Player_Performance_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Necessary Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading The DATA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "batsman_df = pd.read_csv('Data/Batsman_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>18 Dec 2009</td>\n",
       "      <td>ODI # 2933</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>24 Dec 2009</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>27 Dec 2009</td>\n",
       "      <td>ODI # 2936</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>4 Jan 2010</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>5 Jan 2010</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Bat1 Runs BF SR 4s 6s    Opposition   Ground   Start Date  \\\n",
       "0           1  DNB    -  -  -  -  -       v India   Nagpur  18 Dec 2009   \n",
       "1           2  DNB    -  -  -  -  -       v India  Kolkata  24 Dec 2009   \n",
       "2           3  DNB    -  -  -  -  -       v India    Delhi  27 Dec 2009   \n",
       "3           4  DNB    -  -  -  -  -  v Bangladesh    Dhaka   4 Jan 2010   \n",
       "4           5  DNB    -  -  -  -  -       v India    Dhaka   5 Jan 2010   \n",
       "\n",
       "     Match_ID        Batsman  Player_ID  \n",
       "0  ODI # 2933  Oshane Thomas      49619  \n",
       "1  ODI # 2935  Oshane Thomas      49619  \n",
       "2  ODI # 2936  Oshane Thomas      49619  \n",
       "3  ODI # 2937  Oshane Thomas      49619  \n",
       "4  ODI # 2938  Oshane Thomas      49619  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bowler_df = pd.read_csv('Data/Bowler_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>Econ</th>\n",
       "      <th>Ave</th>\n",
       "      <th>SR</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Bowler</th>\n",
       "      <th>Player_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>7.12</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>18 Dec 2009</td>\n",
       "      <td>ODI # 2933</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>5.50</td>\n",
       "      <td>27.50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>24 Dec 2009</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>27 Dec 2009</td>\n",
       "      <td>ODI # 2936</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>7.00</td>\n",
       "      <td>31.50</td>\n",
       "      <td>27.0</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>4 Jan 2010</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>5 Jan 2010</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11113</th>\n",
       "      <td>11114</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>10.25</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>25 Sep 2018</td>\n",
       "      <td>ODI # 4046</td>\n",
       "      <td>Gulbadin Naib</td>\n",
       "      <td>352048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11114</th>\n",
       "      <td>11115</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>2.72</td>\n",
       "      <td>10.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>v Ireland</td>\n",
       "      <td>Dehradun</td>\n",
       "      <td>28 Feb 2019</td>\n",
       "      <td>ODI # 4100</td>\n",
       "      <td>Gulbadin Naib</td>\n",
       "      <td>352048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11115</th>\n",
       "      <td>11116</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v Ireland</td>\n",
       "      <td>Dehradun</td>\n",
       "      <td>2 Mar 2019</td>\n",
       "      <td>ODI # 4101</td>\n",
       "      <td>Gulbadin Naib</td>\n",
       "      <td>352048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11116</th>\n",
       "      <td>11117</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>21.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>v Ireland</td>\n",
       "      <td>Dehradun</td>\n",
       "      <td>8 Mar 2019</td>\n",
       "      <td>ODI # 4108</td>\n",
       "      <td>Gulbadin Naib</td>\n",
       "      <td>352048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11117</th>\n",
       "      <td>11118</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>7.20</td>\n",
       "      <td>24.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>v Scotland</td>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>10 May 2019</td>\n",
       "      <td>ODI # 4131</td>\n",
       "      <td>Gulbadin Naib</td>\n",
       "      <td>352048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11118 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 Overs Mdns Runs Wkts   Econ    Ave    SR    Opposition  \\\n",
       "0               1   8.0    0   57    0   7.12      -     -       v India   \n",
       "1               2  10.0    0   55    2   5.50  27.50  30.0       v India   \n",
       "2               3     -    -    -    -      -      -     -       v India   \n",
       "3               4   9.0    1   63    2   7.00  31.50  27.0  v Bangladesh   \n",
       "4               5   8.0    1   48    0   6.00      -     -       v India   \n",
       "...           ...   ...  ...  ...  ...    ...    ...   ...           ...   \n",
       "11113       11114   4.0    0   41    0  10.25      -     -       v India   \n",
       "11114       11115   7.2    0   20    2   2.72  10.00  22.0     v Ireland   \n",
       "11115       11116     -    -    -    -      -      -     -     v Ireland   \n",
       "11116       11117   6.0    0   21    1   3.50  21.00  36.0     v Ireland   \n",
       "11117       11118  10.0    0   72    3   7.20  24.00  20.0    v Scotland   \n",
       "\n",
       "            Ground   Start Date    Match_ID          Bowler  Player_ID  \n",
       "0           Nagpur  18 Dec 2009  ODI # 2933  Suranga Lakmal      49619  \n",
       "1          Kolkata  24 Dec 2009  ODI # 2935  Suranga Lakmal      49619  \n",
       "2            Delhi  27 Dec 2009  ODI # 2936  Suranga Lakmal      49619  \n",
       "3            Dhaka   4 Jan 2010  ODI # 2937  Suranga Lakmal      49619  \n",
       "4            Dhaka   5 Jan 2010  ODI # 2938  Suranga Lakmal      49619  \n",
       "...            ...          ...         ...             ...        ...  \n",
       "11113  Dubai (DSC)  25 Sep 2018  ODI # 4046  Gulbadin Naib      352048  \n",
       "11114     Dehradun  28 Feb 2019  ODI # 4100  Gulbadin Naib      352048  \n",
       "11115     Dehradun   2 Mar 2019  ODI # 4101  Gulbadin Naib      352048  \n",
       "11116     Dehradun   8 Mar 2019  ODI # 4108  Gulbadin Naib      352048  \n",
       "11117    Edinburgh  10 May 2019  ODI # 4131  Gulbadin Naib      352048  \n",
       "\n",
       "[11118 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_avg_df = pd.read_csv('Data/Ground_Averages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_results_df = pd.read_csv('Data/ODI_Match_Results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_total_df = pd.read_csv('Data/ODI_Match_Totals.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "players_df = pd.read_csv('Data/WC_players.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gulbadin Naib (c)</td>\n",
       "      <td>352048</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rashid Khan (vc)</td>\n",
       "      <td>793463</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aftab Alam</td>\n",
       "      <td>440963</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asghar Afghan</td>\n",
       "      <td>320652</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dawlat Zadran</td>\n",
       "      <td>516561</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Player      ID      Country\n",
       "0  Gulbadin Naib (c)  352048  Afghanistan\n",
       "1   Rashid Khan (vc)  793463  Afghanistan\n",
       "2         Aftab Alam  440963  Afghanistan\n",
       "3      Asghar Afghan  320652  Afghanistan\n",
       "4      Dawlat Zadran  516561  Afghanistan"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Null values and info about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Bat1          0\n",
       "Runs          0\n",
       "BF            0\n",
       "SR            0\n",
       "4s            0\n",
       "6s            0\n",
       "Opposition    0\n",
       "Ground        0\n",
       "Start Date    0\n",
       "Match_ID      0\n",
       "Batsman       0\n",
       "Player_ID     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11149 entries, 0 to 11148\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  11149 non-null  int64 \n",
      " 1   Bat1        11149 non-null  object\n",
      " 2   Runs        11149 non-null  object\n",
      " 3   BF          11149 non-null  object\n",
      " 4   SR          11149 non-null  object\n",
      " 5   4s          11149 non-null  object\n",
      " 6   6s          11149 non-null  object\n",
      " 7   Opposition  11149 non-null  object\n",
      " 8   Ground      11149 non-null  object\n",
      " 9   Start Date  11149 non-null  object\n",
      " 10  Match_ID    11149 non-null  object\n",
      " 11  Batsman     11149 non-null  object\n",
      " 12  Player_ID   11149 non-null  int64 \n",
      "dtypes: int64(2), object(11)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "batsman_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "Overs         0\n",
       "Mdns          0\n",
       "Runs          0\n",
       "Wkts          0\n",
       "Econ          0\n",
       "Ave           0\n",
       "SR            0\n",
       "Opposition    0\n",
       "Ground        0\n",
       "Start Date    0\n",
       "Match_ID      0\n",
       "Bowler        0\n",
       "Player_ID     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11118 entries, 0 to 11117\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  11118 non-null  int64 \n",
      " 1   Overs       11118 non-null  object\n",
      " 2   Mdns        11118 non-null  object\n",
      " 3   Runs        11118 non-null  object\n",
      " 4   Wkts        11118 non-null  object\n",
      " 5   Econ        11118 non-null  object\n",
      " 6   Ave         11118 non-null  object\n",
      " 7   SR          11118 non-null  object\n",
      " 8   Opposition  11118 non-null  object\n",
      " 9   Ground      11118 non-null  object\n",
      " 10  Start Date  11118 non-null  object\n",
      " 11  Match_ID    11118 non-null  object\n",
      " 12  Bowler      11118 non-null  object\n",
      " 13  Player_ID   11118 non-null  int64 \n",
      "dtypes: int64(2), object(12)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "bowler_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ground    0\n",
       "Span      0\n",
       "Mat       0\n",
       "Won       0\n",
       "Tied      0\n",
       "NR        0\n",
       "Runs      0\n",
       "Wkts      0\n",
       "Balls     0\n",
       "Ave       0\n",
       "RPO       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_avg_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 106 entries, 0 to 105\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Ground  106 non-null    object \n",
      " 1   Span    106 non-null    object \n",
      " 2   Mat     106 non-null    int64  \n",
      " 3   Won     106 non-null    int64  \n",
      " 4   Tied    106 non-null    int64  \n",
      " 5   NR      106 non-null    int64  \n",
      " 6   Runs    106 non-null    int64  \n",
      " 7   Wkts    106 non-null    int64  \n",
      " 8   Balls   106 non-null    int64  \n",
      " 9   Ave     106 non-null    float64\n",
      " 10  RPO     106 non-null    float64\n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 9.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ground_avg_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "Result          0\n",
       "Margin          0\n",
       "BR            716\n",
       "Toss            0\n",
       "Bat             0\n",
       "Opposition      0\n",
       "Ground          0\n",
       "Start Date      0\n",
       "Match_ID        0\n",
       "Country         0\n",
       "Country_ID      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_results_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1322 entries, 0 to 1321\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  1322 non-null   int64  \n",
      " 1   Result      1322 non-null   object \n",
      " 2   Margin      1322 non-null   object \n",
      " 3   BR          606 non-null    float64\n",
      " 4   Toss        1322 non-null   object \n",
      " 5   Bat         1322 non-null   object \n",
      " 6   Opposition  1322 non-null   object \n",
      " 7   Ground      1322 non-null   object \n",
      " 8   Start Date  1322 non-null   object \n",
      " 9   Match_ID    1322 non-null   object \n",
      " 10  Country     1322 non-null   object \n",
      " 11  Country_ID  1322 non-null   int64  \n",
      "dtypes: float64(1), int64(2), object(9)\n",
      "memory usage: 124.1+ KB\n"
     ]
    }
   ],
   "source": [
    "match_results_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      0\n",
       "Score           0\n",
       "Overs           0\n",
       "RPO             0\n",
       "Target        676\n",
       "Inns            0\n",
       "Result          0\n",
       "Opposition      0\n",
       "Ground          0\n",
       "Start Date      0\n",
       "Match_ID        0\n",
       "Country         0\n",
       "Country_ID      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_total_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1296 entries, 0 to 1295\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  1296 non-null   int64  \n",
      " 1   Score       1296 non-null   object \n",
      " 2   Overs       1296 non-null   float64\n",
      " 3   RPO         1296 non-null   object \n",
      " 4   Target      620 non-null    float64\n",
      " 5   Inns        1296 non-null   int64  \n",
      " 6   Result      1296 non-null   object \n",
      " 7   Opposition  1296 non-null   object \n",
      " 8   Ground      1296 non-null   object \n",
      " 9   Start Date  1296 non-null   object \n",
      " 10  Match_ID    1296 non-null   object \n",
      " 11  Country     1296 non-null   object \n",
      " 12  Country_ID  1296 non-null   int64  \n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 131.8+ KB\n"
     ]
    }
   ],
   "source": [
    "match_total_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player     0\n",
       "ID         0\n",
       "Country    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 152 entries, 0 to 151\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Player   152 non-null    object\n",
      " 1   ID       152 non-null    int64 \n",
      " 2   Country  152 non-null    object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "players_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = [batsman_df,bowler_df,ground_avg_df,match_results_df,match_total_df,players_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dimensions(df):\n",
    "    for i in df:\n",
    "        print(i.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11149, 13)\n",
      "(11118, 14)\n",
      "(106, 11)\n",
      "(1322, 12)\n",
      "(1296, 13)\n",
      "(152, 3)\n"
     ]
    }
   ],
   "source": [
    "dimensions(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Unnamed: 0</th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>18 Dec 2009</td>\n",
       "      <td>ODI # 2933</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>24 Dec 2009</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>27 Dec 2009</td>\n",
       "      <td>ODI # 2936</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>4 Jan 2010</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>5 Jan 2010</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Bat1 Runs BF SR 4s 6s    Opposition   Ground   Start Date  \\\n",
       "0           1  DNB    -  -  -  -  -       v India   Nagpur  18 Dec 2009   \n",
       "1           2  DNB    -  -  -  -  -       v India  Kolkata  24 Dec 2009   \n",
       "2           3  DNB    -  -  -  -  -       v India    Delhi  27 Dec 2009   \n",
       "3           4  DNB    -  -  -  -  -  v Bangladesh    Dhaka   4 Jan 2010   \n",
       "4           5  DNB    -  -  -  -  -       v India    Dhaka   5 Jan 2010   \n",
       "\n",
       "     Match_ID        Batsman  Player_ID  \n",
       "0  ODI # 2933  Oshane Thomas      49619  \n",
       "1  ODI # 2935  Oshane Thomas      49619  \n",
       "2  ODI # 2936  Oshane Thomas      49619  \n",
       "3  ODI # 2937  Oshane Thomas      49619  \n",
       "4  ODI # 2938  Oshane Thomas      49619  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting The Year into Its Corresponding month and day and year columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As our data set has start date or date column so lets first seaprate them into day, month and year wise and drop the orignal one\n",
    "def seperate_date(df):\n",
    "    for i in range(len(df)):\n",
    "        if 'Start Date' in df[i].columns:\n",
    "            df[i]['Year']=pd.to_datetime(df[i]['Start Date'])\n",
    "            df[i]['Month']=df[i]['Year'].apply(lambda x:x.month) # Extracting Month\n",
    "            df[i]['Day']=df[i]['Year'].apply(lambda x:x.day)  # Extracting day\n",
    "            df[i]['year']=df[i]['Year'].apply(lambda x:x.year)  # Extracting year\n",
    "            print(df[i].head())\n",
    "            print(\"-------------------------\")\n",
    "        else:\n",
    "            print(\"DataFrame\", i, \"does not have 'Start Date' column\")\n",
    "            print(\"-------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0 Bat1 Runs BF SR 4s 6s    Opposition   Ground   Start Date  \\\n",
      "0           1  DNB    -  -  -  -  -       v India   Nagpur  18 Dec 2009   \n",
      "1           2  DNB    -  -  -  -  -       v India  Kolkata  24 Dec 2009   \n",
      "2           3  DNB    -  -  -  -  -       v India    Delhi  27 Dec 2009   \n",
      "3           4  DNB    -  -  -  -  -  v Bangladesh    Dhaka   4 Jan 2010   \n",
      "4           5  DNB    -  -  -  -  -       v India    Dhaka   5 Jan 2010   \n",
      "\n",
      "     Match_ID        Batsman  Player_ID       Year  Month  Day  year  \n",
      "0  ODI # 2933  Oshane Thomas      49619 2009-12-18     12   18  2009  \n",
      "1  ODI # 2935  Oshane Thomas      49619 2009-12-24     12   24  2009  \n",
      "2  ODI # 2936  Oshane Thomas      49619 2009-12-27     12   27  2009  \n",
      "3  ODI # 2937  Oshane Thomas      49619 2010-01-04      1    4  2010  \n",
      "4  ODI # 2938  Oshane Thomas      49619 2010-01-05      1    5  2010  \n",
      "-------------------------\n",
      "   Unnamed: 0 Overs Mdns Runs Wkts  Econ    Ave    SR    Opposition   Ground  \\\n",
      "0           1   8.0    0   57    0  7.12      -     -       v India   Nagpur   \n",
      "1           2  10.0    0   55    2  5.50  27.50  30.0       v India  Kolkata   \n",
      "2           3     -    -    -    -     -      -     -       v India    Delhi   \n",
      "3           4   9.0    1   63    2  7.00  31.50  27.0  v Bangladesh    Dhaka   \n",
      "4           5   8.0    1   48    0  6.00      -     -       v India    Dhaka   \n",
      "\n",
      "    Start Date    Match_ID          Bowler  Player_ID       Year  Month  Day  \\\n",
      "0  18 Dec 2009  ODI # 2933  Suranga Lakmal      49619 2009-12-18     12   18   \n",
      "1  24 Dec 2009  ODI # 2935  Suranga Lakmal      49619 2009-12-24     12   24   \n",
      "2  27 Dec 2009  ODI # 2936  Suranga Lakmal      49619 2009-12-27     12   27   \n",
      "3   4 Jan 2010  ODI # 2937  Suranga Lakmal      49619 2010-01-04      1    4   \n",
      "4   5 Jan 2010  ODI # 2938  Suranga Lakmal      49619 2010-01-05      1    5   \n",
      "\n",
      "   year  \n",
      "0  2009  \n",
      "1  2009  \n",
      "2  2009  \n",
      "3  2010  \n",
      "4  2010  \n",
      "-------------------------\n",
      "DataFrame 2 does not have 'Start Date' column\n",
      "-------------------------\n",
      "   Unnamed: 0 Result    Margin  BR  Toss  Bat   Opposition     Ground  \\\n",
      "0         418    won   85 runs NaN  lost  1st      v India    Kolkata   \n",
      "1         692   lost   85 runs NaN   won  2nd   v Pakistan    Kolkata   \n",
      "2         419   lost   10 runs NaN  lost  2nd      v India      Delhi   \n",
      "3         693    won   10 runs NaN   won  1st   v Pakistan      Delhi   \n",
      "4         121   lost  107 runs NaN  lost  2nd  v Australia  Melbourne   \n",
      "\n",
      "    Start Date    Match_ID   Country  Country_ID       Year  Month  Day  year  \n",
      "0   3 Jan 2013  ODI # 3315  Pakistan           7 2013-01-03      1    3  2013  \n",
      "1   3 Jan 2013  ODI # 3315     India           6 2013-01-03      1    3  2013  \n",
      "2   6 Jan 2013  ODI # 3316  Pakistan           7 2013-01-06      1    6  2013  \n",
      "3   6 Jan 2013  ODI # 3316     India           6 2013-01-06      1    6  2013  \n",
      "4  11 Jan 2013  ODI # 3317  SriLanka           8 2013-01-11      1   11  2013  \n",
      "-------------------------\n",
      "   Unnamed: 0 Score  Overs   RPO  Target  Inns Result   Opposition     Ground  \\\n",
      "0         412   250   48.3  5.15     NaN     1    won      v India    Kolkata   \n",
      "1         680   165   48.0  3.43   251.0     2   lost   v Pakistan    Kolkata   \n",
      "2         413   157   48.5  3.21   168.0     2   lost      v India      Delhi   \n",
      "3         681   167   43.4  3.82     NaN     1    won   v Pakistan      Delhi   \n",
      "4         117   198   40.0  4.95   306.0     2   lost  v Australia  Melbourne   \n",
      "\n",
      "    Start Date    Match_ID   Country  Country_ID       Year  Month  Day  year  \n",
      "0   3 Jan 2013  ODI # 3315  Pakistan           7 2013-01-03      1    3  2013  \n",
      "1   3 Jan 2013  ODI # 3315     India           6 2013-01-03      1    3  2013  \n",
      "2   6 Jan 2013  ODI # 3316  Pakistan           7 2013-01-06      1    6  2013  \n",
      "3   6 Jan 2013  ODI # 3316     India           6 2013-01-06      1    6  2013  \n",
      "4  11 Jan 2013  ODI # 3317  SriLanka           8 2013-01-11      1   11  2013  \n",
      "-------------------------\n",
      "DataFrame 5 does not have 'Start Date' column\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "seperate_date(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
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       "      <th>4s</th>\n",
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       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
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       "      <th>Month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>18 Dec 2009</td>\n",
       "      <td>ODI # 2933</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>24 Dec 2009</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>2009-12-24</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>27 Dec 2009</td>\n",
       "      <td>ODI # 2936</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>2009-12-27</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>4 Jan 2010</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>5 Jan 2010</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Bat1 Runs BF SR 4s 6s    Opposition   Ground   Start Date  \\\n",
       "0           1  DNB    -  -  -  -  -       v India   Nagpur  18 Dec 2009   \n",
       "1           2  DNB    -  -  -  -  -       v India  Kolkata  24 Dec 2009   \n",
       "2           3  DNB    -  -  -  -  -       v India    Delhi  27 Dec 2009   \n",
       "3           4  DNB    -  -  -  -  -  v Bangladesh    Dhaka   4 Jan 2010   \n",
       "4           5  DNB    -  -  -  -  -       v India    Dhaka   5 Jan 2010   \n",
       "\n",
       "     Match_ID        Batsman  Player_ID       Year  Month  Day  year  \n",
       "0  ODI # 2933  Oshane Thomas      49619 2009-12-18     12   18  2009  \n",
       "1  ODI # 2935  Oshane Thomas      49619 2009-12-24     12   24  2009  \n",
       "2  ODI # 2936  Oshane Thomas      49619 2009-12-27     12   27  2009  \n",
       "3  ODI # 2937  Oshane Thomas      49619 2010-01-04      1    4  2010  \n",
       "4  ODI # 2938  Oshane Thomas      49619 2010-01-05      1    5  2010  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2009</td>\n",
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       "      <td>5.50</td>\n",
       "      <td>27.50</td>\n",
       "      <td>30.0</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>24 Dec 2009</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "      <td>2009-12-24</td>\n",
       "      <td>12</td>\n",
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       "      <td>Delhi</td>\n",
       "      <td>27 Dec 2009</td>\n",
       "      <td>ODI # 2936</td>\n",
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       "      <td>49619</td>\n",
       "      <td>2009-12-27</td>\n",
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       "      <td>2009</td>\n",
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       "      <td>63</td>\n",
       "      <td>2</td>\n",
       "      <td>7.00</td>\n",
       "      <td>31.50</td>\n",
       "      <td>27.0</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>4 Jan 2010</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
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       "      <td>0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>5 Jan 2010</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Suranga Lakmal</td>\n",
       "      <td>49619</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0 Overs Mdns Runs Wkts  Econ    Ave    SR    Opposition   Ground  \\\n",
       "0           1   8.0    0   57    0  7.12      -     -       v India   Nagpur   \n",
       "1           2  10.0    0   55    2  5.50  27.50  30.0       v India  Kolkata   \n",
       "2           3     -    -    -    -     -      -     -       v India    Delhi   \n",
       "3           4   9.0    1   63    2  7.00  31.50  27.0  v Bangladesh    Dhaka   \n",
       "4           5   8.0    1   48    0  6.00      -     -       v India    Dhaka   \n",
       "\n",
       "    Start Date    Match_ID          Bowler  Player_ID       Year  Month  Day  \\\n",
       "0  18 Dec 2009  ODI # 2933  Suranga Lakmal      49619 2009-12-18     12   18   \n",
       "1  24 Dec 2009  ODI # 2935  Suranga Lakmal      49619 2009-12-24     12   24   \n",
       "2  27 Dec 2009  ODI # 2936  Suranga Lakmal      49619 2009-12-27     12   27   \n",
       "3   4 Jan 2010  ODI # 2937  Suranga Lakmal      49619 2010-01-04      1    4   \n",
       "4   5 Jan 2010  ODI # 2938  Suranga Lakmal      49619 2010-01-05      1    5   \n",
       "\n",
       "   year  \n",
       "0  2009  \n",
       "1  2009  \n",
       "2  2009  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Drop irrelavent columns from the datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_irrelevant(df): \n",
    "    for i in range(len(df)): \n",
    "        columns_to_drop = ['Unnamed: 0', 'Start Date', 'Year']\n",
    "        irrelevant_columns = [col for col in columns_to_drop if col in df[i].columns]\n",
    "        \n",
    "        if irrelevant_columns: \n",
    "            df[i].drop(columns=irrelevant_columns, axis=1, inplace=True)\n",
    "            print(\"DataFrame\", i , \"after dropping irrelevant columns:\")\n",
    "            print(df[i].head())  # Printing the DataFrame after dropping columns\n",
    "            print(\"-------------------------\")\n",
    "        else:\n",
    "            print(\"DataFrame\", i, \"does not have any irrelevant columns\")\n",
    "            print(\"-------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame 0 after dropping irrelevant columns:\n",
      "  Bat1 Runs BF SR 4s 6s    Opposition   Ground    Match_ID        Batsman  \\\n",
      "0  DNB    -  -  -  -  -       v India   Nagpur  ODI # 2933  Oshane Thomas   \n",
      "1  DNB    -  -  -  -  -       v India  Kolkata  ODI # 2935  Oshane Thomas   \n",
      "2  DNB    -  -  -  -  -       v India    Delhi  ODI # 2936  Oshane Thomas   \n",
      "3  DNB    -  -  -  -  -  v Bangladesh    Dhaka  ODI # 2937  Oshane Thomas   \n",
      "4  DNB    -  -  -  -  -       v India    Dhaka  ODI # 2938  Oshane Thomas   \n",
      "\n",
      "   Player_ID  Month  Day  year  \n",
      "0      49619     12   18  2009  \n",
      "1      49619     12   24  2009  \n",
      "2      49619     12   27  2009  \n",
      "3      49619      1    4  2010  \n",
      "4      49619      1    5  2010  \n",
      "-------------------------\n",
      "DataFrame 1 after dropping irrelevant columns:\n",
      "  Overs Mdns Runs Wkts  Econ    Ave    SR    Opposition   Ground    Match_ID  \\\n",
      "0   8.0    0   57    0  7.12      -     -       v India   Nagpur  ODI # 2933   \n",
      "1  10.0    0   55    2  5.50  27.50  30.0       v India  Kolkata  ODI # 2935   \n",
      "2     -    -    -    -     -      -     -       v India    Delhi  ODI # 2936   \n",
      "3   9.0    1   63    2  7.00  31.50  27.0  v Bangladesh    Dhaka  ODI # 2937   \n",
      "4   8.0    1   48    0  6.00      -     -       v India    Dhaka  ODI # 2938   \n",
      "\n",
      "           Bowler  Player_ID  Month  Day  year  \n",
      "0  Suranga Lakmal      49619     12   18  2009  \n",
      "1  Suranga Lakmal      49619     12   24  2009  \n",
      "2  Suranga Lakmal      49619     12   27  2009  \n",
      "3  Suranga Lakmal      49619      1    4  2010  \n",
      "4  Suranga Lakmal      49619      1    5  2010  \n",
      "-------------------------\n",
      "DataFrame 2 does not have any irrelevant columns\n",
      "-------------------------\n",
      "DataFrame 3 after dropping irrelevant columns:\n",
      "  Result    Margin  BR  Toss  Bat   Opposition     Ground    Match_ID  \\\n",
      "0    won   85 runs NaN  lost  1st      v India    Kolkata  ODI # 3315   \n",
      "1   lost   85 runs NaN   won  2nd   v Pakistan    Kolkata  ODI # 3315   \n",
      "2   lost   10 runs NaN  lost  2nd      v India      Delhi  ODI # 3316   \n",
      "3    won   10 runs NaN   won  1st   v Pakistan      Delhi  ODI # 3316   \n",
      "4   lost  107 runs NaN  lost  2nd  v Australia  Melbourne  ODI # 3317   \n",
      "\n",
      "    Country  Country_ID  Month  Day  year  \n",
      "0  Pakistan           7      1    3  2013  \n",
      "1     India           6      1    3  2013  \n",
      "2  Pakistan           7      1    6  2013  \n",
      "3     India           6      1    6  2013  \n",
      "4  SriLanka           8      1   11  2013  \n",
      "-------------------------\n",
      "DataFrame 4 after dropping irrelevant columns:\n",
      "  Score  Overs   RPO  Target  Inns Result   Opposition     Ground    Match_ID  \\\n",
      "0   250   48.3  5.15     NaN     1    won      v India    Kolkata  ODI # 3315   \n",
      "1   165   48.0  3.43   251.0     2   lost   v Pakistan    Kolkata  ODI # 3315   \n",
      "2   157   48.5  3.21   168.0     2   lost      v India      Delhi  ODI # 3316   \n",
      "3   167   43.4  3.82     NaN     1    won   v Pakistan      Delhi  ODI # 3316   \n",
      "4   198   40.0  4.95   306.0     2   lost  v Australia  Melbourne  ODI # 3317   \n",
      "\n",
      "    Country  Country_ID  Month  Day  year  \n",
      "0  Pakistan           7      1    3  2013  \n",
      "1     India           6      1    3  2013  \n",
      "2  Pakistan           7      1    6  2013  \n",
      "3     India           6      1    6  2013  \n",
      "4  SriLanka           8      1   11  2013  \n",
      "-------------------------\n",
      "DataFrame 5 does not have any irrelevant columns\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "drop_irrelevant(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>ODI # 2933</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>ODI # 2935</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>ODI # 2936</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>ODI # 2937</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DNB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>v India</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>ODI # 2938</td>\n",
       "      <td>Oshane Thomas</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Bat1 Runs BF SR 4s 6s    Opposition   Ground    Match_ID        Batsman  \\\n",
       "0  DNB    -  -  -  -  -       v India   Nagpur  ODI # 2933  Oshane Thomas   \n",
       "1  DNB    -  -  -  -  -       v India  Kolkata  ODI # 2935  Oshane Thomas   \n",
       "2  DNB    -  -  -  -  -       v India    Delhi  ODI # 2936  Oshane Thomas   \n",
       "3  DNB    -  -  -  -  -  v Bangladesh    Dhaka  ODI # 2937  Oshane Thomas   \n",
       "4  DNB    -  -  -  -  -       v India    Dhaka  ODI # 2938  Oshane Thomas   \n",
       "\n",
       "   Player_ID  Month  Day  year  \n",
       "0      49619     12   18  2009  \n",
       "1      49619     12   24  2009  \n",
       "2      49619     12   27  2009  \n",
       "3      49619      1    4  2010  \n",
       "4      49619      1    5  2010  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode_cols_batsman = ['Bat1','Runs','BF','SR','4s','6s','Opposition','Ground','Match_ID','Batsman']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list=[batsman_df,bowler_df,ground_avg_df,match_results_df,match_total_df,players_df]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Feature Engineering (Encoding Categorical Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we seen there are object type of data avilabe in the all the dataset so lets seaprate them and encode them into numeric form .\n",
    "# lets seaprate the catogorical data & numerical data\n",
    "\n",
    "numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "def get_categorical_columns(df_list):\n",
    "    cat_col_list = []  # List to store categorical column names for each DataFrame\n",
    "\n",
    "    for df in df_list:\n",
    "        cat_col = []  # List to store categorical column names for the current DataFrame\n",
    "        features = df.columns.values.tolist()\n",
    "\n",
    "        for col in features:\n",
    "            if df[col].dtype not in numerics:  # Check if the column's data type is non-numeric\n",
    "                cat_col.append(col)\n",
    "\n",
    "        cat_col_list.append(cat_col)\n",
    "\n",
    "    return cat_col_list\n",
    "\n",
    "def label_encode_categorical_columns(df_list):\n",
    "    label = LabelEncoder()\n",
    "\n",
    "    categorical_columns_list = get_categorical_columns(df_list)\n",
    "\n",
    "    for i, cat_col in enumerate(categorical_columns_list):\n",
    "        for col in cat_col:\n",
    "            encoded_values = label.fit_transform(df_list[i][col])\n",
    "            df_list[i][col] = encoded_values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encode_categorical_columns(df_list) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>319</td>\n",
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       "      <td>1579</td>\n",
       "      <td>53</td>\n",
       "      <td>40618</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8890</th>\n",
       "      <td>277</td>\n",
       "      <td>158</td>\n",
       "      <td>88</td>\n",
       "      <td>770</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>38</td>\n",
       "      <td>564</td>\n",
       "      <td>139</td>\n",
       "      <td>56194</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>181</td>\n",
       "      <td>110</td>\n",
       "      <td>104</td>\n",
