13DAwMmDRpElOmTMHb25tt27aRnJzMypUrUVZWlow0SCaTcf/+fXbs2CEyZA4aNEiiJ2qMP15MtbW1FBcXk5ubS1ZWlihVIS9Sl7+CMAdXrlxh165dmJmZMWHCBLp06SLJ5zd8ZsF5i4+PJzIykujoaMLDw9HW1ubSpUuSvNPq6moxQ/ro0SPCwsKoqKhAX1+fAQMGyC3YUVJSQvfu3fnmm28YOHAg/fr1Y86cOTx//pybN2/i4uLC6dOn/9STJAXKy8tJSUkhLCxMdNwyMjLIysrCwcGBkydPypXRNSUlhdDQUIKCgnj8+DH9+/eXrHftvyE/P5+IiAgCAwN58eIF69evfyfMxZWVlWIJc5s2bSQ1+oQ98PTpU1asWMGKFSswNjZm/Pjx3Llzh6NHj7Jjxw52795Nq1atJBv3v6G0tBRfX1+2bt2Ki4sLo0aN4rvvviMmJoZdu3bx5ZdfMnXq1LfO0gtrcvDgwVhYWNC3b1/Wrl1L+/btSUlJISoqCgMDA/bv3y8Xw1qY6+PHj9OrV69GmYDTp08TFBRE79696dSpk1yzXQkJCWKgsKETU1BQQGpqKurq6jg6Ojb7fCwpKcHX15cdO3bg4+NDly5dWLlyJREREWzbto0PPviAFStWyKW394cffuDzzz9vFBS8evUq2dnZ+Pr64uDg8M5Kb6urqwkKCkJRUREDAwNRpk9eKC0tJTU1lYSEBOLj40lMTCQzM5N9+/ZJPlZeXh6qqqpyk2Kqqanh9evXdOjQ4S9/L/AfREZGEhQUxJw5c9DV1ZXLd3kbZGZmkpyc/NaVYoLTlJubS2FhodiqJKzViooKCgsLqaioEPt95YH6+nrREa+ursbQ0BA9PT1MTU0l3Te+vr4sX76c3r17s2DBAiorK0V2ZwsLC/T19SUls/ynWLJkCQ4ODqJTLDU+/PBDxowZw8iRI+nVqxeLFi0SuZ26d+8usnw3Bf+WTzcT9+7dIyEhAWVlZUaMGEHbtm1p27Ytn332mVzGEy7ZL774gpycHKqqqlBRUUFDQwMNDQ3Mzc3lrkUmID09HTMzM/Lz85HJZBgYGJCUlIS3tzc+Pj5cuHBBvLCbYxxkZWURGRmJiYkJtra2dO/eHR8fH7lHVQWN4sDAQAoLCzEwMMDS0hJDQ0NsbW1p167dO+vlTk5OJjo6WizBff/99+nTp4/otEkxB4IRFxwcTG5uLu7u7ri4uODh4SHK9VRXV1NeXg5IQ2KjrKzM06dPiYqKoqSkhFGjRqGuri633hdhnsLDw1FXV+fTTz8lOTkZd3d33nvvPXr16kVlZSUjR46U1CEW9mlWVhZqamo4Ozvj7OzcSKswOztb7CMTyoSaC4G4JDIyEmVlZezs7EQywH79+jF37txmj9EQDXVcN23aRNu2bXF1dcXBwQF9fX18fHzw8fGRqxO+bds20YhesmQJVVVVciMWE2LKT548wd7envbt27Nr1y7s7OxQVlbm448/FiuK5O0UN9Qh1dTUxNfXl8zMTBwcHPD09GTgwIEUFhaKGfu3hYKCAiUlJcTHx7N3715MTU05duwY06dPx9DQkBkzZtClSxe59U4Ld8jJkyfZuHEjEyZMYMSIEZiamjJy5Eg+/PBDueqrCufjyZMnefr0qVg6LvTIWVtbY2FhIToVTb3zGr5HHR0dfHx8RIIkV1dXevbsSVJSElZWVnJxiEtLS3n16hWXL19m5MiRjB49GmtrawYOHCj3zHBDFBYWsnTpUmJjY7GxseHHH38kLi4OGxsbuYyXm5srcpYIZbzyQnFxMWfOnOHKlSsAmJiYYG5ujpubG2ZmZujq6koivxUWFsb06dN59uwZSUlJTJs2jY4dO+Lm5ib22Xp5eeHl5fVOSAAboqqqiidPnnD79m20tLRwdHTE2toaR0dHDA0NMTU1bbJds2nTJk6fPk3Lli2xsbHByckJOzs7LC0t0dXVlZsyi7A/Xr9+zapVqwgLCwMQbXNbW1sxoCgFsdmFCxdEpnt1dXXCw8N5/PgxRUVFokSq8NzffffdO+PXGD16NPPmzcPW1pZWrVpJHsQaPnw4Bw8exNnZmfz8fLF98Pz581RWVjbrrv/XKW4iKioqOHXqFLt376a8vBxlZWW6devGxo0b8fb2lmtvVWFhITdu3OD58+coKSnRuXNnzMzM3kkvSEOYmZnh6urKtm3bWLBgAYqKioSHhwPw+PFjUQ+1KWVkwuHi5+fH9u3bCQ0NFQ0AIyMj3N3dMTY2pl+/frRo0ULaB2uAdevWcf78eQDR2BEIhLKysti0aZPcZFaEeTt37hz79u2jrKwMTU1NkYHTzs4OHR0dhgwZImm54qtXr9i2bRt6enqidq69vT2urq6NmH2b6xRXVVXx22+/iQzPdXV1DBs2jBUrVtCiRQsmTJjQ7Gf5Ixr2igu9pfn5+ZiYmJCQkED79u1p27YtZ8+elZQhWVj/a9eu5cqVK7i7u2NoaIibmxuenp4iKYjQaiBV2adMJuPEiRP8+uuvora1oCXeunVrysrKGDt2rGRMmMKaSEtL4/nz57x+/ZrS0lIUFRXR09PDyckJa2tr2rRpI3lrSVlZGVu2bOHGjRu4urpy//59ampqWLFiBRoaGqxfv17yzL/wvAUFBWK2JzIyUpS9UFVVRVVV9Z2czQ01xm1sbKirq6OoqAgdHR0SExNFCZuHDx8Cb3cuC58dGxuLmZkZpqamZGZmYmZmRlZWFm5ubgwZMoSbN2/K3Wk6fvw4R48e5caNGwQHBzNhwgTatm2LqqoqNTU1KCoqyiVIKjyXTCYjPj6ekpISysvLiYyM5Ndff0VNTQ1HR0fs7e0ZM2ZMkzNRwlxnZWVhbGxMSUmJSLQYHR2NhYUF7u7u3L17V8rHE6GpqcnRo0e5dOkSZ8+eJTo6mjFjxoiB6OrqapSUlOQyx8Kzp6am8uOPP5KXl4e7u7to6P/www+YmJiwc+dOScYT7IyIiAh+/vlnHjx4gLa2NtbW1piamtKhQweUlJSws7Oja9euzR5P2HN37txhz549+Pj4kJubi7+/P3l5eZw8eZKamhq6d+/Orl27mj2es7MzJ0+eBN48q42NDa9fv+b8+fOUlJSgoqIiBnbee+89xo4d2+wx/xeEOTh27Bi7du3C3NxcJI1TUVFBUVGRzMxMJk+ezNSpU9/KMRbOs88//xw3NzciIiIIDQ3l3r17lJaWiq2Na9askYsSgbCeDh06hKamJidOnMDExITAwECCgoKIiIjgyJEjmJqaSuIUN8wCNyQkTUlJEXV8w8LCePHixTsj2UpLS2PPnj3ExMQwffp09PT0RBJeIZnU3Pa7fv368ejRIxYvXkx9fT379u0jISGBsLAwpkyZ0qzKi3+d4reEsOifPXvGuXPnWLRoEaqqqmzcuBETExMMDAzYu3cv3bp1kxuhS15eHoWFhbi6urJ69WpqampEkgR7e3t8fX3FzJ48oaWlxdChQ7l//75IFrFr1y4SExNJTExsVuRRmOedO3dSVVXF9OnT0dbWFktS0tPTuXfvHk5OTpI7xQ1lKW7evCn2bQ0ePJgFCxZw7do1fvvtN9577z256o4KF8Hu3bvx9PTE19eX0tJSUlJSRM29+Ph4evXqJel48Obdmpub4+joSGVlJdevX2fXrl04OTlhbGzMhx9+2GTyIOHdBgYGcuTIEVasWIG9vT1TpkxBX18fS0tLdu3aha+vr+S9iYJh6+LiQnl5uehAFRcXi1m/mJgYyUnqhMt65MiR3L9/H1VVVbS0tHj06BGHDx+msrJSfPaPP/6YDz74QJIsUE5ODps3b6ZHjx64u7uzbt06OnfuzJMnT7h06RJGRkZNIrz4X3B1dWX79u2NGL1jYmK4desWtbW1zJgxQzKnuOF6evToEb/88gsaGhqEh4djZWXF2LFjWbx4MY8ePWoknycFhPU0aNAglixZwqtXr4iKihLn9OnTp4SEhMiFDPDv4O7uTmFhIatWreLTTz+luLiYhIQEunbtSkBAgBjUamrnlIaGBhcvXqRNmzZoaGg0YsktKiqS5Bn+G9TV1Rk3bhwuLi4cP36cJUuW0L9/fyZMmCC2W8ireigvL4+AgAC+/vprvvjiC8rKysjIyODZs2ds3ryZ6upqnjx5woULFzhx4gR2dnZvPYawpoRM4a+//srQoUPJzc2ltLQUeCPrJq+MKbxxjIcPH46DgwMnTpzghx9+wNfXly+++EKusi5Chcy9e/fIz89n3759PHv2jJiYGKytrZk6dSorV67k999/F+VtmjueTCZj7969ZGRksHPnTrZu3YqCggI1NTWsXbsWgPnz50viFAt77u7du/To0YMff/yRbdu2YW5uzsyZM/npp5/IyMhgzpw5zR4L3uwVYZ2Ul5fz3XffYWtrS15eHikpKSQkJBAbG8vLly/JysqSZMx/inPnzjFq1CimT5/OhAkTRKKkrVu30qFDB1GDtil72d7evpGSA0BUVBTTpk3D09OzSfvyn0DYu8HBwSxatEjsm7WwsBClxUA+BHUlJSUkJiZiZGQkVoNJsUf+KYS9e/36dYKCglixYgW6urqkpKSQnp5ORkYGDx8+pLCwsFlOcV5eHhoaGixevJgTJ07g7++Pv78/9vb2LF68uFH1XVPwr1P8lhAO0bt37+Ls7MyAAQPYsGEDVlZWqKqqMmHCBEJCQrh+/TqTJk2Sy3ewtbXl0KFD1NfXk5ubKx5skZGR+Pv7I5PJ5OoUC3OwYcMGhg0bJjplQ4cOJTs7m5cvX/Lxxx+LhmBTMgdCGVxgYCB79+5tVHpYXl5OUVERmZmZfzr4pICwuf39/cVybX9/f1q3bs2IESPo2LEjy5YtY/DgwZKP3RAymUyUCpo7d26jHrra2lpRFF1gAm0uhIvn7NmzfPXVV4wePRp4Y+gmJCSwbNkysTzwu+++Q1VVtZF4+j+FcCE8fPgQKysrBgwYwO7du7G2tkZNTY1x48bx6tUrLly4wOzZsyV5toZj19XV0aJFC1q0aMHt27fx9fWlsrKSrVu30qpVK/z8/Pjuu+8kHVfA8uXLmTVrFgMHDkRfX18kE/n222/x8fFBJpOxdetWrKysmhVJFvZoQEAA6urqrF69mpiYGNzd3Zk1axb9+vVj69atzJo1S8Kn+w80NDT+1FdaV1fHr7/+SlhYWCMDobkQ1tPTp0+xtLTEw8ODnTt3YmNjg4KCAl27dsXHx4d79+5J7hQL47dp0wZvb2/mzZtHRkYGV65c4caNGzx9+pRhw4a9E94B4Zy1tbVl2rRppKenY2RkhKWlJWfPnuXevXvEx8ezePHiRn//T6CgoEB9fT2tWrWiffv2/P7773Tq1ImamhoePnyItbU1p0+flps03h+hqqpKt27d6NatG4cOHeLcuXNcuXKFYcOG8cknnzTSCpYCwn7y9/cnPT1dDHpoaGjg4OCAg4MD6enpKCkpMWPGDKZMmcK5c+eadX5ZWVkxePBg0tLSMDY2xszMjA0bNnDy5EnS09NZtGiRVI/3l1BWVqZDhw64ublx9epVTpw4wfDhw/nwww+ZOHGiXPgehL0ssMlqaGiIgUuAHj16cOrUKUJCQiQx+IU98OzZM5YsWUKPHj04c+YM3bt3Z+DAgcyfPx9HR0fJiAiF8eLj48U1VFlZiampKaampkybNo3FixdLGlwS7JkjR44QHh7OhAkT6Nq1K15eXqKspVBh8S4gjJOcnEy/fv1QVFREX1+fTp060adPHxQUFHj69KmYBW1O5YmwnhQUFHBxcWHWrFkEBgbKrSdd+K79+vUTZeL+7vtIAeFcioyMZM+ePTx79gx4E9QyMzPD2dkZGxsbunbtKhdb+a8QGhpK//79G7HDV1VViTJyTcni5ubmUl5ejpWVFRs3bhTL/b/99lvx86urq0UyxOYQ8P3rFL8lhEWflZUlHtRhYWEioZa1tTXKyspyKyETZDTq6upISkpCWVkZb29vMYpZXl4uar3KC8KzHTlyRJSDAtDW1v5LI+BtDoDq6mrS0tKwtrZGJpPRp08fMjIyGjnF6urqqKurN5ly/Z8iLS0NMzMz4I1jaGBgQHBwsNin8vTpU/r27SvX71BZWcmQIUO4evVqoz51RUVFTExMJM9oxsbGkpqaytChQ8Wf6ejo4OXlxcSJE7l48SKrVq3CwsKC8+fPN8kpFpCeni6WC4eFhYlOlIGBATo6OpJnegRSL+FS/vHHHyktLUVTU5OZM2eyYcMGrl69yuDBg/H19ZVsXCHSHRcXR3Z2NkOHDkVTU1M0Vry9vZk9ezZXrlxh48aNrFq1iqNHjzbLKRYu34iICLE3Li0tDUtLSyoqKujZsye///47jx8/liQD8k8gk8kYNWoUEyZMEDNeUkBYJ5WVleKFGxcXJwaLlJWVKS8vl/S8qK2tpba2FhUVFZE1dtWqVZw7d46HDx+Kl//kyZMZP3683Ih0BERERKCrq4u2tjYqKioMHDhQVCr46quvWLt2LUlJSSxcuFBc2297RwnzPHHiRFJTUzExMWHQoEEsWLCAK1eu4OvrK7e+RGEPlZeXk5aWxpMnT4iKihIJJlNSUlBQUODatWuEhYXxzTffyKWHvLS0FC0tLVHLtmG5trq6Oo8fPwbA09MTPz+/Zo/XkKhm9uzZ/PLLLyQmJjJ16lTJJV2EOU5OTiY4OJhnz56RkpJCTU2NWO3h7OxMUFAQixcvZubMmaINJBWEs1lgQ4Y3zpNApimU1r733nuSjCeTyaivr6e6ulrUThXG1tTUZOrUqcyZM4cJEyZI0gIh7DlLS0uioqKora1FS0uL3NxcKioqsLe3JyEhQVKyK2FOP/roI06ePMmuXbu4ffs2o0ePxtfXF2VlZbn0pv83CC1LioqK1NXVoa2tTWZmJgBdu3blwIEDTZ6DkpIS8vPz0dPTQ1VVtdGz6erqcunSJRYsWCDJczSEsH9yc3NJS0vjxIkTKCsr4+XlhaGhIRoaGpL7BsI9f/DgQcLCwhg+fDgmJiYkJyeLqjhnz55FQUFB7k6xsM4mT57M6dOnSUpKEqsUVFRUMDIyanIgLSYmhuXLl1NVVUVGRgYJCQnU19eLOtSGhoaoqKiwZMkSBgwY0Kyz8V+n+C3RMBK0b98+PvjgAxISEsRIYnh4OAkJCXIjVRH6bdauXUtQUBAZGRkcOnSIpKQkNDQ03gntOrzp3/P19SUtLa0Rg3BzER4ezpQpU7C3t8fQ0JC8vDy2bduGoaEhNjY2ctVeFiBs7nbt2nHz5k2ePXuGoaEhaWlpYu9lRESEpD2nfwcVFRWsrKzYtm0bJSUleHt74+rqKnmks6HRaW5uzvXr1xkyZEij0qXq6moiIyMBcHBw4LfffmvSWML89uvXj82bNxMdHU1sbCw9evQA3qyBsLAwSUtOMzIymDx5Mk5OTlhYWGBra4urqytWVlZoamri7e3N9u3bqaqqkluwpaioCGNjY8LDw/H29m50OdbV1ZGQkICmpia9evVi3bp1zRpL+GwjIyNRpk1FRYX6+nrKysqAN32w+vr6zRrnr1BVVcW6detwcXERCYiEOU1PTycsLEzS1hLhWYcNG8bEiRM5fvw4gYGBovTSrVu3iIiIkFR26vnz51y4cAEPDw+MjY0xMjLC3NycIUOGMGzYMFGT/V0Q8SUnJzN06FDc3NywsbHBxcUFa2trLC0t0dPTw8zMjE2bNkk2noWFhSjx1KdPH1q0aEFaWhr29vZyycA0DEB89NFHREZGimQ8WlpatG7dmm+//RYDAwPy8vL48ccf2bx5Mz/++KNk30dYYz4+Phw/fpxNmzaxYMECcR3fuHGDa9euidVDQg93c5CTk4O/vz86OjpYWlpibm7O999/L7c1JZzz/fr1Q0NDAw8PDzHwOmHCBDp27EhtbS3p6eksW7aMLVu2sG7dOkkDPsJ3EHRdLSwsiImJQU9Pj6qqKg4cOEBJScnfsik3BcXFxXh6enL37l0cHBzQ1NQUS4lVVFTIycmR/JwcPnw4q1evpkePHjg6OnL8+HHGjRuHv78/paWlInGQlGjZsiUtW7YkNDSUEydOMHXqVJycnJgwYQK9evWSayvYH6GsrIynpyerVq1izZo16Ovr8/r1a8aOHUtISAi1tbUoKCi8FbGb0Kt8/PhxNmzYgJeXl3geurq6kpeXx9GjR2nfvr1cnkmwlRITE3n16hW6urosXLgQdXV1zMzMcHR0xMbGhs6dO0sWdBf2y+PHj1myZIlok9bU1FBcXEx+fj6pqalyCRD+HebOnUtUVBTp6en069cPd3d3UVe9qejYsSOffPIJ6enp7Nmzh+LiYvbt2ycG14W+5YCAAD755JNmff9/neImolevXly/fp2lS5eSmppKSEiISGDQs2dPWrZsKbex165dS15eHitWrGDGjBno6upSVFTEzz//jJmZmVwIBAQIh1RCQgKBgYHcunWLhw8figeQYCAaGho26bJUV1dn8ODBlJWVkZWVRXV1NVlZWUybNg1zc3O0tbWxsrLCxMSELl26SHo5/hEdO3bE0dGRO3fu8O2331JQUMDPP/+MiYkJ6enpch1byPIcO3aM/fv3o6enx4ULF9i1axc1NTXo6OigoaHBjBkz+PDDD5s9nnC4uru74+Pjwy+//CKWhaqpqXH79m1OnTolluWHhoY2uy+nS5cunDt3jvnz5xMfH8/r168JCgri2rVrdOvWrcmU+n+F0tJSLC0tKSkp4fXr19y9e5fs7GzMzMwwNDTE3NwcLy8vLCwsKCgokDQDIsyth4cHLi4ufP3118yaNQtXV1eMjY2JjY3l5MmToqxWcHBws/v2hDGHDRvGlStXuH79OqNGjSIkJITHjx+TlJTEq1evGDZsWPMe7i+QkZHB8+fP8fPzo7KyUiSGU1RUJCEhAR8fH7mQfjg7O/PFF19w+vRpEhMTuXnzJleuXMHf359p06ZJagzFx8dz//59njx5goKCArq6uqLxY2tri42NDebm5qiqquLo6ChXCRlDQ0N++OEHYmNjCQoK4vfff6e2thaZTIaRkRFmZmZij52bmxvdunVr0jhVVVWcP3+ec+fOoaKigp2dHQ4ODtjb22NtbS0GAqSGoqKiGEgbN24c7u7uKCoqYmZm9qcyaVNTUz7//HO++uorCgoKJJ93Y2NjJk6cyOrVq+nTpw/6+vpoa2tTWlpKjx49eP/99zl9+jTBwcFNasFoaFivXbuWu3fvoqysTG1tLTo6OiJRnY+PT5M5Hf7X+Pv27cPFxYXa2tq/DELr6uoyatQolixZIrcKCDc3N77//nsOHz5MUVERBw4cYOPGjaSnp7N69WpJ+6l1dHTo2bMne/fuFeWm9uzZQ7t27bh69apcpC19fX2ZNm0ampqaoiM+YsQIUeNVXsjOzkZPT4/58+fz6aefsnr1alatWsXLly9ZuXKl3Mb9I7S0tBg/fjxnz56luroad3d3Ll68yIIFCwgMDBRL49+m91Y4I/r374+GhobYUvjs2TNRj7lXr1589dVXcnkmmUxGTU0Nbdu25dGjR1RVVZGcnExkZCShoaFER0dz/PhxMaEk1ZgAbdu2pa6uTvy5kpIS+vr66OvrixUQ7wJVVVV0794dLy8vAgMDWbp0KeXl5WhoaGBhYYGLiws///xzkz57zJgxFBYWkpmZyeeffy6WY2dkZJCenk5eXh6DBw9uNmv8vzrFzUBGRga7du3iyZMnZGVlYWlpSdeuXfniiy/klsmLjo5m/Pjx3Lx5k+rqavr27cvdu3epqqri+++/R19fnzVr1kg69l8hPDycw4cPU1JSQlRUFMXFxSgoKKCnp4eysjJDhw5lwoQJzSI8EYTXhd7ZpKQkMjMzyc/PJyYmhvHjx0vat/13UUlBFP3evXts3ryZkpISxo4dy7hx4+TWh9NQBL0hm3l2djZZWVmkpKTw6tUrRo0aJXmfZFJSEhs3buTWrVvAm8tGV1eXAQMG8Mknn3Dr1i1OnTrFl19+2WyHvKKigj179vDw4UNycnIwMDCgTZs2TJkyRXJ9PaFM7s6dOxw5coSSkhI8PT1RU1MjKiqK169fo6yszKeffsrMmTMlHVtATk4OGzZs4Pnz52hra1NfX09MTAyDBg1i+vTp3L59m7NnzzJ+/HhJSlFfv34tyjK1a9eO7du3s2/fPurq6hgwYAALFy6UzEFtyFAcHByMnp4e6enppKWlkZGRQUlJCQ4ODiKBT3NRXV1NcXExWlpajbJn9+/f59mzZ0RFRWFoaEifPn3o1q2bZAzQDc+08vJyLl++zObNm9HX10dHR4fy8nISExNRVlbG0NCQ77//XvJS17+DIIVVXV3N6NGjSUxMxN/fn1u3blFUVMSHH37I6tWr34p5WjgXX758yaxZs8RytdDQUNLS0kRn2MfHhwMHDkj6PAkJCWzcuBFfX19atmyJs7Pz337vmpoaZDIZMTEx7N27t9nVFv8Lt2/fJi4ujurqamxsbBg0aBCBgYFMnz6dL7/8kpEjR751Vld4L9u2bePSpUvMmDEDd3d3UlJSiI2NJSYmhlevXtG2bVtWrVol2XP8+uuv+Pr64ubmhoODA5aWln+boautrSU4OJjHjx8zbdo0Sb7D3yE0NBR/f3+ioqIwMTGhT58+uLq6yoVELT4+XkwwLFq0iLi4OIyNjZkyZYrcKvAaqiHcvXsXQ0NDBgwYIFk1gLCerl27JhKGVlVVERwcTEZGBvr6+qiqquLj4yPZenobCGdLRUUFW7du5dq1awwYMIDPP/8cQ0NDSQjzamtryc/Pp6ioCD09PbkEKD/++GO+/PJLevTowc2bN9HW1v7bij4pKyvhjRrNihUrSEhIYPXq1VhZWUmmJtEUCO+spqaGnJwcUlNTiYuLIzg4mIqKiiafy8LnCvKWDVFVVUVVVZUktsy/TnET8UcHqq6ujurqasklP/443sWLF9m3bx8XLlzg5s2bbNy4katXr6KgoMCpU6c4duyYKCEkNWJjY0VCsT8iKyuLmJgYoqKieP78Of369WPo0KFvLcf0v4gIampqyM/PJysrCxMTE7EnVQrcvn2b1atXc+fOHRITE7lw4QIdO3bE3d1dlJeqrKxEUVFRrnqYDTFixAiRhKkhGhIKyOu7CL0blZWV6Ojo0KZNGxISEli5ciUDBw5k8ODBzT7cS0pKxIOsrKxMzILLA8JaXLJkCcrKysyaNQstLS0qKyupqKhg27ZtBAQEsGDBArlWW1RVVRESEkJ4eDhlZWV07doVJycn0tLS2LhxIy1atODjjz9uchamoXM6Z84czp49K55VVVVV+Pv7k5ubS48ePSS9PIXqhiVLluDg4NCoH7K2tpbS0lK0tbVF0qbmGjs3btxg7969dOjQQZQKMzMzEwnb5AkhKi+TyZg6dSqenp5MnDhRPBtTUlL45ptvUFVV5aeffpJLtqkhBENh/PjxtGrVim+++aaR4XD69GkeP37MN998g6OjY5PKEjdu3EhgYCAHDx5s9Pu0tDQCAwOpra0Vez+lgp+fH3PnzqW6ulrM9lhaWmJlZYW7uztt2rTBy8tL1B2VF+u0gIyMDEJCQtDV1cXa2hoDA4M/GWiVlZVir/nbQpjrCRMm0KVLFyZPnvyn35eXlyOTySTbu7dv32b37t1iFqaiogJTU1OMjIxwcXGhRYsWeHp6Ym5ujomJiVznV0B2djYJCQloa2tjbm4uaY/tX6GkpARFRUWRoCc7O1sk15SqZUtYm3l5eVy9epWEhAQMDAyws7PD2toaa2trsQdWqjtdGPPgwYNiskS4u93c3KisrKSqqgpjY2O5tNL8HUpLS4mLi6OkpETU8DUyMpKs3/bq1au8ePECdXV13N3d6dGjh9zsCniji/zRRx9hZmbGqFGjiIuLQ0VFRaxsdHJywsPDQ9SMl3IPvX79msWLF5Oeno6CggJ2dnYYGxuLd2Lbtm0l0bz+p8jLyyMuLg5dXV1MTU0lmXdhHZeUlHDx4kUeP34szqmtrS3W1taUlpZSX1/f7Pa3f8un3wLCi8nMzGTt2rU4OjoyZcoUwsLCuHz5MhoaGowbN04ukShhE+np6YlyQREREdjZ2Ym/CwkJabI24j/BqFGj2LVrF97e3ixZsgRtbW1atWqFs7Mz1tbWdO7cmc6dOzcyht82k9rwsPDz8+PIkSPU1NTg6ekp6myamJggk8kkn2c7Ozvmz58PvIka79mzh/3791NRUYG2tjb29va0bdsWe3t7OnXqJNe5hjfrbfLkyRw4cABjY2OsrKxQUVFBJpOhpKQklx6g2NhY7t69S319PQ4ODiK1v5aWFtXV1djZ2bF3794mf75gjMfGxnLq1CnCw8PR19fHxsZGPMQ1NDSwt7eXnEFWWFv37t1rlCEVtGSnTJnCtGnT5BZkKCgo4PHjx+Tn52NjY0OXLl0wMDBATU0NJSUlTE1NmTdvnkju1lykpKSgoqJCeXm56GCrqKiIBF5Sx0MFgyY9Pf1PPcOKioro6OiIZb1SGAWKiorU1NRw5coVMjMzqa+vR1tbWywXdnR0xMnJCX19fZydnSUNoAnPWlJSwv3791m1ahWqqqqiU2NlZcWMGTM4ffq0ZO/zv0FYs4WFhZibm4t6sjU1NaiqqjJy5EgOHDhAVlYWjo6ObzX/wrNqamr+pUSahYWFKPMkNby8vDhy5IjIZ7B//36io6OxtbXlxYsXnDx5kvLycgA+++wz5s2bJ2aMpTKwG55Za9eu5cGDBygpKVFfXy+eXba2tvTu3Zs+ffo0OTCempoqtk0MGzaM2NhYKisrxc8TSDalbj3o1asXPj4+1NfX8/LlS9atW4eWlpaYoX7w4AG5ubnAG3LCYcOGSZ7tgv8EBM6ePcsvv/xCdnY2BgYGYnuLra0turq6vPfee5KSBgUEBPDrr7+SmZkplnl6enri4uJCcXGx5E7x5s2buXr1Ktra2hQXF1NcXIxMJsPExAQNDQ22bt0qmRShsM+HDx+Ok5MTV65cIS0tTeRJeZfOUkOsXbuWU6dOAf8hYbK1tRX7bj/66KMmZcvLysrYt28fBw8exNrampqaGo4dO4aSkhLz5s1rxIgsJWbMmCEGe+fOnUtBQQFpaWmkpKSQnJzMixcvuHHjBoWFhTx9+rTZQduG2VJnZ2eWL19OYWEh8fHxJCcnk5mZSXBwMFevXmX48OFyf8/CGXnnzh127dpFfn4+GhoaIrmWg4MDWVlZDBkypEnfRSAlPXjwICdOnMDe3l7U8VZQUMDY2JjKykrmzJnT7Aq7f53it4BwqP3888/ExcXRv39/MjIymD9/viido6ioyJQpUyQ3rIXDzdfXlytXrrBu3Tri4uLo0qULsbGx7N27l5CQEGbMmCHpuA2xf/9+3N3dqaurIysri6dPn3L06FGR9dXS0hIXFxecnJz4/PPPm3Vp5ufns2rVKpH4adOmTeKcWlpaoqSkxJUrV6R6NACcnJzEPllra2vRGMvMzCQxMZHY2FhevHjBwYMHmTx5suRyQQKEdRYcHMz06dOBN9kYLy8vXF1dcXR0xMTEBCMjI0kCA8J4KSkpLF++nFevXmFsbExOTg719fXo6upibm5Ohw4dmDdvXrPHAtixYwf+/v44ODiQm5tLcHAwpaWlqKurU1FRwZo1a0TiLakgGMmtW7fm6tWrtGvXrpGjVFlZSUhIiKTOU0NB+3nz5vHy5Utx/aqoqKCrq4u+vj6Ojo7MnDkTMzOzZme7hP9fWVlJSUkJ27Zt45NPPsHY2LiRoSF1xkeYX3t7e65fv46JiQlt2rTB1NRUNAKkajeoq6ujT58+IrFIXV0dkZGRBAcHExQURFhYGE+ePBHZ+NesWdOIUV0q5OfnY21tzcWLF5kwYUKj59PQ0ODx48fvhNlVmPtu3bpx/Phxevbsibm5uTj206dPyc7OFs+3t3n3wt9+/vnnTJ48mR07dtCzZ0+MjY0lb3H4I1RUVERHcffu3bi6urJ8+XKsra2prKwkJSWF1atXI5PJxCy1goKCpAyvgsF37tw50tPT+fnnn3F1dSUpKYmYmBji4uJ4+fIlWlpa9OnTR6yYeBtUVlbSu3dvlJSUMDc3R19fn7CwMExMTOjXrx/GxsZyy9DKZDIxaLZlyxZGjx7NBx98gIGBgUgAOG/ePNzc3ERtcXkEDoXn2717N506dWLw4MFkZGSI/Zi///478fHxtGvXTjKnuLy8nAULFlBXV0fr1q0pLy8nNjYWf39/qqqqqKur4/Lly5JUAAqqIWfPnmXdunViSXZhYSGRkZEEBQXx/PlzuWRstbW18fX1xdfXFz8/P44dO8bkyZPx8PBg4MCBfPDBB3InBWxYwXThwgVWrVqFr68vERERBAcHExISwq1btygpKWH8+PFv9dnCHn38+DE3btxg9uzZDBkyBBUVFdLT09m+fTt79uyhY8eOcklmCGtXQUHhT9wVZWVl5Ofnk56eTkFBgSRVTL/88guDBg3C2dmZwsJCHBwc0NXVFVvpysvLRblWeeqK/xE7d+5ET0+PUaNG8dNPP6GtrU1AQAAXLlzA0tKyyRrCwvyeO3eOMWPGMHXqVMaMGcOgQYOwtrbm66+/pn379pLw/PzrFL8FhIv21q1b7Nq1i/bt27N+/XoUFBTYv38/r169Yt++fYwaNUpuLMkymYxJkyaxdetW6urqOHPmDCdPnsTa2ppvvvmmySQq/wQNGbW3bNlCWVkZBQUFZGRkEB0dTVRUFNHR0QQEBPDll182aYyGepDZ2dn89ttv5OXlMXfuXA4fPsz+/fvZt2+fZOL2DdHQmNmzZw/Tpk0Tyx6rqqooLS2lqqqKmJgYubBDChAOAAcHB06cOEFSUhJ+fn6Eh4dz584dSktLqampYfDgwfz000/NHk+Iwt2/f5/MzEx+/fVX2rZtS3Z2NsnJyURFReHn5ydmZN62JL4hhD308OFDVq9eLWpcwxvHPzo6mtevX0sWKf8rjB07ljlz5rBq1Sp69OiBqakp8fHxnD59Gl9fX7EUUwoIc/vgwQPy8vLYv38/1tbWREZGEhcXR3JyMtHR0WJWvCkG9R8hrJ+oqChKS0s5ePAgFy9exMrKClNTU2xtbTExMWHAgAGSa40WFhby+PFjkpOTWbVqlUg4ZWJigrm5OZaWlpL058lkMvbt2yeSR1lYWODu7o67u3ujbEBJSQkBAQGSS8cIsLa2ZvDgwezcuZOamhq6deuGoaEhDx484NChQ+9MDUDAJ598wo0bNxg4cCBeXl44OTlRX1/PnTt3GDhwYLPW9po1a3jy5InYo2xgYCDq5xoZGTWph/afQDhv7ty5w4YNG0TpNk1NTQwMDFi2bBlLly4Vy4ml5nkQ9pOfnx+jRo0S36mjoyM9evSgpqaGsrIycd82df9u27aNrKws8VxwdXVl3bp1rFy5Eg0NDZExv127dnKRviorKyMuLo4+ffpgYGAgOjEODg4sWrSIFStWiHMsDwddkEcqKSlh8uTJYgCnIW9FVlaWJE6jsKYCAwPJy8vjzp07aGlpUVZWRm5uLhkZGSQlJVFUVCSJQ9ww8GxmZiZKOQpB5w4dOtChQwdJOVIajp2Tk0N0dDQlJSVoa2vTu3dvFBQUuHLlCk+ePJFU6eG/fQ+BF8fFxUUkFDM1NRWDLU2FYDc+evQIV1dXxo4dK/7c1taWGTNmsHDhQu7du8eECROa/Sx/RMPS+Hv37hEcHIyWlhaWlpYic7yDg4Nk1W+3b98Wz6Fx48ZRX1+Pk5MTDg4OIhmflZUVrVu3lgup5R8hBHzCw8O5cOECjo6OnDx5kiVLluDo6MiECRPw8PAQz+6mfD68Kc0W1o1AJNa1a1fmzZtHRESEJFVZ/zrFb4msrCxUVVVp27YtFRUVXL16lVGjRmFubk7btm1ZvHixpJmmv4KzszNbtmwhNjaW0tJSZDIZampqcnXU4I3BHhgYSGVlJZ07d0ZFRQU9PT3s7Ozo1KkT8Cbi3RwNUiGTGBgYSKtWrTA2NiYgIAB7e3sqKyuZMGECGRkZcgk6HDt2jF9++QV7e3sCAgKws7MTS7VUVFREg2/79u3NZrj7bxAco40bN2JqasrkyZNFptGqqiqysrKIiIiQvHw6ODgYX19fkfXZ1NQUU1PTRkRf0DyjUyBK8PT0/JPxKJRhNveC/CMaaonCG1bxxYsXs3PnTr7//ntqamowMzOjU6dOfP3115KOLSAkJISOHTvSuXNngD/1mFZUVADSZGCE5xw4cCDe3t7k5eWRkJBASkoKWVlZvHjxgqSkJDp06CC5U6yjo8PPP/9Mbm4uMTExxMbGkp6eTnBwMPfv38fOzk4SR7G4uJjDhw9TW1tLTU0NGhoamJmZ4eDggJubG66urri4uKCjoyMXzemGGD58OLm5uezZs4f169dTX1+PlpYWQ4cO5YsvvpBs7H/y3UxNTbl48SJHjx7l6dOnvHz5EiUlJUaOHNmktd0wuyMwxevr6xMTE0NycjKJiYkEBgaiqqoqGqJSQ1FRkerqamxtbQkPD//T+9TS0iI8PFxuWWvhvBs6dCg5OTl/InpRUlJqds+rqqoqffr0ob6+XqzwqKiooKCggNTUVBISEoiLixNlGOXhFBcXF+Pg4MDdu3cZP358o3VeXFxMcnKy2HMrL1RUVNC/f38eP378l+oGUtz7DYO6VVVVtG3bVnT2NTQ00NDQwNraWjK2+tDQUMLCwmjRogW1tbW0a9eO3377jVGjRv3pLJGyJ15wFJ89e8Znn30m2oeZmZno6urSvn17vv76a7lXewgQHBt3d3dcXFx4/fq1ZNwdwmcXFRU1auUQ5tLCwoLq6mpR+1pqCMHvEydOcOjQIdTU1FBUVKSsrAyZTIa+vj4KCgpMnTqVAQMGNHu8hlWSCxcuJDAwkNDQUG7cuMGhQ4eAN3exgYEBFy9efCfSgPHx8RgYGODo6EheXh6mpqYkJibi4uLC6NGjOX/+fLO+R3p6OmZmZhQWFmJsbIyWlhZ5eXnAG7mx7du3s2LFimY/x79O8VuisrISJycnVq1aJUY1hUjmy5cvMTAwkCsRRXZ2NocOHSIuLo4WLVowfvx4qqqqxFInKcvGGqK4uJjVq1dz7tw5Jk6cSOfOnamsrOTy5cscOnSIuro6kTG3OZFVYe4aZsxkMhmKiooUFBSIfT4xMTGSPFdDDBs2jLy8PCIiIgDYu3cvmzZtQkFBARMTE5ycnNDR0eHatWssX75c8vEFCO/Q39+/UVSzvr5e1C2WUudVMBDGjBnDuXPnGomuywMqKiqMHTuWnTt3oqWlhbW1tVwDSSdPnsTOzo4uXbqQmppKTU0N7733Hu+99x5FRUUUFhaKEWWQ1jAR3qWzszMJCQkik/kfIQ9iKAcHhz+xPJeUlJCdnU1KSopcgmgKCgpiVlYIAMCbDHJKSopkRomqqiobNmwQK1VSU1NJTU0lOjqap0+fUlpaKhLneHp6snHjRknGVVBQ4P79+1hbW4tza2ZmxpIlS5g1axZhYWHU1dWhpaWFvb39O4nSN/xuRUVFqKmpMXHiRCZOnCjZZwv8FQ35IuCNMZiamkp+fr5kY/0VBEWDFStWkJeXR6dOncQ+skOHDuHq6ipXIp2qqiru3LmDn58fhoaG9O7dG1NTU8nvegUFBdTU1FBTUyM/Px8DAwM8PT0BRIe5urpa0jEFmJqa0r9/f3788UfCw8Np37495ubmpKamcvz4cd577z2geZVC/wvq6uq4ubnx448/EhkZiaenJ3Z2dtja2kqmH3/o0CEiIyNxc3MT9VyvXr0qOUmcgJs3b7Jr1y6UlZXR0tIiPz+fmzdvkp2djYeHB+bm5hgaGqKhoYG6urrkd4+VlRVr1qzBysoKAwMD8dwqKSkR+SzkTVAH/7lXdXV1iY+PZ/r06QwaNAhbW1sxo2poaNikbKrwrN27d2fjxo20b9+eHj16iM8UGhpKSkqK3HtrL1y4ILaZwBtHLiYmRpSGkipT3NDW79u3r1h5AG/mOSoqioCAAJKSkt6JQwxvWLX19fU5c+YMXbt2RUNDg5ycHPH3GRkZzfp8fX19WrRowcKFC9m0aRN6enpERkYCEBYWJlmS6F+n+C1QX1+PtbU1n3zyCdu2bUNZWZnZs2djamrKypUruX//vlw05oQNEBMTw8aNG0lKSiIrK4uamhoSEhJYtGgRo0aNklukHt6UdsXExLBhwwYxk7hz50527tyJl5cXDg4O7N27Fz09vUYb9G0hbPTBgwczYcIEjh49Srt27YiLixOJDCIjI+Wir6qlpcXMmTPx8/NDU1OTadOmkZycTFpaGrGxscTGxpKRkdEk/cm3gTAHw4cP586dO3h6er41OU5TsGLFCkJCQsjPz2fgwIG4ublhYmIiWXZAWMfBwcEcOHCA169fM336dJGh0NLSEhMTEzp37iypRvHdu3dFg2fJkiUEBwfj6ekpkjHZ29tjYWFBSkoKJiYmkl4iwjvLy8vjzJkzFBUV0bNnT6ysrDAyMhJF56VGaWkpFy9e5M6dO2hoaIgOso2NDaampnTs2FFSw1YweLKysjh8+DAxMTG0aNECFxcX7O3tRaNWqotLRUWFdu3aif8WjNu8vDyys7PJzc0lKyuLqKgoyUmg5syZQ3FxsUiO5uzsTMuWLWnbtu3fynDIEw05CHbu3MnTp0/R0NAQmYPd3d1FLoKmwtHRETc3N0JDQ2nRooX4c5lMJjLnyhujRo2iqqqK48ePc+vWLVRVVcnJycHGxkZk1pWXcZ+fn09dXR0uLi6sWbOGFStWoKmpia2tLc7Ozvj4+DS7Z1347q9fv+bo0aNiZsTS0hILCwtsbW3Jz8+nY8eOciFZBPjyyy/R0tLi7NmzBAQEUFFRQWFhIUOHDmXWrFkAcgm8C472wYMH+eWXX9DU1CQkJISAgABqampQV1entraWcePGNdvGio+PJzQ0lNDQUJSVlYmNjSU8PJxr165hZ2eHhYWFGKh1cnJqFidAfX09s2bN4vPPPychIYGIiAgSEhIIDw/n6NGjIg+NmZkZ6urqLF++XHLlA2tra3R0dEhPTxc5UoyMjBrdO++CUVzA9OnTSUpKQl9fn8ePH3P//v1GxKHr1q1rctC/f//+XLp0iUWLFtGtWzdcXV0pKSnh2LFjdOnSRW5OsXCXGhsbN2rVMTc3x9zcnK5du0paGi+0Gggl+deuXRM1qDt06IC3t7f4Pd5VwMPNzY0uXbpw/fp1unTpgrKyMs+fP6dt27acOnWKli1bNulzhe+upqbGp59+yv3791FRUcHV1ZXVq1eTmJhIQkJCs+VBBfwrydQECM6onp4eRkZG+Pv78+uvv9KqVStGjRoluaC9cGGsWrWKpKQktm/fzpYtW0hPT2fdunUsWrSI8PBwfvnlF0n7IRvC19eXFStWiD2gmZmZfPDBB7i7u7N27VpMTEz4/vvvqa2t5fvvv2+2oV9fX8/FixdRUFBgwIABfPTRRxQWFlJdXY2Ojg6//vqrXI3PvLy8v/z8v9JIkweys7Pp2rUrALa2trRu3Vok2rKxscHAwEBSspOqqirWrFlDUVERwcHB4gWqo6ODlZUVLi4urF69ulljCNn/7777jsTERIYOHUpdXR2JiYlkZmZSWFhIQkIC48eP5/PPP5foyRojNDSU27dvExwcTHR0NFlZWWJfl6KiIsePH5eciKO0tJQJEyZQXl5OTk4OysrK6OjoYGRkhLm5ORYWFpIR5AlnxZUrV1i7dq1IchUVFUVhYSFKSkooKCgwYsQIli5dKsmY8J+Ax3fffceTJ09wc3Pj0aNHKCoqUltbi7m5OQUFBezbt08yo6+mpgYFBYX/6twnJCRQW1srWY96bW0t4eHh5OXlkZKSQlxcHDExMSQkJJCVlUVdXR3q6upYWlri4eEhd61c+I/hMHr0aEpKShgwYABVVVVER0eTkJBAbm4uRUVFnDp1Ci8vr7cykoT3unHjRg4fPoyNjQ1Dhw7Fw8MDJyendx4AgDfZl9DQUNLT03F1daVVq1ZykUIsLCxEXV1dPO8rKiooKioSKy3i4uKIj48nMDAQR0dHtm/f3qwsqvBexo0bR35+Pi1btuT8+fMYGxuTm5uLlpYW6urq7Nq1S2498sL7LigoIC4ujuLiYry8vOQu1yPM24cffkj37t0ZN24cBQUFpKSkkJKSQkZGBuHh4YwbN06S9hqhLD0uLo709HTi4+NJS0ujsLCQqqoqlJWVqaio4NChQ83OUCcnJ2NqavqXdkNGRoaoJPL06VPmzZsnWWuWsJ7Onj3Lzp07xUyerq4uhoaGWFhYoKOjw4gRI96ZHFNpaSnt2rXj/PnzuLm5iSSmcXFxJCQkEBsby9atW5tVOZWWlsa5c+e4d+8emZmZ1NXV8cEHHzBt2jS5V+7cv3+f48eP88MPP8i1+k3YpyEhIaxbt46YmBi0tbUpKCigsLCQtm3bsnz5cpydneX2Hf4KWVlZJCQk0L59e+7fv8+8efMoLCykffv2fPfdd2/lGDckKS0rK8PKygo1NTVqa2vFcvVdu3bx7NkzBg4cSP/+/SWRbvs3U9wEZGRkUFZWRm1tLQoKCqJWorxKigQ8fPiQKVOmoKioyKNHjxg8eDAAkydP5ssvvyQqKkouTnFOTg7FxcViZqampoYLFy5QVFTEpEmTxEtj9OjRzJ49u1lRqerqahQVFZHJZAwZMkR0pHbv3s2WLVtQUVFh5MiRcjHGhE1YXFzM/v37cXNzY9CgQSQlJXH79m2MjIzo37+/5OP+FbS0tNixYwdZWVmiA/fgwQOKi4upqanB29ubI0eONGuM7Oxs6uvrxezokiVLgP/0LSclJREbG0tQUJDYJ96cEn3h/4WHh/Pll182Klerrq4mNzeXxMRESUu36+vrKS0tRUtLi5qaGvz8/P7kgMbGxhISEkJcXJxk+0eQLKmvr0dTU5MzZ84Ab4JJ0dHRREREiMZQcnKy+F2bG9EVYpw3b96kU6dOjZyy0tJSYmJieP78uaTl9/Cfd3vz5k1Wr15Nnz59GDhwIMuWLSM7O5v58+czatQoSXvxhaCQMG9lZWUEBgaSk5NDTEwMT548ITg4mB07dkjmFCsqKv6pnLW+vp7a2lpR1zsuLo7IyEgqKyslGfN/QUFBgdLSUsLDw7l8+TI2NjZUV1dTXl5OcXExBQUFpKen4+HhIf79P4XwXrW0tOjZsycJCQn8+uuv4nPr6+uLMjLyCsjCm7X79OlTYmJiMDQ0xMHBgRYtWqChoSE3du+tW7fi6enJ0KFDefLkCQUFBTg4OGBhYYGHh4c4j+Xl5WJJc3OyqIIOp7+/P2fPnsXV1ZXXr1+zf/9+8vPz+fLLL3nvvffkRkKYkpLC+fPnSUlJwdLSEmdnZ2xsbKiqqpLLeA0h2E5GRka0adMGQ0NDDA0NGz1rXV2dqA/eVGRlZXH58mU+//xz9PT0GlU9wJvzOSEhQQxyNdchLiws5LPPPsPU1BRDQ0MsLS1xd3fH2tpa1FU3MzPD19dX0kxiw7tk8+bNdOnShTZt2oj3ekZGBvHx8Y2Ii94F8vPz6dq1q2i/CbwlzWUNzs/PR0VFBU1NTSwsLPj666/56quvKC0tRUdHp9F9LHXWVPjMnJwcHj9+zL179xg2bBjOzs4isaWNjQ1ubm5/amdqzpjwpoJTU1OTbdu2icHmFy9eMG/ePH777TfmzJkjt5bKv4KJiQkmJiZUVVXRo0cPzpw5Q3Z2NlZWVm/NB6CgoMDz589ZuXIl0dHRKCgoMGzYMJYuXSoGmL766ismTpwoaaLqX6f4LfHLL79w8uRJlJWV0dfXR09PD2NjYywtLdHX15cLA6dwYTSMnmVkZIgHuqWlJfn5+XLTw0xOTsba2lq8HPPz87l48SKOjo74+vo2koApKipCU1OzSYdPWFgYR48eRVdXV9zMZWVlBAUFYWhoyPLly+VaCiJ89k8//cS9e/dwdnYmKyuLb775htLSUsrLy1FSUnonjLLq6ur07NkTeBNsgDckEunp6URHR0sSgDl+/DilpaUsWLCAO3fu8PTpU1q3bo2dnR1WVlai7nRDNOeAFf7v1KlTCQ0NpW/fvmKGR1lZWTQQpERFRQV37twBIDc3l127dtG/f3+UlZXR1NREVVUVR0dHHB0defXqlWR7d+3atdTX17N48WJevXpFeno6Xl5eWFpaYmpq2ogsSDD0pNLuhTckG38sadXU1KRVq1aNWOSlgJDliYmJQVVVlQEDBohazBoaGgwZMoTExESx11YKlJeXExkZSVlZGSEhITx8+JDMzEwyMjLEag5LS0vGjh0r+fMKBonQ/+nv709GRgYODg50796dPn36UFZWJrfez79CXl4e7dq1E9eQsrKyWJFgaWn5J+P/bfHFF1+ITnZWVpbYx52WlkZ8fLzcM00rV67k4sWLGBsbi2eGhoYGhoaGYtmp1IFSVVVVcQ+dOnUKf39/DA0N0dLSwtjYWNTONTY2FkmZmrqHhbsnKipKLMFMTk7G1taW9PR0vL29GTt2LJmZmXKRQyouLmb27NlER0fj6elJTEwMV69epb6+HlVVVXR0dDh06JBcyzBra2vp2bMn+/fv/8vstBTa069evWLr1q307dsXa2trMQgvfL7gpHXs2LHZPZDwJsDcq1cvSktLyc3N5fnz5xw4cECUVNTW1haZgu3s7CQLugvvKTY2lrKyMpYtW/anaorMzExSUlLeSbWHEEwXuB527twpBuGbg+LiYk6dOsXTp08pKipi0KBBjB8/noiICO7cuUN5eTn9+/fH09NTbpw7Qtby7NmzXLt2jY8++ghNTU1SU1NJSUkhOjqajIwM3n//fRYsWCDJmMKaffHiBYcOHcLT05P6+nrq6+vp0KED33zzDYcOHSInJ0duSjgNUV9fz4MHD9i6dStKSkrY29vj4OCAi4sLVlZWTTqzCgoKWL16NcbGxnzxxRfEx8dz+PBh3N3d+eSTT8S/k9rf+tcp/gcQLqykpCROnTpFz549adWqlSiUnZiYSEBAAGpqanLt6x05ciS//PIL2tralJWViZH5Q4cOoaqqKjdGZD09PXR1dbl79y6jR4/m5MmTxMfHM3fuXABxMwYGBor6gcJB8U/x+vVr1qxZQ1xcHDNnzkQmkxEVFcXPP//MvXv3AGjTpg1r166VGwmU0Kdx6dIl9uzZg7e3Nxs3bqS0tJStW7dy6dIlTpw4Qbdu3eRehlNbW8uLFy+IiorCyMhIpPV3dHSUrHTO3d1dJH6Iiori/v37PH/+nKqqKtTU1DAwMMDa2hojIyOGDBkiSVlxfn4+mzdvJikpicrKSnr06IG7u7vcLmZVVVXi4uLYtWsXSkpKyGQypk6dio2NDY6Ojjg4OGBiYsKFCxcICAjg0qVLkozbtWtXMYh17do1jhw5gpKSklhaK/R7Ojg40K5dO5H9tLkQjKEZM2Ywe/ZsLC0tad26NYaGhnIh84L/XNCZmZlYWFhQXFxMVVUVWlpaZGdno6CgIOp+C32JzUFmZibffvsttbW1xMXFUV1djYODAz4+PmhpaXH06FF2795Nhw4dJJG4+isoKCiQnZ3Njz/+yMuXL6murqa0tBQDAwMmT57c6NKWJxoam/BGSk4KBk5onAF59uwZ9vb2GBkZ4ezsLGbL6+rqKCsrk9vagjeVSnfu3GHt2rX4+vqSlJQkli4LrRdSEdg0hHC/AUyZMkUsk09KShIrPurq6igoKODkyZOStE0JkjkRERGoq6ujpaVFbm4u8IZo5v79+80eoyH+KIF47Ngx7OzsSE5OJj09XWS+BkROD3k5xnFxcfz0009UV1czePBg3Nzc8PLywsvLC3d3d0mItjw9PTExMWH9+vVs2rQJZWXlPzlL9+7dY+vWrRgbG7Nz585mjWdsbMzChQsBePbsGXv27MHX15f27dtTX19PdHQ0V65cobq6mvHjx0viFOfl5VFYWIidnR2FhYV4e3tTWFj4J+dICAC8S/j5+REaGsq9e/e4du0aTk5OODs74+HhgYODA56enm/l5Gzfvp27d+/SokULLC0t2bZtG+np6Tx//pzs7Gzq6urYu3cv27ZtEzXt5YXHjx8zevRokeW/urqavLw8sURe6gqPvLw8dHV1xftNQUFB3Jve3t6sWLFC7ko4wt6JiIhg+fLlGBoa4uLiQlRUFPfu3aOoqIja2lo8PDw4d+7cW312YGAg5eXlbN++HTU1NWpqaqisrOT8+fN88skncgty/OsU/wM0JMDQ1dX9E/Pwu2Lg7Nu3L69fvxazUD///LPYF7lgwQK5XFb19fXY29vj7e3NsmXL2LNnDykpKXzwwQeiTJBMJuPBgwdcvXqV999/v0nj7Nq1C2dnZ/bv3y86nFu3buXRo0fMnDmT7t27s3TpUg4dOsSCBQvkVqoeFxeHuro63t7e5Obmcv36dYYOHYq7u7vYnyNPh1hYaxs2bODEiRMYGBhQXl6OkZERKioqWFhYEBUVxYYNG8RyyKaiISHaxx9/TNeuXUlPTyctLY2MjAzS0tJITEzk999/p0uXLtja2jbbKKqqqqJDhw64uLhw+/Ztjh8/Tk1NDdra2piYmNC7d29mz57drOdqCJlMxqxZs5g1axbDhg1DUVGRVq1a4e/vz6NHjyguLgbelO199dVXko3bvXt3MaM4c+ZMPv74Y7KysoiJiSE8PFwsY87MzGTPnj107dpVMoOzrKyMnTt38uLFC6Kjo3F0dMTAwABTU1NR9qpfv37NHgcgJiZGJAxzd3dHUVGRxYsX891331FXV0dKSgrw5oKTiowpOjoaPz8/nJ2dWbt2LZ07d6a8vBxdXV2ioqI4ffq0uDfk4RAL72nNmjWkpaXx9ddf4+zsTGFhIffu3WPVqlXo6OiI5+O7wKtXr4iNjeXp06fcuHEDFxeXZhmbDfHgwQMWLlyIuro6urq6YkDJxcUFY2NjXFxc5HImNtQ1NTY2ZsCAASgqKqKnpyd3FlkBQlDFzc3tTxqbeXl5REVFkZSUhKWlZbPGEfa9wKq9b98+kdAtNTUVeKNN2lSdz/+FxMREUecbwMXFpVGQvWF1hLxgaGjI2rVrKSwsJCoqitjYWM6fP8+BAwcoLy+ne/fu7Nq1q8mfLxClfvfdd8yZM4fTp08zatQo0bB++PAh+/bt4/nz5xgaGkpSzlxfX091dTUqKipcuXIFS0tLFi1a1Chre/r0ac6ePfunqqym4vjx49y8eRMPDw9MTU3JyclhzZo1TJo0SWR3lkcP/n+DMMetW7dmwYIFFBYWkpSUREpKCkFBQTx69IikpCR++eUXevfu/Y/vwrNnz7JlyxZRErRr167MnTuXAQMG8Msvv6Curs6sWbO4evUqnTt3liz43BCCLTps2DDS0tJEhQllZWUx8CB1tRK8qQZq2bIl69atY82aNejp6aGiokJ9fT23b9/GxMRE7gRbDfWhjYyM2Lt3r6gCUFVVRW5uLlFRUZSVlb31Z/v5+WFra4uamppY/WVkZCT3cvB/neJ/AGFh1dfX4+rqSmVlZaNDRV4MnOXl5cCbsmlBFmjx4sVcvHgRPz8/0tLSaNu2Lb1796Zbt26Sji1AiA7PmjULd3d3Xr16haWlJYMGDcLAwICnT5/y66+/cv/+fUaMGMGQIUOAty+z9fPz48CBA2K0/fXr1zx48IAxY8YwadIklJSU+PLLLzl48CAZGRnNNkL+iIZ9iZaWlpw+fZrk5GRKS0tFVrvg4GC5lxoJ83327Flmz56Nj48Po0ePpnPnzkRFRXHjxg369u0rWba8sLCQgoICbG1t0dPTEzNA8ObAS09PJyUlRfx5cw9ZExMT5s2bJ2a3MjMzSU5OJiEhQWQalRrCu920aRPGxsaN2LSLioqIioqiurq6SeyI/w11dXWEhobSsmVLsUTbx8dH/H1+fj5paWkiGUZz51a4oPz8/Lhy5QozZsxAXV2dmJgY0tLSSElJ4c6dOzg4OEjmFG/YsAE9PT2mTZuGlZUVM2fOJCwsDD09PaysrDh06BA3b94kLS2NefPmSTKmp6cn8+fP5+LFi2zYsIG4uDhRR/vZs2dYWVlJTnbYEMLZdufOHfbv39+IOKxXr14oKSmJ+1Teuq7Cd2nfvj2Ghobk5+eTlJREamoqwcHBTTY24T9n0bBhw/D19SUmJobAwECCgoK4f/8+p0+fBmD27NlyI8aDN60kjo6OhIaGvjNnGN7cSUuXLsXAwIB9+/ahpKREWloafn5+5Ofn07NnTzp16iQa5FJARUWFr776Sgw2tWnThg0bNrB582bs7OwkDdzBf84cc3NzUcrlryrO3gV7rYGBgSj7JPysoKCA7OxsYmJimp31UlBQoLa2lt69ezNy5Eh27NiBg4MD1dXVbN++nZcvX+Ls7My8efP44IMPJLnrFRQUxMBcSEgIw4YNE21HYS8OHTqUc+fOSda7bW1tjZ2dHfHx8Tx//pzc3FxCQkKIjIzE0tISMzMzkZG6R48e7zRb3KJFi0atHJWVlRQUFJCVlUV8fLyoOvFP1ltsbCzV1dW0bdtW7Blu06YNFRUVTJkyRayknDJlCgsWLJCLQ9wQd+/e5eHDh2hpadGvXz+5zmtdXR3a2toMHz6cpUuXirainp4e9+/fJyYmhi+//FJu4wsQ1nZ+fj6tW7cWHWJBPlRg324KgoKC/kTKGR0dLUpJyss5/tcp/gcQDi8jIyMiIyPZsGEDQ4cOxdDQEH19fbmxEW/dupWcnBxatmyJhYUFRkZGWFhYMH78eMaPH9/ou8kTwuf379+f3r17NyI2ERht582bx7Bhw8RN8TbfKScnB0VFRaytrcWL6/Lly1RXV/Pxxx+LkThHR0ciIyPlUionfF93d3d69OjBtm3bUFRUZNKkSVhaWrJ161Zu3brV5Ez4P0HDvrLa2lrGjh1LaWkpzs7OTJkyBV1dXaZMmSJJZqa+vp7Hjx/z3XffoayszIULF9DW1iY+Pp7Lly+TmppKp06dGD58uKQBCCHwkJSUhJaWlsjSK8y/VDq2fxwTwMbGhrCwMIqKijA3N8fY2BgdHR1J5Z8E+Pn5sWLFCjQ0NNi3bx+amprk5+ezf/9+cnNz8fHxoX///s3u9fwrBAQE0KpVqz8Z0AK7t5S9rh07duTQoUP4+fnx6aefMnbsWLFfeurUqVRXV5OcnMwXX3zRqI+6OdDT0+PTTz+lU6dO7Nq1i59//pmDBw8yf/587ty5Q+vWreV+JiYmJqKrq4uTkxN1dXViX7VMJmPAgAFiQOJdwdbWVnRmBHK5goICcnJySEhIeCtjsyGEvxcIVBpmsxYuXIiioiIDBgyQ6CkaQ2jBSUhI4NWrV0RHRzN69Gjs7e0xNTXFwMAAHR0duZRuP3r0iA0bNqCrq8tnn32GiooK4eHhLFu2jMDAQNTU1Ni7dy+LFi2SnGPC29tbfF9Tp07FysqK9PR0fHx8GgUtpURQUBDPnj1j2rRp9O7dG0dHR6ysrETtWHkGmeA/Gtt37twhISFB1K+3srIS24akqA4TPmPKlCn4+fmxePFi0tLSsLS0ZOPGjXh7e0tecioY7506deLMmTO0atVKrKoRJHUEh1UKNKziq62tJTk5WSSTjI6OJigoiIcPH5KRkcH58+ffmVNcV1dHSEgId+7coaqqClNTU/H9mpmZ4erq+la2dEBAAFZWVmJ2FBDJZs3MzMQzubq6Wu5kcfn5+VRUVODi4sL69ev58ccf0dDQwNbWFldXV9q1a8fIkSMlG09IKvj4+LBgwQJOnTrF8ePHKSoqomXLlsybN69RgEkeWL58Odra2rRp04bWrVtz+PBhnj59io+PjyT3b3x8vKj3LOzbqKgoucixNsS/TvE/gHCobd68maioKOLj43ny5Im4oS0tLdHT02PAgAGSXh4lJSX4+flx48YNKisrxQyIiYkJTk5OODg4YGVlhYaGBm5ubu+kJOaPPTj9+/fH19e3WU5afn4+1tbWhIWF0aVLFxISErh06RI+Pj7Y29uLh5sgBC71BZ2VlYWxsbEY1Z06dSqenp5YWlri5OTEo0ePeP78OT169BAzUvKA4BQnJiZiZ2cn0tGbmJgQFBRE165d6dq1Kw8ePGj2WC9fvmTz5s14e3vz5ZdfoqenR1RUFHPmzBGzBQ8fPiQiIoJFixZJ9mxnzpxh9+7d1NTUoKurK0oTWVlZoa+vz4cffigJrf4fUV5ezubNm7l27Rq6urqoq6tjbGwsGtclJSWSZTIDAgLYvHkz2trajBkzBk1NTWJiYli0aBFhYWE4OjoSGBhIUVGRpBwEwkXUvn17cnJyyM7ObmTg6erqSl7G9emnn9K7d2/27dvH6tWrOXXqFFOnTqVfv35oa2vz/fffSy5jJhhAbm5ubNy4EX9/f7Zt28aiRYsoLy+ndevWYtmrvIKGtbW1GBsbc+LECSZPniyeh/X19cTExMhlDf8d8vLymDhxIi1btsTBwQFbW1sxiOrm5kbr1q0lHU+Y0+nTp8uV3VQwhMLCwjA1NaWgoIBdu3ahpqaGnp4eZmZmaGhoMHPmTMmrtI4fP0779u2ZMmWKmDHcuXMnwcHBbNiwgffff58lS5Zw/fp1STkmIiIiOHbsGJqamri4uIhklvr6+nJZx8JnVlVV0bNnT9LT03n06JGoHaujo4OKigpbtmyRmzaygKVLl3Lr1i3MzMxQVVWlrq4OFRUVdHV1UVFRYd26dc3K3tbU1FBbW4uqqioGBgasXLmSyZMn06NHDzZv3iz+XXV1tShdJyXGjRvHy5cvWbp0qWjbZGRkcPr0aXr16iUZV4gQWJbJZCgqKmJnZ4ednR29e/cW/6a0tFRsrZE3BHsxMjKS77//nuTkZPT19amurhbVGTQ1NWnXrt1b2Rrh4eFYW1tTX19PWVmZqG1tb2/f6L4JCQmRu466trY2P/zwA/X19aJcX3x8vKjekZGRIYlTnJCQQF1dHQ4ODgwbNoydO3fSr1+/RpVfpaWl7yRZJpwVv/76K1VVVdTX17NgwQI6dOhAixYtcHNzw87OrsmJw6ysLK5fv05hYSEODg44OTmRmZkpeUXfH/GvU/wWOHbsGMnJyURERBAaGkpsbCwvX77kzp07lJWVSZ5FbEiYcvr0aVatWkXLli0pLS3l7t27/Prrr+jo6KCurs5vv/32zvpE/mgENdcgEKJp+/bto6amhgMHDqCoqCg6oIqKihQWFnLr1i25lM/NnTuX1atXY2FhwdGjR7GwsMDJyUl8Ll9fXzw8POReOi3Mq7m5OZWVlRw5coRu3bpRWVlJXl4e8KaHU4rMyKVLl3BycmLhwoWisXPkyBGio6NFo+/8+fOcOnWK2NjYZl2eDQ/oLVu2/K08RE5OjuR9mMKFHBAQwOXLlxk9ejSFhYWcPXsWHR0dkUm+R48eko158eJFDAwM+OGHH8TKiZMnTxIcHMz8+fMZPnw4e/bs4ezZs/Ts2RMLCwvJxga4fv06ly5dIjc3lw8++AAXFxdsbW3lxrxpbW3NsmXLGD9+PNu3b2fz5s28ePGCMWPGYGdnh4qKiqSkGA3bWeA/BHw3btzg0qVLHDp0iLq6OqZPny6pXI9AKFhfX4+DgwOdO3dmx44dFBYW4uvri5WVFdeuXePcuXNyj2Y3RGVlJXp6evj7+3P+/HmRKM/W1hZHR0datmzJZ5999lafKezZiooKHj9+jLW1Nfr6+iJjO7xhfg0ICJA7u6lAVFRdXU18fDyRkZFEREQQHx+Pv7+/XDLFr1+/ZsyYMeKZ7+fnx6NHj5gwYYKokztixAiWLVtGfHw8LVu2bLIhKuyNsLAwFixYQEZGBubm5pw7d46ioiLU1NTQ0dGhW7du/PDDD5I+p4D58+cDb+ZY4JJISEggPj6e3NxcuTnEQsA7LS2NO3fusGfPHlq3bi221AjfISsrq9n37/Pnzzl37pzYa2tpaYmvry8vXrwgISEBOzs7ALlJfFlYWLB06VJOnDjBzZs3yc7OxszMjA8//FCs/JMCQjCppqaGs2fPcv78eTZs2IC6ujqXLl0iKyuL0aNHSx4s+zsI6/vatWuoqKhw9+5d9PX1yc7OJj4+nqioKF69eiWWN/9Tre/Xr18TFhbG7NmzMTMzo127dty/fx9bW1vy8/NRUlJCW1ub4OBgubddKCkpYWBgQGZmJubm5iIHDbxRwKioqJBknPDwcLZv346WlhaJiYns379f5I2wtbXFzMwMTU1NJk6cKLY2yQubNm0SFWdSU1NJTU0lLCyM0NBQHj9+TEFBAfX19RgZGb11IqekpIRBgwYRGBjI1atXKS0tRU1NjYqKCrZv306bNm1EiStjY2NJ74B/neK3gIqKitgb2NABzs/PJyMjQ/JyOSHDEhcXx/Hjx1m/fn0jBr2AgABmzpzJoEGD5Lr45Q1Be3jZsmV8+eWX6OvrM2vWLHr37k1hYSF5eXkcP36cBw8e/EljtrkoKyvDxsYGU1NTKioqOHz4MDKZDFVVVfT19UXmZ1tbW/T19enWrZtcI3D19fW0bNmSnj17EhISwuDBg1FRUeHatWukp6dz//59SfrK/P39mTBhgmjs+Pv7i+ziQn96ly5dOHnyJOHh4Tg6OjbZsfm/lIcQHKfHjx/j4uLCN998w+3bt8nKymLLli1cuXKF3bt3S+rEvHjxggkTJogOcVBQEDdv3mTw4MEMGzYMTU1Nxo8fj7+/PwEBAVhYWEgS2RX+v4qKCh07diQyMpJly5ZRV1eHmpoaZmZmmJqasnbtWsnOKoGxHcDBwYGFCxdy48YNTpw4QVBQEJMmTaJfv35yccgbzpeRkRFjx46lRYsWHD58mL1793Lz5k1u3Lgh6XgNx5w4cSL19fVcvXqVY8eOUV5ejoqKCl9//TXjxo2TbNz/BXNzcw4cOCD+W+gnPnjwIDdv3hTLMv+psQn/mduQkBCmTZuGuro6enp6Ypm2uro6v//+O23atJF7RkJgMxeYTV1cXBg8eLDcxhOyHg174a5cuUJZWRkjRowQg6W2trYkJydjZGQENK3vtr6+npqaGlRUVLhx4wZqamocOnQIKysrCgsLycnJISkpicDAQLkxydbX15ORkUFlZSXa2trY2tpia2srN56ShhDWY1paWiPOhT8SfUmBxMREnj9/zuvXr6msrMTAwEDsE//uu+8YOXIkenp6GBgY4ODgIBeZsZYtW4qZrtraWioqKsTKN6mye8I+v3XrFgcOHKBv375oa2uzZ88edu/ejbq6Ok+ePGHHjh3vpHRa6DvNzs6mR48e4rwaGxtjbGxMhw4dGrH1/9Mzau7cudy/f5/Q0FBevHghnoEBAQFER0dja2uLh4cHL168EHlhpIZgE927d4/Dhw+Tn5+Pjo4OxsbGWFhYYGNjg5GREe3atZNkvE6dOhEeHk58fDwKCgpERkbi7+8vVkDo6OiIMoXy9gnU1NRERQ1ra2vq6ur48MMPkclkVFVVkZycTFxcXJMIiLW0tPjhhx+oqakRe84FYraAgABOnjxJbm4ulZWVtGjRgrNnz0r2XP86xf8DwkFVWlrKxYsXuXDhAlZWVrRp0wYnJyc8PT3R1NSUi0yQcJgEBwdTXV1Nnz59qKurE53l1q1bM2bMGLGs+P+vqK+vp02bNpw7d460tDQ0NDTEg/PZs2f8+OOPFBYWsmzZMsn71zQ0NMToe2lpKVOnTgXeOHApKSmkp6cTGRlJaWkp5ubmYpZAXhAuxdmzZ1NUVISOjg7jxo3jxx9/5JdffuHzzz+XhCRJ6OkRcO3aNXJycvjwww9FR9nIyIjU1FQxE9SUC/v/K/IQiYmJYmQ8MzMTXV1dMjMz6du3L5cuXRIz8VKgoqICGxsb8ey4ffs2mZmZjBo1Ci0tLWpra9HS0iI+Pl40qKXE3LlzxZ5SgUVcyP7k5+dLGrz7Y6DEwMCAjz/+mA8//JCtW7cyf/58Dhw4wJIlS5rNlv7fIMx169atad26NR988AGPHj2SdIyrV69iaWmJtbU1BgYG6Orq8u233/LJJ5+QmJgIIJaLySvb9HdoyA5saWmJpaUlBgYGXL9+nY8++gj458QkDcvd27Zty61bt0hJSSEsLIywsDCePn1KVlYWHTt2lDxI2fB5FBQUyMzMZO/evZw4cQIFBQWcnJywsLCgY8eOmJiYoKmpKVmvuoDCwkLc3d3x9/fHwcGB9PR0Ll26RKdOnRpVzCQnJ1NTU9NkIhnhGYW5jo+Pp1u3bmIZraamJhYWFnh5efH++++L71gqCHs3MDCQ9evX4+fnh7q6OmZmZpiYmNC+fXtkMhktWrSQy73n5+dHQkICrq6u5OTkYGFhwatXryRzIBqivr6eMWPGMGbMGAoLC4mLiyMsLIy4uDhMTU2Jj49n8+bNqKioUFZWxpo1aySpHhLecXx8PPv37ycyMhI3NzccHR3F4EN6ejp2dnaSVQwJ6+T69eu0a9eOb7/9lnv37nHnzh3WrFnDgAEDmDhxIo8fP5Z7Rcvp06fR09PD2toaFxcXIiIiSEtLk+RZfXx8GhFXVlVViYSd/v7+hIeH8/vvv1NSUiK3XnzBJtqwYQNaWlq0bduWgoIC0tPTiYiIoK6ujvz8fA4fPiwJD4y+vj6zZ8/Gz88PPT09Jk2aRExMDOnp6aJyiJmZGVOmTJHi8f4rHj9+zMmTJ2nfvj3jxo1DJpORlZVFQEAAhoaGeHp64ujo2ORzS7BTBC3vdu3aiS0QpaWl5Ofnk5ycLOUjAf86xf8TAtnHkSNHOHLkCB4eHty5c4fLly+jqKgo9m9MmzaNb775RtKxhQ2nrKxMZWUlz549o1OnTo1KBQSjV144c+YMlpaWoqyLvDQ/hejmH3s/nJ2dWbRoEd7e3nIpXxYi9crKyty8eZPY2NhGvaUCA3NcXJzcyRoEVFVVUVVVRXl5OTU1NbRv357Dhw+jpKQkSZmIIIt0+/ZtunXrRl5eHhcuXMDb27tRz2liYiIlJSWiTEdTnOL/a3kIwRGwsbHBz89PdIiLiopEhsTExETJ+tSrqqrw8PDg999/p2PHjmRmZnLmzBlat24tGnvC5VFcXCwawM3NEAjGV2pqKvHx8WKEWuhNhzdltk2RRvhvEOZXMLCrq6sJDQ2lsLCQDh06UFBQwNmzZ0lISJCrUyw8Y3V1NTKZjO7du0vKClxUVMT69evR1NREWVkZfX19kRzG0dERGxsbycu4/inKy8tRVlb+09ncoUMHli9fTteuXd+KxGfdunUMHToUT09PUWPa2tq6kQEqbwj37pkzZ7h58yazZ8/m8ePHREdHo6WlxapVq4A3Jcy+vr5vlQX/XzA2NqZdu3Zs2rSJxMREbt68CSCSvsCb7PX58+fF87IpVTQKCgqcOnUKOzs7vLy8GDVqFBcuXCAnJ6dRsEwo2Ze62kIwVnfu3El1dTVHjhzhxx9/REdHBx0dHbZt2wYgBo2lnGN4czfcvn1bZKgNCgoiNjaW0aNHY2Njg4mJCUZGRujo6DT7fFZQUBDfka6uLm3atKFNmzaN/iY9PZ2YmBhev34tWa9tQ4nF8PBwOnTowKlTp1BXVxfbHmpra9m5c6dkTrGwTtLT0xk1ahQA586dw8bGhrZt26Kqqiref/JEVVUVixcvBhA5PJKSkigoKKBXr15YWVmJcn6amppv7TTW1taKzyCTyVBRURErDITnhjd7VV7l/0LCLCUlhRs3bjQK9mdmZpKYmEh0dHSzk2bCOqqqqiIrKwtvb288PT3FNhkBtbW1lJWVyb3///fff2ft2rWUl5eL0p4vXrxg48aNBAcHo6ioSJ8+ffj+++8lrbhQUlJCSUlJ5AWQR0/8v07xP8Tly5cZOXIk06dPZ/LkySLj2ty5c2nTpg1Dhw6VfMyGrM+//fYb8+fPZ9SoUbRr1w4zMzNOnDjB48eP5Ratr66u5ueffxYPUAMDA9q0aYOlpaV4+FhZWaGjo9Nso/7vLlsHBwccHBya9dn/DQoKCuIlEhgY+CdJIJlMJmZe5Anh0CssLGTXrl3s378fDQ0NjI2NsbW1xd3dHXt7e5ydnZvNWKyiokLfvn2ZM2cOGRkZhIWFUVFRwcyZM8W/KS0t5cCBA7i6ujbrgP2jPEReXp4oD2FlZYWpqalc5SGEdTlu3Di++OILHjx4QJs2bVizZg1xcXE8evSIjIwMySLJKioqfPDBB8yePVvsmaqqqmLixImNvtP+/ftxcnKSjJBJWD8nT55k9+7dGBgYYGJigoODA46Ojjg4OGBgYCDKP0mBqKgo0tPTKSkpISIighcvXpCdnY2GhgalpaVUVlbi7e3NkiVL6NKli2Tj/hUEg13I0NbX10sabFFUVGT27Nnk5OSQkpJCXl4e4eHhXL16FRMTE7F/TUdHB3d3d2bNmiXZ2P8LkydPxsHBARcXFxwcHDAxMUFLS4tnz56Rmpoqatv+0zP6999/p6CggJ9++qnR/6mrq2tUQn737l1atmwpt7JegPv37/Pxxx/z2WefkZSURIsWLfjss8/48ccfUVRUZM6cOYD08hzjxo0jPz+f3377DUdHR1HypKCggLCwMA4ePEhiYqLYi9sUVFVVsWnTJoqKiqipqUFdXZ3y8nJKSkro3bs3Xl5eIuuyPErUhTnz9/cXmZdNTEwYOHAgvXv3RkFBATc3N1FmUUqHGGDx4sV89NFHREVFER4ejkwmIykpiT179qCsrIy2tjampqaoq6szb968ZpMlNVwj9fX11NXVAf+xAQTnvGvXrs0a56/GfPjwIVu3bqVbt24EBASwceNGSkpK+OKLLxg6dKhkJFsNx3R1deXBgwdUVVVx8+ZNfv75Z2xsbEhNTSUvL09u2VMBKioqREREiL2mQUFBxMXFERgYyP379wFEwjx3d3dWr179Vp//V+tReK/CfaioqCh3B7GkpIROnToRHx/fyCkWqt86dOjQ7DEUFBQoKSlh586dHDx4kKVLlzJy5Ejq6uq4ffs2oaGhmJiYMGLECLS1tSXl8PgrbN++nV69evHdd9+JP/vpp5+Ij49nwYIFODg4MH/+fE6fPt0omPhPIa9A4D/Bv07x/0DDnheB4lxTUxNzc3N8fHxYuHAhly5dkuvGk8lkLFu2jF27dnHmzBn27NlDeXk5hoaGLFq0SIzUSA0lJSXOnTtHYWGhGAm7ePEiFhYWnD9/nsLCQlRVVbG3t8fKyootW7Y0eay/66d5Fyx6wsbz8vLi4sWLXL58mfbt28tVbuuPqK2tRUlJiWvXrnHx4kW+/vpr3N3dCQ0NJSwsjOvXr5OYmEjnzp3Zv39/s8fr3r07K1as4PTp03Tt2pVBgwbh7e1NXl4et27d4uzZs6Snp4sXVVPfwx/lIdLS0oiKiiI4OJioqCj8/f15+PAh6enpcpGHECLJZmZmrF27FplMhpOTE+3atePzzz9HSUmJyZMnY2ZmJtmYvXr1YvHixVy4cAEdHR3mzZtH3759yc3N5ebNm9y9e5eoqCixIkGKC0z4/9OnT+e9994jIiKCoKAgQkJCuH//PrW1tSgoKLBu3TpJzouUlBQ++ugjVFVVqaioQF9fn44dOzJgwAD09PTw8PCQvCcQ/mP0/NEgUlRUFMvWSktLuXHjBp9//nmTS1v/CE1NTQYNGtToZzt27ODSpUt89dVXJCUlkZmZSXx8PCUlJZKM+U9QUFCAvr4+oaGhXL16leLiYtTU1NDW1qa8vJwPPvjgredg3rx5zJgxg969e4vtKsIara+vJygoiD179nDr1i1JmPD/CsJ6zs7OFoOAioqKaGpqirrYM2fOJDMzEz09PcnvCF1dXRYvXszMmTOpqakRsx0NtYu/++47evbs2ej7vg0UFRX59ddfKSoqIiMjg6SkJNLT0wkNDeWnn36isLAQmUyGgYEBnp6e7Ny5U9JnFIjUAKysrADQ0dGhsrISTU1NJk+ezLfffsv48ePlcg/q6enRvn172rdv3+jnycnJREZGEhUVRVJSEqGhoZIrTggO0x8hOFRSBACEOzM2NhZ1dXW6detGbm4uVlZWFBQU0KlTJ77++mvS09PloqE7depUZsyYwd69exkxYgQDBw7k3r17bN26FVdXV0kd8f8GIanQ8N6prq4mMjKSkJAQXrx4IVbhNbcaoeF7lXcmXICpqSne3t6sX7+eb7/9VpSMkxq//PIL165dY+LEiWKQeefOnWzZsgV9fX3Kyso4c+YMe/fuxdDQUPLxBZSWlhIVFcW2bdvEe+Hu3bsEBwcze/ZsUVFjzpw5/Pbbb4wZM+atqwD+yN/xLvGvU/wPUFhYiIWFBeXl5QCi5iiAh4cHP/74o9wlOCwtLVmxYgWZmZmkpqaipqaGpaWlXMdVUFDAzMwMMzMzcnJyKCwsZP78+QwYMAA1NTXS09PZs2cPV65cEUvr3vZQEy6Ov9sA72JjCGMcOHBA7DUS+iEsLCywsLBAX18fb29vufcKPn78mPfff5+vv/4aoJGMQl5enqQaswMGDGDAgAFUVlaKWbWUlBROnz6NpqYm69evp2PHjkDT34NQ4iTIQwilmA2fq7KykoyMDMllE4KCgrh//z4ZGRn07NmzEUnd5s2bCQwMRFFRUdLsKbwxkEeOHEmfPn0alQ5lZmaKWbh58+aJ+qZSRkOVlJTw9PTE09OTESNGAG/md/LkyWIwQArU19czYsQI2rZtS5s2bTA1NW20joS/kXr/CkZPaWkphYWFlJWV8ejRIwICAigtLcXf3190ShtWPkgBIbNUW1uLsrIy6enpdOzYsRHpU3FxsaR79H9BT09PDEYK+yg6Opr4+Hj09PSapIjw3nvvMWLECFatWkX79u0xMjJCJpPx6NEjLly4wKVLl9DT0+Orr76SG/O0goICNTU1uLm58erVK7p06YKWlpZ475qYmJCamipZ0OPv8Mdgd5cuXbh8+TIKCgrNJrL549lTXV1NRUUFZWVlFBcXk5ubS1JSEpGRkXK7B8vKyvD09OTKlStMnDgRDQ0NsrKygDfrvaioSK4axbW1tdTW1iKTycTyf+GO6NOnDzU1NaKTLhWELBT82fiWR2YqLy8PU1NTkpOTRfmh3NxcAAwNDblw4YLkY8IbAr7t27eTlpaGi4sLhYWF3L59GxMTk0bKJvKGcA9ER0eTl5eHvb09JiYm4j0lcB5A86sRGmYY5W07CrauoNKRmJjI9OnTUVVVRUNDA2tra4yNjfn444+bzX6dm5vL1atXWb9+vRhEiouL45dffqFTp05s3boVmUzG2LFjuXXrVqM5lRoxMTEYGBhgYGCATCajpKSE8+fPo6ury6hRo8T3bWJiQkRExFs7xJGRkaxcuZI5c+ZILiH5T/CvU/xfILxcdXV12rRpw/z589mxYwdmZmY8f/6czz77jICAALlo2gmoq6sjKCiI06dPo6Ojg7e3NzY2NtjY2KCqqip5n88fUV1djbKyMrdv30ZbW5tPP/1U/F66urrMmTMHBQUFcdO/zaUizG9ycjIpKSlYWVnJXU/uv2H9+vWkpKQQGhpKZGQkL168EGnlNTU1xd4yqdGQ2Mba2rqRsdXQsZAHM7OCgkIjR8bLy4szZ85Itq7+LiKfmZlJYWEhxcXF3L9/n9zcXLFPUAo8fPiQtWvXkpSUhJaWFmfPnmXatGlMmTJF3K/yPnD19fUbvT9HR0fmzZuHqampZLqm/w2C4aeqqsrs2bPZsWOHZONaW1uLUjkCVFVV5WqU5Ofnc/PmTaKiokhMTCQmJoaMjAwUFRXFDPKAAQPo1q0bXbt2ldyQFgxoYV5DQkIYPnw48J9MqrxL9QTk5eVx5coV7O3t6dKli7jPbGxsGvWYNRXTp0/Hz8+PVatWMXHiRH766SeCgoLQ09Nj6tSpjdjV5QGhVLt///789NNPdOnSBTc3N5YuXUrv3r159uyZKFX0LqGuri4JUV1DDoCTJ0/i7OzM4MGDxbJhHR0dLC0t6dixI+Xl5XLT2jYwMKBr164cP36c3r17Y2dnx4EDB3BxceHy5ctiz5487AwhIyt8bllZGXV1dTx//pyAgAAyMjJ4+vQpJiYmnDt3rlljZWRkiIoSfxeEl7pkU3jHnp6e6OnpsWzZMlatWkVdXR3x8fHAG5koqdvDGlYeGRkZif3pampqrFy5kqKiondWlip8lxs3brB3717S0tKorKzE0NAQU1NT2rRpQ2VlJcOHD3/r4PQf2zngP0GOvLw8amtrCQ4Opn379nI9l/ft20fbtm1ZuHAhVVVVpKamkp6eTmZmJi9fvqR///7NHiMkJAQlJSUxqF1WVsbp06dFtQNtbW1qa2v58MMP5e4Ul5aWYmhoSExMDO7u7rx+/Zp79+4xePBg9PT0RJ8hLS2tSfNeW1tLdnY2y5cvZ/jw4QwfPhw1NTUxKC3vtfuvU/xf0FDiZNKkSdjY2KCsrEyLFi04ceIEkyZNIi4uThI24D9CuIQePnzIypUrUVNTo6SkhAMHDqCnp4dMJqO2tpauXbuyfv16yccXIFxYlZWVVFZWij9vqKkrEAoAf1na+HdQUFDgyZMn/PTTT8TGxgJvshTLli17J07DH+Hh4YGHh4dYJg9vyhMFdj95oVevXlhbW+Ph4YG6ujpHjhzB1dWVLl26yIXYTMDfGVpSlY/Bm3VSWFhIfX09WVlZ3Lt3j9jYWNLS0oiMjKSoqAhAkoujIfbs2YOXlxdHjx5FV1eXjRs3cvnyZQYMGICTk9M7KcuHxnOsqqoqF2KIhv3o0dHRmJubo62tjZqamljZUF1dzfPnzyUtg/xvRok8cOHCBdasWYOnpycODg4MGjSIFi1a0L59e44dO8bDhw/ZuHGjXMZu+EwNW2qEEvF32fsUGxvLt99+S2RkJGvXrkVBQYGcnBw2b97MjRs3UFdXZ9KkSYwePbpJ77u2thZjY2MWLlzItGnTuHXrFh4eHnzzzTcMHjwYY2Njue8fYT6HDBmCtbU1pqam2NnZ4erqypw5c1BVVRW1XeXdPycPCGW1c+fOJTw8nHnz5jUy+i5dusSNGzeYOXOmKOEjNYR1/Omnn9K7d290dXXp3bs3Dx8+ZPny5VhbW8uVxba6upoXL16Iwa0XL16QmJhIVVUVCgoKojSSFAzJa9euJT8/XyQgtLGxwdraGhMTEwwNDdHQ0JD87BLWpLq6OnPnziUoKAgtLS08PDzYs2cPp06dQklJiQULFkgynrAn/7gXIiMjiY2NJSsri2fPnnHv3j3mzZv31trlTYEwn1u2bMHa2ppJkyaxbNkyWrZsSXp6Ort27UJVVVVssXqbc0V4zpKSElRUVIiOjubFixekpqYSFRVFYGAglZWVvHr1Si7PJuyfvLw8Jk2a1Ohur6mpoaioiOzsbEmClNHR0Tg5OYnPHBMTw40bN+jYsSPe3t6izVZfX09xcXGzx/tvaNOmDfr6+vzyyy8MHTqU7du3i3rb8IYYODMzkwcPHoiVhm8DV1dXNm3axLFjx9iyZQvXr19n1qxZtG3bVvwbed4//zrFf4OkpCRkMhl6enqoqKhgY2PDxIkTRTbOSZMmcePGDSZNmiRuaCkhZCOEaO2aNWv4+eefyc3NZcCAASITqrwp9YVNOHr0aCZOnMjy5csZMmQI5ubmmJqacvr0aRITE5k9e3ajv/8nqKqq4ueff8bU1JTx48eTnp7O3r17sba2Zvr06e+0p6CqqopLly7x5MkTTE1NcXNzw8XFBTs7O7y9vQH5bMSKigrGjBlDUlIS/v7+ZGVlUVpayrRp0zA1NcXJyQk3Nzc8PDxwcnKSi1P1R0j1jPfu3SMoKIiEhASeP39Obm4uMpkMZ2dnYmNjcXZ2Zv78+Xh7e0ue8QkODmbx4sVie8GUKVM4cOBAI3IVeeOvsivyWEPCZ966dYvvv/8eQ0NDTExMcHR0xM3Njerqaq5evSqpdE3D8sN3BRMTEwwMDEQys06dOlFfX4+ysjKFhYVykbeCN+zOQUFBmJubo6uri66uLmVlZdTU1IjM7ALeRbBlzZo12Nrasnr1arGyZtGiRdy/f59Ro0ahra3NwYMHcXd3F8+uf4qGa7Zr166MHz+ew4cPM2fOHJEwRt7VSc+ePePatWtoaGjQv3//RsbQ999/z6NHj/Dw8BDZxeXtEDc8M6R8t+vXr0dfX5/Hjx//qQrI3NycrKwspk2bxrFjx8SeX6mwb98+njx5goWFBUOGDBHXiY6ODgsWLCAyMlIk9ATpSbbmzp3Ly5cv0dLSoqysjNraWoyMjJg8eTK//vorPXv25Ouvv5asPN7MzIyqqioiIyMJDQ0lJSUFFRUVUULNxMQEGxsbtLS0eP/995tN0vfkyROMjY0xMzNDW1tbLBWGN3eRhoYGCQkJDBw4ULKKJQUFBaKiosjOziYtLY1nz54RGhpKfn4+hYWFKCkpYWBgQJ8+fd6JBrXwnUpLS0lMTOTAgQOYmJiwZ88exo4di5mZGQsXLqRbt25vRQZYU1Mj2hRZWVmEh4cTFBREaWmp2Lri4eHBwIED6du3r1zL/wGWLFnCuXPnmDZtmtgbLsy1VNV9JiYmlJaWEhQUhJeXF1evXiUrK0u0u4V5Cw8Pl3uvuLq6OhMmTGDt2rXMnj0bXV1dli1bhre3N5mZmWRlZbFnzx5SUlKaFPBRVFTEzc2NFStW8MEHH7Bt2zbGjRtH3759+eKLL2jRooVc79h/neK/wZdffkldXR2urq7Y29tjb2+PhYWFuNA//fRTPv30U7kZQcJnhoSEMGnSJFG6plWrVgwaNAhNTU2OHTv2TsTX4Y3o/LfffsvBgwfx8/NDU1OT9PR0cnJyWLhwoVg+/TZzERYWRmZmJgcPHhQzwxoaGpw4cYIZM2a8EwNTMPD27dvH0aNH0dbWJioqipMnT1JaWoq2tjYymYyVK1fKhdBMTU2NKVOmIJPJqKmpISsrS2S4jYiIICIighs3brBr1y5sbW25ceOG5N/hrzJ+UmD+/PkUFBTQqlUrxo4dS6tWrVBVVcXNzY3Ro0czZMgQuQR1srKyqK6uxsLCQlxDysrK1NXVyV3QHv6zpoTI7R8zqVJD+MxBgwbh5OREQkICISEhhIWF8eTJE4qLi+nXr5/Ypy7VmO+aCKNPnz5YWVmxb98+ZsyYQatWrfjiiy/w8fEhPDxcbpJBL1++5Ntvv8Xd3R1jY2Ps7e0pLCxETU2N2NhYTExM0NXVFbNN8kROTg6BgYGcPHkSe3t74A350/379/nss8/49ttvqaurIzs7mytXrtCmTZu3cmg++OADUWPSxcWFQYMG8ezZM06ePImXlxdqampydYhPnDjB5s2bxRLlGzdusGbNGtEhF1QP3iX+yulu7pmZnZ3N69evOXXq1F8azl27dqVt27YMHjyY27dvi21LUuCHH37gt99+w83NjRcvXnDv3j22b98uZqSdnJxwcnKSbLy/wq1bt4A3VVIDBw5sFLw5fPgwPXv2xNzcXLIqAIHUsKioiJ9//pmysjJ69epFbW2tqENdWVlJmzZtmn0nFRYWsnjxYoyNjdHR0cHY2Bhzc3OsrKywtLTEwsKC8ePHS1rhUFVVxZQpU1BUVCQ2NpbCwkJ0dXVxd3fH3d2dHTt2iOWo8g5qCWhINGZiYoKJiQnZ2dmYmZlRVFREmzZtGDx4MOfOnXurrPWJEyfYvn07jo6Oohxd37596dOnD/fu3SMwMJAjR46goaEhN7It4d09e/ZMVH8pKCigffv2uLi4YGFhgY6OjmTvt2fPnpw6dYrt27ejqanJlStXGDVqVCNVh3v37hEdHf1ONIp9fX3x8PAgMzMTe3t7UYrw8ePHbNy4kdraWn766ac/kej9Uwjz6+3tzcGDB/n99985cuQIixYtYsSIEQwYMEBuZGL/OsV/gylTphASEkJISAjPnz+nsLBQLOmxtrbGy8uLjz76SG5yQcKhpaamJpL1KCsrU1ZWRmVlJR07dmTVqlXvzDCVyWR8/PHH+Pj4cPv2bVJTU+nVqxc+Pj5NLu/y9/fH0tISLS0tkaTn7/pp5QGBaRLg1KlTjBs3jmHDhqGurk5+fj5ZWVkkJSXh5+cnmYbgX0GQolBVVRWJvby8vEQiptraWpKTk0VNbHmML4wj5WX56aef8vLlS6ytrWnVqlWjAzw7O1vy7IeAkJAQzMzMGkWIw8PD0dXVRVNTU27rqqamRnSG79y5g6KiIj169BB/L6/gg/B5qqqqtG7dmtatWzeSiKuvr6e6ulqy0un/KyIMFRUVvLy8WLduHRcvXmTXrl189tln9O/fn+DgYMmJtQQ4ODjwxRdfkJKSQkxMDAEBARQXF1NeXi4av5aWlujp6dG5c2dJ9ZH/iLCwMLS1tUWHuKCggNOnT6Ovr8/o0aPFdou+ffuyadOmt9rP1dXVvPfeeyQkJPDw4UMxMAhv9lRFRQVeXl64urpiZ2eHnZ2d5M934MABPv30U/r06UNxcTHr1q1j69at7N69G3V1dUnZgf8Jnj59SmxsLIaGhtjb22NpaSkGSpuDiIgI9PT0/mtppbq6Oh999BEPHjyQzClOSUnh7t27rF27lvbt25OWlsbKlSvZtm0bu3btAt6NHMrJkyc5ePAgAQEBAGKwVFlZmYKCAjHYL6XTqKKiIjLyb9q0qVFGLSwsjO+//77JRnxDyGQyxo0bR2ZmJsnJycTGxhIUFERtbS0qKipoa2vj7u7OJ5980mz9WgHx8fE8efIEPT09Jk+ezKBBg9DT06Ouro7S0lJ27dolZoff1d4R7iUlJSX09PQ4d+4cHTt2RF1dnYKCAuDNnSkwoP9T+yMnJ4e8vDysra2ZN28e7du3F//vo0ePsLCwEB1iedmPwro0NTVl8uTJFBQU4O/vz/Xr1yktLUVTUxM1NTU+++wzJk2a1OzxtLW1mTlzJj///DNhYWF8+umnTJ48GX19fYKDg3n9+jXbt2+nT58+TSpZflvU1dX9ZSbczc2NefPm0bNnz2Zl6GUyWaN2ku7du2NjY8P169e5ePEiYWFhfP7553IJ3v3rFP8NhgwZIurzwZsSuujoaIKCgggMDOT27dtcuHCBI0eOSM5eK6C2tpZu3bqxatUqLC0tsba25saNGyI5VU5OjmgcyRt5eXlcvXqV7OxsBg4c2OhCaWrE09/f/0+lHpGRke+sT08mk4ljVFdXM2jQIFFzU0tLC2tra9q1a8eQIUPk9l2qq6vZsGEDv/32G3PnzhUZg+/evcvt27cxNjZm6NChcnvPd+/e5cyZM5SXl2NtbU23bt3o3r07ysrKzb5UpkyZgp2dHXv37uXEiRO0bt2aL7/8knbt2lFWVia3jE9ISIjIEC/ox8bGxmJra9vooJa6BFjQrbWysmL37t20atWKtm3boqKigpqamriGnj59ipmZmSTvtLKykhs3bpCUlISVlZXoDCclJRETE4OdnR0ODg6S9hL/XxJhCPrDI0eOZPjw4ezevZsDBw5QXV3N7du3sbKykjyAZWVl9SetxdTUVGJiYggJCRF1VsPDwzEyMpKrUyz0qAmZoJiYGO7cuUP37t2xs7MTjUOBWAj+ubGprKzM9OnTRUO1sLCQrKws0tPTiYuLIyAggDNnzpCSkoKBgQGPHz+W9NkSExPJyclh3Lhx4Md0KwAAkP9JREFUYhniggULGDdunJg5fpe9w6tWreLcuXMYGRmJvfnq6uoYGRmho6PDwoULm6z+kJeXh6WlJXl5eX9bYimTyVBVVRUDE1Lg9evXaGhoiFVPenp6fPTRRyKDuXCXyzvY7urqysyZM7l27RrHjx/n8uXLfP7557Rs2VKUeZTSqRHWf0BAADo6OqLdIewNDw8POnbsKLKbNwcNCUnhTYtUamoqiYmJxMXFkZSUxI0bN3j69Ck7duyQJDhsZ2fHypUruXbtGnfu3BGTGGpqarx48QI9Pb3/E56W+vp6PDw88Pb25sGDB3Tv3p3a2lquXr2Krq4uJ06cENsj/mlWd9KkSTg7O3PixAkWL17M4MGDGTduHDo6OgQGBoo60+8iYWRvby+WMNfV1ZGTk0N6ejrx8fGSJ1O8vb05duwY8B8C3NraWq5cucJvv/1G3759mT9//jt5z393Dgu8PM2BcAY1HEMmk+Ho6Mj7779PfX09hw8f5v79+5LfQfCvU/y3EKRk4M2Bqq6ujpeXF15eXnzyySeUlZUxZ84cjhw5wvLly+XyHRQVFRkzZgylpaVUVFTQokULNm/ezKpVqwgODqZVq1Zyz6TKZDJiY2P58ccfyc/PJywsjNLSUnx8fLhw4QJff/11k52biIgIFi1aBCAacKGhoaJRKUg1yOMZ09PTuX79Op06dRLLmU6cOMG3337b6O/kna3eunUrd+/eZdiwYbRu3Rp4o3+6a9cuDAwM0NTUJCQkhJUrV0rWXyU80969e9m3bx9ubm4YGBgQFhbGuXPn8PHx4aeffpKk1Lh///7079+fixcvsmPHDubNm4e7uzu6uroiM6HUcyxk+GfNmoW6ujqOjo48efIELS0tIiIi0NfXx8jISPKIudArfe/ePfLz8wkPD+ebb74Re9asrKwwMDBgzpw5rF+/Hnt7+2aV0BUXF7Nt2zZOnz6Nnp4eGRkZnDt3jlmzZvH999+TkpJCXV0d48aN47vvvpPsOf8viDACAgKIjIxk9OjRjbSKJ0+eTI8ePThx4gSnTp0iODiY7777jjZt2kg2tjAeIBLZCNqb3bt3F/+urKxM7tqYrq6uHD58mN9//51u3bqxY8cOVFRURJITRUVFysvLCQgIEM+Tt/lOQquBwIIsGOyCdE5paSm5ubmNSBelQkBAACYmJmhoaIiZvZKSEtHIe1fkePAmG3XlyhWmTJlC27ZtycjIIDU1lbS0NLKyskhMTGwWo62XlxeHDh3iwYMHDB06lJqaGlEHuq6uDmVlZaqrqwkLC2u2kdkQ/v7+YhBfqM5KT08X75Z32RJhamrKp59+io+PD/v27WPr1q3U1dVhZ2fX6L1LAeGMtbKy4sKFC1y9epWBAwc2IhKNioqSJFArvEPhrFBTU8PR0RFHR0d69eoFvFFfmDlzJg8fPuTjjz9u9piqqqqMGDECFxcXDh48yJo1a9i3bx/z58/n6dOnIonnu4awniZNmkRSUhJ6enqMHDmS+fPnM2XKFHr27MnIkSOBfx7wEvq+7ezsOHz4MHv27OHw4cNMnz6d6OhoSe+6v4NwFu3evVvsTRd4dlq1akWrVq0aVWtJgYZVMkKQTiaT8dVXX/H111//nwQ9/ggpEg0ymYyoqChycnIoKSkhJCQEPz8/srOz0dTUFFUehEpKqfGvU/w3+CuDub6+ntraWpSUlNDQ0MDCwoLs7GxJxy0tLaW+vh51dXVkMhmmpqbMmTNH3Aiff/45hw4dwtfXl6+++krSsf8IYYFv2rQJPT09Vq5cyYwZM3B0dMTZ2ZmwsDAOHDjAsmXLmkRMkZSUxIEDB0hMTMTNzY0OHTqQkpIiltnKs8wnIiKCtWvXoqSkRE1NDUpKStTX11NSUkL79u3x8PDAzs5O3NzyMMjKy8s5f/48S5YsETV0MzMz2blzJ15eXixduhSAadOmcfPmTcaPHy/JdxA+Y8+ePcyePZv+/fujq6tLTU0Nr169Ys6cORw7dowvv/yy2e9AiMR/8MEHdOzYkVOnTnH06FHq6uq4dOmSZM/UEMOHD8fe3p6UlBRSU1N59uwZBQUFFBQUsHDhQrS1tTExMUFNTY1JkyZJwg4Jb1odtmzZQmpqKmPGjKFv377k5+eTnp5OQkICjx8/prKyknbt2on9r83JfD19+pT79++zZcsWfH19SUhIYM6cOSxduhQ7OzvmzJnD77//zqlTp/D19aVz586SPOf/BRHGuXPnqK6uFp1iRUVF0XFyc3Pj22+/pUWLFqxfv579+/ezdetWycb+O1mxhu0XMplMzG7KC/X19Xh6etK+fXsWL14snl1z584Ve24B9u7dS3R0tNhHKUV2VWgLUFFRaaS9LSUaVg4JrSIhISEiueC7cNiEcz4iIgJtbW2++OKLP/1NWVkZBQUFzZpXe3t7XFxc2LdvH05OTiIJE/zn3jt16hSxsbFMnz69yeP8EcHBwaIxKcxnaGjo/2PvvMOjqta+fadNeu+9kgSS0Kv0KiCISPOgAlJURKWKUlQEVFBARIpKERCQIr2pdA69hBJKepnUSe9lZpL5/uDb+00UPUJmEgL7vq5zHU3irLX37L3WetrvEQ3vujSKhXsdFBTEV199xeuvv86yZcu4fPkys2bN4q233hIzbWqLcF0DBw7k1KlTfPXVV4SHh9O8eXMMDAzYvHkzSqWSAQMGaGWshwksVl8rhL7ugnGjLZo2bcqyZcsYNWoUy5cvZ+7cuZSVlTFo0CDKy8sxMTGpE+eScJZKTEzE2toaLy8vsad5hw4d+Pnnn5HL5bRq1eqRskDUajVqtRoTExNCQkJYtGgRw4cPZ9WqVWLv5aioKJo0aaLT9Vi4f0ePHqW0tFQMnNnY2ODk5IS7uztWVla88sorWsvSetj90dPTe+xsFV1Q2+dq6dKlHDlyBH19fYqKiigtLcXLy4vWrVvTu3dv7OzsaNGihdbKDh6GnkbXru2njIqKCmJjY4mNjWXTpk0MHTqUkSNHau3zZ8yYQXx8PC1btsTf31+sY7KwsMDc3Fx8werSa96qVSvWr19P8+bN6dSpE19//TUdOnTg5MmTLFmyhPXr1z9yFFOpVLJ48WJRCTI3N1eMxgQGBtKqVSuaNWtGSEgIfn5+WjeQi4uLSU5OpqSkhOTkZDG9KTIyktTUVEpLSzExMcHQ0JCpU6fy6quvanV8gOvXr/P+++9z9uxZDAwMKC8v58cff2TdunWsWbNGdA7s37+fHTt2iKkz2iA5OZkRI0Zw7NgxzM3Na6RY7tu3jw0bNnDgwAGtjSegVqv573//y88//8yFCxcICQnh888/F9UndUFhYSHp6enExcURHx9PcnIyWVlZJCYmsnnzZp3UNp87d66G2nNOTg5JSUlkZmbSrFkzrUT9P/nkE0pLS1myZIloIC5cuJCrV6/yyy+/YGZmRllZGbNnz8bFxUU0krTBnyPcghBGTk6OToQwXn75ZQYOHMgbb7whpo7Nnz+fgICAGutvZmYmeXl5OlfgrG8uXLhAbGwsbm5uokNt27ZtbNq0iaSkJD7++GOGDRum1bR5XTN06FAGDBhQI/V0+vTpODk58eGHH6JWq4G/d1JoA2FfvXz5Mlu2bGH69Ok6qZ2GBw7Q999/n1u3btGiRQuaNWuGi4sLFhYW3Lx5kz179jBz5kxGjhypte+xdevWfPHFFzXaDg4YMIAJEybUKBerS6qXXRQXF/P777+zceNGEhIS2LJli5jx8LgI64VARkYGO3bs4MSJE6SlpVFeXk7Hjh159913CQ0N1em5qqioiMTERE6dOsWuXbvYtGmTVnVpqt/L0tJSfvvtN3bu3ElsbCwjRozgnXfe0bkas1Kp5MCBA8ybNw8AR0dHXn/9dcaOHVvj7woLC9m3b5/YWu3fsG/fPg4ePMj69euprKwUo/FKpZKTJ0+yefNm7t+/z8iRI5k4caLOo6c5OTmkpqYSHx9PfHw8sbGxXL9+HZVKRZcuXVi+fLlWxnlS2s7peh4zZsxAo9Hg7++Pj48PnTp1wtLS8i+aKFJLpieIsrIyvvzyS65du8Z//vMfrfdXbdu2LVVVVVy/fp19+/ZRXFyMiYkJzs7OeHl5ERYWRkBAAE5OTmL9jS7Jzc3F0tISBwcHysrKKCkpERfxRo0akZqaKnoAHwWVSsXbb7+No6MjCoWClJQU0tLSSE9PJzIykhs3bnDkyBGx1cq5c+e0el0WFhY0btyYyspKWrdujVKppLy8XPROKRQK5HI5N2/exN3dXatjC8THx+Pn5ye+3KmpqRw+fJiwsDA6duwoGqpKpVLs51tbhMWksrKSsLAwUcWw+iGzeq87pVKJkZGR1hYgQ0NDunfvTvfu3dm/fz+zZ88mKytLq0axEMETNkwrK6sadWQCCoVCZ+rtzZo14/Tp03h6euLj44O9vb3W1RLv3LnDCy+8UONnGRkZdOjQATMzM9RqNaampuJ3qE3qWggjKSlJFPQTNuULFy6IadJCFo+gcqpt/rwJVz+Q1QfPPfdcjci/RqPBzs6Orl270qlTp8duuSJEtP4sCFcXLbjKyspYtGgRu3btwt/fn86dO3P58mXeeecdAJ32bBcQrj0/P5+oqCjmzp3L8OHDxfRIe3t7rX3nzs7ObN26lc2bN3P69GlOnTqFSqWipKQEJycnli5dSp8+fbTmAFCr1ahUKqZNm4aTkxO+vr6EhIQQHx//l1T1uqT6/bSwsGDIkCGEhISwbNmyWre0iYyMZMeOHXz66aeUlZWRlJSEp6cnkydP5o033kCpVGJhYSFm5+mahIQERowYgYWFBXPnztW6UGv1e2lmZsbLL79MQEAA27ZtY9u2bRw8eJBjx47p9Nx47do1vvvuO55//nmGDBnC/v37+fnnn+nWrRt+fn7Ex8ezdu1aTp48iaGh4SMpcV+/fr2GUS+sRzKZjL59++Lp6cnevXtZv349GRkZLF26VGfXCYj7utB9BR70kf/ss89qiGzWlifBIAbo1q0bXl5eNG7cmCZNmtC4cWNcXFy01tVjyZIlD/25TCZDqVQCD/YBXd4PySh+RMzMzBgzZgxz587VSXRr+PDhDB8+HEA0zqKiokhMTOTevXusXr1a/Nvz58/r3Cg2NDSkadOmrFmzhgEDBmBra4ujoyMlJSX88ccfuLq6YmBg8MgepF9//ZW1a9cyceJEunbtSqtWrWjVqhUVFRUolUpKSkrECJ+uuHTpEr/++ivvvvsuPj4+yGQyrKysyM3NxcfHh44dOzJ48GCdRVusra2pqKgQe/bu27eP1NRUUa1QqJuMjIzUWn9i4dAXFRVFTEwMN2/eJCkpiebNm+Pj40NkZCR//PEHU6dOBXjsa/8nT55g7A8aNIjOnTtr/SD2sOewevqacODXtkEsvANXrlxh1apVxMbGUlZWJjqVQkNDkclkdOzYUSsbplwuF2vUhO8pJiZGjBwKREdHM3DgwFqPJ1DXQhhlZWWUlpaKNdjCc5WdnS3WR+rp6enUaNLT00OpVIpCX3Wl4Pq/qG7A9u3bl549e9ZKJO/v1NHrIitpypQppKWlERcXR0JCAt999x16enrMnz+fb775Bh8fH0JCQggODmbo0KE6+b6FZ/q///0vBgYGJCUlsWLFCqysrMSyC1NTUyZOnFjrbA+NRoOhoSFjx45lyJAhpKamUlRUhIuLi9ZKOv7Mzz//jEKhIDo6mqioKM6cOYOLiwvTpk3DxMQENzc3/P39adq0qVZUc/8XfycCFxwczI8//ljrz8/JyRHVjU+fPs3UqVNxd3fHw8ODxo0bi9l4Qls1XaeiBgUFsX//ftzc3OrMASFo4fTr149ff/1V52fGU6dOERoayvz58zE3Nyc4OJjZs2fz888/4+npyVdffYW5uTl9+vQR09X/bcLq3bt3GTJkCPDw8rqQkBBCQkJo2bJlvdRQA/j7+xMcHExKSopWPq+iooIDBw6Qn59PaGiozloP/i/KysoYNmwYycnJhIeHiwKtJiYmODg4EBAQQNOmTQkNDcXLy+uRM2wyMjJ45513CA0NxdfXlyZNmtCoUSPRMVZXWU+SUfyIyGSyvxw6tY2wQJiZmYk9km/fvk1hYSFOTk4olUr69++vsz5d1edhZWXFqFGj+OSTTzh27Bj6+vps2bKFS5cucf/+fdGL/6hZ+K1ateL69ets27aN33//nUGDBjFw4ECMjY0xNjbG0tISFxcXnSgUV1ZW8ttvv/HRRx/h5uZGaWmp+DuNRsPXX39NVVUV8+fP1+nC2qtXLzZv3sy0adMICgri999/5/nnnxejPEZGRly4cIEbN27w2muvaWVM4dBXWFhIaGgoeXl5nDt3juvXr5OZmUlaWpq4Yefm5mJvb0+nTp0euY5QT0/vbx0l1TczbTW3FxBETv4s0FYXfXWFd+Cnn36ioqKCefPmsWbNGuzs7LCwsGDHjh3Ag40batcCq6KiguLiYrZs2UK7du1o3Lgxnp6e5Ofni2nbQp18enp6DU92balrIYzIyEisra1rrHcKhQJ9fX1cXFxqiGDp4jsuLi7m2LFjXLlyhZSUFFQqFf7+/nTt2pVu3brVa4ryn6+3NgZxeXk5Z8+eJTs7m5YtW4pOX5VKRXR0NG5ubjqrJ4YHbaQ0Gg0VFRWUlpZSUFBAVlYW2dnZJCYmEhcXx82bNzlx4gSvvPKKzuYBsHDhQgoKCsSyi4SEBORyOVlZWSQnJ4u9SWtD9e/ozwaZLtIDBQc3PLjXKpVKrI/OyckhMTFR7LBx5coVxo8fr/Oetn/32dpSsu/YsaNYhtSxY0dWrlxJXFwcd+7c4fTp0+zYsQOVSoVareadd97Rav32wzA2NtbJmeaf1kDBkdi1a9fHziB5FMLDw3nhhRcwNzdHrVZjZ2eHg4MDv/zyCwEBAbz11lsMHjxYNJoE5/+/ITU1FY1GQ2pqKqamppiZmWFsbPyXa9aVCBP8n1N43759Ys2rg4OD2KJIo9EQERFRQ4TxccnNzWXFihXs2LEDe3t7srOz6dWrF8uXL6+TzJnqCM5AQ0ND4uLi+Pnnn7l27RpDhw5FqVRy584d1q5dS1lZGT4+Pvz222+P9PklJSXY2dmRmJjI5cuXKSsro7KyEltbW5ydnWnUqBHBwcE0atQIf39/sTeytpFqih/C/zpk6TKfXRB9ggeemRs3brBmzRrxYNi/f39Gjx6Nra1tnaVUVFZWcuPGDf744w8iIiKIj4/H19eXN998U4xUPQ4FBQVcu3aNgwcPcvr0aaytrRkwYAADBw7UaY1pREQEn376KZ06dWLatGk1vk+lUsmWLVtYtWoVHTt2FFtV6IqkpCRWr15NfHw83t7ezJo1C3t7e65evcr58+fZvn07Xbt25YMPPsDBwUGrY2s0GsrLy0VV8czMTJKTk8nIyCA3N5eioiJSU1P5/vvvH0nN9+rVqyiVSjp27PhE1MJUT6fWpXEsjNGuXTuWLVtGx44defvtt+nbty/9+vVj3rx5mJubM2XKlFpHCbKzs/n0009JSkoiNTWVsrIyUXipW7dutGzZkhYtWiCTyXj11Ve5c+eOVq7xn4QwvL29dSKEsXPnThYuXMisWbOwtrYmLCyMS5cusXHjRg4fPqy1cR5Gfn4+06dP586dOzg4OODu7o5GoyEjI4O8vDwGDhzIO++8Uysl4ieB6Oholi1bRmRkJCYmJmJ6Z1ZWFl988QXl5eW0adOGKVOmaD3l89+gVqtFR1B5ebnOIqkC/7TH/1MbpdqOCXUrdvVnqqqqUCqVVFZW6qz2VDC0T548iVqtrlHfXNcolUqSkpK4efMmjRo1qnX98p9Ld+qTP2dHVf9/XdKqVSu+//572rRpI55nX3rpJUxMTFi+fDkuLi415vhv51RRUUGzZs0wNDTEyckJT0/PGro7Tk5O2NraYmFhgbGxsc6MJmHOL7/8MikpKVhYWGBjY4OjoyPOzs5iH/s1a9bU+hy7b98+1q9fz5gxY+jatSu///47a9asYfLkyaJqd10inOdWrlzJnTt3WLBggdjGtKqqip07d3L48GFmzJhBs2bNHvnzCwoKKCgoQKFQoFQq2bRpE9HR0YSFhZGeni6eSQcPHsyCBQu0fXmAFCl+KP8m/VJXGBoakpKSwtmzZ/n111+5d+8ebdu2ZebMmfTs2VP0RNXlxmlgYEDr1q3F2lttRUasra3p2bMnPXv2JDs7mwMHDnDy5EnOnTtH9+7dRdl9bddDnjlzBkdHR959912g5vcpk8kYO3YsHh4efPnll9y6deuxXu5/i7e3N/PmzUOpVNaIFERERPD777/TrVs30RjQJpWVlWRkZHDhwgUMDQ3Fli7wYPPJyckhPT2dpKSkRxYt2r17N8nJyXTs2PEv71L1yEN+fj4mJiZa3bw2b96MnZ0dnp6euLu74+DgUGeHEz09PSoqKtDX1xcNBysrK5RKJcbGxvznP/9h9uzZWnl37ezsWLBggejUyMzMJDU1ldTUVO7evcsvv/zCd999h0ql0mqD+8zMTFEEUBDCMDc316kQhkqlwt7enu3bt6NWq7G0tCQrKwuNRsM333yDi4sLnp6eou6CttIDy8rKWL58OWlpaaxZs0ZsOVVSUkJKSgqnTp3ip59+wsTEhPfff79ejZnqPM69//HHHzEyMhJF2TZt2sTUqVOxtLSkU6dO+Pr6smbNGgwMDFi8eHGdRMerX4ehoSGGhoY6FQkSxsvLy2PTpk3s2LEDV1dXQkND8fDwICwsTMyE0IZR/Ofv6Ul4foQWQrpEOEft3r2bs2fPsm7dOtq1a1fjfhw+fJiAgACtCuaVlpayc+dOMjIy8PT0JCgoiICAABo1aiSWYdSWh+01f1ap1/b3vHnzZgwNDXF3d8fFxQVXV1esrKzqJDvqz5SXl1NeXk7jxo2B/3umU1JS2LBhg2gQP0y34H9x7949LCwsWL16NeHh4URERPDf//6XnTt3ivoZbm5ueHp60rhxY61kczwMYc7bt29HLpeLferj4+O5c+cO7u7uvPHGG1p5ds+cOUO7du3ElPFXX32VM2fOEBUVBdQu2+xxEIziO3fu4OXlJRrEwjxeeeUVTp48+djnZiFjRnCq79ixgz59+vD++++Tnp5OdnY2CQkJOhXSlIziP1GfC0x0dDRHjhzh3LlzZGRk0LJlS2bPnk1QUJAYiageSa4LTp8+zbJly6iqqiIgIIAmTZoQFBSEv78/Tk5OWjWQx44dy7Bhw9i+fTtbt27lwIEDvPDCC3/pHVxb7t27R+PGjR86d2Gxbt26Nf7+/ty4cUOnRjE8SEsxNTWtEVUdMGAAXbt21VotcXUEdUghPdzBwYHWrVvzyy+/0LdvX5o2bYqbmxtubm60atXqkT+/b9++TJ48mV9++aVGD0Zh4SwvL+fWrVvMnz+fMWPGaM3jWVFRwRdffIG5uTne3t4YGRlhbm6Oi4sLPj4++Pr64uPjg7u7u87aNZSUlBAQEMDmzZuZMGEC1tbWZGZmAg9SW4UU49pS/WDu5uZGSEiIGPkvKyujqKiIvLw8EhMTtZphMGfOHKysrP7yc+Fd0kVv8VdffZV+/fqRkpJCQkICCQkJpKamkpGRwenTp1GpVBgbG1NSUsKcOXO0krYGDxxT4eHhfPnllzRv3lxUsTU3NycoKIigoCDs7OxYvHgxw4YNw83NTSvjPgqFhYXIZLIaKYSPc+/Pnz/PV199RefOnYEHGQHPPfccL730EhMnTkQmk+Hi4sKaNWt0figRqOssLSGN89dff2Xv3r0MHjyYixcvcuDAAezs7Fi2bBkAI0aMIDQ0tNYH0r+7jrp2etc1wh53//59vLy8WLp0KUuWLMHLy0s833z88cfMnz9fa8+ZUqlk2rRpXLlyBR8fH06cOEFxcbHY0sbJyYm1a9fW6jyjUqlYtGgRQUFBeHt7i7XKfxdk0dZ3LNToBgcHi04NBwcHXF1dcXNzw8PDAxcXFxwcHHTu8IiOjqayspKysjIsLCxEoVAjIyOx7Vf1vu+P8v4IPbbbtm1bowUdPOimcefOHSIiIrhy5Qo3btzQ3kX9Cblcjlqtxs/Pj4CAAAICAmqkawv9v7XB3bt3xdarQkCqsLBQ1EOp62wEwfZo3rw5x48fJyMjAxcXF/F7VCgUotL546LRaFCr1RgZGREdHU2fPn2wsLAQnVe6rqmWjOI/UZ8LzIQJE1AoFADMnz9frMuoTl0qcCYnJzN79mxROCA2Npbt27eTnZ2NUqnE2dmZM2fOPPLn5+fnExERQVxcHIWFhejp6ZGdnU1cXBwFBQU4OTkRFBREeHg4J06c0LpR/E9COXp6eqKSa25u7kMNAF1RfYETVHS1uXlWT1vbsmULS5cuJTU1laNHj2Jubk5ERASnT59m69at2NrairVIjzp+t27d+M9//sOqVato27ataNgbGBhw7Ngxtm/fzsWLFzE3N6/Rn7O2lJSU0LFjR8LDw1Gr1QQGBlJeXk5iYiIRERHigcvY2Bh3d3e+/fZbrY0tYGdnxyuvvMKOHTsoLCzExcWF7du3ExYWxi+//CLWk+nCw6unpyc6WOzs7PD29qZ58+aPXO//T1hZWXHmzBmuXbvGhAkTxPdD6EupqzRioV6rem20UC8tl8tJTEzkzp07Wk2rvXjxIi4uLuI9rJ6xInx//fv359y5cxw+fPihPW11gVKp5MiRI+zZswd9fX18fHzw8/PDw8MDJycn7OzsHslALysrIz8/nyZNmohtjwwNDSkpKaFHjx7IZDI0Gg1dunThs88+06kYUVVVVY1158+HvrowFo8dO8ZLL73E1KlT+fjjj+nZsyf/+c9/mDp1Kvb29rz99tu1nktJSQkxMTGo1WrKy8uxsrLCy8sLGxubp9oghgf7nPDuHjp0iI8//pgPP/yQTZs2IZPJqKioQKPRPJZD9s8IjqyIiAhu3rzJN998g5eXFzk5OWRmZopdJkpKSmrt4I+KimLr1q3AA2VxfX19ZDIZ7u7uYtQ7ODgYHx8frWU8FBcX06lTJ06fPk10dLS4n0ZGRnLt2jU0Go1YDmFnZ6fzcrDCwkKxG4GhoSHe3t5iB4SbN2+KAZ7H2ftu3bolRvRVKpW4RhgYGODp6Ymnpyf9+vXT9iXV4ODBgyxZsgRfX19WrVqFqakp+vr6xMTEcO3aNfr27Yutra3Wzm1paWli1wXh+UxPTxezv+prrRg8eDC//vorI0aMoFu3bvj4+KCnp8ehQ4fw9PR87Hf3z86S7Oxs8Vqrn2N0mq2rs09ugNTnAlNRUUHXrl0pLi4mPj6e+fPn88knn2BpaSm2TtBl397qCC/09evXsbe3Z82aNRgbG1NRUUFZWRkFBQUkJyeLbXsetW5006ZN/Pjjj6LoQ05OjliLKIjpmJqaMn78eJ30iOzduzffffcdw4cPx9nZWXwRhZfOwMCA1NRUcnJytGq0PQ7afPmF6zt+/DhNmzald+/eLF68GDc3NzEdd/LkyZw8eZIhQ4bUKjvizTff5MqVKyxZsoSVK1dy7tw51q1bx9WrV2nUqJHY+1mbEVtbW1s+++wzjhw5wh9//IFKpWLYsGG0bNmS1NRUoqKiSEpKIikpSesHe+EduHnzJs7OzkyZMgUHBwf69OnD4cOHeeutt2jTpo3o9dU11TdlbT1DSqWSjRs3snLlSlq2bCm+N/Bg8164cCFdunRh2LBhdeK809PTEzMa2rdvr/XPl8vlf5upYWBggEajwcLCAjMzM3Jzc7U+/p8RnrHr16/z3XffYWdnh4eHBxcvXmT37t2oVCrggYDfypUr//XBLDIyEisrqxpCZhkZGRgbG+Pp6SmOq1AoKCkpqVETqA1UKhVJSUkEBAT8RdVcoLKyUrweXe19wrjp6eni82RgYICZmRn29vZ89NFHfPHFF6Lj4HGjNOfPn2ft2rXcuHGDiooK9PT0MDExEdXbBw0apPWaZYVCQWRkJEFBQdjb22u9JOnfInyH0dHRmJmZ4e/vz7JlyxgxYgQzZsxgxYoVpKamolQqtdLLXbjO+Ph4goODxSwSX19f8W+Edoy1xcbGhj59+nD79m38/Pxo2bIlxcXFJCcnc+3aNY4fP45KpaKsrIwuXbqImQe1wcLCgsWLF3PhwgVOnDiBjY0Nzz//PG3atEGhUHD//n2io6OJi4urEwOqcePGbN68mcLCQuRyObGxsSgUCtzc3MTWS7a2tlhbWzNx4sRH6o0dEBBA69atAR76/AoONdBN8OjIkSOsXr2aZs2a8fbbb9dwbOTl5bFs2TIOHTrEt99+q5XsrOzsbPT09LCxsRGjxCqVisLCQq2l+z8uzs7O7N69m2+//Zbbt28THh5ORUUFvr6+zJ8//7FbNFVfU+tSTLM6klFcjfpcYIyNjZk5cyYlJSVi6qNCoRBVN3Xdt7c6wrUJKS/GxsYYGRlhZGSEhYUFjo6OBAQE1KiTeRSEnqJRUVFMmjSJQYMGYWhoqNV65X9C8GJ9/fXXfPLJJ3+JBhcWFrJq1Sq8vLx0ohT5KGgzUix8TxkZGaJD4tq1a6J31cfHp8Zm8rhjC4qTM2fO5O233+bFF18kKSkJf39/Fi5cSL9+/XRSG6inp4eHhwdvvvkmgwYNYuPGjXz00UcEBQXx5ptv1ujpW1FRodWxhXfhyy+/ZOzYsTz//PPAgzVl06ZNREVF4ezsjKenJ/BoaWMPo7KyEvi/TeLP35MuNo4DBw5w/Phx3nzzTUaOHFlj4xPERpYuXYqNjQ39+vVr8GmgHh4eREVFiRkGf74e4d9jYmJqlS72bxGM0z/++AN/f3+WL19ew6mUlpbGjRs3xHf436q6RkZGoq+vz9mzZzE1NSUoKIhLly7h6OhY4ztOSEgQa8i0yd27d3nllVcwMjLC3t6egIAAmjdvTqtWrcQynbqom9PT00OlUuHj40NSUhIdOnTA1NSUvLw84EGZQnR0dK16cp45c4YlS5ZgYWHBvHnzaNSoESqVitjYWM6dO8eqVau4e/cun376qVazLv773/8yd+5cZDIZFhYWeHl50bRpU8LCwvD29sbT01OnyuICwjMZHh4upoC6uLiwbNkyxo4dy/fff09gYKBWnrMTJ05QVlZG8+bNsbW1xcbGhuTkZHENFpDJZFo5d3h4eDBnzhyOHj3KgQMHCA8P55VXXmHKlCmUlZURGxtLamoq8fHxWhWrs7a2pl+/foSEhLBlyxbeffdd/Pz8eP/99+nWrRvdtNgv938h9O2F/xNtKywsFM+0GRkZyOVyrl27JqYY/9usqffee+8ff/93DjVtsXHjRl544QUmTZr0l32tbdu2/PDDD4wbN46dO3eKXVlqQ0xMDCqViqlTp+Lh4UHTpk2pqqoSO6KUlZUhk8nqpT2g0E7uo48+QqFQUFBQIAZXHofLly+zZ88ese9xSEjIQ/egukBSn/4b5HI5W7ZsYf/+/eICUx/9wYT0qpKSErFFBKC1url/oqqqinfffRdPT0+GDh2KtbU15ubmmJiY1OpFVKlUREVFsWvXLqKjo/H29mbo0KGiFxAeLJS6VHA8c+YMM2bMoLKykrZt29KoUSPs7e1Rq9UcP36clJQUVq9erdVWNn+HQqH4S89cXRoUq1ev5vLly2zatInu3buzcuVKQkJCuHPnDm+99Rbr168nODhYK3PYtGkTX375JVOnTmXcuHEYGhrq9LsV6pWE5zM1NZXt27cTERFBWFgYgwcPxtfXV2f39vXXX2fo0KGP5AHXFtUFXXRxf8eOHUuzZs1EAZM/Px9CWxOlUsnKlSvrrA+nrkhMTOSll15i4cKFYj9NqFk3febMGRYvXszKlSt1rsosGMXTp08nMDCQt956q8bPq/Mo7+7q1atZsWIFbm5uYrpnWloaAOPGjcPe3h4/Pz/Wr19PZmYm33//vVavKzc3l/DwcHJycoiJiSEtLY2LFy9SVlYGPHAYe3l54eXlxYsvvig6nLRJ9Xu4ZcsWvv76azZs2IBcLmfdunVs2LCBc+fOsWzZslr13h47dizu7u7MmTPnL+VXxcXFnD17lpkzZzJx4kQmTZpUq2uqTlFRESkpKWRmZhIVFUVGRga//vorSqUSfX19DAwMxJZbEydO1Nn5QrjP33//PYWFhcycOVN0hm/bto0NGzYgk8kIDg6udST1vffe4+7du5iammJpaUl0dDT+/v4MHDhQFAp0dXXVyT5UVFTEtm3b2LVrF66urowePbpGK09dCiQVFxezdetWjh49ipubGyNHjqR169Y6ryf+twhnWiHY0lBo2bIle/bseWj2orDe/vjjj5w7d45vv/221k6m7OxsfvvtN6Kjo4mMjCQpKYmioiKqqqpwc3PDx8eHwMBAvLy8arTQ0zVVVVXs27ePX375hYiICD7++GO6du2KXC6nRYsWj9XGdOvWrWzYsAFzc3MqKytriGkOHDhQZ2KaD0Myiv8H9bHAPCkRljVr1vDjjz9SVlaGvb09np6e+Pr60qhRIxwdHenZs2et0l+Li4s5f/48u3bt4sKFCwQFBTF06FB69+6Nk5OTFq/k4SQnJ/Pzzz9z48YNCgsLqaioQK1W065dOyZMmFAni0xWVhYffPABP/30k/idV//+b9++rXXDPDs7m+HDh6Ovr09KSgqTJk3Czs6On3/+mcDAQL799tvHfv42btxIfHw8ISEh+Pv74+zszJw5c7C0tGTVqlVavY4/82eDWKCqqorw8HC+/fZb8vPzeeWVVxg6dKjWFlbh+yotLWXy5MkoFArefPNNUbVW26lcW7duZf369XTp0oWQkBDCwsLw9/fX+QGjS5cuLF68+KHOQeEe3Lp1i7lz5/L555/XiUNJl1RWVvLNN9+wd+9eJk6cSN++fcW0OJVKxblz5/j888958cUXdd7ftDpXr17lhx9+YOLEiYSFhWFkZFTr/UKhUBAfH8/9+/eJiooiJSWF9PR0lEql2ALl9u3bfPTRR4wZM0Y7F/IQSkpKMDc354MPPqC4uJhhw4YRHx9PUlISV69e5YUXXuC9997TieCk8AyrVCouXLiAt7c3arVaNE5LSkoYPHgw06dPf2yjpmXLlmzcuPEf343Vq1dz/fp1vvrqqxop7dpCqLMdNmwYXbt2pWfPnmIv5kuXLjFhwgS6deumU8MtIyMDmUwmRpaEe79o0SI2btzI7NmzGTVqVK3GuHHjhqjIn5aWhkKhIC0tjcrKSiorKzE0NMTGxgYTExO++uorrSmKCyJB8ECtf9u2bdy9e5egoCCGDBlSI3VbGzxM+0OtVpOens62bdu4ffs23bp147XXXnssg0UbPCln2sclJSWFUaNG8csvv/wlgFGdiIgIJk6cqLNMztTUVFEj5d69eyQkJBATE8PYsWOZOXOmTsYUEBxaW7Zs4eeff+all15i165djBo1SuzWMmPGjMeu687NzX2omGZhYaHOxDQfhmQU/4mGsMDUBWVlZbRv355x48bRoUMHoqOjuX37Nvfu3SM1NZXS0lKuXbv2WNEgIaJVfcOtHtGzsbFhwIABtGrVSmcpXdUjA9nZ2aSnp1NZWUlAQECdRrgSEhJ44403eOutt2ooNR85coR9+/aRlpbGoUOHtDaesDllZ2ezevVq7t27R15eHiUlJbRq1YrPP/+8Vtf/ySefcOvWLTQaDRUVFZiZmYlRiqFDh9KlSxfc3d3x8PDQmWBPaWkpRUVFWFhYkJiYyL179ygsLCQrK4v9+/eTl5dHeHi41hWo4+LimDlzJhkZGZSUlODs7IytrS1OTk6iYFN1lcrH5fjx4/z666/k5OQgl8spKChAT08PBwcHAgICaNGiBZ07dyYsLExrhoNSqWTYsGF89NFHdOjQ4aGHnKqqKoqKinj++efZt2+f1mtP64P8/HyxVqyyshIXFxfMzc1Rq9Xk5ubSsWNHFi9eXGfzKS8vF3upenl50aZNG5o0aYKfnx/Ozs7Y2NhorR61qqqKpKQkYmJiiIqKIiIigpkzZ2q1xdffMWLECF588UVeffVVqqqqKC8vp6ioCHNzc62vz/v27aNRo0aEhISQm5uLmZlZDad3REQER48epU2bNnTs2BGZTPZY/dezsrLo3r27KDL4sOi+RqMhISGBN998k4MHD+pMJR/gueeeY8WKFWKGlpDuKpPJ6kTVNjk5mZKSElEcDh6sMzt37qRTp05a1xMpKysjOzsbuVxOZmYmaWlpJCcnk52dzbp162r9+f8kTHn16lU+//xzcnJyGDlyJG+99ZbW77FcLkelUqFSqYiMjCQmJoa4uDguX75MWVkZZ8+erZNgw9NIUlISn3zyCYMHD+all16q8bvqe+HJkyeZP38+p0+frvWYmzdvxsDAoEYnnIedmUpLS6mqqtL5uVW4zu7duzNt2jQGDhxIjx49mDt3Lh06dOCDDz6gpKSEZcuWae3c/jAxTV1pDQlIRvHfUB8LjFBMXptUuNoijHX37l2mTJnCkSNHHhqBysnJeSwvdvXDREZGBgUFBZiammJra0tiYiLHjx9n165d5Obm0r9/f62IUTypCNGOtWvXsmfPHj7//HOSkpL4/vvvyc/Px8/PjxEjRvxlEa4t1b+DuLg4SktL8fX11cqiWlZWhlwuJyMjQ/TSZ2Zmio3X1Wo1enp6lJWVsXXrVq0ZThkZGezatYvCwkJKSkpIS0vj5s2b6Onp0ahRIwwMDDAxMSEsLIzmzZvTo0cPrYxbWloq1ttXVlaSlpZGYWEhiYmJJCYmioJtiYmJdOzYkU8++aTWERih5UVpaSkFBQVkZ2eTmppKQkICcXFxpKamUlJSwqeffqo1j2p5eTmff/65uOn9GWHduHHjBm+//TaXL1/WyrhPCuHh4Zw/f57ExESqqqqwt7enQ4cO9OzZs07noVaruXz5MmlpaVy/fp3o6GjkcjmlpaXo6ekREhLCzp0763ROtaX6/iasTc899xzLli3TiYDan3n99dd566236NSpE5988glpaWn4+vqKHSeCg4NxcXGp0fbqcTh16hQLFy7kxIkTNX4uGMPwYO/XxTv05zNEYWEh3bp148iRI3XuvFKr1fz000/s27cPU1NTLCwscHJywtXVFT8/P/T19RkwYECtzzzVz1N5eXkUFhaK6vSCwrWJiQm5ublaFTZLTk4WheFu3LhBVFSUuGYfOXIEMzOzWqXgC+Tm5rJt2zbi4+MpKSlBrVaTnJxMamoq3t7e+Pn5YWlpibOzM8HBwfTt21cLV/fPCM9Zbm4uRkZGOutGUJcI1/Tpp58SFRXF6tWrH/q8ZGZmsmLFCsrKyli6dGmtxw0NDcXMzIzg4GAMDAwwNjbG0dERFxcX3N3dcXNzw9XVFQcHhzoL0BUXF9O5c2dOnDiBtbU1rVu3Zu/evfj4+BAXF8drr73GiRMndOrM0zWS0Nb/598sMLa2towePZrg4GCdeNz+znNYH2knBgYGhIaGcuvWrRq1vgKPm9a1YcMGNm/eLHrjjY2NycrKIi0tTVSg7t69O7dv367Tzbp6LaauETZhIYontOIaOXIkjo6OdO/ene7du4tRCW1z+PBhtm3bRmRkpCgolp6ejr+/f6281/n5+dy6dYuuXbvW6C9ZWVlJXl4e6enppKSkiKls2vx+//jjD1atWoWenh7e3t507tyZd999F29vb/T19XWShgiwePFievXqRefOnYmJiRHbQ4SEhIh/U1xcLKr5Qu17CxoYGGBhYSEeJgUlSqVSSWlpKTk5OXz//fd8//33NG3atNZeW+Hw2LVrVzHV/4UXXsDBwUG8Fj09PTIzM9m4cSPPPfdcrcZ7Erhy5QqVlZU4OTlha2tLixYtaNmyJZWVlVRVVYmOwseJGNYGQ0NDOnbsCCD291YqlaSlpXHv3j1RQE6Xqa/apnrZiGDAlJeX6zQaUJ158+bh7u4OPBBdqqysJDw8XKxpNjIyEnvZuru706RJE3r06PHI5QqRkZFiaraVlZUoTPPn6GJMTIzWBc2Ezxccsffu3cPKykrrKtf/hGBcREZGsmnTJrp27YqBgQF79uyhefPmHDlyRDx3DBw4UCtjCu1yPv74Y4yNjVm9ejVXrlxhw4YNlJaWMmXKFDp37lzrwMPFixf59ddfgQcOxLi4OJKSkvDy8hI1LBwdHZk/f77YYqe2nDp1ipUrV2Jra4ubmxuBgYEMGzaMtm3bYmJiIjpr6xIhLf2bb77BwsKC6dOn10knAl0iPBeCaNq4ceN4/fXXadasGRYWFiiVSkpKSvjxxx+Ry+V89NFHtR6zeiecmJgYQkNDxXfn6tWrNTrh2Nvb67zVlkB+fj6+vr7cuXMHV1dXTExMxPZ/6enpVFVVNWiDGCSjWKS+F5jc3Fx++ukn7OzsGDVqlNjyQ+ilFxAQUCcPm1CTKTRDnzFjBi+99BKBgYFij2Z7e/vHPnAVFRXRunVrfHx8qKqqwsTEBC8vLwICAtDT06OwsBBLS0udqD5XVFRQWlr6UCOhLh0Po0ePJi8vDx8fH7GXbMuWLdm/fz+jRo3SSb9TYdP/+eefWbt2LYMHD+bmzZvk5+cTFxfHrl27WLx4MY0bN37sA8L58+f5+uuvOXToEBYWFuLB3MDAAAcHBxwcHMQDgVKp1Or1KRQKjIyMxDrmtLQ0duzYgbe3Nx4eHjg6OuLk5ISzs7NWe09rNBoxpWnKlCkkJibi7+8vPtP+/v54enqKB2rQ3bMmqKja2NgwefJkBg0apBXlRqF3d69evXjjjTf4/vvvuXTpEh07dhTf48jISH7//XcMDQ2ZP39+7S+mnpk+fTpZWVmiV97X11cU5xHWQCcnpzo1iAXu37/P7t27KSgowMPDg86dO9OyZcsaRmRDMYhzcnLIzs7Gx8dHdBplZmZia2srOs2EchtdifNVb7s1d+5c4EGLleTkZDHjQy6Xo1AoiIqKYt++fSgUCl5//fVHepdLSkooLy/nq6++wsDAAGtraxwcHHBxccHNzQ13d3eCgoK4fPlyDadabRGM8cDAQPEeCy2wBKerENnUpYKvcLa4dOkSLi4ufP7551y+fJnU1FTWr1/Pvn37+OGHHxg7dqzWxhNEvWxtbZkxYwYZGRksX74cKysrzM3N+eGHH/Dx8fmLIvWjsnv3bg4fPoyhoSEtWrRg7Nix9OjRAysrK5111FAoFOI/GxkZUVpaypkzZ4iMjMTLywtHR0ex/ZGzs3OddPYQnp3ExET69ev3UIO4IdYXazQaGjduzOeff85XX33F7NmzxVZtJiYmKBQKrK2tWbhw4UODSI/Kk9ZqS8DNzY1OnTqxdu1aGjdujKurK0qlkpMnT7J161atZeDVJ1L69P9HUOC0tbXF29sbZ2dnzMzMRLUzXS4wcXFxfPXVV1y5coWRI0fywQcfkJuby9q1a9m9ezfGxsb06NGDyZMn15lnd/Xq1Vy6dIni4mJKSkrE9A1bW1v09PSYMWMGjRs3fqzP/jeLoi4Wzp07d3Lv3j3mzZun1c99FKqqqvjll19QKBSimI1CoUClUlFcXIy+vj4tWrSgUaNGNGrUiEGDBmn1gNulSxc++eQTevXqRbt27diwYQNWVlZMmjSJ0NBQ5s2b99jP9q1bt3jzzTcZPnw406dPF3/+53Ttn376icrKSr788kutXBM8cLYkJCSIG0V6ejo5OTnk5+eLwjJWVlZUVlYyZ84crXjrq6qquHXrFoGBgZibmxMfH8+dO3fE/8XHx5Ofny8eNM+cOaO1iPXD3g+NRkNBQQEZGRmcPn2a/fv3c/ToUa2MJ1BcXMz+/fs5cOAAMTExlJaWikJMLVu2ZPz48XWmgqlLCgsLiYqK4tatW9y+fZuoqCjS0tJQqVSYmpri6+vLsGHDGDJkSJ0cNoXv++TJk3z55ZcYGhpia2tLZmYmKSkpdOvWjS+++KJOI3/aYMuWLaxcuRIfHx9sbGwICgoiOjqalJQUNmzYgIODg84PfdXfpYKCAgBMTU0f+r2mpaWxbt06fvvtNy5cuPBI4xQVFRETE0NCQgKJiYmkpKSQnZ0tCskYGBhgY2PD1atXWbRokdbKZmbNmsX+/ftxcnLCysqKkJAQwsPD8fb2ZsGCBf8oHKRNBCfphx9+iLW1NbNnz+bXX3/l+vXrTJ06FWtraz744AM6d+4sZkHUBmHf6dKlC3PnzqVPnz4sWrSI69evs3LlSuzt7Rk0aBDz5s2jTZs2tRrr448/5ubNm9ja2orqyubm5nh6euLh4YGHhwe+vr54enpqtcVMVlYW8fHx4holl8vJzs6mvLwcmUyGvb09+vr6zJw5s9bX+Chs2LCB3377jXHjxtG0aVOsra0xMTGpFyeitqi+TkRHRxMeHk56ejrl5eUEBwczYMAAnQTOnpROOAJpaWk16qYdHBxQqVR06NCBadOm4eXlVW9z0waSUVyN+lpgPvnkE3Jzc3n11Vdp1aoVMpmML774gs2bN/Pyyy/TokUL1qxZw8CBA5k6dWqdetqKi4tFxbu4uDhSUlKIjIzk22+/xdPTs0F5/SZOnIiNjQ1ffvllnac9/hlBlCgvL0+sC01OThZT9tPS0lAqlfz2229aGzM+Pp4RI0Zw5coVMjMz6d+/P8eOHcPOzo6zZ8/y2Wef/aXe7d8iPAcbNmzghx9+YOnSpXTq1KnG2Dt27ODgwYPk5uZqRV30f1FZWSn2RYyPjycxMZGYmBi++OILMeWnNiQkJNCvXz9ROOVhh8uKigqioqK4f/++znvZajQaNm3axNq1a7GxsWH06NEMHz5cZ+MpFAry8/PFCFB99/SuC9LT07l79y6nT5/m999/Z8mSJXXSHk9g4MCBtG/fntdeew0PDw/Kysq4dOkSs2bNYtKkSYwePbrBrMfwIO304sWLFBQUiM+TkD5tbW2NpaUlrq6u2NraMmzYMJ08Y8JeEBcXxzfffMPx48extbXF19cXFxcXcU/28PCgQ4cOXLp0iaNHj/LZZ5/VeuyHrVHx8fHMnz9fzCypLREREURHR5OVlSXqPOTn55OTk4NSqcTExAR3d3fs7Oz48MMPtTbunxH2iKVLl3Lnzh2WLFnClStXOHbsGFOmTMHLy4thw4bxyiuvMGTIEK2N+/zzz7N48WKaN2/Oc889x5gxYxg3bhwVFRV07NiRI0eO4OrqWqsxysvLkcvlYnmQ4OwWnB7l5eUYGBhQWlqqVS2Nh1FdIC86OpqrV68yZ84cna/PgtMjNzdXLKExNDQkODiYoKAg/Pz8xLY6zZo10+lcdMU/ianpmiet1VZiYiK3b98mLy8PW1tbevTo0eDbMIKUPl0DR0dHHB0dadeunfizhy0w2lbNPXXqFPPnzxe9P/Hx8Rw5coQ+ffqIXlUjIyN2796NXC6vE09MQUEBpaWlaDQafHx8atSIVqchHcDi4+OZMmUKULOus7pfqK6uR19fH2tra6ytrSkrKxNriQUFUEHhWxsIhxGhwXp6ejpxcXHY29uLkaXc3FwxIv049YjCfRs1ahQXL15k1apV+Pr6IpPJWLVqFfv370cmk9G5c2edRBPLy8s5ePAgOTk5NGvWjObNm2Nqaoq7uzvu7u468aw6Ojry0UcfsWHDBvbv389rr73GmDFjxPT8qqoqjI2Nadq0KU2bNtWKA0modXVxcRFTAAUBIEFUrH///gwcOFDnLZGcnZ1rOAIakoPs3yCk7QrXZWBggKurK66urvTq1QsjIyO2bdtWZ0axEBWeOHGi+N5aWFjQq1cvUlNT+e233xg9enSdzEUbaDQaOnToIL6b+fn5ZGRkoFAoSE1NJT09nczMTFEtXuhPrG2HpvB569atIyEhgeXLl7N27VqKioowNDRkwYIFAEybNo0OHTrQpEmTx8qSqo81CsDPz0/MjCkrKxOdsFlZWSgUCtLT00lLSyMyMlKnWQ/C2jBy5EjGjRvHH3/8QatWrfjyyy/JyMggPj6e2NhYra5bFRUVdO/enQ8//BAnJyfKysoYOnQoBQUFHDt2DFtb21obxNW1NKobnpWVleTk5IhZYbrQ0ngY+vr6+Pr64uvrS58+fXQ6VnWEM4OdnR3Xrl0jISGB69evc+vWLa5fv87hw4cpLy/H39+fw4cPN8j94mHrjq70G/5sgFtYWDBu3Dj69+/Ptm3bWLNmTZ13wsnPz+fmzZsYGxvj6elJ37596yRTqi6RjOL/ga4XmPLycoqLiwkODhZFMI4cOUJ2djavv/461tbWVFVV0a5dO5YsWaLTptXCIpWens6SJUs4fPiw2EvQy8uLwMBA/P39CQsLIzQ0VGfz0BUKhYKSkhLkcjmWlpZYWVlhYGBQrwvzli1bOHXqFJcuXWLixIk8//zzZGdna/WAJFxfo0aN8Pb2ZsWKFZiamuLn54dGo+HEiRPs2bOHLl26ADWdBI+KoaEhH3zwAWPGjGHevHlcv36dqqoqRo8eTc+ePXViqCUnJ/P1119z6tQpzM3NqaqqYvz48bz55pvigTc7O5sLFy5w/fp1rUR44MEmNWbMGF555RU2btzIzp07+e9//8vYsWPp1q0bFhYWYu9kfX19rTxn1WtdPTw88PPzw8/PDw8PD1xdXWnfvr0oxFTXNLQDzv/iYQeg6gc5Q0PDOhGyEcZMS0vDzc1NrMevPhcPDw9SU1PR09Or9yyYf4tGo+HYsWMEBgaK6dM2NjY1HGaVlZVkZ2eTkpIi7jnavjbh8y5cuMDMmTPp27cvR44coWPHjrzwwgvMmDGDRo0a8dprrwE8liZBfa1RMTExfPfdd6IQj6mpKaampjUMs6qqKoqLi2voI+gSV1dXPv74YywsLAgMDCQsLEzMcBg+fLgoHKgNjI2NmTBhAuXl5ZSXlzNx4kQsLCyYN28eZ86c4e233671GP+kpeHk5ISTk5POtDSeFK5du0ZGRgaNGzfG2dkZCwsLwsLC/lKmlJGRQXZ2NtCwnKj/FCHWlX6DsC49rBNOQkIC9+7d4/r16wwaNKhOjOLi4mIWLFjA4cOHMTIyQq1WY21tLWohhIWF1Wgr2lCRjOJ6JiMjA29vb4qKinB1daWgoICDBw8SGBhImzZtxA2zoKCAoqIindb/CGmQ+/fvJzw8nIULF+Lu7s7t27dF1bstW7bQs2dPVq1a1aAUTnNzcykvL2ft2rX8+uuvoriJEPlxcnLC3t4eGxsbzM3NdToX4TvdtGkTv/76K126dCEqKgoHBwcSEhJYuHAhU6dOZfDgwVoZr7KyksrKSiwsLBg/fjyzZs0iLi4OExMThgwZQnp6Ou3atRPTmWt76AwMDOTjjz/mgw8+oHfv3sydOxdbW1v09fXF+l5tcujQIeRyObNmzaJ58+Zs27aNX3/9le7du+Pi4sK2bds4evQokZGRWukTXJ3KykpMTEx488036datGxs3buSHH37g7t27jBkzBmdnZ1E0TxscPny4Rq3r5cuX2bt3r1jrKqQgDh8+/Knz4D4JlJWVcfjwYa5cuUJUVJTWRIH+CeEg5uzsjLW1NV9//TWfffaZmKqWnZ3NmTNnxOhlQzGKk5OT+eCDD8RMKEdHRzGq6ePjQ6NGjXBwcPhLRoK2Ee5VeXm5aJAJhquFhQWTJ09m5syZvPHGG5iZmT3WYb6+1qjk5GROnjzJmTNn6Nq1q7hnq9Vq0SEsKH6Hh4drbc/5X3To0AGFQkFOTg7ffPMNly5dQq1W66Tu1d7enk8++QR48F3n5ubSpEkTnn/+ea1keQhlDD/88APTp08Xz0R1oaXxpHD06FH++OMP7OzsMDExEQVrhfc4ICBAbCfk4uIiiro1FP7OQQradwY/CZ1wqiM8x1evXuXs2bN89dVXhISEkJqaKmZ33Llzh7i4OMkolqg9Dg4OeHl58fnnnzN58mT27t1LUlKSKAYlvIxnz54lICCgTuZ09+5dXnzxRYYOHQpQI2op1BBWn1tD4Pbt21hZWTFhwgQiIiKIj4/n3r17lJaWoq+vj6WlJU5OTjg4OBAUFMT48eN1NhdhEd20aRPvvvsuL7/8MseOHcPe3p7evXvz+++/c/ToUbp3764VUQ7Baw3QtGlTDh8+zPnz57lx4wbZ2dkMGjSIIUOGiIfsx/leb9++zblz5/D398fV1ZWQkBC6d+9ObGwsVlZW4mfqIrJ25swZ+vTpw8iRIwFYsGABEydOZN26dWRmZnLx4kXatGnDokWL6N27t1bHFu6rvr4+wcHBzJ49m4MHD7J582Z+//13XnvtNcaOHau1jdPKyoo2bdr85fBYvdZVqPevy1rXZwWNRsPZs2eJjo7mtdde0/rz9HeoVCpcXV0ZOnQoX375JaNHj6Z9+/ZYWVnx+++/U1lZycyZM4GGsy5bW1uzaNEiCgoKSElJIScnhxs3brB3715RkM7a2hobGxu6deumE1V+gcLCQkJDQzl//jyBgYGYmZmRlZUFIKrLCinrj/Mu19caJWSOLF++nCZNmuDo6FjDMZmcnMzOnTtZu3YtvXr1YvDgwWLGmq44dOgQ27dvR6lUYm1tTaNGjRg2bBi+vr5aH6u8vJzw8HBSU1OxtLTE3t4eLy8vXn31VdGoqU3EUqPR0KxZM9566y1++OEH2rVrJ2pp6OvrP1RL42nkpZdeIiwsjPT0dLHs4d69e1y7dg0DAwO8vLx47733CAgIaFARYoDffvsNW1tbsT2fcJ7R1TXUdyecvyM6Opp27drx4osvAg9U+zt16iQKy1VWVtb5nHSBZBTXIxqNBgsLC0aNGsWSJUt4//33KS8vZ+zYsaJBWlBQwI4dOzhy5Agvv/yyTucjHPB79uxJTEwM5eXlfynir26kNaSFLTw8nDZt2jB06FDx3sIDYyI2Npb79+8TGxtLREQEarVap3PR09OjpKSEvLw8unfvTnl5OVlZWWI90oQJExg9enStI335+fm0b9+e4OBggoODadasGS1atMDPz4+OHTtqNc327NmzrF69GhcXF4yMjPDy8kKtVhMTE8NHH33E0KFDxb6YDg4OWl3UY2JimDFjBvAgPU0mk5GZmcnp06cZMWIEo0aNonv37lobT6B6JEDY6K2srHj11VcZNmwYW7Zs4dixY5w+fZp169ZpLXL7pNW6PkuYm5vzxRdfYGBgUKf9GI2MjCgrK2Pw4MHY29tz4MABTp8+TU5ODs2bN2fSpEliqmJDMYptbGzo168f8H/v7aeffoq9vT2jR48mOTlZbD+i6/7LVlZWdO3alR9//JE2bdrg5+fH+vXree655zhy5IgoPvW449fXGmVmZsZHH33EyJEjWbduHbNmzcLIyIjY2Fh2797Nnj17KCws5KWXXhIdwbp4foT7tnHjRrZu3Yq/vz++vr5kZmZy5MgRdu3apRPRuiVLlrB9+3ZRTdzGxgZjY2N8fX1JTEzk66+/xs/P77E/v761NJ4U/pwqXVJSQmZmJqmpqdy/f5+jR48ycuRI9u/fX+sa7rqkoKCA2bNni91n/pxh6OzsLIrUaYsnrdWWsB706tWLtLQ0bt68SfPmzcXfCa0gnxYk9eknAI1GQ2RkJOHh4fj5+dGiRQtMTEw4fvw4K1asIDU1VVS4rQt1t8TERIYMGUKPHj3o2LEjLi4u4otoaWlZL16q2vLaa6/RtGlTZs6ciUqlQl9f/28PN3WRfpicnMykSZOYPn06QUFBvPzyy5w4cQJTU1MuX77M+++/z+XLl2s1Rl5eHhs3biQzM5PExESioqJE8S4bGxv8/PxEQ7lx48a17tWoUCiIj48nMjKSe/fuiYfa4uJirKyssLa2xsDAQKvq7WVlZbRs2ZJz586JAlf6+vq0bduW8ePHM3r0aIyNjXXmndZoNFRWVmJoaEhcXBwRERFi79WYmBjOnTuHubk5169f16mHvPpnf/7556Snp7Ny5UqdjCVRd9y4cQMDAwNcXFzo0qULV69exdLS8i9/11BSpqtTPf1QMJrGjx9P27ZtefPNN4EHBmRBQQEymaxO6l1jYmKws7MjPz+fOXPmkJycjLW1NW+++SYvvfTSYxnF9blGCVHh/fv3M3fuXD788EOSkpLYvXs3+vr69OvXjzFjxtTo1awLhOezR48evPrqq4wbN67G7ydNmkRlZSWLFi3SWsui0tJSunTpwuzZs+nUqRPPP/8848aN49atW/z3v/+lV69eLFu2TGsH+ujoaMaMGUNISEidaGk8SQjZg/+kzPyf//yHzp07884779Tl1GpFZWUl169fJyEhgfv37xMXF4dCoaC0tBQDAwOsrKzw8/Nj1KhRtGrVSmvjPomttqKiohg3bhy2trYMHDgQX19fHB0dsbe3x8zMDDs7uwYVKPs7pEjxE4Cenh6NGzf+i6Kll5cXL7/8Mh4eHvTo0aNODj0KhUJs+3T69GnOnTuHqakp9vb2ODk54ePjwwcffKDzeWibZs2aieql1Q1iIeImHNCEnrK6xtPTk969e/Pdd98RGhqKvb09pqamXLhwgW+//VYrUQNbW1smTZqETCbj8uXLbNu2DYVCQd++famsrCQiIoKffvqJn376iXbt2rFp06ZaRWKE2r/q6fZVVVXI5XKio6N1ot4eGRmJtbV1jf6/gqd16NChGBsbo1KptJ4OKNTY5+bmcvv2be7fv49Go8HGxoaqqipsbW3F6ICua36gfmpdJXSLRqMhISGBn376CQsLCwwNDVmzZg1ubm54eXnh7u6Ovb092dnZTJ06lYMHD9b3lB8b4TAVFxdXo3WZTCbD0dGxTuZQVlaGt7e3eOhctmwZ165do2XLlnh4eACPJ6pTX2sU/F+5yqBBg7h27RqLFy/GwcGB4cOH8+qrr9baEfpvEfZUlUpF586dxX8W9uL33nuP119/HZVKVeuxBOfC/fv3kclkvPzyyygUCkJCQpgwYQJGRka8+eabtG7dWqsRrrrW0niSeJgx/OdzVc+ePTl//jzQcJx4BgYGtG3blrZt29b4eXp6OtHR0cTExHD48GE+/PBDfvjhB605l+qrE84/8fHHH2Nra4uenh5r1qyhrKwMmUyGi4sL9vb2rF27VmrJJKFbAgMD66z3p7BIXbhwgZycHPbv34+bmxv3798nOjqaqKgobt++LXoEG8qiJiAYh1DzYCOotQqCI3XJsGHDiIuL4/fff6e8vJwWLVqgVCp5/vnneffdd7UyhnDQOnr0KObm5qxbtw4LCwtUKhVqtZpmzZpx7tw5pk+fDmg/JV5fXx8fHx98fHx0ot4eExNDaWkpv/zyC9bW1oSFhXHp0iUcHR3FlCZdHEYEoRwXFxd8fX2ZMGECHh4e2NraEhYW9heVeF3XUdVXrauE7tDT06NFixakpKRw8eJFjIyMuHDhgtjz1NLSEgsLC9LT08XDZ0MSP6z+PgiGQ35+fq3SWR+Xe/fu8fPPP5Oamoq5uTnu7u6EhYURGBhY6/e2vtao4uJiUlNTcXBwwN7enmnTphETEyOmVAvosvdqcnIyP//8M23btsXDw4OXX36ZH3/8kS+++KKGQVpcXIxSqdSKA0RYa5OTk8X2lZmZmTg6OhIeHi62Abt69Spjxoyp1Vj1qaXxJPEwg/jPZGVliSKmDen8KJQrAeI5UUif7tq1K6NGjWLs2LGcPHlSpxkX9dFqS3iXoqOjuXfvHvv37xevMSsri8jISG7evElMTMxTYRCDZBRL/ImysjI6d+4sepBDQ0Mf2n6poSxoAmZmZqjVas6cOYNKpUImk+Hg4ICfn1+9ND/XaDS4uLiwfPly7t+/z507d1AqlXh4eBAWFqa1GhVhMb99+zb9+/cXFy5BrGHMmDH8/vvvyOVymjRp0uDSX1QqFfb29mzfvh21Wo2lpSVZWVloNBq++eYbXFxc8PT0xNnZGS8vL621NHv77bd54403/lYc5s9GsK7va33VukroFl9fX95//30aNWpEYmIi//nPf7h37x4JCQkkJSWRnJxMu3bteOmll4CGo/OQm5tLVFQU3t7eomKtQqHA3Ny8xsFSiDbpcr9RKpV8+OGHlJSU0LRpUyoqKrhz5w4XLlygsrISjUbDvn37Hvu9qq816vjx46xcuZKwsDBxrwsICODYsWMcPHiQ3r17Y2xsrNN7m5KSwt69e9m7dy9FRUXiz5VKJZ07dyYoKIiIiAiOHTumNVVm4Xp8fX0pLS1l3759NG/enPz8fMrLy4EHThAhlb021KeWxpPCtWvXMDIywsnJCWtra8zMzMR1SPj/kpISoqKixCyBhrJOwd+fdQXDXiaTUVxcrLW0/yeRgoICOnXqJGbMwP9Fs4Xv9GlBMoolgP9bpOzt7UlOTubq1at1VqtQF1y6dIlvvvmGhIQEysvLRe+fk5MTPXv25LXXXsPHx6fO5lNVVcXRo0dRq9UEBQXxwgsv6MSYEaJGzz33HCdOnKBfv36iaAxAUlIScXFxeHt7a33suuDVV1+lX79+pKSkkJCQQEJCAqmpqWRkZHD69GlUKhXGxsaUlJQwZ84crQm5uLm5PfTngjFcH5v+w2pNJRo2ghJwx44dRVGq5557jueee078G0H1syFFX86dOyeKHFlZWeHj40NycjLGxsbcv38fJycnsUZNV++SEFW/ffs2CoWC33//HVtbW4qKisjMzCQjIwO5XE5BQUGt1ub6WqPc3d1p27YtCoWCe/fu8dtvv6HRaCgoKOCLL77gypUrODg4YGNjQ6dOnXQS5Wrbti1//PEHRUVFKBQKcb+5ffs2y5cvJz8/H3jwXXTp0kUrY2o0GjELqmXLlly8eJHevXtjZGTEjh07uH37Njdu3BCFz2rDu+++y7Bhw/6ipeHq6srZs2e5efOmTrQ0niQ+/fRTqqqqcHNzw9nZWRSiEoxkGxsbNm7cKAZcoOEFVR5GTk4OJ0+e5MaNG5SVlT2V362AsbExFRUVrF69mjFjxmBhYfFUOnhAEtqS+BNz587l6NGjqFQqWrVqJapE+vv74+HhUcNT1FA4d+4cS5YswcbGhiFDhuDu7o6enh7p6elcvnyZEydOEBwczPz58//W2NEmxcXFfP755xw7dgxra2tKSkrQ09MTJfiDgoK0XrcdFxfHhAkTsLS0pEuXLjg6OpKXl8fvv/9OQEAAy5cvfyo2KgGNRkN6ejpyuZzExETu3LnD+PHj69TxISFRWwQnS9euXVEoFHh7e+Pl5UVISAgtW7YkNDRUq8qndcXdu3c5efIkWVlZpKamkp+fT35+PkqlEmdnZ6ysrHBycsLMzIwBAwbQsmVLrY5fPZPj3Llz/Pzzz3z//fd16syq6zVKoVCQkJBAYmIiERERxMbGkpeXh1wuZ+XKlfTq1atOHSvl5eXi9UdERNCqVasaehTaorS0FDMzM65cucLixYtJSUlh9OjRjBo1Smcpnw/T0pgzZ06dlcPVFRqNhhMnThAbG0tUVBRJSUnk5uaiUqkwMzPD0tKSgoICCgsL+e6772jfvn19T/mxkMvlODg41HCOxcXFMX78eExMTJgxYwY9e/asxxnqBmE9WLx4MTt37qSiooKgoCB8fHxwcnLCw8MDe3t7OnToUKf1zbpEMoolRDQaDTt37qSgoIDU1FQUCgX5+fkUFxdTWVmJWq1m//79DS49c9KkSVhbWzNz5sy/pLgUFxcTERHBjBkz6NevH3PnztVZ/acgtnHmzBlmz57Nu+++S0BAAFlZWWRkZJCWlkZCQgLGxsasXr1a6+MLh58bN26gVquxsrKiSZMmzJgxo4YIjISExJNDVVUVW7ZsYfHixbRv3x4zMzMSEhKIjY0FHrQTatasGWPHjtWJUVEX5Obmkp6eTnx8PHFxcSQnJ5OTk0NsbCwLFiyge/fuWjXYtmzZQmRkJE2aNEGpVHLy5En+85//iNH4Z4WqqioSEhJwdnbWeU2gRqPh2LFjZGRk4OLigo+PD+7u7mKdqTaoqKjg4MGD5OXl0axZM8LCwjA1NRV/n5ubi5mZGcbGxg0qhbchUVFRQUJCAnFxcaSnp+Po6Ei3bt0anNFUWVnJ4cOH2bBhA0qlkvbt2zNu3Dgx006pVJKSkoKvr+9T/yzdvXuXpKQk0tLSiIuLIy0tjYKCAiorK8nNzWXTpk0EBATU9zS1gmQUS/wtJSUlZGdnk5aWRkpKCvn5+UyYMKG+p/XItG/fnqVLl9KxY8caggnV2bp1K0eOHGHRokVaV+T8s5G9fPlyYmJiWLVqVY2/UyqV5OfnU1lZqbVefsLYlZWVlJWVYWFhgVqtJicnB2Nj46e6DkZCoiEjvLv37t3jww8/5N1336V79+5UVlZSXFzMH3/8wapVqxg0aBCJiYlkZGSwatWqOsl2qQs0Gg2pqamiMr82+fjjj7l27RoGBgaYmJgQGxuLgYEBnTt3Fo01Dw8PseNCQxEve1JRKpXMnTuX3377DU9PT1Gw09TUFAcHB5ycnJg3b16tFLiTk5P5+uuvOXXqFObm5lRVVTF+/HixvRc8cIKfPXuWK1euMG/evNpelgQ1haj09fWfGgPxwIEDrFmzRhTT3LdvH82aNWPZsmVaqUdv6GRnZyOXy4mLi+Oll156atKppZpiCRGlUsm9e/dIS0vD0tISd3d3XFxcGmy9KTwQDisoKCA4OPhvVTY1Gg3du3dn/fr1WhM5qY6enh43b97ExMSE4OBggoKCyMrKoqysrMZhTyaTabV9j3CoTkhIYPv27URFRWFhYYGrqys+Pj64uLhgbW1N48aNteqtl5CQqD1VVVUYGBhw9epVTE1NxZZy8MCYePXVV0lJSaGqqorPPvuMmTNnsmfPHq0p19c3enp6OivXWbBgAdnZ2SQnJ4uRj+TkZFJTU4mNjUWtVmNiYoJKpWL9+vW4uLjoZB5PO0J21J07dzh79iwLFizA29ubtLQ0sa46NTWV7OzsWrekOnToEHK5nFmzZtG8eXOxQ0D37t1xcXFh69atHD16lKioKPr376+lK5R4WPbGn9sxNUT27NlDt27dmDp1KjKZjL59+zJ16lRu3LhBjx49GpTSf20QMmlOnTqFtbU1jRo1ws/PD09PT1q0aKH10pb6RjKKJcSX+/jx4yxdupTU1FQMDAwwNzfHwsKCZs2aoVar6dOnDwMHDqzv6T4SkZGRWFlZ/W16sJCSV1JSQl5ens56yi5ZsgSFQoGNjQ1WVlZERERgYGBA//798fLywtXVVeseVsEo/vrrr4mMjMTPzw+lUsmNGzc4d+4choaGFBUVsWLFCpo2barztkESEhL/HuFd1NfXx9DQkMzMzL+sTzk5OaLyq4+PD0lJSfUx1QaJg4MDDg4OtGjRQvyZRqMhNzeXuLg47t27h1wur5M+408rQvTo/v37BAYGMmjQIACaN28u/k1JSQnFxcW1HuvMmTP06dOHkSNHAg8cHxMnTmTdunVkZmZy8eJF2rRpw6JFi6SWdTqmvsQmtcndu3d57733kMlkKJVKsVex0EqsoRr7/xbBLjhz5gxfffUV+vr6mJubs2vXLjHr0MTEhKFDhzJlypT6nq7WkIxiCZGNGzcSFhbG4cOHGTNmDKGhodja2vLdd99haWnJiBEjgIalchoZGYm+vj5nz57F3NwcJycnbG1txfop4TqSkpJ0VvOi0WgYNmyYWDeck5ODu7s7J06c4NKlSxgbG2NpaYmLiwvOzs5MmTJFKxFr4dpOnz7Ntm3bxINIYWEhKSkpJCYmEhkZKWYCNPRNTELiaUJ4f3v37s3PP//M9OnTGT9+PO7u7piYmHDu3Dlu3brF5MmTgQfCL9pSLn4WEJyA6enp/Pe//yUmJgaZTEZISAg9e/YUD8ESj8e1a9dQKpU0btwYCwsLnJycyMvL+0vqqbm5uVYylWJiYkRFaaVSiUwmIzMzk9OnTzNixAhGjRpF9+7daz2OxD+j0WjE9Hihr29Do6ysjOLiYnx8fKiqqhKzGEpLS3F2dgae/vOSEOk/dOgQYWFhfPbZZ9jY2FBQUIBCoSAuLo4rV640SPHdf0IyiiXEFJCYmBjmz5+PiYkJ1tbWtGvXjt69e5Ofn4+FhYUoOd+QFrm8vDzy8vKYN28exsbGODg44OLiIqaGOzs74+fnx9mzZwkODtbJHPT09EQPOTwwSgsKCkhLSyMxMRGFQkFaWhrp6ekoFIpaG8RVVVWo1WpkMhn5+fkEBwfXaMMkCGw1adJESiOTkHjCcXFx4euvv+bLL79k2rRpODg4YG5uTnx8PGPHjqV169Z89tln5OTk0KlTp/qeboNAcOxGRkaybNkyLly4gK+vL4aGhuzbt4+ffvqJuXPn0qxZs/qeaoNl3bp1JCQk4OjoiL29PZcuXWL27NkMGjQId3d3XF1dsbe314pxUVZWRmlpKb6+vjWMmJSUFKZNm8bo0aMxNjaWsqHqAD09vQafVvywDMOMjAyMjIxwdHT821K8p4nq32GbNm1E/Rlra2usra0JDAx8KoUJJaNYAnhgPFpaWiKTyaiqqsLW1pbc3FwAhg0bxptvvtkga9XeeecdhgwZQnx8PPfv3yc6OpqEhASuX79ORUUFRkZGODs7c+vWLT766COdzaOyslL0mpqbm2NoaIinpyft2rUT/6agoICsrKxaj3X9+nX27NlDSEgI5ubmuLm5sXnzZiZNmoRMJmtQTg0JCQlo1qwZ69at4969e9y7dw+VSkWPHj3w9/fnypUrJCQkMG3aNIKCgup7qg0CwShes2YNarWaI0eO4OXlRVFREYmJicyfP5+1a9fy1VdfNbhuC08KI0aMIC4uDrlcTmpqKk5OTty9e5eEhATMzMzEtluWlpZMmTKlVn3WIyMjsba2rmHEKBQKAIYOHYqxsTEqlarWdcsSf09ycjJHjhzhzJkzmJmZ0aJFC3r27KmzYIMuqZ5haGpqSlBQEJcuXcLR0fGZEScVDP6ZM2cyf/58/Pz8CA4OxtLS8qkR1XoY0gohATzwCjVr1owNGzYwZ84cTExMSElJASA9PV2sL2hIqdMCzs7OODs712hXUlVVRVJSEjExMURFRWFtba3TKItgEMvlcj755BNcXFyYP38+4eHhbN++naqqKmbMmEFAQECtvdnR0dGcPHmSCxcuYGBgQF5eHsePHycuLo7AwEBcXFxwdXXF1tYWHx8frKystHilEhIS2qKqqoqUlBTu3r2LUqnE19eXAQMG1GifExISwhdffPHUqE7XBcIeFhERwezZs/Hy8gLA0tKSsLAw5syZw+TJk7l//z6tWrWSIoyPQffu3WukK+fl5YktXRITE5HL5aSnpxMZGVkrgxgeZLmVlpbyyy+/YG1tTVhYmGjECH28n+aDfH0hnAdv3brFokWLSEpKonPnzpSUlHD48GHOnTvHRx991OAyLqpnGMpkMtzd3UlLSwPg119/FRXTbW1tcXBweGqfrfLycnbv3s2ZM2e4f/8+TZs2xd3dHTc3N1xcXHBycqqhyfA0ILVkkhA5dOgQ27Zt47PPPuPKlSt88803vPrqq1y8eBFPT0+WLl36zCjuaRvhvk2fPp2cnBwmTpyIi4uLGL0tLCykWbNmfPLJJ7Wqba5+eMvKyiImJob4+HjR+M/MzBRVr5VKpdgDVDr0SUg8OQiHzYsXL/L5558TGxuLubk5pqamWFpa4ubmhq2tLb179xZVqRuiw7Ku0Wg03L59G09PT2xsbPjuu++oqqoS12HhHpaXl9OmTRuOHDmi9RZ9zwpCdhT8fcmVRqMhPz+/1i1utm7dyrp167CyskKtVmNpaUlWVhYajYaBAwfi4uKCp6cnzs7OeHl56aTLxLOIULv99ttvY2ZmxocffoiDgwP5+fmkpaWxcOFCDA0NWbFixd+KnT6pKBQKMcMwKiqKlJQU0tPTUSqVGBkZYWdnh76+PjNnzhRLC58WhHXw/PnzzJgxgwEDBmBoaEhkZCSpqamUlJRQXl4uBtKeJqRIsYTIgAEDGDBgACqVCgcHByIiIjhw4AAtWrRg/PjxwNMvLqArhEPBuXPnWLp0Ke3atWPx4sXIZDLWrl1Lfn4+7733HtnZ2bUyivX09MQFzdHREUdHR5577jnx91VVVaSnpxMTE8OFCxcadLstCYmnFeEd3rFjB87Oznz77bdYWFgQERHB/fv3iYuL49KlS4SFhQGgVqul1NB/QXx8PCNGjMDU1BQ7OzsMDQ1JSkrCzs6Ovn374uzsjFwuZ9OmTQwYMEAyiGuB0LM2PT2d77//nt9++w1vb2+Cg4Px8vKiefPmaDQabGxsam0Uv/rqq/Tr14+UlBQSEhJISEgQWz6dPn0alUqFsbExJSUlzJkzRxKk0xKCErNCoWD8+PGiCJW9vT329vYsXbqUUaNGERsbi729fYNyvv+vDMPo6GiuXr2qM4HW+kSIlV65coXQ0FDmzJlT4/c5OTlERkaiUqnqY3o6RdpFJUSioqLQ19fHw8MDW1tbFi1aRHZ2Ng4ODuLfSJGIx0NPT4+ysjJkMhmBgYHAg8j866+/jp2dHXp6eqSmpoqbSm2o/h0lJiayZ88ehg0bhru7O6mpqcTHx9OtWze6detWY34SEhJPBsI7HBUVxZQpU/D39wceHNR69eol/p2g8ioZxP8Od3d3fvnlFzIyMkhMTCQrKwtHR0e+/fZbvvzySywtLTEwMCA/P5958+bV93QbNEKf7R9++IHz588zatQodu7cSUpKCkZGRixZsgSADz74gEaNGtV6PDs7O+zs7GjatKn4M41GQ3p6OnK5nMTERO7cuSM5grVAVVUVO3bswNXVFScnJ0JCQrhx4wYvvPBCjb+TyWQUFBSIQp8N/Zyhr6+Pr68vvr6+9OnTp76nozOE/ScwMJDCwkIxI0DA3t6ejh071tf0dIq0k0oAsG3bNjZu3EhpaSkAtra2BAYG0rhxY9zc3Hj++eeltOnHRIj6VFRU0KJFC2bMmIGdnR0lJSUMHjwYtVrNuXPnarSKqg2CNzYtLY0FCxaQlpbG8OHDCQ8PZ8KECahUKlq3bs3SpUsbXEqThMSzgHAo6datm1jL9k9/J/HvMDExoVmzZrRo0YKTJ0/SqFEjJk6cSHl5uZgumZqays2bN0lISACkKPzjIhhAp06d4t1332XYsGHcunWLPn360KFDB9555x3atm3LkCFDdDoHNzc33NzcaN++vc7GedZISEhgwYIFuLq6Ym1tjUwm4+bNm1RUVNCtWzccHR0pKSnhwIEDdOjQ4alr2/OsIJfLOXbsGJWVlfTr1w9vb29cXV0bvHPjn5BW+mcYwXhKTk7m22+/pVu3brRu3Zq8vDxSUlJITU1lz549KJVKqXVPLdDX16eqqgobGxveeustli1bRlFREQsWLMDOzo558+Zx+vRpRo0apZXxBA/9qVOnyMzMZP369ejp6bFq1So6duzIG2+8wZdffsmpU6cYOnSoVsaUkJDQDsK6nJubS2JiIj/99BNlZWW0aNECZ2dnrK2tMTMzw8TE5Kk+nOgKwUn5448/0q9fPzHy7unpSevWrVGr1ZSXl4viOZJB/HgIDpvi4mKxy4IQiXd3d+fDDz9kzZo1krO9AeLv78+BAwdISEjg7t27xMfHExoayqlTp0TF5uzsbIqLi5k/fz5Ag0qdftYRvqeoqCicnJw4duwYFy5cEDUthHamU6ZMeerq86XV/hlGePCvXbuGo6MjixcvFn9XXl4utgiqqKgApEXtcTh//jze3t54eHigUqkICQlhxYoVGBkZIZPJSEhIwMLCgunTpzNw4ECtjn3r1i3atWuHm5sbP//8M4WFhYwZM4ZWrVrh6enJ/fv3ASTxNAmJJwhhnU1NTeX+/fs4OjqyYsUKDAwMcHBwwMvLC3d3d5577jlefPHF+p5ug0MwckNDQ6mqqqqx/mk0GgwMDLSSsSPxoPbQ39+f6OhovLy8MDMzIzs7GwAHBwdiYmJqrTwtUT8EBAQQEBBA7969xZ+Vl5eTmJjIvXv3kMvlXLp0CblcDvyfs16i4fDpp5+KdkBiYqJYp5+RkUF6evpTZxCDZBQ/s+Tm5orRBkdHR0JDQyktLRV7MpqYmGBiYlKjxlUyiB+dVatW8d577+Hh4cHy5cspKioiKCgIZ2dnnJyc8PHx4cMPP9TqmIKH3tDQkPz8fOLi4ti6dStt27aldevWAKSmptK2bVutjishIVF79PX1UalUhIWFcfr0aeBBD/N79+5x69Yt7t27x4kTJzA0NOTFF1+U0nsfA6VSyfXr19m3bx8qlYru3bvTqFEjaY/TMvb29rRt25ZPP/0ULy8v3NzcOHXqFEOGDOGPP/7A0dGxvqco8ZhoNBrUajVGRkZkZGSQkJBAcnIyvXv3fmhvYskgbnjY2tqKrTsFhW2NRkNBQQHl5eX1PDvdILVkekb54IMPiIyMxNfXFwsLC65du8bLL7/MyJEjpb61WuTkyZO0bdsWCwsLJk2aRGpqqhh5F1JRnJ2dsbS0ZPLkyVq993K5nFmzZpGXl0dpaSkbN27E3t6e1atXc/LkSdasWYOfn5+UASAh8YTwwgsvMGnSJPr378/BgwexsrIiLCxM7LUqoR2ys7NZsGABmZmZREdHU1JSgpGREW5ubvj7+9O+fXutlbM86xQXF3P9+nWCg4NJSkpi1qxZaDQaNBoNb7zxhnSfGzj379/ngw8+QC6Xo1Qq2blzJ5cvX8bU1JQhQ4Zgampa31OUeASql1X+8MMPXLlyhYCAABo1aoSHhweBgYGUlpbi4uKCr69vfU9X60hG8TPKjz/+SEJCArm5uZSUlJCSkkJ2djbu7u44OTmJD7wgsiUtbNohNzeX9PR0EhMTSUhIICkpiYyMDAoKCjhw4IDWxlEqldy+fZvs7GxiY2Pp06cPgYGBLFy4kGPHjjFjxgytp2tLSEjUjq1bt9KjRw9cXV0ZOnQod+7cAcDc3BxPT08aN25M06ZNCQ4OpkWLFvU824aLWq0mPz+fqqoq8vPzSU9PJykpifj4eG7duoWnpycrVqyQSku0wJ+drleuXOHcuXN06dKFli1bSmJxDRilUsno0aPx8fFhzpw5dOzYkb1793Lp0iW++OIL1q9fL9aTSzQMhDVv1qxZXLt2jV69erFjxw4MDAxQq9VUVlYC8MUXXzBgwIB6nq32kYziZ5yCggLS09PJysoiJyeHxMREkpOTycrKori4WDTWzM3N63uqDRJB1KX6P/9Z3l6j0ZCbm6sVJWhhjAsXLrBq1Sq2bt1a4/eCAR4UFFTrsSQkJLRLdQMiPj6eoqIi0tLSiI+PJy4ujvj4eNLT0ykoKODatWtS7Wst0Gg0ZGVlYWpqirm5ubhOq9VqVCqV5AjWAgqFgl27diGXy7GxscHHx4fmzZvj4eGBpaWllKHUQBHWqZs3bzJ58mTOnDlDXFwcI0eO5MyZMxgaGvLBBx+gVqv57rvv6nu6Eo+A8N22a9eOOXPm8OKLLzJy5Ejeeecd/Pz8eP3112nXrp3YReVpQypEekYRHnxra2usra0JDg6moKAAU1NTsbdcfHw8OTk5kkGsBVJTU1m5ciVHjx7Fzs4OLy8vAgICCA4OxsvLS+tGanJyMsbGxjWMcgAXFxdcXFykCIiExBNIdSPBz88PpVJJs2bNxJ+Vl5eTnZ1Ndna2ZBA/BsK+d+HCBTZu3ChmR7m4uODu7o6Xlxc5OTl069ZNMopriVKpZNq0ady/f5+QkBDkcrl432UyGSYmJmzduvWpFOt52hHeo5iYGNEwioiIwNXVFZlMhr6+Pu3atWPPnj2AJObZkNDT06OkpASVSkW3bt1Qq9XY29tTWFiIm5sbM2bM4OjRo0+tQJ5kFD+jVD98nT9/nu3bt5OSkoJMJqNRo0YMGzZMSs/TAoJRunLlSsLDw3njjTcwNzcnIiKCCxcusH//foqKipg5cyZjx47V2rgWFhZkZWWxZMkSBgwYgIuLSw2vnrRBSUg8eQiHzcLCQo4dO8b58+cxMjLCyckJT09PPD09cXV1pXHjxvU91QaJsO8tW7YMPT09WrZsyZYtW3Bzc0OhUGBqaoq1tbWU8lkLVCoVRkZG3L59m9jYWHbt2oWLi4uoWJuWloZcLqesrEwyiBsogqPd398fQ0ND7ty5Q3R0NAEBAejr61NeXs7Zs2dFwS0pIbVhkZubi5eXF3fv3qVdu3Y1VOMdHR0JDw8XW9Y9bUhG8TOK4Lk7fvw43377LYaGhjRr1gyVSkV4eDi//fYb8+fPl/oT1xLhEHb+/HlmzZpFv379avy+uLgYuVyOk5OTVsc7deoUaWlpbN26lVOnTuHq6irWitvY2DBo0CBsbW21MqaEhIR2ENqW7Nixg40bN2JlZYWDgwNRUVFUVFRgZGREaWkpb775JsOHD5dE8h6D7OxsoqKiOHr0KB4eHpw7d45t27aRlZXF+PHjeeGFF/D09KzvaTZYhMNyVlYWoaGh+Pv7Aw8MKOGfJZ4OWrZsiZ+fH7NnzyYnJ4dWrVqxc+dODh48SEVFhejol+rGGxYeHh6Ehoby2Wef8eOPP2JjY8ONGzcYNWoUZ8+exd3dvb6nqDMko/gZZ926dbRu3ZrZs2eLm5lSqWTy5MkcOnSITp06SWrUtUCIyPr4+ODg4CD+XDjMWlhY0KRJE62NJxyQZ86cyWuvvUZiYiJyuZzk5GTx/3Nzc6X+phISTyCCUbx7925ef/113n77bQDS09ORy+XI5XJu3ryJn58fIPWOfxSEexUVFYWbmxseHh7ExcXh4+NDQkICrVq1YsSIEeTk5Ej39DHZv38/GRkZhISEUFRUhJGREREREYSFhdX31CR0xBdffMH333/P4cOHCQ8P5+7duzg7OzN9+nSxBaRkFDcs9PT0eO+990QV8ZYtWzJ37lw6dOiATCZj0qRJ9T1FnSEZxc8ogrGWmJjIvHnzMDQ0RK1WY2BggEwm47333uOdd94hNzdXMoofg+joaN59913at29PYGAgjRs3ZsmSJXz77be4uLjo5NBV/YDs5OSEk5MTzZs3R6PRUFFRQXh4ODt27CA6OvqpFEiQkGjoCI5Jc3Nz8UAJ4OrqiqurK+3atWPYsGHiz6XD5qNTXl6Oqakp9+/fF2tb8/LyADAxMSE+Ph6Q6iAfhxMnTnDnzh3Mzc2xsbHh9u3bxMTE8Pzzz+Pr64uHhwdubm44ODhIWiVPCXp6erz55ptMmjSJxMRE1Go1vr6+0rvTwHF2dhaDJ88//zwymYzr16/TpUsXmjdvXr+T0yGSUfwMU1xcjIuLCxEREQQHB2No+OBx0Gg02Nvbk5OT81SnSegSAwMDGjduTHx8PCdPnqSoqIiKigqGDh1KQEAAjRs3JiQkBF9fX7y9vbUimqOnp0dZWRkGBgZERkZy6dIlUlNTiYqK4v79+yiVSgwNDenduzfAX0S4JCQk6of8/Hx++OEH/Pz8sLe3p0mTJhw+fJjQ0FBMTEzqe3oNnuo1jV26dGHLli1s3LiRadOmUVBQQFpaGmVlZZw4cYKWLVv+5b+R+Hd88sknYrvBxMREnJ2dSU1N5b///S8nT57EyMgIKysrDAwM+Oabb7TScUGi/rh37x6//vor586dIzQ0lAULFhAbGys5lBo42dnZbN68GYVCgYeHB8HBwTRt2pTu3bvX99R0jmQUP4NUT93t1asXS5cuxdnZmcDAQFxcXCgpKeGbb74hKCgIIyMjyXh6DPz9/Vm2bBlKpZLCwkJyc3NJTU0lMjKSmJgYzp49y549eygoKOCVV15h3rx5tRrv2rVrREREEB4eztWrVyksLERfXx8vLy9SU1Px8vJi9OjRNGnSRDyISCmCEhJPBrGxsWzduhVbW1sMDQ3RaDSkpaWRmZlJ06ZNRdV4BwcHHBwcsLa2ru8pNyiqr3VGRkZMnTqV+Ph4HBwcaNmyJYsXL+bLL78kLCyMF154AZCi8I+D8HxWz3JQqVRkZGSgUCiIiYkhOjqa9PR0KVupgSKcB2/cuMHy5cupqKjAwsICjUZDUlISn332GS+88ALjxo2r76lKPAaFhYW89957xMXFERwczP3799m5cydKpRITExOcnJzYtWtXfU9TZ0h9ip9x5HI5CxYs4P79+3h7e1NVVUV0dDRubm58/PHHtG3bVjKKdURWVhYJCQk4OTnh4+NTq896/vnnSUpKon379nTu3BlfX1/09fVp3749EyZMIDQ0lA8//FA7E5eQkNAagpNSrVYjl8uJjIwkNjaWpKQkYmJiyMvLo7KyEgsLCwwNDRk8eDATJkyQ6on/JVVVVZw8eZKgoKC/FdA6evQoWVlZtG3blqCgIOm+PibZ2dls2LABOzs7Ro0ahUwmAx5kQhQUFODt7V3PM5SoLWq1GkNDQ6ZOnYpMJmPx4sV8/PHHGBgYMG/ePD777DPRYK7tuUai7lAqlchkMo4fP87ChQtZtWoVDg4O5OTkkJWVRWZmJnK5HLVa/VSfJaVI8TNGcnIyJiYm2Nvbi5HE7777jkOHDnHr1i1KS0vp1asXHTt2FOX0JYO4dpSVlbF792727duHWq0mKCiIrl270q1bN9q2bauVMV566SXOnDmDtbU1zs7O9OjRQ/xdSkoKgwcPBiRhHgmJJw3hfTQ0NMTPz08U0RIoKioiJSWF2NhYzp49KyrVC6JcEv9McnIy06dPx9raWmxv5ePjQ/PmzfH19cXPz+8vXQEkHp24uDi++uorrly5wsiRI5HJZOTm5rJ27Vp27dqFsbEx3bt35/3339datwWJukc4D96+fVs0jm7evMno0aMBeO+99xgxYgSpqan4+PhIZ44GguDAysjIICwsjJCQEOBBbbGAoE/zNCMZxc8Ys2fPprKyEi8vL+zt7XFzcyMgIIBu3brx8ssvSwawllGpVHz++eccPXqU/v37Y2hoSHR0NJ9//jkRERF88MEHWrnnEydOpHnz5qxdu5YZM2awYcMGRo8eTc+ePcnOziYwMBCQUqYlJJ50SktLOXDgAK1btyYgIAAjIyM0Gg39+vVj4MCB4t9JBvG/w9ramkWLFlFQUEBKSgo5OTncvHmTffv2iaUkNjY22Nra0rVrV8aPH1/PM26YbNq0CSMjI1avXk2rVq0A+P7779m8eTMvv/wyLVq0YM2aNdjb2zN16lTJWGqgCOcVJycn0tLSAFAoFDRq1Ah4IFYn9LkF6czRENi1axfl5eU0b94cS0tLqqqqSE5O/ktmjZ6e3lOvcSEZxc8QarWa+Ph4cnJyKC8vRyaTcfbsWZRKJebm5tjb24sqpw4ODgwaNEj0Hkk8GiqVCiMjI27dusWJEyf46aefCAsLQ6VSUVpaym+//cbSpUtp3LixVtojaTQaOnToQIcOHThz5gzffvstX375JUeOHMHIyEj09kkCGBISTzYrV65k27ZtrFixAkdHR9544w2SkpLw8/Pj66+/llISHxEbGxsxEiykCH766afY29szevRokpOTUSgU3Lt3j/LyckBaJx+HU6dOMX/+fDp06ABAfHw8R44coU+fPnz44YdipH737t3I5XLRaJJomAwfPpwff/wRd3d3KisrCQ0NpaCggKVLl+Lm5ib1+m5AnDhxgoSEBLZt24aFhQXR0dG8/fbb9OnTh0aNGuHr64unp6dWBGGfdCSj+BlCo9EwZ84ctm3bRmxsLH369KFTp07o6+sTERFBTEwMkZGRXL16FSMjoxqtPyQeDaG1ikKhICAggKZNm4o/t7Gx4ZVXXiEuLo6jR49qxSjW09MTPe9du3bFz8+PX3/9lX379mFoaMjOnTuZNGkSBgYGkodeQuIJQ3gnExMT2b17N8uXL6ddu3YsX76cnJwcli1bxooVKzh06BDvvvtufU+3QSHIpujp6YmGbmpqKl26dBGV+JVKJQUFBaITWDKIH43y8nKKi4sJDg4Wa06PHDlCdnY2r7/+OtbW1lRVVdGuXTuWLFmCsbFxfU9ZopZ07dqVM2fOMHXqVPT09OjVqxdqtRpTU1PmzJkDSOVaDYWpU6eSkZFBSkqKKMyamprK8ePHOXLkCHp6epibm2NiYsIPP/zwVBvHklH8DGFkZET//v3p378/J0+eZO/evezatYvBgwczadIkZDIZ5eXlJCcni30bpUXt0dm/fz8ZGRmEhoZSVFSEnp4e169fp1WrVjXuZX5+vlZbUlT/bE9PT6ZOnUq7du1Yt24d3333Hb/99huffvppDWVQCQmJ+kdYZ2/fvo2rqyvdunXjzp07XLx4kVdffZWuXbsil8vZv38/7777riR++JgIa2RcXBwjRowQfy6TyXB0dKyvaTV4MjIy8Pb2pqioCFdXVwoKCjh48CCBgYG0adNGfF4LCgooKiqqUaco0TDIzs7G1tZWdBjZ2dmxfPlyseNFTk4O9vb2tGvXjubNm0trVAMiKCiIoKAg8d8rKyspLy8nNzcXuVwuCj8WFBQ81QYxSEbxM4eQFta9e3eCgoI4dOgQv/zyCxcvXmTEiBGEhoaKtSGSQfx4nDhxgjt37nDo0CFsbW2JiIhg+vTpDBkyhEaNGmFra8vp06fJysrSWdRH2JCee+45nnvuOc6cOcMHH3yAQqGo8XsJCYknh8LCQiwtLUlLS2P37t1oNBr69u0LPMg6cXBwAKT391Govofp6+ujUqnIz8//i6CZxOPj4OCAl5cXn3/+OZMnT2bv3r0kJSWJrQaFZ/Xs2bMEBATU40wlHpfZs2czfPhwevXqxa5duygrKyMkJAQ/Pz+xt3d1pPWp4VBVVYVGo8HAwICKigoSEhK4cuUKVlZWNGnShI4dOwI89SJbILVkeuZ4mKEbGRnJli1bOH78OB06dODtt9+u4TWSeDSys7ORy+UkJiYSGxuLQqEgLS2NkpISAIqLi8nMzKRdu3YsXboUGxsbnc2leupgcnIytra2T72nT0KioZKens7ChQtJTk4mMzOTadOmMXz4cPbt28eGDRt47bXXGD58uFTz+i/Jzc0lKioKb29v7OzsMDExISUlhVdeeYVz586Jf6fRaNBoNNJB/jEQzhTXrl1jyZIlpKSkUF5ezvDhw5k2bRqGhoYUFBSwY8cOjhw5wssvv8yoUaPqe9oSj8jq1at56aWXcHNz46233iI+Ph4DAwOxJMzd3R03Nzf8/f3x9/fHy8sLMzOz+p62xL9AeIdzc3P5+uuv2bt3Ly4uLlhYWKBWq+nRowfvvPPOM3F2lIxiCYqKitDX1+fatWssW7aM5ORk9uzZIwm6aBGVSkVubi7Z2dlcvnyZmzdvcu/ePY4dOyZF4yUkJETOnDnDiRMn6NixI88//zw7duxgxYoV9O/fn4kTJ2JnZydl8fxLDhw4wNdff42fnx9WVlb4+PiQnJxMREQEK1euxMnJCTs7O+leagGNRkNkZCTh4eH4+fnRokULTExMOH78OCtWrCA1NZWRI0fy1ltvPROH66eZ2NhY0tPTxRrU9PR0srOzKSwspLy8HENDQ9zc3Jg+fbrY+ULiyUVwsn7yySfcuXOHSZMm4e/vj1wu5/r16/zwww+89957TJo0qb6nqnMko/gZoaqqivj4eNRqNaWlpdy+fZsbN26Qm5sLPIhQpKWl4eTkhLe3N6tXr8bc3LyeZ90wqZ7aWF5ejlqtRqlUcvnyZRITE0lPT+fy5csA/P777/U5VQkJiSeclJQUkpOTRVVfiX/P3bt3OXnyJFlZWaSmppKfn09+fj5KpRJnZ2esrKxwcnLCzMyMAQMGPDQNVKJ2REdHc+HCBTw8POjRo4cUjW+gCOcatVqNRqMRxUThgVGVl5dHRkYGaWlpxMXFceTIEezs7Ni0aVM9zlri3yA4Wdu3b8/ixYvp2rVrjd9v3LiRgwcPsnLlSlxdXetplnWDVFP8jBAdHc3kyZPJzMykrKyMJk2a0KxZMzw9PbGysiI4OJiQkBD09fXRaDSSQVwLCgsLOXfuHMnJyaSkpHDt2jVSU1NRq9Xo6enh6emJt7c3L730Un1PVUJC4glBpVJx7tw5jh8/jo2NDU2aNMHNzQ03NzdCQkKIjY2V6jEfkZCQEEJCQsR/z83NJT09nfj4eOLi4khOTiYjI4PY2Fg6d+4MSPXa2iYwMFCKFj4FCPGzw4cPs3DhQtq3b4+/vz+BgYH4+vri7OxM48aNCQ0NBR7Umf/444/1OWWJf4menp7YptXZ2ZmqqioqKytFY7lfv36sXLkSS0vL+p6qzpGM4meECxcukJSUhKurKy+88AJubm44OTnRvHlz3Nzc0NfXf+qbctcFM2fO5OrVq9jb21NWVoZarcbV1ZUJEyawYcMGGjduzCeffIK1tXV9T1VCQuIJQEhdu3jxIl9++SV6enoUFRWxfv16AJydnbG2tqZVq1Z8+umnUup0LbCzs8POzq6GoazRaEhNTRU7AUgGsYTEXxE0DBo1asSLL75IamoqBw4cICsri8rKSjw9PZHJZIwbN46XXnqJzp07S9o0DYiKigoaN27M9u3bmTdvXo118OrVq8hksmei7EEyip8RWrVqxeuvv45cLufq1auiipyxsTH29va4u7uLUeNOnTpJEYnH5Nq1a6Snp9OyZUtGjBhB27Ztxd+tW7eOdu3aiT0bpcOXhIRE9QiMp6cn69evZ8WKFWRkZDBu3DimTZtGdnY27dq1Ax5EMiWRLe2hp6eHh4dHfU9DQqJB0KRJE5o0aQI8cOjl5OQQHR3Np59+iomJifguOTo64uLiUp9TlfgXCMrT1tbW9O3bl1mzZpGWlsZzzz2Hk5MT8fHxHD58mJdffrm+p1onSEbxM0KzZs1o1qyZ+O8KhYK4uDgiIyOJjo4mISGBW7duIZfLWblyJQEBAZLh9hhs376drVu3cunSJTZv3kxlZSWtWrVCJpORkZGBp6cnIEUjJCQkHiBEfe/du8e4ceMAKC0txdnZGX9/f6ZPn87+/fvFtETJIJaQkHgSMDAwwMnJCScnJ2bNmsWdO3ekdaqBUf0sOnjwYIyNjdm8eTPbt2+nvLwcpVLJsGHDngmRLZCM4mcWZ2dnnJ2dee6558SfVVVVkZCQgLOzMyAZbo+Dk5MTo0ePxtvbmy1btjB+/HhefPFFevbsCUDjxo3reYYSEhJPEsLh0cTERMzgEf65pKSE9u3bM3fuXLGlm5Q+LSEhUV/8XbCkZcuWTJs27Zkxnp4GNm/ejKGhIe7u7ri4uODs7Ez//v3p1q0bycnJFBYW0rhx42cibVpAMoolRPT19fH396/vaTR47OzsePnll+nZsycbNmxg69at7N27F3t7e1JSUnTal1hCQqLhodFoeOGFF1i1ahUtW7bE3d2dQ4cOYW5uTlxcHIWFhfj5+QFIBrGEhESdIugeXLt2jbfeeov27dsTGBhI48aNCQwMRK1Ws3nzZpycnDAyMpKyDBsIX331Febm5gQHB6Ovr4+xsTFOTk54eXnh6+uLu7s7+fn5GBgYYGpqWt/TrROklkwSElpGo9Gg0WjETSEjI4M1a9Zw6NAh3N3dee+99+jatSsymayeZyohIVFf3Lt3j+vXrzNixAhkMhk5OTn8+OOPdO3aFWdnZ1588UWaNWtGdnY2Xl5erFu3TooSS0hI1BtxcXFs2bIFhUJBbGwsmZmZKJVKqqqqcHNzY8qUKbz44ouiES3x5FJcXMyMGTM4ffo0tra2hIaGYmhoKPab1mg0GBsbY2Vlhb29PStWrKjvKdcJklEsIaFDBI9pcXExly5dYuPGjVy7do2VK1fSq1ev+p6ehIREPbFr1y4+/vhjUc112LBhNbJIjh07xq5duwgMDGTIkCH4+vpKRrGEhESdkpycjImJCfb29jWiv0qlkpycHJKTkyktLcXBwYHg4GAMDaUE1IZCQUEBFy5c4MSJE9jY2PD888/Tpk0bFAoF9+/fJzo6mri4OPT09Fi0aFF9T7dOkIxiCYk6JDMzk2+++YYxY8ZI7QokJJ5h1Go14eHhHDhwgNOnT2NoaEj//v3p168fAQEBmJqakpeXh7m5OTKZTDKIJSQk6pzXX3+dyspKvLy8sLe3x9PTEw8PD7Gt57NUb/q0IpfL2bJlC/v378fPz4/333+fDh061Pe06gXJKJaQkJCQkKhH0tPT2bt3L2fOnMHQ0JDOnTvzyiuvSPoDEhIS9YZaraZr167k5OTQpEkTZDIZJSUlqFQqLCwssLe3x9nZGRcXFxwdHRk0aJBUFtaAKS4uZuvWrRw9ehQ3NzdGjhxJ69atMTExqe+p1RmSUSwhISEhIfEEkJWVxR9//MHmzZtJT09nwIABDBo0iDZt2kjCNRISEnWKSqXi2LFjbNu2jdjYWPr06UOnTp3Q19cnIiKCmJgYcnJyKCwsxMjIiAMHDtT3lCUegaqqKvT09GpkIKnVatLT09m2bRu3b9+mW7duvPbaa5LQloSEhISEhIT20Wg0FBcXo1arsbGxIScnh5iYGEpKSrC0tKSkpISjR49y8OBBXF1dOXXqVH1PWUJC4hnm5MmT7N27l/LycgYPHkyvXr2QyWSUl5eTnJxMXl4ebdu2lco8GiByuRyVSoVKpSIyMpKYmBji4uK4fPkyZWVlnD17Ficnp/qeZp0gGcUSEhISEhJ1yNWrV9myZQsFBQXcuXOHyspK3NzcMDQ0RKFQYGhoSHBwMG5ubnh6ejJhwgSpzYmEhESdIyhJazQa0tLSOHToEOfOncPHx4cRI0YQGhoq/q1kEDcMcnNz2bZtG/Hx8ZSUlKBWq0lOTiY1NRVvb2/8/PywtLTE2dmZ4OBg+vbtW99TrjMko1hCQkJCQqIOWbRoERs3bqR3796MHDkSa2triouLMTY2JiAgAHNzc7HVybNUzyUhIfFk8TBDNzIyki1btnD8+HE6dOjA22+/LQmHNiB2797NnDlzsLW1xc3NjcDAQLp27Urbtm0xMTHByMgIIyOj+p5mvSBpp0tISEhISNQhPXr0IC0tjYsXL5Kfn8+oUaPo3bt3jb+RBGskJCTqm4dFft3d3Zk1axa9e/dm2bJl/Oc//2HPnj34+PjU/QQlHhmFQiH+s5GREaWlpZw5c4bIyEi8vLxwdHTE1tYWa2trnJ2dn6m9SIoUS0hISEhI1AORkZHs2bOHc+fOYWlpyeDBg+nSpQuurq7Ag9RFqe+nhIREXVNVVUV8fDxqtZrS0lJu377NjRs3yM3NBR4o5qelpeHk5IS3tzerV6/G3Ny8nmct8W/JysoiPj6e27dvExUVhVwuJzs7m/LycmQymdiXeubMmbRp06a+p1tnSEaxhISEhIREHaJUKtHX18fQ0BClUsn9+/fZs2cPEREReHl50bdv32eqjktCQuLJIjIyksmTJ5OZmUlZWRlNmjShWbNmmJubY2VlRXBwMCEhIejr66PRaLCzs6vvKUvUkqqqKpKSkoiJiSE6OpqrV68yZ84cAgMD63tqdYZkFEtISEhISNQDf67Xu3btGlu2bOHMmTM4Ozvz6quv8vrrr9fjDCUkJJ5FNmzYwFdffYWrqysdO3bEzc0NJycnmjdvjpubG/r6+pLegcRTh2QUS0hISEhI1BGRkZFERERgYWFBYWEhGRkZpKWlAWBra4uJiQn37t3j7NmzNG3alJ07d4oKsBISEhJ1wa1btzh06BByuZzExEQqKioAMDY2xt7eHnd3dzw9PbGysqJTp04EBATU84wlJGqPZBRLSEhISEjUEV988QVbt27F1NQUAwMDPDw8cHV1paqqiqqqKuzs7GjcuLH48+DgYKkdk4SERL2iUCiIi4sjMjKS6OhoEhISyMvLQy6Xs3LlSnr16iWtUxINHskolpCQkJCQqCMUCgVFRUU4OTlhZWVFYWEharX6b2vypN6fEhISTyJVVVUkJCTg7OyMhYVFfU9HQqLWSEaxhISEhITEE0BVVRV6enqSESwhISEhIVHHSEaxhISEhISEhISEhISExDOLlPwvISEhISEhISEhISEh8cwiGcUSEhISEhISEhISEhISzyySUSwhISEhIdHAkSqhJCQkJCQkHh/JKJaQkJCQkGigFBYW8tFHH3Hp0qV//LvLly8TFBTEhQsXAPjuu+8ICgpCrVbXxTQlJCQkJCSeaCSjWEJCQkJCooFy//599u7d+z8jxU2aNGHr1q2EhYXV0cwkJCQkJCQaDob1PQEJCQkJCQkJ3WJpaUnr1q3rexoSEhISEhJPJFKkWEJCQkJC4gnm/v37jB07ljZt2tC8eXNGjhzJ+fPn2bNnD6NGjQLgjTfe4KOPPgKgR48eLFq0iHHjxtGsWTOmTJnyl/TpP6NQKOjduzcvvPACOTk5wIO+yevWraNPnz6EhobSq1cv1q9fXyMq/dFHHzF69GgWLlxIq1at6NmzJxUVFTq+IxISEhISEtpFihRLSEhISEg8oRQXF/PGG2/QokULvv76awB++ukn3nrrLbZu3crcuXNZuHAhs2bNolu3buJ/t2XLFoYMGcLo0aORyWT/OEZubi5vvPEGRkZGbNq0CXt7ewAWLFjAjh07RIM8PDycJUuWkJOTw8yZM8X//vr166hUKpYvX05RURHGxsbavxESEhISEhI6RDKKJSQkJCQknlDi4uLIy8tjwoQJtGzZEoCmTZvyww8/YGZmRmBgIACBgYH4+PiI/52trS0ff/wx/6+9u2dpJAjAOP54XgIKGgybgIXgGqJYiCAoYmNjtEz8AAGRCAoBC8EQBPGNVCnUQgyiYEC3sxQtRNBGP4GCChKxMCCCqI0ve8VBuOPuPLgcZ479/2Bgm5nZ2WZ55mX38+evr/nj4+Oftn9/f6/BwUFJUjablWEYkqTLy0tZlqV4PK54PC5J6u7uVkVFhRYXFxWNRlVbWytJen5+1uzsrAKBwN9/AAAA/ANsnwYAoEQFg0H5fD6NjIxoenpae3t7crvdSiaTCgaDv6wXCAQKgfg9Q0NDOjk5USKRKARiSTo6OpJt2+rp6dHLy0uhhEIhvb6+fve1a5fLJdM0ixsoAAAfiJViAABKVGVlpSzLUiaT0e7urjY3N+V2u9Xb26upqalf1vs24L7n4eFB9fX1SqfT6urqksvlkiTd3d1JksLh8E/r3dzcFK69Xq8+fWKOHQDw/yIUAwBQwurq6jQ3NyfbtnV6eqrt7W2trq7K4/Gor6+vqLbX19eVy+UUjUaVyWQKW6Wrq6slfT2/XFVV9UM9v99fVL8AAJQSpnYBAChROzs76uzsVD6fV1lZmZqbmzU2NqaGhgZdX1+rvLy8qPYNw1BHR4f6+/u1vLys8/NzSVJ7e7sk6fb2Vi0tLYXy9PSkdDqtfD5f9NgAACgVrBQDAFCi2traZNu2hoeHFYvF5PF4dHh4qLOzM8ViscIq7sHBgXw+37vnjN8zPj6u/f19TUxMyLIsNTY2KhKJaHJyUldXV2ptbVUul9PCwoIMw1BTU9PfHCYAAB+KUAwAQIny+/1aW1vT/Py8ZmZm9Pj4KNM0lUqlFIlE9Pb2pnA4rI2NDV1cXGhlZeWP+vF6vUokEkomk8pmsxoYGFAqlZJpmtra2tLS0pJqamoUCoU0Ojr62988AQDwPymzbdv+6JsAAAAAAOAjcKYYAAAAAOBYhGIAAAAAgGMRigEAAAAAjkUoBgAAAAA4FqEYAAAAAOBYhGIAAAAAgGMRigEAAAAAjkUoBgAAAAA4FqEYAAAAAOBYXwB0YMawTZsjMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.set(font_scale=1.4)\n",
    "# sns.set_style('darkgrid')\n",
    "graph = sns.barplot(x=best_batsmen_in_Chepauk_2021.tail(30).sort_values(by='score', ascending=True).index, y='score', palette='magma', data=best_batsmen_in_Chepauk_2021.tail(30).sort_values(by='score',ascending=True))\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., title='Average', shadow=True)\n",
    "plt.title(\"Worst batsmen in the Chennai leg of IPL 2021\")\n",
    "plt.xticks(rotation=80);\n",
    "\n",
    "for p in graph.patches:\n",
    "        graph.annotate('{:.2f}'.format(p.get_height()), (p.get_x()+0.4, p.get_height()),\n",
    "                    ha='center', va='bottom',\n",
    "                    color= 'black', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "correct-continuity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.set(font_scale=1.4)\n",
    "# sns.set_style('darkgrid')\n",
    "graph = sns.barplot(x=best_batsmen_in_Wankhede_2021.head(30).index, y='score', palette='magma', data=best_batsmen_in_Wankhede_2021.head(30))\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., title='Average', shadow=True)\n",
    "plt.title(\"Best batsmen in the Mumbai leg of IPL 2021\")\n",
    "plt.xticks(rotation=80);\n",
    "\n",
    "for p in graph.patches:\n",
    "        graph.annotate('{:.2f}'.format(p.get_height()), (p.get_x()+0.4, p.get_height()),\n",
    "                    ha='center', va='bottom',\n",
    "                    color= 'black', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "referenced-interim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.set(font_scale=1.4)\n",
    "# sns.set_style('darkgrid')\n",
    "graph = sns.barplot(x=best_batsmen_in_Wankhede_2021.tail(30).sort_values(by='score', ascending=True).index, y='score', palette='magma', data=best_batsmen_in_Wankhede_2021.tail(30).sort_values(by='score',ascending=True))\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0., title='Average', shadow=True)\n",
    "plt.title(\"Worst batsmen in the Mumbai leg of IPL 2021\")\n",
    "plt.xticks(rotation=80);\n",
    "\n",
    "for p in graph.patches:\n",
    "        graph.annotate('{:.2f}'.format(p.get_height()), (p.get_x()+0.4, p.get_height()),\n",
    "                    ha='center', va='bottom',\n",
    "                    color= 'black', fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "pleased-focus",
