EDA of Zomato Bangalore Restaurants .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f7aab88",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96a9aebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e76c19",
   "metadata": {},
   "source": [
    "### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94e9b1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/kamle/Downloads/zomato.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01fe3644",
   "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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1  https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2  https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3  https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4  https://www.zomato.com/bangalore/grand-village...   \n",
       "\n",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "\n",
       "       location            rest_type  \\\n",
       "0  Banashankari        Casual Dining   \n",
       "1  Banashankari        Casual Dining   \n",
       "2  Banashankari  Cafe, Casual Dining   \n",
       "3  Banashankari          Quick Bites   \n",
       "4  Basavanagudi        Casual Dining   \n",
       "\n",
       "                                          dish_liked  \\\n",
       "0  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                        Masala Dosa   \n",
       "4                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                         800   \n",
       "1     Chinese, North Indian, Thai                         800   \n",
       "2          Cafe, Mexican, Italian                         800   \n",
       "3      South Indian, North Indian                         300   \n",
       "4        North Indian, Rajasthani                         600   \n",
       "\n",
       "                                        reviews_list menu_item  \\\n",
       "0  [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1  [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2  [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3  [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4  [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "\n",
       "  listed_in(type) listed_in(city)  \n",
       "0          Buffet    Banashankari  \n",
       "1          Buffet    Banashankari  \n",
       "2          Buffet    Banashankari  \n",
       "3          Buffet    Banashankari  \n",
       "4          Buffet    Banashankari  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3003fc4",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7708686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51717, 17)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display shape of data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee3fa202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['url', 'address', 'name', 'online_order', 'book_table', 'rate', 'votes',\n",
       "       'phone', 'location', 'rest_type', 'dish_liked', 'cuisines',\n",
       "       'approx_cost(for two people)', 'reviews_list', 'menu_item',\n",
       "       'listed_in(type)', 'listed_in(city)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display coulumn names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a718a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   url                          51717 non-null  object\n",
      " 1   address                      51717 non-null  object\n",
      " 2   name                         51717 non-null  object\n",
      " 3   online_order                 51717 non-null  object\n",
      " 4   book_table                   51717 non-null  object\n",
      " 5   rate                         43942 non-null  object\n",
      " 6   votes                        51717 non-null  int64 \n",
      " 7   phone                        50509 non-null  object\n",
      " 8   location                     51696 non-null  object\n",
      " 9   rest_type                    51490 non-null  object\n",
      " 10  dish_liked                   23639 non-null  object\n",
      " 11  cuisines                     51672 non-null  object\n",
      " 12  approx_cost(for two people)  51371 non-null  object\n",
      " 13  reviews_list                 51717 non-null  object\n",
      " 14  menu_item                    51717 non-null  object\n",
      " 15  listed_in(type)              51717 non-null  object\n",
      " 16  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2759b05c",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b90d13b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table   rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes  4.1/5    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No  4.1/5    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No  3.8/5    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No  3.7/5     88  Banashankari   \n",
       "4          Grand Village           No         No  3.8/5    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese   \n",
       "1        Casual Dining     Chinese, North Indian, Thai   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian   \n",
       "3          Quick Bites      South Indian, North Indian   \n",
       "4        Casual Dining        North Indian, Rajasthani   \n",
       "\n",
       "  approx_cost(for two people) listed_in(type) listed_in(city)  \n",
       "0                         800          Buffet    Banashankari  \n",
       "1                         800          Buffet    Banashankari  \n",
       "2                         800          Buffet    Banashankari  \n",
       "3                         300          Buffet    Banashankari  \n",
       "4                         600          Buffet    Banashankari  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping all unwanted columns such as 'url','address','phone','dish_liked','reviews_list','menu_item'.\n",
    "\n",
    "df = df.drop(['url','address','phone','dish_liked','reviews_list','menu_item'], axis = 1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "85df88e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   name                         51717 non-null  object\n",
      " 1   online_order                 51717 non-null  object\n",
      " 2   book_table                   51717 non-null  object\n",
      " 3   rate                         43942 non-null  object\n",
      " 4   votes                        51717 non-null  int64 \n",
      " 5   location                     51696 non-null  object\n",
      " 6   rest_type                    51490 non-null  object\n",
      " 7   cuisines                     51672 non-null  object\n",
      " 8   approx_cost(for two people)  51371 non-null  object\n",
      " 9   listed_in(type)              51717 non-null  object\n",
      " 10  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b306b420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51609, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Duplicates\n",
    "\n",
    "df.drop_duplicates(inplace = True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c47e6116",
   "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>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table   rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes  4.1/5    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No  4.1/5    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No  3.8/5    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No  3.7/5     88  Banashankari   \n",
       "4          Grand Village           No         No  3.8/5    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese   \n",
       "1        Casual Dining     Chinese, North Indian, Thai   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian   \n",
       "3          Quick Bites      South Indian, North Indian   \n",
       "4        Casual Dining        North Indian, Rajasthani   \n",
       "\n",
       "  approx_cost(for two people) listed_in(type) listed_in(city)  \n",
       "0                         800          Buffet    Banashankari  \n",
       "1                         800          Buffet    Banashankari  \n",
       "2                         800          Buffet    Banashankari  \n",
       "3                         300          Buffet    Banashankari  \n",
       "4                         600          Buffet    Banashankari  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "924b617b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',\n",
       "       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',\n",
       "       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',\n",
       "       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',\n",
       "       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',\n",
       "       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',\n",
       "       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',\n",
       "       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',\n",
       "       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
       "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values in Rate column\n",
    "\n",
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa657143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.1\n",
       "1    4.1\n",
       "2    3.8\n",
       "3    3.7\n",
       "4    3.8\n",
       "Name: rate, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning Rate column\n",
    "\n",
    "def handlerate(value):\n",
    "    if(value=='NEW' or value=='-'):\n",
    "        return np.nan\n",
    "    else:\n",
    "        value = str(value).split('/')\n",
    "        value = value[0]\n",
    "        return float(value)\n",
    "    \n",
    "df['rate'] = df['rate'].apply(handlerate)\n",
    "df['rate'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "199c2585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10019"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding Null values\n",
    "\n",
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ea878f",
   "metadata": {},
   "source": [
    "#### note : There are total '10019' null values found, we simply cannot drop this much number of amount. So insted of droping the amount we going to replace it with 'Mean'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aae228bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing null with 'mean'\n",
    "\n",
    "df['rate'].fillna(df['rate'].mean(), inplace = True)\n",
    "df['rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36a2d244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51609 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   name                         51609 non-null  object \n",
      " 1   online_order                 51609 non-null  object \n",
      " 2   book_table                   51609 non-null  object \n",
      " 3   rate                         51609 non-null  float64\n",
      " 4   votes                        51609 non-null  int64  \n",
      " 5   location                     51588 non-null  object \n",
      " 6   rest_type                    51382 non-null  object \n",
      " 7   cuisines                     51564 non-null  object \n",
      " 8   approx_cost(for two people)  51265 non-null  object \n",
      " 9   listed_in(type)              51609 non-null  object \n",
      " 10  listed_in(city)              51609 non-null  object \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55fb1619",
   "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>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table  rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No   3.7     88  Banashankari   \n",
       "4          Grand Village           No         No   3.8    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese   \n",
       "1        Casual Dining     Chinese, North Indian, Thai   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian   \n",
       "3          Quick Bites      South Indian, North Indian   \n",
       "4        Casual Dining        North Indian, Rajasthani   \n",
       "\n",
       "  approx_cost(for two people) listed_in(type) listed_in(city)  \n",
       "0                         800          Buffet    Banashankari  \n",
       "1                         800          Buffet    Banashankari  \n",
       "2                         800          Buffet    Banashankari  \n",
       "3                         300          Buffet    Banashankari  \n",
       "4                         600          Buffet    Banashankari  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping rest null values\n",
    "\n",
    "df.dropna(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd5c3c27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 51042 entries, 0 to 51716\n",
      "Data columns (total 11 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   name                         51042 non-null  object \n",
      " 1   online_order                 51042 non-null  object \n",
      " 2   book_table                   51042 non-null  object \n",
      " 3   rate                         51042 non-null  float64\n",
      " 4   votes                        51042 non-null  int64  \n",
      " 5   location                     51042 non-null  object \n",
      " 6   rest_type                    51042 non-null  object \n",
      " 7   cuisines                     51042 non-null  object \n",
      " 8   approx_cost(for two people)  51042 non-null  object \n",
      " 9   listed_in(type)              51042 non-null  object \n",
      " 10  listed_in(city)              51042 non-null  object \n",
      "dtypes: float64(1), int64(1), object(9)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5d22ff",
   "metadata": {},
   "source": [
    "#### Note : as we can check there is no null value in dataset now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "92cbe077",
   "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>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>Cost2plates</th>\n",
       "      <th>Type</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table  rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No   3.7     88  Banashankari   \n",
       "4          Grand Village           No         No   3.8    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines Cost2plates    Type  \\\n",
       "0        Casual Dining  North Indian, Mughlai, Chinese         800  Buffet   \n",
       "1        Casual Dining     Chinese, North Indian, Thai         800  Buffet   \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian         800  Buffet   \n",
       "3          Quick Bites      South Indian, North Indian         300  Buffet   \n",
       "4        Casual Dining        North Indian, Rajasthani         600  Buffet   \n",
       "\n",
       "  listed_in(city)  \n",
       "0    Banashankari  \n",
       "1    Banashankari  \n",
       "2    Banashankari  \n",
       "3    Banashankari  \n",
       "4    Banashankari  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming the 'approx_cost(for two people)' as 'cost2plates' and 'listed_in(type)' as 'Type'.\n",
    "\n",
    "df.rename(columns = {'approx_cost(for two people)' : 'Cost2plates','listed_in(type)':'Type'}, inplace = True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fdb5f560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar',\n",
       "       'Kumaraswamy Layout', 'Rajarajeshwari Nagar', 'Vijay Nagar',\n",
       "       'Uttarahalli', 'JP Nagar', 'South Bangalore', 'City Market',\n",
       "       'Nagarbhavi', 'Bannerghatta Road', 'BTM', 'Kanakapura Road',\n",
       "       'Bommanahalli', 'CV Raman Nagar', 'Electronic City', 'HSR',\n",
       "       'Marathahalli', 'Wilson Garden', 'Shanti Nagar',\n",
       "       'Koramangala 5th Block', 'Koramangala 8th Block', 'Richmond Road',\n",
       "       'Koramangala 7th Block', 'Jalahalli', 'Koramangala 4th Block',\n",
       "       'Bellandur', 'Sarjapur Road', 'Whitefield', 'East Bangalore',\n",
       "       'Old Airport Road', 'Indiranagar', 'Koramangala 1st Block',\n",
       "       'Frazer Town', 'RT Nagar', 'MG Road', 'Brigade Road',\n",
       "       'Lavelle Road', 'Church Street', 'Ulsoor', 'Residency Road',\n",
       "       'Shivajinagar', 'Infantry Road', 'St. Marks Road',\n",
       "       'Cunningham Road', 'Race Course Road', 'Commercial Street',\n",
       "       'Vasanth Nagar', 'HBR Layout', 'Domlur', 'Ejipura',\n",
       "       'Jeevan Bhima Nagar', 'Old Madras Road', 'Malleshwaram',\n",
       "       'Seshadripuram', 'Kammanahalli', 'Koramangala 6th Block',\n",
       "       'Majestic', 'Langford Town', 'Central Bangalore', 'Sanjay Nagar',\n",
       "       'Brookefield', 'ITPL Main Road, Whitefield',\n",
       "       'Varthur Main Road, Whitefield', 'KR Puram',\n",
       "       'Koramangala 2nd Block', 'Koramangala 3rd Block', 'Koramangala',\n",
       "       'Hosur Road', 'Rajajinagar', 'Banaswadi', 'North Bangalore',\n",
       "       'Nagawara', 'Hennur', 'Kalyan Nagar', 'New BEL Road', 'Jakkur',\n",
       "       'Rammurthy Nagar', 'Thippasandra', 'Kaggadasapura', 'Hebbal',\n",
       "       'Kengeri', 'Sankey Road', 'Sadashiv Nagar', 'Basaveshwara Nagar',\n",
       "       'Yeshwantpur', 'West Bangalore', 'Magadi Road', 'Yelahanka',\n",
       "       'Sahakara Nagar', 'Peenya'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for unique value in location \n",
    "\n",
    "df['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "15c93734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banashankari', 'Bannerghatta Road', 'Basavanagudi', 'Bellandur',\n",
       "       'Brigade Road', 'Brookefield', 'BTM', 'Church Street',\n",
       "       'Electronic City', 'Frazer Town', 'HSR', 'Indiranagar',\n",
       "       'Jayanagar', 'JP Nagar', 'Kalyan Nagar', 'Kammanahalli',\n",
       "       'Koramangala 4th Block', 'Koramangala 5th Block',\n",
       "       'Koramangala 6th Block', 'Koramangala 7th Block', 'Lavelle Road',\n",
       "       'Malleshwaram', 'Marathahalli', 'MG Road', 'New BEL Road',\n",
       "       'Old Airport Road', 'Rajajinagar', 'Residency Road',\n",
       "       'Sarjapur Road', 'Whitefield'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for unique value in location \n",
    "\n",
    "df['listed_in(city)'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffee33fe",
   "metadata": {},
   "source": [
    "#### Note : ''listed_in(city)' and 'location' both are same so lets keep only one of them and drop the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5bfa4e3",
   "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>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>Cost2plates</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table  rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No   3.7     88  Banashankari   \n",
       "4          Grand Village           No         No   3.8    166  Basavanagudi   \n",
       "\n",
       "             rest_type                        cuisines Cost2plates    Type  \n",
       "0        Casual Dining  North Indian, Mughlai, Chinese         800  Buffet  \n",
       "1        Casual Dining     Chinese, North Indian, Thai         800  Buffet  \n",
       "2  Cafe, Casual Dining          Cafe, Mexican, Italian         800  Buffet  \n",
       "3          Quick Bites      South Indian, North Indian         300  Buffet  \n",
       "4        Casual Dining        North Indian, Rajasthani         600  Buffet  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the 'listed_in(city)' column.\n",
    "\n",
    "df = df.drop('listed_in(city)', axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d785477f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['800', '300', '600', '700', '550', '500', '450', '650', '400',\n",
       "       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',\n",
       "       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',\n",
       "       '1,600', '230', '130', '50', '190', '1,700', '1,400', '180',\n",
       "       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',\n",
       "       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',\n",
       "       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',\n",
       "       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',\n",
       "       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding unique values in 'Cost2plates'\n",
    "\n",
    "df['Cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a3a37de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,\n",
       "        900.,  200.,  750.,  150.,  850.,  100., 1200.,  350.,  250.,\n",
       "        950., 1000., 1500., 1300.,  199.,   80., 1100.,  160., 1600.,\n",
       "        230.,  130.,   50.,  190., 1700., 1400.,  180., 1350., 2200.,\n",
       "       2000., 1800., 1900.,  330., 2500., 2100., 3000., 2800., 3400.,\n",
       "         40., 1250., 3500., 4000., 2400., 2600.,  120., 1450.,  469.,\n",
       "         70., 3200.,   60.,  560.,  240.,  360., 6000., 1050., 2300.,\n",
       "       4100., 5000., 3700., 1650., 2700., 4500.,  140.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing comma from the values of 'Cost2plates'\n",
    "\n",
    "def handlecomma(value):\n",
    "    value= str(value)\n",
    "    if ',' in value:\n",
    "        value = value.replace(',', '')\n",
    "        return float(value)\n",
    "    else:\n",
    "        return float(value)\n",
    "\n",
    "df['Cost2plates'] = df['Cost2plates'].apply(handlecomma)\n",
