From cfee89dd21eccb5df2b67b4c0ad18e77b55bc477 Mon Sep 17 00:00:00 2001
From: Prajwal KV <60724395+prajwal-kv@users.noreply.github.com>
Date: Mon, 2 Nov 2020 10:28:59 +0530
Subject: [PATCH] Add files via upload

---
 KKR_RR.ipynb | 10288 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 10288 insertions(+)
 create mode 100644 KKR_RR.ipynb

diff --git a/KKR_RR.ipynb b/KKR_RR.ipynb
new file mode 100644
index 0000000..993b6ec
--- /dev/null
+++ b/KKR_RR.ipynb
@@ -0,0 +1,10288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis Of KKR vs RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Matches=pd.read_csv(\"matches.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>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",
+       "  </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",
+       "\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",
+       "\n",
+       "   result  dl_applied                  winner  win_by_runs  win_by_wickets  \\\n",
+       "0  normal           0     Sunrisers Hyderabad           35               0   \n",
+       "1  normal           0  Rising Pune Supergiant            0               7   \n",
+       "\n",
+       "  player_of_match                                      venue         umpire1  \\\n",
+       "0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
+       "1       SPD Smith    Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
+       "\n",
+       "    umpire2 umpire3  \n",
+       "0  NJ Llong     NaN  \n",
+       "1    S Ravi     NaN  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many rows and columns are there?#  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   id               756 non-null    int64 \n",
+      " 1   season           756 non-null    int64 \n",
+      " 2   city             749 non-null    object\n",
+      " 3   date             756 non-null    object\n",
+      " 4   team1            756 non-null    object\n",
+      " 5   team2            756 non-null    object\n",
+      " 6   toss_winner      756 non-null    object\n",
+      " 7   toss_decision    756 non-null    object\n",
+      " 8   result           756 non-null    object\n",
+      " 9   dl_applied       756 non-null    int64 \n",
+      " 10  winner           752 non-null    object\n",
+      " 11  win_by_runs      756 non-null    int64 \n",
+      " 12  win_by_wickets   756 non-null    int64 \n",
+      " 13  player_of_match  752 non-null    object\n",
+      " 14  venue            756 non-null    object\n",
+      " 15  umpire1          754 non-null    object\n",
+      " 16  umpire2          754 non-null    object\n",
+      " 17  umpire3          119 non-null    object\n",
+      "dtypes: int64(5), object(13)\n",
+      "memory usage: 106.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "Matches.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Match details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         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",
+       "751  11347    2019         Mumbai    05/05/19        Kolkata Knight Riders   \n",
+       "752  11412    2019        Chennai    07/05/19          Chennai Super Kings   \n",
+       "753  11413    2019  Visakhapatnam    08/05/19          Sunrisers Hyderabad   \n",
+       "754  11414    2019  Visakhapatnam    10/05/19               Delhi Capitals   \n",
+       "755  11415    2019      Hyderabad    12/05/19               Mumbai Indians   \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",
+       "751               Mumbai Indians               Mumbai Indians         field   \n",
+       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
+       "753               Delhi Capitals               Delhi Capitals         field   \n",
+       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
+       "755          Chennai Super Kings               Mumbai Indians           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",
+       "751  normal           0               Mumbai Indians            0   \n",
+       "752  normal           0               Mumbai Indians            0   \n",
+       "753  normal           0               Delhi Capitals            0   \n",
+       "754  normal           0          Chennai Super Kings            0   \n",
+       "755  normal           0               Mumbai Indians            1   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "0                 0    Yuvraj Singh   \n",
+       "1                 7       SPD Smith   \n",
+       "2                10         CA Lynn   \n",
+       "3                 6      GJ Maxwell   \n",
+       "4                 0       KM Jadhav   \n",
+       "..              ...             ...   \n",
+       "751               9       HH Pandya   \n",
+       "752               6        AS Yadav   \n",
+       "753               2         RR Pant   \n",
+       "754               6    F du Plessis   \n",
+       "755               0       JJ Bumrah   \n",
+       "\n",
+       "                                         venue         umpire1  \\\n",
+       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
+       "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
+       "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
+       "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
+       "4                        M Chinnaswamy Stadium             NaN   \n",
+       "..                                         ...             ...   \n",
+       "751                           Wankhede Stadium   Nanda Kishore   \n",
+       "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
+       "753                           ACA-VDCA Stadium             NaN   \n",
+       "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
+       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
+       "\n",
+       "            umpire2                  umpire3  \n",
+       "0          NJ Llong                      NaN  \n",
+       "1            S Ravi                      NaN  \n",
+       "2         CK Nandan                      NaN  \n",
+       "3     C Shamshuddin                      NaN  \n",
+       "4               NaN                      NaN  \n",
+       "..              ...                      ...  \n",
+       "751        O Nandan                   S Ravi  \n",
+       "752     Nitin Menon                Ian Gould  \n",
+       "753             NaN                      NaN  \n",
+       "754  Bruce Oxenford  Chettithody Shamshuddin  \n",
+       "755       Ian Gould              Nigel Llong  \n",
+       "\n",
+       "[756 rows x 18 columns]>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'season', 'city', 'date', 'team1', 'team2', 'toss_winner',\n",
+       "       'toss_decision', 'result', 'dl_applied', 'winner', 'win_by_runs',\n",
+       "       'win_by_wickets', 'player_of_match', 'venue', 'umpire1', 'umpire2',\n",
+       "       'umpire3'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of Rows are: 756\n",
+      "No of columns are: 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of Rows are:\",len(Matches))\n",
+    "print(\"No of columns are:\",len(Matches.columns))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  756 rows are there that means we have details 756 matches that takes place between 2008-2019 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of matches that takes place between 2008-2019 were: 756\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of matches that takes place between 2008-2019 were:\",len(Matches))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It looks like between 2011-2013.Total no matches exceeds 70.That because in these periods(2011-2013)there were 10 teams(kochi tuskers kerala and pune warriors india were the new additions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88acc9d48>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "year_wise_analysing=Matches.groupby('season')\n",
+    "year_wise_analysing.count()\n",
+    "year_wise_analysing['id'].count().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "season\n",
+       "2008    58\n",
+       "2009    57\n",
+       "2010    60\n",
+       "2011    73\n",
+       "2012    74\n",
+       "2013    76\n",
+       "2014    60\n",
+       "2015    59\n",
+       "2016    60\n",
+       "2017    59\n",
+       "2018    60\n",
+       "2019    60\n",
+       "Name: id, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "year_wise_analysing['id'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## from the below plot we can find huge missing values are there in umpire 3 columns .we can remove the column it will not create a impact on predicting the matches.#  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88addf848>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Matches.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Removed the columns containing more missing values and have less impact on the predicting matches\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Matchess=Matches.drop(['umpire3','city'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>id</th>\n",
+       "      <th>season</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2017</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  season        date                team1                        team2  \\\n",
+       "0   1    2017  2017-04-05  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "1   2    2017  2017-04-06       Mumbai Indians       Rising Pune Supergiant   \n",
+       "\n",
+       "                   toss_winner toss_decision  result  dl_applied  \\\n",
+       "0  Royal Challengers Bangalore         field  normal           0   \n",
+       "1       Rising Pune Supergiant         field  normal           0   \n",
+       "\n",
+       "                   winner  win_by_runs  win_by_wickets player_of_match  \\\n",
+       "0     Sunrisers Hyderabad           35               0    Yuvraj Singh   \n",
+       "1  Rising Pune Supergiant            0               7       SPD Smith   \n",
+       "\n",
+       "                                       venue         umpire1   umpire2  \n",
+       "0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar  NJ Llong  \n",
+       "1    Maharashtra Cricket Association Stadium  A Nand Kishore    S Ravi  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matchess.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  No missing values are there(well some small amount are still there but will not have impact on Today's match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88aed3c88>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Matchess.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Today's match is between Rajastan Royals(RR) and Kolkata knight riders(KKR).so we only want information between the matches that had take place between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>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>77</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2008-05-01</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>GA Pratapkumar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>126</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Cape Town</td>\n",
+       "      <td>2009-04-23</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>field</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Newlands</td>\n",
+       "      <td>MR Benson</td>\n",
+       "      <td>M Erasmus</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>168</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Durban</td>\n",
+       "      <td>2009-05-20</td>\n",
+       "      <td>Rajasthan Royals</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>4</td>\n",
+       "      <td>LR Shukla</td>\n",
+       "      <td>Kingsmead</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>SJA Taufel</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>187</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ahmedabad</td>\n",
+       "      <td>2010-03-20</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AA Jhunjhunwala</td>\n",
+       "      <td>Sardar Patel Stadium, Motera</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>227</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2010-04-17</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>246</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2011-04-15</td>\n",
+       "      <td>Rajasthan Royals</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>9</td>\n",
+       "      <td>G Gambhir</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>SS Hazare</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>251</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2011-04-17</td>\n",
+       "      <td>Rajasthan Royals</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>8</td>\n",
+       "      <td>L Balaji</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>314</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2012-04-08</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>BJ Hodge</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>BF Bowden</td>\n",
+       "      <td>VA Kulkarni</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>322</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2012-04-13</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Shakib Al Hasan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Asad Rauf</td>\n",
+       "      <td>S Asnani</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>389</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2013-04-08</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SK Trivedi</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>S Das</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2013-05-03</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>476</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>2014-04-29</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>JP Faulkner</td>\n",
+       "      <td>Sheikh Zayed Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>482</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Ahmedabad</td>\n",
+       "      <td>2014-05-05</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PV Tambe</td>\n",
+       "      <td>Sardar Patel Stadium, Motera</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>570</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>Mumbai</td>\n",
+       "      <td>2015-05-16</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SR Watson</td>\n",
+       "      <td>Brabourne Stadium</td>\n",
+       "      <td>RM Deshpande</td>\n",
+       "      <td>RK Illingworth</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>7908</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>18/04/18</td>\n",
+       "      <td>Rajasthan Royals</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>7</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>A.D Deshmukh</td>\n",
+       "      <td>C Shamshuddin</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>15/05/18</td>\n",
+       "      <td>Rajasthan Royals</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>6</td>\n",
+       "      <td>Kuldeep Yadav</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Kumar Dharmasena</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Vineet Kulkarni</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11312</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>07/04/19</td>\n",
+       "      <td>Rajasthan Royals</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>8</td>\n",
+       "      <td>H Gurney</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Chris Gaffaney</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Bruce Oxenford</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season       city        date             team1  \\\n",
+       "77      78    2008     Jaipur  2008-05-01  Rajasthan Royals   \n",
+       "125    126    2009  Cape Town  2009-04-23  Rajasthan Royals   \n",
+       "167    168    2009     Durban  2009-05-20  Rajasthan Royals   \n",
+       "186    187    2010  Ahmedabad  2010-03-20  Rajasthan Royals   \n",
+       "226    227    2010    Kolkata  2010-04-17  Rajasthan Royals   \n",
+       "245    246    2011     Jaipur  2011-04-15  Rajasthan Royals   \n",
+       "250    251    2011    Kolkata  2011-04-17  Rajasthan Royals   \n",
+       "313    314    2012     Jaipur  2012-04-08  Rajasthan Royals   \n",
+       "321    322    2012    Kolkata  2012-04-13  Rajasthan Royals   \n",
+       "388    389    2013     Jaipur  2013-04-08  Rajasthan Royals   \n",
+       "426    427    2013    Kolkata  2013-05-03  Rajasthan Royals   \n",
+       "475    476    2014  Abu Dhabi  2014-04-29  Rajasthan Royals   \n",
+       "481    482    2014  Ahmedabad  2014-05-05  Rajasthan Royals   \n",
+       "569    570    2015     Mumbai  2015-05-16  Rajasthan Royals   \n",
+       "650   7908    2018     Jaipur    18/04/18  Rajasthan Royals   \n",
+       "684   7942    2018    Kolkata    15/05/18  Rajasthan Royals   \n",
+       "716  11312    2019     Jaipur    07/04/19  Rajasthan Royals   \n",
+       "\n",
+       "                     team2            toss_winner toss_decision  result  \\\n",
+       "77   Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "125  Kolkata Knight Riders  Kolkata Knight Riders         field     tie   \n",
+       "167  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "186  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "226  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "245  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "250  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "313  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "321  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "388  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "426  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "475  Kolkata Knight Riders       Rajasthan Royals           bat     tie   \n",
+       "481  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "569  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "650  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "684  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "716  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "\n",
+       "     dl_applied                 winner  win_by_runs  win_by_wickets  \\\n",
+       "77            0       Rajasthan Royals           45               0   \n",
+       "125           0       Rajasthan Royals            0               0   \n",
+       "167           0  Kolkata Knight Riders            0               4   \n",
+       "186           0       Rajasthan Royals           34               0   \n",
+       "226           0  Kolkata Knight Riders            0               8   \n",
+       "245           0  Kolkata Knight Riders            0               9   \n",
+       "250           0  Kolkata Knight Riders            0               8   \n",
+       "313           0       Rajasthan Royals           22               0   \n",
+       "321           0  Kolkata Knight Riders            0               5   \n",
+       "388           0       Rajasthan Royals           19               0   \n",
+       "426           0  Kolkata Knight Riders            0               8   \n",
+       "475           0       Rajasthan Royals            0               0   \n",
+       "481           0       Rajasthan Royals           10               0   \n",
+       "569           0       Rajasthan Royals            9               0   \n",
+       "650           0  Kolkata Knight Riders            0               7   \n",
+       "684           0  Kolkata Knight Riders            0               6   \n",
+       "716           0  Kolkata Knight Riders            0               8   \n",
+       "\n",
+       "     player_of_match                         venue           umpire1  \\\n",
+       "77       SA Asnodkar        Sawai Mansingh Stadium       RE Koertzen   \n",
+       "125        YK Pathan                      Newlands         MR Benson   \n",
+       "167        LR Shukla                     Kingsmead        BG Jerling   \n",
+       "186  AA Jhunjhunwala  Sardar Patel Stadium, Motera       RE Koertzen   \n",
+       "226       JD Unadkat                  Eden Gardens        BG Jerling   \n",
+       "245        G Gambhir        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "250         L Balaji                  Eden Gardens         Aleem Dar   \n",
+       "313         BJ Hodge        Sawai Mansingh Stadium         BF Bowden   \n",
+       "321  Shakib Al Hasan                  Eden Gardens         Asad Rauf   \n",
+       "388       SK Trivedi        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "426        YK Pathan                  Eden Gardens   HDPK Dharmasena   \n",
+       "475      JP Faulkner          Sheikh Zayed Stadium         Aleem Dar   \n",
+       "481         PV Tambe  Sardar Patel Stadium, Motera          NJ Llong   \n",
+       "569        SR Watson             Brabourne Stadium      RM Deshpande   \n",
+       "650           N Rana        Sawai Mansingh Stadium            S Ravi   \n",
+       "684    Kuldeep Yadav                  Eden Gardens  Kumar Dharmasena   \n",
+       "716         H Gurney        Sawai Mansingh Stadium    Chris Gaffaney   \n",
+       "\n",
+       "            umpire2          umpire3  \n",
+       "77   GA Pratapkumar              NaN  \n",
+       "125       M Erasmus              NaN  \n",
+       "167      SJA Taufel              NaN  \n",
+       "186       RB Tiffin              NaN  \n",
+       "226       RB Tiffin              NaN  \n",
+       "245       SS Hazare              NaN  \n",
+       "250       RB Tiffin              NaN  \n",
+       "313     VA Kulkarni              NaN  \n",
+       "321        S Asnani              NaN  \n",
+       "388           S Das              NaN  \n",
+       "426       CK Nandan              NaN  \n",
+       "475    AK Chaudhary              NaN  \n",
+       "481       CK Nandan              NaN  \n",
+       "569  RK Illingworth              NaN  \n",
+       "650    A.D Deshmukh    C Shamshuddin  \n",
+       "684  Anil Chaudhary  Vineet Kulkarni  \n",
+       "716  Anil Chaudhary   Bruce Oxenford  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR1=Matches.loc[(Matches['team1']=='Rajasthan Royals')&(Matches['team2'] =='Kolkata Knight Riders')]\n",
+    "KKR_RR1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KKR_RR2=Matches.loc[(Matches['team2']=='Rajasthan Royals')&(Matches['team1'] =='Kolkata Knight Riders')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>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>104</th>\n",
+       "      <td>105</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2008-05-20</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>7951</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>23/05/18</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AD Russell</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Kumar Dharmasena</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>25/04/19</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>VR Aaron</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Ian Gould</td>\n",
+       "      <td>Anil Dandekar</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season     city        date                  team1  \\\n",
+       "104    105    2008  Kolkata  2008-05-20  Kolkata Knight Riders   \n",
+       "693   7951    2018  Kolkata    23/05/18  Kolkata Knight Riders   \n",
+       "738  11334    2019  Kolkata    25/04/19  Kolkata Knight Riders   \n",
+       "\n",
+       "                team2       toss_winner toss_decision  result  dl_applied  \\\n",
+       "104  Rajasthan Royals  Rajasthan Royals         field  normal           0   \n",
+       "693  Rajasthan Royals  Rajasthan Royals         field  normal           0   \n",
+       "738  Rajasthan Royals  Rajasthan Royals         field  normal           0   \n",
+       "\n",
+       "                    winner  win_by_runs  win_by_wickets player_of_match  \\\n",
+       "104       Rajasthan Royals            0               6       YK Pathan   \n",
+       "693  Kolkata Knight Riders           25               0      AD Russell   \n",
+       "738       Rajasthan Royals            0               3        VR Aaron   \n",
+       "\n",
+       "            venue      umpire1         umpire2           umpire3  \n",
+       "104  Eden Gardens   BG Jerling     RE Koertzen               NaN  \n",
+       "693  Eden Gardens  Nitin Menon  Anil Chaudhary  Kumar Dharmasena  \n",
+       "738  Eden Gardens    Ian Gould   Anil Dandekar       Nitin Menon  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KKR_RR=KKR_RR1.append(KKR_RR2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>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>77</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2008-05-01</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>GA Pratapkumar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>126</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Cape Town</td>\n",
+       "      <td>2009-04-23</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>field</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Newlands</td>\n",
+       "      <td>MR Benson</td>\n",
+       "      <td>M Erasmus</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>168</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Durban</td>\n",
+       "      <td>2009-05-20</td>\n",
+       "      <td>Rajasthan Royals</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>4</td>\n",
+       "      <td>LR Shukla</td>\n",
+       "      <td>Kingsmead</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>SJA Taufel</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>187</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>Ahmedabad</td>\n",
+       "      <td>2010-03-20</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AA Jhunjhunwala</td>\n",
+       "      <td>Sardar Patel Stadium, Motera</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>227</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2010-04-17</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>246</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2011-04-15</td>\n",
+       "      <td>Rajasthan Royals</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>9</td>\n",
+       "      <td>G Gambhir</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>SS Hazare</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>251</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2011-04-17</td>\n",
+       "      <td>Rajasthan Royals</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>8</td>\n",
+       "      <td>L Balaji</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>RB Tiffin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>314</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2012-04-08</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>BJ Hodge</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>BF Bowden</td>\n",
+       "      <td>VA Kulkarni</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>322</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2012-04-13</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Shakib Al Hasan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Asad Rauf</td>\n",
+       "      <td>S Asnani</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>389</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2013-04-08</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SK Trivedi</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>S Das</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>427</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2013-05-03</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>475</th>\n",
+       "      <td>476</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>2014-04-29</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>JP Faulkner</td>\n",
+       "      <td>Sheikh Zayed Stadium</td>\n",
+       "      <td>Aleem Dar</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>482</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>Ahmedabad</td>\n",
+       "      <td>2014-05-05</td>\n",
+       "      <td>Rajasthan Royals</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>Rajasthan Royals</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PV Tambe</td>\n",
+       "      <td>Sardar Patel Stadium, Motera</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>569</th>\n",
+       "      <td>570</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>Mumbai</td>\n",
+       "      <td>2015-05-16</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SR Watson</td>\n",
+       "      <td>Brabourne Stadium</td>\n",
+       "      <td>RM Deshpande</td>\n",
+       "      <td>RK Illingworth</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>650</th>\n",
+       "      <td>7908</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>18/04/18</td>\n",
+       "      <td>Rajasthan Royals</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>7</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>A.D Deshmukh</td>\n",
+       "      <td>C Shamshuddin</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>684</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>15/05/18</td>\n",
+       "      <td>Rajasthan Royals</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>6</td>\n",
+       "      <td>Kuldeep Yadav</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Kumar Dharmasena</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Vineet Kulkarni</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>11312</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>07/04/19</td>\n",
+       "      <td>Rajasthan Royals</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>8</td>\n",
+       "      <td>H Gurney</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>Chris Gaffaney</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Bruce Oxenford</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>105</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>2008-05-20</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>BG Jerling</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>7951</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>23/05/18</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AD Russell</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "      <td>Kumar Dharmasena</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>738</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Kolkata</td>\n",
+       "      <td>25/04/19</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>VR Aaron</td>\n",
+       "      <td>Eden Gardens</td>\n",
+       "      <td>Ian Gould</td>\n",
+       "      <td>Anil Dandekar</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season       city        date                  team1  \\\n",
+       "77      78    2008     Jaipur  2008-05-01       Rajasthan Royals   \n",
+       "125    126    2009  Cape Town  2009-04-23       Rajasthan Royals   \n",
+       "167    168    2009     Durban  2009-05-20       Rajasthan Royals   \n",
+       "186    187    2010  Ahmedabad  2010-03-20       Rajasthan Royals   \n",
+       "226    227    2010    Kolkata  2010-04-17       Rajasthan Royals   \n",
+       "245    246    2011     Jaipur  2011-04-15       Rajasthan Royals   \n",
+       "250    251    2011    Kolkata  2011-04-17       Rajasthan Royals   \n",
+       "313    314    2012     Jaipur  2012-04-08       Rajasthan Royals   \n",
+       "321    322    2012    Kolkata  2012-04-13       Rajasthan Royals   \n",
+       "388    389    2013     Jaipur  2013-04-08       Rajasthan Royals   \n",
+       "426    427    2013    Kolkata  2013-05-03       Rajasthan Royals   \n",
+       "475    476    2014  Abu Dhabi  2014-04-29       Rajasthan Royals   \n",
+       "481    482    2014  Ahmedabad  2014-05-05       Rajasthan Royals   \n",
+       "569    570    2015     Mumbai  2015-05-16       Rajasthan Royals   \n",
+       "650   7908    2018     Jaipur    18/04/18       Rajasthan Royals   \n",
+       "684   7942    2018    Kolkata    15/05/18       Rajasthan Royals   \n",
+       "716  11312    2019     Jaipur    07/04/19       Rajasthan Royals   \n",
+       "104    105    2008    Kolkata  2008-05-20  Kolkata Knight Riders   \n",
+       "693   7951    2018    Kolkata    23/05/18  Kolkata Knight Riders   \n",
+       "738  11334    2019    Kolkata    25/04/19  Kolkata Knight Riders   \n",
+       "\n",
+       "                     team2            toss_winner toss_decision  result  \\\n",
+       "77   Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "125  Kolkata Knight Riders  Kolkata Knight Riders         field     tie   \n",
+       "167  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "186  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "226  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "245  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "250  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "313  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "321  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "388  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "426  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "475  Kolkata Knight Riders       Rajasthan Royals           bat     tie   \n",
+       "481  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "569  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "650  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "684  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "716  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "104       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "693       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "738       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "\n",
+       "     dl_applied                 winner  win_by_runs  win_by_wickets  \\\n",
+       "77            0       Rajasthan Royals           45               0   \n",
+       "125           0       Rajasthan Royals            0               0   \n",
+       "167           0  Kolkata Knight Riders            0               4   \n",
+       "186           0       Rajasthan Royals           34               0   \n",
+       "226           0  Kolkata Knight Riders            0               8   \n",
+       "245           0  Kolkata Knight Riders            0               9   \n",
+       "250           0  Kolkata Knight Riders            0               8   \n",
+       "313           0       Rajasthan Royals           22               0   \n",
+       "321           0  Kolkata Knight Riders            0               5   \n",
+       "388           0       Rajasthan Royals           19               0   \n",
+       "426           0  Kolkata Knight Riders            0               8   \n",
+       "475           0       Rajasthan Royals            0               0   \n",
+       "481           0       Rajasthan Royals           10               0   \n",
+       "569           0       Rajasthan Royals            9               0   \n",
+       "650           0  Kolkata Knight Riders            0               7   \n",
+       "684           0  Kolkata Knight Riders            0               6   \n",
+       "716           0  Kolkata Knight Riders            0               8   \n",
+       "104           0       Rajasthan Royals            0               6   \n",
+       "693           0  Kolkata Knight Riders           25               0   \n",
+       "738           0       Rajasthan Royals            0               3   \n",
+       "\n",
+       "     player_of_match                         venue           umpire1  \\\n",
+       "77       SA Asnodkar        Sawai Mansingh Stadium       RE Koertzen   \n",
+       "125        YK Pathan                      Newlands         MR Benson   \n",
+       "167        LR Shukla                     Kingsmead        BG Jerling   \n",
+       "186  AA Jhunjhunwala  Sardar Patel Stadium, Motera       RE Koertzen   \n",
+       "226       JD Unadkat                  Eden Gardens        BG Jerling   \n",
+       "245        G Gambhir        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "250         L Balaji                  Eden Gardens         Aleem Dar   \n",
+       "313         BJ Hodge        Sawai Mansingh Stadium         BF Bowden   \n",
+       "321  Shakib Al Hasan                  Eden Gardens         Asad Rauf   \n",
+       "388       SK Trivedi        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "426        YK Pathan                  Eden Gardens   HDPK Dharmasena   \n",
+       "475      JP Faulkner          Sheikh Zayed Stadium         Aleem Dar   \n",
+       "481         PV Tambe  Sardar Patel Stadium, Motera          NJ Llong   \n",
+       "569        SR Watson             Brabourne Stadium      RM Deshpande   \n",
+       "650           N Rana        Sawai Mansingh Stadium            S Ravi   \n",
+       "684    Kuldeep Yadav                  Eden Gardens  Kumar Dharmasena   \n",
+       "716         H Gurney        Sawai Mansingh Stadium    Chris Gaffaney   \n",
+       "104        YK Pathan                  Eden Gardens        BG Jerling   \n",
+       "693       AD Russell                  Eden Gardens       Nitin Menon   \n",
+       "738         VR Aaron                  Eden Gardens         Ian Gould   \n",
+       "\n",
+       "            umpire2           umpire3  \n",
+       "77   GA Pratapkumar               NaN  \n",
+       "125       M Erasmus               NaN  \n",
+       "167      SJA Taufel               NaN  \n",
+       "186       RB Tiffin               NaN  \n",
+       "226       RB Tiffin               NaN  \n",
+       "245       SS Hazare               NaN  \n",
+       "250       RB Tiffin               NaN  \n",
+       "313     VA Kulkarni               NaN  \n",
+       "321        S Asnani               NaN  \n",
+       "388           S Das               NaN  \n",
+       "426       CK Nandan               NaN  \n",
+       "475    AK Chaudhary               NaN  \n",
+       "481       CK Nandan               NaN  \n",
+       "569  RK Illingworth               NaN  \n",
+       "650    A.D Deshmukh     C Shamshuddin  \n",
+       "684  Anil Chaudhary   Vineet Kulkarni  \n",
+       "716  Anil Chaudhary    Bruce Oxenford  \n",
+       "104     RE Koertzen               NaN  \n",
+       "693  Anil Chaudhary  Kumar Dharmasena  \n",
+       "738   Anil Dandekar       Nitin Menon  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 20 entries, 77 to 738\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   id               20 non-null     int64 \n",
+      " 1   season           20 non-null     int64 \n",
+      " 2   city             20 non-null     object\n",
+      " 3   date             20 non-null     object\n",
+      " 4   team1            20 non-null     object\n",
+      " 5   team2            20 non-null     object\n",
+      " 6   toss_winner      20 non-null     object\n",
+      " 7   toss_decision    20 non-null     object\n",
+      " 8   result           20 non-null     object\n",
+      " 9   dl_applied       20 non-null     int64 \n",
+      " 10  winner           20 non-null     object\n",
+      " 11  win_by_runs      20 non-null     int64 \n",
+      " 12  win_by_wickets   20 non-null     int64 \n",
+      " 13  player_of_match  20 non-null     object\n",
+      " 14  venue            20 non-null     object\n",
+      " 15  umpire1          20 non-null     object\n",
+      " 16  umpire2          20 non-null     object\n",
+      " 17  umpire3          5 non-null      object\n",
+      "dtypes: int64(5), object(13)\n",
+      "memory usage: 3.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "KKR_RR.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# # Complete details of matches that takes placed between KKR and RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         id  season       city        date                  team1  \\\n",
+       "77      78    2008     Jaipur  2008-05-01       Rajasthan Royals   \n",
+       "125    126    2009  Cape Town  2009-04-23       Rajasthan Royals   \n",
+       "167    168    2009     Durban  2009-05-20       Rajasthan Royals   \n",
+       "186    187    2010  Ahmedabad  2010-03-20       Rajasthan Royals   \n",
+       "226    227    2010    Kolkata  2010-04-17       Rajasthan Royals   \n",
+       "245    246    2011     Jaipur  2011-04-15       Rajasthan Royals   \n",
+       "250    251    2011    Kolkata  2011-04-17       Rajasthan Royals   \n",
+       "313    314    2012     Jaipur  2012-04-08       Rajasthan Royals   \n",
+       "321    322    2012    Kolkata  2012-04-13       Rajasthan Royals   \n",
+       "388    389    2013     Jaipur  2013-04-08       Rajasthan Royals   \n",
+       "426    427    2013    Kolkata  2013-05-03       Rajasthan Royals   \n",
+       "475    476    2014  Abu Dhabi  2014-04-29       Rajasthan Royals   \n",
+       "481    482    2014  Ahmedabad  2014-05-05       Rajasthan Royals   \n",
+       "569    570    2015     Mumbai  2015-05-16       Rajasthan Royals   \n",
+       "650   7908    2018     Jaipur    18/04/18       Rajasthan Royals   \n",
+       "684   7942    2018    Kolkata    15/05/18       Rajasthan Royals   \n",
+       "716  11312    2019     Jaipur    07/04/19       Rajasthan Royals   \n",
+       "104    105    2008    Kolkata  2008-05-20  Kolkata Knight Riders   \n",
+       "693   7951    2018    Kolkata    23/05/18  Kolkata Knight Riders   \n",
+       "738  11334    2019    Kolkata    25/04/19  Kolkata Knight Riders   \n",
+       "\n",
+       "                     team2            toss_winner toss_decision  result  \\\n",
+       "77   Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "125  Kolkata Knight Riders  Kolkata Knight Riders         field     tie   \n",
+       "167  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "186  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "226  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "245  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "250  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "313  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "321  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "388  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "426  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "475  Kolkata Knight Riders       Rajasthan Royals           bat     tie   \n",
+       "481  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "569  Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "650  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "684  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "716  Kolkata Knight Riders  Kolkata Knight Riders         field  normal   \n",
+       "104       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "693       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "738       Rajasthan Royals       Rajasthan Royals         field  normal   \n",
+       "\n",
+       "     dl_applied                 winner  win_by_runs  win_by_wickets  \\\n",
+       "77            0       Rajasthan Royals           45               0   \n",
+       "125           0       Rajasthan Royals            0               0   \n",
+       "167           0  Kolkata Knight Riders            0               4   \n",
+       "186           0       Rajasthan Royals           34               0   \n",
+       "226           0  Kolkata Knight Riders            0               8   \n",
+       "245           0  Kolkata Knight Riders            0               9   \n",
+       "250           0  Kolkata Knight Riders            0               8   \n",
+       "313           0       Rajasthan Royals           22               0   \n",
+       "321           0  Kolkata Knight Riders            0               5   \n",
+       "388           0       Rajasthan Royals           19               0   \n",
+       "426           0  Kolkata Knight Riders            0               8   \n",
+       "475           0       Rajasthan Royals            0               0   \n",
+       "481           0       Rajasthan Royals           10               0   \n",
+       "569           0       Rajasthan Royals            9               0   \n",
+       "650           0  Kolkata Knight Riders            0               7   \n",
+       "684           0  Kolkata Knight Riders            0               6   \n",
+       "716           0  Kolkata Knight Riders            0               8   \n",
+       "104           0       Rajasthan Royals            0               6   \n",
+       "693           0  Kolkata Knight Riders           25               0   \n",
+       "738           0       Rajasthan Royals            0               3   \n",
+       "\n",
+       "     player_of_match                         venue           umpire1  \\\n",
+       "77       SA Asnodkar        Sawai Mansingh Stadium       RE Koertzen   \n",
+       "125        YK Pathan                      Newlands         MR Benson   \n",
+       "167        LR Shukla                     Kingsmead        BG Jerling   \n",
+       "186  AA Jhunjhunwala  Sardar Patel Stadium, Motera       RE Koertzen   \n",
+       "226       JD Unadkat                  Eden Gardens        BG Jerling   \n",
+       "245        G Gambhir        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "250         L Balaji                  Eden Gardens         Aleem Dar   \n",
+       "313         BJ Hodge        Sawai Mansingh Stadium         BF Bowden   \n",
+       "321  Shakib Al Hasan                  Eden Gardens         Asad Rauf   \n",
+       "388       SK Trivedi        Sawai Mansingh Stadium         Aleem Dar   \n",
+       "426        YK Pathan                  Eden Gardens   HDPK Dharmasena   \n",
+       "475      JP Faulkner          Sheikh Zayed Stadium         Aleem Dar   \n",
+       "481         PV Tambe  Sardar Patel Stadium, Motera          NJ Llong   \n",
+       "569        SR Watson             Brabourne Stadium      RM Deshpande   \n",
+       "650           N Rana        Sawai Mansingh Stadium            S Ravi   \n",
+       "684    Kuldeep Yadav                  Eden Gardens  Kumar Dharmasena   \n",
+       "716         H Gurney        Sawai Mansingh Stadium    Chris Gaffaney   \n",
+       "104        YK Pathan                  Eden Gardens        BG Jerling   \n",
+       "693       AD Russell                  Eden Gardens       Nitin Menon   \n",
+       "738         VR Aaron                  Eden Gardens         Ian Gould   \n",
+       "\n",
+       "            umpire2           umpire3  \n",
+       "77   GA Pratapkumar               NaN  \n",
+       "125       M Erasmus               NaN  \n",
+       "167      SJA Taufel               NaN  \n",
+       "186       RB Tiffin               NaN  \n",
+       "226       RB Tiffin               NaN  \n",
+       "245       SS Hazare               NaN  \n",
+       "250       RB Tiffin               NaN  \n",
+       "313     VA Kulkarni               NaN  \n",
+       "321        S Asnani               NaN  \n",
+       "388           S Das               NaN  \n",
+       "426       CK Nandan               NaN  \n",
+       "475    AK Chaudhary               NaN  \n",
+       "481       CK Nandan               NaN  \n",
+       "569  RK Illingworth               NaN  \n",
+       "650    A.D Deshmukh     C Shamshuddin  \n",
+       "684  Anil Chaudhary   Vineet Kulkarni  \n",
+       "716  Anil Chaudhary    Bruce Oxenford  \n",
+       "104     RE Koertzen               NaN  \n",
+       "693  Anil Chaudhary  Kumar Dharmasena  \n",
+       "738   Anil Dandekar       Nitin Menon  >"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of matches that takes place between KXIP and DC: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of matches that takes place between KXIP and DC:\",len(KKR_RR))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Below plot gives the details of the match that takes place between RR and KKR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1e88af439c8>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(KKR_RR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1e88bc7e1c8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.PairGrid(KKR_RR)\n",
+    "g=sns.PairGrid(KKR_RR)\n",
+    "g.map(plt.scatter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  It looks like  KKR win the Toss 10 Times and 10 Times RR won the Toss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c6d3ec8>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAFbCAYAAADIhwX3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbvklEQVR4nO3deVSVdeLH8c8FhZQkVzSNY41LTYNR2YbmApa5RCKYZZPpjGY1ozWU40KUpZLFTFGmznFmaEpzmWZcyNwVtQhaXEbETG3BVDpAmkuACNz7+8PjLX5p4vXKl++979c5ne59bk/P58jDp2/f+32ex+FyuVwCAFgrwHQAAMCFocgBwHIUOQBYjiIHAMtR5ABguXq1fUCn06mSkhLVr19fDoejtg8PAFZyuVyqqKhQSEiIAgKqj8FrvchLSkq0Z8+e2j4sAPiEjh07qlGjRtW21XqR169f3x0mKCiotg/vk/Ly8hQREWE6BvAznJvec/LkSe3Zs8fdoT9V60V+ejolKChIwcHBtX14n8WfJeoqzk3vOtOUNF92AoDlKHIAsBxFDgCWo8gBwHI1KvIffvhBd999tw4cOCBJys7OVmxsrHr37q20tLSLGhAA8MvOWeTbt2/XkCFDlJ+fL0k6ceKEkpKSNGvWLK1YsUJ5eXnatGnTxc4JADiLcxb5O++8o0mTJiksLEySlJubq7Zt2yo8PFz16tVTbGysVq1addGDAgDO7JzryFNSUqq9LyoqUosWLdzvw8LCVFhYeN4HzsvLO+99ats1v/6NQhpeYjrGOXXu3Nl0hBopKT2hz3ftNB3DJ3Buepft5+Z5XxDkdDqrLUh3uVwe3TMlIiLCigsFYp/KMB3BZyx7eYA1v9g24Nz0HhvOzfLy8rMOgM971UqrVq1UXFzsfl9cXOyedgEA1L7zLvLIyEh9/fXX2rdvn6qqqvTee++pe/fuFyMbAKAGzntqJTg4WC+++KLGjBmj8vJy9ejRQ3369LkY2QAANVDjIs/MzHS/joqK0rvvvntRAgEAzg9XdgKA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAchQ5AFiOIgcAy1HkAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsd0FFnpGRof79+6t///566aWXvJUJAHAePC7ysrIypaSkaO7cucrIyNDmzZuVnZ3tzWwAgBrwuMirqqrkdDpVVlamyspKVVZWKjg42JvZAAA1UM/THS+99FI98cQT6tu3rxo0aKCbb75ZN954ozezAQBqwOMi//zzz7Vo0SJt2LBBjRo10tixY5Wenq6RI0fWaP+8vDxPD11rOnfubDqCz9myZYvpCD6Bc9P7bD43PS7yrKwsRUVFqVmzZpKk+Ph4zZ8/v8ZFHhERwVSMH6KAUFfV9XOzvLz8rANgj+fIr7nmGmVnZ6u0tFQul0uZmZnq1KmTxyEBAJ7xeER+++2367PPPlN8fLzq16+vTp06adSoUd7MBgCoAY+LXJJGjRpFeQOAYVzZCQCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAchQ5AFiOIgcAy1HkAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALDcBRV5Zmam4uPj1bdvX02dOtVbmQAA58HjIt+/f78mTZqkWbNm6d1339Vnn32mTZs2eTMbAKAG6nm649q1a9WvXz+1atVKkpSWlqbg4GCvBQMA1IzHI/J9+/apqqpKjz76qAYMGKD58+frsssu82Y2AEANeDwir6qq0ubNmzV37lw1bNhQjz32mJYsWaL4+Pga7Z+Xl+fpoWtN586dTUfwOVu2bDEdwSdwbnqfzeemx0XevHlzRUVFqWnTppKkO+64Q7m5uTUu8oiICKZi/BAFhLqqrp+b5eXlZx0Aezy1Eh0draysLB07dkxVVVX64IMP9Jvf/MbjkAAAz3g8Io+MjNTIkSP1wAMPqKKiQl27dlVCQoI3swEAasDjIpekQYMGadCgQd7KAgDwAFd2AoDlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAchQ5AFiOIgcAy1HkAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5S64yF966SVNmDDBG1kAAB64oCLPycnRkiVLvJUFAOABj4v8yJEjSktL06OPPurNPACA8+RxkT/77LNKTExUaGioN/MAAM5TPU92+s9//qPLL79cUVFRWrx4sUcHzsvL82i/2tS5c2fTEXzOli1bTEfwCZyb3mfzuelRka9YsULFxcUaMGCAjh49qtLSUr3wwgtKSkqq8b8jIiJCwcHBnhweFqOAUFfV9XOzvLz8rANgj4r8X//6l/v14sWL9cknn5xXiQMAvId15ABgOY9G5D8VHx+v+Ph4b2QBAHiAETkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAchQ5AFiOIgcAy1HkAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAcvUuZOcZM2Zo5cqVkqQePXpo3LhxXgkFAKg5j0fk2dnZysrK0pIlS7R06VLt3LlTa9eu9WY2AEANeDwib9GihSZMmKCgoCBJUrt27VRQUOC1YACAmvG4yDt06OB+nZ+fr5UrV2rBggVeCQUAqLkLmiOXpL179+qRRx7RuHHjdOWVV9Z4v7y8vAs99EXXuXNn0xF8zpYtW0xH8Amcm95n87l5QUW+ZcsWPf7440pKSlL//v3Pa9+IiAgFBwdfyOFhIQoIdVVdPzfLy8vPOgD2uMi//fZb/fGPf1RaWpqioqI8DgcAuDAeF3l6errKy8v14osvurfdf//9GjJkiFeCAQBqxuMiT05OVnJysjezAAA8wJWdAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABgOYocACxHkQOA5ShyALAcRQ4AlqPIAcByFDkAWI4iBwDLUeQAYDmKHAAsR5EDgOUocgCwHEUOAJajyAHAchQ5AFiOIgcAy1HkAGA5ihwALEeRA4DlKHIAsBxFDgCWo8gBwHIUOQBYjiIHAMtR5ABguQsq8mXLlqlfv37q3bu35s2b561MAIDzUM/THQsLC5WWlqbFixcrKChI999/v2699Va1b9/em/kAAOfgcZFnZ2frtttuU+PGjSVJd911l1atWqXRo0f/4n4ul0uSdPLkSU8PXasahwSajuAzysvLTUfwKZyb3mPDuXm6M0936E95XORFRUVq0aKF+31YWJhyc3PPuV9FRYUkac+ePZ4eulb9acDlpiP4jLy8PNMRfArnpvfYdG5WVFTokksuqbbN4yJ3Op1yOBzu9y6Xq9r7swkJCVHHjh1Vv379Gv3zAIBTHVtRUaGQkJCffeZxkbdq1UqbN292vy8uLlZYWNg59wsICFCjRo08PSwA+K3/PxI/zeNVK126dFFOTo4OHz6ssrIyrVmzRt27d/c4IADAMx6PyFu2bKnExEQ99NBDqqio0KBBg3Tdddd5MxsAoAYcrjN9BQoAsAZXdgKA5ShyALAcRQ4AlqPIAcByFDkAWI4it1RRUZEkafPmzZo3b55OnDhhOBEgffPNN3r33Xflcrn0zDPPKCEhQTt27DAdy+dR5BaaNGmSXn31VX3xxRd66qmntHPnTiUnJ5uOBWjixIlyOp1av3698vPzNXHiRKWkpJiO5fMocgvt2LFDKSkpWrlypQYNGqQXXnhBX3/9telYgMrLyxUXF6cNGzYoNjZWN910kzV3OrUZRW6hqqoq96ine/fuKisrU1lZmelYgAIDA7V69Wpt3LhRPXv21Lp16xQQQM1cbPwJW2jgwIG6/fbb1aZNG0VGRiohIUH33Xef6ViAJk+erI0bN+rZZ59VWFiYli9frqlTp5qO5fO4RN9CCxcu1ODBg90jncOHD6tp06aGU8GfFRQU/OLnrVu3rqUk/okit9Ddd9+t9957z3QMwC0mJkYOh+OMT69xOBxav369gVT+gyK30MiRI3Xy5ElFRkYqODjYvf1cj9kD4Js8vo0tzLn++utNRwDOKD8/X2+//bZKS0vlcrnkdDp14MABzZs3z3Q0n0aRW2j06NEqLS3VN998o44dO+rEiRNq2LCh6ViAnnzySfXs2VNbtmzRwIEDtXbtWnXo0MF0LJ/HqhUL5eTkaMCAAfrDH/6gQ4cOKTo6WllZWaZjAaqoqNDjjz+ubt266dprr9U//vEPffrpp6Zj+TyK3EKvvPKK5s+fr9DQULVo0ULz5s1Tamqq6ViAGjRooJMnT+rKK6/Uzp07z/qMSXgXRW4hp9OpFi1auN+3b9/eYBrgR/fcc48effRR9ezZU2+//bZGjhypli1bmo7l85gjt1CrVq20YcMGORwOHTt2TPPmzWOdLuqEwYMHKy4uTpdeeqnmzp2rHTt2qGvXrqZj+TyWH1ro0KFDSklJUXZ2tpxOp2677TYlJycrLCzMdDT4uZ49eyo6OloDBw7kYey1iCIH4DVlZWVavXq1li5dqkOHDikuLk733HNPtalAeB9FbpHTV8+dDVfPoS5Zu3atpk6dqmPHjikqKkrjx49X27ZtTcfyScyRW2Tu3LlyuVyaOXOmwsPDFR8fr8DAQC1btkwHDhwwHQ/Qvn37lJGRoeXLl6t169YaO3asevfurY8++kgPP/yw1qxZYzqiT6LILdKmTRtJ0u7duzVt2jT39t///veKj483FQtw+93vfqf4+Hi98cYb7vNVknr06KEPP/zQYDLfxvJDS+Xk5Lhfb9q0SYGBgQbTAKesX79eDzzwgPbs2aN169bpu+++c3+WlJRkMJlvY47cQp999pnGjx+v4uJiuVwutWnTRqmpqawnh3FZWVmaOHGirr/+ejmdTm3btk0pKSmKjo42Hc2nUeQW+/777+VwONS4cWPTUQBJUnx8vF577TWFh4dLkvbv36/Ro0crIyPDcDLfxhy5RZ555hlNmTJFQ4cOPePqlTlz5hhIBfyosrLSXeKSFB4eLqfTaTCRf6DILXL6cW5jxowxnAQ4s9atW+vNN9/UoEGDJEn//e9/q33piYuDqRXLfPXVVwoJCal2/4pDhw7ptdde0+TJkw0mA06di1OmTNFHH30kl8vlvuqYC4IuLorcIjNmzFB6erokaebMmerSpYvS09M1c+ZM3XDDDe7PgLpk69atuvHGG03H8GkUuUV69eqlBQsWqKioSNOnT5fT6VRhYaHGjRunbt26mY4HP7Zt2zZNmzZNjRs31gsvvKDmzZvr4MGDSk1N1caNG7V9+3bTEX0a68gtEhISorCwMEVERCg3N1ft27fX0qVLKXEYN2nSJPXv31/t2rXTzJkztXTpUsXGxqpevXpavny56Xg+jy87LRIQ8ON/d5s0aaIJEyYYTAP8qLKyUsOGDZPL5VJ0dLQ+/fRTpaen64YbbjAdzS9Q5Bb56ZJDnryCuiQoKEjSqXM0ICBAb775ppo3b244lf+gyC2yd+9e9erVS5JUWFjofu1yueRwOLj7IYz56SDjsssuo8RrGUVukdWrV5uOAJxRcXGxZsyY8bPXp40ePdpELL9BkVuECytQV91///1nfI3awfJDALAcyw8BwHIUuYWmTJnys23jx483kARAXcAcuUWefvpp7d+/X3l5edq7d697e2VlpY4fP24wGQCTmCO3yIEDB3Tw4EGlpKQoOTnZvT0wMFDt2rXjvuQw7oMPPlBaWpqOHTsml8vF0thaQpFbqrS0VEePHtVPf3ytW7c2mAiQ7rrrLk2YMEEdOnSotracFVcXF1MrFjp9F8QmTZq4tzHqQV3QpEkTHutmACNyC8XExGjRokXVihyoC/7yl7+osrJS3bp1U3BwsHv7zTffbDCV72NEbqGwsDA1atTIdAzgZ3JzcyWdekD4aQ6Hg8cQXmSMyC1y+rLn3NxcHTp0SN27d1dgYKD7cy6DBvwTI3ILXXfddaYjAGf0v//9T7Nnz1ZpaalcLpecTqcKCgqUmZlpOppPY0QOwGv69eunESNGaMmSJRo6dKjWrFmjZs2aKSkpyXQ0n8aI3EI9evRQUVGRQkNDJUnHjh1TaGiorrjiCk2dOlW//vWvDSeEvwoKClJCQoIOHjyo0NBQpaamKjY21nQsn0eRW+jmm29Wnz59dMcdd0iSNm3apFWrVmno0KF6/vnntXDhQsMJ4a+Cg4N15MgRXXXVVdq+fbuioqJUVVVlOpbP414rFtq7d6+7xKVTI/Tdu3fr2muvVXl5ucFk8HfDhw9XYmKioqOjlZGRof79+ysiIsJ0LJ/HiNxCoaGhWrhwoe655x45nU4tW7ZMl112mb788ks5nU7T8eDH+vbtqz59+sjhcGjRokXKz89nqq8W8GWnhQoLC5WSkqIPP/xQgYGB6tKli5KSkrR69Wq1bdtW3bt3Nx0Rfmr//v1auHChvv/++2q3j5g2bZrBVL6PIgfgNXFxcYqKivrZvVYGDhxoMJXvY2rFIo888ohmz56tmJiYar8kp3GvFZjmcrm4N74BjMgtUlRUpLCwMB08ePCMn3OHOZj23HPPqWvXrurVq5cCAlhLUVsocgtVVlYqKytLR44cqbY9Li7OUCL4u2uuuUYOh8M9L376/xhP3498165dJuP5PKZWLPTUU0+poKBA7dq1qzbFQpHDlM8///ysn508ebIWk/gnitxCu3fv1qpVq0zHAH7mvvvu07///W/3e6fTqYSEBC1btsxgKt9HkVuoXbt27vlyoC546KGH9Mknn0iqPs0SGBioXr16GU7n+5gjt9CIESO0bds2dezYUUFBQe7t3PMZpk2dOrXa82RROyhyC50e+fx/t9xySy0nAar7/vvvtWvXLnXp0kWzZ8/Wzp079ec//1nh4eGmo/k01gdZZN26dZJOFfZP/4qMjNR7771nOB0gjR07Vrt27VJ2drZWrVqlmJgYbmFbCyhyi0yePFkbNmyotm337t0aOHCg9u3bZygV8KOjR49qxIgRWr9+vQYOHKi4uDiVlJSYjuXzKHKLpKena/LkyXr//fclSXPnztWDDz6oe++9V2+99ZbhdMCpVSp5eXlat26doqOjtWvXLm5jWwuYI7fMl19+qVGjRqlNmzY6fvy4UlNT1aFDB9OxAElSTk6O/va3vykmJkbDhw/X4MGD9eSTT+q2224zHc2nUeQWys/P17BhwzR58mT16NHDdBzgrJxOpw4ePMiXnRcZRW6RGTNmuF/v27dP69at00MPPaT69etLkkaPHm0qGiBJWrhwoVJTU1VWVube1qZNG/cX9bg4mCO3VNu2bTVixAh3iQN1wd///ndlZGSoX79+Wrt2rZKTkxUZGWk6ls/jyk6LMOJGXdesWTOFh4fr6quv1p49e/Tb3/5WCxYsMB3L5zEiB+A1DRo00EcffaSrr75aGzZsUHFxsU6cOGE6ls+jyAF4TXJysjIzM9WtWzcdOXJEffr00YMPPmg6ls/jy04f4HK5dODAAVYGAH6KOXILsTIAdc25HkPYsGFDJSQkaPjw4bUfzg8wIrdQTEyM3nrrLb366qtKTEzUpk2btHXrVr388sumo8FPnesxhMePH9fIkSOVlZVVy8n8AyNyC7EyAHXN6Xvjt2jRQps2bXLfX6WqqkoHDhzQE088oenTp5uM6NMocgv9dGXAunXr1KlTJ1YGoE548skndfToUX3zzTe66aab9PHHH+vGG2+UJPff4X2sWrHQM888U21lQN++fVkZgDph9+7dmjNnju68806NHDlSCxYsOOt0C7yHIrdQUVGRkpKSFBAQoNdff12bN29W69atTccC1KxZMzkcDl111VXavXu3wsPDVVFRYTqWz2NqxSIrVqzQyZMnNX36dD3++OPu7ZWVlZo9e7Z69+5tMB0gdejQQVOmTNGQIUM0duxYFRUVifUUFx9FbpGSkhJt3bpVJSUl+vjjj93bAwMDlZiYaDAZcMpzzz2nbdu2qX379hozZoxycnL0yiuvmI7l81h+aKGcnBxFRUWZjgFU89VXXykkJEQtW7Z0b/vuu+80ffp0TZ482WAy38eI3EINGjTQY489ptLSUrlcLjmdThUUFCgzM9N0NPip119/XW+88YYkaebMmerSpYvS09M1a9YsXX/99YbT+T5G5Bbq16+fRowYoSVLlmjo0KFas2aNmjVrxkNuYUyvXr20YMECFRUVafr06XI6nSosLNS4cePUrVs30/F8HiNyCwUFBSkhIUEHDx5UaGioUlNTFRsbazoW/FhISIjCwsIUFham3NxcxcXFafbs2QoMDDQdzS+w/NBCwcHBOnLkiK666ipt375dgYGBPOAWRgUE/FglTZo00YQJEyjxWkSRW2j48OFKTExUdHS0MjIy1L9/f0VERJiOBT/20xtlXXLJJQaT+CfmyC10+kfmcDhUWlqq/Px8NWnSRJdffrnhZPBXERER7tUqhYWF7tcul0sOh0Pr1683Gc/nMUduobFjx+qvf/2rpFO3B926datmzZql7Oxsw8ngr1avXm06gl+jyC3UtGlTJSYmatSoUXr++efVsGFDzZ8/33Qs+LE2bdqYjuDXmFqx1IwZMzRr1ixNmTJFCQkJpuMAMIgit8jEiROrvX///fd1xRVX6Fe/+pUkadq0aSZiATCMqRWL3HLLLb/4HoB/YkRukYKCgl/8nFvZAv6JIrfI6QfbnulHxhIvwH9R5ABgOa7stNDhw4f1pz/9SbfeeqtuuukmjR49Wt99953pWAAMocgt9Oyzz6pTp05av369MjMzFRkZqaefftp0LACGUOQW2r9/v0aMGKFLL71UoaGhevjhh8/5RSgA30WRW8jhcOjbb791vy8oKFC9eqwkBfwVv/0WeuKJJ3TfffcpMjJSLpdL27dv15QpU0zHAmAIq1YsdfjwYeXm5srpdCoyMlKNGjVSUFCQ6VgADGBqxSIvvfSS+3XTpk3Vs2dPxcTE6MiRI7r33nsNJgNgEkVuka1btyotLa3atnfeeUeDBw/WnXfeaSgVANOYWrHIDz/8oIcfflhdunTRsGHD9PTTT2vv3r1KTU3VddddZzoeAEMocsuUlJRo1KhR+uKLL9SvXz+NHz+eR2sBfo6pFcuEhITon//8p66++mq1bNmSEgfAiNwmQ4cOdT/ktqysTDt27NANN9yg+vXrS5LmzJljMh4AQ1hHbpExY8aYjgCgDmJEDgCWY44cACxHkQOA5ShyALAcRQ4AlqPIAcBy/weV7z8dufd+JQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set()\n",
