Practice_google-play-store-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for visting my code. If there is anything missing or wrong, please feel free to inform me. Also like my page on fb: https://www.facebook.com/codemakerz\n",
    "\n",
    "\n",
    "#### Stay tuned for the more updates in the same notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['googleplaystore_user_reviews.csv', 'license.txt', 'googleplaystore.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "# LIKE US ON FB: https://www.facebook.com/codemakerz\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "df = pd.read_csv(\"../input/googleplaystore.csv\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top n Columns using head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2.8M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App      ...        Android Ver\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook      ...       4.0.3 and up\n",
       "1                                Coloring book moana      ...       4.0.3 and up\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...      ...       4.0.3 and up\n",
       "3                              Sketch - Draw & Paint      ...         4.2 and up\n",
       "4              Pixel Draw - Number Art Coloring Book      ...         4.4 and up\n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So we got our dataset\n",
    "df.head() \n",
    "# Here in our dataset we can see Result = 0, 1 that mean 1 mean selected celebrity has cancer and 0 means no cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "      <td>14M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "      <td>25M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "      <td>2.8M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paper flowers instructions</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.4</td>\n",
       "      <td>167</td>\n",
       "      <td>5.6M</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>March 26, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smoke Effect Photo Maker - Smoke Editor</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.8</td>\n",
       "      <td>178</td>\n",
       "      <td>19M</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>April 26, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App      ...        Android Ver\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook      ...       4.0.3 and up\n",
       "1                                Coloring book moana      ...       4.0.3 and up\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...      ...       4.0.3 and up\n",
       "3                              Sketch - Draw & Paint      ...         4.2 and up\n",
       "4              Pixel Draw - Number Art Coloring Book      ...         4.4 and up\n",
       "5                         Paper flowers instructions      ...         2.3 and up\n",
       "6            Smoke Effect Photo Maker - Smoke Editor      ...       4.0.3 and up\n",
       "\n",
       "[7 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also mention the number of rows\n",
    "df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so before visualization lets do some EDA. Exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Out How Many Missing Values are There?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                  0\n",
       "Category             0\n",
       "Rating            1474\n",
       "Reviews              0\n",
       "Size                 0\n",
       "Installs             0\n",
       "Type                 1\n",
       "Price                0\n",
       "Content Rating       1\n",
       "Genres               0\n",
       "Last Updated         0\n",
       "Current Ver          8\n",
       "Android Ver          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find is there any missing values?\n",
    "df.isnull().sum() # We can see in Rating we have 1474 missing values & 1 value in Type, 1 in content rating and others also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get All the Column Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
       "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
       "       'Android Ver'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets see the all columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get All Column Datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                object\n",
       "Category           object\n",
       "Rating            float64\n",
       "Reviews            object\n",
       "Size               object\n",
       "Installs           object\n",
       "Type               object\n",
       "Price              object\n",
       "Content Rating     object\n",
       "Genres             object\n",
       "Last Updated       object\n",
       "Current Ver        object\n",
       "Android Ver        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the datatype of all the columns \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Column wise Record Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               10841\n",
       "Category          10841\n",
       "Rating             9367\n",
       "Reviews           10841\n",
       "Size              10841\n",
       "Installs          10841\n",
       "Type              10840\n",
       "Price             10841\n",
       "Content Rating    10840\n",
       "Genres            10841\n",
       "Last Updated      10841\n",
       "Current Ver       10833\n",
       "Android Ver       10838\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of records in dataset\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe() - Descriptive Statistic for Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.193338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.537431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating\n",
       "count  9367.000000\n",
       "mean      4.193338\n",
       "std       0.537431\n",
       "min       1.000000\n",
       "25%       4.000000\n",
       "50%       4.300000\n",
       "75%       4.500000\n",
       "max      19.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics. It will show you the summary statistic for all the numerical values\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Info Function to Get General Column Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10841 entries, 0 to 10840\n",
      "Data columns (total 13 columns):\n",
      "App               10841 non-null object\n",
      "Category          10841 non-null object\n",
      "Rating            9367 non-null float64\n",
      "Reviews           10841 non-null object\n",
      "Size              10841 non-null object\n",
      "Installs          10841 non-null object\n",
      "Type              10840 non-null object\n",
      "Price             10841 non-null object\n",
      "Content Rating    10840 non-null object\n",
      "Genres            10841 non-null object\n",
      "Last Updated      10841 non-null object\n",
      "Current Ver       10833 non-null object\n",
      "Android Ver       10838 non-null object\n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# info is also used to see some important statistics for dataset.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection & Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select Single Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Photo Editor & Candy Camera & Grid & ScrapBook\n",
       "1                                  Coloring book moana\n",
       "2    U Launcher Lite – FREE Live Cool Themes, Hide ...\n",
       "3                                Sketch - Draw & Paint\n",
       "4                Pixel Draw - Number Art Coloring Book\n",
       "Name: App, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will show you top 5 Name columns. As head return top 5\n",
    "df.App.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook\n",
       "1                                Coloring book moana\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...\n",
       "3                              Sketch - Draw & Paint\n",
       "4              Pixel Draw - Number Art Coloring Book"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also do above same thing with array type\n",
    "df[[\"App\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select Multiple Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  Rating\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook     4.1\n",
       "1                                Coloring book moana     3.9\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...     4.7\n",
       "3                              Sketch - Draw & Paint     4.5\n",
       "4              Pixel Draw - Number Art Coloring Book     4.3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show multiple columns\n",
    "df[[\"App\", \"Rating\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select Last 5 Records for Selected Columns (Use it same as head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10836</th>\n",
       "      <td>Sya9a Maroc - FR</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10837</th>\n",
       "      <td>Fr. Mike Schmitz Audio Teachings</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10838</th>\n",
       "      <td>Parkinson Exercices FR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10839</th>\n",
       "      <td>The SCP Foundation DB fr nn5n</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10840</th>\n",
       "      <td>iHoroscope - 2018 Daily Horoscope &amp; Astrology</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  Rating\n",
       "10836                               Sya9a Maroc - FR     4.5\n",
       "10837               Fr. Mike Schmitz Audio Teachings     5.0\n",
       "10838                         Parkinson Exercices FR     NaN\n",
       "10839                  The SCP Foundation DB fr nn5n     4.5\n",
       "10840  iHoroscope - 2018 Daily Horoscope & Astrology     4.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also watch below 5 using tail\n",
    "df[[\"App\", \"Rating\"]].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Statistic - Mean, Median, Standard Deviation, Variance and Percentiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Age:  4.193338315362448\n",
      "Median Age:  4.3\n",
      "Variance Age:  0.28883240560309886\n",
      "Standard Deviation Age:  0.5374313031477594\n",
      "25th Percentile of Age:  4.0\n",
      "50th Percentile of Age:  4.3\n",
      "75th Percentile of Age:  4.5\n"
     ]
    }
   ],
   "source": [
    "# Basic statistics\n",
    "print(\"Mean Age: \", df.Rating.mean())\n",
    "print(\"Median Age: \", df.Rating.median())\n",
    "print(\"Variance Age: \", df.Rating.var())\n",
    "print(\"Standard Deviation Age: \", df.Rating.std())\n",
    "print(\"25th Percentile of Age: \", df.Rating.quantile(.25))\n",
    "print(\"50th Percentile of Age: \", df.Rating.quantile(.5))\n",
    "print(\"75th Percentile of Age: \", df.Rating.quantile(.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location Base Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category  Rating Reviews\n",
       "0  ART_AND_DESIGN     4.1     159\n",
       "1  ART_AND_DESIGN     3.9     967\n",
       "2  ART_AND_DESIGN     4.7   87510\n",
       "3  ART_AND_DESIGN     4.5  215644\n",
       "4  ART_AND_DESIGN     4.3     967"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# position based indexing\n",
    "# We use iloc for this. Here we mention [row, cols] like this.\n",
    "# : as row means all the rows & 1:4 as columns means 1st index(2nd columns aqs index start from 0), 3rd column and 4th column. 4th index will not included/\n",
    "df.iloc[:, 1:4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>App</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  Rating\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook     4.1\n",
       "1                                Coloring book moana     3.9\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...     4.7\n",
       "3                              Sketch - Draw & Paint     4.5\n",
       "4              Pixel Draw - Number Art Coloring Book     4.3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name based indexing\n",
    "# in below example we are using all the rows(:) and only Name and Age column.\n",
    "# you can use any indexing method but i usually prefer iloc. As i am more comfortable with indices.\n",
    "df.loc[:,[\"App\", \"Rating\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Different Value Counts for Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Free    10039\n",
       "Paid      800\n",
       "0           1\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find out different type of Type column\n",
    "df.Type.value_counts()\n",
    "# we can see the number of free and paid apps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot Univariate distribution for Ratings using histogram. It will show the total number or app according to the ratings.\n",
    "# Total number of app acccording to  the ratings\n",
    "plt.figure(figsize= (15, 10));\n",
    "#plt.hist(x=df.Rating, color='c');\n",
    "df.Rating.plot(kind=\"hist\", bins=30, color='c')\n",
    "plt.title(\"Univariate: Rating Histogram\");\n",
    "plt.xlabel(\"Rating\");\n",
    "plt.ylabel(\"Total Counts\");\n",
    "plt.xlim(right=6)\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FAMILY                 1972\n",
       "GAME                   1144\n",
       "TOOLS                   843\n",
       "MEDICAL                 463\n",
       "BUSINESS                460\n",
       "PRODUCTIVITY            424\n",
       "PERSONALIZATION         392\n",
       "COMMUNICATION           387\n",
       "SPORTS                  384\n",
       "LIFESTYLE               382\n",
       "FINANCE                 366\n",
       "HEALTH_AND_FITNESS      341\n",
       "PHOTOGRAPHY             335\n",
       "SOCIAL                  295\n",
       "NEWS_AND_MAGAZINES      283\n",
       "SHOPPING                260\n",
       "TRAVEL_AND_LOCAL        258\n",
       "DATING                  234\n",
       "BOOKS_AND_REFERENCE     231\n",
       "VIDEO_PLAYERS           175\n",
       "EDUCATION               156\n",
       "ENTERTAINMENT           149\n",
       "MAPS_AND_NAVIGATION     137\n",
       "FOOD_AND_DRINK          127\n",
       "HOUSE_AND_HOME           88\n",
       "LIBRARIES_AND_DEMO       85\n",
       "AUTO_AND_VEHICLES        85\n",
       "WEATHER                  82\n",
       "ART_AND_DESIGN           65\n",
       "EVENTS                   64\n",
       "PARENTING                60\n",
       "COMICS                   60\n",
       "BEAUTY                   53\n",
       "1.9                       1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets see how many categories are there and total number of apps grouped by category\n",
    "df.Category.value_counts()\n",
    "# So it clearly show the count of apps for a particular category. value_counts() is very handly function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# but really saying this data is not convincing... we can make it more appealing by plotting it.\n",
    "# lets plot it.\n",
    "plt.figure(figsize=(15,10))\n",
    "df.Category.value_counts().plot(kind=\"bar\", rot=0, title=\"Pclass Vs Count\", color='c', );\n",
    "plt.xticks(rotation='vertical');\n",
    "plt.title(\"Category Vs App Count\")\n",
    "plt.xlabel(\"Category Name\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now from above example we can see that mostly apps belongs to the family, games , tool category.\n",
    "# and beauty categroy app is least in number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "1.9                    19.000000\n",
       "ART_AND_DESIGN          4.358065\n",
       "AUTO_AND_VEHICLES       4.190411\n",
       "BEAUTY                  4.278571\n",
       "BOOKS_AND_REFERENCE     4.346067\n",
       "BUSINESS                4.121452\n",
       "COMICS                  4.155172\n",
       "COMMUNICATION           4.158537\n",
       "DATING                  3.970769\n",
       "EDUCATION               4.389032\n",
       "ENTERTAINMENT           4.126174\n",
       "EVENTS                  4.435556\n",
       "FAMILY                  4.192272\n",
       "FINANCE                 4.131889\n",
       "FOOD_AND_DRINK          4.166972\n",
       "GAME                    4.286326\n",
       "HEALTH_AND_FITNESS      4.277104\n",
       "HOUSE_AND_HOME          4.197368\n",
       "LIBRARIES_AND_DEMO      4.178462\n",
       "LIFESTYLE               4.094904\n",
       "MAPS_AND_NAVIGATION     4.051613\n",
       "MEDICAL                 4.189143\n",
       "NEWS_AND_MAGAZINES      4.132189\n",
       "PARENTING               4.300000\n",
       "PERSONALIZATION         4.335987\n",
       "PHOTOGRAPHY             4.192114\n",
       "PRODUCTIVITY            4.211396\n",
       "SHOPPING                4.259664\n",
       "SOCIAL                  4.255598\n",
       "SPORTS                  4.223511\n",
       "TOOLS                   4.047411\n",
       "TRAVEL_AND_LOCAL        4.109292\n",
       "VIDEO_PLAYERS           4.063750\n",
       "WEATHER                 4.244000\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets plot the average rating of eah category.\n",
    "df.groupby([\"Category\"]).Rating.mean()\n",
    "# We can see 1.9 category has a rating of 19.0.. which is not possible as our rating is out of 5.\n",
    "# So definitly it may be some data issue. Now what to do?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10472</th>\n",
       "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>February 11, 2018</td>\n",
       "      <td>1.0.19</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           App     ...     Android Ver\n",
       "10472  Life Made WI-Fi Touchscreen Photo Frame     ...             NaN\n",
       "\n",
       "[1 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Category\"] == '1.9'] # We can see Rating has a value of 19.0, which is not possible.\n",
    "# So we have two choices:\n",
    "# 1. Either we remove this row\n",
    "# 2. Correct this value.\n",
    "# i will not prefer removing row as it is the only row related to the category 1.9.\n",
    "# lets replace this value with average rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inplace will save your change to the original dataframe\n",
    "df[\"Rating\"].replace(19.0, df.Rating.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10472</th>\n",
       "      <td>Life Made WI-Fi Touchscreen Photo Frame</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.193338</td>\n",
       "      <td>3.0M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>February 11, 2018</td>\n",
       "      <td>1.0.19</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           App     ...     Android Ver\n",
       "10472  Life Made WI-Fi Touchscreen Photo Frame     ...             NaN\n",
       "\n",
       "[1 rows x 13 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets check again\n",
    "df[df[\"Category\"] == '1.9'] # So we have finally replaced it to the average value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "1.9                    4.193338\n",
       "ART_AND_DESIGN         4.358065\n",
       "AUTO_AND_VEHICLES      4.190411\n",
       "BEAUTY                 4.278571\n",
       "BOOKS_AND_REFERENCE    4.346067\n",
       "BUSINESS               4.121452\n",
       "COMICS                 4.155172\n",
       "COMMUNICATION          4.158537\n",
       "DATING                 3.970769\n",
       "EDUCATION              4.389032\n",
       "ENTERTAINMENT          4.126174\n",
       "EVENTS                 4.435556\n",
       "FAMILY                 4.192272\n",
       "FINANCE                4.131889\n",
       "FOOD_AND_DRINK         4.166972\n",
       "GAME                   4.286326\n",
       "HEALTH_AND_FITNESS     4.277104\n",
       "HOUSE_AND_HOME         4.197368\n",
       "LIBRARIES_AND_DEMO     4.178462\n",
       "LIFESTYLE              4.094904\n",
       "MAPS_AND_NAVIGATION    4.051613\n",
       "MEDICAL                4.189143\n",
       "NEWS_AND_MAGAZINES     4.132189\n",
       "PARENTING              4.300000\n",
       "PERSONALIZATION        4.335987\n",
       "PHOTOGRAPHY            4.192114\n",
       "PRODUCTIVITY           4.211396\n",
       "SHOPPING               4.259664\n",
       "SOCIAL                 4.255598\n",
       "SPORTS                 4.223511\n",
       "TOOLS                  4.047411\n",
       "TRAVEL_AND_LOCAL       4.109292\n",
       "VIDEO_PLAYERS          4.063750\n",
       "WEATHER                4.244000\n",
       "Name: Rating, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Category\"]).Rating.mean() # So we can see now average values are correct for all categries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(15,10))\n",
    "df.groupby([\"Category\"]).Rating.mean().plot(kind=\"bar\", rot=0, title=\"Pclass Vs Count\");\n",
    "plt.xticks(rotation='vertical');\n",
    "plt.xlabel(\"Category Name\")\n",
    "plt.ylabel(\"Avg. Rating\")\n",
    "plt.title(\"Category Vs Avg. Rating\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so now we can see the average rating. As per our data average rating is almost same for all so bars are almost equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category           Installs      \n",
       "1.9                Free              4.193338\n",
       "ART_AND_DESIGN     0+                     NaN\n",
       "                   1,000+            4.366667\n",
       "                   1,000,000+        4.325000\n",
       "                   10,000+           4.445455\n",
       "                   10,000,000+       4.460000\n",
       "                   100+              5.000000\n",
       "                   100,000+          4.211111\n",
       "                   5,000+            4.375000\n",
       "                   5,000,000+        4.366667\n",
       "                   50,000+           4.333333\n",
       "                   50,000,000+       4.500000\n",
       "                   500,000+          4.450000\n",
       "AUTO_AND_VEHICLES  1+                     NaN\n",
       "                   1,000+            3.800000\n",
       "                   1,000,000+        4.361538\n",
       "                   10+                    NaN\n",
       "                   10,000+           4.333333\n",
       "                   10,000,000+       4.300000\n",
       "                   100+              3.500000\n",
       "                   100,000+          4.291304\n",
       "                   5+                     NaN\n",
       "                   5,000+            2.800000\n",
       "                   5,000,000+        4.433333\n",
       "                   50+                    NaN\n",
       "                   50,000+           4.300000\n",
       "                   500+              4.600000\n",
       "                   500,000+          4.160000\n",
       "BEAUTY             1,000+            4.000000\n",
       "                   1,000,000+        4.287500\n",
       "                                       ...   \n",
       "TRAVEL_AND_LOCAL   500+              4.625000\n",
       "                   500,000+          4.222222\n",
       "VIDEO_PLAYERS      1,000+            4.166667\n",
       "                   1,000,000+        4.093939\n",
       "                   1,000,000,000+    4.100000\n",
       "                   10+                    NaN\n",
       "                   10,000+           3.647059\n",
       "                   10,000,000+       4.306667\n",
       "                   100+              4.850000\n",
       "                   100,000+          4.069231\n",
       "                   100,000,000+      4.237500\n",
       "                   5,000+            3.500000\n",
       "                   5,000,000+        4.171429\n",
       "                   50,000+           3.842857\n",
       "                   50,000,000+       4.354545\n",
       "                   500+              4.000000\n",
       "                   500,000+          3.900000\n",
       "                   500,000,000+      4.500000\n",
       "WEATHER            1,000+            3.800000\n",
       "                   1,000,000+        4.233333\n",
       "                   10+                    NaN\n",
       "                   10,000+           4.100000\n",
       "                   10,000,000+       4.327273\n",
       "                   100,000+          4.206667\n",
       "                   5,000+            4.200000\n",
       "                   5,000,000+        4.112500\n",
       "                   50,000+           4.500000\n",
       "                   50,000,000+       4.420000\n",
       "                   500+              3.800000\n",
       "                   500,000+          4.428571\n",
       "Name: Rating, Length: 516, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Category\", \"Installs\"]).Rating.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Free    10039\n",
       "Paid      800\n",
       "0           1\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find out how many apps are paid or free.\n",
    "df.Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bk7you29Z1TcDAACwRq16LCZJd5+Y5MTNlr+SJa5m2t03JDlmC8/zmiSv2e4DAgAA7OIW9aszAAAA2IGJRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGC4nFqtqnqt5dVf9YVRdV1b+rqntU1TlVdcn0fd9p36qqN1bVpVV1QVU9Yu55jpv2v6SqjlvEewEAAFiLFnVk8Y+SfLi7fyzJQ5NclOTlSc7t7kOTnDvdT5InJTl0+johyZuTpKrukeTEJI9MckSSEzcFJgAAAHfMqsdiVd09yU8leWuSdPeN3X1NkqOTnDbtdlqSp0+3j05yes/8XZJ9quo+SZ6Q5Jzuvqq7r05yTpInruJbAQAAWLMWcWTxfkk2JnlbVX2hqt5SVXdLcu/uvjJJpu/3mvbfP8nlc4/fMK1taR0AAIA7aBGxuHuSRyR5c3c/PMl38/1TTpdSS6z1VtbHJ6g6oarOr6rzN27cuK3zAgAA7HIWEYsbkmzo7s9M99+dWTx+Yzq9NNP3b87tf+Dc4w9IcsVW1gfdfUp3H9bdh61bt267vREAAIC1atVjsbv/OcnlVXX/aenIJF9OclaSTVc0PS7J+6fbZyV53nRV1EcluXY6TfXsJEdV1b7ThW2OmtYAAAC4g3Zf0Ov+WpJ3VNWdk3wlyfMzC9czq+r4JJclOWba90NJnpzk0iTXT/umu6+qqlcn+ey036u6+6rVewsAAABr10Jisbu/mOSwJTYducS+neRFW3ieU5Ocun2nAwAAYFG/ZxEAAIAdmFgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAIDBNsdiVe1bVQ9ZiWEAAADYMSwrFqvqE1V196q6R5K/T/K2qnrdyo4GAADAoiz3yOLe3X1dkp9P8rbu/okkj1+5sQAAAFik5cbi7lV1nyTPTPKBFZwHAACAHcByY/GVSc5Ocml3f7aq7pfkkpUbCwAAgEXafZn7Xdnd/3ZRm+7+is8sAgAArF3LPbL4x8tcAwAAYA3Y6pHFqvp3SR6dZF1V/ebcprsn2W0lBwMAAGBxbus01Dsn2Wva7wfn1q9L8oyVGgoAAIDF2mosdvcnk3yyqv6su7++SjMBAACwYMu9wM1dquqUJOvnH9Pdj1uJoQAAAFis5cbiXyb570nekuSWlRsHAACAHcFyY/Hm7n7zik4CAADADmO5vzrjf1bVf6qq+1TVPTZ9rehkAAAALMxyjyweN31/6dxaJ7nf9h0HAACAHcGyYrG7D17pQQAAANhxLCsWq+p5S6139+nbdxwAAAB2BMs9DfXwudt7JDkyyeeTiEUAAIA1aLmnof7a/P2q2jvJ21dkIgAAABZuuVdD3dz1SQ7dnoMAAACw41juZxb/Z2ZXP02S3ZI8IMmZKzUUAAAAi7Xczyz+4dztm5N8vbs3rMA8AAAA7ACWdRpqd38yyT8m+cEk+ya5cSWHAgAAYLGWFYtV9cwk5yU5Jskzk3ymqp6xkoMBAACwOMs9DfV3khze3d9Mkqpal+SjSd69UoMBAACwOMu9GuqdNoXi5Fvb8FgAAAB2Mss9svjhqjo7yTun+7+Y5EMrMxIAAACLttVYrKpDkty7u19aVT+f5LFJKsmnk7xjFeYDAABgAW7rVNI3JPl2knT3e7r7N7v7NzI7qviGlR4OAACAxbitWFzf3Rdsvtjd5ydZvyITAQAAsHC3FYt7bGXbnttzEAAAAHYctxWLn62qX958saqOT/K5lRkJAACARbutq6G+JMl7q+o5+X4cHpbkzkl+biUHAwAAYHG2Govd/Y0kj66qn0ny4Gn5g939sRWfDAAAgIVZ1u9Z7O6PJ/n4Cs8CAADADuK2PrMIAADALkgsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMFhaLVbVbVX2hqj4w3T+4qj5TVZdU1buq6s7T+l2m+5dO29fPPccrpvWLq+oJi3knAAAAa88ijyy+OMlFc/dfm+T13X1okquTHD+tH5/k6u4+JMnrp/1SVQ9McmySByV5YpI3VdVuqzQ7AADAmraQWKyqA5I8JclbpvuV5HFJ3j3tclqSp0+3j57uZ9p+5LT/0UnO6O7vdfdXk1ya5IjVeQcAAABr26KOLL4hyW8l+dfp/j2TXNPdN0/3NyTZf7q9f5LLk2Tafu20/7+tL/GYW6mqE6rq/Ko6f+PGjdvzfQAAAKxJqx6LVfXUJN/s7s/NLy+xa9/Gtq095taL3ad092Hdfdi6deu2aV4AAIBd0e4LeM3HJHlaVT05yR5J7p7ZkcZ9qmr36ejhAUmumPbfkOTAJBuqavckeye5am59k/nHAAAAcAes+pHF7n5Fdx/Q3eszu0DNx7r7OUk+nuQZ027HJXn/dPus6X6m7R/r7p7Wj52ulnpwkkOTnLdKbwMAAGBNW8SRxS15WZIzquq/JvlCkrdO629N8vaqujSzI4rHJkl3f6mqzkzy5SQ3J3lRd9+y+mMDAACsPQuNxe7+RJJPTLe/kiWuZtrdNyQ5ZguPf02S16zchAAAALumRf6eRQAAAHZQYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAgVgEAABgIBYBAAAYiEUAAAAGYhEAAICBWAQAAGAgFgEAABiIRQAAAAZiEQAAgIFYBAAAYCAWAQAAGIhFAAAABmIRAACAwarHYlUdWFUfr6qLqupLVfXiaf0eVXVOVV0yfd93Wq+qemNVXVpVF1TVI+ae67hp/0uq6rjVfi8AAABr1SKOLN6c5D939wOSPCrJi6rqgUlenuTc7j40ybnT/SR5UpJDp68Tkrw5mcVlkhOTPDLJEUlO3BSYAAAA3DGrHovdfWV3f366/e0kFyXZP8nRSU6bdjstydOn20cnOb1n/i7JPlV1nyRPSHJOd1/V3VcnOSfJE1fxrQAAAKxZC/3MYlWtT/LwJJ9Jcu/uvjKZBWWSe0277Z/k8rmHbZjWtrQOAADAHbSwWKyqvZL8VZKXdPd1W9t1ibXeyvpSr3VCVZ1fVedv3Lhx24cFAADYxSwkFqvqBzILxXd093um5W9Mp5dm+v7NaX1DkgPnHn5Akiu2sj7o7lO6+7DuPmzdunXb740AAACsUYu4GmoleWuSi7r7dXObzkqy6YqmxyV5/9z686aroj4qybXTaapnJzmqqvadLmxz1LQGAADAHbT7Al7zMUmem+TCqvritPbbSU5OcmZVHZ/ksiTHTNs+lOTJSS5Ncn2S5ydJd19VVa9O8tlpv1d191Wr8xYAAADWtlWPxe7+myz9ecMkOXKJ/TvJi7bwXKcmOXX7TQcAAECy4KuhAgAAsGMSiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAYPdFDwDAGnXS3ouegJ3JSdcuegIANuPIIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADAQCwCAAAwEIsAAAAMxCIAAAADsQgAAMBALAIAADAQiwAAAAzEIgAAAIOdPhar6olVdXFVXVpVL1/0PAAAAGvBTh2LVbVbkj9J8qQkD0zyrKp64GKnAgAA2Pnt1LGY5Igkl3b3V7r7xiRnJDl6wTMBAADs9Hb2WNw/yeVz9zdMawAAANwBuy96gDuolljrYaeqE5KcMN39TlVdvKJTsVbsl+RfFj3EjqZeu+gJYKfnZ8tSXrnUP+nANvCzhW3xw8vZaWePxQ1JDpy7f0CSKzbfqbtPSXLKag3F2lBV53f3YYueA1hb/GwBVoKfLayEnf001M8mObSqDq6qOyc5NslZC54JAABgp7dTH1ns7pur6leTnJ1ktySndveXFjwWAADATm+njsUk6e4PJfnQoudgTXLqMrAS/GwBVoKfLWx31T1cDwYAAIBd3M7+mUUAAABWgFgEAABgsNN/ZhEAAHY1VfVjSY5Osn9mv2f8iiRndfdFCx1rltudAAAG7ElEQVSMNcVnFmFOVVWS5yS5X3e/qqoOSvJD3X3egkcDdkJV9Ztb297dr1utWYC1o6peluRZSc7I7PeOJ7PfN35skjO6++RFzcbaIhZhTlW9Ocm/Jnlcdz+gqvZN8pHuPnzBowE7oao6cbp5/ySH5/u/C/hnk3yqu1+wkMGAnVpV/e8kD+rumzZbv3OSL3X3oYuZjLXGaahwa4/s7kdU1ReSpLuvnn7wAmyz7n5lklTVR5I8oru/Pd0/KclfLnA0YOf2r0num+Trm63fZ9oG24VYhFu7qap2y+zc/1TVuvihC9xxByW5ce7+jUnWL2YUYA14SZJzq+qSJJdPawclOSTJry5sKtYcsQi39sYk701y76p6TZJnJPkvix0JWAPenuS8qnpvZv8z6ueSnL7YkYCdVXd/uKp+NMkRmV3gpjL77OJnu/uWhQ7HmuIzi7CZ6epiR2b2g/dcVxUDtoeqekSSn5zufqq7v7DIeQDgtohF2ExVPTbJod39tuk01L26+6uLngvY+VTV3bv7uqq6x1Lbu/uq1Z4JAJZLLMKc6cqFhyW5f3f/aFXdN8lfdvdjFjwasBOqqg9091Or6quZPgu9aVOS7u77LWg0ALhNYhHmVNUXkzw8yee7++HT2gXd/ZDFTgYAAKvLBW7g1m7s7q6qTVdDvduiBwLWhun3th6aZI9Na939qcVNBABbJxbh1s6sqj9Nsk9V/XKSX0ryPxY8E7CTq6oXJHlxkgOSfDHJo5J8OsnjFjkXAGyN01BhM1X1fyQ5KrPPFJ3d3ecseCRgJ1dVFyY5PMnfdffDpqsuv7K7f3HBowHAFjmyCJOq2i2zOHx8EoEIbE83dPcNVZWqukt3/2NV3X/RQwHA1ohFmHT3LVV1fVXt3d3XLnoeYE3ZUFX7JHlfknOq6uokVyx4JgDYKqehwpyqOjOzzxKdk+S7m9a7+9cXNhSwplTVv0+yd5IPd/eNi54HALZELMKcqjpuqfXuPm21ZwF2flW1R5IXJjkkyYVJ3trdNy92KgBYHrEISarqoO6+bNFzAGtLVb0ryU1J/jrJk5J8vbtfvNipAGB5xCIkqarPd/cjptt/1d2/sOiZgJ1fVV3Y3T8+3d49yXmbftYAwI7uToseAHYQNXf7fgubAlhrbtp0w+mnAOxsXA0VZnoLtwHuiIdW1XXT7Uqy53S/knR3331xowHA1jkNFZJU1S2ZXf20kuyZ5PpNm+I/6AAA2AWJRQAAAAY+swgAAMBALAIAADBwgRsAdjlV9Z3u3muztRcmub67T1/QWJvm+PEkb5/uHpTk2unrX7r78QsbDIBdjs8sArDLWSoWd0RV9WdJPtDd7170LADsepyGCgBJquqkqvq/ptuHVNVHq+rvq+rzVfUj0/pLq+qzVXVBVb1yWltfVRdV1f+oqi9V1Ueqas9p269X1Zen/c+Y1u5WVadOz/OFqjp6G2Z8Z1U9Ze7+u6rqyVX1gqp6b1WdXVUXV9V/mdvnuKo6r6q+WFVvqir/9gOwLP7BAIDRO5L8SXc/NMmjk1xZVUclOTTJEUkeluQnquqnpv0PnfZ/UJJrkvzCtP7yJA/v7ockeeG09jtJPtbdhyf5mST/rarutsy53pLk+UlSVfsmOTzJ2dO2I5Icm+QRSZ5dVQ+rqgcn+bkkj+7uh2X28ZNjt+2PAoBdlc8sAsCcqvrBJPt393uTpLtvmNaPSnJUki9Mu+6VWSReluSr3f3Faf1zSdZPty9I8o6qel+S901rRyV52qajmEn2yOyziRctY7yPJfnjqrpnkmclObO7b6mqJDm7u6+eZn1fksdm9u/84UnOn/bZM8nly/7DAGCXJhYB4NZqK+t/0N1/eqvFqvVJvje3dEtmUZYkT0nyU0meluR3q+pB0/P8QndfvK2DdXdX1TuSPDvJ/zl9/7fNm+8+vdap3f272/paAOA0VACY093XJdlQVU9Pkqq6S1XdNbPTPX+pqvaa1vevqntt6XmmzwYe2N0fT/JbSfbJ7Gjk2Ul+raZDfVX18G0c8W1JXprkhs2C86iq2mea9egkf5vko0meWVX7Ta91z6o6aBtfD4BdlCOLAOyK7lpVG+buv26z7c9N8qdV9aokNyU5prs/UlUPSPLpqfO+k+Q/ZHYkcSm7Jfnzqto7syN8r+/ua6rq1UnekOSCKRi/luSpyx28u6+oqv+d5IzNNv1Nkr9I8iNJ3r7ptNjpQjwfneL1psw+O3nZcl8PgF2XX50BADuR6WI4FyZ5aHd/e1p7QZIHd/dLFjocAGuK01ABYCdRVU/I7EI4r98UigCwUhxZBAAAYODIIgAAAAOxCAAAwEAsAgAAMBCLAAAADMQiAAAAA7EIAADA4P8Htn01m0ZS+7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10));\n",
    "df.Type.value_counts().plot(kind=\"bar\");\n",
    "plt.xlabel(\"License Type\");\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.title(\"License Type Vs Counts\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#So from our above diagrame we can see we have huge number of free apps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
       "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
       "       'Android Ver'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets try to find out the content rating.. that mean how many adult apps are there or how many non-adult apps.\n",
    "df.columns # it will select content rating column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Everyone           8714\n",
       "Teen               1208\n",
       "Mature 17+          499\n",
       "Everyone 10+        414\n",
       "Adults only 18+       3\n",
       "Unrated               2\n",
       "Name: Content Rating, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Content Rating'].value_counts() # you can see we have 6 categories in content rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10), dpi=100, );\n",
    "df[\"Content Rating\"].value_counts().plot(kind=\"bar\");\n",
    "plt.title(\"Content Vs Total Count\");\n",
    "plt.xlabel(\"Content Type\");\n",
    "plt.ylabel(\"Count\");\n",
    "plt.ylim(bottom=-10);\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Actually you can see last two bars are almost hidden because the value is very less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1 and up            2451\n",
       "4.0.3 and up          1501\n",
       "4.0 and up            1375\n",
       "Varies with device    1362\n",
       "4.4 and up             980\n",
       "2.3 and up             652\n",
       "5.0 and up             601\n",
       "4.2 and up             394\n",
       "2.3.3 and up           281\n",
       "2.2 and up             244\n",
       "4.3 and up             243\n",
       "3.0 and up             241\n",
       "2.1 and up             134\n",
       "1.6 and up             116\n",
       "6.0 and up              60\n",
       "7.0 and up              42\n",
       "3.2 and up              36\n",
       "2.0 and up              32\n",
       "5.1 and up              24\n",
       "1.5 and up              20\n",
       "4.4W and up             12\n",
       "3.1 and up              10\n",
       "2.0.1 and up             7\n",
       "8.0 and up               6\n",
       "7.1 and up               3\n",
       "1.0 and up               2\n",
       "5.0 - 8.0                2\n",
       "4.0.3 - 7.1.1            2\n",
       "2.2 - 7.1.1              1\n",
       "5.0 - 6.0                1\n",
       "7.0 - 7.1.1              1\n",
       "5.0 - 7.1.1              1\n",
       "4.1 - 7.1.1              1\n",
       "Name: Android Ver, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets see apps grouped by os version \n",
    "df[\"Android Ver\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8919</th>\n",
       "      <td>Bible du Semeur-BDS (French)</td>\n",
       "      <td>BOOKS_AND_REFERENCE</td>\n",
       "      <td>4.5</td>\n",
       "      <td>313</td>\n",
       "      <td>6.9M</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Books &amp; Reference</td>\n",
       "      <td>August 4, 2018</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9212</th>\n",
       "      <td>Tamilnadu Electricity Info</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>3.6</td>\n",
       "      <td>216</td>\n",
       "      <td>811k</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Tools</td>\n",
       "      <td>November 9, 2014</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0 and up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               App     ...     Android Ver\n",
       "8919  Bible du Semeur-BDS (French)     ...      1.0 and up\n",
       "9212    Tamilnadu Electricity Info     ...      1.0 and up\n",
       "\n",
       "[2 rows x 13 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Android Ver\"] == '1.0 and up'] # we can verify the above data by using this command. You can\n",
    "# simply replace the value of OS and get the number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(15, 10));\n",
    "plt.xlabel(\"Anroid Version\");\n",
    "plt.ylabel(\"Total Apps\");\n",
    "plt.title(\"Android Version Vs Total Conts\");\n",
    "df[\"Android Ver\"].value_counts().plot(kind=\"bar\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1 and up            0.226149\n",
       "4.0.3 and up          0.138494\n",
       "4.0 and up            0.126868\n",
       "Varies with device    0.125669\n",
       "4.4 and up            0.090423\n",
       "2.3 and up            0.060159\n",
       "5.0 and up            0.055453\n",
       "4.2 and up            0.036354\n",
       "2.3.3 and up          0.025927\n",
       "2.2 and up            0.022513\n",
       "4.3 and up            0.022421\n",
       "3.0 and up            0.022237\n",
       "2.1 and up            0.012364\n",
       "1.6 and up            0.010703\n",
       "6.0 and up            0.005536\n",
       "7.0 and up            0.003875\n",
       "3.2 and up            0.003322\n",
       "2.0 and up            0.002953\n",
       "5.1 and up            0.002214\n",
       "1.5 and up            0.001845\n",
       "4.4W and up           0.001107\n",
       "3.1 and up            0.000923\n",
       "2.0.1 and up          0.000646\n",
       "8.0 and up            0.000554\n",
       "7.1 and up            0.000277\n",
       "1.0 and up            0.000185\n",
       "5.0 - 8.0             0.000185\n",
       "4.0.3 - 7.1.1         0.000185\n",
       "2.2 - 7.1.1           0.000092\n",
       "5.0 - 6.0             0.000092\n",
       "7.0 - 7.1.1           0.000092\n",
       "5.0 - 7.1.1           0.000092\n",
       "4.1 - 7.1.1           0.000092\n",
       "Name: Android Ver, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Android Ver\"].value_counts(normalize=True) # You can get the percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tools                                  842\n",
       "Entertainment                          623\n",
       "Education                              549\n",
       "Medical                                463\n",
       "Business                               460\n",
       "Productivity                           424\n",
       "Sports                                 398\n",
       "Personalization                        392\n",
       "Communication                          387\n",
       "Lifestyle                              381\n",
       "Finance                                366\n",
       "Action                                 365\n",
       "Health & Fitness                       341\n",
       "Photography                            335\n",
       "Social                                 295\n",
       "News & Magazines                       283\n",
       "Shopping                               260\n",
       "Travel & Local                         257\n",
       "Dating                                 234\n",
       "Books & Reference                      231\n",
       "Arcade                                 220\n",
       "Simulation                             200\n",
       "Casual                                 193\n",
       "Video Players & Editors                173\n",
       "Puzzle                                 140\n",
       "Maps & Navigation                      137\n",
       "Food & Drink                           127\n",
       "Role Playing                           109\n",
       "Strategy                               107\n",
       "Racing                                  98\n",
       "                                      ... \n",
       "Strategy;Action & Adventure              2\n",
       "Casual;Music & Video                     2\n",
       "Books & Reference;Education              2\n",
       "Video Players & Editors;Creativity       2\n",
       "Entertainment;Pretend Play               2\n",
       "Card;Action & Adventure                  2\n",
       "Tools;Education                          1\n",
       "Travel & Local;Action & Adventure        1\n",
       "Racing;Pretend Play                      1\n",
       "Health & Fitness;Action & Adventure      1\n",
       "Role Playing;Brain Games                 1\n",
       "Comics;Creativity                        1\n",
       "Puzzle;Education                         1\n",
       "Adventure;Brain Games                    1\n",
       "Music & Audio;Music & Video              1\n",
       "Board;Pretend Play                       1\n",
       "Lifestyle;Education                      1\n",
       "Communication;Creativity                 1\n",
       "Role Playing;Education                   1\n",
       "Books & Reference;Creativity             1\n",
       "Entertainment;Education                  1\n",
       "Strategy;Creativity                      1\n",
