Movie Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Movie Recommendation System </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Movies have been a part of our culture for a long time now. It all started with the plays which dates back to 5th century BC. Short films which where projected during 1890's is considered as the breakthrough in film industry. 20th century can be said as a catalyst for the growth of the industry where the movies and the technologies used evolved. Industry has been through many faces such as \n",
    "- Silent Era\n",
    "- Rise of Hollywood\n",
    "- Golden Era\n",
    "- Appearances of Blockbusters\n",
    "- Modern film industry<br><br>\n",
    "    Now the industry has matured and has turned into a $ 40 Billion dollar industry with USA being the third largest behind China and India in terms of tickets sold.<br>\n",
    "    **USA** has housed world famous production houses namely Warner Bros, Sony Motion Pictures, Walt Disney, Universal Pictures to name a few."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "Movies made by the production houses primarily aims at making there movies Likeable and Profitable. Suppose production houses are interested in answering the following question\n",
    "\n",
    "**What are the factors to be considered to make a successful movie?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "- To analyze the factors affecting the success of the movie like gross, facebook likes, critic reviews, Imdb Score etc\n",
    "- To recommend suitable director, cast, plot based on the choosen genre to make our movie profitable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "- To analyze the mentioned problem [IMDB-5000-Movie-Dataset](https://data.world/popculture/imdb-5000-movie-dataset) was obtained from [data.world](https://data.world)\n",
    "- Our data consist of 5048 movies from the year 1916 to 2016\n",
    "- Each observation represents the individual movie produced with various fields such as title, year, director, cast etc with the total of **5048 rows and 28 columns**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering \n",
    "- Genre column contains multiple values delimited with pipe operator ( ' | ' ) excel was used to make them into individual columns. Top 3 Genres are only considered and named as genre_1, genre_2, genre_3. Now our data set contains 5048 rows and 30 columns\n",
    "<br>These modifications where done using **excel** . After initial modifications the data is loaded onto python using pandas for further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import statsmodels.api as stm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>Color</td>\n",
       "      <td>Joel Schumacher</td>\n",
       "      <td>147.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>673.0</td>\n",
       "      <td>Jami Gertz</td>\n",
       "      <td>967.0</td>\n",
       "      <td>32222567.0</td>\n",
       "      <td>Comedy|Fantasy|Horror</td>\n",
       "      <td>...</td>\n",
       "      <td>339.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2186</th>\n",
       "      <td>Color</td>\n",
       "      <td>Stephen Kay</td>\n",
       "      <td>140.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>Skye McCole Bartusiak</td>\n",
       "      <td>526.0</td>\n",
       "      <td>46363118.0</td>\n",
       "      <td>Drama|Horror|Mystery|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>389.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.85</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Color</td>\n",
       "      <td>Michael Caton-Jones</td>\n",
       "      <td>83.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>Bruce Willis</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>54910560.0</td>\n",
       "      <td>Action|Adventure|Crime|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>227.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>60000000.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>Color</td>\n",
       "      <td>Trey Parker</td>\n",
       "      <td>163.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>Eric Idle</td>\n",
       "      <td>893.0</td>\n",
       "      <td>52008288.0</td>\n",
       "      <td>Animation|Comedy|Fantasy|Musical</td>\n",
       "      <td>...</td>\n",
       "      <td>928.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>21000000.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Color</td>\n",
       "      <td>John Landis</td>\n",
       "      <td>36.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>Jon Tenney</td>\n",
       "      <td>437.0</td>\n",
       "      <td>42610000.0</td>\n",
       "      <td>Action|Comedy|Crime|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>55000000.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.85</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color        director_name  num_critic_for_reviews  duration  \\\n",
       "3331  Color      Joel Schumacher                   147.0      97.0   \n",
       "2186  Color          Stephen Kay                   140.0      89.0   \n",
       "753   Color  Michael Caton-Jones                    83.0     124.0   \n",
       "2133  Color          Trey Parker                   163.0      81.0   \n",
       "981   Color          John Landis                    36.0     104.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes           actor_2_name  \\\n",
       "3331                    541.0                   673.0             Jami Gertz   \n",
       "2186                     19.0                   349.0  Skye McCole Bartusiak   \n",
       "753                     105.0                   460.0           Bruce Willis   \n",
       "2133                    406.0                   406.0              Eric Idle   \n",
       "981                     644.0                   174.0             Jon Tenney   \n",
       "\n",
       "      actor_1_facebook_likes       gross                            genres  \\\n",
       "3331                   967.0  32222567.0             Comedy|Fantasy|Horror   \n",
       "2186                   526.0  46363118.0     Drama|Horror|Mystery|Thriller   \n",
       "753                  24000.0  54910560.0   Action|Adventure|Crime|Thriller   \n",
       "2133                   893.0  52008288.0  Animation|Comedy|Fantasy|Musical   \n",
       "981                    437.0  42610000.0      Action|Comedy|Crime|Thriller   \n",
       "\n",
       "      ... num_user_for_reviews language  country  content_rating      budget  \\\n",
       "3331  ...                339.0  English      USA               R         NaN   \n",
       "2186  ...                389.0  English      USA           PG-13  20000000.0   \n",
       "753   ...                227.0  English      USA               R  60000000.0   \n",
       "2133  ...                928.0  English      USA               R  21000000.0   \n",
       "981   ...                114.0  English      USA               R  55000000.0   \n",
       "\n",
       "      title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "3331      1987.0                  847.0        7.2          2.35   \n",
       "2186      2005.0                  392.0        4.1          1.85   \n",
       "753       1997.0                13000.0        6.3          2.35   \n",
       "2133      1999.0                  795.0        7.8          1.85   \n",
       "981       1994.0                  289.0        5.5          1.85   \n",
       "\n",
       "     movie_facebook_likes  \n",
       "3331                19000  \n",
       "2186                 1000  \n",
       "753                     0  \n",
       "2133                    0  \n",
       "981                   669  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data into the dataframe\n",
    "\n",
    "df = pd.read_csv('movie_metadata.csv')\n",
    "\n",
    "# Displaying 5 samples of the dataset\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4993.000000</td>\n",
       "      <td>5028.000000</td>\n",
       "      <td>4939.000000</td>\n",
       "      <td>5020.000000</td>\n",
       "      <td>5036.000000</td>\n",
       "      <td>4.159000e+03</td>\n",
       "      <td>5.043000e+03</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "      <td>4.551000e+03</td>\n",
       "      <td>4935.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>4714.000000</td>\n",
       "      <td>5043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>140.194272</td>\n",
       "      <td>107.201074</td>\n",
       "      <td>686.509212</td>\n",
       "      <td>645.009761</td>\n",
       "      <td>6560.047061</td>\n",
       "      <td>4.846841e+07</td>\n",
       "      <td>8.366816e+04</td>\n",
       "      <td>9699.063851</td>\n",
       "      <td>1.371173</td>\n",
       "      <td>272.770808</td>\n",
       "      <td>3.975262e+07</td>\n",
       "      <td>2002.470517</td>\n",
       "      <td>1651.754473</td>\n",
       "      <td>6.442138</td>\n",
       "      <td>2.220403</td>\n",
       "      <td>7525.964505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>121.601675</td>\n",
       "      <td>25.197441</td>\n",
       "      <td>2813.328607</td>\n",
       "      <td>1665.041728</td>\n",
       "      <td>15020.759120</td>\n",
       "      <td>6.845299e+07</td>\n",
       "      <td>1.384853e+05</td>\n",
       "      <td>18163.799124</td>\n",