+    "sns.set_style('whitegrid')\n",
+    "KKR_RR['toss_winner'].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c72c3c8>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "sns.countplot(x='toss_winner',data=KKR_RR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RR went on to win the match 4 times by  batting first and 6 times by Batting Second against KKR .so total it is (RR=win-10,loss-10)whereas for KKR they win the match 3 time by batting first and win 7 times by batting first against RR.so it is (KXIP=win-10,loss=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c9e80c8>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.setrecursionlimit(2000)\n",
+    "sns.set()\n",
+    "sns.set_style('whitegrid')\n",
+    "sns.countplot(x='toss_decision',hue=\"winner\",data=KKR_RR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c912988>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set({'figure.figsize':(15,5)})\n",
+    "sns.countplot(x='toss_winner',hue=\"winner\",data=KKR_RR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of times toss won by both teams:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Kolkata Knight Riders    10\n",
+       "Rajasthan Royals         10\n",
+       "Name: toss_winner, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"No of times toss won by both teams:\")\n",
+    "KKR_RR['toss_winner'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Both teams win 10 times each from 20 matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c55ad08>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "sns.countplot(x='winner',data=KKR_RR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# correlation between Columns in Matches Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>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>id</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.863096</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.174604</td>\n",
+       "      <td>0.202509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>season</th>\n",
+       "      <td>0.863096</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.213246</td>\n",
+       "      <td>0.071938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dl_applied</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>win_by_runs</th>\n",
+       "      <td>-0.174604</td>\n",
+       "      <td>-0.213246</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.633845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>win_by_wickets</th>\n",
+       "      <td>0.202509</td>\n",
+       "      <td>0.071938</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.633845</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      id    season  dl_applied  win_by_runs  win_by_wickets\n",
+       "id              1.000000  0.863096         NaN    -0.174604        0.202509\n",
+       "season          0.863096  1.000000         NaN    -0.213246        0.071938\n",
+       "dl_applied           NaN       NaN         NaN          NaN             NaN\n",
+       "win_by_runs    -0.174604 -0.213246         NaN     1.000000       -0.633845\n",
+       "win_by_wickets  0.202509  0.071938         NaN    -0.633845        1.000000"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e88c12f288>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(KKR_RR.corr(),yticklabels=True,annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KKR_RR['winner']=pd.get_dummies(KKR_RR['winner'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>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>77</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>Jaipur</td>\n",
+       "      <td>2008-05-01</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>Sawai Mansingh Stadium</td>\n",
+       "      <td>RE Koertzen</td>\n",
+       "      <td>GA Pratapkumar</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>126</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>Cape Town</td>\n",
+       "      <td>2009-04-23</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>field</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>YK Pathan</td>\n",
+       "      <td>Newlands</td>\n",
+       "      <td>MR Benson</td>\n",
+       "      <td>M Erasmus</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id  season       city        date             team1  \\\n",
+       "77    78    2008     Jaipur  2008-05-01  Rajasthan Royals   \n",
+       "125  126    2009  Cape Town  2009-04-23  Rajasthan Royals   \n",
+       "\n",
+       "                     team2            toss_winner toss_decision  result  \\\n",
+       "77   Kolkata Knight Riders       Rajasthan Royals           bat  normal   \n",
+       "125  Kolkata Knight Riders  Kolkata Knight Riders         field     tie   \n",
+       "\n",
+       "     dl_applied  winner  win_by_runs  win_by_wickets player_of_match  \\\n",
+       "77            0       0           45               0     SA Asnodkar   \n",
+       "125           0       0            0               0       YK Pathan   \n",
+       "\n",
+       "                      venue      umpire1         umpire2 umpire3  \n",
+       "77   Sawai Mansingh Stadium  RE Koertzen  GA Pratapkumar     NaN  \n",
+       "125                Newlands    MR Benson       M Erasmus     NaN  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KKR_RR.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Sweetviz we can get details about the Matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      ":FEATURES DONE:                    |█████████████████████| [100%]   00:05  -> (00:00 left)\n",
+      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating Associations graph... DONE!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sweetviz \n",
+    "my_report=sweetviz.analyze([KKR_RR,'KKR_RR'],target_feat='winner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_report.show_html(\"Report1.html\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Now let get into the Deliveries Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Deliveries=pd.read_csv(\"deliveries.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "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>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>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",
+       "      <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>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": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Deliveries.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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",
+      " #   Column            Non-Null Count   Dtype \n",
+      "---  ------            --------------   ----- \n",
+      " 0   match_id          179078 non-null  int64 \n",
+      " 1   inning            179078 non-null  int64 \n",
+      " 2   batting_team      179078 non-null  object\n",
+      " 3   bowling_team      179078 non-null  object\n",
+      " 4   over              179078 non-null  int64 \n",
+      " 5   ball              179078 non-null  int64 \n",
+      " 6   batsman           179078 non-null  object\n",
+      " 7   non_striker       179078 non-null  object\n",
+      " 8   bowler            179078 non-null  object\n",
+      " 9   is_super_over     179078 non-null  int64 \n",
+      " 10  wide_runs         179078 non-null  int64 \n",
+      " 11  bye_runs          179078 non-null  int64 \n",
+      " 12  legbye_runs       179078 non-null  int64 \n",
+      " 13  noball_runs       179078 non-null  int64 \n",
+      " 14  penalty_runs      179078 non-null  int64 \n",
+      " 15  batsman_runs      179078 non-null  int64 \n",
+      " 16  extra_runs        179078 non-null  int64 \n",
+      " 17  total_runs        179078 non-null  int64 \n",
+      " 18  player_dismissed  8834 non-null    object\n",
+      " 19  dismissal_kind    8834 non-null    object\n",
+      " 20  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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         match_id  inning         batting_team                 bowling_team  \\\n",
+       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "...          ...     ...                  ...                          ...   \n",
+       "179073     11415       2  Chennai Super Kings               Mumbai Indians   \n",
+       "179074     11415       2  Chennai Super Kings               Mumbai Indians   \n",
+       "179075     11415       2  Chennai Super Kings               Mumbai Indians   \n",
+       "179076     11415       2  Chennai Super Kings               Mumbai Indians   \n",
+       "179077     11415       2  Chennai Super Kings               Mumbai Indians   \n",
+       "\n",
+       "        over  ball    batsman non_striker      bowler  is_super_over  ...  \\\n",
+       "0          1     1  DA Warner    S Dhawan    TS Mills              0  ...   \n",
+       "1          1     2  DA Warner    S Dhawan    TS Mills              0  ...   \n",
+       "2          1     3  DA Warner    S Dhawan    TS Mills              0  ...   \n",
+       "3          1     4  DA Warner    S Dhawan    TS Mills              0  ...   \n",
+       "4          1     5  DA Warner    S Dhawan    TS Mills              0  ...   \n",
+       "...      ...   ...        ...         ...         ...            ...  ...   \n",
+       "179073    20     2  RA Jadeja   SR Watson  SL Malinga              0  ...   \n",
+       "179074    20     3  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
+       "179075    20     4  SR Watson   RA Jadeja  SL Malinga              0  ...   \n",
+       "179076    20     5  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
+       "179077    20     6  SN Thakur   RA Jadeja  SL Malinga              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "0              0            0            0             0             0   \n",
+       "1              0            0            0             0             0   \n",
+       "2              0            0            0             0             4   \n",
+       "3              0            0            0             0             0   \n",
+       "4              0            0            0             0             0   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "179073         0            0            0             0             1   \n",
+       "179074         0            0            0             0             2   \n",
+       "179075         0            0            0             0             1   \n",
+       "179076         0            0            0             0             2   \n",
+       "179077         0            0            0             0             0   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind    fielder  \n",
+       "0                0           0               NaN            NaN        NaN  \n",
+       "1                0           0               NaN            NaN        NaN  \n",
+       "2                0           4               NaN            NaN        NaN  \n",
+       "3                0           0               NaN            NaN        NaN  \n",
+       "4                2           2               NaN            NaN        NaN  \n",
+       "...            ...         ...               ...            ...        ...  \n",
+       "179073           0           1               NaN            NaN        NaN  \n",
+       "179074           0           2               NaN            NaN        NaN  \n",
+       "179075           0           1         SR Watson        run out  KH Pandya  \n",
+       "179076           0           2               NaN            NaN        NaN  \n",
+       "179077           0           0         SN Thakur            lbw        NaN  \n",
+       "\n",
+       "[179078 rows x 21 columns]>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Deliveries.describe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filtering the match details that only takes place between Rajatsn Royals and  kolkata Knight Riders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rajastan_Kolkata1=Deliveries.loc[(Deliveries['batting_team' ] =='Kolkata Knight Riders' ) & (Deliveries['bowling_team'] == 'Rajasthan Royals')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>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>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>18250</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18251</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18252</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18253</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18254</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175062</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>JD Unadkat</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>175063</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>JD Unadkat</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175064</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>20</td>\n",
+       "      <td>4</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>JD Unadkat</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>175065</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>JD Unadkat</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175066</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>JD Unadkat</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2353 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning           batting_team      bowling_team  over  ball  \\\n",
+       "18250         78       2  Kolkata Knight Riders  Rajasthan Royals     1     1   \n",
+       "18251         78       2  Kolkata Knight Riders  Rajasthan Royals     1     2   \n",
+       "18252         78       2  Kolkata Knight Riders  Rajasthan Royals     1     3   \n",
+       "18253         78       2  Kolkata Knight Riders  Rajasthan Royals     1     4   \n",
+       "18254         78       2  Kolkata Knight Riders  Rajasthan Royals     1     5   \n",
+       "...          ...     ...                    ...               ...   ...   ...   \n",
+       "175062     11334       1  Kolkata Knight Riders  Rajasthan Royals    20     2   \n",
+       "175063     11334       1  Kolkata Knight Riders  Rajasthan Royals    20     3   \n",
+       "175064     11334       1  Kolkata Knight Riders  Rajasthan Royals    20     4   \n",
+       "175065     11334       1  Kolkata Knight Riders  Rajasthan Royals    20     5   \n",
+       "175066     11334       1  Kolkata Knight Riders  Rajasthan Royals    20     6   \n",
+       "\n",
+       "            batsman  non_striker      bowler  is_super_over  ...  bye_runs  \\\n",
+       "18250   Salman Butt   SC Ganguly   SR Watson              0  ...         0   \n",
+       "18251   Salman Butt   SC Ganguly   SR Watson              0  ...         0   \n",
+       "18252   Salman Butt   SC Ganguly   SR Watson              0  ...         0   \n",
+       "18253   Salman Butt   SC Ganguly   SR Watson              0  ...         0   \n",
+       "18254    SC Ganguly  Salman Butt   SR Watson              0  ...         0   \n",
+       "...             ...          ...         ...            ...  ...       ...   \n",
+       "175062   KD Karthik      R Singh  JD Unadkat              0  ...         0   \n",
+       "175063   KD Karthik      R Singh  JD Unadkat              0  ...         0   \n",
+       "175064   KD Karthik      R Singh  JD Unadkat              0  ...         0   \n",
+       "175065   KD Karthik      R Singh  JD Unadkat              0  ...         0   \n",
+       "175066   KD Karthik      R Singh  JD Unadkat              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "18250             0            0             0             2           0   \n",
+       "18251             0            0             0             0           0   \n",
+       "18252             0            0             0             0           0   \n",
+       "18253             0            0             0             1           0   \n",
+       "18254             0            0             0             0           1   \n",
+       "...             ...          ...           ...           ...         ...   \n",
+       "175062            0            0             0             4           0   \n",
+       "175063            0            0             0             6           0   \n",
+       "175064            0            0             0             0           0   \n",
+       "175065            0            0             0             6           0   \n",
+       "175066            0            0             0             1           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind fielder  \n",
+       "18250            2               NaN            NaN     NaN  \n",
+       "18251            0               NaN            NaN     NaN  \n",
+       "18252            0               NaN            NaN     NaN  \n",
+       "18253            1               NaN            NaN     NaN  \n",
+       "18254            1               NaN            NaN     NaN  \n",
+       "...            ...               ...            ...     ...  \n",
+       "175062           4               NaN            NaN     NaN  \n",
+       "175063           6               NaN            NaN     NaN  \n",
+       "175064           0               NaN            NaN     NaN  \n",
+       "175065           6               NaN            NaN     NaN  \n",
+       "175066           1               NaN            NaN     NaN  \n",
+       "\n",
+       "[2353 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata1         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rajastan_Kolkata2=Deliveries.loc[(Deliveries['bowling_team' ] =='Kolkata Knight Riders' ) & (Deliveries['batting_team'] == 'Rajasthan Royals')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>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>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>18123</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>GC Smith</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>AB Dinda</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>18124</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>GC Smith</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>AB Dinda</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>18125</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>GC Smith</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>AB Dinda</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18126</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>GC Smith</td>\n",
+       "      <td>AB Dinda</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>18127</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SA Asnodkar</td>\n",
+       "      <td>GC Smith</td>\n",
+       "      <td>AB Dinda</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>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175183</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175184</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>R Parag</td>\n",
+       "      <td>hit wicket</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175185</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>175186</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>175187</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2430 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning      batting_team           bowling_team  over  ball  \\\n",
+       "18123         78       1  Rajasthan Royals  Kolkata Knight Riders     1     1   \n",
+       "18124         78       1  Rajasthan Royals  Kolkata Knight Riders     1     2   \n",
+       "18125         78       1  Rajasthan Royals  Kolkata Knight Riders     1     3   \n",
+       "18126         78       1  Rajasthan Royals  Kolkata Knight Riders     1     4   \n",
+       "18127         78       1  Rajasthan Royals  Kolkata Knight Riders     1     5   \n",
+       "...          ...     ...               ...                    ...   ...   ...   \n",
+       "175183     11334       2  Rajasthan Royals  Kolkata Knight Riders    19     4   \n",
+       "175184     11334       2  Rajasthan Royals  Kolkata Knight Riders    19     5   \n",
+       "175185     11334       2  Rajasthan Royals  Kolkata Knight Riders    19     6   \n",
+       "175186     11334       2  Rajasthan Royals  Kolkata Knight Riders    20     1   \n",
+       "175187     11334       2  Rajasthan Royals  Kolkata Knight Riders    20     2   \n",
+       "\n",
+       "            batsman  non_striker      bowler  is_super_over  ...  bye_runs  \\\n",
+       "18123      GC Smith  SA Asnodkar    AB Dinda              0  ...         0   \n",
+       "18124      GC Smith  SA Asnodkar    AB Dinda              0  ...         0   \n",
+       "18125      GC Smith  SA Asnodkar    AB Dinda              0  ...         0   \n",
+       "18126   SA Asnodkar     GC Smith    AB Dinda              0  ...         0   \n",
+       "18127   SA Asnodkar     GC Smith    AB Dinda              0  ...         0   \n",
+       "...             ...          ...         ...            ...  ...       ...   \n",
+       "175183      R Parag     J Archer  AD Russell              0  ...         0   \n",
+       "175184      R Parag     J Archer  AD Russell              0  ...         0   \n",
+       "175185   JD Unadkat     J Archer  AD Russell              0  ...         0   \n",
+       "175186     J Archer   JD Unadkat   P Krishna              0  ...         0   \n",
+       "175187     J Archer   JD Unadkat   P Krishna              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "18123             0            0             0             0           0   \n",
+       "18124             0            0             0             0           0   \n",
+       "18125             0            0             0             1           0   \n",
+       "18126             0            0             0             4           0   \n",
+       "18127             0            0             0             0           0   \n",
+       "...             ...          ...           ...           ...         ...   \n",
+       "175183            0            0             0             6           0   \n",
+       "175184            0            0             0             0           0   \n",
+       "175185            0            0             0             0           0   \n",
+       "175186            0            0             0             4           0   \n",
+       "175187            0            0             0             6           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind fielder  \n",
+       "18123            0               NaN            NaN     NaN  \n",
+       "18124            0               NaN            NaN     NaN  \n",
+       "18125            1               NaN            NaN     NaN  \n",
+       "18126            4               NaN            NaN     NaN  \n",
+       "18127            0               NaN            NaN     NaN  \n",
+       "...            ...               ...            ...     ...  \n",
+       "175183           6               NaN            NaN     NaN  \n",
+       "175184           0           R Parag     hit wicket     NaN  \n",
+       "175185           0               NaN            NaN     NaN  \n",
+       "175186           4               NaN            NaN     NaN  \n",
+       "175187           6               NaN            NaN     NaN  \n",
+       "\n",
+       "[2430 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rajastan_Kolkata=Rajastan_Kolkata1.append(Rajastan_Kolkata2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Complete Details Of Rajastan vs Kolkata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>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>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>18250</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18251</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18252</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18253</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18254</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175183</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175184</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>R Parag</td>\n",
+       "      <td>hit wicket</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175185</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>175186</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>175187</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4783 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning           batting_team           bowling_team  over  \\\n",
+       "18250         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18251         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18252         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18253         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18254         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "...          ...     ...                    ...                    ...   ...   \n",
+       "175183     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175184     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175185     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175186     11334       2       Rajasthan Royals  Kolkata Knight Riders    20   \n",
+       "175187     11334       2       Rajasthan Royals  Kolkata Knight Riders    20   \n",
+       "\n",
+       "        ball      batsman  non_striker      bowler  is_super_over  ...  \\\n",
+       "18250      1  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18251      2  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18252      3  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18253      4  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18254      5   SC Ganguly  Salman Butt   SR Watson              0  ...   \n",
+       "...      ...          ...          ...         ...            ...  ...   \n",
+       "175183     4      R Parag     J Archer  AD Russell              0  ...   \n",
+       "175184     5      R Parag     J Archer  AD Russell              0  ...   \n",
+       "175185     6   JD Unadkat     J Archer  AD Russell              0  ...   \n",
+       "175186     1     J Archer   JD Unadkat   P Krishna              0  ...   \n",
+       "175187     2     J Archer   JD Unadkat   P Krishna              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "18250          0            0            0             0             2   \n",
+       "18251          0            0            0             0             0   \n",
+       "18252          0            0            0             0             0   \n",
+       "18253          0            0            0             0             1   \n",
+       "18254          0            0            0             0             0   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "175183         0            0            0             0             6   \n",
+       "175184         0            0            0             0             0   \n",
+       "175185         0            0            0             0             0   \n",
+       "175186         0            0            0             0             4   \n",
+       "175187         0            0            0             0             6   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
+       "18250            0           2               NaN            NaN     NaN  \n",
+       "18251            0           0               NaN            NaN     NaN  \n",
+       "18252            0           0               NaN            NaN     NaN  \n",
+       "18253            0           1               NaN            NaN     NaN  \n",
+       "18254            1           1               NaN            NaN     NaN  \n",
+       "...            ...         ...               ...            ...     ...  \n",
+       "175183           0           6               NaN            NaN     NaN  \n",
+       "175184           0           0           R Parag     hit wicket     NaN  \n",
+       "175185           0           0               NaN            NaN     NaN  \n",
+       "175186           0           4               NaN            NaN     NaN  \n",
+       "175187           0           6               NaN            NaN     NaN  \n",
+       "\n",
+       "[4783 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# using Match_id we can summarize that 20 matches were played between KKR and RR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "126      267\n",
+       "476      262\n",
+       "570      259\n",
+       "78       249\n",
+       "482      249\n",
+       "314      248\n",
+       "7951     247\n",
+       "322      246\n",
+       "187      244\n",
+       "11334    244\n",
+       "246      242\n",
+       "389      242\n",
+       "168      242\n",
+       "7908     239\n",
+       "427      234\n",
+       "227      225\n",
+       "7942     224\n",
+       "105      224\n",
+       "11312    211\n",
+       "251      185\n",
+       "Name: match_id, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata['match_id'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1e891d26048>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAADM0AAAzCCAYAAAAnUL4PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdebxcdX0//tfcJdslCUsWISxKQM0CVrFWxRUQFJFFKRVFrJYKBIPy1dLa6rdaS3/wxVpkkaVSKrKIbFIrsoiIG7ijAmELAi1Lyn5D7iW5yZ3fH4QIJNDcO3fuPefD8/l45BHmzNx5vz/nzuvMmcm8mUaz2WwGAAAAAAAAAAAAAAAACtIx1g0AAAAAAAAAAAAAAADASDM0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9DMemo2m+nr60uz2RzrVoARJNtQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oR4Mzayn/v7+/MUH35f+/v6xbgUYQbIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG+rB0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDMAT9A82sHGymf6A51q2MOfuCkgzl8dyO25ZY35rqUb9da6qS0ehbjerVKabGytU1Vta7RjvU9ZgEPL86ZLsOPUIVyQ6UqZRsl7IOqqmOr7vrkIk67leAZ6vD8ZZ6qMpjqQp9VKGHqrAvnqmO+6MOPdehR1rjtVd7FPN5hlI+M1HQsayUtbTr925opo0axzw8on+AsbXyWQfgZ19+IekfaOaQK5al+9hHcsgVy2r/JMsL21Aez+24bYn1rake9du1pioZjb7VqF6dYmqsbOaQy1fXuHxZW94EGo0a7VDXYxLw/OqQ7Vr06B+wqKA6ZAcYulKyXdr7/s4FqqWOr7vrkO3lK5tprm6r2XzyMkDd1OF4Sz1U5bFUhT6q0MOaXsb4vLxK+6IK6rg/6tBzXXos4cPjY6WOr2nroJjPM5TymYkaHMvWVylraefv3dAMwHoaaOYZTyo1fU4ZEd2dyZk3rEjy5N/dnWPcELRgKI/ndty2xPrWVI/67VpTlYxG32pUr04xNTqeVaMNr95Ho0Y71PWYBDy/OmS76j36ByyqqurZAYanlGyX9L6/c4HqqePr7jpke7CZHLo6t4desSyDHupADdXheEs9VOWxVIU+qtBDUo3z8qrsi6qo4/6oQ89V77GUD4+PpTq+pq2DYj7PUMpnJip+LBuKUtbSzt+7wxjAeirlSWUkDKxKDpw/LsmTfw+sGuOGoAVDeTy347Yl1remetRv15qqZDT6VqN6dYqpMfisGoP1rNEOdT0mAc+vDtmueo/+AYuqqnp2gOEpJdslve/vXKB66vi6uw7ZLim3wAtXHY631ENVHktV6KMKPSTVOC+vyr6oijrujzr0XPUevW5oXR1f09ZBMZ9nKOUzExU/lg1FKWtp5++90Ww2jVCuh76+vvzFB9+X0796TiZNmrReP9M45uER7aH51xuP6P0BQ8v2UxPoZ96wIgfOH5dTdu3JxO7GKHVaLStXNjPQfPIFxsCqpLuRdHW9MPcF1bW++R7K47kdty2xvjXVo/5Q7nP5ymZWNZNxncmKVUlnIxk/Rsf9oTx3j8bzlRrVqzMaNVaszsRTNTobybgRrvHEymYeX9HMlPGN9C5vZoNxjUyoYY31VbVsAyOnHeflY6XqPT71f7Fc877Fbj2ZWKH+KIvnbijTCzHbJb3v71ygeur4ursO2S4pt9Cq4XyOhWqow/GWsVPH8/Iq9FGFHpJqnJdXZV9URZX2R0nn5VXv0euG1lXpNW3VVe25u5Qao/EYrPqxbChKWUs7f+9dI3IvAC8AE7sbOWXXnpz+jp4MrMoL+kS6q6uRgad9bWUdn1zhKUN5PLfjtiXWt6Z61B/KfY7vaqz5uuJGxm5gZqhG4/lKjerVGY0a456Wiacuj7QJXY00k3Q0kp42vQE5GjXawbkolKkO2a56jxO7Gjllt9XvWwzGh2SpjKpnBxieUrJd0vv+zgWqp46vu+uQ7ZJyC7xw1eF4Sz1U5bFUhT6q0ENSjfPyquyLqqjj/qhDz1Xv0euG1tXxNW0dlPKZidGoMRqPwaofy4ailLW08/duaAZgCJ46ge4ag69PrRr7gpIM5fHcjtuWWN+a6lG/XWuqktHoW43q1SmmRlcZNdqhrsck4PnVIdtV77Gux3XKV/XsAMNTSrZLWUfiXKCK6vg7qUMm6tAjwP/GsYyRUpXHUhX6qEIPSTXOAauyL6qijvujDj1Xvceq91cHVTielaiYzzOU8pmJgo4VpaylXb/3mu8WAAAAAAAAAAAAAAAAWJuhGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKE7XWDfwdI899mh+/KMf5AffvzqHLfx4tthyqzz4wAM5+aQv5Y7Ft2fTzWbl4AULs9VWL06z2cy5Z5+Za67+Xjo7O7PHnntn9z32TJLceMPvcsZXTs3DDz+UefO3z8ELFmaDDTbI448/nlO/fEJuvOG32WjjTfLhgw7OvPnbjfGqAQAAAAAAAAAAAAAAGGmV+aaZ/v7+LDz0I7nuxz/OXXf+fs32s752RsaPn5Bjv3hCttxyq5x28olJkut//ct894rLcuTffiYHHbwgZ515Ru6+686sXLkyJx3/L3ndjm/M0ccelyVL7s8lF12QJLnk4gvy0EMP5uhjj8tr/uR1Oen4f8nKgYExWS8AAAAAAAAAAAAAAADtU5mhmXHjxuWEk0/Lwo//n2dsX3TTjXnDm96cadOn5y077ZI7Ft+e5cuXZ9FNN2bbl748s2dvk1ft8OrMmDEzt9y8KEvuvy+PPPJwdt5l18yYOTOvff2OWbToxjX39SevfX1mzJyZXXbdLY888nCWLLl/LJYLAAAAAAAAAAAAAABAG3WNdQNP6ezszNSpG+aB/1nyjO1Le3szYcKEJElPT8+abb1P254kk3p60rt6e5I//Myknixdve0Z9zXpyfvq7e3NrHX0MzAwkIGnfQtNf3/fCKwSGGuyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBvqqTJDM0PRaAxte55r+/P8zCUXX5ALzz9vSH0B1SfbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqGeKj80M3nylCxbtixJ0t/f/+S2KVMzefLkPPTgA2tu19/fnylTN8zkyVOSJMv6lmXCxInp7+/L1Ckb/uG++p68r77V9zVl9XXPttc++2b3PfZ62v335aOHHDTCqwNGm2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhnqq/NDMnLnz8sNrrs6cOfPy/auvyrbbvizjxo3LnLnzc8Vll+a2W29JX19fltx/f+bMmZuZL9o0G220cb57+WXZaZe35bprf5JX/NGr1tzXdT/5cXZ8w5vyw2uuzsabbJKZL3rROut2d3enu7t7NJcKjALZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg31VPmhmQMO/FBOPun4fPKIhdls1qwcetjhSZJXvmqHvP0de+TYo49KZ1dnDvzzD2fW5lskSQ47/Iic8ZVTc/ll3868+dtnz733SZLs9e59c//99+VTf3VENt54kxy28Ih0dnaO2doAAAAAAAAAAAAAAABoj8oNzUyfMTPnnv/NNZenTZ+ez3z282vdrtFoZP8DDsz+Bxy41nXz5m+XLxx34lrbN9hgg3ziyE+NbMMAAAAAAAAAAAAAAABUTsdYNwAAAAAAAAAAAAAAAAAjzdAMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFCcrrFuYH1976orc+H5X8+yx5dl3vztsmDhx9Lf15+TT/pS7lh8ezbdbFYOXrAwW2314jSbzZx79pm55urvpbOzM3vsuXd232PPJMmNN/wuZ3zl1Dz88EOZN3/7HLxgYTbYYIMxXh0AAAAAAAAAAAAAAAAjqRbfNLN0aW++curJ2fvdf5pj/vm43HvPf+c//+OSnPW1MzJ+/IQc+8UTsuWWW+W0k09Mklz/61/mu1dcliP/9jM56OAFOevMM3L3XXdm5cqVOen4f8nrdnxjjj72uCxZcn8uueiCMV4dAAAAAAAAAAAAAAAAI60WQzPNZtLR0ZFNNtkkG2+8ScaPH5/Ozo4suunGvOFNb8606dPzlp12yR2Lb8/y5cuz6KYbs+1LX57Zs7fJq3Z4dWbMmJlbbl6UJfffl0ceeTg777JrZsycmde+fscsWnTjWC8PAAAAAAAAAAAAAACAEdY11g2sjylTpuS97z8gxx59VLq6ujJz5ouy597vyUUXfCMTJkxIkvT09CRJlvb2pre3d832JJnU05Pe1duT/OFnJvVk6eptzzYwMJCBgYE1l/v7+9qyNmB0tZrt/oFmujuTgVXJxO7GSLdXK/YFVdNKvofyeG7HbUusb031qN+uNY2kOjx3q1G9OmoMocbKZro7koHBZGKXbAOtG63z8rFShx6hHTx3Q3s5Lx9bpawDnq748/IxOm7CWPM5lrLU4XjL6CjlvLwKfVShh6qwL56pjv/WXYffYR16pDVee61bHZ67S6kxGkpZR1LOWtp17KnF0MzDDz2Ui84/Lx866COZvc1Lc+Jx/5xLv/0f67xt4zn2zXNtz3Nsv+TiC3Lh+ecNvVmg0lrJdv9AM4dcsSxn3rAiB84fl1N27an1E0sr7AuqaLj5HsrjuR23LbG+NdWjfrvWNNKq/tytRvXqlFLjiZXNLBtoZkpHI8sGmmk0kgkj/EbkEyubWbaimSnjG1m2oplGRr7Gc6l6toHhG43z8rFShx6hXTx3Q/v0r2zmkMuflpHdekbtQwiyXc464NlKPi8fy/czYKz5HEs56nC8ZfSUcF5ehT6q0MOaXsb4Q+ZV2hdVUMd/667D77AOPdKasXzPquqq/txdSo2n6rRzCKSkY1kpa2nnsafRbDabI3JPbXTtT36UU798Qv79rCcPMuec9dXcdustufeee3LABz+UN77pLbn1lpvz95/+m3z17G/k/PPOyZ2/vyN/93//IUlyxOEL8s537ZWXv3xu/ur/LMyJp3wlm2wyLd+86Pz86he/yD/80zFr1VzXJOBHDzkop3/1nEyaNGm9+m4c8/AIrP4Pmn+98YjeH7wQtZLtlYPNdB/7yB/u6682SldH/Z5URoJ9QRUNN99DeTy347Yl1remetRv15pGWtWfu9WoXp1SaozGm5Bj+UZn1bMNDN9onJePlTr0CO3iuRvax2vusVXKOuDZSj4v98EtXshG4nMsVEMdjreMnhLOy6vQRxV6SKpxrlKVfVEVdXzdXYffYR16pDV+x8+t6s/dpdQYjSGQkh7npaylnevoGJF7abPNZm2eFStW5Nqf/Cj/s2RJfvfb32TW5ltkztx5+eE1V+fBBx7I96++Kttu+7KMGzcuc+bOz6233Jzbbr0lv7n+11ly//2ZM2duXrTpptloo43z3csvywP/syTXXfuTzJk3f501u7u7M2nSpDV/Jk70BgOUoJVsD6xKDpw/LsmTfw+saleX1WdfUEXDzfdQHs/tuG2J9a2pHvXbtaaRVvXnbjWqV6eUGt0dyZk3rEjy5N/dbXj1Pho1nrN2xbMNDN9onJePlTr0CO3iuRvaZ2DwWRkZHL3asj22+x/aqeTz8rF8PwPGms+xlKMOx1tGTwnn5VXoowo9JNU4V6nKvqiKOv5bdx1+h3XokdZ4z+S5Vf25u5Qa3Z3Pek7tHPkaJR3LSllLO489tfimmSS54vJLc8nFF6Zv2bLMm799PnLoR7Ni+fKcfNLxWXz7bdls1qwcetjh2WLLrdJsNvP1s7+Wq7/33XR2dWavvd+Tt+++R5Lkxht+lzO+cmoefvihzJu/fQ45bGF6ejb4X+v39fXlLz74Pt80A4UZSrZXrmxmoJk1X/fW3Ui6XqD/5yr7gjpY33wP5fHcjtuWWN+a6lG/XWtqt6o9d6tRvTqjUWP5ymZWNZNxncmKVUlnIxlfw2+BqcL/7ewpVcs2MHLacV4+Vkbj+A914bkbRlb/yma6O578B8Gx/LaEF2K2n1jZzOMrmpkyvpHe5c1sMK6RCTVcB/xvSjovr9L7GTDWhvM5FqqhDsdbxk4dz8ur0EcVekiqca5SlX1RFVXaHyWdl69Y/X75Uz12NpJxFeuR1lXlPauqq9pzdyk1RuWbZmpwvF1fJa2lXceerhG7pzbbdbfds+tuu6+1/TOf/fxa2xqNRvY/4MDsf8CBa103b/52+cJxJ7alR6BsXV2NDAw0n3H5hcq+oCRDeTy347Yl1remetRv15qqZDT6VqN6dUajxviuRvpX12ikPR+YntjVyCm79eT0d/S07U3I0ajRDnU9JgHPrw7ZHo3jP5SoDvmGsfbUuXhXjb4toZRsT+hqpNlMOhpJT7eBGahDtuv6fgbA09XheEs9VOWxVIU+qtBDUo1zlarsi6qo4/6oQ8/jnvZ++VOXKU8d37OqulI+MzEaNSZ2N3LKrqufU1dlxAdmknocb9dXSWtp17GnNkMzAFXw1BOvE0H7grIM5fHcjtuWWN+a6lG/XWuqktHoW43q1Smmxii8CVnXNzrrekwCnl8dsl2HHqGKZAfKVEq2S1kHjJQ6ZKKu72cAPF0djrfUQ1UeS1Xoowo9JNU4V6nKvqiKOu6POvRchx6hior5PIMalVPSWtrBbgEAAAAAAAAAAAAAAKA4hmYAAAAAAAAAAAAAAAAojqEZAAAAAAAAAAAAAAAAimNoBgAAAAAAAAAAAAAAgOIYmgEAAAAAAAAAAAAAAKA4hmYAAAAAAAAAAAAAAAAoTtdwfujb37pkSLd/57v2Gk4ZAAAAAAAAAAAAAAAAGJZhDc2c/bV/T9JY79sbmgEAAAAAAAAAAAAAAGA0DWto5t377pc0nhyaefihh/L97303O77hTZm56aZrbnPvPffkup/8KPu9930j0ykAAAAAAAAAAAAAAACsp2ENzey73/5r/vsLx/xT5s6bn8MOP2Kt2z36yMO57dZbht8dAAAAAAAAAAAAAAAADENHq3dw86Ibs/HGm6zzumnTZ+SWmxe1WgIAAAAAAAAAAAAAAACGpOWhmfHjJ+TXv/pl7rrz98/Yfvddd+bXv/xFxo+f0GoJAAAAAAAAAAAAAAAAGJKuVu9g57ftmgu+8fV86shPZMstt8zUDTfK0t7Hctddd6bZbGaPPQ8YiT4BAAAAAAAAAAAAAABgvbU8NPPuff8sEyZMzCXfvDB3331XcvddSZKNNto4e+3znuz69t1bbhIAAAAAAAAAAAAAAACGouWhmSTZfY898/bd98hDDz6Qxx57LBtttFE23mRaGo3GSNw9AAAAAAAAAAAAAAAADMmwhmaWL1+e8ePHP2NbR0dHps+YmekzZo5IYwAAAAAAAAAAAAAAADBcwxqa+bu/+WR23e0d2fXtu+fDB+7/v9y6kX8785zhlAEAAAAAAAAAAAAAAIBhGdbQzOCqVRkcHEySPPHEEyPaEAAAAAAAAAAAAAAAALRqWEMz/3TMP2f8hAlJknO+cfGINgQAAAAAAAAAAAAAAACt6hjOD02YODGNRmO9b3/Zpf+Zz3/2M8MpBQAAAAAAAAAAAAAAAEM2rKGZobrvvnuz6KYbR6MUAAAAAAAAAAAAAAAAjM7QDAAAAAAAAAAAAAAAAIwmQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFCcURma2XTTzTJn7tzRKAUAAAAAAAAAAAAAAADpGqk7GhgYSO9jj6bZXPu6t+++R96++x4jVQoAAAAAAAAAAAAAAACeV8tDM/fdd29OPemE3HrrLeu8vtFIzj7volbLZHDVqnzzmxfmmquvyqxZW+TIT306Dz7wQE4+6Uu5Y/Ht2XSzWTl4wcJstdWL02w2c+7ZZ+aaq7+Xzs7O7LHn3tl9jz2TJDfe8Luc8ZVT8/DDD2Xe/O1z8IKF2WCDDVruDwAAAAAAAAAAAAAAgOroaPUOTj/t5Nx6683p6enJlltumS232uoZf7bYcquR6DNf/ffTc+Vl38kBH/hQFnz0Y0mSs752RsaPn5Bjv3hCttxyq5x28olJkut//ct894rLcuTffiYHHbwgZ515Ru6+686sXLkyJx3/L3ndjm/M0ccelyVL7s8lF10wIv0BAAAAAAAAAAAAAABQHS1/08zi22/Li1/8knzuqGPS3d09Ej2t5dFHH81VV16ej3/iyLz6j/9kzfZFN92YD37ooEybPj1v2WmXfO7//m2WL1+eRTfdmG1f+vLMnr1NkmTGjJm55eZF6ezszCOPPJydd9k1G260UV77+h3zq1/8vC09AwAAAAAAAAAAAAAAMHZaHprZYIPJecnWs9s2MJMki2+/NYODg7nuJz/OGaefli222Cp/eciCLO3tzYQJE5IkPT09SZKlvb3pfdr2JJnU05Pe1duT/OFnJvVk6eptzzYwMJCBgYE1l/v7+9qyNmB0yTaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtqKeWh2Z22/2dufKy76S/vz8TJ04ciZ7WsmzZsiSNvPTlL88799w7p518Ys7+2lfXedtGY9338Vzb8xzbL7n4glx4/nlD7hWoNtmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDfU0rKGZIz/xsWdcfvDBB3LEwkMzZerUtW7baDRyzBeOG153q02ZMjXjxo3LrrvtniR53Y5vyA++f3UmT56yeqAm6e/vT5JMnjI1kydPzkMPPrDm5/v7+zNl6oaZPHlKkmRZ37JMmDgx/f19mTplw3XW3GuffbP7Hns97T768tFDDmppHcDYk20ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUE/DGpr57/+6e61tvb2Ppbf3sXXc+rm+4mX9vfSlL0t3d3cu/86386odXp1f/OyneclLts7AwEB+eM3VmTNnXr5/9VXZdtuXZdy4cZkzd36uuOzS3HbrLenr68uS++/PnDlzM/NFm2ajjTbOdy+/LDvt8rZcd+1P8oo/etU6a3Z3d6e7u7vl3oFqkW0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUE/DGpr50kmnjnQfz2tST0+O+OSROeP0f83Xz/la5s6bnwM++KEMrBjIyScdn08esTCbzZqVQw87PEnyylftkLe/Y48ce/RR6ezqzIF//uHM2nyLJMlhhx+RM75yai6/7NuZN3/77Ln3PqO6FgAAAAAAAAAAAAAAANpvWEMz06fPeMblwcHBLL79tsyc+aJMmTo1g6tW5YYbfpf5222fjo6OEWl07rztcuwXj19r+2c++/m1tjUajex/wIHZ/4AD17pu3vzt8oXjThyRngAAAAAAAAAAAAAAAKimlidali7tzWc//an8/ac/lbvvvitJsmJgIEcf9bl8+lN/lccff7zlJgEAAAAAAAAAAAAAAGAoWh6aOe+cs3L77bdm/nbbZcaMmUmSrq6u7PDq1+TO39+Rb3z97JabBAAAAAAAAAAAAAAAgKFoeWjm+l//Ktu+9GX52898LjNm/mFo5hNHfiovnzM3P7vu2pabBAAAAAAAAAAAAAAAgKFoeWhm6dLezHzRpuu8btq06enr62u1BAAAAAAAAAAAAAAAAAxJy0Mzm83aPNf/6pe59557nrH9vvvuzfW//lU233zzVksAAAAAAAAAAAAAAADAkHS1egd77vXunPClf86Rn/hYXvySl2Ty5ClZurQ3d/7+9xkcHMyHDvrISPQJAAAAAAAAAAAAAAAA663loZnX7fiG9EoViAoAACAASURBVD/Rn/POPSt3LL59zfYpU6bmz/Z/f173+je0WgIAAAAAAAAAAAAAAACGpOWhmSTZaee35S1v3Tn33PPfWfb449lgg8nZbNasdHR0jMTdAwAAAAAAAAAAAAAAwJC0PDRzypdPyHbbbZ8d3/jmbLHFls+47orLLs1DDz2U/d//gVbLAAAAAAAAAAAAAAAAwHpr+atgfvD97+X2225ba/vgqlW57tof54rLvt1qCQAAAAAAAAAAAAAAABiSYX/TzPv2e/ea/778sktz+WWXrvN2m2222XBLAAAAAAAAAAAAAAAAwLAMe2hm4sQJaTQa6evrT1dXZ8aNG/eM6zs6OvOiTTfNBz744ZabBAAAAAAAAAAAAAAAgKEY9tDM6V89J0nyvv32yU677Jo///BfjlhTAAAAAAAAAAAAAAAA0IphD8085eAFCzNr1uYj0QsAAAAAAAAAAAAAAACMiJaHZt78lp3y2GOP5sc/+kEee/TRNJvNNdcNDKzIvffckwULP95qGQAAAAAAAAAAAAAAAFhvLQ/N/P6Oxfn//vFzefzxx5/zNoZmAAAAAAAAAAAAAAAAGE0drd7BuWefmWXLHs9bd9o5W8+enSR595/ulze95a1JmnnfBw5stQQAAAAAAAAAAAAAAAAMSctDM3csXpxX//Gf5C8POSzbbvuyJMm+f/reHLJgYV6y9ezcsmhRy00CAAAAAAAAAAAAAADAULQ8NDM4OJjx48cnSSZPnZIkeeSRh5MkW265VRbddEOrJQAAAAAAAAAAAAAAAGBIWh6a2falL8vPfnpdfvmLn2WLzbdM0sx555yV3/7m+vz2N9dn4qRJI9AmAAAAAAAAAAAAAAAArL+Wh2be86d/lsHBVfn5z36aHV79x5k+Y0Z+cM33c/RR/5BHHnk4b935bSPRJwAAAAAAAAAAAAAAAKy3rlbv4KUve3k+949H58EHH0hHZ2c+/fefz8UXnp/HHn0k27/ildntHe8ciT4BAAAAAAAAAAAAAABgvbU8NJMkL9l6dl6y9ewkyfTpM/KRQw4bibsFAAAAAAAAAAAAAACAYRmRoZmHHnwgNy+6KY899liazeZa17/zXXuNRBkAAAAAAAAAAAAAAABYLy0Pzfzsp9fmxC/9S1auXLmOa5tJGoZmAAAAAAAAAAAAAAAAGFUtD81849yzs2rVyuz4hjdm5qabjkRPAAAAAAAAAAAAAAAA0JKWh2YeeOB/ssOrX5PDDj9iJPoBAAAAAAAAAAAAAACAlnW0egczX7Rpuru7R6IXAAAAAAAAAAAAAAAAGBEtf9PMfu99X0780hfz7W9dkrnz5q/zNi/ZenarZQAAAAAAAAAAAAAAAGC9tTw0M3XqhunuHpezv/bVdV7faCRnn3dRq2WSJMf98//LT6/7Sc49/5u56647c+qXT8h9996TrWdvk0MP+1imTZ+egYGB/Nu/npKf/+y6TJrUk/e+/wN5/Y5vTJJc++Mf5evnfC3Llj2eV7/mtfmLvzzEt+QAAAAAAAAAAAAAAAAUqKPVOzj9tJOzbNnj2WTaJpkzd+5af14+Z+5I9Jlf/Pxn+fnPrltz+V9PPilbbz07x37xhHR2duasM89Iklz9vStzw+9+m88ddUzetfe7c+qXT0jvY49l6dLenPLl47PPvvvls58/Or+5/le5+qorR6Q3AAAAAAAAAAAAAAAAqqXlb5q59957MnubbfO5fzw6HR0tz+CsU19fX844/dTs9o535jvf/lZWLF+exYtvywc/fFCmTZ+eN775rWuGZhbdeGNe8cpXZdaszbPpppvlzDO+kjvuuD1J0mwmb37LTmk0GnnlK3fIoptuzK5v370tPQMAAAAAAAAAAAAAADB2Wh6a2WijjbPpZrPaNjCTJOeedWa22/6PMn+77fOdb38rvb29SZIJEyYkSXp6erJ09bbe3t5sMm1akqSjoyMTJk5cc/vxE8an0WgkSSb19GTJkiXPWXNgYCADAwNrLvf39438woBRJ9tQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oZ5aHpp5xx7vyn9cfFEefuihbLzJJiPR0zPcvOim/OLnP82xXzw+t9568/PcsvG81zTXtf25fySXXHxBLjz/vPVtE6gJ2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLIN9dTy0Mzl37k0jz76aD6+8JB0da3r7hr5tzPPGfb9X3zh+Vm6dGk+9tGDs2rVYJLkiMMPTZIsW7YsSdLf15epU6cmSSZPmZy+1dubzWae6O/PlKkbZnBwVZ7o78/g4GA6OjrS39eXKVM3fM66e+2zb3bfY681l/v7+/LRQw4a9jqAapBtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21BPLQ/NLLn/viTJypUrs3LlypYberZDDzs8AwMrkiQ33PC7nHbyifnil07KcV88NldfdWWmTZueH/3wmsyZNz9JMnfu/Fx80fn57//6r9x6683p7OzM7NnbZLDZTEdHR6668vLMnb9drr/+V9nnPfs9Z93u7u50d3eP+HqAsSXbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqGeWh6aOecbF49EH89pw402WvPfU6fenSSZPmNmPnLoR3PaySfmk0cszOxtts1BHzkwSfLWnd+W39+xOJ/9zN9k0qSeHLxgYTaYPDlJcsiCw3Pu2WfmvHPPyh+/5rV581t2amvvAAAAAAAAAAAAAAAAjI2Wh2ZG06t2+OOce/43kyRbbfXiHHX0F9a6TXd3dw5esDAHL1i41nWv2/ENed2Ob2h7nwAAAAAAAAAAAAAAAIytjrFuAAAAAAAAAAAAAAAAAEaaoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAAChO11g3sL6+dcnFufQ//yPLlz+R7f/olTlkweFZsuT+nPrlE3Lfvfdk69nb5NDDPpZp06dnYGAg//avp+TnP7sukyb15L3v/0Bev+MbkyTX/vhH+fo5X8uyZY/n1a95bf7iLw9Jd3f3GK8OAAAAAAAAAAAAAACAkVSLb5r57W+uz3nnnp0FCz+ezx11TG5ZtCiXXfqt/OvJJ2XrrWfn2C+ekM7Ozpx15hlJkqu/d2Vu+N1v87mjjsm79n53Tv3yCel97LEsXdqbU758fPbZd7989vNH5zfX/ypXX3XlGK8OAAAAAAAAAAAAAACAkVaLoZmurq6874ADs932r8gWW2yZTTfbLA8+8EAWL74tb37rzpk2fXre+Oa3ZtGiG5Mki268Ma945asya9bm2XmXXTM4OJg77rg9i2+/Lc1m8ua37JTNt9gir3zlDll0041jvDoAAAAAAAAAAAAAAABGWtdYN7A+5s6bn7nz5idJ7rrz97n9tluz737756rvXpEJEyYkSXp6erK0tzdJ0tvbm02mTUuSdHR0ZMLEieldfd34CePTaDSSJJN6erJkyZJ11hwYGMjAwMCay/39fe1ZHDCqZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk21FMthmae8tBDD+bYo4/KO/fYKzNmzFzHLRrP+bONJM11bX+OH7nk4gty4fnnDadNoMJkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTbUU22GZnp7e/NPn/9s5s3fLvvt//4MrFiRJFm2bFmSpL+vL1OnTk2STJ4yOX2rtzebzTzR358pUzfM4OCqPNHfn8HBwXR0dKS/ry9Tpm64znp77bNvdt9jrzWX+/v78tFDDmrnEoFRINtQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oZ5qMTTzRH9//t8/fT7Tpk3LAR/8UPr6lqXR6MjWs7fJ1VddmWnTpudHP7wmc+bNT5LMnTs/F190fv77v/4rt956czo7OzN79jYZbDbT0dGRq668PHPnb5frr/9V9nnPfuus2d3dne7u7tFcJjAKZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk21FMthmZ++tNrs3jxbUmSj3z4wCTJtOnT88m//rucdvKJ+eQRCzN7m21z0EeevO6tO78tv79jcT77mb/JpEk9OXjBwmwweXKS/5+9ew+zqywPBf7umdm5zJCQhISrl/YctAoBb1VRKQRULl5KpVRPKYaiKCk3tRRREIVysMQARS4SgWiJCAcRqfUcT4kUFa2X1npqRfACCiKiBjIhMDOEPTP7/DHJhBmSMJe99/rWt3+/55lnMnuv7O/91l7v+r699nrXilh24qlxw2dXx403XBcvf8V+ceCSg4vpFAAAAAAAAAAAAAAAAE1TiqKZA5ccvM3ilvMvuPBpj1Wr1TjhxFPihBNPedpzr3rN/vGq1+zf8BhpT5Xl6xr6evUzFjT09QAAAAAAAAAAAAAAoF11FB0AAAAAAAAAAAAAAAAANJqiGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsdBUdAFAeleXrGvp69TMWNPT1AAAAAAAAAAAAAABgM3eaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMiOohkAAAAAAAAAAAAAAACyo2gGAAAAAAAAAAAAAACA7CiaAZiEgVo9BofrMVCrFx1K4awLcjKZ7bkZy+bYvj6Vo/1m9SklrYhbG+m1o41JtDG4qY1BuQ0Urwy5nXqMqcfH9Bm7gZTkktu59IM0lXHsLkNOlCFG8lbG3CY99mU0SirbUgpxpBBDRBrjRCrrIhVlXB9liDn1GFOPrwxS2J9tT1nf42zONcjkfAbnyrQPRTMAEzRQq8eyNX1RXdEby9b0tfXAYl2Qk8lsz81YNsf29akc7TerTylpRdzaSK+dXNp4YrAefbV6DNcj+mr1eKIJB4EGBuux7NZN/bi1L9mDneOVdZ8EbF8Zcjv1GFOPj+kzdgMpySW3nxy3Lx3/N0xHGcfuMuR2GWIkb2XMbdJjX0ajpLItpRBHCjFEpDFOpLIuUlHG9VGGmFOPMfX4yiCF/dn2lPU9zuV8hpa00YJt0Lky7UXRDMAEVTsjVt/5ZESM/K52FhxQgayLp1Olu0XZ1sVktudmLJtj+/pUjvab1aeUtCJubaTXTi5t1CPitK/2x8wLe+O0r/ZHM0bVase4fpTkCEFZ90nQajnPy4uSeoypxxeR/lXpUmfsLp5tGLbIJbeH6jHmy+Qh6U0DlXHsLkNulyFG8lbG3CY99mU0SirbUgpxpBBDRBrjRCrrIhVlXB9liDn1GFOPLyL9Y40p7M+2pwzv8dbkcj5DS9powTboXJn2kthuDCBdtaGIpYtnRMTI79pQwQEVyLoYS5XuFmVcF5PZnpuxbI7t61M52m9Wn1LSiri1kV47ubTRigNAteFx/RhufBvNUNZ9ErRS7vPyoqQeY+rxpX5VujKoDUccu3hGLJhViWON3S33xGA9+p7cdCfAJ5tzJ0Aok1xy25fJNFMZ86QMMZchRvJW1mNqpMW+jEZJZVtKIY4UYohIY5xIZV2koozrowwxpx5j6vGV4Xh56uuwNjTueHli8W1LLucztKSNFoypzpVpL5V6vZ7e3jZB/f398c5jj45V114f3d3dE/o/leXrGhpD/YwFDX09pq/d3uMc+zuZ3B4crEetPvIlWm0oolqJ6OqqtCjStFgXYw0O16O6onf079rp86Oroz3XR0rrYqL5PZntuRnL5ti+PpWj/Wb1qdlSG7u1kV47ubSx+UDp6jufjKWLZ8TKQ3tidhPek4HBelQ7Rg44NeP1Jyq13Iayy31eXpSNg/UYqkfM6Ix4ciiisxIxM6EYB4frsXEoYmZnjP5O6bNpSttlmQ3U6qN5Mrtq7G6lVs3PaE/m5cXZXGw8mtuH9BS6f52uVLYlRgwO1+O3ffVY1F2Jtf312KWnYl7eAKnPy2kPqexvp3IeC2kow/6W4pRxXp5CHCnEEDFy0Y3Hn6zH3JmV2LCxHjvMqMSsNl0XqUhpfeQ0L68N1mPwKTF2VSKqCcXoePn0pfSZdlscL8+7jVaMqa3c3zZ7ey3D2FE0d5oBgGlSpbuFdQFAOxh/AZNmXCSsEhEXHdQdG/9mflx0UHc061DG7K6Rg5tOpoK8mJc3R2XTz/h/p6I2FHHKmr7Yb/WGOGVNX3LvewpX2Sy7gcFxd5FK8OqDOWvFnQBpT2W4smjOOisRKw/pidrp82PlIT3RmdoAPwkDg/X4q1v7YpfL1sdf2ZaSUBuOOOuO/tjt8vVx1h395j8NUo+I/trI3d/6a/WwpVMEx9QA2JZKRHRXK9FRGfltpCBX47ft1Lb12lDEqWv64tAbH4tTHS+fktQ/0zpenr+cxtSBwXr81ZpNx+1sr4XpKjoAACi72dVKrDykJ1Yd3lN45XrRrAsA2kFHRAyN+7vRujoiejYdAOqpVqKrSSdlpnJVTKCxzMuboxX7/+modkZccUjPlitIdRYd0Vizuyqx8tBN26VxZ0rGF22sOryn4Ijay+YvsjffjaI2HE2bo9Fe5HaxhiOir1aPuR2V6KuNXLGyrKodEecf0B1XHTZyBVjFfcWrRMSFB428Jxs21kt9cgcAABOzucC3q6MS/SX/jJGLVlwMj/RUOyMud7x8WlL/TOuYGmVS7Yi4dtP2eu2dT8Y1TdhejXfPzOFSAGiA2dVNV5RyMpp1AUD2hiJGr2Jaj7EnUDdKbSjipE13CzipSVc/ckVryJt5eeMNxshJtcP1kd+DRQc0Tm0oxlxVLbUr50W4GvN0uYtUsVp1J0DaTxmuLJqzrhh3wYKiA5qG2lDEmXf0x8wLe+PMO/qNEwmY1VWJnhmbtq8ZlZhlDtQQXR0RGzdt3xuHFLECAGnp6ojY+bL1sdvl62Pny9abqySgHmO/1/NtWHtwvDx/jpfnr7MjYs7FvfGCqx+NORf3RmeJx9RWbK/Gu2dW4k0IANIxUKvH4HA9BmqmG9YFALnriC23GK9Ecz5YVzsjzjugO7719rlx3gHdTbn60fir77gKMeTFvLzxOmPsSbWJXZguqp3j9uupBci0VTsjPnrASNHGR5s0P2DbnKBLs2y+smjt9Pmx8tAeJ0q0WkfEvIt74x3/5/GYd3Fvqb85NRdIk5OgGq82HHHWHf2x2+Xr46w7+hUbAgBJqQ1HvH3xjFj3RD3e7sIIUBifkacv9QtBOF6ev9pwxDGLZ8S964fjmJKPqTltrwODm76DLuFFWUt86Hfqvv2v34z3nHRCHP+XfxErP3FZ1Gq1okMCoMQGavUxVydo55PSrAsA2sH40a0Zo11taNwJIE240ogrWkO+zMubYzjGXqEptd2mq6rlzwmixWrF/Iz25aT64tSGIv7H4hnxmbtq8T9KPn6aC6SpzCcSpKoSERce1B0PnTwvLnT3NwAgMe5Um57OGHsxvBKfp5yU8YcmUztU6TNy/hwvz19OY2orvl9oxXg3MFiPZbdu+g761r7SHe8q813Gp+SxxzbEyk9cGscdf0Lsuefz4/zzPhxf/ZevxCGHvaHo0ABKo1YfuYXl6jufjKWLZ8TKQ3rab0B5ivFXJ1h1eE/BERXHugCgHXRExNC4vxveRiXiE4f0xDWHRzw5NPJ3o22+ovWqw3uiNhxO0IOMmJc3R2ds+eIvxS9XN1+l6urDemJtf73UV6li62Z3VeLKQ0fmB8bu1mvF/AxovWolYuUhmz4XDY38XVaVysiJBFcf1hMbNtajUuK+5GLziQSj36W4mxQAQPbG36l2Xlte0px20IrvS6ej2jlyLG/V4T2xcSgcL5+C1D/TOl6ev5zG1NnVSly56fuF2tDI32VU7Sj3d9Al3oSm5t57fhb1esSBSw6OZz372fGSl7ws7r7rR0WHBVAqbmE5lqsTbGFdAEBj1COiv1aP4frI72Zdn8MVrSFP5uXtyVXV2oOxuzidHRG9T4zMz3qfqEdn2327AqSuqyNi58vWx26Xr4+dL1sfXfZThRt/IkHVe9IQtnUAIGWO0UEaasMRJ6/pixdc/WicvKZPLk5BGT7TOl6et9zG1NnVTdtrSQtmIkbekzHfQZfsPWm7GwNs2LAhZs6aGZVNl1fq7umJ3/72t09brlarRa1WG/17YKC/ZTECzSO3G2PzCVibK8lrQ9HWX0rMrlbGXI2wzBOb6SpyXchvyJPcJkXjC1iaUdDSGRE91Up0VEZ+51ajLLehuczL21MlIi48qDuuOqwycnX5ogMiK3J75HjYWXf0x5fuqcWb96zGlYf0tPXxMPIgt7fcRW5bf5dJbTji7U89bj/c3sftU7D5RIIi3pOc87s2NG5bb/PvqGgvOec2tDO5nRfH6Hgq+V2cSkSsOKg75s6Ui1NV5Gfa1Mnt1jCmpmd2VyVWHrrpO+gS3uGp7YpmtmZrtyf/4i2fj5tvurH1wQBNNZ3c7uqqRK1WH/N3u1Ik8nSb14EPB8Wti6nm92RyuxnL5ti+PpWj/Wb1qdFSH7u1kV47rWij2lWJwae0UW1WG4Ob2qg0p40ipZ7bkIOc5+VFST3GWV2VqEeMFFzOqMSsxOKj3IzdI/vVKw/piWsOD8fDyIbcjpjRVYmBp/RjRkn7EVH+L61zVOR7kvO83HdUtDPnseSjDPtbWieHeXkKcaQQQ0Qax+hSWRepKON33WV4D1OPcVZXJer1GL1AoePlk+c4w7alPnbn0kYrxtTU92WT0aq+bN4XlPE82Uq9Xm/GRXGT9f++/724eMUFce1nPxcdHR1x9corYuCJJ+LU9542ZrmtVQKevOz4WHXt9dHd3T2htirL1zU09voZCxr6ekxfu73HOfS3EbkNpEl+Q57kNuRJbkO+5DfkSW5DnuQ25Et+Q57kNuRJbkO+5DfkSW5DObXdnWb++57Pj46OjviXr9waey3eJ/7zP78fb/nTtz5tuWq1GtVqtYAIgWaS25Av+Q15ktuQJ7kN+ZLfkCe5DXmS25Av+Q15ktuQJ7kN+ZLfkCe5DeXUdkUzc+fOjWUnnho3fHZ13HjDdfHyV+wXBy45uOiwAAAAAAAAAAAAAAAAaKC2K5qJiHjVa/aPV71m/6LDAAAAAAAAAAAAAAAAoEk6ig4AAAAAAAAAAAAAAAAAGk3RDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQna6iAyiLer0eEREDA/2FxdDfX1zbtEa7vcdT6e/s2bOjUqk0LIYUchsYIb8hT3Ib8iS3IU+Nzu0I+Q2pMHZDnuQ25Mm8HPJl7IY8yW3Ik3k55MvYDXl6ptyu1DdnK9v1yCMPx8nLji86DGh7q669Prq7uxv2enIb0iG/IU9yG/IktyFPjc7tCPkNqTB2Q57kNuTJvBzyZeyGPMltyJN5OeTL2A15eqbcVjQzQcPDw9Hbuy5mzdp+FdLAQH+cvOz4uHzlNTF7dmMnTc0i5tYQc2M0usp3orm9WYrrpCjWxVjWxxZTXRfNyO9f//rBOP19p2T1vuS4relTOaSU25MZu1shl/c7l35E5NOXlPtRdG6nvG4i0o8vIv0YxTd9U4mxGVfOm0x+57peW0l805d6jGWdl6e+Xicql35E5NOXdu+H3G6MXPoRkU9fculHhHl5s6Qeo/imL/UYyzp2N1Lq71FE+jGmHl+EGCeq6NxOYR2kwrrYwroYy7y8OVKPUXzTl3qMZZ2Xp75eJ0o/0pNLX5qV212NCK4ddHR0xE47LZzw8rNndze80rjZxNwaYk7LZHN7s5zXyWRZF2NZH1sUvS46OjpiwYKdkoilGfSpHPSp8aY6drdC0eumUXLpR0Q+fcmlH9uT67w89fgi0o9RfNNXdIxTye+iY56I1GMU3/SlHmPR8eU6dk9ULv2IyKcv+tEYcjuPfkTk05dc+hFRfF/My4shvulLPcai40vheHnR62AiUo8x9fgixNhq7T4vbwTrYgvrYqyi14d5eTHEN32px1h0fO0+dutHenLpS6P70dGwVwIAAAAAAAAAAAAAAIBEKJoBAAAAAAAAAAAAAAAgO53nnHPOOUUHkZuOjo7Ya+99orOzs+hQrryl8QAAIABJREFUJkzMrSHmPFgnW1gXY1kfW6S0LlKKpVH0qRz0qb3ksm5y6UdEPn3JpR/NkPq6ST2+iPRjFN/0lSHG8coQc+oxim/6Uo8x9fi2paxxj5dLPyLy6Yt+FKuscY+XSz8i8ulLLv2IKGdfyhBz6jGKb/pSjzH1+FqhDOsg9RhTjy9CjGVhHWxhXWxhXYxVxvVRhphTj1F805d6jKnHty1ljXs8/UhPLn1pRj8q9Xq93rBXAwAAAAAAAAAAAAAAgAR0FB0AAAAAAAAAAAAAAAAANJqiGQAAAAAAAAAAAAAAALKjaGaC6vV69Pf3R71eLzoUoIHkNuRLfkOe5DbkSW5DvuQ35EluQ57kNuRLfkOe5DbkSW5DvuQ35EluQzkompmggYGBeOexR8fAwEDRoQANJLchX/Ib8iS3IU9yG/IlvyFPchvyJLchX/Ib8iS3IU9yG/IlvyFPchvKQdEMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkp6voAJrhkos+Ft/9zrfihpv+cczjD69dG1de8fH4+b33xG677xEnnHhKPPe5v1dMkAAAAAAAAAAAAAAAADRNdnea+d6//1v8+799Z6vPXfeZT8fMmbNixcWXxXOe89y46srLWxwdAAAAAAAAAAAAAAAArZBV0Ux/f398etUn49DD37jV5+++60ex/wEHxsJFi2LJwa+Ln997T2zcuLFp8QzU6jE4XI+BWn1CjwMAxTA2Q75akd/amGQ7g5vaGWxiX1rQBkDqyjjHLWPMwMTIb8iT3IY8yW2A1rC/JTcpbNMpxJAK62KsMq6PMsYMpCOnc0yaLZd+ROTVl2bIqmjmhutWxz77vjgW77PvVp9/bMOGmDVrVkRE9PT0jD62NbVaLfr7+0d/Bgb6JxXLQK0ey9b0RXVFbyxb0ze6AW7rcaA1ppvbERGV5esa+gM0xlTz29gMaZvO2N2K/NbGJNsZrMeyWze1c2tfU4paWtEG09eIeTmwbUXOcc3LIU+pz8uBqZHbkC/zcsiTY2r5sL/lqXLI7RS26RRiSIV1MZbj5UCjpT5253SOSbPl0o+IvPrSLF1FB9AoP777rvjev383Vlx8afz0pz+e8P+rVLb++Bdv+XzcfNONU46n2hmx+s4nI2Lk96rDe7b7ONAa081tIF1TzW9jM6RtOmN3K/JbG5Nsp6MFfWlBG0yfeTk0V5FzXPNyyFPq83JgauQ25Mu8HPLkmFo+7G95qhxyO4VtOoUYUmFdjOV4OdBoqY/dOZ1j0my59CMir740SzZFM7fcfFM89thj8Z6TT4ihoeGIiHjnsUfHqmuvH11mzpy50dfXFxERAwMDI4/N3XGrr3fEW46KN7zpiNG/Bwb64+Rlx084ntpQxNLFM2L1nU/G0sUzojYU0dWx7ceB1phubgPpmmp+G5shbdMZu1uR39qYZDvD49oZbkJfWtAG02deDs1V5BzXvBzylPq8HJgauQ35Mi+HPDmmlg/7W54qh9xOYZtOIYZUWBdjOV4ONFrqY3dO55g0Wy79iMirL81SqdfrWdx/Z31vb9RqIxVSd975w7jqysvj0is+GTvttDA6OjsjIuKSiz4W/f198e5lJ8cXbv5c/OqXv4y//ejyCb1+f3//aBFOd3f3hP7PQK0e1c6RDXF2tfKMjwOtN5Xcrixf19AY6mcsaOjrASMmk9/GZiiPyY7drchvbUyyncF6VDtGiltmdzWpLy1og8aayrwc2L5U5rjm5ZCnFOflwPTJbciXeTnkyTG1crO/ZVvKmtspbNMpxJAK62KsVNaHeTnkKcWxO6dzTJotl35E5NWXZsimhmje/PmxaOddYtHOu8SOO47cPWbRzrvEB884Lb777W9FRMQxS4+LoaHh+Jv3nRL3/eLn8a5lJzY1ptnVSnR1VJ624W3rcQCgGMZmyFcr8lsbk2yna1M7TSxmaUUbAKkr4xy3jDEDEyO/IU9yG/IktwFaw/6W3KSwTacQQyqsi7HKuD7KGDOQjpzOMWm2XPoRkVdfmqGr6ACa4aUve3nccNM/RkTE8gsvGX184aJFcfY55xUVFgAAAAAAAAAAAAAAAC2SzZ1mAAAAAAAAAAAAAAAAYDNFMwAAAAAAAAAAAAAAAGRH0QwAAAAAAAAAAAAAAADZUTQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkB1FMwAAAAAAAAAAAAAAAGSnq+gAAEhfZfm6hr5e/YwFDX09AAAAAAAAAAAAAIDx3GkGAAAAAAAAAAAAAACA7CiaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMiOohkAAAAAAAAAAAAAAACyo2gGAAAAAAAAAAAAAACA7CiaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMiOohkAAAAAAAAAAAAAAACyo2gGAAAAAAAAAAAAAACA7CiaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMiOohkAAAAAAAAAAAAAAACyo2gGAAAAAAAAAAAAAACA7CiaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMiOohkAAAAAAAAAAAAAAACyo2gGAAAAAAAAAAAAAACA7CiaAQAAAAAAAAAAAAAAIDuKZgAAAAAAAAAAAAAAAMhOV9EBNMrQ0FDceMN1cfttayIi4iUv/cN417KTYsaMGaPL/PQnP46PfOgDY/7fp1ffELNmz25prAAAAAAAAAAAAAAAADRXNkUz3/zG1+P2274Sp3/gQ7HDnDnxP885O26/bU0c9oY3jS6zfn1v7LTTwlh+0SWjjymYAQAAAAAAAAAAAAAAyE82RTMHLjk4DlxycERE9PU9Hl1dXdHZ2TlmmfW9vTFv/vzo6dmhiBABAAAAAAAAAAAAAABokWyKZjZ7/2nviQd/9UDstffieN0hh415rnfduli79ndx6onvjuqMGXHkUW+N1+x/wFZfp1arRa1WG/17YKC/qXEDrSG3IV/yG/IktyFPchvyJb8hT3Ib8iS3IV/yG/IktyFPchvyJb8hT3Ibyim7opkzzjw7Hvr1g3Hp318UX//a7bHkoNeOPvfyV+4X8xcsiL323ie++51vxcorLo299l4c8+cveNrrfPGWz8fNN93YytCBFpDbkC/5DXmS25AnuQ35kt+QJ7kNeZLbkC/5DXmS25AnuQ35kt+QJ7kN5VSp1+v1ooNohPvvvy8eXb8+9n3RiyMi4pKLPxYzZsyME09+z+gyjz66PmbMmBmzZ8+OjRs3xl8e87b40EfOi70X7/O019taJeDJy46PVddeH93d3c3vENAUjcjtyvJ1DY2pfsbTC/dS0459pnyM3ZAnuQ15ktuQL/kNeZLbkCe5DfmS35AnuQ15ktuQL/kNeZLbUE7Z3Gnml/f9Ij51zSfjzLPPjVmzZ8fPfvqTeOObj4jhoaHo6OyMiIhPX3NV9Paui1Pfe1p8//v/EV1dXbHb7nts9fWq1WpUq9VWdgFoAbkN+ZLfkCe5DXmS25Av+Q15ktuQJ7kN+ZLfkCe5DXmS25Av+Q15kttQTtkUzex/wJK4//77YsUF50e9Xo/9Xv2aOOTQN8RFKy6IvfZeHG988xFxzNLj4qqVl8dp7z05dpw3L0485b2xYIG7HQAAAAAAAAAAAAAAAOQmm6KZSqUSxyw9Lo5ZetyYx0//wFmj/164aFGcefa5rQ4NAAAAAAAAAAAAAACAFusoOgAAAAAAAAAAAAAAAABoNEUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANnpKjqARhkaGoobb7gubr9tTUREvOSlfxjvWnZSzJgxY3SZxx9/PD75icviR3f+V8xfsFO84/gTYu/F+xQVMgAAAAAAAAAAAAAAAE2SzZ1mvvmNr8ftt30lTv/Ah+Lc85fHnT/8r9ECms2+eMvn45FHHo4LVlwSr3jlq+KKS/8+Bmu1giIGAAAAAAAAAAAAAACgWbIpmjlwycFxzT9cF3/wghfGvHnzoqurKzo7O8csc/ddP4pX7vfq2HmXXeJ1hxwavb3r4re//U3TYhqo1WNwuB4DtfqEHgcAimFshny1Ir+1kV47LWljcFMbg8YOIE1lnOOWIWb7f5iaMuQ3MHm55LbxHcbKJbcBUmd/S6Oksi2lEgcjvB9jlfFzXxnewzLECClqxT5Jfk5cTuuqjONdK2VTNLPZ+097T7z7HUtj1912i9cdctiY5x7bsCFmzZoVERE93T0REbFhw4atvk6tVov+/v7Rn4GB/knFMVCrx7I1fVFd0RvL1vSNJtO2HgdaY7q5DaRrqvltbIa0TWfsbkV+ayO9dlrSxmA9lt26qY1b+xxwmALzcmiuIue4Oc/L7f9pZ6nPy4GpkdvGd/KV87wc2pljavmwv+WpcpiXpxIHI7wfYxX5uS/neXkZYoRmmdbY3YJ9kvycuJzWleOcz6yr6AAa7Ywzz46Hfv1gXPr3F8XXv3Z7LDnotdtdvlLZ+uNfvOXzcfNNN045jmpnxOo7n4yIkd+rDu/Z7uNAa0w3t4F0TTW/jc2QtumM3a3Ib22k105L2ugwdkyXeTk0V5Fz3Jzn5fb/tLPU5+XA1Mht4zv5ynleDu3MMbV82N/yVDnMy1OJgxHej7GK/NyX87y8DDFCs0xr7G7BPkl+TlxO68pxzmeWTdHM/fffF4+uXx/7vujFsdNOC2OvxYvjrh/dOaZoZs6cudHX3xcREf0DAxERMXfuvK2+3hFvOSre8KYjRv8eGOiPk5cdP+F4akMRSxfPiNV3PhlLF8+I2lBEV8e2HwdaY7q5DaRrqvltbIa0TWfsbkV+ayO9dlrSxvC4NoaNHZNlXg7NVeQcN+d5uf0/7Sz1eTkwNXLb+E6+cp6XQztzTC0f9rc8VQ7z8lTiYIT3Y6wiP/flPC8vQ4zQLNMau1uwT5KfE5fTunKc85lV6vV6Fvff+cbXvxqfuuaTcebZ58as2bPjgvPPjTe++Yg47LA3RkdnZ0REfHb1P8QPfvD/4rT3fzC+8fWvxldvvy0uveKq6Nz0/Pb09/fHO489OlZde310d3dPKKaBWj2qnSNJNbtaecbHgdabSm5Xlq9raAz1MxY09PWaoR37TPlNJr+NzVAekx27W5Hf2kivnZa0MViPasfIgYfZXcaO6ZrKvBzYvlTmuLnNy+3/YUSK83Jg+to1t43vtIPc5uXACMfUys3+lm0p67w8lTgY4f0YK5XPfbnNy8sQI7TCpMfuFuyT5OfE5bSuUhnvUpXNnWb2P2BJ3H//fbHigvOjXq/Hfq9+TRxy6BviohUXxF57L443vvmIOOLIo+I3v3koPnj6+2LBgp3ipFPeN6GCmananDzjK7W29TgAUAxjM+SrFfmtjfTaaUkbXcYOIG1lnOOWIWb7f5iaMuQ3MHm55LbxHcbKJbcBUmd/S6Oksi2lEgcjvB9jlfFzXxnewzLECClqxT5Jfk5cTuuqjONdK2VTNFOpVOKYpcfFMUuPG/P46R84a/TfO+ywQ5z2/g+2OjQAAAAAAAAAAAAAAABaTC0RAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHa6ig5g5Scu2+7zXZ2dsfsez4olB702unt6WhQVAAAAAAAAAAAAAAAAZVZ40cwdX7s9IioRUd/Ks5sfr8T//fKX4n/+3YrYccd5LY0PAAAAAAAAAAAAAACA8im8aObY446P1f+wKt7252+P3ffYY/Tx3/zmobjhutXxpj8+MgYG+uO2Nf8cN/2v6+P4E04sMFoAAAAAAAAAAAAAAADKoPCima999V9i3xe9OP74T4582nM/ufvu+MmP74pzzvu7eOCX98cPf/iDAiIEAAAAAAAAAAAAAACgbDqKDuDBXz0Qs2bN3upzXV1dcf99v4iIiF133S3W9/a2MjQAAAAAAAAAAAAAAABKqvCimZ12Whj/8b1/i29/65tjHv/ud74V//G9f4t58xdERMSvfvVA7Lrb7kWECAAAAAAAAAAAAAAAQMl0FR3AEUceFVddeXlcdsnFcc0nPxHz5s2PDRs2RF9fX0TU48ij3hp3/eiHce89P4u3H/uOosMFAAAAAAAAAAAAAACgBAovmlly0Gtjzpw5cfNNN8Yv778vHnro19HZ2Rl77vm8OPLP3hYvfslLY3h4OI5757vjta8/tOhwAQAAAAAAAAAAAAAAKIHCi2YiIl72h6+Il/3hK2J4aCgee/yxmDt3x6hUKqPPd3R0xOsPPbzACAEAAAAAAAAAAAAAACiTJIpmhoaG4je/eSge2/Bo1OsRv37wwTHPv3CvvQuKDAAAAAAAAAAAAAAAgDIqvGjmJz++Oy675KJYt27dNpe5/nNfaGFEAAAAAAAAAAAAAAAAlF3hRTPXfurqWLfukdjjWc+OXXfdNSIqRYcEAAAAAAAAAAAAAABAyRVeNPPgg7+KF7xwr/jwuecXHQoAAAAAAAAAAAAAAACZ6Cg6gIULF8Uuu+xadBgAAAAAAAAAAAAAAABkpPCimT/50z+L73//e7F27e+KDgUAAAAAAAAAAAAAAIBMdBUdwD/efFP0Pd4Xf33qiVGtVreyRCU+tfr6lscFAAAAAAAAAAAAAABAeRVeNPPQQ78e/ffQ0NC0XutLX7wlvvy//yk2bnwi9n3xS2LZiafGrFmzRp//12/eEZd//OLRv3eYMyeu/tRnptUmAAAAAAAAAAAAAAAA6Sm8aOb6z93SkNf5rx/8Z9x4w2fjjDPPjnnz58dH//Yj8c9f/lL8yZF/NrrM+t7e+IM/eGGc/sGzIiKiUuloSNsAAAAAAAAAAAAAAACkpfCimUbp6uqKo49ZGvvs+6KIiNht993j0fWPjllmfW9vzF+wIHp6digiRAAAAAAAAAAAAAAAAFqkkKKZc87+YLzukMNi/z86MN5/2nu2u2ylUonlF17yjK+5196LY6+9F0dExP33/SLu+dlP4+hjjh2zTG/vuvjZT38SJ53wzpgzd278xdv/crTIZrxarRa1Wm3074GB/meMAUif3IZ8yW/Ik9yGPMltyJf8hjzJbciT3IZ8yW/Ik9yGPMltyJf8hjzJbSinQopm1q79XTzy8MMREfGrB375DEtXJvXajzzycKy44Px445uOiD2f9/wxzx38utfHXov3iec//wVx6//9P3Hp318YV171qeiqVp/2Ol+85fNx8003TqptIH1yG/IlvyFPchvyJLchX/Ib8iS3IU9yG/IlvyFPchvyJLchX/Ib8iS3oZwq9Xq93upG1/7ut7HjjvNixsyZsXbt755x+UWLdp7Q627YsCHO/fCZseeez4tlJ50alcrYgpve3nXR090TM2bOjF898ECc/tenxMcv/2TsvMsuT3utrVUCnrzs+Fh17fXR3d09oXiA9DQityvL1zU0pvoZCxr6es3Qjn2mfIzdkCe5DXmS25Av+Q15ktuQJ7kN+ZLfkCe5DXmS25Av+Q15kttQToXcaWbRzluKVCZaEPNMnhgYiI999LxYuHBhHHPscdHf3xeVSkfMmjkzOjo7IyLiwuUfjYULF8Wx73hX/Os374gddpgTCxZs/cTtarUa1a3cgQYoN7kN+ZLfkCe5DXmS25Av+Q15ktuQJ7kN+ZLfkCe5DXmS25Av+Q15kttQToUUzTzV0NBQ3H7bmrj7rh/Fhg2Pxvj73lQqER/6yHnP+Drf/e634957fxYREe9+x9KIiFi4aFF0d/fEkX/61njlq14d7152Uqy6amW875RlsfMuu8Z7/vr06LLjAgAAAAAAAAAAAAAAyE7hRTOr/2FVfOXWf46I+jaWqEzodQ5ccnAcuOTg7S7z3N/7/fjbjy6fXIAAAAAAAAAAAAAAAACUTuFFM9/59r9Gzw49cfy7/ip22XXXqFQmViQDAAAAAAAAAAAAAAAA21J40UztyVq8cr9XxStf9eqiQwEAAAAAAAAAAAAAACATHUUH8OKXviwefnht0WEAAAAAAAAAAAAAAACQkcLvNLPn854X139mdVy18orYY49nbXWZN775iBZHBQAAAAAAAAAAAAAAQJkVXjRz3bWfjoiIr91+W0RUxj1bj4iKohkAAAAAAAAAAAAAAAAmpfCimSOPemtEZXyxDAAAAAAAAAAAAAAAAExd4UUzR731z4sOAQAAAAAAAAAAAAAAgMwUXjQTEfHAA7+MH999Vzy24dGo18c+V6lEHHnU24oJDAAAAAAAAAAAAAAAgFIqvGjmX75ya3zqmk9uKpapb2WJiqIZAAAAAAAAAAAAAAAAJqXwopl/+scvRKVSide9/tDYZbddoxKVokMCAAAAAAAAAAAAAACg5Aovmlm/vjde/or94rjj3110KAAAAAAAAAAAAAAAAGSio+gAfv/3/3vRIQAAAAAAAAAAAAAAAJCZwu8086Yj3hKXf/zi+OY3vh577PGsrS7z+/9NYQ0AAAAAAAAAAAAAAAATV3jRzMUr/i4iKvGJyz6+1ecrlYjP3viF1gYFAAAAAAAAAAAAAABAqRVeNPOCF+4VlUql6DAAAAAAAAAAAAAAAADISOFFMx8+9/yiQwAAAAAAAAAAAAAAACAzhRTNrF37u5g3b35Uq9V4/LHHnnH5HebMaUFUAAAAAAAAAAAAAAAA5KKQopm//fBZ8frDDo8/PuLIePc7l0ZEZZvLVioRn73xC60LDgAAAAAAAAAAAAAAgNIrpGhm/oIFMW/e/IiI2Gnhwqhsp2gGAAAAAAAAAAAAAAAAJquYO82cv3z035d94uoiQgAAAAAAAAAAAAAAACBjHUUHAAAAAAAAAAAAAAAAAI2maAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO11FB9BIX/riLfHl//1PsXHjE7Hvi18Sy048NWbNmjX6/MNr18aVV3w8fn7vPbHb7nvECSeeEs997u8VFzAAAAAAAAAAAAAAAABNkc2dZv7rB/8ZN97w2TjxlPfGuecvj5/cfXf885e/NGaZ6z7z6Zg5c1asuPiyeM5znhtXXXl5QdECAAAAAAAAAAAAAADQTNkUzXR1dcXRxyyNffZ9UTz72c+J3XbfPR5d/+iYZe6+60ex/wEHxsJFi2LJwa+Ln997T2zcuLEp8QzU6jE4XB/5PTj2Z8xzm37T3sZvF+2g3foLpG0y+6RmLJtj+/pUjvab1aeUtCJubaTXjjbSaqMZyho3sH1lyO3UY0w9PqZvYHDTezxYrvc4l20zl36QHrldrFz6EZFXX3JRxvekDDGXIUbyVtaxm7TYl9EoqWxLKcSRQgypxJFCDCkp4/ooQ8ypx5h6fGWQ+jpMPb5tyeU8AG2k105L2mjB5+FmtZFN0cxeey+ON7zpjyMi4v77fhH3/Oyn8Zo/OmDMMo9t2BCzZs2KiIienp7Rx7amVqtFf3//6M/AQP+EYxmo1WPZmr6oruiNZWv6olaPMT8fW9MXtaEYs0zZdto0zkCtHrV6e20P43Oklf2dTm4DaZtqfk9mn9SMZXNsX5/K0X6z+tRojZyXNyNubaTXjjbSamNbUs9tYOpaMS8vSuoxph4f0zcwWI9lt256j2/ta+kJesbufPpBeuR2sXLpR0RefclFGT93l2E7KkOM5K2sYzdpsS/jqXKYl6cQRwoxpBJHCjGkxLy8OVKPMfX4yiD1dVjG3I7I5zwAbaTXTkvaaMHn4Wa2UanX62ntyabpkUcejo+c9YH4owOWxNuOPmbMc0e/9S3xN2ecGS992cvjgV/eH+8/7T1x+ZVXx04LFz3tdT7/uRvi5ptufNrjq669Prq7u7cbw+BwPaorekf/rp0+f8zzA32Px+yeHZ62TFdHZUJ9JC+DwyMp2E7bw9ZypFX9nU5ub1ZZvq6hMdXPWNDQ12uGduwz5TPV/J7MPqkZy+bYvj6Vo/1m9anRGj0vb3Tc2kivHW2k1ca2pJ7bwNS1Yl5elNRjTD0+ps/YXaxc+kF65HaxculHRF59yUUZ87sM21EZYiRvZcxt0mNfxlPlMC9PIY4UYkgljhRiSEkZx+4yvIepx5h6fGWQ+josY25H5HMegDa53v4qAAAgAElEQVTSa0cbzyybO81ERGzYsCE+et45sffifeKtf/4XT3t+zpy50dfXFxERAwMDI4/N3XGrr3XEW46KVddeP/pz+cprJhxHbShi6eIZETHyuzYUY34uvqOy1WVoT5u3i3baHors73RyG0jbVPN7MvukZiybY/v6VI72m9WnRmv0vLzRtJFeO9pIq41tST23galrxby8KKnHmHp8TN//Z+/e4yQ9yzrhX32aZHoSEoaEyCEBF0QkwVXwgEdc96BAOOjLri95806QYBgICeFkOMpJlBBAICoua9ZN1CACC6i7a2ZZUYRVP+963HBQlwXUVSEwkwS6i6Squ94/Jj1Jz6S7p6frqbruq77fz6c+lZmu7ut3331fz/NM93On+qtHfY9Xx1fbubvOOMhHb09WlXFE1BpLFS3+u7uFddRCRmpr9dxNLo5l3F2F6/IMOTJkyJIjQ4ZMXJd3I3vG7PlakH0OW+ztiDr3AaiRr85Yaozh38Nd1ijzTjNf7fXiJ1/zE7HnlD3x3Oe9IGZnZ2NmZjZOPumkmJ2bi4iIt775jbG8vBSX7H9u/Mf3/Xr83d/8Tbz2p646rq+/vLwcF190wXH/Hzp6/WEszB1ehAtHbXDqD+Ouj935vPvoFzFVBoPhunUxDevh7j0yyfFut7cjpvNdV6ZxzLRvO/29nWNSF6+tWN+Y2qjf1Zi6tJPr8q5yq5Gvjhq5ahyPjL0NjEZX1+WTkj1j9nzsXG8wjIXZw7802D3v3D1uVcZBPnp7sqqMI6LWWKrI8j1xXQ6j1eq5m1wcy9hIq9flGXJkyJAlR4YMmWSZD9fl45U9Xwuyz2GWfBnP3WrkqjGuOmOpMYZ/D3dVY35kX2nC/uiP/iA+/em/joiIS56xLyIizjjzzFhc3BM//H/9m/j27/jOuHDfj8Y7fu7t8aLnXxb3f8AD4tmXXt5ZnrXFNn8P7+WzNulrH7un1zBd5udnjlkX1W3WIwDjtp1jUhevrVjfmNqo39WYMhlHbjXy1VEjV40utJob2FwLvZ09Y/Z87NzaLwda+x5XWZtVxkE+enuyqowjotZYqmjxe9JC5hYyUlur525ycSxjVLKspQw5MmTIkiNDhkxanI8WMmfPmD1fC7LPYfZ8G6lyH4Aa+eqMpcYY/j3cVY0ym2Ye+33fH4/9vu/f9DVnnHlmvPLVrxtTIgAAAAAAAAAAAAAAACalsf11AAAAAAAAAAAAAAAAsDWbZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAAChnftIBRu3WW2+Jj330I/GR3/1wXHrZFXH2OQ9a9/G/+stPxate8ZJ1f/dL178rTt69e5wxAQAAAAAAAAAAAAAA6FCpTTO9Xi8ue/Yl8eAHf2187rOfucfX3HLLobjPfc6Iq9781iN/Z8MMAAAAAAAAAAAAAABALaU2zezatSuuecc7447bb4/LL33WPb7mlkOH4vR73zv27DllzOkAAAAAAAAAAAAAAAAYl1KbZubm5uK0006Pm7/w+Q1fc+jgwbj55i/E5c+5JBZ27Yoffuq/ie/67u895nX9fj/6/f6RP/d6y51kBsZLb0Nd+htq0ttQk96GuvQ31KS3oSa9DXXpb6hJb0NNehvq0t9Qk96GNpXaNHM8vvXbHxP33rs3HnHuI+OP/vC/xy/83NvjEeeeF/e+9951r/vg+98b73vPuyeUEuiK3oa69DfUpLehJr0NdelvqElvQ016G+rS31CT3oaa9DbUpb+hJr0NbZoZDofDSYcYtZu/8Pm4/NJnxRvf/LY4+5wHrfvYrbfeErt2nRS7d++O22+/PZ5+4Y/EK171ujj3vEeue9097QR87v5nxrXX3RCLi4tjGQcweqPo7ZmrDo400/DKvVu/aMKmccy0x7kbatLbUJPehrr0N9Skt6EmvQ116W+oSW9DTXob6tLfUJPehjZNxTvNrK6sxOzcXERE/NIvvjMOHToYl1/xwviTP/njmJ+fj/vd/wHHfM7CwkIsLCyMOyrQMb0NdelvqElvQ016G+rS31CT3oaa9DbUpb+hJr0NNeltqEt/Q016G9o0O+kA4/Dmq98Q/+k3PxgRERfu+9E46aST4oVXPDd+6zfeH8+57IrYu9c7HgAAAAAAAAAAAAAAAFRS8p1mzrzvWfGu93zgyJ9f/JKXH/nvM848M172ytdMIhYAAAAAAAAAAAAAAABjMhXvNAMAAAAAAAAAAAAAAMB0sWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoBybZgAAAAAAAAAAAAAAACjHphkAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcuYnHWCUbr31lvjYRz8SH/ndD8ell10RZ5/zoHUf/8pXvhL/9ueviY/f9Bdx7733iWc881lx7nmPnFBaAAAAAAAAAAAAAAAAulLmnWZ6vV5c9uxL4g8/9rH43Gc/c4+v+eD73xtf+tIX4w1XvzW+7du/I37u7T8Tg35/zEkBAAAAAAAAAAAAAADoWplNM7t27Ypr3vHOuOyKF2z4mk9+4uPx7Y/5zrjvWWfFv/hXPxCHDh2Mz3/+HzvJMxgMN3z0+sMYrK5/ZrodvS6mwbSNF8htO8ekLl5bsb4xtVG/qzFlMo7cauSro0auGl1oNTewuRZ6O3vG9PkGd+Yb5MzXguzf4420mvtoVcZBPq0eH6v0RJVxRLS7liprcX21kLmFjABbcSxjVLKspQw5MmTIkiNDhkxanI8WMmfPmD5fA/+GTz+HyfNtpMp9AGrkq9NqTxytq3GU2TQzNzcXp512+qav+fJtt8XJJ58cERF7FvdERMRtt912j6/t9/uxvLx85NHrLR93ll5/GP1hbPh444Gl6K9E7D+wFAtXH4r9B5aaX6CcuLX1Mk3rodcfTmy8O+ltILcT7e/tHJO6eG3F+sbURv2uxjRqO70u7zq3GvnqqJGrxkay9zZw4sZxXT4p2TOmzzcYxv4b78x341LqXwRm5dw9WVXGQT6TPD7q7TrjiHCuzajFc3cLPdFCRuiK33XX4VjG3VW4Ls+QI0OGLDkyZMjEdXk3smdMn6+Bf8Onn8MGezuizn0AauSrk71nj1eX45gZDodtzsoGbv7C5+PyS58Vb3zz2+Lscx607mPPu/RZ8fjznxQ/8LgnxFd7vfjRfU+LV7329fHwbzj3mK/z3l9/V7zvPe8+5u+vve6GWFxc3DTDYHWL/6P30ldi955TYuHqQ0f+rv/ie8f87Mymn0dNa+tlmtbDYHU4sfHupLfXzFx1cKSZhlfuHenX68I0jpn2nGh/b+eY1MVrK9Y3pjbqdzWmUdvpdXnXudXIV0eNXDU2kr23gRM3juvyScmeUb76nLsnq8o4yEdvT1aVcUTUGksVLfZ3C+uohYzQlVH8rpscHMu4uwrX5RlyZMiQJUeGDJm4Lu9G9ozy7Vz2jC32dkSd+wDUyFcne88ery7HUeadZo7HqafeK5aWlyIiYrnXi4iIe93rnt+d5sk/9NS49robjjx+9hd+8bjr9Fc2f7zlIzPRX4nYd96uiDj83F/Z4eBo1tq6mKb1MMnx7qS3gdxOtL+3c0zq4rUV6xtTG/W7GtOo7fS6vOvcauSro0auGhvJ3tvAiRvHdfmkZM+YPt/qUflWJxyoQc7dk1VlHOQzyeOj3q4zjgjn2oxaPHe30BMtZISu+F13HY5l3F2F6/IMOTJkyJIjQ4ZMXJd3I3vG9Pka+Dd8+jlssLcj6twHoEa+Otl79nh1OY7y7zSzurISs3NzERHxq9f/h/jzP//TeOGPvzR+//c+HB/+nQ/F23/unTF358c3s7y8HBdfdMFx/x86Bpu8XVp/GLEwd/gbu/a8e6G93VyMzmAwXLcupmE99PrDFOPdbm9HTOe7rkzjmGnfdvp7O8ekLl5bsb4xtVG/qzF1abvn7nHkViNfHTVy1TgeGXsbGI2urssnJXvG9PkGw1iYPfwLwd3z+fK1IMv3eFrP3VXGQT5Zjo96u+1xRORZS9wly/pyXQ41ncjvusnDsYyNtHpdniFHhgxZcmTIkEmW+XBdPl7p8zXwb/j0c5gkX8Zztxq5aoyrTpae2KmuxjE/sq+U1JuvfkM84tzz4glPfHI8+YefGv/4j/8QL33x82Pv3vvEpZc9/7g2zJyI+U1OYmuTPj+7/pnpNT8/c8y6qG7tQDYt4wVy284xqYvXVqxvTG3U72pMmYwjtxr56qiRq0YXWs0NbK6F3s6eMX2++dz5WpD9e7yRVnMfrco4yKfV42OVnqgyjoh211JlLa6vFjK3kBFgK45ljEqWtZQhR4YMWXJkyJBJi/PRQubsGdPna+Df8OnnMHm+jVS5D0CNfHVa7YmjdTWOcptmzrzvWfGu93zgyJ9f/JKXH/nvU045JV744y+dRCwAAAAAAAAAAAAAAADGqPG9RAAAAAAAAAAAAAAAAHAsm2YAAAAAAAAAAAAAAAAox6YZAAAAAAAAAAAAAAAAyrFpBgAAAAAAAAAAAAAAgHJsmgEAAAAAAAAAAAAAAKAcm2YAAAAAAAAAAAAAAAAox6YZAAAAAAAAAAAAAAAAypmfdIBWDIfDiIjo9ZYnnASm2+7du2NmZmZkXy9Dby8vT99xJfuY91zz1ZF+vaXLTh7p1+tChjFX7G9Ab0NVehtqGnVvR+hvyMK5G2rS21CT63Koy7kbatLbUJPrcqjLuRtq2qq3Z4Zr3cqmvvSlL8Zz9z9z0jFg6l173Q2xuLg4sq+ntyEP/Q016W2oSW9DTaPu7Qj9DVk4d0NNehtqcl0OdTl3Q016G2pyXQ51OXdDTVv1tk0zx2l1dTUOHToYJ5+8+S6kXm85nrv/mfGzv/CLsXv3aC+auiLzeMg8GqPe5Xu8vb0m45xMirlYz3zc5UTnoov+/vu//z/x4udfVur7UnGtGVMbMvX2ds7d41Dl+11lHBF1xpJ5HJPu7cxzE5E/X0T+jPLt3Ilk7OL/nLed/q46r+Mk385lz9jqdXn2eT1eVcYRUWcs0z4OvT0aVcYRUWcsVcYR4bq8K9kzyrdz2TO2eu4epezfo4j8GbPni5DxeE26tzPMQRbm4i7mYj3X5d3InlG+ncuesdXr8uzzeryMI58qY+mqt+dHEW4azM7Oxn3uc8Zxv3737sWR7zTumszjIXMu2+3tNZXnZLvMxXrm4y6TnovZ2dnYu/c+KbJ0wZjaYEyjd6Ln7nGY9NyMSpVxRNQZS5VxbKbqdXn2fBH5M8q3c5POeCL9PenMxyN7Rvl2LnvGSeereu4+XlXGEVFnLMYxGnq7xjgi6oylyjgiJj8W1+WTId/OZc846XwZfl4+6Tk4HtkzZs8XIeO4Tft1+SiYi7uYi/UmPR+uyydDvp3LnnHS+ab93G0c+VQZy6jHMTuyrwQAAAAAAAAAAAAAAABJ2DQDAAAAAAAAAAAAAABAOXOvfvWrXz3pENXMzs7GI859ZMzNzU06ynGTeTxkrsGc3MVcrGc+7pJpLjJlGRVjaoMxTZcqc1NlHBF1xlJlHF3IPjfZ80XkzyjfzrWQ8WgtZM6eUb6dy54xe76NtJr7aFXGEVFnLMYxWa3mPlqVcUTUGUuVcUS0OZYWMmfPKN/OZc+YPd84tDAH2TNmzxchYyvMwV3MxV3MxXotzkcLmbNnlG/nsmfMnm8jreY+mnHkU2UsXYxjZjgcDkf21QAAAAAAAAAAAAAAACCB2UkHAAAAAAAAAAAAAAAAgFGzaQYAAAAAAAAAAAAAAIBybJo5TsPhMJaXl2M4HE46CjBCehvq0t9Qk96GmvQ21KW/oSa9DTXpbahLf0NNehtq0ttQl/6GmvQ2tMGmmePU6/Xi4osuiF6vN+kowAjpbahLf0NNehtq0ttQl/6GmvQ21KS3oS79DTXpbahJb0Nd+htq0tvQBptmAAAAAAAAAAAAAAAAKMemGQAAAAAAAAAAAAAAAMqxaQYAAAAAAAAAAAAAAIBybJoBAAAAAAAAAAAAAACgHJtmAAAAAAAAAAAAAAAAKMemGQAAAAAAAAAAAAAAAMqxaQYAAAAAAAAAAAAAAIBy5icd4ET85gffH//5t34jbr/9q/GN3/TNsf85l8fnP/+P8W9//pr4h7//P/FPHvLQePalz4szzjxz3ecNh8N4169eH7/34d+Jubm5OP9JT4nHn/+kCY0CAAAAAAAAAAAAAACArjT3TjN/8ed/Fu9+16/Gcy67Il7z+qviLz/5yfjt//yb8e/e8XPxT/7JQ+Lqt1wTc3Nz8SvX/9Ixn/tnf/rH8aEDvx0//rJXxjOf9Zz4let/Kf7mc58d/yAAAAAAAAAAAAAAAADoVHObZubn5+OCC/fFI7/xn8bZZ58T97v//eOLN98cn/70X8dj/9k/jzPOPDO+57H/LD75yY8f87mf/MTH4+se9vB4yEMeGo969LfEfe97Vvzlpz7ZSc7BYLjho9cfxmB1/TPT7eh1MQ1aHW+rubtgLgBowTjOV2rkq6PGNmoM7qwxaOuazrUo1NRCb7eQkdqcuyeryjhgVKr0RJVxRLR7niCXFnqihYwAW3EsY1SyrKUMOTJkyJIjQ4ZMWpyPFjJnz+jfyPW1+j0uc6+BGturM4b1mv24PGnNbZp5xLnnxePPf1JERHzus5+J//XXfxXf+d3fGxERJ598ckRE7NmzJ758223HfO5tt9125DUREYt79sRt9/C6iIh+vx/Ly8tHHr3e8nFn7PWH0R/Gho/LDyxFfyVi/4GlWLj6UOw/sGSBTrG19TJN66HXH05svDvt7Wn6Pm3GXJDRTvo7ImLmqoMjfwA7l/3crUa+Ompso8ZgGPtvvLPGjUtj/WFi9t4GTtyJ9ncLvd1CRmpz7p6sKuOAu9PbdcYRMdnzBPm4Loeadvq7MPJwLOPuKlyXZ8iRIUOWHBkyZNLifWotfA+zZ/Rv5Pr8vFyNVmpEjGe9Zj8uZzAzHA6bnJUvfemL8aqXvyS+53u/L/75v/yBuOw5PxZvfPPb4uxzHhR/8sf/X7zpqp+OG379P677nF/4+Wuit7wcz3/RlRER8bIrXxjf8q3fFj/81B855uu/99ffFe97z7uP+ftrr7shFhcXN802WN18Sp/xn74S//4Jp8TC1YeO/F3/xfeO+dmZTT+PmtbWyzSth8HqcGLj3WlvT9P3aTPmgox20t8R0ckml+GVe0f+NWHaZD93q5Gvjhq5amwke28DJ+5E+7uF3m4hI7U5d09WlXHA3entOuOIqDUWds51OdS009+FkYdjGXdX4bo8Q44MGbLkyJAhkxZ/ptbC9zB7xuz52LkWezuizn0AauSr47i3tflJBzgRt912W/zU614d5573yPg3T/t/on/HHRERsbS0FBERveXlOO200475vFNPPTW+9MWbj/y51+vFvU47/R5rPPmHnhqPP//Jd3vtcjx3/zOPK19/ZfOPnzQ7E/2ViH3n7Yrrb7oj9p23K/orEfPNve8Po7C2XqZpPUxy/e+0t6fp+7QZc0FGO+lvIK/s52418tVRYxs1Vo+qseq6HNi5E+3vFnq7hYzU5tw9WVXGAXent+uMI2Ky5wnycV0ONfldWB2OZdxdhevyDDkyZMiSI0OGTFq8T62F72H2jP6NXJ+fl6vRSo2I8azX7MflDJp7p5mv9nrxk6/5idhzyp547vNeELOzszEzMxuvf+1PxAMfeHb86x+5IK79d++I3Yt74vIrXhgREasrKzE7Nxd/8sf/I972ljfGK171ulheXo6rfup1cfVb3hYPeODZW9ZdXl6Oiy+64Lj/Dx2DTd46qT+MWJg7vEDXnncv2M01zQaD4bp1MQ3rodcfphjvdns7S+4MzAXZbbe/vdMMtCHjuVuNfHXU2EaNwTAWZg//kGb3vOtyYPS2098t9HYLGanNuXuyqowDNqK32x5HRJ7zBPm4LoeatnvuJhfHMjbS6nV5hhwZMmTJkSFDJlnmw3X5ePk3cn1ZvscZz91q5KoRMZ71mv24PGnNvdPMH/3RH8SnP/3XERFxyTP2RUTEGWeeGS+68uXxznf8bLzo+ZfFQx76dfHMSw5/7Ld+8wPxqU98Il505cvimx/16PjBx50fV7/h9TE3Pxf7nv6M49owcyLmN1nQa5O+toPLTi7m52eOWRfVrR2QWxtvq7m7YC4AaME4zldq5KujxjZqzLd5TedaFGpqobdbyEhtzt2TVWUcMCpVeqLKOCLaPU+QSws90UJGgK04ljEqWdZShhwZMmTJkSFDJi3ORwuZs2f0b+T6Wv0el7nXQI3t1RnDes1+XJ605jbNPPb7vj8e+33ff48fe/0b3nTM353/xKfE+U98SkREzMzMxNMu3BdPu3BfpxkBAAAAAAAAAAAAAACYLHuJAAAAAAAAAAAAAAAAKMemGQAAAAAAAAAAAAAAAMqxaQYAAAAAAAAAAAAAAIBybJoBAAAAAAAAAAAAAACgHJtmOjIYDDd89PrDGKyuf2a6Hb0upkGr4201dxfMBQAtGMf5So18ddTYRo3BnTUGbV3TuRaFmlro7ewZs+djelVZm1XGAaNSpSeqjCOi1liYnBbWUQsZAbbiWMaoZFlLGXJkyJCFuVivxfloIXP2jNnzMb3K3GtQ5H6GSseKSmPpgk0zHej1h9EfxoaPyw8sRX8lYv+BpVi4+lDsP7BkgU6xtfUyTeuh1x82Od5Wc3fBXADQgnGcr9TIV0eNbdQYDGP/jXfWuHGpmY0zrkWhphZ6O3vG7PmYXlXWZpVxwKhU6Ykq44ioNRYmp4V11EJGgK04ljEqWdZShhwZMmRhLtZrcT5ayJw9Y/Z8TK8y9xoUuZ+h0rGi0li6YtNMBxbmNn/cvjqMhbmI62+6IyIOPy/MTTg0E7O2LqZpPbQ63lZzd8FcANCCcZyv1MhXR41t1Jg9qkYjPyFwLQo1tdDb2TNmz8f0qrI2q4wDRqVKT1QZR0StsTA5LayjFjICbMWxjFHJspYy5MiQIQtzsV6L89FC5uwZs+djepW516DI/QyVjhWVxtKVRm6JaUt/ZfPHSbMz0V+J2Hferog4/NxfmXBoJmZtXUzTemh1vK3m7oK5AKAF4zhfqZGvjhrbqLF6VI3V0dfogmtRqKmF3s6eMXs+pleVtVllHDAqVXqiyjgiao2FyWlhHbWQEWArjmWMSpa1lCFHhgxZmIv1WpyPFjJnz5g9H9OrzL0GRe5nqHSsqDSWrswMh0Pvv3MclpeX4+KLLohrr7shFhcXt3z9YJO3geoPD+/o6q/c9bx7YWaUcWnMYDBcty6mYT30+sMU491ub2fJnYG5ILvt9vfMVQdHnmF45d6Rfr1RZxx1PhiHjOduNfLVUWMbNQbDWJg9/AOn3fOuy4HR205/t9Db2TNmz0cd03rurjIO2IjebnscEbXGwmi5LoeatnvuJhfHMjbS6nV5hhwZMmRhLtbLMh+uy8crez7qyHjuLlNjDPczVDpWVBpLF+YnHaCq+U2ac23S52fXPzO95udnjlkX1a0dkFsbb6u5u2AuAGjBOM5XauSro8Y2asy3eU3nWhRqaqG3s2fMno/pVWVtVhkHjEqVnqgyjohaY2FyWlhHLWQE2IpjGaOSZS1lyJEhQxbmYr0W56OFzNkzZs/H9Cpzr0GR+xkqHSsqjaULNs0AAHSki3evGaVpe+eaFt5NCAAAAAAAAAAAABgdm2YAABiJ7JuEAAAAAAAAAAAAgOniDXgAAAAAAAAAAAAAAAAox6YZAAAAAAAAAAAAAAAAyrFpBgAAAAAAAAAAAAAAgHJsmunIYDDc8NHrD2Owuv6Z6Xb0upgGrY631dxdMBcAtGAc5ys18tVRoz5zAzW10NstZISM9A7UVKW3q4wDRqWFnmghI8BWHMsYlSxrKUOODBmy5MiQIZMW56OFzC1khIyq3GugRj6VxtIFm2Y60OsPoz+MDR+XH1iK/krE/gNLsXD1odh/YMkCnWJr62Wa1kOvP2xyvK3m7oK5AKAF4zhfqZGvjhr1mRuoqYXebiEjZKR3oKYqvV1lHDAqLfRECxkBthxSE2cAACAASURBVOJYxqhkWUsZcmTIkCVHhgyZtDgfLWRuISNkVOVeAzXyqTSWrtg004GFuc0ft68OY2Eu4vqb7oiIw88LcxMOzcSsrYtpWg+tjrfV3F0wFwC0YBznKzXy1VGjPnMDNbXQ2y1khIz0DtRUpberjANGpYWeaCEjwFYcyxiVLGspQ44MGbLkyJAhkxbno4XMLWSEjKrca6BGPpXG0hWbZjrQX9n8cdLsTPRXIvadtysiDj/3VyYcmolZWxfTtB5aHW+rubtgLgBowTjOV2rkq6NGfeYGamqht1vICBnpHaipSm9XGQeMSgs90UJGgK04ljEqWdZShhwZMmTJkSFDJi3ORwuZW8gIGVW510CNfCqNpSszw+GwufffufXWW+JjH/1IfOR3PxyXXnZFfPnLt8XrXv3KY173qtf+VDz8Gx6x7u9ecPlz4h/+4e+P/Pnpz/ix+IHHPWHLmsvLy3HxRRfEtdfdEIuLi1u+fjDYeFr7w8M7uvordz3vXpjZ8mtS12AwXLcupmE99PrDFOPdbm9nyZ2BuSC77fb3zFUHx5CKaoZX7p10hKmT8dytRr46arQnY28Do7Gd/m6ht1vICOPg3A01TWtvVxkHbMZ1OdS03XM3uTiWsZFWr8sz5MiQIUuODBkyyTIfrsuhpoznbjVy1RiXSmPpwvykA2xXr9eLy559STz4wV8bn/vsZyIi4usf/oj4xf/wK0de84d/8N/jfb/+a/HQr3vYMZ9/yy2H4sUveXl8/cO/ISIiTtp1Uic55+c3Xmxrkz4/u/6Z6TU/P3PMuqhu7YDc2nhbzd0FcwFAC8ZxvlIjXx016jM3UFMLvd1CRshI70BNVXq7yjhgVFroiRYyAmzFsYxRybKWMuTIkCFLjgwZMmlxPlrI3EJGyKjKvQZq5FNpLF1obtPMrl274pp3vDPuuP32uPzSZ0VExNzcXOzZc8qR1/y3/3pj/KsffFzMz68f3le/+tXo9Xpx1llfs+71AAAAAAAAAAAAAAAA1NLcppm5ubk47bTT4+YvfP4eP/7xm/5n/N3f/m289BWvOuZjhw4djIiIa9765vjyl78c//SbvjmefvElsWvXrmNe2+/3o9/vH/lzr7c8ohEAk6S3oS79zSTMXHVwpF9veOXekX69CvQ21KS3oS79DTXpbahJb0Nd+htq0ttQk96GuvQ31KS3oU3NbZrZym/9xvvju7/3sXHqqfc65mOn3eu02Pf0i+MhD/26uOOOO+LtP/OmuP8DHxjnP/Epx7z2g+9/b7zvPe8eR2RgjPQ21KW/oSa9DTXpbahLf0NNehtq0ttQl/6GmvQ21KS3oS79DTXpbWhTqU0zf/s3n4s//7M/jave9NZ7/Pjs3Fx8x3d9T5x++ukREfGI886Lv/nc5+7xtU/+oafG489/8pE/93rL8dz9zxx9aGCs9DbUpb+hJr0NNeltqEt/Q016G2rS21CX/oaa9DbUpLehLv0NNeltaFOpTTO/9RsfiHPP+8Y4+5wHrfv71ZWVmJ2bi098/KZ4y9U/HS//idfGKaeeGn/1qU/F45/4pHv8WgsLC7GwsDCO2MAY6W2oS39DTXobatLbUJf+hpr0NtSkt6Eu/Q016W2oSW9DXfobatLb0KbZSQcYlYNf+lJ87KO/H497whPX/f0f/sHH4mVXvjAiIh716G+JJz7ph+Ktb35jvOaVL41v+uZHxQ/+4BMmERcAAAAAAAAAAAAAAIAONftOM2fe96x413s+cOTPe+9zn/iVX3vvMa97zHd8VzzmO77ryJ9/5IIL40cuuLDzfIPBcMOP9YcRC3MR/ZW7nncvzHSeibwGg+G6dTEN66HXHzY53lZzd8FcANCCcZyv1MhXp0yNwTAWZiP6qxG7511v3Z1rUaiphd5uISNkVKV3XJ/BemV6u8g4YFRa6IkWMgJsxbGMUcmyljLkyJAhIsfPD7LMRRYtzkcLmVvICBmVuZ9Bje3VGcP1gePy5sq800wmvf7hDRAbPZ53YCn6KxH7DyzFwtWHYv+Bpej1N95kQ21r62Wa1kOvP2xyvK3m7oK5AKAF4zhfqZGvTpkag2Hsv/HOGjcuRW+T/zHDtHEtCjW10NstZISMqvSO6zNYr0xvFxkHjEoLPdFCRoCtOJYxKlnWUoYcGTJE5Pj5QZa5yKLF+WghcwsZIaMy9zOosb06Y7g+cFzemk0zHViY2/wxP3d4J9f1N90REYefF+YmHJqJWVsX07QeWh1vq7m7YC4AaME4zldq5KtTpsbsUTX86/0I16JQUwu93UJGyKhK77g+g/XK9HaRccCotNATLWQE2IpjGaOSZS1lyJEhQ0SOnx9kmYssWpyPFjK3kBEyKnM/gxrbqzOG6wPH5a35tU4H+iubPwYrM9Ffidh33q6IOPzcX5lwaCZmbV1M03podbyt5u6CuQCgBeM4X6mRr06ZGqtH1VgdfY1WuRaFmlro7RYyQkZVesf1GaxXpreLjANGpYWeaCEjwFYcyxiVLGspQ44MGSJy/Pwgy1xk0eJ8tJC5hYyQUZn7GdTYXp0xXB84Lm9tZjgcev+d47C8vBwXX3RBXHvdDbG4uLjl6webvHVSf3h4R1d/5a7n3Qszo4xLYwaD4bp1MQ3rodcfphjvdns7S+4MzAXZbbe/Z646OIZUsLnhlXsnHSG9jOduNfLVKVNjMIyF2cM/QNk9X/t6K2NvA6Oxnf5uobdbyAjjMK3n7mm6PmM6TW1vFxkHbMZ1OdS03XM3uTiWsZFWr8sz5MiQISLHzw+yzEUWWebDdTnUlPHcrUauGhHjuT5wXN7c/KQDVDW/yYJem/T52fXPTK/5+Zlj1kV1awfk1sbbau4umAuA0Rv15i2bcMZzvlIjX50yNeZdb23EtSjU1EJvt5ARMqrSO67PYL0yvV1kHDAqLfRECxkBtuJYxqhkWUsZcmTIEJHj5wdZ5iKLFuejhcwtZISMytzPoMb26ozh+sBxeXOmBQAAAAAAAAAAAAAAgHJsmgEAAAAAAAAAAAAAAKAcm2YAAAAAAAAAAAAAAAAox6YZAAAAAAAAAAAAAAAAyrFpBgAAAAAAAAAAAAAAgHJsmunIYDDc8NHrD2Owuv6Z6Xb0upgGrY631dxdMBcAtGAc5ys18tUpU2NwZ41B2zW64FoUamqht1vICBnpHaipSm9XGQeMSgs90UJGgK04ljEqWdZShhwZMmRhLtZrcT5ayNxCRsiozP0MRY4BVcYRUec+lq7YNNOBXn8Y/WFs+HjegaXor0TsP7AUC1cfiv0Hlko0Gydmbb1M03ro9YdNjrfV3F0wFwC0YBznKzXy1SlTYzCM/TfeWePGpU5+4DCOGl1wLQo1tdDbLWSEjPQO1FSlt6uMA0alhZ5oISPAVhzLGJUsaylDjgwZsjAX67U4Hy1kbiEjZFTmfoYix4Aq44iocx9Ll2ya6cDC3OaP+blhLMxFXH/THRFx+HlhbsKhmZi1dTFN66HV8baauwvmAoAWjON8pUa+OmVqzB5Vo4N/vY+jRhdci0JNLfR2CxkhI70DNVXp7SrjgFFpoSdayAiwFccyRiXLWsqQI0OGLMzFei3ORwuZW8gIGZW5n6HIMaDKOCLq3MfSpcbitqG/svljsDIT/ZWIfeftiojDz/2VCYdmYtbWxTSth1bH22ruLpgLAFowjvOVGvnqlKmxelSN1TZrdMG1KNTUQm+3kBEy0jtQU5XerjIOGJUWeqKFjABbcSxjVLKspQw5MmTIwlys1+J8tJC5hYyQUZn7GYocA6qMI6LOfSxdmhkOh229N86ELC8vx8UXXRDXXndDLC4ubvn6wSZvOdQfHt7R1V+563n3wswo49KYwWC4bl1Mw3ro9Ycpxrvd3s6SOwNzQXbb7e+Zqw6OIRWM1/DKvZOOMHIZz91q5KtTpsZgGAuzh3/wsHu+3RrHI2NvA6Oxnf5uobdbyAjj4NwNNU1rb1cZB2zGdTnUtN1zN7k4lrGRVq/LM+TIkCELc7FelvlwXQ41ZTx3V6kxDlXGEVHnPpauzE86QFXzmyyEtUmfn13/zPSan585Zl1Ut3ZAbm28rebugrkAoAXjOF+pka9OmRrzNWp0wbUo1NRCb7eQETLSO1BTld6uMg4YlRZ6ooWMAFtxLGNUsqylDDkyZMjCXKzX4ny0kLmFjJBRmfsZihwDqowjos59LF1pMDIAAAAAAAAAAAAAAABszqYZAAAAAAAAAAAAAAAAyrFpBgAAAAAAAAAAAAAAgHLmJx3gRNx66y3xsY9+JD7yux+OSy+7Is4+50HxsY9+JH72bW858ppTTj01/t2//+V1nzccDuNdv3p9/N6Hfyfm5ubi/Cc9JR5//pPGHR8AAAAAAAAAAAAAAICONbdpptfrxWXPviQe/OCvjc999jNH/v6WQ4fi67/+G+LFL315RETMzBz7Jjp/9qd/HB868Nvx8le9Lm695ZZ401Wvj/Me+Y1xzoMePK74AAAAAAAAAAAAAAAAjMGxO0uS27VrV1zzjnfGZVe8YN3f33LoUNx7797Ys+eU2LPnlFhcXDzmcz/5iY/H1z3s4fGQhzw0HvXob4n73ves+MtPfbKTnIPBcMNHrz+Mwer6Z6bb0etiGrQ63lZzd8FcANCCcZyv1MhXp0oNNmb+oaYWeruFjJBRld7pDe4cx6DtccColOntIuOAUWmhJ1rICLAVxzJGJctaypAjQ4YszMV6Lc5HC5lbyAgZVbmfocoxYGz3yozh9wtVviddaW7TzNzcXJx22unH/P2hQwfjr//qL+PSZ10cL3nx8+N//sWfH/Oa2267LU4++eQjf17csyduu+22e6zT7/djeXn5yKPXWz7ujL3+MPrD2PBx+YGl6K9E7D+wFAtXH4r9B5Ys0Cm2tl6maT30+sOJjXenvT1N36fNmAsy2kl/A3llP3erka9OlRrVZe9t4MSdaH+30NstZISuOHcf/oXW/hvvHMeNSzbOUILerjMOOJrrcqjJ78LqcCzj7ipcl2fIkSFDFuZivRbvU2vhe9hCRuhK9nN3lRrjMLZ7Zcbw+4Uq35MuzU86wKh8/7/4l/GI8x4ZD3vYw+PG//Kf4u0/86Z4xzv/fcwvLGz6eTMz9/z3H3z/e+N973n3CWVZmNv847evDmNhLuL6m+6IiMPP1z5uzwnVon1r62Wa1sMk1/9Oe3uavk+bMRdktJP+BvLKfu5WI1+dKjWqy97bwIk70f5uobdbyAhdce6OWJitMQ64O71dZxxwNNflUJPfhdXhWMbdVbguz5AjQ4YszMV6Ld6n1sL3sIWM0JXs5+4qNcZhbPfKjOH3C1W+J10qs2nmfvd/QDz0oQ+LXSedFD/wuCfEh/7rb8fBgwfjvmeddeQ1p556anzpizcf+XOv14t73cO71kREPPmHnhqPP//Jd3vtcjx3/zOPK0t/ZfOPnzQ7E/2ViH3n7Yrrb7oj9p23K/orEfPNve8Po7C2XqZpPUxy/e+0t6fp+7QZc0FGO+lvIK/s52418tWpUqO67L0NnLgT7e8WeruFjNAV5+6I/upR41htcxxwd3q7zjjgaK7LoSa/C6vDsYy7q3BdniFHhgxZmIv1WrxPrYXvYQsZoSvZz91VaozD2O6VGcPvF6p8T7o0MxwOm3z/nZu/8Pm4/NJnxRvf/LY4+5wHxctf8qI444wz46Jn/Fj81xv/S3zowG8feaeZ1ZWVmJ2biz/54/8Rb3vLG+MVr3pdLC8vx1U/9bq4+i1viwc88Owt6y0vL8fFF10Q1153QywuLm75+sEmb53UHx7e0dVfuet598IGb3nDVBgMhuvWxTSsh15/mGK82+3tLLkzMBdkt93+nrnq4BhSwXgNr9w76Qgjl/HcrUa+OlVqTJOMvQ2Mxnb6u4XebiEjjMO0nrt7g2EszB7+Bdfu+XbHARuZ2t4uMg7YjOtyqGm7525ycSxjI61el2fIkSFDFuZivSzz4bocasp47q5SYxzGdq/MGH6/UOV70pUy7zRzyf5L49p3/kI8/7L9cd+zviae94IXx/zCQvzWb34gPvWJT8SLrnxZfPOjHh0/+Ljz4+o3vD7m5udi39OfcVwbZk7E/CYLem3S13Zw2cnF/PzMMeuiurUDcmvjbTV3F8wFAC0Yx/lKjXx1qtRgY+Yfamqht1vICBlV6Z21X2S1Pg4YlTK9XWQcMCot9EQLGQG24ljGqGRZSxlyZMiQhblYr8X5aCFzCxkhoyr3M1Q5BoztXpkx/H6hyvekK81umjnzvmfFu97zgSN/ftCDvzZe+1NXHfO685/4lDj/iU+JiIiZmZl42oX74mkX7htbTgAAAAAAAAAAAAAAAMbPXiIAAAAAAAAAAAAAAADKsWkGAAAAAAAAAAAAAACAcmyaAQAAAAAAAAAAAAAAoJz5SQcAAAC6MXPVwZF+veGVe0f69QAAAAAAAAAAAKBL3mmmI4PBcMNHrz+Mwer6Z6bb0etiGrQ63lZzd8FcANCCcZyv1MhXR436zA3U1EJvt5ARMtI7UFOV3q4yDnLqDe5cX4N21lcLPdHivAIcrYXjLW3IspYy5MiQISLHtUqWuciixfloIXMLGdmZDMeziqrcazCWGmNYg2O7V6bQWLrW1VzZNNOBXn8Y/WFs+HjegaXor0TsP7AUC1cfiv0HlppfoJy4tfUyTeuh1x82Od5Wc3fBXADQgnGcr9TIV0eN+swN1NRCb7eQETLSO1BTld6uMg5y6g2Gsf/GO9fXjUtN3GTUQk+0OK8AR2vheEsbsqylDDkyZIjIca2SZS6yaHE+WsjcQkZ2JsPxrKIq9xqMpcYY1uDY7pUpNJaudTlXNs10YGFu88f83DAW5iKuv+mOiDj8vDA34dBMzNq6mKb10Op4W83dBXMBQAvGcb5SI18dNeozN1BTC73dQkbISO9ATVV6u8o4yGlh9qj11cBv5lvoiRbnFeBoLRxvaUOWtZQhR4YMETmuVbLMRRYtzkcLmVvIyM5kOJ5VVOVeg7HUGMMaHNu9MoXG0rUu58phrAP9lc0fg5WZ6K9E7DtvV0Qcfu6vTDg0E7O2LqZpPbQ63lZzd8FcANCCcZyv1MhXR436zA3U1EJvt5ARMtI7UFOV3q4yDnLqrx61vlYnHOg4tNATLc4rwNFaON7ShixrKUOODBkiclyrZJmLLFqcjxYyt5CRnclwPKuoyr0GY6kxhjU4tntlCo2la13O1cxwOGzz/XfGbHl5OS6+6IK49robYnFxccvXDzZ5O6D+8PCOrv7KXc+7F2ZGGZfGDAbDdetiGtZDrz9MMd7t9naW3BmYC7Lbbn/PXHVwDKmgbcMr9046Qspztxr56qjRnoy9DYzGdvq7hd5uISOMg3M31DStvV1lHOTUGwxjYfbwL/x3z7fx+7AWeiLLvMKkbffcTS4tHG+ZjFavyzPkyJAhIse1Spa5yCLLfJS7Lm8gIzuT4XjWgozn7jI1xrAGx3avTKGxdK2ruZof2VdinflNvklrkz4/u/6Z6TU/P3PMuqhu7YDc2nhbzd0FcwFAC8ZxvlIjXx016jM3UFMLvd1CRshI70BNVXq7yjjIae0X+y2trxZ6osV5BThaC8db2pBlLWXIkSFDRI5rlSxzkUWL89FC5hYysjMZjmcVVbnXYCw1xrAGx3avTKGxdK2ruWp8WgAAAAAAAAAAAAAAAOBYNs0AAAAAAAAAAAAAAABQjk0zAAAAAAAAAAAAAAAAlGPTDAAAAAAAAAAAAAAAAOXYNAMAAAAAAAAAAAAAAEA5Ns10ZDAYbvjo9YcxWF3/zHQ7el1Mg1bH22ruLpgLAFowjvOVGvnqVKnBxsw/1NRCb7eQETLSO1BTld6uMg4YFT0BMB6Ot4xKlrWUIUeGDFlyZMiQSYvz0ULmFjJCRlXuZ1Ajn0pj6YJNMx3o9YfRH8aGj8sPLEV/JWL/gaVYuPpQ7D+wZIFOsbX1Mk3rodcfNjneVnN3wVwA0IJxnK/UyFenSg02Zv6hphZ6u4WMkJHegZqq9HaVccCo6AmA8XC8ZVSyrKUMOTJkyJIjQ4ZMWpyPFjK3kBEyqnI/gxr5VBpLV2ya6cDC3OaP21eHsTAXcf1Nd0TE4eeFuQmHZmLW1sU0rYdWx9tq7i6YCwBaMI7zlRr56lSpwcbMP9TUQm+3kBEy0jtQU5XerjIOGBU9ATAejreMSpa1lCFHhgxZcmTIkEmL89FC5hYyQkZV7mdQI59KY+mKTTMd6K9s/jhpdib6KxH7ztsVEYef+ysTDs3ErK2LaVoPrY631dxdMBcAtGAc5ys18tWpUoONmX+oqYXebiEjZKR3oKYqvV1lHDAqegJgPBxvGZUsaylDjgwZsuTIkCGTFuejhcwtZISMqtzPoEY+lcbSlZnhcNjc++/ceust8bGPfiQ+8rsfjksvuyLOPudB8elP/6+49p3viL/727+N+93//vGMZz4rvv7h33DM5+674F9Hv98/8ucXv+Tl8ahHf+uWNZeXl+Piiy6Ia6+7IRYXF7d8/WCw8bT2h4d3dPVX7nrevTCz5dekrsFguG5dTMN66PWHKca73d7OkjsDc0F22+3vmasOjiEVtG145d5JR0h57lYjX50qNaZJxt4GRmM7/d1Cb7eQEcbBuRtqmtberjIO2Ey163LgsO2eu8nF8ZaNtHpdniFHhgxZcmTIkEmW+ah2Xd5CRhiHjOduNXLVGJdKY+nC/KQDbFev14vLnn1JPPjBXxuf++xnIiJidXU1fubqN8Sjv/Xb4kU//rL4tRt+OX7h56+Jn3n7z6/73K98+cvR7/fjDW96a5xxxhkREXHyybs7yTk/v/FiW5v0+dn1z0yv+fmZY9ZFdWsH5NbG22ruLpgLAFowjvOVGvnqVKnBxsw/1NRCb7eQETLSO1BTld6uMg4YFT0BMB6Ot4xKlrWUIUeGDFlyZMiQSYvz0ULmFjJCRlXuZ1Ajn0pj6UJz07Jr16645h3vjMuueMGRv7vj9tvju777e+NJT/nh2Huf+8S55z0ybr3llmM+99AthyIi4n5fc7/Ys+eU2LPnlJibmxtbdgAAAAAAAAAAAAAAAMajuXeamZubi9NOOz1u/sLnj/zdybt3x9Mu3BcRESsrK/Hh//ah+O7vfewxn3vo4MGYmZmNn3j5lbG0vBSP+Y7vigsuvChmZo59V5h+vx/9fv/In3u95Q5GA4yb3oa69DfUpLehJr0NdelvqElvQ016G+rS31CT3oaa9DbUpb+hJr0NbWpu08xWfvHf/nx8+Stfjv/7gv/3mI898Oxz4sJ9T49veMS58cUv3hzXvPXN8dCve1h8+2O+85jXfvD97433vefd44gMjJHehrr0N9Skt6EmvQ116W+oSW9DTXob6tLfUJPehpr0NtSlv6EmvQ1tmhkOh8NJhzgRN3/h83H5pc+KN775bXH2OQ+KiIhf/eX/EH/wsY/Gq1/303HGmWce8znLS0sxWFmJe93rXhER8YqXvDj+6Tc/Kv71jzztmNfe007A5+5/Zlx73Q2xuLjY0aiArultqGun/T1z1cEu40EJwyv3jr2mczfUpLehLv0NNeltqElvQ136G2rS21CT3oa69DfUpLehTWXeaeaD739f/M6HDsRLX/Hq2L24O5aWvhInn7w75ubmYnVlJWbn5uKjv/978eu/9qvxE695fXz1q1+Nv/u7v4knPuWH7vHrLSwsxMLCwphHAXRNb0Nd+htq0ttQk96GuvQ31KS3oSa9DXXpb6hJb0NNehvq0t9Qk96GNpXZNPNrN/xyRES88mU/fuTvXvnq18X//t+fjk994hPxoitfFt//z/9lfPazn4nXvurlMTs7Fz/wuPPj2x/znZOKDAAAAAAAAAAAAAAAQEea3TRz5n3Pine95wNH/nz3/767R5z7yDj/iU+JiIj5hYW4ZP+lccn+SzvPNxgMN/xYfxixMBfRX7nreffCTOeZyGswGK5bF9OwHnr9YZPjbTV3F8wFAC0Yx/lKjXx1ytQYDGNhNqK/GrF7vt0aXXAtCjW10NstZISM9A7UVKW3q4wDRkVPAIyH4y2jkmUtZciRIUNEjt+9ZJmLLFqcjxYyt5ARMipzP0OVeyYKHcsqjaULs5MOUFGvf3gDxEaPyw8sRX8lYv+BpVi4+lDsP7AUvf7Gm2yobW29TNN66PWHTY631dxdMBcAtGAc5ys18tUpU2MwjP033lnjxqXobfI/ZshcowuuRaGmFnq7hYyQkd6Bmqr0dpVxwKjoCYDxcLxlVLKspQw5MmSIyPG7lyxzkUWL89FC5hYyQkZl7meocs9EoWNZpbF0xaaZDizMbf64ffXwTq7rb7ojIg4/L8xNODQTs7Yupmk9tDreVnN3wVwA0IJxnK/UyFenTI3Zo2p08K/3cdTogmtRqKmF3m4hI2Skd6CmKr1dZRwwKnoCYDwcbxmVLGspQ44MGSJy/O4ly1xk0eJ8tJC5hYyQUZn7GarcM1HoWFZpLF1p5JaYtvRXNn+cNDsT/ZWIfeftiojDz/2VCYdmYtbWxTSth1bH22ruLpgLAFowjvOVGvnqlKmxelSN1TZrdMG1KNTUQm+3kBEy0jtQU5XerjIOGBU9ATAejreMSpa1lCFHhgwROX73kmUusmhxPlrI3EJGyKjM/QxV7pkodCyrNJauzAyHw07ff2c4HManPvmJOP300+N+939Al6U6tby8HBdfdEFce90Nsbi4uOXrB5u8DVR/eHhHV3/lrufdCzOjjEtjBoPhunUxDeuh1x+mGO92eztL7gzMBdltt79nrjo4hlTQtuGVeycdIeW5W418dcrUGAxjYfbwD4N2z7db43hk7G1gNLbT3y30dgsZYRycu6Gmae3tKuOAzVS7LgcO2+65m1wcb9lIq9flGXJkyBCR43cvWeYiiyzzUe26vIWMMA4Zz91laozjnolCx7JKY+nCfNcFZmZm4qd/8jXxN44gHAAAIABJREFU/7N373GWXmWd6J+6daerwyWdhOsAzhyUWwKi3EFhOJ7xAkwGZJgzkUmEILSEBBBiuCrKiMQAAoJcxgyTCEFuIsg4043KEQFx5OqEywiMAo6CQDoEqopk76p9/qhUd6o7VV3Vtfe7n/Xs7/fzqc+mq3dq/dba63nX2tXvYj/0YQ+P8568f9TNpTG7SXGuDfrs9PpHJtfs7NQx86K6tQtya/1tNfcoGAsAWtDFeqWNfO2UaWO2RhujYC8KNbVQ2y1khIzUDtRUpbar9AOGRU0AdMP1lmHJMpcy5MiQISLHv71kGYssWhyPFjK3kBEyKnM/Q5V7Jgpdyyr1ZRQ6GZbv/4G7xJe//LddNAUAAAAAAAAAAAAAAACj/6SZiIh9+/bFhz/053HJS14ct77NbY75+6mYinOf+KQuogAAAAAAAAAAAAAAADABOjk08+EPfTAiIj79qU9s8AyHZgAAAAAAAAAAAAAAABieTg7NPOWpF3TRDAAAAAAAAAAAAAAAAERER4dmHvqwh6/788LCd2Pv3pO7aBoAAAAAAAAAAAAAAIAJNN1VQ5/4+F/FC55zUZz7M4+LJz/xnIiI+OpXvxKvuPSl8b3vfa+rGJ3p9wcbfi31BtFfWf/IZDt6XkyCVvvbau5RMBYAtKCL9Uob+dop00b/hjb69ltHsxeFmlqo7RYyQkZVasf+DNYrU9tF+hHhOsVwVKoJgMxcbxmWLHMpQ44MGbIwFuu1OB4tZM6e0XtksipzP0Pya8BWVelHRK2+jEInh2Y++GcfiJdd8uvxd3/3tzE9PR2DG16Lr3/tH+Njf/U/4l3v+L0uYnRmqTeI3iA2/Lrw4EL0liP2H1yIuUsPxf6DCyboBFubL5M0H5Z6gyb722ruUTAWALSgi/VKG/naKdNGfxD7D9zQxoEFv8y9EXtRqKmF2m4hI2RUpXbsz2C9MrVdpB8RrlMMR6WaAMjM9ZZhyTKXMuTIkCELY7Fei+PRQubsGb1HJqsy9zMkvwZsVZV+RNTqy6h0cmjmvX/w+3HqaafGa17/O/GjD3344e/f5773jzPvea/46Ec+3EWMzszNbP513cog5mYirrjq+ohYfZybGXNoxmZtXkzSfGi1v63mHgVjAUALulivtJGvnTJtTB/VRmefE5ufvSjU1EJtt5ARMqpSO/ZnsF6Z2i7SjwjXKYajUk0AZOZ6y7BkmUsZcmTIkIWxWK/F8Wghc/aM3iOTVZn7GZJfA7aqSj8iavVlVDpZCv7p61+LM8+8V9zylreMmFr/d6ecckp8+9vXdBGjM73lzb92T09FbzninDN2RcTqY295zKEZm7V5MUnzodX+tpp7FIwFAC3oYr3SRr52yrSxclQbK8Nvo1X2olBTC7XdQkbIqErt2J/BemVqu0g/IlynGI5KNQGQmestw5JlLmXIkSFDFsZivRbHo4XM2TN6j0xWZe5nSH4N2Koq/Yio1ZdRmRoMBiP//J2nn/+UuNnNbh7/8aWXxpsue2O8/8B/jyvf/vuxuLAQF/3ChTE7Nxuves0bRh1jRxYXF+O8c8+Oyy6/Mubn54/7/P4mH+fWG6ye6OotH3ncMze14fOpr98frJsXkzAflnqDFP3dbm1nyZ2BsSC77db31CVXd5AK2ja4eN+4I6Rcu7WRr50ybfQHMTe9+kvdPbO191sZaxsYju3Udwu13UJG6MKkrt2TtD9jMk1sbRfpR4TrFBurti8HVm137SYX11s20uq+PEOODBmyMBbrZRmPavvy7Bm9R6YrGdfuKm10oUo/Imr1ZRRmu2jkRx/28HjXO34vfu1Xfyn6/X5ERLz3Pb8fH/jj98ehQ4fi0Y95bBcxOjW7ySK7Nuiz0+sfmVyzs1PHzIvq1i7IrfW31dyjYCwAaEEX65U28rVTpo1Z+62N2ItCTS3UdgsZIaMqtWN/BuuVqe0i/YhwnWI4KtUEQGautwxLlrmUIUeGDFkYi/VaHI8WMmfP6D0yWZW5nyH5NWCrqvQjolZfRqGTQzOPfsxj4++/+pX4y49+5PD3fu8tb46IQZx5zx+MRz/2cV3EAAAAAAAAAAAAAAAAYEJ0cmhmemYmnv4LF8XnP/eZ+OtPfyquueaa2LNnT9zjjDPjh374vl1EAAAAAAAAAAAAAAAAYIJ0cmjmwx/6YPzwfe4Xd73bPeKud7tHF00CAAAAAAAAAAAAAAAwwTo5NPPaV/9m7Nq1K37wh344Hvigh8S9f+g+sWvXrhP+ed/+9jXx4Q99MD74/30gzr/gGXGHO94pvvmNb8TrXvuq+N9f+mLc9na3j6c89YK4052+75j/9o/e995433v/IJaXl+NHH/Yv4+zHnxtTU1M76B0AAAAAAAAAAAAAAADZTHfRyP0e8MCYnZ2N//HRv4hXveJl8ZTzzonXvOoV8bG/+svo93rb+llLS0txwc8/OT764Q/Hl//ubw9//82/+6bYvfukuPQVvxV3vOOd4o2ve80x/+1Xv/qVePMV/yX2n39BXPSc58fB//5H8clPfHzH/QMAAAAAAAAAAAAAACCXTj5p5hm/8Iuxsrwcn//8Z+OTH/94fOITH4uPfPjP4yMf/lDs2XNS3Oe+94+ff9rTt/Szdu3aFb/1ujfG9dddFxee/5TD3//cZz8T5z7hSXHa6afHwx7+Y/Erv/S8uO6662L37t2Hn/P5z34mbnvb28Y973XviIj4gbvcNT7/uc/ED/3wfYbb4Yjo9wcb/l1vEDE3E9FbPvK4Z86n3Uyyfn+wbl5MwnxY6g2a7G+ruUfBWADQgi7WK23ka6dKG2zM+ENNLdR29oxL/UHMTUf0ViL2zObLx861+hpnrx3gxFSp7Sr9IKcW1+4WaqLFcQU4WgvXW9qQZS5lyJEhQ0SOvUqWsciixfFoIXP2jBlqsXXZxzB7vo1UuZ+hkzY6eI07u1emUF9a1cknzURETM/MxN3vcWb8zDk/G7/8K78Wj/7px8Xc3GwsLS3Fn3/wz7b8c2ZmZuIWt7jlMd//zrXXxkknnRQREXv37j38vRu79tprY/cNz4mImJ/fG9ce9Zw1vV4vFhcXD38tLS1uOeNSb/UAxEZfTz+4EL3liP0HF2Lu0kOx/+BCLPU2PmRDbWvzZZLmw1JvMLb+7rS2J+l12oyxIKOd1DeQV/a1Wxv52qnSRnXZaxs4cSda3y3UdvaMS/1B7D9wQ74DC7G0yf+pD20a52ts7Yaa1HadfpBTi2t3CzVh38sk829hdbRwvaU7FfblGXJkyBCRY6+SZSyyaPE+tRZew+wZM9Ri67KPYYvvuSPq3M/QSRsdvMad3StTqC8t6+STZiIivvqVL8cnPv6x+MTH/yq+9MUvxMrK6otxl7veLR74oIeMpM2pLRyS2ugp73n3O+Nd73jbCbU7N7P538/OrJ7kuuKq6yNi9fGyn9x7Qm3RvrX5MknzYZzzf6e1PUmv02aMBRntpL6BvLKv3drI106VNqrLXtvAiTvR+m6htrNnnJvOnY+dG+drbO2GmtR2nX6QU4trdws1Yd/LJPNvYXW0cL2lOxX25RlyZMgQkWOvkmUssmjxPrUWXsPsGTPUYuuyj2GL77kj6tzP0EkbHbzGnd0rU6gvLevk0MyFT31yfPOb34yI1YMyd77zD8QDHvzgeMADHxL79u0bShs3u9nNY2FhISIilpaWVr9381usf87NbxaLNzxn9XmLcZvb3u4mf95Zj35s/NQjz1r33Kftf9KWsvSWN//7/vJU9JYjzjljV1xx1fVxzhm7orccMdvZ5/6Qydp8maT5MM75v9PanqTXaTPGgox2Ut9AXtnXbm3ka6dKG9Vlr23gxJ1ofbdQ29kz9laOyreSKx87N87X2NoNNantOv0gpxbX7hZqwr6XSebfwupo4XpLdyrsyzPkyJAhIsdeJctYZNHifWotvIbZM2aoxdZlH8MW33NH1LmfoZM2OniNO7tXplBfWjY1GAxG/vk7Zz/u0fF9//xfxAMf9JB4wIMeHKeffqsd/8xv/NPX48LznxK/8fJXxR3ueKd45ct/IxYXF+LJ+58Wv/+ut8fff+Ur8asvuSQiIlaWl2N6Zia++pUvx8XPfkY8++LnxS1ueUq8+JdfEM989sVxrx+893HbW1xcjPPOPTsuu/zKmJ+fP+7z+5t8dFJvsHqiq7d85HHP3BY+Foey+v3BunkxCfNhqTdI0d/t1naW3BkYC7Lbbn1PXXJ1B6mgbYOLh3PgfScyrt3ayNdOlTYmScbaBoZjO/XdQm1nz7jUH8Tc9Oov3/fM5svHzmV5ja3dUNOk1naVfpBTi2t3CzWRZVxh3La7dpNLC9dbxqPVfXmGHBkyROTYq2QZiyyyjEe5fXnyjBlqsXXZxzBLvoxrd5k2OniNO7tXplBfWtXJJ8284lW/Hbe45S3jT95/IK5402Xxve99L25xy1vGmfe8V/zIjzw0pmdmdtzG4895Qrzuta+OZz/zgrjd7W8fP3/+hRER8dG/+HD8wbveES992SvjDne8Uzz+3CfEf3rDb8dyfzl+/Cd+aksHZk7E7CYTem3Q105wOcnF7OzUMfOiurULcmv9bTX3KBgLAFrQxXqljXztVGmDjRl/qKmF2s6ece2X7FnzsXOtvsbZawc4MVVqu0o/yKnFtbuFmmhxXAGO1sL1ljZkmUsZcmTIEJFjr5JlLLJocTxayJw9Y4ZabF32McyebyNV7mfopI0OXuPO7pUp1JdWdXJoZs/8nnj+xc+Kr33taxFx5BNYPvKhP4/3H/hv8fxf+tXYs2fPtn7m6be6dbz1HX9w+M+nnX56vPBFLz7meQ944IPjAQ988OE//9Qj/nX81CP+9fY7AQAAAAAAAAAAAAAAQDM6OTTz1rf8bnzta/8Y97zXvePH/tWPxy1veUocOnR1/OkfH4xPf+qT8ba3vjl+9ok/10UUAAAAAAAAAAAAAAAAJkAnh2Y++fGPxfd93z+Pi5/3wpiamjr8/fvc9/7xgudeFH/1lx91aAYAAAAAAAAAAAAAAIChme6ikeuu+178szvead2BmYiIqampuP3t/1l85zvf6SIGAAAAAAAAAAAAAAAAE6KTQzO3u93t49Of+kR845++vu773/zGN+LTn/5k3Oa2t+0iBgAAAAAAAAAAAAAAABNitotGHvGos+K1v/XKeNYzLogz73mvOGXfvvj2NdfE//zrT8f1118f//5nzukiRqf6/cGGf9cbRMzNRPSWjzzumZva8PnU1+8P1s2LSZgPS71Bk/1tNfcoGAsAWtDFeqWNfO2UaaM/iLnpiN5KxJ7ZdtsYBXtRqKmF2m4hI2SkdqCmKrVdpR8R7b7HI5cWasJcBypo4XpLG7LMpQw5MmTIwlis1+J4tJA5e0bvG8iqzP0Mya8BW1WlHxG1+jIKnXzSzIN/5KFx7hOeFDMz0/HJT3ws/vSPD8bHP/Y/IiLi3//Mf4iH/cv/u4sYnVnqrR6A2OjrwoML0VuO2H9wIeYuPRT7Dy7EUm/jQzbUtjZfJmk+LPUGTfa31dyjYCwAaEEX65U28rVTpo3+IPYfuKGNAwuxtMn/MUPmNkbBXhRqaqG2W8gIGakdqKlKbVfpR0S77/HIpYWaMNeBClq43tKGLHMpQ44MGbIwFuu1OB4tZM6e0fsGsipzP0Pya8BWVelHRK2+jEonh2YiIn78Jx8Rb/idy+O5L3hRnH/BM+IXn/vC+O03/ud41FmP7ipCZ+ZmNv+6bmX1JNcVV10fEauPczNjDs3YrM2LSZoPrfa31dyjYCwAaEEX65U28rVTpo3po9oYwbv3LtoYBXtRqKmF2m4hI2SkdqCmKrVdpR8R7b7HI5cWasJcBypo4XpLG7LMpQw5MmTIwlis1+J4tJA5e0bvG8iqzP0Mya8BW1WlHxG1+jIqnS4Fu3bvjjPvea948I88NH7w3j8U8/PzXTbfmd7y5l+7p6eitxxxzhm7ImL1sbc85tCMzdq8mKT50Gp/W809CsYCgBZ0sV5pI187ZdpYOaqNlTbbGAV7UaiphdpuISNkpHagpiq1XaUfEe2+xyOXFmrCXAcqaOF6SxuyzKUMOTJkyMJYrNfieLSQOXtG7xvIqsz9DMmvAVtVpR8RtfoyKlODwcDn72zB4uJinHfu2XHZ5Vdu6bBPf5OPc+sNVk909ZaPPO6ZmxpmXBrT7w/WzYtJmA9LvUGK/m63trPkzsBYkN1263vqkqs7SAVtG1y8b9wRUq7d2sjXTpk2+oOYm179pe6e2Xbb2IqMtQ0Mx3bqu4XabiEjdMHaDTVNam1X6UdEnvd45FNuX26uQ0Rsf+0mlxaut4xHq/vyDDkyZMjCWKyXZTzK7cuTZ/S+ga5kXLurtNGFKv2IqNWXUZgdd4CqZjdZZNcGfXZ6/SOTa3Z26ph5Ud3aBbm1/raaexSMBQAt6GK90ka+dsq0MVujjVGwF4WaWqjtFjJCRmoHaqpS21X6EdHuezxyaaEmzHWgghaut7Qhy1zKkCNDhiyMxXotjkcLmbNn9L6BrMrcz5D8GrBVVfoRUasvo2BYAAAAAAAAAAAAAAAAKMehGQAAAAAAAAAAAAAAAMpxaAYAAAAAAAAAAAAAAIByHJoBAAAAAAAAAAAAAACgHIdmAAAAAAAAAAAAAAAAKMehmRHp9wcbfi31BtFfWf/IZDt6XkyCVvvbau5RMBYAtKCL9Uob+dop00b/hjb69ltHsxeFmlqo7RYyQkZVaqdKP8in1b1/lZqo0o+IWn1hfMwjgG643jIsWeZShhwZMkTkeI+XZSyyaHE8WsicPWP2fC3IcD2rqMz9DEXumah0rajUl1FwaGYElnqD6A1iw68LDy5Ebzli/8GFmLv0UOw/uGCCTrC1+TJJ82GpN2iyv63mHgVjAUALulivtJGvnTJt9Aex/8ANbRxY8IvIG7EXhZpaqO0WMkJGVWqnSj/Ip9W9f5WaqNKPiFp9YXzMI4BuuN4yLFnmUoYcGTJE5HiPl2UssmhxPFrInD1j9nwtyHA9q6jM/QxF7pmodK2o1JdRcWhmBOZmNv+6bmUQczMRV1x1fUSsPs7NjDk0Y7M2LyZpPrTa31Zzj4KxAKAFXaxX2sjXTpk2po9qw7v3w+xFoaYWaruFjJBRldqp0g/yaXXvX6UmqvQjolZfGB/zCKAbrrcMS5a5lCFHhgwROd7jZRmLLFocjxYyZ8+YPV8LMlzPKipzP0OReyYqXSsq9WVUXMZGoLe8+dfu6anoLUecc8auiFh97C2POTRjszYvJmk+tNrfVnOPgrEAoAVdrFfayNdOmTZWjmpjZfhttMpeFGpqobZbyAgZVamdKv0gn1b3/lVqoko/Imr1hfExjwC64XrLsGSZSxlyZMgQkeM9XpaxyKLF8Wghc/aM2fO1IMP1rKIy9zMUuWei0rWiUl9GZWowGJT4/J3XveZV8cE/+8C679361reJV77m9Yf//Df/6/Pxyy94zrrnvOmKt8ZJe/Yc9+cvLi7GeeeeHZddfmXMz88f9/n9TT4GqjdYPdHVWz7yuGdu6rg/k7r6/cG6eTEJ82GpN0jR3+3WdpbcGRgLsttufU9dcnUHqaBtg4v3jTtCyrVbG/naKdNGfxBz06u/DNozW3u/lbG2geHYTn23UNstZIQuTOraXaUf5JNl76+22+5HRK2+MFzV9uXAqu2u3eTiestGWt2XZ8iRIUNEjvd4WcYiiyzjUW1fnj1j9nwtyHA9a0HGtbtMGx3MwUrXikp9GYXZcQcYlif+3P445wnnHf7zKy59afyL/+vO655zzTWH4tRTT4tLXv7Kw9/byoGZEzG7SXGuDfrs9PpHJtfs7NQx86K6tQtya/1tNfcoGAsAWtDFeqWNfO2UaWPWfmsj9qJQUwu13UJGyKhK7VTpB/m0uvevUhNV+hFRqy+Mj3kE0A3XW4Yly1zKkCNDhogc7/GyjEUWLY5HC5mzZ8yerwUZrmcVlbmfocg9E5WuFZX6MgplhmX37t2xd+/JsXfvyfGtb34z/tfnPxc//hOPWPecaw4diluecsrh5+3de/KY0gIAAAAAAAAAAAAAADBKZT5p5sbe94fvifvc7/5x2umnr/v+oauvjm9845/iwqc+OeZ27YrHPPZx8eCH/OhN/oxerxe9Xu/wn5eWFkeaGeiG2oa61DfUpLahJrUNdalvqEltQ01qG+pS31CT2oaa1DbUpb6hJrUNbSp3aObqb30rPvKhP48XvujFx/zdfe//gDhl3764+z3OjL/86Efi9a99ddz9HmfEKafsO+a573n3O+Nd73hbF5GBDqltqEt9Q01qG2pS21CX+oaa1DbUpLahLvUNNaltqEltQ13qG2pS29CmqcFgMBh3iGF6yxX/JT772avi1176smP+7tvfviZ27dode/bsieuuuy5+9vH/Ll7wyy+Oe5xx5jHPvamTgE/b/6S47PIrY35+fqR9AEZHbUNdO63vqUuuHmU8KGFw8bGHzUfN2g01qW2oS31DTWobalLbUJf6hprUNtSktqEu9Q01qW1oU6lPmllcXIw/+eOD8cSfe8rh760sL8f0zExERLzpd94Yhw5dHRc+41nxiU98PGZnZ+O2t7v9Tf6subm5mJub6yQ30B21DXWpb6hJbUNNahvqUt9Qk9qGmtQ21KW+oSa1DTWpbahLfUNNahvaND3uAMP0J+8/ELtP2h0PeOCDD3/v5Ze+NP7rH74nIiIef84TYvfu3fGsZzwt3vfed8dTL3hG7NvX/f9bNgAAAAAAAAAAAAAAAKNV6pNmHnXWo+NRZz163fcues7zD//v004/PZ73wl/pJEu/P9jw73qDiLmZiN7ykcc9c1Od5CKnfn+wbl5MwnxY6g2a7G+ruUfBWADQgi7WK23ka6dKG2zM+ENNLdR29ozZ8zG5zE2oqUptV+kHDEsLNbHUH8TcdERvJWLPbM6MAMfTwvWWNmSZSxlyZMiQhbFYr8XxaCFz9ozZ8zG5qtzPUKXGqvQjolZfRqHUJ81ksdRbPQCx0dfTDy5Ebzli/8GFmLv0UOw/uBBLvY0P2VDb2nyZpPmw1Bs02d9Wc4+CsQCgBV2sV9rI106VNtiY8YeaWqjt7Bmz52NymZtQU5XartIPGJYWamKpP4j9B27IeGAhljb5P7MEyKqF6y1tyDKXMuTIkCELY7Fei+PRQubsGbPnY3JVuZ+hSo1V6UdErb6MikMzIzA3s/nX7MzqSa4rrro+IlYf52bGHJqxWZsXkzQfWu1vq7lHwVgA0IIu1itt5GunShtszPhDTS3UdvaM2fMxucxNqKlKbVfpBwxLCzUxN31URnc8AA1q4XpLG7LMpQw5MmTIwlis1+J4tJA5e8bs+ZhcVe5nqFJjVfoRUasvo+JXSCPQW978q788Fb3liHPO2BURq4+95TGHZmzW5sUkzYdW+9tq7lEwFgC0oIv1Shv52qnSBhsz/lBTC7WdPWP2fEwucxNqqlLbVfoBw9JCTfRWjsq4MuZAACeghestbcgylzLkyJAhC2OxXovj0ULm7Bmz52NyVbmfoUqNVelHRK2+jMrUYDDw+TtbsLi4GOede3ZcdvmVMT8/f9zn9zf5GOjeYPVEV2/5yOOeualhxqUx/f5g3byYhPmw1Buk6O92aztL7gyMBdltt76nLrm6g1TQtsHF+8YdIeXarY187VRpY5JkrG1gOLZT3y3UdvaM2fNRh7UbaprU2q7SD9hMuX15fxBz06sHaPbM5swIXdju2k0uLVxvGY9W9+UZcmTIkIWxWC/LeJTblyfPmD0fdWRcu6u00YUq/Yio1ZdRmB13gKpmN/nl3Nqgz06vf2Ryzc5OHTMvqlu7ILfW31Zzj4KxAKAFXaxX2sjXTpU22Jjxh5paqO3sGbPnY3KZm1BTldqu0g8YlhZqYu2gTOaMAMfTwvWWNmSZSxlyZMiQhbFYr8XxaCFz9ozZ8zG5qtzPUKXGqvQjolZfRsGwAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMyPS7w82/FrqDaK/sv6RyXb0vJgErfa31dyjYCwAaEEX65U28rVTpo3+DW307beOZi8KNbVQ2y1kzMzatnOtjqHagZrUNhxfi3XSQuZW90QAN9bC9ZY2ZJlLGXJkyBCRY6+SZSyyaHE8WsjcQsbMMlwrjif7a9zCGN6UMvczaGN77XQwX7PX7Lg5NDMCS71B9Aax4deFBxeitxyx/+BCzF16KPYfXDBBJ9jafJmk+bDUGzTZ31Zzj4KxAKAFXaxX2sjXTpk2+oPYf+CGNg4sNPeLvlGyF4WaWqjtFjJmZm3buVbHUO1ATWobjq/FOmkhc6t7IoAba+F6SxuyzKUMOTJkiMixV8kyFlm0OB4tZG4hY2YZrhXHk/01bmEMb0qZ+xm0sb12Opiv2Ws2A4dmRmBuZvOv61YGMTcTccVV10fE6uPczJhDMzZr82KS5kOr/W019ygYCwBa0MV6pY187ZRpY/qoNrx7P8xeFGpqobZbyJiZtW3nWh1DtQM1qW04vhbrpIXMre6JAG6shestbcgylzLkyJAhIsdeJctYZNHieLSQuYWMmWW4VhxP9te4hTG8KWXuZ9DG9trpYL5mr9kMGrlMtKW3vPnX7ump6C1HnHPGrohYfewtjzk0Y7M2LyZpPrTa31Zzj4KxAKAFXaxX2sjXTpk2Vo5qY2X4bbTKXhRqaqG2W8iYmbVt51odQ7UDNaltOL4W66SFzK3uiQBurIXrLW3IMpcy5MiQISLHXiXLWGTR4ni0kLmFjJlluFYcT/bXuIUxvCll7mfQxvba6WC+Zq/ZDKYGg4HP39mCxcXFOO/cs+Oyy6+M+fn54z6/v8lHJ/UGqye6estHHvfMTQ0zLo3p9wfr5sWxEdfpAAAgAElEQVQkzIel3iBFf7db21lyZ2AsyG679T11ydUdpIK2DS7eN+4IKddubeRrp0wb/UHMTa/+AmXPbO39VsbaBoZjO/XdQm23kDGzSVrbRiXLGFq7oSa1DcOXpU7K7cuT7Ilg3La7dpNLC9dbxqPVfXmGHBkyROTYq2QZiyyyjEe5fXkDGTPLcK04nuyvcZYxzLh2ayNXGxHdzNfsNTtus+MOUNXsJhN6bdBnp9c/MrlmZ6eOmRfVrV2QW+tvq7lHwVgA0IIu1itt5GunTBuz9lsbsReFmlqo7RYyZmZt27lWx1DtQE1qG46vxTppIXOreyKAG2vheksbssylDDkyZIjIsVfJMhZZtDgeLWRuIWNmGa4Vx5P9NW5hDG9KmfsZtLG9djqYr9lrdtwMCwAAAAAAAAAAAAAAAOU4NAMAAAAAAAAAAAAAAEA5Ds0AAAAAAAAAAAAAAABQzuy4AwzTr//HF8Vff/pTh//8iEedFY8/5wmH//zNb3wjXvfaV8X//tIX47a3u3085akXxJ3u9H3dBwUAAAAAAAAAAAAAAGCkSh2aOXToUDzhSU+OBz/kRyMiYm5u17q/f/Pvvil27z4pLn3Fb8U73/7WeOPrXhO/9tKXjSMqAAAAAAAAAAAAAAAAIzQ97gDDdM2hQ3GrW9069u49OfbuPTl27Vp/aOZzn/1MPORHHxqnnX56POzhPxb/+0tfjOuuu24kWfr9wYZfS71B9FfWPzLZjp4Xk6DV/raaexSMBQAt6GK90ka+dqq0wcaMP9TUQm23kBEyUjtQU5XartIPGJYWamKpf0PGft6MAMfTwvWWNmSZSxlyZMiQhbFYr8XxaCFzCxkhoyr3M1S5BlTpR0StvoxCmUMz/V4vvvvd78RbfvfyeOqTnxi/+bJL4jvfuXbdc75z7bVx0kknRUTE3r17D3/vpvR6vVhcXDz8tbS0uOUsS71B9Aax4deFBxeitxyx/+BCzF16KPYfXDBBJ9jafJmk+bDUG4ytvzut7Ul6nTZjLMhoJ/UN5JV97dZGvnaqtFFd9toGTtyJ1ncLtd1CRhgVazfUpLbr9AOOVnpf3h/E/gM3ZDyw4OAME8W/hdXRwvWW7lTYl2fIkSFDFsZivRbvU2vhNWwhI4xK9rW7ShtdqNKPiFp9GZXZcQcYmqmp+Nnzfi5uc5vbxfz8fLz2t14Z73z778UTznvy8f6zm/Sed78z3vWOt51QlLmZzf/+upVBzM1EXHHV9RGx+njZT+49obZo39p8maT5MM75v9PanqTXaTPGgox2Ut9AXtnXbm3ka6dKG9Vlr23gxJ1ofbdQ2y1khFGxdkNNartOP+Bopffl0/kzwqj4t7A6Wrje0p0K+/IMOTJkyMJYrNfifWotvIYtZIRRyb52V2mjC1X6EVGrL6NS5tDMyspK3Oc+9499p54aERH3ue/94otf+MK659zsZjePhYWFiIhYWlpa/d7Nb3GTP++sRz82fuqRZx3+89LSYjxt/5O2lKW3vPnf756eit5yxDln7Iorrro+zjljV/SWI2bLfO4P27E2XyZpPoxz/u+0tifpddqMsSCjndQ3kFf2tVsb+dqp0kZ12WsbOHEnWt8t1HYLGWFUrN1Qk9qu0w84Wul9+cpRGVfyZYRR8W9hdbRwvaU7FfblGXJkyJCFsVivxfvUWngNW8gIo5J97a7SRheq9COiVl9GZWowGJT4/J2vfuXL8YvPenpc+Ixnx7+4853jFZe+NO56t7vHuT97XkzPrH6Uxytf/huxuLgQT97/tPj9d709/v4rX4lffcklW/r5i4uLcd65Z8dll18Z8/Pzx31+f5OPge4NVk909ZaPPO6Z2+Ajb5gI/f5g3byYhPmw1Buk6O92aztL7gyMBdltt76nLrm6g1TQtsHF+8YdIeXarY187VRpY5JkrG1gOLZT3y3UdgsZoQvWbqhpUmu7Sj9gM+X25f1BzE2vHqDZM5szI3Rhu2s3ubRwvWU8Wt2XZ8iRIUMWxmK9LONRbl/eQEboQsa1u0obXajSj4hafRmFMp80c4c73inOfeKT4s1XvCmWlhbjzHveK/7tvzs7nnvxs+IxP/24uP8DHxSPP+cJ8brXvjqe/cwL4na3v338/PkXjizP7Ca/nFsb9LUTXE5yMTs7dcy8qG7tgtxaf1vNPQrGAoAWdLFeaSNfO1XaYGPGH2pqobZbyAgZqR2oqUptV+kHDEsLNbF2UCZzRoDjaeF6SxuyzKUMOTJkyMJYrNfieLSQuYWMkFGV+xmqXAOq9COiVl9GocyhmYiIn/jJR8ZP/OQj133vkpe98vD/Pu300+OFL3px17EAAAAAAAAAAAAAAADomLNEAAAAAAAAAAAAAAAAlOPQDAAAAAAAAAAAAAAAAOU4NAMAAAAAAAAAAAAAAEA5Ds0AAAAAAAAAAAAAAABQjkMzI9LvDzb8WuoNor+y/pHJdvS8mASt9rfV3KNgLABoQRfrlTbytVOlDTZm/KGmFmq7hYyQkdqBmqrUdpV+wLCoCTi+pf4NddJXJ5w411uGJctcypAjQ4YsjMV6LY5HC5lbyEhtre7Lq9zPUOUa0Nm9Mh3M1yqvyag4NDMCS71B9Aax4deFBxeitxyx/+BCzF16KPYfXDBBJ9jafJmk+bDUGzTZ31Zzj4KxAKAFXaxX2sjXTpU22Jjxh5paqO0WMkJGagdqqlLbVfoBw6Im4PiW+oPYf+CGOjmw0NwNeuTgesuwZJlLGXJkyJCFsVivxfFoIXMLGamt1X15lfsZqlwDOrtXpoP5WuU1GSWHZkZgbmbzr+tWBjE3E3HFVddHxOrj3MyYQzM2a/NikuZDq/1tNfcoGAsAWtDFeqWNfO1UaYONGX+oqYXabiEjZKR2oKYqtV2lHzAsagKOb276qDpx1w0nwPWWYckylzLkyJAhC2OxXovj0ULmFjJSW6v78ir3M1S5BnR2r0wH87XKazJKjVwm2tJb3vxr9/RU9JYjzjljV0SsPvaWxxyasVmbF5M0H1rtb6u5R8FYANCCLtYrbeRrp0obbMz4Q00t1HYLGSEjtQM1VantKv2AYVETcHy9laPqZGXMgWiS6y3DkmUuZciRIUMWxmK9FsejhcwtZKS2VvflVe5nqHIN6OxemQ7ma5XXZJSmBoOBz9/ZgsXFxTjv3LPjssuvjPn5+eM+v7/JRyf1BqsnunrLRx73zE0NMy6N6fcH6+bFJMyHpd4gRX+3W9tZcmdgLMhuu/U9dcnVHaSCtg0u3jfuCCnXbm3ka6dKG5MkY20Dw7Gd+m6htlvICF2wdkNNk1rbVfoBm6m2L4dxW+oPYm569canPbPt/Fs3ubjespFW9+UZcmTIkIWxWC/LeFTbl7eQkdpa3ZdXuZ+hyjWgs3tlOpivVV6TUZkdd4CqZjeZ0GuDPju9/pHJNTs7dcy8qG7tgtxaf1vNPQrGAoAWdLFeaSNfO1XaYGPGH2pqobZbyAgZqR2oqUptV+kHDIuagONbu8FJnbATrrcMS5a5lCFHhgxZGIv1WhyPFjK3kJHaWt2XV7mfoco1oLN7ZTqYr1Vek1ExLAAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0MyI9PuDDb+WeoPor6x/ZLIdPS8mQav9bTX3KBgLAFrQxXqljXztlGmjf0Mbffuto9mLQk0t1Hb2jNYOsspeO8CJqVLbVfoBw9JCTdj3AhW0cL2lDVnmUoYcGTJkyZEhQyYtjkcLmbNn9L6BrMrcz6CNdCr1ZRQcmhmBpd4geoPY8OvCgwvRW47Yf3Ah5i49FPsPLpigE2xtvkzSfFjqDZrsb6u5R8FYANCCLtYrbeRrp0wb/UHsP3BDGwcW/DL3RuxFoaYWajt7RmsHWWWvHeDEVKntKv2AYWmhJux7gQpauN7ShixzKUOODBmy5MiQIZMWx6OFzNkzet9AVmXuZ9BGOpX6MioOzYzA3MzmX9etDGJuJuKKq66PiNXHuZkxh2Zs1ubFJM2HVvvbau5RMBYAtKCL9Uob+dop08b0UW14936YvSjU1EJtZ89o7SCr7LUDnJgqtV2lHzAsLdSEfS9QQQvXW9qQZS5lyJEhQ5YcGTJk0uJ4tJA5e0bvG8iqzP0M2kinUl9GxVIwAr3lzb92T09FbzninDN2RcTqY295zKEZm7V5MUnzodX+tpp7FIwFAC3oYr3SRr52yrSxclQbK8Nvo1X2olBTC7WdPaO1g6yy1w5wYqrUdpV+wLC0UBP2vUAFLVxvaUOWuZQhR4YMWXJkyJBJi+PRQubsGb1vIKsy9zNoI51KfRmVqcFgUObzd/7wPe+OP3rfe+O6674X9/zBe8f+p14YJ5100uG///CHPhivedUrDv/55JvdLP7Tf/7dLf3sxcXFOO/cs+Oyy6+M+fn54z6/v8nHufUGqye6estHHvfMTW0pBzX1+4N182IS5sNSb5Civ9ut7Sy5MzAWZLfd+p665OoOUkHbBhfvG3eElGu3NvK1U6aN/iDmpld/qbtntvZ+K2NtA8OxnfpuobazZ5yktYPxsnZDTZNa21X6AZspty+374WI2P7aTS4tXG8Zj1b35RlyZMiQJUeGDJlkGY9y+/LkGb1voCsZ125t5GqjK5X6Mgqz4w4wLH/96U/F2976lrj4eS+MW55ySrzkV385/vsf/WH8m8f828PPuebQobjLXe4WFz33+RERMTU1ug/amd1kkV0b9Nnp9Y9MrtnZqWPmRXVrF+TW+ttq7lEwFgC0oIv1Shv52inTxqz91kbsRaGmFmo7e0ZrB1llrx3gxFSp7Sr9gGFpoSbse4EKWrje0oYscylDjgwZsuTIkCGTFsejhczZM3rfQFZl7mfQRjqV+jIKZQ7NzM7OxtmPPyfOvOe9IiLitre7XXz7mm+ve841hw7FKfv2xd69J48jIgAAAAAAAAAAAAAAAB0pc2jm7vc4I+5+jzMiIuLLf/e38cUv/E2c/fhz1z3n0KGr4wt/87/i/KecFze7+c3jZ/7Dzx4+ZHO0Xq8XvV7v8J+XlhZHFx7ojNqGutQ31KS2oSa1DXWpb6hJbUNNahvqUt9Qk9qGmtQ21KW+oSa1DW0qc2hmzbe+9c249KW/Fo945Flx5+//gXV/9/Af+3/i7mecGT/wA3eNA//tv8arf/Nl8bo3/ueYnZs75ue8593vjHe9421dxQY6orahLvUNNaltqEltQ13qG2pS21CT2oa61DfUpLahJrUNdalvqEltQ5umBoPBYNwhhuXaa6+NX/ml58Wd7/z9sf/8C2Nqamrd3x86dHXsnd8bu3bvjr//6lfjol+4IF71mjfErW5962N+1k2dBHza/ifFZZdfGfPz8yPvCzAaahvq2ml9T11y9SjjQQmDi/d13qa1G2pS21CX+oaa1DbUpLahLvUNNaltqEltQ13qG2pS29CmMp80872lpfiNl7w4TjvttHj8uU+IxcWFmJqajpN2747pmZmIiHjZJS+J0047Pc594s/Fhz/0wTj55JvFvn03fePf3NxczN3EJ9AAbVPbUJf6hprUNtSktqEu9Q01qW2oSW1DXeobalLbUJPahrrUN9SktqFNZQ7N/OVf/kV86UtfiIiIJz/xnIiIOO3002N+fm885qcfF/d/4IPiyfvPj8ve+Pp45gX741a3vk08/RcuilkXLgAAAAAAAAAAAAAAgHLKHJp56MMeHg992MM3fc6dvu+fx6++5JJO8vT7gw3/rjeImJuJ6C0fedwzN9VJLnLq9wfr5sUkzIel3qDJ/raaexSMBQAt6GK90ka+drSxjTb6g5ibjuitROyZbWdPZy8KNbVQ2y1khIzUDtRUpbar9IOcWnzfrSYAuuF6y7BkmUsZcmTIkCVHhgyZtDgeLWRuISM70+J72haUuddAG+lU6ssoTI87QEVLvdUDEBt9XXhwIXrLEfsPLsTcpYdi/8GFWOptfMiG2tbmyyTNh6XeoMn+tpp7FIwFAC3oYr3SRr52tLGNNvqD2H/ghjYOLMTSJv/nD5nYi0JNLdR2CxkhI7UDNVWp7Sr9IKcW33erCYBuuN4yLFnmUoYcGTJkyZEhQyYtjkcLmVvIyM60+J62BWXuNdBGOpX6MioOzYzA3MzmX9etrJ7kuuKq6yNi9XFuZsyhGZu1eTFJ86HV/raaexSMBQAt6GK90ka+drSxjTamj2qjkd8Q2ItCTS3UdgsZISO1AzVVqe0q/SCnFt93qwmAbrjeMixZ5lKGHBkyZMmRIUMmLY5HC5lbyMjOtPietgVl7jXQRjqV+jIqLmMj0Fve/Gv39FT0liPOOWNXRKw+9pbHHJqxWZsXkzQfWu1vq7lHwVgA0IIu1itt5GtHG9toY+WoNlaG38Yo2ItCTS3UdgsZISO1AzVVqe0q/SCnFt93qwmAbrjeMixZ5lKGHBkyZMmRIUMmLY5HC5lbyMjOtPietgVl7jXQRjqV+jIqU4PBwOfvbMHi4mKcd+7ZcdnlV8b8/Pxxn9/f5KPIeoPVE1295SOPe+amhhmXxvT7g3XzYhLmw1JvkKK/263tLLkzMBZkt936nrrk6g5SQdsGF+8bd4SUa7c28rWjjW200R/E3PTqLz33zNqXA8O3nfpuobZbyAhdsHZDTZNa21X6QU4tvu9WE9CO7a7d5OJ6y0Za3ZdnyJEhQ5YcGTJkkmU8qu3LW8jIzmR5T5tdxrVbG7na6EqlvozC7LgDVDW7yQKxNuiz0+sfmVyzs1PHzIvq1i7IrfW31dyjYCwAaEEX65U28rWjjW20Mdvmns5eFGpqobZbyAgZqR2oqUptV+kHObX4vltNAHTD9ZZhyTKXMuTIkCFLjgwZMmlxPFrI3EJGdqbF97QtKHOvgTbSqdSXUTAsAAAAAAAAAAAAAAAAlOPQDAAAAAAAAAAAAAAAAOU4NAMAAAAAAAAAAAAAAEA5Ds0AAAAAAAAAAAAAAABQjkMzAAAAAAAAAAAAAAAAlOPQzIj0+4MNv5Z6g+ivrH9ksh09LyZBq/1tNfcoGIv1jMcRxgLIpItrkjbytVOmjf4NbfStqUez34Dja7FOWsicPWP2fEyuKnPT/oxRaXVulantIv0gpxbrW00AdMP1lmHJMpcy5MiQISLHHjDLWGTR4ni0kDl7xuz5WmAMR6PM/QxF7pmoNM+r9GVU/XBoZgSWeoPoDWLDrwsPLkRvOWL/wYWYu/RQ7D+40PwE5cStzZdJmg9LvUGT/W019ygYi/WMxxHGAsiki2uSNvK1U6aN/iD2H7ihjQMLTd1cM2r2G3B8LdZJC5mzZ8yej8lVZW7anzEqrc6tMrVdpB/k1GJ9qwmAbrjeMixZ5lKGHBkyROTYA2YZiyxaHI8WMmfPmD1fC4zhaJS5n6HIPROV5nmVvoyyHw7NjMDczOZf160MYm4m4oqrro+I1ce5mTGHZmzW5sUkzYdW+9tq7lEwFusZjyOMBZBJF9ckbeRrp0wb00e14d37YfYbcHwt1kkLmbNnzJ6PyVVlbtqfMSqtzq0ytV2kH+TUYn2rCYBuuN4yLFnmUoYcGTJE5NgDZhmLLFocjxYyZ8+YPV8LjOFolLmfocg9E5XmeZW+jLIfDfxqrj295c2/dk9PRW854pwzdkXE6mNvecyhGZu1eTFJ86HV/raaexSMxXrG4whjAWTSxTVJG/naKdPGylFtrAy/jVbZb8DxtVgnLWTOnjF7PiZXlblpf8aotDq3ytR2kX6QU4v1rSYAuuF6y7BkmUsZcmTIEJFjD5hlLLJocTxayJw9Y/Z8LTCGo1HmfoYi90xUmudV+jLKfkwNBoM2P3+nY4uLi3HeuWfHZZdfGfPz88d9fn+Tj4HqDVZPQvWWjzzumZsaZlwa0+8P1s2LSZgPS71Biv5ut7az5M7AWKxnPI7IMhbbre+pS67uIBW0bXDxvnFHSLl2ayNfO2Xa6A9ibnr1l0F7ZmvvLzLWNrQuS51sp76zZN5M9ozZ81HHpK7dk7Q/o1tZ5tbE1naRfpBTi/WtJqAd2127ycX1lo20ui/PkCNDhogce8AsY5FFlvGoti/PnjF7vhYYw63JuHaXaaODNbXSPK/Sl1H1Y3ZoP4l1ZjcpzrVBn51e/8jkmp2dOmZeVLd2IWutv63mHgVjsZ7xOMJYAJl0cU3SRr52yrQxa03diP0GHF+LddJC5uwZs+djclWZm/ZnjEqrc6tMbRfpBzm1WN9qAqAbrrcMS5a5lCFHhgwROfaAWcYiixbHo4XM2TNmz9cCYzgaZe5nKHLPRKV5XqUvo+pH48MCAAAAAAAAAAAAAAAAx3JoBgAAAAAAAAAAAAAAgHIcmgEAAAAAAAAAAAAAAKCc2XEHGKa/+PCH4veu/N1YWPhu3Od+D4jzfm5/zM3NHf777373u/GG3/6t+MxVfx2n7Ds1nvikp8Q9zjhzjIkBAAAAAAAAAAAAAAAYhTKfNPOd71wbr//tV8ejH/u4eNGLXxqf/tQn4gN/8v51z3nPu98Z3/rWN+Oll74y7nf/B8ZrX/2b0e/1xpQYAAAAAAAAAAAAAACAUSlzaOZLX/xCDAYRD33Yw+Of3eEOce97/3B87rOfWfecz332M3H/BzwobnXrW8eP/asfj0OHro6vf/1rY0oMAAAAAAAAAAAAAADAqMyOO8CwXHvttbH7pN0xNTUVERHze/fG17/+9XXP+c6118ZJJ50UERF75/ce/u9ufxM/r9frRe9Gn0KztLQ4muBAp9Q21KW+oSa1DTWpbahLfUNNahtqUttQl/qGmtQ21KS2oS71DTWpbWhTmUMzN+WG8zMn9Jz3vPud8a53vO2Y77u4wXjt2bPn8OG4E6G2IS/1DfktLm6/ntQ21KS2oaad1naE+oasrN1Qk9qGmuzLoS5rN9SktqEm+3Koy9oNNR2vtqcGg8Ggwzwj88lPfCxecelL4/K3vD2mp6fjP73+tbH0ve/Fhc941uHnvOA5F8UP3fe+8ZifflxcffXVcf5Tnhgvf+Vr43a3P/azZo4+CXj11d+Ki555QSd9ATZ22eVXxvz8/An/92ob8lLfUJPahprUNtS009qOUN+QlbUbalLbUJN9OdRl7Yaa1DbUZF8OdVm7oabj1XaZQzPXXnttXPDzT4rHn/OEuPsZZ8ZLXvzL8eifflw8/OE/FtMzMxER8ZYr/kt8+tOfjGf94nPjz//sA/GBP/3jePVr3xgzN/z9ZlZWVuLQoavjpJM2P4W0tLQYT9v/pHjN638n9uzZ2aapKzJ3Q+bhGMYp/hvbam2vyTgm42Is1jMeR5zoWIyivv/hH/5PXPTMC0q9LhXnmj61IVNtb2ft7kKV17tKPyLq9CVzP8Zd25nHJiJ/voj8GeXbuRPJOOzajthefVcd1y7Jt3PZM7a6L88+rltVpR8Rdfoy6f1Q28NRpR8RdfpSpR8R9uWjkj2jfDuXPWOra/cwZX+NIvJnzJ4vQsatGndtZxiDLIzFEcZiPfvy0cieUb6dy56x1X159nHdKv3Ip0pfRlXbs8MIl8HNb37z2P/UC+Otb7ki3vbWN8d97/eAeOjDHh4vv/Slcfd7nBGPeNRZcdZjHhtf+9o/xnMvembs23dqnH/BM7d0YCYiYnp6Ok499bQt59mzZ37HJ427JnM3ZM5lu7W9pvKYbJexWM94HDHusZieno59+05NkWUU9KkN+jR8J7p2d2HcYzMsVfoRUacvVfqxmar78uz5IvJnlG/nxp3xROp73Jm3IntG+XYue8Zx56u6dm9VlX5E1OmLfgyH2q7Rj4g6fanSj4jx98W+fDzk27nsGcedL8Pvy8c9BluRPWP2fBEydm3S9+XDYCyOMBbrjXs87MvHQ76dy55x3Pkmfe3Wj3yq9GXY/ShzaCYi4oEPfkg88MEPWfe9i57z/MP/++STT45n/eJzu44FAAAAAAAAAAAAAABAx6bHHQAAAAAAAAAAAAAAAACGbeZFL3rRi8Ydoprp6em4+z3OjJmZmXFH2TKZuyFzDcbkCGOxnvE4ItNYZMoyLPrUBn2aLFXGpko/Iur0pUo/RiH72GTPF5E/o3w710LGo7WQOXtG+XYue8bs+TbSau6jVelHRJ2+6Md4tZr7aFX6EVGnL1X6EdFmX1rInD2jfDuXPWP2fF1oYQyyZ8yeL0LGVhiDI4zFEcZivRbHo4XM2TPKt3PZM2bPt5FWcx9NP/Kp0pdR9GNqMBgMhvbTAAAAAAAAAAAAAAAAIIHpcQcAAAAAAAAAAAAAAACAYXNoBgAAAAAAAAAAAAAAgHIcmtmiwWAQi4uLMRgMxh0FGCK1DXWpb6hJbUNNahvqUt9Qk9qGmtQ21KW+oSa1DTWpbahLfUNNahva4NDMFi0tLcV5554dS0tL444CDJHahrrUN9SktqEmtQ11qW+oSW1DTWob6lLfUJPahprUNtSlvqEmtQ1tcGgGAAAAAAAAAAAAAACAchyaAQAAAAAAAAAAAAAAoByHZgAAAAAAAAAAAAAAACjHoRkAAAAAAAAAAAAAAADKcWgGAAAAAAAAAAAAAACAchyaAQAAAAAAAAAAAAAAoByHZgAAAAAAAAAAAAAAAChndtwBxuE9735X/NH73hvT09PxiH99VjzyUf9m3JEAAAAAAAAAAAAAAAAYook7NPOZq/5nvONtV8bFz/ulmJubi1//jy+K7//+u8Rd7nq3cUcDAAAAAAAAAAAAAABgSKbHHaBrX/zC38Rtb3f7OPOe94q73u3ucbe7nxGf/MTHRtrmUn8Q/ZVBLPUHW/o+k+fGc+F7EzIflno39LnXVn9bzT0KxoJJZf3emnGP07jb345xZt1O2y2NKVCfvSjUpLahLvUNNaltqEltA3TD9ZZhyTKXsuRglX/bbZ+agrq6qG/XkK2zZk6OiZyszPIAACAASURBVDs0c6tb3Tr+6etfi3/8x3+Ia665Jv7p61+La7/97WOe1+v1YnFx8fDX0tLiCbW31B/E/gMLMXfpodh/YOFwUW30fSbP0XPhu9fXv/gu9Qax/+ANfT640OnCvJPaHmfubIwFGQ1r7d6M9Xtrxj1O425/O8aZdTttjzPnMGp76pKrh/oF7Jx9OdR1ovWttiE3azfUpLahLvtyqKmLfwujG6633FiFfXmWHKxq6d/Lq7Mvh5qyr92uIVtnzZwsU4PBYKJe4eXl5Xj5b/x6fOqTH49T9u2LiIiHPuzh8bj/92fWPe+db39rvOsdbzvmv7/s8itjfn5+y+31VwYxd+mhw3/uXXRKzE5Pbfh9Js/Rc+G6Z58S01NRej6Mc/7vpLbV7RHGgoyGtXZvxtzfmnGP07jb345xZt1O262u3WuyH3QZXLxv3BGgc/blUNeJ1rfahtys3VCT2oa67Muhpi7+LYxuuN5yYxX25VlysMrrkYd9OdSUfe12Ddk6YzVZZscdoGszMzPxi899QXz3u9+NXbt2xdPPf0rc4Y53OuZ5Zz36sfFTjzzr8J+XlhbjafuftO32eisR55yxK6646vo454xd0VuJmJ3e+PtMnqPnwrXXDWLvrqnS86G3fNT8X+5u/u+ktseZOxtjQUbDWrs3Y/3emnGP07jb345xZt1O2+PM2UVtj9uwD/U4hEML7MuhrhOtb7UNuVm7oSa1DXXZl0NNk/D78knhesuNVdiXZ8nBqpb+vbw6+3KoKfva7RqyddbMyTJxnzQTEdHv9eLrX/9avOfd74ovffELcekrXh3TMzOb/jeLi4tx3rlnn9D/Q8dSfxBzNxyU2TM7ddzvM3luPBemIuKkCZgPS71BzM2sLtB75sbX3+3WdpbcGRgLstvJ2r0Z6/fWjHucxt3+dowz63bazjKmJ1Lb2T9pJrtRHMLJ/po4eNQ9+3Koazv1rbahHdZuqEltQ1325VDTqP4tjG643rKRVvflWXKwKsu/7bKefTnUlHHtdg3ZOmvm5Ji4T5qJiPjoRz8Sb/qdN8QZZ94znvP8XzrugZmdWiuio0+fbfR9Js8kzoW1hbi1PreaexSMBZNqEq/ZJ2Lc4zTu9rdjnFm303ZLYwrUZy8KNaltqEt9Q01qG2pS2wDdcL1lWLLMpSw5WOXfdtunpqCuLurbNWTrrJmTYyIPzTzkRx4aD/mRh447BgAAAAAAAAAAAAAAACPiXBQAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAOQ7NAAAAAAAAAAAAAAAAUI5DMwAAAAAAAAAAAAAAAJTj0AwAAAAAAAAAAAAAAADlODQDAAAAAAAAAAAAAABAObPjDjAOf/on7493veP3YuG7C3GPM86Mp17w9Ni79+RxxwIAAAAAAAAAAAAAAGBIJu6TZr7znWvjd97wuvg3j/m3ccnLXxn/8H/+Pt733veMOxYAAAAAAAAAAAAAAABDNHGHZgaDiOnp6Tj11FNj375TY/fu3TEzM/xh6PUH8b3+IK674XGpP4j+yuqfl3qr//vGj0y2tfmxdMN8mQStzv9Wc4+CsaCSG1+Hj/vcbcz97fzcURhF+1v9maPq+zjbH9V1bxR92mrWbc1n130gEdckqEltQ13qG2pS21CT2gb4/9m7+zBJr7pO+L/u6R4yk0BIAMFgBHkTYcKbsFF5Xd2LlwCGuJGFwCZLEmDkVRZCAIPCIm6y84AI8QExsxjE5ElCAGX12RkWvVh2V3FVxM2zCr5AiCisZCaQTBehqvt+/ujp6amZdE/XdN1Vv3Puz+e66upJp6bO93fuc+5zumbO1GS43zIuWcZSlhwscz2GTfvvbhwP1xDqNYn57R6ycTX1VYnr3SR17tDMPe5xj3jBi14cuy5/Z1x4/gtjMBjETz7vXx71vH6/HwsLC4cevd7ChtvoDZoYNBF3fLeJxYNfX7HnQNy5GLHYROzceyDmd+2PnXuXv/eKvQeqmGwcn96giZ17Do6JPQfiju/WcfNdT6/fDM2DSda7qbk9xdzZ6AsyOt75feR9eL1N4yhjf5TXbUMb7W/0NduqfZrtt3Xfa6OmjWYdaTwXunYDedmXQ72Oe19ubkNq1m6ok7kN9bIvhzp5v7we7rccroZ9eZYcLHM9hk3z727Yl0Odsq/d7iEbV1NfTfvvKpZgpmmaTvXKvltvjTe87lXxghf963jwQx4WV77nXfHUH/+JeN455w4976PXXxs33nDdUb9/99XXxPbt29dtY7C03KVLTcTszPLXR1z1rfirl54cERHzu/Yfem7/klPi4b++/P/mZmc2Wx4FGiw1Q2PizjecErMzUfV4OLLm/iWnTKzezc7taeXORl+Q0fHO71HGc1vPbUMb7W/0NduqfZrtl1TTNF+zDZtZu1fMXLFv3LE6pbn01LG/ZvZr0kbNDLMvh3pNYl8OTJ61G+pkbkO97MuhTuN4v5wc3G85XA378iw5WOZ6DCvxz7pdQ8gt+9rtHrJxNfVVTbW0ZW7aASbti1/8y1haWoqnP+OsiIh4wpk/El/4/J8ddWjm7HPOjbOec/ah/+71FuJVOy/eUBv9pYhoIg70m9g+PxML/Sae/H1zcedixExEnL9ja3z4pu/G+Tu2xp2LEU/+vrnoL0bMde5zf4hYHi+Hj4lv39nEifMzVY+H/uJwzZMc/5ua21PMnY2+IKPjnd9H3of7S2uP51HG/iiv24Y22t/oa7ZV+zTbb+u+10ZNG8060ngudO0G8rIvh3od977c3IbUrN1QJ3Mb6mVfDnXyfnk93G85XA378iw5WOZ6DJvm392wL4c6ZV+73UM2rqa+mvbfVSxB5z5p5uabvxJvvuR18eqffX08+MEPjV9+1xXx4Ic8NC5+2c+s+/sWFhbiogvO2/C/0NEfNLEYy4dkmoOP+dmIxaXlT56Z37I82Va+bpt3mqvLeoMm5meXb1ozEXHCXP3joddvUoz/Ued2ltwZ6AuyG2V+H34f3naMe/AoY3+U121DG+1v9DXbqn2a7bd132ujpo1mHWk8J7nvj7p2R+T/VJPsfNIMk2BfDvUaaV9ubkMxrN1QJ3Mb6mVfDnU6nvfLycP9lrWUui/PkoNlrsewaf/djRX25VCnjGu3e8jG1dRXWda7rDr3STMPeMAD499c9NL4yIc/FAsHDsQjdzwqnv+CF429nfm5mZi/i+8ffmpr5ddOcrFyc+rSWFhZXEqrudTcbdAX1GSU+/AoY3/a9/c22t/oa7ZV+zTbb+u+10ZNG8060nh23wcScU+COpnbUC/zG+pkbkOdzG2AyXC/ZVyyjKUsOVjmegyb9t/dOB6uIdRrEvPbPWTjauqrEte7SercoZmIiKc/46x4+jPOmnYMAAAAAAAAAAAAAAAAWuIsEQAAAAAAAAAAAAAAANVxaAYAAAAAAAAAAAAAAIDqODQDAAAAAAAAAAAAAABAdRyaAQAAAAAAAAAAAAAAoDoOzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVMehGQAAAAAAAAAAAAAAAKrj0AwAAAAAAAAAAAAAAADVcWgGAAAAAAAAAAAAAACA6jg0AwAAAAAAAAAAAAAAQHUcmgEAAAAAAAAAAAAAAKA6Ds0AAAAAAAAAAAAAAABQHYdmAAAAAAAAAAAAAAAAqI5DMwAAAAAAAAAAAAAAAFTHoRkAAAAAAAAAAAAAAACq49AMAAAAAAAAAAAAAAAA1XFoBgAAAAAAAAAAAAAAgOrMTTvAJL3/yl+J//qZPxj63n3ve794z5UfmFIiAAAAAAAAAAAAAAAA2tCpQzMXvnRnnP+Siw7997t3XR4PevBDppgIAAAAAAAAAAAAAACANsxOO8Ak3e1ud4sTTzwpTjzxpLj1m9+ML/7VX8YznvnsVtoaDJq4c9DE4ODjzkET/YO/7vWbGCwNf6Xb7hx0bzyUWm+pudugL4b1Bgf7Y6A/SjTKeG7juTW2r6Yy2m+rJoC2uSfBsZX4M0oJc7uEjJCRuQN1qmVu11IHjIs5ATAZ7reMS5axlCFHhgxZ6IthJfZHCZlLyAgZTWLu1NLGJNRSR0RdtbShU4dmDvefPvnb8fh/dmbc+z73ucv/3+/3Y2Fh4dCj11vY8Gv3+k30m4jFJqJ/8LHYRAwO/vrVew9EfzFi594DMb9rf+zce8AA7bBev4nFplvjoddvplbvZud2l67TevTFsN6giZ17DvbHngNF/aW0mhzv/B5lPLfx3BrbV1MZ7bdV07htZu0G8rIvh3ZN82eUSezLp6WEjNAWazfUydyupw44Us37cugy75fXw/2Ww9WwL8+QI0OGLPTFsBL/rLuEa1hCRmhL9rW7ljYmoZY6IuqqpS0zTdN0rlf23XprvOaVL4+3vu0d8YMP/6G7fM5Hr782brzhuqO+v/vqa2L79u3rvv5gaf0u/emP3xE3nHNSzO/af+h7/UtOibnZmQ2kpzYr46VL42Gw1Eyt3s3O7S5dp/Xoi2H6I4fjnd+jXL82nltj+2oqo/22ahq3zazdK2au2DfuWJ3SXHrq2F8z+zVpo2aG2ZdDu0pcu0uY2yVkhLZYu6FO5nY9dcCRat6XQ5eN4/1ycnC/5XA17Msz5MiQIQt9Mcz75e0oISO0JfvaXUsbk1BLHRF11dKWuWkHmIb/93c/GQ944APXPDATEXH2OefGWc85+9B/93oL8aqdF2/o9fuLy1+biFgZbof/+l4nzER/MeL8HVvjwzd9N87fsTX6ixFznf3cn27rLy6Pjy6Nh2mO/83O7S5dp/Xoi2H9pSP6Y6nb/TEtxzu/RxnPbTy3xvbVVEb7bdU0bptZu4G87MuhXdP8GWUS+/JpKSEjtMXaDXUyt+upA45U874cusz75fVwv+VwNezLM+TIkCELfTGsxD/rLuEalpAR2pJ97a6ljUmopY6IumppS+c+aWZhYfnmdOFLXx5PevJTR/p9F11w3ob/hY7BoInFiNhy8L8XI2I2lg/O9JuI+S3LA3Tl67Z5p7m67M5BE0tNt8ZDr9+kqHfUuZ0ldwb6Ylhv0MT87PJfTts2pz8yGGV+jzKe23huje2rqYz226qpTaOu3RH5P9UkO580wyTYl8P4ZfkZpa19+bSUkBEmwdoNderq3K6lDlhPbftyYNnxvF9OHu63rKXUfXmGHBkyZKEvhmXpj9r25SVkhEnIuHbX0sYk1FJHRF21tKFznzTz6U/tibudcLf4kR99YqvtzM3NDHXuXf165QSXk1zc7bC/vNKV8bByQy6t3lJzt0FfDFv5S2j6o0yjjOc2nltj+2oqo/22agJom3sSHFuJP6OUMLdLyAgZmTtQp1rmdi11wLiYEwCT4X7LuGQZSxlyZMiQhb4YVmJ/lJC5hIyQ0STmTi1tTEItdUTUVUsbOndo5rlnnxPPPfucaccAAAAAAAAAAAAAAACgRc4SAQAAAAAAAAAAAAAAUB2HZgAAAAAAAAAAAAAAAKiOQzMAAAAAAAAAAAAAAABUx6EZAAAAAAAAAAAAAAAAquPQDAAAAAAAAAAAAAAAANVxaAYAAAAAAAAAAAAAAIDqODQDAAAAAAAAAAAAAABAdRyaAQAAAAAAAAAAAAAAoDoOzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVMehGQAAAAAAAAAAAAAAAKrj0AwAAAAAAAAAAAAAAADVcWgGAAAAAAAAAAAAAACA6jg0AwAAAAAAAAAAAAAAQHUcmgEAAAAAAAAAAAAAAKA6Ds0AAAAAAAAAAAAAAABQnblpB5iGpcXF+MQnbozP/MGn4/73Pz3e+ObLph0JAAAAAAAAAAAAAACAMerkoZmrf2N3/PEf/WFcePHL44ce8chpxwEAAAAAAAAAAAAAAGDMZqcdYNJuu+22+PSn9sRFL9sZTzjzR+Kku9+9lXYGg2bNR6/fxGBp+CvdduS46IJS6y01dxv0xTD9sao3ONgXg3L6YpTr18Zza2xfTWW031ZNAG1zT4JjK3GelJA5e8YSfx6jG7LPnY2qpQ7yKXVslZr7SLXUEWEvkFGJ46uEzCVkpG7ut4yDexnjkmUsZciRIUOWHBkyZFJif5SQOXvG7PlKkL0PS92XT6JftZGrjYjJjNeJ9Nck6mipjc4dmvnbv/lSLC0txR/9j/8er9x5UVz+zn8Xt976zaOe1+/3Y2Fh4dCj11vYcBu9fhP9JtZ8XLb3QPQXI3buPRDzu/bHzr0H0i4qtG9lvHRpPPT6zdTq3ezc7tJ1Wo++GKY/VvUGTezcc7Av9hyY6A8lxzu/R7l+bTy3xvbVVEb7bdU0bptZu4G87MuhXSWu3SXM7ewZp/nzGPWzdtdTB/mUuG5H1DMnaqkjwl4goxLndwlzooSM1K3EPwsjH/cyDlfDvjxDjgwZsuTIkCET+/J2ZM+YPV8JsvdhqfvySfSrNnK1ETGZ8TqR/ppEHS22MdM0Ta47Wcv+62f+ID7wq++Lf3PRxfHQhz08Pvj+K+N7T7t/vOZnXz/0vI9ef23ceMN1R/3+3VdfE9u3b1+3jcHS+l36uVvuiDNPPynmd+0/9L3+JafE3OzMCJVQi5Xx0qXxMFhqplbvZud2l67TevTFMP2xqsT5PUrmNp5bY/tqKqP9tmoat82s3Stmrtg37lid0lx66thfM/s1aaNmhtmXQ7tKXLtLmNvZM2bPR9ms3fXUQT4lrtsR9cyJWuqIqKuWWpQ4v0sYRyVkpG4lzm3ycS/jcDXsyzPkyJAhS44MGTIpce0u4Rpmz5g9Xwmy92GJcztiMrm1kauNSbWjjWPr3CfN3OMeJ8fWrVvj6c84K37gBx4UP/rEJ8VXb/7KUc87+5xzY/fV1xx6XPmBqzbcRn9x/cfHbpqJ/mLE+Tu2RsTy1/7iuCqkNCvjokvjYZr1bnZud+k6rUdfDNMfq/pLR/TF0uTaPt75Pcr1a+O5NbavpjLab6umcdvM2g3kZV8O7Spx7S5hbmfPOM2fx6iftbueOsinxHU7op45UUsdEfYCGZU4v0uYEyVkpG4l/lkY+biXcbga9uUZcmTIkCVHhgyZ2Je3I3vG7PlKkL0PS92XT6JftZGrjYjJjNeJ9Nck6mixjc590szCgQPx2lftjHOf/4J43A8/Pt73K++O+973fvHK17xu/d+3sBAXXXDehv+FjsE6HwfUbyLmtywP0JWv2+bznMBk8gaDZmhcdGE89PpNinpHndtZcmegL4bpj1W9QRPzs8sbmG1zZczvUa5fG8+tsX01ldF+WzW1adS1OyL/p5pk55NmmAT7chi/LPOkrX35tGTPmOXnMerX1bW7ljrIJ8vYMrfLriPCXiCjLOPLvhzGK8v99njeLycP9zLWUuq+PEOODBmy5MiQIZMs/WFfPlnZ85Ugex+Wui+fRL9qI1cbEZMZrxPpr0nU0VIbc2N7pUJsP/HEeN0b3hgf2v3r8f9c85vxiEfuiBdf8JKxtzO3zkVa6fS52eGvdNfc3MxR46J2Kzfk0uotNXcb9MUw/bFqZaNSUl+Mcv3aeG6N7aupjPbbqgmgbe5JcGwlzpMSMmfPWOLPY3RD9rmzUbXUQT6ljq1Scx+pljoi7AUyKnF8lZC5hIzUzf2WcXAvY1yyjKUMOTJkyJIjQ4ZMSuyPEjJnz5g9Xwmy92Gp+/JJ9Ks2crURMZnxOpH+mkQdLbXRuUMzERGPeOQZsevd7512DAAAAAAAAAAAAAAAAFpS2Pk6AAAAAAAAAAAAAAAAODaHZgAAAAAAAAAAAAAAAKiOQzMAAAAAAAAAAAAAAABUx6EZAAAAAAAAAAAAAAAAquPQDAAAAAAAAAAAAAAAANVxaAYAAAAAAAAAAAAAAIDqODQDAAAAAAAAAAAAAABAdRyaAQAAAAAAAAAAAAAAoDoOzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVGdu2gHWsuvyd274uTMzM/GGS9/SYhoAAAAAAAAAAAAAAABKkvbQzOf/7E9GePZMazkAAAAAAAAAAAAAAAAoT9pDM5f9wjumHQEAAAAAAAAAAAAAAIBCpT0084hH7ph2BAAAAAAAAAAAAAAAAAqV9tDM737yt0d6/rOfe3ZLSQAAAAAAAAAAAAAAAChN2kMzv/WbvxERMxt