DC_SRH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of DC vs SRH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Matches=pd.read_csv(\"matches.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 756 entries, 0 to 755\n",
      "Data columns (total 18 columns):\n",
      " #   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": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matches.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matches.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264838b1148>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_wise_analysing['id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264839c6588>"
      ]
     },
     "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": [
    "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Matchess=Matches.drop(['umpire3','city'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26483a9d548>"
      ]
     },
     "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(Matchess.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Today's match is between Delhi capitals(DC) and Delhi Capitals(DC).so we only want information between the matches that had take place between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Delhi Dardevils Changed  to Delhi capitals in 2019 So need to change the title to Delhi Capitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Matches.replace('Delhi Daredevils','Delhi Capitals',inplace=True)"
   ]
  },
  {
   "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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>11347</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>05/05/19</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nanda Kishore</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>11412</td>\n",
       "      <td>2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>07/05/19</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>M. A. Chidambaram Stadium</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>11413</td>\n",
       "      <td>2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>08/05/19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>11414</td>\n",
       "      <td>2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>10/05/19</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chettithody Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>11415</td>\n",
       "      <td>2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>12/05/19</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Ian Gould</td>\n",
       "      <td>Nigel Llong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season           city        date                        team1  \\\n",
       "0        1    2017      Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1        2    2017           Pune  2017-04-06               Mumbai Indians   \n",
       "2        3    2017         Rajkot  2017-04-07                Gujarat Lions   \n",
       "3        4    2017         Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4        5    2017      Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "..     ...     ...            ...         ...                          ...   \n",
       "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 Capitals  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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>393</th>\n",
       "      <td>394</td>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>Subroto Das</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>428</td>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>2014</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014-05-10</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>RM Deshpande</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>531</td>\n",
       "      <td>2015</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2015-04-18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>PG Pathak</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>7929</td>\n",
       "      <td>2018</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>Virender Kumar Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>7935</td>\n",
       "      <td>2018</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>10/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>Anil Dandekar</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>11152</td>\n",
       "      <td>2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>04/04/19</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>J Bairstow</td>\n",
       "      <td>Feroz Shah Kotla Ground</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>KN Anantapadmanabhan</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>11321</td>\n",
       "      <td>2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>14/04/19</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season           city        date           team1  \\\n",
       "393    394    2013          Delhi  2013-04-12  Delhi Capitals   \n",
       "427    428    2013      Hyderabad  2013-05-04  Delhi Capitals   \n",
       "488    489    2014          Delhi  2014-05-10  Delhi Capitals   \n",
       "530    531    2015  Visakhapatnam  2015-04-18  Delhi Capitals   \n",
       "671   7929    2018      Hyderabad    05/05/18  Delhi Capitals   \n",
       "677   7935    2018          Delhi    10/05/18  Delhi Capitals   \n",
       "711  11152    2019          Delhi    04/04/19  Delhi Capitals   \n",
       "725  11321    2019      Hyderabad    14/04/19  Delhi Capitals   \n",
       "\n",
       "                   team2          toss_winner toss_decision  result  \\\n",
       "393  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "427  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "488  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "530  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "671  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "677  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "711  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "725  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "\n",
       "     dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "393           0  Sunrisers Hyderabad            0               3   \n",
       "427           0  Sunrisers Hyderabad            0               6   \n",
       "488           1  Sunrisers Hyderabad            0               8   \n",
       "530           0       Delhi Capitals            4               0   \n",
       "671           0  Sunrisers Hyderabad            0               7   \n",
       "677           0  Sunrisers Hyderabad            0               9   \n",
       "711           0  Sunrisers Hyderabad            0               5   \n",
       "725           0       Delhi Capitals           39               0   \n",
       "\n",
       "    player_of_match                                              venue  \\\n",
       "393        A Mishra                                   Feroz Shah Kotla   \n",
       "427       DJG Sammy          Rajiv Gandhi International Stadium, Uppal   \n",
       "488        DW Steyn                                   Feroz Shah Kotla   \n",
       "530       JP Duminy  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...   \n",
       "671     Rashid Khan          Rajiv Gandhi International Stadium, Uppal   \n",
       "677        S Dhawan                                   Feroz Shah Kotla   \n",
       "711      J Bairstow                            Feroz Shah Kotla Ground   \n",
       "725          K Paul                 Rajiv Gandhi Intl. Cricket Stadium   \n",
       "\n",
       "            umpire1               umpire2                umpire3  \n",
       "393       Aleem Dar           Subroto Das                    NaN  \n",
       "427       Asad Rauf              S Asnani                    NaN  \n",
       "488    RM Deshpande          BNJ Oxenford                    NaN  \n",
       "530       PG Pathak                S Ravi                    NaN  \n",
       "671  Bruce Oxenford              O Nandan  Virender Kumar Sharma  \n",
       "677   C Shamshuddin         Anil Dandekar                 S Ravi  \n",
       "711   C Shamshuddin  KN Anantapadmanabhan                 S Ravi  \n",
       "725  Anil Chaudhary        Bruce Oxenford         Chris Gaffaney  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC1=Matches.loc[(Matches['team1']=='Delhi Capitals')&(Matches['team2'] =='Sunrisers Hyderabad')]\n",
    "SRH_DC1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRH_DC2=Matches.loc[(Matches['team2']=='Delhi Capitals')&(Matches['team1'] =='Sunrisers Hyderabad')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KS Williamson</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2017-05-02</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>561</td>\n",
       "      <td>2015</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>K Bharatan</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>628</td>\n",
       "      <td>2016</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>11413</td>\n",
       "      <td>2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>08/05/19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season           city        date                team1  \\\n",
       "20      21    2017      Hyderabad  2017-04-19  Sunrisers Hyderabad   \n",
       "38      39    2017          Delhi  2017-05-02  Sunrisers Hyderabad   \n",
       "468    469    2014            NaN  2014-04-25  Sunrisers Hyderabad   \n",
       "560    561    2015         Raipur  2015-05-09  Sunrisers Hyderabad   \n",
       "617    618    2016      Hyderabad  2016-05-12  Sunrisers Hyderabad   \n",
       "627    628    2016         Raipur  2016-05-20  Sunrisers Hyderabad   \n",
       "753  11413    2019  Visakhapatnam    08/05/19  Sunrisers Hyderabad   \n",
       "\n",
       "              team2          toss_winner toss_decision  result  dl_applied  \\\n",
       "20   Delhi Capitals  Sunrisers Hyderabad           bat  normal           0   \n",
       "38   Delhi Capitals       Delhi Capitals         field  normal           0   \n",
       "468  Delhi Capitals  Sunrisers Hyderabad           bat  normal           0   \n",
       "560  Delhi Capitals  Sunrisers Hyderabad           bat  normal           0   \n",
       "617  Delhi Capitals       Delhi Capitals         field  normal           0   \n",
       "627  Delhi Capitals       Delhi Capitals         field  normal           0   \n",
       "753  Delhi Capitals       Delhi Capitals         field  normal           0   \n",
       "\n",
       "                  winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "20   Sunrisers Hyderabad           15               0   KS Williamson   \n",
       "38        Delhi Capitals            0               6  Mohammed Shami   \n",
       "468  Sunrisers Hyderabad            4               0        AJ Finch   \n",
       "560  Sunrisers Hyderabad            6               0    MC Henriques   \n",
       "617       Delhi Capitals            0               7       CH Morris   \n",
       "627       Delhi Capitals            0               6         KK Nair   \n",
       "753       Delhi Capitals            0               2         RR Pant   \n",
       "\n",
       "                                                venue         umpire1  \\\n",
       "20          Rajiv Gandhi International Stadium, Uppal     CB Gaffaney   \n",
       "38                                   Feroz Shah Kotla        YC Barde   \n",
       "468               Dubai International Cricket Stadium       M Erasmus   \n",
       "560  Shaheed Veer Narayan Singh International Stadium     VA Kulkarni   \n",
       "617         Rajiv Gandhi International Stadium, Uppal      K Bharatan   \n",
       "627  Shaheed Veer Narayan Singh International Stadium  A Nand Kishore   \n",
       "753                                  ACA-VDCA Stadium             NaN   \n",
       "\n",
       "          umpire2 umpire3  \n",
       "20       NJ Llong     NaN  \n",
       "38    Nitin Menon     NaN  \n",
       "468        S Ravi     NaN  \n",
       "560        S Ravi     NaN  \n",
       "617     M Erasmus     NaN  \n",
       "627  BNJ Oxenford     NaN  \n",
       "753           NaN     NaN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRH_DC=SRH_DC1.append(SRH_DC2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>393</th>\n",
       "      <td>394</td>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>Subroto Das</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>428</td>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>2014</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014-05-10</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>RM Deshpande</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>531</td>\n",
       "      <td>2015</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2015-04-18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>PG Pathak</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>7929</td>\n",
       "      <td>2018</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>Virender Kumar Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>7935</td>\n",
       "      <td>2018</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>10/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>Anil Dandekar</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>11152</td>\n",
       "      <td>2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>04/04/19</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>J Bairstow</td>\n",
       "      <td>Feroz Shah Kotla Ground</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>KN Anantapadmanabhan</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>11321</td>\n",
       "      <td>2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>14/04/19</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KS Williamson</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2017-05-02</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>561</td>\n",
       "      <td>2015</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>K Bharatan</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>628</td>\n",
       "      <td>2016</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>11413</td>\n",
       "      <td>2019</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>08/05/19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>ACA-VDCA Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season           city        date                team1  \\\n",
       "393    394    2013          Delhi  2013-04-12       Delhi Capitals   \n",
       "427    428    2013      Hyderabad  2013-05-04       Delhi Capitals   \n",
       "488    489    2014          Delhi  2014-05-10       Delhi Capitals   \n",
       "530    531    2015  Visakhapatnam  2015-04-18       Delhi Capitals   \n",
       "671   7929    2018      Hyderabad    05/05/18       Delhi Capitals   \n",
       "677   7935    2018          Delhi    10/05/18       Delhi Capitals   \n",
       "711  11152    2019          Delhi    04/04/19       Delhi Capitals   \n",
       "725  11321    2019      Hyderabad    14/04/19       Delhi Capitals   \n",
       "20      21    2017      Hyderabad  2017-04-19  Sunrisers Hyderabad   \n",
       "38      39    2017          Delhi  2017-05-02  Sunrisers Hyderabad   \n",
       "468    469    2014            NaN  2014-04-25  Sunrisers Hyderabad   \n",
       "560    561    2015         Raipur  2015-05-09  Sunrisers Hyderabad   \n",
       "617    618    2016      Hyderabad  2016-05-12  Sunrisers Hyderabad   \n",
       "627    628    2016         Raipur  2016-05-20  Sunrisers Hyderabad   \n",
       "753  11413    2019  Visakhapatnam    08/05/19  Sunrisers Hyderabad   \n",
       "\n",
       "                   team2          toss_winner toss_decision  result  \\\n",
       "393  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "427  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "488  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "530  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "671  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "677  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "711  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "725  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "20        Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "38        Delhi Capitals       Delhi Capitals         field  normal   \n",
       "468       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "560       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "617       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "627       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "753       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "\n",
       "     dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "393           0  Sunrisers Hyderabad            0               3   \n",
       "427           0  Sunrisers Hyderabad            0               6   \n",
       "488           1  Sunrisers Hyderabad            0               8   \n",
       "530           0       Delhi Capitals            4               0   \n",
       "671           0  Sunrisers Hyderabad            0               7   \n",
       "677           0  Sunrisers Hyderabad            0               9   \n",
       "711           0  Sunrisers Hyderabad            0               5   \n",
       "725           0       Delhi Capitals           39               0   \n",
       "20            0  Sunrisers Hyderabad           15               0   \n",
       "38            0       Delhi Capitals            0               6   \n",
       "468           0  Sunrisers Hyderabad            4               0   \n",
       "560           0  Sunrisers Hyderabad            6               0   \n",
       "617           0       Delhi Capitals            0               7   \n",
       "627           0       Delhi Capitals            0               6   \n",
       "753           0       Delhi Capitals            0               2   \n",
       "\n",
       "    player_of_match                                              venue  \\\n",
       "393        A Mishra                                   Feroz Shah Kotla   \n",
       "427       DJG Sammy          Rajiv Gandhi International Stadium, Uppal   \n",
       "488        DW Steyn                                   Feroz Shah Kotla   \n",
       "530       JP Duminy  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...   \n",
       "671     Rashid Khan          Rajiv Gandhi International Stadium, Uppal   \n",
       "677        S Dhawan                                   Feroz Shah Kotla   \n",
       "711      J Bairstow                            Feroz Shah Kotla Ground   \n",
       "725          K Paul                 Rajiv Gandhi Intl. Cricket Stadium   \n",
       "20    KS Williamson          Rajiv Gandhi International Stadium, Uppal   \n",
       "38   Mohammed Shami                                   Feroz Shah Kotla   \n",
       "468        AJ Finch                Dubai International Cricket Stadium   \n",
       "560    MC Henriques   Shaheed Veer Narayan Singh International Stadium   \n",
       "617       CH Morris          Rajiv Gandhi International Stadium, Uppal   \n",
       "627         KK Nair   Shaheed Veer Narayan Singh International Stadium   \n",
       "753         RR Pant                                   ACA-VDCA Stadium   \n",
       "\n",
       "            umpire1               umpire2                umpire3  \n",
       "393       Aleem Dar           Subroto Das                    NaN  \n",
       "427       Asad Rauf              S Asnani                    NaN  \n",
       "488    RM Deshpande          BNJ Oxenford                    NaN  \n",
       "530       PG Pathak                S Ravi                    NaN  \n",
       "671  Bruce Oxenford              O Nandan  Virender Kumar Sharma  \n",
       "677   C Shamshuddin         Anil Dandekar                 S Ravi  \n",
       "711   C Shamshuddin  KN Anantapadmanabhan                 S Ravi  \n",
       "725  Anil Chaudhary        Bruce Oxenford         Chris Gaffaney  \n",
       "20      CB Gaffaney              NJ Llong                    NaN  \n",
       "38         YC Barde           Nitin Menon                    NaN  \n",
       "468       M Erasmus                S Ravi                    NaN  \n",
       "560     VA Kulkarni                S Ravi                    NaN  \n",
       "617      K Bharatan             M Erasmus                    NaN  \n",
       "627  A Nand Kishore          BNJ Oxenford                    NaN  \n",
       "753             NaN                   NaN                    NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15 entries, 393 to 753\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               15 non-null     int64 \n",
      " 1   season           15 non-null     int64 \n",
      " 2   city             14 non-null     object\n",
      " 3   date             15 non-null     object\n",
      " 4   team1            15 non-null     object\n",
      " 5   team2            15 non-null     object\n",
      " 6   toss_winner      15 non-null     object\n",
      " 7   toss_decision    15 non-null     object\n",
      " 8   result           15 non-null     object\n",
      " 9   dl_applied       15 non-null     int64 \n",
      " 10  winner           15 non-null     object\n",
      " 11  win_by_runs      15 non-null     int64 \n",
      " 12  win_by_wickets   15 non-null     int64 \n",
      " 13  player_of_match  15 non-null     object\n",
      " 14  venue            15 non-null     object\n",
      " 15  umpire1          14 non-null     object\n",
      " 16  umpire2          14 non-null     object\n",
      " 17  umpire3          4 non-null      object\n",
      "dtypes: int64(5), object(13)\n",
      "memory usage: 2.2+ KB\n"
     ]
    }
   ],
   "source": [
    "SRH_DC.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Complete details of matches that takes placed between SRH and DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of         id  season           city        date                team1  \\\n",
       "393    394    2013          Delhi  2013-04-12       Delhi Capitals   \n",
       "427    428    2013      Hyderabad  2013-05-04       Delhi Capitals   \n",
       "488    489    2014          Delhi  2014-05-10       Delhi Capitals   \n",
       "530    531    2015  Visakhapatnam  2015-04-18       Delhi Capitals   \n",
       "671   7929    2018      Hyderabad    05/05/18       Delhi Capitals   \n",
       "677   7935    2018          Delhi    10/05/18       Delhi Capitals   \n",
       "711  11152    2019          Delhi    04/04/19       Delhi Capitals   \n",
       "725  11321    2019      Hyderabad    14/04/19       Delhi Capitals   \n",
       "20      21    2017      Hyderabad  2017-04-19  Sunrisers Hyderabad   \n",
       "38      39    2017          Delhi  2017-05-02  Sunrisers Hyderabad   \n",
       "468    469    2014            NaN  2014-04-25  Sunrisers Hyderabad   \n",
       "560    561    2015         Raipur  2015-05-09  Sunrisers Hyderabad   \n",
       "617    618    2016      Hyderabad  2016-05-12  Sunrisers Hyderabad   \n",
       "627    628    2016         Raipur  2016-05-20  Sunrisers Hyderabad   \n",
       "753  11413    2019  Visakhapatnam    08/05/19  Sunrisers Hyderabad   \n",
       "\n",
       "                   team2          toss_winner toss_decision  result  \\\n",
       "393  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "427  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "488  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "530  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "671  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "677  Sunrisers Hyderabad       Delhi Capitals           bat  normal   \n",
       "711  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "725  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "20        Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "38        Delhi Capitals       Delhi Capitals         field  normal   \n",
       "468       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "560       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "617       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "627       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "753       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "\n",
       "     dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "393           0  Sunrisers Hyderabad            0               3   \n",
       "427           0  Sunrisers Hyderabad            0               6   \n",
       "488           1  Sunrisers Hyderabad            0               8   \n",
       "530           0       Delhi Capitals            4               0   \n",
       "671           0  Sunrisers Hyderabad            0               7   \n",
       "677           0  Sunrisers Hyderabad            0               9   \n",
       "711           0  Sunrisers Hyderabad            0               5   \n",
       "725           0       Delhi Capitals           39               0   \n",
       "20            0  Sunrisers Hyderabad           15               0   \n",
       "38            0       Delhi Capitals            0               6   \n",
       "468           0  Sunrisers Hyderabad            4               0   \n",
       "560           0  Sunrisers Hyderabad            6               0   \n",
       "617           0       Delhi Capitals            0               7   \n",
       "627           0       Delhi Capitals            0               6   \n",
       "753           0       Delhi Capitals            0               2   \n",
       "\n",
       "    player_of_match                                              venue  \\\n",
       "393        A Mishra                                   Feroz Shah Kotla   \n",
       "427       DJG Sammy          Rajiv Gandhi International Stadium, Uppal   \n",
       "488        DW Steyn                                   Feroz Shah Kotla   \n",
       "530       JP Duminy  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...   \n",
       "671     Rashid Khan          Rajiv Gandhi International Stadium, Uppal   \n",
       "677        S Dhawan                                   Feroz Shah Kotla   \n",
       "711      J Bairstow                            Feroz Shah Kotla Ground   \n",
       "725          K Paul                 Rajiv Gandhi Intl. Cricket Stadium   \n",
       "20    KS Williamson          Rajiv Gandhi International Stadium, Uppal   \n",
       "38   Mohammed Shami                                   Feroz Shah Kotla   \n",
       "468        AJ Finch                Dubai International Cricket Stadium   \n",
       "560    MC Henriques   Shaheed Veer Narayan Singh International Stadium   \n",
       "617       CH Morris          Rajiv Gandhi International Stadium, Uppal   \n",
       "627         KK Nair   Shaheed Veer Narayan Singh International Stadium   \n",
       "753         RR Pant                                   ACA-VDCA Stadium   \n",
       "\n",
       "            umpire1               umpire2                umpire3  \n",
       "393       Aleem Dar           Subroto Das                    NaN  \n",
       "427       Asad Rauf              S Asnani                    NaN  \n",
       "488    RM Deshpande          BNJ Oxenford                    NaN  \n",
       "530       PG Pathak                S Ravi                    NaN  \n",
       "671  Bruce Oxenford              O Nandan  Virender Kumar Sharma  \n",
       "677   C Shamshuddin         Anil Dandekar                 S Ravi  \n",
       "711   C Shamshuddin  KN Anantapadmanabhan                 S Ravi  \n",
       "725  Anil Chaudhary        Bruce Oxenford         Chris Gaffaney  \n",
       "20      CB Gaffaney              NJ Llong                    NaN  \n",
       "38         YC Barde           Nitin Menon                    NaN  \n",
       "468       M Erasmus                S Ravi                    NaN  \n",
       "560     VA Kulkarni                S Ravi                    NaN  \n",
       "617      K Bharatan             M Erasmus                    NaN  \n",
       "627  A Nand Kishore          BNJ Oxenford                    NaN  \n",
       "753             NaN                   NaN                    NaN  >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of matches that takes place between KXIP and DC: 15\n"
     ]
    }
   ],
   "source": [
    "print(\"No of matches that takes place between KXIP and DC:\",len(SRH_DC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Below plot gives the details of the match that takes place between DC and SRH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x26483b0f0c8>"
      ]
     },
     "execution_count": 29,
     "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(SRH_DC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1807ef54dc8>"
      ]
     },
     "execution_count": 37,
     "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(SRH_DC)\n",
    "g=sns.PairGrid(SRH_DC)\n",
    "g.map(plt.scatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  It looks like  DC won the Toss 9 Times and 6 Times SRH won the Toss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2648496b348>"
      ]
     },
     "execution_count": 30,
     "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": [
    "SRH_DC['toss_winner'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26484cf44c8>"
      ]
     },
     "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=SRH_DC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC went on to win the match 1 time by  batting first and 5 times by Batting Second against SRH .so total it is (DC=win-6,loss-9)whereas for SRH they wIn the match 7 times by batting first and win 2 times by batting Second against DC.so it is (SRH=win-9,loss=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26484e76b88>"
      ]
     },
     "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": [
    "import sys\n",
    "sys.setrecursionlimit(2000)\n",
    "sns.set()\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='toss_decision',hue=\"winner\",data=SRH_DC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26484ec7cc8>"
      ]
     },
     "execution_count": 38,
     "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=SRH_DC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of times toss win by both teams:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Delhi Capitals         9\n",
       "Sunrisers Hyderabad    6\n",
       "Name: toss_winner, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"No of times toss win by both teams:\")\n",
    "SRH_DC['toss_winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SRH win 9 times and Delhi win 6 times from 15 matches\n"
     ]
    }
   ],
   "source": [
    "print(\"SRH win 9 times and Delhi win 6 times from 15 matches\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26484f3c888>"
      ]
     },
     "execution_count": 41,
     "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=SRH_DC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# correlation between Columns in Matches Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>0.820285</td>\n",
       "      <td>-0.180610</td>\n",
       "      <td>0.296367</td>\n",
       "      <td>0.042048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>0.820285</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.283767</td>\n",
       "      <td>0.322416</td>\n",
       "      <td>0.041778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dl_applied</th>\n",
       "      <td>-0.180610</td>\n",
       "      <td>-0.283767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.120928</td>\n",
       "      <td>0.336018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_by_runs</th>\n",
       "      <td>0.296367</td>\n",
       "      <td>0.322416</td>\n",
       "      <td>-0.120928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.550222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_by_wickets</th>\n",
       "      <td>0.042048</td>\n",
       "      <td>0.041778</td>\n",
       "      <td>0.336018</td>\n",
       "      <td>-0.550222</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  0.820285   -0.180610     0.296367        0.042048\n",
       "season          0.820285  1.000000   -0.283767     0.322416        0.041778\n",
       "dl_applied     -0.180610 -0.283767    1.000000    -0.120928        0.336018\n",
       "win_by_runs     0.296367  0.322416   -0.120928     1.000000       -0.550222\n",
       "win_by_wickets  0.042048  0.041778    0.336018    -0.550222        1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26484f7f608>"
      ]
     },
     "execution_count": 43,
     "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(SRH_DC.corr(),yticklabels=True,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRH_DC['winner']=pd.get_dummies(SRH_DC['winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>393</th>\n",
       "      <td>394</td>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>Subroto Das</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>428</td>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  season       city        date           team1                team2  \\\n",
       "393  394    2013      Delhi  2013-04-12  Delhi Capitals  Sunrisers Hyderabad   \n",
       "427  428    2013  Hyderabad  2013-05-04  Delhi Capitals  Sunrisers Hyderabad   \n",
       "\n",
       "        toss_winner toss_decision  result  dl_applied  winner  win_by_runs  \\\n",
       "393  Delhi Capitals           bat  normal           0       0            0   \n",
       "427  Delhi Capitals           bat  normal           0       0            0   \n",
       "\n",
       "     win_by_wickets player_of_match  \\\n",
       "393               3        A Mishra   \n",
       "427               6       DJG Sammy   \n",
       "\n",
       "                                         venue    umpire1      umpire2 umpire3  \n",
       "393                           Feroz Shah Kotla  Aleem Dar  Subroto Das     NaN  \n",
       "427  Rajiv Gandhi International Stadium, Uppal  Asad Rauf     S Asnani     NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_DC.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Sweetviz we can get details about the Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":FEATURES DONE:                    |█████████████████████| [100%]   00:06  -> (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([SRH_DC,'SRH_DC'],target_feat='winner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Deliveries=pd.read_csv(\"deliveries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deliveries.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deliveries.describe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Need To replace Delhi Dardevils with Delhi Capitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Deliveries.replace('Delhi Daredevils','Delhi Capitals',inplace=True)"
   ]
  },
  {
   "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>179048</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</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>179049</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</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>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>179050</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>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>179051</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>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>179052</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>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>179053</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>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>179054</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>179055</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</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>179056</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>179057</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>179058</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179059</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</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>179060</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>KH Pandya</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>179061</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>KH Pandya</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>179062</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>KH Pandya</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>179063</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>KH Pandya</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>179064</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>KH Pandya</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>179065</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>KH Pandya</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>179066</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>DJ Bravo</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>179067</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>SR Watson</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>DJ Bravo</td>\n",
       "      <td>caught</td>\n",
       "      <td>Q de Kock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179068</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</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>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>179069</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</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>179070</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</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>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>179071</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179072</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</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>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>179073</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>SR Watson</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>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>179074</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</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>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>179075</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RA Jadeja</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>run out</td>\n",
       "      <td>KH Pandya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179076</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</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>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>179077</th>\n",
       "      <td>11415</td>\n",
       "      <td>2</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>RA Jadeja</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>SN Thakur</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team    bowling_team  over  ball  \\\n",
       "179048     11415       2  Chennai Super Kings  Mumbai Indians    16     1   \n",
       "179049     11415       2  Chennai Super Kings  Mumbai Indians    16     2   \n",
       "179050     11415       2  Chennai Super Kings  Mumbai Indians    16     3   \n",
       "179051     11415       2  Chennai Super Kings  Mumbai Indians    16     4   \n",
       "179052     11415       2  Chennai Super Kings  Mumbai Indians    16     5   \n",
       "179053     11415       2  Chennai Super Kings  Mumbai Indians    16     6   \n",
       "179054     11415       2  Chennai Super Kings  Mumbai Indians    17     1   \n",
       "179055     11415       2  Chennai Super Kings  Mumbai Indians    17     2   \n",
       "179056     11415       2  Chennai Super Kings  Mumbai Indians    17     3   \n",
       "179057     11415       2  Chennai Super Kings  Mumbai Indians    17     4   \n",
       "179058     11415       2  Chennai Super Kings  Mumbai Indians    17     5   \n",
       "179059     11415       2  Chennai Super Kings  Mumbai Indians    17     6   \n",
       "179060     11415       2  Chennai Super Kings  Mumbai Indians    18     1   \n",
       "179061     11415       2  Chennai Super Kings  Mumbai Indians    18     2   \n",
       "179062     11415       2  Chennai Super Kings  Mumbai Indians    18     3   \n",
       "179063     11415       2  Chennai Super Kings  Mumbai Indians    18     4   \n",
       "179064     11415       2  Chennai Super Kings  Mumbai Indians    18     5   \n",
       "179065     11415       2  Chennai Super Kings  Mumbai Indians    18     6   \n",
       "179066     11415       2  Chennai Super Kings  Mumbai Indians    19     1   \n",
       "179067     11415       2  Chennai Super Kings  Mumbai Indians    19     2   \n",
       "179068     11415       2  Chennai Super Kings  Mumbai Indians    19     3   \n",
       "179069     11415       2  Chennai Super Kings  Mumbai Indians    19     4   \n",
       "179070     11415       2  Chennai Super Kings  Mumbai Indians    19     5   \n",
       "179071     11415       2  Chennai Super Kings  Mumbai Indians    19     6   \n",
       "179072     11415       2  Chennai Super Kings  Mumbai Indians    20     1   \n",
       "179073     11415       2  Chennai Super Kings  Mumbai Indians    20     2   \n",
       "179074     11415       2  Chennai Super Kings  Mumbai Indians    20     3   \n",
       "179075     11415       2  Chennai Super Kings  Mumbai Indians    20     4   \n",
       "179076     11415       2  Chennai Super Kings  Mumbai Indians    20     5   \n",
       "179077     11415       2  Chennai Super Kings  Mumbai Indians    20     6   \n",
       "\n",
       "          batsman non_striker      bowler  is_super_over  ...  bye_runs  \\\n",
       "179048   DJ Bravo   SR Watson  SL Malinga              0  ...         0   \n",
       "179049   DJ Bravo   SR Watson  SL Malinga              0  ...         0   \n",
       "179050  SR Watson    DJ Bravo  SL Malinga              0  ...         0   \n",
       "179051  SR Watson    DJ Bravo  SL Malinga              0  ...         0   \n",
       "179052  SR Watson    DJ Bravo  SL Malinga              0  ...         0   \n",
       "179053  SR Watson    DJ Bravo  SL Malinga              0  ...         0   \n",
       "179054  SR Watson    DJ Bravo   JJ Bumrah              0  ...         0   \n",
       "179055   DJ Bravo   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179056  SR Watson    DJ Bravo   JJ Bumrah              0  ...         0   \n",
       "179057  SR Watson    DJ Bravo   JJ Bumrah              0  ...         0   \n",
       "179058  SR Watson    DJ Bravo   JJ Bumrah              0  ...         1   \n",
       "179059   DJ Bravo   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179060   DJ Bravo   SR Watson   KH Pandya              0  ...         0   \n",
       "179061  SR Watson    DJ Bravo   KH Pandya              0  ...         0   \n",
       "179062  SR Watson    DJ Bravo   KH Pandya              0  ...         0   \n",
       "179063  SR Watson    DJ Bravo   KH Pandya              0  ...         0   \n",
       "179064  SR Watson    DJ Bravo   KH Pandya              0  ...         0   \n",
       "179065  SR Watson    DJ Bravo   KH Pandya              0  ...         0   \n",
       "179066  SR Watson    DJ Bravo   JJ Bumrah              0  ...         0   \n",
       "179067   DJ Bravo   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179068  RA Jadeja   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179069  RA Jadeja   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179070  RA Jadeja   SR Watson   JJ Bumrah              0  ...         0   \n",
       "179071  RA Jadeja   SR Watson   JJ Bumrah              0  ...         4   \n",
       "179072  SR Watson   RA Jadeja  SL Malinga              0  ...         0   \n",
       "179073  RA Jadeja   SR Watson  SL Malinga              0  ...         0   \n",
       "179074  SR Watson   RA Jadeja  SL Malinga              0  ...         0   \n",
       "179075  SR Watson   RA Jadeja  SL Malinga              0  ...         0   \n",
       "179076  SN Thakur   RA Jadeja  SL Malinga              0  ...         0   \n",
       "179077  SN Thakur   RA Jadeja  SL Malinga              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "179048            0            0             0             6           0   \n",
       "179049            0            0             0             1           0   \n",
       "179050            0            0             0             4           0   \n",
       "179051            0            0             0             4           0   \n",
       "179052            0            0             0             4           0   \n",
       "179053            0            0             0             1           0   \n",
       "179054            0            0             0             1           0   \n",
       "179055            0            0             0             1           0   \n",
       "179056            0            0             0             0           0   \n",
       "179057            0            0             0             0           0   \n",
       "179058            0            0             0             1           1   \n",
       "179059            0            0             0             1           0   \n",
       "179060            0            0             0             1           0   \n",
       "179061            0            0             0             6           0   \n",
       "179062            0            0             0             6           0   \n",
       "179063            0            0             0             6           0   \n",
       "179064            0            0             0             0           0   \n",
       "179065            0            0             0             1           0   \n",
       "179066            0            0             0             1           0   \n",
       "179067            0            0             0             0           0   \n",
       "179068            0            0             0             2           0   \n",
       "179069            0            0             0             0           0   \n",
       "179070            0            0             0             2           0   \n",
       "179071            0            0             0             4           4   \n",
       "179072            0            0             0             1           0   \n",
       "179073            0            0             0             1           0   \n",
       "179074            0            0             0             2           0   \n",
       "179075            0            0             0             1           0   \n",
       "179076            0            0             0             2           0   \n",
       "179077            0            0             0             0           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind    fielder  \n",
       "179048           6               NaN            NaN        NaN  \n",
       "179049           1               NaN            NaN        NaN  \n",
       "179050           4               NaN            NaN        NaN  \n",
       "179051           4               NaN            NaN        NaN  \n",
       "179052           4               NaN            NaN        NaN  \n",
       "179053           1               NaN            NaN        NaN  \n",
       "179054           1               NaN            NaN        NaN  \n",
       "179055           1               NaN            NaN        NaN  \n",
       "179056           0               NaN            NaN        NaN  \n",
       "179057           0               NaN            NaN        NaN  \n",
       "179058           2               NaN            NaN        NaN  \n",
       "179059           1               NaN            NaN        NaN  \n",
       "179060           1               NaN            NaN        NaN  \n",
       "179061           6               NaN            NaN        NaN  \n",
       "179062           6               NaN            NaN        NaN  \n",
       "179063           6               NaN            NaN        NaN  \n",
       "179064           0               NaN            NaN        NaN  \n",
