Task 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sparks Foundation\n",
    "\n",
    "## Data Science and Bussiness Analytics intern\n",
    "\n",
    "## Name - Devaangini Mehta\n",
    "\n",
    "### Task 5: Exploratory Data Analysis - Sports ( Indian Premier League )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                        team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2   3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3   4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the dataset\n",
    "matches= pd.read_csv(r'C:\\Users\\omprakash\\Downloads\\matches.csv')\n",
    "deliveries=pd.read_csv(r'C:\\Users\\omprakash\\Downloads\\deliveries.csv')\n",
    "\n",
    "#1st dataset\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the shape\n",
    "matches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 756 entries, 0 to 755\n",
      "Data columns (total 18 columns):\n",
      "id                 756 non-null int64\n",
      "season             756 non-null int64\n",
      "city               749 non-null object\n",
      "date               756 non-null object\n",
      "team1              756 non-null object\n",
      "team2              756 non-null object\n",
      "toss_winner        756 non-null object\n",
      "toss_decision      756 non-null object\n",
      "result             756 non-null object\n",
      "dl_applied         756 non-null int64\n",
      "winner             752 non-null object\n",
      "win_by_runs        756 non-null int64\n",
      "win_by_wickets     756 non-null int64\n",
      "player_of_match    752 non-null object\n",
      "venue              756 non-null object\n",
      "umpire1            754 non-null object\n",
      "umpire2            754 non-null object\n",
      "umpire3            119 non-null object\n",
      "dtypes: int64(5), object(13)\n",
      "memory usage: 106.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the information about data\n",
    "matches.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1792.178571</td>\n",
       "      <td>2013.444444</td>\n",
       "      <td>0.025132</td>\n",
       "      <td>13.283069</td>\n",
       "      <td>3.350529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3464.478148</td>\n",
       "      <td>3.366895</td>\n",
       "      <td>0.156630</td>\n",
       "      <td>23.471144</td>\n",
       "      <td>3.387963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189.750000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>378.500000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>567.250000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11415.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       season  dl_applied  win_by_runs  win_by_wickets\n",
       "count    756.000000   756.000000  756.000000   756.000000      756.000000\n",
       "mean    1792.178571  2013.444444    0.025132    13.283069        3.350529\n",
       "std     3464.478148     3.366895    0.156630    23.471144        3.387963\n",
       "min        1.000000  2008.000000    0.000000     0.000000        0.000000\n",
       "25%      189.750000  2011.000000    0.000000     0.000000        0.000000\n",
       "50%      378.500000  2013.000000    0.000000     0.000000        4.000000\n",
       "75%      567.250000  2016.000000    0.000000    19.000000        6.000000\n",
       "max    11415.000000  2019.000000    1.000000   146.000000       10.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 756\n",
       "season              12\n",
       "city                32\n",
       "date               546\n",
       "team1               15\n",
       "team2               15\n",
       "toss_winner         15\n",
       "toss_decision        2\n",
       "result               3\n",
       "dl_applied           2\n",
       "winner              15\n",
       "win_by_runs         89\n",
       "win_by_wickets      11\n",
       "player_of_match    226\n",
       "venue               41\n",
       "umpire1             61\n",
       "umpire2             65\n",
       "umpire3             25\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking total unique values\n",
    "matches.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "season               0\n",
       "city                 7\n",
       "date                 0\n",
       "team1                0\n",
       "team2                0\n",
       "toss_winner          0\n",
       "toss_decision        0\n",
       "result               0\n",
       "dl_applied           0\n",
       "winner               4\n",
       "win_by_runs          0\n",
       "win_by_wickets       0\n",
       "player_of_match      4\n",
       "venue                0\n",
       "umpire1              2\n",
       "umpire2              2\n",
       "umpire3            637\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null variable\n",
    "matches.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "missing values in columns- city, winner, player_of_match ,umpire1, umpire2 and umpire3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
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       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2nd dataset\n",
    "deliveries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179078, 21)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179078 entries, 0 to 179077\n",
      "Data columns (total 21 columns):\n",
      "match_id            179078 non-null int64\n",
      "inning              179078 non-null int64\n",
      "batting_team        179078 non-null object\n",
      "bowling_team        179078 non-null object\n",
      "over                179078 non-null int64\n",
      "ball                179078 non-null int64\n",
      "batsman             179078 non-null object\n",
      "non_striker         179078 non-null object\n",
      "bowler              179078 non-null object\n",
      "is_super_over       179078 non-null int64\n",
      "wide_runs           179078 non-null int64\n",
      "bye_runs            179078 non-null int64\n",
      "legbye_runs         179078 non-null int64\n",
      "noball_runs         179078 non-null int64\n",
      "penalty_runs        179078 non-null int64\n",
      "batsman_runs        179078 non-null int64\n",
      "extra_runs          179078 non-null int64\n",
      "total_runs          179078 non-null int64\n",
      "player_dismissed    8834 non-null object\n",
      "dismissal_kind      8834 non-null object\n",
      "fielder             6448 non-null object\n",
      "dtypes: int64(13), object(8)\n",
      "memory usage: 28.7+ MB\n"
     ]
    }
   ],
   "source": [
    "deliveries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>wide_runs</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1802.252957</td>\n",
       "      <td>1.482952</td>\n",
       "      <td>10.162488</td>\n",
       "      <td>3.615587</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.036721</td>\n",
       "      <td>0.004936</td>\n",
       "      <td>0.021136</td>\n",
       "      <td>0.004183</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>1.246864</td>\n",
       "      <td>0.067032</td>\n",
       "      <td>1.313897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3472.322805</td>\n",
       "      <td>0.502074</td>\n",
       "      <td>5.677684</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.021263</td>\n",
       "      <td>0.251161</td>\n",
       "      <td>0.116480</td>\n",
       "      <td>0.194908</td>\n",
       "      <td>0.070492</td>\n",
       "      <td>0.016709</td>\n",
       "      <td>1.608270</td>\n",
       "      <td>0.342553</td>\n",
       "      <td>1.605422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>379.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>567.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11415.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            match_id         inning           over           ball  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean     1802.252957       1.482952      10.162488       3.615587   \n",
       "std      3472.322805       0.502074       5.677684       1.806966   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%       190.000000       1.000000       5.000000       2.000000   \n",
       "50%       379.000000       1.000000      10.000000       4.000000   \n",
       "75%       567.000000       2.000000      15.000000       5.000000   \n",
       "max     11415.000000       5.000000      20.000000       9.000000   \n",
       "\n",
       "       is_super_over      wide_runs       bye_runs    legbye_runs  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean        0.000452       0.036721       0.004936       0.021136   \n",
       "std         0.021263       0.251161       0.116480       0.194908   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       5.000000       4.000000       5.000000   \n",
       "\n",
       "         noball_runs   penalty_runs   batsman_runs     extra_runs  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean        0.004183       0.000056       1.246864       0.067032   \n",
       "std         0.070492       0.016709       1.608270       0.342553   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       1.000000       0.000000   \n",
       "75%         0.000000       0.000000       1.000000       0.000000   \n",
       "max         5.000000       5.000000       7.000000       7.000000   \n",
       "\n",
       "          total_runs  \n",
       "count  179078.000000  \n",
       "mean        1.313897  \n",
       "std         1.605422  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max        10.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_id            756\n",
       "inning                5\n",
       "batting_team         15\n",
       "bowling_team         15\n",
       "over                 20\n",
       "ball                  9\n",
       "batsman             516\n",
       "non_striker         511\n",
       "bowler              405\n",
       "is_super_over         2\n",
       "wide_runs             6\n",
       "bye_runs              5\n",
       "legbye_runs           6\n",
       "noball_runs           5\n",
       "penalty_runs          2\n",
       "batsman_runs          8\n",
       "extra_runs            7\n",
       "total_runs           10\n",
       "player_dismissed    487\n",
       "dismissal_kind        9\n",
       "fielder             499\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking total unique value in deliveries dataset\n",
    "deliveries.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "match_id                 0\n",
       "inning                   0\n",
       "batting_team             0\n",
       "bowling_team             0\n",
       "over                     0\n",
       "ball                     0\n",
       "batsman                  0\n",
       "non_striker              0\n",
       "bowler                   0\n",
       "is_super_over            0\n",
       "wide_runs                0\n",
       "bye_runs                 0\n",
       "legbye_runs              0\n",
       "noball_runs              0\n",
       "penalty_runs             0\n",
       "batsman_runs             0\n",
       "extra_runs               0\n",
       "total_runs               0\n",
       "player_dismissed    170244\n",
       "dismissal_kind      170244\n",
       "fielder             172630\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null variables in deliveries dataset\n",
    "deliveries.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we will merge the 2 datasets for better insights from datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  result  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...  normal   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...  normal   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...  normal   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...  normal   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...  normal   \n",
       "\n",
       "   dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "0           0  Sunrisers Hyderabad           35               0   \n",
       "1           0  Sunrisers Hyderabad           35               0   \n",
       "2           0  Sunrisers Hyderabad           35               0   \n",
       "3           0  Sunrisers Hyderabad           35               0   \n",
       "4           0  Sunrisers Hyderabad           35               0   \n",
       "\n",
       "   player_of_match                                      venue      umpire1  \\\n",
       "0     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "1     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "2     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "3     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "4     Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "\n",
       "    umpire2 umpire3  \n",
       "0  NJ Llong     NaN  \n",
       "1  NJ Llong     NaN  \n",
       "2  NJ Llong     NaN  \n",
       "3  NJ Llong     NaN  \n",
       "4  NJ Llong     NaN  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging the 2 datasets\n",
    "df = pd.merge(deliveries, matches, left_on='match_id', right_on='id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>wide_runs</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "      <td>179078.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1802.252957</td>\n",
       "      <td>1.482952</td>\n",
       "      <td>10.162488</td>\n",
       "      <td>3.615587</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.036721</td>\n",
       "      <td>0.004936</td>\n",
       "      <td>0.021136</td>\n",
       "      <td>0.004183</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>1.246864</td>\n",
       "      <td>0.067032</td>\n",
       "      <td>1.313897</td>\n",
       "      <td>1802.252957</td>\n",
       "      <td>2013.444510</td>\n",
       "      <td>0.017914</td>\n",
       "      <td>13.404036</td>\n",
       "      <td>3.261579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3472.322805</td>\n",
       "      <td>0.502074</td>\n",
       "      <td>5.677684</td>\n",
       "      <td>1.806966</td>\n",
       "      <td>0.021263</td>\n",
       "      <td>0.251161</td>\n",
       "      <td>0.116480</td>\n",
       "      <td>0.194908</td>\n",
       "      <td>0.070492</td>\n",
       "      <td>0.016709</td>\n",
       "      <td>1.608270</td>\n",
       "      <td>0.342553</td>\n",
       "      <td>1.605422</td>\n",
       "      <td>3472.322805</td>\n",
       "      <td>3.363947</td>\n",
       "      <td>0.132639</td>\n",
       "      <td>23.261007</td>\n",
       "      <td>3.347033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>379.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>379.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>567.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>567.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11415.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11415.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            match_id         inning           over           ball  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean     1802.252957       1.482952      10.162488       3.615587   \n",
       "std      3472.322805       0.502074       5.677684       1.806966   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%       190.000000       1.000000       5.000000       2.000000   \n",
       "50%       379.000000       1.000000      10.000000       4.000000   \n",
       "75%       567.000000       2.000000      15.000000       5.000000   \n",
       "max     11415.000000       5.000000      20.000000       9.000000   \n",
       "\n",
       "       is_super_over      wide_runs       bye_runs    legbye_runs  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean        0.000452       0.036721       0.004936       0.021136   \n",
       "std         0.021263       0.251161       0.116480       0.194908   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       5.000000       4.000000       5.000000   \n",
       "\n",
       "         noball_runs   penalty_runs   batsman_runs     extra_runs  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean        0.004183       0.000056       1.246864       0.067032   \n",
       "std         0.070492       0.016709       1.608270       0.342553   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       1.000000       0.000000   \n",
       "75%         0.000000       0.000000       1.000000       0.000000   \n",
       "max         5.000000       5.000000       7.000000       7.000000   \n",
       "\n",
       "          total_runs             id         season     dl_applied  \\\n",
       "count  179078.000000  179078.000000  179078.000000  179078.000000   \n",
       "mean        1.313897    1802.252957    2013.444510       0.017914   \n",
       "std         1.605422    3472.322805       3.363947       0.132639   \n",
       "min         0.000000       1.000000    2008.000000       0.000000   \n",
       "25%         0.000000     190.000000    2011.000000       0.000000   \n",
       "50%         1.000000     379.000000    2013.000000       0.000000   \n",
       "75%         1.000000     567.000000    2016.000000       0.000000   \n",
       "max        10.000000   11415.000000    2019.000000       1.000000   \n",
       "\n",
       "         win_by_runs  win_by_wickets  \n",
       "count  179078.000000   179078.000000  \n",
       "mean       13.404036        3.261579  \n",
       "std        23.261007        3.347033  \n",
       "min         0.000000        0.000000  \n",
       "25%         0.000000        0.000000  \n",
       "50%         0.000000        3.000000  \n",
       "75%        19.000000        6.000000  \n",
       "max       146.000000       10.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of matches played in each season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of matches')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(x='season', data=matches)\n",
    "plt.title('Number of matches played in each season', fontsize=15)\n",
    "plt.xlabel('Season', fontsize=15)\n",
    "plt.ylabel('Number of matches', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the above plot we can say that\n",
    "1. Season held in 2011, 2012, 2013 have more matches than other seasons.\n",
    "2. Season held in 2011, 2012, 2013 have more than 70 matches.\n",
    "3. All other seasons have 58-60 matches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team played in each season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Team')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "matches.groupby('season')['team1'].nunique().plot(kind='bar', color='m')\n",
    "plt.title('Team played in each season', fontsize=15)\n",
    "plt.xlabel('Season', fontsize=15)\n",
    "plt.ylabel('Number of Team', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 10 teams played in season 2011\n",
    "2. 9 teams played in seasons 2012 and 2013\n",
    "3. 8 teams played in all other seasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most hosting venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of matches')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fSG9L99x8qp/ne+l2Mb5zknNaa6/flIW11r7RPz+HVdX10v0j4MAkuyc5YBoPaBPcvcYu75Tk+62106e8nBOTPKOqvp9uD4unZcPvewutP69KdwbpU6rq5en+WbdHkqu11l61CT28IN0/ED5QVW9Lt4X3hulOrPbm1tpnq+r96dbvL6d7vz2yv+/4YRIAGxBigS3J59OdWGeXzHP5kD4E3jnd5SZem+7yOuel+9L09U1ZUGvtq1X1xHRn0n1Eui0PB2TDL2BPSfeF8Ynpdgf9RbowtuguoX2o+Hy6E6SM1vxsui2QU9mK1Fp7aVX9JN0xfQel2w3w0+kC9My11j5cVX+W7vi5x6cLbv+ULsjO+Xy6LdK7pjuu7stJHjCyNfXodP+IeHW6wPOWJE9srX2qumuVvijdWXa3Sneyr4/kil0az66qB6dbH45L99o8tp9nJlprF1TVvdOF2fek+wL/oSTPHtk6/7l068xT0p1866wkT2itfbCv8b2qOjhdQHlmuvV+tyW2dlC6Y3EPS7db70vTXepmqXVHvTNdkH1bkmumC+1PnZvYWvtN/5q9tP/ZId068YV0/4jYHE/IFevUdZN8Ld368/nNrLe5Dp1n3FvSvc7T9NR0ly06LN37+O3pnufD5mZYaP3p1827pXu+Xp9uT5Jvp/vMnFhr7cz+s/Zl6dapbdMF4pPS/WMi6fYEeWS6fxpdJd177+GttQWv+Qswp5a+NxoAAAAsD8fEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGIO9xM7222/fdtlll5VuAwAAgBk4/fTTf9JaWzM+ftlDbFX9dbprorV012X8yyQ7pbum43ZJvpTkca21yxars8suu+S0006bcbcAAACshKr64Xzjl3V34qq6YZJnJFnbWvujdBeef3SSf0zymtba7kl+luSA5ewLAACAYViJY2K3TrJtVW2d5BpJzktynyTH9tOPSvKwFegLAACAVW5ZQ2xr7UdJXpnk7HTh9edJTk9ycWvtt/1s5ya54Xz3r6oDq+q0qjpt3bp1y9EyAAAAq8hy7058/SR7J9k1ye8nuWaSB8wza5vv/q21I1pra1tra9es2eD4XgAAALZwy7078V5Jvt9aW9da+98kxyW5a5Lr9bsXJ8mNkvx4mfsCAABgAJY7xJ6d5M5VdY2qqiR7Jvlmkk8m2befZ/8kJyxzXwAAAAzAch8T+8V0J3D6UrrL61wlyRFJDk7y7Ko6K8kNkrxlOfsCAABgGJb9OrGttRcmeeHY6O8lueNy9wIAAMCwrMQldgAAAGCzCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADMbWK93AUq077J1LrrHmoD+fQicAAADMmi2xAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAzG1ivdwGq17vDDl1xjzVOessG48w97+ZLr/t5Bhy65BgAAwBDZEgsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAzGsobYqrpZVX1l5OcXVfWsqtquqk6squ/0t9dfzr4AAAAYhmUNsa21b7XW/ri19sdJbp/k10mOT3JIkpNaa7snOakfBgAAgPWs5O7Eeyb5bmvth0n2TnJUP/6oJA9bsa4AAABYtVYyxD46ydH97zu21s5Lkv52h/nuUFUHVtVpVXXaunXrlqlNAAAAVosVCbFVtU2ShyY5ZlPu11o7orW2trW2ds2aNbNpDgAAgFVrpbbEPiDJl1prF/TDF1TVTknS3164Qn0BAACwiq1UiH1MrtiVOEnen2T//vf9k5yw7B0BAACw6i17iK2qayS5b5LjRka/Isl9q+o7/bRXLHdfAAAArH5bL/cCW2u/TnKDsXE/TXe2YgAAAFjQSp6dGAAAADaJEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYGy90g2wdD96w1OmUueGTzt8KnUAAABmxZZYAAAABkOIBQAAYDCEWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMIRYAAAABkOIBQAAYDCEWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMIRYAAAABkOIBQAAYDCEWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMIRYAAAABkOIBQAAYDCEWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMIRYAAAABkOIBQAAYDCEWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMIRYAAAABkOIBQAAYDCEWAAAAAZj2UNsVV2vqo6tqjOr6oyquktVbVdVJ1bVd/rb6y93XwAAAKx+K7El9nVJPtpau3mS2yQ5I8khSU5qre2e5KR+GAAAANazrCG2qq6T5J5J3pIkrbXLWmsXJ9k7yVH9bEcledhy9gUAAMAwLPeW2JsmWZfkbVX15ap6c1VdM8mOrbXzkqS/3WGZ+wIAAGAAljvEbp3kdkkOa63dNsn/ZBN2Ha6qA6vqtKo6bd26dbPqEQAAgFVquUPsuUnOba19sR8+Nl2ovaCqdkqS/vbC+e7cWjuitba2tbZ2zZo1y9IwAAAAq8eyhtjW2vlJzqmqm/Wj9kzyzSTvT7J/P27/JCcsZ18AAAAMw9YrsMynJ3lXVW2T5HtJ/jJdmH5vVR2Q5Owk+61AXwAAAKxyyx5iW2tfSbJ2nkl7LncvAAAADMtKXCcWAAAANosQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgbL3SDbB6feuNe0+lzs3+6oSp1AEAALAlFgAAgMEQYgEAABgMIRYAAIDBEGIBAAAYDCEWAACAwRBiAQAAGAwhFgAAgMEQYgEAABgMIRYAAIDBEGIBAAAYjIlCbFXtUFW7jgxXVR1YVa+tqofMrj0AAAC4wqRbYt+e5K9Hhl+c5E1J7p/k+Kr6i+m2BQAAABuaNMTeLsnJSVJVV0lyUJK/a63dPMnLkzxrNu0BAADAFSYNsddN8tP+99sn2S7Ju/rhk5PsNuW+AAAAYAOThthzk+zR//6gJGe21n7UD183ySXTbgwAAADGbT3hfG9N8k9VtVe6EPu8kWl3TnLGtBsDAACAcROF2NbaP1TVj5LcIcnT04XaOdslefMMegMAAID1TLolNq21dyR5xzzjnzLVjgAAAGABkx4Tm6q6WlUdVFVvqaqPV9Xu/fhHVdUtZtciAAAAdCbaEltVf5jkxHQncTo9yZ8kuXY/+R7pjpN9/Az6AwAAgMtNuiX29UnOTrJLkvslqZFpn05y9+m2BQAAABua9JjYeyTZr7V2cVVtNTbtgiQ7TbctAAAA2NCkW2IvSbLtAtNumOTi6bQDAAAAC5s0xJ6Y5O+q6roj41pVXS3dJXc+PPXOAAAAYMykuxP/bZL/THJWukDbkrwgyS2TbJNkn5l0BwAAACMm2hLbWjsnyW2SHJ7u5E7fTXcc7DFJbt9aO39WDQIAAMCcSbfEprX2syT/r/8BAACAZTfpMbEAAACw4ibaEltVV03yzHTHvt4oydXH52mt7TBhrR8k+WWS3yX5bWttbVVtl+Q96XZV/kGSR/ZbfgEAAOByk+5O/JokT07ywSSfTHLZEpd779baT0aGD0lyUmvtFVV1SD988BKXAQAAwBZm0hC7X5JDWmuvmlEfeyf5k/73o5J8KkIsAAAAYyY9JraSfG1Ky2xJPl5Vp1fVgf24HVtr5yVJfzvRrskAAABcuUy6JfbIJI9Jd43Ypbpba+3HVbVDkhOr6sxJ79iH3gOTZOedd55CKwAAAAzJgiG2qp46Mnh+ksdW1SfTBdmLx2ZvrbXDJllga+3H/e2FVXV8kjsmuaCqdmqtnVdVOyW5cIH7HpHkiCRZu3Ztm2R5AAAAbDkW2xL7hnnG7ZzkXvOMb0k2GmKr6ppJrtJa+2X/+58meUmS9yfZP8kr+tsTNlYLAACAK58FQ2xrbRbXkN0xyfFVNbfsf2+tfbSqTk3y3qo6IMnZ6U4kBQAAAOuZ9JjYqWitfS/JbeYZ/9Mkey5nLwAAAAzPRCG2qp6R5Pdba4fMM+0fkvyotTbf7sewgdMPf8iSa9z+KR+YQicAAMDQTLrL8FOTnLXAtG/30wEAAGCmJg2xN8nCIfb7SXaZSjcAAACwiElD7M+S3GyBaTdL8ovptAMAAAALmzTEfiDJi6rqVqMjq+qPkrwwLokDAADAMpj07MTPS3LXJF+uqi8nOS/JTklum+QbSTY44RMAAABM20RbYltrFyW5Q5K/SvLdJNv2twcluVNr7Wcz6xAAAAB6E18ntrV2SZJ/7X8AAABg2U20JbaqfldVd1xg2u2r6nfTbQsAAAA2NOmJnWqRaVdN8tsp9AIAAACLWnB34qraOetf//W2VXX1sdmunmT/dNeKBQAAgJla7JjYv0x3+ZzW/xy2wHy/SfLEKfcFm+wzRz5oyTXu+aQPTaETAABgVhYLsW9Kcmy6XYm/luSx/e2oy5Kc3Vq7dDbtAQAAwBUWDLGttXVJ1iVJVe2a5LzW2mXL1RgAAACMm+gSO621HyZJVW2dZOd0x8KOz/PN6bYGAAAA65soxFbVVZO8Pt1JnK62wGxbTaspAAAAmM+kl9h5QZIHJzkg3TGyT0t34qeTkvwgyUNm0RwAAACMmjTEPjLJi5K8tx/+r9baO1prf5rklCR7z6A3AAAAWM+kIfbGSb7dWvtdkkuSXH9k2ruSPGLajQEAAMC4SUPseUmu1//+/ST3HJn2B1PtCAAAABYw0YmdknwqyT2SfCDJkUleWVW7Jbk0yaOSHD2T7gAAAGDEpCH20CTbJ0lr7bVVVUn2TbJtkn9J8pLZtAcAAABXmPQ6secnOX9k+DVJXjOrpgAAAGA+kx4TCwAAACtuoi2xVbVNkmcleXiSGya5+vg8rbUdptsaAAAArG/SY2IPS/LYJCckOTnJZTPrCAAAABYwaYjdJ8mzWmuHz7IZAAAAWMykx8RelOTsWTYCAAAAGzNpiH1JkudU1TVn2QwAAAAsZtJL7BxVVXskObuqTk9y8YaztEdNvTsAAAAYMenZiZ+T5G/TXSv2mkmuOsumAAAAYD6TntjpkCSvT/LXrbU2w34AAABgQZMeE1tJPijAAgAAsJImDbFvT/KIGfYBAAAAGzXp7sTnJnl2VX0iycmZ/8ROh021MwAAABgzaYh9dX97oyT3mWd6SyLEAgAAMFOTXmJn0t2OAQAAYGaEUwAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMBYMsVW1c1VddTmbAQAAgMUstiX2+0lumyRVdXJV3Xx5WgIAAID5LRZif5PkGv3vf5LkOjPvBgAAABax2HViv5zkdVV1Yj/89Ko6b4F5W2vt4Om2BgAAAOtbLMQ+Kck/J9k7SUuyZ5JLF5i3JZk4xFbVVklOS/Kj1tqDq2rXJO9Osl2SLyV5XGvtsknrAQAAcOWw4O7ErbUzW2sPaa3tnqSSPKy1tusCPzfdxOU+M8kZI8P/mOQ1/bJ+luSATX0gAAAAbPkmvcTOrkm+Mo0FVtWNkjwoyZv74UpynyTH9rMcleRh01gWAAAAW5bFdie+XGvth1W1dVU9Ksnd0+32e1GSzyY5rrX2201Y5muTPDfJtfvhGyS5eKTGuUluON8dq+rAJAcmyc4777wJiwQAAGBLMNGW2KraId0xrEen24p60/723UlOrao1E9Z5cJILW2unj46eZ9Y23/1ba0e01ta21tauWTPRIgEAANiCTLQlNsmr020xvVNr7dS5kVV1hyTv66c/bhrP7d0AACAASURBVII6d0vy0Kp6YJKrp7tsz2uTXK+qtu63xt4oyY8nfwgAAABcWUx6TOwDkxw8GmCTpB9+XrqtshvVWntea+1GrbVdkjw6ycmttccm+WSSffvZ9k9ywoR9AQAAcCUyaYi9WpJfLjDtl0m2WWIfByd5dlWdlW6L71uWWA8AAIAt0KS7E38hycFVdXJr7X/mRlbVNdMF0C9s6oJba59K8qn+9+8lueOm1gAAAODKZdIQ+5x0u/yeU1UfT3JBkh2S3C/diZn+ZCbdAQAAwIiJdidurX0lye5JjkiyJsl904XYw5Ps3lr76sw6BAAAgN6kW2LTWvtJkkNm2AsAAAAsatITOwEAAMCKE2IBAAAYDCEWAACAwRBiAQAAGIyNhtiqulpVHVpVt1mOhgAAAGAhGw2xrbVLkxya5HqzbwcAAAAWNunuxF9McvtZNgIAAAAbM+l1Yp+b5N+r6rIkH05yQZI2OkNr7ddT7g0AAADWM2mI/WJ/+/okr1tgnq2W3g4AAAAsbNIQ+4SMbXkFAACA5TZRiG2tvX3GfQAAAMBGTbolNklSVXukO8HTjZO8tbV2flXtluSC1tovZ9EgAAAAzJkoxFbVtZK8Ncm+Sf63v99Hk5yf5O+TnJ3kb2bUIwAAACSZ/BI7r05y1yR7Jrl2khqZ9uEk959yXwAAALCBSXcn3ifJM1trn6yq8bMQ/zDJTabbFgAAAGxo0i2x2yb56QLTrp3kd9NpBwAAABY2aYg9NcnjF5i2b5LPTacdAAAAWNikuxM/P8knquoTSY5Jd83YB1bVX6cLsfecUX8AAABwuUmvE3tKVe2Z5BVJ3pDuxE4vTvKFJHu11k6dXYuwcj76lgdOpc79D/jwVOoAAMCV3cTXiW2t/WeSe1TVtkmun+Ti1tqvZ9YZAAAAjJn0mNhRl6S7VuxvptwLAAAALGriEFtVD6yqz6ULsecnuaSqPldVD5pZdwAAADBiohBbVU9O8oEkv0ryzCT79be/SvL+fjoAAADM1KTHxP5dkiNaaweNjT+8qg5PcmiSf51qZwAAADBm0t2Jb5DkuAWmvS/JdtNpBwAAABY2aYj9ZJJ7LTDtXkk+M512AAAAYGEL7k5cVXuMDL4+yZur6gZJ/iPJhUl2SPLwJA9I8sRZNgkAAADJ4sfEfiNJGxmuJE/uf1o/POejSbaaencAAAAwYrEQe+9l6wIAAAAmsGCIba19ejkbAQAAgI2Z9BI7l6uqrZNsMz6+tfbrqXQEAAAAC5jo7MRVdd2qelNVnZfkkiS/nOcHAAAAZmrSLbFvT3cpnSOTnJXkslk1BAAAAAuZNMTumeTJrbWjZ9kMAAAALGai3YmTnJ3EMa8AAACsqElD7HOTPL+qdp5lMwAAALCYiXYnbq19uKr2SnJWVf0gycXzzHPHKfcGAAAA65koxFbVK5M8K8mpcWInAAAAVsikJ3Z6YpJDW2v/MMtmAAAAYDGTHhP76ySnz7IRAAAA2JhJQ+zrkhxYVTXLZgAAAGAxk+5OvH2SOyX5VlV9Khue2Km11g6eZmMAAAAwbtIQu2+S3ya5apL7zjO9JdloiK2qqyf5TJKr9cs+trX2wqraNcm7k2yX5EtJHtdac/IoAAAA1jPpJXZ2ndLyLk1yn9bar6rqqklOqaqPJHl2kte01t5dVYcnOSDJYVNaJgAAAFuISY+JnYrW+VU/eNX+pyW5T5Jj+/FHJXnYcvYFAADAMEx6ndinbmye1tqbJqy1VbozHe+W5I1Jvpvk4tbab/tZzk1ywwXue2CSA5Nk5513nmRxAAAAbEEmPSb2DYtMa/3tRCG2tfa7JH9cVddLcnySWyxSc/y+RyQ5IknWrl077zwAAABsuSbanbi1dpXxn3QnYXpMkq8m2WNTF9xauzjJp5LcOcn1qmouUN8oyY83tR4AAABbvs0+Jra1dnFr7T1JDk/yr5Pcp6rW9FtgU1XbJtkryRlJPpnuDMhJsn+SEza3LwAAALZck+5OvJjvJ1k74bw7JTmqPy72Kkne21r7YFV9M8m7q+plSb6c5C1T6AsAAIAtzJJCbFXtlOQ56YLsRrXWvpbktvOM/16SOy6lFwAAALZ8k56deF02PNnSNkmuneSSJPtMuS8AAADYwKRbYt+YDUPsJekuh/PR1tpPp9oVAAAAzGOiENtae9GM+wAAAICN2uyzEwMAAMByW3BLbFWdvAl1Wmttzyn0AwAAAAtabHfiSY5z3SnJXbPh8bIAAAAwdQuG2NbafgtNq6qdkxyc5MFJfpLkNdNvDQAAANa3SdeJrardkjwvyZ8nubD//V9ba7+ZQW8AAACwnkmvE3vLJIcm2S/JOUmemeStrbXLZtgbAAAArGfRsxNX1e2r6rgkX0ty2yRPTLJ7a+1wARYAAIDlttjZiT+S5E/TBdhHt9aOWbauAAAAYB6L7U58v/72xkneWFVvXKxQa22HqXUFAAAA81gsxL542boAAACACSx2iR0hFgAAgFVl0RM7AQAAwGoixAIAADAYQiwAAACDIcQCAAAwGEIsAAAAgyHEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGEIsAAAAgyHEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGEIsAAAAgyHEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGEIsAAAAgyHEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGEIsAAAAg7H1SjcAV0bHve3+U6mzz19+dCp1AABgKGyJBQAAYDCWNcRW1Y2r6pNVdUZV/XdVPbMfv11VnVhV3+lvr7+cfQEAADAMy70l9rdJntNau0WSOyf5q6raI8khSU5qre2e5KR+GAAAANazrCG2tXZea+1L/e+/THJGkhsm2TvJUf1sRyV52HL2BQAAwDCs2DGxVbVLktsm+WKSHVtr5yVd0E2ywwL3ObCqTquq09atW7dcrQIAALBKrEiIraprJXlfkme11n4x6f1aa0e01ta21tauWbNmdg0CAACwKi17iK2qq6YLsO9qrR3Xj76gqnbqp++U5MLl7gsAAIDVb7nPTlxJ3pLkjNbaq0cmvT/J/v3v+yc5YTn7AgAAYBi2Xubl3S3J45J8vaq+0o/7uySvSPLeqjogydlJ9lvmvgAAABiAZQ2xrbVTktQCk/dczl4AAAAYnhU7OzEAAABsKiEWAACAwRBiAQAAGAwhFgAAgMEQYgEAABgMIRYAAIDBEGIBAAAYDCEWAACAwRBiAQAAGAwhFgAAgMHYeqUbAKbnnW+/35Jr/PlffGyDcUe+Y+l1n/T4DesCAMCmsiUWAACAwRBiAQAAGAwhFgAAgMEQYgEAABgMIRYAAIDBEGIBAAAYDCEWAACAwRBiAQAAGAwhFgAAgMEQYgEAABiMrVe6AeDK63Xvut+SazzzsR+bQicAAAyFLbEAAAAMhhALAADAYAixAAAADIYQCwAAwGA4sROwRXn5e5Z+sqgkOfRRThgFALAa2RILAADAYAixAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIYQCwAAwGAIsQAAAAzG1ivdAMAQPOfY+0+lzqv2/eh6w484Yel137f3Rzc+EwDAFsKWWAAAAAZDiAUAAGAwhFgAAAAGQ4gFAABgMJzYCWAL9IATnrrkGh/Z+03rDT/w+BcsuWaSfPjhL5lKHQDgysmWWAAAAAZjWUNsVb21qi6sqm+MjNuuqk6squ/0t9dfzp4AAAAYjuXeEvv2JOMXRTwkyUmttd2TnNQPAwAAwAaWNcS21j6T5KKx0XsnOar//agkD1vOngAAABiO1XBM7I6ttfOSpL/dYYX7AQAAYJUa1NmJq+rAJAcmyc4777zC3QAwDQ86/p+nUudDD//bqdQBAFa31bAl9oKq2ilJ+tsLF5qxtXZEa21ta23tmjVrlq1BAAAAVofVEGLfn2T//vf9k5ywgr0AAACwii33JXaOTvL5JDerqnOr6oAkr0hy36r6TpL79sMAAACwgWU9Jra19pgFJu25nH0AAAAwTIM6sRMATOpBx71hyTU+tM/TNqz7viOXXvcRT1pyDQC4sloNx8QCAADARIRYAAAABkOIBQAAYDCEWAAAAAbDiZ0AYBV48LHvWHKND+77+LGa715yza7uo9cbfsixxy+55gf2ffiSawBw5WRLLAAAAIMhxAIAADAYQiwAAACDIcQCAAAwGEIsAAAAg+HsxADAqrD3sR9eco0T9n3gBuMeduzJS677H/veZ73hfd73+SXXTJLjHnGX9Yb3e9/Xp1L3mEfcar3hxx/3wyXXfMc+N9lg3MuOP2/JdZ//8J3WGz7yuAuXXDNJnrTPDusNv+/Yn0yl7iP23X694Y8fvfS6f/qY7Tc+E3A5W2IBAAAYDCEWAACAwRBiAQAAGAwhFgAAgMFwYicAAFhlPveOdUuucdfHr9lg3FePXPqJs27zpB02PhPMkC2xAAAADIYQCwAAwGAIsQAAAAyGEAsAAMBgCLEAAAAMhrMTAwAAm+17rz9/KnVu+ozfm0odtny2xAIAADAYQiwAAACDIcQCAAAwGEIsAAAAg+HETgAAwKpz3j+dM5U6Oz33xlOpw+phSywAAACDIcQCAAAwGEIsAAAAgyHEAgAAMBhO7AQAAFxpnP/qM5Zc4/eefYsNxl3w2tOWXHfHZ61dv+brP7vkmkmy4zPuMZU6q4UtsQAAAAyGEAsAAMBgCLEAAAAMhhALAADAYAixAAAADIazEwMAAFyJXPiGj02lzg5Pu9/6dd/4H0uv+VcP2+g8tsQCAAAwGEIsAAAAgyHEAgAAMBhCLAAAAIMhxAIAADAYQiwAAACDsWpCbFXdv6q+VVVnVdUhK90PAAAAq8+qCLFVtVWSNyZ5QJI9kjymqvZY2a4AAABYbVZFiE1yxyRntda+11q7LMm7k+y9wj0BAACwylRrbaV7SFXtm+T+rbUn9sOPS3Kn1trTxuY7MMmB/eDNknxrwkVsn+QnU2p3ljXVnW3dIfU6tLpD6lXd2dVUd3Y11Z1t3SH1OrS6Q+pV3dnVVHd2Nbf0ujdpra0ZH7n1dPvZbDXPuA3SdWvtiCRHbHLxqtNaa2s3p7HlrKnubOsOqdeh1R1Sr+rOrqa6s6up7mzrDqnXodUdUq/qzq6murOreWWtu1p2Jz43yY1Hhm+U5Mcr1AsAAACr1GoJsacm2b2qdq2qbZI8Osn7V7gnAAAAVplVsTtxa+23VfW0JB9LslWSt7bW/nuKi9jkXZBXqKa6s607pF6HVndIvao7u5rqzq6murOtO6Reh1Z3SL2qO7ua6s6u5pWy7qo4sRMAAABMYrXsTgwAAAAbJcQCAAAwGEIsAAAAg7EqTuw0bVX1zCRvS/LLJG9Octskh7TWPr6ijc2jqq6X5PFJdsnI69Fae8ZK9bQSquoBrbWPjI17Smvt8JXqaVxV3by1dmZV3W6+6a21Ly2x/lZJHpQN14VXL6XuSP3rjNW9aAm13pfkrUk+0lr7vym0NxNVdZ/W2slVtc9801trx01pOTskufpI3bOXWO/2rbXTx8Y9pLX2gSXWvX66y5mNrgdLXW+vneQx2XC9fe5S6g7JLN+7s3jNRmpP7TOhrzfVv2dVtd1i05f4GTazv72zfM2mraoOaK29ZWzcK1prhyyh5r8leVpr7ef98E3SnbBzz6V1e3n9qa63Q1RVt86G6+5U/p5N2yx6rapdWms/GBt3h9baqUup29d5UJJbZv2/6S9Zat1pm+VzMAuz+Du5RYbYJE9orb2uqu6XZE2Sv0wXajc7xFbVg5O8NMlN0j1vlaS11q6zxF4/nOQLSb6eZGphoKrunORfktwiyTbpzvr8P5vbb1X9S5IFzwI2hT/8/6+qLm2tndwv7+Akf5Jks0NsVa1Ncmg2fM1uvZkln5PkSUleNc+0luQ+m1l3zgeSXJLprwtPTvKSJL/JFa9hS3LTJZQ9LN376vVVdUySt7fWzlxSo7NxryQnJ3nIPNNakqX+IX1ouvXh95NcmG5dOyPdH8ClOLKq9m+tfb1fzmOSPCvdOrK5vb40yV8k+W7WXw+Wut5+KMk3Mv31diafuVW1a5KnZ8M/pA9dQtlZvXdn8prN6DMhmf7fs9PT9VVJdk7ys/736yU5O8muS6g9q7+9s3rNpv33bM6+VXVJa+1d/XLelORqS6x5SpIvVtWzk9wwyd+m+/u5JDNcb8eXc/0kN26tfW2JdWbymlXVW5PcOsl/54p1d0l/z4bUa++4/h+7P+qXc68kb0hyq6UUrarDk1wjyb3TbQTbN8l/bWatWX9vnupzUFVfzvz9zq0L827A2QRT/zu5RZ6duKq+1lq7dVW9LsmnWmvHV9WXW2u3XULNs5Lsk+TrbYpPWlV9aQorxnx1T0t3vd1jkqxN9x/n3Vprh25mvf0Xm95aO2pz6o7U3z7JB9P9sbt/kpsneXRr7X+XUPNbfb313jCttR8upddZmVtvZ1D3O0nu0lr7yQxqXzfdVrhDk5yT5Mgk79yc162qPpDFP/CXEjJmoqq+mu7L6Sdaa7etqnsneUxr7cAl1r1pkmOTPDbJ3dO9fx88t2VjM2t+K8mtWmuXLaW3eerO6jNsVp+5X03ylmz4ufDpJdSc1Xt3Vq/ZTD4TZrguHJ7k/a21D/fDD0iyV2tts4PRDHud1Ws2k79nVbVtkven27PmAUkuaq09ayk1+7p3T/LJJD9JctvW2vlTqDnLv2WfSvLQdAHuK0nWJfl0a+3ZS6g5q9fsm621PZZSY56ag+m1r3uHJG9K9w/q2yX5+yQPaa2ds8S6c/lh7vZaSY5rrf3pZtSa9ffmqT4HVfUHi01vrX13c+qO1J/638ktdUvs6VX18XT/pX1ev7vbUlP/OUm+Mc0vU71/q//P3nnHWVJV2/+7ZghDmkEeICAgoCgiApLTU8HAQ0WRII5iAAMYCKJgBoVnALOYQHREMoqgKAiSGTLDECVJEFGU8COMjISB9ftjn5quvnO7Z7rOOTDdz/X53E/fqu677+k6VSfsvfba0geJDdwTzckS9Bjbf5Y03vbTwBRJl2TYynrY5sH+AymqdTbhfd+xwLW+3/Zv81sXGIqS2qAAlecMSW+oQHu/HZhZ2CaS/gvYBXg3MB04lthwvZeIoo8U30g/tweWA45Jx5OBuzq2cdgFSAG651O2H5Q0TtI42+dJOiTTJrbvkPQO4FRi7HmD7X9nmr2BiGDdl9u+Hpwo6d3MOYbl3nO1xtzHbX+vsM1az26tPqsyJlBvPtvQ9h4te2ekiGcOarW1Vp+Vns/aVO0PEGPNxcBBkpbKpGq/G/gC4XxbGzhd0q62r81pM/XuW4BJth+V9AFgiu0DJWVFYincZy1cKmlN238qaHM0tRXbV0rai2BYPg683vb9BUw38+xMSSsAD9KR8fEsrJuLXoPcTeo8oPg8OVY3se8H1gXusD0zLbZ3zbS5PzEQX8DgCS93Efwk8HUiklWSHjNT0kLANZIOBe4FFsu0iaRlgE8BazI4X6ATVUrSDAboYiaoz6sRFKdc6uCBko4EzmFwn3XdbPajpM42Sz495jLgFEnjgKcoR1n/DHCJpMsZfB06U1kk/ZqIlh9NeP7uTb86MbEARowmEibpYNuvav3qNEkXdmzqEh0/N694OHlqLwSOlXQfMKurMUnXMzgavRSRCnC5JDK9mF8Fpku6gcH3QW6E+0HgB8Q49gwDz/IKmXZrjbnflXQgMfG37ebkLNZ6dmv1WfExIaHWfPaApM8Tji0TzrMHM23WamutPis9nzVU7QYi8tXeRP512AHYwvZ9wPGSTgGOItZlOah13wIsIGl54O3EPVECpfuswVHE5vAfyW4J6u+oaGsfxtaiwCPAT9Mcmfuc/U6RL/914Or0XUfmGKywbq56DVKEt0lHXJjosycKzGfF58kxSScGkPQCBrj9ANjuuhAmRXb/xZxUiy9lNBNJtwMbV6B1vZDwBC8IfByYBPzQ9p8z7Z4FnAh8EtiDiLrdb/tTeS0uD0nHEButQbkYtnd77lo1NCTdAWxHefrkFUSOUu+929lLqCSYVKB5/WzfBLzJ9h3peFXgdNsvq/F9OZC0GOG5HUdQfycBx9rutMBOz+2QyKF2SboROJyCNNpk93ZiwXrvXP94ZHZrjblfJdgDtzN4XOics1jx2a3VZ8XHhGS31ny2FHAg0Di3LgS+lBktrNXWWn1WfD5Li8lNbV+c07Z5/K6FnEmxrnXfJts7EdHjqbY/okjp+LrtHTJsVlmDKFIt9qUg9Xe0tFWR9zkkcp+znu9aGJjgjDSeZKfourn2NZB0JeEoPAHYiMjxX8n2AZl2i8+TY3ITm+h8OwN/Ap5Op53jnZB0le0NSrSvx+5vidzPWhSZopA0zfb6bW67pAtsD/tQzYPdV/U7n+l4uN52VpL/MLaLq9dJOhPYxoXVfiVdYnuzwjZrqrH+D3AEcEc6tQqwu+0zM2xOIBgavX2WswAcD5xp+3VdbQxhdxxwne21CtvNfk6HsPs7YAfbT8z1j0dmt9aYezOwdu6CusdmrWe3Vp8VHxOS3VEzn9Vqa8U+qzKfSbrU9qaFbb6EEP97vu21FOq0b7H9v5l2q9y3tVCxz87NcboNYXM0tbXK3Nuyvxlzrm1+kWGv+Lq55jVotXf2PVHi2asxT45VOvF2wEsLL6rOLs3lTniaoPyeRwF6TB864iBk0k0gKAAA96aN3N+BFTNtQggKNJhAeH+mkafoeJkq5GKooHpdD+4Fzpd0BmXpk+dJ+hChDFcq96uKGiuA7T9IWp3wCgPcXOBZPhq4GdiaULd8F6Ei3Bm2n5Y0U9KkXE9tj91nJF0raWVnlurpwbQUhfwt5Wi0EJHoaZJ6aWi5JXZqjbnXUj5nsdazW6vPaowJUHg+a5A2RZ9kzoVlzvxQpa3U67Mq8xlwlqQdCPGaUlGNnxBz+uEAtq+TdByQtYmlwn2rugqytfrs5nQ9e69DDvV31LS11twLoCgP9SJC3Gt2EAzovImlwrq55jUAHlOkI14r6SvE/LZ4AbvF58mxGok9A9jJ9r8K2pxB5JQ+QcGcJw2hXtaVHlOTjpjsvxm4iKiBdxgwkaB1FRUEkLQScKjtyRk2biIGozsplzdSVL2ux+6B/c47nz55Z3+z7pzzpEpqrC37azFn/kiOJ3S6Qz246bMFCS9mbtmLk4BNgD8Cj7XamrtoPxfYkHCOtO3msEnO63PaBa7B7v3O2z48026tMfd8QmzmSgrlLFZ8dmv1WfExIdktOp+17F5LlFubxsDCEvfUUh6hzVptrdVnteaz5jmbRTgm8/PUpCttb6hWVQhJ19jOyomtNJc198HmxJxzYjreCZhm++MZtmv12ZQ+p53JLBo1bU12a829NwFrFnToVFs3V7wGqxEb7QlEaaxJwPdt35ppt/g8OVY3sScD6zBngnqJ5P9RAUmHuIdv3+/c/ApJIiiVnektQ23oC2zkL7e9saTLCCXdBwkV1dVz7I4mKCj751SIkjUD3WuIBcXpRNmHqbZ3zLB5he2NFAJRHwH+AVwxHy/a+9KMXDDf5/8q/nNtRx8aettz3Y7nErXmsxpIgYSPAb+0vZ6kHYH3297mOW7akEjOhzc4lYdLjs6zbG+ZYXM09dmoaStUnXt/CezlghoPkpZxGeXkXru1rsG2wB+cUeLy2cJYpRP/Nr2KQRVyNpPdO+lDZcldXAOvJ9TQ2timz7kRIXlovgtsStBILwU+7iTCk2G3TekZR6gY5srx1/LQFFevg9mTaL97IdeD/55+53Mim9RTY4WgZ68DTLe9q6Tnk399j1AUsP8CMTYsDmSJFEBMFoo6iyvbviXXXstu8Q2VpL7/r/NzuW+i/32bVRuw1phb6drWenZr9VmNMaHmfHaapI8Ap1CORlqlrbX6jHrzGWlsXJ3BzJec5+yjhK7BGpL+RkT3dslqJPXu24QVCDX75p5anHyF9Sp9lqKb/e7dnOjmaGprETGvNjSg+LsE8CeFiFgpdfFL0nhzIsHaeyirsQNtqlXC5+3A9xMj7ATgbEepzizUmCfH5Ca20sKyRs4mQFu4ZAJBYVlqiL+dKyR9mIg0rabBNc6WIGrA5eI4opzG29LxO4DjgY0z7bbLsswCjne+YuLvYXb5ngnAqsAthLhPZ9hu6hOerBC1yVavS/hk6/0EokxB53ItLWzYY/e1xOY7Z+L/JuHIKKrGmvBvR17oLEkTidzFrIWl7WYTfEGurTaSx/IbRGmoVSWtCxyUOeEhaRMGJO4XIsrsPJbpJHis9X4C8GYy84IT2sISzRhWIn+mypirgbJeENd2QfKvba1nt1af1RgToPB81kITcWjfE7llYGq1tVafVZnPFHVR9yZy9K4h6ImXkvGcJaf26xTq7eNsz8hpYwu17luArxGlkRo6+KuBL2barNJnRG3jBhOI9djfM22Oprai0M34KnOmHXUdE74x9z/pBturS9qIWC9/TtKfgBNsH5Njt8I1aD7/boUy85uA3YggwBlu1eruiPLzpO0x9yLqed4C3JmO1wV+W/g7ViI2WjXaPzXjs5MI8YvjiRJDzWupQm27vM+5y57rPp/Htq8HHF7I1mbAO4li7u8B3lOpzRdUsDkp93kAziQWJzX+5x8Swjt7ALcB04ni8zk2F0799VkiAnsAcECBtk5L13N669z1BexeBbw4/e/jiTrXXyl8nRcm8oJr9OFFFWxWGXMJIcCi1zbZrfHsVumzEmPCMLY7z2fP9qtGWyv2WZH5jBDnmwBck47XAE7saGvf4V4VrkHR+xZYDnhrei03v/ZZH7vjgHP/L7WVKLX0WuC6tMb9IpFnmmv3kHk5l2F/acLp8vT8eg1a9scTDM5fAf+v9L2QviNrnhyTesreqwAAIABJREFUkViiIzcCzgewfY2i1mRJ3ANkl8CQtF7rcBzhHV6iqz1HRPARYLKkdYD/Tr+6iAGaTA7Ok/RpgmJgopTR7xV1/HBHelctj1Ibtq9WFHHOguqo19Fcw4RxwPrEpFoaMwnqWA5qqbFi+yPp7Y8l/QGYaPu64T4zD/gN8VxMo9XeAphl+5FI4Z6NIpFp23+WNN5B45ki6ZISdltYlAJRaUlt2nAzhj0v124fFBlze2H71DSmdcaz+OwW6bM+KDEmFJ/PWnaL00hrtbUPqvRZqfkMeNz245KQtLDtmyW9tKOtGtdvOBS5b1sYD9xPsBRfIuklzkxfaKNgn/VidWDlkgZHQVsXsX2OJDnydr8o6SKinnQOiqfiJUbZ24hI7IuItIiNutproco1kPQ6oq2vIxicvyCCAFmoMU+O1U1s8YVlpZxNCFpmg1nAXQQfPQuS9gI+BDQy5sdIOsL2YZmmd04/exVJdyOP3jWFePC+TZSu2ZWgtXSGpH1bh+MIz2KJ5PoNKKxelzCNATrPLCKP6P25Rlu5HhDXYU3gpEyzd6bXQulVDEnU613AarYPkrSypI1s55QxWtH2/xRqYhs3SHonMD45YvYCSmw2Zyok7q+RdCjhNFgsx6AGl98aDyxDlBvKxU9b75sx7B25RmuNuZK2bx02m5fcZ7nWs1ulzyqNCVBpPqNeSkSDknNvrT6rNZ/dkzQeTgX+KOkhOtI9nanGPTdUvG8bscKdgRsZKBtnIKdWfZU+a6VEKP38B/l6J6OmrQmPJ02O2yR9DPgbsGxGO4dLxcud068lnq+DbF+aaauNoteghT0INueetv9dwF6D4vPkWFUn/imhTPxpgnO9F7CgM/jcGqwCNgu4y/k5m9WQHsJNbT+WjhcDLnXFsig5UP/iyhfZ/u+5fXYYm21vVLNIOdn245ltLa5eVxMarMY6C/iL7XsK2V6sucdKQdKPiEXEVrZflkRHzrLd2Sss6QjgMNvXl2pnsrso8DmgKa90JnCwM+vaKpQi/0k4CD5O0OZ+aPvPmTYbzAL+abtE3mYV1BpzNbjkQzMu/MR2ybqxRVCrz2qOCc8GJE0CjnZm7nkNVOyzKvNZz3e8mhhr/mD7yQw7jQDkJsSitZQAZM257BZg7dyxu8dm9T4rhdHUVoAUJb6JSD06mChb83Xbl3W0N4lgEH2V2Ds0mNGVYdiyLcIJ7ZLrpdLXoGX3hYQeh4GbXLZefVGM1U1se2EpBhaW883DKGkX28f0eL9mI5eWmbzBGzb/s6QJwJXOKFnTsvMRYAviBr8I+HGBjeHFBPX5V8C5hEfpa7a70pqKQ4PV69Ylanhmq9dJ2sr2uT0RotlwXgHzKpC0KRGBW9z2yom6vnuLCpxj+2pHWYZ2jcFrba/TwVYTFVmAoDHdQdkaeDvZ/uXczo3Q5isJytGNtrMFYSRNtP1oD5VnNjJSAHay/UuFamw/uz/sYnc0odazW6vPaqH2fNbn+xYkSrC9rMNnq7R1FPbZBCLi8mIiL/anpZxaivJzPyCiORDMjD1t5wpAVoMiNWYn2/96rtsyFCStkeje6/X7ve2rn+02DYWabZW0DJED+mfbD3e1M4z95xEaDLPZql3bm+bHTxFihwJmEDm2WfNjjWsgaQngcELz5VoiIv8Kwgn1IXcUaKu5xh2TdGLbM4lN7OdybUk6yfbbeyhCkL8IbqiBtXJIpgCXSzolHW8H/KyA3V8QD2FDS54MHE0oO+ZgHyJ3aC/Co7QVA4qUI4Kk79jep4d6NBsZ3vta6nWvJjbu2/b5nRmghI8Ikqba3kKD1ViBIuVwvgNsTSplZftaDVESpQOekjSe1OY0WD8z/EeGxJsLtWkofAbo3bD2OzdPUJTn2IWg3Rwq6au2f5LXRI4jrkObytMgJwWgyWVZqXvT5kStMVfS/rYP7aEpz4a71RGv8uxSqc8qjglV57OesXw8ESXoSiOt1dZafVZrPjuKKI92EZHztyahUlwCsn106/iYRHfsZqzuXNZgJpG+cQ6DndMjHhcq9tkngA8ymAo/2ywdFKVHU1uBRk37K8DtREWAD9kuVlJT0kFEOtvtDFyPrtf288SGcMuGhdCwFCQtZft/O7ax1jX4brL5bqeSOomu/EVizf++jnZrzZNjKxI71EPYoMvDKGl52/dqlBWCBhrhii2Igf5CIrLTmSKUbM4REesaJRvC/kRiUuosyS9pfdvTeqhHs+ECdSIVtUsbeusV8yMVsSYkXW574xLR0j6230XkJq1HLLR2BD6fE91MdgcJndnunF8paRvgjUQO3YmtX00k8qU7iTZIupFgUMyU9F8Era+GuMZ8i1pjrqRtbZ+mSgXi/4N6GO305xzUms80OHVnAWIe6xs162D7a8DDDBaAXJiIzs53UWmYI31hNrqMC8/GGqQURlNbASTdQGwK708bwmNtb1rQ/i3AK3LXyi1b6/QyFRUlQK+1/ZKOdqtcA0m32e4rlDbc755LjLVIbBMp256IEjQ1mCYT/P4RwynvsfRmVdL35vK9XaICSPqCUx3TRH+4Op2fBJwFvKaL3RamS9rEiXMvaWMK1J+VtAERPV4iHT8C7GZ72khtNZ+pNfhKejvwdUL9WsBhkvaz/auO9vrS2hpk0NuGrXmYuZD4q6TNACsEiPaiTC1EbB8raRoh3iJgu1xaraS9Ca9wKaGzvxNlcN5CRF4azCByWLvi8cQkwfaDyQuahaHoXA0yaFKHzsXu/l3s1hpzbZ+WfhbbrFZ8dmv1WZUxodZ81vr8BT2Ow9u62qo491bps4rz2VOt75ilwUKYuSgqAFl5LmtsFBsXavXZUHTM1veOOKI1mtqa8KTt+5ONOxT1TEviBiLHtEhgoncDm879W1JXdhnUvwb90HmAqDVPwhjbxDYPoaSDbbepjadJ6qQw14e+0vudXWkszcJ3c4LG00RzdmLwonik+G9JX7Y9m0otaTkiL7hEbuXGwHskNYneKwM3NdS/rlQ/gur8EdsXAUjagtjUjtheHxriIGS0scHniGjZfen7lgHOJvJ5u6Chtb2UWKQ1tJBtyVBGZDCtbWXgofR+SeBuIKfs1B4E9eQFROmTs4CPZtjrxT8JmtsCwCKS1uu6CEx4P7CxB4TODiHyPDptYlMU91pJxxHXtPGo3mL7qaE/OVe8SFLT/+o57krtauhcEwgl3muT7bWBywm2Rhfcnn5uROTNNJHyHYh88U6oNebWYOpQ79mt1We1xoRa8xlQ3HFYq61V+qzifLaOpEebryHG2UcpQNG1XbqkYc25DAAVLPNXsc8aOuayBE313HS8JfFsjHiNN5ramrBijyNq0HGuw4y4B6anaGeu5sk9kl5r+5z2SUlbERUHuqLWNbhM0mdtf6V9UlGC7vKONqHePDm2NrEtLCNptRYHfVVC5n7EsN1EBg8ipMGPJgbPd5FXz/WoZPd9BC3gqXT8Y2JD0BVvAX4l6Vu2900D8xmEYtnhGXYb1ChTAqEAd1FzYHtqWsx2QZMH2WyqmtycdxF5L7kY10MffpBIgO8Ep7IEks4C1muo1JK+SMfcymR31WTnx0RB+NPT8TZE/a/OsP0AcT2LQ9LBRO5Fdk5K2ywDNX1J70uEHjYj8sTvSvZWkvRed68t+Nae4+w8bNtbAkg6gRBnuD4drwV8MsPu4cnOzsAWDf0qTaanZ9itMuZSh6lT69mt1WdVxoSK81mDYo7DWm2t1WdUms9sj89o07BQaBq8CViFweI4nSIuNeeyFkqW+avVZ7sCSPodkbZybzpenkTV7oDR1FaA/XqOs51kPTgKOIQQO8uJlkIw1H4jaSoDjpgNCQda71w/EtS6BnsSNelvBaYT7V0P+BPd82GrzZPA2MqJbSDpf4AjCCVSiIH0Q7Y7T1BKOYBzO9fB7i1EKZz/l46fB1zmDFVehXLjCQRdaFNgH9unDP+pudrspfMYeNiFbiBJ3yaEnY5nIIfmIeBk6EbFknSx7c3ndq6D3a8T3vVGeXFnQikzt07bzUT+xBPpeGEib2KNTLvTbK/fc+4q2xt0sNVXFKdBAS9o0ZyUls19CaGwttDZz21/J9PuNOCdtm9Jxy8Bju+93vMDJF1je925netg9xZgI9uPpONJRH5dlrJ4xTH3wh6mTt9zI7RZ69mt1WfFxoQeG8Xns2Rndv5mOh5HXN/OavsV21qrz6rMZzUg6XTgcXo2As6sI1vrvm3bVtkyf7XWIDfYXqt1PI5Yg6w1zMfmZnPUtLUmJF1gu29+cEd7E4B3Ai8nnCI3Ejms8021lF6kdczs9jbrmwJ2i8+TYy4Smx6QR4lyGs2Fudn5tb+eVgjONEIFkxkc2emKrxHUhfPS8asJJbBO0AD3/Apgf4KSuWpzvqsnlP6Ki4tLuhb4gO27Otpt0EzwB/ac34zuUbjFJG1heyqAIodzsbl8Zq6wvZ+kHQhvmoAjcp0ECUcDVygUpQ28jYjy5eIBhUreMcnuLkT0uAuuSj+r0AYTiuakQNz3ks5nQOhsV9vTC5hesD3A2741OZHmR9wk6UgG3wcl8pi/RYxhjZPwdYQnOxe1xtxiTJ0Waj27tfqs5JjQRtH5rIU/SDqTwY7DztH+hFptrdVnVeazSlgxg4Y6HGrdtwCPp/XjbQol5b8RVNgc1Oqz81vPg4kSRucN/5G5YjS1tSamSfoqQXlt04m75rQ/TpnKIM8abN8K3FrBdPF5cqxGYi91QbWyZHMVIgdwc+LiX0xEOO8qYHs5ItcU4HLb/8iw1bsJHIRcT2if79ueiHLXohl3hqT1icFjUjr1MCEWNd/UUutFanOTO3VhiY1WiqIfCDSRpguBLzlDDCMt/N7QouItCJzVUOoy27sB8BtiM5uVk9KHQTAIOdcg2f8ZMR60KVgLNFSq+QnJI/xhBt8HPyrhEZa0MsH6ALjE9l8L2FyFCmPuEEyd3W2fmWm3xrNbpc9qjAkt28Xmsx6729NS2y/hOKzR1op9NmrmM4XmwDk57Lch7Na8bzcknA1LEmX+JgGHOolYdrRZrc/S89BEibOfh9HU1ppoObXasO2cdKb/IKH0PDlWN7FfAq4Dfl2K7loTica0OoPFBLKSnZ9NSLramdL8CuXJrwAr2N5G0poE1eunBdo3kbjXH8m0M5TgTMladUhalsH3wt3D/PlzglpUvGTrRqLgdi8VbcTqiQoFwHuIshwwmElgdxDt6LG/MJFL1C5l9cNc5oekndxTUqjfufkFkhYnFEfb921ncafaSP1WkqnT2J3vn93aKD2fKfIrz7RdKvexbXvUzb2l5rOWvcWAf9t+JtEI1wDOcIZAnaS3EdHScURaU9E5crShdJ/VxGhq638wOlFynhyrm9gZBA1iFpGXkT2AJu/q+wmeePvi75bZ1g8QBcZXBK4BNgEuHS1en7R4nVog3+cMQljhc7bXUdStm56T85Tsvok5++ygjrZOJQRhfg2c6PJll95CKFyuQFBpVyYW2C/PtLsMQS3vvQ6d7zFJuxLUu0FUPBcoU1AyJ0XSd4myUhcTdKapo8SxNYdjKNdZpILqmz123wN8mlCq/hMhBHFFTi5ZsltlzE2212LO69CZ1lTx2a3VZ8XHhGS3ynymUOh+d8mFdcW2VumzZLvYfNayOY2Ikj0PuIxIGZlpu7Nwn6Q7CN2B60uOt5Xmshqq5W37NfpsE0JZ/2XAQsB44LFcJ8Eoa+syRNm8VRgsHlZifih+HWpA0orEtd2CcPhPBfZ2gRraqd9eYvsXipr1i+U6ZWvMk2MuJxYG1C0L42jgZmBr4CCCNlgiz2VvQq3sMttbSloDKEr5LQH1r/P0PEIN+fsFvmJp2ydJ+gzQ1K3Lyn9TKBkuSigNHgnsSEbpD9vbKURrtgeOSIvsE4ETStCZCArTJsDZtl8paUsiDzAXxxLtfDNRGue9wP05Bm1PSY6Hhor36VK0QQrmpNjeW5KIjey7idIcZxH0vju7NlDSdXP53k75YAq1zTcCL9BgCf2JDESTu6Kk+mYb+xElRS6xvamkVwCfKWC3ypibUi5eQ2wyTge2ISb/nNycWs9urT4rPiYk1JrPHgeul/RH4LHmpPOE5Gq1tUqflZ7P2qZtz5T0fuAw24dKyqXC3wbcUMFhWOO+zVaAHwoV++z7RG7pL4mx9z3Ai3MMjqa2JvyG0Hw5mzJaCUDZ66D65R6nAMcRmiQQOeJTgNfnGFXknW8OvIiYFyek7+la2q1B+XnS9ph8ERusjYjciVcBr8q0Nz39vC79XBA4t0A7r0w/rwEWbt4/19evTzsP7HkdQNAoX1HI/vnAfwFXp+NNgAsybV7X83NxIm+zRHvHpYfvAWDfQjavSj+vJcr4QES0cu1Oa1+H9D7r2iYbRZ+xlt3z+rxKPGtLEguf+4EPZtq6hpCg34+g372w/cqwuw6xMPtL+tm8tgeeV+g+uL517qIC17U9hi2Y3k8vYLfWmHt9en6vTcfPB07LtFn72S3dZ7XGhCrzWc+zMPs1n7a1Vp9Vmc/SOLYpEYV9eW/bO9r8OZFa8Rlg3+ZV8NoWvW+Tna2ARUvYehb67Kq23fT+kv8rbU02qqyTS14HBtYEh6bXK9Lra8ABNa5BoTHsGsLxNr117roCdovPk2MyEjsUTYi8OpNNfsjDiYr2D4LGkIt7JC0JnAr8UdJDwN9zjaacrx2Yk2rRiRLhwoJQfbAvEXl7kaSLCbXQHTNt/jv9nClpBULFMKsoukKxbzJBv5oKvM2t+raZeDjRsy8EjpV0H/nRNxi4d+9NNJm/E89GZ1R6xoCBeoslkPK93kqomS5DUMHXc6bwkO11U+RmMuGh/FP6eZbtzn1m+1pFkfU3uAA1uwc11DcB7ktj2O+BMyQ9SJmoXq0xt8n/m5Xyv+4j8nlzUOvZrdVnxceEhCrzWYVnASq1lXp9Vnw+S9iH2GyeYvtGSauRrx57Z3otlF6lUOu+haiD+eM0fl2UXlNtP5Rhs1afzZS0EHCNpEOBe8lXEh5NbQX4naQ3OtUMLohi18Ep5UzS5h5cqujTaZ2bS1F+QNIuDKi2T6aMWvcTti3JAJIWLWATasyTNTwZz/WL8LRPIHkkiEjJiZk2P0BEnl5FqFreRyhalmz3qwl67oIFbP2BoN3sD3yieT3XfTOXNi9A5CGsVegafIGIvu1ALIDvBQ7OsHcXsWH7NBGBXK/9KtDexYgI0QJEpGEvYKkCdt9MKA6uRSxOpgHbZtos/oy1bO9N0GdF0HmuJjZ1XWw9lvrsU+k+2L79Knjv7kxE5fcrZO8PwEKl2pdsbkh4lVckKEcnAxsX/o6tgbcDEwrYqjLmAj9kICp/GxGJmpJps9azW6XPaowJfb4jez4jHFAfbR1fnu6FO4Ad56e2Pgt9VnQ+ezZewBLA4gXtPRv37Qrp+b0bmDU/9hkR3ZuQ5skDiTJnL/q/0tZkdwaRB/o4UVZzBvBoAbvFrwOxBtmidbwZZSKmKxPBn/vT/HgqGUywlt1PAT8AbifSIaYSlQFy7RafJ7MaNL++qEATAladl3Md7B49L+c62L3hue6HDm3ejCgK/Z7mlWlv4fb7NPktnGHvfFr0VsrTXQ+Zl3Md7G4+L+dGaLMaDZ4BmufWaYBeh0Qz72Dr58RCst/rZ5ntfAHhHJoKnEbk3BZZsBHqzFemCbUIHQ/YaV7OdbD703k518FulTG3x94qwNoF7NR6dmv1WfExIdkoOp8RgmwrtY6vIdJOViZKuMw3bX0W+qz0fDaFKKny7dy29bG9FuEc+kt6TSNRlTPtVrlvk51d0rh7SZp39icU+OebPmvZ2ntezo3Vtj5br+Y6FLCzPkGhvYuBYEh24KPy/74Nkdf/HWCbQjaLz5NjVZ34FMJ7sA9Bb3yI8LC+McNmP7XQabbXz2zrILuKcgLX214z0+4RhEjD9Tl2ni1IOppIIr+GgSR9O0O4o4bCa00M0d7rnJn8X0nptvgz1rJ9ne21FcrC59s+RdJ026/MtV0Kki4gogwnAb8CBgl7Ob/+7IH9zjuD1l/reegzholwouUq89Yac8+x/dq5nRuhzVHz7D6bdnPnM0lX2t6wdfx92x9L7y+zvcn80tah7A517rm2K+nV6e2Tti/NaVsf25cQlQbOS8evAb5ie7NMu9XmdEkPEJGnHwPnObMe9VBtq3gvZM2Ro6mtyYYIsb9VbR8saSVgeXcs7aaoZTsUngDusJ0lLKhy5R4PY3jBqBzBOxR13++3/e90vAghvpqVhlVjnhyTObG235beflFRuHgSQc8bMVLe28uBST03+URa8tsd7H4G+CywiKRHm9PAk8ARGXYbNbQFgF0VUvdPJNvOXVT1+b6PEBz8k52RC0io1q3pAl4VRQH7FxDX9pUMKENOJFTn5itI+jDwEWA1DajeiqCkXZxhd1Miur2MBqtLTyRk7juj5DPWB9MUCsKrAp+RtASterHzCV5IPGe7Ax9qnVc6n5Vj2WxW0/9u2//qakuVFI8l7UdEK5ZMuS0Q//94IgLe1W6tMXcC8fwvragP2h4XVuhos9azW6vPqowJteYzgk4+G80GNmGZLgYrzr21+qzKfOZW3e20SF3Z9i1d7fVgsWYDm77rfIU+QSfUnMsa2F5a0suJ9IUvK0ol3WL73R3aW6XPJE0m2GqrKspONZhIx1zI0dTWHvyQWBdsRaje/ougwG443IeGwbbD/G4B4GWSLumyQVSPRk3sv7PK9lzV8XPzil8Tz1uDZ4i0iI26GKs1T8IY3cQ2kPQCQlwAui+CX0rkYSzJ4Jt8BlGjqhNsfxX4qqSv2i5RjqLBmwvamheIkN1+F5FT1BU3EDVY7y3Qpq0JkYYViZpUzaA8g1i8zG84DjiDqC346db5GZkRvYWIQWIBImrY4FEyRbMUNcRutD3D9gVps/VKImctF+8H1iU8nzMVNcp2LWC3GGyvUtO+QsjoaGCpdPwAQa+/sYO5vxOT3lsIWl+DGcDHM5r5LaJG3aD71vYTQ35i3lBlzCUcDvsQG9ZpDIwLjxKLny6o9ezW6rMqY0LF+exySR+0/ZP2SUm707HsRcW21uqzqvOZpG2JMjMLERuOdYGDnFcf9Q5JXyDGMAiq7p3D/P3cUG0ua5CiZCsTDspVCMds13VjrT67hFgjLZ3sNpgBDFv2bRiMpra2sbHt9ZTKQdl+SCEg1Qm2h11jKMTaujIbfwM8QowLufMjriN018YCtp9sfd8TaSPeFbXmybFFJ04e1gUb74aku4GHiQHwqDR5dbW9aUnKjaQXAg83tAJFvaTtCL78D9o3UOb3LMvggs1ZxYpLQwOFxpcgNi5XMLg2aOeJVNIOtk/ObmRlKJTfnrL9VDp+KeHR/4vtXxew/0IPqOQ9j7jvsh78NHGs19hJA/xVhWhd/Ww8QlyPEoqv8z1q0PEkLWj7KUkLEjlrf7N939w+N4y9FYhJaEY63pxYwN8F/CS3r0qPuS27e9o+rJCt2s9u0T5r2S06JtSaz9L8dSoxJzR1otcnctW2s/3P+aWtLfu1+qzKfCZpGhHNOr+heOZS/NI99SUG6kpeCHzJeUq/Veaylu3rCH2DqcCFtu8pYLPqGiQ5eF8F3G172tz+fi62Rk1bk73LiWjhlWkzuwxRHaBa2pGk5W2PONAi6Qbba1VozzKECNOaDF7nZ1WJkHQO8E0n5WdJbyaEYTtVjqg6T3o+SCAu9SImucVax02dwfGEVHoXmx8EVm/W6oQQwiOEJ6lzYjYRsVohvV+XUDb9BHAUcGSBa/EWQnnzMcID+gwROcu1uyShKPYt4HvNK8Peq4d7dbS5LS2FNqKm7bWEWENRYZhkf3nyBDYubN1jLybyKw8DzgG+lmH3AGCN9H5hQozq/xEqdq/L/J/71SfLriOW7FxGUPuuYsBzeSWhSNpJpbjPd2wAvKD0vVDwnrp2Xs7No60fM1D/cRJRDuh6ovTH5Iw2XkoS3SFq3z0IfA44Afhxht1aY+6GwHKt4/cQHvLv0VEhseKzW6vPqowJ1J/PtgL2TK+tMm1VaWvFPqs6nwGXp5/ZNSGJhfQyfc4/nwzF8lr37RDftVgBG1X6DPgdsFZ6vzwR6Twt3Wud1GNHU1t77L8rtfFvwJeBWyggoFbjRaQpvKKC3bMI5tpNxJr5Z5QRFFydWH/dmV5XAC/JsFdlnrQ99jaxPcfva72f1tHmDSTZfYLfP41QR3wdGQXMGVz4+RvAoen9uK4TSI/9a1M7m438lsARBexeQmxgd6VA0fl0Q/dTHHwVHWXYicXuoun9m4FbCe/9B4AzK9x3Z6cH/RsdP3996/3BRDQAgkHQueg8cCMDbIsPEerK44GXkVlgmsiZ2AtYML32Bk4tdD1PoKVkSXgZpxB5pqUUkI8i1DOLlAWqcE+dQigTr5Jen+96fWk5rwg67anp/XK0Fq4d7LbHsEOb+z/dYzn3ba0x92rSZjWNL38n8pQOBn7V0Wa1Z7dSn1UZE6g8n5V81WprzeeMivMZ8NP0nF1HLF4Po6MTilisz1G6jNhw/Cjn2ta4b3u+Y1Nig3V3Ol4H+OH81Gc999hngV+k90t0vXdHU1v7fMcawEeBjwEvK2GzxivdV08SG+3rCOdWiXX+tKYPW+cuKNjuJYElC9ipMk/aZhxjC4snCg8Atn8Os5OqJ3a0OcspBE484L+w/aDts8kr2KzW+60IjwS2SwnYPGX7QWCcpHEOWuK6BexOsL2v7Sm2j2peGfa+Q+RI9GJm+l0X2PbM9H57otzHNNtH0lEMZC5f9jpigzWlq4nW+62APya7T5InaPSk00hB5L0cb/tph8Jebj78HgSV52/APcDG5OUrtrGGW7mftv8EvNL2HYXsY/u9DtrRB0rZLIzdiHv118SGdhm65wW36ZGvJ+iZ2P5HTgMZegx7mmGUE+cBtcbc8R7Iv9mZcOqdbPsLhDOtC6ouRNoPAAAgAElEQVQ9u633Jfus1phQez4riVptrdVnteezPQkhtSeA44k803062trCfeiBto8lHEddUXMua/CdZPtBANvX0r3Ntfrsqdb71wKnpy9raqZ2wWhqay+WBmba/j7wgKRVC9mdDUkbJH2dHGxDOIjeQES+38zwQlLziuYa3yvpTUmYa8Vco5IWkvR2wmH0EUmflZSTH11rnhxzwk6/Ag6X9LHmoUyKeN9Pv+uCZyQtT5QQeS1BW2iwSEZbz5V0EkGxeB5BjyF9V4l82IclLU6E8Y9VqIeWyCc8WtIHCapIO3e1a3L2KrbnSPK3fZWkVTraVPrfZxJ99sPW7zqrm/Z8wfOAlWg9Q7avHvoTw+I6Sd8gNoQvJigiSFoys5lPJIGgfxKR+E+2fpel0uzI8XpH+5ykDYmi27m4RdKPiIgsxIbj1uSMemrojw0PSWuT1AGbc/0WXDmQ1Ejw/yBNrJ3gyB3bS9Ik4Jk08XfFwymn5W/A5gT9CEkLkDeGXSTpF8QYtixpMyDp+eSNNbXG3PGSFnDk6r6WwarSXefCWs9urT6rNSbUns9KolZba/VZ1fksrZU+l1650DC/ywmaVJvL2rD9V2nQv/D0UH87F9Tqs79K2pNwHK9HqgigUJdecLgPjpG2zoaiDN0GhBDglGTzGOLZK4k9gbUl3Wp75y4GbP9F0jrAf6dTFyUnSS7+N60RPkEwKCaSJyLX4BTgcYIF1fUZaKPWPDnmNrFfIBY8d0v6Szq3MkGX+UJHmwcQ3PDxwG+bCJGixlpOZGgfYnG+POG9bBbny1FmMnkrcRN+nKDyTAK6ynm38STwdaKNjXfFdC8pMtwg2XXi/w5Rb/ZR4CbbVwEkL1W2+rGkgwk1v9sZfA26JtN/kKDjrkLkfDZe0TUJultX7E04b5YhCtrfCSDpjQSVNhuS1iQ2s5OJvMUNCph9HyHHvg+xKJpKLFqeIhYwXdr5M2BtgpbWeP5MRDqLwfbLkoBF5/qVMNsh8DOSEqekR4Dd3E0QY3ci73M5IhepiQy9Fvh9RjP3BN5NjGGv8oAgzkrEuNkVtcbc44ELFErP/wYuSnZfTNy7XVDr2a3VZ7XGhNrzWUnUamutPqs9nzXiim08QjyDh9t+fATm7pO0kXtqdRZwcFafy4hN12aAFSq3exG5hl1Qq8/eT6zjXgfsbPvhdH4TujPBRlNb23gbURHhagDbf1dUSSgK2+8FyLEtaW9irmjWG8dIOsIdBQYlHWL7U8AiDoG6R+i4NhoCL3RZIapa8+TYUidukDw9DT3sz04FezPsLQAs4ZayXorwyhn1G0cjJN1OSJs/UMje8cC5nrOEwvuJm72T5yvRP5YlxHCeSeeWJ3LtshSaJd1CJOnPbxGGZwUKdc/J6TWLKEmwgQsUh68FSX+yvWZF+xMZHOHNk40PpcyP2m42WlsQ+VlF6zzPr6g15ipKQy1PqFg+ls69BFg8g0nxH1RG6rfDiBzIhQgHx2O2u6YJjSpUns++S2wOj0+ndgb+QTiRJ3oEdVIlbQScRNSJbhxuGxAiau+wXaIEWxVIWhr4LrHpEhEt2tuRltXFXrU+K43R1NYGkq6wvZGkqx3qxIsBl5aYI0uzttJ8vmlrzslqq6Triej25S5QEaKP/SOBbzlSueZrjLVILABp09q1nlM/e7MIalv73GOl7NeApO2BQ4iBSenlApP+jQTtpBT2AU6R9C4GT3oLEZ62TrD9N4K60D5XogYthPDMkoQy4v8pKEq/TCKovjvavk3SnSU3sIpSLV8kNsftSaRrtB/gUklrlh6UFfUqDyIieyWYCQ1mNBtYANtTJeVQikcVao25ti/rc+7WXLv/QXV8n2B8/JKBTVHXPOZRh8rz2Sttt3M/T5N0oe1XSRpRXWrbV6SN7EcJRg3EmmFjFygzVBPJMf+ugvZq9llRjKa2tnCSpMOBJVOK227AT+bymbmiEmtLDKblPs3w1Pu54Q+Eqvpikh7t+Z4S6/yNgemS/kykDTZ2i2+YczEmI7H/AaSbb1uH8EFJu6cQIhDnMTgndq9Mu1sSNfUgVO3OzbFXE5I2IEpz3EChmrajBZJ+Q1B4fgscZ/sSSXdkbjB7v+NmggY/KB+jq0c82XwVIe//DwYPylleW0m3ER7WIsyElt1vE7lexxMT6M7Epu5kyMq//g/+g1EHSVfZ3kCt+qWSLnFG3eT/IKDI49+6ibhJWhn4g+01JU13xbqb8wMkDZf6YNsHP2uN+Q9GBEmvJ8SSRCgp/7GAzeKsLUn7EpU8TkmntgN+bruTeKmkhW0/Iek3tt9aqp0t+y/qd9727aW/KxdjMhI7mpCEIE53eRXHf5bewCacml5F4VBPPq+03Uo4iohyX085lb1RAdtvTUICOwBfSvmES/bLg8rAI7bPKGSrwc+I/M3SfXY7ZZkJDRol8QN7zm9GXv51cUj6sO0fze3cWIaknWz/cm7n5hc0i6C5nZuPMDPlKV4j6VAiVy9HqRpJe9v+7tzO/R/AJ4CpKVVIwKqEIulixFw31tGP4bEYkdP5X0RJkPkKkja3ffHczo1VSBpPbFpfR1K6LYjirC3b35J0PrAF8Yztajsnl/tSgk786Nz+sAuazaqkpSgkhtoPkiYQwbbO8+SYjcQmjn8vHfHCDHsiqCar2T4oeSuXy124SzqGqE92MjAld+OZaMQQhY+XIzac7WhhUSGb+R1psHs+g++D3ByiC2y/Ordtfey+BNiPOe/b7A2LQtlxTVoDku1fFLC7LBElnAysZHulAja/RuS8/ZrB927n6KOkc0tcxz52X0mIVFxOQWZCDShUg78CrGB7G4Uo16a2f5pp9+pemlG/cx1tFx3Ha6HWNUiCIFOIMmRHEiyIT9s+K9Nurfa+mVj0N31WhN6W8vDvIxRIP06kNPzQ9p8zbPa7Bp0jj5J2sX1MirrMAdvf6mK3z/csy+BxvPN8JmkcIbQzjai5KeBmj0zMqTpa65q+KLWuUYj37E1sYE8CvplDg1Yosk5xq2RcCZR8ftNGZUg4X99hCn1KrtneLdPub4F3O4SNiqEia6tYRQtJNxACqwcQa8ZByH0eJL0J+DZRrudBYAXgNttr5NhNtscT0fPJREmri2zv2NXemIzESjqEWFj/iQE6oolyM13xQyKCsxWRAzeD2HhumGET27soRGEmA1MkmVi0HO9uZTXatadmEjfL7K8jU41V0p30H5CK0UlLQSHvfiAhy9/ObchN/J8m6asEpbbIJivhl8CPibyOErLmACik6F9DbGJPJ2qWTQWyN7Fpgj8MOCwtNEtg4/SzrXScG328WdJxxORU0qlzOFGio0iEV9K2ROHyv6TjA4io918IkZE7M8z/nBhbGgXWW4ETCfX2Lm3dCXg7sIqiZEmDJehf+3mk9muM40X1AiRtA7wReIGk77V+NZEyJc12s/1dSVszUCt4CqlEwUghaTngBcAiyQHT5GVNpEypku8QtSavd0EPefM8ELnnX8qxJWky8E5g1bQQbrAEqUZoRzSR4X4qptnXQtJbgG8SC8r7CEfBTUR6TyfYfkbSN21vCpQo+VELzbpmWYKN0qQbbQmcT/66ZilgXyJQcRSwnluichm4GThCIVTXrOs6b7okbUr8/8v0OEsmEo7fLphG3J/9cjRL6Dv8rvV+AqF18vdMmxDVN66X9Eda0fQCDuTirC2Vr2ixB3GvLsmc9WZLVF34MlGq6Czbr0y07R1yDCbnwDuBNwFXJPurekCpuBPG5CaW4Ju/tDA1amOHAtp0iDqOid6UDduPSjqZUAPch3jI95P0PY9Qgtv2riXaNAzaG4sJwE7AsJ685xB7E/dBzsKkHxpPfbuMSgmK56xKFMwdgXWA6bZ3TRG5I0t/SWuhmWunpFR8g0WIzWtRpw7RZ30jLx3xZdJ9laJauxAOrlcSDo6tM2wvbfskSZ+BEE+SlOMsuZrYrL6YwRvhGUR5jlzUGMcBDqWcXsDfif/1LQwI00FcgxL1+pqF5RuJiM61iRXUFVsTi6kVgXZk8FEgp5h9g78CN5TawCpUOIe01TE6cglBR16a2BQ2mAHMUbN8XmH78PT27H5Uz652WziYGBvOTgvLLYmxIRdnSdoB+HXBftuAcJb1RuQ7OZCbdY2k3wFrOokOKRR0f5DZ1q8