4ZhMRMw7NAAAAAAAAAAAAAAAAcEjaQzM/de7zI2Y2cmgGAAAAAAAAAAAAAAAAhqU9NHPu81/Y2mv/+198W/zFF/780H8/+7lnx4vPf0lr7QEAAAAAAAAAAAAAADBZaQ/NHKnf78eX/+5v41u33RZNNKvf/+5342tf+/t4/gtetOHX2r9/f7zk4pfFE5/0lIiImJ/fOva8AAAAAAAAAAAAAAAATE8Rh2a+8fV/jMt/6R3xja9/fc3njHJo5rb9++N7vue+ceKJJ40j3l0aDJo1/1+/iZjfEtFfXP26bX6mtSzkNxg0Q+OiC+Oh12+KrLfU3G3QF8P0x6reoIn52Yj+UsS2uTL6YpTr18Zza2xfTWW031ZNAG1zT4JjK3GelJA5e8bs+eguYxPqVMvcrqWOiDLfm61didekhDlRQkaAY3EvY1yyjKUMOTJkyJIjQ4ZMSuyPEjJnz5g9XwlK/Jm2BJMYm9rI1UbEZOZTLfe9tvpqdmyv1KJrPnJ1fOPr/xg7zjgjTjvttIiIeMpTnxaP3LEjIpp47tnnbPi1Bv1+3HHH7fFbv3l1vOJlF8Yv/19XxO23f/uo5/X7/VhYWDj06PUWNtxGr798AGKtx2V7D0R/MWLn3gMxv2t/7Nx7IHr9tQ/ZULeV8dKl8dDrN1Ord7Nzu0vXaT36Ypj+WNUbNLFzz8G+2HMgeuscIh23453fo1y/Np5bY/tqKqP9tmoat82s3UBe9uXQrhLX7hLmdvaM2fNRNms31MncrqeOiOm+N8tdq/398mkpISO0xfvl9XAv43A17Msz5MiQIUuODBky8X55O7JnzJ6vBN5nWFv2tVsbudqImMx8quW+12ZfzTRNk75XXn7RBfGgBz84Ln3Lz8eHdn8wPrXnP8c1138slpaW4o2vf2086MEPiVe86rUbeq3BYBC//+m9cb/7nRbbt2+PX33fe+JRj35MvOSilw0976PXXxs33nDdUb9/99XXxPbt29dvY2n9Lv3cLXfEmaefFPO79h/6Xv+SU2JuttxTXRy/lfHSpfEwWGqmVu9m53aXrtN69MUw/bGqxPk9SuY2nltj+2oqo/22ahq3zazdK2au2DfuWJ3SXHrq2F8z+zVpo2aG2ZdDu0pcu0uY29kzZs9H2azdUCdzu546IuqqpRb25e0oISO0ZRzvl5ODexmHq2FfniFHhgxZcmTIkIl9eTuyZ8yerwT6cG3Z125t5GpjUu3UMmfbrKOIT5q588474+R7nhIREXe/xz0iIuKO22+P2dnZeMhDHxZf+Pyfbfi1lpaW4vGPPzMe9ejHxEMe+rB4/BP+WXz15puPet7Z55wbu6++5tDjyg9cteE2+ovrPz5200z0FyPO37E1Ipa/9hc3/PJUZmVcdGk8TLPezc7tLl2n9eiLYfpjVX/piL5Ymlzbxzu/R7l+bTy3xvbVVEb7bdU0bptZu4G87MuhXSWu3SXM7ewZs+ejbNZuqJO5XU8dEdN9b5a7Vvv75dNSQkZoi/fL6+FexuFq2JdnyJEhQ5YcGTJk4v3ydmTPmD1fCbzPsLbsa7c2crURMZn5VMt9r82+KuKTZt721jfH177293HJpT8X//R/vhG/+r73xE8+76fiqf/8J2LXv//F6H2nF+//4Ic29Fq3fPXmeOPrXxuv+dk3xIMe8pB4967L4+E/9IijPmnmSAsLC3HRBedt+F/oGKzzcUD9JmJ+y/IAXfm6bb6801yMz2DQDI2LLoyHXr9JUe+ocztL7gz0xTD9sao3aGJ+dnkDs22ujPk9yvVr47k1tq+mMtpvq6Y2jbp2R+T/VJPsfNIMk2BfDuOXZZ60tS+fluwZs+ejHtZuqFNX53YtdUTkeW+WVVmuiX051Ol43i8nD/cy1lLqvjxDjgwZsuTIkCGTLP1hXz5Z2fOVIMvPtNllXLu1kauNiMnMp1rue2311dzYXqlFZ5/zL2PX5b8Uv//pT8WFF7887v4b/zF+5xMfj9/5xMcjIuKZZz17w691+vc/IC648OL4yIc/FL3eQpzxqEfHT/+r88aeeW6di7TS6XOzw1/prrm5maPGRe1Wbsil1Vtq7jboi2H6Y9XKRqWkvhjl+rXx3BrbV1MZ7bdVE0Db3JPg2EqcJyVkzp4xez66y9iEOtUyt2upI6LM92ZrV+I1KWFOlJAR4FjcyxiXLGMpQ44MGbLkyJAhkxL7o4TM2TNmz1eCEn+mLcEkxqY2crURMZn5VMt9r62+KuLQzGMf9/h4w6Vvjttuuy22bt0al77lrXHtb/1mfOu2/fGoxzw2nv+CF430es981nPimc96TktpAQAAAAAAAAAAAAAAmLYiDs1ERDzuh59w6NcPevBD4ud+/u1TTAMAAAAAAAAAAAAAAEBmxRyaGfT78dnPfiZu/sqX47b9++Pkk+8ZD3zQg+LJT35qzM3PTzseAAAAAAAAAAAAAAAAiRRxaObvb7klLn/n22Pfvn0Raa8fwAAAIABJREFU0Rz2f2biYzdcF2/6uZ+P+3/f6dOKBwAAAAAAAAAAAAAAQDJFHJr50O4Pxr59t8ZjH/fD8WNPekqcfPLJcfvtt8f/+G+fjT/9kz+OD+3+YFz2C++YdkwAAAAAAAAAAAAAAACSKOLQzN/89RfjoQ/7wbjkTZcNff9Hf+xJ8bbL3hx//aUvTikZAAAAAAAAAAAAAAAAGc1OO8BGnHqve8dpp93/Lv/f/U47LU66+90nnAgAAAAAAAAAAAAAAIDMijg086xnPyc+/2d/Gt/8p38a+v43vv6P8fk//ZN45rOeM6VkAAAAAAAAAAAAAAAAZDQ37QBrufD8Fw7993e+c2e87jU/E/Pz84e+993v9qNpluKP/vC/x3PPPmfSEQEAAAAAAAAAAAAAAEgq7aGZ73znO0d9b3FxMRYXF4/6/pf/7u8mEQkAAAAAAAAAAAAAAIBCpD00c831H592BAAAAAAAAAAAAAAAAAo1O+0AAAAAAAAAAAAAAAAAMG4OzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVMehGQAAAAAAAAAAAAAAAKrj0AwAAAAAAAAAAAAAAADVcWgGAAAAAAAAAAAAAACA6nT20Mx73vUf4oU//bxpxwAAAAAAAAAAAAAAAKAFnTw08yf/84/jf/7xH007BgAAAAAAAAAAAAAAAC3p3KGZhYWF+NDuX4tnPOvZrbYzGDRrPnr9JgZLw1/ptiPHRReUWm+pudugL4bpj1Ul9sUomdt4bo3tq6mM9tuqCaBt7klwbCXOkxIyZ8/YGxzMN8iZj+7KPnc2qpY6YFxqmRO11BFhL8B4lDAnSsgIcCzuZYxLlrGUIUeGDBE59uVZ+iKLEvujhMzZM2bPR3dNYmxqI1cbEZPZH7jvra9zh2au/ciH44xHPSZ2nPGodZ/X7/djYWHh0KPXW9hwG71+E/0m1nxctvdA9Bcjdu49EPO79sfOvQcM0A5bGS9dGg+9fjO1ejc7t7t0ndajL4bpj1Ulzu9RMrfx3BrbV1MZ7bdV07htZu0G8rIvh3aVuHaXMLezZ+wNmti552C+PQf8ZVnGytpdTx1wOHO7njoi7AUYZl8OdfJ+eT3cyzhcDfvyDDkyZIjIsS/P0hdZeL+8HdkzZs9H2bKv3drI1UbEZPYH7nvHNtM0TWd65a/+8n/Hr7x7V+x693vjS1/6q9h1+Tvj2hs+cZfP/ej118aNN1x31Pd3X31NbN++fd12Bkvrd+nnbrkjzjz9pJjftf/Q9/qXnBJzszMbqILarIyXLo2HwVIztXo3O7e7dJ3Woy+G6Y9VJc7vUTK38dwa21dTGe23VdO4bWbtXjFzxb5xx+qU5tJTx/6a2a9JGzUzzL4c2lXi2l3C3M6eMXs+ymbtrqcOOJy5XU8dEXXVwubZl0OdxvF+OTm4l3G4GvblGXJkyJAlR4YMmXi/vB3ZM2bPR9myr93ayNXGpNpx3zu2Tn3SzMdvvCFuv/32eO2rXh7v/eV3RUTERRecd5fPPfucc2P31dccelz5gas23E5/cf3Hx26aif5ixPk7tkbE8tf+4ubro0wr46JL42Ga9W52bnfpOq1HXwzTH6tKnN+jZG7juTW2r6Yy2m+rpnHbzNoN5GVfDu0qce0uYW5nz9hfOiLf0pQDURVrdz11wOHM7XrqiLAXYJh9OdTJ++X1cC/jcDXsyzPkyJAhIse+PEtfZOH98nZkz5g9H2XLvnZrI1cbEZPZH7jvHVunPmnmtv37o9//bkRE3HTT/4oPvv/KeO+v/lrc53vue8zfu7CwEBddcN6G/4WOwTofndRvIua3LA/Qla/b5p3m6rLBoBkaF10YD71+k6LeUed2ltwZ6Ith+mNVlr4YZX6PkrmN59bYvprKaL+tmto06todkf9TTbLzSTNMgn05jF+WedLWvnxasmfsDZqYn11+833bXL581KOra3ctdcBazO2y64iwF2Bt9uVQp+N5v5w83MtYS6n78gw5MmSIyLEvz9IXWWTpD/vyycqej3pkXLu1kauNiMnsD9z31jc37QCTdM9TTjn065NP/mpExIYOzByPuXUG9Eqnz80Of6W75uZmjhoXtVu5IZdWb6m526AvhumPVSX2xSiZ23huje2rqYz226oJoG3uSXBsJc6TEjJnz7jyJnvWfHRX9rmzUbXUAeNSy5yopY4IewHGo4Q5UUJGgGNxL2NcsoylDDkyZIjIsS/P0hdZlNgfJWTOnjF7PrprEmNTG7naiJjM/sB9b32d7ZbH/fAT4tobPjHtGAAAAAAAAAAAAAAAALSgs4dmAAAAAAAAAAAAAAAAqJdDMwAAAAAAAAAAAAAAAFTHoRkAAAAAAAAAAAAAAACq49AMAAAAAAAAAAAAAAAA1XFoBgAAAAAAAAAAAAAAgOo4NAMAAAAAAAAAAAAAAEB1HJoBAAAAAAAAAAAAAACgOg7NAAAAAAAAAAAAAAAAUB2HZgAAAAAAAAAAAAAAAKiOQzMAAAAAAAAAAAAAAABUx6EZAAAAAAAAAAAAAAAAquPQDAAAAAAAAAAAAAAAANVxaAYAAAAAAAAAAAAAAIDqODQDAAAAAAAAAAAAAABAdRyaAQAAAAAAAAAAAAAAoDoOzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVGdu2gEmaXFxMa679iPx+/9lb0REPPZxj4+X7nxlbN26dcrJAAAAAAAAAAAAAAAAGKdOfdLMf/vsZ+L3/8un4pI3XRZvf+cVcdP/+otDB2gAAAAAAAAAAAAAAACoR6cOzTz1aT8eV/3GR+IHH/5Dcc973jPm5uZiy5YtrbQ1GDRrPnr9JgZLw1/ptiPHRReUWm+pudugL4bpj1Ul9sUomdt4bo3tq6mM9tuqCaBt7klwbCXOkxIyZ8+YPt/gYL5BznwlyH6N11Jq7iPVUgeMSy1zopY6IuqqpRYl7n9KGEclZAQ4FvcyxiXLWMqQI0OGLDkyZMikxP4oIXP2jOnzFfDzYvaM2a/xWiaRWxu52phUO6XOiUnp1KGZFW98/WvjZReeH/f73u+Nf/H0Z97lc/r9fiwsLBx69HoLG379Xr+JfhNrPi7beyD6ixE79x6I+V37Y+feAwZoh62Mly6Nh16/mVq9m53bXbpO69EXw/THqhLn9yiZ23huje2rqYz226pp3DazdgN52ZdDu0pcu0uY29kzps83aGLnnoP59hxI+4dsmZU4tyPyj82NqqUOOJy5XU8dEXXVUotp7n/sy6FO3i+vh3sZh6thX54hR4YMWXJkyJBJie+plXANs2dMn6+A98uzZyxxbkdMJrc2crUxqXay3/cymGmapnO9cuut34x//IevxXt/+V1x3r++IJ72z3/iqOd89Ppr48Ybrjvq+7uvvia2b9++7usPltbv0s/dckecefpJMb9r/6Hv9S85JeZmZzZYATVZGS9dGg+DpWZq9W52bnfpOq1HXwzTH6tKnN+jZG7juTW2r6Yy2m+rpnHbzNq9YuaKfeOO1SnNpaeO/TWzX5M2amaYfTm0q8S1u4S5nT2jfPUrcW5H1HPta6kDDmdu11NHRF211KLEtbuEcVRCRmjLON4vJwf3Mg5Xw748Q44MGbLkyJAhE/vydmTPKN/mZc9Y4tyOmExubeRqY1LtZJ+zGXTqk2Zuvvkr8Rdf+PO4173uHTvOeHQ8YseO+N//3013+dyzzzk3dl99zaHHlR+4asPt9BfXf3zsppnoL0acv2NrRCx/7S+OpUQKtDIuujQeplnvZud2l67TevTFMP2xqsT5PUrmNp5bY/tqKqP9tmoat82s3UBe9uXQrhLX7hLmdvaM6fMtHZFvacqBClTi3I7IPzY3qpY64HDmdj11RNRVSy2muf+xL4c6eb+8Hu5lHK6GfXmGHBkyZMmRIUMmJb6nVsI1zJ4xfb4C3i/PnrHEuR0xmdzayNXGpNrJft/LoFOfNPPZz/xB/Merfi3e8ta3xwnbtsXl73x7PPu5Z8dZz/7JY/7ehYWFuOiC8zb8L3QM1vkosn4TMb9leYCufN027zRXlw0GzdC46MJ46PWbFPWOOrez5M5AXwzTH6uy9MUo83uUzG08t8b21VRG+23V1KZR1+6I/J9qkp1PmmES7Mth/LLMk7b25dOSPWP6fIMm5meX/7Bt21y+fCXIco27unbXUgesxdwuu46IumqpRZb9j3051Ol43i8nD/cy1lLqvjxDjgwZsuTIkCGTLP1hXz5Z6fMl+XlxPdkzZrnGGddubeRqY1LtZJkTWc1NO8AkPekpT4ubb/5K7Lr8ndE0TfzIjz0xnv6Ms1ppa26dBWKl0+dmh7/SXXNzM0eNi9qt3JBLq7fU3G3QF8P0x6oS+2KUzG08t8b21VRG+23VBNA29yQ4thLnSQmZs2dMn28ud74SZL/Gayk195FqqQPGpZY5UUsdEXXVUosS9z8ljKMSMgIci3sZ45JlLGXIkSFDlhwZMmRSYn+UkDl7xvT5Cvh5MXvG7Nd4LZPIrY1cbUyqnVLnxKR06tDMzMxMvPj8l8SLz3/JtKMAAAAAAAAAAAAAAADQImeJAAAAAAAAAAAAAAAAqI5DMwAAAAAAAAAAAAAAAFTHoRkAAAAAAAAAAAAAAACq49AMAAAAAAAAAAAAAAAA1XFoBgAAAAAAAAAAAAAAgOo4NAMAAAAAAAAAAAAAAEB1HJoBAAAAAAAAAAAAAACgOg7NAAAAAAAAAAAAAAAAUB2HZgAAAAAAAAAAAAAAAKiOQzMAAAAAAAAAAAAAAABUx6EZAAAAAAAAAAAAAAAAquPQDAAAAAAAAAAAAAAAANVxaAYAAAAAAAAAAAAAAIDqODQDAAAAAAAAAAAAAABAdRyaAQAAAAAAAAAAAAAAoDoOzQAAAAAAAAAAAAAAAFAdh2YAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVGdu2gGm4ZO//fH4vf/0O3Hnnd+JRz3msbHzFa+JE044YdqxAAAAAAAAAAAAAAAAGJPOHZr5iy/8eVx37W/FpW95a9zzlFPil/7dL8R//r1PxvN+6qenHQ0AAIAEZq7YN9bXay49dayvBwAAAAAAAAAAbMzstANM2tzcXJz34vPjjEc9Ok4//fvje087Lb5127fG3s5g0Kz56PWbGCwNf6XbjhwXXVBqvaXmboO+GKY/VpXYF6NkbuO5NbavpjLab6smgLa5J8GxlThPSsicPWP2fHRXLWOzNzhYx6DsOmBcqpnbldQR4T6VUYnXpIQ5UUJGgGNxL2NcsoylDDkyZIjIsQfM0hdZlNgfJWTOnjF7vhJkuJ/VaBJjs5o2JjAGa7pX1FRLGzp3aOYRj9wRZz3nJyMi4uavfDn+5q+/FE988lOOel6/34+FhYVDj15vYcNt9PpN9JtY83HZ3gPRX4zYufdAzO/aHzv3HjBAO2xlvHRpPPT6zdTq3ezc7tJ1Wo++GKY/VpU4v0fJ3MZza2xfTWW031ZN47aZtRvIy74c2lXi2l3C3M6eMXs+ymbtXv7DuZ17Dtax54A/KKYK5nY9dUS4T2U0zWtiXw518n55PdzLOFwN+/IMOTJkiMixL8/SF1l4v7wd2TNmz1eCDPezrLKv3dW0MYExWNO9oqZa2jLTNE0ne+XWW78Zv/Bzb4onP+Vp8a/Oe/FR//+j118bN95w3VHf3331NbF9+/Z1X3uwtH6Xfu6WO+LM00+K+V37D32vf8kpMTc7s8H01GRlvHRpPAyWmqnVu9m53aXrtB59MUx/rCpxfo+SuY3n1ti+mspov62axm0za/eKmSv2jTtWpzSXnjr21+zaNWmjD0tnXw7tKnHtLmFuZ8+YPR9ls3bXUwccztyup46IumqphX15O0rICG0Zx/vl5OBexuFq2JdnyJEhQ5YcGTJkYl/ejuwZs+crgT5cW/a1Wxu52piUmmppS+c+aSYi4tvf/nb80jveFo/ccUY8/4UvusvnnH3OubH76msOPa78wFUbfv3+4vqPj900E/3FiPN3bI2I5a/9xbGURoFWxkWXxsM0693s3O7SdVqPvhimP1aVOL9HydzGc2tsX01ltN9WTeO2mbUbyMu+HNpV4tpdwtzOnjF7Pspm7Y7oLx1Rx9KUA8EYmNv11BHhPpXRNK+JfTnUyfvl9XAv43A17Msz5MiQISLHvjxLX2Th/fJ2ZM+YPV8JMtzPssq+dlfTxgTGYE33ippqaUvnPmnmO71e/OLbfz5OPOnEeNVr/23Mzs7GzMzsMU/3LSwsxEUXnLfhf6FjsM7HQPWbiPktywN05eu2eae5umwwaIbGRRfGQ6/fpKh31LmdJXcG+mKY/liVpS9Gmd+jZG7juTW2r6Yy2m+rpjaNunZHdO9TTcbNJ81snk+aOTb7chi/LPOkrX35tGTPmD0f9ejq2t0bNDE/u/yHddvmyq0D1tLZuV1JHRHuUxlluSb25VCn43m/nDzcy1hLqfvyDDkyZIjIsQfM0hdZZOkP+/LJyp6vBBnuZyXIuHZX08YExmBN94qaamnD3LQDTNrnPveH8bd/+9cREfGyC8+PiIh73+c+8b7/+9fH2s7cOpNzpdPnZoe/0l1zczNHjYvardyQS6u31Nxt0BfD9MeqEvtilMxtPLfG9tVURvtt1QTQNvckOLYS50kJmbNnzJ6P7qplbK78oVzpdcC4VDO3K6kjwn0qoxKvSQlzooSMAMfiXsa4ZBlLGXJkyBCRYw+YpS+yKLE/SsicPWP2fCXIcD+r0STGZjVtTGAM1nSvqKmWNnTu0MxTn/bj8dSn/fi0YwAAAAAAAAAAAAAAANAiZ4kAAAAAAAAAAAAAAACojkMzAAAAAAAAAAAAAAAAVMehGQAAAAAAAAAAAAAAAKrj0AwAAAAAAAAAAAAAAADVcWgGAAAAAAAAAAAAAACA6jg0AwAAAAAAAAAAAAAAQHUcmgEAAAAAAAAAAAAAAKA6Ds0AAAAAAAAAAAAAAABQHYdmAAAAAAAAAAAAAAAAqI5DMwAAAAAAAAAAAAAAAFTHoRkAAAAAAAAAAAAAAACq49AMAAAAAAAAAAAAAAAA1XFoBgAAAAAAAAAAAAAAgOo4NAMAAAAAAAAAAAAAAEB1HJoBAAAAAAAAAAAAAACgOg7NAAAAAAAAAAAAAAAAUB2HZgAAAAAAAAAAAAAAAKhOJw/NfOtbt8Xv/e7vxJsueV3c8tWbpx0HAAAAAAAAAAAAAACAMZubdoBJ6/V68eqfeVk88IE/EDd/5cvTjgMAAAAAAAAAAAAAAEALOvdJM1u3bo33vf+D8eqf/bettjMYNGs+ev0mBkvDX+m2I8dFF5Rab6m526AvhumPVSX2xSiZ23huje2rqYz226oJoG3uSXBsJc6TEjJnz5g9H91Vy9jsDQ7WMSi7DhiXauZ2JXWQU4lrRwlzooSMAMfiXsa4ZBlLGXJkyJAlR4YMmZTYHyVkzp4xe74SlPgzbQkmMTa1kauNiMnMp1rue231VecOzWzZsiVOPvmex3xev9+PhYWFQ49eb2HDbfT6TfSbWPNx2d4D0V+M2Ln3QMzv2h879x4ofoBy/FbGS5fGQ6/fTK3ezc7tLl2n9eiLYfpjVYnze5TMbTy3xvbVVEb7bdU0bptZu4G87MuhXSWu3SXM7ewZs+ejbNbu5T+k2bnnYB17DviDYqpgbtdTBzlNc+2wL4c6eb+8Hu5lHK6GfXmGHBkyZMmRIUMm3i9vR/aM2fOVwPuha8u+dmsjVxsRk5lPtdz32uyrmaZpyuyVTfqn//ONeM0rXx7/4V2/Eqd//wOO+v8fvf7auPGG6476/u6rr4nt27ev+9qDpfW79HO33BFnnn5SzO/af+h7/UtOibnZmQ2mpyYr46VL42Gw1Eyt3s3O7S5dp/Xoi2H6Y1WJ83uUzG08t8b21VRG+23VNG6bWbtXzFyxb9yxOqW59NSxv2bXrkkbfVg6+3JoV4lrdwlzO3vG7Pkom7W7njrgcOZ2PXWQk315O0rICG0Zx/vl5OBexuFq2JdnyJEhQ5YcGTJkYl/ejuwZs+crgT5cW/a1Wxu52phUO7XM2Tbr6NwnzWzU2eecG7uvvubQ48oPXLXh39tfXP/xsZtmor8Ycf6OrRGx/LW/2FYlZLcyLro0HqZZ72bndpeu03r0xTD9sarE+T1K5jaeW2P7aiqj/bZqGrfNrN1AXvbl0K4S1+4S5nb2jNnzUTZrd0R/6Yg6lqYcCMbA3K6nDnKa5tphXw518n55PdzLOFwN+/IMOTJkyJIjQ4ZMvF/ejuwZs+crgfdD15Z97dZGrjYiJjOfarnvtdlXPmlmjU+aOdLCwkJcdMF5G/4XOgbrfBxQv4mY37I8QFe+bpsv7zQX4zMYNEPjogvjoddvUtQ76tzOkjsDfTFMf6zK0hejzO9RMrfx3BrbV1MZ7bdVU5tGXbsjuvepJuPmk2Y2zyfNHJt9OYxflnnS1r58WrJnzJ6PenR17e4NmpifXf5Dm21z5dYBa+ns3K6kDnLKsnbYl0Odjuf9cvJwL2Mtpe7LM+TIkCFLjgwZMsnSH/blk5U9Xwmy/EybXca1Wxu52oiYzHyq5b7XVl/Nje2VGDK3zkVa6fS52eGvdNfc3MxR46J2Kzfk0uotNXcb9MUw/bGqxL4YJXMbz62xfTWV0X5bNQG0zT0Jjq3EeVJC5uwZs+eju2oZmyt/OFN6HTAu1cztSuogpxLXjhLmRAkZAY7FvYxxyTKWMuTIkCFLjgwZMimxP0rInD1j9nwlKPFn2hJMYmxqI1cbEZOZT7Xc99rqq84emrnP99w3rr3hE9OOAQAAAAAAAAAAAAAAQAs6e2gGAAAAJmHmin1jfb3m0lPH+noAAAAAAAAAAFCrwj+ABwAAAAAAAAAAAAAAAI7m0AwAAAAAAAAAAAAAAADVcWgGAAAAAAAAAAAAAACA6jg0AwAAAAAAAAAAAAAAQHUcmgEAAAAAAAAAAAAAAKA6c9MOUIqmaSIiotdbmHIS6LZt27bFzMzM2F7P3IY8zG+ok7ldvoUFfZ3NzBX7xvp6B159wsi/x9yGOo17bkeY35CFtRvqZG5DnezLoV7WbqiTuQ11si+Helm7oU7HmtszzcpsZV233vrNeNXOi6cdAzpv99XXxPbt28f2euY25GF+Q53MbaiTuQ11GvfcjjC/IQtrN9TJ3IY62ZdDvazdUCdzG+pkXw71snZDnY41tx2a2aClpaXYv39fnHDC+qeQer2FeNXOi+PKD1wV27aNd9PUFpknQ+bxGPcp343O7RUZ+2Ra9MUw/bHqePuijfn9D//wtbjkda+u6rrUONbUVIZMc3uUtXsSarnetdQRUU8tmeuY9tzO3DcR+fNF5M8o3+YdT8Y2/uW8UeZ3rf06SfJtXvaMpe7Ls/frRtVSR0Q9tXS9DnN7PGqpI6KeWmqpI8K+vC3ZM8q3edkzlrp2j1P2axSRP2P2fBEybtS053aGPshCX6zSF8Psy9uRPaN8m5c9Y6n78uz9ulHqyKeWWtqa23PjCNcFs7Ozca973XvDz9+2bfvYTxq3TebJkDmXUef2ipr7ZFT6Ypj+WDXtvpidnY1TT71XiixtUFMZ1DR+x7t2T8K0+2Zcaqkjop5aaqljPbXuy7Pni8ifUb7Nm3bG45nf0868Edkzyrd52TNOO1+ta/dG1VJHRD21qGM8zO066oiop5Za6oiYfi325dMh3+ZlzzjtfBneL592H2xE9ozZ80XIOGld35ePg75YpS+GTbs/7MunQ77Ny55x2vm6vnarI59aahl3HbNjeyUAAAAAAAAAAAAAAABIwqEZAAAAAAAAAAAAAAAAqrPlbW9729umHaI2s7Oz8YhHnhFbtmyZdpQNk3kyZK6DPlmlL4bpj1WZ+iJTlnFRUxnU1C219E0tdUTUU0stdbQhe99kzxeRP6N8m1dCxiOVkDl7Rvk2L3vG7PnWUmruI9VSR0Q9tahjukrNfaRa6oiop5Za6ogos5YSMmfPKN/mZc+YPd8klNAH2TNmzxchYyn0wSp9sUpfDCuxP0rInD2jfJuXPWP2fGspNfeR1JFPLbW0UcdM0zTN2F4NAAAAAAAAAAAAAAAAEpiddgAAAAAAAAAAAAAAAAAYN4dmAAAAAAAAAAAAAAAAqI5DMxvUNE0sLCxE0zTTjgKMkbkN9TK/oU7mNtTJ3IZ6md9QJ3Mb6mRuQ73Mb6iTuQ11MrehXuY31MnchjI4NLNBvV4vLrrgvOj1etOOAoyRuQ31Mr+hTuY21MnchnqZ31AncxvqZG5DvcxvqJO5DXUyt6Fe5jfUydyGMjg0AwAAAAAAAAAAAAAAQHUcmgEAAAAAAAAAAAAAAKA6Ds0AAAAAAAAAAAAAAABQHYdmAAAAAAAAAAAAAAAAqI5DMwAAAAAAAAAAAAAAAFTHoRkAAAAAAAAAAAAAAACq49AMAAAAAAAAAAAAAAAA1XFoBgAA+P/Zu/Mwvcr6buDfZzKTlQyBQNhlX8wCxEAISwWpRWUxBiIurfpWUfKCoC2iUKxCUQul1gqCCEVlC4RVarUCUirCq2XJwiqbWFkjmIQkk4nMJPP+AYwMJJhlZs7z3Pl8rmtg5jxnzv27z5zvOYeH53cdAAAAAAAAAAAAKE5z1QX0phdfXJA7br8tt/33rTn2uM9mq7ds3eP1xYsX5zvnnZMH7r83G2w4Mh8/6uiMGTuuomoBAAAAAAAAAAAAAADoK8U8aaa9vT3H/d9P5Zd33JH//c0TK1znhuuvye9//0LOOOtfM3GvvXPu2d9IZ0dHP1cKAAAAAAAAAAAAAABAXyumaWbgwIE559sX5LjP/u1K13nowQey16R9MmqTTfLOg96V+fPnZe7c5/qspvaOrnQu70p7R9cqLQegcTm3/1Ej7otGrBmGODPiAAAgAElEQVRYNf2Rb2PU3zj9MkbnK2N0unYA9JZGuC93/oc10wj5BlZfKdl2fYeeSsk2QL1zvqW31MuxVA911EMN9cK+6KkR90cj1NwINUI96o/3okr5XEZ/KGUeSVlz6QvFNM0MGDAg668/4k3XWbRwYQYPHpwkGTZ0WJJk4cKFK1y3o6MjS5Ys6f5qb1+yWvW0d3Rl2k1taTlrfqbd1NZ9AK5sOdA/1jbbsCLO7X9U5b5Y03z7+0F9W5trd3/k2xj1N06/jNHZlWk3vjLGjW0+WLUGeuO+vHbmvF79AnpHyfflzv+sy+r9vhxYM7Lt+k65Sr4vh3WZ/9ddDudbXquE+/J6qKMeaqgX9kVPPsfSNxqhRugra3Xt7of3okr5XEZ/KGUeSVlz6SvNVRdQtVptxctvuP6aXHv1jDXebsuA5JL7X0ry8r8ves+wN10O9I+1zTasiHP7H1W5L9Y03/5+UN/W5trdH/k2Rv2N0y9jNLl2rC335VCuku/Lnf9Zl9X7fTmwZmTb9Z1ylXxfDusy76mVw/mW1yrhvrwe6qiHGuqFfdGTz7H0jUaoEfrKWl27++G9qFI+l9EfSplHUtZc+so61TQzfHhr2pa0JUmWtLcnSVpbV/x0mslTpubgQyd3/9zeviSfnnbUKo/VsSz56NiBueT+l/LRsQPTsSxpblr5cqB/rG22YUWc2/+oyn2xpvn294P6tjbX7v7ItzHqb5x+GWP568ZY7tqxutyXQ7lKvi93/mddVu/35cCakW3Xd8pV8n05rMu8p1YO51teq4T78nqoox5qqBf2RU8+x9I3GqFG6Ctrde3uh/eiSvlcRn8oZR5JWXPpK7Wurq6inr/z/O/m5vhjj84/ff2b2eotW2f5smVpGjAgSXL5Jd/PnDmzcsLnT87Pf3Zrbv2vn+bscy/IgFdefzNLlizJJz724Vx08fQMHTp0lWpp7+hKy4CXD8QhLbU/uRzof2uSbVgR5/Y/qpd9sTr5rpeagT9tda/d/ZFvY9TfOP0yRmdXWppeflNrSLNrx9pak/vy2pnzerWGri9s2KvbA15W2n258z+8rB7vy4G1t65m2/WddUFp9+XAy/y/7sbmfMvKNOp9eT3UUQ811Av7oqd62R+l3Zc3Qo3QH1b72t0P70WV8rmM/lDKPJKy5tIXin/SzNfPOiOjx4zNIYdNzuTDp+a5557NySf+TTbccGSOPe5vVqlhZk29esC9vlNrZcsBaFzO7X/UiPuiEWsGVk1/5NsY9TdOv4zR7NoB0Nsa4b7c+R/WTCPkG1h9pWTb9R16KiXbAPXO+ZbeUi/HUj3UUQ811Av7oqdG3B+NUHMj1Aj1qD/eiyrlcxn9oZR5JGXNpS8U1zSz8ahNcsXVP+j++cSTTun+fr311ssJnz+5irIAAAAAAAAAAAAAAADoR3qJAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKE5z1QX0pl/ccXuunH5p2toWZ4+Jk/KJT05LS0tL9+sLFizIt7/1r3n4Vw+ltXX9fODDf5V993t7hRUDAAAAAAAAAAAAAADQF4p50syiRQtz/nlnZ8rUI3Pq6WdkzuyZufWWm3usc8P112T+/Pk561/OzkHvOTjf/tbZ+cMf/lBRxQAAAAAAAAAAAAAAAPSVYppmHn/s0XR1JfsfcGC23GqrjB8/IQ89+ECPdZpqTRk8eHA22HBkNtxwZJqbB6Srq6uiigEAAAAAAAAAAAAAAOgrzVUX0FsWLlyYQYMHpVarJUmGDhuWuXPn9ljniPd/ICeecHz++iMfTGdnZ6Yde3wGDx68wu11dHSko6Oj++f29iV9VzzQb2QbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbyiTbUC75hjLJNjSmYppmVuSV/plu1149I+u3rp8Tv3BKZs68O9MvvTh77rlXhg4b9obfveH6a3Lt1TP6qVKgv8g2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbWhMxTTNDB8+PEvb27N8+fI0NTWlfcmStK4/osc6c2bPzP7v+PNss+122XzzLXL1ldPz618/nrHjdn3D9iZPmZqDD53c/XN7+5J8etpRfT4PoG/JNpRLvqFMsg1lkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm1oTMU0zWy/w05pamrKLTffmNFjx2X27JmZcsSRWb5sWZoGDEiSbLHlVpl5z13Ze5/9MmfOrAwYMCCbbbbZCrfX0tKSlpaW/pwC0A9kG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTY0pqaqC+gtra2tmXbM8fnhDdfny6d8Ibvuunv2P+DAfP2sM/KjH96QJPno//l4Bg4cmM/9zXG54bprcvQxx2XkRhtXXDkAAAAAAAAAAAAAAAC9rZgnzSTJ3vvul7333a/HshNPOqX7+5EbbZyTv3hqP1cFAAAAAAAAAAAAAABAfyvmSTMAAAAAAAAAAAAAAADwKk0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFKfSppnly5bl/PPOyc9uvaXKMgAAAAAAAAAAAAAAAChMpU0zTQMGZNbMu/Pwrx6qsgwAAAAAAAAAAAAAAAAKU2nTTJK8/e3vyKyZ92Te739fdSkAAAAAAAAAAAAAAAAUornqAv7fHT/Piy++mM98+ui0tLSsYI1avnvJ9H6vCwAAAAAAAAAAAAAAgMZVedPM/PnzkiTLli3LsmXLKq4GAAAAAAAAAAAAAACAElTeNDP9quurLgEAAAAAAAAAAAAAAIDCNFVdwKueevLJ/OKO2/OfP/ph97IF8+dXWBEAAAAAAAAAAAAAAACNqvInzSxtb8+3zz07d935P93L3nPIYbn1lptzyfcvyqmn/2O23mbbCisEAAAAAAAAAAAAAACg0VT+pJnLLv1+7rrzlxn/tgnZbrvtu5ePn7BnmltaMuOKyyusDgAAAAAAAAAAAAAAgEZUedPMXXf+Mm8dPSYnnnRKtt9xx+7lI0aMyPi37ZFHHn6owuoAAAAAAAAAAAAAAABoRJU3zfxh6dJsPGqTFb7WvmRJOjuX9XNFAAAAAAAAAAAAAAAANLrKm2a23mbbzLrn7sx97tkey++5+87Mmnl3ttl224oqAwAAAAAAAAAAAAAAoFE1V13A1CM/lDO+elpO/NvjM3jwkCTJ5z776TzzzDNJkvcdPrXK8gAAAAAAAAAAAAAAAGhAlT9pZtyuu+WEz5+cDTccmcWLFyXpyjPPPJ3W1tYcc9xnsvv4CVWXCAAAAAAAAAAAAAAAQIOp/EkzSfK2CXvmbRP2zNznns2CBQsyZMiQbLnVW9LUVHlPDwAAAAAAAAAAAAAAAA2o8qaZf/zKqdl73z/LnhP3yiabbpZNNt2s6pIAAAAAAAAAAAAAAABocJU3zdx375zcd++9+e6F52fcrrtl0j77ZY8998qQIUOqLg0AAAAAAAAAAAAAAIAGVXnTzOdP/mJm3nN3Zs+8J7Ne+Wpubsluu4/P3vvulwl7TMygQYOqLhMAAAAAAAAAAAAAAIAGUnnTzO7jJ2T38ROSJE89+WRmzrwrs+55uXnmnrvvyqBBA/O9S6+suEoAAAAAAAAAAAAAAAAaSVPVBbzWgOYBaR7QnKamV8vqSmdnZ6U1AQAAAAAAAAAAAAAA0Hgqf9LMgw/cn5n33JVZ99ydZ599NknS1FTLmLHjMmmf/TJxr0kVVwgAAAAAAAAAAAAAAECjqbxp5iun/X2SpFar5a2jx2TvfffLxL32Tmtra8WVAQAAAAAAAAAAAAAA0Kgqb5rZcaeds8++f5a9Ju2TERtsUHU5AAAAAAAAAAAAAAAAFKDyppnTvnJG9/dL2tqydOnStLa2prmlpcKqAAAAAAAAAAAAAAAAaGSVN80kyayZd2f6ZZfk6aeeSpLUaskubx2dD//Vx7L9Djuu8nZ+ccftuXL6pWlrW5w9Jk7KJz45LS2va7753988kSsuvzSPPPxQ/u7vT8sOO+7Uq3MBAAAAAAAAAAAAAACgek1VF3DfvXPyz2d+LU8/9WSGDRuWzTffPEOGDM1DDz6Q0750Sh579JFV2s6iRQtz/nlnZ8rUI3Pq6WdkzuyZufWWm3us87u5c3Pal07JiA02yOn/eFa22277vpgSAAAAAAAAAAAAAAAAFav8STPXXn1lmpqaMu3Y47Pvfm/vXn7H7bfl/HPPztUzpufkL576J7fz+GOPpqsr2f+AA1Or1TJ+/IQ89OADOejdB3ev86P/uCGbbrppPjXt2DQ1Vd4vBAAAAAAAAAAAAAAAQB+pvGnmiV8/ngl7TOzRMJMk++739tz1P7/MvXNmr9J2Fi5cmEGDB6VWqyVJhg4blrlz5/ZY59GHf5VBgwfn5M//bf6wdGkOPvS9PZpqXqujoyMdHR3dP7e3L1mdaQF1SrahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2OqvGlm4KBB6eh4aYWvdXS8lFd6YNbI63+3ra0tLQMH5lPTjs1jjz6S73/3wowZOy5bbLnVG373huuvybVXz1jzwYG6JNtQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oTFV3jQzevTY3HXn/+QH11+Td77zXVlv+PAsaWvLf91yc2bPmpnxb9tjlbYzfPjwLG1vz/Lly9PU1JT2JUvSuv6IHuu0rr9+9thzr2y/w47ZfocdM/2yi/Pkk79dYdPM5ClTc/Chk7t/bm9fkk9PO2rtJgtUTrahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2OqvGnmQ3/50Txw/3256orpueqK6WluHpDOzmVJkiFDBudDf/mRVdrO9jvslKamptxy840ZPXZcZs+emSlHHJnly5alacCAJMn4CXvk57f9d/acOCmPPvpwOjs785a3bLPC7bW0tKSlpaVX5gjUD9mGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDY2p8qaZTTfbLGf+8zdy9VVX5r45s/Piiy+mtbU1Y3fdLVPf/8Fsutlmq7Sd1tbWTDvm+Fxx+SWZccVl2XPipOx/wIH5+llnZPSYsTnksMk59LD3Zd4LL+SLJ38uQ4YMzaemHZvNt9iij2cIAAAAAAAAAAAAAABAf6u8aSZJRm60caYdc9xKX5/73LOZN29e3jp6zJtuZ+9998ve++7XY9mJJ53S/f3AgQNz1NHH5Kijj1m7ggEAAAAAAAAAAAAAAKhrTVUXsCp+/KMf5vRT/77qMgAAAAAAAAAAAAAAAGgQDdE0AwAAAAAAAAAAAAAAAKtD0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABSnMZpmuqouAAAAAAAAAAAAAAAAgEbSXOXgy5cvz+0//1k23XSz7LTzLitd7z2HHJa99t6nHysDAAAAAAAAAAAAAACgkVX6pJmmpqZ879++k5//7L/fdL1NN9sso8eM7Z+iAAAAAAAAAAAAAAAAaHiVNs0kydhxu+bhhx/K8uXLqy4FAAAAAAAAAAAAAACAQjRXXUBnZ2eeevLJnHTi32TjjUe94fVarZbPfeHvKqgMAAAAAAAAAAAAAACARlV508yc2bOSJE89+ds89eRvV7BGrX8LAgAAAAAAAAAAAAAAoOFV3jTzxS+fXnUJAAAAAAAAAAAAAAAAFKbyppnRY8Z2f79gwYIsmD8v22y7XYUVAQAAAAAAAAAAAAAA0Ogqb5pJkhv/80e57tqrsmjhotRqyeUzrsuDD9yXyy7+Xk465ctpXX/9qksEAAAAAAAAAAAAAACggTRVXcB//PAHufh7/5ZaahkxYkS6ul5e3tzckief/G1mXHFZtQUCAAAAAAAAAAAAAADQcCpvmvnpjT/J5ptvkbPP/U72nDipe/lOO++S3cdPyMyZd1dYHQAAAAAAAAAAAAAAAI2o8qaZefPmZaedd8nAQYOSWs/XhgwZkrbFi6spDAAAAAAAAAAAAAAAgIZVedPMqFGj8sgjD6ezs7PH8t+/8Hxmzbwno0ZtUlFlAAAAAAAAAAAAAAAANKrmqgs46N0H5/vfvTAnfe6zaW5+uZyLLjw/v/x/d6StrS1Tpr6/4goBAAAAAAAAAAAAAABoNHXRNPPCC8/nRz+8IV1dXUmSW26+MUnyjgPfmYMPeW+V5QEAAAAAAAAAAAAAANCAKm+aSZIP/9XHctC7Ds59987OggULMmTokIwZMy5bvWXrqksDAAAAAAAAAAAAAACgAdVF00ySbLTxxtlj4l5ZsGBBRowYkeHDW6suCQAAAAAAAAAAAAAAgAZVF00zN/3kx/n3H1yXefPmdS8budHIvG/K1Pz5X7yrwsoAAAAAAAAAAAAAAABoRJU3zVx37VW5ZsaVSbqyySabZnhraxbMn58XXng+F134nSxevDiTpxxRdZkAAAAAAAAAAAAAAAA0kMqbZm656cYMHjw4n/vC32X0mLHdy++/7958/Z++lpt+8mNNMwAAAAAAAAAAAAAAAKyWpqoLaG9vz9sm7NGjYSZJxo7bNRP23CtLl7ZXVBkAAAAAAAAAAAAAAACNqvKmmR132inz589b4WuLFy3KzruM7ueKAAAAAAAAAAAAAAAAaHTNVRew81tH55oZV+aiC87Ppptt1r38+ed/l3vnzM5fvOvd+dEPb+hefshhk6soEwAAAAAAAAAAAAAAgAZSedPMNTOuSJLc8tMbk9Re80pXkuTmG3/ymp9rmmYAAAAAAAAAAAAAAAD4kypvmjl86pFJrfanVwQAAAAAAAAAAAAAAIBVVHnTzNQjP1R1CQAAAAAAAAAAAAAAABSm8qaZa6+e8aav12rJ4VM/0E/VAAAAAAAAAAAAAAAAUII6aJq5MkktSdfrXql1f7eqTTO/uOP2XDn90rS1Lc4eEyflE5+clpaWljes97+/eSKnnPS5vO/wqZ50AwAAAAAAAAAAAAAAUKDKm2Y+8rGPr3D5M08/nVt+emMOn3rkKm1n0aKFOf+8s/PXRx2dHXbYKV89/Uu59Zabc9C7D+6x3vJly3LB+eemq+v1TToAAAAAAAAAAAAAAACUovKmmfcccthKX3vyt/+bh3/1q1XazuOPPZqurmT/Aw5MrVbL+PET8tCDD7yhaebHP/5hBg4clK233mZtygYAAAAAAAAAAAAAAKCOVd4082Y2GjUqd9/5y1Vad+HChRk0eFBqtVqSZOiwYZk7d26PdX43d25uuO7a/MNXz8g53/yXN91eR0dHOjo6un9ub1+ymtUD9Ui2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbENjqrxp5olfP/6GZV1dXXnuuWcze+bdGbbeemu87Vf6Z7r92wXn5eBD35vNNt/iT/7uDddfk2uvnrHGYwP1SbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2OqvGnmlJM+l6S2kle7ctC7D16l7QwfPjxL29uzfPnyNDU1pX3JkrSuP6L79UcfeTj33Tsnjz36SP7j36/P0qVL89STv01Ly8BMnnLEG7Y3ecrUHHzo5O6f29uX5NPTjlqdqQF1SLahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2OqvGlml7eOTu11j4Sp1WpZf/0R2W382/Jnbz9glbaz/Q47pampKbfcfGNGjx2X2bNnZsoRR2b5smVpGjAgW2+zbc4+9zvd65915tcyZuy4vPOgd61wey0tLWlpaVnjeQH1SbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2OqvGnmS6d9tVe209rammnHHJ8rLr8kM664LHtOnJT9DzgwXz/rjIweMzaHHDY5G4/apHv95ubmDBs2LMOGrdcr4wMAAAAAAAAAAAAAAFA/Km+aSZKFL76YJ574dXbbfXyS5P777s1vf/ubjBu3W7Z6y9arvJ29990ve++7X49lJ550ygrX/dqZX1/zggEAAAAAAAAAAAAAAKhrlTfNPPXkk/nKaX+f1vXXz267j88dP/9Zzj3nm0mSlpbmnHTKl/PW0WMqrhIAAAAAAAAAAAAAAIBG0lR1AVdOvzQLF76YcbvuliS5/rprMmrUqEw5fGpqtVquu2ZGxRUCAAAAAAAAAAAAAADQaCp/0syjjzyct03YMx/52Mfz/PO/yzNPP5XjPnNC9t53v8yd+1zmzJ5VdYkAAAAAAAAAAAAAAAA0mMqfNLNsWWcGDRqUJJkze1ZqtVrG7rrryy/Wauns7KiwOgAAAAAAAAAAAAAAABpR5U+a2W77HXLXnb/M9797Ye6685fZfvsdM3x4a+78n19k5t13Ztvttq+6RAAAAAAAAAAAAAAAABpM5U+a+cCH/irNzS256Sc/zosLFuT9H/xwkuTb3/pmXnqpI1OOOLLiCgEAAAAAAAAAAAAAAGg0lT9pZvsddsyZ//yNPPDA/dlmm22zzbbbJUn2nDgpbz/gwIwdt2vFFQIAAAAAAAAAAAAAANBoKm+aSZKNR22SA0Zt0mPZMcd9tvv72bPuyeOPPZYj3v+B/i4NAAAAAAAAAAAAAACABtRUdQGrYtbMe3Lt1TOqLgMAAAAAAAAAAAAAAIAG0RBNMwAAAAAAAAAAAAAAALA6NM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxGqNppqv7HwAAAAAAAAAAAAAAAPAn1WXTTFdXVxYvXtz9818f9alMv+r6CisCAAAAAAAAAAAAAACgkTRXXcBLL72Uiy74dp555umc/rV/ynPPPpt//Mqpef7557PTTjvnhC+cnOHDW6suEwAAAAAAAAAAAAAAgAZS+ZNmfnDd1fn5bf+dzs7OJMnll34vL7zwfEaOHJlHHvlVrp5xRcUVAgAAAAAAAAAAAAAA0Ggqb5q54/bbsvkWW+bLp301S5cuzexZMzNl6pE559sXZqedd8mse+6uukQAAAAAAAAAAAAAAAAaTOVNMy8uWJCddt4lg4cMya8eeiDLli3PpEn7Jkm22GLLvPjiixVXCAAAAAAAAAAAAAAAQKOpvGlmo402zv33zskTv348N/7njzJixIhsudVWeemll/Lww7/KyI02qrpEAAAAAAAAAAAAAAAAGkzlTTPvPOjdeeGF53PKSSdmzuzZOfAvDkqSnPCZY/PM00/nz/Y/oNoCAQAAAAAAAAAAAAAAaDjNVRfw7oMPTWdnZ+65+65sv8MOOfzw9ydJ2toW5+0HvCOT33dExRUCAAAAAAAAAAAAAADQaCpvmkmSQ9/7vhz63vf1WHbeBd/L4MGDK6oIAAAAAAAAAAAAAACARlZJ08zzz/8uI0ZskJaWlixetGil6y3u6EiSrDd8eH+VBgAAAAAAAAAAAAAAQAEqaZr5hy+dkr9493vy3smH51Of+GiS2krXrdWSy2dc13/FAQAAAAAAAAAAAAAA0PAqaZrZYMMNM2LEBkmSkRttlNqbNM0AAAAAAAAAAAAAAADA6qrmSTNfPbP7+3POu7CKEgAAAAAAAAAAAAAAAChYU9UFAAAAAAAAAAAAAAAAQG/TNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxmqsuoDf94o7bc+X0S9PWtjh7TJyUT3xyWlpaWrpfn/vcs7ng/HPz6COPZOTIkfnwRz6WPSdOqrBiAAAAAAAAAAAAAAAA+kIxT5pZtGhhzj/v7EyZemROPf2MzJk9M7fecnOPdc771jez3nrr5RvnnJeJk/bJuWd/I52dnRVVDAAAAAAAAAAAAAAAQF8ppmnm8cceTVdXsv8BB2bLrbbK+PET8tCDD/RYZ8zYcTni/R/MyJEbZbfdd88f/vCHLG1vr6hiAAAAAAAAAAAAAMXc7HgAACAASURBVAAA+kpz1QX0loULF2bQ4EGp1WpJkqHDhmXu3Lk91jnyg3/Z/f1Pb7oxu4+fkPWGD1/h9jo6OtLR0dH9c3v7kj6oGuhvsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbGlMxTTMr8kr/zBtcc/WVefCB+/PVM85a6e/ecP01ufbqGX1UGVAV2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINjanW1dXVVXURvWHWzLvzL2edkYsvvypNTU258Pxz0750aY7/7Ak91rvpJz/OjCsuyxdP/Uq23Xa7lW5vRZ2An552VC66eHqGDh3aZ/MA+pZsQ7nkG8ok21Cm3sh27cx5vVpT1xc27NXtwbrKtRvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbENjKuZJM9vvsFOamppyy803ZvTYcZk9e2amHHFkli9blqYBA5Ikd9x+Wy675Pv57Amfz6hRo9LWtjiDBg5Kc0vLG7bX0tKSlhUsBxqbbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGxlRM00xra2umHXN8rrj8ksy44rLsOXFS9j/gwHz9rDMyeszYHHLY5MyYflk6Ol7KWWd8pfv3ph1zXPZ/x59XWDkAAAAAAAAAAAAAAAC9rZimmSTZe9/9sve++/VYduJJp3R/f/Z5F/R3SQAAAAAAAAAAAAAAAFSgqeoCAAAAAAAAAAAAAAAAoLdpmgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA4zVUX0Jt+ccftuXL6pWlrW5w9Jk7KJz45LS0tLd2vL168ON8575w8cP+92WDDkfn4UUdnzNhxFVYMAAAAAAAAAAAAAABAXyjmSTOLFi3M+eednSlTj8ypp5+RObNn5tZbbu6xzg3XX5Pf//6FnHHWv2biXnvn3LO/kc6OjooqBgAAAAAAAAAAAAAAoK8U0zTz+GOPpqsr2f+AA7PlVltl/PgJeejBB3qs89CDD2SvSftk1Cab5J0HvSvz58/L3LnP9Uk9nZ1dK/1q7+hK5/Ke/2bd9vrjYl3QqPNt1Lr7gn1BSVbneO6LdUsc35waY/y+mlM96Y+6jVF/4xijvsYAWFWNcE6q9xrrvr7OV+rrrM/6GkG9/41XplHrfr1S5kH9adRjq1Hrfr1S5pGUNZdSNOLfpBFqboQaKZv/tqE3OJfRW+rlWKqHOuqhhnqpox5qqCeNuD8aoeZ6r7Hu62uAe8q634d1Xt/KlPI5AGPU3zj9MkY/nLv6ah7FNM0sXLgwgwYPSq1WS5IMHTYsCxcu7LHOooULM3jw4CTJsKHDun9vRTo6OrJkyZLur/b2JatcS3tHVzq6stKvf7qpLR3Lkmk3taXlrPmZdlNbw5206T2vHi/r0vHQ3tFV2XzXNtvr0t/pzdgX1KM1zffqHM99sW6J45tTY4zfV3PqbfV+7TZG/Y1jjPoaY2XWJttAfeuP+/Kq1HuNdV9fZ1em3fhKfTe21fX/CKxXjXrtrvdjc1WVMg/qj2xXq5R5JGXNpRSNmO9GOI4aoUbKVuV/23hPrRzOZbxWCffl9VBHPdRQL3XUQw31xH1536j3Guu+vgZ4v7zu92EDZjsp53MAxqi/cfpljH44d/XlPGpdXV31dSZbQ7f97NZcevF3c+F3L02SXHbJ9/KbJ36dL3759O51PnPs0Tn40PfmXe85JEvb2/PXH/1QvvwPX80ubx3zhu1dc9UVufbqGW9YftHF0zN06NA3raVz+Zvv0va2xRkybL20nDW/e1nHiRukuan2pr9HmV49Xtal46FzeVdl813bbK9Lf6c3Y19Qj9Y036tzPPfFuiWOb06NMX5fzam31fu12xj1N44x6muMlVmbbL+qdua8Xq2p6wsb9ur2YF3VH/flVan3GtVXvka9dpfyty9lHtQf2a5WKfNIyppLKRox341wHDVCjZStEbNN/XEu47VKuC+vhzrqoYZ6qaMeaqgnjXjtboS/Yb3XqL61V+81NmK2k3I+B2CM+hvHGH9aMU+aGT58eJa2t2f58uVJkvYlS9K6/ojXrdOatiVtSZIl7e1JktbWnuu8avKUqbno4undX986/99WuZaOZW/+9S+31dKxLPno2IFJXv53x7LVnjKFePW4WJeOhyrnu7bZXpf+Tm/GvqAerWm+V+d47ot1SxzfnBpj/L6aU2+r92u3MepvHGPU1xgrszbZBupbf9yXV6Xea6z7+pa/rr7lFRfUgBr12l3vx+aqKmUe1B/ZrlYp80jKmkspGjHfjXAcNUKN/H/27j1OzrK8G/g1uzsJmxCOhUBQDlYFySZBAQ8FJUUr56ZWlEL5hAIRooKHUkR99RXqiYOnVlutLa2mQkSwliJo0KIIVgGVUyQQQATLoW+VQCA7IbO78/4RkmzOe5iZ537u+X4/n5hk9nHu677n+T33M5u9mLwV+d7G99Ty4VrGcDncl6dQRwo1pFJHCjWkxH15a6ReY/L1leD75cmvYQmzHZHPzwEYI71x2jJGG65drZxHNp80s3z58jj77fPi5Lmnxv59M+ITH/1IvOnNb43DD39DdHV3R0TEZQu+EnfeeXuc874PxE03/iB+cMP342//7svR/fzXt6S/vz9OP+WkEf8XOga28JFD9UZEtXv1C7vm995qOh2YtN/AQGO986ITzodavZHEfEeb7VTqToG1IHWjyfdozudWHJvj+OZUjvFbNadWSnHvNkZ64xgjrTFGYrTZjvBJM1AWrbovL0rqNSZf30Ajql2rv6ne25NefWWQymuc4n15O+QyD9KTyrkl2+WeR0Rec8lFKq+J+3JorlTe24zle2qkw7WMzSnrfXkKdaRQQyp1pFBDSlJZD/fl7ZV8fYncU25J8muYSH0p7t3GSGuMdo3TljHacO1q1Tx6mvZMBdtuu+1i/jveFQsvWxBXLPxaHPzKV8dhsw+PT19yYew/vS+OOW5OzPnT4+OJJx6PD5z73thpp53jnWe/d0QNM2PRs4UTYc2i93St/zudq6enstF5kbs1F7KyzbesdbeCtSAnozmfW3FsjuObUznGb9WcUtKOuo2R3jjGSGsMgJEqwzUp9RqTr68n7frKIPXXeHPKWveGcpkH6SnruVXWujeUyzwi8ppLLsr4mpSh5jLUSN68t6EZXMtollTOpRTqSKGGVOpIoYaUlHE9ylBz6jUmX18J7imTX8PE69ucXH4OwBjpjdOWMdpw7WrVPLJpmomIeM0hh8ZrDjl0vcfOff//WfvnbbfdNs553wfaXRYAAAAAAAAAAAAAAABtVrL+OgAAAAAAAAAAAAAAANg6TTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkp6foAsqi0WhERESt1l9wJdDZent7o1KpNO35ZBvSId+QJ9mGPOWY7f5+1xWKN/nzK5v6fCvO3mZUxzc72xFp5BvIc+8GZBty5b4c8mXvhjzJNuTJfTnky94NedpatiuNNWlli373u9/GWfPnFV0GdLxLv3p5TJo0qWnPJ9uQDvmGPMk25Em2IU/NznaEfEMq7N2QJ9mGPLkvh3zZuyFPsg15cl8O+bJ3Q562lm1NMyM0NDQUy5Y9Gdtss+UupFqtP86aPy++8KV/it7e5t40tYqa20PNzdHsLt+RZnuNFNekKNZifdZjnbGuRSvy/dhjj8a57z07q9clx3PNnMohpWyPZu9uh1xe71zmEZHPXFKeR9HZTnltItKvLyL9GtU3fmOpsRX/5bzR5DvXdW0n9Y1f6jWW9b489XUdqVzmEZHPXDp9HrLdHLnMIyKfueQyjwj35a2Seo3qG7/Uayzr3t1Mqb9GEenXmHp9EWocqaKzncIapMJarGMt1ue+vDVSr1F945d6jWW9L099XUfKPNKTy1xale2eZhTXCbq6umLnnX9vxMf39k5qeqdxq6m5PdScltFme42c12S0rMX6rMc6Ra9FV1dX7LTTzknU0grmVA7m1Hxj3bvboei1aZZc5hGRz1xymceW5Hpfnnp9EenXqL7xK7rGseS76JpHIvUa1Td+qddYdH257t0jlcs8IvKZi3k0h2znMY+IfOaSyzwiip+L+/JiqG/8Uq+x6PpS+H550WswEqnXmHp9EWpst06/L28Ga7GOtVhf0evhvrwY6hu/1Gssur5O37vNIz25zKXZ8+hq2jMBAAAAAAAAAAAAAABAIjTNAAAAAAAAAAAAAAAAkJ3u888///yii8hNV1dX7D99RnR3dxddyoipuT3UnAdrso61WJ/1WCeltUiplmYxp3Iwp86Sy9rkMo+IfOaSyzxaIfW1Sb2+iPRrVN/4laHGDZWh5tRrVN/4pV5j6vVtTlnr3lAu84jIZy7mUayy1r2hXOYRkc9ccplHRDnnUoaaU69RfeOXeo2p19cOZViD1GtMvb4INZaFNVjHWqxjLdZXxvUoQ82p16i+8Uu9xtTr25yy1r0h80hPLnNpxTwqjUaj0bRnAwAAAAAAAAAAAAAAgAR0FV0AAAAAAAAAAAAAAAAANJumGQAAAAAAAAAAAAAAALKjaWaEGo1G9Pf3R6PRKLoUoIlkG/Il35An2YY8yTbkS74hT7INeZJtyJd8Q55kG/Ik25Av+YY8yTaUg6aZEarVanH6KSdFrVYruhSgiWQb8iXfkCfZhjzJNuRLviFPsg15km3Il3xDnmQb8iTbkC/5hjzJNpSDphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDs9RRdQhE9+7Py468471v79mOPmxMlzTy2uIAAAAAAAAAAAAAAAAJqqI5tmli1bFqfOOyMOOfR1ERFRrU4ouCIAAAAAAAAAAAAAAACaqavoAorw1LJlseuuU2Py5G1j8uRtY8KE1jbNrBxoRG2gEQNDq39fY1OPAWmr1Z/PbV1uXcMgfWXJaVnq7HRep2K5B4H1yQQAlIu9G/Ik25An2QZoD9dbmsW5BECnsgdSJs7X4nXcJ80M1Ovx7LPPxGX/+tX48hf/Ll7y0n1j3plvjylTtlvvuHq9HvV6fe3fa7X+MY1XG2jEilWNOOcH/bFg8aqY2zchvnTE5IiImL9oxXqP9fZUxj4xYETGk+1avRHzrx+W2zdOjt5qZ+a2NtBwDSM5zdq7c1GWnJalzk5X5Osk2+5ByJP7csjXePfuykVPNrukaJy3U9OfEzqNvRvyJNuQr7HmW7Yhbb5fng/XW4ZzXw75sndD6xS5B8o2o+WeLQ0d1zQTlUr8xelvi912mxaTJk2Kv/v85+Kqb3w9Tj39jPUOu/pbV8U3r7xi3MNVuyK2m1iJBYtXRUTEgsWr4tKjJq/984aPAa01nmxXu+V2jWqXtSA9zdq7c1GWnJalzk5X5Osk2+5ByJP7csiXvRvyZO+GPMk25Gus+ZZtSJv33PlwvWU49+WQL3s3tE6Re6BsM1ru2dLQcU0zQ0NDcdBBr4qddt45IiIOOviV8cD992903Jw3HR9HHztn7d9rtf44a/68UY9XH4pYsaoRc/smrO0Qqw+t/tqGj/V0jW1OwMiNJ9v1wQ1yO9i5ua0PuYaRnmbt3bkoS07LUmenK/J1km33IOTJfTnky94NebJ3Q55kG/I11nzLNqTNe+58uN4ynPtyyJe9G1qnyD1Qthkt92xp6Limmf954vF43znvjne956/iRS9+cdx15x2x38v23+i4arUa1Wp13OP19lSiEhFfOmJyXHrU5KgPrX4sNvMY0FrjyXZvtRJfeuPzuR2Mjv54tN6eimsYyWnW3p2LsuS0LHV2uiJfJ9l2D0Ke3JdDvuzdkCd7N+RJtiFfY823bEPavOfOh+stw7kvh3zZu6F1itwDZZvRcs+Who5rmnnhnnvFKafNi68t+Jeo1fpjxsxZ8ZYTTmrpmNsM+6HC4Z1ha37YULcYlMeazUpuXcOgDMqS07LU2em8TsVyDwLrkwkAKBd7N+RJtiFPsg3QHq63NItzCYBOZQ+kTJyvxeu4ppmIiCOPOjaOPOrYossAAAAAAAAAAAAAAACgRfQrAQAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGSnY5tmPvfpi+PEt/xJ0WUAAAAAAAAAAAAAAADQAh3ZNPOz226N2279adFlAAAAAAAAAAAAAAAA0CId1zTT398f/3LpP8QRRx1TdCkAAAAAAAAAAAAAAAC0SE/RBbTbwq8tiBkzD4i+GTPjO9des9nj6vV61Ov1tX+v1frbUR7QYrIN+ZJvyJNsQ55kG/Il35An2YY8yTbkS74hT7INeZJtyJd8Q55kG8qpo5pm7l1yT/zstlviks/8bSxdeu8Wj736W1fFN6+8ok2VAe0i25Av+YY8yTbkSbYhX/INeZJtyJNsQ77kG/Ik25An2YZ8yTfkSbahnCqNRqNRdBHt8smPXRC/XHx3TJw4IQYHh+K551bGpEmT4tKvXr7RsZvqBDxr/ry49KuXx6RJk9pZNtBEsg35km/Ik2xDnmQb8jXefFcuerLpNTXO26npzwmdxt4NeZJtyJd8Q55kG/Ik25Av+YY8yTaUU0d90szb3/muqNdXRUTE4sV3x5e/+IW48JLPbvLYarUa1Wq1neUBbSDbkC/5hjzJNuRJtiFf8g15km3Ik2xDvuQb8iTbkCfZhnzJN+RJtqGcOqppZocdd1z75+23fyQiInbZdWpR5QAAAAAAAAAAAAAAANAiXUUXUJRXHHhwLLzy34suAwAAAAAAAAAAAAAAgBbo2KYZAAAAAAAAAAAAAAAA8qVpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7PUUXMFI3/uA/44H774/Tz5gfK2u1+OdLvxyP/PqhePmBB8db/+ykqFQqRZcIAAAAAAAAAAAAAABAIkrRNHPjD2+If/jiF+IFL9wzIiKuWHhZ3PyjH0ZExCOPPBK9k3rjj+f8aYEVAgAAAAAAAAAAAAAAkJKuogsYie98+z9i222nxGnzzoihwcG46Uc/iFf/waHx2c9/Mabutlv86Ac3FF0iAAAAAAAAAAAAAAAACSlF08wTTzwRBx38ytjvZdPjwQcfiP7+/jjyqGNi6tTdYv/9p8dvf/u/RZcIAAAAAAAAAAAAAABAQkrRNDNlypT479/8JoaGhuK/fnxTTJw4MX7/xS+JiNUNNb29kwquEAAAAAAAAAAAAAAAgJT0FF3ASBz0ylfFou9cG2ecNjf6+/vjdYfNju7u7vjMJRfGknt+GbMPf33RJQIAAAAAAAAAAAAAAJCQUnzSzFv/7M/jgJcfGCtX1mLvffaJE0+eGxERd991R0ybNi1OOPHPC64QAAAAAAAAAAAAAACAlJTik2Z6e3vjfR/4UAwODkZ3d/fax99zznkxfXpf9FSrBVYHAAAAAAAAAAAAAABAakrRNLPG8IaZiIhZB7y8oEoAAAAAAAAAAAAAAABIWSmaZpYtezIW/MulseSeX8by5cs3+nqlEnHZFf9WQGUAAAAAAAAAAAAAAACkqBRNM//4pb+PO27/eURE9Pb2RqVSGdPzDA4OxhULvxY3fP/6iIh4+SsOirfNf2dMmDChabUCAAAAAAAAAAAAAABQvFI0zdy75J7Yfdoe8ZELPh7bbb/9mJ/n5ptujBu+/7049/0fim2nTImPnf/huOH718eRRx/bxGoBAAAAAAAAAAAAAAAoWimaZiZuMzH22+9l42qYiYg4bPbhcdjswyMiYsWKZ6Onpye6u7ubUeKo1AYaUe2KqA9F9PaM7VNzgGLIL1AmrlmQD3mG9ckEAJSLvRvyJNuQJ9kGaA/XW5rFuQRQTq7fsHVyQjN1FV3ASBw2+/BYuvS+GBwcbMrzve+cd8cZp82N3XbfPd7wxiM3eUy9Xo/+/v61v2q1/qaMXRtoxPxFK6J6ybKYv2hF1AYaTXleYGTGk235hbS1au8uK9csciHb8kye3JdDvuzdkCd7N+RJtiFfY823bEPavOfOh+stw7kvh3zZu9kc1+9yk+32kBOarRSfNPP0U0/F4489Fu8/972x665TN/p6pVKJvzrvgyN+vvM++OF4/LFH428/++m48Yc3xOw/fP1Gx1z9ravim1deMa66N6XaFbFg8aqIWP37pUdNbvoYwOaNJ9vyC2lr1d5dVq5Z5EK25Zk8uS+HfNm7IU/2bsiTbEO+xppv2Ya0ec+dD9dbhnNfDvmyd7M5rt/lJtvtISc0WymaZm784Q0REfHof/8mHv3v32ziiJF95NLDD/86nn7qqZg564DYeeffi/37+uKeXy7eZNPMnDcdH0cfO2ft32u1/jhr/rwx1T9cfShibt+EWLB4VcztmxD1oYieUnzeD+RhPNmWX0hbq/busnLNIheyLc/kyX055MveDXmyd0OeZBvyNdZ8yzakzXvufLjeMpz7csiXvZvNcf0uN9luDzmh2UrRNPOhj3y0Kc/zyK8fin/+p3+ID374gtimtzfuX3pfHHPcnE0eW61Wo1qtNmXc4Xp7KvGlIybHpUdNjvrQ6r8D7TOebMsvpK1Ve3dZuWaRC9mWZ/LkvhzyZe+GPNm7IU+yDfkaa75lG9LmPXc+XG8Zzn055Mvezea4fpebbLeHnNBspWia2X96X1Oe59DXzY6HH/51XHLhx6PRaMSr/+CQeOMRRzfluUdjTXB1vEH5yC9QJq5ZkA95hvXJBACUi70b8iTbkCfZBmgP11uaxbkEUE6u37B1ckIzlaJp5qPnf3iLX69URvZpNJVKJU6ee2qcPPfUZpUGAAAAAAAAAAAAAABAgkrRNLPknsVbOcJHLgEAAAAAAAAAAAAAALBOKZpmPnnxZzb5+GOPPhp//4XPxV+cfkabKwIAAAAAAAAAAAAAACBlpWia2WvvfTb7+M033RjXf/e6eP0b3tjmqgAAAAAAAAAAAAAAAEhVV9EFjFd3d3c88fhjRZcBAAAAAAAAAAAAAABAQkrxSTPXXnP1Ro81ohFPPP54/OLnt8Vuu+1eQFUAY4/BWQAAIABJREFUAAAAAAAAAAAAAACkqhRNM5f961ciorKJrzSiq6s7jj/hxDZXBAAAAAAAAAAAAAAAQMpK0TTzp285YaPHuiqV2H77HaJv5qyYOnW3AqoCAAAAAAAAAAAAAAAgVck3zTQajZg+fUbssMMOsfu0PYouBwAAAAAAAAAAAAAAgBLoKrqAralUKvHJj10Q1337mqJLAQAAAAAAAAAAAAAAoCSSb5qJiHjJS/eNhx9+qOgyAAAAAAAAAAAAAAAAKImeogsYiZ122il+fPNNcdEnPhpTd9tto69XohKnnDavgMoAAAAAAAAAAAAAAABIUSmaZn58848iIuLOO36xmSM0zQAAAAAAAAAAAAAAALBOKZpmznzH2UWXAAAAAAAAAAAAAAAAQImUomnmsNmHb/WYO27/eTz4wAPx5rec0IaKAAAAAAAAAAAAAAAASFlX0QU0y+2/+Hl888orii4DAAAAAAAAAAAAAACABGTTNAMAAAAAAAAAAAAAAABraJoBAAAAAAAAAAAAAAAgO5pmAAAAAAAAAAAAAAAAyI6mGQAAAAAAAAAAAAAAALKjaQYAAAAAAAAAAAAAAIDs5NM001j7PwAAAAAAAAAAAAAAAHS4UjXNrFjxbDxw/9K47dafrn2sXq9HRMSp886Iy7/xraJKAwAAAAAAAAAAAAAAICE9RRcwEoODg7HwsgVx/Xe/EwMDA1GpRFx2xb/FLT/5r1jwlUvjI3/9idh16tSiywQAAAAAAAAAAAAAACARpfikmW98/fK47tv/EdOmTYvdd989Go3Vj79wz72iv39FfH3h14otEAAAAAAAAAAAAAAAgKSUomnm5pt+GC960Yvjk5d8NvpmzFr7+LQ99oiXH3hwLL7rzgKrAwAAAAAAAAAAAAAAIDWlaJp59plnYs+99opKpRJR2fjrK1eubH9RAAAAAAAAAAAAAAAAJKsUTTO7T9sj7r77znj22WfXe/yhXz0Yv/jZrfGCF7ygoMoAAAAAAAAAAAAAAABIUU/RBYzEnD95c3z+bz4d57z7nbFN7zYREXHRJ/46frn47hgYGIyjjjmu4AoBAAAAAAAAAAAAAABISSmaZl5zyKHx7LPPxMLLFsQzzyyPiIg777g9enqq8dY/OzFee9gfFlwhAAAAAAAAAAAAAAAAKSlF00xExB8dcVQcNvvwWLr03njqqaeit7c3XrrvfjFlynZFlwYAAAAAAAAAAAAAAEBiStM0ExExYeLE6Jsxq+gyAAAAAAAAAAAAAAAASFyyTTOnzT1xFEdX4p8XXN6yWgAAAAAAAAAAAAAAACiXZJtmVq5cOeJjp07drYWVAAAAAAAAAAAAAAAAUDbJNs1c/o1vrf3zf35vUVyx8LL46CcvXq9B5rFHH42PfOj9cdQxxxVRIgAAAAAAAAAAAAAAAInqKrqAkbj2mqvjFQcdvNEnykzbY4848KCD47vXfXtUz3fN1d+Kt7/t1Dht7onxuc9cPKpPtQEAAAAAAAAAAAAAACB9pWiaefLJ38UTjz22ya898cTj8eSTvxvxc9115x1xxcLL4h1nvycu+PhFcd+SJfHd665pVqkAAAAAAAAAAAAAAAAkoKfoAkbi91/8klhyzz3x2U9dGIe+bnbssMOOsXz503HTjT+MpffdF9P7+kb8XD09PXHSyXNjxsxZERGx+7Rp8fRTTze95pUDjWhERLUroj4YUe2OqA+t7lIa2vDxwYjeaqXpNUDKagON1TkYiujtKc/5X6s35PZ51oKctOp8Lsu1rug8l2WdclT0a98O7ZhjNmO0KYs5zYXidML1CwByYu+GPMk25Em2AdrD9ZZmcS6t49+HgOFcH6G1csmY+4fOUYpPmjn19DNj+x22j9tuvSU++6mL4yMf+kB8+uIL49ZbfhI77rRjnHr6mSN+rv2n98XRx/5xREQ8/OuH4oH7l8Yhr33dRsfV6/Xo7+9f+6tW6x/xGLWBRjy7qhFvX7Qi/mdFI+ZfvyKqlyyL+YtWxGAj4p2LVsRzg7Hu8etXRK3eGPHzQ9nVBhoxf9G6XNQG2nf+jyvb9YbcPs9akKKx5rtV53OR17rRKDrPZVmnHBX92o9U6nt3NmO0KYs5zYXxST3bwNiNJ99AuuzdkCfZhnyl9v1yoDm8586H6y3DuS9vDv8+RIrs3cVxfaSVZDufjLl/6CyVRqNRild41XPPxQ03fC8eevDBWL786dhhhx3j91/y0jhs9uFRrVZH/Xy/+91v4yP/5/3x2tfNjhNOOnmjr1/1jYXxzSuv2OjxS796eUyaNGmLzz0w1IihRsTuX3gqHj9rh5j4qWVrv1Y/d8d49YLl8dO520X1kvUf7+nSoUZnGBhqFHb+jzfbcruatSBFY813q87nsuSk6DqLHr+TlWXtU9+7jZHeOGU5tztd6tkGxm48+Y6IqFz0ZNNrapy3U9OfEzqNvRvyJNuQr9S+Xw40x3jfc5MO11uGc1/eHNaCFNm7i+OaQCvJdj4Zy2UejExP0QWM1ISJE+PIo45tynMtX748PvHR82N634x464l/vslj5rzp+Dj62Dlr/16r9cdZ8+eN6PnrQxErVjXiuBdX43/7GzG3b0IsWLwq5vZNiFWDETN36Y7nBmO9x+uDET2l+NwfGL/60Abn/1D7zv9xZVtu17IWpGis+W7V+VzktW40is5zWdYpR0W/9iOV+t6dzRhtymJOc2F8Us82MHbjyTeQLns35Em2IV+pfb8caA7vufPhestw7subw78PkSJ7d3FcH2kl2c4nY+4fOkuynzSz6DvXxsv2nx577rV3XHvN1Vs9/pjj5mz1mIiIlbVafOyC/xuTt50cZ737L6Orqysqla6tdvf19/fH6aecNOJOwJUDjWhERLVr9cWh2r06XF0RMbTh44MRvVWdaXSW2kBjdQ6GInp7ijv/R5vtWr0ht8+zFqRuNPlu1fmcyrVua4rOc1nWKUdFv/ZjkeLenc0YbcpiTnOheVLMNtAco823T5qBcrB3Q55kG/KVwvfLgeYb7d5NWlxv2Rz35WPn34dInb27vVwfaZdOzXYuGXP/0DmS/aSZq678erzxiKNjz732jsv+9SsRUYmINf09w0/KRkRURtw0c8stP4kHH7w/IiLOOG1uRET83i67xOf//h+bVPlq2wwLzpqusw27zzb3OHSCNZtL2c7/NZt72epuBWtBTlp1PpflWld0nsuyTjkq+rVvh3bMMZsx2pTFnOZCcTrh+gUAObF3Q55kG/Ik2wDt4XpLsziX1vHvQ8Bwro/QWrlkzP1D50i2aebNx58QL5veFxERb3rzW6PS1ZzurcNmHx6HzT68Kc8FAAAAAAAAAAAAAABAmpJtmjny6GPX/vn6RdfF9OkzYsbMWdE3c1ZMnbpbgZUBAAAAAAAAAAAAAACQumSbZoYbHBiIW2/5Sdx6y08jImKXXXeJGTMPiBkzZsX0vhmx7ZQpBVcIAAAAAAAAAAAAAABASkrRNPNPX7ksfvPIw3HfvUvivnuXxNKl98YN378+bvj+96JSidh7nxfFxy/8VNFlAgAAAAAAAAAAAAAAkIhSNM10dXXFXnvvE3vtvU+84Y+OiHvuWRzfW/TduO3Wn0ajEfHQr35VdIkAAAAAAAAAAAAAAAAkpBRNM88+80zcfvvP4xc/uy3uvuuO6O+vRU9PT8yc9fI48KCD48CDXll0iQAAAAAAAAAAAAAAACSkFE0zZ847JRqNiK6uSrz6NYfEwa96dcw64BWxzTbbFF0aAAAAAAAAAAAAAAAACSpF08z0vhlx75IlMTBQjzvvuD0iIoYGh2LmAQfE5MnbFlwdAAAAAAAAAAAAAAAAqSlF08wHP3xBPPfcc3H3XXfE7T//Wdxxxy/iv358c3R1dcVL9903XnHQwXHscX9SdJkAAAAAAAAAAAAAAAAkohRNMxEREydOjIMOflXMOuAV8cD9S+P6714Xt/z0J3Hvknvi3iVLNM0AAAAAAAAAAAAAAACwVimaZn522y1x371LYul998ZDv/pVDAwMREQjqtVq7Lvfy2LGzAOKLhEAAAAAAAAAAAAAAICElKJp5jOXXBgREZVKJfbae5+YMfOAmDFzZuy73/5RrVYLrg4AAAAAAAAAAAAAAIDUlKJp5vDX/1H0zZwVfX0zY9spU4ouBwAAAAAAAAAAAAAAgMSVomlm3pnvKLoEAAAAAAAAAAAAAAAASqSr6AIAAAAAAAAAAAAAAACg2TTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ6cimmaeffiquu/Y/4v3nvjd+88jDRZcDAAAAAAAAAAAAAABAk/UUXUC71Wq1OPvtZ8Tee+8TD//6oaLLAQAAAAAAAAAAAAAAoAU67pNmJkyYEJ//4pfj7Pf8ZUvHGRhoxKqBRgw8/2vlsD/X6o0YGFr/dzrbcwOddz6Udb5lrbsVrMX6agPPr8eA9Sij0ZzPrTg2x/HNqRzjt2pOKWlH3cZIb5xcxmDzrD9snfcoQErs3ZAn2YY8yTZAe7je0iypnEup1AG0j9xDuvzMBGys45pmuru7Y/vtd9jqcfV6Pfr7+9f+qtX6RzxGrd6IeiNisBFRf/7Xs6saa//8jutXxHODEfOvXxHVS5bF/OtXuGh0sFq9EYONzjofavVGYfMdb7Y76XXaEmuxvtpAI+Yven49Fq3wQ2kFGWu+R3M+t+LYHMc3p3KM36o5NVvqe7cx0hsnlzFyl3q2oeyKfI8ynnwD6bJ3Q55kG/LVju+XA+3nPXc+XG8ZLof78lTqgNTkvHfLPZ0s9Wz7mQnYtEqj0ejIM/V//9//xLveeWZc/Om/iRfuuddGX7/qGwvjm1desdHjl3718pg0adIWn3tgaOMlHWpEdFVW/3m/f3w67n3b9lG9ZNnar9fP3TF61hxAR1lzvnTS+TAw1ChsvuPNdie9TltiLdZnPdIw1nyP5vVrxbE5jm9O5Ri/VXNqttT3bmOkN04uY+Qu9WxD2ZV1746IqFz0ZNNrapy3U9OfEzqNvRvyJNuQr3Z8vxxov/G+5yYdrrcMl8N9eSp1QGpy3rvlnk6Werb9zARsWk/RBaRqzpuOj6OPnbP277Vaf5w1f96I/r/1wY0fW1FvxOTq6gvCa1/QE88NRsztmxALFq+KuX0Toj4Y0dNxn/tDxOrzpRGddT7UCzz/x5vtTnqdtsRarK8+tMF6DHX2ehRlrPkezfncimNzHN+cyjF+q+bUbKnv3cZIb5xcxshd6tmGsivyPcp48g2ky94NeZJtyFc7vl8OtJ/33PlwvWW4HO7LU6kDUpPz3i33dLLUs+1nJmDTfNLMZj5pZkP9/f1x+iknjbgTcGCgEUMRseYaMBDrOpTqjYhq9+qLxprfe6s67DrZcwONGGp01vlQqzeSmO9os51K3SmwFuurDTSi2rX6h9N6e6xHCkaT79Gcz604Nsfxzakc47dqTq2U4t5tjPTGyWWMTpJitqHsUnmPMtp8+6QZKAd7N+RJtiFfrfp+OVCs0e7dpMX1ls0p6315KnVAynLbu+UeVksx235mAjbmk2ZapGeDH0bo2cSf13TV6a5j4rDzpVPOhzWbZNnmW9a6W8FarG/ND6FZj3IazfncimNzHN+cyjF+q+aUknbUbYz0xsllDDbP+sPWeY8CpMTeDXmSbciTbAO0h+stzZLKuZRKHUD7yD2ky89MwMY6tmlml12nxsIr/73oMgAAAAAAAAAAAAAAAGgB/V0AAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAMD/Z+/Oo+Qqy/yBP9Xd1UmnSQJZQEE2BRySsImyLxkGAVlODgxHf8PEIIoQlrDIIKggMAwMGHFBFGRYBBGMgMgwOiMuyKYgmzIsEnYYRQTSSSBdCZVO/f4AOjTphE66qu69b38+5+SErr657/O+937rvkA/KQAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACS05Z1AVn43Z13xA+v/n4sWPBafHib7eIzn50e5XI567IAAAAAAAAAAAAAAACokyH3STOvvjo/LvrO+bH/gR+P0888J/74h/vjll/9IuuyAAAAAAAAAAAAAAAAqKMh1zTz5BOPR60Wsevk3eJ9664bW221dTz6yMN1H2fx4tpyf1WqtVi8pO/vDG3vvC+GgqLOt6h1N4K16Mt6LFXEtViZmhtxbIrjm1Mxxm/UnPKkGXUbI3/jGCNfYzRCUeuGZpITIE9SeU9KZR7kT1HvraLW/U6pzCMirbmkoojXpAg1F6FG0lZZ/OY9uNg9yKrzXka95OVeykMdeaghL3XkoYY8KeJ6FKHmvNeY9/qKIO9rWNR9eSo/B2CM/I3TlDGakLtGzWPINc3Mnz8/hg0fFqVSKSIiRnR2xvz585c5rlqtRnd3d++vSqV7wGNUqrWo1mK5v75y84Ko9kRMv3lBlGd2xfSbF+T2oULjvXW/DKX7oVKtZTbfwWZ7KF2nFbEWfVmPpYqY75WpuRHHpji+ORVj/EbNqd7y/uw2Rv7GMUa+xlievGcbiq6o+Qbyy7M7nXmQP0V9bqeSiVTmEZHWXFJRxHwX4T4qQo2krbK4FtN//uY9+PMFTf0BPf/OnQ7vZbxdCvvyPNSRhxryUkceasgT+/LGyHuNea+vCPK+hkXdl6fycwDGyN84TRmjCblr5DxKtVotX+9kDXbbrbfE96+4LP7jsu9HRMRVV14ezzz9VJxy2pl9jrvuR9fE9dfOWubPX3rF1TFixIgVjrF4ybv8jd4LXouOztWiPLOr97XqiWtEW0tpoNMgIW/dL0Ppfli8pJbZfAeb7aF0nVbEWvRlPZYqYr5XpuZGHJvi+OZUjPEbNad6y/uz2xj5G8cY+RpjefKebSi6ouY7IqJ07py611Q7aUzdzwlDjWd3OvMgf4r63E4lE6nMIyKtuaSiiPkuwn1UhBpJWxGzTf54L+PtUtiX56GOPNSQlzryUEOeFPHZXYRrmPca815fEeR9DYuY7Yh0fg7AGPkbxxjvbsh90szIkSNjYaUSS5YsiYiISnd3jBq9+jLHTdn/wLj0iqt7f11w0SUDHqPas+JfX7utFNWeiGmT2iPijd+rPfWZH8Xz1n0xlO6HLOc72GwPpeu0ItaiL+uxVBHzvTI1N+LYFMc3p2KM36g51Vven93GyN84xsjXGMuT92xD0RU130B+eXanMw/yp6jP7VQykco8ItKaSyqKmO8i3EdFqJG0VZe84x5c0ryx/Tt3OryX8XYp7MvzUEceashLHXmoIU/syxsj7zXmvb4iyPsaFnVfnsrPARgjf+M0ZYwm5K6R8xhynzQzf/78mHHEoTF12iExYdJmcfaZp8X+//jx2P2je67wz3V3d8dnDj5owH9Dx+IVfORQtRZRbn3jwr71e0c5Px2YNN/ixbU+98VQuB8q1Vou5ruy2c5L3XlgLfqyHkvlZS1WJt8rU3Mjjk1xfHMqxviNmlMj5fHZbYz8jWOMfI0xEHnMNhRdXnKysvn2STNQDEP12Z3KPMifvNxbsl3seUSkNZdU5OWaNOq/l2elCDWStsriWpRb3viBoY62YmSb/PFexvIUdV+ehzryUENe6shDDXmSl/WwL2+uvNdXBHlfw6Luy1P5OQBj5G+cpozRhNw1ah5tdTtTQYwaNSqmH3lMXPODK2PWNVfFR7bZLnadvFvdx2lbwY3w1qK3tfT9naGrra20zH2RurfeyIo236LW3QjWoi/rsVQR12Jlam7EsSmOb07FGL9Rc8qTZtRtjPyNY4x8jdEIRa0bmklOgDxJ5T0plXmQP0W9t4pa9zulMo+ItOaSiiJekyLUXIQaSdtbPxjkHmQwvJdRL3m5l/JQRx5qyEsdeaghT4q4HkWoOe815r2+Isj7GhZ1X57KzwEYI3/jNGWMJuSuUfMYck0zERHb77hTbL/jTlmXAQAAAAAAAAAAAAAAQIMMyaYZAAAAAIBVUTp3Tl3PVztpTF3PBwAAAAAAAMBSBftQKgAAAAAAAAAAAAAAAHh3mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5bVkXUBS1Wi0iIiqV7owrgaGto6MjSqVS3c4n25Af8g1pkm1Ik2xDmuqd7Yh85Lu7O9/vLXmvjzR4dkOaZBvSlOq+HPDshlTJNqTJvhzS5dkNaXq3bJdqb6WVFXrllZfj6OmHZl0GDHmXXnF1jBgxom7nk23ID/mGNMk2pEm2IU31znaEfENeeHZDmmQb0mRfDuny7IY0yTakyb4c0uXZDWl6t2xrmhmgJUuWRFfXnBg+fMVdSJVKdxw9/dC44KJLoqOjvpumRlFzc6i5Purd5TvQbL8lj2uSFWvRl/VYalXXohH5/stf/hwnHj8jqeuS4r1mTsWQp2yvzLO7GVK53qnMIyKdueR5HllnO89rE5H/+iLyX6P6Bm9VamzE35y3MvlOdV2bSX2Dl/cai7ovz/u6DlQq84hIZy5DfR6yXR+pzCMinbmkMo8I+/JGyXuN6hu8vNdY1Gd3PeX9GkXkv8a81xehxoHKOtt5WIO8sBZLWYu+7MsbI+81qm/w8l5jUffleV/XgTKP/EllLo3Kdls9ihsKWlpaYuzYcQM+vqNjRN07jRtNzc2h5nxZ2Wy/JeU1WVnWoi/rsVTWa9HS0hJjxozNRS2NYE7FYE71t6rP7mbIem3qJZV5RKQzl1TmsSKp7svzXl9E/mtU3+BlXeOq5Dvrmgci7zWqb/DyXmPW9aX67B6oVOYRkc5czKM+ZDuNeUSkM5dU5hGR/Vzsy7OhvsHLe41Z15eH/16e9RoMRN5rzHt9EWpstqG+L68Ha7GUtegr6/WwL8+G+gYv7zVmXd9Qf3abR/6kMpd6z6OlbmcCAAAAAAAAAAAAAACAnNA0AwAAAAAAAAAAAAAAQHJaTz/99NOzLiI1LS0tMWHiZtHa2pp1KQOm5uZQcxqsyVLWoi/rsVSe1iJPtdSLORWDOQ0tqaxNKvOISGcuqcyjEfK+NnmvLyL/Napv8IpQ4zsVoea816i+wct7jXmvb3mKWvc7pTKPiHTmYh7ZKmrd75TKPCLSmUsq84go5lyKUHPea1Tf4OW9xrzX1wxFWIO815j3+iLUWBTWYClrsZS16KuI61GEmvNeo/oGL+815r2+5Slq3e9kHvmTylwaMY9SrVar1e1sAAAAAAAAAAAAAAAAkAMtWRcAAAAAAAAAAAAAAAAA9aZpBgAAAAAAAAAAAAAAgORomhmgWq0W3d3dUavVsi4FqCPZhnTJN6RJtiFNsg3pkm9Ik2xDmmQb0iXfkCbZhjTJNqRLviFNsg3FoGlmgCqVSnzm4IOiUqlkXQpQR7IN6ZJvSJNsQ5pkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG4pB0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkJy2rAtohG+c95W4+67fxjXX/qTP6y+/9FJc+O1vxlNPPhHvXXudOPzIGbH++htkUyQAAAAAAAAAAAAAAAANk9wnzdx7z+/jnt/f1e/3rvr+5TFs2PCY+bVvxXrrrR8XX3hBk6sDAAAAAAAAAAAAAACgGZJqmunu7o7LL/1u7Pmxffr9/qOPPBw77bJrjBs/Pibvtns89eQTsWjRoqbUVllci8VLalFZXGvKeBSPe6QYXKelrAUpcT8PnLUCiqBSffO9quq9KlWuMaRJtgGgWDy7IU2yDdAc3m+pF///lv64L4B6sF9hKPDMzJ9GXZO2up4tY9dcdWVstvmWMWmzzeO/f3rTMt9/df78GD7i1UtIAAAgAElEQVR8eEREdHZ29r42bPz4ZY6tVqtRrVZ7v65Uule5rsriWkz/+YK48qHXY9qk9rhoz87oaCut8vlIj3ukeQaTbddpKWtBHq1qvt3PA2etyEI99+UMDZVqLabf/Lb3qj06o6PsvSpvBrUvd40h11Z5Xy7bkGv12JeXzp1Tz5KidtKYup4PhiL7ckiXfTmkyX8vT4f3W97Oz7FQb+6L/PDspsjsV5ZPttPhmZk/jbwmyTTN/OnRR+Lee+6OmV87P2bP/tOA/1xpOet44w3XxfXXzqpLbeWWiCsfej0i3vj90o911uW8pMM90jyDybbrtJS1II9WNd/u54GzVmShnvtyhoZyq/eqIhjUvtw1hlxb5X25bEOu2ZdDmuzLIV325ZAm+/J0eL/l7fwcC/XmvsgPz26KzH5l+WQ7HZ6Z+dPIa5JM08wN118br776ahx79OHR07MkIiI+c/BBcekVV/ceM3LkqFiwYEFERFQqlTdeGzW63/NN2f/A2HvfKb1fVyrdcfT0Q1eptuqSiGmT2nu7nqpLItpaVulUJMo90jyDybbrtJS1II9WNd/u54GzVmShnvtyhoZqzzveq3q8V+XRoPblrjHk2irvy2Ubcs2+HNJkXw7psi+HNNmXp8P7LW/n51ioN/dFfnh2U2T2K8sn2+nwzMyfRl6TZJpmjjjqmKhW3+gseuih/42LL7wgzpn59VjS0xMtra0REbHphIlx+623xKabTozf3PKr2HjjD0Z7e3u/5yuXy1Eul+tSW0dbKS7aszMu/VhnVJeEj25iGe6R5hlMtl2npawFebSq+XY/D5y1Igv13JczNHSUS3HRHm++V/WEj4jOqUHty11jyLVV3pfLNuSafTmkyb4c0mVfDmmyL0+H91vezs+xUG/ui/zw7KbI7FeWT7bT4ZmZP428Jsn0Q62+xhoxfs21Yvyaa8Xo0W98esz4NdeKL5x0Qtz9u99GRMTUaYdET8+S+JfjZ8QzTz8Vn51+ZNPq62grRVtLSaBYLvdIMbhOS1kLUuJ+HjhrBRRBR/nN9yr/4S5ZrjGkSbYBoFg8uyFNsg3QHN5vqRf//5b+uC+AerBfYSjwzMyfRl2TZD5p5u0+tPVH4pprfxIREed+9Ru9r48bPz5OPf3MrMoCAAAAAAAAAAAAAACgSZL5pBkAAAAAAAAAAAAAAAB4i6YZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAktOWdQH10tPTE7OuuSp+/cubIyJiqw99OD47/ahob2/vPWb2Y3+K0045uc+fu/zKa2J4R0dTawUAAAAAAAAAAAAAAKCxkmmaueP2W+PXv/xFnHjyKbHayJHxb6efGr/+5c2x19779h4zd25XjB07Ls497xu9r2mYAQAAAAAAAAAAAAAASE8yTTO7Tt4tdp28W0RELFjwWrS1tUVra2ufY+Z2dcXqa6wRnZ2rZVEiAAAAAAAAAAAAAAAATZJM08xbPn/CsfHn/3s+JkycFLvvsVef73XNmRMvvfS3OObIw6Lc3h4HHPjx2HGnXfo9T7VajWq12vt1pdLd0LqB5pBtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik21BMyTXNnPTFU+OFv/w5zv/6eXHrb34dk//+H3q/95Ftt4s1xoyJCRM3i7vv+m1c9O3zY8LESbHGGmOWOc+NN1wX1187q5mlA00g25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik25Am2YZ0yTekSbahmEq1Wq2WdRH18Oyzz8S8uXNj8y22jIiIb3ztK9HePiyOPPrY3mPmzZsb7e3DoqOjIxYtWhSfmvqJOOW0M2PipM2WOV9/nYBHTz80Lr3i6hgxYkTjJwQ0hGxDuuQb0iTbkCbZhnTJN6SpHtkunTunrjXVTlr2L8MCVo7nNqRLviFNsg1pkm1Il3xDmmQbiimZT5p57pmn47JLvhtfPPWMGN7REY/Pfiz22W9KLOnpiZbW1oiIuPySi6Ora04cc9wJcf/990VbW1u8d+11+j1fuVyOcrnczCkATSDbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtqGYkmma2WmXyfHss8/EzHPOilqtFtvtsGPssefecd7Mc2LCxEmxz35TYuq0Q+Liiy6IE447OkavvnocOeO4GDPG30YHAAAAAAAAAAAAAACQmmSaZkqlUkyddkhMnXZIn9dPPPlLvf88bvz4+OKpZzS7NAAAAAAAAAAAAAAAAJqsJesCAAAAAAAAAAAAAAAAoN40zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJCctqwLqJeenp6Ydc1V8etf3hwREVt96MPx2elHRXt7e+8xr732Wnz3O9+Khx96MNYYMzY+fejhMXHSZlmVDAAAAAAAAAAAAAAAQIMk80kzd9x+a/z6l7+IE08+Jc4469x46H8f7G2gecuNN1wXr7zycpwz8xuxzbbbx7fP/3osrlYzqhgAAAAAAAAAAAAAAIBGSaZpZtfJu8Ul37sqPvh3m8bqq68ebW1t0dra2ueYRx95OLbdbodYc621Yvc99oyurjnx4ot/zahiAAAAAAAAAAAAAAAAGqUt6wLq7fMnHBt//r/nY8LESbH7Hnv1+d6r8+fH8OHDIyKic0RnRETMnz8/1unnPNVqNapv+xSaSqW7YTUDzSPbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtqGYkmuaOemLp8YLf/lznP/18+LW3/w6Jv/9P6zw+FKp/9dvvOG6uP7aWQ2oEMiSbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYkqmaebZZ5+JeXPnxuZbbBljx46LCZMmxSMPP9SnaWbkyFGxoHtBRER0VyoRETFq1Or9nm/K/gfG3vtO6f26UumOo6cf2sAZAM0g25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik25Am2YZ0yTekSbahmJJpmnnumafjsku+G1889YwY3tERj89+LPbZb0os6emJltbWiIjYdMLEuOu3d8aOO+0St996S4wZOzbWes97+j1fuVyOcrnczCkATSDbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtqGYkmma2WmXyfHss8/EzHPOilqtFtvtsGPssefecd7Mc2LCxEmxz35TYsoBB8Zf//pCfOHE42PMmLFx1Izjo/XNhhoAAAAAAAAAAAAAAADSkUzTTKlUiqnTDomp0w7p8/qJJ3+p959XW221OOHzX2h2aQAAAAAAAAAAAAAAADRZS9YFAAAAAAAAAAAAAAAAQL1pmgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkpOLppnF1Wo89+wzvV+/8Jc/xz2/vyvmzZubXVEAAAAAAAAAAAAAAAAUVlvWBbzy8ktx9r+dEW1tbXHuV78RD/7xgZh5zlnR07MkVltttfjSaf8a66+/wYDOddONN8TP/us/Y9GihbH5llvF9COPieHDh/d+/847bosLvvm13q9XGzky/uOy79d7SgAAAAAAAAAAAAAAAGQs80+a+eHVV8ULf/lzrPO+dSMi4kc/vDpGjOiMnXbeJbq7F8R1s64e0Hke/OMfYtY1P4gjZxwXZ5x1bjz26KPxPz+7qc8xc7u64oMf3DQu+d5Vccn3ropvXvDdus8HAAAAAAAAAAAAAACA7GX+STMPPfRgTJg4KY457oSYO3duPPXkE/HZw4+Mv/+Hj8bChQvjscceHdB52tra4qCp02KzzbeIiIj3rr12zJs7r88xc7u6Yo0xY6Kzc7W6zwMAAAAAAAAAAAAAAID8yLxpZtHChTF27LiIiPjfB/8QEaXYYqutIyJiRGdnLFq4cEDnmTBxUkyYOCkiIp595ul44vHZcdDUg/sc09U1Jx6f/VgcdfhnYuSoUfHPn/xUb5PNO1Wr1ahWq71fVyrdKzs1IIdkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTYUU+ZNM+uut37c8/u7Yr2bNohbfvWLeN+668aYMWPiySefiAfuuzfe9771Vup8r7zycsw856zYZ98psdHGm/T53m67fzQmTNosNtnk7+Ln//3TOP/rX40LL74s2srlZc5z4w3XxfXXzhrU3ID8kW1Il3xDmmQb0iTbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbUEylWq1Wy7KAB//4QHzl38+KJUuWREQtjj72c7HDjjvHIZ/8f7Fo0esx49jPxfY77jSgc82fPz/O+PIXY6ONNo7pRx0TpVKpz/e7uuZE54jOaB82LP7v+efjxM/NiG9e8N1Yc621ljlXf52AR08/NC694uoYMWLEoOYMZEe2IV3yDWmSbUiTbEO65BvSVI9sl86dU9eaaieNqev5YCjy3IZ0yTekSbYhTbIN6ZJvSJNsQzFl/kkzm2+xVZx+5tnxwP33xfs/8IHY+sPbRETEBhu+PybvtvuAG2YWVirxlbPPjHHjxsXUgw+J7u4FUSq1xPBhw6KltTUiIr567tkxbtz4OPjTn40777gtVlttZIwZ0///WCuXy1Hu5xNogGKTbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQTJk3zUREbLTxJrHRxpv0ee20fz17pc5x992/iyeffDwiIg779LSIiBg3fnyMGNEZB/zjx2Pb7XeIw6YfFZdefFEcP2N6rLnWe+LYz50Ybd64AAAAAAAAAAAAAAAAkpOLppnnn38u/vToI/Hq/HlRq/X9XqkUccCBn3jXc+w6ebfYdfJuKzxm/Q02jH89+9zBlAoAAAAAAAAAAAAAAEABZN4086tf/Dwuu+S7bzbL1Po5ojSgphkAAAAAAAAAAAAAAAB4S+ZNM//5kx9HqVSK3T+6Z6z13vdEKUpZlwQAAAAAAAAAAAAAAEDBZd40M3duV3xkm+3ikEMPy7oUAAAAAAAAAAAAAAAAEtGSdQEbbviBrEsAAAAAAAAAAAAAAAAgMZl/0sy+U/aPC775tbjj9ltjnXXe1+8xG75fYw0AAAAAAAAAAAAAAAADl3nTzNdm/ntElOI73/pmv98vlSJ+MOvHzS0KAAAAAAAAAAAAAACAQsu8aebvNp0QpVIp6zIAAAAAAAAAAAAAAABISOZNM18+46ysSwAAAAAAAAAAAAAAACAxmTfNPPrIw+96zKYTJjahEgAAAAAAAAAAAAAAAFKRedPMmaefEhGlFR5z9Y9+3JxiAAAAAAAAAAAAAAAASELmTTNbf/gj0V/TzEt/ezGee+7Z2GHHnZtfFAAAAAAAAAAAAAAAAIWWedPMCZ//Yr+vL65W48QTjo22crnJFQEAAAAAAAAAAAAAAFB0LVkXsDxt5XJsvPEmce/v7866FAAAAAAAAAAAAAAAAAomt00zXV1z4vHZj0WppZR1KQAAAAAAAAAAAAAAABRMW9YFfHraPy3zWq1Wi0WLXo+IWmy3w07NLwoAAAAAAAAAAAAAAIBCy7xpZuHChf28WorRq4+OLbb8UEz95KeaXRIAAAAAAAAAAAAAAAAFl3nTzNU/uiHrEgAAAAAAAAAAAAAAAEhMS5aDL+npiX8749T46U03ZlkGAAAAAAAAAAAAAAAAicn0k2ZaWlvjhRf+EmOefaYu57vpxhviZ//1n7Fo0cLYfMutYvqRx8Tw4cN7v//ySy/Fhd/+Zjz15BPx3rXXicOPnBHrr79BXcYGAAAAAAAAAAAAAAAgPzL9pJmIiD323Dvuv/f38cTjs+O1V1/t99dAPPjHP8Ssa34QR844Ls4469x47NFH439+dlOfY676/uUxbNjwmPm1b8V6660fF194QSOmBAAAAAAAAAAAAAAAQMYy/aSZiIhZ11wVEaX48pdO7vf7pVLED2b9+F3P09bWFgdNnRabbb5FRES8d+21Y97ceX2OefSRh+PgQw6NcePHx+Tddo8zvvzFWLRoUQwbNmzQ8wAAAAAAAAAAAAAAACA/Mm+aGTtuXJSiNOjzTJg4KSZMnBQREc8+83Q88fjsOGjqwX2OeXX+/Bg+fHhERHR2dva+Nmz8+GXOV61Wo1qt9n5dqXQPukYge7IN6ZJvSJNsQ5pkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG4op86aZb33nP+p6vldeeTlmnnNW7LPvlNho403e9fjScvp1brzhurj+2ll1rQ3InmxDuuQb0iTbkCbZhnTJN6RJtiFNsg3pkm9Ik2xDmmQb0iXfkCbZhmLKvGlmIC6/9OL45c3/Ez+Y9eMVHjd//vw4+8zTY+KkzeLj//TPy3x/5MhRsWDBgoiIqFQqb7w2anS/55qy/4Gx975Ter+uVLrj6OmHruoUgJyQbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQTIVomomIqNVW/P2FlUp85ewzY9y4cTH14EOiu3tBlEotMXzYsGhpbY2IiE0nTIzbb70lNt10Yvzmll/Fxht/MNrb2/s9X7lcjnK5XO9pABmTbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQTIVpmnk3d9/9u3jyyccjIuKwT0+LiIhx48fHiBGdccA/fjy23X6HmDrtkLjw2+fHvxw/I9ZeZ5044qhjsiwZAAAAAAAAAAAAAACABkmmaWbXybvFrpN3W+Ex48aPj1NPP7NJFQEAAAAAAAAAAAAAAJCVlqwLAAAAAAAAAAAAAAAAgHrTNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQnLasCxiIrT60dYwcNSrrMgAAAAAAAAAAAAAAACiIXDTNzO3qip/+143x/HPPxdy5XXHOzK9HpVKJW2/5Vey1976x5VZbx5ZbbZ11mQAAAAAAAAAAAAAAABRE5k0zTzw+O84+87RYuHDhm6+UIiLi9ltviSu/d1m0trXFR/fYK7sCAQAAAAAAAAAAAAAAKJyWrAu4+qorolarxXEnfD522nnX3tf32GvvWH+DDeIXP//vDKsDAAAAAAAAAAAAAACgiDJvmnnqySfiI9tsF9tsu310jBjR53sbvv8D8dcXXsioMgAAAAAAAAAAAAAAAIoq86aZYcOGx4IFC5Z5vVarxVNPPhEdIzoyqAoAAAAAAAAAAAAAAIAia8u6gK0+tHXcfttv4sfXzYpX58+PiIgnHp8dP73pxnju2Wdj5112zbhCAAAAAAAAAAAAAAAAiibzppmDph4cf3r0kbjuRz+MiFJERHz5SydHRC3WGDMmPnHQJzOtDwAAAAAAAAAAAAAAgOLJvGlm1OjRcc7Mr8fPfvqf8eAf/xDz5s6Njo6OmDBps9hvyv4xevTqWZcIAAAAAAAAAAAAAABAwWTeNPPS316M8WuuFQcc+Ik44MBPZF0OAAAAAAAAAAAAAAAACci8aebYo4+ITTb5YOy48y6x7fY7xqhRo7IuCQAAAAAAAAAAAAAAgILLvGlmxIgRMXv2n2L27Mfiyu9dGptvsWXsuPOu8eEPbxPtw4ZlXR4AAAAAAAAAAAAAAAAFlHnTzMWXXRmP/enRuP/ee+L++++NB+6/Lx64//4YNqw9PrLNdrHDTrvE5ltsGS0tLVmXCgAAAAAAAAAAAAAAQEFk3jTT0tISm06YGJtOmBj/PO1T8cJf/hw/+P4Vcf9998Qdt98Wd9x+W4wcNTL22XdK7Ddl/yiVSlmXDAAAAAAAAAAAAAAAQM5l3jQTEfH6okXxxz88EPfcc3c8cP+9seC1BRERsfY668TESZvF7+/+Xfzw6qti3ry58cmDP73Cc82bNzfuvOO2uO03t8RRM46Ldddbv8/3Zz/2pzjtlJP7vHb5ldfE8I6O+k4KAAAAAAAAAAAAAACAzGTeNPPVc8+Oh/73j/H669WIqMXIUaNiz732jp13nRzv/8BGERHxTwd9Mk468bi47Te3rLBpplKpxIwjDosNNtgwnn3m6X6PmTu3K8aOHRfnnveN3tc0zAAAAAAAAAAAAAAAAKQl86aZ+++7J8rlcmy73fax866TY8stPxQtra19jhne0RGbb7FVPPH47BWeq729Pb514cXx+qJFccxRh/d7zNyurlh9jTWis3O1us0BAAAAAAAAAAAAAACAfMm8aeYzhx0R22+/Y4zo7FzxcZ+d/q7nam1tjdGjV4+X/vbico/pmjMnXnrpb3HMkYdFub09Djjw47HjTrssc1y1Wo1qtdr7daXS/a7jA/kn25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik25Am2YZ0yTekSbahmDJvmvmH3feIxdVq3PLrX8azzzwdc7u6YvTo1WODDTeMnXeZHG3lcl3H+8i228UaY8bEhImbxd13/TYu+vb5MWHipFhjjTF9jrvxhuvi+mtn1XVsIHuyDemSb0iTbEOaZBvSJd+QJtmGNMk2pEu+IU2yDWmSbUiXfEOaZBuKqVSr1WpZFvB/zz8f55x1RsyZMyci3l5KKcaOHRsnf+nLsc771l2pc770txfjmKMOj6+c981Yd731+3xv3ry50d4+LDo6OmLRokXxqamfiFNOOzMmTtqsz3H9dQIePf3QuPSKq2PEiBErO00gJ2Qb0iXfkCbZhjTJNqRLviFN9ch26dw5da2pdtKYdz8IWCHPbUiXfEOaZBvSJNuQLvmGNMk2FFPmnzRz+aUXx5w5r8RWH9o6dthplxg9enS8+uqr8ds7bo/77v19XH7pxXHKaWcOaowlPT3R0tr6xniXXBxdXXPimONOiPvvvy/a2trivWuvs8yfKZfLUa7zp9wA2ZNtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik21BMmTfNPPH4Y7HxJh+ME08+pc/r2++wU5x+yhfi8dmPDXqM82aeExMmTop99psSU6cdEhdfdEGccNzRMXr11ePIGcfFmDH+RjoAAAAAAAAAAAAAAICUZN40M2bsuFi7n096iYh4z9prx0sv/22lzzl+zbXimmt/0vv1iSd/qfefx40fH1889YyVLxQAAAAAAAAAAAAAAIDCaMm6gI/ts288cP998fJLL/V5/cW/vhAP3Hdv7PWxfTOqDAAAAAAAAAAAAAAAgKLK5JNmPj3tn/p8vXDhojj+mCOiXC73vvb669Wo1ZbEXb+7M/absn+zSwQAAAAAAAAAAAAAAKDAMmmaWbhw4TKv9fT0RE9PzzKvP/3UU80oCQAAAAAAAAAAAAAAgIRk0jRz9Y9uyGJYAAAAAAAAAAAAAAAAhoiWrAsAAAAAAAAAAAAAAACAetM0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHLasi6gnubNmxt33nFb3PabW+KoGcfFuuut3+f7r732Wnz3O9+Khx96MNYYMzY+fejhMXHSZhlVCwAAAAAAAAAAAAAAQKMk80kzlUolZhxxWNx1553x7DNP93vMjTdcF6+88nKcM/Mbsc2228e3z/96LK5Wm1wpAAAAAAAAAAAAAAAAjZZM00x7e3t868KLY8Zxn1vuMY8+8nBsu90OseZaa8Xue+wZXV1z4sUX/9qQeirVWixeUotFi2ux8M1flcVvvPbW9yqLaw0ZG2ic3vxW5ddakJLeZ7Rn87vKOvtFuVaNqLMh51yJ65n1tV9VzbhnmjJGE9a/qNe4PynNhf65xpAm2QaAYvHshvrdi3cAACAASURBVDTJNkBzeL+lXtxLQFa8/0B+yScsK5mmmdbW1hg9evUVHvPq/PkxfPjwiIjoHNEZERHz58/v99hqtRrd3d29vyqV7gHXUqnW4oibF8SLC2rRU4t47fVavPZ6Lab/fEGUZ3bF9De/d8TPF+T+Bz4hNYPN9vSbl+Z4KG8orAV5tKr5rix+2zPas3mFss5+Ua5VI+psyDlX4npmee0H9exuwj3TlDGasP5Z57ueUppLyuzLIV2rvC+Xbci1wTy7gfyyL4d02ZdDmuzL0+H9lrezL4d0pfzs9v7DUJb3bMsn9K8t6wKyVir1//qNN1wX1187a5XOWW6NuOmJaly8VylaShFtLW8McuVDr/f+/h97dcZNT1Tjko+t0hDAKhpstt+e40s/1lnP0grFWpBHq5rvcov7eaCyzn5RrlUj6mzIOVfiemZ57Qf17G7CPdOUMZqw/lnnu55SmkvK7MshXau8L5dtyLXBPLuB/LIvh3TZl0Oa7MvT4f2Wt7Mvh3Sl/Oz2/sNQlvdsyyf0b0g1zYwcOSoWdC+IiIjuSiUiIkaN6v/Taabsf2Dsve+U3q8rle44evqhAxqn2hOx30bleKm7FmsML0X3m1160ya1x5UPvR7TJrXHS9212G+jclSXRLQl83k/kH+Dzfbbc1ztGbr5tRbk0armu7rkHfezZ/NyZZ39olyrRtTZkHOuxPXM8toP6tndhHumKWM0Yf2zznc9pTSXlNmXQ7pWeV8u25Brg3l2A/llXw7psi+HNNmXp8P7LW9nXw7pSvnZ7f2HoSzv2ZZP6F/yTTNLenqipbU1IiI2nTAx7vrtnbHjTrvE7bfeEmPGjo213vOefv9cuVyOcrm8SmN2lEtx4R6dUW6N6FkSsVr7G580c9GenXHpxzqj2vNGJ9+Fe3ZGR9tyPuoGaIjBZvuiPZbmuKM8dPNrLcijVc13R1tp6TN6SXg2r0DW2S/KtWpEnQ0550pczyyv/aCe3U24Z5oyRhPWP+t811NKc0mZfTmka5X35bINuTaYZzeQX/blkC77ckiTfXk6vN/ydvblkK6Un93efxjK8p5t+YT+Jd80c97Mc2LCxEmxz35TYsoBB8Zf//pCfOHE42PMmLFx1Izjo/XNhpp6e+tNpr/uvLde07kHxbOibA811oKUvPVD7u7nd5d19otyrRpRZ0POuRLXM+trv6qacc80ZYwmrH9Rr3F/UpoL/XONIU2yDQDF4tkNaZJtgObwfku9uJeArHj/gfyST1hWck0z49dcK6659ie9X5948pd6/3m11VaLEz7/hSzKAgAAAAAAAAAAAAAAoIn0kAEAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAwP9n787j5CrLfIE/1UvSnZA9ISEIuCBKEkAGlVWSiVxQlokg4shww6AIGTbhMggiXEAuDJhBEUEWiShbjIBcRFFQQRYXVDYNi+yLLF4gHTrprkCnU/ePkE4aktBJquqc8/b3+/k06a46Oed53zq/95xK8lAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkJymrAuopt//9q744dVXREfHwvjwR7eLL3xxRjQ3N/c8/+jfHolTTjqh1++57PLZ0dLaWu9SAQAAAAAAAAAAAAAAqKFkmmYWLGiPi75zXhx08KGx6aabxRmn/++47de/jF0/sXvPNvPnt8WoUaPj7HPO7XlMwwwAAAAAAAAAAAAAAEB6GrIuoFqeePyxqFQiJk+ZGu/aaKPYeutt4uGHHuy1zfy2thg+YkQMHrxezxcAAAAAAAAAAAAAAADpSeaTZtrb22Ngy8AolUoRETFo8OD4xz/+0Wubtnnz4uWX/18cddgh0TxgQOyz736x4047r3R/XV1d0dXV1fNzudxZu+KBupFtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik21BMyTTNrMyb/TM9PrLtdjFi5MiYMHGLuPsPv4uLLjgvJkycFCNGjHzb773h+mvjumvm1KlSoF5kG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTYUUzJNM0OGDIlF5XIsWbIkGhoaotzZGUOHDe+1zajRo2OD8RtGa2trjFn/U3Htj2bHC88/v9KmmWl77xu77zmt5+dyuTOOmHFwzccB1JZsQ7rkG9Ik25Am2YZ0yTekSbYhTbIN6ZJvSJNsQ5pkG9Il35Am2YZiSqZp5n2bbhYNDQ3x61/eHBMmbRH3339v7P3p/WJJd3c0NDZGRMRll14SbW3z4qijj417770nmpqaYoPxG650f83NzdHc3FzPIQB1INuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNuQJtmGdMk3pEm2oZgasi6gWoYOHRozDjsqbrzh+jjlq8fHllt+KCZPmRrnzDwrfnbjDRERccD0g2LgwIFx7NFHxE9/cn0cduTRMXLk2z9lBgAAAAAAAAAAAAAAgGJL5pNmIiK233Gn2H7HnXo9dtwJX+35fvSYMXHiyafVuywAAAAAAAAAAAAAAADqLJlPmgEAAAAAAAAAAAAAAIBlNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEBymrIuoJp+/9u74odXXxEdHQvjwx/dLr7wxRnR3Nzc8/zChQvj4u98Ox6c+5cYMXJUfP7gQ2PipC0yrBgAAAAAAAAAAAAAAIBaSOaTZhYsaI+LvnNe7L3vfnHq6WfFA/ffG7f9+pe9trnh+mvj1VdfibNmnhsf3Xb7uOC8b8birq6MKgYAAAAAAAAAAAAAAKBWkmmaeeLxx6JSiZg8ZWq8a6ONYuutt4mHH3qw1zYPP/RgbLvdDrH+2LGxy667RVvbvPjHP16qST2LF1dW+VXuqsTiJb1/pX9763nRHxR1vEWtuxbMRW/mY7kizsWa1FyLbVM8vjEV4/i1GlOe1KNux8jfcRwjX8eohaLWDfVUxJwUoea815j7+ha/Wd/ifNZXBHl/jYH+JZU1KZVxRKQ1llQU8TUpQs1FqJG0eW9DNVjLqJa8nEt5qCMPNeSljjzUkCdFnI8i1Jz3GnNfXwHuKXM/hzmvb1VS+XcAjpG/49TlGHVYu2o1jmSaZtrb22Ngy8AolUoRETFo8OBob2/vtc2C9vZoaWmJiIjBgwb3/L6V6erqis7Ozp6vcrmzz7WUuyrRVYlVfn39lo7o6o6YcUtHNM9sixm3dBRu0aZ6lp0v/el8KHdVMhvvuma7P71Oq2MuejMfyxUx32tScy22TfH4xlSM49dqTNWW92u3Y+TvOI6Rr2OsSt6zDUVXxHwXIdt5rzH39S2uxIyb36zv5o5c/0VgXhUx20C+uS9PZxwRaY0lFUW8dhfhPCpCjaQty/c27svTYS1jRSncl+ehjjzUkJc68lBDnrgvr42815j7+grw5+W5n8MCZjsinX8H4Bj5O05djlGHtauW4yhVKpV8rWRr6Y7bb4srfvC9+O73roiIiCsvvyyefurJOOmU03u2+dLhh8bue/5L7PbJPWJRuRwHTf9cnPK1M+KDm0982/6u/dHsuO6aOW97fNYPro5BgwattpbFS1Y/peWOhdE6eL1ontnW81jXcSOiqaG02t9HmpadL/3pfFi8pJLZeNc12/3pdVodc9Gb+ViuiPlek5prsW2KxzemYhy/VmOqtrxfux0jf8dxjHwdY1Xynm0ouiLmuwjZznuN6ktfEbO9otLZ86paU+X4kVXdH/RH7svTGUdEWmNJRRGv3UU4j4pQI2krYrbJH2sZK0rhvjwPdeShhrzUkYca8qSI1+4ivIZ5r1F96y7vNRYx2xHp/DsAx8jfcRzjnSXzSTNDhgyJReVyLFmyJCIiyp2dMXTY8LdsMzQ6OjsiIqKzXI6IiKFDe2+zzLS9941ZP7i65+v8iy7tcy1d3av/+sYdpejqjpg+aUBELP21q3uNh0wilp0X/el8yHK865rt/vQ6rY656M18LFfEfK9JzbXYNsXjG1Mxjl+rMVVb3q/djpG/4zhGvo6xKnnPNhRdEfNdhGznvcbc17fkLfUtybigAipitoF8c1+ezjgi0hpLKop47S7CeVSEGklblu9t3Jenw1rGilK4L89DHXmoIS915KGGPHFfXht5rzH39RXgz8tzP4cFzHZEOv8OwDHyd5y6HKMOa1ctx5HMJ820t7fHkf9xcBww/aCYMGmLOPP0U2LvT+8XU6fuEg2NjRERcdXl348HHrgvjv3yV+LO22+L2279VZx3wSXR+Obzq9PZ2RlfOHD/Pv8fOhav5iOHuioRzY1LX9hlv7Y256cDk/pbvLjS67zoD+dDuauSi/GuabbzUncemIvezMdyeZmLNcn3mtRci21TPL4xFeP4tRpTLeXx2u0Y+TuOY+TrGH2Rx2xD0eUlJ7W6L89K3mvMfX2LK9HcsPQP1Vub8ldfEeTlNV7Ta3eET5qBIuiv9+WpjCMirbGkIi+viftyqK68vLdZm/ty8sNaxqoU9b48D3XkoYa81JGHGvIkL/Phvry+cl9fTu4pVyf3c5iT+vJ47XaMfB2jXsepyzHqsHbVahxNVdtTxoYOHRozDjsqZl91ecyZfWV85KPbxeQpU+OcmWfFhImTYo+9psW0ffaNl156Mb5y3DExcuSoOPzIY/rUMLM2mlZzIiyb9KaG3r/SfzU1ld52XqRu2UJWtPEWte5aMBe9mY/lijgXa1JzLbZN8fjGVIzj12pMeVKPuh0jf8dxjHwdoxaKWjfUUxFzUoSa815j7utrynd9RZD31xjoX1JZk1IZR0RaY0lFEV+TItRchBpJm/c2VIO1jGrJy7mUhzryUENe6shDDXlSxPkoQs15rzH39RXgnjL3c5jz+lYllX8H4Bj5O05djlGHtatW40imaSYiYvsdd4rtd9yp12PHnfDVnu/XW2+9OPbLX6l3WQAAAAAAAAAAAAAAANRZwfrrAAAAAAAAAAAAAAAA4J1pmgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABITlPWBRRFpVKJiIhyuTPjSqB/a21tjVKpVLX9yTbkh3xDmmQb0iTbkKZqZztCviEvUrx2d3ZW99iDv72oqvvrOLKlqvsjf/JwzqSYbcB9OaTMtRvSJNuQJvflkC7XbkjTO2W7VFmWVlbr1VdfiSNmHJx1GdDvzfrB1TFo0KCq7U+2IT/kG9Ik25Am2YY0VTvbEfINeeHaDWmSbUiT+3JIl2s3pEm2IU3uyyFdrt2QpnfKtqaZPlqyZEm0tc2LlpbVdyGVy51xxIyD4/yLLo3W1ureNNWKmutDzdVR7S7fvmZ7mTzOSVbMRW/mY7m1nYta5PuFF56P4445MqnXJcVzzZiKIU/ZXpNrdz2k8nqnMo6IdMaS53Fkne08z01E/uuLyH+N6lt3a1NjLf7PeWuS71TntZ7Ut+7yXmNR78vzPq99lco4ItIZS38fh2xXRyrjiEhnLKmMI8J9ea3kvUb1rbu811jUa3c15f01ish/jXmvL0KNfZV1tvMwB3lhLpYzF725L6+NvNeovnWX9xqLel+e93ntK+PIn1TGUqtsN1WjuP6goaEhRo0a3eftW1sHVb3TuNbUXB9qzpc1zfYyKc/JmjIXvZmP5bKei4aGhhg5clQuaqkFYyoGY6q+tb1210PWc1MtqYwjIp2xpDKO1Un1vjzv9UXkv0b1rbusa1ybfGddc1/kvUb1rbu815h1faleu/sqlXFEpDMW46gO2U5jHBHpjCWVcURkPxb35dlQ37rLe41Z15eHPy/Peg76Iu815r2+CDXWW3+/L68Gc7Gcuegt6/lwX54N9a27vNeYdX39/dptHPmTyliqPY6Gqu0JAAAAAAAAAAAAAAAAckLTDAAAAAAAAAAAAAAAAMlpPPXUU0/NuojUNDQ0xISJW0RjY2PWpfSZmutDzWkwJ8uZi97Mx3J5mos81VItxlQMxtS/pDI3qYwjIp2xpDKOWsj73OS9voj816i+dVeEGt+qCDXnvUb1rbu815j3+lalqHW/VSrjiEhnLMaRraLW/VapjCMinbGkMo6IYo6lCDXnvUb1rbu815j3+uqhCHOQ9xrzXl+EGovCHCxnLpYzF70VcT6KUHPea1Tfust7jXmvb1WKWvdbGUf+pDKWWoyjVKlUKlXbGwAAAAAAAAAAAAAAAORAQ9YFAAAAAAAAAAAAAAAAQLVpmgEAAAAAAAAAAAAAACA5mmb6qFKpRGdnZ1QqlaxLAapItiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtiFNsg3pkm9Ik2xDMWia6aNyuRxfOHD/KJfLWZcCVJFsQ7rkG9Ik25Am2YZ0yTekSbYhTbIN6ZJvSJNsQ5pkG9Il35Am2YZi0DQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACSnKesCauHcc74ed//hdzH7mv/b6/FXXn45LrzgW/HkE4/HBuM3jEMPOzI22eTd2RQJAAAAAAAAAAAAAABAzST3STN//tMf409//MNKn7vyisti4MCWmPmNb8fGG28Sl1x4fp2rAwAAAAAAAAAAAAAAoB6Saprp7OyMy2ZdHLt9co+VPv/wQw/GTjtPjtFjxsSUqbvEk088Hq+//npNaikvrsTiJZUoL670PLZocaXn8RW/X3Eb6C/KXW+e/13FOv+LWnctmAtSsibX5CJdv4uS06LMaVHqpLd6vG6pHKNe6jJfBVn/WHteY0iTbEO65BsAisN1G6A+rLdUi3OJlXFeANVgLaE/cJ7nT61ek6SaZmZfeXlsseWHYtIWW670+QXt7dHS0hIREYMHD+55bGW6urqis7Oz56tc7uxzHeXFlZhxc0c0z2yLGTd3RPnNBpmFbyx9fMDMtp7vV9wG+otyVyVm3PLm+X9LR10vNuuU7QzrzhtzQR6tbb5Xdt2uxrZZK0pOizKnRakzRdW+L6+2VI5RL3WZr4Ksf/2d+3JI11rfl8s25JprN6RpXbK9TOnseVX9AqrDfTmkqRrXbvLBesuKvOem2pwX+eHaTZFZS1ZNttPhPM+fWr4mTVXbU8Yeefih+POf7o6Z3zgvHn30kT7/vlJp5Y/fcP21cd01c9aqluaGiMvnvhERS3+d9cmlDTpDB5bi8rlvxMiWUs/3b90G+oPmxpVnpB7WKdsZ1p035oI8Wtt8r+q6va7bZq0oOS3KnBalzhTV4r68mlI5Rr3UZb4Ksv71d+7LIV1rfV8u25Brrt2QpnXJNpBv7sshTa7d6bDesiLvuak250V+uHZTZNaSVZPtdDjP86eWr0kyTTPXX3dNLFiwIL50xKHR3b0kIiK+cOD+MesHV/dsM2TI0Ojo6IiIiHK5vPSxocNWur9pe+8bu+85refncrkzjphxcJ9q6VoSMX3SgLh87hsxfdKA6FpaTnS8UYnpkwbEFXPfiPbXK2/bpimpz/2BVevqfktGuut3/q9TtjOsO2/MBXm0tvle2XV7VefzmmybtaLktChzWpQ6U1Tt+/Jqv26pHKNe6jJfBVn/+jv35ZCutb4vl23INdduSNO6ZBvIN/flkCbX7nRYb1mR99xUm/MiP1y7KTJryarJdjqc5/lTy9ekVKlUkvgsofltbdHVtbSzaO7cv8YlF54f511wcYwaNToaGhsjIuLcc74enZ0dcciMI+LH1/0o/v7ss/G1M8/u0/47Ozt7mnAGDRr0jtuXF1eiuWHpPwprbVr6cTaLFleiEkv/78qLl0TP9ytuA/1FuasSzY1LF7jW5uzO/zXOdk7qzgNzQd6tSb5Xdt2uxrZZK0pOizKnRakzddW4L6+2VI5RL3WZr4KsfyznvhzStUb35bINheHaDWla02xHRJTOnlfVGirHj6zq/oCl3JdDmtbm2k1+WG9ZFe+5qQbnRT65dlM01pK+ke1ic57nT61ek2T6oYaPGBFj1h8bY9YfG8OGLf30mDHrj42vHH9s3P3730VExAHTD4ru7iXxn8ccGU8/9WR8ccZhNauntakUTQ2lXv8IrKWp1PP4it8X/R+9wdpobX7z/C/YRaaoddeCuSAla3JNLtL1uyg5LcqcFqVOeqvH65bKMeqlLvNVkPWPtec1hjTJNqRLvgGgOFy3AerDeku1OJdYGecFUA3WEvoD53n+1Oo1aarq3nLin7b5SMy+5v9GRMTZ/31uz+Ojx4yJk089PauyAAAAAAAAAAAAAAAAqJNkPmkGAAAAAAAAAAAAAAAAltE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAyWnKuoBq6e7ujjmzr4xbf3VLRERs/U8fji/OODwGDBjQs82jf3skTjnphF6/77LLZ0dLa2tdawUAAAAAAAAAAAAAAKC2kmmauevO2+PWX/0yjjvhpFhvyJD4P6eeHLf+6pb4xO579mwzf35bjBo1Os4+59yexzTMAAAAAAAAAAAAAAAApCeZppnJU6bG5ClTIyKio2NhNDU1RWNjY69t5re1xfARI2Lw4PWyKBEAAAAAAAAAAAAAAIA6SaZpZpkvH/uleP7vz8WEiZNil10/0eu5tnnz4uWX/18cddgh0TxgQOyz736x4047r3Q/XV1d0dXV1fNzudxZ07qB+pBtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik21BMyTXNHH/iyfHiC8/Hed88J27/za0x5Z8/3vPcR7bdLkaMHBkTJm4Rd//hd3HRBefFhImTYsSIkW/bzw3XXxvXXTOnnqUDdSDbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtqGYSpVKpZJ1EdXwzDNPx2vz58eWW30oIiLO/cbXY8CAgXHYEV/q2ea11+bHgAEDo7W1NV5//fX49wM+GyedcnpMnLTF2/a3sk7AI2YcHLN+cHUMGjSo9gMCakK2IV3yDWmSbUiTbEO65BvSJNuQpmpku3T2vKrWVDn+7f+jO2DNuXZDmmQb0iTbkC75hjTJNhRTMp808+zTT8X3Lr04Tjz5tGhpbY3HHv1b7LHXtFjS3R0NjY0REXHZpZdEW9u8OOroY+Pee++Jpqam2GD8hivdX3NzczQ3N9dzCEAdyDakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtKKZkmmZ22nlKPPPM0zHzrDOiUqnEdjvsGLvutnucM/OsmDBxUuyx17Q4YPpBcclF58exRx8Rw4YPj8OOPDpGjvR/rAIAAAAAAAAAAAAAAEhNMk0zpVIpDph+UBww/aBejx93wld7vh89ZkycePJp9S4NAAAAAAAAAAAAAACAOmvIugAAAAAAAAAAAAAAAACoNk0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJKcp6wKqpbu7O+bMvjJu/dUtERGx9T99OL444/AYMGBAzzYLFy6Mi7/z7Xhw7l9ixMhR8fmDD42Jk7bIqmQAAAAAAAAAAAAAAABqJJlPmrnrztvj1l/9Mo474aQ47YyzY+5f/9LTQLPMXiUHTgAAIABJREFUDddfG6+++kqcNfPc+Oi228cF530zFnd1ZVQxAAAAAAAAAAAAAAAAtZJM08zkKVPj0u9fGR/44OYxfPjwaGpqisbGxl7bPPzQg7HtdjvE+mPHxi677hZtbfPiH/94KaOKAQAAAAAAAAAAAAAAqJWmrAuoti8f+6V4/u/PxYSJk2KXXT/R67kF7e3R0tISERGDBw2OiIj29vbYcCX76erqiq4VPoWmXO6sWc1A/cg2pEu+IU2yDWmSbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbSim5Jpmjj/x5HjxhefjvG+eE7f/5taY8s8fX+32pdLKH7/h+mvjumvm1KBCIEuyDemSb0iTbEOaZBvSJd+QJtmGNMk2pEu+IU2yDWmSbUiXfEOaZBuKKZmmmWeeeTpemz8/ttzqQzFq1OiYMGlSPPTg3F5NM0OGDI2Ozo6IiOgslyMiYujQ4Svd37S9943d95zW83O53BlHzDi4hiMA6kG2IV3yDWmSbUiTbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEMxJdM08+zTT8X3Lr04Tjz5tGhpbY3HHv1b7LHXtFjS3R0NjY0REbH5hInxh9/9Nnbcaee48/bbYuSoUTF23LiV7q+5uTmam5vrOQSgDmQb0iXfkCbZhjTJNqRLviFNsg1pkm1Il3xDmmQb0iTbkC75hjTJNhRTMk0zO+08JZ555umYedYZUalUYrsddoxdd9s9zpl5VkyYOCn22GtaTNtn33jppRfjK8cdEyNHjorDjzwmGt9sqAEAAAAAAAAAAAAAACAdyTTNlEqlOGD6QXHA9IN6PX7cCV/t+X699daLY7/8lXqXBgAAAAAAAAAAAAAAQJ01ZF0AAAAAAAAAAAAAAAAAVJumGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkNGVdQETE7bf9Oh5/7LH4wiEzYlG5HN+bdUk8+/RTsfU2H4n9/nX/KJVKWZcIAAAAAAAAAAAAAABAgWTeNHP7b26Niy88P9610cYRETFn9lVx1x2/iYiIZ599NloHtca/TNsnwwoBAAAAAAAAAAAAAAAomoasC/j5T38S6603JD5/8CGxpLs77rzjtthuh53im9++MMaOGxd33HZr1iUCAAAAAAAAAAAAAABQMJk3zbz00kvx4Y98ND64+cR44onHo7OzMz7xyT1i7NhxMWHCxHjllZezLhEAAAAAAAAAAAAAAICCybxpZsiQIfH3556LJUuWxO9+e2cMHDgw3rfp+yNiaUNNa+ugjCsEAAAAAAAAAAAAAACgaJqyLuDDH902bv75z+KQz0+Pzs7O2HnylGhsbIxvzDwrHn7owZgy9eNZlwgAAAAAAAAAAAAAAEDBZP5JM/v967/Fh7beJhYtKse73/Oe+NwB0yMi4q9/uT/Gjx8fn/3cv2VcIQAAAAAAAAAAAAAAAEWT+SfNtLa2xpe/clJ0d3dHY2Njz+NHH3t8TJw4KZqamzOsDgAAAAAAAAAAAAAAgCLKvGlmmRUbZiIitvrQ1hlVAgAAAAAAAAAAAAAAQNFl3jTT1jYvLr9sVjz80IPR3t7+tudLpYir5vw4g8oAAAAAAAAAAAAAAAAoqsybZr570Xfi/vvuiYiI1tbWKJVKGVcEAAAAAAAAAAAAAABA0WXeNPPIww/FBuM3jFNOOyOGDhu2Tvu68Ybr46af/iRef31RbPmhrWPGYUdFS0tLz/O/veuOOP9b3+j5eb0hQ+K737tinY4JAAAAAAAAAAAAAABA/mTeNDOwZWB88IObr3PDzF8euD/mzL4qjj/x5Bg+YkSc+bVT4hc33Rif2uczPdvMb2uLD3xg8zjuK1+NiIhSqWGdjgkAAAAAAAAAAAAAAEA+Zd40M3nK1Ljnz3+K7u7uaGxsXOv9NDU1xf4HTI8tttwqIiI2GD8+Xpv/Wq9t5re1xYiRI2Pw4PXWqWYAAAAAAAAAAAAAAADyLfOmmdfmz48XX3ghTjjumFh//bFve75UKsV/Hn/iO+5nwsRJMWHipIiIeObpp+Lxxx6N/Q84sNc2bW3z4rFH/xaHH/qFGDJ0aPzb//z3niabt+rq6oqurq6en8vlzjUZFpBTsg3pkm9Ik2xDmmQb0iXfkCbZhjTJNqRLviFNsg1pkm1Il3xDmmQbiinzppnbf3NrREQ8//fn4vm/P7eSLUprtL9XX30lZp51Ruyx57TY9P2b9Xpu6i7/IyZM2iI22+yDcfPPfxbnffO/48JLvhdNzc1v288N118b110zZ42ODeSfbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYipVKpVKlgU89ODcd9xm2SfIvJP29vY47X+fGJtu+v6YcfhRUSr1brhpa5sXgwcNjgEDB8bfn3sujvtfR8a3zr841h/79k+4WVkn4BEzDo5ZP7g6Bg0a1Kd6gPyRbUiXfEOaZBvSJNuQLvmGNMk2pKka2S6dPa+qNVWOH1nV/UF/5doNaZJtSJNsQ7rkG9Ik21BMmX/STF8bYt7JonI5vn7m6TF69Og44MCDorOzI0qlhmgZODAaGhsjIuK/zz4zRo8eEwd+/ovx27vuiPXWGxIjR678D9+bm5ujeSWfQAMUm2xDuuQb0iTbkCbZhnTJN6RJtiFNsg3pkm9Ik2xDmmQb0iXfkCbZhmLKvGnm9FNPXu3zpVLESaec/o77ufvu38cTTzwWERGHfH56RESMHjMmBg0aHPt8er/Ydvsd4pAZh8esSy6KY46cEeuPHRdf+l/HRZOFCwAAAAAAAAAAAAAAIDmZN808/NDcd9ii1Kf9TJ4yNSZPmbrabTZ593via2ee3cfKAAAAAAAAAAAAAAAAKKrMm2b+6+vfWOnjLzz/fHzn/HPj379wSJ0rAgAAAAAAAAAAAAAAoOgyb5rZ5N3vWeXjd915e9zyi5vi47vsWueqAAAAAAAAAAAAAAAAKLKGrAtYncbGxnjpxReyLgMAAAAAAAAAAAAAAICCyfyTZn524w1ve6wSlXjpxRfj3nv+FOPGbZBBVQAAAAAAAAAAAAAAABRZ5k0zV13x/YgoreSZSjQ0NMa+n/1cnSsCAAAAAAAAAAAAAACg6DJvmtnnM59922MNpVIMGzY8Jm25VYwdOy6DqgAAAAAAAAAAAAAAACiyTJtmKpVKTJy4RQwfPjw2GL9hlqUAAAAAAAAAAAAAAACQkIYsD14qleK//s9pcdNPb8yyDAAAAAAAAAAAAAAAABKTadNMRMT7N/tAPPPMU1mXAQAAAAAAAAAAAAAAQEKasi5g5MiR8du77oyzzzw9xo4b97bnS1GKAz9/cAaVAQAAAAAAAAAAAAAAUFSZN8389q47IiLigfvvXcUWmmYAAAAAAAAAAAAAAABYM5k3zRx62JFZlwAAAAAAAAAAAAAAAEBiMm+amTxl6jtuc/9998QTjz8en/7MZ+tQEQAAAAAAAAAAAAAAAEXXkHUBfXHfvffEddfMyboMAAAAAAAAAAAAAAAACqIQTTMAAAAAAAAAAAAAAACwJjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACSnKesCqunGG66Pm376k3j99UWx5Ye2jhmHHRUtLS09z7/y8stx4QXfiiefeDw2GL9hHHrYkbHJJu/OrmAAAAAAAAAAAAAAAABqIplPmvnLA/fHnNlXxWFHHh2nnXF2/O3hh+MXN93Ya5srr7gsBg5siZnf+HZsvPEmccmF52dULQAAAAAAAAAAAAAAALVUjKaZSs9/VqmpqSn2P2B6bLHlVrHRRhvHBuPHx2vzX+u1zcMPPRg77Tw5Ro8ZE1Om7hJPPvF4vP766zUpuby4EouXVGLR4kqUu5Z+X1689GdgeUbKBctEUeuuhZ61rctcQH/S1+zXar20DlfXmqzl5n7V6jE39bju1us1rst8OV+T514U0iTbkC75BoDicN0GqA/rLdXiXFrO3w8BK7I+Qm2lkjH3D/1HbppmOjoWxuOPPRp/+uMfeh7r6uqKiIiDDj4krv7R9av9/RMmTord9/yXiIh45umn4vHHHo0dP7Zzr20WtLdHS0tLREQMHjy457GV6erqis7Ozp6vcrmzz2MpL67EjJs7YsDMtlj4RiVm3NIRzTPbYsbNHbHwDcGCZRlZlot6ZqIa2c6i7rwpd62wtt3SUfgbH9KwLvmmb/qa/Vqtl9bh6lqTtbyo1+56qMfc1OO6W6/XuC7zZa0ohHW6L3cvCrm2tvmWbcg3125IU97fcwNrz305pMm1Ox3WW1bkPXd1+Psh8si1OzvWR2pJttPJmPuH/qUp6wK6u7tj9lWXxy2/+HksXrw4SqWIq+b8OO7+/e/i8u/PilO+dmasP3Zsn/f36quvxMyzzog99pwWm75/s3fcvlRa+eM3XH9tXHfNnD4fd0XNDRGXz30jRraUYujAUlw+942IWPrYdz8xOBpWcUzoL5ZlJGLpr7M+Obhux65GtiPqX3feNDeaC/JnXfJN3/Q1+7VaL63D1bUma3lRr931UI+5qcd1t16vcV3my1pRCOt0X+5eFHJtbfMt25Bvrt2Qpry/5wbWnvtySJNrdzqst6zIe+7q8PdD5JFrd3asj9SSbKeTMfcP/UvmTTM/+uHVcdNPfxIbb7xJdHV1xYsvvhgRERttvEl0dnbED2dfGUcdfWyf9tXe3h5nnn5qTJy0Rez3uX972/NDhgyNjo6OiIgol8tLHxs6bKX7mrb3vrH7ntN6fi6XO+OIGQf3qY6uJRHTJw2IK+a+Ee2vV2L6pAFx+dw3YvqkAdH+eiUGDyhFU24+4wfqb1lGluWia0nULRPVyHYWdedNV/db5qK7/84F+bEu+aZv+pr9Wq2X1uHqWpO1vKjX7nqox9zU47pbr9e4LvNlrSiEdbovdy8Kuba2+ZZtyDfXbkhT3t9zA2vPfTmkybU7HdZbVuQ9d3X4+yHyyLU7O9ZHakm208mY+4f+JfOmmbvu/E28972bxun/9fX4/qzv9jTNjN9ww9h6m4/E3L880Kf9LCqX4+tnnh6jR4+OAw48KDo7O6JUaoiWgQOjobExIiI2nzAx7rz9tth884nxm9t+He9//wdiwIABK91fc3NzNDc3r9WYWptKcdFug2PWJwfH4iURF+269PuuJRGliGhp8lEz9G8rZqRrydKf66Va2a533XnT2lxavrZ1L/0ZsrYu+aZv+pr9Wq2X1uHqWpO1vKjX7nqox9zU47pbr9e4LvNlrSiEdbovdy8Kuba2+ZZtyDfXbkhT3t9zA2vPfTmkybU7HdZbVuQ9d3X4+yHyyLU7O9ZHakm208mY+4f+JfOmmYULFsRWW20dpVJpaUfJWyxatKhP+7n77t/HE088FhERh3x+ekREjB4zJgYNGhz7fHq/2Hb7HeKA6QfFhRecF/95zJExfsMN4z8OP6pq43irZcFZseNM9xkst7KMFEFR666FZTc65gL6l75mv1brpXW4utZkLTf3q1aPuanHdbder3Fd5sv5mjz3opAm2YZ0yTcAFIfrNkB9WG+pFufScv5+CFiR9RFqK5WMuX/oPzJvmtlg/Ibx178+EAsXLuz1+FNPPhH3/vmP8a53vatP+5k8ZWpMnjJ1tduMHjMmTj719LWuFQAAAAAAAAAAAAAAgGLIvGlm2qc+Hd/+1jlx7JcOj5bWloiIOPvMr8WDc/8aixd3xyf32CvjCgEAAAAAAAAAAAAAACiazJtmtt9xp1i4cEHMvuryWLCgPSIiHrj/vmhqao79/vVz8bHJ/5xxhQAAAAAAAAAAAAAAABRN5k0zERH/Y7dPxuQpU+PRRx+J+fPnR2tra2z2gQ/GkCFDsy4NAAAAAAAAAAAAAACAAsq8aebC878VO+08JSZtsWVM2mKrrMsBAAAAAAAAAAAAAAAgAZk3zdx5x2/izjtuj2HDh8UOO3wsdvzYzvHe922adVkAAAAAAAAAAAAAAAAUWOZNM/v/zwPjvnvuiUf/9nD8/KYb4+c3/TTGjRsXO+08OXbYcecYt8EGWZcIAAAAAAAAAAAAAABAwWTeNLPnXp+KPff6VHR2dsYD990b993753jg/vvi2h/NiWt/NCfet+mmsePHdo4pUz4eLa2tWZcLAAAAAAAAAAAAAABAATRkXcAygwYNivdt+v5493ve++any1QiohJPPP5YXH7ZrDj6yP+IJ554POsyAQAAAAAAAAAAAAAAKIDMP2nmqaeejD//8e7485/ujueefTYiIhoaGmLrf9omdtp5SkyctGXcefttMfuqK2LWJRfGmWefk3HFAAAAAAAAAAAAAAAA5F3mTTNfPf7YiChFRCXe/Z73xs6T/zl22PFjMXTYsJ5t9thrWvzlgfvib488klmdAAAAAAAAAAAAAAAAFEfmTTMjRo6MnT42JXaePCU2fNdGq9zuU/t8Jtpfe62OlQEAAAAAAAAAAAAAAFBUmTfNnH/hpVEqlSIiYuGCBdE2vy2GDRseQ4cO7bXd5hMmZlEeAAAAAAAAAAAAAAAABZR500ypVIpbbr4pfnL9j2PevHk9j48cOTL+Ze99Ytfdds+wOgAAAAAAAAAAAAAAAIoo86aZH1/3o7h2zg8johLjxm0QQ4cNi4ULFsQLL7wQ3591aXQs7Ii9P/2ZrMsEAAAAAAAAAAAAAACgQDJvmvn1LTfH4MGD44STTon3vW/TnseffurJOONrp8Qvb/65phkAAAAAAAAAAAAAAADWSEPWBSxaVI6tt/lwr4aZiIh3v+e9sfU2H46OjoUZVQYAAAAAAAAAAAAAAEBRZd4088HNJ0TbvHkrfe7VV16JzT6weZ0rAgAAAAAAAAAAAAAAoOiasjjoz268oef79dcfFzf/4qa49OLvxAbjN+x5/Lnnno1HHn4w9j/gwCxKBAAAAAD+P3t3HidXVeYN/KleknQSAumwCrjM4EIScEMWYZRBXnaMYMTB4YVBIkRZxOFlExCU0QEZRgcHRUZEdhAQEVBBBHVkREEWh10WUREQSCAkXUB1d71/QDrprN3pqrrnnv5+P598kqq6qfOcU/d3763QDxcAAAAAAAAASqyQppmLLvhORFQWe6YeN/30J0s99+q258Wuu88Y8nu/8MLzccsvfxG/+NnNcfChh8eGr3/DoNcfevCBOPH4YwY9d+75l8S4rq5hzQEAAAAAAAAAAAAAAIB0FdI0s+fMvSIqlZVvOEzVajUO/eSB8cY3vike/8Njy9zm+efnxpQpa8app3914DkNMwAAAAAAAAAAAAAAAHkppGlm5l57N+V9x4wZE1/7xtnxyssvx2EHH7TMbZ6fOzfWmDw5JkyY2JQaAAAAAAAAAAAAAAAAKF4hTTOLu/Lyy1b4entHe6z/ug3i3Zu9J9ra21e8bXt7rL76GvHMX59e7jZz58yJZ575axz2qQOjc8yY2HPmXrH1Nu9bartarRa1Wm3gcbXas5KZAGUg25Av+YY8yTbkSbYhX/INeZJtyJNsQ77kG/Ik25An2YZ8yTfkSbahnBJomrk0IiqvPaov9sriz1XijW98Y5zw+S9GV1fXiMZ7zxZbxuTu7pg6bZP49a3/E2edeUZMnTY9Jk/uHrTd1VddsdKGHqB8ZBvyJd+QJ9mGPMk25Eu+IU+yDXmSbciXfEOeZBvyJNuQL/mGPMk2lFOlXq/XV75Z83z/e5fHdy+9OD6w/Y7xuvXXH3j+qaeejJ9c/6P4wPY7xrPPPhN333VH7PbBD8XH9tlvpe/5zF+fjsMOPii+fPp/xIavf8Og11544fkYM2ZsdHV1xcsvvxz/tM9H4/gTT45p0zcZtN2yOgEPmT0rzjnv4hg/fvwIZw0URbYhX/INeZJtyJNsQ77kG/Ik25CnRmS7cuqchtZUP7p75RsBK+XcDXmSbciTbEO+5BvyJNtQToXfaebuu+6Mt208NQ44cPZSrz315F/i2Wf/Gkd/9nNx7FH/HLf/5tdDappZUn9fX7S1t0dExLnfOjvmzp0Thx1+RNxxx2+jo6Mj1nvd+kv9nc7Ozujs7Bz+hICkyTbkS74hT7INeZJtyJd8Q55kG/Ik25Av+YY8yTbkSbYhX/INeZJtKKe2ogt45OGHo3vKmst8bdKk1eOhBx+IiIg3velv4rnnnl2lMU4/7ZS47pqrIyJin333j7Fjx8YRhx8S1/7gqvjUoYdHd7f/axUAAAAAAAAAAAAAAEBOCr/TzKRJk+LO394WD//+odjozW8ZeP6RRx6OO357W3R1vXqrqqeffjqmLKe5Zklrrb1OXHL59wceH3nMcQN/XnOtteKzJ3y+QdUDAAAAAAAAAAAAAACQosKbZnbYaZe49OIL4nPHHRMbbLhhTJ7cHS+88Hz88fHHI6Iee8zcKx568IG4/757Y8+P7FV0uQAAAAAAAAAAAAAAAJRA4U0zH/zQnjFm7Jj4/pVXxJ//9Mf485/+GBERkyd3xx4z94rt/8+O8crLL8duH5wRH5yxZ8HVAgAAAAAAAAAAAAAAUAaFN81EROy0826xw467xHPPPhMvvPBCTO7uju7uKVGpVCIiYszYsfGxffYruEoAAAAAAAAAAAAAAADKoq3oAha6+6474lf/c0vc8stfxJQpa0alUonHHnu06LIAAAAAAAAAAAAAAAAoocLvNPP83Lnxb6d+KR599JGIqEdEJfbbf1b8+IfXxoXnnxvHHn9STJu+SdFlAgAAAAAAAAAAAAAAUCKF32nmgvO+HY8++kjssNPOMXXa9IHnt/37D8Tqq68R37visgKrAwAAAAAAAAAAAAAAoIwKb5q5+647453velf808c/EetvsOHA8+O6umLTt78jHnv0kQKrAwAAAAAAAAAAAAAAoIwKb5rp7++LCRNXW+Zrc+bMiYhKawsCAAAAAAAAAAAAAACg9ApvmnnzW94at/36V3H/ffcOPFev1+PHP7o2/vd3d8Wb3/LWAqsDAAAAAAAAAAAAAACgjDqKLuCje+8TXzjxuDj5pBOio6M9IiIO2O9j8dJLL0d7e3t85KN7F1whAAAAAAAAAAAAAAAAZVP4nWb+5m83ihO/8KWYOm1a9PX1RUQ9XnrppXjjm94Uxx5/Ymz05rcUXSIAAAAAAAAAAAAAAAAlU8idZua/+OKgx2uttXYc/s9HRV9/X/T19kU96jF2zNiBbSeutloRZQIAAAAAAAAAAAAAAFBShTTNHHjAfkPetlKJuOiy7zWxGgAAAAAAAAAAAAAAAHJTSNPMlDWnRCUqRQwNAAAAAAAAAAAAAADAKFBI08zXvv5fRQwLAAAAAAAAAAAAAADAKNFWdAEAAAAAAAAAAAAAAADQaJpmAAAAAAAAAAAAAAAAyE5H0QU00gsvPB+3/PIX8Yuf3RwHH3p4bPj6Nwx6ff78+fHNr38t7r3ndzG5e0p8fNZBMW36JgVVCwAAAAAAAAAAAAAAQLNkc6eZarUah37ywLj1llvi8T88tsxtrr7qinjuuWfjlNO+GptvsVWcecZXordWa3GlAAAAAAAAAAAAAAAANFs2TTNjxoyJr33j7Dj08H9e7jb333dvbLHle2PtddaJ7XfYMebOnRNPP/1UU+rp7a1Hrbcevb31qNbq0dtfj5d66/HKYo8X/53R7aXeelR7R9f+UNb9v6x1N4O1GGwgw73Wo4yGsz83Y9tmjT9UZZpTkeObUzmP+62o2xjpjZPLGCyf9YeVK+N3FNmGfMk3AJSH8zZAazje0iip7Eup1AG0jtxDuvzMBCwtm6aZ9vb2WH31NVa4zYvz5sW4ceMiImLC+AkRETFv3rxlblur1aKnp2fgV7XaM+RaqrV61OoRvfWIWj3isBsWRK0vYv4r9eirR8y+YUF0njY3Zr/2/ME3LHDQGMWqtXrMf6Ues69ftF/kvj9Ua/VBOWjlfEea7aLqTo21GKzau1iGr19Qqh9Ky8mq5ns4+3Mztm3W+ENVpjkVOb45OXcbozzj5DJG7lLPNpRdkd9RWnFdDrSeczfkaSTZBtLmuhzy5NydD8dbFpfDd+5U6oDU5HzulntGs9Sz7WcmYNkq9Xo9qz31mb8+HYcdfFB8+fT/iA1f/4ZBr3364INil90+GDvuvGu8VK3G/vvuHSd+4Yvxto2nLfU+V3z3krjy8suWev6c8y6O8ePHr7CG3v7BS/rx6+bHt3edGP31iLZKROdpcwdeqx05ObY8f17cuu+k6GirDGeqZKK3vx799Yix/zZ4v8h5f+jtry+Vg1bNd6TZLqru1FiLwaxHGlY138P5/JqxbbPGH6oyzanI8c3JudsY5RknlzFyl3q2oezKeO6WbUibczfkaSTZXqhy6pyG1lQ/uruh7wejletyyFMjzt2kwfGWxeXwnTuVOiA1OZ+75Z7RLPVs+5kJWLaOogtopdVWmxQLehZERERPtRoREZMmLfvuNDP2mBm77DZj4HG12hOHzJ41pHFqfYMfj22rRK0vYkGtHhM6K7Hv9DFx/j2vxL7Tx0StL2LTtdqj1hfRkc19fxiOhfvGkvtFzvtDrS8Km+9Isz2aPqcVsRaD1fqXWI/+0b0eRVnVfA9nf27Gts0af6jKNKcixzcn525jlGecXMbIXerZhrIr8jtKK67LgdZz7oY8jSTbQNpcl0OenLvz4XjL4nL4zp1KHZCanM/dcs9olnq2/cwELFv2d5rp7+uLtvb2iIi46PzvxN133xlHHHVs/PfPb46bb7oxzjjz7Gh/7fUV6enpiQP2+9iQOwF7e+tRj4hKRNTqEZ3tEb39EW0R0ffa41rfot+7OnXYjWYvvba/dLaNnv2hWqsnsf8PN9up1J0CazFYtbf+aob7I7o6rEcKhpPTdhpqAAAgAElEQVTv4ezPzdi2WeMPVZnmVOT45pTGcT/Fc7cx0hsnlzFGkxSzDWWXyneUZl2XA8Vy7oY8DTfbEe40A2XhuhzytCrnbtLheMvylPU7dyp1QMpyO3fLPbwqxWz7mQlYWvZ3mjn9tFNi6rTpsevuM2LGnjPjqaeejGOP/Ex0d0+Jgw/9zJAaZlZFx2I/jLBwkZfsolv4WHcd4xbfX0bJ/rDwJFm2+Za17mawFoMt/CE061FOw9mfm7Fts8YfqjLNqcjxzamcx7lW1G2M9MbJZQyWz/rDypXxO4psQ77kGwDKw3kboDUcb2mUVPalVOoAWkfuIV1+ZgKWll3TzFprrxOXXP79gcdHHnPcwJ8nTpwYRxx1bBFlAQAAAAAAAAAAAAAA0EL6uwAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACy01F0AY30q1t+GZdefEEsWDA/Ntt8yzjgE7Ojs7Nz4PWHHnwgTjz+mEF/59zzL4lxXV2tLhUAAAAAAAAAAAAAAIAmyqZp5sUX58VZXz8j9p91UGy00Vviiyd/Lm7+6U9ih512Gdjm+efnxpQpa8app3914DkNMwAAAAAAAAAAAAAAAPlpK7qARnnk4d9HvR7x/m23iw023DDe+c53x/333Ttom+fnzo01Jk+OCRMmDvwCAAAAAAAAAAAAAAAgP9ncaWbevHkxdtzYqFQqERExfsKEePrppwdtM3fOnHjmmb/GYZ86MDrHjIk9Z+4VW2/zvmW+X61Wi1qtNvC4Wu1pXvFAy8g25Eu+IU+yDXmSbciXfEOeZBvyJNuQL/mGPMk25Em2IV/yDXmSbSinbJpmluW1/pkB79liy5jc3R1Tp20Sv771f+KsM8+IqdOmx+TJ3Uv93auvuiKuvPyyFlUKtIpsQ77kG/Ik25An2YZ8yTfkSbYhT7IN+ZJvyJNsQ55kG/Il35An2YZyyqZpZrXVVouXqtXo7++Ptra2qPb0xKTV1xi0zZQ114z1Xrd+dHV1xVprfyiu+O4l8Zcnnlhm08yMPWbGLrvNGHhcrfbEIbNnNX0eQHPJNuRLviFPsg15km3Il3xDnmQb8iTbkC/5hjzJNuRJtiFf8g15km0op2yaZv52o7dEW1tb/PQn18fU6ZvEXXfdEXt8eK/o7+uLtvb2iIg491tnx9y5c+Kww4+IO+74bXR0dMR6r1t/me/X2dkZnZ2drZwC0AKyDfmSb8iTbEOeZBvyJd+QJ9mGPMk25Eu+IU+yDXmSbciXfEOeZBvKqa3oAhpl0qRJMftTh8U1V18VJx53dGy66Tvi/dtuF6efdkpcd83VERGxz777x9ixY+OIww+Ja39wVXzq0MOju3vpu8wAAAAAAAAAAAAAAABQbtncaSYiYqutt4mttt5m0HNHHnPcwJ/XXGut+OwJn291WQAAAAAAAAAAAAAAALRYNneaAQAAAAAAAAAAAAAAgIU0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJCdjqILaKRf3fLLuPTiC2LBgvmx2eZbxgGfmB2dnZ0Dr8+fPz+++fWvxb33/C4md0+Jj886KKZN36TAigEAAAAAAAAAAAAAAGiGbO408+KL8+Ksr58Re8zcK046+ZS4+6474uaf/mTQNldfdUU899yzccppX43Nt9gqzjzjK9FbqxVUMQAAAAAAAAAAAAAAAM2STdPMIw//Pur1iPdvu11ssOGG8c53vjvuv+/eQdvcf9+9scWW742111kntt9hx5g7d048/fRTTamnt7e+3F/VWj16+wf/zui25H4xGpR1vmWtuxmsxWDWY5EyrsVwam7GtjmOb07lGL9Zc0pJK+o2RnrjGCOtMZqhrHVDK5UxJ2WoOfUak6+v97X6etOsrwxS/4yXp6x1LymXeZAe+xaQkjIck8pQI3nz3YZGcCyjUVLZl1KoI4UaUqkjhRpSUsb1KEPNqdeYfH0luKZMfg0Tr295cvk5AGOkN05LxmjBsatZY2TTNDNv3rwYO25sVCqViIgYP2FCzJs3b9A2L86bF+PGjYuIiAnjJwz8vWWp1WrR09Mz8Kta7RlyLdVaPWr1WO6vL9+wIGp9EbNvWBCdp82N2TcsKN1Bm8ZZuL+Mpv2hWqsXNt+RZns0fU4rYi0Gsx6LlDHfw6m5GdvmOL45lWP8Zs2p0VI/dxsjvXGMkdYYy5N6tqHsypjvMmQ79RqTr6+3HrOvf62+6xck/R8CU1XGbEekv28OVS7zID1lzTaQNtfl0DxFfrdx7s6HYxmLy+E7dwp1pFBDKnWkUENKyvi9uwyfYeo1Jl9fCf69PPk1LGG2I/L5OQBjpDdOS8ZowbGrmWNU6vV6WkeyVfSLn98cF5z37fivb18QEREXnn9u/OGxR+P4E08e2ObTBx8Uu+z2wdhx513jpWo19t937zjxC1+Mt208ban3u+K7l8SVl1+21PPnnHdxjB8/foW19Pav5P/ovWB+dE2YGJ2nzR14rnbk5Ohoq6zw75GnhfvLaNofevvrhc13pNkeTZ/TiliLwazHImXM93Bqbsa2OY5vTuUYv1lzarTUz93GSG8cY6Q1xvKknm0ouzLmuwzZTr1G9eWvjNmOyOezz2UepKes2V6ocuqchtZUP7q7oe8Ho5Xrcmiesp+7SYNjGYvL4Tt3CnWkUEMqdaRQQ0rKeO4uw2eYeo3qG7nUayxjtiPy+TkAY6Q3jjFWLps7zay22mrxUrUa/f39ERFR7emJSauvscQ2k2JBz4KIiOipViMiYtKkwdssNGOPmXHOeRcP/PrPs7415FpqfSv+9e+/qEStL2Lf6WMi4tXfa33DnjKZWLhfjKb9ocj5jjTbo+lzWhFrMZj1WKSM+R5Ozc3YNsfxzakc4zdrTo2W+rnbGOmNY4y0xlie1LMNZVfGfJch26nXmHx9/UvU119wQSVUxmxHpL9vDlUu8yA9Zc02kDbX5dA8RX63ce7Oh2MZi8vhO3cKdaRQQyp1pFBDSsr4vbsMn2HqNSZfXwn+vTz5NSxhtiPy+TkAY6Q3TkvGaMGxq5ljZHOnmXnz5sWhn5wV++y7f0ydvkl86eQTY48P7xXbbbd9tLW3R0TERed/J+6++8444qhj479/fnPcfNONccaZZ0f7a6+vSE9PTxyw38eG/H/o6F3B7YBq9YjO9ld30IW/d3Wm04FJ6/X21gftF6Nhf6jW6knMd7jZTqXuFFiLwazHIqmsxXDyPZyam7FtjuObUznGb9acminFc7cx0hvHGGmNMRQpZhvKLpWcNOu6vCip15h8fb316Gx79R+8uzrSq68MUvmMR+u5O5d5kJ5U9q3hZjvCnWagLFyXQ2Ol8t1mVc7dpMOxjOUp63fuFOpIoYZU6kihhpSksh6uy1sr+foSuaZckeTXMJH6Ujx3GyOtMVo1TkvGaMGxq1ljdDTsnQo2adKkmP2pw+KSi86Pyy65MN6z+Zbx/m23i9NPOyWmTpseu+4+I2bsOTOeeurJOPbIz0R395Q4+NDPDKlhZlV0rOBDWrjoHW2Df2f06uioLLVf5G7hAbls8y1r3c1gLQazHouUcS2GU3Mzts1xfHMqx/jNmlNKWlG3MdIbxxhpjdEMZa0bWqmMOSlDzanXmHx9HWnXVwapf8bLU9a6l5TLPEiPfQtISRmOSWWokbz5bkMjOJbRKKnsSynUkUINqdSRQg0pKeN6lKHm1GtMvr4SXFMmv4aJ17c8ufwcgDHSG6clY7Tg2NWsMbJpmomI2GrrbWKrrbcZ9NyRxxw38OeJEyfGEUcd2+qyAAAAAAAAAAAAAAAAaLGS9dcBAAAAAAAAAAAAAADAymmaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMhOR9EFAAAAAAAAADRa5dQ5DX/P+tHdDX9PAAAAAACax51mAAAAAAAAAAAAAAAAyI6mGQAAAAAAAAAAAAAAALLTUXQBZVGv1yMiolrtKbgSGN26urqiUqk07P1kG9Ih35An2YY8yTbkqdHZjpBvSIVzN+Qpx2z39KR/XClDjZSb63LIV47nbkC2IVeuyyFfzt2Qp5Vlu1JfmFZW6Lnnno1DZs8qugwY9c457+IYP358w95PtiEd8g15km3Ik2xDnhqd7Qj5hlQ4d0OeZBvy5Loc8uXcDXmSbciT63LIl3M35Gll2dY0M0T9/f0xd+6cGDduxV1I1WpPHDJ7VvznWd+Krq7GXjQ1i5pbQ82N0egu36Fme6EU16Qo1mIw67HIqq5FM/L9l788EUd+5tCsPpcc9zVzKoeUsj2cc3cr5PJ55zKPiHzmkvI8is52ymsTkX59EenXqL6RW5Uam/F/zhtOvnNd11ZS38ilXmNZr8tTX9ehymUeEfnMZbTPQ7YbI5d5ROQzl1zmEeG6vFlSr1F9I5d6jWU9dzdS6p9RRPo1pl5fhBqHquhsp7AGqbAWi1iLwVyXN0fqNapv5FKvsazX5amv61CZR3pymUuzst3RiOJGg7a2tpgyZc0hb9/VNb7hncbNpubWUHNahpvthXJek+GyFoNZj0WKXou2trbo7p6SRC3NYE7lYE6Nt6rn7lYoem0aJZd5ROQzl1zmsSK5XpenXl9E+jWqb+SKrnFV8l10zUOReo3qG7nUayy6vlzP3UOVyzwi8pmLeTSGbOcxj4h85pLLPCKKn4vr8mKob+RSr7Ho+lL49/Ki12AoUq8x9foi1Nhqo/26vBGsxSLWYrCi18N1eTHUN3Kp11h0faP93G0e6cllLo2eR1vD3gkAAAAAAAAAAAAAAAASoWkGAAAAAAAAAAAAAACA7LSfdNJJJxVdRG7a2tpi6rRNor29vehShkzNraHmPFiTRazFYNZjkZTWIqVaGsWcysGcRpdc1iaXeUTkM5dc5tEMqa9N6vVFpF+j+kauDDUuqQw1p16j+kYu9RpTr295ylr3knKZR0Q+czGPYpW17iXlMo+IfOaSyzwiyjmXMtSceo3qG7nUa0y9vlYowxqkXmPq9UWosSyswSLWYhFrMVgZ16MMNadeo/pGLvUaU69vecpa95LMIz25zKUZ86jU6/V6w94NAAAAAAAAAAAAAAAAEtBWdAEAAAAAAAAAAAAAAADQaJpmAAAAAAAAAAAAAAAAyI6mmSGq1+vR09MT9Xq96FKABpJtyJd8Q55kG/Ik25Av+YY8yTbkSbYhX/INeZJtyJNsQ77kG/Ik21AOmmaGqFqtxgH7fSyq1WrRpQANJNuQL/mGPMk25Em2IV/yDXmSbciTbEO+5BvyJNuQJ9mGfMk35Em2oRw0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2ekouoAi/Ou/nBS/u/uugce77j4j9tl3/+IKAgAAAAAAAAAAAAAAoKFGZdPM3LlzY/9ZB8bW27wvIiI6O8cUXBEAAAAAAAAAAAAAAACN1FZ0AUV4fu7cWHvtdWLChIkxYcLEGDOmmKaZam89evvrUe2tFzI+wEg4hkHjyBNAYzmu5s9nDHmSbciXfEOeZBsAYNW5lqJR7EsAjFbOgZSJ/bV4o+5OM721Wsyf/2JcdMF5cfY3zow3v+WtMeugT8Zqq00atF2tVotarTbwuFrtaWgd1d56zL5+QZx/zyux7/QxcdaOE6Kro9LQMYClNTvbo4VjGCkqa77lCVasrNmmOI6r5TCSbPuMIW2rmm/ZhrQ5d0OeZBvyNdJ/U6ucOqeh9dSP7m7o+8Fo5d/L8+FaisW5Lod8OXdD8xR5DpRthss1WxpGXdNMVCrxTwd8ItZd93Uxfvz4OPNrX40rvntp7H/AgYM2u/qqK+LKyy9rWhmdbRHn3/NKRLz6+zk7T2jaWMAizc72aOEYRorKmm95ghUra7YpjuNqOYwk2z5jSNuq5lu2IW3O3ZAn2YZ8+Tc1yJNs58O1FItzXQ75cu6G5inyHCjbDJdrtjSMuqaZ/v7+2GyzLaJ7ypSIiNjsPZvHw7///VLbzdhjZuyy24yBx9VqTxwye1bD6qj1R+w7fcxA11itP6KjrWFvDyxHs7M9WjiGkaKy5lueYMXKmm2K47haDiPJts8Y0raq+ZZtSJtzN+RJtiFf/k0N8iTb+XAtxeJcl0O+nLuheYo8B8o2w+WaLQ2jrmnm6aeejKOO+HQcdvj/i7/ZaKP43d13xds2nrrUdp2dndHZ2dm0Oro6KnHWjhPinJ0nRK0/3GYJWqTZ2R4tHMNIUVnzLU+wYmXNNsVxXC2HkWTbZwxpW9V8yzakzbkb8iTbkC//pgZ5ku18uJZica7LIV/O3dA8RZ4DZZvhcs2WhlHXNLPh698Q+318Vlx4/rlRrfbEJpu+PT7y0Y8VUsvCnV63GFBGjmHQOPIE0FiOq/nzGUOeZBvyJd+QJ9kGAFh1rqVoFPsSAKOVcyBlYn8t3qhrmomI2Gnn3WKnnXcrugwAAAAAAAAAAAAAAACaRL8SAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHZGbdPMV0//cuz9kQ8VXQYAAAAAAAAAAAAAAABNMCqbZm6/7Tdx229uLboMAAAAAAAAAAAAAAAAmmTUNc309PTEued8M3bcedeiSwEAAAAAAAAAAAAAAKBJOoouoNUuufD82GTTd8T0TTaNH113zXK3q9VqUavVBh5Xqz2tKA9oMtmGfMk35Em2IU+yDfmSb8iTbEOeZBvyJd+QJ9mGPMk25Eu+IU+yDeU0qppmHrj/vrj9tl/Haf9+Rjz00AMr3Pbqq66IKy+/rEWVAa0i25Av+YY8yTbkSbYhX/INeZJtyJNsQ77kG/Ik25An2YZ8yTfkSbahnCr1er1edBGt8q//8vm4957/jbFjx0RfX3+8/PJLMX78+DjnvIuX2nZZnYCHzJ4V55x3cYwfP76VZQMNJNuQL/mGPMk25Em2IV/yDXmSbciTbEO+RprvyqlzGlpP/ejuhr4fjFbO3ZAn2YZ8yTfkSbahnEbVnWY+efBhUau9EhER99zzv3H2N/4zTjntK8vctrOzMzo7O1tZHtACsg35km/Ik2xDnmQb8iXfkCfZhjzJNuRLviFPsg15km3Il3xDnmQbymlUNc2sMXnywJ9XX/2PERGx1trrFFUOAAAAAAAAAAAAAAAATdJWdAFFede73xOXXP79ossAAAAAAAAAAAAAAACgCUZt0wwAAAAAAAAAAAAAAAD50jQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdkrTNPPEn/8UN99048Djn9380zjv3G/F7+6+s8CqAAAAAAAAAAAAAAAASFEpmmYeevCBOO6YI+NH110TERE//uG1cfY3zozrf3RdnPqlk+O239xacIUAAAAAAAAAAAAAAACkpBRNM9+99KLo7e2N92+7XURE/PDaH8Qb3/Sm+MTsg6Ora3xcc/VVBVcIAAAAAAAAAAAAAABASkrRNPOHxx6LzbfYMnbdfUb85Ykn4tlnn4kPf+Sj8ffbbR+bvWfz+POf/lR0iQAAAAAAAAAAAAAAACSkFE0z7e3tUavVIiLizjtuj7a29pg6bZOIiHjp5ZeiUimyOgAAAAAAAAAAAAAAAFLTUXQBQ/HWt20ct9/2mzjtlC/Ggw/cFxtPnRZdXV3xw2t/EHfcfntMmz696BIBAAAAAAAAAAAAAABISCnuNLP3PvvGGmusEXfecXv09fXF3v/4fyMi4vLLLo729rbY6x/+seAKAQAAAAAAAAAAAAAASEkp7jSz3nqvi9P/48x49JGHY/31N4g1Jk+OiIiddtk9tvm798X6G2xYcIUAAAAAAAAAAAAAAACkpBRNMxERXV1dMW36JoOe++je7jADAAAAAAAAAAAAAADA0krRNNPX1xc33XhD3H/fvTFv3gtRrw9+vVKJOP7Ek4spDgAAAAAAAAAAAAAAgOSUomnm/O+cEz+5/scRUV/OFpVWlgMAAAAAAAAAAAAAAEDiStE0c+uvbokJEyfErE98MtZZd92oVDTJAAAAAAAAAAAAAAAAsHylaJqpvVKLLbbcKrbY6r1FlwIAAAAAAAAAAAAAAEAJtBVdwFC8413vjmeffaboMgAAAAAAAAAAAAAAACiJUtxpZqM3vzkuvuD8OPusM2P99TdY5ja77j6jxVUBAAAAAAAAAAAAAACQqlI0zVx43rkREfGzm26MiMoSr9YjojKkppm+vr647JIL46Ybb4iIiHe+a7P4xOyDY8yYMY0tGAAAAAAAAAAAAAAAgEKVomlmz5l7RVSWbJYZvl/+98/jpht/Ekcec3xMXG21+JeTToibbrwhdtpltwZUCQAAAAAAAAAAAAAAQCpK0TQzc6+9G/I+7992u3j/tttFRMSCBfOjo6Mj2tvbG/LeAAAAAAAAAAAAAAAApKMUTTNXXn7ZCl+vVCL2nPnRIb/fUUd8Op74859i6rTpsf0OOy1zm1qtFrVabeBxtdoz5PcH0iXbkC/5hjzJNuRJtiFf8g15km3Ik2xDvuQb8iTbkCfZhnzJN+RJtqGcStI0c2lEVCKivsQrlYE/Dadp5ujPnhBP/uWJOOMrp8fPf3ZTbPv3H1hqm6uvumKlzTpA+cg25Eu+IU+yDXmSbciXfEOeZBvyJNuQL/mGPMk25Em2IV/yDXmSbSinSr1eX7ITJTk/uu6aZT7/lyeeiJ/eeH3sOXOvmLnX3it9n8cf/0O88Pzzsenb3xEREV/99y/HmDFj41OHfHqpbZfVCXjI7FlxznkXx/jx41dxJkDRZBvyJd+QJ9mGPMk25Eu+IU+yDXmSbcjXSPNdOXVOQ+upH93d0PeD0cq5G/Ik25Av+YY8yTaUUynuNLPzrrsv97U//fHxePCBB4b0Pn/8w2Px7W99Mz57wudjXFdX/P6hB2PX3Wcsc9vOzs7o7OxcpXqBdMk25Eu+IU+yDXmSbciXfEOeZBvyJNuQL/mGPMk25Em2IV/yDXmSbSinUjTNrMiaa68dt//m1iFtu837to3HH/9DnHbKF6Ner8eW7906dthxlyZXCAAAAAAAAAAAAAAAQKuVomnmsUcfWeq5er0eTz31ZNx1x+0xYeLEIb1PpVKJffbdP/bZd/9GlwgAAAAAAAAAAAAAAEBCStE0c9wx/y8iKst5tR477ORuMQAAAAAAAAAAAAAAACxSiqaZt208NSqVwU0zlUolVl99jXj7O98Vf/e+bYspDAAAAAAAAAAAAAAAgH0PFp8AACAASURBVCSVomnmc5//YtElAAAAAAAAAAAAAAAAUCJtRRewMv39/bHPP3w4vnX2N4ouBQAAAAAAAAAAAAAAgJJIvmmmra0t1l13vXjh+blFlwIAAAAAAAAAAAAAAEBJdBRdwFC8e7PN47prr44fXP29WHfd9Za5zeZbbNXiqgAAAAAAAAAAAAAAAEhVKZpmrvnBVRERcelFFy53m4u/+71WlQMAAAAAAAAAAAAAAEDiStE083fv2zYqlUrRZQAAAAAAAAAAAAAAAFASpWia+eQhn17pNk8/9WTMmTMnNp46rQUVAQAAAAAAAAAAAAAAkLK2ogtolB9ed02cfNIJRZcBAAAAAAAAAAAAAABAArJpmgEAAAAAAAAAAAAAAICFNM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB28mmaqRddAAAAAAAAAAAAAAAAAKnoKLqARtl5191ji63eW3QZAAAAAAAAAAAAAAAAJCDZppnrrrl6WNvvuvuMWHe99ZpUDQAAAAAAAAAA8P/Zu/fwuuo6X/zvnSa9pPQCBVocGUEu0gsgxxFBECrjqAMqogzHYRgZBaVy0VEHRRkVfhz9oYwjgjAggly0XAVhHB6B4XBQkZtc9FguXkZHVKgKSGgSIE1z/oAG0lJM0p3stb55vZ4nT5udtdf38/3u9d5rPen+dAEAAECdVLZp5usXnJukMYwtB5I0ss+b9x3bggAAAAAAAAAAAAAAAKiNyjbNvG3/A5LGcJpmAAAAAAAAAAAAAAAAYKjKNs3sf8DftroEAAAAAAAAAAAAAAAAaqqyTTO/+K+fj2j7LV+61RhVAgAAAAAAAAAAAAAAQN1Utmnm2GP+KUljWNs2GsnXL758bAsCAAAAAAAAAAAAAACgNirbNLPd/AVpNIbXNAMAAAAAAAAAAAAAAADPVdmmmU8e/+lWlwAAAAAAAAAAAAAAAEBNVbZp5vkMDAyku7s7GRgYfOypvr789jcPZNH2O7awMgAAAAAAAAAAAAAAAKqkFk0zT/T25uyzzsjtt92Sp57qe95tll5y+ThXBQAAAAAAAAAAAAAAQFW1tbqA4bjk4qW56XvfSVvbpEydOjXJQDbeeONMnz49yUC238FdZgAAAAAAAAAAAAAAAHhWLZpmbr/tlrxkiy1zxlfOzWv2WJykkVNO/3JO/tK/Zdas2XnZ/PmtLhEAAAAAAAAAAAAAAIAKqUXTzGN/fCwvfelWmTx5cqZ1TkuSPPnkk5k+fYMs3H6H/J/r/7PFFQIAAAAAAAAAAAAAAFAltWiamTtvXu67956sWLEic+ZsnCS5684fJEke/sMf0tPT08ryAAAAAAAAAAAAAAAAqJhaNM0sfu1f5sEHf5uvn//VvHLnXdLePimnnfKFHLHkkNx/372Zv2Bhq0sEAAAAAAAAAAAAAACgQmrRNLP3m96Svd/0lkzfYIPM3nDD/MMh782kSe159JFHssWWW+Yf3n1oq0sEAAAAAAAAAAAAAACgQtpbXcBwNBqNHPTOdw1+v9df/lX22GNxep/ozYwZM1tYGQAAAAAAAAAAAAAAAFVUi6aZ1R568MH86le/zB8ffTSzZs3On2+xxaiaZv79yity9beuypNPPpEdXr5Tlhz+/kydOnUMKgYAAAAAAAAAAAAAAKAVatE009PTky+f8aXcfustGRh49vFGI3nlq3bJe5ccmc7OzmHt60c/vDsXX/j1fPTjn8jsDTfMZ/6/T+XbV/973vq2vxmj6gEAAAAAAAAAAAAAABhvtWiaOfecs3LbLTdn1uzZecUrXpmZs2bl8ccfz5133J7bbrklUyZPyfuO/MCw9tXe3p4DD3pntt9hxyTJZi96UR7742NjWT4AAAAAAAAAAAAAAADjrBZNM3fcfmvmzp2Xz3zuXzNt2rTBx//u7/8hH/vIh/KD228d9r4WLFyUBQsXJUn++5e/yM9++pMceNDBa23X19eXvr6+we97e3vWYwZAVcg2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbainWjTNTJ4yJfMXLBzSMJMkU6dOzfz5C3LbrTePeJ8PP/yHnHTip7PPm/bN1ttsu9bPr7zisnzj0otHXTNQTbIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG+qpMTAwMNDqIv6Ur5795dy77Mc58aQvpG3SpMHH+/r68rGjP5httn1ZDjv8qGHvr6urK8d/8uPZeuttsuSI96fRaKy1zfN1Ah655NCcfd7SdHZ2rt+EgJaRbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzrm+/GZx9paj0DH92oqfuDicq5G8ok21Au+YYyyTbUU2XvNHPSiZ8e/PtTTz2VX//61/no0R/MppvOHXx8+UMP5sEHf5v5CxYNe79P9Pbmc585IRtvvHEOOvhd6enpTqPRttYbVUdHRzo6OtZ/IkClyDaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtqKfKNs3cdecP1nrsN79+IL/59QNrPX79f16bQ967ZFj7vfXWm/Pzn/80SfLed78zSbLxJpvk1NPPWo9qAQAAAAAAAAAAAAAAqJLKNs3886dOGJP97rl4r+y5eK8x2TcAAAAAAAAAAAAAAADVUNmmmQULF7W6BAAAAAAAAAAAAAAAAGqqsk0za/rlL/4rl1y0ND+5/9488cQTmTVrdrbfYce8/YB3ZJNNNm11eQAAAAAAAAAAAAAAAFRIW6sLGI5f/NfPc9wnPpa777ojPT09mTRpUh599JF858Yb8vGPfDi//c1vWl0iAAAAAAAAAAAAAAAAFVKLO81ctPRreeqpp3LAOw7M69+4Tzo7O9PdvSLXXfvtXHLh0ly09IJ86OhjWl0mAAAAAAAAAAAAAAAAFVGLppmf3H9vdnz5/8hb3/Y3g49Nn75B3rrf/vnJfffl3nuWtbA6AAAAAAAAAAAAAAAAqqat1QUMx6RJkzJl6pTn/dnkKZPT19c3zhUBAAAAAAAAAAAAAABQZbVomtlq621zx+235abvfWfI47fe/P3ccftt2XqbbVpUGQAAAAAAAAAAAAAAAFXU3uoChuOAdxyY4z95bE475eScc9YZmT17w3R1daW7uzuTJrXlgHf8XatLBAAAAAAAAAAAAAAAoEJqcqeZbfKpEz6T+QsW5oknnsiDD/42PT3d2Xbbl+WfP3VCtn3Zdq0uEQAAAAAAAAAAAAAAgAqpxZ1mkmSrrbbOJ447ISv7+tL1+OOZPn16pkyZ0uqyAAAAAAAAAAAAAAAAqKDaNM309/fnoYcezONdj2VgYO2fz1+wcPyLAgAAAAAAAAAAAAAAoJJq0TRz/3335tSTP59HHnlkndssveTycawIAAAAAAAAAAAAAACAKqtF08x555yVRx55OH/24s0zb968JI1WlwQAAAAAAAAAAAAAAECF1aJp5je/+XW2m78gnzz+060uBQAAAAAAAAAAAAAAgBpoa3UBw7Hxxptk7tx5rS4DAAAAAAAAAAAAAACAmqhF08xb3/43ufPOH+T3v/9dq0sBAAAAAAAAAAAAAACgBtpbXcBwfPMbl6Z7RXc+9P7D09HR8TxbNHLO+UvHvS4AAAAAAAAAAAAAAACqqRZNMw8++NvBv/f397ewEgAAAAAAAAAAAAAAAOqgFk0zSy+5otUlAAAAAAAAAAAAAAAAUCNtrS4AAAAAAAAAAAAAAAAAmk3TDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMWZkE0zjz32x1z9H1flmKM/mAd+9d+tLgcAAAAAAAAAAAAAAIAma291AeOtt7c3R73vvdliiy3z37/8RavLAQAAAAAAAAAAAAAAYAxMuDvNTJ48Oaf+25dz1D9+aEzH6e0byMpVA3li5dpfvSuf/tnqbXr7Bsa0Fqpv8Jh45hiZCOp6/Ne17rFgLeBPe+77eyv2Odxt61Jnq41oTiN4jxzutnXZZ9WMR93GqN44dT1eGT6vMZRJtqFc8g1lkm0AgNFzLUWzVOVYqkIdVaihKqzFUHX5TELdOM6gukr5XEZJ7zOlzKXO59QJ1zQzadKkzJo1+09u19fXl56ensGv3t6eYY/R2zeQJdd2Z/JJj2bFUwNrfR1+TXee7E+WXNudjpMezZJru2sfAkavd+VAllzzzLFwTXdWPFXPN5ORWJ2RVhz/zci23FoLqml98j0W1nx/b8Z7+0j2Odxt61Jnq41oTiN4jxzutnXZ51io+rnbGNUbx3VKPVQ928DojTbfsg3V5twNZZJtKFfVfl8ONIdsl8O1FM9VwnV5FeqoQg1VYS2GauVnEko+dzvOmMiqnu1SPpdR0vtMKXOpy+f81qUxMDBQr4qb5Pe/W573H3FYPvf5L2bzP3/JWj+/7JIL841LL17r8bPPW5rOzs4X3PfKVQPpOOnRbDS1kQePXLtBZ8FXHst975mVjpMeHXys7+gN097WGMVMqLvVx8tqT/7ThmlrpOjjYc05j+fx34xsrzaRc2stqKL1yfdYGIucjGSfw922LnW22ljNaSxep1bucyxU/dxtjOqNU5f3lYmu6tkGRm+0+ZZtqDbnbiiTbEO51vf35Y3PPtLUegY+ulFT9wcTVdX+LYzRcy3Fc5VwXV6FOqpQQ1VYi6Hq+m/dVec4YyKrerZL+VxGSe8zpcyl7vNob3UBVbXvfvtn7zftO/h9b29Pjlxy6LCe29efvHPR5Fzw46fS9eTaPUmveXF7nnxmm/N//FTeuWhy+vqT9gl33x+SpG/V0GOh68mBTJ/cKPp46Gvh8d+MbMuttaCa1iffY2HN9/e+Veufk5Hsc7jb1qXOVhvRnEbwHjncbeuyz7FQ9XO3Mao3juuUeqh6toHRG22+ZRuqzbkbyiTbUK6q/b4caA7ZLodrKZ6rhOvyKtRRhRqqwloM1crPJJR87nacMZFVPdulfC6jpPeZUuZSl8/5rYs7zazjTjNr6unpySEHHzjsTsDevoF0TEpWrlr7ZwNJOtqeDkHHpKf/nNZRn04rmq935cDTx8SqpJFkanv5x8PqjLT6+B9ttltddxVYC6pupPkeC899f5/WpPf2kexzuNvWpc5WG9GcRvAeOdxt67LPsVbFc7cxqjdOVY5Xhq+K2QaaYyT5lm2oD+duKJNsQ7lGmm93moF6qMK/hTF6rqVYl7pel1ehjirUUBXWYqiqfCahtHO34wyeVsVsl/K5jJLeZ0qZS1XOqaPhTjNjZPUB/UIdVKt/VqcuK8bG6jeOiXQsDCcjVVTXuseCtYA/bSze30eyz+FuW5c6W21EcxrBe+Rwt63LPqtmPOo2RvXGqevxyvB5jaFMsg3lkm8ok2wDAIyeaymapSrHUhXqqEINVWEthqrLZxLqxnEG1VXK5zJKep8pZS51PqdO2KaZTTadmwsv/WarywAAAAAAAAAAAAAAAGAM1LDPBwAAAAAAAAAAAAAAAF6YphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACK097qAlrh5pu+l4uWXpDu7hX5i513ySHvWZKOjo5WlwUAAAAAAAAAAAAAAECTTLg7zTz+eFfOOP2U7Lf/ATnuhBPzw7vvzA3XX9fqsgAAAAAAAAAAAAAAAGiiCdc08/Of/TQDA8mei/fKizffPDvt9Irce8+ypo+zcuXAOr96+wayctXQP5nY1jwuJoK6zreudY8FazGU9XhWHddiJDWPxbYljm9O9Rh/rOZUJeNRtzGqN44xqjXGWKhr3TCe6piTOtRc9RorX9/KZ+pbWc366qDqr/G61LXuNZUyD6qnrsdWXeteUynzSMqaSym8JgCsi3MEzVKVY6kKdVShhqrUUYUaqqSO61GHmqteY+Xrq8Hvyyu/hhWvb11K+RyAMao3Tl0zsaaxmseEa5rp6urKlKlT0mg0kiSd06enq6trre36+vrS09Mz+NXb2zPsMXr7BtI3kHV+fe7a7vT1J0uu7U7HSY9mybXdtT9AGb3Vx8tEOh56+wZaNt/1zfZEep1eiLUYyno8q475HknNY7FtieObUz3GH6s5NVvVz93GqN44xqjWGOtS9WxD3dUx33XIdtVrrHx9Kwey5Jpn6rumu9L/EFhVdcx2Uv1jc7hKmQfVI9utVco8krLmUoq65huoLtkuh/M2z1XCdXkV6qhCDVWpowo1VEkdr8vr8BpWvcbK11eD35dXfg1rmO2knM8BGKN641Q9s8M1lvNoDAwM1HNVRuk7N96QC847J2edc0GS5GvnfzW//MV/5Z8/dcKQ7S675MJ849KL13r+2ectTWdn5wuOsXLVn/gfvbtXZNr0DdJx0qODj/UdvWHa2xrDnQYFWX28TKTjYeWqgZbNd32zPZFepxdiLYayHs+qY75HUvNYbFvi+OZUj/HHak7NVvVztzGqN44xqjXGulQ921B3dcx3HbJd9RrVV746Zjsp57UvZR5Uj2y3VinzSMqaSynqmu8kaXz2kabWM/DRjZq6P5io1jfbVIfzNs9VwnV5FeqoQg1VqaMKNVRJHa/L6/AaVr1G9a2/qtdYx2wn5XwOwBjVG6fqmR2usZzHhLvTzIwZM/JEb29WrVqVJOnt6cnMWbPX2m7f/fbP2ectHfz60hlfGfYYff0v/PWv32mkrz9556LJSZ7+s6+/OfOjflYfFxPpeGjlfNc32xPpdXoh1mIo6/GsOuZ7JDWPxbYljm9O9Rh/rObUbFU/dxujeuMYo1pjrEvVsw11V8d81yHbVa+x8vWtWqO+VS0uqIbqmO2k+sfmcJUyD6pHtlurlHkkZc2lFHXNN1Bdsl0O522eq4Tr8irUUYUaqlJHFWqokjpel9fhNax6jZWvrwa/L6/8GtYw20k5nwMwRvXGqXpmh2ss5zHh7jTT1dWVo953aA5657uyYNH2+cwJn8p+bz8gr/urN7zg83p6enLIwQcO+3/oWPkCt0vrG0g6Jj39wq7+c1pH/bq5aJ6VKweGHBcT4Xjo7RuoxHxHmu2q1F0F1mIo6/GsqqzFSPI9kprHYtsSxzeneow/VnMaS1U8dxujeuMYo1pjDEcVsw11V5WcjNV1eatUvcbK17dyIB1tT/+D4LT26tVXB1V5jSfqubuUeVA9VTm2ZLve80jKmkspqvKajDTf7jQD9TDSbFMtVTlHUD11vS6vQh1VqKEqdVShhiqpynr4ffn4qnx9Nfh9eeXXsCL1VfHcbYxqjTFe41QlE+trrObR3rQ91cTMmTOz5PD358Kvn5+LL/xaXrnzLtlz8V5NH6f9BU5iqxe9vW3on0xc7e2NtY6L0q1+I6vbfOta91iwFkNZj2fVcS1GUvNYbFvi+OZUj/HHak5VMh51G6N64xijWmOMhbrWDeOpjjmpQ81Vr7Hy9bVXu746qPprvC51rXtNpcyD6qnrsVXXutdUyjySsuZSCq8JAOviHEGzVOVYqkIdVaihKnVUoYYqqeN61KHmqtdY+fpq8Pvyyq9hxetbl1I+B2CM6o1T10ysaazmMeGaZpJk1912z6677d7qMgAAAAAAAAAAAAAAABgjNe8lAgAAAAAAAAAAAAAAgLVpmgEAAAAAAAAAAAAAAKA4mmYAAAAAAAAAAAAAAAAojqYZAAAAAAAAAAAAAAAAiqNpBgAAAAAAAAAAAAAAgOJomgEAAAAAAAAAAAAAAKA47a0uoC4GBgaSJL29PS2uBCa2adOmpdFoNG1/sg3VId9QJtmGMsk2lKnZ2U7kG6rCuRvKJNtQphKvy3t6mj/u9FOfaOr+uo+a2tT9wfNx7oYyyTaUqcTrcuBpzt1Qpj+V7cbA6rTygh5++A85csmhrS4DJryzz1uazs7Opu1PtqE65BvKJNtQJtmGMjU724l8Q1U4d0OZZBvK5LocyuXcDWWSbSiT63Iol3M3lOlPZVvTzDCtWrUqjz76SKZOfeEupN7enhy55NB86YyvZNq05l40jRU1jw81N0ezu3yHm+3VqrgmrWIthrIezxrtWoxFvn/729/k6A8eVdTrUuKxZk71UKVsj+TcPR5Keb1LmUdSzlyqPI9WZ7vKa5NUv76k+jWqb/2Npsax+J/zRpLvUtd1PKlv/VW9xrpel1d9XYerlHkk5cxlos9DtpujlHkk5cyllHkkrsvHStVrVN/6q3qNdT13N1PVX6Ok+jVWvb5EjcPV6mxXYQ2qwlo8y1oM5bp8bFS9RvWtv6rXWNfr8qqv63CZR/WUMpexynZ7M4qbCNra2jJnzsbD3n7atM6mdxqPNTWPDzVXy0izvVrJazJS1mIo6/GsVq9FW1tbNtpoTiVqGQvmVA/m1HyjPXePh1avTbOUMo+knLmUMo8XUup1edXrS6pfo/rWX6trHE2+W13zcFS9RvWtv6rX2Or6Sj13D1cp80jKmYt5NIdslzGPpJy5lDKPpPVzcV3eGupbf1WvsdX1VeH35a1eg+Goeo1Vry9R43ib6NflzWAtnmUthmr1ergubw31rb+q19jq+ib6uds8qqeUuTR7Hm1N2xMAAAAAAAAAAAAAAABUhKYZAAAAAAAAAAAAAAAAijPpuOOOO67VRZSmra0tCxZun0mTJrW6lGFT8/hQcxmsybOsxVDW41lVWosq1dIs5lQP5jSxlLI2pcwjKWcupcxjLFR9bapeX1L9GtW3/upQ45rqUHPVa1Tf+qt6jVWvb13qWveaSplHUs5czKO16lr3mkqZR1LOXEqZR1LPudSh5qrXqL71V/Uaq17feKjDGlS9xqrXl6ixLqzBs6zFs6zFUHVcjzrUXPUa1bf+ql5j1etbl7rWvSbzqJ5S5jIW82gMDAwMNG1vAAAAAAAAAAAAAAAAUAFtrS4AAAAAAAAAAAAAAAAAmk3TDAAAAAAAAAAAAAAAAMXRNDNMAwMD6enpycDAQKtLAZpItqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDPWiaGabe3t4ccvCB6e3tbXUpQBPJNpRLvqFMsg1lkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm2oB00zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBx2ltdAAAAAAAAAAAAwETS+OwjTd/nwEc3avo+AQAA6s6dZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAAChOe6sLWJebb/peLlp6Qbq7V+Qvdt4lh7xnSTo6OgZ/vmLFipx5+qlZ9uMfZcON5uTdhx6WhYu2T5Jc/a2r8q2rvpn+/v7ssfi1OfCgg9NoNJIkt97y/Vx5+WVZvvyhnH3e0pbMDQAAAAAAAAAAAAAAgLFVyTvNPP54V844/ZTst/8BOe6EE/PDu+/MDddfN2SbK6+4LA8//IeceNLJ2flVu+a0U76QlX19eeCBX+Vr55+bJUcclaOPOTbXfvvq3HXnHUmSW2/+fk49+V+z+x6L89l/+WIrpgYAAAAAAAAAAAAAAMA4qGTTzM9/9tMMDCR7Lt4rL9588+y00yty7z3Lhmxz7z3L8qpdXp1N587N617/hjz66CNZvvyh3HfPsmy22WbZYcedsvU222bbl22X++59+rnfuPSivP6Nf5293/SWbLzJJq2YGgAAAAAAAAAAAAAAAOOgvdUFPJ+urq5MmTol69DBfQAAIABJREFUjUYjSdI5fXqWL18+ZJvHu7oyderUJMn0zumDz3v6uVMHt+vsnJ6urq709PTkgQd+lbnz5uUDRxyWzunTc/C7Dsl28xc+bw19fX3p6+sb/L63t6epcwRaQ7ahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQz1Vsmnm+TzTPzOqbRpJerq7kySzZm+YD/3PA3P1v1+ZL37hX3L6mecMNuc815VXXJZvXHrxelQMVJFsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YZ6qmTTzIwZM/JEb29WrVqVtra29Pb0ZOas2WtsMzPdPU83wvT09iZJZs6cnRkzZww2yCRPd/DN2+xFmTlzZhqNRl671+vykpdskb96w1/nOzfekMe7ujJz1qy1ath3v/2z95v2HbKfI5ccOhbTBcaRbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGeqpk08xWW2+btra2XH/dNVmwaPvcffed2e/tB2RVf3/aJk1KksxfsDC3fP+m7Lb7HvnujTdkozlzMnfevPT3r8y5Z5+VO++4PbNmb5if3H9/9nnzWzN5ypQsXLR9vn31t/KOv/v7fPfG/5M5czZ+3oaZJOno6EhHR8d4ThsYB7IN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG+qpkk0zM2fOzJLD358Lv35+Lr7wa3nlzrtkz8V75fMnnZgFCxdlnzfvm33ftn8eeujBfOzoD2ajjebkiKM+mEmTJmXzP39JDjr4XTnrzNPTv7I/b3jj3tnx5TslSQ5731E589++lA9/4Mi86EUvyj9++CMtnikAAAAAAAAAAAAAAABjoZJNM0my6267Z9fddh/y2NHHHDv49w022CAf/sjHnve5e+/zluy9z1vWenzjTTbJsZ88vrmFAgAAAAAAAAAAAAAAUDltrS4AAAAAAAAAAAAAAAAAmk3TDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMVpb3UBL+Tmm76Xi5ZekO7uFfmLnXfJIe9Zko6OjsGfr1ixImeefmqW/fhH2XCjOXn3oYdl4aLtkyRXf+uqfOuqb6a/vz97LH5tDjzo4DQajcHnXvvtq/PVs7+cTxx3QhYs3H7c5wYAAAAAAAAAAAAAAMDYqeydZh5/vCtnnH5K9tv/gBx3won54d135obrrxuyzZVXXJaHH/5DTjzp5Oz8ql1z2ilfyMq+vjzwwK/ytfPPzZIjjsrRxxyba799de66847B5z388B9yyUVLx3tKAAAAAAAAAAAAAAAAjJPKNs38/Gc/zcBAsufivfLizTfPTju9Ivfes2zINvfesyyv2uXV2XTu3Lzu9W/Io48+kuXLH8p99yzLZpttlh123Clbb7Nttn3Zdrnv3mefe85ZZ2bPxXuN95QAAAAAAAAAAAAAAAAYJ+2tLmBdurq6MmXqlDQajSRJ5/TpWb58+ZBtHu/qytSpU5Mk0zunDz7v6edOHdyus3N6urq6kiQ33/S9PPTQg1lyxFG5+j+uWuf4fX196evrG/y+t7enORMDWkq2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbEM9VbZp5vk80z8zqm0aSVasWJHzzz07H/jQP6WjveMF93PlFZflG5dePPIigUqTbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQT5VtmpkxY0ae6O3NqlWr0tbWlt6ensycNXuNbWamu6c7SdLT25skmTlzdmbMnJGe7u7B7Xp7ezJvsxfl+uuuyWOP/TEnnfjpwZ997v//dI459pPZbv6CIfved7/9s/eb9h2yjyOXHNr0eQLjS7ahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQz1Vtmlmq623TVtbW66/7posWLR97r77zuz39gOyqr8/bZMmJUnmL1iYW75/U3bbfY9898YbstGcOZk7b176+1fm3LPPyp133J5ZszfMT+6/P/u8+a3Zeptt8urddk+SPPnkUzn6Q0flve87Ii/dauu1xu/o6EhHxwvfjQaoH9mGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDfVU2aaZmTNnZsnh78+FXz8/F1/4tbxy512y5+K98vmTTsyChYuyz5v3zb5v2z8PPfRgPnb0B7PRRnNyxFEfzKRJk7L5n78kBx38rpx15unpX9mfN7xx7+z48p2SJNOnb5AkeeKZO9PMnj07kydPbtk8AQAAAAAAAAAAAAAAaL7KNs0kya677Z5dn7kzzGpHH3Ps4N832GCDfPgjH3ve5+69z1uy9z5vWee+p06blgsv/WZzCgUAAAAAAAAAAAAAAKBS2lpdAAAAAAAAAAAAAAAAADSbphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDijbprpeuyx/PDuuwa///H//VGu/o+r8sCv/rsphQEAAAAAAAAAAAAAAMBotY/mSb9+4IH8r+M/kZmzZmXHl++Um757Y0479YtJko6O9hxz7Kcyf8HCphYKAAAAAAAAAAAAAAAAwzWqO81ctPSCdHU9lu132DFJcsXll2XTTTfNfm/bP41GI5dfdnFTiwQAAAAAAAAAAAAAAICRGNWdZn76k/vzP17xyvz9we/O73//u/z2N7/OUR/4cHbdbfcsX/5Qfnj3Xc2uEwAAAAAAAAAAAAAAAIZtVHea6e9fmSlTpiRJfnj3XWk0Glm0ww5P/7DRyMqVfU0rEAAAAAAAAAAAAAAAAEZqVHeaeelWW+f2227JueecldtvuyVbbbVNZsyYmdtuvTl3/uC2bPnSrZpdJwAAAAAAAAAAAAAAAAzbqO408z//9qC0t3fk2m9fncf++Mf8zTsOTJL825e+mKee6st+bz+gqUUCAAAAAAAAAAAAAADASIzqTjNbbb1NPvsvX8iyZT/OFltsmS22fGmS5JU775I9Fu+VRdvv0NQiAQAAAAAAAAAAAAAAYCRG1TSTJJtsOjeLN5075LHDj/rH9S4IAAAAAAAAAAAAAAAA1teom2buvuuO3LtsWbq6HsvAGj9rJDns8KPWrzIAAAAAAAAAAAAAAAAYpVE1zVx26UW5/NJLkrXaZVZrNKVp5uabvpeLll6Q7u4V+Yudd8kh71mSjo6OwZ+vWLEiZ55+apb9+EfZcKM5efehh2Xhou2TJFd/66p866pvpr+/P3ssfm0OPOjgNBqN3H3Xnfnaeefkd7/7XbbYcssc9r4j82cv3ny9awUAAAAAAAAAAAAAAKA6RtU0c8P112Xy5MnZ/4B3ZO68eWk0Gs2uK48/3pUzTj8l7zr0sGy99bb59AmfzA3XX5fXv3HvwW2uvOKyPPzwH3LiSSfnhv/9nzntlC/klNPOzIMPPZivnX9ujjn2E+nsnJ4TjvvnzF+wKC/bbruc/PnP5W37H5Dddn9Nzjj91Jz31a/k4584vun1AwAAAAAAAAAAAAAA0DqjaprpXrEiO+/y6rzpLW9tdj2Dfv6zn2ZgINlz8V5pNBrZaadX5N57lg1pmrn3nmV51S6vzqZz5+Z1r39Dvnn5pVm+/KHcd8+ybLbZZtlhx52SJNu+bLvcd++yvOQlL8kb3rh3Xv/GvTN16tS8bLv5uf22W8dsDgAAAAAAAAAAAAAAALTGqJpm5i9YmJ7u7mbXMkRXV1emTJ0yeBebzunTs3z58iHbPN7VlalTpyZJpndOH3ze08+dOrhdZ+f0dHV1Zc7Gm+RvD3pnkqS3tzff+86N+cu/esPzjt/X15e+vr7B73t7e5o3OaBlZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk21NOommZ23e01OeuM03LFNy7Nn734xc+7zc6v2nW9Cns+z/TPjGqb5z68cuXKnPz5z2bDjebkr/d58/Nuf+UVl+Ubl1488iKBSpNtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21BPo2qaOeO0U5Ikl1584Tq3WXrJ5aOr6BkzZszIE729WbVqVdra2tLb05OZs2avsc3MdPc8fcebnt7eJMnMmbMzY+aMIXfC6e3tybzNXpQkWbVqVU4/9eQ89sc/5hPH/6+0tz//Euy73/7Z+037DtnHkUsOXa85Aa0n21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahnkbVNPOaPRanMZzbvqyHrbbeNm1tbbn+umuyYNH2ufvuO7Pf2w/Iqv7+tE2alCSZv2Bhbvn+Tdlt9z3y3RtvyEZz5mTuvHnp71+Zc88+K3fecXtmzd4wP7n//uzz5rcmSb76lS/nJ/ffl49/4vgkSXf3ikyb1pm2trYh43d0dKSjo2NM5wiMP9mGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyzf9j787D8yrr/PG/s7R0L1sLiIC2wAgtyi4MgwguaCnbiIwoAwoVCgWUn4j7uDIWi4BsiogosoiAouP4A1wQdNQBQVAWARlZlEWghUoSStLk+wfQkm4mT54k57n7el1XrpCTc879vu/nfHJOYz4+NKaammaOOuZ99c6xnAkTJmT20cfl0osvzGWXXpQddtwpu71+j3xx3txsOW169tp73+z7rwfk0UcfyUc+eHzWXnudzDn2+LS0tGSjjTfJwYe+J+ede04Wdy3Onm+ZkddsvU0ef/xv+cmPr06SfOD9c5aMdcbZ52bS5PUGfU4AAAAAAAAAAAAAAAAMjZqaZm64/rpVfr8pya677V7LqXvZeZd/yc67/EuvbR/88MeW/Pe4cePygRM/ssJjZ+y1T2bstU+vbZMmTc6ll1814FwAAAAAAAAAAAAAAABUW01NM185+4w83xqzcvVomgEAAAAAAAAAAAAAAIBa1NQ0s+db90pT0/JNM488/HBuu/WW7PmWvQYcDAAAAAAAAAAAAAAAAGpVU9PMoe+ZtdLvffiDx2f+/CdqDgQAAAAAAAAAAAAAAAAD1VzvE2688Sb5w+9vq/dpAQAAAAAAAAAAAAAAoM9qeqeZZ/7+9+W29aQnjz7ySG7/w+8zcuQaAw4GAAAAAAAAAAAAAAAAtaqpaeaIww9dxXd78oY37VljHAAAAAAAAAAAAAAAABi4mppm1ll3nTSlqde2puamTJy4Zl6z9bbZe5/96hIOAAAAAAAAAAAAAAAAalFT08yZ55xX7xwAAAAAAAAAAAAAAABQN839PaB78eK875gjc9klFw1GHgAAAAAAAAAAAAAAABiwfjfNNLe0JEkee+zRuocBAAAAAAAAAAAAAACAemit5aDX7bZHfnDVlfn1r36Z9dffYIX7vHLK1AEFAwAAAAAAAAAAAAAAgFrV1DRz5eXfTtKUM08/dYXfb2pKLr7suwPJBQAAAAAAAAAAAAAAADWrqWnmVVtsmaampnpnAQAAAAAAAAAAAAAAgLqoqWnmPz590j/cp63tmXS0d2TdSZNqGQIAAAAAAAAAAAAAAABq1jxYJ/7Oty/JcXOOHKzTAwAAAAAAAAAAAAAAwEoNWtMMAAAAAAAAAAAAAAAADBdNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABSntZaDHv/bY5k0eb1V7rPr616fKVM3rSkUAAAAAAAAAAAAAAAADERNTTPvO+aobL75P2WXXV+X1+68SyZMmLDcPptutnk23WzzmoP9+n9+mW9f8q20tT2T7XfcKYe/d3ZGjBix5PvPPPNMzj3nzNxx+++z1trr5LBZR2ba9K2SJD/64Q/ywx9clcWLF+d1r9897zz40DQ1NeWBB+7PueecmUce/mumTN00R815X9adNKnmjAAAAAAAAAAAAAAAAFRTcy0HjRkzJvfc88dccP55mXPkYZk393P51f/8Is8tWlSXUH//+8J85Zwzsv8BB+ZTn52b2269Jdf99Me99vn+967Ik08+kbnzTs+Or905Z59xWro6O/PQQw/mogu/kdlzjs0HP/yxXHv1j/K7W25Okpz35bMzZcrUzDv1zLS0tOSiCy+oS14AAAAAAAAAAAAAAACqpaamma9+/cJ84lOfy14z98nk9dbP7265OWd96bQcOevQnHPm6bn1d7eku7u75lD3/ene9PQku71+j7x8o42yzTbb5a477+i1z1133pHX7vTPmbzeennjm/fMggXz89hjj+aPd96RDTbYIK9+zTbZdLPNs/k/vSp/vOuOPLdoUe67797stvsbsu6kSdl1t91z1113rCTBwHV19az0o6OzJ13dvT+zelv2ulgdNOp8GzX3YLAWvVmPpRpxLfqTeTD2LXF8c2qM8QdrTlUyFLmNUb1xjFGtMQZDo+aGodSIddIImauesfL5ul7I11XNfI2g6q/xyjRq7mWVMg+qp1GvrUbNvaxS5pGUNZdSNOJr0giZGyEjZfNvG6BKqnJfrEKOKmSoSo4qZKiSRlyPRshc9YyVz9cAz5SVX8OK51uZUv4OwBjVG2dIxhiCn12DNY+ammaam5uzxZbT8q5D3p0vnn5Wvnj6Wdl2u+2zaNGi/PIXN+QLn/9cjjriPfnBVd9NT0//Ay9cuDBrjFojTU1NSZIxY8dm4cKFvfb5+8KFGTVqVJJk7JixS457/thRS/YbM2bsku1Jlh4zdmz+vsw5X6qzszPt7e1LPjo62vucv6OzJ509WenHF65tS+fiZPa1bRkxb0FmX9vWcD+0qZ8Xr5fV6Xro6OwZtvkOtLZXp9dpVaxFb9ZjqUas7/5kHox9SxzfnBpj/MGaU71V/d5tjOqNY4xqjbEyVa9taHSNWN+NUNtVz1j5fF09mX3NC/muaav0/xBYVY1Y20n1r82+KmUeVI/aHl6lzCMpay6laMT6boTrqBEyUrbh/LfNQO7dQHWV8FxehRxVyFCVHFXIUCWeywdH1TNWPl8D/L688mvYgLWdlPN3AMao3jhDMsYQ/OwazHk09dTS1ZLkuUWLctutv8tNN/1vfnfLb9P2TFuSnrxsw5dn2vStcuP//jpPP/V03rrXzPz7oYf169w3XH9dvvXNr+e8r38rSXLRhRfk/j//Xz7+yc8u2ed9c47MjJn7ZM+37pVnOzrynkMOyic/c1LuuP323PzbG/OfJ38xSXLaKSdnzJgxedvb35Fjj35vvvDFL2WjjTfJLTfflFNO/nwu+c53V5jhiu9cmisvv2y57ed/85KMGTNmlfm7ule9pB1tz2T02HEZMW/Bkm2dH1wrrc1NqzyOMr14vaxO10NXd8+wzXegtb06vU6rYi16sx5LNWJ99yfzYOxb4vjm1BjjD9ac6q3q925jVG8cY1RrjJWpem1Do2vE+m6E2q56RvnK14i1nZTz2pcyD6pHbQ+vUuaRlDWXUjRifTfCddQIGSlbI9Y21dV08vy6n7PnQ2vX/ZwMrhKey6uQowoZqpKjChmqpBHv3Y3wGlY9o3wDV/WMjVjbSTl/B2CM6o1jjH+spneaOeXk/8wRhx+S0774hfzyhp+nubk5e75lRj73+Xk55bQz857Dj8hpXzonkyZPyg0/v67f5x8/fnye7ehId3d3kqSjvT0TJq65zD4T0tbeliRp7+hIkkyYsGbGTxif9ra2Jft1dDx/7IQJE5IkbS98r6O9PRMnTlxphn33PyDnf/OSJR9nfeVrfc7fuXjVH6fe0JTOxckh00cmef5z5+I+n57CvHhdrE7Xw3DOd6C1vTq9TqtiLXqzHks1Yn33J/Ng7Fvi+ObUGOMP1pzqrer3bmNUbxxjVGuMlal6bUOja8T6boTarnrGyufrXiZf9zAHakCNWNtJ9a/NviplHlSP2h5epcwjKWsupWjE+m6E66gRMlK24fy3zUDu3UB1lfBcXoUcVchQlRxVyFAlnssHR9UzVj5fA/y+vPJr2IC1nZTzdwDGqN44QzLGEPzsGsx51PROM+88cP+MGDEi2263Q3bd7fXZeutt09zSstx+55/3lfzp3nvy+S+c2q/zL1y4MMceNSsHH/KebDl9q/znZz+Z/d92YPbY441Lxrn4wm/kttt+lw+c+JH84vrrct3PfpIzzv5qHv7rX/KhE96fEz700Uxcc6189pMfz/EnfCiv2XqbfOzDJ+TlL98ob/+3d+b8876c0WPG5rj3f6BPmdrb23P4oe/s8/9DR9cq3nKosycZ0fL8C/vi59EjqtOBydDr6urpdV2sDtdDR2dPJebb39quSu4qsBa9WY+lqrIW/anv/mQejH1LHN+cGmP8wZrTYKrivdsY1RvHGNUaoy+qWNvQ6KpSJ4P1XD5cqp6x8vm6ejKi+flfqo9urV6+RlCV13h1vXeXMg+qpyrXltpu7HkkZc2lFFV5TTyXQ31V5d82/b13Uz3eaYYVadTn8irkqEKGquSoQoYqqcp6eC4fWpXPV5FnylWp/BpWJF8V793GqNYYQzXOkIwxBD+7BmserbUcdPgRR2XnnXfJmLFjV73fe2fXFGrChAmZffRxufTiC3PZpRdlhx13ym6v3yNfnDc3W06bnr323jf7/usBefTRR/KRDx6ftddeJ3OOPT4tLS3ZaONNcvCh78l5556TxV2Ls+dbZuQ1W2+TJDniqGPy1S+flROOPzZTN90ss444pKZ8fdG6igvhxUVvbe79mdVXa2vTctdF6V78QdZo823U3IPBWvRmPZZqxLXoT+bB2LfE8c2pMcYfrDlVyVDkNkb1xjFGtcYYDI2aG4ZSI9ZJI2SuesbK52utdr5GUPXXeGUaNfeySpkH1dOo11aj5l5WKfNIyppLKRrxNWmEzI2QkbL5tw1QJVW5L1YhRxUyVCVHFTJUSSOuRyNkrnrGyudrgGfKyq9hxfOtTCl/B2CM6o0zJGMMwc+uwZpHTU0zP/jelXlqwYK87e3/ttz3vv61c/PQgw/mk585aUDBdt7lX7LzLv/Sa9sHP/yxJf89bty4fODEj6zw2Bl77ZMZe+2z3PZNNnlFTpp7yoByAQAAAAAAAAAAAAAAUH39apq56847kiSPP/63PPjAn5d8/aLFi7ty5+1/yBNPPFG/hAAAAAAAAAAAAAAAANBP/Wqa+eynPp7k+be8uenGG3PTjTeuYK+ebDlteh2iAQAAAAAAAAAAAAAAQG361TSz3fY7JGnKzb+9MZMmT8rGG7+i1/dbWlqy/gYb5K177V3HiAAAAAAAAAAAAAAAANA//Wqa+cCJH02SvPPA/bPNttvn3Ye9d1BCAQAAAAAAAAAAAAAAwED0q2nmRV86+9yMGTOm3lkAAAAAAAAAAAAAAACgLvrcNPONr5/X55M2pSmHHjarpkAAAAAAAAAAAAAAAAAwUH1umrn26h/147SaZgAAAAAAAAAAAAAAABg+fW6aOfLoYwczBwAAAAAAAAAAAAAAANRNn5tmdnv9HoOZAwAAAAAAAAAAAAAAAOqmz00zy3r66ady+x9+n6efeio9PT1Ltnd2PpeH//rXHH3s++sSEAAAAAAAAAAAAAAAAPqrpqaZP//fffn85z6dZ555ZqX7aJoBAAAAAAAAAAAAAABguDTXctClF1+YtrZnsvseb8iUqVOTJP/69gPzutfvnqQn7/z3Q+qZEQAAAAAAAAAAAAAAAPqlpqaZ/7vvvmy/w2vz3tlzstlm/5QkOeDt78jso4/NK6dMzd133VXXkAAAAAAAAAAAAAAAANAfNTXNdHd3Z4011kiSjJ84IUmyYMH8JMnGG2+Su+68vU7xAAAAAAAAAAAAAAAAoP9qaprZbPN/yo3/+5vc/Nsbs9HLN07Sk8suuSi/v+3W/P62WzN6zJg6xwQAAAAAAAAAAAAAAIC+q6lp5m1v/7d0dy/OTTf+b7bbfodMmjw5N1z/88w96TNZsGB+dn/Dm+qdEwAAAAAAAAAAAAAAAPqstZaDNv+nV+XTn5ubJ554PM0tLfn4Jz+b7115eZ5+akFe/Zptsudb96p3TgAAAAAAAAAAAAAAAOizmppmkuSVU6bmlVOmJkkmTZqcI2bPqVsoAAAAAAAAAAAAAAAAGIiam2YeeujB/PGuO/P3hU+np2f577/t7f82kFwAAAAAAAAAAAAAAABQs5qaZn7642vy9a+d+0KzzAo6ZtKkaQYAAAAAAAAAAAAAAIBhU1PTzA+u+m6ampryxjftmfU2WD9Naap3LgAAAAAAAADPDLJ8AAAgAElEQVQAAAAAAKhZTU0zTz21IDvsuFPeM+uIeucBAAAAAAAAAAAAAACAAaupaeaVr5xa7xzLeeCB+3PuOWfmkYf/milTN81Rc96XdSdN6rVPT09PLr34wlx/3c/S0tKSmfvslxkz90mS3HH7H3LB187N/PlPZtr0V+fIo4/NuHHj8tijj+SrXzk7995zT9ZZZ528898PzQ477jTo8wEAAAAAAAAAAAAAAGDoNNdy0Mx998/vbrk5v/zF9fnz/923wo+BOu/LZ2fKlKmZd+qZaWlpyUUXXrDcPrf+7ub85Nqrc+JHP5FZRx6diy68IA8+cH+6urpy9hmnZeddds3ceafnsccezfe/e0WS5JyzvpRx48bltDPPyY47/XPOPuO0dHV1DTgvAAAAAAAAAAAAAAAA1VHTO82cOu/zSZpyzplfWuH3m5qSiy/7bs2hnlu0KPfdd28OPWxW1p00KbvutvsKm2buuvOObLb5qzJ16qZJksmT18vdf7wrLS0tWbBgft7wxjdnzbXWyk7/vEtu+e1NSZJp07fKTjvvknXWWTev2Xrr/OCqK/NsR0fGjR9fc14AAAAAAAAAAAAAAACqpaammVdtsWWamprqEqC7uzsdHe29trW3P//1qFGjkiRjx47N3xcuXO7YhQsXLtknScaMHZuFCxdm4Qv7Ljl+zNLjD3zHu5bs/5Nrr8nW22y3woaZzs7OdHZ2Lvl62YxAY1LbUC71DWVS21AmtQ3lUt9QJrUNZVLbUC71DWVS21AmtQ3lUt9QJrUNjammppn/+PRJdQvw5BOP57g5R/badsCBB61gz7416ay0l2eZ7Vdc/u3cecftOWnuvBXu/v3vXZErL7+sT2MCjUNtQ7nUN5RJbUOZ1DaUS31DmdQ2lEltQ7nUN5RJbUOZ1DaUS31DmdQ2NKaammaS5LnnnsvPr/tJHnrgwTz11IJ84MSPpHvx4txyy83Zfocd+3yeSZPXy6WXX9X73IsW5YrvXJq2trYkSUd7eyZOnLjcsePHj8+TTzy+5OuOjo5MmLhmxo+fkCRpa2/LqNGj09HRnokT1lyy37VX/yj//w9/kI9/6nNZZ91JK8y17/4HZMbMfV9y7vYcM3tWn+cFVJPahnKpbyiT2oYyqW0ol/qGMqltKJPahnKpbyiT2oYyqW0ol/qGMqltaEw1Nc088vBfc9JnPpn58+cn6cmLb+Ny9dX/nYu++Y0cd/wHstPOu9QcauQaa2TK1E1z3U9/nHXXnZRf/uL6bDFt+pLvdy9enOaWlmyx5fRce/WPcu89d6e9vT2PPfpotthiy6y3/gZZa62185Nrrs4eb3xTfvPrX+U1W2+bJPmfX96Qiy78Rt7/gRMzefLktLU9kzVGrpHWESN6ZRgxYkRGLLMNaHxqG8qlvqFMahvKpLahXOobyqS2oUxqG8qlvqFMahvKpLahXOobyqS2oTE113LQt755QZ5++qm8698PzXbbL31XmTfvOSPrr79+fviDq1ZxdN8ccdQx+ctfHsoJxx+b557rzLsOPiRJ8sP/uiqnnnJykmSbbbfLW946M/PmnpSvnHNGDnn3Ydnw5RultbU1c447Pjfd+Jt86IT3Z9Kkydlnv/2TJJddclE6O5/LvLmfy6x3H5xZ7z44//PLGwacFwAAAAAAAAAAAAAAgOqo6Z1m/njXHdluhx2z19775m9/e2zpyVpb86otp+VXdWhC2WSTV+Skuacst33m3vtl5t77JUmamppy0MGH5KAXGmpeatr0rXLK6Wctt/2Mc7464GwAAAAAAAAAAAAAAABUW03vNNPc3JLmphUf+vBf/pLWVm87BQAAAAAAAAAAAAAAwPCp6Z1mpk2fnptu/E1+cf116ezsTJI8/fRT+a/vfy/33HN3dtjxtXUNCQAAAAAAAAAAAAAAAP1RU9PMOw8+NHfeeUe+fPaZS7Yd9d7DkvRk1KjROfCgd9UrHwAAAAAAAAAAAAAAAPRbcy0Hrbf+Bpk777Ts9vrds9baa6W5uSljx47NDjvulM9+/gvZcMOX1zsnAAAAAAAAAAAAAAAA9FlN7zSTJOuss26OPPrYemYBAAAAAAAAAAAAAACAuqi5aaarszO/uOHnuf/+P+fpp57KxDXXzJQpU7PLrrultbXm0wIAAAAAAAAAAAAAAMCA1dTd8peHHsrckz6d+fPnJ+l5yXeacuXll+XDH/tkXrbhhvVJCAAAAAAAAAAAAAAAAP1UU9PMBed/NfPnP5lp07fKbru/IRMmTMiCBQtyw89/lrvuvCMXnH9uPvYfn6l3VgAAAAAAAAAAAAAAAOiTmppm/nTv3XnllKn56Cc+naampiXbd33d6/Pxj3ww995zd90CAgAAAAAAAAAAAAAAQH/V1DSz1lpr52Uv27BXw0ySNDc3Z5NNXpGOjo66hAMAAAAAAAAAAAAAAIBaNNdy0B5venNu/8Pv8+QTj/fa/tSCBfn9bbdmz7fOqEs4AAAAAAAAAAAAAAAAqEVN7zRz/XU/y9NPP53jjzs6ra1LT9HV1ZWursX5zqUX5zuXXvzC1qZ8/cJL6pEVAAAAAAAAAAAAAAAA+qSmpplHHv5rkhebZLqW+/6zzz47sFQAAAAAAAAAAAAAAAAwADU1zVzyne/VOwcAAAAAAAAAAAAAAADUTfNwBwAAAAAAAAAAAAAAAIB60zQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABSndbgDrMwDD9yfc885M488/NdMmbppjprzvqw7aVKvfXp6enLpxRfm+ut+lpaWlszcZ7/MmLlPkuSO2/+QC752bubPfzLTpr86Rx59bMaNG7f0/Pf/OR/78AnZ718PyAEHHjSkcwMAAAAAAAAAAAAAAGBwVfadZs778tmZMmVq5p16ZlpaWnLRhRcst8+tv7s5P7n26pz40U9k1pFH56ILL8iDD9yfrq6unH3Gadl5l10zd97peeyxR/P9716x5LjuxYvz1a+cnZ6enqGcEgAAAAAAAAAAAAAAAEOkkk0zzy1alPvuuze77f6GrDtpUnbdbffcddcdy+131513ZLPNX5WpUzfNttttn8mT18vdf7wrjz36SBYsmJ83vPHNmbzeetnpn3fpdfyPfvRfGTlyjWyyySuGcFYAAAAAAAAAAAAAAAAMldbhDtDd3Z2OjvZe29rbn/961KhRSZKxY8fm7wsXLnfswoULl+yTJGPGjs3ChQuz8IV9lxw/Zunxf3vssXz/u1fmMyfNzZlfOnWluTo7O9PZ2bnk62UzAo1JbUO51DeUSW1DmdQ2lEt9Q5nUNpRJbUO51DeUSW1DmdQ2lEt9Q5nUNjSmYW+aefKJx3PcnCN7bTvgwINWsGdTn87XtLLdXtj+ta+ekxkz98kGL9twlef5/veuyJWXX9anMYHGobahXOobyqS2oUxqG8qlvqFMahvKpLahXOobyqS2oUxqG8qlvqFMahsa07A3zUyavF4uvfyqXtueW7QoV3zn0rS1tSVJOtrbM3HixOWOHT9+fJ584vElX3d0dGTCxDUzfvyEJElbe1tGjR6djo72TJywZu695+784fe35U/33pMf/uB7efbZZ/OXhx7MiBEjs+/+b+t17n33PyAzZu77knO355jZs+o2b2B4qG0ol/qGMqltKJPahnKpbyiT2oYyqW0ol/qGMqltKJPahnKpbyiT2obGNOxNMysyco01MmXqprnupz/OuutOyi9/cX22mDZ9yfe7Fy9Oc0tLtthyeq69+ke59567097enscefTRbbLFl1lt/g6y11tr5yTVXZ483vim/+fWv8pqtt80mr3hlzjj73CXnmXfyf2ba9K3yxjfvuVyGESNGZMSIEUMyX2DoqG0ol/qGMqltKJPahnKpbyiT2oYyqW0ol/qGMqltKJPahnKpbyiT2obG1DzcAVbmiKOOyV/+8lBOOP7YPPdcZ9518CFJkh/+11U59ZSTkyTbbLtd3vLWmZk396R85Zwzcsi7D8uGL98ora2tmXPc8bnpxt/kQye8P5MmTc4+++2fkSNHZtLk9ZZ8tLa2ZuzYsRk7dtxwThUAAAAAAAAAAAAAAIA6q+Q7zSTJJpu8IifNPWW57TP33i8z994vSdLU1JSDDj4kB73QUPNS06ZvlVNOP2uVY/znyV+sT1gAAAAAAAAAAAAAAAAqpbLvNAMAAAAAAAAAAAAAAAC10jQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcVqHO8CKPPDA/Tn3nDPzyMN/zZSpm+aoOe/LupMm9dqnp6cnl158Ya6/7mdpaWnJzH32y4yZ+yRJ7rj9D7nga+dm/vwnM236q3Pk0cdm3Lhxz5/7/j/n0ou/lXvuvisf/cSns+lmmw/5/AAAAAAAAAAAAAAAABhclXynmfO+fHamTJmaeaeemZaWllx04QXL7XPr727OT669Oid+9BOZdeTRuejCC/LgA/enq6srZ59xWnbeZdfMnXd6Hnvs0Xz/u1ckSf722GP59H98LGuutVY++/l5mTJl6lBPDQAAAAAAAAAAAAAAgCFQuXeaeW7Rotx337059LBZWXfSpOy62+4rbJq56847stnmr8rUqZsmSSZPXi93//GutLS0ZMGC+XnDG9+cNddaKzv98y655bc3JUn++4ffz/rrr58jZs9Jc3Ml+4UAAAAAAAAAAAAAAACog2Ftmunu7k5HR3uvbe3tz389atSoJMnYsWPz94ULlzt24cKFS/ZJkjFjx2bhwoVZ+MK+S44fs/T4e+/+Y9YYNSofOfH/y6Jnn82MmfvkzW+ZscJsnZ2d6ezsXPL1sjmBxqS2oVzqG8qktqFMahvKpb6hTGobyqS2oVzqG8qktqFMahvKpb6hTGobGtOwNs08+cTjOW7Okb22HXDgQSvYs6lP52ta2W4vbG9ra8uIkSNzxOw5+dO99+QbXz8v06ZvlQ1fvtFyh3z/e1fkyssv69O4QONQ21Au9Q1lUttQJrUN5VLfUCa1DWVS21Au9Q1lUttQJrUN5VLfUCa1DY1pWJtmJk1eL5deflWvbc8tWpQrvnNp2trakiQd7e2ZOHHicseOHz8+Tz7x+JKvOzo6MmHimhk/fkKSpK29LaNGj05HR3smTlgzSTJh4sRsv8NrM3XTzTJ1081yyUXfzEMPPbjCppl99z8gM2bu+5Lzt+eY2bMGPmlgWKltKJf6hjKpbSiT2oZyqW8ok9qGMqltKJf6hjKpbSiT2oZyqW8ok9qGxjSsTTMrMnKNNTJl6qa57qc/zrrrTsovf3F9tpg2fcn3uxcvTnNLS7bYcnquvfpHufeeu9Pe3p7HHn00W2yxZdZbf4Ostdba+ck1V2ePN74pv/n1r/KarbdNkmyz3fb5xQ0/zw477pR77707XV1d2XjjV6wwx4gRIzJixIihmDIwhNQ2lEt9Q5nUNpRJbUO51DeUSW1DmdQ2lEt9Q5nUNpRJbUO51DeUSW1DY6pc00ySHHHUMfnql8/KCccfm6mbbpZZRxySJPnhf12VP955Z0740Eezzbbb5S1vnZl5c09KS2tLDnn3YUveMWbOccfngq+dm2uu/u9Mm/7q7LPf/kmSmXvvl/lPPJGPf+SEjB49JkfMnpOXbbjhsM0TAAAAAAAAAAAAAACAwVHJpplNNnlFTpp7ynLbZ+69X2buvV+SpKmpKQcdfEgOOviQ5fabNn2rnHL6WcttHzlyZGYdeXRmHXl0/UMDAAAAAAAAAAAAAABQGc3DHQAAAAAAAAAAAAAAAADqTdMMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFCc1uEO0Ch6enqSJB0d7cOcBFZvo0ePTlNTU93Op7ahOtQ3lEltQ5nUNpSp3rWdqG+oCvduKJPahjJ5LodyuXczFNrbXQ9DTW1DmTyXQ7ncu6FM/6i2Nc300bPPdiRJjpk9a5iTwOrt/G9ekjFjxtTtfGobqkN9Q5nUNpRJbUOZ6l3bifqGqnDvhjKpbSiT53Iol3s3y3rHIJzz8EMH4aSsktqGMnkuh3K5d0OZ/lFtN/W82OLGKnV3d2fBgvkZNWrVXUgdHe05ZvasnPWVr2X06Po+NA0WmYeGzPVR7y7fvtb2i6q4JsPFWvRmPZaqdS0Go74ffviv+eDxxxb1upR4rZlTY6hSbffn3j0USnm9S5lHUs5cqjyP4a7tKq9NUv18SfUzyjdwtWQcjP/nvP7Ud6nrOpTkG7iqZ2zU5/Kqr2tflTKPpJy5rO7zUNv1Uco8knLmUso8Es/lg6XqGeUbuKpnbNR7dz1V/TVKqp+x6vkSGftquGu7CmtQFdZiKWvRm+fywVH1jPINXNUzNupzedXXta/Mo3pKmctg1bZ3mumj5ubmrLPOun3ef/ToMXXvNB5sMg8Nmaulv7X9opLXpL+sRW/WY6nhXovm5uasvfY6lcgyGMypMZhT/dV67x4Kw7029VLKPJJy5lLKPFal1OfyqudLqp9RvoEb7oy11PdwZ+6LqmeUb+CqnnG485V67+6rUuaRlDMX86gPtV3GPJJy5lLKPJLhn4vn8uEh38BVPeNw56vC78uHew36ouoZq54vkXGore7P5fVgLZayFr0N93p4Lh8e8g1c1TMOd77V/d5tHtVTylzqPY/mup0JAAAAAAAAAAAAAAAAKkLTDAAAAAAAAAAAAAAAAMVp+dSnPvWp4Q5Rmubm5mw5bau0tLQMd5Q+k3loyFwGa7KUtejNeixVpbWoUpZ6MafGYE6rl1LWppR5JOXMpZR5DIaqr03V8yXVzyjfwDVCxmU1QuaqZ5Rv4Kqeser5VqZRcy+rlHkk5czFPIZXo+ZeVinzSMqZSynzSBpzLo2QueoZ5Ru4qmeser6h0AhrUPWMVc+XyNgorMFS1mIpa9FbI65HI2Suekb5Bq7qGaueb2UaNfeyzKN6SpnLYMyjqaenp6duZwMAAAAAAAAAAAAAAIAKaB7uAAAAAAAAAAAAAAAAAFBvmmYAAAAAAAAAAAAAAAAojqaZPurp6Ul7e3t6enqGOwpQR2obyqW+oUxqG8qktqFc6hvKpLahTGobyqW+oUxqG8qktqFc6hvKpLahMWia6aOOjo4cfug709HRMdxRgDpS21Au9Q1lUttQJrUN5VLfUCa1DWVS21Au9Q1lUttQJrUN5VLfUCa1DY1B0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUJzW4Q4wHLoXL85VV12Z66/7aTbccKOc+JGPD3ckAAAAAAAAAAAAAAAA6mi1bJr55jfOz42/+XUOm3Vktthy2nDHAQAAAAAAAAAAAAAAoM6ahzvAUHvqqafy0x9fk8OPmJ0dXrtTxo0fPyjjdHX1ZFFXT7pe+Fj0kq87OnvS1d37M6u3Za+L1UGjzrdRcw8Ga9FbR9cL69FlPRrx2uhP5sHYt8Txzakxxh+sOVXJUOQ2RvXGKWYMzxcr1ag/k2AoNWKdNGJmoG/UN5RJbUOZGqG2/c4EKEEj/LwFYPg0wn3CczlAtfg7llVb7Zpm7vvTPenu7s5vfvU/mTP78Mw96TN58sknltuvs7Mz7e3tSz46Otr7PEZHZ086e5LFPUnnCx+LX/L1+65tS+fiZPa1bRkxb0FmX9tW6YcbBteL18vqdD10dPYM23wHWtur0+u0Ktait46unsy+5oX1uKatIR8I6qUR67s/mQdj3xLHN6fGGH+w5lRvVb93G6N64xQzRuHPF1WvbWh0jXjvVttQbe7dUCa1DeUq+bm89N+ZwKoM5N5NtTTCz1uGTj1qu+nk+XX9AOrDczmUyXM5VeTvWP6xpp6ensZKPEA3XH9dvnL2mXn34bOy2eavyle/fFY2eNmGOe79H+i13xXfuTRXXn7Zcsef/81LMmbMmFWO0dW96iU97tpncsabx2XEvAVLtnV+cK20Njf1YyaU4sXrZXW6Hrq6e4ZtvgOt7dXpdVoVa9Gb9ViqEeu7P5kHY98Sxzenxhh/sOZUb1W/dxujeuMYozFUvbah0TXivVttQ7W5d0OZ1DaUq+Tn8kbICINlIPduqsXPMl6qHrVd70aXng+tXdfzwerKczmUyXM5VeTvWP6x1uEOMNQmTJiYkSNH5s17zkiS7LzLv+SGn1+33H777n9AZszcd8nXHR3tOWb2rD6N0bn4+c89SV68FF5so2lK0rW4KZ2Lk0Omj8yFtz+XQ6aPTOfipHW1e98fkqXXy+p0PQzn9T/Q2l6dXqdVsRa9dXYvsx7dq+96NGJ99yfzYOxb4vjm1BjjD9ac6q3q925jVG+cYsYo/Pmi6rUNja4R791qG6rNvRvKpLahXCU/l5f+OxNYlYHcu6mWRvh5y9BR21Auz+VQJvduqsjfsfxjq907zbS3teV9x8zOAQe+I9tut33O/NKpWW+99TPnuONXfVx7ew4/9J197gTs6urJ4iQtL3z9Ql9EWpJ09iQjWp6/QF/8PHpE43RaUX9dXT29rovV4Xro6OypxHz7W9tVyV0F1qK3jq6ejGh+/sFgdOvqvR5VuTb6U9/9yTwY+5Y4vjk1xviDNafBVMV7tzGqN04xY6xGzxdVrG1odFWpk8F6LgeGl3s3lEltQ7lKey5fnX5nAqvS33s31dIIP28ZHrXUtneagcbguRzK5LmcqvB3LKu22r3TzJixY3P8CSfmgvPPy7cv+Va2nDY9Bx/6nrqP09ra1GtxV/TfL3ZXNVKXFYPjpdfL6nI9vPgDudHm26i5B4O16O3FBwDr0ZjXRn8yD8a+JY5vTo0x/mDNqUqGIrcxqjdOMWN4vlipRv2ZBEOpEeukETMDfaO+oUxqG8rUCLXtdyZACRrh5y0Aw6cR7hOeywGqxd+xrNpq1zSTJFtO2yrzTj1juGMAAAAAAAAAAAAAAAAwSBqwzwcAAAAAAAAAAAAAAABWTdMMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFCc1uEOAAAAAAAAAAAAAAy/ppPn1/V8PR9au67nAwCA/vJOMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABSndbgDDIfPf+5T+f1tty75eq+9983B/4+9O4+TsyrzBf5UpzuQkLAry4iCgvcOhCVsVwUBcUeUi+KG3DAsYhxxmXEQFxQQdMA4jqPjhiLDlggo6nhn5hJHGUBnFBAEkR1lUcABkkCSriTV3XX/gCxN0p3qdFW/55z6fj+f/jTpflPnd877Pu95hXqsWcdVFwgAAAAAAAAAAAAAAIC26sqmmYULF8ZxJ54UBxx4UERE9PVNrjgRAAAAAAAAAAAAAAAA7dSVTTOLFi6M5z53m9hkk2lVRwEAAAAAAAAAAAAAAKADuq5pZqDRiCVLFselF18Y533tK7HLi/9HnPie98b06ZsOO67RaESj0Vj153q9f6KjAh2gtqFc6hvKpLahTGobyqW+oUxqG8qktqFc6hvKpLahTGobyqW+oUxqG/LUdU0zUavFX5zw7th22+1j6tSp8ZUvfzG+e/l34rgTThp22A+//9343hWXVRQS6BS1DeVS31AmtQ1lUttQLlv6GJYAACAASURBVPUNZVLbUCa1DeVS31AmtQ1lUttQLvUNZVLbkKdas9lsVh1iIq1YsSKWLF4cW261VUREXHrxP8W999wTp3/6M8OOW1cn4MmzT4zzL5wbU6dOndDMQPuobSiX+oYyqW0ok9qGcqlvKJPahjKpbSiX+oYyqW0oUztqu3bugk7Fa4vmqVtWHQEqYe+GMqltyFPXfdLMnx59JD7y4Q/GBz70N/HCnXeOW2/5dfzPP991reP6+vqir6+vgoRAJ6ltKJf6hjKpbSiT2oZyqW8ok9qGMqltKJf6hjKpbSiT2oZyqW8ok9qGPHVd08wOz39BHHv8iXHJRRdEvd4fu++