   "metadata": {},
   "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>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>striker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GJ Maxwell</th>\n",
       "      <td>0.793034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Rana</th>\n",
       "      <td>0.749776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KS Williamson</th>\n",
       "      <td>0.728776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA Lynn</th>\n",
       "      <td>0.719258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JM Bairstow</th>\n",
       "      <td>0.666724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>0.664577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RG Sharma</th>\n",
       "      <td>0.623603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPD Smith</th>\n",
       "      <td>0.618466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL Rahul</th>\n",
       "      <td>0.604399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK Pandey</th>\n",
       "      <td>0.586929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Dhawan</th>\n",
       "      <td>0.585693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH Gayle</th>\n",
       "      <td>0.553022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA Yadav</th>\n",
       "      <td>0.498819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PP Shaw</th>\n",
       "      <td>0.489959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RA Tripathi</th>\n",
       "      <td>0.461808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DA Warner</th>\n",
       "      <td>0.453427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>0.410505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA Agarwal</th>\n",
       "      <td>0.405209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shubman Gill</th>\n",
       "      <td>0.398535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Yadav</th>\n",
       "      <td>0.396052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Shahrukh Khan</th>\n",
       "      <td>0.388069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lalit Yadav</th>\n",
       "      <td>0.387407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR Pant</th>\n",
       "      <td>0.387258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D Padikkal</th>\n",
       "      <td>0.330857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J Suchith</th>\n",
       "      <td>0.330269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KD Karthik</th>\n",
       "      <td>0.306581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>0.296553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA Pollard</th>\n",
       "      <td>0.295795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mohammad Nabi</th>\n",
       "      <td>0.288141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJ Hooda</th>\n",
       "      <td>0.286858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ishan Kishan</th>\n",
       "      <td>0.286401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MC Henriques</th>\n",
       "      <td>0.281363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EJG Morgan</th>\n",
       "      <td>0.279191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakib Al Hasan</th>\n",
       "      <td>0.273598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>0.268073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA Jamieson</th>\n",
       "      <td>0.266211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO Hetmyer</th>\n",
       "      <td>0.258692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abdul Samad</th>\n",
       "      <td>0.257357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q de Kock</th>\n",
       "      <td>0.255226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HV Patel</th>\n",
       "      <td>0.253589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mohammed Shami</th>\n",
       "      <td>0.238925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP Stoinis</th>\n",
       "      <td>0.236272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJ Cummins</th>\n",
       "      <td>0.234568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TA Boult</th>\n",
       "      <td>0.234264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mujeeb Ur Rahman</th>\n",
       "      <td>0.234264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Ashwin</th>\n",
       "      <td>0.233257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KM Jadhav</th>\n",
       "      <td>0.233108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rashid Khan</th>\n",
       "      <td>0.233045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shahbaz Ahmed</th>\n",
       "      <td>0.225876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KH Pandya</th>\n",
       "      <td>0.217815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     score\n",
       "striker                   \n",
       "GJ Maxwell        0.793034\n",
       "N Rana            0.749776\n",
       "KS Williamson     0.728776\n",
       "CA Lynn           0.719258\n",
       "JM Bairstow       0.666724\n",
       "AB de Villiers    0.664577\n",
       "RG Sharma         0.623603\n",
       "SPD Smith         0.618466\n",
       "KL Rahul          0.604399\n",
       "MK Pandey         0.586929\n",
       "S Dhawan          0.585693\n",
       "CH Gayle          0.553022\n",
       "SA Yadav          0.498819\n",
       "PP Shaw           0.489959\n",
       "RA Tripathi       0.461808\n",
       "DA Warner         0.453427\n",
       "V Kohli           0.410505\n",
       "MA Agarwal        0.405209\n",
       "Shubman Gill      0.398535\n",
       "J Yadav           0.396052\n",
       "M Shahrukh Khan   0.388069\n",
       "Lalit Yadav       0.387407\n",
       "RR Pant           0.387258\n",
       "D Padikkal        0.330857\n",
       "J Suchith         0.330269\n",
       "KD Karthik        0.306581\n",
       "AD Russell        0.296553\n",
       "KA Pollard        0.295795\n",
       "Mohammad Nabi     0.288141\n",
       "DJ Hooda          0.286858\n",
       "Ishan Kishan      0.286401\n",
       "MC Henriques      0.281363\n",
       "EJG Morgan        0.279191\n",
       "Shakib Al Hasan   0.273598\n",
       "V Shankar         0.268073\n",
       "KA Jamieson       0.266211\n",
       "SO Hetmyer        0.258692\n",
       "Abdul Samad       0.257357\n",
       "Q de Kock         0.255226\n",
       "HV Patel          0.253589\n",
       "Mohammed Shami    0.238925\n",
       "MP Stoinis        0.236272\n",
       "PJ Cummins        0.234568\n",
       "TA Boult          0.234264\n",
       "Mujeeb Ur Rahman  0.234264\n",
       "M Ashwin          0.233257\n",
       "KM Jadhav         0.233108\n",
       "Rashid Khan       0.233045\n",
       "Shahbaz Ahmed     0.225876\n",
       "KH Pandya         0.217815"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_batsmen_in_Chepauk_2021.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "normal-memorabilia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>season</th>\n",
       "      <th>start_date</th>\n",
       "      <th>venue</th>\n",
       "      <th>innings</th>\n",
       "      <th>ball</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>striker</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>...</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>other_wicket_type</th>\n",
       "      <th>other_player_dismissed</th>\n",
       "      <th>Over Number</th>\n",
       "      <th>Ball Number</th>\n",
       "      <th>isDot</th>\n",
       "      <th>isOne</th>\n",
       "      <th>isFour</th>\n",
       "      <th>isSix</th>\n",
       "      <th>isBoundary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193867</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193868</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193869</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193870</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193871</th>\n",
       "      <td>1254059</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>RD Gaikwad</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198240</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198241</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198242</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198243</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.5</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198244</th>\n",
       "      <td>1254076</td>\n",
       "      <td>2021</td>\n",
       "      <td>2021-04-25</td>\n",
       "      <td>Wankhede Stadium, Mumbai</td>\n",
       "      <td>2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2427 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id season  start_date                     venue  innings  ball  \\\n",
       "193867   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.1   \n",
       "193868   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.2   \n",
       "193869   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.3   \n",
       "193870   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.4   \n",
       "193871   1254059   2021  2021-04-10  Wankhede Stadium, Mumbai        1   0.5   \n",
       "...          ...    ...         ...                       ...      ...   ...   \n",
       "198240   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.2   \n",
       "198241   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.3   \n",
       "198242   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.4   \n",
       "198243   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.5   \n",
       "198244   1254076   2021  2021-04-25  Wankhede Stadium, Mumbai        2  19.6   \n",
       "\n",
       "                       batting_team         bowling_team         striker  \\\n",
       "193867          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193868          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193869          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193870          Chennai Super Kings       Delhi Capitals      RD Gaikwad   \n",
       "193871          Chennai Super Kings       Delhi Capitals    F du Plessis   \n",
       "...                             ...                  ...             ...   \n",
       "198240  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198241  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198242  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "198243  Royal Challengers Bangalore  Chennai Super Kings       YS Chahal   \n",
       "198244  Royal Challengers Bangalore  Chennai Super Kings  Mohammed Siraj   \n",
       "\n",
       "           non_striker  ... player_dismissed  other_wicket_type  \\\n",
       "193867    F du Plessis  ...              NaN                NaN   \n",
       "193868    F du Plessis  ...              NaN                NaN   \n",
       "193869    F du Plessis  ...              NaN                NaN   \n",
       "193870    F du Plessis  ...              NaN                NaN   \n",
       "193871      RD Gaikwad  ...              NaN                NaN   \n",
       "...                ...  ...              ...                ...   \n",
       "198240       YS Chahal  ...              NaN                NaN   \n",
       "198241       YS Chahal  ...              NaN                NaN   \n",
       "198242       YS Chahal  ...              NaN                NaN   \n",
       "198243  Mohammed Siraj  ...              NaN                NaN   \n",
       "198244       YS Chahal  ...              NaN                NaN   \n",
       "\n",
       "        other_player_dismissed  Over Number  Ball Number  isDot  isOne  \\\n",
       "193867                     NaN            1            1      1      0   \n",
       "193868                     NaN            1            2      1      0   \n",
       "193869                     NaN            1            3      0      0   \n",
       "193870                     NaN            1            4      1      0   \n",
       "193871                     NaN            1            5      1      0   \n",
       "...                        ...          ...          ...    ...    ...   \n",
       "198240                     NaN           20            2      0      0   \n",
       "198241                     NaN           20            3      0      0   \n",
       "198242                     NaN           20            4      0      1   \n",
       "198243                     NaN           20            5      0      1   \n",
       "198244                     NaN           20            6      0      1   \n",
       "\n",
       "        isFour isSix isBoundary  \n",
       "193867       0     0          0  \n",
       "193868       0     0          0  \n",
       "193869       1     0          1  \n",
       "193870       0     0          0  \n",
       "193871       0     0          0  \n",
       "...        ...   ...        ...  \n",
       "198240       0     1          1  \n",
       "198241       0     0          0  \n",
       "198242       0     0          0  \n",
       "198243       0     0          0  \n",
       "198244       0     0          0  \n",
       "\n",
       "[2427 rows x 29 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wankhede_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greater-satellite",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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