TjpcjiKoDxSpO2D4SOFLSSwkH1HWSLgZ+klKoRoqFgMWJa9p2ljxKzPNd2rhql8+NwP7J7WNFRYqzC5j+PXmlq4bC3bZ/O/c/GxHeDrzIhSpa2J5KpABclcukGgKzbN8vaZwk2f6jpC/P/WP9Ieke4G7gR8B+tmcoBEGzU7HG6ib2DoJ2VHLx81QKgxtA0jKUi7zsBrwIOBrYyPZ9khYlvKwj2sQqVNrOd6jGilhcNpGc92by8PuJ63xH0lTy6kLOAUlnE3VBf2D7d3P7+yHwV7rXfxwSlTZZEKqQHyGS/9vRwiw6D/Dv5HGflaL+95HvXS2OmnS8is6d8yR9iDkjvF37zK2BfXvgp47asNPSvZGDxxQ1bJsxbBMyng9H3sztwB/SYvLFti9Kzr2uUYE2aozjUFAvwFGw/toU5V8AWNn2LSVsJ0yTdBaRp/iZRHfsPO/YPgo4StIOvYvLQtgfOF3SBQx+Hro+u28u0qoWkrPtL8CmiT2yuu2zFaX5FiGfRXAYka82t3MjxVO2H0wLy3G2z0tshVzsS0SRn5b0b8pQwI8laI6lNQhW8WDV3H8CL8m0+QniXv088LmWj6gUFX48QdVeg1CUvRbYV9Lutt8xElu2LyBqXf/c9l8kLeaClTIS5XV1BtPVS6dvrA6snGskjWU1UIO1VauixVOS3tN70vmpYo8ocuKnAr+QdB95z/HJhFN6Z2Kc+Q0F2CkwdjexMwkBiHMol6f2PWJzsWzySOxIDHq52Iko3j1ooLA9U1KXnIG9CdoghJd2HWLD8krif/jv7k0FSe2JeBwRmS1eYJooobA8g6Od84TWJugO4HxJv6fMgqqx33fDbvugHLuEeh0MznEoQee5StKSBE15GvAvgs4xv6EaHa9WXg5BjwH4TNss3ftMkhYnxrDXEmkMDXIFFvYlKPAvStGAZejovW9D0nuJqOMShDNuNWLR/vpM0zXGcYjn4UTK6gX8D1G0fSGCproucJAzFMuTE/IAop/uSHPCfxERnVxcLOmnFM6PJpgE/yLu1WymUotWf4jtT7V/lzZwn+r7wXlAcvh+iGASvYiITv+YeO662KtB9Wzj4TQ2XAgcmxaW2ZR12zXm7/srRLMg5vMzGVBtfweZgpC2x5VoWD9I+hbB0jgH+IoHNFQOUdSb74oVJJ1BRGVXlrQOsLvtzo5OSR8g1o8rAtcQa69LyWSYKUrDtefef5D33L4VWNH2D9Lx5cQYCbC/7V91tZ1Qg7X1VaJkTemKFu10xgnE2HU1+ali2xF07X2Itfgk5qQtzzNs7y1pH4L+P5nI550o6e2EsG332u8F01bmG6RF1RzI9dxIWoO4SQScU8qbn2xPZHDCd6dIjqRrbK+b3h9HFEP+bjouIdzRnjBmAXcB3ygcfciCIgd0SNjOzan6ROtwAhEtuKnAhqg40kJ4Rdt/TcerEAXsO9Pmkp1Dgf8lctT+QDhL9rF9TIbNFW3fM8TvtrV9Wobtdj7H7LycAhuiokiOq88S9LD7bP9POv9K4jnrtMBu2V8AeCkxht1i+6nMJqNIsdgEuMxJFEfS9bZfkWm31jg+pb/Z7s+vpGnEYu/81jWYXQ4mx67t9XNsDGH3DFJ+tO110n0xvUCfXWV7g7n/5Yjt9hOyybq+kq4BNiLmyOz7VtKrCe2BPYjNcIMZwGm2b+va1mR/MWK8HUfkw00Cju3Djupi+y1AUyv2/Az2U2PvtcRitdcBlS3AJOltDLT1QtunDPf3zyXSeH5CP9qkpEnumB+bNm47Ar9t3bs32F5r+E8Oa/N6YlN0me1103r3S7Z3zrApQvAxS0yzx+bFwDtaa5priHX5YkTKRdYcWQOKesuH08NMSJH1kt8zCTg603k6nthY5qQuze07FiQcv5OBN9heuqutMRmJtX1UogZlU7s0WLntPsIDOPt3uVRPBRXxYGJyaid8d43kPJOofQ8RD3abx75I13Y2qEWlVUFVy9xN6jzYb+dRNQqE2V7n9GB/mNZiAjg8Z6Nh25JOBdZPx3dlNrPBG2zvnxYU9xCMgvOAzptY4BxJW/e2UdKuBOuh8ya2lzqpQnk5/ag86fs6eUJt/0whVLEqQeVp8A/KROA2AlYhnrH1JJWgHj3hqFkHzJ4Es72jaRxfiAG6YJFNdyVq+SzbjygrXbUvLpO0oe0rC9stnR/d4GxJb3CmenIDSR8GPgKsJqnteFsCyC0n8oTtJ1v37QJk3Le9VM/Mtg1CeqZ+4ygp8gwFS98o1OA3JCjAAHtL2sL2pzPM7krQZxdksKhiCRXhq4EZiQK+qKQl3E0EszrSeL52ch63gxS/7rqBbdn4a894k/v8Pm77cUkoym3drMjl7Yy0/jiFtP4ohIWaDWzC1OTIeTA5erKgOqytB2x/b+5/lo2ZBF27M2w/LelJSRNtFynho0i/XMapbFGax09TCMXmjDNjcxOryDMtRe1qK7etTGwORfDb7yYWmznYD3i57Qcy7TQ4gBAaGU946W6E2V7iO7oaVf3yAcVVLSWdxpyD0SPE9Tnc5coILEqZHNMfEZN+QyF9dzr3gUy7NRbCC6afbyQUF/9fgQX8x4E/SnpjE7VIi+x3EiWjSqJIXg4V6DxpcXJqOwLnwXlgnSDpaII2eQ2D1X5zN7GXpnFhYUn/DXyUiM5nQdJriMX6XcSYu5Kk9zozR0vSigTdeXPi/59KKD/3ZQHMI26Q9E5gvKTVgb0IAaFcbAnsIekuQoGzSLkHCudHt/BRYH9JTxCaBrl5hccBZxB0vPZiZ0auA5nYcH6WUGp+PbFZ7uwoa2GmQizo5QzOLexMy0wLy5k50bth8EZgXada9ZKOAqaTt7hcJzeq3w+akwL+AjIo4LUh6WdENYQbKbuZ/6ukzQAnR99ehIZKDu5RpB2dSszDD1FGRbj0+uN57QPbH2sdLkM+aqgpV6lo0bPGHUdUoDhp6E/MM/5FaD2cxWDl564ilocRa9levIBgnr2zz+/mCWNyEwt8kYg4nA9g+xpJnTabTsptkn5MbApPT8fbAK8r0NbbCe9JEdj+nUKsYgkPloi/ikiq7orh8hVLoKiqZcIdxKDWRM93ZkAI4ifEJnHE0GDFzPHpO3LzYQE2tL1O6/hcSSXKHmwJ7C7pL5RbCJ8m6WaCQfCR5GnLcgrYPj0tfs+QtB2xed8QeJUzyx1oIC9H6WdWXk4D23v2fM8kQqAtFzUcDxsQyp6lc0j2IxgEtxPX9EwyFUMTvklE/G8BUNRRPp58r/4UYnO0UzreJZ3LyeHdk1BjfSLZPotgluRimwI2+qFKfrQL51emDdsjwGRF3l+j6XARkLuJ/TRRD/R6YHeCQveTTJsQEc0TiTSTPQitg/sL2K1VUgTCKd9cz0kF7F0mac0m8lIQHyVRwAEcApbLFv6OktjE9poV7O4BfJfYBNxDjDcfzTFo+23p7RcVaWOTKOCMpPz643JJH+x9ViXtTgGtj0qsrVoVLb7Rej8L+EumM7bB2ZRRkG7win7UadtnSvpmvw/MK8ZqTuzltjdWq3C58vNn5shNUoH8H0Wu2xRiUC4pXjKqIGlDYtFXStUSSRfaflW/c5JutN2pvp4G10OdRaidZgtsSLoa2Mmh+oqk1YBfOT+PuW/91hzKm6SFiQj0oylKsBiwuO1/drXZsr0F4Q2+BHh7wYh5dSRK+HW2X5Zp509E7updFHI8SPolsFeJqG6P3bXdk2NdglLab8zOHceTjdm6AcOdG6HNVfrQ4Is4IdLzsLrtKclZtLjz6gU3dgflRwPjXKCckSoonErai4jANRGstwFHeIQl6Hpsru9Q/m6fy8q9Tzam2V6/fa9KusB2FptE9XLEJwNfI9JBRKSzfMb2CRk2byIipXcS83kRBkHv2i7dw1cXYCZUgUI87ZulN/Pqk8omadXccUFBW38+g6nPWfmspdcfyWnRiPI1kcz1gYWB7UqsQXq+76XA722/uKTd0pC0NPBgBSd1NiTdaruvirikW2x3pq2P1UhsDWrXA5I+T+T8mfDel6g/ejhRuLu0FH1RSBqWz19g011U1TJhGUkrN4OwpJWJ2oAAI67XpYEcgd78m4mK3MLcyMB+RMmWO4hJ/4UUyIP0gMLnsuQr3Da4tL25tv2YpIvIKCPREy1dmKCI3afgKXeiJEpaw5Hb07dd8zGdp0YEbmngT5KuoKxC4lGSJtu+GUDS9kRUMjcv8qq0CGwi2+9icC3WrnhA0i4MMDQmkz+W/zptgP4GoCjs/gMgVyjpQCKC/lLC2bkgMQdl1R2V9DNHjleTbrIYEZnNFQ6ronBKsDI2dionolAmvpQRlqDrwU8U9PTrk83JhBpnLqW4ydu+V9KbCCriipk2q5UUsX28pPMJ1ouAT9n+R6bZ/8luWH9coDoU8Fo4iki3+AcFN/MEE2qbtB5B0suAXwI5wk57AgcSbLU29TmrrY5SQHNsjjPs3QdsJmkrgrIPsck8N9c21GNtpbGgN8WgE4NPkf7xNYI9cTAxRy4NjJP0HttZEXRJt9E/L7hrOavbFGlip/d8zzZkpDnC2N3EtqldxxP0tlxq12TiAT+F6NwLKVNofFYGz/zZRLN43JxYqJ+YjneizMJyKdtvmPufjQifIApC304MSKsS1NfF6CaMcRxBEWvnSTfILoVj+5zkdGmiIzcXioy8haBmrkCIk72QyJ8ZcSRa0nIEhWmRxCJorsFEIjLbGaWpiAmfAD5I/P9zfCXzKZ0nTfxzROAyzX4xt11D4B3A8Qq5/M0IanFueR2SnY8STkgRY+4Ph/3EvGE34PvAt4l74JJ0Lge7A6cq9BjWA75C5Brm4m0EFe1qANt/V9SKzcXfJP3I9odT5PT3RIpFLvZmQOF0SyWF0wJ2xWDhmqcZPP52wY7AryS9C9iCKCVRYg76X0VawSeITfZEIt+/EzQ4fWUOFIhuinBerGb7IEkrS9rIA+VguqBWNGgOCjhwZKXvKoGfEWlLpYMUXyE2sm8i1gu/IJx8OdgbeKkLqF23UXFzfC4RACqKGusQRTriogS1+khi7Ml5vr5P5JJOIq7BNrYvS+Pt8eTTwLdovZ9ArPNz0gw+DvwurRGa/cIGwKZk1gIfk3TimpC0uDNqGvWx92Wi8HpvYeVc1eNzCBrL6a1zR9j+UKbd84g8tafS8YLAWc5ULVYoJJ6bS0HsY3dhQiWx2RTOd9RUSVvZPjdFsOaAM8sSKPJqtwLOThSsLYHJXe6FRGl7HzEAXcnAQvJR4Kjctv4HgXYEzvZLJK0A/NJ2pwhc8oSf6VA3LQ5FndFfEV7rt5QcI0cLFHVCDydyF99kOzsPUtIVtjdSKjGTHHCXlqBPpmjmJIKK9zX35IJ1tHml7Q0VZS82dqhWZ1G1k919idzSppzKdsR48+1Muy8hqIl/JaiI/86xVwMtOmaT89hmJszsGs1p2f8RsbnYyvbLklPjLNsbzuWjw9lsNt4iFsGrEurinVJ4WnbfTOQuz7estTYknesMQa+52N4O2J/QKdne+SWczgNe7wKpUT12/0yMBUU3x6VRk7WllFrQ+rk48OuugRsNLqV5k1vpS2qlUZaEpKm2t5j7Xw75+YUJAaeGLXAjcFzumnxMRWLVX412NnJocwoluCMpWFw6oVHl+kzrXHZUj5g0PqXIy2o84SXq961ADJrNJnvxdC4XpVUtG6zPQEmRtZVRUmSowa1BxiD3asKb1q+YdAklw6dsPyhpnKRxts9LC9gRw1H25GhiE3zsXD/wHGMox0CDrpvuZkDXnIXcS923RSNwrqBuKulKBv/vk4gI2bnpOduoo92TbL99qAhU1w2cpP1tHyrpsCHsjjglos+csyghRPTTdA1yqdonSTocWFKhzLobGRHTnufhCuAL6aclbV/ACVVF4dT2txLldQviGduVRIUeKfrcV0sRAn2Xpz7LjWyuRojubEpsDi8FPm67E22ulQ6yeY8T69MKUa5cUcGNk4Nkevq+hxSKt53hHmXiNHfunmMz4R3AdyWdTNQEzVXkrY2bJR3HnEGKrvNO79g1kaBj7pnu3Zy0rjuA8yX9nkK6JAl/pYzyeW3UZG01zrGZySH9IGRVNmk7cXodb9mRSUntMXAcsXfIEnxLDs1TgBvSqVtLBJXG1CaWAWrf9sByDNSsnEwIpOTg28DWpHqgtq9V5D1lwUn9uAIeJihC30sLrV0K2f0aMD157SA2YF/MNVqJwlG6pEgzuE0gHupriQXV2oQwVycvle0D09uD3CPMoI6q2j14OHn+LgSOlXQfQX3tBNvPKJQA5/tNLAOOgWUJqmtDP9qSUC/vtJhoPJKVKNAAT9q2pKYESnb9O8qrm5YaU3qxd/qZRTPqg2bBe1VBm9+Y+590h+1vKHL/HiVogwfY/mOGyV5H2XQiz3ZbCjjMXFjhVNIXbB+cbF9Ncuokuu5ZwGs6mC19X/XiOCIfurkW7yAofhtn2l1MUb91Ksx2rJcYF55Sq7azInWhaKTT9tUK8cZcO7tImkis6aak8XEKUeZtfqwVuwixIWxH3HKes96xq0QqV4O702shCuiSaKAcY63NcVHY/mD6mcUqHAK/S869rxNjmMmjwa8j6VFi/blIeg8DzIdctKsLzCL2T52rmySn2BHAWwmxt3HAC9Omdg/bI9aomW17LNKJNYwqbYbNforH13pwSZSuttci8kzbCd9ZtRt72vk+wsv0PNvZAhOKvMhmQr7c+SIQjRDKHHCGqqVCIbF4SRFJJwBf9oAgyFrAJ22/L9Pu1e5RIlYfVewOdj9AZYYAACAASURBVBcjNjAiKGiTgGNz6D2SvkB4AE9k8IYoV9yqCiT9DvigkzKvpOWBH9geNlI7jL2lhvt97nWQ9ElC4fX1RI3M3QjqTY4aay110/UIr+q/0vESwIttT8+0e4jtT83tXAe7O9n+5dzOjdDmqsC9jWdZ0iLA892jWDxCm1Up4KUhaRyhzN1ZXKaPzbOAK21/rnVuOULn4tctllEX25sANzabn3Tfrmn78sw2X257455zl9neZKjPzKPd9YkcyyYi8jCwWw7NMdl9F7FAXY/QitgR+Hzm89DW+RiXbP+X7a1z2tqyvzThRNuHcE69GPhezvg4mtDM6bafTsfjgYVtZ5drTM+BnZkSokiJGRI5z24N1GJt9fmehYEJpRhRowGSDiICSnv0jLc/IHREvtDZ9hjdxN5E5CTdkY5XJfIoOpe9kPQr4FtEQvUmhNjIBrbfkdnWAwlv8pqESME2wFTbWTX7JO1u+/DW8frARx2KlFlQnRIKbYXBCUQtuGnOyCdRvZIiRUt0KJLxXw4cSigUN5gI7OfMPKIakNRPyt+2c2nwVSDphvbiOnfBnf7/JudrZeCh9H5J4O4SDIsUgWs8+GdlRuCqQVEaagOnPLW0oLqigPOln1OnRImdfnbnODdCm1cBmzUe5eR5vtgZeYXJzm+Bd5de8Eg6Ctjb9sPp+HmEhkLW/CDpWKI8S1ZZjpa9CUSu9a2291UI350BfL09v3W0PR1Yr3FypjHhqq73QcuxtT+xwTyBGCN2JjYYJeoGkyKRKnlPpDnotcQYdk4uTbdnA9NEck7OpQ8qhNN2IxbERxN50fdJWhS4yXbfci7PFdL9+37mVKXNfc4uA17XchwuTswRm2XYXIu4ps19/ADwHtudaPt97C/mpC4+P0LSlPS2L2uri8NblfVOakHSx4Bf2H5UIUq1HjGun9PR3g3ARr1OlnTfXpbj+BxrdOIGHyfoC00Oyirk52MULy6dsCOwDjDd9q6Snk8BtT3bh6unOHyhDWyVEgq2B9HcJK1EbOpyUKukyE2SjmRwuaWcSf+lBMVtSQbT/WYQORpZSAPoIcTgrPSyM/I2S2zSnmWcL+lMgtZnguJ33vAfGRrN/58G+N86CagpJONLRc6uJ+hoTu+z0Np4D0IBx8M4t4RWHPm3C3Y1JunDROmM1SS1688uAVycYXcbQjH4BRpcMmwiGfT6hAXalCjbTyozrzChNAW8wdrNBjbZe0ihNp6L5YEb05jbbm+nMdf245LeBpyQGDCbAvvYPmUuH50XqM3ScaRJ5KyJelXr22sOk1khIUVwdiBpPEjxNc4Udko2blbk949P37WAMwR+KkbZdgK+3es0tz1TUvb6pgKOBm4mUtEOIphQJfJ4J7Qjpbb/lTbyOTgC2Nf2eQCSXkPk33feGCc7mwI/pbyeTFHY3hVms7bW7GVtdTRbW++kFj5k+/uS3kCs9T9M3B9dHdPP9GMJpPs2K5I6Jjextv+QPLZrpFPZpUpsP0C+hHk//DtNnrOSh/U+8kWd0JzF4Y9RqBPn0m1qlVDoxT1k1DxL+GKBdvTDrsRD3eTuXQj8qKsx278BfiNpU9uXFmhfLw4Fts31rreRNikfBhoa+PnA4U6q1fMbbH8sbeYbp84RhRbCG9reo/U9Z0jKjrgkZ9EBxAQo4DBJB9n+WYbZtrBbI5s/LC16HnG3QnSocb59iMit6orjiGjbV4mSGg1mOI+m/Xcip+wtDM4lm0FGCZSE/8/eucfbNpbt/3vtHRG2JIUcE0pCDjmmiIr0FiU2IpX0y1lRKqF6U0gkJSUkJYfKoZxP2/l8LiLVGyklSpHj9fvjfsZeY80919p7jecZ1px7zevzWZ+155hr3nPsMecY47nv+7qv62+S/sf2WQCS3kN0MnLxy/RTGpMkLWD7UZjeRSyxHih6L6jRUq8nOpxXAEtX2503V3d/uk9W1+5PkOFZ+AIU9s4kBHJuolaUbQpJ+wFz1JLgq1P8OQla8cENYh5he0+NILKZW0C2vf0ozzXqErWM19jeUtJ7HKKIPyao8Ln4j6RVnajkiWmXq6w9T5XAAti+TGW0GI6gBT2ZFrFUB3vvr0Ajf1QnvZMqQe4jVOfuJoSA2k2JqdI4XmL7dLNFy5q/n63oxErqk+nfw2acJH3F9mczYi9EdMWWonazL0AL+Tbh97Q1Mbf6b+DW3C996mCs7SFz+CLWDGrPQqGuujcJWAX4g+22xGN6Di1Sj65yQ2uWUWJ+nxCEqeYpPwg8Z/ujJd+n15G6u1cwvCu/vjNnvyTdQ9BTH0mPFwSutr185i53vk+WbH6KsQiRCLwZpnto7+JCNH5Jr2D4+ZBFVZU0R+lii6RlCKGzRYkb9Z8IKt59BWLPDSxh+57cWLWY2xOK+KenTVsSc/4njfyqFx5qca4ufa++SbCIDFxMdHkfbhqzFrsNnYthIxG5UIwBvLm2RrjFYcE2Gbi8yXVB0mppwfuWbs/bvjxzn9civHdfRyTbk4H/5LCK2oSGLLKmEUWSvxCjFlmNCoVI1ikMKX8vAmxlu7HQk0Jk52aGLJy2I8ZE3pu5r63pybQBSd8ixuXqrK37bO/WMN7yRGG3aqr9hiii/7bA7rYCST8kmIzLEcKlk4Bpbj5q8QciWe2WxDrnfJjdOrFbM0RB3Q+oCxO8k0gWm+JMYrF6EcNN17NQo1QcI+k8YIrt20d7zSyiDXN4aMlCgeGqe88SaoONqINq2f4kdfkPZsZFSm4HvSj1SENzGDdK+inxmWXL/Ces0XETukThR9uTaHHxM5Uwcq+6utPStlw8QHQIKzxOJEaNoeEWUZVsfra6ckpWsxY63aCYfzucSAwfBpYkzofcGfGlJBU9f23/DlhLMeMjF1JKTcfgMOI7u7SkVQgV89yO1g8Vc7wbEtfFLWz/usD+Fj3PWqSlkpLVLE2LbtAIOhc0V8WvcLWkNzgJCpaAh88oHpm2PZcKJ03i3ZR+ZyWro+BbxGd2GnH92p4QdepVHJs6UPsTnch507+zYPuGxIJbnjh/7y5QmPswwaT4WYo5jWCd5eJPCiVtpxGL3SlDqW4FJVlbiUr9M4KKeyxxXN9IjDdtYfvaEvtcCpKWSEXiHQnq8H2Jqv9yosHSCLaXKrSLM2B268TWKz3DDH87HzeInd1tHCHuD4nk+ArbdxeM280c/gTbRxR8j7cQSonn9iqNtA1IupJIXL5BzDrsSJxLo3YNZiFuVQmvDLHnINRJG80ba0iooBuc0+FNVfwt0+IdhTfi6U0rdW0jLdhnWPy4pnraS0jXhTcQxTMT0vTXA7+FZjRKDdliwZDYymFNO3yS9rB9pKRD6U4d3LdJ3Fr824gk66J0XmxA+BN/LDNusfNX0na2f6ThaqzTkUl3RdJNxDG4rHZvu8MdPpxjiDfFIdbRlUbufFXtbufZsjksqNJQC37BHfHvYEjnYmUlnQt36D40iPtrImH7PVGMrIqyTX2Tfwu8vvPerZi9vdP2sg1idvV2rlCACXaj7dVVE3iTdLUzBI36CepDoaCUAB1JaEWI0JPZwxnuCP0CSecCX7N9Wcf2twCfsb3JuOzYCFCmwOEocX9NsNVOcUO/7JEwu3ViPcK/uz0eK86RtKmTgEtBnED4ix6VEoFbibb9kTlB3cUc3pmWFwCSTrL9wfQel1fbCDppk3in2v5Al5tf1g06xV4GeMBBeX4rQYv4oWuCJg0xt+2LJclhRH+gpCuIhXEOqsXEY4mO9heCvt4IbncOYx/gUoV4moguWU/Pfdi+T9Jkhy3B8ZKuzo2pGDPYlxkp4FlCZ8Dv0k+FM9Pvxp1Tl/e/eyD9zqbMjoBnbD8iaZKkSbYvlfS1AnFLnr/VzFhbfsHP2v6nNIxEk3Mv+zEhIleJEFVQepytx9DGeVYYbfgF19GKzgXR0S2J04HvStrVSXRFMXb0LYZo5mNF5cFbiV5W1NRtgWz7F+CJ1M27VdIhwEOU8cptBZJ+B1xLNCqmFWA7FBcKUvtzzG3pybSCwmySZToTWIi1s6Rjs3a0HZRga3bDVKK4eaGkvxNU7VNtZ7M4Z7cktrgBcI2SKuCzkp4iko0i1NRUVbucEEvagFBBfj2J2tNwn+v2IVkecl0wjMqnmJ/JsdKoxJHaMKA/A1hd0msIdbyziEXcpplx/5uO8b0KKfIHCeXfXHSjHn0hN6hamLVNScCyDKczZYuNtIi2Fj8nE165mxHn7g7A33KDtkGjlDQ/kahVohqXE9TURlYdts9I5/8SLXW0H0v03GnAyZIeJl9FGAqevw4V+MnAv2x/o8C+deJOSdsAk9P5tjshwNMItjdTZMRvcSEbnA70fJJh++z0ma1oe5+ZvmDsuFExcvM9oljwb4JF0QhV95zh4wUlsD/wv4Qw2x/TtiWIe2UjymsqCiFpXQ/XYfiMpKuIMZkcfJAYhdiVEGNbnFBs7lWsAKxJUFMPSxTg22xv3iSY7QPStetc26cW2seq0HBYoXgASBpt7WIXspxqASUp66Ods71oN9Sp3D8MTVkqtm8DbgP2S0WCrYBrJd1HjA5+r9HeMpvRiV9opEp+1gGUdDFxk7+GqNZd6TLCEqX9+vYjZornZqiiKuBpYmZgvxLvUxIVNULSPoQx+FHKpJWnuGsQ1fyXErYJU4BDbF+Xv9flofDLvRvYhtqsre09Rn3h6DHnIoQq1iOKPFcAxzjTB7AtSFqSUBmck1j8zA8cXdGhM+LeZHu1Dnrb5ba7CpuMIe6ldK+K5/gmnwHcyXAxrpXdwP+uI+6lLXR5q67Qf4nrzLbEZ3ZyLg2ty/k7P3H+Np5PavEYvAT4HOEXLELZ9Eu551n1vS2wi51xu51n33amwJU67GWq7c6wl5F0SQHGRGdMAYvZ/lN6vBSZOheSzknFh7o3dQU7XyRoboYW6ffZzlW5RSH8uKvtK9PjdYjvQVMv9eU9wthDSpgbW2+1CYVl0xpEB3U9YEGiwZBl+Shpmu2iCr9K4yEz2zaGeJ/ssnkeoqC+oO15m8RtGypIWU+F11O6PQV8wPYrM3e3KFIxa8Tig+0TR3quwXu9lRjpWcH2ixvHGSSxswaFvcUXao8nASfZzqJJSPoG0cl8ivBAnEaoCGfdSCRdQlw8i/j11eIeXDJh1YziS8OQ0+mWdB0h7/45wmLm9yqg8KgO5euRto0hXttzdUVnbVPMU4kq44/SpqnAAra3zNnXtlD6Bl2Lca3ttRQqxd8kRM5Ot71MZtx6gjEXsYB/1hlzpuoy199tW4O4hxLJxakMv9aUHr3oWUj6XyJh+ynDj0FpJkwRSDqa0Ei4oYXYcxJKnAbucc0/NyPmeQzZy0wXLLT99YyYXydUSE9j+GeWNVfYVoGgn5CuXz8gzgmAx4APNz0fJD1PdAx3cc0fNT3XyhxfCUh6gvD4PpyY7S8yByppf8JSp/N603imvdtxLFH0T3HmI1h3HyHuE18v0axpAwol6Y0Iy7i/EGySD7mBmrKkHUZ7vmRSWAJtn0upgDyVWM/8gUjwT0uU80aY3ejEbWIJSfvZPjhVhU+jAFXX9l4AiTa3I3A8sDDQuDKRUNqvb0ngsSqBVYisvJf4Ih7ddKFie74U74vEBeMkhjovuXNmOxIUz/9NCezSDCVdOehUvh5p26yi7bm6orO2Cct3XNQvVQ+rExM0386E9UNdto0VX0403U8SczRTgD0zY05X+azhKsXYQQ6elLRerTuyLvneghBm6M8C9Y6uCWXWMUMtqYur3dmvqkpf7wyaEGUaM0bax+mBM4uRxOjKzqny/h8KaBAASHoXcAwxzy1gaUk72z43c38Xs/3OzBideBnwCMM/o0ZzhR24VtIaJQsEqaP3nG1LWpygqN5n+9ZS71ES6fq1smImWG44slDDXcQM/s2Stu9gTbQ1x1cCU4kO7CeAjyrmw6c539O2GgXapbat0Uy7pKkES2tpSWfVnpqPOD8aQyEgtzexnjsRWNXJm7qH0Y2y3oit1GtJ6iwgu+DYDZK+QlCIHyUS13VtPzD6q2Yx9qATO2tINKGTiaraBsRMQvYMlGIm681EN/aPRCf2CtuXFIi9JKEMeVGipU12Q+uH1NXc3PafFTYPFxE2MysRAixZ/qBKXmIz2zaekLQJMU/7AaICWmEKQYl407js2Ewg6aPEfPBKRJFkXmB/29/NiHkCQR++Nj1eE9jBQ5ZRPYHaDXo9gvJcYQrR2dwoM/4MVLYS9DYNV4+dRFwfvukMn9h03p5IdEcE/IP4zEpYevU81LKHZUnU9nELoqhZZzz8wZlqv+neMAOcZhoz4t4NbFbRhxXier+0/drRXznTuMcCR7mgvUxbUChxLkfcz7MLBJJ2Ar5GzNZ+iRDVu5mw6viB7RJiZ8WRChqdOgyN6N8aGg1an+jw/hD4skNAq2c7sRUUs7CbEAXOV9huZGHUBtK1YGliPfeZ2lOPE9TnRjoEiaGzBWEtc3RnB71X0RZrqx+QGBSdArl/dxqPyIh7ADH7Wtwbd5DEzgQa7q04B/BdgvZ7HOTTxRTzmtOAm5peLEaIuxNhsPwy28soREGOsf22hvHq8wGHAc/b3jfRqm8tUMG/GjiaqNKYWKzt0mQOoRZzXeBAQjn3RQwtJhrNEElaGViF6LbU5wYeBy7NrTBKOpGQnn8sPV6AoN00FmBqC5J+Q4g6VTPXSxBzhs9ToKNTCm3doGvxu1GwshdVGj7/9ixhq/HFqouaGXsKgEMsJifOl4E/dRZDJO1JXHeyRMkUAhB3VYW3xFZ5vTNnzxWztk/afj49ngy82EmhdYyx9gb+afu4ju27EUXDLEszdZl967ZtDPHWAF7e2RlV+NH+uQsDYKzxh+1bKv5enrG/lWr9iwjq7/1k2ssoBKfut31Mx/a9gIVtf7rJvtbiFC0QSLqLKMLNR1xjl7T991SYvsF2lm+ypIs71wXdto0x5jHAS4iC//eB9wPX227kNVm/pipEs75N3Ne3BX7Wq0msQodgFULB/UpirXedG860S9qOWLef1LF9J0JB98eZu1wMCgr4U8T9qxijpm20SavudWi4DV+FlxEaB1NLMj8kHetMuzwYJLEzxQgfagU7UxhCLdnAKIQV3kRcMEv4C05/rcIjdD/b56fHtzdZTHTEX4qgdq5LXPCuAva0/YeMmHcTdJDOOapciswctp9RzJauCDzoMmJcM1wocy6eaWF6u4cUI79AzCL8kUiWf5+xr10XahVyOzptQdKChDrv/+Us2BUm5usQlfU6I2MKwVgY8/xM20j/9wMYEuO6kkiMG50PaXG9ksNKpb59MqHAmTt7fgtBP3N6PAm4sUCB4Fpgo6ozkJLjC5oUzCTdmfbx6Y7tLyYSjNzr4m+Adzl56ylGIn5l+3UN411GzHf9oWP7awiBvqb054putzGRXJxKfMe2JOZiu4m8zErc4teZ1CldsSpi1LbXVf2zIekVDO9CNhJZrN8DJN1Wv7Zk3h/mIhLNS4G3wnRa7hSCadboO5ZiV/oL1e95iWTz7Q3jdbs37gB8GXiJ7QWb7mubSEWjmzuvkRnxbgHWdwejLhUmL3XGLLbKWsv0HdpmbfUzJK0OHN60GDlCzCIMisFM7EzgFhQnO9CWDcxTtp9W8hdUzNTkVCwuUYj5PAQsQHiVIWkRCvDo06LqPblxOvDPzo5DDlJ1+SjbdynmIK8hkuOXSfqU7Z9kvsUkSQtUHV0FpTTnHP1fYK0UazNgO6LD/UZibu0dYw2Yqv/P1BLj5Ynv6h/dm0br5xCm4nem7+rNhD/kMqkS2LRTNidBy34Rw2eZ/0V0HZru7xpEd/Mv6fH2DBUeDnSGcAfBcpjGkCXFtgQtvunN2d0WZ7af03Bf06YYpv7uoA6WuGfN5Rq1zfa/0/e6CdyZwKaNT6nMQdgLuEzhxwwxy56jbLpgt8Kgw9s1JxGo+1b+lVBjhbCbWqBp0M4ktTMxbB52eAKbNj5f4jOT9D/A14FFCY/YJYkOatOO6dyS3kiMFcyZ/q30k3MsdiaKcIsShd7q//4vghWVg2rW/glJixKzlUtnxJvBgsP2iQp/58Zidy8AliKcAR6X9HlgVYIG3ZTB13UkzPa/UlE9ByWtZfoRVxPr25cT52+Fx4GskZvE/vgycV6cB6xMNGlKaLS0Dts3pkJUSRQR9hoksbMItSDxn/C87WclbQ4c4WQDkxkT4HJJnyVugBsTwgJnZ8TbkxjMXgRYz3YlFrQwof6bBbXgZUqIDR1KCHVM9zDNuIG82fbH0793BH5r+72SFgbOJQycc/B14GpJldH8lkQi2hT2EEVyC+C41H28SVLT2dXziM/p3lR4uYaYFd9MIWbSa1ZLS9u+M/17R+BC29sr1BKvItSrxwzH/OTlkk6oJfQLEOJnOcWi75KSSsX811eB3QhK2rFkJMgExbfuzfdlSe/NiPeUpKU7O/qpW1hCIOJ+SbsD30mPP0HQSXPxH0mrVtcBxRxQY4ErSa+0/dfObZn7CIDt8xSjINVMaa4f82izeI39XG3v2PS1s4LCieETkpa1fW/HeyxLGaGzLxHFw4scqvAbEMXDpniIULeFEOWrq9X/pWlQx4zfkZJ2s31Uxv51wzkK2u+hROHQBK24EWx/e4Tt9xPijb2K/W2fJmk9omh8GHE9a6r1MYekeWwP8xhN97M583Z1ejFrcipOHq8Y85oQSPfxPwJrwzDW1r+dP+r3dsf43eaEQNmWBAOiL5LYdD8rStu1/c607n+3Gzp7wCCJHQvOZEjiP2cR0YlnEo1hB4aq2bkVNYj5v48QQlQ7E0qhOTcR08XvynaJhBtClfhu4kI/3cs0M2Z1o1i9tq2xYijDF+Ybk9SIbf+lRNPF9g8l3cjQ/m1h+9cZIZWqZ08AbyPmiCo0reAvUFv87UAM6++msNW4iVBp7iU8U/v320gVfduPK2Z2GiFRs0+1fXcqcJ1LJJrPStrG9kUNQ0+udVu3IiieZwBnKEYEcnCppK0JqidEQvzLjHgHAedKOpD47CHOtS8wfP64KT5O2BZ9Pj2+iJjzz8WewGmS/pweL0Ic6yY4FPilwhOxKo6tBhxCLFhLYDWGiqcrS8L2DxvGukhhB/T5erFF0kEkdk0OUgFjN2Ys9uaqKZdMDL9AfG+/zPDv7X4UUBYnmCqPSJokaZLtSyU1Fl9qiw1WY30clR6XZH0ckootZyQ2zFyE5/NEQ8VUeRfwHdtnputlUxwHnC7p/1WMCsUo1tHpuRw8ke7jt6bO4UNkFLb6DS2ytmBoTb8psWb6RyG2UlFIOooZk9WXEaNTexR6j8mE7/lUYr1/Bc2dPQZJ7BjQhsQ/FLaBkbSE7f9LdKnv0YWG06N4je0tJb0n0YR+DJyfE7CFm/9jiZb7IDG7+xGYTtUupTY4B0n4gPxixhHArQQ97De2bwRIdLSHGsasX+A2JBbxJOp646SwRfxJIbLzAEHlOg9A0tzkHd+tiIU1RDI/CViIUCU9kUi4mmCypBelyu/bGJ60Nbpea8iqRoTdQXV9mUQonh7QJK7tsyQ9AHwa2D9tvhPYzpkCQSn+wwS9rShs36BQC12eOCZ315glY431Q0l/IwpvKxLH+S7ggBKjDJJOApYhzuNqQWxCnbUJPkkUM++rFUVWJhZrWQrzCb8gFtNnEyJvpVAsMbR9bmIg7EMk3BDf2/e5jPrxY6l4OA04WdLDhLhNr6FN1sc1xPWWlMw+pdDS6EkBphbxoKTqOH8tFTwnNQ1m+zBJ/yZYQPMS14L/AF+1/Z3RXz1TdLOWed+or5i90AprK+EshUbLk8AnJC1EbxZ1bux4bGIUYG9n6r6ka8w2REHnemINvbQbCCoOi5vHfJs4UJ9I/Gu4it8ZtvviIiTpettvUhhNf4KgSV3vhkrCtbglZf6XIzpDCxPU7xPS9ncQdJFG4iW1+HsAOxFz0gI2Jzpxjalekl4FvIIQ2qnUWBcB5nADoRFJPyI+mweJbtvStp9I1LHL3WOCRooZui8S3bajbV+Qtm8ArGa7UbdMw8VWziCEgb6bHjcWLJD0OaJa+3dC8XlV207U7RNtr9skbj9C0mKE0Egl9nYlIUiW5S+nmB37fwRVDOAy4LtNE9k2oRB2WiGTot4t7qsZouLelWiZJeK2Yosm6SLCl/xgYmbtYWANZ6jXtwUl9WsiIdiWsLQ62ZmCgqWhmkiUpKOBv9k+MD2+1fYqDWIuDLyKKJRtw3CxqGOcabXUb1DM2r8TuMP2vene+4bqPpQZe15iDd/INnGA4ah/5yVdDHzP9imdzzWIO4lgkfwG+JdDM2IeYD4n7YteQ6L5voa47/7ODdW0a/EeIJwsvgP8IjHhfm87Z04+Yg+S2FmDQtHwNYTVRZbEf0fcZYkb8woMT7Sa2sDUF9d9IwuuIS/TNwAnUMbLtKjMf9uQdDuwdjXvki501+R+x0oidTD3IJLCH9i+LW1fB1jGHdL/sysUCrcfJURs7iES4t+n5+7OWawpVCIXIRLj6ruwHDCv8y29FiCsSurXmmk5MduCpAsJkbvqO7UdsK3tjTPjfp/owldG9B8EnnOm13UbkHQasLvtpsyJFxSStiG+XxdQRoegijsP0bkQPZwYdkLSy4FHShchSkChrL2KQ5PjbuBj1bVA0p1uoNKsUAz+EEHPvoGhJPZx4ARniv+psG3eCwUVUqpuE12OLdB8LdpvkHQ2cd16gPAiXtr2Y2nNc6MzrKwkXWN77UK72hoSq/ArwIeJsYJJwGLA8cDnmhZ6JR1JFCHvIO7pZxKFnezv1iCJnUWoPXP4Kwk63zeImdgdic+lEcWvoxNbzARcQ359MzxFmWS+q0BM57Yxxiwq89820jFeo6p6pWrYDW5oizRAe5C0JpEELUR05b+Utm8KfNB2jpBLK0iFoj2Im9KtRHX4GmfahLWFbtXvnIp4LcYwm5KRtvUCFBZv4+y71wAAIABJREFUqxD0q3pSmDtj2gokHUwUBX7HEJ3YvfodK4lUfPoq8A9i1OAkoms8Cdje9nkN4y5JCMb9Mz3egFgQ/hH4lruoY89i3NZYH5Le55jlLwq1ZJvXFjSjINkSxPhClrdvG+i3Y1sabbG2UoyDCIXjn/ViQauCpG8Qbgt7eciffQqh7/Ck7cZzsZJENJSmEtedKcRI3q9ccwsYc9wePp49idIVNUk32V5Nw31Yr7D95obxniNmJETMaVZ88yyD6ZGS+AoFkvluBtM3Oc/37Drba6au2RYEt/9O28vm7GtbkLQ3MV/5c+Lzeg9Rvc6ZxRhgAGCoSAJca3sVxVzoQbabihqN9l5vcOboRaKQnsCQ6vdUYEfbb8uMezOwpe3fpcevBk7PoIBPAt5v+9SZ/vHYY7+l23aHOnbPIS2EV2qaWI0Sdwvga8RoRGUv0/h+1gYUonyfJbrExwKb2L42nWc/acqKknQd4T39Z0mrEPP2BxOe8s/kMAhKsz7Uojd5itcKXb0tSLqN0I4YJkhmu4RAXVH027HtJyh0KeYhigNP0oPXLwBJ9wLLdSbaCjGmu0utndNIzzuJe/rbbb+8aayBsNMsoktFLdf7rcJ/0yLoXkm7ErOGr2gazPbkzP0ZKW5WkjoS0g3+9cD8aaFSYQr5foBFZf7bhu3DJV1GmG2LWLBnqz9LOgw43vZdubEG6Gv81/Z/JSHpxQ5l5eVLBU/J4NbELByE0FEOPkx4F34jPb4qbcvFPoRS8/3EebYkwYBpBIe/6K4MqT4XQ68mq6PgNuClFPIArOEQwoohV7EedVfgnA7buzcM/aJa9+aLtq9N8e5WnhLp3LYrJe3tiDGOr6d1Q5ZiebWPHdt+mxGyuDd5ilUVmErb5rWNokrVdaQRnqUYTv1tKvgG/Xds+wa255v5X/UE3K1T7JjjbdzxVAhZLeTktpFoyWdL+j2ZTgaDJHbWUdr7rcKexNzm7uk9NiS6cT2JVLk9Cngd4Us2GfhPRkVpeWAzYuHz7tr2xwmRo8bwkCfmdJn/ipKVA7XkGSxpGUJk5WZJbwXerBh+fywnLmFddGyadzie6ApkH4d+gtrxIe43PJCKOr8ALpT0KPDnmbxmVCiESrYiroXzEF2ddW3fnbuzieVSnDZr+2KFFkFdnTjXNu1CSZ8CfkowYar3yrEpaeN6O9p7nWN7s8wwrwTulnQDZenPfy2RwCZ0KnCWQl2NudNvNofyVs+ANyTZmKXiSUbYVmCX9yaHaCDUUco2r220olSt8qrlUN6ScICERKXdlpiz/ZKkxYFFbF8/zrvWiV9L2r6zGCJpO2Id2RRHMeT3XserCPbKNl2emyUM6MSzCEk32l490UPemG4g19t+03jv2wuJRJnamvB1Wh3YnrDH+Vxm3LVtX1NgFyvq2YhwvrjEeQx5BtdnRzpvtGONeytxTJcirGDOBpa3vWlO3Fr85YmO01Siq/U925eOMcbZjN7F6NVZvdOIi/A21HyIc2Y8+hmJpjo/cF7GTN0lxHztacAptu9QIcXBFP8Q4MtEQnAeYQWzp+0sg3hJWxL/78clfZ6w/fhyTschVZQ7Yeerq3e73i5r+7M5cUd4r0WcKSBVmv5cu5a/hVCF/wXDk+Osa3lJzGSUZy7bjSy9FKIoixC2aP9D0P2eSQWks22vPmqAFxAKccJ1iP/77wnbosra7de2V8iMP5c7lFIlLegendtUS4JkKqxa3uZIxAAg6TtEkWtD269TCCxeYHuNcd61YVC4WfyMuOfeRKz11iCuZ5vbfrBh3Ls8why4GorIVRh0YmcdbVXUliPobZ2KcD1b/bJ9n6TJtp8Djpd0ddNYknYCLrN9TapWHcfQDM2HGi4sTycqlBXVql6uNnGS5qAtz+DnHUqRWwBH2j5KUjadGKbPNLw2/fydoP3tLWln22Px4mwsbjDOKOpD3HahpDTSIuX26mZRiKb6DKHy+2KGPHdLVkXfbntfSZsTipFbApeS4aOdsL/t0yStR9AbDyOqxI3nwUol7iPELna9ncn7ZCsgt0B/rrNzngDqonxZ1/JEcfs0MzoDNLr3tjXKQ7C1tiIS2fU8pBC6MJBVPG4BbXiT13FGuoY/m+IuDPwSaKyd0Sac5owTThzxD8eOO4nPv4hqeZsjEf2GdF3YiRmZdjmsrTVtr1qt52w/KmnOrB1tASlJXVPShgRrTcC5ti/ODD1aAa9Rca/CIImddbyHqKjtxVBFLYs+mnAaMSvyPWpdvR7GE+nkuzV1Sh4iaIRNsQch3gLRIVwZeDUxQ3Mk0ETg6n3ETX8lQsr7J7bvy9jHTlytAsI1XfCMpKlEt6VavGWd4ACSDicq+BcDX6lRWL4m6Z6xxOrDGb0K1cLvMUkrEl63S2XEe/coz5UolBRFWqTcJmkJF7J3sP0OhYXIB4AjJb0SeKmklWzfXuAtqu/+psQ5/I9C9MnqOvsu4Du2z5R0YG7Q9L3qTIhy6H1Q+Hqr9lXmi9KfbTeeVZ4FnEzQv98FfJwY4/lbi+/XCKnbdkqX7UUKnCVh+weSzid5k9ee+gsZc+c1/AI4XdL7gMWBs4BPFYjbClRYkKzGhJqPoH6WVC1vZSSiD3EmcAUhoFZqTf5MaiQYpifKz4/+kvGD7UuASwqGvFfSprZ/Vd8oaRMgy6N8QCeeCSTtSVAvb6mqf4XjZynwvtBQqBT/lVig7EUk899umiRquMH0j4HrbB+ZHmdZBCUqz3uIhHZBwucqOwlTe57BKxCLqWts/0TS0sBWtr+aGffDBN3ziS7Pze8xzMdKOtX2B0ZaDOceg7agFnyI+w2J/rsGYddSX6QUoYCnOZ+tiWLUSwtQab9KWIk8CbyJmJs/x5kKmor5+AeBjYgOzpOEf3Rjix1JBwBvJZLYXwGbAFfafn/mvpa+3ratMl+U/lxj6tzbhamzQ04ipyFngNur65aky213pUSPFxTKpqMVHnpK4bRtSNqFUDZdCtjZdivMhBKQdB+FBMlSvFG/mznrm7ZGIvoNKmDj1iXmtsQ6dDVi/fF+4PO2Tyv5Pr2KxDg9B7iaoClD3B/WBjZzhpjcIImdCRTKrusQNMzbiQ/hKiLRyK5QpQ7Aw4StSr2iNiGqXwq7i3cBjxILkw2dVHQl/cb26zJiTyZudlsTSqmfsd2YQlqL24pncMd7LAAsXqijValrr58eXm777IZxFrH90AtxDEqhzXmf1H38CrCo7U1SIWJt28eVfq9ctDCvOCfwEncIj6Xv7kI5N6aOWP9yqCO+BJhi+y+ZMV9CXBfuSMnRIsAbnJRlG8a8g2CR3GJ75fS9+L7t0Tr2o8V7L3C17dIqv1X8r9n+9My2NYhbaUfUE8Orba/TMN6dhAbFM5K2AT5JUIrfCBzghlZ0Kfa1ttdKncNvEiJnp9tepmnMAdqBwn5u+kPCi/gOoKJnHj4e+zUzSLrKGX67A7zwkPRl4tr7q5n+8djivhZ4G/H9vbhUYaNfoBBE3YYh14K7gB+7Y8Z9zHEHSeysIS3YVicS2rXTz2POFyroq+qXpHWBA5lxhrfR/ipk+L9L0M7Otr1T2v4WYF/b72oQs1KOfhNBCTmlms0pBUkrM0R1vsL2baP9/SzGvIyg/b6ImCv6G5Fw7j3a62Yh7sHEsTg5bZoK3Gh7v4yYrSyC24KkabbXn/lfjjnuuYTi8+dS8vIiIpF5Q+n3KolEA37EGTcASd8mvp8/7di+I2EOv2vmPhYXYEpxlwEesP2UQgV8JeCHncn4GGNeb/tNkm4iDN0fJzypG1mwSTqduMc8QRRNrwauciGbrG4sl3rimRF3GtHh/j5BIX2I0DZo1OVumamzGUEbXJygQE8hfJPPahpzgHaQmA4jwvZBL9S+jAUKUa7igmSlafsp5kuAvYElbH9MScHd9jk5+9pv0JCn69MMjSFlMx4UGgzL2j4+0YnndaZvcr8j5RPb2N6lcYxBEjtrkDQ/sahYN/1+KVHJb3Nmp+egMLPfixmVeRur7aWF/3y2H61tm4f4fv67Qbznia75lQQNa9iX3M19AKv4exCD/9WNaHPgWNtHZca9xWHf9FGiC3tAoYXl7cAqtp9PjycTiVbjuG0tgtuCpP0J2mhpC5QbbK9RfXZpW3E6Ug7SguerwD8IG6+TgJcDk4DtbZ/XMG5XtdFE+2ycwNXi3G57pXTzP5gQYPpsATpxXQX8fGKuLksFPCX0nyVYH58E/g3cmnt/kLQUUTitiqdLADc03VdJ/w/4BKE78LvaU/MRSfJ2mfu7JMEsmoMy9OfWmDoDlIekl432/ERhmFWQdHyXzXamtVtp2n6K+VNiXbe97RUlzU0wDnvmXtavSEWY1Yn7zHKSFgVOm4hdekmrEI2UrYiRvJ/lrJ0Hwk4zgaRjCZWux4HriIr44fWEq8B7lDatbhP/tH1uyYCOWeNHO7b9Z4Q/nxW0XVj4CKE29x+IDiRwDVEZzcGLErXxA5RXnXwpkcRALCwbob4ITslxhfmIjlGvolo01Ct+JhbzOfiPpAUZEmxYi7Bf6iV8i0iw5ifEGjaxfW2iN/2EsK8pBttOFO5ctCLAxHAV8CNcQAXcduWBeYzCgmtKiVEA239QeBzPnX6qfzfFj4FziaJA3WT+8RIJRm2c4EmgRHfsC4Sv62TgrFoC+xZyBUHaUSGd6KhsObopsDW+3ko6wvaeGsHizT1q7dZmk8PlVcuXsb2VQlwS20+mguSEg4aPX11WoBu9OTECcTOA7T9Lmi8zZt9AMRNb6WU8QjQTZHuD3NiDJHbmWIKwkLiXEAN5AGhMO+uE2jGtbhOXSjqU6ELW6TFZFL+SsF1Syr4bxHDVuufoftMeK75IdIautH2DpFcT37tcHAzcIulSYj/XJ5KaJmh1EdwW3J4Fyt5EJ28ZSVcBCxFWML2EFznNe0r6ou1rAWzfnblG+YekN7pDXCdVWkt8Fx6U9F2Cnvq1NFNTIjkurgKejusXYHriOVnSyba3bRjvs0TndSHgHuBaohjxsbRobQSHiNs/gamJkfFKYh0wr6R53VC5WiOrHlfv24ihYfuc1N0dxtQhEtutmsSsoQ0V0gmNFq+zJ6XffWXx1mKhpLRLBMDTqftaFWSXobbGmyhQCAquwdD41R6S1rP9mVFeNjM8nYq71bHN/az6DXcT19p3V6wcSXuVCDygE88CUjXq9QzRulYkFmnX2B51VmMWYhc1rW4bKRHqhN3DvraloRCZ2IEQ44JQUD3B9hEtvNcatm8oEGcR4sIsYq4sSxynFvcVDLcUKWLfUgpq2c81JVbPAcsTx/YeYJLtnrn516nfnTTwnLnCNM9yMmERVlcc/Biwne2szry6CzCtaPvCzLjFVcAlnQDcY/vg9J04DbjZ9oEN491NUJIrRcfrPAYV8VmIvyuhbfBXhqwe3DTZ1JDQW8V0qJKObYEnbJewoyuKXqP9z25QiLIty/D7w7QCcecm5jbHZA83Hkjd0SuYcfzqjMy4RWn7KebbCQbYCsAFxOjcjra7rflmW7Q0fvUp4lzYmGgAfJgQNcpl7/UFFF7vWxP503mEZdj3SxS9BknsGCBpMeLEXgfYDFjQ9kszY54G7O4CRvP9DkkrMWPFsqf8NitIWhVYj0hcpnV2ozJjr8AQ9eKftlcvFTvFXx74lJOIVsMY7wYOBxYlbqZLEub2WXOQpVGbSXoFcd5W3mcbEDShUZPcWYjfbTY4S3CmNCQ9R8wBi6CiVlZLAuay3bgLKelVwO4MVxw80mGaXgypcr05MNUNxN5mEntxYGvbh2bEEJHQ30F8t861/Y3M/XoZQ4XTtQhbqNsI5cxus3ZjiX0fMRLRWMtghLgzqLF229YLUEsqpAOAQtdhD2AxgmW2FlE0yip2p/vOYcCctpdOrI8v9iqduN8KJWk0Zi3i3nCt7b+P8y694EhJ7FsrZlm6Dl+Wk8SmOBsTyuoCzs8txvYj0n38vcTadkPgRODnznEGGCSxo0PS7sQiYl1CqewqYv7xKqJDkGVYnDqbqxDejaVMq1uDQuDqAGp2LcRNJKtLIOkHhEroXQzvDPTMfJKkKbb/pRHEK3LotKmyOjX9PEskhavb/kNGzJWIG/6ihDriUcC3gTWBr+cssiXdRlyELnKIUW1AJBgfaxqzTSi8QXeqikWpq3d00yRW0sLAq4AfEbLxFS93CnCM7dfm73X/oJrvsf14wZhzApsSx/edhM/vz9zQHqoj9ssJ2vdU4nP8ue1PNYhTL1bMQSitX0X4mRYZs1AI361GXHN3Bpa2PTkz5qXAxi7sfa4QzdrV9pXp8TpEh6jnFvJqSYV0gOn08jWIRGgVxfz9QbazKOAK9e8NiaSiEtLrZUHBooUSje7TboIheITtMxvEvtj222a2bXZHGjX5KlAfv9rP9imF4mc7A8wOSOvoLQkWVOPi1iCJnQkkHc6QvUHxbqkKeze2DUlnAHcSFRQIv7aVC3S0uiqdNoy1r+1DJB1FdxGIRurEks6xvZnCFqketzKdbypacTVBBzqFsAO6V9Lvc6kWkq4DvkMUXd4J7EvMtO7vXG+uIT/I2wgfx+eVbEZy4rYFSXfaXrH2eBJwe33bGOPtAHyIoM/W7ZseJ6jlPckgKA1JnwF2I76/IgTavmn7kIyYGxPJ5TuIhcRPgaNsL5W5r/MR3dxtgOWIcYCtbC+WEXM0ql3jMQuFsEhVPH09Udy7uvqx/bcmcWvxjyMo8L9kePE0y29T0mrADxgSj3sM+HBuMi/pYqLw9qvatmN7tWg20aEh1fZbiY7/UyW6kpKus72mhqvB93ISWxVKniIKJdVaoVGhRDPxaSdU508eSxFVIRz3EuJa+1aGF2TP9QRUAC81fqWWnAEGGMJA2GkmcKZH5yzE78lkdRQsY/t9tccHpRtVLq6RtILtXxeIVZlIF/WGtb1Z+l1avOJvBO3qlYSQy72MIpIyBrzY9gnp3/ekuYzPOEMYpobHJM0LTANOlvQw0UHuVVwm6XxCjdcEXbvxrI9DPOxESe/LnW/qV0jajSiObORk3J6o8N+S9ITtbzUMfT4xR7aek4+ewm8xFw8TjJfPE+JpTrM6jeEC6ooj4ENEwrovcJPtpwvH/7/0M2f6KQLbNwErS5pCFMlLzfEuDXxaoRFQqR5nj1movArpAIEHJL2UYABdKOlR4M8F4t4paRtgssLHdHfiPOlJ2C6qQJsS2MnAcbY36vInf5Q0VjG5nYE9CcbWTQwlsf8Cjm68s30GSa91iB1W7JoH0u9FJS3asBD3gjoDTEQMOrHjBElX2l4vVeq6dfV6ktIk6RpgnxpdbF3gMNtrZ8ZdHzgb+AtRtayOQ89VWNug3SSa9vuIDtRrCEucd9i+PiPm3SledVM6mRr1Nac7kmYbniQqitsSF+mTXXjGriQUIk9vTg+n2f75aH8/hrjvIrpldQGTnhOyKY1E7dvU9l87tr+SqOA3FYx6I1FkeD9ho3IK8AXbI3UfZjXuXinuPAQj4afAhU0ZFCnmqEXO3M5mv0DSdrZ/NNLxKNDhvRl4E/BNYHFgO+DSpt+xFLNThXQqUTDIUSEdoAOJbTY/cF5uMUYh9vY5arOFwJdymUWl0SUhGoYCzISzgA8WLBIhaTdPEKGhbqiYHSOwaxqxaursA3X4WtfZBAM0xyCJHWBMUAgpnMgQffAfwIds35YZ9z7CruQOhmZi676DTWIuBHyaUNurJxhNKX4vCO1Gofi7FbGoWtz24g3jtEV1fC+RaN9h+/wmMWYXSDqG+E5sAHyfSLyut/2Rcd2xFwCS7vIIQl6jPTfG91iXOA/eRwjE/Nz2sZkxX51ibk0oRh6Q4v62QaxR1elrXcOegEbw2azghloMkna2/d2Rjkfuceigj34I+CSwQCYVvLgK6QBD0HALJ6D31OvbQhsJUUf8UwkBpgsJ0b4qcKNRqVrcdZhRXLNX7R5bgaS5Oosi3bbNYqxWnAEGGMIgie0B9OPFPtHFsP2vQvEuyb2wd4l5AdFt+RRhqbED8Dfbn24Ybw+GaDcPMpx2870M+uRo77lkTiJfGpK+TXQdrwbeBpxt+0vju1czR+rCfo1QKRaFGA/VPFbt97yE+NDb8/e6tzHaTbj0DTrNMG9MqAjvWDDuG4iEdivby5SK26vQCBoMFXp1vKVKkmuPVwN2cYbwn1pSIR1g+qjBARSycKrFXY64ny/F8PXShLH4g+maDDMgjbk0jXkSsAxRLKxGjpybGPcbut27mt7P1KIzwACBQRI7zmjrYl8aLwBd7NsEhfZshguNNBbIkXST7dVUE36QdLntURdysxB3wtJuJN1JCHk9l6hdV9hebbz3a2ZInf53O81uFoxbCY1cC2wBPALcaXvZku/Ti5D0LMHEmOEpYH7bxWYtex0K+7WjCCEmA1cCe9h+YNQXzjzuhoTC6xMz/eMeQGK/7MSMSUa2yryklRkaB7iiAPunVRXSiQy1Z+F0G0O+1HXf1ZtGfNE4o1+6m5J+A6zgCZoUaOA40JcYCDuNP/YAlu/lWcKEedLvbkIFJS56cxPJa72DZSBH5bWyTXgozS3+mRBQyoLtoyStyIw05Z67MbWAp52EoWw/IUkze0GP4K+lE9iEc5KAyaHAzcR39nstvE8vYp6Z/8mEwfHEnO2W6fF2advGmXE/BBwj6RFC7OoKQpTq0cy4beFMYh8vopZk5EJhdfcxhu4HP0qUzcbFRNs/kXQZQyqkn3ZDFdIBZsCfgGLzmjU8a/s7LcRtBSN1N4GstYJC1OpgZlyDNJ7vJ1wnFgaKu3D0Cd5BXG8XA+pNmccJcaYBehCDTuw4Qy359bUFSevavmpm23oBkjYjFlSLE12SKYRX3VmZcQ8gZmJXAH4FbEIsLN+fGbfnj62kJ4D7qofEDfo+eliIC6jUbRcm1DKLdPq7vMeLCYpQG4u3noOk04nE7Ze2n5rZ38/OUBf7kG7bMuIvSsxbfwpY1HZPFqBL/p874t4OrG37P+nxPMA1Ta43bYvuDAAqbOGkIW/23QmV8Z93xG3s0d4m2upuSrqSYPB9A3g3sCOxnh91Rn8mMS8FViEU3OvHttGcfL9CE9hxoB/RkzfCiYAaLfd+wv6jqF9fizgK6Lz5d9s2JiTRpI8wo8prDg3tojSM/09CeKcU3g+sTIiA7KhQY/1+gbitHFsASQsQIjb1YzutQah+9YybQsyjFOn0p0r4YUQSfwfwKdsPUjuHJwBOI3yivyPpPMIy4EKXsXBC0km2PzizbQ3i7mH7yJltGyP+Lmk74hhAzNlms2tSzDcDbwD+Tlg2XJEbt0WcI2lT1/xcC0EM7+w+xxDdb6zYm+jqfr3LcwYm1HxlSyht4XQT8dlUn/k+tecM5HQg20Rb3c25bV8sSUkz40BJVxCJbVMcWGbX+hu2z9AEdRzoRwyS2PFDRcttxa+vNCStDawDLNQxFzsFmFzgLU4C7iYoHV8kbFty6Z93SvorseibBlxVqEv2pO3nJT2bBK4eJuMm2vaxlfRRgra+GEFrWgu4hgaLtV4SmRoLSooBJfyAoIRNA/6HKDZsUfg9ehq2fwr8NJ0DmxPfsR9I+gXwE9u5ydYwdeMkgFdi/noHoDNh/VCXbWPBh4kE8xvEovrqtC0XRwC/I+YAL7X9h5xgko6wvadGUClu2nXRkFWcgM9KeooY5yhlGXc8cJ2kyhbrvcBxTQLZ/lj65ybuokLafBcHqOCkRi1pvnjof2fGK+3N3ipq59d8wK8lle5u/jeJ3d0raVdCaPIVOQF7VdTthYZGcBwoEHdJYFnbF0maG3iR7cdz4050DOjE44T0JZ7P9sMd218J/LPz5jreSKqWbyVUfo+pPfU4oVB7b2b8W2y/sabyOgdwfq7qoKQliE7GusCmwGO5dLckQvVZwqLjk8C/gVubJkovwLG9g5j7utb2Kgqj7YNsb5UTtx8gaV/bh0g6iu6L9kbKi520yabqhbMb0qz4ScBKthsVYCTtR5xflZpj1X15GjjW9n4N404lBDvWY3g3cz7gOdsbNYnbNiS9nhAdWo9gU9zTtBstaTXbN2kEleJeXsgm+u96xPdhmu1bMuMVUyEdYDhq14GKBvx3YHvbdzWMtwbwp2pmWdL2hO3WH4EDe41OPNL5VSH3PEvH4zeEGOaXCMvDQ2xf2yBWVYCa4SnKFKD6CmrBcUDSTgT742W2l0lMrmNsv63Ufk9UDDqx44cjgfOYkc64EXGj/n8v+B6NgnTRvVzSCS114yoRpsfSDfAvhKJfYyTF0HWJJHZl4C5CNTQLtj+R/nlMolFOsX17Rrxhx1bSPNXsVyH81/Z/JSHpxWkebPmC8XsZVTf/xsJx55L0RoYSrLnrjyfSXF2iqr+fKOosR1zTGl+/bB8MHCzp4KYJ6wi4mqD1vZzhVNLHgUbn70jFkQpNiyS1+FOAJYAlievh/NR8tMcKJxXX0smqpHcQRdnTO7ZvQ9iaXZgRexJwu+0VCfG0LGhIhXTujnN4CtGBGSAfxwJ7274UQNJbCcG7dRrG+y6xNkLS+oSq9G7EDOexxPWnl/Ag8MouGhfrp+eyYPuG9M9/E/OwObG6iXVOZDyZfj+RtAgeAXKZALsAbwKuA7B9r6SszvkAgUESO35Yr0Zrmg7bJ0vqZSW0JyQdyozzArlzRMemxfDngbOAeYH9M2P+H3AD8BXbH8+MNaogiKRVCyQui0o6l/i/L6GwlNi5ljQ3xQMKBd1fABdKepRQah4zJO0LfL3U3GPbsH12+t3YP28EPMRwBcO/1B5PiLk6SR8kZj9XJayxvkJQXhsnWR34XJoJXdr2lyQtDixiuxG1KxXf/gis3YXaNTeRzI4V9eLIQeTNpHXDlbWfbznTsqeCpHWJGbgliXVA1XVpOhZxECEw04lLCBGexklsGt24TdISLuOfPlAhbR/zVAksgO3LFGJcTTG51m3dimA8AS6jAAAgAElEQVRknAGcIenWnB1tCUfQ/bv0RHqu27kyy1D45e7D0PkLTDy/3JbQzXEgV/PkKdtPK5k5SHoRZVw9JjwGdOJxgqTf2O4qkjPac+MNSRcAPyVUMj9OzJf9zfanM2JOAt5v+9Qyezk97spEV3t9optxL3C57UazVApLh48pVPw64QLU5+uIivJZtt+Ytt2ZOhBFkGhO8wPn2n5mZn/f5fVHE93tXTqrzL0ISS8nqqCPEnOshxKd+d8Bn7R93ygvH2AUSPoJIWTU6Ls0C/G/Q3QdN7T9ulTkusD2GplxW6F2VSMROTFGiV2UnSHpbmAvZvTbbCRGpZoX91ieG0P8S4iRiOuB6cchZ7ZQAxXS1pBml28mKMUQllOr235vw3h3AqvYfjZ9dz/mJExY+h5ZAqPtk6Q7bL8hM37f+eX2I1TIcUDSIcBjwPYEg+ATwK9tfy5/Lyc2Bp3Y8cPDkt7U2VVIsw5/G6d9mhUsaPs4hZpnRYPNoqalSvuuQNEk1vZtkn5HJCxvJm6k65MpCGK7pNJx53v8ScOtV7M7nqopulY0QoV/3Zjn6mzvkjrRR6XFRJVoVM/3Go32x0S3bFliAXw8QeV/M1Fdfeu47Vmfw/bUzm2S9kt04BJY0/aqkm5J7/eopBLid21Ru4pXhBWib8dRnp3xT9vnZu/gEOaS9CJ3WMUptA3mLhD/oAIxhsEDFdI28WHiM/sZaYaZPNrrT4i1xt8JuucVAJJeQzt+tLkYTSCsxPnQV365/QBJG9q+RNIMAo2Scu34PkO4b9wB7ExYM5ZwtJjwGCSx44d9gFMlnUBU0wBWJyo1W4/XTs0Cqo7LQ2kB8GeClpWLCyV9iujy1ivtjQUbJN0IvJiYhbsSWL/EPK+kXYCTbT+WHi8ATLX97czQf5K0DuC0WN+dfIVmKKzyavtmSZ8DziAsZqrFey/SaF9p+7OKysAfbR+att+dPscBymJLoFQS+0z6rhpA0kJkzIPW0E/UriMI+utZML0wt37TYLVRiEvTWMjPGK6a2rQI9TPge5J29XAv12/S0MaqDtuXd1DAX0KmcrtaUiEdIApOxP2rVLz/lXQxsAjBxqjO10lEZ6vXcIOknWx/r75R0kcYWu+NGRryyz1b0ifoE7/cPsFbiPGHblTvxnZ8ML1R8wvgF7Z7uUnVdxjQiccRqfq/C1DRTu4i5p4eHvlV4wtJmxFV0MUJW5EphNLtWZlxf99lc86MFpIWauOCoQ5l2rQtm0qYqK9HEgIWAi4A9sig+HWqvJLiNlZ5Td/ZrxOWQp+wfVuTfXuhoJraqDqURzsfN4gtYDHbfyqwq7MFSlJqJW1LzL+tCpxIJBn7544dlKR2abiy50sYfp7Zmcqekq6zvWb9uEq6zfbKDeN1G4Wo0HgkIhUCvgx8lJg7hhjhOI74zLLo5m1QwNWCCulEh1qycOo3KFwmfk7ca+tNijmBzZ1UlhvE/T3D/XLryF0vbQF8jbDqERNUnbgk0hrhAGBXho7pc8BRA8ZHGQyS2AFma7RBF5N0O7ByVQ1O3aLbbb9+9FfONO7LOiupkpa23S3BH0vcYiqvku4nlCG/5z64eEh6jKCyiaAQT6ueIsTVFsiMf5PtEt6lswUkzeVQwn6D7TsKxHst8Dbi87rYdjYzQTGD/xHg7Snu+Z0dk16BpNMJ8aFvEf7OuxOzhT3J1lGIZL0mPbzP9pOj/f0Y4t5KooDXkvms2cJageBawuP5EeBO28uW2OeJCPWxhVMbkLQBtSaF7UvGc39Gg6T7gHeXuMb2MyR9hbArqjPtPmn78w1i7UVYO36sWsdJejUxhnWe7W+U2/OJiUESO8CYkCh9OxF2D3VVvA8XiL0isALDE84fZsTrShez/ZHM/TyU+P8fQ1RFP0542H0yM+5VwCa2/5Uevw44rYRoRboQL8vwYztt5FeMGGd6dzt9F+hlesxIi6kKuYsqhdDVCR6yPJiwSDfnrQkvVnK/t5I+4g4RNklftf2ZzLiruUMARdK7nZSsewktsDP2Hu1524eP9vx4obMjnTq/NztDMErS/gSb6G3A0SQVUtu5qvgTHgrNjCNntm2AsUEt+uVKusr2umX2tH/RjU3UlLWl0HPY2PbfO7YvRNDiWxECnEgYJLEDjAmSriboxJ2qeFkqj5IOIER2ViCG3jcBrrTd2P+tLbpY6uTszFCH6AJi8ZMlwpS6xvsC7wKWB34IbGs7y0JA0keBPYjZ5VuJjs41GdTBAwgapoiZpGeZoPQYSb8mvFH/SMxyVxSsLDXWfoGkRQjK71RgHmJmbV3bdxeIfS7wI9snp8ffBl5coAh1M7BD1SmWNBXY0/aaufvc60jnLsT1ZQ3SrC0xBzbN9kfHZcdmgpIU8BHiF1EhHSDQbdFfctRgoiJduzay/Q/FbPwpDPnlvq7JeklDQkZvARYmrPjqc7bZM+39hMS0W8P2U+nx3MCNTZh2Gl2luudUtfsRA2GnAcaKlzjDTmcUvB9YGbjF9o5ppiRXva0N0+pqSP84QizKwD25CWyK+0uFmucFwHzAe23fmxuXSGDXAK61vUGiaDZS+0z0mPWIi/wweoykvSYgPWaT8d6B8YLC9mQx4DTgo7bvkPT7EglswhbAWZKeJ47zP5yvygtxrTk9zdyuRyRGPTUHKekLozxt219qEtf2QSn+BcCqth9Pjw8kPsdeRSvqngohvaVIayGFCmlj9s9ERyoIbQMsLamukzEfcf8dIA9t+OXWhYyeYPi1MEvQqE/xI+BiSccT//8PE5oMTfB0w+cGmEUMkthxQscFfgb0sADCOZI2tf2rwnGfTMnhs5KmAA8T4kE56GZanT37JumtxEXtD0TnbXFJOzSh56Z4RzFcBGMKcD+wW1pU5ao8/jfNKSLpxbbvlrR8w1jb00GPsX2/pO2I5HtCJbFOatcKwavRbBVmRzwDzEEogM+RtmVTezSkwAkhFPQL4Crgi93mxseK9H3dOsX9E/D2UrObBdHNE3YeIpFbEGiUxNawBMMXUU8TyVxPQdIStv/P9vPEtbvY7LLCZmwZgp1SFSFNMGAGaIargYeAlxMCgBUeB24flz2avTBZQ1ZWbyPEzio0Ws/bzrE+mu1g+5DUjd0obfqS7fMbhltZ0r+6bBcTb73QCgZJ7PhhbWIB9RPCr7Cb2lwvYg/gs5KeIhaxpRTsbkwJ5/cIqvK/ybQ7qHUrzpB0DuXoYl8nFr73AEhajvgcmwr83NjxuLRh+QPp2P6CsDJ6lLBGaoI5Ouc7IOZiUxe555Hmgx9zgVkKSf9DfB8WJQovSxK2SFkiX/0A2+9IM5sfAI5M7ImXSlrJds6C9SaGFDir3+9KP6ZhcUvSHQxPsl9G2LRcl4pFPUMBtz09AZA0H3Hd3ZGgD359pNeNAScB10v6OXFMNqel5C2TRvoLQp0aSWfYfl+5PWN1YIUS14EBAqmo90difTNAF0g61slzvgFa88uV9M0um/9JUGnPzIndh7iFKMw6/bsRbGfZgA0wcwxmYscJCkXbjYlZspWAXwI/sX3XuO7YKJAkYHHb/9fy+ywFTGm6EG5T/CDFu71zwdttW4O48xBd0+fS48nEDOATo79yTO/xFmB+QhlvzHSW0QQOmooftIlEyzw1dZ9fDJxH0NafBbaxfVFm/NsIb9yLkuDMBoRncNNFSt9C0uKEsNNU4KXOsHtoAwqf0RHhAh7SJZE60nsD2xLMjyMd/pul4q9G0Kkh5mEbL9bagoZbCxWdqZR0GrC77YdKxRwgIGktQjTrdYStzGTgPwWK3X0PdRGWG+Pr12LIL7fyZF4OmNfNfZ6RdCzwWobGCt5H2D4uDtxve8+msfsJkj5AsPcug+muBvvYPn0892uA7hgksT2AtLieSpw4X7R91Djv0ohQi5YiSWBgPaL6daXtnzeMU1z8oCP+D9I+npQ2bUfMqmTRchRWDxvZ/nd6PC9xo1onI+Ykwv6niICApOfoTnUU0enuqW6spLuAFW1b0seI82wjQozpRNtvyox/o+3VUzL7xkSJvz43bj9A0pzEjPxjHdsXABay/dvM+LsAJ3u41cFU29/OjLsWYXdRzYPOR3TkrsuJWxIKBfQtgGOBo6trQuH3mAy8kuEq80ULlJLWJYpFuzR8/Yg+zwX27VLinnA9w4VsenWUp28g6UaioHUa0fHeHniNCwlxDVAeSePg7YmqjEIB/AKi2XKH7RXGc/9eKKR7+ca2H06PFyKK1I28uQdoFwM68TgiJa/vIhbWSwHfpPeH6K+VtIYLW4oolEdfQ9BlAHaWtFHDxU8b4gd1/D9gF8KzUYT3aNbCOmGu+mLV9r8lvSQnYEqqbqtmy3J3sA/pMU/X6ILvAE5Jne7fpJt0Lh5LxYZpwMmSHia6vBMBRwCXAz/t2P5eglq/a2b8nWwfXT2w/aikncg/175Doqgm/KfLtvHGJ4nE6vPA54IEAxQa35C0G3AA8FdiHrSibmdTqiWtQoj7fAD4PXn3tGqmTMDctfmyEsfhwIzXDjAT2L5P0uR0vT1e4WwwIdCnmievIubuK1ryPMCitp9L42MTBZOqBDbhEcKFYYAexCCJHSdIOpEwwT4XOMj2neO8S7OKDYCPS/oDZS1F3kLqmMH043NHw1jFxQ/qcEivHw4cnih/i6VtufiPpFUrSlCi+5UQnFkEuEvS9dS6qD16Iy2NpxT+w38lvrufqj2XVSBIeA/wX2AvgvY5PzBRrIbe6u5qwScw/Dg3xSRJql0TJhPUxFyoVtioCj09dS+03faiaQ9geTf0m+1EojNWVPJHiMKGbG+QE7etoln6Lu1ve6OZ/vEATfBEYmrcqrBHeohIiiYK+lHz5BDi87qM2N/1ga+kMaessZs+w3mSzmeoobIVoYY+QA+ip27cEwwfJBKK5YDdS1faW0RbliL3EIqZ1Vza4jRXM2xN/CDFuQz4H+L8uRX4m6TLbe+dGXpP4DRJlehS5cGZi0Z2OrMJ9gROBxYCvuEhW6BNyRBsqFDNJCU0leGfrZCo2yWSsPOBUyUdQ3QJP07MNOfifkm7E91XCM/R+wvE7Sf8iQLXwhruJq6z77Z9H0y34+pJpO7SE5Lm98Abtg18kOhe7UoU+BYnZiwnChZmSPNkG/pA88T2cZJ+BbyJWId+1na1Ftln/PbshYXtfWqjbSKYfI1G2wZoH4OZ2AHGDEnrAcvaPj7NC8xbJQcNYp1NLFDnJ7xMr0+P1wSublopb0v8IMW5JYn4fJQQujqghLBTij0HsDxx8bzb9jO5MVPchYmbk4EbnESvBmgGSY8zip1MDxehikHSlcBunYJAiU56tO11M+NPIjxB30acDxcA33emJ7PCDumbhCCXgYuBPTsoZLM1FD7XyxOL6/o86OEN421OdGLXIQoNpxCfVbYvd1uQdCqwFnAhwxkquZZmAwwwHX2mefIqQmG/PiffyDqwX6GawKbCinB54NxSa7EBymKQxA4wJkg6gBBqWN72cpIWBU5rumBVqOWOCNuXN4nbJhRWHW8nOm+fs31DThIraUPbl6Tq3wywnTUnnZLtLwCXEMnAW4ib6Q8axpsMnN8PVDxJo3bHmy7aa/G/CPyFEPkSQSmez/YhOXH7AUm052TgGIZsoVYn6Pvb2b5qvPZtgNGRruMzwHYWayMtAN9LLNo3JK6RP7d9QU7cNiBph27bbQ8YFZlI14YDmTEh6inF