xZ7z17UdXHQsAAAAAAAAAAAAAAIA26rqmmYiI173+8Hjd6w+vOgYAAAAAAAAAAAAAAAAd0lN1AAAAAAAAAAAAAAAAAGg3TTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxdE0AwAAAAAAAAAAAAAAQHE0zQAAAAAAAAAAAAAAAFAcTTMAAAAAAAAAAAAAAAAUR9MMAAAAAAAAAAAAAAAAxcmmaWag0YgHH7h/1Z8fefiPccP1v4gnn1xUXSgAAAAAAAAAAAAAAACS1Ft1gFY88fhj8dmzz4ze3t449/NfjFtvuTnmnPOZGBwcimnTpsUnTv90vOAFO1YdEwAAAAAAAAAAAAAAgERk8Ukz35l7STzy8B/jz563Q0REXP6duTF16iZx4MsPiv7+pfHdy+ZWnBAAAAAAAAAAAAAAAICUZPFJM7fddmvsutuM+MCHPhyLFi2K3913b7z7PX8Zr3jlq2PZsmVx1113VB0RAAAAAAAAAAAAAACAhGTxSTPLly2LrbbaOiIifnPrryOiFnvO3CciIqZuskksX7aswnQAAAAAAAAAAAAAAACkJotPmtnh+S+IG67/RTz/RzvG1T/5cTxvhx1iyy23jPvuuzdu/tWN8bznPb/qiAAAAAAAAAAAAAAAACQki0+aectb3x4rVjTi0osvjIcf/mP87zcfFRERZ59xWixevDje8MYjKk4IAAAAAAAAAAAAAABASrL4pJk99pwZZ5z12bj5pl/FC1/0othn3/0jImLHnV4Yhxz6qnjpAQdWnBAAAAAAAAAAAAAAAICUZNE0ExGx8y4vjp13efGwn53+6c9WlAYAAAAAAAAAAAAAAICUZdM089BDD8add9wei596MprN4b+r1SLefNTbqwkGAAAAAAAAAAAAAABAcrJomvnJj6+Kb3/rG880yzTXcURN0wwAAAAAAAAAAAAAAACrZNE0888/uDJqtVq86tWvjW222zZqUas6EgAAAAAAAAAAAAAAAAnLomlm0aKFsd/+L4njTjypba/5xb/7XPzyF/8Z8674QdteEwAAAAAAAAAAAAAAgDT0VB2gFTvt9KK2vt6NN1wfN1z/i7a+JgAAAAAAAAAAAAAAAOnIomnm8COOjJtv+lX87Lpr4ve/u2+dX63q7++PC87/Rrz29W/oYOKIgYHmiF/1RjMGhoZ/p7s9+7roBrnON9fcnWAthrMeq+W4FmPJ3IljSxzfnPIYv1NzSslE5DZGeuMYYwxjDDwzxoDahtLkWCc5ZgZao75hdJ7LoVw51kkOmXPICLA+7mUAjCaHfSKHjIyPc0w3KOm9MqXo1Fpl0TTzhTl/GytWrIivfvkf4hMfPWWtr9M+dkrLrzXvkoti9z32ihm77zHqcY1GI/r7+1d91ev9LY9RbzSj0YwRvz4xf2k0BiNmz18afXMWxuz5SxVBF1t5vXTT9VBvNCub73hru5vO02isxXDWY7Uc63ssmTtxbInjm1Me43dqTu2W+t5tjPTGMcYYxhhoxuyrnhnjqqUT+ga91Gsbcpfj3q22IW32bugcz+VQLs/lnZFDRuiU8ezdpMW9jDWpbSiX53Jy5hyPzN5djpLeK1OKTq5VrdlsJr/ynz79E1Gr1UY95pNnnL3e17nzjtvjH74wJ+Z84Utx9913xpxzPhPzrvjBOo/97uXz4ntXXLbWz8+/cG5MnTp11HEGhkZf0p8/sCQOeMG06JuzcNXPGqdsEb09o8+RMq28XrrpehgYalY23/HWdjedp9FYi+Gsx2o51vdYMnfi2BLHN6c8xu/UnNot9b3bGOmNY4y0xhhJ6rUNucuxvtU2pM3eDZ2T474dobahFTnWdw61nUNG6JTx7N2kxb2MNbWjtmvnLmh3rLZqnrpl1RGgEp7LyZlzPDLP5eUo6b0ypejkWvW25VU67FNnfqYtr/P9710Rixcvjg+e/J4YHByKiIgTjj06zr9w7lrHHnHkUXHY4Ues+nO93h8nzz6xpXEag6P//oe312L/50XMmjE5LrptRcyaMTkagxG9WXzuD+228nrppuuhMVjdfMdb2910nkZjLYazHqvlWN9jydyJY0sc35zyGL9Tc2q31PduY6Q3jjHGMMbQs8YYUttQihz3brUNabN3Q+d4LodyeS7vjBwyQqeMZ+8mLe5lrEltQ7k8l5Mz53hk9u5ylPRemVJ0cq2y+KSZO27/7XqP+fNdd1vvMYsWLoxGY0VERNx222/ivK/9Y3zpK9+I5zx3m/X+3f7+/lUNNq10Ag4MjLysjWZE36SnT+zK71P6dIx1s4GB5rDrohuuh3qjmcR8x1rbqeROgbUYznqslspajKW+x5K5E8eWOL455TF+p+bUSSnu3cZIbxxjjGGMgWb09Tz9Rr0pvWobSpJKnXTquRyolr0b2stzOZQrlTop7bk8h4wwEca6d5MW9zJGsiG17ZNmIA+ey8mNc9waz+V5K+m9MqXo1Fpl8UkzZ51xWkSMPum5l1+53tfZfIstVv3zZps9GBHRUsPMhugd5T9qrFz0lZ1PusXo7a2tdV2UbuWNLLf55pq7E6zFcNZjtRzXYiyZO3FsieObUx7jd2pOKZmI3MZIbxxjjGGMXrUNpcqxTnLMDLRGfcPoPJdDuXKskxwy55ARYH3cywAYTQ77RA4ZGR/nmG5Q0ntlStGptcqiaWafffeLdTXNPPbff4oHH3wgXnbAy8f8mnvvs1/Mu+IHbUgHAAAAAAAAAAAAAABAarJomvnwRz6+zp8PNBpxyoc/GL19fROcCAAAAAAAAAAAAAAAgJRl/SE/vX19scsuL44br/9l1VEAAAAAAAAAAAAAAABISNZNMwsXLoh77r4raj21qqMAAAAAAAAAAAAAAACQkN6qA7Ti+FnvXOtnzWYzli9fERHNeMnLDpz4UAAAAAAAAAAAAAAAACQri6aZZcuWreOntdhs881iz732jmP+z19MdCQAAAAAAAAAAAAAAAASlkXTzNzLv191BAAAAAAAAAAAAAAAADLSU3WA9RkaHIyzz/xk/MuPflh1FAAAAAAAAAAAAAAAADKR/CfN9EyaFI888nBs+cD9VUcBAAAAAAAAAAAAAAAgE8k3zUREvOa1h8WPfnhl3HvP3bHtttut85hp06dPcCoAAAAAAAAAAAAAAABSlUXTzGXzLomIWnzqEx9d5+9rtYhLL7tyYkMBAAAAAAAAAAAAAACQrCyaZrbaeuuoRa3qGAAAAAAAAAAAAAAAAGQii6aZL3/1m1VHAAAAAAAAAAAAAAAAICM9VQdolwvOPy/e9fY3Vx0DAAAAAAAAAAAAAACABBTTNBMR0WxWnQAAAAAAAAAAAAAAAIAUFNU0AwAAAAAAAAAAAAAAABGaZgAAAAAAAAAAAAAAACiQphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDi9VQdol5l77xPTN9206hgAAAAAAAAAAAAAAAAkIJummcWLn4r77r03Fj/1ZDTX8fuDDn5F7DVznwnPBQAAAAAAAAAAAAAAQHqyaJq58YZfxle+9PexfPmKdfy2GRG1OOjgV0x0LAAAAAAAAAAAAAAAABKVRdPMdy69OJYvXx57zdwnttl226jValVHAgAAAAAAAAAAAAAAIGFZNM089th/x8y994lTPnpa1VEAAAAAAAAAAAAAAADIQE/VAVqx3fZ/FptMm151DAAAAAAAAAAAAAAAADKRRdPMO981K2668Ya46847Ysnixev8AgAAAAAAAAAAAAAAgJV6qw7Qis+f+5kYHByKMz/1iXX+vlaLuPSyK9f7OoODg3HZvEvip/8+PyIiZu69b7x79vti8uTJbc0LAAAAAAAAAAAAAABAtbJomtl8iy2iFrVxv87PrrsmfvrvP45TPnpaTJs+Pc4+45Px03+fH6877PA2pAQAAAAAAAAAAAAAACAVWTTNfPmr32zL6xx8yKFx8CGHRkTE0qVLore3NyZNmtSW13625QPNGGpG9E2KGBiKaEZEX0/E0FDE4DM/bww+830oYkrv+JuCyFd9oBl9Pd11LeQ653qjuap+p/Tlk7sTrAUlGcs9aSzXftV10ol7bYmv2ep5qnrv6tR12vL4md73JyL3RFwbpcwjYoLmkun1SuucYyiT2oZyqW8ok9qGMqltgInhfgvAaOwTAO6FtFdP1QHGotlsxpIlS2LJ4sWrvhYsWBC3/eaWMb3ORz78wTjp+Fmx7Xbbxate87p1HtNoNKK/v3/VV73e3/Lr1weaMdiMeO/8pfGnpc1YsqIZs69aGhvPWTjs57PnL42+OQtj9lVLoz7QHNMcKEd94Onro5uuhSrnPK7abqxRt/OXRr1R/rkaibUgRRta32O5J43l2q+6Tjpxry3xNVs9T1Xv1526Tlsev8LrOfW9eyKujVLmETFBc/GckoXUaxvYcBv8XK62IWn2biiT2oZyeS6HMo1n7yYt7resSW1DuTyXQ5ns3RPDvZB2qzWbzeSvomX1epz/za/HDdf/IlasaKzzmLmXX9ny6z3xxOPxyMN/jC/9/d/F0f/n2DjkFa9c65jvXj4vvnfFZWv9/PwL58bUqVNHff2BoaeXdJsvL4pHTt48IiI2+vzC2GebSfGLWZuu+vlGn1+46u80Ttkient0wXWjgaFm9M3prmuhyjmPt7a77VyNxFqQog2t77Fcz506thM6MX6Jr9nqsTmdz6rXtN1S37uNkd44VdcrrUm9toENNxHP5cDEs3dDmdQ2lMtzOZRpPHs3aXG/ZU3tqO3auQvaHautmqduWXUEqITnciiT5/KJ4V5Iu/VWHaAVl182N37+s2tj442nxMYbT4ply+qx9dbPiXq9HkuXLond99irpdd54IH748lFi2KPPfeKrbbaOnadMSNu/+1t62yaOeLIo+Kww49Y9ed6vT9Onn1iS+M0hiKazYg37twXj/U3Y6NJEbNmTI5Lb1sRjcHVP581Y3JcdNuKmDVjcjSGInqz+twf2qUxFF13LVQ553HV9uCzcg+Wf65GYi1I0YbW91juSWO59quuk07ca0t8zVbPU9X7daeu05bHr/B6Tn3vnohro5R5REzQXDynZCH12gY23AY/l6ttSJq9G8qktqFcnsuhTOPZu0mL+y1rUttQLs/lUCZ798RwL6Tdsvikmff/5btj2rTpcebZ58SlF/1T/Hj+/4u5l18ZS5cuib/50Pvj1a97fbz5LW9b7+tcd83V8e1vfSM+/skzY+MpU+Kcz5wZb3jjEXHYG9603r/b398fJxx7dMudgMsHmjHUjOibFDEwFNGMiL6eiKGhiMFnft4YfOb7UMSUXt1v3aw+0Iy+nu66FlKZ81hru95orqrfKX3dca5GYi1I3Vjqeyz3pLFc+1XXSSfutSW+Zqvnqeq9q1PXacvjJ3LfT3Hvnohro5R5REzQXBK5XmldirUNtMeYnsvVNmTD3g1lUttQLs/lUKax7t2kxf2WkWxIbfukGciD53Iok+fyznEvpJ2y+KSZJxc9GXvssVdMnjw5pkydEhERy5cvj002mRa77b5H/MdP/r2lppkDDzokHnjg/phzzmei2WzGS152QLzmtYd1JPNGa7zJbFhn2xr/vPLnOt9Y+abEbroWcp3zyo03t9ydYC0oyVjuSWO59quuk07ca0t8zVbPU9V7V6eu05bHz/S+PxG5J+LaKGUeERM0l0yvV1rnHEOZ1DaUS31DmdQ2lEltA0wM91sARmOfAHAvpL2yaJrZZttt4847bo8lS5bEVlttHRERN990Y7zkpQfEE48/Hv39/S29Tq1Wi2NmHRfHzDquk3EBAAAAAAAAAAAAAACoWBa9V4e84pXxyCMPx6UXXRD77f+S6O2dFF/50t/H+2afEHfdeUf8+a67VR0RAAAAAAAAAAAAAACAhGTRNHPY4W+Kww5/U2wybVpsvsUW8RcnnBSTJvXGwgULYseddoq/OP7EqiMCAAAAAAAAAAAAAACQkN6qA7SiVqvFMbOOW/XnQ1/56jjooEOivqwe06dvWmEyAAAAAAAAAAAAAAAAUpRF08yalixZEtFsRkRELWqxZPHiiIiYNn16lbEAAAAAAAAAAAAAAABISBZNM088/lh88xtfi9t/+5sYGBhc6/e1WsSll11ZQTIAAAAAAAAAAAAAAABSlEXTzLfO+3rcesvN0dfXF1tvvXXVcQAAAAAAAAAAAAAAAEhcFk0zd991Rzz/+S+IM84+JzbeeOOq4wAAAAAAAAAAAAAAAJC4nqoDtKK3ry9euPMuGmYAAAAAAAAAAAAAAABoSRZNMwceeHD89je3xhNPPF51FAAAAAAAAAAAAAAAADLQW3WAVhz5lrfGz667Jv7mQyfHc567zVq/r9Vqce7nv1hBMgAAAAAAAAAAAAAAAFKURdPMP13wrVi8+KmIiPjDQw+u44jaxAYCAAAAAAAAAAAAAAAgaVk0zdx04/UxfdNN4+QP/HVsu912VccBAAAAAAAAAAAAAAAgcVk0zfT09MTMvfeN3ffYs+ooAAAAAAAAAAAAAAAAZKCn6gCt2GvvfeOPf3io6hgAAAAAAAAAAAAAAABkIotPmtls083iP392XZz72bNim223Xev3tajFscefWEEyAAAAAAAAAAAAAAAAUpRF08y//euPIiLill/fNMIRmmYAAAAAAAAAAAAAAABYLYummff85furjgAAAAAAAAAAAAAAAEBGsmiaOfiQQ6uOAAAAAAAAAAAAAAAAQEayaJqJiLjm6p/EvffcEyecNDuW1evx7fPPiwfv/33M3Ge/eNs7jo5arVZ1RAAAAAAAAAAAAAAAABKRRdPMNf/x0/jG1/4xnrfD8yMi4rJ5l8bPrv2PiIh48MEHY8rUKfGmI95cYUIAAAAAAAAAAAAAAABS0lN1gFb82//955g2bXocf+JJMTQ4GNdde3W85GUHxt9/+WuxzbbbxrVX/7TqiAAAAAAAAAAAAAAAACQki6aZRx99NPbdb//4n3++W9x3373R398fr3v9G2KbbbaNXXfdLR5//LGqIwIAAAAAAAAAAAAAAJCQLJpmpk+fHn946KEYGhqK//z5dbHRRhvFi3beJSKebqiZMmVqxQkBAAAAAAAAAAAAAABISW/VAVqx7/7/K676t3+Jk46fFf39/XHQwYfEpEmT4gtzzok7bv9tHHLoK6uOCAAAAAAAAAAAAAAAQEKy+KSZt73jXbHXzH1i2bJ67LjTTvHOY2ZFRMRvbv11bLf99vH2d76r4oQAAAAAAAAAAAAAAACkJItPmpkyZUp85GOnxeDgYEyaNGnVzz/04VNjt91mRG9fX4XpAAAAAAAAAAAAAAAASE0WnzTzvSsui1/ffNOwhpmIiD33mhk33PDLmH/Vv1aUDAAAAAAAAAAAAAAAgBRl0jTznbjl5pvW+btr/+Pq+M6ll4zp9X70w+/He999XBw/653xxS98LpYtW9aOmAAAAAAAAAAAAAAAACSit+oAo/ng+96z6p+vveancdOvbhj2+4HBgVi4YGFsvfXWLb/mrbf8Oi6bd2mc+vFPxuZbbBGf/fTp8f/+9Ufxv9/81rblBgAAAAAAAAAAAAAAoFpJN8089th/P/NPtajX61Gv19c6ZrPNN49jjz+x5dfs7e2No4+ZFbvvsWdERGy3/fbx5KIn2xF3mIGB5oi/azQj+iZFNAZXf5/SV2t7BvIxMNAcdl10w/VQbzSznG+uuTvBWjCSHK+NsWTuxLEljm9OeYzfqTmlZCJyGyO9cUoZg5FZf1i/HOskx8ypqQ80o68nojEUMaXXGpIO9Q1lUtvp8SxAO+RQ2zlkBFgf9zIARpPDPpF6xtTzQe5KeV+Ge0X36Kk6wGi+dcEl8c0LLo6IZhz6qtfEty64ZNjXty+aF18774LYZ9/9W37NXXebEYcd/qaIiHjg/t/HvffcHQe8/KC1jms0GtHf37/qq17vb3mMeuPpBoiRvj40f2k0BiNmz18afXMWxuz5S6PeGLnJhrKtvF666XqoN5qVzXe8td1N52k01oKR5FjfY8nciWNLHN+c8hi/U3Nqt9T3bmOkN04pY5Qu9dqG3OW4d6vt8asPNGP2Vc+s4VVLoz7K/6kPjJW9G8qktsviWYA1lfxcnkNG6JTx7N2kxb2MNaltKJfn8uqkno+82bvLeV+Ge0V3qTWbzeTP8Hev+E7svPMusdfMfdr2mk888Xic/omPxssPOiTefvQxa495+bz43hWXrfXz8y+cG1OnTh31tQeGRl/Sj129JP72FdOib87CVT9rnLJF9PboUOtGK6+XbroeBoaalc13vLXdTedpNNaCkeRY32PJ3IljSxzfnPIYv1NzarfU925jpDdOKWOULvXahtzluHer7fGzhnSSvRvKpLbL4pywppKfy3PICJ0ynr2btLiXsaZ21Hbt3AXtjtVWzVO3rDoCVMJzeXVSz0fePJeX874M94ru0lt1gFYc9dZ3RKPRiLvvujOeXLQomrG6KaWxYkX88Y9/iLe9410tv95TTz0Vnz3rjNhtxu7xtneu++8dceRRcdjhR6z6c73eHyfPPrGl128Mrmf8ZbVoDEbMmjE5LrptRcyaMTkagxG9SX/uD52y8nrppuuhyut/vLXdTedpNNaCkeRY32PJ3IljSxzfnPIYv1NzarfU925jpDdOKWOULvXahtzluHer7fFrDD1rDYesIe1j74Yyqe2yeBZgTSU/l+eQETplPHs3aXEvY01qG8rlubw6qecjb/buct6X4V7RXbL4pJk/PfpInPPZs+JPjz464jFzL7+ypddaVq/H2Wd+KjaZtkmc/MG/jp6enqjVetbb3dff3x8nHHt0y52AA6N85HmjGdE36eliW/l9Sp/OtG42MNAcdl10w/VQbzSTmO9YazuV3CmwFowklWtjLPU9lsydOLbE8c0pj/E7NadOSnHvNkZ645QyRjdJsbYhd6nUSaeey1m3+kAz+nqeftPslF5rSOfYu6FMajt/ngUYSWnP5TlkhIkw1r2btLiXMZINqW2fNAN58Fw+sVLPRzm69bm8lPdluFd0jyw+aWbuJRfGnx59JGbsvkcseOKJePjhh+Oggw+JJ554PH5722/ijUe8ueXX+uUv/yvuu++eiIg46fhZERGx9XOeE1/+6jfbmrl3lH8RvXLRV3aj6Uqjt7e21nVRupWbS27zzTV3J1gLRpLjtTGWzJ04tsTxzSmP8Ts1p5RMRG5jpDdOKWMwMusP65djneSYOTUr3xxrDUmN+oYyqe30eBagHXKo7RwyAqyPexkAo8lhn0g9Y+r5IHelvC/DvaJ7ZNE0c+cdd8See82MUz/+qbjg/PPi4Ycfjtnv+0AMDQ3FRz78wVi4sPVu+YMPOTQOPuTQDqYFAAAAAAAAAAAAAACgaln0RS1fvjw223yLiIiYvummERGxZPHi6OnpiZ13eXHccvNNVcYDAAAAAAAAAAAAAAAgMVk0zey4007xqxuvj7vvujO223a7iGjG//3RD+KRRx6Ou++8I3omZTENAAAAAAAAAAAAAAAAJkgW3SZHHPmWWLpkafz0Jz+O/f7XS2P69E3jn3/w/fjwB0+ORx99NF76sgOrjggAAAAAAAAAAAAAAEBCeqsO0IqZe+8bf3Pqx2LRokUxefLkOPXjn4x5l14cTy5aGHvsNTPe9o53VR0RAAAAAAAAAAAAAACAhGTRNBMRsfc++6365xe+aOf4xKfOrDANAAAAAAAAAAAAAAAAKcumaWag0YjrrrsmHrj/97Fo4cLYbLPNY8cXvjBe/vKDo7evr+p4AAAAAAAAAAAAAAAAJCSLppk/PPRQnPOZM2PBggUR0VzjN7W48orL4qOf+FT82fN2qCoeAAAAAAAAAAAAAAAAicmiaeaC88+LBQueiJl77xMvO/Cg2GyzzWLx4sXxnz+7Ln514/VxwfnnxWmnn1V1TAAAAAAAAAAAAAAAABKRRdPMvffcFbu8+H/EKR89bdjPX/qyA+OM0z4W99x9V0XJAAAAAAAAAAAAAAAASFFP1QFaseVWW8f22//ZOn+37fbbx7Tp0yc4EQAAAAAAAAAAAAAAACnLomnm9W84PG6+6Vfx+GOPDfv5nx59JG7+1Y3xutcfXlEyAAAAAAAAAAAAAAAAUtRbdYCRHD/rncP+vGzZ8virD7w3+vr6Vv1sxYpGNJtD8Yv/+nm88YgjJzoiAAAAAAAAAAAAAAAAiUq2aWbZsmVr/WxwcDAGBwfX+vnvf/e7iYgEAAAAAAAAAAAAAABAJpJtmpl7+ferjgAAAAAAAAAAAAAAAECmeqoOAAAAAAAAAAAAAAAAAO2W7CfNAAAAAAAAAAAAAHSz2rkL2vp6zVO3bOvrAQCkzifNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAmqb9MgAAIABJREFUAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABSnK5tmnnxyUfzrv/xzfPSUv4qHHnyg6jgAAAAAAAAAAAAAAAC0WW/VASZavV6P97/3pNhxx53igft/X3UcAAAAAAAAAAAAAAAAOqDrPmlm8uTJ8eWvnRfv/9Bfd3ScgYHmiF/1RjMGhoZ/p7s9+7roBrnON9fcnWAthrMeq+W4FmPJ3IljSxzfnPIYv1NzSslE5DZGeuMYI60xOiHX3DCRcqyTHDLnkBFSpHagTKXUdinzgHbJoSZyyAiwPu5lAIwmh30ih4zQrbzXANbWdU0zkyZNis0223y9xzUajejv71/1Va/3tzxGvdGMRjNG/Prc/KXRGIyYPX9p9M1ZGLPnL3XT6GIrr5duuh7qjWZl8x1vbXfTeRqNtRjOeqyWY32PJXMnji1xfHPKY/xOzandUt+7jZHeOMZIa4yRpF7bkLsc6zuH2s4hI3SKvRvKpLbLmQc8m+dyKNN49m7S4l7GmtQ2lMtzOZQp9b279PcawIaqNZvNrrxSH/vvP8UH3vee+Nzf/UPs8PwXrPX7714+L753xWVr/fz8C+fG1KlTR33tgaH1/D96L10SUzaZFn1zFq76WeOULaK3p9Ziekqy8nrpputhYKhZ2XzHW9vddJ5GYy2Gsx6r5VjfY8nciWNLHN+c8hi/U3Nqt9T3bmOkN44x0hpjJKnXNuQux/rOobZzyAidYu+GMqntcuYBz+a5HMo0nr2btLiXsaZ21Hbt3AXtjtVWzVO3rDpC9tp9jp2TieG5HMqU+nN56e81gA3VW3WAVB1x5FFx2OFHrPpzvd4fJ88+saW/2xgc/fdfuLYWH3lNxKwZk+Oi21bErBmTozEY0dt1n/tDxOrrpZuuh8ZgdfMdb21303kajbUYznqslmN9jyVzJ44tcXxzymP8Ts2p3VLfu42R3jjGSGuMkaRe25C7HOs7h9rOISN0ir0byqS2y5kHPJvncijTePZu0uJexprUNpTLczmUKfW9u/T3GsCG8kkzI3zSzLP19/fHCcce3XIn4MDAyMvaaEb0TXr6prHy+5Q+HXbdbGCgOey66Ibrod5oJjHfsdZ2KrlTYC2Gsx6rpbIWY6nvsWTuxLEljm9OeYzfqTl1Uop7tzHSG8cYaY3RihRrG3KXSp106rm8KjlkhIlg74YydWttlzIPGI3ncijTWPdu0uJexkg2pLZ90kz5fNJMGTyXQ5lSfC7vpvcaQKt80kyH9PaOfANYuegru+p019HbW1vruijdyk0yt/nmmrsTrMVw1mO1HNdiLJk7cWyJ45tTHuN3ak4pmYjcxkhvHGOkNUYn5JobJlKOdZJD5hwyQorUDpSplNouZR7QLjnURA4ZAdbHvQyA0eSwT+SQEbqV9xrA2rq2aeY5z90m5l3xg6pjAAAAAAAAAAAAAAAA0AH6uwAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDi9VQcAAAAAAAAAAAAAWJ/auQva/prNU7ds+2sCAJAOnzQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUJzeqgMAAAAAAAAAAAAAQO3cBW1/zeapW7b9NQGAfPikGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOL1VB6jCf/38Z/GduRfH0qVLYt/9XxInvHt29PX1VR0LAAAAAAAAAAAAAACANum6T5pZvPip+PpXvxRHHvW2OOOsc+KWX98UV//kx1XHAgAAAAAAAAAAAAAAoI26rmnmvnvviWYz4uBDDo3n7bBDzJy5T9xx+2/bPs7AQHPEr3qjGQNDw7/T3Z59XXSDXOeba+5OsBbDWY+8jeX8deLYEsc3pzzG79ScUjIRuY2R3jjGSGuMTsg1N0ykHOskh8ypZ0w+38Az+QbSzJeD1M/xSHLN/WylzIP05Hp/LKUmSplHRFlzKUWO5ySHzDlkpGy57t2kxb0MgNHksE+knjH5fBk8Uya/honnG0kp7wOYkDEmoE5Keq8Mo+u6ppmnnnoqNtp4o6jVahERMXWTTeKpp55a67hGoxH9/f2rvur1/pbHqDea0WjGiF+fm780GoMRs+cvjb45C2P2/KWKoIutvF666XqoN5qVzXe8td1N52k01mI465GGDa3vsZy/Thxb4vjmlMf4nZpTu6W+dxsjvXGMkdYYI0m9tiF3OdZ3DrWdesbk8w00Y/ZVz+S7amnS/yEwVTnWdkT612arSpkH6any/qi2y5lHRFlzKUWOe3cO11EOGSlbrns3aXEvY01qG8rlubw6yefL4N+XJ7+GGf5v7ohy3gcwIWNMQJ2U9F4Z1q/WbDa7auWvvebquPjCb8c3v31xRERcctEFcf/vfxennX7WsOO+e/m8+N4Vl63198+/cG5MnTp11DEGhkZf0kVLl8Tmm0yLvjkLV/2sccoW0dtTa3UaFGTl9dJN18PAULOy+Y63trvpPI3GWgxnPdKwofU9lvPXiWNLHN+c8hi/U3Nqt9T3bmOkN44x0hpjJKnXNuQux/rOobZTzyhf+XKs7Yhyzn0p8yA9artapcwjoqy5lCLH+s7hOsohI2XLsbZJj3sZa2pHbdfOXdDuWG3VPHXLtr5eJ+bb7ozt1u45d9t8I6qZs+fy6sg3fqlnzPW5vJT3ARgjzXEYXdd90sz06dNjWb0eQ0NDERFR7++PTTfbfK3jjjjyqDj/wrmrvv7x699qeYzG4OhfX7m2Fo3BiFkzJkfE098bg+2ZH/lZeV100/VQ5XzHW9vddJ5GYy2Gsx5p2ND6Hsv568SxJY5vTnmM36k5tVvqe7cx0hvHGGmNMZLUaxtyl2N951DbqWdMPt/Qs/INVRwoQznWdkT612arSpkH6any/qi2y5lHRFlzKUWOe3cO11EOGSlbrns3aXEvY01qG8rlubw6yefL4N+XJ7+GGf5v7ohy3gcwIWNMQJ2U9F4Z1q/rPmnmqaeeive/98Q4ZtZxseuM3eOzZ50eR77lbfGqV7921L/X398fJxx7dMtd/AOjfAxUoxnRN+npIlj5fUqfjrFuNjDQHHZddMP1UG80k5jvWGs7ldwpsBbDWY/0jKW+x3L+OnFsieObUx7jd2pOnZTi3m2M9MYxRlpjtCLF2obcpVInnXour0rqGZPPN9CMvp6n/0PHlN708uUglXPcrXt3KfMgPancH9V23vOIKGsupUjlnHguh/bKde8mLe5ljGRDatsnzYxft33ySrfNNyKNOXsun1jJ50vkmXI0ya9hIvlS/HdqxYwxAXVS0ntlGF1v1QEm2qabbhqz//IDMe/Si+KyeZfEfvu/JA4+5NC2j9M7SnGuXPTenuHf6V69vbW1rovSrbzp5zbfXHN3grUYznrkbSznrxPHlji+OeUxfqfmlJKJyG2M9MYxRlpjdEKuuWEi5VgnOWROPWPy+XrTzpeD1M/xSHLN/WylzIP05Hp/LKUmSplHRFlzKUWO5ySHzDlkpGy57t2kxb0MgNHksE+knjH5fBk8Uya/honnG0kp7wOYkDEmoE5Keq8Mo+u6ppmIiJcecGC89IADq44BAAAAAAAAAAAAAABAh+hXAgAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDi9VQcAAAAAAAAAAAAAKEHt3AVVRwAAYA2aZlrUbDYjIqJe7684CXS3KVOmRK1Wa9vrqW1Ih/qGMqltKJPahjK1u7Yj1Dekwt4NZVLbUCbP5VAuezeUSW2vX39/+nPJIWM7ddt8I8Y+Z8/lUC57N5RpfbVda66sVkb1xBOPx8mzT6w6BnS98y+cG1OnTm3b66ltSIf6hjKpbSiT2oYytbu2I9Q3pMLeDWVS21Amz+VQLns3lEltQ5k8l0O57N1QpvXVtqaZFg0NDcXChQti441H70Kq1/vj5Nknxj9+/VsxZUp7H5o6ReaJIXN7tLvLt9XaXinFNamKtRjOeqy2oWvRifp++OE/xil/9f6izkuJ15o55SGl2h7L3j0RSjnfpcwjopy5pDyPqms75bWJSD9fRPoZ5Ru/DcnYif/nvLHUd6nrOpHkG7/UM+b6XJ76uraqlHlElDOXbp+H2m6PUuYRUc5cSplHhOfyTkk9o3zjl3rGXPfudkr9HEWknzH1fBEytqrq2k5hDVJhLVazFsN5Lu+M1DPKN36pZ8z1uTz1dW2VeaSnlLl0qrZ72xGuG/T09MRWW23d8vFTpkxte6dxp8k8MWROy1hre6WS12SsrMVw1mO1qteip6cnttxyqySydII55cGc2m9D9+6JUPXatEsp84goZy6lzGM0pT6Xp54vIv2M8o1f1Rk3pL6rztyK1DPKN36pZ6w6X6l7d6tKmUdEOXMxj/ZQ22XMI6KcuZQyj4jq5+K5vBryjV/qGavOl8K/L696DVqResbU80XIONG6/bm8HazFatZiuKrXw3N5NeQbv9QzVp2v2/du80hPKXNp9zx62vZKAAAAAAAAAAAAAAAAkAhNMwAAAAAAAAAAAAAAABRn0hlnnHFG1SFK09PTE7vutntMmjSp6igtk3liyFwGa7KatRjOeqyW0lqklKVdzCkP5tRdSlmbUuYRUc5cSplHJ6S+Nqnni0g/o3zjl0PGZ8shc+oZ5Ru/1DOmnm8kueZ+tlLmEVHOXMyjWrnmfrZS5hFRzlxKmUdEnnPJIXPqGeUbv9Qzpp5vIuSwBqlnTD1fhIy5sAarWYvVrMVwOa5HDplTzyjf+KWeMfV8I8k197OZR3pKmUsn5lFrNpvNtr0aAAAAAAAAAAAAAAAAJKCn6gAAAAAAAAAAAAAAAADQbppmAAAAAAAAAAAAAAAAKI6mmRY1m83o7++PZrNZdRSgjdQ2lEt9Q5nUNpRJbUO51DeUSW1DmdQ2lEt9Q5nUNpRJbUO51DeUSW1DHjTNtKher8cJxx4d9Xq96ihAG6ltKJf6hjKpbSiT2oZyqW8ok9qGMqltKJf6hjKpbSiT2oZyqW8ok9qGPGiaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACK01t1gCr87dlnxK23/HrVn9/wxiPimFnHVRcIAAAAAAAAAAAAAACAturKppmFCxfGcSeeFAcceFBERPT1Ta44EQAAAAAAAAAAAAAA/5+9O4+TqyoTBvxWpzukExIgiAKK2zijYgiggCgiiCKCOBFEVGTCKDhEBdFxED8/+cRhxhlkXJERlzgSFWSJ4C5xdxyVxQWIy4DKIou4kI10xVSn6/sjdKebLN3Vfavuuaee5/drO119c897zj3vOSdtvxRAkXrKDqAMK1esiIc//BExa9b2MWvW9jF9enuLZtYNNqM+2IzBoY2fh9UbD77WaG7jbwMpkbebGAsozpbOCSnft2hVibPbeU7lsu+2xnzNn5yAPFm/YXxV3QOrGjewbXIb8iS3YXz+/UoRrLcUxVwCAEifM1v5uu6dZgYbjXjggTXxmU9dHB/98IXx13/zxDjl1NfG7NlzxlzXaDSi0WiMfF2vD0yqvfpgM9aub8abvz0QS5avj4XzpsdFR8yKaEYsWrZ202vPnxX9fbUp9Q0Y31Ryu95oytsHGQtSVNTe3Wn1wWYsumbtmHNCf+/U86ld9y1aVeLsdmU+p6rmdpHsu62xrlSDcznka7L5bf2G8ZW5B9q7IU9yG/I16XO53IZx+Xk5RbDeMppzOeRrqnt37bz7C42nedbcQu8H3cq5nFY5s6Wh64pmolaLvz/5NbHrrrvHzJkz48IL3h9XXv7ZeNXJ/zDmss9fdWUsveKyKTfX1xMxZ7taLFm+PiIilixfH4uPnDXy54e+BrTXVHK7b5q8HWYsSFFRe3en9fW0J5/add+iVSXOblfmc6pqbhfJvtsa60o1OJdDviab39ZvGF+Ze6C9G/IktyFfkz6Xy20Yl5+XUwTrLaM5l0O+7N2QJ7lNq5zZ0tB1RTNDQ0Ox335Pj7k77xwREfvtf0D8+tZbN7tuwTHHxVFHLxj5ul4fiNMWndJye42hiLXrm7Fw3vSRCrHGUEQ0Y+xrGyJ6eybdLWCCppLbjQ3ydpixIEVF7d2d1hiKzc4JReRTu+5btKrE2e3KfE5Vze0i2XdbY12pBudyyNdk89v6DeMrcw+0d0Oe5Dbka9LncrkN4/LzcopgvWU053LIl70b8iS3aZUzWxq6rmjmvt/fG2958xnxhjf+Uzz+CU+Im278WTzpyXtudl1fX1/09fVNub3+3lrUIuKiI2bF4iNnRWMoRt6a96LnP/jahvA2S9AhU8nt/r6avH2QsSBFRe3dndbfW9viOSHV+xatKnF2uzKfU1Vzu0j23dZYV6rBuRzyNdn8tn7D+MrcA+3dkCe5Dfma9LlcbsO4/LycIlhvGc25HPJl74Y8yW1a5cyWhq4rmtnj0Y+Jk159Snx6yX9FvT4Qe83fO176shPa2uaMUT8kGV0ZNjzpVYtBdcjbTYwFFGf4/1ApOp/add+iVSXObuc5lcu+2xrzNX9yAvJk/YbxVXUPrGrcwLbJbciT3Ibx+fcrRbDeUhRzCQAgfc5s5eu6opmIiBcceXS84Mijyw4DAAAAAAAAAAAAAACANlGvBAAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJAdRTMAAAAAAAAAAAAAAABkR9EMAAAAAAAAAAAAAAAA2VE0AwAAAAAAAAAAAAAAQHYUzQAAAAAAAAAAAAAAAJCdri2aef973h2veOmLyw4DAAAAAAAAAAAAAACANujKopkbrr8urr/uR2WHAQAAAAAAAAAAAAAAQJt0XdHMwMBA/Nfij8QRR76wre3UB5sxONSM+mAzIiLWDTZj3WBz5PXRfx6+hu7WbfOh3niwv41q9beqcbeDsSAnrcznKq3X7cjTdvS/KmNalTgZqxPPLZc2OqUj4+Wckj3PGPIktyFf8hu2rao5UtW4oZOqmCdVjBmgiqy3FMVcYkvMCwCYGHtmetr1TLquaObSTy+JvebvE/P2mr/N6xqNRgwMDIx81OsDE26jPtiMRdesjb7zV8Sia9ZGvdGMB9Zv/Fh0zdqYfv6KkT+PXJPBL78xeZvNmcznQ73RjEXLHuzvsrUd3WymlNslxp0aY0GKJpvfrcznKq3X7cjTdvS/KmNalThzVOi5vA3PLZc2OqUj4+WcUgnO5ZCvTpzLgc6zd0P7+Hk55KuK+S23IW1T2btJi/WW0ZzLKZp5kQ57N+RJbufDnpmedj6T3sLuVAG/+uUv4obrr43z3/vBuOWWX23z2s9fdWUsveKySbXT1xOxZPn6iNj4efGRs2JOT23k67kzajFnu9pm19C9tjRnctY3rbz+Tim3S4w7NcaCFE02v1uZz1Var9uRp+3of1XGtCpx5qjoc3nRcmmjUzoyXs4pleBcDvnqxLkc6Dx7N7SPn5dDvqqY33Ib0jaVvZu0WG8ZzbmcopkX6bB3Q57kdj7smelp5zPpqqKZq5ZeEWvWrIkzTjs1NmwYioiIk086IRZffMlm1y445rg46ugFI1/X6wNx2qJTJtROYyhi4bzpsWT5+lg4b3o0NkSsfbDSaeG86fGp5etj9V+aY68Ziujtuvf9Ydhmcybz+dDYsHmOdKq/U8rtEuNOjbEgRZPN71bmc5XW63bkaTv6X5UxrUqcOSr0XN6G55ZLG53SkfFyTqkE53LIVyfO5UDn2buhffy8HPJVxfyW25C2qezdpMV6y2jO5RTNvEiHvRvyJLfzYc9MTzufSa3ZbHbNewmtXLEiGo2N1UfLl98cH/3wh+KDF34kdnn4I8b9uwMDAyMFNjNnzhz3+vpgM/p6Nv5SWH9vLdYNbhzmZmz8rysPDm368/A1dLeHzpnc1RvN6Ju2cYHr7yuvvy3ndiJxp8BYkLpW8ruV+Vyl9bodedqO/ldlTKsSZ+6mei5vh1za6JSOjJdzSuU4l0O+2nUuB8pl74ZipZIjchuKl0qeOJdDnlrdu0mL9ZatcS6nCOZFmlrN79p59xfafvOsuYXeD9jIubza7Jnpadcz6ap3mtlxp51G/rzDDndGREyoYGYyhn/5a7i6acZDfhlsdNWTqjQiNp8zuRteyKrW36rG3Q7Ggpy0Mp+rtF63I0/b0f+qjGlV4mSsTjy3XNrolI6Ml3NK9jxjyJPchnzJb9i2quZIVeOGTqpinlQxZoAqst5SFHOJLTEvAGBi7Jnpadcz6dpH/NSn7R+XXnF12WEAAAAAAAAAAAAAAADQBl1bNAMAAAAAAAAAAAAAAEC+FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQHUUzAAAAAAAAAAAAAAAAZEfRDAAAAAAAAAAAAAAAANlRNAMAAAAAAAAAAAAAAEB2FM0AAAAAAAAAAAAAAACQnd6yA5io5TffFLfc8qs49iXHx4YNG+JzV14ed95xW+z7tP3jsOceXnZ4AAAAAAAAAAAAAAAAJKQSRTM/+fEN8Z53vyse+ag94tiXHB9XLb08rlp6eURE/PiGGyIiFM4AAAAAAAAAAAAAAAAwoqfsACbi6qVXRF/f9HjxscdFRMQ3ll0T8/aaH287+52x4447xrKvfrnkCAEAAAAAAAAAAAAAAEhJJYpm7rrrzjjgwGfEMw86OG6/7bexevWqeNGCY2PeXvNjn32fGr///e/LDhEAAAAAAAAAAAAAAICEVKJoZrsZM2LlivsjIuKGG66L3t6+eOKTnhwREatWrYy+6X1lhgcAAAAAAAAAAAAAAEBiessOYCLmz98n/vt73423vPmM+P2998S+T31aTJ8+PZZ8cnHc+LOfxv4HHFh2iAAAAAAAAAAAAAAAACSkEu8084pXLoxH7bFH3PW7O2P27DlxwoknRUTEt7/59Zg9e068/IQTS44QAAAAAAAAAAAAAACAlFTinWZ23GmnePd7PhB/uO++2Gnu3Ojr64uIiBNPenXsv//TY84OO5QcIQAAAAAAAAAAAAAAACmpRNHMsIc/4hFjvn7u855fUiQAAAAAAAAAAAAAAACkrBJFM/V6Pa5aenn88hc/j9WrV0U0H3JBLeIDH/pIKbEBAAAAAAAAAAAAAACQnkoUzSz+6IfjB//z/di8WmZYrZPhAAAAAAAAAAAAAAAAkLhKFM3c+LOfxty5c+NNZ741dt1116hNskhmw4YNcdmln45vfWNZRETs+9T94jWLXh/Tp08vMlwAAAAAAAAAAAAAAABKVomimahFzN9n3/irv3rClG7z/f/+bnzrG1+PM9/69th+9uz4l3POjm99Y1m84KijCwoUAAAAAAAAAAAAAACAFFSiaObpBz4zbr/tt1O+zyGHHhaHHHpYRESsXftA9Pb2xrRPYR4mAAAgAElEQVRp06Z831bVB5vR1xPRGIro753cu+YA5ZC/QJVYsyAf8hnGkhMA1WT97l6ePeRJbkOe5DZAZ1hvKYq5BADkyjmHIlWiaGb29rPj9ttui3/7l3Nit90fudn3a1GLk159yoTv95Y3nxF33/W72PMp8+J5z3/BFq9pNBrRaDRGvq7XB1qOe0vqg81YdM3aWLJ8fSycNz0uOmKWRIYOmkpuy19IW7v27qqyZpELuS2fyZNzOeTL3s3WWL+rzd4NeZLbkK/J5rfchrT5N3c+rLeM5lwO+bJ3Q57kdmc451C0ShTNfP7qpRERcfNNN8bNN924hStaK5o5621nx7333B0ffN974rvf+VYc+pznbt7mVVfG0isum2zIW9XXE7Fk+fqI2Ph58ZGzCm8D2Lqp5Lb8hbS1a++uKmsWuZDb8pk8OZdDvuzdbI31u9rs3ZAnuQ35mmx+y21Im39z58N6y2jO5ZAvezfkSW53hnMORatE0cyprz0tojb16rA77rg9Vq1cGfP33id23vlhsee8efGLny/fYtHMgmOOi6OOXjDydb0+EKctmnhhztY0hiIWzps+UvnWGIro7ZnybYEJmkpuy19IW7v27qqyZpELuS2fyZNzOeTL3s3WWL+rzd4NeZLbkK/J5rfchrT5N3c+rLeM5lwO+bJ3Q57kdmc451C0ShTNHLKFopbJuPP22+ITH/9IvO3sd8aM/v649Zb/jRe+aMEWr+3r64u+vr5C2h2tv7cWFx0xKxYfOSsaQ+GtoqDDppLb8hfS1q69u6qsWeRCbstn8uRcDvmyd7M11u9qs3dDnuQ25Guy+S23IW3+zZ0P6y2jOZdDvuzdkCe53RnOORStEkUzF/3nBdv8fi0iTn3d6ePe51nPPjTuuOP2OP/f/zWazWYc+MyD4vlHHFVQlBM3nLgq3qB65C9QJdYsyId8hrHkBEA1Wb+7l2cPeZLbkCe5DdAZ1luKYi4BALlyzqFIlSia+d53vjXOFbUJFc3UarU4ceGr4sSFryomMAAAAAAAAAAAAAAAAJJUiaKZfzzzrVt8/Z67747PXvLpeOXfndThiAAAAAAAAAAAAAAAAEhZJYpm9tv/6Vv+xv4Ry2++Ma790Q/ihS9a0NmgAAAAAAAAAAAAAAAASFZP2QFM1fbbz47bb7ut7DAAAAAAAAAAAAAAAABISCXeaea6a3+42WvNZjN+f++9ccP118VOc3cqISoAAAAAAAAAAAAAAABSVYmimfe/590RUdvCd5oREXHUC/+2o/EAAAAAAAAAAAAAAACQtkoUzRz87EOjVhtbNFOr1WKHHXeMvfd5ajx5z6eUFBkAAAAAAAAAAAAAAAApqkTRzEtfdkLMmDEjtp89u+xQAAAAAAAAAAAAAAAAqICesgMYT7PZjDe94bVx2aWfKTsUAAAAAAAAAAAAAAAAKiL5oplarRZ7PPoxcd9995YdCgAAAAAAAAAAAAAAABXRW3YAE/HXf/3E+Pqyr8Xij10Uu+662xaveeGLFnQ4KgAAAAAAAAAAAAAAAFJViaKZry/7akREfPPr10RE7SHfbUZETdEMAAAAAAAAAAAAAAAAIypRNHPsccdH1B5aLAMAAAAAAAAAAAAAAABbVomimeOOf8W41/zm17fG3XffFc8+5DkdiAgAAAAAAAAAAAAAAICU9ZQdQFG+991vx0UXXlB2GAAAAAAAAAAAAAAAACQgm6IZAAAAAAAAAAAAAAAAGKZoBgAAAAAAAAAAAAAAgOwomgEAAAAAAAAAAAAAACA7imYAAAAAAAAAAAAAAADIjqIZAAAAAAAAAAAAAAAAspNP0Uxz5H8AAAAAAAAAAAAAAADocr1lB1CUl778hDh6wYvLDgMAAAAAAAAAAAAAAIAEVKZo5uabboxrf/g/sXLlymg2N72jTKOxPu6955644MMfi+23377ECAEAAAAAAAAAAAAAAEhFJYpmfviD78cF739vRAwXy9RG/Xn4awAAAAAAAAAAAAAAANiop+wAJuILV38uZs6cGa8//Y2x3/5Pj4iIt7/j3Dj1dadHT09PnPGmfyo5QgAAAAAAAAAAAAAAAFJSiaKZe++5J/Y74Olx0MGHxC67PDwiIp70pCfHIYceFk+ZNz++9MWrS44QAAAAAAAAAAAAAACAlFSiaGbGjBmxZvWqiIjYYacdIyLi3nvviYiIOXPmxF2/u7O02AAAAAAAAAAAAAAAAEhPb9kBTMQ++z41vvfd78SXvnB1PPGJT46IZlx4wftj7733jeuv+1E8Ytfdyg4RAAAAAAAAAAAAAACAhFTinWaOf/krY+eH7Rx33fW7eOKTnhx7zd8nbr/tt/H5q5fG4OBgHHf8y8sOEQAAAAAAAAAAAAAAgIRU4p1m5u68c/zH+z4Uf/rTHyMi4i3/5+3xox98P1asXBHz5s2Pxz7u8SVHCAAAAAAAAAAAAAAAQEqSL5oZGhqKq5ZeEY993OPiafsdEBER06ZNi4MOPqTkyAAAAAAAAAAAAAAAAEhVT9kBjKenpye++uUvxE9/fEPZoQAAAAAAAAAAAAAAAFARyRfNRETsd8CBcfPNN8a6devKDgUAAAAAAAAAAAAAAIAK6C07gIn43Z13xB//8Mc447RTY4cddtzs+7VaLc77j/eXEBkAAAAAAAAAAAAAAAApqkTRzG2//U1ERKxZvTrWrF69hStqnQ0IAAAAAAAAAAAAAACApFWiaOYDF36k0Pt98fNXxVe+9IX4y1/Wxfx99o1Fr3tDzJgxo9A2AAAAAAAAAAAAAAAAKE9P2QFMxC9/8fNYvWpV7LLLwzf7+OMf7os7br99wve66cafxWWXfiZed/ob453/el787y9/GV/7yhfbFzwAAAAAAAAAAAAAAAAdV4mimYsu/GB8/3vf3eL3vvaVL8dFF35wwvfq7e2NE05cGHvN3zv22OPRsdvuu8eqlauKCnXEusFm1AebMTjUjHpj0+f1W3kduk1V539V424HY0FO2jWfR/b8wbTzpJX+T/TaVvrermsZX0vPvqJj34n9ShvpteOckj/PGPIkt6fOGJIqcxPyJLfT45lQBPMIoDOstxTFXNrEWABA5+Sy7+bSD8aXdNHMueecHeeec3ZERFx33Q9Hvh7+OOfs/xM/+fH10Te9b8L33PMp8+Koo/82IiLuuP22+PWtt8RBBz97s+sajUYMDAyMfNTrAxNuo95oxgPrm/G6a9bGXzZELFq2NvrOXxGLlq2NDc2I07bwumSjm9QbzdLm/1RzW95uZCxI0WTzu13zuT7YjEXXPHjfa9YmW2jQSv8nem0rfW/XtYyvpWdf4tinvndrI712nFOqIfXcBiYvtXN5NzGGtJO9G/Ikt/PimTCacznkaSp7N2mx3jKac3kxjAUpsndDnuR2PvtuLv1gYmrNZjPZJ3zC8cc8+KdaRGw5zN7e3jjp1a+J5z7v+S3d+89//lO84/++NQ5+9qHxshNO3Oz7V15+aSy94rLNXl988SUxc+bMbd57cKgZQ82IPT++Kn71mh2i7/wVI99rnLlTHHHZmrjmZbM3e723p9ZSH6CqBoeapc3/qea2vN3IWJCiyeZ3u+ZzVfKklTgnem077tnqtYyvKmOf+t6tjfTasVZUQ+q5DUxeaufybmIMaSd7N+RJbufFM2E053LI01T2btJivWU05/JiGAtSNNW9u3be/YXG0zxrbqH3g27lXJ7PvptLP5iY3rID2JZ/e/d7o9lsxtvOenM845nPir998bFjvt8zbVo87GG7RH9/f0v3Xb16dbzr3HPiKfP2iuNf8cotXrPgmOPiqKMXjHxdrw/EaYtOmdD9Gxsi1jaacfCjeuMvGyIWzpseS5avj4XzpkdjQ8Rj5/Rs8fXepN/3B4rTKHH+TzW35e1GxoIUTTa/2zWfG0MPue9QmnnSSv8nem0rfW/XtYyvpWdf4tinvndrI712nFOqIfXcBiYvtXN5NzGGtJO9G/Ikt/PimTCacznkaSp7N2mx3jKac3kxjAUpsndDnuR2PvtuLv1gYpJ+p5lhF134wdhr/t5x0MGHTPle6+r1+Jd3/r+Ytf2sOO2Mf4yenp6o1XrGre4bGBiIk086YcKVgOsGm9GMiL6ejUnVN23j52m1iA1beL2/T2Ua3aXeaCYx/1vN7VTiToGxIHWt5He75nN9sLlxzx+K6O9NN09a6f9Er22l7+26lvG19OwTGfsU925tpNeOc0r1pJjbQDFSOJd3E2NIp9i7IU9yu/o8E7bGuRzy1OreTVqst2yNc/nkGQtS12p+e6cZqIZuPZfnsu/m0g/Gl/Q7zQzrmTYt9t7nqVv83h//cF988hMfizPf+vYJ3evaa38Yv/nNrRER8Q+vXhgREQ/bZZe44D8/VkywD5ox6hcJh6vOHlp9trXXoRsMby5Vm/9VjbsdjAU5add8Hi4sSD1PWun/RK9tpe/tupbxtfTsKzr2ndivtJFeO84p+fOMIU9ye+qMIakyNyFPcjs9nglFMI8AOsN6S1HMpU2MBQB0Ti77bi79YHyVKJr5zre+ETdcd228/JV/F4c99/CIiBgcHIwvfv6q+PxVV8b69Y0J3+uQQw+LQw49rF2hAgAAAAAAAAAAAAAAkIBKFM284Kij4+vXfDU+/pEPx3e++Y047PDnxxev/lzce+890d/fHyec+IqyQwQAAAAAAAAAAAAAACAhlSiaWfj3J8fzDn9BLPnk4rjpxp/Gr399a0REPPfwI+KlLzsh5syZU3KEAAAAAAAAAAAAAAAApKSn7AAmatWqlbF61coHv2pGRMS999wdK1fcX15QAAAAAAAAAAAAAAAAJKkS7zTzvv/497j+uusiohkHPuOgOPyII2PpFZ+NX/x8ebztrDfHoc95bpxy6uvKDhMAAAAAAAAAAAAAAIBEVOKdZq6/7trYbbfd4m1nnxNveNM/xZP3fEq8/R3nxutPf2PMnj0nvvXNb5QdIgAAAAAAAAAAAAAAAAmpxDvNHP/yE+JFC46Ndevq8etbb4kVK+6P/Q84MA46+JDY92n7xxWfvaTsEAEAAAAAAAAAAAAAAEhIJYpmXrTg2Lj0M0ti2de+GoODg1GrRXzmss/FtT/8QSz55OJ4xz+/q+wQAQAAAAAAAAAAAAAASEhP2QFMxOWfvSS+8qUvxO677x677bZbNJsbX9/j0Y+JgYG18dlLP11ugAAAAAAAAAAAAAAAACSlEkUz3//v78TjH/+E+Lfz3xfz9tp75PXdH/nI2Pdp+8fym24sMToAAAAAAAAAAAAAAABSU4mimQfWrIlHP+YxUavVImqbf3/dunWdDwoAAAAAAAAAAAAAAIBkVaJoZrfdHxk333xjPPDAA2Nev+23v4mf3HBdPOpRjyopMgAAAAAAAAAAAAAAAFLUW3YAE7HgxS+JCz7wnnjzGa+PGf0zIiLivHf9c/x8+c0xOLghjnzhi0qOEAAAAAAAAAAAAAAAgJRUomjmGQc9Kx54YE1c+pklsWbN6oiIuPFnP43e3r44/uWviIMPeU7JEQIAAAAAAAAAAAAAAJCSShTNREQcfsSRccihh8Utt/wqVq5cGf39/fE3T3xSzJ49p+zQAAAAAAAAAAAAAAAASExlimYiIqZvt13M22vvssMAAAAAAAAAAAAAAAAgcT1lBwAAAAAAAAAAAAAAAABFUzQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkB1FMwAAAAAAAAAAAAAAAGRH0QwAAAAAAAAAAAAAAADZUTQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkB1FMwAAAAAAAAAAAAAAAGRH0QwAAAAAAAAAAAAAAADZUTQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkB1FMwAAAAAAAAAAAAAAAGRH0QwAAAAAAAAAAAAAAADZUTQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkB1FMwAAAAAAAAAAAAAAAGRH0QwAAAAAAAAAAAAAAADZUTQDAAAAAAAAAAAAAABAdhTNAAAAAAAAAAAAAAAAkJ2uLJpZtWplfOXLX4i3nvmm+N2dd5QdDgAAAAAAAAAAAAAAAAXrLTuATqvX63H6a/8hHvvYx8Udt99WdjgAAAAAAAAAAAAAAAC0Qde908z06dPjgg9/NE5/4z+2tZ3BweZWP+qNZgwOjf1Md1s/2H3zoar9rWrc7WAsxjIe1dbK82vHtTm2r0/VaL9dfUpJJ+LWRnrt5NIGW2f8YXxVzJMqxFyFGCFFueROLv2AouSSE7n0A4pShZyoQowA47GWUZRU5lIKcaQQQyqMxVj1wQfHY9B4AN0hl9+ZyGk/y6UvVd5Tu65oZtq0abHDDjuOe12j0YiBgYGRj3p9YMJt1BvNaDRjqx+nLVsbjQ0Ri5atjb7zV8SiZWsrnwRMXr3RjA3N7poP9UaztP5ONbe76Tlti7EYy3ikYbL53crza8e1ObavT9Vov119Klrqe7c20msnlzZyl3puQ9VVce+uQm5XIUZoF3t3Pv2A0eR2Pv2Ah3IuhzxNZe8mLdYyRsvhXJ5CHCnEkApjMVZ9sBmLrnlwPK5Z29Ff8rV3Q55Sz+1cfmcip/0sl76UuacWodZsNqsVcUH++If74g2vPzXe/Z4PxB6Pfsxm37/y8ktj6RWXbfb64osviZkzZ27z3oND2x7SIy5bE9e8bHb0nb9i5LXGmTtFb09tgtGTk+H50k3zYXCoWVp/p5rb3fSctsVYjGU80jDZ/G7l+bXj2hzb16dqtN+uPhUt9b1bG+m1k0sbuUs9t6Hqqrh3VyG3qxAjtIu9O59+wGhyO59+wEM5l0OeprJ3kxZrGaPlcC5PIY4UYkiFsRirij8vH1Y77/5C42meNbfQ+0G3Sv1cnsvvTOS0n+XSl6r3o+veaWaiFhxzXCy++JKRjw9d9PEJ/93Ghm1/PHZOTzQ2RCycNz0iNn5ubGhXT0jd8LzopvlQZn+nmtvd9Jy2xViMZTzSMNn8buX5tePaHNvXp2q0364+FS31vVsb6bWTSxu5Sz23oeqquHdXIberECO0i707n37AaHI7n37AQzmXQ56msneTFmsZo+VwLk8hjhRiSIWxGKsx9JDxGOpc2/ZuyFPquZ3L70zktJ/l0pcy99QieKeZrbzTzEMNDAzEySedMOFKwMFtvOVQoxnRN21jEgx/7u+rTqUVxVs/2IwNze6aD/VGM4n+tprbqcSdAmMxlvFITyv53crza8e1ObavT9Vov119aqcU925tpNdOLm10kxRzG6oulTxp17m8LFWIETqhW/fuXPoBWyO3q90P2BbncshTq3s3abGWsTVVPZenEEcKMaTCWIxVH2xGX8/GX/bt763Gz8sjvNMMVEWK5/Jcfmcip/0sl76ksqdORm/ZAeSqdxsTYXjQe3vGfqZ7TR81X7plPgwv+lXrb1XjbgdjMZbxqLZWnl87rs2xfX2qRvvt6lNKOhG3NtJrJ5c22DrjD+OrYp5UIeYqxAgpyiV3cukHFCWXnMilH1CUKuREFWIEGI+1jKKkMpdSiCOFGFJhLMYa/qVe4wF0i1x+ZyKn/SyXvlR5T+3aopldHv6IuPSKq8sOAwAAAAAAAAAAAAAAgDaoYJ0PAAAAAAAAAAAAAAAAbJuiGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyE5v2QGU4Yf/8/347CWfirVrH4j9DjgwTn7Noujr6ys7LAAAAAAAAAAAAAAAAArSde80s2bN6rjoPz8Yxxx3fJxz7r/HjT/7SXz7m18vOywAAAAAAAAAAAAAAAAK1HVFM7/59a3RbEYccuhh8ag99oh9931a/PIXPy+8ncHB5lY/6o1mDA6N/Ux3e+i86AZV7W9V424HYzGW8dikimPRSsztuDbH9vWpGu23q08p6UTc2kivHW2k1UY7VDVu6KQq5kkVYwYmRn5DnuQ25EluA3SG9ZaipDKXUogjhRhSiSOFGFJiPICU5PJ7ANpIT059aYeuK5pZvXp1bDdju6jVahERMXPWrFi9evVm1zUajRgYGBj5qNcHJtxGvdGMRjO2+vHuZWujsSFi0bK10Xf+ili0bK0J2sWG50s3zYd6o1laf6ea2930nLbFWIxlPDapYn63EnM7rs2xfX2qRvvt6lPRUt+7tZFeO9pIq42tST23oeqqmN9yG9Jm74Y8yW3Il3M55Gkqezdpsd4yWg7n8hTiSCGGVOJIIYaUVPHn5UDaUt+7tZFWG52SU1/apdZsNrtqVL733W/Hpy7+RHzsE5+KiIhPL/mvuP2238bb33HumOuuvPzSWHrFZZv9/cUXXxIzZ87cZhuDQ+P8F73XPhD9s7aPvvNXjLzWOHOn6O2pTbQbZGR4vnTTfBgcapbW36nmdjc9p20xFmMZj02qmN+txNyOa3NsX5+q0X67+lS01PdubaTXjjbSamNrUs9tqLoq5rfchrTZuyFPchvy5VwOeZrK3k1arLeMlsO5PIU4UoghlThSiCElVfx5+bDaefcXGk/zrLmF3i+iGjFC0VLfu7WRVhudklNf2qXr3mlm9uzZsa5ej6GhoYiIqA8MxJwddtzsugXHHBeLL75k5ONDF318wm00Nmz7473fq0VjQ8TCedMjYuPnxoZi+kf1DM+LbpoPZfZ3qrndTc9pW4zFWMZjkyrmdysxt+PaHNvXp2q0364+FS31vVsb6bWjjbTa2JrUcxuqror5LbchbfZuyJPchnw5l0OeprJ3kxbrLaPlcC5PIY4UYkgljhRiSEkVf14OpC31vVsbabXRKTn1pV267p1mVq9eHae/9pQ4ceGrYs95e8W7zn1HHPOS4+N5hx+xzb83MDAQJ590woSrfAcHtz6sjWZE37SNE3T4c3+faq5uNjjYHDMvumE+1BvNJPrbam6nEncKjMVYxmOTVMailfxuJeZ2XJtj+/pUjfbb1ad2SnHv1kZ67WgjrTYmIsXchqpLJU/adS4HymXvhjzJbciXcznkqdW9m7RYb9maqp7LU4gjhRhSiSOFGFKSyni0mt9VeBeXKsQI7Zbi3q2NtNrolJz60g69ZQfQaXPmzIlFr3tDXPqZJXHZpZ+O/Q84MA459LDC2+nt3fpkGx703p6xn+levb21zeZF7oYX5Kr1t6pxt4OxGMt4bFLFsWgl5nZcm2P7+lSN9tvVp5R0Im5tpNeONtJqox2qGjd0UhXzpIoxAxMjvyFPchvyJLcBOsN6S1FSmUspxJFCDKnEkUIMKTEeQEpy+T0AbaQnp760Q9cVzUREPOOgZ8UzDnpW2WEAAAAAAAAAAAAAAADQJmqJAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALKjaAYAAAAAAAAAAAAAAIDsKJoBAAAAAAAAAAAAAAAgO4pmAAAAAAAAAAAAAAAAyI6iGQAAAAAAAAAAAAAAALLTW3YAVdFsNiMiol4fKDkS6G79/f1Rq9UKu5/chnTIb8iT3IY8yW3IU9G5HSG/IRX2bsiT3IY8OZdDvuzdkCe5DXnK8Vw+MJD+ulJ0jLMuWFfo/daePqPQ+1EOezfkabzcrjWHs5Vt+vOf/xSnLTql7DCg6y2++JKYOXNmYfeT25AO+Q15ktuQJ7kNeSo6tyPkN6TC3g15ktuQJ+dyyJe9G/IktyFPzuWQL3s35Gm83FY0M0FDQ0OxYsX9MWPGtquQ6vWBOG3RKfGhiz4e/f3FHpraRcydIeZiFF3lO9HcHpbimJTFWIxlPDaZ7Fi0I7/vuefuOPNNp2f1XHKca/pUDSnldit7dyfk8rxz6UdEPn1JuR9l53bKYxORfnwR6ccovqmbTIzt+C/ntZLfuY5rJ4lv6lKPsarn8tTHdaJy6UdEPn3p9n7I7WLk0o+IfPqSSz8inMvbJfUYxTd1qcdY1b27SKk/o4j0Y0w9vggxTlTZuZ3CGKTCWGxiLMZyLm+P1GMU39SlHmNVz+Wpj+tE6Ud6culLu3K7t4jgukFPT0/svPPDJnx9f//MwiuN203MnSHmtLSa28NyHpNWGYuxjMcmZY9FT09PzJ27cxKxtIM+VYM+FW+ye3cnlD02RcmlH/H/2bv3ODur+lD4v5nMBJIgt4CAHhUVbxDAC4p9rWL1FQSxlHPwhjYcLmKOomgVsa22WKs1BhEF65VDQQvlpuCtEu+Xeuv5tIAcFQVvIL5WIIGQ2ZK9Z/b7RzKTmSQzmZ29n/2stfb3+/nMZ5g9T571W+tZv7XWTPJjRzl9KaUfcyn1XJ56fBHpxyi+7tUd447kd90xz0fqMYqve6nHWIlEaVAAACAASURBVHd8pe7d81VKPyLK6Yt+9IbcLqMfEeX0pZR+RNTfF+fyeoive6nHWHd8Kfy+vO4xmI/UY0w9vggx9tugn8t7wVhsZixmqns8nMvrIb7upR5j3fEN+t6tH+kppS+97sdwz+4EAAAAAAAAAAAAAAAAiVA0AwAAAAAAAAAAAAAAQHEWnHPOOefUHURphoeH48CDDo4FCxbUHcq8ibk/xFwGY7KZsZjJeGyW0likFEuv6FMe9GmwlDI2pfQjopy+lNKPKqQ+NqnHF5F+jOLrXg4xbimHmFOPUXzdSz3G1OObTa5xb6mUfkSU0xf9qFeucW+plH5ElNOXUvoRkWdfcog59RjF173UY0w9vn7IYQxSjzH1+CLEmAtjsJmx2MxYzJTjeOQQc+oxiq97qceYenyzyTXuLelHekrpSxX9GGq32+2e3Q0AAAAAAAAAAAAAAAASMFx3AAAAAAAAAAAAAAAAANBrimYAAAAAAAAAAAAAAAAojqKZeWq32zE2NhbtdrvuUIAekttQLvkNZZLbUCa5DeWS31AmuQ1lkttQLvkNZZLbUCa5DeWS31AmuQ15UDQzT41GI0496cRoNBp1hwL0kNyGcslvKJPchjLJbSiX/IYyyW0ok9yGcslvKJPchjLJbSiX/IYyyW3Ig6IZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAozkjdAdThq1/5Ulxz1b/E+vvXx0HLDo5Xv/bMWLJkl7rDAgAAAAAAAAAAAAAAoEcG7p1m1q27Lz7+kQ/Fn/33F8XK954fd/7mjvjcZ66rOywAAAAAAAAAAAAAAAB6aOCKZtrtiOHh4Vi6dGnsuefS2GmnnWLBgoEbBhLUaLWjNdGORqtddyh90Whu6m8zr/7mGncVBm3OUja5zXxY9/LkuaWnH8/Ecy+fvRvKJLehXPIb5pbrzzC5xg39lOMemGPMADmy3gJVssYAvWAtAUoyUncA/bbrrrvGS1/+ilj17nfGyMhI7LPPvvGnf/Y/trqu2WxGs9mc+rrRGOtnmAyYRqsdK65fH5fevCGWL1sYHz5qSSwaGao7rMo0mu1YsXpaf49cEotG+9PfbnK7zrhTM2hzljzsaH7LbebDulefrvZuzy05/XgmnnsenMuhXM7lUCZ7N1Snzp9h/MwN1crx78Ps25A2/46lHNZbppPb9Jo1Jh3ym5xZS2aXYm4Prbynp/drn71nT+8HKRi4opl77r47PnXVFXHyaafHow94bFx4/nvjC5//TPzZ8SfMuO66T18d11x1RU1RMmhGhyMuvXlDRGz8fNHRS2qOqFqjC+rrbze5XWfcqRm0OUsedjS/5TbzYd2rT1d7t+eWnH48E889D87lUC7nciiTvRuqU+fPMH7mhmrl+Pdh9m1Im3/HUg7rLdPJbXrNGpMO+U3OrCWzk9uQp4Ermrnllh/HxMREHHnUMRER8dTDnx43/ud/bFU0c9zxJ8Qxxx439XWjMRZnrDitr7EyOJoTEcuXLZyqym1ORIwM1x1VdZrjW/R3vH/97Sa364w7NYM2Z8nDjua33GY+rHv16Wrv9tyS049n4rnnwbkcyuVcDmWyd0N16vwZxs/cUK0c/z7Mvg1p8+9YymG9ZTq5Ta9ZY9Ihv8mZtWR2chvyNHBFMw956H+LDRs2xHe/8+149KMfEz+86cZ49AGP2eq60dHRGB0drSFCBtGikaH48FFL4qKjl0RzYuPXJVs0OhQfPnJTf8ejr2/b101u1xl3agZtzpKHHc1vuc18WPfq09Xe7bklpx/PxHPPg3M5lMu5HMpk74bq1PkzjJ+5oVo5/n2YfRvS5t+xlMN6y3Rym16zxqRDfpMza8ns5DbkaeCKZh7xiP3jf576yvjkpRfH2Pr1cdCyQ+LFL3153WHB1F8oDUo17uQhKrf+5hp3FQZtzlI2uc18WPfy5Lmlpx/PxHMvn70byiS3oVzyG+aW688wucYN/ZTjHphjzAA5st4CVbLGAL1gLQFKMnBFMxERRx51TBx51DF1hwEAAAAAAAAAAAAAAEBF1P8BAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQnJG6A+inD134/vjmN74247V99tk3zr/wwzVFBAAAAAAAAAAAAAAAQBUGqmjmlFeuiOUnnzr19Xmr3h2PevQBNUYEAAAAAAAAAAAAAMAgGFp5T90hwMAZrjuAftppp51iyZJdYsmSXeLuu+6KW37y4zjq+S+orL1Gqx2tiXY0Wu3K2gCoizUOoDesp9B78qp8njGUSW5DueQ3lEluA3Wx/gAlsJYB9I81txrGFYCcDFTRzHSf++x1cdjTDo+99t57m99vNpsxNjY29dFojHV0/0arHSuuXx+jq9bEiuvXOxhAIrrNbTayxpEi+U2OrKfbJ7fplLzKQze57RlD2nY0v+U2pM3eDWWS21Cukn+nZv1hkJWc24PGWsZ0chuqVeeaW3J+28sYZCXnNpRspO4A6nDP3XfHd779rXjbOe+Y9ZrrPn11XHPVFTvcxuhwxKU3b4iIjZ8vOnrJDt8L6J1uc5uNrHGkSH6TI+vp9sltOiWv8tBNbnvGkLYdzW+5DWmzd0OZ5DaUq+TfqVl/GGQl5/agsZYxndyGatW55pac3/YyBlnJuQ0lG8iimX/9/GfjEfvvH497/BNmvea440+IY449burrRmMszlhx2rzbaE5ELF+2MC69eUMsX7YwmhMRIwP7vj6Qjm5zm42scaRIfpMj6+n2yW06Ja/y0E1ue8aQth3Nb7kNabN3Q5nkNpSr5N+pWX8YZCXn9qCxljGd3IZq1bnmlpzf9jIGWcm5DSUbuKKZsbGx+MqXV8cpr3zVnNeNjo7G6OjoDrezaGQoPnzUkrjo6CXRnNj4NVC/bnObjaxxpEh+kyPr6fbJbTolr/LQTW57xpC2Hc1vuQ1ps3dDmeQ2lKvk36lZfxhkJef2oLGWMZ3chmrVueaWnN/2MgZZybkNJRu4opmvfOn62GnnneLpf/SMytuaPAiooAVKZI0D6A3rKfSevCqfZwxlkttQLvkNZZLbQF2sP0AJrGUA/WPNrYZxBSAnA1c088Ljjo8XHnd83WEAAAAAAAAAAAAAAABQITWeAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMVRNAMAAAAAAAAAAAAAAEBxFM0AAAAAAAAAAAAAAABQHEUzAAAAAAAAAAAAAAAAFEfRDAAAAAAAAAAAAAAAAMUZqTuAOkyMj8e1114T3/jaV+KhD31YvPkv31p3SAAAAAAAAAAAAAAAAPTQQBbNXPJPF8UPvvfdOOW0V8UTDjyo7nAAAAAAAAAAAAAAAADoseG6A+i3tWvXxle+dH2cevqKeOrhT49dHvSgStpptdrxQKsdrU0fD0z7utFsR2ti5mcG25bzYhDk2t9c466CsZip0do0Hi3jkePc6CTmKq4tsX19yqP9qvqUkn7ErY302immDeeLWeW6JkE/5ZgnOcYMzI/8hjLJbSiT3AboD+stQP/kuObmGDMApGzgimZuu/WnMTExEd/7zr/Fa1acGu9+59/F3XfftdV1zWYzxsbGpj4ajbF5t9FotqPZjhhvRzQ3fYxP+/rM1eujOR6xYvX6GF21JlasXu9wM8Am58sgzYdGs11bf7vN7UF6TnMxFjM1Wu1Ycf2m8bh+/UD/w9Yc87uTmKu4tsT29SmP9qvqU6+lvndrI712immj8PNF6rkNuctx75bbkDZ7N5RJbkO5nMuhTN3s3aTFest0chuq5fflQK/ZuyFPQ+12e6B21G9+42vx4Q9eEP/z1NPiMY99fHz0QxfGfg95aLzu9W+ccd3VV14e11x1xVZ//qJLLovFixfP2UZrYu4hfd3q++MDR+4So6vWTL3WPGuPGBke6qAnlGJyvgzSfGhNtGvrb7e5PUjPaS7GYibjsVmO+d1JzFVcW2L7+pRH+1X1qddS37u1kV472shD6rkNuctx75bbkDZ7N5RJbkO5nMuhTN3s3aTFest0chuq5fflQK/1Yu8eWnlPr8PqqfbZe9YdAvTcSN0B9Nuuu+4WCxcujCOPOiYiIv7oGX8c3/z617a67rjjT4hjjj1u6utGYyzOWHHavNpojm/83I6IyaPKZBnNUES0xoeiOR6xfNnCuPTmDbF82cJojkeMDNz7/hCxeb4M0nyoc/53m9uD9JzmYixmak5sMR4TgzseOeZ3JzFXcW2J7etTHu1X1adeS33v1kZ67RTTRuHni9RzG3KX494ttyFt9m4ok9yGcjmXQ5m62btJi/WW6eQ2VMvvy4Fes3dDngbunWbG1q+PM89YESe8+KXx5KccFhe8/7zYZ5994zWve8Pcf25sLE496cR5VwK2Wu0Yj4gFm77eVBcRCyKi2Y4YXbDxcDP5edGoSuBB1mq1Z8yLQZgPjWY7if52mtupxJ0CYzFTo9WO0eGN/8B10chgj0cqc6OT/O4k5iquLbF9fcqj/ar6VKUU925tpNdOMW0M0PkixdyG3KWSJ1Wdy4F62buhTHIbyuVcDmXqdO8mLdZbZiO3ofdSWXOdy6FMO7J3e6cZ6L+Be6eZxUuWxBve9Oa4+KKPxb9c9ok48KBl8YqTTu55OyMjQzMGd1v/PVn9qwqY6fNlUObD5GE+t/7mGncVjMVMk/+Q1XjkOTc6ibmKa0tsX5/yaL+qPqWkH3FrI712imnD+WJWua5J0E855kmOMQPzI7+hTHIbyiS3AfrDegvQPzmuuTnGDAApG7iimYiIAw86OFad94G6wwAAAAAAAAAAAAAAAKAi6lABAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAoThZFM+12O66+8vJ473veFRERa9eujbf/zV/HqSedGBd+4H2x4YEHao4QAAAAAAAAAAAAAACAlGRRNPOFz30mPnX1lfG73/0uIiIu++QlcctPfhQPPLAhvvPtb8U1V19Zc4QAAAAAAAAAAAAAAACkJIuima9+5UuxdK+94o1nvSVazWb84HvfiSOff0z80ycuj4c/4hHx/e/+W90hAgAAAAAAAAAAAAAAkJAsimbuvuv3cfAhT4x99t0vbrnlJ7FhQzOOePZzYmR0NA444DGxZs09dYcIAAAAAAAAAAAAAABAQkbqDmA+dt99j7j1p7fE/evWxde/+uVYsmRJ7P/IR0W73Y5f/uIXseuuu3V0v3/4+3PiphtvmPr6BS88Ll6x/OQeRw0AAAAAAAAAAAAAAEBdsiia+eNnHRGfuvrKOP3UkyIi4vnHvCCGhobirW85K37+89vimGNf2NH91qxZEyefdno844+fFRERo6MLex4zAAAAAAAAAAAAAAAA9RmuO4D5OP5/vDied+TRsXTp0njq0w6PF7/kxIiIuOOO2+PAgw6KE1700o7ut3bNmnjwg/eJJUt2iSVLdomFC6stmvlDqx2NVjtaExs/T2o0N73WbM/xp4GUyNvNjAX0zrbOCSnft9dKjLOKPtU9TnW3v6NyjXtL/dh3+zVW/WinlOfO7JxFoUxyG7Yv1zzJNW5gbnIbypRDbucQI2Xz+0d6wVpGr5hLbIt5QT+YZ9TNHAQ6kUXRzIIFC+Lk006PCz70sXjDm86OnRctioiId648N976t++Y+no+Ws1m3H//uvjnT1wSrz79lHjfuStj3br7trqu2WzG2NjY1EejMbZDsTea7bh/QztWXL8+RletiRXXr49Ga+MivWL1ptdWr7doQ590k9vydjNjQYp6tXf3W6O19Tkh5fv2WolxVtGnusepzva72rszmV/b0499t19j1Y92SnnupXMuh3LtaH7Lbdi+OvPE3g1lkttQrpLP5TnESNly/X05abGWMZ1zOb1mXqSj5L3bPKNuuf6+HKjPULvdzma32rBhQ6y7797YVsR77b33vO7RarXiq19ZHfvu+5BYvHhxfPCC8+OQQ58YJ596+ozrrr7y8rjmqiu2+vMXXXJZLF68eN4xtybaMdGO2OncNVOvNc/aIyIiRlfNfG1keGje9wV2TDe53Zpoy9tNjAUp6tXe3W9V5VMueVpinFX0qe5xqrN9e3d/+tGvsSqpL3RHbkO5djS/5TZsn3M50GtyG8pV8rk8hxgpW67nctJiLWM653J6zbxIR8l7t3lG3XI/lw+tvKfXYfVU++w96w4Bem6k7gDm4zd33B4f+uAH4ue33bbN7w8NRfzzFZ+a170mJibisMMOjz2XLo2IiMOe+rS49Wc/2+q6444/IY459riprxuNsThjxWkdx94cj1jfbMfyZQvj0ps3xPJlC6M5ERHtmPnaeMRIFu/7A3nrJreb4/J2krEgRb3au/utORFbnRN6kU9V3bfXSoyzij7VPU51tt/V3p3J/Nqefuy7/RqrfrRTynMvnXM5lGtH81tuw/bVmSf2biiT3IZylXwuzyFGypbr78tJi7WM6ZzL6TXzIh0l793mGXXL9fflQH2yeKeZt//NX8UtP/lxLFmySyxdunRjlcwW3r3qffO61+2//lW8+Y1nxute/6Z41AEHxHmr3h2Pf8KBW73TzJbGxsbi1JNO3KEq3z+02tGOiNHhjb9AWTSyMf5Gsx2jCzYu3otGVdlCHTrNbXm7mbEgdd3s3f3WaLW3OiekfN9eKzHOKvpU9zjV3f6kjvfuROLuVj/23X6NVT/aKeW5DxLncihXJ/ktt2H7UskTezeUSW5DuUo7l+cQI2VL5fePOf1dGFuzljEb53J6wbxIU2l7t3lG3VKZgzuS295pBvovi3ea+cXPfx6PfNSj4+3v+IcYGR3t6l4Pe/gj4qRTTotPXnpxNBpjcfAhh8aLXnJijyLdtp2n/ZJkeiXj5CKtwhbyIW83MxbQO5N/odLrfKrqvr1WYpxV9Knucaq7/R2Va9xb6se+26+x6kc7pTx3ZucsCmWS27B9ueZJrnEDc5PbUKYccjuHGCmb3z/SC9YyesVcYlvMC/rBPKNu5iDQiSyKZnbdddfYf/9Hdl0wM+n5Rx8bzz/62J7cCwAAAAAAAAAAAAAAgPRkUV931NEviJtuuiHG1q+vOxQAAAAAAAAAAAAAAAAykMU7zdx043/GmnvWxBte9+rYbffdt/r+0NBQrDz3/BoiAwAAAAAAAAAAAAAAIEVZFM388KYbIyJi3br7Yt26+7ZxxVB/AwIAAAAAAAAAAAAAACBpWRTNvP+DH6k7BAAAAAAAAAAAAAAAADKSRdHM3ns/uO4QAAAAAAAAAAAAAAAAyEgWRTNnvuZVc18wFPH+C70bDQAAAAAAAAAAAAAAABtlUTTz+9//15zfHxnJohsAAAAAAAAAAAAAAAD0SRbVJh+/+JPbfP3OO38T7/y7v4mTTnllnyMCAAAAAAAAAAAAAAAgZcN1BzAfi5cs2ebHAY95bBz6xCfHdZ++pu4QAQAAAAAAAAAAAAAASEgWRTNzaTQacc/dd9UdBgAAAAAAAAAAAAAAAAkZqTuA+fin//2xrV5rt9vx//32t/HDm26M/fffv/9BAQAAAAAAAAAAAAAAkKwsimZWf/ELs35v8eIl8YqTTu5jNAAAAAAAAAAAAAAAAKQui6KZV736tVu9Njw0FLvtvnsccMBjY/GSJTVEBQAAAAAAAAAAAAAAQKqSL5qZmJiIoaGh2Hff/eKxj3t83eEAAAAAAAAAAAAAAACQgeG6A9ie4eHhuPjjH4lvfePrdYcCAAAAAAAAAAAAAABAJpIvmomIWHbwIXHLLT+OifHxukMBAAAAAAAAAAAAAAAgAyN1BzAfrVYr7rj99njLm/8i9t77wVt9f2hoKN509l/VEBkAAAAAAAAAAAAAAAApyqJo5sYb/jMiIu64/ddxx+2/3sYVQ/0NCAAAAAAAAAAAAAAAgKRlUTTz1r99R8/vef573xPf/9534vKrru35vQEAAAAAAAAAAAAAAKhXFkUzBx60bLvXfPtb34ibf3hTrHj1a7d77f/59x/Ev//ge70IDQAAAAAAAAAAAAAAgAQN1x1Ar/zsp7fEN7/+te1eNzY2Fhdf9JE46ugXVBpPq9We9aPRbEdrYuZnBtuW82IQ5NrfXOOugrGYyXhsluNYdBJzFdeW2L4+5dF+VX1KST/i1kZ67WijgzZam9poyW0oTY55kmPMqcl1Xad88hvmluv6LbfTk+tcKlmOeZJjzAA5st7SK+bSZsZiM2ORP8+we8awe8YQKEkxRTPzdfknL42DD3liLDv4kDmvazabMTY2NvXRaIzNu41Gsx3Ndsz68der10dzPGLF6vUxumpNrFi93qYywCbnyyDNh0azXVt/u83tQXpOczEWMxmPzXLM705iruLaEtvXpzzar6pPvZb63q2N9NrRRgdttNqx4vpNbVy/vq//qCr13Ibc5bh3y+3u1bmuUz57N1THuZxecRZIj3M50Gvd7N2kxXrLdM7lvWEsNjMW6XAur48x7J4xnJ1zOeRpqN1uF7GSXXzRR+NL138xLrvyU7Ne85Mf/yjef96qWHXeB+KnP/1JrHr3O+Pyq67d5rVXX3l5XHPVFVu9ftEll8XixYvnjKU1MfeQ/tuv7o9nPGKXGF21Zuq15ll7xMjw0Jx/jjJNzpdBmg+tiXZt/e02twfpOc3FWMxkPDbLMb87ibmKa0tsX5/yaL+qPvVa6nu3NtJrRxtptTGb1HMbcpdjfsvt7hlDqmTvhurkuG9HyO0UeSbpyTG/zSNIWzd7N2mx3jKdc3lvGIvNjEU6nMvrYwy7Zwxn14tz+dDKe3odVk+1z96z7hCg5wbqnWY+fc1VsW7dujjzjFfFB9733oiIOPWkE7d57XHHnxAXXXLZ1MeFH/74vNtpjs/9cd2PhqI5HrF82cKI2Pi5Od59/8jT5LwYpPlQZ3+7ze1Bek5zMRYzGY/NcszvTmKu4toS29enPNqvqk+9lvrerY302tFGB21MbNHGRO/bmE3quQ25y3Hvltvdq3Ndp3z2bqiOczm94iyQHudyoNe62btJi/WW6ZzLe8NYbGYs0uFcXh9j2D1jODvncsjTQL3TzNo1a6LZ3BARETff/MP46IcujA988COx94P32e79x8bG4tSTTpx3JWBrjrc8b7YjRhds3FQmPy8aVYE5yFqt9ox5MQjzodFsJ9HfTnM7lbhTYCxmMh6bpTIWneR3JzFXcW2J7etTHu1X1acqpbh3ayO9drTRQRutdowOb/zHVYtG5DaUJJU8qepczralsq5TPns39FYq67fczl8qc4nNUskT53IoU6d7N2mx3jIb5/IdZyw2MxZpci7vL2PYPWM4PztyLvdOM9B/I3UH0E+777HH1H/vttuvIyLmVTCzI0bm+EX05KCPDM/8zOAaGRnaal6UbvIQlVt/c427CsZiJuOxWY5j0UnMVVxbYvv6lEf7VfUpJf2IWxvptaONDtoYkdtQqhzzJMeYU5Pruk755DfMLdf1W26nJ9e5VLIc8yTHmAFyZL2lV8ylzYzFZsYif55h94xh94whUJJilrJddnlQ7LXXXvO+/slPeWpcftW1FUYEAAAAAAAAAAAAAABAXYp5p5kXveRl8aKXvKzuMAAAAAAAAAAAAAAAAEhAskUzb37jmfO+dmhoKFaee36F0QAAAAAAAAAAAAAAAJCTZItm7rj91x1cPVRZHAAAAAAAAAAAAAAAAOQn2aKZ93/wI3WHAAAAAAAAAAAAAAAAQKaSLZrZe+8H1x0CAAAAAAAAAAAAAAAAmUq2aGZLP7zpxvj+d/8t1q5dG+12e+r1ZnND/PbOO+OCD32sxugAAAAAAAAAAAAAAABISRZFM9/9zrfjgvPPi4jJYpmhaf89+TUAAAAAAAAAAAAAAABsNFx3APPxmWs/FYsXL47XvPb1cdhTD4+IiLf+7TviVa9+bQwPD8eZb3hTzRECAAAAAAAAAAAAAACQkiyKZn57551x2NMOj2c884jYe+8HR0TE4x//hDji2c+Jg5YdEp/77LU1RwgAAAAAAAAAAAAAAEBKsiia2XnnnWPdffdGRMRuS9RI5wAAIABJREFUe+weERG//e2dERGx6667xh23/7q22AAAAAAAAAAAAAAAAEjPSN0BzMcTn/Tk+OY3vh6f+8y18bjHPSEi2vHBC86PQw99Uvz7D74X++y7X90hAgAAAAAAAAAAAAAAkJAs3mnmxS99eSzda2ncccft8bjHPyEOPuSJ8ctf/Dyuu/aaaLVaccKLX1p3iAAAAAAAAAAAAAAAACQki3ea2XPp0jj3fRfGXXf9PiIi3vyXb43vfefbsWbtmli27JDY/5GPqjlCAAAAAAAAAAAAAAAAUpJF0cyPf/R/Y88994yHPvS/RUTEggUL4hnPPCIiIm6//dfx89tujUc9+oA6QwQAAAAAAAAAAAAAACAhw3UHMB/vOOet8cUvfH6b37vmqn+Jle96R58jAgAAAAAAAAAAAAAAIGVJv9PMh//xgqn//uEPb5zxdUTEeKsVN/zHf8TowtF+hwYAAAAAAAAAAAAAAEDCki6a+ebXv7rpv4bizt/cEXf+5o5tXnfscX/Wv6AAAAAAAAAAAAAAAABIXtJFM39x1lui3W7H+85dGU968lPiT577vBnfHx5eEPvtt1/s95CH1hQhAAAAAAAAAAAAAAAAKUq6aOawpx4eERFPOPCgOPjQJ059vaPGx8fjiss/GV/98uqIiHjSkw+LV654TSxcuLDrWAEAAAAAAAAAAAAAAEhH0kUzk952zt9HRMTaNWvi1lt/Gn/4wx9it912j8c+7vGx0047zfs+3/7WN+KrX/5SnPWWt8YuD3pQ/P05b4uvfnl1PP+YY6sKHQAAAAAAAAAAAAAAgBpkUTQTEXH5P38ivvC562J8fGLqtZ12WhgvfunL4+gXvHBe9zji2c+JI579nIiIWL/+/hgZGYkFCxZUEu8DrXZMtCNGF0S0JiLaETE6HNGeiGhter05vunzRMSikaFK4iAPjWZ7ak4sGh2MuZBrn3ONuwrGgpI0Wu0YHZ7fntzJ3K87T6pov5OxyuWe8x2nKuLsRBVzr6q5n5J+xN2PudGXfvTpGZfUF+rjGUOZ5Hb56j5TUx/5DWWS21CmHHI7hxgBtsdaRq/4fctmqeRVKnGQtxzmUQ4xpsz6DdBfw3UHMB/X/+vn47PXfSqGhobjKYc9NZ77vCPjKYcdFhMTE/GJSy6Ob33z6x3d781vPDNOP2V57LvffvH/Hvn8bV7TbDZjbGxs6qPRGJv3/Rutdoy3I/7X6vXxu/XtuH9DO1Zcvz4WrVoTrWmvr1i9PkZXrYkV16+PRqvdUR8oR6M5bS6sXh+NZvlzoc4+d5XbA/isZmMsSNGO5nejtXGfns+e3MncrztPqmi/k7HK5Z7zHacq4uxEFXOvqrnfa6nv3f2YG33pR5+ecUl9oTup5zaw43b4XC63i1f3mZru2LuhTHIbylXyuTyHGKEq3ezdpMVaxnTd/hs1v2/ZKJW8SiUO0uBczmys33lzLoc8DbXb7eRX2ze+/oy4f926+Lt3rYx99tl36vX/+t3v4m1//ebYc8+l8Q/vOW/e97v77rvit3f+Jj7wvvfGiX9+Ujz7T5671TVXX3l5XHPVFVu9ftEll8XixYvnvH9rYuOQ7nPB2vjtGbtHRMRO566J5zx8JK5/yYOmXt/p3DVTf6Z51h4xMqxadBC1Jtoxumqw5kKdfe42twftWc3GWJCiHc3vTuZzVddWoYr2S7znfK/N6XlW0Sd7tzZyaqfufGV+Us9tYMf141xOnjzjvNm7oUxyG8pV8rk8hxihKt3s3aTFWsZ0zuW9kcpYpBIHaXAuZzbGL2+9OJcPrbyn12H1VPvsPesOAXpupO4A5uP3//W7ePofPWNGwUxExIP32ScOPfRJ8f3vfWde9/nVr34Z965dG4cc+sRYunSvOHDZsvjR/715m0Uzxx1/Qhxz7HFTXzcaY3HGitPm1U5zIqLdjnjhAaPx+7F27LQgYvmyhXHZzRuiOb759eXLFsalN2+I5csWRnMiYiSL9/2h15rjMXMujJc/F+rsc1e5PYDPajbGghTtaH43J2Lee3Inc7/uPKmi/U7GKpd7znecqoizE1XMvarmfq+lvnf3Y270pR99esYl9YXupJ7bwI7b4XO53C5e3WdqumPvhjLJbShXyefyHGKEqnSzd5MWaxnTdftv1Py+ZaNU8iqVOEiDczmzsX7nzbkc8pTFO828ZsWpsfvue8Q7333ujNfb7Xa87S/fHHffc1d86KMXb/c+3/rG1+J/f/wj8Vdve3vsvGhRvPudb48XvPC4OOYFf7rdPzs2NhannnTivCsBH2i1Y6IdMbogojUR0Y6I0eGI9kREa9PrzfFNnyciFo2oEh1kjWZ7ak4sGh2MuZBKnzvN7VTiToGxIHWd5Hej1Y7R4fntyZ3M/brzpIr2OxmrXO4533GqIs5OVDH3qpr7VUpx7+7H3OhLP/r0jEvqC72TYm4DvdHRuVxuF6/uMzW9Y++GMsltKFdp5/IcYoR+6HTvJi3WMmbT8bnc71umpJJXqcRBepzLmc76XY4dOZd7pxnovyzeaeZph/9RXP+vn4+V7/q7+JPnPi/22GPPuPfetfG1r3w5fv7z2+K5zztyXvf542c9O371q1/Gqne/M9rtdjz9/3lGHHnUMZXEvNO0TWxGBehwxOgWr6sQZfLQOEhzIdc+5xp3FYwFJZn84XM+87mTuV93nlTRfidjlcs95ztOVcTZiSrmXlVzPyX9iLsfc6Mv/ejTMy6pL9THM4Yyye3y1X2mpj7yG8okt6FMOeR2DjECbI+1jF7x+5bNUsmrVOIgbznMoxxiTJn1G6C/siiaecnLXhG3/exnceMN/xk33nDDtO+0Y//9HxkvO/HP53WfoaGheMXyk+MVy0+uJlAAAAAAAAAAAAAAAACSkEXRzP3r1sWbzv6ruOGG/4ibbrwh7rt3beyyy4Ni2SGHxmGHPS02bNgQi5csqTtMAAAAAAAAAAAAAAAAEpFF0czrXvOqOOr5x8RJp5wWzzriT2Z87/zz3hM/u+WW+OBHLqopOgAAAAAAAAAAAAAAAFKTdNHMNVddsem/2nHrrT+d9vVG4+Ot+NHNN8f4eKv/wQEAAAAAAAAAAAAAAJCsxItm/iUihiJiKG679da47dafbfO65zz3eX2NCwAAAAAAAAAAAAAAgLQlXTTz5yedEu1oxycvuTge9/gD42mHP33G9xcsWBD77rdfHHLok2qKEAAAAAAAAAAAAAAAgBQlXTRz9AteGBERX/z85+Kggw+e+hoAAAAAAAAAAAAAAADmknTRzKQP/ONH6w4BAAAAAAAAAAAAAACAjAzXHQAAAAAAAAAAAAAAAAD0mqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAiqNoBgAAAAAAAAAAAAAAgOIomgEAAAAAAAAAAAAAAKA4imYAAAAAAAAAAAAAAAAojqIZAAAAAAAAAAAAAAAAijNSdwB1+Ox1n44vfO4z8cADf4hDnvikWPHq18XOO+9cd1gAAAAAAAAAAAAAAAD0yMC908xNN94QV1z+z/Hq174+3v7OlXHLj38cX/zCZ+sOCwAAAAAAAAAAAAAAgB4auKKZkZGROPEVy+PgQw6Nhz3s4bHfQx4S9669t+fttFrtWT8azXa0JmZ+ZrBtOS8GQa79zTXuKhgLZpPj3Ogk5iquLbF9fcqj/ar6lJJ+xK2N9NoppQ1mZ/xh+3LMkxxiTj3G1ONjcJmbUKZScruUfkSU1Rfqk8M8yiFGgO2xltErqcylFOJIIQbolRzmc+oxph4fAP01cEUzBx60LI459k8jIuJXv/xF3Pqzn8Yznvmsra5rNpsxNjY29dFojM27jUazHc12zPrxhtXrozkesWL1+hhdtSZWrF5vYx5gk/NlkOZDo9murb/d5vYgPae5GAtmk2N+dxJzFdeW2L4+5dF+VX3qtdT3bm2k104pbZQu9dyG3OW4d+eQ26nHmHp85M3eDWWS2+X0I6KsvtA953IoUzd7N2mxljFdCefyFOJIIQbYknN5fVKPj7w5l0Oehtrt9kDuBnfffVf87V+/JZ75rGfHS058xVbfv/rKy+Oaq67Y6vWLLrksFi9ePOe9WxNzD+k7vnV/vO2Zu8ToqjVTrzXP2iNGhofmGT0lmZwvgzQfWhPt2vrbbW4P0nOai7FgNjnmdycxV3Ftie3rUx7tV9WnXkt979ZGeu2U0kbpUs9tyF2Oe3cOuZ16jKnHR97s3VAmuV1OPyLK6gvdcy6HMnWzd5MWaxnTlXAuTyGOFGKALTmX1yf1+MhbL87lQyvv6XVYPdU+e8+6Q4CeG6k7gDrcd9998a53nBMHLTs4Xvyyl2/zmuOOPyGOOfa4qa8bjbE4Y8Vp87p/c3zu7//u/qFojkcsX7YwLr15QyxftjCa4xEjA/e+P0Rsni+DNB/qnP/d5vYgPae5GAtmk2N+dxJzFdeW2L4+5dF+VX3qtdT3bm2k104pbZQu9dyG3OW4d+eQ26nHmHp85M3eDWWS2+X0I6KsvtA953IoUzd7N2mxljFdCefyFOJIIQbYknN5fVKPj7w5l0OeBu6dZv7QaMTfv/1vYskuS+KMM/8ihoeHY2hoeLvVfWNjY3HqSSfOuxKw1Zp9WJvtiNEFGzfmyc+LRlWxDrJWqz1jXgzCfGg020n0t9PcTiXuFBgLZpPK3OgkvzuJuYprS2xfn/Jov6o+VSnFvVsb6bVTShuDJMXchtylkidVncvrknqMqcdHOezdUKZBze1S+hFRVl/oLedyKFOnezdpsZYxm1zP5SnEkUIMMBfn8v5KPT7KsSPncu80A/03cO808/3vfzduu+1nERFx+inLIyJir733jgv+8WM9bWdkZPZNdnLQJytXVbAyMjK01bwo3eRBNLf+5hp3FYwFs8lxbnQScxXXlti+PuXRflV9Skk/4tZGeu2U0gazM/6wfTnmSQ4xpx5j6vExuMxNKFMpuV1KPyLK6gv1yWEe5RAjwPZYy+iVVOZSCnGkEAP0Sg7zOfUYU48PSEcVRUwKj9IzcEUzRzz7OXHEs59TdxgAAAAAAAAAAAAAAABUSA0lAAAAAAAAAAAAAAAAxVE0AwAAAAAAAAAAAAAAQHEUzQAAAAAAAAAAAAAAAFAcRTMAAAAAAAAAAAAAAAAUR9EMAAAAAAAAAAAAAAAAxVE0AwAAAAAAAAAAAAAAQHEUzQAAAAAAAAAAAAAAAFAcRTMAAAAAAAAAAAAAAAAUR9EMAAAAAAAAAAAAAAAAxVE0AwAAAAAAAAAAAAAAQHEUzQAAAAAAAAAAAAAAAFAcRTMAAAAAAAAAAAAAAAAUR9EMAAAAAAAAAAAAAAAAxVE0AwAAAAAAAAAAAAAAQHEUzQAAAAAAAAAAAAAAAFAcRTMAAAAAAAAAAAAAAAAUR9EMAAAAAAAAAAAAAAAAxVE0AwAAAAAAAAAAAAAAQHEGsmjm3nvXxhc+/5l4y1lviNt//au6wwEAAAAAAAAAAAAAAKDHRuoOoN8ajUa89n+dHvvv/8j41S9/UXc4AAAAAAAAAAAAAABQu6GV9/T0fu2z9+zp/WBHDNw7zSxcuDAu+NBH47Wv/4tK22m12rN+NJrtaE3M/Mxg23JeDIJc+5tr3FUwFjMZj81yHItOYq7i2hLb16c82q+qTynpR9zaSK8dbaTVRhVyjRv6Kcc8ySHmHGKEFJWSO6X0A3qllJwopR/QKznkRA4xAmyPtYxeSWUupRIHlCKHnMohRgCYNHBFMwsWLIjddtt9u9c1m80YGxub+mg0xubdRqPZjmY7Zv14z+r10RyPWLF6fYyuWhMrVq93cBhgk/NlkOZDo9murb/d5vYgPae5GIuZjMdmOeZ3JzFXcW2J7etTHu1X1adeS33v1kZ67WgjrTZmk3puQ+5yzO8ccjuHGKEq9u5y+gHTye1y+gFbci6HMnWzd5MWaxnTlXAuTyUOSI1zOZTJuRzyNNRutwdyt/r9f/0uXveaV8V73vv+eNjDH7HV96++8vK45qortnr9oksui8WLF89579bEdv6P3uvvj0VLdonRVWumXmuetUeMDA/NM3pKMjlfBmk+tCbatfW329wepOc0F2Mxk/HYLMf87iTmKq4tsX19yqP9qvrUa6nv3dpIrx1tpNXGbFLPbchdjvmdQ27nECNUxd5dTj9gOrldTj9gS87lUKZu9m7SYi1juhLO5anEAalxLocy9eJcPrTynl6HlbT22XvWHcKcqngeqfd5EA3cO83M13HHnxAXXXLZ1MeFH/74vP9sc3zuj/O+ORTN8YjlyxZGxMbPzfGqekLqJufFIM2HOvvbbW4P0nOai7GYyXhslmN+dxJzFdeW2L4+5dF+VX3qtdT3bm2k14420mpjNqnnNuQux/zOIbdziBGqYu8upx8wndwupx+wJedyKFM3ezdpsZYxXQnn8lTigNQ4l0OZnMshT95pZpZ3mtnS2NhYnHrSifOuBGy1Zh/WZjtidMHGg8Pk50WjqmwHWavVnjEvBmE+NJrtJPrbaW6nEncKjMVMxmOzVMaik/zuJOYqri2xfX3Ko/2q+lSlFPdubaTXjjbSamM+UsxtyF0qeVLVubwuOcQI/TCoe3cp/YDZyO28+wFzcS6HMnW6d5MWaxmzyfVcnkockDLncijTjpzLvdNMWrzTzGAYqTuAUo2MzH4ImBz0keGZnxlcIyNDW82L0k0elHPrb65xV8FYzGQ8NstxLDqJuYprS2xfn/Jov6o+paQfcWsjvXa0kVYbVcg1buinHPMkh5hziBFSVErulNIP6JVScqKUfkCv5JATOcQIsD3WMnollbmUShxQihxyKocYAWDSwBbN7P3gfeLyq66tOwwAAAAAAAAAAAAAAAAqoMYTAAAAAAAAAAAAAACA4iiaAfj/2bvv8Ciqto/j3zRCCGn0EHrvvXdEQLFhQQFRRBARBB997Q2UIoqKolhRegcp0hEQEEF6SyD0DiGBkJBKyr5/hCwEEtiQze7s5ve5Lq7nMdns3Gd273PuOTNnRkREREREREREREREREREREREREREREScjhbNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiNPRohkRERERERERERERERERERERERERERERERFxOlo0IyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIk5Hi2ZERERERERERERERERERERERERERERERETE6WjRjIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiDgdLZoRERERERERERERERERERERERERERERERERp6NFMyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIuJ0tGhGREREREREREREREREREREREREREREREREnI4WzYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjT0aIZERERERERERERERERERERERERERERERERcTpaNCMiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJOx93eAYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEhGLp9ftur7md4pZNX3cwR60oyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIg4HS2aEREREREREREREREREREREREREREREREREaejRTMiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLidNztHYA9bN70D7NmTCU2NoZGTZrR96UBeHh42DssERERERERERERERERERERERERERERERERsZI896SZq1ej+emHcTz+1NMMGz6aPbt3sm7NanuHJSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIlaU5xbNHD1yGJMJ2ra7j1KlS1O/fkMOhARbfTvJyaYs/8UnmUhOzfi/krfd+r3ICxy1vY4ad27QvsgoPvn6/kjW/nBE2fk+58ZrnXH7apNjbD+32mQktohb2zDedrQNY20jNzhq3CK25IjHKMptEeel/BZxTsptEeek3BYRsQ31t2It+i6JOCfltoiIiHXluUUz0dHReOb3xMXFBYAC3t5ER0ff9rqkpCTi4uLM/+Lj4yzeRnySiSQTWf77fFUsSSkwYFUsHmMiGbAqVsVNHpb+fclL34f4JJPd2pvT3M5Ln9OdaF9kFJ9sYsDK6/tjZaxDXZTmTO41v7Pzfc6N1zrj9tUmx9h+brXJ2ow+dmsbxtuOtmGsbWTF6Lkt4ujseYxii7pcRGxPY7eIc1Juizgv1eUiziknY7cYi/pbuZnqchHnpbpcxDmpLhdxTC4mkylPjagb1q9j6uTf+fX3qQBMmzKRE8eP8eHQ4RleN2/OTObPnX3b3/82eQYFChS44zaSU++8S8OvxlDUpyAeYyLNP0t6KwB3VxdLmyFOJP37kpe+D8mpJru1N6e5nZc+pzvRvshI+8MY7jW/s/P55cZrnXH7apNjbD+32mRtRh+7tQ3jbUfbMNY2smL03BZxdI6Y38ptEWPT2C3inJTbIs5LdbmIc8rJ2C3Gov5Wbqa6XMR5qS4XcU7WqMtdPr9s7bAMzfROIXuHcEe58XlYu83WjtHon0lucLd3ALbm4+NDQnw8qampuLq6Eh8Xh6+f/22ve+zxp+jy8GPm/46Pj+PVAf0s2kZSyp1///M/LrzTCZ6vlY8p+6/xfK18JKWAe5577o/Aje9LXvo+JKXYr705ze289DndifZFRkmpt+yP1Ly9P+zlXvM7O9/n3HitM25fbXKM7edWm6zN6GO3tmG87WgbxtpGVoye2yKOzp7HKLaoy0XE9jR2izgn5baI81JdLuKccjJ2i7Gov5WbqS4XcV6qy0Wck+pyEceU5540Ex0dzeBX+tHr+T7UqFWbUcOH8viTT3N/x853/Lu4uDj69u5p8UrA5OSsd2uSCTzc0oqb9P/18tBK4LwsOdmU4XuRF74P8UkmQ7Q3u7ltlLiNQPsio/hkEx6uaReneblrfxhBdvI7O9/n3HitM25fbXKM7edWm3KTEcdubcN429E2jLUNSxgxt0UcnVGOUXKrLhcR+9LYLeKclNsizkt1uYhzyu7YLcai/layorpcxHmpLhdxTvdSl+tJM8aiJ83kDXnuSTO+vr4MGDiEmdOnMHvmNBo3aUbbdvdZfTvud7gYIX2np6/+1SpgcXd3ue174ezSi3lHa6+jxp0btC8ySr8ITfvDMWXn+5wbr3XG7atNjrH93GqTkdgibm3DeNvRNoy1jdzgqHGL2JIjHqMot0Wcl/JbxDkpt0Wck3JbRMQ21N+Ktei7JOKclNsiIiLWlecWzQA0b9mK5i1b2TsMERERERERERERERERERERERERERERERERySVahyoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJOR4tmRERERERERERERERERERERERERERERERExOlo0YyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIg4HS2aEREREREREREREREREREREREREREREREREaejRTMiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLidNztHYCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiDgXl88vW/X9TO8Usur7Sd6gJ82IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI09GTZixkMpkAiI+Ps3MkInmbl5cXLi4uVns/5baIcSi/RZyTclvEOSm3RZyTtXMblN8iRqGxW8Q5KbdFnJPqchHnpbFbxDkpt0Wck+pyEeelsdvxxcUZf19b++k61mbtfej9XYJV3y92cP5s/83dctvFlJ6tckeXLkXw6oB+9g5DJM/7bfIMChQoYLX3U26LGIfyW8Q5KbdFnJNyW8Q5WTu3QfktYhQau0Wck3JbxDmpLhdxXhq7RZyTclvEOakuF3FeGrtFnNPdcluLZiyUmppKZORl8ue/8yqk+Pg4Xh3Qj+9/moCXl3WLptyimG1DMVuHtVf5Wprb6Yy4T+xF+yIj7Y8b7nVf5EZ+nzt3lrdeH+xUn4szftfUJsdgpNzOzthtC87yeTtLO8B52mLkdtg7t428b8D48YHxY1R8OXcvMebGnfOyk9/Oul9tSfHlnNFjdNS63Oj71VLO0g5wnrbk9XYot63DWdoBztMWZ2kHqC7PLUaPUfHlnNFjdNSx25qM/hmB8WM0enygGC1l79w2wj4wCu2LG7QvMlJdnjuMHqPiyzmjx+iodbnR96ul1A7jcZa25FZuu1sjuLzA1dWVwoWLWPx6L68CVl9pnNsUs20oZmPJbm6nc+Z9kl3aFxlpf9xg733h6upKoUKFDRFLblCbHIPaZH33Onbbgr33jbU4SzvAedriLO24E2ety40eHxg/RsWXc/aO8V7y294xW8LoMSq+nDN6jPaOz1nHbks5SzvAedqidliHcts52gHO0xZnaQfYvy2qy+1D8eWc0WO0d3xGmC+39z6whNFjNHp8oBhtLa/X5dagfXGD9kVG9t4fqsvtQ/HlnNFjtHd8eX3sVjuMx1naYu12uFrtnUREREREREREREREREREREREREREREREREQMQotmRERERERERERERERERERERERERERERERExOm4DRs2bJi9g3A2rq6u1KhZGzc3N3uHYjHFbBuK2Tlon9ygfZGR9scNRtoXRorFWtQmx6A25S3Osm+cpR3gPG1xlnbkBqPvG6PHB8aPUfHlnCPEeCtHiNnoMSq+nDN6jEaPLyuOGvetnKUd4DxtUTvsy1HjvpWztAOcpy3O0g5wzLY4QsxGj1Hx5ZzRYzR6fLbgCPvA6DEaPT5QjI5C++AG7YsbtC8ycsT94QgxGz1GxZdzRo/R6PFlxVHjvpXaYTzO0pbcaIeLyWQyWe3dRERERERERERERERERERERERERERERERERAzA1d4BiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiFjagRepAAAgAElEQVSbFs2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI03G3dwDOZvOmf5g1YyqxsTE0atKMvi8NwMPDwy6x/LloAcuWLCYxMYE69eozYOAQVixfwuwZ08yvKV+hIqM+/4qYmBh+/uE7gvfvJaBQYV7s9zI1a9UGYNmSxSxZvJCUlBTatGtPz169cXFxyZWYPxsxjL17dpv/+6FHHqN12/b8/MN3nD93lgoVK/HKoNcoUrQoSUlJ/P7rT2zbuoUCBbzp/uxztGjZGrDN5/Dj99+yYf26DD8rXrwE3br35Ptvvzb/rKCPD7/+PtXu8UZFXWHTPxvY8Pc6Bg3+H6XLlCUiPJwfx3/LsaNHCCwZxMsDB1O2bDlMJhMzp09h/bq1uLm58fCjXeny8KMABO/fx8QJP3P58iVq1qrDywMHU7BgwTt+h5xJZvsxr8qsj8mfP7+9w7K5lJQUZs+cxtq/VgFQv0EjXhowiHz58tk5Mvv65qsv+G/Lv8ycu9BuMRhpTLaWzMbJXs/3sV9AOZCdcclRZNamTf9syLQucASZ9fNhYRcyrcvyMmccD43Qh+dEakoKCxfOZ/26NQQFlebt9z60d0j3bO2a1cyfO4vYmFhq1qrNwMGv4e1d0N5h2dzdxnR7H4vcLb6wC+f55afxHD50iMKFC9Pzud40btLMMPGlO3niOB+8+yZdn3iKp57uYbP4LI3x5InjzJw+lUOhB3j/o0+oVLmKYeK7cuUKP37/DaEHD+Dr68czPXvRslUbm8UHdz9WtXeeWMroNbzR5wSMXpc40vGzkeshRzwuNHpuZ4fR+wFLGb2/sIQj9SmWMnLfkxnltvEot41L+W1dRs9Zo+eiI+WZUXPHEWtyazh58oTF5wcSEhJ4+40hFClalI8/GWmoGBMTE5k44Wf+2/Iv+Tw96dj5QZ7q1j3XYzP6HKclMdp7ntOSGNPZc67T1ow+btua0cdhWzPqWGprjnru0Oj5rbr83jlSTQ7G7UsctS43em5bysg5di+M+j3PDkcd7zKTW9fK6EkzVnT1ajQ//TCOx596mmHDR7Nn907WrVltl1j27tnN7JnTGTj4f3wy8nNCDxxgxbI/uRIZSYuWrZkwaRoTJk3j42EjAFi0YB6XLkUwesw3NGnanPHjxpKclMTp06eYNmUSAwYN5q13P2DVimXs2rkj1+KOjIykT7/+5vie7v4sv/44ngoVKjLm6+9wc3Nj2pSJAKxbu5r9+/byycjPeaTrE/z8w3dER0XZ7HN48aUB5jgnTJpGjZq1aNy0GVciI6latbr5599+/7Pd442Pj2fwK/3ZsmkTJ08cN/982tSJeHrmZ8zX31GmTFl++fF7AHbv2sFfq1bw9vsf0e/lgUybMpFTJ0+QnJzM+HFjad6yNaPHfENY2AUW/TEPyPo75Eyy2o95UVZ9TF70z8b1rP1rNW+9+yGfjPyc/fv2mg9q8qrt27aybesWu8ZgpDHZmjIbJx1RdsclR5BVm7KqC4wuq34+q7osr3LG8dAIfXhOTZ70G6tXLKfXc30Y+Opr9g7nnl29Gs2En3+k6xPd+Pyrbzh39gxLFi+yd1g2Z8mYbs9jEUvi++H7bylYsCBjv/uBJs1apMWXnGyY+CBtAu2Xn8ZjMplsEld2Y7wYFsYnH3+Af0AAwz8bQ4UKFQ0V36IF84iMjGTM1+Po9GAXfvx+HImJiTaL0ZJjVUc4Zjd6DW/0OQFHqEsc5fjZ6PWQox0XGj23s8Po/YClHKG/sISj9CmWMnrfcyvltvEot41L+W1dRs9ZR8hFR8kzI+eOo9Xk1pKd8wNzZk3n0qUIG0aXxpIYF86fS2joQUaM/pKXXh7E/DmzOBR6MFfjMvocp6Ux2nOe09IYwb5znbZm9HHb1hxhHLYlI4+ltuaI5w6Nnt+qy3PGUWpyMHZf4oh1udFz21JGz7HsMvL3PDsccbzLTG5eK6NFM1Z09MhhTCZo2+4+SpUuTf36DTkQEmyXWNzd3enZ63lq16lL6dJlCCxZkqgrUVyJjKRwkSJ4exfE27sg+b28ADgQEkzTZi0oVrw493fqTGTkZcLCLnAwJJjAwEDq1K1PpcpVqFK1GgcP5F6brkRGUqxYcXN8mEwcPXqYtu07UKRoUVq3bc+B69s/EBxM3foNCAoqRYf7O5GamsqxY0ds9jl4enqa47wUEUHowQN0fuAhrkRGElCokPl3BQoUsHu8+fLl47sff2Hw/97I8PMDIcG0atOWIkWL0u6++zl29AiJiYkcCAmmcpVqVKxYiQYNG1GsWHFCDx4g7MJ5IiMv0+H+ThQrXpxmLVre+Dyy+A45k6z2Y16UVR+TF7Vtdx8TJk2jarXq+Pv74+7ujpubm73Dspu4uDgm/vYznR98yK5xGGlMtqZbx0mj3mXibrI7LjmCrNqUVV1gdJn18xHh4VnWZXmVs42HRunDc+LKlSusWb2Svv0H0LhpMwr6+Ng7pHtmMoGrqyuFCxemUKHCeHp64uaW9w7hLRnT7XksYkl8NWvV5slu3SlcuAh169UjMTGRhPh4w8QHsGzZn+TL52mXJ7xZEuPSJYsoUaIE/QcMIiioFK42rLctic/VxZX8+fMTUCgtX93d3Wx6Ut6SY1VHOGY3eg1v9DkBR6hLHOH42RHqIUc7LjR6bmeH0fsBSzlCf2EJR+hTLOUIfc+tlNvGo9w2JuW39Rk9Zx0hFx0hz4yeO45Wk1vDtcREi88PHD1ymE3/bKBl67aGjLF4YCDPPteboKBS1KtXH3d3d6KuXMnV2Iw+x2lpjPac57Q0RrDvXKetGX3ctjVHGIdtxehjqS056rlDo+e36vKccYSaHIzflzhiXW703LaU0XMsO4z+PbeUo453mcnNa2Xy3hU3uSg6OhrP/J64uLgAUMDbm+joaLvEUqNmLbo8/CiQ9tjRI4cP0bJ1GyIjL7N1y2Ze6d+HoR++y/HjxwC4Gh1tfjSWdwFvIK09aW268cisAgVyr03JSUnExFxl+tTJDOz/ImO//Jyo6LSO1BybtzdXr28/+qaYXV1dye/ldVPMtv0clvy5iEZNmlKkaFEiIy9z+FAog17uy7tvvc6+vXvsHq+bmxt+fv63/TzD5+7tbf7ZzbHeHFN6XDd/V9I/j6y+Q84kq/2YF2XVx+Rlb//fa/R/8XlKBAZyf6cH7B2O3cycNoXadepRq3Ydu8ZhpDHZWjIbJ69edcw2ZXdccgRZtSmrusDoMuvnW7RK6+czq8vyKmcbD43Sh+fE0SOHSE1NZcu/mxg0oC+jR35qlzsaWoOvry/dn+3FmNEjefH5HiQnJ/No1yftHZbNWTKm2/NYxJL4nu7+LGWun6D9a9VK6tVvaLNJKkviuxgWxqI/5tN/wEC4/jpbsiTGw6EH8cyfn/fefoP/vTqAVSuWGSq+J7s9w6VLEfR5rjvfffMVffq9bNPHj1tyrOoIx+xGr+GNPifgSHWJkY+fjV4POeJxodFzOzuM3g9YypH6C0sYuU+xlNH7nswot41HuW1Mym/rM3rOOlIuGjnPjJw7jliTZ1dqaiqxsTEZ/t3puo2bJScn88tP43n2uRfw9fU1ZIzt2negUeOmAKxb+xcFvL2pXadursUKxp/jtDRGe85zWhqjvec6bc3o47atOdI4nNuMPJbamqOeOzR6fqsutw4j1+Rg7L7EUetyo+e2pRwlxyxh5O95djjqeJeZ3LxWRotmcpm9j8EuXYpgzOiRPPTwY1SqXIVHHnucp57pwfsffYKvry8/fvdNln+bVey51iQXF17o+xLP9X6RN956l1OnTjJ/zqxsRZDVb3Lzc7h86RL//rORB7s8AsB993fkiW7P8N6Hw6hcuSrjxn6Z5WNz7RHv3WT5uWcZbPbfS5zHrX1MXvbO+x/x3odDOXniBOv/XmvvcOzi4IEQtm/7j17PvWDvUDLl8H1SJuPkvEzHSefi6J9bduoCI7q5ny9WrHgmr3DwD8hKnGE8NHofbqnY2FjAhSrVqvHmOx8QFXWF6VMn2zuse3L50iX+mDubPv36M2zEaFJSUli2dLG9wzIES8YGIx5TzZs7i5Dg/fTrP8C2Ad3i1vgm/PIDXR5+lMCSQfYJKBO3xhgbG0tqair9BwziwYceYdLvv3L2zGn7BMft8c2fOxs/Xz+Gj/qCbt17MmPqZOJiY+0TXDY4Qp3lCDEajSPUJUY9fnaIeshJjguV28bgCP2FJYzap1jKIfoeCym3jUG5bRzK77zNEXLRqHlm+Nxxkpr8Ti5FhNPvhV4Z/m34e10mr7y9c/hz0QJ8ff1o07a9YWNMt2/vHqZNmcgrg4aQ38sr94LNgtHnOO+0faPMc4JjzHXamr2/N0bgCONwbjL8WGpjznTuUPmdfUbvD4xak4MD9CVOVJc7cm4bPcfuxvDf82xwpvEuN6+VcbfKuwgAPj4+JMTHk5qaiqurK/FxcfjacUVtdHQ0o4YPo2at2jzd41lMJhPlK1TEz9cPdw8P7ru/E2NGjyI1JQUfH19i49Iu6Ii7/vhUX19/fHx9MlzoER8fR4nAkrkSb2pqKo0aNaVQ4cIANGrcxPzosdjrMcTHxeHn5weQITaTyURCfDy+fv6kpqbY9HNYvvRPypYrR9Vq1QEILBlEpUpVyOfpSecHH+Kv1Su4fPmyYeK9mY+P7419e/1z9/H1w8fHh0sR4ebXxV+P1ccn