    "df['Cost2plates'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef643fe3",
   "metadata": {},
   "source": [
    "#### Note : now we can check there is no comma in middle of value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4c8f2bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites                   19010\n",
       "Casual Dining                 10253\n",
       "Cafe                           3682\n",
       "Delivery                       2574\n",
       "Dessert Parlor                 2242\n",
       "                              ...  \n",
       "Dessert Parlor, Kiosk             2\n",
       "Food Court, Beverage Shop         2\n",
       "Dessert Parlor, Food Court        2\n",
       "Quick Bites, Kiosk                1\n",
       "Sweet Shop, Dessert Parlor        1\n",
       "Name: rest_type, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning 'rest_type' column\n",
    "\n",
    "rest_types = df['rest_type'].value_counts(ascending = False)\n",
    "\n",
    "rest_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a44cd2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bakery                        1140\n",
       "Casual Dining, Bar            1130\n",
       "Beverage Shop                  863\n",
       "Bar                            686\n",
       "Food Court                     616\n",
       "                              ... \n",
       "Dessert Parlor, Kiosk            2\n",
       "Food Court, Beverage Shop        2\n",
       "Dessert Parlor, Food Court       2\n",
       "Quick Bites, Kiosk               1\n",
       "Sweet Shop, Dessert Parlor       1\n",
       "Name: rest_type, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dataframe for restuarent types of which frequency is less than 1500\n",
    "\n",
    "rest_types_less_than1500 = rest_types[rest_types<1500]\n",
    "rest_types_less_than1500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e33c152f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quick Bites           19010\n",
       "others                11273\n",
       "Casual Dining         10253\n",
       "Cafe                   3682\n",
       "Delivery               2574\n",
       "Dessert Parlor         2242\n",
       "Takeaway, Delivery     2008\n",
       "Name: rest_type, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating rest_type which are less than 1500 as 'others'.\n",
    "\n",
    "def handle_rest_type(value):\n",
    "    if(value in rest_types_less_than1500):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['rest_type'] = df['rest_type'].apply(handle_rest_type)\n",
    "df['rest_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1af36411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Shivajinagar                     498\n",
       "Cunningham Road                  490\n",
       "Domlur                           482\n",
       "Old Airport Road                 437\n",
       "Ejipura                          433\n",
       "Commercial Street                370\n",
       "St. Marks Road                   343\n",
       "Koramangala 8th Block            294\n",
       "Vasanth Nagar                    293\n",
       "Jeevan Bhima Nagar               268\n",
       "Wilson Garden                    246\n",
       "Bommanahalli                     236\n",
       "Koramangala 3rd Block            215\n",
       "Thippasandra                     191\n",
       "Kumaraswamy Layout               191\n",
       "Nagawara                         187\n",
       "Basaveshwara Nagar               187\n",
       "Seshadripuram                    165\n",
       "Hennur                           159\n",
       "Majestic                         155\n",
       "HBR Layout                       153\n",
       "Infantry Road                    150\n",
       "Race Course Road                 139\n",
       "City Market                      122\n",
       "Yeshwantpur                      119\n",
       "ITPL Main Road, Whitefield       113\n",
       "Varthur Main Road, Whitefield    109\n",
       "South Bangalore                  107\n",
       "Koramangala 2nd Block            102\n",
       "Kaggadasapura                    101\n",
       "Hosur Road                        98\n",
       "CV Raman Nagar                    89\n",
       "Vijay Nagar                       78\n",
       "RT Nagar                          78\n",
       "Sanjay Nagar                      76\n",
       "Sadashiv Nagar                    63\n",
       "Sahakara Nagar                    53\n",
       "Koramangala                       48\n",
       "East Bangalore                    43\n",
       "Jalahalli                         38\n",
       "Magadi Road                       34\n",
       "Rammurthy Nagar                   32\n",
       "Langford Town                     27\n",
       "Sankey Road                       27\n",
       "Old Madras Road                   22\n",
       "Mysore Road                       22\n",
       "Kanakapura Road                   19\n",
       "KR Puram                          18\n",
       "Uttarahalli                       17\n",
       "Hebbal                            14\n",
       "North Bangalore                   14\n",
       "Nagarbhavi                         9\n",
       "Kengeri                            8\n",
       "Central Bangalore                  8\n",
       "West Bangalore                     6\n",
       "Yelahanka                          5\n",
       "Jakkur                             3\n",
       "Rajarajeshwari Nagar               2\n",
       "Peenya                             1\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dataframe for 'location' of which frequency is less than '500'\n",
    "\n",
    "location = df['location'].value_counts()\n",
    "location_less_than_500 = location[location<500]\n",
    "location_less_than_500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c30403fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others                   8007\n",
       "BTM                      5056\n",
       "HSR                      2494\n",
       "Koramangala 5th Block    2479\n",
       "JP Nagar                 2218\n",
       "Whitefield               2105\n",
       "Indiranagar              2026\n",
       "Jayanagar                1916\n",
       "Marathahalli             1805\n",
       "Bannerghatta Road        1609\n",
       "Bellandur                1268\n",
       "Electronic City          1246\n",
       "Koramangala 1st Block    1236\n",
       "Brigade Road             1210\n",
       "Koramangala 7th Block    1174\n",
       "Koramangala 6th Block    1127\n",
       "Sarjapur Road            1047\n",
       "Koramangala 4th Block    1017\n",
       "Ulsoor                   1011\n",
       "Banashankari              902\n",
       "MG Road                   893\n",
       "Kalyan Nagar              841\n",
       "Richmond Road             803\n",
       "Malleshwaram              721\n",
       "Frazer Town               714\n",
       "Basavanagudi              684\n",
       "Residency Road            671\n",
       "Brookefield               656\n",
       "New BEL Road              644\n",
       "Banaswadi                 640\n",
       "Kammanahalli              639\n",
       "Rajajinagar               591\n",
       "Church Street             566\n",
       "Lavelle Road              518\n",
       "Shanti Nagar              508\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating rest_type which are less than 500 as 'others'.\n",
    "\n",
    "def handle_location(value):\n",
    "    if(value in location_less_than_500):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['location'] = df['location'].apply(handle_location)\n",
    "df['location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91af40bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dataframe for 'cuisines' of which frequency is less than '100'\n",
    "\n",
    "cuisines = df['cuisines'].value_counts()\n",
    "\n",
    "cuisines_less_than_100 = cuisines[cuisines<100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e326a61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others                                 26159\n",
       "North Indian                            2852\n",
       "North Indian, Chinese                   2351\n",
       "South Indian                            1820\n",
       "Biryani                                  903\n",
       "                                       ...  \n",
       "South Indian, Chinese, North Indian      105\n",
       "North Indian, Mughlai, Chinese           104\n",
       "South Indian, Fast Food                  104\n",
       "Italian, Pizza                           102\n",
       "North Indian, Chinese, Seafood           102\n",
       "Name: cuisines, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding 'cuisines_less_than_100' as others\n",
    "\n",
    "def handle_cuisines(value):\n",
    "    if(value in cuisines_less_than_100):\n",
    "        return 'others'\n",
    "    else:\n",
    "        return value\n",
    "    \n",
    "df['cuisines'] = df['cuisines'].apply(handle_cuisines)\n",
    "df['cuisines'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5bb25c",
   "metadata": {},
   "source": [
    "#### Now that data is clean let's go ahead for data visulization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56835d2a",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bd66ee37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34]),\n",
       " [Text(0, 0, 'Banashankari'),\n",
       "  Text(1, 0, 'Basavanagudi'),\n",
       "  Text(2, 0, 'others'),\n",
       "  Text(3, 0, 'Jayanagar'),\n",
       "  Text(4, 0, 'JP Nagar'),\n",
       "  Text(5, 0, 'Bannerghatta Road'),\n",
       "  Text(6, 0, 'BTM'),\n",
       "  Text(7, 0, 'Electronic City'),\n",
       "  Text(8, 0, 'HSR'),\n",
       "  Text(9, 0, 'Marathahalli'),\n",
       "  Text(10, 0, 'Shanti Nagar'),\n",
       "  Text(11, 0, 'Koramangala 5th Block'),\n",
       "  Text(12, 0, 'Richmond Road'),\n",
       "  Text(13, 0, 'Koramangala 7th Block'),\n",
       "  Text(14, 0, 'Koramangala 4th Block'),\n",
       "  Text(15, 0, 'Bellandur'),\n",
       "  Text(16, 0, 'Sarjapur Road'),\n",
       "  Text(17, 0, 'Whitefield'),\n",
       "  Text(18, 0, 'Indiranagar'),\n",
       "  Text(19, 0, 'Koramangala 1st Block'),\n",
       "  Text(20, 0, 'Frazer Town'),\n",
       "  Text(21, 0, 'MG Road'),\n",
       "  Text(22, 0, 'Brigade Road'),\n",
       "  Text(23, 0, 'Lavelle Road'),\n",
       "  Text(24, 0, 'Church Street'),\n",
       "  Text(25, 0, 'Ulsoor'),\n",
       "  Text(26, 0, 'Residency Road'),\n",
       "  Text(27, 0, 'Malleshwaram'),\n",
       "  Text(28, 0, 'Kammanahalli'),\n",
       "  Text(29, 0, 'Koramangala 6th Block'),\n",
       "  Text(30, 0, 'Brookefield'),\n",
       "  Text(31, 0, 'Rajajinagar'),\n",
       "  Text(32, 0, 'Banaswadi'),\n",
       "  Text(33, 0, 'Kalyan Nagar'),\n",
       "  Text(34, 0, 'New BEL Road')])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of restaurants in particuler location.\n",
    "\n",
    "plt.figure(figsize = (16,10))\n",
    "ax = sns.countplot(df['location'])\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "152f3739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of restaurants accepts online order\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "sns.countplot(df['online_order'], palette = 'inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "79f6ea5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of restaurants who provide Table booking service.\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "sns.countplot(df['book_table'],  palette = 'inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc6f3270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='online_order', ylabel='rate'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing ratings for online orders.\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "sns.boxplot(x = 'online_order', y = 'rate', data = df, palette = 'inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b3add0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='book_table', ylabel='rate'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing ratings of restaurants who provides table booking facility.\n",
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "sns.boxplot(x = 'book_table', y = 'rate',data = df, palette = 'inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8f89a37f",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>1763</td>\n",
       "      <td>3293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>397</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>302</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>685</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>243</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>517</td>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>552</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>239</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>226</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>676</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>287</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>584</td>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>697</td>\n",
       "      <td>1329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>911</td>\n",
       "      <td>1307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>552</td>\n",
       "      <td>1364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>350</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>264</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>384</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>459</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>866</td>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>445</td>\n",
       "      <td>682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>389</td>\n",
       "      <td>785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>315</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>520</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>309</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>701</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>255</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>286</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>424</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>557</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>323</td>\n",
       "      <td>724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>289</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>389</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>986</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>3672</td>\n",
       "      <td>4335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name      \n",
       "online_order             No   Yes\n",
       "location                         \n",
       "BTM                    1763  3293\n",
       "Banashankari            397   505\n",
       "Banaswadi               302   338\n",
       "Bannerghatta Road       685   924\n",
       "Basavanagudi            243   441\n",
       "Bellandur               517   751\n",
       "Brigade Road            552   658\n",
       "Brookefield             239   417\n",
       "Church Street           226   340\n",
       "Electronic City         676   570\n",
       "Frazer Town             287   427\n",
       "HSR                     584  1910\n",
       "Indiranagar             697  1329\n",
       "JP Nagar                911  1307\n",
       "Jayanagar               552  1364\n",
       "Kalyan Nagar            350   491\n",
       "Kammanahalli            264   375\n",
       "Koramangala 1st Block   384   852\n",
       "Koramangala 4th Block   459   558\n",
       "Koramangala 5th Block   866  1613\n",
       "Koramangala 6th Block   445   682\n",
       "Koramangala 7th Block   389   785\n",
       "Lavelle Road            315   203\n",
       "MG Road                 520   373\n",
       "Malleshwaram            309   412\n",
       "Marathahalli            701  1104\n",
       "New BEL Road            255   389\n",
       "Rajajinagar             286   305\n",
       "Residency Road          424   247\n",
       "Richmond Road           557   246\n",
       "Sarjapur Road           323   724\n",
       "Shanti Nagar            289   219\n",
       "Ulsoor                  389   622\n",
       "Whitefield              986  1119\n",
       "others                 3672  4335"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating pivot table for location and online order\n",
    "\n",
    "df1 = df.groupby(['location', 'online_order'])['name'].count()\n",
    "df1.to_csv('location_online.csv')\n",
    "df1 = pd.read_csv('location_online.csv')\n",
    "df1 = pd.pivot_table(df1, values=None, index=['location'], columns=['online_order'], fill_value=0, aggfunc=np.sum)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2ab006d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing location which offers online order facility \n",
    "\n",
    "df1.plot(kind = 'bar', figsize = (20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7bc051cd",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>book_table</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTM</th>\n",
       "      <td>4889</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>839</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>632</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>1510</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>668</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>1170</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>1034</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>582</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>385</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>1148</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>706</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>2277</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>1578</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>1903</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>1637</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>692</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>590</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>1186</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>695</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>1787</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>1015</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>1012</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>290</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>546</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>632</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>1642</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>588</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>550</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>522</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>687</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>893</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>451</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>834</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>1852</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>7204</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name     \n",
       "book_table               No  Yes\n",
       "location                        \n",
       "BTM                    4889  167\n",
       "Banashankari            839   63\n",
       "Banaswadi               632    8\n",
       "Bannerghatta Road      1510   99\n",
       "Basavanagudi            668   16\n",
       "Bellandur              1170   98\n",
       "Brigade Road           1034  176\n",
       "Brookefield             582   74\n",
       "Church Street           385  181\n",
       "Electronic City        1148   98\n",
       "Frazer Town             706    8\n",
       "HSR                    2277  217\n",
       "Indiranagar            1578  448\n",
       "JP Nagar               1903  315\n",
       "Jayanagar              1637  279\n",
       "Kalyan Nagar            692  149\n",
       "Kammanahalli            590   49\n",
       "Koramangala 1st Block  1186   50\n",
       "Koramangala 4th Block   695  322\n",
       "Koramangala 5th Block  1787  692\n",
       "Koramangala 6th Block  1015  112\n",
       "Koramangala 7th Block  1012  162\n",
       "Lavelle Road            290  228\n",
       "MG Road                 546  347\n",
       "Malleshwaram            632   89\n",
       "Marathahalli           1642  163\n",
       "New BEL Road            588   56\n",
       "Rajajinagar             550   41\n",
       "Residency Road          522  149\n",
       "Richmond Road           687  116\n",
       "Sarjapur Road           893  154\n",
       "Shanti Nagar            451   57\n",
       "Ulsoor                  834  177\n",
       "Whitefield             1852  253\n",
       "others                 7204  803"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# craeting pivot table for location and table booking\n",
    "\n",
    "df2 = df.groupby(['location','book_table'])['name'].count()\n",
    "df2.to_csv('location_booktable.csv')\n",
    "df2 = pd.read_csv('location_booktable.csv')\n",
    "df2 = pd.pivot_table(df2, values=None, index=['location'], columns=['book_table'], fill_value=0, aggfunc=np.sum)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "999675e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing location which offers table booking facility \n",