       "February 11, 2018                        1\n",
       "Trivia;Education                         1\n",
       "Parenting;Brain Games                    1\n",
       "Health & Fitness;Education               1\n",
       "Strategy;Education                       1\n",
       "Arcade;Pretend Play                      1\n",
       "Card;Brain Games                         1\n",
       "Lifestyle;Pretend Play                   1\n",
       "Name: Genres, Length: 120, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Genres\"].value_counts() # here can find all the genres.. Try its plot yourself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Lets plot top 10 genres\n",
    "plt.figure(figsize=(15, 10));\n",
    "df[\"Genres\"].value_counts().head(10).plot(kind=\"bar\");\n",
    "#df.groupby([\"Genres\"])..head(10).plot(kind=\"bar\");\n",
    "plt.title(\"Top 10 Genres\");\n",
    "plt.xlabel(\"Genres\");\n",
    "plt.ylabel(\"Count\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3702</th>\n",
       "      <td>Cartoon Network App</td>\n",
       "      <td>VIDEO_PLAYERS</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119202</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Video Players &amp; Editors;Music &amp; Video</td>\n",
       "      <td>June 21, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5861</th>\n",
       "      <td>Masha and The Bear</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39779</td>\n",
       "      <td>19M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Video Players &amp; Editors;Music &amp; Video</td>\n",
       "      <td>August 5, 2016</td>\n",
       "      <td>4.0.1</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7582</th>\n",
       "      <td>Cartoon Network App</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>119173</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>10,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Video Players &amp; Editors;Music &amp; Video</td>\n",
       "      <td>June 21, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      App         ...                 Android Ver\n",
       "3702  Cartoon Network App         ...          Varies with device\n",
       "5861   Masha and The Bear         ...                4.0.3 and up\n",
       "7582  Cartoon Network App         ...          Varies with device\n",
       "\n",
       "[3 rows x 13 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can verify using below statement.\n",
    "# So found the genre wise installs\n",
    "df[df[\"Genres\"] == 'Video Players & Editors;Music & Video']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content Rating, Genres, Last Updated, Current Ver, Android Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to change the dtype of review but there is one column whih contains 3.0M\n",
    "df.Reviews.replace(\"3.0M\", '3000000', inplace=True)\n",
    "df[df.Reviews == \"3.0M\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Reviews = df.Reviews.astype(\"float\") # lets change the datatype of Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_new = df.groupby([\"Genres\", \"Category\"], as_index=False).sum()[[\"Genres\", \"Category\", \"Reviews\"]].sort_values(by=\"Reviews\", ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Genres</th>\n",
       "      <th>Category</th>\n",
       "      <th>Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Communication</td>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>815462260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Social</td>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>621241422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>GAME</td>\n",
       "      <td>350303953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arcade</td>\n",
       "      <td>GAME</td>\n",
       "      <td>336990433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Casual</td>\n",
       "      <td>GAME</td>\n",
       "      <td>308028000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Tools</td>\n",
       "      <td>TOOLS</td>\n",
       "      <td>272842708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Strategy</td>\n",
       "      <td>GAME</td>\n",
       "      <td>230117074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Photography</td>\n",
       "      <td>PHOTOGRAPHY</td>\n",
       "      <td>213516650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Sports</td>\n",
       "      <td>GAME</td>\n",
       "      <td>122047448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Shopping</td>\n",
       "      <td>SHOPPING</td>\n",
       "      <td>115041222.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Genres       Category      Reviews\n",
       "46   Communication  COMMUNICATION  815462260.0\n",
       "128         Social         SOCIAL  621241422.0\n",
       "0           Action           GAME  350303953.0\n",
       "8           Arcade           GAME  336990433.0\n",
       "33          Casual           GAME  308028000.0\n",
       "137          Tools          TOOLS  272842708.0\n",
       "133       Strategy           GAME  230117074.0\n",
       "103    Photography    PHOTOGRAPHY  213516650.0\n",
       "129         Sports           GAME  122047448.0\n",
       "120       Shopping       SHOPPING  115041222.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwmMOWXQADeexFll3B2jz2a8uuAAAAFkqPHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgFhrkquqiVfWSqjq7qs6qqhstsj0AAIDN4LAFH//JSV7d3XepqvMnudCC2wMAANjwFhbkquqoJDdL8mtJ0t3fTfLdRbUHAACwWSxyaOVVkmxP8uyqem9VPbOqLrzjg6rqhKraVlXbtm/fvsByAAAANoZFBrnDklwvyd9293WTfDPJI3Z8UHef2N1bu3vrli1bFlgOAADAxrDIIPfpJJ/u7nfO91+SKdgBAACwHxYW5Lr780k+VVVXnzfdOskHF9UeAADAZrHoVSsfnOQF84qVH01y3wW3BwAAsOEtNMh19+lJti6yDQAAgM1moRcEBwAAYP0JcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADCYwxZ58Kr6eJJzknw/ybndvXWR7QEAAGwGCw1ys1t295cOQDsAAACbgqGVAAAAg1l0kOskr6mq06rqhAW3BQAAsCksemjlTbr7s1V1qSSvraqzu/tNqx8wB7wTkuSKV7zigssBAAAY30J75Lr7s/PnLyZ5WZIb7OQxJ3b31u7eumXLlkWWAwAAsCEsLMhV1YWr6siV20l+PskHFtUeAADAZrHIoZWXTvKyqlpp54Xd/eoFtgcAALApLCzIdfdHk/z0oo4PAACwWbn8AAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAazpiBXVTdZyzYAAAAWb609cn+zxm0AAAAs2GG721lVN0py4yRbquphq3YdleTQRRYGAADAzu02yCU5f5Ij5scduWr715PcZVFFAQAAsGu7DXLdfWqSU6vqOd39iQNUEwAAALuxpx65FReoqhOTHL36a7r7VosoCgAAgF1ba5B7cZJnJHlmku8vrhwAAAD2ZK1B7tzu/tuFVgIAAMCarPXyA6+sqt+sqstW1cVXPhZaGQAAADu11h65X50//96qbZ3kKutbDgAAAHuypiDX3VdedCEAAACszZqCXFXdZ2fbu/t561sOAAAAe7LWoZXHrrp9eJJbJ3lPEkEOAADgAFvr0MoHr75fVRdJ8g8LqQgAAIDdWuuqlTv6VpKrrWchAAAArM1a58i9MtMqlUlyaJJrJnnRoooCAABg19Y6R+4vV90+N8knuvvTC6gHAACAPVjT0MruPjXJ2UmOTHKxJN9dZFEAAADs2pqCXFXdLcm7ktw1yd2SvLOq7rLIwgAAANi5tQ6tfFSSY7v7i0lSVVuSvC7JSxZVGAAAADu31lUrD1kJcbMv78XXAgAAsI7W2iP36qr69yQnzffvnuRfF1MSAAAAu7PbIFdVV01y6e7+var6lSQ3TVJJ3p7kBQegPgAAAHawp+GRT0pyTpJ098nd/bDu/p1MvXFPWnRxAAAA/Kg9Bbmju/uMHTd297YkRy+kIgAAAHZrT0Hu8N3su+B6FgIAAMDa7CnIvbuqHrDjxqq6X5LTFlMSAAAAu7OnVSsfmuRlVXXPnBfctiY5f5I7raWBqjo0ybYkn+nuX9rXQgEAAJjsNsh19xeS3LiqbpnkmHnzv3T3G/aijYckOSvJUftWIgAAAKut6Tpy3X1KklP29uBVdfkkv5jk8UketrdfDwAAwI/a0xy5/fWkJL+f5Ae7ekBVnVBV26pq2/bt2xdcDgAAwPgWFuSq6peSfLG7d7soSnef2N1bu3vrli1bFlUOAADAhrHIHrmbJLl9VX08yT8muVVVPX+B7QEAAGwKCwty3f3I7r58dx+d5B5J3tDd91pUewAAAJvFoufIAQAAsM7WtGrl/uruNyZ544FoCwAAYKPTIwcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwhy27ANisrvXcay27hDV5/6++f9klAACwAz1yAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGBcRw7YEM66xjWXXcIeXfPss5ZdAgCwQeiRAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwmMOWXQAAB5enPfANyy5hTX7rGbdadgkAsDQL65GrqsOr6l1V9b6qOrOqHreotgAAADaTRfbI/XeSW3X3N6rqfEneUlX/1t3vWGCbAAAAG97Cglx3d5JvzHfPN3/0otoDAADYLBa62ElVHVpVpyf5YpLXdvc7F9keAADAZrDQINfd3+/u6yS5fJIbVNUxOz6mqk6oqm1VtW379u2LLAcAAGBDOCCXH+juryZ5Y5LjdrLvxO7e2t1bt2zZciDKAQAAGNoiV63cUlUXnW9fMMltkpy9qPYAAAA2i0WuWnnZJM+tqkMzBcYXdferFtgeAADAprDIVSvPSHLdRR0fAABgszogc+QAAABYP4IcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIM5bNkFAMBG9sS7/9KyS1iTh//Tq5ZdAgB7QY8cAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwRy27AIAANbq049487JLWJPL//nPLrsEYIPTIwcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIctuwAAAA68xz72scsuYU1GqRMOND1yAAAAgxHkAAAABiPIAQAADEaQAwAAGMzCglxVXaGqTqmqs6rqzKp6yKLaAgAA2EwWuWrluUke3t3vqaojk5xWVa/t7g8usE0AAIANb2E9ct39ue5+z3z7nCRnJfmxRbUHAACwWRyQOXJVdXSS6yZ55072nVBV26pq2/bt2w9EOQAAAENbeJCrqiOSvDTJQ7v76zvu7+4Tu3trd2/dsmXLossBAAAY3kKDXFWdL1OIe0F3n7zItgAAADaLRa5aWUn+PslZ3f1Xi2oHAABgs1lkj9xNktw7ya2q6vT543YLbA8AAGBTWNjlB7r7LUlqUccHAADYrA7IqpUAAACsH0EOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBHLbsAgAAYHSvf8OPL7uENbn1rT6y7BJYJ3rkAAAABiPIAQAADEaQAwAAGIw5cgAAwEHlMqecvuwS1uTzt7zO0trWIwcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDWViQq6pnVdUXq+oDi2oDAABgM1pkj9xzkhy3wOMDAABsSgsLct39piT/tajjAwAAbFbmyAEAAAxm6UGuqk6oqm1VtW379u3LLgcAAOCgt/Qg190ndvfW7t66ZcuWZZcDAABw0Ft6kAMAAGDvLPLyAycleXuSq1fVp6vqfotqCwAAYDM5bFEH7u7jF3VsAACAzczQSgAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwghwAAMBgBDkAAIDBCHIAAACDEeQAAAAGI8gBAAAMRpADAAAYjCAHAAAwGEEOAABgMIIcAADAYAQ5AACAwQhyAAAAgxHkAAAABiPIAQAADEaQAwAAGIwgBwAAMBhBDgAAYDCCHAAAwGAEOQAAgMEIcgAAAIMR5AAAAAYjyAEAAAxGkAMAABiMIAcAADAYQQ4AAGAwCw1yVXVcVX2oqj5cVY9YZFsAAACbxcKCXFUdmuRpSW6b5CeTHF9VP7mo9gAAADaLRfbI3SDJh7v7o9393ST/mOQOC2wPAABgU6juXsyBq+6S5Ljuvv98/95Jfqa7H7TD405IcsJ89+pJPrSQgtbPJZN8adlFbCDO5/pyPteX87l+nMv15XyuL+dz/TiX68v5XF+jnM8rdfeWPT3osAUWUDvZ9iOpsbtPTHLiAutYV1W1rbu3LruOjcL5XF/O5/pyPtePc7m+nM/15XyuH+dyfTmf62ujnc9FDq38dJIrrLp/+SSfXWB7AAAAm8Iig9y7k1ytqq5cVedPco8k/7zA9gAAADaFhQ2t7O5zq+pBSf49yaFJntXdZy6qvQNomGGgg3A+15fzub6cz/XjXK4v53N9OZ/rx7lcX87n+tpQ53Nhi50AAACwGAu9IDgAAADrT5ADAAAYjCAHAAAwGEEOAA5yVXXDqrrQfPv4qnpCVV1hT1/HzlXVMcuuYSOpqkOXXQNsRhY7WYOq2pLkAUmOzqqVPrv715dV04iq6pXZyUXhV3T37Q9gORuC5+b6qqpKcs8kV+nuP6qqKya5THe/a8mlDcXv+vqrqjOS/HQg5BuEAAAZ70lEQVSSayV5QZLnJLl9d998mXWNqqrekuT8mc7jC7v7q8utaGxV9bEkL0ny7O7+4LLr2Uiq6mJJrtDdZyy7Fg4+C7v8wAbziiRvTvK6JN9fci0j+8tlF7ABeW6ur6cn+UGSWyX5oyTnJHlpkmOXWdSA/K6vv3O7u6vqDkme3N3PrKp7LruoUXX3Tavqakl+Pcm2qnpXphDy2iWXNqprZ7pe8DOr6pAkz0ryj9399eWWNaaqemOS22d6nX56ku1VdWp3P2yphQ2oqt6fH31j8WtJtiX5k+7+8oGvav3okVuDqjq9u6+z7DpgR56b66uq3tPd16uq93b3dedt7+vun152bWxuVfXmJP+c5P5JbpHkC0ne193XWmZdo5uHBN4xyVOSfD1JJfmD7j55qYUNrKpuluSkJBfN1Ev3x9394eVWNZaV/0FVdf9MvXGPqaozuvvay65tNFX1hExvdL9w3nSP+fPXk9y0u395KYWtE3Pk1uZVVXW7ZRexUVTV1arqJVX1war66MrHsusalOfm+vre/MKukx8OXf3Bcksal9/1dXX3TCHjgd39uSSXT/JXyy1pXFV17ar66yRnZeqB/+XuvuZ8+6+XWtyAqurQqrp9Vb0syZOTPDHJVZK8Msm/LrW4MR1WVZdNcrckr1p2MYO7SXc/srvfP388Ksktuvv/ZpqWMjRDK9fmIUn+oKq+m+R787bu7qOWWNPInp3kMZn+Wd4yyX0zvUBh73lurq+nJHlZkktV1eOT3CXJo5db0tD8rq+T7v5skiesuv/JTOeXffPUJM/M1Pv27ZWN3f3ZqvI7v/f+M8kpSf6iu9+2avtL5h469s7jkvx7krd097ur6iqZzjF774iq+pnufmeSVNUNkhwx7zt3eWWtD0MrOeCq6rTuvn5VvX9lWFBVvbm7f3bZtUFVXSPJrTMFjtd391lLLmlYftf3X1V9JTtfOKYyvWlz8QNcEvyIqjqiu7+x7Do2iqq6SXe/dU/b2LOqOjbTnM0jMv3d/HqmIepnJvnF7n7REsvbb3rk1qiqbp9k5V2lN3a3ru599515MvR/VtWDknwmyaWWXNOwPDf3X1WtfjH8xUzzO364r7v/68BXtSH4Xd9/l1x2ARvRRl8AYQkuWFW/HSsor5e/SXK9NWxjD7r73UmuVVUXydSBtXqF2qFDXCLIrUlV/XmmVeteMG96SFXdtLsfscSyRvbQJBdK8ttJ/jjTnIRfXWpFg/LcXDenZXpRV0mumOQr8+2LJvlkkisvr7Sh+V3fT939w9Vo52uf3XS++ybLvO+Xf8uuF0B4TpKhF0BYAisor4OqulGSGyfZUlWrV6g8Kolr9e2DqrpAkjtnfpNhuspQ0t1/tMSy1o2hlWswX7/nOt39g/n+oUnea/Ugls1zc31V1TOS/HN3/+t8/7ZJbtPdD19uZWx2c4/mbyZ5+bzpDkme1t1PX15V46qqt3b3TXa2bfVQYNbGCsrro6punmlV2gcmecaqXeckeWV3mye3l6rq1Zl620/LqjcZuvuJSytqHemRW7uLJlkZXnWRZRYyqqp6Unc/dFcXC3aR4H3mubl+ju3uB67c6e5/q6o/XmZBI6uqU7Lz3/VbLaGc0Z2Q5AYr85Cq6k+TvC3TtQ/Zext6AYQleFVV3W7lTTD2TXefOl+s/lrd/bhl17NBXL67j1t2EYsiyK3NnyV57/yipDLNR3rkcksa0j/Mn10seP14bq6vL80r1j0/UwC5VxJzZfbd7666fXim4S1eJO+bynkr02a+bQXQfXf/JM+qqpXwdk6S+1fVhTP9XWUNquqcnDcs3QrK66C7v7/DvG32z9uq6lrd/f5lF7IIhlau0Xw9j2Mz/bF6Z3d/fsklDWv+R/ntHYYDXqC7v7Xcysbkubl+5n+ej8l5i8e8KcnjLHayfqrq1O6++bLrGE1V/X6S45O8NNPv+h2TnNTd3hjbD7tYAAGWqqqemORqSV6c5Jsr212ofu9V1QeTXDXJx5L8d85b8XdDTEER5Hajqq7R3WdX1U5XCeru9xzomjaCqnpHpnlHK0OEjkjymu6+8XIrG4fnJiPY4V3lQ5JcP8lTuvvqSyppaPMy2j+b6YXIm+bV2NgHVXXpJH+a5HLdfduq+skkN+ruv19yacOqql/JtBhPJ3lzd798D1/CLlTVzq4R2VYB3XtVdaWdbe/uTxzoWhbB0Mrde1imeQk7mxDZmVZgY+8dvvp6M939jaq60DILGpDn5gJU1ZYkv5/kpzINBUxiTtd+WL0a6LmZ3hG931IrGtu3549OYgTD/nlOpguqP2q+/x9J/imJILcPqurpmXo9Vi7d8sCq+rnu/q0lljWs7r7vsmsYXVUd1d1fzzRsesMS5Haju0+Yb962u7+zel9VHb6TL2FtvllV11vpNaqq62d6ccIaeW4uzAsyvZj7pUyrhv1qku1LrWhg3e2yDetk1aqVL8sUjF9UVVat3HeX7O4XVdUjk6S7z60qy+bvu5snOabnYV5V9dwkG3JO0oFQVZfPdN24m2R64+YtSR7S3Z9eamFjeWGm/+Wr31Bc0Umusoyi1tshyy5gEG9b4zbW5qFJXlxVb66qN2d64fygJdc0Ks/N9XWJeWjV97r71HkYyw2XXdSoququVXXkfPvRVXXyroYDs0crq1Y+qrv/IMnPZHqzgX3zzaq6ROZVVavqhpmWKGfffCjTNThXXCHJGUuqZSN4dpJ/TnK5JD+W5JXzNtaou39p/nzl7r7K/HnlY0OEuESP3G5V1WUy/QJdsKqum/PS/FGZLnLLPujud1fVNZJcPdM5Pbu7v7eHL2MVz82FWXkefq6qfjHJZ5Ncfon1jO7/dPeLq+qmSX4h04q1f5sphLB3rFq5vh6W6YXyj1fVW5NsSXLX5ZY0tEskOauq3jXfPzbJ26vqnxOXF9oHW7p7dXB7TlU9dGnVDG4jz98U5HbvF5L8WqYXcn+1avs5Sf5gGQVtBFV1viS/kfNWBnxjVf2dMLdXVj83n5jzXtB9PZ6b++NP5lXsHp5pWMtRSX5nuSUNbWWo2i8m+dvufkVVPXaJ9Qynqg7r7nMzXb7lHVX10nnXnZI8d3mVDe/MTMMBV95Q/FCMUtoff7jsAjaYL1XVvXLenMPj41I4+2Sjz9+0auUaVNWdu/ule34ka1FVz0xyvpz3IuTeSb7f3fdfXlVj8tzkYFZVr0rymSS3ybRi5beTvKu7f3qphQ2kqt7T3debb1u1cp2sPq+72wbLUFVXTPLUJDeaN7010xy5DbHS4oFUVWfmf87fPCTJ+7v7p5Zb2frQI7cG3f3SeZjVjivZ/dHyqhrasTu8kHtDVb1vadWM7fpV9fqVayBV1cWSPLy7H73kuoY0T9B/yA7n84mWfN5nd0tyXJK/7O6vztc8/L0l1zSaHw6fnIOb8LYfDEtfjHmO4d8kuWaS8yc5NMk3XRB833T3J5MYjro+VuZvroTgDTV/U5Bbg6p6RqY/8LdM8swkd0nyrt1+Ebvz/ar68e7+SJJU1VVy3hAs9s5t54UPkiTd/ZWqul0SQW7fXHv1hYHn83ndZRY0su7+VpKTq+pS8zvMSXL2Mmsa0Jaqetiudnb3X+1qHztlysRiPDXJPTJdwHprkvtkuqA1+2B+XfTkTIttdZK3J/md7v7oUgsb04aevynIrc2Nu/vaVXVGdz+uqp6Y5ORlFzWw30tySlWt/EE6OolrpuybQ6vqAt3930lSVRdMcoEl1zSyQ6rqYt39leSHF7T2d3IfVdXtM83hvFySL2Z6V/TsTKMbWJtDkxwRC5usi+5+bpLnGpa+/rr7w1V1aHd/P8mzq8oKyvvuhUmelmkubDKF5JNioah9saHnb3qBsjYr1zj7VlVdLtOEU9dH2kvz/I5Pdffrq+pqSf6/THNnXpPE0Mp98/wkr6+qldWt7hsLIOyPJyZ5W1W9JNO7oHdL8vjlljS0P870jvLruvu6VXXLTJP2WbvPGca//kyZWHffqqrzJzm9qp6Q5HNJLrzkmkZW3f0Pq+4/f76WJHupu0+dh1TfINP/9Xd39+eXXNa6sULT2ryqqi6a5C+SvCfJx5P841IrGtPfJfnufPtnkjwiU+j4QpITl1XUyLr7CUn+JNO8hJ9M8uokV1pqUQPr7ucluXOm5+T2JL+ywz9T9s73uvvLmXo6D+nuU5JcZ9lFDUZP3ALMUybunuTBmc7xXeNv5/64d6bXlA9K8s1M85DuvNSKxnZKVT2iqo6uqitV1e8n+Zequvg8UoQ1qqr7Z5oO9SuZpka9o6o2zLx3q1bupaq6QJLDu9uFQ/dSVb1vZZGTqnpaku3d/dj5/und7QXePqiq6yT5X5l6jz6W5KXd/dTlVjWeeSWrM7r7mGXXslFU1euS3DHJnyW5ZKbhlcd2942XWthAquri3f1fy65jo5mnSlx71ecjkpzc3T+/7NpGU1WHJnlud99r2bVsFFX1sfnmyov01W/o9Ea6oPWiVdWHMk2R+vJ8/xJJ3tbdV19uZetDj9waVNVvzT1ymeciHVJVv7nkskZ0aFWtDOe9dZI3rNpnmO9eqKqfqKo/rKqzMk0y/1SmN2ZuKcTtm+7+QZL3rVqUg31UVVetqpskuUOSb2W6Ft+rMw1Lf/AyaxuNELcw35k/r0yZ+F5Mmdgn85y4LfPQSvZDVR1bVZfp7it395WTPC7JB5K8Msn15+1C3N75dKbFjFack+k104bgxfPaPKC7n7ZyZ17J7gFJnr7EmkZ0UpJTq+pLmeYdvjmZXvQl0cO5d87OdP5+ubs/nCRV5cLV+++ySc6cV7f65rytu/sOS6xpRE9K8gfdvXIOf5BpgYmtSR6b5JeXVRjMXrnDlIlO8v+WW9LQPp7krfNKgCu/91ZV3Xt/l2ntgFTVzTKNZnhwpiHpJ2YaGsje+UySd1bVKzL9nt8hybtWVgMe/TkqyK3NIVVVqy4meGim66SwF7r78VX1+kwvll+zcj4z9Qx7l37v3DnTKlanVNWrM83ZNJdm/z1u1e1KctNYnGNfHN3dP3Kdnu7eVlVHH/hy4DzzMOqV62++dL5wvSkT++ez88chSY5cci0jO3RVL/zdk5w4r6760qo6fYl1jewj88eKV8yfN8Tz1By5Naiqv8i0RP4zMqX5B2ZaffHhy6wLqurCmeYgHZ/kVpkWj3lZd79mqYUNbCdzDk/u7r9ZblVjqaoPd/dV93YfHChV9fbuvtGy64DVquoDSa7T3edW1dlJTujuN63sM4d731XVkZlG2Hxj2bWsJz1ya/O/My2V/xuZ3qV/TaYLg8NSzUPXXpDkBfNKVnfNtBqoILcXquonMvVwHp9pHtc/ZZ5zuNTCxvXuqnpAd/+PoWpVdb8kpy2pJljtNVV150xv1HhHez9V1Stz3sIcK76WZFuSv+vu7/zoV7ETpqCss6o6Jsk/JLn4fP9LSe7T3WcutbB1okcO2PSq6geZ/mHeb9Wcw4+aVL5vqurSSV6W6XIjK8Fta6Yh6XfaSNfwYUxVdU6m65ydm2nhk8r0bv1RSy1sUFX15CRbMgWRZBoW+PkkF0xyVHffe1m1jaaqbpjzpqB8c972E0mO6O73LLW4Ac0Xpn/UfPmbVNUtkvzpRlk9WZDbjap6UXffrarenx99pyndfe0llAWss6q6U6YeuRtnWl3xH5M8c141jH00XwB8ZSjQmd39ht09HhhTVb2pu2+2s21VdWZ3/9SyamNzW33pq/+/vTuLsawqwzD8fjRIgwQxUVsUlVERoQEFJUKM4kCM0TBjVOKsoEGNEwkxYLwzJHrhGBUVjSIi4BQDRJCpJQzd0gJKIiCNKEacCShD+3uxd3UXhRS726padWq/z82pvXafqu9UKtXnr7XWv2Ybm1QWcrNIsmNV3ZXkfx4SWlXrFjqTpPnjnkNpHJJcXFUvf6wxDdMfhXNYVd3RXz8TuKCq9kryi6rav21CjVWS8+k6036zH3oTcEBVHd4u1dxxj9wsququ/tGCTRoB9xxKS1uS5cC2wJOSPJGN3X63B57WLNjk+xBwZZJb6b6nuwDv6f84dmbTZBq7t9F1pD6P7mfzcuCtTRPNIWfkBkhyJPBJ4Cl0PwSupZckacIkeT/wAbqi7ffTbt0DfLmqPtsk2BKQZGtgT7r3SDfb4ESafxZyAyS5he7g5V+3ziJJkjZPkgOBO4Gjq+ozSd5Mdy7n7cDHp53hpU2QZCu6zt5T++QupetW+WCzUBIbGsV8mO4YsQ0rEavq0FaZ5pKF3ABJVlXVwa1zSJKkzZdkDfCKqvprkpfQNTY6CdgPeG5VHd004IRK8hVgKzYuozweWF9V72iXSuoam9CdA70aWD81XlVL4igc98gNc12Ss4HvA/dPDVbVee0iSZKkTbRs2qzbccCXqupc4Nwk1zfMNekOnNEF8JL+DbTU2kNV9YXWIeaLhdww2wP3Aa+aNlZ0GyclSdJkWJZky6p6CHg58K5p93xPtPnWJ9mtqm4FSLIr02Y/pIXWNywD+FGS99CdbTp9MmZJLKP2l9YAVbVkuttIkjRiZwGXJfkz8C/gCoAkuwP/aBlswn0E+FmS2+ianTyLrlug1MpqukmXqc60H55xf9eFjTM/3CM3QJKv8b8PBPeXlCRJEyTJQcCOwEX9kSNTDRG2q6o1TcNNqL5jJcBz6LtWAlTV/Y/6JGkeJXkh8Lupo8SWamMjC7kBkhw17XI5cATwh6p6X6NIkiRJi0KSNVX1/McakxbKWBobubRygH4j9AZJzgJ+2iiOJElSc0meCjwd2CbJ/jz8gPVtmwWTRtLYyEJu8+wBPLN1CEmSpIYOA94C7AR8atr4PcApLQJJvVE0NloyL2Q+JbmHjRsmC/gjcHLTUJIkSQ1V1ZnAmUmOmrl6SWpsFI2N3CMnSZKkzZZkB+BU4CX90GXAJ6pqybxh1uQZQ2MjC7mBkqwEdmbaLKYHgkuSpLFLci5wI3BmP3Q8sG9VHdkulbT0WcgNkOSrwErgJuA//XB5/IAkSRq7JNdX1X6PNSZpbrlHbpiDqmqv1iEkSZIWoX8lOaSqrgRIcjDdviRJ88hCbpirkuxVVb9qHUSSJGmROQH4RpIn0DWG+ytdN0tJ88illQP0Bwn+iK5b5f303SuramXTYJIkSYtEku0BquqfrbNIY2AhN0CSW4APAjewcY8cVbWuWShJkqRFIMnWwFE8sincJ1plksbApZXD3FFVP2wdQpIkaRH6Ad3ZXKvpVi5JWgDOyA2Q5PPADnTLKzf8gvL4AUmSNHZJbqyqvVvnkMbGGblhtqEr4F41bawACzlJkjR2P0+yT1Xd0DqINCbOyEmSJGmTJbmB7g/bWwJ7ALdhUzhpwVjIDZBkF+AkHrmJ93WtMkmSJLWU5Fmz3bcpnDS/LOQGSLIWOINHdq28rFkoSZKkhpIspztDbne690hnVNVDbVNJ42EhN0CSq6vqRa1zSJIkLRZJzgYeBK4AXg2sq6r3t00ljYeF3ABJ3kC39vsiHt61ck2zUJIkSQ0luaGq9uk/3hK4pqqe3ziWNBp2rRxmH+B44FA2Lq2s/lqSJGmMHpz6oKoeStIyizQ6zsgNkORmYGVVPdA6iyRJ0mKQZD1w79Ql3XFN97Gxa+X2rbJJY+CM3DBr6Q4E/1PrIJIkSYtBVS1rnUEaMwu5YVYANye5lofvkfP4AUmSJEkLzkJumNNaB5AkSZKkKe6RGyjJCuDA/vKaqnKZpSRJkqQmtmgdYBIkORa4BjgGOBa4OsnRbVNJkiRJGitn5AZIshZ45dQsXJInAz+tqn3bJpMkSZI0Rs7IDbPFjKWUf8HvnSRJkqRGbHYyzAVJLgTO6q+PA37SMI8kSZKkEXNp5SyS7A6sqKpVSY4EDqE75PJvwLeq6tamASVJkiSNkoXcLJL8GDilqn45Y/wA4LSqem2bZJIkSZLGzH1es9t5ZhEHUFXXATsvfBxJkiRJspB7LMtnubfNgqWQJEmSpGks5GZ3bZJ3zhxM8nZgdYM8kiRJkuQeudkkWQGcDzzAxsLtAOBxwBFV9cdW2SRJkiSNl4XcAEleBuzdX95UVZe0zCNJkiRp3CzkJEmSJGnCuEdOkiRJkiaMhZwkSZIkTRgLOUnSkpFkRZJvJ7ktyeokVyU5onUuSZLmmoWcJGlJSBLg+8DlVbVrVb0AeD2w0xx87mX/7+eQJGkuWchJkpaKQ4EHquqLUwNVta6qPpNkWZLTk1yb5JdJ3g2Q5KVJLk3yvSQ3J/lWXxCS5PYkpya5EjgmyW5JLuhn+q5Ismf/745JcmOStUkub/HCJUnjs2XrAJIkzZHnAWse5d7bgX9U1YFJtgZWJbmov7d//9w/AKuAg4Er+3v/rqpDAJJcDJxQVb9J8iLg83TF46nAYVX1+yQ7zMcLkyRpJgs5SdKSlORzwCHAA8A6YGWSo/vbTwD26O9dU1V39s+5HtiZjYXc2f34dsCLgXP6CTuArfvHVcDXk3wXOG8eX5IkSRtYyEmSloqbgKOmLqrqvUmeBFwH3AGcVFUXTn9CkpcC908bWs/D/2+8t3/cAvh7Ve0384tW1Qn9DN1rgOuT7FdVf5mD1yNJ0qNyj5wkaam4BFie5MRpY9v2jxcCJybZCiDJs5M8fugnrqp/Ar9Nckz//CTZt/94t6q6uqpOBf4MPGMOXoskSbNyRk6StCRUVSU5HPh0ko8Cd9PNqJ0MnEO3ZHJN38zkbuDwTfwSbwS+kORjwFbAd4C1wOlJ9gACXNyPSZI0r1JVrTNIkiRJkjaBSyslSZIkacJYyEmSJEnShLGQkyRJkqQJYyEnSZIkSRPGQk6SJEmSJoyFnCRJkiRNGAs5SZIkSZowFnKSJEmSNGH+C7UqgNg8XI9nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Below diagram shows the ;top 10 famous genres as per there review counts\n",
    "plt.figure(figsize=(15, 10));\n",
    "plt.title(\"Top 10 Most Reviewed Genres\");\n",
    "plt.xlabel(\"Genres\");\n",
    "plt.ylabel(\"Count\");\n",
    "df_new.groupby(\"Genres\").Reviews.sum().sort_values(ascending=False).plot(kind='bar');\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Crosstabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Type</th>\n",
       "      <th>0</th>\n",
       "      <th>Free</th>\n",
       "      <th>Paid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ART_AND_DESIGN</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.046154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTO_AND_VEHICLES</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.035294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEAUTY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOOKS_AND_REFERENCE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.969565</td>\n",
       "      <td>0.030435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMICS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMUNICATION</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.069767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATING</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.970085</td>\n",
       "      <td>0.029915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTERTAINMENT</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.986577</td>\n",
       "      <td>0.013423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EVENTS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.015625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAMILY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.903095</td>\n",
       "      <td>0.096905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANCE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953552</td>\n",
       "      <td>0.046448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOOD_AND_DRINK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984252</td>\n",
       "      <td>0.015748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAME</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.927448</td>\n",
       "      <td>0.072552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH_AND_FITNESS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953079</td>\n",
       "      <td>0.046921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSE_AND_HOME</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIBRARIES_AND_DEMO</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.011765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIFESTYLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.950262</td>\n",
       "      <td>0.049738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAPS_AND_NAVIGATION</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.963504</td>\n",
       "      <td>0.036496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDICAL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.764579</td>\n",
       "      <td>0.235421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEWS_AND_MAGAZINES</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992933</td>\n",
       "      <td>0.007067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARENTING</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PERSONALIZATION</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.788265</td>\n",
       "      <td>0.211735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHOTOGRAPHY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.934328</td>\n",
       "      <td>0.065672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRODUCTIVITY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.933962</td>\n",
       "      <td>0.066038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOPPING</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992308</td>\n",
       "      <td>0.007692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOCIAL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.989831</td>\n",
       "      <td>0.010169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPORTS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOOLS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.907473</td>\n",
       "      <td>0.092527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAVEL_AND_LOCAL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.046512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIDEO_PLAYERS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.977143</td>\n",
       "      <td>0.022857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEATHER</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.097561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Type                   0      Free      Paid\n",
       "Category                                    \n",
       "1.9                  1.0  0.000000  0.000000\n",
       "ART_AND_DESIGN       0.0  0.953846  0.046154\n",
       "AUTO_AND_VEHICLES    0.0  0.964706  0.035294\n",
       "BEAUTY               0.0  1.000000  0.000000\n",
       "BOOKS_AND_REFERENCE  0.0  0.878788  0.121212\n",
       "BUSINESS             0.0  0.969565  0.030435\n",
       "COMICS               0.0  1.000000  0.000000\n",
       "COMMUNICATION        0.0  0.930233  0.069767\n",
       "DATING               0.0  0.970085  0.029915\n",
       "EDUCATION            0.0  0.974359  0.025641\n",
       "ENTERTAINMENT        0.0  0.986577  0.013423\n",
       "EVENTS               0.0  0.984375  0.015625\n",
       "FAMILY               0.0  0.903095  0.096905\n",
       "FINANCE              0.0  0.953552  0.046448\n",
       "FOOD_AND_DRINK       0.0  0.984252  0.015748\n",
       "GAME                 0.0  0.927448  0.072552\n",
       "HEALTH_AND_FITNESS   0.0  0.953079  0.046921\n",
       "HOUSE_AND_HOME       0.0  1.000000  0.000000\n",
       "LIBRARIES_AND_DEMO   0.0  0.988235  0.011765\n",
       "LIFESTYLE            0.0  0.950262  0.049738\n",
       "MAPS_AND_NAVIGATION  0.0  0.963504  0.036496\n",
       "MEDICAL              0.0  0.764579  0.235421\n",
       "NEWS_AND_MAGAZINES   0.0  0.992933  0.007067\n",
       "PARENTING            0.0  0.966667  0.033333\n",
       "PERSONALIZATION      0.0  0.788265  0.211735\n",
       "PHOTOGRAPHY          0.0  0.934328  0.065672\n",
       "PRODUCTIVITY         0.0  0.933962  0.066038\n",
       "SHOPPING             0.0  0.992308  0.007692\n",
       "SOCIAL               0.0  0.989831  0.010169\n",
       "SPORTS               0.0  0.937500  0.062500\n",
       "TOOLS                0.0  0.907473  0.092527\n",
       "TRAVEL_AND_LOCAL     0.0  0.953488  0.046512\n",
       "VIDEO_PLAYERS        0.0  0.977143  0.022857\n",
       "WEATHER              0.0  0.902439  0.097561"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find out category wise application type, how many free and paid apps are there in a category.\n",
    "pd.crosstab(df[\"Category\"], df[\"Type\"]).apply(lambda r: r/r.sum(), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AS we can see there is one app with type 0, which is obviously not the proper type. Lets fetch that row.\n",
    "indexNames = df[df[\"Type\"]== '0'].index # find the index\n",
    "# So we can see its pricing is also not there(everything) and installs section is free. So in this case this data seems to be improper.\n",
    "# as it doesnt have any exceptional infomrmation so we can remove this.\n",
    "# Delete these row indexes from dataFrame\n",
    "df.drop(indexNames , inplace=True) # delete the row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crosstab Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "pd.crosstab(df[\"Category\"], df[\"Type\"]).plot(kind=\"bar\",figsize=(15, 10));\n",
    "plt.plot();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                  0\n",
       "Category             0\n",
       "Rating            1474\n",
       "Reviews              0\n",
       "Size                 0\n",
       "Installs             0\n",
       "Type                 1\n",
       "Price                0\n",
       "Content Rating       0\n",
       "Genres               0\n",
       "Last Updated         0\n",
       "Current Ver          8\n",
       "Android Ver          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle missing values\n",
    "df.isnull().sum() # we can see 4 columns have missing values. Lets start with Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Handle Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle Missing Values in Type Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9148</th>\n",
       "      <td>Command &amp; Conquer: Rivals</td>\n",
       "      <td>FAMILY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Strategy</td>\n",
       "      <td>June 28, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>Varies with device</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            App         ...                 Android Ver\n",
       "9148  Command & Conquer: Rivals         ...          Varies with device\n",
       "\n",
       "[1 rows x 13 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Type.isnull()] # lets fetch first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can clearly see, its price is 0 that mean it is from free type. So we can replace it.\n",
    "df.Type.fillna(\"Free\", inplace=True) # replaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content Rating, Genres, Last Updated, Current Ver, Android Ver]\n",
       "Index: []"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Type.isnull()]  # No null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle Missing Values in \"Android Ver\" Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4453</th>\n",
       "      <td>[substratum] Vacuum: P</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.4</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Paid</td>\n",
       "      <td>$1.49</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>July 20, 2018</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4490</th>\n",
       "      <td>Pi Dark [substratum]</td>\n",
       "      <td>PERSONALIZATION</td>\n",
       "      <td>4.5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2.1M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>March 27, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         App     ...     Android Ver\n",
       "4453  [substratum] Vacuum: P     ...             NaN\n",
       "4490    Pi Dark [substratum]     ...             NaN\n",
       "\n",
       "[2 rows x 13 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets handle android version column missing values\n",
    "df[df['Android Ver'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Android Ver</th>\n",
       "      <th>2.1 and up</th>\n",
       "      <th>2.3 and up</th>\n",
       "      <th>4.0.3 and up</th>\n",
       "      <th>4.2 and up</th>\n",
       "      <th>4.4 and up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current Ver</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>4.4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Android Ver  2.1 and up  2.3 and up     ...      4.2 and up  4.4 and up\n",
       "Current Ver                             ...                            \n",
       "4.4                   1           1     ...               1           2\n",
       "\n",
       "[1 rows x 5 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see we have android ver for both of the records. Lets see a crosstab for android ver and current ver\n",
    "pd.crosstab(df[(df['Current Ver'] == '4.4')]['Current Ver'], df[(df['Current Ver'] == '4.4')][\"Android Ver\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faaf433d2b0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FSmOefPLJjBw5MhtvvHHmz5+fm2++uc/332+//TJr1qwkycUXX9zrmG222SaPPvpoFi1alOeeey5XXnnlSvsvvfTSJMmNN96YzTbbLJttNsB3Cu0HZ+gAAIA+HzMw0ObMmZOvf/3rGTduXMaPH58k+Yd/+IcceuihOe+885IkJ598cr797W/nX//1XzN8+PC89rWvzaWXXrrambBDDjkk5513XnbffffsvPPO2Wefffp8/7PPPjvvec97cvbZZ2fatGm9jhk+fHhOP/30TJo0KaNHj86YMWNW2j9y5Mjsu+++eeqpp9b43b6XWnmhdl9JOjo6amdn52AvA4ABNu5r4wZ7Ca8qd59w92Av4VXl3JOvHewlvGp86LwDB3sJ9MO8efMyduzYwV5Gs6ZMmZIzzzwzHR0d6z1Xb/8WpZS5tdY+J3fJJQAAQKNccgkAAPAiXX/99YO9hCTO0AEAADRL0AEAADRK0AEAADRK0AEAADTKTVEAAIABf7RMfx+tsnTp0nR0dGTbbbdd7cHdSfLcc8/l+OOPz9y5c7PFFlvk0ksvzahRo1Yas3jx4kyePDnPPfdclixZkqOOOiqf/vSnB+JjrGTGjBk5/PDDc9RRRw343OvKGToAAGDQnH322Wt9Ht4FF1yQkSNH5v77789HP/rR/M3f/M1qYzbaaKNce+21ufPOO3PHHXfke9/7Xm6++eaXctmvGIIOAAAYFF1dXbnqqqty4oknrnHMd77znZxwwglJkqOOOio//OEPU2tdaUwpJZtsskmS5Pnnn8/zzz+fUspqc331q1/NXnvtlT322CPTpk3L7373uyTLz7x9+MMfzr777ps3v/nNueyyy5Iktdaceuqp2WWXXXLYYYfl0Ucf7XWNU6ZMSWdnZ5Lk8ccfX3EG8cILL8y73vWuHHLIIdl5551fkrOGgg4AABgUp512Wr7whS9kyJA1Z8mvfvWrbL/99kmSYcOGZbPNNsuiRYtWG7d06dKMHz8+W2+9dQ466KBMmjRptTFHHnlkbr311tx5550ZO3ZsLrjgghX7Hn744dx444258sor84lPfCJJcvnll+cnP/lJ7r777nz1q1/NTTfd9KI/4y233JKLL744d9xxR771rW+tCL+BIugAAICX3ZVXXpmtt946EydOXOu4Vc/GJen17NvQoUNzxx13pKurK7fcckvuueee1cbcc889+eM//uOMGzcuF198ce69994V+4444ogMGTIku+yySx555JEkyQ033JDp06dn6NCh+R//43/kwAMPfLEfMwcddFC22GKLvPa1r82RRx6ZG2+88UXPsTaCDgAAeNnNmTMns2fPzqhRo3LMMcfk2muvzbHHHrvauO222y4LFy5MkixZsiRPPvlkNt988zXO+4Y3vCFTpkzJ9773vdX2zZgxI1/60pdy991354wzzsjixYtX7Ntoo41W/N4zInuLx1UNGzYsy5YtS5KV5uzt+P7M92IIOgAA4GX3+c9/Pl1dXVmwYEFmzZqVAw88MBdddNFq46ZOnZqvfe1rSZLLLrssBx544GpR9Nhjj+W3v/1tkuT3v/99rrnmmowZM2a1uZ5++um86U1vyvPPP5+LL764zzVOnjw5s2bNytKlS/Pwww/nuuuu63XcqFGjMnfu3BVr7OkHP/hBnnjiifz+97/PFVdckf3226/P930xPLYAAADo92MGXg6nn356Ojo6MnXq1HzgAx/Icccdlz/6oz/K5ptvnlmzZiVJHnrooZx44om5+uqr8/DDD+eEE07I0qVLs2zZshx99NE5/PDDV5v3s5/9bCZNmpQdd9wx48aNy9NPP73Wdbz73e/Otddem3HjxuUtb3lLDjjggF7H/dVf/VWOPvrofP3rX1/tssz9998/xx13XO6///685z3vSUdHxzr+rfSu9HZN6mDr6OioA/1lQQAG30A/42hD90r6n69Xg3NPvnawl/Cq8aHzXvz3jHj5zZs3b62PC2D9XXjhhens7MyXvvSltY7r7d+ilDK31tpn/bnkEgAAoFEuuQQAAHgJzJgxIzNmzHhJ38MZOgAAgEYJOgAAgEYJOgAAgEYJOgAAgEa5KQoAAJB5Ywb2EQZj58/r17ilS5emo6Mj2267ba688srV9v/TP/1T/vmf/znDhg3LVlttlZkzZ2bHHXcc0LUmySabbJJnnnlmwOd9qTlDBwAADJqzzz57rc/D23PPPdPZ2Zm77rorRx11VP76r//6ZVzdK5+gAwAABkVXV1euuuqqnHjiiWsc87a3vS0bb7xxkmSfffZJV1dXr+OOOOKITJw4MbvuumvOP//8Fds32WST/P3f/3322GOP7LPPPnnkkUeSJA8++GDe+ta3Zq+99sonP/nJXudcsGBBdttttxWvzzzzzHzqU59KkkyZMiWnnXZa9t133+y222655ZZbXtRnHyiCDgAAGBSnnXZavvCFL2TIkP5lyQUXXJB3vvOdve6bOXNm5s6dm87OzpxzzjlZtGhRkuTZZ5/NPvvskzvvvDOTJ0/OV7/61STJRz7ykZxyyim59dZb88Y3vnGd1v/ss8/mpptuype//OW8//3vX6c51pegAwAAXnZXXnlltt5660ycOLFf4y+66KJ0dnbm4x//eK/7zznnnBVn4RYuXJif/exnSZLXvOY1Ofzww5MkEydOzIIFC5Ikc+bMyfTp05Mkxx133Dp9hheOnzx5cp566qn89re/Xad51oebogAAAC+7OXPmZPbs2bn66quzePHiPPXUUzn22GNz0UUXrTb2mmuuyec+97n86Ec/ykYbbbTa/uuvvz7XXHNNfvzjH2fjjTfOlClTsnjx4iTJ8OHDU0pJkgwdOjRLlixZcdwL29dk2LBhWbZs2YrXL8y5puP7mu+l4AwdAADwsvv85z+frq6uLFiwILNmzcqBBx7Ya8zdfvvt+eAHP5jZs2dn66237nWuJ598MiNHjszGG2+c+fPn5+abb+7z/ffbb7/MmjUrSXLxxRf3OmabbbbJo48+mkWLFuW5555b7S6cl156aZLkxhtvzGabbZbNNtusz/cdaM7QAQAA/X7MwMvh9NNPT0dHR6ZOnZqPf/zjeeaZZ/Jnf/ZnSZIddtghs2fPXmn8IYcckvPOOy+77757dt555+yzzz59vsfZZ5+d97znPTn77LMzbdq0XscMHz48p59+eiZNmpTRo0dnzJgxK+0fOXJk9t133zz11FOZOXPmOn7a9VNqrYPyxmvT0dFROzs7B3sZAAywcV8bN9hLeFW5+4S7B3sJryrnnnztYC/hVeND5x042EugH+bNm7fWxwWwdlOmTMmZZ56Zjo6O9Z6rt3+LUsrcWmufk7vkEgAAoFEuuQQAAHiRrr/++sFeQhJn6AAAAJol6AAAABol6AAAABol6AAAABrlpigAAMCAP7qjv4+vWLp0aTo6OrLtttuu9uDuni677LL82Z/9WW699dYBeVRATwsWLMjhhx+ee+65Z0DnfTk4QwcAAAyas88+u8/n4T399NM555xzMmnSpJdpVe3oM+hKKTNLKY+WUnrN1VLKx0spd3T/3FNKWVpK2bx734JSyt3d+zwpHAAAWKGrqytXXXVVTjzxxLWO++QnP5m//uu/zogRI3rd/8wzz+Ttb397JkyYkHHjxuU73/lOkuVn3saOHZu/+Iu/yK677pqDDz44v//975Mkc+fOzR577JG3vvWtOffcc3ud9/rrr8/hhx++4vWpp56aCy+8MEkyatSo/M3f/E323nvv7L333rn//vtf7McfEP05Q3dhkkPWtLPW+r9rreNrreOT/G2SH9Van+gx5G3d+wf2vCgAANC00047LV/4whcyZMias+T222/PwoULVwqrVY0YMSKXX355brvttlx33XX5y7/8y9RakyQ/+9nP8qEPfSj33ntv3vCGN+Tb3/52kuR973tfzjnnnPz4xz9e5/W//vWvzy233JJTTz01p5122jrPsz76DLpa6w1JnuhrXLfpSS5ZrxUBAACveldeeWW23nrrTJw4cY1jli1blo9+9KP54he/uNa5aq35u7/7u+y+++55xzvekV/96ld55JFHkiSjR4/O+PHjkyQTJ07MggUL8uSTT+a3v/1tDjjggCTJcccdt06fYfr06Sv+XJ8wXB8D9h26UsrGWX4m79s9Ntck/1FKmVtKOWmg3gsAAGjbnDlzMnv27IwaNSrHHHNMrr322hx77LErjXn66adzzz33ZMqUKRk1alRuvvnmTJ06NZ2dK3+b6+KLL85jjz2WuXPn5o477sg222yTxYsXJ0k22mijFeOGDh2aJUuWpNaaUkqfaxw2bFiWLVu24vULc76g5xz9me+lMJA3RfnTJHNWudxyv1rrhCTvTPKhUsrkNR1cSjmplNJZSul87LHHBnBZAADAK83nP//5dHV1ZcGCBZk1a1YOPPDAXHTRRSuN2WyzzfL4449nwYIFWbBgQfbZZ5/Mnj17tbtcPvnkk9l6660zfPjwXHfddfnFL36x1vd+wxvekM022yw33nhjkuVB2Jsdd9wx9913X5577rk8+eST+eEPf7jS/ksvvXTFn29961tf1OcfKAP52IJjssrllrXWh7r/fLSUcnmSvZPc0NvBtdbzk5yfJB0dHXUA1wUAAPShv48ZeDmcfvrp6ejoyNSpU/s1/r3vfW/+9E//NB0dHRk/fnzGjBnT5zH/8i//kve///3ZeOON8yd/8ie9jtl+++1z9NFHZ/fdd89OO+2UPffcc6X9zz33XCZNmpRly5blkksG55tn5YUvC651UCmjklxZa91tDfs3S/Jgku1rrc92b3tdkiG11qe7f/9Bks/UWr/X1/t1dHTUVU+jAtC+cV8bN9hLeFW5+4S7B3sJryoD/QyuDdkrKQxYs3nz5vX5uADWbNSoUens7MyWW2653nP19m9RSpnbnxtL9nmGrpRySZIpSbYspXQlOSPJ8CSptZ7XPezdSf7jhZjrtk2Sy7uvJR2W5Bv9iTkAAAD6p8+gq7VO78eYC7P88QY9tz2QZI91XRgAAMAr1YIFCwZ7CUkG9qYoAABAQ/rz9SteWuv7byDoAABgAzRixIgsWrRI1A2iWmsWLVqUESNGrPMcA3mXSwAAoBHbbbddurq64pFhg2vEiBHZbrvt1vl4QQcAABug4cOHZ/To0YO9DNaTSy4BAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAaJegAAAAa1WfQlVJmllIeLaXcs4b9U0opT5ZS7uj+Ob3HvkNKKT8ppdxfSvnEQC4cAABgQ9efM3QXJjmkjzH/WWsd3/3zmSQppQxNcm6SdybZJcn0Usou67NYAAAA/lufQVdrvSHJE+sw995J7q+1PlBr/UOSWUnetQ7zAAAA0IuB+g7dW0spd5ZSvltK2bV727ZJFvYY09W9DQAAgAEwbADmuC3JjrXWZ0ophya5IslOSUovY+uaJimlnJTkpCTZYYcdBmBZAAAAr27rfYau1vpUrfWZ7t+vTjK8lLJllp+R277H0O2SPLSWec6vtXbUWju22mqr9V0WAADAq956B10p5Y2llNL9+97dcy5KcmuSnUopo0spr0lyTJLZ6/t+AAAALNfnJZellEuSTEmyZSmlK8kZSYYnSa31vCRHJTmllLIkye+THFNrrUmWlFJOTfL9JEOTzKy13vuSfAoAAIANUJ9BV2ud3sf+LyX50hr2XZ3k6nVbGgAAAGszUHe5BAAA4GUm6AAAABol6AAAABoibrneAAAV6klEQVQl6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABrVZ9CVUmaWUh4tpdyzhv3vLaXc1f1zUylljx77FpRS7i6l3FFK6RzIhQMAAGzo+nOG7sIkh6xl/4NJDqi17p7ks0nOX2X/22qt42utHeu2RAAAAHozrK8BtdYbSimj1rL/ph4vb06y3fovCwAAgL4M9HfoPpDkuz1e1yT/UUqZW0o5aW0HllJOKqV0llI6H3vssQFeFgAAwKtPn2fo+quU8rYsD7r9e2zer9b6UCll6yQ/KKXMr7Xe0Nvxtdbz0325ZkdHRx2odQEAALxaDcgZulLK7kn+Ocm7aq2LXthea32o+89Hk1yeZO+BeD8AAAAGIOhKKTsk+bckx9Vaf9pj++tKKZu+8HuSg5P0eqdMAAAAXrw+L7kspVySZEqSLUspXUnOSDI8SWqt5yU5PckWSb5cSkmSJd13tNwmyeXd24Yl+Uat9XsvwWcAAADYIPXnLpfT+9h/YpITe9n+QJI9Vj8CAACAgTDQd7kEAADgZSLoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGtWvoCulzCylPFpKuWcN+0sp5ZxSyv2llLtKKRN67DuhlPKz7p8TBmrhAAAAG7r+nqG7MMkha9n/ziQ7df+clOT/JkkpZfMkZySZlGTvJGeUUkau62IBAAD4b/0KulrrDUmeWMuQdyX517rczUneUEp5U5I/SfKDWusTtdbfJPlB1h6GAAAA9NNAfYdu2yQLe7zu6t62pu0AAACsp2EDNE/pZVtdy/bVJyjlpCy/XDM77LDDAC0LNkyjPnHVYC/hVWXBiPcM9hJePUb77/tAmjdm7GAv4dVlyrmDvQKAF22gztB1Jdm+x+vtkjy0lu2rqbWeX2vtqLV2bLXVVgO0LAAAgFevgQq62UmO777b5T5Jnqy1Ppzk+0kOLqWM7L4ZysHd2wAAAFhP/brkspRySZIpSbYspXRl+Z0rhydJrfW8JFcnOTTJ/Ul+l+R93fueKKV8Nsmt3VN9pta6tpurAAAA0E/9Crpa6/Q+9tckH1rDvplJZr74pQEAALA2A3XJJQAAAC8zQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANAoQQcAANCofgVdKeWQUspPSin3l1I+0cv+/1NKuaP756ellN/22Le0x77ZA7l4AACADdmwvgaUUoYmOTfJQUm6ktxaSplda73vhTG11o/2GP//JtmzxxS/r7WOH7glAwAAkPTvDN3eSe6vtT5Qa/1DkllJ3rWW8dOTXDIQiwMAAGDN+hN02yZZ2ON1V/e21ZRSdkwyOsm1PTaPKKV0llJuLqUcsc4rBQAAYCV9XnKZpPSyra5h7DFJLqu1Lu2xbYda60OllDcnubaUcnet9eervUkpJyU5KUl22GGHfiwLAABgw9afM3RdSbbv8Xq7JA+tYewxWeVyy1rrQ91/PpDk+qz8/bqe486vtXbUWju22mqrfiwLAABgw9afoLs1yU6llNGllNdkebStdrfKUsrOSUYm+XGPbSNLKRt1/75lkv2S3LfqsQAAALx4fV5yWWtdUko5Ncn3kwxNMrPWem8p5TNJOmutL8Td9CSzaq09L8ccm+QrpZRlWR6P/9jz7pgAAACsu/58hy611quTXL3KttNXef2pXo67Kcm49VgfAAAAa9CvB4sDAADwyiPoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGtWvoCulHFJK+Ukp5f5Syid62T+jlPJYKeWO7p8Te+w7oZTys+6fEwZy8QAAABuyYX0NKKUMTXJukoOSdCW5tZQyu9Z63ypDL621nrrKsZsnOSNJR5KaZG73sb8ZkNUDAABswPpzhm7vJPfXWh+otf4hyawk7+rn/H+S5Ae11ie6I+4HSQ5Zt6UCAADQU3+CbtskC3u87uretqpppZS7SimXlVK2f5HHAgAA8CL1J+hKL9vqKq//PcmoWuvuSa5J8rUXcezygaWcVErpLKV0PvbYY/1YFgAAwIatP0HXlWT7Hq+3S/JQzwG11kW11ue6X341ycT+HttjjvNrrR211o6tttqqP2sHAADYoPUn6G5NslMpZXQp5TVJjkkyu+eAUsqberycmmRe9+/fT3JwKWVkKWVkkoO7twEAALCe+rzLZa11SSnl1CwPsaFJZtZa7y2lfCZJZ611dpIPl1KmJlmS5IkkM7qPfaKU8tksj8Ik+Uyt9YmX4HMAAABscPoMuiSptV6d5OpVtp3e4/e/TfK3azh2ZpKZ67FGAAAAetGvB4sDAADwyiPoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGiXoAAAAGtWvoCulHFJK+Ukp5f5Syid62f+xUsp9pZS7Sik/LKXs2GPf0lLKHd0/swdy8QAAABuyYX0NKKUMTXJukoOSdCW5tZQyu9Z6X49htyfpqLX+rpRySpIvJPnz7n2/r7WOH+B1AwAAbPD6c4Zu7yT311ofqLX+IcmsJO/qOaDWel2t9XfdL29Ost3ALhMAAIBV9Sfotk2ysMfrru5ta/KBJN/t8XpEKaWzlHJzKeWINR1USjmpe1znY4891o9lAQAAbNj6vOQySellW+11YCnHJulIckCPzTvUWh8qpbw5ybWllLtrrT9fbcJaz09yfpJ0dHT0Oj8AAAD/rT9n6LqSbN/j9XZJHlp1UCnlHUn+PsnUWutzL2yvtT7U/ecDSa5Psud6rBcAAIBu/Qm6W5PsVEoZXUp5TZJjkqx0t8pSyp5JvpLlMfdoj+0jSykbdf++ZZL9kvS8mQoAAADrqM9LLmutS0oppyb5fpKhSWbWWu8tpXwmSWetdXaS/51kkyTfKqUkyS9rrVOTjE3ylVLKsiyPx39c5e6YAAAArKP+fIcutdark1y9yrbTe/z+jjUcd1OSceuzQAAAAHrXrweLAwAA8Moj6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABol6AAAABrVr6ArpRxSSvlJKeX+Usonetm/USnl0u79/1VKGdVj3992b/9JKeVPBm7pAAAAG7Y+g66UMjTJuUnemWSXJNNLKbusMuwDSX5Ta/2jJP8nyf/XfewuSY5JsmuSQ5J8uXs+AAAA1lN/ztDtneT+WusDtdY/JJmV5F2rjHlXkq91/35ZkreXUkr39lm11udqrQ8mub97PgAAANZTf4Ju2yQLe7zu6t7W65ha65IkTybZop/HAgAAsA6G9WNM6WVb7eeY/hy7fIJSTkpyUvfLZ0opP+nH2gBecr39h4x1dc9gL6C/tkzy+GAvoi+rfv+B9fSTtw/2Cl41Tv3KYK8AXhV27M+g/gRdV5Lte7zeLslDaxjTVUoZlmSzJE/089gkSa31/CTn92fRAPBSKqV01lo7BnsdANCX/lxyeWuSnUopo0spr8nym5zMXmXM7CQndP9+VJJra621e/sx3XfBHJ1kpyS3DMzSAQAANmx9nqGrtS4ppZya5PtJhiaZWWu9t5TymSSdtdbZSS5I8vVSyv1ZfmbumO5j7y2lfDPJfUmWJPlQrXXpS/RZAAAANihl+Yk0AOAFpZSTur8KAACvaIIOAACgUf35Dh0AAACvQIIOAACgUYIOAACgUYIOAFZRSpkw2GsAgP5wUxQANmi9xFtJ/v/27iXEyjKO4/j3Z4EFWrPpQplY0s2EJDSKIrPSIk0IDFxFCYFEaRshWoRtuhDlJrpAuAjyUkYpJegiJjKSIhNLo0WWFkGBGytq6PJvcd6h8TRXiTlzmu8HDpz3eW//mc3L7zzP+zxsB+6g9ZzcN/5VSZI0OgY6SdKkluQvYC/QN6D5mqatquqmjhQmSdIoGOgkSZNakhXAg8BTVbWzafu6qi7sbGWSJI3Md+gkSZNaVW0DlgKLk7yeZCbgr52SpK5gD50kSY0k84ANwBVVdXan65EkaSQGOkmSBkgSYHpVHe90LZIkjcQhl5IkDVAtxwGSLOt0PZIkDcdAJ0nS0BZ0ugBJkobjkEtJktokeaWq7u50HZIkjeTUThcgSVInJdnR3gQsStIDUFXLx78qSZJGx0AnSZrsZgCHgJdpLVcQYD7wTCeLkiRpNBxyKUma1JJMAdYCtwPrqmp/ksNVdVGHS5MkaUQGOkmSgCQzaK1B9wOwvKpmdrgkSZJG5JBLSZKAqvoOuCvJUsA16CRJXcEeOkmSJEnqUq5DJ0mSJEldykAnSZIkSV3KQCdJmvCSnJtkS5KvkhxKsjPJJeNcQ0+S+4fY15vk1ra2h5I8Pz7VSZImKwOdJGlCSxLgTaC3qmZX1RzgEeCcMVzjlLbtk5kUrAcYNNABm4GVbW0rm/YRpcVnsiRpzHx4SJImukXA71X1Yn9DVe2vqveT3Jjk7f72JM8luaf5/k2SR5PsoTV7ZW+Sx5O8B6xNclaSN5J83Hyua85bn2Rjc/zhJGuayz8JzE6yP8nTbTVuA5YlmdpcYxZwHrCn2V7X3ONAksf6j0nyRdOLtw+44L/9t0mSJgOXLZAkTXRzgU9O8tzfqup6gCSrgZ6qWthsbwI2VNWeJDOBXcDlzXmX0QqS04Evk7wAPAzMrap57TepqmNJPgJuA7bT6p3bWlWVZAlwMXA1EGBHkhuAo8ClwL1VNVTPnyRJwzLQSZL+z7YOs30LMKc1ohOAM5JMb76/U1V9QF+SHxnd8M7+YZf9gW5V076k+XzabE+jFfCOAkeqau/o/xxJkk5koJMkTXQHgRVD7PuDE18fOK1t/y/DbE8Brq2qXwce0AS8vgFNfzK65+VbwLNJrgJOr6p9/ZcEnqiql9ruM2uQ+iRJGhPfoZMkTXTvAlOT3NffkGRBkoXAEVq9bFOTnAncPIbr7gYeGHDNfw2lbPMTrSGYg6qqn4FeYCMnToayC1iVZFpzn/OTnD2GOiVJGpKBTpI0oVVVAXcCi5tlCw4C64Hvq+pb4DXgAPAq/wxrHI01wPxmopJDwOoR6jgGfJDk80EmRem3GbgS2DLgvN3AJuDDJJ/RmkBlyGAoSdJYpPWclCRJkiR1G3voJEmSJKlLGegkSZIkqUsZ6CRJkiSpSxnoJEmSJKlLGegkSZIkqUsZ6CRJkiSpSxnoJEmSJKlLGegkSZIkqUv9DV47I4IiNKiFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df[(df['Current Ver'] == '4.4')]['Current Ver'], df['Android Ver']).plot(kind='bar', figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see there is last updated column, we canuse it to extact year and then we can find out which Andriod Version was popular at that time.\n",