       "      <td>1476</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64</td>\n",
       "      <td>1450</td>\n",
       "      <td>56</td>\n",
       "      <td>44932</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10280</th>\n",
       "      <td>137</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>1500</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>126</td>\n",
       "      <td>1724</td>\n",
       "      <td>0</td>\n",
       "      <td>5334</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8066</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>99</td>\n",
       "      <td>880</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>132</td>\n",
       "      <td>1370</td>\n",
       "      <td>35</td>\n",
       "      <td>24598</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10798</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1758</td>\n",
       "      <td>48</td>\n",
       "      <td>440970</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>256</td>\n",
       "      <td>147</td>\n",
       "      <td>47</td>\n",
       "      <td>969</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>978</td>\n",
       "      <td>62</td>\n",
       "      <td>49700</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9183</th>\n",
       "      <td>209</td>\n",
       "      <td>124</td>\n",
       "      <td>88</td>\n",
       "      <td>608</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>1049</td>\n",
       "      <td>129</td>\n",
       "      <td>56143</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9563</th>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>122</td>\n",
       "      <td>1356</td>\n",
       "      <td>28</td>\n",
       "      <td>219889</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4266</th>\n",
       "      <td>152</td>\n",
       "      <td>94</td>\n",
       "      <td>85</td>\n",
       "      <td>1504</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>132</td>\n",
       "      <td>888</td>\n",
       "      <td>121</td>\n",
       "      <td>38699</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Bat1  Runs   BF    SR  4s  6s  Opposition  Ground  Match_ID  Batsman  \\\n",
       "2067    319     0    0     0   0   0          18      23      1579       53   \n",
       "8890    277   158   88   770   2   1          17      38       564      139   \n",
       "2550    181   110  104  1476  18   1           9      64      1450       56   \n",
       "10280   137    85   80  1500  13   1           7     126      1724        0   \n",
       "8066    119    72   99   880   2   1          13     132      1370       35   \n",
       "10798     0     1   86     1   1   1           9      40      1758       48   \n",
       "1251    256   147   47   969   2   1           3      31       978       62   \n",
       "9183    209   124   88   608  21   9           9      38      1049      129   \n",
       "9563    320     0    0     0   0   0           9     122      1356       28   \n",
       "4266    152    94   85  1504  13   2          15     132       888      121   \n",
       "\n",
       "       Player_ID  Month  Day  year  \n",
       "2067       40618      2   10  2017  \n",
       "8890       56194      3   14  2008  \n",
       "2550       44932     10   14  2015  \n",
       "10280       5334      6   13  2018  \n",
       "8066       24598      2   20  2015  \n",
       "10798     440970      9   25  2018  \n",
       "1251       49700      8   22  2011  \n",
       "9183       56143      3   16  2012  \n",
       "9563      219889      1   26  2015  \n",
       "4266       38699      1   22  2011  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Result</th>\n",
       "      <th>Margin</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>3</td>\n",
       "      <td>142</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>478</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>6</td>\n",
       "      <td>110</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>455</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>720</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>6</td>\n",
       "      <td>120</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>721</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>6</td>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>322</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>6</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>488</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>3</td>\n",
       "      <td>120</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>652</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>6</td>\n",
       "      <td>131</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>6</td>\n",
       "      <td>166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>65</td>\n",
       "      <td>708</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Result  Margin     BR  Toss  Bat  Opposition  Ground  Match_ID  Country  \\\n",
       "835        3     142   30.0     1    1           1      75       478        6   \n",
       "794        6     110   13.0     2    2           2      20       455        3   \n",
       "1258       3      97    NaN     2    2           3      86       720        9   \n",
       "1260       6     120   49.0     1    2           6      28       721        0   \n",
       "568        6      81    NaN     1    1           9      10       322        3   \n",
       "855        6      87    NaN     2    1           1      45       488        5   \n",
       "1133       3     120  114.0     2    1          13      27       652        8   \n",
       "309        6     131   10.0     1    2           3      10       170        8   \n",
       "1235       6     166    NaN     2    1           9      65       708        4   \n",
       "64         4       0    NaN     2    2          14      44        34        2   \n",
       "\n",
       "      Country_ID  Month  Day  year  \n",
       "835            7      1   19  2017  \n",
       "794            1     10   12  2016  \n",
       "1258           4      2   27  2019  \n",
       "1260          40      2   28  2019  \n",
       "568            1      6    9  2015  \n",
       "855            5      2    5  2017  \n",
       "1133           8      7   29  2018  \n",
       "309            8      6    3  2014  \n",
       "1235           6      1   26  2019  \n",
       "64            25      3   25  2013  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_results_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result          0\n",
       "Margin          0\n",
       "BR            716\n",
       "Toss            0\n",
       "Bat             0\n",
       "Opposition      0\n",
       "Ground          0\n",
       "Match_ID        0\n",
       "Country         0\n",
       "Country_ID      0\n",
       "Month           0\n",
       "Day             0\n",
       "year            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_results_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Score           0\n",
       "Overs           0\n",
       "RPO             0\n",
       "Target        676\n",
       "Inns            0\n",
       "Result          0\n",
       "Opposition      0\n",
       "Ground          0\n",
       "Match_ID        0\n",
       "Country         0\n",
       "Country_ID      0\n",
       "Month           0\n",
       "Day             0\n",
       "year            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_total_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## performing the iterative imputation to columns match_results ,match_total\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "imputer = IterativeImputer()\n",
    "df_list2 =[match_results_df,match_total_df]\n",
    "\n",
    "def treat_missing_values(df_list2):\n",
    "    for i in range(len(df_list2)):\n",
    "        df_imputed = imputer.fit_transform(df_list2[i])\n",
    "        df_list2[i]=pd.DataFrame(df_imputed,columns =df_list2[i].columns)\n",
    "        print(\"Data Frame\",i,\"after imputation\")\n",
    "        print(df_list2[i].isnull().sum().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Frame 0 after imputation\n",
      "0\n",
      "Data Frame 1 after imputation\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "treat_missing_values(df_list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_total_df = df_list2[0]\n",
    "\n",
    "match_results_df = df_list2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result        0\n",
       "Margin        0\n",
       "BR            0\n",
       "Toss          0\n",
       "Bat           0\n",
       "Opposition    0\n",
       "Ground        0\n",
       "Match_ID      0\n",
       "Country       0\n",
       "Country_ID    0\n",
       "Month         0\n",
       "Day           0\n",
       "year          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_total_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Target</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result</th>\n",
       "      <th>Opposition</th>\n",
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       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>404.0</td>\n",
       "      <td>48.3</td>\n",
       "      <td>165.0</td>\n",
       "      <td>241.723435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>176.027214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score  Overs    RPO      Target  Inns  Result  Opposition  Ground  \\\n",
       "0  404.0   48.3  165.0  241.723435   1.0     4.0         5.0    57.0   \n",
       "1  115.0   48.0   25.0  251.000000   2.0     1.0        11.0    57.0   \n",
       "2   93.0   48.5   18.0  168.000000   2.0     1.0         5.0    29.0   \n",
       "3  120.0   43.4   48.0  176.027214   1.0     4.0        11.0    29.0   \n",
       "4  199.0   40.0  146.0  306.000000   2.0     1.0         1.0    63.0   \n",
       "\n",
       "   Match_ID  Country  Country_ID  Month   Day    year  \n",
       "0       0.0      6.0         7.0    1.0   3.0  2013.0  \n",
       "1       0.0      4.0         6.0    1.0   3.0  2013.0  \n",
       "2       1.0      6.0         7.0    1.0   6.0  2013.0  \n",
       "3       1.0      4.0         6.0    1.0   6.0  2013.0  \n",
       "4       2.0      8.0         8.0    1.0  11.0  2013.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Bat1</th>\n",
       "      <th>Runs</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>319</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>92</td>\n",
       "      <td>759</td>\n",
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       "      <td>49619</td>\n",
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       "      <td>18</td>\n",
       "      <td>2009</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>761</td>\n",
       "      <td>113</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>762</td>\n",
       "      <td>113</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>763</td>\n",
       "      <td>113</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>764</td>\n",
       "      <td>113</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs  BF  SR  4s  6s  Opposition  Ground  Match_ID  Batsman  \\\n",
       "0   319     0   0   0   0   0           9      92       759      113   \n",
       "1   319     0   0   0   0   0           9      78       761      113   \n",
       "2   319     0   0   0   0   0           9      37       762      113   \n",
       "3   319     0   0   0   0   0           4      38       763      113   \n",
       "4   319     0   0   0   0   0           9      38       764      113   \n",
       "\n",
       "   Player_ID  Month  Day  year  \n",
       "0      49619     12   18  2009  \n",
       "1      49619     12   24  2009  \n",
       "2      49619     12   27  2009  \n",
       "3      49619      1    4  2010  \n",
       "4      49619      1    5  2010  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsman_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>49619</td>\n",
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       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>103</td>\n",
       "      <td>77</td>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>761</td>\n",
       "      <td>138</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>762</td>\n",
       "      <td>138</td>\n",
       "      <td>49619</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>406</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>763</td>\n",
       "      <td>138</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>764</td>\n",
       "      <td>138</td>\n",
       "      <td>49619</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Overs  Mdns  Runs  Wkts  Econ  Ave  SR  Opposition  Ground  Match_ID  \\\n",
       "0     49     1    59     1   412    0   0           9      92       759   \n",
       "1     12     1    57     3   316  103  77           9      78       761   \n",
       "2      0     0     0     0     0    0   0           9      37       762   \n",
       "3     55     2    66     3   406  120  70           4      38       763   \n",
       "4     49     2    49     1   349    0   0           9      38       764   \n",
       "\n",
       "   Bowler  Player_ID  Month  Day  year  \n",
       "0     138      49619     12   18  2009  \n",
       "1     138      49619     12   24  2009  \n",
       "2     138      49619     12   27  2009  \n",
       "3     138      49619      1    4  2010  \n",
       "4     138      49619      1    5  2010  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Bat1', 'Runs', 'BF', 'SR', '4s', '6s', 'Opposition', 'Ground',\n",
      "       'Match_ID', 'Batsman', 'Player_ID', 'Month', 'Day', 'year'],\n",
      "      dtype='object')\n",
      "****************************************************************************************************\n",
      "Index(['Overs', 'Mdns', 'Runs', 'Wkts', 'Econ', 'Ave', 'SR', 'Opposition',\n",
      "       'Ground', 'Match_ID', 'Bowler', 'Player_ID', 'Month', 'Day', 'year'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(batsman_df.columns)\n",
    "print('*' * 100)\n",
    "print(bowler_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Of Merging Operations (Inner) On all Sub-datasets based on common columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11118, 22)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets Create a master dataset and the build the model \n",
    "# so first we are going to merge 2 dataset and then further  \n",
    "batsman_join_bowler=pd.merge(batsman_df,bowler_df,on=['Match_ID','Player_ID','Opposition','Ground','Month','Day','year'], how='inner')\n",
    "batsman_join_bowler.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bat1', 'Runs_x', 'BF', 'SR_x', '4s', '6s', 'Opposition', 'Ground',\n",
       "       'Match_ID', 'Batsman', 'Player_ID', 'Month', 'Day', 'year', 'Overs',\n",
       "       'Mdns', 'Runs_y', 'Wkts', 'Econ', 'Ave', 'SR_y', 'Bowler'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "batsman_join_bowler.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Won</th>\n",
       "      <th>Tied</th>\n",
       "      <th>NR</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts_y</th>\n",
       "      <th>Balls</th>\n",
       "      <th>Ave_y</th>\n",
       "      <th>RPO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>16</td>\n",
       "      <td>14</td>\n",
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       "      <td>2</td>\n",
       "      <td>6890</td>\n",
       "      <td>202</td>\n",
       "      <td>7508</td>\n",
       "      <td>34.10</td>\n",
       "      <td>5.50</td>\n",
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       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5345</td>\n",
       "      <td>134</td>\n",
       "      <td>5569</td>\n",
       "      <td>39.88</td>\n",
       "      <td>5.75</td>\n",
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       "      <th>2</th>\n",
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       "      <td>37</td>\n",
       "      <td>762</td>\n",
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       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1059</td>\n",
       "      <td>30</td>\n",
       "      <td>1149</td>\n",
       "      <td>35.30</td>\n",
       "      <td>5.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>763</td>\n",
       "      <td>113</td>\n",
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       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10524</td>\n",
       "      <td>326</td>\n",
       "      <td>12214</td>\n",
       "      <td>32.28</td>\n",
       "      <td>5.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>764</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10524</td>\n",
       "      <td>326</td>\n",
       "      <td>12214</td>\n",
       "      <td>32.28</td>\n",
       "      <td>5.16</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>9074</th>\n",
       "      <td>99</td>\n",
       "      <td>57</td>\n",
       "      <td>104</td>\n",
       "      <td>826</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1758</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8038</td>\n",
       "      <td>208</td>\n",
       "      <td>8369</td>\n",
       "      <td>38.64</td>\n",
       "      <td>5.76</td>\n",
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       "      <th>9075</th>\n",
       "      <td>203</td>\n",
       "      <td>121</td>\n",
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       "      <td>1409</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>1809</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1664</td>\n",
       "      <td>56</td>\n",
       "      <td>1987</td>\n",
       "      <td>29.71</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9076</th>\n",
       "      <td>167</td>\n",
       "      <td>103</td>\n",
       "      <td>119</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>1810</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1664</td>\n",
       "      <td>56</td>\n",
       "      <td>1987</td>\n",
       "      <td>29.71</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9077</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>1817</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1664</td>\n",
       "      <td>56</td>\n",
       "      <td>1987</td>\n",
       "      <td>29.71</td>\n",
       "      <td>5.02</td>\n",
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       "    <tr>\n",
       "      <th>9078</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>46</td>\n",
       "      <td>1834</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1356</td>\n",
       "      <td>29</td>\n",
       "      <td>1171</td>\n",
       "      <td>46.75</td>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9079 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bat1  Runs_x   BF  SR_x  4s  6s  Opposition  Ground  Match_ID  Batsman  \\\n",
       "0      319       0    0     0   0   0           9      92       759      113   \n",
       "1      319       0    0     0   0   0           9      78       761      113   \n",
       "2      319       0    0     0   0   0           9      37       762      113   \n",
       "3      319       0    0     0   0   0           4      38       763      113   \n",
       "4      319       0    0     0   0   0           9      38       764      113   \n",
       "...    ...     ...  ...   ...  ..  ..         ...     ...       ...      ...   \n",
       "9074    99      57  104   826   2   1           9      40      1758       42   \n",
       "9075   203     121  121  1409  22   2          10      36      1809       42   \n",
       "9076   167     103  119   969   1   1          10      36      1810       42   \n",
       "9077     2       2  141   330   1   1          10      36      1817       42   \n",
       "9078   319       0    0     0   0   0          16      46      1834       42   \n",
       "\n",
       "      ...  Span  Mat  Won  Tied  NR   Runs  Wkts_y  Balls  Ave_y   RPO  \n",
       "0     ...     6   16   14     0   2   6890     202   7508  34.10  5.50  \n",
       "1     ...    10   11   10     0   1   5345     134   5569  39.88  5.75  \n",
       "2     ...    12    2    2     0   0   1059      30   1149  35.30  5.53  \n",
       "3     ...     6   23   22     0   1  10524     326  12214  32.28  5.16  \n",
       "4     ...     6   23   22     0   1  10524     326  12214  32.28  5.16  \n",
       "...   ...   ...  ...  ...   ...  ..    ...     ...    ...    ...   ...  \n",
       "9074  ...     6   17   15     0   2   8038     208   8369  38.64  5.76  \n",
       "9075  ...     4    4    4     0   0   1664      56   1987  29.71  5.02  \n",
       "9076  ...     4    4    4     0   0   1664      56   1987  29.71  5.02  \n",
       "9077  ...     4    4    4     0   0   1664      56   1987  29.71  5.02  \n",
       "9078  ...     4    2    2     0   0   1356      29   1171  46.75  6.94  \n",
       "\n",
       "[9079 rows x 32 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets Join ground_df\n",
    "batsman_join_bowler_GrondAvg=pd.merge(batsman_join_bowler,ground_avg_df,on=['Ground'], how='inner')\n",
    "batsman_join_bowler_GrondAvg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Score</th>\n",
       "      <th>Overs</th>\n",
       "      <th>RPO</th>\n",
       "      <th>Target</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Result_x</th>\n",
       "      <th>Opposition</th>\n",
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       "      <th>Match_ID_x</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>year</th>\n",
       "      <th>Result_y</th>\n",
       "      <th>Margin</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>404.0</td>\n",
       "      <td>48.3</td>\n",
       "      <td>165.0</td>\n",
       "      <td>241.723435</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>52.000182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>52.728414</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.689046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>120.0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>176.027214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.548194</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>199.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.527705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>750.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>382.947700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>753.0</td>\n",
       "      <td>49.3</td>\n",
       "      <td>315.0</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.327406</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>187.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>249.0</td>\n",
       "      <td>352.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.815762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>767.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>406.462269</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.063998</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>758.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Score  Overs    RPO      Target  Inns  Result_x  Opposition  Ground  \\\n",
       "0     404.0   48.3  165.0  241.723435   1.0       4.0         5.0    57.0   \n",
       "1     115.0   48.0   25.0  251.000000   2.0       1.0        11.0    57.0   \n",
       "2      93.0   48.5   18.0  168.000000   2.0       1.0         5.0    29.0   \n",
       "3     120.0   43.4   48.0  176.027214   1.0       4.0        11.0    29.0   \n",
       "4     199.0   40.0  146.0  306.000000   2.0       1.0         1.0    63.0   \n",
       "...     ...    ...    ...         ...   ...       ...         ...     ...   \n",
       "1291  750.0   50.0  309.0  382.947700   1.0       1.0         3.0    72.0   \n",
       "1292  753.0   49.3  315.0  341.000000   2.0       4.0        11.0    72.0   \n",
       "1293   27.0   33.0   38.0  211.000000   2.0       0.0         6.0     7.0   \n",
       "1294  187.0   31.5  249.0  352.000000   2.0       0.0         3.0    60.0   \n",
       "1295  767.0   50.0  322.0  406.462269   1.0       0.0        11.0    60.0   \n",
       "\n",
       "      Match_ID_x  Country  Country_ID  Month   Day    year  Result_y  Margin  \\\n",
       "0            0.0      6.0         7.0    1.0   3.0  2013.0       6.0   160.0   \n",
       "1            0.0      4.0         6.0    1.0   3.0  2013.0       3.0   160.0   \n",
       "2            1.0      6.0         7.0    1.0   6.0  2013.0       3.0     3.0   \n",
       "3            1.0      4.0         6.0    1.0   6.0  2013.0       6.0     3.0   \n",
       "4            2.0      8.0         8.0    1.0  11.0  2013.0       3.0    10.0   \n",
       "...          ...      ...         ...    ...   ...     ...       ...     ...   \n",
       "1291       739.0      6.0         7.0    5.0  17.0  2019.0       3.0    99.0   \n",
       "1292       739.0      3.0         1.0    5.0  17.0  2019.0       6.0    99.0   \n",
       "1293       740.0      0.0        40.0    5.0  19.0  2019.0       0.0     0.0   \n",
       "1294       741.0      6.0         7.0    5.0  19.0  2019.0       0.0     0.0   \n",
       "1295       741.0      3.0         1.0    5.0  19.0  2019.0       0.0     0.0   \n",
       "\n",
       "             BR  Toss  Bat  Match_ID_y  \n",
       "0     52.000182   1.0  1.0         0.0  \n",
       "1     52.728414   2.0  2.0         0.0  \n",
       "2     20.689046   1.0  2.0         1.0  \n",
       "3     21.548194   2.0  1.0         1.0  \n",
       "4     19.527705   1.0  2.0         2.0  \n",
       "...         ...   ...  ...         ...  \n",
       "1291   3.000000   1.0  1.0       755.0  \n",
       "1292   3.000000   2.0  2.0       755.0  \n",
       "1293  32.327406   2.0  2.0       757.0  \n",
       "1294  29.815762   1.0  2.0       758.0  \n",
       "1295  31.063998   2.0  1.0       758.0  \n",
       "\n",
       "[1296 rows x 20 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets Join match_results & match_totals\n",
    "OD_Total_result=pd.merge(match_results_df,match_total_df,on=['Ground','Country','Country_ID','Month','Day','year','Opposition'], how='inner')\n",
    "OD_Total_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition_x</th>\n",
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       "      <th>Match_ID</th>\n",
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       "      <th>...</th>\n",
       "      <th>Opposition_y</th>\n",
       "      <th>Match_ID_x</th>\n",
       "      <th>Country</th>\n",
       "      <th>Country_ID</th>\n",
       "      <th>Result_y</th>\n",
       "      <th>Margin</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>140</td>\n",
       "      <td>86</td>\n",
       "      <td>14</td>\n",
       "      <td>677</td>\n",
       "      <td>1</td>\n",
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       "      <td>1225</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>53.000000</td>\n",
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       "      <td>126.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319</td>\n",
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       "      <td>1227</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1227</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>128.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>969</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1660</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>54.142939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>572.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>1039</th>\n",
       "      <td>196</td>\n",
       "      <td>117</td>\n",
       "      <td>95</td>\n",
       "      <td>203</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1753</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.987142</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>535</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1755</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>535</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1755</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>653.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1757</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>52.722313</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1757</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>52.177732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  \\\n",
       "0      140      86   14   677   1   1            15       1      1225   \n",
       "1      140      86   14   677   1   1            15       1      1225   \n",
       "2      319       0    0     0   0   0            15       1      1227   \n",
       "3      319       0    0     0   0   0            15       1      1227   \n",
       "4        2       2   75   969   1   1            15       1      1660   \n",
       "...    ...     ...  ...   ...  ..  ..           ...     ...       ...   \n",
       "1039   196     117   95   203  21   1             4       1      1753   \n",
       "1040     5       3  130   535   2   1            15       1      1755   \n",
       "1041     5       3  130   535   2   1            15       1      1755   \n",
       "1042     1       1   75     1   1   1             4       1      1757   \n",
       "1043     1       1   75     1   1   1             4       1      1757   \n",
       "\n",
       "      Batsman  ...  Opposition_y  Match_ID_x  Country  Country_ID  Result_y  \\\n",
       "0         113  ...          11.0       123.0      8.0         8.0       3.0   \n",
       "1         113  ...          14.0       123.0      6.0         7.0       6.0   \n",
       "2         113  ...          11.0       125.0      8.0         8.0       6.0   \n",
       "3         113  ...          14.0       125.0      6.0         7.0       3.0   \n",
       "4         113  ...          11.0       558.0      8.0         8.0       3.0   \n",
       "...       ...  ...           ...         ...      ...         ...       ...   \n",
       "1039       42  ...           2.0       651.0      0.0        40.0       6.0   \n",
       "1040       42  ...           0.0       653.0      6.0         7.0       6.0   \n",
       "1041       42  ...          11.0       653.0      0.0        40.0       3.0   \n",
       "1042       42  ...           0.0       655.0      2.0        25.0       6.0   \n",
       "1043       42  ...           2.0       655.0      0.0        40.0       3.0   \n",
       "\n",
       "      Margin         BR  Toss  Bat  Match_ID_y  \n",
       "0      154.0  53.000000   2.0  1.0       126.0  \n",
       "1      154.0  53.000000   1.0  2.0       126.0  \n",
       "2       73.0   2.000000   1.0  2.0       128.0  \n",
       "3       73.0   2.000000   2.0  1.0       128.0  \n",
       "4      102.0  54.142939   1.0  2.0       572.0  \n",
       "...      ...        ...   ...  ...         ...  \n",
       "1039    37.0  38.987142   2.0  1.0       665.0  \n",
       "1040    99.0   3.000000   1.0  2.0       667.0  \n",
       "1041    99.0   3.000000   2.0  1.0       667.0  \n",
       "1042    98.0  52.722313   2.0  1.0       669.0  \n",
       "1043    98.0  52.177732   1.0  2.0       669.0  \n",
       "\n",
       "[1044 rows x 48 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets Join batsman_join_bowler_GrondAvg & OD_Total_result\n",
    "batsman_join_bowler_GrondAvg_OD=pd.merge(batsman_join_bowler_GrondAvg,OD_Total_result,on=['Ground','Month','Day','year'], how='inner')\n",
    "batsman_join_bowler_GrondAvg_OD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "players_df = players_df.rename(columns={'ID':'Player_ID'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Player_ID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42</td>\n",
       "      <td>352048</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118</td>\n",
       "      <td>793463</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>440963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>320652</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>516561</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player  Player_ID  Country\n",
       "0      42     352048        0\n",
       "1     118     793463        0\n",
       "2       5     440963        0\n",
       "3      13     320652        0\n",
       "4      30     516561        0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df_after_join = pd.merge(batsman_join_bowler_GrondAvg_OD,players_df,on =['Player_ID','Country'],how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df_after_join['Batting Average'] = master_df_after_join['Bat1']/master_df_after_join['Inns']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df_after_join['Bowling Average'] = master_df_after_join['Runs_y'] / master_df_after_join['Wkts_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering the rows that consist of zeros  in a way by considering only the list \n",
    "# of values into my master_df \n",
    "\n",
    "master_df_after_join = master_df_after_join[master_df_after_join['BF']>0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition_x</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>Country_ID</th>\n",
       "      <th>Result_y</th>\n",
       "      <th>Margin</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "      <th>Player</th>\n",
       "      <th>Batting Average</th>\n",
       "      <th>Bowling Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>1363</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>36</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0   149      93   87  1363  20   1            15       1      1540       36   \n",
       "1     3       2   86   829   1   1            15       1      1540      130   \n",
       "2    87      47   73  1464  18   1            13       5      1226       26   \n",
       "3   119      72   98   889   1   2            15       1      1540       26   \n",
       "4     4       3  152   211   2   1            13       5      1226       59   \n",
       "\n",
       "   ...  Country_ID  Result_y  Margin          BR  Toss  Bat  Match_ID_y  \\\n",
       "0  ...         4.0       3.0    37.0   37.966738   1.0  2.0       450.0   \n",
       "1  ...         4.0       3.0    37.0   37.966738   1.0  2.0       450.0   \n",
       "2  ...         4.0       6.0    73.0  135.000000   2.0  2.0       127.0   \n",
       "3  ...         4.0       3.0    37.0   37.966738   1.0  2.0       450.0   \n",
       "4  ...         4.0       6.0    73.0  135.000000   2.0  2.0       127.0   \n",
       "\n",
       "   Player  Batting Average  Bowling Average  \n",
       "0      36             74.5         0.000000  \n",
       "1     131              1.5         2.466667  \n",
       "2      26             43.5         0.000000  \n",
       "3      26             59.5         0.000000  \n",
       "4      59              2.0         0.416667  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating strike rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate strike rate of Batting for each player\n",
    "master_df_after_join['strike Rate (Batting)'] = (master_df_after_join['Bat1']/master_df_after_join['BF'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Bat1</th>\n",
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       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition_x</th>\n",