    "\n",
    "df2.plot(kind = 'bar', figsize = (20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "59617274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Type', ylabel='rate'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing types of resturants vs ratings\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "sns.boxplot(x= 'Type', y = 'rate', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f1249703",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>Buffet</th>\n",
       "      <th>Cafes</th>\n",
       "      <th>Delivery</th>\n",
       "      <th>Desserts</th>\n",
       "      <th>Dine-out</th>\n",
       "      <th>Drinks &amp; nightlife</th>\n",
       "      <th>Pubs and bars</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</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>BTM</th>\n",
       "      <td>21</td>\n",
       "      <td>83</td>\n",
       "      <td>3053</td>\n",
       "      <td>198</td>\n",
       "      <td>1660</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banashankari</th>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>418</td>\n",
       "      <td>71</td>\n",
       "      <td>356</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banaswadi</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>310</td>\n",
       "      <td>37</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bannerghatta Road</th>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "      <td>828</td>\n",
       "      <td>137</td>\n",
       "      <td>578</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basavanagudi</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>344</td>\n",
       "      <td>66</td>\n",
       "      <td>251</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellandur</th>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "      <td>617</td>\n",
       "      <td>75</td>\n",
       "      <td>479</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brigade Road</th>\n",
       "      <td>25</td>\n",
       "      <td>46</td>\n",
       "      <td>497</td>\n",
       "      <td>108</td>\n",
       "      <td>455</td>\n",
       "      <td>57</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookefield</th>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>339</td>\n",
       "      <td>45</td>\n",
       "      <td>245</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>193</td>\n",
       "      <td>29</td>\n",
       "      <td>215</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic City</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>570</td>\n",
       "      <td>71</td>\n",
       "      <td>516</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frazer Town</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>470</td>\n",
       "      <td>56</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSR</th>\n",
       "      <td>19</td>\n",
       "      <td>49</td>\n",
       "      <td>1694</td>\n",
       "      <td>120</td>\n",
       "      <td>580</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>38</td>\n",
       "      <td>97</td>\n",
       "      <td>1091</td>\n",
       "      <td>140</td>\n",
       "      <td>529</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP Nagar</th>\n",
       "      <td>45</td>\n",
       "      <td>76</td>\n",
       "      <td>1151</td>\n",
       "      <td>166</td>\n",
       "      <td>722</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jayanagar</th>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>1043</td>\n",
       "      <td>182</td>\n",
       "      <td>575</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kalyan Nagar</th>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>366</td>\n",
       "      <td>88</td>\n",
       "      <td>315</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kammanahalli</th>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>329</td>\n",
       "      <td>35</td>\n",
       "      <td>240</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 1st Block</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>716</td>\n",
       "      <td>70</td>\n",
       "      <td>398</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>21</td>\n",
       "      <td>53</td>\n",
       "      <td>464</td>\n",
       "      <td>81</td>\n",
       "      <td>302</td>\n",
       "      <td>62</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>65</td>\n",
       "      <td>146</td>\n",
       "      <td>1075</td>\n",
       "      <td>209</td>\n",
       "      <td>842</td>\n",
       "      <td>84</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 6th Block</th>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>511</td>\n",
       "      <td>70</td>\n",
       "      <td>411</td>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 7th Block</th>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>503</td>\n",
       "      <td>127</td>\n",
       "      <td>417</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavelle Road</th>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>127</td>\n",
       "      <td>50</td>\n",
       "      <td>191</td>\n",
       "      <td>59</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MG Road</th>\n",
       "      <td>51</td>\n",
       "      <td>76</td>\n",
       "      <td>266</td>\n",
       "      <td>68</td>\n",
       "      <td>343</td>\n",
       "      <td>53</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malleshwaram</th>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>269</td>\n",
       "      <td>85</td>\n",
       "      <td>291</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marathahalli</th>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>980</td>\n",
       "      <td>105</td>\n",
       "      <td>630</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New BEL Road</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>338</td>\n",
       "      <td>33</td>\n",
       "      <td>224</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rajajinagar</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>258</td>\n",
       "      <td>55</td>\n",
       "      <td>251</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residency Road</th>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>187</td>\n",
       "      <td>63</td>\n",
       "      <td>289</td>\n",
       "      <td>55</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Richmond Road</th>\n",
       "      <td>63</td>\n",
       "      <td>21</td>\n",
       "      <td>257</td>\n",
       "      <td>78</td>\n",
       "      <td>356</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarjapur Road</th>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>558</td>\n",
       "      <td>82</td>\n",
       "      <td>319</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanti Nagar</th>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>198</td>\n",
       "      <td>39</td>\n",
       "      <td>229</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ulsoor</th>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>456</td>\n",
       "      <td>71</td>\n",
       "      <td>359</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whitefield</th>\n",
       "      <td>28</td>\n",
       "      <td>51</td>\n",
       "      <td>1041</td>\n",
       "      <td>137</td>\n",
       "      <td>768</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>150</td>\n",
       "      <td>225</td>\n",
       "      <td>4062</td>\n",
       "      <td>512</td>\n",
       "      <td>2792</td>\n",
       "      <td>162</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name                                   \\\n",
       "Type                  Buffet Cafes Delivery Desserts Dine-out   \n",
       "location                                                        \n",
       "BTM                       21    83     3053      198     1660   \n",
       "Banashankari               7    36      418       71      356   \n",
       "Banaswadi                  0    24      310       37      262   \n",
       "Bannerghatta Road          9    46      828      137      578   \n",
       "Basavanagudi               7    11      344       66      251   \n",
       "Bellandur                 28    36      617       75      479   \n",
       "Brigade Road              25    46      497      108      455   \n",
       "Brookefield                6    17      339       45      245   \n",
       "Church Street             19    51      193       29      215   \n",
       "Electronic City           23    24      570       71      516   \n",
       "Frazer Town                1    11      470       56      172   \n",
       "HSR                       19    49     1694      120      580   \n",
       "Indiranagar               38    97     1091      140      529   \n",
       "JP Nagar                  45    76     1151      166      722   \n",
       "Jayanagar                 27    77     1043      182      575   \n",
       "Kalyan Nagar               9    45      366       88      315   \n",
       "Kammanahalli               2    27      329       35      240   \n",
       "Koramangala 1st Block      3    26      716       70      398   \n",
       "Koramangala 4th Block     21    53      464       81      302   \n",
       "Koramangala 5th Block     65   146     1075      209      842   \n",
       "Koramangala 6th Block     18    43      511       70      411   \n",
       "Koramangala 7th Block     25    52      503      127      417   \n",
       "Lavelle Road              30    27      127       50      191   \n",
       "MG Road                   51    76      266       68      343   \n",
       "Malleshwaram              11    31      269       85      291   \n",
       "Marathahalli              34    32      980      105      630   \n",
       "New BEL Road               4    29      338       33      224   \n",
       "Rajajinagar               10     4      258       55      251   \n",
       "Residency Road            20    31      187       63      289   \n",
       "Richmond Road             63    21      257       78      356   \n",
       "Sarjapur Road             25    22      558       82      319   \n",
       "Shanti Nagar               9    22      198       39      229   \n",
       "Ulsoor                    16    56      456       71      359   \n",
       "Whitefield                28    51     1041      137      768   \n",
       "others                   150   225     4062      512     2792   \n",
       "\n",
       "                                                        \n",
       "Type                  Drinks & nightlife Pubs and bars  \n",
       "location                                                \n",
       "BTM                                   22            19  \n",
       "Banashankari                          14             0  \n",
       "Banaswadi                              6             1  \n",
       "Bannerghatta Road                      9             2  \n",
       "Basavanagudi                           5             0  \n",
       "Bellandur                             17            16  \n",
       "Brigade Road                          57            22  \n",
       "Brookefield                            4             0  \n",
       "Church Street                         36            23  \n",
       "Electronic City                       21            21  \n",
       "Frazer Town                            2             2  \n",
       "HSR                                   14            18  \n",
       "Indiranagar                           65            66  \n",
       "JP Nagar                              51             7  \n",
       "Jayanagar                             12             0  \n",
       "Kalyan Nagar                          18             0  \n",
       "Kammanahalli                           6             0  \n",
       "Koramangala 1st Block                  7            16  \n",
       "Koramangala 4th Block                 62            34  \n",
       "Koramangala 5th Block                 84            58  \n",
       "Koramangala 6th Block                 51            23  \n",
       "Koramangala 7th Block                 25            25  \n",
       "Lavelle Road                          59            34  \n",
       "MG Road                               53            36  \n",
       "Malleshwaram                          20            14  \n",
       "Marathahalli                          22             2  \n",
       "New BEL Road                           8             8  \n",
       "Rajajinagar                            3            10  \n",
       "Residency Road                        55            26  \n",
       "Richmond Road                         16            12  \n",
       "Sarjapur Road                         19            22  \n",
       "Shanti Nagar                           9             2  \n",
       "Ulsoor                                23            30  \n",
       "Whitefield                            47            33  \n",
       "others                               162           104  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping type of resturants location wise\n",
    "\n",
    "df3 = df.groupby(['location','Type'])['name'].count()\n",
    "df3.to_csv('location_type.csv')\n",
    "df3 = pd.read_csv('location_type.csv')\n",
    "df3 = pd.pivot_table(df3, values=None, index=['location'], columns=['Type'], fill_value=0, aggfunc=np.sum)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b0cd4916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAKgJigAAAABIIigCAAAAoLZTBEWTJkzLpAnT2nsYAAAAAB3aThEUAQAAALB5giIAAAAAkgiKAAAAAKgJigAAAABIIigCAAAAoCYoAgAAACCJoAgAAACAmqAIAAAAgCSCIgAAAABqgiIAAAAAkgiKAAAAAKgJigAAAABIIigCAAAAoCYoAgAAACCJoAgAAACAmqAIAAAAgCSCIgAAAABqgiIAAAAAkgiKAAAAAKgJigAAAABIIigCAAAAoCYoAgAAACCJoAgAAACAmqAIAAAAgCSCIgAAAABqgiIAAAAAkmxBUFRKubGUsqiUMm+dtstLKU+VUprqnxPXeezSUspjpZTflFKOX6f98FLK3Pqx60oppfV3BwAAAICW2pIZRTclOWED7V+oqqqx/vlRkpRSDklyRpIBdZ8vl1K61utfn+T8JAfWPxvaJgAAAADtZLNBUVVV9yZ5bgu3NzrJt6uqermqqt8neSzJsFLKvkl6VlX1i6qqqiTfTHJqC8cMAAAAwHawLecouqiU8mB9aNpeddt+SZ5YZ50n67b96uVXt29QKeX8UsqsUsqsZ599dhuGCAAAAMCWamlQdH2SNydpTPJMks/X7Rs671C1ifYNqqrqq1VVDamqakjv3r1bOEQAAAAAtkaLgqKqqv5YVdWqqqpWJ/lakmH1Q08m2X+dVfskebpu77OBdgAAAAA6iBYFRfU5h9b6f5KsvSLaD5KcUUrZtZTSL2tOWj2jqqpnkrxYSjmyvtrZ2CTf34ZxAwAAANDKdtncCqWUf08yIsnepZQnk1yWZEQppTFrDh9bmGR8klRV9VAp5bYkDydZmeTCqqpW1Zv6YNZcQa1Hkh/XPwAAAAB0EJsNiqqqeu8Gmr+xifU/leRTG2iflWTgVo0OAAAAgDazLVc9AwAAAKATERQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBNUAQAAABAEkERAAAAADVBEQBso4bJDWmY3NDewwAAgG0mKAIAAAAgiaAIAAAAgJqgCAAAAIAkgiIAAAAAaoIiAAAAAJIIigAAAACoCYoAAAAASCIoAgAAAKAmKAIAAAAgiaAIAAAAgJqgCIAdXsPkhjRMbmjvYQAAwA5PUAQAAABAEkERAAAAADVBEQAAAABJBEUAALSQ84MBQOcjKAIAAAAgiaAIAAAAgJqgCAAAAIAkgiIAAAAAaoIiAAAAAJIku7T3AACAbbP2qlNzx81t55EAAHRe617pszN/7jKjCAAAAIAkgiIAAAAAaoIiAAAAAJJsQVBUSrmxlLKolDJvnbbXl1J+Wkp5tL7da53HLi2lPFZK+U0p5fh12g8vpcytH7uulFJaf3cAAAAAaKktmVF0U5ITXtV2SZK7qqo6MMld9f2UUg5JckaSAXWfL5dSutZ9rk9yfpID659XbxMAAACAdrTZoKiqqnuTPPeq5tFJJtfLk5Ocuk77t6uqermqqt8neSzJsFLKvkl6VlX1i6qqqiTfXKcPAAAAAB1AS89R9FdVVT2TJPXtPnX7fkmeWGe9J+u2/erlV7cDAAAA0EG09smsN3TeoWoT7RveSCnnl1JmlVJmPfvss602OAAAAAA2rqVB0R/rw8lS3y6q259Msv866/VJ8nTd3mcD7RtUVdVXq6oaUlXVkN69e7dwiAAAAABsjZYGRT9IMq5eHpfk++u0n1FK2bWU0i9rTlo9oz487cVSypH11c7GrtMHAAAAgA5gl82tUEr59yQjkuxdSnkyyWVJPpPktlLKuUn+J8npSVJV1UOllNuSPJxkZZILq6paVW/qg1lzBbUeSX5c/wAAAADQQWw2KKqq6r0beWjkRtb/VJJPbaB9VpKBWzU6AACADqZhckPz8txxc9txJACtr7VPZg0AAADADkpQBAAAAEASQREAAAAANUERAAAAAEkERQAAAADUBEUAAAAAJBEUAQAAAFATFAEAAACQRFAEAAAAQE1QBAAAAEASQREAAAAANUERAAAAAEkERQAAAADUBEUAAAAAJBEUAQAAAFATFAEAAACQRFAEAAAAQE1QBAAAAEASQREAAAAANUERAAAAAEkERQAAAADUBEUAAAAAJBEUAQAAAFATFAEAAACQRFAEAAAAQE1QBABAq5k0YVomTZjW3sMAAFpIUAQAAABAEkERAAAAADVBEa2qYXJDGiY3tPcwAAAAgBYQFAEAAACQRFAEAAAAQE1QBAAAAEASQREAAAAANUERAAAAAEkERQAAAADUBEUAAAAAJBEUAQAAAFATFAEAAACQRFAEAAAAQE1QBAAAANACkyZMy6QJ09p7GK1KUAQAAABAEkERAAAAADVBEQAAAABJBEUAAAAA1ARFAAAAACQRFAEAAABQExQBAAAAkERQBAAAAEBtl/YeAAB0NpMmTGtevvCGd7TjSAAAYOsIigAAWqhhckPz8txxc9txJAAArcOhZwAAAAAkERQBAAAAUBMUAQAAAJBEUAQAAABATVAEAAAAQBJBEQAAAAA1QREAAAAASQRFAAAAANQERQAAAAAkERQBAAAAUBMUAQAAAJBEUAQAAABATVAEAAAAQBJBEQAAAAA1QREAAAAASQRFAAAAANQERQAAANto0oRpmTRhWnsPA2CbCYoAAAAASCIoglbVMLkhDZMb2nsYAAAA0CKCIgAAAACSCIoAAAAAqAmKAAAAAEgiKAIAAACgJigCAAAAIImgCAAAAICaoAiA7WLShGmZNGFaew8DAADYCru09wDo3Nb9knjhDe9ox5EA7DzWvvZ63QUAYGuZUQTQChomN6RhckN7DwMAAGCbCIoAAAAASCIoAgAAgB2Gmexsb4IiAAAAAJIIigAAAACoCYoAAAAASCIoAgAAAKAmKAIAAAAgSbJLew8AAFrbpAnTmpcvvOEd7TgSAADYsZhRBAAAAECSbQyKSikLSylzSylNpZRZddvrSyk/LaU8Wt/utc76l5ZSHiul/KaUcvy2Dh6gI5s0Ydp6M1sAAAA6utaYUXRsVVWNVVUNqe9fkuSuqqoOTHJXfT+llEOSnJFkQJITkny5lNK1FeoDALATapjckIbJDe09DABoc/MP7p/5B/ffLtveHoeejU4yuV6enOTUddq/XVXVy1VV/T7JY0mGbYf6AAAAALTAtgZFVZI7SymzSynn121/VVXVM0lS3+5Tt++X5Il1+j5Zt71GKeX8UsqsUsqsZ599dhuHCAAArc+MJgA6o2296tnRVVU9XUrZJ8lPSymPbGLdsoG2akMrVlX11SRfTZIhQ4ZscB3gtdZ+WJ07bm47jwQAAIAd0TbNKKqq6un6dlGS72XNoWR/LKXsmyT17aJ69SeT7L9O9z5Jnt6W+gAAAAC0nhYHRaWU3Uspe65dTjIqybwkP0gyrl5tXJLv18s/SHJGKWXXUkq/JAcmmdHS+gAAAAC0rm059OyvknyvlLJ2O9+qquq/Sikzk9xWSjk3yf8kOT1Jqqp6qJRyW5KHk6xMcmFVVau2afTQwa29NPqFN7yjnUcCAAAAm9fioKiqqgVJDt1A+5IkIzfS51NJPtXSmgAAAABsP9t61TMAOgBX3gEAAFqDoAgAYAclJAYAWpugCAAAAIAkgiLo1CZNmNZ8Qm0AAIAdndm025+gCAAAAIAkgiIAAAAAaoIiAAAAAJIIigAAAACoCYoAAAAASCIoAgAAAKC2S3sPoLNZe5m+uePmtvNIAABoS5MmTGtevvCGd7TjSACg5QRFAAB0CoIaANh2Dj0DAAAAIImgCAAAAICaoAgAoBVNmjBtvUOgAAB2JIIiAAAAAJIIigAAAACoCYoAAAAASCIoAgAAAKAmKAIAAAAgiaAIANhGDZMb0jC5ob2HAQBAKxAUAQAAADukSROmZdKEae09jE5ll/YewM5g7S/thTe8o51HAgAAQGtaN6TwnY/OwIwiAAAAAJIIiuhknCcDAKB9OPwDoHNw6BnQKtYN6OaOm9uOIwEA6Nx87gK2JzOKAAAAAEhiRhEAsAPzV3UAgNZlRhEAAAAAScwoAgAAgB3WuieRv/CGd7TjSOgszCgCAFqVKx8BAOy4BEWdjMvDAwAAAC0lKAIA6ETM6AIAtoWgCAAAAIAkgiIAAKATMJsOoHUIigAAANihCQqh9QiKAAAAAEgiKAIAAACgJigCAAAAIEmyS3sPAAAA2PE0TG5IkswdN7fdardXfYDOzIyinYATuwEAAABbQlAEAADAFmuY3LDerK6dzc6+/3R+Dj2jU1t3JtWFN7yjHUcCAACd19rP3T5zw47PjCJgu3HYIwAAwI5FUAQAdCpCagCAlhMUAQAAAJDEOYoAAACAFnJe2M5HUAQAALCDczJp6MQuf92a235vbJNyDj0DAAAAIImgCOjEnNAWAABg6zj0DACAbTL/4P7/986ISe03EABgm5lRBAAAAECSTh4UzT+4//p/4QIAAABarGFyQxomN7T3MNiOHHoGdArrvlnNHTe3HUcCAACw4xIUAQBAJ7DuBRxcIp3ObO0fCP1xELaPTn3oGQDsTBxyDQDAthIUAQAAAJBEUATQaU2aMG29wxAAAAA2R1AEAK3EoV8AAOzoBEUAAAAAO7DPv+fkfP49J7fKtgRFAAAAACRJdmnvAQAAAACwvr6X/DBJsnC3tq0rKAIAYOtc/ro1t/3e2L7jAABanUPPAOg0nEwaAAC2jaCI7cKXNQAAADqrzvydV1AEAAAAQBJBEQAAbJP2+qtyw+SGNExuaPO6AHRuTmYNQKtZ74vSiEntNxAAAKBFzCgCAAAAIImgCDqlznxiNQAAALYfh54BAAC0UPMf5xxyDXQAT15yX/Nyn8+8rUXbMKMIAAAAgCRmFAGwI7v8dWtu+72xfccBAACdhBlFAECrcH40AIAdX+cJii5/3f/9yzIAAO3q8+85OZ9/z8ntPQx2AkJqdlZ+99leHHoGADuidf844tC7nd56XxScUBegTTiROZ2VoGg78YFt5+ZNY+fl//5ORFAD7Iy89gEdhO9c20/nOfQM1mEaJvBqT15y33qXC4XW5r2n/Xju25fnH6BzMaMIgE5t7TlS3tPvn9t5JNC21v7uf+TWO1ptm30v+WGSZOFurbZJgBZri5nca1/3kmThZ07aLjWgoxEUwU5g3ZOJtuYXBjoeh74BHc3amXx9PvO2dh7JzmV7BIUA7cZhr21KUATs2Na+aXjDoIPyZY3twmvfZm3X/3sd7Plvi/N0bM1sMn+g2nl0xD9Qed9lu+hgr/vbm6CI1rGT/cfpUDpguu7EcjuRnfD/vkNvah3wtQcAoL10ppBcUAQAwI5lJwypAaCtCIpaSwf7wNIRp4F2Wv6q3uGZgty5rHdSyZ19Vg/rMZsROh7nqILOy/tu5yUoAgDoxNZ+UU98Wd+RNR/2upVXXep0QU07/XH2/x52/L52qb8pO9WX9Q72x3k6rvZ+7Wvv+ttKUNRZeNFsc+19npL2rt+RtekHJv/3aGP+79fa+/9ee9enzW3tbMa1s1nf0++fk7TilwUzmXduXnu22I7+RX2t9V57tjIoZtvtrJ+7dvigqCVXYFj7hp3s+C8csLU625vm1rxot+Zf1Xfm1x6HfkH72Vk/sG6LzvK+x5Zr7xPKOuS9dXWozx3tHdRtQf2dOaRe9//+up+72XptHhSVUk5I8n+SdE3y9aqqPtPWY2gv3jR2Xht60eo0H1jb+w2TDm+7fWChw2vvL2sdhUO/WkeH+rLIDkNQSFvzna91+QNJ+2jToKiU0jXJpCTvTPJkkpmllB9UVfVwW46D1tHSKdit/qK5A6TbsN0I6tiE7RHU+MAGtAevPe2rJefI2tlD8s4SUm7t/z1BWefQ1jOKhiV5rKqqBUlSSvl2ktFJdtigaEd60+ps/2m9aLWvlhx6lXj+2Xl1lg+MO5L2Dso2dNgpQEt0mEPeW3DoE7DjKVVVtV2xUk5LckJVVR+o778/yRFVVV30qvXOT3J+ffctSX6zDWX3TrJ4G/pvq/asvzPvu/rqq++1R3311d95aquvvvo7b/2ded/VV39b67+pqqrer25s6xlFZQNtr0mqqqr6apKvtkrBUmZVVTWkNba1o9XfmfddffXV99qjvvrq7zy11Vdf/Z23/s687+qrv73qd2ntDW7Gk0n2X+d+nyRPt/EYAAAAANiAtg6KZiY5sJTSr5TSPckZSX7QxmMAAAAAYAPa9NCzqqpWllIuSvKTJF2T3FhV1UPbuWyrHMK2g9bfmfddffXV3zlrq6+++jtnbfXVV3/nrb8z77v66m+X+m16MmsAAAAAOq62PvQMAAAAgA5KUAQAAABAEkER0EmUUk7fkrbtWP+iUspebVWPjqGU0qWU8tZ2rF9KKftvfs3OWb8jKKX03UDb0Dasf/gG2t7V2WurD9D22vtzD23HOYrYZqWU/0yy0V+kqqpOacPhtJtSSknSp6qqJ9q47ovZ9PPfsw2H025KKQ9UVXXY5tq2Y/0rs+ZKjg8kuTHJT6o2fIEtpfROcl6SvlnnQgVVVZ3TVmPYwJjeVFXV421Qp2vWPN/Hbe9aG6n/i6qqjmqP2nX92VVVveYL685Sv72VUh5I8q6qqp6q7789yZeqqmpow/rjqqqaW99/b5IPV1V1RGeuvTPXL6V8MZt+3//Qdq7/j5t6vKqqa7Zn/Z1de//7rzOOrkk+U1XVxW1Rr67Zrp95Symv39TjVVU9t53rj9lM/e9uz/rrjKO9P/e0+e9eR1X/kXr/qqoebO1tt+lVz7a3Usp1m3q8Dd44/66qqls29ga6Pd84SynvqKpq2sZeQLbzC8fn6tsxSd6Q5Jb6/nuTLNyOdZuVUu6vquqYDbyBlCRVW4QlVVVVpZT/SNKmX5iqqtozSUopVyT5Q5Kbs2a/z0yyZ1uMoZSyyTCmqqoHtmPtv0lyYpL9XvUa0DPJyu1V99Wqqvp4KeVfkoxKcnaSL5VSbkvyjaqqftcGQ/h+kvuS/CzJqjao16yUclSS/ZLcW1XVolLKoCSXJHlbku0+26SqqlWllJdKKa+rqupP27veBtxZSvnbJN9ty3BwHb8spQytqmpmO9Ru1/qllH+qquqzG/vi1EZfmMYn+Y96JslhST6dNa9JbeW0JFNLKWcmOSbJ2Kx5HerstXfm+rPq26OTHJLk1vr+6Ulmt0H9tZ8t3pJkaJIf1PffleTeNqjfrJTSK2ue975Z/48k2/szf3u+9rT3v3+S5vfew0sppa3e+zrAZ97ZWfPvXZK8Mcn/Vy/3SvI/Sfpt5/prZyzuk+StSabV949Nck+SNgmK0s6fe9rjdy9p/6BwnXHck+SUrHnNa0rybCnl51VVbTLE31qdKihKMiHJvCS3JXk6a/7jtqXd69s2+XL+Km/PmheLDU15rrIdXziqqvp5kpRSPllV1fB1HvrPUkqbfGCoquqY+rY9nvt1tecXtuNf9VfM60spv0ry2Tao/fn6drckQ5L8Omv+/w1K8qus+QC9vTydNR+aTsn6H5BeTDJxO9Z9jTos/EPWfHhZmWSvrPkS8dOqqv5pO5f/i6qq/nk713iNUsrVSU7Omjeqfy6l3JHkgqz5styWs5mWJ5lbSvlpkj+vbWyjoOAfs+b1f2UpZXnaMKCuHZtkfCnl8azZ97X1B+0E9efXt7M2udZ2VFXVzFLKh5LcmTW/h++squrZNqy/oJRyRpL/SPJEklFVVS3r7LV35vpVVU1OklLKWUmOrarqlfr+DVnze7i96/9rXe/OJIdVVfViff/yJLdv7/qv8qMkv0wyN8nqNqzbbq897f3v/ypzkny/lHJ71n/v3d6BRbt85q2qql/S/Fz/oKqqH9X3/ybJdp/VXFXV2f9/e+cdJllVre/3G5AgSblgJCMKiIAgiogoYMKAXEERURRBRfQCKnp/BkTACGK4qCAiQQQDF1FACYJEyQMzDPESDBgQQZIkCd/vj7Vrurqmu8fQe5+2a73P00/XOTU93+6uqnP2XnutbxW9k4E1bf+xHD8V+Hpt/T66nvdAN++9rgOFPZayfY+knYEjbO8tKTOK5sNTiWj6tsQi7QfA8bbvbCFu+5slFe4e219uodmnvXf5vmNL3QGWlbSK7ZsBJK0MLNtCeKpEeOl2wfRo2dX8PnER245GmSW2NwWQ9H3g3X1p+GsBe1bWng3MlnQscU1bwfb1NTXHoiwU3w7cDhwGfNj2w5JmADcAtQNFJ0t6dW/S0pDXAM+1/WBJf/0DsLbtGxqP46flqzlTIEC9xbDq2z6pfD+qtfYYZdePB+4Gvi2petm1pDkD+ksDCwAXF/1q950utVN/FE8jNid7c5zFy7lWrAD8re/4b0RmT0sWmexd9L+HLq89fXT9+kO89+8ANus7V3WDutDZnLewge1dege2T5G0X0P9lXpBosKfgGe2Ep8C8x7o4L3XdaCwjwVLcPBNwMeridT6j7vA9h3AIcAhkp5OXDSulvTfto9uNIZHJW0JNA0UTZF68Q8AZ0u6uRyvRKTkt2CqRHi7XLC9Bfhq+TLwy3KuJav3gkQAtq+StG4j7VcRZZALASsX3X1rL9b6WAZ4gwc8eWw/Jum1DfR3Bz4m6SHgYdrt7jxg+0FC7E5J13cQJOp6st6rEV+NyKoDwHarjMrflDE8qV+/FV3qjxGsGUXlz/8X5/9PqtLiujIVtVN/hM8DV0g6qxy/BPhUQ/2jgUsknUB8Dv8T+E5DfYCjJb0LOBl4qHey9gZhx9eeHl2//l1uUHc9571d0icIuw0DbyWCFq04W9JpwPeK/puBsyb+kcmly3lP0eoyOaLrQOE+wGnA+SWreRViU3pSmZZm1sUvZTvg5UQA4UDb1zTU/wywFJHR1J8KV9OnZe/ycMx6cds719IeGMfCwOrl8DrbD0307yvojxnhtf2hRvorjHXe9m9b6HeNpO8R7/n+G+fitrdroD2T2FU42/Zzy7krW+3sSjra9tvmd266IekuRntSbNJ/3CpQJ2k14HOEX0P/pGWVBto7E4G65YgSvA2BC21vNtHPTaL+lkT559OA24AVgWttP3u66yuMo8elVxpdeQwrA3/sBUwlLQo82fava2sXvQ2Bq/vKf5YgShIuns7aqT93DE8BeiU4F9u+tZV20V+fkfLyc21f0Vj/fcBngLsYCdy49rV/Klx7yji6fv0XAXYCns3oe29njTRaUCoZ9ibmPBDznn0aVjD0jK1f3NO3fUJD7U7nPWUMywEHEV5dBs4Hdrf9uwbapxG+oP3rnU1sv7KB9gLAbi2ql6ZVoEjSPsQuz7VEKuKptpuZ2faNY6yIrlt8eEq9+NYDk5bjbL+qtnbRW4t5F2rNdpc0RvcdSZfZfl4j/V46uoi/wcrA9Y0WTJ3frMsY3svoG+fBvQVUZe2Lbb9A0hUdBYpGdVgrF/I5ttdspP8d4qZ1nu3rWmgW3akyWT6fmLR9mQiQ70jc4/ae8AcnR3sOEaC/yPa6klYnJozb1tYu+rOJIOkZtp8raVNgO9vvHgb9rpF0GbCR7b+V44WAX9reoJH+FYRPjMvxDOAyN+j42KV26s8dQ6e7+mUMo7IJW26OSboJeIHt21tpTiW6fv0V/jDXEdk8+xKm0tfa3r2ybudz3mGm63lPGcPPgWOJzEaIYM32tl/eQLvTQKGks1xsP2oyrUrPgL2Am4F1ytdnJUFjY88WL9wEdFYvXrKaXkoEin5GlGGdT9s05E5TQT3QDrlkt7UqvzuauFm/kr6bdSNtAEpA6Ms0Lr0sXCXpLcACJbtkN+CC2qKSPgp8DFhU0j2908Rn79Da+n0cSezqHlRSUGcRO0xfrSk6GAiS9DhgLeD3tm+rqT3AorbPlKRSCvUpSecRN/LaPOjwaELSwravk/SsBro9HrZ9h6QZkmbYPkvSF4ZBX/N6xYyi0X1/wV6QqGj+rQSLWjGq64uj3LXV/K5L7aHXH29Xn9GeHTX1B7MJVyDmIU2yGQtXA/c31AOmxrWn69e/8Azbb5T0ettHKfwiT2ug2+mcV9KyhPfkYKCq1WdvQyKbZg3CcmEB4D63M5Puet4DsKztI/qOj5S0RwvhEhCqGgydDxdI+hqVq5emW6ColQ/NfJH0Gua9eOzbQLrLevFtiADdFbZ3lPRkwtS3JdsRC8Ne+uW55Vwn2L5cUpNdZbq7WU+JCRPwX4Sh20PEDsNpwKdri9r+HPA5SZ+z/dHaehOM4xeSziF2eDYlukA+m6jfr4ai3PMg21dLWoqYpD4KLC1pT9vfq6nfx4NlN/8GSe8Hfk+0j23B7xQtmn8M/FzSnYSpdyvukrQ4cb07RtJtREOHYdCfCl4xf5a0pe0TASS9njC1b8XNCjP9g8vxrsSm2XTXTv1YqPR29Tft7eo31N+PCE6MyiZsqA9xv5lVsvn7PYpqd7ycCteerl9/CE9EiPvAWkTX15Ua6HY25y0cQyzSX0vMt94ONOt2CXyN8CU6jug2vAPwjIb6Xc97IJID3kr4NEFce6omB2hqeJMBbFS+98cWzCQHiadb6dnptl8xBcZxCNH9ZFMiULINcIntnRrpd1IvLukS289XeMVsSrQnv6pF2dVUQaNNxWcA6wH/0ahmtff3P5eYrN5KvO9aeLSsONHzHjB4rjyWxWzfN/9/OWl6q5edlDFLDSY7uj/BOM4kWpVeSJSgnd8io0fS1b3PeNnJeantrRS+Caf0ygAbjGMDYjfxCcTiZSlgf9sXtdDvG8dLivap/VkmlTUXI9qyi9hVXQo4xtHgYdrrd42kVYlFw9PLqVuAt9m+qZH+k4D/YWSCeAawR6PPf2faqQ+SLrW9gaRZRPnVQ5Jm2V63kf5ltp+nKD99bsmousT281volzG8fazz7rjBQQu6fv3LGHYGjgfWBo4gOq990vYhlXU7m/MW/Zm211efxYGkc2xPWI4/ifq9z16//gW2N5rfz1YYS/N5T9FdgQiYvbCc+iXhUVRtzaEpYrfQiumWUdSkFfvfwUa21y4f3n0kHUj9NpFzsT1T0i2UbCZJKzSqF7+sRJe/RZiI/xW4pIHuXMqO0jzRz1apoESb0h6PEO26j2+kfaiiVn0vwsx88fK4Ov0X5RI0Ws32GQpT1ybXGUkbEYHZxYEVJK0DvMf2rpWlPwS8i0i/H2TSo/sTcCWwPlH2dTexu3eh7Qcq6/ZPCl5O7G5h+1ZF6W8TbF9aHv6V8CdqhqJWvUev61+zXZiBwGjzxVHX+tBtGn4JCG1Ysqrk4hHYihKUeHNLzamgnfpA97v6vWzC8+gmm7HzgFDHJUBdv/7Y7lUOnAM0CdIUOpvzFnqZVH8sVSR/IEoAW3G/osR5lqT9gT8Sm4VN6HreA3O90Fpl8PQ05waCyhpnBdvXtxxD0X4y8Fngaba3kLQm8ELb355UnWmWUXQzsOd4z9tuEqzRiKnuRcAbiDS4q2yv1kB7zHrx1lk9klYClrR9ZWPdfiPrRYCtgUdsf6TlOIYVRYvadwNL215V4RV0iO3NG2hfTGTvnegRM+urbK9VW3sqUSbtOxLXwqfYXriy3lnENef3RGvW1UuQaEHiurf6hP/B5I1jrHTgu4HLgG+6oqG6pF8DywN3Elk1TyAmbbcB77I9s5Z20b+X8X/3D9muWgrTtX4Zw2WMkYZv++MNtJditKnlOcC+tu+urV30u+z80pl26s8zlqHLZixj6KzjZdHv7NozMI6usjo+OMbpu4GZtme1GkdrJL2WCJAuT1wDliTMjE+c8AcnT39F4E9EcPIDxGv/Dds3NtL/NR3Oe8oYurz3vQ74IrCQ7ZUlrUvc91t1+j2FyOD7uO11ypz7Cg945f6rTLeMoqWIWtGxtrFNu6yek0uE/wDg8qLdyqun03rxEqjqnyw3DRSNcWH6pcK3pSpToWa1LFY+xUirzLOB/VotVgrvA54PXAxg+4aSmt8E27cMZLE8Wluz1EfL9tED599F7CoeW3sMRe/9xGu/PvAb4HBiElOb9xClF08hSi56rXk3JzLqWnEzkVXaq1XflphEPZPIcnxbRe1TgRNsnwYg6RXAq4AfAt9gpHVxLb5E7GYeS9z/3ky8HtcT74OXTnN9AGzfKGkB248CR0iqbmZfOBy4CnhTOX4bMYF7QyP9I4i//RvL8VvLueqdXzrWTn2gZM/27vvntQwS2L6v7GxvQGyKntJByekRjHS83JTS8bLlADq89nT6+heeV75OKsevAS4FdpF0nO39a4h2Pee1fXJ5eDfxvmuK7d+UjKIViMqF620/PJ8fm0y6nvdAt9ffTxHrnbMBbM8qSRKtWMb2DxUNdbD9iKTJX/PYnjZfwOVdj6GMY+H+x0QAa+lG2peV77OBGeXxJY20Pw+cCbyzfP0c+Fzjv/3SfV/LEN0Qrm+g+5Ly9VXC3O515etY4LONfvfjCRPDVcrX3sCPGv/9Ly7fryjfFwSubKT9v4S52+XEDsuewPcb6F4BLDHG+SWJHbVWf/sPEzfmBVu+5lPli/BjG/MccHVl7cvGOwfMavC7XzzGuYvK99nTXb/3WpfP/XeA/Ykd1lba87zGLV73qaA/zL/7FNHfnQhS7lu+5gD/1VD/TcTGxFHls/crYJtW+mUMM8v3OX3nzmuo3+W1p9PXv4zhNGDxvuPFiSDCosA1FXU7nfMSZWYnEAbWfyrjWa6h/kvLZ++c8h78FbBJQ/1O5z3j6TTUHrXeKY+brHeK1tnAf1BiH0SSyDmTrTPdMoqa7iBMwI8kbWX7YYex3BOJnfX15/eDk0CX3WdeDaxr+zEASUcRi+iWnaBmEpk9In7vXwHVTcRdalYl7Wd7k76nTipGey1Y1fbWfcf7FIPDlpwjqdcq/uWEweBJ8/mZyWIXIlD3dOB3wOlEhlNtFvAYniS271G0im+C7QPKzuIuJavqPNuza+tKOoiJs+lqd57psWy/H1sxOVymPFd7h/Uvkv4b+H453ha4U9ICwGOVtQEek/QmIlgKUYLZo0V9edf6EFk8M4D3Ewu15YnS4xY8IGlj2+cDSHoRUNsbrJ/mnV+miHbqx/zmBS4+YZK+QDQ0OKiR/seBDVzMuxUtw89g5FrQgi47XkK3156uX3+IjJb+e+zDwIq2H5D00Dg/Mxl0PeftOpvwQOAVLv44kp5JXIdarDWh+3kPdHv9vUrSW4AFSvnrbkCzTELgg4Q316qSfklk1G8z8Y/840y3QNE8pQWSlgHucAm3NeLHwHGStiZuGCcygXfSJPN6YoL6AUbqxfed8CcmlycAfymPl2qoC4DtlVtrDrCspFVcPDkkrUw7k/WuFysA/4+YuMwhSpJ+RoOyy3Jj+ort7WtrjcHjNEanNUlLELuMTVC0aH43IyW235V0qO3aE8bL+h7vQ+zqdcGHgPMl3UQEilcGdi0eGrXNTt9C/N4/Ltrnl3MLMFKOVJPtiSDpN4jAzEXAW4vR4vuHQB+PGOo/SPv20O8FjiqlECLuge9oqP9OovPLl4m//wXl3HTXTv14v/WXGzxK203TGR7d4e0OImjSkj2ITsO7EfYLmxKtypvQ8bWn69cfIlhykaSflOPXAd8r995rKup2Pedd1vYRfcdHKjq/tuJx7jNRtv1/LTcn6X7eA6OvvxBdz1pdf/+LCJQ/RASqTiOuP02wfXnxJXsW8fevUno43cysNyTKn/5CvFhHEzvKM4AdbJ/acCzvI2o1VyI6LzWJMkp6J5FJcEMLvQHt7Yi//1nEm3YT4KO2vz/hD07uGMbyhLibSElu0Sr4VcChhF8KjLz+pzXQXpdYEPcvVt7uxobiXSHpNOB1blyfL2lPwo/nvbZ/Xc6tBHwdONv2AY3GcSXR8aC3s7gYcKFL29RGY7jCxUi8CyQtDKxOvP+vc0UD62RqURYJnwJWpG8TzI0MbcsYliya97TSTIabYiT8dqIEBmAr4CjbXx73hyZX/wCiLXq/N9wcN2ogUjaJPm/7wy30xhlDZ9eerl//vnE8jzAUFnC+7cvm8yOTobkuHc55JZ0BHMnobJYd3aB5S9E/nAhO9/wx30pkuDft+pp0h6Lb80qMvu58Z1I1plmg6DLgY8RF41BgC9sXSVod+F7tBYxGO/+LyHCaQ5RfYftLNfXLGPYFNibeOJcRZrbnuVHnAUlPJUwNRRgaL9y329JC/6fAC4lgFUQN70WEoe2+HjAcrjSG3mIVYrFaM/V2LP1eS9b7gW1tH9NA84e23yRpDmOUmrQIVkj6JrAekcE3N7un0eduF6LEcnHi97+PmLweXFu7bwxziBKAB8vxIsClnuQOCPMZw+W212ulN4b+Wszb+WZSb5rj6C4LfAR49oD2ZrW1i/4iRCbfoH6TnbWu9csYriMyaWfSt8Puisa6Grvbz1xqX3u6LPvsuuR02PUHxrIeMe8TYTvwf4MZrpX1t2YkSHAu8OOWWfySfgFs3rhyoF+/+bVnQL/T179vHE9i9PX/t410m895i+4KRDbLCxnJJtyt4e+9MGGv0HvtzyG6nrXqeNjpvKeMYX/g00Qm2anAOkRTle9W1PyK7T00ThMjt+t6djSwKjCLkeuOJ/veM91Kzxa0fTpEwMT2RQC2r5OaZGIuMXB8wjjnq2H7kwAl5f9dhMHtV4hUwGpIeiHhDXOu7RMlrU10wnkxUX7XiseANWz/qYzrycDBhMnvuYxE3muyPiMR3nUkVV2slpvk+4i//08If4D3EeWOs4EWN809yvfXNtAajz+UrxmMfOaaTBxtHwIcovAH01ieRbWQdKTtdxC18RdL6t9Z/HarcXSNpL2JwPCaRMnjFkQqdPVAEfEZ+wHx/t+F2OH9cwPdHkcD1xHm/fsSpWDXDpE+wN22T2ms2ezePg7Vd+2nqHbqA5KeDjyVMFC9vCzU9yDKHp/WQH8B4Im2jweOV3RgegdRbrRGbf0+rgB+Iuk4Rm8Step03MW1p/PXv28cWxJ+OU8jWqOvQNwPnl1JbyrMeXuBsLlBAYUf7a7AZxrpP0Sss75U9F9B+OG28kjqet4D4dH0EUn/SXiTvpFIFKgWKGJkTvnFihp/D88D1qwdIJ9uGUVzd7MHd7a73uluhaRPEDs7ixM3z/OJjKI/VtQ8gLhQzAKeAZxMXCw/C3yzZfmHpDn9GRSKCOEc22u1KItpFeEd0PwJcCdhYLg58ETCG2f3hplkl9teT9LRtmu2IZ9oDG+0fdz8zk03Bq57o3YWbV/RQP9eRgJyjyd29ShjsO0lx/zByR/HHGI36Qrb65Qg8WG2X9dAe6bt9SVd2cuek3SO7ZfU1i5aV9h+bk+/+BSc1jCjqTP98p6H8ERYgPDompvFafvy2mNIktYUL5SPAzcS3XW/SiwYvwPsX3POV/TfDHyTCMzcQJReHU20Rd+v5edO0hFjnHbtjMYurz1dv/4DY5kNbAacUe4DmwLb2X53Jb1O57ySlgf2IgJjJxClZ/sCOxDVK7tX1t8MOKTo/5hYa32HmHN9plWAtOt5T9G72vazJX0LON72qZJm216nouaZtjeX9AXb/11L5+8Yx3FEBlvVz/p0yyhaR9I9xIdl0fKYcrzI+D82uSic5/dk3rrBFpP2NxDdvn5KpCFe1CBQ8xrgubYfLBH1PwBruwOfJOA8SScDveDA1sC5Cr+WuxroN4nwDrBKLzgm6TDgdmCFllktwEKS3g5spDF8ohrduD7KyOs+0bnpxuMlPRfmGlieX75L0nq1J+y2u86q6PGA7cckPVJ2HG8jWua2oGcg+EdJryGugcs10u7Xv6uU391K3H+GQf/AgePn9T02sYCpQim525ZYtJxEZPBuAtxELJZvr6Vd9JchdtLvBA4HDiCyeG8CPmT7xumonfpANC54lu2/lBKYG4nW2BdV1u3xCWB92zeWgMmFwJttnzCfn5t03J0nS2fXHrp//ft52PYdkmZImmH7LEX3tVp0Pef9DrG+Op7wor0IuJpY99zaQP9A4vW/kMicvgjYy/ZXG2j30/W8B6Kz9HVE6dmupRyu9pr3qQoT6S0lfR9Gm8fXnnP3lbwtAVwj6RJGB6gntfRtWmUUTRVKdP0Q5q1XntlIfwkiq2BjYqfjT7Y3rqg30/b6fcezbK9bS28+YxERHJprqkdEmZu80VtFeAc0O8+ek7QxUW7yJsIjqJ+qO3uStgBeXbR/0PfUkkTQ7vm1tKcCJaPnUhiz04lbZZV0jaRvEB51byY6oP0VmNViESHptYQf3PJEW+IlgX1sD34WaunvTExa1yZKEBcHPllKIqe9fldI+iExWV6M2NW+iggYbQysa7tqKa6k04kSqCWInfUjiv6Lge1tv3Q6aqf+mPf9q2yvVVNzPvrX2V59op+pOJbOPdJa0/XrPzCWM4hS988RDYRuI/wSN6qk1+mcdzBjRdKfiEBVEz/SMX7/m2yv2kJ7YBydznv6xvFE4B7bj0p6PLBkzYCdpG2I683GzFuCXH3OXYJU42L7nEnVy0DR5DMYOGmsvRYxUXkJsbtxC1F69smKmncR/j89Nuk/nuzo5lRG0lnAukDVCO+A5qOM1OULWJQo/2la+lPGspPtpr44ktYh/ub7Av3v83uBs2zf2Wgc7wOOsX1XOX4ikX79jcq6V7jDTmNTEUXXuSU9JB3/kqDsag4uFvetqHeVo6x5QeB3tp/S91zV9Pd+jbJB8hvbK/Q9V3XDpkvt1AdJtwH9HWXf3H/s+mbev6N4oxQ+2H/sBk0k+sZyHOGJ8xb6PNJqlwANjKH1tafT139gLIsRWRwi/vZLEXOhKmbeXc95SzLASxnZnDur/9j2Xyrr30xUrfT4Yv9xq9KzLhmrcqGfFn8DSXvZ3q+2zgT685S+1SiHm26lZ1OFkyTtStSu9gcLql48Cl8gUiL/h+h49PB8/v1k8PqB48F03GZI2pCIbK9B1CwvANzXMFjyqUY6c7Fd1aj8H+T7Cp+sFWy/W9JqRHr0ybUEbc8GZks61vbDCn+UtYDftwoSFd5l++t947pT0ruAqoGiZASFuefcFsWSNrF97sQ/9S/pTRSAd6tJhKL7ydbMW+5cbaFSdHeY4Gm7QZfJvrEcQnhkbQocBmxDBOxr8jcA249I+sPAc4+O8e8nm0eLviUNlrk9No21Uz/KHPtpkrHex7cYbeY+eNySZ9h+o6TX2z5K0rHAaa3EO7r2dP36z8WjO6wd1UCv6znvUsTfuz+Lu1duZOqXvJ8DvG6cYxNeWdWYIvOe1zG6WU3/a1H9b1D4jKS3EqWQ+5YS0KfYrv3Z7/FyYDAotMUY5/4lMqOoApJ+NcZp227ll9EbxxOB5YdpV13SZcTOynFERtUOxCTi450ObEiQ9APiBrpD2WlfFLiw8s72IcBBtq+WtBRRt/0osDSwp+3v1dIeGMeVwDouF1VFR5grbVfp/NGn+wqXbo/DjMITYVui406/kXzNbL4PjXF6MSIt+T9sL15Le2AcpwJ3M2+5c9WgvaJF+DyniUnc020324zSiJF27/viwI9sv6KiZm9XX8R7r7ejL+BNtp9cS7vo30Vk74rIJO4FRQVsbPuJ01E79ZN+JF1i+/mSziUaqdwKXNJqzt3FtWcqUbI7vgA8iXj/N89mT9oxFeY9Y4zBRMe1822PtQavMYaDiU2BzWyvUdbcp9veoLLue4nr3KqEN1mPJYALbG8/qXoZKJpeSDqbaNe4INF568/AObY/2OGwmiHpMtvP02gX/gtcqVZ6DP2uM5o6pe/vP7ccqnYJhkrXg/J4D+CltreS9BTglFZlWYrufysR/mQm2oXeYnusm2oyyUi6njCTbOITMIb+EsDuxGTph8CBtm9rpN2ZP0XfGHplB/9NBOs+03KTQtLFtl8g6SKiqcMdwFW2V6uo+faJnrdddXe9tVfBVNFO/aQfje2RtpftbzbSb37tmUpIuhF4ne1rux5L0pau5j2S9h7j9NLAK4FP2f7+GM9P9hh63Z6brXeKxlKEJ+LngM8Tdi8QQbJJ73ScpWeVUHgFrcnoeuXvNJBeyvY95cZ5hO29S6bDsHC/pIWAWZL2B/5IRLpb8TXmzWgaislC4W8li6iXVbMqfeWXtTT7Hr+c0uXM9q2xdm3GfwPvAd5L7KidTqShJ224GXgc9d9vo5C0NOHPsT2Rdr9e45JHgAskPcf2nMa6KPx53kEYiF8MbGP7+tbjAE6W9ASi+9TlxDWo6uevdiDo79DvLBjRdSBk2PWTEWz3Pufn0K7TZT/Nrz1TjD9lkGi46HreY3ufCcZ1BqP9u2rxcKkc6K13lqVB2bHtu4G7S2D6u0SZnYCjJH3L9liZ3v80mVFUgRLpfCkRKPoZUTN4vu1tGmjPAV5BfHA/bvvS/uya6Y6kFYE/Edk8HyBqib/h+q1qe/pdZzStCKxm+4wSsFnQ7VqGIunlRNvcNYlAyYuAd9g+u6LmWYQv1u8JU8HVS5BoQWJXr5NOLC0pN6vP2x70LRgaJB0PrAOcyWhvuGqmniWL7A3AocDXbf+1ltZ8xnEN8AzgV8Tv3kv9r3rdVxi47078zT9v+zc19f5eimfTImVClSTJNEbSTUSL8POAc21f0+FYOr/2SNrD9lca6n0VeArwY0bfe2t75byfMM1uvTHTOZJmABvavqAD7Skx7xkPNWrwIml7ouR8PWLNvQ2RyfjD2tpF/0rghS4eYQpT+Qsne96XgaIKlGDNOsAVjq4YTwYOs/26+fzoZGi/EdiLCEztKmkV4ADbWzfQXpbIqhjMpBqK9twApUb+ZcRu0q1ERtM7aqciFu13Ae8Glra9qsJI+hDbm9fW7hvD0sQidcPy/SJgiZo1w5KeSZi3PwX4iu0jy/lXAq+oXfol6Ye231Q+9/NcUFsFaSX9AtjcQ3pRH68MqGbWh6THiInxI8xrrNjMo6EEiOehduCm/P63ESXOY/3+TTcoJG3EvIbeLTJ5k6QTyv3vYODJDl/AtYEtbX+6su5ywEq2zy/HHyRKvgCObbU5V7QXBl5AeEW9CFgdmG37PxuOYcpceyT91n1d+BroHTHGadt+Z2XdTxMZ/JcDhwOntZj/lHnuuLhN4yIkXWj7hS20BnSnxLxnLCRtBnyi1bpT0urA5sTvfmbLzLqy5tjA9oPleBGiidVzJlVnSNcUVdGIsd5MogvCvURmQ1VT266RdDrwA6JN4y7A24E/e5Jb9c1nDC8iOo/N7XwE4HamhisSC6fH0TijSdIs4PnAxX31snMm+6IxnzH8EtjC9j3leA3guK79U2oi6am2/9jVYr1vHAcSZY7HMdI6dihapSaBpCcxOkj/28p6Y77n+/SbZRhJOpowd5zFaDPz6m2iJb3I9i/ndy5JJhtJ5xAdsL7Zd9+v7lkm6XtENsfJ5fh6IsPg8URW76Qaqs5nLAsCGwAvATYG/oNoJPGeRvqdXXvGGc8ttpfvQrs1xRvvFcCOhN3DD4Fv276pouaviACJgBWAO8vjJwC/tb1yLe2BcewDXEkYpw/VYn6cjdmlgT8QzXSuazCGo22/bX7nKup/kFhnn1BObQUcOdnZhOlRVIfLSr3yt4guNH+lfqtMYG5EcSfg2YxeMFSN7Bf+w/a3Je1e6vfPKZOYlnybCNCM6v7Tir6F0QPAmDW0FXnI9t96vjxl8tT65vFZ4CRJryZ29b5D1DBPW2z/sXzvuuxmacJEs38npVWb0M4pGXSfY96Mxi48K5oiaUui/PJpRKB6ReBa4j5QjSnwnu/necCaHU2YDyLSz+d3rgolq+TDzLtBUn1XtUvt1Afg8bYvGfDje6SB7rN6QaLC/S5dFiWd10C/n3uAOcCXgG/ZvqOxfpfXnrFoMg5JH7G9v6L75VjZ1NUDZbYt6VYig/8RwuT3fyX93PZHKmmuDKDouHui7Z+V4y2IioJWfJDwYH1E0oNMgYyehrx24NjAHb0yrEaMml8VC4j1W4nb/pKigdXGxGu/o9PM+t8D27uWh4co2hYv6XbdX44GriOc3/clFumtUuEeLt//KOk1RGR3uUbaPe62fUpjzfGi23NpVIJxjqSPAYsWr6BdgZMa6M7F9k8lPQ74OdGqcSvbN7QcQ1eo4xaxtndsoTOFOQLYG/gykcm5I/EaDAP7EeWeZ9h+rqRNge06HlNrriLKT//YSlDSC4GNgGXL7l6PJYmOl604jui2+C3ab5B0qZ36cLuiaUTPUHUb2nwGFhk47i9x/48G+v1sRyyWdgV2lnQB4VV0ZiP9Lq499zL2nFPAoo2G0VtbXNZIbxSSdiMyKm4n7B4+bPvh4t9zA1AlUNTHBrZ36R3YPkXSfpU152J7iVZaU40uN6kkfRTorbXuYWSe+Tciq7IZti8nSi+rkaVnlZD0dObdYTq3ge4VZaFwpe21y6L9tEY7i68lzASXJ3ZTlyTaFDYLVkj6PDFB/xGjTfXqfpCmQAlGuTnuRKThCjiN8MZqUbM9uKO0GdGF6tfQZmepa9Rxi9iuvCqmCpJm2l6/v9xS0nm2X9z12GqjERP92cBzbT/WK4HuemytUJjar0tk7/Zf+7esqPkSonHFLkSwoMe9wEmtguS9934Lramknfqg8KE8lAhY3kkY2r/V9q8r614MvM32/w2cXx34ThfXnqK9BbAH8CTbVQMmkk4i5j1L0Pjak4CkfYkys3nm15LWqD0Xk3Qaseb5LvE+eCuwie1X1tTt099krPMt1poJSPqc7Y92PY7aZKCoApK+QDihX8PoeuXqN40+f6Rzid2VW4FLWpRfTAWfhrJYGMQN08CHEo1jJNzDDdpIKwwtt2ZeQ8l9a2sX/V/aflELrXH0O/GqmCoUf6wXA/8L/ILogvd528+qrLsV0XFsju3TampNMIYziPr0zwHLEOVnG7hRt8WpQAnazIMbtDGXtGJvsVIC9ou7+LRV1u2Zqu5GvOYnMHqhWs1UtUvt1B9zPIsBM9yoy6mkVxFNJD7DyI72+sRO++4tM7sVHS/XBW4kFu7nEV6ND1bWHfOa06PmtUfSZrZ/UR6v7L6GIZLe4AbehJK+YnuPvoBZPwb+QsxHLqqkvyFwde89L2kJogTw4hp6Y+gvTWQx9wI25wL7tPrsl797j0UIj9KZud6pi6TVbV8naczS8tqJCa3JQFEFFKZ+a9t+aL7/ePK1dwaOB9YmSjEWBz5p+5AJf3BytC+3vd78zk1HBtKAe2mIPbO7quVHU6TsbS6SFgKeWQ6vt/3wRP9+EnVPBe5mwJ+q55vQQL+TFrF9+pfa3kB9rUElzbK9bgv9rpG0AZEK/wSiFGtJouNjlUlq0fwGUad+AVF6cZLtZqnnfeNYDOh5FGxPmOgf08qrQ/M2Eehd96a9PxSApGOJrKJHievPUsCXbB9QWbffVHWQqn//LrVTf66R6bjY/lJN/TKGtYjynp5Xx9XA/ravqq09MI4NgMttd1H6h6QveKBpy1jnJllz7tx6cJ7dat4taX3bMycImC0D7Gd7zUr6VwDr9bLmS5D+smFYc4yFpOWJz1/VsnNJ59veeIzyx6HwSJJ0qO13D0tiQnoU1eFmoutV80CR7cPKw3OAVp2+popPQ288r2FeM++qWSXutla4Z+r2vvL96PJ9e+D+lgOR9FLgKKLkTMDykt7eKBV2OduvaqAzHksSf+9X9J1raSbdlVdF5yhMBN9k+8NE84BWfk2bAOvYflTS44md7OaBIuL3Pcb2ncTnrzWdNhGAubvLBwFrAAsR9577Gk1a17R9j6TtgZ8B/038LaoGijxiqrrIYPaEorHFtNROfSDKnTqlBIR2mALjuFTSRpJWopv29C8nPvP9bDHGuclE4zwe67gKtmeW7+NmTkn6W8UhqBckKuN4TNHEpQml3H9P5s1i7ypQ8Dugega57Y3L986vQV1g+93l+6Zdj6UFGSiaRPp8Wu4HZkk6k9GZBS3a9HZRfrMQkbm0IKMnL/cA21TUnQdFF4LHE2a2hxX9Jh3n+sawMbCa7SMkLQMs0Z8WPNn0lTy8aKD06f+VcpwmpVeFA4FX2L6+jOmZwPdo0wngAknPsT2ngdY8uHsz6fcRXhWrS/o9xaui2yG1oQRq1pc0auLYgL/1drFt3y+pyQR9DJ4CXCrpcuBwwpeu5d+hkyYCA3wNeDNhLvw8YgG7WiPtxyn8ALcCvuYwVG3597+AeTusjXVuumkPrb7t1l1VRyHpxImed0N/Ho3Tnp7oulpT972ExcMqkvob1ixBvAdq4nEej3VcFU3QcdR1PUpvVhhaH1yOdyU26lvRM7I/jA42SDTaG3QGUX45u6F+p+3hpwKSNmLe9XarAHUTMlA0ufSc/2cCE95EK/ITRspvmmQ0ld2EcyQ9YHv//uckvZHoPtCKjRwm3lfa3kfSgTRsDy5pb2KR8iyi9G8hwuiuhXfNYpI2tn1+GctGROvMljyuFyQCsP1/ZQFVjb7SuwWBHSXdTLz3e2mwTUrvJC1HZDS8qIznfMKr4Xct9G3fDLystVfFFOIK4CeSjgPmtkitXPq3Rlkg9AJEq/YdN3vv2f6EpL2IbLYdga9J+iFh9HlTgyGcJekAGjcRGMT2jZIWKMG7IxTdj1rwTSKLcjZwrqK5QQuPoqcATye6rzyXkffhksSGybTUTn2Q9D8TPd9gY/KFwC3ERtDFNMpiGYeu2tMfC5xCBEn+X9/5e13fp2aVEqxT32PK8cqVtQfpquPoLoRP1ieIOdeZwLsb6PZ4xPbB8/9n1ejvNvcI8D039IRl3vbwC9KwPXzXdBWgbk16FFVG0hOB5W1fOd9/PDl6nZnXjlUX3apWuk/vYtsvkHQR8AbgDuAq2012liXNAp5L1Mv3fGKubLFglLQ+kU2wVDl1F/DOlos1SYcTF8r+8rcFa2bbaAp0nCvj+Dkxcez97m8Ftrf98kb6jxKlLh/tq9kfCo8wAElHjHHatt9ZUXNFJvYH+20t7bGQtA4xSX8VcBawIfBz21XbBE+FWn1FA4eXEbu7txJll++wvU6rMfSNRcACth+prPN24B3EQvlSRhZn9wBH1QySTqB9L3Bk5QDtVNav/rfv0x8XV24gUcp9X060pl8b+CmxUL26pu44YzkO2M12p6XWkp7E6Iyaatf/CXyBetrVTfz7xjKUHUclfYqOjewVnqCrE/OQ623XLPXrac5tD8+IvYUo7eE9BJ3AACRdSzcB6qZkoKgCks4GtiQyHGYBfwbOsT2h+eAkaR8KHNSy/EbSFsCrgTcBP+h7akniQ9SsTWrZVT+IMJb9OnHx/JbtTzbS73Wdu9z2eiW748JWmQVlDEsSn+27W2n2aS9MlEBtTNw4zgW+4QbG7uq+A8Y8xtFjnauofyVwKhGo3Nb2X9RnbJ1MPprXzLGfh4CbgI/bPrPyOHYD3g7cTgRKflzKn2YAN9hetab+VKAE7W4j/AE/QATMv2H7xo7Gs6PtsYKXNbS2tn18C62ppJ36o8axmO375v8vq2gvTASMDgD2tX1QI90p0Z5e0uuALwFPI65BKwLX2n72hD84TVB3HUeXBd7FvKU/1TaHBvTHspSwGzVxkPRqIpv1JkYyyd7TqgxcQ9IefjymSoC6NhkoqkBvcaboQLa87b0bZpVcQ7Rq/hWNym/KLva6hBdOf0DmXuAsh8Fqc8rkZZGWARNJexK+GC8n0pHfCRzbcOLU3Mh7jDEsRJTe9XY4WnU967QDhqJF+ZFEKj7ExHlH25s30u8FJ99EpIHvQARJp3VGkaSP2N5fo+v159KgBGNMyo77WoTJdNUsT0n7EmVm82TPSVrD9rU19YtO59eeqYSk39peoQPd79hubjCs8OZ7PjDH9s8b6L2AWIzfI2lRovRnPeAa4LO17/slOPujVqXFE4zjhYSZ/OK2VyjzsffY3rWB9sLAa4h73UqE5cLhtn9fW7voT4msGkmzgc2AM8rcf1NgOxfT20qaqxFZHXcSQapvEcGam4CdbF82wY9P9lgGO44uRXTfqtZxtOheQDSQGOx023ngtgWSrgNe29sMUTQz+ant1RuO4emMdDsFwG2a13TGVAlQtyI9iuqwoKSnEhk2H2+svUVjPWzPBmZLOrZVUGA8yg37B8APHN4cTTvP2f6ipJcT6efPAj7ZYtIMTBUj75fSXdezTjtgEEHBrxF1+ibMLJvsbBUEYPuHkq4mAlbNF6od0AuCNJsY/z04fHJmlwBWba1PwtilD42CRFPh2vNaYpHSm7RWb9Wr0Qa2o54CnlxLt09/0AtRwKaSngB1J6y97Nny+F1EJukJwKcUbbM/X0u7cDjQKyv8KlEC8QUim/gIovS8JvsRDSNuIq61x9n+c2XNsfgK8EqKL6bt2ZI2qS0q6SgiEH4KsI+jA1pTeoGgkrn9QLnnP5MoxWlprv+w7TskzZA0w/ZZkr5QWfMIwgtlScIjag/gP4lg0deBF1TWn4vtS8vDlh1HAR5vu2ZnuQlR+G++l+h+CnA28M2G66DbBjJmbyYy2pog6fNEA4lrGO3RM60DRUTW3IKEL2ana94WZEZRBRQGznsB59veVdIqwAG2t244hma10n2a43Y+qK3dN4YVgW3L12NE0OiHLX7/McayDHBHq/rVXtZa3/fFiR3PV8z3hydvDDOBt3ig65nt6gZ3kn5E3Kj7O2BsanurBtoLEL4UnXUZK4uzmX3HSwJbeZp1YEjmpevShyly7bmRCA7MaXjN/ROxSB/MmhVwge2nVda/nJikH0ZM0EUELd4MdTMq+staJV0KvNr2n8ui/SIXr5KK+tfaXqM8HuXF1qLkt2Swrk/4Ym1L2A3MJP7+P3KjZgIa8WXsfz1mu7I3l6THGGka0P95qx6gHWMsM4kAyROBi4hNg/ttb99I/wyi4+HngGWIa/AGtjeqqDn3PS7pRtvPGOu5mkj6iu09+jIs+jHwFyJwUiWzSNKnievsz2r8/3+H/mFEqXPPD+xtwKO2d26kfzBxr/8h8fd+I3A98Euo3sgDSdcDa7uBtcRUQtIXgY2ANYgGFhcQf/ML3dCfqhWZUVQB28cRbRN7xzcTLeurI2lLokX5qAUDA+70leiq88FcSunF/sD+JXC1F7HLuEBNXYU/zueJG+N+hKHxMsAMSTvYPrWmfuGB8v1+SU8jjLxbd79o3vWsj846YDjasy8raSE3MBMcZwwzJa3FQKB2ujPOJHUu0y0NeBw+TRhXjyp9aKg/Fa49txCNC1rufp1MlPzMGnxC4VVYm+cBuxOZyx+2PUvRfbRFyc0MRbOOGcSm458BbN8nqaqJd+EqjfhAzZb0PNuXlc2JFru8tv0YcDpwernPbUF87r4ILNtgDAC3KDqcWlH2vRsjWZbVsD2jtsY/gGzfL2knwqNzf0VjkVa8nrgGfoBo4LEUYcVQk8f6Hg92WHyMNvQad3xxnOeXITL/1qykvzvwMUl/I4yUWwcpNxgIyP6iVDW0YhHgT0CvBPPPwNLA64g5Ue2OzzcTgbKhChTZ3hPm2mw8jwgavRP4lqS7bNd6v3dCBooqIGkRYCfm9WtoUYayH90tGBa1faYklYDNpySdRwSPmiFpJaLsb1siHbJqx5/C14h68aWItMQtbF8kaXVih7FFoOjkUnJwAHA5caM4rIFuPzMlfZvRXc9mTvDvJ4WS0fMl22+urTUBvwZ+WcpB+tuzf6mFuKS9gZcSk7KfEYuW85lmrTrHoDdJfQPwFOC75Xg74jUZBroofehnrGvPtxrqQ1znfybpHEb7BVT7/NneaYLn3lJLt0/jMeDLClPNL5cMp1bzuqWIa7uIIMVTbN9asslabBDtDHxV0icIE/cLJd1CBAxb7OiP+h1LucmJwIkKz6RW7EKU3j0d+B0RuHpfQ/2pgBReTdsTc2+ovDnYJ7wA8BPbLyMCNFW7zfWxeil9FbBqXxmsgCZZ/L0M5okC0yWIU0t/iVr/99/Jo5JWddhcUKpHHp3Pz0warthN+O/kfmCWpDMZfc/txBeyAxYlSj+XKl9/AJo1kmpFBorqcDRwHZGSvi9x86q+w1PocsHwoEqXHUnvJzofPKmRNhBp2ESE+zjgjSWbqwUL2j69jGHfXqqt7eukNklVtvcrD4+XdDKNjbwLuxCT1N1gpOtZbdGpkNFD3CT+QOyw9yYwLbMbtiE8O66wvaOkJ9M+UNicPp+K/Wz3e3OcpGiZPgzcVRbo5wHHSLoNaJHVAUyZa89nCI+MRYCFGmt3isNQ+Y0KQ/HB7IJamiuN89RjhFdKbf27gXcouluuQsxnf2f7T7W1C9tOMLYHxnuuAmpVYjWF2QP4KHCC7avLgv2sFsJl7nG/pKUaX/PWaKg1IZrAdsL2SRV1RayvVra9n6TlgafabuWP92HgLEk3E/PdFWno0dRxUgKUwHgjrSmDorv4s4mGTRcTpWdfckeNm2qTgaI6PMP2GyW93vZRko4FTmuk3VswnEv7BcMehKHpbkRm02ZEy+aWvN32dY01YXSq7+AksZVfxjzmnZLuJjw7qhvclSDhTEeHpyZZNAP8mg4zeoBrStnpXBR+Za3omXk+ovAnuo1GO4tThGUlrdILDktamXblH12zJfAgkYr/VmKXa59W4pIuI0qPjy2TpS5S0Zdu6Yk0FbH9U+CnHY/hfqLraiu9ewmfiKbY/r/WmuNwgaJN9w+A423f1fF4mlM2C85R+GP17B5aZjU8CMyR9HNGzz2qjcFjdLjskK5sJ75BzL03I9YcfyWMvDdooE2poFiNaFwj4LrGfj1dJiVgu1X23FRjBWBh4AYiIeJ3wF1dDqgmaWZdAZVuIGU3e1fgVuASNzB1LjfKB4ishl6t9DG276itPVVQB22aJT1KTBBEpCPe33uK2F2v7tMj6afACxnZSXspYez4TGBf20eP86OTOYZjgI+6G/PwMUscbTdZMGvAUHW8cxX1v0GUP74Z+BAxaZo1BdKTmyDpVcChRN08RLvm99huFaRvjqR7mTcQ3ZugP0i0Sv647TMrj+MZxOJgW8JI9gjg9JZ+QYoOLL/oZXYmybAg6fnEdX8rwtz8+7a/O+EPTSNK2dm3Cb+wFSStQ1z7d22kP+aG6LAspCXNtL2+pDkuJvaSzrP94sq6l9teT42N3Pv0FwBeQ8w1+tvDt7IbuKJYjPSaSDwOOM32Zo30O29g1BUlm+3ZhD/RRkQHyL8QhtZN7VZqkxlFdThUYfK4F5GWtzjwyRbCtnu7GY+VwEHLrlvPJFIxe+2Je2NqctEqY+ikTbPtJvXw8+ExYI1e6n0pPTqYaJN6LiO+QTV5KnC1pEsYvbNW3VC4VUBoEElbAK8Gni7pf/qeWpJG2XzlpvW5spt8iKRTgSVtj9e+e9ph+9QycVm9nGq9u9eciTwayiR2LeCY8r3mOG4EPi5pL+C1hIHpY5IOB77qNp1A3gd8RNJDhJlx8+5LSdIFpdTmEkmfJbJ5j2LEq20Y+AqRVXEigO3ZkjaZ8CcmkWEJCE1AV7YTD5f7nAEkLUs7I2+AkyjZZI11e/RM++9SNDK5lQhataLzBkZdUdbVV0m6C7i7fL0WeD6NfXlrk4GiCtju+YKcQ6PSD02NrlvHAYcQJqbNDN0G2MgjbZr3kXQg9Z3/pworDfgz3AY80/ZfJLXoAgMNy10GkXQWY5T5NQhU/oHIoui1R+5xL9EFpTq2LenHRLtmbP+6he4UZH1GdvfWkYTt6W7mPSa2HyW6QR3UQk/S2sRE8dXA8USAamPC3H/d2voTBc1qU+6/BxG+IQsRRrr3ZZAqqU0pM/5PIqNoVeAEYrEyVNi+ZcAPstkctMvMCkmbAReVks+u2IN5bSd2aKD7P8T7/UmSPkNsDH+igW6P5Wyv3VBvkM6SEgpTooFRayTtRmQRvYgI1v0SuJDYIEsz62T+SFoY2Jp50xFrlj9Nha5bj9g+uIHOREyFNs1dcV4xku355GwNnFvKEe9qMQD3db+QtAwNM9qAPfseL0L8/tUzemzPJhbkJxCLw0dhbkbHwrX1+7hI0ga2L22oOWWQdDSxUJrFyCLBTP+ubxNi+5u1NSTNJK4x3wb+X18m18WSXlRbv28cazPvfbfFRsHXiIX6cUS73B2AZzTQBboNVHUdJBt2fcKf6cdEefmFjTSnGrdI2ojovrcQEbBo5tVCt5kV7yCyiO8gmhmcB5zf0li3b87xV2BHSQsSZcgXV9Y9ptx7Nif+3lvZbvm6nyLpFV2VO3eRlDBA5w2MOmIl4H+BD9j+Y8djqU56FFWglH3cTWQXzN3VsH1gRc1Zttctj6+1vUbfc3PrdytpL10e7kZksZzA6FaJLcoOemPZi5i0bU6Y2hk4zPZercbQFaX8aGsiyi2iNfrxLQI1E2W0Aa0y2sYa1zm2X9JI6yLgZbb/Wo4XJ3xaNmqkfw1hqvhrRvyy3PGOVzMkXQus2dIXJwnUZyLe4RgOB9YGrmakDMBu0AFG0mW2n9fziijnLmj42b+MMQJVtj8+nbVTP+77w37NK5tSXwVeRtz3Tgd2dyNvzq48egbG8DQio2ZP4Gm2qycClGy29wFPJzJafl6O9wRm2359Jd2lJ3q+1ZpD0n8SJZ4zaFjuLOmttr8r6YNjPG1iHn5i7WChpA2IgOwTiHn/UsD+Ll2fk+lBZhTVYTnbr2qs2WXXrZlFo7eD8uEB7WaRbk+NNs2dUCaL/1u+WtN5RtvA5GEGUYb0lNq6fSzSCxIB2P6rpMfXFpW0gsM8fIvaWlOcq4jXe9rv8EwV+ieqA2UfQNOOgwAb2l6zoV4/95dMhlmS9ifeg4u1HIDtGyUtUDIaj5B0wTBopz7LSPoI8zbwaOYN2TW2byeat3RFZ5kVkt4KvBh4DnA7MRc7r4U2sSF4J1F2szMx91+IyOyZVVG3f82xQhmDiIDFb2lXRXAg0UBmTuNgbe/eMl659crAe4ENaw5iMJOsplbSHRkoqsMFkp5ju2Wt4jqS7qF03SqPKceLjP9j/zq2p0xpl6Id+amOlrkfBtaTtJ/tKzoeWjUknW97Y83bAamlmeuCvfRbSfv2dhRsXzfWArIS/ZOHR4gWzTu1Egfuk7Se7csBJK3PvEHbGvwYWM/2byQdb3vrBppTkWWAaxRG6v0ZjdWN1IeYznyBxuBCSWvavqYD7bcRJUfvJ3zJlieyO1vRZaCq6yDZsOsfA/yAMFLdBXg78OeG+p1R/NfGXaC7Ynv6AfZgXo+eMTuhVeArRHfLQ4CzGvsTrtKXQXUYEahaocy/q9Fbcyia15xo+2fleAsiq6wVNwBXtc7os/3NYm1wj+0vj/VvJFXt9Fw0Om9glNQnS88qUEpAnkEsVB9iSEpAJL1hjNN3E9H22xqNodcmcmPCXPCLwMdsv6CF/rCivjbwGmgJP3g8XSlpuN8nzK0hOsBta3vm+D81KbpzS0trl5lOZSSNWWLoPt+sZPqi6HJ0EtH5ZWjuuwCSViTKvh9HBKqWAr7h6EY3bbVTf1TZU3/ZY7OS6y7ROG3pe3hIupFJejawCdE8YDXgettva6Db6Vyv994fOHeZ7ec10j+SqJg4hdGbU00yaSWdZXvTFlrj6M8mApSDNitV57xJWzKjqA7DWgKyE5GGeVY5filwEfDMkmXSoj1772L1GuBg2z+R9KkGup1S0p6vtF21DfYEdJbR1o/C0HIlRu9uNDEztn1pKbV7FvF7X2e7Rbc5j/N4qMiAUHdIWoS4/g+Wv1T3B+rjcCKzp1mrYklzmDijoUmQytFxBiKDsWnnyS61Ux8YaZH9R0mvITYqlutgHM3pOhAk6cSJnm+RzVp8glYgsjpWIgKVrVq1rzMw11u0bx7YIpv9dkmfIHyCDLyVaGDTil+Vr4XKV2sukPQ1IqPwvt7JXlZ7A6ZCA6OkMhkoqkBv4iDpSTRcJE8BHgPWcGnRLunJwMHAC4BziXrm2vxe0jeJ9NMvKDrQzWig2ym2H5M0u8+vprX+Aq01B1HHXa+KH9EHgRVtv0vSapKeZfvkytITBelalR52xhgll3OfYgh+/ynC0cB1wCuBfQm/kJbdZwB+a3vChVsFXttYbxRdBqq6DpINu34fn5a0FPAhopHHkkQp1LRH0klM/BrUDtS8ELiF8GG8mHadzvo5v+/ra7Z/10p4Csz7tiO6zZ1Qjs8t55pgu4vAcD+9Zgn9ZWYmSh+r0ecHepKkXemwgVFSnyw9q4CkLQmTs6cRKckrAtfafnanA6tMf8eHciyi7GytViUxZbH+qqJ7g6SnAs9xR+0rWyLpF8AGwCWM3l0YCo8Wddz1StIPiBTcHcp7flHgQpduhEkyXeld3/tKfx8HnNbSq0DSNwgz05MYPWn9UasxtKaUPY1LX7bLtNJO/YmRtIftr3Sl34rxyo171M4yLT4xLyeCE2sDPwW+Z/vqmrrjjGUx2/fN/18mk0Xx6NmTebPYp7VHj6RfMbqBUT+23ayBUVKfzCiqw36E2/wZZfK8KQ2j3B1ynqLT2HHleGvgXEmLAXe1GIDt+4EfSXqSpBXK6etaaE8But7d6Jquu16tantbSdsB2H5ADZ28k6RDeuUvd0lai/AJWqnxGBYlAkSv6DtnoHqgSNKGRDbHGkQJwgLAfbWz2boMRnSpnfrz5YOEyfG0putyY0eXu1OBU0v2+nbA2cVq4aAWY5D0QuDbwOLACpLWAd5je9cW+l0g6Su29xgvo6zh5uhxhEfPYfR59LSklJsOlnxXNbL2FGpglNQnA0V1eNj2HZJmSJph+yxJX+h6ULWxvaukrQlDPRElP8eXDI8mhmtjZHOtQASKpnU2F4yeNElaBrijq+yalvRNFpag265XfytZRC7jWrV/HEkyjTlU0hOBTwAnEouWvVoOwHaX7Xm/BryZWDg8D9iBaGjRhK4CVV1rp/74w+pQuzmSViOal6zJ6AVz9cyGEiB6DREkWgn4HxoEp/v4ClHyeyKA7dnF2H8607Ox+GKno+jYo0fR9e3xxPrqMGAboqKgtu4GwC22by3HOxCJAb8BPpWlZ9OLDBTV4S5JixP1ssdIuo1o1z1tGTBTPr7DoQxdNleZqH4e+Avx+x9NtAqfIWkH26d2Ob4GdD1Z6LE3sbu4vKRjgBcB7+h0RElSEUnL2f6d7cPKqXOJLjBIel3jsXRqqG37RkkLlCyDIyRd0EK30GWgqtMgWeqPybTfIBrgCOL++2Vi0bwjDYJlko4C1iK6Xu1j+6rammNh+5aB5OVOslta0euqNbA5+kRgedtX1tafQh49G5VS7ytt7yPpQNoEKXs+sL1uo58H/gtYFziUCFgl04QMFNXh9UQHjA8Qpp5LMdpsbNrRtZlyH8OYzfU14GPE++wXwBa2LyoduL5HBC+mM78Hnmz7l/0nyw3s960GYfvnki4nApUCdrd9eyv9JOmAMyW90vav+09K2pHILjqp4Vi6NNS+X9JCwCxJ+xPlr4s10ga6DVR1HCQbSv35mPgvWlt/irGo7TMlqZQEfkrSeUTwqCZvI/wgnwns1hesadlI4RZFt1eXa9ButG8k0AmSzga2JNays4A/SzrH9gcrS89ktEfPh/ueM2WzpAEPlO/3S3oa0fGtRVnYAn3BsG2BQ20fDxwvaVYD/aQhGSiqQJ+h3GOSfsqQlAABTwWuLqU/XZkpD102F7Bgz6y71MZfBGD7uiGxyPkKESgb5P7yXNXMBknrDZzqeSStUAKnrVqVJklrPgD8XNKrbd8AIOmjwFuACY1mK/AM22+U9HrbR0k6FjitkfbbiJKj9xN/k+WJVPxWdBmo6jpINpT6tpeorfFvxIMlq/0GSe8nNoieVFvU9lToqLsL8FXg6cDvgNOB93U6onYsZfseSTsDR9jeW1L1jKIp5NFzsqQnAAcAlxNBqm810F1A0oK2HwE2B97d91zGFaYZ2fVsEpmoBIjohDStMzvG60DR0nCwGGc/QPzNe9lcx9i+o9UYWiPpctvrDT4e63g6IumqUvI41nOjOvFV0j9rgqc93TtgJMONpM2JVPStgJ2JzouvtX1n43FcYvv5ks4FdiUMtS8Zhg4sig5ctwGPIwJVSwHfsH3jdNZO/QTmeqZcS3Q93I94DfbvbZol0xNJc4jmBUcBH7d9aSnDWruR/vuI9cVd5fiJwHa2v9FCf2AsCwOL2L67gdbHgVcDtxM+sOvZtqRnAEfZflHtMSTtyEDRJCLpMkZKgA5loATIDdrDd02ZNK1m+wxFq/oFbN/b0ViGwtBZ0qNEBlcv5fz+3lPEjeNxXY2tBZJutD2mJ8REzyVJMjlI2hj4MXAB8CbbD3Ywhp0Jf7znAEdSDLVtf7OB9hzmLQO6G7gM+PR03qhIkqQ9kj45wdO2vV+zwXSEpDcSTRN+afu9klYBDrDdJJtT0izb6w6cu6L2Wm8qmEmXxIinAqf3qmgkPRNYPLPopxcZKJpE+i8akq61vUbfc9UvHl0j6V1ECuLStldVdKI4xPbmDbSHOptrmJH0PeAXtr81cH4n4BW2t62s/xHb+5fHb7R9XN9zn7U9Vllckvzb0+eTImBh4GHCSLWZR4eKofY4z73OdnWfpFJy9ChwbDn1ZuJvcDewse3a5a+dBaq6DpINu/4wI+nEiZ5vbHvQFEkfGuP0YoSh/3/YXrzxkIaOUua2Tm8zWtICRFOfql2Wixfmy2z/pXhxfp8RM+k1bKeZdDJpZKBoEskSIM0Cng9c3AuKtSj9KTpDn801rEh6MtF14m+EySBE95mFgP/s7bpU1B/qz32SdImk64FxDbVtr9pgDL8cTLfvnWtU/tpZoGoKBMmGWn+YkfRn4BaiacfFDHQ6a2l70CWSlgB2J4JEPwQOtH1bt6OqT8lgOZhoZrKWpLWBLW1/upH+AcBKwCFEsHgXItNnrCDeZOrOtr1Oefx14M+2P1WO58lySpJ/hTSdmlzWkXQPpQSoPKYcLzL+j00bHrL9t56BsqQFademddgNnYcW238CNpK0KdGqFuCntn/RaAga5/FYx0mSTC5TwVB7cUkvsH1x0X8+UfoGbZopvGggUDWnL1D11mmsnfrDzVOAlwPbEZ/3nxIbg1d3OqpGKNq0f5Dw4zyK8Ipp6g3XMd8iOo59E8D2lYomBk0CRcB/A+8B3kvM9U4HDmugm2bSSTPyDTWJ2F6g6zF0zDmSPkYEyV5OGIq2ao/8WN/jBwaey7S5IcD2WcBExtLVpMd5PNZxkiSTiO2fSXoIOEXSVowYam/ScNG0M3C4ouOmgHuAnUtzhc810O8yUNV1kGzY9YcW248CpwKnFjPf7YCzy2bhQd2Ori4lm+UNRAb9c2z/teMhdcHjbV8ysBnc7DNn+zEio+ngVpqF7xHrrduJ9c55AMVMurqZdTJcZOlZMmko2pPuRHQhENGa+LAWZtLDbuicdEe+95Kke6aIofZSxLzqrsa6GwCHEwGKuYEq4GrgNbZ/OB21Uz8pAaLXEEGilYATgcNt/77LcdVG0mPAQ0RgpH+O3cwfrmsknQK8HzjO9nqStgF2sr1FZd0f2n7TOP5kuEHXtTSTTlqRgaIkSZIkSf4tmSKG2gsTXWdWoi9T2/a+tbUHxtFJoKpr7dQfTiQdRZSbnwJ83/ZVHQ8paUjpcnYosBFwJ/ArYHvbv6ms+1Tbf1R0eZ6H2vpJ0pIMFCWThqQXAZ8CViQmy72J+ipdjitJkiRJaiHpVCLlfyYRpALA9oGN9DsLVHUdJBt2/WGmZNXcVw6HMqsmgVLiO4Mow9rW9jEdjWMB4M1d6SdJDdKjKJlMvk0Yi46aLCdJkiTJNGY526/qUP8njASqHhoi7dQfYmzP6HoMSXskLQm8D3g68fk7oxzvCcwGqgZqBvRPBH5OlMDtCcyqrZ8kLcmMomTSkHSx7Rd0PY4kSZIkaYWkQ4GDbM/pSP8q22vN/19OL+3UT5LhQ9JPiFKzC4muX08EFgJ2tz1ruusnSUsyoyiZTM4qnRh+RN/OWhqrJUmSJNOYjYF3SPoVce/rlb5UNzUtXCDpOR0FqrrUTv0kGT5Wsf0cAEmHAbcDK9i+d0j0k6QZmVGUTBqSxmpNbtubNR9MkiRJkjSga1NTSdcAzyDMXJsGqrrUTv0kGT4kXW57vfGOp7t+krQkA0VJkiRJkiT/IpKeBCzSO7b920a6nQWqpkCQbKj1k2TYkPQoIybmAhYF7qeRiXnX+knSkgwUJZNGdv9IkiRJhg1JWwIHAk8DbiM6f15r+9mNx9FJoKpr7dRPkiRJksknOwYkk8lPgNcDjxDR9t5XkiRJkkxX9gM2BP7P9sqEwekvW4lL2lLSDUT50znAr4FTprt26idJkiRJPdLMOplMum4RnCRJkiStedj2HZJmSJph+yxJX2io3wtUnWH7uZI2BbYbAu3UT5IkSZJKZEZRMplcIOk5XQ8iSZIkSRpyl6TFgXOBYyR9lcisbcXDtu8A5gaqgHWHQDv1kyRJkqQSmVGUTCZdtwhOkiRJkta8HngQ+ACwPbAU0NKbbzBQdRvtAlVdaqd+kiRJklQizayTSSO7fyRJkiTDiqQlGd3I4S+NdBcjAlViJFB1TMl0mbbaqZ8kSZIk9chAUTLpZPePJEmSZFiQ9B4ig+gB4DFGsmlXaTyOTgJVXWunfpIkSZJMPhkoSiaNqdIiOEmSJElaUbpevdD27R3pdxao6jpINuz6SZIkSVKLDBQlk4ak2cBmDHT/sP3ujoeWJEmSJFWQdCrwBtv3d6TfWaBqCgTJhlo/SZIkSWqRZtbJZNJ1i+AkSZIkac1Hia6fFxONHACwvVsj/ZuAToJUHWunfpIkSZJUIgNFyWSS3T+SJEmSYeObwC+AOUT5UWu6DFR1HSQbdv0kSZIkqUKWniWTRun+8QAwg+z+kSRJkgwBki6wvVGH+pcA5zMQqLJ91HTWTv0kSZIkqUcGipIqSFoGuMP5BkuSJEmmMZI+A/wGOInRWSVNOl91GaiaAkGyodZPkiRJklpkoCj5l5G0IfB54C/AfsDRwDJEZtEOtk/tcHhJkiRJUg1JvxrjdMvOW50FqqZAkGyo9ZMkSZKkFhkoSv5lJF0GfIwoNTsU2ML2RZJWB75n+7mdDjBJkiRJpildBqqmQJBsqPWTJEmSpBYZKEr+ZSTNsr1ueXyt7TX6nrsiA0VJkiTJdEbSWsCawCK9c7a/092IkiRJkiRJ/nmy61kyGfR3eXlg4LmMRCZJkiTTFkl7Ay8lAkU/A7YgDI6bBYq6DFR1HSQbdv0kSZIkqUFmFCX/MpIeBe4DBCwK3N97CljE9uO6GluSJEmS1ETSHGAd4Arb60h6MnCY7dc10h8zUGV7m+msnfpJkiRJUo8ZXQ8g+ffH9gK2l7S9hO0Fy+PecQaJkiRJkunMA7YfAx6RtCRwG9DSo2YbYHPgVts7EkGrhYdAO/WTJEmSpBJZepYkSZIkSfLPc5mkJwDfAmYCfwUuaaj/gO3HJHURqOpSO/WTJEmSpBIZKEqSJEmSJPknkCTgc7bvAg6RdCqwpO0rGw6jy0BV10GyYddPkiRJkiqkR1GSJEmSJMk/iaSZttfvSFvAcrZvKccr0ShQ1aV26idJkiRJXTJQlCRJkiRJ8k8i6evAkbYv7Ui/y0BVZ9qpnyRJkiT1SDPrJEmSJEmSf55NgQsl3STpSklzJLXMKrlI0gYN9aaKduonSZIkSSUyoyhJkiRJkuSfRNKKY523/ZtG+tcAzwR+A9wHKOS99nTWTv0kSZIkqUcGipIkSZIkSf4FJK0DvLgcnmd7dkPtzgJVUyBINtT6SZIkSVKLLD1LkiRJkiT5J5G0O3AM8KTy9V1J/9VKvwQlngC8rnw9oVWgokvt1E+SJEmSemSgKEmSJEmS5J9nJ+AFtj9p+5PAhsC7Wol3GajqOkg27PpJkiRJUossPUuSJEmSJPknkTQH2MD2g+V4EeBS289ppH8l8ELb95XjxYALG3kUdaad+kmSJElSjwW7HkCSJEmSJMm/MUcAF0s6oRxvBXy7ob6AR/uOHy3nprt26idJkiRJJTJQlCRJkiRJ8g8iaTnbv7P9JUlnAxsTQYIdgeUaDqXLQFXXQbJh10+SJEmSKmTpWZIkSZIkyT+IpOuBV9r+9cD5dwIft71qZf3lbP+uPF6PkUDVucBytk+ajtqpnyRJkiT1yUBRkiRJkiTJP4ikVwNfBV5t+4Zy7v8B2wNb9AIJFfU7C1RNgSDZUOsnSZIkSW2y61mSJEmSJMk/iO2fAbsAp0haS9JXiBbpm9QOEhU+APxc0mq9EyVQ9QHgJdNYO/WTJEmSpDLpUZQkSZIkSfJPYPtMSe8AzgYuADbvdT9roP0zSQ8RgaqtgJ2BDYhA1Z3TVTv1kyRJkqQ+WXqWJEmSJEnyDyLpXsCEN83CwMOMdL2y7SUbjWNj4MdEoOpNrQJVXWunfpIkSZLUIwNFSZIkSZIk/2Z0GajqOkg27PpJkiRJUpsMFCVJkiRJkiRJkiRJkiRAmlknSZIkSZIkSZIkSZIkhQwUJUmSJEmSJEmSJEmSJEAGipIkSZIkSZIkSZIkSZJCBoqSJEmSJBlqJP11kv+/rSSt2Xe8r6SXTaZGkiRJkiRJLdLMOkmSJEmSoUbSX20vPon/35HAybb/d7L+zyRJkiRJklZkRlGSJEmSJAmg4ABJV0maI2nbvuc+Us7NlvT5cu5dki4t546X9HhJGwFbAgdImiVpVUlHStqm/Mzmkq4o/9fhkhYu538taR9Jl5fnVu/ib5AkSZIkSZKBoiRJkiRJkuANwLrAOsDLiGDPUyVtAWwFvMD2OsD+5d//yPYG5dy1wE62LwBOBD5se13bN/X+c0mLAEcC29p+DrAg8N4+/dttrwccDOxZ79dMkiRJkiQZnwwUJUmSJEmSBBsD37P9qO0/AecAGxBBoyNs3w9g+y/l368l6TxJc4DtgWfP5/9/FvAr2/9Xjo8CNul7/kfl+0xgpX/1l0mSJEmSJPlnyEBRkiRJkiRJoAnOj2XqeCTw/pIdtA+wyD/5//d4qHx/lMg2SpIkSZIkaU4GipIkSZIkSYJzgW0lLSBpWSLb5xLgdOCdkh4PIGnp8u+XAP4o6XFERlGPe8tzg1wHrCTpGeX4bUTWUpIkSZIkyZQhA0VJkiRJkiTBCcCVwGzgF8BHbN9q+1TCd+gySbMY8Q/aC7gY+DkRBOrxfeDDxbR61d5J2w8COwLHlXK1x4BD6v5KSZIkSZIk/xiyx8qkTpIkSZIkSZIkSZIkSYaNzChKkiRJkiRJkiRJkiRJgAwUJUmSJEmSJEmSJEmSJIUMFCVJkiRJkiRJkiRJkiRABoqSJEmSJEmSJEmSJEmSQgaKkiRJkiRJkiRJkiRJEiADRUmSJEmSJEmSJEmSJEkhA0VJkiRJkiRJkiRJkiQJkIGiJEmSJEmSJEmSJEmSpPD/AePawqbl6tCRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing location and type of restuarants are their.\n",
    "\n",
    "df3.plot(kind = 'bar',figsize = (20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ab74433c",
   "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>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Koramangala 5th Block</th>\n",
       "      <td>2214083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>1410176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiranagar</th>\n",
       "      <td>1165909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Koramangala 4th Block</th>\n",
       "      <td>685156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church Street</th>\n",
       "      <td>590306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         votes\n",
       "location                      \n",
       "Koramangala 5th Block  2214083\n",
       "others                 1410176\n",
       "Indiranagar            1165909\n",
       "Koramangala 4th Block   685156\n",
       "Church Street           590306"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping location and votes\n",
    "\n",
    "df4 = df[['location', 'votes']]\n",
    "df4.drop_duplicates()\n",
    "df5 = df4.groupby(['location'])['votes'].sum()\n",
    "df5 = df5.to_frame()\n",
    "df5 = df5.sort_values('votes', ascending=False)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f535366c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34]),\n",
       " [Text(0, 0, 'Koramangala 5th Block'),\n",
       "  Text(1, 0, 'others'),\n",
       "  Text(2, 0, 'Indiranagar'),\n",
       "  Text(3, 0, 'Koramangala 4th Block'),\n",
       "  Text(4, 0, 'Church Street'),\n",
       "  Text(5, 0, 'JP Nagar'),\n",
       "  Text(6, 0, 'BTM'),\n",
       "  Text(7, 0, 'HSR'),\n",
       "  Text(8, 0, 'Lavelle Road'),\n",
       "  Text(9, 0, 'Koramangala 7th Block'),\n",
       "  Text(10, 0, 'Jayanagar'),\n",
       "  Text(11, 0, 'Whitefield'),\n",
       "  Text(12, 0, 'Koramangala 6th Block'),\n",
       "  Text(13, 0, 'Marathahalli'),\n",
       "  Text(14, 0, 'MG Road'),\n",
       "  Text(15, 0, 'Brigade Road'),\n",
       "  Text(16, 0, 'Sarjapur Road'),\n",
       "  Text(17, 0, 'Residency Road'),\n",
       "  Text(18, 0, 'Koramangala 1st Block'),\n",
       "  Text(19, 0, 'Malleshwaram'),\n",
       "  Text(20, 0, 'Bannerghatta Road'),\n",
       "  Text(21, 0, 'Bellandur'),\n",
       "  Text(22, 0, 'Ulsoor'),\n",
       "  Text(23, 0, 'New BEL Road'),\n",
       "  Text(24, 0, 'Kalyan Nagar'),\n",
       "  Text(25, 0, 'Banashankari'),\n",
       "  Text(26, 0, 'Brookefield'),\n",
       "  Text(27, 0, 'Richmond Road'),\n",
       "  Text(28, 0, 'Electronic City'),\n",
       "  Text(29, 0, 'Kammanahalli'),\n",
       "  Text(30, 0, 'Frazer Town'),\n",
       "  Text(31, 0, 'Basavanagudi'),\n",
       "  Text(32, 0, 'Rajajinagar'),\n",
       "  Text(33, 0, 'Shanti Nagar'),\n",
       "  Text(34, 0, 'Banaswadi')])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing location where people gives most votes.\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.barplot(df5.index , df5['votes'])\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6de7f760",
   "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>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisines</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>11542182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian</th>\n",
       "      <td>516310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Chinese</th>\n",
       "      <td>258225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Indian</th>\n",
       "      <td>161975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Mughlai</th>\n",
       "      <td>103706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          votes\n",
       "cuisines                       \n",
       "others                 11542182\n",
       "North Indian             516310\n",
       "North Indian, Chinese    258225\n",
       "South Indian             161975\n",
       "North Indian, Mughlai    103706"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visulising top cuisines\n",
    "\n",
    "df6 = df[['cuisines', 'votes']]\n",
    "df6.drop_duplicates()\n",
    "df7 = df6.groupby(['cuisines'])['votes'].sum()\n",
    "df7 = df7.to_frame()\n",
    "df7 = df7.sort_values('votes', ascending=False)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1bba4b2c",
   "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>votes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisines</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>North Indian</th>\n",
       "      <td>516310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Chinese</th>\n",
       "      <td>258225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Indian</th>\n",
       "      <td>161975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Indian, Mughlai</th>\n",
       "      <td>103706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinese</th>\n",
       "      <td>101728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        votes\n",
       "cuisines                     \n",
       "North Indian           516310\n",
       "North Indian, Chinese  258225\n",
       "South Indian           161975\n",
       "North Indian, Mughlai  103706\n",
       "Chinese                101728"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing 'Other' food categeory.\n",
    "\n",
    "df7 = df7.iloc[1:, :]\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ca3e6d12",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "        68]),\n",
       " [Text(0, 0, 'North Indian'),\n",
       "  Text(1, 0, 'North Indian, Chinese'),\n",
       "  Text(2, 0, 'South Indian'),\n",
       "  Text(3, 0, 'North Indian, Mughlai'),\n",
       "  Text(4, 0, 'Chinese'),\n",
       "  Text(5, 0, 'Cafe'),\n",
       "  Text(6, 0, 'North Indian, South Indian'),\n",
       "  Text(7, 0, 'Desserts'),\n",
       "  Text(8, 0, 'Chinese, Momos'),\n",
       "  Text(9, 0, 'Ice Cream, Desserts'),\n",
       "  Text(10, 0, 'Cafe, Continental'),\n",
       "  Text(11, 0, 'Biryani, North Indian, Chinese'),\n",
       "  Text(12, 0, 'Chinese, Thai, Momos'),\n",
       "  Text(13, 0, 'South Indian, North Indian, Chinese'),\n",
       "  Text(14, 0, 'Desserts, Beverages'),\n",
       "  Text(15, 0, 'Biryani'),\n",
       "  Text(16, 0, 'Finger Food'),\n",
       "  Text(17, 0, 'Bakery, Desserts'),\n",
       "  Text(18, 0, 'Burger, Fast Food'),\n",
       "  Text(19, 0, 'Continental'),\n",
       "  Text(20, 0, 'Cafe, Desserts'),\n",
       "  Text(21, 0, 'Andhra, Biryani'),\n",
       "  Text(22, 0, 'North Indian, Continental'),\n",
       "  Text(23, 0, 'Pizza, Fast Food'),\n",
       "  Text(24, 0, 'Chinese, Thai'),\n",
       "  Text(25, 0, 'North Indian, Chinese, Biryani'),\n",
       "  Text(26, 0, 'South Indian, North Indian, Chinese, Street Food'),\n",
       "  Text(27, 0, 'Mithai, Street Food'),\n",
       "  Text(28, 0, 'North Indian, South Indian, Chinese'),\n",
       "  Text(29, 0, 'Desserts, Ice Cream'),\n",
       "  Text(30, 0, 'Biryani, North Indian'),\n",
       "  Text(31, 0, 'North Indian, Street Food'),\n",
       "  Text(32, 0, 'Fast Food'),\n",
       "  Text(33, 0, 'North Indian, Chinese, Seafood'),\n",
       "  Text(34, 0, 'Chinese, North Indian'),\n",
       "  Text(35, 0, 'North Indian, Biryani'),\n",
       "  Text(36, 0, 'Biryani, Kebab'),\n",
       "  Text(37, 0, 'Italian, Pizza'),\n",
       "  Text(38, 0, 'North Indian, Chinese, Fast Food'),\n",
       "  Text(39, 0, 'North Indian, Chinese, Continental'),\n",
       "  Text(40, 0, 'Desserts, Bakery'),\n",
       "  Text(41, 0, 'Arabian'),\n",
       "  Text(42, 0, 'North Indian, Chinese, South Indian'),\n",
       "  Text(43, 0, 'Pizza'),\n",
       "  Text(44, 0, 'South Indian, North Indian'),\n",
       "  Text(45, 0, 'North Indian, Mughlai, Chinese'),\n",
       "  Text(46, 0, 'Beverages, Fast Food'),\n",
       "  Text(47, 0, 'South Indian, Biryani'),\n",
       "  Text(48, 0, 'Fast Food, Rolls'),\n",
       "  Text(49, 0, 'Bakery'),\n",
       "  Text(50, 0, 'Andhra'),\n",
       "  Text(51, 0, 'Street Food'),\n",
       "  Text(52, 0, 'Kerala'),\n",
       "  Text(53, 0, 'Cafe, Bakery'),\n",
       "  Text(54, 0, 'Biryani, Fast Food'),\n",
       "  Text(55, 0, 'South Indian, Fast Food'),\n",
       "  Text(56, 0, 'North Indian, Fast Food'),\n",
       "  Text(57, 0, 'Bakery, Fast Food'),\n",
       "  Text(58, 0, 'Fast Food, Burger'),\n",
       "  Text(59, 0, 'Cafe, Fast Food'),\n",
       "  Text(60, 0, 'South Indian, Chinese'),\n",
       "  Text(61, 0, 'Beverages, Desserts'),\n",
       "  Text(62, 0, 'Ice Cream'),\n",
       "  Text(63, 0, 'Beverages'),\n",
       "  Text(64, 0, 'Biryani, South Indian'),\n",
       "  Text(65, 0, 'Kerala, South Indian'),\n",
       "  Text(66, 0, 'Fast Food, Beverages'),\n",
       "  Text(67, 0, 'South Indian, Chinese, North Indian'),\n",
       "  Text(68, 0, 'Mithai')])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KAABmSVuccG6rdD/f5rUj3hMAABVHAADwtLDliRe2Snfy1htKkrY68dJW6U/aet3O+wQATxdUHAEAAFrY5sSrW6U7Yes1R7wnADDjUXEEAAAYgW1PvKlVuuO3fsn0/9960h0D0/9sq+Wn/7/nyXcPTH/wlktN//+Ak/88MP1ntnze9P8POvkvA9NL0l5bPqdVOgBPXVQcAQAAMBRHnXRfq3Q7b7WYJOlnJz7QKv1bt1608z4BGA4qjgAAAHhKOOX4dhXNzbeNiuZZP22XfpPtxiqmF/zo/oHpX/X2qa22CzydMB0HAAAAACCLO44AAABAR5ceM/gO5bo7jt2hvObIdt1519hlsc77BIwCFUcAAABgFnXTYX9tle4l711cknT7Ie3SP3+PSH/P19oFQFryQxEA6d6vDA7IJEnP/UgEZfrL125rlf45H1px+v9/+fqNg9N/cOVW28XwUHEEAAAA8JT1129eMzDN4nuvMZb+oMtabXfxvdaJ9Adf1C79nhtIku475LxW6Rfb4zWR/ltntkv//te3SjcqT+sxjma2qZn91sxuN7N9Zvb+AAAAAMBT0dO24mhms0n6tqTXS3qxpO3N7MUzd68AAAAA4Knn6dxVdW1Jt7v7HZJkZj+VtLmkm2fqXgEAAABAR/d9+9RW6Rbb/c1j63znhMHp37dN9vWn7R1HSUtIqo7evSctAwAAAAAUMHef2fswEma2raRN3P3/0vN3SFrb3feopNlV0q7p6UqSftuwuUUltZtBlvSjSD8j3oP0M/89nmnpZ8R7kH7mvwfpZ/57PNPSz4j3IP3Mfw/Sz/z3eLqmX8bdp9Ysl9z9afmQtK6ksyrPPy7p4x23dRXpZ176WXGfnmnpZ8V9eqqnnxX36ZmWflbcp2da+llxn57q6WfFfXqmpZ8V9+mZln5W3Kenenp3f1p3Vb1S0opmtpyZPUvSdpLadQgGAAAAAEz3tA2O4+6Pm9n7JZ0laTZJR7n7TTN5twAAAADgKedpW3GUJHc/Q9IZQ9jU4aSfqelnxHuQfua/xzMt/Yx4D9LP/Pcg/cx/j2da+hnxHqSf+e9B+pn/Hs+09E/f4DgAAAAAgOF4Oo9xBAAAAAAMARVHAAAA4BnKwlIzez8w66OragMzm03S4qqMA3X3PzaknUfShyQt7e7vNrMVJa3k7qcPY/uzIjN7haRlNX7/fzCi91pI0lLufn0mzbaSfunuj5jZpyStIelz7n7NkPdlXnf/V4t0e0k6WtIjkr4naXVJ+7j72UPajxUk3ePuj5nZqyStIukH7v73YWx/lMxsjdzrwz5m6T0HnkMF21o497q7P1izzlYD1jlpsvs1Sl0+c8f3mU3SGzQxb/l6Q/o5JW1dk/6AmrRFx6D0M0/mO2qTn5rZDu7+QzP7YMP2a7+jtO5WktaX5JIudveTc/tqZm+Q9BJJc1W2P+E7LdX1d2BmCw/rHJtZSq6ZZmaS3i5peXc/wMyWlvQcd7+iJm3ped35PKpsY4qk+dz94UFph20G5kWtz7mm77KyT+O+0w7HbIZcP8zsandfs0W6TsegtKzcpZxTeg0pVXLN6bj9GVKWnYyndXCcrsxsD0n7SvqrpCfTYlectHWOlnS1Yu5ISbpH0vGSmn4MrbZvZhe7+/pm9kh6ffpLktzdFxjwOdaXtKK7H21mUxUZ/Z2Z9HMrftC/HbDdYyWtIGmapCcq+9+q4tiyIniBpDcrztFpku43swvdvSmD/rS7H58+8yaSvirpUEkv79vuDRr/XU5/SfGd1h7jdNH/nqT5JC1tZqtKeo+7v69hf3Z294PMbBNJUyXtpDhPaiuOZvaCtL+Lu/vKZraKpDe7++catn+ipLXM7PmSjlRMNfNjSZvVbPuF7n5rU4WtP0PKfEe99E3f0ZclfU7SvyX9UtKqkvZ29x/2Jf1a+juXpLUkXaf4/leRdLmigDtpbc8hM/umu+9tZqep5nO7+5v7Fl2d0lnN27qk5WuWvyn9XUzSKyT9Kj3fSNIFkvoLCkX71CF96TGufualJT2U/n+2pD9KWq5uOx3O69Mk/UfSDRrLG3NOkfSPtH+PDUhbdAxU/pm7fkdt89N509/5Gz9h/fa/I+n5kn6SFr3HzF7r7rs3pD9M0jyK7+V7kraRVFdhOUT5c2jPmsVvqlk2fRVNPAY9l5vZNEUeeqYPucU7XR/frYmFwZ1brLuYxlewJzQAd7hmfkdx/r9a0gGKBsgTJb2sJm3ped31PPqxpPem/b9a0oJm9nV3/0pmnaIKv5mtLOnFGv999n9HnX5nlfdo2yhScs71vsuVFMeoN/XbmyRdVJO+9JiVXj86XcMlXWZmL3P3K5vWTboeg6KysgrKORWl15DSCmrJNad3DfyIpGU0Pm95dcMqrcqyle13zrs6K5348ZnwkHS7pEUK0l+V/l5bWXbdsLbf8TPsq/gB/S49f56k32TSv0nSbyXdmZ6vJunUhrS3KN2tLtifCyQtIGlhRcZytaSvZ9Jfm/7+n6T90//Xt0j/RUlv6z8elXTL5B6Z7V8uaam+Y3xjJv316e9BkrZs2p9K+gslrV2w/WvS349I2iO3fUmHp7/n1zx+lfmOvpweL02PL0n6TGafpqW/W0o6Jh3r3O/gp5JeWnm+sqTv16S7QdL1NY8bWp4T2XNI0prp74Z1j5LzvMXv4HRJz608f66kkya7Tx3Sdz3Gh0narPL89ZK+NsTzuvF4NqRv3NZkj8EkPnNp+uL8tPDz3lTdvmKIyk2DjkHl73ySzq5Jt2N6HC7pYkl7pMdFkr4x5M9gkjZWVH5/L+kLkl6QSb+epHMk/U7SHZLulHRHJv0lkg6U9BbF3YStJW09YJ/eLOk2Sf9K23+y6XstPcYay9+vrSxrzEu7nNcdjsG09Pftkr4uaY5Bv9f0/RyvKOhnP7+izHK+okH9aEl/kXRCJn3R76yyzg8k3Z3e7wZJRw7jnEvrnC1p/srz+RV3j4ZyzNqmV/f8/WZFw8Dv1e4aW5rXlZaVW5dzKusUXUN657ai0vX89Nm/IemMhrRF1xxFw/huiuvgmr1HJv216W+2LFtJ3zrvknRc+ttfpsoe5wnbKf2CnwmPlHnNXpD+EklzV07yFSRdMaztV9ZbTNG6s7TizmAu7bSU8V1bWZbLAK6WtGCb9IoLwXMH7W/fOtemv20rgjekTPFsSS9rkf50Sd9NP/pnS5ozlyF1+O4vr36O9H8uwzs67fttitb7+SVdnUl/Zc32p+X2R9L2km6UtFxaVlyIHvCZJzQ01C2rvHZT+nuEpE1bfEcTPl/DsmVyj2GdQx2/o4UUF4QNeo8B6W/sez5l2MdtxMd4wjmsVBhoSF96Xh8o6XUF+3+4Ko0PLdcpOgYdPnNp+qL8VNGD4RPpsx/Ve2TSn1T9naTfzU8y6Xt53WWKBsc5Jd2WSX++pDkqz+eQdH6Lz/EGSR+V9Jneo+Xn30jSnyT9XdEwsW5NmlsVhdjFJC3Se2S22XhOZta5Lm332sp+HT6kY3y5Yv7pXpliqgYXmEvP69Lz6KZ0bI9XaozS4Mps68qXIr+e0tumYijPaZltF/3O0uutGkW6nHOV827OyvM5Jd06xGNWmr40fy+9xpbmdaVl5eJyjgqvIWmdooZ4FVxz6r6jAemLyrIqyLuU8qDS49z/oKtqvTskXWBmv1DlVrQ395HeV9E1bykz+5GitfNdw9q+mb1Z0b3veZLuUxzkWxTdLZr8193dzDxtY95MWkl63N3/EUMrBlpU0s1mdkXf/vd36aua3cyeq2gV+WSL9zhA0lmKTO5KM1teUQlr8hZJm0r6qrv/Pb3XR5oSm9k6kg6R9CJJz1JcpP/lzd1/707dVd3MniVpT8UxaLKL4q7tHe7+qJktouiu2uSB1F2id7y2kXRvJv1Oim5Dn3f3O81sOUn9XUInKBybOq+Zre/uF1fWzZ1Hp5nZrYququ9LXSj+k0l/i5l9L+23S9pBNd+pu9+V2UbO/opz6OI255CZrSdpP411Kel1X67reioz+z9Je0laUtFQs46kSxXdy5pcYGZnKQpSLmk7RcG7aZ9WVLQ89nffatqnos+g8mP8QBp3UT1mfxuQvuS8vkzSyWkM1f8q+9/0u1xf0rvM7E5FXpTtcp4UHQN1+8wl6Uvz01Mk/VrSuRrr9jhBpdvygorf2hXp+csVBbgmp5vZsyV9RdI1aZ3vZdI/T9Ew1uuSOF9a1qhtd9hK+kUU3+M7FHek9lB0W1tNUZHp7xr3D3c/M7cPfU43s8085n5u63/u/jczm2JmU9z9fDM7sCFt6TE+WNLJkhYzs88rvp9PDdif0vO61XlU8V1Jf1BUmC8ys2UkZcc4epRSz5F0jpltpPhNvM/MrlOM+b+0kvzf7v6kmT1uZgsoyjpN+ZZU/juT4tokSY+a2fNS+qYu5KXnnCQdK+kKMzs5Pd9C0fumSekxK01flL+7+111Q5wy2y89BqVl5S7lnNJriCT9z8y2V/Sg6HULnqMhbek15zQze5/i91z97Td14S4qy6og73L3e9PfrmUqSQTHqWVm+9Ytd/f9M+ssoig4mqTL3P2BYW0/ZbKvlnSuu6+eMuDt3X3XzHt8WNKKita+L0raWdKP3f2QhvRHSjpP0j6KW917KlqR31uTdsOG/b8wsz/bKFqVL3b396VC/FfcfeumdUqY2bHu/o5ByyqvXaXIdI9XjLN7p6Tnu3ttpdbMFlV0O32t4hifLWkvd2/MJFOFf4P09EJ3Py2TdnlFS9YrFOMF7pS0g7v/IbNOqzGplfS142y8fiySzGxNRSv0gmnR3xVjN2sHaVsMGp9H0sPu/kRqrJjP3f/akH4uRReO3nd0kaRD3b22slla2Tez9dz9N4OWVV67VdIHFHffpxekmo5xGkfyMsXvfTUze6Hibvpb69JX1ttSlc/smUAlZnax4mL7DcUFbSdFvl2bh3T4DKXHeOG0P9Vjtn/TRbD0vDazOxSFrRu8xcUpFV4nGHRhLDwGpZ+5NH1Rfmpm09x9tab9HbTdQdvv28ackuZy939k0uykaKzoFWA3lLSfuzcWmM3sendfpfJ3PkWXu9c1pP+dolB+tLvf0/fax9z9wL5lX1LkDydpfGGt6bx+RFGgfkwtC5tmdq7iXP2iomJ4n6Jnwytq0na5Zr5Q0mvSvpzn7rmGyt46Jed1q/NowPvN7u6PZ17vr3z1xqmtJul4d1+ukvY7ijug2ykCqPxTcTeltsG19HeW1vm04hryGknfVmoUcfdP16QtOucqr60h6ZVp279292ub9ielb33MStN3yN/3VZSHVnL3F6TK9fHuvl5D+i7HoKSsvJe7HzRoWd/rRdeQtM6LFRXUS939J6mC+lZ3/1JN2qJrTqpg1iRvbMxVXeXdG+KTdMy7Sm+cTNh7HpN8KFpN5k3/76Do/7/MELff6xd+naQp6f/G2/uV9TZWtBp/VdLGA9LOI+nzkq5Mj88pCgxD+47aLKu8tqSiheY+xQXnRElLZtJf0/d8Nkk3t/hOr68su2SIn/dLior4zulxjqQvtlhvXlXGSGTStR6TWlmn01gqxdjUBVuku6bNsr7Xn6UYe7GyKt3dmo6ZYgzCten47qRoiRzK/ih10Sv4XnrdMKcpdU9Si24jii5Yb5L0RkmLDUh7dfp7Q2XZr4f1GUqPcV/6+QrStz2vz+rlcYX737obf+kxmMRnLkpfsN3PqTKuaATb/72k9/YtO33AOs+RtLli3N9zWrxH6+6w6bfeOB6+YZ3zax4TxnNP8nuaV9FVcHbFnYo9NaTYBYoGvl6e8qq07We3WK8kb2l1Hkn6YO4xYN3fSfq0aq7dkj5W+d8UAfN6z5eVtErL76rT7yydcwsO65yrrLuq4u7k+yWtOsxj1iV95Tuq/ax96aapYIhT6TFQYVlZ9dfwawe8R9dryNyKCnPb9EXXnILt7quC+CQd36OoLNX/oKtqjVTD/6gmRt5q6oJ2qKRVLSJtfkTRwvMDRcvrMLb/99Qie5GkH5nZfZIaW/nSe8yruFCeY2YrSVrJzOZw9//1pevdlXu3x922gd1IO7ZWHKIIKzxoWc/RiuhZ26bnO6RlG/fty8cVrZRzm1mv24xJ+q/iTkeTRy26nE6ziAZ6rzJdOKx9xNCezSSt5u5PpvWPUfxIP96w/b00Nn3HEanVMjd9x36KsXUXSJK7T0utZDk3Kgp4ua6C1X1aUJXWRDO7UNIB3nf3wcyeI2kJxTFYXZoebXQBRYNE0/ZfpejG84e0zlJmtqO710WhkyS5++1mNpu7PyHpaDOb0OXOzNZV3OGaauPDpC+gOFebnG9mX1HLuxSS7rHo0vdzRVeshyT9ObN9mdlbFI05Fyg+8yFm9hF3P6Fhlf+kLje3mdn7FeNsFhvWZ2h7jCvpX6rI2xZOzx+QtKO739iQvvS8vlfRHetMjagbf+kx6PCZS9OX5qd7SfqEmbVqYe6w/f9J2sjMXq6IHP1fxe87Z23FXRYp7rQ09q5IWneH9ei9sOqA7fWvs1FJekmyiPa9osZfkxvzIkXF6fvufrdSd0Qz21U1150Ox6AaTfJ7iu8zG02yQ97S9jwqir5a2Z/ZFA0On6173St37NzdzeznisAh8kxPm8r2W//OLDOdhZnJ+6az6HLOpW3tpYhweaLi+/yhmR3uzT29SvOi0vRF+bsKhziV5nVqWVa26Db6NknLm9mplZfm1+DuyEXXkPR+b1LcYHmWpOXMbDXF9zShK3nHa06baME9Wyqmb7smpfuzmWV/gx3yrlZlqdzKPCbWxs9WjFG7RXFCHyXpwEz63sDaz0japbpsSNufV3Ghad2yqeiqNo/ign+34u7dj2rS3aw48a9TBPpYuPpo2HZda8UXGtKuq+h2crfGt1TupyEETknLpygzqL9hnWUUP7IFFBnr1yWtMGh/1D5i6PXV7y+lzwX36QUE2ETRlWfVAedQXbCeQRHuzld0FzwrvcepytylVFz89leMM1k+fU91Edx2TNt+RBEmvNfCf4qkrQacoytVnr9A+QBCFyky9h8oIsV9oO4YpN/UvooLyL6VxwcV3T9y30+nuxTpPd+swXdNr1OllVgRoCJ3Hr1MMcZkSUUF7CRJ6wzrM7Q9xpX0l0jaqPL8Vcrcqe9wXu9b9xjwfbYKUDKJY1D6mUvTt85PuzxKt6+x69lHFcEplhlwzIp7V2hiAJEFq8tq0n8tnT/vkLRV7zHgPVoH31EEbbtBkT+er2ggzP72FYXGm/uOde33NMlj0DaaZNF5PSMeii62bdN+WymIWcv0rX9niryz6VFbduh4zl2vdEctPZ9XA677JcesQ/rS/P3DirGsdygqwJf2zr/JHoO+8zpbVlbkOa9K779h5bGGBgSWVOE1JK1TFxzyhoa0RdcclUcLvqLvuxp0DnXJu1qVpZoe3HGst4i7H5n6Ul8o6cLUUtPkkXTn6x2SXpla2poG1hZv38dPOJ8baF1lHkFZdpF0iLt/2cyurUl3mOIO2vKKH081Oo6rYXC6t2+teJai4Du7xrdcPqwY8N/kATPbQWNzj22vhpYmjwH1q2a2VWcLj37y/1FkrL3Wwqa+873juZkiIuGDlg8k9EVJ15rZ+YrvdAM13G1MehvbTDGm4jrLv8GNZvY2SbNZBFDZU/mAF1JU1kus4OPHoO5vMa/VOO5+jMX4ye3d/UcF25/DK+Mz3f13Zpb73bxD0UjwfkVGt5TiYt6/PxdajA18qWfGJdesV3SXonK3Xul33BtHWjuuNpni7vdVnv9N8Zma9qk3n9Y/lQ+u1Etfeqel1TGumNfdz6+83wUDWqWLzuuS45WUBCjpKToGKv/MpelL8lNJ5S3Mhdu3tM6XzexqRUPTwpn0Rb0rkkuVepu4+2OSHjOza9TcA2VhxXGq9spxNcz7aIXBdxR333rjlTeyNF45k16Ku/+bSzrezE7wmM8wd26XHINesI53anCwjp7S87rVeWRmB+e24Q1j5JNp6Y7R8YppS3rr1B23jRRzjN6V0vbugDYFHWn9O/OGcZIDFJ1ziWl8oKEnlDknVH7MStMX5e/u/lUz21hRPltJ0dhyTmb7pXldq7KyR5CeexR35QeOxe5bt/QaItUHh/SGtKXXnG0UDabXuvtOZra48sHGjjOz70p6tpm9W9EYd0QmfZe8q64s1TreCBXHer3unPdaTBb7Z0WLf5O3Km6r7+zufzGzpRXdCSa1fYtBr3Un78DBr7G6rauYc2mXtGzC8Xb3gyUdbGaHuvtume1Vte7m2bUQr/ixfEsRFMQVlaKdM+nbTlzbs6MmVhLfVbOspyhiqMcA6wsUP2hTjOf4S2Z/rjazsxWR2j6euiY8mUm/h6Jb8WOKyvVZkmq7BFX2qSgDlvRvGx+RbT2NRaXr3/aTZvYeSSUVx6ssgjIdm56/XdF40aR1Zd+jq1GusFvL2k8OLfV1TUkXwTUHvMUvbSwqnhR5R2M0NCufPLj0M7Q+xskdFgEmesdsB0XAmyZF57XNgG78KjwGKv/MpelLu82XRvMt2r7iboAkyd3PM7NNFPllzrM1FlV1wcy+d+rW3qHg/wofC76zv5l9TfkC/3/c/T9mJjOb091vtRjikeXuf7QIfHOomR2vGCdVp/QY7KTyaJKleUvb8yiXJw9SUvl6feG2S39nktrnjx0rm0dLutwiqqopGhaOzKQvzYtK05fm70oVxVxlsar0GLQuK6dr+KNmtqBngnP163ANkcoa4kuvOUXRgjtU3ovzLh8L5DO9LFWk6VbkM/mhGHS8oCJgx/mKjPPNA9ZZRtJr0//zKBMIosv2O3yGDRXdLD6Wni8v6eAW6w0c8Js+69wa383z+QO2O9TABDXbbzVxreLO5WmK2/qnVh7nK6LW5t5jIUmzpf/n1YAgEJJWUXRfHNjNRdH6s4ZSAARFV4hWwQEKvqN1FIGP/qkYA/qEIgJqU/rVFN0y/iDpLsVdhMZ9UgRB+LCi9Srb3Tmln1PRffQkRVfqDyjfXa1ooLwKuxqp5eTQijspjyguFg+nxyOKAlKbAEhbpd/MNyRtOSDtdSqbPLj1BNcdj/FCiqkCrklpD5K00LDOa82AbvwdjkHpZy5NX5SfpmM6l8a6z79Q0s8GbL+/W35u++epL2iK8l2xtk/nzvcVPWLulLRdQ9odNdat/fzK41Tlf5svSPt1Y3q+iqRPZdKXzkV5sqLyu5+iQHiKGiYAr6xzRN/z3RXTL036GKd1ioJ1dDivi86jynrzawRBn9K2Z0vHa2DQkdLfWVqndf5Yes5V1ltDkQ/tKWn1YR6zDsd4NZXl749o7JrWe/SGOi0/pGNQUlY+TtIfFZXvg3uPAdsvuoZU9qMXHPKq9H9tcEgVXnMkfUeRt7xXMR3YtYreN8P6zXTJu9ZTNA78TtEt+Q415F11D6bjGIJ0O3lXRSF5hdRicZi7v2aI7zGbIppW9a7DH1usN6+P7+ralO5Nisxo3IBfd8/NFdlaavFdUe26rCi1sO6hiXMOThisnNIvU7fc+0Ikp3TLKbqS7lN56RFFRbO25cjM5lFUcpZ2913TMV7J3U9vSH+U4kJzk8busLi71941tegj8XZF5nxAaol7jrvXdq+ysTnaqv6hyPS+6zVTWlj9FCQruvsn6t6jst4CaecfHpDuzprF7vmw089StKq5pN96X/CmlKY3UH59xbxjPfNLesLdX9uw7aMb9qfpGJROEfBFd891x6uVuqqsrfjMV/j4rkf9aa9290F3MTt/hsp6rY5xX/on3f2fA9KVntdXu/uavf1Pyy509w3b7FdbJcegsk6rz9w1fVtmdqW7v8yiy9nL3f0xGzC1QvqdvVBjv7P/ZtLeoSgs/spTLxEzu8bdm7qRymKusV7viss937tCZra1u5+YS9OX/kLFnffvuvvqadmN7r5yQ/rW0y7UrLuhomH3lwO+p3dI+rm7P1JZ9ibPTLvUllWCdbj7cpYJ1tG3XkneUnQeWQT4OFbRIGiS7pf0Tne/KfMeL1AERFnc3Vc2s1UUjeSfq0m7h6Iy91eNv2Y2dVXtrdf6d1aSP5aec5X1VlUMTXFFBOzrBqQvyosmkXe1uYbvr+gB92PFMd5OEVDvt5J2c/dXZbbf5npQVFY2sx3rlnt+qp8Zcg3pwsyWlbSAu1+fSVPX07BXtvuQu9+RWbdt3lU0bVc/uqpWmNlHPcZ1HKKaLqLe3Jd/d8UP+fKU7jYzmxD5sOv2mzJURcWk6bOsq2ilmU/S0ikze4+7v69hlc8p7kiNmyuyb5vHuftbLOavq9v/XAZfOl7g52n/T1O+y2bvve+yFhPXporkXYqgPSWOVvzIenN03aOogNVWHBUBTF5csP3vKD7nqyUdoKjInqgojNW5QzEwvtpl5a+KVtIj1DDOzgvG2VhhRDavzMnVhpm9Su2iql6i6Nq1qOIuYs8jirvLtby8q1HryaHT9j9uZktoYjfSxrFm1jIqno11sy2dPLjoM5QeYyuPold6Xrftxn+xu69fc5Ed2I2/7TGopB9JVNVJ5KdF0XzT93iYojeGKaIGvsfdz2xY5e+KCtfBqYFqh6ZtV7xMY3O5Pal2UVXfpokNg01dqudx9yts/Bikxu5hPhbJ80QzO10Nc1Ga2QLu/rCN79Z+Q/o7n8a639Y5RNKHzGx7H5tjcX9VPvskjvF+KoyaXXpeqzwq9OGK6TfOT+/3KsW1ZsK8lRVHKFW+0ue43sx+rChv9NtL0RjbqvDaIS+SxoaXtMkfi865tA976akdVXVTd3955fnhZnZZavSb0MDc4Ri0Kiv3eMRPKJqvWuVDzVo1xJdec8zshR7dRic0uJnZGt4crf3raq68H6UIGjTZvOsfmfx/MB/S7dKnw0PSm9LfHesemfXGRbhUXAjrukl23f7tKpwfSvHDXErjo0TdmEk/cK5ISc9Nf5epewz5WJTOqbevCua+UXT3uE2ROfS6Gua6bfa+n+r3mYtmdqSkFxfs/zWF27+oaZmkm5rWUUEkLZVHZJtH0qeUurUp7jC/MZO+KKpqh3OotHvbpxVdPrZWRD67V3GRbUr/JUWl94x07p2mwXNpXqcWUfEU3f3uSH/7H41dSjp8hlFHVS09r2dEN/5Wx2ASn7lVeg0hP9VYNN9nZdLcqkq3SMUcgbdm0leP1bsUhZF7BvwOSqOq/lLSzxRjkT7Ue2TSn5n2u3c+bSPpzJp0r05/t6p71KQ/Pf2t+71lu24pupytoehVsm3/dzeZY6xuUbOLzusO51FdPpXdvsbmuq1+jmkNac/XgIiZfemLfpcpTev8se0517fO9XpqR1W9VNJbFEMMpqT/L2s6bqXHoP+8VkNZuZL+TSqfr7rLULODFBW1N6XHDxV3/L8t6di252TfNo+onNf9j1yk8wll38oxuK6yrDjvUuRXayjy7K8obqD0lq3R+rN1+UJ4TDgYX1bMJXirYp7Bk1UwmWaL7RdlqGmdugtPLoM5V9FKcYjiLtZBuQygw2coLcS/LWVyrU5sFU5cq6iMv6hg/y9RjDnpXURWUF/Fui/9BopK6W+VGXNZPV6KfvO97U9VfvzeLaqM/1CMB7m5/5j3rbOMysY6TWuzrPJaryDYO8ZzD0hf17iS+45KK/sXKlo3q+dEY+NJ37qNk0NX0vxWmTGZDevc0Pd8Sv+ytHzdku1O4jOUHuOiwmPpeV3w2RbOPYZxDCbxmYsL2IWf/asqa5S6qO+59S/re/09fc/XVGa6o5S/Tak8ny33O05pWv0OK+mXV1yjHlVEM71YNRUvSfunv0fXPIqmbGqxT71zelFFZfmrgz53wbaPVFwDr1c0wB2i6NI3zPO69Dw6WVHxWjY9PqXoqptbZ2DlS2NTdB2ZjuvHK8s+mNl26e9yiiJoUu95Nn9se871HwNVxsYprre5Y1B6zErTT2uzrO8znybpAUVX5NMU08jMLWn9IRyDorKyCqbJmMxDLRriNYlrTuG+FFXeC7ddV4kdWJntf9BVtaLhdvV03jy+YB/FYNwbJL1HcQdiQrjd0u3b2OTldygmNP2FWk5oKuluM3uFJLcY37KnorLRv0/PV4yd3FzRze0DijFJyyjGGFbTNkV57e1PLsprSZcVSXqporvlqzW+e+6rG9IXTVwr6a8+1r2ojX0VreRLmdmPFIOL35VJf5Ri/29Qi662ikHfJ0tazMw+r7jAfiqT/kOSLjaz6d3PFNFe51XzlC0vlXS3x/jHNpG0SiOyreDub7UYkyh3/7dZdkqR0qiqX1bctW973Iq6GlmMAT1a0o/d/SFVfmsN7lCEEh+UrqptVLxvq3lqgkYdPsOoo6oWndfWfkzU1Yr8wBSNJg+l/5+tCKaQ69Y3S0RVnUR+equkI8xsdsWx/onXd8PcKv17k5mdoQg04ZK2VQSB6E+/gMcYqOP7uj/dqQh6lfNstYiqWnGJmb3U3W8YnFRSBNx4bcrfprj7IxbjAMeNYXf3fdPfVt3U67qR9W2vqTuZFHer5O4PWESePVBxl6O6/a7HuDhqtsrP61bnUcXOiuvGSYrf2oXKXwOl6Jp4uKQXmtmfFOfS2/vS9Kbp+mN6PCs9Bin6XXpEtvya0jAVT9PAZLbf6pzrc7TGoqpK0hZ6ikRVtYijsZu7v6nudUXFuV9p3tiqrFxRMk2GpLJxtRVTzWxpT3FDLMbiL5pe640VLLrmVPLfWt4Q30Px+zhIMczDFQG+dkhddt9f2X5x3uVpui4zW977xkqaWWMsin4Ex6mwGFgqxZ2N52gs/PX2kv7gA4KIDHv7ZrZvbnuemd7CzBZVnHyvVZzcZ0vay/vGD6TxH5/wvsG6ZraWYtLUCZmImR2g6OZxbNr22xWRsb6c2Z/eQPxrfWyg+TRvHoh/qyL6V+MA3770H1a0zG6sCHyzs6Lw3DS24CDFMfi5xlfGG0O2m9kiinGgpmgBeiCT9leeD/9ct84LFWOLTDFxcraCZGZzKgJemKLrWeP0ICn9DxUXzRMVUb0GbX9VRbfWXkHwIUWX6tpxhRbjJV+j6CK8hpmtoCiMrJ3Z/90VQW9M0ZX2O+mCXpf+N+6+Xm6f+9Kfqchoj0/7s41i0uHasO+pEWUnxcW4VwE72xsySTM7UTE/03kafw41jYXurbeVKp/Z3U+uSTP9d1Kiw2coPcYLKQqP66dFFynu8jyU2afW57WVB0E5TNF16Yz0/PWKiH0fanqPlG7gMaikLfrMHdIX56dpvZUUx3p7Sb9RdI06v/L60ZnV3fuCRJnZ6e7+RisMcpUair6kaLU2pTlr3f2nmX2/WXEn407Fb6c3Tqh2zJ/FHI879iqaZradpA/4+PFY1fSLKBr71lcUvi5WdEnsv/71vq+5FAHDrkv7soqi1876GoKux7jD+7Q+ryvrZM+jzHovVHQvfncmzZrufnV/5cuHE0CoS160v+Iu7klNeWIlbdE5V1lvDY0/BtcOSF90zArzrtL8vajc0uUYlEgNy+cpKpxbK26AzOHu782sU3QNSa9vpr5x4JLepxhL+m53/2YlbatrTiX/XUwxDvhX6flGki5w92zFcpDJ5F1WE+zMSgLx+ZBv+T4dHsrctm5I3x/a9k7lxyEVbX/EnzU37rG2S4Dq+2BnxySqcLyAotvjYrlt1qyzsaLf9lclbTwg7dE1j2xXJsX8Y69QFIw2kLRBJu13FH3mt1eLqSDSOq1Dkaf0Kyu6Mbyz92jxHS2gaOm7TNElYlf1hcPuf9+0zgItv/8LFV1cfqQY//eqmnTvr/z/koLje1A6L1p9p6rvarRsi/eZohjv8ydFdMn9VdMVRQVjlSV9ofo9tdiHv2v8dDHjHpP9DKXHuPo9a0C49cmc1yoYE5VemzAmVmk88hCOQdFnVhoLo2igK/l+uuSnsyl6ifxc0RL+MUW3sp+WvPewHpKem863zTVgmqKUfpm6Ryb98oqQ/y9SBB/5tfLdDM9RdKtcLj0+pcx0S5J+qphruPd8ZUnfb0j7zfT3tLa/zdJjrBjacbiiwfdXvUdD2qLzuvQ8UhREz5Z0o+Ku5+KKxsd7FBWp3Pav6ftet+v/3E3fY9P3Wfq77Fv3EUUPoP9pwHCHknOueq4oE6+i6zHrkL7rNbzVFFZdj4HKy8qtp8morFN0DamkmVPRELxa7j1UcM1Jr52uNNY5PX+u8uNM51I0qH9H0XPtKOWHCpTkXS9UVMB/r/Hjv9+lhtgYtdtpm/CZ9FB06Vy+8nw5xdQUTelvVUxeu5hinrJFlJ/XpXT7dRnrsYooZE1zzUxV9CU/PHfySbo98761rynG+71dcdGZkv4fNCi9qBCvaOl5UNFFZ2CBWWlunfT/SopCzBxDPCcOVFSEfqEWgVBUWDFVdE16QBFooc2YyH0VLfx/Tdv+i6QTWn6WRSXtnT7PmYpxg3tUXr+m8v+Jhd/TIpLeoBigvmhDmmvq/m+x7aLvtO/caJwrqi/tKor5sX6r6Gb5ckW34GkN6VvNtVb6mdMx2bDpMdnPUHqMJ3HMSs/r0gamsxSVgmUVlY9PSjprSMegNP3NaR+uU8xv1moMjArzU8XY5NsU3f7X7nvttzXpW40vT+fJdYp5Xi/VgDHgijvZvf8/3vacqKyzvqSd0v9TJS03IP0L0nd8lqS5B6QtLdxNa7MsLV8z/W392+xwjK9Ty/lbJ/HbbHUeKcYpv0txXd1Lcf3+igYU4NO6Aytfle+u1zDYC1DyY1UqTJP9vF0ebc85ja+kjCJvmUz61tdwtbzGTuKcKyor9607m9pVfouDGqV0rRriVXDNSelv7Hs+pX9Z3+vHKxpofq9ojD5b0kGZ9NPaLEvLN0/H9G99x/hgVcb/Dvyu2iZ8Jj0kbaros3xBevxB0iaZ9KURQEu3XxzxSXGhOjD9ELbuPWrS/URxK75/+S5qmAw4/WBO0dgA6p+rxZ2ctG6rQrwKC8yK1tJ5FHcF71aMq/pRJn1psJ7iQCiF50RR5FxFAXyK0kB0RSvwaQPWeXP6Xq5XdOVYLC2fR9JdlXTX1v3fYp/WU4oopxjr8HXVB7C4psv2O3yneylaWk0xjuIaSa8bcA6dpwhKMWffaxNaCFUQ8U2TuPAXfuZWn6H0GE/inCg9r4sCUigqZQcpIlxem/6vraR1OAal31FvHPljKojQqYL8NJ3Ln1aM3617fcGaZa2CRCla9DdWtLxvq0xhqOb7KTpf1TIKtlJDQ+XxF40FHMs1QHxVcXerGmBi/0z6nyjyiFcprjVHKLrZ16WdS9Hw9i1F742BgetKjnFK3zq6dOl5XXoeqa8Qqri+zlawf20rX616YpX+Lms+9w6SPp2eL6WJlebic670GMyA9J2/o5bfY6ftq7ys/GPFNXxeRaXzXkkfGbBO0TUkrbOvWjbEq+Cak9J/K53771JUBM+UdMig77Z3riniKOSisLbOuyrrTCr4HsFxarj7Ly0mJn1hWnSrN4y7Ss43s68oBo1XxzrVDqzvsP3V3X2DyvPTzOwid9/AzJom353H3T+W2WbP3pJONrNqYJK1FIPTt2zY/z8oWi5as5jf6GhF15Aj0jiAfdz97Ib3uNBiotvefG+DJro1d3/UzHZR/Ci/bGbXZtKXBuspCoRiZksqIuGtp7ExNnu5+z0Nq9ytiBba1r89Bvs/bjH57n2KDDNna0nf8L55BtP3Vh3v5A3/D3KopFXTuIqPKO5y/0CRmVU928y2VBToFugfRO4N40w7DHrf2d0PSoErFlOM4zla0YLXv+0pipbZL9RtyOvHI+yn9nOtLWYR7Moq/1e33x/o6g8N22lU+BlKj/HcZra64pjNlf6fHrGgKa9TwXldCc4wLiBFbh2P+Sz3arN9lR+Dos/s7gcr5j481N13a7lPRfmpu7uZbeFj8xT2v173XbcNEjXF3c9J/x9vZh8ftDuD97jRlpJWVzTmyN3/bGbz16R7Y8lGbSwYjSmicvbiCExR3Endt2HVnRR3+Hrn0kWKvKbOMYqujr9W3D15sQacgx2umSXzt5ae16XnUf+5/09Jq1g6oep++zZx3sqFFXeMLjczef1Y1qlWCdqR8tKpNem65kXS+HllP5s+y7c1fl7ZonMuWdLMDk770ft/Op847r30mJWm73QNT+PyJqT3vvHQ6n4MisrKiqi/D6fy6RmKrtRXK+541+1/8TUk2UbRTfVad98plT1rg/YUXnPk7u9PZZxXpkWHe34ca28eyr+b2cqKSuyymfSt8y5Lc8lLelsam96/r9n4DD1UHJutqbHJiVdNmd0PGtL2BkuvVVnmao4AWrr9NhGf+p1uZpt5GsDbxN3/KukVZraRxiLC/cLdf9W0jplNVXQ76e1/b1v9mUtV60J8eo/SyYzNzNZVdAHaJS3Lnd+lk/s+KmmambUNhHK0orVs2/R8h7Rs44b0pZFzr7KYvPkIRUb6T0lXNO18ylCX6K80Vt7nvMrTVc3sYcX3Pnf6XxoLYNEUCfDxVCDZXNLB7n6kme1Yk+5Cxd1PKTK5agAmV1xU6pRW9nsHdzNFMKDrzOqjvKZK+KaSaitdDUoivh2hseiB1f9rNVRUswo/Q+kxvldxB1mKC1n1vMzlda3Pa3d/wszWTP//q8Vn6DUmfFgT86K6/Sk6Bur4mXuVRouJreeqLP9jw2cozU8vM7OXufuEyKgNHrAIVOXp/bZRigja59l9jTjjntc06CxvZqcqzpne/6qkb4pCLrWMgu3ud1Wf93+nNekHHdOm9f6j6N79jRbJX+zuL037c6Qy+W5Ph2Pcyzc/Ut1N1TcOlp7XPW3Po+rvQBr/W2j6HXSpfH1AkVf0oj0uq7ijm9ufkrxIkl7uESjtWkly94csos6PbaDwnEuqx+mqAWml8mNWmr7rNfz0yv9zKRp4/lyTrusxKC0rz2Fmcyii037L3f/XyzPqdLmGJK0b4guvOb3XTlJzuabf4RZBhz6lGKI1n6J3QNO2S/KuXmC6NudoIyqONczsWEUf6WmSnkiLXXH3ZAJPIW5HtX11m3phL0mfMLPHFC0Y2QzDI4ra+S0/wimK1tZzK/s/SOtCfPJJSS/r3WVMF95zJTVVHPdWzP90srvfZBFaOPd52hamenrjLNua6u5HV55/38z2zqT/o1qGIk/f2xfd/e+SDjOzXyr6/l/ftE7KUB81swUb7kpU086Wez3jkXSXYgdJG6TK6hw129+p4/ZLK/tXm9nZit/Lx9MdjScz6c+xiM77M0nTLzoNrfySdKOZvU3SbBY9CPZUdBGfwDMRkIes1WcoPcaleVxF6/M6uTZVQI7X+P1vuuger4iG9z0NyItKj0HXz2wRsv/riu6X9ynGwdwi6SUNq5TmpxtJeo+Z3aX4jrIRSVU/JcI7atJdqPGNONXndQ061TtoX22x31XHmdl3FZXTdyuiYB/RlNjM3qwI3NH2O+1FfFxR4yvvtQ1nFtMU7Je2Wy0M1hUe/1d5/fH8ZWy6omPs7k09F+rSds1bWp1HXX4HXSpf3rIn1iTyIkn6X7ou9a77U9VwTSg559y9qRxWq0NeVJq+0zXc3U+sPjeznyjO2f50nY5Bh/W+q+h9c52ki8xsGUVQo5zSa4hU1hDf+pojTY+Ce6DihokpUxa36DX0sEdU2os0uBdZUd7lKZpx6fk64T3dJ9Pb5OnJzG5RtCpmvxwz28Hdf2h93QZ6mu4Wtd1+3zpFUy+MkmWm0cisc7Ri/OFyii4BsylCEteG/zWzG3qtuul5bzzfS+vS9607RdJ8HnOSNaVZXlGYeoUiRPWdknbw6FI0aWZ2rqTva2y+pe0VgSBeM6Tttw+dPLbOcYrpRM7R+Ay1VfeEFtt/jmJs3ZXu/ut0Z/xV3nwnvXT7pdNrTFGMO7zD3f9uMTfdkk0VbCufhmAeRQPH6xS/y7MkfXYm/zaLPsOsxuqnkPCmOzNdfgejZmbXKVrQz3X31S16c2zv7rs2pC/KT1PhaYKawvqO1QJCamicIuk/kn7g7hO6Ks1IZraxKr8dH+smW5e29Dv9P0Xj6ZKKBtp1JF3adFfAYvqnDygKjdMLg943fUdK+4TG8k9TBMh6VPkCYatjbGavdvdfWcMccAMKv0XankeTfI/aype711b4LeaeXlbjC8BDuX6k7b9dMVXRGopG920UsQ2Or0lbdM49HVlM1fILd3/+JLfTqazcsK3Z3T03H3PpNcQU5YK70/NllWmIL73mmNntKph/2tIwtILtl+Rd2Zsfnu8lMh13HOvdqJjjL3cHSooBu1L7riGl25ckmdk7+xatYg1dW83she5+qzVMDur5/v9tteoG22cXjRXiH02F+Nydp6KJbi26LL5X8cO5WtKCZvZ1d6/tC+8xjmJgP3gzO87d32ITx2z0ttPUyr+zYlD0N9J6l6Rl/dv/prvvbWanNWy/6Ydc2l1NioiwvyhIX+oRRfSvJyy6c7xQY8dvGOrunOyQSb+uIrDDv8xsB0Vh4aCmxCWt/Cn9o4qK4ydL1psMM3uupAfrWuLTPhV9hlHpel53uBtdMhZsRvmfu//NzKaY2RR3P9/MDsykb5WfmtkCqTGszZgdSdrLzOZ098Ol6LqV8rtfKMaezlSpothYWexT+p3upRi3dpm7b2Qx52Durs0/3P3Mlvvd5W5O22vmhoqpN+omYc9142+tw3k0GZ9VVNrHVb4a9qu0J1ZrZraku9/j7j8ys6s1Nq/sFor5ROuUnnNPeTY2RrjnL4pxhZPVqaxs42NjfE8xLnofNQxxksqvIe7uZvZzxfCx3njknNJrzl/bVhqT0p5PrfMuRZnobkW57HJVxqWW4I5jDYuJNVdT3KqunhitauPD3r6ZHVJ5Opci07vG3bepSXu4u+9qY5ODVnlTi2uJlLnMq9j3gd1g0zrrqaYQn2vdNLOtFcFlTIMnup3m7qulFsU1lQZRN1Xs0h3crTWxdfOAvnTPdfd7R9U6a2MTJPcHkOlt/8KG9W5WhEf/g9p1Vxu5dEF+pWIqgssU/egfdfe3N6Sfs78CVLesZr1Wg97N7HrF3e1VFNPXHKmYf2rDvnSdWvmtw1gHM1vO3e8ctCyz/rmKwtWJ7v7hyX6GUSk9r83sy4pGpcP6ln9AMS9gbeGlyx3WyR6DQdIx2kLSlxTh5u9TdLt/RUP6VvmpmZ3u7m9Mn7kXAKZnwmdOjXO/lPRDdz/YolveGZLOc/d9Jv9Ju7OC7lspfe87/aJifP+g7/RKd3+ZmU1TjGt7LHfXz8y+pOgF0zZoR5Eu18wO79HqvC49jya5T1e5+1rp7t3qHuPIrnD3tWvSFvfEKtiP3yoi1/+hb/nOkj7p7ivUrFN0zqV11nP33wxaVnmtKC8add41qzGz69x9VYvYGLsrxvod7X2T16e0na4hKc23FXMfDmyIL73mmNlBihtFP9f4vKWpXFG6/dZ5l0U37Y0VjTerKBoRf+LuTUE2a1FxrNGhEF808L10+zXrL6iYhmMoFdkZoW0hfhLbv0lRGf+xYhD1hb1MpyH9LxXRHvtv739tkvtxcO51H1630NYV2S53TVMGc5a7v7Zgn67x6EK6hyLs+pcHFNau6b8A1C2rvNaqsl+zP5+R9CePYD1177m/u+9r5V1crlOMdeg/h66uS9/0+ay864spClg3VZZ1/QwH9l9Q65ZVXjvP+7pb1y0rlRpCVnb3J/uWT1GEJV+5fs1O71V0DEo/c2rY+LfG5utbUDE10ISuQ6NmEejhTMX4us0lHeoR/XWY77Gt93X1q1vW93pp962i79TMTlb0aNlb0d3wIcW8vps1pB9ZQ2sXpXldWmfSeUvhPmZ7P6Q0rStfZna8pD3dvW1PrNa/SzPbTNHbZDN3vy0t20dxLr3ea6Kdd/kdd7imleZFpelb5+9mNrukJ9zdzWwpRSCb2919Wt220zqleWNpWfl6d18lVb4ucPeTzexad1+9Jm3na4iNsCG+9JrcYfud8q6Ux2yvCEJ5gLsfkktfRVfVGm0rcBWlA99Lt9/vUcWg/ywb8ngBa+j+Wtl2rnW2GnHzIG+IuGkTu0pMf0n5FtrSQdRLuvummdf796ttC/l7FV2Rj1NEI2vVFcAmDnDubb+2lcnd7zKz9SWt6O5Hpwx5vobN98I0t4505wXBdMZ/jAmRbSd067IYC7mExkJ6976jBRRzSjY5RWOV/TbTovSC9bxD0iutOVjPvulvaTfJx929KWT/OBZd5V6i6EJdvSu4gAYEjeg7zosq5kEd10I4ic+wsSZ2RXp9/zIzm0txbBa1CDpSPWbPy+x72/Pa+y/4aeGTqaLcv93aO6uV9Sa05pYeg66f2aNXxTKKY3aMxVjYut9BcX5aUrirfMbDFcF6zpN0T2953XdUx8zWknSvu/+pIcnHFQEjBi2rKuq+5WMREp+0iND7N/fmVm933zL9u18qWC2ouPvalH4yAVcaTeKa2Tqv65K3dKkk1DhW0gpmNq73Q5/NFZWvD2is8tVU+V1U0s1mVu2J5e5eDcLU6Xfp7mdYBAo808y2kPR/iq7MG3gEIqlbp/U5l657r1BEwK+O41tA9b/90ryo6/Wjbf7+bkX55p9m9llFlNhrJK1uZke5+4F96TvljSoPBFYS4K7oGtKnNk5CVZdrTlpedE1O14sPSlrao/fgipJWcvfT69KX5l2pwvgGRaVxWUkHq7ALPBXHiklUWlrNmdh1+zZ+nNAUxbxRxw14r1GMF8jdjRsUBrtVIV5RuHmO4kT+mbfsCppa0ast6XdZjKdocomZvdTdb2izfUlfVrsW8ucqpuB4qyLi588U3QprL04VR6pmgHMTM9tXEdJ6JcUYgDkUc5at15+214Jb/S5TBSRb+FIE0bjBzNoG09lb7SLbbqKYDHdJxTnVy9QfkfSJzP4UVfYVx+Btiqlg/mIRrKdp/qcNJT3kMcXHWyRtIOn3kr6TaVEvGeuwkqLi/myNH7/0iKIFtlbNcX6WGo5zyWcws90kvU8xjUI1CMD8kuq6Vb1HcXyfpzT3XvKwYh60Jm3P60fNbMXe3YDKfq6oKHj2qxsD1tM0Fqz0GFQ/89UaO0+znzkVwnZVzF23gqKR5DDFEIOqovy0tHCn8Z/x1L5lJePl9lCMq/+du7+1sj+vV0TJXsLG97RYQPlox1JEMfyZBnTfMrN1FF1+H1SMlztWUcGYYmbvdPcJlcH+Owy5hlprCNZR2Z/WQTsadL1mluR1Red1h/OofudjrjxTlEea0pRU+Per7qak9VU/HrJTXuTu55nZuxRTfF0i6TVeE8isyzmnyJfnU5Spq+P4HlYE4OlXmheVHuPS/H1vRV41vyJy7DLu/kCqxFypOF+qOuWNaj+/eE9/bIxF1Bwbo/QaMl3Lhviia47FELNc41ZTOepoxXfauyt/j6IRblzFsUveZWbHKKbdO1PS/u5+Y24bTeiqOgRm9jlJl3hZsJiS7Ve7cz4u6S5vnki+t87Ixgt0YQURNy264m4laTtFa9rPJP20oUDeW2dxxfx1z3P315vZiyWt6+5HNqS/WTEo/k5FwSXbNcHMfuPuEwrrOWa2hOLC90FJH3P3YzNpL3f3lze9XpN+mtIE2p66bVjq1lGTtvFCKKnpQiirn4NRPiCUs5nN6y3mUDKzrb0v/PeA9IdLOqSgsq/KnZ9ze3d+vG9spMX4hlUU59pvFReMXyoy7tm8eYxml/F167r7pQX7P00tjnPpZ0i/sYUUXciq490eGfA728MLurS0Pa9TJeQQxZycva6+aykaIvYeZt7a4RiUfuZpktaWdHnlmI2LEt2FRXf89dVQuPOGSJXDYmbzV387ZraqolB3gKTPVJI+Iun8XGOZtey+ZWZXKRqTFlTcOX29u1+W7sD8xGu6rKX1fiTp494wd2Yl3b65133GTaMzTse8rtV5XXoeWYyVbVSXX0zimrOaopzwFsW1+aSm317J77LSaG+S5lSMM31CNY32Xc+5tO4yvQZaaxfdvTQvanuMi/J3q3T/tL4hPtbQNTS9Vpo3FpWVU8PE2yUt7+4HpHLjc9x9wlQZk7mGVBto3f0FZvY8RfT2ojJf3zZry089TeUoGxsX3HhMKvuc2/6EvMvMntTYTYBq3aBszLW78+j4UFwge9HJnlS0avSePzyT9+14Sc+d2d9R3z4tI+m16f95FF3ucumnKCpeD0j64IC0ZyouNtel57NLumHAvkx41KTbKj0OUlRgt68s2yqz/TUUd7emKe66vDiTbg3FRfYriqhXvWVrZLZ/Rfp7Tfo7r6KVvS7tVYqw99sqxvqsk5a/UNK1A77XuRWZaZvju66kmyX9MT1fVXG3qyn9Xoq7E6aImHaNpNdl0t8s6b+KitH1km5o+swp/bsVraW/T89XVAQGmbDd9HcuSX9TVLSU9qvxHOr4G/hy+sxzKO6uP6CYBmZSx3kyn0FRiNwp/b+opOUyaedVTEx8eOU7feMwzmtFS+gxiov+1en/lw7z+0/v84L03d+Ynq+iCMnflH5bpbwqffaTBvw2L09/r01/Z8+dpwX7fW3l/+uaXhvSd7SEotFhg94jk3aOyv8LSVpliPsxrfL/LW0/syIy6SPpOJ+aHqcM+1wa9qOXp6W87n9t87q0bqu8pfQ8UlTg7kh/n0jb/Vv6/86GfWl9zUm/x88oKrEXK+5w39Xiu2qVF82ocy69/uN0DOaVdKsicv5HJnvMJpF+BUlzpv9fpZhr+Nk16W5VNFCumY7D6oq8es3+76BvvVZ5ozqWlSUdqriDeUt6vpCicaMpfadriKKcZn2/jUnn2R3Pv0sU5a7eNX8FpXLArPKgq+okuHvpNBxFbGLXVtNYq5l7/XxRvW6t82vieAH5TAqoU9B9qzc2c3tFhM6LJW3p7r8e8BaLuvtxFt1h5TEx84SucVYeirzaPeFRxcWwp657wv6KLiW3SPqpotU7122rvyvTWn3bb+rKVDKB9uzufnbavwPc/TJJ8pi2pXHHLCYy/6qiG85yqTX4gMw59E1FN9RT0/avM7PcfEQ7u/tBFhHTFlN0QTlazaG2B45D6LO70p2ftD+3WUxE3e8/6fX/mNld7v5Eeu5m9r/+xDa5CKavc/ePmtmWii4o2yq68/6wIX3b41z0GSqfZXpLqwZ0hU2OUotuNOpwXnt0m8m20g7JEYrued9N73u9xXQ+n2tI/2l3Pz51ZdpE8Zs4VDEurM6FZvYJxRjejRVdxk4bwn73xgRPkfQsGxsfbBowTraExZQDb1VUXqrDHC5qWOUci/n6ZlcUwO43swvdfUJXKjP7qEfQrNpuXD6x+1Z1zFJ/d7MJ61dUW9tz3R5nNa3Hoddom7cUnUeepvgxs8Mknerpzk26w9MUPK3kmnOrYtzbm9z99rTOB1p83rZ5Uamu55wUDcQPW0R3P0Mpursahkio/HpQmv5ESWuZ2fMVDdinKiq3/UGi7lWMg5ZiCo5qN8e/NGxbapk3TqKs/HKPAHfXpu08ZGbPako8iWvIf9O1MlpbIzDSzLKforfQUqnnxHqKoT2zDCqOk5AKvPO7+wl9y98m6X7PTGjcUnW83099QLeb5FRJiysy4qoNJTUFN5gRWhXizewPkv6uqHTtqjRWxlKQAW8OJvAvi/7vvR/+OorgAv1+rLg4Xy1NDEUuaVw3Qy8PNvJpRevsqunxhXShrO0K6x2DMrj7V1Oh9GFFof8zmfOt64VwP8UxuyC95zQzW27Aft3dVzDIjWvrJdxMEWL7OqspVXSo7Pc85u7/7W3SIiBE3eddzGK8gFX+7+3f1Jr0G6r7XGu9cb2bKbo9PZirvBcc59LP0LOlUlfY9H5/tghA0GQFd3+rmW2f0v+77ph1Pa9nkHnc/Yq+3c417vTO4TcoopKeYmb7ZdLvoxibc4NiLNAZijvqk9W1cFdqC0UvgzYBqCRpwVRY/j/F73hfGz+uqqo3Rvyqltte1cweVpzHc6f/pQGVZY+o2qtpfLfHw5rSz0IWUzSCjpuXLTXi/VlSbsx/27yl63n0Mnd/b++Ju59pMUayTsk1Z2vFsJTzLaKd/1Tjr8tNWuVFHXQ655I5zGwOxW/oW+7+v15lpCl9+tvqetAh/ZOpEX0rSd9090N6lbCqSeTXrfLGSZSV/2cRD6NXrpuq5uA4k1HSED9S7n62xdRm6yjOub3c/YGZsS9NqDhOzv6qLzz+ShEwY1IVR3ffwsbG+x1hEclq0Hi/zSV9wt3HXbjN7F+S9lW0Ok2axfi9ZTQ+YmtTi7TUvhD/h7R8E8Xdvf6KXdMduA8qKs0rmNlvFIXlCYPS3f2N6W+2AtRj5XMDtdpuZTsfVEzgemTf8j0U3Q2/2bDeBxR98NucY10vhI+7+z/6Lky5i+DdFneLPbUK7qmxgmKdthHTiir7FW3v/ByhsYAG1f+lmgK/pwimiruvd1ZfG1SxVgTUuVVRmHpfuhBOCNBQ2d68kn7l7ueY2UqSVjKzOdy9/y5i0WeoKG1p/a+Zza2xC/kKqon62PW87sLKo0c/kPa79xm2URSmm/wpFSpeK+lAi6h0U5oSe0T2O8IiEMFLFFPBZO9UtMlPh1UZt8FRUu9QFFDbVhxnt5ia4S2SPplL6O6939+jXjOFR036CREpcyzmVt1OcXfxb4rrpc0KDRktr5lfUf3dhVsU4+1yAeha5S2T+C4eMLNPKe5uuaQdFN9xndbXHI/5mU9Oec8WimBai5vZoYpAa009UFrlRaVKz7k+pdHdi64HHdL/L1Ws36mxsmpdUMKu2uaNXcvKB6fXFzOzzyvKdJ+a9F73KWyI73LNac3MTpX0E8Xd/YGxImYGguPUsJZTL1hDMJIWr7Xaft86UxTdhw6R9AVviPZmZjd6w3w1NoQADWk7tV2ZPNMNNlXA/q7IwPZQFOJvdvdsQaNwv2ZX/OhN0m9rCtfVtK3mH7IRzy9nZjcqxgT8t2/5nIq+/E3n0L6KgtqDihbaE9z9r5PZl5r3OFJx13sfRavwnorxTO9tSL+oYizoaxXH4GxFa1nTXGtTNBYx7e8Wd4yX6G/0mMT+T1Hc+ek1QJwl6XuDCvEF2+80b5pF+PKHPaY8mUfSAu5e29KfWh5fqRjbcZniTs2j3hCwp8Nn+LBibNDGikAKO0v6sTcHpNhYceF+seL4rifpXe5+QV+6Tud1h/2vjR7tmflSLaL9Hq7o4vaQ4m7UDt43OXgl/TySNlWMFb0tVZJe2l+gtejKd4hHROEFJV2a9mlhSR929580bL84P52MVKFdRdK4KKmV109U9JY4T+OHOdR+p6nC92lJF7v7+9L3+xV33zqzD0Xz3bVlEfzh15J28bFuj3f4gEntrcO8iYX71eoY567RlpmXuJKmdd5SyiJIzr6KMa9SdF3eP9OIPdn32lbSW71hPrq2edHMZmaze2a4SukxK7x+vFgxRdil7v6T1LD5Vnf/0qQ+1Nj22+aNncrK6fUXKoY0mSJGQetpfNqqNMRng06mtEXXHOs2z/tbFXdxr1A0fp3uNRGAU/qR5l2170nFcSJrOTmxmf1O0af98b7lcygqRbVzLbbdfkrbP97vZ54Z72dmt7v780tfK2Fmv1UEQGjdujfKQnyqcLxNMfBeitbZH9dd0Gxs/qHzFYPFe3evFpB0pru/qC/9Td4QrTD3WsG+5woKAyv6ZraKIpPZWtI97t405qTLvs2juINQPWafbcrAOr7HQoqKy/RW6JpW+F7a4snnU6Ytd79/OHs8/UL2EkWggo9UXlpAEQghe06Y2cqKwk71M9e2VvYK0xZ36ub2GB92rWci+5VKBbDpxzjX0prSL6KxbjSXeU03mq7ndbpjdKikxd195XR+v9nda8cf2iSiR6c7HFO8L8puJv1iGn/M/tj3+vT8wMz2VkSN3sIiovSZTcesS346DNYXJbWyvHaMkA+IptzyPXtTeLxFUSDqWUBxHNee5Pa3VNxxfIVinNBPFdeZbE8Aiy6S/1DftDHunptOo2S/Wh3jyV6/S/KWrsxsAUUXyH8Oc7sd92VgXjSjmdkbFNeH6jFoLMSXHrMZcYxLtcgbu5aVX6pKuc5bTB9Reg1J67RuiC+95pjZJYrGrP68JRtR3qKL7qsVlc5NvXm6vpHmXXXoqlqv7eTEJym6JL3f0y3lVBg5WPlxTq22b93G+11pZu9293H9s81sF42FKJ6s0q5M8piE9eeSfj7kQvyLFN0dzpJ0reIC8jJJn7AIYnJr3yql8w91nhuo4DMs3p9JWUwv0sZ9inEpf1PcwR4ad39UUXEceFfYYs7MPRR3fKWovH8r1/prMSZqL8V8jtMUBYBLNXH+uqLJhs3MFC3j709pzSJQ0iFDaoXrNCdj2rd9FQ0WL1aMfXu9okGo6cJvFpNLv13R8CINP9/+naLF9Fwzm6epQtHbmbTP08Ojm9naXh8evct5XRq45kbFOPBcV9MJ+6WyqXverAj28zzF721pRUCP/gaC6t3VjRWBOuQxh2hul4rz0zbM7IUewUhq7+I1XD+KK4jp97mLJhaW61rU/6y4a/5mjb8ePaLoojgp3r3bY+kcsaXaHuNzLbrkfapaMLUIuvar3Iod8pYiqRD/A8UddJnZA5J2bFOYH4WSvGgG7tNhimvVRoohAtso7ho1pS86Zh3Sr6joSdJf0czega+s/1xJDzY1eBTkjUVlZYseG6dIWkoRVdgkvdTM/ihpc89McaLya4g8pq7Yv9IQf6GZNTXEl15zSueulEUX7DelfVlDER22yajzrol8FgjtOqs8VDj1gqIA9yVFSORe+N/707I5hrD9CxR3xs5XXDTOrz5v+AyLK8L5XqD4QX9N0oWKAvlzJvn9HKL4oZ8o6XbFD/Pg3qNhHVMEWemF8H4wfUefGdIxO0HSW2qWby3pxMx6e7Tc/uvTZ32XpJemx06KwvZmQ9j/dyoKUhsqxqXNr7gwXKG4KDett1s6xjcpxg/UTvcxyX07TWOh7HuPYxWVvbkq6d6g6O63k6KL22qKLo935L4jRfCQuZTCnytaFn9Wk24vjc232QsLf6diHMn7a9J/QDFmYrnKsuUVjQsfGOL3s26HdW5QjAHpTRuzuKTTMuk3TN/7xyqfo/a31vEztJqypJK+VXj0SZzXV6a/11aWTcucm+crupueVT1PB3zm0ql7rpO0iMam19hIaQqAvnTnKxoUVlc0+D2nsv1ba9IX56cN+/dcpZD7fcsPr+xX/6P2+lE5B05QdKu8o/fIpD9eMVff7xURDc+WdNCAfZ5wfRzVQ1HRec+Az3y4RjP1S9ExVkzj8JP0XZ5YWe+nijkBc+9VlLe0PY8qr18iaaPK81cp5uSbIcexZn+KpmqYQft0fd/f+SSdPaxj1iH9xYpuntcrxtfup+he3PbznKu41n614fW2eWNpWflgRYTWKZVlsyl6+RwyYJ9bXUMa1n2OogH8N+qbjkMdrzmKCmvrsqKibvAHRTCvV1e/g4b0I8m7cg+6qlZY/aTEPe7NfZLnVkwmL0m3u3vtnaiu2+8i3QHqjb+7yd2zrZUtt1nbhSlxr+kuYdF3fDNJu3oKJGIxBuZQSb9092+0fO/ali8z+627r9SwTuNr6fVWA5xT15CPaOz7vFGRkZZMznyMYjqPb3tfC23qvrVP2r4rKoNf8r7Ien3rfEkRJGla230oZWYHKYIM9cZmvVVxd3NuxbiKd6R0FyjGMl7Xt/4qikx+w4btX+nuL7OYMP3l7v6YmU1z99Ua0reabNgiatzG3tdtyaLb6tnespunmW0u6S/ufnnD60VjF9I6V7j72hZjFzdS3Gm50Qd3b53XOwyUb/EZpqlgsnob6zp7rWcmJ07Lu5zXZyruFB+f3mcbxXi11/elqz2netz9wsx79M676mfInXe9CZmvk7S6R++JK7yvW2XqInWwovDxTXf/flq+iSKM/of60hfnpw37d65izM2J7v7hNusM2N7Fijv231C0eu8kyXwsKFR/+mvdfXVLY5Usup+d5Q1j09I66ykKscsofju9cf5t74Scq5jn8Nvu3nn6BTO7QXFuzq6oMN+haKDq7c+kxuJ2PcbpGtnLE25y9ztavFenvKWyfvY8qvudN/32Z4SSvGhI7zfwnDOzy9395WZ2meKGwN8Ux6CpK2bRMeuQ/mp3X7Oap5vZr939lQWf2xQN0zfVvNYqb6ykb1tWvlnRtbu/a2uvke9FdeulNK2uIX3r7KYo30xVNJr9zN1v7ktTdM2xsen0TNEg9Jji/MnGNDGzTSWd42lKrcw+jzTvyqGraoWnqRfMbD13/031tXSha1rv34qWoJFsvwt377UsD3Obx0iSme3l7gdVXzOzvRpWe6f6CvHufoeZ7aBomW5VcVTc6VrBzPovarnCdONr1jDAWTVdPnw488t9S9GN4x2KuZ2q2z9TcRekNXffx8zWN7Od3P3oVImZz/uifE7S6u5enYfxNDO7yN03MLPqReQ5/ZXGtI/XW75r4j1m9mxJP1fMBfeQoitbLY9Q4m0q+3P0VxpTuvtTobatlyu6x8zecNE5RTF24Vzlpx2puip95iMUra7/VL4r07qKSMjzSVrazFaV9B53f9+QPkPbaMc9rcOjdzmvFdP2HC7phWb2J6XANTXbvjC9/4He1w3IIhBJY8VR7afu6fm7mc2nCAbyIzO7TzXTd7j77xSBIvqXn6Vone5f3iU/ncDdX9sr3DWladtIlszt7ueZmbn7XZL2M7NfKyqTdXpByP6eGtn+kt4r50hFz4Bx43IKvFNxh2ydDutWTWbexIG6HuNUURxYWexTlLfUvOeg8+gOM/u04losxe9ymNebUjNqqoaeNufc6ekYfEUxxZErP61D6TErTf8fi/gSt5nZ+xVTstVNg7ZwZhuqqzQmrfLGynZalZUV0b7r8tjHzWxQd+9W15A+y0ja2zMN8aXXHO8+d+VFiijzS7v7rhbdjVeqaawYad6V5TPw9uZT5SHpmjbLZtXtz6Tv59qGtDdmttP4WkN6k/SSvmX3KKbi6H98SNLdmW3domhFn+nfZ8djsK+i68Tv0vPnSfrNkN/jFklLV54vrRjIPu54S7o6s40Jr0lap2bZhopxT8/KbOtYRXep7yi6gB2i+u5ejb+lIf+OpxWk3ary/0Lp77KKVtXcepcrxnlUv++i382A7X9Z0icU41I2VoQ+/3wm/dsVXXPukfR5Sb+VtO0wz7v0PvMq5v0alK4uL7p+wDprKLoi/SP9/V3dceid+2lfpigqXTsqogsvMsTP2jo/rbw+W/rNL917ZNK2+t1U0v8mfd6TFC33WyqiVDel/z9FN8ENFJWd+xSNG9nzehLf10KDfjcdtnlsm2Uz8hgXbLtT3tLhPFpIcUf9GkU8gYN67zczHjMiL1L0rlmpRbr3V/5/Sfo7p2KO00kfs67HWBHvYT5FHIGjFV2f666/d2psGMgTGhta9ISkO2vSjzRvVFyPVlfk1dXHmkpdk1tso9U1pJJ+fUk7pf+nqjLUpS9d0TVHNUM/6pZVXvuZpI8qXefTOTgtk36keVfdgzuOFal1/xWSptrYBNpSBOGYzNw+M2T7o2YxH9DbJC1nMddMz/xqns/pvw3LB70mM1tf0ooed9QWVWQC/S1f/XPWVeXmrysOqtGGmX3T3fc2s9NUc+fGhxdif0uVTdzexYckXWxmv1dU2pdTzB01r8YP1l6h73zoMdXPsfgdxUVAZnapu6/rma6FFWupXTSz3hxidfuTncDZyiLWnW5mm7n7GQP2R4qw8b0gAOcppqr4Q4v15O532/jgKo13aCwi4X5IcWF/d6a1sudjioL/wMnqU8v1nYqLWi88+hY+xPDoZvYFSV9297+n5wtJ+pC7f6ov3W6KKX1WsPGTzc+vqPg0bX82RSPFhho8dc/PFcfpX6mnw9bKBykoUslPly/IT2URYXdfSX/V2B0WV0yxUaft76Znb0WAjz0VYxdfrZoeF2b2BXf/hLt/z8w29ojG26qrqWKy968ofhPVKT9qA/ZYdId/s6KAOk3S/WZ2obt/sC59B+O6+qXzJDutThtdj3GhTnlL6Xnk7g9J2tNmgaiqMygvepNinN2zFGWe1RRz99Zdw3dW9CqSoqFmDY9hNU13x0qPWVF6Mzvb3V/n7lea2cfd/YuKLue1PEUdtgjuc2rvmmYx3KAuQMzPNcK8UVEuq51yTtGjoVHba0jfOvsq8smVFBXsORTzla5XSZO75lxSs825FJXXVkH9KlZw97emvEPu/m+zbHS1keRdOVQcx3uWonVmdo2vjDysmsnkm1hzJKpRb3/ULlH8oBdVBN3peUQx+LpOp0J8zQ/5Wer7IUvTo2F1saikm83sCo0vuEy2YtfrxvPVSW5nkNKJ24u5+xmp4vFCxfG61cem4vhmJenmmc3UfQ/VTDBbkevTqrLvHSdwtvKohHspovf+V2Pd9dzrxy5Yw/+D3J26GbqZPUtRmM8Vjo5WdGFaNz2/RxG8ZELF0cbPRZrrTiVpemTkr7n7uooW4VF4vbt/ovKeD5nZZpo46fOPFd1gv6gYR9nziGfmlfOY+2xzj7HVTd2veqrHqW2FqESX/FSK824lb5gftUZRI5m7X5n+/acyhU1Ft9zesTpQzZN413l5+rtW9a3VPMH9gu7+sEUk5qPdfd++wlsnZvZxxWfoTVLfO+b/VXR3m6yux7hE17yl6DyyWSiq6gzKi/ZTjP++IL3nNDNbtsV6bY5B6TErTT+18v+2inyyjZd5ZZ5mdz/TzD47YH86541NZVl336jrNtX+GlLVpiG+9JpTjeBfbRB7WPUR/Hv+azEWtFe2W0E1DRA1eZcUx2VYeVcjKo4V7n6hRWCAl06iQiI1jMdLd1UuNLPve4wdGer2Ry3t810aK5S2WafrndRR31HbbzIrm9n7FC3GJ3qlL767X53+trmDltv+5soENZF0nJl9V9KzzezdihbPgYX/DtbU2NioVcxswh24Dp91SmqBm1L5f/qFKFPwH1Vlv2cbRVTYa919J4vxmY13rb1sDMPcZra64jPPlf6vfubaOy2KyZsPkrSEohJ4tmIMR5PWrZWp8HVdGkvxx5af42wz21rSSQV3sKZrcV7PZmZz9goS6QI6Z82+/8Mi+MBLO+SlvzGzbym6BE0fB11zDLzh/yJNn9nd7zKzeyT9q/A3dLfyYzL7tfrdzMDeEl0KhrOnQuZb1GJ6oIL9+KKkL5rZF93948PabmX7XY/xOBZzx0kRmOVbfS93zVtKz6PvSvqgR/wEmdmrFAXUVxRsY5gmlRe18HjKZ9qkfbbFHKJTJC1gZltVX3T3/iknSo9Zafqu38cDZvYpRSO9K8YG1jUsDCVv1GjKsq2uIX0GNsS7+z8k/cPM+qfWmM/M5uu/hnqMaT7IWgb1q9hXMQftUmb2I8XNknfV7M8XLcZWfs+HGFizDSqOfVKLdHagcIttDBpk/v3eCdq3XmMUusLtj5SNRYuS4k7gHIoLY22UqI5GekctNRIsrhgDIElXuPt9BZswRZ/4tyu6UI1/cZLzJ2lAUBN3/6rFxO0PK+7KfsYHTNxeygoCCBVaUOPn0Kxe9FzNLZj7TfJ9B/l3qkw9btEd677MvkiSLOax6gUQusCbu4RWu978ReO74TTeafEI8vP2lvsvtWytrHiupJtSpaJaiWqqJHxQ0f3mcTP7jwZEiKsxKFjPDyWdZxGB2hUNIrVdoDpWfKWxgu4B1c1p4jHo9ZYwTWzVHcpnTtebR81swVQwaeMOSReY2S80viLY1LVrv5bbLe0tsZjFkAur/D9dZn9kMU/bvhr77Vyo6AbY9B3srwgwdLFH97vlJd3WkLaLM81sg/6F7n7RZDfc8Rj3b+NFFsM1Xl7zcqe8ReXn0by9SmNKd8Gwr8uFJpsXDXKjmb1NURFZUdHbY0KXxORCjZUDLtL4+X1dE+cqLD1mpel7XaNNE7tJ5/L37RW/y5Mrn2X7mnRDyRtHVJZtfQ2pKGmI/0Xabq/X3HKK8bVN0Yv/YWbv7F/Y3wBfWX6OmV2jCMJkioj1E4L9pbRPWgTMm6GYjqOGmX1NEd72eI0vTE2YqLSyzmyKOXWqUetqCzNmVu1/PJdizsHH3f2jw9j+jGZmW0hau9o9YAjb/LDiGGysqIDtLOnHhS03ue2/RRH57ALFj/OVkj7i7icMaftFIe0n+V6LSvrbsFtdUyt3ydiokZtkZX/Qtr+j6PqxnWKc4D8Vg9Jru+tZTInyMkk/Sou2VwQD2qcufcd9KpryIzUmfEpxIT5bqbXS3S9oSL9h3fLJ3jGfDItxNb1xS2d7RCVtSvsrxTFoW/GVmS3vfdMb1C2bUczsOEUh4RyN/wx7NqSvzUN8cr1kijXtR09uf8zsREUX2l6B7h2SVnX3rRrS10Yi71/WVbrL2jOXoovi1W0bc1tsv+gYzwil55GZnaxo5KtGVV3L3bcYyQ7OZBbjxT8p6XVp0VmSPudjwzVmWU35es+g/N1GOI51RpRlS64hlXU2VhxrU0wn1Koh3szWUAQDe0/D69Uy61xpv65x9wnD0yyimj+RbpospWgo+r27X5t5/29L+r6PDTEYOSqONax+vkXPFNZqB5l7wTwqFgP9a3/sw9j+qJnZZe4+2dDo/dvs9ENO62a7xFnMO7Rxr+KRCujnesMcUGY2p6KCv6zGZ3gHNKQvnj/JWoTMt5g64EuSHlQErjhW0RVtiqR3uvsvm7ZfysyOl7Snuw81gFBXo6zsp1bPJd397vR8WcVclY3jkCzGWK3m7k+m57MpurkO7XdpZpcopvwYN22Bu5+YWWcRjbVWXtbUWpnS1oYW719Wee08d3/NoGV9r5dMBVGkS8XX0vxvfcuudvehBBSwwgBF1jDXn6epHIawP+soIqm+SNFDZDbV9BCxsXnBag35vJ7mffNm1i2rvFZ3zCYsG+L+LaUIsFF3t6XL9lodYzO72N3Xt/G9eqTh300rZjGkYH9FTxsp7kbt7xE0Z2bsT3Fe1HK7x7r7O6xmCpWnO+sbx6qIrrqjD2kc62TLsjYD4nt0aYgvyYtSb4tj+xs3053OAxUN1p9VzB1+jWLI1lHufmDD9m6W9ALFMLJ/aSyvYB7HGanpDkNG6SDzalfYKYpxZM8Z1vZHzcb335+iCHAw7Ltd80r6Vbptv5KklcxsDq+PflhnUJe4KX13q/6m+CxNTlGMB7la+a5/Pa3mT+qx9t1Cv6W4K7agpF8pBoJfZmYvlPQTRd/4YZnUmEKL6Gb/UPTBH8a5+0nF4P1xlX3FhL1t9qdxAufUwvdzpWhk3jLiqaRnKyrxUhyTYZunqRJXx2I+2Gnu/guLuVI/YWYHefM4wI3VN6+oIihQf2VyLkWkzaIIcW3P65qC8vSXlCkwe0GX8/QbeYmkBfvysAVUFqRpkNYBiqSoPFgEPnpBWtQU5VXS9PP+o4rPUu0G33R37FuKu+jHK/Lqdyp6c/SbkfOC/dvM1nf3i6Xp5+2EycBt5kUiv0fSysPaWNtj7O7rp7/DjpA9QdvzyMy2cveTPIKM7DuzKoqV/emUFxVY08yWkbSzmf2gsn1J2TH4TwejHsc62bJs45jILteQXEO8mdU2xPflQ1MUEeLvL/gMj6o+/91bca2cXxEAbxl3fyA1RF6pqFTWqSvfjhQVxxpmtqSihXY9xYl4saKf8T0Nq5QOMr9aY32kH1eEld4lk750+6NW7b//uKQ/KB9Zs4uLJL0yXRjOlXSVpLeq5XgvH9xt9pdmdpaisqW07dxk5Uu6+4TJvTP2VouQ9hVtQ+bP7u5nS5KZHeDul0mSu99q7Qbxl9hvkutfocgIv6EorE5WaWW/36AJnC8zs5cVdPn4oqRrzex8xW95A0nDDrBRMuWHJB2qGH+yqqLF8ihFJW3cnTkbCy2+vLWbzqIaIa46PnVQhLhW53XXgnLNXehDzKzpLvRKisrRszU+D3tE0R14WIrCqafC2TGKfNQUQRF29ObxdT9SBPZ5oyJ40o4aUHBx99vNbDZ3f0LS0elOdn+ayQRsK/VeST9Ire+S9JBqAkBoSJHIB7HoTtY7R6dIWk3SdUPc/qtUdox76y2m8ZW6YXbpa3seTZgKYoj70EXXvKitwxQNsMv3bV/Kj8F/Ohj1ONZJlWU9Myay4zWkS0N89X0eV4x5zPUAqgYbm03R8+O4mqT/TY0yD5nZ7b2eQu7+qEXk9loeAbhWVfTAkqRfu/vQ8q46dFWtYWbnKELvVvvyv93dN25If6SiUNJ2kHnp/ox0+7Oi3q3/1LVhbnf/spld6+6rN6SfTdIbNLFLXC5Aw1aKbjcm6SJ3PzmT9nBJh7j7DZ0+0ADWsltotUtEf/eIUXbdasPMFh5la6zFvG+raHxl//rcHTmLQDFLu/tvW2y/uMtH6jrzspT2cnfPzjHVsH5j15vUijqv4nf/v8o+1d6Bq/xuPiPpT+5+ZEM3vwUVE3oXTWdhhRHi2p7XXVlhl/OUZl13v3QU+5O2f4liHMtv0rFYQdJP3H3thvRXS3pb7xw1sxek9LVdZ22sG/z1vXPT8kMdLlLMxfY9RWCNexXjXpu65bfq2joMFmOp5O51Uzb10swm6WdeMyZoiPtRbdR7XNIffEjjJ9P2S4/xmxXTdzxPEaRrGcXE500BOOq2MShvaXUeVa+7uWvwjFaaF3XY/qHuvtskt7GWpHvd/U8t0xd1xSxN32J7Ix3HWlqWTXnnPe7+WGp8WUXSDzzN0TiE/ZnePd7MbnH3F1Vey57rFlH+3QeMA7Xxwykel3RX3U0oM7tVESdhiiLAz9sU13uT9MPqvvWtt5ei4bPXuLOlpMNH+dvgjmO9qe5eHef4fTPbO5P+j+nxrPTIMrM5JO2mSjRGSd+t67rSZfuj1uGObMe3sXUVdxh7d2Nz5+tpkv6jmMj8yaZEZvZ8SYu7+288gh2dlJZvYGYruPvvG1ZdX9K7zOxORYaXrVSkgsFHFBf8akW2qTtZ226huWhmQ+luZ93H2VxuZtMUXfXObHH3tP99a0POV47ZR/oq+5dqLDBN3fZKJnCWWnb5MLNNJM3v7iekCtGpafnbzew+L4tumw1H3qEV9RGL+Z3eobhjP5si6nG//ykqltunfV9J0maKSvOEIGBm9jJJd/cuRhZR4rZO6ffLVDZHPYVK67vQFmNILnD3S1Or9ZEa+wzv8uZpC0q1CqdeMUe1YcPdf5euEU1614l7zewNkv4saclM+ncovpP3S/qApKUUn7tJXdfW52fSj2OZ8eUW3bz+4e5HSmMVxtRAOJu7f7N/HR9CpPNBvKYr6ZDfovQYf1bRM+Jcd1/dzDZSfXTLnEFTHbQ9j7pO9zESk8iLivQqjTa5u757KKax+p27v7VF+tLpKYrS2+DhIzsrxrGepNSgrvxcrv3bbxwOkpSWZU+UtFYqAxypuNb+WHGtGoZqWbG/q3xt+cXMVlZ87wun59lxoD5xOEVTNOhc5Nxcg/Qukl7u7v9K+3Ogomw0soqj3J1H30PRNXIHRUvrbOn/84a4/e8puq28Oj2OVvyQZ/pnb7n/5ygyk9nT412Szhnye2yoyCQ+lp4vL+ngTPrrW273dEmr1CxfS9JpmfWWqXtk0l+naBxYWzFubk1Jaw74vBMeM+n4Nn6uAeuZYszcTyT9XtIXJL2gcBuLSHrDkI7Z1YouKNe2PU/S7/15kpbuPWrSXKZoXOpf/hxJl3b83l7St+yF6e8adY/Mtp6jCFP/yvR8aUXQpP50F0laMf3/fMUYj0MUXdG+VJP+GkkLp/83UBQyt1YUcE+YWee1opvqWSkPepeiu/mBDWlvVBTgpWjNvTqdb69VdO8Z5m9oEUUPiDdKWnRA2qMUhaJXpccRiknum9K/MZ3XK0s6P32ONw9IP6Vg36/yvt+KpEsK1v+CoiHvzIZj8Kya5XPmfpuKu2+nKirBW/UeQzxer1JUPC5Mv407JW0wxO2XHuPeMbiud+wU43dL33dC3lJ6HqXXmh6/GtZ3VPCZOuVFHd7nTYpC/r/S+fCkpJs6bmv+YRyzyaaXtIUicNcPBqRbQNJ8HT7n8xRlnd2HdazT349I2iP9f+0Qj/ETii7OjyjuBj5cef6/hnUukbRR5fmrcvmjYu7ZuxRl/h+kc2mbIX6GGyTNVXk+l6QbhrX9ugddVWuY2dKKVtd1Fa0OlyjuqNWOAbHCYAVmdp33dROqW9Z1+6NmhRHxJvle83pqSRmQ7kBF5f7sAeludPfaoAdWiYCaWb9V66MNMUrjjGbju8Oe6O65uxNN29hI0d1iXkXhZx+vdA9Md8LOcvfXtthWp2NmZpe7+8v7ulpN75ZVk75VxLcB22h8rS9d9jwys8PdfVeL8ZP9PPfbtwjssKK7n2sxsH42d3+kL0012u9nFQWx3dNdl6v7v9Nq/mQR/vt+d98vPR/Jb78ti0nA15PyXc77uiX9WNG1+KD0fGjdvG0sQNG/LAIUrSGpMUCRRcTm3VXpNi/pOz687mc/VFzLTlRUVm4ZkL6oa2vhvuR+r7nXjq5Z7D6kia+tsCtph+0XHeN052YLRVfyRRXdVV/m7o1BSkbdrW9WMaPyIotu8K9W311fd9+1Ju0LPeIM1OYhnrkra4XTU5Skt8LhI9YhqqqVDQcpLStfLumbisB4b3L3O3PlgRmhQ/m9eDhF4f58UDE+uXfd20IxPcc3h7H9WqOslT5THor50nZRRELaUNG6WNvqndJfowig0Hu+vFLLyjC2PwM+70jvyKb3WFfSzZL+mJ6vqrjQNqXfUtEy+G+NtRg9XJPu9sw2cq+9WS1aHxUZ7sKKwDLvUwRj6S1bOLP9dRSRs/4p6b9KLWEz6fheW/d/i/UWUURNu0oxhmErxcVtLUl31qQ/VdKCLbbb9ZgdqbizdL0iitkhkg7LbUvSIi3253eKIEX9y+eQdNuAdVudR5M4du9O59Hv0/MV636bGn836TeStqg8v64m/Y29zyzpVlXuxki6MbM/kzqvU15zpqQ3DuG7uSb9HudSNA68pPLaLUM8BtcrKgerKhpN9pJ04RC2+9H09xBJB/c/Bqy7gCKoyGWKbky7quEuiKI3xVxpnX0VXaZWyGx7HkmflnRE5ZyrPV6K1vHFa5YvrhG3kg86Zm2WzcD9mVdxbZ1dUSjcc1DepIhcPLuiB8HvFUHJzhjmeTQrPLrmRR3ep/VdX8WYMqnwrqyiK+sDkm5Kv40bcuddh/S3KbqcbybFjaIBn7n0btqbFN2670zPV5N0aiZ9aVn5xem83D49X07RCD0zz7+TU363bHp8StLPM+lv6Hs+Zdh5neIu756Ka83qo/4OGONYYRFQoom7+2cbXlvEIwjFXh7zh11oZrkJVj8i6Xwzu0NRwFhG+X7kpdsftZ0Vd2S/kZ7/Ji0bpm9K2kRp/Ji7X2dmG2TSf01R2bzB0y+pwZVm9m53P6K60Mx2UXTVadJ2zEk1Yq4Ux7rH1RyRrW3I/BnBG/4f5FJF3/8tfPx416vM7LCa9P+RdINFMKrcpNhdj9keipbKxxTjIs6S9LlM+rYR306SdISZvd/HxhXMq7jATRgf2Kd47JKVzYO4u6J79OUp3W3p7ma/683sq4ppYp6vuKDLzJ7dsN2fKPKdBxSNM79O6Z+v/Hc22fO6NhKuxVjjpnPT3X2FmuWfUTRqzKYo3NyUtrWhpDsK9mmQx93dLcb6HZzy7h37E6W7ybnP0D8fXe9O4VWlO+TuD5vZiZLmVkSk3FLSR8zsYJ8YRGELjzux/1GMd+oFYGia0+5otZ9+5CuSfmFmH1JU5KUo9HxZMR65VroDeKii0rmyma2i6FaZ+z2XuMoicEcvKMjblc9bWulwjHsvVHvZHNPy7Z5098fNbEtJ33T3Q8zs2pp0nc+jWUTXvKjU381sPsXd4R+Z2X2K7owTeLoL6e4bFb7HXiqbnqI0/QsUvQd2VkSc/pnibtTvGtKXRlXdT3G9uSCln2YxB3KTorKsu99sZh9TDLmQu9+pmD5jZiodB1oXwb9tlPRW3P1qDSG/aouK43h1XSLnVbSQLKIo9NUpClbg7udZmhRaceLd6vluSaXBEEbKo1vEsIJb5N7nbhsfxf6JprSKlrUbB1QapSg0nWxm1YLBWoqB2ltm1vufu//NzKaY2RR3P9+ie2y/t3nHiI3eImT+DJILwONePx/SbJJOb2pc8frJa3+RHoPsrYJjZmkCZ0nvdvdPKiqPjWxsXqY7JF1gZoMivn1KUQG9y8x63Q+XVtzh/PSAz9L2PJr+WdRufs+ex9z9v73fjZnNrvqC67sVhZBlJb3O3R9Ny1+smgK8u3/ezM5TVOLOrvzOpigq6I26ntcWU/Es2nBRXKvv+RTFWJIPS6orLMvdT7foxju/j5+LrjfVz7C0DVBUF8xiHUVXrglzUbr7aelv24qEJMkiQudOivPoWElru/t9Ft2Yb9HEIAo7amIl8V01y3paTz/i7j8ws/slHaAYW+eKuyf7untuOqQjFI1w303bud6iu/GwKo67KRpd9lSlK+kQtlt0jK3jfKbJ/9Ix2FFj081MOO+6nkezisnkRYU2V1RMP6BoSFhQcd5mFTb0lU5PUZQ+fTfnSDrHxoaPvC91nxw3fCS5w8w+rfFRVe/MvMXj7v6Php97naKyrJUHuBu5dO3YM+3fQpL+nit3+sSgfod7JoJ/P2uInNuXV1jl/9kV48hHVr+j4ljh7l/r/W8RancvxQX3p4o7Wk0+ZxHe/kOKi/ACisxmHBuLBvYXj3EIqylFAzOz/by5L3qr7Y+ajUUlvC0VDEYVlVCS7k4ZsFuMu9pTYy2lde5VFPrPVKbQ7+5/lfSKlIn2+sn/wt1/NWB/2rY+flvd5rl6NH3OaWb25fR5Ws+fZIOjmbXm7rN1WOcJi7mEStY5xlqMj+hwzEoncO5FLm0V8c3dH5e0j5ntr7Fok7e7+4QJzGu0bsVO2s7v2XOhmX1CUeHfWNFd+rSaz/Bv1bTcuvsliu5KE3iaM7RvWVPLdU/ReW1mFygapWZXVJbvt5gi4IPVdL0WdzOboqigfSSlf4O739y0/XTsHupbNnAMdaG3KrpI7+zuf7EYM/+Vmn2ZXhlOdz0/rQgS8966SpSNnw9sgkxhamtJ3/C+OQM95geb3lMkVTrepiignVpJOr8iWm2T/6bfsaftrKBKHlyzn2cqP2dunXnc/Yq+Amrud1MkFcq+bmbfUoy/+tOAxty22y06xt5xPtNkJ8V8jJ/3GAu2nKKiME7X88jMzuu/Q1q3bEbomBe1YpXI62nRk5KOsejx9GxlfgsdGvraNlZ2Sm9miygqf+9QdM/fQ9GLazVFr4Dl+lap3k2TBt9Nu9HM3iZptnQzZE81XD+S0rLsfpp4R7N/n2cIix6Jx3mMZZ1TkYetKukJM3ubu5/btK67n2QxdvyVijJGidrIuf15RaqzvE8xJKF1xbQLguP0sQj7/UFFC9MxiqAGD+XXar3tayS91t0fTJnQTxU/5NUkvchHOE/VMJjZjYr+0/9LmcWHJL1O0uqKFuNXZjdQ9l6LKlq4X6so+J+tCFBUm2mb2b51y919/yHtz7yKrlumsdbHH/Xvj3Wc5ypVdO5TtBB/IG3/O+5+e8v1n6fUpc/dhzEJcjEz+5qiG+LxGt/1tLb7ZrU10d2H1ppoZnsq7iAsr+iKWS1turs3dRceuXQe/VvROt54HlXSF82DmCpSuyh+l6bonvu9gornUJWe173fj5n9n6Sl3H1fqwk4ZDGVwc5pmxdL+qI3T6Uzw1mLAEUp3SaKysR/FAX+8/vTVNJumP7dShE9t1cp2F4x7+AnatYpCUK1jKIgOWFuT8U4qtqKWmqg+JTibvXZStOPuPsFg96zrdQg+H5Jx3vMjbmNpF3cvdUUOpntHqaYn/emVKC9VFHgX1jSh939J9kNtHuP1se4b72ioClpnYENcaXnkZnNpRjHer5izFsvP11AETm3dn65pyozO13SJ9z9+r7laynKOW+qX1OymFKqdUNfabmlQ/rfKSoeR3vfdGlm9jFPPYHMbKveddrMFmpb5k152ycV1xspDQdx9/+0Wb/F9osC3I2Smd0kaWV3dzPbVdHI9hpFd+BjvG+e3nQe7ePuN6a7htcoercsrxgP/s2C9zbFeXVTzWvPVvTKeqdiSM43msoTw0LFscJigvGtJB2uuHMzaGLPj3pMTH+IalrwvG+slhVGAyvd/qjZDIpK+FRmZn9XtNLVmmylqOE9F1IUsq8fmHiErDDyoUUkw1cr7mL3LgoDI9sW7E+rCZwncSenyz59WjHG5O7Ksl3d/fCGfZpf0bA0qnkQZylmdoOiEHKMpE+6+5UNFcd7FHecvqmaFtymxooZwaJnxq6KYFgrpJb4w2ru1lwpaaribuSE7u3e0IPDzC5y9w0GLau8dqqkd7j7MMd/9b/HIooumCbpMnd/YMjbX15xXX6F4o7xnZJ2cPc/THK7N7n7S9L/e0t6lbtvYWbPUVSKihsB+7bf9Ri3ivDct05RQ1zb88hifOveiqkWqg1xDysKwN/S04hNIvJ6aUPfKKWGh694X2+NhrTVSOoDy3KWhoNYjFVs6sJeTd+pLGsx7vg8RUPW1oo7mnO4+3sHveew9VVeT1R0k/5uej7hO+vLWz6hmGLrnenO4G9qrmkLK8P7ekqlmysfUvRwOUrRADayPL6KrqrjfUhROPuUpE/aWLeYpvEFpYPMZzOz2VPL7WsUhYueumMxqw1ifzK1nDyk2P/PV16be5hvZBGy+N2aOFagqRIykilLzOxid1/fJo49aTon7le+W3P/9o9z97ekAnNdhto07cMFatGlb0Zy91yXljp14yOG1pLl7Sdw7o3pq22BH9b+JHtI2t7Mdq/ceXivolBct09FLKaC2E8RcGt2jZ2nM/Qua9fzWtFN6ixJF6dK4/KqnzD53LTdVdNj3OY1OEhRdV9rx5BMQtsARf9SRJvdRlEoGndnXNGoUmeqmS3v7nek/V9OUTlp0ioIVU0eN/0lZcbX2dj0I7+wmH7kE2bWOP1IF+mzvtbijv0Ur7l729F/K/9vrOgtIY8uxsPYftdjXBoERSrv1tfqPEoVg4PMbA+fGEjp6WiuzGuDyjmLSrrZzFo19JWWW0rSe9nwEWv4v0npcJCuZdlqgLufKK4NTbFGRu0xM1tZ0ZizkcaPX56nJv3/Kv+/RjFOW+7+iJk9WZO+GlhxaUU52xTdo/+oid2K71KUN4+W9KikXap5ljd3d540Ko4V7j6lMH3pIPOiaGAdtj9qMyoqoSSdovh+zlU+KE7PjyT9TDGp8XsVAQLun+xOuPv66W/bsSePeEQLa2uv9PeNRTsW01g8bNGl72hPXfoKtzFUVh75sHR8ROn+vEkxlcDzFN0ll1FcwF5STdc7Xmb22b7W9tMsxiW0fb82FZA/KYIuHG9mJ7j7V1R/of6Txo+z6b3HBum1Jkcqum9erXa/m3HM7AuKvOh7bQqt1jy2tut5fW+1Uunud5jZhAugu7+rcLs5tWNIJqFVgCJ3f1XH7X9AMc6pl+cuqxjX0qRVEKqCPK7foYqAWqsqxpoepRjTtWF2rQqLCLR/cffLG17fS1FAekQR0XgNRTew7Ly9LfzdzN6o+E2tp+jm3Ttmk24MncQxLg2aIpU3xJWeR38xs/lTwfdTirH8n2u6a/oU1jWKtxSV9xKl5ZbS9NNSj4NBw0fmNrPVFUMo5kr/WyV9/zE+TNIvFd0ur9bEBpFxDZVdy7IeQdsGBribQfaSdIKiceUbHhFeZWabqT4g292p58A9it/KL1P6uVUftGq59PphivL1Gen56xVDtvp9RWO/78mMjS5GV9VJsA5d3MxsHY1FA+uF8n+BpPn6f5xdtj9q6YI6LiphagU2H9C1t/B9JnTdHZD+andf0yrd2tIduFaFl6YCcIfuAye5+1Zt97sra9mlb0ayCKv9EUnfrXTpyHX7qY6P6I3H+6wPb3zEdWo5gXNKf4siuEq1Bf4MbzmGJ51DK0hqrIDY2Bi+uRQF7vkkvdTdX9iXrtM4G0tjQtrsb8P6W6TPsKq7v7NF+qGOrW3o8jPybvBmzWNIOmzry5L+rhhzsociYMHNHhF+h8IiOEPvnBkUlXukesfHInjEnzzC7Rcds9Rg8VLF/HwTxi1aGuZhMV5wd8WYwaMne16ka+/Bip4G33T376flmyiiDX9oMtvvsD+9HiMvUURdbxs0pVO3vpLzqHd9MbP1FeNgv6rIozrnN7MiM1tcEVzkv6qJ4u3ufxniexWVWzqkbzV8xGLamCaeuQM60uEgDev9Q3ED47vDKiuMgkUvkwMU18dv9xq5UjlkTXev7VXUO8Z9y65y9/5I4jMNdxwnp7iLm5dFA5uRXeha8RkTlVCSTjezzXqtLi1MdsqS2vniVNh9oLTSaM3dw3rbawq/3rZL34xUFPlwBrQmFk19ofIW+HHc/bW9Ckgm2VUp7X8k7WRmuyvmseu3bH+lMa13leXnyTrfYqz2SRpf4Gx1V8Ddf94mXSX9nxW/tXEt8aXntZmtqxi/NrVSeJYiAEdxlN9BrD7wyKQrjck+ijtXNyjOnzMkfe//27vzMEmqMl3g79stiLKNSKuMuLAJLgiCqMhcURY3cBlQkU0EXMYZERBlZlzZ9CrIKOAMwshFRGVXFB0RZBVQkU12lQFcEUREEBSlee8f52RXVFZEZJzIiIyozvf3PP10V2Rk5KmuqMyzfd/X0LUHNsbMNv4NSEJDKf9Zf7twqqrlRwopJ7HPkMGbyqsRBow/JsffSxo/e1+Zc/w7CO+vk5aU4XlInW19I++jjMEOhm0AHC3p6yQPqNi2eUP1M68PFgaOAvBMhJ/bQgAPlHyOp/ZbUku/VQofUXr9ycHz2g4HuRVhhS9bA/FOhIQ0/43wntNLku5CWBUePn4BQqKpInfHFf0vIbxv74LyrNYT5xXHBjAxWUHfrt9HseO5PMKH4N8wOs5mW4StrU/BTJrnAyV9I+/8+JyRGegy5+ZuH2hqRprkQQB+i7BtbpC5dUVJhxacv5nmbmOcc2ySmJj5sO3ZxLgC+HqE2fFVEbarbiLpxSXPSVrJKRiAFGY+JLkrgDOVidEi+RrFrTyZY7dIWnvOBUY/lveBVDZjXGl7cckgZPB7WRSLW+m+Ztju/lKED9rPZR66H8BZkhqbFGGNxCN9woKU/5obs7iapDsYYpHmUEMxiAyJZHYC8CNJ32MoP/LSkgHIYLfBfgjvv29nrGusglJCceXkyQgTdRsgdMgvHJ6Zt+qq3keZ87+JsKV3K4QB558BXK6Y8M/CyhCANyNsDX0+woT0OkUTI6n9lhrnp4aPJGFBOIhiUpic81MTexWez0zymaUJwy63jwIYfN8XI/yMh+NGO+OBYwM45ha3rq8/jZiega7V7QPM2WKYdyzzWCdb+sowP/PhzkUdVJJHYO5s4m8RYotWkjTWbCITS1/E51Qu3lxnAMKQdfd2hC2zN8VjeT/LkwCcr/w4m5dLaqRgPStuL647CEm5r+Mg/BSNUZaIYSvvHZIK40BJ3gLghWX3wTg4ZoIijoiVZWLK/6Hnrgrg93WeO+K6lcqPZM4/BWGV+i2xQ/sYAN9XQXgCQ5mZDQHcKune2LlaXR1nkq6rws84eVIt9Tmp91H8ub4SwHUKCZ9WQ9hmP26c6VJj0Cfg7K2kl5VNVrbcnqTwkRrXbzUcJJ7/isFkbJyUOlvSs1iz9Nl8QXIlAI+owRCwpnirajPG2uLWg+uPZdSHYOK11lMosJo7AFJx+vLUmbUDMDcD3dNLmjbW9oEK/0eLSe6MUNtTCFs45iQ34YS39CV6nMJ2zSWZD+MAvWhl43nKSUYzmE2s2wjWLOBcNAOP4uLNdTIf3oawjfF0kgdIOg3ITY6zD4CvxXtiTpxNTtt3kfSloXtiCRXHRlXaXqyYXr7GKlWl+zpeezFHxBRXsBeA55L8acnguk7ikRRjJSjC6GQ91yNs9ypN+c+wbe4TAO5B2LJ4IsLK+wKSb5F0do225b3OkvIjCL8/T0ZYNS4rDL+WpB1I7ggAkv5Mlm493RQhc+sDDJlbN0Ko8ztfjfoZ19mil/qcSvfRgEJowVdJPiF24AHg5irPnSIPklwWISnNoQj/t8sPn8T0Um51S7MlhY/U0HY4yH4ALiH5vwifk2sA+OfYx+hF0sgqk5WJ11sfoc+xSvz6bgC7Sbq+4vNLE401wQPHBkg6m2GrTSvJCtq+fgOazEr4XoROSF5Ji7L05f+NOLMGAJKuZag1WTRwzMtAV2ZHhNWlr8WvL47Hqhr1f7QTQkfoCITv89J4bNiyCAlVHoXZmbTuQ0j53qX/JrmbpOsAgOSbET4ozio4fxHJpw7NJq4aH/trwXOq+AyAvK1BD8bHigo4Px9pKzl1BiCSdBXD1syTSL4QOQN+pcfZDDonqdnV7ia5FmJnhGF7cWFHkukxPFXv64GrWS0LYC5Ju8V2lv0/3IrQeamceCTRHyV9u+6TNTpWtmrK/88i/B6sDOB8AK+S9AOS6yEMLhoZOKJ6+ZGsv8ZVxsF9txYy30uObObW/REG50mZW1O03fmq8DOuM6mW+pzU0hGvRfhcHmxLfCrCwHGp2y44hl0Rdri8G+Gz7ykIiYqGpZanqFvOIvX9/TzNrTc751jGvSRXQOgPfZnkXSjPa5DUl5X0P5nzGc8frJx/puh5E1ZlsnIJkv+MMHl9hkLOkGHHAHivYrkuki/FzE6uKl4IYH2G0n+5YULj8sCxOSlB5n28fm0VPgRTrvWO+HdqsHbqzFpSKQiF/eV7Fz0+yqj/I4VC1q+rcJ2LSF6CsEXowLrtackbEFbSdgbwDwjxHS8vOf+9aGc2sW5imaQZeNQbgAxW7u5myNz4ScwMDOfQ6ED6wXmDCZPUe+JfED6U1iP5a8TC6iXnfxZzY3hy4y1je25Hhfs6YxWED9XsBNGcuox1dyZEdRKPpEhOUMSQqXIdScczbCddUcXJeg6o2I5HaSaT30GKidni/1vFS1RSqfzIkI8iDFyfQvLLCKUw3lpy/sOSFAd0Ryhkbt1t/KYXarzzxbSETHUm1VKfc0Dad4CDEZLHzdqWmHiNpd36AH4ZBzeF78VKLE+Ren5G3vv7zsMnMWT5fiyAVUk+DjO7YFZCmCgo8jqEcJB9MRMOctCINqX2ZddByDC8HMIArTd9X6DyZGUWEfpHOyPU4h62vGZqPEPShbFPVLU9oxKNjc0DxwYwfYtbr65fs01tZiUcvEbleDMkzqxhdga6ryBkoCsMGGfYCvu+nPYUrYAmdQYZCvu+Pef6ewyf29CWvsYp1Nx7M4AzEVbjXi7pz3nnMsQsrYjwodD0bGLdAs5JM/CoMQCRtE3m348grJK/v8pzq0i5j+Lx5MLqkm4huVDSYgDHkyyccKnRnkpZAFF/Z0KdwXWqQfxmNv65sE0kPxrPXRehVuGyCFviN8s7X9XrxGaLTA//HjYZ43gRyQ8g1ILbGqH8SNEug/Di0rkkr0IYiBDA3pLuLnnK2JlbUzTd+WJBPDSAonjoOlv0kp6TcB8NpG5LnEZvBnAEyTMQsv/elHcSE8tTpJ6fUTV85J0I4RF/j9l1Ge8DMKfMEicUDhLfG1+KMOH+PwBeBeCSovPbNOZkZfa8UWWrbiX5YYRdakCYyL2tpF1Jicaa4OQ4DeAYyQr6cP1URR+CajArYdEbjIozvlVKzELyREm7ktxbUuUYGYYg8M9hKG5JUm5B4GxnUNIzGOrdnSYptzMYO9/fy7n+GQXnH44w6Kq1pa9JnJtp8wkIWzgfim0qyrjZSmZg1kwsw7B9dI4aHay8a39G0j5FHYCSD/7U10m9j2YVVkeIHSssrE7yYoSsip9HSGR0B4C3qiCzYo32tJoFML7GIoTtjs/G7BTyhYPNNpG8BsDzAFylmQQWc2qysrjESW7GaZKLEd4biDBh8mDm/OUkNTLwipNAe2J2PdbPl31eMSQQmhOzOPx+nTk/OXNr4vfQaueLNRIycSbD8/Ck2ljPSb2PMs9LzlI9jRiSmuwIYHeE/+fjAZyk2Zm0B581ueUphicuUs/PPO8qhPi4WeEjKk66t5ekoyp8j3XrDKcmZLoOIYvy1Qp1XJ+I8N5SFG7SGpLHSnoHEzOXx+dug7mfN7krs3HF90CEVUkibAM+QJna6UPnJyUaa4IHjg0geRqA9ygmkJhv109V50OwxmtUfoOJs8+fkPT+USsnJG9EmLX6BsJM1qw9WypIecycrKoj2nQNKnQGs+en/KKzYmHfSWBBps2Bks7ghxFWQk7B7MHvWGmn2VAB59i53UnSvxQ8XnkAQnJjSVe2OTiNr5N6HyUVVo8/6zsR/i/3Rdia9F+SbmmoPclZAJm2MwEkz0G4596HUP5jNwC/k/SvVdtZcN1aCYpIXi7pBYzZdeN72PebnIjrG5LXInQIn4uwevD/AGyngkLm8TlJmVsT29Nq5yt2NrdWfkxT3vlvyTs+aqBM8jkIqzPZ96OxBteM219ZI0v1tGLYYbQLwireTQjb+Y8cHpTlTZ6WTajWOH9NAKcj/LwG4SPbSsqNzSf5RoSspfczJAPcCMAhGlpNK3tPJnmdpPULHkvqy2beG68E8DKECc7rNY/KcDCUcnssQvs/jxDSc7mkPUc8r1JWVc5k8r0685n546LJ3CZ4q2ozUre49e36qdrOSggkxJspbN3cOP77gRGnfw4htmZNzN6SAYTZwaKU+WcxBDV/DbN/BkWDnL9KEsnB1tlRe9S/SfLVinUiR1H1LX2tGx4YcqgYcInBIDc7MCv7GVRtzzgFnDdEWNl4E8Kqde7KWPRlhAHItsgMQArOvYHkPggdiOsAHFe1E5ko6T7CzP1fqbB65mddGsMzRnuSYpVZbxv/4xVi5PaOA/aL4oB1XHUTFJ1K8hgAf8eQoXQPhNXfeYH1yo9kYxaP1IiYRdbL3JoiNctrqtR46E0y/14O4fu8CiX3Ndvb1ncmgI0UVofPkLQ9epLRsm8YEgjtjnCPngjgBZLuihMdNyEkFstaRHJNzS5PsajkJZLOV0L4SPRhSacxhNm8AqFk2dGY2X4/MKlwkCtI/h3C++GVAP4E4PKS609E4mTliyU9Ny4cHMiwW6xwZxjTs6qmJhobmweOzThgnl8/VWtZCTmzlW9FpL3BVMrGKOlIAEeSPFrSuxKaNujUZOPRygY5qZ3BvQF8gORDAP6G0duGWt/Sl4pzs+49DeHDMnd2UNIabbZHFRPLxP/LNyNs+/k9wmCQGp2gKWUAcgLCz/V7CB26Z2GMZEvDOLP9jEi4jwBcGVfg1gDw7wwB/o8UnJs3SADCCxT9HiTd10iPVU7NhIvYDgC4g2EL0W8ArJ7w/FyqmaBI0qcYYgPvQ4hz/Iikc8dtzwTVKT+SGrNYJ3NrirY7X0nx0JL2yn5NcmXMxDwVeQNmtvXtzritr15zZ8kOoMea1JsC2wP4tKSLswclPUgybzdQanmKSudzbvjIKggZsH/IkFymaDfD4Pd3GwBHS/o6yQNyzvsRybcrPxwkN3wnyrtWrjhx838l3QvgcyTPRqjv3Gnt1hqTlYOB+oMMIUu/R/i8LZKaVTU10djYPHBsQFPbzLq6fg1tZiX8VM3nVcrGuOSBOGgcXh1TzEiXc37SICe1MygpdZUitfzIJCRl3au7HasFNyMM6F6juOWS5L4VnpcyAHnWYPsOyePQ8KxpjftnYE/MFFZ/kOTjEWbMiyQNEmq0KzXLa2omXAA4JHbE90NYAVgJ4XtqBBMTAsXdCOcrJIxZF8C6JJeR9Le883uoTvmRHRBW9veQ9FuGmMXDSs6vk7k1Raudr9TJhBwPIsS0l/mzpEdIPhy3ud2FZgZ6Kvi3ZcTJjycPDxoHJJ2Xcyy1PEXV87dNa/0Sv44T3lsB+CRDzOyCnPP2QUKd4Uz7K/dl446EMxGysA4ydPdB6mTlN+Oq6WEIuwaE8gmdpKyqSk80NjYPHMfAmkHmfbl+XQ18CJb5NWZn6wIAMGTrKiywmrp1kyGz2H9gxOoYye3KrjO8opl5XqXOIAsydGWuX5Spq+3CvnWkZt1L3o7Vku0RVhwviLOaJ2Mo9rVAygBkyc9d0sPN7oIDGGIUV5R0+tDxnRDi94omLYSw+rktQhr15VG+DanSIKHufa30LK/J2/g1k/DkjwhxJ037OsJExHdRbQXuYoRVt8fF51yBMLCakza/p5LLj8TB4hmYGQzdjZkauXkuYmLm1hRtd76YmJCJs5NoLUD4HT11xMu0ta1vA5L3Ify/PCb+G+i4H9I3CiEzD5JcWQUxhAVSy1OMPF