    "# Seems like a good finding.\n",
    "# Lets create one function to extract year from the Last Updated.\n",
    "def get_year(date):\n",
    "    year = date.split(',')[1]\n",
    "    year = year.strip()\n",
    "    return year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2018\n",
       "1        2018\n",
       "2        2018\n",
       "3        2018\n",
       "4        2018\n",
       "5        2017\n",
       "6        2018\n",
       "7        2018\n",
       "8        2017\n",
       "9        2018\n",
       "10       2017\n",
       "11       2018\n",
       "12       2018\n",
       "13       2018\n",
       "14       2018\n",
       "15       2018\n",
       "16       2018\n",
       "17       2017\n",
       "18       2018\n",
       "19       2018\n",
       "20       2018\n",
       "21       2018\n",
       "22       2018\n",
       "23       2018\n",
       "24       2018\n",
       "25       2018\n",
       "26       2017\n",
       "27       2018\n",
       "28       2018\n",
       "29       2017\n",
       "         ... \n",
       "10811    2018\n",
       "10812    2018\n",
       "10813    2018\n",
       "10814    2015\n",
       "10815    2018\n",
       "10816    2018\n",
       "10817    2015\n",
       "10818    2016\n",
       "10819    2017\n",
       "10820    2018\n",
       "10821    2018\n",
       "10822    2017\n",
       "10823    2018\n",
       "10824    2018\n",
       "10825    2018\n",
       "10826    2018\n",
       "10827    2018\n",
       "10828    2017\n",
       "10829    2016\n",
       "10830    2014\n",
       "10831    2018\n",
       "10832    2014\n",
       "10833    2014\n",
       "10834    2017\n",
       "10835    2016\n",
       "10836    2017\n",
       "10837    2018\n",
       "10838    2017\n",
       "10839    2015\n",
       "10840    2018\n",
       "Name: Last Updated, Length: 10840, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Last Updated\"].map(lambda x: get_year(x)) # we get the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets create a new column for year\n",
    "df[\"Year\"] = df[\"Last Updated\"].map(lambda x: get_year(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.1</td>\n",
       "      <td>159.0</td>\n",
       "      <td>19M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>January 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coloring book moana</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>3.9</td>\n",
       "      <td>967.0</td>\n",
       "      <td>14M</td>\n",
       "      <td>500,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Pretend Play</td>\n",
       "      <td>January 15, 2018</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>87510.0</td>\n",
       "      <td>8.7M</td>\n",
       "      <td>5,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>August 1, 2018</td>\n",
       "      <td>1.2.4</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sketch - Draw &amp; Paint</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.5</td>\n",
       "      <td>215644.0</td>\n",
       "      <td>25M</td>\n",
       "      <td>50,000,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Art &amp; Design</td>\n",
       "      <td>June 8, 2018</td>\n",
       "      <td>Varies with device</td>\n",
       "      <td>4.2 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>4.3</td>\n",
       "      <td>967.0</td>\n",
       "      <td>2.8M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Creativity</td>\n",
       "      <td>June 20, 2018</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 App  ...   Year\n",
       "0     Photo Editor & Candy Camera & Grid & ScrapBook  ...   2018\n",
       "1                                Coloring book moana  ...   2018\n",
       "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ...   2018\n",
       "3                              Sketch - Draw & Paint  ...   2018\n",
       "4              Pixel Draw - Number Art Coloring Book  ...   2018\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Android Ver</th>\n",
       "      <th>1.0 and up</th>\n",
       "      <th>1.5 and up</th>\n",
       "      <th>1.6 and up</th>\n",
       "      <th>2.0 and up</th>\n",
       "      <th>2.0.1 and up</th>\n",
       "      <th>2.1 and up</th>\n",
       "      <th>2.2 and up</th>\n",
       "      <th>2.3 and up</th>\n",
       "      <th>2.3.3 and up</th>\n",
       "      <th>3.0 and up</th>\n",
       "      <th>3.2 and up</th>\n",
       "      <th>4.0 and up</th>\n",
       "      <th>4.0.3 - 7.1.1</th>\n",
       "      <th>4.0.3 and up</th>\n",
       "      <th>4.1 and up</th>\n",
       "      <th>4.2 and up</th>\n",
       "      <th>4.3 and up</th>\n",
       "      <th>4.4 and up</th>\n",
       "      <th>4.4W and up</th>\n",
       "      <th>5.0 - 7.1.1</th>\n",
       "      <th>5.0 - 8.0</th>\n",
       "      <th>5.0 and up</th>\n",
       "      <th>5.1 and up</th>\n",
       "      <th>6.0 and up</th>\n",
       "      <th>7.0 - 7.1.1</th>\n",
       "      <th>7.0 and up</th>\n",
       "      <th>7.1 and up</th>\n",
       "      <th>8.0 and up</th>\n",
       "      <th>Varies with device</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>185</td>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "      <td>11</td>\n",
       "      <td>797</td>\n",
       "      <td>2</td>\n",
       "      <td>1046</td>\n",
       "      <td>1944</td>\n",
       "      <td>300</td>\n",
       "      <td>180</td>\n",
       "      <td>828</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>547</td>\n",
       "      <td>18</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Android Ver  1.0 and up         ...          Varies with device\n",
       "Year                            ...                            \n",
       "2018                  1         ...                        1192\n",
       "\n",
       "[1 rows x 29 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets plot crosstab for year and android ver\n",
    "pd.crosstab(df[df[\"Year\"] == '2018'].Year, df[\"Android Ver\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faaf4157cc0>"
      ]
     },
     "execution_count": 66,
     "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": [
    "pd.crosstab(df[df[\"Year\"] == '2018'].Year, df[\"Android Ver\"]).plot(kind='bar', figsize=(15, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so we can see in year 2018 mostly app were using android version 4.1 and up. So this seems a fair analysis. Let replace with this value.\n",
    "most_frequent_andoid = df['Android Ver'].value_counts().idxmax()\n",
    "df[\"Android Ver\"].fillna(most_frequent_andoid, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content Rating, Genres, Last Updated, Current Ver, Android Ver, Year]\n",
       "Index: []"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check again for null values in android version\n",
    "df[df[\"Android Ver\"].isnull()] # So no records... its done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App                  0\n",
       "Category             0\n",
       "Rating            1474\n",
       "Reviews              0\n",
       "Size                 0\n",
       "Installs             0\n",
       "Type                 0\n",
       "Price                0\n",
       "Content Rating       0\n",
       "Genres               0\n",
       "Last Updated         0\n",
       "Current Ver          8\n",
       "Android Ver          0\n",
       "Year                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check again for null values in whole data frame\n",
    "df.isnull().sum() # so we can see rating has a big number of missing values. Lets deal with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows  = 15  # it will display always 15 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handle Missing Values in Rating Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mcqueen Coloring pages</td>\n",
       "      <td>ART_AND_DESIGN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7.0M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Art &amp; Design;Action &amp; Adventure</td>\n",
       "      <td>March 7, 2018</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>4.1 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Wrinkles and rejuvenation</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5.7M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>September 20, 2017</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0 and up</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Manicure - nail design</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3.7M</td>\n",
       "      <td>50,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>July 23, 2018</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.1 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Skin Care and Natural Beauty</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>654.0</td>\n",
       "      <td>7.4M</td>\n",
       "      <td>100,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>July 17, 2018</td>\n",
       "      <td>1.15</td>\n",
       "      <td>4.1 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Secrets of beauty, youth and health</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.9M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>August 8, 2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.3 and up</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Recipes and tips for losing weight</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.1M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone 10+</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>December 11, 2017</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0 and up</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Lady adviser (beauty, health)</td>\n",
       "      <td>BEAUTY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.9M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Mature 17+</td>\n",
       "      <td>Beauty</td>\n",
       "      <td>January 24, 2018</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10822</th>\n",
       "      <td>PLMGSS FR</td>\n",
       "      <td>PRODUCTIVITY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1M</td>\n",
       "      <td>10+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Productivity</td>\n",
       "      <td>December 1, 2017</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10823</th>\n",
       "      <td>List iptv FR</td>\n",
       "      <td>VIDEO_PLAYERS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9M</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Video Players &amp; Editors</td>\n",
       "      <td>April 22, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0.3 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10824</th>\n",
       "      <td>Cardio-FR</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>82M</td>\n",
       "      <td>10,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>July 31, 2018</td>\n",
       "      <td>2.2.2</td>\n",
       "      <td>4.4 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10825</th>\n",
       "      <td>Naruto &amp; Boruto FR</td>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.7M</td>\n",
       "      <td>100+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Teen</td>\n",
       "      <td>Social</td>\n",
       "      <td>February 2, 2018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10831</th>\n",
       "      <td>payermonstationnement.fr</td>\n",
       "      <td>MAPS_AND_NAVIGATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.8M</td>\n",
       "      <td>5,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Maps &amp; Navigation</td>\n",
       "      <td>June 13, 2018</td>\n",
       "      <td>2.0.148.0</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10835</th>\n",
       "      <td>FR Forms</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.6M</td>\n",
       "      <td>10+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Business</td>\n",
       "      <td>September 29, 2016</td>\n",
       "      <td>1.1.5</td>\n",
       "      <td>4.0 and up</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10838</th>\n",
       "      <td>Parkinson Exercices FR</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.5M</td>\n",
       "      <td>1,000+</td>\n",
       "      <td>Free</td>\n",
       "      <td>0</td>\n",
       "      <td>Everyone</td>\n",
       "      <td>Medical</td>\n",
       "      <td>January 20, 2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2 and up</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1474 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       App  ...   Year\n",
       "23                  Mcqueen Coloring pages  ...   2018\n",
       "113              Wrinkles and rejuvenation  ...   2017\n",
       "123                 Manicure - nail design  ...   2018\n",
       "126           Skin Care and Natural Beauty  ...   2018\n",
       "129    Secrets of beauty, youth and health  ...   2017\n",
       "130     Recipes and tips for losing weight  ...   2017\n",
       "134          Lady adviser (beauty, health)  ...   2018\n",
       "...                                    ...  ...    ...\n",
       "10822                            PLMGSS FR  ...   2017\n",
       "10823                         List iptv FR  ...   2018\n",
       "10824                            Cardio-FR  ...   2018\n",
       "10825                   Naruto & Boruto FR  ...   2018\n",
       "10831             payermonstationnement.fr  ...   2018\n",
       "10835                             FR Forms  ...   2016\n",
       "10838               Parkinson Exercices FR  ...   2017\n",
       "\n",
       "[1474 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets fetch all the missing value rows for Rating\n",
    "df[df[\"Rating\"].isnull()] # there are total 1474 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genres                                 Category     \n",
       "Action                                 GAME             4.3\n",
       "Action;Action & Adventure              FAMILY           4.3\n",
       "                                       GAME             4.3\n",
       "Adventure                              GAME             4.3\n",
       "Adventure;Action & Adventure           FAMILY           4.5\n",
       "                                       GAME             4.5\n",
       "Adventure;Brain Games                  FAMILY           4.6\n",
       "                                                       ... \n",
       "Video Players & Editors                VIDEO_PLAYERS    4.2\n",
       "Video Players & Editors;Creativity     FAMILY           4.1\n",
       "                                       VIDEO_PLAYERS    4.1\n",
       "Video Players & Editors;Music & Video  FAMILY           4.0\n",
       "                                       VIDEO_PLAYERS    4.0\n",
       "Weather                                WEATHER          4.3\n",
       "Word                                   GAME             4.4\n",
       "Name: Rating, Length: 149, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #Lets get the median review according to genre and category\n",
    "df.groupby([\"Genres\", \"Category\"]).Rating.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NOy5ydDKPH++4yNF93/jJ8d8u83/7942LHJ3Mz2/eUWEvraovGvi2qjYewOuBn0bXnwQ+Oei9CpzrSvCWxO5kHj85uu8bPzk6mZ/fvOMiRyfz+PGOixzd942fHJ3Mz2/ecZGjk3n8eMdFju77xk+OTubnN++4yNHJPH684yJH933jJ8d/u8z/7d83LnJ0Mj+/eUtjp0dOuL9rgRVFZFkRmQPYATgv472OOuqoo4466qijjjrqqKOOOuqoo4466qijjjrqqKOOOqqk2QYxqOozIvIh4KfAVOAEVb29uGQdddRRRx111FFHHXXUUUcdddRRRx111FFHHXXUUUcd/dfSwEEqAFX9CfCTIf/ruEK8JbE7mcdPju77xk+OTubnN++4yNHJPH684yJH933jJ0cn8/Obd1zk6GQeP95xkaP7vvGTo5P5+c07LnJ0Mo8f77jI0X3f+Mnx3y7zf/v3jYscnczPb97S2H0kIWZgRx111FFHHXXUUUcdddRRRx111FFHHXXUUUcdddRRRx3NMsrZk6qjjjrqqKOOOuqoo4466qijjjrqqKOOOuqoo4466qijjkZK3SBVRx111FFHHXXUUUcdddRRRx111FFHHXXUUUcdddRRR7OcsvakGoZE5AXAS4C/AzNU9dkaPgFWiXhvV9U/DIvb0XAkIgsAi2Pp/JA2xIf05Mk45J+3zD0fyZknz3l6iMgUYNVEhsdmpQyRLEXKc8l0LlGvSuZJG2wRmVNV/zmK/48ws/Vc4J8X+Ieq/meUcjzfyJt/3vKZk9cty1Cx9mdM2jZXec7EdOutcUiL/x8oN7/HoY0fJxone2NcyNO+Onmf0zbTU/Zb9Ee7/mugcbEXx8nOLlX2c3Fb2kjPqcxOzOJ6PFfXlbC9SlMp+6GU/89D42bzlOi/lsQtgT1OeTIGdklR3fV81EfPV3K0x895nsyCcjeSelVkTyoRWRDYE9gRmAN4HJgLWAy4Gvimql4aeJcB9gU2BR6IeFcEngS+BXxfVdWDG7CXBHYA1mciI24DfgxcmDaYIrIW8K7Av3jCf6qqPj0E9moVvJeo6p/byuCVIxdbROYHPgjsBMwHPBHSeRHgypDOVwReT1678s+TboEC9HCzAAAgAElEQVT39Q3f9/3eO54yF2EvCqybyHFdRT4vDmxfkx8Xp8ooV+bAm5XXLeqJKz1alNGBeSgiywP7AW8C7o1keBnwN+DbwElJefaUjax8KVmeS6Vzy3q1CLBOkhY3JmXenSclsINBux3wTuB1wLPhOx8JaXG8qt4fYefqAY+em4LVvXcCawH/BOYMcv8EOE5V7yWhnLQIfHMA/8Pk8vkTVb2rLW/gX7OC9+eq+seUN5ffmX+eOpWd197yWbL9KYydq/ezy3PgL9Jelm7nA3+p9rgUrvf7ctpMj/5aBr/Nk1s+vWkxF7B5Fb+q3t4W28nbxt7I0uWBN6scedK5xTfm1m+PzvXwuttMpz08cv3VQtd5sIv1X511ytPP9eRHmzqVq2899kZ2ngTsLHvKace4yn5u/rXA9aRbm/qaYysWs50L91E8us5le+XKEPhG7uPxylxKz5X0PZS0eQJvVr+qYP/VhVsSu7DvzdMGDWxPhtBHnrJRRHc5dEYxfdQyPUaadqVlFpHZsDJaZRPcF/Fll6UW/XOP3vf4j4rYaW3r1SAqNUj1M+Bk4HxVfTJ5tgawM3Crqn5XRM7AFNP0GkfAO4EnVHWaE/dEYAngAuA64PdMZMSGwBrA/qp6eXj/AuAPwI9q+DcDvqSqF3iwReRdwEeBh4HrE97XAzcAB6vqwx4ZgsweOTzf93PgFOA8jWY3hIZr7ZDON6jqCc488fBmp1t4/0Lgdw3ftwXwFVU9z1nmNgT2BxYGbkxwlwfOBI5U1adE5HhgOUyJVMmwKrCvql7ZQmZPXmenc7jnSQ9POfKU/dOAY4ErKhrcRTHF/idVPalF2cjOl1LluXA6e2ReH/gk8GLgpgR3aeB04Kuq+hdPnoR7RbBFZDpwWUiLmzXMzAh8G2KdoR+q6inOOuXRc5cBPw+4t+nEwMDCAXcn4BxV/X6LtDgQ2Ba4nMnleUNAgI+r6m1O3t2AD2OdgZR3XczQOEhVHwwyZ/M7889TPj157S2fJdufIthOve8pz0Xay1mQzkXa44K43u/zlA1PfnvaHo8MXpvnEKxsTa/A3jCcf0xVb3Gms1cOj/7y6HJPOSppx3jq93Tyda6H19tmemT26q9jgcsyyr5X1z3n/VdnnfLYldlpHL7BU6ey60kLbE+eeOwpT/5ll31n/nnrlCfdPDLvRr6tWNJ2LtlHmU6+rvO0xR4Zivh4gmyl7IdS/j/v95WyeXbD16+aTpn+azZuuF/KJijZl8jCBp4m3+7y6iNP2SjlA8nGDe8X0Uct0qNU2pWUeX9ssOWqCt4NgH8Dn1DVO5xtpidPPHo/24YJ/1fETvPWq2xS1ef0AOYrhLvKgOdzACtE14tlYC7qxQY+AszbwLsm8GavDC3k8Hzf1DEoF9npFq5fmIH5wvCbXeaALwMvrXk2G7AVsG24XnUA1lzAy1rK7CrPzrT2pIenHLnysGDZcOVLqaNUOjtl+CqwXEMZejvw9pbfVwQbmDODZ47w66lT2XoOmN3D40kL4G0DcBcH1m7BuycwdwPvasDG0bWLv8ThyevCchTRXV5sj953luci7WXpdKZQe1wQ1/t9nrLhyW9P2+ORwWvzbDaAf1FgzRbp7JXjdY708OhyTzkqZsc467enffXwettMj8zZvJ7Dq+tK6UZ8et9Tpzz2e5E0Du9l15OSeYjDnnLKkF32nfnnqlMFZc62FVvogbHoozh1nact9shQxMfTQuZSeq5Yn9iJ7bF5vP2qUv1XV1+pFLYT19u+ZmHjs7u8+shTNkr5QFy4pfRRi/QoknaFZd6WsICnhndJQv/BU5aceeLR+0VsmPBusXqVLUMbwTMEeVd0vm7y7EPJ9X25ldaJu0ADzqREB75OpgL1YANrOdItW4YWcni+7/rcQg28tOkYgjc73VqUz+wy58Tdp0m5Zby/CLA1sEbFs32BKZk42fXEmx7OcuQp+/tG59slzw4fpmx48gWbhbFgdL0hcFTAmCPhHZd09ujFpUrkSUls4PwqnV2Du1J0Pmfy7HXJtUfPbRSdL5s822bItJj0/ih4Sx7Ycu4XR9e7YDNovg4snPB69L4nrxcAVozLUZBjFyo6tE45vDrmOW/bPOXZietqL8chLQL2UO3xKHBblCNPm+nRX562xyODK42r2o0RpbNXjhscvKV0eUk75mIHrkfnenjnod/Z83JsNmtlGuFru3etwZgdOC25t0bcHmCztc/CZmAvlPB6dV2R/mt0fy5s746Vgbk85SW8P1ty7bErs/Mj3HtD0+GQedLgFD570ZMnnvqa3TeoeHcJJtrA2XL/M0OmRlycNpLjfz2D/D8BlnHwj0sfxaPrPG2xR4YiPp4WMpfy0xXpE7fALmmHluq/ZuOWxHbijsz3BlzV8j2vXeKxh0v5QLJxA38RffRcHQwYiGnLW/P+lg7e7LLkyZOKd2cHXkPFxPS6ctuAle27ceK6fGTZuIUK1A1V5zXXywLnARemHzYkbsx7SRNvuHcAcA/wDuf3NWIDNwPH0GBstJGhhRye71snVKhjSTpxFby3AreE31uj60eB/wzBm51ugf8B4P7oiK9/M0SZ26XpSHi/jS0bzTLgsaWcq4TzxUM6nA/cAeyd8B6DLWtdNwM3u560SA9POfKUfU/d9paN7HwBrgFeEs5Xw+LGfgw4CfjOmKazJ+1+A3ycjFkdLb6vCDa2TPheLI5uI7YT16PnSqbFRVgIp6VHzHtGdP7F5NkkJybwPoJDA1sifiLwFKajV0+/mWDQYE6o32EzkD4DnJnwevS+J6+PA3aLru8Djga+A3yrgr9k+1MEG7gU+EXNkbb5nvJcpL2cBelcpD0uiOv9Pk/Z8OS3p+3xyOC1eTyDQ550LilHq3atQNnwpMeNDjk8OtfDezkT7ckKwB8x/XwJ8PmmtEvTseoa2CO5Ny/wM+C7yf3rgUXC+XpBD20PHE7URobnXl1Xqv86G/AlzP68Hgu18ni4N3vCe2V0/r0BuEXsynDv/IrjPOC3JHp/wP8+OGTaefLEY095+gafBD4dfxPWBt4FfDLhXY+ojcFC6PTa+I3a4obn2TYSFjLrqXA8HV3/DXgm4b0R00eN7U7gfUcoc59Ky24N/7j0UTy6ztMWt/2+kfl4WshcSs8V6RO3wPbYPCsTObKxVR0nhGP1Cv5S/dds3JLYpfI743seis6/Fp1/JOGbllx77RJP2SjlA8nGDfxF9FHgfw8Tq3gEOD6k4Q3Aam15B/znJJvAw0s0cYnJA4AXJtc/wwZtBg7eecqSM0++BawczhfEfMK3YnvC7ZjwZtswvbJFpu8m8EwlWtGIreLaA7izbXn2HK1eykiEG6vOq66j+1sA/wecC5zdO9ritpThpdgMu59iy9i27B1DyDE1KIB70sI1jAxtvtGJPQXYCzNqv4bNOvwKFlu2Sf5lQiW8F9irLW+LdFskOV6ELct+ADhriDJ3dMXxDazj9UwF5lrY5qDfxjoyr+4dFby3R+cHACeH8/mBWyr4V8fipH4XW566eu8YtuznpoeznmTnoUdmb9nw5Euc7sARWIzrXn24pa3MhdPZk3YLhTJ8I7DOqPJkFmDPDxwZsPfGYoJ/GPjwkLhZeq5kWgT+txMcDeHdBXpHW95E5tRoqJL5NiaWpu9EcO5hG2xekfDeHJ0fAxwSXd804FuXoaGN8OQ10cqC5HuvbJJhkBy00DElsLFVAOmxJ9b+XDtEeS7SXpZOZwq1xwVxvd/n5XfZaTl52EIGj81zM/ACLMb5pGNIbA/vk5izpPJIeD3tmmeQqqQdcz+wTd1RgZulcz282L4jvfPPAMeE8zniZxGPx45ZGPh17z8x/XUt8IWqMhedfwM4tOqZt560qFee7/sqNpAwf3RvAWzQ4agG3Jx2fuR2ZU2arIc5I68GtnDUi4cq7hXxKQS+XHvK0ze4gSi8UE9GrM5fmfBeArwyrjdYO/8G4KK2uL3ntLSRsHq+H6ZLjkyeTcHq/j3Azhl5Oi/wRUz/fxxbfbYPsE8F71j0UaI0yNWLubZXye/L9vF4ZPbUKa/Mzrrq+j6HzB6b/Pw43zAH8rbYnjLnDlOOWuS3p3yORZ/bk98D8uHB6Nwz4OO1Szxlo0jd9uBG74xcHwX+2E+wQ3hvMWBT4PK2vAP+c5JN4OFN0jrHRtoBuBv4BDAftmJqHmCeIctSbp7EvuG9CXoF24OrSl6P/yjbdxPS4c/YQNZl2Orxh4FzGJHfedAxG2VIa86rrhGRFbGMuxpLtGdTnha4LhkA1Db5PAuLw7hdJIdinVc3ttqGg0eIyE+AX4nIsQFX7LEu3FIG9zc6sRcEXoWNDN9OfZ4AM/PwU8BrsYbtw6r677a8LdLtDwF7CmYsfAJbebSZqt5RI8PAMqeqe0XvCBYqZL/w3ucq+K8VkX2xxndlJvJBsU5HTPE3b4zNMkBVnxaRSfKo6g0i8inMSFs+wd4oZq05r7rufVtuHcwuR848LFanwju5+SLR+UaY0kdVn7Xsr5XrOUtnjxxqG9nuJSJrAT8TkRn0p93qbXBLYwN/xzbNnQdzSI2ijYB8PVcyLVDVM0XkbuBKbLBgJi/WMWvDW1kGG549E+nhzbFB8z8APxeRLyW8U0VkNlV9BtNde0TPKu0KRxuRm9ezabCAAu0cnS9U806R9qcUtqpeH2G/ETgImBP4gKpeWCFCVnku1V6WTIvAX6Q9Lojr/T5vucu20xw2j1dmj82zEjb4PakxDfzLtcV2yvE4ViYHklOXryQit1TA9HhfHeGWtGMWxHR4XTqfndzL1bke3lg3b4TZMajqv6rs24R/kB3zRxF5E3ChiLwEeBtwrKp+vQJ3qohMDem9MfCB+FnK7NV1hfqvm2P7i828r7Y59QcxZ8RHGt5twi1iV8YkIhtj7ZRis5Z/1iBflsxeWZx2dq495ekboKp/jS6PCvf+IyJzJ6wLJG3uvb12X0Q+PwQutLCRRGQhzDG2C3AqFu7qDzGP2qboXxORizHd9U0svXq6a4EE9t/AXzHbZX6a82Nc+ijg04u5tlfJ7/P4eLJlhmJ6rlSf2IXtbIsXV9VfRtdPqepZ4f/eXyN6qf6rp3yORZ/bk98isk3dI2Du5LrqvIpcdomzv1akbrfQW1BGH0G/n2AL4CRVfQy4SEQOH4K3iZpsnBxer410uojchi0K+Bj9NsFLat7NsXFz9e2/ovM3Az8MuP9XY2tk+4/w+W4OxLafuU9EVgd+BeygqudUyNzKXhxI2nJ0q+nAlof3wr30znvXf014P4sZ3ZuPGPdhbKbOx6Lz3nXVSOtKWGidM4ElB8jhxd4Vm0W8NxZbcmrvaCuDVw7n970XWwL6IQbsg4TFSz8t5MO70m9qy+tJt8A7O/D+UJa+AyzfgJtd5gL/bCFN7gSmAS+v4XshFiLraiqWfVfwn4811lsDfyIsAcUa39sT3kWB72FKc9AG2tn1pEUd9JbR3LL/HybCTDxDf9iJfw9ZNrLzBev0nRF+H2BiFsjiwHXjmM4t5HhjeP41YEVswHP5tM5486QUNtaQ3obNXq3d/DLw/h6LrXt0dN67fizh9ei53sz78+mfhX8+8Kch02IO4OBQNrYaIIeH9y4sjvEamO56Dbbycg2SpdqB/4ZQzucCHiMsNw/P0qXdn8J00Y+IZutiS96vSng9bYQnr28miq0c3V+C6pWoJdufktibYMbnz4ENGzA95blke1ksLQJ/qfa4FK73+3LbTE9+e/MwVwZvWnjC0Hna7WJyBP5cXX47tndN5TFM2XCmh2dFl0fneni/H/g+irUn84T7C1Gxgglf291bFbYr5kg7g5qVYsCnsbAsZ2FtxpRwf3ngl0PWk1L913sacO5Jru/H+hDb0r+Cblsmh2712JXZ+RH4NwN+ia2eagxLzkQI2PS4FfhnBb/HXvTkicee8vQN7qEitB02SHNvcu/ehv+8ry1uuJ9tI2H65fOhDB1ItP9WjWy7Y7rrQzRvNL8pttrkCyQz0RveGYc+ikfXZbfFThmK+Hi8MjvrVCn/n/f7Stk8dzdgTNLbznLkaQOzcUtiO3G9eXJi0xHx9VboL8Lk1fo3J5guu8RTNpx12+vzysL11u0W2L3VUHNiq+FWiZ6lfgIv7w0Vx43AP9ryBv67sMGhVbH+3auwCASrVsgxO+bfuJsB+6d5ypInTzA9tznms3mS0IZjfdS7Et5sGybwe3w36aqzuxpw3T6ynKPUSqpXOHhnB16jqn9PH4jIa1X1mpa4x2MzdtJzMKdMSj/C9gGqmp3cGltELscq5xtV9XcDcD0yuORwYr8ZWE9VH00fiMhLku+4GXgIi4m5NrB2PNKrqh9uw+tMN7BOwzOYkn0QWFVEVo2w45mj2WVORPbEZi1eAmyqqr9tkOHXmMJ6j4ZaO4B2Bw7DwmltrzajAeB1WCMc06+wcAm7ZGB76gn46mB2OfLkoapOmtE6CtxAnnzZG9uvYHGsDvRmgbwYU+4xjUU6e+QQke9js9V3UdWbmng9eVIY+zPATqpaNUM9pU9E59clz9Jrj557W3R+RMKeXrvSAtOL52OOx7+NkPf/sKXk6XnvOqVPY2k0FQt5dTvMXMFzf8yoqp8TkUuwenJxVK96y9lTmXPbCE9efxk4X0Q+hhlbYINwR4RnKZVsf4pgi8i12CzGL2NtAGFGUw/3hojdU56LtJeBiqVzqfa4IK73+zz8nvz22DweGbw2j4c82F45HsgVwqnL/zWg7MS4Je2YQbOJY/LoXA/v+7A6tQzwlqi9eiUVbSa+tnuL6Py85J4SrRRT1cNE5BdYW3WR2ioQsDoRtz29ex5dV6r/eoeI7KKqJyeY78KcETFdhoW86p3HaXN5wuuxKz35AWaXPIwNGu6XzvhV1S2jy80z/j9+12MvevLEY095+gZnAt8WkQ/1cEVkXizc5JkJ710ispmq/jiRdXPMUdYWF3w20m+xFaYnYoMLuyft9kz7UUR+CcwA1lfVKlsypgOB7Xr25CAalz4KPl2X3RY7ZSjl43HJTDk9V6pP7MJ2tsW/q2oLROR1WFislEr1Xz24JbE9uK72VVXfXfenIrJYdLkg/Sv0435Raiu57BJnf62I7nLqDCinjwAOYSJ9L1TV2wLO+ky2qz28b8/47za8YPtHfrPivHcd001YONE1VPUvA3A9Zcmjb9+PDfS+GNN5vTZ2Y6xfHZPHhvH6bhYVkX2i6/ni69gmwOkjyyXRkfcrR0ci8qCqpkvVSv3X3FWKMzy7SlXXbYm7qapeVPNsb1X9WmkZRomd5omI7EbDUj5VPaklb3a6hXvTGrBVVd9T978JTvp9z2KzQx5P8CeFbxGRxdSWslbh/kBVt8+RIfD3lmP2rn+sqpvlvj8qqkiP7HLkLPtrYZvzXZjwbQH8TvvDXnnLRut8EZFFsDA6D8YyjJqGSWfn/3wQ2zB5Ul0RkW01hC0I1wunPDGp6h9nFXYdlWojhsV1psWrVPXWGpwjVPXjbXhbyj0btg/Gn6J782Azf/4S3aurr1sCjyT1ddem/4z1/gDZJuWJiGyK7ee3crh1G7YvyaSOrEeOFjqmCLaITKe5Xduo5lmKm+qXaQNwW7WX4V7JdC7SHhfE9X6fi7+OPPqromx4yqerbRWR3VR1WqZcnnT2yvFGmm3RyyNejy7/hqp+qOm7It5idoyIrKyZTuEBMg5Tjqap6m6O/5oLa3seT+4vioVU+kcuVgXuv9XC9iAiKwBvBX6rquc1vtyP47IJhrEhRGQJbKDt75jzTbH9yOYGtlbVRzJx0vI5qn7gJFsj1KlaUtXLMnDXxZyjeyb3i9iLTttrJVW9K5zPqar/jJ69TlWvjq6nYmFi34sN/giwFLaf8IFJ324FzPH0SyYcemtgG61vrqr3tMGN3smykUTkEJp14qER75s1M4yjiNykqqvl8Ab+seijDJBxGL04Ehkq3h1Zn3GY7xsVlewTD2nzrA38AFttH9fXXbHJxr9uK8eoqBRuSewcXBFZEFshvBPwClVdosX/eO0ST9koortGqTNGoY9EZA5spe3j0b35gdVU9Yq2vDXyvhazCT4ySt4GjNW0ZrBORL6gqvtn4qyrqldl8nr07bwahfz12DADcBcC9lTVz0X3Dm56J7YJSlGRQSoReZqKjn7vVyfHK67DeUhVl2qDKyJfb8LW/tnbXjlGgu0smH0yjFiOSdij4C1FhRvgNK+XbuLX/FmzVc68K1V1vXD+PVXdOXp2g0axYNPrAf81kvoXsIqUjYqGcjqwm6rOSPhWAI7Tls7YFnJcAOyvqreJyOKYEXodtvT5uMQgGYt0HqG+rRqgfRhbcdHD7ZGqat/+Ic8FdkVaNDqdtH82rwe3cUaZRk7sDOwiHcCKNN6miV/7V8sgNktbVPV7yf33YWE6To3uTadFfRWR+eyv+/ZVyKJRtj1DytGYJyWx21KpdnsQ7qjTolR7XLKdHwVvC+xZ0m57eUXkRJoHR3efRXKcX/X/WPiPJTVzlmtFOdqVZkfvyXXPBsnslOMBqm2CIIYun4k7jF2SbbMG/uOwlU5pu/RObAbqB6N7uzRAadyGichlwPtU9R4RWR64FnMyvgILb5KugqmTz6VDR2GnichG2OCCYCHAL8n9//B+KZ1Riys2KLgC9m2/0QGDiyKyGuZ0fAc2u/psVT064SliLw7gTevUzPKc2z8T2ydqhXB5n9Y72efE9kHsDSTdDpxal3a5uKVIRI6mWc/Fq6VdemDA/86yPsoA3lnVL57lPh4vf8H+aDG/1LC6TmwVz57019djtGYSSa4cpfqvJbFLyhzuz42tFN4JWwU6P7AVcLmGVdESRZeokWHmyqqS+sjDO8I2zWsrFrH3vfwDbIhVsPzeHludeJbWTNDL4RWbPFtLmjlZqSIPp2J2yxKY7Xqb2AroA4C5VfU1mbhV9XUJbLXTLWp7XC2KrebeTVVfUoUzSN5wbylsdfMS2N62p2IrJ3cGTtOWA3yj9JHFVCrc3yXYMrWzgdNV9cGWOKkR5MH9ADZj6Ays4HrCXwySY1TYnveqDMJRyeEZqezjFZH1gOV6nW8ROROLAwvwWVX9RRveATTpO0VkSWAZVb0yXO8DzBcen6qq92Vi931frnOqJc0bna+cPEu/cR4ReU3FfWBS2KdR1T8YomwMoPQ7Fkkd3gBqG/YtMgSul5bVsCQZeDfwM1XdRWzmx1VYeKwejUs6j0qONO2OBjbAvvs04ErV1rMaSmGn77weCzV2GnBNxf+2xX023DsVW1o9jFPAI9MwvGdiS9dvqnjeFxIp0MewVYMpnQ5Mx769R676KjZb65MEnScifwG+qKrfTHkbKG17Pt3Eq6qfKSRHZZ6Uwg7Gaa9DrNj+Dseo6u8duFWz5FbBQnXEuEdozaysXNyAXSQteu2xiCwbyX2nqt6f8nqoFO4A8uqmYe3FUfAO275eUHHvpVjnyxW6aRhS1TgkWs82/RTwKBYzPpfS9FizhmcLrFOYNUhVgeulVI4pWGf640yE/cqhYcqRx2YFG4jao4LvFBE5ILm9VgVknM7xRIuFdWI1yq6YjfS/YWDgOiaHaqsjr33S2k6TiVnWcfs9875mrhyinM6oaqdmAw4H3oOt8pkCLBkGpj+lE+HxEJGXATsAO2LhAX8AiKpuWPN/pezFJkq/UTKfISJVdtRaEsLnabRSM1z/EzhhoEBOXK+NJCJvxdrtVzJhE3xRVX+SvBuH8ToU2xOjjtKQQakQX6l7VkGzso/SRLOqfX0ufDxe/lL90VJp7OWvskMfw8KjD0uzqv9aEruYzCJyCtYnvRgLafoLbFB+esJ6HTZQ2Futk/Z140mTXrukiYbpn49Kd3nTu6S93zo9RGQ5zCbYCfgLZhPMrqrrT3rRwRvoXMyO6vn20vKRu6I+/b7vYiuZfw18XUR+i9WH/VX13EzMngwTfyKyN2aX3gfMKSJHYds1nIyt2sylqvw4GQsLfTa2X+TVWN15tSahe0XkDFV9Rzj/oqruFz27WFXfErGP0kc2k4oMUqnqVmLLMrcBjg8zq36ANVjp8uvzqa40gm2C1woXG4HcDhtdfSbwnaVRGKNEjrpZ54KFWGiN3UBpwfTI4JLDg90wS0qwDeFiOpT+OJYvB3bDnFMHYI1KG94mqpLty8Ap0fX7geOAecL/vnPmRzjKnIjsjnVyvxyuH8Fmcgiwr6oeG/HWzc4QLBZvznfUPVsCOJJqpdPXCDvriTc9vGW0jtL/a3p33oZng3C9+fLv6HxjLA44qvp0mPky8Udjks5eORooHaD9iFjvdwNslsXRInIxcKyqPuDAHQq7oYMrTAxE9+jFWOzfHTED5sfY7JBJoY88ek5VVxORlQLuqVjn/VQspu+kECsDKNX7dcv+hcmGXDYvFhZhe2yD0B9h6dA0WD9VVZ+eJKyV/bSeZNdXETkQC12zQc/pH4zMo0RkYVX9bMTryeuq1TnzYnv9LYLNDmolxwCq0jFFsMXCH52KhRY5GUuH1YFfi8g7NQon4CnPIvI2LE7055loV9YAzhaRj6vqjyLebL0V+Eum8wLYXgxrYp0OwfbTuh7YXVWfiniz9X4p3AE0lGPFmd+uPHTI4EoL7Q93shxm970B+ALW4WuF3TZPRGRj4CDsuw7XzBBWEaXt2kz7NrRv7wT2wzqCnyOfhrJjVPUP4Z0pWPv6Caxcb6aqdyS42TrXqZ+zbdYIo46m9L3sS+c4LTcKMqGq/0xtuha6rlT/9QkaZllje0jkUOs+ptPWAOuDzY9N9Ho6YCyAtTNHYHs39Ogu4Apgi55NIiIfrf0In73osbM936g151XXn2AyzVypSTQgL5NXPfa9o/2rHrNxA2XbSGIr5t8P7MvEINSawBdEZElVPS4S6qTovb21OWTzVEw3DDvwDrOwj+LUix6fSbYMFPLxeGUupedK+h4K2lZxMR0AACAASURBVDyX1uCC1deNE/5S/VcPbklsD643T1YB/gTcCdylqv8Rkar3P4b1d/+OTao8R+v3E/LaJU3UeiB1hLqrylYspY9GyZ/y3ofZBNv0JhWJSLpHUhteMP25PbAScA6miyrTONgslY8q7q2JDe48G/TcE8AK6WBPwPXkyR7Ay1X1jyLyUux736BRSOEI12unLayqh4Tzn4rIY8BaGoUvjmjF6PzNmI3doxfFjCP2kc2k4ntShU7S9tio8eHprBlpGcd6EG7CuwSWcPsA+2kSzijwnDhAjne3wZbJS59nPsKWA84W8baSIVOObGzx7SlxraquFV2frarbhPN0jyIPb3a6Bf40/MKNGpZaisgV8Qi7p8yJbVy/adTpv1FVXxMU0sWq+oaI99IBuH2zBEXkfqxxnYJ18HqxQwX4Utw5ib/HQzn1xJkennLkKfvfwmZUHhjPJhGRQ4HFNZph26JsZOdLMKIuxhwEJ2Ad7ifFlpxfp6rpirfee89ZOnvkEJFbqU+7l6nqnDW4C2EzVz4DHKCqx1fwFMGWlnFxxWZK74jVrcN0cgiZbD1Xgb09cAw2yzTdgNqVFpGTom5m17JteKN35sU2tdwe6wh8qqpdFZE7gTU1Cc0mtorwWlVdKbrnqa93A6tqErom1KmbVfVl0b22eT0/5gTbHZt1eqQmK42ccnh1TBFsEbka+KCq9q1+EAuR9G1VfW10z9Nu3wy8TSeHa1wG+JGqrhrdc9lohdN5GrZh+2E6EepDsIGGFVR1l4jXo/dL4Xq/z1M2PPntaXs8MrhsnvDOK7BZgq/BdPP3qzoxznT22l6bBRn+jK3ir40d723XxFaU7IbZdtcAn1fVuytwS9oxs2MrWj4KXBlk+E3Vex6d6+R12axiYfk+ocmeHmL7Hx4Z29rhfm46n4at7HkEq8/LqupfxRypV2j/fnNeXVek/yo2c3YDMmZZO20Nj/3eZGuoJuGIROTe8H+pU3cq5mBcMbq3NWbzrQNchDkWv1Nlv0z6qMH2okfXeWyv3wc5Bcu703uPgHeo6mINMvdWar4A+Jyqnh89S52z8arHG1R12za4FbyNNpKI3IGtZkwHEhbByt8ranAbw2cNel7BPxZ9FKeu87TFbb9vZD6eFjKX0nPF+sQFbZ6qlQyvwwZ3f6+Rjyvwl+q/tsItiZ2B6y5HYo7vXji332MDDa/S6oGAZcP/vw1r8w/XZH+hFnaJp2yU8oF4bdBi+khEzmngf4uqztuSdzssDdbGVuL8AJhW49PI5k3emx/YGitL82O+kHQPrYdotnsqQwBXXSe4njxJcW9T1VVqcF0+odD33yDivzS+1v690NwhjqPnjT6yXCo2SCUi62DKYn2sk/SDtDDMClyx2Yc7YqOA12OG2R11/E5ZimE/X+QQkXvjzkfy7D5VXaENbws57lDVV0bXC/cqW/rMiXu9qq4RXR+gqoeH875BtxbYng6jt2EtUv9KkZgj/TtYo9MzKlbFZvS9V+tnxYxajkWBw7AZbMeo6sXh/obAGqp6RMI/FumcK4c49l6R/sGNF2HLg3+gqg/VyFAM20PBUN4MS49lsKXcJ2jmZuMNuEtghtHW2OyuM6iZseVJi9IUnESbYrKvgi1F/2kF38ex1YMf1DB4ITZocQwwPTY0PPVVRO5W1ZfXyHaXRoNfLb5tYazT/k7gJOAorV8tXVKOIthNbdeQ7VoR3PB+yXRusiFqnz1XuB31k4j8EJt9eASmP/8TP08dpAXl6O0HcDMVnWiN9lFwtmt7Yo7gS4AvzEo9H5OI9FbhfA2YFG5Jk32fCsngtVnXxsrENKwfA1ZWdgF2UNVrIt7sdA5t1Ucxm+67GsL5iK1SXVFVpzk+ayjy2IsiM2dZ74i1s3Urh4rYGiIyVVX/M5hzJv89Gk1AyHkW8mYr7Bs3wtrwc3p2d8JXxF7MJY+TKXone6WmTF71eHhdP96Jm2UjicidDQNRTc8GOau8emAs+iilqI0M4+JryqVx6ReXpjDochAwJ1YPLxwBZpH+a0nskjIn/7MmNmD1duBhVV2ngmdlrK+7Mxbt6IzkeasJ35nyFdFdJfWWFzu0PU38l7Thjd6ZH1uFuSPwRszPcI5WbAfj4Q38UzAdugMWkWT/YeqsiPwNW+UENsizfLgW+7x2+zDJxISYHu0QX2vmXoQ12DOw0HwDJx+JyF1Y2k4Bvo/VPQnH91ObwOMjy5a3xCBVSIQnsUT9BRMhCwDSTezqRnF7vPEsNw/uocDm2DLR07FNzWqXnElDzOSA/ZWINxtb6pfi9XDjUctsGVrI4fm+uiW5Pd4tE95vqeqPk//bHHN2btaSNzvdAv81wM46EXu+d38l4GRVXTu65ylzlYNnQdndl1TouuXoPdxsx4CIbKv9oXHe3NQRSd6dQWY9Cfye9PCUI1cehneWI9qMVCv2BGlRNkaSLyIyW1y/xiidXXLkkoj8FbgXm8l7H4nswzi6PNji2DxYRE7CBmIuxJZ031b3nlPPXYbNvDkD2+vpjwlva+eq+DZ99fBuyISD6+dYelxX92545wPYngS9kA5/wZyAx9bw59TXS7AO3CXJ/Y2Ag7R/BYAnr7+MGajHYQPKjYaQUw6vjimCLba6bZ3UqRQwfqn9q9s85flmLNRSnwM7dFjOT3RRtt4K/CXTuXYySzqY5NH7BXG93+cpG5789rQ9HhlcbWtoq3py9H5lgr2dPdVCjlYzwweR2ODX77G9EeL0ntRpLWnHiK0MrMtvVdX3RLwenevhzbZZo3cWA/4Xa8PBYuV/QyevivWk87yarA6Oni0RO9Ja6Loi/dfkPwbNsv4m5mx5qur9hNdjV96E9ct+NQg38J8LnK1hz+Ho/ruwlUaNm5eH+vB2bEByo+SZx1705Em2PeUh8a3U9Kx6zMYN/Nk2UuhD76GqNyf3VwWOT/rQvdUFgoVa+1vvEVb/Foh4X0QyGSGmIW3nkn0Uj67LboudMhTx8QT+UvbDDMr4/7zfV8TmCfybYINT/8BWMNauci7Yf83GLYntxHW1rw04goU/uyxc9/Yoehu2P9bpwAWaRHMIvG/RZBJE9GzdVKe28WNlfkNJ3VVEHyX/MQcWdliBB1T1X6PgTd57IbaqeAdNVtJ7eEVkfcwXsg62v/bpWhE6L/A2lkFVvSXi9QxKevStZ9VVERsmYE+nWea4L1/ER1ZqkGo6/R3ReMROYwPUmcke3GeB+5nYvCvuEGuqDMW3tDsbWxwhEzwytJDD832eZdIrYDFofwn0KsMamDLYXKNBIyevN9TEpsDXsXj0MfYBwEc0Gi13lrlvAn9U1QOT//ss8EJV/UB0r2llVJ9jYBCJyIPav6w0bdwVi396KbbZ/T8i3ulk1pPA70kPTznylP2XVvDEzA9GvN6ykZ0vInKlqq4Xzr+nqjtHz9LlrtMZj3TOlkOal66nnctpNbw93L7yXArb2WA/y0Qc/irnVSyDR8/NoD+NU9y0zHnSoraDw+T88/A+C9yCOT40lSftxCTyz4fZB5P2qArPPYNlK2N7Yl2JzQJVbOP7dbGQc7dHvN68/ifWCW7M6xZyeHVMEWwR2QN4HyH0T7i9BvBFbKbityNeT3neCvgSttF9LO/+WCiZcyNe70y7kul8EvAb4DOqfWEmD8JCU8T62qP3S+F6v89TNjz57Wl7PDKMzOZJyZnOLjlEZAGtceqLyEsTe8Ojy4ukc+AvktZOnevhvZTmNr5xlm0TOdM5DlnSt9lzhU3n1XVt+68VrH1tt2eW9b5YG3Gwqp7aJI/TrnwtFqbrZmxGeuOey2KzaM/G+qOx3p8b2Fr7BwPXwvpPFyYYWwKPqOr1yf1p5NuLnjzx2FMnDpBh94jXs1Ize9WjBzfiz7KRxEIHngKcSH/+7Qq8S1WvrPn2Rmqh58alj+LRdZ622CNDER9PC5lL+emK9IlbYHtsnmsxnfxlYNIAvk6enFqq/5qNWxLbiettX+v28enxfziS4Ras3/FU+o72T76Yig1mLIEN+t4mNlH+ACx8X98qK2fZKOUDycYN/EX0UeCfik2Y2QMLoTwFW6V+HPBp7Z/I7eFdA7MJ+qK8hLx5RKOQ9x7ecL9XPi7DVhGl5WOfiLdpxadq/xYvK6nqXeF8To32dRKR12k0EObJkyaSyZPls22YwP8uVf1+OO8blBWRD6nqN3LkqJBrBg4fWTauFt6TatIfisyuqv8ujetVhs7/Kob9PJVjTiyUwMwZ9cCpWj2LIZu3hRyrYHGBY+wvacPMjgzMXlirtbCOAcyCMHQi8pCqLhVdV+X1wlgHYl5VfV8mbpH6NyzJxCBcn0GLGYOLqmq6IXApOeK9zGr3OcvAGYt0LqhvF1PVx0aNWxr7/ydq0YnxzLL2GkZzYUvFV8bq+O3AKaPQ+x4qKUcp7GB4p+3al7Vh/4lM3FWx/VxieY/QZCZ1S+xSabEA8F0sVMNNWBvxGuBGrD1+cpxwO+qnUh2kFnLEAxeXaDRgkrb7TtzsTmtJEpGvqere4fwjqnpU9Gyaqu42C2Tw7t1RZFArsen6bDiPTTcrSZyzrMMA0VeAFwLHYo6YSl6nHAJ8AJskcWGCWznJRWzF7Ey9r9UhfaYDu+nkPRFXAI5L7YcBMs4Se1FEqvaGeimwNzBVVZeMeD1OwmnkOytH4uiqIxF5MbaSMW63j9Fk/5fQvn8AWAFz/p2gQ2yOXoL+G/oR4+LjGRWNS794GBLHZIOOhqPcPqyIHEJznhwWYU4DlgJ+DbwW27vq9dhq5HMrEWYxjbPuEpEjMDvjI6r653BvIeBI4ClV/WhL3l9gfa2+aCwi8jIszPHGbXjD/d1pIFX9bu73J7it92wagJs9Wb6kzDLC6GBtaZYMUgVDd0PMabGFRhuMVoziChPO6kmjuLm4De+sC+ykqnsm911LZ3OxxTfjfGgZGuTwLN+9ZQBv1pLcYciTbi2w3WVO+sNa3aEV4Rg8Tt4MGftWUg3gbexoD6onnvRwlqPWeSgiywD7AW8Cvq7RBpxeXKfzfZiNAp+TdPbIIUMsXRfbaHzbgPsKVV0ieV4EW0TOG4A7KIxMb9+DnbQ/pOhQek5ElsdCDOyoyaaWnrTwGAKjMBqCg2ELVf1hct81U7EEefJ6mPKWIUfJ9qdUiKEi7fYwNloGdqu0CHXvlUw4QkfSHo8at0Vb5bEXs/Pb2fZ4ZPCG4PF0kDzp7JUje+DCqcs93zcWdoxT57ZqiyVj7w5xDGpVlOeZj5hcnj1p4dJ1LfgXBfbE+hIK3IENAqThDKfVfB8kAxfRO7tgkSR+wcRgUh+v164UkUWwFQOvBL5N/yBVOsnFU09uVdVXVfGJyM2qumoT1gB70aPrWtlToT94APAG4KvYXmdZIYxmJZWykUTkB8C/gSuAtwK/VdWP1PDege1ncXrqVKzhH4s+ilMvetriofNkWB9P4C9iP1TIOhL/X4vvK2LzeKlg/3Uo3JLYDbje9nIuYH5VfTy5vyg2yDFw0puIrKWq10bXtwOvUtVnA/4TwAqaDMRH/B57uJQPxBuOspg+EpF7gZer6rPJ/dmAO7U/NLqHt8kmuCWROZu3icTCEG6mqudE9xrLtqqeF/F6+hGePMmeLO+1YZwyx9EbtgDiibGVdmjyX7U+slyarc1LuSQWLmAnbBOthTHj/BMJ2yXAi7EwAadrskfCELgx/2qB/x3AA+G/UvoAcBsWT/F3ULm0sw32ddhspJ6STVeKxLMuWsmQKYcHu7cc8lSsUP69jtHZWfQsK/WkG+KLwZpd5pIGqheuYsHe/cR4OQKbhX0hFmKhMf+kPj6vAAMHXCOaUoOfW088ddBTjlx5GGReEYu5/lpstsWHdfLMKy+uJ18WEpGtsTRdKGoABFiw6oUxSGePHE9g4UJ6sx7TtEtDb8wNbBlwV8dizm4FXF4hQins12Nxpk8DriEvLeYA/idgbwqcBXwrYcvWcxHu4lgInp2AVwOfx2Idp+RJizOx8nlTDe/ZLXljuacCbwmyboI5F/oGqdQxCOUxjJx635PXvZA0lWEYmFzeSrY/RbAlM9xFIE+7XaS9DNgl07lIe1ywnfe2VR5+j/7y5KFHBpfNkzxPedNrD7ZXDq05r7r26HLP95W0Y5rkSMmjc11tsTj27tAoxFsyqPWBdFBLVedv+t+EFhWRDwdZe+eE6xclvN7+qKcvsS5WV6cBJ4f/Xx34tYi8U6NVhepY6SYWXvVYzE5cW1UfbWDPtivF9qf8BDZItbvqwBmtcXtc5YiM68ncDTjz1siTay968tBlT4nIK7D+yWuwdPmAVqwgaujbGXD7/We9e7pk20gD+qOaYL+y5yQUke9iqxLqaEfMUfUzEXkC0x1nqOrvavjHpY/i0XWettj1fT0asY/HK3MpP13JPnERm6fFwHap/qsbtyR2Jq63HH0duIjJZf3NwHrAB2tkeSXBOY7t37dm9PifvUETVf2HiNxTN0AVyGOnldJdXp1RUh9pOugUbj4jIpNsaQdvk00wzxC8fSQiU4CNsbLxP8DVwDkRy7mYPdCLwJWmx3nJddV51bUnT5psrfSZ1yeULbOqvrt3Hgaw3s0AcvjIsqjUnlSfwxrSBzEldw5wnaouW8O/ILa55w7AXMAPMAWWjuBm44ot++spqT8EzI+rauXyabEZY9thiftM4D9LK+Jwe7BF5KPY6Pifsc38ztGaEHEeGVrI4cVeKeBugc32OxW4uMogL0GedAv8rjAIjjKXHdYqGJE7YA309VgZvaSucye+mMlVszleALwL+Iuq7hXxuupfeCc3PTz1xFP2V8E6fytje6WcpqqVm+22KBvZ+SLN+z6kSntc0tmjF48CNgCuCrxXNpTPU7CZohczsQnufQ16vAh2GGB5M6aPXo3ta3eaRnvbRLw9vk2w/dp+ABytqsvUyJGl50TkfYFvSayTdAbwoxGlxdZYPq+AxdQ+TVXvG5Y38L8BMxY2wxwJ6wLLqerfKng9qySfpcEw0v7Z29khPpx5vbQ6wp045fDqmCLY4g/ZmFuei7SXgbdkOhdpjwvier/Py59tpznaHk/59No8nlUtnnT2yvEwFh5NgI+Gc8L13tofatmjyz3fV9KOuTnIPAVrWzdgQkdfqtFKFafO9fC69u4I72QPalWRWCiZPVX1c9G9zzS9o6oHJRjZus7DLyJXAx/UyfslrAZ8W1VfG93LDtcotlJlb63ZDD75L49d+X1gH01WeTVgZ7fHIvItrM96YFx+ReRQYHFV3SPh99qiuXnisb1+iDk7j8Dsv77+ifavwvH07bJXsHtwe/yOPPHI3Cq0kYi8DkvvbbEwlqep6vEJz7j0UbJ1XeDPtb08MhTz8XhkDrwj99M5cdt8Xwmbp8lH0Nf3CfxF+q8tymcR7BZ9bk9f4g5VfWUNzu2qunJ0vXSQY0esfCwNrKmTQ8r+DdM9YDbR8uG6ajDeWzZK+UCycaN3Rq6PAv+PsP0xT03u74itnNuiJe9xwKOqenDC+2ngpar63ja80bN1MF/IllgY99cBy6d5KSI9HfNSTHedrqoP1KTF77G8k/DO6b1HwDt0cjSl3Dy5HwvFPwWznz8e4X5JVZePeL0+oV75j8t+D3s5Va2bJDQokpTLR5ZNqjryAxtxvhJ4OzBXuHd/xntTwkc+gRnHrXGxUcvLsGWc5MoQ+JbACsXvgJ1HgQ0si4UHuCZk3mrDyDDMN+ZgJ/zbhzz5RMWzeYDZo+uXY53+rYfhbZtu0XuzY7PdFh2mzA1RB9bBNh2+E9iyhmcFYN2K++tjyjO+d2ly/AJbCbFnnKbeejJMeuSWo5w8xDp8M4BjsNkzfceoykZOvjjyeCzS2SsH1hhtiG1eeRM2KLhsBd/NWLz5jwNL5XxfSezAMyewW/jmvSqe93TistG93Dxp0nP/Crhr5uLmpkXEPy9mRP0o5Ocbh+HFZkf9EtgZC5kA8EAD5r+AG4D9gV2w/e5mHgnv1phBdh3mVFyhAfcGT71y5LULt40cZOqYUthYaCx32mWU52ktMQfqrcLp/PqWcjfq/VK43u9ryz8ov7152FLmHJvnb5jevzU6713/ddh0dshxcNNRwZ/brv0es1mOjs5714+NMK8HlecZwP3YjPv0aLINGnWuhxeYzmS7dab9WsF/bZB7T2xWcd+R8C4V8uIC4L1Y3+LIIMtRbepy23qSy4+FCa97947k+oaq85rrn7X8vkF2ZbH2FbNfTgN+g824PwtzlpwOzFfB39ZezNV1OfbUjLgONdUpzPGUmxYfcvBm47bIE4/M/wGeCsfTmFO4d/5UxvsbYI7Cf9Y8f877KMk72Xox8De2xY7vmyU+nhyZI76R+ek8uMN8n0MPtPI3OepNkf5rTvkshT0Ebk5f4s6G9++Mzn+JrXY6CFgx3Hug5r2lm45hy4ajbrt0Vy5uzbsj0UeBd0lsktTPgS8CX8BWyF3f+46WvPNjPs17sIHLH4TzMwn+iza8gf+32ESpdwMLNpWP5D92wQZoLwfWr+DZtelomyfAiU1HDV6W/2iI8t9oS9DCR5ZzlAr392ImQgt9LcxSnVtEZtPqGRrrBN71Q+JurapXDIm7LTZSf6mIXMTEaGcjia1Y2RGbRXAhVplScmOr6gNhVHluzGn4MiZmobeRoZUcudhiG/HugDkj/4QNJp1TwXoRsDtwr9imt78CTgE2F5HXqur+LXmB/HQLM/OOVtXbw0yNX2GG9MIi8nFVPS3hzypzIrIW8JCG5cBicd+3xZTeIVo9++NF2ADZqzBHcd1MxK9hjV5Kfw/PZs4yUNUNazAQkcWAeJNFV/0LGLl1sMefW0Zz83B3mpe4tsWNZR6YLyF/G/5Svxddj0s6u+RQaz0uFZEbsTr+GWyT7uMTvlXDzI+dgJ+HWSPzi8iLtWZ5fClsEZkTWw20I7AM5virCm23Rvjfn4fZKKcDU6tkDbi5eu4l2Cy+r4T6dgY2EF5LuWkR0T+w2VpPYbN45mqAz+E9CwsdsD3wn1BXmurY4mTOVFSL4XyOWOzxtwFHhpmOn9LJm3dnhaGYyZyf1y7cFvweHVMKe1Oq24hKcpTnrJjdEa5Hb5VM52Mwp3U2ZbbHpXABf1vlsHty89vd9jht1ty0eEXd/9WRJ51zedW5v55Dl8fhjK5LnqXXPeyR2zFaM4u5ATNX52bzquoGHhmAvwJ/wZyb29KvR5T+sDonYx3iszAdeTXmpHpVaj+ISJP+VFX9fMLvtdFy+UVEXpC2p2J7QqQhu6XmvIoWGfC8SpBs+90Dm8uoqn8FdpT+vX5v15o9i1rYi648JMOectapNIxkE70H+EYBXPC1xdnYqlprU9cKYn3pHbG6PQNziP6wincc+ihBZo9ezG6LHTIU8/F4ZS7kpyvqeyhh84iFMTwOW4VwK/AeVb2zDjO8U6r/ml0+C2J7cT158nsRWVtV+8KJBl0S71P1ODYgshimx+6lpq+rYUWoiCzLxN6Qd9a1PdF7WXZaKd3l9ScU0keo6sPAGiLyFiz9BBvUujjgtOV9GthObPVozyY4SFXvqZAhmzfQBZg/9W3AX2VAuPtAf8X8qr/HytZ8FTx/V9UzBuDMJEeeHKKOKDGBcv1H+wH7q+pTGfLG6bScJHvVaf+2AG4fWQ4VCffX9we2Md3mmFJaDwtPsVP0fAbwJBPLHfsaMa3fOLsRN+LrbeC3I9bJOQlbrnlxwndowLszyHJRnaPZgx2M8B2wyvFQwL5AKzb8ayODQ45sbBG5DBtFPgMbme4biNGaDXDFwmosrKp7isWnvV6jze2cvNnpFvhnLv0Vkb2BDVR1KxF5MXCh9m8GN4PMMiciNwBvUtU/ioXOOh3YC1gN29zw7RHvuzEH71wh3c7QhlAZInKb1mwmJw0bA4bnjRvERnwD64kzPTzlyJWHDd/aZ9y2KBvZ+SK2B8yk21gDt4SqVg7sP5fp7JEjGlTYHjPmzsaWYz+Ugb0mVt7eDjysquskz4tgi8hJwCpYh+R0Vb2tGmUS5rpMdIpvwnTicdHzbD2X4C7JRAiOeQLuAQlPdlqIyIYBa21sxtHpqlrp0PTwBv7eDKle/OUFsAHhn2hzaKklwjv7APtp/+BszDcVcxLugOXR/qr604SnF1qrkrR/z4XsvJaJpfZ1uOlGyh45vDqmCLZMhO2qdEwkbbGn3b4Ly9863FgXzcBhoxVO576NXZvIqfdL4Xq/z1M2PPk9g/y2xyODy+bxkDOdvbaXJ7Rp63atiQrbMZ7Nvj0618O7r6p+KZxvp6o/jJ4dnraZTSRJCFERuVn7QxY+hoV5+WfFu/tVQM6Nzap9karOE/HOwKfrsvlFZA/gfdjM6d79NbAZxieo6rfj7yM/XOP9TISDmUTavz+kx373tq/Z/J7yWSNbk704g/w88dhenjrlyZOsUHle3MDvyRMXdoVsPT/ETqq6WXT/cExv/SnIcnpwXjbhPOd9FKeu87TF7u8btY+nhcwzKOCnK9knLmjzXAd8EltdsSXwXlXdpEGOUv3XVrglsTNwZ+BrX9fGyuc0JgYj18RWuOygqtdEvD2f2I5YlKKFgE108gDXAsB3As5NWLu6asDfXRPnvbNslPKBuHBL66Pw3vpMDPLdoc0Dy1m8IjI7ltYzeYMs/xqGN/BPAd6ElY+3YL6QXTE98reIb/3Asw4WDeB0Vb26BvMCYDbgf3XAIKczT+7DyugRg/x+LXxC+2J26MGahGGs4H1j03OdPCG5995AH1kuFR+k6vszkfmBbTTaR0FEpjMxUqf0O01Uo70APLg1fAsTZoqnuGJ7bNzPxGZmPZmEijiludgB9xZsCd5TEa79Sb/DZigZMuTIwg4NiSZ8MW+82eotvXdF5Crgy6p6brhOO5Ie3ux0C/wznUwi8mPgh6o6LX0WrqeTWeZiuUTkGOBxVT0kXN+kqqslMt+KxWJO0y4dJ0/VYAAAIABJREFUdUZE7lPVFaigqmfSsMmiVmxOWIFZWU+c6eEpR56yf6WqrhfOv6eqO0fP0vjn3rLhypfoPQHeic08uAPbI+GWKt7kvVmazh45ROSv2AyZ07DwKmlaNHZEA4YAb0gbqFLYIS3+2oOJ2Q1WFxiAOdM40f49xWaQqecasF8WcA9N7menRVSerwx8KW+6F1QWb4WsswNvxYyHt6jqC2v44pmK1wNHquodCY/HufMotrF73YDIoRFvdl6LyG+BT9d9b0X988rh0TFFsEXkn8AjNbhpWzyDzPIsIk9j4bXqcGNdND3BbbTRCqfzk1RviN7j3zLizdb7BXHbtFW5ZWMG+fk9ncw8bGGzemyep6tkjWSO67c3nT1y/IuGTdjbtpmSzDRMaQRlw5MetZt9V+R3rs718Gbvz1VFwRbYELN3t9Aovr9MHry/NL7W+gkm8wEfAvbAZrB+WaNZyy10nZd/c2BfotVDQYbzE74ZWBilHL3/B6wM1fHG+0N67Hdv+5rNL449AJuoxl6cjl/X5dhe2TI78+QZLPTppM9jcp3Kxg38njxxYYd35sAmP+2ETVY6Czg7Ls9ie26dpvWz3FPMseijOHXdDMhui4f6vlH4eFrIPJ3MOtUg91D+vxbf58XOtXlcbVnB/utQuCWxG3Cn4yxHYqsy/hcbNANrM7+hzZOQFsMGXXbAwsrFe4xOw1ZyHqbBbxb0RS9s/S4JlqdslPKBuHBL6iMRWRzT84r5BwTzR07B6vejLXlXAs7H9s6OedfG7L+72vBWUWi3NsfKx0Ya+UKi/L4Ms8HS9NgnwdoK+Dy2v9Sx4Z0ebzrQn5sn8wOHYRMS9lLV2r5pG5+Q2CTkrwAvrJA5bgOnabT/aRuq85HlUpFwf2KjaMuo6pXheh8mlsr1jdxpQ0iI4FRrhVuBtQS2BPVC4IQKlmWb3h8C+zAmCk26XDAdIWwtQ4Yc2djqC2twi4gcgTnSVsA2AURsA+NheD3pBvBk6AA+AqyLrRZARGbDZk1OvOwoc8BUmVjJszHWue1RWn82rMOtoWtF5H06eePY3UmWsEv/JovfYGKTxekV3+CqJ8708JRRTx7Gm/WtnDxLO03esuHKl1BmdsM2LrwGeLuq3l3BNxbp7JTjh1garRSOPhGJlvOLyMrY3mjnheuvAguGx1XhSYpgq2oaCqeWxFb2zK1hlZDYRs1zYEuh+xprj54Tm321mKreG663Y0KvfKviley0wGZ055KHt/9PbSb6ecB5YgPefSSTZyp+Uutn8VzChGE0J7CLRKEyE8PoUVU9LFPG7LwG/pA6ygZQthz4dUwp7Ds0c4WPs92+L9cR6NRbUDadH8f2nskhj94vhev9vmx+T34789Ajs9fmuQQLB3Q2NrD9YAOvB9srR3ZoU3y6/PXYbNvTMNuhcqA2UEk75mPYrOK/M2Czb4/OdepnqTmvup54YOGUdsJCoiyM7VH1iYRtQSacEz3qzcJWoG+CSehj7I3NnD0FWEtV/5D+t1fXteC/AAs900hOXf7bqgGEGvLY7972NZtfG8KXV5HTXtygASfNk2x7yimzJ09uzW3jnbjgy8NsbBF5MzZJaRNsgPh7wNqxQzqi24BVRKQyakiF43Ys+ihOvbhMLq9HhpRG5eOBcvZDKf8f/u/zYHva4oVEZJu667Q8F+y/etriYthO3A0acCpDg6nqYyLyecxnqMBvdEA0HlV9jLAfqIgsnTxeN3W8q6oCh4nIvRVwnrJRSne5dEZhfXQMtvL7O/FNEXkPNuCxVUveo7EBmYsS3k0CzsYteSeR2mqrs4GzxVaSxfS+pncrsM4VkQewSY7xtiV9dqhT3z4NfFRE1gAuEYtO0pu0pNo/IO/2CanqI2KLOT6HRYnqDVKl+T1wMnyPWvjIsoUd+YF10DaPru/GOk0HAacMeFew0cPvkGw07MHFluN+Orp+EJuFeBcWkij3W9YFjimEvVZbGUYlRx12Bd/ywKeA25L7cwP7A0cBq0b31yHZ0NLD6003LD7sRdjy3d2i+5tgKwHalrlPAVdhsyhuhJmrD1cArsqUdymqN8dbDNvwcTrmIDsSG73/FfDihDd7k8Vh6t+g9Bi2HNXlIY6Nor1lw5MvmEPkHqwBXXrA+2ORzsPKEZfH5Pp8YJ3o+g7M8bUzcG4ubglsbFDzncCPk/tHAPtG1w+E//oZ8IUM3Do9dxz9euU+zFD6DvCtYdKigW8uYLu2vNgS/j2j62uwmYj3YwOvKcaz4btuDcct4bgVuCXh3bXpSHhv9KSPI6+vduIMJUeEU9X+FMEeQdrVlefWuIP0VuF0zm4LGnCr9H4RXO/3Dctfl9/ePBxW5qa0wDri7wZ+itk7/4uFfh55Oufw0mKT+ejdtF3rhT89CbMXPwus3KIsDWXHRM+WpcVG8HU618OL06bDOsz3YgOZ78X2WnrAm3YVuJ8HfhPqxQLOd131pIkfsxe+XndUYM2GORA+EY7NgNkq+IbWt1Tbld721cu/CBY6/ZhwfKhODzCEvdhG11FtT52GhVav4v9e2zwpxevNE6ccz2K6e9noXl1/9MSG4wTn98yyPkoNj0cvZrXFA75vlvl4PDIP0HNF/H/Dfl8b7PBeapMPXZ7ryhHD919ry2cp7GFwB+UJ1gZ+CZtEdj1mUz0e7s0e8a0H7BJdn4lN5P4FtlImxryvQZ57c8tFVdkYwFtEd6W4DXxD66Nw7+4G/nuG4L2rgfeutrzh3ubAB6PrqzA/3z3Ynmg5aTFHyotN0P0MNrF38xyc3DwJ9eLWUL+WBZbuHZnYlf4jbBHA5djktcUHYNyF7X+7etWR8I7MR9aH2/bFAR+WdkhujM6vqHnntdjgxYPY5rm7Ai9oi4vNqps35cU6kVcOkH81TAnOwGYJ7TVC7FdiI/P3Ate1lWEYOXKwA9/i2OzDX2OzIg7GNiaOeWo7fViM+Fa8bdPNWU4HlrnA9zpsVmec1i9LK2nyzguBDwZl8Bsstmgd74ZYZ20vkgY14VsppMHdwBVYY/3iCj53/fOkh7cc5eQh5jjfGmuk7we2Cce22MyZkZSNQfmCdb7+j34nfZ2jfizSua0c4fmC2MbNPwceSZ6leXR1dN6o50pgY0bCVpiz7SmsU7BF+u1EjhwmdKI04ObouZkD1BVpPFRaJHxTsZB8J2Obdp7ZlhczxJaKrm/CHEMvxWLDp3hLNx0NcsxHpBsrnk9yPGEdnndR09nPyeua95YHDqTa6HPLEfE16phS2ERGX3K/dgAzszy/pebdJod3bntZMp3Pbnh39oZng/R+EVzv97Xhz8lvbx62kblFWkzBZuM/AewzgNdjT3l4Vwe+jOnG7wKvzMiTXF0+J7Yi+3EG2EbesuH8xpWxjvQM4B0NfNk6N4cX+E949jS2Wu2p6PrfFZiPYytz3w7MFe5VOr7Ds9xBnF7ooj9h+wD0jj8BfxxFPcnhp38ixwyaJ3a8BLPzpwNfBb6GDQrcDbwk4V0luV4Es6XXGFCGBtmVywALRtcbhm/cB5hjUHkO71S2x8ArgEexvUY+gumvk7CB4pUqcNrYi948HGRPPQ78lmjiT/TsxuQ6e2AaOMDB6xrwxmy92qOpHEX3qwYwX4PtpfYbzBm9O7YSq+r9bTwyV7z/nPRRIh6PXsxuizO/r6iPxytzTp2ikP9viO8rZvM4y3Gp/mtW+SyF3RI3ty/xVczJPX90bwHMIX5UdO8SItsN89esgUUfuijBPAkLgSrJ/YNIJhsMWzYopLuacBO+keqj8LxykC/k931D8N5LhV2B2dH3tuXtpSeRDwOb9L8YttJpki8k4puCbXtwAuYXPDd5fjdwOLaSMFcP5PTPT8d8vI15VYE90H+EDahV9v8reJ/GBnovrTh+kfAO5SOrlaHtiwM+7I7keuGGZ9mz55y4aUO5W3R+fQX2yzDFdWco0HtRb3R5sZfGVhDdjM0GeAJbDt1aBq8czu97XyiY92AzQV/dkCfxbMlLBsiXzetJt8B7RnT+xeTZxUOUuY2i82WTZ9sk1/NjGzpehA20HIltgFiXfws3HXXvhXfXDPgPAr9sW09apIe3jOaW/RObjiHLRna+4HDUj0s6t5BjbizM0Y+w0ERPEjbpTviyZ8GUxGbCSHgE+D7mmJpR8+7NyfVbovObkmcePXdrcr1KdF45Myk3LQLvG7Al0Q9hMZz/D5inBjeLF7g2uf5GdF47wxabtbM55vRbroHvg5j++UM4fottIFrHn9Phyc7r6B2XIZ4jR+DL1jGzCHuQEy27PCfvDRrAabXCoWRaRO82zeh1tcelcL3fl8vvyW9vHjpkcKcxtnL+aGxg6BvA+jV8nnbbmyeHhu/6PqbrJg1uJPweXT4nNsHmh9i+bwcBSwxbNpzpsRwTK6jOJBr4qeD1tK9u/Zx70K/fHsZCiD1alTf4BnHmCNiVx5D1pK1ubFy1gg3e7F1x/8PAScm9Cwj2CNYWPorN0r4jxcBnV17TS0vMKfwEtiLiJOA7DbLnOGHOpGLAFJuUdlbFfY+96M3DXHvqRixU6U9Dmr8wepb2dR9gYtX6/cn1bxLeg0OeVB0HtcUN/L3JdrfSv0L+UeA/DWniGQRYF9Pjj2Jh6PZInrtXKTMefRSPXnTZXg4Zivh4vDJTzk9X0vdQyuZ5beD5Cxb1pnJ1Zcty5Om/utriUthOXG+e3EsymBTuTyUajGByX/fs6Pyq5NkCmG32G0zfnxnOzySalNGmbATeIrorFzfwFtFHgffrWHs5d3RvHuCbRD6GFrwHY9sRLBndWxLbO/SQtrw15ePY6PxXFfzrYG3agyFNHgPmq+CrnNRG9Upsj759X0NeVa1u8/iP5qy5XzUZxbO62u0jy8Jt++IAYa8BXlZxfyXg18m97NlzTtx7qJgBS/1Ia2/5+grRvTo5srGxcG63Yx3VFcO9uoKZLUMLOTzf96/Au2YG741V54OuM3iz060Cu3YmT4sylx2yBIv/fxmwPhNhAZvyr9fBiDsaD/Su695LMAR4Y3Ivu560SA9POXLlYe7Romy48sUhx7iks0cvnoI1ZN/FDNGpdWmHdU5fW3H/dcD0ivtFsPGFFrmTaNZVdH9BJi8Z9+i5m6letbgEyQq7FmnxcCjTO/dkHxFvU1iDKofGAtiAwv1YXOJzwvkPSVbBYrOjf0I0iIU5Rs8HDkx4PR0eT157DXGPHF4dUxI714nmKc8eh7d3hUOxtIjey5nR69b7o8Ztkdcee9GT3562xyOD1+aZgQ1O7Y9tctwUPsKTzl45PKFNPbr8JMyJ8VlqViYMUTY86fFsSOdDsYGFfeKjgjdX53p4L8D2lqrsLA9Im7lCWT0LcxCcmjyfRv4gjidctFfXufhzZaI5nM3dyfXt0fkBwMnhfP6KsuyxK2+Jzo8AvhTOp6S44b7HCdPkoJv0DJ+96NF1Hnsq7gvug+myt4brtI+5SHK8CAsl/gDJIFyon+lxEDbp5y9tcWu+YRkslPm9TF455xoEqMCegoXXPyG57xqkYnz6KB5d52mLPTIU8fG0kLmUn65In7gFtsfmuS7k25zYvpY/HVCeS/Vfs3FLYjtxve1r5UBz+iytCwlf3Wqe5bG+yZbY3lB173vKRikfSDZu4C+ijwL/HNikoMexun418Ptwb862vIF/b6zf+BjWz32ECjuvBW+2LwRrd3+FhSRfsCm/k/cGTSLNzpOaulS3Ii/bhqnAHbSa3jNI5fKRZeO2fXGAsJtijeuuwKvCsVu499aGjB00e86DezjmKJknujcvNpv38xXYW2ObJz8EHI9tvFaZ0R5sbBT2QWxUdp2mgumRoYUcnu+LZ1bfjYUKeaiG1zOI4+HNTrcW2J4y5xlY+yimiG/DOovLN8nsrFOelWLZ9aRFenjKkTcPp9I/O3EOYA/gziFxs/MFW976VMXxNPDUmKazRy969jZbG+v8HowZc1sAh4R7a1fwF8HGF1pkH2zw5KXRvaXDvY8lvB499y5sVvwbMOfP/MAbw71J+5g40+IozDDqOfXmHRHvKVTMyAHeD5xWcX9ayIMp0T3BHBcnJ7x3UzEzH5uRlc5s9nR4PHntMvqccnh1TBFsfE40T3n2OLyz9dYsSGfPzFuP3i+F6/0+T9nw5Len7fHI4LJ5sJUvl9YcafgITzp75Vi66Uh4Pbr8WcxWSO2IKvuhpB1zCNa2Vh4Jr0fnenjfhu1P8jhmy2xFZpi4BGd+JofD8wzieDraXl3n4o/eGzRIVStz+oxotjqmv3aoehauPXblrbG8wCbRddUglccxVvv9Vc/w2YseXeexp9J+5KrYoPbRRAOFCc8UzC6/DZuw0RhONJT1A8N3fRFYdES4K2L23Z1Y+1Y18OEaBKj5n5cDxyf3/kZ/6PTaCQGBf1z6KB5d52mLPTIU8fG0kLmUn65In7gFtsfmce2Z7SxHnv5rNm5JbCeut309l2ivqej+u4Dzouvzgc0q+DZn8h5ai2KDJBdg+1U27lPpLBulfCDZuC3qthd7rfA7HxP7FU1aZeTlTd57Af0rMN82DC9mh76n4v7uwA+Se8eE/D4XeAfm02hKj9xJpNl5EvhzV+Rl2zCB37OaPntbAJw+styj1UtZwLAKpoiuD8dJDJ5Z2Dh7zoOLKcMvYMsye7yPh3u1HQgmNge8ADOujk0zyovNxCjozzAF9CcqDCiPDG2/MRc74l8SU17Xh0J9ePL8YayR+lh03rt+qC2vN92Y2OBtjSBnTyGuQTLI4SlzODd/DveXwzbDuxULd7Ef1TOLKjejo3pWcfZKsbb1L7cOOstoVh4COwB/xmLSX4bFv38YW80xae8vT9nw5ovnGKN0zpaDzL3NAu+igfescBxGw0adJbHDO42hRQLPB5gIQ/cE1oB/cABuo54LPJtiBkYP9zIqBiNbpkUvvNjx2IygpzEDqWqJeRZvSN9fYg7gI8MxHZslVLVkvGk2Wjprs2kmdDp7ztWZys1r/Eaft1PnaX+KYOM0QHPLMy0nVJBno5VMZ/fKBTL0filc7/e14c/J7xZ56JV55G1rG+xScuDQ5U7csbBjIuyB7auXl4nO9jlYB/4E4M0VfLs0HQmvZxDnYWyFVeUxTD3x8NM/cJnu0ZUOYN7PxL6s8TFpj1bMQbcX5sT9E7BQlO51gyc5/dyjsJXVR4WyOXu4vzjV+wV6HGNx/y8+KvuC4Z029mKOrsu1p6r6WXNhTq1nk/uzY5OB7sJCxtbO1A/8C2Orzx7AHJWVe+a0wF0Fc9DdgjmRpjbwegYwXw1cjNkPn8X29zgr5OtHE97bce51ypj0UaJ3PHoxpy+Ru790cR9PrszOOlXE/zfE943M5mGybu67HrYc0a7/mtsWF8FuiZuTJ0tg/ZTpWP/1CKzf/WuiEMrAClhdOpGJ/d2nYQOjqa1/ETYpbRNsgsG0Jjk9ZcNTtwNvtu7y4HrrtlNmz8p0d6jXGpwHh+HFwvReE/Lvi+H4eShHi1fwTwHeEspTzybYhmTwiZarmAblCf7VbR7/UavJKGTsg4vTR5Zz9GbQjh2JyALA1qp60pA4c2MKDGzJ398d7y6MLefdXlU3GgW2iCyKddh2xEatlxpGhrZy5GIn/C/HZukdGt07uOmdtrwV/92YbiJy6QDsDZueB4xJZU5EnsQqnWCzzi/vPQLWU9UXDMB8VZB5e1VdvkHmNTCFFYk8kScicoOqrp6eV12PinLrYG45aspDEbkN2EpV7xOR1TFH+g6qek6GnK46Fd6pzBcRmQfb1Pvf4frlwP9g4aoGytKGRp3Ozv9eE0uH7bBwX+uMAncWYE/BGu8dVPXdNTzzYatEnnZiT9Jzw5InLURkdqyx3xHrfL1wGF4R2QhYOVzerqq/qMG6T1VXqHl2r6quGF1fghlWl1T810F1+lZE1g2ybouFozpHVY+r+77wTk5eL8WEDpgn4B7QgOmSw6NjRo0tIoIN2u+I6aIFsIGfn6jqX+pwo/dry7OILBdwd8BmWx8c5L0nA3eg3iqQFlOxzsOOmFF+KfCmwPtMhsx1er8Irvf7huUP72Trr8w89MrcZPO8oeFVVdUr2mI75XgAiDs/El3rgDws0q6N2I75dMNrqqqfGYA7UOe25H015rB8tapOTZ4dXfUKNsN4CVWdLeK9H+vkV/F/KUmLR7HOu1TJpKoHNckcMFz90WH6ryLyAuArTTxxOodycxg2eHSMql4c7m8IrKGqRwz4v0q7MrQ72wfcM1T1kXD/NdgKn582YC6JtSmV7fEwfcG2lKnrau0pEZmiqs/WvLe4qj76/9h7z7BLiuL/+1MLkrMiOQfJQVkBwQBGFCTnLKL+lBwEEwgqCIIiIAgILFElKaCgCBIkKksGSQKSRQyIIPn7vKievfvM6ZnTfcKy/h/rus513zNTU9PT3VNdVV0hOn4c34g8CjfedoCkCyLc7+AGsBPx8Wtc00voBvzXcWPXL4HXE/i7JZ4xIx71WK2Fp+Hjd1mEcxO+OXAD3l9fBM7GZb+XavRulbRy0zv1gilJRynhdQG/51qc04bJZePJbXOEOxQ7Xb90+3y/gWQeMzu1hbwkfSqjDSPRX3Pn56hoD6Bzt45JpMMarsNekcCZFt+8nKTr4htfdX50m6SVouMi+1mhLjgqWbEvusPgRyX9NSzbpJk9liMP98I1s4/QaQu5LIVXu2cavD73lsDaNZng+/haeSe+/l2IR6AvmtPWQCNlV78dn+un45Fej5nZQzl0e9mEzGxDXDZ7D75h+xO8xugiCVoz484rW+ERWD/Dee38ue83MGgIu5yD/Ih2samldJjM7dgo+j/pxdQn3baorYUmRxtKaRPt6pLweJxM45Hdb33QnhD9v30P3Pe3/Wq4qw3Qpl6FlPuKFBtBf5TMo5K5X48Oa0vnUjQ3SsYF34Ss8g4vDvwd97a5Avj2lNjPw/hBd22zKYU2sEv0/7JDbNdk53OpviBKk5DAn75f3D7adhoeBm61818DzqidWxav6TIB91rbJdz/YM4YMeapVK9fMPBY4ylnDszEbWrHwOvPKGjjntTr4QLxs7VrA81nPC3LIXR76k+I/t++zzEZRV/0ilzoaz0eJt3S9yvBLxnvkjEsbENRH+NRH/XfRbjn7esD9HNpOwaq7RJopHh5FS3zPJ1p/14EXhtwbpT0R6rOzQGk69xk89wS3AhvLnyNuA73Oj8MWCmjb7fBlf+f4pta8fVT23413BLP3+zvpB/8zDaUtLf1mfU5Rpn8vlT0f73WRMlcXJLM9XhI/Zc9JgwgT9FeJ2JCy/ysr4Fv4Ol3c1KEZtMN+DvgadeSv4y+nAOP3KqnYq2nkXyMhigt4Nghjetk1VEK+eLAusSg71fybZe2eRR8rpRuH+9XQnuU9qZR6a8joTviNhfNI9zB4S3R8TvwLBAbDtCG2wkp4sKv43gUc2NUvKuJ7ij5EZ527oKmX7+4PdoyUCRV4XvP13JtxoZ+yonELhoT8qNt+5JhyIumL67rPOzfZHtQS0c1pjGbzO1oTOs2RLrHvBltKKVd2g7cy/t83HPhbuA84AOD4Jb0W4T3VlwZ/kH47UJ60elrzuGGjDkz++2Gfsen4fqVbb8B50Z2fwwwj3rN/Xr6j47jQeZGybjQmYf/G7hnI3h9rDt7PevN6OfCNlwW/f+lYdEdJe3JxJuHzW+z+6LWjlYDaQluH22eBTgXD+U+P/DmP4W/syXwp8ONNEfint87ka5T9VFgk8T5rakJa6Vj0sDz39qA2287cnjMSGjja05XvQk8ncqCLXR78ZfFgTUS599HlAYgnCtaL0fZzy3PTNWt6Xs9HhbdPubRSOTFkjEcoA3FfQysiaeQuRFYb0j9nN0OMmq7MMC6FubPfvjmzJFDnBsl79ha56ZwHpXgfgb4La64H5PiN4l7psZr5vwRN8i/o3RO1ehtT1lNqlJeN3T9tbC9twDXRsd1Z5LsusAlY526F5cfloiON2UsXeNcNdzsGruD9F/hO/aUp8isE/Hf8KN7A/OzNBi1qBWkZ8xpskpPHztO1lPV7w3slKC5a51uffxL+W3GO/crlw+TL04RelJhm0dipxvgWx0qb6Z8LV4Od8q7Ga+5chqw/DDaParxnhJol84j2p2GD+2zDY/g8tjDiV+XAb5kbozw2y6iO0p+hNfu/WDTbwDcW3F5pv67FXipX9w++jruu3MK721zIu37mwJWwe0sjwLXt9DtyyZEszNKX2UBhvkbx5sPerMbEMAa/h8m3TXepDaMjLaZfQLPMV/VzNgaL5h4ipl9vF9cyvoNM1sa/5DeheehfQAYD9xlZkvV0LPnnDkcaGbP4oL5/Wb214Z0KnGbp8t9Rg5IWqvtNyj5AtySeVQyhicxVmxv5sRxv3Tr+L3GJe6LtfEctkh6Bfd4HARG1c8lMGf0/6ZDpDtq2hUMmy+OCkr6In6nXuHcJbhFIOlfkjbFI14m4KHmH5G0iaR/djTCbHE8ldApkvaWtJekk4HxZlZPlXUQ7o1ThytwT6EmaB3rFp5/Zwihr0NJO0p5zKhoH0PnXKpgPrwuQb9wFO7xVYcXge/VzpXKaCPrZzPby8x2SlzaAfeGbKLdyvdHRZfB1qoc/Fzod+0pkVmzZR4z+6CZXYU7gnxX0mqSLh6AdlE7zOwtZvZZ4B7cS3B9SdtIuieBXryumdlsZvZ13Et3ZryA9N4tbR62HIOZzWFm38Tr0UyNG4/3k/RM5jNK2pOC1XEetYCkXSVd16O9X8DH413AxyTtIOm+gvakYHfgw2Y2S9Ovhl/K60ahv5Z+qzNGx8smrjcdl8jvbXQqOILOeXwovh6/D18TYlgi+v/DtWup9a4E+uV1rfKUmZ2FyxkfwWu0LAz8Q9JVqqUBNLOjov93r12bUDuOUywuUru2Ub90w7lro//PqF3+fe34KGCima1EN2xXO34Kd0zuR9QVAAAgAElEQVSqap0+HR3X00vuCNSfDZ7eMJUabUrUUf4bdLBR2o9GZacbpU48EpnHzNbH015djc/fT4f/LwjXcp8zTBilXjxM2qXzaHZJD4T/twd+LGlXYB1g3b4aIC0saVFJiyR+qTWgRE4b1bc9JfHE5yVd0fQbAHeT8Pz6bxO65ZoS3FKIx3uJRqwESHpV0sWStsIdtIYCkm4O+sPCuCNxDAPbhCT9XdIJjKWUrc5/T9KqwCfDc34OzGtm+5nZkv08qxSm7o0yPDCzz+MFtc7XWJ7/+c3saLwDqv8ngRI5kzPpNuHOA/xd0su1S9OHXNvjgOnC/5MGX9ItGe1I0S5hygO3oaUdJbTfbmZ7hevV/0S4cb70ffFaQrdH524zs5txA9slfeKWLmbfAHaXdE580sw2xoskbhydLplze+DevuMlPRxoLgocb2Z7SooNeuPM88mPi/6P+/jvtbYdE71nz7lvZgsBL0h61sxWC+16UNLPG3ul8/6m76SkP0rmUfYYqiwHfencKBmXO8zsCNz7d3G8ODBmNlvuw96Efi5px8DKhpkdAjyH57H9W/wKI6I9W8ijOw6Ypa64q5aHv4HuKsBTCnUVApTwuSa66wNPS7qpdqmkL9Tw/6C4RWBmH8WLf1YRVNX5rYFnJP0mQj8K96ypw3/CtfWiczNI+msdUdLT5vUPYigZ6zaefwidPL+0HaV9Oyray0vq2vCR9GszO7J2umQ+LyzpjgTdm81s4drpUhltlP38Kdxbuw4n4h6tR0XnSvj+qOiO0uhdMt4lY1jShlKZ5xPAV3Ae/5UeGxcltIvagXvNxrVdVjSzFSP8mM+UODW9DY8c2Bx3yFpZ0nMN6COTY6yzzs3yaq9dV8JzS3Cv0Fh9pDXisTazXSQdW2vHMcAzuFx7sdmkVzMnrRVa3qEJDPeuFZ3KfHUsYMHofCmvG1h/HRBK5AXoX36v00k9dzzujVvB88Gg2LFZktHuku8tJS/2y+t6PXc54B941NC9kl43s6Z74tp72wPfj47r8/gIxtae8+lch76Kp0bqhy6UbWDei9eW+rmZHavOWmYduCpzjJTcya9+8mWLPvL4UgHtJAxJRynhdSVr8TDeb1AbT2mbR2WnG6VOPCqZ52A8I8Aj0bnbzey3eITlhTX8UemvA9MdJe0GuqXzqO40/J2A94qZ9eU0HOxo/6zkMvPajRvgEVY/SPCqfnX5vqCBd5XSHSU/emwUuJL+1BvLZQlJaw4bN25Kw/+lsFrteGB7k6Q3zGxPOh1JR2YTip77EG5H/5aN1cG9FI+saoUWG1kWVDkGJwuYe8othefx/GQ4t33bPcooyJii24J7Od6x50vaJzp/ZXszehdlTNE2sxfxOh0Wrj1YoVNTvIbRhpZ2ZNO2gqK2ZnavpHqkUvJaIW52vwX8+ySlvOe7rpXMOTO7FRdInq3RnBMPk105OvcIHm2TFLxV89IobMcBuFIivNDdh4CrgFWB2yXt0UYr0Eh+J4XtKJlHJXP/HEmbhf8Pk7RfdO0ySR/ph27Af4TMcTEvULs7Xiz6FIUNVTN7D7CYpJRHYAdM7n4uaYeZ/RMPoTfcg/yaGuFW/hlobID3+4qStovOj4S2DadQ7Wm4En+/pM3DuWw+10L3ELyWz9SS1onOZ/eFeXHrFwLu9Hg0C4zN51n6wW1p8+XAq7hA/ovofJVy6681/LnxwtmrR+fukrRcA/07JS0fHd+Pp9F6rYb3FuAeSUtE57LHuoTn99GOUh4zEtpmdr+kpMdSYl0rWbcflLR4Cq9+rVRGG3E/d8yttmuFfH9UdEvfr2RulIx3ydpT0oZHKJN53sBT+d5OQqGq8cVs2n20Y0Lq+RF+zGdKePkLeO74U0lEKipSREcsx7wBvIxvxMXvmVpTSnhuCe6kotlWK6BdPw7nFmqhjaQ/t11PQeo5PfBLed3A+muiDbfGekUvXLw20t64QfE7QKXXGnC4pMUi/BL5/Rlc1zB80/UnEd3NJM1Va0udTy4n6a7wf4e8YGb34oaOcXiaza0CXQPOlLR05vun5MUSXlckT5ln5dgK749ncPl6eUlP1/AmjWF9PBPfQhtu43EvuvVzvb7B6th8o/1HeAToNpKeSuDOgqdwfCAcbxr6D+DXkv4S4d4JfCg+F87PBVxeX3enFB2lkNeVrMXDeL+BbDx9tHkkdrpR6sQjlHnukbRMimbq2gj114HpjpJ2A93S9fVMPErzCWB/YBFJL5o7DV8tacUEmXobXsR13WotugmvafWkedTo5XjU7wrAq5I+XaNRMjdGZQMpoju5+dHkhFL5qA3XzH7FmC3kV+Hc6/gmoeFr4L8qdHy858h89qOSFoyOB7Y3BTqPSVogOh7YJtTS5qUk3Rv+n1aRU4SZrS7phgyaSRtZdps0GTepphQwM8ONKHePmvYoFK9+2jEqMLOJkt6Vc60Qt6jf2pTRUkW1dm+bMbbx2qBgZlMrMvKZ2T3ASnghyUfxAnovmtnUeBHbkbRjECgZw5ry1ajEldL9H3SCmb2/7boSURtTAu1hgZnNLCmV5mzYz5li+8LM5sU3YVeT9IPo/B1q8FavX7OyTY5vA3PhxXhfCOdmBI7G8zbvl6KT8R5FPL+kHX2sPyOhbWa/xAXoOMoYM1sH2K0foS/c/2M8//RJtfM74SkeN++HbqAxyn4uMngVtHlUdEvf701f20bZhimZLzZBSZvNU/w1KlU148CbPtajhBLj+yjbAJwo6fhwPEnpfrPA3BO1csj7Y2U8i67Poe7IvyZaxwIz0T7nduyznaUGxduBj6p7w2Y+4NKa/HBVjzaXROq8KWAeKbAVnmLocUnvia7dTqhVhaf9+QBM2ly+MjauWtlGUjbdgP8Q+RuY9Wd9Do+W3xOPeo2vnYjXxpgQjh/EPaunB16T9LkIdztgt9COKtrlXcDhuGxTn0f/T+sow2rD5LLx/P8BCmXy23FnvkcTNC5u0qGGAaPUX0dFexC6NqDTsJmNxyOl313pHbEua5415w1JXzSzcbgtre4cVDI3ir/tnPX+/3WeWAIl9txeuGa2ADAvbgv5fjg3VRtNSa9H9zfRNuAXkubJaWcJ1DeS+rh/r6ZL+Do/R4Rb5Gg2ChjpJpWZTYun3FmYKLWgpLY6FDl058LT+cwraR0zWwZYXV4TI4W/Jl5871RzL6GZFVK3DQqjpP3f0A4b24XvugSsKWn2fnD7aMfjeE7sFO09FO08F9IdyeZXuH9SKKqZnSFp2ybaNWbRyzOviQkBeWGlkxumBGYYnnUn3eGzzwJXAkdIeinCnaL62cxmBb6Oe8OA58k+WM3phnrRi9NRdoGGkMrGPK/t8bhX5nJmtgLwSUnfHJDuGrjA+YKZbYOnUPn+oIY/89SkpwJnS/rHILQmF5jZusAlqtVNSOCVRMBkb3KYb6R/E8/d/mecJy8AnAx8TdKrfb5XEc8fVTtGSTt8H78ArgcmhtOr4PVe1pV0f59058Lz6r9SozsN7mX4dNO9GbRH2c9FBq83m+7/YMqEKW3tHjaYWau3Z+4myIBtKI2kep60vFHsERrRPBZ4T1M7JicE+exCnBfegb/X8rjT2fqS/lXDfwfwGaINLeAkDV6nK7e9U9dlgR742+AGxb3xFIvgctcRwNG9DIqZzzgNT/H7z3A8O3CkMqMGRgFhs+B9tc3qR2iOekTSIhFukyd7SofOphvw2yIiOjYwUxvH5pFjZwHLSZo2xsVr3Kl+ryVSLJk71eyPp0wEryX6bUmXtrUvF0alR0wp0MDPnx9Elho1NKyxzwETJd02udszLDCPdDkct0FOxNes8fj83k+Z5RcynjMfsBCdttOU/ayUruH14BeVdLCZLYg7Pddr1PVDeyRt7rMt44CZEuvqpIhfM7sF+JKkX4fjRkfNEbbzAeA23K5wacVT/wdpGOYm1RDa0hbd2bezTSQLV+t8NScMmF5S36WarCzKriTKeyNaQJnpR7vaO+JNql8RFiVg0u6jpHothVK6l+If9FckrRiMIrcq4e0aBmQV4B2SljT3Ij9X0sBFqUdJ+7+lHSW78CP2BhhKKGWCbhVK2XUJmE7SW/qhG2iXRA89hHvBGS4g7Ru1o+4RV/XFO3Dh6aJwvB5wjWrhzFMC2JDSfwyhHSmvmTnwVIszSto5wp2i+tnMzseVv8qgui0eMt66eLTQG3oqm8Qzrsbn8gnRtzBwhKKZ3QGsiIfwn4EbyDeS1MqDMugujheC3hyoNqwum5IFS/OUCavj9Q5OlfTHBrySCJjiTQ5zr7gqwupBSf8Z8L364vnDbseoaZs7+2zFmIHnbnyT9KXmu7JprxXTlTS0Yq+j6udRGbxGbUj7HySdQCZdIpHiboTtaOMdUp+OdFarsZAgPOoaRVU7HqZTya01I1kgfNhtaEuTs6iken26UvpZmzhtDl6TE8LceAX4YuUwEgxp38YND7tGuKvjtYhOwDd8DFgZ2BmXY26McCdI2iH8v/2wNtRr/XZM3L6Wez6GR99U9Y+SPLRfg0bDRsrkispbFvfevygcfw9PtQhwrDJrRifoThGe7Gb27pShOjgpbSzpJ9G57NSOkwNGpUdMKRA2JxfAa6IZMBvwFJ5ycmdJE5vvfnPAzM7GdYKLw6lP4PU9l8LtU4e/WW0bFMzrV+6N8znDZfIj1FlvfRD6h+E65j2M2U6lIaReM7Pj8Y3utSUtHTb6L5M0fkC6I2tzQRvOBj4Xnj8R58/flfSdCOf7eGTWU8AngSUlvWpe5+1iSatMrvaG9hhevuNTwLuBnwIT1KcD4psF5iVQnlOi/uAguIl7+073Z2aL4bWVlgGmq86rIaX+/8tgZm9TrYxNAqckyrtyiHk78B480htgLeCqvu2QI96kGomQYGZ/kDS+ZuS/TdJKCdzbcOH+lgh3KLvlo6T939iONjCz8yXVC9kPjNtHO74k6dAR0J1dhZEVfTKAJCiR0sPMLsOVi+fD8cy4cPixknZODrD/gvQfTYvjlNLPKR7YxBenFCjh5YV0q/z6BwBPSDq5/k0NSH8csC7uvfkGcAoeqTVyD/V+wLyGwJb4BpvwzbUfK0rDYN0RMOCpEhojYHI2OUblYVMKiXZUUZK3acA0F6OkPQqwbs9c4cWEBxYI/9v64n8w+aDBCWQSaApIcWdm4yX9oc97R+7Y8d8CpWNtZhtVa0EvebpwE+chPMJnHB55u2etHRcxGcA8ZfcK6o5Unhq4U5Ejlrkj5mGSrqrhvh/YX531Lxud3QZs70joBnpv4N7jVURFvJkqNURGWUh3V82NsI5drT7TsZaAmV0MHCrp+nB8D/A1PAX7xpI2yKDxDmAfRc5uPfDXkHTdIHTN0xfNXhmkzGwaYAdgz9qcax3feBPOylI71jMyTMpOIenaXu+WA6PSI6YUMLMf4nVhq4iPjwAfA87B9Y5V38z2pcDMfo1/F/8OxzMB5wEb4tFUybpO/wMws/vwteLlnsjltCvdOP5WbleP+k4ZdEfW5oI23CZpJTPbGs+EsB8+12J+ZPhm2jzAOZKeCOdXBt5efWNvBgR9+kxgRrxu6/7KqPszJYB5VNEiwE8k7d8vbrCfVc6ND1T8I7q+osbSPmbjhuPf4TaOI4ANcHvIG5IOKHrZMXpL4Gl0FwfuxNfgJ/qhVaM7Hb7ZujgedX9KXW4cgPa6uP3nVdx2tVkl0yRwi+qShnt+gTtOPBWO58GzkfS1SdV3yFgmXG9my0u6s/RGM/s88De8MGR9cF4ws7cSBB8zWw2P2ErBK5JkZhVukfde6OC/NzDegWgPqQ0DtSODdoy7PvC0pJty6UdQ4rk5Si/PTfEiiV3QY871givw1BYlMJuZbYgrz7NFxj1jzEMP6Duv/IK452YFr+CpN7OgpD9K5lEKJH2gn/smM4xrOD+l9PN/zGzNSvEzT3k3jEiOOXFhr+59snbjTfnwrLl3S8W7NsE9nHLatQrwVINQ8LyZfQmPJntvUNSzoh578TnzVCI7Ah/Ho5POAtbEPUemSKVY0r/MI+2mB/bAFcV9zexoSccEnNeA/c3sIDIjYCRdiafCbIP12pqGGxtbocdY50KqHXMAK5jZTqkNtjebtnWnwIrTj+4n6W8ZNFLzuUpTYtHfmYLx6dOSHsmg28S3RtbPozJ4TQ5D2uSCEjltQJmnGJo2ocJatRXwhVG3oeH5ywBb4Bv5z+Ee4MXQtAkVlM42PjhyCOvsFsCWynAeLOG5Kdw+Nhy/ytha0EuePgB/j6uicz83s98CBwJxrb7rgM3C/9fjOsCkZjIWAd8Ipd9JA/4rqXslvWZmdXlusfoGVcC92rweUMfpXu3JaG9Kriyim+ChHaDOKMKNcaPHCngKxB9LejB5YyccidsVzgvHm+Ke0TntG5TXzVMz5vxL0vmB9mdrz1oBN4TNC/wcOAY4Dlg1vEOMOxU+P+cDfiXprmBI+jIur63cD92AvwW+kfuCeVqpr+OZBf6Ap/yKoS3DjYBY3v8OcLGZpVI7fqd2780JenMA3zGzn0o6quW5udC3HlEKhXxxEJtJDKsoqvMl6TIzO0TSXuaR9m1tKNLNhyg/1PXiV4GFJP0nwe9K6NZxS99vID4QNqvb+FxW5FCPefQQrq8W21My5uergedU38qcuLH6zWzzsOTQt5hHfm6AR7e+6ntSYyBJjBnb4/O31s9NDgi27G1wW8VfgF1xmWQl4Fx8M2dYzxoWP+oCSWuZO+/2dBhJ4Zo7TxyHr4WP4Pa2+c3sHOALCk6ykm4vwa09egZJvzazIyT9Cfhq2LjqF04BTsdT9H4SX4+LNmMaxuQ0nF/+DrczLYs7Wg0DDgHeK+leM1sVz8zVFM29b/R/fR1PresAC1cbVAH+AvQdqTbqTao1gR3MU0+8DEUpPSzcvzU++DHshX/Ei5nZdcCcePHSFJxjZifgmwA74yGVJzXgpuCM8JzzJe1Tu9Y3bfPc2i/iO4x39UBva8NA7cigHcOqwPLmucpLC7aXKDyNuIX9liTR41rTnOubrplV6bV+IOnY6NLV0XOuptOIMYw8vmcAvzezn+F9uiFjqeByoKQ/sudRagyDwP3l8P+HJf2moJ2NdHvgd42Lpb0JZ8eFiKYxmVL6+XPA6ea1Dwz4O+4x2bsBZofgxrkfJQzfZ+Eh6J8Iz9ge+GsO3QzaXwBOBJYysyeAh+lWnptgV9zwfb+iGkgBNseNnp+S9LR53u268twEjXzOzCYC/8Sji/aPFKObgqG1FXr0RR33clxQ+YGkX/SLa2br4WvCYvj8ebekZ8xsBjxF0jExftiUKnYsaYI+N9jr0DbWA7XD3OP/HHzcW6GJx4yKtqSZE3iz49/1D+k0uDZB13xWrXZFRHujQDcnCjTJt0bZzwzB4NWwHo+KbhNu6VpVgl8ip2WvPSVtyOkLM1sJ59Gb4Xw/K6KysJ8bccN83DL8XsNrKaySs0Eb7m/l5cEQ9JFA/6O4wnluBt2B5Zjo2jyMrYUr4A5aW/aiGaCE5w7Mn+mUodvkdCjYxFFU67X14WbbSDqzpW0lukEKfzpzb+36uxlQNzS3RZzW04/Pb55K0KL/J4HyUkym5MqlzNMmW7h2R9TelB7fZLToAkk/A35m7lC5PnBkMNZ9RS3p7SSdbl4XdO3Qjo0k3ZP52BJel5KnOtZiSatFh2+vkTgJj7K/AV9LbwHOBrZWd5rek/FUbr8HjjazP+PpmfdXd32bErrgG7/vkvRg0G1uALYI/d8BaslWYe4IHOOeaWbP4l7pcWrHA1RL7ajmjfsf4pvGWZtUfegR2+TQzaBdhxJel70W92jD381sP8aM65sD/whrTK8NhhIbT1Gbaf+mzgZuNLMLw/F6wI/DN9/rmx2J7aGUdsNafERE5yQ880Q/0DaPXgRuM7MriDZ9Mnl5r/l5NJ6q/e1m9i3cdvrVN7nNRetry7dyAr5pcTtwTZDx+qrJndGGEnm47du+AZ/DG0h6PDp/c+CR/dJNwbD4EWa2MPCkpFfMbE1cvjwzsTFU4Rtuq4/rlcW4XwFmAuZXqCMWbFnH4fPzwD5xY3g5tONPZvY54Am61+4kmJcvqmSCX4XTM2usLvd3zGuclUJqTJbRWN20k3HZoAhabEKvSboXQNJN5tFoTXA7cHvY2M2Fq8yjaH+M20O3oLcjcyOMOt1fMt2DBkjpYb77uho+aO/Amdt9aikgaWYfxpVFA35dagAPk3oZSXcPi7aZjcc9Td6tqNZHP20YpB05tIcBNqRid6X91kbbzKaS9Hqve0rpNlx/K7CapF9G5+aS9JdhPL/lue/Ei/GC10kambdI7jxKjaG1pD4seH7x3DCztwGrVuNi3YUQhXv4XAWc2MRnpqR+Nk/thmoFQ3vQ3ADfxFhR0na1axMlvcuiFKJmdrUy6zs10Q68fBNJ5wTFZZz6SAdmZjOn7gvrzxKSLjffkJmqH/o1motKeqh2bhFJD2fe39jPCdx58ZQEq0n6Qb+4ZnY6Lmh2bbKa2QclXZHT9n7BzNreUyooqN401oNCLs/pk8eMhHa/fLJfusNYL0fYF9MD1ys/V3nXejyZ6Za+30ByT0Sn7zHso80pmWdJxiKW/oY7P+wjqTU1XA7twnZcj0er/wRPOfKAmT2shs3bBrpN69r78E2hT+B6yhp4DaYXM+kOQ47ZGe/j+fGN4XOAC0veL6KdzXMH4c/WXJcU6Eo3NlHSuxro9Cs/jrrI9lVkprS2sTQrXWSopVmxIaWYrMuVTfp7RLeernET4BcqqJVobmT/GM4TlsM3ZrrSLZnZLPJo8Hqa2qotQ02znJKngm6wv2pe6OYbON9WlA3CaqnmzOwx3MO4i/ea2V14mqw3zCMunwUWV7quZzbdcL2eNv5eSUulcNvAzB6VtGDpfRl0S+qL9JSd+9UjSuTy6J6hyqI9dLC34YbXNXEecC1wEG5EXlA9ohAnh42n4bmr4OufAddKyt7ILnzOSN6v11pcMn9bntE1j5p4ei4vb6IbXVsK+CA+LleooU5xCe2SNg+qS7TIXh26eJgXi0t6oN9ntbQhW07r8W2b1J8hvh++NSza5mVmqj74DfBLYBFJ6yZwPw8cjMv81aa6FKX8NK9Xu7q6U/bNjOtgy/eDW7u+Kr5BPjsegT0rnlY5J6XuAngE82qSvh/OVTJrJaeeRSS3qv86la3lXzJpJG1CZvY4nva6gr3iY0nfjXBvBhbBnWGuw51KbuxlWzTPEPa+cHiNEk4x2e/R57eR/wAvMFgZb3+n5l3WufAwtHklrWOefmN1SScncG+QtHrGs6fCN2w+VNjmNXHj5qlBOJi5boTsl3a4dxwwU9tAB/pz0bnr/Gjpsxpo93y/gHcznrvybBXWW0rQ6rvYXTg3o6S6B+FA7TCP8DsPOFX53nhNdONNll2As3r1mZk9jUcs/BgPcx6ax0eYY3eooCZc4Te4GPC4pJfN7AO4F8Xpkv6ZwJ1UVLfl2cWbVDl0Rw1TWD9Pi6dRWZhOvtFXEfiI7o2SVjP3jjgaeBI4T9Jig9ANtK+R9L7emGAeqXSbpBfMbBs8vcj368aSgLszXlR9DkmLmecO/qGkDyZws/lcam62GcxKwTyMvQqNbnS+ME8Hc4lCAfYpGczTAHWdxj0r55PUFdFtnprwFLy+QdE7WmFKA/N6DhNyZIpSyKHdz9pmntKiI9d6dG1TPG3Q82b2Vfw7+YYyNs7N6wZcq3R9z4HWy1H2c6DfKGMEnlsVhv69pGcGpZu7zg8COXOjZLyHKfPU6ObKPG/gEUU7VYY1M3tIUmOKZzM7rG4ESJ2LrsXOCdMDUycMKhfiabQuwvn+9b3akQNBAXwUj3b4eRiTh9WyOTQKOcbMXsE9dPeuDIM572dmn2RMubxKDRG8g/Dnlme3eVtKUXphK9jEKXh+rBtky2gBf6iyV5PBL6I7cG2zYeuY5pkE1gB+hes0lzUZIs1rb2yJF4q/HN8objRgm9kvJK0b+FdstKiiurrmtbln9dcZsz9cDRzcpGf1kr3MbFJRe9xoA173ZHtgc0m/j3CzjVclBqlSo1iJQaoNzOwxSQvUzq0D7I9HUgk3/h0m6ZIMelPj6a02ktR3GlQz26vteu779XhGid4xNJvJIJBr4wm4JfpPqr+fw2XR2xL4WTxmVDpxwM/mzaVrcanx2Lxm20K1diQztBTogoZHIC0q6WDzrCFzx/wowu3HXjGKNo9KDh2pfh7o5cjkcyjTccLMUumGn8Mjk09QgdNHgvbI+JGN1TbbF3hZ0tEtutKD+PfcmIHHzO5U8+bSJAfpUtxRQonMGt3Tc0zM7HXGIuYNT/v7ImPyziwN9/X8/sysKcqsavRBNfwZcDntPeE3HngauE7S52u4fe+LNMFI0/2Z2e54IdsqjceZZnaiQg2MGkzAB+4r4fh+XCBMKQWXmdnGwAVS8y6bpNfN7EUzm7VJME20+UA8H/07Qnumwb36OlI5ldI2s7PxVFmv4/UgZjWz70rqSkFlZrviXjN/Idp1xhfjGK9er6IDUhM59/0CbIHXXvlD9GFd1tTn5rltaWBEdSNDFq6ZvQf4ER7auaD5pudn6x9HAcTpVlbA3/FHYfE+BVeWsiNQIohTeMyN99ktgeavG/psPuBDoQ2HmtkNuHJ3kVrqwHQ8tCFHsNwr73YzW7BA8ZxA/jd4PrCKmS0erl+Eh/d/PIH7w8A8J+CMOSVMvj0IwBb9H79PStnIoTsJzFNZHYaH9xoNDN+8OPU/JN1hZpvhRps/Aceplvd6CuvnCwkKAw15oa2sbkAF3wwK/954arhZqBUfrz1jSdxIN5ek5czz6H9S0jcT6L8xs33w958k+DUIeMcDKwYe8EW8P04nnU/3C/jCelOg94CZNYV19+Rz5p5ny+J8O845PAtRna46BD63M90KUldh8KBwnYanKzBgATPbvkEh2AL4fjAWnvgz9lsAACAASURBVKoWTzhzT99jgKVxfj8V8EKToJO4v6RuYVeIuaRdo+uVQrUfcCPN9SSOx8fkaDM7F9/cuDenvTSkNLB0Lvk5cG+jZGqYMJf3pVtJW7uG1w/tnmtbba5VMDue7uW8xDWAr0k6NxgqPoqnJ/khUZq9BmPD7HiqjaYUalnr5Sj7ueHeyuD1eMP1zfBUn1fh39UxZravpKb+y6JLxjrf0BeTQA31Cwrlnp7jHUG2zFM4Jrkyz8bh+Veap8+oivK2wYepyY94zaGUZ/Mk5wTcC3R+vC86nBMkrR/WtI2Bg8L6OpuZvTtl3Am0D8fTW/0HN8SvCOyhzhRx5+P1EDYHXjffDOvlCTgKOWZePBXod4MB8Bx61GQ0s0PxNfOscGp3M1tD0pcS6H3zZ2tOmdqYbiwB+7Zc69dbPx6nCeTLaJAne7U648TrvMo85ot5TI6O2aJfJuVmSRuaR/FvCOwGnBzm/48TMswVeEHwa/FUh9tZFHFdl0MVvLNVFgl4Cp6CrqpHti0+pl1rao7sJen35p7YuzCWRvtu3FO5nhHjKTo3h56Ojuv1naq0ioRnV6kVU2kVS+iCpyObueG47xT8gc9+FpfDq+9tFeDbZja/pBMj3GoexXz+RXzTsKOWV+0ZOXpEW6qiRijUUUr0jmybSaFusCSwTwI3ZQQtsfEUtTnQXQW4OBx/Aq9v9jkzO1fS4VE7Yh7zOkyqf5oyIE9gNDoxZPDmCHquxdYZzTmVefrtOOI3uTlhZofhcsE9eH+A90cqw8UHyNcFj8N5+Np4tMrzeB+NryOW2itG2OZcXSLLXtGvfl57Vmv6vkKZ/CbzSKNTcUeeNl77MJ4G78fheHPG6vichK9bVRtK+BaMiB8FeM3cQW5bXOaFZhnzcbz8RBu8YR4JldIH6u0twcXMjpS0t42V5Oi8QUrJBKvh/KvSf6r1eMnoviyZ1TpLmPQcE0lTZdKdXWGjK/f7U20TqoX2lyQdKs8AcZWZ/QG3p60BbEeiJID62HPp2Y72b2dA4i5ora6w62wein2D0t6/f5A03jq92TpC2yPc54EZ8TzyL9Gyu2heSG01PBwxNoIm86QGxrIycEvUjuTObAnt6l3MbGvc+2o/mj2hH8RTd+TkGMXMDsaF1TPwvtga95w5PIGb/X7RPeOAdXHm+Aa+oHxf0t+D0fFAXHA3PFXHa8AxqnmqlOBG99yE58y9KGrvXUp4gph75+2KC2fg9VaOVSJ3feLe9+GLxGy48e8bikLozWx5oEqT8Mf6ImY1z4nwrh/BmdEquJHgZHmxvtTzp8ENL1sAa+Eh2D1r84SFdQWgK0eweRHp8XjKmXh+NhnGSr7B2IviJUnHWLsn+xJ4XZxNQ3smSLosul60u59Lt4b7ILCe2g36P8D7czrgPlwg+RXuQTBVakymlH5u+i5qOJPDQ/dq3Ih0QsY3m/Luk9KesVVfHAA8Ielka05NdpOkVau+Mjc43zIAn1sfF8Q+SWeh9edx4fr6Or1A83o8cmAiY0I+CgW3a7gTga0k3ReOl8QNPE1pjWbBvWp3xAWvUwN+PWrgZpyvnIvzou3wFAhfIQPMN54Ww6M9e9Wbawoxnxo37OyNCzqHVu/Zg96s4R2/AjyGC+1nqiW9bwutulGhSuX5gKRXErdgZrfjRu76+E0cAu2ea5uZndpA9yo1pDqL5vyhwJ2Szq7zjAS/reheI6lnPbK29XLE/dxm8NpD0pMNtD+sED0VlLHLJa04CN1wX+s6n+iLDlBD/ZVCuafneDc8o5fMkzUmEX62zBN0gQ3wb3ttXLn6WU0m+D/g88CiuJNIBTPjXnxdG55Bvn03cFPUb41el9F9b8eNA1sCC6gWNVDRDjL8hqHtewJXxvMo6ocqUuTjuKFkJzzy9d8kYNhyTA1/fsZSLM6A9/OXE3h3ACspREaZe0Xe2mPNLObPNqTUlcOGmjyWLaOFazmy18WJ08I3O+dXZJgIfL9JOZeknSLcYh5TqmP2A+ZpNjfBv+E54m+qXznU0lEtRykdndE1Xi1ydpHsVbt3AbzOU1bNUzN7S/x9WGFaxVy6GfjjJf0hOm7a7DRgbUkzRrj3AGuqZpAPY36tpKVz29HSvhI9YgFJj9XOza1EysQ+aGfrHdE9jbpEhFOiG2SvxdaHjaegzb8GNq7WMfPI+/PwjemJ6kzflc1jRmx76Mmba/i9bBVVNGfSQJ7SXcN99+FpPXOc/bL5UdQfcd/dXpdLIvxse8Wo2ly7r02XyFon+tXPa+3oldqxRCY33AH9U7g8+lN8Ht2fwO3KJlOdM7O7JS0bnc/mWzV6Q+VHAX954P/w9Hpnmtki+Ph3OZ6a2Y+AJYBf0Fmv7OgI5/HQtqbvasF+cAP+u+VOJl2ZdMINXSUPzGu9fpHu/igu0ZJaL3LGpITuIDJME23c4fE9wEr4uFUbVTe0rK1Fey69YKSRVPgEisP9K2+KFLwQBBwBmO9iJnfilCgm3gK/DL9ceEWSzKxqx4wtuCW032KeomcDfOPk1eoZCXiMsmJ/H5UUe80eHxhq1yYVZe+H+U79jriyfT7uZbkm8Ft84u6B76yOVwgnN7NFQxv2lPS9iFwJ7iSQ9Jjz/EmQyuv9CdwD/GA8V7PhCswpZraLEikIghL+ifB+CwNHhvd7L3AJsGRQwi/EC9tW3m3Lm9mjwPoKnh91xhL6+Gl88/A13Ev9PDP7jaQvJt7xlSD4/xHfxFymjtPQN9uHd0l9E1k75hFkf4PAq2a2JZ7qokrX0OipK49k+SrudXc0sHJYyL8s6QJl7u6X0q2h/yXDsLOWpGXMc8M/Abxd7h1wAj7+KZhS+vl6M1teLUbmJuW/DZoMJmr2sJkhCATxudca2lPiGfu8mX0J99x5b/h+m/riajP7MjC9eb2+zzPm/dcFvficpAuBC81sdUk3FLR5hpTA2wBvUbRxI+n+sGYkQV6j4Xw8FHwPXFHc18yOVi1aWV44u8oDfmoQSLNA0ofCN9XFk6yWdlBuzH8SF+wqnC8Au+Pe0x8rMLq8FY+82Ra4lbEx2R74QISXijZ6DjfYT0rrpmCsM7PZcIEZPG1IcuMkwGuSju/V1j5p91zbJO3Y69kJeCLwqw8Bh5mnOxlXo1vMb3PWy0B7lP3cj+f0OHWm9/sb3f3Rl0d2r3VeDZtQmbR7yj0Beo53BbljGCBrTKL29pR5zKMbX5M7rp0FnGXumbwpnjoq3pg5G7gUODRcq+D5FkXu5SBLVe87NWnPykMUbdSE+XEMHmXXZDSuePHHceXv77XxmdQP+Lrx28C/P4Zv5BwHvC1FeNhyjJmtJunGQPtxXNk8wjzl5hYtt87GmMfrrD2ekcWfa/eMw9OQ/AFfZ/uCJpkkgBRt4hTAjdH/JTIa5MleHWnNzCMfv4JHx+xSQ0+lWVwQX+c7vGzbeIyZ/RTfaK9DqY5Zp9tao8g8umAjfON3Dmpj3Y8cGiAV1XIG6aiW/5jZmpKuDW1aA4+CTEGR7GWeQm1T/LueD2ituRC+5bXw1Hzr4SnQqmcl5aHQ3q3wrADFdBvwl2Fsw/o53JmggiNabq1fsxQPlvS3FE8MfHgdxhw978GjbZN6QYBsPQJ4yMzOw9PIVrX/LsFtAIPSLtE7cmwmcRtydYOStbjIxlPY5gWBWI57FVhI0n/MrL6RUcJjRmZ7IIM3x5BhqyjRWWN4KLSz54YPZfzo1TAnq76bk7Ho2BSUyP4jaXOBLpG1Tki60Mx+Aewn6ZCce2rtyZJLcmXyIAP+Bs8UsxYeyfh5883m/Ws2hDktimwzT9dYyYl1namEb1XvNgp+RPiePh8dP0xzZpSnwq8xe4uk+QuenY0b8KvsCDPgqdFznDn+JanRZlQIHQNWMCYldItkmEzaJwL34g4S1yixyZqA0j2XdpA0sh+e//h2PC/014HbcK/UFO478cJcz4W/9+M7+Cnc96V+Q2rzPsAJOHPeGc/rvusQ6O6GG70vwQd/IbxGVwr3ZDwNwpdCH+4F7NVC+3o8emoq3DixNb67PdD74YbGK3ABeNratQvC31uBtyXunRP3wozPZeNG187Dd3JvwcPW98E9I+p4V+EF/urnVwCubqD9UOjr9ySuHV39xQX0cdG1cfgG4DEtYz0R+DWuyLwluu9PNdwFcc+IW/DInYOApVvG+nx8cR/XhDPAHC35BpcJfbNlOF4EX3xTuCsA3wv0fgC8M5yfF/hz+P+cCP+w2v2X9Uu3hv993KNlS1yB3gjPiR7j3JL6P3U8BfbzPbhQcx++oXYnnn86hTtnmNeXEAxqwG8bcDeOflvj3+TRLe93KR55c0s43gQPeU/hbpf6NeDOjfPC90bfThPuOJy/nRvauzOuWKdwc/jcF8PfY8J4dPxa+uKbwMcz58UpOD/6QPidhKfyS+GuhxtG7sD5x9vD+Rnqcx9PzTANnqLkcDwC4PaWdixW9UNox27AbA24Z+JRDofTwLdwhamqv3dH9GubnxeE+fwlYJ7atZtrx7/EDavnh9/fwrkHgG0jvCqNxz9wfntb+P8UYJqGdnwdF8TnwY1tc+Be4XW8fmjnrm1rhfe6O/zOAz7QMn4z4LxtiXA8D/CRBN724dkvhN/NNHxPAb/nejnqfg64U+Pzf9/w+wRed6ip3d/B1+Idwu9S4PAh0C1Z55cI43ZP6MeHgIdaaGfNjZLxLhnDPsYkqy/ocw3FZdt5cZ6/IF4sPoV3OPBlXLH6MM4jv5XAK24H8O1A91bccDMnHrEV40xouX/6hvMjlWMK3m9L4M/4t3sanoZmiwbcEv58Nm6cmDH031PAvv3Mg4jmxonfnqH9jyfwF8dTJF4Yft+uvpcG+tkyWsAvkb0+iOsrV+IRnr3edVE8zdD9uOdykoc23Ptow/kiHTNx/2OJczPjhvxL8PX+RDxKMil39TnulUx5AL4h0TjXcWPP7Xj6m0fCd9ulIwbcnrJXeL/t8MwKD+GG1a65Vrtn1fC9Pgr8G19zZ2/BXwnnYY+E+dGkm2fTxW0N+4e+mAg8Cyxc0OcL1L9X3Js6pW+viNd8jM/NG76Lq3A+dxS+cXofXoOo6bklesSt+Fo1EVisOjck2iV6R09dIjou0Q2+Tv5aXGTDKmzz13CZ5MDwuxn/FmfEa1L2xWMYkU4crpfw5hxbxULArNE9a+Hf4p608GZchn8wjE2r7kiZLrg1Hj30OL5JcB+wac68yph3o2pzthwazmXZK/DI9tx3K5JLKJPJ34o7Zt6M66Ib4frFKsDDNdyP43z8SpxH/hnXPWakZi+ngG/18W1n8SOc197S9Otx7/Q0y8Hztv36xa3dd0bo31Px1OhTteAeGn7jcb6wAi0yYI/3ju2K2WNSSDf7+8uljetd78SdqM4Obf8F7ly1dsu90wDLhd9b+m2DpNGm+wMws3fiO4SG78SlijmPw8PDfo+najPai+7Fu5vT4eGUE5XOz/sw6SiAtkLNH8bTlhju7fObBrxi2rX7p1bCk8gaUp+pOeXZwvgiuUZoz3U4c3ukAT/3/RaV9FCPd2gMNa1fK8GNzr8tvNuHQnsvA3ZXLYTczO6VtFT9/rZrsZdddG4NSddFx/fgjOm1Gt7UuKd+V1oD8/SLJytdWHVpBS/YEM0wH25Iby0cHN3/IXwHfrVw3wQ11AKwglo0pd9guGd63FjUmrbLzK7BGeZ5qtXaMrNtJZ1hnaHq9ULCTentetKtnTs10TwpigiysULDhgudVb53w7+pVBqgKaWfF0qdb5iHl+GGrn3wWnnbA39VhidNeIfLU/w2XF8UN1C8BzdOPwxs3dCOOOJnOtyAc4ukTRpoL4Qbly43L+g4lWrp7UohxefMbBFFhYbNbD1JF1tD+gE1p6epUtO+gnsdBvTk3JgW95qdtF6SqIMWcE8HfqREvm8z+6Ci8PXQZ8/ghtU9cQ/54xSl9qrdfxsuTC+MG50vAt4hKZnz3XqkHWyJTAAa5+fHVYt+NbNpG/riYuDTCmH45jVYjgc+jcscy4Xz38ANfp+L2jYzroz+WdLXErQfrp8jkdKjT9o91zbrjBC+JeC9E/gqkIwQDvfFhbPnBGaqzeft8LmwV43ud/CUA6enaPZaLwfoi9x+nhdX5p7ClSXDU9vMjUfBNqXl24hOOfRntevFdHPX+XB8LW7U+R6+EbYjbrxNynq5ck+E3zreMV7OGIbzWWMScHNlnp5pCBP374Ib6Trq5yidKnscnlpvknyL80nV8G7HFblkZgc115WYHfewfD14qM+sKO1FXXbJfL9RyDHF7Qj3zYMr5YZvwDWl9Fhb0m8zaWanOm9pU2NNxCBvfBl3VvwePg9fia6vim9M/YjOb3tHPBvCH2r0+pHRespegZd/BTfEfjP1zdVoLh3wV8b58pkpfbEHjWTEU6mOmUPXzJ7Fv7efkO+tXATm6Y5+hY/d+4C/4un/GtN5BvkEtdQazpG9zOw/+Jz4Kp7STmb2UAM//BZeC+tRPJXVz/DN264oDPO0PFV0098IcrmkrjlVQjfgX4/Lez/BdcwHzOzhJvzovq5IMUWpnsN6cxYu603E5b7xuB6xTbzGmNkEfIyOqj1jN+BdCtlAEm1I6RHbKGHXsLGUZ2vgvHQ/4KAmHliiowT8LL0jR5eIzpfoBtlrccDPsvE0tbkNzOtgrxFoX6sGu0UujxmlThxwS/TiHFvFTcCGkp40s5WAy3Fj9grAq5I+3dCObN2xRBcM+EvhurPhpSLaShrEtQanwfXCJnvFSNpsZjOpIfVxQ5uz7BWBP85Kd43rWxI0i+SSEpnczO7HN0ROlUeyx9f2k3RY7dy0eJSpAfdKeqmhDaV8a+j8yMwWSz2rAqXTey+DOz7NE97xcWCHmo70R7rTaArfkJ9TnemQs3ETbZkW3wTcHOc7l0r6XALvd+nX60zNmAPWmZYve0wK6RbxjAzaXbpasK1sgtsOFkn1syVqYwHbK12brjdogB2uph8wS/g7R+rXcM8NAzxvAdwolrr21ug3H54u4eAG3Klw42vuc0toz4Xvcl4ajpcheIK10J8ZNzYMa1yK3i/c8wk8rcIB1a92vXHnvH6tBDdq756Z7ZxYeq3hmfU239ZCt/FauP52Wjx/8RQVPb0MgS8lzs2KL9aP4ZF0O1Lbsca9OBbHFfOpAs4hLc/J/gZxQ9t9BK8Q3APwogHmZnEEE4moTFxoGOQbObDt13DPm9rP9MdvJ4a/d0TnkhGHiXvfATzYcG0csFn4vzLilfT/rE3zCPcK/APBMx+PTriiAXfdMB5/B/6F56b+V6+5V++fxPmVB5lfGe8/Pb4hNLJnZLSh8tLal+CBSYtXarj+Nnz9ewT39nqAASKQG8akiQ/cWTs24K56u/Ei6jMk7p+pwh+gvUW0yVzb6C9C+EA8teX94XhevIZPjHMjCW9qfGPyxkHGZMT9PIE0398NOK3lvoWAD4X/Z6jzpX7pBpzWdT7gVPz2zuhcUyR9ttyTO96lYzjA+PSSeR4n8qqu/xpoPgi8NePZU+GG/Jx2vox78z6c+CUj3MK8+SpwYjheAli3hnMvvqnwztSvge4o5Jh/4s4FyV/DPYan7zsgHFf1GZqesRxuMO8VAX03bgg7F3h/OJf0ZG+4//IwLkfUzi+NR/LejUdIJqMece/rDybOr4Wnqk3dU6yP4lEku4Rfime/gW8uXNxrTEJfPYwbHeakRaZrmmu44e2pHm1u1DFbvtO98U3Dru+jgU5XJM4A87okqqVI56aH7IUbZm7C17cv4x7tTbzir3gUySbAdOFcE+4beGTR4tG5gemGaxeGOXcsIWqhhXZRpFjo34PxaIsLgG8Acyfw7m2hcV/GmPfUI+iU8+bBDXQvNuAW6SiU6R3ZusQofvRh4wn3tdp5Es/oGdVc+PyR2h7owZsL2xrrzEcQovLDvGpd1xhihEH0zEFl6g1ot1cMtc2B5nT42nYcHgFyCnBKC36WvQJ3NKv/mjLEZMsllMvk2dHDjMmVJ4XjLrkyGusi28qo+RGu938s/LqyZEV41xJFjeMbfdf2oL0A7vz9YK++L8GNxnMdPDquS5YZ9o8oQmqYY0J3trKh2Y9wGWcF3M58eujbR/EN4N2BVRrumxi3AU/d2fecG1VNqrNxI2HlYVOBheOU98dlZrYxPphKXG+Dx3EG2gXq3uU+Kni1HpDAfd3MXjSzWSX1zKVbQhs3gpyKe8aBhxP/FBeiO8DMlsN34ecIx8/igvjdqXaY18/ZCVgWZ/5V+zpqxpS+n5n9EGega+FeiJvg3i4xrGhmKQ81i9vSB27V3vVx78hesJiZXdRAu+6NvTruiTCnme0VXZqFWr53YDozW5luj1sDpk01xMzWwyNw5sUjGBbCa00tG+Mpv17FprinTkU/uxaAymrRlHyDX8cjGK8Kz7nNvHBiF5gXIj0UVxLj+RmPywyhn8fhdYSqPjec8aZgOzx1RAw74N4uqXbMjy9kVcThtbgxaJKni/qvjfVm9nOd39a9S1L8tvKUeSp4+D4JJPP81jyvwFO5JCOuJL0RPN/PkdcdKYUXGatjU4cv4H1xU3jWA+YF71NwFB5if2dTHwfvs2WBWa2zrtEsJPhRgO8Gz+4q+jHJk2vP+STu9QtwlaRUvYkK7zu4UrBI8NI7WOmCtj2j98zsTprrdqBmT/bsnO+hzTviBpszcKPmM8Hb9I94jZf6/BGecuZKPId4HDk0N+7wEfMA8DGZoaG9vzPPR35uON4YuMY80uGfEd4bGqtXMNYY6d/WUB/SPKfz/xGNH160tu7lWUS7YG2bW9LtifvvCF5NKdiQUDg74D5p3TULZ1HCI1nSI5XneQWF6yWMtp9Xk7RDgvbR5kWeU7R3Bj6Dy1OL4fPrh7jn6SB0s9b5AC8Fj+EHAn98At/Q6YJCuQcyxruPMSwZk5K+mArfrExGMDVAVm2L0G9zmtk06lELDrhHhRFdjEUNvCccP47znJifz4cbd1PvJzz9WR2GLsfgxuwjm18lCcfhBvO1cePz87gBenyiDQfisuYy+CbQOqEdXRGYeKqgR/B0Y9cEz/bsekhK1EQ0s3PxaN8j8M2D14FZLNRqUGck3OJKFMaWdGXQcVJQpI+a2e64MbuqH3ammZ2oztqQa/WiE8F4fGz3wTeFYGxO1WW6tnFuyrKQo2O21enrmpsxz7fCmk3RfZ/Ho4nOVyJqTB7Z993o+FHScw7KdO6espe8ZvL3gjf7lsDPgXnNbD880iiu1zA3HsmyJW4XuBKXaVLZUzbGI6muNLNf4VFPTfyxhC6S1jevq7wxcJCZLQ7MZqGQfA39GbojxTZMNcLMJoT1MmXrqENTHTBweb9Oe68UYvRtfzdxeZ3qH0lPmdnajPHpDuhDR+mpd/SpS/TUDSxErFq67iqq1SsstfGEZ+TYeSrcXXGnmL8wVmNeuDGzwjlK0h7mGQ5Ssm+XPsOIbA+hPTm8ucLNsVXE3+baeDrDal41NtoSEQZmtr2iCAMzO0fSZk26W11nC8+83aKaRqUg6edmtn/q2ijaHOAMfG36KC5rbI3Li02QZa+QVLLGZsslfcjkFybmwnO4Q/MJ6oyUquTK1cNxSq4s4luj4kc13I3x/vgdPjd+aGZ7qpalIsDMiqI55RGpSbnFxiLj3xvo790kz5fgBvwP4Wvth/CMY6fjafdSuDPj6U2r/rgaj4B/vob3dnyNWBaf//fgEUx/id53o37GxLye2SS6kq6soXwwwi2xH62D861lojYfpigzi6RDzOwWvJ8uBb6mvHriQ62NNZJNKknrhr+tIeU12AvfHX7NzF4iLH5Kh6AewxgzHMdY7ukuME83SIS7Cu3C90vAnWb2GzrDRXcbkPbbJJ1jXoATSa+ZWVMh7BNxr9Irw3M+gIcgJ4Uuyhh+9vvhnlcrmNkdkg4KTKUuFDWGVdahBDeC68zsWHqH767fQqNe9HUa3FAyNZ3j9S9cQIuhQylKXEvBN/Ew0sslrRwYzZYt7esFk1Y7M7sADws+A1hP0lPh0k/NrB52/6KZTQPcZmaH46mM2oqoZn+DeCHX52oLcZNweSpjqY7WIqQ6quE8xVg/1/u8o5/NDehb4Yw43picGVdwm+BUfENn03C8TTj34ZZ7cuBN7ec++e03g/K6N27wmgU39nSBpDZ+mYLfmNk+dH+zXSmUaorMOHzRPLeOF+BlSa9UfWGecrNpzj2Ge5i1KTzvwDf3ZmNsMwbcOLdz6gZJa5lvpGwGnGhu0P+ppG+m8M3s27jB6axwanfzlFsppeBAupWvhRvafiwubFXGuu3waL4Y1m24txfsiHvPfEvSw0EBPLMBdxPge6qFckt60cw+Ff7vmj/mabN2wDcLNo0ufTScn59OHvA8Loym4Au4EaZKQXI6buQSnYZBheemNMmmQsPH4xt0x4XjbRlLJRhDP7Rz1rY2ZaTpWk7h7DbDUf1ayXoJo+3nIoNXgJzN7X7olqzze+CGoN1wj/O18U3gJsiVeyBvvEvHEPLHBPL74ilJBzc8rwkeAq4ys18SFfBuMFY+gvfdRXT2W5MMVwKLSdo8yB/Ii8XX5/iDakiDW4cRyzHPK98JqoJV5SmzbgWQ9I8g16RgE9w7/VZJO5pvmP+oAfdiSUdXB2b2KPCpBtwKJ05f+TbcyBFvoJRs4rSlA27ioSUyGriT4KqV8cjMDsNrwUwyhDaNh5ktgK/lV0e4C7e0uQMKDXMV9NQxVeiwFYw6G+Jzekl8Y2pRlRU5N9zhbmsgZVxJpar6t6RZE7RKdO5s2UuequdbwLfMbHn8fataIRXO6+HcpeZOpOvi/P8JM7tC0lYR7s+AnwW+vQEuh89lZsfjm1+X9UM3uuc5QqRCWPc2xze4FlBn+vIv4/PweOBsM/tpQ19BtCmRAXWjXAWG6x11KNU3wN//O3jWEYWxPgqPKExBto5Cnt5RrEtk6gbvsKWZ2wAAIABJREFUx+vvrFe/P7ThgsT5EhsPZNh5Itgd95JvW5+q9LR1u0sbjMr2ABm8OYIcW8VvzewcXM+fHR8fzJ0X25xjjsRrhd4X8JfEU3a+K8LZPfwt0d3mAe42s9/TOd6pzUBq32Jlt2zqv1G1eXFJm5rZ+pJOM7Oz8VSxTZBlrwg4B9K5sXCw0hu2pXJJiUz+MB4B/eNwvDm+sbskvs5uG+HmyJUV5PKtUfGjGA4Axqszzf5lpB1SHgnrcMUbtsFrQ8XPXxpfg6q0859TQ3rjEtwafA53AtlVtXSeCTgFd2zZLhxvi/OHSfqSeXrZs3GHmNNhUtr8m8xsa3WmdM4eEzObD+fBL+EbmAZsFnjXhpKegK5xz5JhzJ02P4tHzlY241WAb5vZ/JJOrHDVR8pw4GYzO5mxsd46vENfMKpIKgCC0PTBXueg2BAaG+Nfw1P9NeX3PrKG+zBuYGyCX4ZfDpTQfsE8AqYyJKxGszfhjIp2TCVd1WB4qKCE4Ze8X/URv2hes+FvQIkhfBhQKU2xYaPLI7VEIQ+4V5t7g7XuDEv6QC7dCF6V9DczG2dm4+Qem4f1vq25GdH/P1JDrRZJq9Tu2xYXQnbBF/QFcGNu+iFl3+BdZrYVMJW599FueNrBFEwv6Qozs9DfXzfP93pg9OwSRft6XEB8G53f4PN4YdQmmFPSqdHxBDPbo+C5TTBF9HNYMG+T9IKZbYMvlkcp4WGlMQ+Z5+jh4dtA9/st304l6H0hfiTpiK5YkXkNr1nzeAIP/Jv9Mu45+mG8kPDFDbhfBC4xr2GQNGxKuhD3eFpd0g0NdLpA7s17tLkXa5UiI7lJhRdFXUnSGwBmdhoe/ZgS/FLKV1s7WqP3JP3ZzDbAN6/ulNSmBMTwZzxkvjLoPIoXzE3BU/UNKjM7TNJ+SnivR237B+6VvG3t/GnAaWa2saTzcxorSXjY/nk9UGdlTODrItNwz3hJK0bHvzWvZzMM2jlrW3aEcATnmNkJuMf0zvj3eFINZ2kzS/HKLrol62WAkfZzocEL8oxM/dDNXuc1Vvfm37jhoxdkyT0Beo53H2MI+WMC+X1REkFVwaPhN034tcGT4TeOwkiQCsxsoYY+esW8FkYlwy9GtLb0AaOUYx7poz2vmtlUjL3fnDRvKv9H7tn7mrmjxjM086PziYzFkmRmP6HT0DUJzKO0VsGV+lPxMT8Td0KoaCxc8F4LmFlqk9LwKJ8uKJTRKlrxBkgVZZBG7iPSKMy3LYAt1VDbtwBKdcwcyI7EaQJJP+hxvR4hugFumElBic5dJHtF7bkT90j+UgvOSwT5JHwryT4JRvSzgLPMbA58fuyPG/76phu+44XwDfRncCPvMVar1aOySLEq80Wyw2qG26tJb7KAp+Wr33tQaPcCkh6rvcvcDXTuxnn+ZWa2eTDetQ1miY7SU+/oU5foqRso1KyUlCMzVFBi44EyO0/PqGZJlUFyZjydatMaEt8zKtsDlPHmnrYK3OFoc3xzaE2NRZbPzVjUZgp6RhhozOn48+qut3QY6Qwmpdlf4m/xNVxWaHLyHlWbqz77p3lU79N4qvEkFNgrTsHTsVZ22GpjISXbF8kllMnkK6uzdtHFZnaNpPeZWT37SolcmcW3RsWPajBOUbQQHr0/rqXd38Cj7g3n+3WedhfOXy7EnSAOj9djSXv1iRufb3LIS8ESkmIH2q+Z1+uO4UhgA0m3RucuNLOf4ZF6q0bPLhmTY4HjJU2IT5rXkj6O9PeaK8PsifOteIPrt+bRVdfiDkyDwP/h83M3xsb6uNY7WmAkm1TmHj4zAG+zTo/aWfB0IKl7ksXI6gawALNJ6lAyzWz3+rkAOylRpKyp7UoUBGyBEtp74bnHFzOz6/Bd9qYP5iEz+xqdu84Pt7Qjm+EXvt8vzGw2fKf6FpwZdnhL2phnW/xlCJ9b00iauh/cqL1ZmxfBYNxkAJOijVEL4ejAsZZOxfTJCLe1SF7D/Pynmc2Ef5xnmdkzuDDQL8T99U2c0cdwAwmPsWCkngbPG30+vYuRlnyDu+IC2cu4t8iv8UUoBdmpjhJt+jDwRUmTvISD8PhnxsKjc+HZsMlSebdUhYr7gqD8zSnpnnDqJTylxnK0CPIj7Ofj8ZSaK+KbJ5U3w/sTbTiVxPeiWorQFrqnp+gGGiUb2R9PCbb1cwH2x73i7sQ9QS6h2Xv7W7hBeDoaDJtm9kVJhwNbWfBiikHp6NmlcQVlE3zu/IQxT+4mmA2vjQVuwG+CEuWrZ/SemR2Hh4pfD3zDPMVL09yJ4Qo8HL4qbDs9bihJRfJ+mG4lZJ3EuS4Iys7UtXPbSDoTWNgSaV+UiIgImwuH4TzFaPDCLDRsVvC6mS2mUAw2GHC6vLH7oZ25tpVECFd0jwh881+4ofcAdRfOXjqvlWXrZTheOJd2BFn9jK+p2QavAD2NTH3SzV7nzT1Q98WNhZPmvBqibnLlnoDbc7xLxzBA7phAfl90OagF2rMBX5D0rUTbKqPljOqR5kSZ0R+SJpinQJwPuEaeonQFfI15L+5oUocD8XotC5jZWfimyQ41nEa+Z2ZrKHKmG6UcIymZHiolT0VwNL5Z8nbzAuSb4BsOKbg5jNlJ+Ib0v6mliLI+U86Ql6409W5NmziNGwg0ROcWymjghrCbgnECPCKmI62c9RFpZO6dv3m4ZwU8FdUgWRkqKNUxcyA7Eie1tseQWucb8BpTVVGmc/eUvaw7bbExps92yBtmtipu7FkMl1d3knSPpH/h6bNiutPhHt6LB9yTgwHphPCjH7oB/9PAIcCf8IjNz0i6CCbxny5QRqQYBWlNCzdYYnjIzM7D37GKZr6EdHTUa5K+aGab4emft6MluqZQR+mpd/SjSwTI0g3M7BC89tE/w/HseGqrLv5caOOBDDtPBCVRzVsA3zez84FTJTWmcxuhTgwZvDmCnrYKScL1vnpbb62fq0FJhEG2XqXyiOkfqebUb+6I+kwCdyRtxrOQzI6nU7sIj/LvSh1afVfWmT1rEiS+q8UkxQ7CB9U3FvqVS0pkcjyt9qQUjGa2IO6MBN3Rdl+nW65M8sxcvjVqfhTgMjO7BI8kAv/ek46w8sjLz/eg95ke1/vFxcyulvR+M/sHiTVc0hyJ216yaEPJ3MnlpRrOLKnvXh7FVHeoKRmTZSR1OZ1IOt3MmjbCc+1HpkTEsNzJsIF0bzB3GLpe7gjzXZozkJXRVXH5pwyingN2D3xD6gnGBJl/4cXhjk3cExsNpsO9oyamlHgzu0W1MDQzu1WJHPMNuBMlNXnxPUyaGXZ52PRBe2rciGC0bBgE5n0Qnvag2on8utz7PIX/aXwTYnk87HAm3FDRlW+95P1q902LF2pt9aIJH+bncWHuZ5IaDbg5uGaWzHmtWsoYM0v1+Wq4Uf0ZSeNjXEkTzazJyH51hJuK1BCe6mR+JVIYmnskViHrW+PM/iy1h8g3QjCwnYIrBmfiSkO88ftDSUsl7vsAtXzCwPZNinbJN1jY/vF4+snZcGFyVlzgvjHCWRtP/TUv7sF3CGPhs99SlHvbzK6VtGaT0lg3Tkf3LYh7KKwe7rser+XQ07PcPB/x05Juis79BPd2uLqG+1G8n5ty3Y6qn2+Rp+s5AHhC0skpHhVwY0FuOtx48mRKeCmhG/C3S52X1FU7oIGH3qHmeklZYGY3qzuysI6znqSLzSyZdiul7JnZjbhidK6kJzPasSXwbbz+kuFpCL4kqUvJMa/j9BW85oARlC915q+ucBfClYq34J4xs+J5kB+McO7CiwS/Hmj/rmltqtG+TdJKbefM7P9w3r0YXlCzgpmB6yRtE+GmDKaz48a3a2NebmaflXSCuTd9F6SM0Gb2IJ76tC2nebwB1mUwNrNdGuSSD+JK7kP4mCwE7Khabug+afdc28xslmCASr1Pax56c+/qeEPk79G1pSTdG/6fVtLL0bXVarw5e70M+CPr534gGB12ovO7+pEGFH5L1nnzCKQf4gr+pI0ejXkd1/Gz5J7aPW3jXTSG4Z7sMcntC/PUZl9jbJ0/G5cJtsUzIuxODcw3k04GZpK0oLmzxGcldSm91uCsVF9bzTf21wNuw43Dv8D52SF01wyI73srLlcacKOkZ2vXp8K9eOcDfiXpLjNbFzfgTx/rKKOUY8zTLZ5AhjxVo70UvpFowBW9eGq4Z2FcWb+jdn593CD4SdwQVcHzeD3Hpojw30t6dyR3zAjckJIJLL2Jc4E8wqVv6EdGM0//PklnqxsvzOw/dEcaPdSgW+6Mb0bND5wTfheqzLjeCKU6ZiHtKhJnC7y+6IHUInGa1vcKUut8uC+Vqur9kpIbvQU6d0/Zy8x+jkdLXIDP37a192Z8c/QafP5/WtJHG3B/ijua/g436v45xQdL6Qb8u4C1JP01jMtZTX1VAk32lhb85XAnjbhuxxFt36l52tGT8LV7M0l/arHzTDpvZsviMvqCkmZroJ2to+RAn7pEiW7Q9d4tul1fNp5wb6udp0QuD/iz4Lxgx9CmU/F1vl7XZSQ6cUS/lTdHeD1tFQO0YVo8wiDmucfVZO9Kr1oU31iuoEuviu5JpUDtqE9cw0/p3E1zaSRtzoXS78rMbgD2lXRtOF4D5zOrRzj9yiXZMrmZfRyX9/+E99sieB9dBews6agafqtcGeFl8a1R86OAb3i0bzw3zov1KjM7UtLe5hvEKZ6UdKaqPWca4BNK17rKwjXP7vBGkM+7QGNZY+J73olvzk6Lv9+LeN3OWyOcP+LpUv9Ru3cOfMNmqehc9piY2YOS6uUbKl32/oZrWfYjM7sJ+Ixqta6DXnWSpKbI9FYwdyhZHe+n63C95Dpl1G1vpTugnt5O3GxXJYoTZt67AL44bBmdq3K4r4kLdBXMDLwu6UMRbrVbfjguHFUwC87EUsWtK2ZRwXT4RziHpAMinGLa4eP4BB7hFBsShrLbmAuZ79fKOFIKrrknzh54/s6z8TolyU2ZQtx446rKwf1HpSM+qnvejxtCpgUOkXRpA96GeDh6dsoW81z5X8GNrN+S1JRuLJfeWrhn0DvCqT8Cx0q6qoa3Pe61uwqd6S6fByY0jMlEYCvV8gkrw1Ad8Lu+wejaKrjhZWE653NfmwtBIdkTjwpbBzeofE3p6MjJDuaebMsDU0taJ5y7u4WP3KXMlCzD6mfz1Ha/whWC9+Hh17dJWj6jDePweiIpp4AiuuZeTxVMhxu9blEUat2nML4urjhU0QiNxjzz/Mq/VZTPvwnMbOUmpWUYEAxp4/H23iRPFzhyqCseTYpI4r7r8JzNt4Tjd+E8KRb0Z8V54KF0pgN4XjUvHfOovRiEe/5fJSmZlsTM5pT0115trdoraY0MvEnvX9I3QVGrDF33ptaLfmjnrG01uh2pklvofhZPSfEfPF1X9Z0smrq3oL1Z6+WI+7mKCMJq0fM2Vsy9GEZFN6LR6LzUgJ8t9+SMd4RbJPPkjEkJmG8iXY2v8x/D14a78fSiSb4YlKpNgIs0ZohMrq/W6aw0HZ529zVJX6zh3QO8U9JL5gb7J4EVJD3Qo/0r0L0Wxw40E3BnoN/jqT6qSKn9Jf28jfYwoUSeCgp1I6hzs7N1/VCiPoMVptM1r7mwBO6ZfSieKubsWJe0gk0cMzuJ9iwLn81oU1JGs+BE0NSHtb7bE9+4mRHXe34K/KbhO30FH7u9Jd0cziU3tFraPA/w90G/2QTdLoetFtwqEmczSYv1ws+gF8sSVaqqk+TeuxVOsf5a8PxZ8dRRW+D85ae4YbMu85Sse3dWsrT5ptrvW3CLZLrCdtQ3Nyw6Vjx+VrBJFebLEfi3fHOg+y58s20feRqkxrabG5pPwiMyDmqQTd6lyOHDfHNkAzVsOuXoKBFuid5RpEvk6gb/H3nvHW1JUfV/f/YMGRkyiIhDkCiSM0gwg6CIIAygBAOYQRADCAoKEpUk6iNJUJKAj+BDFIYocZghSxpEJZkQxIDofv/4Vs+t06e6u6rvvcpvvbVWr3O6e3d1dYVdO29TaOZ1q7VsCg92R4OsqVPGE+DGbZ3U3rMI8tTcB8k3Xg+c4C2ywbHgiUtw8yullPBVLXVsC6zn7l+qXd8QRcPYB+XcqsoklOcmDu08Lm22MfKgbWnLGsgwe360pv6IDIbrBjQTgc+7++EFdRfJIgPtvBIjtHOT4VN2WpwSvBXgxwUfRfCLMJIf9A4fNtpaz91vMxm8DRVvSAkQ5FFvQTTeVkhxt+1oYMOYTyud5wF/mKeNDz+K8kntT/D8R3vbkcBp7v7dxDOdY2Jm30TOJvv4SC69edG6/bs3e8LlfM8mKKzw6cho0tEY7gbs6kHB21HH4Sha1Pfr/WIyWtsoHBuiSF63u/tWvdrr46uk+gSy3IldlKe4e2d8QjMz4G6PBKEm6/FlSCDDAPtyBNtLW97QlhvdfZPR1G1yi/w7chmfFaPXIwuUSlBisirJDclSTZgsV/DM76sLFWvNGBCiLYLCXe2IvH1O9GYrnGzYlrbOiYQVQ9ZjJg+WL6N+/rp3WGGH73wzsgA4F7jCmxP1vSXU7UjxVQ+fhA2HM6zGMEnUmtm7kEXsoQjBGQplcBDwSa/lngrPZOdqsYRHSupay/NDazC690ukoK3P59iaNzmPI9g4rGKdmXq0i6k1s+PRemsVgFi5y3hWMbOH3H2Fhnu/dPcVU/cSsKPq5wj21UggcLu732CyuN68iVGrPbsi8DNPW2j0rjc8Pz9wVm28i4lxk8fMdii/UuvGFdbivCgkxD9pZyyvRfHFL0Dzacjyw8zOd/f3m9k9pK3e6+tsJXd/sEmo55Ewr3Cd1N9fh10tgv0rI15OxojXU7LN0XPrInxYeYotAexYFwKMJwNoZg+j8EPnIcv4RgvvgAdejTwG4hAkF9XgYmvbASFL4vzN7n5NEyM/mrpbvmNob+tTb+i7DesMw2jbm7tfjnM/ZyvACtdKSb1F+3x45ivI8/FiBudoruChje7pHO8ItnMMS8akB80zI2YSzewZZPHeKEg3s1vdff3avBqop+Obr3P3zWrXBpSGlvAeTdRzGvLWuY+RvbhOD98HvNFltTkX8HuUM7aR2R8POqaEnrIR4bQhhvJP4f8CwBMeKX7CXtlU3NNGLosiRn5pBoWKbYZmbyOyCK3T21agxDGzHROvWBIlfJ/d3ZMh6Gt1JGk0M7vU3be2BgF/Q3tyPI3inFWLIyXc7u6eCkPZ1Oar0Z5/obvvH6714jFr9Q4ZbPUtYY18CBl9zgq11DY3Muqs+NfFkKDkmnC+BTKM2S6Czaa9au+YgPjYExE/eFzt/mNIcFWVY+LzGg4tUSRl1xvgn2UwNNlO8XkNZ8TKDZCn2vtD/dM8CqNlZm939yvD+L0e9eGjnvb6nwG8x90fr11fGimWk3i8hu+XQLTgOu4+TwJ2VJ5RKR4lulfCd+TwEtm8QfTMAUjeVIVp3xPRA0d1fpyeH5DxhGslcp5ivGFm24R2Loc8Es50hdWdBwn3J7e0d9Q8cQlu7osHckoL71jV3SiPMbPFGMSLjd6bteducfcNatc2AzZHoUXjKEsvAJd4ZKAzXm22/h60VwE7+KCM89wUPRzuTwr1JaNQBJhrvSyEX/35Npp8HhRudrK7f8QUfm1FH8mtVe198yDPpc0ZoaEnAZe5e2dI9ja8Fe6PCz4Kz70PKU1uCG3fCBmbDXk8WSKCRsO1jZCs6d0oH9YGKITjX6iVEtjomXOQYcRvW2CmuPs5ZpaUC7r7CTX4rVHErthL+GhvcGDIHJPZkWxsd2To5shI4kzgS+7+UgRbjLvMbHHkIfkGNHb3ASe38Sm157dFeH11dx/ae02OPBujObEBimbWa62NS06qqHzEo0So7v4nkwXckJKqxnhNANYABtzRvCCGu/dLHEdtoVbhBAZiS/as+7VtiD2UKu5rMtdES9nSI6uJ0M9bkYgnn/l9exS8+1fIs+J05Ob3IRtMYHdcT9imMg+J5KZmdjuKOX40Yl4HvjWFaN19j4AMtkTI7ttmdpW7fziq913Ic+rPwIFei+Vbq6802fLnkLVXPM+nm0I6nMhw7imAVU3hDOrvToUBKoknnLUGo/I7D7HNW0rJPF6gJhSz+LzOfIUyDSUzrOL7n1cJLGqlClmTupcsZrYDCtfzgpkdhJSHh/mgBcTDZraV15SJpgSEA7nqavfHup8BCBvMcdH5E8iCOtWGeoihp2nIp1FSb0P5KxLGxHX+Ga2pKSYLl8XRfvQqM3tVAzH+a+DeLkYx1J+9Ft19C5Mi7v0oVvYkNJe+FoFV4Ve2zqz2syhu8rGpVzKYbLVaJ9shZcvZ4XwKshSOS+77oSDv0EDj3G8PREbsQVEPk/Oj0JbKEsfiKohwtJmdQEvxhJLY3Zc3s/WQUOVAk+fDuR7CyNXKJDTH3l5rQx1neMP/1PlmSLiVylM02rqbSmpv61Pvo6g/2kpxvTn7ZY+6S/vZGv6nSrVWqiTD8T5Y75/senvs8yArNRj0vB9YJx0lSfeEkjPeemHeGGaPSZ++sME8tU8D85isBJuUdr8ODKmbwnl8mpE9vV53rDSfgKwaX50AXc7M4n116fi8genfwN1XafisqvzDQ+Jpl5fWQxmM33jQMdn0lAcllJl9Bwld/i+cb4lyExLB9mE0/xcJMq6mOadZ3NBlUGjaq8L53Ga2dE3I/RqkxDkuMNznozBHQ8XdZ+VGMhk6fhFZ3h6DPDRSbcii0dx96/CbHYbPh3P+TKGW88elcD4FOMXMXov2wWdN4WUu9pqVfMN73hqEvfGc7ctjxvU25fHK9sSptedB4B3IWG8XEmu7hIao+FczuxTldngqnC8BnFx7tIT2qgRjU1DeuhuRB8INdTjkLbpNw3l9X1vdzCphqqHcic9DUtFfUi8M7jfQwv95sIg2KeA+EJ6djsIn3V8Dv9YUMnVPxNdPAF4bFB8H1ujF2Wtrt3rf42EvaipbRrBPmULCp/KigizBqzLLw4B8PmWIR4lKCd+Rw0uU8AZVvUcFhUEVivUwd0/mf8mR8YQ6S+Q8ffDGDihKzkB6AXf/q5kNKKHHgycuxM298WFVzOxMNI9Odvd7o1ulvGOl4DsO7XPPIgH1A0ioXIdNhUBNKZauQ/lZz/DuFAfj0mYfyS+6iGcYVUVlUQ8KqlDPn4IyrN6GhZHRxyaIXrwRONTTkZpuNrOTkPJ7Vq7TJqVMorTR5JWXSiWr/g1SSlwawezFSFqcOxmhiZ9neJ9qKm14a9zwUSgHI+/OZ2CW4uNKRMPWy57IID8uH4qvmdmvkGHs99Ae8mczm9mgoMqGrZVFgAdMYSHjMY/X0ILhd9HE86l1dSmD49pacsYk7J/7m/KGvh7NjUd8JDdjXEppmElhzFI54IZSCAT53KfdfZb3pSeiQpjS0myI+u2XwC1ofD/qiXCKuWW8PanuRpo2D+cTkXVECtHuFp2+DDzepBCwzETpAbbIis8GLQVfRtbcx3oImda3bjM7EsV4zwk/NRBupuladK/EFbzz+2w4Oeue3hCb3mQh3BZK49A+sNEzsSXHRLQADvOaq7iZTY3ghoSm3h5LfnYUemYP4E3uvmh0799og5mRarsPejnECXDvRu6eSc+sAP+gJ3JJtd2zsjBAnfGEa/Ala7Byr/057d4La6J5dF/THApw2VZdiWcXQmF9dkJW2clN28x2cPcLuq6F63e7+2om19gj0EbwJXdfP4JZAW1ONzPC/K2DEPXWHlnG1uoe834OsBsg5ebKKDb1ROAv7t6VALO1lNZrg5YdE5CQ5AJ3H1KCmZLTfgV4hkHr9FQ4w3VR2I3r6E7aWwlDl2fQqiuZjy165o3IKmZHd58jcf/I+nekrkX35vLhmMBD18L1691904xr2xKSbDcxqn2KFXq1FNT7EnAvEiQ+SU0J4B2Jnk1W5ccBu3giB2BBOyrPstirjHC+rLvPm3hmGXefmXGtT92de5uZ/QZ9u6HwXdVcNxQKYMiyPuDc04FbGVwnsdV0ZWFtyCK8sq42FJpp8Xq90bON++Uo+iK3n2cgq8MJSJGyOSPz6VpPWGVbIhxk/VpJvaX7fJ+SS/cE2M7xTjzTOoYBpnNMetA8jzMSkrBe3NOeJ4sAxyOFiSFG+DMpwYMNegVV9O2hXgthYQ25uaKGXJeo+1REK9cFtjFML+/V8OyY0TGmsINttHaKXhwKSWm13I4mi+CjCfsPHVap4ZlOL7X6O1Gc/5fC+RwoBPC6DfCVEmcKEhwNKXFCuw9EIRiPRVb9yfxEAT6bRgvw2SF7uoqZ/cKbcyytCOzkI8K+t3nkZRbo1eXd/fSwbuarr+EAl81jhvE4HYVcbPNozvbEiZ65y93XjOjt2ZHnXD2HXDENYbWQoCbly92eDhPaSXsF3PUc2iuvQfMibkOucPMVWULf74nojBuBI9z90QbYbyLFx74e8gsFYdsxwN88yqkV9tZtfFj4NRl5cDR59P8L4Zovus+SIeWGrO7yMCjhUYr4jui5Ll4imzcoKbkyrBI5T/TM1ihc8L/b4Hq0eVx44gCfhZtzZRUt37Au8kRer2EeZfOOYc28GYXgX9OUFmKKu380ARvLTpIhUGvwKyCcvDSDcsuUF/SYtjnMn9NRdJN/I16jM6qVKXXFeyscEnDHxXVcYPK4up4RQf0uKOrLgLFNgE15hXuqHwJ8CU1+h7uvYxne/1aQFqcEbyWeHVN8ZFGY2nA+AZhRu7Yjos02Rx5jVZkPeWJvEcGejAwupiEj2EvQWkzxBNmwteeyww5aLTdz6poFj8Pwf2BdmNmV7h4bzqbakxwTC1ETwv86bX94nb4N13PlR31SCEx19807vuVB4C+MyERv9cJIacl6fXyVVEcjRPgdtLD2Bn7t7vslYEuI5qxE6QH2ZmTFV09YnQyZZmbLuizd4mtDzHpp3aZcAGcjxNIVfiqV2LBapVE5AAAgAElEQVQxZJAVuILnfJ8VJmdtKma2rrvfPhrYsBlV5WXgGS8UCJnZ7CmG1MzeiRDoFiih4XnAlT4YNjJbmGEFCXADfGOeirZ7NbhGl+PSUrgGz0bxdttC3xyMYlHfiYQDR7h70nK1oI271RlSk8fFjigE5/3unrIAb1pXTUi5Yp6PQMqAH6XWYOj/nYGK8b0PMfONjMZY93MEeweazxcgZdkHkcAitaFtjPJKvWhmuyJPseM9HUYwu94AH6+Zl9E6+E0D7CPA+t6Qk64GeyXaBBtDpkawH0bWYK9F1qAboOTrKUJ8ZTR/tke5ks5FIXKGiPyGOdQYQrNwzj2ArFYfC+fLIIZw5Qjm28gy7WZkVXmJux+WendpMbOvuvshllYYN825/0X99b+etvKphFc7oD5+GeHZC71d4DUJeC+ad8sh66zzfTDkYFEYz9pekvrA1NxPjV9KmNun7s69zXqExzCz25CQqb5Ozoxgdqs/V6s3JfTr3C8D3Hj28+OUKzmmo/C5VTLljZCxxhoRTHa9pft8eGZ24GMopx+o/76boksCfDbdkzPeEWzWGAbYzjHp0xfjWRoY7Tm9ZphjIVxpQx1D1oTh+qaIGX4aCceGFE995n707JjTMSXFzK5A43g2wqe7Apv6YPjRG5B3QsUbbOgdya/N7GsoiXQqMkAKfkip1STgSTw7pMRB/FClnDqHYeXC0DzIpdFsDEL2JN5dku8nFjgcguizFd19BTN7DRJgDeVrLOExzez1SJm9I/LiOx3hjKTwwIY9cQ73BsWumd3m7uuZ2fUoR+nTKCfTsjW4PjTESchI6Rw0n3dC1sifSsDm0F5T6WkI+f9CMRnEvAx8CxjCfz4YovBhYIX6HDAZIj/okYLdZFR1FHA4gzkwvoDywiRz9ZmMby8H1kRCvD/mro2w397dtP4KeZQSvmO0vEQTb5AyFHzR0/KjLBlWHzlP4Ec3BC4ETvd2w9OSNo85T1yCm8dDVpFodzbvaCNKjhnAmq7wwbe5+3oJ2I29ptBLXYvuzUAy2brccsjLcqzbHNb0+13h5dZHaUpaZWzhuXcihWolb9sUeWhcUYPrNLTpWwpp8oo/v8mVV285lA9+aPwC/KpI4RQb1A55gZbgrQA/Lvgo3DsWre0fhUs7Idy/fwSzDOLfUyl67qrzP4F2eCtSQr8drdXdUFSjv44CtlNhlPi+HP4nVkLWw/Y20VOdY2IF4eej6500TKLNuaH+v47yvLV6HZoM7ap8VBugvFozEP3f5ojQXNx93A6kkNkb+DHa1PYCJjTATktcu6sB9qaCNkwvbHOqHXeOtm4U/ms1kGKwAWYKYoL/hHJdVce1yDqhrf53IiumY4F3jOb76jCpZ1rqXwWFbXgYeXONChZZQ3VeS8AYsur4PtpMUjDnIoHAnCVzJDy7FPC52rV7ov+zdfUbssr7aeK4BPhTZjsWBB6uXVseOANZ3b8WhRL5C0IW6xbOjaY1eE9G2+4D5gn/F0Y5jYr6ua2NyJvyYcTI7Aks0PDMlohYfgY4ITrOQAxx6plLge+iUEoLAHMiK5GBOZYzD8e7nyPYO8Lv3dG1mxtg7w5rZPXw/zPAdaOttxqXnGvh+rXIoib7+zJh70EE3/RwvhJyqU7B3hK+/zUt9X0s1Pli6K/qmAmcnYB/NQo19QBistcKx+aIkEu9451IODA1HI9Tw+XImnhi+D8PDXvTf+pAocG+jUK+XIAIr7la4JdEVnxPAh9ogZuJ4l1v2AKzTfjdLXWM8rtWQl4NjyI3+urYHVlsjUXf9drbMuptXJujrLf3fvlf7ue10d73eDimA2uNor6ifT7AfR/FE39zOE5HSWdHPTdKxjtnDEvGpLQvUGLe6v/GtXufbHhmGUTLXEREJzXApvbW1msowkErfLj+CBLmLYNC2UxGOQcG+i76P2ft3gYN9Y45HQN8K/r/mdq9MxrqXwh5rN0VjuOBhWow02vnOXP/BSRM/DsKZfMC8HwL/FXAu6Pz99THqGC+T0OREH4djifCMeu8YB4N0WiIbpiJlJaPhf8zEb5JzuecNhfA3hX9n45ouvja3TX40fCYE8L8/23ou6/G8wOFW9wLhe/7PsoP0dX+DyMeZtPQf88Ce3c8k0VDBNjtEB3xTWSJ3wTXSXsVjMnb+jz33z4CHjm94TitBvtQSz1D9xCf8QMkGK/C8K3e0Z5p4ff9iJdcu2lthDldzeVLw1z6RkvdJTxKCd+Rw0v04Q3uQN6rdyFlzx4o73Zjv9WuDfEJdbimvk08Nyms81tQaoWPIo/NsW7zqHhiCnAzPWQVtTlXHWeF984VwRXxjuGZq5GA90SkZD+eZl4+i+ZpmwsJmHFpc985F2AXQZGDtgYWaYA5BilLJjDiyfvVBtj5EV15RziOBeZveX8JTf52pFD7HfBDtJ9s0QB7CNp/n0G49mngxw2w2Xgr3BsXfBSes9C/J4Qx34EMmVjBeM+BDFXPB34/GlgacElDXeuFPvs1Ci1eHQcxTE9NS/1vm9uZY3JXU9ubvoVMGqZnm69NHNe0tH82pOz/HOKb/tV3HoxrTiqXS/B3woGZLQXsh1y4CdemIE+EZWwwRvx8SMuYKncEC87WROmhXGqJvDH1YsrB8QZgfhsMdzSJSLvdp+5QHqY7rvHNwFMIGcfxQV9AG0Sq3RNReIS3IiY3WQq/rx7PfuC83s/BwmBKOF5GzPs6nohDXQIbykDIQjObDSHUZAnWGTsjhLUQCndXj8tdfcdOTfU01B0nM16S4dirs6wC3P1ls5RR9kB5T8u9ZIxkS7sc18Mkno6YgEkoBNA+qD/ehGKErh8D91yDt5jZKt4S+gb4uweLBnf/Q7B8GG2JO3UmeQnjn0REyLsZjMn+AgptkSrvJyh+3f05Uzz7+jy61swuRF4kcXLQOVCIxd0QMj8jXB+vfq7KX8O7p5vixT8FDIXWCuVld3czew/yoDrVmr0rSuoFeBvD+a22TFwDMQ9TzexndIfSuNpCwuaWd1fl766cIJUl/YMmK+uh4rUEsw3lR0jZO2QR5Ok8Ku9AQt3XEuXzQnMu6YHm7pebwhJVYT4f9OHQnC95iO/riu3eiWRKi5ktgLzllmYwHEQqd1QV53wiEr5/BDgN4Z56vWsh3Pk21JeN+RFQWLi2vRIfSUz6V0+Ev2p7NqOsiJihBRjMAfEC+saxKEV7W0G51sw+ipjoeE2l5ml2Kd0vM8u497PLQnR1k3ee+ehDEJTu8yDjkNgT5JpgcdpUSuZG9nhnjmHJmJT2xWcZCcVyImKGq5KKWw+i809F35cMM2SK8b4kyueyJoNW0/OkHon+L9RyLy5PeHcujB8x8k2/YPD7vl07r8p40DFxiI/dkMCoKkmv3zBfurzg5qr170B/ezr3a2nesr2BH5o8YQwJC4aSM2cWc/fXZgMX0mguS//jrSBkzxiXeI98KdB0kiCFPG+1UsxjhrpWQ0LmrZDB6Q8RjXsNyh8DmsexJ87qZjYL56X4c3f/fvh7PRn5+QppiOqdnWGKM2mv3HIkUrT+P1XcffcC8PvN7INes/Y3RWV4MFH3DBJr2Mwme7N3aYVTzjez+5Dg+3UNsDG/3OlhQBmPks13ZPISxbxBqPsRM5sY6P/Tg7fGrNJDhlUk54muPx/43rkZkS18zsxOqOPAjDaPG09ciJv7yCoeQ/KXc8L5jkjRsALKdfiBcL2UdwTJhv6O9vVdkEJlQM5jZhsij4VFzeyz0a1JSDbUVC4xs48j+VUTvdi3zX9razOwWK2tA+cNfH9VNmKQrknlAdqLQRpzAvBieIf7oBffacjo8/3h/ANIdtbkGZ5Nk7v7laYQhRsgPPaZFvpue6TEv8uVM3ZxZOSRKiV4a1zwUeWVFPjz88PRWkyhMCuvyjlRn/zDE16VUdtfQnP04piWsSjEXgFsHSfW3xXjunkRfTQbg3mpXkDy37jME+jfCQzSwobwY+pdOWPiDf9T51W9uTRMteaMwfVnpPNw4Rm5aM3s3WiNbozWyn2I3twv/PYq4xruD9KCfR90B5yMLBNT7oB3ezoMyemJV7mnwxG9gCbdS+FIhtkLwtptEQMYb5YvoITtQ52cW3eAPQMR4ZdRENc4p4TN/QNtwpeS72vo36oM9HMgOuZHFrrnuvvDpgR2Q8kqC2G/iBDk3IwkBTfUz99z9y/W4L+ONpsnENFwMbKAStV9o7tvEsYvXgBD42dm8yEibGdEfFyMQg8MMb6mGNovRnVVbW+cF6XF8kJEzQqXYmaPuPvrU/dqdZauwQeQG29lqZQKffMcYj4J998UneMN8cLbiplNA3YOSoekG3JKUBKeTYZ9bIBdDviNu//DzDZHgp0feJS80xROYE9EkC2DPOPmRhvWlSiB6vQIflz6uVb/M8iqZF+01r7t7o8kYK9DSu09EOH3O2Ql/cYEbFa9ZvYxFLJlWWSFX5X5kPfrrom6D6lfg8ZQGhW+/QfdIVMvDt+2D1Ke/AklcN4qgjnf3d9vg4pfaOnj6NnFGHTNHwqPEuDe5w2hZSOY7FxQVpDzJPFd9XqbQhTejKx9OsOHBfi5kTB7RySEvdSjkDpm9lVC7jyE+y9PzfUA+y1338cGY2/HbR7CGTZO4a9CPRu6+y9GW0+tzqK9rUf9MxOX3TvidbfUl71f9i3j1M+7uvvZNaZ4VulLe/XZ58O+tYOHHB9mtiyylqzP2+K5kTPefcYwZ0xK+8L6hZq41aNckA3t2A0x2usgRU5VXkCeQ3XDqj7hNL6NFHd1ZWCMn7O/z8xWGi86pqMd9e/NxrmWzuMQgSbD6RqBRnL3w0zGiku4+20d3/AqxKO+0AbXUUfTWE5GFtc7+2C+uWIaLXo2K2RPRpv7hvvbH0VReFto/54o9PSolGdB2PYcUhRfGAs+zOwiD+EerV8etMNRyKfnwvmCwH7uflANroSGKOHvxjwPZ9v4mdk6wFPekcetxzuL6jUJqf+AxjMOb78qMsZ7A+q7+5Gh3j2155dEyr+/MRjCb27ksfbbGvyGSP5zvbs/a1J6fgHlQxzKqxmeWdsHwztPAratrykryNHak0fp5Dv68BI5vEEEez0KbfV95GnxFLB7DXcVybBK5DzRM9sgvLIc8ho6M4znPCg39uQINqfN48oTR8+04uY+sgpryQFjZvd5Iid8gMniHbuKKfTb5sio4zvRrRdQGPiHG54r5g9y2mwKL/aUh3DLgS9c3CMD9CZ+P6p3iO8Pz30D4ZcfhktTkHyvN69kaVlY6lofmjw7R6WNhLy9E4XhfgE5NLwhginCW+OJj0rok+iZ21E4zXORp9LuwFLufnBJPaXvt5G0HX8A/hcaw7mncN1Q2NQETBs9PKDcKRmTiK+KeaoKdi53nz2CLaJh+qxBk+L0cOT9taWZrYIM606NYC5CyqibkLfmS23vyS3joqSyAsH+K62Mh7Ak1FsijM2O5Rvgz0ca+6sYjBc5ZPk+1t9nykmyJiKKfuTuN5vZY6kNrwQ2euaInI3IzH4H/BJZ8V3q8qJorTunmNnfgNuQq+eN7u5jUW+o+1rambrGpMttREMfAUyPtk9OXffIKs56JCfPeO9dyBX/ow0bRFJQEp5dHhHDdWK1KY/JOsib5Ao0Z1f0SMlRg58dWV/8zSNF1mhLZj8vCizqNcuyQJg/4+6/S9T7aoSfb3f3G8zsdSjBaEy4F9VrSla8IGXWV9Wz87r7i20wfUuYh/MjwcZL0fUl3P2pnD6OntkGWRu9BoWmmYyYsyRDEp55F2L64zl3aHQ/OxdUU1tTbY5gPxF+zwq/uyDvo7qVW/VcNo4weTSvjxSe5wNTvZZU2cz+jawO/1Y1s7rFMHG2trvf2YQ7fDAH4JbIsvv9KFZyVSYBq3hD/O+SYlJEf4jh8RsianvUnbW3/f+hjEc/m9le7v7dEtprvIopYe/paB0Ywht7uHuSyXklzI1xGpM+yqGdkfD9SgaVQ0NKnAJG+zcIjxsyvKgUlgbs4wnBaSZ+zv4+M/veeNExJi+9zZHBzDUM5uS41gcFhdk4t08xs1OQscOb3X1lkyLiSndftwF+ThRucmkGPXmT+1XHu+PxWAztFTsjY4qjgYvc/a7SehPvOQT18SrA/yHr5hvdffsIZiV3fzD8H8iTZlFCbjNb1d3vzXzvLCVROH8bCjVkKMJG0qOnhMfMEdaUFIvyyqYETik8UEJDFLalOA9nRp1teTzORAZvD7n7jh31XI2UIie7e8proFe9Af4TyOJ6sgcBvEnJcQzCLXegvl0b5S3a393/N1HPm9H+YCgUbCoB/dFIwTgdKZMuRQLXw1FexmT+XjNLek/WeJSiHK2j4VHaSh9eIjzXyhtEcCUGiOMiwwp1/wCFKb4+ce8t8fiHNj+LwoC2trmwDaV9nIObi2UVJmXZOyrZS+ChL3f3VRrwWifvaMOK9Vm3aDYmmtz07aMtJfyuKcfZRhWPbYq+clPTPl/YjruBNSre0hS5464K74c58ZwHQ30z2wIpbB9H+HNIYG5mv0ApO6pctRsjhfyGDW3opMmtR47KgMO+hIxm9kOpOaa7+x4RTBHeGk98ZGaPoXC7yeIJww4LuZzM7B4PhtBmdrO7b9RUT1MplE9Mc+UEK5Z7mhxs9mO4P4pyW0X19RqTjHrHnIZJvOMyxL8e6O6rm7wI7/LIqN3MzL1doZQDM/RMIXxepT0E+yYt4JHAYjDLXa4JKa8AnIK09KuarHLe7e5fS8AWWfGZBLMfYZhBSmlae1kIdpWA7HdCOT7WQW7yr3f3AxvgU2G63NOJ97K/r6C98yOmcgoiQhdAm/dQP5TARs8siTbHuL3X12AmIuZsCvKcuBZZ8SzlzRZ3Z7n7B9qumdm+aCzmRS7Q5wFXtc3l3GJmKVfhDYADgGdTm7vJpfJY2gmdytsi9rQgnC/r7slQbSVrMHpmTKyCcouZneTunwz/U0nSh65F925E8X+/iTw/9kA4cEiIGW1uByDF04kpwrPnN4xpP5vZucApdULazN6BcvPs3LOdvesN63FxBtdsyvpqQ2Sh+yp3f50pPMxe7v7xhnoXRALLuC+GGKYIfklGQh88mcIFZnaku3++61q4PgPhl6tdFjpbAFPc/aMN7/8OIlq3QBaF26P8IR+KYBZ392eavmG0xcxu8lry9NS16N6+iFC+lI7wYaaEtld5CEHYUF+2Yi16pjORcpgra6CQErFF1gtIENuYVL1W75nIUulkrwkHzewCFL5m5/CeXRC+7QqL1Vl3uN+5tzXU+x7gaXe/teF+L6t+a7CwDvc698uOusetnxvet46739EN+Z8pJuH7igjfd4aTKpkbueNdMobjNCbFtImZHYFCsTzKYKL0lNdOloLDelr0dhUzexZZixryLD23uoUShi+eeGbM6Rgzexz1VZPlaKzQ2h/la/x17neWlIiWir27Zvhg+MsY/nLgzwwndj82Bd/x7ouQ59sUZIX8Y2RMcaEnoixEzxXRaCbr2Cpkz+oWQva4+zYRTIkCc13g1+7+dDj/IJrXvwK+ktqLS0oPHjNLmJ757rgf7kahUP8RzudGFvL10EolxjmXEqJpeC2BeqItY0575QjEzGw+7/AQNLPXAEugXHYnZ767s96WZ2cA7/Fa6H0zWxqFNI8V2/UQqQMlnp9mdj/KAfn3QL8/CazmDZ4e0XOxB+BcSAk1zQeVC/ei3Fb/Mnnz3OBRYvuO+rN4lACbxXcU8hKdvEFH+zd295sS18dcxjPeZTxlDzm4uWebt0IeTI+G9i6DlK9TgY+4+7dq8EW8Y0E7VkBKg6UZHO8mI5d5UEi817mMZJZHBrhDivCSNlvaC6lxny8pYZ/YvMIrAf9M9REl1a3Ig/NJM1sD5cc6Ainu/+nuH07UuQbKEzs/Gr8/IrlGW9jbVprczD6Dore8BuVurOiv54H/cfeBcNZmZsBrK9or4NpJHW0owVtjjo+sn1dS5VV5Gop29RRaI8XGJTn7ax22j+wu0KEXIwX7J1DY7Kfd/YAa3MKIR6rC7D2AnDCSNFrhmLwxqvd+d78vAVNMw4R1/Mlam09y96kN8Le7+7o1Gn5gvZvZVBQKujX9ibufUdJWfIySnMUHGtRbUbzPLyFm9LGOZx4BVs6s/zrkMhgnF7u3AfYU4GTEXIO00Y1JEZFFzpHI4u591THaulGsx6ORJcc11dEAe0f4vTtuV0H/L4UsBEb1fT3HfjHgU+E9vx4tLPANZA3xf4jZvISGpNnRM3MhBHshsj76UQNcPWncbAgRpGCXBQ5EIbD+jmLArjCG/bYZ2lhvALZsgZuBEnveFc63QC7HMczktqOl7pI1+G6UZ+1F5Hb/b0aZ6B4J8I4FfhaOYxDx1ATfK2Eog8neb2iAvRUJNu4FlgnXkjimx3eOaT+39XtTm5Ey9HakjHgJCYP+PNp6w71PAr9HMWnvCcfdDbC3InyVg8s/HOr6E1JC/40aDkVWnwdH50+gXAsPAl8smEdN7a1w8wxgQvh/Wwo2rif6fRWyIo9hnkZesHvSkrh1FPNtOrBJdL4RstRqgv8ECu/zOCPJhh+rwbw5/G6XOsagzSWJlGcf5bvWRftgKilthWer8Zu9PudGUXfx3hY9e3iAvyxx7xAyE/E2jP2JqXbUx4SW/fK/0M8PhOOTcZ0Idx6KPOv6zI2heguf77VOSuZGyXiXjOFox6ShzmLaBOHuOTLrvxwZEh2ArCD3Q+HD6nDF44noyk+g3FKnVUcNZre2I2dMmq5F9zrpmKa+bKjvmyjv0/UoYXoyKfkoxvxWZCgyLZwvSksiazLpLEQHfwrxYCcjumPhBNw/Ec+4ZnRtzPjRAH9bNTbIarryLBlaT/X/DefTgIXC/02RYP99wGH1tY2MMp6Pfp+Pzxvam81jIkHsD8IcOQTRYKeOYj7E/XAAcCPy2Nwz/D8g8UxnQvYKBuVGOQeFsD4PWdQn8QfjQHsh77zq/1qJYzlgto46FkSKnDaYJRnJ07IpsGkL7Pxhnd8RjmPr30vLXl6/R6AJw+/fw//qvE4r3lk7b6Q9O753fmp7IJmJ3xN1lfAonXxH2/tb6s3hDSYiPnR/YNVwbWskM2mih8dNxkOab3y+BnMP4rmSR0O94yZ7IAM3j6I/5kQKsDVQGK422GzeEeVeGzoaYGegfXs95Pm4NrB2Szsq+ujecD5305osbPNVyFmgOn8P8PMx6ucpyEDjDKRYmgnsVF9L4f8xKIQsyJM8Oeci+ElIMdTVhhKa/FMF33ZnAWw23grwY4qPmurMaPeyiH5eANEwJ9BTfkoL7dgEC7wxA9Zq5xWdXfWHIcVoDLMyUridgfK57hPm55PASn3HBO11U9GeejHKy/so2n8m1WCLaBjgXWH97MEI7tozvGurhmemInq7ouE3AK6rwcyFlPQ3he+/P9T5K5Sjb41e493nocKJmSXYR26hufXeXp+sNCPZaQnYGS11ZxNQJXWjUCUfQgKPzRCDOySsCbDXI7fuHwBHIaVfY5vDM4ugjer6MJmPGe33jcH4T47+n9gHFoXwm3MUbZhEJBxAwoIvIgbuZQYZuj8AR2TU+UYkJHx0DProHYgxuxrYIgO+k2iggKGrXStZg53KssJ+2BAh+68g4mZb4KsB2W1Qg301IsQeQOEjK+Zvc2Sh3vSOmxDRchHa7N8L/LIBdhW0kU4J58sAXxjteI9HP6MQH03PN33fHciT8S7ECO0BHD7aesO9R0gIixpgbw2/OTj0HrQRTg/nKyEL8BhmGjBvdF7120Tk1RvDfizU+SKDjNRM4OyGNlyNiLeTkCDkeFoMCKLvuwVZV80JPFyDmYjwwOlI0PwTZIU/9xjNt7XDPHo8HNORZWsT/KN0CCqBr4bf0xNHXXCbFJ6REKIhZuQSJBD4aXRci6z5Um1ZHlnIV0TRY3QIIQv6rmJwrwdWRftsZ93xHGxbQ4xib2up9x6E52aE88VRGJwU7MSM+ka1X45nP9fqWBh4V+3aikiwen+Y95+nQIDfVG/Bs9nrpO/cyBnvPmM4FmOSqHOI6c145jxgsUzYXAVHH0b7AsRgP4royCuB42swhxfUN250TOn3IQZ8M2R49yTKnftBYL7RjHeoexeEw38DfD3M7R1a4L9Hh1CBAuEAg8Zw9wd80GVAl02jBfgqX9neSIB6F3B605jUxydxPiP6fzLynqrOR83DUcBjkim8Knh3/Vu3RILFY1FUjdQzU8MYvq52fQ5k6X8myncT35sb0VEXI0HOacDbajBFtBeZitEI/hYkzL8DCcn/gYT8jwFvT3zjJGAhZFx1J3BcQ71HUmDgggw2v4pkMsuGNXBRDWZGvX/D9cm0C0JbhYbI4Kmi4y6pnWcZ5YR6ZicYBEfX/soI3X5PdN4lvC3hUXL4jj68RA5vcAbwc+QVck2Ypw+i3FxN7R03GQ9pvvHrifnSeDTUO26yBzJw8yj6YyPkRfHB6miBrXjHE+ngHRlRQNwT2vwyDYo1CpQc1RiG3xyeu6TNy4W5/AQyaLgZeeeO1dxbAiko3w28ut5f0f9pRPtIEx4Ic+iEAH9n+LY2XF7EryGa+f1dcwPtI+tm1pmFtxgnfFSfNwV9MReRzBHRr61K3drzP4z+NxrxN8HSj364JfxeiWiEN1KT+SK5w/sTbXgf8tbvNSZhXh5DkPFGfXYUNVk65TTMVOR9XL++GjXFU3RvLcR3/Dn8PkSLEQ3aq5cAFiidK0N1jbaCgknWKtgPCOI8JKRqtTRFDNRyjCiJtidhURzulVrxfY0GbeJo6qamlQ3/mybE5LCoJyFi8jgSyB4lzfsgsh59DBH5v+loc+f30WD12zYmGX2VzTQzyMxdhkKBjdU8jOseEwFbqOsXPZ65HTEanyBhcdfwTKeQnB4IOdwvWYNFHiUZfXEZcueuX9+svrYJbqOEsF7R8dO2+Yks+ULGtO4AACAASURBVF8FvBYh9AupKcBq8HPT4sk1im8d035GXmdDaxox/014sdOStk+94f61dFiKRrA/RoT+tDA/90dJfpPrJfxOJxCL1BiyOp6J5znDFp3zoxAJ5zDISC3U0t55EbEwW5iHn6adsP0yYpDex0jy4MNa4OdgxBL4aSKCawzm3STyLG1+CsyTWecyXdcQ0XQLst5LWgNGsJORkPYXYe1Xx1pNcwop+d+CCL7JSNH91ZZ3rIAse66k26v5w8iqeVO0xz4L7N1S90ZIEPpEOF8dxeFPwWbvbcAOBIExCqN8EZFnQA0223IUEchHk+FpROF+OZ79HJ5ZHFkVb02GEiOMxRFIyZAUiiCh44Il3zlW62QUc6NkvLPHsM+YZNTZh8GdikKxVLkhG4WbZCg4AlwfJVWnZ1lJvYwjHdOnn6NnK6b3LpS7ML6X8gxppVnDcyshOveTdFjMI/z5EhIKJQXOFAgHavcno5wOM0K9hzbAZdNoiWeXJsHAh/VzAhL4Vf+r82dqsPcS9jsklN40vtfw3mPI9Bglk8cMsFnCq9J1VPjMqKx0kQDmLuBfLTCttBf9rKbPBd4Qna8S1uyyDNOuFX75MCPGDU0C1lKB6ZDiIvH+bZHwaXckr1kVKSF+SbtSpBXnMUjHDR0tz13CCL6/NIz3NxLzuPFoqbuER8nhO/rwEp28AcIDFd83F/JgenVHe7NkWPSQ85DpgRnm0v40KJ0T8P8R2QMNuLnPgXL83oyUYCeG44QW+HnR3prFO9aeXQvlb0vd+wrCjUsgBfdCHfPuZiTbqOSWyzX1H4X8bnjmVTQYt6Awg41HAn4yEc+KFJLHB/g5ouvHozC+xyOeZvZwfYlqviTqviqswWXCcRANhpABvoQmP4T86Ab3IyXko3Qo2MnEW4wTPgpwfbySfhHPCSS7LokO9sRoYOlBPyBl6PyIdrgB4ZvtajBtxtp1w7HsMQntGxrnsA4faHlnp/yIdgO4tnuzoZDPqzLK6DUlx3jlpCpOoGUFSb/MbFnEjG6ELK5nArt4OrfFLkiruBYiKLcHDnL3Cxra9QJCzC+Foy03VnbdZnaLu29gZlcgxuRJhLSWS8DOi/LgxEkC5/RafG3rl/ur8/uisVgM9fE14XwL5O64XVP9Le8tjiMa/l+IhEs/ZzA/yqdL2xDqG4hNmhtrOqdeZGHUOO/rcyjE8KzgncEYr+7pnAvzonADE5CF6vwIEf0hgpkLuW/ugjbf59D3TUQCw5PdfXqi7pI1eDUiRI9AFtbPImuQjSKYS2jvj3dHsA+5+wopODP7pbuvmLielSS9TzElDD0GEULLhPjFh8ZtHkXdY93PKyAm7mYkqATlGdgQ2NrdH0rUW8UI/j4jBMnuPhh3vrje8NypyIPhZwyu2eMSsIsgwvKtaP5fCXwmns8R7MWIad4HKVv/hDbLrSKYh5BQ4J+1Z+dEwp3lU20OMLkxzicDy7v71SG+90TPyAMQ2jCXh+SuLXDLI2ZtV5TMPMZX99C+poZiPFthIvrQz29ABHErzk3hdQtJUmvX5keM506oj89DyshR5daI32eDSVlvcPc3NcDPQCGM6jlP7kzBF7blVkQH/NRHYjff6+6rJmCz9zYzu9vdVzOzTRAuOAb4kruvn4DtTMQbwc4X4PZAe8ppaFyeb/i+7P1ynPv5/Ui5NhXhjTehEMc/boCfgBSZU4CtkJXctgm4r6H+mIb64oo6LWvNya2B4X0+PJO1TqJ7JXMje7wD/JjQPKGuor6wfkmXN2uATSUzvx/RYTNRv1X07Wo1uJeR1f1QFTTT+7e5+3ph7/w42jdv88H8TjMYTJhdb3Mqr9+Y0zE2khsrWZro5xADfyfE1/wBOMej3Bpmdm34OxeiBWagb10NKTM2SdR5PPI8uDmz7ZMb2hznHUrShF33anCrIE/5L4fzN7v7NeF/No0W4N+LFJZV4vYFkNHVTyKY3dra4+5nRrAHIjz1exTqaa3A370eONMT+STN7MMIj8+GBGPnNNEauTxmuPdlJIB9C7L6dpTT5ctt39NUzOwkJBTaJIE/cnLRzI5o4b+5+3MtcIsjS/adkLDyAtQnQ7xP9Ewb7fVj4Hx3P7/2zPuAnd39fYn6Unlaprv7GomcDvegvMpnogTlt1f7fqLey5A34l+avqUG/wu0P94YzjdG0VY2rMGtzkiyeENKkmPdfUZL3Tk5uNZEwvD73P2BzDbHeP9l4Ffu/psaTK9k7YU8SiffkXimKFdzE29Q79vMvs6SYfWR82Tyjd9G8+dmhDMucffDOto8pjxxDT4HN2fLKqJnHkBGAWMvRE2UprE3s5kJcPcGOaCZvR1FuFoF8dsbozGcmoBdBnjKQ35MU87Axb2Wty7cO7h+LTTk0AjmkPB3RWRs89Nwvg1wvddyR1lmrikzq3KALoFw9G/D9TWRAdsVifam+NQ73H2d1HcU0uTZedBy6J0INhtv1Z4bE3wU7k2lMPdQ2z7Y1o4I9gl3f91YwObSD5nvasTHXbi6bUza+qar39pomHC/je9su7cRwzKkzjzXoy3jpaSayngk0BqpZ6IrUea8yJKiKwHpSmizNBQjNYtIymxLVt1mtjXSxi6FCP5JyFrqpwnYW4C3VgSomVXhFTaqwe2LCPB5gR8hwd9VTZtTj2+7FCW3eyqcL4EUHP9JJVWSsYsZur7tCEzdZ5BF6nQUZ/MX3pBwsqDeQxEBdxaaF7sgK4KjCuqb3WuC9uhetpB8LBFyrd4cZVnFZGyHQtucHc6nAI+7+5ci2Dbk2LYRZCd0NrOrEGP3XDhfEAlk35GAvRMxJFN9RNg8Swj+nyo5/Rzg5kRhByph+H0oF1tT8vXJyMpnDhTqZX7k8fFIDa6o3vDMIanrnkhGb2aLuvvvmupqecdmoc2Xu/tL0fXD0Vz7ZCVwCX14Ekp6+cVEXdsgS+LXIIZnMrJWeUMC9iPAR5H1y3KBIPiOu7+loZ2fQOMVz7kp7v7tGtzrEJE9BeHzc9HcfKAGVxG0nwi/Z4XfXZDV+9Dct8JE9Dk4N+x7b0Du55+LwCYhYchQ34XnJoTvPBGFxkoS12a2QYBZGc3RiYjgSgmPb0JKih8jRvu3yOK2SYjZiGsSsIej+Obx+O3n7gc1wN/q7uvbYILRZPLgkr2tqs/MjkChLX5kGclgLSMRbwS7KbLAWgD15WExPijdL8e5n2eg0E3PhvNFkRXk6jW4N6E1tS0SuJ2LPC0aFcWB4X07Ekytgyw0T3X3R2twnfv8KNZJL7qna7xLxrBkTHJpHuuRdLmk5DL8OWsnUfeHEV+zGlICvAr4srt/N4L5B4MJs2vNaBQcjSkdY2a/ApJCo1B3jMuXR3zEFLQ/nIuE+Y81PW9m56IwT/eE81WB/d199wTsbgjnr4BCr53n7nc01R0918bE9xYOtLyv13Ph2ZQQJgc/Lwg85wlGPOyBSyDe78VwbQVkzT2tpc4VEe6agiyH/8fdr63BZPGYibpbDW3Cuz/KYELu/3H3X7bVO5Yl0GhTkDDvIrQ2bmqBz6W9ihWjZnYe8gKtFMY7In7sA8iwdN0IdgdkzX6Tu3/MZIx7tKeVX0XGmyYB75mIZgYpWnb3FuVTeC45P83ss9HpZxH9PKvEdJ1JeL0roj/XR968/9Px3m2RscE9nhAwR3BT6SFrKuFRas8l+Y7ofgkv0ckbmNlfUYgv0J6yXDhPGl/0KSVynrC/Pou8iJN8o5ndi8JJ/SvIJ27IpQMz25vFE0fwnbi5RFYRPXMB8Omq31ra22TI02YQE6+vCcgQfuGUrKJPMbOFEe1nyGDr9w1wdwAbVXM9rKubYrwVwe4Xnc6FIhw8kKLpzOxKlCfthXA+H3CBu7+zBjdLSW9mxwD/dvcDAh85PbrXxzHiGBS+sjI62B4ZuCZxQyG/Vhk03YmUvi8gI9kUHjjL3T/QdS1cL8JbY42PwvViI3gzuxn4WLXfhP3oO+6+QQTThMsM4dsl+sD2LSaF4H61/jjK3T8SwfyG2t4XtWMfd18qUW/nmJjZgwj31PkIQ6EBV67VmUXDBNjnULjnVJs3cfcFE8+chfae6YzIkLyJ3hjT4uPgnkU/17oVEMFVJfNbDXklpep/AnlSvQXa8+8gK/2NCtpuiKD6cjhfClhvLOouaEOna37tXknur5Lvu7d2PqF+reCbipPdRedjFnaNwTi8nbGmC+qNwwjemrg/dK1hbN6MrJSeaYD5CAoT+Gg4X56xS06ZvQYL672+6xqD4U/iYygUSvRMUULn1BxsmpekcyW1JuD8b/VzFw6MYVBI0qGQMEgJtWjfehvu5eTleZiRfH1Z8WtRwugqmexstXsTUYLT3yOm+E6UQPsbddjomewY52iTnqOOR1ramsLldfx2M9objwHWyeyDoXBlqWvhejHODt+4Kg2u3cil/HRkbX96dJxAYk9Elponhv47CXhTx/s7Y99HsPXwVxfRHsbzK2SGx2jAGY3hbSgIXxngs/Y25NH4XRQSYgEUcmlGDWYiUTgKxIRWSdWbwm9MRGENLg59/VkURm97ajnpKNwvx7mf76mdT0hc+zWiQT+FrD9L5v/qwLdQqK1TQt8cVYPp3OdL10nJ3Og53tljWDImOX3RNaYt37kd2if+TEMuuxr8JsAe4f+ikAy12DscXkdb+4QyG3M6pqSfEW/2dTLCuETPFPEo4f5CiH79OS3h4gI+ehjF7p8J/Jta+EqU3yoVMmg/OvJN5fQrhTQaCdowgY8OJoSFQ/j7GqTAeBYpjGLYN0f/6+Fz20JBTgw45yeI9vk8Cpt2bg0uJ/xbUTgwyvLKLtR29Bm/qO7TkZHBhAzYbNqrbU013UM4fD9Gkp/vD8yD9qveYeyR8mXoyHhuErXE6z3n5yFtRw32PkL4aERn397Rxm8D1yGPidsIsooG2NGGgezkUSLYRr4jginiJRLX6nh8ctvRUG+2jCfcHzM5T2ottK2bCGZcZA+hrk7cHF3vlFVE169Fyt7OMMQ92hyvpwORQiCZwyfgk4OqeYZkQlu31P1TgjA7ox2pOZrMX5WAmxNFIkjde5AoXGmAHQo1RmauKfrlHHoB0RYvh+Pf4VojfUk+v5adBy2xXiYC93fUn4W3GGN8lLiflXsIGSc8xkhI60frOAk5cTQefWH7HqlvT4xV9j5YMiZhPl/bdNRgi+RH9Ai/i4yNOuWB43GM/wvyJ/F1wHoMMgpNsbfnRm78F6GcPichDWAKdjeUXPQRFB6mixA9BYU0eCCcL0gDUZVTN7KOfHd0/k0URuY0mvMO3RTfQ8mVs3Ie0Z37q+T7TkIb8O7hWy9jOGlbVqLoUEc2bPR/GxQTe2Y4X4MEIUB+vPiTov+dsaYL5nk8b29GRMVERqx9GuOvIgR+PFK+/iX0dTIvBoVC8sJv6FyDSJnwuej8t4wIjj7WUO8DwLLR+TIMJ8Ddre1oqLcooTNi2l8XnU+mmbk8FXkQ3Y2IvhOR5ccrrp8pINCQhcVmiTa9A3lI9aq3dn9DMvPyhPvrIcuSx5Awftfa/S8CB0fnT4RxeRD4YkOdcyNc+EYaEkhGsNkxzqkpL5Hrc1uC5rthIGHoRIYFbptRSAAgPLBJdL4RDbiLzDwtEfzmiOi5DlnczCTKiVEf64z6Hg/t/UIY6848JmTGvu9zhO+pH4+1jF/MTM1dH78a/CLAD5GX4rPIIjMZw53MvS3cmwcJB5cP50swnHj9GOCA2ndegmKvH9lQ72MI16UUiyfUzov2y3Hu56MZoU12R7RJXYk0ucfc+DTaJ65AecCq2PYTGE6am73P56yT0rnRc7yzx7BkTHL7gn5KnEfoyGMUwR4S+uChcP4a0gr9LxW8/7UM4trPIoHuwdTy+PT8vjGnYwhJn8frQB6X30d7xWZIIHxOxzProby5j6IQUE1wOUz8IW1Hz2+KDc2y+dFw7zREwyyHjAa/CZxRg7mPEWOhjyKBw0TkLVzPMzot9T91Hl0/DgnEvsuwAKieH6GTx2RQqV4/Tku8vySv7Ey098xElrm/R4r8fxFw3n/ioID2YhwUo7X6SxWjrYZENdjF0T5/WThfBfhQx/yc2jQ/o2cWyfiuei7YOzvg70XRQUB0Tyt89Fx2snYyeBT68R0lvEQOb1BsKEiBjCfcz5Hz3BPamzxqsH+N7t0Tnbfl2hkX2UOA68TNEWynrCK6t1nqaGnH61JHztzuGP/zUL7fCm/MTTtdvhlSovwKhUDdnmYF2FUMyi/fQ6ZRdJh3SWMUpHibgQwaDkH06BBNRmauKUaprM78nlxZpAFLRedLk85R+cUwd1+O5vELaB9M5o6lXLYypvholP03Z+izNYnyib1Sj9BncT60BRkDWWvJmGTWl03DjOIdFwBL/FfG4b89EaJOqJjneJPqVBiEifMDWhKiBrhcK75piXa0Wg601Y0Y5o2i8/tRfpAPAD9pqG9dxMhVmuFHgLUTcH0ImKLvQ4Kxb4bjvU39lTnGfaxo70Tu3K1KmZ51X4ysHb6ChLH/C/xfDaZRWFCDWzX6v3So6/fIi+MnwNKJZ76OmMqfo2S5C9PBoFEoJC/sj841iLy4Fo7Oq3bMRbPF0TsRgT81HI+TmVA1sy+yEjpH7TgrHL9qagdijr4evveO8D9JyP23+5k0gTaTBIFGu+C3zhRk11sfF2S9lyXgiWAWIYHLkQXVvNF51RcTUciU0Y7H1UgweBISvh1Ps7D5KJT/5UHgbQiHJD18AvzRaIN/C1LsnY9i/I+2zWsjIudxRpRATQqfzkT0Nfg7iazFkPAkKSgIc+QTiOmpjC9Oq8FMpdkq6JqGeq9HQpgfhD7fl8Q+hZjqacjq/kW0Vj842v6N6j8AuBExyHuG/wc0wE4E9i2oO2tvi+61eogga73Z4vPwa03rhIRxD7BxA2znfvmf6Ofome2Q8CFJm/Rsx6E0WyivXDtfmox93jPXSenc6Dne2WNYOPez+gKFUykdk6SHaAPs9PD9rR7QSDBycMPx5RrsOUSWyQiP7odCc/2wBrt7j+8bczqGBoEYYycYmwvh5IvDsS/Ngq4jEZ17OfKI7TJYHFMmvuCbYsVQET+KQqx8A+0/dyIvkHlrMHFdFwJ7pd6dgK17VzR5/+9J8FhJ3Ju/dp7FYxb230Mt95IJxpEX4VbR+ZaMAX00TvPjkLaj4ZmNkaD3ISQ0fYxmQ40SQ93NyTQkCvCXIcPeGeF8NhL7ScH83Brh+SeR8q7RKxiFg6o8TS6pnaeEvMWeOD3GspNHoQffQRkv0ckb0M9DpI8Mq0vOM7nt6AsbPTMusodwvxM3R7DjIqsIdd8THQ8j5UST0c86aF+dRoMyMIKt9svs8Y7m8dvCvGvyHFoO0SVPIE/vm6kZ5tS+r2rrfchI75Mt718bhZ7+DLBmA4yhUMT7AktG19dsGhc6lNVhHsbKhy3QOt2XFuUJBfwamYr1AJtUSDXAFslWGGN8NIq5v3PqqMHsF/3frnbvsL6wo2jzHkh2UvEK9zOMa08kHQHqBGpGniVjQqEXe+F3lRgcXIL26WsZJ6/RrmNWAqxXQPm9mS1HiN1qZtuj0AHJEmLI7oiI2tsRAdZWXo9CmyyNJltT+acpiWzVjkWRG2jfupfwwYTBz3tIkmxme6UqcyVMXQnF1DbkBpvKT3RtiE3dGo8ZOKPv97kSWQ8ls47KxBCrMydRdAlsVV529z8rTcQIaEt7sou7vzf8/YopIfT8iJGOy6KZdd0b/X8cWZx0lY8igccpwKXu/ncz6/q268zsS8DcZvY2pES4JKeNGSVnDU7wwdjPFwCEts+dqtTdLw95D6o49Q+6+z9iGFPi1KZvd3f/UOL6paYkqEcjYs6RdW9TJZeb2VqMxGLe1xtiMbvyGR0YjrEuY9rPrvxQ3wa+bd05yGZvadfAvcJ6B4q7/7q2Zv+VgjOzScB7ESG6HCLM10vU92J0eny49q+mOVdY3oNinO/DSIzzZD4Q5A30IbTR74U8aRvnHAq3sxfwMTTnruyAzyrufieweug/85YcO2iPLCmze5RDwt0fCuOfKmchhd07UJ/tgqwR47ZuXvh+kBHHBOCTiHlYChl3zCpm9kE0Zp9F69+Qd9bRZoY3JPUM3/IxFBYNxIx+N7XHuvtRpiS4Vd7Jw7whR0KYj+9BjH5Oyd7bQizydRBNcDpaq2cjIVhVJrj7y9H550O7POQcSZUTUJ/F5cTEtdz9Mm7zuPRzVO5E9NTVZjaPmc3nHflJu4q7HxzaPpQPx2vxvQv2echYJ7WSMzeKx7tkDAvn/uPk9cWlNRrHGPkud/flEs/cEXK7/ITB/CspuvSl8P3V3jpvQzv+krg2DyPGQnGi9xXd/dLo/K8e8vmZ2Q21OjYz5XZLlf8kHfOzUE88gRzRs4shAVXvEmiDSqjZVWYiT8IkrZUoz4X5ez3wQzN7Fgn0BoqZbYmskVdB33Y/8iD8v8z31Muvo/9F/GigT77QUf8/TLm7nkGCsf2je/PUq2z4nzqvyi7uflp8wcx+7u5vqdMHOTymma2PvLCXQ/TOnnUcWCttuPfFhuvruvveUbsuM7PDGmD/q8U78hU1lFMR/TKQD7ShzOPut9Vw/tC8D+VY5En9S6DKVXYOEvymyiLufr6ZfRHA3V82s3p7Subn4Shk84NhnhyFLLpTpb4vHNMAV5WVzKzKqWjAcuF8zPIwQR6P0oPvKOElcniDdyLl8zlmlsr/8k2v5X+hhwyrS87j7r+yzDxhXsv/mFnGRfYQ7ufg5go2R1Zxo7tvYsO5phpzTIW6B/JZhz08KQNE0Rg+h/Bul/zxpfD9Vd8tR0QnpUqA3wbJUNdCys5Umx8FNgj7sXXQ11tH/19GaRqa8BfIoOgppDDHzF4XyzGjNpybuHZX9C0DuabCPtaWJ+x8JHP4c8iNdAFSXK6B5B0fbniuRBZ5i5mt6+63NzXClN/tOQ95ss1sCxQi93GU12ko5x3ky1ZCGWt81Le8Kfo/F1KC3Qn8KLq+C9rXQOErY3z0LmQU1ge2V3H3020kN70BO3rIwRqVztyqiZIzJtu0NY12mXxX2bobZFbp2qvHvbySlFSfQATxSmb2W8TY7JICNLOZCMGdj9x/mwhgzOxIpH18NMAf1iFkPQEJShczs68jV9im5N05dc8Xn3iUKA4xi6l6P1i7tGaD0K0PAVPyfdshK8jF0CJNbcIrIWSTTBSN3Kv7wFblXjPbGSm4lkfheG5OwMWE7cBn0ELYmtkmKITS6YGYWxLNvarMH/ohWVKCklDPR5DScrYItp5A8tUodvoU4FtBaDS3mc3WsrmXCslLSs4anD8+cffDAUyJLBduqXttRvpj9cR8vjTxzOsQIk8KVNy9YmYvNCV/bUzoHNpYCY+eD7+rhHYMJREMTN/+DI/hUIL5HmXc+jmDQHvYzLaqC3KCwKcxUXpGvXH5tZltBLhJYf5pmgWyM5Dw8VB3/0UDzKvMbPZKiOIhCbIpiXeSISgp7v5iIBiXd/czTcl+h+ZcYPzOdPddkSdZTt3/RkroU0bbzlpb5kRKm6WB2Sqi1d2HCNDAYNbxXJPSAiQUPhUJ1mEk6XWqvN7ddzCz94S++xGytqm3dzE079/AiEDxZHd/NlVpaPPcyMijSTj0cWT1+Xh07Rozex8Ka5lUUqGxmB0xJCCF2Ck0MCfufhmyRM4pN5nZSSgExyy6xNOJ7nP3NhBTtSYSYuPuT5qSDcdljlhR4+5XApjZ/EQKl3BtQxQiclEbTNI8iRYBdsZ+GZdx62cz+wgy8lgICVGXRFb5b8l5vqXeZFJbNG/rsLn7PGSuk6jkzI3s8a61O3sMc8ekoC/WqZ1PQAZm+yPPsFSZhEIGvT1uGmlG7Xwz+y6wQJgje5LA1ZWSKbR9PmTNuyfCG8fWwOt9Gc+x+l78iqBjEgKxpZEQ4q1IwJxVzGxhTySkN7ONkTfeZAbHe9kIZld3P9vdvxPgfx/d+6S7n9Tw2veg3Lr70sDEh7HdC3n7VYKCdYBvmNlr3f17Eey7277R3X8afmNhejY/Gt5xLQmBVY1e3AflLFwU8WYzw7NbMTz3lzWznyL+pfpPOF+m9u65kBJhERs0ApyE8FiqvTk85sloXV6P8oR9CynZm8pSZnZC6nUIx6TK783sIGRw4YjWGJpvoylmtnKlXDOzDdz9lp71HNxy26N1HJc/BxyaU0oUoyWGRAAvmtnCUd0boBx/cfkM+fPzZXd/MLz71gQtMqu4+3Ut7UqVlbtBRl1yeJRiviOXlwiwnbyB9zMUzJbxhO/plPOY2bcRDXQzcJiZrdcw3/uWceOJM3FzXFplFe6+SfhtnPM5xd2nmdm6Dbd/V+1LGeUryNBoKTP7ITJc270J2GTws3545mRgapiLKdiDa+dV2w+Nri0U/tYVWJNC3w0Zn5vZp5CHyjNIyVIZK9XldH0M8bvK3O7+ZPi/K4pocGyYR3V5aVxK+LUtgL3M7FeID0zJIfsoy0pkK2OOj/oWd/9YfB7olDNqYNbwv+u8C7ZXMbPXIJr1/PhaNHdAY3tpwNNZJXNMvuDuz4yi+W3lcyi1R9PcnVWqvdvMjnT3z8f3TPqP0r29uFTxh//rxcwmuixU5kUWE40aezOb5O7PN92vwe4N/NjzrfgwWZhV1qM/9wbrsZy6wwb5BXe/tXZ9A+AbnrAyN7MTo9O5Qlumufv2Le/J9nQo+L5HgG2a7geYu9x9zbb39YGNnpkHebNUAoorgK/VkYKZ3Qds1VSPJ6x7LLJOd/cVAlK6wN03jmD+gMLYJBVrKYGUmd2MQmgMWM958KBr+M65kIZ7Ctp8f+7uOzfBj0fJWYOBWP2jux9Uu/41ZK23d+KZs5AgcToj/eHu/umGdiyLQqptiix1T/WEVUmYG/uhUAgfCYRD3eo5ho89zuZCHjt3pohVM5uBhJ71MWwS1meX8ernzHev0R3YcgAAIABJREFUgARpNzOieFgHxTre2t0f6lNv7R2LIKvDtzJikfOZBmHXgBVUQ32HI4XuJ10ebpWF/EnA0x4skRLPLcmwEC2lkJwl8Hb35cI8+o67Dwm8zewKhBOTVk4J+JmkGaSUQr565kjgfHe/08y+6e77JmAuR0KG+vysC1iz8FwNfk7ENG6Cxu864BSvWRQG2NvcfT0zux4pjZ5GoZliYeXGyGLqDEaMFNZCDMYu7n5Tot5tkBXPHO6+TCDiD3X3d0cw97v7Kg3f0HZvhruv3nUtXI+tJedASpcXvcFaMuz39eINOCZrbwuwVT9Pc/e1wvz/Rcz0mJRNbwX2rhi6QAyfgvaTWDC/GQoZtDfCc1V5AeWLeTjRhtJ5NJ79PB3h71srmsLM7vGacL72zPlICfEzRKS/LwEzA1nOXe3ua5qsG6e4+0cTsNn7fM46qcF3zo2S8Y6eyR7DkjEppXmCQOADiGGajvKVtkU4yC4mD/O3Izxzhbtf1QC3EPLC3AVZEh/v7n9KwN0KfKC+NwY6+gfuPuT5G+6/EuiY5dE8Wh8p3870dFSG+JlH0Ro5G+XuGMKjZvYgCQ+ReI+vcFX9f+q8tJjZ/ShU6R9r1xdGobhWjq6dFYFsyaDS1d29rrAp4kcDfOzBMhcyIHnZ3Q/I/qjB+pq8UqpGXxfBfgYpwF6D8rRUvMrzwP94QhmYw2OWjpmZ7dbR5iFr/bAGD0FrxJFC7ND6uI6mmNnPkID7p8CH3X2FFthG2svM9ks8Mi8yGlzY3YcMf8zsG0gAdRGDXqBDRisBX3wPGY/8CQnqd/VBI5wK9jTUX9Xc3gWFft2j4bvWQh7Sq6KcT4sC27t7yrCzs5jZb5AxR1U+G5+7+3FDD+XXncMTdMJ0PN/Jo/ThOwp5iWLeoOD7smQ8ATZHznMvsHrAifMAN7h7k9den/aOG09cgpt7yCrWQnySo32nydCmoteqMgHxQAu7+5Di38zegmRBP6fbe7za9yrP6lu8XSb5TuAqd+/y7KzjvEpG9YBHcq9oHhsyyPlT+L8Ayp00YFQRnnkEWD8lE6jBzYWMh3YBUob4J/uwIX7XN83iE8xsGsovd0U4v9ubDdpL+LXJqTo8kkPG7zKzY4B/u/sBgTaenmpHDt6qwb8i8FHiXVW42ZhOy6YXx5O2jOp5gJH+mBtFc3nU3VeMYC5GSuHLkSfzlV3rKmdMzOxp5IhwDnCht0fKqdffKj8K9OJOKCTmeSiXbOsaSvVp21oZ0+L/gZiCOQeKefo9wsbaAJMd/5Facvb60dKO42mJr1xaN2IgZyJCfJtwfCVcW6+rX0Id8zNG8R9zvi+C7cwFQEGi6BLY6JlkrNoxqrszfwH9cl115lLreH4SsFt0vhuwPBLyHocSeV+GQtbMQCEzxmJu5KzBeRHifATFLb8w/D8XeFXDMw801VeDWxkJRu5DlkCzdcAXJQxNPL8UDcm+KYgp/Erp54L3z4ni7R4bjj0Zo3xbof5FM2CqWLfJowY7EcUU/z0Sit2J4uF/o2mOIMvAx5Gn4SXV+xpgpyMhbE6s6e+i8LJfJkqe3fKdC0fHkkiYdGhH3+wQxvkeGvLWkJHjq/Z9OXlaFgVWSVxftWlMkbXXgijUy2PI+2TvGswtJPA4shi7taHeVPzvOm5uXKMd96YBy0Xny5KJ51E4hsPHaJ1k7W0Bdv8w9x5DHiu/AD6VgNsb4Zc/hPXyK9qTSk8e63n0n+hneuRmRHlYTgz9kswjR1mi4ZK9plonmzatkz5zo8d4F41h7pjk9gVSdO2FQh9+P54fCdgDwm+S7m94Zl/gtRntOBpFQPg8HfspiljwEKLD3hiO3cO1LRPw/3U6BuHsc1Cc+V2Bibn1Rf34Lxry+9GAt2swxXmVwr3tUM6OPzOSUPz5GkwymX3GvSw+gQwaLaOO62rnn60d+yJF7TJ96k+8b2g/KHh2iMcMeCrOhTBwPhZtjt41Kpq2VtfSwKTatU8jYe/7Op7tpL0C3HzIO2UmwQulAe7axJHMwxk9My8wXwfMnGEOXcRITrg5O56ZDXnDrIo8sVIwE5HAvzqfAwnUHqjBHdJ2jHL8plKYh6nHO3J4lD58RwkvUcwbZH5btownwOfIeXrlCUN72YoZcP9Rnpgabo6uZ8kqAuzBAU98NRwzgINa4OM1ciBSvDTlcTwbeQificJ7n04zL/hTpNBK5tmK4Ead5ybgnSsa7mXnF0R4sJUuSjzTmmuqoJ7jkXfM8Qh/zx6uL0Gg/xueK+HXzuq6FuMFxCu9Izpvyj/Wibdq8P91fBTqvhjtVRehyDkPA0fXYP4F/BEpOV8O/6vzf/aFHasDyfJPSVyfhHiDy1COxlNozw/ZOSZo73lHWPfPhD7bEXkBdrUzl4aZjPifuxDeOxhYoQbzsVDPiwzmrppJLR/veB2vpHB/KyIFzieAU01hL8519xsjmCqsw8YoDvl54XwHhsMRVVakcyHL0RmIOV8NJZ/bpKEd04CDTB4HFwPnuXs97mR23a4Y0+uj3Bq7h8v3ARt4vjvfX5GSYixKzvdVJScXwPFNLzKzyT7owVQCW5XjzGwJ5Ap7rrvf11DFkDV+RsnJX9DHdfRSS4RUyy0uL8HY6vAzSDD5A4QQb0UbyHtRrNeTkKXsaEvnGnSF1pwSrP6qEEj3u2IXN5V7kUVaW465C9BaOoYRAckkG3EvT1lWLufuO5rZlADzNzMrGa/fIIYtVS4xs4+jNRLP/bGw8Byvfu4swQLxH2jza4PxUbzm5mCVcx6yAkl5dlaxbrdDc+PscD4FKZdmFZdlyhfM7KsoJjrAI+7+t5Y2bIuYo9bY3KH8w91fqqZOsPJp+v4nwzGBWijXVPFhC6dvmdmNiCCo3rc38H8+Es7gUkT0/BERc6lys5m90YdjJKdKbp6WE0m7+i+JPAKGPDvdvQo1eh3pcK0ggdGQhaG7T7fmMDGp+N/1srI1h3hts/76HAoj8ViAnYyUtp3F3X9iZo3x7a0hJJAnwjCSv7fh7seYPESeR/jjYE94iLj7d4DvWEcMeTP7lrvvA5xkiTyIHnmsRSV3HlVl3PqZjNyMptwm34/oiscQo3E5YrZTJSsfTijZ+3y0Tq6nfW5WJWtu5I53VErHMH5X25jk9sVM1J/fQoKp1c1slmddjbasLLtL4r5PAq4wsz8iRu3HDXT2fmhfPwg4MMIzQ6GOXHkqtkOKpMqi+l4k2LmXqLyC6JgZKMfSz9CcXy+u0iPLcDO7EvhItU5MUR72RsrErUmHTb3WzI6m3UPEG/6nzuNyFB2W/cDzZra6u8+IL4a51LYGcumaHH40fu9C0ekEFDLq1TWw1F63NJp/X/Eo94YpF1xjWz1twfq0hfCfphB6ayFL71So2XpJ8ZjXMZgbIT53auE2rUdeWVPoou+j8MOvC+O3l7t/PKPNTeVCpMyo3vFpJOBZA4W3ujC6V0R72bD35Vqe8L6sirtvkdtoU166DzIcwnnIiyPQtccx6M2UqrMpVP0KplBcF0WwOyFDmBfN7GFkTHsWMsoaCL3m/fJz5ZY+aQxKSyeP0pPvyOYlcniDnqVExgN5cp7iPGEWRUMAlrFENISojBtPnImbq9Ipq4jKFKS4+Ht4zzdQ338tBVy4Zlb3lqgAtXIswm/fMLPb0JxOhSCrcPdiyFvzmnC+BVIM5+S5mYdm+rUkv+BjwFSTl2s85xrxmZelHGgr+6D+WgJ5Y1ee5a+mPQd5Nr9GLTy4KVVA3fPwGlN0h6eQAds1AXYJoClSS45sJS6vBHwEklFW5WXgVz7sITxHQX0lsGNSgix/SD4SyWvPNHk0bg+caGYLuftSiao6xyTsPVcgXmYOpPDdCTjelGd01n7cU35EoPePBI40szWB05ACPQ49+COkfDuCwbx+L4yRLLSzvGLC/cXFFK/yeBQKKJUb5FqUNPSf4Xx25GY3RBCa2bnIavaecL4qsL+7797RhoWQW/BOyKJnSEnUt+6uYgrnUQ3MBKSQO9/ds5I/Zr4j5/tSgmz3Wog7U36LJYHr3f1ZM1sNTeg31RdpCWz0zKtR3oIdkRDiPHcfIgTCRrCgB1fnsLh3R4mlh+Jcm9n+iDF7G1qEe6IwQCdGMKvWBRHRu3Zy9x8m7r2ArH5eCkdrQs2uYmZ3obW6Rjh/xN1fH92fXt0bq9K1BgvruhYxiLcxSJDEobseZ2TOV782AjrscmwKMfQWZAm2lime+zneHH7nRAbX1RrA464cQ3XYmYkqku0YTRnLfs5831TEpLfGePYQf30U71kP4ZZtUf6hc9397ATc9e6+ade1Hu+/DNjB3f+SAXsUYoQ/iCw4P46YnwMjmLmQdevvas8ujvIOJGMSm8JBVGUCEmB+zKOQZzYYfmBBJGy/CIWIujU1n03hjl6PhL7/oJ1ZTOG5c9z9hBrcfe4+lHsn3LvX3YcUuuH7Dwde4+5bmtkqwIbufmoE8wCy7PxT7dmFgJvdfSVqxZQT6+dob3gfEg7PHjNB1hBSoSreksDZFNIwThqfVGbWhDzV+G3m7hs2wHeGx6jBZ+1tEfwkBsNX9iIUzWxtVziAzVL3PZFHIme/TDwzXv08AYVZmhXWDSmkPIKJw2ksjdbVV939x2Z2u7sP5QMwKW3+Huqs8uH8MMHAFe3zptBBR1VMZVjn+3ktdE3tmaK5kVNKxrBkTHL7wszOoF2QnVwnpSXQlTsi3PEbd3/rWNSb8d7HeQXQMWa2O839jEeh12L60czehbzMtnXluWlaJ9emq/VYOfBXZOluKITSI9UtYFl3TypIzewmbwghGsFsgpLLn44MFB15Su6GQqQ1KZOKQ8Hk0Gg2GPLoZbQvH9rUjtqzC6HwonHImuK9rcJ3oW+OQALiL7n7kAHbePCYplyQ/x97bx5/21j+/z+vc8wOMlYyT5llChFRPiWzRCcKhUiINPeJUokMCZnSQULHPJSZQwg5zmSeDokoJUTm6/fH617nfe+177X3vdbe+33en8fvez0e67H3Wuta97rXPV7zVYYZ+djcfbHEM3cioc7lPhS2NUlr1KhHvO7/BOVy/JS7v2pmEz0KU1aH9jIpZXdAHh8ndaItLeRjs9YQXzPAE8LYsA7cgSyX34lw47k63t13sgolZpn+i3j4pGDa3XeIcO9F8/7RQLf+CfG4l1R9Zy8Q+uZFtG9XhavKTmPQ4P1ZPErNMrvyEhFuV96gx7p0lfEEvK5ynobr0USkLJ4Qze2uIaL6zRPXWZtzZBUR7lUoFHRB070LOMfdt6qoxzpICbIkrTR8imc7HSljs0MgB5nUZsig+RNV8iaTAnAvd/9bOH8vWs/aFNqldWY0irbxQ0+HkL0GhXyO8wtu7Olwhoem6uaDVX4X724cTrQbTW5m30YGnXMiw4+C9nsDOM2jEKFmZgwpy8a7+9Ph+prIMzeZs7bOujWS1qNuYGaXIqXI5VXylCa4PdQnNg6Zodx2980r8OdHtMxYxGNd5DIELeNl90n0zPKh3F1RyPU1o3u15UcBd1ZkEPIZxH/cjPiOSzvUYxGiHL0eyRAHBSNKSRUEJjsjreGf0QKQiu//EBKE/Sucz4/isL4/gdsmwE9dSzz3wVCX7dAA2jqB06jsblASHBVa57/2UmbiHV2/L7Oco5CFxmQkNL0STbqfAKd6aw6FbNyKd62GrFl3dvfZSvd2RszDK0h7fBhDVmCHe4U1oXXJXxCEgvshxdrlwHXIK+4QFJJlWwYMpri5+IBjsIaysuZggzLbwOsn1C2XuzmyhF4ZWdhtiMJATKjA3y06fQsJdpp44PUMg2jnzPemYjzPiTbhRjGeu7xvIWTtWWVw8ACwpbs/Hs6XRlYhPSVPNiVbXYP2mN5tVqmZAu/TgKu9FBPczHZB1lgtiUGj+7Ew7y3kJXa0R0mvTfk91kbM+KUoRMI54V6VcijJNKaYxYDfNU+LmT3sFfkazOyhiv31KiQo/K67r2GyCprkkRWgme2NGKdDkKUh4XuPRO7opybKzcnJU4vZMLPN3P1Gq7AuLvdteCZm4Iv+O93d/97pvdHzsyNCulPC+Y57W7j/JeCHwH+RAKtQAvSkNDez7dF8y/E4zB1Hw97OFXW9D62vSwBnIIbrxsAc3usVCtlBgCXycebu293GRoO65OZs6nufNAEzWxiFpViZVgapKvF5IUz4NGLA5kkIbzdz98JydWl3nx7d2yE1RgcFw03HmNks7v5WdH4nStS9ODIGWNPdnw607x1ekdsv4z2NjAjM7Hhk1dzJsr/o4y8jq2VDESpOcvdnS3iXMCRo24whQX1RbnKdGk4aLbU+VOB1Mo6b5MqjdwQKH3NuVbmD5jEtPx/bne6+XlxPq8hbWOPdlyC6djHkTbaKuz9rZishIXKspMqmvczsHTQe36JVQdSmkDezL7n7qRXCWPeEZ3XOfmBm73X3vzWg/7oKphN85YOeMCDqF5jZdkh5vYYn8sINF3TjUWqW1ZWXiHC78gY91qUvMp4e3p+a251y/swUnjhRhzZIySqCoHxdJBNyZPhzKwrl3MZrBrnl12lXQqcUfA+gudHVCDHgz4lkazujNe9Kd9+/Are8ro1C4eW68ZlvAc/FtEMJt3Z+QTOb2+UhN2xgfTDUzeDXjvCKXNkRTk+593LWrZm9HpnZC3Q2llogwi0U6pugOXUekrm0jbc6uD3UPfYCLNrjAg/5CQPOPGh9HYvm3eUogsNNHfotq0/MbAk0n8ciA8DzkULygRJeLflR4DfGAlsiZfz5wKWd5qHJK/ZYlPf070jR/sBw8NAjRkllsnaYjGKFXt6lwfZASohiUm2CLGTPTOCehxQXsXZ/jLuPrSj7SGQt9Vioy8VeYcFTt+yRADW/bwUU/und7r6qyTp1G2+1HLgfhTx4zaQsfAZY3dOJ17Nxo2dWQhN1R5R34Xykof57Ca+WFVhg9q7xLha2ZnYZinP6J6Rtnh+5mh7oFcL8IATbBVja3Q83s8WB97r7XZ3e1aEOk5A1em2r1JrvyZ6DDcpeElje3a8PAujR3j0Z9bJoIxqbWGgNMaGvkpkwNLOetQWsDd4xsHauWY+BWCkG4db2qO+WRSEnxrt7OSQrpiSupyH3f1Coky95hRVRjTrslrrupeTdYR04yxPedCW8+6uEddbBAymzrnuhEBGjEQNzNlKu7QZ8wN1TFsqYLKaXd/dxQZA7Jha2RnhHuvs3M679Hgku/lC6vgVwgLtvkSj7z+6+bokRTRlvbIWI+lXQXnk/ikl9RaLM0cBP3f3rqe+O8CZQg9kwsx+4+6GW6SHcDwj73F2e9lTO2tsC7iPIMKen9S1R7jgkuL0lvP+aCqYga78MuANrZ6sRBisQ1schK8YHUCiXGxCdNsrd94xwX2bI2pboHZ28o7L3eVNonHU9KAODUOHuqnWjztjIhTp92KDsrLawoTCTmNmB7n58dO9MT0QhMIWj+x1Scu+D5vU/yutXwN0XtdvCwIVI0NVmjWwDSrxswYMi/N/QI+WRmX3FSxbIA6RjbnX3jcL/37j756J75e9dDnmsvoH4gi3QerAtEuoflyg/x4O2kRCmzrphMrpZDs3XxzydxLwtSXip4BsSz2TRaFV0YlR2V3rRzDZDuUxiL7TaxnEmRcTTKKn62sig4S4fJkvoUIeVkHHJmsgj75xOgiMzuxAJP05E4/8AYB13/0wPdZgNWbu/gejKM5FgZUWU6/e6CLcR7VWjLi1rQNW1cP0glGf4SrqEGM+l6aJ7XQXTZvZXWsMHHhyfe4dQXDlgCn80LCGCukEdHqVGmVm8xKChjown4HeV8zSsR9doCBFu33nipmtzrqyiiseMyi/zmjP25G5gNZTQplCN66Ew1uOR59o7ZbwI/0Tk6XEe2jfHAo94pNSy1hCJqXpkzWMrGcRE1zdAhmNj3L1fYV6zwNKGunE40aShbg5NHvrt3+7+YjjfFCkxngjlvhHhTqCmsqymbGWmr0ehDoaUl/9AjgMFrzCXux+ZeGZu1GafQd5cVyAPnzbv/Tq4gwAzex4pmc5HSrI3u+DnyptuR7RfEVqyMlxrXRrGpIw8F43d3Hk8BckJrncZQm2K5LJ75zzfC4wkJdW8rtiOufjvYSgPz51esp6L8OZAyb+K8FG3AL/06rAz+6A49l2ZxLplZ5RXCEqSkBKUNHhHne+7GVl+nOoVoRisPXxCpSdZHdwI5w60mV7g7s90wKttBWZmlwOfKzaUCpzYlXI0SqK6RIpoiZ45GVnKbObuKwVB5bWeCJuSA4Go+FknnBTx0uA9defgaODdtLqut7l/hkV0b2ABd1/W5Lp6iru3CQ9MFn47o/w3q6OwJRd7IvdOeTx1qGcdwebABdmDaueRAoHhuBQRT3/KwJ8dCRAAHgTeqdrszex9tIdLuKXH+l6DcmBUxYHGzB7wCu+u1D2rCPNSQJnhD30M+q4jUNLMe1Co0ra12mShuw7Ku7WCmS2K1si2MEkpoaslLBoDs3olcDtDOR7XATYAtnL3hxNlT0AM6HWuUFXrA0e6e9IiMRfM7Ebv4CkRcJoyGy2eE6lr1hpSqw084ZEXnkuFxzjc0+HUsva2gHs1yoHzaie8CP9DhNwWUZ1TuWUwKau3QOvuRqgv90zgdd0vS/h9b+eIed8v/P4m/O4CvOrp3F+FMmB/NK8moTDNnXJLdIU6+7yZfQPYBnkdOhq3l7v7URVlZ4+NgJ/V35k0T+2xn9sW1kA5VOzz1hrG6+bUGmPKC3F+at6X8GKleou3Sfm8DvTyfRll16Fj4u8r16Pj95nCzXwMecReX4GT40E7gQGFFg7v+wnKcfcX5Am+WFSnNyPcMzyRD6lL+Vk0mtULp5bqvwWQsd7n3f3BCLeJcdxcKHzLNHd/JNDSq7n7tRHOwHhMa83HNh7lY4vLTilbFkKhvT6GhFfXom9Mhn9rWK85gNWQILZNWF+X9io9WwjJPuvuWybup2ivqnVgP+DHiI4p+sg9HSI0i6aL7pUF059B+ZViwXQyBFcBnhGKy8y2BZ519zsT9x5BiohxwFU+E4VPDXiULL4jk5eoxRvUhToynoDfVc7TsB5xNITCY+BwTxsS9J0nrrM2R89kyyoC/myId3XgoS79/lGkECpH94jzws3r7i9ZhZKoYg39BKLZ3048UlWX7Ynkll4y5rbWEIlLoL3IgHcBf3H3pSPcbIOY6Hrfw7w2AathqJtDk4fv2t7dnzHlYLse7SmrA296q2Fcbf61wbo109ej8I47vRR22MzucPf1uzy3GlK6rO5dvFzr4HYpJ/a8b4MSTTdXLk8ePZPTJ5ugeZm1R/ZCw2SWf7e7r2NSVq3p7u+Y2V1eEUqwnzBLd5TBQswQWyJXcJUwyKWUuszkbbGnmX0mtcCFDfG4cGCy8DyAktDfhuJx3oUSuC5RKqctXFxu2eHeCogQKBM6m0X/5wm4PwSepVXrXJVgPguafB/SdN9V6peyVcSyQfBRwFLxubfG8q2DW1zruIhFsEhpsR0Tn1cstK8B08zsOuQRV+DGY+7N6PrbZjbduycnX88lsJ0UnnshEDNtYMpj9g0U6qXwMDjG3Yskpbj7V8x6cw3u8lztOWhm+yPriOcYcl13tBmXYT+UvPvOUN4jptimcXl7IQJuMcTg7omEG50YozvMbF13/3On70N5YYp6QEmwGSO6+6Hhd48uZdaGYWjnkQLL1BmH7v66ydNgU5TYemvEgLSAyUJwZzRHCoK8CCtQ4NSK2R/gCeC2sBbF60C8ZvzdzD7o7Z4B6yILoTIU6/X7UUiIYp3bOq5v9K7ie95GFqzdYHtkrVyEAn3G5Hoe121fFA5pGRtKelzUrc2S15WDZDWkIC720puRhVtVKNaD0bcta2a3IaXMjqV6HAU87u6nlK4fhGI8p6x/J4X+uIDWPrk4+v8aClP1yzrMBhKclhmnC2lNbFtYLm2I1ubfhfNPM6TAS0Ecj75jeIwaexvAt1HS3DvpHr7yN8jKbjKt8ySppHL3N4PQ2VHoz23R+luGnP0yhr63swdjDJM1eqyQ/VYYf0klVViPfhGONggM4z7IK2MqCkPZLXRE9j7v7keFNemjiKY73Dt4i9YZGzX7O6cPm4z93Lawiv+doKDB/mbKmfQMohOGChoS6hxVOifUpyzc8Yr/qfPiHQujsKVL0UrDx4Yrnb6v6nv7TsfQgcnucg93n4QUuZ1gIXcfb8rBgLu/ZWZlIdknCPkPTWF8y0KY42IhjJl9I8yTpJK0NEZ/hvaxZQp63GRlfHQ4DoxwsxWOdWm0gk40eTGt7KVwaqXHy7lKHPinp70GlvEh47hfkWEc58q59HekAHwE7T+PlHCyeUyr74mwbvimQ4AiN2PsmdqmbAkClF3K1/sJgU6onFt1aa+wrn0S0UmfQPtcmbbZAAnGFy7xpfPSmpg8hoOB5ToJlerSdAUEHnIH4MPh0mllwXQXXisX1gNWM3lQlD3vV0DKyC+gxPK/A870hPHTMEA2j5LDd0TwBN15iVq8QS40lPFAnpwnfs+caC3qGAYsCG+/G46qsgbGE9dcmwvoKquI6vFJ4FTksWbA0qYwn1dVlL0HUmjNWqp3vI6ei/aJIs9i3Cgta2hpfd623H6J9Tm+dwnywsHMNjKzk9x9v+j+0uHeKUiR9IdwvgWawzHEUXzKMthKGs/dnyrVOVvJ1i9wGbP8LRM3hyaf04cUWLsiPuIYk+dqi8KpIf9aS7bCTFyPSuCmlCzj3b34nwST4UoRqnspxDPu1StuDSiiHWyLwtsVoZXHork+A7ymgirAE3TpE6+ZCqWB/Kgu/NvMxqCcc78NdGbfQit2gpmupKIBQ2xpb4vKEHvRQB6LXOhSIeCOCb9zIGuwKWiBXR1tWEk33cyyQcK2U4DT6b4Yf9xbtc4nB+FU0uo2E5p83/MmJWBBROxI+4JeDjtxDNVCjAfZAAAgAElEQVSQjVslZC4gIWw+nVYmKz6vKuf34egEa5hZYeFjwJzhvDIMEPCmSbNdtNvCRDGICzBZnR3NUJJjQwK8i8zsEHe/LEK/yZRnp6NVKgptUReaCKUORF4cORaPr7v7GwVBYrKCLffJSchq9LMeXFvNrNtmvCnwJTN7Ei32ybjNTQSbZnYgsvh7GY2ltYBveWSV2gAG3c4zFSxKxl3BcKSSz66H1vHtkVXxfkiZn4LtUFt08lQthFPJ5LUV8Ew4RlFtDPB1YLyZnUmrl9HnEYHUAgXDbwpVtVYkSDsM7QW9whuB2CvaOxXu81zgKrS+xAnRX04IbQsl9+to3Cch4BTvHIX2k00QgWvImrDsBbcV7cwLyIJ6Kso3U4YFUEiF2JuqzNAN3chgNsxsRRRucL4SgzcvUa6bUN5Z4ZndgU2LbwpMW6c14EceWRKGZ8rWhXX3NhAzfCOlePYVsA5iyrsyMyYrzM8QrEuBX6FQSSnI2S+Hq53nNrONPCS/NnkS9RLy9iykDPkjEkCuQqugOwVZ+3wBQXhRJcAglNFkbGT3Nxl92LBPcttilMnLalT0v9gsqoS3PzKz+ZDg+wQ0jsoJiWOhTpvlL7JUjWGZwCRa9J9wXsYt4DI0Pq6nmoavrfxiMHTMu0zW0qPC/2IeGjBfRT3qwCtmtiBD/b0+0OKd10AIU8TarwxtEsFWwArxmHdZoO+LvLHjuTuXyfgiKTDzyCCM5sYJSxVC0ADPIaF8/J6i/zalNextKjRNbeM4i7yr0R4+KwpF3+ZdTR6PWeSwSXoiUNqL3X2pTvUr1TVpLBCVVWX4MNPAhnI5fBz12W+AD3raoG02YAySscQ05UuUjHgiuI92ZXMZatF0MQShdaXg2oKBV/jfEjrQzK519/+pejZ6x3c63HMUtvK6MAfOAb5sss7+lmd4BvQKTXgU8viOArryEgPkDRrJsMiT8xDubY1kFbMhpcwHUN6hbSKcGW2cglIbDwdP3HVtjiBHVlHAsYg+ejTgLotoqyoabw2PPI1T4O5bhd8qGiSGWutzDKHfxiI56vQOuOt6FJ7R3a+y1lw90IzmeSrQ7B5kWAcwtP+PKKhJk8eLymbIuBCX50nlO7rxrw3XLZi561EMn0W0+8mm3I53UDJOMaXxGYu8ni8Fvk+FN1Ed3LrgIfyzmR3q7oW3IaYcdLWURxWQI28aabAtCh/9VdRv81FhENpvmOlKqjoMsdXwtjBZlW+PJscKSHm0jLsvVsYN9dg0PHc+sLeH8GImT5dDeik7wFvufnKH+zG8bWa7oDiXHr65JyuDOt8XwX4oX8yKZvY02sxaFpYqja/Jq+wzRJO6Di71rEY7WoGZvB3i84WBhb09ZvCqiICJy23iNvoLNCYWMbMfI6bkfxN4PwQ2d/cnomtTzOxGJBCJlVS1rFLrQEOh1FOUhBId4GYz+w5S8G2OLAHLuWgWRYTpsaZ8B+MRk90J2nLkdIE6gs0vuPvxZvZxRATugRj/xkqqYWjnmQ1Hh98dUOLzc8L5WGQ9MgPCvNgJCRDPQ3Ph7vKcLMHjaExUMosFM+IZ4S9N3hPzlNeOMP7KAre7TImI9wN2D5fvQx4EnfLELIFyIxTwBrL46RXGm9mpSAC5F1obflWq84voO8Zaa/6qhSwRio2aivBAeB/j7hugtqgC90SM9PB8leCw756MSHi3FRJcx0mkX6ba+mpRREgWAqAx4VoVtOQYCkxuOZRXrb0twFvunmshdS+afzkWgrsjOuNLVUKYOvtlgOFo5y8Cvw7KC9B+2Eso1pV9yHPhDGSN3A1y93msNczWbGgde8XbjVyajI2u/d2gD6Fen+S2xXxI+FTM+9i6u4rBfCFaywo6tkXw7vUsf6HVYOro0r3yeQFzeUXOlwhWNHk4GPIuLRQgRsKTJMAg6JibUXjJ4n88D/thHZvyoP10FXKOEQFBuNeFBoiKbBdIBIVO+fr7kBFUaq9xhsIe9aI0n2AK4xKHU2tRPplChl2MvBmLObCTyVNje3d/OkJvYhzX1bs6gq48pjfzRGgBq84ruw9at8YjgU2uV+XMhGuQknqjgnYys+NTiIHXvdmUZ68rLRrgbWCyKWdE0ls6punC+xdBfOAYMxvj7SHPbnX3jaw9zGNqHMV5Mzen1Xho4VSFzexuxBed6+4vdPo4k1J7V+BzaM/ZH60hH0DC0BzBfK+QzaNE0JXvqMNLRNBX3qChjAcy5DwRHIY8jSaEd042s3Kdq/bPVJ2HgyfuujZHkCOrKODvhYIqwOMo910V3GFmK3siR2YBViOnUd312RTN6TNorP8TKQStGDcV8LyZfQ/NE0fzt6wcbGIQsw8yUnwf8FfU1wPPR9UQ6tDkN5rZeETrzE9QHIY+qQzvlgG11q2RsB7F4O6PA23hcEuwGfBzFCa8m5dOHdymsIiZLRXJZ5egYh/MgYZ9MiLA3V+xoVx9Z1nI1TdcLx8RB/AQigNbnM+PrLJjnDcQw7VOdO3xivL+G3A/DDNybyVxS89N7natSdloc/8y8F5kJb5A/L0l3KWQguJ5FErqUmQN0o927vp90fWlw+/caHLNuFaBvxDK0XULcos8uldc4LacawmclZHg+xEk/I7vnQ9sknjm44jY7lb23GiD+n0HnBXRpvYVYKUKnPs7PN/p3qxhHL2rH2MiKrfrHIzunQHciixFDi6OCtxRSDh5AcEdt0s9FkNE9URkXfOT0v11gS0Sz20NrN2h3LWRddkTiBCfjCxHUrhTw+/xSIAAyrkwYtt5pBzIoqXjtbCu3YqEmXOEa93W0IuAR5FXSRG66xcVuOujEC//QfvG28BLJZzTUJ6f8rO7ACf3qS2+G8bcYShExWTgO30qe3MU9uhopOyuwjsUMVoPh/NFSa+rc6A96jYkOLo/zJMnkTfhBxLP/ADlpLIO7/8zIm7K15enfW0+CtgngXsQynXVj3bboAbuHuH7zwzHdGD3BN63kRLmLWQt/VI4/ydwREXZ2XsbylWxN3n0w03Ik+QaJAS6HAnum7ZXo/1yEO2ceG5eYL4+jIl7Op13eK7rPl/x3HaU9rUexkbX/m7Sh3X7pGlb1O2bTv0DTExcu7tP9fgR8MkuOEt2Okq4A6djan7f3MCo6HwUUsylcGdHRo6rIC/ZWYHZ+9XPwAldcC9FeZzK13dNjP3adBs1aLQIZ3uGwr9vn7h/SWr+IG/sy/rQf3fF7Rj6c2oF7lJk8pjAvaXzUeVrpfvvRRa3dyGF3KEoN1aMsyASVN6EvGv2BObvtQ1K71ifwLeG83mQUVHT8tYEjkT86nXIUOLJLs+sgOjMa5HA8kbgxgrc3VJHBe7WiLd9JawB7wD39dhe96T+p86j68sh2uRRtMd8nApaEHgYGS4slrj3zX72fca3duVRoutd+Q4a8BIMiDeghown3MuW86D87xCtqVVrTLg3G9ofVgVm7YA3UJ6YLmtzhJctqwBOBv6ADL12Q/l8j0GKhNRYeADxog+hCBLTym2HPN4WDf8/gNbnryFP/19V1CNrfQ5rxM0opGhxrRvPvQCSgUxCxg8/p8R3ICV15VFR7oY510bSQQZNjhRzn0H86vui62si7+Ve65C1bjFC1iPgvOh/WY53VcbzSyKP4Sn9xM2s+5ZIIX59OJ6kC/0ffysy8liwlz5J4G5LDzRMl7KvR4ZiWyXu7YXkOI+F8+WBGwZRj7Z3D8dLMhuoK0NMq2LjIeBw4KmK8g5CC/69wHdQzP4cJdV5yCL9IyiM0enxRGtadvie8tG1PgNo567fF+GmhAMTS+fzICbramRJcgzw14rysnGjZyYjy7Xi/ENUK9VmLFJIwfE8CcaLDsQ8FcwXIra2Q5Z/L6ENeOsK3N9kXpuCYjunvqOS8Bvg2MgWSqFNrO2owD0w51rFs++Py0WC+QkV/bocFQxgCa+rYJMhr6lHgLnC2G0TgI2kdh4pByLGl4nOlwYeKOGMRhbkZyNLqt8g66NZOpS7W+qowL07jIdJ4V17AD8u4XRSBCfXCOCw0nkbMZJ4Zi0UouJAlHSyCu8nRIpnxKj9qAK3TWGTuhauT0bEcxZjGe5nKcKRIuYdxHwVipmyMnALxOTvjtzzVwv98TAlog8pxkYl3tNRMFZzfM6BhOm/BH5dHB3w34OIw21RDq1OZScVUh36JXdvy6Yf0L7edpRwbo367yVaFWvl/qu9Xw6qnYFdw+/BqaPimbMS8+rXJZy3S23wFhXtET2Ttc93+N47+jQ2cvq7aR9mjf1e26KizA2QgOapUj8fRgUjihR130NC+CUR431NL/WIyi7Wuv92GxuZ5U1gwHRMzfrcAYyJzscAt1fgZisOa7x/Um5ZyAL7ztCGxyBDjZuRYuR9VeXWqEuuccKK0f/ZS/fWL51XKrk63YtwOhrHIcOuUxFvtRcKn71/H8bFiWFe7Y5orqtIKBHDO29E+/qPUJix6Rnlvy/U/Rngc30cz5OIFCaIhqhStmTTXuH+hqFd/hbaY+8KvClIZvFBpFxemw4K6BrfNgUp+iaF801Rnqky3pVhzCSVzSXcB5EwdW1Ew6+JaNe1KdHviWdHIc/Np9F6/QPahdk7JZ77dL/6u2b7deVRontd+Q4a8BLhXhZvUPPbsmU8Ab+rnCe6fgaKIDQVCSpPAE6pwP0IWkNvRjK76cDGFbh954mpsTZH17NlFXRWzrTRueQZrkyN/h8NHBX+j6La4KC8Pl9Nen3eHnlPPRXGxEfJWJ8T5VTy6DXK6Dv9MOiDDJqcDsaadXA6PJu1bo2U9YgONB0VdBmKXPQV4HZk5HJ4VV3q4Das/5wM7dtz1nhuO8S7nN1rn5TwfoIMjtsUfNSkYRLPLxq+c7/EvclIBh7357R+tXPHeg3HS2o0Uh1hUEdviwhvGcSoTguD+JsolnlVuXMgJVSRWPAgKiwE65ad8f3jo/9Hlu5d26c27vp9yCL2U8hibIfo2L08majhVVYHN3omthp9ggqr0bBI3YcstZYP16ZXlPlwh/eVvfc2R8K1p5GL7dbAE13qXF6MR6cWKLSQPcyQ8HZVRKw9BGzXj/5uMD6y52CNMlMESSOvJGTNU7k4khBeAT+P/h9YundmRTmj0Ib9rnC+ILD6SG7nkXKg0JR/QQKkCWHeVloRhTVpR2Sx+BwZ3owZdbg7/MZE/+0lnErGu+oeJcU0CWIkXN8s+r906V6bNU243jYnUnOn6jrVTEy2hfUAx8SqSFkwMRxnUbKuDnidCOieLIWjci5AxOxjiKm7Fji+hLNr9H/D0r2vdCn/fYiB2bg4KvCy9raZfVBjvxx0O6OwhFDPSCI1r3r2ii3PQSr2+XAvpqN2BH4K/KlD2X0dG3X6sMnYr9MWNeq8SejXv5X6+WASnpnhmdjyd1L431cPjX4dDAMdU7M+OVEk3kO7EHstJJB8sMf3V3pydHhmMxQ27ADgoxU4bd5q0b3fdbiXozTP9j4BHq0oY1SHe12N44j4N7p4V9OQxwzrVkdPBGpEOonurxXqOxkJwFfudRx3Gc9VNFI27ZXou48TeQwAq0T/sw3bkND/QmSo83hxVOAW9O0UglEPgc4r4W2LlBb/QALq7YDZKsqcgDzbkkeHeq8exsVDyMtoPUQTl9eOESOYpiaPklFeNi9BA96gZl2yZFjUkPNEz8yFPOf+jAwBf0yIhJHAnYhyRxXnK3SaD/SZJ6aZZ2DfZBXR8/OG3wVSRwl3WlyXeEzS2WMty1Ms4BbGDleicHUnA/9Twrk1+v+b0r2qtlurdL4O7QYjtQ2PRspBBk0e1pL9KRmfo318M8T37t5DHbLWLUbIelRnDiLZ57XIoPWniDaYXlFuNm6DOn+t6vuBw3sot7a8qWb5tWkY8j1dWzxoURSFYZEhDfwFDRt7WWQNmWU5TcnbogPeakjb+FiNuiwOfL1p2cUCQCsRMOOoGmSJCdyXUGM534eIhHEoVNG46PgF8KESbrZXWR3cxLMzrEaBdyfuX4YW7xOLOnaox+9JuG0ii/+rStcKF+mlo2tV5ZZDPr1M95BPayBvkomIKDkbJdgckXOQIChB2vzLy0cJd2zAe6GEdxNwfcN6TaKCoQ/32+7RjFjdOHWMxHYeiQcKB7RGOGanwwZYem5e2q0Ux4ffaciCr+WoKOcWtAGfjULIHUSJCA7z+oOJZ9elIvRHje9vMuam0ipwmpN2o4B9Qzu8UmqH6cA5FeX23cIaCWZ2TFz/LB1CD2aUmx0asId3FIRWEdJzVkqeC036L9z7KWIc/hDm7hXd5itd9rYIb1WUx+3zxVG6n+0dFT3T1QOGGvvloNuZSABao7+nECkqkHCgsRUYzfb5mI46HRk3LZLxrsqxUae/6/RhnT5p0hY123o0cGEPz89Bj9b6BItshhQyLUcP5Q6cjqlZn9vi70FCmT+VcHZD9NvLtAqxL6N3gcarDIVBKv5XhUXaIfrfWAkJ/CUDp5IfpZVnm1R1L5wfF+b+3NG1uVEYmHL4sGzjuKLvyfBcZIA8JvUinfwA8TznoJwfPVvnJ95xMVJezhqOA4FLK3C70l413hvP08PID/N/K/JumIq8LA4DflCBez3ydDwBKaGOp8LrMfqenZHS4tkwthrTaVG5E4EbEN1XNna9OPxuEer5HFG4POQx06ZYG66DLjwKNfgOavASDHgdT9QhKcOihpyn4XvbeLPUtQROX3hi6q3NtWUVyFD+EpSH6jlkZJkKZXll+J2OeLDpVERDCPN4fPidXoxJtH5k8T8of/BJmbgLAF+inS7Pbrvo+uml87OQvOZ30bVNqGl4NNIOOtPkqZD5j9MhZH6D93eVrTBC1iPknbtaqOsD4f/qxXkJ981Q7zWja1Wy1mzcBnVuIrs5KoyLWdF++DyRsV/dPonu3Y2ikXSlcalJw1DP0/UoJLN/ENGml1CKTDSoY+AvqDEwusaxrlleY7dLauRWynjHD8LvuMRRDjkzLMRL7vdRL6dEtldZHdzomflQYvTrgae74FwXJtwLFYvCCsiD6Uxk9bA/2lAfLteDZnHIs4QzdMhJMTOOnDlICFVBXoihJcNC+KcS3lo0ZEqRIu8UZMFlpXs/IB3yohPBVbXpXBEd16HEhl1D8MyMdh6pBwoxtxkKPfFcL+0Vjae2o+KZJRHBOG9o32OJYnEHnA8ihcJhSAi0dRhD0ynF/UVM9i+qjppjrorI/wYSVHwRrWO3At8o4cyHQlmdV2qHpOAjei4rf1WNPrkDWDhx/T108BDJKDc7NGAP7yg8y25Bip+FaGcWa/dfuPcQNXOzkLe3HYoY5ucQ7fAsPQjvo3LLdMYslDxgqLFfDrqdEdP7CMo3mWV1jxR6DyCh6eGI0O45rBR9UML0Y2xkllOH5mmydg2sLai579IaSrbWPKEilnz