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       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>Result_y</th>\n",
       "      <th>Margin</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "      <th>Player</th>\n",
       "      <th>Batting Average</th>\n",
       "      <th>Bowling Average</th>\n",
       "      <th>strike Rate (Batting)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>1363</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>36</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171.264368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.488372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.178082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>2.631579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0   149      93   87  1363  20   1            15       1      1540       36   \n",
       "1     3       2   86   829   1   1            15       1      1540      130   \n",
       "2    87      47   73  1464  18   1            13       5      1226       26   \n",
       "3   119      72   98   889   1   2            15       1      1540       26   \n",
       "4     4       3  152   211   2   1            13       5      1226       59   \n",
       "\n",
       "   ...  Result_y  Margin          BR  Toss  Bat  Match_ID_y  Player  \\\n",
       "0  ...       3.0    37.0   37.966738   1.0  2.0       450.0      36   \n",
       "1  ...       3.0    37.0   37.966738   1.0  2.0       450.0     131   \n",
       "2  ...       6.0    73.0  135.000000   2.0  2.0       127.0      26   \n",
       "3  ...       3.0    37.0   37.966738   1.0  2.0       450.0      26   \n",
       "4  ...       6.0    73.0  135.000000   2.0  2.0       127.0      59   \n",
       "\n",
       "   Batting Average  Bowling Average  strike Rate (Batting)  \n",
       "0             74.5         0.000000             171.264368  \n",
       "1              1.5         2.466667               3.488372  \n",
       "2             43.5         0.000000             119.178082  \n",
       "3             59.5         0.000000             121.428571  \n",
       "4              2.0         0.416667               2.631579  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df_after_join['Economy Rate (Bowling)'] = (master_df_after_join['Runs_y']/master_df_after_join['Overs_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the total Maiden Overs for each player\n",
    "master_df_after_join['maiden Overs Total'] = master_df_after_join['Mdns'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
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       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>BR</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "      <th>Player</th>\n",
       "      <th>Batting Average</th>\n",
       "      <th>Bowling Average</th>\n",
       "      <th>strike Rate (Batting)</th>\n",
       "      <th>Economy Rate (Bowling)</th>\n",
       "      <th>maiden Overs Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>37.966738</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>36</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171.264368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.488372</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>262</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.178082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>37.966738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>0.732601</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0   149      93   87  1363  20   1            15       1      1540       36   \n",
       "1     3       2   86   829   1   1            15       1      1540      130   \n",
       "2    87      47   73  1464  18   1            13       5      1226       26   \n",
       "3   119      72   98   889   1   2            15       1      1540       26   \n",
       "4     4       3  152   211   2   1            13       5      1226       59   \n",
       "\n",
       "   ...          BR  Toss  Bat  Match_ID_y  Player  Batting Average  \\\n",
       "0  ...   37.966738   1.0  2.0       450.0      36             74.5   \n",
       "1  ...   37.966738   1.0  2.0       450.0     131              1.5   \n",
       "2  ...  135.000000   2.0  2.0       127.0      26             43.5   \n",
       "3  ...   37.966738   1.0  2.0       450.0      26             59.5   \n",
       "4  ...  135.000000   2.0  2.0       127.0      59              2.0   \n",
       "\n",
       "   Bowling Average  strike Rate (Batting)  Economy Rate (Bowling)  \\\n",
       "0         0.000000             171.264368                0.000000   \n",
       "1         2.466667               3.488372                0.840909   \n",
       "2         0.000000             119.178082                0.000000   \n",
       "3         0.000000             121.428571                0.000000   \n",
       "4         0.416667               2.631579                0.732601   \n",
       "\n",
       "   maiden Overs Total  \n",
       "0                 262  \n",
       "1                 262  \n",
       "2                 262  \n",
       "3                 262  \n",
       "4                 262  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "batting_average = master_df_after_join['Batting Average']\n",
    "bowling_average = master_df_after_join['Bowling Average']\n",
    "strike_rate_batting = master_df_after_join['strike Rate (Batting)']\n",
    "economy_rate_bowling = master_df_after_join['Economy Rate (Bowling)']\n",
    "maiden_overs = master_df_after_join['maiden Overs Total']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalizing The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_max_scaling(x):\n",
    "    return(x - x.min()) / (x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step 2: Normalize the selected performance metrics\n",
    "# You can use Min-Max Scaling or Z-score normalization\n",
    "normalized_batting_average = min_max_scaling(batting_average)\n",
    "normalized_bowling_average = min_max_scaling(bowling_average) \n",
    "normalized_strike_rate_batting = min_max_scaling(strike_rate_batting)\n",
    "normalized_economy_rate_bowling = min_max_scaling(economy_rate_bowling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Assign weights to each performance metric\n",
    "\n",
    "batting_weight = 0.3 \n",
    "\n",
    "bowling_weight = 0.25\n",
    "\n",
    "strike_rate_weight = 0.2\n",
    "\n",
    "economy_rate_weight = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: Calculate the composite performance score for each player\n",
    "master_df_after_join['Player Performance Score'] = (\n",
    "    batting_weight * normalized_batting_average +\n",
    "    bowling_weight * normalized_bowling_average +\n",
    "    strike_rate_weight * normalized_strike_rate_batting +\n",
    "    economy_rate_weight * normalized_economy_rate_bowling )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Bowling Average</th>\n",
       "      <th>strike Rate (Batting)</th>\n",
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       "      <th>maiden Overs Total</th>\n",
       "      <th>Player Performance Score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.488372</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>262</td>\n",
       "      <td>0.170293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.178082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.044236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.059436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>0.732601</td>\n",
       "      <td>262</td>\n",
       "      <td>0.080625</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>505</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1341</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.092896</td>\n",
       "      <td>262</td>\n",
       "      <td>0.145447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>189</td>\n",
       "      <td>114</td>\n",
       "      <td>108</td>\n",
       "      <td>1525</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1750</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>42</td>\n",
       "      <td>189.0</td>\n",
       "      <td>1.866667</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.304614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>196</td>\n",
       "      <td>117</td>\n",
       "      <td>95</td>\n",
       "      <td>203</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1753</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>42</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>206.315789</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.320705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>535</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1755</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.266667</td>\n",
       "      <td>3.846154</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.281958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1757</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.260314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0     149      93   87  1363  20   1            15       1      1540       36   \n",
       "1       3       2   86   829   1   1            15       1      1540      130   \n",
       "2      87      47   73  1464  18   1            13       5      1226       26   \n",
       "3     119      72   98   889   1   2            15       1      1540       26   \n",
       "4       4       3  152   211   2   1            13       5      1226       59   \n",
       "..    ...     ...  ...   ...  ..  ..           ...     ...       ...      ...   \n",
       "505     0       1   86     1   1   1            16       1      1341       42   \n",
       "506   189     114  108  1525   1   1            18       1      1750       42   \n",
       "507   196     117   95   203  21   1             4       1      1753       42   \n",
       "508     5       3  130   535   2   1            15       1      1755       42   \n",
       "509     1       1   75     1   1   1             4       1      1757       42   \n",
       "\n",
       "     ...  Toss  Bat  Match_ID_y  Player  Batting Average  Bowling Average  \\\n",
       "0    ...   1.0  2.0       450.0      36             74.5         0.000000   \n",
       "1    ...   1.0  2.0       450.0     131              1.5         2.466667   \n",
       "2    ...   2.0  2.0       127.0      26             43.5         0.000000   \n",
       "3    ...   1.0  2.0       450.0      26             59.5         0.000000   \n",
       "4    ...   2.0  2.0       127.0      59              2.0         0.416667   \n",
       "..   ...   ...  ...         ...     ...              ...              ...   \n",
       "505  ...   1.0  2.0       247.0      42              0.0         1.333333   \n",
       "506  ...   2.0  1.0       662.0      42            189.0         1.866667   \n",
       "507  ...   2.0  1.0       665.0      42            196.0         2.000000   \n",
       "508  ...   2.0  1.0       667.0      42              5.0         4.266667   \n",
       "509  ...   1.0  2.0       669.0      42              0.5         4.000000   \n",
       "\n",
       "     strike Rate (Batting)  Economy Rate (Bowling)  maiden Overs Total  \\\n",
       "0               171.264368                0.000000                 262   \n",
       "1                 3.488372                0.840909                 262   \n",
       "2               119.178082                0.000000                 262   \n",
       "3               121.428571                0.000000                 262   \n",
       "4                 2.631579                0.732601                 262   \n",
       "..                     ...                     ...                 ...   \n",
       "505               0.000000                1.092896                 262   \n",
       "506             175.000000                0.560000                 262   \n",
       "507             206.315789                0.600000                 262   \n",
       "508               3.846154                1.280000                 262   \n",
       "509               1.333333                1.200000                 262   \n",
       "\n",
       "     Player Performance Score  \n",
       "0                    0.074915  \n",
       "1                    0.170293  \n",
       "2                    0.044236  \n",
       "3                    0.059436  \n",
       "4                    0.080625  \n",
       "..                        ...  \n",
       "505                  0.145447  \n",
       "506                  0.304614  \n",
       "507                  0.320705  \n",
       "508                  0.281958  \n",
       "509                  0.260314  \n",
       "\n",
       "[444 rows x 55 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(444, 55)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detecting and removing Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bat1                       -0.233887\n",
       "Runs_x                     -0.346075\n",
       "BF                         -0.445824\n",
       "SR_x                       -0.039165\n",
       "4s                          0.578048\n",
       "6s                          2.952126\n",
       "Opposition_x               -0.516344\n",
       "Ground                      0.970068\n",
       "Match_ID                   -0.326713\n",
       "Batsman                    -0.105322\n",
       "Player_ID                   0.979422\n",
       "Month                      -0.134240\n",
       "Day                        -0.024541\n",
       "year                       -0.322862\n",
       "Overs_x                     1.262778\n",
       "Mdns                        1.473151\n",
       "Runs_y                      1.019444\n",
       "Wkts_x                      1.420556\n",
       "Econ                        0.740863\n",
       "Ave_x                       1.770862\n",
       "SR_y                        1.372929\n",
       "Bowler                     -0.097655\n",
       "Span                       -2.101193\n",
       "Mat                         3.289249\n",
       "Won                         3.289249\n",
       "Tied                        0.000000\n",
       "NR                          0.000000\n",
       "Runs                        2.546417\n",
       "Wkts_y                      2.866807\n",
       "Balls                       2.933363\n",
       "Ave_y                       3.219416\n",
       "RPO_x                       1.697543\n",
       "Score                       0.291136\n",
       "Overs_y                    -2.122394\n",
       "RPO_y                       0.256769\n",
       "Target                      0.058698\n",
       "Inns                        0.218229\n",
       "Result_x                   -0.044760\n",
       "Opposition_y               -0.301409\n",
       "Match_ID_x                 -0.326713\n",
       "Country                    -0.195242\n",
       "Country_ID                  2.054187\n",
       "Result_y                   -0.044760\n",
       "Margin                     -0.995123\n",
       "BR                          1.052424\n",
       "Toss                       -0.126806\n",
       "Bat                         0.218229\n",
       "Match_ID_y                 -0.337863\n",
       "Player                     -0.085164\n",
       "Batting Average             0.423427\n",
       "Bowling Average             1.558220\n",
       "strike Rate (Batting)       5.879068\n",
       "Economy Rate (Bowling)      1.403963\n",
       "maiden Overs Total          0.000000\n",
       "Player Performance Score    0.489816\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.skew() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4s                        0.578048\n",
       "6s                        2.952126\n",
       "Ground                    0.970068\n",
       "Player_ID                 0.979422\n",
       "Overs_x                   1.262778\n",
       "Mdns                      1.473151\n",
       "Runs_y                    1.019444\n",
       "Wkts_x                    1.420556\n",
       "Econ                      0.740863\n",
       "Ave_x                     1.770862\n",
       "SR_y                      1.372929\n",
       "Mat                       3.289249\n",
       "Won                       3.289249\n",
       "Runs                      2.546417\n",
       "Wkts_y                    2.866807\n",
       "Balls                     2.933363\n",
       "Ave_y                     3.219416\n",
       "RPO_x                     1.697543\n",
       "Country_ID                2.054187\n",
       "BR                        1.052424\n",
       "Bowling Average           1.558220\n",
       "strike Rate (Batting)     5.879068\n",
       "Economy Rate (Bowling)    1.403963\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.skew()[(master_df_after_join.skew() >= 0.5) & (master_df_after_join.skew() >= -0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew_col=['4s','6s','Ground','Player_ID','Overs_x','Mdns','Runs_y','Wkts_x','Econ','Ave_x','SR_y','Mat','Won','Runs', 'Wkts_y', 'Balls', 'Ave_y', 'RPO_x','BR','Country_ID','Bowling Average','Bowling Average','Economy Rate (Bowling)']\n",
    "len(skew_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x2000 with 55 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's see the how data is distributed or Graphical analysis of all features\n",
    "plt.figure(figsize=(15,20))\n",
    "plotnumber=1\n",
    "for column in master_df_after_join:\n",
    "    if plotnumber<=60:\n",
    "        ax=plt.subplot(10,6,plotnumber)\n",
    "        sns.histplot(master_df_after_join[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jDh8+jJ49ewJ4/xfdurq62L9/P7y9veHk5ARjY2N4enoiICCg5BdbRMnJyVAoFBo/b1mS+/RRRX8KSS6X51nwtrRp5O74/PnzCA0NhZ+fH3x9ffHpp5/Cy8sLbdu21cTpiagMGTRoEDp06IDRo0ejSZMmePHiBUaNGoXly5ejcuXKhTrGr7/+ChcXF3h4eCA6Ohq1atXC559/jrFjx+ZbPzAwEPPnzy/NyyDSOi9fvsTEiROxadMmAMD169dRt25dTJw4EbVq1cL06dMljlDzKnqnnB3y1zTRIS9vBEGQOgQieoeWLVuiZcuWWL58Ofbt24fQ0FA4OzvDwcEBXl5eGDVqlMpIViIq25RKJYDXA8fOnj1boifBALx3WrbCfNFtZ2eHgwcPliiOkkpOTsbwESORnZX5/soVwMKFC6UOQVJ6+gbYumWzWvvtGkmit2jRAt9++y2WL1+OX3/9FWFhYejYsSMaNGiAMWPGYMSIEahRo4YmQiGiMiIrKws5OTnIyclBzZo1i/SEyu3btxEcHAw/Pz/MmDEDZ8+exf/+9z/o6+vn+WYeeP24mp+fn7idmpoKGxubUrkOIm3h7++PixcvIioqCr179xbLe/TogXnz5hU6iT5v3rw8X1o1bNhQXA8hIyMDU6ZMwY4dO1RGtrzZ2UlISIC3tzeOHTsGExMTeHp6IjAwUKMj49kp/w875OrvkBMRqYMgCMjOzkZWVhYEQUDVqlWxevVqzJ49Gxs2bMCnn34qdYhEVATx8fHizxkZGRV+lgeFQoHsrEyk1+0MpSG/GKzIdDIUwO1oKBSK8p9EF09WqRIGDhyIfv36Yc2aNfD398fUqVMxY8YMDBo0CEuWLEHNmjU1GRIRadiOHTvg7e2Njz76CNevX8eFCxcwevRoHD58GFu2bCnU+glKpRKtW7fGokWLALwebXPlyhWsXbs23yS6gYEB52Yjeo89e/bgxx9/RPv27SGTycTyJk2a4NatW0U6VpMmTXD06FFx+83k9+TJk3HgwAHs3LkTcrkcvr6+GDhwIE6ePAng9byO/fr1g5WVFU6dOoXExESMHDkSenp6YpvXBHbKCdBch5yIqDTFxsYiNDQU27dvh4GBAUaOHImgoCDUq1cPAPD999/jf//7X4VIovOpMj5VBmjPU2VKpRILFy7E2rVrkZycLD45Onv2bNSpUwdeXl5ShygJpaEcSuOSjc4nKgyNJtHPnTuHjRs3YseOHTA2NsbUqVPh5eWF+/fvY/78+XB1dcWZM2c0GRIRaZiXlxe++eYbeHt7AwB69uyJy5cvY/z48WjRokWh5iuvWbNmnlW/GzVqhF9++UUtMRNVBP/++y8sLCzylL948UIlqV4YlSpVgpWVVZ5yhUKBkJAQhIeHo1u3bgCA0NBQNGrUCKdPn0b79u1x5MgRXL16FUePHoWlpSVatGiBr7/+Gl999RXmzZsHfX394l1gMbFTTkTqkpGRgYSEBEljKCsJNltb2wo/orK0NGvWDP/88w969eqFkJAQfPzxx9DV1VWpM2TIEHzxxRcSRag5fKrsP3yqTDueKluwYAE2bdqEpUuXqkxl2rRpU6xatarCJtGJNEUjSfQVK1YgNDQUcXFx6Nu3LzZv3oy+fftCR0cHwOt5ncLCwoq0mjARlU/nz59Hw4YNVcqqVq2Kn376CVu2bCnUMZydnREXF6dSdv36ddjZ2ZVanEQVTevWrXHgwAFMnDgRAMTE+Q8//AAnJ6ciHevGjRuwtraGoaEhnJycEBgYCFtbW8TGxiI7Oxs9evQQ6zo4OMDW1hYxMTFo3749YmJi0KxZM5WbHBcXF3h7e+Pvv/9Gy5Yt8z0nFxAmovImISEB48aNkzoMANIn2NavX48GDRpIGoO2GDRoEMaMGYNatWoVWKd69eriHMvajE+VEaBdT5Vt3rwZ69evR/fu3TFhwgSxvHnz5uLUiUSkPhpJogcHB2PMmDEYNWpUgdO1WFhYvHdxAyIq/95OoL9pxIgR4s+mpqa4cOFCvtO7TJ48GR06dMCiRYswaNAgnDlzBuvXr8f69evVEjNRRbBo0SL06dMHV69exatXr/Dtt9/i6tWrOHXqFKKjowt9nHbt2iEsLAwNGzZEYmIi5s+fj48++ghXrlxBUlIS9PX1YWZmpvIeS0tLJCUlAQCSkpLy3ODkbufWyQ8XECbSnOHDh8PU1FTqMMo9W1tb9l3+P1tbW6lD0BqzZ88uVL139bW1DZ8qI23x4MEDcVqmNymVSmRnZ0sQEVHFopEk+o0bN95bR19fH3/++Sf69etX4pWGiaj8EwShwH1t2rTB7t274e/vj4CAANjb22PVqlUYNmyYBiMk0i4dO3bExYsXERgYiGbNmuHIkSP48MMPxZHhhdWnTx/xZ0dHR7Rr1w52dnb46aefYGRkpI7QAXABYaLScOjQIZiYmKBjx44AgKCgIGzYsAGNGzdGUFAQqlatCuD1ABkqOUNDQ46+Jsm8q69NRGVT48aN8ccff+R5Avvnn38u8GlNIio9Gp0T/X22bt2KqVOnMolORO/Vv39/9O/fX+owiLRCdnY2xo8fj9mzZ2PDhg2lemwzMzM0aNAAN2/eRM+ePZGVlYWUlBSV0ejJycniHOpWVlZ51kdJTk4W9xWECwgTldy0adOwZMkSAMDly5cxZcoU+Pn54dixY/Dz80NoaKjEERIREVVcc+bMgaenJx48eAClUoldu3YhLi4Omzdvxv79+6UOj0jr6UgdwJv4bTgREZHm6enpqW1h3rS0NNy6dQs1a9ZEq1atoKenh8jISHF/XFwcEhISxHnXnZyccPnyZTx69EisExERAVNT0zwLChNR6YqPjxfb2S+//IL+/ftj0aJFCAoKwm+//SZxdERERBWbq6sr9u3bh6NHj8LY2Bhz5szBtWvXsG/fPvTs2VPq8Ii0XpkaiU5ERETScHNzw549ezB58uQSHWfq1Kn4+OOPYWdnh4cPH2Lu3LnQ1dXFkCFDIJfL4eXlBT8/P5ibm8PU1BQTJ06Ek5MT2rdvDwDo1asXGjdujBEjRmDp0qVISkrCrFmz4OPjw5HmRGqmr6+Ply9fAgCOHj2KkSNHAgDMzc25WC8REVEZ8NFHHyEiIkLqMIgqJCbRtVRGRgYSEhKkDqNMsLW1haGhodRhUBHJZDKpQyCqUOrXr4+AgACcPHkSrVq1grGxscr+//3vf4U6zv379zFkyBA8efIENWrUQMeOHXH69GnUqFEDALBy5Uro6OjA3d0dmZmZcHFxwZo1a8T36+rqYv/+/fD29oaTkxOMjY3h6emJgICA0rtYIsqXs7Mz/Pz84OzsjDNnzuDHH38EAFy/fh21a9eWODoiKk3v6msHBgZi165d+Oeff2BkZIQOHTpgyZIlaNiwoVgnIyMDU6ZMwY4dO1Q+z99cHDwhIQHe3t44duwYTExM4OnpicDAQFSqxDQEERGVP/z00lIJCQkYN26c1GGUCevXr+eiTeUQp3ci0qyQkBCYmZkhNjYWsbGxKvtkMlmhk+g7dux4535DQ0MEBQUhKCiowDp2dnY4ePBgoc5HRKUnKCgIPj4++PnnnxEcHIxatWoBAH777Tf07t1b4uiIqDS9q68dHR0NHx8ftGnTBq9evcKMGTPQq1cvXL16VfySffLkyThw4AB27twJuVwOX19fDBw4ECdPngQA5OTkoF+/frCyssKpU6eQmJiIkSNHQk9PD4sWLdLINRJpm6pVq+b7BZhMJoOhoSHq1auHUaNGYfTo0RJER6T9mETXUra2tli/fr1k57979y4WLlyImTNn5lk5WtNsbW0lPT/l7/HjxwBQ4ELCv/32m3jzTkTqFx8fL3UIRCShV69eISoqChs2bMiziO/KlSslioqIiqskfe1Dhw6pbIeFhcHCwgKxsbHo1KkTFAoFQkJCEB4ejm7dugEAQkND0ahRI5w+fRrt27fHkSNHcPXqVRw9ehSWlpZo0aIFvv76a3z11VeYN28e9PX1S/FqiSqGOXPmYOHChejTpw/atm0LADhz5gwOHToEHx8fxMfHw9vbG69evcLYsWMljpZI+5SpJPrw4cNhamoqdRhawdDQsEyMvrazsysTcVDZkJKSgpkzZ+LHH3/Es2fPALz+Nn3w4MFYsGABzMzMxLodO3aUKEoiyh2dxmmViCqOSpUqYcKECbh27ZrUoZCGpKWlITAwEA8fPoS1tTX8/f1hYmIidVhUAurqaysUCgCv10cAgNjYWGRnZ6NHjx5iHQcHB9ja2iImJgbt27dHTEwMmjVrpjK9i4uLC7y9vfH333+jZcuWec6TmZmJzMxMcZtrMRCpOnHiBBYsWIAJEyaolK9btw5HjhzBL7/8AkdHR3z33XdMohOpgcaS6H/88QfWrVuHW7du4eeff0atWrWwZcsW2Nvbix/gwcHBmgqHiDTs6dOncHJywoMHDzBs2DA0atQIAHD16lWEhYUhMjISp06dQtWqVSWOlKji2rx5M5YtW4YbN24AABo0aIBp06ZhxIgREkdGRJrQtm1b/PXXX5I/RUjqN2HCBPzzzz/idnx8PPr37w8HBwesXbtWwsiouNTV11YqlZg0aRKcnZ3RtGlTAEBSUhL09fVVkvIAYGlpiaSkJLHOmwn03P25+/ITGBiI+fPnFyk+oork8OHDWLJkSZ7y7t27Y8qUKQCAvn37Yvr06ZoOjahC0EgS/ZdffsGIESMwbNgw/PXXX+K3ywqFAosWLeK8p0QVQEBAAPT19XHr1q08HeqAgAD06tULAQEBfGScSCIrVqzA7Nmz4evrC2dnZwCvR7tMmDABjx8/xuTJkyWOkIjU7fPPP8eUKVNw//79fBcYdnR0lCgyKk1vJ9Df9M8//2DChAlMpJdD6upr+/j44MqVKzhx4kRphpsvf39/+Pn5idupqamwsbFR+3mJygtzc3Ps27cvT79837594pMiL168QJUqVaQIj0jraSSJvmDBAqxduxYjR45UWXDM2dkZCxYs0EQIRCSxPXv2YN26dXk69QBgZWWFpUuXYsKECUyiE0nk+++/R3BwMEaOHCmWDRgwAE2aNMG8efOYRCeqAAYPHgwAKgsJy2QyCIIAmUyGnJwcqUKjUpKWlqaSQK9Tpw7GjRuH9evX486dOwBeJ9LT0tI4tUs5o46+tq+vL/bv34/jx4+jdu3aKsfLyspCSkqKymj05ORkcU0FKysrnDlzRuV4ycnJ4r78GBgYwMDAoNDxEVU0s2fPhre3N44dOybOiX727FkcPHhQ/PIzIiICnTt3ljJMIq2lkSR6XFwcOnXqlKdcLpcjJSVFEyEQkcQSExPRpEmTAvc3bdq0wEc7iUj9EhMT0aFDhzzlHTp0QGJiogQREZGmcYFh7ffmVBm//vqruB5Vhw4dkJqaigEDBoj1li1bJkmMVDyl2dcWBAETJ07E7t27ERUVBXt7e5X9rVq1gp6eHiIjI+Hu7g7g9T1/QkICnJycAABOTk5YuHAhHj16BAsLCwCvk3umpqZo3LhxcS6RqMIbO3YsGjdujNWrV2PXrl0AgIYNGyI6Olrsx+dO61KR6KSnSB0CSUxT/w9oJIluZWWFmzdvok6dOirlJ06cQN26dTURAhFJrHr16rhz547KKJY3xcfHi4+gEZHm1atXDz/99BNmzJihUv7jjz+ifv36EkVFRJrEudC13/nz5wG8vj9LSUmBu7s7srOzoaenh5CQEFhaWiI5OVmsR+VHafa1fXx8EB4ejr1796JKlSpi8l0ul8PIyAhyuRxeXl7w8/ODubk5TE1NMXHiRDg5OaF9+/YAgF69eqFx48YYMWIEli5diqSkJMyaNQs+Pj4cbU5UDNnZ2Rg/fjxmz56N7du3Sx1OmWIUf1zqEKiC0EgSfezYsfjiiy+wceNGyGQyPHz4EDExMZg6dSpmz56tiRCISGIuLi6YOXMmIiIioK+vr7IvMzMTs2fPRu/evSWKjojmz5+PTz/9FMePHxfnRD958iQiIyPx008/SRwdEWnKli1bsHbtWsTHxyMmJgZ2dnZYtWoV7O3t4erqKnV4VEJKpRLA64Ud35y+Kzs7W2U7tx6VH6XZ1w4ODgYAdOnSRaU8NDQUo0aNAgCsXLkSOjo6cHd3R2ZmJlxcXLBmzRqxrq6uLvbv3w9vb284OTnB2NgYnp6eCAgIKP5FElVgenp6+OWXX5hDy0e6fScojcykDoMkpJOeopEvUzSSRJ8+fTqUSiW6d++Oly9folOnTjAwMMDUqVMxceJETYRARBILCAhA69atUb9+ffj4+MDBwQGCIODatWtYs2YNMjMzsWXLFqnDJKqw3N3d8eeff2LlypXYs2cPAKBRo0Y4c+YMWrZsKW1wRKQRwcHBmDNnDiZNmoSFCxeKc6CbmZlh1apVTKJrAXNzczx58kTcrly5Mry8vBASEoKXL1+q1KPypTT72oIgvLeOoaEhgoKCEBQUVGAdOzs7HDx4sNDXQETv5ubmhj179nCtorcojcygNK4udRhUAWgkiS6TyTBz5kxMmzYNN2/eRFpaGho3blzixWoWL14Mf39/fPHFF1i1ahUAICMjA1OmTMGOHTtUvhHPb4EVItKc2rVr49SpU/Dx8YG/v7/YOZfJZOjZsydWr14NGxsbiaMkqthatWqFrVu3Sh0GEUnk+++/x4YNG+Dm5obFixeL5a1bt8bUqVMljIxKy/z58+Hr6ytuOzs7o1mzZnB2dkZERIRKPSpf2Ncm0n7169dHQEAATp48iVatWsHY2Fhl/5sLgxNR6dNIEn3r1q0YOHAgKleuXGqLiJw9exbr1q2Do6OjSvnkyZNx4MAB7Ny5E3K5HL6+vhg4cCBOnjxZKuclouKrW7cufvvtNzx79gw3btwA8HoeZo52IpLewYMHoaurCxcXF5Xyw4cPQ6lUok+fPhJFRkSaEh8fn++TJwYGBnjx4oUEEVFpe3v0YkREhEry/M16+ZVT2ca+NpF2CwkJgZmZGWJjYxEbG6uyTyaTMYlOpGY6mjjJ5MmTYWFhgaFDh+LgwYPio6HFlZaWhmHDhmHDhg2oWrWqWK5QKBASEoIVK1agW7duaNWqFUJDQ3Hq1CmcPn26pJdBRCV0584dbNiwAdu3b0flypXRtm1bduqJyojp06fn+/ksCAKmT58uQUREpGn29va4cOFCnvJDhw6hUaNGmg+ISl12djaA18mW/OSW59aj8oV9bSLtFh8fX+Dr9u3bUodHpPU0MhI9MTERhw4dwvbt2zFo0CBUrlwZHh4eGDZsGDp06FDk4/n4+KBfv37o0aMHFixYIJbHxsYiOzsbPXr0EMscHBxga2uLmJgYcaXwt2VmZiIzM1PcTk1NLXJMRPRux44dQ//+/ZGeng4AqFSpEjZu3Ijhw4dLHFnxJScnQ6FQSB2GZO7evavy34pKLpdrxZRhN27cyPdpMQcHB9y8eVOCiIhI0/z8/ODj44OMjAwIgoAzZ85g+/btCAwMxA8//CB1eFQK9PT0kJ2dDSMjI2zatAm+vr5QKBSQy+VYvXo1PD098fLlS+jp6UkdKhWRNva1iYiIyhKNJNErVaqE/v37o3///nj58iV2796N8PBwdO3aFbVr18atW7cKfawdO3bg/PnzOHv2bJ59SUlJ0NfXh5mZmUq5paUlkpKSCjxmYGAg5/0jUrPZs2ejZ8+eCA4OhqGhIWbNmoUvv/yy3Hbsk5OTMXzESGRnZb6/spZbuHCh1CFISk/fAFu3bC73iXS5XI7bt2+jTp06KuU3b97MM98iEWmnzz77DEZGRpg1axZevnyJoUOHwtraGt9++y0GDx4sdXhUCkJCQjBy5Ei8fPkSgiDgxx9/FPc9evRIXFw0JCREqhCpmLStr01E+bt//z5+/fVXJCQkICsrS2XfihUrJIqKqGLQSBL9TZUrV4aLiwuePXuGu3fv4tq1a4V+77179/DFF18gIiIChoaGpRaTv78//Pz8xO3U1FQuukJUyq5cuYJTp06hZs2aAIBly5Zh3bp1ePLkCapVqyZxdEWnUCiQnZWJ9LqdoTSUSx0OSUQnQwHcjoZCoSj3SXRXV1dMmjQJu3fvxgcffADgdQJ9ypQpGDBggMTREZGmDBs2DMOGDcPLly+RlpYGCwsLqUOiUmRrawsdHR0olUrxCeGRI0di8+bNYgJdR0cHtra2EkdKRaVtfW0iyisyMhIDBgxA3bp18c8//6Bp06a4c+cOBEHAhx9+KHV4RFpPY0n03BHo27ZtQ2RkJGxsbDBkyBD8/PPPhT5GbGwsHj16pPLHIScnB8ePH8fq1atx+PBhZGVlISUlRWU0enJyMqysrAo8roGBAQwMDIp1XURUOKmpqahevbq4XblyZRgZGUGhUJTrjr3SUA6lcfX3VyQq45YuXYrevXvDwcEBtWvXBvD6y+tOnTrhm2++kTg6ItKEBQsWYNiwYbC3t0flypVRuXJlqUMiNfj999/RrVs3KJVKvHz5EmvXrhX36ejo4Pfff5cwOioube1rE9F//P39MXXqVMyfPx9VqlTBL7/8AgsLCwwbNgy9e/eWOjwiraeRJPrgwYOxf/9+VK5cGYMGDcLs2bPh5OQE4PU35oXVvXt3XL58WaVs9OjRcHBwwFdffQUbGxvo6ekhMjIS7u7uAIC4uDgkJCSI5yMi6Rw+fBhy+X+jtpVKJSIjI1X+DnDEK5E05HI5Tp06hYiICFy8eBFGRkZo3rw5PvroI6lDIyIN2blzJ+bOnYt27dph+PDhGDRokEpSrrCCg4MRHByMO3fuAACaNGmCOXPmoE+fPgCAjIwMTJkyBTt27EBmZiZcXFywZs2acv9ET3ny+++/IyEhAV5eXsjOzoaenh5CQkI4Ar2cY1+bSLtdu3YN27dvB/B62uT09HSYmJggICAArq6u8Pb2ljhCIu2mkSS6rq4ufvrpJ7i4uEBXVxfPnz/H+vXrERISgnPnziEnJ6dQx6lSpQqaNm2qUmZsbIxq1aqJ5V5eXvDz84O5uTlMTU0xceJEODk5FbioKBFpjqenZ56y8ePHiz/LZLJC/z0gotIRExODJ0+eoH///pDJZOjVqxcSExMxd+5cvHz5Em5ubvj+++/5xBZRBXDx4kX8/fff2LZtG7755htMmjQJPXv2xLBhw+Dm5lbokem1a9fG4sWLUb9+fQiCgE2bNsHV1RV//fUXmjRpgsmTJ+PAgQPYuXMn5HI5fH19MXDgQJw8eVLNV0hvsrW1RUREhNRhUCliXzt/OukpUodAEtKmf39jY2NxHvSaNWvi1q1baNKkCQDg8ePHUoZGVCFoJIm+bds2AMDx48cREhKCX375BdbW1hg4cCBWr15dqudauXIldHR04O7urjKyhYikpVQq31sndy5OItKcgIAAdOnSBf379wcAXL58GWPHjoWnpycaNWqEZcuWwdraGvPmzZM2UIlo040XFV1F/Pdv0qQJFi1ahEWLFuHkyZMIDw/HpEmTMGHCBKSmphbqGB9//LHK9sKFCxEcHIzTp0+jdu3aCAkJQXh4OLp16wYACA0NRaNGjXD69OkCB75kZmYiM/O/xbwLGwtRRcG+dsGM4o9LHQJRiQQEBGDKlClo3749Tpw4gUaNGqFv376YMmUKLl++jF27dlXogaM6GQqpQyCJaer/AbUn0ZOSkhAWFoaQkBCkpqZi0KBByMzMxJ49e9C4ceMSHz8qKkpl29DQEEFBQQgKCirxsYlIMzIzMxEUFISlS5ciKSlJ6nCIKpQLFy7g66+/Frd37NiBtm3bYsOGDQAAGxsbzJ07t8Im0XnjTRWZsbExjIyMoK+vj+fPnxfrGDk5Odi5cydevHgBJycnxMbGIjs7Gz169BDrODg4wNbWFjExMQUmAQIDAzF//vxixUBU0VX0vna6fScojcykDoMkopOeUu77c/Pnz8eECROwYsUKpKWliWVpaWn48ccfUb9+faxYsULiKDVPLpdDT98AuB0tdShUBujpG6hMaaYOak2if/zxxzh+/Dj69u2LVatWoXfv3tDV1VVZvIaIKobMzEzMmzcPERER0NfXx5dffgk3Nzds3LgRs2bNgq6uLiZPnix1mEQVzrNnz1TmIY6OjhbnLQaANm3a4N69e1KEVibwxrti04Yb76KKj49HeHg4wsPDERcXh86dO2P+/Pn45JNPinScy5cvw8nJCRkZGTAxMcHu3bvRuHFjXLhwAfr6+jAzM1Opb2lp+c7knr+/P/z8/MTt1NRU2NjYFCkmUpWeno5169bh/v37qF27NsaPHw8jIyOpw6JiYl+7YEojMyiNi76+A1FZIQgCAKBu3bpimbGxcYXPrVlaWmLrls1QKCr2SPS7d+9i4cKFmDlzJuzs7KQORzJyuVzt6+uoNYn+22+/4X//+x+8vb1Rv359dZ6KiMq4OXPmYN26dejRowdOnToFDw8PjB49GqdPn8aKFSvg4eEBXV1dqcMkqnAsLS0RHx8PGxsbZGVl4fz58yqjPZ8/fw49PT0JI5QWb7ypImnfvj3OnDmD5s2bY/To0RgyZAhq1apVrGM1bNgQFy5cgEKhwM8//wxPT09ERxd/pJiBgQHXZihFM2fOVJmD/ty5c9izZw+cnZ2xcOFCCSOj4mJfm0i7yWQyqUMokywtLbkw+f9nZ2eHBg0aSB2GVlNrEv3EiRMICQlBq1at0KhRI4wYMQKDBw9W5ymJqIzauXMnNm/ejAEDBuDKlStwdHTEq1evcPHiRXYIiCTUt29fTJ8+HUuWLMGePXtQuXJlfPTRR+L+S5cu4YMPPpAwQiLSlO7duyM0NBQ1atQAAFSvXvwvkPT19VGvXj0AQKtWrXD27Fl8++23+PTTT5GVlYWUlBSV0ejJycmwsrIqUfxUOLkJdD09PXh4eKBv3744ePAgdu7ciZMnT2LmzJlMpJdD7GsTabcGDRq8ty0/ffpUQ9EQVUxqTaK3b98e7du3x6pVq/Djjz9i48aN8PPzg1KpREREBGxsbFClShV1hkBEZcT9+/fRqlUrAEDTpk1hYGCAyZMns1NPJLGvv/4aAwcOROfOnWFiYoJNmzZBX19f3L9x40b06tVLwgiJSBNSUlLw7NkzfPTRR3j27BkAoGrVqhg8eDAWLFiQZ/qVolIqlcjMzESrVq2gp6eHyMhIuLu7AwDi4uKQkJAAJyenkl4GvUd6erqYQD9w4ID4937cuHEYNWoU+vXrh5MnTyI9PZ1Tu5Qz7GsTabf58+erfb5nIno3tS8sCryeq2nMmDEYM2YM4uLiEBISgsWLF2P69Ono2bMnfv31V02EQUQSysnJUUnMVapUCSYmJhJGRETA65Gmx48fh0KhgImJSZ5HvXfu3Mm2SqTlnj59CicnJzx48ADDhg1Do0aNAABXr15FWFgYIiMjcerUKVStWrVQx/P390efPn1ga2uL58+fIzw8HFFRUTh8+DDkcjm8vLzg5+cHc3NzmJqaYuLEiXBycipwUVEqPevWrQMAeHh4qPTLgNdPD3zyySfYvn071q1bh0mTJkkQIRUX+9pE2m3w4MGwsLCQOgyiCk0jSfQ3NWzYEEuXLkVgYCD27duHjRs3ajoEIpKAIAgYNWqUOJ9pRkYGJkyYAGNjY5V6u3btkiI8ogqvoJEt5ubmGo6EiDQtICAA+vr6uHXrVp55RQMCAtCrVy8EBARg5cqVhTreo0ePMHLkSCQmJkIul8PR0RGHDx9Gz549AQArV66Ejo4O3N3dkZmZCRcXF6xZs6bUr4vyun//PoDXU3llZWVh7969ePjwIaytreHq6oq+ffti+/btYj0qP9jXJtJefKKEqGzQeBI9l66uLtzc3ODm5iZVCESkQZ6enirbw4cPlygSIiIietOePXuwbt26fBfmsrKywtKlSzFhwoRCJ9FDQkLeud/Q0BBBQUEICgoqVrxUfLVr18a5c+ewcOFCXL9+HTk5OeK+tWvXon79+mI9Kl/Y1ybSXoIgSB0CEUHCJDoRVSyhoaFSh0BEGhAYGIhdu3bhn3/+gZGRETp06IAlS5agYcOGYp0uXbogOjpa5X3jx4/H2rVrxe2EhAR4e3vj2LFjMDExgaenJwIDA1GpErsuRKUtMTERTZo0KXB/06ZNkZSUpMGISF3Gjx+PPXv24Nq1azA1NcW4cePg5OSEmJgYrF+/Hv/8849Yj8oX9rWJtJdSqZQ6BCICoCN1AERERKQ9oqOj4ePjg9OnTyMiIgLZ2dno1asXXrx4oVJv7NixSExMFF9Lly4V9+Xk5KBfv37IysrCqVOnsGnTJoSFhWHOnDmavhyiCqF69eq4c+dOgfvj4+M5tZOWeHPdi/T0dDx48AAvX77EgwcPkJ6enm89IiIiImISnYjKoXnz5kEmk6m8HBwcpA6LiAAcOnQIo0aNQpMmTdC8eXOEhYUhISEBsbGxKvUqV64MKysr8WVqairuO3LkCK5evYqtW7eiRYsW6NOnD77++msEBQUhKysr3/NmZmYiNTVV5UVEhePi4oKZM2fm274yMzMxe/Zs9O7dW4LIqLTt3bsXAPDBBx8gOzsb27dvx4gRI7B9+3ZkZ2fjgw8+UKlHRERERK/xmWgiKpeaNGmCo0ePituc4oGobFIoFADyLlC6bds2bN26FVZWVvj4448xe/ZsVK5cGQAQExODZs2aqczP7OLiAm9vb/z9999o2bJlnvMEBgZi/vz5arwSIu0VEBCA1q1bo379+vDx8YGDgwMEQcC1a9ewZs0aZGZmYsuWLVKHSaXg4cOHAIClS5eicuXKWLduHe7fv4/atWtj/PjxePHiBT755BOxHhERERG9xqwTEZVLlSpVgpWVldRhENE7KJVKTJo0Cc7OzmjatKlYPnToUNjZ2cHa2hqXLl3CV199hbi4OOzatQsAkJSUlGeBw9ztguZl9vf3h5+fn7idmpoKGxub0r4kIq1Uu3ZtxMTE4PPPP4e/v7+4gJlMJkPPnj2xevVqtictYW1tDeD1l5X9+/fHpEmTVPbnDlDIrUdERERErzGJTkTl0o0bN2BtbQ1DQ0M4OTkhMDAQtra2+dbNzMxEZmamuM1pHog0w8fHB1euXMGJEydUyseNGyf+3KxZM9SsWRPdu3fHrVu3xKkEisrAwAAGBgYlipeoIrO3t8dvv/2GZ8+e4caNGwCAevXqcS50LePq6oq1a9ciJCQEvXv3VnmS79WrV9i4cSN0dXXh6uoqYZREREREZQ/nRCeicqddu3YICwvDoUOHEBwcjPj4eHz00Ud4/vx5vvUDAwMhl8vFF0fTEamfr68v9u/fj2PHjqF27drvrNuuXTsAwM2bNwEAVlZWSE5OVqmTu80nUIjUq2rVqmjbti3atm3LBLoW0tfXh4eHB549ewYPDw/s27cPjx8/xr59+1TK9fX1pQ6ViIjUJDAwEG3atEGVKlVgYWEBNzc3xMXFqdTJyMiAj48PqlWrBhMTE7i7u+fpnyckJKBfv36oXLkyLCwsMG3aNLx69UqTl0KkUUyiE1G506dPH3h4eMDR0REuLi44ePAgUlJS8NNPP+Vb39/fHwqFQnzdu3dPwxETVRyCIMDX1xe7d+/G77//Dnt7+/e+58KFCwCAmjVrAgCcnJxw+fJlPHr0SKwTEREBU1NTNG7cWC1xExFVFBMmTMDgwYORmpqK5cuX45NPPsHy5cuRmpqKwYMHY8KECVKHSEREahQdHQ0fHx+cPn0aERERyM7ORq9evfDixQuxzuTJk7Fv3z7s3LkT0dHRePjwIQYOHCjuz8nJQb9+/ZCVlYVTp05h06ZNCAsLw5w5c6S4JCKN4HQuRFTumZmZoUGDBuIo1rdxmgcizfHx8UF4eDj27t2LKlWqiHOYy+VyGBkZ4datWwgPD0ffvn1RrVo1XLp0CZMnT0anTp3g6OgIAOjVqxcaN26MESNGYOnSpUhKSsKsWbPg4+Oj8bask6HQ6PmobOG/P2mrCRMmYMyYMdi7dy8ePnwIa2truLq6cgQ6EVEFcOjQIZXtsLAwWFhYIDY2Fp06dYJCoUBISAjCw8PRrVs3AEBoaCgaNWqE06dPo3379jhy5AiuXr2Ko0ePwtLSEi1atMDXX3+Nr776CvPmzePnCWklJtGJqNxLS0vDrVu3MGLECKlDIarwgoODAQBdunRRKQ8NDcWoUaOgr6+Po0ePYtWqVXjx4gVsbGzg7u6OWbNmiXV1dXWxf/9+eHt7w8nJCcbGxvD09ERAQIDGrkMul0NP3wC4Ha2xc1LZpKdvALlcLnUYRKUud2oXIiKq2BSK14MGcqdxi42NRXZ2Nnr06CHWcXBwgK2tLWJiYtC+fXvExMSgWbNmsLS0FOu4uLjA29sbf//9N1q2bJnnPFyrjMo7JtHVJDk5WfxDVBHdvXtX5b8VmVwuV/lgoZKbOnUqPv74Y9jZ2eHhw4eYO3cudHV1MWTIEKlDI6rwBEF4534bGxtER78/MW1nZ4eDBw+WVlhFZmlpia1bNlf4z/KFCxdi5syZsLOzkzocyfBznLSVQqHAzJkzkZycDEtLSyxcuJBfGJFW4lNFFRv//d9NqVRi0qRJcHZ2RtOmTQEASUlJ0NfXh5mZmUpdS0tL8SnTpKSkPP2j3O3cOm8LDAzE/PnzS/kKiDSHSXQ1SE5OxvARI5Gdlfn+ylpu4cKFUocgOT19A2zdspk34KXo/v37GDJkCJ48eYIaNWqgY8eOOH36NGrUqCF1aESkRSwtLfm3G6+/0GjQoIHUYRBRKRo2bBgePHggbv/7779wdXVFrVq1sG3bNgkjIyo9fKqMcvGpsoL5+PjgypUrOHHihNrP5e/vDz8/P3E7NTUVNjY2aj8vUWlhEl0NFAoFsrMykV63M5SG/ENdkelkKIDb0VAoFEzElKIdO3ZIHQIRERFRufRmAr1t27YYOXIkNm/ejDNnzuDBgwcYNmwYE+mkFfhUGZ8qy8WnyvLn6+uL/fv34/jx46hdu7ZYbmVlhaysLKSkpKiMRk9OToaVlZVY58yZMyrHS05OFvflh2uVUXnHJLoaKQ3lUBpXlzoMIiIiIiIiKBQKMYF+8OBBVK5cGQCwdOlSvHz5En379sWDBw+gUCg4apO0Ap8qe41PldGbBEHAxIkTsXv3bkRFRcHe3l5lf6tWraCnp4fIyEi4u7sDAOLi4pCQkAAnJycAgJOTExYuXIhHjx7BwsICABAREQFTU1M0btxYsxdEpCE6UgdARERERERE6jdz5kwAr0egZ2dnw9fXFx4eHvD19UV2djbatGmjUo+IiLSPj48Ptm7divDwcFSpUgVJSUlISkpCeno6gNcj9728vODn54djx44hNjYWo0ePhpOTE9q3bw8A6NWrFxo3bowRI0bg4sWLOHz4MGbNmgUfHx+ONietxZHoREREREREFUDuo/a3bt2Cq6urWJ47J7q5ublKPSIi0j7BwcEAgC5duqiUh4aGYtSoUQCAlStXQkdHB+7u7sjMzISLiwvWrFkj1tXV1cX+/fvh7e0NJycnGBsbw9PTEwEBAZq6DCKNYxKdiIiIiIioArC0tMS///6LJ0+eAMg7J/rTp0/FekREpJ0EQXhvHUNDQwQFBSEoKKjAOnZ2djh48GBphkZUpjGJTkREREREVAFMnz4dw4cPBwD8/PPPqF799fpNS5cuxePHj/HJJ5+I9YiIiIjoP5wTnYiIiIiIqAJYvHix+PMnn3yCadOm4dKlS5g2bZqYQH+7HhERERFxJDoREREREVGFkDvXefXq1fH48WOcPXsWZ8+eFffnlnNOdCIiIiJVHIlORERERERUAeTOdV63bl3s3bsXTZs2RY0aNdC0aVPs3bsX9vb2KvWIiIiI6DWORCciIiIiIqoAFi5cCFdXV5w5cwZ6enpYvXq1uO/ly5fiqPSFCxdKFSIRERFRmcQkOhERERERUQUgl8tRq1YtPHjwAH379kWbNm0wYsQIbNmyRUyg16pVC3K5XOJIiYiIiMoWJtGJiIiIiIgqiG3btmHYsGF48OBBnjnRa9WqhW3btkkYHREREVHZxCQ6ERERERFRBbJt2zYoFArMnDkTycnJsLS0xMKFCzkCnYiIiKgATKITERERERFVMHK5XGVOdCIiIiIqGJPoREREREREFUx6ejrWrVuH+/fvo3bt2hg/fjyMjIykDouIiIioTNKROgAiIiIiItIegYGBaNOmDapUqQILCwu4ubkhLi5OpU5GRgZ8fHxQrVo1mJiYwN3dHcnJyRJFXPHMnDkTffr0wZ49e3Du3Dns2bMHffr0wcyZM6UOjYiIiKhMYhKdiIiIiIhKTXR0NHx8fHD69GlEREQgOzsbvXr1wosXL8Q6kydPxr59+7Bz505ER0fj4cOHGDhwoIRRVxwzZ87EyZMnoaenh6FDh2Lr1q0YOnQo9PT0cPLkSSbSiYiIiPLB6VyIiIiIiKjUHDp0SGU7LCwMFhYWiI2NRadOnaBQKBASEoLw8HB069YNABAaGopGjRrh9OnTaN++vRRhVwjp6eliAv3AgQPQ19cHAIwbNw6jRo1Cv379cPLkSaSnp3NqFyIiIqI3cCQ6ERERERGpjUKhAACYm5sDAGJjY5GdnY0ePXqIdRwcHGBra4uYmJh8j5GZmYnU1FSVFxXdunXrAAAeHh5iAj2Xvr4+PvnkE5V6RERERPQak+hERERERKQWSqUSkyZNgrOzM5o2bQoASEpKgr6+PszMzFTqWlpaIikpKd/jBAYGQi6Xiy8bGxt1h66V7t+/DwDo27dvvvtzy3PrEREREdFrTKITEREREZFa+Pj44MqVK9ixY0eJjuPv7w+FQiG+7t27V0oRViy1a9cGABw8eDDf/bnlufWIiIiI6DUm0YmIiIiIqNT5+vpi//79OHbsmEpS1srKCllZWUhJSVGpn5ycDCsrq3yPZWBgAFNTU5UXFd348eMBADt37kRSUhJGjRqFjz/+GKNGjUJSUhJ+/vlnlXpERERE9BqT6EREREREVGoEQYCvry92796N33//Hfb29ir7W7VqBT09PURGRoplcXFxSEhIgJOTk6bDrVCMjIzg7OyM7OxsDB48GHfu3MHz589x584dDB48GNnZ2XB2duaiokRERERvqSR1AEREREREpD18fHwQHh6OvXv3okqVKuI853K5HEZGRpDL5fDy8oKfnx/Mzc1hamqKiRMnwsnJCe3bt5c4eu137dq1Eu0nIiIiqog4Ep2IiIiIiEpNcHAwFAoFunTpgpo1a4qvH3/8UayzcuVK9O/fH+7u7ujUqROsrKywa9cuCaOuGJ4+fYqnT58CeD2li5ubG1q3bg03Nzfs3LkzTx0iIiIieo0j0dVIV3EfOukp0pxcyIEs66U05y5jBP3KgExXknPLstIkOS9pjmTtnG1cxDauvYKCgrBs2TIkJSWhefPm+P7779G2bVupw9KYjIwMJCQkSHb+u3fvqvxXSra2tjA0NJQ6DCokQRDeW8fQ0BBBQUEICgrSQESUy8/PDwDQuHFj1KhRA5MmTVLZ36hRI1y7dg1+fn4ICwvTfICkdfhZzs/yXPwsJ20kdRsHyk47rwhtnEl0NZDL5dDR0YXhg/NSh0JlgI6OLuRyudRhUCljO6dcbOPq8eOPP8LPzw9r165Fu3btsGrVKri4uCAuLg4WFhZSh6cRCQkJGDdunNRhYOHChVKHgPXr16NBgwZSh0FU7j158gQA4OXlle/+0aNH48svvxTrEZUEP8v5Wf4mfpaTNiorbRyQvp1XhDbOJLoaWFpaYs2aINy7d0+yGLKzs/H48WPJzl+WVK9eHXp6epKd38bGBpaWlpKdn9RD6nbONv4ftnHttGLFCowdOxajR48GAKxduxYHDhzAxo0bMX36dImj0wxbW1usX79e6jDKBFtbW6lDINIK1apVw/PnzxESEoJWrVrl2R8aGirWIyqpon6WZ2ZmIjMzU9xOTU3VWKzqws/y//CznLQR2/h/KkIbL3dJ9MDAQOzatQv//PMPjIyM0KFDByxZsgQNGzYU62RkZGDKlCnYsWMHMjMz4eLigjVr1mg0yeHg4AAHBweNnY+INI/