8jbRRfPkLOAHth8cz/0aCZK+RjC/7gKeT5s9QcaOpkPSTYQi8QLAtYQF4hO2tx3XHRugKwZJ7ABjQhJGeiNws4esD4p0IVOsKQy/6WXZ4bQBSVsC+xMKyp+Q9GrgUDf0MJR0UOrmHt/ladv+cOb+3gOsU82/SVqQ6HIvnxHzLOCDvU7FG2mxXqHAov0622vObNvsilTE2oOY74dYAB2Zs1CTdKrtD2hGX1eAxn6ukvZ1GNkfNULcQQeuIJJewJbAVrY3HO/96QZJcwNLOHl+D1AGku4maMQ3EcJhAJSawe51dGienNIPmidpnbBSIX2PvkWlgp7E7+ZO94yi9l4DlMNgJnaAseLpNPNWia0UEWtQ2J98iZhhfZ4htcyeq9zaPo0hLzVs30/GvE9KYCcRlJVTC+xiJx4AHq89fpyYicvBf4E7JPU0FS83SZ0FPCdpW4I+aaLqnkV37SekmalP17dJeoWk3TJoc3uk35tl7dyM+E36fWPhuH2HNAayL/B6YK5qe8lkMxUgv5t+eg6S3g0cRoiFLZ1o8F+caJ2nlvBP2+eO906MI/pR8+R+YA5q4wUTFJK0NsGq+kjaNsiVehSDD2aAseJUSd8FXqqwu/gw8P0CcfcBXm/77wVitYqktvhlIuE+D1iZmKn7UdOYSSF1V6BYEluj0j4IXCfpTCLReg8xe5yDX6afiY5tgCPTj4Gr0rYJBUnzE76mU4HXAWc2jWX7oURZP64kZd322SnuirYnjFDJCDiZUBDejBDM2gH427ju0QuPAwmdgMsAbN8qqWdnePsMlyq8jn/G8JnrLD2KfsELoC7eBp4g1IkvZvhn1lOF6RcAewL7EWMQdyWm3aXjvE8DjIBBEjvAmGD7MEkbA/8iBt6/YPvCAqF/R1xE+wFvt71vEjN5gKDMXQo0TmITLpT0KWJxWe9uNqVUz5d+/y79VGicYFSwfWLq5mB7oi1+gemzwZvbfs9478t4IFEx300k7asCZxOdvcWcOafSlnpsirtaqXh9jAWTGukeSXfgckk9pz/QMp61/c9alwxGEWsbYEyoxilWr20zMSc9QG/irPQzoVFdD2G6uODfJ2Ai3zcYJLEDjAmSvmb704SiY+e2HOwHXC3pOnq/CjhH+r0pIZv/j46FUFNUs6+71LY1plS3QaVV/EcPIKwTRHh5PgscZXui+KMC0xOi9wDfGO99GSc8DNxMsBIuScdj09wEtoa2KOu3pJnu0zriZgmo9Rkqpc2HJL0L+DOw2Djuz3jgTknbEL7iywK7A1eP8z71PdLC/zstjcYM0BIGgmYBST8m2CnPETPd80s63Pah47tnA3TDQNhpgDGhGnrv2JYt7CTpeuBK4A6GlPF68sIq6auEAueTBB3tpcA5vSrmI+lSugvZjLkqrvB+3BT4mIc8V19NeG6eZ7snE7qkFPk+QiWyLhyWlXhL+l/CHqqzez7b0+Yk7UfYqjxNeDOfCkwrpUDalnpsWwJq/QRJmxG+rosDRwFT/n979x5v61juf/zzXch52ZQilXaSQs5CpBSVSNnO6lepNrU7oF3t2ip02Cmd7SKSbXcgkkQRlShny2k5VbZCJ3KWY4vv74/7HtZY05xzzTnGmPMZc4zv+/WarzmeZ8x5z2sdxpjP/dzXfV3AwbZ7vhIj6Ujbe/d63G5JWgo4gFJpHkpf4k8Me2GbXpB0ru0tm44jFm6qCunNVJKusL1erXWxIaXmw5xh+3uYKTKJjQmR9E7g3yirgu2pqcsC59l+Y5fjn2/7xd2MMZ0kLQ/cW1eflqKk53VULEnSGymvxW+NOP+vwP22v9tlrO3pk0tQJnPzbH+wg7EuB7YZuXe5phaf2a8V/CSdAdzDE6tlfr7LcUfbK+N+rcY6FSS9gJJSvBtlUvQhyn6imzscb0VgRdvXjji/NnDrsKavz1SSNrQ9Z+FfOb0k7VKL9I17LiZP0kcpN3l7tTUmpoiklWsdglVHe972TdMdU5MkXQOsB3wX+G/b5/SyA0f0ViaxMSG1cMvywKcpF6kt9/XiF1Nd0bqJsq+uPZ24b3/p1dTarSgX8K+1/bQOx7kc2NL2fSPOzwbOtt3zPXySzrE9bo/eMb7vattrT/a5pvVzbINE0saU4k672H5mh2McT0lHPGfE+VcBb7bdUeGsWpDtRttHjDi/P7BSD7ZE9D1JawGrtVZcJX2RkkkA5YJt4DMIWsbIKnrCuZg8Sb8f5bR7laURU0elBd+WwM39ePNpqkl6L2X19UpKn99nAd+2/ZJGA4tRZRIbk1YL2jyNBdMyO1p1aRtzxvzSk7QJZeK6I7ACZQ/rj2zf1eF4Y97l61Gq9gpth7MoKTJfcQd9Yse7yOvnC0BJR1L27c6dgrG344mtSoZqf/BIktTp3lhJ19hea4znOr4ZIelaSmXix0acnwVcNQw3OSSdCnza9vn1+FpKz+ulgJ1sv77DccdNQ3Yfta2RtC1lS8SulJXCltnAmrZf1EhgEQ2QdBrwIdtXS1qZUufgUmA14EjbX2o0wD4gaRV30fs8pk4KO8WkqLSBOQi4lfl7Vw10NdGy3fetDepq8a7AzZQ9gB8HLu3Bvt3FJC1t+/72k5KWpfQw7NYcyr+RgHnA75nf/2yy1pV07yjnRdskrg9tAbyl3ix5mPn9+rq9QXAEZQKwFaXV1M50375oxuuyuNNiHT63MB45ga0nH1OPKrPNACu3JrDVvbZPApC0TxfjbkbpPX0ccBHl9dWv/ky5SN+B8t7Ych+wfyMRDZi6xeZ9wLNs710LZ61h+7SGQ4sn+mfbV9fHewFn2X5Tvf44DxjKSWzNPtyJsmDxAmCVZiOK0WQSG5O1H+WX0R29HrjueVuTBVe0/rfXP6cLewO/oRQxOs32Q5J6kcpwNPB9Se+0/QcASc8Gvlqf60ovbxDYXqRXY02zbado3BfbXqeumB8s6fOU3ojRud/VKsc/aT9ZV9Bu7GLcByStbvt3I8ZdnbJ/bxgs235ge9O2w6d2Me5KwDaUVPI9KT2kj7N9TRdjTgnbVwJXSvqu7X8s9BuiE8dQbhC06lz8kVINPJPY/tP+GngFcBSA7fskPeGm3yBTaRu3A/Pbxi1LKeJ5bpNxxdgyiY3JuoVSIKenJB0IvIwyif0JZdLxa6CfJrErUSpZ7gF8qRb1WVLSorbndTqoS+/dv1N6NS5DWTW9HzjE9uGdjlv3J95i+6/1+E2UO4s3AQf1837jXrN9k6R1gda+ll/Vi9lutSY/D0h6OnAH0PdZBb0kaWvbP+vhkPsDp0nalfkrZRtRVvu272LcjwGnS/rkiHE/TLk5Nwz+LGkT2xe1n5S0KWWFsiO2HwXOAM6olcD3AH4p6eO2D+sq4qnzIkkHAatSroVa2Rl9t4VlBlrN9m6S9gCw/eAQZTvMNLdIeg/lRsMGlNdxa0LXTebLjCLpO5S9wGcC/w38ArjB9i+bjCvGl0lsTNaNlIuTH7NgAaYvdDnuzsC6wOW295L0NEp6Zt+oF2qnUy6El6BcUC8F/EnSzzstOFPHPgI4ok5iNbLIU4e+DmwNIGlL4BDgPZTKe0dS/s6HgqR9gX9l/irpt1Vaf3R7gX2apH8CDqXsJTL1TvYQeYekwymppMe4tl7qlO3fSnoh5W54a5/qOcA+th/qYtzTJb0e+ADldQBwNWUvaM/3Svep/wC+J+l/KP9foeyRfzOlunTH6uR1O8oE9tnAV+jvrISjKTdMFqhYHj3xSJ0EGUDSarRdL0RfeRtla9TWwG62767nN6WsqA+LtYG7gOuA6106T6RoUJ9LYaeYlLpi+gS2D+5y3Ittv0jSHMr+wvuAq8cq8NJPahXhHXuwN7anJF1pe936+KvA32wfVI+vsL1ek/FNJ0lXAZu19h1LWhq4oJdl8+tF/BK2e56p0O9qRcs3Am8B7qZc/Jxoe1jSdGcMSU8F3k0pRgZwDfBV27d2MeaxlIvA04Hj2/bY9S1JF7lPe3vPdJJeSenBuyZlZWtzYC/bo7Uki+gLkp7P/HZxtwHPB17YymaL/pNJbPQFSV8D/hPYHfh34O/AFbb3ajSwGUzS1cB6tudJuh7Y2/a5reeGoRpri0oj941bK3l1Jf0S2y/scLykao9Qb+bsSXkd/x+lZ+xnbR/ZaGAx5ereuVZhuvaLilaK7uzpj2p8kg4BFqGsFrdnFQ1Nm6GpVG9sbUr5P3ChR/QWj+hnkjaitosD/mj7xQv5lmhAJrExIbU1w5j/WXrZQqEWNZpt+6pejTmMJB1AaSVxO6XX2Qa2Lem5wLG2N280wGkk6X2UlMmT66nXA//TafsASZcBW9u+s6ZqH8/8VO0X2B6mVO1tgLdStgO0Uor/WKtbXm171UYDjBhFrWkwkm2/fNqDGTB1e80rFnYuot/VvdxbekTf8ugPmcTGhEh66XjPd/oClzRuX9F+vCsuaXHbDy/sXIdj97RCcy3YsjJwZlsq7fOAZfrx73Yq1f9rW1BWBs61fXkXYyVVu5J0AmX/+lkjW+tI2tb26c1EFhHTqWa4LAWcTSnU2CrmNBs43fYLGgotIgZQJrHRqLa74UtQKoVeSfnFtw5wke0tmoptLJIus73Bws51MO6oFZqHaVWv1yTNtn2vpBVGe77TtN+kak+92vrm0zzxpk5X1WMlrUgp8vVs2oob2n5rN+POJJLW7+YmzkxWszLG1IMihUOrFtDbD3g68CfmT2LvBY6y/d9NxRbjy/tizESpThyNsr0VgKTjKROBufV4beD9TcY2kqSVKA2vl5S0PgveZV6qBz+i7ys0z0DfpVSRnsMoe/WATidEx1FaIt1OabPzK4Caqj0UhZ0k/Y3xtxh003cUSnGoA4EvUoq97cX811w3TqH8e/2M4a1K+wVJK1N6dx7fj/1cp9CyC/+S6ITtLwNflvSePm6tFKPL+yIg6Z9HVtgf7Vz0h6zERl8YLQWz39IyJb2ZUn11I+DStqfupewx7aqdxEyu0DyMhj1Vu1ZjFvAR4E7gW/WpNwKL2z6ky/Hn2N5Q0txWAS5Jv7L9koV970LG7av3labUm3K7Uipxzga+Z/uTzUYVg0LSi3niql4/9X2PNnlfLMbItJtje8OmYoqxZSU2+sV1kr4BfJuyuvNGSr+uvlFb6BwraSfbJ03Bj7i09hw9irJy+Hfg4in4OUNnKgqN2L5wlHO/7XS8maa1B1zS1rY3bXvqi5IupPQl7sZDkmYBv5P0bkp6Yreru9xDmQ4AACAASURBVFB6+77G9k96MNaMVStrf6Vu6fgg8DEgk9jomqRvAasBVzB/Vc9AJrH9a6jfF2t7nbWA5ST9S9tTs2nbzhL9JSuxMSGSvmR7v7GqFHdbnbgWhHgnsGU9dS5weKslSj+pKxifAp5ue1tJa1J6kB7dxZgCnmH7lnr8bFKhuWspNDL1JF1EaaVzUj3eCfhgtz04axuj64B/Aj5B+Tc7dLSbBxMc7z7Ke5eApSltVf5BH7eBmSqSXkBZgd0ZuINSXfsk27c1GlgMBEnXAWuOLPQW/au+Pw7t+6Kk11G6FuwA/KjtqfsoWy7ObySwGFcmsTEhkja0PWesKsXDVH5c0umU/XoH2F5X0qKUfawd9RxtGzcpKz2WQiNTr+4D/iqwMWWSeAnwHtu/62LMRYBDbH+gN1FGu7pSfhxwou0/Nx1PDBZJJwLvtf2XpmOJmAxJm9m+oOk4YmIyiY1GSTrB9q6S5jL6Cu86DYQ1LkmX2N5Y0uW216/nut5PUlu1/I/tS3oSaDwuhUZmHkm/AF7R69UcSZsDV9i+X9IbgQ2AL9m+uZc/J/qTpDfa/vZYVYpTnbh7NUV9Pcp2mMdbz3WbsRVTS9LywOosWA3+3OYimn61rsXhwNNsry1pHWCH1AvoT9kTG5NSLwAPAlal/P9ppZx0WuV13/p5++6jmzb3S3oyddJdC/z0oiLtVsA+km4C7mf+323fTeRnGtuH9boH77CTdCjjVyf+YJc/4nLglLqqc3/buF0VUKNcoKwraV3KXtCjKUWpxu2FPQjGuWk4TO81S9fPqVI8dQ5qOoCYHElvp1yPPYOyl3lT4ALg5U3G1YCjgA8AXwewfZWk75J6AX0pk9iYrKOB/SmFh7ouw95KN7J9U7djTaP3UfZMrCbpPGBFyt6ybm3bgzFiFGP14CWFRrpxQ/38IuCFlHYtADvRm4JkK1D2a7ZfRBnodhI7z7brHqgv2z66Vh4fBjPxpmFP2W5dnB7cdCyDapi2Fw2QfSlbQi60vVUtdDSMr5GlbF9cypQ8bl5TwcT4MomNybrH9um9HrRWg/sMpfqo6OOiArYvq3uD16DE+RtgVg+G/qTt/9d+olZ5/H9jfH1MXHrw9lhrMiBpN2AL24/U469QbhR06xu2z2s/UTNBunWfpA9TXlcvqftvF+vBuH1vht40nBK16NvbKBVJ27Mz3tpYUDNcW/G0JzxFn/4+j8c9ZPshSUha3Pb1ktZoOqgG3C5pNeZn2u0MZG93n+rFhXcMAUkbSNoAOFvSoZI2a52r57v1Wcq+g+Vsz7a9bL/+wpP0TdvzbF9j+2rgSfTmon2BfrD14jqFnnrjQduPAfMkzQZuAzpNgY8FrQIs2Xa8BCUlrVuj7WHuxb7m3Sj79N5a28ysAhzag3H7nqRf18/3Sbp3lI/fS/q3puOcJt8CVgJeBZxD+T97X6MRzXCt39ujfPTt7/N43B9ri78fAmdJOgUYxqJv76KkEj9f0p8ohSHf2WxIMZasxMZEfX7E8UZtj033+yZutd1XfWHH8SdJh9t+Zy2E8GPKPoqO1FWh/wSWlHRv6zTwCHBk19EGpAfvVPoCcLmkM+vx1pSsio5I2gx4MbDiiOI7s4FFOo6ysv1XSSdRCpgA3A6c3O24M4HtLernUfeD1r3+5wNfm864GvJc27tIep3tY+u+t582HVREE2zvWB8eVAtzLQec0WBITfmT7a0lLQ3Msn2fpBWaDipGl+rE0ai2ptIvpdwV/yELVjPsdv/blJD0Gcqb/IaUViAn9WDMT9v+cNfBxbjSg7f3JD0L2Kwent/qd9zhWC+l7F9+B3BE21P3Aad207qnjv+vwN7ACrZXk7Q6cITtV3Qz7kwk6aksmEp7s6SVh6E1iqSLbb9I0rnAvwF/BS7uokhhxIwl6XPAN21f23QsTZL0Y+B1tufV45WAH6f9YX/KJDYmZKx2BC2dtiWQdMz4w/bP/qS2CTeUldKPUlbzzoDeTLglrcL8ys/UcYeqxH0vSXp+3dszasq77cumO6ZBVPcNrWb7M5KeATzF9hVdjrnqVOzdlHQFpRjVRW0tsuZ22+d5JpG0AyW75umU1PpVgetsrzXuNw6QWo31JGAdSt/vZYCPtvZ6RwyT+nrYi3LtcQxwnO1edF2YUepNzu0oBQqfSSni+X7bZ477jdGITGJjQmp1VyjFjDamvLABXguca/vtjQQ2jaZ6wi3pEGB34FrmV352eut1TtKRtveu6VEj2fawtQ/ouVrIaWngxbZfUFNST7f9oi7HfR7wfuDZLHhTp6t/M0kX2d6k1edZ0qLAZUPSXgYASVdStoD8rP4dbAXsYXvvhkOLAVQLsu1p+11NxxLjq8Wc9gL2AM4DjrI92u/PgSXpXcCrKb979rF9frMRxViyJzYmpNWOoO5728D2ffX4IOa31pg0SZ8FbrR9xIjz+wMr2f6PjoPuMdt7TfGP2BFYw/bDC/3KmJDWRbntrZqOZYC9pE6ELgewfYekxXsw7omUdOJv0IN2Xm3OkdTag74NJZX01B6OPxP8o/47zZI0y/bZdYvE0Kg3Ww4CNqfUdfgV8AnbdzQZ16CQtB6wJ7Ar8Hu6b40VU6wWk3x+/bgduBJ4n6R9bO/eaHBTbES2oSirsFcAm0ratNNsw5haqU4ck/UsSsGhlkcod6s6tT2jFy/6MiWlo+9IOrYWCWodLy/pmz0Y+kaGpNXHdJP0rlH+zYalCutUe0TSLOa3JFiB0dtsTNY824fbvtj2nNZHD8b9EPA3YC6wD6Wy+Ed6MO5McrekZYBzge9I+jLD1wvxeEoq9U6UFly3A99rNKIZTtLzJH1M0nXAfwO3UDL+trLdi8riMUUkfYHSLvA1wH/Z3tD2Z2y/Fli/2eimxbJtH8tQiv3d0HYu+lDSiWNSJB1AubN6MuVCdUfgBNv/1eF414y1D2u855rUSkNc2LlJjHcY5e9yFUov05+zYHGr93YRblD2Qdpeb8S5jv/NYj5Je1FuRm1Mqf68O/Bp29/ucLxWJcj3UiYZJ7Pg6+HOrgIOauXNByk3st9AKVL3nWFahZQ0Z2SxFkmX2t5orO+J8Ul6jLKi/TbbN9RzN6ZYVv+T9FbgeNsPjPLccsO4Pzb6X9KJY1Jsf0rSGcAW9dReti/vYsgHJK0+suJorRj6YBfjTqVZkpa3fRc8ftHdzWvp0vp5DvP3GkdvzZIk17t2NW3qSQ3HNBBsHyNpDrANJQ1rT9tXdjHkHMpNHdXjD7T/ODrs7ytpLuOsEA/Tnljb99eHjwHH1tfD7sB3motq2p0taXfghHq8M6VdWnRuJ8r/o7PrdcLxzH8dRx+z/c2aobQ2C1YsP3eYJrCSzgJ2sX13PV6eMrl/VbORxWiyEhuTVi94nsaCxVZu7nCsbYHDgE9SLl6h9KD9MLCf7Z90F23vSXoTJb7v11O7AJ+y/a0e/ozlgWemDUxvSDqUkvZ+BGUi8w7gFtv/3mRcg6imqe5je2Rv6UZJWrU+bBWXab1e3wA8YPvj0x/V9JI0m/LnX4Vyw+ysevwB4Arbr2swvGkh6T7m3yRZmjKRh7Iq/Xfbs5uKbVDUlf7XU4oDvRw4Fjg5FV77V61OvC/wDOpeUOCCYSt+mKytmSWT2JgUSe8BDgRupRRbEaXKa8erGPXO3weAteupq4HP2Z7bZbhTRtKalF/OAn7ei95qkn4J7EC5OXAFZd/eObbHbW8UC1f3bO4DvILyb3Ym8A3bvSwYNFQkrUzZX7oKpb/z9yltp/6VcsG6T5fj7wKc4dJs/iPABpTCO91kfiDpPNubL+zcIJJ0CnAXcAHltbA8JSNh325bIkWMpmYq7QzsPmwTopmkZqpsDFxoez1JzwcOtr1bw6FNq5pVtGNrYabe/DzZ9qht+qJZmcTGpEi6AdhkmPZOtUiabfvetj17C+h2r15by4+3U1ZhD5R01TClOU4lSU+itIgy8Bvb/2g4pBlN0s8oqfAXAK+i9F69mTIhuqUH419lex1JWwCfBj4H/KftTboc9wrg3bZ/XY9fDHxt5N33QaS2frg1o+Z24FmtavPDRNKWo513+nJ3TNLGlB7Rp484vwPwpx4VZospIOkS2xvX98dNbD882qrkoJP0akqx0XPqqS2BvW3/tLmoYizZExuTdQswNPsjRvgupYBNa89ei+hir16bRevq1q7AAV2OFW0kvYyS0vYHavl8SW/OBWtXVrT9ofr4FEm3AVvYfqhH47dWybcDDrd9Sm3p1a23Ad+UtFw9vhvoqsfzDPL4jRvbj0r6/TBOYKv2vdZLUG7CzKFk2ERnDgXeMsr5aykTg/zd9q8/1gr+PwTOknQX8OeGY5p2ts+QtAElnVrA/rZvbzisGENWYmNSJB1NWc36MQtWDB2KHlqSRFkl7WgP8ELG3oWSjvlr2/8m6TnAobZ36vXPGjY1RWhP27+px88DjhtZnTQmrt6x34z5hVvObz8ercrlJMc/DfgTsDWwIaXQ28W21+1m3LbxZ1N+Bw7NTTlJjwKtok4ClgQeYP62kKHdDyrpmcBnbe/RdCwzVftK/yjPXdmr125MLUkvpVQsP8P2Iwv7+kEg6fm2r68T2Cewfdl0xxQLl0lsTIqkA0c7b/vgKfhZG9u+pNfjdmu01gzR30ZLy06qdnck/ZVSFKe9+mirYI5tP73L8ZcCXg3Mtf27mqXwwl4Uh5G0HbAWC1bhHPjCTjG2eoPyqrEmYbFwkm6w/dzJPhfNqjUjrrK99kK/eEBJOtL23pLOHuVpZz93f8okNvpKLZi0O6Wq4T392LNP0leB/+n1BFvSEpRUx5EX18OS6jhlJH2TMsFqVaR9I7CI7b2aiyomQtJTWfD10FUWhKQjgKWArYBvUIrOXGz7bd2MGzOL5vfnhlKZeD3gD7bf2FxUM1t9bd0BfMRtF5eSDgZWtr13Y8HFuCR9B/jwVGSZzRR1Mr+Z7fOajiUmJpPYmBRJKwIf5IkTrY7vUtXqb3vUj3nAqsBGtv/QVbBTRNK1wPOAmyipeV1XaK7jnghcD+wJfJzS+uM62/t2F3FIWpzSSmQLyr/XuZRiPg+P+43RmFoM5vPA04HbgGcB19teq8txWwWjWp+XAX5g+5XdRx0zhaQ3tx3Oo0xgc/Hahdpa5xuU/cWtatfrAZcAb7f996Zii/FJ+gWlOvHFzN9ygO0dGguqAZIusL1Z03HExGQSG5Mi6Uzge8D7Kb023wz8zfZ/dDje+ZS9F8dTGkr/rhYb+edexdxrbf0mF2D7pi7HbVUnbl1cLwb8NGksvVWrSz/D6cHb1yRdSSkE87P6utgK2KPb1RxJF9t+kaQLgX+hrBxdbXv17qOOmaTelMX235qOZZDUeg6tm03X2L6xyXhi4eo+2Cewfc5o5wdVzRq4inJjMxOkPpfqxDFZT7Z9tKR965vbOZK6eZP7G6W59tOAFYHfsWDl374xTvuA11Kq+HU1iWV+5dC7a+/cvwLP7nLMYPQevJLSg7e//cP2HZJmSZpl+2xJn+nBuKfWKpyHApdR3m+O6sG4MQPUva8HAu+mZGXMkjQPOCz7ortTb/DeXSetN9YbT/tKugn472EpEjQTDdtkdRzvA5YG5kl6iBS962uzmg4gZpzWROsvkraTtD5lEtoR268DXki5mDxY0u+B5SW9qPtQe+5Q4LpRzl9Xn+vWkZKWBz4C/IjSlqAXF+0By9m+l7LydkwtzLV1wzENFEmzJT219dGDIe+uqb7nAt+R9GVK2mc3Mc4Cfm77btsnUbYuPN/2x7oPN2aI/YDNgY1tP9n28sAmwOaS9m82tBnvBMoEAEnrASdSekevC3ytwbhiISRtKukSSX+X9IikRyXd23Rc0832srZn2X6S7dn1OBPYPpV04pgUSdsDvwKeCRwGzAYOtv2jHo3/NGA3SnGnZ9p+Zi/G7YWpbB9QL653tn1CxwHGmCTNBV5J6RV7gO1LUp24N1Saw3+Zsmf1buCpwO+7rURa99c9SLnZ+gbKtoPv2L6jy3Gz52mISboc2GZk78eaWnym7fWbiWzma39PlfQ54DHbH6y/367I+23/knQp5brrRGAj4E3A6rb/s9HApomkVwHL2v7+iPN7UrbMndVMZDGerMTGpNg+zfY9tq+2vZXtDXs1ga3j32r7K7ZfTCnC00+WHOe5pbsZ2PZjlPS2mBofB34K3FAnsM+hpK5H9w4BXkIpurQysB3l77pjkhYBTrH9mO15to+t7wtdTWCrMyXtVNNKY/gsNnICC4/vi12sgXgGSftr6uXAz+Hx32/R52zfQKna/6jtY4CXNRzSdDoYGC2l+heU64foQ9kTGxMiaS1gtdaEVdIXKSsjUPa6dNQIWtLCJsD9VBnvZ5I+xejtA37Rg/HPkvR+SuGs9uqAd/Zg7KFm+0TKHebW8Y3ATs1FNFDm2b5N0iKSZPuM+promO1HJT0gaTnb9/Qq0Cp7nobbePsys2ezO2dLOoFSz2F56u/F2uM5f7f97QFJTwKukPRZ4C90eXN+hllqtAJvtv9as4KiDyWdOCZE0qnAp22fX4+vBT5K6be4k+3Xdzju34BbgOOAi1jwTm5fFRsYo33AusCl9KB9QN0PPJJtP6ebcQPqL+VPUtJTz6D8u+1n+9uNBjYAamuG7SntcJYEbgVeZnuTLsc9AdgUOIsFb+q8t5txY7hJepS2/0/tTwFL2M5qbBck7Q6sBJxg+8/13HrAU22f2WhwMaZalOtW4EnA/pRFiq/V1dmBJ+m3wJq25404vxhwbarX96dMYmNCJF1qe6O24wttb1of/9p2R6m/NW1wG0qP2HWAHwPH2b6mB2FPibQPmHkkXWF7PUk7Aq+n/JI+u5t9zFHUSr9/p2xP2Yty8XOs7Vu7HPfNo523fWyH4z0V+E/guZQWCofUYl8R0QOS7qNU+xYLdhkQ8DBwA6Umwc8bCC9iTJIOoXTJeLft++u5pYGvALe7wzaSMbUyiY0JkfQb22uM8dxvbT+vBz9jccpk9lDg47YP63bMmaa21lkTWKJ1zvb/NhfRYJB0je21JB0FnFRTXrsqxhWP34Q61fZrmo5lYSSdAcyhVDvenlLE4y2NBhUxJOp7xdqU4mxrNx1PFJLOZuy2hrb9iumMpymSFqVka72d+e0SnwUcDXzU9j/G+t5oTvbExkT9WdImti9qPylpU0qP1I7Vyet2lAnssyl3vn7QzZgzkaQDKYUU1gR+AmwL/BrIJLZ7p0q6npJO/G+1EulDDcc049W9q49JWtb2fb0cu6bXP+Hiqov0+pVsH1Af/1RSR/v4I2LybD8KXClp6G5O97n3j3JuU+CDwG3THEtjahrxh2o9h1Zl/RtsP9hgWLEQWYmNCal9W78H/A+lpyvAhsCbgd1sX9zhuMdS7s6eDhxv++ruo52ZahuYdYHLba9b2w19w/ZrGw5tINQevPfWiddSwJNt39J0XDOdpO9QLnrOYMG9qx/sctwntx0uAewCrNBpT1dJV1JuErX23Z/dfpwCahExzCS9lFLrZHHgv2yf3nBIEePKJDYmrO4pezdt+0GBr3az903SY8y/8B25h6ZvK4ZK2oDSAsjAeZ1WZx4x5sW2XyRpDrAVcB9wte21FvKtMUG1rcpWwJ7Aa20/reGQZjxJ+4x23vbXp+BndbP//g/AY4woHlelgFpEDKXaI/WjlOykT9k+u+GQIiYk6cQxYbZvAzpaBRlnzBnXq1jSxyirQq2U52MknWj7k10OfWktknMUZe/e34GOVrhjQZI2oUxcdwRWAN4FfKDRoAbEVExW4fEbRS2zgI2AZTsdz/azu40pImKQSLoEWJFSi+SCeu7x995e3KCPmCpZiY2YJEnXAevbfqgeLwlcZvsFPfwZzwZm276qV2MOo9rXd1fgZkobp5OBS23/c6OBDRBJ/wx8gicWJFuzy3HbVwPmAX8APmf7N92MGxERhaRfMj8LrlVZusW2Xz7tQTWsbj1anQV/n53bXEQxlqzERkzeHyhvbq3CQIsD/9eLgSWtAqxKfW1K2jJvnl3ZG/gNcDhwmu2HJOXOXW8dC3ymfuxIabPTdSVH21t1O0ZERIzN9suajqGfSHo7sC/wDOAKSr2HC4Chm8zPBJnExqRIWt/25U3H0YRaVdGUfnfXSDqrHm9DqSLc7fifAXYDrgUeradNaQkSnVkJeCWl8vWX6urekpIWHdnUPDq2tO0fSzqkrpJ+SNKvejGwpO0oe/Db74h/vBdjR0REjLAvsDFwoe2tJD0fOLjhmGIMmcTGZH1B0srAiZRqwtc0HdA0urR+nkNJS235ZY/Gfz2whu2HezTe0KttHU4HTpe0BKU/6FLAnyT93PaejQY4GB6uBbNurHex/0RpGt8VSUdQ/q22Ar4B7EwXe8QlrTDe86lOHBEx9B6qGVtIWtz29ZLWaDqoGF32xMakSVqJss9wN2A28L0eFDUaepJOB3ax/femYxl0kmYDO9o+tulYZjpJLwbmAk8GPg0sB3zadlersZKusr1O2+dlgB/YfmWH47X6zqY6cUREVW9CPiMt50DSyZQtMftRUojvAhaz/ZpGA4tRZRIbHZP0QkpD7N1sP6npeKaLpNUpF+sjC9l0dBHclqa8CqVP7M8pKcutcd/bTbwRM5Gki2xvIulC4F+AOygtp1ZvOLSIiIEiaY7tDZuOo5/UvrnLAWfYfqTpeOKJkk4ckyLpBZQV2J0pF5XHA//eaFDT7xjgQOCLlFTHvRh9dWei2tOUf9RdaBHTS9JzKHetn03b75Qe3Lk+rbacOhS4jHKj56guxwRSfTIiYoQLJW1s+5KmA2mapEUoW2J+X0+tROlwEH0mK7ExKXVV5DjgRNt/bjqeJrTuWEqaa/uF9dyvbL+k6dhidHVvy8MLOxeTJ+ly4H8pN2FaBcmwfV4Pf8biwBK27+nBWKNWnxzGVhIREQCSrgXWoHRfuJ9yY96212kyrukm6T2URYpbgcfq6aH7e5gpshIbk2J706Zj6AMPSZoF/E7SuymFbJ7a7aC9TlOOBVwAbDCBczF5tv3FXg0maWPgFtt/rcdvAnYCbpJ0UA8KMKX6ZETEgrZtOoA+sS+lwOYdTQcSCzer6QBiZpB0Qv08V9JVbR9zJV3VdHzTbD9K1dT3AhsC/w94cw/GPYbSz3QeJU35f4Fv9WDcoSVpJUkbUtrqrC9pg/rxMsq/YXRI0lKSlgJ+KOmtkpZvnavnO/V14JH6M7YEDqG8Fu4Bjuw68Fp9so6/uO3rKSsQERFDyfZNwDOBl9fHDzCcc4RbKL9rYgZIOnFMiKSVbf9F0qqjPV/f9KILSVPuPUlvBt4CbMT8vccA9wLH2v5BE3ENAkl/4YnVflvHtv30Dse90va69fFXgb/ZPqgeX2F7vS7jTvXJiIg2kg6k/J5cw/bzJD2dsm1s84ZDmxaS3lcfrkW5qfljFiyw+YUm4orxJZ04JsT2X+rnoZ2sSvqS7f0knUq5WG9n4E7g67Yv7PBHTEma8jCrLXSOlbST7ZOajmeQ2F55rOckdVOtfBFJi9qeB7wC2Lvtua5/Z9nesT48SNLZ1OqT3Y4bETGD7QisTymih+0/S1q22ZCmVevPenP9eFL9iD6WSWxMiKRf295C0n08cQIHpVLxoba/Ns2hTadWau/nxnj+KcA3KXtaO9GepvwJyipRL9KUA86TdDTwdNvbSloT2Mz20U0HNtNJOtz2O9uOlwROBl7d4ZDHAedIuh14EPhVHfe59CjNK9UnIyIW8IhtSzKApKWbDmg62V6gLkLtJW/b9zUUUkxA0omjJyQ9GTjf9lDvLZP0WtunNh1HLEjS6ZQ9xwfYXlfSosDlrbTt6Jykz1LScfevv/hPA75r+4guxtwUWBk40/b99dzzgGVsX9ZlvKk+GRHRRtL7KW3HtqEUmHwr5X38sEYDm2aSNqJcK7RWZu8B3mp7TnNRxVgyiY2OSHoqC1bQvbm1b7bBsKaFpM2Bg4BVKdkMrT2AHVURljRub1jbO3Qybswn6RLbG0u63Pb69VzX+yujkPTl+nAT4Cu2v9tkPOORdAOwSapPRkTMJ2kb4JWUa5qf2j6r4ZCmXS1U+i7brQygLYCv5SZnf0o6cUyKpB2AzwNPB26jTOSuA9YahglsdTSwPyP6YnZhM0pFvOOAi1iwUE70xv01W6CVKrUpqUDYFUnthZDOAj4JXAjcLek1tn/STGQLleqTERFP9FvKDfmf1Srzyw5hOu19rQksgO1f12100YeyEhuTIulKyl7Nn9leX9JWwB62917Itw4MSRfZ3qSH4y1CSeHZA1iHUhXvONvX9OpnDDtJGwCHAWsDVwMrAjvbHrb2UD0j6bhxnrbtPactmAlI9cmIiNFJ+ldKEb0VbK9W+9YfYfsVDYc2Leo1ApSWiUtRFhUM7AbcZfuApmKLsWUSG5Mi6VLbG9XJ7Pq2H5N0se0XNR3bdJF0CLAI8AMWvAjuaq9eHXtxymT2UODjw7YfZSrVfbBrUFa6fwPMsv3w+N8Vg6K2kBjTyMIeERHDQtIVwIuAi9q23Dze7m/Q1Ur1Y7Htl09bMDFhSSeOybpb0jLAucB3JN0GzGs4punWWoXdqO2cKSvUHamT1+0oE9hnA1+hTJKjByR90/ZbgWvq8dLAjygtXKILkp5DWeVeqfY5fiGwre3PNhzaAjJJjYgY08O2H5HKbqZ603doVrlsb1VbHO5s+4Sm44mJyUpsTEq9+H8QmAW8gdJj8TspktI5ScdS0lxPB463fXXDIQ0cSZ8AnmL7nZKWp6SSHmX7mIZDm/Ek/YJS6OzLdYuBgLm21242stFJOgvYxfbd9Xh5yuvuVc1GFhHRjFpl/m7gTcB7gH8Drh22NFpJ59resuk4YmIyiY2u1P2cu9v+TtOxTBdJHxvtvO2PdzjeY8D9rWHanyrDenYn48aCJH2GctNlQ+AQ2yc1dMG9ywAAEl9JREFUHNJAmGmVn0eLrT32iIhhU1ch30ZbdWLgGx6ySYKkj1IWar7H/OsybN/ZWFAxpqQTx4TU/o/vAlahpGGeVY8/AFwBDM0klrY3Nkqboe0pFZo7YntW1xHFqCT9S9vhxcBH62dL+hfbSdnu3p2SnsX8ys+vpfRg7VePSnqW7ZsBJK3KEKXNRUS01P6w37N9C3BU/Rhmb62f39V2zkBHLRRjamUlNiZE0inAXcAFlH2EywNPAva1fUWTsTWt7mf9UdIR+4+k8dKFXffJRhdqFcujgI2Bv1LeJ3a1fWOjgY1B0quBI4Fz6qktgb1t/7S5qCIipp+kLwI7A7+nVOQ90fbtzUYVMTGZxMaEtFepqynEtwPPGsIeYk9Q99RdbHv1pmOJaErtw6uZcAEk6SnAppS0uQtmQswREVOh1jHYEtgdeB1wJWVCe/IwXuNJWhtYk5JpB4Dt/20uohhL0hhjov7RemD7UeD3w/jmBmVCL+mq+nENpV3Ll5uOK8Ym6VhJ/9R2vLykbzYZ06CQ9E5Js2txt4MknS/pZU3HtRCPArcB9wBrSkohj4gYSi7Osf1O4JnAl4D96e9tIVOitmI7rH5sBXwW2KHRoGJM2RMbE7WupHvrYwFL1uNhLD60fdvjecCttoetzdBMs06rGi2A7bskpZBPb7zD9uGStgZWo1z8fI1SQKvvSHo7sC/wDMp+/k0p2yTSBzAihlZtj7Y7sBtwB/CfzUbUiJ2BdYHLbe8l6WnANxqOKcaQSWxMiO1Fmo6haZKWAN4BPBeYCxydyeuMMUvS8rbvApC0Ann/67XXUF4TF9VKl/1qX8r+3Qtrb8DnA+khGxFDp9Y02J3So/5R4Hjglf1a02AaPGj7MUnzakHT20hRp76Vi7iIiTuWklb9K2Bbyp6JfRuNKCbq88D5kr5fj3cBPtVgPIPkakk/orwePippGfq72u9Dth+ShKTFbV8vaY2mg4qIaMBPKftfd7M9t+lg+sCldevRUcAc4O+UjgbRh1LYKWKCRhS3WpRSzGmDhsOKCZK0JiVlVMDPbV/bcEgDoRZ62wT4ne2/SVoRWNX2pQ2HNipJJwN7AftR/j/cBSxm+zWNBhYREX1D0rOB2bavajiUGEMmsRETJOmy9knryOPoP7Xg0L01ffgJ0sC8N+rq63NYsJpj39+9lvRSYDngDNuPNB1PRERMP0njXsvZvmy6YomJyyQ2YoIkPQrc3zoElgQeYDiLW80Ikk6zvb2k37Ngimvr3yx7Xbok6U3Ah4BVgGuBDShZCi9pNLBx1LZYz6RtS00uUiIihpOks+vDJYCNKG2GBKwDXGR7i6Zii7FlEhsRA632wHum7ZubjmUQSZpLSSc+3/Z6tcLlh23v2XBoo5L0CeAtwI3AY/W0bac6cUTEEJN0PPCp1v7g2jP2/bbf0mhgMaoUdoqIgWbbdR9kX7Z8GQAP2X6gFkpazPZcSS9oOqhx7AqslvThiBh29SbkmKtZtteZxnD6wfPbC1zZvlrSek0GFGPLJDYihsGFkja2fUnTgQyg22o1xx8Dp0u6A/hbwzGN52rgnyitEyIihlmr7/276udv1c9voGyXGjbXSfoG8G3K5P6NwHXNhhRjSTpxRAw8SdcCzwNuouxrbu2JHba7zFNK0qsohZJOtf1g0/GMRtJGwCmUyezDrfO2d2gsqIiIBkk6z/bmCzs36CQtAbwT2LKeOhc43PZDzUUVY8kkNiIGnqRVRztv+6bpjmVQ1B6rD0taapSn3ceT2GuArwNzmb8nFtvnNBZURESDJF0BvNv2r+vxi4Gv2U4qbfStpBNHxMCStDHwFNunjzj/WuDPlJXZ6MypwCuB/6OkXameN6We1mLAYbYPbii+sdxu+ytNBxER0UfeBnxT0nKU9/B7gLc2G9L0kXSC7V3H2iOcrK3+lJXYiBhYkn4JvMX2H0acfy5wZCrSTp06iZ1r+/lNx9JO0hcoacQ/YsF04rTYiYihJmk2ZW5wT9OxTCdJK9v+S7K2ZpasxEbEIHvyyAksgO0bJD25gXgGkqRlgOdQeuwBYPvi2m6n36xfP2/ads5AbmhExFCS9DTgv4Cn295W0prAZraPbji06XKApO/aPr/pQGLiMomNiEG25DjPLT1tUQwwSW8G/gNYBbgW2AC4GHiJ7X80GdtobG/VdAwREX3mf4BjgAPq8W+B7wHDMon9HfB5SStT/tzH2b6i4ZhiIZJOHBEDS9IRwB3AR9z2ZifpYGBl23s3FtyAqHuINgHOt71eXX39sO09Gw5tAZLeaPvbkt432vO2vzDdMUVE9ANJl9jeWNLlttev564YtsJONZ149/qxBHAccLzt3zYaWIxqVtMBRERMoX+npLneIOmk+nEDsAYw6mQmJu0h2w9A2QdbG8W/oOGYRtNaeV92jI+IiGF1f91iYwBJm1KKOw0V2zfZ/kydyO8J7Ej6xPatrMRGxMCT9BxgrXp4je0bm4xnkEj6MfAG4AOUFdk7gOVtv7LRwCIiYkIkbQAcBqxN6aG9IrCz7asaDWya1YKEr6asxL4COIeSWvzDRgOLUWUSGxERPSHpVcBywI/6rTm8pHHb6th+73TFEhHRbyQtSslSEvCbfqxpMFUkbQPsAWxHqelwPPBD2/c3GliMK5PYiIjomVqpeB/bn286lna1AFXLwcCB7c/bPnZ6I4qI6A+SlqJssVnV9r9KWh1Yw/ZpDYc2LSSdDXwXOMn2nU3HExOTSWxERExareL4IUpV4h8C3wc+Crydcgd7nwbDG1d78ZKIiGEn6XvAHOBNtteWtCRwwbAVdoqZJS12ImIo1D0/W1AKV5xn+7KGQ5rpvgVcWj+/CtgPuBnYyPYtTQY2Abl7GxEx32q2d5O0B4DtByWp6aAixpNJbEQMPEkfA3YBflBPHSPpRNufbDCsmW5F2x+qj0+RdBuwRb/thY2IiIV6pK6+tqoTrwY83GxIEeNLOnFEDDxJ1wHrtyZY9Zf1Zbb7sRXMjCDpCmAzShEQgPPbj1ttd/qFpPuYvwK7FNCKT4Btz24ksIiIhtXCRh8B1gTOBDYH3mL7l03GFTGerMRGxDD4A6VxeWuVcHHg/xqLZjCsRPk7bE85ax0beHoTQY3FdnrBRkSMwvZZki4DNqW8h+9r+/aGw4oYVyaxETGwJB1GmVA9DFwj6ax6vA3w6yZjm+lsr9R0DBER0TNLAHdR5gZrSsL2uQ3HFDGmpBNHxMAa0VblCdJWJSIihp2kzwC7AdcAj9XTtr1Dc1FFjC+T2IiIiIiIISXpN8A6tlPMKWaMpBNHxMCrjds/TSlasUTrvO3nNBZUREREf7gRWIxUJI4ZJJPYiBgGxwAHAl8EtgL2YsGCRBEREUOlrW7EA8AVkn5O20TW9nubii1iYZJOHBEDT9Ic2xtKmmv7hfXcr2y/pOnYBpGkk2zv1HQcERExttSNiJksK7ERMQwekjQL+J2kdwN/Ap7acEyD7H1NBxAREePLJDVmsqzERsTAk7QxcB3wT8AngOWAz9q+sNHABki9SbAG8BfbdzcdT0RETEzqRsRMlElsRERMmqSvAF+3fY2kZSl9d5cClgXebfv7jQYYERETIunXzK8b8Vpq3QjbBzYaWMQ4MomNiIEl6Uu295N0KqV4RTsDd1ImYlmRnSRJ19heqz5+D/Aq29tLegbwI9sbNBthRERMROpGxEyUPbERMci+VT9/boznnwJ8k5JCFZPzSNvjVwInAdj+o6RUfo6ImDlSNyJmnExiI2Jg2Z5TP58z1tdIemSs52Jc90ramnKx8xJgHwBJiwBLNhlYRERMyn6U7SDvpdSNeDkwbuXiiKYlnTgiBp6kzYGDgFUpN+8EOEUrOidpTeBrwErAF21/vZ5/NbCd7fc0GV9EREQMrkxiI2LgSboe2B+YAzzaOm/7jsaCioiI6AOSngd8gPk3egGw/fLGgopYiExiI2LgSbrI9iZNxzEsJG1j+6ym44iIiIWTdCVwBE+80TunsaAiFiJ7YiNiGJwt6VDgB8DDrZO2L2supIH2ciCT2IiImWGe7cObDiJiMrISGxEDT9LZo5x2UqU6J2m27XubjiMiIjojaYX68L3AbcDJLHij984m4oqYiExiIyJi0iT9H3CA7eObjiUiIiZP0u8pPdNHa4uW4ofR1zKJjYiBJ+ljo523/fHpjmVQSFoV+BKwDPBO2zc0HFJEREQMieyJjYhhcH/b4yWA7YHrGoplINi+CdixttQ5T9IlwGNtz+/QWHAREbFQkjYGbrH913r8JmAn4CbgoKQTRz/LSmxEDB1JiwM/sv2qpmOZySStARwO3Al8lQUnsec0FVdERCycpMuArW3fKWlL4HjgPcB6wAts79xogBHjyEpsRAyjpYDs9emCpEOAHYB/t3160/FERMSkLdK22robcKTtk4CTJF3RYFwRC5VJbEQMPElzKcUrABYBVgSyH7Y7jwIb2H6o6UAiIqIji0ha1PY84BXA3m3PZY4QfS3/QSNiGGzf9ngecGv9pR0dsn1A0zFERERXjgPOkXQ78CDwKwBJzwXuaTKwiIXJntiIGFiSlgDeATwXmAscnclrREREIWlTYGXgTNv313PPA5axfVmjwUWMI5PYiBhYkr4H/INyd3lb4Cbb+zYbVURERER0I5PYiBhYkubafmF9vChwse0NGg4rIiIiIrowq+kAIiKm0D9aD5JGPH1q24aIiIiIKZGV2IgYWJIeBe5vHQJLAg/Ux7Y9u6nYIiIiIqIzmcRGRERHJC0C/NT21k3HEhEREcMj6cQREdER248CD0harulYIiIiYnikT2xERHTjIWCupLOYn7qN7fc2F1JEREQMskxiIyKiGz+uHxERERHTIntiIyKiK5JWBLD9t6ZjiYiIiMGXPbERETFpKg6SdDtwPfBbSX+T9LGmY4uIiIjBlklsRER0Yj9gc2Bj20+2vTywCbC5pP2bDS0iIiIGWdKJIyJi0iRdDmxj+/YR51cEzrS9fjORRURExKDLSmxERHRisZETWHh8X+xiDcQTERERQyKT2IiI6MQjHT4XERER0ZWkE0dExKRJepS2vrDtTwFL2M5qbEREREyJTGIjIiIiIiJixkg6cURERERERMwYmcRGRERERETEjJFJbERERERERMwYmcRGRET0gKTTJM0d5/n/lnSXpMWnM66IiIhBk0lsREREbxwHrC1prZFPSFoE2Bn4ge2Hpz2yiIiIAZJJbERERG+cAjwA7D7Kc1sBT6NMdCMiIqILmcRGRET0gO2/A6cBu43y9O7ArcDZAJJWlfS9ml78gKTTJa3e+mJJz5VkSTtJOkrSPZL+KOljktT2dd+WdGH7D2r73le3nVtE0gGS/k/Sw5Kul/T/evxXEBERMS0yiY2IiOid44DVJW3YOiFpMWBH4ATbj0p6CnAe8Fxgb8qk95+As0bZL/t54G5KKvJxwMF1rMn6GvAh4HBgO+BU4Nj2iW5ERMRMsWjTAURERAyQ0ymTzt2BOfXcq4AVmJ9K/O/A4sArbN8NIOl84A/AW4Cvt433C9sfqI/PkrQt8C/ADyYakKQ1KJPlN9r+Tj39M0mrAAcCZ0zizxcREdG4rMRGRET0SC3adDKwa1va727ATUAr7Xdr4KfA3yUtKmlR4B7gMmCjEUOeOeL4WuAZkwxra+AfwCmtn1d/5s+BDSTlWiAiImaUrMRGRET01nHAXsBmki4DXgd81bbr80+hTFbfMMr3Pjji+O4Rx48AS0wynqcAiwH3jfH8U4G/TnLMiIiIxmQSGxER0Vu/oBRx2h1YGViWBasS3wlcDvzXKN977yR/1kPAk0acW2HE8Z2Uye8WgHmiOyb5MyMiIhqVSWxEREQP1eJNJwK7AKsA19m+qu1Lfk5ZnZ3bg56xfwR2krR421jbjPiaX1AmusvYPrvLnxcREdG47IOJiIjoveOAlSiVhL874rnPAUsDv5C0h6SXStpN0tck7TrJn3MypbLxUZK2lvQfwJvav8D2NcBRwImSPijpFZK2l/Qfkr4+ypgRERF9LZPYiIiI3ruAUm1YwPHtT9i+DdgUuAH4EqV402coacdzJ/NDbF8JvB3YHDilfn7bKF/6Dkr68luAnwDHANsCv5rMz4uIiOgHml9nIiIiIiIiIqK/ZSU2IiIiIiIiZoxMYiMiIiIiImLGyCQ2IiIiIiIiZoxMYiMiIiIiImLGyCQ2IiIiIiIiZoxMYiMiIiIiImLGyCQ2IiIiIiIiZoxMYiMiIiIiImLGyCQ2IiIiIiIiZoz/D2IIBPLLeCgYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(x='venue',data=matches ,order=df['venue'].value_counts().index)\n",
    "plt.title('Venue which hosted most number of IPL matches', fontsize=15)\n",
    "plt.xlabel('Venue', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Number of matches', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eden Gareden of Kolkalta hosted maximum number of matches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most winning team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Season-wise Winner                 winner team\n",
      "2008       Rajasthan Royals\n",
      "2009        Deccan Chargers\n",
      "2010    Chennai Super Kings\n",
      "2011    Chennai Super Kings\n",
      "2012  Kolkata Knight Riders\n",
      "2013         Mumbai Indians\n",
      "2014  Kolkata Knight Riders\n",
      "2015         Mumbai Indians\n",
      "2016    Sunrisers Hyderabad\n",
      "2017         Mumbai Indians\n",
      "2018    Chennai Super Kings\n",
      "2019         Mumbai Indians\n"
     ]
    }
   ],
   "source": [
    "winning_teams = matches[['season','winner']]\n",
    "winner_team ={}\n",
    "for i in sorted(winning_teams.season.unique()):\n",
    "    winner_team[i] = winning_teams[winning_teams.season ==i]['winner'].tail(1).values[0]\n",
    "    \n",
    "winner_IPL = pd.Series(winner_team)\n",
    "winner_IPL = pd.DataFrame(winner_IPL, columns=['winner team'])\n",
    "print(\"Season-wise Winner\",winner_IPL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of seasons')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "winner_IPL['winner team'].value_counts().plot(kind='bar', color='b')\n",
    "plt.title('Winner of IPL across 11 seasons', fontsize=15)\n",
    "plt.xlabel('Team', fontsize=15)\n",
    "plt.ylabel('Number of seasons', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Mumbai Indians have won 4 times\n",
    "2. Chennai Super Kings have won 3 times\n",
    "3. kolkata knight riders have won 2 times\n",
    "4. Rajasthan Royals, Sunrises Hyderabad and Deccan Chargers have won 1 time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toss Decision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision taken by captain after winning toss')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "(matches['toss_decision'].value_counts()).plot(kind='pie',autopct = '%1.1f%%', colors=['r','orange'], fontsize=15)\n",
    "plt.title('Decision taken by captain after winning toss', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of times team choose to field after winning the toss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toss decision affecting winning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matches['tosswin_matchwin'] = np.where((matches.toss_winner == matches.winner), 'Yes','No')\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(x='tosswin_matchwin',data = matches, hue='toss_decision', palette='viridis')\n",
    "plt.title('Toss decision affects match result', fontsize=15)\n",
    "plt.xlabel('Winning toss and winning match', fontsize=15)\n",
    "plt.ylabel('Frequency', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teams winning toss and electing to field first have won most of the times "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individual team toss decision after winning toss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(x='toss_winner',data = matches, hue='toss_decision', palette='rocket')\n",
    "plt.title('Individual team toss decision after winning toss', fontsize=15)\n",
    "plt.xlabel('Teams', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Frequency', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most teams field first after winning the toss except chennai supur kings, who has mostly choosed bat first. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top players with most man of the match "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.countplot(x='player_of_match',data = matches,order=df['player_of_match'].value_counts().head(15).index , palette='rainbow')\n",
    "plt.title('Top players with most man of the match ', fontsize=15)\n",
    "plt.xlabel('Players', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Frequency', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chris Gayle has so far won the maximum man of the match awards followed by AB de Villers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top run getters of IPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total runs scored')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using merged dataset (mathces and deliveries)\n",
    "df.groupby('batsman')['batsman_runs'].sum().sort_values(ascending=False).head(10).plot(kind='bar',figsize=(16,8), color='orange')\n",
    "plt.title('Top Run Getters of IPL', fontsize=15)\n",
    "plt.xlabel('Batsmen', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Total runs scored', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of top run getters are either openers or 3rd, 4th positions to bat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most consistent batsman of IPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x249435a9ba8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cons_batsman = df[df.batsman.isin(['V Kohli', 'SK Raina', 'RG Sharma', 'DA Warner', 'S Dhawan','CH Gayle','MS Dhoni',\n",
    "                                         'RV Uthappa', 'AB de Villers', 'G Gambhir'])]\n",
    "[['batsman', 'season', 'total_runs']]\n",
    "\n",
    "cons_batsman.groupby(['season','batsman'])['total_runs'].sum().unstack().plot(kind='box', figsize=(16,8), grid='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the highest and lowest season totals and spread of runs, It seems Raina has been most consistent among all. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top wicket takers of IPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Wicket Taken')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('bowler')['player_dismissed'].count().sort_values(ascending=False).head(10).plot(kind='bar', figsize=(16,8), color='purple')\n",
    "plt.title('Top Wicket takers of IPL', fontsize=15)\n",
    "plt.xlabel('Bowlers', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Total Wicket Taken', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Malinga has taken the most number of wickets in IPL followed by Bravo and Amit Mishra\n",
    "2. In Top 10 bolwers, 5 are spinner while other 5 are medium and fast pacers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of Dismissal in all seasons of IPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApO087wLWmv1utf+8S1jXzr3w0/MuAQAA4Br0+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmrHnyr6vyq+nhVnVVVp49tu1fVyVV17ni929heVfXSqjqvqj5WVXedeZzDxu3PrarDVvt5AAAAsD7Mq8f3vt19l+4+cFx+epJTunu/JKeMy0nywCT7jZcjkrwiGYJykqOT3D3JQUmOXgjLAAAAMGutDHV+SJITxtsnJHnoTPure3BakptW1Z5J7p/k5O6+tLsvS3JykgesdtEAAACsffMIvp3kXVV1RlUdMbbdoru/nCTj9c3H9r2SfHHmvheMbZtqv4aqOqKqTq+q0y+55JIVfhoAAACsBzvPYZ8Hd/eFVXXzJCdX1ac3s20t0dabab9mQ/exSY5NkgMPPPBa6wEAAJi+Ve/x7e4Lx+uLk7wlwzm6F41DmDNeXzxufkGSfWbuvneSCzfTDgAAANewqsG3qm5QVTdauJ3k0CSfSHJSkoWZmQ9L8tbx9klJHjvO7nyPJN8Yh0K/M8mhVbXbOKnVoWMbAAAAXMNqD3W+RZK3VNXCvl/b3f9RVR9OcmJVHZ7kC0keMW7/9iS/lOS8JN9N8vgk6e5Lq+rZST48bndMd1+6ek8DAACA9WJVg293fzbJAUu0fy3JLy7R3kmO3MRjHZ/k+JWuEQAAgGlZK19nBAAAADuE4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkCb4AAABMmuALAADApAm+AAAATJrgCwAAwKQJvgAAAEya4AsAAMCkzSX4VtVOVfWRqnrbuHybqvpgVZ1bVW+oquuO7dcbl88b1+878xhHje3nVNX95/E8AAAAWPvm1eP75CRnzyy/IMmLu3u/JJclOXxsPzzJZd192yQvHrdLVd0hyaOS/EySByR5eVXttEq1AwAAsI6sevCtqr2T/HKSV43LleR+Sd40bnJCkoeOtx8yLmdc/4vj9g9J8vru/kF3fy7JeUkOWp1nAAAAwHoyjx7flyT54yRXjss/nuTr3X35uHxBkr3G23sl+WKSjOu/MW5/VfsS9wEAAICrrGrwraoHJbm4u8+YbV5i097Cus3dZ3Z/R1TV6VV1+iWXXLLsegEAAFj/VrvH9+Akv1JV5yd5fYYhzi9JctOq2nncZu8kF463L0iyT5KM62+S5NLZ9iXuc5XuPra7D+zuA/fYY4+VfzYAAACseasafLv7qO7eu7v3zTA51X9192OSvDvJw8fNDkvy1vH2SeNyxvX/1d09tj9qnPX5Nkn2S/KhVXoaAAAArCM7b3mTVfG0JK+vquck+UiS48b245K8pqrOy9DT+6gk6e5PVtWJST6V5PIkR3b3FatfNgAAAGvd3IJvd78nyXvG25/NErMyd/f3kzxiE/d/bpLn7rgKAQAAmIJ5fY8vAAAArArBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJi0ZQXfqnp9VR1aVbWjCgIAAICVtNwe332S/EeSL1TVc6rqtjugJgAAAFgxywq+3X1wktsleU2SxyY5p6pOrarHVdUNdkSBAAAAsD2WfY5vd5/b3c9Icuskv5TkgiR/m+TLVXVcVd17hWsEAACAbbbNk1t1dyc5Nck7knwyyQ0zBOFTq+qMqjpgZUoEAACAbbdNwbeqDq6qVyb5SpKXJTkryT27e88kd0nyzQzDoQEAAGCudl7OxlV1VJLHJbltkg8keUqSN3T3dxe26e6PVdUzM/QGAwAAwFwtK/gmeXKSVyc5rrvP2cx2n05yxDZXBQAAACtkucF37+6+fEsbdffXkhy3bSUBAADAylnuOb73rqrHLrWiqn6rqu6zAjUBAADAillu8H1eklttYt0tx/UAAACwZiw3+N4xyembWHdmkp/ZvnIAAABgZS03+F6ZZLdNrPvxbXg8AAAA2KGWG1Tfn+QPq2qX2cZx+alJ3rdShQEAAMBKWO6szs/IEG7PrarXJflykj2TPCrJ7kl+fmXLAwAAgO2zrODb3R+tqnsk+bMkT8gw7PmyJKckObq7P73iFQIAAMB2WG6Pb7r7k0kesQNqAQAAgBVnMioAAAAmbdk9vlX10CQPS7J3kl0Xr+/ue23mvrsmOTXJ9cZ9v6m7j66q2yR5fYbzhM9M8lvd/cOqul6SVye5W5KvJfn17j5/fKyjkhye5IokT+rudy73uQAAADB9y+rxrao/TfLmJD+b5JIkn1nisjk/SHK/7j4gyV2SPGA8Z/gFSV7c3ftlOGf48HH7w5Nc1t23TfLicbtU1R0yTKj1M0kekOTlVbXTcp4LAAAAG8Nye3yPSPIX3f20bdlZd3eSb4+Lu4yXTnK/JL8xtp+QYfKsVyR5yHg7Sd6U5G+qqsb213f3D5J8rqrOS3JQkg9sS10AAABM13LP8b1Rkndtzw6raqeqOivJxUlOztBL/PXuvnzc5IIke42390ryxSQZ138jyY/Pti9xn9l9HVFVp1fV6Zdccsn2lA0AAMA6tdzge2KSQ7dnh919RXffJcM5wgcluf1Sm43XtYl1m2pfvK9ju/vA7j5wjz322NaSAQAAWMeWO9T5P5L8ZVXtnqG39uuLN+jureoR7u6vV9V7ktwjyU2rauexV3fvJBeOm12QZJ8kF1TVzklukuTSmfYFs/cBAACAqyw3+L5pvD48V09ANauTbHKSqaraI8mPxtD7Y0kOyTBh1buTPDzDzM6HJXnreJeTxuUPjOv/q7u7qk5K8tqqelGSWyXZL8mHlvlcAAAA2ACWG3z328797ZnkhHEG5uskObG731ZVn0ry+qp6TpKPJDlu3P64JK8ZJ6+6NMNMzunuT1bViUk+leTyJEd29xXbWRsAAAATtKzg291b+rqiLd3/Yxm+Cmlx+2cznO+7uP37SR6xicd6bpLnbk89AAAATN9yJ7dKVe1SVU+oqr+vqrdX1W3H9odX1e1WvkQAAADYdsvq8R1D7ruS3CzJmUl+PsmNx9X3TfLgDOfkAgAAwJqw3B7flyb5SpJ9M0xMNfu1Qv+dIQgDAADAmrHcya3uk+SR3X3pOEHVrK9kmLwKAAAA1ozl9vj+IMn1NrHuVlnie30BAABgnpYbfE9OclRV3WimratqlyS/n+Q/VqwyAAAAWAHLHer8R0n+J8l5Sd6ZpJP8SZKfSXKDJI9c0eoAAABgOy2rx7e7v5DkgCTHJ9k/yeczTHR1UpK7dfeFK10gAAAAbI/l9vimu7+W5KgdUAsAAACsuOWe4wsAAADryrJ6fKvqyxnO692k7r7VdlUEAAAAK2i5Q52Py7WD7+5J7pfk+klOWImiAAAAYKUsK/h29zOXaq+q6yR5Y5LvrkRRAAAAsFJW5Bzf7r4yySuTPGklHg8AAABWykpObnXrJNddwccDAACA7bbcya2OWKL5uklun+SxSd68EkUBAADASlnu5FZ/t0Tb5Um+lGGo87O2uyIAAABYQcsNvrssbujuK1aoFgAAAFhxy53VWcgFAABgXVnuOb6/sZztu/u1yysHAAAAVtZyhzr/U5Ieb9dM+6baBF8AAADmarlfZ3T3JJ9P8udJ7pzkluP1MWP73ZPsNl52X7kyAQAAYNsst8f3BUle0d1/MdN2cZJPVNV3k7ywu++7YtUBAADAdlpuj+89knx0E+s+lqHHFwAAANaM5QbfC5I8bhPrHpfh+3wBAABgzVjuUOdnJnltVd0hyUkZhjnfPMmvJLlTkkevbHkAAACwfZb7Pb4nVtX5SZ6e5PFJbpHkoiQfTvK73f3BFa8QAAAAtsNye3zT3R9K8rAdUAsAAACsuOWe45skqaqbVNU9q+qRVXXTsW2XlS0NAAAAtt+ygm9VXaeqnpdhEqv3J3ldkp8cV59UVUevcH0AAACwXZbb4/vcJEcmeWqSn05SM+v+NcMkVwAAALBmLPcc38OSPL27X1lVOy1a95kkP7UyZQEAAMDKWG6P725Jzt3Eul2SLA7DAAAAMFfLDb6fTPLgTay7f5KPbF85AAAAsLKWO9T5eUlOrKrrJXljkk5yx6p6cJLfS/LQFa4PAAAAtsuyeny7+81JHpvkl5OcnGFyq39M8rtJHt/d71jpAgEAAGB7LLfHN9392qp6XZLbJ7lZkkuTfKq7r1zp4gAAAGB7bXXwrapdk5yZ5Knd/c4kn9phVQEAAMAK2eqhzt39/Qw9vL3jygEAAICVtdxZnV+X4RxfAAAAWBeWe47vZ5I8vKpOS/L2JBflmj3A3d2vXKniAAAAYHstN/i+ZLzeM8lBS6zvJIIvAAAAa8Zyg+8uO6QKAAAA2EG2eI5vVb2rqm6XJN19RXdfkeQ+SXZdWJ697OiCAQAAYDm2ZnKrQ5LcZGGhqnZKcnKS2+2oogAAAGClLHdW5wW1olUAAADADrKtwRcAAADWha0Nvr2VbQAAALCmbO2szu+sqssXtZ2yRFu6++bbXxYAAACsjK0Jvn++w6sAAACAHWSLwbe7BV8AAADWLZNbAQAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaasafKtqn6p6d1WdXVWfrKonj+27V9XJVXXueL3b2F5V9dKqOq+qPlZVd515rMPG7c+tqsNW83kAAACwfqx2j+/lSf6wu2+f5B5JjqyqOyR5epJTunu/JKeMy0nywCT7jZcjkrwiGYJykqOT3D3JQUmOXgjLAAAAMGtVg293f7m7zxxvfyvJ2Un2SvKQJCeMm52Q5KHj7YckeXUPTkty06raM8n9k5zc3Zd292VJTk7ygFV8KgAAAKwTczvHt6r2TfKzST6Y5Bbd/eVkCMdJbj5utleSL87c7YKxbVPtAAAAcA1zCb5VdcMk/5LkKd39zc1tukRbb6Z98X6OqKrTq+r0Sy65ZNuKBQAAYF1b9eBbVeZwzngAACAASURBVLtkCL3/3N1vHpsvGocwZ7y+eGy/IMk+M3ffO8mFm2m/hu4+trsP7O4D99hjj5V9IgAAAKwLqz2rcyU5LsnZ3f2imVUnJVmYmfmwJG+daX/sOLvzPZJ8YxwK/c4kh1bVbuOkVoeObQAAAHANO6/y/g5O8ltJPl5VZ41tz0jy/CQnVtXhSb6Q5BHjurcn+aUk5yX5bpLHJ0l3X1pVz07y4XG7Y7r70tV5CgAAAKwnqxp8u/t9Wfr83CT5xSW27yRHbuKxjk