4vSlxNKYJGAY6JlFGm3VzdG9dROf+MRyPUhGaDPjUAPo5ub+TDotU2+uq4p1JJRWZ/GineiTOZ0X77/MMeRT/I1ybrYRbxzjuXqQ8LHtEpAwhmwhgdkBr7otkhrmke17Zd8L4mkarEqBNIdlD3y6C8iQVQuRzqTAMIIP2qvHeeJ5OTxxV/Tgx/MbeFH+swJ0beXHNEvr+ADqEnC49uzraR98uXV8e5Sa7F9GXyXWi9MwyGThrhDo+SWvIvB2YyV6udOBRqMF3UI+XaERb1vyuOvnBu8p5qFAM0cFoEilCz0AygI8gIf24Cty+88TUW5uXpKasAskF9ghzcBbEs1zXh/FYO6cRyl91ZBiDN9G7EWLfZJFEeQHj9g6/c9cpayQcZNLkDCh3fDROKmUrI2U9YoiWTB4l3EUQX3I7Uu4dSrWRSzZugzo34X8mh9/tEV+1AO1G0dl9Ej1TJ+djLRqGGp6uJHL1VdWj30cRcm2mgZnthawYFkMWBONREtmleyx3ArJsuMzd/xJdnw1ZGuyGXNjPDNfnQQPss6izLgF2dvfFeqlHzTo/iNpiFLIw+yxajAxZya/UQ9m1v8/MVkCuwO9291XNbHVgG3f/UZd3rRa+Y2d3X7YprpnNieJcfxYRC/OgcAW3uPs7XcpdBFmOjQUWd/fFS/dnD+WuGi7dh8KMvdahzA1DeZ9Crr6XuPtpCbxRoeyl3f1wM1scEbx3lfDucfe1On3HcMCg5mDpHUsiS5nrQ7/O4u4vNyjnHhQu8ldo0Z8cbq2BrA72dPf/lJ6ZggjQUShm/kfQnAKtAWtkvHdxFB96bN06R2UMvJ1HApjZemj8b4826/0QI/PCML1/NHCWu++agbsIql+8Dpzo7n8v4e3WqRx3P6uEP2Nul+d5p3lvZlsga1pD4Xeu6VD3jdCcGmdmCyFmYHoJZzTKyfKxTvWvC2Y21d1Xr3svs+xVga8z1Cf3IiZ7WtMyS+XviWiD1ZDQfgzwfXc/JcJp2n9XIc+N/6TuR3i19jYzm4bWuEnuvoaZvRslUt66wzsWQYpaAEp00LeRZdScyGsBNObeQALyb5fKarJfDqSdzez9iJHfOdT3POB8d3+yQ11WRkydoYSv91fh5kLOPm9mJwCVRLa7H5AotxHd06m/w/2sPmzYJ1k0T4e6PxD+nuTuJ5buHYMEqBcgD9Li+y4u4W0c6rAlEnZtiISor5IJZrYdsuRew90/n/tcRrlnoXl2krvfG67NzfDTMWchIfXD7r5z4v6sKDKFofB9b1aU8x7EPC/q7luE+bWBu5/RrQ4d6mbehSEtcMxskruvGa7N+F/xTBWNbcjK/b0Rbi0azcz+joQHhtaj86Oyd3L3dyeemRMJHwx5zLWNTzNbE61xOyJL7PPR2rlkAncjFL5pJyQ0jsHd/QsRbm0e08weRSEGHyjfy4FivXb3H4TzzZFyvBI6reWDgjq0V5dyGvF1ZnYb4m0uRPP7aeCn7v7+BO7SwN+KtTuMqXe7+xMVZb8bjY/PIMHpBShP0eQI549IsX8L2n82cPcdutR5dsQPL4UE9QC4+w8TuLO4+1udyhsuGASPUoOXaERbZry/kQwrR85jZpt0KsPdb06UOztqj43QnLoF+KW7vx7hDIwnbrI2h+eyZBVmNtndP5BxbUng3+7+YjjfFNFzTyB64I0It84euAKaz2ORN9zvgENSe0RdMLPHUVi+Uci48ZDiFpKDJOV6ZvY+pPCL14JbEngbIAXmGHdfwszWQKG8v9xr3QcBvcgi+1yP7HVrZq9HvUCYM2MZolUucffv94qb+e4m/M997r6KmZ0OXOTuV5vZlDJNntsnifJHIY/zk5GBz69R6Px/lfDqyI/a5DTla4Efur1b/QYJI0FJ9QayXviau98drj3u7sv0WO4cSJu4C7A08G/EwI9GsSxPKhFo/0WM7fdQGBXvRz1KddoSWIVWQcIPo/s3dXre3Tft4d21v8/MbkbCwlMjhvBed1+16pl+gZn9FoVYuxYRFzcihq428WJmS/aT6QkLxuaI8dojcb9YSDZz95XMbH60YKxbwiuEDlYuA6C8AA0KmsxBM1seOAJYmdbx3PZMIET3Rt4ey4ZnT3H3jzao68UF42Rmy6D5BHJrfbzimSdQf6Ta2XPmuJkZsvBcrW6dozIG2s4zG8zsx4gR/gsSHF+CQhT0RQlnZusjj6aVUCi/0cAr7j5vAvcaJFh5o3xvOMDM/o2YMkOCh4JQN5R8df4eyz8UWcW/391XMLNFgQvcfcME7uXIc+TFXt5ZKvNh5MnyVun6rMgTZ/l+vWtmgJm9iqyXDAmtHy1uIcH33BXPXYQEzTcAM5hxj5QRTfY2M7vL3T9oZhNRkvSXUTiUVRK42wDHoESof0dM4wMVuEd4SSE1nNC0naPn10CM+k7As/H4N7N53f0lM1sg9Wyv+2vOPm9Dyu0N0Rr+u3D+aWS1dlCpzCZjI7u/M7+rdp/k0jxd3rsgSmj++9L1cQn0svD9r2jfORmF9HrZzKb3a++J3rMq7Xvx2RnPrQssgTz7v1m6N2x0TFTWPIXgzcw2c/cbzSwpkPaSMjA8cxXy6PyuS2k+C1Kg90IfTSDTsNCqlS1Fne+Jns/mq+rSaFbDeKWqfSPctnYOz+Uax33RuygJm/CYZnZbiq5oCiZDs7VzhbENyv+Gux9lFQYCnjAM6CeUhFxzoVBWS7j73oGef7+7X5l4bl3kdfYu5PE7HxII35HAvRuFZHsjnM8G3JbgMQslwPtR+MPz3f22inq3CNhzBJRmdjXysJsIvF1cd/djIpzx7r6TydAm1R+NDZrqQhMepQ7fkVmHgfAGTWVYdeU8YaytEE4f8gpDhopnN4zH3yB54jprc/RMtqzCzK5HxlfnhUtjgT3KuGZ2J8oT9YyZfQB54ByBjEXedPc9I9wJ5O+B7yCPlC+6+6MBry8yywqaawZUyL2ORMrA+xlaC9zdt0ng3okMMC4fbtliXeinLLKHOgxMtjJoWUV4x0Lu/nx0vjDwYo58xsxWAsZ6huKpDm6HMt5Ge5ohZeRLxS2kVJ018cwRSHH4X2R09i5kALVe03pEZa+OPDY/CVwD/BatBZ/zkkK8Zrm/Rvvxb8KlXZBCfo8I50KUR+5VFMLydkRn3Nf0vXVhlu4oA4dFEcN+rMnaZzxyk+wKJq3hBHf/r5ntEBP5LgujXwK/DIKzhYD/uvu/K4r7DhJ2nAyca2a/q8DrVqcF3f2fieunoKSTmyLryR0RQTEDUgxCH6HJ983l7ndJPj8DhssSalUUru8BZNH5tpk10qh6hYLKzE5z972j8zZr14ry3kGLRZWWej13X8vMJgX8FwKRUYYVEXGfFDqgsIjDAU3m4DjkYnscGtN7UKFsQ5YDH0T5yHD3R0wWBTMgMGhPufuz4fzziCl/EjisECh6ZNkXhDlJgU4M7r5UN5wylBjcUcidfkrdckow6Hae2bA3Cnd2MtqgX8uds2a2LyJYnzazr7r7zxNoJ6I17AKkoPk8skhOwRPAbUFBE8LkVqAAACAASURBVFvfH5t492Huflh0/hNEpPyqvJYH4uqbtDNIm5WK3Tb6f3TpXsu5md3q7huZ2cu0MvGmopPM8PYoBMQ94f3PmCwpU/AaMM3MrqO1LXoR1lwMnG5mX3H3V8J3zI3CHyaFbWUwsy8jC8CLvF3ZtXcsiOuEWxdC/x5V0AJBoP41d/9ehNbUa7kIgdIJmuxtd5vZu1DIlInAfyjRDxEcDqyPYumvabLcTHqAuvu3w/cvT+t4Tlk/1tovB9zOmIxFFgHejcIf/aOEci6yPptIYl4R7a+JudcCFXOw6z5fCELMbHdg00KgE+jBaxNlNhkb2f2d2YdN+iSX5inee6SXlDUoNEX5WlIgkoCLkGXrzsDbZnYZHfozqsdRwI8Qc3k1UjB/1d3PSeAeioyKVkY557ZAITXalFRmtmo8J9z9zyjf3kVl3EHSMaEu2yAhC4hfutJbLcM3QYKXlFemk17PF3L38SaPTNz9rcDc9wKfQIaF55k8RcqGhcf5kGHh31AIX1BIx3hfd+Q1SahbJV8VeMMYatFoKUFnB9i69P+KUp2T+2YQ6t5mZgcg47idUR6r4hs2c/cbgRcsoQgr8cVNeMy7A794Ka2GF1n7fAIMuMlk0NFRGIuEwHWh8Pi6u2tFmtFe3SAWvI1D+8+HwvlfEf3apqQKawRob++25s0SC/jc/Y2K9fZDKE/m9d7d4n8OkwdfwV/MGZ97pPiNYDF3/0SXcg8Mv1t1wRsOaMKjZPMdmbxENm9QE5rKsLLlPGb2ERTO6gk0LhY3s91ietEUvWEn4H3A1e5+r5ltxZDnfuz1OjCeuObaXEBXWUUEX0Bj4zi0dtwerpVhTnd/JvzfFaX5OCbQr5NLuHX2wE+h/r7JpCwuvMaywMzGePDWNrPlCkVX+O4cmqsM2yEF/OtdMfWOp0pjrlf6YVDQN1lkD9BItjKT16MYzkFju4DxwNJmdr67f6vLs+9BoahzoA5uFVTyLSkI8/gKlLfppTA+XqW1XWP8OvKmiWgNOAP4VjS37jQZL/VCw+yL1rsDAu4tSGcyA9x9x/COpRAt8SHgS2a2BPBnd/9kVbv0DXwYYgrmHnSJY53APwnlxvglfcrZRLN8SY8h4dwHKeVziHCmln7HIEvTFO57SufvpWaei358H3AVsqQtEvPuSIdE6QMYDyui3BMPIYuRf5Tbpsfy1y6dr4s2/iN7LPdORFQU7bYwFfk1hqsta9Q9aw5SL376nfH3IuV4OfHsPYS8Okig8kzoi8OpyClRnvOhvg8AX+lDO+wWHbsAG470dp7ZB625QP6KLDT+Rkb+MWTJdwMS/l1TgXN3+J0aXUsmi6Y1KeuMowJ369L5dijUwdkJ3GtRzN8HkHDv172uFw3busj3U6wxc5fnVIS7W+ro8f2zIOFHOb/GT0kkcq0oYz9koZpKevylXNwGdU+txeW1pJeclnMSxXquwGm8t6HQOqt3uF/MkynAqHi8JHD3RLTAC0go+F8q8uxQc7/MaeeG/fdhRPM9E+bjFwiJjPtQ9g9R4uN5UD67famI603mPh/uPUSUNw6YH1kh9zw2avb3TKV5Oo0DqtevFdDecG84Xx34XgKviNN/OgqV9TISlo3pUI+useQj3GnIYGVKOH83cEUF7q1IifxluuQkKLcT/aVjjght9wWG8rX2nD8MmAAsGPX3+sDNvZYblT/T8jlEeNn8KLB36fzLSJlUlc+kEe2PPGLKCeqz8x5Hz2TzmHXKzfyGexiGBPOld44C5u2xjCWJ9hkkID8eeUrNVvFMsTbHeS7KuSo2Aj4fnRfh/m5Enqmpcq9DIdmK821RGNtevu+mDkcVTXAambnDKeXZmRkHDXgU6vEd2bzEAL+xlgyLGnIeMvKYIMXyDWjvuTGsFw8C23Wp90B4YmqszWTIKhr0R1zPe4hyS3Uqm8w9EPF+uyDF96tIkfE/GfWaggwPdgIeq8BZq3TeKaflVXSgtUq4FyKB9z1IKXAI8vIcljnSoA8HKovMeH8j2cpIWI861G0UCqtdnG+C6IB/hzXk/Ui+PwWF0KcJ7jB9S3Ye7jp9QiLnIwzlKe1DvbvKKiLcFZHs6wwUpvCmYWnb4e7MGo33fkpCRWA9Ssnage8jC+vPDKAOq6FEyskFvIR7ELIE+HzF/UKoeAeyHpkdeKQC9/el8+tRcrXK5JeD+D5E7FwfNr6nEeO95EwaD+ugkDZ/oYJAHCkHIhguD5vJj8PG1rZwMgKVVKX6tc3B6N5tYZO5GPgKEvJUCd2OQlZUDyJL0EuAH5dwpkT/T0LeU8X55Bp1XhDYsuLewJRaI6GdR9KBhBA7Isvx51Duk/j+tiifRXFuiLh+hBJTEeHcggjas8OYOogKgeIAv6tgkGKGtVI4F4/jcP4TxDS2JblGjOLs4f9HkIVLkkFBRP2pSLCzFwqZ0ZYsF1kt7gisNKD2mDPsI6shi8GZPvYy6jyVSCAXvuG+Es4ElJh1idL12ZCA8yxg90TZW4f1fno4/wBdFGt02NsQczImOl8fKfE3JpGQOOBcj4xgTkChIY4vlxvhTgtztRDWrwj8brjaObp3Wun8rLAerFq6/lRYE/dHORRy6rEhIUEzsmQ9ttyvEe6dOdfC9ax9PuDugQSwZ4Zjemr81BkbTfq7Zv9l9UmdtkBKv2nIo3NqdExHOXFS9bgZGYDFgt57u9R91jAXzwWe74B3X/g9HfhE+F+lpCpo+IlIgWlV4zngFWGJHg316Co4ip7tRMdkK7VC246KzkdTrQw8MPquXyEBUrLOKC/DbcgC9DaUY6hScT4SDsQ/Hh/m0n+Qscb8mc9W0mjhfi2jCroo65Ei9lqUj/FHSCF6UZhfB9X45k9VXB8WHjPn2xmQQjLMuXmREPdBJNT7egVuV9oLKeIXDf8/gAx0vobWxV9VlHs72vfuid5zVwnnBhQ6uTifBqyN9virO9T3jjCWnwrvWW7QfVeq41QkKHwTrfdTi+sVzxyKhFt/DPMja/8e4Dd05FEivIHwHdTgDXp4R1cZFjXkPKm+LV8La1ZhLDMHWmtrCfTpI09MjbWZDFlFhLtwwD0NGSv+moTyHu0548PvdIIRH1rz7u7zmF4A+BIJpTKK5DRL6dq+SG5ZRbeWDSLOAiaR4BHCPHoU8aW/KI6KchdCYcueQyGqz+nnuB/kwUyWReauW8V4qFHuYaXzvq1HyLBytvB/I6QonreEMwn4GNqvd0S05cEV5WXj9lDnS0vnVyOPqU8kcH+AjP26GrfWrEPKmG9iBW62/CjgbEMXWUVY365AtMZZYb1YExjdz+/s2AbD9aI+ddgUYI7o/Fi0US2MYvAOVz2uJdrEkeDoIWSVPL7imf9FcSo/hcJT/A34YY13GrDKMH7jKJRckrAQJAViHZ6vFGj0iGvAJjXq0clqZgUknLiWIcu1pMVYg/ZbERFDX6FCQEyGkGqkHsgCewyyfhqHNsz1O4ylvVC4hAvDfyvh3Fv0ESIQN47vdajHksDHwv85G4zTFmEQEiydGdaWxZB10H/C2rPuSG7nkXogQcFupWszhGdISHEhIuLnAO7o0NdzhPIODX20XAlnoXDvgNBuJ4exdVkC9wQiYrp8VNThjvB7DbAl2rA7MYB1rGYmI8u95ZB37nHAHzqUvTlKans0sHni/veR8PA8gjJrAH1bxyJ7dpQ75Duhbt9HyeB7wm1Q528gRvyLyLvgVkreMjS09EYC7PloFahPy6xX294W+vYb0fl0RDReR7UH09xozZ0FCWIPoILRQC77xdgrCNykUQA198ucdo5wszx8aGAkg9YaQ6HcpiJhfFKxjIR9uyBh/qjwv5IRJWOfj+cKUs5vW543TcZGnf4OY+byqqOXPqnTFmFuLIXWpCWjo5KZjsZoPKfqGK5UKs+R5+eDiOmdFfESVUrJXyIafh9kTDEJGNfl3aNDmz1NCBcD7FDCaWvPqjbu8J6kUiuM99iDbwGqhciFh9jHw7hYgzSTPApZQc+CcmmtSqb37Mw4kNL0EaQM2DO01fSZXKduSqo7gd2RsPbAMH5+RsT/Zr7nLzVwkzwmmZ6MNd5z8TC1cWF4sQuiFWftMPa70l60GiYdjULZFvOhqtzNkZL9H0go+wTwkRLOn6vaB+V/6PSNY6jJ8/SpbZfsdHR5dvUwJx9EYQiHte4VdWrjUUrf2pHviHCz5wojwMuBmnIepIg5AwlBP4LowXElnLISui9RjqLyBsYTkyGriHBvB45EnkifKo4EnqGwfC2ehIh3/Hg/6p35bXcQ0Z1IuTcVCft/X7OstnFCZsQORBNlG1qM1IOassgB1aFy3Qr3Hwlj+ZNV4zjCHdh6hPbXWZEi5XEke7myhFM2vnq8w9zLxu2hzouVzhdHhk4HJnBfRvli30Q5rF5Gof9S5R4V+m1WtFc8D+xawlkxrCePATtEx+5UG3rWlR+lZBVlg4MHUdjkw4D/oU8RS+ocFiryfwLM7D53X8WUpPdMZPmxj7u/YxlJPvtYjxkJRs1sS8Q8bOfuD5vZn709eekotIneHs5nR8xGZSJ7M9sIWN7dx5nZQmhTmD6ob6qowy3uvnF3zOSzlYmie8FtUI/90IRf0ksJHM1sCnAK7UlfJ2aUW8Q9P8ndT4yuFwxL1wSQZnYFrTFEHS1YN3kiJ8JIgRBz+qfu/vVM3LPcfdcueN9FG+nzaCys5e5uZsuF59sSN1uNJKfRM0uieXW9mc2JFGMvR/eLHBPzIqLyq0jA92HgR96HRIi5UKed/69BmD+roLBaFyNh94/DvSnuvkYJP3ccXYs21XmAjyImpui/Xdz9IxHubp3K8nRi3a2QJejiiNCaF4Xb6ZaHqCsUe5iZfR14zd1PMLNJHhLLRnijUUjEj3Up7z6kWH3VzBZEVrm9xmsuv+P37r5ldH49IkQvcvdDSrhdk2w3wW1Y7y3Q+DAUdrcqv2CRt6RbTssC9053Xy/uNzOb6g2Tg4ccP+t6yMNVlGsK5v5Hd9+ohJ81NiL8S5CXz1eRh9gLSODcFmu6yX5Zp50HBdG8+j7wtLufUUUvhtjbxyPvK0dKyq+6+xMlvKx93sx2LfZya08a/pWYdmj4bblrwSad7rv7zT3UIZvmKT03GnmIzMiN61F+mgjvKqT4uiD0444oWfgWTetcKn9+hmLJF4K6Z0s4hpjWp8L5UsgSdGpFmUWy4y2RQvkMd7/HzBZFoUGWjHDbxmKnNcMS+bxS18L1sUgRdxOagxsD33b38xO4U919dTM7HuWuuiS1/wTcP7n7Bqn6jTQws38gI8KfM5TPoS8J5qN3HIhojZeRF9qaKIfAtRFOTO9vzFCCcgBi/iTmMcP5U8BS7l4rb4eZPeXui1fcy+IxzexmFI75VO+Q5N4y88oOFwT65wPIo+pEd785RVsG3K60l5lNc/fVCnw0j64J523ztVgzkJxifTT/7vAogXzAe8Tdl6/4hkfdvS3/UdjL2sDdf1jdImBmK7n7A+H/+u5+Ryf8HDCztZB1vCOlWip3VYz/HpSH6DNozDWijYYDcvmOCD9rrowkqCPnCbKr/VB/z8hj4lEeIlM+liK/kSF+4FGGcqQ07u8mPLGZHZy4/CLySJgc4dXt65Y1ugOeuXcWtObg9Arx2mdmeyMF3Cfd/R9mdre7r1Px3PuQojam09py1gbc2ZCiFuTd9mYF3oSYF/9/MBgIe9DHkJHgB4HfAWe6+8MJ3AUGtUeX9tfX3f0Xif31ccSHFvDz+DyWsdTBHWlQrBtmtj1SBB6EZL5rRDjbhnvb0Jrj+mUUFvP2RLlZ8qMIP0tWYWYLMJSPan1kIDAFGW+Oa9wQmTBLd5QRBbea2Q3IInUe4KMuBdUmKJfCcMHrQcC5OLJaXdPdnzazIrRAC4Q6HgNsEM5fJ0pAWwZTguZ1kBXdOORqfg4SnAwnXGdmh6CF7ZXiYs5C5h0SRRdgZnO7+ys5uLlgZqNjRs7dT+qA/pa7n9zkPe6+UhD8rl+6/o6ZTTGzJVIClxKkkhIuAOxqSrzdLaHgTIEgzFk7h7AKuAub2WweJfpN4P04zO33ImFmUe4oFNosBXWSnLYotRDhvBgSusZKrTHuflrA38fdLwjXrzOzn3X61n5DnXb+Pwi/Q1Ya8yB3/9sDQfW5cN4CueMIhRD5TijrSXcv+uxBk8I6LrN2Yl13LxJev4hyEiTBzE6gVQFdLueAxOU3g1BxN4aSrLclEQ5t8aqZzecdDB0QofJqeOafQZjcV/BIQRXOPxbafuUEek6S7Sa4tcHdr0Jekjm4byLP5xy418w+C4w2Kc0PQBaXTWGUBwVVgG+GOrmZjUnUNXdsFPjbh7+HmdlNyLLq6gr02vtlbjub2QpIuFNmhjer874KeNnMvo1C/W0chBHJ5NwuZVQy4W0JL3efPxjRbiCldqyMKBJvN4bc/vYaSihrN54pl7VN6bwOzVO84yvIOu85ZIFIeGdKgLUfCqmzopk9jbwJd8l5T0Y95grlL4Fog0UR3X1ljBfm26UoDFcxTjrBicjK/DvuPoM3cfdnzOx74d37Im/NZcwsVnbNg5SjVbA5YR2IYIvENdz9PDObgKzPDfimlxRwEUw0GXksDXzbzOZhqG/KcK2ZfQp5fYwY2sTM3gv8y1uTt78HWYGOBX4e1rk5zWyW0traC3zB3Y83s48jb7w9EO92bYQT0/vdjC3mMLM1UZ+BvPlXD3sr3RQBEST7piaPOZe732WtSe5T7XYqEohhZhsj5ej+SFF0GgrLM5xwKvJcmgLcYjJQe6kCN4f2usnMxiNaYH7kSVyMuTaatFgz3H1t4Pcd6vmgmW3p7i04JoOohyqeeSX6PwewFfLU7AZHm9l8SPC1J0MC5UYQlGWfRoZmAOPM7AJ3/1ECd18U1WRhgpeKu9/fy/sHDTX4jgJy50pB85yMeJZVTYYN26TabsCQLecJ6+qx4QBkfEPrfrXSoCrakCdeJxxXhPMtkbxpnzBWj4rKrtPXV5rZJ939D13wbjKzi4DLYvooKHQ2QmvOTcjwfpDwz7DuL468Mt4fFFTvRet/G5jZkWjO3s+QYZpTMrAIuB9BUZGeQPvW4ma2W4VC6zYzO5H2MZe7r/0/yIAwR65Dc3xTtMd/2WRw+C13/1OEfqeZTUb0wFV9puveMrNPI/nOduFaeX+9De0lqXOnVVlTB7cRmNmyyON3ZbTHqnD35J5pZtsg4yOQkdeVKTyGvvuTwHnu/q/SfoG7XwZcZmYblPqoE2TJjyLIklWEfeBKk+FwEYb4S4h//f+fkspkeTvB3f9rZju4e0H84O5fMll/vYGY2wtNFmAgi61B1WlBd/9ndGkX4FuhHkcCZ5nZLUi4cXpFMXWYuu2RJd49MIOxnaeXb+gEie8r4AvhNxbuOophXC4jW8hkZh9C1oZjgCXMbA0UN/jLjT9iCB41swuRC3o3AvgKM/syijs8g6lNEWdBqPJbd38hwvsnaebjvcB9ZnYXrRtwWbiTFByZ2eXIWn2mKKk6zcEIJqFF9AJavzGF+wQiSi4v4caE7v+6++GJZ59DDO5HEvded/c3igXe5GHZaW7lKLVioUyZoa0S2DSCAbTz/xlw98PM7FeIgXsdhZC4DAkUPl/x2BN0GUcEQjoICJ5vfTzdf2a2MBLwlYmR1No1jsQYc/cvlC7dXfENnWAPFErqx+4+3cyWZkjAXYbXgGlmdh2tbRErv5YNbQXBojE6b1uPmkAg5P7q7q8HJmV1FB7gvgT67Wa2mrtPyyi6Dm4tMLOXGerD2RAh94q7z9uH4vdHSatfRxbc16CcIk1hNjObx4O3pwfL/CBomqPimZyxMQOs1aJ+YeB9SBFQhuz9MpRbp50vQAYDpxN5afUJdkahI7/o7s+a2RLI+z1V54WRhelStNIx5fkNefu8VfxPnTeF7P4OzMgRtK91MU2XMp7pBlk0TwRfRUKSFN1ZBg/K77mR0vblsDZmgZnN4e6vVdweh2itD4Xzv6KxmGIw7zCzdV1GVd3gYnf/TakeB7r78dH1c5EC9whaab2XK2jQ2kqtoNT4KErA/EMzW8LMPujudyXQv4gUCo/7kPftHhXfdzAyyHvLzF5jyEq+H2toL/AbtM/N8OR1Ga1dBVxlZoVAfy7gaTO7wd0/W1VYJo0GQ3P5k4j3mFIolAqoovcr4G9EgmAUIr44d+T1WtRxGmm615CnYgrq8JjPh33ew/t2JG20MToatzujnHYXARcF4dewgrsXoZsLeDII6lKQQ3sdiL7rvcBGPuQl8B6056cgZ804CPh9aNdCSLs2WpO2Sj3gJY9yMzuahGDO5PH5L3d/KTy3pZkdgPa/ynFfKiOl+C1gLDLSfS3g/jR8Q4rmWRJ5JQ/7WOgRnqA731FA7lwB0TpfR8pU3H2qmZ1Lb/RiEjrIeCBDzmMy7NkJ0YZXu/u9JiXqd1CY/dha/y/dZFxVSqYB8cQLosgs/wnvOBQpSTdGe/9REe4TdJdVFHStAd8xs9cZUkSm9sBPoDY+L6wr/0a012hkxHBcP+aEmY2JvnE5d3+0hPJplFPmYUTjXh2UFZtSvX5th+i0SqP6CI5BOSwfCnVYAYV1XjuBW9Bbsedny772/6B3CDTcrkg59BziTy9HdN4FyCCpgBUY8ro6wcwqva4awBfR2DvK3R8P8+C8Es6FQTmTA3Vwm8KZaC0+GhmB7UG1DOmnyBDst+HSgWa2kacdDS43sweRY82XA8/Zwp+Y2TdcyvPPBsVTC1Tw8nXkR9AqqzgPySpaZLAmxduHkPHSKiin5O0oDGQvxrfZMOLC/ZnZSWhRuweFyOsYws/MFnb3fwygHo8hBcQ5aKKmLMML3DXR5J7k7tdX4LxMYOrQgKxk6szsLnf/oA25782NQoT0zS2+zvdllpcdCsjM7kRWdZd7pku8yaJjvLtPNLPj3P2gCrx5UBiBPZAXzq+Re2SbBZ2ZpQRx7okwIGb2o1DuPaHMa6oIMasIrVOHSbVMV/JBQM4cDML6MnhKmBeIwhTyDyKca1Fs9u9G196DFs6LPBHGwsyOQgTf59GC+2Xg/riMEn6Le2tQat0TzysbClUQhykgnC/j7m2ekk2h3+080iAI1S53eZl+1d1/3mN5OePo38jKy1CIv8KKy5BgYf5Eudcii65D0Ca/G/APT4dPio0h5kDCnmeqFACDAqsIVeiRd1jVOhThNg7xFb1jMrJSXArN1csRU5MKF3c/ipc8HRFGleE/6uD24Ru2Q6Fmv9OHstZ090k1n6nc20yhSj6Gwhr/JVxbElng3lAWVoX7XcdGhDvDot7dVzCFI7vA0+FVs/fLiu+sbGczm+iyOK8FJsv28xEtc66792SsZGa3o3CeZTqmzcs7Z5+3KJSblcK6lc8r6tOV7qnZ37eivBrHIWu7PRAfkFxbc6EuzWPyZtncMzxZUu3UbbyYlGXnI+brwtR4Dnh3u/s61hryoiok2P2IkX8SCbA6rV+pOncKvdE19KFJMT0/mUqt8MzJiLHezOX9Pz/yVG8L+2ryfmkDrwjtM1LBTJ68njaUiPHmBbZPzZMIJ4sfDXTa+5DQZw0kgJzQZE2rC2E/qAR3fzLxTDaPaWbLIEOxD6FwsNNR6OQnS3j3olyNbwUhzN7F2OnG3w0CrGFIvA7lfRUpgyflrFvhmaw1wxRGbRckCAIJg871auV6+T3zA3d5KWygmU1Ec//FcH4AUrTtiULVdxUKW+cQzlcBYz2EQTazdwHnuPtWEc687v6SKWxQG1StXcMBOTxKDt8R4abmyq6e8Ly1kBqitPf0je/vp4zHzM5EHjh3obwsT6LIQN9y90tLuBNQVJyOnkPufmbiPX3niU2h5dfw4B0V5trksB+WQ45l93UTsBqhwxuUPQWNt3OBI9x92S74iyLh81QPiqUEzlXApz0ov7qUlwoV1jjU+UiFXFnkgOuQJVsxs4eR4c44d/9r6d433f3IiucKr6u5keFw2euq75DDDzXB7aE+E919bWsN8/tHd/9wAncqon3eCeejEZ1Qng+jUPStB+gQYtzMtnb3K+rwdoMAM7sYKaNuQ+FRczxM+woz3ZPKzNZDlnv/AHD3/QJxeSCtlh1J8AEoqEK5y5rZQcCfqLYmLHAnIeuOTjh1PKHGm9mpwLtMIcq+QLWHViPo9H0ma9ujEWE6DTjE3Z/uUmStUEDu/pS1Ghp2s56+G/i6ma2CXLWryn0ZtdXpgek+DzjO5F11uEfWJe6ebY3r7t8zs/9FYUP2AE4MArIz3P2xAi8I4pYDpnmX/BsVRPv8SOnSkcHuJzSZg+7ecU5EZS+MCOVHuxBl2yDPyGPd/eAwBq8Cfubup1Y88y1koTENuZ/+AXnoVcHNZvYdFOplc6TUuqKEM7BQBYNs5xEKY4CzTZZmo1Hc4MaQySjEobrKHgFVHgILuvLUHBiEqjebYsun6tAirDaz84CkYUK4X8dLazppL61lSnhrIoHHfR7yC1TUtWclVAa8E4RS2wM/9xAHuQK3Tg6ZvuSbyQF3v9TM+uW1eqzJ8vgCZByRs45X7m3ufqxJcX5rIGYd9f1Pq/bbmgRstkV9nf2y4vlO7VzLSyuCn6H98jSCRXIZLO3R9R93ny+BPpdn5MWssc+vGBiYwpOx8IAxEh7pCehK99Ts7znd/QYzsyBkPszM/ogUVy1geV5XtWieCB4HJpjZ72nt79hieUUktJ3PzHaInp2Xai/CAj6J8lg9SXW4YIA3TLkpC8v3ZakOxd11TTJZPX4WWNoir1Xk7ZS0ZLfM0IdB0PwiMNZalVpjTFbUqVCL6wUlxKRQxgtBWJiCOM/HHMjrfCKtXjuLIOv55VDS9Z96wgBsuMDMvujuZ5QuH+GRBauZ/dzdvxr+H+juxwMEwfmmKERRgduUHy17oS1AF74x49uWRJ6nz5vZ+kjA+2hZKEwzz4U6PKZ7nifjeYh2C5FvGQAAIABJREFUeh5ZCf8xvHs5NG6HG7JD4mXSXouhfIXFml4Ib/7UYZ/KomNcngq/zsEN9Y2950ajEHop5dusPqSg+gna6zcPYzS1/6Xq1imE8+vIg/a6UJ/NEa3yi/DsAUhovhVaSwoPlBnFk7cPDgq68ih1FBTu/jjQMlc6oNfxuqoN3WRYNeU86wCru0L7zoFyRy/n6dCx2Z5Dw8QTn4s8GgvPi61D3eZGYewIdcmSVYR1+d/RvNoUeRw9gRS/ndIa1Akd3hFMoYrf8KAwd/c1gvLiPGRQ3RHc/RnEp3SCV4HJplQMMZ2WMsq828zOQEoRkNK9xUg99PdpDI25L3TiYUcoZMkiBwy5spX3V9EGZQWV1fO6yoJAd3YKHT5QRVOP8HrY9x4zs32Ap4HKdCLAu4CCDkjurWH9PMajfK7u/gqttAruXsglp3qm0WsN+VGdcO6fakBb9hVmupIKLVjrFSdmdiyyyl4RCSzaEvwOAkzW9HsFxp3AFOyDhN9bAWf3UPYswNvu7ma2OPreR73Cxdfdjw5C9JdQzPDvu/t1Td8f6lDn+34dzm9ByoMTUAzbTlBHyPSUKeSfB4b5AErMQ1gU/hAx3lciC5x/AY90+M7RKObwHmgcHYNcMD+MlBgrRLipb3oRCVtSeXHczJ5FoTfeQgqlC83sOnf/hpn9EglVbgcON4VVSYWwK6BMtDsSZExArrHDBdlzMBCoOyMrsSuQYGNj4DGkBHw+wt0T+Em4t7SZ7e0VCQ1dyay3B843s/ORldZX3f2SDvWeE/i1u58e3jc6XHu1Aj9HqdU4VEEGDKSdRwqYkj3+ORC/IAZoGeQJ0jiflymk636oLX4dyvowaouveaviuYlipgjb8jcz2xJ4BgkkcmB5lM+kCn6LvLS2JPLSqsCNE9fOgcIztCiyAwO3KyFMhZkdUYz/KjCzw9z9sOj8J2id+5XnhdvqBNlxkN39SVNo18IS6Y/uPqVX3LpQWvdHoXbvC5Hl7puaPEB3Ak4zWev/zqM8A3X3Nnc/BTjFlIPKqgQfQejw3VDOsUjoWMyTL7p7KgTlG2FfKwQllZ6iJivQfYnibqME4VXJkeu0c2ExFgvK2wRYZnY4GreFFf/jSJh+NTB7qmAvGQhZ8OiqqEfXPAM19/laRg91xkbD/n7NZM33SFCOdGK8xjHkdbUpweuqVIe6NE8BfwnHbFTkQ0C071aI+ds6uv4yClcT12MccFg0LuZD6+dRpPNcFXAoGjuLm9lvkWXx7inEiHaeCwlsn/R2I7nbkRBqIVrzDr2MlDopqBP6MFupFeDNQBcV83thKkKWuHvcxgRe5agS2tlo7zkB9c0vqGivYYIdzew1d/8tzBiP5XUg9hDbDSkaCii3WVN+dANknf+Kme2Kcs8dX4HbFcI+vxvikc5HNNQEYEsz+0ihdAtQO+dJTR7zIhQuKxakXEgpjJN3ziu7f53v7wd4Zki8AF1pLw9eRKFd10HeMl9ABpH/9oSnSrRmLEJ3xXodiMMAvgU852nvrsfC2rgYGpOrBAVVx33J0p6dKYObS8JRwIQyggevKu/RyKUfUIdHqcN3mDzeU+8DWo0vIkjlWty1h2+rK8OqI+d5w4OXQODVH65QUOHy/vsl8Evr7jk0cJ7Y3Q83eQRtiOiXfSLaaJdQdrasAhiPjLteNLNCgH8EEub/EnkpDgfciJRjzwIE+cm+wMdRCNFuCqgcuJz8/D77ojF9AGrnW1B7xHASMhoqxtzPQ31HLDSVRfa5Dk1lK5dZKd8R4v3vRrxb7Kn7J6Rg3M5bva7uNrNTGla9Th7KFc0slZOs8D5eqyFuUzgIKQMPQLmp5mMoNGoZjgAmmSJEGFqXqiKz1En9U8fotSsNE6Aw1t4BhSouQgKORYr2GGZ+Pj13n6kHsgYHEUPnoE1rVLh2zzDWY3L0f0tkYbFCOP9zD+XuhRazv4T/D6ON9yGUxLiMPxq4fmZ+X4yb2w+IwCofj1fgLoSEt88Bfw/9vmAJZ1r0f37gVhQH31BYg6p6PI7y23woce8XpfPfh765KBxFjqlHgM+VcA9AzPk1aPLPGq6PAh4L/+9FcdlBMe8n9tBfsw7j2M+eg4hA+y1wKXAzIjo+gWK3XlnCvRdYOPxfBlkcVtXh4HB8M/TDpdG1gyueuQMYE52PAW7v8I65i/4J56OR5XyMMwEx1EuUrs+GLIrPAnYfSe08Ug4khCu+Z1YkyDgebZh39FDutYiBOAGtW19HTMxeKKRO6pnDSuc/CWNrwQTuVogAWRVtuBNRAuNUuS8jwU5xPIysTarqPrFom+jazTW+/dbyGCrGLIq13nVvArYunW+HYgqf3Yc+XxkJKseG86VRaIAU7oFhTfhhOKYB+/eK26DO46LjdCToX6Rf8yB6z2qI6H+jdL3R3pYzVoC9ERP4NNqn5kDWzXdWPHMI8kB6PMynP3Xok1+h9W+zcIxDCqNha+fSPFoqjIsdw3k2nUbFehTm9zsoJPNLxXwv4WTv80ip2K0uFv3PHhsN+3tdtE8uFvrlYhRWJ4U7MVGnPzZti4p3zJ2Bs0EGzpTo/9qIvt4wnHecU2gd3RLtAwsl7m+DmLd7kIfWdER7PAvs1st4DuXfBMxSA/9REvtYBe4uSMj0V8RoP4TC9+Q8a3Hfh2u1eYNBHsgo6TrEYJ+NvHnLOJNS/1P1pyE/imgfQ6H+pqL9K2ufJ0GbIDpnNqSgfYmhPX8W4N7S83OgqAC3IQOb+9F6/iRadz/QsG1XRLmeH0MCjeLYvWinRD2+CpyIBOPZY3qYxsr8wCM18G+tuD4fosUPR170d6NwSincbRA/+UpYN95JtV2NOi3Q6Ujgz4YUHzshIda9SLj9DPKoSr1jf+Qpcx/aX6cR7bs91P2GnGsDHgPZPAo1+A5k7FB5dKlTEe6p12+rJcOixlqODD+nhmNadN7T2GCYeGLE6y+KDAqXoJ2/ryOriGnQo1GeHZAsqOd5UqPtYppnb+TVU3zD3X18z2yIN16VHuVSiT6dqfRDZp0Hwq/VrEMj2UrAORcZeW0d5tjRYe78poTblVfp8RsWCvP1E6Tp7PuQh13yaIo7jOPk/2PvvcNlKaru4bXuBQSECyIoiMRLEkEUQUkvIIi+JAERkCwiLxgBRRRFSSqIogQFBUmSk0gOEi8ZueScwQQYEK4Er8D6/bGqz6npqZ6pmjNzzuH7XM8zzznds7unuru6atcOa88Hz/cbAZi3g1y1xvwPGtaYNfl5YfvzDWG83bugTUkdJnw3pds+DEi3LPmMh0yq60P01bwwJcbackrcGjBlQBIk54cLccaRPiPhTv83zf+4ANwhPiDzfU6ClYhesRv84swOZwstJNM3zApPKi0plzJH5csk51BIJ+4TSq5vZppSqnLBzxJvS2rzYCszSipEaG0raesuojOGqO65YaXkEEknh3PM2uG47SRdX/vNVSXdoPYU5TcAvEfSs0HunXCtjw/DkR5x4eu5AXxSNR720FerqLbpcpFmyNFqbSEMnRDkPwLTxWyI5qLH/UbJO7i0pGXo7MA/Sloj7K+KcMaYruE0/sdpLugmxNHuhyf2pTCzIq5kSf/q0jeuhCNPqmNmgRciq0QygyxyOqj7PF4wIwDQNCK/AXCVpO+HfbM0HURnVS6M1rE8jvp7p6RvhffjKUlV5NCDJJsoeOq18G6Fx+GfwvRgQ5B0Yfj3Bfj9a4TKaFuBgiwtknH0T5V5Uv+9VyW9HNry95AV0REaTh2vtut0QSPBUwB2r8Y9OBjj8AbZHWH6qZcAgOb2vgk2AoxEtggaIIVmiFDeAo4g+zsckPK1mlivc1s3zCbp6HCeXSRVkZS/I5mMtlNZRP2Kaq3Vc1WnsajkPhdkaU0kWRkajgXweUlXhbEhee9KMroy3++Seb40Eq2kb/TyvCt6kn+hOx1ZTtZVTzoPyZXh5zcbgAXprMmdJX0hIf4oTdO7MFrniDiqUTS984KwYXFdSfcFnaPbM50fnt9nALA6Sai1CPsBMM3zHPCzel/QZ94B6xQxXdz1klZjK8UkMBzh2VaDFhnUhzX8AZn0aZJOoevSrB3asLEa6HVIHhG1eQIcGV5/v0nXv6me88R4W6NUX4atVNmfg9+VGwDsT3KuWjsmhDZOiP4fan/t1D2tR2G6c4WI58Nk+uDtMy8npZu8KtNGTSf5WDTnv0ayhU5KBZkLUb8kuvfP7EzGgBNhfec6mOZuadhZNyZgPiVelu5F8mg4a3QagFvgrMmfSHq+QzMOgGtQXCHXwf0I7FDtFTH7xoJwVgnhZ/Q0apRMoQ8NFU8nuQIcPPNIqn8E7IrMzE5mUMKGDJhZAcxde/cmwY6D0UTJGiV73aFACUhyAUl/iL+jM+vj7V6yrnJQasMqsfMMigZ/4Gtikl+GnYXPwmUlqrEvzqItsVXEOs5aAPYKx71RaPIZKf5O19BaAA4eWFLSX0PmRVN2ehFIrgmP60/C170Aye1jWyvJMyVtXhtvh6DWujxz1vTxlu2a3jVeMKj1WlEbwu8V2VbgMSDOJL+A5BRJq5OsZ+WUZF0Vgc4c+imsGxBmBtldrUxJ0xWVTumCEtki0HR8XyN5LtL9uS3blOSVktZGlHUY7asfX2RDkjNWD6eztPYE8F3YMV9vQ479KMY8JBeVKWoR7J3z1H67JCt2IBhzJ5WknUmuBmA6PImcTadZA47iakMwWG0Be/Uq45hgx0Kv2BqmA5sOO45OJDkF9oom6ZRIrgrTblTOskrRjylqpgcl9nmSjyqkJIcFfRN37asA7qF5nocoFhJOlkFd319g+pgKz0TbQsRRX4GZ1HmyE24jeMDqhEPgxftE2Hs8YzBObQ9HgzbhcJjWIMYRiX0AsHDloAp4Do48+gfJFuOYpO8C7bQNkp6OFv0VVzmAlhoUyWK5FWie3q3gFPK54JTpr6dkB4HCd3B6OOY1kn+ufVevK/ZuBm7y1Hbcn9VbYdKXSC5fKdMkP4jOhoSuTq1BDsoDvM/jBWcAeBCeGJ8DcGNY4G0btttA8iTYQHMnWsfy2ElVGUFF1zuI0URdlO2YoelQUspIW2p3GPPbaH2UKE4e8L2gWH4NHocmwWnkKcT0NK/BC4PNazKTOVzrhLVtKOITrhkd2zDC+aRCjuN3qElo7bvVgjGFEtksjNL9OB7mhP+YhqkZ6uh1buuG+F2o14lpe0/ogJHLJH0UzkbohtdJTq4WB3SR8LaxqMf7fBS8EKvoQbYN++rUKd+EI8Gnw9Gva5B8DY4WbyrsGxtYq/dqo5RgGK+2BrCITBOzAID5JN0aiZXM86VBDyV9o+h5h+tbAtYt6gFebTodHGA1K2zwOgDW++qG9550HgzTvJwffv8u2smUwnnw4vYKNM99O8OZQtOD/J7B+LUFOtDVkDwONlbdh1bqvNhY8oakh4P8E9WiTtJzoe8NQdJq4W/JQjSH+jBGTj2v2InzHDwmDX3X4EyK6SFfA3CapBtqMnPAhvJ4LK6MmcLo1ZeJjfXV3/XDp96Oeptj42vLONXLejRgGsm94HHrf8LYmqS9raNBN6mMdwQwKVpfEQ31DsK5OtY8KemXks6DDVcrK69o+tIaLjB+LOx8G0vkUuIBebrXgjCV5COww/6P8HjeCf9RCCYiOUHS1cF+0YYce4JCMChNv3S+Ai0tyXVhPawjwvqmWx2VbCc4Mihh4bF5N9ghFb+HL8IR/aOJkjVK8boDwON0/esdK8cyTCsf2x9KA91yUWrDKrHzDIQGf5TWxDlO12xbBRycdSZ8/94G66MIzqHGelQDwGZwYNfDcNBA5az7CMxakESYS34IBxwRnQNoDoHXMg+FY5eAdYmY6rUKRNgA3XEtWvXxeLuud40XDGq9VoJi20rAPCQXVAiQC+2u3q96X30CdlJUuuIW8Du5BDx+bDuC9n8XDnKMEwIuRytV7M0F5yuRLcUZ4e/PugmyxwAMkp9AFJCp4UDpulxO0GuFHB0mxu7wOuLxsL0wPFcn0U23HBRYOKeMCkjOo3au9/j7h+BoxqYCx/1owwdgpe8OSVc0yDwIP+ipiCbJeDIMMlvCns2TYWdENTGcLKktQoUNEXgqK5DdETnXV3i+i2Be9qvDrjXhgWQJAPtLOimSrfg9z0CrE64lQyss9AAr7AfCRo3b4cj9v9VkV4YNo7uh1QE2CcAmao0Ar445El54VBHIm8ILj6/D6eMfiWQ3hBW4d8GTwkIAHpD03khmoeTNGb6+FkN2uA+bwwaK0+AB+zaND+7u5DtI8jl4oCQ8eFac0QSwuaR3RrIdI0nr/TkstPaCo/IEO6F/qIb6ICRXDL9fKazzAdhCUj2LppK/Aaayip1aP1NUxHC00Y/7PJ5A8t3wBPlvOFL+o3A09naSnkjIPwAbNxonIpL/hAMQCHPCV8EIBLCapLdFssUGcjrKp8LMsLP4zw2yd8OUPu+DMy2PhTMs16jLDgJ0hGEjFNXkKn3/emzPnZLe321f2P9VWLGvFNONAZwgqa3oa4lsQVur+7EqPMZUyuhmME1Zk/Ow7yiZ26JjOmYcknwZpgIj7PitaiYQwKKS2qJpg4NzW2VkbZNcGzZKPR7OuRCAHSRdXZMrvs8k76rP0al9te8JUxN9HMAdAL4vqVOQQleQPAo2QK0l6T1h4XG5pBUjmaJ5PjouK+ght2/0+LzvAvALtOusyTmzG0ZwL26R9GGSd0j6QNW2Bj0tOZ50adcnMNwvjm2aX0jer0QtmZrMXbA+OwE2SK2J4cXo1Q1tPgzmkM8x7lfHvFWtdX+a5PZJ7VcU6MPhIsrJjIvxoGO+WdBtPRpk5oXXdr+XdF0wBq2p1ozwSnYJ2AH/Tjkz4H0wvXBct/D4Tr+nEWYD0/WZjpN0f4Zs1/YGudsV1YKob48Wag7aNjQ4aHPPTTibapXwWQamjL9JUtt7SfIKWHc5EB77n4ONdW1BPDn2hEh2qqQP1vbdJmmFumwpgoNxSZj6vmNmZ9UOkvdEDsrrJP1PQvbLkkacCT9S5K5RStYd0TF3wAbdHeF12mPxHFeTTWZdqaHWUyn6aeMheQ1cEqFjRrikE0byO+GcfV0T0xkI66jZQV20VgpjwBawveFMSX8K+z8A01lf1ulcgwLJd8E6992VU6lB7lGYAj6ZUV2TvVu1QKPUvrD/h5K+0W3fmxG9rNcG0IYi20o4Zj1Y338Mfk8WgenbroHr1x0ayU5Ra9bV0D6S98W2zh7aPjQ/hO0JMF3lsh0OG1MEm++laqi5HGR2xXAARuw4fxHAMZLaHF0kD4Jp108Ju7aE18XfTMjeDNuGz1Jz0GvPoDNGlwqbDw7Sp9IrxqWTqhvoIoibKcqKGKN23CLpw11kru70fewIGW+oK0x0pMg/Uh2Z5AUAPqd26rzPwTyXy0SyqXsipaN5c9u6Bmw82AUelCtMA3CBpLYCh0HZ2BTDBTWvB3BOyqARDBVroUbbIOn/RtDmv8KRGIfCTrFXST6u1ky8cYVBGb5J7gR78ffEcETvCgAOguueHN1w3IzwgorwINtpQilyao0lRsPBMB5A8iwAX5HUGKEx2o6ZoEBdkRqPKsMLXdj8TzKtT6MxhhlZWmyg/4hke6X/GDhY6PilU9JXg9/XKZLu6HDubNnCNl8NRwf+J2zPCDsiep6L2UB1USG1qCs8fzLjUJEjtReHAR0RuhKcSdU1azsotfF426jUltxnugjuZmrN0jp7JEZOtkbGtiF1jdH73dV5Mtbo8Xm3GTc7nL8k66oIdLT5T+BoxZXgbK0VJH06Ifs9uNZkMlhlhO04FqZvaTTWk3wSdlymMjmV0tfCPLQFHKR1LoAzNFysvS47RH0oqRv1YXxcV6cWGzIuJH0tkrm76XB0zoYbF2B3533HMUQJ+vIe27EQgMUlXUFn6E+UNC0hdy38Xv0yGmPujddIgwbJz8FZLzMgZP+qIVAht70kX8fwHEI4q/plDPejVKR+30HyDTjYsDJKx+9ty/vaq+4VDIarwo6qDeB6YnMm5N4KsztMgDNd5gBwSoPjqas9IZK9DM4uPRnWO7YBsLqkj+cc3+XcXZ3gkewNsAPnbNiB/ycAB0laMiH7Rfja/xm23wavoY+sy44HlKw7omMq/WFV2Fn1DQD7pfQYmq2lJeuq01piUMix89AZA59FyDJHe0b4z9U7DX5uO3ta27HA6ZrZjq4ZYzkyYwWSN0haNVP2OHh8qYLMt4ZrDbYFSaT6bgeH1hADTtheAcBfFBx+/0X/EDkiqjVbkraPDhj+uFqzri6VtDQbHO0FbTgEpgw9Nez6dGjLHr2ec9AIa+7V4XntdNgmlMzYZEEARtC33y/pjbA9EQ4k6EnPLtVhSK4lU+Sn2M/GHeXmm9VJdQ4czX4lWiedftD1lLTjIHiS/k2tHSNa9HA4CrIFo+28IHmRpPWj7StgQ9k59cEl4SknTPW3zEgHuMI2L5QyzvThvLdJWiE4qz4g8w/fKulDCdmjY+cVyRPhxdrPJd0b7Z8I1zrYEnaAXQ1HRyygDlE/bzaQ/L/YyUTyC3Dq6jnVdZK8H45O+0ft2LfDxf+SnNjdDBQJ+Wyn1n8xeARD9vthaph4DP1E40GDb9OSAC6StFjiu2sBXAobeFYH8FeY/i8ZEcSMLK1gVLkTwCXwPWgxhjYYB/aVtG+0/QOYouVXdQMIyXngxXK9bkA/jM1dHb8kJ0l6kQ0RzvE7XyI7gjY/BGDl6lzBWHJzyrBScM7KYVDVKogXdS9LStbCKDh/14zDHs/bNWu7V6W25D4zM0urBDSd8r1wwe0/o/29ajNqkLwFNjz+Phib5oEda6lI6Kx5fjyB5L5wNP+5aB1v294rFmRdld4LmtLnMFjfIWzo2rXBeDsNrqnxb7jmTd+M3jTF4AUw1VE19vbNMRPGsU3hRfmCkhZPyNwC03mcn+O0KHFqpZySrGVckLwTXm+cCt+LlmzElD5d1+nD+AT4eXelSekXmOe8j8eQD6K1XqX6NA/uBBevn0vSZLpWzy+UqElA8veSVmSrI7wlW7BX50kP7V4S1mW2hGt6HVMfc3PaO55AZzGuCV/PafD6oSmTMlv3oqOmV4adU/8J578p/L2nMjpF8hsDWCx81zW7osSeEMaVfWAdtCp1sH+TfkRyJQD3KThNSc4O6xO3dGjP7P755mDgoP89AGdoHgAzl/xIUhsdU6rPjKZtYDRQe0fmg7PIV5DUVruGBVlXA25ztp0nfD8mtUl6Bcucrjm2imswSlllg0AYH+eF6yvF40ybHh8cHF9EFCwI4Ei1OjA/D2fnLApn7FSYHcANkrZJnPcYSTtF2yfC7CQPS9piRBf4XwyBDpb5KoCFJO0U9JIllaCXY0HWVQ/tIMykEfejs/u9nu03Qv9fHw44WwnAJZJ2ib4vXhsHJ9Wa0bp4Lpjy732RTFOdt7b1San9iOR+kvZhOltfSpS5GEuMeU2qHnE+OvDMjyKqqKc4xb6pZlN2VlLtfDPDL3dHCoNBIFZcwvZHw2CToke5juSFaKXOm0JHkrUoMnQWQur3ejbkkTxU0m4AfkYy5eCLa7X0Utz6nyRngwfXU+jU8yZH0i9r2z+DKVe2hY3FVZtehweWS+hIpQ1gftM/0UX3tup81WOHHGUuFk9srwYbcKvnwtQCS+Zzb2pDTj2jOlbEsFPrA3SR9E7yY4rC+/xmxL65goNyzCTGgWcQvac1bAHT+uwo6Rk62uhHDbKQdE7tt06Da6vEWB42Yq4PG9BOA3BlFwWubihOFV+vcAq8WF4fzjTdHnaujRiSfk9yKXR2/J4Kj21VHZEKRHv9kBLZXnEQgDsi4+UaANoWrCWoDLkkV1VrlOI36WjjETmpYGfLvMjghM5xGIT3Y566o4bkMjAPeYw14GiyDdGOTjzy2fdZ0pXVAgronqWViflg3WkLeK4+Ax43OxW6Pxx24LyDpuP9FIDvNMhmzfODRqGDqHJKxjUvm96r1yQdldmMonshU6RsnXNiFRYaLsRxcBvvQXOdkZFgMTiKdWGYvjgJSX+o6TmdamyU1PP6G8m90Zpx0TJXSnp/GMO3hMff+8Pfy5t0jLohVabGfDu8kB9NrIAuznu10nbfocGwV3wRwIcA3BJ+8xG6fm0KfyM5GWF+I/kptI/rnfp8Xww7dJDcUuHzN5g66Kskd1ZrRmNOe5t+460w3d1W9fXkoCBp17BOXRN+t48geTmAo9ROi1Siey0EZ77srg6Z/wBAU8m/F8CNAA4g+SFJB3RperY9IayVdo33kexkzzkKrXWRXkrsq86zDBxkM1fY/htMKXVfTW4i7Fz5OlyTtBsF5QRyOMMkHJ9Tg29MUbLuALBu9Y+kv5BcC+n6rEFER9LBIBeQ/Ab69G6XoNDOM2a1SSqUrolTzqhOp09s120VpXVGxxsmwfrhx6J9ST0+6OA/QWvtsjpOhe1YB8I10SpMa3Kaxw6qsL09MOQY/y/6h+Phea1iNvkjbKNtc1JJujiswVJZVz07qMK5RfIqmNlKcFmTrLGO5PsBPKMMGtQS2RxI+jfJ8+DArYlweZZdIpFe1sYHYnhdTDjQ5Fs1mZI6b6X2oyOBkVNGjxokjZsPgPnhCX316tNBdiaYD3oZADOOddszru2i2vYVcKG6H2cef/0YtXs1OKIZcPTMIg1yhA06P4UHtE/BjoeU7Neiz7fhaLTjRtjOD4a/a6Q+fbgPb4UHqRlgQ89XYIqHQdzzSQC2H6PnnfUOwpG78fYXARwBRwP38ru3AFgusX85ALc2HPNAUx9rkD8JXjQeGdp6BIDD//90n8fjB8A74cl4A5jTu0luw9r2xmEM+XVC9nI4QvGBMAYcB9c3G+trXRLAox2+XyU83wfgug/9+M2p4e/d0b5rR3jOtcLfT6Y+Y32fM9o/L1xQeiMA8/bxvHfCGaHx87yzD+e9Gq4rcxnXV+wRAAAgAElEQVSGg3SSY0A1F0bbK8JBIz+M9p2emhdh4/epDedtm/tT+0ru82j1ozDe7gFnVG3bRXapMM5+CcB7xrKfZl5b1+fd43n3hSMp54MNlnPBmSL9aPMisOHjN039GcA20f+r1r77Up/acVWB7B217QfCp60tcGHyR+Cs288CmLPDec8O48Tt8LpmD7ieVZP8LfX2wPz+Kdm54Iy1O8LnsG7PEHbq/g3A1zvItPWtkfa3Hp/fWQDmK5C/vUC2ZD3a8kzgdcLdDbKLwuu/l2F6tOsBLFzQrt36cN9+EvrnLwF8qPbdQxntXajDuWeCdbMz4doMx6Omu41i/5gTNir9FY4G7yTbUfdCmKvC/4vUvvtkbftemO4RcODh1D5dz/XR/yfVvmvs20joIB36540APhJtrwnTraZkr0LmGgwO5joLwNqw4+1MmGp11PtF4T0vWXe8DgfnMNqXfC618Xs+OAD25TG4vrkSn1G3qeWOt8hcEwM4NPy9AJGOgQ66cw9tnjE8u8b5/c34gWttAQ7eubv+6XLsO+AApQXh7PERPe//fkb0HG8Lf3N0xVkB7A1nUwPA4gA26FM7NgXwNBwweyqAJwFsknnsyaHfJdelvcpmnOujAH4V2noK7KSeqU/3Y75wvo72BxTq2ciwH8FB2L+D1yVzjGX/zPmMG7o/kj+EF0f3o5W2oY32ieSaAE6EOw8BLAAb9afUZXtox6rw4nwheKFRZdekeOfnwHDKPQBcC6fcdy1CHo4nHAVYj1CKo5smwJFVn1cf6iIUXt8+4beXlLQEXZjxLGXy2Ra06S2w0tDGpx2+2xTtlG7JyHSSmwC4WBlR2CE68I+yt3xNON341xphCjsHWMthkCh5BzPPl01ZQnI1eCI4HsNZFCvCDsFtJF2fOH/XekY1+YFQZpWi3/d5PIKZNIwkN4cXr9cAQwWKvy7p7BH+flXQeYgTm+S1ktZIyK4KL+JfIrkNHJlymNI0RyvBSsB7YGPMRAD/kjRHQztSWVp7qZZhFWTngSN1NoPpZL6jGm0KySPQIdpS6To7N0taia5jcDhsqD9b0uSm83RDLynjITt07W77SmUL2ryNpJPD/6tKuiH67kvqA00VXZPrOLj2BOAoy88qTdmTPbexoT6CEnURMtvZWAiXDVRjTHPOp+jEsu9zL/0oHHciTA8X17c4pKHfLQ9niawDzy2NNYhIniRp2277wv5B1mwq0nsKzjsjgM9jWGe9Bq4100Z7S9NO19GiK9K1SDuNR8k5LUSPH4taBpNa6wsO9bd630v1xbC/6JmEjIc5YUNWR+qbhuPfDmAlSRfV9u8Cj7Fdi2qzgPowyGfX88oFyfnhaMxNYGf4mQDOVQPVV8NYkKw/MUiwkC64qd8k5Ip0NJIHw2P9dgC+DDt375f07Q6/8VYAE5SoW9WlbU9LWrDkmMQ5Pgs7Ql9OfDdHtYal63N+StKZ3dpLch14nP04HFRxBoAjJC08kraWIrRzI/j5zQM7ws+Q9IcOx+ToXtnjUe54Fb7bRtLJTeul2jopppOrU242UsWR/A081leZsV+AHVEbJ2TbajCm9oX9h8DGzLPQWtMyRXM0Aa47vDaGx7lfqaHOx6CRu0YpPOfdcGDCB2Da6380PRe2s+vMAGCVkdqxSmw8Qf5J2Ib2fJCdE86Ueg527A68ZvMg1sQkPyhpaonu3PAOvgA7mcdldhRNNfZD2DlEoCMjEOh6ekfAtKWCgw52lfTHSGY+ORNwodQ5GtbGG8J6ybvgvrMQgAdS64w3ow1kUDp5D+3IHrdI3giPtzfI9OWT4fqTqTIlZ8Dro+3kMi2zALhJfaD1Dfr+xyQ9G7bfCWfqZ9u0Sc6pTNtsiWyHc5wNB3NeJOmVBpliWuZCO0hJnbeuOkyQmwivNT4NYD04SeQ02AafvM6xxHii+9sYdobkULwcAnf4h4ChhelpMN/4SHEsgN1R4+FvwHFwxNTmYXtb2Mie5KcMhvjFJR0fFqazq+agCjgk+v81OONq84RcLyi5vk1gZet2AJD0Z9bScdkbdV4ds6KZxuk8BCUB0SK0Az4B4FCSU+AB5jI106KdA2AFkovB9+V82Mu/3tBFDF9XlQZeXWOn6zsL5nY9Bt3v8XhC13ewcFCu+sqSsMOpoujcEI4ai4+7nuSH4cXTZ+D7ex9sAGpK3Z0bwP0kc+sZZVNmDRj9vs/jCiyjYfw2gBUlPReOnQeO2h1yUvXimIEnaQD4C8n1YcfMuxtOcRSA5ejaHnvCY8Gv4QysOn4GT+5nwQ787eBFelPbutIXkNwBVthnhq978+p+JHBbt/Ml8D06oOJr8OJkEjwH9AxJ+4S/XVPGaSrTWQHMHZwJ1Vg6CV7Q9CTbA74KR1oBvg+x8vdZ+NmOCGFBvxzJSQAo6YWgkKeQPbelFtRNyDTUz9jhFC3f0VRg7wUwB1u5tychotKMkH2fq34EB/e0OEVoKpUmvC9ehEh6nmSLIYjkfnB25gOwLrBXB12gQsuCOij0TXrlIOf5rn2jRwfRUfDzrYrVbxv2fS5xfKf7X+HHGTIpvCrp8C4ybPg/tV2h9JnMAt/frtQ3JH8oqU5duGe8j+RSkh6EnSYL0nSwwydOOKtVQH0YsAvs1Jofpm+5HMO18Kp2HCppt6Y+olb662thXe1MWPeqKHpmIjmXWmsGDtWfCAbZCrPDtXlGG/t2E6jpD+8m2dLvGvSHkvUoYJqjHWGn684ALoajcFPtmRPWGxYGMAMDzWNDO5KnyJTrhK0lHVdr15WS1lYUZCnX3v0SHFn/UttZWnEZgOvgTOInwjkP60NbS/EcnCV2GoBHEQLe6PpJLQ6UQt2rZDxaKno/CGBy2E7VvHtr+JtDdaWG/1PbMXaBA5T2DnJXwjXUUnic5HcwXFdzG9gGkcJcMNVarFskx0+5ZtdRGHaUjRlK1ihBnzoKwDuD8fZ9cJT69xKnfk3SnnTw3XUkt0Pzc/kTyR/BeokkvUbyUCQoGAtRYuMB7FQ7V6FuGsmPwZR2Z8J6woc7HNsvZI+3uY6kyLk2Oxy4nEPnu0L4XBC21wfwewC7kDxL0sEZ5xhtHAxn+z3QVdI4HrZzbRa2twn71qkENBz4+4W6zhMcTCk66+/BQTNXSPoAyY/AQQsplM6v4wGltsi+o9C2Alg/uhTAAiRPgR2TTWv2yZK2ILklAEh6hWyotVGOCZWDKuCvcALGEMK42ghJd6f2k/w03Pbvk1wAZuMZsWNd0qcyxLJtnIV2kGw9u1CHQQgKuQzAZXQdvXVhm9ZhQQcsWYsMHOPJSfU4vHjOeflnrBxUACDpYTpCtB94QdIlmbKTJW0abe9HFyFuA6OsJHhCmAk25KSyknaU9Hjt+ByDQQ5Krm+6JDHUeKIj1FogabXwN5tLlq3F4CbC0W5NfN3vlvS/ueeWtEPoC+vCtWOOJPk7SW1GGABvBMVwEzg1/Ai6mGl8vl44cktqOYwn5LyDJY6n/QCA5oNfXsNFe/fFcO2yWP4Zmu97Mbh/PKZhTtwU9u18OW0odWoNCn29z+MQXetERJhQm1D/jprygsE7Zl4L49xGcAbVsSS3bzqxpEdJTgyT/fEhWikJ5mVpVRkFT8NRyB+LdcO4f6pWQygHGi6S+gKAvtbjqBvcot+MDW47A9gNVsSmYlg5exHAz2unLJEtbm7D/6ntkYIANiW5FZx1N39Cpuvc1mMQSI6h/hGS60m6uPZ768LjU4wlYWfPnGjl3p4GYCe0o5f7fA7ajTNno9lBNIHk2xTqS9HFZ+v67Hfga1kufH4Q3qtU8dm9YF7wWUi+GLVzOoCjkcYg5/kcvacXB9GKao1evIqOcmwDM7KuSpynNRwWdOLL0ToXx06cXgyyRc8kx8keYR20G2fWre37Kmz8PQTtENL1aheBs28WRusYmtRLMp1alYE5p48sFNq2M1oN1001A4vqTwwSmf0v1h9yjRcl69HKAH9M+HTDxQBuRu910HpmAugxCOR3JPeAM6PibJn68/4gbOy4guTjcGDAxF7bOgKcBd+jqt5WjLoDJVv3Qtl49J7cxkr6ZfibUztnzrBenRD+r4JGiOHs7dRvPAc/mxx8Fq4d+Ztw3iloMGyWjJ90Zm7KYd6POqOlKFmjHAMH/VTP6W6Sp8JG+ToYZM4keR/sKG3KerwPfo6Xk9wivE/90ENLbDyAs3CH6qxIupzkDyR9lc4eGQ2UjLeljqTKAHsOgOO7OHPeDtsq/gUM2e3OhnWgqbBDaLzh2QIHFeBatDFzwQkkd2uQzdF5KvxHriE+geQESVcHh1YKRfPrOEGRLXJAKBm3qnd5Kuw8JJwx15TdP53OnqrsvZPRv+dzOcmLYf0R8Dt5WU2m0/peGF6HDIHkz+B+tDqA78P6yS9gm1lPYGDcIfk80mvuuYYaVWbjjG0b8TonZdso0bNLdJgWSJpO8n44kPODaKhDOJYYT06qlwHcSfJKtC5aU9Flt5E8FsMLsa2Rv/johqtDdMtv0Lx4rvAKydUUqMiCQbIpXa5rVlKEs1FmsClByfWdSfKXsDK8E6y8JhdhLKPOi4vBvQZPsk0RzjeSXFbSPd0uLLqW/5C8BB5gZoGpH1JOqv+EqIHtMWx8q0eRzwxHoS0Gc50e16GtFS6gi3mei9Z7nLWIJ7kCgL9I+lOOfB/R9R0sdTwFLAgb+ypMhw0yQ6CpDn4AL4aehpX3d9M0UN9Wgo5I0rV0Ovrikq4gOSs6L4r37fDdaGJQ93m8oCRj7VKahu60sL0FbMQZwig4ZqYFI/U2AFansyeagh5eDtEnd9IUP3/BcCRsCjlZWsWOIzrj7BuwUjGUzaIErVV4h1LGgSSVWiG6GtwkHQYvEr8s6YhOJyuR7QG9RiFnISj4n4CDI5aHHc0bo9mp3HVu6yUIBHmG+t0BXEhH/Fa60wpwgd2WYq2SzgNwHsmVJd2U8fvZ95nlWVoVDoHvX5VxuRm8SImRHdgj6UAAB5I8UNJemYeNaJ7vgpy+0YuD6HWSkyU9BgAkF0WzIzM764ouuHwg2sejJgPksuF8a2F43Kg7capshDgTAWG76bxFzyTodzvCfTBu92cjmeyoRkmVk2fdeoBN+K0UfgvPCxcgw2mR6dT6CMnncvqICujY5CybFwBsGebJd4Y2zEZyNklP555rJChx3veiP6BsPVpKsTWzpI6Z8onrGvoKXtP0il6CQKp3Ic7WqzsvIamqe/aNcD+2hLPxLoEzNZqc/X2FpM8UiJfoXouSPB9h/An/I2zX55qnuxkSSTKWyRmL4JICn4j+j4NG2vQMkntKOpgNbASp/hwCP7Ky+ljLSgx4Aa6Fcl5t/wrR/zPDc/ZcGBuUrFFmlXQrW5MKmmwAQ3OjpPto9pw2SsXqHMrPuipBiY0HAP5B8huwUxnwGuz5ML734kTvBSXjbZEjSdI2NLPBlnBQoeBA8dPUTl1at1X8B66/9wrJ8epQuY2mavst8iiL/0YHTFZr7i3h4NAhlOg8Ef5JcjZ4HDqF5HNofk+K5tdxgmJb5ABQxAbEYRq5ixL76tgX+VlXpdgDHu9Xg+fLExGx5QCApP/p4byryDSGd4Rz/CPYZ0aCSieYu+CYrjbOQjvIkJ4NACTfAc+ZKT27F/vRgvA4vyVsvzodwEaFzu5RwXiqSZWMXE8tMEJ0xxcx3OGnADhSfUgdpTnOE81IGv/eD79sc4R2/APAZyS1RaaSvFXShxg4JumspJvUGs1bGWwOhiN3KkyC67Qka0iUoOT6gvw6MBUKYeq83zXI3QkroQtjuLj7kpLWS8iW1H24H3YQPQFPZim6hFj+f2Ev/UfgyN8zYO7TtsmS5NKwA+omSaeFxf8Wkg6KZM6AFZXr4AiSpyTtmvrt6JiutRy6HH8i7OR7WNIWOcf0A4Xv4IMAlqveufBO3iWpHrkIkt+G6SrPhZXwTWCO+AMjmZ/CCtDukUNmEhwJ/ErqngfH6f/BhcAnB0PZLxom4OqYNqdWQlEdKAZ1n8cLWF4nYlNYISKAKZLObZAbiGOG5LywY+H3kq4LE/iaStfQWgjAs3Am7O7w2H+kpEcb2lyN998F8Cc5SyurLkYnBOflGbDytwvsaP+r2umoqvtbYWb4/ftzPxYEpddCchm0P78mPu1s2czffhmm/yFMmVA9MwJYVFInZ2O3c58CL5IvhxW+qwA8qg6UaaVzWzimUlYBC7cZhWlH9nPoYqgPY8lWAKr6U/fBBWeT2auZRrSi+0xnL24MG97Oj04zDa6Z0ilLcWnYqUEAV6qhzlQJ6LoZWwFYRNIBNH3EfJJuTciOaJ7v0o7svlHiICK5NmygeTyccyEAO0hq0wtZVpvkerg+609hw+kO8Ppin7pskH8Qpmycnvo+yCTrIVRQui5C0TOh61o+CD/z/eGAtwdifYPOyH0bCrKHUuNi01hJ8hZJ2ZRKzKvn9VMAn4L7z2lwLdmu9bEK2vAl2KjxbNSGjmPXmwklOlqQfxAJii0l6oqR3B3AvwBciP47t7OQYyjp0+9MgKPxP62yrMV+/PbysWGeIwz+Y0Ntmwq19+8aODv4vHiODga01WB97WpJJ0TfdR2LemjzhpIuyOnPLKAIjY45Gs5WqwLnNoX1iAUAPC6pKUOjOv56hUCc0UTJGoV2sn4JHkOXJ/kpmO1m3YTsdqnfa1hLxPXF3ouQdSVpzt6uaui8pTaeueG5u7KnXQ9n0r0Q2pNc2/QThWviB+A18fSw/RaYreI97FyXbW44EHE3OHNgMQCHx+MgTXO5CUztBliXOR8Oijpa44wKCxha69ah1Fo3yC8IU26vDL/nN8IZNk9FMr3oPG8F8Crch7aG18anNMyBRfPreEAv67UBtCFr3OJwxvTVANYMbQVsS75EUjLTl6HGapC/eaQ6I8nLJX2su2TbcUuhfT1zakLuFrgf3xbG5rcj0E2OoNmgHfS3p9Y6DfIpG+eZkn4Qyawl6Sq2BmMOQek6jtl13nJBM//MD8/Zp0vqhalo1DBunFTAkAK3RNh8SInsifGIYEyHpBc7yOwB1y5ZBx74PwsbhOIJsmeDzVgjMsZ+Ha43cESTwlBfrNNZNHdLaks1bDJUpAwUQf502Eh4ifL4jWeBFbGHGr6/R9KyUTtvHamBORckZx8DB0rWO5jjeKrJLw+gipSYIkddxt8/AmAJ1QakMFk8KKmt7g/tGP0QgFsiZX/oeSXki51ag8Kg7vN4QNNiXr1TQ1XnHTPHTHCQzVM3hAdHyrOS/tpw3LVwdNIOsBPjr/CCqq2PktxX0r7R9g/gheKv6oo+yamSPsioiCZDmnrGtUyAFbnkorUEJQY3OuJxTVj5vBh2+l+vBPdziWxBW4uN3gXnvgtW7H+NUKSd5OOdHBYlcxvJT8AL5ZyixANxnuQa0Xp0LmRlaZGcJOlFmt4vde5UvztawxkuoINAXgbwc0n31mSPgo3uawWjx9vgIJee6SN6QWHfKHUQvQWmcCQ8tzbVvLodwGZqzbo6u8HRUo1Hsb50nRqiI+ngny+rA3c62Zpp0KtMl+PvkOsn3C3pfTTF4WUdDHpx9hCAVkcxHfAwP0zlvRVajQO/UDqIZyt4bdCJ+jCWz3JqkSQ853waZhO4CzaEnpvSK+v6ejAGAn5PflaTfRTAh1MGqNFCmMfulrRMV+Hezp+9Hs19JkH2i3DW5z8x7AwY8fhcApKbAbhU0jSSe8OZv99Tq1NncThIbDLsEN1DHRw8QcdvRFN/HhRIHiNpp2i7MfivRPfK/O2Z4TX+1gAWgZ/1zDDTw+XwO3Vn7ZjssYgjcMCF92a2ur2C5AclTS3R30leBdcHfy1szxCubx0A98Rr+lr/mAAHtX4+1xDYTxRe46Iw5e8qAJ6HjdTbSHoyIRs7fmcGsDZs7EzpuB9UVDsl2JE21ggCsd7MKFgTFzmSgqH3s/A4dhKAEyU9RwepPiBpoZr8ChgOmrx+vBtxRwPMCI7r4ZxvKntvqS1yQG3IGrdI7orhjOk/AS0Z08fU9blwTFuGVWpfYXsbncYdjtkbToxYCk54+Dj8HrY5d+iggE3gueQ42Fa2n6TT67I9tP00dNF5avLdbJz7SdqHBU7lYFdYC7U6b/F6NpLN0mFCH5oyknXTaGLcOKloirgTATwJv1ALANhe0pRI5kxJm7O1ptEQ1AePNh1BsA+G+S+vhYt6vxDJbCPpZKYLOELSTxrOnZuVlEurU4zM62uimgAAKFEHg/ZoHwrg23ARxydI3hsvIBnVfYCNRIDvxXRYuUjS7NBUWdXLf50SmWq9ICgvPwYwk6RF6My4/dVaVLruUOuaOcC0p/wFWGlvM8owr27NwJHzDtbkOw7KQSbLkEDyYUlLlHxXGQaixd0M8IKgKcuuyKk1KAziPo83kHwnhnmBb633e/ZQa4cDcsyQXAmuW/UeOENqIoB/SZojkjkdwFEJZfDj8LPbquF3S7K0NpR0QbS9MbywWk7SdjXZmyWtRFMlHg7gz7ABeXLGvVgSwEWSFusmm3GubINbmLeXA3CHpOVCH/mVpA1HIlvQ1oEavenor63gNPrnYCV7WUnPdDgma24rUVYL21ziwCky6Be2IzdL60JJG7C9vkUjvVbCGLQiTM/wIbUXha6CbeIo56bsoa41m0aCgr7R1UHE3qL4SrKubghtPRvOIvwTgIMkLdnQ5mtgg/Hv0RARyt6yEYqeCYcZDqbA9DbPwPNVqh91zR6iI4Q/Ay+aY+PWNAAnNNznA2Hqw8dq521ylBU5tcIxEwF8FMBBMMPBrE2ytePeDmAlSRfV9l8NYB11p74eKOgM1r3UZ5rBHnS0g2C9oSvFFsnHYAdf3zLbShGN4avBQZM/BvAtRY42ktfBQRdT4MDJlZUwFEXyb8BZNFXATsyP1tifRxtMBP/l6F6hzzfpBlKDMS+MSXPDjBAp+vtKrmQsynbAhe9PhQO7Xoez/eYA8BNJP0rI7ipTE3XcF/Y/BM+jL4TtOeD11VJsd3rH88Zr8Lv1YzUEiA4a3dYoCfm3wjV0swNHw/04SekMreysqxLk2Hhq8kvAgX8LozX4YtTe1x7G22xHEslfw2uHFB3m2pKurO3rGIgy3kDy3fD6dVV4fLoezoz6Y02umPqTGZkciTX80FdoXsuviYLnPV4wKFtkYRuyxy1mZEyzx6yrzLY+Do8tSTTow/fA2WK3h7X/fLAO38TE815YtyW8Pr43JddD238H4MMAbkJrLc5PRjJrSboq/L+IpCei7z6Zur7CNtwmaYWw/v+ApDcqPSEhm20/ejNhPNWkOgSOyHkIGJo4T0NrHaYqYncDDA7Hwbyfm4ftbeGFeqycV3Q1qToRbYN1mPQuk/RRAEnHVA2PkvwW2pWGftQQ6Xp9CvUvSO4PK8onAUMpvE21MXaAleDvyw6qReBo0iGoh7oPdETAThgudntyMKwdUZPrpcD8vrDT4prQvjtDu2MsRxdSr84VF1ZvOu+OcApqpZSvCddtWYLk/pJOqsnn1K0ZDeS8g3XHU8foyDCo3kVywS6K3v0kt6sr6LTT7sGGY64N78kstAP4CxgupprCv+VCgdW5Z0B/OMBL0ff7PJ5Ac6z/CH6vCOAIkl+XNMRBrN5q7VSGxr+QXB92zLw789jF0VzA+GdwtPlZsHFxuyAfY1kloiwlXUbykKYflR0UP4m2n4bf7ZTsBbXt3zadF8D3wmL0a/ACZRJMN9SGxJj4DNKFb3vBVwEslmlweyWMB6/RUaPPobm2TIlsLq6miyd3NHoDOKGXk0t6EMB3AXw3LKK3BHAryT9KWqUunzu3BWQXJS401P+ytv0z+D3ZFu19pDr+n3QG4TOo8W7X2pHtAIN1jAfhqLmhLK36OSVtEP420igmjpla2/497Bw5JyH+n6CvKbR5HjTXZciu2VSKwr7xapgrHgmOlD8BeEdNZg3YeZRy8ir6neGd0pV0NkXXrCs4anNWuJbJAbBDNUnpEpDM8qrhf+EI6IqKuZ6N8FPVshFQ/kyOprPlvgNHY88W/k9hN9jB05hZIdPVnEhyU0mp/pXCJjANZiP1YQ059byGQHJZeH7bAq498a0GuR+qPSt5z8Q+wI7La0hehFanTDJAb4CYD8B9JG9FqyGhsVh0Bbp22d8BnJNwtmXpaBEq505cd6fpmdyH4SC9sUJFSbg+HHxzHk0TG2N2SVUN4h/RmZWd8DWY7u0VmM3iXIW6MWMFOgN5aB6UdGHKyZCpe6WMbSvBa7ZGQ2GYd3NqiFRj0d7oMhbFDqqwvT1gB1zDuZeWM5C3hjPTvwE7q9qcVPC4XXdIfSaxD3BpgjvpgIIqc/MHwaFzRa2NxbUzBoWcNQobgpCr9WPmWPcy2tcSFeLs7KGsKzSsDwqQY8OKcRaAXwD4FZrrUw4apePtHfD6b4Yg32hf6GSgTTiovgzrJs/C94LwOD6eaWyPB3AqXPMHMKXh8XA2Y4xKny7JDPsePMa1BMfFAoVr+Aqlz3vMUaiTD6oNXcetGDKjVTfa/F7qVOZiDthez8R3yXUHvPZ/Paz9Z4fXmalAjZiS774RtjOFg7qL4MdwUgHg9WScwLA3outrmk8qNMwnVZ2369Clzluh/ehNg/GUSTUUHd9pX9jftphqWGD10o47Jb2/276wf1VJN3TbF/afD2BbNUSz1GRvhDtlnd88d9Hb6dwl19dGYZHaF33XkTqvJjs/hosMAwBSURR00caVJb0UtttqefUK1jJxqt8b6blpTu/PSXo2bL8Tw4aSKaplFXFAdWt6aHfJO5gdwUpTQqwI8+gmDQmhP/wGXuBOhSewFeGsu02USLkNhrkdEWUnwhFTyUGN5MGwoWs7uPD4FwDcL+nb3a6hnxjUfR4voKM+1lGI8AmG3iuUzkYoqU+3ATwuLoBhx8x+ks5PyKYcM3ulxlAOR6vEGVo3xo4Fds70e0jNGQM5WVrJyC0FAQwAACAASURBVLYKGsfFZMO89mlJXY1uJI+EDaSfhg1a/4IzSNtqVZTIFrS1mIJnpKAtGqunHJwlcxvJK2Aq4APhqOznAKzY4Pz6FWyor/jdtwXwuqQROU9Ifg5WxJeFHXmzAfiupF80yJdkMJXSrqWyjw9NjZNhAfx1tOsbqazKrWFj/vLw/fsUgL0lnZWQza7ZVIrCvrEibICYE3YQzQHgYEk3J2Rbov1S+9hD1tWgwfxshEE+k6LsITqQop4ZuH9Criv1YU0+p57X4vDYuSW8hjgdLhT/eIdjUnW0mvSSpJNR0n4519AvcATUwnQW8FIAFqo7tUp0tPDdovV7m9oX9p8L94urMUZF40leCDuzPwobBl+Bo7KXi2QehPtPZWA6BRGFpZrpKBcJx20E4CkAP+j3vJoDOrttRbjdCG26TYkASTqAYtdqbAnOokPUXNdlDdiB9Bb4+i4ZYVsnAPiUpDMz5VNrwxfgmsmp+sv3wdHppwL4maRr6+MiyS3h57sarGdXmB3WHz7a0Jb54GBPwn3oz7XvezHODRQ5a5SmMa5Caqxjaz2vCbBx+ExJ36zLJo5tzLoqQYmNJ3w3VdKYOgcK18RJR1KHsTm1BntJ6QyfMaexLUUPz3uzuj6b2hf2l2RyJINAG3Tyovl1PGCQtsiCNmTbVsL3JRT7fa9T2Ysdk+Qv4SCKreGgtxfh7L02ZzMLKfkyfz+7jlbNdlzPHK5vV/PJkrBeUtmtNoTtwm3r89DHXoHnkm513g6GncqvwCUmlgOwm6ST67JvJoynTKrbSB4LR9QCfiBTG2TXQXuU77qJfb3gFZKrSboeGDKGvNIgewRaPadN+wAXFLyHTiGMDfWphcmsdSNOH1Fyfa8Ho83psOJVLXjbwIg6D8AiTFDnRbIHwQvo+6PzCaaVaBOv/WallCRRYvQGcC9NnTIxLOq/AheRHCkWVnBQBTwH11v6B8lUNPs0mgpxWwD/Q0cIzNiHdpSi5B0siWDtargIk8yHSa4FL+AJpxpfmZIP9+hESdsAOCYlk8A3YafWPXD0yMVw9NhoY1D3ebxgQs3g9nd4kk2hpaYOnd2WXDBJujD8+wKAjlGZKovuepnOprkzTPR/wXC2bIVHSK4n6eJae9eFo8qbkJOlVcx5TvMapygbUrzGg6QTfR2+b10NbpK+EP79BclLAUySdHfqpCWyuZD0KpxhcWSu0XukkCSYbiWFkrltI3ie3h3DymqbwTtgxdqi5aqwuGlvQIEDR1I1Vk5BRlabCjOYwt+sLC2ks49PQjr7uIoUPgZdIoUlnUJyKhzVTLhGRFtGV8DrJCertWZTvyKRs/tGuK+AHbndnLj1aD/AFH3xmFucdVXSj4L8JwH8EM74ItAxM70kGyHrmdAUOQtHevBXYacr4FqxqWLx2dlDJH8BZ5Z9BNYxPgUH6aTwTgAPkmykPqzhLtgh2cmpdRkcmbyFpHs6yIHk5+GAnUWDIabC7ADaAu5C2/YLx761MtqMEe7C8Hz6sDKCACtI6hQlXKKjAX6H6u/VWUjrMr8Nn2LQwQr/gQMqLuwm3wGbw5mKP5b0z+Bo+HpN5i+IssDhMbnabszck5k0zoMDzLaF646MupMKwHoA3i/pDWDIEXUHgBSLx/tiPUDS8yRT9ZQ/DjunXoVZQ9qoT3tBMP5+CUCWkwrWY5YHcDc8di4T/n87yV0kXV6T/yVMrXUXgCl0fZV6De0b4Wc+N5zpUGFaOHcTVsQwBdbrcIZLjEoXTxrnOpx3kOi6RonGuAUk/SH+jqbxTuHH0f+vwU7DPzbI1tEp66oEJTYeALiAzio9F13qyg4QJePtruiS0VxDag3WRHX+B3h9+WbC38K67rSwvSXcn5uwF3wvuu0DhjM5pqBLJgeAmBJ4ZjgQ8CHU1vgBpfPreECRLXJAKLGtANY7K9r8HRho81OCysu6KkXx/ZG0c/j353Q5g0lNATHwXPUAyUZKvh4wT4GsGv5v247mk8sBLK+Q0U1nsKfePQSbzUIAFpd0Il1Hb2JDWz4maU+SmwD4I5xZeTVqjGZ10IwsZ8r1KH8qKcnIM1YYT06qzwP4IuwoIDwoHhkLRIupyYnFVD+cC1U7TgxRLQTwDzjVPW7HynARzXnYGiU0Cc0d6CK0DuKdcGHKINondL2+CFvBKf6HwS/cDWFfCvuiO3VehU1gJaOJOibG8QBuoaMPAUeTH9tBPtvoDWfTfBtWzE6DF/YHZLSpG66jIxWrgWdTeGHwVjh6v44t4Pv6WUnPhIiUFA3DoNH1HYyQHTGrjKjWSPYq2Kg6K4ClSc4j6a8JuddJzkNyJmXQ5PTo1BoUBnKfxxEuDcpFpTRvATsEh8CoPh1b6TSnw0WK2zBAx8y2sKL3JdgJsAD8zsbYHR6XN8ewMr0CTOvZkX5W0qMkJ0p6HcDxdKZs/P2JDYd2QmygmhkeU+vGgQqDpBPNNrgF5ekqSS9IepLknCQ3ViItvUS2FxQYvQeJrLktjF3nyZHMb2A4Q6oJJc6TbAcOXYj1YLVGm39N0t4N8iWOixTV0Xc7NOc1SSK5EfxeH0vXA2qSParTtYX2xvSqTRSzMb4OU0i21GzKOC4H2XpPzn2m66W9F8AcbM2QmoRoQRqOqyL+9lci66qhvdn9KOBguHZpkwOwV+Q+kx9hOMMCcNDK0bBjaT/YaFLH0+EzU/h0wipyRuDdkvajKWGbMtByqA9jdHVqKVHHpgNOBXAJnKUZR/xPazJUhjXQsfB7umCYW3aOggsGihBUcjTsvH8Cnr8XCu/LLimdkKbrOR42uv8KwAcAfDNh0AcydbSS96pCMDb0WjR+OzhwaaVM+SQkvRwMjqsBeAQ2PD5SkymiZwvzzKfhZ/IHOLjx+yE4ZKwwJ7zGBRzY0YQJJN8m6XkAIDkXavaR8L7NA48dN4V9Q47JDoa0XPyO5B4AzkCrwS31Dj4JYEdJ94V2LA2PfQfA40xLn5Z0OFy7tMJTNHVXLPMUnPm2cm6D2Z6t9hWSqyjKVuvFODcK6LpGifA4ybPh+10xBlyMmlOargGyGFx7+rJuDWBD1lXRVaRRYuMBhml5Yye1MHJ67RKUrImLHUnd1mARxguNbQk+Czvifgo/txvDvhbQAZXrAZifZDwWTEKz42kj2CHfNThOtdreYWzcOSWLsuc9XlBqixwESsYtoIA2nw1ZVxgZ/WjXWkhkax1omqL32rD2fzSs/TdoCMrJoeQrRV2Xa4FamSQWpdlkGP2PsN20VloQtnNVmI6GgEySOwH4PwBzwfWl5ofXWan6l1Viw3owa8I/yCwf4W0Avk7X9vp9N+HRxrih+8tBmHTfhoLF1Ah+axIASKpHG1Wp/mvCNZhiqptpAC6Q9Ej9mMLfngZH8k8Pn45Rpj3+RuP19Xi+bOo8kpcA2EyZfOVhslsNYTKTdEdCZsjojWG+9yGjtzJrYPUD9MiwKaLCnjDvfePLFnnLr6i85Soo0DqewYx0+zAxHQ4r1HvDXLjPwoP3N1KGfDoteHnYqBkv6pIKZZjcN8xxav0XIwPJuP9PkXRug1xJfbrYcTTkmFG64OvdcATR++BorWMBfFLSGpHMPADmkXR/7dhlADxbd46SfAvsTK7oOu+Do+4bjTB0EeyPwkaxZ2DHyGeUpj6cB84GrkcydS1gHAzsV6RkOWA60VyDG9O0FC0p8b3IvpmRM7cFuRK64LXhBVWLoV6JqG8WUL2k7n+nfkRnb/0C7bTFI46YJHktTGmwA1wH46+wU3rZhOy+8OKsa6QwC+lVw5iQU7OpGAV9o+t9Ds68jQF8AsOR7IB11tMltRltUs+2qb+U9KMgf4OkVXPlS5DzTOrXVtNbr5P0P/VjItmu2UORPnwzXAvk7wDulTTiSHmOgOIu49xZReNJ3gJH6Z4f3bd7VaOyHhToermTYYdUZfSeHdYbn5LUVsuHgd6Mzob5IpwRc/xI5sEe36s1UVA0fgQOrU7t3gcOsllS0hIk3wXgrNQ7SXJeubZmtT0fgH/U3yuSb8AZN+fBWTr1SOJRNfTS9HUHwdHEVb2kvSSdnpDdDs4mqGp7bAY72E6KZK7B8DUpnLOCcvS0Lu19IrFbKYdzg350p6T3N3yXDPhQmn60hB7tbrRmq02Eo/ZT6/4H4SLu/w7bbwFwl6SlUm0bNArWKHfAwRc7Athc0mN1XYimp34v7CBYG7YDdQx4rY3jpVlXXdFvG894AZ2BsyQc9N3VkVS4BhsXNLaDAB1I8n7YyRSPB9MAXK3goO/zb4562YpBIlcnH3AbssatIFtCsX8PhrOulmPIupKUYlTIbes1MHtDxzrQkk6IvhvTtT/Jv8M6TLKOlqKg6CZdPBJO0fx/G85kPxfWIzaBM5l+kJC9E07+uCXSs+9pWOceGM71SjhmTgAXqr1kzy4ALq6eB12m5yw42edSSQd2uqbRxphnUpE8U9Lm4QVJRcm/L/r/BQAvkDwMVpKHFickPyzplhG0YxtJJ7PGn8xEkczQ8a4leYIyKZOCApq6vjYFVL0VIuz2+9nXF303M6yY1Xn1UzzdJdR5L8MUUVeigSKK5CS50Otc8GLuyei7ueoGpvBiHZhr9KajrneFlR3ANR0O18hSW6u2CF7sJIsZJtpS4i3vO0reweiY7IUM8ijPDoBrS80BLyrfJ+lxku8AcCXS2QN/Dp8JGKaU6IQnAdwQDL5dnVr9xijc53EDufZTTg29W0nOURnfSc4JYE0lMmZUqydF8xFfUZcLyMm0OALOMqpjflixG8oaJcmwuD6+6UKCTP255mRpVTgFjqJdHw6A2B42vudgcThCJ4WKTnQbAKuzj3SiKYMbySaDW4qWoEkHKZEdd2CHFPrSuS0gmy5Y0pVhDs5xnpRQvUwk+ZbIyDQLXJOjCVkZTOFcRVlaGM4+3lHds49LIoW70quyuWbTZJIjqtnUY9/oep8lnQfgPJIrS7qpSxuKs0NQThl0G12L6bc1+Z7uXQ/PpH4dsa719obfKMkeujDMZT8CcDvc35I0KyynPhyxM6qhHV+CGRGehTM2ATQXjZf0B7ZGavaL6jIHn4Rr2w3VQpQ0LfTBm2EHVB1VY9eDnVN3sXYBpTpayXsVIbtofOH8WoJN4Eyy28N1/Jl28qVwLKyTVDgJfq/OkbRHtH9/DN+z2TDGkHRaMJKtCN+7byhyttVkf03yNpjCkHBA0/01mTUH3N6myOsUHiJ5FJytBng+fDg4flJOzNipPjOc/d+UxVpCjwbkZ6udBOv7cSZCLywCfUHBGkWSjqSDQS4g+Q20jw2rww641+kg0+vQgZWFhVlXOSi18XSYMyv5gdee7GVNjLKMZsBrsInIWIO9mZxRJPeUdDAbahrX1waS7gJwV3j/XpKzyirHcosez/aazkNfoUE3qfW7CXAAcT3Qs5fnPaboUScfGHLHraDbHBjWVDm0+dlZVwX4Xzir7zSahaFeB/qnaq9Xmb32r/XTGcJ5/z1C+9hTDTbuNvSii0v6Pp2oUQXC7aBmZ+e/JU2vxnCaGaztvaGDlC+AGSpeDPPQy3AmZB1fVKgjHdbZF8DZ1z8FcAucBDRuMB6MPruGvx0pk2o4Cq2p1i8l9pWiqkGSUtSbMmB+RRccjA0rp0v6eEJ2hej/meFIrblSJw2Dy9YAFpF0AMkFAMwnqYnTPge9XN9JMO3Nx+EFyNZoVmxLqPPOR2vUYQqnwn1iaq19RIdUdEl7heewOFoda0OLOjpqbjcAX4UXaYT7zo+CQePXkWzTZF2dN84Gul7SaoljumXCfRHBWx7O+UhwzowWenkHcxxPQ1D3dPs3JD0M2KGrUHBa0nMkk6noKq+LUOrU6jcGfp/HEj32/30URQLJ9RH2QR6F3EgdM8umlAxJl9H0TDGuJtk1IgjACWF/PUvrVQD70VlaTdkwbw/OtF01HAiRVIIS9/gZNNdkLDHolyLb4AYbpn8CR7sLnjOasmpKZMcjOqXQ9zK3daUL7tF5UuLAORnAlRym3fwsOhuZShwX60r6ViTzPMn14KzaNgRjYxw49DQaaCkKjX85Rorimk0F6KVvlNznO0h+EZ0Dj5YMbZgTrdc4DcBODe0upQyaBAcrxQWKR3LvSp/JNJJLVDpHda+Cg64pw/9QWBc+PxxzF8nVU4IajqI/h6Z+nlnNWZBF1IelTq3E8W9Xup7Hbsiv9fEHkqsAUJgDv4LmtcEg8EbsoKog6V8km/T1qTTl2CIA9gpOmTdqMkU6WmUoBLAVnblTb0+q5vCM1XwZZB6maySmUDK/lmC6JFX3iqYiT0LS+rXtj4Y16tK1/fuOsE19AcmlJD3IYSq+KjvlXSTfpYiWr2aAfAYef6vvRs0ASTNpvCTpb3Rg2moAHlUzvfFn4NIHuwFDbB17wA6qNppGSS36LMkfo8MaPGO9VuFAeE6Js9W+lRKsGeeEzsa5gaDHNQrhL2+gs9TPAFDP/ppeGf1lKs1GniW2Zl0dQPJD6pJ1lYlSG88g9ZhcFK+JSx1JGg4kfwUN+h3JQyXtxlYKxvgc47EGdDXfltY0vhzOLKv0nFnCvlUqAfUWKB8f8xq8Xqk7U3qxgYw1erJF9hO9jFthfv8tgq4g6ckuP3MbHVh1DHyt/0JzHdUsqLc60LfTtcHjtX9ynoj7aXDUfBLOBhsJBlJnrFqfh83nJR0WfffJhvX5tSSrshjrwHP+BXWh4Fw8RNLK0b6X0BqcUmHGoO/NDdvZDpF0cmjHrL1e38AgaVx8APwwZ1/Yf2di3919aseqOfvC/jty9nX4resb9h8Fv6APhO23Afj9WF1fdW9hI+9VfWrHLPCCuN/96HMA7gHwPGw0fqXeZjjScuHEsQsDuLnhvPvDA8TssIHl8wD27FObb6nd7xn61Z8L21HyDt4W943w/40NslPgiKdfwwaZ3WGah1jmrtDP3x79P1f43NVw3pUB3A/g6bC9HIAjM67zraN9b0fjPr8ZP6l+DkcXpmSnwTQy1edhAJs2yM4LO6H/J2wvCGC7mszDHdr1UG175vD+3wA7Ou+Ha2E8BSt176/Jnw5gjcR5Pw5TBKZ+8+bw9zI4cvkDAB4b62fUw/NLjl3wIvogeFE1FTZwJN/FEtnx8IEz3xaMtmeB64ZdC1MMjUYb9gt/j098juvTb6wLFwc/BMDHu8g+kfg83tSPALyldv/u63DueCx4Fc7ieKFBdkbYiF5lN38JNhTX5TaGDX0dryuSXyRn3yg895L7fBYcPPQY7Fi6HM40TcmuPNrX0od7kfVM4AjPh8M9WDZ8PhP2rdtw7hY9LfzfpJvMCmfzHBO2FwewQYPsDYXX+CiA9xQe8xhMp/whAPc3yFwNYIbM880NZ/4+C0fcngwHWYzWc67riPGn6ZlU0d1zhu254Iz9lGyWjgY7FxH6Udun4dzHwdlJa4bPMXBmV0o2e34tvH97APglTAm7E1xj6csd5FeDnQrVsx/1ca7g2qp37urEp74WvDD8fSLci+rTOIYOoL3fCe/nowC+B69PK5rCQwf0m28D8EjDd13XazX5+WC6y40AzNvld5eDjY5fgjOPxry/ZNyreWvbMwBYvbbvZViHuRu2P7wc/X93TfZemM4f8Dwxtc/tzbbxjJdPznhbvQuwkfb8+idx/D3RM2n71GQ/GP6ukfqM9f3pcu82y9kXfZeyn7btC/sXTH1G43n/99OXvvFzACtmyBHAAtH2wmjQjUahzbPBa8w7YT3vRwBmKzg+acMtOH7ZnPvVw3lvT/2f2o72T4D1s7PgtetOTb8NO+A37da2cI5nAfwN1jF2CO/1d+CyNGPeb1vaO9YN6PSQ6hNJtP83sNFhxvDZFcBvB9iOpg40Fa2GqYU6yC4ffVaAjVpNi6nbw9+ui+EBX9+t4e8UuA7L3Ego7vCC7HbYa/sSbFjcrkMbNgTwEIAnwvb7kVAywndX5uyLvrsHNijfGbaXAnBGTSa5SO/0HYKBotu+sH8ygsENXoR+BWFh3CB/MBx99iCAdeCI6O/343n3oW80vYPZC5nwXswMO/f2gSPgF6vJPInhxWH902RwuwVO3Y/fk3s7XF9PTq03y30eLx8AJ+XsC/uPC/1hMhyR9FMAJ4xSOy8CsF5i/7oALulw3IzwwrzTO93JwJ7so3C01hzwWHs1PL98okF2VQSnDZwt9hMACzXIrgRn8/wLrtHXaNDv4R7WDW6/QoPB7f/LH0SOVdgAdD3sJCXCPJo4puvcBhu3TwjP990ALgnP8S4AKzScN9t5gkwHzijcvz3DPdsRztC6HgVBILCD6QcN3/0KzvhaK3yOh7nWY5kjYYfigXD04HcyfjM1jvfF4JTTN3o8b3bgETxnfzHcm+Oqz0j6UfVMYarVw+ufPlxf9jMJ4+yvwzg7NfSRZTqc+2w42vh2eE7eA2ZOSMmeEfr0vWF7FjQbgg4L8lvCkaCfhOnGmtpR5NSKjtsdHvuTujk8jl8P1+b5avXpR3/u9we96Yslc2aWjoZMp17tmLeEe/sbWNffHZGDviab7dDK/e3o/3VgA9CPAazT4Zh9YMPww2H7Xb32wf9+kvf3/jCezAkHXcxa9S0064qrAvgd7FQfcq51+I3YYH8f7Fj+UoNs1/VaJJs9T8F2mnthY9r+oU2NjtEB3/OSNcrrsNOQ0b66kXGhTp+abJaBcgTXlm3jCd/9ANFaBtZfvzfKz6PreItCR1LJM4mO2QDAhLHok6P4vG8AsHx8XwHc1CB7T/R5BM6QSq5tYbvmubB+lHQGljzv8fYpGesG2IbscSt8d394Zo+hwWkeyfbVWd6Ha50VwCxdZD4RfTaGgzySdtmC370GDqRYsLZ/Jnj9eCJc067beWaubd+R+j+1HfZNBHByQbunwcwA/4H1iGkw9V9KdmL4vAWe3++DmdPmHuvnXv+MOd0fyc/DEeqT6SKcFWZHc02jXeBF7d5wOuCVaKYhyW3HyvACdJ4at+ok+GGm8G0A10eUTKsD2LlBNk63fw1eTG3eIPufQE+l0LZ50E5LUYQer+/oQJ23NxytMhtaCy4WUedF2BeO6LwGACTdGfhK4/PODA9Sc4c2VCmYk+BFUhNelfQqSdD1Mx4kuWRN5pUOxzd99zrJreEMCcEGhSYO/nMArEByMXiBeT6cMrxeg/w3YePcPXD/uRgN9QsGgR7fwexaO5KeCpQwC8L3pq34s6SFe2m7yuoiZNP1DAKDvs/jCO+NN2ge3SZ6mi/DERxnwO/45fA9agPJVWFD30skt4HHmcOUqAvIdC2vf0mKOfN3h2uHbI5hKrkVYGdmIx1B6Lt/afo+oFPNp+R3ki4M/76ABGVLDUcBWI6ui7InPM78Gl6w1TFIysjPw4bsr8DP71rU6nyVUGm8SWk3gIIU+sK57Xj4uU6CnfK7wfVE/geOkvsw2nEO2qmPz0b6HTwK7o9Hhu1tw77P1QVrVBMzheMa6+MFeofPw3oR4Pn+l/WxHwBkbv174NpABHCACmo1SPotyW82fL2iWotkX0XXl4iRXVOCvdVsykIvek/JfcZwvZJ/BurRZ+CoyRRK6J5z+1GvFDUd0cszkXQvPBbmYhfYoTQ/TCF2OTz2pTBZ0hYVBZykVzpQQJVSH3at5xVo7Xaq5sYwH+4C65cbIE2NmV3rI+jrX4b7ztA6crTG5x71xa5zZg862q0IYy3JIyR9OaPt/4YNAzm1UOvz6xQMv2O94CYAy5M8SdK2sKOjG0rqV4052FBjp4IStDoNuuWhiqidO/zefHCN7Ka6j93wqqTpAKaTfEyBxlLSaySnNxxzLKy/TkVeLbhYn30NwLOSkjTq8DzyW0kvItCjkdwAzvRC2O5lfb4jgA8rULPTdTtvgvX00UbJGuU+eA12OcktZArI+lj+tIL1rwnkUM3apaKxhRgeayrqrp7q8vRo4wEKqZb7iZLxVtLU6LuLJXW0iQW7Q2ntr08DOIymdj9emRS8YwGS68I2pflJHh59NQl+x5uwG4CzSP45bM8HX3cbJC1b+83l0WzjPAWme74HDfbK6HkvmnjeN3Ro85hhBLbIQaBk3AIccJuLm0muKKlOTz+qoCnyT4D7JUn+Ec7ivj8hvln0/2tw8FKqDlMJeqmjVbX9VthGfBq85l41+loN/6e2Edai85CcKegHHaECik4FalpYd/hqJ9mxxpg7qWDj/SVwBGtsZJimBj5oSc+hNqiSXBH5ReZTmAl2wsyAVm7VFwF8qqEdl4ZBeyV44Npd0t8azr+jQp2dqM1NtRIOhyMS3kHy++H3R6ow9HJ9laNkCpp5V78AYBO18p1eRXJT+GVNLYZfk/RCbc1ef0l3hifTd8GKeCX8Imyca8IfA6/qbwH8juTzMD1XjPfUJsgKRPN1bgUbKA4Lbb0h7EvhjbDA2ARe6BxBspF7Oyhbx4TPWKCXd7Cr46kCB1f8ubguQqFTq9/Ivs/srabRmIKuAVXx575Y7Yazd45OHRMWrEP3IiiEG8IOlTr66piRa0EsC7/Hy4Td1wLYWeZSHgkeIbmepIvjnWFh8XjqAA7X+mmB0kU8X5MkkhvBjrpjSW7f1Bjl1xjIQq1/DhncQv+chNa5+KTw98cZpy6RHU84BH6uE+FF2ox07a/t4azhGCVz22ySjgYAkrtIqt6L35FsqSvWo/Mkx4EDoF0JDgaADzWcFyhwgIXzXwKPj11Ru74J8DveZCR6neRkSY+FYxdF+7ifXVMCvdVsykUvek/Jfe4aeBRhMUmbkdxI0okkT4WpSFPI6keSLgh/O9Uy6wWDfCYAgKDbb50pPp3kLBgONJuMyKFUO+8OhU3JcWq9I3JQrQ9nzGwY5rykkUllNT5/C8+/F2CEAXSjiJw5s1QXjseJVRPfDws2FIuvkDJOFzq0cjBTuOZVUs6clAMHBfWrUgj3+xlJt/TU4nJU7/87YKN9VQfiI7ADP3WNKd3yJKR1yzpOFO6NwwAAIABJREFUgo3s50jao4f2zhmeBQFMip4L4cz6FF4I82VH0LW2AI+DMSbRQaSpPn0EgK+R3DIy0u8P0xdXiOep26P9neYponXefR0Dqv/RhF7WKPC4sScdzHYdHZhbf49Lata+p1/XU0OxjSdgIh3Q+28ACPPWWwbUxjqKbQ/IdCSxh9pfkrYhOQkOQj4+jHnHAzhNUv0dGmv8GQ72+QRa6/VOgx3YSUj6fVgrLAn3/Qdzf1DS7cHWmsJfJeXUmi993mONXm2RfUOP4xbgjMhta+c6CV4j1PERADuTfApmxBqR03wE+CWAb0n6HQCQ/Ch8jaslZH8u6eZ4Bx2Q9UyvP67e6mhVWA8OKH8KZlqIsSjJ8xHszOF/hO0mX8CTAG4IskM6uaSkPkjyE4iCFTUc+PymBbsEf4waQse6r5oI6EitpTsptiSXhiesLWGlbYU+tGMhJSLzM46bHNrxaUnLJL6/XdLytX1TJSW94GESqaKKr+xXREfJ9ZH8AYCDq5czGDe+JmnvSOZ+SUs3HJ/8juSxcPbbN+GskK/A1DC7JGS/LKmnSCuSa8BK/qWxJ5ouUtuIXp5/7XdvgbN2vg0bBp4geW+9X/SyaB0kSt7BlOMJ5uBvczyRnApgK9WKPzf1/YL2zg07DT+K4SycXdVQ+Jvk2fBi/2ewY/krMGVWMopoUMi5zyRPB3CUpGtrx34cvs9NDtIxB8kDJe1VID8RNrhtGf5eL6ltUVWNoSS/C+BPwcjUNq4G2dskrUDy7uo9InmjpFUimSqysVPbuso0HLcEvKi/EYksLUkPJ46JM+RmhiOY/6xE8XU6e/dSmE94ddgpdKdqkW9Bdgr8jvwKVt7+Aqer91xg9M3cPweF0I//H3tnHnfdVPf/94fkNt2UDJVZJE9IGaOU5kGRMksoaRANhiZEiOopyVD4qYcylkTKPDwSwn2bUhE9TZqehJKEz++P79rXtc8+a5+z9znXua5ze/q8Xtfrvs8+a++zzj5rr7W+0+cD4SQ4gqhAuZma5JUma1t5fFfHeub1mwnKgzfRKYr+EEFL1hWYlHQzwV9fDuCck3umavp3ne2Nat67pTrGcsfS8bZVWqeUXhYZdCc6EpiqbV9BOBruIdaJFYnMvCtKbR5mMlNcBP3o3fQw1CRtbPtHuf4Nizb7njb3uWUfbrC9QZo/3kvMHTfY7krkaTuOFEHu/YE1KQVQbW8+ZJ9H+Zs0rh5SiBx/gvh+FxMBjHfYvrLUZj9HBeEx5JMTuub9Fn29njC0lyf2Oeva/m1ywF1Xsy/fmHDOL2p7heSwf7ftrspmSdfbzlVxji1Ka+ZuRCVqrzWz0V641/ycuWZhd4hENVx+P2d3KCpYDiXmrKcwOR9l58V+kLQpEWjdhs41InWhOyFG0keIBJ9XEevaboSuZtP56XBC8+0ptttkdg8FSRcQ1YT3pdfPJJxaXcG5NnvLms8SMT7uGKCfp/R635kgtqTPEAkx36azmvLmSrsniKrPorKiHBRyzVw+h6h6OhU42PbZkubYXjfTts069SEiWHNuOrQlQe39xSbnTyXa2Cjl767I8j+doIJaotRmFvFc7Eg4HKvZ98c6Zd+P0u5I57byYUnaj9gzFklyuxHyC0cN8vmDoK3/T5OBpF2JPncFkiTdTqU6vqnPIfkWdiKCE3cS1VhfGtQXNUqke/F3pySrZIcs6FSR2eM8EYGJHQg/1TKZNuUqi0LTcUnbr8m0fQXxm1xGTYV35pyl6dz/9a1cnSkM44ucwj609a1UbcT5iarC3P4v6xcd1h/aFi3txlZ+9alGWrsP9mRC2KrEvupcQsvwnaW2PRNeqr6UdM5BNW0/lWn7GWB9oqIR4lm8yXYdw8i8AY8B52Bai+dAB+fvfOR5S1ckghu3EM6/PwMrTWE/LqGbn/eimrbPJBaxG4hKh4OoiK4RmkhbE5ygbyn9vYN6btejgReP6D63+X45nswqn3Itj2nde0Tp7GGETsqPCR7RWTVt35fp73v7fMeyyO9SMLzIb7rOx4iIfj99hjWJarjt0+uVgQNqxnIrzuRR/jV9BovfFnhu6fXqPX7vKRV/Jgls0kMctOa8GRX7bnOf6+aG9F6t7tY4/BHBlcVLr5cAtsy0eylwAvBrohrv9yQ+/prrXkXoZfwcWJZUtVLTtq+WF1PEPdyjvwsSRtTn099udfNczfnzUa8XsyxRpv2S9HoF6rVGVqShxkCLvg2iuVXVUbiX3vohjdrOq380WNvoFOCuinP/vea6G7fowysIiq8r0/P1S+DlNW3L+5e3EjoNWS771P5mgvaseL1KdZ7rcW6txtSA93pBYG1Ch7BL/4XB9AsaazaNYmwMcp9poT9BVGI9jZin7yHWzD2HHUep/cWEI/ROolrh/zEFwtnEnF39flP1m9xCBHxeTm8tjCJpZ0ngDUSFVxfXO+EYgnDcdv1l2jfW8yKcaielsflh4FKiYu4mImie+36NNT4Jx9ZBRNLFhN7uVNznUf3Rbs1sao/2mp9r97h1z2em3d3EvNVaqLvPdXdv2b6RftU4/VXHbvoN68Zzm71lK83hEX23KzJ/XXtFwpdwS5oHXtJkHBVjk7CXLkm/eZ2OScFg0UjTMs0THyD0qdadwbHRyEZJ772o8np23byR3u+pWcvo7Y7GPp5Sm9el3/nzwGtm4Pdo7HsotXkG4X/7JVGdcxcljbPq+U3mXKIK89w0h+9LVCRD+Kz+Z6bGa58+X0cklhSvFwWu7dF+wzQv/IrQt90FeFpN24NKfx8ngrB1vrrTiMqurxNBw1Oo95FtkX6vvxO23RP0sCnH4Y8BfJEj6ENT38pHieTEx5jUJ3oI+F/giJprt9K7GuF3PC/1f7n0dwARNC+32YBYQ35NrCfF3yeYRm0zSj4lgnbxZ8Am6XVWi7rhdWcRTDXV48v0eP5upaSnR+xhxlrnrcnfOFVSzbX9gsqxiSz49PpaojLmDCIz+C5J99quK5UbpB9dGUPVY5LeRUQplwPOSn/n5foxYHbzLsC2hOP/XOBM21PC4d/k+5WO30rQuJTLwG+0/R+lNuUM5I7TgVVsd1FDSFrXdi39XaVtblxk+5veO4ioWHiu7dUlPQs423ZPKo4G/biW0Kno4AC3/a2a9gsRm9Aq1VOu7crAfU4UY+ncZdxJoThyNHkGex3v0fb/EdlOBZXXjkRWZVuam+J6txHGzvVukOko6Ujb+0t6myfpsmYMDee6n9teveb8n9mu6qyNDZo8swqe4V8RNCvfsf1Qv7lc0rKEc+zHtv9bQaf2Mmd071JW0B8Iw++DxLpxnO0qr341+3EhEv88pezHAe7B0NmSCi2979l+zoB9qFJGFsefT2gSDEyPO8j4lPRTMjoKzlQ+tmk7r6Lhc7Jir2s4n30/iwgA/AedWYo52kgkLUiJ+sM12hpqUb2U2vetYOoFZaq01Mm93wWXqk8kbW77ctVolLhHhmfD/p1NUKXsQEmzyfbew1w3XbvxvqfNfa7Z/zWuGOjT50bjKLW9yfaL1FnpepXtzYbsQ+P9bXpvKYIOcCU6q6NyFSWNq4dGkc0paQvb56uG1tU9KBQlrUtU086xfWlNm+ttb1ipHKjLYD2CoIz5BZN0f/aQlXCDQtLznNgmJG3kCgVMqd2KwGq2L03Z9fM7Q+PUdC88yPyczmv0zEm6AniF++ivNMWo58TS57yY7mcqR/0+Mkj6MlEBdjphf2wH3O2MbljLveVcwsZciaA//S5hb9ZpDjft7wtdqoSStB5hF/52yOuKCKZtTzj3Liaq4O+taf89229I/58POJJgUJkv0/YkIihTzD07A4+7lEFeus6tzrDMzARarq9Z3cJBx3ON3ZGtuhrw+q3WwHFAS9/DFsT9W5XwKXzd9h/TfH6n7RVTu0Gq4/8LOMl5RphX2L5smO85CtTcu9yxw4gK2l8Rc+K5hD9vSvynkm5zpiq5pu0tRED2UtvrSno5kdS9x1T0ZRRo64schz6oXcVo46qrUULSkkT1eEHvdzVwoEt0kGm8bE4k0p1UOv0hwhff1+86RX2dSwTHViASAF9n+45kC821PRC1q6SvEgxg364c3xHY1PZ7MufcSuxZ/pJeP52g/MsycqkBU9o4YBw0qQrcI+kDTIquv5du7Y4/EYGhZYjKlrvoQZc2IJ6QtIJT2WkyQqqfcSwh+LmDU/BIia+7CtvnAeepBQ1JMja/ngbZ1sCRqU9TIXbf5PsVOA24TJNaKbsxuRktMMhD+J8K6oWziUBdL4qE+crO3DRx9hJ1HpXI78K292/SMG2iPkf0c2VJLwAOcb2o9NkEd3qBx9OxOu7fUaHJM1jgRgVtYznwdFNN24HFnyUVNJfH2v5y+v8PiArKRRT8vCLGZx0VyuslfYLIzpjxIBXN7nNrTaMxQpcxS/da8y0ieL8toRlzHn3mctu/p6TNkOawDkMxE5ip1fLycNzD/dCGo754r0x5BlFZlp1zFPQYxxDz71MJA/dvtssaBscwOcbKeDZRFToMJd8g47ORjsIAbedV9F3b6pycfXAqETx5DaXgSblBD2flqgqtii5npVsmFdi+TNJqNAuANdWY2hO4nUgM+l26bh02I/RItsi8ZzLaJJK+WjaUJX2dqJA41vbtleZtNJvaovG+p819poX+RBMjZpBxlFDoV96n0Ez6HbG3HxbzSXqa7ftT/55ObzvnPCL56FL661MerUiCupgeFFsJjUWo1ZD60EPoeTmSwvolhrXR+NyKSELrK+g8TficpMWJgME7iQS/DiiSC/cAnk44LJ9NVHK/InO9RnvhNvOzQr+4wEKKwOHE/FUzjvYDLlRQFZbH3KAaVY3nxNJ+pNhfT7xFD8pBhebFqsBcJp8pk9cnHhlsvz/NSy9Jh75q+9yatn33liW00hxugffQqZ+3F7C2IiFoWwBJO9k+TZ00XOXv0TUu0hpyRerjdoQD8C5qNJCLAFX6/xNERcm+NX1uqkX4hKRbyr6HGUYTG6VA2Q6fRcwXNzPgeB6x3QHtfDx1tkQt1fKI0Mb38DbgC9VAkkNLtJxc0to3ZTsbkEzvjV2AKuHvKgW4Jb0I+Eem3R5EpcfxwAW2H6nzWxZQBMo/ziTdLFArRXGdpDVdSYqswb9s/6+k+STNZ/sKSUc2OG8m0dYXOZI+ZI51zVvpmf9rEaBKQZ0ticTCY90pf1LVuyr2JP30rkYCRyJqB8W0gsrugFKbK4g17RTbM+kPezfBCvYoYUvsJ+kywq/VT5+tFzbNBWxtf0PSx2rOOQKYo0hsEsGAUdcWIqA28b7t+yW9nqhGGxuMUyXV0gRtxebEgnoZoS/zp0q7xYnAzfYEpcUSRHnyDVPUj9cSD2bBD/lSgpf9B6U2zyAWyu2JgNlZRHn28j2u2zhjs3TOBsRg3xL4ie2cYdEKTb5fpv2E5o/tKXHCKDLXtiG+32yiWuzTmXafJe7ZCcS42BP4te0P11y30FEoOMYXISiJGuk7SVrS+cz+TxMl1BdmTqu2vYkYx1d6MiO1NstE+eyIoTUl2qLpM5jaLkgEnjalFHjq4Rwbpl9LAhvZ/l7l+Hm239zg/M8SG7RFCIdjv6DWSNHkPmsATaNxgaJy7q9EMN+Esf002++otCs4sbcn9BlmExUgF9r+W+a6fQMzGhOtJI2wSitd/0bC4XA2MS7eTmSIf6zU5g6Xql4r53dp5LX8/EE0txrpKLRtO6+izdrWJniilFmnlImaHCEXlZ3ekj5l+yDl9TBc3peoRjOn1LhDO6dH4KJonwsONarSSmvB24h9w2PAmcC3iqBEDpJWdiVrPHcsHX+R7ZtKr9cnsuQ2cCVJRS00m9qiydgY8D431p9Qg6qrNuOocp03EsGh5Yk5fTbB7X5+rn1TKLLeP0pQT5nYYx7umqz33N6rx7UbVw9J+gkRKOkrQi3pYmIcf4T4nXchRMjrEhRGpefVWONT0pkEtVK2inLUkLQS8BfbD5aOfYCgpNvBGYYDRdbrBkQFfs99eZu9cIs+96ogrRtHFxOUTLcxOeZwRpNgWEjaOnffBrjOnYSezHg4FxpAeU3EatJP0baR5vAU9m0xT2r1vNv2V5TXqrDtQyrnLgK8mVgvlyL2U2fa/nXmc75oex9J55PXyMtp7zXWIpR0ORHwuYFOEfi65M2RoamNUnPu4gQN1rT3uwkG8PHkbInn2P74NHS36MOUz7cD9mMcAnatkPaoZxCJPhBUk9u5wr6kTu3nzYkkyVcCy9t+jAwk/YwIUFfXnxyDw51EcsK9hM3Wa89zKeHXPIII0v6RCHi/uNp2XNDWFzmiPjT1rVwPbOVI0n8BkYR1BEEd/C9XKl3TOa30rqYTkn5le4XM8ecQFM4r0elXf/X09a6jP28ikkPnACc33QNJmuXEpJVe3+maKqw+7z2TWGNF7HN/3+Mz+zKljQPGJkiVg/pkIqaFbVti0l3ePYJELT/3GcBGxA/9I2dEz0ttlyMW+O0J3tpzy47CUrvGdHGKjIK3EMbwWcC3PXVZNo2+X1rQLrL9yqn63Jq+rEVkC25ruysrQUETsAedhvOJrqG/0AAiv5J+QQgZn0YIueZEBR8ighyPpr/aAIfytCnZ0vX03iXAMba/m16/GfiA7VyG57Si3zPY59zVCEPuL0SW4olEVuMvgHfWXVedlCwLEdSAXZQsA7RtFNSaCeTucwoE7gAUxu8dxFh+pHr+OCEZxp+k85k91D2EXJMj/bXEPPpq28/ItJnRwMygUMNsSUmbECXif5e0E0FpeXSNQXCj7fXUSZd1bXmTrxFTRrYdnzVOujrnXOO244R0T7ame9N8SKZt47VtVMGTJgEcTdKLbUI4xs9Mr99G6BB+sHL+QIGLtpD0bGK++BCwv+1Ta9qNRFxX0juJitC1iIrIRQlaihOGuW66dt+xMUSA6HVENnjPxKM2RkyTcVR5bxPbP+x3bBBIWpNwwgi4zD0ye9Uu+einwNpuUD2kFiLUakl92DaoNQpIupJwePyYziSCaXHcKiWC2X4gvf4AYQu+kwjc59aUjn25pKcQOiVNE9gG3gsPimKdn6bPqnMEfY7QFmmSIY+CBvUDtu+b6j62QU3gqZHDWdKWxNqas+XXJJ67H9k+XUHXvq3tzwzZ3xz94wOEFs5jlbaN5k9Jfyeqpk4nqM46HD4uJTIUewzVCLw7L+zehmq28XVHjUFslNK5CxDUhQNROU0HWvqw+toSM4G6+XaUgaQaG3NaA3aDII3JiUp6ANv/6tF+FqGVuT2RaHyZMwmckq6xvWn1eM012+x5FiFYTkQkci4OfMNjTOfe1hc5oj40mrcqz/LniOrf/dJ3mOtOaYmi6qrYS9VWXc0UJP06599XJB6dTLdf/fpp7N5AkHQDEVw+nUjs2KT03lXAvq4U3yS7//O2X5q53mVVn3HuWOm9xgmLM4mxC1KlDWAR9Hmg6QZd0oq5yXDIvqya+rFdE+emQkNkO2ey3NQuY3NPYtDWbiymAv2+n6TvAjsXk9cUfu7zCIPyrYSQ3xlENnTfrExJy6f+frZHm1cRGSMiAm2XNLjuBwmKvl09JHe6ggbvMqI8dWuCNmUB23vWtF8V+AbwrNTnXxPCrDmtr5Gj1zPYJvAk6RqCEmE2oS+zD3B+av9pZ/QdVKJksb1q+rwTchNtm7alcxoHtUaNPvd5aE2jcUHaEG/hhnpgkhay3UVXMA6BmVEiOYXXIZx/pxKbr7fknJUpAPFKgo/598B9RDXvOqU23yM2mTlKvg/Yft0QfX3SjM+phKQfEI6l6qb58w3O7bu2NexD4+BJmwCOInD46sL4TYbxxbZfXtOPJgGwVlVapfNeSMybryLu9eerTlRJaxC6XEfRSVc0mzAAcsGW1VPbKr3JjAZHe42NtgGiFp/ZpuqqVSCwpv2UaGOVrrcIQUu3vUsUVpU2RfLRPwkKwl7JR32rh5IR+QxXqEoVFNC/cynQXHrvOtsbSbqIyCb/HbH/X7XmMxoHtdI9+EfhSElOilk5Z2xytu9Fd4A9V0Exow7nync/nKD43tpB+VQ3fx1FZCG/nfie7yUYKmodkIPao+ncLCtDGygqii+3ffEw12n4WXWOoHcCuxJj4hTg9F42YVonXkBUzEx7ALMOvQJPNe27NBFL7zXWHG7Rv+uIxKRbiXno+en/SwJ7lsdA0/lT0teoX1/tKUgYUQMtwnTvn0NonEwVJe6UoZeNos7KsvmIRJ2zbB9QbTtuaOLDamJLTBeazLejDCQ1sTHHFdIEM8kOxFhepuF5ixF2ZheNsCIIvT3hzyrP5eXg9mzbDyqolbvgkpbQkwlTZa8N2YfsvKVShbii2vWjxbyrbv3z1lVXI/oudUFmAbfX7E2m1GaYTigSCd5PUPJ9xPaXSu9tQBSofI1Oppq3E2Pu+lLbWUSBzBWE9mRB1zgb+L57JFOoYcLiTGIsNKmS43j79PcY4SBYz/Yva9rvYfurpdfvBf5X0m9dU7baoi/PJAIoOxAP6RGpX7m2y7qznO5BoC6b6gJlNDwq1ysethuAFRTirRPwFFAdtfl+RKbDbYpKn3J5ftZxlPmsOiPtFCJ6/Grbv8u8X71OmV7x2YTgY65dufqrNjClyER9l1NQU5GZsyfBL/pGMlzTaQOwI7Cy7UPTAvVM52km9yICOf9M3/Miggc8CwdVwkaSFgU0E0GTFs/gKUwGnq4nAk9bEYGnLwPlwNOixXMqac/SQnqJonQ6h/eRKFkAbN+lqJgctm1HUIsoS1+Oek2CkaDFfW6taTROUCe1wKuBa8jogUk62PbBpdeHAQ9KOikzdzycvv/c5HC6j3AwljEva3k9ZtuKSsqjbZ+sySqWKnYmDOb3EwHg5YmAeBkfJNadbchQ8g3Z10E0tw7MXcj5KqPGbccMy9l+bdPGLda2xsET24WQ7NVAlnquFMBZXJ10cbMpUYhV8CxgMSJBASL49ayathCBsqoBcQ5QdiAXlCTZKq1Mvz9FjN07ieSWj/bY8z03tV2CTg2Wh+jU/ijjbGJNOJE+GkUasfBs07FBs/tcXLNxdYHtoyTdxqQRc2jViGk7jiRtTOhvLqVOXZXZREb0UEjzz+uJ/e1riXtTW9lmu41e6TLATyX1qh76LPCOzLl3EhRMuWDnpxUUUh9mkvpwnx79aKPndRnhgCzocxcmsm9zTrfvEIkR51Oi9slhuoJRPfALRQXhcsTY/48UoOpV3XAAQSd8G7HXv5BO0W2gvT1aObeDlYGY04bB+widg74MDlOAbDAjrScnKZIwdwVulfRDIos8V/F88Aj6NjRsf0dSNrCg5pqIRcC5jeZwU/wS2N1Jozk57PclbMdvAxe3nT/dgL6uQJrreyWM1FUcvojJwPY6Ci3CCRta0nHEGnEtcKikDWzX2sPThaY2CvFbF3iMqGz7zeh7OBha+nigmS0xMgwy39q+W9L8th8HTlEwFfX6jKZB5SY25lhB0obEb70V4dt4H/UacqikX5XwXCIwkcOuwBrEPnGC4phOPddvEvvsm5iUUaDUdsIGUbfu8sRbzID0Qlu02JOPsg9N5q3LJZ1FjN+nETqUxdxQrYxaqOSH3Ymomv68UtXVaL5FFneQHz9VTcwyzpO0B/E7lPfkD9a0nzGk/erBniymWZwYS0cR8/QEbN+QAlXvY9KeuAPYMJMk927CXngWoZVY4EGCFrIWKZlurLW/ZzxIlRaXxQmHw1uTo/nePgZBVSxbhGNsRyLzc5B+vIt46JcjIpjvJBxwvbi/TwbKGZqnEoLR37L9kUrbvYGP9TE2imzrWcQm+ZbUbm3CEd+o7DaHAb/f99JfGf2y5/saaXXZaZXrLEYsujsQ3P7nEkLNteLath+X9LCkxXtl+gFLlwJUbyAcC1vY/rmkd9eccxyxSG9OGA1/IyaA9asNHVmqH09/vb5jVgQ34mF5EdxRoOUz2CbwVHZ0VBeNOifIP20/WtwDBSVL3Zhr0xZaBrWmGi3v82uJzPWCTqSqafQFD6FpNCpIeinxzL6BCLZvQjy3dTQaVUf0j4kA4heIrJEyZjowM2o8pBAx3Ql4adqMLlBuoNAjWcqTVSOPAJ+S9HyigmcCaT5bi05KvqsIfvphKSMHGZ9/L/2/oJu4s+b6bdqOE66VtJbt2+oaDLK2MfXBk0ECOJ9hUpgVYDMgVzXeOHBRZG9Kegfwck9WaZ1AjKMqPkkEm9dJf4en+b+L/972eYQRs7HtH9V8pyoes318/2bACIRn24yNQQKN1aCMUnVBXX8aGDFtx9FTieDmU4iAZ4EHicr6gaCont+e4IO/gtiLb2B715r2a9j+qfIUW3UJYTkdmCqWzK3nyam2ZM0596f96gNEJjQK6tc6tAlqzXJJ39H23yQtXNP2EZeyOXshjbcjgaWJZ2+6nUzbEnpjjxLzwaWS/kg41LKJHY5qshPTXxYD2qPlz1hVwcrwI8LBNxRaBlH7okcgQkQQtu68+Yl7uwbwZ8I2/ZBCI2m7clvbV0lahknb6IaMY2XkaBN4onPuKjQR66jBDybmzCsBbM9Ne6BhsUYRoErX/YmkdW3fU9g4DDh/Vh3TktYD7rP921Kz1vtjSacS+/W5TO5LTGei50uBdZJ9vjAhezBjQao2NorGvAKsjAF9PGU6tkdI+7m0/oycyWXA+bZVIKllUHlnItg7IwG7NlAkdG4D/IpIhj6EoGPuqoiq4D107s32AtZWsJBsW2m7jmu01AvYfmP6t+8cONXr2XRgQHttFP1o41vZh9gjPRPY1JPUj8vS7Zcs+9M3JzRdsf1Ead0ZOTyYXE9R5fXJ8qUIKvwZQQruLZoJlL2w5Hd+ERHc3c32DxXUfx1Ie6a+Nofto4GjJe3lHrI2pf5dY3vTTMB4LAPFMx6kAv5ELKrLEMKed9EnEGL7K5XXPaOFDXEsYVjs4CQ4KKlfP97YWxBNAAAgAElEQVRQef1KxVOdC8z0nZydaHMknQHsUTi6kgOyGvRqi0G+X8dip1Te2uecWiOth4FUnFuOJv+RmIg/AVxj25K26vXZCU2qv/6pqFBYnqDiW9f2bxXlpnWbnQ1tv1DSnHS9+9NGqQPpunsTzhsIx+qXnKcQLD5rphfuNs9gm8DTGgoKMxHB21vTcVGT4Q9cJeljwELJ8fReIrN32LbQPqg11Wh8n1MQ4TjgODXUNJppSPoNsWE+nqDTeigZHLU877bPr7z+Tua64xKYGTWK7Mfdbf9eUUlbDfweQ9zfKp5NlI1P8IpLkoN6JadZU27T+hkYZHy6Qnmn4Mr+7rBtxwybAu+Q1Es8eJC1bUqDJ4MEcGyfIun7TFbLHuC8MOsgAbCmVVqDOAPnSHofEdCZCN44T3V0vqIyv5qZl6MsmV/Sgu7UbFpwgP6V0WZsDHKfO+De1QV9q67ajqPkwL4GWKuf86wlLiKcn5s60RxKOrpH+w8RVdU5Gk6TqXhys+qhhXq8V7e3PIbuSrjcsQJtglp/Lzuok3HcRaWbcLSkg4jgcHns5wJ2RxGJXTOSOODQSTiteJ2c7msBd9WtQekeHcxkNWoxN5f3oq3sUQ3AylA693XAlbb/IektLlEoldq0YXBogkECEf9JzC+XA4eXPvtISV2VCYoEoc8SQRwBx0ja1/Y5A/Z5UDQOPLkmmF2Dx2w/UHHgTYUd8TNJxxMOe4j94M8VdHr/Sv28irB7vuZ28gZ9HdMtr1dgPWDNPnvIRx0VLziqHafP81lBGxtFY1oB1gOtfDwp8LwNYTv8wPbtkt5I2BELERSqo0Zr/x/tA0kH0x1UXinXsPQM/INMAtaYYQ/gZ8RYvsD2I/18egC231V5vQtMBGOquE7Smu6t69la00gVlqhSX36VOz7DGNQXOWUY0LdyRubYnNI1C9u/TdXVWGHAwNaUQ9I3iX3f40Ry9OKS/tOdVJBOgcYVgMMJO/2OtL5nfcDqZho6nNj3TzANSdrc9uXAb9WZmBMfWtlXOmnMNYlJjAPGQpNKkRW4NZEF8hzC6H5N3UZcLcTJW/ShXMa5DJGJ8o5+D4GkTQmNm1PSNRZzRgugjbGhjH5V7tg0fb+u8laXqsRqjLSvE0bKq21vU2pbCCu+L/1bCJ3vCDxc/v1SoGs7wrD/JkEDdIkz4u+V/tZlUX691OY5BPXHo4SW0usIaqQ3A6fZ/kLmutcTNAs/TsGqpQgOz3VLbd5ObJo+RJRdinA0fJag7xpK62qUaPoMSnqYyLASkUFXZFuJyOpYpNQ2K6RZIGcUKbIQdqekKUZMyF0TVZu2qX1rTYKpRtu5bl5CcgpuSdDpfBM4j8hA7Hpm1UKLRhG0P77qJJT0GmAXlwRfmwRdBg3MjAMk3eGMlk5673aXeOcV4vZ9Kflsf22kna6BosLnBturTWXbmUTdnFee6wZZ2yQdTBhLfYMnimSA9SvBkxtz40bBZ707PQI4ShW/6f8d4uyS3m/7yzV9bhwAk7Qr4UzoqNKairEp6WxCTHoHItt0R+BO23tn2uZ0nKqO7KLtlAvPDjg22tznXHXBZrY3bnBuraZLk3FUaX+5p1DnS9K6xH17K1FZcwahw9ZzD9LyM/pWDykqAP8X+ER5jVHQVD7T9h6lYwV11z5E5XCB2YRGQFYTRC30vBQaWWcQlIAQmbXbOq+NdQThAPwFJWqf3O8k6YcuCT3PC5D0U2J/XtUL/N9Ku8Z7tLJdpklWhi0dyTI/tt3FtFA691iCKu1mYKOa3+94EoOD7eeldfDiXtfthUH2R5J2A86oceR3sVZIugV4lVP1VLKVLq0bz9MJSeu7Uze3Z+WgM9T2aqk53KJvCxE2yabE3HINkQj0CLCwSxWRCvrfj9DtA2k1p0pazBV6+WTDHwM8j0hOmJ8aSti0tn7A9n09PqOwGaHTbswl8IwULW2U26lUgLlGY3Ec0NbHo9ArW55wwG8I/A/BOHGAM8mCo8KobWJJ19veUNKcwl+jbl2eQakuZwzqpH3bnNg7vxJY3n1kTyQ9m27q8Ksz7e4kntfapDsNoGmU7neBWcDKwM/qbNuZxKC+yCnuQ+N5K7W/koa2f/JNF1VXZzlV16Y99dIe4wrStGbuDaxo+z3Jt7uaK5qw09CPubZfIGlHYk+3P3BT5TnZEDiMSb/zbGIfsS2hubV/5rpbuJTInWywVYl16e3p2KdsH6SgE6zCPWywVYHf2P6npJcRz+t/ecwS4cciSFWGgn5rW2LiXT63wGoIcfKGfViOSfHGhYnATM4wP4gw8p9re3VJzwLOzhlvbYwNSacTVUCnEQvnTkT5YC9e4Sn7fsqXt27rPOVMayMtZ+DWGb2SVkn93A5YjSh/PNf2zyvtqtUWxfHnA3+w/ace92NdYnGfYzvLzZsmn22JoNPXCWfIJ22fVWpzHSFq98vKuSsRRl6dCO9SRJbbSnRuGoYWtR0EvZ7BOidsgYozdhCDeBGCdubx9Hp+YMGcgVxzvQ4nauW9VkGtUaPJXDevIW14Xk58p9cTC/HuwIUVI7tOawnoCio/aQIzvVDjHPib7cVLbX5ue/Wa839m+7ml17MIx/mOhBFQpeQ71tNIGVkxBOcnMicPcSbI0abtuEHSOoRGH4Rj45aado3WttR2JMETNQjglB3gVWd4nXM8vdc2cLEsk1Va1ztfpVW0/ao7Hf5fBx4mxvTtlbZzbK9bOCYUlX8XtXXo1fRjJMKzLcdG4/tcMWSK6oIT3ZCOS9J1uX1Mk3FUaf/59L3OprPivauapC0UFTPbE46vucR9+2qP9s8n2A/K9y6nS3o3faqH0v7lJCJzu5hb1yG0195ZWQM3I4SO96RTN+sh4Hzbd1WuPWhQawGi6k7ATz1J/VJt91NgbWcynzNtjyaoY75DjaD6uKFwVrY8p+ceLTnojiPPynCd7TVLbTcE7inbIgrtxb2B9zmT+VzMrxUH6y11v3WD73MlLfdHki6z/YrKdbqOld6bEG1Pr+cDbnEf2qhRQaHtVNi7D9her/Teo8DthEP/d9ApJeAMdVYKWHycTjviUE9jpb4iEHgC3T6QruBzap9box8g9JUeq7S9kbhfZzMp1v4cl5L5JJ1P7C0WA15ABDqyOn1tbMbpQAsbpfFeZ9zQxIelCMKt7aD1mkXQeD6n175r1OjjexgokKQGQeVxG6NtkX6/NxL3bVPgMpeSNyttjyTu8U8oUXQ6Q39Yd18qfp6JgJ+C8eIJ2/uleX9u3e9S+ZwXEmwndXIbM442e/IRfX6jeSu1zdn+swhbusP2H8RPNy5IvvLbiOrR56e1+YcuFQ9MUz/uINbBbwJfdjBG9NynSXoTQU8+Bzh5uu+vpLnE+r4SsYf5LhHLeP109qMfxi5IVYakFXOLQ9UxOeI+PJcIPOS0F+YSJdE3uyZDo9S2sbGRJpj3EFzOEFU+xzllRk8lct9P0j/oLm+9p8Yh1thIK50zF3i/7WvS6xen79ezUkyTNF7b2F618l7jaotBodCAKBxSl1WdFZJ+kvu+Dd67lqCqqRoc3xq2z8Oi+gy2WdAGNIivA15ZLLqSFiWcfy8utelJU9BmgVKPoNZ0om6um5eRnGOvJQl82n7GgNeZZwMzbVDjHFjNnQkE3yO+w4WVc19HZLS+rubaM04ZWTF4HiOSB7IZf23ajhMk7U0kHBTO2q2Ar7oPV3SvtW3AfjQKnjQJ4FT2LHPcWT08p26+bRK40OBVWi8qO+QUFSMrEJU++1fa3mB7A0lXE5nqvyeq8nL7mQXo3HtdCXzFNY796UC/sdHkPg/4uY2rrpqMo0r7Vll/gyA5SV5F7G/rtKkOIgJFawIXElX119ju0ndRi+qh5NAoEivusH1PTbv5gTNzn5dp2ziopUQBogz9B+SDSZLOBPZyg4DldPx+Uw1JnyGcNN+mP51h7vyuPZpasDKkwMKGTsEMBY3eSgQ14LlONCyV6/dlcGiDls6rWYSD+wpi3BUBnNnA920/r+YzPktk5Z6eDm0L3Fqdl0eJtH/YPv09RlQNrOfuBMIlieqTbVO7M4Fv2b5/uvpa6kuVjhKAmnXqJreo6kl21QuBgn79+en/SwJ72r641PZG2+tVnM/XVmywzXp9XtkOH2cnaC8bRWNUATYM6nxYGvMgXMb3MFAgSQ2DypqH9Md6QZFk/hbXaFMpKFrX7uVPlDTb9oOSnp573yUGB5WSEiTdDHy0uH91/tCazxyr8dcLU22vDfD5jX0r/Wz/Qfx00wFJqxV7WgXVaq6SvViryjbqUKxjA/Z1L2IfeAuhGbYCsf97Sc8T+1/3KODTBP3oD4iEt30Kmzm1+VCva9j+z5prFzGJfYnCgGN62fMzhbEJUknaw6VsR4UuwP8SG8Zqps9XgWPcQ5x8iH4s61ImiYKX8y+5Cb3k/Ch+7EWAH+Um5WGMDSUtKHfyWw6EJt9PLcpb2xhppXNeBPw/QjATwlDaramxWPO9GldbDHj9U23v3OtYL8Ohz3vTPqnW9KPvM9hmQWtjEJeu05fqUi1pCjSFQa2pQJu57skCSQvZ7tLCSPPg/nRnsped5PNsYKYNGjoHVgcuIHjyCyf9esT4f6OnKatrUCgqiJen0wmTnffbtB0XKKj2Nrb99/S6dk/Q8rojCZ40CeBo8EqqJgGwga7d8ju+k1iz1gK+RuhdHWj7hEzbkwjtpcLA3xl43HnKkr6aTdOBhve5Mb1q6ZzGVVdNxtE4QpGdvQ5RRb+OpGWI6uotMm1HUj2kFtSHahjU0gAUIGlvtzbwY2qqIsYFdc6zAs7ToF6Rb5qlMxxoj6YerAyFjaLQQv0aUfm5p6OSoY6uMcfg8AnbZ9f1oSkaOK/2Jqr2ngX8lskg1YPEPFBb1Sxpa0LYXcDVts8dtr9NoUj6W5yguTzD9l0K/Y6eeoYKCqztCbr2/W2fWtNuSqj2MtdtREeZ2h5MQ/rf1P4MwjF/R3q9JrAvcCjw7Yp9dTUxhk8i5vH7CNq4dSrXbOTUH1cnaBVVG0XzaHVNUx+WxigI13S+bTrmBvj8sv7YK4ikj3HWH5uASpqT6fV6wH1OtG2Z9t8H3uZK9U2lzQW236hgcDB0VJi6Yh8cTVDF3UcwOKxu+19p3J3vUuVq6ZyyY30+Yn1b0vZrGnzlf6OEOt9Ki/Nb++mmA5IuIlWBAu9xhuo/rfWbA9cmv/rKxP54g2ns53zAW93JqiVg/mH9eZqkEdyKoHv8ILFWrlNqc1D673OB9ZnU7t6C2Ht12a7pvOuBLxIB/C1s3zsVvvKpxlP6N5k2VMU0RWxgdiQmvjKaiJMPipOJSGiBU4FVJX3LJS2mhLMkfQVYQtK7iAf9xJrrfol42JaWdBjJ2KjrhDJaUIN8mQz6fj9HYOkLmixv/Q7wLEn7UylvtX03MPEQSLqc2ODuXzXSSufcBKyjqLaSQ4R2mVxbNaf1WaDHd+71XlN0BMCSs6AadHpeclJWIaCXs+YCSa+vOuFnAE2ewdcS4/z0tCBUF7QvFAuaI1PpOOC4FgGDJkLf69GOpuBkJoNaX5I0I9zbJbSZ6+Y5qFvs8TDgQUkTYo8lfIMIgr+ByBDfhRDTLeODxDOyDZnATF0/kgO/lit/DPFwMtznKjJo7iMSBSbgoFEtsriKzcRVBFXCtNHNDAJJhwLvIJIZCoe5iU3mwG3HDKLkYEr/zwqFt1jbIARzFyDmU4jgyfGU1t7SddoET76agoGfIDa3iwIHVtqskdY1EXuFYo3rt64VAbS/KijVfk849jq6W/P/3OvJN8JRuC/dWedd48P2Sem/V/fpL4SWV9kZd7miAqILrgjPJgfKlBhHLcdGk/t8Y/p3EyIh4Mz0+m1MzqkdcE31UQ2ajKMJpN/veGAZB03H2sCbbH+6xWfmrlut9Cuq3Y+tcar/I+0jHkv70T9SP0ZmE7/Bq0vHzGTV5KD9mCPpuzSgPnToo/QM0KR2B6V/2/yGB/VrIGk/20fVBT1zwc4R4SYmnWcrAPen/y9BCIznAhK7u1LRlmycHAbaozmEyefUvH2NpMuIQOdiwCvS2NuM7j1ucb1vSLqJyarYLd2DbrIN+u2PbB8NHC1pL/epBM6c+y0iMDET+BOwHKHLsxRwFz0C9BCOXsLWfRXwfWrmxISziUrGk+hc64fFA26upVHQZe9bOmbq5641igAVgO2fSFrX9j3hT+vAzoTj+P3E3nt5giZtAhWn/qGKTPc6p35jm3G60NBG+ZU9nhVgfdDUh5WthJwh9J1vW445NElJmYU7ky9eSkV/jAjgzgt4D8HeUGAvYG0FA8m2mfYPEzbmZXQGuD9Q+v8b0789A/sJ+zCpabSpJxPnliUc4DmU986PAd9j5taLRmi5Jx9VH9r4VhphQD/dlENRjPFXJ41E26+R9H7gP4m1OYdDiQqj5dLvsRlBgThtSHu49xOUwcUxE+N6WBS+69cDp9v+S3W9dqqQlXQx8MLi/ikSWXolM+1K+NwOSwGqlQmJobHC2FRStUFdhsuoMltSVHTN8iav9N6rKJUR276kx3X60cU11oKaSvT6fqU2axETxbaeovJWTQpm7gA8z/azM20a0fpogGoLSXsnQ6z2mKSPkipuiAWpmCEeJWicPlpq2yrzquRMFOGMfjT9dQlyjyNGsaCpgdC3Wmbcawy5t5/MUAOxx9J7N9l+kTqrh66yvVml3YJ0BmbuAL457oGZNkjzxx+IwMIHiWzg41IiQNFmbKlT+kFBM7GWm2meNG47TlBkCO7CZFLJlsDXbH8x07YNZV0XNXDuWE2ftkzX7NLVbIK261rpvL4VTBq8SquxJoekw4GjijUqBVM+bLsrSUhBV/I2279Ir1cBzum1vlTOz2o2tUXLsdGmUuwKgh7kX+n1AkRF/8tLbVpXXbWFpKsIB+tXPEnTMZIsPgWl10a2v5d57zhif7cd8GHgb4SOQpvgzlD9UHvB48Z6XopqmFMISsATiYzlA1yi92r5Hbawfb5qNCVdQzE0Kkg6gdDbuzC9fh1BF/3hTNuu+UQtadOGhaRNiT3+H4BziP0zwNbOVAknu2uN9PLO6XKGVfrwNoKB4CFJnyDG0Ker/ZV0je1N1ZkkATNgz5Rsy+2JyoslgNe4Qhkk6VNEotOdhN3xA/fJfh7VmNGQdJR9rn0m8BfiO0I4lZ9BBKSucUY7us/1bqfi1G9yT2bSCVrpRxNB+iuZByrAmiDn45nXbIm2Y07tKCnHmvpwEEharHBYV473XbvTnv+vth9Ir19O2DK/JPxsj5bazlPjaFC02ZOPsA+NfSvzGpSScUp22nuJ9eldwNGu18BcimApE1FR1Uhfdyoh6ZNEotGZdO7Js5XNpfPmAxa1/WDN+0cQMYF/EAmQSwAXOKOtqqjEXsepYjb5zG6xvUa17byEsQlSpRu6Nd0l9IfUtG8kTj5gXzYltEBOUVQ0LWb73kqb+Ymg1CsbXvNoogzx2h5tGmtBDYN+32+Ui46khYjMmB0IY2cxYvG72vYTba9Xum5rGqwaozXLySnpCJcCUk8maEBO01FBfYS+1ZKmYFw2oON2n8cBhVNXUdr9JSI4eY5LgfAn+yY4bbKWsv2TyvHnE1pMZaH1K5lHDWdJ3yLK9ptonjRuO25QZGVvChNUR3WZ9W2uOZLgSZMAzoj3A8VcXp7HSa9Xsb1IzXmNHYW5Nb1uDZD0CsKpf0/qw4rArra7qMLUQrNpXKAI/m5cGE/p977Onbp+hRMjW3Vl+4OZ6zYOBKb3f2x7fU0xl7ykI6sOg9yxdFzAcrZ/nV6vBMy2fWulXevqoTb9aIs2QS2lQLZCm/V9wCeBU2rG/luAI4GlibE/9slSuXlAiTa39HoNIgP/KDorT2YD+zpDE67O4N5JhPbwwMG9Hv1fqry+l44vDpxHVLEUVaxrEVVib65zaowCmqQR3RQ4Avgc8LGco2QcIWlpIiizPbC87eVL7z1BzPVFJVvxfNeys6gl1V6Lfraho1yYoCVcwfYeklYjRM8vqLn2QgQNa7EvuYbInn8EWNgl6i810MYaF5tqlNCY0mD1g/KVtg9l7OgrGRNbosY2foDYbxQaeQOPufSdCl3jnw3rUxg3KKhKq8/r1T3a97sf1wNb2f6dpBcAlxJz/9rAv1yiEBtkHCkoCT+e6fNY3+cC/YILI/zcpw+7zowr1JmofAiwETEG/15n7yU/wRlE4GZgysNhoWB1q8I5372kbxIVTI8TvurFgf90Rc4njbGNiASaBx3B+UUIf31Xgr2kjxOyJucS+5itgLNsH96jzzl7Zqwo2scpSPUD0qJEZ2bs5zNtBxInb9iPgwhnw3Ntry7pWcDZzggmKyg6dnbKNuhz3V2IjXJRIXWm7RsrbRprQQ2KJt9vVJsXSd8gyqovJiaWy4G73aOkWC1ofdSw2kLS9qndSwgKoAKLEfoTXYHHNGHsAKxs+1BFaeoznRHza4vkLNlxFNdu+PkDcZqOsD8vpjtY/V+l99tWrI3FBnTc7vNUo86JV6DGmfdGglpheeAYwnH0KdvfLbW5kjExpkYBhV7A8S5l9qXjrwF2sb1D6VjOcF6IcJKPreEME4bJecDt9NE8adN2HKAWQsOlc9qsbSMJnjQJ4Az6/DUMgA1apXUwDR2FCnrC9T2ZYbYQcGPOOZ3eX5DOJImswLRaaDY1hdpR1BTntKkU25VwQBbjZjNivv1apm3fqqtS28aBwPTe9wk6qbMdXPJvJejYsvqCTZH7TPUQ764zfittWlcPtemHRkR9WP5MRZLclbbPzf1Wqe3dBDd9Xzo5NdCSnA4oklv+m6ApMbAT8FKXtC0kvZlIhHsTk3suiADUGc4kDrYJ7o0Ckr5EVFzt55S8p0iMPILQUd1rOvqRPrfQvDuC0IL5Zt0YSu376vfOFCStWF5TBll/2jikRgVFZdRNwNvTnLEQoX05tLaxGmhjjYtNNSjazrkakwqwJpD0S8KeKlOg3kfsl97lVA0yTkG45LhdDyiqRN5AaCOuQewRjhp0zEl6GaHn98vUdnnCrrq61Gae1B8DkHQk4Vv8CZPPq+tspYb3oxww+BzwhO39kh9sbvleDzKOFMlS+wK3ARPJ6WN+nxsFF0bch7uAuYQt+H2PiwN/CpD86X8gqHo3ICo//yDpuQTVXV1S4bbAa4gChTOBCz3GDCya1JnakZCM2Z8Ixudsgx/l7PYe134hk8U7PRNkFcwOBWYRSYhPt11L0z4TGKcgVWOqD41InDxday6RNXezJzM864zLs4hI5yV0lvjV0qEkJ9bWRDBqBefF4AotqO0IWo+DqGhBDYom32/ARacJdd4txKL4X0SQ7tfqUymmFrQ+TZE2JCsTBt8BpbceAm51hu5B0vHEYrq57eclR9DFbkmTUNOfkV27ZT8uJmhHCk7TxYgN4munsQ+nEpvPuXRuuAamGBq3Deg43OdRoM6JVyDnzGt43Xk2MNMESoLqNe/VrovzkuEM8T2Br9BtmFw1TNtxgLqFhifeoj6jqtXaNorgSZMAzqDOjIYBsIGqtNo4CiXtRzioTyF+m90IirCjSm02t325OgN85Qt3UamNAmpBUVM6p22AaFmgqIK43jW0t2pQdVVq2zYQuArwVYKm437gXmDHQddiSe8hKgVWIXTsCiwG/ND2TjXnHUvQcf54kM+din6oJfVhGwdrmgueTTy36xDP7JXOZ6X+0JlkvJo+XEw4BD5CSUvS00R7U+rH0wn76KXp0NVE0DUXrN7Y9o8aXrdxcC+1r1af1M77DT//JwRF9WOV408hAkXTpicj6QLgt4TWcKERe4NrqGYzc/xTCLtqzenobxsMuv5McR92sn2aapgWnGFYUKoWVGclai39b2Z8FtfOrZfXu0+V3LjZVG3Rds6dl6CgQD3X9kXp9asJXbCzCNqsHFXUjNoSimSDrZ0q+iQtStChbkU4cNccdMwpaMR2sP2z9Hp1wuk9bTSvo0Tap61dZw9k2ve9H5Jus71W+v/NwEdL46lX0k+jcaREDdvsG44H2gQXRtgHEevwbkQg50xi/zq0b3imkezM7YjknHuICvbfEVXwuxXjr+bcpxB6krsTdM9LjL7HHZ/fuLI5+TZeQBSifNn2VXVrt4KO+Fbg23V7kMJ2Tf9f2Z2saG9pY7uO43P5lP5Npg3XSlrL9m0N2jYWJx8Aj9q2JMNEAKwO30t/bfAcIjtkJSLzoQsOcd/DgMM0qQX1fcJ5Pyz6fj8PJqS3C3B05dg7ysccmYlrEBVJl0r6I7CYpGXrHCXAY7aPb/jdUANxQ9v/I+k3hJh8U6fnho6M3znpGvcrMsmb9mtJ1wsbDnXtKcQKxAJR4FG6BdhHjfWIDIopMwrH0GAah/s85RgkCJWcaLmS491K/x8LYc8RYoFB3nMf4fMxxJ9tf2kEbWccbic0XKDv2tYjeLKqpGzwxO30dE4DLis9h7sRWZbl6w36/M0vacFK4GLBSpsrFJQNPau0CK2lcp8a32dHFu5tTOqBHpoxeDYjKru3yF2Cyap9NELNphb7kTL63ufCEZpermr7vNJ777f95cx1PwPM0SQN1WbAp2r60HccVWDbr0z7z/kcejdtnp0qvknskbsSj3IBixJeDrxb0v8QiWa9aL6aVA8N0o+Fbd+gTkHkXro4J5IcrOnzb1Vk+eaqAHYnDOJ7bD+syJ6smx9uVFRofIfO6sSckbuk7ZMViWhXAVclx++0It3TvSXNJrK9/1Zto0TXCOygYFGoXiP3vN6UAnErAx9VJBL1oiM/mUz1yRB4tBqgSn19TFIjZ+QUYhvCyf0523+V9Ew6aRMBUEm/V1JBgySSfu90dbYlGq8/PdZiYKhEhsIGXyx32ZpzHk3zfGHHr0rpmc2gzfi8QtJn6a2N9auZDu4NibZz7ryE9WzvWbywfbGkw21/SJHo1IUxsCWqNvG/gBVt/367s28AACAASURBVKOY74aw4xcoAjLpOj9P+9gnC+4h7MSm60KT+3G5Ign/PuBpxN6YNPfXVqm0GEcHSToJuIz+e41xwQLpPm1JBBf+VZk/Ro40n14CXKLQCTsNeK8i4fEAN0zCGUckO/NrxWtJGxCJVT/rtYdPc9obiIqq9YDTR9vTLE4h1tYXp9e/ITRjc/S7JxCJo7cAV6fgex1t5IeI/cHjCjmgHAX35wjpHAjGk3Jy4ico2a5lKKquChSMK7k9yIxinIJUmwLvUGTI/pMexiIxIK6XVBYnP3mK+nGWpK8AS0h6F2Fsn5hr2MYpqyjJfQuRXXkW4Sj5a6VN16YuBe1uIzb/U7Hxa/z90uf3XHQ0SZ23iqJcs8BiQFdQxvZPgQOBAxWUTtsDN0j6je0XV9sD5ysE9Jryf3+l8vrLxAZoZ8LBUJz/uKSHJS3uBnSNwL8UdBuFUbAUvY1WJP2CCGKeRky+dZmEra89IpxK/BZlTtNpFcIm6L2WZd5yvrfFONznkaGhM69AeRGfRdyL39VdewyMqVHgLkmvdxJ/L6AQgb9nhvo0CtykoAz6Lv2Fwdu0HRtIuswVgdfcsYQma9tIgycNAzjl9m2evyaBi9em46enIEW1SusLzldpLQC8h8kKiiuJrOh/Vdumfn+fCB7Ufa+CivUQd+uPVoMnBU1zVrOp7jPaQJGJdwTdc2iuMqPJff5QagdBq1o2UHYj9kkdcGiWfp/JqqsD6pKJ2o4jkjHlxIaQcA6RodoaaQ/3ALB92kstQ9g3i0patOyArqANveA3iN/6DZSqh6agH39OTuZi//dWej9jbRysRVbk2g0cKrOJhK5Xl78SeSO3eM7uk/QGYs1ert8HTDUUSXz/BTw9vf4zQV10e6lZQV94I81RDe49nfrgHsADaY5p0ueC3nsV24dIWgFY1p303rMkrUt38qXoDvSPFOn7/5EYS3cRY+2uTLsjgCM0b+n3tll/Gq/FbWC7sFkvtf3D8nuKCqgcDgJ+ACyvoNHfhEgKrUPj8cnkfL9e6ZiB8v59oOSSMULbOXdewl8k7U9IKkA4b+9P69FM+BWa4JvAdZKK5JktiGdyEWqSuVvgRkknE3Y3xNw7Jfu0McHDwFxJ1YBPXbJUk/uxDzFunglsWtpXL0toSQ2LXYlk/QWYHJMDz6HThK/QHVxo4jucMqQko50If+YfgL0IG/kFRFBkmESvsYLth4GeQbe09m1KBO5OJioEpyJJqC1Wtb1tkQSVgutdG24FXeYfbD+7dOxXRLJcF2w3CRqp5v+512WUpZQKxpVtGnzetGKc6P6ypbx12RMagTh56dqvIgw1ARfZvqSmXWPhMUl7EkLnf+7xuVcyDdorTb9fw2u1ps7LXEMEj3yOymZk/N9qQdeoKPHdlnDufB14K/AJ22f3+YwPEpHuXV3SVZqKa48CasFpOqLPv4JYcG9gAC0a9a5YGxvM9H0eJTQEFVBaxC+tCWg9KaGgW7iA4FQujIX1gI2BN/pJUMoPE892Fc791m3ajgMUVAULE46ZlzG5OZxNcId30TO1WdtUKePPHdMk3WY2eGL7g62+1BQgBVqLwMXFvQIXalGllbIwF2AyGLMzoSfZpesn6SEm92lPTef9vZKNVrTNaQnVifY21mxqC0nXEI7ILxAOm12J/fpBNe173md10kJ10JZlXk9UXUnapOw4VX3VVdPvtQZB33EUndUYs4F9XUMP2OL67ydorf5AyflRk+xWPm9h4pn5H9t/qmlzk+0XqVOv4SrbXRSNbfqhltSHaqHnpdA4KzCLoIi5adh5VHktyYNtn9/zxCmGpGuBjztp8yn0Ng4vJ7xJekoTO6Ry3U0I7Y2/S9qJ2JsfXf1NNJmNug0R2OhVfVKc05feu2b9m8BUzDFNoRY6zaVznkZQ1ZcD7FfXtR8lkp13BpEw+E3bW9e0m2nKs9zakzsmIiD8MGG/iqBh7eVb+AwNx2fDvo6NntEgqJlzd7L9y5ns11RA0jOIvUPhH7uGqIB+gKCjurvH6TMGRdLyJqQ+u6LZPsR1FyR0BSf8hcBxrqfLXoi4Tz/LvT9uUAutzNS+7/2QRkuDqhKd4LyCjL0l4Dm2uxI2RtiHnxPBxVNs/6by3v62j5yuvowD0j70ItckJ05jP64l7K8fpj35qgSF5gaZtlfbfmnXReqv/SZKyZiuUAiW9wjV/UJu/zCvYWyCVACS1mHScfvftm+pvN9anLzl589PDPhXNmzfV3hMnSV1XShvEufVjV/b+zbivrQRo88t7u4RTFqDSUfQZa4ITCfn/LsKQ1bSRoQT7bOEM6s2St3v2qNGCg7c6hnm5FaNJkcugFk6p6NizS2476c7qDUu93mUaOPMy5z7XOB7tp8z8o6OEZLhsANQjIs7CIfKIzPXq3+jKSTtTWQfPovQ7yiCVA8SelADO/bT9UcSPGkTwBknKMPhnTtWc+6WwAa2P1Y61jp4ohaaTW1RmkPL2gD/bfsl/c6tuV5jQ2YQo6fpOJL0ZoL54E1EBmiBh4AzbF87yPcrXf9ugj6555qeDL8vAX8hKDGOJQJKKwH75xw8kq6zvZFCP+NLRPXQOba7aLib9iO1Xdn2vapQH7oSlC61H1jPS9LywFG2ty8d289RCZetxHQ+aasjeFl3bNRoMg9UxvMxtvdqcN1bCaqZtQmn0MnAW6p7mD7BJNfYHTcnR0YjPaGZhlroNKf33gnsTQRS5hKBlB8NGxgdFJLWB95OsHZ8xfZQlQCSHidsuo8WTtphnEGSNiae5X2IpIQCs4GtcuOibu3v8RltEoQWp1Pn7SqiwjhbNTDTwb1hUJ5zZ7ov/9ehzspjAFxTAT1sIKlurZK0BZFU/FTbK0t6ATH2GyXJzhQUieyrp5c/G9ZprxEnzEs6kahSHbZKbtrQxgYbYR/GmUJ12pD277Ww/d1e7081FLp/HycS3S4mVTbbvjLT9pOErueZdBZH5HRUPwOsT7A4QOxhbrJ9QKnNX4lAs4j4SZEMJKIK8mmVa2a1L0v96NLAnEmMDd1fcvK8i8lyz9MU+kLHlJp9E3gjkW3eJU5OiBUPDLekgMsYoF9UZMAeWDpWlNTNIrLRbkn9XRu4nkk6jpFqr1ScCF0YxinV9r61gfL83w8Q4sFdQvBEyesJBIVhz7LPqiMiGfDbZfpQDiz8tMclly4FqN5AGDJbODh/35257veIMf0dBw1ir2uPFLafkHSLpBXqNobT1I+rFNV5q9m+VJHhPH+fc1ZVVKz9iN6ULEB3UIt6GsYpx7jc5xGjMRVQZl76PSVazv8LSBvPfxI0tr3azNObU0lLEM6ileg0RHNO0MZtxwG2jwaOlrRXZc9SCzWgrCsFTxavrIWzKWWpV/Asgm632PQumo7l+t1BJ1AEcJr0vx9GHAB7XNKqtn+RPmsVGmrB2P6OpAMqh59L7C2XoJPO6SFiX5pDG82mtngk7TvuUlTl/BZYOtew4X1eIzneReiZ3VqcTve+uTV9RNNx5NDCOk/Sxh4Nf/6vaUbBcijBJrA4Uf24tu17JC1N6CTkspA/nRy4H2ayemifIfsB7akP7cH1vH7DZCJEgUHo8KqUkXXHRo17ktFfUBftRATtyiiP2drqnwoes+0UVD3aob/VldRWBP4lreLQEp780JiTchgXeu+maKPTDBGgWp8I2L88rWFTNS/2haRDgZNKQdt7iLnoB0wNVeIdhIbDxZK2Tc6lYcRJnkqs0U+hUxPiQYJVI4frJK1v+8dNPsDtKu/+H0G7XiRV7kzsTeu0uOYZCu46B50SM9O4OegGgSJR9yN0753HkoUAQNJeRGD0D0xqzJvwk1XbTgSSgNpAUppjtwGeDfzA9u2KyouPAQsRgfcqDibmiisBbM+VtNLQX3CEUFQPf52g6xJBAbqLK5Wrks6yvY2CljmXjFK+1wPRcLfApsAuaibvMqMY0AYbFc5TN4vcA8Te7Sueh5NaFbqitbBd1m16W/r3GUSCx5Xp9WZEUsW0Bqkcun83MVnZvLfrK5sLvfX3lS9BPn7xeuAFtp8AkPR1YA6drGVvLv3/c5Xzq6+LY3MJ+vvi2RtbjE2QiuAA37Aw1BQaTj8iDB+AQcXJ2+IR4DZJTSjg+gqPlYyYM4A9HBpTSHo+sZHIYqo3foUTQdIhhCP4VGJw7ljt84BofN9aYneC9qpwBL0MuA5YXdIhtk+ttO8rRl+GojT+bUSE+tmEPkgHWgQW/pkM2eWBDwDr2v5tmnxzht1XiaDYFyVdTgj+XWi7VphyxHgmcIekG+j8Dacti0ihk7YHoTGwKvGbnEBUmRVtchVrewLvJhyN2Uq4Am2DWiPAjN/nESPnzMtSjVWdm/9HMa/z+zfFhcTcfRv9nXJt2o4TnpC0RJFYoqiu2d72cZm2xxMBheK9ndOxMmXdtAZPagI4A2GUATCi2ukKSfcQ+5gVqZnLK4ZlsU+ran+2Dp64hWbTANiHoI/8ABFQ2ZyYB3L9aHKfu+gme8A1/8+9zl+g/zi6W9LH6Hak7VZ7RjPcA1yZEoDKtFZV5+MTTjSqku4tAgy2/yipjhrufk9qThX7+rqgR99+DOH8aBzUUmd11HwElXIHQ4UTRV81aSsHTVZ9LFVx+M6mTzLRiLAbMbd9Gyaoi6rzwCDJHQ9J+igxJ78kOTyr4vJlnEN3gO5s8oHGLxF2xtKSDiPRew/Qx+lCKx1j4BHbj0hC0oK2f6qokJ8uvNn2JwGSg/l84FO2z5HUKKjTB4/Z3k/SNsB/S3o7g40xYIIl4ipJX3ODasiElwPvlvQ/hA2RdfIqUbfWBWdqgjKrupMS8VOKaronA/4v2BtFou5JNEzcGQPsTdCJNmE1OZhmgaSTCV/MDcCX0rOyMbFP+07NtR+z/UAmEDDO+DzBnPAzmAhSnk732rN3+veN/S44yoT5hNdO0XWmA4PYYKPCvcBSxO8LIRPyB6KK7kRivzKv4g5iHS0/fMVrAytMHLR3BpD0XWBN279Nr59N7K+mFZLOIZI7vl8ElOowQPxiCSYTThfPXK+WZaoGLyT8zm8gCn5OJ9i7xjIJepyCVKJzQS2yKbobthMnb4vvpb8mqAqP3Uu98NgaRYAKIGV1vGCwLg6F19jesPT6eEnXEzQ3wyB33/px2h4JnGX7JklfcF4z4wngebb/kM5ZhnDkbUgYpNUgVV8xekmLAVsR9Fqrp7ar2O4l/NwksLAjEeF+FDgS+Lqkq4lId5dRV3KMLUTQ3+wCnCDpQoLPdGCtsAExbdmOPfA+YgN6PYDtu1J2cxltK9aGCmqNAONwn0cGT3LmTjjz6qCG2g9PcuSy1hYiZesyfNbauGCW7Z6l5gO2HSe8y/axxQvb9yfHXi5Itb47qXwul1R1II80eNIkgDNVmOIA2GWSViMMSAE/dY3GAJ2GZSEQ++Z8U+ZIeh8RPCjrqUwET1TSbCIceueV3htKs6n0eYUz9W+0TKSouc+/6meESBPVmm2qropz246j8whNo0uZWkfar9LfU9NfHeZLAeT5iMDy05i0N+arOadN9VCTfrRyfgwY1CpXRz1G7CuzlHyKip79icry8tgvZ+APUvUxMti+H/hASgR7wvbfMs16jee67O1tCftgN9u/l7QCsc/swCC/ie1vpKzbgt57S08zvXcTpADTP21/TqFj/CAxZg/sY5v8RlEJ/R3gEkn3E9X004X50++1AuGofo/tyxWe54Wn4PoCsH2WpDsIJ88KvU9phAUlfZVmFTBd+nM1KJIjc8GZuvn5H5I2tX0NTOzR/9Hw88Yatj8FwZpi+9fl9yQtOzO9mnK0StQdE7SpPG4aSFqPqJB+QiGl8WdCQ6hXItHtknYg5pDViCShoSiIpwELuER7mPwgXQkVtouk9/e6og+dfHFZBhNPYcK8kmQLsceZJ2D7PEkXEDTQh89wd9Z1p57R+UoaR2ktmmdhe/kBTlulCFAl/I7Yo0w3TiDstGMknU3IjmSZsRTsUB8i6Er3KOxYV7SmEo5gMuFUBOvKxzLtGiP5kuYCB0h6MVGgcYxC02xaK9CaYJyCVKcA10sqKlm2JDaYE9CkOPkzKkblbGrobNqiSTZhCbu7m+KhLkp6p0Ls+zRic7gTkzQb04nHJe1ICMmaGKBDOwmq90011HkV3AjsK+k/gLoMt5WKAFXCH4HVbf9FUo53t8g2LutKVEsp/0hk13yCEOi0pK369LVvYMEhSDqRBa+ojnolsbhd2uO8gp/0TElrE6XbuzDNmakDRORHgX/afrTYgEp6Ct3GVNuKtVZBrVFjTO7zyCDpFPJ0Arks+eOBdRR6hPsRc/5/EdUf/ycwDVlr44JTU8DmAmoSCAZsO06Yr+TsLyhH6pzUbSjrRhU8aRPAaYVRBMAkbZ4cjlXqoVUlYfvb1XNstwnynErQ7r4GOIRIPKnu0z5E7OOgO0ixGzB0kErttDWb3Oc21Zptqq4KtB1HC1cdJVOBkhNyEXdWGlWxOJFFWNgQN5fe67h3GqB6qEk/BghAt87obWnPfIPYh76BSOLZBfhT5XpXKSjN1yq+40xC0lrEfuHp6fWfgV1s315q1no8p8DUt4DV0qE/k2FZYIDfRCGqfa/tYxVUTa+SdF+v9V7S6wjR7H9IektunhsBfgS8UNKpjszlRklztgtb6uDkXFmcoNqbLhwAXE4kC94ObKaojtyJ+E7DYsLGs32HpE0Jf8WwaFwBU7JnlqZH1aXtr6T/XloNTqu+CvQ9RILl4sT8+BfgHQ36Py/hHkX2++62H07HLmT66UpHgb6JumOIphXQ0DyQ9KhTVYOjsvPnfQJUAHsR2jL/JILPFxGV7OOMGyWdzGTC9o7E3qYOr6I7IPW6zLFRoCrZUq2aGUqyZVRwSJq8CpjpINVSKjE6pWSMZ6T3ZoqBacohaTsiAHW4pOWAZWznxvTVac44nUl/9tWZdiNF8u9emtbM7YnknF8TxQmnuVMj7hRi/L84vf4NsfZ3Balsn67Qh1ufeFb2bzCHNUJKSlsXWCv1ISefM+NQn+TKaYWCPm9TEm2D7TmV90cqTp4+417yDtauyVPtxMxn0ak/cTVwXI/s35FAURZ9NMHNbuCHwD62fzkF1+6izrP9kdL7exJ0dsUEuxDxcC5GcAYfkbnmcUSW2tnp0NbEA7UvcIHbcW0X1/wgEUBbhFg0zwQuyf3Gqf2WwHMIHayL2n5eg/4sQ1TgbUdUbJ1NZLxOa/WEosLoGMKofyrhgJkqDZGmfTiKqCR5O7FhfC/wE5fEjiU9h8mKtV8QG6yiYu0021+oXPN6IgiQC2pdZ3vaNKlSf2b8Po8SkspUIbOIqsXfOU+ZWoiIHwj81qH9MLAI9b8xvkhBlsOI57tYY12ztjZuO06Q9FkiE/oEot97Ar+2/eFM21cQG9YOyjrbXSLnKTvrp0R2/0TwxPbepTYTz031GZqJZyoFqwsUgYsTndeSbHrNT9k+qHLtAq4E7cpUZ7nGufloju11Jd1qe+0UNL6oHBwq2lT/n3s9KBQVdScQxsyEwzJnqDW5z2n/uRsxbnIaA8cW+41ykLVH/4bSyJP0aeBa2xcOeo2a625MJDosanuFlPzwbtvvHeKamxE003sSv0mBh4Dzbd81TD+SsfguGlIfNglqabJaqOstaqqHCtulGPvp2FW2uxJGJF2eC5hONyRdC3y8mDNT0Odw2y/ueWL/607QTjvooVcDTnANW0eLQCMK6rT1iN/7BwQd3XNtv77HOccS9E03AxtNx1wu6XYioetAOpP+ALIJAem8p2cOP1Rx1EwLJImwI15DaDkc5kgKHPa6z6e74nAoRoY630FN2zcRTC7PIpxLKxL7gf+oaZ/zVfTcEyT7qKoF8qSApDmEA3F3YBvbv5iqtXumkXxYVYz13lnSQbnjuUQIRSXCxwlNSZECSa7o8Uh6GLi7eEnIB9xN7wraeQ6SFiQYaCZ8p2R8i5LeQ/hTViH8JgUWA35oe6fp6fG8CQU17+KEv7DMpnRz7UlT34fXE3vQXxC/9crEb3olweLxxenqy6gg6csEtfJLbT8v7Scusr1+pq2ICv4Jv7rts6vtpgOSliQSYXYmKrq+QTyTa9l+WandjbbXq9iRt7iTVaVoOxRrnKRZmXlxV4IpYBZBU33WMDb5qDHjQSql8s+ajW02+0MtxMkH6M+SpZeziKDL020fWGpTUDwcRefmfTawb91GsfI5ywPb2e6ikJiXoDx13rbOUOdJus32Wun/TyOMs28DXwCut92lV5Emoa2JoJqAa4Bv1TlI1J1hDVFGflv1QVRkrm9PBIdWI4Q7z3XSKkhtjiN+62sJeo7zbU9JZk0yhLcnsjG/DZzhGiqW6YCkG4l7cTZhRL8dWM32UOWlLfswH2E4lDegJ/VyiElal6hYm+NMxVrboNaoMQ73eTqRftNLc04tSVcRjppdiY3Gnwj6v7Wmt5f/xqgh6ReE7mSdoOhAbccJaazvQcxHIgIAJ7qGpzoZmH0p66Y6eDJIAGecIGll2/f2OqaotoXYO6xJGJcQe7qbnKEXlnSD7Q0UNL3vJfQ7byg7eKYjGNjGYTnAtXtWayoy9/pWXdn+2qDjSNJDRJLQP4F/Mek4GipRQ5GQ8lbgu6Vn4Xbbzx/yuvMDZ9puRGnXph8p0PLfdAckv1Vz7b5BrRQIMZGEdT4Vui5n6HQlXWd7I0kXEbz+vwPOsb1qpu3niT3z2XQ6bKajwqfcjy7jvs7gb3nduSTa6dLvN2G/lNoNEggvEnP2I57BYzLz84bAPbb/VDp2IKEr8j7bZwzz/ZpAUSG0I5FAV6WBcY8g6i+JhLD7ied6CYIu6o+EI61Xlv/YIznUX0asKRcS9sQ1TeeGHtc9mLhHfStgFEkMmxP76nUlvZzQvtyj0q6oAt2HsLMLzAa2qnGM7U0kzzxEBHJeSFAGXzzM9xsnlJ7BTYjvuD+hW/bv5LgnCSSt2Ov98hoo6Xx6z+PzvF60osrjaQSFWJkO+qHcHDOiPqwI/NWh7Umat7YkEquO9czpsfeFJjV+y3DOrzHifiwIrMGkzfhIn1PmKZTm5r5BnMy5GxHr4N792k4lJH2b+E1OJaj+7iu9d6Pt9UqvC1/yD9P3XJUoStig1KZgjbuC2GuUWeO+b7uWHUAhSXMGUV12ju1NKu8/QWh9F7ZdVZ95rOa6caD7q5Z/FijE0nLZH23EyVvB3cKNX1TQWxxYOjaQkJ4ylUbD9rct0uDfnR60QS3RhjpvAUmLEA6S7wCfd6InStkxXUjBiXPSXxPsTohjFgvKy4DrgNUlHWJ7QsPKQdV4GHCYgjZke+D7RMZNgZcC6zjKfRcmnAlTVf79YkLk/tI6J+Z0w/bdkua3/ThwSppQpwXJEfR1R0ZPL2HmDjgqLuf0eH8gGsZRYibv8wxgNeo5+wvth93dQ/vh3/j/7J153D1j/f+fL1vWj1IqFdmixZYIUSElQkT2Flq0IWn19f2RFlGSRKUF2SJtKJG9heRjT+lbaI9WlJ3X74/3NZ97ztwz5z5z7rPMfX/m9Xicxzkz55o57zPLNdf1Xl6vWYFfAPdN2ap+28Yg9eNfSK8sGeVActe0+qCsIxz5AP9WZHH/lXBSd/x8xeey5UyrpjSAU/L7PWNEAbBvMpma52xyYtFOVGeS3ghs5pTJL+kLRPCwDCek8eTBhGN2STrHftCHZlMf6EVbs6/j7Kk1Bso08vJVV3mNvL6uI9tDE7C3/Qd16lVUUmeVBAcyasfjnGNmSOO/0kS6AdhRl/pwSj0v2+sokul2I+ZYt6T3C20/UrHfjyZH1oFEpfccwrldhmWAfxCO8nk/SyRbjRK3SfpfJqiO9iS0gaeLXminoVPzq1c8LGk3Ijkpmz8WNUROYEJTEEmfJvr7ZxN9wtCDVA5Noh8nJ8tXptxgAj8gkv0uAJD0CqJPOYtgNNigy7YzATsBaxNJcXspmDC+PID99kJVn+Fh2/+QtICkBWxfqtCVKaIfDbm9bR8jaUvgyUQC2YlUPzNnIjJdsZ8oKtrPJO6tGYsu40pg9AkEvUDSZ2y/uypIlHeY9hFIqqPD+al6lo8fks6yvbOkmyg/dmsVlu8mErZ3S9tnVKFLSloyn5A0RJxFJLXfLWkdIsnlcGAd4tnw5i7bjhXug7Vp0NCEntEzbb9F0rMkVekZzVQ8rEj2zCjznwhU+kfTfDgrNvgzMTccNT5n+5KyL/IBqoRDiTHS8pJOI+ZObyy02YcJ1rh8pd49wHF0x9bAu4DfAe8t+X7s13EdjD1IZXub9F6l5VSGOuLktaCgHMyQ8ft3TKhdg0te5ZVGK7uk0mhE6EVzoQ4OIjqHzwOnSzqzS9ujCGqjBYlI7sLJKf0G4NZ8Q0k/tr2JIuN2UvDS1Rm3jwHPcdKxShOIzxMToysIrZNJVDW2b0o2HZS2y9o8lAIJ2L5PmlqtU9L+to+Zap3raWWMAvcpsqWvV9Du/YVyjaehIDmClpW0yDAzaqYKao0AYz3Ow0bJPftXqkVZ/wp8Orf8e0JjosXsw6PENX8pnY73Mmd6nbaNQg/JKC8lNDO2ZTKqHL0DDZ70GcDpFcMMgGVV7EsXnDFzqNbmeBoxhssCPEtSoWFqO3M2XkF1wKkfzaa66MVhOZTj7BoaeXWvI+V00yRt7FzluKp10+rgDwoxYKdn7H50Gd+6QO/koBd5IrBhSfPrJJ1Db9VDdew4T9LW7p36sKeglkO4+RDgEEm7EM/VI6hOAvlXzpG1GcQ5qth3U8auexN6sdk5uIJwqvcESU8sSUwEuFzSQcBiCi2KdxAVaR1wPc2vDHsR1JEfs317CgSfWmizkENHZSFCJ+4+YCfbj1Ul9A0amdMb+FeZ47uL03s922/LtbtQ0sdtv0eRBT7TcX86D48oKPHuYgDJCTV9IP+WtCRxvZ8m6S6C6rW4z8uJa/kkl1RPViCbeso7AQAAIABJREFU424NnGj7hl7mvTMMW2UfbP9F0uZMaITMVPQzrhw3suSCXoJEdQNJPetwOqcTnb5fLS3e6jHQlPaIrFpkmzobSdqWmHN3UIUS4+phYzHbf06f9wS+avuoFJQYqbxFXaQEnkOYoJa7HDgsjZlGhUzPaKO0XKlnNINxHBFoWlbSh4lK7g7aTwUL1q6EX/0/xNxnYdsvHrGtAKTkgJ4ogNN4aC4xxxCwvwuMMclXfIx6YI1T0L0fmnu+L03MAY8EJlGa5vu6mYCx0/1lUA3uxeR8WTsLNCgqMG50DzR7PdiRL+l8hMjKO8r2rSVte6HduJ/JlUa3eUz8wOqBNqjP/U5JnZfaZULTCxEZFFsSkeIDijdqn3Z0UHKkwfVNttfI/ffL6J3Opjavscr5vxvPd60oxb6TyL47gOjsjndUIo3Khi8SGfLn0OkIKhNQnZFownFuClSuz/Uf20uP1bAWA4cm6Nc6UOboq9O2CahIRimlvc1tMyVlXU0beqY3yW1zK7CRU4VOCoRdZXv1fmwo7PtS4BW5wMXCRCVH35lckl5N0INsRycF1b0EXe6kilQFB/ehTFRXv5Sg9jmppO3HgSPdWaV/oO2Dc22GrtlUB8M4zn3Y0NN1pCFTJaYA8TF00m3uXxGIQNIRxYBP2bq0fkodtH7sUE3qQ/Wo5yXp6cR4fAeCeu0sYkz+n4r2PevWSFqNSP56ShpbrwVsZ/uj3WwaFCQt5OqKsKm2/S3wPSIwdJJLNElVk3Y6zQU/wGQnRV/zqjQOXhV4KhFgf5nt/1Poo33cBQqXYUA19P8K210IXMxEtdcuwMuJaqqfT/ceHzcUFPBZcuaBhJPs+ukGbjWRJb+C7bcqdNBKs+QVjCT3E4m0exBziNO69HOrEVnVK9Lpqyij4D6RSK5ZiagYWxC4zEOinx0HJD1KBOs/lPMjtVq4Y4KkbQi98p4YZXoJJKmGDmdum02Bkwn6ORG0pW+wfUX9fzUa1BnDpO96ogodkq15yY9rifsvq7idp4XZRKSA583E9QGhPbS27dLKxSHZ0LOe0UyGpOcxMXa+yPbNhe8fI9gE3pL5mMfsV++ZAljS2cBXgR9U9XeqURWbP/+SXkAwJuztqBK+2iUyOqntobYPzS1/nEhQ+3LVOGIcGHuQSn1wL6qGOHkf9qzsoIHLryt1HKkHLnlJBxCD2SWIi+dM4IdjvJmm1FwYwG9k1Hm7uITPvua+VgH+aPvBNIBYC/iaS7J6U/vjCWqxTDxvRyLb4H3AebY3qzN4qeP4U1B47A68mMhwy7AU8KjtLWr89bGgl8HfkH+/ZwHVwnZPAJa3XSUYPnYkR8aytm8prF8DuNM5/YGZDEX29fW2/ytpTyLoeEyFk3y+0uea31Gnfxl3X1QH/SSjVDiFS7WIhhU8qRPAqYshB8CmrGIvtH8qEzRTP3NUcJa1m5RMUhJMuYwek1x6ta/EjoWBtzORtXkZ8MUKZ8zQjnOv6PU6Ug3dtFGg4h5surNkyqCWQutxKSIwdTYTVYTAJNrIfnRrLifG1F/0ALW/ekUh2Hms7X1rbn8AkZm/l0uyXfuw50JibvdeYj76BuBvFcHO2ymnZ1q50G4TQkf1TuIcPil9taNHKNZeBkk7uloz7UlEkuImME9H+MOEA2QFjygZS9LJRGA4/8w8qiq41udvrAjMGcS8Q8FCMhd4fQr8LgZcaXudQrvtiQDmTZmDt4d930D4S4q+irmFdgKeASxLaKL9W1FZ+vQmz63qQpHo/APg+YSf4p/jeP4MA72MFZsGSacS1SHfJKr3Kquf+wkkaYqK8Fy7ucDuTknpKbh7RpMDtHXHMLlAxw3A8x1VoZXO7AHbegywHMEesx2wmu2HJS1H6L0XqdEaA0nXl/TFk9YN2YYp9YxmOhR6p2favrpLm9cSfqMXEhXuZxIJR3WqkQcGBeVmRgG8thIFsO1JVa2StiCq6Tck/F4nOVgP8m16ThBS6KfuR/i9Pw5sZfsXiqr168tiKGm7bW2fm1venijCWNv262sdgGHC9lhfRMnq