tnEg9srKyEBsbC39/f7FMR0cHPXr0QExMTL7v0cYbb0NDQ60fzUFEmrVixQoMHDgQV69eRVpaGkxMTMR9aWlp4qKiK1askCpE0hLF+SwPDAzE/PnzNRWiRvCznEi7sY1XLOVuYdHo6Gj4+Pjg9OnTiIiIQHZ2Nnr16oUXL16IdSZPnox9+/Zh586diI6OxsOHDzFw4EAJoyYiIqLCevz4MXJycvJ8+W1paYmkpKR83xMYGAi5XC6+bGxsNBEqEVG5Ym5uDnNzcwBA//794e3tjTNnzsDb2xv9+/fPU4eouIrzWe7v7w+FQiG+pHyym4iI6G3lbiT6oUOHVLbDwsJgYWGB2NhYdOrUCQqFAiEhIQgPD0e3bt0AvH4ssVGjRjh9+jTat2+f55jaOHqNiIioIvH39xcXzANef5YzkU5ElNeuXbswcOBAPH36FNeuXcOXX34p7jM3N8euXbskjI4qMgMDAxgYGEgdBhERUb7KXRL9bQqFAgDE0RKxsbHIzs5Gjx49xDoODg6wtbVFTExMvkn0gh4bYzKdpJb7/6AgCBJHol1yf59s4yQ1tvH8Va9eHbq6ukhOTlYpT05OhpWVVb7vefvGm+2cygq2c/VgGy+ZsLAwPH36FDNmzMDTp09hbm6ORYsWwdzcnL/TImIbz19xPsvfxnZOZQXbuXqwjVNZUdg2Xq6T6EqlEpMmTYKzszOaNm0KAEhKSoK+vj7MzMxU6r7vsbE3R689ePAAjRs35gg2KjOeP38OuVwudRha4/nz5wDANk5lBtu4Kn19fbRq1QqRkZFwc3MD8PozPzIyEr6+voU6Bts5lTVs56WLbbz0bd++XeoQyjW2cVX8LCdtxHZeutjGqax5Xxsv10l0Hx8fXLlyBSdOnCjRcd4evWZiYoJ79+6hSpUqkMlkJQ2zQsp9jP7evXswNTWVOpxySxAEPH/+HNbW1lKHolWsra3ZxksB23nJsY0XzM/PD56enmjdujXatm2LVatW4cWLFxg9enSh3s92XnJs46WD7Vw92MZLB9t5ybGNF4yf5dJjGy8dbOfqwTZeOtjOS66wbbzcJtF9fX2xf/9+HD9+HLVr1xbLrayskJWVhZSUFJXR6EV5bExHR0flmFR8pqambMQlxG+6Sx/beOliOy8ZtvH8ffrpp/j3338xZ84cJCUloUWLFjh06FCeBcoKwnZeetjGS47tvPSxjZcutvOSYRvPHz/Lyw628ZJjOy99bOOli+28ZArTxmVCOZvUSRAETJw4Ebt370ZUVBTq16+vsl+hUKBGjRrYvn073N3dAQBxcXFwcHAocE50Kn2pqamQy+VQKBRsxERaiu2cSLuxjRNpP7ZzIu3GNk6k/djONafcjUT38fFBeHg49u7diypVqojznMvlchgZGUEul8PLywt+fn4wNzeHqakpJk6cCCcnJybQiYiIiIiIiIiIiKhIyl0SPTg4GADQpUsXlfLQ0FCMGjUKALBy5Uro6OjA3d0dmZmZcHFxwZo1azQcacVmYGCAuXPnqsw1T0Tahe2cSLuxjRNpP7ZzIu3GNk6k/djONafcTedCRERERERERERERKQpOlIHQERERERERERERERUVjGJTkRERERERERERERUACbRiYiIiIiIiIiIiIgKwCQ6EREREREREREREVEBmEQnIiIiIiIiIiIiIioAk+hERERERERERERERAVgEp2IiIiIiIiIiIiIqABMohMRERERERERERERFYBJdCIiIiIiIiIiIiKiAjCJXkGdOXMGMpkMMpkMAQEBUodDRCWQ25ZzXzo6OpDL5Wjfvj1WrVqF7OzsfN83atSoPO81MjKCg4MDJk+ejKSkpALPef/+fUydOhXNmjWDqakpjI2N0aBBA3h7e+Pq1avqulQi+v+OHTsGd3d31KpVC/r6+qhatSoaNmwIDw8PrF69GgqFQuoQiegtT58+xbx589C6dWtUrVoVRkZGsLe3h6enJ2JiYqQOj4jUiP11Iu31dht936tOnTpSh0zFJBMEQZA6CNK8iRMnYvXq1QCABg0aIC4uTuKIiKi4ZDIZAMDT0xMAkJOTgzt37uDUqVNQKpXo3r07Dh06hEqVKqm8b9SoUdi0aROcnZ1Rr149AMCjR49w+vRpPHv2DFZWVoiJicnzIb9t2zaMHTsW6enpqFWrFtq0aYNKlSrh4sWLuHHjBnR1dbFo0SJ8+eWX6r94ogooICAAc+fOBQA0atQIDg4O0NPTQ1xcHC5fvgylUomYmBi0b99e4kiJKFdkZCQ8PDzw7NkzVKtWDR06dEDlypVx7do1XLp0CQDwxRdfYMWKFdDR4TgnIm3D/jqR9ho1alSeshMnTuDWrVto3rw5WrRoobKvevXq+OabbzQTXCnK/Xt07NgxdOnSRepwpCFQhZOVlSVUr15dACBYWVkJAITTp09LHRYRFRMAIb8/56dPnxYMDQ0FAMKWLVvy7Pf09BQACKGhoSrljx49Elq3bi0AENzd3VX27d69W5DJZIK+vr6wbt06QalUquzfu3evIJfLBQDCd999V/KLIyIV586dE2QymaCnpyfs3r07z/7ExERh2bJlwrVr1zQfHBHl68yZM4K+vr4gk8mEgIAAISsrS2X/H3/8IdSuXVsAIPj5+UkUJRGpE/vrRBVLbtudO3eu1KGUmtxrOnbsmNShSIbDHCqgQ4cO4fHjx3B2dsbnn38OANiyZYvEURFRaWvXrp34rfjhw4cL/b4aNWpg+fLlAIADBw6Ij5empaVh7NixEAQB69evx7hx48RRNbkGDBiA3bt3QyaTYdq0abh7927pXAwRAQB27doFQRAwaNAguLm55dlvZWWFqVOnwsHBQfPBEVEegiDA09MTWVlZmDt3LmbPng09PT2VOh07dsSRI0dgaGiIlStX4vTp0xJFS0Saxv46EVH5wSR6BbR161YAwPDhwzF8+HAAwI8//ih+8J4/fx4ymQzt2rUr8Bjff/89ZDIZ/Pz8VMpfvXqF4OBgODk5wdTUFEZGRmjRogVWrVqFV69eFSveb775BjKZDDNmzCiwTq9evSCTyXDs2LFinYNIWzVp0gTA68c+i6Jly5YAgIyMDDx+/BgAsHnzZjx+/Bht2rQRH0XNT9euXTFw4EBkZmYiKCioyDGnpaWhXr16kMlkOHDgQJ7927Ztg0wmg6OjIzIzM4t8fKLy7N9//wXw+ua5KF68eIElS5agdevW4ryoDg4O8PHxwfXr1/PU37JlCzp27AhTU1NUrlwZjo6OCAwMREZGRp66ufO1RkVF4fDhw+jatSvMzMwgk8mQkpIi1jt06BD69euHGjVqwMDAAHXr1oWfnx+ePHlStF8CUTny22+/4dq1a7C2tn5nX7ZRo0bw8fGBIAhYsWIFAMDR0REymQz//PNPvu958uQJ9PX1YWlpmaef/eeff8LDwwM1a9aEvr4+ateujc8++wwJCQl5jjNv3jzIZDKEhYXhzJkz6N+/P6pVqwaZTIYLFy4AeP23Z/r06WjcuDFMTEwgl8vRoEEDjBw5EmfOnCny78XX1xcymQzr168vsE7Dhg2ho6OD27dvF/n4ROVJeeyv8x6dqHhu3ryJefPmwcnJCVZWVuJn9MiRI/PtkwMQ51HPyspCQEAAHBwcYGBgoDKg5s6dOxg6dChq1KgBY2NjtG7dGjt27MCdO3cgk8kKnH6lsP1zmUyGTZs2AXj99+PNOd7v3LlTGr+acoFJ9ApGoVDg119/hb6+PgYNGgR7e3t06NABjx8/xqFDhwAAH374IRwcHHDmzBncunUr3+Ns27YNAMQkPACkp6ejV69e+Pzzz3H9+nW0b98ePXv2RGJiIiZPngx3d3colcoixzxq1CgYGBggNDQ030R8fHw8jh49ivr166Nr165FPj6RNnv+/DkAwMLColjvAwADAwMAEBPaQ4cOfe/7hw0bBuB18qCoTExMsHXrVujq6mLMmDEqNxR3796Fj48PDA0NER4eLsZGVFHY2NgAAH755ZdC32wnJiaiXbt2mD59Om7fvo0uXbqgb9++MDY2xtq1a3Hw4EGV+uPHj8fIkSMRGxuLjz76CP369UNiYiJmzJiBbt264eXLl/meJzw8HH369MGLFy/Qp08ftGnTRhz9Nn36dPTp0wdHjx5Fw4YNMWDAAFSqVAkrV65Eu3btkJycXILfClHZlfvZ6eHhkWcE+ttyPzuPHDkCpVIpbuf2u9+2c+dOZGdn49NPP1WZR3nNmjXo0KEDdu3aBTs7O7i5uaFatWoICQlB69atce3atXyPd/z4cXTs2BF37txBr1690KlTJ+jo6OD58+do164dlixZgrS0NPTs2RO9evVC1apVsWPHjjx/Qwpj/PjxAIANGzbkuz86OhrXr19H9+7dUbdu3SIfn6g8KY/9dd6jExXPDz/8gICAALx48QJt2rTBgAEDYGpqii1btqBNmzbiOilvUyqVcHNzw9KlS/HBBx/A1dUVNWvWBPA6Md+2bVts374dZmZmGDBgAIyNjTF06FCsWrWqwFiK0j/39PTEBx98AABwcXGBp6en+DIxMSm9X1BZJ+lkMqRxP/zwgwBAcHV1FcvWrFkjABA8PDzEsq+//loAIAQEBOQ5xs2bNwUAgoODg0r5559/LgAQPv30UyElJUUsT01NFfr27SsAEIKDg4sV99ChQwUA+c7/OnPmTAGAsGTJkmIdm6i8QwFzLAqCIHTq1EkAIGzdujXPvoLmWBQEQVi7dq0AQKhVq5ZYVqtWLQGA8Mcff7w3prt37woABJlMlmfu18KaN2+eAEDo37+/IAiCkJOTI3z00UcCAGHVqlXFOiZReXfr1i3ByMhIACBUqVJF8PT0FDZs2CCcP39eePXqVb7v6d69uwBAGDRokPD8+XOVffHx8cLFixfF7Z9//lkAIFhbWwvXr18Xy1NSUoSOHTsKAIQpU6aoHCP3bwkAYceOHXnO/9NPPwkAhKZNmwo3btwQy5VKpTBnzhyx70CkjZydnQuc6/ht2dnZgr6+vgBAuHnzppCQkCDIZDLhgw8+yLd+bpt8c22jmJgYQVdXV6hVq5Zw7tw5lfq59wHt2rVTKZ87d67YhvPrT2/cuFEAIAwYMEDIyclR2ffo0SPh8uXL7722/HTo0EEAIPz111959g0bNkwAIPz444/FOjZRWaON/XXeoxMVrKA50WNiYoTbt2/nqZ/7Wdu1a9c8+3L/ftSrV0+4f/9+nv25ff0JEyao3A8cOnRI0NPTEwAInTt3VnlPcfrnnBNdEJhEr2A6d+4sABB27twplj1+/FjQ09MTDA0NxeT37du3802UC4IgzJ8/XwAgfP3112JZcnKyoKenJ9jY2AgvX77M857ExERBX19fcHR0LFbcx48fFwAIffv2VSl/9eqVUKtWLUFPT09ITk4u1rGJyru3O+U5OTnCzZs3hQkTJohfmmVnZ+d5X36d8kePHgkbN24UTE1NBQDCggULxH25ix79888/740pPT1djCspKalY1/Xq1SvByclJACAEBQUJCxYsEAAIvXr1yrNAElFFcvToUcHGxkZsY7kvMzMzwdvbW3j48KFY988//xQACBYWFkJqaup7j517I79u3bo8+y5evCjIZDLBxMRESE9PF8tz/5b069cv32M2b95cAJBvok2pVAotWrQQdHV1hX///bcwl09Urjg4OAgAhEOHDhWqvqWlpUpiPLfvHhMTo1Lvzp07gkwmE+rVq6dS7urqKgAQ9u3bl+/xBwwYIAAQzp8/L5blJtGbNWuW7+frkiVL1PIF9ubNmwUAwueff65S/vTpU8HQ0FCoUaOGkJmZWarnJJKKNvbXeY9OVLDiLCzq7OwsyGQylUGpgvDf348383i5bty4Id4HvD1YRhD++1L67SR6cfrnTKJzYdEKJSEhAcePH4eZmRk+/vhjsbxatWro27cvMjIysHPnTgAQp3n5559/cP78eZXj5D5Smvv4FwBERUUhOzsbvXv3hpGRUZ5zW1lZoX79+rh8+TLS09OLHPtHH32EJk2a4NChQ7h3755YfvDgQTx48ACurq5FfvyNSNvkzkmmq6uLevXqYe3atRg7dix2796t8pj320aPHi2+18LCAmPGjEFqaio8PT0xffp0DV6BKl1dXWzduhVVqlTB1KlTMX/+fFSrVg2hoaF5Fkgiqki6d++OmzdvYteuXZgwYQI+/PBDVKpUCSkpKQgODkaLFi0QFxcHADh69CgAYMiQIahSpco7j5udnS0uaPjmZ3wuR0dHODo6Ii0tTZwn+U0DBgzIU/bo0SNcvHgR9evXR9OmTfPsl8lkcHZ2Rk5ODmJjY9977UQVTW5bDA8PVykPDw+HIAgqbVWpVCIyMhKVK1eGi4tLvsf76KOPACDfecz79++f7+drq1atAADLli3Djh07VKaQKAkPDw9Uq1YN27ZtU5kmauvWrcjIyICnpyf09fVL5VxEZYU29dd5j05UPGlpadi+fTu++uorjB07FqNGjcKoUaOQmJgIQRDynVZZJpOp5PFynTx5EgDQu3fvfKdV+fTTT/OUsX9efEyiVyDbtm2DIAj45JNP8swjnDu3ee6iowDynYfx3LlzuH79Ojp06AB7e3uxPHchgQ0bNqgsMPDm6++//4YgCHj69Gmx4h8/fjyUSiU2btwoluXOozh27NhiHZNIm+TOSfbpp5/CwcEBwOs2krsASEGcnZ3F944fPx6LFi3CX3/9hbCwMOjq6or1qlWrBuC/hQ3fJXeuZplMBnNz8+JeEurWrYsFCxYgPT0d2dnZWLNmDaytrYt9PCJtoa+vj//7v/9DcHAwYmNj8e+//yI4OBhVq1bFo0eP4OvrCwDiTW3uHIbv8uTJE2RlZaF69eowNjbOt06dOnUAAA8ePMizz9bWNk9Zbv/gxo0bBfYPchc0y10UjUibFOWz89WrV3j27BkAoHr16gAg9tt//PFH5OTkiHXzG9Ty+PFjpKWl4eXLl9DX18+3vU2bNk2s+7b82jDw+ou7yZMn4+HDhxgyZAjMzc3Rrl07zJo1q0SLfhoaGsLT0xMKhUIcyAO8ni8WAD777LNiH5uorNK2/jrv0YmK5vfff0fdunUxdOhQLF26FD/88AM2bdqETZs2iZ+p+X1ZbWFhke96YImJiQD+Wzfpbeyfl66Cv+okrbNlyxYAr0eNd+zYUWVfVlYWgNcLCt29exd2dnb49NNPMWnSJOzYsQPLli2Djo5Ovh12AOKCoS1atEDz5s3fGUdxFwIcOXIkpk+fjo0bN2L27NlISkrCwYMHUadOHfTs2bNYxyTSJmFhYSrby5Ytw5dffgkfHx907doVdnZ2+b7vs88+w6hRo957/ObNm+PBgwc4d+5cnr8hb8t9gqVJkybvXUjtXZRKJX7++Wdx+9y5cxg0aFCxj0ekrczMzDBhwgRYW1vD1dUVx44dK3AB0JJ411MghoaGecpy+wdWVlYFjozNVdDfKKLyrHnz5jh58iTOnTsnDlopyJUrV5CVlQW5XC4OVqlatSr69u2L3bt34+jRo3BxccHFixfx999/o02bNqhfv774/tz2ZmJiAnd393eeq0mTJnnK8mvDuVasWIHx48dj7969OHr0KE6ePIkzZ85g6dKl2L59+3vPV5Dx48dj5cqV2LBhAzw9PXHmzBlcunQJnTp1QsOGDYt1TKKyTNv667xHJyq8tLQ0DBo0CE+fPsWcOXMwePBg2NnZwcjICDKZDEOHDsX27dshCEKe977rM7qo2D8vPibRK4jY2Fhcu3YNwOuVe2/evJlvPUEQsG3bNsyYMQPVqlWDi4sL9u/fj6ioKHTu3Bk7duyAnp5enkdCateuDQDo2LEjvv/+e7Vcg1wux+DBg7Fx40YcPnwY58+fR05ODj777DNO7UCUj2nTpuHo0aM4cuQI5s+frzJCpDj69u2LgwcPYvv27Zg0adI76+Y+dt6nT58SnXPJkiX4448/0KVLF9y8eRPLly9H37590aVLlxIdl0hbdevWDQCQk5ODlJQUcVRKfo+Fvq1atWrQ19fH48eP8eLFi3xHo+eOXKlVq1ah4sntH1SvXj1P4oCoIujbty/WrFmDn3/+GcuWLXtnoir3s7NXr17Q0fnvgeFhw4Zh9+7d2LZtG1xcXMRBLW8n5atXrw5DQ0Po6OioZeqzhg0b4ssvv8SXX36JjIwMrF69GtOmTYO3t3exk+gNGjRA165d8fvvv+PatWviCNZx48aVZuhEZVZ576/zHp2o8P744w88efIEn3zyCebPn59nf3Ge7qpZsyYAqEyp9Kb8ytk/Lz5O51JB5E7TMnXqVAivF5TN84qKilKpC6jOw/j7778jKSkJLi4u4mNiubp27QpdXV3s378f2dnZaruOCRMmAADWrVuHkJAQ6OrqYvTo0Wo7H1F5t3jxYgCvn0S5e/duiY7l6emJatWq4cyZM+KTLfmJjo7Gzz//DAMDA/j4+BT7fLGxsZg7dy7Mzc2xbds2hIWFQRAEjBw5EikpKcU+LlF5lt/IlDflfkmur6+P6tWro0ePHgCA7du3Iy0t7Z3v1dPTQ/v27QEAO3bsyLP/ypUruHjxIkxMTNCiRYtCxVu7dm04ODjg6tWruH79eqHeQ6RN+vTpAwcHBzx48ED8TM5PXFwcVq9eDZlMBj8/P5V9/fv3h1wux549e/DixQts374durq6eQa1VKpUCV26dEFqaioiIyPVcj25DA0NMXXqVNSsWRP//vuvOC1EceT271esWIEdO3agatWqxU7KE5VH5bm/DvAenaiwcqdsy01iv+nmzZt51iMsjA4dOgAADh8+jBcvXuTZ/9NPP+UpK27/PHedklevXhU5Tm3BJHoFkJOTg+3btwN4vbBYQT766CPUqlUL165dExcPcHV1RZUqVfDLL7+I34rnt9hYrVq1MGbMGNy5cwdDhgxBcnJynjo3b97EL7/8UqJradOmDT788EPs3bsX8fHx6NevH+dHJnqHli1bws3NDa9evcLSpUtLdCwTExP88MMPkMlk+OyzzxASEpInobd//364ublBEAQsXbq02I9/paenY/jw4cjOzsb69ethbW2N7t27Y9KkSbh37x68vb1LdC1E5dXs2bMxbdq0fEeWP3jwAOPHjwfwepFPfX19tG3bFl27dsWjR48wbty4PJ3rO3fu4PLly+L2xIkTAQDz5s1TGQ3z/Plz+Pr6QhAEjB8/vkiPlM6ePRtKpRLu7u75Lkj65MkTcfQpkbbR0dHB5s2boa+vj7lz52LRokV5bj5PnTqFnj17Ij09HZMmTRK/zMplYGCATz75BM+fP8fUqVNx//599OjRA5aWlnnON3PmTOjo6GD06NHiAJk3paWlYePGjUhPTy/0NezZs0dcdPhNsbGxSE5OhomJCczMzAp9vLe5ubnBysoKP/zwA9LS0jBixIhSfWydqKwrr/31XLxHJyqcBg0aAAB27dqlsm5BSkoKvLy8ijUgtX79+ujevTuePXuGr776SpyqBQAiIiLyHRgDFK9/ntuu4+Liihyn1hBI6x08eFAAIDRo0OC9df38/AQAwhdffCGWjRw5UgAgABCqVKkivHz5Mt/3vnz5UujZs6cAQDA2NhacnZ2FIUOGCAMGDBDq1asnABBcXV1LfD3r168X49m/f3+Jj0dU3uW2h4JcuHBBkMlkgqGhoZCYmCiWe3p6CgCE0NDQIp1vy5YtgpGRkQBAqF27tjBw4EDBw8NDaNCggQBA0NXVFRYvXlzcyxEEQRC8vb0FAMLo0aNVyjMyMoRmzZoJAIQtW7aU6BxE5dEXX3whtvkGDRoIbm5uwuDBg4WOHTsKenp6AgChXr16wv3798X33L9/X2jYsKEAQDA3NxcGDBggeHh4CB9++KGgo6MjrFy5UuUc48aNEwAIRkZGQr9+/QQPDw+hRo0aAgChffv2wosXL1Tq5/4tOXbsWIFxz5gxQwAg6OjoCB9++KHg4eEhfPLJJ0LLli0FXV1dQS6Xl+JviajsiYiIEKpWrSoAEKpXry4MGDBA+PTTT4XmzZuLbXrixIlCTk5Ovu///fffxXrv+wwMDg4WdHV1BQBC06ZNhYEDBwqffvqp0K5dO8HAwEAAIDx79kysP3fu3Hf2B3L/7tSqVUvo37+/MHToUKFLly7iOZYvX16SX40gCP/9jQAgXLlypcTHIyprtLG//ibeoxOpym27c+fOVSnPzZmZmZkJbm5ugpubm2BmZibUq1dPcHV1zbdPDUCws7Mr8FzXr18X++r169cXhgwZInTu3FnQ0dERfH19BQBCz54987yvqP3zc+fOiX+nXF1dBS8vL8HLy0t4/PhxMX9L5Q+T6BXAkCFD8m28+Tl79qwAQLCwsBCys7MFQRCEw4cPix+II0eOfOf7X716JWzatEno1q2bYG5uLujp6QnW1taCk5OTMH/+fCEuLq7E13Pz5k2xM/Dq1asSH4+ovHtfp1wQBGHgwIECAGHatGliWXE75YIgCAkJCcKUKVOExo0bC8bGxoKRkZHwwQcfCOPHjy/xze+BAwcEAELdunWF58+f59l/6dIlwcDAQDA1NRXi4+NLdC6i8ubff/8VtmzZIgwfPlxo1qyZUK1aNaFSpUqCubm54OzsLCxdulRIS0vL877U1FQhICBAcHR0FIyMjAQTExPBwcFB8PX1FW7cuJGn/ubNm4UOHToIJiYmgqGhodCkSRNh4cKF+X6RXpgkuiAIQnR0tODh4SFYW1sLenp6QrVq1QRHR0fB19dXiI6OLvbvhKi8ePz4sTBnzhyhZcuWgqmpqWBgYCDY2toKI0aMEE6dOvXO9+bk5Ai1a9cWAAiVK1fO9/PxTX/99Zfg6ekp2NnZCfr6+oKZmZnQpEkTYcyYMcL+/fsFpVIp1n1fEv2vv/4SpkyZIrRp00awsLAQDAwMBDs7O+Hjjz8Wjh49WuTfQ36OHj0qABCcnJxK5XhEZY229dffxnt0IlUFJdFfvnwpzJw5U6hfv75gYGAg2NjYCBMmTBAeP35cYJ/6fUl0QRCE27dvC0OGDBGqVasmGBkZCS1bthS2bNkinDhxQgAgDB48ON/3FbV/vm3bNuHDDz8Uv6QDUKHuyWWC8J7JNYnKmMDAQMyYMQNz587FvHnzpA6HiIiIiIhKYPz48Vi/fj1CQ0MxatQoqcMhoiLiPTpR2bR48WL4+/tj8eLF+Oqrr6QOp9xjEp3KldTUVDg4OODJkyeIj4/nXGtEREREROXY3bt30ahRIxgbGyMhIQFGRkZSh0RERcB7dCJpZWRk4Pbt22jcuLFK+bFjx+Dq6or09HTcvHmzxOsfEFBJ6gCICiM0NBTR0dE4fvw4EhMTMWnSJH44ExERERGVU8uWLcOlS5cQERGB9PR0BAYGMoFOVI7wHp2obEhJSUGTJk3QsGFD1K9fH4aGhrhx4wYuXrwIAPjmm2+YQC8lHIlOkijsY5rVq1fHN998g1GjRmHTpk2oUaMGBg0ahOXLl8PAwEC9QRJRqSlqmyciIqLy4/Hjx5g6dWqh6jo4OGD69Ono0qULoqOjYW1tjbFjx2Lu3LmQyWRqjpSICsJ7dKLyKT09HXPmzEFERATu3buH1NRUmJmZoU2bNpg4cSL69OkjdYhag0l0kkRhO8h2dna4c+eOeoMhIrVjmyciItJed+7cgb29faHqdu7cGVFRUeoNiIiKjP11IqJ3YxKdiIiIiIiIiIiIiKgAOlIHQERERERERERERERUVjGJTkRERERERERERERUACbRiYiIiIiIiIiIiIgKwCQ6EREREREREREREVEBmEQnIiIiIiIiIiIiIioAk+hERERERERERERERAVgEp2IiIiIiIiIiIiIqABMohMRERERERERERERFYBJdCIiIiIiIiIiIiKiAjCJTkRERERERERERERUACbRiYiIiIiIiIiIiIgKwCQ6EREREREREREREVEBmEQnIiIiIiIiIiIiIioAk+hERERERERERERERAVgEp2IiIiIiIiIiIiIqABMohMRERERERERERERFYBJdCIiIiIiIiIiIiKiAjCJTkRERERERERERERUACbRiYiIiIiIiIiIiIgKwCQ6EREREREREREREVEBmEQnIiIiIiIiIiIiIioAk+hERERERERERERERAVgEp2IiIiIiIiIiIiIqABMohMRERERERERERERFYBJdCIiIiIiIiIiIiKiAjCJTkRERERERERERERUgEpSB1AWKZVKPHz4EFWqVIFMJpM6HKrABEHA8+fPYW1tDR0dfudVWtjGqaxgG1cftnMqK9jO1YNtnMoKtnH1YTunsoLtXD3YxqmsKGwbZxI9Hw8fPoSNjY3UYRCJ7t27h9q1a0sdhtZgG6eyhm289LGdU1nDdl662MaprGEbL31s51TWsJ2XLrZxKmve18aZRM9HlSpVALz+5ZmamkocDVVkqampsLGxEf+fpNLBNk5lBdu4+rCdU1nBdq4ebONUVrCNqw/bOZUVbOfqwTZecrt27cIPP/wgbn/22WcYOHCghBGVT4Vt40yi5yP3MRJTU1M2ZCoT+GhT6WIbp7KGbbz0sZ1TWcN2XrrYxqmsYRsvfWznVNawnZcutvGS6dKlCwCgUqX/UrthYWEICwtDVFSUNEGVc+9r42V6MqfAwEC0adMGVapUgYWFBdzc3BAXF/fe9+3cuRMODg4wNDREs2bNcPDgQQ1ES0RSWbx4MWQyGSZNmiR1KERERERERERaJb977oyMDPj4+KBatWowMTGBu7s7kpOTpQuyAslNoOeqXr36O/dT6SjTSfTo6Gj4+Pjg9OnTiIiIQHZ2Nnr16oUXL14U+J5Tp05hyJAh8PLywl9//QU3Nze4ubnhypUrGoyciDTl7NmzWLduHRwdHaUOhYiIiIiIiEirFHTPPXnyZOzbtw87d+5EdHQ0Hj58yKlENOCnn34Sf54+fTqioqLw888/IyoqCtOnT8+3HpWOMj2dy6FDh1S2w8LCYGFhgdjYWHTq1Cnf93z77bfo3bs3pk2bBgD4+uuvERERgdWrV2Pt2rX5viczMxOZmZnidmpqaoljT05OhkKhKPFxiiszMxNJSUmSnb8ssbKygoGBgWTnl8vlsLS0lOz82iwtLQ3Dhg3Dhg0bsGDBggLrqaONA9K2c7bx/7CNk7bKyMhAQkKC1GGUCba2tjA0NJQ6DKJSxTb+H7Zx0lZs5/9hOy+fCrrnVigUCAkJQXh4OLp16wYACA0NRaNGjXD69Gm0b98+z7HUdV9e0axZs0b8uXfv3ir7evfujcWLF4v1Bg0apNHYtF2ZTqK/LTdZZW5uXmCdmJgY+Pn5qZS5uLhgz549Bb4nMDAQ8+fPL5UYgdeJteEjRiI7K/P9lUnr6ekbYOuWzUyyqYGPjw/69euHHj16vDOJXtptHGA7p/+wjZO6JCQkYNy4cVKHUSasX78eDRo0kDoMolLFNv4ftnHSVmzn/2E7L58KuueOjY1FdnY2evToIZY5ODjA1tYWMTEx+SbR1XFfXpG9PYVLrqpVq+LZs2cajqZiKDdJdKVSiUmTJsHZ2RlNmzYtsF5SUlKeRIalpeU7R2z6+/urJN5zV2UtLoVCgeysTKTX7QylobzYxykR5SvoZKZJc+4yRmlgAuhI87+6ToYCuB0NhULBBFsp27FjB86fP4+zZ8++t25pt3GgDLRztnER2zhpK1tbW6xfv16y89+9excLFy7EzJkzYWdnJ1kcwOvfBZG2kbqNA2WnnbONk7aSup2XlTYOsJ2XR++6505KSoK+vj7MzMxUyt+Vf1PHfXlF9vjx43zLmUBXn3KTRPfx8cGVK1dw4sSJUj+2gYGBWqYCUBrKoTTO/5shTVBWkezURGp17949fPHFF4iIiCjUI4HqauOAtO2cbZxIuxkaGpaJEVt2dnZlIg4ibVNW2jjAdk6kLmWlnbONU1EV9Z67MNR5X16RfP755+KULocOHVKZ0uXNabE///xzjcem7cpFEt3X1xf79+/H8ePHUbt27XfWtbKyyrMacHJyMqysrNQZIhFpUGxsLB49eoQPP/xQLMvJycHx48exevVqZGZmQldXV8IIiYiIiIiIiMqn991zHz58GFlZWUhJSVEZjc78m/oNGjRITKIvXrwYixcvzncKF86HXvp0pA7gXQRBgK+vL3bv3o3ff/8d9vb2732Pk5MTIiMjVcoiIiLg5OSkrjCJSMO6d++Oy5cv48KFC+KrdevWGDZsGC5cuMAEOhEREREREVExve+eu3Xr1tDT01PJv8XFxSEhIYH5Nw2IiopS2X47gf72fiodZXokuo+PD8LDw7F3715UqVJFnFdJLpfDyMgIADBy5EjUqlULgYGBAIAvvvgCnTt3xvLly9GvXz/s2LED586dk3y+QSIqPVWqVMmzNoKxsTGqVav2zjUTiIiIiIiIiOjdCnPP7eXlBT8/P5ibm8PU1BQTJ06Ek5NTvouKUumLiorCTz/9JI5KB15P4cIR6OpTppPowcHBAIAuXbqolIeGhmLUqFEAXq92raPz34D6Dh06IDw8HLNmzcKMGTNQv3597Nmzh4k1IiIiIiIiIiKiUrBy5Uro6OjA3d0dmZmZcHFxUUnokvoNGjSISXMNKtNJdEEQ3lsnv0cUPDw84OHhoYaIiKis4uNKREREREREROrx9j23oaEhgoKCEBQUJE1ARBpWpudEJyIiIiIiIiIiIiKSEpPoREREREREREREREQFYBKdiIiIiIiIiIiIiKgAZXpOdCIiItI+x48fx7JlyxAbG4vExETs3r0bbm5u4v5Ro0Zh06ZNKu9xcXHBoUOHNBwpEWm75ORkKBQKqcOQzN27d1X+W1HJ5XJYWlpKHQYRERGVYUyiExERkUa9ePECzZs3x5gxYzBw4MB86/Tu3RuhoaHitoGBgabCI6IKIjk5GcNHjER2VqbUoUhu4cKFUocgKT19A2zdspmJdCIiIioQk+hERESkUX369EGfPn3eWcfAwABWVlYaioiIKiKFQoHsrEyk1+0MpaFc6nBIIjoZCuB2NBQKBZPoREREVCAm0YmIiKjMiYqKgoWFBapWrYpu3bphwYIFqFatWoH1MzMzkZn532jS1NRUTYRJRFpAaSiH0ri61GEQERERURnGhUWJiIioTOnduzc2b96MyMhILFmyBNHR0ejTpw9ycnIKfE9gYCDkcrn4srGx0WDEREREREREpM04Ep2IiIjKlMGDB4s/N2vWDI6Ojvjggw8QFRWF7t275/sef39/+Pn5idupqalMpBMREREREVGp4Eh0IiIiKtPq1q2L6tWr4+bNmwXWMTAwgKmpqcqLiIiISq5OnTqQyWR5Xj4+PvnWDwsLy1PX0NBQw1ETERGVLibRiYiIqEy7f/8+njx5gpo1a0odChEBePDgAYYPH45q1arByMgIzZo1w7lz58T9giBgzpw5qFmzJoyMjNCjRw/cuHFD5RhPnz7FsGHDYGpqCjMzM3h5eSEtLU3Tl0JEhXD27FkkJiaKr4iICACAh4dHge8xNTVVec/du3c1FS4REZFaMIlOREREhRIaGoqXL1+W+DhpaWm4cOECLly4AACIj4/HhQsXkJCQgLS0NEybNg2nT5/GnTt3EBkZCVdXV9SrVw8uLi4lPjcRlcyzZ8/g7OwMPT09/Pbbb7h69SqWL1+OqlWrinWWLl2K7777DmvXrsWff/4JY2NjuLi4ICMjQ6wzbNgw/P3334iIiMD+/ftx/PhxjBs3TopLIqL3qFGjBqysrMTX/v378cEHH6Bz584Fvkcmk6m8x9LSUoMRExERlT4m0YmIiKhQpk+fDisrK3h5eeHUqVPFPs65c+fQsmVLtGzZEgDg5+eHli1bYs6cOdDV1cWlS5cwYMAANGjQAF5eXmjVqhX++OMPGBgYlNalEFExLVmyBDY2NggNDUXbtm1hb2+PXr164YMPPgDwehT6qlWrMGvWLLi6usLR0RGbN2/Gw4cPsWfPHgDAtWvXcOjQIfzwww9o164dOnbsiO+//x47duzAw4cP8z1vZmYmUlNTVV5EpHlZWVnYunUrxowZA5lMVmC9tLQ02NnZwcbGBq6urvj777/fe2y2cyIiKsuYRCciIqJCefDgATZt2oTHjx+jS5cucHBwwJIlS5CUlFSk43Tp0gWCIOR5hYWFwcjICIcPH8ajR4+QlZWFO3fuYP369RzBRlRG/Prrr2jdujU8PDxgYWGBli1bYsOGDeL++Ph4JCUloUePHmKZXC5Hu3btEBMTAwCIiYmBmZkZWrduLdbp0aMHdHR08Oeff+Z73sDAQMjlcvHFhYOJpLFnzx6kpKRg1KhRBdZp2LAhNm7ciL1792Lr1q1QKpXo0KED7t+//85js50TEVFZxiQ6ERERFUqlSpXwf//3f9i7dy/u3buHsWPHYtu2bbC1tcWAAQOwd+9eKJVKqcMkIjW6ffs2goODUb9+fRw+fBje3t743//+h02bNgGA+KXa2198WVpaivuSkpJgYWGhsr9SpUowNzcv8Es5f39/KBQK8XXv3r3SvjQiKoSQkBD06dMH1tbWBdZxcnLCyJEj0aJFC3Tu3Bm7du1CjRo1sG7duncem+2ciIjKskpSB0BERETlj6WlJTp27Ijr16/j+vXruHz5Mjw9PVG1alWEhoaiS5cuUodIRGqgVCrRunVrLFq0CADQsmVLXLlyBWvXroWnp6fazmtgYMApnYgkdvfuXRw9ehS7du0q0vv09PTQsmVL3Lx585312M6JiKgs40h0IiIiKrTk5GR88803aNKkCbp06YLU1FTs378f8fHxePDgAQYNGqTWRBoRSatmzZpo3LixSlmjRo2QkJAAALCysgLw+m/Fm5KTk8V9VlZWePTokcr+V69e4enTp2IdIip7QkNDYWFhgX79+hXpfTk5Obh8+TJq1qyppsiIiIjUj0l0IiIiKpSPP/4YNjY2CAsLw9ixY/HgwQNs375dnPvY2NgYU6ZM4ePXRFrM2dkZcXFxKmXXr1+HnZ0dAMDe3h5WVlaIjIwU96empuLPP/+Ek5MTgNdTPaSkpCA2Nlas8/vvv0OpVKJdu3YauAoiKiqlUonQ0FB4enqiUiXVB9pHjhwJf39/cTsgIABHjhzB7du3cf78eQwfPhx3797FZ599pumwiYiISg2T6ERERFQoFhYWiI6OxpUrVzBp0iSYm5vnqVOjRg3Ex8dLEB0Rvc+8efPyXbdAoVBgyJAhhTrG5MmTcfr0aSxatAg3b95EeHg41q9fDx8fHwCATCbDpEmTsGDBAvz666+4fPkyRo4cCWtra7i5uQF4PXK9d+/eGDt2LM6cOYOTJ0/C19cXgwcPfuc8y0QknaNHjyIhIQFjxozJsy8hIQGJiYni9rNnzzB27Fg0atQIffv2RWpqKk6dOpXnKRYiUg9BEPD48WM8efJE6lCItAqT6ERERFQoISEh4kjSgshkMvTv35+j0YnKoJCQEHTs2BG3b98Wy6KiotCsWTPcunWrUMdo06YNdu/eje3bt6Np06b4+uuvsWrVKgwbNkys8+WXX2LixIkYN24c2rRpg7S0NBw6dAiGhoZinW3btsHBwQHdu3dH37590bFjR6xfv770LpaISlWvXr0gCAIaNGiQZ19UVBTCwsLE7ZUrV+Lu3bvIzMxEUlISDhw4gJYtW2owWqKKKSkpCSNHjkTVqlVhaWkJCwsLVK1aFWPGjMkzzRoRFR0XFiUiIqJSdefOHWRnZ0sdBhG95dKlSxg/fjxatGiB5cuX4/r16/j2228xbdo0zJ8/v9DH6d+/P/r371/gfplMhoCAAAQEBBRYx9zcHOHh4UWKn4iIiPKXmpqKDh06IC0tDaNHj4aDgwMEQcDVq1exfft2nDhxAufPn4eJiYnUoRKVW0yiExERERFVAFWrVsVPP/2EGTNmYPz48ahUqRJ+++03dO/eXerQiIiIqAS+/fZb6Orq4u+//0aNGjVU9s2aNQvOzs747rvvMGPGDIkiJCr/OJ0LEREREVEF8f333+Pbb7/FkCFDULduXfzvf//DxYsXpQ6LiIiISuDAgQOYMWNGngQ68HpdI39/f+zbt0+CyIi0B5PoREREREQVQO/evTF//nxs2rQJ27Ztw19//YVOnTqhffv2WLp0qdThERERUTFdv34dHTp0KHB/hw4dEBcXp8GIiLQPk+hERERERBVATk4OLl26hE8++QQAYGRkhODgYPz8889YuXKlxNERERFRcaWmpsLMzKzA/WZmZkhNTdVcQERaiEl0IiIiIqIKICIiAtbW1nnK+/Xrh8uXL4vb27dvx4sXLzQZGhEREZWAIAjQ0Sk4xSeTySAIggYjItI+XFiUiIiIStW6detgaWkpdRhEVATVq1cXfx4/fjzatWuHunXrShgRERERFZYgCGjQoAFkMlmB+4moZJhEJyIiokKLjIxEZGQkHj16BKVSqbJv48aNAIChQ4dKERoRlRLeaBMREZUvoaGhUodApPWYRCcijfr333/zXTEcAC5fvoxmzZppOCIiKqz58+cjICAArVu3Rs2aNQsc6UJERERERJrj6ekpdQhEWo9zohORRjVr1gwHDhzIU/7NN9+gbdu2hTpGcHAwHB0dYWpqClNTUzg5OeG3334r7VCJ6C1r165FWFgY/vzzT+zZswe7d+9WeREREVHZ4enpiePHj0sdBhERkVbgSHQi0ig/Pz+4u7tj9OjRWLFiBZ4+fYqRI0fi8uXLCA8PL9QxateujcWLF6N+/foQBAGbNm2Cq6sr/vrrLzRp0kTNV0BUcWVlZaFDhw5Sh0FERESFoFAo0KNHD9jZ2WH06NHw9PRErVq1pA6LiNSgatWqhXpK9OnTpxqIhkg7MYlORBr15ZdfomfPnhgxYgQcHR3x9OlTtGvXDpcuXYKVlVWhjvHxxx+rbC9cuBDBwcE4ffo0k+hEavTZZ58hPDwcs2fPljoUIiIieo89e/bg33//xZYtW7Bp0ybMnTsXPXr0gJeXF1xdXaGnpyd1iERUSlatWiV1CERaj0l0ItK4evXqoWnTpvjll18AAJ9++mmhE+hvy8nJwc6dO/HixQs4OTnlWyczMxOZmZnidmpqarHOlR+d9JRSOxaVPxXt3z8jIwPr16/H0aNH4ejomOfme8WKFRJFRkSlyc7Ojsk1Ii1Ro0YN+Pn5wc/PD+fPn0doaChGjBgBExMTDB8+HJ9//jnq168vdZhqk5ycDIVCIXUYkrl7967KfysquVwOS0tLqcNQq6LOib59+3YMGDAAxsbGaoqISPswiU5EGnXy5EkMHz4c5ubmuHTpEk6ePImJEyfi4MGDWLt2LapWrVqo41y+fBlOTk7IyMiAiYkJdu/ejcaNG+dbNzAwEPPnzy/NyxAZxXOeSao4Ll26hBYtWgAArly5orKPi4wSlX2CICA2NhZ37tyBTCaDvb09WrZsmaf9vt2+iaj8S0xMREREBCIiIqCrq4u+ffvi8uXLaNy4MZYuXYrJkydLHWKpS05OxvARI5Gdlfn+ylpu4cKFUocgKT19A2zdslnrE+lFMX78eLRr1w5169aVOhSicoNJdCLSqG7dumHy5Mn4+uuvoaenh0aNGqFr164YPnw4mjVrhvv37xfqOA0bNsSFCxegUCjw888/w9PTE9HR0fkm0v39/eHn5ydup6amwsbGplSuJ92+E5RGZqVyLCp/dNJTKtQXKceOHZM6BI3i6DWOXgO0Z/TasWPH4OXlhbt370IQBAAQE+kbN25Ep06dJI5QOhXtqSJSpc3//tnZ2fj1118RGhqKI0eOwNHREZMmTcLQoUNhamoKANi9ezfGjBmjlUl0hUKB7KxMpNftDKWhXOpwSCI6GQrgdjQUCoVWfJ6Xlty+AJVvUVFRmDdvnrg9b948dOnSRbJ4tB2T6ESkUUeOHEHnzp1Vyj744AOcPHmySCMk9PX1Ua9ePQBAq1atcPbsWXz77bdYt25dnroGBgYwMDAoWeAFUBqZQWlcXS3HJirLcr/wql27tsSRqAdHr/2Ho9fK/+i1mzdvon///mjXrh1WrlwJBwcHCIKAq1ev4rvvvkPfvn1x6dKlCjsarSJ9GUoVS82aNaFUKjFkyBCcOXNGfJrsTV27doWZmZnGY9MkpaGc/XUi0jr5JctzE+pRUVEajaWiYBKdiDTq7QR6Lh0dHZXFCps1a4aDBw8WesS4UqlUmfeciEqfUqnEggULsHz5cqSlpQEAqlSpgilTpmDmzJnQ0dGROMLSw9FrBGjP6LVVq1ahffv2iIyMVCl3cHDA//3f/6FHjx5YuXIlvv/+e4kilBafKqvYtPmpspUrV8LDwwOGhoYF1jEzM0N8fLwGoyIiopJ6O4HeuHFjXL16VWU/E+mlj0l0IiqT7ty5g+zs7Hz3+fv7o0+fPrC1tcXz588RHh6OqKgoHD58WMNRElUsM2fOREhICBYvXgxnZ2cAwIkTJzBv3jxkZGRo5Yhljl4jbRAVFYXAwMB898lkMkyaNAn+/v4ajqrs4FNlpI2ys7MxevRotGzZEk2bNpU6HCIiKiVvJscXL16M9u3bi9unT5/G9OnTxXqc2qV0MYlOROXOo0ePMHLkSCQmJkIul8PR0RGHDx9Gz549pQ6NSKtt2rQJP/zwAwYMGCCWOTo6olatWvj888+1MolOpA0SEhLQrFmzAvc3bdq0ws99T6Rt9PT0YGtri5ycHKlDISKiUvTmHOhvJtDf3p43bx5Ho5cy7XnumogqjJCQENy5cweZmZl49OgRjh49ygQ6kQY8ffoUDg4OecodHBzw9OnTQh/n+PHj+Pjjj2FtbQ2ZTIY9e/ao7BcEAXPmzEHNmjVhZGSEHj164MaNGyUNn6jCSktLQ+XKlQvcX7lyZbx8+VKDERGRJsycORMzZswo0mc0EVUMdnZ20NPTkzoMKoHGjRvnW16/fn0NR1JxcCQ6ERERFUrz5s2xevVqfPfddyrlq1evRvPmzQt9nBcvXqB58+YYM2YMBg4cmGf/0qVL8d1332HTpk2wt7fH7Nmz4eLigqtXr75zXlciKtjVq1eRlJSU777Hjx9rOBoi0oTVq1fj5s2bsLa2hp2dHYyNjVX2nz9/XqLIiEhdPD094eXlhU6dOr2z3pUrVzQUEanLm3Ogv4mDj9SHSXQiIiIqlKVLl6Jfv344evQonJycAAAxMTG4d+8eDh48WOjj9OnTB3369Ml3nyAIWLVqFWbNmgVXV1cAwObNm2FpaYk9e/Zg8ODBJb8Qogqoe/fuEAQhT7lMJoMgCJDJZBJERUTq5OrqyrZNVMEoFAr06NEDdnZ2GD16NDw9PVGrVi2pw6JSNG/ePHFKl9OnT+eZE/3NelS6mEQnIiKiQuncuTOuX7+OoKAg/PPPPwCAgQMH4vPPP4e1tXWpnCM+Ph5JSUno0aOHWCaXy9GuXTvExMQUmETPzMxEZmamuJ2amloq8RBpg/j4eKlDICIJMIFCVPHs2bMH//77L7Zs2YJNmzZh7ty56NGjB7y8vODq6sopXLTAm4uF5i4iWr9+/Twj0LmoaOnjnOhEpFGbN29WSXTlysrKwubNm8XtdevWwdLSUpOhEVEhWFtbY+HChfjll1/wyy+/YMGCBaWWQAcgTjfxdvu3tLQscCoKAAgMDIRcLhdfNjY2pRYTUXlnZ2dXqBcRaZe6deviyZMnecpTUlJQt27dQh9n3rx5kMlkKq/81kh5086dO+Hg4ABDQ0M0a9asSE+sEVHJ1KhRA35+frh48SL+/PNP1KtXDyNGjIC1tTUmT57M6T60wNsLhr79b8oFRdVDbUn0TZs24cCBA+L2l19+CTMzM3To0AF3795V12mJqIwbPXo0FApFnvLnz59j9OjR4vbQoUPzzNtIRJp36dIlKJVK8ed3vaTk7+8PhUIhvu7duydpPERlyfvabkna8OLFiyGTyTBp0iSxLCMjAz4+PqhWrRpMTEzg7u6O5ORklfclJCSgX79+qFy5MiwsLDBt2jS8evWqJJdJRG+5c+cOcnJy8pRnZmbi/v37RTpWkyZNkJiYKL5OnDhRYN1Tp05hyJAh8PLywl9//QU3Nze4ublxDmYiDUtMTERERAQiIiKgq6uLvn374vLly2jcuDFWrlwpdXhUQlFRUXmeOJo3bx4T6GqktulcFi1ahODgYACv50sNCgrCypUrsX//fkyePBm7du1S16mJqAwraN7V+/fvQy6XSxAREb1LixYtkJSUBAsLC7Ro0UKcP/ltMpks3xv1orKysgIAJCcno2bNmmJ5cnIyWrRoUeD7DAwMYGBgUOLzE2mjd7XdXMVpw2fPnsW6devg6OioUj558mQcOHAAO3fuhFwuh6+vLwYOHIiTJ08CAHJyctCvXz9YWVnh1KlTSExMxMiRI6Gnp4dFixYV/QKJSMWvv/4q/nz48GGVPnZOTg4iIyNhb29fpGNWqlRJ/Ix+n2+//Ra9e/fGtGnTAABff/01IiIisHr1aqxdu7ZI5yWiosnOzsavv/6K0NBQHDlyBI6Ojpg0aRKGDh0KU1NTAMDu3bsxZswYTJ48WeJoqaS6dOnCpLkGqS2Jfu/ePdSrVw/A6zmZ3N3dMW7cODg7O3NeHqIKqGXLluLjn927d0elSv/9+cnJyUF8fDx69+4tYYRElJ/4+HjUqFFD/Fnd7O3tYWVlhcjISDFpnpqaij///BPe3t5qPz+RNlJH201LS8OwYcOwYcMGLFiwQCxXKBQICQlBeHg4unXrBgAIDQ1Fo0aNxMWvjhw5gqtXr+Lo0aOwtLREixYt8PXXX+Orr77CvHnzoK+vn+d8XPeAqPDc3NwAvP5yzNPTU2Wfnp4e6tSpg+XLlxfpmDdu3IC1tTUMDQ3h5OSEwMBA2Nra5ls3JiYGfn5+KmUuLi7Ys2fPO8+hrnauk55SKseh8qmi/fvXrFkTSqUSQ4YMwZkzZ/IdhNK1a1eYmZlpPDai8k5tSXQTExM8efIEtra2OHLkiPghamhoiPT0dHWdlojKqNzO/IULF+Di4gITExNxn76+PurUqQN3d3eJoiOigrw5T/Ldu3fRoUMHlS/BAODVq1c4depUoedUTktLw82bN8Xt+Ph4XLhwAebm5rC1tcWkSZOwYMEC1K9fH/b29pg9ezasra3FvyNEVDRFne/8888/R0BAAKpXr15gHR8fH/Tr1w89evRQSaLHxsYiOztbZXFgBwcH2NraIiYmBu3bt0dMTAyaNWumsvaBi4sLvL298ffff6Nly5Z5zhcYGIj58+cX6TqIKqrcadjs7e1x9uzZd7blwmjXrh3CwsLQsGFDJCYmYv78+fjoo49w5coVVKlSJU/9pKSkIq9tAqivnRvFHy/1YxKVVStXroSHhwcMDQ0LrGNmZsZFx4mKQW1J9J49e+Kzzz5Dy5Ytcf36dfTt2xcA8Pfff6NOnTrqOi0RlVFz584FANSpUweffvrpOz/Uiahs6tq1KxITE2FhYaFSrlAo0LVr10JPBXHu3Dl07dpV3M79ot3T0xNhYWH48ssv8eLFC4wbNw4pKSno2LEjDh06xL8bRBqydetWTJ06tcDE244dO3D+/HmcPXs2z76kpCTo6+vnGeH2ZgKtoARb7r78+Pv7q4xsTU1N5QLCRO9RWkmyPn36iD87OjqiXbt2sLOzw08//QQvL69SOQegvnaebt8JSiOzEh+Hyied9JQK80VKdnY2Ro8ejZYtW6Jp06ZSh0OkddSWRA8KCsKsWbNw7949/PLLL6hWrRqA16NThgwZoq7TElEZ9/YjpURUfhS0psGTJ0+KtBBwly5d3js3c0BAAAICAooVZ2mqaI8Ak6qK+u//rvZ57949fPHFF4iIiNDoF1tc94CoeCIjIxEZGYlHjx6JI9Rzbdy4sVjHNDMzQ4MGDVSeKnuTlZVVnsWEk5OT3zunurraudLIDErjko3GJyoP9PT0YGtrWyrrFBFRXmpLopuZmWH16tV5yov6eNbx48exbNkyxMbGIjExEbt3737n49xRUVEqo9tyJSYmFnohFCIqXVWrVs038Zafp0+fqjkaIiqqgQMHAnid3B41apTKDW5OTg4uXbqEDh06SBWeWlWUkUtEhRUbG4tHjx7hww8/FMtycnJw/PhxrF69GocPH0ZWVhZSUlJURqO/mUCzsrLCmTNnVI6bm3Bjf52o9MyfPx8BAQFo3bo1atasWej++PukpaXh1q1bGDFiRL77nZycEBkZiUmTJollERERcHJyKpXzE1HBZs6ciRkzZmDLli0wNzeXOhwiraK2JDoApKSk4MyZM3m+9ZbJZAV+4L7txYsXaN68OcaMGSPexBdGXFycuPIwgDyPnhOR5qxatUrqEIioBORyOYDXo1OrVKkCIyMjcZ++vj7at2+PsWPHShWeWvER8IqtIj0CXljdu3fH5cuXVcpGjx4NBwcHfPXVV7CxsYGenh4iIyPFtU7i4uKQkJAgJtCcnJywcOFCPHr0SOyjR0REwNTUFI0bN9bsBRFpsbVr1yIsLKzQ994FmTp1Kj7++GPY2dnh4cOHmDt3LnR1dcUnzEeOHIlatWohMDAQAPDFF1+gc+fOWL58Ofr164cdO3bg3LlzWL9+fYmviYjebfXq1bh58yasra1hZ2eX52nR8+fPF+o4gYGB2LVrF/755x8YGRmhQ4cOWLJkCRo2bCjWycjIwJQpU7Bjxw5kZmbCxcUFa9asyTNlG5G2UFsSfd++fRg2bBjS0tJgamqq8q13UZLoffr0UZmDrbAsLCwKvdqwulYBJ6LXOIULUfkWGhoK4PWaBlOnTi3S1C3lHR8BJ1JVpUqVPPOsGhsbo1q1amK5l5cX/Pz8YG5uDlNTU0ycOBFOTk5o3749AKBXr15o3LgxRowYgaVLlyIpKQmzZs2Cj48Pp2whKkVZWVml8qTY/fv3MWTIEDx58gQ1atRAx44dcfr0adSoUQMAkJCQAB0dHbF+hw4dEB4ejlmzZmHGjBmoX78+9uzZwzmaiTTA1dW1VJ46iY6Oho+PD9q0aYNXr15hxowZ6NWrF65evSreC0yePBkHDhzAzp07IZfL4evri4EDB+LkyZMlPj9RWaS2JPqUKVMwZswYLFq0CJUrV1bXaQrUokULZGZmomnTppg3bx6cnZ0LrKuuVcCJKK+EhIR37re1tdVQJERUVLkLBBMRvcvKlSuho6MDd3d3lZFpuXR1dbF//354e3vDyckJxsbG8PT0LBPrIBBpk88++wzh4eGYPXt2iY6zY8eOd+6PiorKU+bh4QEPD48SnZeIim7evHmlcpxDhw6pbIeFhcHCwgKxsbHo1KkTFAoFQkJCEB4ejm7dugF4PfCmUaNGOH36tPjFOZE2UVsS/cGDB/jf//6n8QR6zZo1sXbtWrRu3RqZmZn44Ycf0KVLF/z5558qcze+SV2rgBNRXnXq1HnnN+NcBIWobPnwww8RGRmJqlWromXLlu9sv4V9PJSIyrbhw4erTIv4Pm8n0AwNDREUFISgoKAC32NnZ4eDBw8WN0QiKoSMjAysX78eR48ehaOjI/T09FT2r1ixQqLIiEhd6tati7Nnz6JatWoq5SkpKfjwww9x+/btYh1XoVAAgDjPemxsLLKzs9GjRw+xjoODA2xtbRETE5NvEp2zQFB5p7YkuouLC86dO4e6deuq6xT5atiwococTR06dMCtW7ewcuVKbNmyJd/3qGsVcCLK66+//lLZzs7Oxl9//YUVK1Zg4cKFEkVFRAVxdXUVPyPftbA3EZUPBa1ZBLye1xgAgoODpQiNiErZpUuX0KJFCwDAlStXVPaV1iKjRFS23LlzJ9+BaZmZmbh//36xjqlUKjFp0iQ4OzuL0zIlJSVBX18/zzTKlpaWSEpKyvc4nAWCyju1JdH79euHadOm4erVq2jWrFmeb70HDBigrlPn0bZtW5w4cUJj5yOigjVv3jxPWevWrWFtbY1ly5YVaQFhIlK/N6dw4XQuROXb+9Ysyk2iE5F2OHbsmNQhEJGG/Prrr+LPhw8fhlwuF7dzcnIQGRkJe3v7Yh3bx8cHV65cKXFejbNAUHmntiT62LFjASDfuQ1lMplGp2y4cOECatasqbHzEVHRNWzYEGfPnpU6DCJ6h3v37kEmk6F27doAgDNnziA8PByNGzfGuHHjJI6OiN5H6jWLiIiISD1ynxiVyWTw9PRU2aenp4c6depg+fLlRT6ur68v9u/fj+PHj4v3AABgZWWFrKwspKSkqIxGT05OhpWVVb7H4iwQVN6pLYn+9uOhxZWWloabN2+K2/Hx8bhw4QLMzc1ha2sLf39/PHjwAJs3bwYArFq1Cvb29mjSpAkyMjLwww8/4Pfff8eRI0dKJR4iKpm35z0TBAGJiYmYN28e6tevL1FURFQYQ4cOxbhx4zBixAgkJSWhR48eaNq0KbZt24akpCTMmTNH6hCJ6B2kWrOIiKTRtWvXd07b8vvvv2swGiJSp9wcnL29Pc6ePYvq1auX6HiCIGDixInYvXs3oqKi8oxib9WqFfT09BAZGQl3d3cAQFxcHBISEuDk5FSicxOVVWpLopeWc+fOoWvXruJ27qMfnp6eCAsLQ2JiIhISEsT9WVlZmDJlCh48eIDKlSvD0dERR48eVTkGEUnHzMwsT2deEATY2Nhgx44dEkVFRIVx5coVtG3bFgDw008/oVmzZjh58iSOHDmCCRMmMIlOVMZJtWYREUkjdz70XNnZ2bhw4QKuXLmSZ6QqEWmH+Pj4UjmOj48PwsPDsXfvXlSpUkWc51wul8PIyAhyuRxeXl7w8/ODubk5TE1NMXHiRDg5OeW7qCiRNlBrEj0yMhKRkZH5Lly0cePGQh2jS5cuEAShwP1hYWEq219++SW+/PLLIsdKRJrx9tyMOjo6qFGjBurVq4dKlcr893pEFVp2drb4CObRo0fF9U0cHByQmJgoZWhEVIA350gtS2sWEZH6rVy5Mt/yefPmIS0tTcPREJGmlEYuLneR8S5duqiUh4aGYtSoUQBe/43R0dGBu7s7MjMz4eLigjVr1pQ4fqKySm0Zq/nz5yMgIACtW7fG/2PvvsOiuNo2gN+7lAVEFhABCyzYezeIGsUGWGOJCTawJwo2NCpRY0ViN4koGhVsaEwsSYyxK8aIscdYE5WIBbAFsFF3vj/8mNcVVgF3mWW5f9fFFWbmzJlnDWd39plTypUrx9W/iQgA0Lp1a6lDIKJCql27NiIiItC5c2fs378fs2fPBgDcu3cPZcqUkTg6IspLzhyprzKENYuISDr9+/fHe++9h4ULF0odChHpmK5ycW/qzJrDwsIC4eHhCA8PL9Q1iIobvSXRIyIiEBUVhQEDBujrEkRUDK1btw4ODg7o3LkzgJejR1atWoVatWph8+bNUKlUEkdIRNrMmzcPPXr0wIIFCxAQEID69esDeNnTNWeaFyIyLLpap4iIjEdsbCwsLCykDoOI9IC5OCL90VsSPSMjA82bN9dX9URUTM2dO1ccGhYbG4tly5Zh6dKl2LVrF8aNG4ft27dLHCERaePl5YWHDx8iNTUVdnZ24v7hw4dzoUIiIiID07NnT41tQRCQkJCA06dPY9q0aRJFRUT6xFwckf7I9VXx0KFDER0dra/qiaiYun37NqpUqQIA2LlzJz788EMMHz4cYWFh+O233ySOjojexsTEBFlZWTh27BiOHTuGBw8ewM3NDY6OjlKHRkRvMXr0aHz99de59i9btgxjx44t+oCISK+USqXGj729Pby8vLB7925Mnz5d6vCISA+YiyPSH731RE9LS8OqVatw4MAB1KtXL9fCRYsXL9bXpYnIgFlbW+PRo0dwdXXFvn37EBwcDODlfGovXryQODoiepNnz55h1KhRWL9+vThFhImJCfz9/fHNN9+wNzqRgdu2bZvGQqM5mjdvji+//BJLly4t+qCISG8iIyOlDoGIihhzcUT6o7ck+oULF9CgQQMAwMWLF/V1GSIqZjp06IChQ4eiYcOG+Pvvv9GpUycAwKVLl+Dm5iZtcET0RsHBwYiJicHPP/+MFi1aAACOHTuG0aNHY/z48eJUTURkmB49egSlUplrv42NDR4+fChBRERUFM6cOYMrV64AeLlIeMOGDSWOiIj05U25uMIuMkpEL+ktiX748GF9VU1ExVh4eDimTp2K27dvY9u2bShTpgyAlzf3ffr0kTg6InqTbdu24YcffoCXl5e4r1OnTrC0tMRHH33EJDqRgatSpQr27NmDoKAgjf2//vorKlWqJFFU0pOnpUgdAknImP//379/H35+fjhy5AhsbW0BAMnJyWjTpg22bNmCsmXLShsgEekcc3FE+qPzJPrri5fkRSaTYdu2bbq+NBEVA7a2tli2bFmu/TNnzpQgGiIqiOfPn8PJySnXfkdHRzx//lyCiIioIIKDgxEUFIQHDx6gbdu2AICDBw9i0aJFJXIqF6VSCTNzBXAzRupQSGJm5oo8R2kUd6NGjcKTJ09w6dIl1KxZEwBw+fJlBAQEYPTo0di8ebPEERIRERUfOk+iG+PNBxHp1m+//YaVK1fi5s2b+P7771GhQgVs2LAB7u7uaNmypdThEZEWnp6emD59OtavXw8LCwsAwIsXLzBz5kx4enpKHB0Rvc3gwYORnp6O0NBQzJ49GwDg5uaGFStWwN/fX+Loip6TkxM2bliPlBTj7Yn8Nrdu3UJoaCimTJkClUoldTiSUSqVeT4kLu727NmDAwcOiAl0AKhVqxbCw8Ph7e0tYWREpC9t2rR547Qthw4dKsJoiIyLzpPoXLyEiN5k27ZtGDBgAPr164ezZ88iPT0dAJCSkoK5c+di9+7db60jLCwM27dvx9WrV2FpaYnmzZtj3rx5qF69ur7DJyrRvvrqK/j4+KBixYqoX78+AODPP/+EhYUF9u7dK3F0RJQfI0aMwIgRI/DgwQNYWlrC2tpa6pAk5eTkZJTJ04JSqVSoVq2a1GGQjqnV6lyLCgKAmZmZuEA4ERmXnPnQc2RmZuL8+fO4ePEiAgICpAmKyEjobU50IqK8zJkzBxEREfD398eWLVvE/S1atMCcOXPyVUdMTAwCAwPRtGlTZGVl4fPPP4e3tzcuX76MUqVK6St0ohKvTp06+Oeff7Bp0yZcvXoVANCnTx/069cPlpaWEkdHRG/Ttm1bbN++Hba2thpzIaempqJ79+7snUZkZNq2bYsxY8Zg8+bNKF++PADg7t27GDduHNq1aydxdESkD0uWLMlz/4wZM/D06dMijobIuDCJTkRF6tq1a2jVqlWu/UqlEsnJyfmqY8+ePRrbUVFRcHR0xJkzZ/Ksm4h0x8rKCsOGDZM6DCIqhCNHjiAjIyPX/rS0NPz2228SRERE+rRs2TJ069YNbm5ucHFxAQDcvn0bderUwcaNGyWOjoiKUv/+/fHee+9h4cKFUodCVGwxiU5ERcrZ2RnXr1+Hm5ubxv5jx46hUqVKhaozZy5Te3v7PI+np6eL08YAL3vcEVHhXLt2Dd988w2uXLkCAKhZsyaCgoJQo0YNiSMjIm0uXLgg/n758mUkJiaK29nZ2dizZw8qVKggRWhEpEcuLi44e/YsDhw4II4gq1mzJtq3by9xZERU1GJjY8U1jYiocJhEJ6IiNWzYMIwZMwZr166FTCbDvXv3EBsbi/Hjx+OLL74ocH1qtRpjx45FixYtUKdOnTzLhIWFYebMme8aOlGJt23bNvj5+aFJkybiQqInTpxA3bp1sWXLFvTq1UviCIkoLw0aNIBMJoNMJkPbtm1zHbe0tMQ333wjQWREpA+HDh1CUFAQTpw4ARsbG3To0AEdOnQA8LLzSe3atREREYH3339f4kiJSNd69uypsS0IAhISEnD69GlMmzZNoqiIjAOT6ERUpCZPngy1Wo127drh+fPnaNWqFRQKBT777DMMHTq0wPUFBgbi4sWLOHbsmNYyISEhCA4OFrdTU1PFIa1ElH8TJ05ESEgIZs2apbF/+vTpmDhxos6S6DNmzMj14Kt69epiLzoiKpi4uDgIgoBKlSrh5MmTGvOhm5ubw9HRESYmJhJGSES6tHTpUgwbNgw2Nja5jimVSnzyySdYvHgxk+hERkipVGpsy+VyVK9eHbNmzYK3t7dEUREZBybRiahIyWQyTJkyBZ999hmuX7+Op0+folatWli5ciXc3d01hpi/TVBQEHbt2oWjR4+iYsWKWsspFAooFApdhE9UoiUkJMDf3z/X/v79+2PBggU6vVbt2rVx4MABcdvUlLcsRIWlUqkAvBy9RUTG788//8S8efO0Hvf29ua8yERGKjIyUuoQiIwWv5ESUZFIT0/HjBkzsH//frHneffu3REZGYkePXrAxMQE48aNy1ddgiBg1KhR2LFjB44cOQJ3d3c9R09EAODl5YXffvsNVapU0dh/7NgxnfdmMzU1hbOzs07rJKKXLl++jPj4+FyLjHbr1k2iiIhIl5KSkmBmZqb1uKmpKR48eJDv+sLCwrB9+3ZcvXoVlpaWaN68OebNm4fq1atrPScqKgqDBg3S2KdQKJCWlpbv6xJR4Z05c0Zcw6h27dpo2LChxBERFX9MohNRkfjiiy+wcuVKtG/fHsePH0fv3r0xaNAgnDhxAosWLULv3r3zPZQ8MDAQ0dHR+PHHH1G6dGmx97pSqYSlpaU+XwZRidatWzdMmjQJZ86cQbNmzQC8nBP9+++/x8yZM/HTTz9plH0X//zzD8qXLw8LCwt4enoiLCwMrq6uWstzAWGit7t58yZ69OiBv/76CzKZDIIgAHg5Sgx4ucjo2+QnmZaWlobx48djy5YtSE9Ph4+PD5YvXw4nJyexTHx8PEaMGIHDhw/D2toaAQEBCAsL46gTIh2oUKECLl68mOuhd44LFy6gXLly+a4vJiYGgYGBaNq0KbKysvD555/D29sbly9fRqlSpbSeZ2Njg2vXronbOe81RKQ/9+/fh5+fH44cOQJbW1sAQHJyMtq0aYMtW7ZoTOlGRAXDu1QiKhLff/891q9fj27duuHixYuoV68esrKy8Oeffxb4hnrFihUAXvaKfVVkZCQGDhyoo4iJ6HUjR44EACxfvhzLly/P8xjw8ktyfpJx2nh4eCAqKgrVq1dHQkICZs6ciffffx8XL15E6dKl8zyHCwgTvd2YMWPg7u6OgwcPwt3dHSdPnsSjR48wfvz4fE/tkJ9k2rhx4/DLL7/g+++/h1KpRFBQEHr27Inff/8dwMtkfefOneHs7Izjx4+LU0WZmZlh7ty5env9RCVFp06dMG3aNPj6+sLCwkLj2IsXLzB9+nR06dIl3/Xt2bNHYzsqKgqOjo44c+YMWrVqpfU8mUzGUWVERWzUqFF48uQJLl26hJo1awJ4OQItICAAo0ePxubNmyWOkKj4YhKdiIrEnTt30LhxYwBAnTp1oFAoMG7cuEL1SMnpOUdERauo5lPu2LGj+Hu9evXg4eEBlUqFrVu3YsiQIXmewwWEid4uNjYWhw4dgoODA+RyOeRyOVq2bImwsDCMHj0a586de2sdb0umpaSkYM2aNYiOjkbbtm0BvHzIXbNmTZw4cQLNmjXDvn37cPnyZRw4cABOTk5o0KABZs+ejUmTJmHGjBkwNzfPdV2ONiHKv6lTp2L79u2oVq0agoKCxJEiV69eRXh4OLKzszFlypRC15+SkgIAsLe3f2O5p0+fQqVSQa1Wo1GjRpg7dy5q166ttTzbOdG727NnDw4cOCAm0AGgVq1aCA8P58KiRO9ILnUARFQyZGdna3wpNjU1hbW1tYQREVFxYWtri2rVquH69etayygUCtjY2Gj8EJGm7OxscTSHg4MD7t27B+DlwqOvTrlQEK8n086cOYPMzEy0b99eLFOjRg24uroiNjYWwMtkft26dTWmd/Hx8UFqaiouXbqU53XCwsKgVCrFHz4kI9LOyckJx48fR506dRASEoIePXqgR48e+Pzzz1GnTh0cO3ZMo/0VhFqtxtixY9GiRQvUqVNHa7nq1atj7dq1+PHHH7Fx40ao1Wo0b94cd+7c0XoO2znRu1Or1XmuiWBmZsYFxoneEXuiE1GREAQBAwcOhEKhAPByvtRPP/001zyK27dvlyI8IsqngwcPYsmSJeJCRTVr1sTYsWM1Ema69vTpU9y4cQMDBgzQ2zWISoI6dergzz//hLu7Ozw8PDB//nyYm5tj1apVqFSpUoHryyuZlpiYCHNzc3Ee1hxOTk7iGiaJiYm5Eng52zllXsfRJkQFo1KpsHv3bvz333+4fv06BEFA1apVYWdn9071BgYG4uLFizh27Ngby3l6esLT01Pcbt68OWrWrImVK1di9uzZeZ7Ddk707tq2bYsxY8Zg8+bNKF++PADg7t27GDduHNq1aydxdETFG5PoRFQkAgICNLb79+8vUSREVFjLly/HmDFj8OGHH2LMmDEAXi4s2qlTJyxZsgSBgYE6uc6ECRPQtWtXqFQq3Lt3D9OnT4eJiQn69Omjk/qJSqqpU6fi2bNnAICZM2eia9eueP/991GmTBls2bKlwPXlN5mmCwqFQnwQT0T5Z2dnh6ZNm+qkrqCgIOzatQtHjx5FxYoVC3SumZkZGjZs+NZRZWznRO9m2bJl6NatG9zc3MSHULdv30adOnWwceNGiaMjKt6YRCeiIhEZGSl1CET0jubOnYslS5YgKChI3Dd69Gi0aNECc+fO1VkS/c6dO+jTpw8ePXqEsmXLomXLljhx4gTKli2rk/qJSiofHx/x96pVq+Lq1at4/Pgx7OzsCrxGibZkmrOzMzIyMpCcnKzRGz0pKUlcYNDZ2RknT57UqC8pKUk8RkSGRRAEjBo1Cjt27MCRI0fg7u5e4Dqys7Px119/oVOnTnqIkIhyuLi44OzZszhw4ACuXr0K4OXIUX2OGiUqKZhEJyIionxJTk6Gr69vrv3e3t6YNGmSzq5TmB6xRKTd4MGD81Vu7dq1by3ztmRa48aNYWZmhoMHD6JXr14AgGvXriE+Pl6c2sHT0xOhoaG4f/8+HB0dAQD79++HjY0NatWqVZCXRkRFIDAwENHR0fjxxx9RunRpcdolpVIJS0tLAIC/vz8qVKiAsLAwAMCsWbPQrFkzVKlSBcnJyViwYAFu3bqFoUOHSvY6iIzZoUOHEBQUhBMnTsDGxgYdOnRAhw4dALxcv6R27dqIiIjA+++/L3GkRMUXk+hERESUL926dcOOHTvw2Wefaez/8ccf0aVLF4miIqK3iYqKgkqlQsOGDSEIwjvV9bZkmlKpxJAhQxAcHAx7e3vY2Nhg1KhR8PT0RLNmzQC8fPBWq1YtDBgwAPPnz0diYiKmTp2KwMBATuVAZIBWrFgBAPDy8tLYHxkZiYEDBwIA4uPjIZfLxWP//fcfhg0bhsTERNjZ2aFx48Y4fvy4JA/K5GkpRX5NMhwl5f//0qVLMWzYMNjY2OQ6plQq8cknn2Dx4sVMohO9AybRiYiIKF9q1aqF0NBQHDlyROxReuLECfz+++8YP348vv76a7Hs6NGjpQqTiF4zYsQIbN68GXFxcRg0aBD69+8Pe3v7QtWVn2TakiVLIJfL0atXL6Snp8PHxwfLly8Xy5qYmGDXrl0YMWIEPD09UapUKQQEBGDWrFmFiomI9Cs/D9+OHDmisb1kyRIsWbJETxHlj1KphJm5ArgZI2kcJD0zcwWUSqXUYejVn3/+iXnz5mk97u3tjYULFxZhRETGh0l0IiIiypc1a9bAzs4Oly9fxuXLl8X9tra2WLNmjbgtk8mYRCcyIOHh4Vi8eDG2b9+OtWvXIiQkBJ07d8aQIUPg7e1doPnQ85NMs7CwQHh4OMLDw7WWUalU2L17d76vS0RUUE5OTti4YT1SUkpGT+S83Lp1C6GhoZgyZQpUKpXU4UhGqVTCyclJ6jD0KikpCWZmZlqPm5qa4sGDB0UYEZHxYRKdiIiI8iUuLg4A8PDhQwCAg4ODlOEQUQEoFAr06dMHffr0wa1btxAVFYWRI0ciKysLly5dgrW1tdQhEhHpnJOTk9EnT/NDpVKhWrVqUodBelShQgVcvHgRVapUyfP4hQsXUK5cuSKOisi4yN9ehIiIiEq65ORkBAYGwsHBQfxC6uDggKCgICQnJ0sdHhEVgFwuh0wmgyAIyM7OljocIiIiekedOnXCtGnTkJaWluvYixcvMH36dK5hRPSO2BOdiIiI3ujx48fw9PTE3bt30a9fP9SsWRMAcPnyZURFReHgwYM4fvw47OzsJI6UiLRJT08Xp3M5duwYunTpgmXLlsHX11djMUAiIiIqfqZOnYrt27ejWrVqCAoKQvXq1QEAV69eRXh4OLKzszFlyhSJoyQq3phEJyIiojeaNWsWzM3NcePGjVxDomfNmgVvb2/MmjVL8gXEiChvI0eOxJYtW+Di4oLBgwdj8+bNnI6JiIjIiDg5OeH48eMYMWIEQkJCxDVMZDIZfHx8EB4ezqmN9CAtLQ3x8fFSh2EQXF1dYWFhIXUYesUkOhEREb3Rzp07sXLlyjxvvJ2dnTF//nx8+umnTKITGaiIiAi4urqiUqVKiImJQUxMTJ7ltm/fXsSRERERka7kLNr933//4fr16xAEAVWrVuVoUT2Kj4/H8OHDpQ7DIKxatcro115gEp2IiIjeKCEhAbVr19Z6vE6dOkhMTCzCiIioIPz9/SGTyaQOg4iIiIqAnZ0dmjZtKnUYJYKrqytWrVolaQy3bt1CaGgopkyZApVKJVkcrq6ukl27qDCJTkRERG/k4OCAf//9FxUrVszzeFxcHOzt7Ys4KiLKr6ioKKlDICIiItK5pKQkpKSkSB0GAZJPa6NUKvU+ZRGT6ERERPRGPj4+mDJlCvbv3w9zc3ONY+np6Zg2bRp8fX0lio6IiIiIiEqapKQk9B/gj8yMdKlDMQihoaFShyApM3MFNm5Yr9dEOpPoRERE9EazZs1CkyZNULVqVQQGBqJGjRoQBAFXrlzB8uXLkZ6ejg0bNkgdJhERERERlRApKSnIzEjHi0qtobZQSh0OSUielgLcjEFKSgqT6ERERCSdihUrIjY2FiNHjkRISAgEQQAAyGQydOjQAcuWLYOLi4vEURIRERERUUmjtlBCXcpB6jCoBGASnYiIiN7K3d0dv/76K/777z/8888/AIAqVaoY/Vzo8jTOsViS8f8/EREREREBTKITERFRAdjZ2eG9996TOgy9UyqVMDNXADdjpA6FJGZmroBSySHCREREREQlGZPoRERERK9xcnLCxg3rkZJScnsi37p1C6GhoZgyZQpUKpXU4UhGqVTqdW5FIiIiIio8+YtkqUMgiRXV3wCT6ERERER5cHJyYvIUgEqlQrVq1aQOg4iIiIgoF8u4o1KHQCUEk+hERERERERERERU7LxwbwW1pa3UYZCE5C+Si+RhCpPoREREREREREREVOyoLW2hLuUgdRhUAsilDoCIiIiIiIiIiIiIyFAxiU5EREREREREREREpAWT6EREREREREREREREWnBOdCIiIiIiIiIiIip25GkpUodAEiuqvwEm0YmIiIiIiIiIiKjYUCqVMDNXADdjpA6FDICZuQJKpVKv12ASnYiIiIiIiIiIiIoNJycnbNywHikpJbsn+q1btxAaGoopU6ZApVJJHY5klEolnJyc9HoNg0+iHz16FAsWLMCZM2eQkJCAHTt2oHv37m8858iRIwgODsalS5fg4uKCqVOnYuDAgUUSLxHpX2HeF4io+AkPD8eCBQuQmJiI+vXr45tvvsF7770ndVhEREQlUkE/l7///ntMmzYN//77L6pWrYp58+ahU6dORRgxERk7JycnvSdOiwuVSoVq1apJHYZRM/iFRZ89e4b69esjPDw8X+Xj4uLQuXNntGnTBufPn8fYsWMxdOhQ7N27V8+RElFRKej7AhEVP9999x2Cg4Mxffp0nD17FvXr14ePjw/u378vdWhEpEPh4eFwc3ODhYUFPDw8cPLkSalDIqI8FPRz+fjx4+jTpw+GDBmCc+fOoXv37ujevTsuXrxYxJETERHphsEn0Tt27Ig5c+agR48e+SofEREBd3d3LFq0CDVr1kRQUBA+/PBDLFmyRM+RElFRKej7AhEVP4sXL8awYcMwaNAg1KpVCxEREbCyssLatWulDo2IdIQPy4iKj4J+Ln/11Vfw9fXFZ599hpo1a2L27Nlo1KgRli1bVsSRExER6YbBT+dSULGxsWjfvr3GPh8fH4wdO1brOenp6UhPTxe3U1NTdRKLScodyF8k66SuAhOyIct4Ls21DYxgbgXITCS5tizjqSTXJU36auOAhCuBq7MgT+ffFwCoFdaAXJqPM64Erx8ZGRk4c+YMQkJCxH1yuRzt27dHbGxsnufos51LJS0tDfHx8ZJd/9atWxr/lZKrqyssLCykDoN07NWkHPCyM8wvv/yCtWvXYvLkyRpl2cb1w1DaOdu4YSvM53JsbCyCg4M19vn4+GDnzp1ar8N2rnuG0sYBtnNjVpKnYJS6jQOG085LQhs3uiR6YmJirvmQnJyckJqaihcvXsDS0jLXOWFhYZg5c6bOYlAqlZDLTWBx96zO6qTiSy430fsKwfRmum7jAFcCp/8pilXAS5qHDx8iOzs7z8/zq1ev5nmOPtq51OLj4zF8+HCpw0BoaKjUIWDVqlWc49HIFDQpxzauX1K3c7Zxw1aYz2Vt38sTExO1XoftXH+kbuMA27mxyhlVFhERAQ8PDyxduhQ+Pj64du0aHB0dpQ5P7wyljQPSt/OS0MaNLoleGCEhIRpPyVNTU+Hi4lLo+pycnLB8eThu376ti/AKJTMzEw8fPpTs+obEwcEBZmZmkl3fxcWFC11ITNdtHJB+JfD09PQ3fgkpSZydnaFQKCS7flGsAk5vp492LjVXV1esWrVK6jAMgqurq9QhkI4VNCnHNm7c2MYJYDs3dmznxqkgo8qMEdv4/5SENm50SXRnZ2ckJSVp7EtKSoKNjU2evdABQKFQ6DwBU6NGDdSoUUOndRJR4eijjQPSrwRet25dya5NpE8ODg4wMTHJ8/Pc2dk5z3P01c6lZGFhYfS9OYjyi22cSDqF+VzW9r1cW3mA7ZyouCnoqDJjnLKJbbxkMfiFRQvK09MTBw8e1Ni3f/9+eHp6ShQRERERFYS5uTkaN26s8XmuVqtx8OBBfp4TGYnCJOWISBqF+Vzm93Ii4/emUWV5jZoOCwuDUqkUf4r7SBMqeQw+if706VOcP38e58+fBwDExcXh/Pnz4sT9ISEh8Pf3F8t/+umnuHnzJiZOnIirV69i+fLl2Lp1K8aNGydF+ESkB297XyCi4i84OBjffvst1q1bhytXrmDEiBF49uyZOFSUiIo3PiwjKl7e9rns7++v0Rt1zJgx2LNnDxYtWoSrV69ixowZOH36NIKCgqR6CUQksZCQEKSkpIg/Uk6BTFQYBj+dy+nTp9GmTRtxO2eOtICAAERFRSEhIUEjcebu7o5ffvkF48aNw1dffYWKFSti9erV8PHxyfc1BUEAYBxDS6h4y/kbzPmbpJfe9r7wNmzjZCjYxrX7+OOP8eDBA3zxxRdITExEgwYNsGfPnnxPocR2ToaC7Vy74OBgBAQEoEmTJnjvvfewdOnSfD8sYxsnQ1FS2vjbPpfj4+Mhl/+vj17z5s0RHR2NqVOn4vPPP0fVqlWxc+dO1KlTJ9/XZDsnQ1FS2nlBFXRU2etTNrGNk6HIbxuXCXwXyOXOnTscVkIG5fbt26hYsaLUYRgNtnEyNGzjusd2ToaG7Txvy5Ytw4IFC8Sk3Ndffw0PD4+3nsc2ToaGbVz32M7J0LCd5+bh4YH33nsP33zzDYCXo8pcXV0RFBT01oVF2cbJ0LytjTOJnge1Wo179+6hdOnSkMlkUodTLOWspH779m3Y2NhIHU6xJQgCnjx5gvLly2v07KB3wzauG2zn745tXH/Yzt8d27husJ3rB9u4brCdvzu2cf1hO393bOO6wXau3XfffYeAgACsXLlSHFW2detWXL169a0jSNnGdYPt/N3lt40ziU56kZqaCqVSiZSUFDZiIiPFdk5k3NjGiYwf2zmRcWMbp6JQ2FFlpBts50XH4OdEJyIiIiIiIiIiIsMTFBTERYOpROA4FCIiIiIiIiIiIiIiLZhEJ71QKBSYPn26xsrLRGRc2M6JjBvbOJHxYzsnMm5s40TGj+286HBOdCIiIiIiIiIiIiIiLdgTnYiIiIiIiIiIiIhICybRiYiIiIiIiIiIiIi0YBKdiIiIiIiIiIiIiEgLJtGJiIiIiIiIiIiIiLRgEp2IiIiIiIiIiIiISAsm0YmIiIiIiIiIiIiItGASnYiIiIiIiIiIiIhICybRiYiIiIiIiIiIiIi0YBKdiIiIiIiIiIiIiEgLJtFJg0wmg0wmkzoMohIvpy3m/MjlciiVSjRr1gxLly5FZmam1CHmy8CBAyGTyXDkyBGpQyEqtMePH2PGjBlo0qQJ7OzsYGlpCXd3dwQEBCA2Nlbq8PRqxowZkMlkiIqKKpLziAzJq5/Db2rrW7duFcu5ubkVXYD55Obmprf7+3///TfP152z/9UfhUIBR0dHNG7cGJ988gn2798PQRD0EheRVJ49e4bFixejTZs2cHJygrm5Oezs7ODp6YkvvvgC8fHxUodIVKKxjRaNnHuPf//9N8/9OT+mpqawt7dHjRo10LdvX6xbtw5paWnSBJ0PTKITERmwgIAABAQEoF+/fqhXrx5OnTqFcePGoWPHjsjKypI6vELT5xd6Il06ePAgqlSpgpkzZ+Lff//F+++/jw8++AA2NjZYv349mjdvjrFjx0KtVksdapHy8vLK88aYyFht2rRJ67GNGzfq9FrG9BCqVKlS4r3MRx99BA8PDzx+/BirVq2Ct7c3mjZtir///lvqMIl04vjx46hSpQrGjx+PkydPok6dOvjwww/RvHlz3LhxA7Nnz0a1atVw4MABqUN9K3aEIWPENmo4evXqJeY5WrduDQsLC2zduhUDBw6Em5sbfv31V6lDzJOp1AEQEZF2r3+B/uOPP+Dl5YWDBw9iy5Yt6N+/vzSBEZUAp06dQqdOnZCZmYlZs2Zh8uTJMDMzE48fO3YMffr0wVdffQUTExMsWrRIwmj1IygoCH5+fihXrlyRnEdkaExMTFCrVi189913WLp0KUxNNb8+PXr0CHv27EGjRo1w9uxZiaI0XA4ODnk+DPjzzz8RHByMQ4cOoVWrVjh16hRcXFyKPkAiHTl//jzatWuHtLQ0TJo0CdOmTUOpUqXE42q1Gjt37sTEiRNx584dCSMlKpnYRg3LwoULc41iS0xMxJw5cxAeHo4uXbrgl19+ga+vrzQBasGe6ERExYiHhwcGDhwIANi7d6+0wRAZMUEQEBAQgIyMDEyfPh3Tpk3TSKADQMuWLbFv3z5YWFhgyZIlOHHihETR6o+DgwNq1KgBpVJZJOcRGaJ+/frh4cOHeX7ufvfdd8jMzORD7QKqX78+9u3bB29vbyQlJWHMmDFSh0RUaIIgYMCAAUhLS8OMGTPw5ZdfaiTnAEAul6Nnz544c+YMmjRpIlGkRCUT22jx4OzsjGXLlmH27NlQq9UYNGgQ0tPTpQ5LA5Po9FY5cxp6eXnhxYsXmDx5MlQqFRQKBapUqYJ58+blOZ9hzvyI2dnZmDdvHqpVqwaFQgEXFxdMmjQpz8bw4MEDTJ48GbVq1YK1tTWUSiWqVasGf39/nDx5siheLpHBq127NgDg/v37uY4JgoDNmzejbdu2sLOzg4WFBWrWrIkZM2bg+fPnuco/ffoUYWFhqF+/PpRKJaytrVG5cmX07t07V7LgTXO9RkVFQSaTYcaMGW+M/ciRI5DJZLh165ZYZ2Hnkc3OzkaLFi0gk8mwfPnyXMd/++03mJiYoHz58nj06FGB6ib69ddfceXKFZQvXx6ff/651nI1a9ZEYGAgBEHA4sWLxf2vTneyceNGNG7cGFZWVnB0dERAQADu3r2bq65Xp3D4448/4OPjA1tbW9jY2KBDhw5vTNLv3r0bHTp0ENt99erVMXnyZCQnJ+cqKwgCNm3ahJYtW8LJyQkWFhZwcXFB+/btER4erjUm4H/3BDExMQAAd3d3jXas7bxXPXr0CJ999hmqVq0KCwsL2Nvbw9fXF/v27cvztRXmfiI/Fi5cCJlM9sb/v97e3pDJZDh8+HChrkHGoW/fvpDJZHlO27Jx40ZYW1vjgw8+yPPcnM9lPz8/VKtWDaVKlULp0qXx3nvvYfny5bmmgnJzc8PMmTMBAIMGDdJoX68P2f7jjz/g5+eHChUqQKFQoFy5cmjXrh2+/fZbra9l9erVqFevHiwtLeHs7IxPPvkkz/eJomBiYoJly5ZBJpNh586dhZqHdvfu3ZDJZKhcuTKePHmicUwQBPj4+EAmkyEsLExXYRPlsmfPHly8eBEVK1bElClT3lhWqVSiTp064vbz588xe/Zs1KlTB5aWllAqlWjVqhW2bNmS5/mFuR9/9Z5k586daNasGUqVKgV7e3v06dMnV69bmUyGdevWAQDatGmj8T6UM43bq5/zJ0+eRJcuXVCmTBnIZDL88ccfcHBwgJWVldb3l+PHj0Mmk6F169Zv/Pci0gW20eLVRkNCQqBSqZCYmIjvv/++yK//JkyiU75lZGTA29sb3377LZo0aYI2bdrg7t27mDx5MqZNm6b1vL59+2LOnDmoXr06vL298eTJE8yfPx9DhgzRKPfkyRN4eHhg3rx5ePr0KTp06ABvb2/Y2dlhy5Yt2L17t75fIlGxkPMl0dHRUWO/Wq1Gv3790LdvX5w6dQoNGjRAp06d8OzZM8ycORNt2rTBixcvxPLZ2dlo3749Pv/8c9y7dw9eXl7o3LkznJ2dsXv3bmzevFnnsTs7OyMgIEB88p8zT2pAQAA+/PDDAtVlYmKCDRs2oHTp0pgwYQKuXr0qHktJScGAAQMgCALWrVuHMmXK6PR1kPH75ZdfAAC9e/fO1QP9df369QMA7Nu3L1dCbOHChfD39xeTbKVKlcL69evRrFkzrUNFjx8/jlatWuHOnTvo2LEjqlevjgMHDqB169Z5JprDwsLQuXNnHDlyBI0bN0b37t3x/PlzzJs3Dx4eHkhKStIoP3HiRPTv3x+nT59G/fr10bNnT1StWhUXLlzAggUL3vhara2tERAQACcnJwD/m88w5+dt7t69i/feew8LFy5ERkYGunfvjoYNG+LAgQPw8fHBkiVLtJ6b3/uJ/Bo4cCAUCgUiIyPzXGMiLi4OBw4cQNWqVdGmTZtCXYOMg4uLC1q1aoWffvoJT58+FfffvHkTsbGx6NGjB6ysrPI8Nz09HX379sWBAwfg7OyMrl27olmzZrh06RICAwMxePBgjfIffvgh6tevDwBo0aKFRvtydnYWy3311Vdo3rw5vvvuO5QrVw49e/ZEnTp1cPHiRXz22Wd5xjJx4kQEBgaiXLly6NixIwRBwKpVq9CtWzfJFvisWrUqGjduDEEQxIdzBdGpUycEBgbi5s2bGD16tMaxr776Cvv27UOrVq0wadIkXYVMlMur9wyvT/n0Jk+ePEGrVq3wxRdf4P79++jSpQtatGiBkydPok+fPjofobF8+XJ8+OGHsLS0RKdOnWBtbY0tW7agbdu2Gt8RAgICULlyZQCAj4+PxvuQtbW1Rp1Hjx5Fy5Yt8e+//8Lb2xutWrWCpaUlAgIC8OLFC63rSeQ87Bs+fLhOXyNRXthGi1cbNTExQe/evQHA8DqyCESvACC8/mcRFxcn7m/durWQkpIiHjt16pRgYmIiWFlZCU+ePMmzrpo1awoJCQni/ps3bwq2trYCAOH69evi/rVr1woAhG7dugnZ2dkadd2/f1/466+/dPlSiQxaXm0xR6tWrQQAwsaNGzX2z58/XwAgeHl5abS59PR0YciQIQIAYdKkSeL+Q4cOCQCEpk2bCi9evNCoKyUlRTh9+nSumFQqVZ4xRUZGCgCE6dOna+wPCAgQAAiHDx/W2K9SqbS+voKKiooSAAgNGzYU0tPTBUEQhL59+woAhLFjx+rkGlTytGjRQgAgbNiw4a1lMzMzBXNzc43PtdatWwsABFNTU+GXX34Ry2ZkZAj9+vUTAAgffPCBRj3Tp08X2/6UKVMEtVotHlu+fLkAQChXrpzw/Plzcf/JkycFuVwuWFtbCydOnBD3p6WlCb179xYACL169RL3v3jxQlAoFELp0qWFmzdv5nodR48ezTOmyMhIjf05ry8uLi7PfxNt53Xp0kUAIPTt21dsr4IgCL/99ptgZWUlmJiYCOfOndM4p6D3EwWR816xY8eOXMemTJkiABDmzZtXqLqp+AMgmJiYCIIgCN9++60AQFi3bp14fNasWQIAYe/evUJCQkKen5OZmZnCjh07hIyMDI399+/fF5o0aSIAEGJiYjSOaWs/OWJiYgSZTCaULl1aOHDgQK7rvfqeIwj/+8x1dnYWrl69Ku5/8OCBUKVKFQGAcPDgwXz9m7wu53vC669b2/68DB06VAAghISEFCqG58+fCzVr1hQACN9//70gCIJw4cIFQaFQCEqlUvj3338LVS9RfhXknuFVQUFBAgChTZs2Qmpqqrj/ypUrgqOjowBA+PnnnzXOKcz9eM5ntpWVlXD8+HFx/7Nnz4TmzZsLAIQ1a9ZonKPtHj7Hq/cseX1OXrt2TZDJZEL9+vVzHUtJSRGsrKwEOzu7XN9BiPSBbbTo22jOvcfr3xW07X/dxo0bBQCCp6dnoa6vL+yJTvkml8uxcuVK2NjYiPuaNGmCjh074vnz5zh9+nSe53399dcaPWfc3d3FeSN/++03cf+DBw8AAG3btoVcrvmnWbZsWY0hNUQljVqtxo0bNzBixAgcPXoUH3zwAT7++GPxeFZWFubPn49SpUphy5YtGm3O3Nwc33zzDZydnbFq1Sqxp2xOm2vRogUsLCw0rmdjY4PGjRsXwSt7dwEBAejduzfOnTuHqVOnYtOmTYiOjkadOnXw5ZdfSh0eFVM5UwCVLVv2rWVNTU1hZ2cHAHj48KHGsY8++gidOnUSt83MzPDVV1/BysoKP/30E27fvp2rPpVKJQ7BzDFixAh4eHggISEB27ZtE/cvW7YMarUao0aNgoeHh7hfoVBg2bJlsLS0xI4dO8TrpKamIj09HZUrV4a7u3uu1/H++++/9fUW1s2bN7Fr1y5YW1vjm2++gbm5uXisZcuW+PTTT5GdnZ1rSpkc+b2fKIhPP/0UAHJNf5GdnY2oqCiYmZmJ61BQyfbhhx9CoVBo9NjatGmTOIWKNqampujevXuuES1ly5YVpxj58ccfCxTLl19+CUEQMGXKlFzXNjU11XjPedXs2bNRvXp1cdvBwUFsA0ePHi1QDLrk4OAAAPjvv/8Kdb6lpSU2bdoEc3NzfPLJJ7hx4wb69euH9PR0hIeHQ6VS6TJcolwKcs+Q49mzZ1izZg3kcjmWL1+O0qVLi8dq1KiBqVOnAng5okJXxo0bB09PT3HbysoKwcHBAAr/HlC3bt08R79Uq1YNbdq0wZ9//olTp05pHIuOjsbz588xYMCAXN9BiPSBbbT4tdF3vTfQFybRKd9UKpXGjXeOatWqAQASEhJyHTMzM8tzCHRe5+Qk7BYsWIAtW7bkmteQqCTKmdvMxMQEVapUQUREBIYNG4YdO3ZoDEU7e/YsHj58iObNm4vTLLzK0tISjRs3xn///Yd//vkHANCgQQPI5XJERkbi22+/Ldbzhq9cuRIVK1bEokWL8Omnn4qJDoVCIXVoVML5+fnl2lemTBl4e3tDEAQcO3Ys1/FevXrlOdS0T58+ADQTxjm/50wp8ypHR0d4e3tDrVbj999/F/dVrFgR58+fx+TJk3Hz5s3CvbBCyHmtvr6+sLe3z3V8wIABAPJOiBfkfqIg3n//fdSuXRt79uzReKCxe/du3L17Fx988EGuqbOoZLK1tUXnzp1x8OBBJCYm4tSpU7h27Rr8/PxgYmLy1vPPnz+P+fPnIzAwEIMGDcLAgQOxYsUKABA/l/MjKytLnBu9oEOsvb29c+171zakC8L/TyXz6oPDgmrYsCHmzJmDx48fo1GjRvjrr7/Qp0+fPN8biQzBmTNn8OLFCzRq1Ag1atTIdTznM/H333/PNVVcYenjPaBLly5a2662B9WcyoWKA7ZRaduoLu4N9IFJdMq3ihUr5rk/54lcXgt7OTs75/nFIq9z2rVrh3HjxuHevXvo06cP7O3t4eHhgalTpxbpl3wiQ5Izt9nHH38sfnh/++234kIiOXIWENm/f7/GwiKv/uTMBZfTU7ZatWqYP38+nj9/juHDh8PR0RH169dHcHAwLly4UHQvUgfs7OwQHh4OtVqNp0+fYtasWahXr57UYVExljOPfs6IjTfJysoSe0nk9JrIoa0HZM6CQ/fu3ct1rCDn5PyubQGjnP2vLmS6bt06lC1bFvPmzUPlypXh5uaGgIAA/Prrr3nWoSuFiTVHQe4nCuqTTz6BWq3G2rVrxX05Xx6GDRtW6HrJ+PTv3x/Z2dnYsmWLuMhozmgIbTIyMtC3b180bNgQkyZNwvLlyxEVFYV169Zh+/btAFCgjiOPHj3CixcvYG9vL46Aya+87uV10YbeVc59SV4P1wpi/PjxaNy4MVJTU1GuXLk8Fx0n0oeC3DPkeNtnoq2tLZRKJV68eKGznpj6eA9wdXXVeqx79+5wdnbG5s2bxfUkzp49i7Nnz8LT0xO1a9cu1DWJCoptNG+G3EZ1dW+ga0yiU769PsWKPs5ZvHgxrly5gnnz5qFNmza4ePEiQkNDUaNGDY3h60QlRVRUFKKiorBlyxZcuXIF8+fPBwAEBgbi1q1bYrmcp99VqlTRWFgkr59XF9kcP348bty4ga+//hqdO3dGfHw8lixZggYNGhRoaJqunr6/i++++078/cyZMxJGQsYgZ2E/bVOVverixYvIyMiAUqnMNUWK1PLqvdG2bVtcv34dmzZtwoABA6BWq7F+/Xp06tSpwAv86tKbepoU5h4kv/z9/WFlZYW1a9dCrVbj3r172L17N9zc3NChQwe9XZeKn06dOsHW1hbr16/Hd999h5o1a6JRo0ZvPGfx4sXYvHkz6tati19//RVJSUnIyMiAIAi4du0aABTZop76bEfv4ty5cwCAWrVqvVM9ly9fxsWLFwG8fNiQ08GASN8aNGgA4GXySZcK2gPzbffj+ngPeNNUD2ZmZhg8eDCePn2KLVu2AABWr14NgA+pqWixjebNkNuoru4NdM0w76SoRKtevTomTpyIffv24dGjR1iwYAEyMzMxYsQIqUMjktxnn30Gb29vPH/+HDNnzhT35zy1rlGjhph41/bz+nA0FxcXjBo1Cj/99BMePHiADRs2wMTEBBMnTtR4qm5mZiY+oX5dXvM6F6XNmzcjOjoatWvXRsOGDbF161asX79e0pioeMuZU/iHH35AZmbmG8tGR0cDeDkE8/Wb31cfduW1v3z58lqP5eecnN+1nZOTRKpQoYLGfhsbG/Tt2xfr169HfHw8YmNjUbFiRWzbtg27d+/Os653VdhY9U2pVMLPzw/x8fHYu3cvIiMjkZ2djaFDhxrcEFKSlkKhENfgSEpKemsvdADYsWMHgJefU76+vnB0dBTnRy/MSEsHBwdYWlri8ePHSE5OLvD5huaff/7BuXPnIJfL0apVq0LXk56eLs6D3r9/f2RkZKBfv35IS0vTYbREeevcuTMA4Pvvv0dWVla+znnbZ2JKSgqSk5NhaWmpMerEkO/H8zJ8+HDI5XJ8++23eP78OaKjo2FjY6OxthORvrGNameIbTQ7Oxs//PADAOQ5naOUmEQng2ZhYYEJEyagXLlyePDgAe7fvy91SESSy1ksc8OGDeKHetOmTaFUKhETE4PHjx8Xum5TU1P0798fTZs2RUZGhsY8reXKlcOjR4/ynDv9wIEDBbpOzoKC+b2JeZPbt29j5MiRUCgUiI6OxsaNG2FhYYFRo0YhLi7uneunkqljx46oUaMG7t69+8YFaq9du4Zly5ZBJpOJC/+8auvWrbn2PX78GPv27YNMJkOLFi1yHd++fTuys7Nz7c/pIdKyZUtxX85CoJs3b85V/sGDB9i7d6/W67yqWbNm4tyOOT0536QwbTgn7j179uSZ/MuZHkOfi5tqkzMn5MqVK7FmzRqYmJhg0KBBRR4HGb4BAwagTJkycHBwyNd82zkPo/Maop3X+wPw5vZlYmICLy8vAMCqVavyG7ZBys7ORlBQEARBQK9evbROHZkfISEhuHDhAvz8/LBhwwb4+/vj8uXLeS6mRqRrvr6+qF27Nu7cuYPQ0NA3lk1NTcWlS5fQuHFjWFpa4syZM3mui5DzmdiiRQuNB/S6vB9/E13dq6tUKvj6+uLkyZOYOnUqUlJS0K9fP1hZWekiTKJ8YRvVzhDb6Ny5cxEfH48KFSqgV69eksWRFybRyWDs3LkTJ06cyLX/zJkzSEpKgrW1NWxtbYs+MCID07BhQ3Tv3h1ZWVni9C4KhQITJ07EkydP0LNnzzx7t929excbNmwQtw8fPowDBw7kGlYWFxeHK1euQCaTaXyhbd26NQBgzpw5GuXnz5+f5+KIb5LzZD9nKHthqdVq+Pv7Izk5GaGhoahXrx5q1aqFefPmITU1FQMGDMgzGUn0NnK5HOvXr4e5uTmmT5+OuXPn5rpJPX78ODp06IAXL15g7NixaNasWa56vvvuO+zdu1fczsrKwrhx4/Ds2TN06dIlz3kK//33X42RJsDLZFlsbCycnJw0biYDAwMhl8vx9ddfa0w9k5GRgVGjRuHFixfo2bMnXFxcAADx8fGIiorC8+fPNepPS0vD4cOHAUAs+yaFacOVKlVC586d8eTJE4wZM0ajh39sbCxWrFgBExMTBAYG5rtOXWnatCkaNWqEH3/8EXFxcejcuXOeowSI3n//fTx8+BAPHjzQun7Bq3IWBIuIiNDY/8MPP2gdMfW29jVp0iTIZDKEhoaK7TZHVlaW3kaT6NKFCxfg7e2Nffv2oVy5cli6dGmh6zp48CCWLl0KFxcXcbHWb775Bm5ubli2bBn27Nmjo6iJ8iaTycROHDNmzEBISAiePXumUUYQBPz0009o0qQJTp06hVKlSmHw4MFQq9UIDAzUKP/333+L99ujR4/WqEeX9+Nvoqt7deB/D6qXLFkCQPppIqjkYRt9M0Npo4mJiRg1ahS++OILmJiYIDIyUnxYYDAEolcAEF7/s4iLixMACK1bt87znOnTpwsAhMjIyFx1qVSqPM+JjIwUAAjTp08X940ZM0YAIFSoUEHo0qWL0LdvX8HLy0swMTERAAiLFi16h1dGVLzk1RZfdf78eUEmkwkWFhZCQkKCIAiCkJ2dLQwYMEAAIJibmwseHh6Cn5+f0LNnT6F27dqCTCYT6tevL9axZMkSAYBQtmxZwdfXV+jXr5/g7e0tKBQKAYAwatQojWtevHhRsLS0FAAIDRo0EHr16iVUq1ZNsLS0FEaOHJmrTQuCIAQEBAgAhMOHD2vsX7RokQBAcHJyEvz8/IQhQ4YIkyZNKvC/07x58wQAQrt27QS1Wi3uV6vVgre3twBAmD17doHrJcqxf/9+wc7OTgAgODg4CN26dRM+/vhjoX79+mI7HTVqlJCdna1xXuvWrQUAQmBgoCCTyYTWrVsLfn5+gru7uwBAKF++vHDr1i2Nc3I+T4cNGyaYmZkJtWvXFvr06SM0bdpUACCYmZkJv/76a64YQ0NDBQCCqamp0L59e8HPz09wcXERAAhVq1YVEhMTxbLnzp0TAAhWVlZCq1athL59+woffPCBULZsWQGA0KRJEyEtLS1XTK9/xm/btk0AINjY2AgffvihMGTIEGHIkCFvPe/OnTviv4FKpRL8/PyEdu3avfGzvqD3E4W1atUq8f/prl273rk+Kv4ACCYmJvkqm5CQkOffakxMjPj33bhxY6FPnz5CkyZNBADChAkT8rzHvnv3rmBhYSGYmJgIvr6+wuDBg4UhQ4YIV69eFcssWLBAkMlkYrvt06eP0KFDB8HR0VFQKpUa9alUKq33FIcPHxYACAEBAfl6na/L+Z7w+uvO2V+qVCkhICBACAgIEPr37y907dpVqFSpktjWmjZtKvzzzz+FurYgCMLjx4+FChUqCHK5PNe9xm+//SbI5XLB2dlZePDgQaGvQZRfx44dE5ycnMTP2Xbt2gl9+/YVOnfuLO63sLAQDhw4IAiCIKSmpgqNGzcWAAiOjo5C7969hU6dOgkWFhYCAGH06NG5rlGY+/Gce5K4uLhc9Wn7rn/69Gnxu8YHH3wgfs4/fPhQEATtn/N5ycrKEu9LmjRpkq9/SyJ9YBvNmz7aaM69x+uvKWd/r169hICAAMHf31/o0aOH0KBBA/F+ydnZWdizZ49O4tA1JtFJg5RJ9HPnzgnjx48XmjZtKjg6OgoKhUJQqVRC165dxTcxopLibUl0QRCEnj17CgCEzz77TGP/jz/+KHTu3FlwdHQUzMzMBEdHR6Fx48bCxIkThTNnzojl/vnnH2Hq1KlCixYthHLlygnm5uZChQoVhHbt2gnbtm3TSErniI2NFby8vAQrKyvBxsZG6Nixo3D+/HmtiSxtSfTMzExh6tSpQuXKlQUzM7M3vl9oc+7cOcHc3Fyws7MT7ty5k+v4vXv3hDJlygimpqbCH3/8UaC6iV718OFD4YsvvhAaNmwo2NjYCAqFQnB1dRUGDBggHD9+PM9zXr0ZjoyMFBo0aCBYWFgIZcqUEQYMGCDcvn071zmvfp4eP35caNeunVC6dGnB2tpaaNeunfD7779rjXHXrl1Cu3btBKVSKZibmwtVqlQRJk6cKDx+/FijXGpqqrBo0SKhU6dOgpubmxhTkyZNhCVLlgjPnj3TGtPrlixZItSqVUt88Pbqe9abznv48KEwfvx4oXLlyoK5ublga2sreHt7C3v37s3ztRVVEv369esCAKFixYpCVlbWO9dHxZ8ukuiC8PKzs23btoKdnZ1QunRpoXnz5sK2bdveeI+9d+9eoUWLFoK1tbXYvl7/LD169KjQo0cP8fO+XLlyQrt27YTVq1drlJMyif7qj5mZmeDg4CA0atRIGD58uLBv37487zUK4qOPPsrzXihHSEiIAED44IMP3uk6RPn15MkTYeHChULr1q2FsmXLCqampoKtra3g4eEhTJ8+Pdfn/9OnT4WZM2eKn6elS5cWWrZsKURHR2u9RkHvxwuToBMEQdi0aZPQqFEjMSH4ah0FSdAJgiD0799fACCsXLkyX+WJ9IVtNG+6bqNvS6Ln/MjlcsHW1laoXr264OfnJ6xbt0548eKFTmLQB5kgFNFy8EREREQlhJeXF2JiYhAXFwc3N7d8nTNjxgzMnDkTkZGRGDhwoF7jo9zCwsLw+eefY/r06ZgxY4bU4RARERmF58+fo0KFCsjKysK9e/dQunRpqUMiolewjeYf50QnIiIiohItNTUV33zzDczNzTF8+HCpwyEiIjIa4eHhSE5ORkBAAJNzRAaIbTT/TKUOgIiIiIhICpGRkYiJicHRo0eRkJCAsWPHckFRIiKid/To0SNMmjQJSUlJ2L17N6ytrTF58mSpwyKi/8c2WjhMohMRkcGYMGECHj58mK+yUVFR+g2GiIqd/E6D4+DggIULFyImJgbr1q1D2bJlERgYiC+//FK/ARIZOKk/h48dO4bVq1fnq2z37t3RvXt3ncdARO/uyZMnWLNmDczNzdGwYUMsXLgQFStWlDosIvp/BWmjq1evxrFjx/JV7+TJk1GjRg1dhmpQOCc6EREZDDc3N9y6dStfZfnxRUSvk8lk+SqnUqnw77//6jcYomJI6s/hqKgoDBo0KF9luX4BERGR/g0cOBDr1q3LV9nDhw/Dy8tLvwFJiEl0IiIiIiIiIiIiIiItuLAoEREREREREREREZEWTKITEREREREREREREWnBJDoRERERERERERERkRZMohMRERERERERERERacEkOhERERERERERERGRFkyiExERERERERERERFpwSQ6EREREREREREREZEWTKITEREREREREREREWnBJDoRERERERERERERkRZMohMRERERERERERERacEkOhERERERERERERGRFkyiExERERERERERERFpwSQ6EREREREREREREZEWTKITEREREREREREREWnBJDoRERERERERERERkRZMohMRERERERERERERacEkOhERERERERERERGRFkyiExERERERERERERFpwSQ6EREREREREREREZEWTKITEREREREREREREWnBJDoRERERERERERERkRZMohMRERERERERERERacEkOhERERERERERERGRFkyiExERERERERERERFpwSQ6EREREREREREREZEWTKITEREREREREREREWlhKnUAhkitVuPevXsoXbo0ZDKZ1OFQCSYIAp48eYLy5ctDLuczL11hGydDwTauP2znZCjYzvWDbZwMBdu4/rCdk6FgO9cPtnEyFPlt40yi5+HevXtwcXGROgwi0e3bt1GxYkWpwzAabONkaNjGdY/tnAwN27lusY2ToWEb1z22czI0bOe6xTZOhuZtbZxJ9DyULl0awMt/PBsbG4mjoZIsNTUVLi4u4t8k6QbbOBkKtnH9YTt/N7dv30ZQUBAyMzNhZmaGZcuW8UtOIbGd6wfbOBkKtnH9YTsnQ2Fs7dzNzQ23bt3KtX/kyJEIDw+Hl5cXYmJiNI598skniIiIELfj4+MxYsQIHD58GNbW1ggICEBYWBhMTfOfZmQbJ0OR3zbOJHoecoaR2NjYsCGTQeDQJt1iGydDwzaue2znhde2bVuo1WoAgKmpKQRBQGBgIORyOQ4dOiRxdMUX27lusY2ToWEb1z22czI0xtLOT506hezsbHH74sWL6NChA3r37i3uGzZsGGbNmiVuW1lZib9nZ2ejc+fOcHZ2xvHjx5GQkAB/f3+YmZlh7ty5+Y6DbZwMzdvauFFO5nT37l30798fZcqUgaWlJerWrYvTp09LHRYRERGRQXs1gW5lZYVRo0aJX5rUajXatm0rZXhkoMLCwtC0aVOULl0ajo6O6N69O65du/bW877//nvUqFEDFhYWqFu3Lnbv3l0E0RJRQbGNExmXsmXLwtnZWfzZtWsXKleujNatW4tlrKysNMq8muTet28fLl++jI0bN6JBgwbo2LEjZs+ejfDwcGRkZGi9bnp6OlJTUzV+iIoTo0ui//fff2jRogXMzMzw66+/4vLly1i0aBHs7OykDo2IiIjIYMXHx4sJ9K1bt2L37t3o1asXdu/eja1btwJ4mUiPj4+XMkwyQDExMQgMDMSJEyewf/9+ZGZmwtvbG8+ePdN6zvHjx9GnTx8MGTIE586dQ/fu3dG9e3dcvHixCCMnovxgGycyXhkZGdi4cSMGDx6s0Qt306ZNcHBwQJ06dRASEoLnz5+Lx2JjY1G3bl04OTmJ+3x8fJCamopLly5pvVZYWBiUSqX4w6kCqbiRCYIgSB2ELk2ePBm///47fvvtt3yfk56ejvT0dHE7Zy6clJSUQg8pSUpKQkpKSqHO1YX09HQkJiZKdn1D4uzsDIVCIdn1lUqlxodLQaSmpkKpVL7T3yLlZgz/rmlpaUxk/T9XV1dYWFhIHUahGMPfoqHiv23BdejQAZmZmbCyssqzt2CnTp3w/PlzmJmZYf/+/RJEWDyVxL/FBw8ewNHRETExMWjVqlWeZT7++GM8e/YMu3btEvc1a9YMDRo00JhzNYc+7tcB4OrVq7h9+3ahz38XmZmZePjwoSTXNjQODg4wMzOT7PouLi6oUaNGoc5lG9dNGwf4vdzYSfm9/F2+kwPG3c63bt2Kvn37Ij4+HuXLlwcArFq1CiqVCuXLl8eFCxcwadIkvPfee9i+fTsAYPjw4bh16xb27t0r1vP8+XOUKlUKu3fvRseOHfO8lr4+y6XE7+X/UxK+lxvdnOg//fQTfHx80Lt3b8TExKBChQoYOXIkhg0bpvWcsLAwzJw5U2cxJCUlof8Af2RmpL+9MBk9M3MFNm5Y/04f2kSvi4+Px/Dhw6UOwyCsWrUK1apVkzoMomIvMzMTADBkyJA8j/v7+yMiIkIsR6RNTsLK3t5ea5nY2FgEBwdr7PPx8cHOnTvzLK/r+3Xg5T37yJGBUKuz316YjJpcboLNm6N5v55P+mjjAL+Xk/7wO7l2a9asQceOHcUEOgCN75l169ZFuXLl0K5dO9y4cQOVK1cu9LUUCoWkHRz1gd/L/6ckfC83uiT6zZs3sWLFCgQHB+Pzzz/HqVOnMHr0aJibmyMgICDPc0JCQjQ+4HOehhVWSkoKMjPS8aJSa6gtlIWu552osyBPfyrNtQ2MWmENyKX5U5enpQA3Y5CSksIPbNIpV1dXrFq1StIYbt26hdDQUEyZMgUqlUqyOFxdXSW7NpExMTMzQ2ZmJtasWYNevXrlOr5+/XqxHJE2arUaY8eORYsWLVCnTh2t5RITE3PdGzk5OWntsanr+3Xg5T27Wp2NtAqNIJhbv1NdhSJkQ5bx/O3lSgDB3AqQmUhybVnGU1jcPcv79XzSVxsH+L3c2En1vZzfybW7desWDhw4IPYw18bDwwMAcP36dVSuXBnOzs44efKkRpmkpCQAL0cclCT8Xv4/JeF7udEl0dVqNZo0aSKuCNywYUNcvHgRERERWpPo+noaprZQQl3KQef15vv6pSW7NBHpmYWFhcE85VWpVAYTCxEV3po1a+Dv74/nz5/j/v37cHR0FI/dv39fnAtzzZo1UoVIxUBgYCAuXryIY8eO6bReffZey1ZWlPSenaQlf/YQuHtW6jCKDX21cYDfy4mKWmRkJBwdHdG5c+c3ljt//jwAoFy5cgAAT09PhIaGatwv7t+/HzY2NqhVq5ZeYzY0/F5eshhdEr1cuXK5Gm3NmjWxbds2iSIiIiIiMnyurq6Qy+VQq9X46KOPYGVlBX9/f6xfv15MoMvl8hLRy4QKJygoCLt27cLRo0dRsWLFN5Z1dnYWe63lSEpKKnE92IiKE7ZxIuOhVqsRGRmJgIAAmJr+LzV448YNREdHo1OnTihTpgwuXLiAcePGoVWrVqhXrx4AwNvbG7Vq1cKAAQMwf/58JCYmYurUqQgMDDS66VqIXiWXOgBda9GiBa5du6ax7++//5Z0SAMRERFRcXDo0CHI5S9vD58/f46IiAiNBPqhQ4ekDI8MlCAICAoKwo4dO3Do0CG4u7u/9RxPT08cPHhQY9/+/fvh6emprzCJqJDYxomMz4EDBxAfH4/Bgwdr7Dc3N8eBAwfg7e2NGjVqYPz48ejVqxd+/vlnsYyJiQl27doFExMTeHp6on///vD398esWbOK+mUQFSmjS6KPGzcOJ06cwNy5c3H9+nVER0dj1apVCAwMlDo0IiIio3f06FF07doV5cuXh0wmy7WA2MCBAyGTyTR+fH19Nco8fvwY/fr1g42NDWxtbTFkyBA8fcr5RIvKoUOHsH79enHuczMzM6xfv54JdNIqMDAQGzduRHR0NEqXLo3ExEQkJibixYsXYhl/f3+EhISI22PGjMGePXuwaNEiXL16FTNmzMDp06cRFBQkxUsgojdgGycyPt7e3hAEIdf0Hy4uLoiJicGjR4+QlpaGf/75B/Pnz4eNjY1GOZVKhd27d+P58+d48OABFi5cqNGjncgYGd1feNOmTbFjxw6EhIRg1qxZcHd3x9KlS9GvXz+pQyMiIjJ6z549Q/369TF48GD07NkzzzK+vr6IjIwUt18f9tmvXz8kJCRg//79yMzMxKBBgzB8+HBER0frNXb6H1dXV+zfv1/qMKiYWLFiBQDAy8tLY39kZCQGDhwIAIiPjxdHOQBA8+bNER0djalTp+Lzzz9H1apVsXPnzjcuVEhE0mAbJyIiMsIkOgB06dIFXbp0kToMIiKiEqdjx47o2LHjG8soFAqtc6JeuXIFe/bswalTp9CkSRMAwDfffINOnTph4cKFKF++vM5jJqJ3IwjCW8scOXIk177evXujd+/eeoiIiHSJbZyIiMgIp3MhIiIiw3bkyBE4OjqievXqGDFiBB49eiQei42Nha2trZhAB4D27dtDLpfjjz/+0Fpneno6UlNTNX6IiIiIiIiIdIFJdCIiIioyvr6+WL9+PQ4ePIh58+YhJiYGHTt2RHZ2NgAgMTERjo6OGueYmprC3t4eiYmJWusNCwuDUqkUf1xcXPT6OoiIiIiIiKjkMMrpXIiIiMgw+fn5ib/XrVsX9erVQ+XKlXHkyBG0a9eu0PWGhIQgODhY3E5NTWUinYiIiIiIiHSCSXQiKlJHjx7FggULcObMGSQkJGDHjh3o3r27eHzgwIFYt26dxjk+Pj7Ys2ePuP348WOMGjUKP//8M+RyOXr16oWvvvoK1tbWRfUyiEhHKlWqBAcHB1y/fh3t2rWDs7Mz7t+/r1EmKysLjx8/1jqPOvBynvXXFyglIiIiIiLjlZSUhJSUFKnDkNStW7c0/ltSKZVKODk56fUaTKITUZF69uwZ6tevj8GDB6Nnz555lvH19UVkZKS4/XpirF+/fkhISMD+/fuRmZmJQYMGYfjw4YiOjtZr7ESke3fu3MGjR49Qrlw5AICnpyeSk5Nx5swZNG7cGABw6NAhqNVqeHh4SBkqEREREREZiKSkJPQf4I/MjHSpQzEIoaGhUocgKTNzBTZuWK/XRDqT6ERUpDp27IiOHTu+sYxCodDa4/TKlSvYs2cPTp06JS48+M0336BTp05YuHAhypcvr/OYiSj/nj59iuvXr4vbcXFxOH/+POzt7WFvb4+ZM2eiV69ecHZ2xo0bNzBx4kRUqVIFPj4+AICaNWvC19cXw4YNQ0REBDIzMxEUFAQ/Pz+2byIiIiIiAgCkpKQgMyMdLyq1htpCKXU4JCF5WgpwMwYpKSlMohNRyXLkyBE4OjrCzs4Obdu2xZw5c1CmTBkAQGxsLGxtbcUEOgC0b98ecrkcf/zxB3r06JGrvvT0dKSn/+/pdGpqqv5fBFEJdfr0abRp00bczpmnPCAgACtWrMCFCxewbt06JCcno3z58vD29sbs2bM1Rpxs2rQJQUFBaNeunThl09dff13kr4WIiIiIiAyb2kIJdSkHqcOgEoBJdCIyKL6+vujZsyfc3d1x48YNfP755+jYsSNiY2NhYmKCxMREODo6apxjamoKe3t7JCYm5llnWFgYZs6cWRThE5V4Xl5eEARB6/G9e/e+tQ57e3tOzySxx48fIzg4GI8ePUKZMmWwePFi2NvbSx0WERERERGRJJhEJyKD4ufnJ/5et25d1KtXD5UrV8aRI0fQrl27QtUZEhIi9oYFXvZEd3FxeedYiYiMUc+ePfH48WNx+8mTJ+jZsyfs7e2xfft2CSMjIiIiIiKShlzqAIiI3qRSpUpwcHAQ51h2dnbG/fv3NcpkZWXh8ePHWudRVygUsLGx0fghIqLcXk2g16pVC4sWLUKtWrUAvOydrm1BaCIiIiIiImPGnuhEZNDu3LmDR48eoVy5cgAAT09PJCcn48yZM2jcuDEA4NChQ1Cr1fDw8JAyVCKiYu3x48diAn3Xrl2wtrYGADRu3BhPnz5Fly5dxDKc2oWIiIiIiEoS9kQnoiL19OlTnD9/HufPnwcAxMXF4fz584iPj8fTp0/x2Wef4cSJE/j3339x8OBBfPDBB6hSpQp8fHwAADVr1oSvry+GDRuGkydP4vfff0dQUBD8/PxQvnx5CV8ZEVHxljPtVa1atcQEeg5ra2vUrFlToxwREREREVFJwSQ6ERWp06dPo2HDhmjYsCGAl8mYhg0b4osvvoCJiQkuXLiAbt26oVq1ahgyZAgaN26M3377DQqFQqxj06ZNqFGjBtq1a4dOnTqhZcuWWLVqlVQviYjIKDx69AgAMGTIkDyPDxo0SKMcERERERFRScHpXIioSHl5eUEQBK3H9+7d+9Y67O3tER0drcuwiIhKvDJlyuDJkydYs2aNOF3WqyIjI8VyREREREREJQl7ohMRERERFi9eDAC4fPkynj59qnHs6dOnuHLlikY5IiIiIiKikoI90YmIiIgI9vb2sLe3x+PHj9GlSxfUrFkTgwYNQmRkpJhAzylDRERERERUkrAnOhEREREBALZv3y4mya9cuYKJEydqJNC3b98uZXhERERERESSYE90IiIiIhJt374djx8/RnBwMB49eoQyZcpg8eLF7IFOREREREQlFpPoRERERKTB3t4eUVFRUodBRERERK9wc3PD4MGDMXDgQLi6ukodDlGJwiQ6EVEhJSUlISUlReowJHPr1i2N/5ZUSqUSTk5OUodBRERERERGbuzYsYiKisKsWbPQpk0bDBkyBD169IBCoZA6NCKjxyQ6EVEhJCUlof8Af2RmpEsdiuRCQ0OlDkFSZuYKbNywnol0IiIiIiLSq7Fjx2Ls2LE4e/YsoqKiMGrUKIwcORJ9+/bF4MGD0ahRI6lDJDJaTKITERVCSkoKMjPS8aJSa6gtlFKHQxKRp6UAN2OQkpLCJDoRERERERWJRo0aoVGjRli0aBGWL1+OSZMmYcWKFahbty5Gjx6NQYMGQSaTSR0mkVFhEp2I6B2oLZRQl3KQOgwiIiIiIiIqITIzM7Fjxw5ERkZi//79aNasGYYMGYI7d+7g888/x4EDBxAdHS11mERGhUl0IiIiIiIiIiIiA3f27FlERkZi8+bNkMvl8Pf3x5IlS1CjRg2xTI8ePdC0aVMJoyQyTkyiExERERERERERGbimTZuiQ4cOWLFiBbp37w4zM7NcZdzd3eHn5ydBdETGjUl0IiIiIiIiIiIiA3fz5k2oVKo3lilVqhS8vb3x7NkzlCpVqogiIzJ+cqkDICIiIiIiIiIiojd7WwI9xyeffIKkpCQ9R0NUsjCJTkREREREREREZCQEQZA6BCKjY1BJ9MjISDx//lzqMIiIiIiIiIiIiIiIABhYEn3y5MlwdnbGkCFDcPz4canDISIiIiqRbt68ibZt28LLywtt27bFzZs3pQ6JiIiIiHRgxowZkMlkGj81atQQj6elpSEwMBBlypSBtbU1evXqlWtqmPj4eHTu3BlWVlZwdHTEZ599hqysrKJ+KURFyqAWFr179y5+/vlnREVFwcvLC5UqVcKgQYMQEBAAZ2fnfNczY8YMzJw5U2Nf9erVcfXqVV2HTEQlnPxFstQhkIT4/5+MkZeXl8a2Wq3G4MGDAQBHjhwp+oCIiIiISKdq166NAwcOiNumpv9LD44bNw6//PILvv/+eyiVSgQFBaFnz574/fffAQDZ2dno3LkznJ2dcfz4cSQkJMDf3x9mZmaYO3dukb8WoqJiUEl0U1NT9OjRAz169EBSUhI2btyIdevWYdq0afD19cWQIUPQtWtXyOVv70D/pjcEIiJdsYw7KnUIREQ682oC3czMDAMGDMCGDRuQmZkpHmcinYiIiKh4MzU1zbOzakpKCtasWYPo6Gi0bdsWwMupl2vWrIkTJ06gWbNm2LdvHy5fvowDBw7AyckJDRo0wOzZszFp0iTMmDED5ubmeV4zPT0d6enp4nZqaqp+XhyRnhhsZtnJyQktW7bE33//jb///ht//fUXAgICYGdnh8jIyFy9pF6n7Q0hL/pqyOyhSPwbMH4v3FtBbWkrdRgkEfmLZD5IIaPx6pQt0dHRKF++PADA398f9+7dQ9++fcVylSpVkiRGIiIiIno7lUoFMzMzrcf/+ecflC9fHhYWFvD09ERYWBhcXV1x5swZZGZmon379mLZGjVqwNXVFbGxsWjWrBliY2NRt25dODk5iWV8fHwwYsQIXLp0CQ0bNszzmmFhYblmjSAqTgwuiZ6UlIQNGzYgMjISN2/eRPfu3bFr1y60b98ez549w6xZsxAQEIBbt269sR5tbwh50VdDZmKFyPipLW2hLuUgdRhERO9s6NChAF72QM9JoOcoX748zMzMkJmZiaFDh+LQoUNShEhERERUogmCgDNnzuDff/+FTCaDu7s7GjZsCJlMplHu4sWLWuvw8PBAVFQUqlevjoSEBMycORPvv/8+Ll68iMTERJibm8PW1lbjHCcnJyQmJgIAEhMTNRLoOcdzjmkTEhKC4OBgcTs1NRUuLi75et1EhsCgkuhdu3bF3r17Ua1aNQwbNgz+/v6wt7cXj5cqVQrjx4/HggUL3ljPm94QSpcunau8vhoye6iSsfVS/eeff3D48GHcv38farVa49gXX3whUVRERKQLOe/rAwYMyPO4n58fNmzYkOv9n4iIiIj07/DhwxgyZAhu3boFQRAAQEykr127Fq1atcpXPR07dhR/r1evHjw8PKBSqbB161ZYWlrqJXYAUCgUUCgUOq+XMwBQUf0NGFQS3dHRETExMfD09NRapmzZsoiLi3tjPW96QxgyZEiu8vpqyOyhSsbk22+/xYgRI+Dg4ABnZ2eNJ90ymYxJdCICABw9ehQLFizAmTNnkJCQgB07dqB79+7icUEQMH36dHz77bdITk5GixYtsGLFClStWlUs8/jxY4waNQo///wz5HI5evXqha+++grW1tYSvKKSQy6XQ61WY8OGDfD39891fMuWLWI5IiIiIio6169fR5cuXeDh4YElS5agRo0aEAQBly9fxtdff41OnTrhwoULhZpyz9bWFtWqVcP169fRoUMHZGRkIDk5WaM3elJSkjhlsrOzM06ePKlRR1JSknisqBlTx0UybAaVRF+zZs1by8hkMnTp0gW7d+/Od2/xV98QiKhw5syZg9DQUEyaNEnqUIjIgD179gz169fH4MGD0bNnz1zH58+fj6+//hrr1q2Du7s7pk2bBh8fH1y+fBkWFhYAgH79+iEhIQH79+9HZmYmBg0ahOHDhyM6OrqoX06Jsnr1agwePBiZmZm4d++expQu9+7dExcXXb16tVQhEhEREZVIS5cuRbNmzXDw4EGN/TVq1ECPHj3Qvn17LFmyBN98802B63769Clu3LiBAQMGoHHjxjAzM8PBgwfRq1cvAMC1a9cQHx8vdnj19PREaGgo7t+/D0dHRwDA/v37YWNjg1q1ar3jKy04zgJBRTULhEEl0fPr33//Fb/I5cerbwhEVDj//fcfevfuLXUYRGTgOnbsqDEi7FWCIGDp0qWYOnUqPvjgAwDA+vXr4eTkhJ07d8LPzw9XrlzBnj17cOrUKTRp0gQA8M0336BTp05YuHBhrrm6SXde7bnUt29fmJmZwc/PD1u2bNG47+KiokRERERF68iRIwgLC8vzmEwmw9ixYxESEpKvuiZMmICuXbtCpVLh3r17mD59OkxMTNCnTx8olUoMGTIEwcHBsLe3h42NDUaNGgVPT080a9YMAODt7Y1atWphwIABmD9/PhITEzF16lQEBgbqZZaHt+EsEFRUjHI87oQJExATE4N///0Xx48fR48ePcQ3BCIqnN69e2Pfvn1Sh0FExVhcXBwSExPRvn17cZ9SqYSHhwdiY2MBALGxsbC1tRUT6ADQvn17yOVy/PHHH1rrTk9PR2pqqsYPFdyRI0fE3zMzM7FhwwaNBPqrx4mIiIioaMTHx6Nu3bpaj9epUwe3bt3KV1137txBnz59UL16dXz00UcoU6YMTpw4gbJlywIAlixZgi5duqBXr15o1aoVnJ2dsX37dvF8ExMT7Nq1CyYmJvD09ET//v3h7++PWbNmvduLJDJwxbIn+tvkvCE8evQIZcuWRcuWLTXeEIio4KpUqYJp06bhxIkTqFu3LszMzDSOjx49Ol/1cL5kopIrMTERAODk5KSx38nJSTyWmJgoDgvNYWpqCnt7e7FMXsLCwjBz5kwdR1wyHTlyBDdv3sTQoUOhVqshl8uxevVq9kAnIiIiksjTp09hZWWl9biVlRWeP3+er7py1rnRxsLCAuHh4QgPD9daRqVSYffu3fm6HpGxMMok+tveEIio4FatWgVra2vExMQgJiZG45hMJst3Ep3zJRORPoSEhCA4OFjcTk1NzffaKYYqLS0N8fHxkl0/IiJC/D0rKwt///23ZLG4urqKnwFEREREJdHly5e1dip5+PBhEUdDVPIYZRKdiHQvLi5OJ/VwvmSiksvZ2RkAkJSUhHLlyon7k5KS0KBBA7HM/fv3Nc7LysrC48ePxfPzolAoJJmDUZ/i4+MxfPhwqcMwCKtWrUK1atWkDsOoZWdn46+//oJKpYKdnZ3U4RAREdFr2rVrB0EQcu2XyWQQBAEymUyCqIhKDibRichgvG2+ZD8/v7fOl9yjR49c9aanpyM9PV3c5lzJRNJwd3eHs7MzDh48KCbNU1NT8ccff2DEiBEAAE9PTyQnJ+PMmTNo3LgxAODQoUNQq9Xw8PCQKnRJuLq6YtWqVZJd/9atWwgNDcWUKVOgUqkkiwN4+W9BujV27FjUrVsXQ4YMQXZ2Nlq3bo3jx4/DysoKu3btgpeXl9QhEhER0f/TVac2Iio8g0qir1+/Hh9//HGunmQZGRnYsmUL/P39AQArV67MNZ8qEelecHAwZs+ejVKlSmlMk5CXxYsXv/P19DVfsj7nSpanpeilXioe+P8/t6dPn+L69evidlxcHM6fPw97e3u4urpi7NixmDNnDqpWrSpO2VS+fHlxbYSaNWvC19cXw4YNQ0REBDIzMxEUFAQ/P78SN9LEwsLCIHpfq1Qqg4iDdOuHH35A//79AQA///wz4uLicPXqVWzYsAFTpkzB77//LnGERERElEPqDg1EZGBJ9EGDBsHX1zdXguzJkycYNGiQmETv27evFOERlTjnzp1DZmam+Ls2hj5sTB9zJSuVSpiZK4CbMW8vTEbNzFwBpVIpdRgG4/Tp02jTpo24ndP2AgICEBUVhYkTJ+LZs2cYPnw4kpOT0bJlS+zZs0djvutNmzYhKCgI7dq1ExcP/vrrr4v8tRAZs4cPH4pTJO3evRu9e/dGtWrVMHjwYHz11VcSR0dERESvunDhQr7K1atXT8+REJVcBpVE1zaH0507d5igIJLA4cOH8/xdX/Q1X7I+5kp2cnLCxg3rkZJScnsiG9JUD1JSKpUcHfUKLy+vPOdqzCGTyTBr1izMmjVLaxl7e3suFEykZ05OTrh8+TLKlSuHPXv2YMWKFQCA58+fw8TEROLoiEhX8jvam4gMW4MGDcS5z7WRyWTIzs4uwqiIShaDSKI3bNgQMpkMMpkM7dq1g6np/8LKzs5GXFwcfH19JYyQiIpCcZsv2cnJiclTcKoHIqLiaNCgQfjoo49Qrlw5yGQycT2SP/74AzVq1JA4OiLSlfyO9iYiw8Y50YmkZxBJ9Jx5UM+fPw8fHx9YW1uLx8zNzeHm5oZevXpJFB0RAUCPHj3yHCkik8lgYWGBKlWqoG/fvqhevfob6+F8yURERNKbMWMG6tSpg9u3b6N3795iL1UTExNMnjxZ4uiISFc42pvIOBR05O/IkSMxa9YsODg46CkiopLHIJLo06dPBwC4ubnh448/1pgXlYgMg1KpxM6dO2Frayv2AD979iySk5Ph7e2N7777DvPmzcPBgwfRokULrfVwvmSi4ichIQGhoaFYtmyZ1KEQkQ59+OGHGtvJyckICAiQKBoi0iWO9iYq2TZu3IgJEyYwiU6kQwaRRM/Bm3Yiw+Xs7Iy+ffti2bJlkMvlAAC1Wo0xY8agdOnS2LJlCz799FNMmjQJx44d01oP50smMkyXLl3C4cOHYW5ujo8++gi2trZ4+PAhQkNDERERgUqVKkkdIhHp0Lx588QOLADw0UcfYdu2bShXrhx2797NhcmIijmO9iYq2d70nZuICkfyJLqdnV2ew8vy8vjxYz1HQ0TarFmzBr///ruYQAcAuVyOUaNGoXnz5pg7dy6CgoLw/vvvSxglERXGTz/9hA8//BBZWVkAgPnz5+Pbb7/FRx99hMaNG2PHjh3srUZkZCIiIrBp0yYAwP79+7F//378+uuv2Lp1KyZMmIB9+/blu66jR49iwYIFOHPmDBISErBjxw4xgZeXI0eOaIxKy5GQkKB1kXAiKhhdj/ZmOyciopJO8iT60qVLpQ6BiPIhKysLV69ezbWA5NWrV8UVwC0sLPL9UIyIDMecOXMQGBiI2bNnY/Xq1QgODsbo0aOxe/duNG3aVOrwiEgPEhMT4eLiAgDYtWsXPvroI3h7e8PNza3AC3U/e/YM9evXx+DBg9GzZ898n3ft2jXY2NiI268vfEhE705Xo73ZzomIqKSTPInOKVyIiocBAwZgyJAh+Pzzz8Wk2qlTpzB37lz4+/sDAGJiYlC7dm0pwySiQrh27Rqio6NhbW2NUaNGYcKECViyZAkT6ERGzM7ODrdv34aLiwv27NmDOXPmAHg5/Dvn4Xh+dezYER07dixwDI6OjrC1tc1X2fT0dKSnp4vbqampBb4eUUmUnZ2NJUuWYOvWrYiPj0dGRobG8fyO9mY7JyKikk7yJPqr4uPj33jc1dW1iCIhotctWbIETk5OmD9/PpKSkgAATk5OGDduHCZNmgQA8Pb25pQPRMXQkydPxF5iJiYmsLS05BzoREauZ8+e6Nu3L6pWrYpHjx6JybFz586hSpUqRRJDgwYNkJ6ejjp16mDGjBlvXJg8LCwMM2fOLJK4iIzJzJkzsXr1aowfPx5Tp07FlClT8O+//2Lnzp344osv9H59tnMiIjIWBpVEd3Nze+NUEAXtFUNEupGVlYXo6GgMHToUU6ZMEXuFvDo0E+CDLqLibO/evVAqlQBeLhp88OBBXLx4UaNMt27dpAiNiPRgyZIlcHNzw+3btzF//nxx0cGEhASMHDlSr9cuV64cIiIi0KRJE6Snp2P16tXw8vLCH3/8gUaNGuV5TkhICIKDg8Xt1NRUcToaItJu06ZN+Pbbb9G5c2fMmDEDffr0QeXKlVGvXj2cOHECo0eP1st12c6JpNW/f/9c39eJ6N0YVBL93LlzGtuZmZk4d+4cFi9ejNDQUImiIiJTU1N8+umnuHLlCoDcyXMiKv5en17tk08+0diWyWR8mE1kRMzMzDBhwoRc+8eNG6f3a1evXh3Vq1cXt5s3b44bN25gyZIl2LBhQ57nKBQKKBQKvcdGZGwSExNRt25dAIC1tTVSUlIAAF26dMG0adP0dl22cyL9SU5OxsmTJ3H//n2o1WqNYzlTra5YsUKK0IiMmkEl0evXr59rX5MmTVC+fHksWLCgQAuYEJFuvffeezh37hxUKpXUoRCRjr1+801EJcONGzewdOlS8SF5rVq1MHbsWEmmc3rvvfdw7NixIr8ukbGrWLEiEhIS4OrqisqVK2Pfvn1o1KgRTp06VeQJa7Zzonf3888/o1+/fnj69ClsbGw0ZnOQyWRiEp2IdM+gkujaVK9eHadOnZI6DKISbeTIkRg/fjzu3LmDxo0bo1SpUhrH69WrJ1FkRKQrjx49QpkyZQAAt2/fxrfffou0tDR07doV77//vsTREZEu7d27F926dUODBg3EOYp///131KpVCz///DM6dOhQpPGcP38e5cqVK9JrEpUEPXr0wMGDB+Hh4YFRo0ahf//+WLNmDeLj44tk5Mmr2M6J3t348eMxePBgzJ07F1ZWVlKHQ1SiGFQS/fXVtwVBQEJCAmbMmIGqVatKFBURAYCfnx8AaMybKJPJIAgCp3kgKub++usvdO3aFbdv30bVqlWxZcsW+Pr64tmzZ5DL5Vi8eDF++OEHdO/eXepQiUhHJk+ejHHjxuHLL7/MtX/SpEkFSqI/ffoU169fF7fj4uJw/vx52Nvbw9XVFSEhIbh79y7Wr18PAFi6dCnc3d1Ru3ZtpKWlYfXq1Th06BD27dunmxdHRKJX2/jHH38MlUqF48ePo2rVqujatWu+62E7JzIMd+/exejRo5lAJ5KAQSXRbW1tcy0sKggCXFxcsGXLFomiIiLg5Y0yERmniRMnom7duti0aRM2bNiALl26oHPnzvj2228BAKNGjcKXX37JJDqREbly5Qq2bt2aa//gwYOxdOnSAtV1+vRptGnTRtzOWRgwICAAUVFRSEhIQHx8vHg8IyMD48ePx927d2FlZYV69erhwIEDGnUQkW68Psps9+7dePHiBZo0aVKgetjOiQyDj48PTp8+LcnUa0QlnUEl0Q8fPqyxLZfLUbZsWVSpUgWmpgYVKlGJw7nQiYzXqVOncOjQIdSrVw/169fHqlWrMHLkSMjlcgAvk+jNmjWTOEoi0qWyZcvi/PnzuUZ7nj9/Ho6OjgWqy8vLC4IgaD0eFRWlsT1x4kRMnDixQNcgooJ52yizJUuWFGiUGduPBdfLAAEAAElEQVQ5kXR++ukn8ffOnTvjs88+w+XLl1G3bl2YmZlplO3WrVtRh0dUYhhUZrp169ZSh0BEb3H58mXEx8cjIyNDYz8/rImKr8ePH8PZ2RkAYG1tjVKlSsHOzk48bmdnhydPnkgVHhHp0KxZszBhwgQMGzYMw4cPx82bN9G8eXMAL+dEnzdvntjDlIiKL44yIzIeebXTWbNm5drHaVaJ9Mugkujr1q2Dg4MDOnfuDODlB/+qVatQq1YtbN68mT1hiSR08+ZN9OjRA3/99Zc4FzoAcQomflgTFW+vT6f2+jYRGYeZM2fi008/xbRp01C6dGksWrQIISEhAIDy5ctjxowZGuufEFHxxFFmRMZDrVZLHQIRwcCS6HPnzsWKFSsAALGxsVi2bBmWLl2KXbt2Ydy4cdi+fbvEERKVXGPGjIG7uzsOHjwId3d3nDx5Eo8ePcL48eOxcOFCqcMjonc0cOBAKBQKAEBaWho+/fRTlCpVCgCQnp4uZWhEpEOvPgQfN24cxo0bJ440KV26tJShEZEOcZQZERGRbhlUEv327duoUqUKAGDnzp348MMPMXz4cLRo0QJeXl7SBkdUwsXGxuLQoUNwcHCAXC6HXC5Hy5YtERYWhtGjR+PcuXNSh0hEhRQQEKCx3b9//1xl/P39iyocItKz10eaMHlOZJw4yozI+IwePRpVqlTJNWps2bJluH79eoEXByei/DOoJLq1tTUePXoEV1dX7Nu3T5yP0cLCAi9evJA4OqKSLTs7W/yS7eDggHv37qF69epQqVS4du2axNER0buIjIyUOgQiKkLVqlV7azLt8ePHRRQNEekLR5kRGZ9t27ZpLDSao3nz5vjyyy+ZRCfSI4NKonfo0AFDhw5Fw4YN8ffff6NTp04AgEuXLsHNzU3a4IhKuDp16uDPP/+Eu7s7PDw8MH/+fJibm2PVqlWoVKmS1OEREelcUlISUlJSpA5DMrdu3dL4b0mlVCrh5OQkdRg6NXPmTCiVSqnDICI94igzIuP06NGjPD/DbWxs8PDhQwkiIio5DCqJHh4ejqlTp+L27dvYtm0bypQpAwA4c+YM+vTpI3F0RCXb1KlT8ezZMwAvv3x37doV77//PsqUKYMtW7ZIHB0RkW4lJSWh/wB/ZGawp15oaKjUIUjKzFyBjRvWG1Ui3c/PD46OjlKHQUR6xFFmRMapSpUq2LNnD4KCgjT2//rrryW2c5s8reR2eqGXiupvwKCS6La2tli2bFmu/TNnzpQgGiJ6lY+Pj/h71apVcfXqVTx+/Bh2dnacX5GIjE5KSgoyM9LxolJrqC3YY7ekkqelADdjkJKSYjRJdH5mExERFV/BwcEICgrCgwcP0LZtWwDAwYMHsWjRohI3lYtSqYSZuQK4GSN1KGQAzMwVeh9paVBJdAD47bffsHLlSty8eRPff/89KlSogA0bNsDd3R0tW7aUOjyiEmfw4MH5Krd27Vo9R0JEVPTUFkqoSzlIHQaRzgiCIHUIREREVEiDBw9Geno6QkNDMXv2bACAm5sbVqxYUeKmaHJycsLGDetL9PSLwMupF0NDQzFlyhSoVCqpw5FMUUzBaFBJ9G3btmHAgAHo168fzp49Ky52kpKSgrlz52L37t0FrvPLL79ESEgIxowZU+KeyhHpQlRUFFQqFRo2bMgv3kRERMWcWq2WOgQiIiJ6ByNGjMCIESPw4MEDWFpawtraukDnh4WFYfv27bh69SosLS3RvHlzzJs3D9WrVxfLeHl5ISZGs4f3J598goiICHE7Pj4eI0aMwOHDh2FtbY2AgACEhYXB1LToUo1OTk5GM1rwXalUKlSrVk3qMIyaQSXR58yZg4iICPj7+2vMsdyiRQvMmTOnwPWdOnUKK1euRL169XQZJlGJMmLECGzevBlxcXEYNGgQ+vfvD3t7e6nDIiIiIiIiIipR2rZti+3bt8PW1hZly5YV96empqJ79+44dOjQW+uIiYlBYGAgmjZtiqysLHz++efw9vbG5cuXUapUKbHcsGHDMGvWLHHbyspK/D07OxudO3eGs7Mzjh8/joSEBPj7+8PMzAxz587V0aslMiwGlUS/du0aWrVqlWu/UqlEcnJygep6+vQp+vXrh2+//bZQCXgieik8PByLFy/G9u3bsXbtWoSEhKBz584YMmQIvL29ObcqERk1+YtkqUMgCfH/PxERERmSI0eOICMjI9f+tLQ0/Pbbb/mqY8+ePRrbUVFRcHR0xJkzZzRyclZWVnB2ds6zjn379uHy5cs4cOAAnJyc0KBBA8yePRuTJk3CjBkzYG5uXoBXRVQ8GFQS3dnZGdevX4ebm5vG/mPHjhV4leHAwEB07twZ7du3f2sSPT09XZw6Bnj5BI+I/kehUKBPnz7o06cPbt26haioKIwcORJZWVm4dOlSgYePEREVF5ZxR6UOgYiIiIhKuAsXLoi/X758GYmJieJ2dnY29uzZgwoVKhSq7pw5xV8fcb5p0yZs3LgRzs7O6Nq1K6ZNmyb2Ro+NjUXdunU1plLx8fHBiBEjcOnSJTRs2DDXdZh7o+LOoJLow4YNw5gxY7B27VrIZDLcu3cPsbGxGD9+PL744ot817NlyxacPXsWp06dylf5sLAwzJw5s7BhE5UocrkcMpkMgiAgOztb6nCIqBiaMWNGrs/d6tWr4+rVqwBe9qQZP348tmzZgvT0dPj4+GD58uWSzHf4wr0V1Ja2RX5dMgzyF8l8kEJERESSa9CgAWQyGWQyGdq2bZvruKWlJb755psC16tWqzF27Fi0aNECderUEff37dsXKpUK5cuXx4ULFzBp0iRcu3YN27dvBwAkJibmujfP2X41wf8q5t6ouDOoJPrkyZOhVqvRrl07PH/+HK1atYJCocBnn32GoUOH5quO27dvY8yYMdi/fz8sLCzydU5ISAiCg4PF7dTUVLi4uBTqNRAZo/T0dHE6l2PHjqFLly5YtmwZfH19IZfLdXqt4pRcK8lu3ryJTz/9FADw6aefYvXq1QUeMUQlW+3atXHgwAFx+9UFiMaNG4dffvkF33//PZRKJYKCgtCzZ0/8/vvvRR6n2tIW6lIORX5dIiIiIqIccXFxEAQBlSpVwsmTJzXmQzc3N4ejoyNMTEwKXG9gYCAuXryIY8eOaewfPny4+HvdunVRrlw5tGvXDjdu3EDlypUL9RqYe6PizqCS6DKZDFOmTMFnn32G69ev4+nTp6hVqxZWrlwJd3d3rU+zXnXmzBncv38fjRo1EvdlZ2fj6NGjWLZsGdLT03O9sSgUCigUCp2/HiJjMHLkSGzZsgUuLi4YPHgwNm/eDAcH/SaUiktyraTy8vLS2Far1Rg8eDCAl3P0EeWHqalpnnMspqSkYM2aNYiOjhZ72URGRqJmzZo4ceIEmjVrlmd9HB5KRERERMZKpVIBePndS1eCgoKwa9cuHD16FBUrVnxjWQ8PDwDA9evXUblyZTg7O+PkyZMaZZKSkgBA6zzqzL1RcWcQSfT09HTMmDED+/fvF3ued+/eHZGRkejRowdMTEwwbty4fNXVrl07/PXXXxr7Bg0ahBo1amDSpEmFejJHVJJFRETA1dUVlSpVQkxMDGJiYvIslzOsSxd0nVwj3Xk1gW5iYoLs7GzxvznHmUin/Pjnn39Qvnx5WFhYwNPTE2FhYXB1dcWZM2eQmZmJ9u3bi2Vr1KgBV1dXxMbGam3nHB5KRERERCXF5cuXER8fn2uR0W7dur31XEEQMGrUKOzYsQNHjhyBu7v7W885f/48AKBcuXIAAE9PT4SGhuL+/ftwdHQEAOzfvx82NjaoVatWAV8NUfFgEEn0L774AitXrkT79u1x/Phx9O7dG4MGDcKJEyewaNEi9O7dO9/J79KlS2vM4wQApUqVQpkyZXLtJ6K38/f3h0wmK9Jr6jq5Zow9VNPS0hAfH1+k17x37574+4wZM5CZmYnQ0FBMnjwZZmZmmDFjBoCXvdHLly9fZHG5urrme/ouMgweHh6IiopC9erVkZCQgJkzZ+L999/HxYsXkZiYCHNzc9ja2mqc4+Tk9MYRaRweSkRERETG7ubNm+jRowf++usvca0yAOJ39vysWxYYGIjo6Gj8+OOPKF26tHiPrVQqYWlpiRs3biA6OhqdOnVCmTJlcOHCBYwbNw6tWrVCvXr1AADe3t6oVasWBgwYgPnz5yMxMRFTp05FYGAge5uT0TKIJPr333+P9evXo1u3brh48SLq1auHrKws/Pnnn0WevCMiTVFRUUV6PX0k14yxh2p8fLzGPHVFLSdhDgChoaFajxWFVatWoVq1akV6TXo3HTt2FH+vV68ePDw8oFKpsHXrVlhaWhaqTg4PJSIiIiJjN2bMGLi7u+PgwYNwd3fHyZMn8ejRI4wfPx4LFy7MVx0rVqwAkHuazsjISAwcOBDm5uY4cOAAli5dimfPnsHFxQW9evXC1KlTxbImJibYtWsXRowYAU9PT5QqVQoBAQGYNWuWzl4rkaExiCT6nTt30LhxYwBAnTp1oFAoMG7cOJ0l0Dm1AFHxoY/kmjH2UHV1dcWqVauK9Jo5SfuuXbuia9euuY7v3LkTu3fvBoAijc3V1bXIrkX6YWtri2rVquH69evo0KEDMjIykJycrPHALCkpSev8ikREREREJUFsbCwOHToEBwcHyOVyyOVytGzZEmFhYRg9ejTOnTv31jpyeq9r4+LionUa11epVCrx+x9RSWAQSfTs7GyYm5uL26amprC2tpYwIiIyFLpIrhljD1ULC4si730tl8uhVquxZ88ejB8/Ptfx/fv3i+XYM5wK4unTp7hx4wYGDBiAxo0bw8zMDAcPHkSvXr0AANeuXUN8fDw8PT0ljpSIiIiISDrZ2dkoXbo0AMDBwQH37t1D9erVoVKpcO3aNYmjIzJuBpFEFwQBAwcOFJNcaWlp+PTTT1GqVCmNcrpcuJCIigcm1wzH6tWrMXjwYGRmZuLevXsa857fu3cPmZmZYjmiN5kwYQK6du0KlUqFe/fuYfr06TAxMUGfPn2gVCoxZMgQBAcHw97eHjY2Nhg1ahQ8PT25eDARERERlWh16tTBn3/+CXd3d3h4eGD+/PkwNzfHqlWrUKlSJanDIzJqBpFEDwgI0Nju37+/RJEQkdSYXDNcr96U9e3bN1/liPJy584d9OnTB48ePULZsmXRsmVLnDhxAmXLlgUALFmyBHK5HL169UJ6ejp8fHywfPlyiaMmIiIiIpLW1KlT8ezZMwDAzJkz0bVrV7z//vsoU6YMtmzZInF0RMbNIJLokZGRUodARAaCyTXDduTIkVwL0Lx+nOht3naDb2FhgfDwcISHhxdRREREREREhs/Hx0f8vWrVqrh69SoeP34MOzs7na0rSER5M4gkOhFRDibXDNubEug5x5lIJ2MiT0uROgSSEP//ExERkSEYPHhwvsqtXbtWz5EQlVxMohMRUb6cPHlS/P3rr79GvXr1xO0LFy5g9OjRYrn33nuvyOMj0iWlUgkzcwVwM0bqUEhiZuYKKJVKqcMgIiKiEiwqKgoqlQoNGzaEIAhSh0NUIjGJTkRE+TJx4kTx91cT6K9vT5w4kb3RqdhzcnLCxg3rkZJScnsi37p1C6GhoZgyZQpUKpXU4UhGqVTCyclJ6jCIiIioBBsxYgQ2b96MuLg4DBo0CP3794e9vb3UYRGVKEyiExFRgXh6eua5v0mTJjh9+nQRR0OkP05OTkyeAlCpVKhWrZrUYRARERGVWOHh4Vi8eDG2b9+OtWvXIiQkBJ07d8aQIUPg7e3N+dCJioBc6gCIiKh4iY2NzXM/E+hERERERET6oVAo0KdPH+zfvx+XL19G7dq1MXLkSLi5ueHp06dSh0dk9JhEJyKifJk/f774+4ULFzSOvbr9ajkiIiIiIiLSLblcDplMBkEQkJ2dLXU4RCUCp3MhIqJ8eXWx0JxFRPOawoWLihIREREREelWenq6OJ3LsWPH0KVLFyxbtgy+vr6Qy9lHlkjfmEQnIqJ8O3LkCLy8vMTt1xPoXFCUiIiIiIhIt0aOHIktW7bAxcUFgwcPxubNm+Hg4CB1WEQlCpPoRERUIEeOHMHJkycxceJEcd/8+fPZA52IiIiIiEgPIiIi4OrqikqVKiEmJgYxMTF5ltu+fXsRR0ZUcjCJTkREBfbee++x1zkREREREVER8Pf3h0wmkzoMohKNSXQiIiIiIiIiIiIDFRUVJXUIRCUeVx4gIiIiIiIiIiIiItKCSXQiIiIiIiIiIiIiIi2YRCciIiIiIiIiIiIi0oJJdCIiIiIiIiIiIiIiLZhEJyIiIiIiIiIiIiLSgkl0IiIiIiIiIiIiIiItmEQnIiIiIiIiIiIiItKCSXQiIiIiIiIiIiIiIi2YRCciIiIiIiIiIiIi0oJJdCIiIiIiIiIiIiIiLZhEJyIiIiIiIiIiIiLSgkl0IiIiIiIiIiIiIiItmEQnIqICi4+PR4cOHeDl5YUOHTogPj5e6pCIiIiIiIiIiPTCVOoAiIioeGnbti3UarW4nZmZCX9/f8jlchw6dEjCyIiIiIiIiIiIdI890YmIKN9eT6C/Sq1Wo23btkUcERHpQ2JiIiZMmAAAmDBhAhITEyWOiIiIiIiISDpGmURfsWIF6tWrBxsbG9jY2MDT0xO//vqr1GERkY6Fh4fDzc0NFhYW8PDwwMmTJ6UOyajFx8drTaDnUKvVnNqFqJjz9fWFn58fUlNTAQCpqanw8/ODr6+vxJGRoTp69Ci6du2K8uXLQyaTYefOnW8958iRI2jUqBEUCgWqVKmCqKgovcdJRIXHdk5ERCWdUSbRK1asiC+//BJnzpzB6dOn0bZtW3zwwQe4dOmS1KERkY589913CA4OxvTp03H27FnUr18fPj4+uH//vtShGa1BgwZpbJuYmGDAgAEwMTF5YzmiwuKDsqLn6+uLtLQ0AECZMmU0/puWlsZEOuXp2bNnqF+/PsLDw/NVPi4uDp07d0abNm1w/vx5jB07FkOHDsXevXv1HCkRFRbbORFpw3t2KimMck70rl27amyHhoZixYoVOHHiBGrXri1RVESkS4sXL8awYcPEhG1ERAR++eUXrF27FpMnT5Y4OuOUnZ0t/r5x40ZUrFgRADBkyBDcuXMH/fv3z1WOqLByHpRFRETAw8MDS5cuhY+PD65duwZHR0epwysSaWlpRTqyIzk5WUygz58/HykpKQgNDcWnn34KpVKJiRMnIi0tDSdPnoStrW2RxQUArq6usLCwKNJrUv517NgRHTt2zHf5iIgIuLu7Y9GiRQCAmjVr4tixY1iyZAl8fHz0FSYRvQO2cyLKC+/ZqSQxyiT6q7Kzs/H999/j2bNn8PT0zLNMeno60tPTxe2c4cvvSp6WopN6CkWdBXn6U+mub0DUCmtALs2fuqR/A0YsIyMDZ86cQUhIiLhPLpejffv2iI2NzVVeX228JMtJoGvbJnpXBX1QZoztPD4+HsOHD5fk2hMnThR/Dw0N1XqsqKxatQrVqlUr8uuSfsTGxqJ9+/Ya+3x8fDB27Fit5+izjUt2v8b7dRHv142PIbVzk5Q7kL9I1kldBSZkQ5bxXJprGxjB3AqQmby9oI7JMvg+q08FuWc3xvv1ou70kpdbt25p/FcqJaHTi9Em0f/66y94enoiLS0N1tbW2LFjB2rVqpVn2bCwMMycOVNn11YqlTAzVwA3Y3RWJxVfZuYKKJVKqcMwKg8fPkR2djacnJw09js5OeHq1au5yuu6jRNw//59jZ4FnEaHdKmgD8oA42znrq6uWLVqVZFdb+TIkcjKysKwYcPQtGnTXMdPnDiBtWvXwtTUFMuXLy+yuICX/xZkPBITE/P8DE9NTcWLFy9gaWmZ6xx9tHHes1MO3q/rniG0c6VSCbncBBZ3z+qsTiqe5HITtnE9KOg9uzHer0vZ6eV1r3d8KWolodOL0SbRq1evjvPnzyMlJQU//PADAgICEBMTk2ciPSQkBMHBweJ2amoqXFxcCn1tJycnbNywHikp0vVqSE9PR2JiomTXNyTOzs5QKBSSXV+pVOa6gaSipes2TsBHH30EKysr+Pv7Y/369Xj+nD1sSHcK+qAMMM52bmFhUaQ3oqVLl8Z///2Hn3/+Gf369ct1/IsvvhDLGfsNMhkefbRxqe/Zeb/+P7xfJ0A/38uXLw/H7du3dRFeoWRmZuLhw4eSXd+QODg4wMzMTJJru7i4sI3rQUHv2Y3xfr2oO70YspLQ6cVok+jm5uaoUqUKAKBx48Y4deoUvvrqK6xcuTJXWYVCofObNicnJ8nfpOvWrSvp9Yn0xcHBASYmJkhKStLYn5SUBGdn51zl9dHGS6LVq1dj6NCh4vbz588RERGRZzmiosZ2/u5WrFgBPz8/JCYmIjk5WWPe8+TkZDHZt2LFCokiJGPh7Oyc52e4jY1Nnr1TAf21canv2Xm/TsbKUNp5jRo1UKNGDZ3WSUSFY4z360Xd6YWkJZc6gKKiVqs15l4iouLL3NwcjRs3xsGDB8V9arUaBw8e1Lr2Ab27nAeTuipHpE1BH5SRbjg7O4vzGHbv3h1+fn7Y/3/s3XlcTfn/wPHXbReKkAol+xpZhiaTdWQZYxujEbJv2cc6IhnZZsY2ZBdDGOswGDtlyb4zmEkjW1maEEnL/f3h1/nOVVGk0/J+Ph49pvs5n/s579v43HvP+3yWvXtxc3OjTZs2wOsLBfl/ID6Uk5OTzmc4wN69e+UzXIgcRPq5EDmffGcXuU2OTKKPHTuWoKAg/vnnHy5dusTYsWM5dOhQilOThRDZ0/Dhw1myZAkrV67kzz//pH///jx//lzZ0ER8HIcOHfqg40KkhdwoU8+uXbuURHp4eDi+vr7KCHQTExN27dqlZngii4qOjub8+fOcP38egNDQUM6fP69stDV27Fi6du2q1O/Xrx83b95k1KhRXLt2DT8/P9avX8+wYcPUCF8IkQbSz4UQb5Lv7CK3yZHLuTx48ICuXbty//59zM3NcXBwYPfu3Xz++edper5WqwVyxk7BIntL+jeY9G9S/E/Hjh15+PAhEyZMIDw8nOrVq7Nr1640TcmWPv5htm3bRkhICIMHD0ar1aLRaJg7dy6lS5eWv2k6SR9P3fDhw/Hw8KBWrVp88sknzJ49O103yqSfv7/169cTERHBsGHDeP78OXnz5mXWrFnKhnAifXJDPz99+jQNGzZUHietd+rh4cGKFSu4f/++kmgDsLe3Z8eOHQwbNow5c+ZQvHhxli5diqura5rPKX1cZBW5oY+D9HORu+WWfv4+PuQ7u/RxkVWktY9rtPIukMydO3ey/eYGIme5ffs2xYsXVzuMHEP6uMhqpI+nbN68efzwww/KjbK5c+dSp06dND1X+rnIaqSfZyzp4yKrkT6e8aSfi6xG+nnK3vc7u/RxkdW8q49LEj0FiYmJ3Lt3j/z586PRaNQOJ1tK2mX59u3bmJmZqR1OtqXVann27Bk2Njbo6eXI1ZdUIX08Y0g//3DSxz8e6ecfTvp4xpB+/nFIH88Y0s8/nPTxj0f6+YeTPp4xpJ9/HNLHM4b08w+X1j4uSXTxUTx9+hRzc3OePHkinViIHEr6uRA5m/RxIXI+6edC5GzSx4XI+aSfZx65hSaEEEIIIYQQQgghhBBCpEKS6EIIIYQQQgghhBBCCCFEKiSJLj4KY2NjvL29MTY2VjsUIcRHIv1ciJxN+rgQOZ/0cyFyNunjQuR80s8zj6yJLoQQQgghhBBCCCGEEEKkQkaiCyGEEEIIIYQQQgghhBCpkCS6EEIIIYQQQgghhBBCCJEKSaILIYQQQgghhBBCCCGEEKmQJLoQQgghhBBCCCGEEEIIkQpJogshhBBCCCGEEEIIIYQQqZAkuhBCCCGEEEIIIYQQQgiRCkmiCyGEEEIIIYQQQgghhBCpkCS6EEIIIYQQQgghhBBCCJEKSaILIYQQQgghhBBCCCGEEKmQJHo2pdFodH709PQwNzenbt26zJ49m7i4OLVDTJNu3bqh0Wg4dOiQ2qEIka38t/8HBwenWm/9+vVKvZIlS2ZegOlUsmRJNBqN2mEIkeO9+f1Bo9FgaGiIjY0N7du359ixYyk+r0GDBsmelzdvXipVqsS3337Lw4cPM/mVCJH7pNR/3/aTlT/3hRDp876f30LkdjnluvljXi//888/Kb7upPL//hgbG2NpaUnNmjXp27cve/fuRavVfpS4siIDtQMQH8bDwwOAhIQE/vnnH44dO8aJEyfYvn07u3btwsAge/4vLlmyJLdu3cpVnVGI9xUQEICTk1OKx1avXp3J0QghsoOk7w8Az54948KFC2zevJktW7awevVqOnXqlOLzXF1dsbKyAuD+/fscP36cmTNn8uuvv3LixAmKFSuWKfELkRv9t98mOXLkCCEhIVSrVo3q1avrHCtcuHAmRSaEyCzv+/mdVv/88w/29vbUr19fBrqJHCczr5snTpyIj48P/v7+dOvWLUPbzmx58+blq6++Al7nHqOiorh8+TKLFy9m8eLF1KxZkzVr1lCuXDmVI/34smeGVShWrFih8/jEiRM0aNCA/fv3s27dOjp37qxOYEKIj05fX59KlSrx66+/Mnv27GQ3zR4/fsyuXbuoUaMGZ8+eVSnKtNm/f3+2mUEjRE7w5veHxMREvvvuO6ZPn87gwYPp0KEDhoaGyZ43ZswYGjRooDy+f/8+jRs35s8//8Tb25ulS5d+5MiFyL3e7LfwelZnSEgIbdq0YeLEiZkekxAic73v57cQuVlOum5WQ+HChVP8DnLhwgWGDx/OgQMHcHFx4dSpU5QoUSLzA8xEspxLDlOnTh3lLtfu3bvVDUYI8dG5u7vz6NGjFPv7r7/+SlxcXLa4mVa6dGkqVKigdhhC5Fp6enpMmjQJAwMDHj9+zJUrV9L0PGtra7y9vQH53iGEEEJktvf9/BYit8kp181ZSbVq1dizZw9NmzYlIiKCIUOGqB3SRydJ9ByocuXKADx48CDZMa1Wy9q1a2nUqBEFCxbExMSEihUrMnHiRF68eJGsfnR0NFOnTqVatWqYm5uTL18+SpcuTYcOHZK9+bxt7agVK1ag0WjeOULm0KFDaDQabt26pbT5vutSbdy4EY1G89YpbX369EGj0eDv75+utoXIKjp16oRGo0lx+tnq1avJly8frVu3TvG5Se8Hbm5ulCtXjrx585I/f34++eQT/Pz8SExMTPaciRMnotFoWLFiBSdPnuSLL76gUKFCaDQazp8/r7S7ePFiqlWrRp48ebCysqJnz548ePAg1X0QUlrjLWkNtgYNGhATE8OYMWOws7PD2NiYMmXKMH36dFnySYgMZGRkhLm5OQDx8fFpft7bvncIIdTz+PFjRo4cSdmyZTExMcHCwoJmzZqxZ8+eFOvfunWL/v37U65cOUxNTbGwsKBy5cr07duX69ev69S9fPkynTt3plSpUpiYmFCkSBGqV6/O0KFDuX//fma8PCHE/3vb5/f58+cZNWoUNWvWpEiRIhgbG1OqVCkGDBjAvXv3dOpOnDgRe3t7AAIDA3WuxbP7chRCZOZ1c8mSJfHx8QGge/fuOn3pzevgEydO4ObmRrFixTA2Nsba2prGjRuzZMmSVF/L0qVLcXBwUK61+/btS1RUVPr+IBlEX1+fefPmodFo+O233wgLC0t3G1WqVEGj0ST7rpHk9u3b6OvrY29vr/r1vyznkgM9e/YMAEtLS53yxMREOnfuzNq1a8mXLx+1atWiYMGCnD59Gh8fH/744w8OHTpEnjx5gNdrHTVp0oQTJ05QuHBhGjRogImJCXfu3GHnzp3kzZsXV1fXDI3dysoKDw8PNm7cyPPnz3XWfEvvuo6tW7fGysqKzZs38/jxYwoVKqRzPDo6mrVr12JmZkbHjh0zJH4hMluJEiVwcXFh27ZtREdHky9fPgBu3rxJcHAwXbp0wdTUNMXnxsbG0qlTJwoVKkSlSpWoUaMGjx8/5tixY3h6enLy5MkUp20BBAUF0adPH8qVK0fTpk25d+8eenqv78sOHz6c2bNnY2RkRMOGDTE3N2fnzp0cOHAABweHdL/GV69e0bRpU65evUqDBg14/vw5gYGBjBkzhmfPnjF58uR0tymESC40NJTHjx9jaGhImTJl0vy81L53CCHUc/fuXVxcXLh58ya2tra0adOGhw8fsm/fPnbv3s3MmTMZNmyYUv/27dvUqFGDyMhIypYtS4sWLUhISODWrVssWbIEJycnypcvD8CZM2eoV68eL1++xMHBgdatW/PixQtu3rzJnDlzaNOmDdbW1mq9dCFynbd9fk+bNo1Nmzbh4OBAvXr1gNeJ9QULFvDbb79x+vRpbGxsAKhevTrt27dn06ZNFC1alGbNmintJD1XiOwqM6+bv/rqK/bt28eFCxdwdnbW6ZdJewsBzJkzh+HDh5OYmEjNmjVxcXHh0aNHXLx4kZEjR9K7d+9ksYwaNYo5c+bQoEEDypQpw9GjR1m8eDF//vmncvMrs5UtW5aaNWty+vRpAgMD6dKlS7qe37dvXwYPHszSpUv54Ycfkh1fvnw5iYmJ9OrVS5XXp0MrsiVAm9r/PhcXFy2gXb16tU75jBkztIC2QYMG2vv37yvlsbGx2p49e2oB7ejRo5XyAwcOaAFt7dq1tTExMTptPXnyRHv69OlkMdnZ2aUYk7+/vxbQent765R7eHhoAe3Bgwd1yu3s7FJ9fenx3XffaQHtrFmzkh1bsmSJFtD279//g88jRGYDtPr6+lqt9n//lleuXKkcnzRpkhbQ7t69W3v//v0U+2dcXJx2y5Yt2levXumUP3jwQFurVi0toA0MDNQ55u3trbz/TJ8+PVlchw8f1gJaCwsL7aVLl5Ty58+fa11dXZXnpqXPh4aGKvXr16+vffLkiXLs1KlTWn19fa2pqan22bNn7/6DCSG0Wm3K3x+ePXumPXz4sNLvBw8enOx59evXT7HvarVa7YQJE7SAtlevXh8rbCFEKpK+S7/5HfuLL77QAtpOnTppY2NjlfLDhw9rTU1Ntfr6+tpz584p5Un9eODAgcnOcevWLe3ff/+tPO7atasW0P7444/J6v7555/ae/fuffgLE0LoeN/P7wMHDmjDw8N1yhISErQ+Pj5aQNu9e3edY0nfv+vXr5/hr0EINah93ezv759iXIGBgVqNRqPNnz+/dt++fcnOt2PHDp2ypOtlKysr7bVr15Tyhw8fasuUKaMFtPv370/T3+RNSf3+zdedWnlKevXqpQW0Y8eOTff5o6KitKamptoiRYrofGfRal+/X9na2mr19fW1d+/eTXfbGU2Wc8khEhMTCQkJoX///gQFBdG6dWud0dXx8fHMmDGDvHnzsm7dOp27X0ZGRvz8889YWVmxePFiZSrKw4cPAXB2dsbExETnfGZmZtSsWTMTXtmH6dOnD3p6eilOhUna/Cylu3tCZCdfffUVxsbGBAQEKGUBAQHKVLDUGBgY0KZNm2SbDxUpUoSpU6cCsHXr1hSfW7VqVUaOHJmsfOHChQAMGzaMKlWqKOWmpqbMnTtXGa2eHnp6eixatAgzMzOlrFatWjRv3pwXL15w+vTpdLcpRG7332ml+fPn57PPPuP69ev8/PPPzJ49O01t3L9/n3nz5jFjxgzKlCnDpEmTPm7QQog0uXnzJtu3bydfvnz8/PPPGBkZKcfq1atHv379SEhIYP78+Up50vf+Jk2aJGvP1taW0qVLp6luhQoVZBS6EB9Rej+/GzZsSNGiRXXK9PT0mDBhAsWKFWPbtm2ZFLkQ6lPjujk106ZNQ6vVMm7cuGTnNjAwoEWLFik+7/vvv1dmhsHrFRv69esHvJ4trpaklSP+/fffdD/X3NwcNzc3Hj58mOzvuGfPHsLCwmjZsqUya0ZNspxLNpfSVIbevXuzaNEinWNnz57l0aNHfP7558k+RAHy5MlDzZo12bFjB3/99Rfly5enevXq6Onp4e/vT6VKlWjXrl2yJVGyOjs7O5o1a8bOnTs5duwYn376KQCXLl3ixIkT1KpVC0dHR5WjFOLDFChQgJYtW7J161bCw8O5ffs2169fZ9iwYejr67/z+efPn2fPnj3cunWLFy9eoNVqleUZ/vrrrxSf88UXX6T4/nP06FEAOnTokOxYuXLlqF69erp3PLezs9P5ovDf9gBZe1WI9/Df5dJiY2O5desWJ06cYNKkSZQuXZrmzZun+LyGDRsmK6tRowYHDx7UudElhFDPkSNHAGjWrBkWFhbJjnfp0oWZM2dy+PBhpSxpcMx3332Hvr4+TZo0STaI5r91//jjDzw9PZk8eTL16tXDwEAuK4XIDO/z+f348WO2bdvG5cuXiYqKIiEhAYC4uDgeP35MZGRkiu8VQuQ0alw3pyQ+Pl5ZG71Pnz7peg1NmzZNVpYVrou1/79W+fsut9KvXz+WL1/OkiVLdHIJSQNi0/t3+ljk2042l/Qh+vLlSy5cuMC1a9dYsmQJn376qc7mH//88w8Ae/fufec/6kePHlG+fHnKlSvHjBkzGDt2LH369KFfv35UqVKFxo0b061bt/da21gN/fr1Y+fOncrfBf7XEWUUusgpOnfuzObNm1m3bh2hoaFK2du8evWKbt26sXbt2lTrJH0peJOtrW2K5Ukf3CVKlEj1eelNohcvXjzF8vz58wOvLyCEEOmT0n4H586do379+nz55Zdcvnw5xZtXrq6uWFlZkZCQQGhoKMeOHePs2bMMGTJENukWIotI2iywZMmSKR5PKr97965S1q1bN/bs2cP69etp1aoVJiYm1K5dm2bNmtGjRw+dWawjR47kyJEjHDp0iIYNG5IvXz6cnJxo2bIl3bp1UzY4FEJkvPR+fq9du5Y+ffoQHR2dapvPnj2TJLrINTL7ujkljx8/JiYmBgsLCwoWLJjm50HK18ZZ4br40aNHAO/9XlK7dm1q1KjBvn37CA0Nxd7enoiICH7//XeKFy+us0eDmmQ5l2xuxYoVrFixgnXr1vHnn38yY8YMADw9Pbl165ZSL2mJljJlyuDh4fHWn/+ONv/2228JCQlh7ty5tGzZkrCwMGbNmkX16tWZM2dOmuN8c7fizNSiRQtKlCjB+vXrefr0KS9fvlR2X/7mm29Ui0uIjNSiRQsKFCjAL7/8wq+//krFihWpUaPGW58zc+ZM1q5dS9WqVfnjjz+IiIjg1atXaLVaZWdsbSq7X6c2Ou1jeJ8lYIQQ6efo6Ejfvn2Jj49nwYIFKdYZM2YMK1asYNWqVUoSzcTEhBUrVrBx48ZMjlgI8T5SGlCjr6/Pr7/+ytmzZ/H29qZ27dqcOHGCcePGUa5cOY4dO6bUNTMz48CBAxw+fJhRo0ZRqVIlDhw4wNChQylfvny6RuMJIT5cap/ft27dolu3brx69YrZs2fz119/KaNntVotTk5OQOrf94XIiTL7ujmjZdVr43PnzgFQqVKl926jX79+aLVali1bBsDKlSuJi4ujR48eaZopkBmy5l9fvLeRI0fStGlTXrx4gY+Pj1KedLeqQoUKSuI9tZ8KFSrotFmiRAkGDRrEtm3bePjwIatWrUJfX59Ro0bprHdkaGiY6h3u27dvf4RXmzb6+vr07t2bFy9eEBAQwKZNm/j3339xc3NT7tgJkd0ZGxvToUMHzp07R0RExDvvpgNs2bIFeD1CpVmzZlhaWirrvN28efO94khaBzW1Pq/me4EQ4t3s7e2BtE9JdXFxYcKECcDrZSCSpogLIdSTtGbofwfU/FfSDNVixYolO+bo6MjEiRMJCgri4cOHDBs2jGfPnjF06FCdehqNhnr16jF9+nROnDjBvXv3+Oabb4iIiGDcuHEZ+nqEEO+W0uf3zp07efXqFYMHD2bIkCGUKVOGPHnyKMff9/u+ENlZVrhuLly4MHny5CEyMpKoqKh0Pz+r+euvvzh37hx6enq4uLi8dzudOnXCzMwMf39/4uPjWbp0KXp6evTs2TMDo/0wkkTPgaZNmwbAqlWrlC/PtWvXxtzcnMDAQCIjI9+7bQMDAzp37kzt2rV59eqVzoe0tbU1jx8/5vHjx8met2/fvnSdJ2kDpPj4+PeO9b969eqFgYEBS5YskaVcRI7VpUsXChUqROHChXF3d39n/aSbYClNCVu/fv17xeDs7AzApk2bkh37+++/lTvUQoisKelCIF++fGl+ztChQ7GysuKvv/7i119//VihCSHSqF69egDs2rUrxYvz1atXA/DZZ5+9tR0zMzOmTp2KRqPh8uXLb61raWnJxIkTAd5ZVwiR8VL6/H7bd/2goCAiIiKSlWf0dbgQWVFmXDe/rS/p6+vToEEDABYvXpzWsLOkhIQEBg4ciFarpX379qkuxZoWefPmpXPnzty7d49Ro0bx119/4erqmupSsmqQJHoO5OjoSJs2bYiPj1eWdzE2NmbUqFE8e/aMdu3apXi37O7du6xatUp5fPDgQfbt25dsKZbQ0FD+/PNPNBqNTgepX78+AJMnT9apP2PGDGWDo7RKGkGTNDXmQ1lbW/Pll19y7tw5AgMDcXBw4JNPPsmQtoXIKj777DMePXrEw4cPsbOze2f9pA1IFi5cqFO+ceNGfvnll/eKoW/fvsDrKW9Xr15VymNiYhg8eLCqSzsJId7u3Llzyhf5Fi1apPl5efLkYcyYMQBMnTpVpoULobJSpUrRsmVLnj17xpAhQ4iLi1OOBQcHs2DBAvT19fH09FTKV61alWLy+48//kCr1ersdbJw4UJlHdn/2rlzJ5D6vihCiI8jtc/vpO/6q1ev5vnz50r53bt36devX4ptFS5cGENDQ0JCQmR2mcixMuO6+V05rdGjR6PRaPD19eXgwYM6x+Lj45XP1Kzs4sWLNG3alD179mBtbc3s2bM/uM2k96ZZs2YBWW/wq2wsmkNNnDiRrVu3snz5csaPH4+VlRVjxozh2rVrrFq1iooVK+Lo6Ii9vT2vXr3i+vXrXL16FQcHB7p06QLAhQsXGDZsGEWKFKFmzZoUKlSIhw8fEhgYSGxsLIMGDVLeGOD1m8DGjRuZPXs2hw4donTp0ly6dInbt28zYMAA/Pz80hz/l19+SWBgII0bN6Zhw4bkzZuXwoULK6Ps30e/fv3YvHkzkHV29hVCTaNGjWLXrl2MGTOGDRs2UK5cOf766y9Onz7NiBEj+PHHH9Pd5meffcbQoUOZPXs2NWrUoGHDhpiZmXH48GGMjIxo1aoVv//+u3JnXgihjv9uPv7q1Stu3brF8ePHSUxMpFWrVsp3gbTq27cv06dP5/Lly2zbto3WrVtncMRCiPRYtGgRn332Gb/88guBgYE4OTnx8OFDDh06REJCAj/99BPVq1dX6m/atImuXbtSunRpqlatSp48eQgNDeXEiRPo6enpDJJZuHAh/fv3p1KlSlSsWBEDAwOuXbvGhQsXMDExUZZ4EkJkvPR8fn/55ZdUrlyZ06dPU6ZMGZydnXn58iUHDx6kevXqfPrppzr7HcDr0bPNmjXj999/p1q1atSoUQMjIyOcnZ3p3r17Zr1MIbKU97lubtq0KSYmJsyaNYvLly9jY2ODRqNh5MiRlC9fnvr16zNjxgxGjRpFo0aNqFWrFmXLluXRo0dcuHCB2NjYLLPUy6NHj5T3noSEBJ48ecKVK1eUwbm1a9dmzZo1OvnB91W1alXlvcnKyopWrVp9cJsZSiuyJUD7rv997dq10wLakSNH6pRv3bpV27JlS62lpaXW0NBQa2lpqa1Zs6Z21KhR2jNnzij1/vrrL62Xl5fW2dlZa21trTUyMtIWK1ZM27hxY+2mTZu0iYmJyc4ZHBysbdCggdbU1FRrZmambd68ufb8+fNaf39/LaD19vbWqe/h4aEFtAcPHtQpj4uL03p5eWlLly6tNTQ01AJaOzu7dP2N3hQTE6M1NDTU5smTR/vvv/9+UFtCqA3Q6uvrp6nu/fv3U+1DwcHB2kaNGmkLFiyozZ8/v/bTTz/Vbtq0SRsaGqoFtPXr19ep7+3trQW0/v7+qZ4vMTFRu3DhQm3VqlW1xsbGWktLS62Hh4f2/v372iZNmmgB7bVr13SeY2dnl+w9LbUY0hOLEEJX0veH//7o6elpLSwstA0aNNAuW7ZMm5CQkOx59evXT/Hz+r/mzp2rBbS1a9f+iK9ACPFfSd+l3/yOrdVqtY8ePdJ+++232tKlS2uNjIy0BQoU0DZt2lS7e/fuZHUDAwO1np6e2urVq2sLFSqkNTEx0ZYqVUrr5uamPXXqlE7dbdu2aXv06KGtXLmytkCBAlpTU1NtuXLltL169Ur2+S6EyBjv+/kdGRmp7d+/v7ZkyZJaY2NjbalSpbSjR4/WPn/+XPlsDw0N1XlORESEtkuXLlorKyutvr6+FtB6eHhkzgsVIoOpdd2s1Wq1u3fv1jo7O2vz5cun9Ns3v0sHBQVp27Ztq+TnrK2ttY0bN9YuXbpUp15K18tJDh48+EH9NOk1vPm6k8r/+2NoaKgtXLiwtkaNGto+ffpo9+zZk2Ju8EN4eXlpAe3YsWMztN2MoNFqZc6tyB3Wrl1Lp06d8PDwYMWKFWqHI0SuEx0djb29PS9fviQqKirL7LAthBBCCCGEEEIIdWm1WipWrMiNGzf4+++/KVWqlNoh6ZA10UWuEBcXx/Tp0wF01n8UQmS8P//8kxcvXuiUPX36lD59+vDo0SPc3NwkgS6EEEIIIYQQQgjFxo0buX79Oi1atMhyCXQAGYkucrRt27bx22+/cfLkSa5cuUKbNm3YsmWL2mEJkaP169eP1atXU7NmTaytrXn06BHnzp0jMjKSUqVKcfz4cYoUKaJ2mEIIIYQQQgghhFBZr169iIqKYvv27cTHx3PixAlq1qypdljJyMaiIlsZMWIEjx49SlPdFStWcPbsWfz9/SlYsCCdOnXi559//sgRCiHatWtHeHg4Z86c4eTJkwDY29vTq1cvRo0aRaFChVSOUAghhBBCCCGEyHnSmzfLaEeOHGHp0qVpqtumTRvatGnDsmXLMDAwoGzZskyaNClLJtBBRqKLbKZkyZLcunUrTXXln7YQQgghhBBCCCGEyC3UzputWLGC7t27p6mut7c3EydOzPAYPhZJogshhBBCCCGEEEIIIYQQqZCNRYUQQgghhBBCCCGEEEKIVEgSXQghhBBCCCGEEEIIIYRIhSTRhRBCCCGEEEIIIYQQQohUSBJdCCGEEEIIIYQQQgghhEiFJNGFEEIIIYQQQgghhBBCiFRIEl0IIYQQQgghhBBCCCGESIUk0YUQQgghhBBCCCGEEEKIVEgSXQghhBBCCCGEEEIIIYRIhSTRhRBCCCGEEEIIIYQQQohUSBJdCCGEEEIIIYQQQgghhEiFJNGFEEIIIYQQQgghhBBCiFRIEl0IIYQQQgghhBBCCCGESIWB2gEIIYQQQgghhBAia4iLiyM8PJwXL15QpEgRLCws1A5JCCGEUJ2MRBdCCCGEEEIIIXKxZ8+esWDBAurXr4+ZmRklS5akYsWKFClSBDs7O3r37s2pU6fUDlMIIYRQjUar1WrVDkIIIYQQQgghhBCZb+bMmfj6+lK6dGlatWrFJ598go2NDXny5CEyMpLLly9z+PBhfvvtN+rUqcPPP/9M2bJl1Q5bCCGEyFSSRBdCCCGEEEIIIXKpb775Bi8vLypXrvzWerGxsfj7+2NkZESPHj0yKTohhBAia5AkuhBCCCGEEEIIIYQQQgiRClkTXQghhBBCCCGEEEIIIYRIhYHaAQghhBBCCCGEEEJ9bdu2RaPRJCvXaDSYmJhQpkwZOnXqRPny5VWITgghhFCPjEQXQgghhBBCCCEE5ubmHDhwgLNnz6LRaNBoNJw7d44DBw4QHx/Pr7/+SrVq1Th69KjaoQohhBCZStZEF0IIIYQQQgghBGPGjOHp06fMmzcPPb3XY+4SExMZMmQI+fPnx9fXl379+nHlyhWOHDmicrRCCCFE5pEkuhBCCCGEEEIIIShSpAhHjx6lXLlyOuU3btzg008/5dGjR1y6dInPPvuMqKgodYIUQgghVCDLuQghhBBCCCGEEIL4+HiuXbuWrPzatWskJCQAYGJikuK66UIIIUROJhuLCiGEEEIIIYQQgi5dutCzZ0++++47ateuDcCpU6eYMmUKXbt2BSAwMJDKlSurGaYQQgiR6WQ5FyGEEEIIIYQQQpCQkMC0adOYN28eERERABQtWpRBgwYxevRo9PX1CQsLQ09Pj+LFi6scrRBCCJF5JIkuhBBCCCGEEEIIHU+fPgXAzMxM5UiEEEII9UkSPQWJiYncu3eP/Pnzy1pvQlVarZZnz55hY2ODnp5sYZBRpI+LrEL6+Mcj/VxkFdLPPw7p4yKrkD7+8Ug/F1mF9POPQ/q4yCrS2sdlTfQU3Lt3jxIlSqgdhhCK27dvy3TJDCR9XGQ10scznvRzkdV8zH4eGhrK4cOHuXXrFi9evKBIkSI4Ojri5OSEiYnJRzmn2qSPi6wmp3yWR0REMGLECPbv38+DBw94c8xd0uai77JgwQIWLFjAP//8A0DlypWZMGECzZs3T3Ms0s9FVpNT+nlWIX1cZDXv6uOSRE9B/vz5gdd/PJm6JtT09OlTSpQoofybFBlD+rjIKqSPfzzSzz9MTEwM/v7+3L17l2LFitG9e3fy5MmjdljZ0sfs5wEBAcyZM4fTp09TtGhRbGxsyJMnD5GRkYSEhGBiYoK7uzujR4/Gzs4uw8+vJunjHy4hIYHLly/z77//UrBgQapUqYK+vr7aYWU7Oe2zvFu3boSFhTF+/Hisra3fe3Ro8eLFmTZtGmXLlkWr1bJy5Upat27NuXPn0rwpqfTzDyN9POPktH6eVUgfF1lFWvu4JNFTkPRFwczMTDqyyBJkalPGkj4ushrp4xlP+vn7GzduHEePHlUeX7p0iV27duHs7Iyvr6+KkWVvGd3PHR0dMTIyolu3bmzatCnZSK7Y2FiCg4NZt24dtWrVws/Pjw4dOmRoDGqSPv5hgoKC8PPzIzw8XCmzsrJiwIABuLi4qBhZ9pVTPsuPHDnC4cOHqV69+ge106pVK53Hvr6+LFiwgOPHj6c5iS79/P1JH/84cko/zyqkj4us5l19PEsl0YOCgvjhhx84c+YM9+/fZ8uWLbRp00Y53q1bN1auXKnzHFdXV3bt2qU8joyMZNCgQfz+++/o6enRvn175syZQ758+TLrZQghhBBCZEtJCXRDQ0M6dOhAixYt2LlzJxs2bODo0aOMGzdOEulZxLRp03B1dU31uLGxMQ0aNKBBgwb4+voqSyoIERQUhLe3N05OTowfPx57e3tCQ0MJCAjA29sbHx8fSbLlYiVKlEi2hMuHSkhIYMOGDTx//hwnJ6dU68XGxhIbG6s8TtrYVKRPUh+vW7cuHTt2xNjYmNjYWE6ePCl9PJtp165dmutu3rz5I0YihIAslkR//vw51apVo0ePHqm+WTRr1gx/f3/lsbGxsc5xd3d37t+/z969e4mLi6N79+706dOHNWvWfNTYhRBCCCGys5iYGCWBvmPHDoyMjADo06cP3bp1o2XLlhw9epSYmBhZ2iULeFsC/U2FChWiUKFCHzEakV0kJCTg5+eHk5MTkydPVjbPqly5MpMnT8bLy4sFCxbg7Owsyz7kUrNnz2bMmDEsWrSIkiVLflBbly5dwsnJiZcvX5IvXz62bNlCpUqVUq0/depUfHx8PuicuV1SHy9XrhyhoaEEBwcrx6ysrChXrpz08WzE3Nxc7RCEEP+RpZLozZs3f+dGI8bGxlhZWaV47M8//2TXrl2cOnWKWrVqAfDzzz/TokULfvzxR2xsbFJ8Xk684/3y5UvCwsLUDiNLsLW1zbGbaoncS/r4/0gfFyJjLFq0CIAOHTooCfQkRkZGfPXVV6xdu5ZFixYxdOhQFSIUqUntu6tGo8HY2DjZ/0+Re128eJHw8HDGjx+vJNCT6Onp4e7ujqenJxcvXsTR0VGlKIWaOnbsyIsXLyhdujSmpqYYGhrqHI+MjExzW+XLl+f8+fM8efKEjRs34uHhQWBgYKqJ9LFjxzJ8+HDlcdIatSLtkvp4REREirNNgoOD0Wq10sezif8OIBVCqC9LJdHT4tChQ1haWlKwYEEaNWrE5MmTlZE1wcHBFChQQEmgAzRp0gQ9PT1OnDhB27ZtU2wzJ97xDgsLo0+fPmqHkSUsXryYcuXKqR2GEBlK+vj/SB8XImPcuXMHgBYtWqR4vEWLFqxdu1apJ7KOAgUKvHUNx+LFi9OtWze8vb2TJU5F7pKUALW3t0/xeFJ5ehKlImeZPXt2hrVlZGREmTJlAKhZsyanTp1izpw5yk3bNxkbGyebaS7S59GjRwB88sknKc42GTt2LCdOnFDqCSGESLtslURv1qwZ7dq1w97enpCQEL777juaN29OcHAw+vr6hIeHY2lpqfMcAwMDLCwsdDbUeFNOvONta2vL4sWLVTv/rVu38PX1Zdy4cdjZ2akWB7z+WwiR06jdxyHr9HPp40JkjOLFi3P69Gl27tyZ4k26nTt3KvVE1rJixQrGjRtHt27d+OSTTwA4efIkK1euxMvLi4cPH/Ljjz9ibGzMd99999a2SpYsya1bt5KVDxgwgPnz59OgQQMCAwN1jvXt25eFCxcqj8PCwujfvz8HDx4kX758eHh4MHXqVAwM/nfpcejQIYYPH86VK1coUaIEXl5edOvW7QP+CiItLCwsAAgNDU1xc8fQ0FCdeiL38fDw+GhtJyYm6swAFxkvKioKgM8++yzF2Sb16tXjxIkTSj2RvWzcuJH169cTFhbGq1evdI6dPXtWpaiEyD2yVRLdzc1N+b1q1ao4ODhQunRpDh06ROPGjd+73Zx4x9vExCRLjMy0s7PLEnEIkdNklT4O0s+FyCn69u3Lb7/9xoYNG+jWrZvOEiCvXr1i48aNSj2RtaxcuZKffvqJr7/+Wilr1aoVVatWZdGiRezfvx9bW1t8fX3fmUQ/deoUCQkJyuPLly/z+eef06FDB6Wsd+/eTJo0SXlsamqq/J6QkEDLli2xsrLi2LFj3L9/n65du2JoaMiUKVOA14nali1b0q9fPwICAti/fz+9evXC2to6XWu9i/RzcHDAysqKgIAAnVGq8DrBGRAQgLW1NQ4ODipGKTLb06dPMTMzU35/m6R67zJ27FiaN2+Ora0tz549Y82aNRw6dIjdu3d/cLwidQUKFADg8OHDtGjRIlkfP3LkiE49kX3MnTtXuWG+detWunfvTkhICKdOncLT01Pt8ITIFbL1fM5SpUpRuHBh/v77b+D1RhkPHjzQqRMfH09kZGSq66gLITLXggULcHBwwMzMDDMzM5ycnPjjjz+U4y9fvsTT05NChQqRL18+2rdvT0REhE4bYWFhtGzZElNTUywtLRk5ciTx8fGZ/VKEEB/R3bt36dy5M4UKFSJPnjxUrVqV06dPqx1WjpYnTx6cnZ2Ji4ujZcuWLFq0iNu3b7No0SJatmxJXFwczs7OsqloFnTs2LEU17Z1dHRUNpWrV69emvbSKFKkCFZWVsrP9u3bKV26NPXr11fqmJqa6tT5b1Jtz549XL16ldWrV1O9enWaN2/O999/z/z585VRcwsXLsTe3p6ffvqJihUrMnDgQL766itmzZqValyxsbE8ffpU50ekn76+PgMGDCA4OBgvLy+uXLnCixcvuHLlCl5eXgQHB9O/f3/ZcDCXKViwoHIdXaBAAQoWLJjsJ6k8rR48eEDXrl0pX748jRs35tSpU+zevZvPP//8Y70MARQuXBiAEydOMG7cOLZs2cLOnTvZsmUL48aN48SJEzr1RPbh5+fH4sWL+fnnnzEyMmLUqFHs3buXwYMH8+TJE7XDEyJXyFYj0d90584dHj9+jLW1NQBOTk5ERUVx5swZatasCcCBAwdITEykTp06aoYqhPh/xYsXZ9q0aZQtWxatVsvKlStp3bo1586do3LlygwbNowdO3awYcMGzM3NGThwIO3atePo0aNA2ka4CSGyt3///RdnZ2caNmzIH3/8QZEiRfjrr7/SdfEu3k/SEk1Hjx5l7dq1rF27Vjnm7OyMr6+vitGJ1JQoUYJly5Yxbdo0nfJly5YpSxQ+fvw43X3o1atXrF69muHDh+usuR4QEMDq1auxsrKiVatWjB8/XhmNHhwcTNWqVSlatKhS39XVlf79+3PlyhUlsd+kSROdc7m6ur51w9qcuIeRWlxcXPDx8cHPz09n9KK1tTU+Pj64uLioGJ1Qw4EDB5QlfA4ePJghbS5btixD2hHpkzTbJGlfuKQbqfB6ORcbGxu0Wq3MNsmGwsLC+PTTT4HXAx+ePXsGQJcuXahbty7z5s1TMzwhcoUslUSPjo5WRpXD66me58+fx8LCAgsLC3x8fGjfvj1WVlaEhIQwatQoypQpo0z7rFixIs2aNaN3794sXLiQuLg4Bg4ciJubGzY2Nmq9LCHEf7Rq1Urnsa+vLwsWLOD48eMUL16cZcuWsWbNGho1agS83pG8YsWKHD9+nLp16yoj3Pbt20fRokWpXr0633//PaNHj2bixIk6yw8IIbKn6dOnU6JECfz9/ZWy1DbBExnP19eXmJgYFi1axJ07dyhevDh9+/aVEehZ2I8//kiHDh34448/qF27NgCnT5/m2rVryjI8p06domPHjulq97fffiMqKkpnrfJOnTphZ2eHjY0NFy9eZPTo0Vy/fp3NmzcDEB4erpNAB5THSXsUpVbn6dOnxMTEpPhvLSfuYaQmFxcXnJ2duXjxIpGRkVhYWODg4CAj0HOp/840+e/vIvvR19enQYMGrFu3joIFC/L5559jY2PDvXv32Lt3L/fu3cPNzU36ejZkZWVFZGQkdnZ22Nracvz4capVq0ZoaCharVbt8ITIFbJUEv306dM0bNhQeZz0RdnDw4MFCxZw8eJFVq5cSVRUFDY2NjRt2pTvv/9eZz3zgIAABg4cSOPGjdHT06N9+/bMnTs301+LEOLdEhIS2LBhA8+fP8fJyYkzZ84QFxenMzqtQoUK2NraEhwcTN26ddM0wu1NsbGxOpsYyRRwIT6ew4cPs2jRIkJCQti4cSPFihVj1apV2NvbU69evTS1sW3bNlxdXenQoQOBgYEUK1aMAQMG0Lt371SfI/08Y+XJk+eto4JF1vLll19y7do1Fi1axI0bNwBo3rw5v/32GyVLlgSgf//+6W532bJlNG/eXGcwyn83na1atSrW1tY0btyYkJAQSpcu/WEv5C1y4h5GatPX10/xe5PIfS5evJjmujKCOWtLSEjg0KFDlC9fnqioKNavX68cs7Kyonz58gQGBtK7d29JpGczjRo1Ytu2bTg6OtK9e3eGDRvGxo0bOX36NO3atVM7PCFyhSyVRG/QoMFb76ClZRMSCwsL1qxZk5FhCSEy2KVLl3BycuLly5fky5ePLVu2UKlSJc6fP4+RkVGyjW6KFi36ztFrScdSIlPAhcgcmzZtokuXLri7u3Pu3Dklqf3kyROmTJnCzp0709TOzZs3WbBgAcOHD+e7777j1KlTDB48GCMjIzw8PFJ8jvRzkdvZ29snW87lQ9y6dYt9+/YpI8xTk7Rk4t9//03p0qWxsrLi5MmTOnWS9jZJ2qPIysoq2X4nERERmJmZyYyHTJSQkCAj0QUA1atXR6PRvHM0q0aj0dl4WGQ9Fy9eJDw8nFatWvH7778nO+7i4sKSJUu4ePGi3ETLZhYvXkxiYiKAsofYsWPH+PLLL2XTdyEySZZKogshsq6QkBD8/f0JCQlhzpw5WFpa8scff2Bra0vlypXT1Vb58uU5f/48T548YePGjXh4eBAYGPiRIpcp4EJklsmTJ7Nw4UK6du3KunXrlHJnZ2cmT56c5nYSExOpVauWss+Bo6Mjly9fZuHChakm0aWfi9wuKiqKkydP8uDBA+UiO0nXrl3T3Z6/vz+Wlpa0bNnyrfXOnz8PoLNHka+vLw8ePMDS0hKAvXv3YmZmRqVKlZQ6b95U27t3L05OTumOU7yfoKAg/Pz8dAYgWFlZMWDAAFkTPRcKDQ1VOwSRQSIjIwFYunQpTk5OjB8/Hnt7e0JDQwkICGDp0qU69UT2cefOHZ3vtm5ubri5uaHVarl9+za2trYqRidE7iBJdCHEOwUGBtK8eXOcnZ0JCgrC19cXS0tLLly4wLJly5T1VtPKyMiIMmXKAFCzZk1OnTrFnDlz6NixI69evSIqKkpnNHpERITO6LV3jXB7k0wBFyJzXL9+PcXki7m5OVFRUWlux9raWkm2JalYsSKbNm1K9TnSz0Vu9vvvv+Pu7k50dDRmZmY6m4BqNJp0J9ETExPx9/fHw8MDA4P/XS6EhISwZs0aWrRoQaFChbh48SLDhg3DxcVFWeKhadOmVKpUiS5dujBjxgzCw8Px8vLC09NT6aP9+vVj3rx5jBo1ih49enDgwAHWr1/Pjh07MuCvId4lKCgIb2/vFBNs3t7esrloLmRnZ6d2CCKDJF1DValShcmTJ6OnpwdA5cqVmTx5MkOGDOHSpUvJZv6KrM/e3p779+8rN6iTREZGYm9vL7NEhMgEemoHIITI+saMGcPkyZPZu3evzsadjRo14vjx4x/cfmJiIrGxsdSsWRNDQ0P279+vHLt+/TphYWHK6DQnJycuXbrEgwcPlDpvjnATQqjDyspKZ4PwJEeOHKFUqVJpbsfZ2Znr16/rlN24cUMu8oVIxbfffkuPHj2Ijo4mKiqKf//9V/l5n9GG+/btIywsjB49euiUGxkZsW/fPpo2bUqFChX49ttvad++vc6SAfr6+mzfvh19fX2cnJzo3LkzXbt2ZdKkSUode3t7duzYwd69e6lWrRo//fQTS5cuxdXV9f3/CCJNEhIS8PPzw8nJicmTJ1O5cmVMTU2VBJuTkxMLFiyQZEwuZmtrS9euXVm2bBkhISFqhyOE+H9arVbnJnmS6OhoTExMVIhIiNxHRqILId7p0qVLKe41YGlpyaNHj9LV1tixY2nevDm2trY8e/aMNWvWcOjQIXbv3o25uTk9e/Zk+PDhWFhYYGZmxqBBg3BycqJu3bpA2ka4CSHU0bt3b4YMGcLy5cvRaDTcu3eP4OBgRowYwfjx49PczrBhw/j000+ZMmUKX3/9NSdPnmTx4sUsXrz4I0YvRPZ19+5dBg8ejKmpaYa017Rp0xTXRi5RokSall+zs7N75x4IDRo04Ny5c+8do3g/Sesljx8/XhmhmkRPTw93d3c8PT1lveRcbMqUKQQFBTF9+nR69+5NsWLFqF+/PvXr16dBgwaULVtW7RDFWyTN/Lt06RJeXl64u7vrzDa5dOmSTj2R9SUtV6jRaBg/frzOZ31CQgInTpygevXqKkUnRO4iSXQhxDsVKFCA+/fvY29vr1N+7tw5ihUrlq62Hjx4QNeuXbl//z7m5uY4ODiwe/duPv/8cwBmzZqFnp4e7du3JzY2FldXV/z8/JTnJ41w69+/P05OTuTNmxcPDw+dEW5CCHWMGTOGxMREGjduzIsXL3BxccHY2JgRI0YwaNCgNLdTu3ZttmzZwtixY5k0aRL29vbMnj0bd3f3jxi9ENmXq6srp0+fTteMD5E7Jc1MePM7XZKkclkvOffq3LkznTt3BuD+/fsEBgayfft2BgwYQGJiosxSyOIsLCyA1wMbfv/9dzw9PZVj1tbW9OrVi6VLlyr1RNaXdMNZq9Vy6dIlnZnhRkZGVKtWjREjRqgVnhC5iiTRhRDv5ObmxujRo9mwYQMajYbExESOHj3KiBEj0r3O6rJly9563MTEhPnz5zN//vxU66RlhJsQIvNpNBrGjRvHyJEj+fvvv4mOjqZSpUrky5cv3W198cUXfPHFFx8hSiFynpYtWzJy5EiuXr1K1apVMTQ01Dn+5ZdfqhSZyGqSEmehoaEpbgyftMGkJNhytxcvXnDkyBEOHTrEwYMHOXfuHFWqVKFBgwZqhybewcHBASsrK65cucKqVau4fPkykZGRWFhYUKVKFby9vbG2tlb2sRBZ38GDBwHo3r07c+bMwczMTOWIhMi9JIkuhHinKVOm4OnpSYkSJUhISKBSpUokJCTQqVMnvLy81A5PCJHFGBkZyR4FQmSi3r17A6Q4K0uj0cjIUaFISrAFBATobDoIr/eoCQgIkARbLvfpp59y7tw5KlasSIMGDRgzZgwuLi4ULFhQ7dBEGujr6zNgwAC8vb3x9vbG3d0dJycnQkND8fb2Jjg4GB8fH/T19dUOVaSTv7+/8vudO3cAKF68uFrhCJErSRJdCPFORkZGLFmyhPHjx3P58mWio6NxdHSUNRGFEDratm2b4oZHGo0GExMTypQpQ6dOnShfvrwK0QmRcyUmJqodgsgm/ptgS2m9ZEmwiWvXrpE3b14qVKhAhQoVqFixoiTQsxkXFxd8fHzw8/NLtpyLj48PLi4uKkYn3ldiYiKTJ0/mp59+Ijo6GoD8+fPz7bffMm7cuGT7XAghMp4k0YUQaWZra4utra3aYQghsihzc3N+++03ChQoQM2aNQE4e/YsUVFRNG3alF9//ZXp06ezf/9+nJ2dVY5WCCFyJ0mwibd5/Pgxly5d4tChQ+zevZtx48ZhZGRE/fr1adiwoTLzRWRtLi4uODs7c/HiRWU5FwcHB7lBlo2NGzeOZcuWMW3aNOV79JEjR5g4cSIvX77E19dX5QiFyPkkiS6EeKekHcHf9N/Rpa1bt5b1M4XI5aysrOjUqRPz5s1TRsMkJiYyZMgQ8ufPz7p16+jXrx+jR4/myJEjKkcrRPY2d+5c+vTpg4mJCXPnzn1r3cGDB2dSVCK7kASbSI1Go8HBwQEHBwcGDRrEmTNnmDdvHgEBAfz666+SRM9G9PX1cXR0VDsMkUFWrlzJ0qVLdfY5cXBwoFixYgwYMECS6EJkAkmiCyHe6dy5c5w9e5aEhARlGYYbN26gr69PhQoV8PPz49tvv+XIkSOyDrIQudiyZcs4evSoznRSPT09Bg0axKeffsqUKVMYOHAgn332mYpRCpEzzJo1C3d3d0xMTJg1a1aq9TQajSTRRYokwSZScvbsWQ4dOsShQ4c4cuQIz549o2rVqgwaNIj69eurHZ4QuVZkZCQVKlRIVl6hQgUiIyNViEiI3EeS6EKId0oaZe7v76/sBv7kyRN69epFvXr16N27N506dWLYsGHs3r1b5WiFEGqJj4/n2rVrlCtXTqf82rVrysaGJiYmKa6bLoRIn9DQ0BR/F0KID/HJJ5/g6OhI/fr16d27Ny4uLpibm6sdlhC5XrVq1Zg3b16y2Wfz5s2jWrVqKkUlRO4iSXQhxDv98MMP7N27V0mgw+u1jydOnEjTpk0ZMmQIEyZMoGnTpipGKYRQW5cuXejZsyffffcdtWvXBuDUqVNMmTKFrl27AhAYGEjlypXVDFOIHGfSpEmMGDECU1NTnfKYmBh++OEHJkyYoFJkQojsJjIyUuc7vxBCXaVKleLUqVPMmDGDli1bsm/fPpycnAAIDg7m9u3b7Ny5U+UohcgdZPteIcQ7PXnyhAcPHiQrf/jwIU+fPgWgQIECvHr1KrNDE0JkIbNmzWLo0KHMmDEDFxcXXFxcmDFjBsOGDWPmzJkANG3alHXr1qkcqRA5i4+PD9HR0cnKX7x4gY+PjwoRiewgISGBc+fOsX//fs6dO6fMGBK5W1IC/cyZM6xevZrVq1dz9uxZlaMSIvf6559/SEhIoH79+ly/fp22bdsSFRVFVFQU7dq14/r16+leKnHq1KnUrl2b/PnzY2lpSZs2bbh+/brOOTUaTYo/GzZsUOqFhYXRsmVLTE1NsbS0ZOTIkcTHx2fYaxciq5GR6EKId2rdujU9evTgp59+0hldOmLECNq0aQPAyZMnky3hIITIXfT19Rk3bhzjxo1TbrC9OZrN1tZWjdCEyNG0Wm2KyyRduHBBNv0WKQoKCsLPz4/w8HClzMrKigEDBuDi4qJiZEJtDx48oGPHjgQGBlKgQAEAoqKiaNiwIevWraNIkSLqBijSLCEhQTYPzmGKFSuWIRuIBgYG4unpSe3atYmPj+e7776jadOmXL16lbx581KiRAnu37+v85zFixfzww8/0Lx5c+D1v6+WLVtiZWXFsWPHuH//Pl27dsXQ0JApU6Z8cIxCZEWSRBdCvNOiRYsYNmwYbm5uyp1lAwMDPDw8lM3MKlSowNKlS9UMUwiRhchUcCE+voIFCyojw8qVK6eTSE9ISCA6Opp+/fqpGKHIioKCgvD29sbJyYnx48djb29PaGgoAQEBeHt74+PjI4n0XGzQoEFER0dz5coVKlasCMDVq1fx8PBg8ODBrF27VuUIRVoEBQUxf/58IiIilLKiRYvi6ekp/Tsb2r179zv3Jvjyyy/T3N6uXbt0Hq9YsQJLS0vOnDmDi4sL+vr6WFlZ6dTZsmULX3/9Nfny5QNgz549XL16lX379lG0aFGqV6/O999/z+jRo5k4cSJGRkbJzhsbG0tsbKzyOGnQjRDZhSTRP5KIiAiePHmidhiquXXrls5/czNzc3OKFi2qdhgfJF++fCxZsoRZs2Zx8+ZN4PXabEkfoADVq1dXKTohRFayceNG1q9fT1hYWLIlnmQ6ePYRExPDokWLuHPnDsWLF6dv377kyZNH7bDEG2bPno1Wq6VHjx74+PjoXGAbGRlRsmRJZd1UIeD1zRU/Pz+cnJyYPHkyenqvV/esXLkykydPxsvLiwULFuDs7CwjVnOpXbt2sW/fPiWBDlCpUiXmz58v+x9lE0FBQUyYMAFjY2Od8qioKCZMmMCkSZMkkZ7NeHh4vPW4RqP5oCW5knJXqc1eO3PmDOfPn2f+/PlKWXBwMFWrVtXJdbi6utK/f3+uXLmCo6NjsnamTp0qy8yJbE2S6B9BREQEnbt0Je5V7Lsr53AZMdUouzM0Mmb1ql+yfSIdXifTHRwc1A5DCJFFzZ07l3HjxtGtWze2bt1K9+7dCQkJ4dSpU3h6eqodnkijcePGcfToUeXx6dOn+e2333B2dpbP9Swm6aLa3t4eZ2dnDAzkq714u4sXLxIeHs748eOVBHoSPT093N3d8fT05OLFiykmQETOl5iYiKGhYbJyQ0NDEhMTVYhIpEdCQoKyD02NGjXo3LmzMttk9erVBAcHM3PmTLlRls2Eh4djaWn5UdpOTExk6NChODs7U6VKlRTrLFu2jIoVK/Lpp5/qxPRmjiPp8X+XCvuvsWPHMnz4cOXx06dPKVGixIe+BCEyjXzT/giePHlC3KtYYkrVJ9Hk7VNuRM6m9/IJ3AzkyZMn2T6Jfvr06VRHl27evFmlqIQQWYmfnx+LFy/mm2++YcWKFYwaNYpSpUoxYcIEIiMj1Q5PpEFSAt3Q0JAOHTrQokULdu7cyYYNGzh69Cjjxo2TRHoW1KhRI+7fv5/sAvvx48dYWlrKhpFCkfRebG9vn+LxpHJ5z869GjVqxJAhQ1i7di02NjYA3L17l2HDhtG4cWOVoxPvcv78eaKioqhatSq+vr46s018fX0ZMmQIly5d4vz589SsWVPlaEVapLTnSUby9PTk8uXLHDlyJMXjMTExrFmzhvHjx3/wuYyNjZPNkBAiO5Ek+keUaGJOYt7CaochxAdbt24dXbt2xdXVlT179tC0aVNu3LhBREQEbdu2VTs8IUQWERYWpoxQyZMnD8+ePQOgS5cu1K1bl3nz5qkZnniHmJgYJYG+bds2rl27xvXr16lduzZdunThyy+/5OjRo8TExMjSLlmMVqtNsTw2NjbFNUlF7pU0VT80NJTKlSsnOx4aGqpTT+Q+8+bN48svv6RkyZLKCNHbt29TpUoVVq9erXJ04l3Onz8PQPfu3dFqtZw7d05nY9Fu3brx7bffShI9G0ntMz4jDBw4kO3btxMUFETx4sVTrLNx40ZevHhB165ddcqtrKw4efKkTlnSGvxvrqcuRE4hSXQhxDtNmTKFWbNm4enpSf78+ZkzZw729vb07dsXa2trtcMTQmQRVlZWREZGYmdnh62tLcePH6datWqEhoZ+1AsAkTEWLVoEQJ06dejevbvOVFwrKyvq1KnDkSNHWLRoEUOHDlUpSvFfc+fOBV6PUlu6dKnOXiUJCQkEBQVRoUIFtcITWZCDgwNWVlYEBATorIkOr6f0BwQEYG1tLcv35WIlSpTg7Nmz7Nu3j2vXrgFQsWJFmjRponJkIj0uXrzIjBkzkn2Wu7q6qhiVeB8eHh4ZPnhBq9UyaNAgtmzZwqFDh1KdnQSvl3L58ssvKVKkiE65k5MTvr6+PHjwQJkJt3fvXszMzKhUqVKGxitEViFJdCHEO4WEhNCyZUvg9UZlz58/R6PRMGzYMBo1aiSbgwghgNdTwLdt24ajoyPdu3dn2LBhbNy4kdOnT9OuXTu1wxPvcOfOHQCOHDmCk5MTHTt2xMTEhJcvX3Ly5Ellmm9SPaG+WbNmAa8vhhcuXKizvm3SxqILFy5UKzyRBenr6zNgwAC8vb0ZN24cn3zyCcbGxsTGxnLy5EmOHz+Oj4+PrJWcy2k0Gj7//HM+//xztUMR6VS9enVWrVrFihUrcHJyYvz48Tproq9cuVKpJ7IHf3//DG/T09OTNWvWsHXrVvLnz6/cbDE3N9dJ2P/9998EBQWxc+fOZG00bdqUSpUq0aVLF+WGjZeXF56enrJki8ixJIkuhHinggULKssyFCtWjMuXL1O1alWioqJ48eKFytEJIbKKxYsXK5uOeXp6UqhQIY4dO8aXX35J3759VY5OvEvS2rcFCxbk5s2bBAcHK8eKFi1KwYIF+ffff5V6Qn1JS280bNiQzZs3U7BgQZUjEtmBi4sLHTt25Ndff9Xp53p6enTs2BEXFxcVoxNqevbsGTdu3KB8+fLky5ePs2fPMnv2bGJiYmjTpg3u7u5qhyjeoWrVqujp6ZGYmJhsFmDSYz09PapWrapGeCKLWLBgAQANGjTQKff396dbt27K4+XLl1O8eHGaNm2arA19fX22b99O//79cXJyIm/evHh4eDBp0qSPGboQqpIkuhDinVxcXNi7dy9Vq1alQ4cODBkyhAMHDrB3717ZYEgIAUB8fDxTpkyhR48eypqKbm5uuLm5qRyZSCtnZ2e2bdvGv//+S506dZgwYYIyeu2XX37hxIkTSj2RtRw8eFDtEEQ2EhQUxLp165KVJyYmsm7dOipVqiSJ9FwoKCiIL774gujoaAoWLMjatWv56quvKFasGPr6+mzevJkXL17Qu3dvtUMVb3HlyhVlQMO5c+c4fvy4cixpdHBiYiJXrlzB0dFRlRiF+tK6zOKUKVOYMmVKqsft7OxSHKUuRE4lSXQhxDvNmzePly9fAjBu3DgMDQ05duwY7du3x8vLS+XohBBZgYGBATNmzEi26ZDIPp4+far8fvbsWUqVKoWZmRlHjhzh7NmzKdYTWcedO3fYtm0bYWFhvHr1SufYzJkzVYpKZDUJCQlMmzYNeD3rpGfPnjg5OREcHMyyZcv4999/mT59Os7OzrKkSy7j5eVFhw4dmDRpEsuXL6djx44MHDhQSaBNnjyZ+fPnSxI9i4uMjAReX7MtW7ZMZ010CwsLevToga+vr1JPCCFE2kkSXQjxVvHx8Wzfvl3ZhEZPT48xY8aoHJUQIitq3LgxgYGBlCxZUu1QxHuIiooCoHTp0oSEhLB27VrWrl2rHE8qT6onso79+/fz5ZdfUqpUKa5du0aVKlX4559/0Gq11KhRQ+3wRBZy9uxZXrx4Qf78+dmwYQMGBq8vB7/44guaNWtG27ZtefbsGWfPnqV27doqRysy08WLF1m8eDHFihVj9OjRTJw4kY4dOyrH3dzcmD59uooRirSwsLAAXi/RFhAQwMWLF4mMjMTCwgIHBwdls9ikeiL7evr0KQcOHKB8+fJUrFhR7XCEyBX03l1FCJGbGRgY0K9fP2UkuhBCpKZ58+aMGTOGESNGsHbtWrZt26bzI7K2AgUKAK9vlr55cW1hYYGenp5OPZF1jB07lhEjRnDp0iVMTEzYtGkTt2/fpn79+nTo0EHt8EQWsmfPHgC6d++uJNCTGBgYKGvhJtUTucfTp0+V934jIyNMTU3Jnz+/cjx//vyyF1I24ODggJWVFQEBAWg0GhwdHWncuDGOjo5oNBoCAgKwtrbGwcFB7VBFOn399dfMmzcPgJiYGGrVqsXXX3+Ng4MDmzZtUjk6IXIHGYkuhHinTz75hPPnz2NnZ6d2KEKILGzAgAFAyktHaDQaEhISMjskkQ6FCxcG4K+//kp2LDIyUpn6nVRPZB1//vmnMmvAwMCAmJgY8uXLx6RJk2jdujX9+/dXOUKRVcTExABgbW2d4nErKyudeiL30Gg0aDSaVB+L7EFfX58BAwbg7e2Nl5cX7u7uyv4mAQEBBAcH4+PjI8s1ZUNBQUGMGzcOgC1btqDVaomKimLlypVMnjyZ9u3bqxyhEDmfJNGFEO80YMAAhg8fzu3bt6lZsyZ58+bVOS4jGYQQgLKRlcieKleujEajeetmUxqNhsqVK2diVCIt8ubNq6yDbm1tTUhIiPL/6dGjR2qGJrKYqlWrcuTIEZYuXUqdOnWUGSbw+j182bJlSj2Ru2i1Who3bqzMUHjx4gWtWrXCyMgIeL3EY3pMnTqVzZs3c+3aNfLkycOnn37K9OnTKV++fIbHLnS5uLjg4+ODn58fnp6eSrm1tTU+Pj6ycXA29eTJE2W2yK5du2jfvj2mpqa0bNmSkSNHqhydELmDJNGFEO/k5uYGwODBg5WypESLjC4VQqTk5cuXmJiYqB2GSIcLFy68NYEOr5MsFy5ckLWSs5i6dety5MgRKlasSIsWLfj222+5dOkSmzdvpm7dumqHJ7KQtm3bsmjRIkJCQvjuu+/o0qWLMkp11apV3Lx5Ez09Pdq2bat2qCKTeXt76zxu3bp1sjrpGekaGBiIp6cntWvXJj4+nu+++46mTZty9erVZANyRMZzcXHB2dk52ZroMgI9+ypRogTBwcFYWFiwa9cu1q1bB8C///4r37mFyCRZKokeFBTEDz/8wJkzZ7h//z5btmyhTZs2ynGtVou3tzdLliwhKioKZ2dnFixYQNmyZZU6kZGRDBo0iN9//x09PT3at2/PnDlzyJcvnwqvSIicITQ0VO0QhBDZQEJCAlOmTGHhwoVERERw48YNSpUqxfjx4ylZsiQ9e/ZUO0TxFrt3705zPUmiZy0zZ84kOjoaAB8fH6Kjo/n1118pW7ZsissridzLyMiIr7/+mnXr1nHy5EmOHz+uHEsalf71118ro49F7vFmEv1D7dq1S+fxihUrsLS05MyZM6mOhI6NjSU2NlZ5/PTp0wyNKbfR19fH0dFR7TBEBhk6dCju7u7ky5cPOzs7GjRoALzOo8nsISEyR5ZKoj9//pxq1arRo0cP2rVrl+z4jBkzmDt3LitXrsTe3p7x48fj6urK1atXlTtv7u7u3L9/n7179xIXF0f37t3p06cPa9asyeyXI0SOIWuhCyHSwtfXl5UrVzJjxgx69+6tlFepUoXZs2dLEj2Lu3v3rvL7jh07uHHjhjJ6rVy5crRs2TJZPZE1lCpVSvk9b968LFy4UMVoRFbXr18/ANavX69TrtFocHNzU44LkZGePHkCkGzj6v+aOnUqPj4+mRVSpnj58iVhYWFqh5El2NrayojpDzBgwADq1KlDWFgYn3/+uXLjs1SpUkyePFnl6ITIHbJUEr158+Y0b948xWNarZbZs2fj5eWlTC375ZdfKFq0KL/99htubm78+eef7Nq1i1OnTlGrVi0Afv75Z1q0aMGPP/6IjY1Nim3LHW8h3m3VqlUsXLiQ0NBQgoODsbOzY/bs2djb26c43VMIkfv88ssvLF68mMaNG+skYapVq8a1a9dUjEykxcOHD4HXSVh9fX3+/vtv7t27h42NDRUrVsTU1JQXL14o9UTWdfPmTWJiYqhYsaLOmtdpMXHixGRJrPLlyyt9+OXLl3z77besW7eO2NhYXF1d8fPzo2jRokr9sLAw+vfvz8GDB8mXLx8eHh5MnTpVWWsZ4NChQwwfPpwrV65QokQJvLy86Nat2/u/aJEu/fr1o0ePHmzdulXp561bt5YR6LlUs2bNmDhx4juXf3r27Bl+fn7ky5dPZ63td0lMTGTo0KE4OztTpUqVVOuNHTuW4cOHK4+fPn1KiRIl0nyerCgsLIw+ffqoHUaWsHjxYsqVK6d2GNlSXFwcFSpUYPv27cmW20oa5CCE+PiyVBL9bUJDQwkPD6dJkyZKmbm5OXXq1CE4OBg3NzeCg4MpUKCAkkAHaNKkCXp6epw4cSLVtf1y4h1vITLSggULmDBhAkOHDsXX11dZA71AgQLMnj1bkuhCCOD1COUyZcokK09MTCQuLk6FiER6JK2T+vz5c5o1a6ZzbP78+cnqCfXFxcUxefJkzp49S926dRkzZgydO3dWRhiXL1+enTt3UrJkyXS1W7lyZfbt26c8/m/ye9iwYezYsYMNGzZgbm7OwIEDadeuHUePHgVeL+vUsmVLrKysOHbsGPfv36dr164YGhoyZcoU4PX3+pYtW9KvXz8CAgLYv38/vXr1wtraGldX1w/8q4i0MjIyokOHDmqHIbKADh060L59e8zNzWnVqhW1atXCxsYGExMT/v33X65evcqRI0fYuXMnLVu25IcffkhX+56enly+fJkjR468tZ6xsTHGxsYf8lKyHFtbWxYvXqza+W/duoWvry/jxo1TfXaxra2tqufPzgwNDXn58qXaYQiR62WbJHp4eDiAziiXpMdJx8LDw7G0tNQ5bmBggIWFhVInJTnxjrcQGennn39myZIltGnThmnTpinltWrVYsSIEWluZ+rUqWzevJlr166RJ08ePv30U6ZPn0758uWVOhk1wk0IkfkqVarE4cOHk12kbdy48YPW5Jw2bRpjx45lyJAhzJ49+wOjFKkpXbo0ERERaaonsoYxY8awatUqWrduzfLlyzl58iTXr19nzZo16Onp8f333zNu3DgCAgLS1a6BgQFWVlbJyp88ecKyZctYs2YNjRo1AsDf35+KFSty/Phx6taty549e7h69Sr79u2jaNGiVK9ene+//57Ro0czceJEjIyMWLhwIfb29vz0008AVKxYkSNHjjBr1ixJoguhgp49e9K5c2c2bNjAr7/+yuLFi5XlVzQaDZUqVcLV1ZVTp05RsWLFdLU9cOBAtm/fTlBQEMWLF/8Y4WdpJiYmWWL0tZ2dXZaIQ7w/T09Ppk+fztKlS+W6VwiVZEjPi4+PZ82aNbi6uiZLcmcHOfGOtxAZKTQ0NMUEmLGxMc+fP09zO4GBgXh6elK7dm3i4+P57rvvaNq0KVevXiVv3rxAxoxwE0KoY8KECXh4eHD37l0SExPZvHkz169f55dffmH79u3v1eapU6dYtGgRDg4OGRyteNOIESNS3JMmpXoia9i4cSMrVqygRYsW3LhxgwoVKrBjxw5leURLS0vc3d3T3e5ff/2ljEJ1cnJi6tSp2NracubMGeLi4nRmhlaoUAFbW1uCg4OpW7cuwcHBVK1aVeeawNXVlf79+3PlyhUcHR0JDg7WaSOpztChQ1ONSZZfFOLjMjY2pnPnznTu3Bl4fdMsJiaGQoUKYWhomO72tFotgwYNYsuWLRw6dAh7e/uMDlmIXOXUqVPs37+fPXv2ULVqVeX6OcnmzZtVikyI3CN9iySmwsDAgH79+n3U6SVJo2HeHCEVERGhHLOysuLBgwc6x+Pj44mMjExxNI0QIm3s7e05f/58svJdu3alazTKrl276NatG5UrV6ZatWqsWLGCsLAwzpw5A/xvhNvMmTNp1KgRNWvWxN/fn2PHjnH8+HEAZYTb6tWrqV69Os2bN+f7779n/vz5vHr1KsXzxsbG8vTpU50fIUTGa926Nb///jv79u0jb968TJgwgT///JPff/+dzz//PN3tRUdH4+7uzpIlSyhYsOBb60o//3D79+/P0Hri47t37x7VqlUDoFy5chgbG+ssqVSuXLm3zsZMSZ06dVixYgW7du1iwYIFhIaG8tlnn/Hs2TPCw8MxMjKiQIECOs95c2ZoSjNHk469rc7Tp0+JiYlJMa6pU6dibm6u/Mis0Q+XkJDAuXPn2L9/P+fOnVOW6xMCXi+damVl9V4JdHg9anb16tWsWbOG/PnzEx4eTnh4eKp9XAjxdgUKFKB9+/a4urpiY2Oj85lobm6udnhC5AoZNgfkk08+4fz58x9tnS17e3usrKzYv38/1atXB16PQDlx4gT9+/cHwMnJiaioKM6cOUPNmjUBOHDgAImJidSpU+ejxCVEbjB8+HA8PT15+fIlWq2WkydPsnbtWqZOncrSpUvfu92kaaIWFhYAGTbC7U2y74EQmeezzz5j7969GdKWp6cnLVu2pEmTJkyePPmtdaWff7i7d+9maD3x8SUkJOgkuAwMDHTWrNfT00Or1aarzaRR7AAODg7UqVMHOzs71q9fT548eT486Pckyy9mrKCgIPz8/HRuslhZWTFgwABcXFxUjEzkFAsWLACgQYMGOuX+/v6yibAQ78Hf31/tEITI9TIsiT5gwACGDx/O7du3qVmzZrKpJWmZhh0dHc3ff/+tPA4NDeX8+fNYWFhga2vL0KFDmTx5MmXLlsXe3p7x48djY2NDmzZtgNfrKTZr1ozevXuzcOFC4uLiGDhwIG5ubtjY2GTUSxUi1+nVqxd58uTBy8uLFy9e0KlTJ2xsbJgzZw5ubm7v1WZiYiJDhw7F2dmZKlWqAGTYCLc3yYW3EJmjV69edO7cOdkF8/tYt24dZ8+e5dSpU2mqL/38wyUmJmZoPZE5du/erYxAS0xMZP/+/Vy+fBmAqKioD26/QIEClCtXjr///pvPP/+cV69eERUVpfNZ/ebM0JMnT+q0kTST9L91UppdamZmlmqiXpZfzDhBQUF4e3vj5OTE+PHjsbe3JzQ0lICAALy9vfHx8ZFEuvhg6b2BJ4R4t/j4eA4dOkRISAidOnUif/783Lt3DzMzM/Lly6d2eELkeBmWRE9KpA0ePFgp02g0aLVaNBpNmqYHnj59moYNGyqPky6GPTw8WLFiBaNGjeL58+f06dOHqKgo6tWrx65duzAxMVGeExAQwMCBA2ncuDF6enq0b9+euXPnZtTLFCLXcnd3x93dnRcvXhAdHZ1sE9/08vT05PLlyxw5ciSDIkydXHgLkTkePnxIs2bNKFKkCG5ubri7uyuzx9Lj9u3bDBkyhL179+p8xr+N9PMP99+/tbm5Ob1798bJyYng4GCWLFmizB5K6/8TkTk8PDx0Hvft21fnsUaj+aD2o6OjCQkJoUuXLtSsWRNDQ0P2799P+/btAbh+/TphYWE4OTkBr2eG+vr68uDBA+W7wt69ezEzM6NSpUpKnZ07d+qcZ+/evUob4uNJSEjAz88PJycnJk+ejJ7e69U9K1euzOTJk/Hy8mLBggU4OzvrzGoQQgihrlu3btGsWTPCwsKIjY3l888/J3/+/EyfPp3Y2FgWLlyodohC5HgZlkQPDQ394DYaNGjw1jvWGo2GSZMmMWnSpFTrWFhYsGbNmg+ORQjxP5MnT8bd3R17e3tMTU0xNTX9oPYGDhzI9u3bCQoKonjx4kq5lZVVhoxwE0KoY+vWrfz7779s2LCBNWvWMHPmTCpUqIC7uzudOnWiZMmSaWrnzJkzPHjwgBo1aihlCQkJBAUFMW/ePGJjYyW58xH8dzZg0jIgST9JibY36wl1fYxZASNGjKBVq1bY2dlx7949vL290dfX55tvvsHc3JyePXsyfPhwLCwsMDMzY9CgQTg5OVG3bl0AmjZtSqVKlejSpQszZswgPDwcLy8vPD09lRtd/fr1Y968eYwaNYoePXpw4MAB1q9fz44dOzL89QhdFy9eJDw8nPHjx+v0a3jd793d3fH09OTixYspLpEnhBBCHUOGDKFWrVpcuHCBQoUKKeVt27ald+/eKkYmRO6RYUn0j7UWuhBCfRs2bMDb25s6derQuXNnvv76awoXLpzudrRaLYMGDWLLli0cOnQIe3t7neMZNcJNCKGeggUL0qdPH/r06cOdO3dYu3Yty5cvZ8KECcTHx6epjcaNG3Pp0iWdsu7du1OhQgVGjx4tCfSPJDIyEgBTU1OePn3KTz/9pBzT19fH1NSUFy9eKPVEznTnzh2++eYbHj9+TJEiRahXrx7Hjx+nSJEiAMyaNUuZ7RkbG4urqyt+fn7K8/X19dm+fTv9+/fHycmJvHnz4uHhoTMIxt7enh07djBs2DDmzJlD8eLFWbp0Ka6urpn+enObpP775newJEnl0s9zt6ioKDZu3EhISAgjR47EwsKCs2fPUrRoUYoVK6Z2eELkSocPH+bYsWMYGRnplJcsWVL2qxEik2RYEh1g1apVLFy4kNDQUIKDg7Gzs2P27NnY29vTunXrjDyVECITXbhwgStXrhAQEMCPP/7I0KFD+fzzz3F3d6dNmzZpHpnu6enJmjVr2Lp1K/nz51fWMDc3NydPnjwZNsJNCKG+uLg4Tp8+zYkTJ/jnn3+S7WXwNvnz51f2SkiSN29eChUqlKxcZJyktTRfvHhBnTp1KF68OLGxsRgbG3Pnzh1OnDihU0+o6/jx48pn47u8ePGC0NBQKleu/M6669ate+txExMT5s+fz/z581OtY2dnl2y5ljc1aNCAc+fOvTMekbGSNnNP7d9D0uzipHoi97l48SJNmjTB3Nycf/75h969e2NhYcHmzZsJCwvjl19+UTtEIXKlxMTEFJdJvnPnDvnz51chIiFyH713V0mbBQsWMHz4cFq0aEFUVJTSuQsUKMDs2bMz6jRCCJVUrlyZKVOmcPPmTQ4ePEjJkiUZOnRoupZQWbBgAU+ePKFBgwZYW1srP7/++qtSZ9asWXzxxRe0b98eFxcXrKys2Lx5s3I8aYSbvr4+Tk5OdO7cma5du751mSchROY5ePAgvXv3pmjRonTr1g0zMzO2b9/OnTt31A5NvIOzs7Py+9mzZ9m0aRPbt29n06ZNOsnO/9YT6unSpQuurq5s2LCB58+fp1jn6tWrfPfdd5QuXZozZ85kcoQiK3JwcMDKyoqAgIBkywElJiYSEBCAtbU1Dg4OKkUo1DZ8+HC6devGX3/9pbMHRosWLQgKClIxMiFyt6ZNm+rk1jQaDdHR0Xh7e9OiRQv1AhMiF8mwkeg///wzS5YsoU2bNkybNk0pr1WrFiNGjMio0wghsoC8efOSJ08ejIyMePbsWZqf97Y9D5Jk1Ag3IUTmK1asGJGRkTRr1ozFixfTqlWrDJshcujQoQxpJzt5+fIlYWFhmXa+atWqKb/HxcXpHPvv42rVqnHjxo1MiwvA1tZWNjR9w9WrV1mwYAFeXl506tSJcuXKYWNjg4mJCf/++y/Xrl0jOjqatm3bsmfPHqpWrap2yCIL0NfXZ8CAAXh7e+Pl5aXseRMaGkpAQADBwcH4+PjIslm52KlTp1i0aFGy8mLFiimzSIUQme+nn37C1dWVSpUq8fLlSzp16sRff/1F4cKFWbt2rdrhCZErZOjGoiltPmNsbJzq6BghRPYRGhrKmjVrWLNmDdevX6d+/fr4+Pjw1VdfqR2aECKLmDhxIh06dNDZGBher626evVqBg4cqE5g2VRYWBh9+vRROwxA9ybogAEDMv38ixcvply5cpl+3qzM0NCQwYMHM3jwYE6fPs2RI0e4desWMTExVKtWjWHDhtGwYUNZlkMk4+Ligo+PD35+fnh6eirl1tbW+Pj44OLiomJ0Qm3GxsY8ffo0WfmNGzeUvRGEEJmvePHiXLhwgXXr1nHx4kWio6Pp2bMn7u7u5MmTR+3whMgVMiyJbm9vz/nz55NtMLpr1y4qVqyYUacRQqigbt26nDp1CgcHB7p3784333wjmwoJIZLp3bu3zuP9+/ezbNkytmzZgqmpqSTR08nW1pbFixdn+nk3btzI3r17dRLnGo2Gzz//XLUbp7a2tqqcN7uoVasWtWrVUjsMkY24uLjg7OzMxYsXiYyMxMLCAgcHBxmBLvjyyy+ZNGkS69evB16//4eFhTF69Gjat2+vcnRC5F4vX77ExMSEzp07qx2KELlWhiXRhw8fjqenJy9fvkSr1XLy5EnWrl3L1KlTWbp0aUadRgihgsaNG7N8+XIqVaqkU56YmMjOnTv54osvVIpMXRERETx58kTtMFRz69Ytnf/mVubm5unaNDOnu337Nv7+/vj7+xMWFoabmxtbtmyhcePGaoeW7ZiYmKgy+vq7775jxIgRLF26lPXr1/P111/Tq1cvjIyMMj0WIcTHo6+vn+JMYpG7/fTTT3z11VdYWloSExND/fr1CQ8Px8nJCV9fX7XDEyLXsrS0pG3btnTu3JnGjRujp5dhWxwKIdIow5LovXr1Ik+ePHh5efHixQs6deqEjY0Nc+bMwc3NLaNOk63oxUSpHYJQWU75N/DmF+a///6b5cuXs2LFCh4+fJhs7dzcICIigs5duhL3KlbtUFSX2y+oDI2MWb3ql1ydSI+Li+O3335j6dKlHD58mGbNmvHDDz/wzTffMG7cuGQ34ETWZ2RkRJMmTVi/fj1NmjSRBLoQQuQS5ubm7N27l6NHj3LhwgWio6OpUaMGTZo0UTs0IXK1lStXsmbNGlq3bo25uTkdO3akc+fOMhNNiEyUYUl0AHd3d9zd3Xnx4gXR0dFYWlpmZPPZTp5Q2b1c5BwxMTFs2LCBpUuXcvToUT777DMmTJhA27Zt1Q5NFU+ePCHuVSwxpeqTaGKudjhCJXovn8DNQJ48eZKrk+jFihWjQoUKdO7cmXXr1lGwYEEAvvnmG5UjE0IIIUR6/PLLL3Ts2BFnZ2ecnZ2V8levXrFu3Tq6du2qYnRC5F5t27albdu2PHv2jI0bN7J27Vrq1q1LqVKl6Ny5MxMmTFA7RCFyvAxLont7e9OjRw/s7OwwNTXF1NQ0o5rOtmLsXUjMU0DtMISK9GKisv3NlFOnTrF06VLWrVtH6dKlcXd359ixY/j5+cnoUiDRxJzEvIXVDkMIVcXHx6PRaNBoNLKerhBCCJGNde/enWbNmiUbEPfs2TO6d+8uSXQhVJY/f366d+9O9+7duXr1Ku7u7vj4+EgSXYhMkGFJ9K1bt+Lr60v9+vXp2bMn7du3x9jYOKOaz5YS8xSQ5JrI1hwcHHj69CmdOnXi2LFjVK5cGYAxY8aoHJkQIiu5d+8emzZtYtmyZQwZMoTmzZvTuXNnNBqN2qEJkeskbTwmhBDvQ6vVpvj5fefOHczNZfalEGp7+fIl27ZtY82aNezatYuiRYsycuRItcMSIlfIsCT6+fPnOXfuHP7+/gwZMgRPT0/c3Nzo0aMHtWvXzqjTCCEy0fXr1+nYsSMNGzaUUedCiFSZmJgoS7qFhITg7+/P4MGDiY+Px9fXl27dutGoUSMZpS7ER5KYmIivry8LFy4kIiKCGzduUKpUKcaPH0/JkiXp2bOn2iEKIbI4R0dHZVZZ48aNMTD4X6ogISGB0NBQmjVrpmKEQuRuu3fvZs2aNfz2228YGBjw1VdfsWfPHlxcXNQOTVV///03oaGhqp3/xYsXhISEqHb+rKR06dKqrkpib29PmTJlPuo5MnRNdEdHRxwdHfnpp5/4/fff8ff3x9nZmQoVKtCzZ0+6desmd6+FyEZu3rzJihUr6N+/PzExMXzzzTe4u7vL6FIhRKpKly7N5MmTmTRpErt372bZsmV88cUX5M+fn0ePHqkdnhA50uTJk1m5ciUzZsygd+/eSnmVKlWYPXu2JNGFEO/Upk0b4PXgOFdXV/Lly6ccMzIyomTJkrRv316l6IQQbdu25YsvvuCXX36hRYsWGBoaqh1SlvDzzz9z4cIFtcMQWUC1atWYM2fORz1HhibRk2i1WuLi4nj16hVarZaCBQsyb948xo8fz5IlS+jYsePHOK0QIoMVK1aMcePGMW7cOA4cOMDy5ctxdnYmPj6eFStW0KtXL8qVK6d2mEKILEhPT4/mzZvTvHlzHj58yKpVq9QOSYgc65dffmHx4sU0btyYfv36KeXVqlXj2rVrKkYmhMguvL29AShZsiQdO3aUZaGEyGIiIiLInz+/2mFkOYMGDZKR6FlEVhiJ/rFlaBL9zJkz+Pv7s3btWoyNjenatSvz589XhtP//PPPDB48WJLoQmRDjRo1olGjRjx58oSAgACWL1/Ojz/+SJUqVbh48aLa4QkhsrAiRYowfPhwtcMQIse6e/duitNXExMTiYuLUyEikR0kJCRw8eJFIiMjsbCwwMHBQZbdEnh4eKgdghAiBU+fPsXf358bN24AUK5cOdq3b0+xYsVUjkxdZcqU+ehLeAiRJMOS6FWrVuXatWs0bdqUZcuW0apVq2Rfwr755huGDBmSUacUQqjA3NycAQMGMGDAAM6fP8/y5cvVDkkIIYTI1SpVqsThw4exs7PTKd+4cSOOjo4qRSWysqCgIPz8/AgPD1fKrKysGDBgQK5fXze3S0hIYNasWaxfv56wsDBevXqlczwyMlKlyITIvfz8/Bg+fDivXr3CzMwMeJ1UHzVqFDNnzmTAgAEqRyhE7qCXUQ19/fXX/PPPP+zYsYM2bdqkOIqhcOHCJCYmZtQphRAqq169OnPnzlU7DCGEECJXmzBhAgMHDmT69OkkJiayefNmevfuja+vLxMmTFA7PJHFBAUF4e3tTalSpZg/fz47d+5k/vz5lCpVCm9vb4KCgtQOUajIx8eHmTNn0rFjR548ecLw4cNp164denp6TJw4Ue3whMh1duzYweDBgxk4cCB3794lKiqKqKgo7t69y4ABAxgyZAg7d+5UO0whcoUMS6KPHz8+108jEUIIIYQQIrO1bt2a33//nX379pE3b14mTJjAn3/+ye+//87nn3+udngiC0lISMDPzw8nJycmT55M5cqVMTU1pXLlykyePBknJycWLFhAQkKC2qEKlQQEBLBkyRK+/fZbDAwM+Oabb1i6dCkTJkzg+PHjaocnRK7zww8/MGbMGH788Uesra2Vcmtra2bOnMno0aOZMWOGihEKkXtkWBId4M6dO/j5+TFmzBiGDx+u8yOEEEIIIYT4OD777DP27t3LgwcPePHiBUeOHKFp06ZqhyWymIsXLxIeHo67uzt6erqXgnp6eri7u3P//n3Z7yYXCw8Pp2rVqgDky5ePJ0+eAPDFF1+wY8cONUMTIlc6e/YsXbp0SfV4ly5dOHv2bLranDp1KrVr1yZ//vxYWlrSpk0brl+/nqxecHAwjRo1Im/evJiZmeHi4kJMTIxyPDIyEnd3d8zMzChQoAA9e/YkOjo6XbEIkZ1k2Jro+/fv58svv6RUqVJcu3aNKlWq8M8//6DVaqlRo0ZGnUYIIYQQWVRqN801Gg0mJiaUKVOG1q1bY2FhkcmRCZGzlSpVilOnTlGoUCGd8qioKGrUqMHNmzdVikxkNUnrWdvb26e4sai9vb1OPZH7FC9enPv372Nra0vp0qXZs2cPNWrU4NSpUxgbG6sdnhC5TkJCAoaGhqkeNzQ0TPfsocDAQDw9Palduzbx8fF89913NG3alKtXr5I3b17gdQK9WbNmjB07lp9//hkDAwMuXLigcwM26cbr3r17iYuLo3v37vTp04c1a9a834sVIovLsCT62LFjGTFiBD4+PuTPn59NmzZhaWmJu7s7zZo1y6jTCCGEECKLOnfuHGfPniUhIYHy5csDcOPGDfT19alQoQJ+fn58++23HDlyhEqVKqkcrRA5xz///JPiBXRsbCx3795VISKRVSXdxNyyZQu///57so1FW7VqpVNP5D5t27Zl//791KlTh0GDBtG5c2eWLVtGWFgYw4YNUzs8IXKdypUrs3Xr1lT732+//UblypXT1eauXbt0Hq9YsQJLS0vOnDmjbC49bNgwBg8ezJgxY5R6Sd/vAf7880927drFqVOnqFWrFgA///wzLVq04Mcff8TGxiZdMQmRHWRYEv3PP/9k7dq1rxs1MCAmJoZ8+fIxadIkWrduTf/+/TPqVEKITJba5qH/HV3q4uKS4obCQojcI2mUub+/P2ZmZgA8efKEXr16Ua9ePXr37k2nTp0YNmwYu3fvVjlaIbK/bdu2Kb/v3r0bc3Nz5XFCQgL79++nZMmSKkQmsioHBwcKFCjAkiVLqFu3Lh07dsTY2JjY2FhOnDjBkiVLKFCgAA4ODmqHKlQybdo05feOHTtiZ2fHsWPHKFu2rHKTRQiReTw9Penfvz/Gxsb06dMHA4PXabz4+HgWLVqEl5cXfn5+H3SOpGWbkm6gPnjwgBMnTuDu7s6nn35KSEgIFSpUwNfXl3r16gGvR6oXKFBASaADNGnSBD09PU6cOEHbtm2TnSc2NpbY2Fjl8dOnTz8obiEyW4Yl0fPmzcurV6+A1xschISEKHfDHj16lFGnEUKoYNasWTx8+JAXL15QsGBBAP79919MTU3Jly8fDx48oFSpUhw8eJASJUqoHK0QQi0//PADe/fuVRLoAObm5kycOJGmTZsyZMgQJkyYIOs0C5FB2rRpA7y+qe3h4aFzzNDQkJIlS/LTTz+pEJnIDs6dO6ezUWTSUh0ajUatkEQWVLduXerWrQvA6dOndRJmQoiPz8PDg0uXLjFw4EDGjh1L6dKl0Wq13Lx5k+joaAYPHky3bt3eu/3ExESGDh2Ks7MzVapUAVCWgZs4cSI//vgj1atX55dffqFx48ZcvnyZsmXLEh4ejqWlpU5bBgYGWFhY6Mxy+q+pU6fi4+Pz3rEKobYM21i0bt26HDlyBIAWLVrw7bff4uvrS48ePZQPXSFE9jRlyhRq167NX3/9xePHj3n8+DE3btygTp06zJkzh7CwMKysrGSKpxC53JMnT3jw4EGy8ocPHyojTQoUKKDcdBdCfJjExEQSExOxtbXlwYMHyuPExERiY2O5fv06X3zxhdphiizk4sWLREVFvbXOv//+KxuL5mLR0dE6GwcCnD9/nlatWlGnTh2VohIid/vxxx85duwY3bp1w8rKCmtra7p3787Ro0eZNWvWB7Xt6enJ5cuXWbdunVKWmJgIQN++fenevTuOjo7MmjWL8uXLs3z58vc+19ixY3ny5Inyc/v27Q+KXYjMlmEj0WfOnKnswuvj40N0dDS//vorZcuWZebMmRl1GiGECry8vNi0aROlS5dWysqUKcOPP/5I+/btuXnzJjNmzKB9+/YqRimEUFvr1q3p0aMHP/30E7Vr1wbg1KlTjBgxQhkxe/LkScqVK6dilELkPKGhoWqHILKJpBnCderUYfLkyVy+fFnZWLRKlSp4eXlx4sQJmUmcC92+fZuvv/6akydPoq+vz8CBA5k8eTL9+vXj119/pW3bthw7dkztMIXItf47KySjDBw4kO3btxMUFETx4sWVcmtra4BkexhVrFiRsLAw4PU+Gm8OnomPjycyMhIrK6sUz2dsbCwbFItsLcOS6KVKlVJ+z5s3LwsXLsyopoUQKrt//z7x8fHJyuPj45WpWjY2Njx79iyzQxNCZCGLFi1i2LBhuLm5Ke8ZBgYGeHh4KKNkKlSowNKlS9UMU4gc6fnz5wQGBhIWFpZstsfgwYNVikpkNUmj0D/77DMMDQ1xdHTUOV6vXj1OnDjxztHqIucZOXIkL1++ZM6cOWzevJk5c+Zw+PBh6tSpQ0hIiE6CTQiRvWm1WgYNGsSWLVs4dOgQ9vb2OsdLliyJjY0N169f1ym/ceMGzZs3B8DJyYmoqCjOnDlDzZo1AThw4ACJiYkya0XkWBmWRBdC5FwNGzakb9++LF26VLnYOnfuHP3796dRo0YAXLp0KdmHrxAid8mXLx9Llixh1qxZylqKpUqVIl++fEqd6tWrqxSdEDnXuXPnaNGiBS9evOD58+dYWFjw6NEjTE1NsbS0lCS6UBQoUACAw4cP06JFC/T0/re6Z2JiorI8Z1I9kXsEBQWxefNm6taty9dff42VlRXu7u4MHTpU7dCEEBnM09OTNWvWsHXrVvLnz68MjDM3NydPnjxoNBpGjhyJt7c31apVo3r16qxcuZJr166xceNG4PWo9GbNmtG7d28WLlxIXFwcAwcOxM3NDRsbGzVfnhAfzQcl0QsWLJjmjWciIyM/5FRCCBUtW7aMLl26ULNmTQwNDYHXo9AbN27MsmXLgNfJM9m8TAgBr98PHBwc1A5DiFxj2LBhtGrVioULF2Jubs7x48cxNDSkc+fODBkyRO3wRBZSuHBhAE6cOIGXlxfu7u7Y29sTGhpKQEAAJ06c0Kknco+IiAhlQIylpSWmpqbKiFMhRM6yYMECABo0aKBT7u/vr2xSOnToUF6+fMmwYcOIjIykWrVq7N27V2eJ14CAAAYOHEjjxo3R09Ojffv2zJ07N7NehhCZ7oOS6LNnz86gMIQQWZmVlRV79+7l2rVr3LhxA4Dy5ctTvnx5pU7Dhg3VCk8IkUU8f/6cadOmsX//fmWTw/9KGp0uhMhY58+fZ9GiRejp6aGvr09sbCylSpVixowZeHh40K5dO7VDFFmEg4MDVlZWmJubExISgqenp3LMysqK8uXL8/TpU7kRmkv9d2aCnp4eRkZGKkYjhPhYtFptmuqNGTOGMWPGpHrcwsKCNWvWZFRYQmR5H5RE9/DwICEhgR9//JFt27bx6tUrGjdujLe3N3ny5MmoGHVMnDgRHx8fnbLy5ctz7do1AF6+fMm3337LunXriI2NxdXVFT8/P4oWLfpR4hEiN6lQoQIVKlRQOwwhRBbVq1cvAgMD6dKlC9bW1mmerSaE+DCGhoZK8svS0pKwsDAqVqyIubk5t2/fVjk6kZXo6+szYMAAvL29ldmFSSIjI4mIiMDHxwd9fX2VIhRq0Wq1lCtXTvnsjo6OxtHRUSexDumbYR4UFMQPP/zAmTNnuH//Plu2bFE2GhdCCCGymw9eE33KlClMnDiRJk2akCdPHubMmcODBw9Yvnx5RsSXosqVK7Nv3z7lsYHB/17GsGHD2LFjBxs2bMDc3JyBAwfSrl07jh49+tHiESKnS0hIYMWKFamOLj1w4IBKkQkhspI//viDHTt24OzsrHYoQuQqjo6OnDp1irJly1K/fn0mTJjAo0ePWLVqFVWqVFE7PJEFabXaZDc6NRpNmkcnipzH398/w9t8/vw51apVo0ePHjIjRogPFBERwYgRI5Rr8jffrxMSElSKTIjc44OT6L/88gt+fn707dsXgH379tGyZUuWLl2a7K51RjEwMMDKyipZ+ZMnT1i2bBlr1qxRNjv09/enYsWKHD9+nLp1636UeITI6YYMGcKKFSto2bIlVapU+aDRpe8akaLVavH29mbJkiVERUXh7OzMggULKFu2rFInMjKSQYMG8fvvvytrr82ZM0dn80IhROYrWLAgFhYWH9zO1KlT2bx5M9euXSNPnjx8+umnTJ8+XWcJKSHE/0yZMoVnz54B4OvrS9euXenfvz9ly5ZV9i5Jq7T0vwYNGhAYGKjzvL59+7Jw4ULlcVhYGP379+fgwYPky5cPDw8Ppk6dqjP45dChQwwfPpwrV65QokQJvLy8lLVYxceRkJCAn58fn376KT4+Ply+fJnIyEgsLCyoUqUK3t7eLFiwAGdnZxmNnst4eHhkeJvNmzdP17rqsbGxxMbGKo+fPn2a4TEJkV1169aNsLAwxo8fLzM+hVDJByfRw8LCaNGihfK4SZMmaDQa7t27R/HixT+0+RT99ddf2NjYYGJigpOTE1OnTsXW1pYzZ84QFxdHkyZNlLoVKlTA1taW4ODgVJPo8mEtxNutW7eO9evX6/T19/WuESkzZsxg7ty5rFy5Ent7e8aPH4+rqytXr17FxMQEAHd3d+7fv8/evXuJi4uje/fu9OnTR9ZjE0Jl33//PRMmTGDlypWYmpq+dzuBgYF4enpSu3Zt4uPj+e6772jatClXr14lb968GRixEDlDrVq1lN8tLS3ZtWvXe7eV1v7Xu3dvJk2apDz+b59PSEigZcuWWFlZcezYMe7fv0/Xrl0xNDRkypQpAISGhtKyZUv69etHQEAA+/fvp1evXlhbW+Pq6vre8Yu3u3jxIuHh4YwfPx5DQ0McHR11jru7u+Pp6cnFixeTHRPiY5s6dWqypVuFEK8dOXKEw4cPU716dbVDESLX+uAkenx8vJLYSmJoaEhcXNyHNp2iOnXqsGLFCsqXL8/9+/fx8fHhs88+4/Lly4SHh2NkZESBAgV0nlO0aFHCw8NTbVM+rIV4OyMjI8qUKZMhbb1tRIpWq2X27Nl4eXnRunVr4PVsl6JFi/Lbb7/h5ubGn3/+ya5duzh16pSSNPj5559p0aIFP/74IzY2NhkSpxAi/X766SdCQkIoWrQoJUuWTLbe7tmzZ9PUzpsJwBUrVmBpacmZM2dwcXHJsHiFyOnOnj3LhAkT2L59e5qfk9b+Z2pqmuLMUIA9e/Zw9epV9u3bR9GiRalevTrff/89o0ePZuLEiRgZGbFw4ULs7e356aefAKhYsSJHjhxh1qxZKSbRZdBLxkhaz9re3j7F40nl6Vn3WoiMMnbsWIYPH648fvr0KSVKlFAxIiGyjhIlSsiSW0Ko7IOT6Fqtlm7dumFsbKyUvXz5kn79+umMVtm8efOHngpAJ/nm4OBAnTp1sLOzY/369e+9mal8WAvxdt9++y1z5sxh3rx5H3XaWGhoKOHh4TqzSczNzalTpw7BwcG4ubkRHBxMgQIFdEbdNWnSBD09PU6cOEHbtm2TtfsxL7z1YqIyrC2R/cj/f10fa7OwJ0+eALx1qRhJsIncavfu3ezduxcjIyN69epFqVKluHbtGmPGjOH333//4FHdqfW/gIAAVq9ejZWVFa1atWL8+PHKaPTg4GCqVq1K0aJFlfqurq7079+fK1eu4OjoSHBwsM7nfVKdoUOHphiHDHrJGEn/H0NDQylbtixbt27l3r172NjY0Lp1a0JDQ3XqCZGZjI2NdfIKQoj/mT17NmPGjGHRokWULFlS7XCEyJU+OIme0tppnTt3/tBm06xAgQKUK1eOv//+m88//5xXr14RFRWlMxo9IiIi1ZEyIB/WQrzLkSNHOHjwIH/88QeVK1dONro0o26SJc0Y+e9Fd9LjpGPh4eFYWlrqHDcwMMDCwiLVGScf88I7T2jQR2lXiOzI29s7w9tMTExk6NChODs7v3WDREmwidxo2bJl9O7dGwsLC/7991+WLl3KzJkzGTRoEB07duTy5ctUrFjxvdtPrf916tQJOzs7bGxsuHjxIqNHj+b69evK94Hw8PAUP8uTjr2tztOnT4mJiUk2OEYGvWQMBwcHrKys8PX1JSIiQmcjuoULF1K0aFGsra1xcHBQMUohhBBv6tixIy9evKB06dKYmpomuyaXGURCfHwfnET/GLt4p0d0dDQhISF06dKFmjVrYmhoyP79+2nfvj0A169fJywsDCcnJ1XjFCI7K1CgQIojvLOLj3nhHWPvQmKeAhnSlsh+9GKi5EbKR+bp6cnly5c5cuTIW+tJgk3kRnPmzGH69OmMHDmSTZs20aFDB/z8/Lh06VKG7E2UWv/r06eP8nvVqlWxtramcePGhISEULp06Q8+b0pk0EvG0NfXp3Tp0hw9ehRDQ0M6duxIixYt2LlzJxs2bODevXuyqWgud/DgQRo2bKh2GEKIN8yePVvtEITI9T44iZ7ZRowYQatWrbCzs+PevXt4e3ujr6/PN998g7m5OT179mT48OFYWFhgZmbGoEGDcHJySnVTUSHEu2XWzbKkGSMRERFYW1sr5REREcoGKlZWVjx48EDnefHx8URGRqY64+RjXngn5ilAYt7CH6VtIbIDCwsLbty4QeHChSlYsOBbl3xK7wiZgQMHsn37doKCgt6ZEJQEm8iNQkJC6NChAwDt2rXDwMCAH374IUMS6Onpf3Xq1AHg77//pnTp0lhZWXHy5EmdOhEREcD/PuutrKyUsv/WMTMze+8lGsW7vXr1iuPHj5M3b15MTExYs2aNsjF74cKFiYmJ4fjx47x69QojIyOVoxVqaNasGcWLF6d79+54eHh80A3p6Oho/v77b+VxaGgo58+fx8LCAltb24wIV4hcI6VVIIQQmSvbJdHv3LnDN998w+PHjylSpAj16tXj+PHjFClSBIBZs2ahp6dH+/btiY2NxdXVFT8/P1Vi1Xv5RJXziqxD/g2kj729PVZWVuzfv19Jmj99+pQTJ07Qv39/AJycnIiKiuLMmTPUrFkTgAMHDpCYmKhcxAshMs+sWbPInz8/kHEjZLRaLYMGDWLLli0cOnQo1Q3whMjtYmJilHXINRoNxsbGOjeh38f79L/z588DKOd2cnLC19eXBw8eKEuw7d27FzMzMypVqqTU2blzp047e/fuldmjH9nWrVtJSEjAysqKkJAQnWOPHj2idOnShISEsHXrVuUGjchd7t69y6pVq1i5ciU+Pj40atSInj170qZNm3TfWDl9+rTOqPakGWMeHh6sWLEiI8NOVUREhLK3Q25069Ytnf/mVubm5smWEMuOQkJC8Pf3JyQkhDlz5mBpackff/yBra0tlStXVjs8IXK8bJdEX7du3VuPm5iYMH/+fObPn59JESVnbm6OoZEx3AxULQaRdRgaGWNubq52GOlWo0YN9u/fT8GCBXF0dHzr6NKzZ8+mud13jUgZOnQokydPpmzZstjb2zN+/HhsbGyUDQsrVqxIs2bN6N27NwsXLiQuLo6BAwfi5uaGjY3Ne79eIcT7+e+omIwaIePp6cmaNWvYunUr+fPnV9ZQNjc3lxGqQrxh6dKl5MuXD3g9M2vFihUULqw7Q2rw4MFpbu9d/S8kJIQ1a9bQokULChUqxMWLFxk2bBguLi7KOtpNmzalUqVKdOnShRkzZhAeHo6Xlxeenp7KjJF+/foxb948Ro0aRY8ePThw4ADr169nx44dGfFnEam4d+8e8DoRY2hoSIcOHXSWc0lKrCfVE7lP4cKFGTZsGMOGDePs2bP4+/szYMAABgwYQKdOnejZsyfVqlVLU1sNGjRAq9V+5IhTFxERQecuXYl7Ffvuyjmcr6+v2iGoytDImNWrfsnWifTAwECaN2+Os7MzQUFB+Pr6YmlpyYULF1i2bBkbN25UO0Qhcrxsl0TPDooWLcrqVb/k+jvevr6+jBs3Djs7O7XDUVV2vevdunVr5UK3devWb02ip8e7RqSMGjWK58+f06dPH6KioqhXrx67du3CxMREeU5AQAADBw6kcePGysyTuXPnZkh8Qoj0efr0aZrrmpmZpaneggULgNcX3//l7+9Pt27d0nw+IXI6W1tblixZojy2srJi1apVOnU0Gk26kujv6n9GRkbs27eP2bNn8/z5c0qUKEH79u3x8vJS6urr67N9+3b69++Pk5MTefPmxcPDg0mTJil17O3t2bFjB8OGDWPOnDkUL16cpUuX4urqmp4/gUinpNm7+vr67NixQxlZ3KdPH7p160bz5s1JSEhQ6oncrUaNGlhZWVGoUCGmTZvG8uXL8fPzw8nJiYULF2b5ka9Pnjwh7lUsMaXqk2iS/QY1iYyh9/IJ3AzkyZMn2fK6PMmYMWOYPHkyw4cPV2aBAjRq1Ih58+apGJkQuYck0T+SokWLZus36IxiZ2dHuXLl1A5DvAdvb2/l94kTJ2ZYu+8akaLRaJg0aZLOhfabLCwslPU7hRDqKlCgwDtvsmm1WjQaDQkJCWlqU81Ra0JkJ//880+Gt/mu/leiRAkCA98929LOzi7Zci1vatCgAefOnUtXfOLD3L9/HwADAwP09PR0junp6WFgYEBCQoJST+ROcXFxbN26leXLl7N3715q1arFvHnz+Oabb3j48CFeXl506NCBq1evqh1qmiSamMseRiLbu3TpUorXwJaWljx69EiFiITIfSSJLoR4p1KlSnHq1CkKFSqkUx4VFUWNGjW4efOmSpEJIdR28OBBtUMQQgiRRknJ8djYWL766iuaNm2KjY0N9+7dY8+ePcTGxurUE7nPoEGDWLt2LVqtVlmSqUqVKsrxvHnz8uOPP8oyikJksgIFCnD//v1ke5WcO3eOYsWKqRSVELmLJNGFEO/0zz//pDiCNDY2ljt37qgQkRAiq6hfv77aIQghhEij4sWLc/r0aYoVK8bdu3dZv369zvGk8uLFi6sUoVDb1atX+fnnn2nXrp2ytOObChcuLDfRhchkbm5ujB49mg0bNqDRaEhMTOTo0aOMGDGCrl27qh2eELmCJNGFEKnatm2b8vvu3bt1NkhNSEhg//79ye6ECyFyLxcXFxo0aED9+vVxdnbW2csgO4qIiMj1+5v897+5VXbd20SIlPTt25fffvuNu3fvYmhoSFxcnHLM0NCQu3fvKvVE7rR///531jEwMJCb6EJksilTpuDp6UmJEiVISEigUqVKJCQk0KlTJ519SYQQH48k0YUQqWrTpo3yu4eHh84xQ0NDSpYsyU8//ZTJUQkhsqqmTZsSFBTEzJkziY+Pp1atWjpJdVNTU7VDTLOIiAg6d+lK3KtYtUNRna+vr9ohqMrQyJjVq36RRLrIEYyMjDAwMCA+Pp64uDhq1qyJo6Mj586d48yZM8DrBGnShqMid7p37x5HjhzhwYMHJCYm6hxLz0bFQoiMY2RkxJIlS/6PvfsOi+Jq2wB+71J26aBIU5rYUaxRsWKJGLtYUDF2E7sGE0tMRI3G9r6WGKNRoybGGmsssQTFbmyxYy+ggooIKFXgfH/47bysuzTdZRHu33Vx6Z6ZOefZ2T1Tnpk9g2+//RZXrlzBq1evULNmTZQvX97QoREVG0yiE1G2VAfNnp6eOHPmDOzt+UAeIsqe6i6Y9PR0nDlzBocPH0ZYWBjmzJkDuVyOlJQUA0eYd/Hx8Xidlorksk2RqbTJfQEqkuQp8cDdw4iPj2cSnYqECxcuID09HUqlEikpKTh37pyUPAcglV+4cAG1a9c2YKRkKKtXr8bnn38OU1NTlCxZUu3h4TKZjEl0IgNzc3ODm5ubocMgKpaYRCeiXE2dOhVWVlYa5WlpadiwYQPHYCMiNXfv3sXly5dx8eJFXLp0CVZWVmjSpImhw3onmUobZFrwAiIVfnfu3MGqVatw584dLFy4EA4ODvjrr7/g5uYGb29vQ4dHhcSFCxcAvHmuzUcffYTnz58jISEB1tbWKFmyJM6ePSvNxyR68fTtt99i8uTJmDhxIuRyuaHDISrWgoOD8zzvvHnz9BgJEQFMohNRHvTv3x+tW7eGg4ODWvnLly/Rv39/JtGJCADQq1cvHD58GKmpqWjSpAmaNm2KCRMmwMfHR+1ONiLSrcOHD+OTTz5Bw4YNceTIEcyYMQMODg64ePEifvnlF2zevNnQIVIhofqVYYkSJXD+/HnpwfExMTF48OABSpQogefPn2sM4UHFR1JSEnr06MEEOlEh8O+//6q9Pn/+PNLT01GxYkUAwM2bN2FkZMSLnkQFhEl0IsqVEEJrAuzhw4dqDxslouJtw4YNsLe3x6BBg9C8eXM0atTogxoHnehDNWHCBEyfPh3BwcFqvxxr3rw5fvzxRwNGRoWNtbU1AOD58+cax3aZmZl4/vy52nxU/AwcOBB//PEHJkyYYOhQiIq9Q4cOSf+fN28erKys8Ouvv8LOzg4A8OLFC/Tv3x+NGzc2VIhExQqT6ESUrZo1a0Imk0Emk6FFixYwNv7fJiMjIwP37t1D69atDRghERUmz58/x9GjRxEWFoaJEyciPDwcNWrUgJ+fH/z8/NCqVStDh0hUJF2+fBnr1q3TKHdwcEBMTIwBIqLCKmty3NLSEg4ODkhLS4OpqSmePn2Kly9fasxHxcvMmTPRrl077N27F9WqVYOJiYnadA4ZQWQY//3vf7F//34pgQ4AdnZ2mD59Olq1aoWxY8caMDqi4oFJdCLKVqdOnQC8GRfT398flpaW0jRTU1N4eHigS5cuBoqOiAobOzs7dOjQAR06dAAA3L59G9OnT8fcuXMxe/ZsadgAItItW1tbREVFwdPTU63833//RenSpQ0UFRVG4eHh0v9fvnwpJc21zffJJ58UVFhUiMycORP79u2Thot4+8GiRGQYCQkJePbsmUb5s2fPst2WE5FuMYlORNkKCQkBAHh4eCAwMBBKpdLAERFRYfb8+XMcPnwYYWFhCAsLw7Vr12Bra4v27dujadOmhg6PqMjq0aMHxo8fjz/++AMymQyZmZk4fvw4vvzySz63hNSohmvR1XxU9Pz3v//FypUr0a9fP0OHQkRZdO7cGf3798d///tf1K1bFwDwzz//4KuvvkJAQICBoyMqHphEJ6Jc9e3b19AhENEHwMHBAfb29mjcuDEGDx4MPz8/VKtWzdBhERV533//PYYPHw5XV1dkZGSgSpUqyMjIQK9evfDNN98YOjwqRExNTdVelylTBlZWVnj58iUePnyY7XxUfCgUCjRs2NDQYRDRW5YuXYovv/wSvXr1wuvXrwEAxsbGGDhwIObOnWvg6IiKBybRiShXGRkZmD9/PjZt2oSIiAikpaWpTY+NjTVQZERUmFy6dAne3t6GDoOo2DE1NcXy5csxefJkXL58Ga9evULNmjVRvnx5JCcnw8zMzNAhUiERFRWl9jpr4jyn+aj4GD16NBYtWoQffvjB0KEQURbm5ub46aefMHfuXNy5cwcA4OXlBQsLCwNHRlR8MIlORLmaOnUqVqxYgbFjx+Kbb77BpEmTcP/+fWzfvh2TJ082dHhEVEioEujPnj3DjRs3AAAVK1ZEqVKlDBkWUZE3atQo/PDDD3B1dYWrq6tUnpiYiHbt2uHQoUMGjI4Kk6xJc5lMhvLly8PFxQWPHz/GrVu3IITQmI+Kl9OnT+PgwYPYtWsXvL29NR4sunXrVgNFRkQAYGFhAR8fH0OHQVQsMYlORLlau3Ytli9fjrZt22LKlCno2bMnvLy84OPjg1OnTmHUqFGGDpGICoHExESMHDkSv/32GzIzMwEARkZG6NOnDxYtWgRzc3MDR0hUNO3evRt2dnaYOnWqVJaYmIjWrVsbMCoqjLI+GFIIgZs3b+LmzZs5zkfFi62tLcdXJiokAgICsHr1alhbW+faL3mBi0j/mEQnolxFR0dL4xpbWloiPj4eANCuXTt8++23hgyNiAqR4OBgHD58GDt37pTGUz127BhGjRqFsWPHYsmSJQaOkKho2r9/Pxo3bgw7OzuMGTMGL1++hL+/P4yNjfHXX38ZOjwqRMqUKYPw8HAAb8bSTU9Pl6ZlfV2mTBmDxEeGt2rVKkOHQET/z8bGRrqoaWNjY+BoiIhJdCLKVZkyZRAVFQU3Nzd4eXlh//79qFWrFs6cOQOFQmHo8IiokNiyZQs2b94MPz8/qaxNmzYwMzND9+7dmUQn0hMvLy/s3bsXzZo1g1wux/r166FQKLB7926OlUpqvLy8pCR61gT626+9vLwKNC4qfDg0G5HhZb2oxQtcRIYnN3QARFT4de7cGaGhoQCAkSNH4ttvv0X58uXRp08fDBgwwMDREVFhkZSUBEdHR41yBwcHJCUlGSAiouLDx8cHu3btwtdffw1zc3P89ddfTKCTBmPjvN1Dldf5qOhJTEzEgAED4OzsjCZNmqBJkyZwcXHBwIEDuS8nIqJijUdHRJSrWbNmSf8PDAyEm5sbTp48ifLly6N9+/YGjMzw5Cnxhg6BDIifvzpfX1+EhITgt99+g1KpBAAkJydj6tSp8PX1NXB0REVLzZo1tY5brVAo8PjxY2lIJQA4f/58QYZGhVjFihV1Oh8VPRyajajw2rx5MzZt2oSIiAikpaWpTeO+nkj/mEQnonzz9fUt9gkxGxsbmJgqgLuHDR0KGZiJqYJjFP6/hQsXwt/fH2XKlEH16tUBABcvXoRSqcS+ffsMHB1R0dKpUydDh0AfoLi4OLXXCoUCcrkcmZmZSE1NzXY+Kj44NBtR4fTDDz9g0qRJ6NevH3bs2IH+/fvjzp07OHPmDIYPH27o8IiKBSbRiShXz58/R8mSJQEAkZGRWL58OZKTk9GhQwc0btzYwNEZhqOjI35f85v0kNXi6MGDB5gxYwYmTZoEd3d3Q4djMDY2NlqHMCmOqlatilu3bmHt2rW4fv06AKBnz54ICgqCmZmZgaMjKlpCQkIMHQJ9gFRjXKtkTZznNB8VHxyajahw+umnn7Bs2TL07NkTq1evxrhx41C2bFlMnjwZsbGxhg6PqFhgEp2IsnX58mW0b98ekZGRKF++PDZs2IDWrVsjMTERcrkc8+fPx+bNm4vt3XCOjo5MngJwd3dHhQoVDB0GFRLm5uYYPHiwocMgIiItVBc4dTUfFT0cmo2ocIqIiECDBg0AAGZmZnj58iUA4NNPP0X9+vXx448/GjI8omKBDxYlomyNGzcO1apVw5EjR+Dn54d27dqhbdu2iI+Px4sXL/D555+rjZdORMXb8+fPpf9HRkZi8uTJ+Oqrr3DkyBEDRkVUNJUoUQIxMTEAADs7O5QoUSLbPyIVuTxvp395nY+KnoULF+L48eMoU6YMWrRogRYtWsDV1RUnTpzAwoULDR0eUbHl5OQk3XHu5uaGU6dOAQDu3bsHIYQhQyMqNngnOhFl68yZMzh48CB8fHxQvXp1LFu2DMOGDZNOrEaOHIn69esbOEoiMjT+aoWo4M2fPx9WVlYAgAULFhg2GPpgVapUCfXr18epU6d49zkB4NBsRIVV8+bN8eeff6JmzZro378/vvjiC2zevBlnz55FQEBAvuqaOXMmtm7diuvXr8PMzAwNGjTA7Nmz1R4q7efnh8OH1Z//9fnnn2Pp0qXS64iICAwdOhSHDh2CpaUl+vbti5kzZ8LYmKlGKpr4zSaibMXGxsLJyQkAYGlpCQsLC9jZ2UnT7ezspJ+REVHxpfrVytq1a7FmzRrpVyvLly8H8OaC26xZs5hEJ9Khvn37AgDS09Mhk8ng7+/PIcYoV9HR0Wqvr1+/rjV5/vZ8VLxwaDaiwmfZsmXIzMwEAAwfPhwlS5bEiRMn0KFDB3z++ef5quvw4cMYPnw4PvroI6Snp+Prr79Gq1atcO3aNVhYWEjzDR48GNOmTZNem5ubS//PyMhA27Zt4eTkhBMnTiAqKgp9+vSBiYkJvv/++/d8t0SFE5PoRJQjmUyW42siIv5qhchwjI2NMWTIEISHhxs6FMqnlJQUREREFGibqgRMXua7efOmnqP5Hzc3N2n8bTK8W7du4dChQ3j69KnGd2by5MkGioqoeJPL5WpDbfXo0QM9evR4p7r27t2r9nr16tVwcHDAuXPn0KRJE6nc3Nxcuqnubfv378e1a9fw999/w9HRETVq1MB3332H8ePHY8qUKTA1NdVYJjU1Ve2B1gkJCe8UP5GhMIlORDnq168fFAoFgDcne0OGDJGuTmfdARJR8cVfrRAZVt26dfHvv//C3d3d0KFQPkREROCzzz4zdBjZKsjYli1bxoeUFxLLly/H0KFDYW9vDycnJ7UbaGQyGZPoRAaUkpKCS5cuab3A1aFDh3euNz4+HgA0nqOydu1a/P7773ByckL79u3x7bffSnejnzx5EtWqVVP7FZy/vz+GDh2Kq1evombNmhrtzJw5E1OnTn3nOIkMjUl0IsqW6qfiKr1799aYp0+fPgUVDhEVYvzVCpHhDBs2DGPHjsXDhw9Ru3ZttZ9iA4CPj4+BIiv8njx5IiUPClpqaiomTZpUoG2uXbsW9+/fl17b2dnhxYsX0r8qHh4eCAoKKrC4UlNTC/TO97fZ2NhwOKT/N336dMyYMQPjx483dChElMXevXvRp08f6aHiWclkMmRkZLxTvZmZmRgzZgwaNmyIqlWrSuW9evWCu7s7XFxccOnSJYwfPx43btzA1q1bAbwZ9uvt7abqdXZDgk2cOBHBwcHS64SEBLi6ur5T3ESGUGST6IsXL8bcuXMRHR2N6tWrY9GiRahbt66hwyL6oKxatcrQIeSI/Zyo8NDHr1bYx4nyRvVz7lGjRkllMpkMQoj3OrEuCIbs50+ePEFQ70+R/jqtQNorjFSJ86wJdAC4f/8+ZsyYYYiQDMLYxBRrf1/DRDrefBe6deums/q4LyfSjZEjR6Jbt26YPHmyTrdVw4cPx5UrV3Ds2DG18qy/RqpWrRqcnZ3RokUL3LlzB15eXu/UlkKhkM4XiD5E8txn+fBs3LgRwcHBCAkJwfnz51G9enX4+/vj6dOnhg6NiHSE/Zyo8Ojbty8cHBxgY2MDGxsb9O7dGy4uLtJrBweHfP9qhX2cKO/u3bun8Xf37l3p38LK0P08Pj6+WCfQ6X/SX6cZ7BcJhU23bt2wf/9+ndRl6D5OVJQ8efIEwcHBOk2gjxgxArt27cKhQ4dQpkyZHOetV68eAOD27dsAACcnJzx58kQjRtU0oqKoSN6JPm/ePAwePBj9+/cHACxduhS7d+/GypUrMWHCBANHR0S6wH5OVHjo41cr7ONEeffgwQM0aNAAxsbqh/bp6ek4ceJEoR0r3dD93MbGBsYmpkykE4xNTGFjY2PoMAqFcuXK4dtvv8WpU6dQrVo1mJiYqE3P+ouX3Bi6jxMVJV27dkVYWNg73wWelRACI0eOxLZt2xAWFgZPT89cl7lw4QIAwNnZGQDg6+uLGTNm4OnTp3BwcAAAHDhwANbW1qhSpcp7x0hUGBW5JHpaWhrOnTuHiRMnSmVyuRwtW7bEyZMntS5TFJ8QnJKSgoiICIO1/+DBA7V/DcnNzQ1KpdLQYZAO5befs4/rR2Hp5+zjRU9h2pcbxT+EPDlOJ3Xlm8iALC3JMG0XMsLUHJAZFXi7srRXBd7mu2jWrBmioqKkk1iV+Ph4NGvWrFAO51IY9uWOjo5Y+/sag46Jnt24sQUhJSUFmzZtQmRkJFxdXdG9e3eD7U+dnJwM+hN/jon+P8uWLYOlpSUOHz6Mw4cPq02TyWR5TqJzX/7/uC+XcF/+fn788Ud069YNR48efe8LXMOHD8e6deuwY8cOWFlZSfsiGxsbmJmZ4c6dO1i3bh3atGmDkiVL4tKlS/jiiy/QpEkT6TkrrVq1QpUqVfDpp59izpw5iI6OxjfffIPhw4dzyBYqsopcEj0mJgYZGRlaH3Bw/fp1rcsUxScER0REqI1hZSiFYSzFZcuWoUKFCoYOg3Qov/2cfVy/DN3P2ceLnsKwL7exsYFcbgTlo/M6q5M+THK5UaG/Q1U19vnbnj9/rvGQ0cKisOzLHR0dDZY8vXnzpsH3oSqRkZH473//a7D2uS8vPO7du6eTergvp8LkQ9iX52b9+vXYv38/lEolwsLC1Pb7+bnABQBLliwBAPj5+amVr1q1Cv369YOpqSn+/vtvLFiwAImJiXB1dUWXLl3wzTffSPMaGRlh165dGDp0KHx9fWFhYYG+ffti2rRp7/dGiQqxIpdEfxdF8QnBbm5uWLZsmaHDKBTc3NwMHQIZGPt40cY+ToDu+7mjoyN++mkxIiMjdRHeO3n9+jViYmIM1n5hYm9vr3HHVUFxdXUttHeoBgQEAHhz8pz14b4AkJGRgUuXLqFBgwaGCk+nuC8v2rgvL5yEEACg9SKdPnBfXrRxX/5+Jk2ahKlTp2LChAmQy9/v8Yaqvp0dV1dXjV+iaOPu7o49e/a8VyxEH5Iil0S3t7eHkZGR1gccZPdwg6L4hGClUsm7OajIym8/Zx8n+rAUln15pUqVUKlSJZ3WSaRLqrvqhBCwsrKCmZmZNM3U1BT169fH4MGDDRVejrgv576cCq/ffvsNc+fOxa1btwAAFSpUwFdffYVPP/00z3VwX06kW2lpaQgMDHzvBDoRvbsi1/tMTU1Ru3ZthIaGSmWZmZkIDQ2Fr6+vASMjIl1hPycq2tjHifJm1apVWLVqFUJCQvDLL79Ir1etWoWff/4ZEydOhL29vaHD1Ir9nKhwmjdvHoYOHYo2bdpg06ZN2LRpE1q3bo0hQ4Zg/vz5ea6HfZxIt/r27YuNGzcaOgyiYq3I3YkOAMHBwejbty/q1KmDunXrSuM4qZ4KTkQfPvZzoqKNfZwo78aNG6f20+wHDx5g27ZtqFKlClq1amXAyHLGfk5U+CxatAhLlixBnz59pLIOHTrA29sbU6ZMwRdffJHnutjHiXQnIyMDc+bMwb59++Dj46MxNM68efMMFBlR8VEkk+iBgYF49uwZJk+ejOjoaNSoUQN79+7N8xhYqpMQXT0NnOhdqb6DuY1ZVhy9Tz9nH6fCgn08e9yXU1FREP28Y8eOCAgIwJAhQxAXF4e6devC1NQUMTEx0l2lhRH35VQUFLV9eVRUlNZnKTRo0ABRUVH5qov7cioqCkM/v3z5MmrWrAkAuHLlitq0gnpuga6xj1Nhkdc+LhNFZW+vQw8fPvzgH1RERUtkZCTKlClj6DCKDPZxKmzYx3WP/ZwKG332c3t7exw+fBje3t5YsWIFFi1ahH///RdbtmzB5MmTER4erpd2DYl9nAqborIvr1q1Knr16oWvv/5arXz69OnYuHEjLl++XGCxsJ9TYVNU+nlhwT5OhU1ufbxI3on+vlxcXBAZGQkrK6sP9oqeoamepB4ZGQlra2tDh/PBEkLg5cuXcHFxMXQoRQr7uG6wn78/9nH9YT9/f+zjulEQ/TwpKQlWVlYAgP379yMgIAByuRz169fHgwcP9NauIbGP6wb7+fsravvyqVOnIjAwEEeOHEHDhg0BAMePH0doaCg2bdpUoLGwn78/9nHdKGr9vLBgH9cN9vP3l9c+zjvRSS8SEhJgY2OD+Ph4dmKiIor9nKhoYx//cPj4+GDQoEHo3Lkzqlatir1798LX1xfnzp1D27ZtER0dbegQqZBiPydtzp07h/nz50u/YqlcuTLGjh0rDSVBHw72caKij/284PBOdCIiIiKiD9jkyZPRq1cvfPHFF2jRogV8fX0BvLkrnUkvIsqv2rVr4/fffzd0GERERIUKk+hERERERB+wrl27olGjRoiKikL16tWl8hYtWqBz584GjIyIPjR79uyBkZER/P391cr37duHzMxMfPLJJwaKjIiIyLDkhg6AiiaFQoGQkBAoFApDh0JEesJ+TlS0sY9/WJycnFCzZk3I5f87vK9bty4qVapkwKiosGM/p7dNmDABGRkZGuVCCEyYMMEAEdH7YB8nKvrYzwsOx0QnIiIiIiIiIpiZmSE8PBweHh5q5ffv34e3tzcSExMNExgREZGB8U50IiIiIiIiIoKNjQ3u3r2rUX779m1YWFgYICIiIqLCgUl0IiIiIiIiIkLHjh0xZswY3LlzRyq7ffs2xo4diw4dOhgwMiIiIsPicC5EREREREREhPj4eLRu3Rpnz55FmTJlAAAPHz5E48aNsXXrVtja2ho2QCIiIgNhEp2IiIiIiIiIALx5iOiBAwdw8eJFmJmZwcfHB02aNDF0WERERAbFJDoRERERERERERERUTY4JjoRERERERFRMdamTRvEx8dLr2fNmoW4uDjp9fPnz1GlShUDREZERFQ48E50IiIiIiIiomLMyMgIUVFRcHBwAABYW1vjwoULKFu2LADgyZMncHFxQUZGhiHDJCIiMhjeiU5ERERERERUjL19bx3vtSMiIlLHJDoRERERERERERERUTaYRC8GZDKZ2p9cLoetrS0aN26MFStWaNxlMGXKFMhkMqxevdowARMVgLf7hUwmg4mJCVxcXNClSxecOHHC0CFK/Pz8IJPJcP/+fUOHkqOoqCgYGxtDJpNhwIABhg6HiqEPqV8DgIeHB2QymUa5TCaDh4dHwQeUT5s2bZLW82+//WbocIgkq1evhkwmw5QpU9TK+/XrB5lMhrCwsAKPSbUvz/pnYWGBKlWqYOzYsXj27FmBx6RLcXFxKFmyJLp166ZWrvos3v4zNzdHlSpVMG7cOMTGxhooanX379+HTCaDn5+fQdpPTk6Gs7Mz2rRpo9d2tH0eMpkMQgg4OjpCJpOhX79+0rxUMFTn4Fn/FAoF3N3d0bt3b1y8eNHQIb635s2bo0yZMkhNTZXKVP3u7T9TU1O4u7ujT58+uHr1qgGj1qTtOK24bD8Ko3c5bjbk8UBehYeHY9iwYahYsSIsLCxgbW2NqlWrYuzYsYiMjDR0eHqlbXuY219B5S+NC6QVKhT69u0LAMjIyMCdO3dw/PhxHDt2DKGhoVi/fr2BoyMyDFW/AICXL1/i4sWL2Lp1K7Zt24bff/8dvXr1eq/679+/D09PTzRt2jTbnbRMJoO7u3uhT5LnZt26ddI4mVu2bMFPP/0EpVJp4KioONJ3v6Y31qxZI/3/999/R58+fQwYDRUVYWFhaNasGfr27Vskb+jw9/eHk5MTgDcXn0+dOoV58+Zh48aN+Oeff1C6dOn3qt9Q62/GjBl48eIFQkJCtE738vJCo0aNALwZJkT13ufOnYtNmzbh1KlT0np5V1OmTMHUqVOxatUqKQmc1erVq9G/f3+EhIRoXGApDMzMzDBu3DgEBwfj4MGDaN68uV7by7qvBIBff/0VpUuXhpGRES5duoSUlBQMGTIEFhYWAKCW+CT9qV69OmrUqAEAiI+Px9mzZ7F27Vps2rQJu3btQqtWrd67DUOce+zevRuHDh3C4sWLoVAoNKZbWFiga9eu0uvY2FicP38ea9aswaZNm7Bv3z40bdq0wOL90BT09oP0a86cOZg0aRLS09NRrlw5tGnTBunp6Th79izmzZuHJUuWYPny5QgKCjJ0qHpRo0YNjX3Uq1evsGXLFgCa+y8AKFeuXJ7rz+14ISdMohcjbx9IHzhwAG3atMGGDRsQFBSEdu3aGSYwIgN6u19kZmbi66+/xuzZszFq1Ch069YNJiYmhgnu//32229ISkp67xNrfVMl1JydnREVFYUdO3YgMDDQwFFRcfQh9OuchIeHF+r4AODZs2fYu3cvzM3NYWxsjNDQUDx+/BguLi6GDo0InTt3Rv369WFvb2/oUDRMmDBB7U7FqKgotGjRAuHh4QgJCcGKFSsMF9w7ioqKwqJFi9C+fXtUrVpV6zyNGjXS2DY/e/YMfn5+uHbtGr7//nv88MMPBRBt9kqXLo3w8HCYm5sbLIYhQ4Zg2rRpmDhxIv755x+9tvX25/H2XefVq1fXWIYXS/WvU6dOahd5UlNT0a9fP2zYsAHDhg3D7du3DRfce/j6669RqlQpDBo0SOt0e3t7je9kWloaBgwYgLVr12LkyJG4dOlSAUT6borb9qMw+RCOm/Pjhx9+wPjx42FtbY1ff/0VnTp1kqYJIfDLL79g+PDh+PTTT2FpaYmOHTsaLlg96dSpk9r7Bt7cnKhKohvyJgsO51KMffzxx/j0008BANu3bzdsMESFhFwux7Rp02BsbIznz58Xip8Purm5oVKlSoX64ODKlSu4ePEiXF1dMXPmTADqd6kSGVJh7Nc5qVSpEry8vAwdRo42bNiA9PR0dO7cGV27dkVmZibWrVtn6LCIAAA2NjaoVKlSoUyiv83Z2Vm6e3vfvn0GjubdrFy5EqmpqflOsJYqVQpjx44FABw5ckQfoeWLiYkJKlWqBDc3N4PFYGZmhi5duuD06dP4999/C7TtVatW5emPCpZCocD8+fMBAHfu3MGdO3cMHFH+HT9+HJcuXUJgYCBMTU3zvJypqSmmTZsGALh8+TLi4uL0FOH7K+7bD0P6EI6b8+rBgwcYN24cZDIZtm/frpFIlslkGDRoEH7++WcIITBo0CAkJiYaJthiikn0Yq5mzZoAkKcxlW7fvo0pU6bA19cXTk5OMDU1RZkyZdCnTx/cvHlTbd6oqCiYmJjA1dVVGt7hbevWrYNMJtP4KYYQAuvXr0fz5s1hZ2cHpVKJypUrY8qUKUhKStKoJ+t40evWrUP9+vVhZWUFW1vbPK4FInWmpqawsbEBAKSnp6tNu3DhAsaNG4fatWujVKlSUCgUKFu2LIYNG4bHjx+rzTtlyhR4enoCAA4fPqw2Zle/fv2kcUKBNzvMrNOz3qWW3ZjoqvHfMjIyMHv2bFSoUAEKhQKurq4YP358tj+7vXTpEtq3bw9bW1tYWVmhSZMmOHDgAMLCwtTGwcwPVcK8V69e6NKlC8zNzbFv3z61MV6fPn0KY2NjuLi4IDMzU2s9O3fuhEwmQ0BAgFq5PrYLu3fvxoABA1C5cmVYW1vDwsIC1atXx/fff5/tunv9+jVmz56NihUrQqlUws3NDcHBwXj16lWOY9dHRkZixIgR8PLyglKpRIkSJdCuXbtCN0Z3UZZTvwbefEaff/453N3doVAo4ODggICAAJw5c0ZtvpSUFCiVSq1jL3bq1AkymUwasiCrOnXqQC6X52ncY21jO2btn7GxsRg6dCicnZ2hUChQtWpVrFy5Mtv6tm7divr168Pc3Bz29vbo1q2btE9/1zEEf//9dwBA79690bt3bwCaF862bt0KmUyW4y9Sxo4dC5lMpnEHalJSEmbOnImaNWvC0tISlpaWqF+/Pn799Vet9ajWWVpaGqZNm4ZKlSpBoVBIJx8pKSn45Zdf0LFjR5QtWxZmZmawtbVFkyZNsGHDhmzji4mJwdChQ+Hi4gIzMzNUrVoVixcvhhAixzE4//nnH3Tr1g3Ozs7S8dKgQYMQERGRbVtF3ZUrV9C7d2+ULVsWSqUSpUqVQo0aNTBmzBhERUUBeDNGabNmzQC8GVoi635RdWdm1nFnExISEBwcDE9PT5iYmGDMmDEAsh8TPTtpaWno1q2btP/Jug8oiM/S29sbwJv95NuOHj2KESNGwMfHB3Z2djAzM0OlSpUwYcIEjYRSXtafiq72S6q74qysrNC2bdt8LQsADg4OADS3y6r9fo8ePVChQgVYWFjAysoKdevWxU8//aRxHOHh4YGpU6cCAPr376/23sPCwuDn54f+/fsDAKZOnap1HNXsxjTO+n2KiIhAr169UKpUKZiZmaFOnTrYuXNntutm2bJlqF69OszMzODk5ISBAwfi6dOnOY7HqxpybNmyZflal/r2/PlzTJo0CdWqVZPG561WrRrGjRsn9WGV9PR0LFq0CLVr15a24XXr1sWSJUu0nhtmPYbavn076tevDwsLC5QoUQI9e/bEw4cPtcaUlJSE7777DlWrVoWZmRlsbGxy3K5nfRbJ4sWLpeU8PT0xZ84c6Vlh58+fR/v27VGiRAnpTs8HDx6o1dWuXTvIZDLs378/29hUx9ovX77MeeXmwsnJCSVLlgSguZ3Izzl6Xs89VPHnZz+cE9UvbHr27JnvZVXbCED78du1a9cQFBQkbaNLly6NPn364MaNG2rz5TRmeY0aNSCTyaTjGRUhBEqVKgVbW9tscxq51V+Utx9Z33NiYiKCg4Ph6uoKMzMz1KpVS+29/fHHH6hXrx4sLCzg6OiIUaNGITk5WaPO/Jxrq+R0PLZy5UrUqFFDWof9+vVDdHR0ju8rNjYWEydORJUqVaTtSvPmzbFr164c10FycjImTJggnUuUK1cOs2fP1ngGYU4WL16M1NRUdOnSRdqfa9OvXz/UqVMHMTExUp8sbOfaeTnu06U9e/bg448/luKvWLGi1mOl3I4XgJzPGyCoyAMgkM1HPWPGDAFAtG/fXioLCQkRAMSqVavU5h0/fryQyWSiWrVqol27dqJLly6icuXKAoCwtrYWFy9eVJs/ICBAABC7du3S2rafn58AII4dOyaVZWRkiJ49ewoAwtLSUvj5+YnOnTsLV1dXAUDUrVtXJCUlqdXTtGlTAUB89tlnQi6Xi8aNG4sePXqIhg0b5mc1UTGTU7+4e/euACBMTEzEixcv1KYFBgYKY2NjUatWLdGpUyfRqVMn4eHhIQAIZ2dn8ejRI2nebdu2iS5duggAwtHRUfTt21f6W758uTh69Kjo27evACAsLCzUps+cOVOqR/Udv3fvnsZ7cHd3F927dxeWlpaiXbt2ol27dsLGxkYAEEFBQRrv7cSJE8Lc3FwAED4+PqJHjx7io48+EnK5XIwcOVIAEH379s3XuszIyBClS5cWAMTly5eFEEL06tVLABA//PCD2rytW7cWAMTff/+tta7AwEABQGzZskWtfn1sFxwdHYW1tbVo0KCB6N69u/D39xd2dnYCgGjevLlIT09XqzMzM1N07txZ+rzat28vAgIChK2trfjoo4+Er6+v1s/pxIkTUr0VK1YUAQEBonHjxsLY2FgYGRmJDRs25Gt9U/betV9funRJ2NvbS59Rjx49RIMGDQQAYWxsLDZt2qQ2f5MmTTQ+64yMDOlzNjU1FYmJidK0uLg4IZfLhbe3t1o97u7uWuNV9e2sDh06JACIjh07igoVKggXFxfRrVs30axZM2FkZCQAiOXLl2vUtWDBAgFAyOVy4efnJ3r06CE8PT2FnZ2d6NOnj9b9fW5u3LghbdfS09NFZmamKFOmjAAgLl26JM2XkpIibGxshJmZmXj58qVGPRkZGcLFxUUYGRmJJ0+eSOVPnjwRPj4+AoBwcnISbdq0EZ988om0bRsxYoTWdebq6io++eQTYWFhIdq0aSO6desmhgwZIoQQIjw8XAAQLi4uolmzZiIwMFA0bdpUmJiYCAAiJCREo85nz56J8uXLS8t1795dfPzxx8LExESMHj1a6+ckhBCLFy8WcrlcyOVyUa9ePdGtWzfp/ZQqVUpcu3YtX+u7KDh79qxQKpXSvqd79+6iXbt2okqVKgKAOHTokBBCiOXLlwt/f38BQHh5eantF7dt2yaEEOLevXvStr9GjRrCzs5OdOrUSQQEBIgpU6YIIYRYtWqV1s9Vtc9VtSeEEC9fvhQtW7YUAMSAAQPUtv26/CxV+6WsbaucOHFCABBlypTRmFavXj2hVCpF3bp1RZcuXUTbtm2Fs7OzACC8vb3V+lZe1p+qPV3tl65cuSIAiI8//ljrdNVnkd2xheq8o2vXrmrlycnJAoAoWbKkaNy4sQgMDBQtW7aUjmHerm/s2LGievXqAoBo2LCh2nsPDw8XM2fOFA0bNhQARPXq1dWmHz16VAjxv+9W06ZNs30PDg4OwsvLSwQGBkr7fblcLvbt26fx3saMGSPtE/z9/UX37t2Fk5OT8PDwEB06dMj2+5CcnCxMTEyEq6ur9pX+nnLaV2bn2rVr0nbeyclJdO7cWXTu3Fl4e3sLAGrfr/T0dNGmTRvpHLFTp06iY8eOwsrKSgAQnTt3FhkZGWr1q/rHV199JYyMjISfn5/o2rWrdKxXvnx5jWO9hIQEUbt2bak/du3aVXzyySdCoVAIAGLUqFEa70O13x0zZowwMzMTbdq0Ee3atZNimzx5sjh27JgwNzcXtWrVEt27dxflypWT+lTWGP7880+t312V1atXCwBi0KBBeVrHqr6gbX+UmZkpbUNv376tNi0/5+h5Pfd4l/1wTkqVKiXMzMzE69evNaap+p22/akQ/zv+KVWqlMa0v//+W5iZmQkAombNmqJHjx6iRo0a0jnDkSNH1OZ3d3cXCoVCJCcnS2XPnz8XMplM6zb40qVLAoBo27atWrm2eIvL9iMr1Xv29fUV9erVEw4ODqJr167Cz89PyOVyYWRkJA4cOCDmzZsnjI2NRYsWLUTnzp1FyZIlBQDRq1cvjTrzc66tkt33Z/z48dKxf6tWrUS3bt2Eg4ODcHNzE+3bt9e6Dm/cuCFtdzw8PETHjh1F8+bNpX3P3Llzs10HjRo1EiVKlBABAQHC399f6rOTJk3K8zpVHRdl3aZmZ968eQKAaNeunVRWWM6183rclx+qda1t//X9999L520tWrQQgYGB0j6rQoUKIjo6Wpo3t+MFIXI5b8h35PTBye6LlpmZKW28s3bs7JLoJ0+eFHfv3tWoZ+XKlQKAaNasmVr5/v37pRP+t926dUsAEJUrV1YrnzNnjgAg/Pz8RFRUlFSempoqBg4cKACI8ePHqy2j6sBKpVKEhYVlux6IstLWL16+fCmOHj0q6tSpk+3B98GDB9U2wkK82fFMnTpVABD9+/dXm5bdAdXbsWR34ChEzkl0VT/K2l/u3r0rbG1tNQ60MzIyRIUKFQQAMWPGDLW6VqxYIdWX3yT633//LZ2UquzZs0cAEB999JHavGvWrJESFW9LSEgQZmZmwsbGRqSkpEjl+toubN++XesJWbt27QQA8euvv2qN3dPTU0RGRkrlMTEx0gH7259TfHy8cHZ2FkZGRuL3339Xq+/MmTPCzs5OWFpaiqdPn2qNkfLnXfp1ZmamqFatmgAgxo0bJzIzM6VpmzdvFnK5XFhaWorHjx9L5ZMnT9bYT54/f15KagEQBw4ckKapTrSHDx+u1va7JNEBiB49eqj1kW3btgkAws3NTW2ZO3fuCFNTU2FqaioOHjwolb9+/Vr0799fqi+/SfRvvvlGABCjR4+WysaNGyclQLJS9dHffvtNox7VtqN169Zq5arky+jRo9XeZ3R0tPQ5/vXXX2rLqN5LuXLlxMOHDzXaiomJEQcOHFD7fIV4s7308PAQcrlcYxurir1Dhw5qJ9znzp2TEglvf04nT54URkZGonTp0uLs2bNq01Tb2Xr16mnEV9SpLtj85z//0ZgWHh6u1r9U3/Xs9kVZT6J8fX01LooJkfckekxMjKhbt67W766uP8uckuiqbYq2ZNuePXtEXFycWllKSor47LPPBAAxdepUtWm5rT9d75eWLFkiAIivv/5a63RtSfTMzEwRFRUlli1bJszMzIRCoRAnT55UW+7169di27ZtIi0tTa386dOn0nbg8OHDatOyO4d5OxZtSUohck+CARBjx45VSwDPnz9fABCNGzdWW+bo0aMCgChRooR0g4EQQiQmJkoXOnJKJKiSw9rOvd5XdueG2Xn9+rWoWLGiAN4kn1NTU9WmX7lyRe148z//+Y+0P8x6zPz48WOpnkWLFqnVoeof5ubm4sSJE1J5YmKidFH7l19+UVtmxIgR0jloQkKCVB4eHi4cHBwEALFz5061ZVT7XRcXF7WYw8PDhUKhEObm5sLDw0MsWbJEmpaamiqaN28uAIiVK1dK5enp6cLV1VWYmJioXQhWUV20+eeff7Sv2LfklEQ/ePCglAx6ez+W33N0IXI/93iX/XB2VMmoBg0aaJ2eXRL9xYsX4q+//pIuYsybN09t+qtXr4Sjo6MAIH788Ue1aarkYpkyZdT236p9UdZ+t3XrVrXjt1u3bknTfvjhB62JU23xFpftR1ZZ98fNmzcXr169kqap3ne5cuWEnZ2dOHPmjDTt0aNHUh+9c+eOWp35PdcWQvvncfLkSSGTyYSNjY04f/68VP7y5UupP7+9DtPT06Vzgjlz5qh9Vrdu3RKenp7CyMhI7TPJug6aNm0q4uPjpWlnzpwRRkZGwtzcXOvNJG9LTU2VLuhkPdfMzuHDh6XvuUphOdfOz3FfXmWXRD99+rR0vnbq1CmpPCUlRXTr1k0AEF26dFFbJrfjhZzOG5hELwbe/qKlp6eLmzdvin79+gkAQqFQqB1E5PaF0qZhw4ZCJpOpHeRnZmaKcuXKCWNjY41OoroqmHVn+Pr1a2Fvby8sLCw0NpxCCJGUlCScnJyEnZ2d2gZN1YHfTk4Q5UTVL7T9WVlZiUWLFmlsNHNTunRpUbJkSbWygkiiZ03WqahOLLL24wMHDgjgzd08b98BJMT/Dvbzm0RXJSWyHmC+fv1aOji6fv26VP7q1SthYWGhsfMWQohff/1VABADBw5Uq6egtwuqi3wBAQFq5ar1s2bNGo1lVOv27c9JdXA8duxYrW2pDvLfPjGgd/Mu/Vp1Yurm5qaRrBHif7+qmj59usYyWfuK6rPcuHGjANQvTgcHBwsAGne0v0sS3draWsTExGgsU7VqVY3v36RJkzT6lMqLFy+EpaVlvvf3mZmZwtPTUwBQOyFS3a1VunRptb6oitvf31+jLlUiP2uf+vfffwXw5gKctu2U6mJFhw4d1MpVn/Mff/yR5/eisnz5cgGo/3Lm5cuXQqlUCiMjI41trxD/W7dvf04dO3bUmrhRUd09lvWErjj45JNPBABx4cKFXOfNTxI963cwq7wk0R8+fCjdEZX1DkwVXX+W2pLojx8/FosWLRJKpVKUK1cuXyeVSUlJ0t16WeW2/nS9Xxo6dKgANC88q2RNIGn7++ijj8Tp06fz1JaKap8bHBysVq7vJLqnp6dGAvn169fCzs5OmJiYqE0LCgoSAMR3332n0c6NGzeEXC7PMQmmWn7r1q1ap7+PnD4P1V/WuyBV+zVvb2+NX+lp4+bmJgBovbtWdVG5XLlyauWq/qHtjs3NmzdrfKdfvXolzMzMhFwul+4czEqV/GzZsqVauWq/u2LFCo1lVL82bNSokca0HTt2aO1X06ZNkxJuWakSxz4+Php1ZUdbEj0uLk7s3LlTuLm5CXNz83zfMKbtHF2InM893nU/nB3V90db8lMI9W26tj9HR0exfv16jeVUFwl8fX211qtKJGe9WKhaJus6HjVqlNrxW9Zf9amOAd/eRmlbf8Vl+5GV6j3L5XJx48YNtWkZGRnSrzy/+eYbjWW/+OKLfB+DajvXFkL756FK4k6ePFlj/qtXr0rJ6qzrUHVTytsJVxXVBZesN+RkXQdZz3lVVDdn5eXO6+joaOl7//Z5sjaq7YxSqZTKCsu5dn6O+/IquyS66rOeOHGixjJPnjyR9hURERFS+bvkPFWMQcXG209cBwArKyv8+uuveX4Qw6tXr7Bz505cuHABsbGxeP36NYA3Y6ALIXDnzh3UqlVLau+zzz7DuHHjsGrVKnz99dcA3owpvHr1aigUCrUHEJ0/fx4xMTH4+OOP4ejoqNG2mZkZateujd27d+PWrVuoWLGi2vQOHTrkbUUQZZF1TP7U1FQ8ePAA//zzD6ZNmwYvLy988sknGss8f/4cf/75J65cuYK4uDhpjLzXr1/j+fPniI2NRYkSJQokfhMTE63jpVWoUAEA1MYbO378OACgS5cukMs1H4kRGBgozZNXycnJ2Lp1K+RyuTQGHwAYGxujZ8+eWLhwIdasWYPp06cDACwsLNCxY0esW7cOu3fvVhuPbe3atQCgNh6hvrcLt27dwp49e3D79m0kJiYiMzNTGrfu1q1b0nyvX7/GmTNnIJPJ0LVrV416WrZsiRIlSiA2NlatXDVO5tvjzqk0btwYAHD69Okc46T8yU+/Pnr0KACge/fuWh/e++mnn2Lr1q3SfABQv359KBQKtbEow8LCYGVlhS5dusDd3V1jGgCt43DmV+3ataVxUbOqUKECrly5gqioKGlcSFV/7tatm8b8tra2aNWqFbZu3Zqv9o8fP4579+6hUqVKqFOnjlRerVo1VK9eHRcvXsTBgwfRsmVLAECTJk1QpkwZhIaG4unTp9LYpikpKdiyZQssLCzQuXNnqR5Vn+nUqZPW7ZRqbFZtfUYmk6F9+/Y5xn/s2DGEhYXh0aNHSElJgRBC2k5m7fPnzp1DSkoK6tevr3WczcDAQMyYMUOtLDMzE6GhoTA3N4e/v7/W9hs3bow///wTp0+flp5LUxzUrl0bf/31F4YPH47p06ejUaNGMDZ+v9MQZ2dnte9gfty6dQv9+vVDZGQkli1bhsGDB6tN1+dnqW2fXatWLRw6dAjW1tZal3n06BF27tyJ69evIyEhQRrr1NTUVO17mxe63i+pxme2s7PLcT4vLy+150W8ePECly9fxpkzZzB+/Hhs3LgRpUqV0ljuwoUL2L9/Px48eICkpCQIIaTxpfP73t+Xn5+fxkMRjY2N4enpifPnz+P58+dwdnYGkPP2t0KFCqhRowbOnz+fbVuq48i8PEfjXb39XKqssj4c8e+//wYADBo0CEZGRjnWGRERgYiICJQqVQqtWrXSmN6uXTvY2tri9u3biI6OhpOTk9p0bctoO6Y9d+4ckpOTUadOHVSqVEljmU8//RSjRo3C8ePHkZmZqbE/0dZO2bJlc5329ji+gwYNwrRp07BixQp89dVXUvny5csBAJ999plGXbmZOnWqNF6vip2dHU6dOoVq1appXSY/5+i5eZ/9sDZ53UZYWFioHWMnJyfj9u3bOH/+PCZOnIhy5cqpbfNVx2VBQUFa6+vduzfOnTuHo0ePSvM0bdoUADSO0SpXrowOHTpIx3aDBg2CEAJHjhyBtbV1ntddTora9iMrDw8PqZ+qyOVyuLu7IyYmJl99CtDNubbq+9GjRw+NaVWqVEH16tVx4cIFtfL32T+6u7trnIcC2rdf+lRYzrX1cdyXnZy2BQ4ODmjVqhV27NiB48ePa/0+5ETbeQOT6MWI6kBJLpdLD4IJCAjIdYemcvDgQfTo0SPHjfHbD03p378/vv32W/zyyy+YOHEiZDIZdu7ciSdPnqBnz55qiQDVw/gOHDigNeGfVUxMjEYHNuSTsOnDpe2Bev/++y+aNm2KDh064MqVK2rftfXr1+Ozzz7Dq1evsq3z5cuXBZZEd3Jy0npCY2VlBQBqD0dT7bxdXV211vUufWj79u14+fIlWrZsCRcXF7VpvXv3xsKFC7F27Vp89913Ur8OCgrCunXrsHbtWmnH/uTJE4SGhqJMmTJvHtjx//S1XRBC4Msvv8T8+fOzfdhL1u3Z8+fPkZaWhlKlSkGpVGqd383NTSOJroq/YcOGucZOupOffq16SFF2DyRSlT969EgqMzMzQ926dXH06FHcv38fbm5uOHr0KBo3bgwjIyP4+flh/fr1SEpKwuvXr3HhwgVUqVJFa4Iov8qUKaO1vKD6vOrhoW8/fEtVdvHiRfz+++9SEl0ul6Nnz56YO3cuNm7ciJEjRwIAdu3ahYSEBPTq1QsWFhZSHao+M2nSJEyaNCnbOFJSUjTKHBwcoFAotM4fHx+PgIAAHDx4MNs6s/b5d1l3MTEx0r7h7RNlbfMWJ1999ZV0ItKsWTNYWlrC19cXbdu2Rb9+/aSH/ubH+xz3DRs2DOnp6Zg9e7ZGAh3Q72fp7+8PJycnZGRk4N69ezhx4gTOnz+P0aNHY9WqVRrzz5s3DxMmTJCSYu9L1/ul+Ph4AP/bBmWnUaNGGtvmzMxMTJs2DVOnTkWXLl1w5MgRaVpaWhr69euH9evXZ1vn+z6sMb90vf3NKQmmuqDy9gPRdCmvD5WOjIwEgDzddKXap7q7u2udLpPJ4O7ujri4ODx69Egjia5tHWtbv7ntu21tbWFjY4P4+Hi8ePFC4+Jz6dKlNZaxtLTMddrbD553dnZGhw4dsHXrVhw+fBhNmzZFWloafvvtN5iZmWWb4M1J9erVUaNGDQgh8PTpU4SFheHFixfo1asXTp48KcWi8i7n6Dl5n/2wNnndRtjb22v9Tu7btw/t2rXDxx9/jDt37kjnWO9y/Fa2bFm4ubnh1KlTSElJQVJSEi5fvoyhQ4dCqVSifv36OHz4MIA3D0WMiYlBmzZtcr14lBdFbfuRlbY+A7xbn9LVuXZu2yIPDw+NJLrqux8UFJRj39W2f8zP55sdOzs7yGQyCCHw7NmzbOtUUV2gsre3VysvDOfa+jjuy867bAtyk9N5A5PoxUheD5S0efXqFbp3747Y2FhMnjwZPXr0gLu7O8zMzCCTydCrVy+sX79eIxllb2+PLl26YN26dQgNDUXLli2lp3Nru+sHAMqVK5frgb22u/CyS2wR5VfNmjXx+eef4z//+Q+WLFmCBQsWAHjzFPt+/foBABYsWIC2bduidOnSMDMzAwA0aNAAJ0+ezNcTuN+XtjtECpIqoXbjxg21O8xUVE/tPnbsmHT1vlWrVihVqhR2796N+Ph42NjYYMOGDcjIyEDPnj3V3pO+tgsbN27EvHnz4Orqivnz58PX1xelSpWCiYkJ0tLSoFAodPI5quLv2rWrWqLwbdruoiLdyq5f5ya7A0o/Pz8cPXoUYWFhqF69Ol68eCHdae7n54dff/0VJ06cQHJyMjIzM6W7n96XIft8amoqNm3aBADYsGED/vrrL7XpqpP0LVu24KeffoK5uTmAN8n1uXPnYt26dVISXXU3zNsnKao+06hRozz/Sk4lp+OA8ePH4+DBg2jatCmmTp2KqlWrwtbWFkZGRti/fz/8/f3fu8+rYre0tESXLl1ynNfb2/u92vrQWFtb4+DBgzh+/Dh27tyJsLAwHDx4EAcOHMDMmTNx9OhRlC9fPl91vs9xX2BgINatW4f58+ejQ4cOGttgfX6WEyZMUPtVypEjR+Dv74/Vq1ejbdu2andinjp1CmPHjoWNjQ0WLlwIPz8/ODk5SReLXFxc8n13m673S6oT4XdJaMvlcoSEhGDJkiU4evQoLly4gBo1agB4c/Fg/fr1qFatGubMmYNatWrBzs4OJiYmuHnzJipWrFigx1uqeAuKKvFoa2tbYG0WlJwSNbpcx+/aTn5jGDJkCLZu3Yrly5ejadOm2L59O2JiYtCnT593+vw6deqEKVOmSK8fPXqEZs2a4cqVK5gwYQJ+/PFHadq7nqPn5H32w9q8zzYCeHPhsVOnTti8eTN+++03jBkzJk/LZff5N23aFGvWrMGpU6fw4sULCCHUjt8OHz6M27dvS3erf4jHbwW9/cjtveX1vRv6XFv13W/durXWu7JV3k5aA7r5fE1NTVG5cmVcu3YNZ8+ezTWJrrqQotpvqhSGc219HPe9q9wuDmiT03kDk+iUJ0ePHsXz58/RtWtXjZ+XAcDdu3ezXXbIkCFYt24dli9fjgoVKmDfvn0oX768xs9ZVRuJSpUqvVfCn0gXPD09Aaj/VHjPnj1IS0vDl19+idGjR2ssk1M/KAxUPxFU3VH0tuzKs/P06VMcOHBAWjan5desWSMl0Y2NjREYGIgff/wRW7ZswYABA7T+vAzQ33Zh27ZtAIAlS5agbdu2atO0fY4lS5aEiYkJYmJikJKSovWAQdv7L1OmDG7cuIEJEyagdu3aOoqe3pW2fq36BcWDBw+0LqO6Q+Ptu2iaNm2K7777TrpDDIDaSRjw5ifCycnJamUFydnZGTdu3EBkZCSqVKmiMT2/fX7Xrl3SXU1XrlzJdr5Xr15h+/bt0hBPPj4+qFq1Kk6dOoW7d+/Czs4Oe/bs0fpzf1Wf79SpE8aOHZuv+HKybds2GBkZ4c8//9QYMkNbn3+X7aW9vT2USiXkcjlWrVr1TgftRZlMJkOjRo2kC65Pnz7FmDFjsH79ekyaNEm6QFMQBg0ahIYNG2LYsGFo3rw5wsLC1H6KXpCfZZMmTTB58mR8/fXX+Prrr9G5c2fprkfVvmrGjBkaQ28kJycjOjo63+3per+kGqLp7V9i5ZXqzuSnT5/i1q1bUjJA9d7Xr1+vcaGisB9vAW+2Iffv30dkZKTWn/jntv1V7Vd08Qum96W6G/bOnTu5zpvbPjXrtOzuXs2L3NqJj49HXFwczMzM8vyr63fVsmVLlCtXDlu2bMGiRYveaygXbUqXLo3Vq1ejYcOG+PnnnxEcHCwNhfE+5+jZ0fV++H23EYDuj9/WrFmT7fHb1KlTERYWptOh+PKrKG0/8kOX59qqdfjgwQNUrlxZY7q2743quz9o0KBcL6DrS5s2bXDt2jWsX78enTp1ynHedevWAYDG8LOF4VwbKLjjPhcXF9y7dw8PHjzQer6T3bYgJzmdNxj2Fkb6YKg2xNquhqnGKstO48aN4e3tje3bt2POnDnIzMzEoEGDNOb76KOPYGNjg8OHD7/XTpZIF1Q76aw/mcypHxw5cgRPnjzRKFf9DDw9PT3btkxMTHKcriuqq8vbtm3TegU/vzuy9evXIz09HV27doUQQuvfvXv3AAB//PGH2s/YVHefrlu3Drdu3cKZM2dQtWpV+Pj4qLWhr+1CTp+ltvVgYmKCunXrQgihdQzpgwcP4vnz5xrlH3/8MYD/JQPIsLT1a9XFnT/++EMaczGr33//XW0+lQYNGsDU1FQ60co6ZqaHh4c0Lrqu72TKD1Wf37Jli8a0+Ph4aezHvFKtix9//DHbPq86AFf9SkUla5/fvHkz0tLSEBgYqDE+or76zIsXL2Btba11zGltfb527dpQKpU4e/YsIiIi8rSMsbEx/Pz8kJCQgNDQUN0EXoQ5ODhId1tmvSiTl/2mLgwdOhQ//vgjoqKi0KxZM9y+fVuaVtCf5ZgxY+Dk5IRbt25h48aNUnlO+6o//vhD6748t/Wn6z5WvXp1AG9+kfYuMjMzpRPcvB5zZXe8ktt7L6jvFpDz9vf27dv4999/c1w+PDwcgOYdhoagGp7rl19+ke5azI6bmxvc3Nzw7NkzrX1n9+7dePHiBcqVK6cxlEt+1K5dG2ZmZjh37pzWsfFV+6uGDRvq/Q5g1XPAUlJSMG3aNISGhqJy5cq53tWZHw0aNEDHjh2Rnp6OWbNmSeXveo6e07lHYdtGADkfv2U35FN2x29Zb3QICwuDt7e3lGxWPfPm0KFDOHLkCKysrAxyE0xR2n7kx7uca2dH9blr219cv35dYygXoHCctw0fPhwKhQJ//PGH2hBnb1uzZg3OnDkDe3t7tecMqhj6XFub7I773ldO24Jnz55h3759kMlkatvk3I4HcjpvYBKd8kR1d87WrVvVxluLi4vDwIEDcx2n8fPPP0daWhoWL14MExMT6Wc6WSkUCowbNw4vX75EQECA1iuNjx490jgxJ9K1f//9F8uWLQPw5mqwiqof/P7770hMTJTKHz16hCFDhmity97eHiYmJrhz547WBB3w5urpkydP9D5uXfPmzVG+fHncuHEDc+bMUZu2evVqtQcn5oWqL/bs2TPbeTw8PODr64u4uDjs2rVLKq9fvz68vLxw6NAh/Oc//wGg/WEg+touqD7LZcuWqSUhjh49irlz52pdRvUZT548WW1MtdjYWLWHSWX1+eefw8HBAXPmzMGyZcs0Tj7T09Oxb98+nR5IkHbZ9Ws/Pz9Uq1YN9+/fx+TJk9W+D9u2bcPWrVthaWmJAQMGqNWnGhf9wYMH2L9/vzQeetZ6T58+jQsXLqBSpUo5/ixUX/r37w9TU1P89ttvagfiGRkZGDt2bL5+Wh0bG4s9e/bAyMhI64OuVDp37gylUokDBw6onez06tULMplMGqMR0N7n69Wrh48//hjHjx/H8OHDkZCQoDHPxYsXsXfv3jzHDrzp8y9evFBLUALA/PnzcejQIY35LS0tERQUhPT0dIwePVrtIuDFixexaNEire1MmjQJcrkc/fv3V3twmcqrV6+wcuVK6RcKxcXSpUuli6pZ7dmzB4D6uK+quwvfJ+GSV8OHD8fChQvx+PFjNGvWTO1O24L8LM3MzDBhwgQAwMyZM6XtkGpf9csvv6gda1+7dg3jx4/XWldu60/X+yXVyeuZM2fyNH9WmZmZmDp1Kp49ewZra2u1E1zVe1+6dKnaMqohHbTJ7b0X5Hfr888/B/BmWJpr165J5cnJyRg1alSOyeiUlBRcvnwZrq6u0h24hhQQECA9vHrcuHEa531Xr15VOz5TDdsVHBysdt4YHR0tHS9pu8s0PywsLDBgwABkZmZi+PDhasflN2/elB5oP2rUqPdqJ6/69+8PhUKBBQsWQAih9VkL72vKlCmQyWT49ddfpePQdz1Hz+ncQ9f74YoVK8LBwQEXLlx4pwtY+/btw/bt2wGoH791794djo6OOHbsmHR8p/LDDz/g7NmzKF26tMYdxV5eXihTpgxOnjyJy5cvq91prhoXfevWrYiJiUHDhg11Mh56fhWl7Ud+vMu5dnZU8y9YsAAXL16UyhMTEzFy5EitF6G7dOmCKlWqSM/zensccyEEjh8/Lj34VR88PDwwe/ZsCCHQsWNH/PnnnxoxrFq1CoMGDYJMJsOKFSs0npMAGP5cOz/Hfe9r+PDhkMvlUr9XSUtLw8iRI5GcnIyAgIB8HWvmdN4AQUUeAIF8fNQhISECgFi1apVa+ccffywACFtbW9GpUyfRqVMnYWtrK8qVKyc6duwoAIhDhw5prTMuLk6Ym5sLAKJr167Ztp2RkSE+/fRTAUCYmpqKevXqiR49eoiAgADh7e0tZDKZqF69utoyTZs2FQDEvXv38vweiVT9om/fvtJfz549RYMGDYRcLhcARPv27UVGRoa0TGpqqvD29hYAhJOTk+jSpYto27atMDc3Fw0aNBANGjTQ+l1s3769ACC8vb3Fp59+KgYOHChWrlwpTR85cqQAIDw9PUVQUJAYOHCgmDNnjjQ9u+84AOHu7q71/a1atUoAECEhIWrlJ06ckPpi9erVRc+ePUXdunWFTCYTw4cPFwDE4MGDc11/165dEwCEtbW1SE5OznHeH374QQAQHTt2VCufPHmy9DnIZDLx4MEDrcvrY7tw48YNYWFhIQCIKlWqiB49eojGjRsLmUwmvvzyS63rNjMzU3Tu3FkAEJaWlqJjx44iICBA2NnZiTp16oj69esLAOLRo0dqy508eVLY29sLAMLV1VV88sknolevXqJ58+bC1tZWABDbtm3LcR1S3rxLvxZCiEuXLomSJUsKAKJy5cqiZ8+eomHDhgKAMDY2Fhs3btTa3qRJk6Q2586dqzZN1QcBiCFDhmhd3t3dXev+Wdv379ChQ9J706Zv375a98MLFiwQAIRcLhfNmjUTPXr0EGXLlhW2traid+/eAoBYu3at1jqz+umnnwQA0apVq1znDQgIEADE/Pnz1cqbNGkirRMvL69sl3/y5ImoWbOmdMzh5+cnevXqJdq2bStcXV0FADF69Gi1ZXLaHgohxO+//y613bhxY9GzZ09RpUoVIZfLxRdffKF13T579kyUK1dOABClS5cWgYGBwt/fX5iYmIgRI0YIAKJ8+fIabS1ZskQYGRkJAKJq1aoiICBABAYGinr16gmFQiEAiBcvXuSyFouW6tWrS9vbLl26iMDAQKlMqVSKY8eOqc3v4+MjAIiPPvpI9OvXTwwcOFDs2LFDCCHEvXv3BADRtGnTbNvLbh+YXT+ZP3++tI2+e/euVK7Lz1K1X8ruWDk5OVk4OzsLAGL79u1CCCFiYmKEk5OTdIzQvXt30bJlS2FiYiK6deuW7TYkp/UnhG73S5mZmcLT01NYWVlpPR5QfRZeXl5q2+aOHTsKT09PAUAYGRmJdevWqS13+PBhad3Xrl1b9OzZU9SpU0cAkPbTb38HHj16JJRKpTAyMhKtW7cWAwYMEAMHDhTXr1+X1rGDg4O0bP/+/cXAgQPF8ePHhRDZf7ey+z6pZHfMMWbMGAFAKBQK0bp1a9G9e3fh7Ows3N3dpWNDVdtZ/f333znuO96Xtn3l23/ffvut2jKXL1+WvovOzs4iICBAdO7cWVStWlXj+5Keni4++eQTAUDY2NiIzp07i06dOgkrKysBQHTq1EljP5zTcVt2n0tCQoKoXbu2ACAcHBxEt27dRJs2bYRSqRQAxKhRozTqyq7PCJH9OXBOMWTVq1cv6fOOiYnJdr7sqNrP7nsmhJCOQ8eMGSOVvcs5em7nHu+yH85Jv379BACNbb0Q/1u3FhYWat/B7t27i1q1aknf12HDhmks+/fffwszMzO17YQqbktLS3HkyBGt8QQFBUn1/vHHH2rTVJ8DADFr1iyty2s75igu24+scusXOfVrbevlXc+1szsGVO0rTExMhL+/v+jevbtwdHQUbm5u0jp8u2/cvHlT2jc5ODiIli1bil69eolWrVpJ+4+sx7e5rYOctis5mTVrlrQPLF++vOjWrZsICAiQ+p9SqRRr1qzJsQ5Dnmvn97gvL1TrWts2fMaMGdJ5W8uWLUWPHj2kdVW+fHkRHR2tNn9uxws5nTcwiV4MZPdFy052HT0pKUlMmjRJlC9fXigUCuHq6iqGDBkiYmJisj0pyapRo0YCgNi3b1+uMezYsUO0bdtWODg4CBMTE+Hg4CBq164txo0bJ86dO6c2L5Po9C5U/SLrn1wuFyVKlBB+fn7il19+0TjAF0KI2NhYMXToUOHh4SEUCoUoW7asGD9+vEhMTMz2u/jkyRPx6aefCicnJ2lnmDVZ8+rVKzFixAjh6uoqjI2NNXbEukyiCyHEhQsXRLt27YS1tbWwsLAQDRs2FH/99Ze0s5gwYUKu62/ixIk5JvSyio6OFkZGRsLExETtpOLGjRvSum/SpEmu9eh6uxAeHi7at28vHBwchLm5uahZs6ZYtmyZECL7dZuWliZmzpwpypcvL0xNTUXp0qXFyJEjRUJCgihXrpyQyWQiKSlJY7moqCgxbtw44e3tLczNzYW5ubnw8vISHTt2FKtXrxYvX77M9f1T7t61XwshxIMHD8TgwYOFq6urMDExEfb29qJTp07in3/+yba9AwcOSO2cOXNGbVrWA70NGzZoXb4gkuhCCLF582ZRt25dYWZmJuzs7ERAQIC4ceOGGDRokAAg9u7dm+17VPH19c3zScAff/whAIhatWqplf/888/SOpk8eXKOdSQnJ4sffvhBNGjQQNjY2AhTU1Ph6uoqmjZtKubOnSsiIyPV5s9pe6iye/duUb9+fWFlZSVsbW1Fy5YtRVhYWI7r9unTp+Lzzz8XTk5OQqFQiMqVK4sFCxaIiIgIAUDUr19fa1v//vuv6Nu3r3B3dxempqbC1tZWeHt7iwEDBohdu3aJzMzMHGMtav78808xYMAA4e3tLWxtbYW5ubmoUKGCGDRokHTCktWtW7dEp06dRMmSJaULYKr9mT6S6EII8Z///Ef6HmXdd+jqs8wtiS7E/y46f/TRR1JZZGSk6NWrlyhdurRQKpWicuXKYtasWSI9PT3bbUhO609Fl/sl1cnrpk2bNKZlvaCY9c/U1FS4u7uL3r17a+zDVU6ePCmaN28u7OzshJWVlWjQoIHYsmVLjt+Bffv2iYYNGwpLS0uprazr/MyZM+Ljjz8WNjY2QiaTqW3XdJ0Ey8zMFEuXLhXVqlUTCoVCODg4iL59+4qoqCjRsmVLAUDr93/AgAECQLbr5X1p+zze/ns7YSLEm+PZL7/8UlSoUEEolUphY2MjqlWrJsaPHy+ioqLU5n39+rVYuHChqFmzpvT9qlOnjli8eLFIT0/XqPtdkuhCvDmGnjp1qqhSpYpQKBTCyspKNGrUSOOijIo+k+grVqwQAETPnj2znScneUmiX7x4UchkMmFubi6ePXsmhHi3c/Tczj2EyP9+OCfHjx/PNhGe9Xgp65+RkZFwcHAQn3zyidiyZUu2dV+5ckX07NlTODo6ChMTE+Hs7Cx69+6ttW+pLF++XEosPn36VG2a6pgAgDh16pTW5QsiiV5Ytx9Z6TqJLsS7nWvndAy4fPly4ePjI63D3r17i0ePHuV4PBAXFyemT58uatWqJSwtLYVSqRQeHh7C399fLF68WOp7eVkH75pEF0KIq1eviiFDhohy5coJc3NzYWFhISpXriy++OKLbBPiWRnyXDu/x315kVMSXQghdu3aJVq0aCFtr8qVKyfGjRsnYmNjtc6f2/FCducNMiG0/I6BSMciIyPh6ekJV1dX3L17lw/bIiqEhgwZgp9//hkbNmxAYGCgocP5oDx8+BCenp4oV66cNA4hUWGWkZEBHx8fhIeH4/Hjx+81Nm1xtGHDBvTs2RNDhgzBkiVLDB0OkUFFR0fD09MTLVu2xM6dOw0dTqH36tUreHp6IiUlBXFxcWrDRSQnJ8PFxQUVKlTAP//8Y8AoKb/8/f2xf/9+HDp0yCAPoyzsatasiYcPH+Lhw4dQKBSGDueDxe0HkWFxTHQqELNmzUJGRgaGDx/OBDqRAcXGxkoP8Mpq48aNWLFiBWxtbdGuXbuCD+wDcenSJY3xJZ88eYJ+/fohPT1d44nnRIZ2584djTFPU1NTMW7cOFy7dg0tWrRgAj0H586d0yi7cOGCNK4v+zwR4OTkhJEjR2L37t24fPmyocMpNMLDw5GUlKRWlpCQgM8++wwxMTHo0aOHxnjLS5cuRVxcHGbOnFmQodJ7On36NA4cOABvb28m0LMxY8YMxMTEYPny5YYO5YPA7QdR4cQ70Ulvbty4gblz5+LevXs4ePAgypQpg/DwcK0PPiCignHq1Cn4+vrCx8cHZcuWBfDmIO3GjRswMjLCunXr0L17dwNHWXi1bt0ap0+fRo0aNeDo6IioqCicO3cOr169wkcffYQjR45AqVQaOkwiyaxZsxASEoLatWvD1dUVCQkJuHjxIqKiomBvb49jx46hYsWKhg6z0FIqlXByckLlypVhbW2Ne/fu4dy5c8jMzMSIESOyfcAoUXETFxcHLy8vNGvWDJs3bzZ0OIXCkCFD8Pvvv6N27dpwdnZGTEwM/v33X8TGxqJs2bI4deoUSpUqJc2fnJyMsmXLombNmtLD16hwmzBhAiIiIrB7924kJCRg+/bt6Nixo6HDKrSaN2+Omzdv4s6dO7wbPRfcfhAVTkyik96EhYWhWbNmMDMzw0cffYRFixbBx8fH0GERFWtPnz7FtGnTcPDgQTx+/BiJiYmwt7dHgwYN8OWXX8LX19fQIRZqGzZswKpVq3D58mXExsbCyMgIFSpUQNeuXfHFF1/A3Nzc0CESqTlz5gzmzZuHU6dO4dmzZ0hPT0fp0qXh7++PiRMnqj2pnjRNnToVe/bswd27dxEXFwdLS0vUqFEDgwYNQlBQkKHDI6JCbP/+/fjpp59w7tw5xMTEAAA8PT3Rvn17jBs3DiVLljRwhPS+PDw8EBkZCXd3d3z55ZcYNmyYoUOiIoLbD6LCiUl0IiIiIiIiIiIiIqJscEx0IiIiIiIiIiIiIqJsMIlORERERERERERERJQNJtGJiIiIiIiIiIiIiLLBJDoRERERERERERERUTaYRCciIiIiIiIiIiIiygaT6ERERERERERERERE2WASnYiIiIiIiIiIiIgoG0yiExERERERERERERFlQ29J9PT0dPz999/4+eef8fLlSwDA48eP8erVK301SURERERERERERESkUzIhhNB1pQ8ePEDr1q0RERGB1NRU3Lx5E2XLlsXo0aORmpqKpUuX6rpJIiIiIiIiIiIiIiKd08ud6KNHj0adOnXw4sULmJmZSeWdO3dGaGioPpokIiIiIiIiIiIiItI5Y31UevToUZw4cQKmpqZq5R4eHnj06JE+miQiIiIiIiIiIiIi0jm93ImemZmJjIwMjfKHDx/CyspKH00SEREREREREREREemcXpLorVq1woIFC6TXMpkMr169QkhICNq0aaOPJomIiIiIiIiIiIiIdE4vDxaNjIxE69atIYTArVu3UKdOHdy6dQv29vY4cuQIHBwcdN0kEREREREREREREZHO6SWJDgDp6enYuHEjLl68iFevXqFWrVoICgpSe9AoEREREREREREREVFhpvMk+uvXr1GpUiXs2rULlStX1mXVREREREREREREREQFSudjopuYmCAlJUXX1RIRERERERERERERFTi9PFh0+PDhmD17NtLT0/VRPRERERERERERERFRgdDLmOidO3dGaGgoLC0tUa1aNVhYWKhN37p1q66bJCIiIiIiIiIiIiLSOWN9VGpra4suXbroo2oiIiIiIiIiIiIiogKjlzvRiYiIiIiIiIiIiIiKAr3cia7y7Nkz3LhxAwBQsWJFlCpVSp/NERERERERERERERHplF4eLJqYmIgBAwbA2dkZTZo0QZMmTeDi4oKBAwciKSlJH00SEREREREREREREemcXpLowcHBOHz4MHbu3Im4uDjExcVhx44dOHz4MMaOHauPJomIiIiIiIiIiIiIdE4vY6Lb29tj8+bN8PPzUys/dOgQunfvjmfPnum6SSIiIiIiIiIiIiIindPLnehJSUlwdHTUKHdwcOBwLkRERERERERERET0wdDLnegtWrRAyZIl8dtvv0GpVAIAkpOT0bdvX8TGxuLvv//WdZM6lZmZicePH8PKygoymczQ4VAxJoTAy5cv4eLiArlcL9e8iIiIiIiIiIiIKAd6SaJfuXIF/v7+SE1NRfXq1QEAFy9ehFKpxL59++Dt7a3rJnXq4cOHcHV1NXQYRJLIyEiUKVPG0GEQEREREREREREVO3pJogNvhnRZu3Ytrl+/DgCoXLkygoKCYGZmpo/mdCo+Ph62traIjIyEtbW1ocP54GRkZODKlSt48eIF7OzsULVqVRgZGRk6rA9SQkICXF1dERcXBxsbG0OHQ0REREREREREVOzoLYn+IUtISICNjQ3i4+OZRM+nI0eO4KeffkJ0dLRU5uTkhGHDhqFJkyYGjOzDxO8iERERERERERGRYellkOWZM2di5cqVGuUrV67E7Nmz9dEkFQJHjhxBSEgIypYti8WLF2PPnj1YvHgxypYti5CQEBw5csTQIRIRERERERERERHli16S6D///DMqVaqkUe7t7Y2lS5fqo0kysIyMDPz000/w9fXF9OnT4e3tDXNzc3h7e2P69Onw9fXFkiVLkJGRYehQiYiIiIiIiIiIiPJML0n06OhoODs7a5SXKlUKUVFR+miSDOzSpUuIjo5GUFAQ5HL1r5VcLkdQUBCioqJw6dIlA0VIRERERERERERElH96SaK7urri+PHjGuXHjx+Hi4uLPpokA4uNjQUAeHp6ap2uKlfNR0RERERERERERPQhMNZHpYMHD8aYMWPw+vVrNG/eHAAQGhqKcePGYezYsfpokgysRIkSAIB79+7B29tbY/q9e/fU5iMiIiIiIiIiIiL6EOglif7VV1/h+fPnGDZsGNLS0gAASqUS48ePx8SJE/XRJBmYj48PnJycsHbtWkyfPl1tSJfMzEysXbsWzs7O8PHxMWCURERERERERERERPkjE0IIfVX+6tUrhIeHw8zMDOXLl4dCodBXUzqVkJAAGxsbxMfHw9ra2tDhfDCOHDmCkJAQ+Pr6IigoCJ6enrh37x7Wrl2LkydPYurUqWjSpImhw/yg8LtIRERERERERERkWHpNoqs8ePAAiYmJqFSpksZDJwsjJi7f3ZEjR/DTTz8hOjpaKnN2dsbQoUOZQH8H/C4SEREREREREREZlk6T6CtXrkRcXByCg4Olss8++wy//PILAKBixYrYt28fXF1dddWkXjBx+X4yMjJw6dIlxMbGokSJEvDx8YGRkZGhw/og8btIRERERERERERkWDq9LXzZsmWws7OTXu/duxerVq3Cb7/9hjNnzsDW1hZTp07Nd72LFy+Gh4cHlEol6tWrh9OnT+c4f1xcHIYPHw5nZ2coFApUqFABe/bsyXe79G6MjIxQs2ZNtGjRAjVr1mQCnYiIiIiIiIiIiD5YOn2w6K1bt1CnTh3p9Y4dO9CxY0cEBQUBAL7//nv0798/X3Vu3LgRwcHBWLp0KerVq4cFCxbA398fN27cgIODg8b8aWlp+Pjjj+Hg4IDNmzejdOnSePDgAWxtgz5GRQAAM6hJREFUbd/rvRERERERERERERFR8aPTJHpycrLakBMnTpzAwIEDpddly5ZVGys7L+bNm4fBgwdLyfelS5di9+7dWLlyJSZMmKAx/8qVKxEbG4sTJ07AxMQEAODh4fEO74aIiIiIiIiIiIiIijudDufi7u6Oc+fOAQBiYmJw9epVNGzYUJoeHR0NGxubPNeXlpaGc+fOoWXLllKZXC5Hy5YtcfLkSa3L/Pnnn/D19cXw4cPh6OiIqlWr4vvvv0dGRka27aSmpiIhIUHtj4iIiIiIiIiIiIhIp3ei9+3bF8OHD8fVq1dx8OBBVKpUCbVr15amnzhxAlWrVs1zfTExMcjIyICjo6NauaOjI65fv651mbt37+LgwYMICgrCnj17cPv2bQwbNgyvX79GSEiI1mVmzpz5TmO1ExEREREREREREVHRptMk+rhx45CUlIStW7fCyckJf/zxh9r048ePo2fPnrpsUkNmZiYcHBywbNkyGBkZoXbt2nj06BHmzp2bbRJ94sSJCA4Oll4nJCTA1dVVr3EWZRkZGbh06RJiY2NRokQJ+Pj48OGiRERERERERERE9EHSaRJdLpdj2rRpmDZtmtbpbyfVc2Nvbw8jIyM8efJErfzJkydwcnLSuoyzszNMTEzUkraVK1dGdHQ00tLSYGpqqrGMQqGAQqHIV2yk3ZEjR/DTTz+pjX3v5OSEYcOGoUmTJgaMjIiIiIiIiIiIiCj/dDomuq6Zmpqidu3aCA0NlcoyMzMRGhoKX19frcs0bNgQt2/fRmZmplR28+ZNODs7a02gk+4cOXIEISEhKFu2LBYvXow9e/Zg8eLFKFu2LEJCQnDkyBFDh0hERERERERERESUL4U6iQ4AwcHBWL58OX799VeEh4dj6NChSExMRP/+/QEAffr0wcSJE6X5hw4ditjYWIwePRo3b97E7t278f3332P48OGGegvFQkZGBn766Sf4+vpi+vTp8Pb2hrm5Oby9vTF9+nT4+vpiyZIlOT7glYiIiIiIiIiIiKiw0elwLvoQGBiIZ8+eYfLkyYiOjkaNGjWwd+9e6WGjERERkMv/dy3A1dUV+/btwxdffAEfHx+ULl0ao0ePxvjx4w31FoqFS5cuITo6Gt9++63a5wG8GeYnKCgIw4cPx6VLl1CzZk0DRUlERERERERERESUP4U+iQ4AI0aMwIgRI7ROCwsL0yjz9fXFqVOn9BwVZRUbGwsA8PT01DpdVa6aj4iIiIiIiIiIiOhDoNfhXNLS0nDjxg2kp6frsxkqBEqUKAEAuHfvntbpqnLVfEREREREREREREQfAr0k0ZOSkjBw4EBpTOyIiAgAwMiRIzFr1ix9NEkG5uPjAycnJ6xdu1btoa7Am4fBrl27Fs7OzvDx8TFQhERERERERERERET5p5ck+sSJE3Hx4kWEhYVBqVRK5S1btsTGjRv10SQZmJGREYYNG4aTJ0/im2++wdWrV5GUlISrV6/im2++wcmTJzF06FAYGRkZOlQiIiIiIiIiIiKiPJMJIYSuK3V3d8fGjRtRv359WFlZ4eLFiyhbtixu376NWrVqISEhQddN6lRCQgJsbGwQHx8Pa2trQ4fzQTly5Ah++uknREdHS2XOzs4YOnQomjRpYsDIPkz8LhIRERERERERERmWXh4s+uzZMzg4OGiUJyYmQiaT6aNJektKSoo0jE5BcnJywpQpU3Dr1i3Ex8fDxsYG5cuXh1wux82bNws8HgBwc3NT+0UEERERERERERERUV7pJYlep04d7N69GyNHjgQAKXG+YsUK+Pr66qNJektERAQ+++wzQ4dRKCxbtgwVKlQwdBhERERERERERET0AdJLEv3777/HJ598gmvXriE9PR0LFy7EtWvXcOLECRw+fFgfTdJb3NzcsGzZMoO1/+DBA8yYMQOTJk2Cu7u7weIA3qwLIiIiIiIiIiIionehlyR6o0aNcOHCBcyaNQvVqlXD/v37UatWLZw8eRLVqlXTR5P0FqVSWSjuvnZ3dy8UcRARERERERERERG9C70k0QHAy8sLy5cv11f1RERERERERERERER6J9dHpXv27MG+ffs0yvft24e//vpLH00SEREREREREREREemcXpLoEyZMQEZGhka5EAITJkzQR5NERERERERERERERDqnlyT6rVu3UKVKFY3ySpUq4fbt2/pokoiIiIiIiIiIiIhI5/SSRLexscHdu3c1ym/fvg0LCwt9NElEREREREREREREpHN6SaJ37NgRY8aMwZ07d6Sy27dvY+zYsejQoYM+miQiIiIiIiIiIiIi0jm9JNHnzJkDCwsLVKpUCZ6envD09ETlypVRsmRJ/Oc//9FHk0REREREREREREREOmesj0ptbGxw4sQJHDhwABcvXoSZmRl8fHzQpEkTfTRHRERERERERERERKQXekmiA4BMJkOrVq3QqlUrfTVBRERERERERERERKRXekuih4aGIjQ0FE+fPkVmZqbatJUrV+qrWSIiIiIiIiIiIiIindFLEn3q1KmYNm0a6tSpA2dnZ8hkMn00Q0RERERERERERESkV3pJoi9duhSrV6/Gp59+qo/qiYiIiIiIiIiIiIgKhFwflaalpaFBgwb6qJqIiIiIiIiIiIiIqMDoJYk+aNAgrFu3Th9VExEREREREREREREVGL0M55KSkoJly5bh77//ho+PD0xMTNSmz5s3Tx/NEhERERERERERERHplF6S6JcuXUKNGjUAAFeuXFGbxoeMEhEREREREREREdGHQi9J9EOHDumjWiIiIiIiIiIiIiKiAqWXMdGJiIiIiIiIiIiIiIoCvdyJDgBnz57Fpk2bEBERgbS0NLVpW7du1VezREREREREREREREQ6o5c70Tds2IAGDRogPDwc27Ztw+vXr3H16lUcPHgQNjY2+a5v8eLF8PDwgFKpRL169XD69Ok8xyGTydCpU6d8t0lEREREREREREREpJck+vfff4/58+dj586dMDU1xcKFC3H9+nV0794dbm5u+apr48aNCA4ORkhICM6fP4/q1avD398fT58+zXG5+/fv48svv0Tjxo3f560QERERERERERERUTGmlyT6nTt30LZtWwCAqakpEhMTIZPJ8MUXX2DZsmX5qmvevHkYPHgw+vfvjypVqmDp0qUwNzfHypUrs10mIyMDQUFBmDp1KsqWLZtrG6mpqUhISFD7IyIiIiIiIiIiIiLSSxLdzs4OL1++BACULl0aV65cAQDExcUhKSkpz/WkpaXh3LlzaNmypVQml8vRsmVLnDx5Mtvlpk2bBgcHBwwcODBP7cycORM2NjbSn6ura55jJCIiIiIiIiIiIqKiSy9J9CZNmuDAgQMAgG7dumH06NEYPHgwevbsiRYtWuS5npiYGGRkZMDR0VGt3NHREdHR0VqXOXbsGH755RcsX748z+1MnDgR8fHx0l9kZGSelyUiIiIiIiIiIiKiostYH5X++OOPSElJAQBMmjQJJiYmOHHiBLp06YJvvvlGH00CAF6+fIlPP/0Uy5cvh729fZ6XUygUUCgUeouLiIiIiIiIiIiIiD5MekmilyhRQvq/XC7HhAkT3qkee3t7GBkZ4cmTJ2rlT548gZOTk8b8d+7cwf3799G+fXupLDMzEwBgbGyMGzduwMvL651iISIiIiIiIiIiIqLiRy9JdJWnT5/i6dOnUiJbxcfHJ0/Lm5qaonbt2ggNDUWnTp0AvEmKh4aGYsSIERrzV6pUCZcvX1Yr++abb/Dy5UssXLiQY50TERERERERERERUb7oJYl+7tw59O3bF+Hh4RBCqE2TyWTIyMjIc13BwcHo27cv6tSpg7p162LBggVITExE//79AQB9+vRB6dKlMXPmTCiVSlStWlVteVtbWwDQKCciIiIiIiIiIiIiyo1ekugDBgxAhQoV8Msvv8DR0REymeyd6woMDMSzZ88wefJkREdHo0aNGti7d6/0sNGIiAjI5Xp5PioRERERERERERERFXMy8fat4jpgZWWFf//9F+XKldN11QUiISEBNjY2iI+Ph7W1taHD+SDdvHkTn332GZYtW4YKFSoYOpwPFr+LREREREREREREhqWXW7hbtGiBixcv6qNqIiIiIiIiIiIiIqICo5fhXFasWIG+ffviypUrqFq1KkxMTNSmd+jQQR/NEhERERERERERERHplF6S6CdPnsTx48fx119/aUzL74NFiYiIiIiIiIiIiIgMRS/DuYwcORK9e/dGVFQUMjMz1f6YQCciIiIiIiIiIiKiD4VekujPnz/HF198AUdHR31UT0RERERERERERERUIPSSRA8ICMChQ4f0UTURERERERERERERUYHRy5joFSpUwMSJE3Hs2DFUq1ZN48Gio0aN0kezREREREREREREREQ6pZck+ooVK2BpaYnDhw/j8OHDatNkMhmT6ERERERERERERET0QdB5El0IgbCwMDg4OMDMzEzX1RMRERERERERERERFRidj4kuhED58uXx8OFDXVdNRERERERERERERFSgdJ5El8vlKF++PJ4/f67rqomIiIiIiIiIiIiICpTOk+gAMGvWLHz11Ve4cuWKPqonIiIiIiIiIiIiIioQenmwaJ8+fZCUlITq1avD1NRUY2z02NhYfTRLRERERERERERERKRTekmiL1iwQB/VEhEREREREREREREVKL0k0fv27auPaomIiIiIiIiIiIiICpRekugAkJGRge3btyM8PBwA4O3tjQ4dOsDIyEhfTRYqT548QXx8vKHDMJgHDx6o/Vuc2djYwNHR0dBhEBERERERERER0TuQCSGEriu9ffs22rRpg0ePHqFixYoAgBs3bsDV1RW7d++Gl5eXrpvUqYSEBNjY2CA+Ph7W1tb5Xv7Jkyfo/WkfvE5L1UN09KExMVXg9zW/vVMi/X2/i0RERERERERERPR+9HIn+qhRo+Dl5YVTp06hRIkSAIDnz5+jd+/eGDVqFHbv3q2PZguN+Ph4vE5LRXLZpshU2hg6HDIgeUo8cPcw4uPjeTc6ERERERERERHRB0gvSfTDhw+rJdABoGTJkpg1axYaNmyojyYLpUylDTIt7A0dBhERERERERERERG9I7k+KlUoFHj58qVG+atXr2BqaqqPJomIiIiIiIiIiIiIdE4vSfR27drhs88+wz///AMhBIQQOHXqFIYMGYIOHTroo0kiIiIiIiIiIiIiIp3TSxL9hx9+gJeXF3x9faFUKqFUKtGwYUOUK1cOCxcu1EeTREREREREREREREQ6p7Mx0RMSEmBtbQ0AsLW1xY4dO3D79m2Eh4cDACpXroxy5crpqjkiIiIiIiIiIiIiIr3TWRLdzs4OUVFRcHBwQPPmzbF161aUK1eOiXMiIiIiIiIiIiIi+mDpbDgXS0tLPH/+HAAQFhaG169f66pqIiIiIiIiIiIiIiKD0Nmd6C1btkSzZs1QuXJlAEDnzp1hamqqdd6DBw/qqlkiIiIiIiIiIiIiIr3R2Z3ov//+O6ZMmYI6deoAALy9vVG9enWtf/m1ePFieHh4QKlUol69ejh9+nS28y5fvhyNGzeGnZ0d7Ozs0LJlyxznJyIiIiIiIiIiIiLKjs7uRDczM8OQIUMAAGfPnsXs2bNha2v73vVu3LgRwcHBWLp0KerVq4cFCxbA398fN27cgIODg8b8YWFh6NmzJxo0aAClUonZs2ejVatWuHr1KkqXLv3e8RARERERERERERFR8aGzO9FVXr9+jYiICERFRemkvnnz5mHw4MHo378/qlSpgqVLl8Lc3BwrV67UOv/atWsxbNgw1KhRA5UqVcKKFSuQmZmJ0NBQncRDRERERERERERERMWHzpPoJiYmSElJ0UldaWlpOHfuHFq2bCmVyeVytGzZEidPnsxTHUlJSXj9+jVKlCiR7TypqalISEhQ+yMiIiIiIiIiIiIi0nkSHQCGDx+O2bNnIz09/b3qiYmJQUZGBhwdHdXKHR0dER0dnac6xo8fDxcXF7VE/NtmzpwJGxsb6c/V1fW94iYiIiIiIiIiIiKiokFnY6JndebMGYSGhmL//v2oVq0aLCws1KZv3bpVH81qmDVrFjZs2ICwsDAolcps55s4cSKCg4Ol1wkJCUykExEREREREREREZF+kui2trbo0qXLe9djb28PIyMjPHnyRK38yZMncHJyynHZ//znP5g1axb+/vtv+Pj45DivQqGAQqF473iJiIiIiIiIiIiIqGjRSxJ91apVOqnH1NQUtWvXRmhoKDp16gQA0kNCR4wYke1yc+bMwYwZM7Bv3z7UqVNHJ7EQERERERERERERUfGjlyQ6AKSnpyMsLAx37txBr169YGVlhcePH8Pa2hqWlpZ5ric4OBh9+/ZFnTp1ULduXSxYsACJiYno378/AKBPnz4oXbo0Zs6cCQCYPXs2Jk+ejHXr1sHDw0MaO93S0jJf7eqCPDmuQNujwoffASIiIiIiIiIiog+bXpLoDx48QOvWrREREYHU1FR8/PHHsLKywuzZs5GamoqlS5fmua7AwEA8e/YMkydPRnR0NGrUqIG9e/dKDxuNiIiAXP6/56MuWbIEaWlp6Nq1q1o9ISEhmDJlik7eX16Z3TtSoO0RERERERERERERkW7pJYk+evRo1KlTBxcvXkTJkiWl8s6dO2Pw4MH5rm/EiBHZDt8SFham9vr+/fv5rl9fkj2bINPM1tBhkAHJk+N4MYWIiIiIiIiIiOgDppck+tGjR3HixAmYmpqqlXt4eODRo0f6aLJQyjSzRaaFvaHDICIiIiIiIiIiIqJ3JM99lvzLzMxERkaGRvnDhw9hZWWljyaJiIiIiIiIiIiIiHROL0n0Vq1aYcGCBdJrmUyGV69eISQkBG3atNFHk0REREREREREREREOqeX4Vz++9//wt/fH1WqVEFKSgp69eqFW7duwd7eHuvXr9dHk0REREREREREREREOqeXJHqZMmVw8eJFbNy4ERcvXsSrV68wcOBABAUFwczMTB9NEhERERERERERERHpnM6T6KdOncLOnTuRlpaG5s2bY86cObpugoiIiIiIiIiIiIioQOg0ib5582YEBgbCzMwMJiYmmDdvHmbPno0vv/xSl80QERERERERERERERUInT5YdObMmRg8eDDi4+Px4sULTJ8+Hd9//70umyAiIiIiIiIiIiIiKjA6TaLfuHEDX375JYyMjAAAY8eOxcuXL/H06VNdNkNEREREREREREREVCB0mkRPSkqCtbW19NrU1BRKpRKvXr3SZTNERERERERERERERAVC5w8WXbFiBSwtLaXX6enpWL16Nezt7aWyUaNG6bpZIiIiIiIiIiIiIiKd02kS3c3NDcuXL1crc3Jywpo1a6TXMpmMSXQiIiIiIiIiIiIi+iDoNIl+//59XVb3wZOnxBs6BDIwfgeIiIiIiIiIiIg+bDofzoUAGxsbmJgqgLuHDR0KFQImpgrY2NgYOgwiIiIiIiIiIiJ6B0yi64GjoyN+X/Mb4uOL713IDx48wIwZMzBp0iS4u7sbOhyDsrGxgaOjo6HDICIiIiIiIiIionfAJLqeODo6MnEKwN3dHRUqVDB0GERERERERERERETvRG7oAIiIiIiIiIiIiIiICiudJ9HT09Px22+/4cmTJ7qumoiIiIiIiIiIiIioQOk8iW5sbIwhQ4YgJSVF11UTERERERERERERERUovQznUrduXVy4cEEfVRMRERERERERERERFRi9PFh02LBhCA4ORmRkJGrXrg0LCwu16T4+PvpoloiIiIiIiIiIiIhIp/SSRO/RowcAYNSoUVKZTCaDEAIymQwZGRn6aJaIiIiIiIiIiIiISKf0kkS/d++ePqolIiIiIiIiIiIiIipQekmiu7u766NaIiIiIiIiIiIiIqICpZcHiwLAmjVr0LBhQ7i4uODBgwcAgAULFmDHjh36apKIiIiIiIiIiIiISKf0kkRfsmQJgoOD0aZNG8TFxUljoNva2mLBggX6aJKIiIiIiIiIiIiISOf0kkRftGgRli9fjkmTJsHIyEgqr1OnDi5fvqyPJomIiIiIiIiIiIiIdE4vSfR79+6hZs2aGuUKhQKJiYn5rm/x4sXw8PCAUqlEvXr1cPr06Rzn/+OPP1CpUiUolUpUq1YNe/bsyXebRERERERERERERER6SaJ7enriwoULGuV79+5F5cqV81XXxo0bERwcjJCQEJw/fx7Vq1eHv78/nj59qnX+EydOoGfPnhg4cCD+/fdfdOrUCZ06dcKVK1fe5a0QERERERERERERUTGmlyR6cHAwhg8fjo0bN0IIgdOnT2PGjBmYOHEixo0bl6+65s2bh8GDB6N///6oUqUKli5dCnNzc6xcuVLr/AsXLkTr1q3x1VdfoXLlyvjuu+9Qq1Yt/Pjjj7p4a0RERERERERERERUjBjro9JBgwbBzMwM33zzDZKSktCrVy+4uLhg4cKF6NGjR57rSUtLw7lz5zBx4kSpTC6Xo2XLljh58qTWZU6ePIng4GC1Mn9/f2zfvj3bdlJTU5Gamiq9TkhIyHOMhVVKSgoiIiIM1v6DBw/U/jUkNzc3KJVKQ4dBREREREREREREHyC9JNEBICgoCEFBQUhKSsKrV6/g4OCQ7zpiYmKQkZEBR0dHtXJHR0dcv35d6zLR0dFa54+Ojs62nZkzZ2Lq1Kn5jq8wi4iIwGeffWboMDBjxgxDh4Bly5ahQoUKhg6DiIiIiIiIiIiIPkB6SaKHhIRgwIABcHd3h7m5OczNzfXRjM5MnDhR7e71hIQEuLq6GjCi9+fm5oZly5YZOoxCwc3NzdAhEBERERERERER0QdKL0n0HTt2YMaMGWjatCkGDhyILl26QKFQ5Lsee3t7GBkZ4cmTJ2rlT548gZOTk9ZlnJyc8jU/ACgUineKrzBTKpW8+5qIiIiIiIiIiIjoPenlwaIXLlzAmTNn4O3tjdGjR8PJyQlDhw7FmTNn8lWPqakpateujdDQUKksMzMToaGh8PX11bqMr6+v2vwAcODAgWznJyIiIiIiIiIiIiLKjl6S6ABQs2ZN/PDDD3j8+DF++eUXPHz4EA0bNoSPjw8WLlyI+Pj4PNUTHByM5cuX49dff0V4eDiGDh2KxMRE9O/fHwDQp08ftQePjh49Gnv37sV///tfXL9+HVOmTMHZs2cxYsQIvbxPIiIiIiIiIiIiIiq69PZgURUhBF6/fo20tDQIIWBnZ4cff/wR3377LZYvX47AwMAclw8MDMSzZ88wefJkREdHo0aNGti7d6/08NCIiAjI5f+7FtCgQQOsW7cO33zzDb7++muUL18e27dvR9WqVfMVM/BmbHQiQ1J9B1XfSSIiIiIiIiIiIipYMqGn7Ny5c+ewatUqrF+/HgqFAn369MGgQYNQrlw5AMCiRYswffp0jfHLC4OHDx9+8A8WpaIlMjISZcqUMXQYRERERERERERExY5ekujVqlXD9evX0apVKwwePBjt27eHkZGR2jwxMTFwcHBAZmamrpt/b5mZmXj8+DGsrKwgk8kMHc4HKSEhAa6uroiMjIS1tbWhw/lgCSHw8uVLuLi4qP3igoiIiIiIiIiIiAqGXoZz6d69OwYMGIDSpUtnO4+9vX2hTKADgFwu512/OmJtbc0k+nuysbExdAhERERERERERETFlt6Gc6HiLSEhATY2NoiPj2cSnYiIiIiIiIiIiD5Yenuw6MOHD/Hnn38iIiICaWlpatPmzZunr2aJiIiIiIiIiIiIiHRGL0n00NBQdOjQAWXLlsX169dRtWpV3L9/H0II1KpVSx9NUiGjUCgQEhIChUJh6FCIiIiIiIiIiIiI3plehnOpW7cuPvnkE0ydOhVWVla4ePEiHBwcEBQUhNatW2Po0KG6bpKIiIiIiIiIiIiISOf0kkS3srLChQsX4OXlBTs7Oxw7dgze3t64ePEiOnbsiPv37+u6SSIiIiIiIiIiIiIinZPro1ILCwtpHHRnZ2fcuXNHmhYTE6OPJomIiIiIiIiIiIiIdE4vY6LXr18fx44dQ+XKldGmTRuMHTsWly9fxtatW1G/fn19NElEREREREREREREpHN6Gc7l7t27ePXqFXx8fJCYmIixY8fixIkTKF++PObNmwd3d3ddN0lEREREREREREREpHN6SaITERERERERERERERUFehkTnYiIiIiIiIiIiIioKNDZmOh2dnaQyWR5mjc2NlZXzRIRERERERERERER6Y3OkugLFizQVVVERERERERERERERIUCx0QnIiIiIiIiIiIiIsqGTsdEl8lkGn82NjZo3LgxVqxYgbfz9VOmTIFMJsPq1at1GUahtHr1ao11Y2JigjJlyqBnz544f/58gcSRkZGByZMnw8vLC6amppDJZOjXr1+BtE15FxUVhS+//BLe3t4wNzeHmZkZ3N3d0bRpU3zzzTe4cOGCoUMkIiIiIiIiIiIqFnQ2nEtWLi4uMDIywuPHj6FQKHD8+HEcO3YMoaGhWL9+vT6a/GB4eXmhUaNGAIBXr17h7Nmz2LBhA7Zs2YLNmzejQ4cOem1/4cKF+O677+Di4oKAgAAolUopHiocLly4gBYtWiA2NhYlSpRA48aNUbJkSTx58gRnzpzBkSNHEBMTg6VLlxo6VCIiIiIiIiIioiJPL0n0R48eAQD+/vtvtG3bFn/99RfatWuHDRs2ICgoCO3atdNHsx+ERo0aqd15//r1awwbNgwrVqzAkCFD0Lp1a5iamuqt/e3btwMAjh49irJly+qtHXp3ffr0QWxsLPr27YvFixfDwsJCmpaWloa9e/fi+fPnBoyQiIiIiIiIiIio+NDpcC5va9myJWQyGby9vfHpp58C+F8Sl94wMTHBggULYGlpiaioKJw+fVqv7T18+BAAmEAvpG7duoXLly/D2NgYS5YsUUugA4CpqSk6dOiA/v37GyhCIiIiIiIiIiKi4kWvSXTgTZL49evXqFmzJgAgMjIy12Vu376NKVOmwNfXF05OTjA1NUWZMmXQp08f3Lx5U23eqKgomJiYwNXVFRkZGVrrW7duHWQyGfr27atWLoTA+vXr0bx5c9jZ2UGpVKJy5cqYMmUKkpKSNOrx8/ODTCbD/fv3sW7dOtSvXx9WVlawtbXN49rQzsLCAhUqVACgvn7S09OxZMkS+Pr6wtraGmZmZqhRowYWLFiA9PR0jXo8PDwgk8kghMCiRYtQvXp1mJubo0aNGujXrx9kMhnu3bsHQH38+vv370t1REZG4vPPP4e7uzsUCgUcHBwQEBCAM2fOaLR3//59yGQy+Pn5ISEhAcHBwfD09ISJiQnGjBmjFhMALF68GFWrVoWZmRk8PT0xZ84caZz88+fPo3379ihRogQsLS3RsWNHPHjwQKPNqKgozJkzB02bNkXp0qVhamoKJyenbGN8O4YVK1bAx8cHZmZmcHJywueff464uDity71+/RpLly5Fo0aNYGtrCzMzM5QrVw79+/fHuXPnNOYPDw9Hv3794OrqCoVCAUdHR/To0QNXr17VWr82z549AwBYWVnBzMwsz8up7N27Fx06dICjoyMUCgVcXV3Rrl07bNmyRWPea9euISgoCM7OzjA1NUXp0qXRp08f3LhxQ2PesLAwafz86OhoDBo0CGXKlIGxsTEWLFggzRcZGYkRI0bAy8sLSqUSJUqUQLt27XDixIl8vxciIiIiIiIiIqLCQC/DuQQEBEj/T0lJwZAhQ6Q7oC9dupTr8itWrMCcOXNQtWpVfPTRR1AoFLh27RrWrFmDHTt24OjRo/Dx8QEAODs7o0OHDti6dSv27t2Ltm3batS3fPlyAMBnn30mlWVmZqJ3795Yv349LC0tUadOHdjZ2eHs2bOYOnUq/vrrL4SFhWlNZM6cORMrVqxAw4YN0a5duzxdGMjNy5cvAQAKhQIAkJycjLZt2+LQoUMoUaIE6tevD6VSiX/++QdffPEFDh06hG3btkEu17wOMmTIEKxatQpNmzZF5cqVkZaWJo17vnnzZiQmJqpdULC0tAQAXL58Gc2bN0dMTAwqVqyIgIAAREREYNu2bdi5cyfWrVuHbt26abSXnJyMpk2b4sGDB2jatClq1aoFOzs7tXm++OIL/Pzzz2jWrBk8PT1x+PBhjB8/HomJiWjVqhVatWqFSpUq4eOPP8b58+fx559/4urVq7h8+bLaZ7Bjxw6MHz8eFStWhI+PD6ytrXHr1i1s27YNu3btwq5du9CqVSut63jcuHFYuHAh/Pz8UK5cORw/fhzLli1DeHg4Dh8+LCXaASAxMRFt2rTBkSNHYGFhISXS79+/j7Vr18LGxga1a9eW5t++fTt69OiB1NRU1KhRA/Xr10dkZCQ2bdqEnTt34q+//kKTJk1y/hIAKFOmDADgxYsXWL9+PXr27JnrMipjx47FvHnzIJfL4evrCzc3Nzx+/BjHjx/Hw4cP0aVLF2ne0NBQtG/fHsnJyahZsyb8/Pxw/fp1rFmzBtu2bcOePXvQuHFjjTaePXuGjz76COnp6WjUqBFSUlJgbm4OADh58iTatm2LFy9eoGLFimjbti2ePXuGffv2Ye/evVi7di0CAwPz/H6IiIiIiIiIiIgKBaFDAAQA0a9fP42/UqVKCQDCx8dHmj8kJEQAEKtWrVKr5+TJk+Lu3bsa9a9cuVIAEM2aNVMr379/vwAgOnbsqLHMrVu3BABRuXJltfI5c+YIAMLPz09ERUVJ5ampqWLgwIECgBg/frzaMk2bNhUAhFKpFGFhYXldLUIIIVatWiUAiL59+2pMu3r1qjAyMhIAxK1bt4QQQgwbNkwAEIGBgSIuLk6aNyEhQbRp00YAEEuWLFGrx93dXQAQ9vb24sqVK1rjUM3ztszMTFGtWjUBQIwbN05kZmZK0zZv3izkcrmwtLQUjx8/lsrv3bsnfea+vr7ixYsX2bbn4uIibt++LZWHh4cLhUIhzM3NhYeHh9p7SU1NFc2bNxcAxMqVK9Xqu3Tpktb3tnfvXmFqaiq8vLzUYs8ag5OTk7h+/bpU/uzZM1GuXDkBQISGhqoto/oONGnSRDx9+lRtWnR0tDh16pTaerD4v/buP6bK6o8D+PtyIUQuCIkg8uMORZTEIKCZiQR1b9mWkuAYiBFpCuVWW5ZgLo3NZdO0KTU3kML8QUt+eLNmeBM0pNUgJGjALUQoEDAFW4gCXs/3j/bcL7d7L7+8JLX3a/MPnnPO8zn3PI//fJ6zz3F0FAqFQmi1WqO+p06dEnZ2dsLHx0f09/ebzNucp556yrCuUVFRYufOnUKr1Rq9B393+PBhwzpfuHDBqK2vr0+cPn3a8Hdvb6/w8PAQAMQHH3xg1Hfv3r0CgPD29hY3b940XC8rKzPMaeXKlUZtQgjxxx9/CE9PTyGXy8WRI0eM2iorK4Wrq6tQKBQma0lERERERERERDTZTUgSXXL79m3x888/i5SUFAFA2NvbGyVSLSXRh7NkyRIhk8mMEop37twR/v7+wtbW1ijJK4QQ6enpAoDYu3ev4drg4KBwc3MTjo6OorOz0yRGX1+fmDlzpnB1dRV6vd5wXUqib9y4cdTzlZhLovf29oqvv/5azJs3TwAQKpVKCCFEV1eXIfHa19dncq+Ojg5x3333GX2QEOL/yeLdu3dbnIelJHppaakAIHx9fcXAwIBJe2xsrAAgduzYYbg2NIleWVk5bLyDBw+atK1cuVIAEBERESZtGo3G4kcHS5KSkgQAUVtba3YOOTk5JmPee+89AUBs377dcK29vV3I5XJhb28vWlpaRoz76quvCgAiKyvLbPsrr7wiAIiioqJR/Y6rV6+KZ555xrC20j+5XC6io6NNEvVCCBEYGCgAiE8//XTE+0sfoxYvXmy2PSwsTAAwSoZLSXR7e3vR1tZmMub9998XAMSmTZvM3lNKzg/9f0hERERERERERPRvMCE10aVa27a2tggICEBeXh6cnJyQn5+POXPmjOoevb29yM/PR3p6OtavX4+UlBSkpKSgo6MDQghcvHjRKN6GDRtw+/ZtfPzxx4brg4ODyMvLg729PZKTkw3Xq6urcfXqVTz66KPw8PAwie3g4ICwsDD09PTgl19+MWlfsWLFWJbDyKFDhwzro1AooFKpoNPpEB4ejsOHDwP4q/704OAgli1bZraczMyZMzF37lzU1dXh5s2bVplfeXk5ACA+Ph52dnYm7dLBsFK/oTw9PREeHj7s/c2VWJEONx2uraOjw6Stv78fGo0GW7duxYYNGwzvRl1dHQCYfWaW4ki16IfGOXv2LPR6PZYtWwalUjns7wKA06dPAzAuYzSUVBZltIfGTp8+HSdPnkRNTQ3efvttqNVquLq6Qq/Xo6ysDGq1Gnv37jX0v3z5MhoaGuDi4oL4+PgR7y89w6SkJLPta9asMeo3VGhoKLy8vEyuW3sNiIiIiIiIiIiIJosJqYku1du2sbGBs7MzFi5ciNjYWJM62ZaUlpYiISHBcMiiOVINcckLL7yAt956C7m5udiyZQtkMhlOnjyJrq4uJCYmYvr06Ya+0kGaWq3WqA62OVJ98KF8fX1H9TvMmTNnjqE+uZ2dHTw8PLB06VKo1WpDfXNpfjk5OYZ67pZ0d3ebJDXHM7/Lly8D+OsQTnOk6+3t7SZto4lnLvEq1WIfrq2/v9/oel1dHVasWGF0GOrf/f3dkEj1xodycnIyiSPVuB/tBx9pLuZ+x1BXr14d1f0kwcHBCA4OBgDo9XpUVFRgy5Yt+Pbbb5Geno64uDgolUrDfGfPnj3i+wxMzLOW1mDJkiXDxh7rGhAREREREREREd1rE5JEz8vLG/fY3t5exMfHo7u7G9u2bUNCQgKUSiUcHBwgk8mwevVq5OfnQwhhNM7NzQ1xcXE4duwYzpw5A5VKhYMHDwIA1q9fb9T3zp07AAB/f/8Rk35Dk++SKVOmjPv3RUREjLg+0vxCQkIMSVRLpINIrTU/S4ZLzo4mnrkDUEfTNpQQAvHx8WhpaUFaWhrS0tIwe/ZsKBQKyGQyvPnmm9i5c6fJuzHWOGMlPa+hh7Was2jRonHHkMvliIyMhFarxbx589DW1oaSkhKjw3KtZTzPWlqDVatWwdHR0eL4+fPn393kiIiIiIiIiIiI/mETkkS/G+Xl5bh27RpWrVqFzMxMk/bm5maLY9PS0nDs2DHk5OQgICAAJSUlmDt3LqKjo436STuS58+ff1cJ/4kizS8iIgJZWVn/SMxZs2YBAFpbW822j3a39URqbGxEY2MjwsPDceDAAZP24d6NsfDx8QEAo5JBw/H29sbFixexZ88esx9drGnq1KlYtGgR2traDLu6pfk2NzdDCDHibvSJeNbe3t7Q6XTIyMhAWFjYqMcRERERERERERFNdhOzNfcu9PT0ADBfeqOpqQnV1dUWxy5duhQLFizAiRMnsGvXLty5cwcvvviiSb+HH34Y06ZNw7lz59Dd3W29yVtJdHQ05HI5vvjiCwwODv4jMaWa1cePH4derzdpP3LkiFG/e2G4d6OnpwdardYqcaKioiCXy1FSUmIolTIctVoNACguLr7r2JZ20Q/V1NQE4P9J7lmzZiEwMBDXr1/H8ePHRxwvPcP8/Hyz7eN51tZcAyIiIiIiIiIioslk0iXRpYMei4qKjGqiX79+HevWrRsxqZyamoqBgQF8+OGHsLOzQ0pKikkfe3t7bN68GX/++SdiY2PN7mBub283HPT5T/Py8sLatWvR0tKCxMREdHV1mfRpampCYWGh1WJGRUVh4cKFaGlpwbZt24ySucXFxSgqKoJCocDatWutFnOs/P39YWNjg9LSUqPDQ2/duoW0tDSrfRCZNWsWkpOTcevWLTz//PO4du2aUfuVK1fw/fffG/7etGkTHBwc8Prrr6OoqMjkfv39/SgoKEBbW9uIsWtra/Hkk0+ipKTEUCJFMjg4iMzMTPz444+YOnUqnn76aUNbRkYGAOC1115DbW2t0bhbt24ZfWCIj4+Hh4cHzp8/j+zsbKO++/fvR1VVFby8vBAXFzfifCWpqalwd3fHrl27kJ2dbTL327dvo6SkBD/99NOo70lERERERERERDQZTLpyLuHh4VCr1dBqtQgICEBUVBQA4OzZs3Bzc0NMTAw0Go3F8cnJycjIyEBfXx9iYmLg7u5utl9GRgYaGxtx+PBhBAYG4qGHHoKfnx8GBgag0+lQX1+PBx98EM8999xE/MwR7du3Dy0tLSgsLMRXX32FkJAQ+Pr64saNG6ivr0dTUxNiYmLGlOgcjkwmw9GjRxEdHY133nkHxcXFCAkJwa+//oqKigrY2toiNzcXnp6eVok3Hu7u7li3bh1ycnIQHByMxx9/HA4ODigvL4der0dKSorVyvPs27cPOp0OZWVlUCqViIyMhLOzM1pbW1FdXY2XXnrJUOPc398f+fn5WL16NeLi4uDv74/AwEA4Ojqivb0d1dXVuHHjBi5cuGB2F/1QQghotVpotVrcf//9CA0Nhbu7O7q7u1FTU4POzk7Y2toiOzvb6N1OTk5GVVUVsrKyEBoaisWLF8PHxwcdHR2oqamBUqlETU0NAMDR0RFHjx7F8uXLkZqaiuzsbAQEBKCxsREXLlyAQqFAfn7+mGrru7i4QKPRGO65Y8cOBAUFwdXVFZ2dnaiursb169dRXFyMoKCgsT8QIiIiIiIiIiKie2TS7UQHAI1Gg61bt2LGjBk4deoUfvjhByQkJOC7776Di4vLsGOnTZuG0NBQAKYHig5lY2ODTz75BBqNBmq1GpcuXUJhYSHOnz+PKVOm4I033sBHH31kzZ81Jg4ODjh16hQOHTqERYsWoaGhAQUFBaiqqsKMGTOQmZmJXbt2WTXmwoULUV1djfXr16O3txcFBQXQ6XR49tlnUVFRgfj4eKvGG48DBw5gz5498PPzw5kzZ1BeXg6VSoWqqioolUqrxXFyckJZWRn27duHBQsWoLy8HJ9//jl+//13JCUlITk52ah/TEwMamtr8fLLL0Mmk0Gr1eLLL7/ElStXsHz5cnz22Wd44IEHRowbFBSE0tJSpKenY/78+dDpdCgoKMA333wDV1dXpKamoqamBklJSSZj9+/fD41GA5VKhfr6ehQWFqKpqQkRERHYvn27Ud8nnngClZWVSExMRFtbGwoKCtDZ2Yk1a9agqqpqXGV7HnnkEdTV1WHz5s1wdnbGuXPncOLECbS2tuKxxx5DXl4eVCrVmO9LRERERERERER0L8nEaIow/4v89ttv8PPzg4+PD5qbm0c8ZJGIiIiIiIiIiIiIyJJJuRP9brz77rvQ6/XYuHEjE+hEREREREREREREdFf+EzvRdToddu/ejUuXLqG0tBTe3t5oaGiAQqG411MjIiIiIiIiIiIion+xSXew6Hh0dHQgNzcXDg4OiIyMRFZWFhPoRERERERERERERHTX/hM70YmIiIiIiIiIiIiIJsJ/riY6EREREREREREREZG1MIlORERERERERERERGQBk+hERERERERERERERBYwiU5EREREREREREREZAGT6EREREREREREREREFjCJTkRERERERERERERkAZPoREREREREREREREQWMIlORERERERERERERGTB/wBtlE60jtt3qgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x2000 with 55 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now almost All skewness we removed so move further\n",
    "#Let's check for the Outliers\n",
    "plt.figure(figsize=(15,20))\n",
    "plotnumber=1\n",
    "for column in master_df_after_join:\n",
    "    if plotnumber<=60:\n",
    "        ax=plt.subplot(10,6,plotnumber)\n",
    "        sns.boxplot(master_df_after_join[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So from graph we can see the outlier are present in the some columns so lets remove them first \n",
    "outliers_col=['Margin', 'BR','Overs_y','Target','Ground','Span','strike Rate (Batting)','Player Performance Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition_x</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "      <th>Player</th>\n",
       "      <th>Batting Average</th>\n",
       "      <th>Bowling Average</th>\n",
       "      <th>strike Rate (Batting)</th>\n",
       "      <th>Economy Rate (Bowling)</th>\n",
       "      <th>maiden Overs Total</th>\n",
       "      <th>Player Performance Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>1363</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>36</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171.264368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.074915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.488372</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>262</td>\n",
       "      <td>0.170293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.178082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.044236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.059436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>0.732601</td>\n",
       "      <td>262</td>\n",
       "      <td>0.080625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0   149      93   87  1363  20   1            15       1      1540       36   \n",
       "1     3       2   86   829   1   1            15       1      1540      130   \n",
       "2    87      47   73  1464  18   1            13       5      1226       26   \n",
       "3   119      72   98   889   1   2            15       1      1540       26   \n",
       "4     4       3  152   211   2   1            13       5      1226       59   \n",
       "\n",
       "   ...  Toss  Bat  Match_ID_y  Player  Batting Average  Bowling Average  \\\n",
       "0  ...   1.0  2.0       450.0      36             74.5         0.000000   \n",
       "1  ...   1.0  2.0       450.0     131              1.5         2.466667   \n",
       "2  ...   2.0  2.0       127.0      26             43.5         0.000000   \n",
       "3  ...   1.0  2.0       450.0      26             59.5         0.000000   \n",
       "4  ...   2.0  2.0       127.0      59              2.0         0.416667   \n",
       "\n",
       "   strike Rate (Batting)  Economy Rate (Bowling)  maiden Overs Total  \\\n",
       "0             171.264368                0.000000                 262   \n",
       "1               3.488372                0.840909                 262   \n",
       "2             119.178082                0.000000                 262   \n",
       "3             121.428571                0.000000                 262   \n",
       "4               2.631579                0.732601                 262   \n",
       "\n",
       "   Player Performance Score  \n",
       "0                  0.074915  \n",
       "1                  0.170293  \n",
       "2                  0.044236  \n",
       "3                  0.059436  \n",
       "4                  0.080625  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_after_join.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Bat1</th>\n",
       "      <th>Runs_x</th>\n",
       "      <th>BF</th>\n",
       "      <th>SR_x</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>Opposition_x</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Match_ID</th>\n",
       "      <th>Batsman</th>\n",
       "      <th>...</th>\n",
       "      <th>Toss</th>\n",
       "      <th>Bat</th>\n",
       "      <th>Match_ID_y</th>\n",
       "      <th>Player</th>\n",
       "      <th>Batting Average</th>\n",
       "      <th>Bowling Average</th>\n",
       "      <th>strike Rate (Batting)</th>\n",
       "      <th>Economy Rate (Bowling)</th>\n",
       "      <th>maiden Overs Total</th>\n",
       "      <th>Player Performance Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>1363</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>36</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>171.264368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.074915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.488372</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>262</td>\n",
       "      <td>0.170293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87</td>\n",
       "      <td>47</td>\n",
       "      <td>73</td>\n",
       "      <td>1464</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>26</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>119.178082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.044236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>119</td>\n",
       "      <td>72</td>\n",
       "      <td>98</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1540</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>262</td>\n",
       "      <td>0.059436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1226</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>0.732601</td>\n",
       "      <td>262</td>\n",
       "      <td>0.080625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bat1  Runs_x   BF  SR_x  4s  6s  Opposition_x  Ground  Match_ID  Batsman  \\\n",
       "0   149      93   87  1363  20   1            15       1      1540       36   \n",
       "1     3       2   86   829   1   1            15       1      1540      130   \n",
       "2    87      47   73  1464  18   1            13       5      1226       26   \n",
       "3   119      72   98   889   1   2            15       1      1540       26   \n",
       "4     4       3  152   211   2   1            13       5      1226       59   \n",
       "\n",
       "   ...  Toss  Bat  Match_ID_y  Player  Batting Average  Bowling Average  \\\n",
       "0  ...   1.0  2.0       450.0      36             74.5         0.000000   \n",
       "1  ...   1.0  2.0       450.0     131              1.5         2.466667   \n",
       "2  ...   2.0  2.0       127.0      26             43.5         0.000000   \n",
       "3  ...   1.0  2.0       450.0      26             59.5         0.000000   \n",
       "4  ...   2.0  2.0       127.0      59              2.0         0.416667   \n",
       "\n",
       "   strike Rate (Batting)  Economy Rate (Bowling)  maiden Overs Total  \\\n",
       "0             171.264368                0.000000                 262   \n",
       "1               3.488372                0.840909                 262   \n",
       "2             119.178082                0.000000                 262   \n",
       "3             121.428571                0.000000                 262   \n",
       "4               2.631579                0.732601                 262   \n",
       "\n",
       "   Player Performance Score  \n",
       "0                  0.074915  \n",
       "1                  0.170293  \n",
       "2                  0.044236  \n",
       "3                  0.059436  \n",
       "4                  0.080625  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the outliers by using Z score\n",
    "from scipy.stats import zscore\n",
    "\n",
    "z_score=zscore(master_df_after_join[outliers_col])\n",
    "z_score_abs=np.abs(z_score)\n",
    "filter_entry=(z_score_abs<3).all(axis=1)\n",
    "master_after_join=master_df_after_join[filter_entry]\n",
    "master_after_join.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 'Player Performance Score' as the target variable for the prediction model\n",
    "x = master_after_join[['Bat1', 'Runs_x', 'BF', 'SR_x', '4s', '6s', 'Opposition_x', 'Ground',\n",
    "          'Match_ID', 'Batsman', 'Player_ID', 'Month', 'Day', 'year', 'Overs_x',\n",
    "          'Mdns', 'Runs_y', 'Wkts_x', 'Econ', 'Ave_x', 'SR_y', 'Bowler', 'Span',\n",
    "          'Mat', 'Won', 'Tied', 'NR', 'Runs', 'Wkts_y', 'Balls', 'Ave_y', 'RPO_x',\n",
    "          'Result_x', 'Margin', 'BR', 'Toss', 'Bat', 'Opposition_y', 'Match_ID_x',\n",
    "          'Country', 'Country_ID', 'Score', 'Overs_y', 'RPO_y', 'Target', 'Inns',\n",
    "          'Result_y', 'Match_ID_y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = master_after_join['Player Performance Score']  # Target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(408, 48) (408,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing stadardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scale=StandardScaler()\n",
    "x_scaled=scale.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### splitting the data into training and testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the data into model and performing training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### making some predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perfroming model evaluation methods to see how well the model performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.026260716919972147\n",
      "Mean Squared Error: 0.0010815997971432808\n",
      "Root Mean Squared Error: 0.032887684581667964\n",
      "R-squared: 0.9402293334532789\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "r_squared = r2_score(y_test, predictions)\n",
    "\n",
    "print(f\"Mean Absolute Error: {mae}\\nMean Squared Error: {mse}\\nRoot Mean Squared Error: {rmse}\\nR-squared: {r_squared}\")\n"
   ]
  }
 ],
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