/1w0feBOCVAD4l6V6SqyEn67cSw0HG6Mv+gOQmkn5Uoe2TkjRZKeng+M8zGJIKLTfi/kjNqjpINPYthkRjHyBZmGisCU6OY8lSPwQTr52UrYvkoQirJZ8bOr4vgCepIOEFQ5bULTC0OqZYRzJzXl4SmgGVrCJcCeD/AHgcgB8gdAYflLTz0HllWylV0rH4NkKR4dMUEmq8AcCeaqngaxUcM+veYDtWWae/TXGw8nqEVdItELaXfk0FGUYTrz3IcAlgVpbLpiaZfoCwYvq7oeNPQvgeNi143tEIW1O3kPTMOHg5R9ImQ+cNBoJvQtjyVDpIGOO+Ts3yWjnZEMn9JR1K8ijkZ7bNzdiciukJgQZJcfYC8JjYxqsVEx3knL8dQv3PJyDcP5124At+1oU/4/icJTGLktaKqyifU0GhcdbI3JqCiVlea1w/KSHT0H39MICfS/pVyfUJYHVJv4xfPx0jtvX17T5aGpA8FWHy4VzMDpkpygafmj0+9fzc8BGNSC5TdSdW2xiSGa4L4HbMZIiWOkwcFt/vNkSYlCmcrBxj0SGbVRUIE4sHqjiranKisXF54GjJUj8EE6+dlK0rvrE8R6EmXvb4AgDXllxrkInqxwCep7DF53JJLxj3e4jXT+oM1rh+XvmRXdThdg6OmXWP5DIIP7NnttfKahjqZL4RwA7DHeBJDUBSsDxjb9ljg/t0yb3JnIxsdQeCqZiY5TXx2q+RdBYLkrAovbh20escAuAyVUwIRPJqhBW0TyNM/tyQ916XOf8WhEmC3Bpx8wFD1ukXAPhh5r4r/J7j44sAYHhypKH2tNr5YszKnf1dJHlRk507pmf+nvf3Ud+kvrcwvTxF6vmVJsgz5w8PNJ+KsB22kyymLMjY3uZq2ihVJytTFx1IbjcYTJJ8XNFAMac9g8/wjwD4tULehaua+Mws4q2qVkdbWQmB9GxdGh40xoOPxFnYIveSXAFhNufLJO9Cs1s9SXJThAHUIO1yY79vqlG4vW2ayWj7CEPipN+XfcCx3nasiVDIlntM/DNs0NG6YnItGmk5ko/SUKbWOBgvy3L3N4bYnEFSkEXISY6jmCiImYx+mddoMmFGUpZXki9C2Cb8TIT4moUAHshbNZF0Vvy7lYyQrJ+gaB8A/46wMnxD/P8sG6jf2YfOPmuWH4kqxSzGn/1HEXZXMB5aDOAoFdRBqykpy2sNleKhOXdLHzFzT41aDUzd1teL+2hpUuO9JTVGO/X81PCRpDwFbZP0c4YMr+tIOj5+Pq3QVXtimyr1dZWe+f5DmMnJcR7CrocqUhONjc0DR6ujlayEUWq2rgdJriPpZ0Pnr4OZbFZ5Xhcf3xczq2NNdkT2QVpnMAmHtvQxbCUs3NLXpth5/wSAexA+eE5E2Kq6gORbJJ1d8NRsIqSR27H6ou0BSE1fRbgP3j0YwMdJhSNRkvo7Pv41AE8g+TGEzIwfKjn/dMz9QDsNMYFBA5KyvAL4LEJ86mkISQvegoIkIkMTFXMMbzVKpZoJijKTb8vHr28FMGfVOrP16QqGuoNnYvZWqbKfcxvqlh8Bqscs7oOQqGYTSbcBSyYqjia5r6RP13jtPG13vqrGQ5+HMDD4KoCTlbZF8GUA/onk7SjZ1tfD+2jeI3mqpDdxbjZTACjLYpoao516fuoEeepAs1UMJWaej7Bd9XiE38kvIbwndNWmypOVmedsg7mhXcP9TRb8e5TURGNj81ZVS0ZyW4TkD0/BzIfggZK+0cC1k4q3k3xVbMMhQ+f/O4B9hreLkVwbJcl3JP3vuN/D0HWX1+jaknWu29qWvhptuQLABxAG38cCeJWkH5BcD8BJqrA9l6FgcukKZV+0PQCpI67YHALgbQAG23ieipAF9cMqydAZf05bInxYnZe3ChHPeTaAQzE7WcJKAN7f1FYmhi3mGyLELd/LsGV4dRXEanFmy3l2C+BlyomrzWwx2g6hc/6l+PWOAG6X9IEx214rQVHcmXAcgBUkPZXkBgDeKemfh86rFW/dR6wYs8iwjXdrDSWqiSsP51R5b6nYnichdL5+JOl7sfP1Uk0+wzPiAHM7hAmR5RDCQk5Wcc3gwfMqbetbmu6jviC5mqQ7qv4MMs+rHKNd8/yk8BGOmaegaQxb2p8H4CrNbGkvDL2YUJuuQM5kZdHnB8nPAXgswsTO5xEmZy+XtOfQeTcjfBYtQPhs2gmZAaRKko3F+24dSd9lqBm6sM1daB44NoAtB5m3ff0+4uxsXTeopHg7yecgdGYH51+PkBXsupxzk5LvDJ1Tuehr1c7g0HOejLn18XJTew/ePEkeAeBCSV9jgzGUKZhJBkLyJmViFPPaVLZCCaBshbIX2h6AjIOhFt3a8ctbVF7sefCchQCeiNn33S+GznkdQofitQCyE0T3I3RqLyu5fsp9nZSkhOTFCKnjPw/gtwhbuN6qoRjN4edIesmoY6lYP0HRDxE6E9/IdI7KYr03y5n4mnNsUphYfiTx2mX/D4WP1XytxjtfHCMeOg6ud0CYGP24KtRJZs62vsFKbc65vbqPljZ9mwwtaw/Jp0r6RepAs0YbkvqyjHknOBPHtzyA73c9cKw6WRkfG/TVBn+vAOCrkl4+dF7dhHJJicaa4K2qzTgU7QaZt339Ssb5EEylisXb47nXIyToqeLpeasXkq5gyESXi+lFXz8D4BWInWyFWK3CjmncDrIDgBuHrp/bwUb6lr42ZV93eKCS96H5WcysUJ6PoRVKhHpqvTWY3SV58NBg46w4kOlMHCjOmTApwpC86aMA7kS47wYxVbM+mCV9HcDXSW4q6fsJ10+9r49GSDG/AULm5uMQfseKkojsitDJeTfC1r+nIJRXKbOImVhNkmsAWFTpGyr32OFBI7Ck9ERhHa54zi85O5SzrIzHUZi7XTjv2KSklh8ZTBAcgJkJhUEHcjhe9q8llyl7LEm284XwPv9kAJ9DWIkfR3I8dJyg3BEhK/clCDttvlfheanb+vp2H81bZZOhzAnXYGJ5ihrnp4aPnAlgozhhd4ak7RGyijcttS97KkNdyb+Lv6N7IGTb7tKDJJcFcA1DVv87MLNtP89fMs/7e4R643PqgivmEajhXxATjcXr/IwhK25rPHBsRttB5n0JYu9jUpBUqcl3BlKLvqZ2Bl8PYF2VFP8dMly4fRWUF25vU1mdr7z/70cpxmKSPEjSDwBA0s1suL5hy9oagEzS3gj3XemM8mDSCMBOJOckSyiZNHo90u7rbJKSIzQ6Scn6AH4p6S8IKcyr2BfAhSQHSX6eDuCdFZ9bpm6Col/GwYJih+Q9mHmvzV5nU4Qsyos4OyHNSghxNl15rNIzah+H8HO4EuXvixtk3k+yit5b6mql86XEeGiG+MR7EWrJvgMxHo2xHI5KtqshFF5/HkItXEj6TZxQHH6Nvt5H81nSZKgS46FTz09tDzArpq7JZGfDkvqykj7FEAN9H8KEyEdUXJN4UlInK89iqK96GMLvptDs4LdSorEmeeA4BrYcZN729VOlfgj2VGrynYHUbGaVOoMZtyLMEFftYG+KnC19FZ/bKEmpnY3UFcq+amsAMkm/BFClWHXdSaPU+zo1ScmbARzBUEz++CqdEklnx+0868VDNycMbMvUTVD0Twi/u08G8CsA5yAMZIYti5BR8FGYnZDmPoStrl35JslXq2L5keiPkr496qQa7y11tdL5Yno89O3x/FdgJv5zyekIpRWK/DVOugwyJBetgvT1PprP+jYZmtoeFfy7EXX7sgz1uE/rwWAxq/JkZdxufp6kewGcwRAqtZykKp+5VV3EaonGGuMYxzGw5SDztq+fqsaHYO8wPfnO4HteERWKvmaetypCZ3ArhA//cwDsPbyyk9n2+2SEOmLnDV2/qLBvtu7YiQgz+K0WfW0KQzr9Qda/xwB4cPAQwptqq6mkm0Ty0Wh+ANIYkqsBuKeoXSSPQ5jJ/RZm33cj46lGvG7d+zo5SQnJlRC29u0eX/N4hKRMhfFpTIhXropjJChKeI2FAE6R1HkHn7PLjyyP8POtUn4EJD+BsLr1Vcy+L8pW1FoTt5zdi5DoYi+EzteNkj445nUnFg9N8n0IGYW3RkhusgeAr0g6quD8p6nDenhLE2bq5nGoht7w131sz4jP5NLf5YrtqdWXjduv34Sw5fZkAKdLunOctoyL5JcQJu4rTVaS/L4K4tsbak+lRGONvqYHjuNjy0HmbV8/oR19TgryDIT4qCdKeg7J5wJ4raRDCs6vlHyHBVnMBlSxpk+REdvwVNSZZQdFX22uNgYgTWLIkrcWgDMkvS/n8Y/mPU/SgUPnlWZMHp5AqXtfx+cmJymJEzW7IJRvuAkhQdCReZ1mFsQrl2y3TcLEBEVMTC5D8nwVJEqYL5ifCEJdfV9td77YUkKmnNfZGpnvoWylJv4M8nIVzOt7qwt9mwztW3sy7arVl439uR0QtoT+StJWLTZzpJTJSpIHArgWISHOyPcTkudpKLFN3rEueeDYgIIZnMY68W1fv0Z7JvIhmNimixAyqx6jCpkJa1z/kxqK48k7lnkstTO4t6QjRh3LPHYRQpzCHghJFH6HsHV1/crflI2l7QFIUxj2Jj1L0g1jXON3CNtaT0KIA5u136loAqXGfZ2UIY7kaxE+vNdCWHk/QdJdccB5k6Q56fFJ3oTEeOU2kbwMIbnMrHg/SWcUnH84wsrSaQidw8H5Ew1dYM3yI9Mo3nPbaHY89P8ok4G6gdcYbOurVAuXZLb26nIInfKHJe3fVJvMsur2ZeNOlDcihCasqA6zqg5UnayMOzOWR3hv/zMKVnFJLodQtuMCAC/FzGfsSgC+XfReweqJxhrjGMcxsOUg87avP4Y+JgV5rKTLOXv/flmh21RbAxgeJL4q59hAaqbB3TA3RvGtOccGJl701eZITpg0Ccwvr3HD0DmfkbRP0fbz4RVEhB0GWyPMsu6EsLX1pAqD0dT7OjVJyfYAPq2h8h4KCaOKtvKnxiu3LTW5zCoImfmyK0NCeRxlGw4EkFe+6HyEcIA5A0eSu0j60tDn2RIac4t0XRPofE0iHnolAN8hWWlbn6ThmP5L44SkTQjTy1PMy9JsdfuyJN+F0NdZBOB0AG+XdGObbR0lZ7LyBdnJSoTMxEuoemKjdyIMQv8eYRJx0Jm9D8B/ljyvaqKxxnjgOJ62g8z7GsTex6Qgd5NcC7ETTPINaKBjGN+4/hnAWgxxhQMrAijbXlGpM8iQoXInAGsObQdcEaFzmCsOFs9AWHkAgLsROms2OX0bgICzy2sMkhDNKa+B8IEHAJ+qcl1JixFWuM9miOvcEeE94KCC7aCD+3qNlPsaCUlK4gD5ycODxkybzyt4jVUB3EiyUrzyBCQll5HUVfbkYXXKjwyOp2aJbFurnS+NmZCJI2KV42scCODAzLa+i0gWbutjyMQ9sADAxgjvZzY5qeUpelGarYa6fdmnAdhH0jXtNS1Z0mRl3PGzM4A1JB1M8ikAVpN0+dDzj0BI9LZX3mdqiUqJxprkrapjYsvJCtq+fl3sWVIQkmsCOBZhVusPAG4DsIuk28e87soAHoeQbODfMg/dL+mekucdAuCyUZ3BGM+1Rt71AVyrodT+medNvOirzRZjhDZExYRJE2rTLQBeqIYKNg9d+9EAtkEYND4doUbp/5P065xz697XSUlK4qB0VyVkqWNB3HLRdttxjer0Z7YyVUouQ3J1hFntzRAG1ZcgJN6qtEWxKSR/irDinld+5EZJ6+Q/s39I/lDSC1t+jdrx0BwRqzx0bqVtfSRvw0xyo4cRPjMPknRJlTbZ+EheKqmozubY5/cNayRkIvkPCDHvx8cQoBUk3dZOC0e2ZSFC7HDlGEuSRyNM4m4h6ZkkHwfgHEmbFJz/RgBnS7qf5IcQMuYfooLEYewg0ZgHjg1gy8kK2r5+HeN8CLYpznQv0IhkGonXXIDQ2a0cL5nSGaz5ZnQN4pY+zcR0XifHOE7MpAcgVcTB7NZFA7Oc8ytt0SN5AkIyqW8DOFnS9U22O/M6SUlKSJ4K4EUI2yKz8X69iTNN6fRXvN65AL6CmVXjXQDsLGnrca+d2I5PIGyJzis/cnfZjgsmxoC3re3OFxuIh44rF4Wxyjnb+k7peluf5eNMeYrNEVZ5z0RJeYrU8/uKiQmZGJK3PR+hFvAzSP49QhxvZ4Pn1MlKziQyvDrTV/uxpA0Kzr9W0nPjgPn/IuwK+kDRxBY7SDTmrarNuDreTG0lK2j7+kmKPgQBdDZwJPlxAIcq1MtBnNXZT9KHxr22pEdI/pjkUyX9ouJzKm/FkrSY5IMkV05YOZl40VebrcsBYolbEbaQVi2vUXWL3q4I7z3PAPAezsQSj1odG5RsAMJ2pWUAPFB0fvxdOxPAmXnbIHN8K/4Zaagtsx5Ci3FCkrYadPqH2rOeQl213MQQJYOWRZKy6e2/QHKfZlqb5EMI5Ud+TnJO+ZERz02NAW/boFP2/MyxUXUTUyTHQ7NCrPKQpG19cWX4XQAGSe0uREguN3bZGBspGxv8IMJE2UBevHLq+X2VnThbkpCp5Px/BPA8AFcBgKTfkOx6m/tfAFwXJ/CqTFb+Lf4uD8KoFmF2Lethg/fDbQAcLenrJA8oOlnSyxLa3ggPHJvRdrKCviRDGOhjUpBXKVMORNIfSL4aoXPThNUA3BDjorJvFsNlCOp2BlPfjC7ihIu+WtDVAKSiX8Q/y8Y/o1QtxL6gTmOGJ1BIvh5hpRxDx4kQm/luxKQPDCnlj5J0UMn1T6jbljZV7PS/F2G7+eE5lygbtNxNcheEDLdA2Drc+NbkUeKq9r8xpJuvXH4kSk0I1KoJdL6S4qFZPVZ5CUn/RvIfSO5ecVvf0QgTOf8Vv941HntblTZafYpxyiwoTzHu+X2l9IRMf5UkkoNBV1Hs9CRVnqyMjkTIP/EEkh9DiOks65f+muQxCDXAPxlDROZ8/rLDRGPeqmrJSJ4G4D2S+pQU5FoAmyjGETHUUrtC0rMbun6lbYkkj5X0jtTtAyyoe1fUMU7d0meWlZnYeBMmHR9B/kDSi4aO7Qvg1QDeMejoMsQtH40Q7/HpofNPlfQmktchf+tTZ+naizr9TbWJIYPyZxGKUAvAZQgxjvOmmDsrxoBPoB0T6XwxMR6aNWKVU7f15W2XK9tCZ81jYnmK1PP7hvkJmY6UtG7B+e9DSAC4NcK2zT0AfEVpyWM6QXJ1xbhzkusB2BKhr3YegLUl5U70M2RnfSWA6xSyiq8GYH1J5wyd905Jx7BiLeYmeeDYALacrKDt69doTx+TguwP4LUIhViF8AbzDUmHNvgaTwQwCGi+XNJdTV07Xn9ZhK2AAPCTUVuG4owyKm7psykQ74n9ATwbYSsQgLkxJAUTG5nTm4mPyMTmAKGj8HwAm0vadOi8qxFiM+8eOr4IIZHA84aOrybpDoYkPHnfQGeDqJqd/l7GjDcts1pPJCQEarE9E+l8VZ14zJyfFKscn3MN4rY+zcRSXVs0YUHyKgBvlPS/8es1EUp4zItByHzGmfIU+wDIToqtBOAfcwb0Sef3FWskZIo7qpZMkKuj+rCpk5UkfwLgFRpK0MiQefWDktYa8XpPwOzP8EphUpPgrarNOB4hWcEb49e7xGNNJSto+/qpDujodQtJOjT+Qg9mdQ6W9J2mrk/yTQh1Ei+M1z+K5Ps1VPx66DmVO4MkXwrgBAC3x+s/heRuGkr5XHdLn02NLwM4BcC2AP4JoY5iXsmEScVFZGNzHka4v1+Xc94yw4NGIEyKxFis4eN3xL+XDBAZCjL/vger7r8EkJLltVLMOMmPlFxGkg5Oa+bkTXK7cBWSjol/tzY7H6+fGg+dGqsMpG/rez+ACxjKahEhRrIvpV6WdqnlKfpami2JpDVqPOdcAOcO3t+bb1Vle8e/t614/r4I7X61pJ8BAMl/QyjNkTuRFM95LUL4wt8DuAshbvxmhMngvPMnnmjMK44NIHmNpA1HHevr9W00kj9GmAG+K369CMB3i2b6ijqDKohZJHklgJ0k/SR+/QyEAusbD52XtKXPpgvJKyVtnF1pIHmRpM2HznsvQnzjcUPH9wKwUNJnJtZo1Nqe9SIAnwBwD4CDETKMroqwqvkWSWe33ORCJI8DsC5CHMzITj/Jm1AhZpzkfjmHl0fYsv54SSvUbnSDWFJ+hOQrEEpEnD50fCcAv+twNaGVzhdrxkPXWQGts60vxk+tG9vTeVmtacOE8hTsaWm2FKyYkKnP7+9ZoyYrSW4J4BgAr0eIHd4EwLaS/lByzR8jxLd/V9LzSL4MwI6S3lFw/mUIicZmJbiTdEatb6oCrzg2o+1kBb1IhlD3Q7BNE2zTgqGtqb9HTsByRmoCoWUGg0YAkPTTvJUWhPp2s7b0Sbo13h/nYPY2Fps+gw/gO0huA+A3AFbPOW8PhPpQw44F8CMAn2miMQnb7DcgeV/eJZDZrpPxWQAfALAygPMRkmP9IMaSnASgsGMRt89+EsAT4vWbfq9ITVBUKXGKpCVJdBgyC+6NsEJ0MvIT7HTlRABrkcwrP3IgZq9CD5yPkECik4EjWsryWneFtc4KqKRPxW199yEMBj+SNxAnuQmAX0r6raSHSG6IkN3y5yQPUEl9YmvcFwYrxFl5oQIK2ddXGT4+z1RNyFT7/b0tZYNZkrmDWUnnkXwrwgD5MgBbSvrLiJf6m6Tfk1xAcoGkC0h+suT8iSca88CxGXsg3OifxkyygiaXidu+fiV922YETLRNZ5P8DmYG7zsAKEvskJRFD8AVcaViUJttZ4QZpGFJW/qsPRMYgNRxCMmVAeyHMGBbCWHLzDBJ+mvOwYfiduimVNpmL2lh4nUfpZgsgORBkn4Qr3NzheYfCuA1km5KfM1Kqnb6SZ6F8H6+IoAbGTI2l8aMx47jexHeH04AsFHZ7HUXVFB+JHqscmKyJf22wtbKNvUqyysrxioPq7itb5CxESRfgtAZ3gshb8GxmEdbH5cCqeUpelWarYZNhnZpnR9X2IaN8/7elqTBLGfHcz8aIYzqrvjeWNZPuJfkCgAuBvBlkneh/J74ZtwOO7FEYx44NkAhaLW1xDBtX99Gk/R+ktsjrJwQwLGSvjZ8Xp3OYPQuAP8C4D3x+hdjZlYua05nv+Jj1rxWByB1SPpm/OcfAZTGMZJ8oqQ7h4813KS2ag5m62ANl38Ytcp/Z5s/s4RO/6cSr3sYgO0QOvfrS/rTmE1tDKvXHFyO5KM0lPQlTno9pt1Wlpp452uESrHKQK2VkIWZVcUdED7LzgBwBkOCHZsQpZen6FtptlSLSa6l2QmZ8lb4x3l/b0vSYDZ1UYMzdcJfh/A974swQbgygDn5K4YGph8gObFEY45xHANbTlbQ9vWteSzInjegMYvGMyTCeSDvIQDLSfKq44SQvFQFqe4njeT+CgmijkJ+xrf3DJ3/FoRJiv0QiysjpEY/FMB/KqE+4oh2fRfAFzB7m/3ukrYc87qD3wMiDDgeHDyEgt8DzmR43RxhN8CZmD2p00jni+Q5CJ3+9yHT6R9e0SK5NoAnam5dtpcA+PWgc5U5/khs78OY/TPudKWbCeVHSH4CYYD5bkkPxGPLI9Q6u3vSq37sWZbXTLsqxSrH41dgZiXkWAythGhuRuLrAWwo6WGSNyPEy188eEzSc1r+9ixiYnmK+S7G/B2PkPxpSUImSRcMnZf8/t42ZmLtORR3P/x1A9c/Q9L247W4PV5xHE9eB35JsgKE2b8+X99G4Ez66DzS3JTKv0ZJZzDn+heMuP6WQwdSt/RZwzIDkCtInoKWBiCJBitoV1Q5WdIXSf4OYSbzOQj34A0APirp2w22K7vNHgAuRQPb7Gv+HmRj6x5ESPG+5JJobtb+8ZKOI7l3nCi6qGAV4TMIHf5hD8bHZsUCSiqLqe7S3gi1A6vE3X8IwCEI8XSDpCBPBXAcgA+31L5CfQy/iKrGKgPp2/pOQrgn70ZY2fhefO7aSMgGbI24EnPLU+xZdHJCzHgvxZi/dTAiIVNP+zmDOHwCeEwmJr8oDj9V9pd1zZEnd5hozCuODckkK9gTwKkADleDdf7avr7lI/n4oUMLEIqmvw+hXtb2Q+d/E8AHJF07dPz5CJ3y1wwdn5U1NXoRwla3uyRtkvO4dYjk8SUPSy2mwbbxkdwsZ2JnzrExrv8DSS+KMdFHInT6Tx+eZCpb3SF5naT1m2hP21iv5uBjAKwdv7xF0vB2tImYVOcrNR6a5LYIA7qnYCZW+UBJ38g5N3klJG5vXQ2hRupg5fcZAFaQdNXw+dYPJM9FiBkf5ELYBcDOkroqzVYJMwmZ4tdvQUzIBMAJmVD+e1xw/g8QQmV+N3T8SQC+pqFayY221QPH8XBusoIj1GCygravX7NNfUwK0iqSCxAygL0focTGxyXdmHNe7c5g3Ob6YYRA6o83vPJjDWt7AJLYlkFsbS4Vx9a2guTbAVwo6WcxGcBxmOkovLXLzmneh3ITW40y16rU6Sd5i6S1cy5R+ljfMLH8SJ9MqvNF8ha0FA/dx219Vg0rlqfInD8vS7ORvArAVpLuibuvTsZMQqZnah6XGGnKiN/jOf3r7Db2nGsVPtYEb1UdA1tOVtD29cfQu6QgbYlv7HsgBCpfAuB1w7FHQ8q2LOQmf4iz3h8G8BcAHxve72+9dRTmlrTIOzYJg0Qr2yHE730pfr0jgNs7aM/eCLGNgzZsgLD95nkAjgDwfybdIJKbAngxgEUMdSwHVgLQ2NYoVU9Q9COSb5f030Pt3BP5GZX7KrX8SJ9MKstrpYRMqbHK8Vgft/VZNVXLUwz0ojRbDU7INEKN3+POEo154Die/RBmWD8E4IOZeIKmVuDavn5drWYl7JnbEGIPPoPQOdqA5JJ00jnxbEmdQZI/ArAIwGEAvh+PLRl4eNtQ/0xqAJIixtKB5MGSXpJ56CySF3fQpIczs+bbAvhijIH7LslDO2gPEAY1KyB87mVj2+5DAyUIanT69wHwNZLZ0jvPj+38x3HbMymqUXOwR1rtfNWIh06KVbZ5r2p5ioFelGarYWHm92xLANli9h6H1PNVAP9NMi/RWKt5FvwDG0PbyQr6lgyhp0lB2vZdhDfoDeKfrLyEGvsgrTP4AIA/IXRct8fsAGlhdtpt64dWByBjWkRyTUm3AgDJNRAmJioh+ToAv5X0wzHb8QjJ1QD8AaGj8LHMY52UXZB0EclLEHZvtDHYSU1QdCeAF5N8GUKCIgD4lqTzW2hba1iz5uDQNVYDcE9eooyWtd35SkrIJOms+HcjWY2t96qWpwAwr0uzOSFT8zpLNOYYR6vMSUGqG+oM3jDfOoNWjqFu3Sl9i80g+UqEre23xkNPB/BOSd+p+PyPA1gfIVPjq8Zox7YIhcYXAjhL0tvj8c0B7C9pm7rXHhfJ81MGNVaOFcuPjLjGdwGsBeAMSe8bdX5TSD4KofP1NoT4WyDT+SqKNavxOpXiofsWq2ztYvXyFPO+NJsTMrWji0RjHjhasj4lBTHrSl8HICQfDWC9+GVuuvMJteNRCBkr/5A5tjzC505n8dokDwewDoDTkCl5NO6OiWnt9DOh5uCI6xDAsyTd0EpDy1+71c5X1YRMnKkDnBurLCmvfIvNY/H9urQ8Bcn9cp66pDSbpBXabaXZDG9VtTr6lBTErCtXk/wGGh6ANGBjhJXGRyHE5ELSF4tOJvnizPkAUHp+VTGe5Q9Dx/Jq007aKggJJbKD/ibqOPYtQdGkpNQcXCKu2j8Rs++7iQ8a4+v+GcB1TV83NR66h7HK1gJmylNIeojkhohZp0nOKU8h6fDMcwel2XZHyE56OMwmyANHq6yPSUHMOtTWAKQ2kicibPm7BjOxMgKQOxBMPX9pIGn3lq47rZ3+Q0iujJDMbVB+ZN+yJ5DcC8BHAdwJ4JF4WABaSyHfkbrx0GPFKlvvHQNgKwCI5Sk+gZnyFMci597g3NJsG6nj0mw2nTxwtBR9TgoyUSSfD+AOSb9u6fpdJYuwitoagIzp+Qjb/arGIKSeP++RXB1hgLMZwmDlEgB7S/pVQy8xVZ3+hPIjWXsDWDdm2l1qxcmEi0h+QdLPRz5hxr4ALiQ5K1a56fZZZ5LKU7C/pdmsZybRd3SMoyXpa1KQSSN5AsLs+E8l7dDC9TtJFmHVTWAAUqdNpwF4j6Q72jh/XH2YECF5LoCvADgxHtoFwM6Stm7o+mMlKJovapQfyT73AgBbD5fB6Ium79P4/eb9HxXGSPclVtmaR/J6ABtKepjkzQDeIeniwWOSnjN0/iMIGewfxuz7qOvSbNYzk+g7esXRkkhaHLdMTDVJuwFL4g3auP5Wg2QRbVzfGnE8wgDkjfHrXeKxRgYgNa0K4EaSl2N2qZyixCyp54/rRABrkexyQmSRpGyG6C+Q3Kepi0s6m+Q6WPo7/ePUHLwVYUXtW5h93/1HEw1rQNP3afYayyHEs40aNCfFKtu8klSeom+l2ay/JtF39IqjJWsrK2EfkVxP0s0kcxP/NJlGuiBZxC+aur41i+Q1kjYcdWySMlkZZxnE3417fhO6zJ4ZX/+7AL6A0HkDQvKa3SVt2eBrtJJwaGlB8qN5x9VOfc1a2r5PyzLPFsUel63i2vzi8hTWlEn3HT1wtGTMr+coLYV1HEkeK+kdcavRMDVVjqEoWcQgvb31zyQGIEuDvk2IkHwqgM8C2BRh29dlCFuMU2LQyq4/FZ3+pa38SJv36dAunQUIq4lHSlq34PybMGWxx2aWrou+oweOZj1A8hYAL1zak0UsTdoegCS25X7kd+JLY2DirPdRAJ6JkPxqIYAHmoqZmcYJkWnp9I9Tc5DkIgD7A3g2wtZNAOUxf21q+z4leRvC7ycRtqjeBuAgSZcUnD/R2GMzm5+66Ds6xtGS9TEpyCS0vP3sl8iJbbD+iqsRvVhVkVQ31vazAN6MsO38+QDegrANvSm9yZ5J8iMlD0vSwQ291PUIA6mlutM/ZvmRLwM4BcC2AP4JwG4AftdKQ6tp9T6VtEbiUyYde2xm89PE+44eOFodfUwK0qoJ1Lvre7IIiyY4AJkISbeQXChpMYDjSV7W4OX7NCHyQM6x5QHsCeDxAJr6uU1bp79O+ZHHSzqO5N6ZkhWtxdVW0Op9SnIZAO8CMBhgXwjgGEl/K3jKAW21xcyWKhPvO3rgaHW0mpWwp9qud/eL+GfZ+Mf6a1IDkEl4kOSyAK4heSjCKtnyDV6/NxMikg4f/DtmQ94bwO4ATgZweNHzajigwWvNB3VqDg4GTHeQ3AbAbwCs3k7zKmn7Pj0awDIA/it+vWs89ra8k9tMTmVmS5WJ9x09cLQ67ia5C2YnBel8K1rLWt1+1qdsglZuggOQSdgVIa7x3QgDgKcglApoSq8mRGKSkvcC2BnACQA2kvSHJl9j2jr9NcuPHEJyZQD7IYQ9rIRw/3Wl7ft0E0kbZL4+n+SPh0+qG6tsZtOpi76jk+NYsj4lBZmUmFV1QwCtbD/rW7IIK5czADmi6QGINYvkYQiJXI4F8J+S/tTw9ae20+/yI+VIXgXgjZL+N369JoDTJeWWeTIzq6KLvqMHjmYVtF3vjuQ5CMki3odMsghJ/9rE9a05bQ9AxkFyOwCfBPAEhAFL7qCF5KmS3kTyOuQMdhrMJtmbCRGSjyBM+jyM2d/zUj+wa1NK+RGS+0s6lORRyL/vOilZ0vZ9SnJLhDwAtyLcb09DKN2TV+bJzKySLvqOHjhaZUtbUpA+IXmlpI1JXjvotJcViLbu9HkAElNzv0bSTSPOW03SHSSflvd4gzUNPSGylEspP0LyNZLOIrlb3uOSTmi8gRVM4j4l+WgA6yK8T1TZzmtmVqqLvqNjHC3F0pQUJEnb9e7Qv2QRVkDSgq7bUOLOUYNGABjUh5vA9vK+Zc+05lWO/5Z0Vvy7kwFiiVbuU5KbAPilpN9KeojkhggxxD8neYCke8Z9DTObahPvO3rgaJUtZUlBUrVd765vySJsHolbVAHgCpKnADgTs2Nxvzp0flE83uB8T4hYVZXLj5A8C+X3XVclS9q6T48BsBUAkHwJgE8A2AshXv5YAG9o4DXMbHpNvO/ogaMlmURWwr5qs96dpG/Gf/4RwMuauq5Njddk/v0ggJdnvhaAWQNHSSsCAMmDAPwWwIkIW+h2BrBig+3yhMjS74CEcz8V/94OYZXyS/HrHQHc3lyTkrV1ny7MrCruAOBYSWcAOIPkNUVPqhqrbGbTrYu+o2McrbI+JwVpG8mLEWaOP4/Q0b4DwFuHUqzXuW4vk0XY/ERyM0mXjjqWeeyHkl446phV507/aCQvlvSSUcfmO5LXA9hQ0sMkbwbwDkkXDx6T9JyC51WKVTaz6dRl39ErjpZiP4StSB8C8EGSg+PT0DHaFcACNF/vbtAxuKKBa5kdBWA4xX/esYHFJHdG2G4uhJWfxQXnVjblEyKHYgo6/WOWH1lEck1Jt8ZrrQFgUQvNLDWB+/QkhHjJuwH8GcD34uuujbBCUKRSrLKZTa3O+o4eOFplPU8K0rb1EZIc/AVAYwVXe5wswuYRkpsCeDFCh/y9mYdWQkjkVGQnAEfEPwJwaTw2rmmeEJmKTv9gu3NN+wK4kOSt8eunA3jn2I1K1+p9KuljJM8DsBqAczKZZxcgxDrOkhqrbGbTqcu+o7eqmlVA8ksANgVwBoDjm+oY9jhZhM0jsc7oSxFKCXwu89D9AM6S9LMu2jVNMp3+zRHi986EO/2FYnmK9eKXLk8BgOTxJQ9L0h4Ta4yZ9VaXfUcPHM0qIrkSwla+3RF+YY8HcJKk+8e45qDWTm6yCEkfqN9imyYkFwI4RVLlTI2x8PnbEVZ8luxAGbeDOo0TIu70pyH5Ysy977444Tb08j5NjVU2s+nSZd/RA0ezBCRXBbALgH0QtjmtDeBISUeNed2pSBZh7SJ5vqQtEs6/DCHu6kpkYhtj5sdx2jG1EyLu9I9G8kQAawG4BjP3nSYd+9rX+5TkVZI2GnXMzKZbF31HxziaVUDytQgrjWshlC54gaS7SD4WYQA51sARPUkWYfPe1SS/gVBv9IHBwZJtko+V9K9NNyIWUQfJg4c+wM6KGYqXZqkJiqbR8wE8Sx3PXPftPh0jVtnMptPE+44eOJpVsz2ATw9SqQ9IepBkE1vQ+pIswua3VQD8HkB21XFOHceMb5J8taT/aak9UzMhMq2d/prlR65HWOG7o/0WVtKX+3RZACsg9M2yyYfuA1B5C7qZTY2J9x29VdVshBg79h1JW7X8Ok4WYRMVSyosj5DE5W9ouLQOyVci1H2d9aEm6TtNXL9PpjVBUZ2agyQvALAhgMsxO4FQVzGFvblP68Qqm9n0mnTf0QNHswri9r9dJZXV3hr3NTpPFmHzG8nVEbZFboaw0ngJgL0l/arDNk3NhMg0dvpJXipps8TnbJ53fLB1tAt9uk9TY5XNbHpNuu/orapm1fwFwHUkz8Xs2LFGkjkUJYsA4IGjpTgewFcAvDF+vUs8tnX2JJKl8XaSrmqwTRtj5kNtA5JL7YSIpMUkV+m6HZMwTs3BLgeIJfp0n6bGKpvZFOqi7+gVR7MKSO6Wd7yp4qskb0IPkkXY/EbyGkkbVjh2Qcll1NRqR1+yZ04SycMBrIOlvNNfp/xI3Bqd9x7X6BbpVH27Twv+b13Sxcxm6aLv6BVHswqaGiCW6FuyCJuf7ia5C4CT4tc7IiTLmUXSyybUnl5kz5yw1ARF85Kk3YHi8iMFz1kx73gP9Oo+HfzfmpmNMPG+oweOZiVInirpTSSvQ85MuaTnNvRSqwK4kWQvkkXYvLUHgM8C+DTC/XpZPNaVqZsQmcJO/9JQfqRX92kfY5XNrJcm3nf0wNGs3N7x721bfp0DWr6+TQFJvwDQp8mGqZsQmZZO/1JWfqRv92mlWGUzm3oHTPoFPXA0KyHpjvj3zwfHSK4K4PdNbmvqabIImydIfqTkYUk6eGKNme2Ajl63S9PS6V+aag4e0HUDhiySlI1z/ALJfbpqjJn1Uxd9RyfHMStB8kUAPgHgHgAHAzgRYXZ6AYC3SDp7zOv3MlmEzS8k98s5vDyAPQE8XtIKJc99MoCnYXYq74sbb+SUqJqgaGkwjeVHJoHkdwF8AbNjlXeXtGVnjTKz3uiy7+gVR7NynwXwAQArAzgfwKsk/YDkeggf6mMNHHucLMLmEUmHD/5NckWELda7AzgZwOFFzyP5SQA7ALgRs1N5jzVwnPIJkUoJipYGdcuPxFIenwTwBIR7opP7osf3ad9ilc2sR7rsO3rF0axEdqWA5E2Snpl57GpJz+uscWYZsQP/XgA7AzgBwBGS/jDiOT8B8Nwui50vbUg+FaHTvylmOv17Z7e7L03qlB8heQuA10i6qf0WmplZU7ziaFbukcy//zz0mGddrBdIHgZgOwDHAlhf0p8qPvVWAMsgkxDExtPDBEVtq1N+5E4PGufqcayymRkArzialSK5GGEWnQAeA+DBwUMAlpO0TFdtMxsg+QjC4O9hzJ7QyN1yR/KoeN6TAWwA4DzMzibZSeHz+cyd/tHiFlUA2Byh/MWZmH3fLVW1LlONE6tsZjYJHjiamU0ZkruVPCxJX5xYY5YS09rpTyk/QvL44WMZkuQ4vigTq7wngFMBHC7prm5bZWbTzgNHsx7oS7IImy4k95Z0xKhjlmaaOv0kz0UoP3JiPLQLgJ0lFZYfIbmZpEtHHZtGdWKVzWw6ddF39MDRrAecLMK6QPIqSRsNHWss6dO0TYhMY6e/TvmRgvtuzrFJ6ct9OhSr/J8JscpmNoW66Ds6OY5ZPzhZhE0MyR0B7ARgTZLfyDy0IpotHXEopmRCZIwERfNd5fIjJDcF8GIAi0i+N/PQSgAWttrKcn25T/dDiPn8EIAPkhwcX6onXMyston3HT1wNOtQJlnEFSRPgZNF2GRcBuAOAKtidp3H+wFc2+DrTNOEyLR2+lNqDi4LYAWEvke2Dtl9AN7QYhtH6cV9KmlB120ws/7rsu/orapmHXKyCOsKyYUAviNpqxau7eyZlived6dI6nKgOGiL71Mzm3e67Dt6xdGsQ5J2B4qTRXTTKpsGkhaTfJDkypL+2PDlX5P594MAXp59aZTX+LN5oG75kXjfrdJSs1L5PjWzeafLvqNXHM16oG/JImw6kDwVwIsAnItQrxRAc3UcnT1z6TVO+RGShwNYB8BpmH3fdTJQ831qZvNRF31HrziadajHySJsOnwr/mnLUQCGP8Dyjtk8I2lJbGym/MjuAE7G7LjZPKsgJNDZIntJdLfC5/vUzOaNLvuOHjiadauvySJsCkg6geSyAJ4RD/1E0t/Gva4nRKZDTvmRjaqUHxlss+qa71Mzm6c66zt64GjWIUkXAbiI5Bck/bzr9th0IflShA7/7QjZP59CcjdJF495aU+ILOXGKT9CcnWEFb3NEFYaLwGwt6RftdHWEr5PzWze6bLv6BhHsx4geQFCB2oWSVvknG7WCJJXAthJ0k/i188AcJKkjRu4dm+yZ1rzSD6CkIH0Ycx+7xpZfoTkuQC+AuDEeGgXADtL2rql5hbyfWpm81UXfUevOJr1w/sy/14OwPYIHTKzNi0zGDQCgKSfklymiQv3LHumNWzMmoOLJGXTyX+B5D5jNqkW36dmNo9NvO/ogaNZD0i6cujQpSQv6qQxNk2uIHkcZlZ+dgYwfC+O42qS30BPsmdab9xNchcAJ8Wvd0RIltMV36dmNu900Xf0wNGsB4ZmvBcA2BihILVZm94F4F8AvAdhi+HFAP6rwev3LXum9cMeAD4L4NMI98Nl8VhXfJ+a2bzTRd/RMY5mPUDyNoSOChG2GdwG4CBJl3TaMDMzMzPrnS76jh44mplNmaKA+kiStmzodfqSPdN6gORHSh6WpIMn1pgM36dmZtV44GjWAzEhybsAvCQeuhDAMU3U1DMbRjIva+qLAOwP4C5JmzT0Or3JnmndI7lfzuHlAewJ4PGSVphwkwD4PjWz+amLvqMHjmY9QPLzAJZBqKkHALsCWCzpbd21yqYByc0BfBjAowF8XNK3G7z2NZI2HHXMpg/JFQHsjTBoPBXA4ZLu6qgtvk/NbN7pou/o5Dhm/bCJpA0yX59P8sedtcaWeiRfgTBg/AuAj0m6oIWX6Vv2TOtYTObwXoQMvicA2EjSH7ptle9TM5uXJt539MDRrB8Wk1xL0v8CAMk1ASzuuE22lCL5IwCLABwG4Pvx2EaDxyVd1dBL9S17pnWI5GEAtgNwLID1Jf2p4yYN+D41s/lo4n1Hb1U16wGSWwI4HsCtCNmxngZg95ZWgWzKkbwQM8lxBhnZBiRpizlPMhsTyUcAPISQ/S/b+SDCfbdSJw0zM5uHuug7euBo1hMkHw1gXYRf/pslPdRxk8xq6Wv2TLMs36dmNt9Nuu+4oM2Lm1k5kpuQfBIAxF/2DQEcBOCwocKuZvPJAzl/gJAI5V+7apTZEN+nZjbvdNl39IqjWYdIXgVgK0n3kHwJgJMB7IXwJvBMSW/osn1m4+pT9kyzIr5PzWy+6LLv6OQ4Zt1aKOme+O8dABwr6QwAZ5C8prtmmY2np9kzzWbxfWpm81BnfUdvVTXr1kKSgwmcLQGcn3nMEzs2USRXi/ES417nMAA/AnA/QvbMA9wZt77xfWpm81RnfUdvVTXrEMkPAng1gLsBPBVhtlsk1wZwgqTNOm2gTRWS3wWwFoAzJL1vjOs4e6b1nu9TM5uPuuw7euBo1jGSLwKwGoBzJD0Qjz0DwAoN1tMzq4QkATxL0g1dt8XMzMzm6qrv6IGjmdkUI7kQwBOR2d4i6RfdtcjMzMz6yDFUZmZTiuReAD4K4E4Aj8TDAvDczhplZmZmveQVRzOzKUXyFgAvlPT7rttiZmZm/easqmZm0+uXAP7YdSPMzMys/7xV1cxset0K4EKS30LILgkAkPQf3TXJzMzM+sgDRzOz6fWL+GfZ+MfMzMwsl2MczczMzMzMrJRXHM3MphTJRQD2B/BsAMsNjkvaorNGmZmZWS85OY6Z2fT6MoCbAawB4EAAtwP4UZcNMjMzs37yVlUzsylF8kpJG5O8VtJz47GLJG3eddvMzMysX7xV1cxsev0t/n0HyW0A/AbA6h22x8zMzHrKA0czs+l1CMmVAewH4CgAKwHYt9smmZmZWR95q6qZmZmZmZmV8oqjmdmUIbm/pENJHgVgzuyhpPd00CwzMzPrMQ8czcymz03x7ys6bYWZmZnNG96qamZmZmZmZqW84mhmNmVInoWcLaoDkl47weaYmZnZPOCBo5nZ9PlU/Hs7AE8C8KX49Y4Abu+iQWZmZtZv3qpqZjalSF4s6SWjjpmZmZkt6LoBZmbWmUUk1xx8QXINAIs6bI+ZmZn1lLeqmplNr30BXEjy1vj10wG8s7vmmJmZWV95q6qZ2RQj+WgA68Uvb5b0UJftMTMzs37ywNHMbIqRfDHCSuOSHSiSvthZg8zMzKyXvFXVzGxKkTwRwFoArgGwOB4WAA8czczMbBavOJqZTSmSNwF4lvxBYGZmZiM4q6qZ2fS6HqGOo5mZmVkpb1U1M5teqwK4keTlAJYkxZH02u6aZGZmZn3kgaOZ2fQ6oOsGmJmZ2fzgGEczMzMzMzMr5RVHM7MpQ/J+hOypcx4CIEkrTbhJZmZm1nNecTQzMzMzM7NSzqpqZmZmZmZmpTxwNDMzMzMzs1IeOJqZmZmZmVkpDxzNzMzMzMyslAeOZmZTiuR2JH9G8o8k7yN5P8n7um6XmZmZ9Y+zqpqZTSmStwB4jaSbum6LmZmZ9ZtXHM3MptedHjSamZlZFV5xNDObMiS3i//cHMCTAJwJ4KHB45K+2kGzzMzMrMc8cDQzmzIkjy95WJL2mFhjzMzMbF7wwNHMbEqR3EzSpaOOmZmZmXngaGY2pUheJWmjUcfMzMzMHtV1A8zMbLJIbgrgxQAWkXxv5qGVACzsplVmZmbWZx44mplNn2UBrIDwGbBi5vh9AN7QSYvMzMys17xV1cxsSpF8mqSfd90OMzMz6z8PHM3MphTJCwDM+RCQtEUHzTEzM7Me81ZVM7Pp9b7Mv5cDsD2Ahztqi5mZmfWYVxzNzGwJkhdJ2rzrdpiZmVm/eMXRzGxKkVwl8+UCABsDeFJHzTEzM7Me88DRzGx6XYkQ40iELaq3Adiz0xaZmZlZL3mrqpmZmZmZmZXyiqOZ2ZQiuQyAdwF4STx0IYBjJP2ts0aZmZlZL3nF0cxsSpH8PIBlAJwQD+0KYLGkt3XXKjMzM+sjDxzNzKYUyR9L2mDUMTMzM7MFXTfAzMw6s5jkWoMvSK4JYHGH7TEzM7Oecoyjmdn0ej+AC0jeipBZ9WkAdu+2SWZmZtZH3qpqZjbFSD4awLoIA8ebJT3UcZPMzMysh7xV1cxsypDchOSTACAOFDcEcBCAw0iu0mXbzMzMrJ88cDQzmz7HAPgrAJB8CYBPAPgigD8COLbDdpmZmVlPOcbRzGz6LJR0T/z3DgCOlXQGgDNIXtNds8zMzKyvvOJoZjZ9FpIcTBxuCeD8zGOeUDQzM7M53EEwM5s+JwG4iOTdAP4M4HsAQHJthO2qZmZmZrM4q6qZ2RQi+SIAqwE4R9ID8dgzAKwg6apOG2dmZma944GjmZmZmZmZlXKMo5mZmZmZmZXywNHMzMzMzMxKeeBoZmY2AST/nuTpI865bFLtMTMzS+EYRzMzMzMzMyvlFUczM7MxkHwLyWtJ/pjkiSS/QPINmcf/FP9+Osnr47+fTfJyktfE564zdO5LSV5I8nSSN5P8MknGxzYmeRHJK0l+h+Rq8fh7SN4Yr3fypP8fzMxs6eY6jmZmZjWRfDaADwLYTNLdJFcB8B8VnvpPAI6Q9GWSywJYmHPO8wA8G8BvAFwKYDOSPwRwFIDXSfodyR0AfAzAHgD+DcAakh4i+Xfjfm9mZmZZHjiamZnVtwWA0yXdDQCS7okLg6N8H8AHSa4O4KuSfpZzzuWSfgUAJK8B8HQA9wJ4DoBz4+ssBHBHPP9aAF8meSaAM2t9N2ZmZgW8VdXMzKw+AhhOFvAw4udr3F667PCTJH0FwGsB/BnAd0hukXPthzL/Xoww2UsAN0jaMP5ZX9LL4znbAPhPABsDuJKkJ4fNzKwxHjiamZnVdx6AN5F8PADEraq3IwzeAOB1AJYZfhLJNQHcKulIAN8A8NyKr/cTAItIbhqvs0yMl1wA4CmSLgCwP4C/A7BC3W/KzMxsmGcjzczMapJ0A8mPAbiI5GIAVwP4VwBfJ3k5wsDygZyn7gBgF5J/A/BbAAdVfL2/xsQ7R5JcGeFz/DMAfgrgS/EYAXxa0r1jfXNmZmYZLsdhZmZmZmZmpbxV1czMzMzMzEp54GhmZmZmZmalPHA0MzMzMzOzUh44mpmZmZmZWSkPHM3MzMzMzKyUB45mZmZmZmZWygNHMzMzMzMzK+WBo5mZmZmZmZX6/8rdMXgdiQDLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "sns.barplot(df7.index , df7['votes'])\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf39818f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}