       "      <td>2.013576</td>\n",
       "      <td>377.982886</td>\n",
       "      <td>2.061149e+08</td>\n",
       "      <td>12.474599</td>\n",
       "      <td>4042.438863</td>\n",
       "      <td>1.125116</td>\n",
       "      <td>1.385113</td>\n",
       "      <td>19320.445110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.180000e+02</td>\n",
       "      <td>1916.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>5.340988e+06</td>\n",
       "      <td>8.593500e+03</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>371.500000</td>\n",
       "      <td>988.000000</td>\n",
       "      <td>2.551750e+07</td>\n",
       "      <td>3.435900e+04</td>\n",
       "      <td>3090.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>2.000000e+07</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>166.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>194.500000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>6.230944e+07</td>\n",
       "      <td>9.630900e+04</td>\n",
       "      <td>13756.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>4.500000e+07</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>813.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>640000.000000</td>\n",
       "      <td>7.605058e+08</td>\n",
       "      <td>1.689764e+06</td>\n",
       "      <td>656730.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>5060.000000</td>\n",
       "      <td>1.221550e+10</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>137000.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>349000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_critic_for_reviews     duration  director_facebook_likes  \\\n",
       "count             4993.000000  5028.000000              4939.000000   \n",
       "mean               140.194272   107.201074               686.509212   \n",
       "std                121.601675    25.197441              2813.328607   \n",
       "min                  1.000000     7.000000                 0.000000   \n",
       "25%                 50.000000    93.000000                 7.000000   \n",
       "50%                110.000000   103.000000                49.000000   \n",
       "75%                195.000000   118.000000               194.500000   \n",
       "max                813.000000   511.000000             23000.000000   \n",
       "\n",
       "       actor_3_facebook_likes  actor_1_facebook_likes         gross  \\\n",
       "count             5020.000000             5036.000000  4.159000e+03   \n",
       "mean               645.009761             6560.047061  4.846841e+07   \n",
       "std               1665.041728            15020.759120  6.845299e+07   \n",
       "min                  0.000000                0.000000  1.620000e+02   \n",
       "25%                133.000000              614.000000  5.340988e+06   \n",
       "50%                371.500000              988.000000  2.551750e+07   \n",
       "75%                636.000000            11000.000000  6.230944e+07   \n",
       "max              23000.000000           640000.000000  7.605058e+08   \n",
       "\n",
       "       num_voted_users  cast_total_facebook_likes  facenumber_in_poster  \\\n",
       "count     5.043000e+03                5043.000000           5030.000000   \n",
       "mean      8.366816e+04                9699.063851              1.371173   \n",
       "std       1.384853e+05               18163.799124              2.013576   \n",
       "min       5.000000e+00                   0.000000              0.000000   \n",
       "25%       8.593500e+03                1411.000000              0.000000   \n",
       "50%       3.435900e+04                3090.000000              1.000000   \n",
       "75%       9.630900e+04               13756.500000              2.000000   \n",
       "max       1.689764e+06              656730.000000             43.000000   \n",
       "\n",
       "       num_user_for_reviews        budget   title_year  \\\n",
       "count           5022.000000  4.551000e+03  4935.000000   \n",
       "mean             272.770808  3.975262e+07  2002.470517   \n",
       "std              377.982886  2.061149e+08    12.474599   \n",
       "min                1.000000  2.180000e+02  1916.000000   \n",
       "25%               65.000000  6.000000e+06  1999.000000   \n",
       "50%              156.000000  2.000000e+07  2005.000000   \n",
       "75%              326.000000  4.500000e+07  2011.000000   \n",
       "max             5060.000000  1.221550e+10  2016.000000   \n",
       "\n",
       "       actor_2_facebook_likes   imdb_score  aspect_ratio  movie_facebook_likes  \n",
       "count             5030.000000  5043.000000   4714.000000           5043.000000  \n",
       "mean              1651.754473     6.442138      2.220403           7525.964505  \n",
       "std               4042.438863     1.125116      1.385113          19320.445110  \n",
       "min                  0.000000     1.600000      1.180000              0.000000  \n",
       "25%                281.000000     5.800000      1.850000              0.000000  \n",
       "50%                595.000000     6.600000      2.350000            166.000000  \n",
       "75%                918.000000     7.200000      2.350000           3000.000000  \n",
       "max             137000.000000     9.500000     16.000000         349000.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To understand the data we can use describe function which gives important metrics for all numerical values\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5043 entries, 0 to 5042\n",
      "Data columns (total 28 columns):\n",
      "color                        5024 non-null object\n",
      "director_name                4939 non-null object\n",
      "num_critic_for_reviews       4993 non-null float64\n",
      "duration                     5028 non-null float64\n",
      "director_facebook_likes      4939 non-null float64\n",
      "actor_3_facebook_likes       5020 non-null float64\n",
      "actor_2_name                 5030 non-null object\n",
      "actor_1_facebook_likes       5036 non-null float64\n",
      "gross                        4159 non-null float64\n",
      "genres                       5043 non-null object\n",
      "actor_1_name                 5036 non-null object\n",
      "movie_title                  5043 non-null object\n",
      "num_voted_users              5043 non-null int64\n",
      "cast_total_facebook_likes    5043 non-null int64\n",
      "actor_3_name                 5020 non-null object\n",
      "facenumber_in_poster         5030 non-null float64\n",
      "plot_keywords                4890 non-null object\n",
      "movie_imdb_link              5043 non-null object\n",
      "num_user_for_reviews         5022 non-null float64\n",
      "language                     5031 non-null object\n",
      "country                      5038 non-null object\n",
      "content_rating               4740 non-null object\n",
      "budget                       4551 non-null float64\n",
      "title_year                   4935 non-null float64\n",
      "actor_2_facebook_likes       5030 non-null float64\n",
      "imdb_score                   5043 non-null float64\n",
      "aspect_ratio                 4714 non-null float64\n",
      "movie_facebook_likes         5043 non-null int64\n",
      "dtypes: float64(13), int64(3), object(12)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#To understand about the missing values in the data\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gross                        884\n",
       "budget                       492\n",
       "aspect_ratio                 329\n",
       "content_rating               303\n",
       "plot_keywords                153\n",
       "title_year                   108\n",
       "director_name                104\n",
       "director_facebook_likes      104\n",
       "num_critic_for_reviews        50\n",
       "actor_3_name                  23\n",
       "actor_3_facebook_likes        23\n",
       "num_user_for_reviews          21\n",
       "color                         19\n",
       "duration                      15\n",
       "facenumber_in_poster          13\n",
       "actor_2_name                  13\n",
       "actor_2_facebook_likes        13\n",
       "language                      12\n",
       "actor_1_name                   7\n",
       "actor_1_facebook_likes         7\n",
       "country                        5\n",
       "movie_facebook_likes           0\n",
       "genres                         0\n",
       "movie_title                    0\n",
       "num_voted_users                0\n",
       "movie_imdb_link                0\n",
       "imdb_score                     0\n",
       "cast_total_facebook_likes      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To better understand the count of the missing values\n",
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Gross and Budget are important fields with many missing values.. Lets look into the distribution of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 48468407.53\n",
      "Median: 25517500.00\n"
     ]
    }
   ],
   "source": [
    "# Distribution of the Gross\n",
    "sns.distplot(df.loc[df.gross.isna()==False,'gross'],color='g')\n",
    "plt.title('Distribution of Gross')\n",
    "plt.xlabel('Gross in USD')\n",