9y/MpVBwAAwBTNbVZnAAAAWA2CLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpO8+7ANiUg+/y0HmXsK69/6x/nXcJAACwJujxBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNIEXwAAACZN8AUAAGDSBF8AAAAmTfAFAABg0lY1+FbV8VV1cVV9YqZt96o6uarOHa93G9urql5aVedV1ceq6q4z9zls3P7cqjpsNZ8DAAAA68tq9/j+Y5IHLGp7epJTunu/JKeMy0nywCT7jZcjkrwiGYJykqOT3D3JQUmOXgjLAAAAsNiqBt/uPjXJpYuaH5LkhPH2CUkeOtP+6h6cluSmVbVnkvsnObm7L+3uy5KcnGuHaQAAAEiyNs7xvUV3fzlJxuubj+17JfnizHYXjG2bar+Wqjqiqk6vqtMvueSSFS8cAACAtW8tBN9NqSXaejPt127sPra7D+zuA/fYY48VLQ4AAID1YS0E34vGIcwZry8e2y9Iss/MdnsnuXAz7QAAAHAtayH4npRkYWbmw5K8dab9sePszvdI8o1xKPQ7kxxaVbuNk1odOrYBAADAtey8mjurqtcl+YUkN6uqCzLMzvz8JCdW1eFJvpDkEePmb0/yS0nOS/LdJI9Pku6+tKqeneTD43bHdPfiCbMAAAAgySoH3+5+9CZW/eIS23aSIzfxOMcnOX4FSwMAAGCi1sJQZwAAANhhBF8AAAAmTfAFAABg0gRfAAAAJk3wBQAAYNJWdVZnYP36pQceNe8S1q23v+P/zbsEAIANTY8vAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaTvPuwAAlufQx79k3iWsW+/6h6fMuwQAYA70+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaYIvAAAAkyb4AgAAMGmCLwAAAJMm+AIAADBpO8+7AABYrw55+svnXcK69Z/P/9/zLgGADUSPLwAAAJMm+AIAADBpgi8AAACTJvgCAAAwaSa3AgDWvUNe9Mp5l7Cu/ecfPGHeJQDsUHp8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASRN8AQAAmDTBFwAAgEkTfAEAAJg0wRcAAIBJE3wBAACYNMEXAACASVvXwbeqHlBV51TVeVX19HnXAwAAwNqzboNvVe2U5G+TPDDJHZI8uqruMN+qAAAAWGt2nncB2+GgJOd192eTpKpen+QhST4116oAADawX3ntCfMuYV076TcOm3cJMEnV3fOuYZtU1cOTPKC7f2dc/q0kd+/u35/Z5ogkR4yLt0tyzqoXuvJuluSr8y6CJI7FWuJYrC2Ox9rhWKwdjsXa4VisHY7F2jKF43Hr7t5jqRXruce3lmi7Rorv7mOTHLs65ayOqjq9uw+cdx04FmuJY7G2OB5rh2OxdjgWa4djsXY4FmvL1I/Huj3HN8kFSfaZWd47yYVzqgUAAIA1aj0H3w8n2a+qblNV103yqCQnzbkmAAAA1ph1O9S5uy+vqt9P8s4kOyU5vrs/OeeyVsOkhm6vc47F2uFYrC2Ox9rhWKwdjsXa4VisHY7F2jLp47FuJ7cCAACArbGehzoDAADAFgm+AAAATJrgC6w7VXW9rWkDAIBkHU9uBWxoH0hy161oA2CDq6qDu/v9W2pjx3Ms1oaq+oPNre/uF61WLatJ8F3jquoR3f3GLbXBRlBVt0yyV5Ifq6qfTVLjqhsnuf7cCiNV9d4kpyZ5b5L3d/e35lwSzEVVvSzJJmcO7e4nrWI5DF6Wa38wulQbO55jsTbcaLy+XZKfy9VfCfvgDH/LJ0nwXfuOSrI45C7Vxg5UVR/P5v+RufMqlrOR3T/J45LsnWT208hvJXnGPAriKocluXeSX0vyF1X1gyTv7e6nzresjaGqvpXN/4668SqWs9GdPl4fnOQOSd4wLj8iyRlzqWiDqqp7JrlXkj0W9XDdOMNXYbJKHIu1pbv/PEmq6l1J7rrwYXVV/VkmnDEE3zWqqh6Y5JeS7FVVL51ZdeMkl8+nqg3tQeP1keP1a8brxyT57uqXszF19wlJTqiqX+vuf5l3PVytuz9bVd9L8sPxct8kt59vVRtHd98oSarqmCRfyfA7qjL8jrrRZu7KCht/T6WqHpfkvt39o3H575K8a46lbUTXTXLDDP/vzv4cfDPJw+dS0cblWKxNP5Hhb/aCHybZdz6l7Hi+x3eNqqoDktwlyTFJnjWz6ltJ3t3dl82lsA2uqt7f3QdvqY0dq6qOzhK9W919zBzKIUlVfSbJV5O8NsNw57O6+8r5VrXxVNUHu/vuW2pjx6uqc5Lcs7svHZd3S3Jad99uvpVtLFW1U5I3dLdwtQZU1a27+/NVdYPu/s6869noqupPkjwyyVsy/F/1q0lO7O7nzbWwHUSP7xrV3R9N8tGqeu3Cp8WsCTeoqnt39/uSpKruleQGc65pI/r2zO1dM/TInz2nWhi8NMNQ50cn+dkk/11Vp3b3Z+Zb1oZzRVU9JsnrM/wT8+gkV8y3pA3r+Uk+UlXvHpfvk+TP5lfOxtTdV1TV7vOug6vcqqrekaH39yfGjp7f7e7/Pee6NqTufu54PH5+bHp8d39knjXtSHp817iqOjjDH8pbZ/igopJ0d//kPOvaqKrqbkmOT3KTDP9UfiPJb3f3mXMtbIMbv8ropO6+/7xr2eiq6oZJHp/k/ybZu7udu7WKqmrfJH+d4fzSTvL+JE/p7vPnV9XGNU7It9Db/sHu/so869moquqvkuyX4dzFq3oZu/vNcytqg6qqD2YY2nxSd//s2PaJ7r7jfCvbWLb0YdDCSJWp0eO79h2X5KkZJsTwqf2cdfcZSQ6oqhtn+ODoG/OuiSTDjM4+DJqj8R/Le2f4FP+0DKdovHeuRW1AY8B9yLzrIKmqSnJIkp/s7mOq6ieq6qDu/tC8a9uAdk/ytST3m2nrJILvHHT3F4cfj6v4/3b1nZHhZ2D2QCwsdyb6P5Xgu/Z9o7vfMe8iGFTVLZI8L8mtuvuBVXWHDOdwHTfn0jaURbNs75RkjwznwzM/pyV5YXdfNO9CNrKq+ukkr0hyi+6+Y1XdOcmvdPdz5lzaRvTyJFdmCFvHZJij418yfHUIq6i7Hz/vGrjKF8fTxLqqrpvkSXGq0qrr7tvMu4Z5MNR5jaqqhe8ze2SGf+zfnOQHC+sNrZ2P8TyIf0jyJ919QFXtnOQj3X2nOZe2oVTVrWcWL09yUXeb7XzOquphGXp9O8n7uvstcy5pw6mq/07yR0n+3jDC+aqqM7v7rlX1kZlj8dHuPmDetW00VfUPWXpCxN+eQzkbWlXdLMPpGIdk6F18V5Ind/fX5lrYBjWOTHlMktt097Or6ieS3HKqI1P0+K5df7Vo+cCZ251rDtdh9dysu0+sqqOSpLsvrypDdFbZOCPkAbl6MoZTk3xsjiVteFX18iS3TfK6sel3q+qQ7j5yM3dj5V2/uz+0aBihD4Xm40fjjMKdJFW1R4YeYFbf22Zu75ph5toL51TLRndldz9mtqGqbpNhKDqrb3ZkyrMz8ZEpgu8a1d33nXcNLOk7VfXjufofmXtkmOCKVVRVT07yhFx9ftY/V9Wx3f2yOZa10d0nyR17HEZUVSck+fh8S9qQvlpVP5Wrf0c9PMmX51vShvXSDF8RcvOqem6GCX2eOd+SNqbF3/teVa9L8p9zKmej+7eqemB3fzNJqur2GSYdMyplPu6+MDIlSbr7snEI+iQJvmtcVf3BEs3fSHJGd5+12vWQP0hyUpKfqqr3Zzi31HcDrr7DM/yy/k6SVNULknwgieA7P+ck+Ykknx+X94le+Hk4MsmxSfavqi8l+VyGYWyssu7+56o6I8kvZhjS+dDudi7j2rBfht9XrL7nZQi/v5zkdkleHb+j5mlDjUwRfNe+A8fLv43Lv5zkw0meWFVv7O4Xzq2yDai7z6yq+2T4ZV1JzvE9y3NRueYskFfkmjMTskqq6t8y/MG8SZKzq+pD4/Ldk/zPPGvbiLr7s0kOqaobJLlOd39r3jVtNIu+JuTiXD38P1W1+1S/JmQtq6pv5Zoz1n4lydPmWtQG1d3/XlW7ZDi390YZPhA6d85lbWQbamSKya3WuKp6Z5Jf6+5vj8s3TPKmDOennNHdd5hnfRvFOGnPJvkuwNU1joQ4LMMv6yR5aJJ/7O6XzK+qjWn8IGiTuvu/V6sWkvFUjKMzM8lYkmNMHLN6qupzuebXhCz8o1VJursn+TUhsDlV9bJcc4Kx+yX5bJLzk6S7nzSHskhSVfvn6pEpp0x5ZIrgu8ZV1dlJDujuH47L10tyVnfffnamSHascUbITWkzQ66+cebze2f4RX1qd39kziXB3FXVyRkme/unsekxSX6huw+ZX1Uwf1W1V5JbZ2a0Y3efOr+KNpaqOmxz67v7hNWqhaSqbtzd31w0QuUqUx2ZIviucVX1pxl6d986Nj04wzmmf5Xk2MUz4wGsppkhhNdaleFDoRuvckkbWlWd0d13W9R2encfuKn7sGNU1auTvDfJe7v70/OuZyMb54H49SSfytWnyXR3/8r8qtqYxtMwvt/dV4zLOyW5Xnd/d76VbSxV9bbuftDMCJWrVmXCI1ME33Wgqu6Wq3u23tfdp8+5pA2rqj6T5LQM/8yc2t2fmnNJAFepqr9McnqSE8emhyf5me4+en5VbUxVdb8Mf7t/PslPJjkrw9+Nv55rYRtQVZ2T5M7d/YN517LRVdVpSQ5ZdArfu7r7XvOtbGOpqnt39/uqatfu/v6861ktgu8aN36R9LV09xdWuxauGmp+9wz/yBycZP8kH+3uX51rYQC5qgf+Brl6Vs7rJPnOeFsP/Cobe7N+Lsl9kzwxyfe6e//5VrXxVNU7kjxiIWwxP1V1VnffZUtt7FgLo4Oq6szuvuu861ktZnVe+/49Vw9B+LEkt8nwtSE/M7eKNrYrkvxovL4yyUUZZu0EmLvuvtG8a2BQVadk+BDiAxlGCf1cd/t7sYpmJlT6bpKzxmNyVa+vCZXm4jtVddfuPjO5alTj9+Zc00b0o3H+mr2r6qWLV071Z0PwXeO6+06zy+OEPr87p3JIvpnk40lelOSVZkqdj3GW7RckuXmGUwCcTwqjqrpzkn1zzUl8zDy/+j6W5G5J7pjkG0m+XlUf6G7/5K+ehVPDzsgwPwrz95Qkb6yqC8flPTOcf83qelCSQzLMrn3GnGtZNYY6r0MbbVjCWlJVD8lwztZBSX6Y4XtKT+3uU+Za2AZTVeclefCUp9yHbVFVxye5c5JP5urhzmaen6PxHMbHJ/m/SW7Z3debc0kwV+P3+N4uw4fWn+7uH825pA2rqg7o7o/Ou47/v707j7O7qtM8/nnCGhAwIOLCJjSCaWUNEARlEWkQRBZBGR1AbEC6lXXGhbaFVmSQaW0ZHO1GkQ5uw6pI6AbZZA9LIOwwCBJsdpUlhi2Ep/84v0puKpUE7HvvKe7veb9e9ar6Lcl96tYrlfu953vO6ZcUvqNcs1/pkDHAxsBKtv+qUqRgzp5nO1HeuXyz7bGVI7WKpGtsb1k7R8RoI+mu7O8+Okj6LGU9iE2A6ZRtpq6yfVnVYBEVSNrO9mVNx9Z80pUS/ZBW59Gvc77Wy5Q5v+dUytJ6ks4BNgR+A1wN7AtcXzVUO90k6QzgF8w7Xyv/cUbbXSdpfFacHxXGUqbFTLX9cu0wEZVtDVxG2ZZzOAP5/zt6LiO+Ea+BpE2Bm4f2n4s6mgUZhks7Z7SepPcD5wOPUd4UGpr/vn7VYC0laQPKqC+U0d7WtBRGRIw2KXxHOUkrA5+nrOK89NB529tVC9VizbyUQ4D3N6euAP4581MiYjRo5r8fSVmEb2iOL7anVwvVUpIOBQ5i7kjW7sAptk+ul6pdJJ3P3J0x5mN71z7GCUDS/cAUykrnV6Y7pS5JqwDHA2+zvZOk8cAWtk+tHK0nUviOcpJ+BZxBWRTjM8B+wJO2v1A1WEtJ+gGwBDCpOfXfgdm2/7peqvZpRnzn++WVEd9oO0mX5Y3R0UHSbZQXkDOb42WB6zL63j+Stm6+3AN4C/Dj5ngf4EHbR1cJ1mKSlgI2p3RCbAmsB9xqe/eqwVqq2eP6NODvbG8gaXHgluG7ygyKzPEd/Vayfaqkw2xfAVwh6YraoVpsU9sbdBxfJimta/03uePrpSkjKY8s4N6INrlH0k8p7c6Z/16XKHu+D5ndnIs+aV43Ielrtt/fcel8SVdWitV2s4FZzedXgMeB7G9dz5tsnynpSwC2X5Y0sNP5UviOfkMttI9K2pny4n7VinnabraktW3fDyBpLeZ9YRN9YHueBd4k/Qy4pFKciNFkLKXg3aHjXBaOqeM04HpJP6cUvB8BBrJ98HVgZUlr2X4AQNI7gJUrZ2qrZylTMb4FfN/2HyrnabuZklai6aKTNJGy7/hASqvzKCdpF8o8iNWAk4HlgWNtn181WEtJ+gDlxcwDzak1gU/ZvrxaqEDSusAFtv+idpaIiCGSNqbs/Q5lcatbauZpK0k7Aqcw7//dB9u+qFqolpL0Ecq/ic2Al4BrKXN9L60arKWa31EnA+8G7qC8IfRR27dVDdYjKXxHOUmTgMNsP90crwj8Y+Yy1iFpaeAo4APNqYuBf7L9Qr1U7SNpBvPO8X0M+NLwkeCItsn899GleVH5PkpL5zW2b64cqbWauaXrNYf32H5xYfdHb0laD9gJOBx4s+2xlSO1VjOvd11KZ8q9g7xgawrfUU7SLbY3WtS56A9JZ1LadH7SnNoHGGd7r3qp2kWSgNVsP1Q7S8RoI2nPjsM5899tH1opUmtJ+gqwF3AO5QXlbsBZto+rGqyFJC1DWe18DdsHSloHWNf25EX80egySecAGwK/oXQ0XgVcnwGEeiS9l9IFMWcKrO3TqwXqoRS+o1yzcNI2tp9qjlcErhjU1dZGO0m3DlvcasRz0VuSptrepHaOiNFO0hjgkqz03H+S7gY2GnpBL2ksZR/4d9VN1j6SzgCmAvvafnfzs7jO9oaVo7WOpE0p/w6yPsooIOlHwNrANOauWeNBfbM0i1uNft8ErpV0NqV9bW/g63UjtdotkibangIgaXPgmsqZ2miKpE1t31g7SMQotw6weu0QLfUgZdR9aCRrKeD+amnabW3bH5O0D4Dt55vuoeiz/L896kwAxrslI6EpfEc526dLugnYjtIqtUc2++4/SbdT3nhYAthX0kPN8RpAfh79ty1wsKTpwEzKvw1nf8xouwXMf8++730k6WTKz+BF4E5JFzfHHwSurpmtxV5qRnmHVq5dm47tviJa7A7KHteP1g7SD2l1jngVJK2xsOu2p/crSyz455GfQ0TUJmm/hV23PalfWaKQ9EHgy8B44FfAlsD+tn9dM1dELZLOp7wRtBxlzvUNzLv3+66VovVUCt+IiIgBIelS2x9Y1LmItmhamlcFngMmUjqEptj+fdVgLZXfUaODpK0Xdt32Ff3K0k9pdY6IiHida7ZaWwZ4k6RxlBf3UPZ+f1u1YBGV2bakXzQLIl5QO09b5XfU6DJU2Er6hu15psNI+gYwkIXvmNoBIiIi4r/sYMqqtes1n6cCNwHnAd+pmCtiNJjSrCYc9XT+jrqZub+nzgP+b8VcbffBEc7t1PcUfZJW54iIiAHR7B37bdvPSvp7YGPga7ZvrhwtohpJdwHvBLIgYmWSPmf75No52k7SIcDfAGsx72rzywHX2P5klWA9lsI3IiJiQEi6zfb6krYCjqdsiXe07c0rR2uNjkVjRjSoi8aMZlkQsT5J29m+TNIeI123fW6/M7WZpBWAccD/Ar7YcWmG7T/WSdV7meMbERExOGY3n3cG/tn2eZKOrZinjf6x+bwHZZuQHzfH+1D29o0+kbS87WeBGbWzBFsDlwEfHuGagRS+fWT7GeAZyu+l1siIb0RExICQNBl4GNge2AR4HrjB9gZVg7WQpCttv39R56J3JE22vYuk31KKK3Vctu21KkWLiApS+EZERAwIScsAOwK3275P0luB99j+VeVorSPpbmBn2w80x+8A/s32u+omi6hH0lLAnsCadHSe2v5qrUzRHml1joiIGBC2n6OjZdD2o8Cj9RK12hHAryU90ByvSVnZNvose8eOKudRWmynAi9WzhItk8I3IiIiostsXyhpHcr2LQD32M4L/T7K3rGj0qq2d6wdIgpJM5h/Mb5nKNvhHTXUsTIoUvhGRERE9MYmzG3p3EAStk+vG6lVDgYOpxS5nVt6PUv2jq3lWknvsX177SABwLeAR4CfUt4Y+jhlUb57gR8C21RL1gOZ4xsRERHRZZJ+BKwNTGPuatu2fWi9VO2UvWPrk3Q7ZWRxcWAd4AFKq3P2VK5I0vXDt7uTNMX2REm3DtrCiBnxjYiIiOi+CcB4Z4RhNPihpC8Dq9s+qGlBX9f25NrBWmSX2gFiRK9I2hs4uzn+aMe1gfvdlcI3IiIiovvuoLQMZnGx+n5IWUzpvc3xfwBnASl8+8T29NoZYkSfAE4CvkspdKcAn5Q0FvhszWC9kFbniIiIiC6TdDmwIXADHavX2t61WqiWknST7QmSbrG9UXNu4No4I2LhMuIbERER0X3H1g4Qc7zUjGAZQNLaZCudCCStDBzI/PsqH1ArUy+l8I2IiIjoMttX1M4QcxwDXAisJuknwJbA/lUTRYwO5wFXAZcwdxG+gZVW54iIiIgukzQROBl4F7AksBgw0/byVYO1jCQBqwLPARMpqwhPsf37qsEiRgFJ02xvWDtHv4ypHSAiIiJiAH0H2Ae4DxgL/HVzLvqoWVX7F7b/YPsC25NT9EbMMVnSh2qH6JcUvhERERE9YPs3wGK2Z9s+DdimcqS2miJp09ohIkahwyjF7/OSnpU0Q9KztUP1Sub4RkRERHTfc5KWBKZJOpGyrdGylTO11bbAwZKmAzMp7c62vX7dWBF12V6udoZ+yhzfiIiIiC6TtAbwOGV+7xHACsB3m1Hg6KPmZzGf7C0bbSVpPdv3SNp4pOu2b+53pn5I4RsREREREdESkk6xfVCz3/hwtr1d30P1QQrfiIiIiIiIlpG0tO0XFnVuUGRxq4iIiIiIiPa59lWeGwhZ3CoiIiKiyyTtZfusRZ2LiOg3SW8B3g6MlbQRZcE3gOWBZaoF67G0OkdERER0maSbbW+8qHMREf0maT9gf2ACcCNzC99ngUm2z60UradS+EZERER0iaSdgA8BewNndFxaHhhve7MqwSIihpG0p+1zaufol8zxjYiIiOieR4CbgBeAqR0fvwT+qmKuiIjhNpH0xqEDSeMkHVczUC9lxDciIiKiyyQtYXtW7RwREQsi6RbbGw07N7BTMrK4VURERET3bSbpWGANyustUfbHXKtqqoiIuRaTtJTtFwEkjQWWqpypZ1L4RkRERHTfqcARlDbn2ZWzRESM5MfApZJOAwwcAEyqG6l30uocERER0WWSrre9ee0cEREL0yzI9wFKV8qvbF9UOVLPpPCNiIiI6BJJQ3Pj9gYWA84FXhy6bvvmGrkiItouhW9EREREl0i6fCGXbXu7voWJiFgISTMoLc4ASwJLADNtL18vVe9kjm9EREREl9jetnaGiIhXw/ZynceSdgMGdq/xjPhGREREdJmkI0c4/Qww1fa0fueJiHg1JE2xPbF2jl7IiG9ERERE901oPs5vjncGbgQ+I+ks2ydWSxYRAUjao+NwDOV31sCOimbENyIiIqLLJF0E7Gn7T83xG4Czgd0po77ja+aLiGi2MRryMvAg8H3bT9RJ1FsZ8Y2IiIjovtWBlzqOZwFr2H5e0osL+DMREX0haTHgNtv/VDtLv6TwjYiIiOi+nwJTJJ3XHH8Y+JmkZYG76sWKiADbsyXtCrSm8E2rc0REREQPSNoE2AoQcLXtmypHioiYQ9LXgRWAM4CZQ+cHdb/xioO9twAACptJREFUFL4RERERXSZp9ZHO236o31kiIkaygH3HB3a/8RS+EREREV0m6Xbmro46FngHcK/tv6yXKiJiLklr2X5gUecGxZjaASIiIiIGje332F6/+VgH2Ay4unauiIgOZ49w7qy+p+iTLG4VERER0WO2b5a0ae0cERGS1gP+Elhh2F6+ywNL10nVeyl8IyIiIrpM0pEdh2OAjYEnK8WJiOi0LrAL8EbKivNDZgAHVknUB5njGxEREdFlko7pOHwZeBA4x/YLdRJFRMxL0ha2r6udo19S+EZERERERLSMpBOB44DngQuBDYDDbf+4arAeSeEbERER0WWSVgY+T5lHN2fO3KBuExIRrz+SptneUNLuwG7AEcDltjeoHK0nsqpzRERERPf9BLiHso3RP1BanW+sGSgiYpglms8fAn5m+481w/RaCt+IiIiI7lvJ9qnALNtX2D4AmFg7VEREh/Ml3QNMAC5tOlUGdh2CFL4RERER3Ter+fyopJ0lbQSsWjNQREQn218EtgAm2J4FzAQ+UjdV72Q7o4iIiIjuO07SCsBRwMmU/TGPqBspImIuSUsDnwK2kmTgauB7dVP1Tha3ioiIiIiIaBlJZ1L27h1axXkfYJztveql6p20OkdERER0maRJkt7YcTxO0g9rZoqIGGZd25+2fXnzcRDwztqheiWFb0RERET3rW/76aED208BG1XMExEx3C2S5iy6J2lz4JqKeXoqc3wjIiIium+MpHFNwYukFcnrrogYBSTdDpiyndG+kh5qLq0O3FUtWI/lF3BERERE930TuFbS2ZQXmHsDX68bKSICgF1qB6ghi1tFRERE9ICk8cB2gIBLbQ/sSEpEvD5J2gB4X3N4le1ba+bppRS+ERERERERLSPpMOBA4Nzm1O7AKbZPrpeqd1L4RkREREREtIyk24AtbM9sjpcFrrO9ft1kvZFVnSMiIiIiItpHwOyO49nNuYGUxa0iIiIiIiLa5zTgekk/b453A06tmKen0uocERERERHRQpI2BraijPReafuWypF6JoVvREREREREDLTM8Y2IiIiIiIiBlsI3IiIiIiIiBloK34iIeF2T5FfxsU3tnN0iaXFJ35f0RPO9fXEB953Q8f3PlvSUpOslfVXSysPuXa+5b/s+5P9M81h9W2Czn99fRESMTlnVOSIiXu+26Ph6LHAZcBxwQcf5u/qaqLc+DhwA7A/cBzy0kHufBHZtvn4jMAH4DHCQpB1s39Zce5DyPPbjeToXmGb75T48VkREBJDCNyIiXudsTxn6WtIbmi/v7zw/YNYDHrf9o1dx70vDnocLJX0PuBr4maR3u3gB6MvzZfsJ4Il+PFZERMSQtDpHRMTAk7SKpFmSPjbs/GKSHpZ0fHN8gqT/kLSNpGmSXpA0VdLEEf7OQyTdLelFSb+VdPiw6xtIurhpMf6TpDslHbiInG+Q9N2mjfn5pjV5247rU4C/A97a0cb8ltfyXNj+A/AlYDywdfP3ztcKLGlPSbdIek7SHyVdJ+m9zbWlm/v/RtJJzfUnJB3aXD9Q0oPN9/4vkpbs+HvnaXVW8RVJDzTP92OS/k3SSh2P9W1Jv2ue64clnSNpTHN9NUmTmp/B85LulXSMpCVey/MSERGDLSO+EREx8Gw/Lmky8CngjI5LOwBvA/6149zywGmUdukngC9SRkrXbopGJP098GXgBOAqYCJwoqQ/2f5BU5RdANwE/DfgJeBdwAqLiDoJ2J5SmD4IHAJcJGkr2zcAnwa+AOzI3BbmP7ymJ6O4FHCT+9fDL0oaD/w/4H8DRwLLUNqkxw279UvAecDHgD2AkyStCmwE/C2wNvBN4G7g2wvIciBwFPD55r6VKc/B2Ob6V4A9gaOB6cBbgV0oe04CvBl4DDgceJryPB8LrAgctqgnIiIi2iGFb0REtMWpwHmS3m774ebcp4BrbP//jvuWA/a3fS6ApKuA3wGfA46VtCKlCPuK7W80f+YSSctTirQfUIrptwPb2r6vuefShYWTtCGlePy47TOacxcB91BGeT9i+05JjzB/C/NrYnuGpGeAVRZwy8bAE7aP7jh3wQj33WH7s03Wy4G9Kc/pGrafa85vD+zOggvfzYDJtv+l49w5w66fPqy1e86bF7anAlObxxJwDeWNhpMkHWl79gIeNyIiWiStzhER0Rb/ThkZ3BdA0jjKqOlpw+6bDfxy6MD205QFszZrTr0PWBo4S2WF5cWbtt1LgdUkrQI83jzW9yXtNXwV5QXYrHnsczseezZwNrDVa/xeXw0t5NptlHbqH0jaXtIyC7hvTjHfLFY1Hbh+qOht/IbyJsCCTAN2a9qdJwy1MA+7fqCkoyS9e75vQhoj6X9Kugd4HphFeZPjDZTR4YiIiBS+ERHRDk0ROYmyGjKUFuSXgTOH3frUCCsOP8HcIupNzef7KUXW0MeFzfnVbM8CPkhpvZ0EPCbp15Les5CIb20ee9aw848zf4vxf4mk5Sgt3Y+PdL1Z7XkPStvwRcDvJZ3ejHZ3enrY8UsLOLf0QuJ8D/gH4BPAjZTn6piOAnhoFP0w4HZJD0k6pOPPfwE4njIK/GHKGwhHNNcW9rgREdEiKXwjIqJNTgXWaRZp2h842/aMYfeM0/x7zL4ZeLT5+o/N5x2ATUf4uBvA9h22d6NsI7QjZX7v+QvJ9mjz2MMXZVoFeOpVfXev3vaUEd/rFnSD7V/Y3hJYCTgY2Bn4VpdzYHu27RNtrwusCZwMHEMzMm/7OdtH216dsqL1ecB3NXdv5r2An9g+xvbFtm+kjPxGRETMkcI3IiJaw/b9wBWURakmMH+bM8BizF04CkkrANsBNzSnrqaMYr7F9k0jfMwc9pgv2b4Y+D/AGpKWXUC8G5rH3r3jsRejLOx09Wv/bkfWjNoeD9wJXLmo+20/3cyvnUxZCbpnbE+3/TXKnOr5Hsv2vZTR3Fc6ro8FXhx26yd6mTMiIl5/srhVRES0zanAjyityiMVfjOAbzVzgIdWdX4F+A6A7SclfR34nqS/oBSliwPrAu+1vbekzSjtu2cCv6W0Rx9Fmf86c/gDNn/vNEnnAqc0xel0yqrOa/LnF3JLau5WTCtQiv1Dmrw72PZIf6jZlmh94GLKSPR6wG6UtuSuknQa8DCl8H+WMpK+GnB5c/0CyoJV0ygF7scpc6Gvav6Ki4FPS7qZ8pztB6za7ZwREfH6lsI3IiLa5peUrXz+dQGF37PAAcBJwDuBu4CdbP9+6AbbX5X0O+BQyhzT54B7gZ82tzxMaU/+Cs3cXeASShG9MPtRthD6GmV16VuBHZv23T/HypR25lcoBf19lML/O7afXMifmwbsRFmJeRzwCKXwP/bPzLEw11Ke778Flmwy7m/735vr1wAfZe5zdyewm+3bm+MvNxlPoHyfZwH/g3lXho6IiJbTAt7sjYiIGEiS9qAUR2va/t2waycAn7SdEcOIiIgBkhHfiIhoBUlvp4zgHgf8fHjRGxEREYMri1tFRERbfI4yH/Rp4PDKWSIiIqKP0uocERERERERAy0jvhERERERETHQUvhGRERERETEQEvhGxEREREREQMthW9EREREREQMtBS+ERERERERMdBS+EZERERERMRA+0/iRpvObqE0RgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "dismissal_type = deliveries.dismissal_kind.value_counts()\n",
    "sns.barplot(x = dismissal_type.index, y = dismissal_type,palette=\"mako\")\n",
    "plt.title('Types of Dismissal in all seasons of IPL', fontsize=15)\n",
    "plt.xlabel('Types of Dismissal', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Frequency', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caught is the most dismissal in the all seasons of IPL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best strike rate of batsman over the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>season</th>\n",
       "      <th>batsman</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>11</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>145.640370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>10</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>141.751527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>10</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>139.523249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>11</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>136.945813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>9</td>\n",
       "      <td>JA Morkel</td>\n",
       "      <td>136.938202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season     batsman  strike_rate\n",
       "92       11    CH Gayle   145.640370\n",
       "213      10  KA Pollard   141.751527\n",
       "112      10   DA Warner   139.523249\n",
       "444      11   SR Watson   136.945813\n",
       "188       9   JA Morkel   136.938202"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we will consider batsmen, which played most of the seasons\n",
    "no_of_balls = pd.DataFrame(df.groupby('batsman')['ball'].count())\n",
    "runs = pd.DataFrame(df.groupby('batsman')['batsman_runs'].sum())\n",
    "seasons = pd.DataFrame(df.groupby('batsman')['season'].nunique())\n",
    "\n",
    "strike_rate = pd.DataFrame({'balls':no_of_balls['ball'], 'run':runs['batsman_runs'], 'season':seasons['season']})\n",
    "strike_rate.reset_index(inplace = True)\n",
    "strike_rate['strike_rate'] = (strike_rate['run']/strike_rate['balls'])*100\n",
    "highest_strike_rate = strike_rate[strike_rate.season.isin([9,10,11])][['season','batsman','strike_rate']].sort_values(by='strike_rate',ascending = False)\n",
    "highest_strike_rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Strike Rate')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "sns.barplot(x='batsman', y='strike_rate', data=highest_strike_rate.head(10), hue='season')\n",
    "plt.title('Highest Strike rate player of IPL', fontsize=15)\n",
    "plt.xlabel('Player', fontsize=15)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Strike Rate', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gayle and Pollard have the highest strike rates in IPL."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Scoring runs over the season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total runs')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('season')['batsman_runs'].sum().plot(kind='bar',figsize=(16,8), color='y' )\n",
    "plt.title('Total scoring runs over the seasons of IPL', fontsize=15)\n",
    "plt.xlabel('Seasons', fontsize=15)\n",
    "plt.ylim(10000,22000)\n",
    "plt.ylabel('Total runs', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest total runs in a season have scored in the season 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}