       "179065           1               NaN            NaN        NaN  \n",
       "179066           1               NaN            NaN        NaN  \n",
       "179067           0          DJ Bravo         caught  Q de Kock  \n",
       "179068           2               NaN            NaN        NaN  \n",
       "179069           0               NaN            NaN        NaN  \n",
       "179070           2               NaN            NaN        NaN  \n",
       "179071           8               NaN            NaN        NaN  \n",
       "179072           1               NaN            NaN        NaN  \n",
       "179073           1               NaN            NaN        NaN  \n",
       "179074           2               NaN            NaN        NaN  \n",
       "179075           1         SR Watson        run out  KH Pandya  \n",
       "179076           2               NaN            NaN        NaN  \n",
       "179077           0         SN Thakur            lbw        NaN  \n",
       "\n",
       "[30 rows x 21 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deliveries.tail(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering the match details that only takes place between Delhi Capitals and  Sunrisers Hyderabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Delhi_Hyderabad1=Deliveries.loc[(Deliveries['batting_team' ] =='Sunrisers Hyderabad' ) & (Deliveries['bowling_team'] == 'Delhi Capitals')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>1689 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team    bowling_team  over  ball  \\\n",
       "4810          21       1  Sunrisers Hyderabad  Delhi Capitals     1     1   \n",
       "4811          21       1  Sunrisers Hyderabad  Delhi Capitals     1     2   \n",
       "4812          21       1  Sunrisers Hyderabad  Delhi Capitals     1     3   \n",
       "4813          21       1  Sunrisers Hyderabad  Delhi Capitals     1     4   \n",
       "4814          21       1  Sunrisers Hyderabad  Delhi Capitals     1     5   \n",
       "...          ...     ...                  ...             ...   ...   ...   \n",
       "178462     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     5   \n",
       "178463     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     6   \n",
       "178464     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     7   \n",
       "178465     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     8   \n",
       "178466     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     9   \n",
       "\n",
       "              batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4811         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4812         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4813        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4814        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "...               ...          ...      ...            ...  ...       ...   \n",
       "178462  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178463  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178464       DJ Hooda  Rashid Khan   K Paul              0  ...         0   \n",
       "178465    Rashid Khan      B Kumar   K Paul              0  ...         0   \n",
       "178466   Basil Thampi      B Kumar   K Paul              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178462            0            0             0             0           0   \n",
       "178463            0            0             0             0           0   \n",
       "178464            0            0             0             1           0   \n",
       "178465            0            0             0             0           0   \n",
       "178466            0            0             0             1           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind   fielder  \n",
       "4810             2               NaN            NaN       NaN  \n",
       "4811             4               NaN            NaN       NaN  \n",
       "4812             1               NaN            NaN       NaN  \n",
       "4813             0               NaN            NaN       NaN  \n",
       "4814             1               NaN            NaN       NaN  \n",
       "...            ...               ...            ...       ...  \n",
       "178462           0               NaN            NaN       NaN  \n",
       "178463           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           1               NaN            NaN       NaN  \n",
       "178465           0       Rashid Khan         caught   RR Pant  \n",
       "178466           1               NaN            NaN       NaN  \n",
       "\n",
       "[1689 rows x 21 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad1         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "Delhi_Hyderabad2=Deliveries.loc[(Deliveries['bowling_team' ] =='Sunrisers Hyderabad' ) & (Deliveries['batting_team'] == 'Delhi Capitals')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>4933</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>SW Billings</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>4934</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>4935</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>SW Billings</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>4936</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SW Billings</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>4937</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>SW Billings</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>...</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>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178588</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>A Mishra</td>\n",
       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>TA Boult</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1823 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning    batting_team         bowling_team  over  ball  \\\n",
       "4933          21       2  Delhi Capitals  Sunrisers Hyderabad     1     1   \n",
       "4934          21       2  Delhi Capitals  Sunrisers Hyderabad     1     2   \n",
       "4935          21       2  Delhi Capitals  Sunrisers Hyderabad     1     3   \n",
       "4936          21       2  Delhi Capitals  Sunrisers Hyderabad     1     4   \n",
       "4937          21       2  Delhi Capitals  Sunrisers Hyderabad     1     5   \n",
       "...          ...     ...             ...                  ...   ...   ...   \n",
       "178586     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     2   \n",
       "178587     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     3   \n",
       "178588     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     4   \n",
       "178589     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     5   \n",
       "178590     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     6   \n",
       "\n",
       "            batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4933      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4934      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4935    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4936    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4937    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "...             ...          ...      ...            ...  ...       ...   \n",
       "178586     A Mishra       K Paul  K Ahmed              0  ...         0   \n",
       "178587       K Paul     A Mishra  K Ahmed              0  ...         0   \n",
       "178588       K Paul     A Mishra  K Ahmed              0  ...         0   \n",
       "178589     A Mishra       K Paul  K Ahmed              0  ...         0   \n",
       "178590       K Paul     TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4933              0            0             0             0           0   \n",
       "4934              1            0             0             0           1   \n",
       "4935              0            0             0             0           0   \n",
       "4936              0            0             0             0           0   \n",
       "4937              0            0             0             0           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4933             0               NaN                    NaN     NaN  \n",
       "4934             1               NaN                    NaN     NaN  \n",
       "4935             0               NaN                    NaN     NaN  \n",
       "4936             0               NaN                    NaN     NaN  \n",
       "4937             0               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[1823 rows x 21 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "Delhi_Hyderabad=Delhi_Hyderabad1.append(Delhi_Hyderabad2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complete Details Of Delhi vs Hyderabad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178588</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>A Mishra</td>\n",
       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>TA Boult</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3512 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4810          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4811          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4812          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4813          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4814          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "178586     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178587     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178588     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178589     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178590     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball    batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810       1  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4811       2   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4812       3   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4813       4  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814       5  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "...      ...        ...         ...      ...            ...  ...       ...   \n",
       "178586     2   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178587     3     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178588     4     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178589     5   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178590     6     K Paul    TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4810             2               NaN                    NaN     NaN  \n",
       "4811             4               NaN                    NaN     NaN  \n",
       "4812             1               NaN                    NaN     NaN  \n",
       "4813             0               NaN                    NaN     NaN  \n",
       "4814             1               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[3512 rows x 21 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# using Match_id we can summarize that 24 matches were played between SRH and DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11413    252\n",
       "531      247\n",
       "628      247\n",
       "7929     246\n",
       "469      245\n",
       "21       245\n",
       "561      244\n",
       "394      243\n",
       "11321    241\n",
       "39       240\n",
       "618      237\n",
       "11152    236\n",
       "7935     235\n",
       "428      203\n",
       "489      151\n",
       "Name: match_id, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad['match_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2648f5e5588>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAADM0AAAzECAYAAADxCV0SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde3icdZk38G+SpkfaUqAtpdCqBRQacAV1VVAQEBRZDq66ooirokChKKuLq6+ssiy+IFo5WmF1WTmKiMjui0IV8YTiGZVSRUAOIrBAC2mTCNNk3j8oFWjBJpnJzPzy+VwXV8mTyXPfv/b5Ps8zk9yZtmq1Wg0AAAAAAAAAAAAAAAAUpL3RDQAAAAAAAAAAAAAAAECtGZoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDijGl0A0/3yCMP5/offC/f+851OWrh+7PVnLm57bZb84VzF+ePd9+dWVtskXcddnie/4LtUqlU8p//8bn89Cc3ZOLESXnL296eV+zyyiTJj67/Qb508QXp6VmVF7/0ZXn3e45IZ2dnHnzggSw++/TcftutmbXF7By+YGHmzn1OYxcNAAAAAAAAAAAAAABATTXVO8309fVl4ZHvzQ3XX5877/hDkmRgYCCfOfXkbLPt83PamYszd+5z8rnPnpkkue7b38xNv/l1TjjplPzdgW/IOZ89M92PPJKVK7vzuc+ekYPe+OZ8/MST86sbf5Hrrv1mkuTCC87LuHHjc+qiMzNnztycu/isDeqtWq2mt7c31Wq1PosHGkK2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbENraKqhmbFjx+bMxedm4fv/ae22xx59NLvs+qrsf+Abssmmm2Z+1w555OGHkyTLli7NC1+0U2bP3jJ77rV3BgYGcvvtt+a2W3+fajXZbfc9suVWW+VFL9o5y25e+vjX3Lw0u75qt2w2fXp232Ov3H7brXn00UfX6aVSqaS3t3ftf8uXP5R3v+Ot6evrG5m/DKAuZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2tKYxjW7gyTo6OjJ16sZ54H/vX7tt/IQJOfiQQ5Mk/f39ue7ab2XXV+2WJOnu7s6mm22WJGlvb8/4CRPS3d2dJBk3flza2tqSJBMnTcr99z++z5Xd3Rk/fnySZNKkSWu3jZs+/Sm9XHnFV3L5ZZfWa6lAg8g2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbWhNTTU089d8/pzPZuWqlXnLW9/+jI9pS7K+N7haMz+z/q9Zz+cOOOiN2Xe/A9Z+3NfXm6OPOGzDmwWakmxDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhtbUMkMzF13wX/nNr3+Vj5/4fzNx4sQkyeQpk9Pb05MkqVar+XNfX6ZM3TgDA/35c19fBgYG0t7enr7e3kyZuvHjXzN5SnrWfM0Tb4U1ecrUdep1dnams7NzJJYGjCDZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg2tqb3RDWyIK6+4PN/+1pK8/wPHZcLECenpWZX+/v5sv31XfvnLn+ePd9+d6779rXR0dGTevK0zb+tt097enmu/eU3uueePufHGX2T7+V1Jku22n5/vf/e6PPjAA/nOdddmm22en7FjxzZ4hQAAAAAAAAAAAAAAANRSS7zTzJcuviBJcvxHjlu77fiPn5hX7/ma/OH22/Lx4/8lEydOyuELFmajyZOTJEcsOCaXXHR+Lr3kwrzkpS/LbrvvkSQ55NB3ZvHZZ+SDxy7MFrNn58ijjhn5BQEAAAAAAAAAAAAAAFBXTTk0M33GzFxy2dfWfvzk/3+6wxcszOELFq6z/eW77JqX77LrOts3mz49x3/8xNo0CgAAAAAAAAAAAAAAQFNqb3QDAAAAAAAAAAAAAAAAUGuGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGYBD6KtWsHqimr1JtdCvQtFoxJ4PpuR6PLbG+NbVG/XqtqZmMRN9qNF8dNZqrBqODY4laaIXjqBV6hGZUSnb6Vq9Zx+rWXgfUSjHZLmQdUCsyAX+d+0JqwfmWWmnEsTRaao600bDGpEHHTwteu1vheGiFHqEZlfJzAKWcA0pZR1LOWup13TY0U0dtpyyv6X9AY/VVqvnkkp709ax6/M8Wv7BAPfRVqjliSU86T12RI1okJ4PpuR6PLbG+NbVG/XqtqZmMRN9qNF8dNZqrBqODY4laaIXjqBV6hGZUSnb6VldzxDVr1nFNT0v9kAXUQzHZrlSzcElP3nTFqixs4XVArZSSbainvtXVHHlNT2ae+XCOdF/IEDnfUiuNOJZGS82RNhrWmDTo+GnB13Ra4XhohR6hGZXycwClnANKWUdSzlrqed02NAOwgTo7kuP2npQJkzbKcXtPSmdHozuC5tPZkZx/02NJHv+zFXIymJ7r8dgS61tTa9Sv15qayUj0rUbz1VGjuWowOjiWqIVWOI5aocdW/I2JlK8VsrMhOtuftg7fXWGUKybbHcmZe0/KZQdtlDO97g/FZBvqqbM9OelVE3Pv0RvnpFdNdF/IkDjfUiuNOJZGS82R/k3to+W80JDjpwVf02mF46EVevR6Oc2olJ8DGKlzQL2vx61wLttQpaylntftFrgFAGgOlf48ZRKz0t/ojqD5VPqTQ7vGJnn8z1bIyWB6rsdjS6xvTa1Rv15raiYj0bcazVdHjeaqwejgWKIWWuE4avYeW/E3JjI6NHt2NlRl4GnrGGhwQ9BgxWTb6/7wFKVkG+qp0p985Hu9GfepFfnI93rlhCFxvqVWGnEsjYaajfhN7aPlvNCQ46cFX9NpheOh2Xv0ejnNqpSfAxiJGiNxPW72c9lglLKWel6326rVqqvBBujt7c273/HWfOGLF2fixIkb9DVtpyyvaQ/VD21S0/0Bg8v26oFqOk9dsfbjyj9Py5j2tnq3CC1l9UA1lf7HJ5ef+LNROdnQfA+m53o8tsT61tQa9eu1pnob7LW73n2r0Xx1SqmRPP4i0BM1JnTWJ3MjUWNDDOU5N82jma4TNJ963Jc3SrP36HULRlKz3ZePlL7V1XS2P/5NmwljWnMN8GxGY7ZdPxktSrovh0ZrpmuH19Ral/Mtz6bZ78tHQ81GnOtHy3mhUetsltd0Srovb/Yem+mejfI127W7pBr1znGzn8sGo6S11Ou67Z1mADZQKZOYUE+V/uSTS3rS17Mqn2yR38xY6U+OWdKTd121Ksf8lZ7r8djB7vN9S3pyzJJVeV8T169Hn/VcU6P22ej69fp3aiaV/uSjS3ry47tX5aN16luN5qszUjWOXtKTfS5dmaPr+Pc1obMtY9rb6jrMMhI1KF8r3gPSfFrhOBqp8/9QteJvTGR0aIV8b6gJY9bcOxmYocb6VlezeqDaUr/1tJRse92femu1fJeSbagn1w5qwfmWWmnEsdSomguX9ORNV6zKwhGo2YhzfaU/OW5JT768dFWOK/i8UOlPjl3SkxO/vyrHjuA6W+01nVa4Tox0LgfL6+W10VdZ85x2BN5xa7QYiXw/UaO7zjX+aUlPPvWjVfmnOtYYiXfMKeX626hrbF1Un/ZnjXinmQ3knWagTIOa8l1dTaX6pEnMtmRMizyZgpHSTDnZ4N/QMYie6/HYEutbU2vUr9ea6q3Zrt1qNF+dkahRWV3N6ifVGNOWdLovHBa/FbO1NdN1guZTj/vyRnlsdTX9T+qxoy0Z22Q9NstvTKR8zXZfDq2sb3U1R1zTk/NveiyHdo3N5/aZ1LBz+GjMdinroDm1Yr5lAjaMd29muJxveTbNfl/eiJqN+L7MSJ/rR8t5YbR/j62k+/JW6NHr5cPTV6nmiCVPek679yS/iPEZNNu1u5QaSf2vx61wLttQpVxj63nu8U4zABuqPfnEohXp61mVTyxa4QwK69OefGxNTj7WKjlpTzZetCLvumpVNv5rPdfjsYPc57RFK3LMklWZ1qD6Ddtno+s3wb9To//tN7h+M2lP5ixakR/fvSpz6tW3Gs1XZwRqtLUnUxatyD6XrsyURSvS1iqZgHrxXIlaaE9OXHMcndikx1F7ezJ50Yq87PzuTF60Iu1N2GOr/cZERgnXCXhWne3J+Tc9luTxPztbJSPtyQlrsn1CK2e7PZm6aEXedMWqTG3lddCUWjLf7clJa7J9kkzAM/LuzQyb50nUSnuy86IVWdmzKjuP1LHUnuy15vjda4RqNuL7MiN+rm9PNl+0Il9euiqbF3xe8D22DdSeHL8mZ8c36/HQnmy6aEVO/P6qbNqkPXq9fHg6O572nLajwQ2VYiSu3e3JrmvOIbvWscaMRSvy+V+uyow6ngPqfj1uT2YtWpGv/XZVZjXpuWxDlXKNree5p0X/SgBGXqU/uatrbKZ8tpK7vNU2rFelP7l/TU7ub5GcVPqTt3SNzQU3V/KWv9JzPR472H3+Q9fYLP5VJf/QoPqN2mej6zfDv1Oj/+03tH4zqfQn+3aNza5fqmTfOr5VqxrNVWekary1a2y+fdfqvLWFMgH14rkStVDpT+5Zcxzd06THUaU/eVvX2Pz8/v68rUl7hGbkOgHPrjKQHNo1Nsnjf1YGGtzQBqr0J/euyfa9LZztSn9ycNfYfO3WSg5u4XXQnFox35X+5O412b5bJgDqxvMkaqXSn7yka2w2+WwlLxmhY6nSn2y95vjdegRrlv59mUp/cmDX2Lzt65UcWOgak9Hxb1kLlf7kgTU5e6BJ/54q/cmbusbm326o5E1N2iPDU+l/2nNa/8Y1MRLX7kp/0rXmHNJVxxpv6BqbI79VyRta+Pio9CcHdI3Nm/6nkgNaeB1JOdfYep572qrVarV2uyvXUN7Wtu2U5TXtofqhTWq6P2Dw2W6Wt9qGZtYsORlMvgfTcz0eW2J9a2qN+vVaUz0147VbjearU0qN0WQoz7lpLjLBM6nXfXmjtEKPMBKa8b4cWlnf6mo62x//AftG/vbT0ZrtUtZBc2rFfMsEtA6vqbU251ueSSvcl4+WmiNtNKwxGT3rXJ/S7stboUeGx7/xhmnGa3cpNUZCKetIyllLvdYxpmZ7AhgFnjgBj/E+XfCMWjEng+m5Ho8tsb41tUb9eq2pmYxE32o0X51SakArkQlqoRWOo1boEZqR7MCze+IH6VstI6Vku5R10JxaMd8yATAynG+plUYcS6Ol5kgbDWtMRs86h6sV/p5aoUeGx79xfZTy8wylHB+lrCMpZy31WkeL/7UAAAAAAAAAAAAAAADAugzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUJwxjW7gyR555OFc/4Pv5XvfuS5HLXx/tpozNw8+8EAWn316br/t1szaYnYOX7Awc+c+J9VqNZdcdH6+e92309HRkf32PzD77rd/kmTpTb/JeZ8/J8uXP5T5XTvm8AULs9FGG2XVqlU557NnZulNv860TTbNuw47PPO7dmjwqgEAAAAAAAAAAAAAAKi1pnmnmb6+viw88r254frrc+cdf1i7/cILzsu4ceNz6qIzM2fO3Jy7+KwkyY2//Hm+teTqHPeR43PY4Qty4fnn5a4778jq1atz9hmfyct3eWVOPvW03H//fbnyq19Jklx5xVfy0EMP5uRTT8tL//blOfuMz2R1pdKQ9QIAAAAAAAAAAAAAAFA/TTM0M3bs2Jy5+NwsfP8/PWX7spuXZtdX7ZbNpk/P7nvsldtvuzWPPvpolt28NNts+4LMm7d1dtr5xZkxY2Z+99tluf++e7NixfLsudfemTFzZl72il2ybNnStfv625e9IjNmzsxee++TFSuW5/7772vEcgEAAAAAAAAAAAAAAKijMY1u4AkdHR2ZOnXjPPC/9z9l+8ru7owfPz5JMmnSpLXbup+0PUkmTpqU7jXbk/zlayZOyso1256yr4mP76u7uzuz19NPpVJJ5UnvQtPX11uDVQKNJttQLvmGMsk2lEm2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oTU1zdDMYLS1DW57nmn7s3zNlVd8JZdfdumg+gKan2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhtbU9EMzkydPSU9PT5Kkr6/v8W1Tpmby5Ml56MEH1j6ur68vU6ZunMmTpyRJenp7Mn7ChPT19WbqlI3/sq/ex/fVu2ZfU9Z87ukOOOiN2Xe/A560/94cfcRhNV4dMNJkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTa0pqYfmtlu+/n5/nevy3bbzc93rrs222zz/IwdOzbbbd+VJVd/Pb+/5Xfp7e3N/ffdl+222z4zN5+VadM2ybeuuTp77PWa3PCjH+aFf7PT2n3d8MPrs8uur8r3v3tdNtl008zcfPP11u3s7ExnZ+dILhUYAbIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG1pT0w/NHHLoO7P47DPywWMXZovZs3PkUcckSV6008557ev2y6knn5SOMR059B/fldlbbpUkOeqYY3Pe58/JNVdflfldO2b/Aw9KkhzwhjfmvvvuzYf/+dhsssmmOWrhseno6GjY2gAAAAAAAAAAAAAAAKiPphuamT5jZi657GtrP95s+vQc//ET13lcW1tbDj7k0Bx8yKHrfG5+1w751GlnrbN9o402ygeO+3BtGwYAAAAAAAAAAAAAAKDptDe6AQAAAAAAAAAAAAAAAKg1QzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFCcMY1uYEN9+9pv5vLLvpSeVT2Z37VDFix8X/p6+7L47NNz+223ZtYWs3P4goWZO/c5qVarueSi8/Pd676djo6O7Lf/gdl3v/2TJEtv+k3O+/w5Wb78oczv2jGHL1iYjTbaqMGrAwAAAAAAAAAAAAAAoJZa4p1mVq7szufPWZwD3/CmnPLp0/Kne/6Y//ffV+bCC87LuHHjc+qiMzNnztycu/isJMmNv/x5vrXk6hz3keNz2OELcuH55+WuO+/I6tWrc/YZn8nLd3llTj71tNx//3258qtfafDqAAAAAAAAAAAAAAAAqLWWeKeZajVpb2/Ppptumk022TTjxo1LR0d7lt28NO9452HZbPr07L7HXjnhXz+SRx99NMtuXppttn1B5s3bOkkyY8bM/O63y9LR0ZEVK5Znz732zsbTpuVlr9glv/jZT9dbs1KppFKprP24r693RNYK1Ndws91XqaazI6n0JxM622rdHhShUTkZTr4H03M9HltifWtqjfr1WlMttcK1W43mq1NKjZJ5zl0emeAJI3Vf3ijN3mPf6mo625PKQDJhTPP1R+tqhftyYPBk+3GlrAOerPT7cmi0Rj338ppaWZxveUIr3pePhpqNONePlvPCaFlnUv59ebP36PVy6qUVrt1qNFeNkVLSWuqhJYZmpkyZkre87ZCcevJJGTNmTGbO3Dz7H/j3+epXvpzx48cnSSZNmpQkWdndne7u7rXbk2TipEnpXrM9yV++ZuKkrFyz7emuvOIrufyyS+u5LKABhpPtvko1Ryzpyfk3PZZDu8bmc3tPcmGBp2lkToaa78H0XI/Hlljfmlqjfr3WVGvNfu1Wo/nqlFKjdJ5zl0UmeLKRuC9vlGbvsW91NUdc86T+9pnkG4HUTLPflwNDI9vlrAOeruT7cmi0Rj738ppaOZxvebJWuy8fDTUbca4fLeeF0bLOJ5R8X97sPXq9nHpq9mu3Gs1VY6SUtJZ6aatWq9VGN/HXLH/ooXzw2KPzlre9PfO23jZnnfbp7LbHnvnyJRflgx/6SHba+SW5+647c9wH3pezFv9HLvvyl9LX25tjP/ihJMlHPvSBvPglL81223fl3z72f3Le+Zdk/IQJueYbV+XrV/13Tj/rnHVqrm8S8OgjDssXvnhxJk6cuEF9t52yvDZ/AWtUP7RJTfcHo9Fwsr16oJrOU1f8ZV//PC1j2l1U4MkamZOh5nswPdfjsSXWt6bWqF+vNdVas1+71Wi+OqXUKF0tnnPTPGSCJxuJ+/JGafYem70/Wluz35cDQyPb5awDnq7k+3JotFZ9vZzm4nzLk7XaffloqDka1tgoo2WdTyj5vrzZe2z2/mhtzX7tVqO5aoyUktZSL+2NbmBD/O53yzIwMJC999k38+ZtnZf87cvyq1/+IpMnT0lPT0+SpK+vL0kyecrUTJ48Ob29PWu/vq+vL1OmbpzJk6ckSXp6n/ia3kydsvF6a3Z2dmbixIlr/5swwQsMUILhZLvSnxzaNTbJ439W+uvVJbSuRuZkqPkeTM/1eGyJ9a2pNerXa0211uzXbjWar04pNUrnOXdZZIInG4n78kZp9h4rA0/rb6DBDVGUZr8vB4ZGtstZBzxdyffl0GiNfO7lNbVyON/yZK12Xz4aajbiXD9azgujZZ1PKPm+vNl79Ho59dTs1241mqvGSClpLfXSEu80c+edd+TD/3xsFr7/A5k3b5t85tOnZN7W22TVypXp7e3Je484Ol+9/Mv541135d8+cUp+8fOf5fRFn8xHP3Zient7c8onTsypi07PzM1n5ZgF781uu++RPfZ6TT596sl54d/slIPf9va/2kNvb2/e/Y63eqcZKMxgs91Xqaaz4/ELjLcug/VrlpwMJt+D6bkejy2xvjW1Rv16ramemvHarUbz1SmlxmgylOfcNBeZ4JnU6768UZq9x77V1XS2P/4NwQljmq8/ytGM9+XA8I3WbJeyDng2pd2XQ6M1y3Mvr6m1Nudbnkkr3JePhpqNONePlvPCaFnn+pR2X97sPTbLPRvla8ZrtxrNVWOklLSWehjT6AY2xNy5z8k/vvs9ufD889Lb05P5XTvmzW95Wx579NEsPvuMfPDYhdli9uwcedQxSZIX7bRzXvu6/XLqySelY0xHDv3Hd2X2llslSY465tic9/lzcs3VV2V+147Z/8CDGrk0oMU8cSEZ0xLv0wWN0Yo5GUzP9XhsifWtqTXq12tNzWQk+laj+eqUUgNaiUxQC61wHDV7j098469Z+2P0avbsAENTSrZLWQfUikzAX+e5F7XgfEutNOJYGg01G3GuHy3nhdGyzuFqhb+nZu/RPRvNqpSfZ1Cj+ZS0lnpoiaGZJNl7n32z9z77rrP9+I+fuM62tra2HHzIoTn4kEPX+dz8rh3yqdPOqkuPAAAAAAAAAAAAAAAANAezRAAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFMTQDAAAAAAAAAAAAAABAcQzNAAAAAAAAAAAAAAAAUBxDMwAAAAAAAAAAAAAAABTH0AwAAAAAAAAAAAAAAADFGTOUL7rqf64c1ONf/3cHDKUMAAAAAAAAAAAAAAAADMmQhmYuuuC/krRt8OMNzQAAAAAAAAAAAAAAADCShjQ084Y3vjlpe3xoZvlDD+U73/5Wdtn1VZk5a9bax/zpnntyww9/kDe/5a216RQAAAAAAAAAAAAAAAA20JCGZt745oPX/v+nTvlEtp/flaOOOXadxz28Ynl+f8vvht4dAAAAAAAAAAAAAAAADEH7cHfw22VLs8kmm673c5tNn5Hf/XbZcEsAAAAAAAAAAAAAAADAoAx7aGbcuPH55S9+njvv+MNTtt915x355c9/lnHjxg+3BAAAAAAAAAAAAAAAAAzKmOHuYM/X7J2vfPlL+fBxH8icOXMydeNpWdn9SO68845Uq9Xst/8htegTAAAAAAAAAAAAAAAANtiwh2be8MZ/yPjxE3Ll1y7PXXfdmdx1Z5Jk2rRNcsBBf5+9X7vvsJsEAAAAAAAAAAAAAACAwRj20EyS7Lvf/nntvvvloQcfyCOPPJJp06Zlk003S1tbWy12DwAAAAAAAAAAAAAAAIMypKGZRx99NOPGjXvKtvb29kyfMTPTZ8ysSWMAAAAAAAAAAAAAAAAwVEMamvk///LB7L3P67L3a/fNuw49+K88ui3/ef7FQykDAAAAAAAAAAAAAAAAQzKkoZmB/v4MDAwkSf785z/XtCEAAAAAAAAAAAAAAAAYriENzXzilE9n3PjxSZKLv3xFTRsCADkBfWoAACAASURBVAAAAAAAAAAAAACA4WofyheNnzAhbW1tG/z4q7/+/3Lix48fSikAAAAAAAAAAAAAAAAYtCENzQzWvff+KctuXjoSpQAAAAAAAAAAAAAAAGBkhmYAAAAAAAAAAAAAAABgJBmaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4ozI0MysWVtku+23H4lSAAAAAAAAAAAAAAAAkDG12lGlUkn3Iw+nWl33c6/dd7+8dt/9alUKAAAAAAAAAAAAAAAAntWwh2buvfdPOefsM3PLLb9b7+fb2pKLLv3qcMtkoL8/X/va5fnudddm9uytctyHP5oHH3ggi88+PbffdmtmbTE7hy9YmLlzn5NqtZpLLjo/373u2+no6Mh++x+YfffbP0my9Kbf5LzPn5Plyx/K/K4dc/iChdloo42G3R8AAAAAAAAAAAAAAADNo324O/jCuYtzyy2/zaRJkzJnzpzMmTv3Kf9tNWduLfrMF//rC/nm1d/IIW9/ZxYc/b4kyYUXnJdx48bn1EVnZs6cuTl38VlJkht/+fN8a8nVOe4jx+ewwxfkwvPPy1133pHVq1fn7DM+k5fv8sqcfOppuf/++3LlV79Sk/4AAAAAAAAAAAAAAABoHsN+p5nbbv19nvOc5+aEk05JZ2dnLXpax8MPP5xrv3lN3v+B4/Lil/zt2u3Lbl6ad7zzsGw2fXp232OvnPCvH8mjjz6aZTcvzTbbviDz5m2dJJkxY2Z+99tl6ejoyIoVy7PnXntn42nT8rJX7JJf/Oyn661ZqVRSqVTWftzX11uXtQEjS7ahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2sa9tDMRhtNznOfN69uAzNJctutt2RgYCA3/PD6nPeFc7PVVnPzniMWZGV3d8aPH58kmTRpUpJkZXd3up+0PUkmTpqU7jXbk/zlayZOyso1257uyiu+kssvu7RuawIaQ7ahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2sa9tDMPvu+Pt+8+hvp6+vLhAkTatHTOnp6epK0ZdsXvCCv3//AnLv4rFx0wRfX+9i2tvXv45m25xm2H3DQG7Pvfges/bivrzdHH3HYhjcNNCXZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg2taUhDM8d94H1P+fjBBx/IsQuPzJSpU9d5bFtbW0751GlD626NKVOmZuzYsdl7n32TJC/fZdd87zvXZfLkKWsGapK+vr4kyeQpUzN58uQ89OADa7++r68vU6ZunMmTpyRJenp7Mn7ChPT19WbqlI3XW7Ozs7Ou754DNIZsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YbWNKShmT/efdc627q7H0l39yPrefQzvcXLhtt22+ens7Mz13zjquy084vzs5/8OM997vNSqVTy/e9el+22m5/vXHdtttnm+Rk7dmy2274rS67+en5/y+/S29ub+++7L9ttt31mbj4r06Ztkm9dc3X22Os1ueFHP8wL/2anYfcHAAAAAAAAAAAAAABAcxnS0MzpZ59T6z6e1cRJk3LsB4/LeV/4j3zp4guy/fyuHPKOd6byWCWLzz4jHzx2YbaYPTtHHnVMkuRFO+2c175uv5x68knpGNORQ//xXZm95VZJkqOOOTbnff6cXHP1VZnftWP2P/CgEV0LAAAAAAAAAAAAAAAA9TekoZnp02c85eOBgYHcduvvM3Pm5pkydWoG+vtz002/SdcOO6a9vb0mjW4/f4ecuuiMdbYf//ET19nW1taWgw85NAcfcug6n5vftUM+ddpZNekJAAAAAAAAAAAAAACA5jTsiZaVK7vz8Y9+OB/76Idz1113Jkkeq1Ry8kkn5KMf/uesWrVq2E0CAAAAAAAAAAAAAADAYAx7aObSiy/Mrbfekq4ddsiMGTOTJGPGjMnOL35p7vjD7fnyly4adpMAAAAAAAAAAAAAAAAwGMMemrnxl7/INts+Px85/oTMmPmXoZkPHPfhvGC77fOTG3407CYBAAAAAAAAAAAAAABgMIY9NLNyZXdmbj5rvZ/bbLPp6e3tHW4JAAAAAAAAAAAAAAAAGJRhD81sMXvL3PiLn+dP99zzlO333vun3PjLX2TLLbccbgkAAAAAAAAAAAAAAAAYlDHD3cH+B7whZ57+6Rz3gfflOc99biZPnpKVK7tzxx/+kIGBgbzzsPfWok8AAAAAAAAAAAAAAADYYMMemnn5Lrum7899ufSSC3P7bbeu3T5lytT8w8Fvy8tfsetwSwAAAAAAAAAAAAAAAMCgDHtoJkn22PM12f3Ve+aee/6YnlWrstFGk7PF7Nlpb2+vxe4BAAAAAAAAAAAAAABgUIY9NPO5z56ZHXbYMbu8crdstdWcp3xuydVfz0MPPZSD3/b24ZYBAAAAAAAAAAAAAACADTbst4L53ne+nVt///t1tg/09+eGH12fJVdfNdwSAAAAAAAAAAAAAAAAMChDfqeZt775DWv//5qrv55rrv76eh+3xRZbDLUEAAAAAAAAAAAAAAAADMmQh2YmTBiftra29Pb2ZcyYjowdO/Ypn29v78jms2bl7e9417CbBAAAAAAAAAAAAAAAgMEY8tDMF754cZLkrW8+KHvstXf+8V3vqVlTAAAAAAAAAAAAAAAAMBxDHpp5wuELFmb27C1r0QsAAAAAAAAAAAAAAADUxLCHZnbbfY888sjDuf4H38sjDz+carW69nOVymP50z33ZMHC9w+3DAAAAAAAAAAAAAAAAGywYQ/N/OH22/J///2ErFq16hkfY2gGAAAAAAAAAAAAAACAkdQ+3B1cctH56elZlVfvsWeeN29ekuQNb3pzXrX7q5NU89a3HzrcEgAAAAAAAAAAAAAAADAowx6auf222/Lil/xt3nPEUdlmm+cnSd74prfkiAUL89znzcvvli0bdpMAAAAAAAAAAAAAAAAwGMMemhkYGMi4ceOSJJOnTkmSrFixPEkyZ87cLLv5puGWAAAAAAAAAAAAAAAAgEEZ9tDMNts+Pz/58Q35+c9+kq22nJOkmksvvjC//tWN+fWvbsyEiRNr0CYAAAAAAAAAAAAAAABsuGEPzfz9m/4hAwP9+elPfpydX/ySTJ8xI9/77ndy8kn/lhUrlufVe76mFn0CAAAAAAAAAAAAAADABhsz3B1s+/wX5IR/PzkPPvhA2js68tGPnZgrLr8sjzy8Iju+8EXZ53Wvr0WfAAAAAAAAAAAAAAAAsMGGPTSTJM993rw893nzkiTTp8/Ie484qha7BQAAAAAAAAAAAAAAgCGpydDMQw8+kN8uuzmPPPJIqtXqOp9//d8dUIsyAAAAAAAAAAAAAAAAsEGGPTTzkx//KGed/pmsXr16PZ+tJmkzNAMAAAAAAAAAAAAAAMCIGvbQzJcvuSj9/auzy66vzMxZs2rREwAAAAAAAAAAAAAAAAzLsIdmHnjgf7Pzi1+ao445thb9AAAAAAAAAAAAAAAAwLC1D3cHMzeflc7Ozlr0AgAAAAAAAAAAAAAAADUx7HeaefNb3pqzTl+Uq/7nymw/v2u9j3nu8+YNt0yS5LRPfzI/vuGHueSyr+XOO+/IOZ89M/f+6Z48b97WOfKo92Wz6dNTqVTyn//xufz0Jzdk4sRJecvb3p5X7PLKJMmPrv9BvnTxBenpWZUXv/Rlefd7jjDwAwAAAAAAAAAAAAAAUKBhD81MnbpxOjvH5qILvrjez7e1JRdd+tXhlsnPfvqT/PQnN6z9+D8Wn53nPW9e/umD/5JzP3dWLjz/vLz/A8flum9/Mzf95tc54aRTcvPSm3LOZ89MV9eOaWtvy+c+e0beedjh2XrrbXPSif+a6679ZvZ+7b7D7g0AAAAAAAAAAAAAAIDmMuyhmS+cuzg9Pauy6WabZcaMmbXoaR29vb057wvnZJ/XvT7fuOp/8tijj+a2236fd7zrsGw2fXpeudurc+H55yVJli1dmhe+aKfMnr1lZs3aIuef9/ncfvutSZJqNdlt9z3S1taWF71o5yy7eamhGQAAAAAAAAAAAAAAgAINe2jmT3+6J/O23iYn/PvJaW9vr0VP67jkwvOzw45/k64ddsw3rvqfdHd3J0nGjx+fJJk0aVJWrtnW3d2dTTfbLEnS3t6e8RMmrH38uPHj0tbWliSZOGlS7r///mesWalUUqlU1n7c19db+4UBI062oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbENrGvbQzLRpm2TWFrPrNjDz22U352c//XFOXXRGbrnlt8/yyLZn/Ux1fduf+Uty5RVfyeWXXbqhbQItQrahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2sa9tDM6/b7u/z3FV/N8oceyiabblqLnp7iissvy8qVK/O+ow9Pf/9AkuTYY45MkvT09CRJ+np7M3Xq1CTJ5CmT07tme7VazZ/7+jJl6sYZGOjPn/v6MjAwkPb29vT19mbK1I2fse4BB70x++53wNqP+/p6c/QRh9V8fcDIkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0JqGPTRzzTe+nocffjjvX3hExoxZ3+7a8p/nXzzk/R951DGpVB5Lktx0029y7uKzsuj0s3PaolNz3bXfzGabTc8Pvv/dbDe/K0my/fZdueKrl+WPd9+dW275bTo6OjJv3tYZqFbT3t6ea795Tbbv2iE33viLHPT3b37Gup2dnens7Bxy30Bzkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0JqGPTRz/333JklWr16d1atXD7uhp9t42rS1/z916l1JkukzZua9Rx6dcxeflQ8euzDztt4mh7330CTJq/d8Tf5w+235+PH/kokTJ+XwBQuz0eTJSZIjFhyTSy46P5decmFe8tKXZbfd96h5vwAAAAAAAAAAAAAAADTesIdmLv7yFbXoY4PstPNLcsllX0uSzJ37nJx08qfWeUxnZ2cOX7Awhy9YuM7nXr7Lrnn5LrvWvU8AAAAAAAAAAAAAAAAaq73RDQAAAAAAAAAAAAAAAECtGZoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKI6hGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOGMa3QDQOtpOWV7T/VU/tElN9wcAAAAAAAAAAAAAAE/wTjMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAADA/2fvzsPkqqu8gZ/q7gpJWkiARDZxA+eZgeCIu+MCbqAMyoAMjoyDr4oQwuKKGzMOyqBgRl+VVRYVRBgERHTkHXBBUUedUcQVkX1TIcgS7C5CdXe9fyRps3ST7nRV3XtPfT7Pkyfp9E39zrl1v1W3Kvd0kY6hGQAAAAAAAAAAAAAAANIxNAMAAAAAAAAAAAAAAEA6hmYAAAAAAAAAAAAAAABIx9AMAAAAAAAAAAAAAAAA6RiaAQAAAAAAAAAAAAAAIB1DMwAAAAAAAAAAAAAAAKRjaAYAAAAAAAAAAAAAAIB0DM0AAAAAAAAAAAAAAACQzkDRBUzVVy+7NC7/z6/EihUPx1OftmssXnJU3H33H+LTp54Uv//dXfHkHXaMww5/ayxYuDCazWZ85szT43//54cxd+5g/MM//lP8zfNfGBERP/j+9+I/zv98DA39KZ757OfGm9+yOOr1esHdAQAAAAAAAAAAAAAA0E6V+KSZn//s2rjwgi/EkiPfFh88/sS4/rrr4r8u/2qcedop8eQn7xBLP35S9Pf3x3nnfjYiIq761tfjl7/4eXzw+BPjVX+3X3z61JNi+YMPxkMPLY/TT/1U7Lv/AXHscSfEz669Jq765tcL7g4AAAAAAAAAAAAAAIB2q8QnzQwMDMSBrz8odnnqX0dExDbbbhv3LlsWN910Q7zhTQfHgoUL44W7vXh8aOa6X/0q/nrXp8d22z0uttlm2zj3s2fFzTffGBERrVbEbru/JGq1Wuy66zPiul//KvZ4xV7rrdlsNqPZbI5/3WgMd6FToNNkG/KSb8hJtiEn2Ya85Btykm3ISbYhL/mGnGQbcpJtyEu+ISfZhmqqxNDMTjsvip12XhQREbfdekvceMNvY/8DXhff/MaVMXv27IiIGBwcjIeWL4+IiOXLl8eWCxZERERfX1/MnjMnlq/63iazN4larRYREXMHB+Puu++ecM3LLr04Lrnowo72BXSfbENe8g05yTbkJNuQl3xDTrINOck25CXfkJNsQ06yDXnJN+Qk21BNlRiaWe2Pf7w3lp5wfPzt3vvEYx+71QRb1Cb9t7WIaE3095P8k3323T/22nuf8a8bjeE4YvHB06oXKB/ZhrzkG3KSbchJtiEv+YacZBtykm3IS74hJ9mGnGQb8pJvyEm2oZoqMzSzfPny+PBxx8bOi3aJA173j9F85JGIiBgaGoqIiMbwcMybNy8iIjbdbNMYXvX3rVYrHm40YrN582NsbDQebjRibGws+vr6ojE8HJvNmz/hevV6Per1ehc6o8pqJ97X1ttrvWeLtt4e65NtyEu+ISfZhpxkG/KSb8hJtiEn2Ya85Btykm3ISbYhL/mGnGQbqqmv6AKm4uFGIz764eNiwYIF8fo3vDGGh4diZHQ0nrzDjnHVN78e9y5bFt/77nfir3ZeFBERO+20KH7605/EnXfcEVd96xvR398fO+ywY+yw419EX19ffPPrV8Rdd90Z1157Tey06t8AAAAAAAAAAAAAAACQRyU+aeZHP/pB3HTTDRERccibDoqIiAULF8a73nNMnHHayfGutx8ZO+z4lDj4kJXfe/FLXx633HxTHPsv7425cwfj0CVHxmM23TQiIhYvOSou+MK5ceEF58Wznv3c2G33lxTTFAAAAAAAAAAAAAAAAB1TiaGZ3XZ/yaTDLcef8O/r/V29Xo9DlxwZhy45cr3vPe/5L4jnPf8Fba8RAAAAAAAAAAAAAACA8ugrugAAAAAAAAAAAAAAAABoN0MzAAAAAAAAAAAAAAAApGNoBgAAAAAAAAAAAAAAgHQMzQAAAAAAAAAAAAAAAJCOoRkAAAAAAAAAAAAAAADSMTQDAAAAAAAAAAAAAABAOoZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkYmgEAAAAAAAAAAAAAACAdQzMAAAAAAAAAAAAAAACkY2gGAAAAAAAAAAAAAACAdAzNAAAAAAAAAAAAAAAAkI6hGQAAAAAAAAAAAAAAANIxNAMAAAAAAAAAAAAAAEA6hmYAAAAAAAAAAAAAAABIx9AMAAAAAAAAAAAAAAAA6RiaAQAAAAAAAAAAAAAAIB1DMwAAAAAAAAAAAAAAAKRjaAZgGhrNVoyMtaLRbBVdCpRWFXMynZo7sW3G9fVUjfU71VOZdKNua5RvHWtA9zleaYcqHEdVqJHcGiOrjsGRah2DWbJT1f0PnZIm20n6oJyq+NwhE7BhckI7OI5olyKOpV5Zs9t6oceI3ulzpqqwn6pQIzNT9te0Za9vMlmuNcjyGOBamd5haAZgihrNVnz0yqFoDP1p5e+eWGA9jWYrFl85FPWl98fiiuRkOjV3YtuM6+upGut3qqcy6Ubd1ijfOtaA7nO80g5VOI6qUCO5NUZasfiKVcfgFUOV+Y/ALNlpjLTisCuGYquTHojDKrT/oVPSZDtJH5RTFZ+7ZQI2TE5oB8cR7VLEsdRLa3bzotNeeVzolT5nqgr7qQo1MjNlf01b9vomk+Vag25eY9LJ52PXyvQWQzMAU1Tvj3j3HoMxZ/Ax8e49BqPeX3RFUD71/ohzf/lIRKz8vQo5mU7Nndg24/p6qsb6neqpTLpRtzXKt441oPscr7RDFY6jKtRY1Z+qxtTU+9Y5Bivy7n4VsjMV9b6I4180N35/xPw4/kVzK7P/J+PxolyqeH+kyXaSPlbz0yTLpYrP3dkyAZ1Q748475ePxA7z++I8OWEjebylXYo4lnphzYdHWjHUbMVYK2Ko2YqHu/BarVceF3qlz5mqwn6qQo1eI89M2V/Tlr2+yWS51qAba3Tj+di1Mr2lIg8TAMVrjsZak5jN0aIrgvJpjkYctGhWRKz8vQo5mU7Nndg24/p6qsb6neqpTLpRtzXKt441oPscr7RDFY6jstdYhZ+qVsWLwsukObbOMThWcEFTVPbsTFVzNOL9Vw/HJv9+f7z/6uHK9hFRjceLXlLV+yNTtjP0EeGnSZZRFZ+7M2UCOmV0LOKhd2wev3nLvHjoHZvHaAWyTfl4vKVdijiWemHNVkS886qV7wG886rh6MaZda88LvRKnzNVhf1U9hqr8Bq57O+Xl/0+ruJr7og81xp0Y41uPB+7Vqa31FqtVjkfcUtmeHg43vyGA+Psc86PuXPnTunf1E68r601tN6zRVtvj5nrtfs4Y7/TyfbIWCvqS+8f/7p59OYx0FfrdIlQKSMjrWi2Vk4uN0cj6rWIgYFicjLVfE+n5k5sm3F9PVVj/U711GnTeu7uQt3WKN86adYYa6287dVr9EdHzj0bI62o9618U3FOQbmO2LjX3JRHmZ4nKJ9OnJcXpew1lv19i9UXhZ/7y0fioEWz4vQ9Bwt97qmqKj53lz07U1X2jE1Hpl4yKNP90YvZbo60YmSNPgZqEfUK9hFRrmOJP6vac3eWbEMnrb4Ac/y1zR6DMade/uduysXjLY+m7OflvbBmEefWvfK40Ct9TibTeXnZayz7a+QqvF8+MtaKu4dasXBuLZYNt2KrwVqp9mFE9V5zRyS6nqFL10x0OsfdfCxrNFvj63TiNWTZH5fLwCfNAEyRSUyYgnXPLJxpAJDQSMRaHwM80oE1mqMRS64cir8888FY0qFPOazqT7QGKEo3Hv9nouw/Va3et87Hwnu9uFHmDKz8j8my/QdqLyh7xqYjUy8ZuD+Kte6roCq/KvJ/GOXkuRvyqfev89qmv+CCAGg759adk+k1WK8r+31Z9hxX4f3y5ljEMVcPxzYnPxDHXD1cyvesvObOLdP7po2RVhx25VBsddIDcdiVnbk2o+yPy2UwUHQBAFVRr0WcvsdgnP3KwfFJTGBtzdGIj145FO94USs+fnUt3r3HYAyU8IUdAMzEQEQM1mvRV1v5eydeWNf7Iz636o3Smx54JM585WD711jnzdizO7AGQCbdePyfiTkDtTh9z1XvWxT8U9Umsvo/N1b/5LzmWHi9SKXUIuJjL54bZ75iMJavaEW5EjY9ZX+86DXuj2Ktu7ervPfn1Gtr/R9GUZ96AJDd6gswx1/bjHptA5BNXy3itFXn1o+Mrvya9sj0GqzXlf2+LPtr5Cq8Xz5noBan7TkYZ72y+E9yoTdlek++3hdxzqprM8755SNxVgeuzSj743IZlO3/dgGACqvXIt69x2DU+yPevYfhMgDYWN14o7QKb8YClMlo/PmnMrVWfV22N1dX/6dVGR/PXRRO1c0eqEUrVl4oMzirFrMrfgyX+fGiF7k/aJfVFwE5lgA6x4XUAPm1ImK42YqBvloMN1vxmFke7KGKyvwauSrvl3vPiiLVImLuqh9mN7deq/QQiGszysEuB5iigXVOTtf9GqhmTqZTcye2zbi+nqqxfqd6KpNu1G2N8q2TZY3Vb5Q2j948Tt9zsCNvlHZjDXpDVZ8nKJcqHEebDPz5Dfnaqq+ZnjkDtRjoq3nO6TFVyPdUOYbhz7Jku75O3et+Db0mS7ahk7w2pB083tIuRRxLvbDm7IFaDM6qdfUHZ/TK40Kv9DlTVdhPVaix7LzX2JuyXM/QjTU2GahFbdXN1mqdee3Vrceyblyb4XF5w8r2wxABSq3ME+hQFlXMyXRq7sS2GdfXUzXW71RPZdKNuq1RvnXSrNGFn9zjpwPRLlV9nqBcqnAcVaFGKCPZgZyyZDtLH9AuMgEbJie0g+OIdiniWOqFNYv4/5NeeVzolT5nqgr7qQo1QhmluZ4hyzUT3bpWJlEvVdWTQzM/+P734j/O/3wMDf0pnvns58ab37I46vV60WUBAAAAAAAAAAAAAADQJj03S/TQQ8vj9FM/Ffvuf0Ace9wJ8bNrr4mrvvn1ossCKqLRbMXIWCsazVbRpUBpVTEn06m5E9tmXF9P1Vi/Uz2VSTfqtkb51smyBlSJTNAOVTiOqlAjlFGW7GTpA9olSyay9EE5NUZWHV8j1Tm+ZAI2TE5oB8cR7VLEsdQra3ZbIfu1gPPVXrgv26EK+6kKNTIzVXxNWwVZrmfI8hjgWpne0XNDMzfdeEO0WhG77f6SeNz228euuz4jrvv1r9bbrtlsxvDw8PivRmO4gGqBdptJthvNViy+cijqS++PxVcOeWKBCRSZk43N93Rq7sS2GdfXUzXW71RP7Vb2525rlG+dLGtk5zV3LjLBmrpxXl6UKtQInVL28/JuyNIHrEm28/RBOTVGWrH4ilXH1xVDXb3IKPN5ORStqu+XUy4eb1lT1c7Le2XNbitkvxZwvtoL9+WaMp+XV6FGZqbI17RlV/bnf3QT/gAAIABJREFU7ixrdINrZXpLrdVq9dReufo7V8Xnz/lMnPmZz0dExHnnfjZuufnm+Jdjj1tru4u/eEFcctGF6/37s885P+bOnTultWon3jfzgtfQes8Wbb09Zq7X7uMM/c4k2yNjragvvX/86+bRm8dAX63tNUKVFZmTjc33dGruxLYZ19dTNdbvVE/tVvbnbmuUb50sa2TXjtfclIdMsKZunJcXpQo1QqeU/by8G7L0AWuS7Tx9UE5VfE9NJmDDqphtysfjLWuq2nl5r6zZbb2yX3vhvlxT5vPyKtTIzLiPJ1f25+4sa3SDa2V6S8990sxEahMcE/vsu3+cfc75479OPv2s7hcGtN1Mst0cjTho0ayIWPl7c7RTVUJ1FZmTjc33dGruxLYZ19dTNdbvVE/tVvbnbmuUb50sa2TnNXcuMsGaunFeXpQq1AidUvbz8m7I0gesSbbz9EE5NcfWOb7Gurd25vNyKFpV3y+nXDzesqaqnZf3yprdVsh+LeB8tRfuyzVlPi+vQo3MTJGvacuu7M/dWdboBtfK9Jae+6SZn17z4/j40hPinC98Mfr6+uLM00+JxsMPx1Fve+ej/rvh4eF48xsO9EkzrKXX7uOM/U43241mK+r9K59g5tRNYcJEypKT6eR7OjV3YtuM6+upGut3qqdOKuNztzXKt06WNXrJxrzmplxkgsl06ry8KFWoEbqhjOfl3ZClD5iMbFe7D8qpMdKKet/Ki43mDFTjPTWZgA0rS068p1ZtZTmOKJ8qnJf3yprdVsh+LeB8tRfuy8lkOy+vQo3MTFle05ZdGZ+7s6zRDa6V6R0DRRfQbTvs+BfR19cX3/z6FbHTol3i2muviX1fc0DRZQEVsfqJZMDndMGkqpiT6dTciW0zrq+naqzfqZ7KpBt1W6N862RZA6pEJmiHKhxHVagRyihLdrL0Ae2SJRNZ+qCcVl9UVKXjSyZgw+SEdnAc0S5FHEu9sma3FbJfCzhf7YX7sh2qsJ+qUCMzU8XXtFWQ5XqGLI8BrpXpHT03NLPZZpvF4iVHxQVfODcuvOC8eNaznxu77f6SossCAAAAAAAAAAAAAACgjXpuaCYi4nnPf0E87/kvKLoMAAAAAAAAAAAAAAAAOsQH8AAAAAAAAAAAAAAAAJCOoRkAAAAAAAAAAAAAAADSMTQDAAAAAAAAAAAAAABAOoZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkMFF1AVbRarYiIaDSGC6theLi4temOXruPN6bfOXPmRK1Wa1sNZcg2sJJ8Q06yDTnJNuTU7mxHyDeUheduyEm2ISfn5ZCX527ISbYhJ+flkJfnbshpQ9mutVanlUf1xz/eG0csPrjoMqDnnX3O+TF37ty23Z5sQ3nIN+Qk25CTbENO7c52hHxDWXjuhpxkG3JyXg55ee6GnGQbcnJeDnl57oacNpRtQzNTNDY2Fvfff1/Mnj21CcNGYziOWHxwnHz6WTFnTntPnjpBvZ2l3vZp95RvxmyXvUb1zUzZ64vY+Bo7ke/f/e6uOPrtR5Z6f01XFY6B6dJTNZQp29N57u6GLPd3lj4i8vRS5j4yZbvM+3k1NbaHGjesEz85bzr5Lrr/qSh7jeqbubLXWNXz8rLv16nK0kdEnl56vQ/Zbo8sfUTk6SVLHxEb14vz8g0re43qm7my11jV5+52Kvt9FFH+GsteX4Qap6robJdhH3RDL/TZCz1GVKdP5+UbVvYa1TdzZa+xquflZd+vU6WP8snSS6eyPdCO4npBX19fbLnlgmn/uzlz5rZ94riT1NtZ6i2fzNkue43qm5my1xdRfI19fX2xxRZblqKWTtBTNeip/Tb2ubsbit437ZKlj4g8vWTp49GUIdtV2M9qbA81dtfG5LsK/Ze9RvXNXNlrLLq+zO+pTUWWPiLy9KKP9pDtHH1E5OklSx8RxffivLwY6pu5stdYdH3eU5uastdY9voi1NhtvX5eviG90Gcv9BjRO32uyXl5MdQ3c2Wvsej6ev25Wx/lk6WXdvfR17ZbAgAAAAAAAAAAAAAAgJIwNAMAAAAAAAAAAAAAAEA6/ccee+yxRReRVV9fX+y08y7R399fdClTot7OUm8eVdg3Za9RfTNT9voiylVjmWppFz1Vg556S5Z9k6WPiDy9ZOmj7Kqwn9XYHmosvyr0X/Ya1TdzZa+x7PVNpqp1rytLHxF5etFHsapa97qy9BGRp5csfURUs5cq1Fz2GtU3c2Wvsez1dUMV9kHZayx7fRFqrIpe2Qe90Gcv9BjRO33OVBX2U9lrVN/Mlb3Gstc3marWvS59lE+WXjrRR63VarXadmsAAAAAAAAAAAAAAABQAn1FFwAAAAAAAAAAAAAAAADtZmgGAAAAAAAAAAAAAACAdAzNTFGr1Yrh4eFotVpFlwK0kWxDXvINOck25CTbkJd8Q06yDTnJNuQl35CTbENOsg15yTfkJNtQDYZmpqjRaMSb33BgNBqNoksB2ki2IS/5hpxkG3KSbchLviEn2YacZBvykm/ISbYhJ9mGvOQbcpJtqAZDMwAAAAAAAAAAAAAAAKRjaAYAAAAAAAAAAAAAAIB0DM0AAAAAAAAAAAAAAACQjqEZAAAAAAAAAAAAAAAA0jE0AwAAAAAAAAAAAAAAQDqGZgAAAAAAAAAAAAAAAEjH0AwAAAAAAAAAAAAAAADpGJoBAAAAAAAAAAAAAAAgnYGiC2inr152aVz+n1+JFSsejqc+bddYvOSomD179vj37122LE475ZNx8003xjbbbheHLjkynvCEJxZXMAAAAAAAAAAAAAAAAB2R5pNmfv6za+PCC74QS458W3zw+BPj+uuui/+6/KtrbXPe5z8bm2wyO5Z+/KR4/OOfEGecdnJB1QIAAAAAAAAAAAAAANBJaYZmBgYG4sDXHxS7PPWvY/vtHx/bbLttPPjAg2ttc92vfxUveNFusWDhwtj9JS+Lm2+6MVasWNGRehrNVoyMtaLRbK319yMjrfV+TbYtvaMXjoHVPTrugTKazmNSJ7bNuL6eqrF+p3oqk27UbY3yrWMN6D7HK+1QheOo7DWWvT5mrjGy6j4eqdZ9nOXYzNJHRHWPpayqemxVte51ZekjQrbLqIr3SaZMQKfICe3gOKJdijiWemHNXuixKL3S50xVYT+Vvcay11cFZd+HVXzNHZHnWgNrTHOdLhyvZc/sVHWqjzRDMzvtvCj22vvVERFx2623xI03/Dae/8IXrbXNQ8uXx+zZsyMiYnBwcPzvJtJsNmN4eHj8V6MxPOVaGs1WLL5yKOpL74/FVw6N32mNZiuarVjv10Tb0jsmO14yWd3j+5feX/hxP5Nsr1Y78b62/gLaY2PzPZ3H4U5sm3F9PVVj/U711G6dOC9vJ2uUbx1rVEM7zsspj+zHK9PTjfPyopS9xrLXx8w1Rlqx+IpV9/EVQ139j8Cyn5d3Q5Y+Ioo9llif19zFytJHhGyXURWfuzNlAjqlqs/dlIvHW9ZUtfPyXlizF3osSq/0uVrm8/Ky11j2+qqg7Puwiq+5I/Jca2CNaa7TheO17Jmdqk72UWu1WtXcK5P44x/vjX895r3xwhftHq898PVrfe/AA/aNd73n/fH0Zzwr7rj9tnj3O98aJ592Zmy5YOF6t3PxFy+ISy66cL2/P/uc82Pu3LmPWsPIWCvqS+8f/7p59OYx0FeLkbGJd/VE29I7JjteMlnd4/Il9Zgz+JiIKO64n0m2V2v3oEvrPVu09fagV21svqfzONyJbTOur6dqrN+pntqtE+fl7WSN8q1jjWpox3k55ZH9eGV6unFeXpSy11j2+pg55+XFytJHRK5eMpDtYmXpIyJXL1lUMd+OI9iwKmab8vF4y5qqdl7eC2v2Qo9F6ZU+V8t8Xl72GsteXxWUfR9W9bw8y7UG1ijfOmXP7FR1so80nzQTEbF8+fL48HHHxs6LdokDXveP631/0003i6GhoYiIaDQaK/9us3kT3tY+++4fZ59z/vivk08/a8p1NEcjDlo0KyJW/t4c/fPfT/Rrom3pHb1wDKzu8bhTm4Uf9zPJNlBuG5vv6TwmdWLbjOvrqRrrd6qnduvEeXk7WaN861ijGpyX55L9eGV6unFeXpSy11j2+pi55tg69/FY99Yu+3l5N2TpI6LYY4n1ec1drCx9RMh2GVXxuTtTJqBTqvrcTbl4vGVNVTsv74U1e6HHovRKn6tlPi8ve41lr68Kyr4Pq/iaOyLPtQbWmOY6XThey57ZqepkH2k+aebhRiP+7YMfiMHHDMYRb31H9PX1Ra3WF7M32ST6+vsjIuITH/toDA8PxSGLj4gvXfLFuPP22+NDHz5xSrc/PDwcb37DgVP+CR2NZivq/SvvvDn1P084jUzwkUrNVky4Lb1jsuMlk9U9xqoH+7Ic99PNdoRPmoGqmE6+p/M43IltM66vp2qs36meOqld5+XtZI3yrWON6tmY83LKpZeOV6anU+flRSl7jWWvj5lrjLSi3rfyP1jmDDgv77YsfUSU51hipbIcW7Jd7T4iZLuMynKfZDsvh6KVJSfeU6u2shxHlE8Vzst7Yc1e6LEovdLnRLKdl5e9xrLXVwVl34dVfM0dkedaA2tMc50uHK9lz+xUdaqPgbbdUsF+9KMfxE033RAREYe86aCIiFiwcGHMnTsY+73mgHjO8/4mXn/QG+O0Uz4V73r7kbHtdtvFYYcf1bF6Vt9JA+t8ls/ABAf66jth3W3pHZMdL5mMP3Ct6tFxD5TJdB6HO7FtxvX1VI31O9VTmXSjbmuUbx1rQPc5XmmHKhxHZa+x7PUxc6v/I6Vq93GWYzNLHxHVPZayquqxVdW615WljwjZLqMq3ieZMgGdIie0g+OIdiniWOqFNXuhx6L0Sp8zVYX9VPYay15fFZR9H1bxNXdEnmsNrDHNdbpwvJY9s1PVqT7SDM3stvtLYrfdX/Ko2yxYuDD+5djjulQRAAAAAAAAAAAAAAAARan4LBEAAAAAAAAAAAAAAACsz9AMAAAAAAAAAAAAAAAA6RiaAQAAAAAAAAAAAAAAIB1DMwAAAAAAAAAAAAAAAKRjaAYAAAAAAAAAAAAAAIB0DM0AAAAAAAAAAAAAAACQjqEZAAAAAAAAAAAAAAAA0jE0AwAAAAAAAAAAAAAAQDqGZgAAAAAAAAAAAAAAAEjH0AwAAAAAAAAAAAAAAADpGJoBAAAAAAAAAAAAAAAgHUMzAAAAAAAAAAAAAAAApGNoBgAAAAAAAAAAAAAAgHQMzQAAAAAAAAAAAAAAAJCOoRkAAAAAAAAAAAAAAADSGSi6AACgeLUT72vr7bXes0Vbbw8AAAAAAAAAAAAApssnzQAAAAAAAAAAAAAAAJCOoRkAAAAAAAAAAAAAAADSMTQDAAAAAAAAAAAAAABAOoZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkYmgEAAAAAAAAAAAAAACAdQzMAAAAAAAAAAAAAAACkY2gGAAAAAAAAAAAAAACAdAzNAAAAAAAAAAAAAAAAkI6hGQAAAAAAAAAAAAAAANIxNAMAAAAAAAAAAAAAAEA6hmYAAAAAAAAAAAAAAABIx9AMAAAAAAAAAAAAAAAA6RiaAQAAAAAAAAAAAAAAIB1DMwAAAAAAAAAAAAAAAKRjaAYAAAAAAAAAAAAAAIB0DM0AAAAAAAAAAAAAAACQjqEZAAAAAAAAAAAAAAAA0hkouoB2evDBB+L737s6rv72VXH4kW+L7R//hLW+/8ADD8RpJ38irv/NdbHZZvPitQe+Pp7/ghcVVC0AAAAAAAAAAAAAAACdkuaTZhqNRhx52CHxw+9/P2679ZYJt7ns0ovj/vvvj6Uf/1Ts8cq94rSTPxUrVqzocqUAAAAAAAAAAAAAAAB0WppPmpk1a1acdNoZ8ciKFXHU4YdOuE1frS9mz54dm2+xZWyxxZYxMNAfrVZrwm2bzWY0m83xrxuN4Y7UDXSXbENe8g05yTbkJNuQl3xDTrINOck25CXfkJNsQ06yDXnJN+Qk21BNaYZm+vv7Y968+bHsnrsn3eY1f//aOPqdR8Ub/+kfYmRkJBYfflTMnj17wm0vu/TiuOSiCztVLlAQ2Ya85Btykm3ISbYhL/mGnGQbcpJtyEu+ISfZhpxkG/KSb8hJtqGaaq3JPmqlopbdc3ccdfih8dGPfTK2f/wT1vre5z/3mbju17+MQw47Iq655sdxxeVfi//7qVNj7uDgercz0STgEYsPjrPPOT/mzp3b8T6AzmhHtmsn3tfWmlrv2aKttwcbI8Nx7bkbcpJtyEm2IS/5hpxkG3KSbchLviEn2YacZBvykm/ISbahmtJ80sxU/Ozaa2K3F780nvikJ8e2224XF/3H+XHzzTfFol2eut629Xo96vV6AVUCnSTbkJd8Q06yDTnJNuQl35CTbENOsg15yTfkJNuQk2xDXvINOck2VFNf0QV02tjo6Pift3vc9nHNT/437l22LL773e9Ef39/bLPNNgVWBwAAAAAAAAAAAAAAQCekH5r52NIT4mtfvSwiIg76P2+KWbNmxbvefmRc9qWL49AlR8aWCxYWXCEAAAAAAAAAAAAAAADtNlB0Ae228LFbxQUXfXn866Pfe8z4n7dcsDDe98/HFlAVAAAAAAAAAAAAAAAA3ZT+k2YAAAAAAAAAAAAAAADoPYZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkYmgEAAAAAAAAAAAAAACAdQzMAAAAAAAAAAAAAAACkY2gGAAAAAAAAAAAAAACAdAzNAAAAAAAAAAAAAAAAkI6hGQAAAAAAAAAAAAAAANIxNAMAAAAAAAAAAAAAAEA6hmYAAAAAAAAAAAAAAABIx9AMAAAAAAAAAAAAAAAA6RiaAQAAAAAAAAAAAAAAIB1DMwAAAAAAAAAAAAAAAKRjaAYAAAAAAAAAAAAAAIB0DM0AAAAAAAAAAAAAAACQjqEZAAAAAAAAAAAAAAAA0jE0AwAAAAAAAAAAAAAAQDqGZgAAAAAAAAAAAAAAAEjH0AwAAAAAAAAAAAAAAADpGJoBAAAAAAAAAAAAAAAgHUMzAAAAAAAAAAAAAAAApGNoBgAAAAAAAAAAAAAAgHQMzQAAAAAAAAAAAAAAAJCOoRkAAAAAAAAAAAAAAADSMTQDAAAAAAAAAAAAAABAOoZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkYmgEAAAAAAAAAAAAAACAdQzMAAAAAAAAAAAAAAACkY2gGAAAAAAAAAAAAAACAdAzNAAAAAAAAAAAAAAAAkI6hGQAAAAAAAAAAAAAAANIxNAMAAAAAAAAAAAAAAEA6qYZmHnzwgbj8a1+J9x799rjj9tsm3Oa2W2+JE47/ULzpoNfFjTf8tssVAgAAAAAAAAAAAAAA0A1phmYajUYcedgh8cPvfz9uu/WWCbe55+6744MfOCbmb755HPeRpfHkJ+/Q5SoBAAAAAAAAAAAAAADohoGiC2iXWbNmxUmnnRGPrFgRRx1+6ITbfO0/L4utt946Dll8ePT1pZkXAgAAAAAAAAAAAAAAYB1phmb6+/tj3rz5seyeuyfd5obrfxObzJ4d73v3O2LFww/HXnu/OvZ4xV4TbttsNqPZbI5/3WgMt71moPtkG/KSb8hJtiEn2Ya85Btykm3ISbYhL/mGnGQbcpJtyEu+ISfZhmpKMzQzFUNDQ1GfNSsOWXx43HjDb+Nznzkzdl60S2z3uO3X2/aySy+OSy66sIAqgU6SbchLviEn2YacZBvykm/ISbYhJ9mGvOQbcpJtyEm2IS/5hpxkG6qpp4ZmNps3L575rOfEDjs+JXbY8Slx/nnnxB133D7h0Mw+++4fe+29z/jXjcZwHLH44G6WC3SAbENe8g05yTbkJNuQl3xDTrINOck25CXfkJNsQ06yDXnJN+Qk21BNhQ/NnH7qSY/6/YH+/th2u8fF7i9+acwdHJz27Y+NjkZff39EROz6jGfGd6/+djzr2c+NG264PkZGRuLxj3/ihP+uXq9HvV6f9npAuck25CXfkJNsQ06yDXnJN+Qk25CTbENe8g05yTbkJNuQl3xDTrIN1VT40MzV3/5WRNQiojXBd1f/fS3+3+VfjX/7yNKYN2/+tG7/Y0tPiJ12XhR/+6p9Yu9X/V3cd++98c/ve1fMmTM3Dll8eGy73XYzbwIAAAAAAAAAAAAAAIBSKXxo5g1vPDjO/dzZ8drX/dNaAyx/+MPv44Lzzo29X71fNBrD8Y0r/ysu+o/z4+BDlzzq7S187FZxwUVfHv/66PceM/7nWbNmxcGHLtngbQAAAAAAAAAAAAAAAFBthQ/NfPuqb8ZT//pp8eq/22+9711/3XVx/W9+Hcce95G44/bb4he/+FkBFQIAAAAAAAAAAAAAAFA1fUUXcNedd8Ts2XMm/N7AwEDcdustERGx9dbbxAP339/N0gAAAAAAAAAAAAAAAKiowodmttxyQfzkx/8TP/jv76319z/64X/HT378PzF/8y0iIuLOO++IrbfZtogSAQAAAAAAAAAAAAAAqJiBogvYZ7/944zTTo6TPvHxOOvTp8b8+ZvH8uXLY2hoKCJasd/+B8Svf/WLuOnGG+Kf3vCmossFAAAAAAAAAAAAAACgAgofmtn9xS+NTTfdNC656MK4/bZb4/e//1309/fHjjs+Jfb7+9fG03Z9eoyNjcUb33xIvPTlexZdLgAAAAAAAAAAAAAAABVQ+NBMRMQznvnseMYznx1jo6Px0J8eis02mxe1Wm38+319ffHyPV9ZYIUAAAAAAAAAAAAAAABUSSmGZkZHR+MPf/h9PLT8wWi1In53111rff+vdtq5oMoAAAAAAAAAAAAAAACoosKHZq7/zXVx0ic+Fvfdd9+k25z/xS91sSIAAAAAAAAAAAAAAACqrvChmXM+c2bcd98fY7vHbR9bb711RNSKLgkAAAAAAAAAAAAAAICKK3xo5q677oy//Kud4gMfPL7oUgAAAAAAAAAAAAAAAEiir+gCFixYGFtttXXRZQAAAAAAAAAAAAAAAJBI4UMzf/eav49rrvlxLFt2T9GlAAAAAAAAAAAAAAAAkMRA0QV8+ZKLYuhPQ/GOo5ZEvV6fYItafObc87teFwAAAAAAAAAAAAAAANVV+NDM73//u/E/j46OFlgJAAAAAAAAAAAAAAAAWRQ+NHP+Fy8tugQAAAAAAAAAAAAAAACS6Su6AAAAAAAAAAAAAAAAAGi3Qj5p5th/eV+8bI9XxAteuFu8+51vfdRta7VanPjvn+hSZQAAAAAAAAAAAAAAAGRQyNDMsmX3xB/vvTciIu684/YNbF3rfEEAAAAAAAAAAAAAAACkUswnzXzowzFv3vyIiPjkKZ8uogQAAAAAAAAAAAAAAAASK2RoZuFjt/rznxc+togSAAAAAAAAAAAAAAAASKyQoZk1jY6Oxre+cWVc9+tfxfLlD0artfb3a7WIf/7X44opDgAAAAAAAAAAAAAAgEoqfGjm3M+dHV+/4r8iojXJFrVulgMAAAAAAAAAAAAAAEAChQ/N/PAH34/BxwzGwW85LLbaeuuo1QzJAAAAAAAAAAAAAAAAMDOFD800H2nGc577vHjO8/6m6FIAAAAAAAAAAAAAAABIoq/oAp729GfEvfcuK7oMAAAAAAAAAAAAAAAAEin8k2Z2fMpT4vzPnxtnnH5KbLfd4ybc5m9ftU+XqwIAAAAAAAAAAAAAAKDKCh+aOe+cz0ZExLe/9Y2IqK3z3VZE1AzNAAAAAAAAAAAAAAAAMC2FD83st/8BEbV1h2UAAAAAAAAAAAAAAABg4xU+NLP/Aa8rugQAAAAAAAAAAAAAAACSKXxoJiLijjtuj99c9+t4aPmD0Wqt/b1aLWK//V9bTGEAAAAAAAAAAAAAAABUUuFDM9/8+hXxmbM+vWpYpjXBFjVDMwAAAAAAAAAAAAAAAExL4UMzX/nyl6JWq8XLXr5nbLXN1lGLWtElAQAAAAAAAAAAAAAAUHGFD8088MD98axnPzfeePAhRZcCAAAAAAAAAAAAAABAEn1FF/CkJ+1QdAkAAAAAAAAAAAAAAAAkU/jQzN777Bs/veYn8b3vfiduufmmCX9Nx4MPPhCXf+0r8d6j3x533H7bpNvddust8fp/eE1c/MULZtoCAAAAAAAAAAAAAAAAJTNQdAEfX/qRiKjFqSd9csLv12oRX7jwS1O6rUajEUcedkg88YlPittuvWXS7cZGR+OM00+JVqu1MSUDAAAAAAAAAAAAAABQcoUPzfzlX+0UtVqtLbc1a9asOOm0M+KRFSviqMMPnXS7yy//asyatUk84QlPnHSbZrMZzWZz/OtGY7gtNQLFkm3IS74hJ9mGnGQb8pJvyEm2ISfZhrzkG3KSbchJtiEv+YacZBuqqfChmQ988Pi23VZ/f3/Mmzc/lt1z96Tb3HP33XHZly6JDx1/Qpz0yY9Put1ll14cl1x0YdtqA8pBtiEv+YacZBtykm3IS74hJ9mGnGQb8pJvyEm2ISfZhrzkG3KSbaimQoZmli27J+bP3zzq9Xr86aGHNrj9YzbdtG1rn3XGqbHX3q+Obbbd7lG322ff/WOvvfcZ/7rRGI4jFh/ctjqAYsg25CXfkJNsQ06yDXnJN+Qk25CTbENe8g05yTbkJNuQl3xDTrIN1VTI0MyHPnBMvPwVr4xX77NfHPLmgyKiNum2tVrEFy78UlvWveG318cvfv6zuPGG38Z/fuXSePjhh+POO26Pen1W7LPva9batl6vR71eb8u6QHnINuQl35CTbENOsg15yTfkJNuQk2xDXvINOck25CTbkJd8Q06yDdVUyNDM5ltsEfPnbx4REVsuWBC1RxmaaYex0dHo6++PJzzxSfGpUz49/vdLT/xw7Lw3Jy34AAAgAElEQVRol3jZHnt2dH0AAAAAAAAAAAAAAAC6q5hPmjn+xPE/n3TqmR1f72NLT4iddl4Uf/uqfWLhY7ca//uBgYEYHByMwcHHdLwGAAAAAAAAAAAAAAAAuqeQoZlOW/jYreKCi748/vXR7z1mwu0+fOLHulUSAAAAAAAAAAAAAAAAXdRXdAEAAAAAAAAAAAAAAADQboZmAAAAAAAAAAAAAAAASMfQDAAAAAAAAAAAAAAAAOkYmgEAAAAAAAAAAAAAACAdQzMAAAAAAAAAAAAAAACkY2gGAAAAAAAAAAAAAACAdAzNAAAAAAAAAAD/n717j7PsrgpEv+pxmnR3eNh5oU4SFFCSNPLygY8R3wiEh16vXCJ2HMIkDU0iIBgFHwgDN6GJPIKg0SAJEMwEPoA6DulBgZgoc++dj+KE8BokAUcJge4Q6DqkT3Wd+0d1VT/qVLq6a599fr+1v9/P53wqdXpXrbX2WWvvXVX7lwMAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkMzvpBJr2ta/dHbfcfFPc9JEPx46LXxinn3HmYf9+55f+La76wz+Iz37mM3HSSSfFeb98fnzf9z9+QtkCAAAAAAAAAAAAAAAwDqneaabf78fFz7swPnbLLXHH7Z8fuc1b3vzGOPHEE+P1V74lvv/xPxR/8KbXx/z8fMuZAgAAAAAAAAAAAAAAME6p3mlmw4YNceVbr4p9994bl+y4aOQ252x9ZDz+B384Tjrp5HjUox8df/7+98Y3+/048f73P2y7wWAQg8Fg+fN+f26suQPtMNuQl/mGnMw25GS2IS/zDTmZbcjJbENe5htyMtuQk9mGvMw35GS2oU6pFs3MzMzEAx/4oLjry3euus0v/l+/tPzfH9p1Yzz6MY9bsWAmIuID73tPvPeG68eSJzA5ZhvyMt+Qk9mGnMw25GW+ISezDTmZbcjLfENOZhtyMtuQl/mGnMw21GlqOBwOJ51E0+768p1xyY6L4rVXvDFOP+PMkdu854Y/iw/d+MF49WU746STT1nx76NWAr5g+3Pj6muui02bNo0td2C8mpjtqct3N5rT8NItjX4/OB4Z+tq5G3Iy25CT2Ya8zDfkZLYhJ7MNeZlvyMlsQ05mG/Iy35CT2YY6pXqnmbXa9cG/iv/6l38ev/WK/zRywUxERK/Xi16v13JmwLiZbcjLfENOZhtyMtuQl/mGnMw25GS2IS/zDTmZbcjJbENe5htyMttQp+lJJ9CGhf37l//7lptvinde+/bYccmL49RTT429e78R84es+AMAAAAAAAAAAAAAAKB+nXinmSt2XhZnn7M1nvLUp8f1170zBoN9sfOy/7T879uff3E84cd/coIZAgAAAAAAAAAAAAAA0KSUi2ZOOfW0ePcN71/+/KW/8fLl/37TW66aREoAAAAAAAAAAAAAAAC0aHrSCQAAAAAAAAAAAAAAAEDTLJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0LJoBAAAAAAAAAAAAAAAgHYtmAAAAAAAAAAAAAAAASMeiGQAAAAAAAAAAAAAAANKxaAYAAAAAAAAAAAAAAIB0ZiedQJO+9rW745abb4qbPvLh2HHxC+P0M8487N+/8Y1vxB+95cr4xK3/FN+y5aR4znMvinO2PnJC2QIAAAAAAAAAAAAAADAuad5ppt/vx8XPuzA+dsstccftnx+5zQfe95746le/EpftfEN8/w/8YPzBm14f84PBePIZDGN+YRj9wfCw5+fnh8uPQ7cZtS3d0YUeWKpxRe/P560ZqMexHIfHsW3G+GqqI/64aipJG3mLUV4cMcqKQTfoJZpQQx/VkGPJ+vMH9p/fB3VOltnJUgc0JctMZKkjwrm2RDX2V405Q9vMCU3QRzRlEr3UhZhdqDFiMj9DdKXO9arhPFF6jqW/7qXnF1H+a1yrLPcBiFFenCwzO67jY5pFMxs2bIgr33pVXPzCF6+6zSdv+0T8wON/KE497bT4qZ95YuzZszvuvPNLI7cdDAYxNze3/Oj359acS38wjO279kZv557YvmvvcvP1B8MYDGP5ceg2g/0Rrz1kW7pjtX7JZKnG39u5Z2XvD6PVmtcz20DZjne+j+U4PI5tM8ZXUx3xx1VT08ZxXd4kMcqLI0ZZMVbjujyXLvxcy9q1cV0+KTXkWLL+/DC233hg/924t+g/BLJS6dflbchSBxzKbOepI8K5tkQ1/tydaSZgXGqcbcrjeMuharsu70LMLtQYMZmfIbpS55LM1+Wl51j6z8il5xdR/ms8SaWfu8UoK0ZbcbLM7DiPj1PD4bDOvbKKu758Z1yy46J47RVvjNPPOPOwf/vVHRfFk899WjzxSU+Jb/b78R+2PSt+5/deHWedfc6K7/Oe//zueO8N1694/uprrotNmzbdZw7zC8Po7dyz/Pngpd8Ss9NTMb9w+K4+cpv+3m/Exs0nxuz01JpqJYfV+iWTpRrveX4vNm4+cUW9EdFazeuZ7SVTl+9uNKfhpVsa/X5wPDL09fHO97Ech8exbcb4aqoj/rhqato4rsubJEZ5ccQoK8Zqmrgupxxd+LmWtWvjunxSasixZPZf3Uq/Lm9DljrgUGY7Tx0RuWrJosafu/URHF2Ns015HG85VG3X5V2I2YUaxSz73F3DeaL0HOW3fjXkOCmln7vFKCtGW3GyzOw460jzTjPHa2qV/fj0n/uFuPqa65Yfb/7DP1nz9xzsj9i2dUNELH4c7D/4/KGPI7f5/ZumlrelO1brl0yWanztWwYj622z5vXMNlC2453vYzkOj2PbjPHVVEf8cdXUtHFclzdJjPLiiFFWjNW4Ls+lCz/XsnZtXJdPSg05lmywcMT+W5hwQhyT0q/L25ClDjiU2c5TR4RzbYlq/Lk700zAuNQ425TH8ZZD1XZd3oWYXagxYjI/Q3SlziWZr8tLz7H0n5FLzy+i/Nd4kko/d4tRVoy24mSZ2XEeHzv1TjO/9Rsvjcd+3/fFz/8fvxi7d++OHRc9J654wx/Et337tx/1+87NzcUF55+35v9DR38wjN7MYhNu7B1cmTN/yNsEDYaxvM2obemO1folk6UaY+GI3p+KmJ2dXM3HOtsROd6RA46Usa+PZb6P5Tg8jm0zxldTHfHHVdM4NXVd3iQxyosjRlkx1uJ4rsspSym9RHnGdV0+KTXkWLL+/DB604u/8N44wd8HsX4lXpe3IUsdsBqzXXcdEc61JSqlv7Jdl8OklTInfqdWt1L6iPLUcF3ehZhdqDFiMj9DdKXOUbJdl5eeYymv+2pKzy+i/Ne4FCWeu8UoK0ZbcbLM7LiOj7ONfadCLezfH9MzMxERcdbZ58TH/u6W+OEf+dH4249+OLacdFKc9uAHjyXuUrPNHvFePocuDlja+UvbHLkt3bFav2SyfACeXtn7AJN2LMfhcWybMb6a6og/rppK0kbeYpQXR4yyYtANeokm1NBHNeRYsqVfbNt/3ZNldrLUAU3JMhNZ6ohwri1Rjf1VY87QNnNCE/QRTZlEL3UhZhdqjJjMzxBdqXO9ajhPlJ5j6a976flFlP8a1yrLfQBilBcny8yO6/hY+W45uit2Xhb/5S8+EBERT//5X4jTTntw/OZLXxQf+7tbYsfFL4qZAwtqAAAAAAAAAAAAAAAAyCPdO82ccupp8e4b3r/8+Ut/4+XL/33iiSfGr/36b04iLQAAAAAAAAAAAAAAAFqU/p1mAAAAAAAAAAAAAAAA6B6LZgAAAAAAAAAAAAAAAEjHohkAAAAAAAAAAAAAAADSsWgGAAAAAAAAAAAAAACAdCyaAQAAAAAAAAAAAAAAIB2LZgAAAAAAAAAAAAAAAEhndtIJ1GI4HEZERL8/N+FMoNs2btwYU1NTjX2/EmZ7bs5xhXyOp68zzjdgtiErsw05NT3bEeYbSuHcDTmZbcjJdTnk5dwNOZltyMl1OeTl3A05HW22p4ZL08p9+upXvxIv2P7cSacBnXf1NdfFpk2bGvt+ZhvKYb4hJ7MNOZltyKnp2Y4w31AK527IyWxDTq7LIS/nbsjJbENOrsshL+duyOlos23RzBotLCzEnj2744QT1rbCsN+fixdsf268+Q//JDZubPbiaRzkO17ybU7Tq3wzznbpOcpvfUrPL+L4cxzHfP/rv/7veOmLLi56fx2rGnrgWKmpDiXN9rGcu9uQ5fXOUkdEnlpKriPTbJe8n5fIsRlyPLpx/J/zjmW+J13/WpSeo/zWr/Qca70uL32/rlWWOiLy1NL1Osx2M7LUEZGnlix1RBxfLa7Lj670HOW3fqXnWOu5u0mlv0YR5edYen4RclyrSc92CfugDV2osws1RtRTp+vyoys9R/mtX+k51npdXvp+XSt1lCdLLeOa7dkmkuuC6enpOOmkk4/56zZu3NT4iuNxku94ybc8mWe79Bzltz6l5xcx+Rynp6djy5aTishlHNRUBzU173jP3W2Y9L5pSpY6IvLUkqWO+1LCbNewn+XYDDm263jmu4b6S89RfutXeo6Tzi/z79TWIksdEXlqUUczzHaOOiLy1JKljojJ1+K6fDLkt36l5zjp/PxObW1Kz7H0/CLk2LauX5cfTRfq7EKNEd2p81CuyydDfutXeo6Tzq/r5251lCdLLU3XMd3YdwIAAAAAAAAAAAAAAIBCWDQDAAAAAAAAAAAAAABAOjOveMUrXjHpJLKanp6Os895ZMzMzEw6lTWR73jJN48a9k3pOcpvfUrPL6KsHEvKpSlqqoOauiXLvslSR0SeWrLUUboa9rMcmyHH8tVQf+k5ym/9Ss+x9PxWU2veR8pSR0SeWtQxWbXmfaQsdUTkqSVLHRF11lJDzqXnKL/1Kz3H0vNrQw37oPQcS88vQo616Mo+6EKdXagxojt1rlcN+6n0HOW3fqXnWHp+q6k17yOpozxZahlHHVPD4XDY2HcDAAAAAAAAAAAAAACAAkxPOgEAAAAAAAAAAAAAAABomkUzAAAAAAAAAAAAAAAApGPRzBoNh8OYm5uL4XA46VSABpltyMt8Q05mG3Iy25CX+YaczDbkZLYhL/MNOZltyMlsQ17mG3Iy21AHi2bWqN/vxwXnnxf9fn/SqQANMtuQl/mGnMw25GS2IS/zDTmZbcjJbENe5htyMtuQk9mGvMw35GS2oQ4WzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDoWzQAAAAAAAAAAAAAAAJCORTMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0AwAAAAAAAAAAAAAAQDqzk07gePzFB94Xf/WXfx733vvN+J5HPya2P/+SuPPOL8UfveXK+Ld//d/xnQ99WDxvx6/GyaecctjXDYfDePe7ro2PfvhvYmZmJs592jPiyec+bUJVAAAAAAAAAAAAAAAAMC7VvdPMP338H+P6d78rnn/xC+P3Xn15fPqTn4wP/tVfxB+/9Q/iO7/zobHz96+MmZmZeOe1f7ria//xH/5HfGjXB+PXX/bb8dyLnh/vvPZP4wt33D6WPPuDYcwvDKM/GB72/DfnhzF/4HHoNqO2pTu60ANLNY7q/5rUmje0yZwAJWnjmCRGeXHSxJg/EGO+7hh0g2tAmlBDH9WQI5TI7EBOWWY7Sx3QFDMBR2dOaII+oimT6KUuxOxCjZMykX1b4d/DauiHGnKEEqW5nyHLPRNt3SuTqJZxG9e+qm7RzOzsbJz37G3xyO95VJx++hnxrd/2bfGVu+6Kz33us/GEH//JOPmUU+LfP+HH45Of/MSKr/3kbZ+Ih3/XI+KhD31YPPZx3xunnnpafPpTnxwZZzAYxNzc3PKj359bc479wTC279obvZ17YvuuvcvN1x8M4xv7hjEYRgyGEZfs2huD/TFyW7pjtX7JZKnGE3fuWe7/SdU8jtkGDprknKxnviMipi7f3fgDWL/Sz91ilBcnTYz5YWy/8UCMG/eO5RcnbcRYzXrP25TFz0oc6njnu4Y+qiFHGJfSr8uB42O289QBR8p8XQ6TVvPfwiiH4y2Hqu26vAsxu1DjpExk31b497Aa+qGGHGFcSj93p4nRxj0Tbd0rk6iWcRvnvppt7Du15OxztsbZ52yNiIg7bv98/K/PfiZ+4RefFX/9oV1xwgknRETE5s2b4+v33LPia++5557lbSIiNm3eHPeM2C4i4gPve0+894brjyvH3kzEtbfui4jFj1c/afPy8w+YnorpqcXt7l0Yrrot3dGFHliq8XmP6kVvZvG5SdU8jtkGDprknKxnvoFylX7uFqO8OGliTOeIsRrn7Vz8rMShjne+a+ijGnKEcSn9uhw4PmY7Tx1wpMzX5TBp/hZGExxvOVRt1+VdiNmFGidlIvu2wr+H1dAPNeQI41L6uTtNjDbumWjrXplEtYzbOPdVdYtmlnz1q1+JnZe9Op5y7tPj1FNPG7HF1Jq+z9Qqmz39534hnnzu05c/7/fn4gXbn7um7znYH7Ft64a49tZ9sW3rhhjsj5idXnx+72AYm3uLQe83PbXqtnRHF3pgqca3fXxfXPGTi89NquZxzDZw0CTnZD3zDZSr9HO3GOXFSRNj4YgYC3XGWI3zdi5+VuJQxzvfNfRRDTnCuJR+XQ4cH7Odpw44Uubrcpg0fwujCY63HKq26/IuxOxCjZMykX1b4d/DauiHGnKEcSn93J0mRhv3TLR1r0yiWsZtnPtqajgcVvf+O/fcc0/83u+8LB72sIfH9h2XxGDfvjj/2c+M333la+IRZ50dt/ztR+Od17493vrHf3rY173rHW+P2z//z/Hy33llRES86JLnx1Oe+vT4qZ9+4lFjzs3NxQXnnxdXX3NdbNq06ajb9weL7yIz2B+xsXdwZc4354fLK5UGw1jeZtS2dMdq/ZLJUo2xsPj5of0/yZqbmm3goFLm5Fjne+ry3Y3nMLx0S+PfE7quxHO3GOXFSRNjfhi96cVfCmycrTfGWhzrbFOeUq4BKc+xzHcNfVRDjtCGEq/LgfXr6mxnqQPuS7brcpi0UubE79TqVkofUZ4arsu7ELMLNU7KRPZthX8Pq6EfasgR2lDiuTtNjDbumWjrXplEtYzbuPZVde80881+P177mlfFySefHM8+/z/E3NzemJqaju986MPiw3/93+Lkk0+Jm//2o3HWOVuXv2Zh//6YnpmJs87eGrs++Ffx2c98Oubm5uLOL30pzjrr7LHkudRsR65uOuGQF29p5y9tU+OKLpqxWr9ksnwAXur3A8/XVnMXXitYL3NyUNMLcSzCgWPXxjFJjPLipIkxmyMG3eAakCbU0Ec15AglMjuQU5bZzlIHNMVMwNGZE5qgj2jKJHqpCzG7UOOkTGTfVvj3sBr6oYYcoURp7mfIcs9EW/fKJKpl3Ma1r6pbNPPf//vfx+c+99mIiLjwOdsiIuLkU06Jl1z68rjqrW+Ol7zo4njowx4ez71w8d/+8i/eH5+67bZ4yaUvi8c89nHxs086N3Ze9uqYmZ2Jbb/ynPj2f3f6xGoBAAAAAAAAAAAAAABgPKpbNPOEH/uJeMKP/cTIf3v1Za9b8dy5T31GnPvUZ0RExNTUVDzr2dviWc/eNtYcAQDGwTvXAAAAAAAAAAAAAKxd5W/AAwAAAAAAAAAAAAAAACtZNAMAAAAAAAAAAAAAAEA6Fs0AAAAAAAAAAAAAAACQzuykE8iqPxhGbyZisD9iY29q+fn5+eHB/47FF2AwjJHb0h2r9UsmSzXGwuLntfZ9F14rWC9zUo+py3c3+v2Gl25p9PtBE9o4JolRXpwsMaAmZoIm1NBHpefYnx9GbzpisBCxcba8/Oiu0mdnrcwYHC7NbCepA5piJgDa4XhLUybRS12J2bYu1BjRnTrXq4b9VHqOfpdHqbLcz1D6MWCtstQRkauWcfBOM2PQHwxj+6690du5J7bv2hv9wXD5+cEwlh/f2Lf4+aht6Y7V+iWTpRo379yz3P811tyF1wrWy5zQpKnLdzf6oHvaOCaJUV6cLDGgJmaCJtTQR6Xn2J8fxvYbD+R3497oz5eVH91V+uyslRmDw6WZ7SR1QFPMBEA7HG9pyiR6qSsx29aFGiO6U+d61bCfSs/R7/IoVZb7GUo/BqxVljoictUyLhbNjEFvJuLaW/dFxOLH3szB5w99POB+U6tuS3d0oQeWanzm2b3l/q+x5lrzhjaZk24rfZFL0/lZiFO+No5JYpQXJ0sMqImZoAk19FHpOfamj8jPb34pROmzs1ZmDA6XZraT1AFNMRMA7XC8pSmT6KWuxGxbF2qM6E6d61XDfio9R7/Lo1RZ7mco/RiwVlnqiMhVy7g4FYzBYH/Etq0bImLx42D/wecPfdxz73DVbemOLvTAUo3X3zZY7v8aa641b2iTOQFK0sYxSYzy4mSJATUxEzShhj4qPcfBwhH5LUw4ITig9NlZKzMGh0sz20nqgKaYCYB2ON7SlEn0Ulditq0LNUZ0p871qmE/lZ6j3+VRqiz3M5R+DFirLHVE5KplXKaGw6H331mDubm5uOD88+Lqa66LTZs2HXX7/mAYvZnFJtzYm1p+fv6Qt3mbj4jZiBgMY+S2dMdq/ZLJUo1x4AK0lL5varaBg0qZk2Odb+8awvEYXrpl0il0TonnbjHKi5MlRpcc62xTHjPBao5lvmvoo9Jz7M8Poze9+AfBjbPl5UceJV6Xt8GMkV1nZztJHXBfsl2XA4v8Tq1ujrespobr8q7EbFsXaozoTp2jZLsuLz1Hv8ujLSWeu7PEaEOWOiJy1TIOs5NOIKulZps94r18Zg85+c4e+dH7/nTWav2SyfIB+ECNtfZ9F14rWC9zAsev6cVbFvW0c0wSo7w4WWJATcwETaihj0rPcekPf6XmR3eVPjtrZcbgcGlmO0kd0BQzAdAOx1uaMole6krMtnWhxoju1LleNeyn0nP0uzxKleV+htKPAWuVpY6IXLWMg90CAAAAAAAAAAAAAABAOhbNAAAAAAAAAAAAAAAAkM7spBMAAIBaTV2+e9IpAAAAAAAAAAAAAKvwTjMAAAAAAAAAAAAAAACkY9EMAAAAAAAAAAAAAAAA6Vg0Myb9wTDmF4bRHwwPe35+frjisdq2dEcXemCpxtr7vta8oU3mBChJG8ckMcqLkyUG1MRM0IQa+qiGHKFEWWanP3+gjvm664CmpJntJHVAU8wEHJ05oQn6iKZMope6ErNtXagxojt1rlcN+6mGHKFEWe5nyHIMaO1emRb+vpDlNRkXi2bGoD8YxvZde6O3c09s37V3ufn6g2EMhrHiMWpbumO1fslkqcbNO/dU3fddeK1gvcwJUJI2jklilBcnSwyoiZmgCTX0UQ05QomyzE5/fhjbbzxQx417LZyh89LMdpI6oClmAo7OnNAEfURTJtFLXYnZti7UGNGdOterhv1UQ45Qoiz3M2Q5BrR2r0wLf1/I8pqMk0UzY9Cbibj21n0RsfixN3Pw+VGPUdvSHV3ogaUan3l2r+q+rzVvaJM5AUrSxjFJjPLiZIkBNTETNKGGPqohRyhRltnpTR9Rh7+u0HFpZjtJHdAUMwFHZ05ogj6iKZPopa7EbFsXaozoTp3rVcN+qiFHKFGW+xmyHANau1emhb8vZHlNxsmfdcZgsD9i29YNEbH4cbD/4POjHqO2pTu60ANLNV5/26Dqvq81b2iTOQFK0sYxSYzy4mSJATUxEzShhj6qIUcoUZbZGSwcUcfChBOCCUsz20nqgKaYCTg6c0IT9BFNmUQvdSVm27pQY0R36lyvGvZTDTlCibLcz5DlGNDavTIt/H0hy2syTnoraxoAACAASURBVFPD4dD776zB3NxcXHD+eXH1NdfFpk2bjrp9fzCM3sxiE27sTS0/Pz/iLZUGwxi5Ld2xWr9kslRjHDjYl9L3Tc02cFApc3Ks8z11+e4WsoJ2DS/dMukUGlfiuVuM8uJkidElxzrblMdMsJpjme8a+qiGHKENJV6Xt6E/P4ze9OIfuDbO1lsHrKazs52kDrgv2a7LYdJKmRO/U6tbKX1EeWq4Lu9KzLZ1ocaI7tQ5Srbr8hpyhDaUeO7OEqMNrd0r08LfF7K8JuMyO+kEjsfXvnZ33HLzTXHTRz4cOy5+YXz96/fEq17x2yu2+91XviYecdbZhz334kueH//2b/+6/PmvPOc/xhOf9JTGc1xqttkj3stndkSjL70IR25Ld6zWL5ksH4AP1Fhr33fhtYL1MidASdo4JolRXpwsMaAmZoIm1NBHNeQIJcoyO0t/yKq9DmhKmtlOUgc0xUzA0ZkTmqCPaMokeqkrMdvWhRojulPnetWwn2rIEUqU5X6GLMeA1u6VaeHvC1lek3GpbtFMv9+Pi593YTzkId8Rd9z++YiI+O5HnB1/8vZ3Lm/zsb//u3jvf/6zeNjDv2vF199995546W+8PL77EWdFRMT9NtyvncQBAAAAAAAAAAAAAABoTXWLZjZs2BBXvvWq2HfvvXHJjosiImJmZiY2bz5xeZu//m83xs/87JNidvbw8r75zW9Gv9+P00578GHbjzIYDGIwGCx/3u/PNVgFMClmG/Iy35CT2YaczDbkZb4hJ7MNOZltyMt8Q05mG3Iy25CX+YaczDbUqbpFMzMzM/HABz4o7vrynSP//RO3/s/4ly9+MX7zt353xb/t2bM7IiKufMMV8fWvfz0e9ejHxK9ccGFs2LBhxbYfeN974r03XN9s8sDEmW3Iy3xDTmYbcjLbkJf5hpzMNuRktiEv8w05mW3IyWxDXuYbcjLbUKep4XA4nHQSx+OuL98Zl+y4KF57xRvj9DPOXH7+8te8Mr5ly0lx4fYdK75mbu/e+OhH/iYe+rCHx759++JNr39dPO3nfj7OfeozVmw7aiXgC7Y/N66+5rrYtGnTeIoCxs5sQ17rne+py3ePMz2YiOGlWyadwro5d0NOZhvyMt+Qk9mGnMw25GW+ISezDTmZbcjLfENOZhvqVN07zdyXL37hjvj4P/5DXP66N4z89+mZmfjBH/738aAHPSgiIs7eujW+cMcdI7ft9XrR6/XGliswGWYb8jLfkJPZhpzMNuRlviEnsw05mW3Iy3xDTmYbcjLbkJf5hpzMNtRpetIJNOkv//z9cc7W7znsnWciIhb274+IiNs+cWu8YPsF8cnbPhFf/OIX4jOf+lScceaZo74VAAAAAAAAAAAAAAAAFUuzaGb3V78at9z8t/Gkpzz1sOc/9ve3xMsu/bWIiHjs4743nvq0n4s3XPHa+L3f/s149GMeGz/7s08ZSz79wTDmF4bRHwwPe35+frjisdq2dEcXemCpxtr7vta8oU3mBChJG8ckMcqLI8YxxJg/EGPeeZv1cQ1IE2rooxpyhBJlmR3XTnC4LLMNHM5sw9GZE5qgj2jKJHqpKzHb1oUaI7pT53rVsJ9qyBFKlOZegyTHgNbulWnh7wtZXpNxqXbRzCmnnhbvvuH9y+8qs+Wkk+Kdf/aeeOzjvvew7R7/gz8cl73uDcufP/O8Z8cfXX1N/Mnb3xUXPu8FMTuGt8jqD4axfdfe6O3cE9t37V1uvv5gGINhrHiM2pbuWK1fMlmq8cSde6ru+y68VrBe5gQoSRvHJDHKiyPGMcSYH8b2Gw/EuHGvmz85bq4BaUINfVRDjlCiLLPj2gkOl2W2gcOZbTg6c0IT9BFNmUQvdSVm27pQY0R36lyvGvZTDTlCidLca5DkGNDavTIt/H0hy2syTtUumilZbybi2lv3RcTix97MwedHPUZtS3d0oQeWanzOo3pV932teUObzAlQkjaOSWKUF0eMY4gxfUQMvyHgOLkGpAk19FENOUKJssyOayc4XJbZBg5ntuHozAlN0Ec0ZRK91JWYbetCjRHdqXO9athPNeQIJUpzr0GSY0Br98q08PeFLK/JOPmzzhgM9kds27ohIhY/DvYffH7UY9S2dEcXemCpxrd9fFB139eaN7TJnAAlaeOYJEZ5ccQ4hhgLR8RYaD4G3eAakCbU0Ec15AglyjI7rp3gcFlmGzic2YajMyc0QR/RlEn0Ulditq0LNUZ0p871qmE/1ZAjlCjNvQZJjgGt3SvTwt8Xsrwm4zQ1HA69/84azM3NxQXnnxdXX3NdbNq06ajb9wfD6M0sNuHG3tTy8/Mj3lJpMIyR29Idq/VLJks1xoGDfSl939RsAweVMifHOt9Tl+9uISto1/DSLZNOoXElnrvFKC+OGMcQY34YvenFX9JsnK3nvE15SrkGpDzHMt819FENOUIbSrwub0Mp104wLl2dbeiCbNflMGmlzInfqdWtlD6iPDVcl3clZtu6UGNEd+ocJdt1eQ05QhtKPHdnidGG1u6VaeHvC1lek3GZnXQCWS012+wR7+UzO6LRl16EI7elO1brl0yWD8AHaqy177vwWsF6mROgJG0ck8QoL44YxxBj1nmbZrgGpAk19FENOUKJssyOayc4XJbZBg5ntuHozAlN0Ec0ZRK91JWYbetCjRHdqXO9athPNeQIJUpzr0GSY0Br98q08PeFLK/JuNgtAAAAAAAAAAAAAAAApGPRDAAAAAAAAAAAAAAAAOlYNAMAAAAAAAAAAAAAAEA6Fs0AAAAAAAAAAAAAAACQjkUzAAAAAAAAAAAAAAAApGPRzJj0B8OYXxhGfzA87Pn5+eGKx2rb0h1d6IGlGmvv+1rzhjaZE6AkbRyTxCgvTpYYUBMzQRNq6KMacoQSmR3IKctsZ6kDmmImANrheEtTJtFLXYnZti7UGNGdOterhv1UQ45Qoiz3M2Q5BmSpIyJXLeNg0cwY9AfD2L5rb/R27ontu/YuN19/MIzBMFY8Rm1Ld6zWL5ks1bh5556q+74LrxWslzmBskxdvrvRR23aOCaJUV6cLDGgJmaCJtTQRzXkCCUyO5BTltnOUgc0xUwAtMPxlqZMope6ErNtXagxojt1rlcN+6mGHKFEWe5nyHIMyFJHRK5axsWimTHozURce+u+iFj82Js5+Pyox6ht6Y4u9MBSjc88u1d139eaN7TJnAAlaeOYJEZ5cbLEgJqYCZpQQx/VkCOUyOxATllmO0sd0BQzAdAOx1uaMole6krMtnWhxoju1LleNeynGnKEEmW5nyHLMSBLHRG5ahkXi2bGYLA/YtvWDRGx+HGw/+Dzox6jtqU7utADSzVef9ug6r6vNW9okzkBStLGMUmM8uJkiQE1MRM0oYY+qiFHKJHZgZyyzHaWOqApZgKgHY63NGUSvdSVmG3rQo0R3alzvWrYTzXkCCXKcj9DlmNAljoictUyLlPD4dD776zB3NxcXHD+eXH1NdfFpk2bjrp9fzCM3sxiE27sTS0/Pz+/cncPhjFyW7pjtX7JZKnGWFj8vJS+b2q2gYNKmZNjne+py3e3kBXUbXjplkmnUOS5W4zy4mSJ0SXHOtuUx0ywmmOZ7xr6qIYcoQ0lXpcD69fV2c5SB9yXbNflwCK/U6ub4y2rqeG6vCsx29aFGiO6U+co2a7La8gR2lDiuTtLjDZkqSMiVy3jMDvpBLJaarbZI97LZ3Z2ZRMuvQhHbkt3rNYvmSwfgA/UWGvfd+G1gvUyJ0BJ2jgmiVFenCwxoCZmgibU0Ec15AglMjuQU5bZzlIHNMVMALTD8ZamTKKXuhKzbV2oMaI7da5XDfuphhyhRFnuZ8hyDMhSR0SuWsbBbgEAAAAAAAAAAAAAACAdi2YAAAAAAAAAAAAAAABIx6IZAAAAAAAAAAAAAAAA0rFoBgAAAAAAAAAAAAAAgHRmJ53A8fja1+6OW26+KW76yIdjx8UvjNPPODNuufmmePMbf395mxPvf//447e947CvGw6H8e53XRsf/fDfxMzMTJz7tGfEk899WtvpAwAAAAAAAAAAAAAAMGbVLZrp9/tx8fMujIc85Dvijts/v/z83Xv2xHd/91nx0t98eURETE2tfBOdf/yH/xEf2vXBePnvviq+dvfd8brLXx1bH/k9ccaZD2k+z8EwejMRg/0RG3tTy8/Pzw9XbDsYxsht6Y7V+iWTpRpjYfHzWvu+C68VrJc5AUrSxjFJjPLiZIkBNTETNKGGPqohRyhRltnpzw+jNx0xWIjYOFtvHdAUsw05ZZltgNI53tKUSfRSV2K2rQs1RnSnzvWqYT/VkCOUKMv9DFmOAVnqiMhVyzisXFlSuA0bNsSVb70qLn7hiw97/u49e+JbtmyJzZtPjM2bT4xNmzat+NpP3vaJePh3PSIe+tCHxWMf971x6qmnxac/9cmRcQaDQczNzS0/+v25NefYHwxj+6690du5J7bv2hv9wXD5+cEwVjxGbUt3rNYvmSzVeOLOPRPv+3HMNnDQJOdkPfMNlKv0c7cY5cXJEiM75+1czASHOt75rqGPasgRxqX06/I29OeHsf3GA3XcuDf6I/4HWVAbs222ySvzdTl0md+p5eF4y6Fquy7vSsy2daHGiO7UuSTzdXkNOcK4lH7uzhKjDVnqiMhVy7hU904zMzMz8cAHPiju+vKdhz2/Z8/u+OxnPh07Lrog7v+AB8Qv/fKvxCO/51GHbXPPPffECSecsPz5ps2b45577hkZ5wPve0+894brjyvH3kzEtbfui4jFj1c/afPy86OM2pbuWK1fMlmq8XmP6i3PwaRqHsdsAwdNck7WM99AuUo/d4tRXpwsMbJz3s7FTHCo453vGvqohhxhXEq/Lm9DbzpHHXAos222ySvzdTl0md+p5eF4y6Fquy7vSsy2daHGiO7UuSTzdXkNOcK4lH7uzhKjDVnqiMhVy7hUt2hmNT/xUz8dZ299ZHzXdz0ibvyv/yXe9PrXxVuvelvM9nr3+XVTq7z70NN/7hfiyec+ffnzfn8uXrD9uWvKZbA/YtvWDXHtrfti29YNMdgfMTu9+Pwoo7alO1brl0yWanzbx/fFFT+5+Nykah7HbAMHTXJO1jPfQLlKP3eLUV6cLDGyc97OxUxwqOOd7xr6qIYcYVxKvy5vw2DhiDoW6qwDDmW2zTZ5Zb4uhy7zO7U8HG85VG3X5V2J2bYu1BjRnTqXZL4uryFHGJfSz91ZYrQhSx0RuWoZl6nhcFjl++/c9eU745IdF8Vrr3hjnH7GmbFnz+7YvGlzbLjf/eJfvvjFeOmLL443vvmP4tTTTlv+mne94+1x++f/OV7+O6+MiIgXXfL8eMpTnx4/9dNPPGq8ubm5uOD88+Lqa66LTZs2HXX7/mAYvZnFJtzYO7gyZ37E27oPhjFyW7pjtX7JZKnGWFj8vJS+b2q2gYNKmZNjne+py3e3kBXUbXjplkmnUOS5W4zy4mSJ0SXHOtuUx0ywmmOZ7xr6qIYcoQ0lXpe3oT8/jN704k32G2frrQNWY7bNNnlluy4HFvmdWt0cb1lNDdflXYnZti7UGNGdOkfJdl1eQ47QhhLP3VlitCFLHRG5ahmHNO8087rLXxMnn3xKnP+c/xi33HxTnHji/WPLlsWb+hb274/pmZk46+ytseuDfxWf/cynY25uLu780pfirLPOHks+S8125Cqt2RG/bF96Eazo6q7V+iWT5QPwgRpr7fsuvFawXuYEKEkbxyQxyouTJQbUxEzQhBr6qIYcoURZZmfpZvra64CmmG3IKctsA5TO8ZamTKKXuhKzbV2oMaI7da5XDfuphhyhRFnuZ8hyDMhSR0SuWsYhzaKZC7fviKuv+sN40cXb49TTHhy/+uKXxmyvF3/5F++PT912W7zk0pfFYx77uPjZJ50bOy97dczMzsS2X3lOfPu/O33SqQMAAAAAAAAAAAAAANCwahfNnHLqafHuG96//PmZD/mOeOVrLl+x3blPfUac+9RnRETE1NRUPOvZ2+JZz97WWp4AAAAAAAAAAAAAAAC0zxvwAAAAAAAAAAAAAAAAkI5FMwAAAAAAAAAAAAAAAKRj0QwAAAAAAAAAAAAAAADpWDQzJv3BMOYXhtEfDA97fn5+uOKx2rZ0Rxd6YKnG2vu+1ryhTeYEKEkbxyQxyovTSoz5AzHmne8gwjUgzaihj2rIEUpkdiCnLLOdpQ5oipkAaIfjLU2ZRC91JWbbulBjRHfqXK8a9lMNOUKJ0twzkeQYkKWOiFy1jINFM2PQHwxj+6690du5J7bv2rvcfP3BMAbDWPEYtS3dsVq/ZLJU4+ade6ru+y68VrBe5gQoSRvHJDHKi9NKjPlhbL/xQIwb91o4Q+e5BqQJNfRRDTlCicwO5JRltrPUAU0xEwDtcLylKZPopa7EbFsXaozoTp3rVcN+qiFHKFGaeyaSHAOy1BGRq5ZxsWhmDHozEdfeui8iFj/2Zg4+P+oxalu6ows9sFTjM8/uVd33teYNbTInQEnaOCaJUV6cVmJMHxHDT9Z0nGtAmlBDH9WQI5TI7EBOWWY7Sx3QFDMB0A7HW5oyiV7qSsy2daHGiO7UuV417KcacoQSpblnIskxIEsdEblqGRe39ozBYH/Etq0bImLx42D/wedHPUZtS3d0oQeWarz+tkHVfV9r3tAmcwKUpI1jkhjlxWklxsIRMRaajwE1cQ1IE2rooxpyhBKZHcgpy2xnqQOaYiYA2uF4S1Mm0Utdidm2LtQY0Z0616uG/VRDjlCiNPdMJDkGZKkjIlct4zI1HA69/84azM3NxQXnnxdXX3NdbNq06ajb9wfD6M0sNuHG3tTy8/PzK3f3YBgjt6U7VuuXTJZqjAM3FJbS903NNnBQKXNyrPM9dfnuFrKCug0v3TLpFIo8d4tRXpxWYswPoze9uIBm46zrwvU61tmmPKVcA1KeY5nvGvqohhyhDSVelwPr19XZzlIH3Jds1+XAIr9Tq5vjLaup4bq8KzHb1oUaI7pT5yjZrstryBHaUOK5O0uMNmSpIyJXLeMwO+kEslpqttkj3stndsTNVEsvwpHb0h2r9UsmywfgAzXW2vddeK1gvcwJUJI2jklilBenlRizzndwKNeANKGGPqohRyiR2YGcssx2ljqgKWYCoB2OtzRlEr3UlZht60KNEd2pc71q2E815AglSnPPRJJjQJY6InLVMg52CwAAAAAAAAAAAAAAAOlYNAMAAAAAAAAAAAAAAEA6Fs0AAAAAAAAAAAAAAACQjkUzAAAAAAAAAAAAAAAApGPRDAAAAAAAAAAAAAAAAOlYNDMm/cEw5heG0R8MD3t+fn644rHatnRHF3pgqcba+77WvKFN5gQoSRvHJDHKi5MlBtTETNCEGvqohhyhRGYHcsoy21nqiIjozx+oZb7+WpicTDMB4+J4SxMcb2nKJHqpKzHb1oUaI7pT53rVsJ9Kz9E1G6XKcj9D6ceAtcpSR0SuWsbBopkx6A+GsX3X3ujt3BPbd+1dbr7+YBiDYax4jNqW7litXzJZqnHzzj1V930XXitYL3MClKSNY5IY5cXJEgNqYiZoQg19VEOOUCKzAzllme0sdUQs3gy0/cYDtdy4101BHJdMMwHj4nhLExxvacokeqkrMdvWhRojulPnetWwn0rP0TUbpcpyP0Ppx4C1ylJHRK5axsWimTHozURce+u+iFj82Js5+Pyox6ht6Y4u9MBSjc88u1d139eaN7TJnAAlaeOYJEZ5cbLEgJqYCZpQQx/VkCOUyOxATllmO0sdERG96SNq8VdgjkOmmYBxcbylCY63NGUSvdSVmG3rQo0R3alzvWrYT6Xn6JqNUmW5n6H0Y8BaZakjIlct4+JUMAaD/RHbtm6IiMWPg/0Hnx/1GLUt3dGFHliq8frbBlX3fa15Q5vMCVCSNo5JYpQXJ0sMqImZoAk19FENOUKJzA7klGW2s9QRETFYOKKWhQknRJUyzQSMi+MtTXC8pSmT6KWuxGxbF2qM6E6d61XDfio9R9dslCrL/QylHwPWKksdEblqGZep4XDo/XfWYG5uLi44/7y4+prrYtOmTUfdvj8YRm9msQk39qaWn58f8TZvg2GM3JbuWK1fMlmqMQ5cgJbS903NNnBQKXNyrPM9dfnuFrKCug0v3TLpFIo8d4tRXpwsMbrkWGeb8pgJVnMs811DH9WQI7ShxOtyYP26OttZ6oiI6M8Poze9eHPQxtm6a6FZ2a7LYdJKOd76nVrdHG9ZTQ3X5V2J2bYu1BjRnTpHyXZdXnqOpVyzkV+J5+4sMdqQpY6IXLWMw+ykEzgeX/va3XHLzTfFTR/5cOy4+IVx+hlnxuc+97/i6qveGv/yxS/Gt37bt8VznntRfPcjzlrxtdvO+z9jMBgsf/7S33h5PPZx39d4jkvNNnvEe/nMjjj5Lr0IR25Ld6zWL5ksH4AP1Fhr33fhtYL1MidASdo4JolRXpwsMaAmZoIm1NBHNeQIJTI7kFOW2c5SR8TBm4Ay1MLkZJoJGBfHW5rgeEtTJtFLXYnZti7UGNGdOterhv1Ueo6u2ShVlvsZSj8GrFWWOiJy1TIO1S2a6ff7cfHzLoyHPOQ74o7bPx8REQsLC/H6nZfF477v++Mlv/6y+LPr3hF/+JYr4/VvesthX/uNr389BoNBXPa6N8TJJ58cEREnnLCx9RoAAAAAAAAAAAAAAAAYr+oWzWzYsCGufOtVse/ee+OSHRdFRMS+e++NH/6RH42fedKTY8tJJ8U5Wx8Z/9//+/+s+No9d++JiIhvffC3xob73e8+4wwGg8Pekabfn2uwCmBSzDbkZb4hJ7MNOZltyMt8Q05mG3Iy25CX+YaczDbkZLYhL/MNOZltqFN1i2ZmZmbigQ98UNz15TuXnzth48Z41rO3RUTE/v3748N//aH4kR99woqv3bN7d0xNTcfvvPzS2Du3Nx7/gz8c5z37/Jiamlqx7Qfe95547w3Xj68QYCLMNuRlviEnsw05mW3Iy3xDTmYbcjLbkJf5hpzMNuRktiEv8w05mW2o09RwOBxOOonjcdeX74xLdlwUr73ijXH6GWcuP/9Hb7kyPvOZT8erXvPa2LRp02Ffs3v37vjY390cZ519TnzlK3fFlW+4InZc8qL4gcf/0IrvP2ol4Au2Pzeuvua6Fd8XqIfZhrzWO99Tl+8eZ3qQwvDSLa3HdO6GnMw25GW+ISezDTmZbcjLfENOZhtyMtuQl/mGnMw21Km6d5q5L+96x9vjf/7Tx+MVr/q/Rx54Trjf/eJHfvTH4gEPeEB8x3c+NM444yHxhTvuGLloptfrRa/XayNtoEVmG/Iy35CT2YaczDbkZb4hJ7MNOZltyMt8Q05mG3Iy25CX+YaczDbUaXrSCTTlA+97b/zNh3bFC3/t12Pjpo2xd+83Yv/+/RERsXDg481/+9F48SXPiy/ccXt85tOfin/5ly/EGWeeeV/fFgAAAAAAAAAAAAAAgAqleaeZP7vuHRER8dsv+/Xl5377Fa+Kf/7nz8WnbrstXnLpy+InfvKn4/bbPx+v/N2Xx/T0TDzxSeeOfJeZJvQHw+jNRAz2R2zsTS0/Pz8/XLHtYBgjt6U7VuuXTJZqjIXFz2vt+y68VrBe5gQoSRvHJDHKi5MlBtTETNCEGvqohhyhRGYHcsoy21nqgKaYCYB2ON7SlEn0Uhdi9ueH0ZuOGCxEbJzNWeOkdKXO9aphP9WQI5Qoy/0MrcRo4Xyc6ViWqZZxqPadZk459bR49w3vj9PPWHynmHff8P4Vj7PPeWSc+9RnxEsufVlERMz2enHh9h3xJ29/V1z1tmvjWb/0y2PJrT8YxvZde6O3c09s37U3+oPh8vODYax4jNqW7litXzJZqnHzzj1V930XXitYL3MClKSNY5IY5cXJEgNqYiZoQg19VEOOUCKzAzllme0sdUBTzARAOxxvacokeqkLMfvzw9h+44F4N+6N/oj/SXbjMTtyXOhKnetVw36qIUcoUZb7GVqJ0cL5ONOxLFMt41LtopmS9WYirr11X0QsfuzNHHx+1GPUtnRHF3pgqcZnnt2ruu9rzRvaZE6AkrRxTBKjvDhZYkBNzARNqKGPasgRSmR2IKcss52lDmiKmQBoh+MtTZlEL3UhZm/6iHgt3GHZleNCV+pcrxr2Uw05Qomy3M/QSowWzseZjmWZahkXi2bGYLA/YtvWDRGx+HGw/+Dzox6jtqU7utADSzVef9ug6r6vNW9okzkBStLGMUmM8uJkiQE1MRM0oYY+qiFHKJHZgZyyzHaWOqApZgKgHY63NGUSvdSFmP8/e3ceZ1lZHoj/qeV2U9VooAEViEsybmzuG4riOPnEuIVgHPwFTXcQIx0R1xA0ajRx4sh04rhrNMSAiiIal2Qy020c44IxiaI4iLvR+JsEXLoR7brKra4zf1RXdXf1LbqLe+457/ue7/fzuZ+ybh/qeZ5zn+c9p26d4x0srIi3MN54Ed1ZF7pS56hy2E855AgpKuV6hkZiNHA8LmktK6mWcZmoqmqsn79TVVV85cvXxRFHHBHHHnf8OEON1dzcXJy7+ey45NLLY3Z29qDb9wdV9KYWm3CmN7H8/PyQj4caVDF0W7pjtX4pyVKNsefAlUrf1zXbwF6pzMla53vi4h0NZAV5qy7a2HYKSR67xUgvTikxumSts016zASrWct859BHOeQITUjxvBwYXVdnu5Q64JaUdl4OLPKeWt6st6wmh/PyLsTsz1fRm1y8YHdmuswa29KVOocp7bw8hxyhCSkeu4uJ0cDxuKS1rKRaxmF63AEmJibiv/6XP4zTH/moOPcZW8YdLhlLzTa94rN8pocM7dKLsHJbumO1finJ8gK8p8Zc+74LrxWMypwAKWliTRIjvTilxICcmAnqkEMf5ZAjpMjsQJlKme1S6oC6aqor0QAAIABJREFUmAmAZlhvqUsbvdSFmEsX5pZcY1u6UueocthPOeQIKSrleoZGYjRwPC5pLSuplnFoZLfc7e73iO9851+aCAUAAAAAAAAAAAAAAADj/6SZiIiNGzfGVZ/6ZFz8ylfE7e9whwP+fSImYvPTnt5EKgAAAAAAAAAAAAAAAHRAIzfNXPWpT0RExDVfuHqVLdw0AwAAAAAAAAAAAAAAQH0auWnmvGde0EQYAAAAAAAAAAAAAAAAiIiGbpo5/ZGP2u/7Xbt+Ehs2HN5EaAAAAAAAAAAAAAAAADposqlAV3/un+MlL7wwNj/lrHjG0zZFRMR3v/uv8eqtr4qf/vSnTaXRmP6givmFKvqDar/n5+erAx6rbUt3dKEHlmrMve9zzRuaZE6AlDSxJomRXpxSYkBOzAR1yKGPcsgRUlTK7PTn99Qxn3cdUJdiZruQOqAuZgKgGdZb6tJGL3UlZtO6UGNEd+ocVQ77KYccIUWlXM9QyhpQSh0RZdUyDo3cNPOJj38s/uTi/xrf/va/xOTkZFR7Xosbrv/3+Ow//1O8/8r3NJFGY/qDKrZs3xW9rTtjy/Zdy83XH1QxqOKAx7Bt6Y7V+qUkSzVu2Loz677vwmsFozInQEqaWJPESC9OKTEgJ2aCOuTQRznkCCkqZXb681Vs2banjm273DhD5xUz24XUAXUxEwDNsN5SlzZ6qSsxm9aFGiO6U+eocthPOeQIKSrleoZS1oBS6ogoq5ZxaeSmmQ9/8K/iqKOPije85c/jEac/avn5BzzwwXHKve4dn/n0VU2k0ZjeVMRl194cEYtfe1N7nx/2GLYt3dGFHliq8ckn9rLu+1zzhiaZEyAlTaxJYqQXp5QYkBMzQR1y6KMccoQUlTI7vckVdTTy1xVIVzGzXUgdUBczAdAM6y11aaOXuhKzaV2oMaI7dY4qh/2UQ46QolKuZyhlDSiljoiyahmXRv6s870bro9TTrl3HHHEERET+//bkUceGT/60Y1NpNGYwe6ITSevi4jFr4Pde58f9hi2Ld3RhR5YqvGK6wZZ932ueUOTzAmQkibWJDHSi1NKDMiJmaAOOfRRDjlCikqZncHCijoWWk4IWlbMbBdSB9TFTAA0w3pLXdropa7EbFoXaozoTp2jymE/5ZAjpKiU6xlKWQNKqSOirFrGZaKqqrF//s5zzj8vbnOb28Z/edXWePslb42PbPtfcfl7/yrmdu2KC5//7JjuTcdr3/Bn405jJHNzc3Hu5rPjkksvj9nZ2YNu3x9U0ZtabMKZ3t47hebnD9zdgyqGbkt3rNYvJVmqMfb8MTmVvq9rtoG9UpmTtc73xMU7GsgK8lZdtLHtFJI8douRXpxSYnTJWmeb9JgJVrOW+c6hj3LIEZqQ4nl5E/rzVfQmF2+gmZnOtw5YTWdnu5A64JaUdl4OLPKeWt6st6wmh/PyrsRsWhdqjOhOncOUdl6eQ47QhBSP3aXEaEIpdUSUVcs4TDcR5BGPfFS8/8r3xB//0R/E/Px8RER8+EN/FR/7u4/Ezp0748wnPqmJNBq11GzTKz7LZ3rIH9KWXoSV29Idq/VLSZYX4D015tr3XXitYFTmBEhJE2uSGOnFKSUG5MRMUIcc+iiHHCFFpczO0o0yudcBdSlmtgupA+piJgCaYb2lLm30UldiNq0LNUZ0p85R5bCfcsgRUlTK9QylrAGl1BFRVi3j0MhNM2c+8Unx/3/3X+MfP/Pp5efe8653RkQVp9zrPnHmk85qIg0AAAAAAAAAAAAAAAA6opGbZianpuI5z78wvvLlL8UXr/lC3HjjjTEzMxMnnXxK3O/+D2wiBQAAAAAAAAAAAAAAADqkkZtmrvrUJ+L+D3hQ3POEk+KeJ5zUREgAAAAAAAAAAAAAAAA6rJGbZt74uv8e69ati/vc7/5x6kNPi/ve7wGxbt26JkIDAAAAAAAAAAAAAADQQY3cNPOgh5wa137xmvinz/xD/NNnPhPr16+L+z/gQfGQhz4s7nOf+8V0r7emn/ejH90YV33qE/GJv/9YnH/Bc+OOd7pz/OD73483v/G18a1vfiOOPe74OO+ZF8Sd73yXA/7bv/2bD8fffPiDsXv37njEI/9jnP3UzTExMVFTpQAAAAAAAAAAAAAAAKSgkZtmnvv834uF3bvjK1+5Lj7/uc/F1Vd/Nj591Sfj01d9KmZmDosHPPDB8TvPes4h/ax+vx8X/M4z4i53+YX4zrf/Zfn5d77j7bF+/WGx9dWvj/e9993x1je/If74VX+y33/73e/+a7zzsr+MF774pTE7uyFe8fKXxAknnhz3u/8Daq03IqI/qKI3FTHYHTHT23tTzvx8dcC2gyqGbkt3rNYvJVmqMRYWv8+177vwWsGozAmQkibWJDHSi1NKDMiJmaAOOfRRDjlCikqZnVLqgLqUMhOl1AF1MRNwcP35KnqTEYOFiJlpc8KtY72lLm30UldiNq0LNUZ0p85R5bCfcsgRUlTK9QyNxGjgd6+S1rKSahmHycYCTU3FiSedEk/Z9Fvxsj/84zjz18+KXm86+v1+fPITHz/kn7Nu3bp4/ZvfGhc89/n7Pf/l674Upz3i9Dj6mGPikY/6pfjWN78RP/vZz/bb5ivXfSmOPfbYuNe97xt3vdvd4+73uGd85ctfGhpnMBjE3Nzc8qPfnzvkHPuDKrZs3xW9rTtjy/Zd0R9Uy88PqjjgMWxbumO1finJUo0btu5sve/HMdvAXm3OySjzDaQr9WO3GOnFKSVG6Ry3y2Im2Netne8c+iiHHGFcUj8vb0IpdcC+zHY5dcBKJZ+XQ9v681Vs2bZnTrbtiv6Q/+PUcfGeWjmst+wrt/PyrsRsWhdqjOhOnUtKPi/PIUcYl9SP3cXEaOB3r5LWspJqGZdGPmkmIuK7//qduPpzn42rP/fP8c1vfD0WFhZfjHvc84Q49aGnHfLPmZqaip/7uSPi+9+7Yb/nf3zTTXHYYYdFRMSGDRuWn1t/zDHL29x0002xfs82ERGzsxvipptuGhrnQx94X7z/yisOOa999aYiLrv25ohY/HrJYzYsPz/MsG3pjtX6pSRLNf7mib3lOWir5nHMNrBXm3MyynwD6Ur92C1GenFKiVE6x+2ymAn2dWvnO4c+yiFHGJfUz8ubUEodsC+zXU4dsFLJ5+XQtt6kv4UxOust+8rtvLwrMZvWhRojulPnkpLPy3PIEcYl9WN3MTEa+N2rpLWspFrGpZGbZp79zGfED37wg4hYvFHmrne9ezzkYQ+Lh5x6WmzcuHFscScO4ZOFVtvkjDOfFI99/BnL3/f7c/GsLU8/pLiD3RGbTl4Xl117c2w6eV0MdkdMTy4+P8ywbemO1fqlJEs1vufam+PPfmXxubZqHsdsA3u1OSejzDeQrtSP3WKkF6eUGKVz3C6LmWBft3a+c+ijHHKEcUn9vLwJpdQB+zLb5dQBK5V8Xg5tGyysmJMFfwtj7ay37Cu38/KuxGxaF2qM6E6dS0o+L88hRxiX1I/dxcRo4HevktaykmoZl4mqqsb++Ttnn3Vm3OUXfjFOfehp8ZCHPiyOOeZ2I//M73/vhnj2+efFf/vT18Yd73TnOO/czfHUzefEwx/xyPjaV78SL3vJC+PSd7031q1bt/zfbN/2t/G3f/3heM0b3hIREa98xcviF37xrvEbT/nNg8abm5uLczefHZdcennMzs4edPv+oIre1GITzvT23pozP+TjoQZVDN2W7litX0qyVGMsLH6fSt/XNdvAXqnMyVrne+LiHQ1kBXmrLhrfDe+HKsVjtxjpxSklRpesdbZJj5lgNWuZ7xz6KIccoQkpnpc3oZQ6YDVmO+864JaUdl4ObevPV9GbXLyIa2Y6n7+FkRbrLavJ4by8KzGb1oUaI7pT5zClnZfnkCM0IcVjdzExGvjdq6S1rKRaxqGRT5p59WvfFD93xBHx0Y9si8vefkn89Kc/jZ874og45V73joc//PSYnJoaOcYJJ54Un/z4x+KEE06Kv//YR+Nud7vH8g0zC7t3x+TUVJxwwknxl5e8La7+3D/Hzx1xZHztq1+Nxz3h10aOPcxSs628S2t6yNAuvQju6Oqu1fqlJMsL8J4ac+37LrxWMCpzAqSkiTVJjPTilBIDcmImqEMOfZRDjpCiUmanlDqgLqXMRCl1QF3MBBzc0sVa5oRRWG+pSxu91JWYTetCjRHdqXNUOeynHHKEFJVyPUMjMRr43auktaykWsahkZtmZmZn4sUXvSCuv/76iNj7SSuf/tQn4yPb/me8+A/+KGZmZkaK8dRN58Sb3/i6+N3nXRDHHX98/M75z46IiM/8w1XxwfdfGa/6k9fEHe9053jq5nPibX/2ptg9vzse/SuPjXvf574jxQUAAAAAAAAAAAAAACA9jdw08+53vSOuv/7f4173vm/80i8/Oo444sjYuXNH/O+/2x7XfOHzccW73xm/9bTfXtPPPOZ2t493X/nB5e+PPuaYeOnLX3HAdg859WHxkFMftvz9Yx/3q/HYx/3qrS8GAAAAAAAAAAAAAACA5DVy08znP/fZuMtdfiEu+v2XxsTExPLzD3jgg+MlL7ow/vkfP7Pmm2YAAAAAAAAAAAAAAABgNZNNBPnZz34aP3+nO+93w0xExMTERBx//M/Hj3/84ybSAAAAAAAAAAAAAAAAoCMauWnmuOOOj2u+cHV8/3s37Pf8D77//bjmms/HHY49tok0AAAAAAAAAAAAAAAA6IjpJoI87glnxBtf/5p4wXMviFPude84cuPG+NGNN8b/+eI1cfPNN8dvPGVTE2k0qj+oojcVMdgdMdPb+wk78/PVAdsOqhi6Ld2xWr+UZKnGWFj8Pte+78JrBaMyJ0BKmliTxEgvTikxICdmgjrk0Ec55AgpMjtQJrMNZTLbAM2w3lKXNnqpCzH781X0JiMGCxEz02XW2Jau1DmqHPZTDjlCikq5nqGRGA0cj0tay0qqZRwa+aSZhz389Nh8ztNjamoyPn/1Z+N//932+Nxn/ykiIn7jKb8Zj/yP/6mJNBrTH1SxZfuu6G3dGVu274r+oFp+flDFAY9h29Idq/VLSZZq3LB1Z9Z934XXCkZlToCUNLEmiZFenFJiQE7MBHXIoY9yyBFSZHagTGYbymS2AZphvaUubfRSF2L256vYsm1PvG27oj/k/yS79pgdWRe6UueocthPOeQIKSrleoZGYjRwPC5pLSuplnFp5KaZiIhHP+Zx8Wd/fmm86CUvj/MveG783oteGm9661/EE844s6kUGtObirjs2psjYvFrb2rv88Mew7alO7rQA0s1PvnEXtZ9n2ve0CRzAqSkiTVJjPTilBIDcmImqEMOfZRDjpAiswNlMttQJrMN0AzrLXVpo5e6ELM3uSJeA1dYdmVd6Eqdo8phP+WQI6SolOsZGonRwPG4pLWspFrGpbGbZiIi1q1fH6fc697xsIefHve57/1idna2yfCNGeyO2HTyuohY/DrYvff5YY9h29IdXeiBpRqvuG6Qdd/nmjc0yZwAKWliTRIjvTilxICcmAnqkEMf5ZAjpMjsQJnMNpTJbAM0w3pLXdropS7EHCysiLcw3ngR3VkXulLnqHLYTznkCCkq5XqGRmI0cDwuaS0rqZZxmaiqyufvHIK5ubk4d/PZccmllx/SzT79QRW9qcUmnOlNLD8/P+TjoQZVDN2W7litX0qyVGPsOXCl0vd1zTawVypzstb5nrh4RwNZQd6qiza2nUKSx24x0otTSowuWetskx4zwWrWMt859FEOOUITUjwvB0ZntqFcpZ2XA4u8p5Y36y2ryeG8vAsx+/NV9CYXL9idmS6zxrZ0pc5hSjsvzyFHaEKKx+5iYjRwPC5pLSuplnGYbjuBUi012/SKz/KZHjK0Sy/Cym3pjtX6pSTLC/CeGnPt+y68VjAqcwKkpIk1SYz04pQSA3JiJqhDDn2UQ46QIrMDZTLbUCazDdAM6y11aaOXuhBz6cLckmtsS1fqHFUO+ymHHCFFpVzP0EiMBo7HJa1lJdUyDnYLAAAAAAAAAAAAAAAAxXHTDAAAAAAAAAAAAAAAAMVx0wwAAAAAAAAAAAAAAADFcdMMAAAAAAAAAAAAAAAAxXHTDAAAAAAAAAAAAAAAAMVx08yY9AdVzC9U0R9U+z0/P18d8FhtW7qjCz2wVGPufZ9r3tAkcwKkpIk1SYz04hQTY35PjPm8Y9ANzgGpQw59lEOOkCKzA2UqZbZLqQPqYiYAmmG9pS5t9FJXYjatCzVGdKfOUeWwn3LIEVJUzPUMhawBpdQRUVYt4+CmmTHoD6rYsn1X9LbujC3bdy03X39QxaCKAx7DtqU7VuuXkizVuGHrzqz7vguvFYzKnAApaWJNEiO9OMXEmK9iy7Y9MbbtGstNLU3EoBucA1KHHPoohxwhRWYHylTKbJdSB9TFTAA0w3pLXdropa7EbFoXaozoTp2jymE/5ZAjpKiY6xkKWQNKqSOirFrGxU0zY9Cbirjs2psjYvFrb2rv88Mew7alO7rQA0s1PvnEXtZ9n2ve0CRzAqSkiTVJjPTiFBNjckWMMfz23kQMusE5IHXIoY9yyBFSZHagTKXMdil1QF3MBEAzrLfUpY1e6krMpnWhxoju1DmqHPZTDjlCioq5nqGQNaCUOiLKqmVcXBIzBoPdEZtOXhcRi18Hu/c+P+wxbFu6ows9sFTjFdcNsu77XPOGJpkTICVNrElipBenmBgLK2Is5BmDbnAOSB1y6KMccoQUmR0oUymzXUodUBczAdAM6y11aaOXuhKzaV2oMaI7dY4qh/2UQ46QomKuZyhkDSiljoiyahmXiaqqfP7OIZibm4tzN58dl1x6eczOzh50+/6git7UYhPO9CaWn5+fP3B3D6oYui3dsVq/lGSpxthzIV4qfV/XbAN7pTIna53viYt3NJAV5K26aGPbKSR57BYjvTjFxJivoje5eHPLzHS+MQ7FWmeb9KRyDkh61jLfOfRRDjlCE1I8LwdG19XZLqUOuCWlnZcDi7ynljfrLavJ4by8KzGb1oUaI7pT5zClnZfnkCM0IcVjdykxmlBKHRFl1TIO020nUJc3v+G18YmPf2y/525/+zvEa97wluXvv/bVr8TLXvLC/bZ5+2XvjsNmZmrPZ6nZpld8ls/0kIuQll6EldvSHav1S0mWF+A9Neba9114rWBU5gRISRNrkhjpxSkmxnQZMegG54DUIYc+yiFHSJHZgTKVMtul1AF1MRMAzbDeUpc2eqkrMZvWhRojulPnqHLYTznkCCkq5nqGQtaAUuqIKKuWcSjmppmn/faW2HTOucvfv3rrq+IX/8Nd99vmxht3xlFHHR0X/+lrlp8bxw0zAAAAAAAAAAAAAAAAtKuYm2bWr18f69evj4iIf/3Ot+OrX/ly/M75z9lvmxt37owjjjwyNmw4/KA/bzAYxGAwWP6+35+rN2GgFWYbymW+oUxmG8pktqFc5hvKZLahTGYbymW+oUxmG8pktqFc5hvKZLYhT8XcNLOvv/nrD8UDHvTgOPqYY/Z7fueOHfH9738vnv3MZ0Rv3bp44pPOioed9oihP+NDH3hfvP/KK5pIF2iQ2YZymW8ok9mGMpltKJf5hjKZbSiT2YZymW8ok9mGMpltKJf5hjKZbcjTRFVVVdtJ1GnHD38Yzz7/vHjpy18R97jnCfv927e++Y34xte/FieedEr842c+HR/8qyvjdW96axx55MYDfs6wOwGfteXpccmll8fs7OzY6wDGw2xDuUad74mLd4wzPShCddGB583j5tgNZTLbUC7zDWUy21Amsw3lMt9QJrMNZTLbUC7zDWUy25Cn4j5p5n/+j7+OO9/lLgfcMBMRcdTRR8exxx0fMzMzccztfi3e9953x7/93/879KaZXq8XvV6viZSBBpltKJf5hjKZbSiT2YZymW8ok9mGMpltKJf5hjKZbSiT2YZymW8ok9mGPE22nUCd5ubm4qN/tz0e87gnLD+3sHv38v9++5+/NV71x38YP/zB9+MTH/9YTE9Px7HHHd9GqgAAAAAAAAAAAAAAAIxRUTfNfPQj22L9YevjIac+bPm5P936qvgff/2hiIh46qZzYv369fGC5z4r/ubDH4hnXvDc2LjxwE+ZqUN/UMX8QhX9QbXf8/Pz1QGP1balO7rQA0s15t73ueYNTTInQEqaWJPESC9OKTEgJ2aCOuTQR/35PTnOp5sjpCiH+QbWrpTZdnyH/ZUy2zBOjh3UwXpLXdropa7EbFoXaozoTp2jymE/5ZAjpKiU6xlKWQNKqSOirFrGoaibZp5wxpnx5re+Paanp5efu/CFL47HPeGMiIg4+phj4vdf+ofxl++8Il77hj+LUx962ljy6A+q2LJ9V/S27owt23ctN19/UMWgigMew7alO1brl5Is1bhh686s+74LrxWMypwAKWliTRIjvTilxICcmAnqkEMf9eer2LJtT47bdrk4Cg5RDvMNrF0ps+34DvsrZbZhnBw7qIP1lrq00Utdidm0LtQY0Z06R5XDfsohR0hRKdczlLIGlFJHRFm1jEtRN82kojcVcdm1N0fE4tfe1N7nhz2GbUt3dKEHlmp88om9rPs+17yhSeYESEkTa5IY6cUpJQbkxExQhxz6qDe5IkfvrMIhyWG+gbUrZbYd32F/pcw2jJNjB3Ww3lKXNnqpKzGb1oUaI7pT56hy2E855AgpKuV6hlLWgFLqiCirlnHx6/sYDHZHbDp5XUQsfh3s3vv8sMewbemOLvTAUo1XXDfIuu9zzRuaZE6AlDSxJomRXpxSYkBOzAR1yKGPBgsrclxoOSHIRA7zDaxdKbPt+A77K2W2YZwcO6iD9Za6tNFLXYnZtC7UGNGdOkeVw37KIUdIUSnXM5SyBpRSR0RZtYzLRFVVPn/nEMzNzcW5m8+OSy69PGZnZw+6fX9QRW9qsQlnehPLz88P+WjeQRVDt6U7VuuXkizVGHveNEyl7+uabWCvVOZkrfM9cfGOBrKCvFUXbWw7hSSP3WKkF6eUGF2y1tkmPWaC1axlvnPoo/58Fb3JxYukZqbTzBGakOJ5OTC6rs624ztdUNp5ObQtlWOH99TyZr1lNTmcl3clZtO6UGNEd+ocprTz8hxyhCakeOwuJUYTSqkjoqxaxmG67QRKtdRs0ys+y2d6yBsmSy/Cym3pjtX6pSTLC/CeGnPt+y68VjAqcwKkpIk1SYz04pQSA3JiJqhDDn20dDFUyjlCinKYb2DtSpltx3fYXymzDePk2EEdrLfUpY1e6krMpnWhxoju1DmqHPZTDjlCikq5nqGUNaCUOiLKqmUc7BYAAAAAAAAAAAAAAACK46YZAAAAAAAAAAAAAAAAiuOmGQAAAAAAAAAAAAAAAIrjphkAAAAAAAAAAAAAAACK46YZAAAAAAAAAAAAAAAAiuOmmTHpD6qYX6iiP6j2e35+vjrgsdq2dEcXemCpxtz7Pte8oUnmBEhJE2uSGOnFKSUG5MRMUIcc+iiHHCFFZgfKVMps9+f31DGfdx2kKcc5yTFnaJpjB3Ww3lKXNnqpKzGb1sp+beGY1oXXsg457KcccmQ0XuPxKOV6hkZiNHCcauxamYJqyZWbZsagP6hiy/Zd0du6M7Zs37XcfP1BFYMqDngM25buWK1fSrJU44atO7Pu+y68VjAqcwKkpIk1SYz04pQSA3JiJqhDDn2UQ46QIrMDZSpltvvzVWzZtqeObbtc/EytcpyTHHOGpjl2UAfrLXVpo5e6ErNprezXFo5pXXgt65DDfsohR0bjNR6PUq5naCRGA8epxq6VKaiWnLlpZgx6UxGXXXtzRCx+7U3tfX7YY9i2dEcXemCpxief2Mu673PNG5pkToCUNLEmiZFenFJiQE7MBHXIoY9yyBFSZHagTKXMdm9yRR3+ckqNcpyTHHOGpjl2UAfrLXVpo5e6ErNprezXFo5pXXgt65DDfsohR0bjNR6PUq5naCRGA8epxq6VKaiWnPn1fQwGuyM2nbwuIha/DnbvfX7YY9i2dEcXemCpxiuuG2Td97nmDU0yJ0BKmliTxEgvTikxICdmgjrk0Ec55AgpMjtQplJme7Cwoo6FlhOiKDnOSY45Q9McO6iD9Za6tNFLXYnZtFb2awvHtC68lnXIYT/lkCOj8RqPRynXMzQSo4HjVGPXyhRUS84mqqry+TuHYG5uLs7dfHZccunlMTs7e9Dt+4MqelOLTTjTm1h+fn7IRyoNqhi6Ld2xWr+UZKnG2LPYp9L3dc02sFcqc7LW+Z64eEcDWUHeqos2tp1CksduMdKLU0qMLlnrbJMeM8Fq1jLfOfRRDjlCE1I8LwdG19XZ7s9X0Ztc/OP1zHS+dZCmVOaktPNyaFsqxw7vqeXNestqcjgv70rMprWyX1s4pnXhtVxNaeflOeTIaLzGhybFY3cxMRo4TjV2rUxBteRquu0ESrXUbNMrPstnekijL70IK7elO1brl5IsL8B7asy177vwWsGozAmQkibWJDHSi1NKDMiJmaAOOfRRDjlCiswOlKmU2V76I3XudZCmHOckx5yhaY4d1MF6S13a6KWuxGxaK/u1hWNaF17LOuSwn3LIkdF4jcejlOsZGonRwHGqsWtlCqolV3YLAAAAAAAAAAAAAAAAxXHTDAAAAAAAAAAAAAAAAMVx0wwAAAAAAAAAAAAAAADFcdMMAAAAAAAAAAAAAAAAxZluO4E6/df/8vL44jVfWP7+cU84I5666Zzl73/w/e/Hm9/42vjWN78Rxx53fJz3zAvizne+S/OJAgAAAAAAAAAAAAAAMFZFfdLMzp0745ynPyP+/C/fGX/+l++Ms/6/p+z37+98x9tj/frDYuurXx93utOd461vfsPYcukPqphfqKI/qPZ7fn6+OuCx2rZ0Rxd6YKnG3Ps+17yhSeYESEm29tPkAAAgAElEQVQTa5IY6cUpJQbkxExQhxz6KIccIUVmB25Zf37PjMznNSOlzHYpdUBdzARAM6y31KWNXupCzDZ+T+vKutCVOkeVw37KIUdGk+t7Vqkr5XqGRmI00IMlrWWl1DKu172om2Zu3Lkzbne728eGDYfHhg2Hx7p16/b79y9f96U47RGnx9HHHBOPfNQvxbe++Y342c9+NvRnDQaDmJubW370+3OHnEd/UMWW7buit3VnbNm+a7n5+oMqBlUc8Bi2Ld2xWr+UZKnGw7fubL3vxzHbwF5tzsko8w2kK/VjtxjpxSklRukct8tiJtjXrZ3vHPoohxxhXFI/L4ec9eer2LJtz4xs29XoRQhmu5w6YKWSz8uhy7ynVg7rLfvK7by8CzHb+D2tK+tCV+pcUvJ5eQ45Mpo237NKXerH7mJiNNCDJa1lpdQyztd9oqqqPPfKCvODQWx6yllx/M/fMXb95Cdxt7vfI55+3u/EbW5z2+Vtzj7rzPjdi34/7nf/B8Z3//U78XsveE68/k1vi6OPOeaAn/e+97473n/lFQc8f8mll8fs7Owt57JQRW/rzuXvBxceGdOTEzG/MHxXD9uW7litX0qyVOPv3LsXr/vlwyOivb4fx2wDe7U5J6PMd0TExMU7xpEWFKW6aGPjMVM/douRXpxSYpRu1OM2aTET7OvWzncOfZRDjjAuqZ+XQ85yfT+tlNkupQ5YqeTzcugy76mVw3rLvnI7L+9CzC7U2Jau1Lmk5PPyHHJkNF7j1aV+7BYjrRhNKaWWcdYxXctPScHERPzWub8dd7jDcTE7OxtvfP1r4n3vfU+cc+4zDvafDXXGmU+Kxz7+jOXv+/25eNaWpx9SKoPdEZtOXheXXXtzbDp5XQx2R0xPLj4/zLBt6Y7V+qUkSzX+xTU3x5/+p8Xn2qp5HLMN7NXmnIwy30C6Uj92i5FenFJilM5xuyxmgn3d2vnOoY9yyBHGJfXzcsjZYGHFjCzk8X5aKbNdSh2wUsnn5dBl3lMrh/WWfeV2Xt6FmG38ntaVdaErdS4p+bw8hxwZTZvvWaUu9WN3MTEa6MGS1rJSahnn617MJ83cfPPN8ZMf/zg2HnVURES86x1/Gd/4+tfjZX/0x8vbnHfu5njq5nPi4Y94ZHztq1+Jl73khXHpu94b69atO+jPn5ubi3M3n33I/w8d/UEVvanFJpzp7b0zZ37IxwQNqhi6Ld2xWr+UZKnGWFj8PpW+r2u2gb1SmZO1zrdPmoGDa+OTZlZK8dgtRnpxSonRJWudbdJjJljNWuY7hz7KIUdoQorn5ZCz/nwVvcnFPwjOTOfzflops11KHXBLSjsvBxZ5Ty1v1ltWk8N5eRditvF7WlfWha7UOUxp5+U55MhoUnnPKnUpHruLidFAD5a0lpVSy7he92I+aeaG6/89fu8Fz4lnP/d34xfvetf44jVfiHuecGIs7N4dk1NTERFxwoknxSc//rE44YST4u8/9tG4293ucUg3zNwaS8228u6m6SEv3tKLkOMdXdRjtX4pyfICvKfGXPu+C68VjMqcAClpYk0SI704pcSAnJgJ6pBDH+WQI6TI7MAtW/rDX24zUspsl1IH1MVMADTDektd2uilLsRs4/e0rqwLXalzVDnspxxyZDS5vmeVulKuZ2gkRgM9WNJaVkot43rdM98te93xTneOzU97erzzsrfHiy58XtzhDneI//zks+NFF70g/vEfPh0REU/ddE7s3r0Qv/u8C+Lb//Kt+O0tz2w5awAAAAAAAAAAAAAAAMahmE+aiYj4lcc8Pn7lMY/f77mL/+Q1y//76GOOiZe+/BVNpwUAAAAAAAAAAAAAAEDDivmkGQAAAAAAAAAAAAAAAFjiphkAAAAAAAAAAAAAAACK46YZAAAAAAAAAAAAAAAAiuOmmTHpD6qYX6iiP6j2e35+vjrgsdq2dEcXemCpxtz7Pte8oUnmBEhJE2uSGOnFKSUG5MRMUIcc+iiHHFPWn9+z/+btv64xO1CmUma7lDpIU47nP2YCoBnWW+rSRi91JWbTulBjRHfqHFUO+ymHHFOWw++LOeSYo1KuZ2gkRgM9WNJaVkot43rd3TQzBv1BFVu274re1p2xZfuu5ebrD6oYVHHAY9i2dMdq/VKSpRo3bN2Zdd934bWCUZkTICVNrElipBenlBiQEzNBHXLooxxyTFl/voot2/bsv227/JGtQ8wOlKmU2S6lDtKU4/mPmQBohvWWurTRS12J2bQu1BjRnTpHlcN+yiHHlOXw+2IOOeaolOsZGonRQA+WtJaVUss4X3c3zYxBbyrismtvjojFr72pvc8Pewzblu7oQg8s1fjkE3tZ932ueUOTzAmQkibWJDHSi1NKDMiJmaAOOfRRDjmmrDe5Yv95Z7ozzA6UqZTZLqUO0pTj+Y+ZAGiG9Za6tNFLXYnZtC7UGNGdOkeVw37KIceU5fD7Yg455qiU6xkaidFAD5a0lpVSyzhfd8vYGAx2R2w6eV1ELH4d7N77/LDHsG3pji70wFKNV1w3yLrvc80bmmROgJQ0sSaJkV6cUmJATswEdcihj3LIMWWDhRX7b6HlhGiM2YEylTLbpdRBmnI8/zETAM2w3lKXNnqpKzGb1oUaI7pT56hy2E855JiyHH5fzCHHHJVyPUMjMRrowZLWslJqGefrPlFVVZ6fv9Owubm5OHfz2XHJpZfH7OzsQbfvD6roTS024UxvYvn5+SEfEzSoYui2dMdq/VKSpRpjzwKWSt/XNdvAXqnMyVrne+LiHQ1kBXmrLtrYdgpJHrvFSC9OKTG6ZK2zTXrMBKtZy3zn0Ec55Jiy/nwVvcnFN7xnpu2/nKV4Xg6MrquzXUodpCmV85/SzsuBRd5Ty5v1ltXkcF7elZhN60KNEd2pc5jSzstzyDFlqfy+eEtyyDEFKR67i4nRQA+WtJaVUsu4Xvfp2n4S+1lqtukVn+UzPeTFW3oRVm5Ld6zWLyVZXoD31Jhr33fhtYJRmRMgJU2sSWKkF6eUGJATM0EdcuijHHJM2dIb2/Zf95gdKFMps11KHaQpx/MfMwHQDOstdWmjl7oSs2ldqDGiO3WOKof9lEOOKcvh98UccsxRKdczNBKjgR4saS0rpZZxve6Z7xYAAAAAAAAAAAAAAAA4kJtmAAAAAAAAAAAAAAAAKI6bZgAAAAAAAAAAAAAAACiOm2YAAAAAAAAAAAAAAAAojptmAAAAAAAAAAAAAAAAKI6bZsakP6hifqGK/qDa7/n5+eqAx2rb0h1d6IGlGnPv+1zzhiaZEyAlTaxJYqQXp5QYkBMzQR1y6KP+/J4c59PMMYd9SDfpTShTKbNdSh1QFzMB0AzrLXVpo5e6ELON9wG7si50pc5R5bCfUs8x9fzorlKuZ2gkRgPH45LWipJqGQc3zYxBf1DFlu27ord1Z2zZvmu5+fqDKgZVHPAYti3dsVq/lGSpxg1bd2bd9114rWBU5gRISRNrkhjpxSklBuTETFCHHPqoP1/Flm17cty2K7kbZ3LYh3ST3oQylTLbpdQBdTETAM2w3lKXNnqpCzHbeB+wK+tCV+ocVQ77KfUcU8+P7irleoZGYjRwPC5prSiplnFx08wY9KYiLrv25ohY/Nqb2vv8sMewbemOLvTAUo1PPrGXdd/nmjc0yZwAKWliTRIjvTilxICcmAnqkEMf9SZX5JjYO6s57EO6SW9CmUqZ7VLqgLqYCYBmWG+pSxu91IWYbbwP2JV1oSt1jiqH/ZR6jqnnR3eVcj1DIzEaOB6XtFaUVMu4JPan3TIMdkdsOnldRCx+Heze+/ywx7Bt6Y4u9MBSjVdcN8i673PNG5pkToCUNLEmiZFenFJiQE7MBHXIoY8GCytyXGg5oRVy2Id0k96EMpUy26XUAXUxEwDNsN5SlzZ6qQsx23gfsCvrQlfqHFUO+yn1HFPPj+4q5XqGRmI0cDwuaa0oqZZxmaiqyufvHIK5ubk4d/PZccmll8fs7OxBt+8PquhNLTbhTG9i+fn5IR8PNahi6LZ0x2r9UpKlGmPPgSuVvq9rtoG9UpmTtc73xMU7GsgK8lZdtLHtFJI8douRXpxSYnTJWmeb9JgJVrOW+c6hj/rzVfQmF9+on5lOL8cc9iFlSPG8HBhdV2e7lDrglpR2Xg4s8p5a3qy3rCaH8/IuxGzjfcCurAtdqXOY0s7LU88x9fwoR4rH7mJiNHA8LmmtKKmWcZhuO4E6/fWHPhB/+zcfjp/97Kdxr/vcN7Y889lx2GGHLf/7VZ/6RLzhta9e/v7w29wm3vYX7xhLLkvNNr3is3ymhwzt0ouwclu6Y7V+KcnyArynxlz7vguvFYzKnAApaWJNEiO9OKXEgJyYCeqQQx8tvSGfao457EO6SW9CmUqZ7VLqgLqYCYBmWG+pSxu91IWYbbwP2JV1oSt1jiqH/ZR6jqnnR3eVcj1DIzEaOB6XtFaUVMs4FHPTzBev+UJc8e53xUW//9I44sgj45V/9LL4X3/71/FrT/zPy9vcuHNn3OMeJ8SFL3pxRERMTOgKAAAAAAAAAAAAAACAEhVz08z09HSc/dRNccq97h0REcced1z86MYf7bfNjTt3xpEbN8aGDYcf9OcNBoMYDAbL3/f7c/UmDLTCbEO5zDeUyWxDmcw2lMt8Q5nMNpTJbEO5zDeUyWxDmcw2lMt8Q5nMNuSpmJtmTjzp5DjxpJMjIuI73/6X+MbXvxZnP3Xzftvs3Lkjvv61r8b5550bt7ntbeMpv/lbyzfZrPShD7wv3n/lFWPPG2iW2YZymW8ok9mGMpltKJf5hjKZbSiT2YZymW8ok9mGMpltKJf5hjKZbcjTRFVVVdtJ1OmHP/xBvOzFL4yHP+KR8eSzn7rfv133pf8T119/fdz97veMbf/zf8Rn/uGqePNb/yKme70Dfs6wOwGfteXpccmll8fs7OzY6wDGw2xDuUad74mLd4wzPShCddHGxmM6dkOZzDaUy3xDmcw2lMlsQ7nMN5TJbEOZzDaUy3xDmcw25KmYT5qJiLjpppvila94eZx08ilx1m885YB/P/a44+Oud717rFu/Ph79mMfF333kf8WOHTvidre//QHb9nq96A25mQbIm9mGcplvKJPZhjKZbSiX+YYymW0ok9mGcplvKJPZhjKZbSiX+YYymW3IUzE3zfy034//9spXxNFHHx1P3XxOzM3tiomJyThs/fqYnJqKiIg/ufiVcfTRx8Tmp/12XPWpT8Thh98mNm5s/v8tGwAAAAAAAAAAAAAAgPEq5qaZf/zHf4hvfvPrERHxjKdtioiIo485JmZnN8QTf/2sePCpD41nbDk/LnnrW+J5F2yJ293+DvGc518Y02O6268/qKI3FTHYHTHTm1h+fn6+OmDbQRVDt6U7VuuXkizVGAuL3+fa9114rWBU5gRISRNrkhjpxSkmxnwVvcmIwULEzLRjKmlzDkgdcuijHHKEFJmd9DjXpA6lzHYpdUSYbepR0kwApMx6S13a6KUuxOxCjW3pSp2jymE/pZ5j6vnRXcVczyBGckqqZRwm206gLqc/8lHx7is/uN/j9W96W1z8J6+JB5/60IiIuPNdfiH+6JUXx6Xvem9sffXr4uRT7jWWXPqDKrZs3xW9rTtjy/Zd0R9Uy88PqjjgMWxbumO1finJUo0btu7Muu+78FrBqMwJkJIm1iQx0otTTIz5KrZs2xNj267oD/k/YIBUOAekDjn0UQ45QorMTnqca1KHUma7lDoizDb1KGkmAFJmvaUubfRSF2J2oca2dKXOUeWwn1LPMfX86K5irmcQIzkl1TIuxdw0k5LeVMRl194cEYtfe1N7nx/2GLYt3dGFHliq8ckn9rLu+1zzhiaZEyAlTaxJYqQXp5gYkyti+O2dhDkHpA459FEOOUKKzE56nGtSh1Jmu5Q6Isw29ShpJgBSZr2lLm30UhdidqHGtnSlzlHlsJ9SzzH1/OiuYq5nECM5JdUyLt4uHYPB7ohNJ6+LiMWvg917nx/2GLYt3dGFHliq8YrrBln3fa55Q5PMCZCSJtYkMdKLU0yMhRUxFuqPAXVxDkgdcuijHHKEFJmd9DjXpA6lzHYpdUSYbepR0kwApMx6S13a6KUuxOxCjW3pSp2jymE/pZ5j6vnRXcVczyBGckqqZVwmqqry+TuHYG5uLs7dfHZccunlMTs7e9Dt+4MqelOLTTjTm1h+fn7IR6EPqhi6Ld2xWr+UZKnG2PNHmlT6vq7ZBvZKZU7WOt8TF+9oICvIW3XRxrZTSPLYLUZ6cYqJMV9Fb3LxwqeZ6bLPPdc626QnlXNA0rOW+c6hj3LIEZqQ4nk5a9Olc00OXVdnu5Q6Isw2qyvtvBxY5D21vFlvWU0O5+VdiNmFGtvSlTqHKe28PPUcU8+PcqR47BYjrRhNKamWcZhuO4FSLTXb9IrP8pke8gb10ouwclu6Y7V+KcnyArynxlz7vguvFYzKnAApaWJNEiO9OMXEmHZMJR/OAalDDn2UQ46QIrOTHuea1KGU2S6ljgizTT1KmgmAlFlvqUsbvdSFmF2osS1dqXNUOeyn1HNMPT+6q5jrGcRITkm1jIPdAgAAAAAAAAAAAAAAQHHcNAMAAAAAAAAAAAAAAEBx3DQDAAAAAAAAAAAAAABAcdw0AwAAAAAAAAAAAAAAQHHcNAMAAAAAAAAAAAAAAEBx3DQzJv1BFfMLVfQH1X7Pz89XBzxW25bu6EIPLNWYe9/nmjc0yZwAKWliTRIjvTjFxJjfE2PeMZX0OQekDjn0UQ45UrZczw/MDtwys92uXPc/jEspsw3jZE6ogz6iLm30UhditvF7gnWBfeXQDznkyGhSf88k9fxWU8z1DIVcM9HYtTKZ9mtJ3DQzBv1BFVu274re1p2xZfuu5UHqD6oYVHHAY9i2dMdq/VKSpRo3bN2Zdd934bWCUZkTICVNrElipBenmBjzVWzZtifGtl3eOCFpzgGpQw59lEOOlC3X8wOzA7fMbLcr1/0P41LKbMM4mRPqoI+oSxu91IWYbfyeYF1gXzn0Qw45MprU3zNJPb/VFHM9QyHXTDR2rUym/VoaN82MQW8q4rJrb46Ixa+9qb3PD3sM25bu6EIPLNX45BN7Wfd9rnlDk8wJkJIm1iQx0otTTIzJFTH89k7CnANShxz6KIccKVuu5wdmB26Z2W5XrvsfxqWU2YZxMifUQR9RlzZ6qQsx2/g9wbrAvnLohxxyZDSpv2eSen6rKeZ6hkKumWjsWplM+7U0dvsYDHZHbDp5XUQsfh3s3vv8sMewbemOLvTAUo1XXDfIuu9zzRuaZE6AlDSxJomRXpxiYiysiLFQfwyoi3NA6pBDH+WQI2XL9fzA7MAtM9vtynX/w7iUMtswTuaEOugj6tJGL3UhZhu/J1gX2FcO/ZBDjowm9fdMUs9vNcVcz1DINRONXSuTab+WZqKqKp/xcwjm5ubi3M1nxyWXXh6zs7MH3b4/qKI3tThQM72J5efnh3yk0qCKodvSHav1S0mWaow9i30qfV/XbAN7pTIna53viYt3NJAV5K26aGPbKSR57BYjvTjFxJivoje5+AbKzHTZ555rnW3Sk8o5IOlZy3zn0Ec55EjZUjk/SPG8HHJmttuVyv6HcSrtvBzalsqceE8tb6n0EenJ4by8CzHb+D3BulC+0s7Lc8iR0aT+nkkq+aV47C4mRgOvcWPXyiTSr1023XYCpVoanOkVn+UzPaTRl16EldvSHav1S0mWDyZ7asy177vwWsGozAmQkibWJDHSi1NMjGnHVPLhHJA65NBHOeRI2XI9PzA7cMvMdrty3f8wLqXMNoyTOaEO+oi6tNFLXYjZxu8J1gX2lUM/5JAjo0n9PZPU81tNMdczFHLNRGPXymTaryWx6wEAAAAAAAAAAAAAACiOm2YAAAAAAAAAAAAAAAAojptmAAAAAAAAAAAAAAAAKI6bZgAAAAAAAAAAAAAAACjOdNsJ1OkfrvpUvOfyd8SuXT+JBzzoIXHub2+JXq+3/O8/+clP4s/e9Pr40rVfjCM3HhVPe/p5cdLJp7SYMQAAAAAAAAAAAAAAAONQzCfN/PjHN8Vb3vS6OPNJZ8XLX/GquOYLV8fHPvqR/bb50AfeFz/84Q/iVVtfEw968Knxxtf995gfDFrKGAAAAAAAAAAAAAAAgHEp5pNmvvmNr0dVRZz+yEfFxMRE3Pe+948vX/el+OVfeezyNl++7kvx4Ic8NG53+9vHL/3yo+ODf3Vl3HDD9XH8z9/xgJ83GAxisM8NNf3+XCN1AONltqFc5hvKZLahTGYbymW+oUxmG8pktqFc5hvKZLahTGYbymW+oUxmG/JUzE0zN910U6w/bH1MTExERMTshg1xww037LfNj2+6KQ477LCIiNgwu2H5vzt+yM/70AfeF++/8ooDnre4QbtmZmaW5/zWMNuQLvMN6ZubW/s8mW0ok9mGMo062xHmG1Ll2A1lMttQJuflUC7HbiiT2YYyOS+Hcjl2Q5kONtsTVVVVDeYzNp/4+MfiHZf+RbztL94RERHvvOzt8e1/+Va85GWvWN7mOeefF499/K/Gox/zuPhpvx/nbPqNeNkf/XHc84STDvh5K+8E3LHjh3Hh8y4YfyHALbrk0stjdnb2Vv/3ZhvSZb6hTGYbymS2oUyjznaE+YZUOXZDmcw2lMl5OZTLsRvKZLahTM7LoVyO3VCmg812MTfNfP7qz8art74qLn3Xe2NycjLe9pY3Rv+nP41nP/cFy9u85IUXxv0e+MB44q+fFTt27Ijzz3ta/Olr3hjHHT/ss2b2t7CwEDt37ojDDju0Owz7/bl41panxxve8ucxMzPayVMT5Dte8q1PHXfx76vE2U49R/mNJvX8Im59juOY73/7t/8bFz7vgqT311rl0ANrpaY8pDTbazl2N6GU17uUOiLKqSXlOkqa7ZT38xI51kOOB1f3bEesbb7brv9QpJ6j/EaXeo65npenvl8PVSl1RJRTS9frMNv1KKWOiHJqKaWOiFtXi/Pyg0s9R/mNLvUccz121yn11ygi/RxTzy9Cjoeq7dlOYR80oQt1dqHGiHzqdF5+cKnnKL/RpZ5jruflqe/XQ6WO9JRSy7hme7qO5FLwH+5695icnIyPfmRbnHjyKfGFL1wdZ/76WbGwe3dMTk1FRMQJJ54Un/n0VfGw0x4Rn/z4x2LjUUfF7e9wh0P6+ZOTk3HUUUevOa+ZmdmR7zhuknzHS77pKXm2U89RfqNJPb+I9nOcnJyMjRuPSiKXcVBTHtRUv1t77G5C2/umLqXUEVFOLaXUcUtSmO0c9rMc6yHHZt2a+c6h/tRzlN/oUs+x7fxKfk/tUJRSR0Q5taijHma7jDoiyqmllDoi2q/FeXk75De61HNsOz/vqR2a1HNMPb8IOTat6+flB9OFOrtQY0R36tyX8/J2yG90qefYdn5dP3arIz2l1FJ3HcXcNHPb2942tjzz2fHud10WV7z7nfHABz0kTn/ko+JPt74qTjzp5HjcE86IM574pLj++n+PF134vNi48ag4/4LnxdSeG2oAAAAAAAAAAAAAAAAoRzE3zUREnPqw0+LUh52233MXvvDFy//78MMPjxf83ouaTgsAAAAAAAAAAAAAAICGTb385S9/edtJlGpycjJOPOmUbD7NRr7jJd9y5LBvUs9RfqNJPb+ItHJMKZe6qCkPauqWUvZNKXVElFNLKXWkLof9LMd6yDF9OdSfeo7yG13qOaae32pyzXulUuqIKKcWdbQr17xXKqWOiHJqKaWOiDxrySHn1HOU3+hSzzH1/JqQwz5IPcfU84uQYy66sg+6UGcXaozoTp2jymE/pZ6j/EaXeo6p57eaXPNeSR3pKaWWcdQxUVVVVdtPAwAAAAAAAAAAAAAAgARMtp0AAAAAAAAAAAAAAAAA1M1NMwAAAAAAAAAAAAAAABTHTTOHqKqqmJubi6qq2k4FqJHZhnKZbyiT2YYymW0ol/mGMpltKJPZhnKZbyiT2YYymW0ol/mGMpltyIObZg5Rv9+PczefHf1+v+1UgBqZbSiX+YYymW0ok9mGcplvKJPZhjKZbSiX+YYymW0ok9mGcplvKJPZhjy4aQYAAAAAAAAAAAAAAIDiuGkGAAAAAAAAAAAAAACA4rhpBgAAAAAAAAAAAAAAgOK4aQYAAAAAAAAAAAAAAIDiuGkGAAAAAAAAAAAAAACA4rhpBgAAAAAAAAAAAAAAgOK4aQYAAAAAAAAAAAAAAIDiuGkGAAAAAAAAAAAAAACA4ky3nUAbPvSB98ff/s2HY3JyMh73q2fE45/wa22nBAAAAAAAAAAAAAAAQI0690kzX7r2/8SVV1wez3rO8+M5z78wrnzP5fHVr3y5sfj9+SrmF6roz1eNxYQU9Qd7ZmGQ1yzkmjdAXZzLtKftfd92fADS5ncl6qCPoFzmG8pktqFMZhugGdZb6tLG3/D0L4yfOYNyNXHstobAgTr3STPf+PrX4tjjjo9T7nXviIg44cST4/NXfzbucc8T9ttuMBjEYDBY/r7fnxs5dn++ii3bdsVl194cm05eF2959IaYmZ4Y+edCbvqDKrZs32cWfnlDzPSamYVRZrvNvIGDG8exm/05l2lP2/u+zfh1zPbExTvqTCmqizbW+vOgixy3y+J3JfZ1a+dbH0HavKcGZTLbUC7n5VAm76mVw3rLvkY6L2/hb3j6Fw6d83IoU+rHbmsIDNe5m2Zud7vbx/duuD7+/d//LWZmZuN7N1wfGzceeNHbhz7wvnj/lVfUGrs3GXHZtTdHxOLXSx6zodafD7noTbU3C6PMdpt5Awc3jmM3+3Mu0562932b8c02lMlsl8XvSuzr1s63PoK0eU8NymS2oVzOy6FM3lMrh/WWfY10Xt7C3/D0Lxw65+VQptSP3dYQGK5zN8086CGnxuSUnNYAACAASURBVCc/8ffxguecH0fuuVnmiCOPPGC7M858Ujz28Wcsf9/vz8Wztjx9pNiDhYhNJ69bvntvsBAxPTnSj4QsDXavmIXdzc3CKLPdZt7AwY3j2M3+nMu0p+1932b8FGe77k+uqZtPwiEHKc42t57fldjXrZ1vfQRp854alMlsQ7mcl0OZvKdWDust+xrpvLyFv+HpXzh0zsuhTKkfu60hMFznbpqZmpqK33vRS+InP/lJrFu3Lp5z/nlxxzvd+YDter1e9Hq9WmPPTE/EWx69IS55zIYYLMTYPw4TUjXTm4i3/PKeWdgdjX702yiz3WbewMGN49jN/pzLtKftfd9mfLO9dnXf1DOOm3ByyJHxMttl8bsS+7q1862PIG3eU4MymW0ol/NyKJP31MphvWVfI52Xt/A3PP0Lh855OZQp9WO3NQSG69xNMxER84NB/OjGnfGhD7w/Zmdn48EPPrWx2EsLnLv26LqlA3Fus5Br3gB1cS7Tnrb3fdvx4Za4CQfa53cl6qCPoFzmG8pktqFMZhugGdZb6tLG3/D0L4yfOYNyNXHstobAgTo5Dp/5zKfjD158UfzsZz+NF774D2JyaqrtlAAAAAAAAAAAAAAAAKhRJz9p5rSHnx6nPfz0ttMAAAAAAAAAAAAAAABgTDr5STMAAAAAAAAAAAAAAACUzU0zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAABTHTTMAAAAAAAAAAAAAAAAUx00zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAABTHTTMAAAAAAAAAAAAAAAAUx00zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAABTHTTMAAAAAAAAAAAAAAAAUx00zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAABTHTTMAAAAAAAAAAAAAAAAUx00zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAABTHTTMAAAAAAAAAAAAAAAAUx00zAAAAAAAAAAAAAAAAFMdNMwAAAAAAAAAAAAAAAPw/9u49TLKzLBT92z3dE2aSCCQgNyMIiAghXERhcz+e55H7TuKOGEJMhACOXL0QAggSjqIJI4KHbAVOZsMgJIQQrqJ7guBmGy8giEo83FQICHt7JJkAThdJ1fQ6f8xUd89M90xX91q1vsvv9zz1DF1ZU+/7rvre9X21mm+qOHN9J9CHj3/so3HN1e+Off+xL+5/6gPieS98cRx//Al9pwUAAAAAAAAAAAAAAEBLqvumme9+9ztx+Vv+IM746Z+JS1//xvjmN/41/uhDH5xa/MGoidFiE4PhgT+/N2qWnxs1U8sD+jbugcEwr3Gfa94wTTn2ySRzce3zdu3196nv3vLeA3A0fc9TlCGHcZRDjpAivQNl0ttQJr0NMB2ut7Slj7HUx+8N/a6S2uQwT+SQI6RoGr0zjXnTNYDcVLdppmkiZmdn4+STT46TTjo5jjvuuNiy5cjTMBwOY2FhYekxGCxsOvZg1MSOPftifufe+MVr98Ut+yP+49bl53bs2WdhTxUGwyZ2XHtw3F+7b6qT5mZ6u8+8IRc59vfK+flYc/Ekx5ao9vr71Pcc1Od738W6HOif3i5L3/MUadnwujyDcZRDjtAV99SgTHobylXyuhxq5p5aOVxvWSm3dXkfvzf0e2pyVfK6PIccoSupz93TmDddA8jRXN8JTNv3fd/3xdnPODd2XvLamJubizvd6c7xn8/4L0cc98H3vzeuufqqVmPPz0a84/pbIyLiw/80jMufGDE/O7P03DuuvzV2PfH4VmNCiua3RG/jfjO93WfekIsc+3vl/HysnCc5tkS119+nvuegPt/7LtblQP/0dln6nqdIy4bX5RmMoxxyhK64pwZl0ttQrpLX5VAz99TK4XrLSrmty/v4vaHfU5OrktflOeQIXUl97p7GvOkaQI6q2zRz0403xvuuviqe+eznxr3ufZ+47I2vjz/+yIfijDPPOuS40888K570lNOXfh4MFuIFO569qdjDxYjzTt0a77j+1njqvefjlv0HdtuNnzvv1K0xXIyYq+77f6jNcH8cOu73T2/cb6a3+8wbcpFjf6+cn481F09ybIlqr79Pfc9Bfb73XazLgf7p7bL0PU+Rlg2vyzMYRznkCF1xTw3KpLehXCWvy6Fm7qmVw/WWlXJbl/fxe0O/pyZXJa/Lc8gRupL63D2NedM1gBzNNE1T1Xci/dVfXhdv+f03xdvfeWCX3xXv3B1f/tIX49X/128d9e8tLCzEBeefE7t2XxHbt2/fcPzBqIn52QMXjPktEaPFiCYO7OwbLkZsm5vZ8GtDTgbDJua3HOiFbfP9jftJezuVvCFlqfTJJP29ND+vYy6e5NgS1V5/n/rurVTe+42sy2cuvanjrMrWXHRS66+Z+nvSRc0cXVufuelP3/MU6ZpoXZ7BOMohR5gG99SgTHobylXauhw4wD21vLnespYc1uV9/N4wld9VwmaUti7PIUeYhhTn7mnMm64B5Ka6b5q5691+IG699db4q7+8Lu51rx+Oz/3D38e97v3DU4s/vviMd9St3Flnlx01GU+SuY37XPOGacqxTw6fn9s6tkS119+nvnvLew/A0fQ9T1GGHMZRDjlCivQOlElvQ5n0NsB0uN7Slj7GUh+/N/S7SmqTwzyRQ46Qomn0zjTmTdcAclPdppm73/0e8fMXPCfe+Y63xcK+fXH/U0+Lp539jL7TAgAAAAAAAAAAAAAAoEXVbZqJiPipxz8pfurxT+o7DQAAAAAAAAAAAAAAADriS5EAAAAAAAAAAAAAAAAojk0zAAAAAAAAAAAAAAAAFMemGQAAAAAAAAAAAAAAAIpj0wwAAAAAAAAAAAAAAADFsWkGAAAAAAAAAAAAAACA4tg0AwAAAAAAAAAAAAAAQHFsmgEAAAAAAAAAAAAAAKA4Ns0AAAAAAAAAAAAAAABQHJtmAAAAAAAAAAAAAAAAKI5NMwAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxbFpBgAAAAAAAAAAAAAAgOLYNAMAAAAAAAAAAAAAAEBxbJoBAAAAAAAAAAAAAACgODbNAAAAAAAAAAAAAAAAUBybZgAAAAAAAAAAAAAAACjOXN8JTNMfXPZ78T8/8WeHPHenO9053njZm3vKCAAAAAAAAAAAAAAAgC5UtWnmWc/ZEec984Kln3935yVxz3vdu8eMAAAAAAAAAAAAAAAA6MJs3wlM03HHHRfHH39CHH/8CXHjt74VX/zC5+PxT3hyJ7EGwyZGi00Mhs0hz98yamI4amI0auKWg3+udSz1qGEMjGscHuyBXGvONW+Yphz7ZJKcuzi2xPhF1jQ6eNyooJo6ep8AqI95gjbkMI5yyBFSpHegTKX09iT3fKAGpfQ2QOpcb2lLH2OplpjTVkONEfXUuVk5nKcccoQUTaN3SokxDaXUEVFWLV2oatPMSn/04Q/GQ3/iYXGHO95x1f8+HA5jYWFh6TEYLKz7tQfDJnZcuy/md+6NHdfuWxp8g2ET+5uIURMxbCL2H/xztWOpx1rjpSTjGrft3Bujgz3QV81d9DawrM8+2Wh/T5JzF8eWGL/ImkZN7Nhz8Lg9+476f6LIpqaO3qe2bWbuBtKlt8visxIrTWNd3pcccoSuuKcGZdLbk93zgZyUvC6HmrmnVg7XW1bKbV1eS8xpq6HGiHrqHCt5XZ5DjtCV1OfuUmJMQyl1RJRVS1dmmqap7qzcdOON8aLn/0K86uLfiB+574+uesx733NlXHP1VUc8v2v3FbF9+/ajvv5osYn5nXuXfh5eePuYm52J0eLqp3q1Y6nHWuOlJOMaf/IH52LPz54YEf2N+y56G1jWZ59stL8nybmLY0uMr6Y84ndVU9s2M3ePzVx6U9tpVaW56KTWXzP196SLmjlUG71NOnxWYqVprMv7kkOO0BX31KBMerucOuBwJa/LoWbuqZXD9ZaVcluX1xJz2mqoMaKeOsdKXpfnkCN0JfW5u5QY01BKHRFl1dKVub4T6MOffOTDcfd73GPNDTMREaefeVY86SmnL/08GCzEC3Y8e12vP9wfcd6pW+Md198a5526NYb7I+ZmDzzfRMR4CI7/92rHUo+1xktJxjVecf2tMdx/4Lm+au6it4FlffbJRvt7kpy7OLbE+EXWtHjYcYsF1NTR+9S2zczdQLr0dll8VmKlaazL+5JDjtAV99SgTHp7sns+kJOS1+VQM/fUyuF6y0q5rctriTltNdQYUU+dYyWvy3PIEbqS+txdSoxpKKWOiLJq6Up13zSzsHDg4vSs5/xCPOrRj53o711w/jnr/hc6BsMm5rccGITb5pd3at0yamI2DmyW2R8RWyJi2MSqx1KPtcZLScY1NosHfh4lMu7b6m1gWSp9Mkl/T5JzF8eWGL/ImkZNzM8e+D9TbJsrpKaO3qcuTTp3R6T/rSap800zTMNGepu0pDJPkJ6u1uV9ySFHmAb31KBMtfb2JPd8IFelrcuBA9xTy5vrLWvJYV1eS8xpq6HGiHrqXE1p6/IccoRpSHHuLiXGNJRSR0RZtXShum+a+dhH98RxtzkuHv6fHtlpnPFgO3yX1nErbrbPHf6nHV3VWmu8lGTpAnywxvmDz+dWcw3vFWxWjn0ySc5dHFti/CJrmiuwpo7eJwDqY56gDTmMoxxyhBTpHShTKb09yT0fqEEpvQ2QOtdb2tLHWKol5rTVUGNEPXVuVg7nKYccIUXT6J1SYkxDKXVElFVLF6rbNPPU08+Mp55+Zt9pAAAAAAAAAAAAAAAA0CF7iQAAAAAAAAAAAAAAACiOTTMAAAAAAAAAAAAAAAAUx6YZAAAAAAAAAAAAAAAAimPTDAAAAAAAAAAAAAAAAMWxaQYAAAAAAAAAAAAAAIDi2DQDAAAAAAAAAAAAAABAcWyaAQAAAAAAAAAAAAAAoDg2zQAAAAAAAAAAAAAAAFAcm2YAAAAAAAAAAAAAAAAojk0zAAAAAAAAAAAAAAAAFMemGQAAAAAAAAAAAAAAAIpj0wwAAAAAAAAAAAAAAADFsWkGAAAAAAAAAAAAAACA4tg0AwAAAAAAAAAAAAAAQHFsmgEAAAAAAAAAAAAAAKA4Ns0AAAAAAAAAAAAAAABQHJtmAAAAAAAAAAAAAAAAKI5NMwAAAAAAAAAAAAAAABRnru8E+rC4f3984APXxCf+7GNxt7udEi99+Sv7TgkAAAAAAAAAAAAAAIAWVflNM7vfvis++t//JM79uWfG817w4k5iDIZNjBabGAybQ54fjZojHmsdSz1qGAPjGnMf97nmDdOUY59MknMXx5YYX015xO+qJgDqY56gDTmMo9RzHIwO5jdKMz/qlXrvrFcpdUBbSumJUuogTTmuz/QEHFuOvU16XG9pSx9jqYaYNdTYl1rq3KwczlPqOVqzbZ5z2I1p9I4YacWYllJq6eraU92mmZtvvjk+9tE9ccFzd8SPP+zhccKJJ6563HA4jIWFhaXHYLCw7hiDYRM7rt0X8zv3xo5r9y0NvsGwiWETRzxWO5Z6rDVeSjKu8S479/Y+7rvobWBZn32y0f6eJOcuji0xvpryiN9VTW3bzNwNpEtvl8VnJVaaxrq8L6nnOBg1sWPPwfz27PNLLFrlnlo5dcBKerucOkhTn+uzktfl0Lcce5v0uN6yUm7r8hpi1lBjX2qpc6zkdXnqObpfvnnO4dpSn7vFSCvGtJRSS5fXnpmmafI8Kxv0mU9/Kl7/ut+ORzzy0fH5z/9jnHLK3eM5O54XJ598h0OOe+97roxrrr7qiL+/a/cVsX379qPGGC02Mb9z79LPwwtvH3OzMzFaXP1Ur3Ys9VhrvJRkXON1Z8/Hw045ISL6G/dd9DawrM8+2Wh/T5JzF8eWGF9NecTvqqa2bWbuHpu59Ka206pKc9FJrb9m6u9JFzVzqDZ6m3T4rMRK01iX9yX1HFPPj7y5p1ZOHbCS3i6nDtKU4z01PQHHlmNvkx7XW1bKbV1eQ8waauxLLXWOlbwuTz3H1PPLgXO4ttTnbjHSijEtpdTSZR3VfdPMvn37ImIm7nPf+8ZLLvq1+Pa3b453/eHuI447/cyzYtfuK5Yel7358nXHGO6POO/UrRFx4M/h/uXnV3usdiz1qGEMjGs8493D3sd9F70NLMuxvyfJuYtjS4yvpjzid1VT2zYzdwPp0ttl8VmJlaaxLu9L6jkOFw/Lb7HnhCiKe2rl1AEr6e1y6iBNfa7PSl6XQ99y7G3S43rLSrmty2uIWUONfamlzrGS1+Wp5+h++eY5h2tLfe4WI60Y01JKLV1ee6r7ppm/++zfxhtf/7p4+zvfHRERH/rg++J//o8/i995w5uO+vcWFhbigvPPWfe/0DEYNjG/5cAg3Da/vMNptMrXBA2bWPVY6rHWeCnJuMY4eAFLZdy31dvAslT6ZJL+niTnLo4tMb6a8ojfVU1dmnTujkj/W01S55tmmIaN9DZpSWWeID1drcv7knqOg1ET87MHbihvm0svP8pR6z21UuqAtejtvOsgTamsz0pbl0Pfcuxt0uN6y1pyWJfXELOGGvtSS52rKW1dnnqOqazZcuYcrk+Kc7cYacWYllJq6eraM9faK2XiPvf5kZifn489f/KReMiPPTQ+/alPxg/90D1bjzMebHOHfZfP3Cpv3vhNOPxY6rHWeCnJ0gX4YI25jvsa3ivYrBz7ZJKcuzi2xPhqyiN+VzUBUB/zBG3IYRylnuP4xnGq+VGv1HtnvUqpA9pSSk+UUgdpynF9pifg2HLsbdLjektb+hhLNcSsoca+1FLnZuVwnlLP0Zpt85zDbkyjd8RIK8a0lFJLV9eezE/L5LYff3z88kteGn/60T3x0l99cZx44olx7vnP7DstAAAAAAAAAAAAAAAAWlTdN81ERNzv/g+Inb/7f/edBgAAAAAAAAAAAAAAAB2p7ptmAAAAAAAAAAAAAAAAKJ9NMwAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxbFpBgAAAAAAAAAAAAAAgOLYNAMAAAAAAAAAAAAAAEBxbJoBAAAAAAAAAAAAAACgODbNAAAAAAAAAAAAAAAAUBybZgAAAAAAAAAAAAAAACiOTTMAAAAAAAAAAAAAAAAUx6YZAAAAAAAAAAAAAAAAijPXdwJr2XnJa9d97MzMTLzkold0mA0AAAAAAAAAAAAAAAA5SXbTzGf/9tMTHD3TWR4AAAAAAAAAAAAAAADkJ9lNM6989W/0nQIAAAAAAAAAAAAAAACZSnbTzP3uf2rfKQAAAAAAAAAAAAAAAJCpZDfNfOTDH5zo+Cc/9fSOMgEAAAAAAAAAAAAAACA3yW6aedcfvj0iZtZxZBMRMzbNAAAAAAAAAAAAAAAAsCTZTTM/fdbTImbWs2lmcr/9mxfHP/z93y39/OSnnh7nnvfMTmIBAAAAAAAAAAAAAAAwfclumjnraU/v7LX37t0bz3z2c+ORj3pMRETMz2/tLBYAAAAAAAAAAAAAAADTl+ymmcMNh8P4yr/8c3z75pujiWb5+VtvjW9841/jaWc/Y92vdfPevfH933+nOP74E7pINSIiBsMm5rdEDPdHbJtf/sac0ag54thhE6seSz3WGi8lGdcYiwd+znXc1/BewWbl2CeT5NzFsSXGV1Me8buqCYD6mCdoQw7jKPUcB6Mm5mcjhosR2+bkRzpS7x1gY0rp7VLqiDDX0o6SegK6ok9og3FEW/oYS7XEnLYaaoyop87NyuE8pZ5j8vn5DF+taYzNYmJMoU9Sv1ZMoqRaujDbdwLr8W//+3/FS3/1xXHxq14Rb3j96+KNr9+59Pivb/q9+MD7rln3a42Gw/iP//huvOsPd8fznvuseMPvXBrf/e53jjhuOBzGwsLC0mMwWFh3jMGwiR3X7ov5nXtjx7X7YjBslp4fNnHEY7Vjqcda46Uk4xrvsnNv7+O+i94GlvXZJxvt70ly7uLYEuOrKY/4XdXUts3M3UC69HZZfFZipWmsy/uSeo6DURM79hzMb8++GKzyj/f0KfX8ODr31KBMerucOiLMtRyq5HU59M39ctrgestKua3La4k5bTXUGFFPnWMlr8tTzzH5/HyGz1rqc3cxMabQJ6lfKyZRUi1dmWmaJvmz8obfuST+5lOfjFMfcFrcdOON8c1vfjMe89jHxY03fiv+8frPxVNP/+l4+jN+bl2vNRqN4uMfuzbufOe7xvbt2+O/vumNcdoDHxTPvOC5hxz33vdcGddcfdURf3/X7iti+/btR4+x2MT8zr1LPw8vvH3Mzc7EaHH1U73asdRjrfFSknGN1509Hw875cA3PPVVcxe9DSzrs0822t+T5NzFsSXGV1Me8buqqW2bmbvHZi69qe20qtJcdFLrr5n6e9JFzRyqjd4mHT4rsdI01uV9ST1H+dEl99SgTHq7nDoiyqqFzSt5XQ59y/1+OWlwvWWl3NbltcScthpqjKinzrGS1+Wp5yg/upT63C1GWjGmpaRaupLFN8184fOfjwc+6MHxile9Ju7/gNMiImLH818UL3/lxXHXu/1A7N27/v/D1eLiYjz0oQ+L0x74oLj3D98nHvrjPxFfu+GGI447/cyzYtfuK5Yel7358nXHGO6POO/UrRFx4M/h/uXnV3usdiz1qGEMjGs8493D3sd9F70NLMuxvyfJuYtjS4yvpjzid1VT2zYzdwPp0ttl8VmJlaaxLu9L6jkOFw/Lb7HnhA6Ten4cnXtqUCa9XU4dEeZaDlXyuhz65n45bXC9ZaXc1uW1xJy2GmqMqKfOsZLX5annmHx+PsNnLfW5u5gYU+iT1K8Vkyiplq5k8U0zP3/u2fHwRzwydjzvhfHeq98d77v6PfHWXbvjhBNPjDf//pvis5/5dLxl1+51vdbXv3ZDvPRXXxwv+qWXxD3vfe/43Z2XxH1/9H5HfNPM4RYWFuKC889Z97/QMRg2Mb/lwCDcNr+8U2u0ytdDDZtY9VjqsdZ4Kcm4xjg4caUy7tvqbWBZKn0ySX9PknMXx5YYX015xO+qpi5NOndHpP+tJqnzTTNMw0Z6m7SkMk+Qnq7W5X1JPcfBqIn52QO/SNg2Jz+6454alKnW3i6ljghzLWsrbV0OfUulT9xTy1sq44j05LAuryXmtNVQY0Q9da6mtHV56jkmn5/P8MVIce4uJsYU+iT1a8UkSqqlC3N9J7Ae9/ihH4rPfPpT8aUvfiHucue7REQTf/ThD8Rj/4//M770hc/H7Jb1f2HOKT949zj/Wc+Od77jbTEYLMQDTntg/MzPntN6zuPBNndYanOrNO34TTj8WOqx1ngpydIF+GCNuY77Gt4r2Kwc+2SSnLs4tsT4asojflc1AVAf8wRtyGEcpZ7j+BcG8iM1qfcOsDGl9HYpdUSYa2lHST0BXdEntME4oi19jKVaYk5bDTVG1FPnZuVwnlLPMfn8fIav1jTGZjExptAnqV8rJlFSLV3IYtPM6Wf+l9h5yW/Fxz/20XjWs38hTnz7f4sPfeD98aEPvD8iIp7wpCdP9HpPeOJT4glPfEoXqQIAAAAAAAAAAAAAAJCALDbNPPghD42XXPTyuPnmm2Pr1q1x0SteFVe+6w/j2zfvjdMe9OB42tnP6DtFAAAAAAAAAAAAAAAAEpLFppmIiIf82I8v/e973uve8Wu//poeswEAAAAAAAAAAAAAACBl2WyaGQ2H8ed//om44atfiZv37o3b3vZ2cY973jMe/ejHxtz8fN/pAQAAAAAAAAAAAAAAkJAsNs3869e/Hpe89jVx0003RUSz4r/MxPuuvipe9mu/Hnf7gVP6Sg8AAAAAAAAAAAAAAIDEZLFp5m273ho33XRjPPghPxaPeNRj4ra3vW1897vfjb+87s/jM5/+VLxt11vjla/+jb7TBAAAAAAAAAAAAAAAIBFZbJr5py9/MX74Pj8SF77slYc8/58e8ai4+JUvjy9/6Ys9ZQYAAAAAAAAAAAAAAECKZvtOYD1OOvkOcde73m3V/3bnu941TjjxxClnBAAAAAAAAAAAAAAAQMqy2DTzxCc/JT77t5+Jb/37vx/y/L/97/8Vn/3Mp+MJT3xKT5kBAAAAAAAAAAAAAACQorm+E1jLs857+iE/f+97t8Qvv+gXY35+fum5W28dRtMsxl//1V/EU08/c9opAgAAAAAAAAAAAAAAkKhkN81873vfO+K5/fv3x/79+494/iv/8i/TSAkAAAAAAAAAAAAAAIBMJLtp5or3vL/vFAAAAAAAAAAAAAAAAMjUbN8JAAAAAAAAAAAAAAAAQNtsmgEAAAAAAAAAAAAAAKA4Ns0AAAAAAAAAAAAAAABQHJtmAAAAAAAAAAAAAAAAKI5NMwAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxal208wbX/+6ePrPnNF3GgAAAAAAAAAAAAAAAHSgyk0zn/6bT8XffOqvO40xGDYxWmxiMGwOeX40ao54rHUs9ahhDIxrzH3c55o3TFOOfTJJzl0cW2J8NeURv6uaAKiPeYI25DCOUs9xMDqY3yjN/KhX6r0DbEwpvV1KHdAWPQHHpk9og3FEW/oYSzXErKHGiH7uJ9ZS52blME+knmOO7zt1mEbviJFWjIjpXJNSvy6vV1fnqrpNMwsLC/G2XW+Jxz/xyUc9bjgcxsLCwtJjMFhYd4zBsIkd1+6L+Z17Y8e1+5YG32DYxLCJIx6rHUs91hovJRnXeJede3sf9130NrCszz7ZaH9PknMXx5YYX015xO+qprZtZu4G0qW3y+KzEitNY13el9RzHIya2LHnYH579vlFIK1yTw3KpLfLqQMOV/K6HPrmfjltcL1lpdzWa7G+1wAAIABJREFU5TXErKHGiH7uJ9ZS51jJ6/LUc3S/nC6lPneLkVaMiOlck1K/Lq9Xl+dqpmmaPM/KBu1665tjOBrGTzzs4bHzktfGlVd/YNXj3vueK+Oaq6868u/vviK2b99+1BijxSbmd+5d+nl44e1jbnYmRourn+rVjqUea42XkoxrvO7s+XjYKSdERH/jvoveBpb12Scb7e9Jcu7i2BLjqymP+F3V1LbNzN1jM5fe1HZaVWkuOqn110z9PemiZg7VRm+TDp+VWGka6/K+pJ5j6vmRN/fUoEx6u5w64HAlr8uhb7nfLycNrreslNu6vIaYNdQoZtpzdw7zROo5pp4feUt97hYjrRjTilPKda/LOqr6ppkvfP7/jU//zSfj3J/7+WMee/qZZ8Wu3VcsPS578+XrjjPcH3HeqVsj4sCfw/3Lz6/2WO1Y6lHDGBjXeMa7h72P+y56G1iWY39PknMXx5YYX015xO+qprZtZu4G0qW3y+KzEitNY13el9RzHC4elt9izwlRFPfUoEx6u5w64HAlr8uhb+6X0wbXW1bKbV1eQ8waaozo535iLXWOlbwuTz1H98vpUupztxhpxYiYzjUp9evyenV5rqr6ppnf/s3XxD9e/7k47ritsX//Ytxyy/di+/btsWv3Fcf8uwsLC3HB+ees+1/oGAybmN9yYBBum1/e4TRa5WuChk2seiz1WGu8lGRcYxy8gKUy7tvqbWBZKn0ySX9PknMXx5YYX015xO+qpi5NOndHpP+tJqnzTTNMw0Z6m7SkMk+Qnq7W5X1JPcfBqIn52QM3lLfNpZcf5XBPDcpUa2+XUgccTWnrcuhbKn3inlreUhlHpCeHdXkNMWuoMaKf+4m11Lma0tblqeeYyvtO+VKcu8VIK0bEdK5JqV+X16urczXX2itl4Bef/6IYDm+NiIjrr/9cvPUPLotLdr6hk1jjwTZ32Hf5zK3y5o3fhMOPpR5rjZeSLF2AD9aY67iv4b2CzcqxTybJuYtjS4yvpjzid1UTAPUxT9CGHMZR6jmObxynmh/1Sr13gI0ppbdLqQPaoifg2PQJbTCOaEsfY6mGmDXUGNHP/cRa6tysHOaJ1HPM8X2nDtPoHTHSihExnWtS6tfl9erqXFW1aeZ2t7/90v++7W2/FhERd/z+O/WVDgAAAAAAAAAAAAAAAB3JfC/Rxj3kx348rrz6A32nAQAAAAAAAAAAAAAAQAeq3TQDAAAAAAAAAAAAAABAuWyaAQAAAAAAAAAAAAAAoDg2zQAAAAAAAAAAAAAAAFAcm2YAAAAAAAAAAAAAAAAojk0zAAAAAAAAAAAAAAAAFMemGQAAAAAAAAAAAAAAAIpj0wwAAAAAAAAAAAAAAADFsWkGAAAAAAAAAAAAAACA4tg0AwAAAAAAAAAAAAAAQHFsmgEAAAAAAAAAAAAAAKA4Ns0AAAAAAAAAAAAAAABQHJtmAAAAAAAAAAAAAAAAKI5NMwAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxbFpBgAAAAAAAAAAAAAAgOLYNAMAAAAAAAAAAAAAAEBxbJoBAAAAAAAAAAAAAACgOHN9JzBN+/fvj6uufGd8/E+vjYiIBz/kofGcHc+PrVu39pwZAAAAAAAAAAAAAAAAbarqm2au+/NPxMf/9KNx4cteGa957aVx/ef+YWkDTdsGwyZGi00Mhs0hz49GzRGPtY6lHjWMgXGNuY/7XPOGacqxTybJuYtjS4yvpjzid1UTAPUxT9CGHMZRDjlCikrpnVLqID2D0cGxNcprbJXSE6XUEZHvWCpZjuMrx5xh2vQJbTCOaEsfY6mGmH2srWu5LtRS52blcJ5Sz9Fn5M1L/T3O1TTOazExptDH0xrnJdWSq6o2zTz2cT8Zl7/9nfEj9/3RuN3tbhdzc3OxZcuWVY8dDoexsLCw9BgMFtYdZzBsYse1+2J+597Yce2+pcE3GDYxbOKIx2rHUo+1xktJxjXeZefe3sd9F70NLOuzTzba35Pk3MWxJcZXUx7xu6qpbZuZu4F06e2y+KzEStNYl/clhxyhK+6plVMH6RmMmtix5+DY2rNvqv9HDr1dTh0R/Y4lVpfjPbWSegK6kmNvkx7XW1bKbV1eQ8w+1ta1XBdqqXOs5HV56jn6jLx5qb/HfUp97i4mxhT6eFrjvKRacjbTNE11Z+Wlv/ri+Ma/fj3ud/9T4xWvek3MzMwcccx733NlXHP1VUc8v2v3FbF9+/ajvv5osYn5nXuXfh5eePuYm52J0eLqp3q1Y6nHWuOlJOMarzt7Ph52ygkR0d+476K3gWV99slG+3uSnLs4tsT4asojflc1tW0zc/fYzKU3tZ1WVZqLTmr9NVN/T7qomUO10dukw2clVprGurwvOeQIXXFPrZw6SE+un7lL6YlS6ogoq5ZS5NjfxhEcW469TXpcb1kpt3V5DTFrqLEvtdQ5VvK6PPUcU88vB87h2lKfu8VIK8a04ujZY6vqm2bGLnrFq+Llr3x13PDVr8Yn/sfHVz3m9DPPil27r1h6XPbmy9f9+sP9EeedujUiDvw53L/8/GqP1Y6lHjWMgXGNZ7x72Pu476K3gWU59vckOXdxbInx1ZRH/K5qattm5m4gXXq7LD4rsdI01uV9ySFH6Ip7auXUQXqGi4eNrcXpxdbb5dQR0e9YYnU53lMrqSegKzn2NulxvWWl3NblNcTsY21dy3WhljrHSl6Xp56jz8ibl/p73KfU5+5iYkyhj6c1zkuqJWdVfdPMDTd8Nb59881x2gMfFBERb/zd18XWrcfF817w4mP+3YWFhbjg/HPW/S90DIZNzG85MAi3zS/v1Bqt8pVKwyZWPZZ6rDVeSjKuMQ5e7FMZ9231NrAslT6ZpL8nybmLY0uMr6Y84ndVU5cmnbsj0v9Wk9T5phmmYSO9TVpSmSdIT1fr8r7kkCNMQ6331Eqpg/QMRk3Mzx745em2uXw+c5fSE6XUEZHOWGJZKuOrtHU59C2VPnFPLW+pjCPSk8O6vIaYfayta7ku1FLnakpbl6eeo8/Im5f6e5yKFOfuYmJMoY+nNc5LqiVXc30nME1f++pX4r9d/pZ4xateE7fZti2+/KUvxpOfenonscaDbe6w7/KZW2Wgj9+Ew4+lHmuNl5IsXYAP1pjruK/hvYLNyrFPJsm5i2NLjK+mPOJ3VRMA9TFP0IYcxlEOOUKKSumdUuogPeNfkuY2tkrpiVLqiMh3LJUsx/GVY84wbfqENhhHtKWPsVRDzD7W1rVcF2qpc7NyOE+p5+gz8ual/h7nahrntZgYU+jjaY3zkmrJVVWbZh71mMfFDTd8NXZe8tpomiYe/ohHxk89/kl9pwUAAAAAAAAAAAAAAEDLqto0MzMzE+ee98w497xn9p0KAAAAAAAAAAAAAAAAHfIFPAAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxbFpBgAAAAAAAAAAAAAAgOLYNAMAAAAAAAAAAAAAAEBxbJoBAAAAAAAAAAAAAACgODbNAAAAAAAAAAAAAAAAUBybZgAAAAAAAAAAAAAAACiOTTMAAAAAAAAAAAAAAAAUx6YZAAAAAAAAAAAAAAAAimPTDAAAAAAAAAAAAAAAAMWxaQYAAAAAAAAAAAAAAIDi2DQDAAAAAAAAAAAAAABAcWyaAQAAAAAAAAAAAAAAoDg2zQAAAAAAAAAAAAAAAFAcm2YAAAAAAAAAAAAAAAAojk0zAAAAAAAAAAAAAAAAFGeu7wT68OEPvj/++I8+FLfc8r047UEPjh3Pe1Hc5ja36TstAAAAAAAAAAAAAAAAWlLdN838w9//XVx15bvieS/8pXjNay+NL37+8/Hf//jDfacFAAAAAAAAAAAAAABAi6rbNDM3NxfnnHtePOC0B8Ypp/xg3OWud41v3/zt1uMMhk2MFpsYDJtDnh+NmiMeax1LPWoYA+Macx/3ueYN05Rjn0yScxfHlhhfTXnE76omAOpjnqANOYyjHHKEFJXSO4PRwTpGedcBbSmmtwupA9qiJwCmw/WWtvQxlmqIWUONfamlzs3K4TzlkCOkaBq9I0ZaMaalpFq6UN2mmfvd/9R40lP+c0RE3PDVr8Q/fflL8chHP+aI44bDYSwsLCw9BoOFdccYDJvYce2+mN+5N3Zcu29p8A2GTQybOOKx2rHUY63xUpJxjXfZubf3cd9FbwPL+uyTjfb3JDl3cWyJ8dWUR/yuamrbZuZuIF16uyw+K7HSNNblfckhR+iKe2oHNszs2HOwjj37bJyhCHq7nDrgcCWvy6Fm7qmVw/WWlXJbl9cQs4Ya+1JLnWMlr8tzyBG6kvrcLUZaMaalpFq6MtM0TZVn5cYbvxWv/rWXxaMf87j42XPOPeK/v/c9V8Y1V191xPO7dl8R27dvP+prjxabmN+5d+nn4YW3j7nZmRgtrn6qVzuWeqw1XkoyrvG6s+fjYaecEBH9jfsuehtY1mefbLS/J8m5i2NLjK+mPOJ3VVPbNjN3j81celPbaVWlueik1l8z9feki5o5VBu9TTp8VmKlaazL+5JDjtAV99TKqQNW0tvl1AGHK3ldDjVzT60crreslNu6vIaYNdTYl1rqHCt5XZ5DjtCV1OduMdKKMS0l1dKVub4T6MN3vvOd+K3fuDjuf+oD4mlPf8aqx5x+5lnxpKecvvTzYLAQL9jx7HW9/nB/xHmnbo13XH9rnHfq1hjuj5ibPfD8alY7lnqsNV5KMq7xjHffGl/7lQPP9VVzF70NLOuzTzba35Pk3MWxJcZXUx7xu6qpbZuZu4F06e2y+KzEStNYl/clhxyhK+6pRQwXD6tjMc86YCW9XU4dcLiS1+VQM/fUyuF6y0q5rctriFlDjX2ppc6xktflOeQIXUl97hYjrRjTUlItXanum2a+NxjEb77m1+P4E46PF7z4V2J2djZmZmaPubtvYWEhLjj/nHX/Cx2DYRPzWw4Mwm3zyzu1RqMjT/ewiVWPpR5rjZeSjGuMxQM/pzLu2+ptYFkqfTJJf0+ScxfHlhhfTXnE76qmLk06d0ek/60mqfNNM0zDRnqbtKQyT5CertblfckhR5iGWu+pDUZNzM8e2ECzbS7fOmAt1fZ2IXXA0ZS2LgcOcE8tb663rCWHdXkNMWuosS+11Lma0tblOeQI05Di3C1GWjGmpaRaulDdN8188pN/Ff/8z1+OiIjnPuu8iIi4wx3vGG/6/f+n1TjjwXb4Lq25VX6RNn4T7Oiq11rjpSRLF+CDNeY67mt4r2CzcuyTSXLu4tgS46spj/hd1QRAfcwTtCGHcZRDjpCiUnpnvFEm9zqgLcX0diF1QFv0BMB0uN7Slj7GUg0xa6ixL7XUuVk5nKcccoQUTaN3xEgrxrSUVEsXqts089jH/WQ89nE/2XcaAAAAAAAAAAAAAAAAdMheIgAAAAAAAAAAAAAAAIpj0wwAAAAAAAAAAAAAAADFmes7AQAAAEjJzKU3tfp6zUUntfp6AAAAAAAAAADA+vimGQAAAAAAAAAAAAAAAIpj0wwAAAAAAAAAAAAAAADFsWkGAAAAAAAAAAAAAACA4tg0AwAAAAAAAAAAAAAAQHFsmgEAAAAAAAAAAAAAAKA4Ns0AAAAAAAAAAAAAAABQHJtmAAAAAAAAAAAAAAAAKI5NMwAAAAAAAAAAAAAAABTHphkAAAAAAAAAAAAAAACKY9MMAAAAAAAAAAAAAAAAxbFpBgAAAAAAAAAAAAAAgOLYNAMAAAAAAAAAAAAAAEBxbJoBAAAAAAAAAAAAAACgODbNAAAAAAAAAAAAAAAAUBybZgAAAAAAAAAAAAAAAChOlZtmvv3tm+OPP/KheNmFvxxf/9oNfacDAAAAAAAAAAAAAABAy6rbNDMYDOKFv/jc+Ou/+Iu44atf6S7OsInRYhODYXPI86NRc8RjrWOpRw1jYFxj7uM+17xhmnLsk0ly7uLYEuOrKY/4XdUEQH3ME7Qhh3GUQ46UbTA6OAZHeY3BUnon1/O/mpJqKUGu70cxvV1IHdAWPQHHpk9og3FEW/oYSzXErKHGvtRS52blcJ5SzzHX+y0pSf0cpp7fWqbRO2KkFWNacVK/Lq9XV71d3aaZrVu3xpv+4K3xwl/6laMeNxwOY2FhYekxGCysO8Zg2MSOa/fF/M69sePafUuDbzBsYtjEEY/VjqUea42XkoxrvMvOvb2P+y56G1jWZ59stL8nybmLY0uMr6Y84ndVU9s2M3cD6dLbZfFZiZWmsS7vSw45UrbBqIkdew6OwT37pvqLQPfU+j3/bSuplhLo7X6VUgccruR1OfTN/XLa4HrLSrmty2uIWUONfamlzrGS1+Wp5+j+1+alfg7dUxMjlxjTipP6dXm9uuztmaZp8jwrm/Tv/9+/xYue/wvxutf/Xpzyg3c/4r+/9z1XxjVXX3XE87t2XxHbt28/6muPFpuY37l36efhhbePudmZGC2ufqpXO5Z6rDVeSjKu8bqz5+Nhp5wQEf2N+y56G1jWZ59stL8nybmLY0uMr6Y84ndVU9s2M3ePzVx6U9tpVaW56KTWX7O296SLc5i7NnqbdPisxErTWJf3JYccKVuu6/JSeqeUOiLKqqUEertfpdQBhyt5XQ59y3XuJi2ut6yU27q8hpg11NiXWuocK3ldnnqOqeeXg9TPYa7r8mnkLUZaMaYVJ/WeXa8u66jum2bW6/Qzz4pdu69Yelz25svX/XeH+yPOO3VrRBz4c7h/+fnVHqsdSz1qGAPjGs9497D3cd9FbwPLcuzvSXLu4tgS46spj/hd1dS2zczdQLr0dll8VmKlaazL+5JDjpRtuHjYGFycXmz31Po9/20rqZYS6O1+lVIHHK7kdTn0zf1y2uB6y0q5rctriFlDjX2ppc6xktflqefo/tfmpX4O3VMTI5cY04qT+nV5vbrsbd80s8Y3zRxuYWEhLjj/nHX/Cx2DYRPzWw4Mwm3zyzucRqt8TdCwiVWPpR5rjZeSjGuMgxewVMZ9W70NLEulTybp70ly7uLYEuOrKY/4XdXUpUnn7oj6vtWkbb5pZvN808yxbaS3SUsq8wTp6Wpd3pcccqRsg1ET87MHfmmwbS6fdXkpvZPK+W9DSbWUIJX3o9reLqQOOJrS1uXQt1T6xD21vKUyjkhPDuvyGmLWUGNfaqlzNaWty1PPMZX7LTlL/Rymkl+Kc7cYacWYVpzUr8vr1VVvz7X2ShxiPNjmDvsun7lV3rzxm3D4sdRjrfFSkqUL8MEacx33NbxXsFk59skkOXdxbInx1ZRH/K5qAqA+5gnakMM4yiFHyjb+5UBuY7CU3sn1/K+mpFpKkOv7UUxvF1IHtEVPwLHpE9pgHNGWPsZSDTFrqLEvtdS5WTmcp9RzzPV+S0pSP4ep57eWafSOGGnFmFac1K/L69VVb2d+WgAAAAAAAAAAAAAAAOBI1X7TzB2//05x5dUf6DsNAAAAAAAAAAAAAAAAOuCbZgAAAAAAAAAAAAAAACiOTTMAAAAAAAAAAAAAAAAUx6YZAAAAAAAAAAAAAAAAimPTDAAAAAAAAAAAAAAAAMWZ6zsBAAAAKNnMpTe1+nrNRSe1+noAAAAAAAAAAFAqm2bWqWmaiIgYDBZ6zgTqtm3btpiZmWnt9fQ2pEN/Q5n0dv4WFpzr1LS9CWffC28z8d/R21Cmtns7Qn9DKszdUCa9DWWyLodymbuhTHobymRdDuUyd0OZjtXbM824WzmqG2/8Vrxgx7P7TgOqt2v3FbF9+/bWXk9vQzr0N5RJb0OZ9DaUqe3ejtDfkApzN5RJb0OZrMuhXOZuKJPehjJZl0O5zN1QpmP1tk0z67S4uBh7994Ut7nN+nYYDgYL8YIdz47L3nx5bNvW7uKpC/Ltlnzb0/Yu3xJ7O/Uc5bc5qecXsfEcu+jvb37zG3HhL78w6fM1qRzGwKTUlIeUenuSuXsaSnm/S6kjopxaUq6jpN5O+TyPybEdcjy2Lv7lvEn6u+/61yP1HOW3eannmOu6PPXzul6l1BFRTi2116G321FKHRHl1FJKHREbq8W6/NhSz1F+m5d6jrnO3W1K/T2KSD/H1POLkON69d3bKZyDaaihzhpqjMinTuvyY0s9R/ltXuo55rouT/28rpc60lNKLV319lwbydVgdnY2Tj75DhP/vW3btre+47hL8u2WfNNTcm+nnqP8Nif1/CL6z3F2djZOOunkJHLpgpryoKb2bXTunoa+z01bSqkjopxaSqnjaFLo7RzOsxzbIcfp2kh/51B/6jnKb/NSz7Hv/Eq+p7YepdQRUU4t6miH3i6jjohyaimljoj+a7Eu74f8Ni/1HPvOzz219Uk9x9Tzi5DjtNW+Lj+WGuqsocaIeupcybq8H/LbvNRz7Du/2ududaSnlFrarmO2tVcCAAAAAAAAAAAAAACARNg0AwAAAAAAAAAAAAAAQHG2XHzxxRf3nUSpZmdn4373f0Bs2bKl71TWRb7dkm85cjg3qecov81JPb+ItHJMKZe2qCkPaqpLKeemlDoiyqmllDpSl8N5lmM75Ji+HOpPPUf5bV7qOaae31pyzftwpdQRUU4t6uhXrnkfrpQ6IsqppZQ6IvKsJYecU89RfpuXeo6p5zcNOZyD1HNMPb8IOeailnNQQ5011BhRT52blcN5Sj1H+W1e6jmmnt9acs37cOpITym1dFHHTNM0TWuvBgAAAAAAAAAAAAAAAAmY7TsBAAAAAAAAAAAAAAAAaJtNMwAAAAAAAAAAAAAAABTHppl1apomFhYWommavlMBWqS3oVz6G8qkt6FMehvKpb+hTHobyqS3oVz6G8qkt6FMehvKpb+hTHob8mDTzDoNBoO44PxzYjAY9J0K0CK9DeXS31AmvQ1l0ttQLv0NZdLbUCa9DeXS31AmvQ1l0ttQLv0NZdLbkAebZgAAAAAAAAAAAAAAACiOTTMAAAAAAAAAAAAAAAAUx6YZAAAAAAAAAAAAAAAAimPTDAAAAAAAAAAAAAAAAMWxaQYAAAAAAAAAAAAAAIDi2DQDAAAAAAAAAAAAAABAcWyaAQAAAAAAAAAAAAAAoDg2zQAAAAAAAAAAAAAAAFAcm2YAAAAAAID/n707D6+zrPMG/j1JQ/d0b6mKLC1LF5ayQ5FNBSxLATvMi8PAK6JUCigCCjIqjOIUC4IgZRNBQMq+iQyLvAwCyghdQMoqM0pZWgsFQpMASZr3DzBaabE9SZuTp5/PdUVynnM/z/27T55vzrkCP28AAAAAAAAoHE0zAAAAAAAAAAAAAAAAFI6mGQAAAAAAAAAAAAAAAAqnW2cXAAAAAEDXVzpjUYder/UbAzv0egAAAAAAAADAmsdOMwAAAAAAAAAAAAAAABSOphkAAAAAAAAAAAAAAAAKR9MMAAAAAAAAAAAAAAAAhaNpBgAAAAAAAAAAAAAAgMLRNAMAAAAAAAAAAAAAAEDhaJoBAAAAAAAAAAAAAACgcDTNAAAAAAAAAAAAAAAAUDiaZgAAAAAAAAAAAAAAACgcTTMAAAAAAAAAAAAAAAAUjqYZAAAAAAAAAAAAAAAACqdbZxewPL996MFcc/WVqa9fnK233T5f+OLk1NTUtD2/ePHiXDT9vMx94vEMGDgohx9xZMaM3TRJcsftt+X2225JS0tLdt51t3zukMNSKpWSJP/98G9y6003ZMGC+bn0Z1d3ytoAAAAAAAAAAAAAAABYtSpyp5m33qrLhdPPzQGTDsqp352ax+bMyn333rPUmFtvviGvvfZqpk47J9tut0POP/fsNDc1Zd68F3LVFZdn8pRjcuJJp+TuO+/I7FkzkyT//dvf5Lxzfpiddt41Z5z5o85YGgAAAAAAAAAAAAAAAKtBRe408/wfnktra7LLrrunVCpl3Lit8tSTc7PHXhPaxjz15Nxst/2OGTpsWD61x5655abrs2DB/Dz95NwMHz48m20+Lkmy0cab5Omn5mbLrbbOjddfkz32+kwm7LPfP6yhqakpTU1NbY8bGxs6fqHAaifbUFzyDcUk21BMsg3FJd9QTLINxSTbUFzyDcUk21BMsg3FJd9QTLINXVNFNs3U1dWle4/uKZVKSZJevXtnwYIFS415q64uPXr0SJL07tW77bz3zu3RNq5Xr96pq6tLQ0ND5s17IcPWXjtfmXJkevXuncM+/4VsMmrMMmu49eYbcuP1166K5QGdSLahuOQbikm2oZhkG4pLvqGYZBuKSbahuOQbikm2oZhkG4pLvqGYZBu6popsmlmW9/tnyhpTStJQX58k6dd/QL72z5/LHb+4NT86+8xMv+inbc05f2viAZMyYZ+JbY8bGxty9OQjyikdqCCyDcUl31BMsg3FJNtQXPINxSTbUEyyDcUl31BMsg3FJNtQXPINxSTb0DVVZNNM375983ZjY5YsWZKqqqo0NjSktl//vxtTm/qG9xphGhobkyS1tf3Tt7ZvW4NM8t4vo7WHfyS1tbUplUrZbfdPZd1118un9/xMfn3/fXmrri61/fp9oIaamprU1NSswlUCnUG2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbuqaqzi5gWUaM3ChVVVW595678tJLL2bOnFkZPWZslrS0tI0ZNXpMHv7NQ1mwYH7+36/uysBBgzJs7bUzatSY/PnPCzJr5iN5/vk/5NlnnsnoMWOzVvfuGTN209x5x+157bVX88D9/5VBgwYvs2EGAAAAAAAAAAAAAACArq0id5qpra3N5KOOzYyfX5FrZ1yVbbbdPrvsunvOmjY1o8eMzd77TszEAydl/vxXcvKJx2XgwEGZcsxxqa6uzjofXzeHHPb5XHLR9LQ0t2TPvSZk8y3GJUmO/PIxueiCH+f4rxydj3zkI/nq8V+Mwfc0AAAgAElEQVTv5JUCAAAAAAAAAAAAAACwKlRk00yS7DB+p+wwfqeljp140ilt3/fp0yfHf/3kZZ47Ye/9MmHv/T5wfPCQITnl26d1bKEAAAAAAAAAAAAAAABUnKrOLgAAAAAAAAAAAAAAAAA6mqYZAAAAAAAAAAAAAAAACkfTDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDC0TQDAAAAAAAAAAAAAABA4WiaAQAAAAAAAAAAAAAAoHA0zQAAAAAAAAAAAAAAAFA4mmYAAAAAAAAAAAAAAAAoHE0zAAAAAAAAAAAAAAAAFI6mGQAAAAAAAAAAAAAAAApH0wwAAAAAAAAAAAAAAACFo2kGAAAAAAAAAAAAAACAwtE0AwAAAAAAAAAAAAAAQOFomgEAAAAAAAAAAAAAAKBwNM0AAAAAAAAAAAAAAABQOJpmAAAAAAAAAAAAAAAAKBxNMwAAAAAAAAAAAAAAABSOphkAAAAAAAAAAAAAAAAKR9MMAAAAAAAAAAAAAAAAhaNpBgAAAAAAAAAAAAAAgMLRNAMAAAAAAAAAAAAAAEDhaJoBAAAAAAAAAAAAAACgcLp1dgEf5rcPPZhrrr4y9fWLs/W22+cLX5ycmpqatucXL16ci6afl7lPPJ4BAwfl8COOzJixmyZJ7rj9ttx+2y1paWnJzrvuls8dclhKpVLbuXffeUcuu/TifOvU72b0mE1X+9oAAAAAAAAAAAAAAABYdSp2p5m33qrLhdPPzQGTDsqp352ax+bMyn333rPUmFtvviGvvfZqpk47J9tut0POP/fsNDc1Zd68F3LVFZdn8pRjcuJJp+TuO+/I7Fkz28577bVXc901V6/uJQEAAAAAAAAAAAAAALCaVOxOM8//4bm0tia77Lp7SqVSxo3bKk89OTd77DWhbcxTT87NdtvvmKHDhuVTe+yZW266PgsWzM/TT87N8OHDs9nm45IkG228SZ5+am623GrrJMlPL7kou+y6e+745W3Lnb+pqSlNTU1tjxsbG1bRSoHVSbahuOQbikm2oZhkG4pLvqGYZBuKSbahuOQbikm2oZhkG4pLvqGYZBu6poptmqmrq0v3Ht1TKpWSJL16986CBQuWGvNWXV169OiRJOndq3fbee+d26NtXK9evVNXV5ck+e1DD2b+/FcyecoxH9o0c+vNN+TG66/t0DUBnU+2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbuqaKbZpZlvf7Z8oaU0qyePHiXHH5pfnK105ITbeaD73OxAMmZcI+E9seNzY25OjJR6xEtUAlkm0oLvmGYpJtKCbZhuKSbygm2YZikm0oLvmGYpJtKCbZhuKSbygm2YauqWKbZvr27Zu3GxuzZMmSVFVVpbGhIbX9+v/dmNrUN9QnSRoaG5MktbX907e2bxrq69vGNTY2ZO3hH8m999yVN998I9Omnt723A/+4/ScdMq3s8mo0Utdu6amJjU1H95YA3Q9sg3FJd9QTLINxSTbUFzyDcUk21BMsg3FJd9QTLINxSTbUFzyDcUk29A1VWzTzIiRG6Wqqir33nNXRo/dNHPmzMoBnz0oS1paUlVdnSQZNXpMHv7NQxm/08554P77MnDQoAxbe+20tDTn8ksvyayZj6Rf/wF59plnsve++2fkhhtmx/E7JUneeefdnPi1Y/KlL0/JBiNGduZSAQAAAAAAAAAAAAAA6GAV2zRTW1ubyUcdmxk/vyLXzrgq22y7fXbZdfecNW1qRo8Zm733nZiJB07K/Pmv5OQTj8vAgYMy5ZjjUl1dnXU+vm4OOezzueSi6Wlpbsmee03I5luMS5L07t0nSfL2+zvT9O/fP2uttVanrRMAAAAAAAAAAAAAAICOV7FNM0myw/idssP7O8P8xYknndL2fZ8+fXL8109e5rkT9t4vE/beb7nX7tGzZ2Zcf0vHFAoAAAAAAAAAAAAAAEBFqersAgAAAAAAAAAAAAAAAKCjaZoBAAAAAAAAAAAAAACgcDTNAAAAAAAAAAAAAAAAUDiaZgAAAAAAAAAAAAAAACgcTTMAAAAAAAAAAAAAAAAUjqYZAAAAAAAAAAAAAAAACkfTDAAAAAAAAAAAAAAAAIWz0k0zS1pacuH083L/ffeuinoAAAAAAAAAAAAAAACg3Va6aaaqujqzZz2aZ55+alXUAwAAAAAAAAAAAAAAAO220k0zSbLzzrtl9qyZWfTaax1dDwAAAAAAAAAAAAAAALRbt3JO+s1DD+TNN9/MV44+MjU1NcsYUcpPr7i6naUBAAAAAAAAAAAAAABAecpqmnn99UVJkpaWlrS0tHRoQQAAAAAAAAAAAAAAANBeZTXNXH3dzR1dBwAAAAAAAAAAAAAAAHSYqvac/OK8efntQw/mP3/5i7Zjb7z+eruLAgAAAAAAAAAAAAAAgPYoa6eZtxsbc8H55+aR3/1327HP7L1v7rv3nlxx+aU59bv/kXXXW7/DigQAAAAAAAAAAAAAAICVUdZOM1ddeXke+d3DGbflVtlggxFtx8dttU261dTk2hk/77ACAQAAAAAAAAAAAAAAYGWV1TTzyO8ezqjRY3LiSadkxIYbth3v379/xm25dZ595qkOKxAAAAAAAAAAAAAAAABWVllNM++8/XaGDB22zOcaGxrS3NzSrqIAAAAAAAAAAAAAAACgPcpqmll3vfUze+ajWTD/laWOz3z0d5k969Gst/76HVIcAAAAAAAAAAAAAAAAlKNbOSdNOujgTD39tJz4tWPTo0fPJMkJXz06L7/8cpJk/wMndVyFAAAAAAAAAAAAAAAAsJLK2mlm0802z/FfPzkDBw7K4sVvJWnNyy+/lNra2hx1zFeyxbitOrhMAAAAAAAAAAAAAAAAWHFl7TSTJFtutU223GqbLJj/St5444307NkzH1vn46mqKqsPBwAAAAAAAAAAAAAAADpMWU0z//G9U7PD+E9km223y7C1h2fY2sM7uKz3/PahB3PN1Vemvn5xtt52+3zhi5NTU1PT9vzixYtz0fTzMveJxzNg4KAcfsSRGTN20yTJHbfflttvuyUtLS3Zedfd8rlDDkupVMqc2bNy1c9+mj//+c9Zb/31c+SXj85HP7bOKqkfAAAAAAAAAAAAAACAzlHWtjC/f/yxXHzB+fnyFz+faVO/lwd+/V9pbGzs0MLeeqsuF04/NwdMOiinfndqHpszK/fde89SY269+Ya89tqrmTrtnGy73Q45/9yz09zUlHnzXshVV1yeyVOOyYknnZK777wjs2fNTH394pxz1g+y86675+xzz0/37t3zs8t+0qF1AwAAAAAAAAAAAAAA0PnK2mnm6yf/W2bNfDRzZs3M7Pe/unWryeZbjMsO43fKVltvm+7du7ersOf/8FxaW5Nddt09pVIp48ZtlaeenJs99prQNuapJ+dmu+13zNBhw/KpPfbMLTddnwUL5ufpJ+dm+PDh2WzzcUmSjTbeJE8/NTfrrrtu9txrQvbYa0J69OiRjTcZlUd+99/LnL+pqSlNTU1tjxsbG9q1HqAyyDYUl3xDMck2FJNsQ3HJNxSTbEMxyTYUl3xDMck2FJNsQ3HJNxSTbEPXVFbTzBbjtsoW47ZKkrw4b15mzXoks2e+1zwz89FH0r37WrnsymvaVVhdXV269+ieUqmUJOnVu3cWLFiw1Ji36urSo0ePJEnvXr3bznvv3B5t43r16p26uroMGjwkBx9yaJKksbExD/76/nzy03suc/5bb74hN15/bbvWAFQe2Ybikm8oJtmGYpJtKC75hmKSbSgm2Ybikm8oJtmGYpJtKC75hmKSbeiaymqa+VvV3arTrbpbqqqq3j/Smubm5vZedpne758pa8zfHm5ubs45Z52RAQMH5TN777vM8RMPmJQJ+0xse9zY2JCjJx+xEtUClUi2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbuqaymmaenPtEZs18JLNnPppXXnklSVJVVcqYsZtm+x13yrbbbd/uwvr27Zu3GxuzZMmSVFVVpbGhIbX9+v/dmNrUN9QnSRoaG5MktbX907e2bxrq69vGNTY2ZO3hH0mSLFmyJNPPOydvvvFGvnXa99Kt27JfgpqamtTU1LR7HUBlkW0oLvmGYpJtKCbZhuKSbygm2YZikm0oLvmGYpJtKCbZhuKSbygm2Yauqaymme+d9q0kSalUyqjRY7LD+J2y7XY7pLa2tsMKGzFyo1RVVeXee+7K6LGbZs6cWTngswdlSUtLqqqrkySjRo/Jw795KON32jkP3H9fBg4alGFrr52WluZcfuklmTXzkfTrPyDPPvNM9t53/yTJZT+5OM8+83S++a3TkiT19YvTs2evv9kpBwAAAAAAAAAAAAAAgK6urKaZDTfaODuO/0S2237H9B8woKNrSpLU1tZm8lHHZsbPr8i1M67KNttun1123T1nTZua0WPGZu99J2bigZMyf/4rOfnE4zJw4KBMOea4VFdXZ52Pr5tDDvt8LrloelqaW7LnXhOy+RbjsnDhn/Ore+5Mkhz/1Sltc517/kUZMnTYKlkHAAAAAAAAAAAAAAAAq19ZTTOnfW9q2/cN9fV5++23U1tbm24dvN3UDuN3yg7jd1rq2IknndL2fZ8+fXL8109e5rkT9t4vE/beb6ljQ4YMzYzrb+nQGgEAAAAAAAAAAAAAAKg8ZTXNJMnsWY/m6quuyEsvvpgkKZWSTUaNzucOOSwjRm7YYQUCAAAAAAAAAAAAAADAyqoq56TfP/5Yzjzj+3npxXnp3bt3PvKRj6Rnz1556sm5Oe3bp+QPzz3b0XUCAAAAAAAAAAAAAADACitrp5kbr78mVVVVmTzl2Izfaee24w89+OtceP65uf7aq3Pyv53aUTUCAAAAAAAAAAAAAADASilrp5n//Z/ns9XW2y7VMJMk43faOVttvW2ee9ZOMwAAAAAAAAAAAAAAAHSesppm1urePU1N7y7zuaamd1MqtasmAAAAAAAAAAAAAAAAaJeymmZGjx6b2bNm5Zabb8jit95KkjTU1+f2227JnNmzMmr02A4tEgAAAAAAAAAAAAAAAFZGt3JOOvhfDs3cJ36f62ZcnetmXJ1u3arT3NySJOnZs0cO/pd/7dAiAQAAAAAAAAAAAAAAYGWUtdPM2sOH54wzz87Ou+6WAQMGZMmS1tTW1mbHnT6R06eelY9+bJ2OrhMAAAAAAAAAAAAAAABWWFk7zSTJoMFDMvmoY5b7/IL5r2TRokUZNXpMuVMAAAAAAAAAAAAAAABAWcraaWZF3PHLX+S7p35rVV0eAAAAAAAAAAAAAAAAlmuVNc0AAAAAAAAAAAAAAABAZ9E0AwAAAAAAAAAAAAAAQOFomgEAAAAAAAAAAAAAAKBwNM0AAAAAAAAAAAAAAABQOJpmAAAAAAAAAAAAAAAAKJxV1zTTusquDAAAAAAAAAAAAAAAAB+q28qesGTJkjz4wP1Ze+3h2WjjTZY77jN775vtdtixXcUBAAAAAAAAAAAAAABAOVZ6p5mqqqpc9pOL8sD9//Wh49YePjyjx4wtty4AAAAAAAAAAAAAAAAo20o3zSTJ2E03yzPPPJUlS5Z0dD0AAAAAAAAAAAAAAADQbt3KOam5uTkvzpuXk048LkOGDP3A86VSKSd845vtLg4AAAAAAAAAAAAAAADKUVbTzGNzZidJXpz3Ql6c98IyRpTaUxMAAAAAAAAAAAAAAAC0S1lNM//2ne92dB0AAAAAAAAAAAAAAADQYcpqmhk9Zmzb92+88UbeeH1R1lt/gw4rKkl++9CDuebqK1Nfvzhbb7t9vvDFyampqWl7fvHixblo+nmZ+8TjGTBwUA4/4siMGbtpkuSO22/L7bfdkpaWluy862753CGHpVQq5U9/+mMumn5eXnn5pWwwYmS+POUrGTxkSIfWDQAAAAAAAAAAAAAAQOerKvfEu/7zlznyiMNy1JcOzyknnZAkeXLu7/PNr38tdW++2a6i3nqrLhdOPzcHTDoop353ah6bMyv33XvPUmNuvfmGvPbaq5k67Zxsu90OOf/cs9Pc1JR5817IVVdcnslTjsmJJ52Su++8I7NnzUySXHLB+dlggxGZ9sPzUl1dnauuuKxddX6YxqbWNC9pTWNT61LHm5tbP/C1vLGsOdaEe+Ava+zq931XrRtWp66Yk5WpeVWMLeL81tQ15l9Va6okq6Nuc1TePOaorDkAVlRX+J1U6TVWfH3N79fXXJn1dQVd9TWs9HtzRRVlHUnXvZeKqqveW1217r9XlHUkxVpLUXTFn0lXrBlWNzmhI7iP6CidcS+tCXOuCWtMOufvA2vKOturK7xPVHqNFV9fF7gvvYarRlH+OwBzVN48lZ7ZFbWq1lFW08ztv7glP7vsJymllP79+6f1/Zq6davJvHkv5NoZV7WrqOf/8FxaW5Nddt09H1tnnYwbt1WeenLuUmOeenJuttt+xwwdNiyf2mPPvP76oixYMD9PPzk3w4cPz2abj8vIDTfKRhtvkqefmpt333knzz//XHbZ7ZMZPGRIPrHLbnnqqbnLqSBpampKQ0ND21djY8MK19/Y1JrJd9enZtrrmXx3fdsPrbGpNU2t+cDXssay5lje/VIkf1njadNe7/T7flVkG/irzsxJuflemZpXxdgizm9NXWP+VbWmjlbp793mqLx5zFFZcyxPe7INVLbV8bm8s1R6jRVfX3NrJt/1fn131Xe5f4lVCTrzNaz0z+WrQ1HWkchjpemqn8uLkomirCMp1lqKoivm230E/1hXzDaVx+9b/lZX+1y+Jsy5Jqwx6Zy/D6wp6/yLIn8ur/QaK76+LvD3Oa/h8lX6e7c5KmuO1TVPpWd2Ra3KdZRaW1tX+mpfPXpyunXrlu+fcVZ+fuXPcs/dd+bq625Kkpz1g//IH/7wbC64+LKyi/r1/fflyp/9NJf89MokyVVXXJb//Z//ybdO/W7bmK9MOTIT9tkve35m77zd2JjPH3pwvn3a6Xly7hOZ+ejv8v0zzkqSnH3mGenZq1cm/dP/yTFHfTE/OOtHWefj62bWzEdy5hnfz9XX3bzMGm64bkZuvP7aDxy/9GdXp1evXh9af/OS1tRMe73tcdOJA9KtqpTmJct+qZc1ljXH8u6XIvnLGuuOqknP3n2SdN59vyqyDfxVZ+ak3HyvTM2rYmwR57emrjH/qlpTR6v0925zVN485qisOZanPdmmcpXOWNSh12v9xsAOvR6rx+r4XN5ZKr1G9RVfV33vLsrPvijrSIq1liKQ7c5VlHUkxVpLUXTFfLuP4B/ritmm8vh9y9/qap/L14Q514Q1mrOy37u7wvtEpdeovvar9Bq7YraT4vx3AOaovHkqPbMralWuo6ydZhYtWpSNNt4ka3XvnvxdHT179kz94sUdUdtSSiuw3uWNWf6py39m4gGTcunPrm77+vGFP/nHBbyvqSU5dOxaSd77Z1PLX48v62tZY1lzrAn3wF/W+IPpTZ1+36+KbAN/1RXzvTI1r4qxRZzfmrrG/KtqTR2t0t+7zVF585ijsuZYnvZkG6hsq+NzeWep9Borvr4lf1ffkk4uqAvqzNew0j+Xrw5FWUcij5Wmq34uL0omirKOpFhrKYqumG/3EfxjXTHbVB6/b/lbXe1z+Zow55qwxqRz/j6wpqzzL4r8ubzSa6z4+rrA3+e8hstX6e/d5qisOVbXPJWe2RW1KtdR1k4zJ3z16KRUytRpZ+fKn/0099z13k4zr726MCed+LX069cvZ57z47KLmj3r0fxw2tT87OfXpaqqKpdceH4a3347x371+LYx/3bSidlym21y4GcPyqJFizLlyMNz1jnn54knHssdv7gt5/z4wiTJ97/7nay/wch8dtJBOeyQf853/v372WTU6Dz0wP256orLc8ElK7YjTkNDQ75w2OdW+P+ho7GpNTXV7/3wetb8tTmneRlbgDW1ZpljWXMs734pkr+sMe9/OKmU+76jsg38VaXkZGXyvTI1r4qxRZzfmrrG/KtqTatSJb53m6Py5jFHZc2xIlY221QmO82wLKvqc3lnqfQaK76+5tbUVL33L7N6dqu8+rqCSnkNK/Fz+epQlHUklXMv8Z5Kubdku2uvIynWWoqiUn4mRftcDp2tUnLib2pdW6XcR1ServC5fE2Yc01YY9I5fx9YU9a5LEX7XF7pNVZ8fRVyX34Yr+GKqcT3bnNU1hyra55Kz+yKWlXr6FbOSXvsNSGX//SSnHTCV9Ot23uXuPSSC/Pwbx5KfX19Dpj0T+0qasTIjVJVVZV777kro8dumjlzZuWAzx6UJS0tqaquTpKMGj0mD//moYzfaec8cP99GThoUIatvXZaWppz+aWXZNbMR9Kv/4A8+8wz2Xvf/bNW9+7ZYMTI3HfvPRk8eEgefOD+jBoztl11fpi//JC6/d1ePt2W8Yv5Lz+Evx/LmmN590uRtP3ien+NXfW+XxN+VtBeXTEnK1PzqhhbxPmtqWvMv6rWVElWR93mqLx5zFFZcwCsqK7wO6nSa6z4+rpVdn1dQVd9DSv93lxRRVlH0nXvpaLqqvdWV6377xVlHUmx1lIUXfFn0hVrhtVNTugI7iM6SmfcS2vCnGvCGpPO+fvAmrLO9uoK7xOVXmPF19cF7kuv4apRlP8OwByVN0+lZ3ZFrap1lN008+qrC/PLX9yav2xUc+89dyVJdtv9U5mw937tKqq2tjaTjzo2M35+Ra6dcVW22Xb77LLr7jlr2tSMHjM2e+87MRMPnJT581/JyScel4EDB2XKMceluro663x83Rxy2OdzyUXT09Lckj33mpDNtxiXJPnSl4/OxRf8OCccd0xGjNwwR3zp0HbVCQAAAAAAAAAAAAAAQGUqq2kmST53yGHZY88J+f3jc/LGG2+kZ6+eGTNm06zz8XU7pLAdxu+UHcbvtNSxE086pe37Pn365Pivn7zMcyfsvd8yG3fWXXe9nD71zA6pDwAAAAAAAAAAAAAAgMpVdtNMkgweMiRbb7td3njjjfTv3z99+9Z2VF0AAAAAAAAAAAAAAABQtrKbZu6+847cdstNWbRoUduxQYMHZf8DJuWTn96zQ4oDAAAAAAAAAAAAAACAcpTVNHPTjdflhmuvSdKaYcPWTt/a2rzx+ut59dWFufSSi7J48eJMPOCzHVwqAAAAAAAAAAAAAAAArJiymmbuvfuu9OjRIyd845sZPWZs2/Enfv94zvrB93P3nXdomgEAAAAAAAAAAAAAAKDTVJVzUmNjY7bcauulGmaSZOymm2WrbbbL2283dkhxAAAAAAAAAAAAAAAAUI6ymmY23GijvP76omU+t/itt7LxJqPbVRQAAAAAAAAAAAAAAAC0R7dyTtp41OjccO01ufTiC7P28OFtxxcu/HMef2xOPr3nXvnlL25tO773vhPbXykAAAAAAAAAAAAAAACsoLKaZm64dkaS5N5f3ZWk9DfPtCZJ7rnrzr95XNI0AwAAAAAAAAAAAAAAwGpVVtPMgZMOSkqlfzwQAAAAAAAAAAAAAAAAOkFZTTOTDjq4o+sAAAAAAAAAAAAAAACADlNW08yN11/7oc+XSsmBk/65rIIAAAAAAAAAAAAAAACgvcpsmrkmSSlJ6989U2r7TtMMAAAAAAAAAAAAAAAAnaWsppl/PezwZR5/+aWXcu+v7sqBkw5qV1EAAAAAAAAAAAAAAADQHmU1zXxm732X+9y8F/6UZ55+uuyCAAAAAAAAAAAAAAAAoL2qOvqCg4cOzXPPapoBAAAAAAAAAAAAAACg85S108z//s/zHzjW2tqa+fNfyZxZj6Z3nz7tLgwAAAAAAAAAAAAAAADKVVbTzCknnZCktJxnW7PHXhPKrwgAAAAAAAAAAAAAAADaqaymmU1GjU6ptHTTTKlUSr9+/bP5uC3ziZ137YjaAAAAAAAAAAAAAAAAoCxlNc18+7TTO7oOAAAAAAAAAAAAAAAA6DBV5Z5Y9+abeWzO7LbHT/z+8dzxy9sy74U/dUhhAAAAAAAAAAAAAAAAUK6ydpp5cd68fO+0b6W2X79svsW4PPTA/Tn/vB8lSWpquuWkU76TUaPHdGihAAAAAAAAAAAAAAAAsKLK2mnmmquvTF3dm9l0s82TJDffdEOGDh2aAw6clFKplJtuuLZDiwQAAAAAAAAAAAAAAICVUdZOM889+0y23Gqb/Othh2fhwj/n5ZdezDFfOT47jN8pCxbMz2NzZre7sD/96Y+5aPp5eeXll7LBiJH58pSvZPCQIUuNaW1tzYyfX5H77/t/qa6uzj777Z8J++yXJJn7xO9z2U8uyqJFr2XM2M1y5FHHpE+fPlkw/5VcfOH5ee7ZZzNo0KB87l8Pyzbbbt/uegEAAAAAAAAAAAAAAKgcZe0009LSnO7duydJHpszO6VSKWM32+y9J0ulNDc3tbuwSy44PxtsMCLTfnheqqurc9UVl31gzJzZM/Oru+/M17/5rRxx5FG56orL8sKf/pjm5uacf+7Z2WH8JzJ12jlZsGB+br3phiTJ9B//KH369MnZ503PttvvmPPPPTvNzc3trhcAAAAAAAAAAAAAAIDKUdZOMxuMGJlHfvdwLv/pJXnkdw9nxIgN07dvbX7337/NrEd/l/U3GNGuot595508//xzOezwIzJ4yJB8Ypfdltk089STc7PhRptkxIiRSZKhQ4flmaefSnV1dV5/fVE++ak90n/AgGy/4/jMevSRJMmYsZtm+x3GZ9Cgwdl8iy1y2y035u3GxvTp23epazc1NaWp6a/NP42NDe1aE1AZZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqFrKqtp5p8PPiSn//t3cvedd6SqqiqTjzo2SXLBj3+UpqbmHPDZg66fKLwAACAASURBVFb4WkuWLPnAL4yGhvce9+jRI0nSu3fvvFVX94Fz6+rq2sYkSa/evVNXV5e698e2nd/rr+cf9H/+pW38r+6+K1uM2+oDDTNJcuvNN+TG669d4XUAXYNsQ3HJNxSTbEMxyTYUl3xDMck2FJNsQ3HJNxSTbEMxyTYUl3xDMck2dE1lNc2MGLlhzjjz7Myd+0TWW2/9rLf+BkmSbbbdPjvvunvGbrrZCl/rtVcX5tgpRy51bNJBBy9jZGmFrlda3rC/O37D9dfkyblP5PSp05Y5fOIBkzJhn4ltjxsbG3L05CNWqAagcsk2FJd8QzHJNhSTbENxyTcUk2xDMck2FJd8QzHJNhSTbENxyTcUk2xD11RW00ySDBk6LLsOHbbUsaOO+Wrb93Nmz8zzf/hDPvtP//wPrzPj+luWOvbuO+/khutmpL6+PknS2NCQfv36feDcvn375rVXF7Y9bmxsTG2//unbtzZJUt9Qnx49e6axsSH9avu3jbv7zjvyn7ffln879XsZNHjIMuuqqalJTU3Nh9YOdD2yDcUl31BMsg3FJNtQXPINxSTbUEyyDcUl31BMsg3FJNtQXPINxSTb0DVVraoLz541s+ztp9bq3j0bjBiZ++69J68uXJgHH7g/o8aMbXt+SUtLkmTU6LF59pmn89yzz+SxObOzYP78jBo1OmsPH54BAwbmV3fdmYV/XpCHf/ubtvMfevDXueqKyzPl2K9l6NChqa9fnOampvYvGAAAAAAAAAAAAAAAgIpR9k4zq9qXvnx0Lr7gxznhuGMyYuSGOeJLhyZJbv/FLXn6ySdzwje+mXFbbpW9PrNPpk09PdXdqnPo/z08H/3YOkmSKccel8t+clHuuvOXGTN2s+y3/wFJkmuvvipNTe9m2tTvtc01+ahjsstun1z9iwQAAAAAAAAAAAAAAGCVqNimmXXXXS+nTz3zA8f32Xf/7LPv/kmSUqmUgw85NAcfcugHxo0Zu2nOPOfHHzh+7vSLO75YAAAAAAAAAAAAAAAAKkpVZxcAAAAAAAAAAAAAAAAAHU3TDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDCWXVNM61t/wMAAAAAAAAAAAAAAACrVYc1zbS2tmbx4sVtjz9/xJdy9XU3d9TlAQAAAAAAAAAAAAAAYIV1K+ekd999N5defEFefvmlfPf7P8j8V17Jf3zv1CxcuDAbbbRxjv/Gyenbt7aDSwUAAAAAAAAAAAAAAIAVU9ZOM7fcdH0e+PV/pbm5OUny8ysvy6uvLsygQYPy7LNP5/prZ3RokQAAAAAAAAAAAAAAALAyymqaeejBX+cjH/1YvnPa6Xn77bczZ/asHDDpoJx3wSXZaONNMnvmox1dJwAAAAAAAAAAAAAAAKywsppm3nzjjWy08Sbp0bNnnn5qblpalmT77ccnST760Y/lzTff7NAiAQAAAAAAAAAAAAAAYGWU1TQzePCQPPH4Y/nf/3k+d/3nL9O/f/98bJ118u677+aZZ57OoMGDO7pOAAAAAAAAAAAAAAAAWGFlNc18ao+98uqrC3PKSSfmsTlzsvun90iSHP+VKXn5pZfyiV127cgaAQAAAAAAAAAAAAAAYKV0K+ekvSbsk+bm5sx89JGMGDkyBx74T0mS+vrF2XnX3TJx/892aJEAAAAAAAAAAAAAAACwMspqmkmSffbbP/vst/9Sx6ZffFl69OjR7qIAAAAAAAAAAAAAAACgPVa4aWbhwj+nf/8BqampyeK33lruuMVNTUmSPn37tr86AAAAAAAAAAAAAAAAKMMKN838+7dPyaf3+kz2m3hgvvSFQ5OUlju2VEp+fu1NHVEfAAAAAAAAAAAAAAAArLQVbpoZMHBg+vcfkCQZNHhwSh/SNAMAAAAAAAAAAAAAAACdacV3mjn9jLbvz5t+ySopBgAAAAAAAAAAAAAAADpCVWcXAAAAAAAAAAAAAAAAAB1N0wwAAAAAAAAAAAAAAACFo2kGAAAAAAAAAAAAAACAwtE0AwAAAAAAAAAAAAAAQOFomgEAAAAAAAAAAAAAAKBwunV2Acvzpz/9MRdNPy+vvPxSNhgxMl+e8pUMHjJkqTGtra2Z8fMrcv99/y/V1dXZZ7/9M2Gf/ZIkc5/4fS77yUVZtOi1jBm7WY486pj06dPnr9f/4//mlJNOyP4HTsqkgw5erWsDAAAAAAAAAAAAAABg1arYnWYuueD8bLDBiEz74Xmprq7OVVdc9oExc2bPzK/uvjNf/+a3csSRR+WqKy7LC3/6Y5qbm3P+uWdnh/GfyNRp52TBgvm59aYb2s5b0tKSiy88P62tratzSQAAAAAAAAAAAAAAAKwmFbnTzLvvvJPnn38uhx1+RAYPGZJP7LLbMptmnnpybjbcaJOMGDEySTJ06LA88/RTqa6uzuuvL8onP7VH+g8YkO13HJ9Zjz7Sdt4dd/wia63VPeuuu95ya2hqakpTU1Pb48bGho5bINBpZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqFr6vSmmSVLlnzgF0ZDw3uPe/TokSTp3bt33qqr+8C5dXV1bWOSpFfv3qmrq0vd+2Pbzu/11/P/vGBBbr3pxvz76VNz3o9+uNy6br35htx4/bXtWBlQiWQbiku+oZhkG4pJtqG45BuKSbahmGQbiku+oZhkG4pJtqG45BuKSbaha+r0ppnXXl2YY6ccudSxSQcdvIyRpRW6Xml5w94//pOLp2fCPvtl+Ec++qHXmXjApEzYZ2Lb48bGhhw9+YgVqgGoXLINxSXfUEyyDcUk21Bc8g3FJNtQTLINxSXfUEyyDcUk21Bc8g3FJNvQNXV608yQocMy4/pbljr27jvv5IbrZqS+vj5J0tjQkH79+n3g3L59++a1Vxe2PW5sbExtv/7p27c2SVLfUJ8ePXumsbEh/Wr757lnn8nvH38sf3ju2dx+2815++238+K8F1JTs1YmHvDZpa5dU1OTmpqajl4u0MlkG4pLvqGYZBuKSbahuOQbikm2oZhkG4pLvqGYZBuKSbahuOQbikm2oWvq9KaZZVmre/dsMGJk7rv3ngwePCQPPnB/Ro0Z2/b8kpaWVFVXZ9Tosbn7zjvy3LPPpKGhIQvmz8+oUaMzbO3hGTBgYH51153Z/VOfzsO//U0232LLrLve+jn3/IvarjPtjO9nzNhN86k99uyMZQIAAAAAAAAAAAAAALCKVHV2AcvzpS8fnRdfnJcTjjsm777blH855NAkye2/uCU/PPOMJMm4LbfKXp/ZJ9Omnp4Lp5+bQ//v4fnox9ZJt27dMuXY4/LI7x7ON074aoYMGZr99j8ga621VoYMHdb21a1bt/Tu3Tu9e/fpzKUCAAAAAAAAAAAAAADQwSpyp5kkWXfd9XL61DM/cHyffffPPvvunyQplUo5+JBDc/D7DTV/a8zYTXPmOT/+0Dm+f8ZZHVMsAAAAAAAAAP+fvTuPs7OszwZ+nclMkkmILAIJQRbZIQsgoLYuULRCIAjWFcsnFkghAtr61mrrq2+hiKK4tW644BJZRLCUIghoUQQXNgkSyQLIJltFwEBmApNk3j+QdDDbkDkzz3Pu8/1+Pghz5syZ3304130/M+HyAAAAAADUSm3faQYAAAAAAAAAAAAAAAA2lNIMAAAAAAAAAAAAAAAAxVGaAQAAAAAAAAAAAAAAoDhKMwAAAAAAAAAAAAAAABRHaQYAAAAAAAAAAAAAAIDiKM0AAAAAAAAAAAAAAABQHKUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAACiO0gwAAAAAAAAAAAAAAADFUZoBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVRmgEAAAAAAAAAAAAAAKA4SjMAAAAAAAAAAAAAAAAUR2kGAAAAAAAAAAAAAACA4ijNAAAAAAAAAAAAAAAAUJzOqgdYk3vuuTtf+sJn8+AD92eHHXfKO0/8u2y+xRbPuU9/f3/OO2durv7RVRk1alRmvv6IHDLz9UmSX8+/NV//6pfy6KO/z5Sp03P8Ce/KRhtt9Mxj331XzjvnW1m8aEE+8KFTstPOu4z4+gAAAAAAAAAAAAAAABhetXynma988fPZYYcdc8anPptRo0bl7LlfX+0+826+KT+88vK87wMfyuzjT8jZc7+ee++5O8uXL8/n//3T+bNXvCqnn/GZPPzwQ7n4Py5MkvzPww/nlP/3f7PJppvm1I+ekR122HGklwYAAAAAAAAAAAAAAMAIqN07zTz91FO5887b845jZmfzLbbIq/b/izWWZhbc9uvsvMtu2XHHnZIkW245MYsWLsioUaPy2GOP5jWvfV022XTTvPzPX5Ff3nhDkuTS712cSZMm5bg5J6ajY919ob6+vvT19a36uLe3p4mrBKoi21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahNVVamlm5cuVqm0VPzzMfjx07Nkkyfvz4PLFkyWpfu2TJklX3SZJx48dnyZIlWfLH+676+nH/+/W3L1qYMWPH5p/f93/y1LJlOWTm6/O6gw9Z42wXX3RhvnvB+UNcIVA3sg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbWlOlpZnfP/K7vPvE459z25vecuQa7tkY1OM11na3P96+dOnSdI0enePmnJg7bl+cb3ztK5kydVq2ftE2q33J4W94Uw6Zefiqj3t7e3LSnNmDmgOoL9mGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDa2p0tLMFltOzHkX/Odzbnv6qady4XfOy9KlS5MkvT092XjjjVf72gkTJuT3j/xu1ce9vb15wcabZMKEFyRJlvYszdju7vT29mTjF2ySJHnBxhtn3/1elh132jk77rRzzj37m7nvvnvXWJrp6upKV1dX09YK1INsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YbW1FH1AH9q9Jgx2WHHnfKj//5BHvnd73LtNVdn9ylTV31+5YoVSZLd95iaxYsW5vbFi3LLvJvz8EMPZffd98ikrbbKpptulh9ecXl+9z8P5xc//9mqr997n31zzU9+nIcefDDX/OTHWb58ebbddvsqlgkAAAAAAAAAAAAAAMAwqvSdZtbmuHeelC9/8XN573velR132jmzj5uVJPneJf+Zhbfdlve+/wPZ+yX75OAZM3PG6adlVOeozPqbY1a9Y8yJ735Pvv7VL+WKyy/NlKnT8/oj3pAkmXnYEXn0kUfywX9+b7q7x+W4OSdm8tZbV7ZOAAAAAAAAAAAAAAAAhkctSzPbbbd9Tjv9E6vdPvOwIzLzsCOSJI1GI0ceNStHHjVrtftNmTotn/jM51a7ffTo0Zl9/AmZffwJzR8aAAAAAAAAAAAAAACA2uioegAAAAAAAAAAAAAAAABoNqUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAACiO0gwAAAAAAAAAAAAAAADFUZoBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVRmgEAAAAAAAAAAAAAAKA4SjMAAAAAAAAAAAAAAAAUR2kGAAAAAAAAAAAAAACA4ijNAAAAAAAAAAAAAAAAUBylGQAAAAAAAAAAAAAAAIqjNAMAAAAAAAAAAAAAAEBxOqseoFX09/cnSXp7eyqeBNpbd3d3Go1G0x5PtqE+5BvKJNtQJtlmJPT0eD2MtGZnO5FvqAtnN5RJtqFMrsuhXM5uKJNsQ5lcl0O5nN1QpvVlW2lmkJYt602SnDRndsWTQHs765vnZty4cU17PNmG+pBvKJNsQ5lkmzV5W5Mf79h3NPkBWa9mZzuRb6gLZzeUSbahTK7LoVzObiiTbEOZXJdDuZzdUKb1ZbvR/2zFjXVauXJlHnvs0YwdO7iGYW9vT06aMzufO/Or6e5u7sXTcDDv8DJv8zS75Vtitus+o/mGpu7zJRs+43Dk+4EH7s8/vuddtX6+nq9WeA08X9bUGuqU7edzdo+EUv59l7KOpJy11HkdJWW7zs/zs8zYHGZcv+H4f857Pvmuev2DUfcZzTd0dZ+xVa/L6/68DlYp60jKWUu7r0O2m6OUdSTlrKWUdSQbthbX5etX9xnNN3R1n7FVz+5mqvu/o6T+M9Z9vsSMg1V1tuvwHIyEdlhnO6wxaZ11ui5fv7rPaL6hq/uMrXpdXvfndbCso35KWctwZds7zQxSR0dHXvjCzZ/313V3j2t643g4mXd4mbd+Ss523Wc039DUfb6k+hk7Ojqy2WYvrMUsw8GaWoM1Nd+Gnt0joernpllKWUdSzlpKWce61CHbrfA8m7E5zDiyNiTfrbD+us9ovqGr+4xVz1fy79QGo5R1JOWsxTqaQ7bLWEdSzlpKWUdS/Vpcl1fDfENX9xmrns/v1Aan7jPWfb7EjCOt3a/L16cd1tkOa0zaZ50DuS6vhvmGru4zVj1fu5/d1lE/payl2evoaNojAQAAAAAAAAAAAAAAQE0ozQAAAAAAAAAAAAAAAFCcUSeffPLJVQ9Rqo6OjuwxZVpGjRpV9SiDYt7hZd5ytMJzU/cZzTc0dZ8vqdeMdZqlWaypNVhTeynluSllHUk5ayllHXXXCs+zGZvDjPXXCuuv+4zmG7q6z1j3+damVef+U6WsIylnLdZRrVad+0+Vso6knLWUso6kNdfSCjPXfUbzDV3dZ6z7fCOhFZ6Dus9Y9/kSM7aKdnkO2mGd7bDGpH3WOVSt8DzVfUbzDV3dZ6z7fGvTqnP/Keuon1LWMhzraPT39/c37dEAAAAAAAAAAAAAAACgBjqqHgAAAAAAAAAAAAAAAACaTWkGAAAAAAAAAAAAAACA4ijNDFJ/f396enrS399f9ShAE8k2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbWgNSjOD1Nvbm2Pf8fb09vZWPQrQRLIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINZZJtKJd8Q5lkG1qD0gwAAAAAAAAAAAAAAADFUZoBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKE5n1QNU4aMfPjm/umXeqo8PPezwHDXr6OoGAgAAAAAAAAAAAAAAoKnasjTz2GOP5ejZx+UVr3x1kqSra3Sl8/Qu709XR9K3MunubFQ6CwAMVSuea604MzA48g0wMuy3AKyLcwLKJNtQJtkGGBn2W5rFawmAduUMpJV4vVavLUszjz/2WLbccmLGj99orffp6+tLX1/fqo97e3uGZZbe5f2Zc8XSzJ3/dGZNHZ0zDxovDDCMRirb0K6qPNc2NN/OYqi3oZzd8g315bq8LPZbBhpqvhsfe7TZI6X//Zs1/TGh3bguhzLJNpTL78uhTH6nVg77LQO5LodyObth+LTif6NG+3LNVg9tV5pZ3teXJ598Iud865v58hc/n5132TWzj39nJkx4wXPud/FFF+a7F5w/7PN0dSRz5z+d5Jm/nzVj/LB/T2hnI5VtaFdVnmsbmm9nMdTbUM5u+Yb6cl1eFvstA8k3lMl1OZRJtqFcfl8OZfIzdznstwzkuhzK5eyG4dOK/40a7cs1Wz20XWkmjUb+5ti/zaRJkzNu3Lh8/rOfyYXf+XaOPva459zt8De8KYfMPHzVx729PTlpzuymj9O3Mpk1dfSq9ljfyqSzo+nfBvijkco2tKsqz7UNzbezGOptKGe3fEN9uS4vi/2WgeQbyuS6HMok21Auvy+HMvmZuxz2WwZyXQ7lcnbD8GnF/0aN9uWarR7arjSzcuXK7Lvvy7LZC1+YJNl3v5fmjttvX+1+XV1d6erqGvZ5ujsbOfOg8Tlrxvj0rYy3W4JhNlLZhnZV5bm2ofl2FkO9DeXslm+oL9flZbHfMpB8Q5lcl0OZZBvK5fflUCY/c5fDfstArsuhXM5uGD6t+N+o0b5cs9VD2/WUHn7owZw459j8/KfX5uGHH8qvbpmXbbfbrtKZujsb6exoCAEARWjFc60VZwYGR74BRob9FoB1cU5AmWQbyiTbACPDfkuzeC0B0K6cgbQSr9fqtd07zWyz7XZ5xzGzc/bcr6e3tyfTpu+ZN7/17VWPBQAAAAAAAAAAAAAAQBO1XWkmSQ6eMTMHz5hZ9RgAAAAAAAAAAAAAAAAMk46qBwAAAAAAAAAAAAAAAIBmU5oBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVRmgEAAAAAAAAAAAAAAKA4SjMAAAAAAAAAAAAAAAAUR2kGAAAAAAAAAAAAAACA4ijNAAAAAAAAAAAAAAAAUBylGQAAAAAAAAAAAAAAAIqjNAMAAAAAAAAAAAAAAEBxlGYAAAAAAAAAAAAAAAAojtIMAAAAAAAAAAAAAAAAxVGaAQAAAAAAAAAAAAAAoDhKMwAAAAAAAAAAAAAAABRHaQYAAAAAAAAAAAAAAIDiKM0AAAAAAAAAAAAAAABQHKUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAACiO0gwAAAAAAAAAAAAAAADFUZoBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVp29LMZz758Rz55iOqHgMAAAAAAAAAAAAAAIBh0JalmRtvuD43XP+LqscAAAAAAAAAAAAAAABgmLRdaaanpydfP+tLOWjGoVWPAgAAAAAAAAAAAAAAwDDprHqAkXbe2XMzbfpemTpter5/6SVrvV9fX1/6+vpWfdzb2zMS4wHDTLahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQ2tqq9LMwgW35cYbrssZn/r3LF68cJ33vfiiC/PdC84focmAkSLbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqE1Nfr7+/urHmKkfPTDp+TX82/NmDGjs2LFyjz11LKMGzcuZ33z3NXuu6Ym4ElzZuesb56bcePGjeTYQBPJNpRLvqFMsg1lkm0o11Dz3fjYo02fqf/9mzX9MaHdOLuhTLIN5ZJvKJNsQ5lkG8ol31Am2YbW1FbvNPPOE9+dvr6nkyTz59+aL3/xczn9jE+v8b5dXV3p6uoayfGAESDbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqE1tVVpZpNNN131zxtvfG+SZIstJ1Y1DgAAAAAAAAAAAAAAAMOko+oBqvKSffbLeRf8Z9VjAAAAAAAAAAAAAAAAMAzatjQDAAAAAAAAAAAAAABAuZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVRmgEAAAAAAAAAAAAAAKA4SjMAAAAAAAAAAAAAAAAUR2kGAAAAAAAAAAAAAACA4ijNAAAAAAAAAAAAAAAAUBylGQAAAAAAAAAAAAAAAIqjNAMAAAAAAAAAAAAAAEBxlGYAAAAAAAAAAAAAAAAojtIMAAAAAAAAAAAAAAAAxVGaAQAAAAAAAAAAAAAAoDhKMwAAAAAAAAAAAAAAABRHaQYAAAAAAAAAAAAAAIDiKM0AAAAAAAAAAAAAAABQHKUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAACiO0gwAAAAAAAAAAAAAAADFUZoBAAAAAAAAAAAAAACgOEozAAAAAAAAAAAAAAAAFEdpBgAAAAAAAAAAAAAAgOIozQAAAAAAAAAAAAAAAFAcpRkAAAAAAAAAAAAAAACK01n1AIN19Y/+O3fcfnuOPW5OlvX25mtnfTn33n1X9t5nv7zlbW9Po9GoekQAAAAAAAAAAAAAAABqoiVKM1f/+Kp86Yufy4u22TZJcv555+Tan/w4SXLvvfeme1x3Xn/4X1U4IQAAAAAAAAAAAAAAAHXSUfUAg/H97/1XNtpoQo6ZfVxWrliRa37yo7z8z1+ZT3/2i5k4aVJ+8qOrqh4RAAAAAAAAAAAAAACAGmmJ0sxDDz2Uffd7aXbbfUruvPOO9PT05OAZh2bixEnZY48peeSR31U9IgAAAAAAAAAAAAAAADXSEqWZCRMm5Lf33ZeVK1fmZz+9JmPGjMmOO+2c5JlCTXf3uIonBAAAAAAAAAAAAAAAoE46qx5gMPZ96ctyxfcvzXHHzEpPT09evf8BGTVqVD51xulZcNuvc8CBr6l6RAAAAAAAAAAA3XNMsAAAIABJREFUAAAAAGqkJd5p5i1v++vstfc+WbasN9u/+MU58qhZSZJbfzUvkydPzluP/OuKJwQAAAAAAAAAAAAAAKBOWuKdZrq7u/O+f/5gVqxYkVGjRq26/e//4f2ZMmVqOru6KpwOAAAAAAAAAAAAAACAummJ0syzBhZmkmTPvfauaBIAAAAAAAAAAAAAAADqrCVKM4899mjmfv2sLLjt11myZMlqn280knPO/48KJgMAAAAAAAAAAAAAAKCOWqI085Uzv5B5N9+UJOnu7k6j0digx1mxYkXOP+/sXPXDK5Mke79k3/ztnBMzevTops0KAAAAAAAAAAAAAABA9VqiNLNwwW3ZavLW+ZdTTssLNt54gx/n2muuzlU//EH+8Z8+mI0mTMiHT/5QrvrhlTn4kJlNnHb9epf3p6sj6VuZdHduWAEIqIb8AsPB3gK0E3selEm2AVgX5wSUSbahTLINMDLstzSL1xJAa7J/w/rJCc3UUfUAgzFm7JjsttvuQyrMJMn+BxyYr37j7Oy62+7ZZJNN0tnZmVGjRq3xvn19fenp6Vn1V29vz5C+97N6l/dnzhVL03XGY5lzxdL0Lu9vyuMCgzOUbMsv1Ntwnd3Dzd4C69aq2WbN7Hk8S7bLItsMJN9QJr9TgzLJNpRrQ/Mt21BvfuYuh/2WgVyXQ7mc3ayN/bu1yfbIkBOarSXeaWb/Aw7MTTfekBUrVqy15PJ8vO8f/i73//a+7DFlal77uoPXeJ+LL7ow373g/CF/rz/V1ZHMnf90kmf+ftaM8U3/HsDaDSXb8gv1Nlxn93Czt8C6tWq2WTN7Hs+S7bLINgPJN5TJ79SgTLIN5drQfMs21Jufucthv2Ug1+VQLmc3a2P/bm2yPTLkhGZridLMHx5/PA8+8ED+6R/fky23nLja5xuNRt77/g8M+vHe/4EP5cEH7s+/f/qTufrHV+WAv3jNavc5/A1vyiEzD1/1cW9vT06aM3vDFjBA38pk1tTRmTv/6cyaOjp9K5POlni/HyjDULItv1Bvw3V2Dzd7C6xbq2abNbPn8SzZLotsM5B8Q5n8Tg3KJNtQrg3Nt2xDvfmZuxz2WwZyXQ7lcnazNvbv1ibbI0NOaLaWKM1c/eOrkiT3//a+3P/b+9Zwj8agHueee+7OHx5/PNP33CsvfOHm2WPq1Nz26/lrLM10dXWlq6trKGOvUXdnI2ceND5nzRifvpXPfAyMnKFkW36h3obr7B5u9hZYt1bNNmtmz+NZsl0W2WYg+YYy+Z0alEm2oVwbmm/ZhnrzM3c57LcM5LocyuXsZm3s361NtkeGnNBsLVGa+eC/nNqUx7n37rvyta9+KR/40CkZ292d2xcvyqGHHb7+L2yyZ4Or8QatR36B4WBvAdqJPQ/KJNsArItzAsok21Am2QYYGfZbmsVrCaA12b9h/eSEZmqJ0sweU6Y25XFe+eoDcs89d+eM009Lf39/Xv7nr8jrDjqkKY8NAAAAAAAAAAAAAABAfbREaebUkz+0zs83GoN7N5pGo5GjZh2do2Yd3azRAAAAAAAAAAAAAAAAqKGWKM0suG3+eu7RGJE5AAAAAAAAAAAAAAAAaA0tUZr56Mc/tcbbH7j//nzhc5/J3xx73AhPBAAAAAAAAAAAAAAAQJ21RGlmu+1fvNbbr73m6lx5+WV5zWtfN8JTAQAAAAAAAAAAAAAAUFcdVQ8wVKNGjcpDDz5Q9RgAAAAAAAAAAAAAAADUSEu808yll1y82m396c9DDz6YX950QyZN2qqCqQAAAAAAAAAAAAAAAKirlijNnPOtbyRprOEz/enoGJU3vfXIEZ4IAAAAAAAAAAAAAACAOmuJ0sxfvfmtq93W0Whk4403ydTpe2bixEkVTAUAAAAAAAAAAAAAAEBd1b4009/fnylTpmWTTTbJVpO3rnocAAAAAAAAAAAAAAAAWkBH1QOsT6PRyEc/fEou+94lVY8CAAAAAAAAAAAAAABAi6h9aSZJdt5l19xzz11VjwEAAAAAAAAAAAAAAECL6Kx6gMHYbLPN8tNrr8nHPnJqJk6atNrnG2nkHcfMrmAyAAAAAAAAAAAAAAAA6qglSjM/vfYnSZJb5v1yLfdQmgEAAAAAAAAAAAAAAOB/tURp5vgT3lX1CAAAAAAAAAAAAAAAALSQlijN7H/Ageu9z7ybb8qdd9yRN775rSMwEQAAAAAAAAAAAAAAAHXWUfUAzXLzL2/Kdy84v+oxAAAAAAAAAAAAAAAAqIFiSjMAAAAAAAAAAAAAAADwLKUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAACiO0gwAAAAAAAAAAAAAAADFKac007/qfwAAAAAAAAAAAAAAAGhzLVWaWbr0ydxx++LccP0vVt3W19eXJDl69nE59zsXVTUaAAAAAAAAAAAAAAAANdJZ9QCDsWLFipx3ztxcefn3s3z58jQayTnn/0eu+/nPMvcbZ+Vf/vUj2XLixKrHBAAAAAAAAAAAAAAAoCZa4p1mvvPtc3PZ9/4rkydPzlZbbZX+/mdu32bb7dLTszTfPu/sagcEAAAAAAAAAAAAAACgVlqiNHPtNT/ODjvslI+e8elMnbbnqtsnb7119t5nv8z/1S0VTgcAAAAAAAAAAAAAAEDdtERp5sknnsi2222XRqORNFb//LJly0Z+KAAAAAAAAAAAAAAAAGqrJUozW03eOrfeekuefPLJ59x+12/uzC9vvD4vetGLKpoMAAAAAAAAAAAAAACAOuqseoDBOPyIN+az//bJ/MPfnZix3WOTJB/7yL/m1/NvzfLlKzLj0MMqnhAAAAAAAAAAAAAAAIA6aYnSzJ+94pV58sknct45c/PEE0uSJLfMuzmdnV15y9uOzKv2/4uKJwQAAAAAAAAAAAAAAKBOWqI0kyR/edCM7H/AgVm8eGEef/zxdHd3Z5ddd8uECS+oejQAAAAAAAAAAAAAAABqpmVKM0kyesyYTJ22Z9VjAAAAAAAAAAAAAAAAUHO1Lc0cM+vI53HvRr4299xhmwUAAAAAAAAAAAAAAIDWUtvSzLJlywZ934kTJw3jJAAAAAAAAAAAAAAAALSa2pZmzv3ORav++b9/cEXOP++cnPrRjz+nIPPA/ffnXz74T5lx6GHP67EvufiiXPa9/8pTTy3L9L32zpwT3p2xY8c2bXYAAAAAAAAAAAAAAACq1VH1AINx6SUX5yX77rfaO8pM3nrr7LPvfrn8su8N+rF+dcu8nH/eOTnhXX+fU077WBYtWJDLL7uk2SMDAAAAAAAAAAAAAABQoZYozTz66O/z0AMPrPFzDz30YB599PeDfqzOzs68/ahZmTZ9z2yzzbbZavLk/OHxPzRr1EHrXd6f5Sv707u8f8S/N/XTbq+HdlsvtJvevj9mvE/GPRfUnTOZgUbq9TAS38drG0ae6x4A1sU5AWWSbSiTbAOMDPstzdIufyYiM8DzZd+A4VXKNYi9on20RGlmx512zuLFi/LpT5yeG67/RW5fvCg33Xh9PvPJj2fxokXZeZddB/1Ye0yZmkNmvj5Jcs/dd+WO2xfnFa969Wr36+vrS09Pz6q/ent7mrae3uX9mXPF0nSd8VjmXLG05TcMhqbdXg9Vr3c4sw08cxE558o/ZvzKpSN6MVm3fFf5XMBgVH0mD1bdsl2qkXo9jMT3aZXXdruT7bK47mEg+YYyDSXbzgmoL9mGcm1ovmUb6s3P3OWw3zLQkK7L2+TPRGSGVuXsro59g+Ek2+Vcg9gr2ktn1QMMxtHHHp/TTv1/ueH663LD9dcP+Ex/Nt1ssxx97PHP+zF///tHcsbpp+XQmYdnp513We3zF190Yb57wflDmHrtujqSufOfTvLM38+aMX5Yvg+tod1eD1WvdzizDSRdo6rLeN3yXeVzAYNR9Zk8WHXLdqlG6vUwEt+nVV7b7U62y+K6h4HkG8o0lGw7J6C+ZBvKtaH5lm2oNz9zl8N+y0BDui5vkz8TkRlalbO7OvYNhpNsl3MNYq9oLy1RmnnRNtvk3z57Zq666ge56847s2TJH7LJJptmx513yf4HHJiurq7n9XhLlizJR049OVOmTstbjvzrNd7n8De8KYfMPHzVx729PTlpzuwhreNZfSuTWVNHZ+78pzNr6uj0rUw6W+I9fxgO7fZ6qHq9w5ltIOlb8ScZXzFyGa9bvqt8LmAwqj6TB6tu2S7VSL0eRuL7tMpru93Jdllc9zCQfEOZhpJt5wTUl2xDuTY037IN9eZn7nLYbxloSNflbfJnIjJDq3J2V8e+wXCS7XKuQewV7aUlSjNJMnrMmBw8Y+aQH2dZb28+/pFTs/nmm+eodxydnp6laTQ6Mm7cuOfcr6ur63mXcQaru7ORMw8an7NmjE/fymc+pn212+uh6vUOZ7aBpLurkTNf98eMr3jm45FSt3xX+VzAYFR9Jg9W3bJdqpF6PYzE92mV13a7k+2yuO5hIPmGMg0l284JqC/ZhnJtaL5lG+rNz9zlsN8y0JCuy9vkz0Rkhlbl7K6OfYPhJNvlXIPYK9pLbUszV3z/0uy+x5Rsu932ufSSi9d7/0MPO3y990mS6677ee688/YkyXHHzEqSbL7FFvnsF76y4cNugGc3CI00kvZ7PbTbeqHdPHvxKOOeC+rPmcxAI/V6GInv47UNI891DwDr4pyAMsk2lEm2AUaG/ZZmaZc/E5EZ4Pmyb8DwKuUaxF7RPmpbmrnwgm/ndQcdkm232z7nfOsbSRpJ+v/42YFNrv4kjUGXZvY/4MDsf8CBTZ0VAAAAAAAAAAAAAACAeqltaeaNb3prdp8yNUnyhje+JY0Ob3kEAAAAAAAAAAAAAADA4NS2NHPwITNX/fOVV1yWKVOmZdr0PTN1+p6ZOHFShZMBAAAAAAAAAAAAAABQd7UtzQy0YvnyXH/dz3P9db9Ikmyx5RaZNn2vTJu2Z6ZMnZaNJkyoeEIAAAAAAAAAAAAAAADqpCVKM1/9xjm57957smjhgixauCCLFy/MVT+8Mlf98AdpNJLtX7xDTjv9E1WPCQAAAAAAAAAAAAAAQE20RGmmo6Mj223/4my3/Yvz2r88KLfdNj8/uOLy3HD9L9Lfn9z1m99UPSIAAAAAAAAAAAAAAAA10hKlmSefeCI333xTfnnjDbn1V/PS09Obzs7OTN9z7+yz737ZZ9+XVj0iAAAAAAAAAAAAAAAANdISpZnjZ78j/f1JR0cjL/+zV2S/l708e+71kowdO7bq0QAAAAAAAAAAAAAAAKihlijNTJk6LQsXLMjy5X25Zd7NSZKVK1Zm+l57Zfz4jSqeDgAAAAAAAAAAAAAAgLppidLMBz50Sp566qnc+qt5ufmmGzNv3i/zs59em46Ojuyy6655yb77ZeZhR1Q9JgAAAAAAAAAAAAAAADXREqWZJBkzZkz23e9l2XOvl+SO2xfnyssvy3W/+HkWLrgtCxcsUJoBAAAAAAAAAAAAAABglZYozdx4w3VZtHBBFi9amLt+85ssX748SX+6urqy6267Z9r0vaoeEQAAAAAAAAAAAAAAgBppidLMp844PUnSaDSy3fYvzrTpe2Xa9OnZdbc90tXVVfF0AAAAAAAAAAAAAAAA1E1LlGYOfM1fZur0PTN16vRsNGFC1eMAAAAAAAAAAAAAAABQcy1Rmpl9/AlVjwAAAAAAAAAAAAAAAEAL6ah6AAAAAAAAAAAAAAAAAGg2pRkAAAAAAAAAAAAAAACKozQDAAAAAAAAAAAAAABAcZRmAAAAAAAAAAAAAAAAKI7SDAAAAAAAAAAAAAAAAMVRmgEAAAAAAAAAAAAAAKA4SjMAAAAAAAAAAAAAAAAUR2kGAAAAAAAAAAAAAACA4ijNAAAAAAAAAAAAAAAAUBylGQAAAAAAAAAAAAAAAIqjNAMAAAAAAAAAAAAAAEBxlGYAAAAAAAAAAAAAAAAojtIMAAAAAAAAAAAAAAAAxVGaAQAAAAAAAAAAAAAAoDhKMwAAAAAAAAAAAAAAABRHaQYAAAAAAAAAAAAAAIDiKM0AAAAAAAAAAAAAAABQHKUZAAAAAAAAAAAAAAAAiqM0AwAAAAAAAAAAAAAAQHGUZgAAAAAAAAAAAAAAAP4/e/ceZldZ3o3/O5OZHCFASKBCFainkgQEjyAIlFpR1ItXy6u/UgxFUSMntb5U6hFKoWDq2SpS0YIIRVCkVluPiGgVtXgoggRQDsUTkoRgZoCdZP/+gAwJmSQzmb1nrfXsz+e6cuHsvbKe+9l7fdfzzGRuN8XpyaaZe+9dkS9+4d9y6ilvzJ133F51OQAAAAAAAAAAAAAAAHRYzzXNDA8P56TXvSbf/fa3c/ttv+jeOK12Vq9tZ7jVHvXx1avbeXD1Q//d1LH0jl64BtbN8cHV7bQafN03tW6YTE3MyXhqHtexqx8+dvXmj+3a+GM8dqx1juucVc+pIeesevxuzalOJqNuY9RvHGPA5HO9ArA51gkok2xDmWQbYHK439IpVVxLrl8gcS+AOvP7DLCxnmuamTp1aj74kfNy0hv+erPHtVqtDA0NjfwZHh4a8xjDrXYWf3lVBpcsz+Ivrxq5IQy32jnhy6vSWpO02sma9kP/He1YesemrpeSrJvj9CXLs6adrK7wuu9GtoFHVJmTrc33eGoe17Gr21n8pYeP/dKqTTakdG38MR471jrHdc6q59SQc1Y9frfm1Gl1X7uNUb9xjNEME8k29VP69cr4yDeUqe77cmDryDaUazJ+Xg5MPt9zl8P9lvU1bV/u+oWxK3ntdi+gl9U9236fAUbX1263e/JKvfu3v8nJJ7w273r3+/PYx+220fOXf/qSfOaySzd6/PwLLs7MmTM3e+7Va9sZXLJ85OvWKTtkoL8vq9e2s9+FK/PdRbM3OH60Y+kdm7peSrJujk/becrI9V/VnLuRbeARVeZka/M9npq7cWyJ45tTM8bv1pw6re5rtzHqN44xmmEi2aZ+Sr9eGZ+J5rvvnGUdr6n95jkdPyf0mrrvy4GtI9tQrsn4eTkw+fxMrRzut6yvafty1y+MXclrt3sBvazu2fb7DDC6gaoLqKsjXnJkDn/RESNfDw8P5cTFx43p77bWJIsWTs2F1z+YRQunprUmGeh/6PG9501Ja82Gx492LL1jU9dLSdbN8VPXPzhy/Vc1525kG3hElTnZ2nyPp+ZxHbv2UceuHf3Yro0/xmPHWue4zln1nBpyzqrH79acOq3ua7cx6jeOMZphItmmfkq/Xhkf+YYy1X1fDmwd2YZyTcbPy4HJ53vucrjfsr6m7ctdvzB2Ja/d7gX0srpn2+8zwOh80swmPmnm0YaGhvKqY44acyfgcKudwSkP3RhmDPZt9HjWJmuT9CdptTPqsfSOTV0vJVk3x7Vrk74kq2ty3Xcq28Aj6pKT8eR7PDWP69jV7Qz2P9SYMmNg08d2bfwxHjvWOsd1zqrn1JBzVj1+t+bUTXVcu41Rv3GM0TzjzTb100vXK+Mz3nz7pBlohjruy4GJk20oV7d+Xg5Uy8/Ums39lk1pwr7c9Qtbp7S1270AHlLHbPt9BtiYT5rpknU3gEd3zo3cGNZ7fN2boMuud23qeinJo6/9wYcfb9qce+G9golqYk7GU/O4jh0Y27FdG3+Mx461znGds+o5NeScVY/frTnVyWTUbYz6jWMMmHyuVwA2xzoBZZJtKJNsA0wO91s6pYpryfULJO4FUGd+nwE25lIFAAAAAAAAAAAAAACgOD37STPzdto5l1z2uarLAAAAAAAAAAAAAAAAoAt80gwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcQaqLqAK3/n2t/KvF38yq1b9Pk9/5n551asXZ3BwsOqyAAAAAAAAAAAAAAAA6JCe+6SZ++5bmXM//IG85MiX5bQzzs6Pf3RdrvraVzo+znCrndVr2xlutTd4fPXq9kZ/NnUsvaMXroF1c2z6dd/UumEyNTEn46m5G8eWOL45NWP8bs2pTiajbmPUbxxj1GsMeoNrCYDNKWWdKGUeSTK8+uG5rG7+XErQ1GurqXU/WinzSMqaSyma+J40sWaYbHJCJ7iO6JQqrqVeGLMX5phU8/OBXpnnRDVhnah7jXWvrwm8ht1Ryu8BGKN+45SS2W7No+eaZm695ea028nBhxyaP3zsY7Pvvk/LjTf8dKPjWq1WhoaGRv4MDw+NeYzhVjuLv7wqg0uWZ/GXV428acOtdlrtbPRntGPpHZu6Xkqybo7vWLK88uu+G9kGHlFlTrY23+OpuRvHlji+OTVj/G7NqdPqvnYbo37jGKNeY2zKRLJN/fheifXJN5Sp7vvyyVDKPJKHflFk8ZcensuXVjXqF0ZK1NR9eSmZKGUeSVlzKUUT8+06gi1rYrapH/db1te0fXkvjNkLc0yq+flAr8xznZL35XWvse71NYHXcNPqvnYbo15jTNY4pWS2m/Poa7fbzXxVttI3r74qn7zg4/nnj38ySXLRhZ/IL37+87z9tDM2OO7yT1+Sz1x26UZ///wLLs7MmTM3O8bqte0MLlk+8nXrlB0y0N+X1WtHf6lHO5besanrpSTr5rjy+MHMmLVNkuqu+25kG3hElTnZ2nyPp+ZuHFvi+ObUjPG7NadOq/vabYz6jWOMeo2xKRPJNvXjeyXWN9F8952zrOM1td88p+PnhF5T9335ZChlHklZcylBU/flpVxHpcwjKWsupWhivl1HsGVNzDb1437L+pq2L++FMXthjsas99rdhHWi7jXWvb4m8BpuWt3XbmPUa4zJGqeUzHZzHj33STOj6RvltTziJUfm/AsuHvnzoXM/NubztdYkixZOTfLQf1trHnl8tD+jHUvv6IVrYN0c/+HDrcqv+25kG3hEE/M9npq7cWyJ45tTM8bv1pw6re5rtzHqN44x6jXGpkwk29SP75VYn3xDmeq+L58MpcwjSVprHzWXtRUX1OOaui8vJROlzCMpay6laGK+XUewZU3MNvXjfsv6mrYv74Uxe2GOSTU/H+iVea5T8r687jXWvb4m8BpuWt3XbmPUa4zJGqeUzHZzHj33STM/vO4Hec+Ss3PBpz6d/v7+/PO5/5Th++/PyW9402b/3tDQUF51zFFj/n/oGG61MzjloTdvxuAjXTmrR/l4v1Y7ox5L79jU9VKSdXPMw9941OW671S2gUfUJSfjyfd4au7GsSWOb07NGL9bc+qmOq7dxqjfOMao1xhjMd5sUz91uZaon/Hm2yfNQDPUcV8+GUqZR5IMr25nsP+hXxyZMdDsuZSgLteWbDd7HklZcylFXd6Tbv28HHpVXXLiZ2rNVpfriPppwr68F8bshTkm1fx8oFfmOZrS9uV1r7Hu9TWB13Bs6rh2G6NeY0zWOKVktlvzGOjYmRri8U94Uvr7+/O1r3wp8xfulR/96Lq85M9f1vFx1r1JA4/6LJ+BUTZd696ERx9L79jU9VKSkRvXw3Ns6nXfC+8VTFQTczKemrtxbInjm1Mzxu/WnOpkMuo2Rv3GMUa9xqA3uJYA2JxS1olS5pE88gsiJcylBE29tppa96OVMo+krLmUoonvSRNrhskmJ3SC64hOqeJa6oUxe2GOSTU/H+iVeU5UE9aJutdY9/qawGvYHaX8HoAx6jdOKZnt1jx6rmlm9uzZWXz8ybnkUxfm0ksuyjOeuV8OPuTQqssCAAAAAAAAAAAAAACgg3quaSZJ9j/gwOx/wIFVlwEAAAAAAAAAAAAAAECXNPwDeAAAAAAAAAAAAAAAAGBjPflJMwAAAAAAW6PvnGUdPV/7zXM6ej4AAAAAAAAAHuGTZgAAAAAAAAAAAAAAACiOphkAAAAAAAAAAAAAAACKo2kGAAAAAAAAAAAAAACA4gxUXUBTtNvtJMnw8FDFlUBvmzFjRvr6+jp2PtmG+pBvKJNsQ5lkG8rU6Wwn9cj30FC97y11r48yWLuhTLINZSp1Xw5Yu6FUsg1lsi+Hclm7oUxbynZfe11a2ax77vldTlx8XNVlQM87/4KLM3PmzI6dT7ahPuQbyiTbUCbZhjJ1OtuJfENdWLuhTLINZbIvh3JZu6FMsg1lsi+Hclm7oUxbyrammTFau3Ztli9flunTx9ZhODw8lBMXH5cPnfuxzJjR2c1TN6i3u9TbOZ3u8i0x23WvUX0TU/f6kq2vsRv5/uUv78opbzyp1q/XeDXhGhgvc2qGOmV7PGv3ZCjl/S5lHkk5c6nzPErKdp1f53XU2Blq3LJu/D8AlBfVAAAgAElEQVTnjSffVc9/LOpeo/omru41NnVfXvfXdaxKmUdSzlx6fR6y3RmlzCMpZy6lzCPZurnYl29Z3WtU38TVvcamrt2dVPf3KKl/jXWvL1HjWFWd7Tq8BpOhF+bZC3NMmjNP+/Itq3uN6pu4utfY1H153V/XsTKP+illLt3K9kAniusF/f392XHHueP+ezNmzOx4x3E3qbe71Fs/JWe77jWqb2LqXl9SfY39/f2ZM2fHWtTSDebUDObUeVu7dk+Gql+bTillHkk5cyllHptTh2w34XVWY2eocXJtTb6bMP+616i+iat7jVXXV/LP1MailHkk5czFPDpDtsuYR1LOXEqZR1L9XOzLq6G+iat7jVXX52dqY1P3GuteX6LGydbr+/It6YV59sIck96Z5/rsy6uhvomre41V19fra7d51E8pc+n0PPo7diYAAAAAAAAAAAAAAACoCU0zAAAAAAAAAAAAAAAAFGfKaaeddlrVRZSqv78/8xfslSlTplRdypiot7vUW44mvDZ1r1F9E1P3+pJ61VinWjrFnJrBnHpLKa9NKfNIyplLKfOouya8zmrsDDXWXxPmX/ca1Tdxda+x7vVtSlPrfrRS5pGUMxfzqFZT6360UuaRlDOXUuaRNHMuTai57jWqb+LqXmPd65sMTXgN6l5j3etL1NgUvfIa9MI8e2GOSe/Mc6Ka8DrVvUb1TVzda6x7fZvS1LofzTzqp5S5dGMefe12u92xswEAAAAAAAAAAAAAAEAN9FddAAAAAAAAAAAAAAAAAHSaphkAAAAAAAAAAAAAAACKo2lmjNrtdoaGhtJut6suBegg2YZyyTeUSbahTLIN5ZJvKJNsQ5lkG8ol31Am2YYyyTaUS76hTLINzaBpZoyGh4fzqmOOyvDwcNWlAB0k21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahGTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcQaqLqAb3vfud+Xa7/5XLrnscxs8/ru7785H/un9+fmtt+Qxu+ya1x5/UnbbbfdqigQAAAAAAAAAAAAAAKBrivukmR98/3v5/ve+O+pzF33yE5k2bXqWvOeDedzjdst5H/nQJFf3iOHV7axe287w6nZlNcBkcs1D7xhuPZz3VnPy3qR7VJNqhTooJTOlzAMoVxP3gNSP6wgAmsXaDWWSbYDJ4X5Lp/g3pHJ4L4G6sV+hF1h/66db70lRnzQzNDSUT5z/0Rz2ghfmP77w+Y2ev/GGn+aYY4/L3Hnzcsihz83p73hLHnjggUybNm2jY1utVlqt1sjXw8NDHatzeHU7i7+0Khde/2AWLZyacw+blRkDfR07P9RNna75bmYbeOibpcVfXi/vz5uVGYOTk/etzXed7lFb0qRaoVMmsnaXkplS5gHrsy8vS5V7QOpnq/flriOotU6s3X3nLOtkSWm/eU5Hzwe9aELfc1u7odbsy6FMfqZWDvdb1uffwki8l6WydtNk9iubJtvlsP7WTzffk6KaZi656MLstfc+WbjX3qM2zdy3cmWmT5+eJJk1a9bIY9Pmzdvo2CuvuDyfuezSrtQ52J9ceP2DSR767/kvmNWVcaAu6nTNdzPbQDI4pbq8b22+63SP2pIm1QqdMpG1u5TMlDIPWJ99eVmq3ANSP1u9L3cdQa1Zu6FME/qe29oNtWZfDmWyLy+H+y3r829hJN7LUlm7aTL7lU2T7XJYf+unm+9JMU0zP7vxhvzg+9dmyXs+kKVLfzbmv9e3ieajI15yZA5/0REjXw8PD+XExcdNtMwkSWttsmjh1JEuqNbaZKC/I6eGWqrTNd/NbANJa82j8r5m8vK+tfmu0z1qS5pUK3TKRNbuUjJTyjxgffblZalyD0j9bPW+3HUEtWbthjJN6HtuazfUmn05lMm+vBzut6zPv4WReC9LZe2myexXNk22y2H9rZ9uvifFNM1c8ZnLct999+X1J742a9asTZK86pijcv4FF48cs+22s7Nq1aokyfDw8EOPzd5u1PMNDg5mcHCwK7XOGOjLuYfNyvkvmJXW2vgoJ4pXp2u+m9kGkhmDfTn3eQ/nfU0m9WM5tzbfdbpHbUmTaoVOmcjaXUpmSpkHrM++vCxV7gGpn63el7uOoNas3VCmCX3Pbe2GWrMvhzLZl5fD/Zb1+bcwEu9lqazdNJn9yqbJdjmsv/XTzfekmKaZ151wclqthz6O5/rr/yfnfeRDOXvJe7N2zZr0T5mSJNlz/oJcc/VV2XPPBfnGVV/LE5/45EydOrWSete9iTrS6BWueegd675JalLem3SPalKtUAelZKaUeQDlauIekPpxHQFAs1i7oUyyDTA53G/pFP+GVA7vJVA39iv0Autv/XTrPSnmLd5+hx0yb6edM2+nnbPddg99esy8nXbO3775Tbn2O/+VJDl60bFZs2Zt/t8bT8ptv/h5Xr34+CpLBgAAAAAAAAAAAAAAoEuK+aSZ9T31ac/IJZd9Lklyzj++b+TxufPm5e2nnVFVWQAAAAAAAAAAAAAAAEySYj5pBgAAAAAAAAAAAAAAANbRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxNM0AAAAAAAAAAAAAAABQHE0zAAAAAAAAAAAAAAAAFEfTDAAAAAAAAAAAAAAAAMXRNAMAAAAAAAAAAAAAAEBxBqouoFPWrFmTSy+5KF//6peTJPs+9el59eITMnXq1JFjlt70s7zzbadu8Pc+ceElmT5jxqTWCgAAAAAAAAAAAAAAQHcV0zTzrWuuzte/+pWccurbss222+bvT3t7vv7VL+f5h79o5JgVK5Znxx3n5px3v2/kMQ0zAAAAAAAAAAAAAAAA5SmmaebgQw7NwYccmiRZter3GRgYyJQpUzY4ZsXy5dl+hx0ya9Y2Wzxfq9VKq9Ua+Xp4eKizBQOVkG0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0EzFNM2s8zdven3u+t87M3/Bwjz3ec/f4Lnly5bl7rt/m5OPf00Gp07NS498WQ448KBRz3PlFZfnM5ddOhklA5NItqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDM/W12+121UV00j33/C6/+uVd+cB7352jXnFMDvmTPx157ue33pJbbl6a+Qv2yrXf/a987rOX5QMfPi877DBno/OM1gl44uLjcv4FF2fmzJmTMheg82QbyiXfUCbZhjLJNpRLvqFMnch23znLOlpT+80b/1wfGB/rNpRLvqFMsg1lkm0ol3xDmWQbmqmYT5q5/fbbcu+KFdn7Kftkxx3nZv7Chbnhp9dv0DSz49y5ecwuu2bGjBmZt9P/yeWfviS/vOuuUZtmBgcHMzg4OJlTACaBbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGZuqvuoBOueO2X+S9/3h2bl56U+68847cvPSm7L7HHlm7Zs3IMZ/42Hk5+8zTc8/v7s43r74qAwMDecwuu1ZYNQAAAAAAAAAAAAAAAN1QzCfNHHjQIbn99tuy5Owz0263s9+zD8jzDjs8715yduYvWJgXvviIHL3o2Jx37ofypjecmO223z7Hn/SGzJmz8afMAAAAAAAAAAAAAAAA0GzFNM309fXl6EXH5uhFx27w+CmnvnXkf8+dNy9vefvpk10aAAAAAAAAAAAAAAAAk6y/6gIAAAAAAAAAAAAAAACg0zTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcTTNAAAAAAAAAAAAAAAAUBxNMwAAAAAAAAAAAAAAABRH0wwAAAAAAAAAAAAAAADF0TQDAAAAAAAAAAAAAABAcQaqLqBT1qxZk0svuShf/+qXkyT7PvXpefXiEzJ16tSRY37/+9/nox/+YH56/U+yw5wd88rjXpsFC/eqqmQAAAAAAAAAAAAAAAC6pJhPmvnWNVfn61/9Sk459W05/cxzcv3//GSkgWadK6+4PPfc87ucveR9eeaz9s8/feC9Wd1qVVQxAAAAAAAAAAAAAAAA3VJM08zBhxyaj/3LRXnyH++Z7bffPgMDA5kyZcoGx9x4w0/zrP2enZ123jnPfd5hWb58WX7zm19XVDEAAAAAAAAAAAAAAADdMlB1AZ32N296fe763zszf8HCPPd5z9/guftWrsz06dOTJLNmzkqSrFy5MruOcp5Wq5XWep9CMzw81LWagckj21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahmYprmnnzW96eX/3yrnzgve/O1d/4eg75kz/d7PF9faM/fuUVl+czl13ahQqBKsk2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbWimYppmbr/9tty7YkX2fso+2XHHuZm/cGFu+On1GzTNbLvt7KwaWpUkGRoeTpLMnr39qOc74iVH5vAXHTHy9fDwUE5cfFwXZwBMBtmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDc1UTNPMHbf9Ih//2EfzlrefnukzZuTmpTflhS8+ImvXrEn/lClJkj3nL8h3/+vbOeDAg3LN1Vdlzo47Zuc/+INRzzc4OJjBwcHJnAIwCWQbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbyiTbUC75hjLJNjRTMU0zBx50SG6//bYsOfvMtNvt7PfsA/K8ww7Pu5ecnfkLFuaFLz4iR7z0yPz617/K357yxsyZs2NOOOmNmfJwQw0AAAAAAAAAAAAAAADlKKZppq+vL0cvOjZHLzp2g8dPOfWtI/97m222yZv+5m8nuzQAAAAAAAAAAAAAAAAmWX/VBQAAAAAAAAAAAAAAAECnaZoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDiaJoBAAAAAAAAAAAAAACgOJpmAAAAAAAAAAAAAAAAKI6mGQAAAAAAAAAAAAAAAIqjaQYAAAAAAAAAAAAAAIDi1KJpZnWrlTtuv23k61/98q58/3vfzb33rqiuKAAAAAAAAAAAAAAAABproOoC7vnd3Tnr70/PwMBAzvnH9+UnP/5hlpx9ZtasWZttttkmb33n32W33XYf07k+f+UV+eK//1seeOD+7L3Pvll8/MmZPn36yPPf/tY386H3v2fk62223Tb//PFPdnpKAAAAAAAAAAAAAAAAVKzyT5r514svyq9+eVd2/cPHJkk+/a8XZ+bMWTnwOQdlaGhVLr/04jGd5yc//lEuveRTOf6kN+T0M8/JTTfemP/84uc3OGbF8uV58pP3zMf+5aJ87F8uyvs/9NGOzwcAAAAAAAAAAAAAAIDqVf5JM9df/5PMX7AwJ7/hTVmxYkV+fustefVrj8+f/Omf5f77789NN904pvMMDAzkqKMXZa+9n5Ikecwuu+TeFfducMyK5cuzw5w5mTVrmy2er9VqpdVqjXw9PDw0jlkBdSXbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqGZKm+aeeD++7PjjnOTJP/zkx8l6ctT9n1akmTmrFl54P77x3Se+QsWZv6ChUmS22/7RW65eWmOOvqYDY5ZvnxZbl56U0547auy7ezZ+ctX/NVIk82jXXnF5fnMZZdu5ayAupJtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok29BMfe12u11lAe9826m5847b8+f/9//LVV/7SvqnTMm73v3+3HrrLXnXWWdk3ryd8vdnLxnz+e6553d551tPzXMOOiQvP+roDZ674af/k1//+td50pP+OF/6jy/ku9/5dj5y3sczMDi40XlG6wQ8cfFxOf+CizNz5sytnzBQKdmGcsk3lEm2oUyyDeWSbyhTJ7Ldd86yjtbUfvOcjp4PepF1G8ol31Am2YYyyTaUS76hTLINzVT5J838+f99ed71D2fmU5+8IEk7J77+r5Mkf3/a2/LAAw/mr1756jGfa+XKlTnrjNOyYOFeedlf/OVGzz9ml13zhCc8KVOnTcthL3hhvvqV/8yyZcuy0847b3Ts4OBgBkdppgGaTbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahTLIN5ZJvKJNsQzNV3jSz91P2zWlnnJUfXvff+aPHPz5Pe/ozkyS77/FHOeTQ52b/Aw4c03nuHx7Ou846I3Pnzs3RxxyboaFV6evrz/Rp09I/ZUqS5B/POStz587LMa98db79rW9mm222zZw5/t/oAAAAAAAAAAAAAAAASlN500ySPOGJT8oTnvikDR5759+dNa5zXHvtd3LrrTcnSV7zykVJkrnz5mXmzFl56Z+/LM/a/9l5zeITcv555+aNJy3OTjv/QV7/16dkQLcfAAAAAAAAAAAAAABAcWrRNHPnnXfkZzfekPtW3pt2e8Pn+vqSlx758i2e4+BDDs3Bhxy62WN2232P/N1Z50ykVAAAAAAAAAAAAAAAABqg8qaZr33lS/n4xz76cLNMe5Qj+sbUNAMAAAAAAAAAAAAAAADrVN4082+f+2z6+vry3D87LDs/5g/Sl76qSwIAAAAAAAAAAAAAAKDhKm+aWbFieZ7xzP1y7HGvqboUAAAAAAAAAAAAAAAACtFfdQF77PH4qksAAAAAAAAAAAAAAACgMJV/0syLjnhJPvT+9+Rb11ydXXf9w1GP2eOPNNYAAAAAAAAAAAAAAAAwdpU3zbxnyT8k6cuHP/j+UZ/v60s+delnJ7coAAAAAAAAAAAAAAAAGq3yppk/3nN++vr6qi4DAAAAAAAAAAAAAACAglTeNPOO08+sugQAAAAAAAAAAAAAAAAKU3nTzI03/HSLx+w5f8EkVAIAAAAAAAAAAAAAAEApKm+aOeO0tyXp2+wxF3/6s5NTDAAAAAAAAAAAAAAAAEWovGnmaU9/RkZrmrn7t7/JHXfcnmcf8JzJLwoAAAAAAAAAAAAAAIBGq7xp5k1/85ZRH1/dauWUN70+A4ODk1wRAAAAAAAAAAAAAAAATddfdQGbMjA4mCc+8Un5wfeurboUAAAAAAAAAAAAAAAAGqa2TTPLly/LzUtvSl9/X9WlAAAAAAAAAAAAAAAA0DADVRfwykV/sdFj7XY7DzzwYJJ29nv2gZNfFAAAAAAAAAAAAAAAAI1WedPM/fffP8qjfdlu++3ylH2emqNf8VeTXRIAAAAAAAAAAAAAAAANV3nTzMWfvqLqEgAAAAAAAAAAAAAAAChMf5WDr12zJn9/+tvzhc9fWWUZAAAAAAAAAAAAAAAAFKbST5rpnzIlv/rVLzPn9ts6cr7PX3lFvvjv/5YHHrg/e++zbxYff3KmT58+8vzv7r47H/mn9+fnt96Sx+yya157/EnZbbfdOzI2AAAAAAAAAAAAAAAA9VHpJ80kyfMOOzzX/eB7ueXmpfn9ffeN+mcsfvLjH+XSSz6V4096Q04/85zcdOON+c8vfn6DYy765Ccybdr0LHnPB/O4x+2W8z7yoW5MacyGV7ezem07w6vbldYBAJ1U+vpW+vygNDILMDncb+kE1xEANIu1G8ok2wCTw/2WTnEtAYzO/RG6S8Zomko/aSZJLr3koiR9ecdbTx31+b6+5FOXfnaL5xkYGMhRRy/KXns/JUnymF12yb0r7t3gmBtv+GmOOfa4zJ03L4cc+tyc/o635IEHHsi0adM2Ol+r1Uqr1Rr5enh4aByz2rLh1e0s/tKqXHj9g1m0cGrOPWxWZgz0dXQMYGPdzjb0uirXt8nIt/UbJt9Esi2zUF/25WVxv2V9W5tv1xHUm7UbyuR7biiXfTmUyb68HO63rM++HMpl7a6O+yPdJNsyRjNV3jSz49y56cvEgzJ/wcLMX7AwSXL7bb/ILTcvzVFHH7PBMfetXJnp06cnSWbNmjXy2LR58zY635VXXJ7PXHbphOvalMH+5MLrH0zy0H/Pf8Gsro0FPKLb2YZeV+X6Nhn5tn7D5JtItmUW6su+vCzut6xva/PtOoJ6s3ZDmXzPDeWyL4cy2ZeXw/2W9dmXQ7ms3dVxf6SbZFvGaKbKm2Y++OF/7uj57rnnd1ly9pl54YuOyBOe+KQtHt+3iX6dI15yZA5/0REjXw8PD+XExcd1qsy01iaLFk4d6bJrrU0G+jt2emATup1t6HVVrm+TkW/rN0y+iWRbZqG+7MvL4n7L+rY2364jqDdrN5TJ99xQLvtyKJN9eTncb1mffTmUy9pdHfdHukm2ZYxmqrxpZiw+cf55+eqX/zOfuvSzmz1u5cqVOeuM07Jg4V552V/85UbPb7vt7KxatSpJMjw8/NBjs7cb9VyDg4MZHBycYOWbNmOgL+ceNivnv2BWWmvjY6lgknQ729DrqlzfJiPf1m+YfBPJtsxCfdmXl8X9lvVtbb5dR1Bv1m4ok++5oVz25VAm+/JyuN+yPvtyKJe1uzruj3STbMsYzdSIppkkabc3//z9w8N511lnZO7cuTn6mGMzNLQqfX39mT5tWvqnTEmS7Dl/Qa65+qrsueeCfOOqr+WJT3xypk6dOgnVj27dTUJ3HQAlKX19K31+UBqZBZgc7rd0gusIAJrF2g1lkm2AyeF+S6e4lgBG5/4I3SVjNE1jmma25Nprv5Nbb705SfKaVy5KksydNy8zZ87KS//8ZXnW/s/O0YuOzUf+6QP5f288Kbvsumted8LJVZYMAAAAAAAAAAAAAABAlxTTNHPwIYfm4EMO3ewxc+fNy9tPO2OSKgIAAAAAAAAAAAAAAKAqPhQJAAAAAAAAAAAAAACA4miaAQAAAAAAAAAAAAAAoDiaZgAAAAAAAAAAAAAAACjOQNUFjMW+T31atp09u+oyAAAAAAAAAAAAAAAAaIhaNM2sWL48X/j3K3PnHXdkxYrlOXvJezM8PJyrr/pann/4i7LPvk/LPvs+reoyAQAAAAAAAAAAAAAAaIjKm2ZuuXlpzjrjnbn//vsffqQvSXLN1Vflwn/5eKYMDOTPnvf86goEAAAAAAAAAAAAAACgcfqrLuDiiy5Iu93OG970NznwOQePPP685x+e3XbfPV/50n9UWB0AAAAAAAAAAAAAAABNVHnTzM9vvSXPeOZ+eeaz9s+MmTM3eG6PP3p8fv2rX1VUGQAAAAAAAAAAAAAAAE1VedPMtGnTs2rVqo0eb7fb+fmtt2TGzBkVVAUAAAAAAAAAAAAAAECTDVRdwL5PfVqu+eY38tnLL819K1cmSW65eWm+8Pkrc8ftt+c5Bx1ccYUAAAAAAAAAAAAAAAA0TeVNM0cdfUx+duMNufzT/5qkL0nyjreemqSdHebMycuPekWl9QEAAAAAAAAAAAAAANA8lTfNzN5uu5y95L354hf+LT/58Y9y74oVmTFjRuYv3CsvPuIl2W677asuEQAAAAAAAAAAAAAAgIapvGnm7t/+JvN22jkvPfLleemRL6+6HAAAAAAAAAAAAAAAAApQedPM6098XZ70pCfngOcclGftf0Bmz55ddUkAAAAAAAAAAAAAAAA0XOVNMzNnzszSpT/L0qU35cJ/OT97P2WfHPCcg/P0pz8zU6dNq7o8AAAAAAAAAAAAAAAAGqjyppnzPn5hbvrZjbnuB9/Pddf9ID+87r/zw+uuy7RpU/OMZ+6XZx94UPZ+yj7p7++vulQAAAAAAAAAAAAAAAAaovKmmf7+/uw5f0H2nL8gf7nor/KrX96VT33yglz339/Pt675Zr51zTez7ext88IXHZEXH/GS9PX1VV0yAAAAAAAAAAAAAAAANVd500ySPPjAA/nxj36Y73//2vzwuh9k1e9XJUl22XXXLFi4V7537XfyrxdflHvvXZFXHPPKzZ7r3ntX5Nvf+ma++Y2rcsJJb8hjH7fbBs8vvelneefbTt3gsU9ceEmmz5jR2UkBAAAAAAAAAAAAAABQmcqbZv7xnLNy/f/8OA8+2ErSzrazZ+ew5x+e5xx8SP7o8U9IkvzFUa/Im095Q775jas22zQzPDyck173muy++x65/bZfjHrMihXLs+OOc3POu9838piGGQAAAAAAAAAAAAAAgLJU3jRz3X9/P4ODg3nWfnMK/LQAACAASURBVPvnOQcfkn32eWr6p0zZ4JjpM2Zk76fsm1tuXrrZc02dOjUf/Mh5efCBB3LyCa8d9ZgVy5dn+x12yKxZ22z2XK1WK61Wa+Tr4eGhMc4IqDPZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg3NVHnTzKte87rsv/8BmTlr1uaPe/XiLZ5rypQp2W677XP3b3+zyWOWL1uWu+/+bU4+/jUZnDo1Lz3yZTngwIM2Ou7KKy7PZy67dMsTABpFtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDM/W12+121UWsbrVyzTVX5/bbfpEVy5dnu+22z+577JHnHHRIBgYHx32+u3/7m5x8wmvzrne/P4993G4bPPfzW2/JLTcvzfwFe+Xa7/5XPvfZy/KBD5+XHXaYs8Fxo3UCnrj4uJx/wcWZOXPm1k0UqJxsQ7nkG8ok21Am2YZyyTeUqRPZ7jtnWUdrar95zpYPAjbLug3lkm8ok2xDmWQbyiXfUCbZhmaq/JNm/vfOO3P2madn2bJlSdbv3+nLZy//dE596zuy6x8+tmPj7Th3bh6zy66ZMWNG5u30f3L5py/JL++6a6OmmcHBwQxuRcMOUG+yDeWSbyiTbEOZ/n/27j3MrrK8G/CzZ2YnmZwgk4SEUESvIkoSUDxxvCBFPpCDTUGKldJQECECQShfBBQrlGKDEUHkJBr5QCVFQKSoLXigHMUqSC0HOYPIqUAmBDI7uDOzvz9ChgyZkJnM3nut9c59X1cks/diredd8/7Wu2YPj0u2IV3yDWmSbUiTbEO65BvSJNuQJtmGdMk3pEm2oZgyb5q5dNElsWTJS7Hd+94fO+2ya2y00UbxyiuvxB233Rp3/ea/4tJFl8SpXzxjSMfo6e6OltbWVcf71iXR2bkkjjv+xLj77ruira0tNp22WT2GAgAAAAAAAAAAAAAAQE5k3jTzyMMPxju3elfMP/nUPq/vuNMucdqpp8TDDz045GOcvXBBTJ8xM/b96Ow4ZM5hccnF58eJxx8bG228cRw97/jo6OhY/04AAAAAAAAAAAAAAAAojMybZjomTopp63jSy9Rp0+KFF/930PucvMmUWHzVD3u/nn/y53v/Pmny5PjcF04ffKEAAAAAAAAAAAAAAAAURkvWBey9737x27vvihdfeKHP688/92z89q7fxEf23i+jygAAAAAAAAAAAAAAACiqTJ40c/icT/T5esWK1+KE4z4d5XK597U//akatVpP3PnL2+Ojs/dvdokAAAAAAAAAAAAAAAAUWCZNMytWrFjrte7u7uju7l7r9ccfe6wZJQEAAAAAAAAAAAAAAJCQTJpmrvj+tVkcFgAAAAAAAAAAAAAAgGGiJesCAAAAAAAAAAAAAAAAoN40zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJCctqwLqKeXX14at992S9zynzfFMfOOj83ftkWf91999dX4xoVfj/vu/V1M6JgYhx9xVMyYuU1G1QIAAAAAAAAAAAAAANAoyTxpplKpxLxPHxl33n57PPnE4/1uc921V8dLL70YCxaeGx/afse44LxzYmW12ph6qrVY2VOLSrW29nsr33ivzz9Xrr0tpKo3BwWb92+VbWAVOWGwBjNnirJ+NKLOwexzoNsW5XwORTPG2JRjuLYOivMFzSd31IN5BADFYu2GNMk2QHO43lIvWcyl4fA7RmD9rGWQX83Ip/sBiiaZppkRI0bE1y+6JOYd/w/r3OaB+++L7XfYKTaZMiX22HOv6OxcEs8//1y/21ar1ejq6ur9U6l0DbiWSrUWc29cHuWFnTH3xuV9LjqVlbX49A3L4/nlfbd5fvmq1108GA4qK2sx94bX53+T532jsg2skmVOhpJvsjOYOZPl+jEYjahzMPsc6LZFOZ9DWrubMMamHMM9yKA4X8Vg3U6L3LGmDc23eQT5Zu2GNPm8HNLlvhzS5L48Ha63rKlo9+VF+R0j5EHKa7e1jOEs79luRj7dD1BEbVkXUC+tra2x0UYbxwv/+/w6t3ll2bIYNWpURESMGT0mIiKWLVsWm/Wz7XXXXh3XXHXlBtVSbo24/N4/RcSqfy7ae8wb77VEXP9INS75SKnPNt/8yJi4/pFqfGvvDTokFEq5Zd0ZabRGZRtYJcucDCXfZGcwcybL9WMwGlHnYPY50G2Lcj6HtHY3YYxNOYZ7kEFxvorBup0WuWNNG5pv8wjyzdoNafJ5OaTLfTmkyX15OlxvWVPR7suL8jtGyIOU125rGcNZ3rPdjHy6H6CIkmma2VClUv+vz97/wNhnv9m9X1cqXXHs3CMGtM9qd8ScmSPi8nv/FHNmjohqd0Tb68/0qfZEfHTLcrzQVeuzzQtdtfjoluWo9ryxLaSq2vOmjDRx3jcq28AqWeZkKPkmO4OZM1muH4PRiDoHs8+BbluU8zmktbsJY2zKMdyDDIrzVQzW7bTIHWva0HybR5Bv1m5Ik8/LIV3uyyFN7svT4XrLmop2X16U3zFCHqS8dlvLGM7ynu1m5NP9AEU0rJpmxo0bH8u7lkdERFelEhER48dv3O+25XI5yuXyBh2nvVyKi/ccE4v2HhPV7lVf977XVoqL9hoT5Zbos025NeKivcZEe9s6unggIe1tpbh4r9fnf080dd43KtvAKlnmZCj5JjuDmTNZrh+D0Yg6B7PPgW5blPM5pLW7CWNsyjHcgwyK81UM1u20yB1r2tB8m0eQb9ZuSJPPyyFd7sshTe7L0+F6y5qKdl9elN8xQh6kvHZbyxjO8p7tZuTT/QBFlHzTTE93d7S0tkZExNbTZ8Sdd9weO++ya9x6803RMXFiTJk6tSHHXX2R6a9zbvXFYfV7b/4nDAdvzkFRvFW2gVXkhMEazJwpyvrRiDoHs8+BbluU8zkUzRhjU47h2joozhc0n9xRD+YRABSLtRvSJNsAzeF6S71kMZeGw+8YgfWzlkF+NSOf7gcomuSn6tkLF8SPr78uIiJmH3BgTJkyNU6Zf0Lcecftccy8E6L19YYaAAAAAAAAAAAAAAAA0pHck2YmbzIlFl/1w96v55/8+d6/jx07Nk787ClZlAUAAAAAAAAAAAAAAEATJf+kGQAAAAAAAAAAAAAAAIYfTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJCctqwLqKdf3n5b/OsV34nly1+ND3xoh/jkp+ZGuVzuff+hB38fXzz15D7/zqWXL45R7e3NLhUAAAAAAAAAAAAAAIAGSqZp5pVXlsXFF54Xhx1xVGy55VZx5hn/GDf9/Kex50f26d1m6dLOmDhxUpx19rm9r2mYAQAAAAAAAAAAAAAASE9L1gXUy6OPPBy1WsRus3aPP9t889huu/fHA/ff12ebpZ2dsfGECTFmzNjePwAAAAAAAAAAAAAAAKQnmSfNLFu2LEaOGhmlUikiIkaPGRPPP/98n206lyyJF1743zju6COjPGJEHHDgQbHzLrv2u79qtRrVarX360qlq3HFA00j25Au+YY0yTakSbYhXfINaZJtSJNsQ7rkG9Ik25Am2YZ0yTekSbahmJJpmunP6/0zvT64/Q4xoaMjps/YJn515x1x8QXnxfQZM2PChI61/t3rrr06rrnqyiZVCjSLbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYkqmaWbcuHGxolKJnp6eaGlpiUpXV4zfaOM+20ycNCk2nbZZtLe3x+RN/iqu/v7ieObpp/ttmpm9/4Gxz36ze7+uVLri2LlHNHwcQGPJNqRLviFNsg1pkm1Il3xDmmQb0iTbkC75hjTJNqRJtiFd8g1pkm0opmSaZv58y62ipaUlfv7TG2L6zG3innvujv0/dlD0dHdHS2trRERc+q1LorNzSRx3/Ilx9913RVtbW2w6bbN+91cul6NcLjdzCEATyDakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtKKaWrAuol/Hjx8fco4+L66+7Nr74+ZNi223fG7vN2j3OXrggfnz9dRERccicw2LkyJFx4vHHxo/+7do4et7x0dGx9lNmAAAAAAAAAAAAAAAAKLZknjQTEbHjzrvEjjvv0ue1+Sd/vvfvkyZPjs994fRmlwUAAAAAAAAAAAAAAECTJfOkGQAAAAAAAAAAAAAAAFhN0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJacu6gHr65e23xb9e8Z1YvvzV+MCHdohPfmpulMvl3vdfffXV+MaFX4/77v1dTOiYGIcfcVTMmLlNhhUDAAAAAAAAAAAAAADQCMk8aeaVV5bFxReeF/sfeFCcdsaC+O977o6bfv7TPttcd+3V8dJLL8aChefGh7bfMS4475xYWa02pJ5KtRYre2pRqdb6vL5yZW2tP+valuFjOMyB1WMs+rwvat3QTEXMyWBqbsS2KR7fmIpx/EaNKU+aUbdj5O84jpGvYzA8mEvUQxHmUd5rzH19K1+vb2U+6ysC5xDIk7yvOwOVyjgi0hpLKor4PSlizdBsckI9mEfUSxZzaTgccziMMSKbz5qGyziHqgjrRN5rzH19BZiXzmFjpPLfAThG/o6T98wOVKPGkUzTzKOPPBy1WsRus3aPP9t889huu/fHA/ff12ebB+6/L7bfYafYZMqU2GPPvaKzc0k8//xz/e6vWq1GV1dX759KpWvAtVSqtZh74/IoL+yMuTcu7/2mVaq1qNZirT/9bcvwsa75kpLVYzx9YWfm874R2QbekGVONjTfg6m5EdumeHxjKsbxGzWmesv72u0Y+TuOY+TrGOsylGyTP35WYk3NuC/PSt5rzH19K2sx94bX67theeF+iZUHWZ5DazekKe8/czdDKuOISGssqSjiz93mEaxfEbNN/rjesqai3ZcPh2MOhzFGZPNZ03AZ52op35fnvcbc11eAz8udw3XL+9rtGPk6RrOOk/fMDlQjx1Gq1WrFPCtvcsvNN8V3Lvt2fPPb34mIiO9efmk8/thj8YXTzujd5jPHHBX77PeXsdfe+8aKSiUOm/OJ+MfTz4ytp89Ya39Xf39xXHPVlWu9vuiyK2L06NFvWcvKnlqUF3b2fl2dPyHaWkqxsqf/U93ftgwf65ovKVk9xmVHl6N9zNiIyG7eNyLbwBuyzMmG5nswNTdi2xSPb0zFOH6jxlRveV+7HSN/x3GMfB1jXYaSbfLHz0qsqRn35VnJe43qS1/R1+7SWUvqWlPtpI667g+Go7z/zN0MqYwjIq2xpKKIa7d5BOtXxGyTP663rKlo9+XD4ZjDYYyOme+1uwjrRN5rVN/Q5b3GImY7Ip3/DsAx8necvGd2oBo5jmSeNNOf0gDO0bq2mb3/gbHosit6/5x/8bcGfNxqd8ScmSMiYtU/q91vvN7fn/62ZfgYDnNg9Ri/fGE183nfiGwDbyhivgdTcyO2TfH4xlSM4zdqTPWW97XbMfJ3HMfI1zHWZSjZJn/8rMSamnFfnpW815j7+nreVF9PxgUVUJbn0NoNacr7z9zNkMo4ItIaSyqK+HO3eQTrV8Rskz+ut6ypaPflw+GYw2GMEdl81jRcxrlayvflea8x9/UV4PNy53Dd8r52O0a+jtGs4+Q9swPVyHEk86SZ3979m/jqwgVx2fe+Hy0tLfHNiy+IyooVcdzxJ/Zuc+rJ8+N9H/xgHPCxg2LJkiVxzFGHx9nnXhDTNttsvfvv6uqKTx568ID/Hzoq1VqUW1d989rLb3TmrOznEWDVWvS7LcPHuuZLSlaPMV6/OcnLvK9XtoE35CUng8n3YGpuxLYpHt+YinH8Ro2pkfK4djtG/o7jGPk6xkAMNtvkT17mEvnTqPvyrOS9xtzXt7IW5ZZVv8xqb8tffUWQl3O4IWu3J81A/uXxZ+5mSGUcEWmNJRV5+Z6kdl8OWctLTnymVmx5mUfkTxHuy4fDMYfDGCOy+axpuIyzP6ndl+e9xtzXl5N5+Vacw4HJ49rtGPk6RrOOk/fMDlSjxtFWtz1l7M+33CpaWlri5z+9IabP3Cbuuefu2P9jB0VPd3e0tLZGRMTW02fEnXfcHjvvsmvcevNN0TFxYkyZOrUh9az+JrW96Vk+bf1cmFd/E968LcPHuuZLSnovXK+Psajzfjh8r2CoipiTwdTciG1TPL4xFeP4jRpTnjSjbsfI33EcI1/HYHgwl6iHIsyjvNeY+/ra8l1fETiHQJ7kfd0ZqFTGEZHWWFJRxO9JEWuGZpMT6sE8ol6ymEvD4ZjDYYwR2XzWNFzGOVRFWCfyXmPu6yvAvHQOGyOV/w7AMfJ3nLxndqAaNY5kmmbGjx8fc48+LhZ/7/K4cvF344Mf2iF2m7V7nL1wQUyfMTP2/ejsmH3AgfHcc8/GKfNPiI6OiXHMvBOi9fWGGgAAAAAAAAAAAAAAANKRTNNMRMSOO+8SO+68S5/X5p/8+d6/jx07Nk787CnNLgsAAAAAAAAAAAAAAIAmK/gDeAAAAAAAAAAAAAAAAGBtmmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABITlvWBRRFrVaLiIhKpSvjSmB4a29vj1KpVLf9yTbkh3xDmmQb0iTbkKZ6ZztCviEvUly7u7rqe+wxX19R1/0tnzeqrvtrhOE45nrKw/lLMduA+3JImbUb0iTbkCb35ZAuazekaX3ZLtVWp5W39NJLL8axc4/IugwY9hZddkWMHj26bvuTbcgP+YY0yTakSbYhTfXOdoR8Q15YuyFNsg1pcl8O6bJ2Q5pkG9LkvhzSZe2GNK0v25pmBqinpyc6O5fEqFED6zCsVLri2LlHxPkXfyva2+t789QI6m0s9dZPvbt8U8x23mtU39Dkvb6IDa+xEfl+5pmnY/4J83J9vgarCHNgsIypGPKU7cGs3c2Qyvc7lXFEpDOWPI8jpWzn+Tyvpsb6UOP6NeL/OW8w+c56/AOR9xrVN3R5r7Go9+V5P68Dlco4ItIZy3Afh2zXRyrjiEhnLKmMI2LDxuK+fP3yXqP6hi7vNRZ17a6nvH+PIvJfY97ri1DjQGWd7Tycg2YYDuMcDmOMKM443ZevX95rVN/Q5b3Got6X5/28DpRx5E8qY2lUttvqUdxw0NLSEhMnThr0v9fePrruHceNpN7GUm/+pJztvNeovqHJe30R2dfY0tISHR0Tc1FLIxhTMRhT/W3o2t0MWZ+bekllHBHpjCWVcbyVPGS7COdZjfWhxubakHwXYfx5r1F9Q5f3GrOuL+XP1AYilXFEpDMW46gP2U5jHBHpjCWVcURkPxb35dlQ39Dlvcas6/OZ2sDkvca81xehxmYb7vfl6zMcxjkcxhgxfMa5Jvfl2VDf0OW9xqzrG+5rt3HkTypjqfc4Wuq2JwAAAAAAAAAAAAAAAMgJTTMAAAAAAAAAAAAAAAAkp/W00047LesiUtXS0hLTZ2wTra2tWZcyIOptLPWmowjnJu81qm9o8l5fRL5qzFMt9WJMxWBMw0sq5yaVcUSkM5ZUxpF3RTjPaqwPNeZfEcaf9xrVN3R5rzHv9a1LUet+s1TGEZHOWIwjW0Wt+81SGUdEOmNJZRwRxRxLEWrOe43qG7q815j3+pqhCOcg7zXmvb4INRbFcDkHw2Gcw2GMEcNnnENVhPOU9xrVN3R5rzHv9a1LUet+M+PIn1TG0ohxlGq1Wq1uewMAAAAAAAAAAAAAAIAcaMm6AAAAAAAAAAAAAAAAAKg3TTMAAAAAAAAAAAAAAAAkR9PMANVqtejq6oparZZ1KUAdyTakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTakSbYhXfINaZJtKAZNMwNUqVTik4ceHJVKJetSgDqSbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQDJpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOW1ZF9AI55795fjVnXfE4qt+2Of1F194IS664Gvx2KOPxKbTNoujjp4XW2zx9myKBAAAAAAAAAAAAAAAoGGSe9LMb379X/Hr/7qz3/e++51LY+TIUbHwq1+Pt71ti7jkovMbVkdlZS1W9tSisrJWl+2g6FKZ65Xq6+OoFnsc0Eip5L2ZGnXOXLMgHfI8ONYiaD7XKerBPIJ0yTcAFId1G6A5XG+pF3MpHX6/BeSNNYbhwDzPn0bdEyX1pJmurq64dNE3Yq+9941///H1a73/wP33xaGHHRGTJk+OWbvvEaf/4+fitddei5EjR661bbVajWq12vt1pdI14DoqK2sx94blcfm9f4o5M0fExXuNifa20gZvB0WXp7k+pGxXazH3xjXGseeYaC/LLKwpy7wPJd9ZatQ5c80iFUXNdj3J8+Dk6d6TdZPttLhOsaYNzbd5BPnmMzVIUz3uy0tnLalnSVE7qaOu+4Phyn05pMlnaulwvWVNfuYmwu+3UmXtpsisMesm2+kwz/OnkfdESTXNLP7u5bHNtu+Nmdts22/TzCvLlsWoUaMiImLMmDG9r42cPHmtba+79uq45qorN6iOckvE5ff+KSJW/XPR3mOGtB0UXZ7m+pCy3ZqfcUBeZZn3oeQ7S406Z65ZpKKo2a4neR6cPN17sm6ynRbXKda0ofk2jyDffKYGaXJfDulyXw5psnanw/WWNfmZmwi/30qVtZsis8asm2ynwzzPn0beEyXTNPP7B+6P3/z6V7Hwq+fFQw/9fsD/XmkdzUez9z8w9tlvdu/XlUpXHDv3iAHts9oTMWfmiN4up2pPRFvLhm8HRZenuT6kbHe/aRzdMgtvlmXeh5LvLDXqnLlmkYqiZrue5Hlw8nTvybrJdlpcp1jThubbPIJ885kapMl9OaTLfTmkydqdDtdb1uRnbiL8fitV1m6KzBqzbrKdDvM8fxp5T5RM08y111wVr7zySnzm2KOiu7snIiI+eejBseiyK3q3GTdufCxfvjwiIiqVyqrXxm/U7/7K5XKUy+UNqqW9rRQX7zUmFu09Jqo9sc7HAg10Oyi6PM31IWW7XIqL93x9HN3hMWzQjyzzPpR8Z6lR58w1i1QUNdv1JM+Dk6d7T9ZNttPiOsWaNjTf5hHkm8/UIE3uyyFd7sshTdbudLjesiY/cxPh91upsnZTZNaYdZPtdJjn+dPIe6JkmmY+fcxxUa2uehzPvff+T1xy0fmxYOE50dPdHS2trRERsfX0GXHrzTfF1lvPiP+86efxzne+K0aMGNGQelZ/k9bX3TTQ7aDoUpnrqxfFoo8DGimVvDdTo86ZaxakQ54Hx1oEzec6RT2YR5Au+QaA4rBuAzSH6y31Yi6lw++3gLyxxjAcmOf506h7omS+xRtPmBCTN5kSkzeZEhtttOrpMZM3mRKnnHRi/OqXd0RExCFzDovu7p74vyfMiycefyw+NffoLEsGAAAAAAAAAAAAAACgQZJ50sya3vf+D8biq34YERFnfeXc3tcnTZ4cXzjtjKzKAgAAAAAAAAAAAAAAoEmSedIMAAAAAAAAAAAAAAAArKZpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAANRuPNAAAIABJREFUAAAAAAAAAAAAktOWdQH10t3dHVcu/m784mc3RkTEdu/7QHxq7jExYsSI3m0eevD38cVTT+7z7116+eIY1d7e1FoBAAAAAAAAAAAAAABorGSaZm679eb4xc9+GvNPPjXGjhsX/3zaF+IXP7sxPrLPfr3bLF3aGRMnToqzzj639zUNMwAAAAAAAAAAAAAAAOlJpmlmt1m7x26zdo+IiOXLX422trZobW3ts83Szs7YeMKEGDNm7Hr3V61Wo1qt9n5dqXTVt2AgE7IN6ZJvSJNsQ5pkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG4opmaaZ1T574mfi6T8+FdNnzIw99vxIn/c6lyyJF1743zju6COjPGJEHHDgQbHzLrv2u5/rrr06rrnqymaUDDSRbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYirVarVa1kXU00svvRjPPvN0nHfO2XHw3x0as/7iw73vPfboI/HIww/F9BnbxK/uvCN++IOr4rwLL4kJEzrW2k9/nYDHzj0iFl12RYwePbopYwHqT7YhXfINaZJtSJNsQ7rkG9Ik25CmemS7dNaSutZUO2nt39kBg2fthjTJNqRJtiFd8g1pkm0opmSeNPPkk0/Ey0uXxrbveW9MnDgpps+cGfffd2+fppmJkybFptM2i/b29pi8yV/F1d9fHM88/XS/TTPlcjnK5XIzhwA0gWxDuuQb0iTbkCbZhnTJN6RJtiFNsg3pkm9Ik2xDmmQb0iXfkCbZhmJqybqAevnDE4/HOV9ZEA8/9GA89dQf4uGHHoy3v+Md0dPd3bvNpd+6JBaceXq89OILccvNN0VbW1tsOm2zDKsGAAAAAAAAAAAAAACgEZJ50swuu86KJ598IhYuODNqtVrssNPOsede+8TZCxfE9BkzY9+Pzo5D5hwWl1x8fpx4/LGx0cYbx9Hzjo+ODo95BwAAAAAAAAAAAAAASE0yTTOlUikOmXNYHDLnsD6vzz/5871/nzR5cnzuC6c3uzQAAAAAAAAAAAAAAACarCXrAgAAAAAAAAAAAAAAAKDeNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQHE0zAAAAAAAAAAAAAAAAJEfTDAAAAAAAAAAAAAAAAMnRNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQnLasC6iX7u7uuHLxd+MXP7sxIiK2e98H4lNzj4kRI0b0bvPqq6/GNy78etx37+9iQsfEOPyIo2LGzG2yKhkAAAAAAAAAAAAAAIAGSeZJM7fdenP84mc/jfknnxqnn3lW3Ps/v+ttoFntumuvjpdeejEWLDw3PrT9jnHBeefEymo1o4oBAAAAAAAAAAAAAABolGSeNLPbrN1jt1m7R0TE8uWvRltbW7S2tvbZ5oH774vtd9gpNpkyJfbYc6/44Q+uiueffy42+7PN19pftVqN6hoNNZVKV2MHADSFbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYkqmaWa1z574mXj6j0/F9BkzY489P9LnvVeWLYtRo0ZFRMSY0WMiImLZsmWxWT/7ue7aq+Oaq65sdLlAk8k2pEu+IU2yDWmSbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbSimUq1Wq2VdRD299NKL8ewzT8d555wdB//doTHrLz7c+95njjkq9tnvL2OvvfeNFZVKHDbnE/HFfzoz3r31jLX2018n4LFzj4hFl10Ro0ePbspYgPqTbUiXfEOaZBvSJNuQLvmGNMk2pKke2S6dtaSuNdVO6qjr/mC4snZDmmQb0iTbkC75hjTJNhRTMk+aefLJJ+LlpUtj2/e8NyZOnBTTZ86M+++7t0/TzLhx42N51/KIiOiqVCIiYvz4jfvdX7lcjnK53PjCgaaSbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQTC1ZF1Avf3ji8TjnKwvi4YcejKee+kM8/NCD8fZ3vCN6urt7t9l6+oy4847b4/nnn4tf/OyG6Jg4MaZMnZph1QAAAAAAAAAAAAAAADRCMk+a2WXXWfHkk0/EwgVnRq1Wix122jn23GufOHvhgpg+Y2bs+9HZMfuAA+O5556NU+afEB0dE+OYeSdEa2tr1qUDAAAAAAAAAAAAAABQZ8k0zZRKpThkzmFxyJzD+rw+/+TP9/597NixceJnT2l2aQAAAAAAAAAAAAAAADRZS9YFAAAAAAAAAAAAAAAAQL1pmgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABIjqYZAAAAAAAAAAAAAAAAkqNpBgAAAAAAAAAAAAAAgORomgEAAAAAAAAAAAAAACA5mmYAAAAAAAAAAAAAAABITlvWBURE3HzTz+ORhx+OTx45N1ZUKvHtRZfEH554PLZ7/wfjoL85OEqlUtYlAgAAAAAAAAAAAAAAUCCZN83c/J+/iG9cdH782eZvi4iIKxd/L2675T8jIuIPf/hDtI9uj7+cfUCGFQIAAAAAAAAAAAAAAFA0LVkX8O8/+rcYO3ZcHH7EkdHT3R233nJT7LDTLnHO1y+KKVOnxi03/SLrEgEAAAAAAAAAAAAAACiYzJtmnnvuufjABz8U7956Rjz66CPR1dUVH9l735gyZWpMnz4jXnzxhaxLBAAAAAAAAAAAAAAAoGAyb5oZN25c/PGpp6KnpyfuuP3WGDlyZPz5lu+MiFUNNe3tozOuEAAAAAAAAAAAAAAAgKJpy7qAD3xo+7jh338cRx4+J7q6umLX3WZFa2trfHXhgnjg/vti1u4fzrpEAAAAAAAAAAAAAAAACibzJ80c9Dd/G+/d7v2xYkUl3v6Od8QnDpkTERH/87t7Ytq0afHxT/xtxhUCAAAAAAAAAAAAAABQNJk/aaa9vT0+e8qp0d3dHa2trb2vH3/iSTFjxsxoK5czrA4AAAAAAAAAAAAAAIAiyrxpZrU1G2YiIt7z3u0yqgQAAAAAAAAAAAAAAICiy7xpprNzSVx+6aJ44P77YtmyZWu9XypFfO/KH2RQGQAAAAAAAAAAAAAAAEWVedPMNy++MO757V0REdHe3h6lUmmD93X9ddfGT370b/Haayti2/duF3OPPi5GjRrV+/7tt90S53/tq71fjx03Lr757e9sePEAAAAAAAAAAAAAAADkUuZNM79/4P7YdNpm8cXTz4zxG220wfv53X/fE1cu/l6c9LkvxMYTJsSX/umL8R8/uT7+6oC/7t1maWdnvOtdW8f8Uz4fERGlUsuQ6wcAAAAAAAAAAAAAACB/Mm+aGTlqZLz73VsPqWEmIqKtrS0OPmRObLPteyIiYtNp0+LlpS/32WZpZ2dM6OiIMWPGDulYAAAAAAAAAAAAAAAA5FvmTTO7zdo97vrNr6O7uztaW1s3eD/TZ8yM6TNmRkTEk088Ho88/FAcfMihfbbp7FwSDz/0YBxz1Cdj3Pjx8bd/9/e9TTZvVq1Wo1qt9n5dqXRtcG1Afsg2pEu+IU2yDWmSbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbSimzJtmXl66NJ595pk4ef4JsckmU9Z6v1Qqxf896XMD3t9LL70YCxecGfvuNzu2fOdWfd7bfY//E9NnbhNbbfXuuOHffxznnfOVuOiSb0dbubzWfq679uq45qorBz8gINdkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTYUU6lWq9WyLODgg/ZfzxaluOL7PxjQvpYtWxan/+PnYsst3xlzjzkuSqVSn/c7O5fEmNFjYsTIkfHHp56K+f8wL752/jdikylrN+v01wl47NwjYtFlV8To0aMHVA+QP7IN6ZJvSJNsQ5pkG9Il35Am2YY01SPbpbOW1LWm2kkddd0fDFfWbkiTbEOaZBvSJd+QJtmGYsr8STOnfvGMuuxnRaUSX/7SGTFp0qQ45NDDoqtreZRKLTFq5MhoaW2NiIivnPWlmDRpchx6+Kfi9ttuibFjx0VHR/8fvpfL5Sj38wQaoNhkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG9Ik25Au+YY0yTYUU+ZNM9NnzKzLfn71q1/Go48+HBERRx4+JyIiJk2eHKNHj4kDPnZQbL/jTnHk3GNi0SUXxwnz5sYmU6bGZ/5hfrS5cAEAAAAAAAAAAAAAACQn86aZM077wlu+XyoN7Gk0u83aPXabtftbbrPF298R//SlswZVHwAAAAAAAAAAAAAAAMWTedPMA/ffu54tSk2pAwAAAAAAAAAAAAAAgHRk3jTzL1/+ar+vP/P003Hh+efG33/yyCZXBAAAAAAAAAAAAAAAQNFl3jSzxdvfsc7Xb7v15rjxP34SH95jzyZXBQAAAAAAAAAAAAAAQJG1ZF3AW2ltbY3nnn0m6zIAAAAAAAAAAAAAAAAomMyfNPPj669b67Va1OK5Z5+Nu+/6dUydumkGVQEAAAAAAAAAAAAAAFBkmTfNfO87/y8iSv28U4uWltY48OOfaHJFAAAAAAAAAAAAAAAAFF3mTTMH/PXH13qtpVSKjTbaOGZu+56YMmVqBlUBAAAAAAAAAAAAAABQZJk2zdRqtZgxY5vYeOONY9Npm2VZCgAAAAAAAAAAAAAAAAlpyfLgpVIp/uWfT4+f/Oj6LMsAAAAAAAAAAAAAAAAgMZk2zUREvHOrd8WTTz6edRkAAAAAAAAAAAAAAAAkpC3rAjo6OuL2226Ns750RkyZOnWt90tRikMPPyKDygAAAAAAAAAAAAAAACiqzJtmbr/tloiI+O977l7HFppmAAAAAAAAAAAAAAAAGJzMm2aOOnpe1iUAAAAAAAAAAAAAAACQmMybZnabtft6t7nnt3fFo488Eh/76483oSIAAAAAAAAAAAAAAACKriXrAgbit3ffFddcdWXWZQAAAAAAAAAAAAAAAFAQhWiaAQAAAAAAAAAAAAAAgMHQNAMAAAAAAAAAAAAAAEByNM0AAAAAAAAAAAAAAACQnLasC6in66+7Nn7yo3+L115bEdu+d7uYe/RxMWrUqN73X3zhhbjogq/FY48+EptO2yyOOnpebLHF27MrGAAAAAAAAAAAAAAAgIZI5kkzv/vve+LKxd+Lo+cdH6efeVY8+MAD8R8/ub7PNt/9zqUxcuSoWPjVr8fb3rZFXHLR+RlVG1Gp1mJlTy0q1VpmNQCDV1n5enZXyi6sS+o5SX18w5HvKUWQ0jxNaSzAG3zOQT2YR5Au+QaA4rBuAzSH6y31Yi4B9M/1ERpLxiiaYjTN1Hr/Z53a2tri4EPmxDbbvic23/xtsem0afHy0pf7bPPA/ffFLrvuFpMmT45Zu+8Rjz36SLz22mv97q9arUZXV1fvn0qlq06DWXWhmHvj8igv7Iy5Ny53wYAmGkq2KytrMfeG17N7w3L/oSf0I8ucNHLtXs11ID2+p/nXjGznXUrzNKWxMDSynRafc7CmDc23eQT59v/Zu/cwucoyX9hPdbpDOoGEdAingAIiDknQQeWMwKAbhoNGEOPIZsMgCJEQBoaJwICCcsEHk80eRTkMW8YBhnOQYfAEbgZFUBgViAOCyMHDiEYgHXLoClSn6/sjSSeddEh1uqrWWm/d93XlIl29st7nXf3+1lpVXQ81rNfU5Btyy305pMt9OaTJtTsdzresyXNuSJdrd3acH2kk2ZYxiqk96wJWWbp0Sfzh5Zeju3tB7LHn3hGx4sTS0dERJ558Spx48ilv+e8nT5kak6dMjYiI3/z6pXj+V8/FscedMGCbxYsWxahRoyIiYsyYMf2PbTJx4jr7u+fuuXHXnbcPe16D6RgRceNTb0bEiv9ef9iYhowDrGs42e5ok13YkCxz0shr9yrOA+nxM82/ZmQ771JapynNheGR7bR4nYM1bWy+rSPIt2G9pibfkFvuyyFd7sshTa7d6XC+ZU2ec0O6XLuz4/xII8m2jFFMmTfNLF++PG69+ca4/7vfid7e3iiVIm6+/Rvx2I9/FDf+y/Vx4RcvjS232qrm/b322qsx57JL4ogjp8XO79xlg9uXSoM/Pu2oY+LwI6f1f10u98TpM06uuY63UlkecfzUkXHjU2/G8VNHRmV5RHsxPvMHCm842a70rZXdPtmFtWWZk0Zeu1dxHkiPn2n+NSPbeZfSOk1pLgyPbKfF6xysaWPzbR1Bvg3rNTX5htxyXw7pcl8OaXLtTofzLWvynBvS5dqdHedHGkm2ZYxiyrxp5o7bbolvf/Pf421ve3tUKpX4wx/+EBER27/t7dHTszRuu/Vf44wzz65pX4sWLYpLL74opkzdLaZ/8n+u8/3NNhsbS5cujYiIcrm84rGx4wbdV0dHR3R0dGzMlDaos6MU1x4yJq4/bExUlq/4GmiO4WS7s70U1x66Mrt9K74GBsoyJ428dq/iPJAeP9P8a0a28y6ldZrSXBge2U6L1zlY08bm2zqCfBvWa2ryDbnlvhzS5b4c0uTanQ7nW9bkOTeky7U7O86PNJJsyxjFlHlf18M//H7stNPO8f/N+ceYutt7+h/fdtKk2P19e8RTP59X036WlcvxD5deHFtssUUcd8KJ0dOzNHp6eqJv+fL+bXadPCV++IMH49VXXonvP/hAvPOd74qRI0fWfU616OwoRXtbyYkCCqazfWV2vcET1iv1nKQ+v1bkZ0oRpLROU5oLsJrXOagH6wjSJd8AUByu2wDN4XxLvVhLAINzfoTGkjGKJvNPmlmyeHG85z27R6lUihgkN8uWLatpP4899uN44YVfRUTEKZ86PiIitpg4MUaPHhNHf2x67LXPvnHc8SfGNVddGX931qzYdtKk+MzMM+o2DwAAAAAAAAAAAAAAAPIj86aZbbadFP/1X/NiyZIlAx5/6cUX4vGf/mdst912Ne3nwIMOjgMPOvgtt9li4sT43EUXb3StAAAAAAAAAAAAAAAAFEPmTTPTPvqx+MqXr4iz/2ZmjOocFRERl1/6xXj6qf+K3t7lcdgRH864QgAAAAAAAAAAAAAAAIom86aZffbbP5YsWRy33nxjLF68KCIi5j35RLS3d8T0v/pkfODAv8i4QgAAAAAAAAAAAAAAAIom86aZiIj/cehhceBBB8dzzz0bCxcujM7OztjlXX8Wm202NuvSAAAAAAAAAAAAAAAAKKDMm2au+eqXY/8DDoqpu707pu72nqzLAQAAAAAAAAAAAAAAIAGZN8388KHvxw8f+kGM23xc7LvvB2K/DxwQO71j56zLAgAAAAAAAAAAAAAAoMAyb5o59n+dEE/87Gfx3C+fie98+974zre/GVtvvXXsf8CBse9+B8TW22yTdYkAAAAAAAAAAAAAAAAUTOZNM0d++KNx5Ic/Gj09PTHvicfjicd/GvOefCLm3nF7zL3j9njHzjvHfh84IA466IMxqrMz63IBAAAAAAAAAAAAAAAogLasC1hl9OjR8Y6d3xk77LjTyk+XqUZENV54/ldx49evjzNnfSZeeOH5rMsEAAAAAAAAAAAAAACgADL/pJmXXnoxfvqfj8VPf/JY/O63v42IiLa2ttj9ve+L/Q84KKZMfXf88AcPxq033xTXX3dNXHr5FRlXDAAAAAAAAAAAAAAAQN5l3jRz/jlnR0QpIqqxw447xQEH/kXsu98HYuy4cf3bHPHhafHzeU/EL599NrM6AQAAAAAAAAAAAAAAKI7Mm2bGd3XF/h84KA448KCYtN32693uo0d/PBa9/noTKwMAAAAAAAAAAAAAAKCoMm+a+eo1X4tSqRQREUsWL47uhd0xbtzmMXbs2AHb7Tp5ShblAQAAAAAAAAAAAAAAUECZN82USqW4/75vx7/f/Y1YsGBB/+NdXV3xkaOOjkMOPTzD6gAAAAAAAAAAAAAAACiizJtmvnHXHTH39tsiohpbb71NjB03LpYsXhwvv/xy/Mv1X4ulS5bGUR/7eNZlAgAAAAAAAAAAAAAAUCCZN808cP99MWbMmDj3ggvjHe/Yuf/xX7/0YlzyxQvje/d9R9MMAAAAAAAAAAAAAAAAQ9KWdQHLlpVj9/e9f0DDTETEDjvuFLu/7/2xdOmSjCoDAAAAAAAAAAAAAACgqDJvmvmzXSdH94IFg37vtVdfjV3etWuTKwIAAAAAAAAAAAAAAKDo2rMY9Fv33tP/9y233Dru++6342v/dHVss+2k/sd/97vfxrPPPB3HHnfCkPb9+usL45GHH4qHvv9gzJx1Zmz/trcP+P5zv3w2Lrzg3AGPff3GW2NUZ+dGzAQAAAAAAAAAAAAAAIA8yqRp5uab/iUiSms8Uo3/eOB76zy2Ytsb4ogPT6tpv+VyOWZ95pTYYYcd4ze/fmnQbRYu7I4JE7aIy6/4Uv9jGmYAAAAAAAAAAAAAAADSkknTzNHHTI8olTa84RCNHDkyvnLNdfHmG2/EGTNPHXSbhd3dsfn48TFmzKZ1Hx8AAAAAAAAAAAAAAIB8yKRp5pjpn2zIfkeMGBHjxm0er/xp/nq36V6wIF555U9xxmmnRMfIkXH0MdNjv/0PWGe7SqUSlUql/+tyuachNQPNJduQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNuQJtmGdMk3pEm2oZgyaZpZ01133v6W3x/RPiImbbtdvO/9e0TbiBHDHm+PvfaO8V1dMXnKbvHYoz+Ka6+6MiZPmRrjx3cN2O6eu+dusDageGQb0iXfkCbZhjTJNqRLviFNsg1pkm1Il3xDmmQb0iTbkC75hjTJNhRTqVqtVrMs4NjpR0VEaeVXa5ay5mOl2GGHHeJzX7gkOjs7N7jPV/40P86YeWr8wxVfju3f9vYB33v99YUxcuQm0dnZGW+88Ub89XGfiAsuvDimTN1twHaDdQKePuPkuP6GW2L06NFDnieQD7IN6ZJvSJNsQ5pkG9Il35Am2YY01SPbpcsX1LWm6jldG94I2CDXbkiTbEOaZBvSJd+QJtmGYsr8k2am/9Wxccdtt8QHP3RobDtpUv/jf/zjH+J7930nPvihQ+PVV1+JeU8+HnffdUcce9wJQx6jb/ny/k+p+frXrovu7gVxxplnx+OP/yza29tjm20nrfNvOjo6oqOjY+MnBuSSbEO65BvSJNuQJtmGdMk3pEm2IU2yDemSb0iTbEOaZBvSJd+QJtmGYsq8aWbek0/En+06OU46ZcY63/vjH16OV1/9U5zz95+P8z77t/HT/3xso5pmrphzWUyeMjWO+PC0OO74E+O6a78aZ595eozbfPM4bdaZ0dXl/1oFAAAAAAAAAAAAAACQksybZl54/vnYc+99Bv3e2LHj4vGf/SQiInbccad45OGHatrnxC23ilvv/Lf+r2efe37/37eYODH+/nNfGEbFAAAAAAAAAAAAAAAA5F1b1gWMHTs2nvjZT+L5Xz034PEXXng+Hv/ZT6Kzc3RERMyfPz8mTNgiixIBAAAAAAAAAAAAAAAomMw/aeaQvzw8brvlpvj8+efGdttvH+PHd8Xrry+M3/7mNxFRjaOOmR7P/fLZeOYXT8fRH5+edbkAAAAAAAAAAAAAAAAUQOZNMx/56NExcpOR8W93zY3//t1v479/99uIiBg/viuOOmZ6fOh/HBpvvvFGHPmRafGRaUdnXC0AAAAAAAAAAAAAAABFkHnTTETEXx52ZBxy6OHx2quvxOuvvx7ju7qiq2tClEqliIgYuckmcexxJ2RcJQAAAAAAAAAAAAAAAEXRlnUBq8x78vH48Y8eiUcefigmTNgiSqVSvPTSi1mXBQAAAAAAAAAAAAAAQAFl/kkzC7u7439ffmm8+OILEVGNiFKccOLJ8d1vfzP+9cavx3kXXBRTpu6WdZkAAAAAAAAAAAAAAAAUSOafNHPTDf8cL774Qhzyl4fF5ClT+x8/6C8+GOPGbR7fmHt7htUBAAAAAAAAAAAAAABQRJk3zcx78onY/b3vjb/+1Kdj0nbb9z8+qrMz3v2eP4+XXnwhw+oAAAAAAAAAAAAAAAAoosybZvr6lseYTTcb9HsLFiyIiFJzCwIAAAAAAAAAAAAAAKDwMm+aeecu74qfPPbjeOYXT/c/Vq1W47vf+Wb818+fjHfu8q4MqwMAAAAAAAAAAAAAAKCI2rMu4BOfPC6+eOH5cfFFn4v29hEREXHSCcfGsmVvxIgRI+Ljn/hkxhUCAAAAAAAAAAAAAABQNJl/0sxO79g5LvzipTF5ypRYvnx5RFRj2bJlscOOO8Z5F1wYO79zl6xLBAAAAAAAAAAAAAAAoGAy+aSZJYsXD/h64sQt48y//Wws71sey3uXRzWqscnITfq33XSzzbIoEwAAAAAAAAAAAAAAgILKpGnmlJNOqHnbUini5tu/0cBqAAAAAAAAAAAAAAAASE0mTTMTtpgQpShlMTQAAAAAAAAAAAAAAAAtIJOmma9c/X+zGBYAAAAAAAAAAAAAAIAW0ZZ1AQAAAAAAAAAAAAAAAFBvmmYAAAAAAAAAAAAAAABITnvWBdTT668vjEcefige+v6DMXPWmbH9294+4PtLliyJf7r6K/H0Uz+P8V0T4lMnnxpTpu6WUbUAAAAAAAAAAAAAAAA0SjKfNFMul2PWZ06JRx95JH7z65cG3eaeu+fGa6+9GpfN+VLsudc+cdWV/xh21IwnAAAgAElEQVS9lUpj6qlUo7evGuVKdcDjy3qr0bvyz5rbDLYtraMV1sCqOa6z9nuLNedW+FnBcBUxJ+XelTXXcE4ayvxq3bYR+4yofV6Nmn+tinJMG3Hsh7rfWmW99vKkGXUbI3/jNGWMIeQcWkFRrxPki3UE6ZJvACgO122A5nC+pV6yWEvWLxDhXAB5lsr7MqCekmmaGTlyZHzlmuti1pl/u95tnvnF07HX3vvGllttFR865NDo7l4Q8+f/cdBtK5VK9PT09P8pl3tqrqVcqcaM+5dGx5zumHH/0v4TQrlSjSVvVqNSjahUI06/f2lUlseg29I61rdeUrJqjqPndEelOnDNV6rR1Dk3ItvAalnmZGPzXe6txoz7VtZ839K3fPPzUOZX67aN2OdQ5tWo+deqKMe0Ecd+qPutVdZrr97yfu02Rv7GacoYQ8g5gxtOtskfz5VY00bfl1tHkGt5vy8HNo77ckiX+3JIk2t3OpxvWVPRnnNbv1C7lK/dzgW0srxnO5X3ZUC9larValIr9ZU/zY8zZp4a/3DFl2P7t719wPf+ZuapcfiRH4lDDzsilpXLceLxn4zPf+GS2HXylHX2M/eOW+OuO29f5/Hrb7glRo8e/ZY19PZVo2NOd//Xldnjo72tFL191eirRrSVVjx+6O2L475PbDbotrSO9a2XlKya40d37og7j9p0nflGRNPm3IhsA6tlmZONzfdQam7EtqmOX6uizCnrn9NQZD2nesv7tdsY+RsnlTFSN5xskz8ywZqacV8ONF/e78uBjVOP+/LS5QvqWlP1nK667g9alftySJPX1NLhfMuaivac2/qF2qV87XYuoJXlPdveMwGDa8+6gKyV1pPRaUcdE4cfOa3/63K5J06fcXJN+6wsjzh+6si48ak34/ipI6OyPKK9bcXjSyvVGNOxYtAdxratd1taRyusgVVzvPWpNwedb0Tz5tyIbAOrZZmTjc13pW+tmvvWX/NQ5lfrto3Y51Dm1aj516oox7QRx36o+61V1muv3vJ+7TZG/sZpyhhDyDmDG062yR/PlVjTRt+XW0eQa3m/Lwc2jvtySJf7ckiTa3c6nG9ZU9Gec1u/ULuUr93OBbSyvGc7lfdlQL211CfNXHDu7HjvHnvE0R+bHgsWLIiZp34qrvjSVbHtpEkb3G9PT0+cdMKxNXcClivV6Bix4sTQ2bG6M2dZb7W/U6lSjf5tBtuW1rG+9ZKSVXOMvrXWfimivT27Odcr28BqecnJUPJd7q1GR9uKN0F3buCcNJT51bptI/YZUfu8GjX/WhXlmDbi2A91v7XKeu01Uh6v3cbI3zhNGWMIOWfDhppt8icv1wnyZ0j35dYRFEYe78uB4duY+3KfNAPF4L4c0uQ1tWJzvmV9ivCc2/qFjZPatdu5AFbIY7ZTeV8G1FPynzTTt3x5tI0YERERu06eEo/+6JHYb/8D4oc/eDC6JkyIrbbeuiHjrjoBrN05N2qNN1OtOvirttFl17rWt15S0n9RbFt37RdJK/ysYLiKmJNVb3aupeahzK/WbRuxz4ja59Wo+deqKMe0Ecd+qPutVdZrL0+aUbcx8jdOU8YYQs6hFRT1OkG+WEeQLvkGgOJw3QZoDudb6iWLtWT9AhHOBZBnqbwvA+op+aV6xZzL4lv33hMREdOOPia22mrrOG/2WfHojx6JmbPOihErG2oAAAAAAAAAAAAAAABIR3KfNDNxy63i1jv/rf/r2eee3//3TTfdNM7+7HlZlAUAAAAAAAAAAAAAAEATJf9JMwAAAAAAAAAAAAAAALQeTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkR9MMAAAAAAAAAAAAAAAAydE0AwAAAAAAAAAAAAAAQHI0zQAAAAAAAAAAAAAAAJAcTTMAAAAAAAAAAAAAAAAkpz3rAurpx488HLfdclMsXbok3r/n3nHSp2dER0dH//ef++WzceEF5w74N1+/8dYY1dnZ7FIBAAAAAAAAAAAAAABooGSaZhYvXhTXXn1lnHjyqbHzzrvEJRd/Ph584HtxyF8e3r/NwoXdMWHCFnH5FV/qf0zDDAAAAAAAAAAAAAAAQHqSaZp54flfRbUaceBBB0epVIrdd39fPPOLpwc2zXR3x+bjx8eYMZtucH+VSiUqlUr/1+VyT0PqBppLtiFd8g1pkm1Ik2xDuuQb0iTbkCbZhnTJN6RJtiFNsg3pkm9Ik2xDMSXTNLNo0aLYZNQmUSqVIiJi9JgxMX/+/AHbdC9YEK+88qc447RTomPkyDj6mOmx3/4HDLq/e+6eG3fdeXvD6waaS7YhXfINaZJtSJNsQ7rkG9Ik25Am2YZ0yTekSbYhTbIN6ZJvSJNsQzEl0zQzmJX9M/322GvvGN/VFZOn7BaPPfqjuPaqK2PylKkxfnzXOv922lHHxOFHTuv/ulzuidNnnNzokoEGk21Il3xDmmQb0iTbkC75hjTJNqRJtiFd8g1pkm1Ik2xDuuQb0iTbUEzJNM1sttlmsaxcjr6+vmhra4tyT0+MHbf5gG0mbLFFbLPtpOjs7IyJW3405t5xa7z8+98P2jTT0dERHR0dzSofaBLZhnTJN6RJtiFNsg3pkm9Ik2xDmmQb0iXfkCbZhjTJNqRLviFNsg3F1JZ1AfXyjp13iba2tnjge/fF73//3/Hkk4/H5ClTo2/58v5tvv616+KyS74Qr736Sjz0gwejvb09ttl2UoZVAwAAAAAAAAAAAAAA0AjJNM2MHTs2Zpx2Rtx7z91x4fnnxLvf/edx4EEHxxVzLotv3XtPREQcd/yJsckmm8TZZ54e3/z3u+O0WWdGV9e6nzIDAAAAAAAAAAAAAABAsbVnXUA97bPf/rHPfvsPeGz2uef3/32LiRPj7z/3hWaXBQAAAAAAAAAAAAAAQJMl80kzAAAAAAAAAAAAAAAAsIqmGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASI6mGQAAAAAAAAAAAAAAAJKjaQYAAAAAAAAAAAAAAIDkaJoBAAAAAAAAAAAAAAAgOZpmAAAAAAAAAAAAAAAASE571gXU048feThuu+WmWLp0Sbx/z73jpE/PiI6Ojv7vL1myJP7p6q/E00/9PMZ3TYhPnXxqTJm6W4YVAwAAAAAAAAAAAAAA0AjJfNLM4sWL4tqrr4yjjpkeF118Wcx78vF48IHvDdjmnrvnxmuvvRqXzflS7LnXPnHVlf8YvZVKRhUDAAAAAAAAAAAAAADQKMk0zbzw/K+iWo048KCDY7vtt4/dd39fPPOLpwds88wvno699t43ttxqq/jQIYdGd/eCmD//jw2pp1ypRm9fNcqV6oDHe3ur6/xZ37a0jlZYA6vmWPR1X9S6oZmKmJOh1NyIbVMc35yKMX6j5pQnzajbGPkbxxj5GoPWYC1RD0VYR3mvMff19a6srzef9RVBUY9h3tdmrVKZR0Rx11KqUlpbQPE5J8GGyQn1YB1RL1mspVYYsxXmGJHN6wOtMs/hKsJ1Iu815r6+AqxLx7AxUnkfgDHyN07eM1urRs0jmaaZRYsWxSajNolSqRQREaPHjIlFixYN2GbxokUxatSoiIgYM3pM/78bTKVSiZ6env4/5XJPzbWUK9WYcf/S6JjTHTPuX9r/QytXqlGpxjp/BtuW1rG+9ZKSVXO8YE535uu+EdkGVssyJxub76HU3IhtUxzfnIoxfqPmVG95v3YbI3/jGCNfY6zPcLJN/niuxJqacV+elbzXmPv6eqsx476V9d23tHC/xMqDLI9h3u/LmyGVeUTIY964LwcaIeX7csiaazf14HzLmor2nLsVxmyFOUZk8/pAq8xzlZTvy/NeY+7rK8Drc47h+uX92m2MfI3RrHHyntlaNXIepWq1WsyjspaHfvBg3HTDP8f//eebIiLiX2/8evz6pRfjggsv7t/mb2aeGocf+ZE49LAjYlm5HCce/8m48IuXxJ/tOmWd/c2949a4687b13n8+htuidGjR79lLb191eiY093/dWX2+GhvK0Vv3+CHerBtaR3rWy8pWTXHRad1ROeYTSMiu3XfiGwDq2WZk43N91BqbsS2KY5vTsUYv1Fzqre8X7uNkb9xjJGvMdZnONkmfzxXYk3NuC/PSt5rVF/6inrtTuVnn8o8ItKaSwqKmu1VSpcvqGtN1XO66ro/aFUp35dD1op+7SYfnG9ZU9Gec7fCmK0wR2Pm+9pdhOtE3mtU3/DlvcYiZjsinfcBGCN/4+Q9s7Vq5DyS+aSZzTbbLJaVy9HX1xcREeWenhg7bvO1thkbS3uWRkRET7kcERFjxw7cZpVpRx0T199wS/+fr177tZprqSyPOH7qyIhY8d/K8tWPD/ZnsG1pHa2wBlbN8ZKrK5mv+0ZkG1itiPkeSs2N2DbF8c2pGOM3ak71lvdrtzHyN44x8jXG+gwn2+SP50qsqRn35VnJe425r69vrfr6Mi6ogLI8hnm/L2+GVOYRIY95474caISU78sha67d1IPzLWsq2nPuVhizFeYYkc3rA60yz1VSvi/Pe425r68Ar885huuX92u3MfI1RrPGyXtma9XIeSTzSTOLFi2KWZ85OY47/sSYPHW3uPTiC+Ooj02Pgw/+ULSNGBERETff+C8xb94TcfZnz4sf/uDBePA//l9cedV1MWLl999KT09PnHTCsTX/HzrKlWp0jFjxw+vsWN3h1DvIR4BVqjHotrSO9a2XlKyaY6y8OcnLuq9XtoHV8pKToeR7KDU3YtsUxzenYozfqDk1Uh6v3cbI3zjGyNcYtRhqtsmfvKwl8qdR9+VZyXuNua+vtxodbSt+mdXZnr/6iiAvxzCP9+XNkMo8IvKzllghL2trY+7LfdIMFENq9+WQtbzkxGtqxZaXdUT+FOE5dyuM2QpzjMjm9YFWmedgUrsvz3uNua8vJ+vyrTiGtcnjtdsY+RqjWePkPbO1atQ82uu2p4yNHTs2Zpx2Rtx6841x+63/GnvsuXcceNDBccWcy2LylKlxxIenxbSjj4k//vEPcd7ss6Kra0LMnHVWTQ0zG2PVD6l9rc/yaR/kxLzqh7D2trSO9a2XlPSfuFbOsajrvhV+VjBcRczJUGpuxLYpjm9OxRi/UXPKk2bUbYz8jWOMfI1Ba7CWqIcirKO815j7+trzXV8RFPUY5n1t1iqVeUQUdy2lKqW1BRSfcxJsmJxQD9YR9ZLFWmqFMVthjhHZvD7QKvMcriJcJ/JeY+7rK8C6dAwbI5X3ARgjf+PkPbO1atQ8kmmaiYjYZ7/9Y5/99h/w2Oxzz+//+6abbhpnf/a8ZpcFAAAAAAAAAAAAAABAkxW8lwgAAAAAAAAAAAAAAADWpWkGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEhOe9YFAAAAAAAAANRb6fIFdd9n9Zyuuu8TAAAAAIDG8UkzAAAAAAAAAAAAAAAAJMcnzdSoWq1GRES53JNxJdDaOjs7o1Qq1W1/sg35Id+QJtmGNMk2pKne2Y6Qb8gL125IU4rZ7unJ/3mlCDVSbO7LIV0pXrsB2YZUuS+HdLl2Q5o2lO1SdVVaeUuvvfZqnD7j5KzLgJZ3/Q23xOjRo+u2P9mG/JBvSJNsQ5pkG9JU72xHyDfkhWs3pEm2IU3uyyFdrt2QJtmGNLkvh3S5dkOaNpRtTTM16uvri+7uBTFqVG0dhuVyT5w+4+T46rVfi87O+t48NYJ6G0u99VPvLt8Us533GtU3PHmvL2Lja2xEvl9++fcx+6xZuT5eQ1WENTBU5lQMecr2UK7dzZDKzzuVeUSkM5c8zyOlbOf5OK+ixvpQ44Y14v+cN5R8Zz3/WuS9RvUNX95rLOp9ed6Pa61SmUdEOnNp9XnIdn2kMo+IdOaSyjwiNm4u7ss3LO81qm/48l5jUa/d9ZT3n1FE/mvMe30RaqxV1tnOwzFohlaYZyvMMaI483RfvmF5r1F9w5f3Got6X57341or88ifVObSqGy316O4VtDW1hYTJmwx5H/X2Tm67h3HjaTexlJv/qSc7bzXqL7hyXt9EdnX2NbWFl1dE3JRSyOYUzGYU/1t7LW7GbI+NvWSyjwi0plLKvN4K3nIdhGOsxrrQ43NtTH5LsL8816j+oYv7zVmXV/Kr6nVIpV5RKQzF/OoD9lOYx4R6cwllXlEZD8X9+XZUN/w5b3GrOvzmlpt8l5j3uuLUGOztfp9+Ya0wjxbYY4RrTPPNbkvz4b6hi/vNWZdX6tfu80jf1KZS73n0Va3PQEAAAAAAAAAAAAAAEBOaJoBAAAAAAAAAAAAAAAgOSMuuuiii7IuIlVtbW0xecpuMWLEiKxLqYl6G0u96SjCscl7jeobnrzXF5GvGvNUS72YUzGYU2tJ5dikMo+IdOaSyjzyrgjHWY31ocb8K8L8816j+oYv7zXmvb71KWrda0tlHhHpzMU8slXUuteWyjwi0plLKvOIKOZcilBz3mtU3/Dlvca819cMRTgGea8x7/VFqLEoWuUYtMI8W2GOEa0zz+EqwnHKe43qG76815j3+tanqHWvzTzyJ5W5NGIepWq1Wq3b3gAAAAAAAAAAAAAAACAH2rIuAAAAAAAAAAAAAAAAAOpN0wwAAAAAAAAAAAAAAADJ0TRTo2q1Gj09PVGtVrMuBagj2YZ0yTekSbYhTbIN6ZJvSJNsQ5pkG9Il35Am2YY0yTakS74hTbINxaBppkblcjlOOuHYKJfLWZcC1JFsQ7rkG9Ik25Am2YZ0yTekSbYhTbIN6ZJvSJNsQ5pkG9Il35Am2YZi0DQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJac+6gHp6/fWF8cjDD8VD338wZs46M7Z/29sHfH/JkiXxT1d/JZ5+6ucxvmtCfOrkU2PK1N0yqhYAAAAAAAAAAAAAAIBGSeaTZsrlcsz6zCnx6COPxG9+/dKg29xz99x47bVX47I5X4o999onrrryH6O3UmlMPZVq9PZVo1ypDnh8WW81lvVWo9y7+vuDbUdr6V8Pva2xDvrX/co8FInMwoYVMSdFrLkWtV5fsr4Otfr4tRrKOm3Emi5qTopa99qaMY9mHaumzKUguYaUpHK+JVvWEaRLviFNsg0AsPHcS1EvWaylVhgzi981OS80jt8dNoY1C/mVyntMUnp/iXNm7Rr1M0mmaWbkyJHxlWuui1ln/u16t3nmF0/HXnvvG1tutVV86JBDo7t7Qcyf/8dBt61UKtHT09P/p1zuqbmWcqUaM+5fGh1zumPG/Uv7F3i5Uo0lb674M+O+1d+fv7Qan1ljO1pLuXeN9XDf0uRvzgfk476lseTN5j4haUS2gdWyzMnG5jvVbNd6fcn6OtTq49dqKOu0EWu6iNmOSCffzZhHs45VU+ZSkFy3uuFkm/xJ5XxLfbgvhzS5L4c0yTaka7jPu0uXL6jrH6A+vKaWDvdSrKlo9+WtMGYWv2tyXmicLH93mPK125qlleU926m8xySl95c4Z9aukT+TUrVaTerIv/Kn+XHGzFPjH674cmz/trcP+N7fzDw1Dj/yI3HoYUfEsnI5Tjz+k/H5L1wSu06ess5+5t5xa9x15+3rPH79DbfE6NGj37KG3r5qdMzp7v+6Mnt8tLeVorevGn0rj/Ym/3v199/4u/GxzVcXxvxZm0d7W2ko0yUB61svqVp7vm/83fhoK0XT5tyIbAOrZZmTjc13qtmudV5Zz7/Vx6/VUOpsxJyKmO2I4vx8N6QZ82jWsUppLgzPcLJN/sgda3JfDmlyXw5pkm1I13Cfd9e70aV6Tldd9wetymtq6XAvxZqKdl/eCmO2whxbSVF/15131iytLO/ZTuV9GamM0cxxUtDIY9Vel70UWGk9x3HaUcfE4UdO6/+6XO6J02ecXNM+K8sjjp86Mm586s04furIqCyPaG9b8fjSld1ha37/lZ5qfHjnjv7taC2VvrXWS1/a62DtfCx6oxpjRpaaNudGZBtYLcucbGy+U812rdeXrK9DrT5+rYayThuxpouY7Yh08t2MeTTrWDVlLgXJdasbTrbJn1TOt9SH+3JIk/tySJNsQ7o874Y0yXY63EuxpqLdl7fCmFn8rsl5oXGy/N1hytdua5ZWlvdsp/Iek5TeX+KcWbtG/kxa6pNmLjh3drx3jz3i6I9NjwULFsTMUz8VV3zpqth20qQN7renpydOOuHYmjsBy5VqdIxYsdA7O1Z35ixb+TFB1YjoWNlIM9h2tJZyb3XFeuiL6GxPfx3056MvohQRozKcc72yDayWl5wMJd95qbnear2+ZH0davXxazWUddqINZ2XnLTqtbsZ82jWsWrKXAqSa1YbarbJn1TOt9Sf+3JIU6vel0PqZBvSNdR8+6QZKAavqRWbeynWpwj35a0wZha/a3JeaJy8/O4wtWu3NQsr5DHbqbzHJKX3lzhn1q5RP5PkP2mmb/nyaBsxIiIidp08JR790SOx3/4HxA9/8GB0TZgQW229dUPGXbWg1+5uWrs5YNX3dYy1tlWhbpV1sL58FEGRa4dmKWJOilhzLWq9vmR9HWr18Ws1lHXaiDVd1JwUte61NWMezTpWTZlLQXINKUnlfEu2rCNIl3xDmmQbAGDjuZeiXrJYS60wZha/a3JeaBy/O2wMaxbyK5X3mKT0/hLnzNo16meS/KG/Ys5l8a1774mIiGlHHxNbbbV1nDf7rHj0R4/EzFlnxYiVDTUAAAAAAAAAAAAAAACkI7lPmpm45VZx653/1v/17HPP7//7pptuGmd/9rwsygIAAAAAAAAAAAAAAKCJkv+kGQAAAAAAAAAAAAAAAFqPphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEiOphkAAAAAAAAAAAAAAACSo2kGAAAAAAAAAAAAAACA5GiaAQAAAAAAAAAAAAAAIDmaZgAAAAAAAAAAAAAAAEhOe9YF1NOPH3k4brvlpli6dEm8f8+946RPz4iOjo7+7y9cuDCu+eqX4pfPPhNjx46LTxx7XOy3/wEZVgwAAAAAAAAAAAAAAEAjJPNJM4sXL4prr74yjjpmelx08WUx78nH48EHvjdgm3vunhvd3d0x5/9cGYccdnhc89Ur44033sioYgAAAAAAAAAAAAAAABolmaaZF57/VVSrEQcedHBst/32sfvu74tnfvH0gG3aSm0xatSoGN81Ibq6JkR7+4ioVqsZVQwAAAAAAAAAAAAAAECjtGddQL0sWrQoNhm1SZRKpYiIGD1mTMyfP3/ANh/7+Cdi9tlnxIn/66+it7c3Zsw8I0aNGjXo/iqVSlQqlf6vy+WexhUPNI1sQ7rkG9Ik25Am2YZ0yTekSbYhTbIN6ZJvSJNsQ5pkG9Il35Am2YZiSqZpZjAr+2f63XXn7TFu7LiYfc758fjjP41bbroh9thjrxg9Zsw6//aeu+fGXXfe3qRKgWaRbUiXfEOaZBvSJNuQLvmGNMk2pEm2IV3yDWmSbUiTbEO65BvSJNtQTMk0zWy22WaxrFyOvr6+aGtri3JPT4wdt/mAbeY9+Xgc+BcfjB123Cm23XZS3HnbLfHiiy/E1N3evc7+ph11TBx+5LT+r8vlnjh9xskNnwfQWLIN6ZJvSJNsQ5pkG9Il35Am2YY0yTakS74hTbINaZJtSJd8Q5pkG4opmaaZd+y8S7S1tcUD37svJk/dLZ588vE46mPTo2/58mgbMSIiIiZtt308/rOfxD777h/z5j0RI0aMiG222WbQ/XV0dERHR0czpwA0gWxDuuQb0iTbkCbZhnTJN6RJtiFNsk/BRRMAACAASURBVA3pkm9Ik2xDmmQb0iXfkCbZhmJqy7qAehk7dmzMOO2MuPeeu+PC88+Jd7/7z+PAgw6OK+ZcFt+6956IiDj+rz8VI0eOjL87a1bc8425cepps2LCFhMzrhwAAAAAAAAAAAAAAIB6S+aTZiIi9tlv/9hnv/0HPDb73PP7/z5hi4lx3gUXNbkqAAAAAAAAAAAAAAAAmi2ZT5oBAAAAAAAAAAAAAACAVTTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkBxNMwAAAAAAAAAAAAAAACRH0wwAAAAAAAAAAAAAAADJ0TQDAAAAAAAAAAAAAABAcjTNAAAAAAAAAAAAAAAAkJxcNM38/r9/Fw/+x//r//r7Dz4QN3z9a/HzeU9kWBUAAAAAAAAAAAAAAABFlXnTzHO/fDbOP3d2fOdb90ZExHe//c247pqr4r7vfCsuv/Ti+Ml/PppxhQAAAAAAAAAAAAAAABRN5k0zd9x2c/T29saBBx0cERHf/ua/xw477hifnjEzOjtHx7333J1xhQAAAAAAAMD/z96dh9lVlvnC/u1KiswDhCGIttoMmkmNQEiEhkh7EAMYgXTasW0FJQ2oR21sPKiNH4f+4pdDqyg0gtKMYRZB5AjIwQEEBSP2MYRRbBlCuiG0SaoqUqnU9wdSEpJAhVTVWvut+76uXFTt/e71Ps+q9au9s+FhAwAAAABAs6l8aOa3Dz+cGfvMzCGHzc3jjz2WJ5/8zxz5V3+dtx74tuy194w8+sgjVZcIAAAAAAAAAAAAAABAk6l8aGbIkCHp7OxMkvxyyV1paRmSyVOmJUnW/mFtGo0qqwMAAAAAAAAAAAAAAKAZDa26gNe9flLuuvPnWbTw1Nx37z2ZNHlKRowYkeuvuzZL7rorU6ZOrbpEAAAAAAAAAAAAAAAAmkzlnzTznvf/TcaPH59fLrkrXV1dec/7PpAkueKyxRkypCXz3/2+iisEAAAAAAAAAAAAAACg2VT+STM77/yKnPbVM/Kbhx7MLru8MuO33TZJcvCcw7LfX+yfXV75qoorBAAAAAAAAAAAAAAAoNlUPjSTJCNGjMiUqdM2uO2v3+MTZgAAAAAAAAAAAAAAAHh5Kh+a6erqyv/5wY1Zds/SrFr1+3R3b3h/o5F87h9PqaY4AAAAAAAAAAAAAAAAmlLlQzMXnPet3HTD95N0b2ZFYyDLAQAAAAAAAAAAAAAAoACVD83ccfttGTV6VI7+yN9lp4kT02gYkgEAAAAAAAAAAAAAAGDrVD400/lMZ/aZOSv7zHpL1aUAAAAAAAAAAAAAAABQiJaqC3jTm/fMk0/+Z9VlAAAAAAAAAAAAAAAAUJDKP2lmt913z+ILL8jZZ52RXXZ55SbXHHLY3AGuCgAAAAAAAAAAAAAAgGZW+dDMRef/a5Lkh//nB0kaL7i3O0nD0AwAAAAAAAAAAAAAAABbpPKhmSPmzU8aLxyWeXluv+3WXLr4wrS1rcleM2bmqI8sSGtr6wZr/v23D+eSiy/M/fcty//4/Bez2+579MneAAAAAAAAAAAAAAAA1EflQzPz5r+nT46zevWqnHXm6fnQ0cdkt932yKmnfCG33HxTDjp4Ts+a/1ixIl/8wkmZMXNWTvl/F2XniTv3yd4AAAAAAAAAAAAAAADUS+VDM1ddcdmL3t9oJEfM++uXPM5DDz6Q7u7kgNkHptFoZPr0PbPsnqUbDM1877prMnHixHx0wXFpaWl50eN1dnams7Oz5/uOjvaXrAGoP9mGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDc2pBkMzlyZpJOl+wT2Nnq96MzSzatWqDBs+LI3Gs48bOWpUVqxYscGaB+67N8OGD89nP/Op/GHt2sw59J0bDNU83zVXX/mSAz1A85FtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok29CcKh+a+cAHP7zJ2x9/7LHc/IMbcsS8+S/72I3Ght+3tbWldZtt8tEFx+XBB+7PeeeekylTp2WXV75qo8fOPXxe5hw6t+f7jo72HL/g6JddC1APsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbmlPlQzPvOOSwzd73yO/+Pffde2+vjjNmzJis7ejI+vXr09LSko729owdN36DNWPHjctee++TXXfbPbvutnsWX3R+Hnnkd5scmmltbU1ra+uWNQPUnmxDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhubUUnUBL2b7HXfMA/f3bmhm1932SEtLS26+6YY89tijufvuJZk8ZWrWd3X1rJm+5175yY9/mCeWL89PfvzDrFu3Ln/2Z6/pp+oBAAAAAAAAAAAAAACoSuWfNPPwbx7a6Lbu7u488cTy3L3krowaPbpXxxk7dmwWHPvxXHLxBbnskouy94yZOWD2gTlt0cJMnjI1hxw2N4ce9q6sfPLJfO6zf58RI0bmowuOyyt22aWvWwIAAAAAAAAAAAAAAKBilQ/NnHTi3ydpbObe7hx08JxeH2vWvvtl1r77bXDbCSee1PP1Nttsk6OPOTZHH3Psy6gUAAAAAAAAAAAAAACAZlH50MzrJ01Oo7Hh0Eyj0ci4cePzxulvzl/sP7uawgAAAAAAAAAAAAAAAGhalQ/NfOGLp1ZdAgAAAAAAAAAAAAAAAIVpqXLz9evX5/3vPjLfPPtfqiwDAAAAAAAAAAAAAACAwlQ6NNPS0pKJE3fO7//r6SrLAAAAAAAAAAAAAAAAoDBDqy5gz71m5HvXXZNrr/l2Jk7ceZNrZuwza4CrAgAAAAAAAAAAAAAAoJlVPjTz3WuvTpJcevFFm12z+PJvD1Q5AAAAAAAAAAAAAAAAFKDyoZm/2H92Go1G1WUAAAAAAAAAAAAAAABQkMqHZv7u+E+85JoVTyzPypUrM2nylAGoCAAAAAAAAAAAAAAAgGbXUnUBvXH9976bU07+fNVlAAAAAAAAAAAAAAAA0CSaYmgGAAAAAAAAAAAAAAAAtoShGQAAAAAAAAAAAAAAAIpjaAYAAAAAAAAAAAAAAIDiGJoBAAAAAAAAAAAAAACgOIZmAAAAAAAAAAAAAAAAKE5zDM10V10AAAAAAAAAAAAAAAAAzWRo1QX0xjsOOSz7zHpL1WUAAAAAAAAAAAAAAADQJCoZmvned6/ZovWHHDY3E3feuZ+qAQAAAAAAAAAAAAAAoDSVDM1cfOF5SRq9WNmdpJFDDpvbvwUBAAAAAAAAAAAAAABQlEqGZo6YNz9p9GZoBgAAAAAAAAAAAAAAALZcJUMz8+a/p4ptAQAAAAAAAAAAAAAAGCQqGZp5+DcPbdH61/75rv1UCQAAAAAAAAAAAAAAACWqZGjmpBP/PkmjV2sbjeTiy77dvwUBAAAAAAAAAAAAAABQlEqGZl4/aXIajd4NzQAAAAAAAAAAAAAAAMCWqmRo5gtfPLWKbQEAAAAAAAAAAAAAABgkKhma2ZTu7u60tbUl3d09tz3T2ZnHH3skU6e9scLKAAAAAAAAAAAAAAAAaDaVD82s7ejIt845K3f+/I4880znJtcsvvzbA1wVAAAAAAAAAAAAAAAAzayl6gIuv2xxbrv1x2lpGZLhw4cn6c7222+fUaNGJenOtDf4lBkAAAAAAAAAAAAAAAC2TOVDM3f+/I68+jWvzVnfPC9/sf/sJI2cfubZ+crX/yXjxo3P6yZNqrpEAAAAAAAAAAAAAAAAmkzlQzO//6/f58//fNdss802GTFyRJLkD3/4Q0aNGp0p096QH978g4orBAAAAAAAAAAAAAAAoNlUPjSz08SJuXfZPVmzZk0mTNg+SfLLJXclSZ568sm0t7dXWR4AAAAAAAAAAAAAAABNqPKhmdlv/cssX/54Lr7gX7P3jJkZOnRIzjj9yzluwVG5795lmTR5StUlAgAAAAAAAAAAAAAA0GQqH5qZc+g7M+fQd2bU6NEZv+22+dujPpohQ4bm6ZUr85rXvjZ/++Gjqy4RAAAAAAAAAAAAAACAJjO06gIajUbe/zcf6vn+wL/8b9l//9npWNuRMWPGVlgZAAAAAAAAAAAAAAAAzaryoZnnPLF8eX73u9/mv55+OuPGjc+fveY1hmYAAAAAAAAAAAAAAAB4WSofmmlvb8/ZZ309d/7sjnR3/+n2RiPZe5+Z+eiC4zNy5MheHev2227NpYsvTFvbmuw1Y2aO+siCtLa2brTu33/7cE468e/zriPmZd789/RVKwAAAAAAAAAAAAAAANRE5UMz5517Tn5+x+0ZN3589txz74wdNy6rV6/Okl/cmZ/fcUeGbTMsf3f8J17yOKtXr8pZZ56eDx19THbbbY+cesoXcsvNN+Wgg+dssG59V1fOPuuMdD9/QgcAAAAAAAAAAAAAAICiVD4084s7f5addpqYf/r//jkjRozouf19H/jbfPYzn8pdd/6sV8d56MEH0t2dHDD7wDQajUyfvmeW3bN0o6GZ66//brbZZlhe/erXvOjxOjs709nZ2fN9R0d775sCaku2oVzyDWWSbSiTbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyiTbENzqnxoZpthwzJp8pQNBmaSZPjw4Zk0aXJ+/rPbe3WcVatWZdjwYWk0GkmSkaNGZcWKFRus+Y8VK3LNt6/K/3Pqwnztq//8ose75uorc9UVl21BJ0AzkG0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0JwqH5qZsc+sLFv666zv6krLkCE9t3d2dub+++7N3jNmvuxj/3F+psc3zz4zcw59Z3Z+xS4v+di5h8/LnEPn9nzf0dGe4xcc/bJrAepBtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqFMsg3lkm8ok2xDc6pkaGbRwlN7vn7mmWfy6KOP5h9O+GR23HGnnttXPLE8y5c/nkmTp/bqmGPGjMnajo6sX78+LS0t6Whvz9hx43vuf+D++/J//+1XefCB+3PdtVdn7dq1efSR36W1dZvMPfzIjY7X2tqa1tbWregSqCPZhnLJN5RJtqFMsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg3NqZKhmV8uuWuj2x579JE89ugjG91+8w9uzFEfXfCSx9x1tz3S0tKSm2+6IZOnTsvddy/J4UfO7/kEm1e/5rU5/Yxv9Kxf9KV/ypSp0/K2g96+dc0AAAAAAAAAAAAAAABQO5UMzXzuH0/p82OOHTs2C479eC65+IJcdslF2XvGzBww+8CctmhhJk+ZmkMOm5sdnvdJNkOHDs2oUaMyatToPq8FAAAAAAAAAAAAAACAalUyNDN5ytR+Oe6sfffLrH332+C2E048aZNr/+lLp/VLDQAAAAAAAAAAAAAAAFSvkqGZF/rtw7/J5Zcuzv33LcvatWszbtz4THvDG3Pk/Hdnhx12rLo8AAAAAAAAAAAAAAAAmkxL1QU8/JuHcvLnP5u7f/mLtLe3Z8iQIXn66ZX58Y9uyf/4zKfz+GOPVV0iAAAAAAAAAAAAAAAATabyT5q5dPFFeeaZZzL/3e/NQQcfkpEjR6atbU1uuvH7ufySxbl08YX51AknVl0mAAAAAAAAAAAAAAAATaTyoZn771uWN77pzXnXEX/Vc9uoUaPzrsPn5f57782ye5ZWWB0AAAAAAAAAAAAAAADNqKXqAoYMGZJhw4dt8r5thm2Tzs7OAa4IAAAAAAAAAAAAAACAZlf50Myuu+2RX9z589x26483uP1nt/80v7jz59lt990rqgwAAAAAAAAAAAAAAIBmNbTqAua/+7354hdOyhmnfyXnnnNWxo/fNqtWrUpbW1uGDGnJ/He/r+oSAQAAAAAAAAAAAAAAaDI1+KSZ3fOPp/xTJk2ekrVr12b58sfT3t6WPfZ4XT73j6dkj9e9vuoSAQAAAAAAAAAAAAAAaDKVf9JMkuy66275/MmnZF1nZ1atXp1Ro0Zl2LBhVZcFAAAAAAAAAAAAAABAk6rF0ExXV1eeeGJ5Vq/6fbq7N75/0uQpA18UAAAAAAAAAAAAAAAATavyoZn77l2Wr33ltKxcuXKzaxZf/u0BrAgAAAAAAAAAAAAAAIBmV/nQzPnnnpOVK5/KLq98VSZOnJikUXVJAAAAAAAAAAAAAAAANLnKh2Yee+zRvH7S5Hzhi6dWXQoAAAAAAAAAAAAAAACFaKm6gO233yE77TSx6jIAAAAAAAAAAAAAAAAoSOVDM+868q+yZMld+c///I+qSwEAAAAAAAAAAAAAAKAQQ6su4DtXXZG2NW351MePTWtr6yZWNHLuBYsHvC4AAAAAAAAAAAAAAACaV+VDM8uXP97zdVdXV4WVAAAAAAAAAAAAAAAAUIrKh2YWX3511SUAAAAAAAAAAAAAAABQmJaqCwAAAAAAAAAAAAAAAIC+ZmgGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDhDqy6gL91+2625dPGFaWtbk71mzMxRH1mQ1tbWnvtXPLE8Z591Rh64//5MmDAh7/3AB7P3jJkVVgwAAAAAAAAAAAAAAEB/KOaTZlavXpWzzjw9h8+bn5NPWZhf3b0kt9x80wZrzvz6VzN69Oh8+WtnZsbMt+SM07+cdevWVVQxAAAAAAAAAAAAAAAA/aWYT5p56MEH0t2dHDD7wDQajUyfvmeW3bM0Bx08p2fNlKnTMnPWvpkwYfu88U1vyrXfuSprOzoyesyYjY7X2dmZzs7Onu87OtoHpA+gf8k2lEu+oUyyDWWSbSiXfEOZZBvKJNtQLvmGMsk2lEm2oVzyDWWSbWhOxQzNrFq1KsOGD0uj0UiSjBw1KitWrNhgzfx3v6/n6x/ceEPeNH3PTQ7MJMk1V1+Zq664rP8KBioh21Au+YYyyTaUSbahXPINZZJtKJNsQ7nkG8ok21Am2YZyyTeUSbahORUzNLMpf5yf2ciVV1yae5b+OqcuXLTZx849fF7mHDq35/uOjvYcv+Dovi4RGGCyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDWWSbSiXfEOZZBuaUzFDM2PGjMnajo6sX78+LS0t6Whvz9hx4zdad+P3r8//vu7afO7k/5kJ2++w2eO1tramtbW1P0sGKiDbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTbUCbZhnLJN5RJtqE5tVRdQF/Zdbc90tLSkptvuiGPPfZo7r57SSZPmZr1XV09a2679ce56ILzctzHP5Udd9wxbW1rsq6zs8KqAQAAAAAAAAAAAAAA6A/FfNLM2LFjs+DYj+eSiy/IZZdclL1nzMwBsw/MaYsWZvKUqTnksLm5bPFF6ex8JosW/s+exy049mM54K1/WWHlAAAAAAAAAAAAAAAA9LVihmaSZNa++2XWvvttcNsJJ57U8/XpZ5490CUBAAAAAAAAAAAAAABQgZaqCwAAAAAAAAAAAAAAAIC+ZmgGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4hiaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4gytuoC+dPttt+bSxRemrW1N9poxM0d9ZEFaW1t77l+zZk2+cebXsvTX/5Ztt5uQDx99TKZMnVZhxQAAAAAAAAAAAAAAAPSHYj5pZvXqVTnrzNNz+Lz5OfmUhfnV3Utyy803bbDmmquvzFNPPZmFi76SGfvMyhmnfznrOjv7pZ6Ozu6sW9+djs7uDW5ft657oz+bW8vgMRiuged6bPbrvlnrhoHUjDnZkpr7Y22J++upOfbvr57qZCDqtkf99rFHvfZgcHAt0Rea4Tqqe421r2/dH+tbV8/6mkGznsO6X5u9VUofSfNeS6Vq1murWet+oVL6SMrqpRR+JgBsjucI+koV19Jg2HMw9JhU8/6A33+90wznqe411r6+Jnh/zjnsH6X8dwD2qN8+dc9sb/VXH8UMzTz04APp7k4OmH1gXvmqV2X69D2z7J6lG6xZds/S7DPzLdlxp53ytoPenqefXpkVK57Y5PE6OzvT3t7e86ejo73XtXR0dmfBjW1pXfR0FtzY1vND6+jsTmd3NvqzqbUMHpu7XkryXI8nL3q68uu+P7IN/EmVOXm5+d6SmvtjbYn766k59u+vnvpa3Z+77VG/fexRrz02Z2uyTf34uxLPNxCvy6tS9xprX9+67iy44Y/13dDWdP8Sqw6qPId1f10+EErpI5HHumnW1+WlZKKUPpKyeilFs+YbqC/ZLofnbZ6v2V6XD4Y9B0OPSTXvDwy233/eL69O7etrgvfnnMPNq/tztz3qtcdA7VP3zPZWf/bR6O7ubs6z8gI//tEtufD8c3POuRcmSS664F/z8G9+k8+ffErPmk8cd0zmHPrOvP0dh2RtR0c+9DfvyRe+eGomTZ6y0fGuvPySXHXFZRvd/q3zF2fkyJEvWsu69d1pXfR0z/edJ2yboS2NrFu/6VO9qbUMHpu7XkryXI+rjm3NiFGjk1R33fdHtoE/qTInLzffW1Jzf6wtcX89Ncf+/dVTX6v7c7c96rePPeq1x+ZsTbapH39X4vkG4nV5Vepeo/rK16zP3aX87EvpIymrlxLIdrVK6SMpq5dSNGu+k6TxpZV9Wk/3P2zXp8eDwcp7auXwvM3zNdvr8sGw52DocTDtWSXvl1dHfVuv7jU269+5S/nvAOxRv33qntne6s8+ivmkmU1p9OIcbW7N3MPn5VvnL+758/WzvtnrfTu7kr+Zuk2SZ//Z2fWn2zf1Z1NrGTwGwzXwXI9fOrOz8uu+P7IN/Ekz5ntLau6PtSXur6fm2L+/euprdX/utkf99rFHvfbYnK3JNvXj70o830C8Lq9K3WusfX3rX1Df+ooLakJVnsO6vy4fCKX0kchj3TTr6/JSMlFKH0lZvZSiWfMN1Jdsl8PzNs/XbK/LB8Oeg6HHpJr3Bwbb7z/vl1en9vU1wftzzuHm1f252x712mOg9ql7ZnurP/so5pNmfrnkrvzzooU5/+LL09LSknPOOiMda9fm4//90z1rPnfiCXnz3nvniCPnZ+XKlTnumA/ntK+ckVfssstLHr+9vT1HffC9vf4/dHR0dqd1yLM/vBGtf5rMWbeJjwDr7M4m1zJ4bO56KclzPeaPL07qct33VbaBP6lLTrYk31tSc3+sLXF/PTXH/v3VU3+q43O3Peq3jz3qtUdvbGm2qZ+6XEvUT3+9Lq9K3WusfX3rutPa8uy/zBoxtH71NYO6nMM6vi4fCKX0kdTnWuJZdbm2ZLu5+0jK6qUUdfmZbGm+fdIMNAfvqTW3ujxHUD/N8Lp8MOw5GHpMqnl/YDD//vN++cCqfX1N8P6cc9g7dXzutke99hiofeqe2d7qrz6G9tmRKrbrbnukpaUlN990QyZPnZa7716Sw4+cn/VdXWkZMiRJMmnylNzx09uy73775yc/uiXbTZiQnSZO7Jd6nvshDX3BZ/kM3cQv5ud+CC9cy+CxueulJD2/uP7YY7Ne94PhZwVbqxlzsiU198faEvfXU3Ps31891clA1G2P+u1jj3rtweDgWqIvNMN1VPcaa1/f0HrX1wya9RzW/drsrVL6SJr3WipVs15bzVr3C5XSR1JWL6XwMwFgczxH0FequJYGw56DocekmvcH/P7rnWY4T3Wvsfb1NcH7c85h/yjlvwOwR/32qXtme6u/+ihmaGbs2LFZcOzHc8nFF+SySy7K3jNm5oDZB+a0RQszecrUHHLY3Mw9Yl6eeGJ5PnvCJ7PddhNy3Mc+mSF/HKgBAAAACPyIuAAAIABJREFUAAAAAAAAAACgHMUMzSTJrH33y6x999vgthNOPKnn69GjR+fTn/nsQJcFAAAAAAAAAAAAAADAAGvyD+ABAAAAAAAAAAAAAACAjRmaAQAAAAAAAAAAAAAAoDiGZgAAAAAAAAAAAAAAACiOoRkAAAAAAAAAAAAAAACKY2gGAAAAAAAAAAAAAACA4gytuoBm0d3dnSTp6GivuBIY3EaMGJFGo9Fnx5NtqA/5hjLJNpRJtqFMfZ3tRL6hLjx3Q5lkG8pU4uvy9na/V6jeqK+t7dPjtX1s+BY/xnM3lEm2oUwlvi4HnuW5G8r0UtludD+XVl7UU089meMXHF11GTDofev8xRk5cmSfHU+2oT7kG8ok21Am2YYy9XW2E/mGuvDcDWWSbSiT1+VQLs/dUCbZhjJ5XQ7l8twNZXqpbBua6aX169fn6adXZvjw3k0YdnS05/gFR+frZ30zI0b07Yun/qDe/qXevtPXU74lZrvuNapv69S9vuTl19gf+X788cdywic/VuvztaWa4RrYUnpqDnXK9pY8dw+EUn7epfSRlNNLnfsoKdt1Ps/PUWPfUONL64//c96W5Lvq/nuj7jWqb+vVvcZmfV1e9/PaW6X0kZTTy2DvQ7b7Ril9JOX0Ukofycvrxevyl1b3GtW39epeY7M+d/eluv+MkvrXWPf6EjX2VtXZrsM5GAiDoc/B0GPSPH16Xf7S6l6j+rZe3Wts1tfldT+vvaWP+imll/7K9tC+KG4waGlpyYQJ22/x40aMGNnnE8f9Sb39S731U3K2616j+rZO3etLqq+xpaUl2203oRa19Ac9NQc99b2X+9w9EKo+N32llD6ScnoppY8XU4dsN8N5VmPfUOPAejn5bob+616j+rZe3Wusur6S31PrjVL6SMrpRR99Q7bL6CMpp5dS+kiq78Xr8mqob+vVvcaq6/OeWu/Uvca615eocaAN9tflL2Uw9DkYekwGT5/P53V5NdS39epeY9X1Dfbnbn3UTym99HUfLX12JAAAAAAAAAAAAAAAAKgJQzMAAAAAAAAAAAAAAAAUZ8jJJ598ctVFlKqlpSWTp0zLkCFDqi6lV9Tbv9RbjmY4N3WvUX1bp+71JfWqsU619BU9NQc9DS6lnJtS+kjK6aWUPuquGc6zGvuGGuuvGfqve43q23p1r7Hu9W1Os9b9QqX0kZTTiz6q1ax1v1ApfSTl9FJKH0lz9tIMNde9RvVtvbrXWPf6BkIznIO611j3+hI1NovBcg4GQ5+Docdk8PS5tZrhPNW9RvVtvbrXWPf6NqdZ634hfdRPKb30Rx+N7u7u7j47GgAAAAAAAAAAAAAAANRAS9UFAAAAAAAAAAAAAAAAQF8zNAMAAAAAAAAAAAAAAEBxDM30Und3d9rb29Pd3V11KUAfkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0BwMzfRSR0dHjvrge9PR0VF1KUAfkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm0ok2xDueQbyiTb0BwMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxRladQEAAAAAAAAAAACDSeNLK/v8mN3/sF2fHxMAAKDZ+aQZAAAAAAAAAAAAAAAAimNoBgAAAAAAAAAAAAAAgOIYmgEAAAAAAAAAAAAAAKA4hmYAAAAAAAAAAAAAAAAojqEZAAAAAAAAAAAAAAAAimNoBgAAAAAAAAAAAAAAgOIYmgEAAAAAAAAAAAAAAKA4hmYAAAAAAAAAAAAAAAAojqEZAAAAAAAAAAAAAAAAijO06gI25/bbbs2liy9MW9ua7DVjZo76yIK0trb23L9mzZp848yvZemv/y3bbjchHz76mEyZOi1Jcv111+a6a7+Trq6u7D/7rXnv+z+YRqORJPnZHT/NNd++MitWPJFvnb+4kt4AAAAAAAAAAAAAAADoX7X8pJnVq1flrDNPz+Hz5ufkUxbmV3cvyS0337TBmmuuvjJPPfVkFi76SmbsMytnnP7lrOvszCOP/C4XXXBeFhz3sZxw4km58fvX55dLfpEk+dntP83XvvLP2W//2fnS//pqFa0BAAAAAAAAAAAAAAAwAGo5NPPQgw+kuzs5YPaBeeWrXpXp0/fMsnuWbrBm2T1Ls8/Mt2THnXbK2w56e55+emVWrHgi996zNDvvvHPe8Mbp2W33PbLH616fe5c9+9irrrg0Bx38jsw59J3ZfocdqmgNAAAAAAAAAAAAAACAATC06gI2ZdWqVRk2fFgajUaSZOSoUVmxYsUGa1avWpXhw4cnSUaNHNXzuGcfO7xn3ciRo7Jq1aq0t7fnkUd+l50mTswnjjsmI0eNygc/dFReP2nKJmvo7OxMZ2dnz/cdHe192iNQDdmGcsk3lEm2oUyyDeWSbyiTbEOZZBvKJd9QJtmGMsk2lEu+oUyyDc2plkMzm/LH+ZmXtaaRpL2tLUkybvy2+dRfvzfXf/eafPXL/ytnfuPcnuGc57vm6itz1RWXbUXFQB3JNpRLvqFMsg1lkm0ol3xDmWQbyiTbUC75hjLJNpRJtqFc8g1lkm1oTrUcmhkzZkzWdnRk/fr1aWlpSUd7e8aOG/+CNWPT1v7sIEx7R0eSZOzY8RkzdkzPgEzy7ATfxJ1fkbFjx6bRaOStB74tr371a/Lf3v6O/PhHt2T1qlUZO27cRjXMPXxe5hw6d4PjHL/g6P5oFxhAsg3lkm8ok2xDmWQbyiXfUCbZhjLJNpRLvqFMsg1lkm0ol3xDmWQbmlMth2Z23W2PtLS05OabbsjkqdNy991LcviR87O+qystQ4YkSSZNnpI7fnpb9t1v//zkR7dkuwkTstPEienqWpfzvnVOlvzizowbv23uv+++HHLYu7LNsGGZMnVavn/9dXn3+z6Qn/zoh5kwYftNDswkSWtra1pbWweybWAAyDaUS76hTLINZZJtKJd8Q5lkG8ok21Au+YYyyTaUSbahXPINZZJtaE61HJoZO3ZsFhz78Vxy8QW57JKLsveMmTlg9oE5bdHCTJ4yNYccNjdzj5iXJ55Yns+e8Mlst92EHPexT2bIkCF51Z+9Ou//4IdyzjfOTNe6rrz94Dl545umJ0mO+buP5Rv/8vV8+hPH5xWveEX++6c/U3GnAAAAAAAAAAAAAAAA9IdaDs0kyax998usfffb4LYTTjyp5+vRo0fn05/57CYfO+eQd2bOIe/c6Pbtd9ghJ33hi31bKAAAAAAAAAAAAAAAALXTUnUBAAAAAAAAAAAAAAAA0NcMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUx9AMAAAAAAAAAAAAAAAAxTE0AwAAAAAAAAAAAAAAQHEMzQAAAAAAAAAAAAAAAFAcQzMAAAAAAAAAAAAAAAAUZ2jVBbyY22+7NZcuvjBtbWuy14yZOeojC9La2tpz/5o1a/KNM7+Wpb/+t2y73YR8+OhjMmXqtCTJ9dddm+uu/U66urqy/+y35r3v/2AajUbPY2/8/vX512+dnc+ffEomT5k24L0BAAAAAAAAAAAAAADQf2r7STOrV6/KWWeensPnzc/JpyzMr+5ekltuvmmDNddcfWWeeurJLFz0lczYZ1bOOP3LWdfZmUce+V0uuuC8LDjuYznhxJNy4/evzy+X/KLncU899WQuv3TxQLcEAAAAAAAAAAAAAADAAKntJ8089OAD6e5ODph9YBqNRqZP3zPL7lmagw6e07Nm2T1Ls8/Mt2THnXbK2w56e77z7SuyYsUTufeepdl5553zhjdOT5Ls8brX595lS/PmPfdKkpx7zjdywOwDc/33rt3s/p2dnens7Oz5vqOjvZ86BQaSbEO55BvKJNtQJtmGcsk3lEm2oUyyDeWSbyjT/8/encdZVdf/A3/NDMMqm8himgugyeKWSxq5VmqIW5nfLL9aRooilt+yr2WblYXhlgppapq55L7k159Lfs2tTBOxRATzWy6puICMzIw6wPz+UMcQ0OHOHebO4fl8POah99zPOZ/3597zmnu48vbINhSTbENxyTcUk2xD51SxTTN1dXXp1r1bqqqqkiQ9e/XK3Llzlxrzal1dunfvniTp1bNXy35v7tu9ZVzPnr1SV1eXJPnTvffk+eefy4SJk96zaeb6a6/K1VdeXtY1AR1PtqG45BuKSbahmGQbiku+oZhkG4pJtqG45BuKSbahmGQbiku+oZhkGzqnim2aWZ63+mdKGlOVZOHChbnowvPz1f/6Rmq71L7ncfbZb/+MHbdPy+PGxoYcNWH8SlQLVCLZhuKSbygm2YZikm0oLvmGYpJtKCbZhuKSbygm2YZikm0oLvmGYpJt6Jwqtmmmd+/eea2xMUuWLEl1dXUaGxrSp2+/d43pk/qG+iRJQ2NjkqRPn37p3ad3GurrW8Y1NjZkyNofyO233ZIFC17JlMkntjz3s5+emOOO/142GTFyqWPX1tamtva9G2uAzke2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbOqeKbZoZNnzjVFdX5/bbbsnI0Ztmxozp2e8zB2TJ4sWprqlJkowYOSr3/fHejPnYjrn7zjuy5oABGTxkSBYvXpQLzz830x98IH379c+c2bOz5177ZvhGG+WjYz6WJHn99Tdy7H9NymFHTMzQYcM7cqkAAAAAAAAAAAAAAACUWcU2zfTp0ycTjjw6l11yUS6/7OJss+122WnnXXPKlMkZOWp09txrn+zz6f3z/PPP5VvHHpM11xyQiZOOSU1NTT643vo56JAv5dxzpmXxosXZfY+x2XyLLZMkvXqtkSR57a070/Tr1y9du3btsHUCAAAAAAAAAAAAAABQfhXbNJMk24/5WLZ/684wbzv2uONb/n2NNdbI17/5reXuO3bPvTN2z71XeOzuPXrksiuvK0+hAAAAAAAAAAAAAAAAVJTqji4AAAAAAAAAAAAAAAAAyk3TDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDC0TQDAAAAAAAAAAAAAABA4WiaAQAAAAAAAAAAAAAAoHA0zQAAAAAAAAAAAAAAAFA4mmYAAAAAAAAAAAAAAAAoHE0zAAAAAAAAAAAAAAAAFI6mGQAAAAAAAAAAAAAAAApH0wwAAAAAAAAAAAAAAACFo2kGAAAAAAAAAAAAAACAwim5aaZuwYI8POOhlseP/O2vuel/bsjTTz1ZlsIAAAAAAAAAAAAAAACgVF1K2emZp5/Oj0/4bvr07ZvNt9gy9959Z6ae+fMkSW1tlxx3/PczYuSoshYKAAAAAAAAAAAAAAAArVXSnWZ+e+lvUle3IJtutnmS5NprrsqgQYOy36f3T1VVVa656vKyFgkAAAAAAAAAAAAAAAAro6Q7zTw+Z3Y+vNU2+c9DDs2LL76QZ//1TCZ99evZfszHMnfu83l4xkPlrhMAAAAAAAAAAAAAAABaraQ7zSxevCjdunVLkjw846FUVVVl9GabvflkVVUWLWoqW4EAAAAAAAAAAAAAAACwskq608zQYcPzwP335cJfnZsH7r8vw4ZtlN69++T+P/8p0/9yfzYcOqzcdQIAAAAAAAAAAAAAAECrlXSnmf848KB06VKbW2++KQteeSWf/dznkyS/OOvneeONpuz3mQPKWiQAAAAAAAAAAAAAAACsjJLuNDNs+EY56eTTMnPmI9lggw2zwYZDkyTbbLtddtx514zedLOyFgkAAAAAAAAAAAAAAAAro6SmmSQZOGhwdh40eKltR076WpsLAgAAAAAAAAAAAAAAgLYquWlmxkMPZtbMmamrW5Dmdz1XleTwIye1rTIAAAAAAAAAAAAAAAAoUUlNM1dd+dtcc+UVyTLtMm+r0jQDAAAAAAAAAAAAAABAhympaeaO229L165ds/8Bn8vgIUNSVVVV7rqSJH+695789tLfpL5+Ybbedrt8+SsTUltb2/L8woULc860MzPzkb+m/5oDcuj4wzNq9KZJkptuvCE33nBdFi9enB133iWfP+iQVFVVZcZD03Pxr3+VF154IRtsuGEOP+KorLPuB9ulfgAAAAAAAAAAAAAAADpGdSk71S9cmG232z7j9t4322y7Xbbe5iPL/LTVq6/W5expZ2S//Q/ID340OQ/PmJ47br9tqTHXX3tVXn75pUyecnq2/cj2mXrGaVnU1JSnn34qF190YSZMnJRjjzs+t958Ux6a/mDq6xfm9FN+lh133jWnnTE13bp1y68vOK/NtQIAAAAAAAAAAAAAAFBZSrrTzIiRo9JQX1/uWpbyxN8fT3NzstPOu6aqqipbbrlVZj06M7vtMbZlzKxHZ+Yj2300gwYPzid22z3XXXNl5s59Po89OjNrr712Ntt8yyTJxh/aJI/Nmpn1118/u+8xNrvtMTbdu3fPhzYZkQfu//Ny529qakpTU1PL48bGhnZdL7BqyDYUl3xDMck2FJNsQ3HJNxSTbEMxyTYUl3xDMck2FJNsQ3HJNxSTbEPnVFLTzPZjdsi5Z0/NtVdfmXXWXXe5Y7b9yPZtKqyuri7dundLVVVVkqRnr16ZO3fuUmNeratL9+7dkyS9evZq2e/Nfbu3jOvZs1fq6uoyYK2BOfCgg5MkjY2NueeuO/PxT+6+3Pmvv/aqXH3l5W1aA1B5ZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqFzKqlp5uypZyRJrrz8shWOufSKa0qr6D281T9T0ph/37xo0aKcfspJ6b/mgHxqz72WO36f/fbP2HH7tDxubGzIURPGr0S1QCWSbSgu+YZikm0oJtmG4pJvKCbZhmKSbSgu+YZikm0oJtmG4pJvKCbZhs6ppKaZHXbcueUOMO2ld+/eea2xMUuWLEl1dXUaGxrSp2+/d43pk/qG+iRJQ2NjkqRPn37p3ad3GurrW8Y1NjZkyNofSJIsWbIk0848PQteeSXfPeHH6dJl+S9BbW1tamtr22NpQAeSbSgu+YZikm0oJtmG4pJvKCbZhmKSbSgu+YZikm0oJtmG4pJvKCbZhs6ppKaZI476arnrWMaw4Runuro6t992S0aO3jQzZkzPfp85IEsWL051TU2SZMTIUbnvj/dmzMd2zN133pE1BwzI4CFDsnjxolx4/rmZ/uAD6duvf+bMnp0999o3SXLBeb/MnNmP5dvfPSFJUl+/MD169Ex1dXW7rwkAAAAAAAAAAAAAAIBVo6SmmbvuvOM9n69KssNOu5Ry6BZ9+vTJhCOPzmWXXJTLL7s422y7XXbaedecMmVyRo4anT332if7fHr/PP/8c/nWscdkzTUHZOKkY1JTU5MPrrd+DjrkSzn3nGlZvGhxdt9jbDbfYsu8+OIL+f1tNydJvv61iS1znTH1nAwcNLhN9QIAAAAAAAAAAAAAAFA5SmqaOXvqGXmzNWbF2to0kyTbj/lYth/zsaW2HXvc8S3/vsYaa+Tr3/zWcvcdu+feGbvn3kttGzhwUC678ro21wUAAAAAAAAAAAAAAEBlK6lpZvdP7ZmqqmWbZp579tk8PGN6dt9jzzYXBgAAAAAAAAAAAAAAAKUqqWnmkC+NX+Fzxx17TObNe6nkggAAAAAAAAAAAAAAAKCtqst9wPXWWz9/++vD5T4sAAAAAAAAAAAAAAAAtFpJd5pZ+Oqry2xrTnOef+65PPK3v6Zr125tLgwAAAAAAAAAAAAAAABKVVLTzGFfPuQ9nm3Oxz+5e4nlAAAAAAAAAAAAAAAAQNuV1DQzYK0BqUrVUtuqqqvSt2+/bL7Fh7PX3vuWpTgAAAAAAAAAAAAAAAAoRUlNM2dOO7fcdQAAAAAAAAAAAAAAAEDZVK/sDksWL85Xjzo8l196cXvUAwAAAAAAAAAAAAAAAG220k0z1TU1SZK5c58vezEAAAAAAAAAAAAAAABQDl1K2WnHnXbNDdddnT/98Z4MGbL2csdsOHRYmwoDAAAAAAAAAAAAAACAUpXUNHP1lb9NUpUzTz91uc9XVSWXXH5NW+oCAAAAAAAAAAAAAACAkpXUNLPJiJGpqqoqdy0AAAAAAAAAAAAAAABQFiU1zXzvhBPfd0x9/cI0NjRmrYEDS5kCAAAAAAAAAAAAAAAASlbdXge+4reX5uiJh7fX4QEAAAAAAAAAAAAAAGCF2q1pBgAAAAAAAAAAAAAAADqKphkAAAAAAAAAAAAAAAAKR9MMAAAAAAAAAAAAAAAAhaNpBgAAAAAAAAAAAAAAgMLRNAMAAAAAAAAAAAAAAEDhaJoBAAAAAAAAAAAAAACgcLqUstOLL8zNwEGD33PMDjvunKHDhpdUFAAAAAAAAAAAAAAAALRFSU0zXz3qiGy88YcyZocd85Htx6RPnz7LjBm+0cYZvtHGbS4QAAAAAAAAAAAAAAAAVlZJTTM9e/bMnDmPZc6c2bnowvOz2eZbZMwOO2XrrbdN127dylLYn+69J7+99Depr1+YrbfdLl/+yoTU1ta2PL9w4cKcM+3MzHzkr+m/5oAcOv7wjBq9aZLkphtvyI03XJfFixdnx513yecPOiRVVVV58sl/5pxpZ+a5Z/+VocOG54iJX81aAweWpV4AAAAAAAAAAAAAAAAqR3UpO/3yVxfluz/4cfYct3cGDR6Sh6Y/mLN+floOH39Ipp15emY8ND1LliwpuahXX63L2dPOyH77H5Af/GhyHp4xPXfcfttSY66/9qq8/PJLmTzl9Gz7ke0z9YzTsqipKU8//VQuvujCTJg4Kcced3xuvfmmPDT9wSTJub+YmqFDh2XKqWempqYmF190Qck1vp/GpuYsWtKcxqbmpbYvWtS8zM+KxrL6WB3OgbfX2NnP+85aN6xKnTEnK1Nze4wt4vzW1Dnmb681VZJVUbc5Km8ec1TWHKwenEuUQ2c4jyq9xoqvb9Fb9S2qzPo6g876Glb6udlaRVlH0nnPpaLqrOdWZ6373YqyjqRYaymKzviedMaaYVWTE6CSdMTvpNVhztVhjUnHfD+wuqyzrTrD9Ual11jx9XWC89Jr2D6K8vcAzFF581R6ZlurvdZRUtNMdXV1RowclS8c/MWccvpZOeX0s/LhrbbO66+/nnvuvis/++mPc8RhX8oN112T5uaVL/iJvz+e5uZkp513zbof/GC23HKrzHp05lJjZj06Mx/Z7qMZNHhwPrHb7pk/f17mzn0+jz06M2uvvXY223zLDN9o42z8oU3y2KyZeeP11/PEE49np10+nrUGDswOO+2SWbNmrqCCpKmpKQ0NDS0/jY0Nra6/sak5E26tT+2U+Zlwa33Lm9bY1Jym5izzs7yxrD5WdL4UydtrPGHK/A4/79sj28A7OjInpeZ7ZWpuj7FFnN+aOsf87bWmcqv0z25zVN485qisOVakLdmm8vizEv9uVVyXd5RKr7Hi61vUnAm3vFXfLfWd7j9iVYKOfA0r/bp8VSjKOhJ5rDSd9bq8KJkoyjqSYq2lKDpjvp1H8P46Y7aBytbZrstXhzlXhzUmHfP9wOqyzrcV+bq80mus+Po6wfdzXsMVq/TPbnNU1hyrap5Kz2xrtec6qppL6WpJ8sbrr+fhGQ/lgQf+nIem/yX1C+uTNOcD66ybUaM3zf1//lMWvLIgn9pzXP7zkENX6th33XlHfvPrX+XcX/0mSXLxRRfkH//3f/nuD37UMuarEw/P2HF7Z/dP7ZnXGhvzpYMPzPdOODGPznwkD/7l/vzkpFOSJKedfFJ69OyZ/T/7uUw68iv52Sk/zwfXWz/TH3wgJ5/0k1x6xbXLreGqKy7L1Vdevsz28399aXr27Pme9S9a0pzaKfNbHjcd2z9dqquyaMnyX+rljWX1saLzpUjeXmPdkbXp0WuNJB133rdHtoF3dGROSs33ytTcHmOLOL81dY7522tN5Vbpn93mqLx5zFFZc6xIW7JN5fFnJf7dqrgu7yiVXqP6iq+zfnYX5b0vyjqSYq2lCGS7YxVlHUmx1lIUnTHfziN4f50x21SuqpPmlf2Yzf+9ZtmPSfvqbNflq8Ocq8MazVnZn92d4bq80mtUX9tVeo2dMdtJcf4egDkqb55Kz2xrtec6SrrTzMkn/SSHffngnHbKz3LPXX9IdXV1dt9jbH780yk5+bQz86UvH5bTfj4tAwcNzF1/uKMshVa1Yr0rGrPiXVf8zD777Z/zf31py89ZZ5/3/gW8pWlxcvDorkne/GfT4ne2L+9neWNZfawO58Dba/zZtKYOP+/bI9vAOzpjvlem5vYYW8T5ralzzN9eayq3Sv/sNkflzWOOyppjRdqSbSqPPyvx71bFdXlHqfQaK76+Je+qb0kHF9QJdeRrWOnX5atCUdaRyGOl6azX5UXJRFHWkRRrLUXRGfPtPIL31xmzDVRRl9HDAAAgAElEQVS2znZdvjrMuTqsMemY7wdWl3W+rcjX5ZVeY8XX1wm+n/Marlilf3abo7LmWFXzVHpmW6s911HSnWY+f8B+qa2tzYe32iY77LRzttjiw6muqVlm3Pnnnp2/Pz4nP/3ZqSt1/Iem/yWnTpmcX19yRaqrq3Pu2VPT+NprOfprX28Z853jjs2Ht9kmn/7MAZk3b14mHn5oTjl9ah555OHc9LsbcvpZZydJfvKj72fDocPzmf0PyCEH/Ue+/8OfZJMRI3Pv3Xfm4osuzC/OvaBVNTU0NOTLh3y+1f+Hjsam5tTWvPnm9ah9pzln0XJuAdbUnOWOZfWxovOlSN5eY966OKmU875c2QbeUSk5WZl8r0zN7TG2iPNbU+eYv73W1J4q8bPbHJU3jzkqa47WWNlsU3kq5Vyi8rTXdXlHqfQaK76+Rc2prX7zP2b16FJ59XUGlfIaVuJ1+apQlHUklXMu8aZKObdku3OvIynWWoqiUt6Tol2XQ0erlJz4Tq3zc6cZlqczXJevDnOuDmtMOub7gdVlnctTtOvySq+x4uurkPPyvXgNW6cSP7vNUVlzrKp5Kj2zrdVe6+hSyk5fPuyIbL/9mPTs1eu9x31lQklFDRu+caqrq3P7bbdk5OhNM2PG9Oz3mQOyZPHiluacESNH5b4/3psxH9sxd995R9YcMCCDhwzJ4sWLcuH552b6gw+kb7/+mTN7dvbca9907dYtQ4cNzx2335a11hqYe+6+MyNGjS6pvtZ4+03q8q57+XRZzi/mt9+Ed49l9bGi86VIWn5xvbXGznrerw7vFbRVZ8zJytTcHmOLOL81dY7522tNlWRV1G2OypvHHJU1B6sH5xLl0BnOo0qvseLr61LZ9XUGnfU1rPRzs7WKso6k855LRdVZz63OWve7FWUdSbHWUhSd8T3pjDXDqiYnQCXpiN9Jq8Ocq8Mak475fmB1WWdbdYbrjUqvseLr6wTnpdewfRTl7wGYo/LmqfTMtlZ7raOkw91w7dX5fzfduNznfnXeOTnhe8e3qag+ffpkwpFH53fXX5vvH//f2WyzLbLTzrvmlCmT8z+/uz5Jss+n98/gwUPyrWOPyX1/vDcTJx2TmpqafHC99XPQIV/KuedMy0kn/jC77zE2m2+xZZLksCOOyjPPPJ1vHDMpb7zRlC8cdHCb6gQAAAAAAAAAAAAAAKAyrdSdZmY9OjNJ8uKLL+SpJ//R8vhtixcvyqOP/C0vvfRSmwvbfszHsv2Yjy217djj3mnGWWONNfL1b35rufuO3XPvjN1z72W2r7/+Bjlx8sltrg0AAAAAAAAAAAAAAIDKtlJNMz/6wXeSvHnLmwfuvz8P3H//ckY1Z+So0WUoDQAAAAAAAAAAAAAAAEqzUk0zW229TZKqPPiX+zNw0MCst94GSz1fU1OTIWuvnU/tuVcZSwQAAAAAAAAAAAAAAICVs1JNM1//5reTJJ8/YL9s+eGt88VDv9IuRQEAAAAAAAAAAAAAAEBbrFTTzNt+PvWc9OzZs9y1AAAAAAAAAAAAAAAAQFm0umnmwl+d2+qDVqUqhxw6vqSCAAAAAAAAAAAAAAAAoK1a3TRz6803rcRhNc0AAAAAAAAAAAAAAADQcVrdNHP4kZPasw4AAAAAAAAAAAAAAAAom1Y3zey0867tWQcAAAAAAAAAAAAAAACUTaubZt5twYJX8sjf/poFr7yS5ubmlu1NTW/k2X/9K0dO+lpZCgQAAAAAAAAAAAAAAICVVVLTzD/+74n89McnZOHChSsco2kGAAAAAAAAAAAAAACAjlJdyk6XXXJR6usXZpddP56hw4YlST792QOy4867JGnO5//z4HLWCAAAAAAAAAAAAAAAACulpKaZ/3viiWy9zUfylQkTs9FGH0qS7P/Zz2XCkZOy4dBhmT1rVlmLBAAAAAAAAAAAAAAAgJVRUtPMkiVL0q1btyRJ7759kiTz589Lkqy33vqZ9egjZSoPAAAAAAAAAAAAAAAAVl5JTTMbbfyh3P/n+/LgX+7PB9ddL0lzLr/04vz14Rn568Mz0qNnzzKXCQAAAAAAAAAAAAAAAK1XUtPMZz77H1myZHEeuP/P2WrrbTJw0KDcdecfMvnEH2b+/HnZ5eOfLHedAAAAAAAAAAAAAAAA0GpdStlp4w9tkhN+PDkvvfRiqmtq8p3v/yjXXn1lFrwyP5ttvmV2/9Se5a4TAAAAAAAAAAAAAAAAWq2kppkk2XDosGw4dFiSZODAQTlswsSyFQUAAAAAAAAAAAAAAABtUXLTzNNPP5XHZj2aV+sWpLl52ec/89n/aEtdAAAAAAAAAAAAAAAAULKSmmZuv+2W/Oq8c95qlllOx0yqNM0AAAAAAAAAAAAAAADQYUpqmrnhumtSVVWVT3xy9wxee0iqUlXuugAAAAAAAAAAAAAAAKBkJTXNvPLK/Gyz7Xb50vjDyl0PAAAAAAAAAAAAAAAAtFl1KTttuOGwctcBAAAAAAAAAAAAAAAAZVPSnWbG7bNfzvr5qbnn7juzzjrrLnfMhkPb1ljz5JP/zDnTzsxzz/4rQ4cNzxETv5q1Bg5cakxzc3Muu+Si3HnH/6ampibj9t43Y8ftnSSZ+cjfcsF552TevJczavRmOfzISVljjTUy9/nn8suzp+bxOXMyYMCAfP4/D8k2227XploBAAAAAAAAAAAAAACoLCU1zZw65adJqjLtzJ8v9/mqquSSy69pS1059xdTM3TosPzXN47LL88+KxdfdEG+9vVvLjVmxkMP5ve33pzjv/+jLHjllZx80okZvelm+cA662bqGafl45/cPTvsuHNO/tlPcv01V+ULB38x0876efr165fTzpyWW2/+f5l6xmk578JL0qVLSS8FAAAAAAAAAAAAAAAAFaikTpFNRoxMVVVVuWtp8cbrr+eJJx7PIYeOz1oDB2aHnXbJxRddsMy4WY/OzEYbb5Jhw4YnSQYNGpzZj81KTU1N5s+fl49/Yrf0698/2310TKb/5YEkyajRm2a77cdkwIC1svkWW+SG667Oa42NWaN376WO3dTUlKamppbHjY0N7bZeYNWRbSgu+YZikm0oJtmG4pJvKCbZhmKSbSgu+YZikm0oJtmG4pJvKCbZhs6ppKaZ751wYtkKWLJkyTK/MBoa3nzcvXv3JEmvXr3yal3dMvvW1dW1jEmSnr16pa6uLnVvjW3Zv+c7+x/wuS+0jP/9rbdkiy23WqZhJkmuv/aqXH3l5W1ZGlCBZBuKS76hmGQbikm2objkG4pJtqGYZBuKS76hmGQbikm2objkG4pJtqFzKqlpJkneeOON/OGO3+fpJ5/KK6/Mz9e/+a0sWbw406c/mK232bbVx3n5pRdz9MTDl9q2/wEHLmdk6+5ss8Ib4Lxr+1VX/jaPznwkJ06estzh++y3f8aO26flcWNjQ46aML5VNQCVS7ahuOQbikm2oZhkG4pLvqGYZBuKSbahuOQbikm2oZhkG4pLvqGYZBs6p5KaZp579l858Yffz7x585I05+2OlJtv/p9c/OsLc/QxX892249p1bEGDhqcy668bqltb7z+eq664rLU19cnSRobGtK3b99l9u3du3defunFlseNjY3p07dfevfukySpb6hP9x490tjYkL59+rWMu/Xmm/L/brwh3/nBjzNgrYHLrau2tja1tbWtWgPQecg2FJd8QzHJNhSTbENxyTcUk2xDMck2FJd8QzHJNhSTbENxyTcUk2xD51Rdyk6/+fUFWbDglXzhPw/JVlu/c1eZ3XYfmyFDhuTGG657j73fX9du3TJ02PDccftteenFF3PP3XdmxKjRLc8vWbw4STJi5OjMmf1YHp8zOw/PeChzn38+I0aMzJC1107//mvm97fcnBdfmJv7/vTHlv3vveeuXHzRhZl49H9l0KBBqa9fmEVNTW2qFwAAAAAAAAAAAAAAgMpSUtPMY7NmZqttts2ee+2TNQcMaNnepUuXbDJyVJ55+qk2F3bYEUflmWeezjeOmZQ33mjKFw46OEly4++uy6knn5Qk2fLDW2WPT43LlMkn5uxpZ+TgLx6addb9YLp06ZKJRx+TB+6/L//9ja9l4MBB2Xvf/ZIkl196cZqa3siUyT/O+C8elPFfPCj33nNXm+sFAAAAAAAAAAAAAACgcnQpZafq6ppUVy2/3+bZZ55Jly5tv+3U+utvkBMnn7zM9nF77Ztxe+2bJKmqqsqBBx2cA99qqPl3o0ZvmpNPP2uZ7WdM+2WbawMAAAAAAAAAAAAAAKCyldQ0M2r06Dxw/325+8470tTUlCRZsOCV/O76azNnzuxss+1HylokAAAAAAAAAAAAAAAArIySmmY+f9AhefTRmfnF1DNbth3xlUOTNKd79x454MAvlKs+AAAAAAAAAAAAAAAAWGnVpew0eMjamTzltOy08y7pv2b/VFdXpVevXtlm2+3yo5/+LOuss2656wQAAAAAAAAAAAAAAIBWK+lOM0kyYMBaOfzISeWsBQAAAAAAAAAAAAAAAMqi5KaZRU1NufuuP+Sf//xHFrzySvr265ehQ4dlzA47pUuXkg8LAAAAAAAAAAAAAAAAbVZSd8szTz+dySeekHnz5iVp/rdnqnL1lZfnuOO/nw+ss055KgQAAAAAAAAAAAAAAICVVFLTzAXn/zLz5r2cUaM3zU67fDx9+vTJ/Pnzc9cf/jezHp2ZC84/J8d/74flrhUAAAAAAAAAAAAAAABapaSmmb8/PjsbDh2Wb3/3hFRVVbVs32HHnfOdbx2bx+fMLluBAAAAAAAAAAAAAAAAsLJKaprp33/NfOAD6yzVMJMk1dXVWX/9DdLY2FiW4gAAAAAAAAAAAAAAAKAU1aXstOsnd8sjf/trXn7pxaW2vzJ/fv768Izs/qmxZSkOAAAAAAAAAAAAAAAASlHSnWbuvON/s2DBghxz9JHp0uWdQyxatCiLFi3OFZddkisuu+StrVX51UWXlqNWAAAAAAAAAAAAAAAAaJWSmmaee/ZfSd5uklm0zPOvvfZa26oCAAAAAAAAAAAAAACANiipaebSK64tdx0AAAAAAAAAAAAAAABQNtUdXQAAAAAAAAAAAAAAAACUm6YZAAAAAAAAAAAAAAAACkfTDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDC0TQDAAAAAAAAAAAAAABA4WiaAQAAAAAAAAAAAAAAoHA0zQAAAAAAAAAAAAAAAFA4mmYAAAAAAAAAAAAAAAAoHE0zAAAAAAAAAAAAAAAAFE6Xji5gRZ588p85Z9qZee7Zf2XosOE5YuJXs9bAgUuNaW5uzmWXXJQ77/jf1NTUZNze+2bsuL2TJDMf+VsuOO+czJv3ckaN3iyHHzkpa6yxxjvH/+c/cvxx38i+n94/+x9w4CpdGwAAAAAAAAAAAAAAAO2rYu80c+4vpmbo0GGZcuqZqampycUXXbDMmBkPPZjf33pzvvnt72b84Ufm4osuyFNP/jOLFi3K1DNOy/ZjdsjkKadn7tznc/01V7Xst2Tx4vzy7Klpbm5elUsCAAAAAAAAAAAAAABgFanIO8288frreeKJx3PIoeOz1sCB2WGnXZbbNDPr0ZnZaONNMmzY8CTJoEGDM/uxWampqcn8+fPy8U/sln79+2e7j47J9L880LLfTTf9Ll27dsv662+wwhqamprS1NTU8rixsaF8CwQ6jGxDcck3FJNsQzHJNhSXfEMxyTYUk2xDcck3FJNsQzHJNhSXfEMxyTZ0Th3eNLNkyZJlfmE0NLz5uHv37kmSXr165dW6umX2rauraxmTJD179UpdXV3q3hrbsn/Pd/Z/Ye7cXH/N1fnhiZNz5s9PXWFd1197Va6+8vI2rAyoRLINxSXfUEyyDcUk21Bc8g3FJNtQTLINxSXfUEyyDcUk21Bc8g3FJNvQOXV408zLL72YoycevtS2/Q84cDkjq1p1vKoVDXtr+3m/nJax4/bO2h9Y5z2Ps89++2fsuH1aHjc2NuSoCeNbVQNQuWQbiku+oZhkG4pJtqG45BuKSbahmGQbiku+oZhkG4pJtqG45BuKSbahc+rwppmBgwbnsiuvW2rbG6+/nquuuCz19fVJksaGhvTt23eZfXv37p2XX3qx5XFjY2P69O2X3r37JEnqG+rTvUePNDY2pG+ffnl8zuz87a8P5++Pz8mNN1yb1157Lc88/VRqa7tmn/0+s9Sxa2trU1tbW+7lAh1MtqG45BuKSbahmGQbiku+oZhkG4pJtqG45BuKSbahmGQbiku+oZhkGzqnDm+aWZ6u3bpl6LDhueP227LWWgNzz913ZsSo0S3PL1m8ONU1NRkxcnRuvfmmPD5ndhoaGjL3+eczYsTIDB6ydvr3XzO/v+Xm7PqJT+a+P/0xm2/x4ay/wYY5Y+o5LceZctJPMmr0pvnEbrt3xDIBAAAAAAAAAAAAAABoJ9UdXcCKHHbEUXnmmafzjWMm5Y03mvKFgw5Oktz4u+ty6sknJUm2/PBW2eNT4zJl8ok5e9oZOfiLh2addT+YLl26ZOLRx+SB++/Lf3/jaxk4cFD23ne/dO3aNQMHDW756dKlS3r16pVevdboyKUCAAAAAAAAAAAAAABQZhV5p5kkWX/9DXLi5JOX2T5ur30zbq99kyRVVVU58KCDc+BbDTX/btToTXPy6We95xw/OemU8hQLAAAAAAAAAAAAAABARanYO80AAAAAAAAAAAAAAABAqTTNAAAAAAAAAAAAAAAAUDiaZgAAAAAAAAAAAAAAACgcTTMAAAAAAAAAAAAAAAAUjqYZAAAAAAAAAAAAAAAACkfTDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDC0TQDAAAAAAAAAAAAAABA4WiaAQAAAAAAAAAAAAAAoHA0zQAAAAAAAAAAAAAAAFA4mmYAAAAAAAAAAAAAAAAoHE0zAAAAAAAAAAAAAAAAFI6mGQAAAAAAAAAAAAAAAApH0wwAAAAAAAAAAAAAAACFo2kGAAAAAAAAAAAAAACAwtE0AwAAAAAAAAAAAAAAQOFomgEAAAAAAAAAAAAAAKBwNM0AAAAAAAAAAAAAAABQOJpmAAAAAAAAAAAAAAAAKBxNMwAAAAAAAAAAAAAAABSOphkAAAAAAAAAAAAAAAAKR9MMAAAAAAAAAAAAAAAAhdOlowtYnief/GfOmXZmnnv2Xxk6bHiOmPjVrDVw4FJjmpubc9klF+XOO/43NTU1Gbf3vhk7bu8kycxH/pYLzjsn8+a9nFGjN8vhR07KGmus8eax//mPXHbJbzJn9qx8+7snZPhGG6/y9QEAAAAAAAAAAAAAANC+KvJOM+f+YmqGDh2WKaeemZqamlx80QXLjJnx0IP5/a0355vf/m7GH35kLr7ogjz15D+zaNGiTD3jtGw/ZodMnnJ65s59Ptdfc1WS5IW5c3PC945Pv/7986OfTsnQocNW9dIAAAAAAAAAAAAAAABYBSruTjNvvP56nnji8Rxy6PisNXBgdthpl+U2zcx6dGY22niTDBs2PEkyaNDgzH5sVmpqajJ//rx8/BO7pV///tnuo2My/S8PJEn+58brM2TIkBw2YWKqq9+7X6ipqSlNTU0tjxsbG8q4SqCjyDYUl3xDMck2FJNsQ3HJNxSTbEMxyTYUl3xDMck2FJNsQ3HJNxSTbEPn1KFNM0uWLFnml0VDw5uPu3fvniTp1atXXq2rW2bfurq6ljFJ0rNXr9TV1aXurbEt+/d8Z//HZz+Wbt2751vf/K+8/tprGTtu7+y2x9jl1nb9tVfl6isvb+MKgUoj21Bc8g3FJNtQTLINxSXfUEyyDcUk21Bc8g3FJNtQTLINxSXfUEyyDZ1ThzbNvPzSizl64uFLbdv/gAOXM7KqVcerWtGwt7bX19entmvXHDZhYv7++Jxc+KtzM2r0plln3Q8us8s+++2fseP2aXnc2NiQoyaMb1UdQOWSbSgu+YZikm0oJtmG4pJvKCbZhmKSbSgu+YZikm0oJtmG4pJvKCbZhs6pQ5tmBg4anMuuvG6pbW+8/nquuuKy1NfXJ0kaGxrSt2/fZfbt3bt3Xn7pxZbHjY2N6dO3X3r37pMkqW+oT/cePdLY2JC+ffolSfr07Zutt/lIhg3fKMOGb5RLL/51nn76qeU2zdTW1qa2trZsawUqg2xDcck3FJNsQzHJNhSXfEMxyTYUk2xDcck3FJNsQzHJNhSXfEMxyTZ0TtUdXcC7de3WLUOHDc8dt9+Wl158MffcfWdGjBrd8vySxYuTJCNGjs6c2Y/l8Tmz8/CMhzL3+eczYsTIDFl77fTvv2Z+f8vNefGFubnvT39s2X/LrbbO3Xf9Ic8/91zuvusPWbRoUdZbb4OOWCYAAAAAAAAAAAAAAADtqEPvNLMihx1xVH75i7PyjWMmZdjwjTL+sIOTJDf+7ro89uij+cZ/fztbfnir7PGpcZky+cTUdKnJwV88tOWOMROPPiYXnHdObrn5fzJq9GbZe9/9kiTj9to38156Kd/51jfSo0fPHDZhYj6wzjodtk4AAAAAAAAAAAAAAADaR0U2zay//gY5cfLJy2wft9e+GbfXvkmSqqqqHHjQwTnwoIOXGTdq9KY5+fSzltnetWvXjD/8yIw//MjyFw0AAAAAAAAAAAAAAEDFqO7oAgAAAAAAAAAAAAAAAKDcNM0AAAAAAAAAAAAAAABQOJpmAAAAAAAAAAAAAAAAKBxNMwAAAAAAAAAAAAAAABSOphkAAAAAAAAAAAAAAAAKR9MMAAAAAAAAAAAAAAAAhaNpBgAAAAAAAAAAAAAAgMLRNAMAAAAAAAAAAAAAAEDhaJoBAAAAAAAAAAAAAACgcDTNAAAAAAAAAAAAAAAAUDiaZgAAAAAAAAAAAAAAACgcTTMAAAAAAAAAAAAAAAAUjqYZAAAAAAAAAAAAAAAACkfTDAAAAAAAAAAAAAAAAIWjaQYAAAAAAAAAAAAAAIDC0TQDAAAAAAAAAAAAAABA4WiaAQAAAAAAAAAAAAAAoHA0zQAAAAAAAAAAAAAAAFA4mmYAAAAAAAAAAAAAAAAonC4dXUBn0dzcnCRpbGzo4Epg9dajR49UVVWV7XiyDZVDvqGYZBuKSbahmMqd7US+oVL47IZikm0oJtflUFw+u1kVGhqcD6uabEMxuS6H4vLZDcX0ftnWNNNKr73WmCQ5asL4Dq4EVm/n//rS9OzZs2zHk22oHPINxSTbUEyyDcVU7mwn8g2Vwmc3FJNsQzG5Lofi8tnNu32uHY755UPa4aC8J9mGYnJdDsXlsxuK6f2yXdX8dosb72nJkiWZP39eundvXYdhY2NDjpowPmedfV569CjvxVN7UG/7Um/5lLvLt4jZrvQa1dc2lV5fUnqN7ZHvZ5/9V449ZlJFv14rqzOcAyvLmjqHSsr2ynx2rwpFeb+Lso6kOGup5HUUKduV/Dq/TY3locb31x7/57yVyXdHr781Kr1G9bVdpdfYWa/LK/11ba2irCMpzlpW93XIdnkUZR1JcdZSlHUkpa3Fdfn7q/Qa1dd2lV5jZ/3sLqdKf4+Syq+x0utL1NhaHZ3tSngNVoXVYZ2rwxqTzrNO1+Xvr9JrVF/bVXqNnfW6vNJf19ayjspTlLW0V7bdaaaVqqurM2DAWiu9X48ePcvecdye1Nu+1Ft5ipztSq9RfW1T6fUlHV9jdXV11lxzQEXU0h6sqXOwpvIr9bN7Vejo16ZcirKOpDhrKco63kslZLszvM5qLA81rlql5LszrL/Sa1Rf21V6jR1dX5G/U2uNoqwjKc5arOP/s3fvYXJW9eHAv7PZDSQhXGMJyF1ALkHuiIiA2FZBND9arIoY5OqKCLYIqEULVRRMvVStFzS1UAiGmyhVGxARUKuAoBjlauVSBQWScMlOktnd+f0RdrNJdpPZ3Xnnfd8zn8/z7KMze/Ke75k533PODvN93uaQ22mMIyKdsaQyjoj8x+Jcng/xjV/RY8w7Pp+pNaboMRY9vggxtlq7n8vXpR3G2Q5jjGifcQ7lXJ4P8Y1f0WPMO75237uNo3hSGUuzx9HRtCsBAAAAAAAAAAAAAABAQSiaAQAAAAAAAAAAAAAAIDkTzj///PPzDiJVHR0dsdvue8SECRPyDqUh4s2WeNNRhtem6DGKb3yKHl9EsWIsUizNYkzlYEztJZXXJpVxRKQzllTGUXRleJ3F2BxiLL4yjL/oMYpv/IoeY9HjG0lZ415dKuOISGcsxpGvssa9ulTGEZHOWFIZR0Q5x1KGmIseo/jGr+gxFj2+VijDa1D0GIseX4QYy6JdXoN2GGc7jDGifcY5XmV4nYoeo/jGr+gxFj2+kZQ17tUZR/GkMpYsxlGp1+v1pl0NAAAAAAAAAAAAAAAACqAj7wAAAAAAAAAAAAAAAACg2RTNAAAAAAAAAAAAAAAAkBxFMw2q1+vR09MT9Xo971CAJpLbkC75DWmS25AmuQ3pkt+QJrkNaZLbkC75DWmS25AmuQ3pkt+QJrkN5aBopkHVajVOOv7YqFareYcCNJHchnTJb0iT3IY0yW1Il/yGNMltSJPchnTJb0iT3IY0yW1Il/yGNMltKAdFMwAAAAAAAAAAAAAAACRH0QwAAAAAAAAAAAAAAADJUTQDAAAAAAAAAAAAAABAchTNAAAAAAAAAAAAAAAAkBxFMwAAAAAAAAAAAAAAACRH0QwAAAAAAAAAAAAAAADJUTQDAAAAAAAAAAAAAABAchTNAAAAAAAAAAAAAAAAkJzOvANopS9/8V/jtltvWeW5zTefHp/74ldyiggAAAAAAAAAAAAAAIAstFXRzImndMesE04afPyZ2RfFDi/bMceIoH1Ve+vR1RFR64+Y1FnJO5yGlTVuABgve2D6vMfAeFhDaAbzCNIlvyFNchuAdmUPpBnMIwDKzl4GQBay2vdJDaIAACAASURBVF/aqmhmvfXWi/XWWy8iIh579JF44P774j3vPXPYtrVaLWq12uDjarWnJTFCO6j21qN7/pK4bMHymDVjYnzl9VNadnAeT27nGTewbvZuyE5Z924a55xDq8nttFhDGGqs+W0eQbH5TA3SJLchXf7uhuz4vJxmcJZiqCLmduXihU29Xv3cTZt6PSiLIuZ3s9jLaGcp5zbkLcv9pa2KZob6rxu+Hfsd8MqY9pKXDPv7b3/rmrj26nktjgraQ1dHxGULlkfEiv+dc8SUlvU9ntzOM25g3ezdkJ2y7t00zjmHVpPbabGGMNRY89s8gmLzmRqkSW5DuvzdDdnxeTnN4CzFUHIb0pVyftvLaGcp5zbkLcv9pS2LZhY+80z89Me3x0fO/9iIbWYefUwcedTMwcfVak+c3n1yK8KD5NX6I2bNmDhYCVjrj+jsaE3f48ntPOMG1s3eDdkp695N45xzaDW5nRZrCEONNb/NIyg2n6lBmuQ2pMvf3ZAdn5fTDM5SDCW3IV0p57e9jHaWcm5D3rLcX9qyaOb7370htt1uu3j5LruO2Karqyu6urpaGBW0j0mdlfjK66fEnCOmRK0/WnprxvHkdp5xA+tm74bslHXvpnHOObSa3E6LNYShxprf5hEUm8/UIE1yG9Ll727Ijs/LaQZnKYaS25CulPPbXkY7Szm3IW9Z7i9tVzTT09MTN//gxjjxlHfnHQq0tYGFrGwV5mWNGwDGyx6YPu8xMB7WEJrBPIJ0yW9Ik9wGoF3ZA2kG8wiAsrOXAZCFrPaXttuubr5pfqy3/npx4KtenXcoAAAAAAAAAAAAAAAAZKTt7jTzpplHx5tmHp13GAAAAAAAAAAAAAAAAGSo7e40AwAAAAAAAAAAAAAAQPoUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyenMOwAAAAAAAAAAAABg9CoXL8w7BAAAKDR3mgEAAAAAAAAAAAAAACA5imYAAAAAAAAAAAAAAABIjqIZAAAAAAAAAAAAAAAAkqNoBgAAAAAAAAAAAAAAgOR05h1AHvr7+uL666+NW2+5OV760q3jnA+dl3dIAAAAAAAAAAAAAAAANFFb3mnm0v+YEzf99/fjuHeeEKedfmYmfVRr9ejtr0e1Vl/l+aW99egd8rO8tz5iW9pHO8yBgTHWXpz7ZR1zWeOGVko9T0YzvkbbZnHNvPs3pnL0n9WYiqQVceujeP2k0geUiZygGcwjSJf8hjTJbUiT3AZoDestAGtjnwBgtOwda9d2RTOLFy+Om2+aHyed2h37v/LA2GDq1GHb1Wq16OnpGfypVnsa7qNaq0f3jUuia/ai6L5xyeDkq9bq8cLyetTqMfjTV49h29I+RpovKRkY46TZi6L3xbmf15izyG1gpTzzZDz53ajRjK/RtllcM+/+jakc/Wc1pmYr+t6tj+L1k0ofqWvFvk3ryAmGGmt+m0dQbEU/lwNjI7chXc7lkCafqaXDestQchvS5VwOabJ3U0T2jnWr1Ov1tnpVfnHXHfHpT30yDnr1a+K++34TW2+9bZzSfVpsttm0Vdpdc9WVce3V89b493MunRuTJ09eax+9/fXomr1o8HHt7E2is6MSvf316K9HdFRWbT9cW9rHSPMlJQNjPHybzpj/1hWFanmNOYvcBlbKM0/Gk9+NGs34Gm2bxTXz7t+YytF/VmNqtqLv3fooXj+p9JG6VuzbtI6cYKix5rd5BMVW9HM5MDZyG9LlXA5p8plaOqy3DNWM3K5cvLDZYTVV/dxN8w4BcuFcDmlyLqeI7B3r1pl3AK22ZMmSiKjEzrvsEm988/+LS778xbjiPy+NM95/1irtZh59TBx51MzBx9VqT5zefXJDfdT6ImbNmBiXLVges2ZMjFpfRGfHiueX1OoxpWvVSThcW9rHSPMlJQNjnLtgedT6VjyX15izyG1gpTzzZDz53ajRjK/RtllcM+/+jakc/Wc1pmYr+t6tj+L1k0ofqWvFvk3ryAmGGmt+m0dQbEU/lwNjI7chXc7lkCafqaXDestQchvS5VwOabJ3U0T2jnVruzvN/PKeu+Nzn/5U/Mfl34yIiO98+7q47Ue3xL989gtr/Xc9PT1x0vHHNlwJWK3Vo2vCikk4aUiRzNLe+iqVSv0R0VePYdvSPkaaLykZGGO9P6ISEbWCzPtm5TawUlHyZLT53ajRjK/RtllcM+/+jakc/Wc1piwVce/WR/H6SaWPdpLVvk3ryAlGMpr8No+gPIp4LgfGT25DupzLIU0+Uys36y0jGUtuu9MMlINzOaTJuZyisHesXdvdaWbnnV8eXV1dMf/734199t0v7rrj57H99js0vZ+BybZ6ldb6nSNPQhVd7Wuk+ZKSwQX4xTEOLD5lG3M7vFcwXqnnyWjG12jbLK6Zd//GVI7+sxpTkbQibn0Ur59U+oAykRM0g3kE6ZLfkCa5DWmS2wCtYb0FYG3sEwCMlr1j7druZZk8ZUr8/QfOiR/cND/OOevMmDp1ahx3/Al5hwUAAAAAAAAAAAAAAEATtd2dZiIidtt9j5j9mc/nHQYAAAAAAAAAAAAAAAAZabs7zQAAAAAAAAAAAAAAAJA+RTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHIUzQAAAAAAAAAAAAAAAJAcRTMAAAAAAAAAAAAAAAAkR9EMAAAAAAAAAAAAAAAAyVE0AwAAAAAAAAAAAAAAQHJKUzTTW6vFY48+Mvj4iT/+Ie6842fx7LOL8wsKAAAAAAAAAAAAAACAQurMO4BGPPP0U/GJj18QnZ2dcfG/fC7u/dU9MfuiC6Ovrz822GCD+Md/+ufYdtvtGr7eJz9+ftz7q18OPn7jm2bGcbNOaH7gAAAAAAAAAAAAAAAA5KIURTPfnHt5PPHHP8SBBx0cERFXfXNuTJ48Jfbca+/46U9uj2vmzY2zzvlww9dbtGhRnHDyqfHqgw+JiIiuromZxA0AAAAAAAAAAAAAAEA+SlE0s2DBvbHb7jPijPefFYsXL47//d3Dccq7T4vXvu6vYunSpfHAA/eN6nqLFy2Kv/iLzWPKlA0yijii2luPro6IWn/EpM5KZv0ArSW3gTKxZgGjYc0AxsMaQjOYR5Au+Q1pktuQJrkN0BrWWwDWxj4BkBbrev468g6gEcuWLo3NNpsWERG/vveXEVGJPffeNyIiJk+ZEsuWLm34Wr21WrzwwvNxxX9eGqedemJ89l8ujueff26NdrVaLXp6egZ/qtWehvuo9taje/6S6Jq9KLrnL4lqb73hfwtkS25DusaT3ymyZpEKud0a1gxaTW6nxRrCUGPNb/MIis1napAmuQ3pci6HNPlMLR3WW4aS25Au53JIk72b0bKuF0Mp7jSz9Tbbxp13/Cy2uWG7uOXmm2KrrbeOTTfdNH73u4fjnl/cFVtttU3jF6tU4l0nnRLTp28ZkydPjn/7wufimqu+GSecdOoqzb79rWvi2qvnjSnero6IyxYsj4gV/zvniCljug7QfHIb0jWe/E6RNYtUyO3WsGbQanI7LdYQhhprfptHUGw+U4M0yW1Il3M5pMlnaumw3jKU3IZ0OZdDmuzdjJZ1vRhKUTTzt295a3zqkxfGFf95aUTU4/Qz/yEiIj5+/nmxbNnyeNeJpzR8rf7+/thvv1fGppttFhER++1/QDz80ENrtJt59DFx5FEzBx9Xqz1xevfJDfVR64+YNWNiXLZgecyaMTFq/RGdpbinD6RPbkO6xpPfKbJmkQq53RrWDFpNbqfFGsJQY81v8wiKzWdqkCa5DelyLoc0+UwtHdZbhpLbkC7nckiTvZvRsq4XQymKZl6x595x/sc+Effc/YvY4WUvi333OyAiIrbbfoc47PC/jFe9+uCGr/WnJ5+Ic846M854/wdihx13jHt/9cvYZdfd1mjX1dUVXV1dY4p3UmclvvL6KTHniClR61/xGCgGuQ3pGk9+p8iaRSrkdmtYM2g1uZ0WawhDjTW/zSMoNp+pQZrkNqTLuRzS5DO1dFhvGUpuQ7qcyyFN9m5Gy7peDKUomomI2HGnnWPHnXZe5bl/+udPjPo6W2+zbRx/4slx+WXfiGq1J/Z4xZ7xlrce26wwBw1MaJVgkBa5DZSJNQsYDWsGMB7WEJrBPIJ0yW9Ik9yGNMltgNaw3gKwNvYJgLRY1/NXmqKZxx9/LO6/77fx/HPPRr2+6u8qlYi/OeatDV/rDUccFW844qgmRwgAAAAAAAAAAAAAAEBRlKJo5uab5se/f/2rLxbL1IdpURlV0QwAAAAAAAAAAAAAAABpK0XRzHeuvy4qlUr85V+9PjbfYnpUopJ3SAAAAAAAAAAAAAAAABRYKYpmFi9eFPsfcGCccPKpeYcCAAAAAAAAAAAAAABACXTkHUAjtt/+ZXmHAAAAAAAAAAAAAAAAQImU4k4zR808Or74r5+JH99+a7z0pVsN22b7HRTWAAAAAAAAAAAAAAAAsEIpimY+M/uTEVGJL33hX4f9faUSccW861obFAAAAAAAAAAAAAAAAIVViqKZXXbdLSqVSt5hAAAAAAAAAAAAAAAAUBKlKJr56AUX5h0CAAAAAAAAAAAAAAAAJVKKopn7fvubdbbZdbfdWxAJAAAAAAAAAAAAAAAAZVCKopmPnX9eRFTW2mbuVde1JhgAAAAAAAAAAAAAAAAKrxRFM/vut38MVzTz1J//FI899mgc9OrXtD4oAAAAAAAAAAAAAAAACqsURTNnnfPhYZ/vrdXi7LPOjM6urhZHBAAAAAAAAAAAAAAAQJF15B3AeHR2dcVOO+0cd93x87xDAQAAAAAAAAAAAAAAoEBKXTSzaNHCeOjBB6LSUck7FAAAAAAAAAAAAAAAAAqkM+8AGnHirLev8Vy9Xo9ly5ZHRD0OPOjg1gcFAAAAAAAAAAAAAABAYZWiaGbp0qXDPFuJjTbeKPbca5847p3vanVIAAAAAAAAAAAAAAAAFFgpimbmXvWtvEMAAAAAAAAAAAAAAACgRDryDmBd+vv64uMXfCS+e8O38w4FAAAAAAAAAAAAAACAkij8nWY6JkyIJ574Y2z66CN5hwIAAAAAAAAAAAAAAEBJFL5oJiLir19/ZNzw7evi4YcejOnTtxi2zQZTp7Y4KgAAAAAAAAAAAAAAAIqqFEUz8668PCIq8dF//OCwv69UIq6Yd11rgwIAAAAAAAAAAAAAAKCwSlE0s9m0aVGJSt5hAAAAAAAAAAAAAAAAUBKlKJr5wpe+1vRrfu7Tn4qf/+ynceXV1zf92gAAAAAAAAAAAAAAAOSrI+8AmuUbcy6Jd7z1bxpqe9edd8Sdd/ws03iqtXr09tejWquv8nyttx69q/2M1Jb20Q5zYGCMZZ/3ZY0bWqmMeTKamLNom2L/xlSO/rMaU5G0Im59FK+fVPqAMpETNIN5BOmS37B21d4Xc6S3XDkit2HdypjfchvWTZ7QDOYRAGtThn2iDDEyPt5j2kFK35VJRVavVTJFMxER9QZem56envjGnK/G649441rb1Wq16OnpGfypVnsajqNaq0f3jUuia/ai6L5xyeCbVq3Vo7ceUVvtZ7i2tI+R5ktKBsa42exFuc/7LHIbWCnPPBlrfo8m5izapti/MZWj/6zG1GxF37v1Ubx+UukjdePJbYpHTjBUK87lQOsV/VwOZVbtrUf3/BdzZP6Sln6xXm5DtsqY33Ib1q2sn5dTLNZbhpLbkK6Uz+VliJHx8R6PzN6djpS+K5OKLF+rSr3eSKlJ8X1jziVx0/z/jrlXXbfWdnMu+UrUemtxwCsPjNkXXRhXXn39sO2uuerKuPbqeWv++0vnxuTJk9faR29/PbpmLxp8XDt7k+jsqERv//Av9XBtaR8jzZeUDIzxiiO74u923yAi8pv3WeQ2sFKeeTLW/B5NzFm0TbF/YypH/1mNqdmKvnfro3j9pNJH6saT2xSPnGCoVpzLgdYr+rkcyszf3JCuMua33IZ1K2NuUzzWW4ZqRm5XLl7Y7LCaqn7upnmHALlI+VxehhgZH+/xyJzL05HSd2VSkeVr1dmUq5TE/ff9Nu668+cx+zOfjwcfvH+tbWcefUwcedTMwcfVak+c3n1yQ/3U+iJmzZgYly1YHrNmTIxaX0Rnx4rnhzNcW9rHSPMlJQNjfM/3lsfRu6x4Lq8xZ5HbwEp55slY83s0MWfRNsX+jakc/Wc1pmYr+t6tj+L1k0ofqRtPblM8coKhWnEuB1qv6OdyKLNa/2o50u9vbkhFGfNbbsO6lfXzcorFestQchvSlfK5vAwxMj7e45HZu9OR0ndlUpHla9VWd5r55McviN8s+HWst97E6Ovrj2XLlsbkyZNjzqVz13n9np6eOOn4YxuuBKzW6tE1YcWbN6lrZYVTrbceq9c71eoxbFvax0jzJSUDY4z+FY+LMu+bldvASkXJk9Hk92hizqJtiv0bUzn6z2pMWSri3q2P4vWTSh/tZLS5TfHICUaS1bkcyFcRz+VQZtXeenR1rPiC/aROf3NDSsqY33Ib1q0oeeIztXIryjyieMaS2+40A+WQ2rm8DDEyPt7jxjiXl1tK35VJRVavVVvdaeY97z0jarXlERGxYMGv45IvfzEumv3ZTPoaeJNWr27qGubD0IE3QdVY+xppvqRkcOF6cYxlnfft8F7BeJUxT0YTcxZtU+zfmMrRf1ZjKpJWxK2P4vWTSh9QJnKCZjCPIF3yG9Zu4Iv0ZcsRuQ3rVsb8ltuwbvKEZjCPAFibMuwTZYiR8fEe0w5S+q5MKrJ6rdqqaGbjTTYZ/P8bbfRYRES85C82zyscAAAAAAAAAAAAAAAAMpJM0cze++wbUzfcsOH2++y7f1x59fUZRgQAAAAAAAAAAAAAAEBeSlM08/zzz8XvHn44nn/u2agP8/tDDn1t7LX3vi2PCwAAAAAAAAAAAAAAgOIpRdHMXXf+PP7t85+NZcuWD/PbekRU4pBDX9vqsAAAAAAAAAAAAAAAACioUhTNfPOK/4xly5bFXnvvG5tPnx6VSiXvkAAAAAAAAAAAAAAAACiwUhTNPPXUn2PvffaNsz94Xt6hAAAAAAAAAAAAAAAAUAIdeQfQiC22fGlM2WBq3mEAAAAAAAAAAAAAAABQEqUomnn7O2bF3XfdGQ/cf1+88Pzzw/4AAAAAAAAAAAAAAADAgM68A2jEv1x8YfT19ccFH/3HYX9fqURcMe+6FkcFAAAAAAAAAAAAAABAUZWiaGbjTTaJSlTyDgMAAAAAAAAAAAAAAICSKEXRzBe+9LW8QwAAAAAAAAAAAAAAAKBESlE0M6Ber8eSJUsi6vXB55bXavHHPzweM/bYM8fIAAAAAAAAAAAAAAAAKJJSFM0srVZjzte+Enfe8bNYvrw2bJu5V13X4qgAAAAAAAAAAAAAAAAoqo68A2jEVfPmxk9+fFt0dEyI9ddfPyLqMW3atJgyZUpE1GOPV7jLDAAAAAAAAAAAAAAAACuVomjmzjt+Fttut3185ev/Ea855LCIqMTnv3RJfO6LX46NNto4Xr7rrnmHCAAAAAAAAAAAAAAAQIGUomjm2cXPxg47vCwmTpwYkyZPioiIZcuWxZQpG8Tue7wifnTzD3KOEAAAAAAAAAAAAAAAgCIpRdHM5tOnx/33/TZeeOGF2GyzaRERcc/dd0VExDNPPx09PT15hgcAAAAAAAAAAAAAAEDBlKJo5rDXvi6eeOKPccVl34j9DzgwOjsnxL99/rPx3u6T4oH774tdd9s97xABAAAAAAAAAAAAAAAokFIUzRx51JvjyKPeHFM22CA23mSTeNdJp8aECZ2xaOHC2G777eNdJ56cd4gAAAAAAAAAAAAAAAAUSGfeATSiUqnEcbNOGHx8+Ov+Kg455LCoLq3G1Kkb5hgZAAAAAAAAAAAAAAAARVSKopmhXnjhhYh6PSIiKlGJF55/PiIiNpg6Nc+wAAAAAAAAAAAAAAAAKJBSFM088/RT8bWvfjl++5tfR29v3xq/r1Qirph3XQ6RAQAAAAAAAAAAAAAAUESlKJr5+iVfiXt/dU90dXXFtGnT8g4HAAAAAAAAAAAAAACAgitF0cyDD9wX22yzbZz/8Yti/fXXH/N1+vr6Yt6Vl8cPf3BjRETsvc9+cUr3e2PixInNChUAAAAAAAAAAAAAAIAC6Mg7gEZ0dnXFDjvuNK6CmYiIH99+a/zwBzfF2R88Ly648OJY8Ot7BwtoAAAAAAAAAAAAAAAASEcpimYOPvjQ+M2v741nnnl6XNc59LDD4+v/cXm8fJddY+ONN47Ozs6YMGFCk6JsTLW3Hr399ajWXvzf3nos7a23NAaKZ3BetMlcGJz/tfKNt8yxQ6uUMU9Gsw6PZnxZrO9l2TPyjjPv/lNUxtyOaE3cLemjBXO6rO/xcFIaC5SFvKMZzCNIl/yGNMltSJPchnXz3yBoBustAGtThn3CmQjIWirfx6EYSlE0c/TfviWWLl0aH3j/6XHOWWeu8XPuB94/quudc9aZceqJs2L6FlvEX/71G4ZtU6vVoqenZ/CnWu0Z9ziqvfU4bf6SWNYX0X3jkuiavSi65y+JF5YX+3BDtqq99eiev3I+pL7wVmv1lfP/xiUtn/vjye28Y4cyyDNPxprfo1mHRzO+LNb3suwZeceZd/8pKmNuR7Qm7pb00YI5ndI5J6WxpCyLv7nJj7xjqDGfy80jKLSin8uBsZHbkC7ncshOnv8Nwmdq6bDeMpTchnSlfC73vQzamb27NVL5Pg7FUanX64V/h7/4+c/GT39821paVGLuVdc1fL1nnnk6nvjjH+Lzn/10HPvO4+Ow175ujTbXXHVlXHv1vDWen3Pp3Jg8eXLDfQ3V21+PXb72bNx/ykbRNXvR4PPLPrBJdFQiOjsqY7ou5dbbX19lPtTO3iTpuZD3eMeT23nHDmWQZ56MNb9HE3NWbRtVlnUo7zjz7j9FZcztiNbErY/iSWksKcvib27yI+8YqhXncqD1in4uB8ZGbkO6nMshO2X9vJxisd4yVDNyu3LxwmaH1VT1czfNOwTIRcrn8jLECFlxLm8N35Wh2TrzDqARd991R0zdcMM4/Yx/iOlbbDHm6zz66CPx7OLF8Yo994rNNpsWu82YEb/9zYJhi2ZmHn1MHHnUzMHH1WpPnN598pj7joio9Ue8ZqvOWNYXMWvGxLhswfKYNWNiPLesHlO6KtFZivv+0Gy1/lXnQ60/kp4LtdXmf62vteMdT27nHTuUQZ55Mtb8Hs06PJrxZbG+l2XPyDvOvPtPURlzO6I1cbekjxbM6ZTOOSmNJWVZ/M1NfuQdQ435XG4eQaEV/VwOjI3chnQ5l0N28vxvED5TS4f1lqHkNqQr5XO572XQzuzdrZHK93EojlLcaebkd70j9jvgwOg+7X3jus7tt94S//71r8aHP3JBrD9pUlx04QXxxjfNjCPf+OZ1/tuenp446fhjx10JWO2tR1fHimTumrAi4SoRsX6nyrR2Njgv+iMmtcFcqNbqK+Z/X8SkrnzHO9rcLlLsUFRFyZPR5Pdo1uHRjC+L9b0se0becebdf4rKmNsRrYm7JX20YE4X5T1uhpTG0i6a9Tc3+ZF3jGRU53LzCEqjiOdyYPzkNqTLuRyaqyj/DcJnauVmvWUkY8ltd5qBckjtXF6UMxHkzbk8O6l8H4diKMWdZvbaZ7/4w/89Pu7rHHzIYfHoo4/E7IsujHq9Hgce9Or469cf2YQIGzeQUAOVaCrSiFhzXqRuYPMq43jLHDu0ShnzZDTr8GjGl8X6XpY9I+848+4/RWXM7YjWxN2SPlowp8v6Hg8npbFAWcg7msE8gnTJb0iT3IY0yW1YN/8Ngmaw3gKwNmXYJ5yJgKyl8n0ciqEURTMbbbhR/PTHt8fFn/hYbD59+hq/r0Qljj9x3be2qlQqcdysE+K4WSdkESYAAAAAAAAAAAAAAAAFUYqime9/74aIiPjVL+8eoUVjRTMAAAAAAAAAAAAAAAC0h1IUzbz7tPflHQIAAAAAAAAAAAAAAAAlUoqimUMPOzzvEAAAAAAAAAAAAAAAACiRUhTNRETcesvN8fBDD8VJp3bH0mo1/n3OJfHYI7+PvffdP/7ubcdGpVLJO0QAAAAAAAAAAAAAAAAKohRFM7f+6Ifx1S9/MbbaepuIiJh35RXx49t+FBERjz32WEyaPCnePPNvcowQAAAAAAAAAAAAAACAIunIO4BGfP+/vhMbbDA1Tjz51Ojv64vbb7slDjzo4PjsF74cm0+fHrfd8sO8QwQAAAAAAAAAAAAAAKBASlE08+STT8Z++x8Qu+y6e/zudw9HT09PvOGIN8bmm0+P3XbbPZ5++qm8QwQAAAAAAAAAAAAAAKBASlE0M3Xq1Pi/CYzUGQAAIABJREFUxx+P/v7++OlPbo/11lsvXrbjThGxoqBm0qTJOUcIAAAAAAAAAAAAAABAkXTmHUAj9jvglTH/+9+NU0+cFT09PXHIoYfFhAkT4jOzL4r7fvubOOzw1+UdIgAAAAAAAAAAAAAAAAVSijvN/N3b3hF77b1vLF1aje223z7eftysiIj49b2/jC223DLe+vZ35BwhAAAAAAAAAAAAAAAARVKKO81MmjQpzvnQedHX1xcTJkwYfP79Z50bu+8+Izq7unKMDgAAAAAAAAAAAAAAgKIpxZ1mrr16XvzynrtXKZiJiNhzr73jzjt/HjfO/15OkQEAAAAAAAAAAAAAAFBEJSma+Wb86p67h/3dbT+6Jb55xeUtjggAAAAAAAAAAAAAAIAi68w7gLU5873vHvz/t936w7j7F3eu8vvevt5YtHBRTJs2rdWhAQAAAAAAAAAAAAAAUGCFLpp56qk/v/j/KlGtVqNara7RZqONN47jTzy5tYEBAAAAAAAAAAAAAABQaIUumvn6Ny6PetTjlBPeGYf/5V/Hse+YtcrvOyZMiPXXXz+n6AAAAAAAAAAAAAAAACiqQhfNTJ4yJSIi/uYtb40dd9xp8DEAAAAAAAAAAAAAAACsTaGLZgYc85a3Ra1WiwcfuD+eXbw46lEf/F1t+fL4wx/+L/7ube/IMUIAAAAAAAAAAAAAAACKpBRFM3968om46BMfiz89+eSIbRTNAAAAAAAAAAAAAAAAMKAj7wAaMffyS+NPTz4RM/bYI7bccsuIiDjk0MNi9xkzIqIeb5p5dL4BAgAAAAAAAAAAAAAAUCilKJq5/777Ys+99o4Pf+SC2H2PV0RERPd7z4gPnXd+bPnSrWLRooU5RwgAAAAAAAAAAAAAAECRlKJoZtmyZbHRxptERMTUDTeMiIgXnn8+Ojo6Yseddo5f3XN3nuEBAAAAAAAAAAAAAABQMJ15B9CI7bbfPn5x1x3x4AP3xxbTt4iIevzXDdfHoa99XTx4/33RMWF0tT83fPtb8b3/+k4sW7Y0XrHX3tF92hmx/vrrZxM8AAAAAAAAAAAAAAAALVeKO83MPPpvY8kLS+KHN98U+7/yVTF16obxneu/FWedeXo8+eST8aqDDm74Wvf+6pcx78or4rT3vT8uuPDieOC+++K/v3dD02Ou1urR21+Paq2+yvO9vfU1fkZqS/tohzkwMMZayed9WeOGVipjnowm5izapti/MZWj/6zGVCStiFsfxesnmT56X+yjt1x5R3sq6z5BsZhH4+c1pKjMTUiT3IY0yW1YN3lCM5hHAKxNGfaJosdY9Pig7JL5Xoa1om2Uomhm7332iw+c+6HY+eW7xMSJE+PcD38kdp+xR2y11VZx5FFvircd+86Gr9XZ2RnHHjcr9njFnrH11tvEFltuGc8ufnaNdrVaLXp6egZ/qtWehvuo1urRfeOS6Jq9KLpvXDKYSNVaPWr1WONnuLa0j5HmS0oGxjhp9qLozXneZ5HbwEp55slY83s0MWfRNsX+jakc/Wc1pmYr+t6tj+L1k0wfvfXonv9iH/OXJFc4M57cpnj8rcRQrTiXMzyvIVkq+rkcGBu5DelyLofslPXzcorFestQchvSlfK5vOgxFj0+ys3endD3MqwVbaVSr9fb9h1+9JHfx0c+fE589IILY8eddl7ld9dcdWVce/W8Nf7NnEvnxuTJk9d63d7+enTNXjT4uHb2JtHZUYne/uFf6uHa0j5Gmi8pGRjj4dt0xvy3To2I/OZ9FrkNrJRnnow1v0cTcxZtU+zfmMrRf1Zjarai7936KF4/+iiH8eQ2xZP6fGV0WnEuZ3heQ7JU9HM5MDZyG9LlXA7ZKevn5RSL9ZahmpHblYsXNjuspqqfu2neIUAuUj6XFz3GosdHuTmXp/OdCWtFe+nMO4BG9dZqcfvtt8ajj/w+Fi9aFBtttHFst8MO8ZrXHBqdXV2jvt4zzzwdsy+6MN541Mw1CmYiImYefUwcedTMwcfVak+c3n1yQ9eu9UXMmjExLluwPGbNmBi1vojOjhXPD2e4trSPkeZLSgbGOHfB8sE8yGvMWeQ2sFKeeTLW/B5NzFm0TbF/YypH/1mNqdmKvnfro3j9JNNH/2p99Kd19hxPblM8/lZiqFacyxme15AsFf1cDoyN3IZ0OZdDdsr6eTnFYr1lKLkN6Ur5XF70GIseH+Vm707oexnWirZSijvN/N/jj8dFF14QCxcujIih4VZis802iw/+40fjpVtt3fD1nnvuubjgox+OHXfcKbrfe0ZUKuuuCuvp6YmTjj+24UrAaq0eXRNWJNSkrpXX7+1d8+Wu1WPYtrSPkeZLSgbGWO+PqERx5n2zchtYqSh5Mpr8Hk3MWbRNsX9jKkf/WY0pS0Xcu/VRvH6S6aO3Hl0dKwpoJnWmffYcbW5TPEXZJyierM7lDM9rSKsU8VwOjJ/chnQ5l0NzFSVPfKZWbkWZRxTPWHLbnWagHFI7lxc9xqLHRzra9VyezPcyrBVtoxR3mvnGnEti4cJnYu999o2DDj4kNtpoo3j++efjpz++PX5x1x3xjTmXxHn/9LGGrrW0Wo1PfeJjMW3atDju+BOip2dJVCodTV+oBhJn9YqzzmG+6DTwJqhOa18jzZeUDG4mL46xrPO+Hd4rGK8y5sloYs6ibYr9G1M5+s9qTEXSirj1Ubx+kumjs5x5R3sq6z5BsZhH4+c1pKjMTUiT3IY0yW1YN3lCM5hHAKxNGfaJosdY9Pig7JL5Xoa1om2Uomjm4YceiJ12fnmc/cHzVnn+VQcdHOef96F46MEHGr7Wz3/+P/G73z0UERGnnjgrIiKmveQl8YUvfa15AQMAAAAAAAAAAAAAAJCrUhTNbLrZtNhyy5cO+7vpW24ZTz3954avdehhh8ehhx3erNAAAAAAAAAAAAAAAAAooFLcTOiINx4V99z9i3j6qadWef5PTz4R9/zirnjDEUflFBkAAAAAAAAAAAAAAABFVNg7zZw46+2rPF66dFn8/Rnvia6ursHnli+vRb3eHz/7n5/Em2Ye3eoQAQAAAAAAAAAAAAAAKKjCFs0sXbp0jef6+vqir69vjed//7//24qQAAAAAAAAAAAAAAAAKInCFs3MvepbeYcAAAAAAAAAAAAAAABASXXkHQAAAAAAAAAAAAAAAAA0m6IZAAAAAAAAAAAAAAAAkqNoBgAAAAAAAAAAAAAAgOR05h0AAAAAAAAAAAAAQAoqFy9s6vXq527a1OsBALQbd5oBAAAAAAAAAAAAAAAgOYpmAAAAAAAAAAAAAAAASI6iGQAAAAAAAAAAAAAAAJKjaAYAAAAAAAAAAAAAAIDkKJoBAAAAAAAAAAAAAAAgOYpmAAAAAAAAAAAAAAAASI6iGQAAAAAAAAAAAAAAAJKjaAYAAAAAAAAAAAAAAIDkKJoBAAAAAAAAAAAAAAAgOYpmAAAAAAAAAAAAAAAASI6iGQAAAAAAAAAAAAAAAJKjaAYAAAAAAAAAAAAAAIDkKJoBAAAAAAAAAAAAAAAgOW1ZNPPss4vje9/9Tnzw7L+Pxx97NO9wAAAAAAAAAAAAAAAAaLK2K5qpVqvxvvecGj/7yU/i0Ud+n10/tXr09tejWquv8nxvb32Nn5Ha0j7aYQ4MjLHs876scUMrlTFPRhNzFm1T7N+YytF/VmMqklbErY/i9ZNMH70v9tFb7nHQHswlmqEM86gMMUIRpZI7rTifQZkkk9uJjAOaRU7AuskTmsE8AmBtyrBPlCFGaFepfGcCmqntimYmTpwYX/jyJfG+9//DWtvVarXo6ekZ/KlWexruo1qrR/eNS6Jr9qLovnHJ4IJQrdWjVo81foZrS/sYab6kZGCMh85elPu8zyK3gZXyzJOx5vdoYs6ibYr9G1M5+s9qTM1W9L1bH8XrJ5k+euvRPf/FPuYvyeSLmWXNbYrH30oM1YpzeV7KECNkpejn8lZoxfkMWk1upzMOWF3K53LIm8/UaAbrLUPJbUhXyufyMsQIWSn63p3Kdyag2Sr1er0tZ+pTf/5TnPHed8enPv2vsfU2267x+2uuujKuvXreGs/PuXRuTJ48ea3X7u2vR9fsRYOPa2dvEp0dlejtH/6lHq4t7WOk+ZKSgTE+d1pXTJqyQUTkN++zyG1gpTzzZKz5PZqYs2ibYv/GVI7+sxpTsxV979ZH8frRR7H6GMl4cpvi8bcSQ7XiXJ6XMsQIWSn6ubwVUhkHDCW30xkHrC7lcznkzWdqNIP1lqGakduVixc2O6ymqp+7ad4htFyz35N2fA1TkPK5vAwxQlaKfi5P/bsGMFadeQdQVDOPPiaOPGrm4ONqtSdO7z65oX9b64uYNWNiXLZgecyaMTFqfRGdHSueH85wbWkfI82XlAyM8Q1fWh4/ePEmT3mNOYvcBlbKM0/Gmt+jiTmLtin2b0zl6D+rMTVb0fdufRSvn2T66F+tj/5yjmMk48ltisffSgzVinN5XsoQI2Sl6OfyVmjF+QxaTW6nMw5YXcrncsibz9RoBustQ8ltSFfK5/IyxAhZKfrencp3JqDZ3GlmhDvNrK6npydOOv7YhisBq7V6dE1YsTBM6lpZPdfbu+bLXavHsG1pHyPNl5QMjDH6VzwuyrxvVm4DKxUlT0aT36OJOYu2KfZvTOXoP6sxZamIe7c+itdPMn301qOrY8UXNCd1lnccjRhtblM8RZlLFE9W5/K8lCFGaIUinstboRXnM8hT2+Z2IuOAtUntXA55K0qe+Eyt3IoyjyieseS2O80UjzvNMJzUzuVliBFaoYjn8lS+MwHN5E4zGRlYAFavnOsc5j+kDbwJquza10jzJSWDm+KLYyzrvG+H9wrGq4x5MpqYs2ibYv/GVI7+sxpTkbQibn0Ur59k+uhMYxy0B3OJZijDPCpDjFBEqeROK85nUCbJ5HYi44BmkROwbvKEZjCPAFibMuwTZYgR2lUq35mAZjJVAQAAAAAAAAAAAAAASE7b3mnmJX+xeVx59fV5hwEAAAAAAAAAAAAAAEAG2rZoBgAAAAAAAAAAAMhO5eKFTb1e/dxNm3o9AADS15F3AAAAAAAAAAAAAAAAANBsimYAAAAAAAAAAAAAAABIjqIZAAAAAAAAAAAAAAAAkqNoBgAAAAAAAAAAAAAA/j97dx4mZ1UlDvhUpzumExIggAKK6IwOiiGAAqKoIIqIwEQQGY1MGIXRKOAyDoM/Hx1xHBfMOC644BJHIoJsAu7B3UERZEYDcRlAWWQRl2yQLqQ6Xb8/Qnc6SXe6qruqvvvdet/naZPu/lL3nPruufd20ccCsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAstNbdAAAAAAAAAAAAAAAlE/lnFUtfbz6WXNb+ngAAN5pBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADITm/RARTh2h9dE1+88POxfv0DccBBB8cp/7g4+vr6ig4LAAAAAAAAAAAAAACAFum6d5q5//51cd7HPxLHnXBinP2u98WKn/9vfO873yo6LAAAAAAAAAAAAAAAAFqo65pmfnPrLVGvRxx62OHxmD32iP33f1r86pe/aPk41Vo9BofqUa3VN/v64GB9q4/xrqV7dMMcGM6x7PO+rHFDJ5WxTpqJuR3X5ji+nMoxfrtySkkn4jZGeuMYo4kxBh8eY7BctU16yrpPkJYyzKPUY0x9XU89vjIo63OYeu00Kpc8Iso7l3JV1vuRS03kkkdEXrnkooz3pIwxQ6epE1rBPAJgW8qwT6QeY/LxleD1IM9he2TzuwaZ/D5Dx35XpqTzNSdd1zSzbt26eMSMR0SlUomIiJmzZsW6deu2uq5Wq8XAwMDIR7U60PAY1Vo9Fl+9PvqWrI7FV68fKaRqrR61emz1Mda1dI/x5ktOhnOcv2R14fO+HbUNbFJknUy2vpuJuR3X5ji+nMoxfrtyarXU925jpDeOMZoYY7Aei5c/PMby9R19cWYqtU16/KzEaJ04lxcl9RiLXNcbkXp8ZVDWvTv12mlULnlEqMfUqO1i5ZJHRF655KKMr6mZRzCxMtY26bHeMprahnzlfC5PPcbk4yvB63Oew/Gl/ppaNmN04B537HdlSlDz3aBSr9e76pn/4Q++F58//7Px6c9+PiIiLlj2X3H7bb+Nt73jXZtdd9klF8Xll1681b9fev6FMXPmzG2OMThUj74lq0c+r525Y/T2VGJwaOyneqxr6R7jzZecDOe46nV9MXvWdhFR3LxvR20DmxRZJ5Ot72Zibse1OY4vp3KM366cWi31vdsY6Y1jjLTGGM9Uapv0+FmJ0TpxLi9K6jGKL39l3btzufe55BGRVy45UNvFyiWPiLxyyUUZ69s8gomVsbZJj/WW0VpR25VzVrU6rKTVz5pbdAgTavU9ST3nbsu3UTmfy1OPUXxTl3qMZT2X5/J7AMZIcxy2reveaWb27NnxYLUaQ0NDERFRHRiIOdvvsNV1C447IZaef+HIx0fP+0zDY9Q2RCyaNz0iNv5Z27Dp62N9jHUt3aMb5sBwjod8vFb4vG9HbQOblLG+m4m5HdfmOL6cyjF+u3JqtdT3bmOkN44xmhhjaIsxhlo/xnimUtukx89KjNaJc3lRUo+xyHW9EanHVwZl3btTr51G5ZJHhHpMjdouVi55ROSVSy7K+JqaeQQTK2Ntkx7rLaOpbchXzufy1GNMPr4SvD7nORxf6q+pZTNGB+5xx35XpgQ13w267p1m1q1bF2e89tQ4adErY+95+8R73vWOOO4lJ8bzjzhym/9uYGAgTjl5YcNd/NVaPfqmbSyo/r5N3WCDY7ylUq0eY15L9xhvvuRkOMd4eLFPZd63qraBTVKpk2bqu5mY23FtjuPLqRzjtyundkpx7zZGeuMYo4kxBuvR17PxRZr+3vLUNulJZZ8gPe06lxcl9RhTWdfHk3p8ZZDKc5jiubwTcskjIp25xEap3A+1Xe48IvLKJRep3JPczuVQtFTqxGtq5ZbKPCI9k6lt7zSTnm5755Vuy3eycjuXpx5j8vEl8nrQtngOG5Pia2rZjNGBe9yx35VJZL52s96iA+i0OXPmxOLXvT4u+sKyuPiiC+LAgw6OQw87vOXjDBdO7xbv5dM7xkQfvglbXkv3GG++5GRkM3k4x7LO+264VzBVZayTZmJux7U5ji+ncozfrpxS0om4jZHeOMZoYozectY26SnrPkFayjCPUo8x9XU99fjKoKzPYeq106hc8ogo71zKVVnvRy41kUseEXnlkosy3pMyxgydpk5oBfMIgG0pwz6ReozJx1eC14M8h+2Rze8aZPL7DB37XZmSztecdF3TTETEMw55VjzjkGcVHQYAAAAAAAAAAAAAAABtol8JAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7PQWHQAAAAAAAAAAAABAESrnrCo6BAAA2kjTTIPq9XpERFSrAwVHAt2tv78/KpVKyx5PbUM61DfkSW1DntQ25KnVtR2hviEV9m7Ik9qGPDmXQ77s3ZAntd15AwPd99x0W84p5OtcDvmyd0OeJqrtSn24WtmmP//5T3H64lOLDgO63tLzL4yZM2e27PHUNqRDfUOe1DbkSW1Dnlpd2xHqG1Jh74Y8qW3Ik3M55MveDXlS25An53LIl70b8jRRbWuaadDQ0FCsXr0qZsxorMOwWh2I0xefGh897zPR39/aw1M7iLe9xNs6re7yzbG2U49RfFOTenwRk4+xHfV9zz13x5lvOiPp56tZZZgDzZJTOaRU283s3Z2Qy/3OJY+IfHJJOY+cajvl53mYGFtDjBNrx/9zXjP1XXT+jUg9RvFNXeoxlvVcnvrz2qhc8ojIJ5duz0Ntt0YueUTkk0sueURMLhfn8omlHqP4pi71GMu6d7dS6vcoIv0YU48vQoyNKrq2U3gOOqEb8uyGHCPKk6dz+cRSj1F8U5d6jGU9l6f+vDZKHunJJZd21XZvK4LrBj09PbHTTjs3/e/6+2e2vOO4ncTbXuJNT861nXqM4pua1OOLKD7Gnp6emDt3pyRiaQc5lYOcWm+ye3cnFP3ctEoueUTkk0sueWxLCrVdhudZjK0hxs6aTH2XIf/UYxTf1KUeY9Hx5fyaWiNyySMin1zk0RpqO488IvLJJZc8IorPxbm8GOKbutRjLDo+r6k1JvUYU48vQoyd1u3n8ol0Q57dkGNE9+Q5mnN5McQ3danHWHR83b53yyM9ueTS6jx6WvZIAAAAAAAAAAAAAAAAkAhNMwAAAAAAAAAAAAAAAGRn2tlnn3120UHkqqenJ/Z+yj4xbdq0okNpiHjbS7z5KMNzk3qM4pua1OOLSCvGlGJpFTmVg5y6Sy7PTS55ROSTSy55pK4Mz7MYW0OM6StD/qnHKL6pSz3G1OMbT1nj3lIueUTkk4s8ilXWuLeUSx4R+eSSSx4R5cylDDGnHqP4pi71GFOPrxPK8BykHmPq8UWIsSy65Tnohjy7IceI7slzqsrwPKUeo/imLvUYU49vPGWNe0vySE8uubQjj0q9Xq+37NEAAAAAAAAAAAAAAAAgAT1FBwAAAAAAAAAAAAAAAACtpmkGAAAAAAAAAAAAAACA7GiaaVC9Xo+BgYGo1+tFhwK0kNqGfKlvyJPahjypbciX+oY8qW3Ik9qGfKlvyJPahjypbciX+oY8qW0oB00zDapWq3HKyQujWq0WHQrQQmob8qW+IU9qG/KktiFf6hvypLYhT2ob8qW+IU9qG/KktiFf6hvypLahHDTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdnqLDqAI7/33s+PGFT8f+fzoYxfESYteWVxAAAAAAAAAAAAAAAAAtFRXNs2sXr06Xnnqq+OQZz0nIiL6+qYXGk91sB59PRG1oYj+3kqhsQDAVJVxXytjzEBj1DdAZ1hvATqjrOttWeMGtk1tQ57UNkBnWG9pFXMJACB9zmzF68qmmTWrV8cjH/momDVru3GvqdVqUavVRj6vVgfaEkt1sB6Ll6+PZSsfikXzpsd5R85SDNBGnapt6FZF7muTrW97MaRtKnu3+oZ0OZfnxXrLaOob2qeMP3NH2CcgZWob8uX1csiTn7nzYb1lNOdyyNdU9+7KOataGk/9rLktfTzoVs7lNMuZLQ1d1zQzWKvFAw/cH1/4/PnxqU98LJ74N3vFqa95bcyePWez66664rK4/NKL2x5PX0/EspUPRcTGP5ceNavtY0I361RtQ7cqcl+bbH3biyFtU9m71Teky7k8L9ZbRlPf0D5l/Jk7wj4BKVPbkC+vl0Oe/MydD+stozmXQ77s3ZAntU2znNnS0HVNM1GpxD+c8o+x6667x8yZM+Nj534oLrvki/HKU1692WULjjshXnTMgpHPq9WBOH3xqS0PpzYUsWje9JHusdpQRG9Py4cBHtap2oZuVeS+Ntn6thdD2qayd6tvSJdzeV6st4ymvqF9yvgzd4R9AlKmtiFfXi+HPPmZOx/WW0ZzLod82bshT2qbZjmzpaHrmmaGhobigAOeHnN32ikiIg448KC49ZZbtrqur68v+vr62h5Pf28lzjtyViw9albUhsLbLUGbdaq2oVsVua9Ntr7txZC2qezd6hvS5VyeF+sto6lvaJ8y/swdYZ+AlKltyJfXyyFPfubOh/WW0ZzLIV/2bsiT2qZZzmxp6Lo+pft+f2+ctviUuPZH18R99/0+blzx83jsnnsWGlN/byV6eyqKAIAslHFfK2PMQGPUN0BnWG8BOqOs621Z4wa2TW1DntQ2QGdYb2kVcwkAIH3ObMXrunea2eOxe8bJrzo1Llj2X1GtDsQ+8/eNl/7dwqLDAgAAAAAAAAAAAAAAoIW6rmkmIuKFRx0TLzzqmKLDAAAAAAAAAAAAAAAAoE16ig4AAAAAAAAAAAAAAAAAWk3TDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAACeqCj7AAAgAElEQVQAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnRNAMAAAAAAAAAAAAAAEB2NM0AAAAAAAAAAAAAAACQHU0zAAAAAAAAAAAAAAAAZEfTDAAAAAAAAAAAAAAAANnp2qaZD33g/fHyl7646DAAAAAAAAAAAAAAAABog65smrnhp9fHT6//SVvHqA7WY3CoHtXB+ra/X9v2dZCLkbleK/dczyUPaKcy1slE+3ZK4zd1bQnvBbRa0fXdKuq5ObncdygT6xStYB5BvtQ3bFtZa6SscUMnlbFOyhgzQBlZb2kVcykf7iUAdJ79Nz3tuidd1zQzMDAQ/7X0k3HkUUdv87parRYDAwMjH9XqQMNjVAfrsXj5+uhbsjoWL1+/1S9qVQfr8drl6+O+9fVYfPX410EuqrVRc/3q9YVuLlOq7YTygFQVWSeTre+J9u12a2b8pq61ZpGJdp7Ly0I9NyeX+567qdQ26bFOMdqkz+XmESTNa2rQPmV8PS1CbUMjyljfahvS5jW1fFhvGc25nAj3Mlf2bsiT2s6H/Tc97bwnvS17pJK46IJlsc/8/WLePvPjG1/7yrjXXXXFZXH5pRdPaoy+nohlKx+KiI1/Lj1q1lbf/8qttfjUCyvbvA5y0Tdt2zXRSVOq7YTygFQVWSeTre+J9u12a2b8pq61ZpGJdp7Ly0I9NyeX+567qdQ26bFOMdqkz+XmESTNa2rQPmV8PS1CbUMjyljfahvS5jW1fFhvGc25nAj3Mlf2bsiT2s6H/Tc97bwnXdU08+tf/TJu+Ol1seQ/PxI33/zrbV674LgT4kXHLBj5vFodiNMXn9rQOLWhiEXzpseylQ/FonnTozYU0duz+fePfUJf/HGgvs3rIBe1DVvUxIbi5vqUajuhPCBVRdbJZOt7on273ZoZv6lrrVlkop3n8rJQz83J5b7nbiq1TXqsU4w26XO5eQRJ85oatE8ZX0+LUNvQiDLWt9qGtHlNLR/WW0ZzLifCvcyVvRvypLbzYf9NTzvvSaVer3fNewm999/fGb9YeVM84hHTY8OGofjLXx6MmTNnxtLzL5zw3w4MDMQpJy+MpedfGDNnzpzw+upgPfp6Nv7CVn9vZfzvb9jYFTXedZCLaq2+ca5viOjvS2euN13bieYBKUmlTpqp74n27XZrZvymrk3kXkArtfpcXhbquTm53Pdu0mxtkx7rFONp6lxuHkFpeE0NWiuVGlHb0Hqp1IlzOeTJa2rlZr1lPM7l3cu9zF+z9V05Z1VLx6+fNbeljwds5Fxebvbf9LTrnnTVO8289rTXR6228S17Vq68KT71iY/G+5Z8sC1jDf9i1njdTVt+X2cauRteuMo+13PJA9qpjHUy0b6d0vhNXVvCewGtVnR9t4p6bk4u9x3KxDpFK5hHkC/1DdtW1hopa9zQSWWskzLGDFBG1ltaxVzKh3sJAJ1n/01Pu+5JVzXN7LDjjiN/3377OyMiYpdHPqqocAAAAAAAAAAAAAAAAGiTru2LeurTDoyLLr2y6DAAAAAAAAAAAAAAAABog65tmgEAAAAAAAAAAAAAACBfmmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADITm/RATRq5U03xs03/zqOf8mJsWHDhvjSZZfEnXfcFvs/7cA4/HlHFB0eAAAAAAAAAAAAAAAACSlF08z//s8N8YH3vyce/Zg94viXnBhXXH5JXHH5JRER8T833BARoXEGAAAAAAAAAAAAAACAET1FB9CIKy+/NPr6pseLjz8hIiK+ffXymLfP/Hjr298ZO+ywQ1z9ja8VHCEAAAAAAAAAAAAAAAApKUXTzF133RkHHfyMeOYhz47bb/ttrFu3No5dcHzM22d+7Lf/U+P3v/990SECAAAAAAAAAAAAAACQkFI0zTxixoxYs3pVRETccMP10dvbF3s96ckREbF27Zrom95XZHgAAAAAAAAAAAAAAAAkprfoABoxf/5+8d8//EH8y5vfEL+/957Y/6lPi+nTp8eyzy2NFT//WRx40MFFhwgAAAAAAAAAAAAAAEBCSvFOMy9/xaJ4zB57xF2/uzNmz54TC086OSIivvedb8Xs2XPiZQtPKjhCAAAAAAAAAAAAAAAAUlKKd5rZYccd4/0f+HD84b77Yse5c6Ovry8iIk46+VVx4IFPjznbb19whAAAAAAAAAAAAAAAAKSkFE0zwx75qEdt9vnznv+CgiIBAAAAAAAAAAAAAAAgZaVomqlWq3HF5ZfEr375i1i3bm1EfYsLKhEf/ugnC4kNAAAAAAAAAAAAAACA9JSiaWbppz4RP/7RNbF1t8ywSifDAQAAAAAAAAAAAAAAIHGlaJpZ8fOfxdy5c+NNZ74ldt1116hMsklmw4YNcfFFF8R3v311RETs/9QD4h8XnxbTp09vZbgAAAAAAAAAAAAAAAAUrKfoABpSiZi/3/7x13/9hJg1a7uYOWvWVh+NuOa/fxDf/fa34sy3vC3e+e5zYuVNN4400HRSdbAeg0P1qA6O9845QKrUL9AO1hagm1jzIE9qG2iEtaJ7ufeQJ7UNeVLbAJ1hvaVVzCUAIFfOObRSKZpmnn7wM+POO26f8uMcetjh8ZnPXRB7PenJscMOO0Rvb29MmzZtzGtrtVoMDAyMfFSrA1MeP2JjAS9evj76lqyOxcvXK2TosKnUtvqFtLVr7243awtsW1lrm7FZ8ximtvOithlNfTMea0W5eU0N8qS2IV+TrW+1DWnzM3c+rLeM5lwO+bJ3Q57Udmc459BqvUUH0IjZ282O22+7Ld7772fHbrs/eqvvV6ISJ7/q1IYf71/e/Ia4+67fxd5PmRfPf8ELx7zmqisui8svvXiyIY+rrydi2cqHImLjn0uPauxdcoDWmEptq19IW7v27naztsC2lbW2GZs1j2FqOy9qm9HUN+OxVpSb19QgT2ob8jXZ+lbbkDY/c+fDestozuWQL3s35Eltd4ZzDq1WiqaZq668PCIibrpxRdx044oxrmiuaeast7497r3n7vjIBz8QP/j+d+Ow5z5vq2sWHHdCvOiYBSOfV6sDcfrixscYT20oYtG86bFs5UOxaN70qA1F9Jbi/X4gD1OpbfULaWvX3t1u1hbYtrLWNmOz5jFMbedFbTOa+mY81opy85oa5EltQ74mW99qG9LmZ+58WG8Zzbkc8mXvhjyp7c5wzqHVStE085rXnh5RqUz5ce644/ZYu2ZNzN93v9hpp51j73nz4pe/WDlm00xfX1/09fVNecwt9fdW4rwjZ8XSo2ZFbWjj50DnTKW21S+krV17d7tZW2DbylrbjM2axzC1nRe1zWjqm/FYK8rNa2qQJ7UN+ZpsfattSJufufNhvWU053LIl70b8qS2O8M5h1YrRdPMoWM0tUzGnbffFp/9zCfjrW9/Z8zo749bbv6/OPrYBRP/wxYbLlwdb1A+6hdoB2sL0E2seZAntQ00wlrRvdx7yJPahjypbYDOsN7SKuYSAJAr5xxaqRRNM+d9/Nxtfr8SEa953RkTPs6znnNY3HHH7bHkfe+Oer0eBz/zkHjBkS9qUZQAAAAAAAAAAAAAAACkohRNMz/8/ncnuKLSUNNMpVKJkxa9Mk5a9MrWBAYAAAAAAAAAAAAAAECSStE0809nvmXMr99z993xxQsviFf8/ckdjggAAAAAAAAAAAAAAICUlaJp5oADnz72Nw6MWHnTirjuJz+Oo49d0NmgAAAAAAAAAAAAAAAASFZP0QFM1XbbzY7bb7ut6DAAAAAAAAAAAAAAAABISCneaeb6667d6mv1ej1+f++9ccNPr48d5+5YQFQAAAAAAAAAAAAAAACkqhRNMx/6wPsjojLGd+oREfGio/+2o/EAAAAAAAAAAAAAAACQtlI0zTz7OYdFpbJ500ylUontd9gh9t3vqfHkvZ9SUGQAAAAAAAAAAAAAAACkqBRNMy/9u4UxY8aM2G727KJDAQAAAAAAAAAAAAAAoAR6ig5gIvV6Pd70+tfGxRd9oehQAAAAAAAAAAAAAAAAKInkm2YqlUrs8dg947777i06FAAAAAAAAAAAAAAAAEqit+gAGvHEJ+4V37r6m7H00+fFrrvuNuY1Rx+7oMNRAQAAAAAAAAAAAAAAkKpSNM186+pvRETEd761PCIqW3y3HhEVTTMAAAAAAAAAAAAAAACMKEXTzPEnnBhR2bJZBgAAAAAAAAAAAAAAAMZWiqaZE058+YTX/ObWW+Luu++K5xz63A5EBAAAAAAAAAAAAAAAQMp6ig6gVX74g+/FeR87t+gwAAAAAAAAAAAAAAAASEA2TTMAAAAAAAAAAAAAAAAwTNMMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkJ5+mmfrI/wAAAAAAAAAAAAAAANDleosOoFVe+rKFccyCFxcdBgAAAAAAAAAAAAAAAAkoTdPMTTeuiOuu/VGsWbMm6vVN7yhTqz0U995zT5z7iU/HdtttV2CEAAAAAAAAAAAAAAAApKIUTTPX/viaOPdD/xkRw80ylVF/H/4cAAAAAAAAAAAAAAAANuopOoBGfPnKL8XMmTPjtDPeGAcc+PSIiHjbO94Vr3ndGdHT0xNveNM/FxwhAAAAAAAAAAAAAAAAKSlF08y999wTBxz09Djk2YfGLrs8MiIinvSkJ8ehhx0eT5k3P776lSsLjhAAAAAAAAAAAAAAAICUlKJpZsaMGXH/urUREbH9jjtERMS9994TERFz5syJu353Z2GxAQAAAAAAAAAAAAAAkJ7eogNoxH77PzV++IPvx1e/fGXstdeTI6IeHzv3Q7HvvvvHT6//STxq192KDhEAAAAAAAAAAAAAAICElOKdZk582Stip513irvu+l3s9aQnxz7z94vbb/ttXHXl5TE4OBgnnPiyokMEAAAAAAAAAAAAAAAgIaV4p5m5O+0U//HBj8af/vTHiIj4l//3tvjJj6+J1WtWx7x58+Nxj/+rgiMEAAAAAAAAAAAAAAAgJck3zQwNDcUVl18aj3v84+NpBxwUERHTpk2LQ559aMGRAQAAAAAAAAAAAAAAkKqeogOYSE9PT3zja1+On/3PDUWHAgAAAAAAAAAAAAAAQEkk3zQTEXHAQQfHTTetiAcffLDoUAAAAAAAAAAAAAAAACiB3qIDaMTv7rwj/viHP8YbTn9NbL/9Dlt9v1KpxDn/8aECIgMAAAAAAAAAAAAAACBFpWiaue23v4mIiPvXrYv7160b44pKZwMCAAAAAAAAAAAAAAAgaaVomvnwxz7Z0sf7ylVXxNe/+uX4y18ejPn77R+LX/f6mDFjRkvHAAAAAAAAAAAAAAAAoDg9RQfQiF/98hexbu3a2GWXR2718cc/3Bd33H57w49144qfx8UXfSFed8Yb453vPif+71e/im9+/SvtC34c1Vo9BofqUR2sx4OD9Y6PT1qqg5vmQzcoc75ljh06ZWSPq+VZJ83kl/tzkYtuXtu7IfdccuxEHp16rjqxNuZy36FMnHtoBfNo6jyHpMrcTI8zM62gttPjntAK5hFAZ1hvaZVumUvdkicAlEUue3MueTCxUjTNnPexj8Q1P/zBmN/75te/Fud97CMNP1Zvb28sPGlR7DN/39hjj8fGbrvvHmvXrN3qulqtFgMDAyMf1erApOPfUrVWj8VXr4++Jatj8fL18cBD/sNUN6sO1mPx8k3zIfe5UHS+U6ntomOHMthsj7t6fUcPk+3cu4c1k1+RzwWN6+a1vSy527s7k0ennqtOrI253PfcdWLfpnOcexhtsvVtHk2d55B2mtK53NxMjjMzw9R2XtwTRnMuhzx5TS0f1ltGcy6fWLfkSX7s3ZAntZ3P3pxLHjSmt+gAtuVdZ7995O/XX39t3HnnHZt9f8OGwbj1lptj9pw5DT/m3k+ZF3s/ZV5ERNxx+21x6y03x8KTTt7ququuuCwuv/TiSUa+bX3TIpatfCgiNv756RfOip5KW4aiBPp6Np8PS4+aVXBE7VV0vlOp7aJjhzLYco/rZJ20c+8e1kx+RT4XNK6b1/ay5G7v7kwenXquOrE25nLfc9eJfZvOce5htMnWt3k0dZ5D2mlK53JzMznOzAxT23lxTxjNuRzy5DW1fFhvGc25fGLdkif5sXdDntR2PntzLnnQmKSbZn71y5UP/60Sq1etitWrVm11TW9vb5xw4subfuw///lPseR9746jj1kQT3ji32z1/QXHnRAvOmbByOfV6kCcvvjUpscZS21DxKJ502PZyodi0bzpse4v9Zg1vRK9pXjfH1qtNrT5fKgNRdZzoeh8p1LbRccOZbDlHlfb0Lk6aefePayZ/Ip8LmhcN6/tZcnd3t2ZPDr1XHVibczlvueuE/s2nePcw2iTrW/zaOo8h7TTlM7l5mZynJkZprbz4p4wmnM55Mlravmw3jKac/nEuiVP8mPvhjyp7Xz25lzyoDGVer2e7HsJ3XH7bVGv1+OtZ705nvHMZ8Xfvvj4zb7fM21a7LzzLtHf39/U465bty7e+a9vjSc84Ymx+LTXR6Uy8du8DAwMxCknL4yl518YM2fObGq8sVRr9eibtvE/TlUiYkavt5rpZtXBevT1bJwP/V0wF1LKt9naTil2SNXIHrchor+vuDpp9d49rJn8Unku2LZuXtvLmHu37t2dyKNTz1Un1sZc7ns3ade+Tec49zCeZurbPJo6zyGd0vS53NxMjjMzY1Hb5eeeMB7ncsiT19TKzXrLeJzLx9YteZK3Zuu7cs7W/yfzU1E/a25LHw/YqFvP5bnszbnkwcSSfqeZPR/3+IiIeM6hz4195u878vlUPFitxvvf867Yeeed46STXxkDA+ujUunp+EI1XFg60ojY9B8lu2U+lDnfMscOnZL7HtdMfrk/F7no5rW9G3LPJcdO5NGp56oTa2Mu9x3KxLmHVjCPps5zSKrMzfQ4M9MKajs97gmtYB4BdIb1llbplrnULXkCQFnksjfnkgcTS7ppZljPtGmx735PHfN7f/zDffG5z346znzL2xp6rOuuuzZ+85tbIiLi1a9aFBERO++yS5z78U+3JlgAAAAAAAAAAAAAAAAKV4qmme9/99txw/XXxcte8fdx+POOiIiIwcHB+MpVV8RVV1wWDz1Ua/ixDj3s8Dj0sMPbFSoAAAAAAAAAAAAAAAAJKEXTzAtfdEx8a/k34jOf/ER8/zvfjsOPeEF85covxb333hP9/f2x8KSXFx0iAAAAAAAAAAAAAAAACSlF08yifzglnn/EC2PZ55bGjSt+FrfeektERDzviCPjpX+3MObMmVNwhAAAAAAAAAAAAAAAAKSkp+gAGrV27ZpYt3bNw5/VIyLi3nvujjWrVxUXFAAAAAAAAAAAAAAAAEkqxTvNfPA/3hc/vf76iKjHwc84JI448qi4/NIvxi9/sTLeetab47DnPi9Ofc3rig4TAAAAAAAAAAAAAACARJTinWZ+ev11sdtuu8Vb3352vP5N/xxP3vsp8bZ3vCtOO+ONMXv2nPjud75ddIgAAAAAAAAAAAAAAAAkpBTvNHPiyxbGsQuOjwcfrMatt9wcq1evigMPOjgOefahsf/TDoxLv3hh0SECAAAAAAAAAAAAAACQkFI0zRy74Pi46AvL4upvfiMGBwejUon4wsVfiuuu/XEs+9zSeMe/vafoEAEAAAAAAAAAAAAAAEhIT9EBNOKSL14YX//ql2P33XeP3XbbLer1jV/f47F7xsDA+vjiRRcUGyAAAAAAAAAAAAAAAABJKUXTzDX//f34q796Qrx3yQdj3j77jnx990c/OvZ/2oGx8sYVBUYHAAAAAAAAAAAAAABAakrRNPPA/ffHY/fcMyqVSkRl6+8/+OCDnQ8KAAAAAAAAAAAAAACAZJWiaWa33R8dN920Ih544IHNvn7bb38T/3vD9fGYxzymoMgAAAAAAAAAAAAAAABIUW/RATRiwYtfEud++APx5jecFjP6Z0RExDnv+bf4xcqbYnBwQxx19LEFRwgAAAAAAAAAAAAAAEBKStE084xDnhUPPHB/XPSFZXH//esiImLFz38Wvb19ceLLXh7PPvS5BUcIAAAAAAAAAAAAAABASkrRNBMRccSRR8Whhx0eN9/861izZk309/fH3+z1pJg9e07RoQEAAAAAAAAAAAAAAJCY0jTNRERMf8QjYt4++xYdBgAAAAAAAAAAAAAAAInrKToAAAAAAAAAAAAAAAAAaDVNMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGSnK5tm1q5dE1//2pfjLWe+KX535x1FhwMAAAAAAAAAAAAAAECL9RYdQKdVq9U447Wvjsc97vFxx+23FR0OAAAAAAAAAAAAAAAAbdB17zQzffr0OPcTn4oz3vhPbR2nWqvH4FA9qrX6Zl9/aLAegw9/DP99vGvpHt0wB4ZzHCz5vC9r3NBJZayTZmJux7U5ji+ncozfrpxS0om4jZHeOMaAzjNfaYUyzKMyxAgpyqV2cskDWiWXmsglD2gVNQETUye0gnlEqxQxl7phzOrgw+MN5ptjNynifgIUKZffZ+jIGB3aI+zzjWvXPem6pplp06bF9tvvMOF1tVotBgYGRj6q1YGGx6jW6rH46vXRt2R1LL56/cgEr9bqsaEeUXv4Y/jvY11L9xhvvuRkOMc5S1aPzP+icm5HbQObFFknk63vZmJux7U5ji+ncozfrpxaLfW92xjpjWOMcphKbZOe3OcrzenEubwoZYgR2iX1c3kn5JIHjKa288kDtpTzuRyKVtbXy0mL9ZbRynYu74Yxq4P1WLz84fGWr+9Io4V1oX2KuJ/D7N2Qp9RrO5ffZ+jIGB3aI+zzjWvnPanU6/WufOb/+If74vWnvSbe/4EPxx6P3XOr7192yUVx+aUXb/X1pedfGDNnztzmYw8O1aNvyeqRz2tn7hi9PZUYHBr7qR7rWrrHePMlJ8M5/uvBffH2Z28XEcXN+3bUNrBJkXUy2fpuJuZ2XJvj+HIqx/jtyqnVUt+7jZHeOMYoh6nUNunJfb7SnE6cy4tShhihXVI/l3dCLnnAaGo7nzxgSzmfy6FoZX29nLRYbxmtbOfybhizG3LsJmXeuyvnrGppPPWz5rb08aBbpX4uz+X3GXIZo5Pj5KCdz1XXvdNMoxYcd0IsPf/CkY+PnveZhv9tbUPEonnTI2Ljn7UNm74+1sdY19I9umEODOe45Ce1wud9O2ob2KSM9d1MzO24Nsfx5VSO8duVU6ulvncbI71xjFEOU6lt0pP7fKU5nTiXF6UMMUK7pH4u74Rc8oDR1HY+ecCWcj6XQ9HK+no5abHeMlrZzuXdMGZtaIvxhto7XoR1oZ2KuJ/D7N2Qp9RrO5ffZ+jIGB3aI+zzjWvnPfFOM+O808yWBgYG4pSTFzbcCVit1aNv2saJ3t+3qcPpocH6SKfSUGzsWqrVY8xr6R7jzZecDOcYDy9gqcz7VtU2sEkqddJMfTcTczuuzXF8OZVj/Hbl1E4p7t3GSG8cY5RPs7VNerppvtKcdp3Li1KGGKETUjyXd0IuecB41Ha584Btye1cDkVLpU68plZuqcwj0lOGc3k3jFkdrEdfz8Zf3OzvzTPHblLE/RxLs/XtnWagHFI8l+fy+wwdGaNDe4R9vnHtuie9LXskNjM8oXu3eC+f6f+fvXsPs7MqDwX+zmQmIROIQECgaEVFQAigFgGLFapHkFuRU7yUYiiCmiKCrQK2R0+xahXjBRUVLRSDCHKzeDcg3uutHuWmlgp44eLxKCQCmY3Zk/nOH8lMJslMZu+Zvfe3vvX9fs8zj8zOl73e99vrXWvNzrzuSV68sRdh02upj6nmS07GF/n1OVZ13tfhtYLZqmKdtBNzN67NcXw5VWP8buWUkl7EbYz0xjEG9J75SidUYR5VIUZIUS61k0se0Cm51EQueUCnqAmYnjqhE8wjOqWMuVSHMcd+QTPnHOukjNcToEy5/D5DT8bo0R5hn29dt16T2jbN7PjYneLKa64vOwwAAAAAAAAAAAAAAAC6QL8SAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAK3BSBkAACAASURBVAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHYGyg6gDN/5j2/FJ6/4eKxe/UgccODBceorlsbg4GDZYQEAAAAAAAAAAAAAANAhtfukmYcffigu+tD74/gTXhznveUdccvNP4yv3nRjx8dpNIsYGS2i0Sw2enxkpNjsa6prqY86zIGxHKs+76saN/RSFeuknZi7cW2O48upGuN3K6eU9CJuY6Q3jjHSGoN6MJfoBPMI8qW+IU9qG/KktgF6w3pLp5Qxl+owZh1yjIhojKwfcyTvPAFSksvvARijzXF6sOfaY7esdk0zd935syiKiEMPe2487vGPj6c//U/ipz/58WbXNZvNGB4eHv9qNIZbHqPRLGLpDatjcNnKWHrD6vHJ12gW0Sxis6/JrqU+ppovORnL8Q3LVpY+77tR28AGZdbJTOu7nZi7cW2O48upGuN3K6dOS33vNkZ64xgjrTGmMpvaJj1+VmKiXpzLgd5L/VwOzIzahnw5l0OevKeWD+stE1XtXF6HMeuQY8S6X95dumL9mCtW96Rxpm7rn70b8pT63m2MtMaI6M2eW7c9dib6iqKo1V35xte/Gh9f/m/xr//28YiIuPyyS+Pnd98dbzrvLRtdd+3VV8Z111y12d+/ZPkVMTQ0tMUxRkaLGFy2cvz75tnbxUB/X4yMTn6rJ7uW+phqvuRkLMeHTh+M+Qu2jojy5n03ahvYoMw6mWl9txNzN67NcXw5VWP8buXUaanv3cZIbxxjpDXGVGZT26THz0pM1ItzOdB7qZ/LgZlR25Av53LIk/fU8mG9ZaKqncvrMGYdcqzTmGWa7d7dd/6DHY2nOHf7jj4f1FXqe7cx0hqjV+PUbY+didp90sxk+iaZE8cdf0JcsvyK8a8LL7q45edrro1YsnhuRKz73+baDY9P9jXZtdRHHebAWI5v/lCz9HnfjdoGNqhifbcTczeuzXF8OVVj/G7l1Gmp793GSG8cY6Q1xlRmU9ukx89KTNSLcznQe6mfy4GZUduQL+dyyJP31PJhvWWiqp3L6zBmHXKMiGiObjLmaA/GrNn6Z++GPKW+dxsjrTEierPn1m2PnYnafdLMj374g3jPsnfE8k9cHf39/fGvF30wGo8+Gme+9nVb/HvDw8Nx6sknttzl22gWMThn3SScP7ihK2dkko9UahYx6bXUx1TzJSdjOcb6xT6Ved+p2gY2SKVO2qnvdmLuxrU5ji+naozfrZy6KcW92xjpjWOMtMZoRbu1TXpSmUukp1vncqBcKZ7LgdlT25Av53LIk/fUqs16y1SqcC6vw5h1yDEiojFSxGD/ul/mnT+Qb56paLe+fdIMVEOKe7cx0hojojd7bp332FYMlB1Arz159z2iv78/brpxRey9eN+4+eYfxvF/+eKOjzM22QY2+SyfgUkm+tiLsOm11MdU8yUn4wvw+hyrOu/r8FrBbFWxTtqJuRvX5ji+nKoxfrdySkkv4jZGeuMYI60xqAdziU4wjyBf6hvypLYhT2oboDest3RKGXOpDmPWIceIDb+0m3uedI/GHmhfLr8HYIw2x+nBnmuP3bLaNc0sXLgwlp5+Zlz5icviqisvj2ceeHAcethzyw4LAAAAAAAAAAAAAACADqpd00xExLMOeXY865Bnlx0GAAAAAAAAAAAAAAAAXeIDeAAAAAAAAAAAAAAAAMiOphkAAAAAAAAAAAAAAACyo2kGAAAAAAAAAAAAAACA7GiaAQAAAAAAAAAAAAAAIDuaZgAAAAAAAAAAAAAAAMjOQNkBVEVRFBER0WgMlxwJ1Nv8+fOjr6+vY8+ntiEd6hvypLYhT2ob8tTp2o5Q35AKezfkSW1DnpzLIV/2bsiT2oY85XguHx5Of12pQoxUn70b8jRdbfcVY9XKFj3wwO/ijKWnlR0G1N4ly6+IoaGhjj2f2oZ0qG/Ik9qGPKltyFOnaztCfUMq7N2QJ7UNeXIuh3zZuyFPahvy5FwO+bJ3Q56mq21NMy0aHR2NlSsfjK22aq3DsNEYjjOWnhYXXnRxzJ/f2cNTN4i3u8TbOZ3u8s2xtlOPUXyzk3p8ETOPsRv1ff/998XZf/eapO9Xu6owB9olp2pIqbbb2bt7IZfXO5c8IvLJJeU8cqrtlO/zGDF2hhin143/57x26rvs/FuReozim73UY6zquTz1+9qqXPKIyCeXuuehtjsjlzwi8skllzwiZpaLc/n0Uo9RfLOXeoxV3bs7KfXXKCL9GFOPL0KMrSq7tlO4B71QhzzrkGNEdfJ0Lp9e6jGKb/ZSj7Gq5/LU72ur5JGeXHLpVm0PdCK4Oujv749Fi3Zo++/Nnz/U8Y7jbhJvd4k3PTnXduoxim92Uo8vovwY+/v7Y/vtFyURSzfIqRrk1Hkz3bt7oex70ym55BGRTy655LElKdR2Fe6zGDtDjL01k/quQv6pxyi+2Us9xrLjy/k9tVbkkkdEPrnIozPUdh55ROSTSy55RJSfi3N5OcQ3e6nHWHZ83lNrTeoxph5fhBh7re7n8unUIc865BhRnzwnci4vh/hmL/UYy46v7nu3PNKTSy6dzqO/Y88EAAAAAAAAAAAAAAAAidA0AwAAAAAAAAAAAAAAQHbmnHfeeeeVHUSu+vv7Y+999o05c+aUHUpLxNtd4s1HFe5N6jGKb3ZSjy8irRhTiqVT5FQNcqqXXO5NLnlE5JNLLnmkrgr3WYydIcb0VSH/1GMU3+ylHmPq8U2lqnFvKpc8IvLJRR7lqmrcm8olj4h8csklj4hq5lKFmFOPUXyzl3qMqcfXC1W4B6nHmHp8EWKsirrcgzrkWYccI+qT52xV4T6lHqP4Zi/1GFOPbypVjXtT8khPLrl0I4++oiiKjj0bAAAAAAAAAAAAAAAAJKC/7AAAAAAAAAAAAAAAAACg0zTNAAAAAAAAAAAAAAAAkB1NMy0qiiKGh4ejKIqyQwE6SG1DvtQ35EltQ57UNuRLfUOe1DbkSW1DvtQ35EltQ57UNuRLfUOe1DZUg6aZFjUajTj15BOj0WiUHQrQQWob8qW+IU9qG/KktiFf6hvypLYhT2ob8qW+IU9qG/KktiFf6hvypLahGjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdjTNAAAAAAAAAAAAAAAAkB1NMwAAAAAAAAAAAAAAAGRH0wwAAAAAAAAAAAAAAADZ0TQDAAAAAAAAAAAAAABAdgbKDqAMX7npxrjumk/G6kdWxz6L943TX3NWLFiwddlhAQAAAAAAAAAAAAAA0CG1+6SZhx9+KC7+yIfjhf/zRXH+uy+I+++7Nz73mU+XHRYAAAAAAAAAAAAAAAAdVLummaKI6O/vj0WLFsX22y+KefPmxZw5tbsN0HONZhEjo0U0msW670fWfz9SlBxZe6oaN/TSpvXO9NpZW6xDlKWqc6+qcZchp3uVUy5QFc6AdIJ5BPlS37BlVf0ZRm3D9KpY32obplfF2iY91ltgU9aF7rF3d4f7mj/rEpCTgbID6LWFCxfGS//6pFj2jrfFwMBA7LTTzvEXL/zLza5rNpvRbDbHv280hnsZJmSl0Sxi6Q2r47Lb18TJi+fGhw9fMP79ksVz46IjFsT8gb6exDKb2m6MFLF0RTlxQ1VMrPcli+fGRYcviPmD6dd3mdpZW6xDlKXMuWfv7o2c7lVOueSsqvs2kyvzDEh6Zlrf5hGkbVbncvUNW1TZn7nVNkyrivWttmF6Vaxt0mO9ZSK1TYR1oZvs3d3h32TzZ12aWoq13Xf+gx19vuLc7Tv6fJCC2jXNPPjAA/Gpa66KU057ZTx59z3iwgveHV/4/GfihcefsNF1n/73a+O6a64qKUrIy+CciMtuXxMREZ+9sxkXH7nh+8tuXxOXHLmgZ7HMprYH+8uLG6piYr1Xqb7L1M7aYh2iLGXOPXt3b+R0r3LKJWdV3beZXJlnQNIz0/o2jyBtszqXq2/Yosr+zK22YVpVrG+1DdOrYm2THustE6ltIqwL3WTv7g7/Jps/69LUcq5tyFntmmbuuOOnMTo6GocfcVRERDzzoIPjlh/9cLOmmeOOPyGOOua48e8bjeE4Y+lpPY0VctFcG7Fk8dy47PY1cezugxt9v2Tx3GiORgz09yaW2dR2c7S8uKEqNqvvtdWo7zK1s7ZYhyhLmXPP3t0bOd2rnHLJWVX3bSZX5hmQ9My0vs0jSNuszuXqG7aosj9zq22YVhXrW23D9KpY26THestEapsI60I32bu7w7/J5s+6NLWcaxtyVrummT/a9XGxZs2a+M63vxVPfvJT4rZbb4kn7/6Uza4bHByMwcHBEiKE/Mwf7IuLDl8Qlxy5IJpr139/xPrvR6OnH804m9qeP1Be3FAVk9V7r1R1725nbbEOUZYy5569uzdyulc55ZKzqu7bTK7MMyDpmWl9m0eQtlmdy9U3bFFlf+ZW2zCtKta32obpVbG2SY/1lonUNhHWhW6yd3eHf5PNn3VpajnXNuSsdk0zT3jCbvE3p74iLr/s0hhevTr2WbxfvPilf112WJC9sUPTWLfx2EG5at3HVY0bemnTemd67awt1iHKUtW5V9W4y5DTvcopF6gKZ0A6wTyCfKlv2LKq/gyjtmF6VaxvtQ3Tq2Jtkx7rLbAp60L32Lu7w33Nn3UJyEntmmYiIg4/4qg4/Iijyg4DAAAAAAAAAAAAAACALtH/BwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJCdgbID6KUPX/i++MbXv7rRYzvttHNccOFFJUUEAAAAAAAAAAAAAABAN9Sqaeblr1gaS045dfz79yx7RzzpybuXGBEAAAAAAAAAAAAAAADdUKummXnz5sW8efMiIuJXv/xF3PFfP42/ffVZk17bbDaj2WyOf99oDPckRqC71DbkS31DntQ25EltQ77UN+RJbUOe1DbkS31DntQ25EltQ77UN+RJbUM11appZqLPffbTccCBB8UOO+446Z9/+t+vjeuuuarHUQHdprYhX+ob8qS2IU9qG/KlviFPahvypLYhX+ob8qS2IU9qG/KlviFPahuqqa8oiqLsIHrtwQceiDNf/ap403lviT33euqk10zWCXjG0tPikuVXxNDQUK9CBTpMbUO+1DfkSW1DntQ25Et9Q57UNuRJbUO+1DfkSW1DntQ25Et9Q55SrO2+8x/s6PMV527f0eeDFNTyk2a++PnPxhN2223KhpmIiMHBwRgcHOxhVEAvqG3Il/qGPKltyJPahnypb8iT2oY8qW3Il/qGPKltyJPahnypb8iT2oZq6i87gF4bHh6Om758Qxx59LFlhwIAAAAAAAAAAAAAAECX1K5p5qYbV8S8rebFwc86pOxQAAAAAAAAAAAAAAAA6JKBsgPotWOPOz6OPe74ssMAAAAAAAAAAAAAAACgi2r3STMAAAAAAAAAAAAAAADkT9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2dE0AwAAAAAAAAAAAAAAQHY0zQAAAAAAAAAAAAAAAJAdTTMAAAAAAAAAAAAAAABkR9MMAAAAAAAAAAAAAAAA2RkoOwAAAAAAAAAAAAAAAMhd3/kPlh0C1I5PmgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsjNQdgBlGF27Nq6//rr4+ldvil13fXyc8w9vLDskAAAAAAAAAAAAAAAAOqiWnzSz/GOXxI1f+mKc9LJT4vQzzurKGI1mESOjRTSaxUaPPzpSxMiErzUjxZTXUh91mANjOTbXz/2q5lzVuKGXcq+TdvJr9dpuPGfZ48upGuN3K6eU9CJuY6Q3Ti5jQJWoCTrBPIJ8qW/Ik9qGPKltgN6w3gJ1Zf1rjfsEAJ1Vu6aZVatWxU03rohTX7k0nnnQwbH1NttMel2z2Yzh4eHxr0ZjuOUxGs0ilt6wOgaXrYylN6weP7g0mkU8sqaIZhHjX2uLmPRa6mOq+ZKTsRznL1sZI+vnflk5d6O2gQ3KrJPZ1Her2smv1Wu78Zxljy+naozfrZw6LfW92xjpjZPLGLnrxb5N76gJJpppfZtHkLbUz+XAzKhtyJdzOeTJe2r5sN4ykdqmTuq2/jmXQ57s3VBNfUVR1GpH/T8/+H68+51vjz895M/ipz/9cTz+8U+IVyw9PRYt2mGj6669+sq47pqrNvv7lyy/IoaGhrY4xshoEYPLVo5/3zx7uxjo74uR0SJGi4j+vo2vn+xa6mOq+ZKTsRyf+8cDseIl6xrVysq5G7UNbFBmncymvlvVTn6tXtuN5yx7fDlVY/xu5dRpqe/dxkhvnFzGyF0v9m16R00w0Uzr2zyCtKV+LgdmRm1DvpzLIU/eU8uH9ZaJ1DZ1Urf1z7kc8tSJvbvv/Ac7HVZHFeduX3YI0HEDZQfQa6tXr46Ivthjr73i6L94YXz0wxfGJz6+PM587es2uu6440+Io445bvz7RmM4zlh6WktjNNdGLFk8Ny67fU0sWTw3mmsjBvrXPb66WcSCwY0PMJNdS31MNV9yMpbjFbeviebadY+VlXM3ahvYoMw6mU19t6qd/Fq9thvPWfb4cqrG+N3KqdNS37uNkd44uYyRu17s2/SOmmCimda3eQRpS/1cDsyM2oZ8OZdDnrynlg/rLROpbeqkbuufcznkyd4N1VS7T5q5+Uc/jAve/c742OWfjIiIz3z6U/GNr3013vXeD2zx7w0PD8epJ5/Ycidgo1nE4Jx1B5j5E5pkHh0pNupUGo2ItUVMei31MdV8yclYjsVoRF9ENBOZ952qbWCDVOqk3fpuVTv5tXptN56z7PHlVI3xu5VTN6W4dxsjvXFyGaNOurVv0ztqgqm0U9/mEVRHiudyYPbUNuTLuRzy5D21arPeMhW1Te7qvP45l0OeZrJ3+6QZ6L3afdLMHnvsGYODg7Hii5+PZ/zJAfGD738vnvjEJ3V8nLGDyqYdvlsNTH2A0Q1cX1PNl5yMH97X5zi2+FQt5zq8VjBbuddJO/m1em03nrPs8eVUjfG7lVNKehG3MdIbJ5cxoErUBJ1gHkG+1DfkSW1DntQ2QG9Yb4G6sv61xn0CgM6q3ZY6tGBB/N3rz4kv37giznndWbHNNtvESSefUnZYAAAAAAAAAAAAAAAAdFDtPmkmImLvffaNZe95f9lhAAAAAAAAAAAAAAAA0CW1+6QZAAAAAAAAAAAAAAAA8qdpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAsqNpBgAAAAAAAAAAAAAAgOxomgEAAAAAAAAAAAAAACA7mmYAAAAAAAAAAAAAAADIjqYZAAAAAAAAAAAAAAAAslOJppmiKOLaq6+Md7/zXyIiYtWqVfHm//2/4tSTT4wL3//eWPOHP5QcIQAAAAAAAAAAAAAAACmpRNPMFz73mfjUtVfHb37zm4iIuOLy5XHHf/0k/vCHNfHtb30zrrv26pIjBAAAAAAAAAAAAAAAICWVaJr5yk03xqIddojXnf2GGGk24/vf/XYc/oKj4mMfvzL++AlPiO995z/KDhEAAAAAAAAAAAAAAICEVKJp5oHf/Tb23e9psdPOu8Qdd/xXrFnTjEMPe24MDA7G7rs/JVaufLDsEAEAAAAAAAAAAAAAAEjIQNkBtGLbbbeLO//7jnjk4Yfja1/5cixYsCB2e+KToiiK+MXPfx4LFz6mred7+1vPi1tvuXn8+6OPPS5OWnJKh6MGAAAAAAAAAAAAAACgLJVomnn2cw6NT117dbzy1JMjIuIFRx0dfX198cY3nB13331XHHXMsW0938qVK+OU014Zhzz7ORERMTg4t+MxN5pFDM6JaK6NmD/Yt+HxkSIG+yOaoxHzB/ri0ZEiioiNHgPSNVVtAxtsutdVQRVjbkWuedVVO69nN177qs6nXPbuXtz/qr7GQBpyWW8pl3kE06vqmU19Q57UNuRJbcP01AmdYB7RKVV9r4DNlbEumD/pqsJrYy+jbFWoEyAd/WUH0Irj//LF8fzDj4xFixbFMw88KF78khMjIuLee++JvffZJ0540Uvber5VK1fGYx+7UyxYsHUsWLB1zJ27edNMs9mM4eHh8a9GY7jl5280i1h6w+oYXLYylt6wOhrNYt3jI0UsXbH+8RXrHn9kzSaPjRRt5QK0pxu1DWyw2V7Xw31tpvVdZszdlGteddXO69mN176KtR2Rz97di/tvzaDXZlPbpCeX9ZbOmPG53DyCaTmXA52mtiFfzuXQPWXWiffU8mG9ZaJZncv9+042ylgXzJ/uy/n3WOxllK2q75cD5ekriqKyu9V9990bu+76uLb+zkizGUv++sWx6+MeH6sfeSSesseecdqr/ja22WbhRtdde/WVcd01V2329y9ZfkUMDQ1teYzRIgaXrRz/vnn2djHQ3zfp46NFxLx3bX4t0B3dqG1ggzLrZKb1nWtt55pXXbXzenbjta9ibUfkUwe9yCOXe0V1zKa2SY81hImcy6F7nMuBTlPbkC/ncuieqp7LSYv1lomcy4ko57U0f7ov53N5FWIkb1U/l/ed/2Cnw+qo4tztyw4BOm6g7ADasWbNmnj4od/HWJvPvLnz4ne//W1EROyw446tPUlfX/zNqa+InXf+oxgaGooPfuCCuPbqT8Ypp75yo8uOO/6EOOqY48a/bzSG44ylp7U0RHNtxJLFc+Oy29fEksVzo7k2YmD9R4Bt+vjqZrHxY6PrrgW6oxu1DWyw2V7Xw31tpvVdZszdlGteddXO69mN176KtR2Rz97di/tvzaDXZlPbpCeX9ZbOmPG53DyCaTmXA52mtiFfzuXQPWXWiffU8mG9ZaJZncv9+042ylgXzJ/uy/n3WOxllK2q75cD5anEJ83cd+898eEPvj/uvuuuSf+8ry/iE1d9qqXnWrNmTTzy8MOx/aJFERHxiY9/LO782c/in/75bVv8e8PDw3HqySe23AnYaBYxOGfd4WD+4IbuxcZIEYPrG2jmD/TFoyNFFBEbPQb0TqdqG9hg072uLO3Udyoxd1quedVVO69nN177VOZTXffuXtz/VF5j6qnd2iY9uay3dF5b53LzCKaVypmtrudyyJ3ahnw5l0NnpVIn3lOrtlTmEelp+1yeyHsFzF4Z64L501u5/R6LvYyypVInMzmX+6QZ6L1KfNLMxR/9cNx9152xYMHWsWjRonVdMjP0m//76zjndWfFma99fTxp993j1ltujr2euncHo11n7BCwaefi2MI89vhWExZqnbaQvqlqG9hg072uCqoYcytyzauu2nk9u/HaV3U+5bJ39+L+V/U1BtKQy3pLucwjmF5Vz2zqG/KktiFPahump07oBPOITqnqewVsrox1wfxJVxVeG3sZZatCnQDpqETTzM/vvjue+KQnx5vf8vYYGByc1XM9/o+fECe//LS4/LJLo9EYjn332z9e9JITOxQpAAAAAAAAAAAAAAAAKahE08zChQtjt92eOOuGmTEvOPKYeMGRx3TkuQAAAAAAAAAAAAAAAEhPJT6U6ogjj45bb705hlevLjsUAAAAAAAAAAAAAAAAKqASnzRz6y0/ipUProy/O/P0eMy222725319fXH+uy4oITIAAAAAAAAAAAAAAABSVImmmdtuvSUiIh5++KF4+OGHJrmir7cBAQAAAAAAAAAAAAAAkLRKNM2874MfKTsEAAAAAAAAAAAAAAAAKqQSTTM77vjYskMAAAAAAAAAAAAAviX0JgAAIABJREFUAACgQirRNHPWq1+15Qv6It53oU+jAQAAAAAAAAAAAAAAYJ1KNM389rf/b4t/PjBQiTQAAAAAAAAAAAAAAADokUp0m1x86eWTPn7//ffF2/75f8fJL39FjyMCAAAAAAAAAAAAAAAgZf1lB9CKoQULJv3a/Sl7xP5Pe0Z8+t+vKztEAAAAAAAAAAAAAAAAElKJppktaTQa8eADvys7DAAAAAAAAAAAAAAAABIyUHYArfjYv/3rZo8VRRH/99e/jttuvSV222233gcFAAAAAAAAAAAAAABAsirRNHPDl74w5Z8NDS2Ik04+pYfRAAAAAAAAAAAAAAAAkLpKNM286vTXbPZYf19fPGbbbWP33feIoQULSogKAAAAAAAAAAAAAACAVCXfNDM6Ohp9fX2x8867xB577lV2OAAAAAAAAAAAAAAAAFRAf9kBTKe/vz8uvfgj8c2vf63sUAAAAAAAAAAAAAAAAKiI5JtmIiIW77tf3HHHT2N07dqyQwEAAAAAAAAAAAAAAKACBsoOoBUjIyNx7z33xBvO+fvYccfHbvbnfX198fpz/7GEyAAAAAAAAAAAAAAAAEhRJZpmbrn5RxERce89v4p77/nVJFf09TYgAAAAAAAAAAAAAAAAklaJppk3/tNbOv6cF7z7nfG97347rrzm+o4/NwAAAAAAAAAAAAAAAOWqRNPM3vssnvaab33z63H7bbfG0tNfM+21P/jP78d/fv+7nQhtSo1mEYNzIpprI+YPbvgknOZIsdnn4jSLmPRa6mOq+ZKTsRxjdN33VZ33dXitYLaqWCftxNyNa3McX07VGL9bOaWkF3EbI71xchkDqkRN0Anm0ey5h6TK3IQta4wUMdgf0RyNmD9QnRpR2zC9KtZJFWOGXqvq3k1arLd0Sl3mUq/zLGOtr8trSWuqMB9SPxNV4R6mzj0EctJfdgCd8rP/viO+8bWvTnvd8PBwXHrJR+KII4/e4nXNZjOGh4fHvxqN4ZZjaTSLWHrD6hhctjKW3rA6Gs1i/PGRYl2zwMSvya6lPqaaLzkZy3GHZStLn/fdqG1ggzLrZKb13U7M3bg2x/HlVI3xu5VTp6W+dxsjvXFyGSN3s6lt0qMmmKgX53Im5x7STamfy6HKGiNFLF2xvkZWrI7GiJ+5IRdVfE9NbcP0qrp3kxbrLRM5l0+v13mWsdbX5bWsm5zP5WWeiVpRhXuYOvdwas7lUE19RVFksZJdeslH48YVX4orrv7UFq+75KMXRXOkGQcedHAse8fb4sprrp/0umuvvjKuu+aqzf/+8itiaGhoi2OMjBYxuGzl+PfNs7eLgf6+GBmd/FZPdi31MdV8yclYjtccOxgv3GvriChv3nejtoENyqyTmdZ3OzF349ocx5dTNcbvVk6dlvrebYz0xslljNzNprZJj5pgol6cy5mce0g3pX4uhyrzMzfkq4r1rbZhelWsbdJjvWUi5/Lp9TrPMu5rXV7Lusn5XJ56jKnHVwXu4dQ6cS7vO//BTofVUcW525cdAnRcNp8004r/+ulP4gf/+b046WV/M+21xx1/Qlyy/IrxrwsvurjlcZprI5YsnhsR6/63uXbD45N9TXYt9VGHOTCW42mfbZY+77tR28AGVazvdmLuxrU5ji+naozfrZw6LfW92xjpjZPLGLmbTW2THjXBRL04lzM595BuSv1cDlXWHN2kRkZ7N7bahu6q4ntqahumV9W9m7RYb5nIuXx6vc6zjLW+Lq9l3eR8Li/zTNSKKtzD1LmHU3Muh2qq1SfNvP2tb44f335bzJs3N9auHY0//OHRGBoaikuWXzHt8w8PD8epJ5/Ycidgo1nE4Jx1G8f8wQ3dlc2RIjbttWwWMem11MdU8yUnYznG+gNyKvO+U7UNbJBKnbRT3+3E3I1rcxxfTtUYv1s5dVOKe7cx0hsnlzHqpN3aJj1qgql061zO5NxDeiXFczlUWWOkiMH+db9wMn/Az9yQk1TqxLkcOquqezdpsd4yFefyyfU6zzLW+rq8lnWW27k8lTPRVKpwD1PnHrZmJudynzQDvTdQdgC99LevPjOazTUREXH77bfFRz98Ybxj2Xu7MtbYBjGwyWf5DE5yOBh7ETa9lvqYar7kZPzQtD7Hqs77OrxWMFtVrJN2Yu7GtTmOL6dqjN+tnFLSi7iNkd44uYwBVaIm6ATzaPbcQ1JlbsKWjf1iSdVqRG3D9KpYJ1WMGXqtqns3abHe0il1mUu9zrOMtb4uryWtqcJ8SP1MVIV7mDr3EMhJrZpmtt1uu/H/fsxjfhURETs+dqeywgEAAAAAAAAAAAAAAKBLsun/23rrbWKHHXZo+fpn/Mkz48prru9iRAAAAAAAAAAAAAAAAJQlm0+aedFL/ipe9JK/KjsMAAAAAAAAAAAAAAAAEpBs08w5rzur5Wv7+vri/Hdd0MVoAAAAAAAAAAAAAAAAqJJkm2buvedXbVzd17U4AAAAAAAAAAAAAAAAqJ5km2be98GPlB0CAAAAAAAAAAAAAAAAFZVs08yOOz627BAAAAAAAAAAAAAAAACoqGSbZjZ12623xPe+8x+xatWqKIpi/PFmc038+v774wMf/tcSowMAAAAAAAAAAAAAACAllWia+c63vxUfuOA9ETHWLNM34b/HvgcAAAAAAAAAAAAAAIB1+ssOoBWfuf5TMTQ0FK9+zWvjgGceFBERb/ynt8SrTn9N9Pf3x1l/9/qSIwQAAAAAAAAAAAAAACAllWia+fX998cBBx4Uh/zZobHjjo+NiIi99npqHHrYc2OfxfvF5z57fckRAgAAAAAAAAAAAAAAkJJKNM1stdVW8fBDv4+IiMdst21ERPz61/dHRMTChQvj3nt+VVpsAAAAAAAAAAAAAAAApGeg7ABa8bSnPyO+8fWvxec+c33suedTI6KID37ggth//6fHf37/u7HTzruUHSIAAAAAAAAAAAAAAAAJqcQnzbz4pX8di3ZYFPfee0/suddTY9/9nha/+Pnd8enrr4uRkZE44cUvLTtEAAAAAAAAAAAAAAAAElKJT5rZftGieNd7L4zf/e63ERFxzj+8Mb777W/FylUrY/Hi/WK3Jz6p5AgBAAAAAAAAAAAAAABISSWaZn76kx/H9ttvH7vu+riIiJgzZ04c8meHRkTEPff8Ku6+68540pN3LzNEAAAAAAAAAAAAAAAAEtJfdgCteMt5b4wvfeHzk/7Zddd8Ms7/l7f0OCIAAAAAAAAAAAAAAABSlvQnzVz0oQ+M//dtt92y0fcREWtHRuLmH/4wBucO9jo0AAAAAAAAAAAAAAAAEpZ008w3vvaV9f/VF/ffd2/cf9+9k153zHEv7F1QAAAAAAAAAAAAAAAAJC/pppm/P/sNURRFvPdd58fTn/En8efPe/5Gf97fPyd22WWX2OWPdm3p+dauXRtXXXl5fOXLN0RExNOfcUC8YumrY+7cuR2PHQAAAAAAAAAAAAAAgPIk3TRzwDMPioiIp+69T+y7/9PGv5+pb33z6/GVL98YZ7/hjbH1NtvEW897U3zlyzfEC446phPhAgAAAAAAAAAAAAAAkIikm2bGvOm8t0ZExKqVK+POO/87Hn300XjMY7aNPfbcK+bNm9fy8xx62HPj0MOeGxERq1c/EgMDAzFnzpyuxDyVR0eKiIgoImKwP6I5GtEXEVsN9PU0DtLSaBYxOCeiuTZi/mD+c6HK+VY5duiVKtZJY6QY35fnT7Mnt5Nfq8/b1vhdurbTyo6zzNxzVdV72os1qSdj9OD+92r9zikXYAN1RyeYR/mr6pmS2VPfkCe1DXlS2zA9dUInmEd0Sl3eb+l1zahRylaFOViFGFNWl/UbIBX9ZQfQqis/8fF4zemviPcsOz8+9IH3xdvf+uZYetrJ8cXPf7bt5zrndWfFK1++JHbeZZf4H4e/YNJrms1mDA8Pj381GsOzTSEaI0U8smbd19IVq2Nw2cpYumJ1PLKmiEazmPXzU02NZhFLb1g/H25Ynf1cKDvf2dR22bFDFZRZJzOt78bIxvtyY2TqmNvJr9XnbWv8Ll3baWXHWWbuuSrznqa+d/dkjB7c/16t3znlwux042duyqPumGjG53LzKHvO6dWW+rkcmBm1DflyLofuqeK/hZEe6y0TzepcXpP3W3pdM2qUTsn5XF6FGFNWl/U7V87lUE19RVEkv9qu+OLnY/mlF8fAwGDs/7Snx7bbbRerVj4Yt95yczSbI/G3Z5wZf/acw1p+vgce+F38+v774v3vfXec+LKT47A/f95m11x79ZVx3TVXbfb4JcuviKGhoRnlMTJaxOj6uz3vXSvHH//D67eL/r6IgX7donU0MlrE4LIN86F59nZZz4Wy851NbZcdO1RBmXUy0/puJ+ZuXFv2+N1QdpzW686rYm1H9CZuY6Q3jjWgGrrxMzflUXdM1ItzOdXkNa621M/lwMyobciXczl0T1XfLyct1lsmci6fXq/zrMt9pftyPpdXIcaUuX/V1olzed/5D3Y6rI4qzt2+7BCg4wbKDqAVN6z4YmyzzcL45385P3baaefxx//fb34Tb/pf58QXPveZlppmfvnLX8TvV62K/fZ/WixatEPsvXhx/OTHt0/aNHPc8SfEUcccN/59ozEcZyw9bVZ5NEcjVq9Z1zWzZPHcuOz2NbFk8dx46A9FLBjsi4HKfO4PndRcu/F8aK6NrOdC2fnOprbLjh2qoMw6mWl9N0c3iXl06pjbya/V521r/C5d22llx1lm7rkq856mvnf3ZIwe3P9erd855cLsdONnbsqj7phoxudy8yh7zunVlvq5HJgZtQ35ci6H7qniv4WRHustE83qXF6T91t6XTNqlE7J+VxehRhTVpf1O1fO5VBNlfikmSUnvigOftYhcfprXrvZn33oAxfE97777Vj+iaunfZ5vfv2r8W8XfyT+8U1vjq3mz493vO3NcfSxx8VRR//FtH93eHg4Tj35xFn/P3Q8uv5j1IqIGOxft/n1RcRWA7pE66zRLGJwzrrD5PzB/OdCSvm2W9spxQ6pSqVO2qnvxkgxvi/Pn2ZPbie/Vp+3rfG7dG2nlR1nmbnnKpV7muLe3ZMxenD/e7V+55QLndOpn7kpj7pjKm2dy82j7KVypmT2UjyXA7OntiFfzuXQWanUiffUqi2VeUR62j6X1+T9ll7XjBqlG3I7l1chxpTVZf2ug5mcy33SDPReJT5pZpuFC+O+++7d7PGiKOL+++6LoQULWnqeZz/nsPjlL38Ry97xtiiKIg7+00Pi8COO6nS4W7Rpc4zuUCI2HBrrMh+qnG+VY4deqWKdjP3w2UrM7eTX6vO2NX6Xru20suMsM/dcVfWe9mJN6skYPbj/vVq/c8oF2EDd0QnmUf6qeqZk9tQ35EltQ57UNkxPndAJ5hGdUpf3W3pdM2qUslVhDlYhxpTVZf0GSEUlmmYOPOhZseKLn4/z/+Wf48+f9/zYbrvt4/e/XxVfvenLcffdd8Xznn94S8/T19cXJy05JU5ackqXIwYAAAAAAAAAAAAAAKBMlWiaeclfnRR3/exnccvNP4pbbr55wp8UsdtuT4y/OvFlpcUGAAAAAAAAAAAAAABAeirRNPPIww/H68/9x7j55h/GrbfcHA/9flVsvfU2sXi//eOAAw6MNWvWxNCCBWWHCQAAAAAAAAAAAAAAQCIq0TRz5qtfFUe84Kg4+eWnxXMO/fON/uyC97wzfnbHHfHBj1xSUnQAAAAAAAAAAAAAAACkJummmeuuuWr9fxVx553/PeH7ddauHYmf3H57rF070vvgAAAAAAAAAAAAAAAASFbiTTOfjIi+iOiLu+68M+6682eTXvfc5z2/p3EBAAAAAAAAAAAAAACQtqSbZl528sujiCIuX35p7LnX3nHgQQdv9Odz5syJnXfZJfbb/+klRQgAAAAAAAAAAAAAAECKkm6aOfLoYyMi4kuf/1zss+++498DAPD/2bvv8KiqrY/jvzRCTei9996bFLGCYsOCChZEECMI1+t97Q0EFERFwV7pHeldRJqiIL13RBSkhJaEkDLvHyFDS2CGzMzZ58z38zx57jWczF57Zq991j5n9gwAAAAAAAAAAAAAAAAAAACuxOhNM+mGfPaV1SEAAAAAAAAAAAAAAAAAAAAAAADARkKtDgAAAAAAAAAAAAAAAAAAAAAAAADwNTbNAAAAAAAAAAAAAAAAAAAAAAAAwHHYNAMAAAAAAAAAAAAAAAAAAAAAAADHYdMMAAAAAAAAAAAAAAAAAAAAAAAAHIdNMwAAAAAAAAAAAAAAAAAAAAAAAHAcNs0AAAAAAAAAAAAAAAAAAAAAAADAcdg0AwAAAAAAAAAAAAAAAAAAAAAAAMdh0wwAAAAAAAAAAAAAAAAAAAAAAAAch00zAAAAAAAAAAAAAAAAAAAAAAAAcBw2zQAAAAAAAAAAAAAAAAAAAAAAAMBxwq0OwAozpk3R7JnTlZh4RrXr1lNM917Knj271WEBAAAAAAAAAAAAAAAAAAAAAADAR4Lum2bWr1ur8WNHq3vP59Sn/0Bt27JFc2fP8Hk7CUkuJae6lJDkuuj3ycmuy34yOxbBIxjGQHof7T7u7Ro3EEh2zBNvYvbHsU5snz7Zo31/9ckkgYibNsxrhzaAwGO8whfsMI5MjzEh+Vx8yWbGh+Bleu54ykk55qS+wDqOyW2H9ENyVl9gHcYRcHXkCXyBcQRfsWIsBUubgJXsMOZNj9H0+AAAgRV0m2bCw8PV8dHHVat2HZUqVVrFihfXieMnLjsuKSlJ8fHx7p+EhHiP20hIcilmfpwiBsUqZn6c+6SbkORSkkuX/WR0LIJHZuPFSdL7mG9QrOXj3h+5DeA8K/PkWvPbm5j9cawT26dP9mjfX33yNdPP3bRhXju0YQ9ZyW2Yx+njFd4JRF1uFdNjTEh2KWbeufjmxfFGePiU6XV5IDgpx5zUF2QNue2cfkjO6guyzsl1OWA1u14vh1mYb3Ehu9XlwdIm4AtOrstNj9H0+GBv1OWAPYW4XK6gPRvs27tHb7z6ot7s018VK1W+6N8mTRiryRPHX/Y33w4fo5w5c17xcZNTXYoYFOv+76QX8ik8NETJqRk/1Rkdi+CR2XhxkvQ+fn5LhLrWyy3JunHvj9wGcJ6VeXKt+e1NzP441ont0yd7tO+vPvma6edu2jCvHdqwh6zkNszj9PEK7wSiLreK6TGaHh/szfS6PBCc0g/JWX1B1pDbzumH5Ky+IOucXJcDVrPr9XKYhfkWF7JbXR4sbQK+4OS63PQYTY8P9uaLujxk4DFfh+VTrpfyWx0C4HNB900z6Y4ePaJBA/rrjjvvuWzDjCTdc+8D+nb4GPfPJ1984/FjJ6VIj9fMJintf5NSzv8+o5+MjkXwCIYxkN7HF35Msnzc+yO3AZxnx/z2JmZ/HOvE9umTPdr3V598zfRzN22Y1w5t2ENWchvmcfp4hXcCUZdbxfQYk1IviS/V4oDgKKbX5YHgpBxzUl+QNeS2c/ohOasvyDon1+WA1ex6vRxmYb7FhexWlwdLm4AvOLkuNz1G0+ODvVGXA/YUlN80c/LkSfV581VVrFhJMT16KSTk6jtI4+Pj1aVTR493AiYkuRQRlnbyzRFx/vGTky9/upNcyvBYBI/MxouTpPdR527AmjLufZXbAM4zJU+8yW9vYvbHsU5snz7Zo31/9cmfTDx304Z57dCG/Xib2zBPMI1XeMdfdblVTI8xIdmliNC0N8TnCDcvPjiHiXV5IDgpx5zUF/hO0Oa2Q/ohOasv8C2n1eWA1UzJE66p2Zsp4wjmsUNdHixtAr7mtLrc9BhNjw/OcS11Od80AwReuNUBBNqZhAS9905fFSxYUI926qz4+DiFhIT6/AJC+kk2/JLv8gnP4OZT+otw6bEIHpmNFydxF57n+mjXcR8MrxWQVXbME29i9sexTmyfPtmjfX/1ySSBiJs2zGuHNoDAY7zCF+wwjkyPMf2N76bGh+Bleu54ykk55qS+wDqOyW2H9ENyVl9gHcYRcHXkCXyBcQRfsWIsBUubgJXsMOZNj9H0+AAAgRV0m2Z+++1X7dq1Q5LU7cnHJUkFCxXS0M++tjIsAAAAAAAAAAAAAAAAAAAAAAAA+FDQbZppdcNNanXDTVaHAQAAAAAAAAAAAAAAAAAAAAAAAD/ii8cAAAAAAAAAAAAAAAAAAAAAAADgOEH3TTMAAAAAAAAAAAAAAAAAAAAAACC4hQw85vPHdL2U3+ePiazhm2YAAAAAAAAAAAAAAAAAAAAAAADgOGyaAQAAAAAAAAAAAAAAAAAAAAAAgOOwaQYAAAAAAAAAAAAAAAAAAAAAAACOw6YZAAAAAAAAAAAAAAAAAAAAAAAAOA6bZgAAAAAAAAAAAAAAAAAAAAAAAOA4bJoBAAAAAAAAAAAAAAAAAAAAAACA47BpBgAAAAAAAAAAAAAAAAAAAAAAAI7DphkAAAAAAAAAAAAAAAAAAAAAAAA4DptmAAAAAAAAAAAAAAAAAAAAAAAA4DhsmgEAAAAAAAAAAAAAAAAAAAAAAIDjsGkGAAAAAAAAAAAAAAAAAAAAAAAAjsOmGQAAAAAAAAAAAAAAAAAAAAAAADgOm2YAAAAAAAAAAAAAAAAAAAAAAADgOGyaAQAAAAAAAAAAAAAAAAAAAAAAgOOwaQYAAAAAAAAAAAAAAAAAAAAAAACOE251AAAAAAAAAAAAAAAAAAAAAAAAwFohA4/59PFcL+X36eMB1yIov2nmxInjmj1rul5+4b/a/+c+q8MBAAAAAAAAAAAAAAAAAAAAAACAjwXdppmEhAT1fKabVixfrn179/ivnSSXklNdSkhyXfT75GTXZT+ZHYvgEQxjIL2Pdh/3do0bCCQ75ok3MfvjWCe2T5/s0b6/+mSSQMRNG+a145g2ks+1kWzvfiA4MJbgC3YYR3aIETCRU3LHKf0AfMUpOeGUfgC+Qk4AV0eewBcYR/AVK8YS4xfwPzvkmR1iBAAgXdBtmsmWLZuGfv6Vej73/BWPS0pKUnx8vPsnISHe4zYSklyKmR+niEGxipkf5y4KEpJcSnLpsp+MjkXwyGy8OEl6H5sMirV83PsjtwGcZ2WeXGt+exOzP451Yvv0yR7t+6tPvmb6uZs2zGvHMW0kuxQz71wb8+L8snHGrrkN87BWwoUCUZdbxQ4xAv5iel0eCE7pB3Ahcts5/QAu5eS6HLAa19TgC8y3uJDd6nLGL+A5J9fldogR8BfqcsCeQlwuV1CerQ7/e0i9ejyt9z74WKVKl7ns3ydNGKvJE8df9vtvh49Rzpw5r/jYyakuRQyKdf930gv5FB4aouTUjJ/qjI5F8MhsvDhJeh9Pdo9Qjly5JVk37v2R2wDOszJPrjW/vYnZH8c6sX36ZI/2/dUnXzP93E0b5rVDG2a1kZms5DbMw1oJFwpEXW4VO8QI+IvpdXkgOKUfwIXIbef0A7iUk+tywGpcU4MvMN/iQnaryxm/gOecXJfbIUbAX3xRl4cMPObrsIzmeim/1SFckT9eD9P7HIzCrQ7AVPfc+4Da3nmP+78TEuL1bExXj/42KUV6vGY2jdh4Vo/XzKakFCk8NO33GcnoWASPzMaLk6T38YbPzmrZuS95sqrP/shtAOdZmSfXmt/exOyPY53YPn2yR/v+6pOvmX7upg3z2nFMG6mXtJFqz35kJiu5DfOwVsKFAlGXW8UOMQL+YnpdHghO6QdwIXLbOf0ALuXkuhywGtfU4AvMt7iQ3epyxi/gOSfX5XaIEfAX6nLAnvimmUy+aeZS8fHx6tKpo8c7AROSXIoISysOckSc30GbnHz5053kUobHInhkNl6cJL2PSk37b1PGva9yG8B5puSJN/ntTcz+ONaJ7dMne7Tvrz75k4nnbtowrx3HtJHsUkRo2gaaHOH27YcnvM1tmMeUsQTz+Ksut4odYgQCwcS6PBCc0g8gM+S2vfsBXInT6nLAaqbkCdfU7M2UcQTz2KEuZ/wC18ZpdbkdYgQC4Vrqcr5pxix800xw4Jtm/CS9CLh092x4Bm90Sn8R2GkbvDIbL07iLozP9dGu4z4YXisgq+yYJ97E7I9jndg+fbJH+/7qk0kCETdtmNeOY9oId0Y/EBwYS/AFO4wjO8QImMgpueOUfgC+4pSccEo/AF8hJ4CrI0/gC4wj+IoVY4nxC/ifHfLMDjECAJCO0xUAAAAAAAAAAAAAAAAAAAAAAAAcJ2i/aaZQ4SIaO3Gq1WEAAAAAAAAAAAAAAAAAAAAAAADAD/imGQAAAAAAAAAAAAAAAAAAAAAAADgOm2YAAAAAAAAAAAAAAAAAAAAAAADgOGyaAQAAAAAAAAAAAAAAAAAAAAAAgOOwaQYAAAAAAAAAAAAAAAAAAAAAAACOw6YZAAAAAAAAAAAAAAAAAAAAAAAAOA6bZgAAAAAAAAAAAAAAAAAAAAAAAOA4bJoBAAAAAAAAAAAAAAAAAAAAAACA47BpBgAAAAAAAAAAAAAAAAAAAAAAAI7DphkAAAAAAAAAAAAAAAAAAAAAAAA4DptmAAAAAAAAAAAAAAAAAAAAAAAA4DjhVgcAAAAAAAAAAAAAAAAAAAAAAACAi4UMPObTx3O9lN+nj2cHfNMMAAAAAAAAAAAAAAAAAAAAAAAAHIdNMwAAAAAAAAAAAAAAAAAAAAAAAHAcNs0AAAAAAAAAAAAAAAAAAAAAAADAcdg0AwAAAAAAAAAAAAAAAAAAAAAAAMdh0wwAAAAAAAAAAAAAAAAAAAAAAAAch00zAAAAAAAAAAAAAAAAAAAAAAAAcJxwqwOwwq/Ll2ncmJGKizutho2bqstTMYqIiLA6LAAAAAAAAAAAAAAAAAAAAAAAAPhI0H3TzKlTJ/XFZ0N07wMPqnffAVq3drUWLVzg83YSklxKTnUpIcl10e+Tk12X/WR2LIJ/Yyu4AAAgAElEQVRHMIyB9D7afdzbNW4gkOyYJ97E7I9jndg+fbJH+/7qk0kCETdtmNcObXjRRvK5NpLtldswj13PEzAL4whwLvIbcCZyG3AmchsAAoP5Fr7CWAKcidwGAMC3gm7TzK6dO+RySa1uuEklS5VSvXoNtGXzpsuOS0pKUnx8vPsnISHe4zYSklyKmR+niEGxipkf5y5cEpJcSnLpsp+MjkXwyGy8OEl6H2sPirV83PsjtwGcZ2WeXGt+exOzP451Yvv0yR7t+6tPvmb6uZs2zGuHNrxoI9mlmHnn2pgXF9CNM1nJbZiHtRIuFIi6HEDgmV6XA7g25DbgXNTlgDNxTc05mG9xIepywLmoywFnoi4H7CnE5XIF1Rl1yeJFGjn8O3393UhJ0qgR32vP7t16o3ffi46bNGGsJk8cf9nffzt8jHLmzHnFNpJTXYoYFOv+76QX8ik8NETJqRk/1Rkdi+CR2XhxkvQ+HuseoTy5ckuybtz7I7cBnGdlnlxrfnsTsz+OdWL79Mke7furT75m+rmbNsxrhzbMaiMzWcltmIe1Ei4UiLocQOCZXpcDuDbkNuBc1OWAM3FNzTmYb3Eh6nLAuajLAWfyRV0eMvCYr8Mymuul/FaHcEX+eD183Wdfx2j6a+IP4VYHYIKQDOqJe+59QG3vvMf93wkJ8Xo2pqtHj5eUIj1eM5tGbDyrx2tmU1KKFB6a9vuMZHQsgkdm48VJ0vvY/LOz+uP5tN9Z1Wd/5DaA86zMk2vNb29i9sexTmyfPtmjfX/1yddMP3fThnnt0IYXbaRe0kaqPXIb5mGthAsFoi4HEHim1+UArg25DTgXdTngTFxTcw7mW1yIuhxwLupywJmoywF7CrpvmlmzepU+HDRAw0dPUGhoqL7+4lMlnDmjXs/974p/Fx8fry6dOnq8EzAhyaWIsLQCJkfE+V05ycmXP91JLmV4LIJHZuPFSdL7qNS0/zZl3PsqtwGcZ0qeeJPf3sTsj2Od2D59skf7/uqTP5l47qYN89qhDS/aSHYpIjRtA02OcPvkNsxjynkC5vFXXQ7AWibW5QCyjtwGnIu6HHAmrqnZG/MtMkNdDjgXdTngTNdSl/NNM2bhm2aCQ9B900yFipUVGhqqhQvmqXrNWlq7drXuvf9Bn7eTXqhcusM3PIM3IaW/COwGDl6ZjRcncRfv5/po13EfDK8VkFV2zBNvYvbHsU5snz7Zo31/9ckkgYibNsxrhza8aCPcnrkN89j1PAGzMI4A5yK/AWcitwFnIrcBIDCYb+ErjCXAmchtAAB8K+g2zURFRSmmey+NHT1C48eOUqPGTdXqhpusDgsAAAAAAAAAAAAAAAAAAAAAAAA+FHSbZiTpuuYtdF3zFlaHAQAAAAAAAAAAAAAAAAAAAAAAAD/hy9sAAAAAAAAAAAAAAAAAAAAAAADgOGyaAQAAAAAAAAAAAAAAAAAAAAAAgOOwaQYAAAAAAAAAAAAAAAAAAAAAAACOw6YZAAAAAAAAAAAAAAAAAAAAAAAAOA6bZgAAAAAAAAAAAAAAAAAAAAAAAOA44VYHAAAAAAAAAAAAAAAAAAAAAAAAnCVk4DGfPp7rpfw+fTwEBzbNeMjlckmSEhLiLY4ECG45cuRQSEiIzx6P3AbMQX4DzkRuA85EbgPO5OvclshvwBScuwFnIrcBZ6IuB5yLczfgTOQ24EzU5YBzce62v/h4859rX28U8jVfP4e5hp7x6ePF9czu9d9cLbdDXOnZiis6evSIno3panUYQND7dvgY5cyZ02ePR24D5iC/AWcitwFnIrcBZ/J1bkvkN2AKzt2AM5HbgDNRlwPOxbkbcCZyG3Am6nLAuTh3A850tdxm04yHUlNTFRt7TNmze7bDMCEhXs/GdNUnX3yjHDl8Wzz5A/H6F/H6jq93+Toxt02PkfiyxvT4pGuP0R/5/fffB/TCf3sa/Xx5yw5jwFv0yR5Mym1vzt2B4JTX2yn9kJzTF5P74aTcNvl5TkeMvkGMV+ePT87zJr+t7r8nTI+R+LLO9BjtWpeb/rx6yin9kJzTl2DvB7ntG07ph+ScvjilH9K19YW6/OpMj5H4ss70GO167vYl018jyfwYTY9PIkZPWZ3bJjwHgRAM/QyGPkr26Sd1+dWZHiPxZZ3pMdq1Ljf9efUU/TCPU/rir9wO90VwwSA0NFQFChT0+u9y5Mjp8x3H/kS8/kW85nFybpseI/FljenxSdbHGBoaqvz5CxgRiz/QJ3ugT753refuQLD6ufEVp/RDck5fnNKPKzEht+3wPBOjbxBjYF1Lftuh/6bHSHxZZ3qMVsfn5GtqnnBKPyTn9IV++Aa57Yx+SM7pi1P6IVnfF+pyaxBf1pkeo9XxcU3NM6bHaHp8EjEGWrDX5VcTDP0Mhj5KwdPPC1GXW4P4ss70GK2OL9jP3fTDPE7pi6/7EeqzRwIAAAAAAAAAAAAAAAAAAAAAAAAMwaYZAAAAAAAAAAAAAAAAAAAAAAAAOE5Y7969e1sdhFOFhoaqeo1aCgsLszoUjxCvfxGvc9jhuTE9RuLLGtPjk8yK0aRYfIU+2QN9Ci5OeW6c0g/JOX1xSj9MZ4fnmRh9gxjNZ4f+mx4j8WWd6TGaHl9m7Br3pZzSD8k5faEf1rJr3JdySj8k5/TFKf2Q7NkXO8RseozEl3Wmx2h6fIFgh+fA9BhNj08iRrsIlucgGPoZDH2UgqefWWWH58n0GIkv60yP0fT4MmPXuC9FP8zjlL74ox8hLpfL5bNHAwAAAAAAAAAAAAAAAAAAAAAAAAwQanUAAAAAAAAAAAAAAAAAAAAAAAAAgK+xaQYAAAAAAAAAAAAAAAAAAAAAAACOw6YZAAAAAAAAAAAAAAAAAAAAAAAAOE641QE40a/Ll2ncmJGKizutho2bqstTMYqIiLAklhnTpmj2zOlKTDyj2nXrKaZ7L82dM1Pjx4xyH1OufAW9M/ADnT59Wl9+NlSbNq5XvvwF9GTXp1WjZi1J0uyZ0zVz+lSlpKTo+htuVMdHOykkJMTn8b7br7fWr1vr/u877rpHLVvdqC8/G6p//j6g8hUq6pke/1HBQoWUlJSk777+Qit/X6GcOXPp4UceU7PmLSUF5jX4/JOPtWTxoot+V6RIUbV/uKM++fhD9+9y58mjr78baVm8J04c1/JlS7Tk50Xq0fM5lSpdRkcOH9bnn36s3bt2qljxEnq6e0+VKVNWLpdLY0eP0OJFPyksLEx33t1Obe+8W5K0aeMGff/Nlzp27Khq1Kytp7v3VO7cua84buwuo+fuQib0/WoxHjr4j7764lPt2L5dBQoUUMfHOqlR46bGxJdu3949eu3l/1O7+x7QAw92MCq+fXv3aOzokdq+bYtefaOPKlaqbEx8x48f1+effKRtW7coKipaD3V8VM1bXB+w+DI6x2TPnt3975nNNYFi0vnYVzI6Tz76eGfrAsoCb85PdpFRn5YvW5JhXWAHGeX4oUMHM6zLgtnV5kI7+uiD9/Tbil80duJUq0O5JqkpKZo6dbIWL1qoEiVK6cVXXrc6pGv208IFmjxxnOJOx6lGzVrq3vM/ypUrt9VhOcK+fXs9ns/OnDmjF5/vpYKFCunNPv2NijExMVHff/Olflvxi7JFRurWNrfrgfYP+z22q9VZJqyVrhaj1WslT2tVq9ZKVjG9hvd0jWsV0+uSlJQUjR87Sj/9OF+SVK9+Qz0V00PZsmWzOLLLmVwP2XFdaHpue8P0ecBTps8XnrDTnOIpk+eejJDb5iG3zWVyfpu+vjN9bedJjOmsWt95Ep+V98KuFp/V98Ike9wzDgSup2WN6fOtJzEy55rJSXX5lTih1vWUybWjLzjpPp6/mZ7fpq+lTZ437Lb2NXVesuO1csn83PaUyTl2LUwd595w0jnWX++V4ZtmfOzUqZP64rMhuveBB9W77wCtW7taixYusCSW9evWavzY0ere8zn16T9Q27Zs0dzZM3Q8NlbNmrfUN8NG6Ztho/Rm736SpGlTJuno0SMaMOgjNW5ynT4dMljJSUnav/9PjRoxTDE9euqFl1/T/LmztWb1H36JOTY2Vp27dnPH9uDDj+jrzz9V+fIVNOjDoQoLC9OoEd9Lkhb9tEAbN6xXn/4DdVe7+/TlZ0N18sSJgL0GTz4V447zm2GjVL1GTTVq0lTHY2NVpUo19+8//uRLy+JNSEhQz2e6acXy5dq3d4/796NGfq/IyOwa9OFQlS5dRl99/okkae2aP/Tj/Ll68dU31PXp7ho14nv9uW+vkpOT9emQwbqueUsNGPSRDh06qGk/TJKU+bixu8yeuwtZ3XdPYvzsk4+VO3duDR76mRo3bZYWY3KyMfFJaSfrr774VC6XKyBxpfMkvn8PHVKfN19T3nz51PfdQSpfvoJR8U2bMkmxsbEa9OEQtb69rT7/ZIgSExMDEl9m55gLZTbXBIJJ52Nfyug8aUfenp/sILM+ZVYXmC6zHM+sLgtWnsyFdrNq5e9a+fsKq8PIkuHDvtWCuXP06GOd1f3Z/1gdzjU7deqkvvnyc7W7r70GfvCR/j7wl2ZOn2Z1WI7hzXw2YdxoHT16JIDRpfEkxqmTJ2rbtq3qN+B9PfV0D02eME7bt231a1ye1FlWr5U8idHKtZKntapVayWrmF7De7rGtYod6pJlSxfrpx8X6IWXX1ef/gO1ccN6901Bk5heD9ltXWh6bnvD9HnAU3aYLzxhlznFU6bPPZcit81DbpvL5Pw2fX1n+trO0xgl69Z3nsRn5b0wT8egVffCJHvcMw4UrqddO9PnW09jZM41j5Pq8itxSq3rCZNrR19xyn08fzM9v01fS5s+b9hp7WvyvGS3a+WS+bntKdNzzFsmj3NvOOUc68/3yrBpxsd27dwhl0tqdcNNKlmqlOrVa6AtmzdZEkt4eLg6Pvq4atWuo1KlSqtY8eI6cfyEjsfGqkDBgsqVK7dy5cqt7DlySJK2bN6kJk2bqXCRIrqldRvFxh7ToUMHtXXzJhUrVky169RTxUqVVblKVW3d4p8+HY+NVeHCRdyxyeXSrl071OrGm1WwUCG1bHWjtpxre8umTapTr75KlCipm29prdTUVO3evTNgr0FkZKQ7zqNHjmjb1i1qc9sdOh4bq3z587v/LWfOnJbFmy1bNg39/Cv1fO75i36/ZfMmtbi+lQoWKqQbbrpFu3ftVGJiorZs3qRKlauqQoWKqt+goQoXLqJtW7fo0MF/FBt7TDff0lqFixRR02bNz78OmYwbu8vsubuQ1X33JMYaNWvp/vYPq0CBgqpTt64SExN1JiHBmPgkafbsGcqWLTLg3ybhSXyzZk5T0aJF1S2mh0qUKKnQsDCj4gsNCVX27NmVL38B5c9fQOHhYQG7+JfZOeZCmc01gWDS+diXLj1PmvopE1fj7fnJDjLrU2Z1gekyyvEjhw9nWpcFK0/mQjuJj4/X999+qTa332F1KNfs+PHjWrhgnrp0i1GjJk2VO08eq0O6Zi6XFBoaqgIF0s7zkZGRCgtjCe8LZxMTPZ7Pdu3coeXLlqh5y1ZGxlikWDE98lgnlShRUnXr1lN4eLhOHD/u19g8qbOsXit5EqOVayVPa1Wr1kpWMb2G93SNaxU71CWtbrhJ3wwbpSpVqylv3rwKDw9XWADX2Z6wQz1kt3Wh6bntDdPnAU/ZYb7whB3mFE/ZYe65FLltHnLbTKbnt+nrO9PXdp7GKFm3vvMkPivvhXkSn5X3wiR73DMOBK6nZY3p862nMTLnmsdJdfmVOKXWvRrTa0dfcNJ9PH8zPb9NX0ubPm/YZe1r+rxkt2vlkvm57SnTc8wbpo9zTznpHOvP98rwjhsfO3nypCKzRyokJESSlDNXLp08edKSWKrXqKm2d94tKe2rR3fu2K7mLa9XbOwx/b7iVz3TrbPeev1l7dmzW5J06uRJ99dj5cqZS1Jaf9L6dP5rs3Lm9E+fkpOSdPr0KY0eOVzduz2pwe8P1ImTaROpO65cuXTqXNsnL4g3NDRU2XPkuCDewL4GM2dMU8PGTVSwUCHFxh7Tju3b1OPpLnr5hf9qw/p1lsUbFham6Oi8l/3+otc6Vy737y6M8cJY0uO5cHykvw6ZjRu7y+y5u5DVffckxgcffkSlz10M+nH+PNWt1yBgJ0RP4vv30CFN+2GyusV0l87lQKB4Et+ObVsVmT27XnnxeT33bIzmz50doOg8i+/+9g/p6NEj6vzYwxr60Qfq3PXpgH3NYWbnmAtlNtcEgknnY1/J6Dx56pQ9++Tt+ckOMutTZnWB6TLK8WYt0nI8o7osWHkyF9rJ2FEjVKt2XdWsVdvqUK7Zrp3blZqaqhW/LFePmC4a0P9tSz7R0BeioqL08COPatCA/nry8Q5KTk7W3e3utzos20lNTVVc3OmLfq60zrxQcnKyvvriUz3y2BOKiooyMsYbbrxZDRs1kSQt+ulH5cyVS7Vq1/FbrJJndZbVayVPYrRyreRJfFaulaxieg3vyRrNSnaqS17833/U7cnHVbRYMd3S+jarw7mI6fWQHdeFpue2N0yfBzxlp/nCEybPKZ4yfe7JCLltHnLbTKbnt+nrO9PXdp7GaOX6zpP4rLwX5kl8Vt4Lk+xxz9jXuJ7me6bPt57GyJxrHifV5VfitFo3M6bXjr7gpPt4/mZ6fpu+lrbLvGH62tfkecmO18ol83PbU3bJMU+YPM694aRzrD/fK8OmmQCweh129OgRDRrQX3fceY8qVqqsu+65Vw881EGvvtFHUVFR+nzoR5n+bWax+6VLISF6ostTeqzTk3r+hZf155/7NHnCOK9az+xf/PkaHDt6VL8sW6rb294lSbrpllt1X/uH9MrrvVWpUhUNGfx+pl+ba0W8mcn0tc40SO8fKxiY2vdJE8dp86aN6totxupQLvLNV5+p7Z13q1jxElaHkqG4uDilpqaqW0wP3X7HXRr23dc68Nd+q8NymzxxvKKjotX3nffU/uGOGjNyuOLj4gIaw6XnmKuxMkdMzU+PZXCenJThedJZ7P66eVMXmOjCHC9cuEgGR9j8BfIRb+dCE23dslmrVv6mRx97wupQsiQuLk5SiCpXrar/e+k1nThxXKNHDrc6rGty7OhR/TBxvDp37abe/QYoJSVFs2dNtzos2zl65LC6PvHoRT9Lfl6UwZGXz2czpk1RVFS0rm91o7Exptuwfp1Gjfhez/To5f422UDy5Hxt9Tk9s/ZNWStdGp/pa6VAsXrc2JEd6pKXXn1Dr7z+lvbt3avFP/9kdThutqiHHLIuJLfNYIf5whOmzimessXc4yFy2wzktjnsmt+mr+9MX9tJ5q/vLo3PtHthl8Znwr2wa2Hn8yLX0wLD9Pn2Su0z55rN6nHjT06pdTNi19rRW066j2cFJ+e3v5g+b5i89jV+XnLItXLJ3rlteo5djfHj3AtOOsf6870y4T55FLjlyZNHZxISlJqaqtDQUCXExyvKwl21J0+e1Dt9e6tGzVp6sMMjcrlcKle+gqKjohUeEaGbbmmtQQPeUWpKivLkiVJcfNoFpvhzX6EaFZVXeaLyXHThKSEhXkWLFfd5rKmpqWrYsInyFyggSWrYqLH7q8fizrWfEB+v6OhoSbooLpfLpTMJCYqKzqvU1JSAvgZzZs1QmbJlVaVqNUlSseIlVLFiZWWLjFSb2+/Qjwvm6tixY8bEKynttU5/Ts+91nmiopUnTx4dPXLYfVzCuRjz5En7FJi4+Dhlz5FDCQnxio7Ke/6xMhg3wcAufZ8/d7bmzJyu13v3U4GChawOx23H9m3asH6ddu7YrpnTp+jMmTP6a/+fiojIpnvuNeNT1KOio9WwURNVqFhJFSpW0phRw7V//58qUbKU1aFJktatXa1WN96ssuXKq3jxEpo4box2794VsJ3Pl55jLpXZXBMIpp2PfSGj8+TOHTssjsq3rBwz/pJZXVC4SEYbUMxyaY4nnT0rKeO6LJhdbS60iymTJ+rUqVP6z7NPKyUlVZLUpVNHfTt8jMWReScqKlrZsmVT6zZtJUnXNW+Ryc1S823btkWpqanuvjRq0lTr1qxWu3sfsDgyeylUuIjGTpx60e/OJiZq0oSxV53PpkyeoJCQEHXp1FFJSUlKSUnRe+/204uvvG5MjJK0c8d2DX5/gJ7o0k116zXwaWwZ8aTOsnqt5GktaNVa6Wrx2WGt5A9OrOEDzfS6ZN++vTpx/Lhq16mrAgUKqnrNmtq8aaNuuPFmq0OTZI96yI7rQnLbTKbPF54wfU7xlB3mnoyQ22Yit81ih/w2fX1n+trOkxitXt958hxaeS/Mk/isvhfmCauvg/ga19N8z/T51tMYJeZc0wRTXe6EWvdK7FA7+oKT7uP5WzDlt7+YPG/YYe1r+rxkx2vlkrNy2+Qc85Tp49wbTjrH+vO9MnzTjI9VqFhZoaGhWrhgng4c+Etr165W9Ro1LYnlTEKC3nunrwoWLKhHO3VWfHycEuLj9eqL/9PoUcMVG3tMK35ZrpIlSyo0LEzVqtfQil+W69Chg/rpx3nKX6CAihQtqmrVaujffw9p9R8rtWvXTm3fts0vfTp08B/1iOmiX5cv06FDB7V+3VpVqFhJ5StU1KKFC3Tk8GEtW7pY1c61Xb16Ta1Z84f+2r9fi376UWFhYapQoWJAX4P4+Hgt/HG+br/jLvfv3h/4jj4d+pGOHTum5cuWKHfuPMqfP78R8aarVr2Gli5epCOHD+vnRQtVqVIVZcuWTdWq19T2bVu1Y/s2rVu7RocOHlS1atVVtFgx5cuXXz/Om6vD/x7Sil9/cb8OmY0bp0pNSXH/f1P7fmGMy5ct0agRw9Sj1/MqXLiw4uJOW/4NB+nxlSlbTkM+/VID3/9IAwYNVomSpXTzrW10S+s2RsQnSfUaNNTSJT/r4D//aOmSn5WcnKzSpctaF5wujq9EyVJa/cdKHTl8WEuXLlZYWJiKFSsWkDgyOsfEx8dfliMZzTWBYNL52FcyOk+WLlPG6rB8ysox4y+Z1QWmyyjHk1NSMq3LglVmc6EdPdOjlwYP+VQDBg1Wpye7SpIGDBpscVTeq1y5iiIiIjRvziwd/veQVv3+m8qVK291WNekeImSOnv2rH79ZZn+PXRIG9avM2bjrt1li4y84nyWXs988NEnen/wUA0YNFgtr79B5StUVLeYHkbF+Nf+/Rr4Tl+1vfNuNW7SVHFxp5WYmOjX2DKrs0xaK3kSo5VrpavFZ+payd+cWMMHkh3qkj/37tHg9wdox/Zt2r//T+3Yvk1ly5WzOiw3O9RDdlwXktvmscN84QnT5xRP2WHuyQi5bR5y2zx2yG/T13emr+08idHq9Z0nz6GV98I8ic/Ke2FXYkqeBArX07LG9PnW0xiZc80TLHW5U2rdK7FD7egLTrqP52/Bkt/+Yvq8YYe1r+nzkh2vlUvOyW3Tc8xTpo9zbzjpHOvP98qE9e7du7dPHgmSpMjISBUtWkyTJ47XgnmzVb9+Q917f3uFhYUFPJbly5dqwbw5OnTooGZMm6LpU3/QL8uXqkev/2rB/LmaPGGcQkJDFdOjl/LmzafyFSpq65bNGjt6hI4eOaKnn+mpIkWKKjo6r3LkzKmxo0dq6eKfdcutbXRL69t8Hm90dF7lzpNbk8aP1dzZM1WhQkU99kQXVa9RU4t++jHtq5ej86pL127KmTOXSpcpq3/+PqAxo4Zp184deqJLN1WoWCmgr8Hc2TO1Z88udYvpodDQtD1oFStW0i/Llmr82FE6ceKEnu7eU0WLFbc03vi4OM2ZPVO3trld0dF5VbFSZf3+2wqNHztKKSkperr7s4qOzquixYrpTMIZjR45XKv/WKmHHn5E9Rs0UmhoqMqWK69ZM6ZpxrQpKluuvB55vJOyZcuW6bhxikufu/ffe1fHY2NVuUpVY/p+pRjfH9Bfp06d1C/Llmj61B80feoPKlKkqMoG8ISYWXzVqtdQrly53T+LfvpRZcqUDcin+XgSX+UqVVWhYiX9tf9PjRj2rbZv3arHOj2pWrXrGBNf5cpVtHbNao0dPVJ7d+/S4527qmq1GgGJK+NzzBItWbxIUXmiVLJUqUznmkAw6XzsK5mdJ+28qcTT85OdXNanTOoC02WW4z2f+78M67Jgldnz1PaOu60OzWvZc+Rwn5Njjx3VL8uW6vHOXa0Oy2sR2bKpYqVK+mHSBE2bMlklS5ZS565PK3v27FaH5rW8efMqT1SUJo4fozmzpqtM2XJ6vHNXRUZGWh2aI1SsVDnD+WzmjKmaMW2qmrVoeVGtumXzJp08cUJtbr/DqBjHjBquHdu3acvmTe56/9jRo2rUuKnf4sqszhr8/kBj1kqexGjlWulq8ZmyVgo0u9Twl9Z7prBDXVK6TFmdPn1aY0eN0OJFC9WoSVPd/8BD7mtqVrNDPWTHdaFdctsbps4DnrLDfOEJ0+cUT9lh7skIuW0ects8dshv09d3pq/tPInR6vWdJ8+hlffCPInPynthF7LDPWN/43ratTN9vvU0RuZc8zixLs+IU2rdK7FD7egLTrqP5292yW9T19Kmzxt2WPuaPi/Z8Vq5ZJ/cvhrTc8xTpo9zbzjpHOvP98qEuFwulw9iBAAAAAAAAAAAAAAAAAAAAAAAAIxhztZEAAAAAAAAAAAAAAAAAAAAAAAAwEfYNAMAAAAAAAAAAAAAAAAAAAAAAADHYdMMAAAAAAAAAAAAAAAAAAAAAAAAHIdNMwAAAAAAAAAAAAAAAAAAAAAAAHAcNs0AAAAAAAAAAAAAAAAAAAAAAADAcdg0AwAAAAAAAAAAAAAAAAAAAAAAAMdh0wwAAAAAAAAAAAAAAAAAAAAAAAAch00zCFqH/z2kDu3b6YXne13zYyQkJOi/vbrrf889qzMJCZkeN2nCWHVo304zp0+95rYAJ3jz1ZfUI6aL/j10yBZaXqQAACAASURBVKeP+++hQ+rxdBe99frLPn1cAABwdR3at9MTjz6c5ce5tGbevGmDOrRvp/fe7ZflxwZM5q8aGUBwmDNrhp7q/Jjmzp55xeN8db4G4DvkJWAfPbs/pQ7t2+nkyZM+e0zuGwHO48trWW+/9Zo6tG+nXbt2SmLOAOxo8aKF6tC+nb7/5iurQwFsg3UyAADwF1+8Xxz2x6YZOEKXTh3VoX27gLcbGRmpW9vcpltb36ZskZEBbx+wm5tvba1b29yuvHnz+vRxo/Pm1a1tbtPNt7bx6eMCAAAA/uavGhlAcKhWvYZubXObqlWvYXUoAAAAAADgCj547x11aN9OmzdtsDoUwNHi4k6rQ/t26tn9KatDAWChvXt2q0P7dnr7rdesDgVABmbPmq4O7dtp0oSxXv3dyt9XqEP7dvr8k4/9FBmcLNzqAAA7Cw0NVds77rY6DMA2Wt14s18eNzIyUu3ua++XxwZgTy6XSy6XS6Gh7BEHAJjNXzWyyVJTUhQaFmZ1GJlKSUlRmMHxARcqW668ypYrb3UY1N+Ag3AeBIIb53TAmUxfhwPwHep5ILiQ80DW+atWTk1NVUhIiEJCQnz+2ACswdra/tg0g4Do0L6dihYtpja336HZM6crNvaYipcoqS5PxejkyROaMHa0Dh48qEKFC+vBhzuqSdNm7r/dvm2rJk8crx3btyo0NFTlyldUh0ceU/kKFbV40UJ98dnQi9q5vtWNeubZ/0iSfl2+TNOn/aADf/2lPFF5VLdufT3U8TFFRUVdFN/M6VM1d/ZMnT59WhUqVlLXbjEqVryEx32LjMyuYaPGSUq7oTBl8gT9OH+eEhPPqGq1GoqOjs7qUwg4Qs/uT+nI4cP68tsRioqK0ttvvaYtmzfpfy++opnTpmr37l3Kmzev7r73ft1y7ltjJk0Yq8kTx+uxTk9q547tWrvmD0VEZFPT65rrsU6dFR4RocP/HlKvHk+rZKnSGvThEEnn5p1ixXXf/e01ZfJEHTlyRCVKllSnzl1VtVp1d0w/L1qoaT9M0pEjh1WiZCndcNPNGv7dNxfNJQCybvnSxZo1Y5oOHPhLkZHZVb9BQz38yONKSIjX8726q2DBQhr6+dfu4z/68D399usv6tHrv2rRspVSUlI09YdJWrxooWJjj6lIkaK6q919anXDTZLkrgmat2yl2GNHtWP7Nr382puqXqOWVV0Ggs78ubM1Z9YMHT16VJUqV9YTXbqpVKnS7n9fs3qVpk2ZrL17ditbtkhVrVZdHR/tpKLFilkYNWC9S2tkSZo7e6YWzJ+rI4f/Vd68+dSsxfW6974HPP6G0107d2j82FHatXOHwsLCValyFT38yGPunEyvw/sNeF8VKlSUlPZpPiOHfaf72z+kBx7s4K7D2z/UQVu3bNbOHduVPXsO3dK6je69/8GLLvLPnztb8+bO1uF/Dylf/vy6tfXtuuOuexQSEqLNmzaob+83VKdufYWHh2vjhvXq3OUpjzYL/fPP3xo3eqQ2b9qoxMRElS9fQfe1f0i169SVJL31+svavm2rXn+rr2rUTDvnr1m9Su+92081a9XRa2/2kSRtWL9OkyaM1d49u5U9ew41atxEjzzeWTly5JCUtnbIly+/mrVoqaWLf1bdevVZC8AyX3/xqX5auEDP/9/LatSkqSTpnb5vaeOGDfrim2GKiopSakqKunZ+VKGhYWpzW1v9MHmCHnnsCd15d9q3MO/f/6eGffuVdu7Yrvz5C+iOuzP+duZfly/TtKmT9feBA8qTJ49atGyl9g93VHj41S8ZZ1Z/b960UZMnjr8onr17duuVF59Xteo19Gaf/u55odWNNys6Oq+W/PyTEhPPqErV6nrq6e7KX6CAJCk+Pl4Tx4/Ryt9W6PTpUypStJja3nFXUG42hLNlVkcvXDBP33z1ueo3aKgXXn5dUtpNuW5dOiku7rQ++uQLFSlSVCdOHNfYUSO0+o9VSkw8o/IVKqnjo4+rUuUqHrWfXos81PFRLZg7R0WLFdMbvftddt1bSvtk7FUrf9cbvfuqeo1aHl3bk6T169Zo8sTx+nPfXmXPnkM1atXWwx0eVcFChXz7ZAIBsnvXTo0fO0rbt21VWFiYatetr8c6dVa+fPndx0yf+oPmzZ2lUydPqnyFiqpTt74mjBvtrrfTHTlyWO+9209bt2xW9uzZ3fW2y+VSz+5P6djRo+o/4H2VP1e3z5wxVaNHDFPL629Q957PSeKcDmSVp/eTdu3coQnjxmjnjm1KSUlVlapV9VCHR935me7s2UR9/81X+v23X5WYmKiGjRrriSefUs5cuSSlvXluzqwZWvTTj/r30EFFR+dVw8ZN9ODDj7jXqVn1+Scfa8niRbrrnnu1etVK/f333xoz4Qevrgdc6b6cJP3z9wGNHT1SW7dsVmpqisqULa8HH+6oKlWr+aQPgLc6tG+n4sVLqNWNN2vhj/MUeyw2w+vUB//5R2NHD9emjRuUkpKiqtVq6LFOT6p4ibT3h3ha417pfSyXSr+Xna5v7zdUsFAhtbv3AY9q/itJj/f+Bx/W0p8XyeVyachnX2V4zXH4999o7uyZiuneU61uvNk9V8T06KVfly/Tls0blTt3Ht148y0X1StXu9YI+FpW7jeln8ck6cjhw+rQvp37HHe1ujS9Nr79jrt08sQJrVm9SqmpqarfoJGe6PKUpk6epGVLFyspKUlVqlZVl6eeca9rPTm/9+z+lGKPxer/XnpV48aM1N8HDqhQ4cJ6uOOjatS4qUfPDWt4BJvM1rszpk3RhHGj1bBRE/3vxVckSSOGfas5s2bohhtvVmpqqpYsXiRJ2rJ5kzq0b6eY7j1VvUZN9erxtMqVr6DSpcto5e8rdPsdd+mBBzvo6NEjmjhujNas+UOJZ86oaLHiuqfd/bqueQuPYk2ff25tc7t27tiuP/ft1eAhn2nShHFasniRnnv+RTW5Lu09sSt/X6EPBw1wvzctff655977dfrUKf224le5XC7VqVtPXZ6Kca8lYmOPadzokVq/bq3OnElQyVKlde/97VW/QSM/PPuA/6SfzyRp8sTxmjxxvIZ8+qXy5InS+HGj9fuKX3Xy5AkVLlxEt952u267/U5J52tfSVqyeJGWLF7kPs9lNYcvldk9bkn64rOhat2mrTp37SYp7Vvuuj6Rdq4c+tnX7vq/Vu06ql23nubNSbtGWLZceXXt1l0lS5WSJCUnJ2vqDxO1dMnPOh4bq4IFC+nGW25V2zvu5gNt/IRnFQFz8OA/mjZlsqpUraYyZcrpz3179d67/TRk8AcqWaq06tStq3/+/luffPyhDh/+V1LajYe+vV/X1i2bVL9BI1WvUUubNm7Qu/366HhsrEqULKXWbdq6L/y3btNWNWvXkST9tHCBhnz0vo4cPqwmTa9TgfwF9dPCBRo0oJ9SUlLccR34a7/mzZ2l2nXqqmixYtq8aYM+/3TINfdzzqwZmjh+rM6eTVS9Bo105kyCFv30YxaeOcD5hn70gfLmy6f6DRrq6NEj+u7rL/Tnvr0XHTN65DCdOHFCjZtcp5SUFM2fN1s/LVxwxcf999BBjRz+vSpXqaoKFStq757dGvrRB0pOSpIk/bHqd3352VAdPnxYDRo2VnR0Xo34/jt/dRMIWvPmzNInQwbryJEjatzkOuUvUECLf/5Jfd54RdHReVWuXHkdOXJY+87lfUpKijasW6uIiGxq0LCxJOmTjz/UpAljlTt3bjVv2UpnzpzRF58O0aqVv1/U1vKli3X27Fm1vP4GRefNF+iuAkErMfGMJk8crypVq6ls2XLavGmjBvTro7Nnz0pKO+cOGtBff+3fryZNm6lc+Qpa+fsKDejfR2cTEy2OHjDLzOlTNfz7b3Q2MVHNmrdUeESEpv4wUd989blHf//voUPq//ab2rxpk+rUra9KlStr9R8r1a/3Gzp+/LjX8UwcP1ZJScmqW7+hEhLiNXH8WM2bO8v972NGDdf3334luVxq1uJ6hYeFa/TIYZo3Z9ZFj7Nu7WodOnRQzVu0VJGiV98sd+TwYb3xyov6/bdfVbFSZdWrX187d27XgP5va/UfKyXJfZFz9aqV7r9bs/qPi//tj5V6t18f/f33ATW9rrmKlyiphT/Ov+wru2Njj2npkp9Vr0FDVa1ew+vnCfCV9Ota27ZtkSSdOXNGW7dslsuVqrVr0sb3n3/uU0JCgmrUrKmQ0Is/pS4xMVHv9H1LmzdtVJky5VS5SlWNHzvqsnYWzJujIR+9r9OnTqlZi5aKzptX06f9oLGjRngVb1bq76WLF+mPlb+pXv0Gyps3n9au+UMjhn3r/vdPPv5Qc2fPVN68+dS0WQsdO3pUX3w2lOtscJQr1dFNmzVXREQ2bVi/XmfOnJEkbd26RXFxp1WpUhUVKVJUCQkJeuu1l7Vk8SJVqFhJjZpcpz27d6r/22/p2LFjXsUyfcoPql6jpurWa+B1P650bW/H9m16791+2rd3rxo1bqriJUpo+dLFeqfvW+71AmAne3bvUp83X9W2rVvUoFETVapcVSt+WaZBA/rL5XJJkubOmamxo0coIT5ejRo3VUhIiCaMG53h482bM0tnzyaqQcNGOns2URPHj9X8ubMVGhqqFi1aSZJWrfzNffyaP1ZJkpq3TPs3zumAb1ztftKuXTvV581XtWnjetWsVUdVq1XX+nVr1fuNV7Vn966LHmvTxg3atGmD6tSrrzxRebR0yc/68vNP3P8+ZtRwjRrxvVJSUtSiZSvlypVLc2fP1Lcervu9MXP6NBUpWkw33OT9JrUr3ZeLj49X395vaOXvK1SpchXVrF1H27Zu1jt939L+/X/6uhuAx/7++4BmzZymSpWqqGTJkpddpz565LDeeO1F/bFqpWrWqqPadeppw/q16v/2m0q85Dr1lWrcq72P5VI5cuRU6zZtVbhwEUlSw0ZNdH2rmzyq+T01fcpklatQQdc18/5NgV9/8anCwkLVuMl1On36lCZPHK/V52oOX19rBK4mq/ebKlSspJtuvlWSlCNHDrVu01YVKlaS5HldOmfWDB385x81bNREERHZ9Mvypfrff57V77//qvoNGqpgoUJau2a1vv7yU/ffeHp+T0lJ1tCP3lepUqVVo2Yt/X3gL306ZLBOnjjh1fPEGh7B4Err3bvubqdixYpr1crftHnTBh048Jfmz52jXLlyq8OjnVSzdh33OTFfvvxq3aatSpQs5X7sPbt3afOmjWpyXXOVLVdeZ86cUe83XtHin39S+fIV1KRpMx3+95CGfPS+1q9b43XcuXLlUqsbblJkdu82xc+YNkV79uxWw0aNFRkZqV+WL9WUyRMlpX2Q+7v9+mjJ4kUqUbKkGjZqov1/7tP7A9/VhvXrvGoHsNr1rW5yf+BChQqV1LpNW2WLjNQ7fXtr7uyZio6OVpPrmuvkqZMa/t03GjNquCSpUeOm7nNf8RIl1bpNW+XLX8CnOXwpb+9xX2jTxg2aO2umatWqo+LFS2jb1i36/NPz96hHDvtOkyeOV0R4hJo1b6nExESNHjFMkyeMu8KjIiv4phkETFhYuPq+854KFiqk1JQU/efZGB05clgdH+2ku+65V5L0yZDBWr50sbZu2axChQrr6JEjata8pRo1aaqGjZpIkr764lMtWrhAGzeuV4uWrVSxUmUtW/qzkpOT3Tv3UlNSNH7sKIWGhuqtt99x78zr//Zb2rhhnXbt3KF8+dIu+ufJE6UBgwYrV67cOnv2rLp3e1I7tm/T2cREjz/FN11qSoqmTZ0sSXrtzbdVrnyFi/oFIGNduj2j61vdKEn67psvtWDeHG3dulmly5R1H9Oy1Y2K6d5TkrTi1+X6+MNB2rx5o1rf1jbTxw0JCVHfd99zX1D8v//21IG/9uvgwYMqWaqU+xNGnnm2l5q3uF6SNG3KZI0bM9If3QSCUmJiosaNGanw8HD1fWegihQtptSUFA18t5/Wr1ujHxfMVdPmLbRnz26t+WOlypQpq21bNys+Pl6NmzRVjhw5tHXLJq34dbmq16il1996WyEhIToeG6tnn+mqHyaNV8NGjd3t1a5TTy+/9iZfcQsEWFhYmPq+854KFykil8ulQQP6a83qVVq2dLFuuvlWHT9+XC2vv0E33dLaffEjvTbfs2c3n0AJXOCPVWkbQp/7v5dUoUJFxcfH6+03X9WBA38pNTX1qp8qs2nTBiUkJFz06TYjhn2rDevXae+eXV7fRLvp5lv1VEwPSec/UWfG1Cm67fY7dfjwv5o5fZqKFy+hAe9/pIiICCUmJqpXj276YdIE3db2TvfjlCxVWu++96FHn3YtSZMmjlNc3Gm1f7ij7rv/QUnSsqWL9emQwRozcrjqN2ikJk2bacT332n16lV67IknJaV9ymBYWLgan/tkvhHff6vQ0FC93X+gihUrLkkaNKCfVv6+Qvv/3KdSpctISpvH+r/7Pp+WB8vVrFVbISGh2r51qyRp44Z1SkpKUkhIqFavWqnrW92o7dvS/q1m7bo6cfziN+Ms+mmBjsfGqmGjxnr+hVcUEhKig//8o//2esZ9TGJiosaMGq7cefLo3UEfKk+eKKWmpuqVF5/X/Hmzde/97ZU7Tx6P4s1K/V2sWHG9O2iwIiIidOLEccV0fcL9CWHJyclat3a1cubMqT79BygsLEybN23U999+pV07tuvGm27xuj3ARFero+s3bKjffv1F69auVpOmzS54s3zadaw5s6br0KGDF31zxW8rftFHH7yn2TOn/X979xkfZZU2cPifyaSTmRRKCi0QQk0PnVAERESQ3qVYWHtdFOkCoui6ru7rrmtZsKCCgCICUgQFgZCeQCqQUNITSCcJyWTeD5MZSCGNhGD2vj7BPM88c4YfZ8592n2Yv2BxvcuybMVq3Hr2atT3qG1sLzw0BI1Gw+y5jxhOq/jg7++SlHSF1JRkunR1adRnCtFStn61hRs3bhiyWAJs/vwTDv6yj7DQYLy8fPhx5w4Alq1YY6hXn33yb349dKDa80bdN4YlTz0L6LK4r3x9KT/9uItx4yfgP2IUP+3eRXDgaWbOnsf1wkLiYmNRq21wd/eQNl2IJlTXfNI3X31BaWkpTz/3Iv7DRwLww87v2f7dVrZ/t5XXlq82PMvZuSNvv/N3lCYmXL9+nVdeeIagwABSU5JxdHJGq9UyfMQo5i9cjLW1ihslJTz5xCICTwdQrtGgMDZusu81c85cJk+Z3qj31jYvl3DhPNnZ1/D28WPpshWALgnI778d4Vx8nJw+IVqMsbGSDRvfoV37DpRrNGzcsJaos2c4fuw3Ro+5nx3bv6MgP99w0grcPMXttyOHGTd+guFZtcW49VnHcqs21tYsfnwJ772zkYyMdMZPeMgQR9QV89fXX55+zjDf3VBTps1k2oxZAHTe3ZVvvv6CmOiz+Pj6NflYoxB1udP5Jm8fP9x69uLIr4ewatPG8P+2IXHprbFxZEQ4b21Yy40bJby56W+0a9ee4qIiljy2kKizZw1j9g1p3//62gp6VyRu2rRxPeFhIVy4cA5vH796/ztJH160dvXp7y5+/C9sXL+Gr77YjEqlRqMpY/a8R1CpVPgPH0mnTp05dfIPHBwdDb8FmRnpQMV60b/9A0tLS0CXdL1Pn344d+zEpMlTgZtzUgGnTuLh6V3vso8ecz+P/+XpRn3vPn378frKtSgUCsMYgb5/nZGRzpXLl+jYqTMr16wHdAkwfvxhJ/FxsbhXJOQS4s9gxqw5WLWxIi42Bi8fH6bPnMOx349y7lwcHp5evLZ8NQqFgvS0VJa+/Dz7fv6JBx6cyPgJEys2r4bg6trDULebsg5XVXWOOz0ttd7vtbSy4u1336eNtTWlpaU89cRiEi6cp6SkBDMzM8O6gNXrNqJSqUhNTeHv777NxYsJjS6vqJ1smhF3jVKpNCw+URgb0659e7KyMunTz91wj4ODbiAyN1eXkaL/wEH4+PUnJDiQXTu2kZeXx+WLFwHIyameoUMvOTmJvNxcunV3NWyYAXh8yZNkZGTQvoMDpTd0mUJUajVWVm0AMDU1xb5tWwoLC8jNy6Vdu/YN+o5ZWVnk5ebSqXMXw4aZW7+XEKJmt+7md6jYkZuXl1fpno633KPftZtf5Z6qlEqTShl4HBwcSU66Ql5eLhqNE5cuJmJhYVEp447+6G0hRNOIi42huLgYbx8/Q91VGBsz5v5xREaEER8by8LFj/Ht118SGhzM5KkzCAvRZdAeVFE3I8LDAdCUlbHl808Nz1YqTUi6ctmQxRN09Vw2zAhx9ymVJrTvoMuSZ2RkxNBh/oSFBnMpMRHQDQ76+48gMDCAM5Hh5Ofnc+3aVaD2uF6I/0XdursSGxPN9999w7jxD9K7d1/e/ts/6v3+ri7dUCgUBAUG0LFzZ7y8vFmw6LFGl8fR6WZ83KevOx06OJCenkZebi5nIiPQassxVir5+ovNhvsUCgU5udlkZ9/McN+uXft6b5gBiAzXZf0ZM/YBw2tDhw1ny+efkpycREFBAba2dvTu04foqLOkJCejKdeQlZmJl7cPbaytSUtNJT09jTZtrPll78+G5+izYF6+ZdOMUmkiG2bEPcHaWkXXrl1JTLzAjZISwkKCsbKywsPLh/DQEMrKyoiL1Z1C4+7uyR/Hf6v0/oQLuizX/iNGGeJiB8fKma/i42IpLi6mnbWKHdtuZqsqvXGDsrIyUlKS6z3pfifxd/sODpiYmACgVttgYWFJXp4uq6ZSqaRLl64kJibw9RebGTx0GD179ebdvzf+dGgh7kV1xdH+w0dy+tRJggNPM3DQEEJDgzE2NmbQkKEARFb0l5OTktj82ScAXL9eCMCVy5caVBaHis2ljVHb2J5Ld1cAjv56CLVajbuHFy+8vLTRnyVESyovLyc6KgpjYyWnT53i9KlTACQlXQF08WXHjp3Jzc2hY6fOldrT2407Ozl3NPy5u2sPHBydSEtNIS8vj46dOuHi0o3ExATS01JJuHABjaaMwUOGoTA2Jj7qrLTpQjSR2uaTSksdiImOwsLCotKC9HEPPMj277YSV7HhXa99BweUFXXC0tISH18/jvx6iMuXLuHo5MwjCx8lO/saASdPkJWVRXFREQClpTcovF6ItbWqyb6Xo0Pj2/fa5uWcO3bEzMyc2Jhoft7zI94+fjw0abJhca0QLUWpVNKu4jQXhbExI0aNJursGS5d1MXXkRG6+DkuNsbQf752LQuoHj/XFuM2dh1LTeqK+evrTuL5jjV8V319b+qxRiHq0lzzTQ2JS2+NjR0rxtXate9gWENmbmGB2kZNVmYmhYUFWFurGtS+12d9TF2kDy9au/qMYbt7eDJo8FACTp0AdKdV6E+aqovaxsawYQZ09eKpZ18gNiaan3bvIic7m8yMDIBqiavqcif108mpoyF5XgdD/dT1r21t7bC1tSM1JYUd27/Ft/9AhgwbbjiJVog/O/3c8H2j769UDzy9vAkOCuTCuXjsBg6q8b1NWYeraugc961sbGwNCW1MTEwMa9PzKtamd3d15erVLL7c8jkjR42WMbO7QDbNiHtTxdrXzIx0NqxbTUZ6+m3vqUlhYQFAtUHFDg6OhoBCv3O4KsOUgLaWD7gNfZBiY2PT4PcKISroK2EtdfDmLQ2rpzfn/LTk5+dTXl6O2sa2zmzdQojGu9mBt630uq2tHQDFxUW0a98B1x5unD9/jtzcHEJDgzEzM8PHtz9wswMTFxdDXFxMtc8oLChozq8ghGgElVoXD+fk6BbMJ1w4z6aN6w2/CZU0POwWolWbM28B5uYWHDl8kHfe2oCxsRIvbx/mLVhkOCmlNi4u3Xht+Sq2f/cNmz/7D1qtFmfnjkyeNqNalsvGUKnUpKenkZOTbZiEvHL5Uo0Lc3OyGz8ImZeXi7GxEpXqZr/eyMgItY0NhYUFlBQX0aZNGwYNGUZ01FlCQ4Mo15QDNzfe6stXUJDPwQP7mrR8QjQndw8vEhMTOH/+HOFhoXh4+TBw4GBOnThOTHQU8XGxtGvXvtpmGIC8XP3YlG21a3r6upGZmVFz3WihDa1V1+m++voqvtn6JUePHOKX/T9jaWmJ/4hRzJozHwsLixYpoxDNrWoc7eXti0qlJiw0hJTkZFKSk/Dy9kFtuE9XX/WLA26V3VLtXJWxPb/+A3j2+Zf48Yed/Ov/PgB0ixhmzJ6Lp1fjM/wJ0RI0mjK02nI0mvLbxpe3GwurL7VKTVpqCjnZ13TZcUeMIjExgaCg04aFgvrM89KmC9F8Ks0n5eWh1VafT7K0ssLExJTi4uJan6Vv37Mr2vcjvx5i82f/oaysrNq9jZieviuqzsvZ2tqxau16vt36Jd9+/SVbv9xC23bteHDCJMZPmNhyBRWiCpVKDdycZ9K3jUePHK52b63xc5UYt7HrWGpSV8x/11V8V319b+6xRiHq0pTzTc0Rl+rb7ka37/VYH9OspA8v7lH17e+OHTfeMC42euz9jV7/VVZWxrtvv0lkRFi1ay1WPfVtcsWPm6mpKave2MA3X33B7h92svP7bajUasaMfYAp02Y0elG/EPeK3Ir5LVs7u0qv29jcXF92O/diHa5J1bXpf3n6eezs2nL82FFOHP8dU1NTBg4awtz5C7Fp5NiiqJ38Uop72g+7dpCRno7/8JHMmjsfW1s7du3Yxs7vt9X6Pqs2ut15+fmVd+KXlZZSVlaGialps5RXvyChoRkAhBB3n0qlwthYSX5eLlqtVk6mEKKZtGmjO81N37nRy76mG9jUD/oPHjKM8+fiObh/HynJSQweMgwzMzMALCx0GT5mz32Eh6dMu1tFF0LcgYL8fODmZMa3W78iLy+XiQ9P4aGJk7FWqfj4ow859vvRliymEPckpVLJjFlzmD5zNklXLhNw6iQ/7PyeS5cS+eCfH6MwNq7zGR6eK/TPSAAAFKxJREFU3nh4eutOgzkTwfbvtvLRh++jVtvg7uGJkVHFpEEjRgn1i3xsbGyxrGijR48dx+NLnqrx/uioMw3+DACrNm3Iy82loKDAEE9otVpyc7IxMjLCumLRw8CBg9ny+SeEBgeh1WpRKpX07z8QAIuKLGFOTs6898FHjSqHEC2hn4cnP+3excED+7h27Sq+vn54enljYmLC4YP7ycrKZNRtMubpB9FrXDhQQR9fe3h68/rKNU1adv3vS0OTXNTExtaWp599gSV/eZrz589xYP9eDuzfS2FBAc88/9IdP1+Ie1HVONrY2JjBQ4dxYP9etn61BaBS9kh9W7fuzU30cOvZpGXRjZU1zYziUP8RDPUfwdWrWYSHhbLtm6959+0NbHrvA5xvOWVDiHudtlxXJ8zMzPn8i60Y1xCbX83KBKqPhdVXfoHud0BdkZxtyDB/tn61hcCAU6SlpuLg4IhrDzdA2nQh7hZLKyuMjBTV5pMKCgooLb1R50IWffuuVttQWlrKl5s/R6vV8tyLr+Dt44eFhQXPPf0EWZmZzf5d7mQ8oKrurj1YuWY91wsLiY4+y87vt/Hlls8xNze/bX9FiLstp+IUZHXFOg4LC0sKCwv41yf/NSR3a4zGrmOpSV0xf2MpmrC+1zXWKERzasr5puaKS1u6fQfpw4vWp7793V07bra9P+3+gWHDRxpOQm2IgFMniIwIo0tXF5557kUcnZyJj4th/dpVDS98FUYVG3m0TVBHHR2deOXV1ykpKSEuNpo9u39g145taLXlzJw9746fL0RLMqwvy8mp9Hq2Iaa//aby5qzDVTVlnba0tGTho4/zyMLFJF5M5NjRIxw8sI+MjHTWrn/rjp8vqpPU+uKeps/6OtR/BPb2bVEoFGRlZVW7T79oSL9j39nJGZVazcXEBFKSkw33ffD+31i8YA5nz0Q2S3nt7O2xtbXj8qWLhiN+AVJSUprl84QQjadQKHDp1o3CwkJOB5w0vJ6entaCpRKi9XFz64WZmRlnIsPJzNQdfVleXs6RXw8CugWBAIMGD8XIyIg9P/0I6DbR6PXp1w+AI78eNJwmBxAUGFBjVnshxN1XWnqDtNRUw99PnjgOQFeXbsDNbD8jR41BpVaj1Wq5du3q3S+oEPe44qIiHl0wh2effJzSGzfo1LkL02fOxs7OjqzMTAqvF9b5jI8+fJ9F82dzOuAkKrWaocOGM3SYLhP15UsXgZuDimmpN/uqqck191v1R1cDnD0TSVZmJnb29qjUavr01bXRASf/qDT5Fx8XS9TZxm2W0XN318UI+pgBdL8thYWF9OzVG9OKZBgqtZq+/dyJj4slPi4WTy8fLK2sAOjUsRMqtZqUlGSCgwINz8nJyeHIr4fuqHxCNKeevXpjYmJKYMApFAoFnl4+mFtY0LtPPwJPBwDcdlFKd9ceABy/ZaHArWNjAG49e2FiYkLU2TMkXDhf6b6aTqtoiBp/XxoxLhYeFsqi+bN57523UJqY0Kt3H2bPnQ/ApYrfMiFag7riaAD/EaMACA0JwszMDL+KzaGAoS3eu2c35eW6E9e0Wi0H9u/l+vXrd1Q2tdqGkpISrl3Vxe1lpaVk3BIX1Ncbq5ezaP5sLpw/h719W0aPuR9PL280Gg3JSVfuqIxC3G2mZma4uHSjpKSYQwf2G14vLipi/949aLVabO1uzhOdPxdvuCc9reZx51vHti4mJpCakoytrZ0h0YxabYOHpzfn4uPIz88znDID0qYLcbeYm5vTw82NwsLCSnXr8KFfgJv9V730tFRu3LgBQHFxMaGhQRgZGdGliwtFRUWUlBSjUqkZPGQYFhYWFBYWUHT99hlzm1JDxgNqs+2br1k0fzZ79+zG0soKv/4DuX/ceEDqtmhZZWVlhkV15RqNYRG9S0V83bdivunnirko/Xv2/fxTjadD3E5917FUpVDo17VoKr1eW8zfWPpYIi1N19/QarWV+h71VZ+xRiGaUlPMNxlX1DXNLXWtOePSlm7fQfrwovWpT3/3xPHfiTp7hj59+zF67DjSUlPYs3uX4V59oguNpnK7WxN92+7p5UOnzl1QKpVkNtGmN0ObfIf9618PHWDR/Nl8sfkzzMzM8PD0NiSdvXTxYpOUVYi7qWp7rV8/dvTIYcNYd0Z6OpER4ZiZmeHm1kv3vhrqdnPW4aqaqk4nJ11h0fzZrFj2VzAyont3V+YvXIyxsbHE2c1ITpoR9zQPLy/CQoP5z7/+iYeXN5cvXSQx4QIAN0pvGO5zdHDiXH4cb65bw8DBg3lg/EPMmjOfTz/+iDWrluHl5UNWVhaxMVF06epCP3cPrl2te9CioRQKBQ9PmcaW/37K+rWr8PH1IzMzk9iYqCb/LCHEnZs2YxabNq7now/fJ+DUCTRlZYQEB7d0sYRoVSytrJg6Yxbffv0lK5ctxcPTi+TkJBITLuDSrTv+w0cCuo2nbj17ERcbg4WFBZ7ePoZn+Pj2x8PTi8iIcJa+9DzuHp7k5GRzJjICX7/+vPLq8hb6dkIIPa1Wy6oVr+Lr25+01FTi4mKws7M31HEPTy+Srlzm3U1v0rNnL86fiyc5OQmoHNcL8b/O3MKC/gMGcez3oyx/7RXcevUmJTmJq1ez6NGjJ9bWqjqfMWLUfZw8cZyPP/qQoNMBaLVagoNOY2JiYhhs9PTy5tSJ4/z3s/8QFhpCZmYG8XGxNT7v4IF9pKWlYm1tbdh4MnnKdAA6d+nKqNFjOfrrIZYtfQlvH1+Ki4sICw2lY6dOvPXO3xv9bzFt5mxCQ4L5butXxEZHY2JiQkhwEEqlkrnzF1a6d/CQYZyJjKj481DD6wpjY+YvWMy//vkP3v/b2/j4+mFhYUlYaAilpTfw8vbFzq7xWUWFaC6mpqb07NWbs2ci6NmrN22sdScq+/bvT2REGEZGRvTt51Hje0eMvI8fd35P4OkA1q58nQ4ODoQEB1W6R6VSMWXaTLZ/t5U3Vi/H128AAKEhwVhYWuDj44dpxamPDeXu7oGxsTFHj/xKXl4e5eUaIsLDG/ycvv3csbe3JzjoNOvXrqSDgyMxUWcB8Pbxa1TZhLgX1RVHA3Tv7oqzc0eSk5Pw6z8Qc3Nzw7WJD08l4OQJTgecZPmrL9OtuysJCRe4dDERTbmGBydManTZPL28+f23I6xbs4Iebj05fy7esOCuIUaOGk1sTDSbNq7Hx9eP69evExIciJVVG8NEpxB/JvMWLOatDW/wxebPCAsNxtbOnsjwMHJycujZqzfdursyZfpM/vvpx2xcvxYfv/7k5eYY4tWqjh/7jaysTOzt2xIaEoxWq2XSlKmV7vEfPpKwUN24tX6RKkibLsTdNGfeAja8sZqPPvwHp0+dpKSkhIjwUCwtLZk+c06le1NTU3j91Zdxc+tJbGwM165eZcDAQTg5OwO6xfuJiQmsW7MCe/u2nD0TaUgWVdrM42QNGQ+ozVD/Eezft4fvvvmKc/FxmJmZERIciJGREV7evs1QciHqR6MpY/lrr+Dp5WNYW2Jv35ZhFfH1zNnzOBMZwb6ff+L8uXicnTsSExNNWmoK7dq3p/+AQfX6nPquY6nKwdEJgC82f0afvv147Ikngdpj/sby8Pbm3Lk43n9vE/36eXDl8qVGbQyoz1ijEE2pKeabzC0ssLGxJTv7Gps2rsN/xCj6DxjUbHGpSqVq0fYdpA8vWp+6+rseHl58/eUWjIwUPLLwUezs7Dn5x3F+3LWTof4j6NDBAfu27VAqlZw/F89777zFuPET6NChQ42f19fdA4VCwS/79nD1ahb5ebmGpOx3Woc9vbz56ced7NqxnUsXL1JQUEDU2YYnfPcbMJDt277hwP59pKeloVarCQ8PBcDbV/rX4s9HHxsfPnyA1NQUFj22hMMHfyEsNJhVy1/FycmZyIhwSktvMH/BYswtLHTvc9C9LzjoNP947x2mTp/ZrHW4KtcePbCysuJMZARvbViLubkFEeFhDX6Oc8dOdHd1JTrqLKuWv0rXri5cuHAejUaDl4yZNRs5aUbc08Y9MIHpM+egUCgIDDiFra2dYXFMSlKS4b75Cxfj5ORMYsJ5w675+0aP5bkXX8Hevi0Bp06QkpzE6LHjWLH6DcNuw2Yp8/gJzJozDxNTE4KDAjE3N2fG7LnN9nlCiMbz8vblqWdfwN6+LeGhIeTm5vLQpIcBMFbKvlIhmsqkh6ey5MlnUKnVBJw6QXb2NcaOG8/K1etQ3lLX9KfL+PoNMGSPB91x0n99bQWTp85AqVRy4o/jJCVdYfyEiTz9XOOPyhZCNJ0uXV2YOGkKkRHhJCScp3efvixbucZQl2fNnscD4x+i6Pp1QkKCcOnWnYkPTwEqx/VCCHjiL08zfeYcysrKOP77UTIzMxg9dhx/fa1+m0T7uXvy2vJVdHXpRnDQaSLCQ3Ht4cZry1fTpUtXQLfobvKU6ZiZmRN4OgATExOmTptZ4/PGjhuPQmFEaEgQFpYWzJw9j7EV2WMBHl/yFPMXLEatVnPq5AnOxcfjP3wEy1aswcjIqNH/Do6OTrzx5tt4+/gSGxNFZEQYvfv0YdXaDfRw61np3v4DB2NsrMTU1NQwcaLnP3wkS5etxLWHG5ER4QQHBdLdtQdr1m2UDTPinubuqVt4cutkl/7/d5euLqhUNW+iMzU1ZfmqtfTp605CwgXi42KZPms27atMBk6ZNoMnn3keRydnggIDOBMZgZePD2vXbWz04lqADg6OPPPcSzg6OhIRHkpGRgaLHnuiwc8xMTFh1doNjBw1mqSkK/xx7HcUCgWz5s5nloyziVakrjhaT79Rbsgti+UBbGxsWLdxE/7DR3Lt2jWOH/sNjUbD4seWMP7BiXdUtnkLFjFo8FAKCgoIDQnCrWcvBg/1b/BzRowazbMvvEzbdu04eeIPYmOi8fL2YeWaddjY2t5RGYVoCX37ubP6jQ24e3gSHxdLwMkTdHBw4PWVa+jW3RWAsfc/wNz5C7GwsCC0YhH56DH3A2BsXHncee78hRgZGRFw6gQmJiZMnzmHcQ9MqPaZAN26u+Lo5FzpmrTpQtwdvXr3YeWadfTs1Yuw0GDOxcfi49ufdW9uqhZrj7pvDD16uHE64BS5OdkM8x/BkqeeNVx/aekyfP0GcPnSReJiYxg7brzhJMnkZh4na8h4QG06durEqjfepE9fdyIjwgg8fQoHRydeeHkpXrckxBLibjMzM2fSw1OJjY4i6coV+vTtx+sr12BiYgLoFqate/Md+g8YRNKVy/xx/BiWlpY89+Ir9d4wA/Vfx1LV+AkT6dO3H1mZGVy5dKnStdvF/I01cdIURo8dR7mmnKDA07Rr34H7H3iwwc+pz1ijEE2pqeabHlvyJG3btiM2Jpr8vLxmj0tbsn0H6cOL1qm2/u72bd+Qk5PNyFH30dWlGyq1minTZlBaeoMtn38KgKWlJQsffQJraxXRUWcoKrr9qcwuLt144eWldOjgQHBgAAUFBTz7wstA9ZPcG6pP334sXPw4Nja2BAedpqjoerXkcPWhVtuwdv1bDBg4iPi4WE6eOI61tYrHljzFmLHj7qiMQrQED08vRo0ei6asjLjYGMrLy1mxeh2jx47jalYWpwNOYmNry5PPPM+EiQ8b3ufk7MzU6bNQKk2IijpDaWlps9bhqqys2vDiK6/RpasLMdFRXLyYwMLFj1Ub86uPV15dzoMTJpGTk83xY79RdP06D02azJInn2nSMoubjLRarbalCyHEvWr/3j23PaLWwdGR8RPubOJRCNGyyjUaYmKiDZOOAP/97D8cOrCfeQsW8dDEyS1YOiGEEEIIIarb/Nknt73m5ePTZNmad2z/lp3fb2PeI4t4aFLTxsWpqSn8svfn215/YMJDOFZkFxJCtIy79VsjhKhbcVERL73wNJoyDf/+dHODEkKFhQYTHhp62+uLH1/SFEUUQlRRUlLCpYuJuPW8mYl508b1hIeF8OIrrzJw0JAGPW/vnt18/eVmHln0aINPkJI2XYjWR/rUQtRszozJmJmZs+Xr71q6KA12u5j//Ll4jv/+223fN2PWHMPptEKIPz/pwwtx7yrIz+f7bd/e9rr/iJG49nC7iyUSQtyp77d9S0F+fo3XXN3cKp0GL1oPSaMvRC2CAgOIiY6q8VrvPn1l04wQf3KHDx9g82ef0N21Bx07dSY9LZXYmGjs7O0ZOWpMSxdPCCGEEEKIag4e2Hfba9Yq6z/Forfsa1dr/R4DBw+WBT5CtLDW8FsjRGvw+acfExkeRk52NrPmzGvwCeoXzp+rtT7LghshmseO7d+yd89uevfpS/v2Hbh4MZGLiQl06dK12smItYmOOsvuH3YQdfYM1taqRo1ZS5suROsjfWohWpfaYv7kpCu11veHJj0sm2aEaEWkDy/Evauo6Hqt9bNb9+6yaUaIP5ljvx8hKzOzxmvFxUWyaaaVkpNmhBBC/M/SaDQc2L+X344cJi0tFRsbW/q5ezBz9jw5WlYIIYQQQvxPa86TZoQQQghRP8uWvkRWZgb+I0bxyILFKBq4aUYI0TJKSkr46cddnPzjGFevZmFv3xbf/gOYOm0mllZW9X5OwKkT/Pv/PsDRyZnHlzwlC3CEEEKIWvxZT5qRmF8IIYQQQggh7g7ZNCOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFaHUVLF0AIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIpiabZoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUSrI5tmhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRKsjm2aEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFEqyObZoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUSrI5tmhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRKsjm2aEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFEqyObZoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUSr8//XH1UTrK1gFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 3250x3250 with 182 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(Delhi_Hyderabad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# It is difficult to interpret covariance.Let's try correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>2.154718e+07</td>\n",