    "plt.ylabel('Frequency in Log')\n",
    "plt.show()\n",
    "print(f'Mean: {df.gross.mean():.2f}')\n",
    "print(f'Median: {df.gross.median():.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 39752620.44\n",
      "Median: 20000000.00\n"
     ]
    }
   ],
   "source": [
    "# Distribution of the Budget\n",
    "sns.distplot(df.loc[df.budget.isna()==False,'budget'],color='g')\n",
    "plt.title('Distribution of budget')\n",
    "plt.xlabel('Budget in USD')\n",
    "plt.ylabel('Frequency in Log')\n",
    "plt.show()\n",
    "print(f'Mean: {df.budget.mean():.2f}')\n",
    "print(f'Median: {df.budget.median():.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is clear that both the distribution are highly skewed to the right and imputing median will be a better approach.\n",
    "- Since the data spans over a period of 100 years imputing values with the median of the entire series will be wrong as the money value changes over time.\n",
    "\n",
    "**Imputing with the median of the corresponding year will be a better approach**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping by title_year and imputing gross and budget with median.\n",
    "df.loc[df.gross.isnull(), 'gross'] = df.groupby('title_year')['gross'].transform('median')\n",
    "df.loc[df.budget.isnull(), 'budget'] = df.groupby('title_year')['budget'].transform('median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                         19\n",
       "director_name                104\n",
       "num_critic_for_reviews        50\n",
       "duration                      15\n",
       "director_facebook_likes      104\n",
       "actor_3_facebook_likes        23\n",
       "actor_2_name                  13\n",
       "actor_1_facebook_likes         7\n",
       "gross                        130\n",
       "genres                         0\n",
       "actor_1_name                   7\n",
       "movie_title                    0\n",
       "num_voted_users                0\n",
       "cast_total_facebook_likes      0\n",
       "actor_3_name                  23\n",
       "facenumber_in_poster          13\n",
       "plot_keywords                153\n",
       "movie_imdb_link                0\n",
       "num_user_for_reviews          21\n",
       "language                      12\n",
       "country                        5\n",
       "content_rating               303\n",
       "budget                       100\n",
       "title_year                   108\n",
       "actor_2_facebook_likes        13\n",
       "imdb_score                     0\n",
       "aspect_ratio                 329\n",
       "movie_facebook_likes           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping values with both gross and budget that is not available\n",
    "\n",
    "df.drop(df.index[(df.gross.isna() == True) & \n",
    "    (df.budget.isna() == True)], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4946, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aspect_ratio                 309\n",
       "content_rating               264\n",
       "plot_keywords                140\n",
       "num_critic_for_reviews        42\n",
       "gross                         33\n",
       "actor_3_facebook_likes        19\n",
       "actor_3_name                  19\n",
       "num_user_for_reviews          15\n",
       "color                         15\n",
       "facenumber_in_poster          13\n",
       "duration                      12\n",
       "actor_2_name                  11\n",
       "title_year                    11\n",
       "director_facebook_likes       11\n",
       "actor_2_facebook_likes        11\n",
       "director_name                 11\n",
       "language                       9\n",
       "actor_1_name                   7\n",
       "actor_1_facebook_likes         7\n",
       "budget                         3\n",
       "country                        1\n",
       "movie_facebook_likes           0\n",
       "genres                         0\n",
       "movie_title                    0\n",
       "num_voted_users                0\n",
       "movie_imdb_link                0\n",
       "imdb_score                     0\n",
       "cast_total_facebook_likes      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping these 97 observation actually reduced the number of null values in director_name and director_facebook_likes. Since the number of Null Values is within 1% of the total observation for prime factors we can proceed with our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 1 : Is Gross of the movie related to budget ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the year 2009.0 there were about 260 movies released, which maximum as per our data\n"
     ]
    }
   ],
   "source": [
    "print(f\"In the year {df.groupby('title_year')['gross'].count().idxmax()}\",end='')\n",
    "print(f\" there were about {df.groupby('title_year')['gross'].count().max()} movies released, which maximum as per our data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting regplot for 2009\n",
    "sns.regplot(df.loc[df.title_year==2009,'budget'],df.loc[df.title_year==2009,'gross'],scatter_kws={'alpha':0.3})\n",
    "plt.title('Gross vs Budget for the 2009')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that there is linear relationship between the gross and the budget. This is only for the year with maximum number of movies for the given year. In order to simulate the time series over all the years we use **Tableau**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEaCAYAAAD+E0veAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eZQl11ng+ftif1u+93KpqqzKWlWS0GKEZXnFNjKbZYOxD+1mLAO9QGOa0zTLDNPN6elzoM/p7oGmuxmPYZp2D2CGGdtjTMNgLIQNsmzjRZYsq6zSUiqp1qzKPd/+Yo87f0RkVmZWZlbWkpmVpfido6N6ETdu3Ih87373fqsopcjJycnJyQHQtnsAOTk5OTk3D7lQyMnJyclZJBcKOTk5OTmL5EIhJycnJ2eRXCjk5OTk5CySC4WcnJycnEVyoZCzLYjI4yLyT7Z7HLcCImKLyPMisme7x7IaIvKgiIwv+XxGRL4/+/cviMhvbN/oclaSC4WcDZH9kF0R6YpIQ0Q+KyL7b4Jx/SMR+bstuteoiPw3EbmYvYdTIvIxEfmOrbj/OnwI+JJSanLhgIi8RUQeE5GOiLRE5DMicvc2jnEtPgr8hIjs2u6B5KTkQiHnaniPUqoMjAJTwEe2eTxbhogMAV8FisDbgApwP/BF4AfWuMbYouH9LPDHS+77ZuBzwP8H7AUOA8eAr4jIkRt5Y0m55nlEKeUBfwX8gxs3qpzrIRcKOVdN9kP+NLC48lypDlq5gheRHxCRF7NV6+8AsuScLiL/SURmReS0iPy8iKiFSVVEqiLy+yIyISIXROTfZtfcBfwe8OZs5d5cOVYR+YCIPLXi2C+LyF9k/353pnrpZH3/yhqP/ctAG/hJpdQrKqWplPpDpdRHsr4OZeP+aRE5BzyWHf8REXlORJrZe7pryVj+ZXbfjoicEJHvy46/QUSeEpG2iEyJyH9ebVAicgC4DXhiyeH/APxfSqkPK6U6Sql5pdS/Br4O/Hp23Qsi8sNL+jGy939/9vlNIvLVbMzHROTBJW0fF5F/JyJfAfrAERH5x1mfnWwH9bNrvMfVeBz4oaton7OJ5EIh56oRkSLwP5BOMhtpPwz8KfCvgWHgFeC7lzT5GeBdwHeRrr7ft6KLPwIi4CjwWuAHgX+ilHoB+KfA15RSZaVUbZXb/wVwp4jcvuTYB4GPZ//+feBnlVIV4F6yiXwVvh/4M6VUcuUn5nuAu4B3isgdwCeAXwJGgEeAz4iIJSJ3Aj8PvD67/zuBM1kfHwY+rJQaIJ30P7XGvV4DnFJKRbD4t3kL8CertP0Ul3Y1nwAeXnLuncCsUuppEdkHfBb4t8Ag8CvAn4rIyJL2P0mqtqoAZ4Fp4IeBAeAfA7+9IGA2wAvAfRtsm7PJ5EIh52r482w13iadXH5rg9e9G3heKfVppVQI/G/A5JLzP0Y6AY4rpRrAouFRRHaTCoxfUkr1lFLTwG8DH9jIjZVSfVI1ysNZf7cD30EqLABC4G4RGVBKNZRST6/R1fDSMWer/2a2Mv7cira/no3VJRWen1VKfT579v8IFEgn7hiws/ubSqkzSqlXlozrqIgMK6W6Sqm1BHAN6Cz5PEj6u55Ype1E9hyQCsUfyYQILBeUPwE8opR6RCmVKKU+DzxF+ndc4GNKqeeUUpFSKlRKfXbJDuqLpOqrt60x5pV0gOoG2+ZsMrlQyLka3petxm3SFe4XZWMeL3uB8wsfVJqF8fxa51f8+yBgAhPZJNwE/itwNYbJj3NpVfxB4M8zYQHw90gnu7Mi8sVMH78ac6S2lIVn+IvsXfwyYK1ou/LZzi65LsnO71NKvUy6g/h1YFpEPikie7OmPw3cAbwoIk8uVfWsoEG6Wl/6OVk61iWMArPZOF4mXaG/JxMMP8IloXAQ+PsL7zt7529d0efSZ0RE3iUiXxeR+az9u7kkgK5EBWhtsG3OJpMLhZyrRikVK6X+O+lK963Z4R6pEXaBpcJiAlj0VBIRWfo5Oz+25PPSc+cBHxhWStWy/waUUvcsDGcDQ/4cMCwi30UqHBYmP5RSTyql3ksqZP6ctdU0fwu8b4NG1aVjukg6yQLLnv1Cdv+PK6XemrVRwG9mx08qpR7OxvWbwKdFpLTKvb5NqtM3sut6wNeAv79K2x/LnmOBBRXSe0l3ci9nx88Df7zkfdeUUiWl1FLX0cVnFBGbVD34H4HdmbB8hCV2oytwF6khPOcmIBcKOVdN5nHyXqBOutoEeAb4UREpishR0pXuAp8F7hGRH80mr19gudD4FPCLIrJPRGrAv1w4oZSaIJ3U/5OIDIiIJiK3icj3ZE2mgDERWblaZ0kfEalh/LdI1Sufz57DEpEfF5Fqptppkwq61fjP2fP+cXZ/EZEKqR1kPT4F/JCIfJ+ImMD/RCrkvioid4rI92aTqge4C/cXkZ8QkZFsZ7FgQL9sbEqpceAk8IYlh38V+IeSxgBURKQuIv8WeDPwb5a0+ySpfebnWCIogf+bdAfxTkkN+o6ksQZLBfdSLNLd4wwQici7sn43yveQeiDl3ATkQiHnaviMiHRJJ89/B/xDpdRz2bnfBgLSSfqPgP9n4SKl1CzpyvU3SNUwtwNfWdLvfyOd+L8NfIt0lRlxaRL8B6QTz/Ok6pFPc0mV8RjwHDApIrPrjP3jpMbiP1kwymb8JHBGRNqkRuufWO3i7BneRDp5/x2pHvwZUtXHz611U6XUiazPj5Cqbt5D6tobkE6kv5EdnyTdFfyr7NKHgOey9/1h4AOZ19dq/NfsORbu+XekhuMfJd2FnSU10L9VKXVySbsJ0l3FW4D/d8nx86S7h39FOtGfB/5n1pgvlFIdUkH/KdK/zwe5ZLNZFxFxSFVNf7SR9jmbj+RFdnJuNrKV5u8ppQ5esXHOgvrmW8D3ZRP9jkFE/jmwXyn1L7Z7LDkpuVDI2XZEpAC8g3S3sJtUP/11pdQvbevAcnJeheRCIWfbybxfvkjqKuqS2iB+USnV3taB5eS8CsmFQk5OTk7OIrmhOScnJydnkVwo5OTk5OQsslVZHDeF4eFhdejQoe0eRk5OTs6O4pvf/OasUmpktXM7WigcOnSIp5566soNc3JycnIWEZGza527adRHInK3iHxKRP6LiLx/u8eTk5OT82pkU4WCiPyBiEyLyPEVxx+SNHf8yyLyq9nhdwEfUUr9HHnBjZycnJxtYbN3Ch8jDddfRER04HdJhcDdwMOSlgn8Y+ADIvJbwNAmjysnJycnZxU2VSgopb4EzK84/AbgZaXUqSz/yyeB9yqlppVS/4w0mdd6OWxycnJycjaJ7TA072N5LvZx4I0icog0AVeJdYq3iMiHSCs+ceDAgU0bZE5Ozs3BRNPl2HiT+V7AYMnivrEao7XCdg/rlmU7hMJqOdaVUuoM2WS/Hkqpj4rIBPAey7Jed6MHl5OTc/Mw0XT5/PNTVByD4bJNz4/4/PNT/MDdu3PBsElsh1AYZ3kRlTHSQiQbRin1GeAzDzzwwM/cyIHl5ORsDyt3A6MDDhNtjy+9NINtaNw1OoAmQsUxATg23syFwiaxHULhSeB2ETlMWn3qA6T51zeMiLwHeM/Ro0c3YXg5OTlbycrdwIWGy2eemeB1h2rMdT0uNH0ee3Gag0Ml3np0iEPDZWa7/nYP+5Zls11SP0FaxONOERkXkZ/OCpz8PPDXpFW7PrWkUMuGUEp9Rin1oWo1r/Wdk7PTOTbepOIYVBwTTYSZrketZPDchRYvz/QJ45iqYzLb8fncc1M8d7HJYGnNQns518mm7hSyGrOrHX+EtLrWNZHvFHJybh3mewHDZXvx82TLpecni8IiUSCS/meZGk+dafKB1+f1lzaLmyaiOScn59XJYMmi56cVUud7PjOdgI4fAlAwdDQRgigGgQHboGQbuT1hE9mRuY9yQ3NOztrc7C6cqxmVj423ADg102O4bHN+3qVaMIhVgqFp9IKQkqbxwmSHWsHk2LkG9x2ob/OT3JrsSKGQk5OzOsfONfjEk+eJE8VQycILY6bb/g114dyI0FmrzVKjsibCE6fmmOsF7Ks6vORHPHuhxZ4Bh8PDDs+c93ll3kVUQhBDxdER0agVDD78Nyf5xe+/PRcMm8COFAq5TSEn53Immi6fePIcuiYMlWy8MOHl6R5Hd5VumAvnRuIGVmvzsa+cpuuHvDTdxdQ0qo7ObD/C1jX6Yci3zjZwbJ2ioTHV8fjWeZeSqVPQhZYHCZDEinpJp+JYdPyQTz11LhcKm8COFAq5+ign53KOjTezHYKNiFCwdACm2z6Oqd+weyx4CgFUHJNGP+B3HnuJnp8w1/fxgpjdFZt6yQaBfhDx/IU2IwM2OsJUx+fl6YDdAw7tWNHoBSRKUXIMpto+s72AKAEIF++rAZahUy859MOYwYLJyenuDXmmnOXsSKGQk5NzOakXj4UXxRTM9KftmBrTHZ/vOlC7gfe45Cl0aqbD55+fYrzhUikY1Aomk22PM7M9BKFoa3T8GEMgShLcSCEoolhxbr5P2THpBRGWLgRhxNyiQFhOAsz2Q/y4TZTAgGOwp2pf3jDnutmRQiFXH+XkXM5gycIPk2wFHeEYOk03QNeE+8auXiisZhdY8BSqOCbzPZ8vn5yl7YWYumBpGq1+RLMf0PMTTA2CWKcfxkQxdP2QgYKFHyV0vIhQQRAmuLGiDzTc+Ipj8qMEpWC+GxAliv/yhZd432v331SG9J3OjhQKufooJ+dy7hurMd2e4vZdZaY7LtMdD10THn79gWWT5rFzDR45PsFU22P3gMO77x29TDe/mkH4T785TtnWaXsRBwaLNHsBEy2PVj/ENIQwSej6MV6YoIBYQS+ICbOVfxiDrml0vZBQpcfcOP2H2uAzxjFoGpiGRtnS+cbpBiXbynMh3UB2pFDIyclJuXw1X2Wi7WGbGvftr1/mGXTsXIOPfuk0tZLB3mqBlhfy0S+d5kNvZ5lgePzENKdnu7TdiOmuT71g0vUj/CimVrSYaXs8Pd6ibGkMlS3muj5nZnsgECZp1ksRSFRqD0iAIIHJpkd0Hc+rAFMTBhyDWEE/jKk4Rp4L6QayI4VCrj7KebWwnvvnal4+x8Zb666aHzk+gWlAoxtyoelSNAwsU3jk+MSiUJhounz1lTlGKhb9ICKIYp4+38PUoGgZjA4UaEYJtw2XSBQ0+gH9ICZOWMyBrLgkHJbuAq5HICz0G8SKnh/hmDq1gkXJNvJcSDeQHRnRnOc+ynk1sDDpu0HMcNnGDWI+//wUE00XuDxnUMUxF1fNK/t59PgEH3/iLF8/NceFhkuYKIqmQZgo5roBp2d7i+0+8thJul5Iz4tpuiFdLyJOEhIFhqZxseUy1XIZqdhMtT3muj6GLmjZzsBYkhx/o2qhjaKASEE/TIgSxesO1uj5UZ4L6QayI3cKOTmvBlZz/1w4PlorMN8L0EQ4eW6ejhdRcQwODJZww0sG25W7iTiBOTfA1DW8KCGIEvpBhCbw7x95geGyxVzXw48TvnZ6Di+I0jQTcULfj9hXddBEI8i8h3YPOHT9EMfUUltCoriyufj6MQRGqzYjFYeOF/GmI3kF3xtFLhRycm4ilqqLnrvY4jX7qlQwF88vVZUI8I3T89RLJgOOiRfFfOP0PK8/dMk2sFKw7K8VePZii1dmu+wuO/hxTLMfYutCwRTcIOHlmT4VW8fUhal+iGiCqSlCBccutilbOrYutF3B0sELErwwIkjTE7GKR+kNRQNqJQvT0ClYOm86MpTbE24guVDIydlENuLps8DKVb2lazx5psEbDw8yWLKZ7wW8MNEiiBMePT5Bs+8jAl034oLn0vYikiSh2S8u9pnuJuBbLzU4O9dnpuMTxzFuGDPZ8VDAgK3R6Id87vlpdBE0EiabMQohAVSk8IGCIVga9PyI2SDB1KHsmMRJKhAUN15dtBqGDprASMW+6fI63QrkQiEnZ5PYqKfPYvsVq/q7Rqt8/dQcL0y0uXPPAN84PY8IvP5QHTeIeWGyS9XReepMEz9OqDom++oOT59r8YknzqAQnrvQ5ELLxQ8VJcugZOucmospWRpjtQIvTraYC9MVvqkr4kQtuouunOL9WOFH8aJ6KIkhdsNFl9OtomBojFYL1Atm7nW0CexIoZB7H+XsBB45PkGtZFAvppG3C/9f6umzlJXRwoMlizccHuTrp2b57LMXiWLF0ZHyolHZMTSeON1gtFqkZOuEseJiy8cLIz733BT76kVOz/Z5ZbrLQNGkrYd0/QhbF7ww4bmLbeLkUq0CP1bIahXUM+IV24AFD6OtZmTA5t59VRKlmO8FWz+AW5wdKRTy4LWdy82e1vlGMtX22Ftd/mxVx+RiK/UeWvkuBLUYLbyAG8Q4psGeAQ3H1JhoeLw41Wa4aPHyTJfxhocfxuytOeiGzoX5HlECSQL7B4sEcYIfJcx3fZQIPS9EqdRLqGBpeKFK00pkE77aCv3PdTDkCGP1MqahkSQq9zraBHakS2rOzuRKLpa3GrsHHFpeuOxYywvZPeCs+i5meyHj8306XkiiFB0v5MRUmzt2lymYOs9d7DDT9ZhqeXzl1Bwz3YCqk+4QXprucX62lwZ3GRq2oXNmrk8/iDF0oeNF+GFMGKdBZJGCjp9sy0r/eigXHIIootELGSzb15S+I2d9cqGQs2Vs1K/+VuHd947S7EU0+j5JktDo+0w2PWoFk488dpLTs13COFl8F2O1AsMVm4KlM9v1KVg6BwaLjNWL9PyI6bbHfD9kvhcQRglRlGBbBvWSha2nP2VNS+MFypZGqx8w1XJp9EKCBPpBsumeQZuFKeAYgqYJYax4/aE6779/7JbdZW4nO1J9lLMzWakzBzY9GvV61VVLr0/V7QqFrNrXam0HSwbfHm8RxDEjFYd9tQJ7qgUuNF0E4ZnzLb5rf5XBkk3JNnDDmPvGajx+YpovvTTDy9Md/ub5SU7P9IlUgiGCHyXoAkqE+W7ArgGHVj+g7UfYhk7fC3nmQoskSYgTFg3DN7lmaF1CBXtKFj/7PUdIFDz8xrxG82Zx0wgFETkA/A4wC7yklPqNbR7Sq5LN1PkvzbC5wGZGo26kIMxGr9dEePT4JBeafYqWxmDR5ptnG/zUdx9etaLYN07P44YxpibcO1YlSSCKY4JYEcYJAwUTP0oomDpnZvsMltLxtfsB/+tfvcD5OZeirTHT8ZhuB7hhgiHgkqp+EkAkIUqEUzNd/DDC0HT2V22OTwZEsUqNyJvyZrceAfYOFrCz2ISczWNT1Uci8gciMi0ix1ccf0hETojIyyLyq9nhO4DPKqV+Crh7M8eVszqbrfO/b6xGx4uW6cw7XrRpeuHrVVctvf7J03OcmeuhCWiikag0cOzPvzV+Wdtz8z3qJZMwiukFEfWiTdEymOoGVB2TM7N9Dg2VcYMERULbC+l4IeNNl6fONTg51cENQ87M9ZntBhQsHU0gUJc8gGLAjSBOFIYmKCXUiwZnWx4qUTimjqalGUVvBRTQ6Aab+n3JSdnsr8zHgIeWHhARHfhd4F2kk//DInI38C3gAyLyGPCFTR5Xzipsts5/tFbgB+7evUxnvpkpj+d7ASV7+Wa4ZBsbdmNcev0Lkx1Klk7ZNgljRdkxqRYMvvrK3GVtO15ayyBMEqJsFndMDVGAKDp+anw2NHhhosO5+R5eGGEITDRcWm5IP0ho9VPbAYCuCYaWBm1ppGkeNNLJ0g0iojghSqDrpn37YUyUbI/L6GYx2fa4b6ya2xE2mU1VHymlviQih1YcfgPwslLqFICIfBJ4L2ntvV/Lrvk08IebObacy9kKnf9orbBlP+rrVVctvT5MEgqGTpQoLCNdS5maxmwv4NHjExy/0MLOgqqmOz6nZrqX0lFcEHQRhstWVn8g4MWJ9mICuaJp8JfPTjLZ7NEPElBCIoowgSCGsB+CSiN5Y5XuEiT7TymIlUIBUx2PYCsSD20DBukO7dh4i10DTi4YNpHtsCnsA84v+TwOvBH4PeDXReSDwJm1LhaRDwEfAjhw4MDmjfJVyFbr/Nfiau0aa7W/b6zG55+fAlLh1vOjq0qetvT6vQMO5+ZdLENnb9UmiBJmuj6WoeEGMd85VuOLL03z9Lkm++sObhDT9dPcD/1CSBgrptouvWyXECUKP1ZYukYYWRQdg9luSNUx6YUxgR+joRaL1eiAii+lntaBhf1OECqUQHSLCgRId1qOpee1E7aA7dA4rmb7Ukqp40qp9yul/qlS6lfWulgp9VHg3wBPW1YeuHIj2Wqd/2pcrV1jvfbXq65aev09e6uUbYMBRydRCi+MSBR899Ehwjjh3FyfRi8gCGOmuz6VgsnBoSKjVYeeHxMnijhJGCzbiKbRDxPCMGa+53Nyuovnp9P9XC+g44V0g7Se8QK6pEJAgJIJxpL81JFi1brGtxKWqTFUNHlpqs2jxyd49PjELRvfst1sx05hHNi/5PMYcHEbxpGzgoVJ8Nh4k9muz2DJ2vIMlFdKF71yV9Do+eu2X8l027uqXciCuuuhe0d55z0N/vArpzl+sQ2iKBo6YZTwzPkWBVPHNjX21QrM9HxERTiWjlIa440+RdskiGK8KMlKUkaZgRjCJOG5iXZqIBAuUwFppBM/6WmCGKoFg36Y7jp2sqvpRhFJ7SptL2LPQGFR+OdlOG8827FTeBK4XUQOi4gFfAD4i6vpIC+ys3ksTIAffONBHrp3dMt/cOsZh1fbFXzt1Dz+Cr3JWu0nmh4f/dJpLjbda/auCmN445FBfvCuPVSLFp99dpKul3oIlSyDThDhhjH9IKHvx5yc6tILYgwdekHExYbLVKuf9pWk6iFN0n6TJLURGJLuDBb2AqaxfHvtGMJYvfSqiTzVAS+MCaKYrhdxZKR0ywc+biebulMQkU8ADwLDIjJOakj+fRH5eeCvSf/ef6CUeu4q+80T4t2irGfXWG0XMVSyODHZYfioc8X2M12PWslgthNwYLC0bFex8P/1dhArE9ztGXB4YaLNk2fnCeJUPXRmto9jCiqBqY6LLkLR0phuezimQWTFeIFCz4zMIhqaQERazNjQUo8hpS7tAILo0r8ToBMonnmVTIZFQzAMLfW4MgyqBZPBUvr+8zKcm8Nmex89vMbxR4BHrqPfPCHeLcp6xuEvnJi+rNLYaNXh2QttOl64avul3lQdL6LqmIsuoQCvTLX56+en6YURgwWLt98xTMHUV1VNTLU9KrbBKzMdmr2QphtScXT6fsJc12euFzJYNNKqZnFCGCVUCyZNN8QLE0qWMFy0GQ88YpWu+E1TxwvS8Sy4j4qQuq9mvBrUQ6uhA5ahc3RXiUhB0dKXvYu8DOfmcNNENF8N+U5ha9nKzKbr2TVWqzT24mSXe0Yri8bkpe0HSxbfeGWWp841me16+GFCrWhyZFeF+V7A+bkuf/bMBAmK4ZJJECv+8tgE7/iOCE3T+J0vtHnb7SOLz1uydI5daIGCRi81+yqlKBfSFWzB0mn0U+H02t0Vvn5qlpabFphPYz80Wl5IydToBQndQKEHYRadDGQG41erEFhJTJrJtWib1IoGUy0fQxMSpa7akyxn4+xIoZDvFLaO600VcS2sHcuQ5ftXmYZdCSJQLZo8dO/oZa07fZ/PHp/CMlKDbgKcb7go0pX9hYaLrgkVW8cyDGxTiBLF37wwwzvv3Y0gywyaNcdkrhNQKaQ/m4QEN4w5urtMkCgEoWjq7KrYGLpQL6RxCZqkwkMTRRAn1AsmIiENL1l0MV1MXX2D3+VORgO8IGbAMTB1ne/cX+PIcHHbnCBeLexIoZDvFLaOK3kDbSUK4fWH6pybc2l7IRXb5PWH6iRrzKSff2GG0ZpNox8iiUbZEuI4ptmP2TWg0fFDhkoWrp/Q931sQ8OPIvwoQdAYcLRlz9vwIu4/WGO86dHyQixNY3/dYrqTBv0lSjFWL1CyDY5faPHKTBdRCjsrah8nMVGcMN9TuFGCxubXM96JFE2Noq2TZCk82l7Iu44O8eCduafRVrAjhUK+U9g8VqqKTs10GSxZnDzbpeOnE/GBoQJuuPFIqfXUT6udm257PHJ8ghcnOjT7AZYhqXFXFPtqRe4aHchqFvu8MNFmvhdwdq5HrZAWqjnf6NMLYp4+12B/1SaKkyzuIkIpCOIktUWcF87Pe4urc0NLPYAKtsZXT81ysO6AKA4MlnDDmNmuxzNnG8z1o8UcRBdJ219s9Gl6l96JQRqBXLANyrYFhLT6AV4MxLkoWAsN0EUwdaFetrEMjYfuHc2zom4hO1Io5GwOq6mKXpzsEEYJe2vFVI8fJnzxxAxDZYuPP3H2ijaGiabLp58eZ77rE8YJpq5xcrrL++8fA7js3JdPTHNmrk8viDg/30cTwY8TBhw38/l3mWx5HB4u8vS5Jm03omTriMDpmR4nJtt4cUJB1+gFEc9eDLEtIU4gihVJFh38uecmcVfUF1gw9Hp+ghuEPHnW46un5tCA0WqBiWafpr98WxICoX/5JB+RRhh7/YhGP7rsfM7qJEAviCnZQqygH8RIrlTbUnakq7OIvEdEPtpqtbZ7KLcUqyXEcwwtTSCXucN0/YCLLY8gSjbk6//4iWlOz/TQNKFasNA04fRMj8dPTK967mun5hlv9DODrUkCBFFC31fUCgZxophqe/zlsQlMXWPXgE3RMpnvhbw42WK+H2KIhmiCY+hEQC9IC9InmV1BSSoA1pqqI6DjxfS8kDBSeBGcnOpdJhByNoc0j1PERKPL6IDDrZMAfGewI3cKufpoc1gtIZ5laNSKFuMNl2Y/wIsS9tcL2Ka+KDhgbRvDsxdaGJriYtPFDRIE8MOQP3umh23ojFYdCqbBVNvl5Zke480+Qpr8TNMgzHTvfpxgmzpBEFEvWjTckO8cq/HcxTbzXZcz8y4dP1XfxHGMbRlECRQN6EdpRTKFQlepp09avH7td9H3Y2IFKlGYmsp1/1uMkBbWedd37iW52QtH32LsSKGwk7mZC9evDByb7wWcmu1xdq7PYNHC0GCm4zPX8TkyUmK+5y9WDFsaRDTRdHn8xDRPnJ7jyydniGNFyTKxTKHnxyRKUbYNRIPz8z3KtslsJ2CgaKQeJwkjY98AACAASURBVDHAckOsBDGvzPQoWhqnoi5umPDiZJuLjS6n5zyW1l3pR9CPIowst7QG1AsmvSAGldYlDq+QPc5fMARLWtM4Fwpbh6lBrZj+vWxDy4vqbDE7Un20U7nZC9cvTYg32/X5+qk5Wv20tGQ/jJhoecRx6obZ82OeOd9ivucvCyJasCF86aUZJlseOkI/iGl7AbNdDzeM8MIEIXXh7Hgx4/N9NE3R89LC8gssnYgVECcJ/SCm0QsYKVv0g5hzTQ9NFCKXf5WFSzmDekFMkqTpqB1DI1ml/VIUqRopUWl0ca7A2DpiBV6U4Bh6XlRnG9iRQmGn2hSup4jNRNPl0eMTfPyJs5uWIXJpVtBvjzepFgzKtsHBoRJKCaIJtpUGanX8EMfQeGGiveyHe2y8yXzXJ4gTKo6FYWqYukaYKHq+IowUjil4sWJfvUg1C0LTs3QPC4JgtS+mSDqhVwomjqlzcKhEkqjFtiuvWcgcmpCqg4I4QSUKhWKouH4k7JIkpCjy+IHNZKXAVQqCSPH22wfzhHfbwI4UCjs1Id61VgLbyh3GQkK8e/dVefNtwzimQcHUKNppAZmCqWPpQtuNODndodEPlv1w53sBYZwQJwpTl6w4jGBogp6Vh9Q1jThOqNgGe2sFipaBY+sMV2x0DSzJIny5VExG16BoGRi6xnDJYv9gicGSSckyMEydIyMl7CxHzgJLJ5tKIU0/YRigi8ZY3cFe49svwGjNWewrFwiby0IVuQVsA37muw/w2w8/kAuEbSC3KWwh11rEZjsCyBbGenCoxMnpDhpC1w/p+hEF0+DOPRXG6oXLXO4HSxamrqFrQhgrdEmndSfbMSwEmllG6jIaK8Wbjgwx3Q6wzLTkpB9lgoB0Qk4AS4OxepGuHxIlaTK6+w/UmW97PPr8NP0wJkqSNOso4BhZYrkkzThaz3YGfhRjGhpHd1c4NdOj6YbpPZbYDUwNbEun4uj4cUIQKoQ07ULO5lAwNYZKJnfsGeAtR4cxNG2xJkbO1rIjdwo7lWstYnO9tYavZ6yHhouMVGwKpsZcx8cxdIZKJnsqDkoJd+wuL1N/3TdWY7BsY+kaHS9A1wQ9c2ctWjpVx6BkpQLOj9JcRG+/Y4S33TGEiGBlOwrbkMXdAoCpazimjh8lxEpxYKhAxwu5e6zOP3vwMCUzjVUwNChbgmWmydP07FgUJ1kQG9iaRrMXUS+Z7KpY2Ia2eK2jQ9HW6boRBVPH0TUqtnbZ+8+5doRLuwNdwNA1HrxzFz983xg/eM+eLINtnhZ7u8i/6VvItRax2YwymSu9oEYHHCba3jKvqIWxemHMaNXBMnWKhkHJ0Rgq2xwaLlIrWss8j0ZrBd5//xiPlyyeOD1Hqx9StosMlm2GyyYly8SPYgxd40C9wPlGn6+fmmP3gMO/+ME7+d3HX2Gq2afphrS9iCCK01iBMOHMXI89ZZOSYy5e8+4s59FsP6IXxHS8AC+IcMPU/dXQoVKwKNoGXT8tcK/rwr37KrRdn/HIRZcYSxd0TcMyoGAaDJctRqsOxy+2afRDoigPQLuRJApMHeys3vXb7hhmuHwp/XmeFnv7ELUDfYCX5D76mZMnT273cDadpZHGS9NDX6sRbmV/FxouT51p8LpDNcbqxTX7f/T4BG4QLxNOHS+kYOmrJqRbb+z3jVU5Nt667PiJyRb9IMGPYs7OdTk372EbGmVbZ6jscLHp8a7X7OJNR0bo+RHPX2hxaq7PnqrNRKPP107No2nCcMmk6aaC4mDdoV52OD3bQ0M4PFJgpOww3nA5MdnGMnUMST2VkkTxi997G7ES/vTpC8x0fcIoJojVYsRzzvVhSRpAaBkalq5RL1m89kCdNx4eXKyVcKXvVc71ISLfVEo9sNq5Hak+2qmG5mvlemsNr2SlF9TS4jPreUVdi/prrbFPtL1VPbH214sEkaJesjA1g8GShaFrDJZsEmC0ZnOh4S9e8/JMlyCKqRdtGl7MnloBx9RpeTFjg0UODxXphYozsz0qls7eqk3BNGl7EePNPnECcaJww9T31DE0vvzKPC9OtpnvB9imlnof7by1081LVlnO1DX2DDj80L17UApemGhvW23wnEvk6qMdwtrppK+elZHLqxWfWW37fq3qr9XGvrIAzsI9B4oWH3r7YR45PsFsz2O4bHN0V4U91QLHLzYp6Brz7iVbSi+MKWQqiJ4fMli0qBdMOn7EO+7cTZIkPHl2nkNDJbpeRJQoLEMniBI6/QhdF4qmwUDBIFHghREvT3cZqxcYcHRENDpulNkl1pYMC+UzRdJ8d44h9KNckqzEylKdV4smtq7zvtfu4/BwmX1dn29fR23wmzkodKeRC4VXISttFBXHoOWGVJ1LNoq1bBY3SjitZye570Cd+w7UAej60WL5y6Kp03RDaoVL4yqZOlHm0lSy05gHUUI5Mwy3vJCCqTNctrjYdKlm15q6oDJjpxIQEVSS4AYxilRQLuxG2m6IhuCtY9jXsxiLWsEkjBOCPBPqZeiZQNhbL3Lv3gGqBZPDw2UgtS28/Y6Ra1IXbUfNj1uZHak+yrk+VqqBRsoOzV7EcMXaku37RNOl0Qt47MVpvvLyDLNdb9V7vvveUZq9iEbfJ0kSbEOn1Y/4jtHy4jiPjpSxDJ1G3+e2oSKNbsBE20UlCcfON5hserz5yCC7KgU0TegFEUopen5MydJJlCKJFUEU0+iHBHHCvprDbSMlekFCo+djGYIfr++QWi9ZWLpQKRgMOCbhOruKVyuWIRRtg6GCSbMXMVJ21vy+XU2w5vUEheZcTi4UXoWs1POP1hw+9PbD7K0VbojNYj0mmi6ffuo8L011MHThzGyPv3lhCi+83LB934E6H3r7Ycq2wcWWy64Bm//x+49y12h1cZw/9bYj/ML3HqVsG7T9mOGKzcG6g2XpGJpwZFeZt9w2gq4JDxysEyeK2a5PrBJed7DOngEHx9Jo9iN0DcbqBR44NMzb79jNkeEiYaLwshgIZ419taOBY+o8ePswRdOgG8TsrljkGXtSDEn/M0Xo+hETbY+Dgw6OqS37vkHqzPB/fOEkH/7bl5hoehsK1twOl+1bmZtGfSQibwN+nHRMdyul3rLNQ7qlWU0NdN8Gr70e/e3jJ6Y4M9+nVrDYWy0wWLRpZjaCtQrv/KO3HF63/9FagfsO1Nf0jppoe4u2EEvXaboB1YJJvWhydFeFIE547mKLim1ScUzu3TcAwEjZRiHcNlImiBLm+z6npjpcaHkEcbqiKlpwz74acQLT3ZAff9MBGv2Qo7sq/OInvom7hieroy8k/lsdXeBW2GwYWvosUQyOpbOvXuT23WUmOwEFx+T9948xWissUwG13QhdE05OdynZxqIac61gzRvhsp3bJC6xqUJBRP4A+GFgWil175LjDwEfJg1a/T+VUr+hlPoy8GUReR/w5GaOK+fauV797fGLbaqOuZj5smDpKGVy/GL7qvtfrUrc0V2VZW0WDOZr2UIW+vCjBNvQFqu6PX1unnLBZF+9xP0H68z3Ap46M8d0weKgqRPHCZqmLebAGCqnKTrqJZt6KR33ggurcHmqjLVKiC5QNIVOsPOlgp4Z3k0DyrbJ0ZEyuyoF3DBivusvTvRLVUC9IKJWsPCimDNzXQZLg+vGLdw3VuPzz08BLHNvftORoQ2NMbdJLGez1UcfAx5aekBEdOB3gXcBdwMPi8jdS5p8EPjEJo8r5xq5Xv2tUiwW7FlE1KLL50b7Xy0f1Ln5PuON/rJ2V1oxLuR6+uffezuHh8tZKg7FTMcnSeDQcJH5XsBXX5nl+Yk2zV6AGybMdAM6boiQJm9zw5iWG3J6prdos5GsyttqaJqseS6tOaF2fGZWDagWLI7uKnFkpMKRkTK9rOaFY+iEcbKo4lmqAqrYaYW/hSypsP7f8XpdtnObxHI2VSgopb4EzK84/AbgZaXUKaVUAHwSeC+AiBwAWkqp9lp9isiHROQpEXlqZmZms4aeswbXq799zb4qzX6EG6YGXzeMaPYjXrOvelX9r/ZDvnP3AC9Nda86jQhcPrHYhkYUxzx7ocVnn73IubleWguC1AsqjBJ6QUQ/jGm6AUqRGuzdYLGvatFIU21oYGZpNABsLfVS0tb59YVq5ybiM0l3OreNFKkWTcIEClb6sP0sMtyLYkxdW5zoF1RAkApiN3uvZVvfcDzMQ/eO8sE3HuShe0evaoWf2ySWsx2G5n3A+SWfx7NjAD8N/OF6FyulPqqUekAp9cDIyMgmDTFnLZb+eBe4Gv3tg3fu4vBImvK65QYkieLwSIkH79x1Vf2v9kPeVy9wcLB4zSvGhYnlHXfuYne1QKzSAvITLZe2F+EGEQVLo+KYlOw0riGMExKl2Fst0Asinj7X4Jc++TQf++ppHjhQw9KzH9lCrh/gjUcGee2B+ppCQbGz6zeIBrsqNiBUbIO7RwfQRGi5ARpCPwhp9EIGy/biRL/UI65WTHcXcQIDjrWpjg9w/d/pW43tMDSv9n1XAEqpX9tQB5fSXNzIceVsgOvV3y7kRlrLqLeR/ieaLs9fbPHKTA/T0Dg4WOA1++qYunB4pHRVvu6rGRiPjTcZqxXYXXE4M9dFAZYmFCwdQZjteCQqzQBbcQwqtkGYxJyf6zMyYLO3WqDlhZye9xirO0x0AsIowTQ0dtkGXT/mfMMlXCedkp6VnUtIf6Q7JfNSmm9KEFL335EBG1MX4kQRZQUu5nsBbzw8xIN37lr8u68MjNxbK/Cuq1zxXyvX+52+1dgOoTAO7F/yeQy4uA3jyLkGrjWqeWUfa7W/Uv8Lld3mugFKKUjg5FSXmXbAPWNV3n//2IbHsZaBsedH3LarjCbCYGmQrhfx0lQHv5MQxoo4UYgIjplGQ9+zr8aFpkvZMTg8XEbTNOpFm1gpmm7MbcMV4iTN0DrTcRlv9On60bolPuMErMxDaacIBFtPx+2HCl0T9lcLCMI3Ts+zv17gxx7Yj53ZCZYKhAVuZNT+1XAjvtO3EtshFJ4EbheRw8AF4AOkxuUNo5T6DPCZBx544Gc2YXw5V2Czf7zr9b9Q2W1vvcDuAYfptk/TSxPoDZfMqxrXsfEmUZJwcqpLxw+p2CbDFYumGyxzcbx3X42Zjs98P8D3I1BC2dKpFkzaXsC3xxu03JDdFYezc30KlkbJMmj2fBq9iI6XVghMVFoNrmhp+OH6FoOCKRQsA68XrtvuZiJK0loUoglBnDDfDXHMhHrR5PbdlWVZUDezFsi1sF0C6WZks11SPwE8CAyLyDjwa0qp3xeRnwf+mlTF+gdKqeeust9cffQqZaGyW9WyEFMoOyZKKVpuyNX665ya6XJ+3qVopVHIXpgKiGrBoONFNPsh0x2X2W6AaWjsrli0TZ0kSY3GvTBmsGgzMmDhRqlH0iHboOvGqaBxo7QwT5J6XS3sDPpBcsWRBpEiTEI0UlvETtgtJAqCGAZMwQ0TkkTR9lJhO932FtvlabFvbjZVKCilHl7j+CPAI9fRb75TuIVZL5BoobKbF8UUzPTrO9cNmGq7KBSPHp/YcOBRyw3RNJbFTHhRhALuG6vyiSfPEyeKoZLNrgGbv31xmt0DBrqmcX7OpWAYmIYw2Qm4a88Az55vMdXxcQwNhMU4BU2WxyWo7Nh67kUilwTJTsqilABKCZomHBou0Q8T2l7I2TmX+Z7PYBbD8Wo14u4EdmSaCxF5j4h8tNVqbfdQcm4wV6pHvVDZrdEL6Qch0x2Pb19o0uwH9P2IJ07N8emnzm+ofnWtYJEkLHOPTZL0+ETb442HB3n9oUE0DU7P9mj3Al642KHvxygSoiRmthsShgn760XuHi3TdkPOzvcIo3ixNGicXF7KU9dkzd1CydTYNWDvuDQZQvq8vTBmqKgzULAoWQYqVlimcGqml6fF3gHsSKHwaqun8GriSoFEC95Lrz9UT6uxzXaxdeGO3QPsrRVxg4SvvDLLbz76whUTqR0eKXHH7gq2odH2QmxD447dFQ6PlJjvBfhRwjPn02jnAcdECYSJYqbrM98P6foxAwUd09SY6wXMuxF37qnw+kNDDJXttA4DgFzucmfqy4WCkKqkbA0S0kpz3k7aIpCOf6hkULJ0EJ35nkfFNrhn3wB7BhxOzXZ5cbJNz484Nt7ckODO2XpumtxHV0NuU7h1WVnrAS7XQY/WCjz8xoM8DPwvf/ZtbF1Py216ERdbLpahMd8LF3cZC8nWVqqk7hurMd2e4vZdlRVV4VK31L99fpL5fkicBV/FcUKcJSQ6NFTk9GyPs3MhowMOJyZbGJrGvlqaJ+n5iTampEFosiIQzdTSALgoSvCziV8BSQK6DlGk6O0IK8IlLIH9g0WqRYtW36dWNBlwUsO/G8YcqVhMtn2+Y8/A4rt+NaeSuJnJdwo5NxVXG0i0NG3GdMclQdHshUy0XE5OdYmShMdPTK+qkgLWTI8wOuDwwlSXMFIUTI2+nzDTCxCVMD7f57kLadB9xTYIlSJOYKxWQAGDJZvBgsFg2UTjklplgXoWzRxzKQ1GFpZAGEOtaDJWc9gJ6AI1R6gWLfZUHPbXC7z7NaNoIkx3fExdOLqrxGTb447d5TyVxA5gR+4Ucm5drjaQ6DX7qnzxxAxh0ufUTJd+EBPHinrJ5MRkh/GGjkgaSX2pqFD6/2PjzTVTIky0Pe7eU2GuF+BGMZomiFL0wgQEDE1wgwTd0bhrqMhoLa1tXbHTCnZ+AroIBVMDFEGkMCTdEVimMN0JFr2YSqaOLkLLjzA1KDs65+ZuTtVKxdYWjeY1xyQBhkoWlqFx34E6h4aLqVAsOUy2XQ4MFRksWXhhzFi9uKyv3Avp5mRHCoVcfbRzuVKK4qsNJLp7dIC/eWE6La/pBnT9GF3XGBQTP4ppewEirJrbZr0Jab4XcP/BQb493qJgaVxsuGiahiLG1DRMPV37B2FEP0oYrTr896cvMFLx0AT6Xsh0N0hdSrU0ylcJJJGi0Q+JMsuzAvwwxjS1RZfOuU5w03kcGQJRltYDwDE0NE145z27qJccSBRjg0VKtkHHC9E1WZby/NHjE4w3+sx2gmUxIXu3WHWUp8i+MjtSKOQuqdvLtf6wbkSK4pX3bvR83np0mFOzHZ4db6NpCTrQ6IXESqg6BoYul+Xbv9BwmWy7fPyJs8ueYaH/4xda2IbGoaEiTTdgvp8mRyuYOpqmEat0cgzChPOzfR6PZ9AFLEOj50foug4KdEOy2s0CKklVRJFaNEIn2ezvZ8YFBbQ3EMew1UQqSwGu0kjr0VqR1x2o8YbDw4ueROsJ8tEBh888M0GtZFB1TFpewNm5Pq97e33LniFPkb0xdqRQyNk+rueHtdSzCJarcZamsVirf+Cyc187Nc9bbhtC0LANoWBbJInCj9J6y/Wiwd5aYTEFsx8lPH12nmPnmwyW0qCqWtHi5GSHt98xwrHxFhXH4NBQkcdOzPDM+Rb3jFY4UC9wseFi6RoKRcdX+GEaldbxI3rTHYZLFmP1IjNtn4pj0coio3VdUEoRRKkdwdGF8AoFFW7GDKlFU+PO3SXecffoYnGcpaz3959oezxwqM5M16PjRVQLJkdHKky0vQ0Xd7peNvL9y8mFQs5Vcj0/rI14Fq3Xf/p5+bmhksXTZxtZkZ50onYMncGShZO5iv69+/fz4J27ePzENF99ZY65rkfR0rEMk9lugKXrtL0+k0+d44FDQ4Sx4sycy6GhEjNtj1dmu4xUHAqm0A8VuqYRZzmQDE2wTcHSNYIIzs51QdKcSLWiRdcNUSJ4kVqspGbqGm64fs3nzWBlEN1G0UltH4YhVIo2Y1lRnKtNT72vXmD/4CW7QqLUltoUNvL9y9mh3kd58Nr2cT255zfiWbRe/6udG606fHu8yVzPB4EoVnS8NBgNBbauA4ovnJjmzFyP1x6oYRo6Jcug44fMdHxemu6gw2L5x+MXmky1U/WSpWuMVgu8485d7KkWEMCLEpIEipZOyTbZVbY5NFzGjSKOnW/x4kSbZ8ebNHs+ImAbOmVbW/RECqLksp3AVqiLNElVP1eNgGgaOuCF8TXVGrgZ0lNv1hgmmi6PHp/g40+cvWJszE5gR+4UcpvC9nE99XA34ll0pf5Xnnt5ukvBSovZNPohJdsgThI0ERxTp14ycUyDkm3wrXMN2m5EK/MoKpgmRUunHyaca/RRpLaGFyc7DDgmRVNnvhdwZq6HUoqZbsBdeysYovOt843UrTRRzHZDNE2n1QvQdI2KbdJ2Q5r9iJGKgWOa+LFGyVb0/RgE4iQmWGJNXk1IqHU+XwtRcm3CRwSSJKHjwWTL44lTsxi6xn/4qxd4eboLojg8XObd945y34HVbQQ3Q3rqzRjDdtgpNttYviN3CjkbYzNWMEuLoVxvdbPViqes1/9q507N9Lh9V4k3Hxnh0FBa6cvQNTp+hIjwmn21Rd/4kYqNpqVRyakTTZrawvVDLrY8NAVPn5vHNlI30o4bMdv1GSgYtPoRRUvnYtOn5fo4pkbbjYiiBMfWmWp7GKbOngGb4YrF8ICNrkPLi4iShIptUC/aVBwTW9e5Y1dpzfdkAGVLcIxLk/iNsjFcSz9RkqbqMAwNWxceeXaKi/M9vnxyljPzPU7N9Hn6bIP//bGXOXausWof11sy80awGWPY6lKeV0oDcyPYkTuFnCuzWSuY6809f6UUxVfqf+W5scECZduiaBscHCrx3MU2RUvH1DVGqw5TbZ+9tTQR26GhMt8610BEGCyaNNyQZi+gWjSpFy2GKw5umDBcthhvenhBzO4Bi4Jp0vJCDg0VOTndo+PFGLosJr5TSUIYJ9QLqY3D1HX2Vi3mOh6zvZCuFxEnCYam4UcJh4eLWKbOoSBmqu3hRst3AkVLo1QwafdDIMExQCH40daYn/XM9rBwN01SI/NQ2aJUsCgXFM+Mt9G1VGSFiSJsx/hRwp988/yau4WbIT31jR7DVtsptsJYnguFW5TN/PJsZz2FlecaPZ+nzjYREbpeyEjZJogUR3eVqBQMJpoef/viNLsqNhXHoGRq6JKmrw7ihN0DDgcGSwwUDAZLNm5WDu3Nt43w2Wcvpit1BXftqQAQxQlNNySMFaahoYmibFtEcUCiFOMNj4NDJSxDw9R1NELQ0uA1sRSawAMH6pyc7bF/sMTtu6uYuhDGipemWsx2A/YPlgiTmEYvWLy/H2+dP5LK3E+rjkaUKEqOiWPqPHCwzmwvoKBrvDjZoVYwsA0DW0/rJ3hhxLMXbm47341WvVyPOvVa2AohtCOFQh68dmVuJU+L9X7ID965m9lOGkcw1w8omya7BkxeM1al5YY88co8ugG3DZeYaHq8NNXlTUfq9IOEV2Y62IZOxTHQNI1Dw0USBV95eRZTF+4erdD2IpQS7t1X5bEXp2l5ETqg6ZJ68oiGF0Z85/4a3z7fIogj5ns+XS+g6QbUSyZKCVGS8P+z9+Yxlmbned/vfPt397q1V+/7zLDJGc4MORxSFCnRiihCjhxbsizFThAZYZxAiZA/osRIAAdwABkIEsCwBSOMJCuLRSWgHYeyqKFFidQwQ3I2ztaz9PTeXdXVtd39fvt3Tv44t25X9VR3V/V0dVeN6iEGnLpT97unvnu+857zvu/zPJNln301l2aUgYJcKRxLZ3AdS2gTGgXVgsVcK8M2BZahKHsuC934njqH7gVFRxPTakWHKJUYAnzLoJ/kFGyTVpgOuRemoa02XUszx5N0p9Hubs6fi0s9rjYCTk1W2Dfi35fT84OulTyIILQrawp72kd3x07o9rgfuFsOdbrm84tPH+CZo6McHy9zaKzA546PUS+6NIOEA6M+I75LN84I0pwTk0Vcy+KTB0cYKbrEmd75P3GgSr3o4loGnz02iu+YVDyHXMLxiSK1gsNsI8AxDU5MVTg0VsQ0DdphylIvwbdMTk2VGC06tMKMpX5KWed96MUpQSLpRjFIXbg9Nl4kTiW9KEVK/f8AP3lynCNjRWZqHiXXxrGswaL84OhsaS5RSqGk4rGpEr5tkSldJHctk5VuTMEWtMKUlV5CmGSYAuJMcWB0Z/X7r50/nTDDNATnFnu0gvS+5P8fdK3kw9T0NotdeVLYw92xE7o97hVrTwZXVvpMVfw7psFWU0qrf7NtGkilWOrG+LbFZ4/VqBdd/uL9RcqubkWtFx2+9Mgkr11tkklFreAMH7C1D/UbV5t868w8b8626MYZVc8ilYpWkFJwTDqh7kC63g75/IkxvvToFN98fY5ulLLSj1kOE0zDwBvoHXWjJn/9kzM8fnAE1zZ590aXRpBQKzh88mANzzL4s/cWsU0DzzYIk4x+Iim4JmmeretYuhWronqbff12iHPIlcQIU85c7ww2GJJWmHCg7vPkoRGuNAIuLvZIcqlTYwIO13Xr7kbf48OSlFibRu0nGTXfIcpyLq/0qBfr9+X0/CBrJQ/CT3ovKHxEsV2TZ7sf9FsL5K9dbdEJM4quNTzl3OlBtk145UoDpcC1DMZKLpdX+rw112axG9ONUmaqmkBVLzpMVV3enuvwrbeuM1nx+MoagTz9t7aZqvgYAt6xDVaClMVujEIMx1RybU5OVphtRrTClFaY0g0TogGRzjKFlrmQ2tD+7fkuQhi8fLlBLhUlz6LqWYRJzoWlHp5t4loGUgkc28BA21t6tkGeSPJBzl/wwcXe5IOGPluFQrvGtaIcS+gW2qNjRT53bJyxsoNnW3zyYJ3vvHOD+XZIP82xTYMTE2W+eGpyw+/xYUlKrE2jll1tuerZJp3ByWw3np63OwjtBYWPMO735LmXB32rQeTWAvl4yaUdJcOdHWz8IK8d2xdOTtCPM96Za/PWXIepqkvVs+lFGe8v9Dg0WkAqxVwz5KULDSSSViRY7MScX+zy1KE6IwWbt693aPZTOlHGTM3lxFiJFy6ukEnJwRGfXAm6kWJfZR1/kwAAIABJREFUzaQXp1xc6nFyqkzNtzEF9NOQsbJDP86J8hzLMJC55NJSn/ag82nQwMPVLKDRj/FsiyPjJS4v9xEDhnac5ghhMFlxsK2cTj/Fdw2STJHkCokOEKahO4dMIMt1wNgoSGyF86CAomMjJRQ9izPXO3zh5IQ+KbkWExWPLJckuaKy5jvZKZISa3Pwh8cKvH6tTZRlVDxreDLcDafnB4kdExSEEAbwD4EK8IpS6n97yEPawy3Y6oN+L0Hk1gL54bECr11NWOrGSKVumwZ7Y7ZFJiWvXW1yZSXQLmlpRr3gUPUcunHKdNXn0GiBMM1Z7sW8ca1BI0yYKPsYQnG1GZJmOZYhKHkOF5Z6VD0LQwjm2zGm0F1N55f6rPRTZmo+J8ZLuI7B23Nt+mlOvZfwxIEqb8y2udEOWerGugiLIEkzQiWomgZSKhzbJIhzulHKwXqJRj9mrGxgm4LJisvFpR5xmpPkIJHMtyN8S1ApmISpPi54tqBkm6yE2kpUsd7XeaNTw1bq1a4lsE3oRCnfPjNPJ8po9VMypaj5Fvunq4RJjmsZ6+Qvdkqjw9o0aq3gcHyiyPsLPSqeg++Y9z318lHAtgYFIcTvAT8PLCqlTq95/cvAP0ZvZH5HKfWPgF8A9gENYHY7x7WHe8NWH/R72S3e2l1RL7qcmCyx0InumAa7uNTjvRtdlroxRUdP62udhH6c84VHJqkX9bhX9XZ+9ZlDPHdmflhjWO7GGAJc0+DtuS4/e3qa0aLDpeU+tYJDkKSawOaa+JZJmisKtqG9hxcDbMvkJw7WcGyT2VZEyTGJMkk/lqRS4pqGXshRhEnGslIgBJYQRKlECOhEGbWCRZhIGv2Esmsy19K6Q76pSWStWFG0FZYp8G2TMJWYlokgQxiaZHY/+3/SXDHXjpkqOyx2EybKLjfaEZmUBLENSmCaBqemSuvmwoNu1bwdbk2jztR8fu42Hhp70Njuk8LvA/8U+N9XXxBCmMBvAz+DXvxfFkJ8EzgF/FAp9b8IIb4B/Nk2j20PW8RWH/R72S1uVCC3DGOdNv8q1qamXr/aohkkuJZJI0hIMk0o6ydweTkYBoW1420FyUAnSdCNEyxhIoQizRWebVAraLkKxzKwhKATpXTClPGiQ9XXYnpXGiEHRjyOT1RwLJM8Vyx0tOBexbfJcolCt20iwDaEPjVkujZQdCwkisVuTJpL5loRn9g/wmyzz41ujGsJXFNgWgYylphIolRStS0yqaW8+0mGELoOcL+RSigK6Mc5lYLNo9MVokzy3nxn2Ln1pUcnqBddulE6vLc7qdFhJ5DmdhO2tSVVKfU8eue/Fp8GziulLiqlEuAP0aeEWWCVI//gJST3cFdstR3uXtpiN9vit7bV0BCC5X7M2YUO7y90hi2StikIs5wrK/0Nx+tbJu0wQ6JwTJMcRZDkuLZBlEpaQcqR0SK5VMy2Q6TShsvhwFRnqupjmYJjE2U+vr9KmObMNvsUHK2CmuZKp5gmy0xUfKYqHrZloADLMFBK0Y0TZK4IkozJssNY0eXlK02WuhFBkuEYColASpBKkqMfjjiVSCkpuhZSyoFsx+aw1eZWcyAN8omZCqWBMu2x8RLHxktUfHtd59bqvd0JshZ7uDc8jJrCPuDamp9ngWfQ6aR/IoT4PPD87d4shPgq8FWAgwcPbuMw93ArttrRdK+7xc3s7FZTU2mueHO2zUjBoeBYw93rWMlhf61IkuekudxwvNWiTaFtDrqCdJ+9bRnUCzatMGGln3B8rEi+3KcdGtjCwBAQprqlsVpwGfFt4kxRL7o8caDKv3krIMskZc9mquIOC8orQYxtmCRpjmObFBwTYUA3yih6JgfqPmXP4kYrQkhBkOQoCd0MTJGTr5GdEIBrG6S55hIoBPo8crOIfLu6gQBqvkknzDe183INGC175JnCMvVyEWU5k1WX8ZK23Lytsc7eDn1X4mEEhY02KkopFQB/925vVkp9TQgxD/xVx3Geuu+j28MdsZUHfTt7qldTU69fa+I7BgdGCpxf7CIVjBRsBIJMaRmLR6cr/Oozhz5wjfGyxxMHDeaaMf0kZaRo4FkC0zB4+lAN3za4sNRnpOhQ9S1udGKaQUrVNwfidg6dKKHZj3jh/JJ2aOvGSOD4RAkDyXIvIc0lea6wDN1WOlK0qfg2BWnh23rsJgZXVgJMIcjynEwqBurf3Cp55Oo4Ri512skcyGELtPXnqpvbRsqrvi1IcoVtQr6JqOBagoprUx+1mWsFmKa+/v5agV6Ucni0uCMNgfZw79hUUBBC/Abwz4Eu8DvAJ4H/Rin1b+/hM2eBA2t+3g9c38oF9qSzPzweFLFou3aLq6mpbpRR8WyELdg3UmClFwOCMM15/ECVo2Nlpmvehtc4PVPhlSstnjxYwFtNGYUJTx+q8SvPHGa+FfJbf/IuY0WXXCquNEKyXFLz7EGaKafqWyC0BedsI6TomgSJpBOlLPdT3IE9p2ebIKDgWBwcLVEvOLy/0GWq4pNKhWXoGsf+WoHZfkqznyIMgSlvmvMA+BYghA4YSiFRjHouQRIPNYvE4LhgDP7dtXVHEwhcyySXEmUKsjgnu0vaqZcoPjZTwncc3r7eYqmjfSvOLypKrj30ZZ5rhnzv7PscrBc4Ol66p/m0E8hue9j8SeHXlFL/WAjxs8A48B+hg8S9BIWXgRNCiCPAHPC3gF/dygX2tI8+HHYKsejDYDU1ZRmCMM0QGEyUXMZLLiXPouJZnJysrMtz37roPDZd5eJin/NLXfppTtE2OT5eGhKwpms+zx6t8/5CD4livKT5DlqHKKdasAhiRZCkLPVi5EAeohNmvHI5QSntzDZSdPBtE6kUaS7pBMnA/7nIF05N8Pq1Fr5j8P5Ch3fnO/TiDKUYFqYdoQXnUCCEgWub7B/xWGhHKCX4288e5v9+5SrXmiGCAUlOQNm3GS+7VHyLc4t9slzXIYIkI80VjikwBCR3ENsTwGvXOpQcg4VuwkTF49hYkSSXLPUSjuWa3X1usYdpCDphxnwrGgaIkYINCBTccaHfrjm5F2i2js0GhdWUz1eAf66UekMIcdd6lRDi68AXgTEhxCzwD5RSvyuE+HXg2+iW1N9TSr299aHfG/Ymyc4hFn0YrKamvndW8IMLK4yVHD5zbJQgyTfsQ99o0Xn+/DIYgsNjRdJcYpvGOgIWaMG9NGegY6S0h7NrUXBMmkFKO0gpOSYyEXQHLFnHMnEtCDNAwpGJArZh4FgmvSjDsw0qvsVo0aVedHjiQI0XLy7TizPigStbrnSKKEVRsAW2IYhzfTIoOSbtvu6amqq6vHq1xYGRIgudiDRT2g9CaGe4U5NlEIJWoK+d5DlRahCpHHNgJ5rcIY3k2oJmP+HScspj0xWKjsWFpYAgTTk1VebySg8A3zHwLJP5dkQn0hpD15shF5b6KAWfPlIf6lZttNBvx5z8KGx+HgY2GxReFUL8W+AI8PeFEGU20Q6tlPqV27z+LeBbmx7lB99/T+mjvUmi8TCJRfczKE/XfH7lmUN88dTE8Jq360PfaNFp9LQQ2ueOjw8c1npcXenz+z+4NGyBXVsXWejGtMOUqYpLkuvCbphKfNvAMAykAtMQWIZelD1TEmeQZopc5CSZ3qV3I8VKXyCARl/f8/PLfTzH1CeCWyClxLFNcpXjmyZpltONc0wD5tt6p+45JpZpopCMFR1MwyDJJO8tdFBK4FqC/SNFZlsBuYQ016Q4dRdJ7jhVdKMEUwgKrkUzSOgnKWGcM9sMcS1d0Kh4WkKin2Ta7c4yeet6mxPjZRCKq40+Tx6sD7+LW7+f7ZiTH4XNz8PAZoPC3wWeAC4qpQIhRB2dQnoouNf00d4k0XhYxKLtNP652/sbA0mJc1d6dOOUsmvTChKKrq15DoMUznjJZamXrBvX6j/fP7eEYxpcb4XMtUKKrjUgqSlKrl68pRK4lkRIodtElaTZTzg0WqCfZvSTjMmKz8emSrxyrc3b16/gOwbLnQglB7t8IEwkttAnBssyMITgif1lKr7LucU+I6ahd/2Z9oVYTQlJCUGSY5n6FFDxHMZKDu/d6OI7GQXbInWgF8REm7i3OdBPJLYJl5d7VH2Hgm2RpDlXVwL213xGSw6tMEEpQXFwYohSiVDg2frvWdUaut1Cvx1zcqewqncbNhsUngVeV0r1hRB/G3gS3UL6UHCvJ4W9SaLxsIhF9xKU79fJQqB4+XKLmu8Md7Xz7YgDI4LLKz18x8C3tSjdeMkdSiqvpp7emG1xZq5NoxeTK0jSnHywqy86FmXfph0kKBh6DFR9m5V+QpJLrbDqW4wVPR6dLnNpOeDwaJG351q0ggSEIM5yqr5NP86xDUkmFbmS1HyHv/HUPqYqPv/mzeuMlR2STHJ+sa9d0RxtDbq651/pSwqOgWUIHMvgqcMjdKIMxzJ4ZKrAi5caRFtgAuUD3Yx+nFH1XXIp8WyHqg+OZVDxHJpBxsnJIkvdaBggDo0WiFIJQun0G7df6D/snNxontxLoNlLL2+evPbPgEAI8Tjwm8AV1rCUdws+Kh4Dm8XtPJofFrGo0U8ouuv3IUXXotFPbjv+++dHqzt2+kk2kMRoE2eSZj/V+kSmQZjkhGnO4bHCcFxrxyAULPUSWoEm7wWJXuwzKTlcL1DxLJRSNHoxUZKx0o2p+haOJbjWCHjlcouZikMzSIY5+FwqhBDMVD3NwpYSqSRSKSxDn1zKvs101UehrTHDJCeTEtcSOLYgyyGRulV0tdAnlSCIJWGScXi0RL3o0OilGAYcGy98oM31TvAsgRSQS12o7kQZRdfgZ09P8dhMlf/0p47zG186wUzNX+dB8dhMhVaY0OynHKwX70h2/DBz8nbzZLribYlsudX5th0e6DsBmz0pZEopJYT4BeAfD4rF/+F2DuxOuNf00U6i3m837paqeRjEotWgnOaSy8sB3TjFMgQnJ0sb/v5mTxab2d0p4JGpEt8/v4KUCts08C3NVI6znCDOOTRa5NRUaZ1kw1qS3PV2SC4VmVKkQu+SkQMHNqXlMSxDEClJPwXbUJQyA9dzcC1JK8j4f9+YZ6ZWYH/dZ6mrT6i+ZVIt2FR8i16UEsRaAqPkKqQU1H2Lly83OT5epBNm9OIM2zRwbYMkVchVYoJgcFIBQwgsC3IlqBd1Cun9hS5/9u7ioD11cxDAaMlluReTSqiVHB4/UOPj+6v6Hjq6prB2Pq39Pp4+VAMEUqm7CtDd65y83TyZ70Rb4sls5ST7Ua5PbjYodIUQfx/4O8DnB/pF9l3es2241/TRgzCo2CnYifWTx/fX+MYr17jcCKh6Nrapu2KW+ynzrfCeio+bfTjrRYdzC11OTpbIc7i43MO2TGYqFmNll36cM1Z21kk2fOboKN89u4ghBG/OtglTiWXoE4dE4ZoGpq0Ly8u9GAyYqvrkymWll9JPUhZ7Cftsm14s8S2TIM1Z6Uc0g4iRokPZt1FKkWYwVnK4spJpMprUAnidOMNzLIIk49UrDaIsp9FLsEwD01BEWY6UN4/8udRBIUozTAPyXPGDC4u8cK5BrWAxUXaZbQab/s4cEzKpGC051Isu/94T++66oXrQG447zZOtjGUr6eWd+HzdL2w2KPwymkvwa0qpG0KIg8D/uH3D2j78ZaHeP+j6yWZ269M1n7GySyNISKWk7Fl85qg2rt/oYdpMTnizD+fj+2t86615xoouC51gKAdRL9osdCLKnsWPLqzwxMGco+Ol4WahXnR48eIKvmPoWoHS7aEohWUa5EppzaN2hG+btMOUINHSGnEqMQ0tWWEIqJYc4jSjF+dEaU6YSD59ZATLMLnejuhGGdZg920agjTX1pjv3egwVnBYGaStPMckzSS9OMcxBY5j0g50WtQ0AAEGgjxXxFnGv3z1OgJJlDq4jsli985zYK3fgmeb+LbBwdESnzkyMkzv7KQN1f0qUm/lOh/l+uSmgsIgEPwL4FNCiJ8HXlJKPbSawh557e54kB1GWzlKK+DZY2MYa2guq3LWt2Iz6b7bPZwXFns8d2Z+XZBaJaI1gpSRgkPJtbi41CPNtVpQkivyXK0LaGuDyYhvs9iNyXOFbWpCmaFgZsSnF6fc6KVYpokhFJbQRjeg0ztlV9cPFFD1bU5Nlri6EvDGbId6yebUpO7531f1dfupVEgFN9oBvSgjSjT72DR02qrouDSDiDSTKCWH9zZbPTWY2uWt7FosdhNyBUESkW1CWvtg3WO5l2AZglOTFQ6PFTgwWuQXPrl/RwSBW3G/0sJbuc5OkQbfDmyq0CyE+JvAS8AvAX8TeFEI8YvbObA7QSn1R0qpr1ar1Yc1hB2PB2HwvYq1u3VDCNJccmm5xz/583MfKMBtpdi/meLjRteba4ZcaQQfKBg+Nl3lyFiJx6YrzNQ85tvhQMvIxjZNCrbF5UbA984urBvDs0frKBSTVQ/PFmS5pBvnRGmGY8GJiZImnElYFSyKpcJ3BAXbpOia2KaJIcA0TGZqHtWCjWEYTFRcRnwXqSCMc3KlCWYL3YjZVkA/zgFBJrWhzmol2TYFaSZJ8lUpC4Ft3BTDc00DIRRRpscjGRSj7/JdWgL2jRT47LFR/t4XjvHXntzHTz86yS8++eEDwnYVZu9X48RWrvMgn68Hjc2mj/5b4FNKqUUAIcQ48B3gG9s1sD18ODzI+sna3XqjH/P6tTaeZQw7ZdaeGra6q7tbum+j651d6HBysqwJagNS2lI3Zq4V8JXT09imZkEv9WJmKi6+YxFnOUfGiphCcOZ6Z91nrLKaoyTjnettLEtgKhBC0IolV1d6jJddpFS0w4wk16qrM1Wfim9rbkM7IsokUyWHybLPXCvg0ZkyeaZohAnHxkucmizyzo0+ExWHetHm0lKfNFdMlJ1hy6ppGARJzmjJQKINeBzLJE6zAXktRyntMrbYjYiyHLUF/82Sa/IfPHt4272373dh9n6lhTd7nY9yfXKzQcFYDQgDrLDNXgx3wl76aHN4EPWT+VbIlZU+r11tMV7SXTu+bdJLUlpByo+vNrEMwffOCn7lmUP3/WHa6HoH6wX2jxQ2JKW9MdvmZx6b5IunJvjNb7xBsx8z14lgsNvbV3WpFNwNP+NfvnqNomuT5BLLhKKt//39pT4nJ0rYlkm1YJDlkurAYGe06LJvxGeq6nFuoUemBLYpGC87lDyb640IFFxY7tFLcnTHriDJFK5lUfUFh8aKgOBaMyRJc2xTk9lMISj7FiNFm7mWTiN5lkGQStqhJouZQmCZBtndlO8GMA0x3O3emn77MHPpo1iY/ajWJzcbFJ4TQnwb+Prg51/mQ8hUfFjsqaTuDKzu/qYqPp0wox0lvH+jy2TF5UY74fhkiYpnE6YZP7iwwhdPTWxLO+yt13vuzDz9OLsjKe3Lp6c5PVPhm2/OU3JNCoMOn9dmY/7dT0xv+BkImKq4SAS2qfdEUkr6SUp3ICBUL9iMllxyCfPtgFrB5nPHxwF9ovrRxRUsU1B0LN653tES2FnOtShloRtTsA2yPKfiOdhVQSZzbrQjqgUbKSWGIZipedSLLo1+jIlmLQe+pBmmhKkeh1S6rpHkOY5hbIq9DNAOMn7t91+kXvR4+nCNj83UNrWrv1ujwUe5MPtRw2YLzf+VEOKvAz+Bzmp+TSn1/2zryPaw47F291d0LS6v9LAtg8srIaf3VYeLgMBgrORs265w7YLUCRLOLnQ5v9QnznJOT1egqKW0b/UR7sYZo4NaRppLXMvEtUy6t9QoVjFZ9ji/1GOkcLP+EaQ50xUfyzJ45ECZbpDRjlNMITgyVtRchgHqRYdPH6nz5myLIMnIckU7jAkSiWNqI4QwBdexqPgWFd/npYsNKr6BZ1mMl6EdppRdi8OjBT42XeLbby9ycbmPYwgEioJj4luGJuplgJQEyeZt2TLgeisiV/Dd95YoOhZHx8vA7fkhl5b6XGkEnJwssX+ksGEQ+SgXZjeD3cSUvmtQGHASvq2U+ivAv9r+Ie3hYWIrk3ft7q9edKgX6xysF/mDl65QsE2UUkSpJExzPrG/clvm8ocZ33TF08qlniZ+/cmZBRxLcGKiyLvzXV663OTTh0f4zLGxD/gI9+KMxw/UWOnFBGlOwTYZHaTANsIvP72f//6P3qEdpVRciyDVpLd/59EJolxqwbs0xzINDo0UiDMtgvfjqw26UUbZsxgvefzkyXEuLvWQCl69HGMa2tjHNLWIXZzknF3oMVFyGSnZTJQ9Do4WKLs2S90QIQxyqXhzrksqpe50koqZqs9I0eHScp8ok4wXHbpxRi6zdYY6dysvZAPp7X6S88KFFY6Ol+/ID+lECaYB5xf7lFxr6Ie9Viak2Y/54cUGo0WHU1MVXMtYV0vaTYvmRrjT+Hcb0e2uQUEplQshAiFEVSnVfhCD2sP2Y6NJDGxp8m60+3Mtg0/sq6JQdCItPHdqqrSO/fphxnzr+L7+8jWmqy43OiHfO7uk+QEZnL0hmar4WEZEM0g/QEoDmKx49OJsuBPuRRkXlroIoVNQty5MX/rYNI1+zO++cIWlXky94PATj45ycKzEvqrLv3jxGgpFnOS8cqVBP0rxXZtHp8vMVH3mWxHPn1tmuuIy2wwpexYFx6DgWtimydWVFDWQ4wCI85yKZzNWcvjCyQkAvvVmyIXlDgXHouiaeLaJYxr0k4xnjo5S8R1WejG5TGkGKVGuHdwcQ3ceebZJkOTcSRw1ySXXGhH1os31Zkijn/DufJskl8P7svaU2Itzar5DlGqmer3oDoPI2u/ss8dGOXujywvnl/nssdHhvNpti+atuNv4d1s9ZbM1hQh4Swjxp0B/9UWl1H+xLaPaw7bidpPYNtnS5L1dJ9EvPXVguHu/n3IiGz1c3TBlttnn1FSFIMkGZjSCkguWaeA5Jiv9eMOi9ldOT/O15y8BWhbi3fkOaa74udOTt9X+/6VPH+YnTk4O0yatMKEXp3z3/Q5BqhfiRj/BtUwEAs8UrPRS4lSy0k8QSpFLRdGxWOomZLkkkwkFxyaT2iaz6JnMVH1826IVJfTXpH+aYUqSSibKFo5lUnQsenGKJQQXlnpUPYdWkOrgmOcYCDJuMp1XGdl3wurJY7ETMV5y+dHFFYSATx0eGd6XfpxxbKI0+B4soizHs82hGupqamjtd1bGZuy4p5sRHHN4X3fbonkr7jb+3VZP2WxQ+OPBP7DeP/yhYK/76MPhdpP4lSuN4Y50FXeavHfqJJqoePe9XW/14Wr046F20mwzBCS+bSEG/zMMQSoVjmVgmwYjvrOhR/PjB0f44sk2/+eL17jWDCg6Fj/9yBjHJyrr7tVGzGyAxU7MVNWj6Fp8550FFrux9j6wTCxD0Iszwszk6ckys62A8sARLpMwUXGxLINmPyZKJYYA1zJRAoquzYF6gTiXLHQiRnytHdSPtXmNbd604yzY1tBP4dpKiBoF0xQYuaZMGEIv8hL9wIbJxvWStVBKIIQiUxAkWt310enKMC0EMNcKhqfEw6MlXr/WIkpzyq79AZmQuy2Iu23RvBV3G/9uq6fcMSgMBPD2K6V+e/DzS2g7TgX819s/vI2x1310E/eSi73dJFaKLU/e23USbUe7Xr3oMNsMOL/Yx7dNKp4NKHqxZKkTMVKwmW2EGCb4lt5Bx4nkif23Edy72uS5d5ao+Db1xMExDV671mHfSHGYR7+VGT1d8ZjvRDz//hKuZfDodAVDCLpJRpLmZApqvjFgF+vWUM82aAUJvm1iGQa+I5go+3SjjNGSy2TFo+bbvDXbxjWhEWV8/9wSlYLDmGcy2wz47e+eY7LscWK8yIhv0Qi0nEbJNzni+Jyd1w5o/SSn5Fp4lknYDQkzhWUKLEPv5fKBd7MpIFPgmGIg0X3zvkS5wkD7QU9UvQ8w0OMsZ6ETMd+OhjWCExMlzi50qPjWOuG7uy2IN1uam4yX3aGi605eNG/F3f7G3SbEeTeuwW8C31zzswM8hbbY/HvbNKY9bBL3Ki19O1bxx/dVdzRL8/H9Nd5f6CGEwrMNoiynUnA4PlGiFaaUPZtDYwWqvo0SAs82+clTY3ziwM3xr2XV/k9/epbZRo9OmBJnim6c0QoSXriwAnyQGT3fivja85e43goxBAgEr19ra/c0BWLwNEkFILBNLVfRChNqvk0uFf0kY6LsUfIs9tUKuKZBwTF55ugov/z0PlqRZKTg8MhkmTyTvHytQ7Vg8bljYxRck++fX+HySjAw63GxDcG78z06UYpCsdSJWO7GdKMU19A2mQItr1FwTQ6NFjgw6mlCG6BQGBuc+RUQS3AMMZwrjX7CX7y/wP/18jXaYcZj0/pE9cL5ZTzb4De+dJL/7KdODGsOf/DiFZr9hNlGsOGcWp2/kxUPyzBohymvXW1yrRHsqHl3N9yN3fywpOrvFXdLHzlKqWtrfv7/lFINoCGEKG7juPawCdxrLvZ2O5efeWxy+P6dyNKcrvkcqhfoRIkuYnsWP3lijItLfTKp+KlHJnjpUmOY/3Ytc93DeWst5dKgbXW0ZFDzLRr9lFxJriz16EbpOmY0wFIvola0WO4mVHybOJP4tjkorjoEcU6SZTSCaCCbYVJwTS6vBBhAlOaYAq41dHeQlIqPz1T5+cdnmO9EfPOtG5RcE9cUhLmkl+aUPYurjYg0b7HcjcmVIspgouTyxrUmjb7WNTIMWO7GA9E+RZ4BBniWxLdt4iyj6FqMFBz9e0K/J5OrQewmhH4rudR2o90ooxWkvL/QZa4V4NkmExWXq42QJw7UsE0xrBFsVK/CEERpRpjm6+bUc2fmh/O35FpcXg5Y6sXc6IRDO9TdgM0QMncT0e1uQWFk7Q9KqV9f8+P4/R/OHraCe83F3m0S3w+Nm+1qLzwyXiRMvHVHdcswuNEJafYTCo5udXxrrs3pmcq6HdmtQVRWj/PNAAAgAElEQVQBhqHZw8WizWhJsNyNCXOJ75hDZvQqulFG1bPpxikf31fjhfNLdKOUXqJd2KIsY7ToIoQiTCRRKqkVHL70yAT7Rwq8O9/mT99dJMlTSp7FZNXDMATPv7/E/nqBOM2puDaJVEyUXN6Z6xCnGcIwcExBK0yJs0wXjYWgH+eESU6t4LDSz0lzSHJ9KjAHtYQ4A9uUFByTOJVcawQUHJOqZ9EMM2wD0ny9JpJi0KlkwXIv4Wcem+T3f3CJTCoEgpMTRcq+Q5hqguATB0aGc26jjcr+GviOyZdPrycFrm9pdqkX3aE44m5ZQFexmxb9u+FuQeFFIcR/rJT6X9e+KIT4T9ACefcNQogvAv8QeBv4Q6XU9+7n9T+K+DAFrO2axNvdXrjRKcc0BF85Pc0bs22eOlRfd/pZi1uD6FjZYbYR6mJqwSaXYFsGp6dKfPn09JAZvaqhtNiNefVyg36a8+rlFRIJJdukUrDJDIFvmfSTlEyCbxtUCwaPTpc5WC/S6Ce8M9/FRPsej5U8fMfizdkWcSYZr3gstCMuZQHR0G9Zq7EWLIOFToSUShfQTYFnWcw2Q8JEAom2veSmIJ5SMFJwBnaYAjG4XxXf5mqjTz/WGklKaEG9teSF1WySUJArpU9oo0WeOuTy+lU9XgDP0t1Ga+fcVjYq9zp/dzunYafjbkHhvwT+tRDiV4EfD157CnCBv3a3iwshfg/4eWBRKXV6zetfRns8m8DvKKX+EXpa9gAPmN3i3/GXEjuxgLXd7YW3O+Vs5nNvXYQ+sa9GN8xohynXmgG+ZTBT9fipUzqNtnp/V1MnUZxxZaWHMAwWB/LZbcvk8yfGOLfUJ1OQZArH0imZbpxzebnP//Gjy1xZ7hOmOZYBSQbXG8GgGK3wTEEnTFgJEvqxRAjNK1BAP1VYZkqQKKQE2xL4joVtGUMp7jjX5j+ZUggtiopjC6JMYVmCgqNVWouOSZJL4kyilA5cYSY/kD4yhC5IhzmMl02eOzPPmbk2rmUwXfW4vKJrVgr9uWvn3FYW+nuZv7ud07AbcMegMBDB+6wQ4qeBjw1e/mOl1J9v8vq/D/xT1vg5DxjSvw38DHrxf1kI8U3g+0qpvxBCTAL/M/Dvb+UP2anYzl3NTlRqfBDthRudcjbT+njrIjRacrAMgyNjRQqOiVRacXS1gLp6f1dTJ3OtAMs0sU2DQGbkUpEDL11u0ItzpJQUHIvJitY+urzS4+3rHQ6NFqh4Nq0oodHPcS0D17YGxjc5YaIwTROU9kNOct0N5FgGIpNIJTCE/izT0ATBLNcLu4U21DFNSNNBSgwo2CZpPpDPEALf1oXcSkHLm6uBf8RA6Xsd1KBDSQDVgkeY5Hxif42XLjXoRD1OTBRZ6MQs9xI+e2x0qGm10T2+m0PbVufvbuc07AZsVvvoz4HNBoK173teCHH4lpc/DZxXSl0EEEL8IfALSql3Bv+9iT6JbAghxFeBrwIcPHhwq0N6oHgQu5qdlst8WD3Zm/ncWxehIMn5uY9PkeRynQzFfCfi8TXvWU2dfP/cEhXPxrUHzmhSUbRNrjRCPNtACC1lraHNdeTAy8Aa+DkrpcXqBErn8QdMsjDNkUphGgaeoa9T9W1aQYrvWszULOZbEQz8KpRSFBxraLAjDANhaIkMIcC2TCbKFnGuqBVsSq6NANJM4RgmkZIDc6H1sAQgdNeRIaAbp/zhy1epFRxOThTpxTnXmiGfPzF+W3e9rSz0W52/u53TsBuwWfLa/cQ+YG1H0yzwzEBw72eBGvp0sSGUUl8Dvgbw9NNPb1Il/uFgN+5qPuzJ5mGktLS2TsIPLqwwVnI4NVUedh7d+rlrF6E/ePEKYyX3ti5wq/diNXWS5xLb0FIdtmmQSW2KAwLXMlBYlByLOM9xTRPLNCi4WqU1SHIsU+AYkEs1VDNdbWVN8xyp9Emg5JhEmV60fdfCM3V77b6aR5TlCCEYLbpkUqeCqr5NO0xxTBNDCMqeScmziZIM2zRYCRKuLPdY6MQIoUlvhnGT5cxa8x2hTyIAWa7tROsFhyDJ+eGFBn/lsUkqvv2BovHt7vH9xm4jgu1GPIygsBETWiml/hWbFNzbLYzmh72r2eoCfz9ONg86pbV2zJ87PsbZGx1+cGGFZ4/W7zruOy0wa6+7mjrxbFP3oqMwhEHFs+iEGfvrHkdGC5xd6DFa8ig4BkGiF9TJssvj+6tcXO6TpjmdMNUPgFJkUp8WLMC3TEyhkEr/M1p06CUZQsFTh+p85tgYAH/x/iIXl/r0kpSTk2U808BzLVphwkJb8xMmKx7PHK3TDFIMAf/6tTlaYYpjaaJakumHcJBZol60idOUXgJSamZ1luUow0BJhWEYlDwdKF68uMIvf3rjE/qDKADvxDraRw0PIyjMAgfW/LwfuP4QxrHteJi7mntZ4O/XyeZBprTWa+vA2PHxD2jr3A53WmBuve5njo6SpBk/vNQgTHIgw3dsjo4V+e9+/jEmKh6/9/2LnF/qsRIkFG2Tzx0dpRPlpFJyaNTn6kofhV6Qc6XbSgUKyzQYKTlYhpb4znMoeRaf2F8dLvpvzrZJspz5ZshIwSFTilaQMlX1ODlZohVkuNZN+erZZsAL55e53goJ09WUkoWtFIKMKFV4pkHJt9lX8zm30MUWOa5jMFP1udroIZTkRjem2AgYLdlYhmCpH29IKntQBeCdWEf7qOFhBIWXgRNCiCPAHPC3gF/dygV2i8zFw9zV3MsC/7BPNveCRj/BEHDuSo9urFVZD476N9Mzd8CdFphbC9f1osNPnJygGaZalTTNsISg4Fq8cGGZo+OlIQlt7U55sRPxrTPzLHQiLEtwZLSAaRjc6ESDGoRgouzxk6fG6YQpUsF//tMnhkSwb/x4lkYvBhTnF7tc7yQcqpuMFh2CRPLOfJeDdX9gv2lwabnP5eU+7TBlouzx7vUOaS6RCAQSYQiKrkUmU0xTp8tmB4HDNPUpQQh9OmDg7iaA2WbIiG/ziX3VDefPg0yV7rQ62kcN2xoUhBBfR0tijAkhZoF/oJT6XSHErwPfRrek/p5S6u0tXndXpI8e5q7mXhb43ZivFShevtyi5jtUPJsolbx8ucnThzYnkXC7BWaje3H2hmY4f+74OJeWezx/bplOmPHG1TaebbLYiT+wM56u+Tx+UHNA/87v/AghoOw5FFyLXEKaZUgFTx6sb6geur/mM1n2uLzSY7YZkeSSq42AVCpqvo1nC77x6ixFz2as6HJgxGeuGdFPck5MFnEcE5Uqslyzk33bIBsUxLMcKr5B2TXpBDGphCjNOL/YGxbEK77NTM2jHaVEqeSXnjrARtiNG4o9bIxtDQpKqV+5zevf4kPYee6WkwI8vF3NvSzwuzNfO5CCFrrnoJ9kzLUC8lwyUnQ3VUfZKA++0b1Y6esWzEY/4flzSxhCMFp0aEcZ5xf7HJ8o3tadrNFPiLOcdNV72bOYa4fkOdRL1ge8HmD1FCR4c7aNVIpwcDrpxxkL7YjZZkCW5SRSsc/UBLd+nGEMTgNzrZCpkstyX9AOEmxTM7ijNMc0DD5zuM5iP+V6K8QyTNI8RyEwDN0UJaWi7Jg0Aq3d9Pi+4jDA3YrduKHYw8a4myDejoQQ4q8KIb7Wbu95/twOdxPp2gi7TbgLdI/9p4/UcS2DG+2IuVbIwXqBkmffVSBwNT3z4sUV3rne5sWLK3zjx7PMt8JBYKjy3o0O33rrOu/d6PDoVAnXMrm80kMOPBEyCVXfwrdNLi72+P65Jf7gxSs8d2aeN6421wkWHh4tEueSIErJFYyVXMqeyXTV3/Be14sOZ2908B2DTphQ8R1NkJNSy2tEKe0oR0moFxwyqbQlaZjSiRKurAS4jolAIYQ+GaDgSL3AwXqBIJc8dXiEx2YqlH0T2xRDIpshwLdNDoyV+BtP7mem5tOOMp47M7/h/byX+baHnQmh7ua4sYPx9NNPq1deeeVhD2PH4i+DHMBzZ+YJk5yyZ/Pjqw0twaB0i+iTh0aGKZmNWii//uIVXr7cZKRo41kmUZbT7Kd86vAIXzw1MSycrp4UZlshSMXVZkA3TAlTiQKOjBVRSvHWXIcnDlR59tgY/TjjxUsrnJzUMhegd/7ffW+BKM3ZN1LANnW75y8+fWDD72W+FfJbf/IuY0WXC8tdwjjnzPUOeS4xTYM01z4MRddkouyxr1ZkqRfR6MeYhslURaeTvnN2EUsIjo8XcW2bZhATxhlBmjNR9QnijNlGQCZ1e23BNclzhecYjBUdHp2podTN4LsqnnjrmP8yzLePCoQQryqlnt7ovz2MQvOHxm6pKTxs7MSC3P1eONameTqDXv0oyzk1pT0U4kzy8uXGhp/31lybWsHCt/Vj4NsWqqB4a67NSNGh7FmkueL1a026UYY1aDFdVUSVSjJTcym5Fm/NtXBtg5maP/z9q40AyxDDoFAvOnzh1ARvzrZ4bKaCGPQifffs4ob3Yrrm8+zROu8v9EAJyr5NvWgTpFphVQAlz6RecGmFKQfqgvGyQ5TlHBsvMVZ0udoImCq7ZLmkm+T4roVlCIIkIweCOCVKc6JMggLbUIN0HFhC0A4zKp7Fo9PVdamg25kP7bT5toetY1emj5RSf6SU+mq1Wn3YQ9nDFnCv/g93wtqUl1TaH+CJA1XqRZdGP+GlSw1cy9jw87QQ3C20GSUQgkENQPL6NS0AV/FshIC357t85fQ0zxwd5acfmaBedFjsRrTClJpn8qfvLHD2RhchBJ5l8Pb1rvZbGECfaizaQcobsx3CNMcQghcvrvBbf/IuX3/x8rr78dh0lTCVlFyLTpgM9ZOmKx7jZYeCY+nTgmMilaTRy6h5Nl84Oc4XTk1wcLTAp4+OMV3TPhOuZVLyLDIE0xWfoqttQF1LINEkNtcycC2DKJPsr/s8e2xsXUAouhaNfnLP39kedjZ25UlhD7sT29W2uLpDXT012KaBVIp359sIwdAdbe3ngeYLvHatRb3gsG/EI8kkl1f6TFY8rqz0afYTyr49PEkIDMZKDvOdaNhV5toGB+tF4jSnGaTD4HF5uU+tYNGPc96d7/DssTHmmiGvXG7y1OEaS90I04A3r7VRKMbKLmNFl/cXeqQ5a7wt2pyaLHNxqcvllT6mITAQCCGoFRzSXBGlktGSw8F6gScOuowV9eKv77FFnEn21Qo0w4RmkFByLGYq2ujHsy3iVEtwu4PVwDAFDgZKKTphxg8vLK+z49wrIH+0sSuDwl76aHdiu9sWb20BTnLJpw6PrPMWXrXYXOzEHBsvc2Wlz3s3Orx6ZQXbNDg2UeTZY2MEScYL51d4bKai6w2pJExzPrG/QqOfrEuVPHdmnicOjvAnZ+ZxLS2Yl2SSZj/lpx6Z4GojYHlgHvPU4RoH60UuLfep+Q4L7Q4KOFDXdYlV86DVwLUaRJd6EZ85Nko/yrm41KXs25qLoeDouMOj0xWOjpeGhd1VdddumPLW9Q6eZfDFU+MsdGI6Uca+EZ8kk3TCjGwg0e1YJgZaiK8b5xQs7SnRDjNevNRYZ1y0szvS9vBhsCuDwm5qSd3DTdzattjox7w73yHOJM+dmb8vhclbF2vNPr6JfpzRChOmqh7LvZh2qL0UlFIoAUEi6YQpR8dLPDpVYqkT4VoGZdfm1FQJ2zTwHXPdNRv9hP0jBR6ZrHC9FREkOb5jUnAMJiseh8eKfPn09FBrCfRiH2Wa7RynkouLPVpRQtm1ibN8SL5b/f1ulFHxbLySSZIXmaq6HyC7rcXj+6t8/eVr5FJxeqZKqx/znXd07cK1TfaPeCx2EkZKNo9YZc4tKFpBRtGz6MQZCkXBM5mseJzeV+Pd+TZvzbX5/InxPQbxRxy7MijsYXdibVE4znJevtwcdrWs5vvvZwvs7XgXVd+m6Fo89/YNRooOJdfmsugilMFIweHVq02Ojpd46nCdH1xY4cmDI3fkbawGu4/v1x7XvTilEybEmcVsK+QXn9y/7vfKns3h0RKvX2sRJTrt5FoWphDUfHtIvhsparmINFcsdmMuLvXxLZOZEe8DZLdbC/jNfswzR+pDg6DXr7WoFh3Krs1kxePsQgdLwGInASEouzYIwUjBxTATRoo2Ut78+549NsZyL76jEN7tsNeVtLuwKwvNe9idWFsUfmuuTcWz+MzR0UG/vr0ubXK/P28t7+LoeEmfGIIEIeBGO6TV1z32QihagS6iupbJs0frt+VtzLdCnjszz6WlPi9eanC9FSKVIsm1wul4yVlngLy2l79WsJmsOKz0UzphSqMfM1FxKfnWQE1b8Pj+GrOtkB9dXKHm24RJxqWVPq9fbfLtM9eZbYU8vr+2YQH/hxcbxJk+bVxe6eE7BjXfoZ9kHKgXmKy4XFoJsE0DoaCX5BQdi6mKw1jZxbdMTkyWh14L91pH2I7mgj1sL3blSWGvprB7sZreWa0vrJWt3g5ZhNu1Sf7pOwuYhuDiYg+lBKnMiVLFm1db1CsuL5xfuiuHYJXHcGyihGebfOe9BWqezemZGofHCtSLLt0oHRbS19Y8Liz2mG/HnJosUy/ZXG9FXG2EFB2LTx+pIwc2mGNFm2srcGUlYKUf41kmEsW7810aQcrYYKG+tYA/WnQ4e6OLMW3w9nWdouvHGQXbABTvzXfpxymNfkIqFVGSI4DFXsLnj49zeSUgl5KKtzHbevUe3O0EsBvl4/+yY1cGhb2awu7Hw5RFWF2cXzi3RDNIsYSBaSqiTJFLyeSqurvxQZX31YXw+feXcC1j2Nl0oF5gpuoxWnR58tBNKYhbA91qcHjuzDyubfDixQYXlgKqvsXRsQIlz8K1btYtmkGKaRiUXJMTE2XiVHK9HVEtWBwaLfD+QpflXsLnjo9RXjPOU1MVvvPuAu0wI8tylroxAt0me3Gpx1wzQirJaMml6lkI4EYnohkmrHRjvIGR0KcO65PSrXWEW1VR55oh3zv7PgfrhWHBe23wX4s9TaSdjV0ZFPaw+/GwdZama1pa4tBogW6kfZp928B3bCoFm88NJLjX7mjfuNrkd1+4xFI3ZqkbMVlyafZTPnt8lHrRZazksNxb379/u0B3canHtUbISMEmSDKCWLLc63Nmrs1LFxvsr/s0+zFzzQBj1X8ZmGtH9OOUXEmyXHs0j5W0HMbY8fHh9V3LYKriYpsG11s6i+Wags5g159KiYE27xFCYAo91l6iyHLF8YkSZdfGd627ngAa/YRziz1MQ9AJs3X1oT1NpN2HvaCwh4eC7VKQ3UpRcyXQv3NotMSVRg/XNAmTjEaQ8uOrjWF3z6oE9v/wx++w0I2pedpbYKGfEGaSypzN6X1VVnoJl5f7vHB+iVNTlaEkxEaBrh2mGAaMFDw82+RKo8/1ZoRE8fnjFUquwytXWiy0Q+pFlyyTLPViwlS7qZnC4Nxij+MTRU5NVfjBhRWuNQIWuyHLvQTT0EXrJw/VaQ/+jjiVpFLXGUxDkEvNccil5GojJJMK3zIYKdiYhkGt6NDoxRumetaeAFZrFp5lDlpqb6aIHnbw38PWsRcU9vDQcL9lETYyevnGj2cZK9ooxAeCxGjBZaEbkWT5UB4jyvQiGWcSxzRRKL7xyjXOLfVoBCk130IIgcogz3PCVHB2oU0vzgjTnP0j/3979xoj13kedvz/nDNz5rqz910uudzlnRJFmbJEWzUd2XRtObRh2akhxJZdtAaMCDbsoh/rBAGMoh/qov3i1EYSWVGVoKldwUhd2ZXtpIkYJZGqiJFFiRJFSuZ1yRX3MnuZmZ37vP0wM4fD4ex9Z2cO9/kBC3LP7Jx558XBec57e94gb783zxvX5vjEXQN87v3DDb9jV8gpP1XnC0QCPiwRIgGbWMjPQCwMgIgQT5a7cny2Rb4Ajm3h2BbhSveSYBHw2dy9Lcq5GwmKJUNvJMBALMD5GwnGZhZIVabI9neUF+gNdBQYm8mQSGcZm0mRzBYxBhwbbNuiWDKUTIm5hRy+qNNw9XJtC6A6XTaTL5VnMXGzi0g3xfEeTwYFHWhWjdQPauaLhouTKeJJn5ukrnba64N7unnhnWnyhSKOLSSz5YCwozMIRsgUity3s5Oz4/NMJrIEfRaChd+2wPHhL1rkSyWmkjlGe6I4ttATDbC9K8JsOsel+OIzbHb3Rwj6bSaTGeYzefJFQyzov6X/Pei3CPptLkwlKRkY7g2RyuSZTOYYCDjsGYiSzhdJZAp0hQME/eVFbolMgakkDHUGOX8jid8SFkqGZCWD6fauIIl0nomEwRbBJ1AwkClCoFQinTdcm83QEy6yrTPYsKuntgUQDdjMpnMYI27OqdouIs2J5C2enJKquY9UI/FUjkjg5nPOpekkXWEfhZJx01zUTns9fnCQQ9s62NkTZt9AB/cNd9MZ9DPYGSLgs9wcSrPpHHMLOTL5EpOp8p4FtpST7XWHHe7d0Ulv1F+eyukvtyS6Qg7Fkll0iu2R4S5sS9g/0MFD+/vZ2x8pp5xO5zhzfZYLE0muxlPEUzn29EXpizpkckXCjo/Pvm+IIyPdZPJFeiJ+Hj40yOxCnvM3Em6epngqzyuX4lyfXSBXKOG3hKBj09/h0BsNAOUMrcUS+HwW4YDgs2AhX8KYIgvZPNOpHD7bapj+una6byxYPs++gQhdYUfTZnucJ1sKSjVSP6iZyBTwWxYdNYGidubLUFeIR4/udMcgBPDbULsIOp7KMj6bKbdAEG7Ml5hL51jIlZPG3bu9kz0DEV69MstAR9B9XyZfojfSuOul+tm13SqDHQFCfptssUTQgWS2wJWZFLt