7W4wsXGx5PfyIj4+Dj9f/xvvlcl3SJzXrX1MXnXy5AmirlyhTt16FC5chBq1ahESvJ927TvYOzSbWzB/LlevXuW1V18mJSUVgL69e/Lb5Bk2j8VoY7I1ZDZOHjl82M5RWVdW45Ijy6ouKFY8swUoxnJrP5907RqQeV2WlznLeGikPjwnfH39yJcvH506dwGgectWWZwsNb7Q0AOkpqaa29K4aTP27NpJ18efsnNktmXJmG7PYxFLa45VK5axfMliPhw2gsJFitokNkviO3wolH1793Dk8CGWLF5AQkICZ06fwsMjH489bpsnG1myD339/GjUuCkVK1WmYqXKzJg2mdOnTxFUqrQh4tuzeydt23egXPkKlCwZxNxZMzh27Kih7kjkCMfszljD25rR6xKjHz87Qj3kiMeFym1jMnp/YQmj9ymWcoS+JzPKbWNSbhuL8jvvMnouGj3PjJ47jliTZ1fRYsWZOXdhhp9dS0xk3pyZdz0/sGD+HFxcXOjbuydJSUmkpKTwxWcjePu9Dw0TI8CRw4cY++VoXujbn3r1G1o1tswYfY7T0hjBfvOclsRohLlOW9O4fTujj8O2YPSx1NYc9dyh8jvnjNwfGL0mB+P3JY5alztTbhs5xyxl9O95djjqeJeZ3LxWRk+asaKKlarg6urKmtUrOXv2DLt376RGzVp2iSUhPp4vRg2nSJEi9Ordh7i4WOLj4nj/7f9j+rTJREZeZsu/myhVqhSubm5Ur1GTLf9uIizsAmv/WkmhwoUpXqIE1avX5OLFMHbu2MbRo0c4FBqaa20Ku3CeQQP6snnTP4SFXWDvnt1UrFSZChUrsW7NaiLCw/ln43qqX99+jRq12LVrB2dOn2bd2r9wc3OjYsVKNv0c4uLiWPPXKh586BHzz778fBTjv/uGy5cvs+mfDRQs6EOhQoUMEe+tqteoycb164gID+fvdWuoXLkq+fLlo3qNWhwKPcjhQ6Hs2b2LsAsXqF69BiUCAwkIKMRfK1cQfjGMLZv/NX8eWX2HxDll1sfExcXZOyy7OHXiOGO/HM3hQ6GcPn2Kw4dCKVe+vL3DsotXBg1h7LjxjB4zlt4v9gNg9JixdonFSGOytWQ2TpYpW9beYVlVVuOSI8uqLjC6zPr55JSULOuyvMqZxkMj9eE5UaVKVTw8PFi5fCnhF8PYvvU/ypevYO+w7knJoFJcu3aNzf/+w8WwMPbt3WOTBQJGk9WYnpqSYn6NPY9FLIlv0z8bmDZlEoOGvEGxYsWIjY2x2VPH7hZf2XLlGTf+Zz7/8htGjxlLUKnSdOjYmfs7dbZJfJbECFC/YSM2bvibC+fPs3HD3yQnJ1OmTDnDxBdUqjQ7d2wjIjycjRvX4+bmRmBgoE3iuxOj5ImlnLGGtyVHqEuMfvzsCPWQIx4XKreNxxH6C0sYvU+xlCP0PZlRbhuPctt4lN95kyPkotHzzOi544g1uTXk8/S84/mB9DmIr775ni/HfsfoMWNp3aYdFSpWov+AQYaK8czp03w+ajhdHn6UJk2bERsbQ2JiYq7GZvQ5TktjtOc8pyUxGmGu09Y0bmfkCOOwLRh9LLU1Rz13qPzOGaP3B0avycH4fYmj1uXOkttGzzFLGf17nh2OOt5lJjevldGTZqzI19eXAQOHMHP6FGbPnEbjJs1o2+4+u8Ty33+bOXo0beVk/xefB6BI0aIMef1NJk+cwNq/VlG6TFkGDXkdgMeeeIoLF87z3luvU6hQYQYNfh03NzdKlylLr959+PXnH0hJTqHzA12oW69+rsRcukxZer/Yj2lTJhIfH0ftOnXp9kxPLl2K4Jcfv+fN1wdTsVJl+vVPa0/7Dh05fuwowz56lwIFvHl54GAK+vgA2OxzWLN6JZ75PWnWvKX5Z/0HDOK3X37i9cEDKFa8BK+98RbuHh6GiPdWvZ7vw4/jx/Hm64MpGRTEK4OGAFC/QUMeePBhxoweiZu7G8+/8KK50xk05HUmTviZlSuWUrNWHR7t+jiQ9XdInFNWfcx3P/xqz7DsolWbdpw8eYIxo0diMplo1qKleZVrXuMfEGD+/35+p4C0Oz3Zg5HGZGvJapx0JlmNS44sq7rA6LLq599854NM67K8ypnGQyP14TlRwNub1998m4m//cqsGVOpUbMWvXr3sXdY96Rs2XK80Pclpk2ZSFxsLDVr1eHp7o55h5acyGpM/2rMaGrUrMVDjzxm12MRS+KbPWMaSUnXGDN6hPnvBgwcTFsb3C3KkvhuznV3d3e8vb3x9i6Y67FlJ8aHH+nK5YgIPnzvTby8CtB/wCBKBgUZJr7nX3iRX34az5uvD8bX15eXBw62+Z02M2OUPLGUM9bwtuQIdYnRj58doR5yxONC5bbxOEJ/YQmj9ymWcoS+JzPKbeNRbhuP8jtvcoRcNHqeGT13HLEmt5b+r7ya6fmBJX8u5GBICG++836Gz8qrQAE8PDwyfKZGiHHJ4gXExFxl/tzZzJ87G4A2bdvzyquv5VpcRp/jtDRGe85zWhqjvec6bU3jdkaOMA7bgtHHUltz1HOHyu+cMXp/YPSaHIzflzhqXe4suW30HLOU0b/n2eGo411mcvNaGReTyWSyyjuJiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIGISrvQMQERERERERERERERERERERERERERERERERsTYtmhERERERERERERERERERERERERERERERERGno0UzIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi4nS0aEZEREREREREREREREREREREREREREREREScjhbNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiNPRohkRERERERERERERERERERERERERERERERFxOlo0IyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIk5Hi2Ykzwq/GEaPbl15640h9/we8fHxvD5kIP/3v1dJiI/P8nXz5sykR7euLFm88J63JeIMPn7/HQYN6MvFsDCrvu/FsDAGvdyXoR++a9X3FRERkbvr0a0rL/TqnuP3ubVmDgneR49uXfnisxE5fm8RI8utGllE8oblS//kpT7PsWLZkju+zlrjtYhYj/JSxHEMHvgSPbp1JTo62mrvqfNGIs7HmnNZnw79gB7dunL06BFAfYaII1q/bg09unVl4oRf7B2KiMPQcbKIiIjkFmtcLy6OT4tmxCn07d2THt262ny7np6edOz8AB07PUA+T0+bb1/E0XTo2ImOnR/E39/fqu/r5+9Px84P0KFjZ6u+r4iIiIhIbsutGllE8obqNWrSsfMDVK9R096hiIiIiIiIiMgdfPXFKHp060pI8D57hyLi1GJjY+jRrSuDB75k71BExI5OHD9Gj25d+XToB/YORUQysWzpYnp068q8OTOz9Xfbtm6hR7eu/Pj9t7kUmTgzd3sHIOLIXF1d6fLQo/YOQ8RhtG3fIVfe19PTk65PdMuV9xYRx2QymTCZTLi6ao24iIgYW27VyEaWmpKCq5ubvcPIUkpKCm4Gjk/kZuXKV6Bc+Qr2DkP1t4gT0TgokrdpTBdxTkY/DhcR61E9L5K3KOdFci63auXU1FRcXFxwcXGx+nuLiH3o2NrxadGM2ESPbl0pUSKQzg8+xLIli4mMvEzJoFL0fWkA0dFRzJk5nQsXLlC0WDGe7t6Tps1amP/2UOhB5s+dzeFDB3F1daV8hUr0ePY5KlSsxPp1a/jph+8ybKdN2/a88uprAGze9A+LF/3B2TNn8PH1oV69BjzT8zl8fX0zxLdk8UJWLFtCTEwMFStVpl//AQSWDLK4bZ6e+Zk0bRaQdkJhwfw5/LVqJYmJCVSrXhM/P7+c7kIRpzB44EtEhIfz829T8PX15dOhH3AgJJj/e/s9lixayLFjR/H39+fRx5/k/utPjZk3Zybz587mud4vcuTwIXbv2oGHRz6aNW/Jc7374O7hQfjFMIYMeplSpcsw5utxwPV+J7AkTzzZjQXz5xIREUFQqVL07tOPatVrmGP6e90aFv0xj4iIcIJKlabdfR2Y/PuEDH2JiOTcpo3rWfrnIs6ePYOnZ34aNGxE92efJz4+jjeGDKRIkaJ89+Ov5td/8/UX/Lf5XwYNeZ1WrduSkpLCwj/msX7dGiIjL1O8eAke6foEbdvdB2CuCVq2bkvk5UscPhTKux98TI2ate3VZJE8Z9WKZSxf+ieXLl2icpUqvNC3P6VLlzH/ftfO7SxaMJ8Tx4+RL58n1arXoGev3pQIDLRj1CL2d2uNDLBi2RJWr1pBRPhF/P0DaNGqDY8/8ZTFTzg9euQws2dO4+iRw7i5uVO5SlW6P/ucOSfT6/ARo7+kYsVKQNrdfKZO+p0nuz3DU0/3MNfh3Z7pwcEDIRw5fIj8+b24v1NnHn/y6QyT/KtWLGPlimWEXwwjoFAhOnZ6kIceeQwXFxdCgvcxfNhH1K3XAHd3d/bv20ufvi9ZtFjo/PlzzJo+lZDg/SQmJlKhQkWe6PYMderWA2Doh+9yKPQgHw4dTs1aaWP+rp3b+eKzEdSqXZcPPv4EgH179zBvzkxOHD9G/vxeNG7SlGef74OXlxeQduwQEFCIFq1as3H939Sr30DHAmI3v/40nrVrVvPGm+/SuGkzAEYNH8r+ffv4acIkfH19SU1JoV+fXri6utH5gS78MX8Ozz73Ag8/mvYU5tOnTzHpt184cvgQhQoV5qFHM3868+ZN/7Bo4XzOnT2Lj48PrVq3pVv3nri7333KOKv6OyR4P/Pnzs4Qz4njx3jv7TeoXqMmH38y0twvtG3fAT8/fzb8vZbExASqVqvBSy8PpFDhwgDExcUxd/YMtv23hZiYqxQvEUiXhx7Jk4sNxbllVUevWb2SCb/8SIOGjXjr3Q+BtJNy/fv2JjY2hm++/4nixUsQFXWFmdOmsHPHdhITE6hQsTI9ez1P5SpVLdp+ei3yTM9erF6xnBKBgXw0bMRt896Qdmfs7du28tGw4dSoWduiuT2AvXt2MX/ubE6dPEH+/F7UrF2H7j16UaRoUevuTBEbOXb0CLNnTuNQ6EHc3NyoU68Bz/XuQ0BAIfNrFi/8g5UrlnI1OpoKFStRt14D5syabq6300VEhPPFZyM4eCCE/Pnzm+ttk8nE4IEvcfnSJUaO/pIK1+v2JX8uZPqUSbRu046Bg/8HaEwXySlLzycdPXKYObNmcORwKCkpqVStVo1nevQy52e6a9cSmTjhF7b+t5nExEQaNW7CCy++RAFvbyDt4rnlS/9k3dq/uBh2AT8/fxo1acrT3Z81H6fm1I/ff8uG9et45LHH2bl9G+fOnWPGnD+yNR9wp/NyAOfPnWXm9KkcPBBCamoKZctV4OnuPalarbpV2iCSXT26daVkySDatu/Amr9WEnk5MtN56gvnzzNz+mSC9+8jJSWFatVr8lzvFykZlHZ9iKU17p2uY7lV+rnsdMOHfUSRokXp+vhTFtX8d5Ie75NPd2fj3+swmUyM++GXTOccJ0+cwIplSxgwcDBt23cw9xUDBg1h86Z/OBCyn4IFfWjf4f4M9crd5hpFrC0n55vSxzGAiPBwenTrah7j7laXptfGDz70CNFRUezauZ3U1FQaNGzMC31fYuH8efyzcT1JSUlUrVaNvi+9Yj6utWR8HzzwJSIvR/LmO+8za8ZUzp09S9FixejesxeNmzSzaN/oGF7ymqyOd/9ctIA5s6bTqHFT/u/t9wCYMuk3li/9k3btO5CamsqG9esAOBASTI9uXRkwcDA1atZiyKCXKV+hImXKlGXb1i08+NAjPPV0Dy5dimDurBns2rWDxIQESgSW5LGuT9K8ZSuLYk3vfzp2fpAjhw9x6uQJxo77gXlzZrFh/Tr+98bbNG2edk3stq1b+HrMaPO1aen9z2OPP0nM1av8t2UzJpOJuvXq0/elAeZjicjIy8yaPpW9e3aTkBBPqdJlePzJbjRo2DgX9r5I7kkfzwDmz53N/LmzGTf+Z3x8fJk9azpbt2wmOjqKYsWK0/GBB3ngwYeBG7UvwIb169iwfp15nMtpDt8qq3PcAD/98B2dOnehT7/+QNpT7vq9kDZWfvfDr+b6v3adutSpV5+Vy9PmCMuVr0C//gMpVbo0AMnJySz8Yy4bN/zNlchIihQpSvv7O9LloUd1Q5tcor0qNnPhwnkWLZhP1WrVKVu2PKdOnuCLz0YwbuxXlCpdhrr16nH+3Dm+//ZrwsMvAmknHoYP+yAvxmEAACAASURBVJCDB4Jp0LAxNWrWJnj/Pj4b8QlXIiMJKlWaTp27mCf+O3XuQq06dQFYu2Y14775kojwcJo2a07hQkVYu2Y1Y0aPICUlxRzX2TOnWbliKXXq1qNEYCAhwfv4cfy4e27n8qV/Mnf2TK5dS6R+w8YkJMSzbu1fOdhzIs7vu2++wj8ggAYNG3HpUgS///oTp06eyPCa6VMnERUVRZOmzUlJSWHVymWsXbP6ju97MewCUydPpErValSsVIkTx4/x3TdfkZyUBMCO7Vv5+YfvCA8Pp2GjJvj5+TNl4u+51UyRPGvl8qV8P24sERERNGnanEKFC7P+77V88tF7+Pn5U758BSIiwjl5Pe9TUlLYt2c3Hh75aNioCQDff/s18+bMpGDBgrRs3ZaEhAR+Gj+O7du2ZtjWpo3ruXbtGq3btMPPP8DWTRXJsxITE5g/dzZVq1WnXLnyhATvZ/SIT7h27RqQNuaOGT2SM6dP07RZC8pXqMi2rVsYPfITriUm2jl6EWNZsnghkydO4FpiIi1atsbdw4OFf8xlwi8/WvT3F8PCGPnpx4QEB1O3XgMqV6nCzh3bGDHsI65cuZLteObOnklSUjL1GjQiPj6OubNnsnLFUvPvZ0ybzMTffgGTiRat2uDu5s70qZNYuXxphvfZs3snYWEXaNmqNcVL3H2xXER4OB+99zZb/9tMpcpVqN+gAUeOHGL0yE/ZuWMbgHmSc+f2bea/27VzR8bf7djGZyM+4dy5szRr3pKSQaVY89eq2x7ZHRl5mY0b/qZ+w0ZUq1Ez2/tJxFrS57VCQw8AkJCQwMEDIZhMqezelfb9PnXqJPHx8dSsVQsX14x3qUtMTGTU8KGEBO+nbNnyVKlajdkzp922ndUrlzPumy+JuXqVFq1a4+fvz+JFfzBz2pRsxZuT+nvj+nXs2PYf9Rs0xN8/gN27djBl0m/m33//7desWLYEf/8AmrVoxeVLl/jph+80zyZO5U51dLMWLfHwyMe+vXtJSEgA4ODBA8TGxlC5clWKFy9BfHw8Qz94lw3r11GxUmUaN23O8WNHGPnpUC5fvpytWBYv+IMaNWtRr37DbLfjTnN7hw+F8sVnIzh54gSNmzSjZFAQmzauZ9TwoebjBRFHcvzYUT75+H1CDx6gYeOmVK5SjS3//sOY0SMxmUwArFi+hJnTpxAfF0fjJs1wcXFhzqzpmb7fyuVLuXYtkYaNGnPtWiJzZ89k1YpluLq60qpVWwC2b/vP/PpdO7YD0LJ12u80potYx93OJx09eoRPPn6f4P17qVW7LtWq12Dvnt0M++h9jh87muG9gvfvIzh4H3XrN8DH14eNG/7m5x+/N/9+xrTJTJsykZSUFFq1bou3tzcrli3hNwuP+7NjyeJFFC8RSLv7sr9I7U7n5eLi4hg+7CO2bd1C5SpVqVWnLqEHQxg1fCinT5+ydjNELHbu3FmWLllE5cpVKVWq1G3z1Jciwvnog7fZsX0btWrXpU7d+uzbu5uRn35M4i3z1Heqce92HcutvLwK0KlzF4oVKw5Ao8ZNadP2PotqfkstXjCf8hUr0rxF9i8K/PWn8bi5udKkaXNiYq4yf+5sdl6vOaw91yhyNzk931SxUmXu69ARAC8vLzp17kLFSpUBy+vS5Uv/5ML58zRq3BQPj3z8u2kj//faq2zdupkGDRtRpGhRdu/aya8/jzf/jaXje0pKMt998yWlS5ehZq3anDt7hvHjxhIdFZWt/aRjeMkL7nS8+8ijXQkMLMn2bf8REryPs2fPsGrFcry9C9KjV29q1alrHhMDAgrRqXMXgkqVNr/38WNHCQneT9PmLSlXvgIJCQkM++g91v+9lgoVKtK0WQvCL4Yx7psv2btnV7bj9vb2pm27+/DMn71F8X8uWsDx48do1LgJnp6e/LtpIwvmzwXSbuT+2YhP2LB+HUGlStGocVNOnzrJl59/xr69e7K1HRF7a9P2PvMNFypWrEynzl3I5+nJqOHDWLFsCX5+fjRt3pLoq9FM/n0CM6ZNBqBxk2bmsa9kUCk6de5CQKHCVs3hW2X3HPfNgvfvY8XSJdSuXZeSJYMIPXiAH8ffOEc9ddLvzJ87Gw93D1q0bE1iYiLTp0xi/pxZd3hXyQk9aUZsxs3NneGjvqBI0aKkpqTw2qsDiIgIp2ev3jzy2OMAfD9uLJs2rufggRCKFi3GpYgIWrRsTeOmzWjUuCkAv/w0nnVrVrN//15atW5LpcpV+Gfj3yQnJ5tX7qWmpDB75jRcXV0Z+uko88q8kZ8OZf++PRw9cpiAgLRJfx8fX0aPGYu3d0GuXbvGwP4vcvhQKNcSEy2+i2+61JQUFi2cD8AHH39K+QoVM7RLRDLXt/8rtGnbHoDfJ/zM6pXLOXgwhDJly5lf07ptewYMHAzAls2b+PbrMYSE7KfTA12yfF8XFxeGf/aFeULxzdcHc/bMaS5cuECp0qXNdxh55dUhtGzVBoBFC+Yza8bU3GimSJ6UmJjIrBlTcXd3Z/iozyleIpDUlBQ+/2wEe/fs4q/VK2jWshXHjx9j145tlC1bjtCDIcTFxdGkaTO8vLw4eCCYLZs3UaNmbT4c+ikuLi5ciYzk1Vf68ce82TRq3MS8vTp16/PuBx/rEbciNubm5sbwUV9QrHhxTCYTY0aPZNfO7fyzcT33dejIlStXaN2mHffd38k8+ZFemx8/fkx3oBS5yY7taQtC//fmO1SsWIm4uDg+/fh9zp49Q2pq6l3vKhMcvI/4+PgMd7eZMuk39u3dw4njR7N9Eu2+Dh15acAg4MYddf5cuIAHHnyY8PCLLFm8iJIlgxj95Td4eHiQmJjIkEH9+WPeHB7o8rD5fUqVLsNnX3xt0d2uAebNnUVsbAzduvfkiSefBuCfjesZP24sM6ZOpkHDxjRt1oIpE39n587tPPfCi0DaXQbd3Nxpcv3OfFMm/oarqyufjvycwMCSAIwZPYJtW7dw+tRJSpcpC6T1YyM/+1J3yxO7q1W7Di4urhw6eBCA/fv2kJSUhIuLKzu3b6NN2/YcCk37Xa069Yi6kvFinHVrV3MlMpJGjZvwxlvv4eLiwoXz53l9yCvm1yQmJjJj2mQK+vjw2Ziv8fHxJTU1lffefoNVK5fx+JPdKOjjY1G8Oam/AwNL8tmYsXh4eBAVdYUB/V4w3yEsOTmZPbt3UqBAAT4ZORo3NzdCgvcz8bdfOHr4EO3vuz/b2xMxorvV0Q0aNeK/zf+yZ/dOmjZrcdPF8mnzWMuXLiYs7EKGJ1f8t+VfvvnqC5YtWUSv5/tYHMu7H3xMlarV7qkdd5rb271zBykpKXTv+Zz5aRXffj2GM2dOc/7cWcqWK39P2xSxl+lTJ3Ht2jXzXSwBJv72C6tWLGPXzu3Uq9eAhfPnAfDuB0PNeTXhlx9Zs3rlbe/X/r776f/Kq0DaXdw/fO8tFi/8g84PPkTrtu1ZvOgPtm/9j6e7P0tcbCyhBw/i5+dP7dp1NKaLWNHdzifNmDqZpKQkBg7+H63btANgwfy5zJk1nTmzpvPO+x+b3ysoqBSjv/gadw8P4uLi+L/XBrFt6xbOnztLYMkgTCYTbdq2p1fvPvj4+HItMZEBL73A1v+2kJqSgqubm9Xa9XSPnnR9/Kl7+ts7nZc7dvQIkZGXqd+gEW+9+wGQdhOQ9X+v5fChUD19QuzGzc2dEaO+oGix4qSmpDBqxDCC9+9j44a/6XB/J+bNmUXM1avmJ63Ajae4/b32Lzo/+JD5ve5U41pyHcvNCvr40Kdff776YhQXL4bx4EMPm+uIu9X8lnp54GDz+e7sevzJp3my2zMAlFlUjhnTJnMgZD8NGjay+lyjyN3k9HxT/QaNqFK1GmvXrMa7YEHz9zY7denNtfHePbv5bMQwrl1LZOTnX1K0aDES4uPp37c3wfv3m+fsszO+v/nOB1S/fuOmz0cNZ/euHRw9epj6DRpZvJ90DC/OzpLj3T79XmbU8KFMnTwRX18/UlKS6f7sc/j6+tK6TTtKly7D5n//oURgoLkvCL8YBly/XvTLbyhQoACQdtP1GjVqEVSqNI92fQK4cU5qy+Z/qVO3vsWxd7i/E/1eHnhP7a5RsxbvfTgMV1dX8xxB+vH1xYthnD51klKly/Dh0OFA2g0wFi6Yz6HQg9S+fkMuEUfQ7ZkeeBf0JvTgAeo1aMBTT/dgw/p1HD4cSp269Xjn/Y9xdXUl7MJ53npjCMuWLOaBLo/w4EOPXF+8uoNKlSqbc9uaOXyrW89xh104b/HfFvD2ZvSYsRT08SEpKYlXXurDsaNHSExMxNPT03xdwMefjsLX15fz58/x9ZjRnDhx7J7jlTvTohmxGXd3d/PFJ65ubhQtVoyIiHBq1Kptfk2JEmkTkVFRaXekaNy0GQ0aNWbH9q38MW820dHRnDpxAoArV26/Q0e6s2fPEB0VRYWKlcwLZgD69R/AxYsXKVa8BEnX0u4U4uvnh7d3QQDy5ctH4SJFiI2NISo6iqJFi2WrjREREURHRVG6TFnzgpmb2yUimbt5NX+J6ytyo6OjM7ym1E2vSV+1e/WW19zK3d0jwx14SpQI5OyZ00RHR5GSUpKTJ47j5eWV4Y476Y/eFhHrCD14gISEBOo3aGTOXVc3N+7v1Jm9e3Zx6OBBevfpy8xpU9i5fTtdn+jGrh1pd9Budj039+zeDUBKcjKTfvvV/N7u7h6cOX3KfBdPSMtzLZgRsT13dw+KFU+7S56LiwstW7Vm187tnDx+HEibHGzdui1bt25h397dXL16lcuXLwF3rutF8qIKFStx8EAIc2fNoPODXahevSajv/zG4r8vV74Crq6ubNu6hVJlylCvXn2ef6HvPccTWPJGfVyjZm2KFy9BWNgFoqOi2Ld3DyZTKm7u7kybPNH8OldXV65ERRIZeeMO90WLFrN4wQzA3t1pd/25v+MD5p+1bNWGSb/9ytmzZ4iJiSEgoBDVa9QgJHg/586eJSU1hYjwcOrVb0BBHx8unD9PWNgFChb0YcXSJeb3Sb8L5qmbFs24u3towYwYgo+PL+XKleP48aNcS0xk147teHt7U6deA3bv3EFycjKhB9OeQlO7dl3+2fh3hr8/djTtLtet27Y318UlAjPe+epQ6EESEhIo6uPLvNk37laVdO0aycnJnDt31uKT7jmpv4sVL4GHhwcAfn7+eHkVIDo67a6a7u7ulC1bjuPHjzFt8kSat2xF1WrVGfP1vT8dWsSI7lZHt27Tjv82/8v2rf/RtFkLdu7cjpubG81atARg7/Xj5bNnzjBxwi8AxMXFAnD61MlsxVLi+uLSe3Gnub3yFSsBsG7Navz8/Khdpx6vvfHWPW9LxJ5SU1MJCQ7Gzc2d/zZv5r/NmwE4c+Y0kFZflipVhqioK5QqXSbDeJrVvHPJoFLm/1+xUmVKBJbkwvlzREdHU6p0acqXr8Dx48cIu3CeY0ePkpKSTPMWrXB1c+NQ8H6N6SJWcqfzSUlJJTgQEoyXl1eGC9I7P9CFObOmE3p9wXu6YsVL4H49JwoUKECDho1Yu2Y1p06eJLBkEM/1fpHIyMts+XcTERERJMTHA5CUdI3YuFh8fHyt1q7AEvc+vt/pvFxQqVJ4eubn4IEQlvy5kPoNGvHwo13NF9eK2Iu7uztFrz/NxdXNjbbtOxC8fx8nT6TV13v3pNXPoQcPmI+fL1+OAG6vn+9U497rdSyZuVvNb6mc1POlMmlrer5be65R5G5y63xTdurSm2vjwOvzakWLFTdfQ5bfyws/fz8iwsOJjY3Bx8c3W+O7JdfH3I2O4cXZWTKHXbtOXZo1b8mWzZuAtKdVpD9p6m78/P3NC2YgLS9eefU1Dh4IYfGiP7gSGUn4xYsAt9246m5ykp8lS5Yy3zyvuDk/046vAwIKERBQiPPnzjFvzkwaNm5Ki1ZtzE+iFXF06eeG7+vQKUMe1K1Xn+3btnL08CEKNW2W6d9aM4dvld1z3Dfz9w8w39DGw8PDfG169PVr0ytWqsSlSxFMmfQb7dp30JyZDWjRjBjT9Wtfwy+GMeLTj7kYFpblazITGxsDcNukYvESgeaCIn3l8K3MpwRMd9hAFtKLFH9//2z/rYhcl56Ed8jBGy/JXp7eOOdn4urVq6SmpuLnH3DXu3WLyL27cQAfkOHnAQGFAEhIiKdoseJUqlyFI0cOExV1hZ07t+Pp6UmDho2BGwcwoaEHCA09cNs2YmNicrMJInIPfP3S6uErV9IumD929Aifjxpu7hMyyH7ZLeLUejz7PPnze7H2r1V88dkI3NzcqVe/Ac8+/4L5SSl3Ur58Bd55/yPmzJrBxAk/YzKZCAoqRdcnu912l8t74evrR1jYBa5ciTSfhDx96mSmF+Zeibz3Scjo6Cjc3Nzx9b1xXO/i4oKfvz+xsTEkJsRTsGBBmrVoRUjwfnbu3EZqSipwY+FtenwxMVdZtXKZVeMTyU2169Tj+PFjHDlymN27dlKnXgOaNm3O5k0bORASzKHQgxQtWuy2xTAA0VHpc1MBt/0uXXpuhIdfzDw37LSg9dbrdN9+7yNmTJ/CurWrWbF8CQUKFKB12/Y806MXXl5edolRJLfdWkfXq98QX18/du3cwbmzZzl39gz16jfAz/y6tHxNvzjgZpH2Gudumdtr1LgJrw55nYUL5vPD998CaRcxdOvek7r17v0OfyL2kJKSjMmUSkpKapb1ZVZzYZby8/XjwvlzXIm8nHZ33LbtOX78GNu2/We+UDD9zvMa00VyT4bzSdHRmEy3n08q4O2Nh0c+EhIS7vhe6eN75PXxfe2a1Uyc8DPJycm3vfYeTk/bxK3n5QICCvHRsOHMnD6FmdOmMH3KJIoULUqXhx7lwYcesV+gIrfw9fUDbpxnSh8b163967bX3rF+vqXGvdfrWDJzt5rf5q63NT3fc3uuUeRurHm+KTfq0vSx+57Hdwuuj8lVOoYXg7L0eLdj5wfN82IdOna65+u/kpOTGTN6JHv37Lrtd3ZLz/Qx+Xrnli9fPj76ZAQzpk5m0YL5zJ87G18/P+7v+ACPP9ntni/qFzGKqOvntwIKFcrwc3//G9eXZcWIOZyZW69Nf3ngEAoVKsLGDevYtHE9+fLlo2mzFvTs1Rv/e5xblDtTTymGtuCPeVwMC6N1m3Y807MXAQGF+GPebObPnX3Hv/MumLY67+rVjCvxk5OSSE5OxiNfvlyJN/2ChOzeAUBEbM/X1xc3N3euRkdhMpn0ZAqRXFKwYNrT3NIPbtJFXk6b2Eyf9G/eohVHDh9i1fJlnDt7huYtWuHp6QmAl1faHT6693yOxx5/0lahi0gOxFy9Ctw4mTFz+lSio6N45LHHefiRrvj4+vLT+HFsWL/OnmGKGJK7uzvdnunBU09358zpU2zZ/C8L5s/l5MnjfPvdT7i6ud31PerUrU+duvXTngazbw9zZk1n/Lix+Pn5U7tOXVxcrp80uIdZwvSLfPz9AyhwfYzu0LEz/fq/kunrQ4L3ZXsbAN4FCxIdFUVMTIy5njCZTERdicTFxQWf6xc9NG3anEm//cLO7dswmUy4u7vTuHFTALyu3yWsZMkgvvp2/D3FIWIPterUZfGiP1i1chmXL1+iYcNG1K1XHw8PD/5atZyIiHDaZ3HHvPRJ9EwvHLguvb6uU7c+73041Kqxp/cv2b3JRWb8AwIY+Opr9H95IEeOHGbl8qWsXL6U2JgYBg15PcfvL2JEt9bRbm5uNG/ZipXLlzJ96iSADHePTB/rPh35OZWrVLVqLGlzZdY5o9iydVtatm7LpUsR7N61k9kzpjFm9Ag+/+pbgm56yoaI0ZlS03LC0zM/v02ejlsmtfmliHDg9rkwS12NSesH/K7fnK1Fq9ZMnzqJrVs2c+H8eUqUCKRS5SqAxnQRWyng7Y2Li+tt55NiYmJISrp21wtZ0sd3Pz9/kpKSmDLxN0wmE4P/93/Ub9AILy8vBg98iYjw8FxvS07mA25VsVJlPhw6nLjYWEJC9jN/7mymTPqN/PnzZ3m8ImJrV64/Bdnv+nUcXl4FiI2N4Ydffjff3O1e3Ot1LJm5W81/r1ytmO93m2sUyU3WPN+UW3Wpvcd30DG8OB9Lj3f/mHdj7F28aAGt2rQzPwk1O7Zs3sTePbsoW648gwb/j8CSQRwKPcDwYR9lP/hbuFxfyGOyQo4GBpbk/95+j8TEREIPhvDnogX8MW82JlMqT3d/NsfvL2JP5uvLrlzJ8PNIc02f9aLy3MzhW1kzpwsUKEDvF/vxXO8+HD9xnA3r1rJq5TIuXgxj2PDPcvz+cjvdWl8MLf2ury1bt6Vw4SK4uroSERFx2+vSLxpKX7EfVDIIXz8/Thw/xrmzZ82v+3bsl/R5vgf79+3NlXgLFS5MQEAhTp08YX7EL8C5c+dyZXsicu9cXV0pX6ECsbGx/LflX/PPw8Iu2DEqEedTpUo1PD092bd3N+HhaY++TE1NZe2aVUDaBYEAzZq3xMXFhT8XLwTSFtGkq1GrFgBr16wyP00OYNvWLZne1V5EbC8p6RoXzp83//e/mzYCUK58BeDG3X7atb8fXz8/TCYTly9fsn2gIgaXEB/Pi8/34NUB/Ui69v/t3Xlc11W+x/EX+zYsgqWIpsgq++pSmZqRmltuiEkuY3lrqmlxXHJfysbu7Ta3uc00k5PNTNNetzI1J1tcUPZN2V0wQUhQyQVE/cn9A/kliggIovR+/uWD7/I78PD8zucsn3PO0uOOnkyMjsHZ2ZnysjJOV56+5jtef+1VZsTGkBC/EwdHR+66+x7uurt2J+ofDhYCPw8qlpb83FctKW6431p3dDXAnt2ZlJeV4ezigoOjI37+tW10/M4d9Sb/8vNyydrTsmSZOoGBtTFCXcwAtd8tp0+fxse3D5YXN8NwcHTEPyCQ/Lxc8vNyCQ4Jw9bODoAe3Xvg4OjI4cPFJCclGt9TUVHBt998fV3lE2lLPr59sLCwJDF+F6ampgSHhGFtY0MfvwASE+IBrrooxcPTC4DtlywUuHRsDMDbxxcLCwuy9uxm/7699e5r6LSK5mjw+6UF42LpaanMiI3hlZdfwtzCAt8+fsQ8FAvAwYvfZSIdwbXiaICBg4YAkJqShJWVFREXk0MBY1u8Yf3nXLhQe+JaTU0NmzdtoLKy8rrK5ujoRHV1NceO1sbt58+d48glcUFTrVi6kBmxMezbW4CLS2eG3nc/wSGhGAwGiosOXVcZRW40Sysr3N17U119hq83bzL+/ExVFZs2rKempoZOzj/PE+0tyDfe82Npw+POl45tFR7YT8nhYjp1cjZuNOPo6ERQcCgF+XmcPHnCeMoMqE0XuVGsra3x8vbm9OnT9erWlq+/An7uv9b5sbSEs2fPAnDmzBlSU5MwMTGhZ093qqqqqK4+g4ODIwPuvBsbGxtOnz5FVeXVd8xtTc0ZD2jMB+++w4zYGDas/xxbOzsiIvtx/7ARgOq2tK/z588bF9VdMBiMi+jdL8bX/hfnm768OBdV98zGL79o8HSIq2nqOpbLmZrWrWsx1Pt5YzF/S9XFEqWltf2Nmpqaen2PpmrKWKNIa2qN+Sazi3XNcElda8u4tL3bd1AfXjqepvR347ZvJWvPbvz8AxgaNYzSksOs//xT4711G10YDPXb3YbUte3BIWH0uKMn5ubmlLVS0puxTb7O/vU3X29mRmwMf1+3FisrK4KCQ42bzh4sLGyVsorcSJe313Xrx777dotxrPvIjz+SmZGOlZUV3t6+tc81ULfbsg5frrXqdHHRIWbExrBowe/AxAQPD09ip8/EzMxMcXYb0kkzclMLCgkhLTWZv/zpjwSFhPLDwUIO7N8HwNlzZ433uXbtRsHJPF5cuYx+AwYwfMQoJk+J5c03XmfZkgWEhIRRXl5Obk4WPXu5ExAYxLGj1x60aC5TU1PGjpvA22+9yarlSwgLj6CsrIzcnKxW/ywRuX4TJk1mzepVvP7aq8TvisNw/jwpycntXSyRDsXWzo7xkybz3jv/YPGCuQQFh1BcXMSB/ftw7+3BwHsGA7WJp94+vuTl5mBjY0NwaJjxHWHhkQQFh5CZkc7cZ39LYFAwFRXH2Z2ZQXhEJHPmLWyn305E6tTU1LBk0TzCwyMpLSkhLy8HZ2cXYx0PCg6h6NAP/OeaF/Hx8WVvQT7FxUVA/bhe5JfO2saGyL792bb1OxbOn4O3bx8OFxdx9Gg5Xl4+2Ns7XPMdg4bcy8647bzx+mskJcRTU1NDclICFhYWxsHG4JBQdsVt5621fyEtNYWysiPk5+U2+L5/b95IaWkJ9vb2xsSTB8dNBOCOnr0YMjSK7775mgVznyU0LJwzZ6pIS02le48evPTyf7f4bzEhOobUlGTe/9c/yc3OxsLCgpTkJMzNzXkodnq9ewfceTe7MzMu/vsu489NzcyInTaTP/3xD7z6X78nLDwCGxtb0lJTOHfuLCGh4Tg7t3xXUZG2YmlpiY9vH/bszsDHtw+/sq89UTk8MpLMjDRMTEzwDwhq8NlBg+/ls08+IjEhnuWLn6dL166kJCfVu8fBwYFxE6L58P1/sWLpQsIj+gKQmpKMja0NYWERWF489bG5AgODMDMz47tvv+HEiRNcuGAgIz292e/xDwjExcWF5KQEVi1fTJeuruRk7QEgNCyiRWUTuRldK44G8PDwxM2tO8XFRURE9sPa2tp4bfTY8cTvjCMhficL5z1Hbw9P9u/fx8HCAxguGHhg5JgWly04JJSt33/LymWL8PL2YW9BvnHBXXMMHjKU3Jxs1qxeRVh4BJWVlaQkJ2Jn9yvjRKfIrWTqtJm89MIK/r5uLWmpyXRydiEzPY2Kigp8fPvQ28OTcROjeevNN1i9ajlhEZGc+KnCGK9ebvu27ykvL8PFpTOpKcnU1NQwZtz4evcMvGcwaam149Z1i1RBbbrI3KU07gAACsBJREFUjTRl6jReWLGU11/7Awm7dlJdXU1Geiq2trZMjJ5S796SksM8P+85vL19yM3N4djRo/Tt159ubm5A7eL9Awf2s3LZIlxcOrNnd6Zxs6hzbTxO1pzxgMbcNXAQmzau5/13/0lBfh5WVlakJCdiYmJCSGh4G5RcpGkMhvMsnD+H4JAw49oSF5fO3H0xvo6OmcruzAw2fvkFewvycXPrTk5ONqUlh7nt9tuJ7Nu/SZ/T1HUsl+vq2g2Av69bi59/ALMefQxoPOZvqaDQUAoK8nj1lTUEBARx6IeDLUoMaMpYo0hrao35JmsbG5ycOnH8+DHWrF7JwEFDiOzbv83iUgcHh3Zt30F9eOl4rtXfDQoK4Z1/vI2JiSkPT/81zs4u7Nyxnc8+/YS7Bg6iS5euuHS+DXNzc/YW5PPKyy8xbMRIunTp0uDn+QcGYWpqylcb13P0aDknT/xk3JT9eutwcEgoX3z2CZ9+/CEHCws5deoUWXuav+F7RN9+fPjBu2zetJEfS0txdHQkPT0VgNBw9a/l1lMXG2/ZspmSksPMmDWbLf/+irTUZJYsnEe3bm5kZqRz7txZYqfNxNrGpva5rrXPJScl8IdXXmb8xOg2rcOX8/Tyws7Ojt2ZGbz0wnKsrW3ISE9r9nvcuvfAw9OT7Kw9LFk4j1693Nm3by8Gg4EQjZm1GZ00Ize1YcNHMjF6CqampiTG76JTJ2fj4pjDRUXG+2Knz6RbNzcO7N9rzJq/d2gUTz0zBxeXzsTviuNwcRFDo4axaOkKY7Zhm5R5xEgmT5mKhaUFyUmJWFtbMynmoTb7PBFpuZDQcB5/8mlcXDqTnprCTz/9xKgxYwEwM1deqUhrGTN2PLMfewIHR0fid8Vx/PgxooaNYPHSlZhfUtfqTpcJj+hr3D0eao+T/t38RTw4fhLm5ubE7dhOUdEhRowczW+eavlR2SLSenr2cmf0mHFkZqSzf/9e+vj5s2DxMmNdnhwzleEjRlFVWUlKShLuvT0YPXYcUD+uFxF49D9+w8ToKZw/f57tW7+jrOwIQ6OG8bv5TUsSDQgMZv7CJfRy701yUgIZ6al4enkzf+FSevbsBdQuuntw3ESsrKxJTIjHwsKC8ROiG3xf1LARmJqakJqShI2tDdExU4m6uHsswCOzHyd22kwcHR3ZtTOOgvx8Bt4ziAWLlmFiYtLiv4OrazdWvPh7QsPCyc3JIjMjjT5+fixZ/gJe3j717o3sNwAzM3MsLS2NEyd1Bt4zmLkLFuPp5U1mRjrJSYl4eHqxbOVqJczITS0wuHbhyaWTXXX/v3v2csfBoeEkOktLSxYuWY6ffyD79+8jPy+XiZNjuP2yycBxEybx2BO/xbWbG0mJ8ezOzCAkLIzlK1e3eHEtQJeurjzx1LO4urqSkZ7KkSNHmDHr0Wa/x8LCgiXLX2DwkKEUFR1ix7atmJqaMvmhWCZrnE06kGvF0XXqEuXuvGSxPICTkxMrV69h4D2DOXbsGNu3fY/BYGDmrNmMeGD0dZVt6rQZ9B9wF6dOnSI1JQlvH18G3DWw2e8ZNGQoTz79HJ1vu42dcTvIzckmJDSMxctW4tSp03WVUaQ9+AcEsnTFCwQGBZOfl0v8zji6dO3K84uX0dvDE4Co+4fzUOx0bGxsSL24iHzoffcDYGZWf9z5odjpmJiYEL8rDgsLCyZGT2HY8JFXfCZAbw9PXLu51bumNl3kxvDt48fiZSvx8fUlLTWZgvxcwsIjWfnimiti7SH33oeXlzcJ8bv4qeI4dw8cxOzHnzRef3buAsIj+vLDwULycnOIGjbCeJJkcRuPkzVnPKAx3Xv0YMmKF/HzDyQzI43EhF10de3G08/NJeSSDbFEbjQrK2vGjB1PbnYWRYcO4ecfwPOLl2FhYQHULkxb+eLLRPbtT9GhH9ixfRu2trY89cycJifMQNPXsVxuxMjR+PkHUF52hEMHD9a7drWYv6VGjxnH0KhhXDBcICkxgdtu78L9wx9o9nuaMtYo0ppaa75p1uzH6Nz5NnJzsjl54kSbx6Xt2b6D+vDSMTXW3/3wg3epqDjO4CH30su9Nw6OjoybMIlz587y9t/eBMDW1pbpv34Ue3sHsrN2U1V19VOZ3d178/Rzc+nSpSvJifGcOnWKJ59+DrjyJPfm8vMPYPrMR3By6kRyUgJVVZVXbA7XFI6OTixf9RJ9+/UnPy+XnXHbsbd3YNbsx7kvath1lVGkPQQFhzBkaBSG8+fJy83hwoULLFq6kqFRwzhaXk5C/E6cOnXisSd+y8jRY43PdXNzY/zEyZibW5CVtZtz5861aR2+nJ3dr3hmznx69nInJzuLwsL9TJ8564oxv6aYM28hD4wcQ0XFcbZv+56qykpGjXmQ2Y890apllp+Z1NTU1LR3IURuVps2rL/qEbVdXV0ZMfL6Jh5FpH1dMBjIyck2TjoCvLX2L3y9eRNTp81g1OgH27F0IiIiIiJXWrf2r1e9FhIW1mq7NX/84Xt88tEHTH14BqPGtG5cXFJymK82fHnV68NHjsL14u5CItI+btR3jYhc25mqKp59+jcYzhv485vrmrUhVFpqMumpqVe9PvOR2a1RRBG5THV1NQcLD+Dt8/NOzGtWryI9LYVn5syjX/87m/W+Des/551/rOPhGb9u9glSatNFOh71qUUaNmXSg1hZWfP2O++3d1Ga7Wox/96CfLZv/f6qz02aPMV4Oq2I3PrUhxe5eZ06eZKPPnjvqtcHDhqMp5f3DSyRiFyvjz54j1MnTzZ4zdPbu95p8NJxaBt9kUYkJcaTk53V4LU+fv5KmhG5xW3Zspl1a/+Kh6cX3XvcwY+lJeTmZOPs4sLgIfe1d/FERERERK7w780br3rN3sH+llj0dvzY0UZ/j34DBmiBj0g76wjfNSIdwd/efIPM9DQqjh9n8pSpzT5Bfd/egkbrsxbciLSNjz98jw3rP6ePnz+3396FwsIDFB7YT8+eva44GbEx2Vl7+Pz/PiZrz27s7R1aNGatNl2k41GfWqRjaSzmLy461Gh9HzVmrJJmRDoQ9eFFbl5VVZWN1s/eHh5KmhG5xWzb+i3lZWUNXjtzpkpJMx2UTpoREZFfLIPBwOZNG/j+2y2Ulpbg5NSJgMAgomOm6mhZEREREflFa8uTZkRERKRpFsx9lvKyIwwcNISHp83EtJlJMyLSPqqrq/nis0/ZuWMbR4+W4+LSmfDIvoyfEI2tnV2T3xO/K44//+//4NrNjUdmP64FOCIiIo24VU+aUcwvIiIiIiJyYyhpRkRERERERERERERERERERERERERERERERDoc0/YugIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEhrU9KMiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIdDhKmhEREREREREREREREREREREREREREREREZEOR0kzIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi0uEoaUZEREREREREREREREREREREREREREREREQ6HCXNiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiISIejpBkRERERERERERERERERERERERERERERERHpcJQ0IyIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIh2OkmZERERERERERERERERERERERERERERERESkw/l/7HPDedMq2SEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 3250x3250 with 182 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(Rajastan_Kolkata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It is difficult to interpret covariance.Let's try correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>match_id</th>\n",
+       "      <td>1.549019e+07</td>\n",
+       "      <td>-51.926500</td>\n",
+       "      <td>23.584749</td>\n",
+       "      <td>-60.728007</td>\n",
+       "      <td>-10.430489</td>\n",
+       "      <td>-27.581622</td>\n",
+       "      <td>-3.371207</td>\n",
+       "      <td>2.617832</td>\n",
+       "      <td>-3.215663</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>348.473511</td>\n",
+       "      <td>-31.550660</td>\n",
+       "      <td>316.922851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inning</th>\n",
+       "      <td>-5.192650e+01</td>\n",
+       "      <td>0.268014</td>\n",
+       "      <td>-0.229059</td>\n",
+       "      <td>0.000756</td>\n",
+       "      <td>0.009328</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>0.000125</td>\n",
+       "      <td>0.000839</td>\n",
+       "      <td>-0.000180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.010065</td>\n",
+       "      <td>0.003051</td>\n",
+       "      <td>0.013116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>over</th>\n",
+       "      <td>2.358475e+01</td>\n",
+       "      <td>-0.229059</td>\n",
+       "      <td>32.190656</td>\n",
+       "      <td>0.003036</td>\n",
+       "      <td>-0.043745</td>\n",
+       "      <td>-0.007723</td>\n",
+       "      <td>0.004392</td>\n",
+       "      <td>-0.002168</td>\n",
+       "      <td>0.019269</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.519339</td>\n",
+       "      <td>0.013770</td>\n",
+       "      <td>0.533108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ball</th>\n",
+       "      <td>-6.072801e+01</td>\n",
+       "      <td>0.000756</td>\n",
+       "      <td>0.003036</td>\n",
+       "      <td>3.307420</td>\n",
+       "      <td>-0.000717</td>\n",
+       "      <td>-0.008298</td>\n",
+       "      <td>0.004982</td>\n",
+       "      <td>0.000964</td>\n",
+       "      <td>0.004010</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.009225</td>\n",
+       "      <td>0.001658</td>\n",
+       "      <td>0.010883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_super_over</th>\n",
+       "      <td>-1.043049e+01</td>\n",
+       "      <td>0.009328</td>\n",
+       "      <td>-0.043745</td>\n",
+       "      <td>-0.000717</td>\n",
+       "      <td>0.004787</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>-0.000021</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>-0.000030</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.005391</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.005469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wide_runs</th>\n",
+       "      <td>-2.758162e+01</td>\n",
+       "      <td>0.002266</td>\n",
+       "      <td>-0.007723</td>\n",
+       "      <td>-0.008298</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>0.054240</td>\n",
+       "      <td>-0.000163</td>\n",
+       "      <td>-0.000848</td>\n",
+       "      <td>-0.000233</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.037991</td>\n",
+       "      <td>0.052995</td>\n",
+       "      <td>0.015003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bye_runs</th>\n",
+       "      <td>-3.371207e+00</td>\n",
+       "      <td>0.000125</td>\n",
+       "      <td>0.004392</td>\n",
+       "      <td>0.004982</td>\n",
+       "      <td>-0.000021</td>\n",
+       "      <td>-0.000163</td>\n",
+       "      <td>0.009391</td>\n",
+       "      <td>-0.000100</td>\n",
+       "      <td>-0.000028</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001852</td>\n",
+       "      <td>0.009100</td>\n",
+       "      <td>0.007248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <td>2.617832e+00</td>\n",
+       "      <td>0.000839</td>\n",
+       "      <td>-0.002168</td>\n",
+       "      <td>0.000964</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>-0.000848</td>\n",
+       "      <td>-0.000100</td>\n",
+       "      <td>0.040258</td>\n",
+       "      <td>-0.000143</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.016522</td>\n",
+       "      <td>0.039167</td>\n",
+       "      <td>0.022645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noball_runs</th>\n",
+       "      <td>-3.215663e+00</td>\n",
+       "      <td>-0.000180</td>\n",
+       "      <td>0.019269</td>\n",
+       "      <td>0.004010</td>\n",
+       "      <td>-0.000030</td>\n",
+       "      <td>-0.000233</td>\n",
+       "      <td>-0.000028</td>\n",
+       "      <td>-0.000143</td>\n",
+       "      <td>0.010417</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000524</td>\n",
+       "      <td>0.010013</td>\n",
+       "      <td>0.009488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <td>0.000000e+00</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>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <td>3.484735e+02</td>\n",
+       "      <td>0.010065</td>\n",
+       "      <td>0.519339</td>\n",
+       "      <td>0.009225</td>\n",
+       "      <td>0.005391</td>\n",
+       "      <td>-0.037991</td>\n",
+       "      <td>-0.001852</td>\n",
+       "      <td>-0.016522</td>\n",
+       "      <td>-0.000524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.383277</td>\n",
+       "      <td>-0.056889</td>\n",
+       "      <td>2.326387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>extra_runs</th>\n",
+       "      <td>-3.155066e+01</td>\n",
+       "      <td>0.003051</td>\n",
+       "      <td>0.013770</td>\n",
+       "      <td>0.001658</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>0.052995</td>\n",
+       "      <td>0.009100</td>\n",
+       "      <td>0.039167</td>\n",
+       "      <td>0.010013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.056889</td>\n",
+       "      <td>0.111274</td>\n",
+       "      <td>0.054385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_runs</th>\n",
+       "      <td>3.169229e+02</td>\n",
+       "      <td>0.013116</td>\n",
+       "      <td>0.533108</td>\n",
+       "      <td>0.010883</td>\n",
+       "      <td>0.005469</td>\n",
+       "      <td>0.015003</td>\n",
+       "      <td>0.007248</td>\n",
+       "      <td>0.022645</td>\n",
+       "      <td>0.009488</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.326387</td>\n",
+       "      <td>0.054385</td>\n",
+       "      <td>2.380772</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   match_id     inning       over       ball  is_super_over  \\\n",
+       "match_id       1.549019e+07 -51.926500  23.584749 -60.728007     -10.430489   \n",
+       "inning        -5.192650e+01   0.268014  -0.229059   0.000756       0.009328   \n",
+       "over           2.358475e+01  -0.229059  32.190656   0.003036      -0.043745   \n",
+       "ball          -6.072801e+01   0.000756   0.003036   3.307420      -0.000717   \n",
+       "is_super_over -1.043049e+01   0.009328  -0.043745  -0.000717       0.004787   \n",
+       "wide_runs     -2.758162e+01   0.002266  -0.007723  -0.008298       0.000030   \n",
+       "bye_runs      -3.371207e+00   0.000125   0.004392   0.004982      -0.000021   \n",
+       "legbye_runs    2.617832e+00   0.000839  -0.002168   0.000964       0.000100   \n",
+       "noball_runs   -3.215663e+00  -0.000180   0.019269   0.004010      -0.000030   \n",
+       "penalty_runs   0.000000e+00   0.000000   0.000000   0.000000       0.000000   \n",
+       "batsman_runs   3.484735e+02   0.010065   0.519339   0.009225       0.005391   \n",
+       "extra_runs    -3.155066e+01   0.003051   0.013770   0.001658       0.000078   \n",
+       "total_runs     3.169229e+02   0.013116   0.533108   0.010883       0.005469   \n",
+       "\n",
+       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
+       "match_id      -27.581622 -3.371207     2.617832    -3.215663           0.0   \n",
+       "inning          0.002266  0.000125     0.000839    -0.000180           0.0   \n",
+       "over           -0.007723  0.004392    -0.002168     0.019269           0.0   \n",
+       "ball           -0.008298  0.004982     0.000964     0.004010           0.0   \n",
+       "is_super_over   0.000030 -0.000021     0.000100    -0.000030           0.0   \n",
+       "wide_runs       0.054240 -0.000163    -0.000848    -0.000233           0.0   \n",
+       "bye_runs       -0.000163  0.009391    -0.000100    -0.000028           0.0   \n",
+       "legbye_runs    -0.000848 -0.000100     0.040258    -0.000143           0.0   \n",
+       "noball_runs    -0.000233 -0.000028    -0.000143     0.010417           0.0   \n",
+       "penalty_runs    0.000000  0.000000     0.000000     0.000000           0.0   \n",
+       "batsman_runs   -0.037991 -0.001852    -0.016522    -0.000524           0.0   \n",
+       "extra_runs      0.052995  0.009100     0.039167     0.010013           0.0   \n",
+       "total_runs      0.015003  0.007248     0.022645     0.009488           0.0   \n",
+       "\n",
+       "               batsman_runs  extra_runs  total_runs  \n",
+       "match_id         348.473511  -31.550660  316.922851  \n",
+       "inning             0.010065    0.003051    0.013116  \n",
+       "over               0.519339    0.013770    0.533108  \n",
+       "ball               0.009225    0.001658    0.010883  \n",
+       "is_super_over      0.005391    0.000078    0.005469  \n",
+       "wide_runs         -0.037991    0.052995    0.015003  \n",
+       "bye_runs          -0.001852    0.009100    0.007248  \n",
+       "legbye_runs       -0.016522    0.039167    0.022645  \n",
+       "noball_runs       -0.000524    0.010013    0.009488  \n",
+       "penalty_runs       0.000000    0.000000    0.000000  \n",
+       "batsman_runs       2.383277   -0.056889    2.326387  \n",
+       "extra_runs        -0.056889    0.111274    0.054385  \n",
+       "total_runs         2.326387    0.054385    2.380772  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata.cov()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>match_id</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.025485</td>\n",
+       "      <td>0.001056</td>\n",
+       "      <td>-0.008484</td>\n",
+       "      <td>-0.038306</td>\n",
+       "      <td>-0.030091</td>\n",
+       "      <td>-0.008839</td>\n",
+       "      <td>0.003315</td>\n",
+       "      <td>-0.008005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.057353</td>\n",
+       "      <td>-0.024032</td>\n",
+       "      <td>0.052187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inning</th>\n",
+       "      <td>-0.025485</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.077984</td>\n",
+       "      <td>0.000803</td>\n",
+       "      <td>0.260444</td>\n",
+       "      <td>0.018796</td>\n",
+       "      <td>0.002496</td>\n",
+       "      <td>0.008078</td>\n",
+       "      <td>-0.003399</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.012593</td>\n",
+       "      <td>0.017667</td>\n",
+       "      <td>0.016420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>over</th>\n",
+       "      <td>0.001056</td>\n",
+       "      <td>-0.077984</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>-0.111442</td>\n",
+       "      <td>-0.005845</td>\n",
+       "      <td>0.007988</td>\n",
+       "      <td>-0.001905</td>\n",
+       "      <td>0.033277</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.059292</td>\n",
+       "      <td>0.007275</td>\n",
+       "      <td>0.060896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ball</th>\n",
+       "      <td>-0.008484</td>\n",
+       "      <td>0.000803</td>\n",
+       "      <td>0.000294</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.005702</td>\n",
+       "      <td>-0.019592</td>\n",
+       "      <td>0.028269</td>\n",
+       "      <td>0.002642</td>\n",
+       "      <td>0.021606</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.003286</td>\n",
+       "      <td>0.002733</td>\n",
+       "      <td>0.003878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_super_over</th>\n",
+       "      <td>-0.038306</td>\n",
+       "      <td>0.260444</td>\n",
+       "      <td>-0.111442</td>\n",
+       "      <td>-0.005702</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.001870</td>\n",
+       "      <td>-0.003150</td>\n",
+       "      <td>0.007168</td>\n",
+       "      <td>-0.004272</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.050471</td>\n",
+       "      <td>0.003395</td>\n",
+       "      <td>0.051231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wide_runs</th>\n",
+       "      <td>-0.030091</td>\n",
+       "      <td>0.018796</td>\n",
+       "      <td>-0.005845</td>\n",
+       "      <td>-0.019592</td>\n",
+       "      <td>0.001870</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.007241</td>\n",
+       "      <td>-0.018153</td>\n",
+       "      <td>-0.009822</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.105667</td>\n",
+       "      <td>0.682144</td>\n",
+       "      <td>0.041751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bye_runs</th>\n",
+       "      <td>-0.008839</td>\n",
+       "      <td>0.002496</td>\n",
+       "      <td>0.007988</td>\n",
+       "      <td>0.028269</td>\n",
+       "      <td>-0.003150</td>\n",
+       "      <td>-0.007241</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.005147</td>\n",
+       "      <td>-0.002785</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.012377</td>\n",
+       "      <td>0.281505</td>\n",
+       "      <td>0.048475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <td>0.003315</td>\n",
+       "      <td>0.008078</td>\n",
+       "      <td>-0.001905</td>\n",
+       "      <td>0.002642</td>\n",
+       "      <td>0.007168</td>\n",
+       "      <td>-0.018153</td>\n",
+       "      <td>-0.005147</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.006981</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.053340</td>\n",
+       "      <td>0.585188</td>\n",
+       "      <td>0.073145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noball_runs</th>\n",
+       "      <td>-0.008005</td>\n",
+       "      <td>-0.003399</td>\n",
+       "      <td>0.033277</td>\n",
+       "      <td>0.021606</td>\n",
+       "      <td>-0.004272</td>\n",
+       "      <td>-0.009822</td>\n",
+       "      <td>-0.002785</td>\n",
+       "      <td>-0.006981</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.003327</td>\n",
+       "      <td>0.294093</td>\n",
+       "      <td>0.060251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <td>0.057353</td>\n",
+       "      <td>0.012593</td>\n",
+       "      <td>0.059292</td>\n",
+       "      <td>0.003286</td>\n",
+       "      <td>0.050471</td>\n",
+       "      <td>-0.105667</td>\n",
+       "      <td>-0.012377</td>\n",
+       "      <td>-0.053340</td>\n",
+       "      <td>-0.003327</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.110471</td>\n",
+       "      <td>0.976643</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>extra_runs</th>\n",
+       "      <td>-0.024032</td>\n",
+       "      <td>0.017667</td>\n",
+       "      <td>0.007275</td>\n",
+       "      <td>0.002733</td>\n",
+       "      <td>0.003395</td>\n",
+       "      <td>0.682144</td>\n",
+       "      <td>0.281505</td>\n",
+       "      <td>0.585188</td>\n",
+       "      <td>0.294093</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.110471</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.105663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_runs</th>\n",
+       "      <td>0.052187</td>\n",
+       "      <td>0.016420</td>\n",
+       "      <td>0.060896</td>\n",
+       "      <td>0.003878</td>\n",
+       "      <td>0.051231</td>\n",
+       "      <td>0.041751</td>\n",
+       "      <td>0.048475</td>\n",
+       "      <td>0.073145</td>\n",
+       "      <td>0.060251</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.976643</td>\n",
+       "      <td>0.105663</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               match_id    inning      over      ball  is_super_over  \\\n",
+       "match_id       1.000000 -0.025485  0.001056 -0.008484      -0.038306   \n",
+       "inning        -0.025485  1.000000 -0.077984  0.000803       0.260444   \n",
+       "over           0.001056 -0.077984  1.000000  0.000294      -0.111442   \n",
+       "ball          -0.008484  0.000803  0.000294  1.000000      -0.005702   \n",
+       "is_super_over -0.038306  0.260444 -0.111442 -0.005702       1.000000   \n",
+       "wide_runs     -0.030091  0.018796 -0.005845 -0.019592       0.001870   \n",
+       "bye_runs      -0.008839  0.002496  0.007988  0.028269      -0.003150   \n",
+       "legbye_runs    0.003315  0.008078 -0.001905  0.002642       0.007168   \n",
+       "noball_runs   -0.008005 -0.003399  0.033277  0.021606      -0.004272   \n",
+       "penalty_runs        NaN       NaN       NaN       NaN            NaN   \n",
+       "batsman_runs   0.057353  0.012593  0.059292  0.003286       0.050471   \n",
+       "extra_runs    -0.024032  0.017667  0.007275  0.002733       0.003395   \n",
+       "total_runs     0.052187  0.016420  0.060896  0.003878       0.051231   \n",
+       "\n",
+       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
+       "match_id       -0.030091 -0.008839     0.003315    -0.008005           NaN   \n",
+       "inning          0.018796  0.002496     0.008078    -0.003399           NaN   \n",
+       "over           -0.005845  0.007988    -0.001905     0.033277           NaN   \n",
+       "ball           -0.019592  0.028269     0.002642     0.021606           NaN   \n",
+       "is_super_over   0.001870 -0.003150     0.007168    -0.004272           NaN   \n",
+       "wide_runs       1.000000 -0.007241    -0.018153    -0.009822           NaN   \n",
+       "bye_runs       -0.007241  1.000000    -0.005147    -0.002785           NaN   \n",
+       "legbye_runs    -0.018153 -0.005147     1.000000    -0.006981           NaN   \n",
+       "noball_runs    -0.009822 -0.002785    -0.006981     1.000000           NaN   \n",
+       "penalty_runs         NaN       NaN          NaN          NaN           NaN   \n",
+       "batsman_runs   -0.105667 -0.012377    -0.053340    -0.003327           NaN   \n",
+       "extra_runs      0.682144  0.281505     0.585188     0.294093           NaN   \n",
+       "total_runs      0.041751  0.048475     0.073145     0.060251           NaN   \n",
+       "\n",
+       "               batsman_runs  extra_runs  total_runs  \n",
+       "match_id           0.057353   -0.024032    0.052187  \n",
+       "inning             0.012593    0.017667    0.016420  \n",
+       "over               0.059292    0.007275    0.060896  \n",
+       "ball               0.003286    0.002733    0.003878  \n",
+       "is_super_over      0.050471    0.003395    0.051231  \n",
+       "wide_runs         -0.105667    0.682144    0.041751  \n",
+       "bye_runs          -0.012377    0.281505    0.048475  \n",
+       "legbye_runs       -0.053340    0.585188    0.073145  \n",
+       "noball_runs       -0.003327    0.294093    0.060251  \n",
+       "penalty_runs            NaN         NaN         NaN  \n",
+       "batsman_runs       1.000000   -0.110471    0.976643  \n",
+       "extra_runs        -0.110471    1.000000    0.105663  \n",
+       "total_runs         0.976643    0.105663    1.000000  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlation between Features.From the correlation plot we can clearly see that extra runs,wide runs ,bye runs are correlated and apart from that batsman runs column and total_runs columns are highly correlated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e894cf0508>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Rajastan_Kolkata.corr(),yticklabels=True,annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.0</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "      <td>4783.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2477.243362</td>\n",
+       "      <td>1.495296</td>\n",
+       "      <td>10.095129</td>\n",
+       "      <td>3.627430</td>\n",
+       "      <td>0.004809</td>\n",
+       "      <td>0.037215</td>\n",
+       "      <td>0.004391</td>\n",
+       "      <td>0.022789</td>\n",
+       "      <td>0.006272</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.183567</td>\n",
+       "      <td>0.070667</td>\n",
+       "      <td>1.254234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3935.758128</td>\n",
+       "      <td>0.517701</td>\n",
+       "      <td>5.673681</td>\n",
+       "      <td>1.818631</td>\n",
+       "      <td>0.069185</td>\n",
+       "      <td>0.232895</td>\n",
+       "      <td>0.096907</td>\n",
+       "      <td>0.200645</td>\n",
+       "      <td>0.102061</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.543786</td>\n",
+       "      <td>0.333578</td>\n",
+       "      <td>1.542975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>78.000000</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>0.0</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>187.000000</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>0.0</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>389.000000</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>0.0</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>570.000000</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>0.0</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>11334.000000</td>\n",
+       "      <td>4.000000</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>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           match_id       inning         over         ball  is_super_over  \\\n",
+       "count   4783.000000  4783.000000  4783.000000  4783.000000    4783.000000   \n",
+       "mean    2477.243362     1.495296    10.095129     3.627430       0.004809   \n",
+       "std     3935.758128     0.517701     5.673681     1.818631       0.069185   \n",
+       "min       78.000000     1.000000     1.000000     1.000000       0.000000   \n",
+       "25%      187.000000     1.000000     5.000000     2.000000       0.000000   \n",
+       "50%      389.000000     1.000000    10.000000     4.000000       0.000000   \n",
+       "75%      570.000000     2.000000    15.000000     5.000000       0.000000   \n",
+       "max    11334.000000     4.000000    20.000000     9.000000       1.000000   \n",
+       "\n",
+       "         wide_runs     bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
+       "count  4783.000000  4783.000000  4783.000000  4783.000000        4783.0   \n",
+       "mean      0.037215     0.004391     0.022789     0.006272           0.0   \n",
+       "std       0.232895     0.096907     0.200645     0.102061           0.0   \n",
+       "min       0.000000     0.000000     0.000000     0.000000           0.0   \n",
+       "25%       0.000000     0.000000     0.000000     0.000000           0.0   \n",
+       "50%       0.000000     0.000000     0.000000     0.000000           0.0   \n",
+       "75%       0.000000     0.000000     0.000000     0.000000           0.0   \n",
+       "max       5.000000     4.000000     4.000000     5.000000           0.0   \n",
+       "\n",
+       "       batsman_runs   extra_runs   total_runs  \n",
+       "count   4783.000000  4783.000000  4783.000000  \n",
+       "mean       1.183567     0.070667     1.254234  \n",
+       "std        1.543786     0.333578     1.542975  \n",
+       "min        0.000000     0.000000     0.000000  \n",
+       "25%        0.000000     0.000000     0.000000  \n",
+       "50%        1.000000     0.000000     1.000000  \n",
+       "75%        1.000000     0.000000     1.000000  \n",
+       "max        6.000000     5.000000     8.000000  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>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>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>18250</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18251</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18252</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>18253</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18254</th>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SC Ganguly</td>\n",
+       "      <td>Salman Butt</td>\n",
+       "      <td>SR Watson</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175183</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175184</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>R Parag</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>R Parag</td>\n",
+       "      <td>hit wicket</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175185</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>AD Russell</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>175186</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>175187</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>J Archer</td>\n",
+       "      <td>JD Unadkat</td>\n",
+       "      <td>P Krishna</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4783 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning           batting_team           bowling_team  over  \\\n",
+       "18250         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18251         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18252         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18253         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "18254         78       2  Kolkata Knight Riders       Rajasthan Royals     1   \n",
+       "...          ...     ...                    ...                    ...   ...   \n",
+       "175183     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175184     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175185     11334       2       Rajasthan Royals  Kolkata Knight Riders    19   \n",
+       "175186     11334       2       Rajasthan Royals  Kolkata Knight Riders    20   \n",
+       "175187     11334       2       Rajasthan Royals  Kolkata Knight Riders    20   \n",
+       "\n",
+       "        ball      batsman  non_striker      bowler  is_super_over  ...  \\\n",
+       "18250      1  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18251      2  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18252      3  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18253      4  Salman Butt   SC Ganguly   SR Watson              0  ...   \n",
+       "18254      5   SC Ganguly  Salman Butt   SR Watson              0  ...   \n",
+       "...      ...          ...          ...         ...            ...  ...   \n",
+       "175183     4      R Parag     J Archer  AD Russell              0  ...   \n",
+       "175184     5      R Parag     J Archer  AD Russell              0  ...   \n",
+       "175185     6   JD Unadkat     J Archer  AD Russell              0  ...   \n",
+       "175186     1     J Archer   JD Unadkat   P Krishna              0  ...   \n",
+       "175187     2     J Archer   JD Unadkat   P Krishna              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "18250          0            0            0             0             2   \n",
+       "18251          0            0            0             0             0   \n",
+       "18252          0            0            0             0             0   \n",
+       "18253          0            0            0             0             1   \n",
+       "18254          0            0            0             0             0   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "175183         0            0            0             0             6   \n",
+       "175184         0            0            0             0             0   \n",
+       "175185         0            0            0             0             0   \n",
+       "175186         0            0            0             0             4   \n",
+       "175187         0            0            0             0             6   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
+       "18250            0           2               NaN            NaN     NaN  \n",
+       "18251            0           0               NaN            NaN     NaN  \n",
+       "18252            0           0               NaN            NaN     NaN  \n",
+       "18253            0           1               NaN            NaN     NaN  \n",
+       "18254            1           1               NaN            NaN     NaN  \n",
+       "...            ...         ...               ...            ...     ...  \n",
+       "175183           0           6               NaN            NaN     NaN  \n",
+       "175184           0           0           R Parag     hit wicket     NaN  \n",
+       "175185           0           0               NaN            NaN     NaN  \n",
+       "175186           0           4               NaN            NaN     NaN  \n",
+       "175187           0           6               NaN            NaN     NaN  \n",
+       "\n",
+       "[4783 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      ":FEATURES DONE:                    |                     | [  0%]   00:18  -> (00:00 left)\n",
+      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:02  -> (00:00 left)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating Associations graph... DONE!\n"
+     ]
+    }
+   ],
+   "source": [
+    "RR_KKR=sweetviz.analyze([Rajastan_Kolkata,'Rajastan_Kolkata'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RR_KKR.show_html()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many Wickets ll fall in total during the match?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "caught               128\n",
+       "bowled                52\n",
+       "run out               31\n",
+       "lbw                   15\n",
+       "stumped               13\n",
+       "caught and bowled      4\n",
+       "hit wicket             1\n",
+       "Name: dismissal_kind, dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rajastan_Kolkata['dismissal_kind'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Rajastan_Kolkata['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Total_Wickets=244//20\n",
+    "Total_Wickets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average number of wickets fall in a single match 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average number of wickets fall in a single match\",Total_Wickets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e8a232e648>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set()\n",
+    "sns.set_style('whitegrid')\n",
+    "sns.countplot(x='dismissal_kind',palette='Set3',data=Rajastan_Kolkata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1e8a085dec8>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='dismissal_kind',hue='match_id',palette='Set3',data=Rajastan_Kolkata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jofra Archer Economy and Boundary hits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Jofra_Archer_Economy_Boundary_hits=Rajastan_Kolkata1.loc[(Rajastan_Kolkata1['bowler']=='J Archer')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>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>161943</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>J Archer</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>161944</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>J Archer</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>161945</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>J Archer</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161946</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>J Archer</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161947</th>\n",
+       "      <td>7942</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>J Archer</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>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175056</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>J Archer</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175057</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>J Archer</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175058</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>J Archer</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175059</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>J Archer</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175060</th>\n",
+       "      <td>11334</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>KD Karthik</td>\n",
+       "      <td>R Singh</td>\n",
+       "      <td>J Archer</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>95 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning           batting_team      bowling_team  over  ball  \\\n",
+       "161943      7942       2  Kolkata Knight Riders  Rajasthan Royals     3     1   \n",
+       "161944      7942       2  Kolkata Knight Riders  Rajasthan Royals     3     2   \n",
+       "161945      7942       2  Kolkata Knight Riders  Rajasthan Royals     3     3   \n",
+       "161946      7942       2  Kolkata Knight Riders  Rajasthan Royals     3     4   \n",
+       "161947      7942       2  Kolkata Knight Riders  Rajasthan Royals     3     5   \n",
+       "...          ...     ...                    ...               ...   ...   ...   \n",
+       "175056     11334       1  Kolkata Knight Riders  Rajasthan Royals    19     2   \n",
+       "175057     11334       1  Kolkata Knight Riders  Rajasthan Royals    19     3   \n",
+       "175058     11334       1  Kolkata Knight Riders  Rajasthan Royals    19     4   \n",
+       "175059     11334       1  Kolkata Knight Riders  Rajasthan Royals    19     5   \n",
+       "175060     11334       1  Kolkata Knight Riders  Rajasthan Royals    19     6   \n",
+       "\n",
+       "           batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
+       "161943  RV Uthappa     CA Lynn  J Archer              0  ...         0   \n",
+       "161944  RV Uthappa     CA Lynn  J Archer              0  ...         0   \n",
+       "161945  RV Uthappa     CA Lynn  J Archer              0  ...         0   \n",
+       "161946     CA Lynn  RV Uthappa  J Archer              0  ...         0   \n",
+       "161947     CA Lynn  RV Uthappa  J Archer              0  ...         0   \n",
+       "...            ...         ...       ...            ...  ...       ...   \n",
+       "175056     R Singh  KD Karthik  J Archer              0  ...         0   \n",
+       "175057  KD Karthik     R Singh  J Archer              0  ...         0   \n",
+       "175058     R Singh  KD Karthik  J Archer              0  ...         0   \n",
+       "175059  KD Karthik     R Singh  J Archer              0  ...         0   \n",