       "      <td>45.805117</td>\n",
       "      <td>423.447028</td>\n",
       "      <td>41.443133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.876628</td>\n",
       "      <td>-0.647197</td>\n",
       "      <td>-5.226715</td>\n",
       "      <td>-1.815862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.250214</td>\n",
       "      <td>0.186854</td>\n",
       "      <td>46.437068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inning</th>\n",
       "      <td>4.580512e+01</td>\n",
       "      <td>0.249457</td>\n",
       "      <td>-0.139723</td>\n",
       "      <td>-0.004562</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001461</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>-0.001991</td>\n",
       "      <td>-0.000471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011037</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>0.011723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>over</th>\n",
       "      <td>4.234470e+02</td>\n",
       "      <td>-0.139723</td>\n",
       "      <td>32.640185</td>\n",
       "      <td>-0.026250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.020134</td>\n",
       "      <td>-0.013127</td>\n",
       "      <td>-0.015573</td>\n",
       "      <td>0.012712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.806537</td>\n",
       "      <td>-0.036123</td>\n",
       "      <td>0.770414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>4.144313e+01</td>\n",
       "      <td>-0.004562</td>\n",
       "      <td>-0.026250</td>\n",
       "      <td>3.180869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006723</td>\n",
       "      <td>-0.004546</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>-0.001482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018086</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.018853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_super_over</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>wide_runs</th>\n",
       "      <td>7.876628e+00</td>\n",
       "      <td>0.001461</td>\n",
       "      <td>-0.020134</td>\n",
       "      <td>0.006723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033009</td>\n",
       "      <td>-0.000119</td>\n",
       "      <td>-0.000425</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.020649</td>\n",
       "      <td>0.032367</td>\n",
       "      <td>0.011718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bye_runs</th>\n",
       "      <td>-6.471975e-01</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>-0.013127</td>\n",
       "      <td>-0.004546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000119</td>\n",
       "      <td>0.012224</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>0.012002</td>\n",
       "      <td>0.010791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>legbye_runs</th>\n",
       "      <td>-5.226715e+00</td>\n",
       "      <td>-0.001991</td>\n",
       "      <td>-0.015573</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000425</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>0.028465</td>\n",
       "      <td>-0.000069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.013173</td>\n",
       "      <td>0.027886</td>\n",
       "      <td>0.014713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>noball_runs</th>\n",
       "      <td>-1.815862e+00</td>\n",
       "      <td>-0.000471</td>\n",
       "      <td>0.012712</td>\n",
       "      <td>-0.001482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000098</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000069</td>\n",
       "      <td>0.003972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.007061</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>0.0</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>4.625021e+01</td>\n",
       "      <td>0.011037</td>\n",
       "      <td>0.806537</td>\n",
       "      <td>0.018086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.020649</td>\n",
       "      <td>-0.001210</td>\n",
       "      <td>-0.013173</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.453005</td>\n",
       "      <td>-0.031757</td>\n",
       "      <td>2.421248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extra_runs</th>\n",
       "      <td>1.868540e-01</td>\n",
       "      <td>0.000686</td>\n",
       "      <td>-0.036123</td>\n",
       "      <td>0.000767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032367</td>\n",
       "      <td>0.012002</td>\n",
       "      <td>0.027886</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.031757</td>\n",
       "      <td>0.076041</td>\n",
       "      <td>0.044283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_runs</th>\n",
       "      <td>4.643707e+01</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>0.770414</td>\n",
       "      <td>0.018853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011718</td>\n",
       "      <td>0.010791</td>\n",
       "      <td>0.014713</td>\n",
       "      <td>0.007061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.421248</td>\n",
       "      <td>0.044283</td>\n",
       "      <td>2.465531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   match_id     inning        over       ball  is_super_over  \\\n",
       "match_id       2.154718e+07  45.805117  423.447028  41.443133            0.0   \n",
       "inning         4.580512e+01   0.249457   -0.139723  -0.004562            0.0   \n",
       "over           4.234470e+02  -0.139723   32.640185  -0.026250            0.0   \n",
       "ball           4.144313e+01  -0.004562   -0.026250   3.180869            0.0   \n",
       "is_super_over  0.000000e+00   0.000000    0.000000   0.000000            0.0   \n",
       "wide_runs      7.876628e+00   0.001461   -0.020134   0.006723            0.0   \n",
       "bye_runs      -6.471975e-01   0.001686   -0.013127  -0.004546            0.0   \n",
       "legbye_runs   -5.226715e+00  -0.001991   -0.015573   0.000073            0.0   \n",
       "noball_runs   -1.815862e+00  -0.000471    0.012712  -0.001482            0.0   \n",
       "penalty_runs   0.000000e+00   0.000000    0.000000   0.000000            0.0   \n",
       "batsman_runs   4.625021e+01   0.011037    0.806537   0.018086            0.0   \n",
       "extra_runs     1.868540e-01   0.000686   -0.036123   0.000767            0.0   \n",
       "total_runs     4.643707e+01   0.011723    0.770414   0.018853            0.0   \n",
       "\n",
       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "match_id        7.876628 -0.647197    -5.226715    -1.815862           0.0   \n",
       "inning          0.001461  0.001686    -0.001991    -0.000471           0.0   \n",
       "over           -0.020134 -0.013127    -0.015573     0.012712           0.0   \n",
       "ball            0.006723 -0.004546     0.000073    -0.001482           0.0   \n",
       "is_super_over   0.000000  0.000000     0.000000     0.000000           0.0   \n",
       "wide_runs       0.033009 -0.000119    -0.000425    -0.000098           0.0   \n",
       "bye_runs       -0.000119  0.012224    -0.000084    -0.000019           0.0   \n",
       "legbye_runs    -0.000425 -0.000084     0.028465    -0.000069           0.0   \n",
       "noball_runs    -0.000098 -0.000019    -0.000069     0.003972           0.0   \n",
       "penalty_runs    0.000000  0.000000     0.000000     0.000000           0.0   \n",
       "batsman_runs   -0.020649 -0.001210    -0.013173     0.003275           0.0   \n",
       "extra_runs      0.032367  0.012002     0.027886     0.003785           0.0   \n",
       "total_runs      0.011718  0.010791     0.014713     0.007061           0.0   \n",
       "\n",
       "               batsman_runs  extra_runs  total_runs  \n",
       "match_id          46.250214    0.186854   46.437068  \n",
       "inning             0.011037    0.000686    0.011723  \n",
       "over               0.806537   -0.036123    0.770414  \n",
       "ball               0.018086    0.000767    0.018853  \n",
       "is_super_over      0.000000    0.000000    0.000000  \n",
       "wide_runs         -0.020649    0.032367    0.011718  \n",
       "bye_runs          -0.001210    0.012002    0.010791  \n",
       "legbye_runs       -0.013173    0.027886    0.014713  \n",
       "noball_runs        0.003275    0.003785    0.007061  \n",
       "penalty_runs       0.000000    0.000000    0.000000  \n",
       "batsman_runs       2.453005   -0.031757    2.421248  \n",
       "extra_runs        -0.031757    0.076041    0.044283  \n",
       "total_runs         2.421248    0.044283    2.465531  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>0.019757</td>\n",
       "      <td>0.015967</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009340</td>\n",
       "      <td>-0.001261</td>\n",
       "      <td>-0.006674</td>\n",
       "      <td>-0.006207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006362</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.006371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inning</th>\n",
       "      <td>0.019757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048966</td>\n",
       "      <td>-0.005122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016103</td>\n",
       "      <td>0.030540</td>\n",
       "      <td>-0.023622</td>\n",
       "      <td>-0.014959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014109</td>\n",
       "      <td>0.004983</td>\n",
       "      <td>0.014948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>over</th>\n",
       "      <td>0.015967</td>\n",
       "      <td>-0.048966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019398</td>\n",
       "      <td>-0.020783</td>\n",
       "      <td>-0.016156</td>\n",
       "      <td>0.035306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090136</td>\n",
       "      <td>-0.022929</td>\n",
       "      <td>0.085880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>0.005006</td>\n",
       "      <td>-0.005122</td>\n",
       "      <td>-0.002576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020748</td>\n",
       "      <td>-0.023056</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>-0.013187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.006732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_super_over</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>wide_runs</th>\n",
       "      <td>0.009340</td>\n",
       "      <td>0.016103</td>\n",
       "      <td>-0.019398</td>\n",
       "      <td>0.020748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>-0.013879</td>\n",
       "      <td>-0.008528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.072568</td>\n",
       "      <td>0.646052</td>\n",
       "      <td>0.041075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bye_runs</th>\n",
       "      <td>-0.001261</td>\n",
       "      <td>0.030540</td>\n",
       "      <td>-0.020783</td>\n",
       "      <td>-0.023056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>-0.002770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.006990</td>\n",
       "      <td>0.393660</td>\n",
       "      <td>0.062161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>legbye_runs</th>\n",
       "      <td>-0.006674</td>\n",
       "      <td>-0.023622</td>\n",
       "      <td>-0.016156</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.013879</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049851</td>\n",
       "      <td>0.599392</td>\n",
       "      <td>0.055539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>noball_runs</th>\n",
       "      <td>-0.006207</td>\n",
       "      <td>-0.014959</td>\n",
       "      <td>0.035306</td>\n",
       "      <td>-0.013187</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008528</td>\n",
       "      <td>-0.002770</td>\n",
       "      <td>-0.006514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033185</td>\n",
       "      <td>0.217823</td>\n",
       "      <td>0.071354</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>0.006362</td>\n",
       "      <td>0.014109</td>\n",
       "      <td>0.090136</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.072568</td>\n",
       "      <td>-0.006990</td>\n",
       "      <td>-0.049851</td>\n",
       "      <td>0.033185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073531</td>\n",
       "      <td>0.984543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extra_runs</th>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.004983</td>\n",
       "      <td>-0.022929</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.646052</td>\n",
       "      <td>0.393660</td>\n",
       "      <td>0.599392</td>\n",
       "      <td>0.217823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.073531</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_runs</th>\n",
       "      <td>0.006371</td>\n",
       "      <td>0.014948</td>\n",
       "      <td>0.085880</td>\n",
       "      <td>0.006732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.041075</td>\n",
       "      <td>0.062161</td>\n",
       "      <td>0.055539</td>\n",
       "      <td>0.071354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.984543</td>\n",
       "      <td>0.102273</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  0.019757  0.015967  0.005006            NaN   \n",
       "inning         0.019757  1.000000 -0.048966 -0.005122            NaN   \n",
       "over           0.015967 -0.048966  1.000000 -0.002576            NaN   \n",
       "ball           0.005006 -0.005122 -0.002576  1.000000            NaN   \n",
       "is_super_over       NaN       NaN       NaN       NaN            NaN   \n",
       "wide_runs      0.009340  0.016103 -0.019398  0.020748            NaN   \n",
       "bye_runs      -0.001261  0.030540 -0.020783 -0.023056            NaN   \n",
       "legbye_runs   -0.006674 -0.023622 -0.016156  0.000241            NaN   \n",
       "noball_runs   -0.006207 -0.014959  0.035306 -0.013187            NaN   \n",
       "penalty_runs        NaN       NaN       NaN       NaN            NaN   \n",
       "batsman_runs   0.006362  0.014109  0.090136  0.006475            NaN   \n",
       "extra_runs     0.000146  0.004983 -0.022929  0.001560            NaN   \n",
       "total_runs     0.006371  0.014948  0.085880  0.006732            NaN   \n",
       "\n",
       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "match_id        0.009340 -0.001261    -0.006674    -0.006207           NaN   \n",
       "inning          0.016103  0.030540    -0.023622    -0.014959           NaN   \n",
       "over           -0.019398 -0.020783    -0.016156     0.035306           NaN   \n",
       "ball            0.020748 -0.023056     0.000241    -0.013187           NaN   \n",
       "is_super_over        NaN       NaN          NaN          NaN           NaN   \n",
       "wide_runs       1.000000 -0.005903    -0.013879    -0.008528           NaN   \n",
       "bye_runs       -0.005903  1.000000    -0.004509    -0.002770           NaN   \n",
       "legbye_runs    -0.013879 -0.004509     1.000000    -0.006514           NaN   \n",
       "noball_runs    -0.008528 -0.002770    -0.006514     1.000000           NaN   \n",
       "penalty_runs         NaN       NaN          NaN          NaN           NaN   \n",
       "batsman_runs   -0.072568 -0.006990    -0.049851     0.033185           NaN   \n",
       "extra_runs      0.646052  0.393660     0.599392     0.217823           NaN   \n",
       "total_runs      0.041075  0.062161     0.055539     0.071354           NaN   \n",
       "\n",
       "               batsman_runs  extra_runs  total_runs  \n",
       "match_id           0.006362    0.000146    0.006371  \n",
       "inning             0.014109    0.004983    0.014948  \n",
       "over               0.090136   -0.022929    0.085880  \n",
       "ball               0.006475    0.001560    0.006732  \n",
       "is_super_over           NaN         NaN         NaN  \n",
       "wide_runs         -0.072568    0.646052    0.041075  \n",
       "bye_runs          -0.006990    0.393660    0.062161  \n",
       "legbye_runs       -0.049851    0.599392    0.055539  \n",
       "noball_runs        0.033185    0.217823    0.071354  \n",
       "penalty_runs            NaN         NaN         NaN  \n",
       "batsman_runs       1.000000   -0.073531    0.984543  \n",
       "extra_runs        -0.073531    1.000000    0.102273  \n",
       "total_runs         0.984543    0.102273    1.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad.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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2649fef2308>"
      ]
     },
     "execution_count": 77,
     "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(Delhi_Hyderabad.corr(),yticklabels=True,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.0</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.0</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "      <td>3512.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3703.606492</td>\n",
       "      <td>1.475228</td>\n",
       "      <td>10.240604</td>\n",
       "      <td>3.585991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024487</td>\n",
       "      <td>0.004841</td>\n",
       "      <td>0.017369</td>\n",
       "      <td>0.003986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.050683</td>\n",
       "      <td>1.300683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4641.893560</td>\n",
       "      <td>0.499457</td>\n",
       "      <td>5.713159</td>\n",
       "      <td>1.783499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.181683</td>\n",
       "      <td>0.110561</td>\n",
       "      <td>0.168716</td>\n",
       "      <td>0.063020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.566207</td>\n",
       "      <td>0.275755</td>\n",
       "      <td>1.570201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</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>428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</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>561.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</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>7935.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</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>11413.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</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   3512.000000  3512.000000  3512.000000  3512.000000         3512.0   \n",
       "mean    3703.606492     1.475228    10.240604     3.585991            0.0   \n",
       "std     4641.893560     0.499457     5.713159     1.783499            0.0   \n",
       "min       21.000000     1.000000     1.000000     1.000000            0.0   \n",
       "25%      428.000000     1.000000     5.000000     2.000000            0.0   \n",
       "50%      561.000000     1.000000    10.000000     4.000000            0.0   \n",
       "75%     7935.000000     2.000000    15.000000     5.000000            0.0   \n",
       "max    11413.000000     2.000000    20.000000     9.000000            0.0   \n",
       "\n",
       "         wide_runs     bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "count  3512.000000  3512.000000  3512.000000  3512.000000        3512.0   \n",
       "mean      0.024487     0.004841     0.017369     0.003986           0.0   \n",
       "std       0.181683     0.110561     0.168716     0.063020           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     1.000000           0.0   \n",
       "\n",
       "       batsman_runs   extra_runs   total_runs  \n",
       "count   3512.000000  3512.000000  3512.000000  \n",
       "mean       1.250000     0.050683     1.300683  \n",
       "std        1.566207     0.275755     1.570201  \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        7.000000     5.000000     8.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J Yadav</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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178588</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</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>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</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>A Mishra</td>\n",
       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>TA Boult</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3512 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4810          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4811          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4812          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4813          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4814          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "178586     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178587     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178588     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178589     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178590     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball    batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810       1  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4811       2   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4812       3   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4813       4  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814       5  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "...      ...        ...         ...      ...            ...  ...       ...   \n",
       "178586     2   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178587     3     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178588     4     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178589     5   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178590     6     K Paul    TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4810             2               NaN                    NaN     NaN  \n",
       "4811             4               NaN                    NaN     NaN  \n",
       "4812             1               NaN                    NaN     NaN  \n",
       "4813             0               NaN                    NaN     NaN  \n",
       "4814             1               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[3512 rows x 21 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delhi_Hyderabad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many wickets ll Rashid Khan take in this match?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rashid_Khan_wickets= Delhi_Hyderabad.loc[(Delhi_Hyderabad['bowler']=='Rashid Khan')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Rashid_Khan_wickets['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "run out    2\n",
       "lbw        2\n",
       "bowled     2\n",
       "caught     2\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rashid_Khan_wickets['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average wickets taken by Rashid Khan in a single match against Delhi Capitals 0.8571428571428571\n"
     ]
    }
   ],
   "source": [
    "print(\"Average wickets taken by Rashid Khan in a single match against Delhi Capitals\",6/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average wickets taken by Rashid Khan in a single match against Delhi Capitals 0-1\n"
     ]
    }
   ],
   "source": [
    "print(\"Average wickets taken by Rashid Khan in a single match against Delhi Capitals 0-1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many Runs ll david Warner score in his last seven balls?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "David_Warner_Runs_in_last_seven_Balls=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='DA Warner')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "David_Warner_Runs=David_Warner_Runs_in_last_seven_Balls.loc[(David_Warner_Runs_in_last_seven_Balls['noball_runs']==0)&(David_Warner_Runs_in_last_seven_Balls['wide_runs']==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J Yadav</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>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning         batting_team    bowling_team  over  ball  \\\n",
       "4813        21       1  Sunrisers Hyderabad  Delhi Capitals     1     4   \n",
       "4814        21       1  Sunrisers Hyderabad  Delhi Capitals     1     5   \n",
       "\n",
       "        batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4813  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "\n",
       "      legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4813            0            0             0             0           0   \n",
       "4814            0            0             0             1           0   \n",
       "\n",
       "      total_runs  player_dismissed dismissal_kind fielder  \n",
       "4813           0               NaN            NaN     NaN  \n",
       "4814           1               NaN            NaN     NaN  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "David_Warner_Runs.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(David_Warner_Runs['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 0\n",
      "21 1\n",
      "21 1\n",
      "21 1\n",
      "21 0\n",
      "21 0\n",
      "39 0\n",
      "39 4\n",
      "39 0\n",
      "39 0\n",
      "39 0\n",
      "39 0\n",
      "39 0\n",
      "39 0\n",
      "39 4\n",
      "39 2\n",
      "39 0\n",
      "39 0\n",
      "39 6\n",
      "39 1\n",
      "39 1\n",
      "39 2\n",
      "39 4\n",
      "39 1\n",
      "39 1\n",
      "39 4\n",
      "39 0\n",
      "469 0\n",
      "469 0\n",
      "469 1\n",
      "469 1\n",
      "469 6\n",
      "469 0\n",
      "469 1\n",
      "469 0\n",
      "469 0\n",
      "469 1\n",
      "469 1\n",
      "469 0\n",
      "469 1\n",
      "469 0\n",
      "469 0\n",
      "469 1\n",
      "469 1\n",
      "469 1\n",
      "469 1\n",
      "469 0\n",
      "469 2\n",
      "469 0\n",
      "469 0\n",
      "469 0\n",
      "469 0\n",
      "469 0\n",
      "469 0\n",
      "469 4\n",
      "469 2\n",
      "469 2\n",
      "469 1\n",
      "469 4\n",
      "469 1\n",
      "469 1\n",
      "469 6\n",
      "469 1\n",
      "469 2\n",
      "469 1\n",
      "469 6\n",
      "469 1\n",
      "469 2\n",
      "469 0\n",
      "469 4\n",
      "469 1\n",
      "469 1\n",
      "489 0\n",
      "489 2\n",
      "489 1\n",
      "489 1\n",
      "489 1\n",
      "489 1\n",
      "489 4\n",
      "489 1\n",
      "489 1\n",
      "531 0\n",
      "531 0\n",
      "531 0\n",
      "531 1\n",
      "531 0\n",
      "531 1\n",
      "531 0\n",
      "531 1\n",
      "531 2\n",
      "531 1\n",
      "531 2\n",
      "531 2\n",
      "531 0\n",
      "531 4\n",
      "531 0\n",
      "531 4\n",
      "531 4\n",
      "531 2\n",
      "531 4\n",
      "531 0\n",
      "561 1\n",
      "561 0\n",
      "561 4\n",
      "561 1\n",
      "561 0\n",
      "561 0\n",
      "561 0\n",
      "561 3\n",
      "561 0\n",
      "561 0\n",
      "561 6\n",
      "561 0\n",
      "561 1\n",
      "561 1\n",
      "561 0\n",
      "618 1\n",
      "618 1\n",
      "618 0\n",
      "618 2\n",
      "618 0\n",
      "618 0\n",
      "618 1\n",
      "618 1\n",
      "618 4\n",
      "618 4\n",
      "618 1\n",
      "618 4\n",
      "618 4\n",
      "618 6\n",
      "618 0\n",
      "618 1\n",
      "618 1\n",
      "618 1\n",
      "618 1\n",
      "618 1\n",
      "618 2\n",
      "618 0\n",
      "618 4\n",
      "618 0\n",
      "618 0\n",
      "618 1\n",
      "618 0\n",
      "618 1\n",
      "618 4\n",
      "618 0\n",
      "628 0\n",
      "628 0\n",
      "628 0\n",
      "628 1\n",
      "628 0\n",
      "628 0\n",
      "628 0\n",
      "628 4\n",
      "628 4\n",
      "628 1\n",
      "628 1\n",
      "628 0\n",
      "628 1\n",
      "628 4\n",
      "628 2\n",
      "628 4\n",
      "628 0\n",
      "628 1\n",
      "628 0\n",
      "628 2\n",
      "628 4\n",
      "628 4\n",
      "628 0\n",
      "628 0\n",
      "628 0\n",
      "628 2\n",
      "628 0\n",
      "628 1\n",
      "628 0\n",
      "628 1\n",
      "628 0\n",
      "628 1\n",
      "628 1\n",
      "628 1\n",
      "628 1\n",
      "628 2\n",
      "628 1\n",
      "628 4\n",
      "628 1\n",
      "628 1\n",
      "628 0\n",
      "628 6\n",
      "628 1\n",
      "628 2\n",
      "628 4\n",
      "628 0\n",
      "628 0\n",
      "628 1\n",
      "628 1\n",
      "628 1\n",
      "628 1\n",
      "628 2\n",
      "628 1\n",
      "628 1\n",
      "628 2\n",
      "628 0\n",
      "11152 0\n",
      "11152 0\n",
      "11152 0\n",
      "11152 1\n",
      "11152 2\n",
      "11152 0\n",
      "11152 0\n",
      "11152 4\n",
      "11152 1\n",
      "11152 1\n",
      "11152 1\n",
      "11152 0\n",
      "11152 1\n",
      "11152 1\n",
      "11152 0\n",
      "11152 2\n",
      "11152 0\n",
      "11152 0\n",
      "11321 0\n",
      "11321 0\n",
      "11321 2\n",
      "11321 0\n",
      "11321 0\n",
      "11321 1\n",
      "11321 4\n",
      "11321 0\n",
      "11321 0\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 0\n",
      "11321 0\n",
      "11321 1\n",
      "11321 0\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 0\n",
      "11321 1\n",
      "11321 6\n",
      "11321 2\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 4\n",
      "11321 1\n",
      "11321 4\n",
      "11321 1\n",
      "11321 2\n",
      "11321 0\n",
      "11321 0\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 1\n",
      "11321 0\n",
      "11321 1\n",
      "11321 2\n",
      "11321 0\n",
      "394 0\n",
      "394 0\n",
      "394 0\n",
      "394 0\n",
      "428 0\n",
      "428 0\n",
      "428 1\n",
      "428 0\n",
      "428 2\n",
      "428 0\n",
      "428 1\n",
      "428 0\n",
      "428 1\n",
      "428 1\n",
      "428 1\n",
      "428 0\n",
      "428 1\n",
      "428 0\n",
      "428 0\n"
     ]
    }
   ],
   "source": [
    "match_id=David_Warner_Runs['match_id']\n",
    "runs=David_Warner_Runs['batsman_runs']\n",
    "for i,j in zip(match_id,runs):\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In match_id_21 David Warner only faced 6 balls\n",
      "In match_id_39 David Warner scored 13 runs in his last seven balls\n",
      "In match_id_469 David Warner scored 15 runs in his last seven balls\n",
      "In match_id_489 David Warner scored 10 runs in his last seven balls\n",
      "In match_id_531 David Warner scored 18 runs in his last seven balls\n",
      "In match_id_561 David Warner scored 8 runs in his last seven balls\n",
      "In match_id_618 David Warner scored 6 runs in his last seven balls\n",
      "In match_id_628 David Warner scored 8 runs in his last seven balls\n",
      "In match_id_11152 David Warner scored 4 runs in his last seven balls\n",
      "In match_id_11321 David Warner scored 6 runs in his last seven balls\n",
      "In match_id_394 David Warner only faced 4 balls\n",
      "In match_id_428 David Warner scored 3 runs in his last seven balls\n"
     ]
    }
   ],
   "source": [
    "print(\"In match_id_21 David Warner only faced 6 balls\")\n",
    "print(\"In match_id_39 David Warner scored 13 runs in his last seven balls\")\n",
    "print(\"In match_id_469 David Warner scored 15 runs in his last seven balls\")\n",
    "print(\"In match_id_489 David Warner scored 10 runs in his last seven balls\")\n",
    "print(\"In match_id_531 David Warner scored 18 runs in his last seven balls\")\n",
    "print(\"In match_id_561 David Warner scored 8 runs in his last seven balls\")\n",
    "print(\"In match_id_618 David Warner scored 6 runs in his last seven balls\")\n",
    "print(\"In match_id_628 David Warner scored 8 runs in his last seven balls\")\n",
    "print(\"In match_id_11152 David Warner scored 4 runs in his last seven balls\")\n",
    "print(\"In match_id_11321 David Warner scored 6 runs in his last seven balls\")\n",
    "print(\"In match_id_394 David Warner only faced 4 balls\")\n",
    "print(\"In match_id_428 David Warner scored 3 runs in his last seven balls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Runs scored by David Warner in his last seven balls against DC in 12 matches: 9\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Runs scored by David Warner in his last seven balls against DC in 12 matches:\",(13+15+10+18+8+6+8+4+6+3)//10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How many balls more/less ll DC take to score 20 runs after reaching 30runs as compared to SRH?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DC_take_to_score_20_runs=Delhi_Hyderabad2.loc[(Delhi_Hyderabad2['over']<=19)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DC_take_to_score_20_runs.groupby('match_id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Delhi_Hyderabad2['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In match_id 21 DC took 10 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 39 DC took 9 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 394 DC took 28 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 428 DC took 27 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 469 DC took 13 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 489 DC took 10 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 531 DC took 16 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 561 DC took 12 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 618 DC took 12 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 628 DC took 16 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 7928 DC took 10 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 7935 DC took 28 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11152 DC took 22 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11321 DC took 12 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11413 DC took 12 balls to score 20 runs after scoring 30 runs\n"
     ]
    }
   ],
   "source": [
    "print('In match_id 21 DC took 10 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 39 DC took 9 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 394 DC took 28 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 428 DC took 27 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 469 DC took 13 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 489 DC took 10 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 531 DC took 16 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 561 DC took 12 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 618 DC took 12 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 628 DC took 16 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 7928 DC took 10 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 7935 DC took 28 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11152 DC took 22 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11321 DC took 12 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11413 DC took 12 balls to score 20 runs after scoring 30 runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Balls Taken by DC to score 20 runs after scoring 30 runs 15\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Balls Taken by DC to score 20 runs after scoring 30 runs\",(10+9+28+27+13+10+16+12+12+16+10+28+22+12+12)//15)"
   ]
  },
  {
   "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>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",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>117</td>\n",
       "      <td>1161</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>4</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>116</td>\n",
       "      <td>1171</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>7</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>232</td>\n",
       "      <td>1158</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>3</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>172</td>\n",
       "      <td>648</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>116</td>\n",
       "      <td>1143</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>5</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>60</td>\n",
       "      <td>78</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>230</td>\n",
       "      <td>1142</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>4</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>117</td>\n",
       "      <td>1168</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>4</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>118</td>\n",
       "      <td>1185</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>10</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>115</td>\n",
       "      <td>1150</td>\n",
       "      <td>406</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7929</th>\n",
       "      <td>238</td>\n",
       "      <td>1191</td>\n",
       "      <td>434</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>6</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7935</th>\n",
       "      <td>228</td>\n",
       "      <td>1129</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11152</th>\n",
       "      <td>228</td>\n",
       "      <td>1113</td>\n",
       "      <td>405</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>11</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11321</th>\n",
       "      <td>234</td>\n",
       "      <td>1167</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11413</th>\n",
       "      <td>119</td>\n",
       "      <td>1198</td>\n",
       "      <td>437</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>7</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          inning  over  ball  is_super_over  wide_runs  bye_runs  legbye_runs  \\\n",
       "match_id                                                                        \n",
       "21           117  1161   420              0          1         0            1   \n",
       "39           116  1171   413              0          2         0            5   \n",
       "394          232  1158   413              0          1         0            1   \n",
       "428          172   648   308              0          3         0            0   \n",
       "469          116  1143   413              0          2         0            3   \n",
       "489           60    78   116              0          4         4            2   \n",
       "531          230  1142   406              0          1         2            1   \n",
       "561          117  1168   420              0          3         0            1   \n",
       "618          118  1185   427              0          3         0            6   \n",
       "628          115  1150   406              0          1         1            1   \n",
       "7929         238  1191   434              0          5         0            0   \n",
       "7935         228  1129   400              0          1         0            1   \n",
       "11152        228  1113   405              0          3         4            4   \n",
       "11321        234  1167   420              0          4         0            0   \n",
       "11413        119  1198   437              0          5         0            2   \n",
       "\n",
       "          noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \n",
       "match_id                                                                   \n",
       "21                  2             0           170           4         174  \n",
       "39                  0             0           159           7         166  \n",
       "394                 1             0           106           3         109  \n",
       "428                 0             0            78           3          81  \n",
       "469                 0             0           169           5         174  \n",
       "489                 1             0            33          11          44  \n",
       "531                 0             0           154           4         158  \n",
       "561                 0             0           148           4         152  \n",
       "618                 1             0           128          10         138  \n",
       "628                 0             0           146           3         149  \n",
       "7929                1             0           150           6         156  \n",
       "7935                0             0           191           2         193  \n",
       "11152               0             0           131          11         142  \n",
       "11321               1             0           116           5         121  \n",
       "11413               0             0           151           7         158  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_take_to_score_20_runs=Delhi_Hyderabad1.loc[(Delhi_Hyderabad1['over']<=19)]\n",
    "SRH_take_to_score_20_runs.groupby('match_id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In match_id 21 SRH took 10 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 39 SRH took 8 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 394 SRH took 26 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 428 SRH took 30 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 469 SRH took 12 balls to score 20 runs after scoring 30 runs\n",
      "******************In match_id 489 SRH total score was only 44 runs**************************\n",
      "In match_id 531 SRH took 6 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 561 SRH took 20 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 618 SRH took 10 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 628 SRH took 19 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 7928 SRH took 9 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 7935 SRH took 12 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11152 SRH took 9 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11321 SRH took 24 balls to score 20 runs after scoring 30 runs\n",
      "In match_id 11413 SRH took 18 balls to score 20 runs after scoring 30 runs\n"
     ]
    }
   ],
   "source": [
    "print('In match_id 21 SRH took 10 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 39 SRH took 8 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 394 SRH took 26 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 428 SRH took 30 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 469 SRH took 12 balls to score 20 runs after scoring 30 runs')\n",
    "print('******************In match_id 489 SRH total score was only 44 runs**************************')\n",
    "print('In match_id 531 SRH took 6 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 561 SRH took 20 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 618 SRH took 10 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 628 SRH took 19 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 7928 SRH took 9 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 7935 SRH took 12 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11152 SRH took 9 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11321 SRH took 24 balls to score 20 runs after scoring 30 runs')\n",
    "print('In match_id 11413 SRH took 18 balls to score 20 runs after scoring 30 runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Balls Taken by SRH to score 20 runs after scoring 30 runs 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Balls Taken by SRH to score 20 runs after scoring 30 runs\",(10+8+26+30+12+6+20+10+19+9+12+9+24+18)//15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Balls Taken by DC to score 20 runs after scoring 30 runs 15\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Balls Taken by DC to score 20 runs after scoring 30 runs\",(10+9+28+27+13+10+16+12+12+16+10+28+22+12+12)//15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Which Batsman ll score the most boundaries in the match?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# David_warner_boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "David_warner_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='DA Warner')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    114\n",
       "1    110\n",
       "4     31\n",
       "2     28\n",
       "6      8\n",
       "3      1\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "David_warner_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(David_warner_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by David Warner in a single match 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by David Warner in a single match\",39//12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rishab_Pant_boundaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rishab_Pant_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='RR Pant')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    95\n",
       "0    55\n",
       "4    30\n",
       "6    16\n",
       "2    11\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rishab_Pant_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Rishab_Pant_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by Rishab_Pant in a single match 5\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by Rishab_Pant in a single match\",46//9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shikhar_Dhawan_boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "Shikhar_Dhawan_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='S Dhawan')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    128\n",
       "0    109\n",
       "4     47\n",
       "2     14\n",
       "6      8\n",
       "3      2\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Shikhar_Dhawan_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Shikhar_Dhawan_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by Shikhar_Dhawan in a single match 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by Shikhar_Dhawan in a single match\",55//14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manish_Pandey_boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "Manish_Pandey_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='MK Pandey')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    35\n",
       "0    24\n",
       "4     5\n",
       "2     5\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Manish_Pandey_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Manish_Pandey_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by Manish_Pandey in a single match 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by Manish_Pandey in a single match\",5//3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shreyas_Iyer_boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "Shreyas_Iyer_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='SS Iyer')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    105\n",
       "0     92\n",
       "4     21\n",
       "2     14\n",
       "6     12\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Shreyas_Iyer_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Shreyas_Iyer_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by Shreyas_Iyer in a single match 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by Shreyas_Iyer in a single match\",33//9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kane_Williamson_boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kane_Williamson_boundaries=Delhi_Hyderabad.loc[(Delhi_Hyderabad['batsman']=='KS Williamson')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    107\n",
       "0     64\n",
       "2     21\n",
       "4     20\n",
       "6      9\n",
       "5      1\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kane_Williamson_boundaries['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Kane_Williamson_boundaries['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Boundaries hit by Kane_Williamson in a single match 4\n"
     ]
    }
   ],
   "source": [
    "print(\"Average Boundaries hit by Kane_Williamson in a single match\",29//7)"
   ]
  }
 ],
 "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
}