7cSE67b0+XbgBuBdFdssQEwEziYeavsACwzInmtL1s2taHt9jf2uDPwPQSn3AJF1t9oYjvebCfHEl6TjfRfwtmnsT4No02Xb64nMr1WB3xKT6O93+y2Ca/to4DPpc+XvE1zwywGPH8CxfSYRaL2ScNJkr3UJ+o6ptn9O7vOGY7g2NiV4TC8ngmy3Ay8ZtR3JliV6aHMZ4UhZhhABnAt8usf9PwFYa8T/6evAS0vWbwmcPo7jPKT/mVGOrZ0+7w9cXtH2mmyb3Lqfjvs/tK+hXBc99y9N6ot6/G8HAD8jMu0OIgZ7t1W0fTaRPPFb4DW51xuBX1Rsc13JumsLy5cB+xKOwPz6RYjMyZOBN5bs56nAq9PrqQM8Jnulc3hSet1e9vt97ntR4J0ELd1Xs1ehzZ65zxsXvqsaW94IPC63vFjxnKTffgfwE4ID/RZiLPU7QktxnQH8vy+n87V5ep1ITHpGepxr2jzldZS/Zkuu30lj7z5sWImgsvkWUWl3DnBOSbu3E2Px/6Zznr1uB06t2PdqRIXIzWl5LeDgfu3o5/qscRzuYGIudVvudTuFfokYox5COI4Oyb3eQySMlO3/5+n9uty6nudDAzjP+d/tet0QiWfPzC1vSNCLvxk4q2KbjYEfAr+uOm6F9nPTe34cUzXmeWLu9XQiOHhYD/952VEd3x5s+f24bahzjXRb18d+1y15rUIPc7wp9ntN0UbghkKb44kx0eFEUsz/9rjvUh/GdNvO1FfWZxBUUr8gtCyn/fxpwqvium/8fyPm8vsQGuJXAm8FlippN5eoMMyWVxvUNZvvv7uta8KLPsYwabuLCMrrY4EziIrvkcy5Cb/ArsR86em59c8Hthz3MZ3C9iuBTXLLGxNJBKO04RWp//8bcBoxztts3MdmwP/xTWnM9uv0nKucTxGJWG8gEg7uT9f05mOw+er0Pjf1Y6JiLp/bZmlSgQ3wU2Iet3Afv71Buqe/n/7/yUSy9/eAI8Z9Pqd9bMdtQO5A7zuNbZcH3jfN3+/HcfRRYOs+fmtNIuL523Ef9wGct8vowzFWY//ZYPJ92TXCACYafdhxQmH5ZILuZI3C+gWJ8tR+fuN7RNbh+4Ffj+E/Dm3wV8OGjQin3+/T8toEFV5Z2+vS+5uJrG3oMqBkGkGtAf23yocWyfE1G165e/b/AW/Krytpe0XqK75GcOgeQGFi3r5mx6tO/9KEvqjP/zhlMgrhxD8R+Ed6z16fBV5Usd+BBk8YooO8sK9hBcC+AXyEGK+9gZjUHFNoUzsgkp69PyYmSntnz+MudgwsyaWw30l9YLd+cVjHuQc7a11HhLbOjen+yD5ny/8dxHEjsgo3I5coVNJuaYKN4QwiuSh7LdNl35cTmZt5J3Lpc7sXO+pen8O8Z4lx69k12p9POOazZ/1OBPPFqK67ymNX0vb63OdXpX5xtbT884ptfkVQtjyZXFCpy29cld4vSL/xfGrM74jq25EcuwEd/z+UrPtMej+XXGCWikDxCGy8AXhCbnkZovpouvu9iqhwu4YYpzwI/Jx41r5iGvv9KfFcz+6pVUjOr1ybm4EF0+fF6XFMRFS5voN4Vi2TvSraHgesP+5rbMjXRr4Pfx7xDPr3uO0a0H+bcqzY1BdR7fRuwvl+PvB/FHyDDDGQRCQ7fYVIktuUGDefOO7jUmFr7TFM2m4J4nm/EDF23q/bs23ANg81qX3Itq+Tnil3EHOq6xhxonOy44lpjLEN8KRxH5ch/s9liUDsZcCvemj/JCJx8Yox2Ho88Hgi6PR/6do4cYpzuH8aQ5xDUCIfS9DqQiSIVb6msGW79Ax/c7d7ifC3zSHmsBcDfyc3x2jKa+wG5A7YO8lN9IlKh3dMcUG+nXBy/hb41DR/vx/H0b3AY4Qz6p60fE+hTaM6ZSI4VjzOH53G/oaaVUxkp+9GDM5XSusmOQZIk7zsHORek85Jn3a8oLC8PhHUnBSpTp3O0j3sc0WCKiK/bj9i0rPjqK6J3G+PPYsone/l6c0RdBMx6bqQNKnqZi81g1pD+G+VgUeCzmyk53uI//Ny4ENEJsxTiQFxqXOAGFQvmh6WhxDZ56uO+z+0r6FcFz33L03oiwbwf9ckMsFKnZUERVuv+xpo8IQhVrQwggBYri+/Mfd/LylrU/xctlz4biuCCuwoxpTdSeh0rpJbXrnkPI0k0DiVnXWuIzqdKZNeA7DnZ31ssyAhIr5C9qpo13P1UC921L0+h3nPpn1cUqPtykT25n3An1J/NO3zV+P3uwU7byy0/RnhjDuYYI54elo/B7hlENcR4TBaGliDoLycS2h0lbXNV+CsRzg2ZlRiDiWVVKQ5Ep0sEvNeY7Dx9cAviWSGjxCBx9cNYL9fB56XW34u4StYuao/6HG/L2dylvymhTZ93fdMVFXmX1WV3rcQlIC/rbqnZvqLyfP5OQTN4thtG8B/qzVWbMIL2JbQcb2RSEh+clq/OPC7QtuhBZKAxxGO4G8lew4gF/Br8otIqOg6hhn3iyEntY/oP8yh4Lcb4W9f3Mu62fBK46MjiKDPyBKgBmD3inQJXqa+5RbCR7Zc4busmvqQ9Do9/f+j0uvXVDBq9GHn9el9h3TfLUMDx6HzBDQbgLfYPi5bsP0vSW8hIpQAqFycfGV3ESfvFba/C3y3jtaB7aV6aHZpEtzrql9A0LSMAls5p/eSjvPWxASuNmw/QJyj42vw/j6OCPCsSKeI62ElzfciJnEfs327pJUIweOiHZuk917OSW24wN1t++dE9lwZL/sDwE2SfkiUYmfb7Fdo903iwQyApP2IiPo6RCS8lPN9iLhG0leAU9LyHsRke6Sw/YegRp+HRyuaHkZkrv7Y9s8lrUx06FVYKHEf70xUO4wa/ydpaxcEzyVtRQR2Zwt2IfroN9n+q6QVCJHieSjR53oA+HDS57p7pNa2GBWK/cueVPcvddo2ErZvIgaiH6pocp2kdxKZvIvmtpukmWH7yMR7/TKCSuAj7qJH4dAu+ksX81TxuWy5Lt7DxDP6WGKykWFv4HPT3D+EFg/Av1Of8VdiPJGHKz6XLU98YZ9PZPGOE+8jxo63EefjmcRYKI9RHOepUPc6+r3TzKhyhz1oq3XBMUnX8kIi2QcAV2j4SHoXUWVwJ5FwBnFtlOk5/D1pADhtuxPV91gvdtS9PmvfsyW6X5nWx3G2i9fHdZLOIaoU8+PWbzEZtr2FpCUIqvV709h8VCjl2a/AHsAHieqXI4CTFZq8ryYcnGW4VKF9/C16uI5sn5c+3k1Uz3XDUbnPjxDBiJ2n2GbkSM+bquvwKcWVuTnSMgQl+4PFNqOE7a8lzdPNCZtf44IebJ94tu1f5H7nFknPt31bYd7SM5IW1K8I9pYNk7372/578beTnhKpzSppOdOlK9WhcT2tjq1qGT8z8TxJzxu3EcNA3bFiQ/Ba4GgXdKVs3yepOB5+O5HUvh/x/64g5yecDlKf9en0AuZpLP9kEPsfBiRtS9j7NCIJ45lEcP55hXb3Ut2f2zlNyyHilcTY9Iw0XijqwR/tpAffNCRtvkMIv60l/Zig6f3HCH57USJg+6SkMZc9aOYQ533WQNLHiMr8PxBaUxs4p6HaJEhat9t3FePFz9m+pGwbJ0022x9O+7gQWNf2vWn5UGJ8PggsnN63JnTN/tnv+GWYaFKQaoH85FTSgkR0PY+7mCxOvsOA7fiNpIOYHECZ5DhKA8s9iAqfj0hanoiM5gXfmtYpLyjpcdkEIomzPm4QO+7BMZbhu8RkLqNK6LbPWyR9gMgOwfbtwCeq2icnwh9tP5gENtcCvtYtaNYLkoDm+4gBQP662Lyk+ffSq+OvlLRb2Pbdaf8fJyhCXp4GZktPx94+MbTBXw38QdKLiEHAIsmWKhHVi23P67DTJPG9XfZdN6g1aBwAnCdpZyYc7usRFIfbjNCOocL2X8kN8lNwvihOfixBl1nE0wlNn92HZmCLcaHYv1xO+TVQt21jIOkE22/NLZ9MZPofZ/vmQvNTCMfUlkTftAfVfd2ggyd9BXB6xDADYBlOSJO1g4nK5SUJetE8Mode3pmX2bBy2U4LE/lFiIH8f0c0gZ8H2xdLehawOmHvr0qcvqM4zlOh7nU07KStNYHXEY7pfNCpbJwGQS20eo+OhncCJxDX1Z+IaoQ9pmFH3euz9j2bD1Cl5eckZ8uGJc2XIZgk8jaaCNQU8U1i8vzf3LqzCW2XUaDnYKft3xDV89n6S4AtgA/Yvqhi8w3Se17IvfI6SvODzwNPsb2GpLWISqqPFtvaniqIld/vxkTG638l7UkEoo+x/bte9zEN9Dsm3Q74TAoEfh24wPYjgzOrOyTNsX2PpGWI5IXTc98tMwCH162SPk/8N4ikrF+n5MuHqzerRvJlfMf2C5g8d8yjTnB2HiQtTiQ1rGD7rdmzJRdczdvyO0mbEHp0J6aEsiX7+d0GY/3c50WJgM61TJ6nzEg0JNGmZ9h+fZfvLi4sDzyQlPyNOxPzzx/YvlnSNsRcdDHCN9NUfJR4nl9k+/mSNiPYhzowrATuOugnqb1B+DrhE9sxLe9BBFG2GMFv70OMVZ9G+I6y8f09REL7bMJfCP3pO8dtSA84qst3peNF25ekxMrn0pmcWvbsWYFIrsrwEJOTMfvFOZJ+RWh5vSM95x8Y0L4HBvWfsDhYpKy1FYEvECf3bQTv9YG5NgcQontLEAPPM4Ef2i51OPRpx0+BHxEdwbwKDtuTqlrSQPUxQqjtOclpcqHt9YttU/uxd8qS3k9MIk4kjvPeBF/4kSO04Wbba/TYdluCemcR2ytJWofIXtiuov31xMRyRSIgcQ4xGN96mjbfQFybxetiyuz+FLzc1XaxmuTbRNDyGcTk83mOypPnEMKXo5rwNwaSnkQIeW5BPIgvJDIKJzmRJP2EqAy8Jy0/lxDCLr22yiaoklZKgc+RIE1kdydoYSBEe09Pg7dZAUkbEkGo5xCO3gWB/9heOtfmF7ZLMxnr9A8tmg9NrprL1q8B3Gn7b/20bSIkvSD/TJC0PjHQfKHtDxTaXpcmlTfaXiuNDy4oS3wYdPBE0n3Ab0gO8vSZtLyy7SX62W/a97W21y1+LlseJiQ9s9v3vTh6JW1PnLuDpmo7CEjaPE1iXlP2vXNVLU04znWvo5QRujcpuYvJSVvHTSdpK0261rL90JSNo/2lRGLQlE70bKygQvVQ2fihFzvqXp/93LOSjijpdyat6xWSnk1kaB9JJG1lmEPoAo+kOkHSZUSgrGuw0/ZJfe5/Zdu3TbUu993lxPH4YhYYrBrHpAS0Q4CXpFWXE3OaSRXkKXC5NpFsdwpBc/Ua2y/t53/VQRbk66dNepZtRQRwNiHm6W8uthsGJJ1nextJt9MZvM0qBqblL0iJne8g/pcIOrXjCQfP4rb/0+d+jwNOcrB0VLXp65xIOpOYu74+BVEXA660vU7J9ocQc+jVba8m6WnAN2xv3MffmhFI9+QpVX6FmYSK+dfIE23qoBebpwokFRMyav7+SYTMwNVEgsLviOTRD9r+Tr/7HQUkXWN7veSjer7txyRdbfuFFe1XKFuff462mAxJc4s+uezYj9CGfW0fO6rfGxdSf7wKnUGcn3Zpv7jt+9LnynFaE5Cer5sSQarvE+OkH9veqaTt/xB93reJscwOhI/z41P8xgLAkplvtOL7DYmk2HtsP5rmNUs5kswbgyZVUn0AeCuRQZ05pzuoGGwfDRytqIDYDfgO8DRFpc23bf96AHYsXmMCt4HtdSVdl+z7V5oklcK9VxoNDY5S8BuZCAKMoxT8p5LWdNAhTYVDCbHqywBsX6/utCKP2X5EUWH3GdvHZudnmnjEds+Z/CnY8lriOn060ckUsQvRAT1E0L1dJOku4NnEJHskSFl1/wP8k8hO+hLwYoKT/M3dJk0DtCFznGxmuyo7uYiPA+dKehWRcf41qjObSW0nBbWYCBgNFWny+CARIO7WphmZA/3jc0QywTeIye7rgWcV2ixc3KjH71rMPNSpmpvRFXbFpAV3p4XthbIu209HFmQWPJmGqX1lZPeI2hVMdaGoPD4yS/ZJCUIH2s7TFk+bWs72dyR9cBA294iXApcQGg2TzKGzqmXox7kH1LqOPPxM2hsIAeO7emx/G3CZpO/RSev26ZK2daqHerGj7vXZzz37cmJulcdWJet6rQZanaiweTyd1+i9wFv6sK9fDJuh4mw66TMhxjNViWOL275anXQpVYHPrxIauxnF3+uIMWFZYPoR25b0aqKC6iuSRjU36Lvq0fbDks4n+qzFCGrFkQSpbG+T3odCP2n7fiY0IoroK0CVsBmwj6TfEXSbZRR+/Z6TVWzvImm37D9Ildw+OxCVI9emtn9WSC3MZtzH5DnKTEXZ/GvVsVo0NXqx+StMBJI+m+6TQQWS1iOSSh5LiTR/J7SRG+WwrcC/JS1JVPmclnxI3ZJu8pWaixLJQrdSoAdsMQmXStqV8BlBUNJ1q3odOJI/s9cqnBkJBb3ngcSc/yai6vUqIrBThatTYtjpRALVSPq75Dv9ZPq9m4D32v7TFJvtRCQeXWd7L0lPAb5c1tD2x9I46sVp1V62S/3Zkk4ninseJRJSlpb0aReKI9J+H5N0lO2Ncuv+S47muyloTJDK9mNEpcoXYF71yYEUtExS29uAjwEfk7QmEQg4n4i8ThfnqUQ3pgIPp+yOjKJwWSaoPRqHZOsFtrcAfjBGUzYB3pgy3R6kfDCe4RHbdxfG090m9g+ngfgbmJhED8Lpfa6kdxDBprwzY15ljmpqpjmybE/Nbb8eQRPzfwN02vSCE4kAzxxCZPrdxP94MTF43KB604Fha0kHE/otPXGu2v5ecnRdCCwFbG+7G31f3aDWoNE0fbqhwfZvJC1o+1HgREWFah7ziz5XC1jT9uXFlbYvkFR09NRp2zioHi1sL5R1pRhA8GSY2kDDDIBl6EVbs3Z/q84KpgUI58XIkgZsH5I+HuZChU5Jcs4ojvNU6Ps6GlLS1lOAX0n6OZ3jtKoM+d+n1yJMphcHOqqHli5cH3PIOQr6sKPu9dnzsZb0dqLaY+Vc8BJinFRFi/QlUjVQsvXGNPGdF6RyH9q9w8Cwgp19nmuop1e2iu0dc8sfVjBAlOFeSR8iAlkvTnO4USXx9BUIlPRKwuG8GZFc+GXGoLmlcqrEz3iaFQPJMXU4kx2F000M6EULqt/g7EOK6qns+lyFaqr9h1IfkrXtu6q6qZB0LhPP9QWIc3lW9RYzCz3MvxqHHmweZiDpoeSDxPYDkn49QwJUEAkADxByAnsASxP04aWwvWZ+WaGrs88wDZwl2IdOHdgFgP9Keg8j0vRSRRUOs4SmNOEA4l6/0vaLFdqB+XldxsjwcOorSElVbyd8h7uO0NavEsf+CoKh7FjKk43yuD/1YY9ImkMksnWMHZQYNdLiv2wfk/vuNS7XiX2ug+Z4D+La+AARrJoUQ0m4UNKOwLeanBjfmCAVTF19UjbZdVTj3ERkWQ+iEmF/4CBJDxEVLt1EBT+bbHyyJsTeDi5p1wg4Svruk7S0S6glRog6wqw3S9qd0NJ6FqFR0m3AtRcRTf6Yg5plJXKBoGkgy17MU5yYzs5lWpppaeI99KqlEixp+wQASW/zhM7TDxU0nKPAD4hB5xKS7iHdd5Tcf5KOpdNxOIcIbOwrCdv7lf1AH0GtQaNsgrkYMdhptGhoTdyXHG3XSzqScNYUJ7rzhT5XC6Be1dxMr7D7BpFo8yVytLBlsJ1lT13BFJUvQwieDDNgPswAWIZetDX7cejlq0MeAe4gnACjxjeZXMlRrNoZxXGeCk1LvDhk6iYT8IRA8RLurJDKo5/qoV7sqHt91jnWpxOJe4cD+WD2va7W5KlTDdSzdu+wMeBgZ7+VYmV6ZXtWtL1f0ia2fwzzgin3V7TdhUh629tBBb4C1U6HgWIagcA3Evod+3iyjt4o8XlgbUlrA+8nKjFOIapVp4MTifv7aCIQtxdMXwPQid5T0pOpCIhO45wcQsyxlpd0GrAxcZ7KcJakLwKPl/QWoo/6UkXbmYpP5T4/AvzO9h/HZcyA0cv8q2noxeZhBpKyqnTorEzvlkDdCBTGLSf3sf21ClryFl3gBmh6UaMKZwbjAUelL5IWsf2LlDyUx2VEMOjPAApZmHcCrwLeRY+J7gPAUrazZ+MnJV3bwzbXSHo88UydS1RfX11o8ykm5n/FueDBlOvELpzGBNsDn3NUs3eb+72H6GMflXQ/3WMdY8PYNalUXn2yi0uqTzRkHvJ+kG6elxEn+GLblcLnTYCkswguyh+SK+2rcuwP0Y61mShh/JHtGyraLU5Q0b2COMYXEBSFlRo+yWG1gu1bB2t1d2gEmmnDgBqgbZH7ve/a7uoU1BR0J7Y7BmolQa3NiaDWHan9SK/9ZNPY9emGBYXWxp1EZvoBRGbX8Q4B83y7Wa/P1QIUVFrHubxqbj/bW/XTtolQCW95l7a9UNZlbfMUoVnw5Eu2e6U1K+5vaNpAoxinqaa25kzpb1VD86cJ4+FhXkejgKSNCOf1krZXSOPSfWy/o6yth1Q91Mv12e+xVlTfPIXOYNKkahIFrci7CP2ZdRXVQG8q63NVQ7u3KUjBoEOZqHKt1Cjq91wrp1fWpc06hDNx6WTDP4E3dpkDPRN4lu2L0nxowW77bxHI5i6S/h/wJwdV4rTnM9kzXtJNTlUJkn5k+8VTbTvFfrcjKASfRiQ8PhP4paep86aIOj+DoLTbkLjmrrL99y7bvJzcnNv2D6djQ5OgoEpeFbjJo5c6GDp6nX81Ccnmu4hEtFKbNaHJCHToMk47kKQB6JeOGurUqe34ii7OZkXlT4YFCAf4E21vOXgrZz7StfFvp8R+SZuPOvclAAAgAElEQVQRgYA7iDFXT/qnA7LlatsvlDSXSJC4F7h5us+IJkHSOQTd54HEPOafwBK2X5lrc4PttdPnvYkA1da279QIdcIUFIO7MZGkchrh0xJEAHiK7VcE5ti+sbD+Ok9om877XLacW78vkZB2AxGsWwE4dbrjknGjCUGq+5lcfXJbxcRh6BPiNKDbA1jJ9kcUtIPL2S5GOpF0DHCmuwi6NQ0VDn57hJymkvYnshKzaPAOwAkegCBgiqh/CljE9kppQniYpymIqnIh87uJge5dhbaZZtquBM/1IQxOM23gUB+C3EO2Jz8pXwxYqGpSnhxyq6XFWx1ZtcU2tYJaLfqDgu50Wdu3FNavAdxp+2+5dX2LcreYWVBQ4J1HVMBOqprL94t12jYRkg4lJtuVtLC5tpMGm6NOCki/OdAAzqgCFylwmSUIXTgdh1NJIkMHRpXIoNCe2Z4IwJ2T++pe4Ov5sWbTAkRNCASmcdoRwJOJ62Iqh83PiOzUc3KTwpttT9KpTM+3t9BD9VBdO+qi12Mt6V1EYOZOJqjISx16adx6AvAi4F9ENdAeZQ46SdfbXme6/2OUSA6FA5gcWPtHrs37Hbq9pf1BsR8oOPwmweXaZtm2c1KbUnHr1OYthFbzMrZXUbBJfMH2y7r97jghaUOC9uY5hKN8QeC/g7r2a9hxOVE9tBfwEuBvBP3fml03nHq/PyESLM8m9AP/BHzC9urT3O8NRALdRbafn5yhu9l+63T2m/bdU/LMfBDAOZ5IAvkpMXY41/ZHxmvV8CFpY9tVNK8zAsMMJM1P81EFZVyGLOHtm26TQ0uRxog7OLT51gEuIirU1yIo50aitZhsOZ5gDduVCOL8h3im7TUqG0YJSS8jAtbfc64qOyXo/ZDQqNsZWN323xSVZRdN9xlfw75Lu3xt52j+FbSa3Rpfm2tbq3BA0gLATrbPyq0TkdBUqU+XEmNekhYvs31eNxvHgSYEqfqqPhnWhFjS54nJ3Oa2n6PIbr7Q9qRy2OT83oWJCrAzbV8zKFtGgRSE29Ul4mpD/M0bgY2cypRT9uGVxYlzOr77ExQcAL8EPtstoJYyDDYnbrjM6XDTdDstRYb/RgSdCgQv7FXEuT/M9ikV22WaabvYrtRMU4+VZcNAk7KI6kzKJW1KZKTeQTiBlgfeYPuKLvufMqjVoj9I+jrweRc0hSRtSZyX3XPrLqNhVbEthgfVqJqr07ZpUOgsFuGKpJsbgfXdSVl3jTurZRoRPOkXTQhc9IJcIsPGBN/7mWn5tcBc2weM2J5alRwz5TgPG5J+A2zrHhkNJP3M9gaFzMV5WZqFtj1XD9W1Y1hIdmyQD8R0abuSgyJ7XjVQtq6k7UeBn7o37d5GIDvXU7TZ1va5VYlNxYSmgsNvH5KeV679h3Ntawe0FFpVLwR+Nsj5zDAh6RpiTv8NIsHk9YR2zP+M2I6nEuOIn9v+kYIqcdNu88ce97s+MRd9PPARwol2pO2rprnfa2yvl4JVz3foVlxt+4XT2W/a93HASbYr6eTnhwCOpJuBtR3SB4sTc+2eKt+bDkXF7M6EVMYPbN8saRvCqb1YMSGqCZB0E93Ht2vl2g4tkDST56OpX5sET1N7r0VA0o3ZdSjpU8Bjtt+fAgPXlyX8DMkOAc+w/Ye0vCIlVTgzGakPu7Zs/F1otyxRPfUQ8FuCdeJa4rl1aJU/dpyoGdD6NyEDIMIvnPk0BWxi+wkl+7/C9kuK67vY8wlgfaL6C8JPPdf2dLSuB46xB6kyqCHVJ5qgCJhy0prbZhlgR8L2FWw/a0Tm9gWVaH/Zfu8If/8mwjn3QFpelJhIrJlr83oi6/E9ROcjoiz5k8AxVRONCqfDjdN9kCiEVt9s+860/BSC8/zNwBUO4b6+BlEaYmVZL2hSFlGdSXkKSO7uROuoqMI4o2rS0U9Qq0XvkPQLV5Sdq5CdrvIqgLw+V6Npolr0jjr9S5P6omFDPVDWNS140hSok+pkEYImZtrZ+mki8YoseSEFfy60vdl09tuHHYsCbyKchvP0STwGzZ+ZBEk/sb1xjfZnA58GPkdQYe0HrGd7kviyalQP1bVjWEjX88vdJZsy17YsQ7O0AiPdf0sQ1aIPQzP57KEjg3VnoqrnW3RWueYzWBfq5VhV/E4pDUvu+54DWrltOuYzkhYinDiN1UjJBVvyzr2f2n7RuG1rMiRdRFTRHk4kHNxFzJOnfdwk3UIk5/2OoPmfRJE2mwM4GYp9XFmfN1Mh6SRiTns1sAFxrjcCPmj7O2M0rRKqkSA7zEBSxXx0RtAWJ39ahkUJ+2/tMhdfj5DQyGhvgc6AYIsJqJPW9VrgQ05VpoPwLda0pWc6+ZkKSWcA77X9pxrbPIMI5tzgApPPTISkrrqZLiSCp23+l9A2PZNOKZ9SDVpFkuw6Tjp/KUB4XdP6gYWmbjIa2L4N+BjwMU1Un5xP0I+NEg+nk2WYF7F9rPsmrAo8m6ABaeQNonLtr5Vdov01ApwI/EzSt9Py9oQuQB7vIEps78itu0TSjoQob1U23M2SdieE1Z9FOB0GQce4YhagSrgLWM32PyVl1Tj9ioi/ich2zSrLjgCuJCgzRoEmiZ8/aPshJfHuNCmvckgv7JzumO1fJ6diFY4iHJAdQS06xehb9I9ux77jO/cvAN1i5qFO/9Kkvqg20rX8dnIl9MAXXVKx6aCVuokJyrqPuECx45S5L+mNwGa54MkXiMnzfAkXRIwVNEXTzjgn9ECWInjQAZZM60aNU4BfAVsChxHOk0brnTYE10g6E/gOnYGIMqFhgLcBxxDJWn8k7ql3VrQ9T9LW7q16qK4dw8JtwGUKJoC8HfOqdjShg7a0Ommt55ALkOZRvP8ajqMKy3m9AhPMCxmuJolUSzrW9r41fqdr4oQ7q6q2LwtKleBySQcBiyl0gt4BnFvDpnHgvvS8vl7SkcBfiIDmSFGRyPAf20v3ub9zun3vaVLKA68mnEwHEP390kTfPwj0ouX5kO1HAWzfp2wSNrvw7OScgxhzrZKWp61r1ACsB6zlqMBbFPg7UcH41zHbVQnbv1PvFJOvJAJJZ0gqCyQd3W8gaSbPR4sJvCkpY58um5xGVJ7cxNS+zRbhdzyLeI49gaB4RdJyRCXPKHGVpPXdpSJ2FuBJwC8lXUlnsKVMciX77o+EL6+xSP7oT5L6OroE4sqCUD0gS2DMz18MdGOlezwTc92+xkXDxtgrqZqWOS1pD4LCb12i6mIn4GDb3yhpewTwGqLc8CzgW019qKmG9teI7FmXcDqKqES6rvD9LbafW7Ftt+8WJ7JE5om+Es6/aVFFKagQViAoLCAq5/5IPOzPs71Zv9k46qGybJhoUhZRmtT+m6AI2ZeYlN/iEqoQSV8lOuGstHcPQr+qlJ+3LOtl1JkwsxnJEXZc0Ymn0I7ZzyUC7C1mPyr6l9KquTptmwhJXyacYRkt1OuARz1N3nJJtxIUuf9My08gxM+npYMxmyDpKtsbTnMfexEaPhk1w0uBD/eTnTtNO7LKiRttr5UcJxc4RwnRYjIknViy2h5ABVqd6qFh2lEHheqdvCH5gEkdHbQ9bZ+aPnfonEh6l+3PDfgvDAySVk7JkJXr1MnAUKvKok77XtsqKIXeROd85sujmg/3g1QdcScRGDqAcH4cb/s3XTccvl3bAy+0fVCf2/8N+APhDPsZ0BHE6dOxlLdt6FpQkp5MZ2VuPhEo0yYGOvSJZ0MAB6hXuTPTUOxT6vZf44D6pJicaYGkUaPbuZf0Y9ubjNqmmYoUrN8FWA44KwssSHo+8ORh9tcltkxZETvTodChmgTbF4/alkFC0o+I4ooriLH2Rt0CbyOwZzfgE8RcV0Ri7UG2GxXsa0KQ6jIaxgWbMguz7OaLXcErL+ltwNm2/z4q2/qF+tT+GrANc2zfo6BHnATnyhLVpay123fDQnpQ7UjQLgn4MSE2WXoD1RlEKbjq30BUt0E4DE62ffSAzO8Z4x781ZmUK7Rr3kku2ElMhh8stk3tawW1WtRDqkw7j5hwzE2r1yMoJ7bxiKlbWzQPNfvFGTcRVQk1cNm6tL5nyrqmBE+agkLFxwJEP/NS2xsNYN9PJehyIGhnR56JrKRFIukKIlHjr8DV40oqmq1IGdn7EiwIeeqb6VZFNAqSlnCq1O/SZkodNNUUdG4SyuwrziW6/b+KfeY1VVal08lf6Txq+rGarZhOIoOCYeXlBMvLWsD3CHrxX0zTpqFrQSkE0o8iqoLvIqi+fulO/ctZG8DJ0LSk6EFiJgYZNR9QTA4b6tQ6XIBIsH+i7S0r2r+M6MMuZrxV3jMCTeozqvroWdI3X2j7FeO2o1+kZ2zGoHK57XML33fQhQ96DJj6z/cQkkNvVVRurW77vC7bLEfoUokxzXWnQhOCVI2p4kj2HAOcmc8eLGnT9cJyjuO8adAYtb8knWd7G4W4fP7CywZRK+fa5gdcFNqubHsSfYRCv2N/IMsu/yXwWU9TKHcU0BSVZfMzihm7he8WIc63CR7mSbRauba1glot6iMd492BTH/qF8DpnmYlY4sWMwEKzvLX2v5tWl6ZSGTpJWu+a6Z3E4InTUGhUuURQmfwS7bv6nN/jaoQkfRmInlrTYLackng/9n+wijtmCmQ9H4HfeaxlNCu2d6vYrsbCKrpDuqbfFVEnWujXzuGBUkbEf9vSdsrSFob2Mf2O0raLktoo65IZ8Bu71ybfKVRhwZTcbkp0ASd4ZEE80GGOcD7Co76bN6Rd/JChaO3jmO/TkBL0lm2dy5sk99vEx3Ol1JNeWjbpRnSQ7RnmIkMjyPm0Z8EDvM09INH4ahP/dzmwEWOCt3NgN1svzXXpjHO2GGhiUnRg8JMDDLOtESHJkKd1dLZWPibVXNuSacS8iS/YGLMY7d6p6VoUp8h6RTbr5tq3UxEU8ePvUDS4QTd/Glp1W7ANbY/lGvzq7Q+q8A+jfCVCXqPG0hatOzeVtCLzwVeb3sNSYsBV7pCR1fSxcUxWdm6cWPsQao8mpA5nQIduzCh23Sm7WsKbbJs5kWJge8NxIW2FuE8mhGltJrQ/trF9qi1v7qi7oBL0usJaon3ANcS52NdYhJxTL+BKqXS6ELWO1BN9dLHb8zaB0+vSFmKOxPaED+wfbOkbYCDgMXKHl6SNiVote4gzsfywBtsX9Hld3oOarWoh/lhktuiRTekLMUTCS0YERnLe9m+tOuGE9t3ZHo3LXgyW9GtgqJ1nDQfkra1fW4av0+Ck7ZbyXY/s71B2Xe5Nj1fG/3aMSxI+hlBWX5OLrh0s+01Str+FPgRMdF9NFtv+5u5NjPuPlE9OsO6846exzw1A1rL2f7LTMqcllQWWNkQeD9wl+31R2zPQBMZ0j4fB7yKmDevSFxPX3UNkfeSfQ79PpJ0je31UrDq+Q7doqttvzDX5jIa4owdFtSwpOhBYibOvzQDq79mOiTd5BFJScwGNKnPKHlWLEhQxJbKn8wkSLoNeG/V9y6p9Etju08QFcJigH7ZOlDoGq5j+7G0vCBwXSHxqJsPwO5C5S7pauDrBNXw2bY3LmmTPePziWRlzC6LAosTrCybMhE0mwOcb/s5U/7hEaJRQaomQUFJtyNRcbSC7WeVtPk68DHbN6XlNQgxtDeO0tZe0KQBzDAiuJKuAna1fUdh/YrERHRaWhXDxGx+8PQKSScRQaariWqB3xE0cR+0/Z2KbeYCu9u+NS2vRtBvVNFEbkrNoFaL3jE/THJbtJgKyZG1OtHH/MrV9KNTZnrPRKfwMKGKCpUM7rNSRQ2rEJH0ceDILFlLoUF2oO2DR2nHbIek3QlGgQvppL65NtemUddGHWRBuKkmrml9Bx1Jxf66VRqVMhw0BeqBzrCPfV5Gj2OeunOwNA+4wPYWg7R5FJD0UuB/gccBH7d9/phNmjYknUwwBJxPzClvHtB+h+6ol3QREag9nEgEvovQQX5Rrk1jnLGjgBqQFD1IzMT5V92kgBaTIWk9Qof9mXRWQFdRzX4JONr2LaOxcPZgXH2GpA+REraB+5gILDwEnOBcxc5MhaR/AN9l4r/lYZdU+kn6DbBD5oMfF1KQalNPaEYvA1w2qCC7pCcB7yKugffa/mxJm4wu+Ce215W0CuEPfWGh3f7Au4nA3p9zX91DJPE0KvF1oambzLdYlSiJXRGo6syfnb85HNUfXSd5Y8SlkqYcwBDUMkNBLoL7pOR0yUdwnzbN3c8pBqgAbN8haRDVTqsAf7T9YAp2rAV8bToPqvyDR9I9FB480zR5pmE9YK2U4bco8HdgVXentFo4C1AB2P51GkRU4SjgFcWgFtByYA8GryQmuWcotD7+TQyqFiAmuUfPpkluixYZJG1u+5JC4AlgFUlVfO/b5j5nmd6vLu664nPZ8vyArKp9Y+C5hLYmwGuZ0MHrB674XLY8CmzlHO2j7X9J2hpog1RdoKCs+wBxbSyare+Spbgm8DqCCmse9U1aJrdc9rlsuV87hoU/SHoR4DTW34+gwS7DeZK2tv39LvtrVJZlL8gHthVi0R3oN7CdUDbmyTv282OeWnMwB/3bfZKWtn33NGwcGSRtSQSnHiASOHuqIB6wDZMcOHlM43y/jhCrXw3YT5r3+J1u9vYo7qlXA/cTbCN7AEsDh+UbOCiEjgeOn20BnDI4WDT+Mm47Bog6fVEj0AahBoLTCBrbDsriLtgEeINCduNB2qq1njGuPsP24cDhkg6fDQGpCvyuLBA1Be4cd4Aq4XDgulQtJUKbqu/zpKgCPzTXPy5NzHGPJHzPZTgU+AGwvKTTiDnyG4uNbB8DHCNpX0+DpnhUaCupCpB0BPAa4LfAWcC3qgZpks4gBq2nEpOgPQnu90kToXGjCVlShQjun5hwsk07gquCAHKv39XY//VEIGVF4AKC5mF121tPZ79p37P5wdMT+qkSkPRV4r47Ja3aA1jI9l4V7W8sDsTK1rWYPuaHSW6LFhkkfdj2IeqkGMpQmgXW437bSqoSpMnAK9KkMetvLrS9WZ/7a1SFSMrMWz+rwlPwi1/jnH5Oi8mQdCERuHwv8DbC8f832x+oaP8rIjnmoS77rH1t1LVjWEgZmMcAWxD2Xgjsb/sfJW3vBZYgHFcPU+J8r1sN1ASognoxgwdEwTjVmKefOZikswjKvB8Sc83M5pFqm/UCST8HliUo1idVrHlEWs2SHgJuJubvf6aQzDGo8z0oDPueUmhdrkqwc1zQzz5azCy086/5B0qSFDXazxgK2RaBdM7+nSWrKDQFtycSG4/rNn6dKeiHlUDSZ4gxx3foZEI4p3KjIUHScsD6xHhjWprRebYDBY3y6cDetn+iAk1vYbsnEuNFAVfZ/ntJm6qEWqCcVnGcaINUBUh6G8H5OOnklrRdFHg7ETUFuAI43hX0Pk3BuAcw043gSnpicZJdoEzo+IoBOJkyh6Ck9wEP2D62n061Yt8vKVvv+YiGTn1QXihotd5JZAaJKe6/ukGtFi1atKgDSSvZvr3bOtWgrGta8KQpkHQrsJEn6BWeQAzKV+9zf42inZH0fkJD50TiWtmb0BU6cpR2zDRkCUn55BNJl9t+aUX7M4F93UWnpp9ro64dMwWagZRSg0LZvGMa++ppDlYVYGtaoAXmXRvZc810Boc8qirC5Kh5LaEt/QgRLP6m7X+N4vfrYpj3lKTjgecBGRXQubY/MgCzW7Ro0QAodHB3Ay6m01H/rUK7ObbvUVCRTUI2lm7RPCi0RXew/WcFW9dFRPXOWsDDtt88VgMHAElrTlUVVUzWkHRKSTPbfv3ADewCRVn1HsSc/DBJKwBPtX11n/u7nmA9WAH4OMGs8Yvk87zeJbpRks4Gvgr8wEkbq2LfQ0moHRbaIFWCpK4Zyb1kgUlantBF+uTADJuFkPRO4DR36i3sZvv4Ltv8FvgeUbV2kgt6TcN2MqWHxGcI7t9tbd+uCgHqPvZ9bm5xUeCFwNxRTeqagFE4CesGtVq0aNGiDsqqm4qVvDnHYyllne0Dcm0bFTxpCiTtRdAbZHRSLwU+3K9zvIkVIpK2IhyLIqrE2iz4KSDpKtsbSroA+CxRSXG27VUq2l9GTPR/TqeDZ7tcm9rXRl07hgUF7dO+BANAXq8i///2tH1q+ryx7Z/kvnuXcwwH/VQDNQXqg4JxqnlHi2ZD0tMJB+57gA/YLnNqjRXDvKck3Qys7aCOXBz4kafJKtKixTCgqBZfwTkK/xZTQ9KphDTJL8hRFhedzZLOs72NguavLIlg5ZEY3KI2CslOnwIes/1+SQsQQYsZzwY0kxOgJH2euPc2t/2c5NO+0Pb6Fe23Y6K45XLb5xa+3wD4GCH98ltCEudiIvHmZpcwMkjaAtiLqKT6BjFe/dUg/t840QapEhT0MRCDw/WAG4hOfC2idK+0nFZBp/FaYiD8dODbtt87fItnLlQi0NxLVZKkA4BPAXvZ/towbSz57ecStC1X2s44n3ex/Ykh/NbyhGh642gjmwBJzyKChf8EPg18CXgx0Zm/2fbPx2heixYt5jNIejaRsXwkwQ+fYQ7wPpfQtKkHyromBk+aAklPBTZIi9OlV7iMGTpBajEBSdsAPwKWB44l7r9Di5PAXPvSyibbl+faXEbNa6OuHcOCpBuAr1DQqyj8v74oRXutBmoK1CcF45jnHc8iMqaLgbXWoTgFUuLpbsDLCb3Co2xX6Us3AoO+p+rczy1ajAuStiX62EVsr5SqRQ7LJ1O0KIekm2yvOW47WgwP+XMs6VrgQ1nSmmaJZEU/yRqSnkbQWWf++SuAA2z/eVR2Jzsypq15fmzlKPsKbQ8nChFOS6t2I6jcKyVfUlBrS+A64Cvd5vuSlk77/B/gD4R/9NScn+E93f6L7U93+37UWGjqJvMHMseQpK8Db83KDiWtQUxq5kHSUsAOwO6EiOq3iTK/Z4zU6JmLBfKONUkLAovkG6QJ5VuyTHFJGxITy32AbYCRThZt3yLpA0T5JQ76poEHqBL+CEy7QmsW40Ti/M8BfkbonO1ABKo+x4TjEmiDWi1atBg6VieeS48Hts2tvxd4S8U2TwOWIvolgCXTujwulTSlgxw4aZr2zwjkqz6AVWx/N/ddR9VHTTRKdFyhD5RNRBYBFgb+65w+UItS/MvB2383kI3pN65qnA/WdEE/10YtO4aIB2x/doo2qvhctjwPHpOI+DTwRNtfkbR/Ou+XS+o4/02bdxBj3UOAo4nraC+6nJMWQWdDnKtfAl8nHHqPDOF3lrT9n/R5VdtldPO1MIR76tkKfUOI62aVtFxJo96ixRhwKOG4vQzA9vWSVhyfOTMKV0l67lQBeM0HukazGJco9Cn/AjwBuARAoYM0K86b7QeA44HjayRrnAicDeyZll+X1m05TFtL8HDyY2c+7WXJJYUV8CpgHSdKPkknE8GnyiCVQ2NrSp0tBdXxnsRxuI4IhGV+gk1Ts6XS++qEhla2322JIF+j0FZSFVBR5dOxTtL9wNXAwcCPbVvSbW12W2+Q9EmCfuQLxE39NuAPtg/MtZl3zCW9ihDj3d72ryX9vKqMcog2Dy3TR50aJQsA6wB32N6zeqv5F4Vr4ze2Vy37Lrfux0wEtQ4gglrnEoGqj9ruCGq1aNGiRT+QtJHtSaLxFW2npKwbJhXQTES/VR81f6NxFSKStgdeaPugcdvSZJRdA1NUA70GOAJ4MuG4zZy3pcHAXq+NunYMC5J2B55F9BV5OsNrc22Gfk81AeqBgrGB845M2yyfSf0j2y8epR0zCZIeA24D7k+rsrnVQAMzqUrxdkLU/HCPmMqzF6ilC24xAyDpZ7Y3KFQizIoKkWFD0i8JvdrbiWd8aT+n+UDXaLZCkgiqt+WAs2z/Ka1/PvBkz6dU4L3460dkxx7E+VkXOBnYCTjY9jdK2t4IbOoJPeVlgMum29dJ+hZB+3kKQfX3l9x319her9D+QmBH2/em5aWAb9h+5XTsGDTaSqrJ+KWkLxMc5Caikr8stDkI2BX4PHC6Qny5Re/4APBW4O3EA/VCoroljwcV2h3LEwJyz7f9J0lzgFpi8RqM4PGhTM70WWma+8xwTe7zI8AZzukCzO8oOX/5DIV7Cs3LsheWtH1C2tfbcg+OH6aAaYsWLVoMAtcpNBefRyc90yQxUtsnSjqficrPD7pAWddndtlsRl9VH3XQxAoR29+R9MFx29FUSNoIeBGwbIHOYg4R0K3CkYTGaHGMX4qpro1p2DEsrElkVW5OTq8iLWfIqi3ylRak5dmUePfRRIVyIBMUjAcU2gxs3jEgPKDQnfg/Se8C/kQEVBsPhabeZbbvl/Qa298a0U8Pal7WAYWm00NZVZbttSW9HTiD8Ac0Eb/PGEuqkGc1adFiTLg5JVQsmJhP9gN+OmabZgp6dSov5gkatD2Br9o+Kj1f5ptEt5kK218vWXdd9nk+7cf/KWlXJnSdd2aCmWRksH2apLlMaAhv32VOcTjhJ7g0tX0JXaqoauBzti+psG+9ktUr0FmF9xBRPNIotEGqydiLCJ7sn5avIJxE82D7aOBoSSsT3I/fAZ6moIP7tu1fj9DeGYdU5viF9EKhwXQgkbWYYQ/gg8SNcwRwsqQrgFczOaA1CSoIHhOc7tPBI7bvjoSGib8yzX3GTuyT88uSlpf0PtvzbQBlivNX16lSN6jVokWLFv3gFOBXBN3AYcRzrGOwqj4p65oYPBkDXPG5bHnGIlX4ZFiA0EmdNf9vCFiEoMtciAk6C4jn/U5dtruz1wDVkO0YFnYgqMi7UcI8Z1TGjBO2z0sf51EwlmBa844h4N3A4oTT9iOE3W8Ygx39YBvgEIWGxobAqIJUwwrMXELQY/017WMHwlewJRHsnJQ13QC0dMEtZgL2JSj5HySCvhcQ/V2LCkiaY/seglK8p01ynzcnOcZtP1bwa7VoHtp+vBx7E/7544j50eMFh08AACAASURBVFXAm0b146kKKsNdRN8177usWioP22codG7XJ+7JDxSTUyt+awEi4b7ow8z2e4lCnqioX1pFUX0KcLWkbxPHbgdGT2c9JVq6vymQAii7ThUwkLQmEbDapYll/02DpCcBryWO2dOJ4N57u7R/PrAFcJ3ti3r8jYEJHkv6CnAxMYHdkZg0Lmz7bdPZb27/tY7H/ICq81eXwkLSfcBvSEGt9Jm0vLLtcWTItmjRYpYhoyvJqEpS9dMFtjfPtZkv6LWGgfmlL5d0Ym7xEUI34Eu27xqPRc2HghP+TNs9B4MkHQM8lUg0y9Ph9e1Q78eOYSGxPOzb7brpxWE/G7J0Ja1GsF88xfYaktYCtrP90S7b1J53DArpOvqE7feN8nf7haQNgNts/y237v8RCZ/vLMsEH5IdlwFTOvSco9Xtcb/zhNAlvZXQmtza9t/K6HSaALV0wS1azEpIOs/2NpJuJ5zM+UiTXZAfSWOd5YhEt+2A1Ww/rNA1OreJ/VeLQNuPNxOFe28F4F/p8+OJZJlJVd2JunEPYr56mKQVgKfavrqk7emEHM6jwFxgaeDTZfEISYcQulPPBb4PbEXIEVXOQyStS8ieAFyRr8xrCtogVQmmChjML5O6QSNxXu4A7A6sBnybCOo9YwD7LhM8PpmoznqF7Z2nuf/FiUyfVxCd0AXARxIdU7/7HNrxmGmoc/7q3n91g1otWrRo0Q8kXW37hSn7/h1E1vXV+QmjOnn3530uW27RibYvb9ENki7JB4R7aH9iyWq7hJ5zmHYMC8lhvxbwczqDcNsV2gzcqd80SLoceB/wxVz/e7PtNcZrWTUkXQK8bCbMJRUaTRtkcyJJnyboY/Yh5tCbjMiOoTj00rm4nKCCfA2wegpQLUckojRaP0ctXXCLhkHSuXSpEPcANL9bBJJzvNU1muFo+3GQdKCDqvJoSvoP2+8p2WyY9nwBOMf299PyVsAWtg8saft5gsFpc9vPkfQE4EKX6J0q6WspNK9eQEjlzC0ba0i6CVibSKhaW9JTgC/b3rbQbnMnWkBJK9m+PffdKGmZe0JL95dQETBYuSJg0JZe9oe7gKuBg4kIrxNlwiDw5FyAIxM83tYheLzPdHdu+z4iSPU/091XDsM8HjMNdc5f3fuv5WVv0aLFKHBCGnQeDJxDUH/9v0Kb+YKybkiY1X25pGPp7rTZb4TmzERcJ+kcgnrrv9nKqomX7b2aYMcQcUgPbV5JOPXPUOisFp36R8+SLN3FbV+tTmqjR8ZlTI+4DviupHFfR71gIdsPSFqIGHvfB+zkoJNafFRGeHg6jq8l6P1+TVRR/SAF5jZjsPPCocAtXXCL5uFT4zZgpiIlbP3b9t1peTOCjvQOIhA/ieK3rJrVra7RjELbjwPw2/R+81itmMD6zrFq2T5fUhVd6Qa215V0XWr7r+S7LMPCaQyzPaE59bCkqvvz/jTWekShoXoX5fInnwIytpZv5j5D+C0aNbZsg1QTqBMwmF8mdYPGQYTA7OeB0xMVyaAwNMHjtN/9gdXTql8Cn/U0KQQZ7vGYaahz/uref21QuUWLFkOH7S+nj1dQPkCE+pp6LSYw2/vya9L7xgRtQzYmeC1B99CiO5YB/kFoLmQwhYmXpPfbPrIqKDiAYGBPdgwbti/voc2wnPpNw98lrUI635J2ovnOnkZcRz3ix5IuJugzlyIqwB6T9FLg/nEYNEiHnu1/APOoISVdSfTTR9i+dRC/0aLF/IT88ymN4VZLi7eme7dFNc4iEuvvlrQOkRBzOLAO8Tx/c6H9bB87t5hPYPs76eO/igk76tTzHRX+Lulg4FRifLYnMW4rw8MKKudsHLosUVlVhi8QQecbgCtSYLpUkwq4RtLjCe3UucB/iJhGEar4XLY8drR0fwkK/ZtdCYf46YRz4Icu8LqWbDebJ3VDgaSVCSrFXYFnEdme37b965K2+9s+pod1qzIhePxbgo8zEzw+1fbRfdr6ekIU9z3AtcRNvC5R6XPMAAJVtY7HbEW/56+X+29Y9B8tWrRokYekjwNHZn1Rqqo60PbBuTYtZV2fmF/6ckmXEjS3D6flhQlKiM3Ga9nsgKRtbZ+bEmMmwfbJo7ZpGEgT9iOAJxNjVxF0hnPGatgYkMbZJwAvIrQDbgf2tH3HOO2aTZC0CTGGvxM4mxibA+xo+9qxGdaiRYvGQtKmBL3/HcQzanngDbavGKNZjYaS7m36/CngMdvvl7QAcH2REmx+GTu3mH+gEg1nSXNtv2DEdixD+G1fklZdAXzY9j9L2u5B0G6uS/R5OwEH2/5God0CRCX6Wbl1Aha03ZUBQNKKwBzbN5Z8N6M0sdsgVQFtwGC0kLQmQbG4s+1VSr4v64Sm1O3QgASPJV0F7FqcyKZO4Ou2N+x33xW/tyZx/e1SdjzmFwzq/JXstw0qt2jRYigoezaVDARbTcsBYDb35ZJuBTbKJjkp2HmV7dW7bzl/Q9JqRGX6U2yvIWktYDvbH51i09lqx28I2uRfjvJ3mwxJSwAL2L63S5uNgUOBZxKMI1lwb6SVrmk+egywIZF5eyXwbud0BJoMScva/tu47WjRokVzIWkusHtWkZien2eM2tk8kyDpJttrps/XAh9y0pXKB7Aqtp21Y+cWsx+StiQYlXYHTst9NQdY2yX6Tk2CpGcDLyPGlRdXjc8lXWH7JWXf5dp0DSoVk4Mk/ZsIogl4cfpMWt7E9hN6+hMjQhuk6oI2YDA+SNqN6IDyNxEEjcSjtrcYkR232H5u3e963HfrrGzRokWLWYJE3be+7QfT8mLANbafl2tzGcEF3ZV2w/ZJIzS9RYMgaS/CSX5pWvVSIjPvpHHZNBMg6XLgfcAXs2CxpJttr1HRfllCjPi5RFYxALY3L2s/LDuGBUk/sb3xKH+zaZDUVUTb9qdLtvkVwaAwF3g017aKwmUoSElyxwFnpFW7Avva3mCUdrRo0aLFsFAWVJkq0DK/Q9IxwHIEnel2wGoOzZrlgHNtrzdWA1u0GBJSEvu6hN7zYbmv7gUusf33EdnxGdvvlnQu5bTh2+XaLtNtXxVVV/9LUCWfSacm6T9zbS4tbte52865TKJf7rbBlBTho0SrSZVQFgywfRNwE6Ed1AYMBgRJJ9h+a275ZEJk9zjbmRDeT4mH75OAo3Kb3wtMKmEcIrpxqU+XZ73lCG7RokWL2YNTgYslnUgMWvcmSvrzaDUtW3SF7RMlnQ9kzugP2v7rOG2aIVjc9tXBijEP3agxTiMmgK8C3kaMuQZR+VHXjmHhGoXW6XeAB7OVLvD4z3Islfu8D/DFHra52/b5Q7KnDmT7lNzyqZLeNTZrWmSVeJlI+WrAs4Hz3WrotGjRL66R9BUg6+v2oNXgnArvJmjDliMqILL+56nA/4zNqhYthgzb1wHXSToDeMD2YzCPIm+REZqS9Vef6qHtXMInIGAFgnJawOOB3xMUnEXsnd7fmVtnctrVdSngmxaEmgptJVVCm908Okh6ge25ueX1iZv2hbY/kFu/IHDBqKqmyiDpPuA3ZV8BK9teYhr7bjmCW7Ro0WIWQdJWTJTyX5hRcFS0bWk3WsyDpD1tn5o+b2z7J7nv3mX7c+OzrvlIgb13Ad+wva6knYA32d6qov1c2y8o6DtcbrtrtuGg7RgWUrC8CNveu2T9rMdUVOE56pSdiXH4t+gM7o1EVymXdft+Yl7wdcI5sQvwONsfGYUdLSYjUZO9GHgCcBVwDXCf7T3GaliLFjMUkh5HOGI3IcbNVwDHZ4wELSajZeJpMb9D0pWEdu+9aXkpwmf8ohH9/nuBM23/ocY2XwDOsf39tLwVsIXtA/u04VnAJ4FViaKa99r+Uz/7aiLaIFVCGzBoJiSdA7zO9t09tt/f9jFTravx+yMRuW+dlZORdDiWd4n4X4sWLVq0aDGboBkmats0JA2fE4AXEZmKtwN7VI3TJF1le0NJFwCfBf4MnD1deu+6drQYDaa6h+pSpwwLkm5nIuu2zI6RamPVQdLzut72fyXtSdDyHDNbrv3sGpK0L7CY7SOnCn62aNGiHopJOi060SbWt5jfIel62+tMtW6Iv380sBMxvj+DSErrSjWYJcYV1l1TRs8paXHgPcAKtt+aAlKr2z4v1+ZHwNeIwP52hJbxa6b51xqDlu4vwfYDwPHA8W3AYLhIFAnvY0KUGKjUAXgAuEnSD+nk5NyvYvdvIISG83hjybqeMKqJVSrV/ssofqvJSAOv7Yjr4nrgbymzuauuQIsWLVqMG5LuZYKbehFgYeC/tueMz6oWMwiq+Fy23GIybHuLRMm1gO17E6VmFT4qaWngQOBYQnj53WOwY6CQ9P7kPD+Wcq78qvHzfI2MOkXSyrZvy3+XAo+jsmNk18oQ8HlgbUlrE5VgXyGcKNOqTmwQJGkjIqH1TWld60tp0aImElvOzsDTgR/YvlnSNoTExmJAG/itRksb3mJ+x32S1rZ9A4CkdQif8Uhg+4CkefoSQi/0fyXdQASsvp1VeBXwd0kHE9IABvYEqrROTyRoArPKsD8C3wDOy7VZyvaX0udPSuq72l/SoikW0hi0A6sStAGDoeMbwBeAL5ETJa7A99Irj0mTbkm7AbsDK6fqqwxLUd0BtGgelrZ9j6Q3AyfaPkRSW0nVokWLxsN2Xv8ESdsD/7+9O4+Wq6yzPv7dBDCCBF6GRhdCA2ESZB5kFiFNQ6sBFJuEQQTbVtsR5W0cUIR2YDWtoAjia6sBkRAREVBaUIGEORACBERRQXAI0kwaESTAfv94zoXKpe5cVafq3v1ZKyt1hjq1KyGXqvN7nt+zU01xovd4gMfNtuPFLgS2s/1Ew77vAtsPcP5j1Sz9PwF9BYrdasjRandXv9/SodfrWpIW8cK/nY0aPk+KUkzcqsnTvkuZAdToAjr09ydpb9tXSmo6IrbL1xR7xrYlHUCZQfV1SUfWHaqFPgB8lHIT6q6qeDnYDLyIaO7rwLrAfOBLku4HdqGswfn9WpN1uQysj+AY4KLq5waUZWMO7WSAqp3mXGButV7oNOBkyj3ulZo8ZSZwAnBRtT2v2tfMVNuHVPe3sf2k+i10C0yWtC0vDGJ8aeP2UC2qJc2ntJOeTfnc24rvPy2TIlXU4RnbXxnOibaXWXRe0rqUinV/11MKi2sCn2/YvwRIkaN3LC/pFZTRVVn8MyJ6lu3vS/pI3TmiZ2xW3UQXMLXfDfWubfFVN0mbAVsAq/a7sT+FMrp4IKfz4mJEs33tztFSti+tfj97qHMngDcM98Ru+fujzDq6Enhjk2OmrJXVrZZI+ihwBLBHNVtihZoztdLvbE/v26hm3GVmYsTI7QBsZfu5asmNh4GNbD9Yc66ekoH1MRHZvknSq4BXUb4j3cXQEx/aQtKWlHvTh1AmRnys2Xm2H6UMdBmOpyW9lGqQlaSpNKyRWlkMfKFh+8GGbQNDtaj+J8r6ufcDxw4zV8ekSBV1uFTSv1EqyY2LEj/a7GRJawJvoVSb1+GFCvTzbN8v6XeU1kpz25L6xbnWsJ1ZWq11EnA5cK3tm6tRir+sOVNExJD63dhcjvIlPDNgYrheVXeAHrUppRixGsve2F8CvKP/yVW7rl2Btap2HX2mUNrldCRHu0laCzgO2JyGIkun1lbqEg+MYIH5rvj7s31C9ftR/Y9JenOncozSIZTRzEfbflDSepSFvceLWZLWAW6mjIK+xvaimjNF9KKnbT8HZWaQpHtSoIqI4bL9N+A2SXtS1pU9EHh5J167WiNqBuXe9LOUGUn79m8VXZ17mu0PSrqU5i24p/ffB3wK+BGwrqRvU2Y5va3f8143wszfBD7VsJTNqpT76/8JNOsqUCsN8dk9ouWqRYH7W2YxYEmrAAdRvuxsQilMHWL7lUNc+xLgiKqFS8tJ+jWl/eC5wCzbm7fjdSYqSav3L1ZK2sB2s/9mIiK6RvUBsM8zwG+Ar9l+qJ5E0UsabpaP6ZyJStIutm8YxnmvBfYC3kVpy9FnCXCp7TENjBlujnaTdAUwhzJC8l2UNVv/1/ZxtQbroNEsMN8tf3/NSHrA9np15xiMpJdT2twauHm83Xiu/tvZkfIz5J3Ay2yvXmuoiB4j6a/Ar/o2ganV9mCtWCMikLQ95R7xm4G1KDOaL7b9cIde/15Km7zzhxqoIml72wuq7x4vMtDkCklrADtTfibeONb3Jul221v3ZQLOowwouk7SfNtdtTxBilTRlSQ9SelTfDxlVo0l3dtYyBrged+h/IP+MfD8egCtXCha0jHAfwFH2T6nVdcNkHQdsL/tP1fbmwPfsf3qepNFRES0z2huqMcLqplD7wDWp6FThO2jm5w7CZhj++A6c7STpAW2t5d0R98NP0lzbTf9ojweVW2kjgYOA5otMH9G3wLzkk5nkJmvrfweMVqSfmt73bpzDKRaT/aTlHaForQuPMn2N2oN1iKSdgf2qH6tBtxGmU01u9ZgET1G0t8PdrxhtH9EBACSTqTM2P4jpUh0ITDf9gYdzrFc30zQQc5Rdf/6WMr3jd+O4PrfBb4B/Gio1xnBNW+jFPPWAz5Lud96l6SXALfZ7qpuHmn3Fx03wGLAfwIWNYw4/xhlGuVXgPMkzRnm5X9Y/Wo06kpsNRL1HX0fliTtTBmR+k5KW5AUqVrrs5R2kK+ntF45h3JzISKiK/XCzc3oCftRbqjPltTshvqpfTfUo6mLgWuAnzBEb3rbz0pq1+yHYedos6XV74urz1R/AAbtRjDejHCB+Vs6Gm50un1k6f8Ftu1rhV6NBL6ecrNlPJhL+e/kc8Bltp+uOU9ErxpJK9aICChrKN0FnEr1/2BJdfyMuFLSkIMKgVmUpWqurzqJzQYuGMasqLOAo4DTJV1A6d718zFmfifwGeBpyveUf5f0U0rR75IxXrvlMpMqOk7SD4FdKP94obRMuJHS1u8k299qOHdDSr/PGcDGwAnARbbvGeZrrQvMsD2qnuiSbrO9TfX49ZTe6gfavkfSzbZ3HM11Y2CSDgT+HVgFeNNYW+9ERLSTpCOrh7tR1n/pG1TxFmCB7WNqCRY9axg31KOfxs9rwzz/85TPlRew7Mz773UyR7tIegOlWLYucDplza1P2b601mAxKEmLaF6MErCJ7Zd0ONKwVTc89u8r3lQ3bC6zPa3eZK0haTXK/+f3pLT8ew64wfYnag0W0WMyczwiRqr6brQf5d7wnpTOWfsB67RqxtEwcwx7ln51vqq8M4ADgNspBauLbC8Z5HVWpbzXjwO/Bb4GnGt7ab/zplfXB5g7nM/51XP+EVgIfL3bBgSkSBUdVy0c9y+2/1htr02ZMfUvwLyBWrtJ2pLyD/UQ21MHuf6alJuDMynV64tsHzvKrDdRRmGuS5kiua3t30uaQukPmjWpWqDJTIS9gXspa7pkJkJEdD1JV1EWTl1aba8AXDHSxU0jYuQkfRq43vZlwzz/m012e6xt+Uaao10k7Wb7uqH2xbKqdo3HUQYcTO7bb3vvDr1+z7bBknQOsCVllK4pN2PmA/cA2P5CfelaQ9KrKG0M9wB2pcwImTAtNCNaYaQ3eSMiGklaCZhOud+7E/Bj22+tIceIBhVW7canAScDm9peaYDz1gAOB46gdEL4NqWAv6XtvRrO+xzl/X+72jUTuMX2R0f7nrpBilTRcZIW2d6yYVuUVn+vlrTQ9rYjXUBc0irAQZRF9DYBLqIUs8bU2kTSRsBHKFMjfw3sD8yjfPE61/apY7l+FA0zEZqyfXanskREjIakXwC72H602v4/lMEMm9abLGL8k7QEWBn4G6XVXd8C7FMmaI5bbW831L5YVtXmew5wLKW995HA/9o+rkOvP6LvP91E0gmDHbd9YqeytIOkXwO/AK6lzFK8KS3/IsYmM8cjYiyqWc5vtv31urMMpppwMYPSYu8RYLbt05qc9z1gM+BblFZ/ixuO3WJ7h4btO4Bt+maSVUWwhX1r0faqrEkVdbhG0g8oLVYA3gzMk7QyZSQNwFUj6PUJ8BBltN7xwLXVQnUHjTWo7V9RZnj1vf6VlOr3cbZ/MtbrR9FYhKr+jjepNn/Rf0prRESXOhlYWM2ogjLauqdvykX0CturjOR8SZtQZvGvXQ2S2gqYbvvTnczRapJ2oczwWEvShxoOTaGMUo/BrWH765I+YHsuMFfS3A6+/ki//3SNXi9CDcPGnWwpFDERVN/zFw95YkQEIGkr23c07Fof+GFNcQYlaWNKYWomZZ3a8yldV+4d5Glftn1lswONBaoGqwGPVo9XHUPcrpGZVNFx1cypN1P6eosyIu3CxlGBo+j1eQzlB8DKwHmUUZA/tr1hJ95TtIakvYCzKW3+RGmzeKTteTXGiogYFkkvB15Tbd5k+8E680SMd5IOt31u9XiZdnaS3mv7ywM8by7wf4Gv2t622nfnQC2n25Wj1SS9lrLW67soiy/3WQJcmnU+ByfpRts7S7oc+BKlzcp3B2sz3uLX79k2WFWrxH8HtqCGVont1q7CdkRERAyPpG80tuaWdC6wFXCn7UPrS/Ziku6lrD91vu1FI3jeq3lx2+lzmpw3kzJI9irKvdM9gY/aPn8Yr7Ec8DLbfx5urk5JkSq63kimgUvakFKpnkFZEPsEyppU97Q9aIyZpAXAobZ/UW1vQpkKu329ySIimuuWm9MRE1FjC7v+7ewGa28n6WbbO/a1ma723WZ7m07maIeq3ccc2wd36jXHC0lvoLRyWxc4nTID7UTbl9SQpafaYNXdKrHdWl3YjoiIiNaQtFq3fVaStNxQM7D7t3CuWifvRSlSXUZZbubagT7TS3oFsCOlSDXoAFlJ51E+nz0LLKDMvPqC7VNG8r7abbm6A8TEIena6vclkv7c8GuJpAEruLaX2l48nB86tu+1/ZlqzasdKf/w/qdlbyLabYW+AhVAVVxcocY8ERFDaWypdXq/Y0cTEe2kAR432270sKSpQN/apgcztpZDo83RcrafBVbv5GuOF7Z/YPtPtu+0/Trb29dRoKqyDPv7T5dYo1oTYqntudVI553rDtVCK9me32/fM7UkiYiImEAkbTXYry79rHSlpPdJWq9xp6QVJe0t6WzKgJ5GBwP7AA/aPgrYGnhJs4tXHcr2oaxLdTGwoqSdBsmzeTVz6kBKAWw94IjRvLF2yppU0TG2d69+b3m//maLCFdTKhcBHxvonBFc/wO2vzjUvhizWyR9nbJQIJR2JwtqzBMRMZSuuTkdMQF5gMfNthu9B/h/wGaSfg/cR/nM0ekc7bJQ0iWU9V+feD6I/b0asvSMtHQbk741ZBdLej2lVeIra8zTaq0ubEdERMTwnDHIMVNa3XWb/SgDVmdLatbC+dQmLZyftP2cpGckTQEeAgZawuZM4Dlgb+AkSmvvCymTNZpZoZqlfyBl7aulkrqutV6KVNFx1Qf839n+W7UG0VbAOWOsfrd7oeEjgf4Fqbc12Rdj827KjaP3U27uzqP88I2I6FbddnM6YiLZTNIdlM8MU6vHVNuDrUtq29MkrQwsZ3tJ9QWy0znaZXXgEcoX1z4GUqQa3NeoWroB2L6jao+SItXQPi1pVeDDvNAq8Zh6I7VUs8L24fVGioiIGP9s71F3hpGy/RTlXuaZI2jhfIuk1SifRxcAfwH6z+Lu8xrb20laWL3eY9X974GcBfwGuB2YJ+nvgaxJFSHpNmAHYH3gcuASYFPb/zSGa7ZloeFqMbpDgT0oBZM+qwDP2p422szRXPWDdVPKzZRf2F46xFMiImoj6a/Ar6huTlePqbY3tL1yXdkixrvqC9aAbN8/wPNetE6UpAWjXQNztDmiu7R6rbIYfxoL23VniYiImGgkbUZZs2ly3z7b59WXqD0krQ9MsX3HAMdvAnYFbq6KVWsBV/R9fu137nLAwba/07BPwCTbXdW6ODOpog7P2X5G0kHAabZP76v+jtYoq9TDcT2llcOawOcb9i8Bmv6wiNGrZtadTanwC1hX0pG25w32vIiIGr2q7gARE9gDQ7Vybmz3XH2x3QJYVdKbGk6bQsOX3XbnaLe0rRu1tHQbIUlfGuy47fd3Kks7SHrrAPsBsH1ORwNFRERMUJKOB/YFNqNMePhH4Fqgp4tUkrYb7JjtW5sc+hJwEfB3kj5DWc/q+GbXqFoIvhf4TsM+04Vra2YmVXRcVfE9Dfg48Ebb90m60/ara47WlKRJwOWZNdV+khYAh9r+RbW9CTB7tCObIyLabTg3njt5czpiIpF0NaX/+qDtnm3PqvYfQOnFPp0yk7/PEuB829d3Ike7SZpL1bauYUZQ137W7haSNqS0dNsVeIyqpZvt39SZq5tJehq4k3Lj4w/0W4vR9tl15GoVSac32w28EVjHdgb9RkREdICkRcA2wK22t5b0Cspn3ek1RxsTSVcNcti29252oBp8tw/lc8lPbd89yGt8AngSmMOy69U+OqrQbZIiVXScpM2BdwE32O5bRO4Q2yfXHG1A1eLTR9j+U91ZxjNJd9jeaqh9ERHdottuTkdMJKNt9yxpF9s31J2jXdK2bmzS0m34JK0BvAU4hDIidw5woe3Hag3WBlVrnMOA44CfAZ8ZqA1PREREtJak+bZ3qga370VZs2nRRBqEJWn1wY4PVHSSdF/z013H2rkDSpEqaiHppcB6fTNmup2k7wA7Az9m2apzT7ew6DaSvkFpsfKtatdhwPK2j6ovVUTEwLrt5nTERDWSds9V3/Z3UNZHfX4mhO2jO5mjXST9D/Be4IKqT/3BwNtt719Hnm4n6UODHbf9hU5l6WWS1gFmAh8CjrP9rSGe0hMkLQ+8DfgwcBPwuV75DhsRETFeSPoqZaDIYcD7gT8Dd9tu2pq3V0jaGDgF2AhYBBxr+/cDnHsf5Z6pgPUoM/8FrEZpP75BR0K3SYpU0XGS3gj8F7Ci7Q0kbQOc1M1TNCUd2WS304e8p6aPXgAAF/RJREFUtSS9BHgPZfaBgHnAmbb/VmuwiIhh6Iab0xExNEnXA9cAC4Bn+/bbvrC2UC00QNu6w2zfX2uwLiXphIbNdwJfbTxu+8TOJuo91XoKM4F/oPy7+rztn9WbauwkvQf4APBT4OT8G4qIiKifpI2AKQOs19RTJF0DnEO5/zkd2MX2m4Z4zlnAJbYvq7b3B6bZ/vAA569EGUS0nu1/rQpjm9r+QQvfypilSBUdV03N3Bu4uqEFySLbW9abbPgkrQvMsH1K3VkiIiIiYvjGe+s7SRtUa74+37aub1/d2bpdY4vEGJqkE4E3AHcD5wM/st11C3GPlqTngIeA/6WMXH7+EGXAYlqSR0REdICk6cDcvmVYJK0G7N5thZaR6v+9RNKttrcb4jkLbG/fb98ttncY4Pw5lEFEb7X96qq72Q3d9n0oC31GHZ6x/afS1vt5XV8tlbQmpef6TGAd4KJ6E40fVRX/48CjwBeArwF7AL8G/sX2zTXGi4iIiPHlB5L+qW/04Th0IbCd7Sca9n0X2H6A8+MFXf+dpMt8ArgX2Lr69dnqO954KeL0dNuciIiIceSkxqKK7ccl/QfQ00UqYLKkbSmfnQBe2rg9wGyxhyUdD5xL+ex6OPDIIK8x1fYhkmZW13xS/W7Kd4MUqaIOd0o6FJhUFSfeD1xfc6amJK0CHAQcCmxCKUxtaPuVtQYbf75Jmd46hdLr/YOUP/c9gC8Dr6kvWkRERIwzHwA+JulvwFJeuKE+pd5YYyNpM2ALYFVJjW1CplDWyototfFexHnAQ7SekaShzomIiIgxW67JvvFQ11hMGazf58GGbVM6kfU3EziBFyZPzKv2DeTpavaUASRNBbpuWZW0+4uOq3phfhzYl3JT4HLgP2w/VWuwJiQ9CcwHjgeutW1J99resOZo40rj9FZJv7K9UbNjEREREdGcpAOAAyn97C9pOLQEON92Vw4Kq5ukRbwwg2oj4Fd9hxgfs4HaZjgFml4u4ki6mjIz8WLbDzTsX5Gyhu6RwFW2Z9USMCIiYoKQNIvSgvcMyue29wFr235rnbl6gaR9KffhNweuAHYD3mb76jpz9ZciVcQgJB0DzABWBs4D5gA/TpGqtRp7rvbvvzqcfqwRERERQ5F0uO1zq8e72b6u4dh7bX+5vnStI2kX2zfUnaNXSPr7wY7bvr9TWXrNeC/iSJoMHA0cRpk19jhlVuIkyk2eM2zfVl/CiIiIiUHSy4BPAdMoA4muAE60/Zc6c3WSpNNsf1DSpTRpUW17+iDPXQPYmfJnd6Pth9uXdHRSpIqOknQkpcXKptWuu4Ev2T6nvlRDk7QhZerkDGBjqmmVtu+pNdg4IemvlFGrAqay7AjWDW2vXFe2iIiIGB8myqAYSWsB7wDWp6ENiu2j68rUzcb7bKB2mkhFHEkrAGsCT9p+vO48ERERE1HVncu2n6w7S6dJ2t72AkmvbXbc9twBnvdd4BvAj2w/186MY5EiVXSMpLcCxwAfAm6lFCC2A04Bvtjthao+krakFKwOsT217jzjQUawRkRERLtJWmh72/6Pm233MknXA9cAC4Bn+/bbvrC2UF1svM8G6pQUcSIiIqJdJG0BzAJeQbmf/DvgKNs/qzNXJ0k6Fphj+7cjfN404CjKTKoLgFm2f96GiGOSIlV0jKQbgRm2f9Nv//qUPvk71xBrUBlZ2Rn5c46IiIh2m0AzqbKe5whMpNlAEREREb1I0rWU9n4/rranAZ+yvXu9yVpH0nRgz2pzru1L+x0/FTgYuA+YDVwwkrZ9klalTLr4OPBb4GvAubaXtiD+mKVIFR0j6We2Nx/psTplZGVn5M85IiIi2m2itBeW9GngetuX1Z2l12Q2UERERET3kXS77a2H2terJH0O2An4drVrJnCL7Y/2O0+UQtYM4ADgdkrB6iLbSwa5/hrA4cARwB+q19kd2NL2Xi19M6OUIlV0jKQFtrcf6bE6ZWRlZ+TPOSIiItptorQXlrQEWBn4G7CUUoSz7Sm1BouIiIiIGAVJFwM3At+qdh0O7Gp7en2pWkfSHcA2fWtGSZoELLS91SDPmQRMA04GNrW90gDnfQ/YjPJnN8v24oZjt9jeoXXvZPRSpIqOaRi9+qJD9MDo1Yys7Iz8OUdEREQ7pL1wRERERETvqWYC/Qdl9g/APOCTth+tL1XrVEWqvfrej6TVgasHKlJJ2pIym+oQ4BFgtu3TBjh3b9tXtid566RIFR0zUUavRkRERET3Ge/thSUdbvvc6vFutq9rOPZe21+uL11EREREROtIOtn2R+rO0QqSZlJmRF1FmcyxJ/BR2+c3nLMxpTA1E3gWOJ9SnLp3GNd/NbA5pWsVALbPaeV7GKsUqSIiIiIiYtwb7+2FJd1qe7v+j5ttR0RERET0MkkP2F6v7hytIukVwI6UItVNth/sd/xeyvpT59teNILrngDsRSlSXQbsD1xr++AWRW+J5esOEBERERER0W62nwLOBM4cp+2FNcDjZtsREREREb1s3Hy+lSRgH8pyOCdJWk/STrbnN5y2Ud+aVYNdp0nr8oOBrSlrXB0laW3gv1v6BlpguboDREREREREdJLtpbYXj6MCFYAHeNxsOyIiIiKiq0maMsCvVevO1mJnArtQWvkBLAHO6HfOlZLeJ2mZ2WOSVpS0t6SzKe3L+3uyKm49I2kK8BCwYWvjj11mUkVXkLSG7UfqzhERERER0aM2qxZdFjC1eky13XVfRCMiIiIihnAXZbBV46ypvu3xNAjrNba3k7QQwPZj1bq5jfajtC6fLalZ6/JTB2hdfouk1YCvAQuAvwDzm5xXq6xJFbWR9Gvgh8C5wCzbm9ccKSIiIiKiJ0n6+8GO276/U1kiIiIiImJ4JN0E7ArcXBWr1gKusL3tAOePqnW5pPWBKbbvGOLUjstMqqiN7amSjgFuAI6qO09ERERERA97oEkP+mUM0Kc+IiIiIiLq8yXgIuDvJH2Gso7U8QOdbHspsHiwC0rabrBjtm8dZda2yEyq6BhJVwDv6BvFKWln4GzgFGBf2/9cZ76IiIiIiF4l6WrgQuBi2w807F8R2J3So/4q27NqCRgREREREU1J2gzYh9LK8Ke27x7j9a4a5LBt7z2W67dailTRMZJus71N9fj1lOLUgbbvkXSz7R3rTRgRERER0ZskTab0qT8MaNan/owB+tRHRERERESHSVp9sOO2H+1UlrqlSBUdU/XXPBNYF3g/sK3t30uaAtyYNakiIiIiIsZutH3qIyIiIiK6laSNbf+yeryT7fl1ZxoLSfcBpsyeWg94rHq8GqWV9wZjuPbGlAkiGwGLgGNt/37ModskRaroGEkbAR8BngZ+DewPzAMOAM61fWqN8SIiIiIiIiIiIiKiC0m6HFiJsn7Tu21vXHOklpB0FnCJ7cuq7f2BabY/PIZrXgOcQ7n3Ph3YxfabWpG3HVKkitpI2haYBiy0/ZO680RERERERERERERE/SStCzxue0nDvvcCXwBm2r6wtnAtJGmB7e377bvF9g5juObzy+5U27fa3m4sOdtp+boDxMRleyGwsO4cEREREREREREREdFVvg/s07ch6d8o669uB3wRGBdFKuBhSccD51La/x0OPDLGa06uJoio2n5p47btW8d4/ZbKTKqIiIiIiIiIiIiIiOgaku6wvVX1+CRgZ+Ag2080m33UqyStDpwA7FntmgecaPvRMVzzqkEO2/beo712O6RIFRERERERERERERERXUPSJcAfgVcCOwGb2/6jpE2B2d3cvi5GJkWq6DhJH7D9xaH2RURERERERERERMTEI2kyMAN4GrgX+G/gD8AWwNG2L68x3phJOs32ByVdSmnztwzb02uIVYsUqaLjmi3UJmmh7W3ryhQRERERERERERER3UnSSsDWwC/G0gqvW0ja3vYCSa9tdtz23E5nqsvydQeIiUPSTOBQYMNqumafVRj7YnARERERERERERERMQ7Z/itwQ905Wuh1kh6aSMWogaRIFZ10PbAYWBP4fMP+JcAdtSSKiIiIiIiIiIiIiOisdYDrJd0HzAYusP1wq19E0nRgz2pzru1LW/0aY5V2f9FRkiYBl9ueVneWiIiIiIiIiIiIiIg6SBKlgDQDOAC4nVKwusj2khZc/3PATsC3q10zgVtsf3Ss126lFKmi46pWf0fY/lPdWSIiIiIiIiIiIiKiu0iaMthx23/uVJZOqCZ3TANOBja1vVILrnkHsI3t5xpeY6HtrcZ67VZKu7+ow1PAIkk/Bp7o22n7/fVFioiIiIiIiIiIiIgucRdgQA37+rYNrFdHqHaQtCVlNtUhwCPAx1p4+dWAR6vHq7bwui2TIlXU4YfVr0aZ0hcRERERERERERER2F637gztJGljSmFqJvAscD6wr+17W/gynwMWSrqKUtzbE+iqVn+Qdn/RBSStC8ywfUrdWSIiIiIiIiIiIiKie0iaAWxo+7OSXgmsbXtB3bnGQtK9lPWnzre9qI2v8wpgR0qR6ibbD7brtUZruboDxMQkaU1J75Y0D7gaWLvmSBERERERERERERHRRSR9GXgdcES166/AWfUlapmNbH98sAKVJA10bDiq5+9DWZfqYmBFSTuN5ZrtkCJVdIykVSS9VdKPgPnARpQK+FTbx9YcLyIiIiIiIiIiIiK6y6623wk8BWD7UWDFeiO1xJWS3idpmbW1JK0oaW9JZwNHjvE1zgR2obQUBFgCnDHGa7Zc1qSKTnqIUpw6HrjWtiUdVHOmiIiIiIiIiIiIiOhOSyUtBxhA0hrAc/VGaon9gKOB2ZI2AB4HJgOTgCuAU23fNsbXeI3t7SQtBLD9mKSuK/ClSBWd9DHKYnBfAc6TNKfmPBERERERERERERHRvc4ALgTWknQi8M/AifVGGjvbT1FmOp0paQVgTeBJ24+38GWWSprECwW+tejCAp9s150hJhhJG1KmGM4ANgZOAC6yfU+twSIiIiIiIiIiIiKiq0jaApgGCPiJ7TtrjtQTJB0GHAJsB5wNHAwcb/uCWoP1kyJV1ErSlpSC1SG2p9adJyIiIiIiIiIiIiK6g6TPA3Nsz687Sy+StBmwD6XA91Pbd9cc6UVSpIqOkSQP8R/ccM6JiIiIiIiIiIiIiPFP0tsps4HWp7T9m9OCtZrGNUmrD3bc9qOdyjIcKVJFx0i6mvKD5GLbDzTsXxHYHTgSuMr2rFoCRkRERERERERERETXqdZTOphSsHq57c1qjtS1JN1HWYdKwHrAY9Xj1YAHbG9QY7wXWb7uADGh7AccDcyWtAHwODAZmARcAZyaKnhERERERERERERE9LMuZTbVOsCv6o3S3fqKUJLOAi6xfVm1vT9lba+ukplUUQtJKwBrAk/afrzuPBERERERERERERHRXSR9hjKD6rfAHODCbmtX160kLbC9fb99t9jeoa5MzWQmVdTC9lJgcd05IiIiIiIiIiIiIqJrLQb2tP3HuoP0oIclHQ+cS2n/dzjwSL2RXiwzqSIiIiIiIiIiIiIioitJWhWYSlk6BgDb19eXqDdIWh04Adiz2jUPOLHbZqKlSBUREREREREREREREV1H0tHAhylrUS0CdgRutL1XnbmiddLuLyIiIiIiIiIiIiIiutExwA7ADbb3kLQFcHzNmbqapNNsf1DSpZQ2f8uwPb2GWANKkSoiIiIiIiIiIiIiIrrRU7aflISkFW3fJWmzukN1uW9Vv/9XrSmGKUWqiIiIiIiIiIiIiIjoRoslrQZcClwu6VHgjzVn6navk/SQ7bl1BxmOrEkVERERERERERERERFdTdI+wKrAD23/re483UrSqcDBwH3AbOAC2w/Xm2pgKVJFRERERERERERERERXkTQJuNX21nVn6TWSBOwJzAAOAG6nFKwusr2kzmz9pUgVERERERERERERERFdR9Js4Fjbv687S6+qin3TgJOBTW2vVHOkZWRNqoiIiIiIiIiIiIiI6EZrAndLugF4om+n7TfVF6l3SNqSMpvqEOAR4GP1JnqxFKkiIiIiIiIiIiIiIqIbnVx3gF4jaWNKYWom8CxwPrCv7XtrDTaAtPuLiIiIiIiIiIiIiIiuIekK2/vWnaMXSbqXsv7U+bYX1Z1nKJlJFRERERERERERERER3WStugP0sI1sPzfYCZLkLpnBlCJVRERERERERERERER0k1UlDbjulO3vdTJMj7lS0oXAxbYf6NspaUVgd+BI4CpgVj3xlpV2fxERERERERERERER0TUkPQJcDKjJYds+usOReoakycDRwGHABsDjwGRgEnAFcIbt2+pLuKwUqSIiIiIiIiIiIiIiomtIutX2dnXn6HWSVgDWBJ60/XjdeZpJu7+IiIiIiIiIiIiIiOgmzWZQxQjZXgosrjvHYJarO0BERERERERERERERESDtw51gqQUssaBFKkiIiIiIiIiIiIiIqKbnC7pfZLWa9wpaUVJe0s6GziypmzRQlmTKiIiIiIiIiIiIiIiuoakycDRwGHABsDjwGRgEnAFcIbt2+pLGK2SIlVERERERERERERERHQlSSsAawJP2n687jzRWilSRURERERERERERERERMdlTaqIiIiIiIiIiIiIiIjouBSpIiIiIiIiIiIiIiIiouNSpIqIiIiIiJ4iaW1J50m6V9ICSTdIOqgLcm0i6TJJv5J0t6TvSFp7kPPXl3RoJzNGRERERER0kxSpIiIiIiKiZ0gS8H1gnu0NbW8PzABe2YJrTxrDcycDPwS+Ynsj268CvgKsNcjT1gfaXqQay/uKiIiIiIhopxSpIiIiIiKil+wNPG37rL4dtu+3fbqkSZJOkXSzpDskvRNA0l6Srpb0XUk/l/TtqtiFpN9I+qSka4G3SJoq6UfVDK1rJG1WnfcWSXdKul3SvCa5DgVusH1pQ66rbN9ZzZi6RtKt1a9dq1NOBvaQdJukYwbJv5ykMyXdJekH1Wytg6tj+0haKGmRpG9IekmT9/URSbf25ZK0saQFLfsbiYiIiIiIGKXl6w4QERERERExAlsAtw5w7O3An2zvWBVrrpN0RXVs2+q5fwCuA3YDrq2OPWV7dwBJPwXeZfuXkl4DnEkpjH0S+Efbv5e0WpPXfjUwUOHnIeAfbD8laWNgNrAD8BHgWNtvqF77XwfIvz1l1tWWwN8BdwPfqGZvzQL2sX2PpHOAdwOnNXlf0yRtY/s24KjqeREREREREbVKkSoiIiIiInqWpDOA3YGngfuBrfpmGQGrAhtXx+bb/l31nNsoRZ++ItWcav/LgF2BC6qJVgAvqX6/Dpgl6TvA90YYcwXgy5K2AZ4FNhngvH0HyL87cIHt54AHJV1VHd8UuM/2PdX22cB7eKFINafh2v8NHCXpQ8AhwE4jfA8REREREREtlyJVRERERET0kruAN/dt2H6PpDWBW4AHgPfZvrzxCZL2Av7WsOtZlv0u9ET1+3LA47a36f+itt9Vzax6PXBbNSvpkX65XjtA5mOAPwJbV6/x1ADnaYD8rx/k/ME80fD4QuAE4EpgQb/sERERERERtciaVBERERER0UuuBCZLenfDvpWq3y8H3i1pBQBJm0haebgXtv1n4D5Jb6meL0lbV4+n2r7J9ieBh4F1Ja1TtQcEOA/YtbGgJGk/SVtSZkQtrmZCHQFMqk5ZAqzSEGGg/NcCb67Wplob2Ks6/+fA+pI2qraPAOYO8N6eqq7/FeCbw/0ziYiIiIiIaKcUqSIiIiIiomfYNnAg8FpJ90maT2lzdxylpd3PgFsl3Ql8lZF3jzgMeLuk2ymzow6o9p8iaVF13XnA7cArgGeqXE8CbwDeJ+mXkn4GvI2yHtWZwJGSbqS0+uub4XQH8Iyk2yUdM0j+C4HfAX37bqKsXfUUZX2pCyQtAp4DzhrkvX0bMHDFIOdERERERER0jMp3vIiIiIiIiBgJSe8FHrB9SQde62W2/yJpDWA+sJvtB0d4jWOBVW1/oi0hIyIiIiIiRihFqoiIiIiIiC4n6WpgNWBF4D9tzxrh8y8CpgJ723645QEjIiIiIiJGIUWqiIiIiIiIiIiIiIiI6LisSRUREREREREREREREREdlyJVREREREREREREREREdFyKVBEREREREREREREREdFxKVJFREREREREREREREREx6VIFRERERERERERERERER33/wEWYBm6tJO7YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby([\"Genres\", \"Category\"]).Rating.median().plot(kind=\"bar\", figsize=(30, 10));\n",
    "# so we can see Ratings for all the genres and category are simillar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets find out the median rating of total df\n",
    "df.Rating.median() # in this case our median is also simillar to category and genres wise grouped data. so lets replace with this data only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_rating = df.Rating.median()\n",
    "df.Rating.fillna(median_rating, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App</th>\n",
       "      <th>Category</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Size</th>\n",
       "      <th>Installs</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Content Rating</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Last Updated</th>\n",
       "      <th>Current Ver</th>\n",
       "      <th>Android Ver</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [App, Category, Rating, Reviews, Size, Installs, Type, Price, Content Rating, Genres, Last Updated, Current Ver, Android Ver, Year]\n",
       "Index: []"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Rating.isnull()] # so finally we replaced all the missing values. Lets check it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "App               0\n",
       "Category          0\n",
       "Rating            0\n",
       "Reviews           0\n",
       "Size              0\n",
       "Installs          0\n",
       "Type              0\n",
       "Price             0\n",
       "Content Rating    0\n",
       "Genres            0\n",
       "Last Updated      0\n",
       "Current Ver       8\n",
       "Android Ver       0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # so finally we got rid of missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for visting my code. If there is anything missing or wrong, please feel free to inform me. Also like my page on fb: https://www.facebook.com/codemakerz"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}