+       "175060  KD Karthik     R Singh  J Archer              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "161943            0            0             0             0           0   \n",
+       "161944            0            0             0             0           0   \n",
+       "161945            0            0             0             1           0   \n",
+       "161946            0            0             0             6           0   \n",
+       "161947            0            0             0             4           0   \n",
+       "...             ...          ...           ...           ...         ...   \n",
+       "175056            0            0             0             1           0   \n",
+       "175057            0            0             0             1           0   \n",
+       "175058            0            0             0             1           0   \n",
+       "175059            0            0             0             6           0   \n",
+       "175060            0            0             0             6           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind fielder  \n",
+       "161943           0               NaN            NaN     NaN  \n",
+       "161944           0               NaN            NaN     NaN  \n",
+       "161945           1               NaN            NaN     NaN  \n",
+       "161946           6               NaN            NaN     NaN  \n",
+       "161947           4               NaN            NaN     NaN  \n",
+       "...            ...               ...            ...     ...  \n",
+       "175056           1               NaN            NaN     NaN  \n",
+       "175057           1               NaN            NaN     NaN  \n",
+       "175058           1               NaN            NaN     NaN  \n",
+       "175059           6               NaN            NaN     NaN  \n",
+       "175060           6               NaN            NaN     NaN  \n",
+       "\n",
+       "[95 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Jofra_Archer_Economy_Boundary_hits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Jofra_Archer_Economy_Boundary_hits['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    45\n",
+       "1    25\n",
+       "4     9\n",
+       "6     8\n",
+       "2     8\n",
+       "Name: total_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Jofra_Archer_Economy_Boundary_hits['total_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "7942      3       6\n",
+       "          5       6\n",
+       "          14      6\n",
+       "          18      6\n",
+       "7951      2       9\n",
+       "          4       6\n",
+       "          15      7\n",
+       "          19      6\n",
+       "11312     3       6\n",
+       "          5       6\n",
+       "          12      6\n",
+       "11334     6       6\n",
+       "          12      6\n",
+       "          15      7\n",
+       "          19      6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Jofra_Archer_Economy_Boundary_hits.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN)Boundaries Hit on Jofra Archer 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN)Boundaries Hit on Jofra Archer\",17//4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Jofra Archer in a single match 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Jofra Archer in a single match\",125//16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many bowlers ll have economy less than 8?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# R Tewatia Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R_Tewatia_Economy=Rajastan_Kolkata1.loc[(Rajastan_Kolkata1['bowler']=='R Tewatia')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(R_Tewatia_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "482       8       6\n",
+       "          11      6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R_Tewatia_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    8\n",
+       "0    2\n",
+       "6    1\n",
+       "4    1\n",
+       "Name: total_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R_Tewatia_Economy['total_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF R Tewatia in a single match 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF R Tewatia in a single match\",18//2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ben _stokes_Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ben_stokes_Economy=Rajastan_Kolkata1.loc[(Rajastan_Kolkata1['bowler']=='BA Stokes')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Ben_stokes_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "7908      8       6\n",
+       "          10      7\n",
+       "          18      6\n",
+       "7942      2       6\n",
+       "          4       6\n",
+       "          6       7\n",
+       "          16      6\n",
+       "11312     14      6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_stokes_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10    7\n",
+       "6     7\n",
+       "18    6\n",
+       "16    6\n",
+       "14    6\n",
+       "8     6\n",
+       "4     6\n",
+       "2     6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_stokes_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    20\n",
+       "0    19\n",
+       "2     6\n",
+       "4     4\n",
+       "8     1\n",
+       "Name: total_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_stokes_Economy['total_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_stokes_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Ben stokes in a single match 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Ben stokes in a single match\",56//7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# varun_Chakravarthy_Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "varun_Chakravarthy_Economy=Deliveries.loc[(Deliveries['bowler']=='V Chakravarthy')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(varun_Chakravarthy_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "11142     2       6\n",
+       "          7       6\n",
+       "          15      6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "varun_Chakravarthy_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15    6\n",
+       "7     6\n",
+       "2     6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "varun_Chakravarthy_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "varun_Chakravarthy_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF varun Chakravarthy in a single match 11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF varun Chakravarthy in a single match\",35//3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Patt Cummins Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Patt_Cummins_Economy=Deliveries.loc[(Deliveries['bowler']=='PJ Cummins')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Patt_Cummins_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "5         3       7\n",
+       "          6       6\n",
+       "          12      6\n",
+       "          19      6\n",
+       "9         2       7\n",
+       "                 ..\n",
+       "547       3       6\n",
+       "          14      6\n",
+       "          18      8\n",
+       "550       1       8\n",
+       "          3       6\n",
+       "Length: 61, dtype: int64"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Patt_Cummins_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18    45\n",
+       "6     43\n",
+       "3     31\n",
+       "13    25\n",
+       "16    24\n",
+       "8     24\n",
+       "1     22\n",
+       "20    21\n",
+       "19    20\n",
+       "2     19\n",
+       "12    18\n",
+       "11    18\n",
+       "17    14\n",
+       "10    13\n",
+       "5     12\n",
+       "9      6\n",
+       "14     6\n",
+       "7      6\n",
+       "15     6\n",
+       "4      6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Patt_Cummins_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "525"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Patt_Cummins_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Patt Cummins in a single match 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Patt Cummins in a single match\",525//63)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sunil_Naraine_Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sunil_Naraine_Economy=Deliveries.loc[(Deliveries['bowler']=='SP Narine')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "109"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Sunil_Naraine_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "3         3       6\n",
+       "          5       6\n",
+       "          13      6\n",
+       "          15      6\n",
+       "7         5       6\n",
+       "                 ..\n",
+       "11343     17      6\n",
+       "11347     6       6\n",
+       "          8       6\n",
+       "          11      7\n",
+       "          14      6\n",
+       "Length: 429, dtype: int64"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Sunil_Naraine_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5     301\n",
+       "17    217\n",
+       "6     213\n",
+       "18    197\n",
+       "15    195\n",
+       "16    179\n",
+       "14    175\n",
+       "19    147\n",
+       "11    145\n",
+       "12    132\n",
+       "4     116\n",
+       "13    108\n",
+       "10     97\n",
+       "7      90\n",
+       "9      73\n",
+       "20     68\n",
+       "8      67\n",
+       "2      43\n",
+       "3      25\n",
+       "1      12\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Sunil_Naraine_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2939"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Sunil_Naraine_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Sunil Naraine in a single match 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Sunil Naraine in a single match\",2939//433)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Jofra Archer in a single match 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Jofra Archer in a single match\",125//16)#already found it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kuldeep Yadav Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Kuldeep_Yadav_Economy=Deliveries.loc[(Deliveries['bowler']=='Kuldeep Yadav')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Kuldeep_Yadav_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "3         7       6\n",
+       "          9       6\n",
+       "          11      6\n",
+       "          16      6\n",
+       "7         6       7\n",
+       "                 ..\n",
+       "11320     13      6\n",
+       "11326     8       6\n",
+       "          10      6\n",
+       "          14      6\n",
+       "          16      7\n",
+       "Length: 138, dtype: int64"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Kuldeep_Yadav_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14    97\n",
+       "11    92\n",
+       "10    91\n",
+       "8     84\n",
+       "9     79\n",
+       "7     78\n",
+       "12    73\n",
+       "16    56\n",
+       "18    38\n",
+       "13    36\n",
+       "6     27\n",
+       "17    24\n",
+       "15    20\n",
+       "5     13\n",
+       "2     12\n",
+       "19     6\n",
+       "3      6\n",
+       "20     6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Kuldeep_Yadav_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1173"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Kuldeep_Yadav_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Kuldeep Yadav in a single match 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Kuldeep Yadav in a single match\",1173//139)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shivam mavi Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shivam_mavi_Economy=Deliveries.loc[(Deliveries['bowler']=='S Mavi' )]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(shivam_mavi_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "7903      15      6\n",
+       "7906      3       7\n",
+       "          5       6\n",
+       "7908      5       6\n",
+       "          9       6\n",
+       "          12      6\n",
+       "          20      9\n",
+       "7911      1       6\n",
+       "          3       7\n",
+       "          11      7\n",
+       "7919      4       6\n",
+       "          7       6\n",
+       "          17      7\n",
+       "          20      7\n",
+       "7922      6       6\n",
+       "          16      6\n",
+       "          19      7\n",
+       "7926      3       6\n",
+       "          9       6\n",
+       "          16      7\n",
+       "7942      1       6\n",
+       "          3       6\n",
+       "          13      6\n",
+       "          15      6\n",
+       "7952      1       7\n",
+       "          3       6\n",
+       "          5       6\n",
+       "          19      6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shivam_mavi_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3     32\n",
+       "1     19\n",
+       "5     18\n",
+       "20    16\n",
+       "19    13\n",
+       "16    13\n",
+       "15    12\n",
+       "9     12\n",
+       "17     7\n",
+       "11     7\n",
+       "13     6\n",
+       "12     6\n",
+       "7      6\n",
+       "6      6\n",
+       "4      6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shivam_mavi_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "288"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shivam_mavi_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF shivam mavi in a single match 9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF shivam mavi in a single match\",288//29)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Shreyas Gopal Economy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Shreyas_Gopal_Economy=Deliveries.loc[(Deliveries['bowler']=='S Gopal' )]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Shreyas_Gopal_Economy['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id  over\n",
+       "501       9       7\n",
+       "          11      6\n",
+       "          13      6\n",
+       "          15      6\n",
+       "505       7       6\n",
+       "                 ..\n",
+       "11340     2       6\n",
+       "11344     5       6\n",
+       "          8       6\n",
+       "          12      6\n",
+       "          16      6\n",
+       "Length: 99, dtype: int64"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Shreyas_Gopal_Economy.groupby(['match_id','over']).size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9     85\n",
+       "11    78\n",
+       "8     73\n",
+       "7     72\n",
+       "13    60\n",
+       "12    37\n",
+       "10    36\n",
+       "16    36\n",
+       "15    30\n",
+       "14    30\n",
+       "17    13\n",
+       "4     12\n",
+       "6     12\n",
+       "3      6\n",
+       "2      6\n",
+       "5      6\n",
+       "20     1\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Shreyas_Gopal_Economy['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "753"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Shreyas_Gopal_Economy['total_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Shreyas Gopal in a single match 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Shreyas Gopal in a single match\",753//98)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average(MEAN) Economy oF Shreyas Gopal in a single match 7\n",
+      "Average(MEAN) Economy oF shivam mavi in a single match 9\n",
+      "Average(MEAN) Economy oF Kuldeep Yadav in a single match 8\n",
+      "Average(MEAN) Economy oF Jofra Archer in a single match 7\n",
+      "Average(MEAN) Economy oF Patt Cummins in a single match 8\n",
+      "Average(MEAN) Economy oF Sunil Naraine in a single match 6\n",
+      "Average(MEAN) Economy oF varun Chakravarthy in a single match 11\n",
+      "Average(MEAN) Economy oF R Tewatia in a single match 9\n",
+      "Average(MEAN) Economy oF Ben stokes in a single match 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average(MEAN) Economy oF Shreyas Gopal in a single match\",753//98)\n",
+    "print(\"Average(MEAN) Economy oF shivam mavi in a single match\",288//29)\n",
+    "print(\"Average(MEAN) Economy oF Kuldeep Yadav in a single match\",1173//139)\n",
+    "print(\"Average(MEAN) Economy oF Jofra Archer in a single match\",125//16)\n",
+    "print(\"Average(MEAN) Economy oF Patt Cummins in a single match\",525//63)\n",
+    "print(\"Average(MEAN) Economy oF Sunil Naraine in a single match\",2939//433)\n",
+    "print(\"Average(MEAN) Economy oF varun Chakravarthy in a single match\",35//3)\n",
+    "print(\"Average(MEAN) Economy oF R Tewatia in a single match\",18//2)\n",
+    "print(\"Average(MEAN) Economy oF Ben stokes in a single match\",56//7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Values=[7,9,8,7,8,6,11,9,8]\n",
+    "explode=[0,0,0,0,0,0,0.2,0,0]\n",
+    "labels=['Shreyas Gopal Econ','shivam mavi Econ','Kuldeep Yadav Econ','Jofra Archer Econ','Patt Cummins Econ','Sunil Naraine Econ','varun Chakravarthy','R Tewatia','Ben stokes']\n",
+    "plt.pie(Values,labels=labels,explode=explode,autopct='%.2f')\n",
+    "plt.title(\"Economy Of Bowlers\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Which batsman ll have the highest runs scored throught boundaries-runs scored off 1's,2's and 3's?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ben_Stokes_Ratio=Deliveries.loc[(Deliveries['batsman']=='BA Stokes')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Ben_Stokes_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    231\n",
+       "0    148\n",
+       "2     44\n",
+       "4     43\n",
+       "6     25\n",
+       "3      2\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_Stokes_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_Stokes_Total_Runs=((1*231)+(2*44)+(4*43)+(6*25)+(3*2))//32\n",
+    "Ben_Stokes_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ben_Stokes_Boundary_Runs=((4*43)+(6*25))//32\n",
+    "Ben_Stokes_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ben Stokes Runs scored ratio 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ben Stokes Runs scored ratio\",Ben_Stokes_Boundary_Runs/Ben_Stokes_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Samson_Ratio=Deliveries.loc[(Deliveries['batsman']=='SV Samson')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>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>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>1118</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>SW Billings</td>\n",
+       "      <td>B Stanlake</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>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1120</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>SW Billings</td>\n",
+       "      <td>B Stanlake</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1121</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>SW Billings</td>\n",
+       "      <td>YS Chahal</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1123</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>SW Billings</td>\n",
+       "      <td>YS Chahal</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>1124</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>SW Billings</td>\n",
+       "      <td>YS Chahal</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177187</th>\n",
+       "      <td>11344</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>L Livingstone</td>\n",
+       "      <td>TA Boult</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177188</th>\n",
+       "      <td>11344</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>L Livingstone</td>\n",
+       "      <td>I Sharma</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>177189</th>\n",
+       "      <td>11344</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>L Livingstone</td>\n",
+       "      <td>I Sharma</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177194</th>\n",
+       "      <td>11344</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>M Lomror</td>\n",
+       "      <td>AR Patel</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177195</th>\n",
+       "      <td>11344</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>M Lomror</td>\n",
+       "      <td>AR Patel</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>SV Samson</td>\n",
+       "      <td>run out</td>\n",
+       "      <td>P Shaw</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1728 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning      batting_team                 bowling_team  over  \\\n",
+       "1118           5       2  Delhi Daredevils  Royal Challengers Bangalore     6   \n",
+       "1120           5       2  Delhi Daredevils  Royal Challengers Bangalore     6   \n",
+       "1121           5       2  Delhi Daredevils  Royal Challengers Bangalore     7   \n",
+       "1123           5       2  Delhi Daredevils  Royal Challengers Bangalore     7   \n",
+       "1124           5       2  Delhi Daredevils  Royal Challengers Bangalore     7   \n",
+       "...          ...     ...               ...                          ...   ...   \n",
+       "177187     11344       1  Rajasthan Royals               Delhi Capitals     3   \n",
+       "177188     11344       1  Rajasthan Royals               Delhi Capitals     4   \n",
+       "177189     11344       1  Rajasthan Royals               Delhi Capitals     4   \n",
+       "177194     11344       1  Rajasthan Royals               Delhi Capitals     5   \n",
+       "177195     11344       1  Rajasthan Royals               Delhi Capitals     5   \n",
+       "\n",
+       "        ball    batsman    non_striker      bowler  is_super_over  ...  \\\n",
+       "1118       4  SV Samson    SW Billings  B Stanlake              0  ...   \n",
+       "1120       6  SV Samson    SW Billings  B Stanlake              0  ...   \n",
+       "1121       1  SV Samson    SW Billings   YS Chahal              0  ...   \n",
+       "1123       3  SV Samson    SW Billings   YS Chahal              0  ...   \n",
+       "1124       4  SV Samson    SW Billings   YS Chahal              0  ...   \n",
+       "...      ...        ...            ...         ...            ...  ...   \n",
+       "177187     7  SV Samson  L Livingstone    TA Boult              0  ...   \n",
+       "177188     1  SV Samson  L Livingstone    I Sharma              0  ...   \n",
+       "177189     2  SV Samson  L Livingstone    I Sharma              0  ...   \n",
+       "177194     1  SV Samson       M Lomror    AR Patel              0  ...   \n",
+       "177195     2  SV Samson       M Lomror    AR Patel              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "1118           0            0            0             0             3   \n",
+       "1120           0            0            0             0             1   \n",
+       "1121           0            0            0             0             1   \n",
+       "1123           0            0            0             0             0   \n",
+       "1124           0            0            0             0             1   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "177187         0            0            0             0             1   \n",
+       "177188         0            0            0             0             0   \n",
+       "177189         0            0            0             0             1   \n",
+       "177194         0            0            0             0             2   \n",
+       "177195         0            0            0             0             0   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
+       "1118             0           3               NaN            NaN     NaN  \n",
+       "1120             0           1               NaN            NaN     NaN  \n",
+       "1121             0           1               NaN            NaN     NaN  \n",
+       "1123             0           0               NaN            NaN     NaN  \n",
+       "1124             0           1               NaN            NaN     NaN  \n",
+       "...            ...         ...               ...            ...     ...  \n",
+       "177187           0           1               NaN            NaN     NaN  \n",
+       "177188           0           0               NaN            NaN     NaN  \n",
+       "177189           0           1               NaN            NaN     NaN  \n",
+       "177194           0           2               NaN            NaN     NaN  \n",
+       "177195           0           0         SV Samson        run out  P Shaw  \n",
+       "\n",
+       "[1728 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Samson_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "88"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Samson_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    712\n",
+       "0    609\n",
+       "4    171\n",
+       "2    136\n",
+       "6     89\n",
+       "3     11\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Samson_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Samson_Ratio_Total_Runs=((1*712)+(2*136)+(4*171)+(6*89)+(3*11))//88\n",
+    "Samson_Ratio_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Samson_Ratio_Boundary_Runs=((4*171)+(6*89))//88\n",
+    "Samson_Ratio_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samson Runs scored ratio 0.52\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Samson Runs scored ratio\",Samson_Ratio_Boundary_Runs/Samson_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nitish Rana Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nitish_Rana_Ratio=Deliveries.loc[(Deliveries['batsman']=='N Rana')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "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>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>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>288</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>JC Buttler</td>\n",
+       "      <td>Imran Tahir</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>AT Rayudu</td>\n",
+       "      <td>BA Stokes</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>AT Rayudu</td>\n",
+       "      <td>BA Stokes</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>296</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>AT Rayudu</td>\n",
+       "      <td>BA Stokes</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>298</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>AT Rayudu</td>\n",
+       "      <td>A Zampa</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>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177973</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>JJ Bumrah</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>177974</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>JJ Bumrah</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177976</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>JJ Bumrah</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177979</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>SL Malinga</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177980</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>N Rana</td>\n",
+       "      <td>RV Uthappa</td>\n",
+       "      <td>SL Malinga</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>N Rana</td>\n",
+       "      <td>caught</td>\n",
+       "      <td>KA Pollard</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>835 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning           batting_team            bowling_team  over  \\\n",
+       "288            2       1         Mumbai Indians  Rising Pune Supergiant     7   \n",
+       "292            2       1         Mumbai Indians  Rising Pune Supergiant     8   \n",
+       "295            2       1         Mumbai Indians  Rising Pune Supergiant     8   \n",
+       "296            2       1         Mumbai Indians  Rising Pune Supergiant     8   \n",
+       "298            2       1         Mumbai Indians  Rising Pune Supergiant     9   \n",
+       "...          ...     ...                    ...                     ...   ...   \n",
+       "177973     11347       1  Kolkata Knight Riders          Mumbai Indians    17   \n",
+       "177974     11347       1  Kolkata Knight Riders          Mumbai Indians    17   \n",
+       "177976     11347       1  Kolkata Knight Riders          Mumbai Indians    17   \n",
+       "177979     11347       1  Kolkata Knight Riders          Mumbai Indians    18   \n",
+       "177980     11347       1  Kolkata Knight Riders          Mumbai Indians    18   \n",
+       "\n",
+       "        ball batsman non_striker       bowler  is_super_over  ...  bye_runs  \\\n",
+       "288        4  N Rana  JC Buttler  Imran Tahir              0  ...         0   \n",
+       "292        2  N Rana   AT Rayudu    BA Stokes              0  ...         0   \n",
+       "295        5  N Rana   AT Rayudu    BA Stokes              0  ...         0   \n",
+       "296        6  N Rana   AT Rayudu    BA Stokes              0  ...         0   \n",
+       "298        2  N Rana   AT Rayudu      A Zampa              0  ...         0   \n",
+       "...      ...     ...         ...          ...            ...  ...       ...   \n",
+       "177973     1  N Rana  RV Uthappa    JJ Bumrah              0  ...         0   \n",
+       "177974     2  N Rana  RV Uthappa    JJ Bumrah              0  ...         0   \n",
+       "177976     4  N Rana  RV Uthappa    JJ Bumrah              0  ...         0   \n",
+       "177979     1  N Rana  RV Uthappa   SL Malinga              0  ...         0   \n",
+       "177980     2  N Rana  RV Uthappa   SL Malinga              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "288               0            0             0             1           0   \n",
+       "292               0            0             0             1           0   \n",
+       "295               0            0             0             0           0   \n",
+       "296               0            0             0             0           0   \n",
+       "298               0            0             0             0           0   \n",
+       "...             ...          ...           ...           ...         ...   \n",
+       "177973            0            0             0             0           0   \n",
+       "177974            0            0             0             1           0   \n",
+       "177976            0            0             0             1           0   \n",
+       "177979            0            0             0             6           0   \n",
+       "177980            0            0             0             0           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind     fielder  \n",
+       "288              1               NaN            NaN         NaN  \n",
+       "292              1               NaN            NaN         NaN  \n",
+       "295              0               NaN            NaN         NaN  \n",
+       "296              0               NaN            NaN         NaN  \n",
+       "298              0               NaN            NaN         NaN  \n",
+       "...            ...               ...            ...         ...  \n",
+       "177973           0               NaN            NaN         NaN  \n",
+       "177974           1               NaN            NaN         NaN  \n",
+       "177976           1               NaN            NaN         NaN  \n",
+       "177979           6               NaN            NaN         NaN  \n",
+       "177980           0            N Rana         caught  KA Pollard  \n",
+       "\n",
+       "[835 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nitish_Rana_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Nitish_Rana_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    329\n",
+       "1    322\n",
+       "4     85\n",
+       "6     60\n",
+       "2     37\n",
+       "5      1\n",
+       "3      1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nitish_Rana_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nitish_Rana_Ratio_Total_Runs=((1*322)+(2*37)+(4*86)+(6*60)+(3*1))//41\n",
+    "Nitish_Rana_Ratio_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Nitish_Rana_Ratio_Boundary_Runs=((4*87)+(6*60))//41\n",
+    "Nitish_Rana_Ratio_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nitish Rana Runs scored ratio 0.6538461538461539\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Nitish Rana Runs scored ratio\",Nitish_Rana_Ratio_Boundary_Runs/Nitish_Rana_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Eoin Morgan Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Eoin_Morgan_Ratio=Deliveries.loc[(Deliveries['batsman']=='EJG Morgan')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "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>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>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>3465</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>HM Amla</td>\n",
+       "      <td>S Nadeem</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>3466</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>HM Amla</td>\n",
+       "      <td>S Nadeem</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>3467</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>HM Amla</td>\n",
+       "      <td>S Nadeem</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3469</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>HM Amla</td>\n",
+       "      <td>CH Morris</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>3470</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>HM Amla</td>\n",
+       "      <td>CH Morris</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148368</th>\n",
+       "      <td>628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>NV Ojha</td>\n",
+       "      <td>Z Khan</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>148369</th>\n",
+       "      <td>628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>NV Ojha</td>\n",
+       "      <td>Z Khan</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148371</th>\n",
+       "      <td>628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>NV Ojha</td>\n",
+       "      <td>NM Coulter-Nile</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>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148372</th>\n",
+       "      <td>628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>NV Ojha</td>\n",
+       "      <td>NM Coulter-Nile</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148373</th>\n",
+       "      <td>628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>EJG Morgan</td>\n",
+       "      <td>NV Ojha</td>\n",
+       "      <td>NM Coulter-Nile</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>EJG Morgan</td>\n",
+       "      <td>caught</td>\n",
+       "      <td>CR Brathwaite</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>725 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning         batting_team      bowling_team  over  ball  \\\n",
+       "3465          15       2      Kings XI Punjab  Delhi Daredevils     4     3   \n",
+       "3466          15       2      Kings XI Punjab  Delhi Daredevils     4     4   \n",
+       "3467          15       2      Kings XI Punjab  Delhi Daredevils     4     5   \n",
+       "3469          15       2      Kings XI Punjab  Delhi Daredevils     5     1   \n",
+       "3470          15       2      Kings XI Punjab  Delhi Daredevils     5     2   \n",
+       "...          ...     ...                  ...               ...   ...   ...   \n",
+       "148368       628       1  Sunrisers Hyderabad  Delhi Daredevils    17     4   \n",
+       "148369       628       1  Sunrisers Hyderabad  Delhi Daredevils    17     5   \n",
+       "148371       628       1  Sunrisers Hyderabad  Delhi Daredevils    18     1   \n",
+       "148372       628       1  Sunrisers Hyderabad  Delhi Daredevils    18     2   \n",
+       "148373       628       1  Sunrisers Hyderabad  Delhi Daredevils    18     3   \n",
+       "\n",
+       "           batsman non_striker           bowler  is_super_over  ...  bye_runs  \\\n",
+       "3465    EJG Morgan     HM Amla         S Nadeem              0  ...         0   \n",
+       "3466    EJG Morgan     HM Amla         S Nadeem              0  ...         0   \n",
+       "3467    EJG Morgan     HM Amla         S Nadeem              0  ...         0   \n",
+       "3469    EJG Morgan     HM Amla        CH Morris              0  ...         0   \n",
+       "3470    EJG Morgan     HM Amla        CH Morris              0  ...         0   \n",
+       "...            ...         ...              ...            ...  ...       ...   \n",
+       "148368  EJG Morgan     NV Ojha           Z Khan              0  ...         0   \n",
+       "148369  EJG Morgan     NV Ojha           Z Khan              0  ...         0   \n",
+       "148371  EJG Morgan     NV Ojha  NM Coulter-Nile              0  ...         0   \n",
+       "148372  EJG Morgan     NV Ojha  NM Coulter-Nile              0  ...         0   \n",
+       "148373  EJG Morgan     NV Ojha  NM Coulter-Nile              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "3465              0            0             0             0           0   \n",
+       "3466              0            0             0             0           0   \n",
+       "3467              0            0             0             1           0   \n",
+       "3469              0            0             0             0           0   \n",
+       "3470              0            0             0             1           0   \n",
+       "...             ...          ...           ...           ...         ...   \n",
+       "148368            0            0             0             0           0   \n",
+       "148369            0            0             0             1           0   \n",
+       "148371            0            0             0             6           0   \n",
+       "148372            0            0             0             2           0   \n",
+       "148373            0            0             0             0           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind        fielder  \n",
+       "3465             0               NaN            NaN            NaN  \n",
+       "3466             0               NaN            NaN            NaN  \n",
+       "3467             1               NaN            NaN            NaN  \n",
+       "3469             0               NaN            NaN            NaN  \n",
+       "3470             1               NaN            NaN            NaN  \n",
+       "...            ...               ...            ...            ...  \n",
+       "148368           0               NaN            NaN            NaN  \n",
+       "148369           1               NaN            NaN            NaN  \n",
+       "148371           6               NaN            NaN            NaN  \n",
+       "148372           2               NaN            NaN            NaN  \n",
+       "148373           0        EJG Morgan         caught  CR Brathwaite  \n",
+       "\n",
+       "[725 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eoin_Morgan_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "45"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Eoin_Morgan_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    312\n",
+       "1    255\n",
+       "4     72\n",
+       "2     49\n",
+       "6     34\n",
+       "3      3\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eoin_Morgan_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eoin_Morgan_Ratio_Total_Runs=((1*255)+(2*49)+(4*72)+(6*34)+(3*3))//45\n",
+    "Eoin_Morgan_Ratio_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Eoin_Morgan_Ratio_Boundary_Runs=((4*72)+(6*34))//45\n",
+    "Eoin_Morgan_Ratio_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eoin Morgan Runs scored ratio 0.5555555555555556\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Eoin Morgan Runs scored ratio\",Eoin_Morgan_Ratio_Boundary_Runs/Eoin_Morgan_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Steve Smith Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Steve_Smith_Ratio=Deliveries.loc[(Deliveries['batsman']=='SPD Smith')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "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>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>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>393</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>AM Rahane</td>\n",
+       "      <td>MJ McClenaghan</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>394</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>AM Rahane</td>\n",
+       "      <td>MJ McClenaghan</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>AM Rahane</td>\n",
+       "      <td>MJ McClenaghan</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>AM Rahane</td>\n",
+       "      <td>MJ McClenaghan</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>399</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>AM Rahane</td>\n",
+       "      <td>JJ Bumrah</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175644</th>\n",
+       "      <td>11336</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>B Kumar</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>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175645</th>\n",
+       "      <td>11336</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>B Kumar</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>175648</th>\n",
+       "      <td>11336</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>K Ahmed</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>175649</th>\n",
+       "      <td>11336</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>K Ahmed</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175651</th>\n",
+       "      <td>11336</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Rajasthan Royals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>SV Samson</td>\n",
+       "      <td>K Ahmed</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>SPD Smith</td>\n",
+       "      <td>caught</td>\n",
+       "      <td>S Kaul</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1616 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning            batting_team         bowling_team  over  \\\n",
+       "393            2       2  Rising Pune Supergiant       Mumbai Indians     4   \n",
+       "394            2       2  Rising Pune Supergiant       Mumbai Indians     4   \n",
+       "396            2       2  Rising Pune Supergiant       Mumbai Indians     4   \n",
+       "397            2       2  Rising Pune Supergiant       Mumbai Indians     4   \n",
+       "399            2       2  Rising Pune Supergiant       Mumbai Indians     5   \n",
+       "...          ...     ...                     ...                  ...   ...   \n",
+       "175644     11336       2        Rajasthan Royals  Sunrisers Hyderabad    16   \n",
+       "175645     11336       2        Rajasthan Royals  Sunrisers Hyderabad    16   \n",
+       "175648     11336       2        Rajasthan Royals  Sunrisers Hyderabad    17   \n",
+       "175649     11336       2        Rajasthan Royals  Sunrisers Hyderabad    17   \n",
+       "175651     11336       2        Rajasthan Royals  Sunrisers Hyderabad    17   \n",
+       "\n",
+       "        ball    batsman non_striker          bowler  is_super_over  ...  \\\n",
+       "393        2  SPD Smith   AM Rahane  MJ McClenaghan              0  ...   \n",
+       "394        3  SPD Smith   AM Rahane  MJ McClenaghan              0  ...   \n",
+       "396        5  SPD Smith   AM Rahane  MJ McClenaghan              0  ...   \n",
+       "397        6  SPD Smith   AM Rahane  MJ McClenaghan              0  ...   \n",
+       "399        2  SPD Smith   AM Rahane       JJ Bumrah              0  ...   \n",
+       "...      ...        ...         ...             ...            ...  ...   \n",
+       "175644     6  SPD Smith   SV Samson         B Kumar              0  ...   \n",
+       "175645     7  SPD Smith   SV Samson         B Kumar              0  ...   \n",
+       "175648     3  SPD Smith   SV Samson         K Ahmed              0  ...   \n",
+       "175649     4  SPD Smith   SV Samson         K Ahmed              0  ...   \n",
+       "175651     6  SPD Smith   SV Samson         K Ahmed              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "393            0            0            0             0             0   \n",
+       "394            0            0            0             0             1   \n",
+       "396            0            0            0             0             2   \n",
+       "397            0            0            0             0             0   \n",
+       "399            0            0            0             0             1   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "175644         0            0            0             0             2   \n",
+       "175645         0            0            0             0             0   \n",
+       "175648         0            0            0             0             4   \n",
+       "175649         0            0            0             0             1   \n",
+       "175651         0            0            0             0             0   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
+       "393              0           0               NaN            NaN     NaN  \n",
+       "394              0           1               NaN            NaN     NaN  \n",
+       "396              0           2               NaN            NaN     NaN  \n",
+       "397              0           0               NaN            NaN     NaN  \n",
+       "399              0           1               NaN            NaN     NaN  \n",
+       "...            ...         ...               ...            ...     ...  \n",
+       "175644           0           2               NaN            NaN     NaN  \n",
+       "175645           0           0               NaN            NaN     NaN  \n",
+       "175648           0           4               NaN            NaN     NaN  \n",
+       "175649           0           1               NaN            NaN     NaN  \n",
+       "175651           0           0         SPD Smith         caught  S Kaul  \n",
+       "\n",
+       "[1616 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Steve_Smith_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Steve_Smith_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    730\n",
+       "0    510\n",
+       "4    181\n",
+       "2    139\n",
+       "6     49\n",
+       "3      7\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Steve_Smith_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Steve_Smith_Ratio_Total_Runs=((1*730)+(2*139)+(4*181)+(6*49)+(3*7))//72\n",
+    "Steve_Smith_Ratio_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Steve_Smith_Ratio_Boundary_Runs=((4*181)+(6*49))//72\n",
+    "Steve_Smith_Ratio_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Steve Smith Runs scored ratio 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Steve Smith Runs scored ratio\",Steve_Smith_Ratio_Boundary_Runs/Steve_Smith_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rahul Tewatia Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Rahul_Tewatia_Ratio=Deliveries.loc[(Deliveries['batsman']=='R Tewatia')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "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>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>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>11221</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>CR Woakes</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>11222</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>CR Woakes</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>11223</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>CR Woakes</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>11224</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>19</td>\n",
+       "      <td>6</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>CR Woakes</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11225</th>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>AS Rajpoot</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168496</th>\n",
+       "      <td>11152</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>SS Iyer</td>\n",
+       "      <td>S Sharma</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>168497</th>\n",
+       "      <td>11152</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>SS Iyer</td>\n",
+       "      <td>S Sharma</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>168498</th>\n",
+       "      <td>11152</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>SS Iyer</td>\n",
+       "      <td>S Sharma</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>R Tewatia</td>\n",
+       "      <td>caught</td>\n",
+       "      <td>Mohammad Nabi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169640</th>\n",
+       "      <td>11311</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>Mohammed Siraj</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>169641</th>\n",
+       "      <td>11311</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>19</td>\n",
+       "      <td>4</td>\n",
+       "      <td>R Tewatia</td>\n",
+       "      <td>AR Patel</td>\n",
+       "      <td>Mohammed Siraj</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>93 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning     batting_team                 bowling_team  over  \\\n",
+       "11221         48       1  Kings XI Punjab        Kolkata Knight Riders    19   \n",
+       "11222         48       1  Kings XI Punjab        Kolkata Knight Riders    19   \n",
+       "11223         48       1  Kings XI Punjab        Kolkata Knight Riders    19   \n",
+       "11224         48       1  Kings XI Punjab        Kolkata Knight Riders    19   \n",
+       "11225         48       1  Kings XI Punjab        Kolkata Knight Riders    20   \n",
+       "...          ...     ...              ...                          ...   ...   \n",
+       "168496     11152       1   Delhi Capitals          Sunrisers Hyderabad    11   \n",
+       "168497     11152       1   Delhi Capitals          Sunrisers Hyderabad    11   \n",
+       "168498     11152       1   Delhi Capitals          Sunrisers Hyderabad    11   \n",
+       "169640     11311       2   Delhi Capitals  Royal Challengers Bangalore    19   \n",
+       "169641     11311       2   Delhi Capitals  Royal Challengers Bangalore    19   \n",
+       "\n",
+       "        ball    batsman non_striker          bowler  is_super_over  ...  \\\n",
+       "11221      3  R Tewatia    AR Patel       CR Woakes              0  ...   \n",
+       "11222      4  R Tewatia    AR Patel       CR Woakes              0  ...   \n",
+       "11223      5  R Tewatia    AR Patel       CR Woakes              0  ...   \n",
+       "11224      6  R Tewatia    AR Patel       CR Woakes              0  ...   \n",
+       "11225      1  R Tewatia    AR Patel      AS Rajpoot              0  ...   \n",
+       "...      ...        ...         ...             ...            ...  ...   \n",
+       "168496     3  R Tewatia     SS Iyer        S Sharma              0  ...   \n",
+       "168497     4  R Tewatia     SS Iyer        S Sharma              0  ...   \n",
+       "168498     5  R Tewatia     SS Iyer        S Sharma              0  ...   \n",
+       "169640     3  R Tewatia    AR Patel  Mohammed Siraj              0  ...   \n",
+       "169641     4  R Tewatia    AR Patel  Mohammed Siraj              0  ...   \n",
+       "\n",
+       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
+       "11221          0            0            0             0             0   \n",
+       "11222          0            0            0             0             4   \n",
+       "11223          0            0            0             0             4   \n",
+       "11224          0            0            0             0             1   \n",
+       "11225          0            0            0             0             1   \n",
+       "...          ...          ...          ...           ...           ...   \n",
+       "168496         0            0            0             0             4   \n",
+       "168497         0            0            0             0             0   \n",
+       "168498         0            0            0             0             0   \n",
+       "169640         0            0            0             0             0   \n",
+       "169641         0            0            0             0             1   \n",
+       "\n",
+       "        extra_runs  total_runs  player_dismissed dismissal_kind        fielder  \n",
+       "11221            0           0               NaN            NaN            NaN  \n",
+       "11222            0           4               NaN            NaN            NaN  \n",
+       "11223            0           4               NaN            NaN            NaN  \n",
+       "11224            0           1               NaN            NaN            NaN  \n",
+       "11225            0           1               NaN            NaN            NaN  \n",
+       "...            ...         ...               ...            ...            ...  \n",
+       "168496           0           4               NaN            NaN            NaN  \n",
+       "168497           0           0               NaN            NaN            NaN  \n",
+       "168498           0           0         R Tewatia         caught  Mohammad Nabi  \n",
+       "169640           0           0               NaN            NaN            NaN  \n",
+       "169641           0           1               NaN            NaN            NaN  \n",
+       "\n",
+       "[93 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rahul_Tewatia_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Rahul_Tewatia_Ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    43\n",
+       "0    29\n",
+       "4    11\n",
+       "2     7\n",
+       "6     3\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rahul_Tewatia_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rahul_Tewatia_Ratio_Total_Runs=((1*43)+(2*7)+(4*11)+(6*3))//12\n",
+    "Rahul_Tewatia_Ratio_Total_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rahul_Tewatia_Ratio_Boundary_Runs=((4*11)+(6*3))//12\n",
+    "Rahul_Tewatia_Ratio_Boundary_Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rahul Tewatia Runs scored ratio 0.5555555555555556\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rahul Tewatia Runs scored ratio\",Rahul_Tewatia_Ratio_Boundary_Runs/Rahul_Tewatia_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Steve Smith Runs scored ratio 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Steve Smith Runs scored ratio\",Steve_Smith_Ratio_Boundary_Runs/Steve_Smith_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eoin Morgan Runs scored ratio 0.5555555555555556\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Eoin Morgan Runs scored ratio\",Eoin_Morgan_Ratio_Boundary_Runs/Eoin_Morgan_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nitish Rana Runs scored ratio 0.6538461538461539\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Nitish Rana Runs scored ratio\",Nitish_Rana_Ratio_Boundary_Runs/Nitish_Rana_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Samson Runs scored ratio 0.52\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Samson Runs scored ratio\",Samson_Ratio_Boundary_Runs/Samson_Ratio_Total_Runs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ben Stokes Runs scored ratio 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ben Stokes Runs scored ratio\",Ben_Stokes_Boundary_Runs/Ben_Stokes_Total_Runs)"
+   ]
+  }
+ ],
+ "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}