6whzd1Q0Ir12dRcRQKMHBbR1si4XcVs9sOodllTO9JjMFLkwmmE2XZ1Yd3tHJVKK8mK26f/OVmfIaBZ8NtrFI58vTVfPFEqlciaDP5vD2GHv6Ios+5de2AKpjOdpF5H0aFJTLKytPV7pbms8S5jJ57hq6OVlzsZkvcwt5LscX2BYLcmM+y1wmx6+u5CgUi3SFHIJ+uzJTBy5Op8hlC7x/Zy+fObKdgViQ02NzzKZzdIUcN0/Sju7IkrNs6lNyOH6bN6/PcXY8Qb5YQjB0Rxw3d9Ou3jD/dDnOmWvlJHqfPjzkvr8z5Hdn/lyamieeKg9kx0J2ud8/W+DXEwmCfrv8r89mLpPHFsgVy+myC8USIcdHTzjAFz64k9HeyIqnjmoX0Z3Dk91HauO108rT6krh6g5mtWVYqpz1K5gPDHawqyfsZk2t79YYn03z41NXefnCNC9dmOL67AIXp1Ls7ovQGfJTKJV4bz7L8bsGOLavF8dnUTRwz1CMh/b38dGDg5weK+/+99gHRiiWYCKRxW8L+wYi+KzGXS+NxFM5wo6PgM/Hh/b28slD29jdF+XSdJp4Kks8leXS9AJ9HQGO7e3lrm0xTo/NuXWzpz/KQMxhbGaBt8YT5IolOgN+esLBciqMWBBE+NKDo6SyJTqD5QAnUm7xlAzkS7C3L8qxfb3s7ovq1NEtSlsKCmiflaeNZhDVDg4vV876J9alujVOnrvBpfgCXSEHEfBbNhPzOWLBBe4d7sIYw6XpFOfem+fuoRjRoM37hjvdnd2qn33y3ATdEYfRnjCz6RyxkI/tlZbLSuuuJ+Lw8oVpQo7lpuoe6AiRyiY5Oz5PwGcjYjBG6IkEeGciwWQiy7XZBb5ybDdDsSA/fW2cvg6HvmiAojHMLOTZ1Vfe4AepbJ4DTC9k6ekIEnT8XJxOUSxB2C+IWOzsjXBoe+eiq5jVna+tgoKIRIAXgG8bY37W6vJsJe2y8nS5m/5qy9koSPzizDjxVI6fv/Geu4gt4pR3WYsEbM7fSFA0IGI4OBhzU0eXSiX8Povx2Qz9HQ6vXoZYyMeb1+f553cNsHcg6s7DX23X25HhLp57Y5y+SABjyusHLEvc1NszCzmGOoP0RAJcnFog5Fj0RwNMJnOVPSTg6K5uJpOZ8kBzOkdvh0MiWyCWLzA+m6E36vA/Xr5MNl8kmS3gt2x3ALtkfEQci7Bj8f8uTDEYC97SPaW2jqYGBRF5CvgMMGGMOVxz/ATwXcAGnjTGfKfy0r8DnmlmmVRj7bLydLmb/nrKWd8KKZQMV6ZThP0+BjpCXJhKAsZNjGeMcGRnJyCcHZ/jzetzWCKM9kTo6wiQyZc4eW6Cnd2hdbewarfenM/kb0nVXX3aT+eKvDORcFsT6VyR/miAjqCPU5fjfPTAADt7wuzqjfDiu9MkMuVEe7Ggj1zRsKc/Sl80QH/E4cJkCp9dTuPhtyxsG4Z7Izww2uMuMjs9NsdALLghgcEr41Wq+WMKTwMnag+IiA18H/gUcAh4TEQOicgngLeAG00uk2qgNoNno/73zVK96deqvemvp5y1rRBLhP0DUQolw9WZFJGAzfauELliuTXQEfC7U1KrqaO3d4XY3hUmGqo8S4khWygR8N+6v8Jat6s8fnCQ3X1R7h/pZqQ3zNnxeZ4/N8FMKstQLEgiU2AykSVgW6Rz5T0XdvWFiQTKmVWr9dYTCXBsXy8jveWptt0Rhw/v62WkJ4IlQtDx4dg2PkvojQToCPnJFyFY6RJrNH13PdppvEotr6ktBWPMCyKyq+7wB4F3jTEXAETkR8DngCgQoRwo0iLynDGmVH9OEXkceBxgZGSkeYXfYla78rRZT37LpUVYzwrZ+lbI4R2dzKbzlc12coT8Fh/e109fxE/Q77utNRIJ+Lh3RxdX4inmM3kE6AjYvDWeIBZ03MyotUHs9JUZnjszzsWpJBhh30CU9+3scuurvh53dAZ4/vwkr1+bozfs8OCeXoJ+H6fH5jgy3Mm12QUmkzn6owEObou6mVjv3VHey6Fab37bYndflIcPDfL8uYlbvvdUMsfu/jCzC3n6YwHCfpuJ+QxzmVs3JNqo7sN2Ga9SK9OKMYUdwNWa38eAB40x3wQQka8AU40CAoAx5gkRGQcecQYFcEwAABKpSURBVBzngWYXditZ6bTC5QaD11uG5W76a53+WN/11BMJcN/OLm7MZxjtjbjBDWgYmA5vL+czun+kx924pjcapJS4OYV1/2AUn2Xxz/b0cvrKDE+8cBG/D9K5EkVjePlCnIDfYmI+y5HhTk6Pzd2SafTUpRkiAYujIz2IBVfi5dxHHUEf4/MZvnJst1v3kcDNtNY393S+vd7qv7eRcu6j0d4we/rL03Wz+RIL+dt3qduI7sN2Ga9SK9OKoHB7PmLcvTwwxjy93Ak0dXZrNfvJr1lz3hu1QnyWVZ69U/d59YFpT1+Et8bn+Zu3J+iLOuSLRUSEkOPjIwdizKZzTCay3KjcuIe6Qjz94kW6IuWupKDfh+OzSNp53hpPcOKeCM+dGeeubTG3/iaTGboiPsbiafp3BBERoMCl6ST37exeUS6hRvVW/70How7nJ1Js7wy7g9qO3yZcCTIbnbiuXcar1Mq0IiiMATtrfh8Grq/mBJr7qLW8+uS3mq6n+tW61afze3fEeOlCnPM3EuztjfAbB/rY01/O91Myxr1xA9yYz7C9M8S1mYybwC7it5leKKfjuDGf4YHRHvczE5kCnUE/VyVNJl8i5Nhu5tH15BKq/973j3bTFQlQKJaYS+fx28KhbR185EA/4/OZDV+VrJlSvaUVQeEVYL+I7AauAV8EvrSaE2hLobVa+eS33rGM1XSRVT/n8nSKbbEQ+aLh0nSaPf0RLCBfKnFpOk1X2LltLAFgMBZkLpMn5FjkiwbHJ6TyRbrC5fobjAVvqceOoI+5dJ7R7jDpSleOoYTPkkVvoiutj8XWb9S+D2B8PrPiulwpzZTqLc2ekvpD4DjQJyJjlNcf/ImIfBP4JeUpqU8ZY95c5Xm1pdBCrXryW2ws48hwJ+PzmQ0b9K7/nF9dmWU+XcBvizsddLg7zFvvzSNiuDCZwm/fvnfCpw8P8cQLFwn4LJKZHKmcIZsrcf9IeQbVpw8PuSuiIwEf/dEgl6fSPLCri7Bjc+698q5qx/b2cvzgwG3faaW7nzXSKEjUnmtsZoGT5yYZ7Qmzuz+y7jrVNBjeIcaY5f+qTR09etScOnWq1cXYklox77yc8iLjpofuqGxdOT5XTvZWG6DWM+j9izPjpHNF9wn+1cszzGVyXI2nuXdHJyJCOlckXywRCViMz2U4cXioYR389Zvj/M9TY1ydSeHYNkeGOzm2v999Mj95boI3rs0hAoe3xzg0tPIAV1vO6sC3iKmscehYVT3ceq4sr12dQ8QQC/o4MBhbd52q9iIi/2SMOdrotbZa0bxS2lJYu426mbfiye/iZIor8QXCAbu8qUuhyOtjs3QGnQ0d9K4fM9nVFy4nxyuVSOcLCBbpfHmvBb9tcWRnNycOD912nvHZNNfmsnzq3qFbAlbtDKeOoI/jBwfc1wZiQY6MdN92ruXKudTuZyuZVvzm9Tl2dod5ZyLBmevzOJawvTtMMlvUKaRbjCcT4ul+Cmvj9UVEtemhRYSQ30euUN6ruNZaF49V1S+g64kE2D8YZU9fhMlEjpIp8b7hmNtltNjCufrFcrULwpZ6bS3lTGQKBH32bbufLVYP9ddCvlDi52feYzqZRSrV+c6NhDtVcL11qrxDWwpbiNcXEdWmhw76LTL5EgGf7aZrqFrvoPdiU1e/8bH9AO7TdSZfxG/D8+cmGra6lpul1ei1X08k3dxMy7XkVrP7Wb36ayHot/HbwlQiT8ixSedK7jlevRJnMlEeIK5mo1V3Lm0pbCH1O5OBt54A9/RH2T8YJeCzmM/kCfgsHhjtpiPk39D0HPUpuEOO7fanD3WFOHF4iI8dHCBfNAT9vkVbXUul7Gj02rWZNJfjCytuydXvfpZI58kXCrw+Nsc/vDvJWHxh0XqovxYMcPdQB4VSibBjUTIleqMOl+MLzKXz+CyLwVjQUy1LtTaebCmotfH6IqIjw11MzGfZPxi9pY/+YwcHNnx+/XJjJo1aXbMLeZ5+8aK7OnqouvnOQp6JRJqpZA7bEh77wAgDlRss3GyNnLsxz4HBjlW15KrlHJ9NUzCGeDJLvlgCBKxG60TL6q+F6nTYe7Z3cv9oN/FUlp+/MY7ftuisSeGRyOQ907JUa+PJoKDdR2vj9UVES813P7LJZanvGoqncpy/kaBQMjwwGnCzjO7oDPBXb09SLBl6IwEGYgFOj83x8KHgbd9lpCfMcHf4ls9Z6aLA02OzDHeFuHtbzD221A28/lqoTofd2x+lZAx+26Ij5Oc39/bSF725zagXFimq9fFkUNDFa2tzJywiapf57vVP2pemk1gW9IcD7sAxwD9enuHB3T23tM6qN+sTdfsV/OLM+JpbcitZZV4720gwjM8u8H/H50nnixwc7ODz7x8ib3CvjQ/t6SHguzUDrJdalmptPBkU1Nq1y03V6+qftCcTWXyWxa6+MPFUjkvTSebTec5PJNjdF6GDmzf6xZ6219OSW65rsHZxmiXwt+emuD6X4eC2jvIg9UKBt24kefT+4dvSe6ylPMq7PDnQrNRKLbXf83rUD0b3RBz2D0YB4bWrs2QLpfKeBSL85FfXeO6Na7x6eYZ4Krvo0/ZSA9zLWW6fidoxkCvTaXIFQ1fITzKTJ+z46Y74iSezt0yJXU95lHd5sqWgYwpqJVaT4nsti/oaJc07Oz5H0C9ghHgqS7Sy2nh2IU9XqMjLF+Ps6gnz6NGdy55zNZbrGqztXkpk85VZRrabLjvos5lL526biaYty63Hky0FnZKqVmKlC8Q2YlFf9aacK5bIFQwBn0VHyGZnT5h7hjoplsoJ9GJBH30dgabcaKvTZb/04Oht4xW1U2A7An58lsVCrki4smtcplDEb1s6XqC8GRSUWomVrsvYiNXFUL4pP7S/nwdGu7l/tBsQgj4bn21xz/ZOPnpggA/t7aMV2cZqu5dGekM4PmE2nSca9LOQyzOTytMTDWz69quq/WhQUHes5fZ7rtrIRX21N9/qKuPqXsqLff5mqB0fKBn4yIE+TtwziN8WMvkSH9jVfcsgs9q6PDmmoNRKrHQ2z0Yu6qvt248FHWYWChwYjNAVdtzB31bM3mlFVlvlTZ5sKYjIIyLyxNzcXKuLotrYSmfPLDdzZ6WqM52ePzcBwG+9fwf/9uP72d4VaunsHa8nQlSbS/dTUIr1P0nXznTaqH0dNkr9/hBQXkAXcuyGKb/Vne+O209BqY1SHww+1mCHs5Vo5wy0Xt1TW7WGJ7uPlNoIG9mt0s4ZaFc64K4UaEtB3QHW2vWzkU/37ZyB1uuJENXmapuWgojcLSJ/JCI/FpGvt7o8yhvW87TfrKmoG7Wvw0bRdBVqNZraUhCRp4DPABPGmMM1x08A3wVs4EljzHeMMWeBr4mIBfygmeVSd471PO03aypqO2ag1XQVaqWa3X30NPA94M+qB0TEBr4PPAyMAa+IyLPGmLdE5LPAtyrvUWpZ6xlE3ehuFb3xqjtBU7uPjDEvAPG6wx8E3jXGXDDG5IAfAZ+r/P2zxphjwJebWS5151jPIOpW7lZpVvZY5X2tGGjeAVyt+X0MeFBEjgOfBwLAc4u9WUQeBx4HGBkZaV4plSes92l/Kz7dryZ7rNp6WhEUGm0ca4wxJ4GTy73ZGPOEiIwDjziO88AGl015zGb35S8108krqSSq4zD5YonXrsySyObxWcLJc/DYg7taXTzVYq2YfTQG1CaTHwaur+YEmjpb1VoqZfRGWmqmk5dSScRTObKFIq9dnSNbKBEL+hGEly7E27K8anO1Iii8AuwXkd0i4gBfBJ5dzQk095FqhaVSbG9U+u3N0BNxOPdegpDfJuTYiAhiQW/Eacvyqs3V1KAgIj8EXgIOisiYiHzVGFMAvgn8EjgLPGOMebOZ5VBqIyy1rqGdVzTXOzLcxVQyh6GEMYZ0vkA6V+LgtlhblldtrqaOKRhjHlvk+HMsMZi8gvP+FPjp0aNHf2et51BqtZZb19CuK5rrDXWFOLa3l/M3Esxn8nQEfRwcjOG3hZBjt7p4qsXaZkXzamj3kWqFpVYtt/OK5kaOHxxgd1+U+0e6uW9nN35b2rq8avNo6mylVuFOmH1U5bXyqo2jqbOV2iBLrWvw2poHr5VXbQ7tPlJKKeXyZFDQdQpKKdUcngwK2lJQSqnm8GRQ0JaCUko1hyeDglJKqebQoKCUUsrlyaCgYwpKKdUcngwKOqaglFLN4cmgoJRSqjk0KCillHJpUFBKKeXyZO4jEXkEeGTfvn2tLopSbUGT26mN4smWgg40K3WTl7YCVe3Pk0FBKXWTl7YCVe1Pg4JSHuelrUBV+9OgoJTHVbcJrdWuW4Gq9qdBQSmP89pWoKq9tVVQEJHfEpEfiMj/FpFPtro8SnnBUFeIhw8NEnJsppJZQo7Nw4cGdfaRWpOmT0kVkaeAzwATxpjDNcdPAN8FbOBJY8x3jDE/AX4iIt3AfwH+stnlU+pOoFtrqo2yGS2Fp4ETtQdExAa+D3wKOAQ8JiKHav7k9yuvK6WU2kRNDwrGmBeAeN3hDwLvGmMuGGNywI+Az0nZfwJ+box5tdH5RORxETklIqcmJyebW3illNpiWjWmsAO4WvP7WOXYvwE+ATwqIl9r9EZjzBPGmKPGmKP9/f3NL6lSSm0hrUpzIQ2OGWPMHwB/sOybNc2FUko1RataCmPAzprfh4HrLSqLUkqpCjHGNP9DRHYBP6vOPhIRH3Ae+DhwDXgF+JIx5s1VnncSuLyhhd14fcBUqwvhUVp3a6P1tnZbpe5GjTEN+983Y0rqD4HjQJ+IjAHfNsb8iYh8E/gl5SmpT602IAAs9qXaiYicMsYcbXU5vEjrbm203tZO624TgoIx5rFFjj8HPNfsz1dKKbVybbWiWSmlVGtpUGi+J1pdAA/Tulsbrbe12/J1tykDzUoppbxBWwpKKaVcGhSUUkq5NCgopZRyaVDYZCJyt4j8kYj8WES+3uryeIWIHBeRv6vU3fFWl8dLROShSr09KSIvtro8XiIih0TkGRH5QxF5tNXl2QwaFDaAiDwlIhMicqbu+AkROSci74rItwCMMWeNMV8DfhvY0otkVlNvgAGSQJBympQtbZXX3N9VrrmfAX/aivK2k1Ved58C/qsx5uvAv9r0wraCMUZ/1vkDfAS4HzhTc8wGfg3sARzgNHCo8tpngRcpp/Zoefm9UG+AVXl9EPjzVpe91T+rveYqrz8DxFpd9lb/rPK6G6C8t8t/Bv6h1WXfjB9tKWwAs4o9Iyp//6wx5hjw5c0taXtZTb0ZY0qV12eAwCYWsy2t9poTkRFgzhgzv7klbT+rvO4mjDHfAL7F1siJ1LLU2VtBoz0jHqz0h3+e8o1N03zcbrF6+zzwm0AX8L1WFMwDGtZd5f9fBf7bppfIOxa77nYBvwdEKLcW7ngaFJpnsT0jTgInN7conrJYvf0F8BebXRiPaVh3AMaYb29yWbxmsevuEvD4JpelpbT7qHl0z4i10XpbO627tdO6q9Cg0DyvAPtFZLeIOMAXgWdbXCYv0HpbO627tdO6q9CgsAEqe0a8BBwUkTER+aoxpgBU94w4Czxj1rBnxJ1M623ttO7WTutuaZoQTymllEtbCkoppVwaFJRSSrk0KCillHJpUFBKKeXSoKCUUsqlQUEppZRLg4JSdUSkKCKvichpEXlVRI6t8v3HReRn6/j831vre5VaLw0KSt0ubYy5zxhzBPhd4D9u8udrUFAto0FBqaXFKKfrvq0FICLfE5GvVP5/QkTeFpG/p5wFt/o3/SLyV5UWxx+LyGUR6au89i9F5B8rrZI/FhFbRL4DhCrH/nwzv6hSoEFBqUaqN+W3gSeB/7DUH4tIEPgB8AjwELCt5uVvA39jjLkf+F/ASOU9dwNfAD5sjLkPKAJfNsZ8i5stlS2934ZqDU2drdTt0pUbNSLyIeDPROTwEn9/F3DRGPNO5T3/nZvpln8D+BcAxphfiMhM5fjHgQeAV0QEIARMbPQXUWq1NCgotQRjzEuV7p5+oMCtretg7Z8ucopGefqrx//UGPO76y+lUhtHu4+UWoKI3EV5/95p4DJwSEQCItJJ+Wkf4G1gt4jsrfz+WM0p/h747cq5Pgl0V47/NfCoiAxUXusRkdHKa3kR8TfrOym1FG0pKHW7kIi8Vvm/AP/aGFMErorIM8DrwDvArwCMMRkReRz4PyIyRTkQVLub/j3wQxH5AvC3wDiQMMZMicjvA38pIhaQB75BOfA8AbwuIq/quILabJo6W6kmEpEAUDTGFCrjE39YHa9Qqh1pS0Gp5hoBnqm0BnLA77S4PEotSVsKSimlXDrQrJRSyqVBQSmllEuDglJKKZcGBaWUUi4NCkoppVwaFJRSSrn+P+0+pFl4Q5oIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the reggression line for all the years\n",
    "plt.scatter(df.budget,df.gross,alpha=0.3)\n",
    "plt.title(r'Budget vs Gross (Overall)')\n",
    "plt.xlabel('Budget')\n",
    "plt.ylabel('Gross')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph it is clear that our hypothesis holds because we can see a linear relationship between the Budget and the Gross."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 2: Best genre to make successful movie in the current era"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before getting into current era lets explore the best movie genre of all time by plotting the highest gross movies and understanding the number of movies in individual genre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![png](/projects/movie/tree.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the tree represents the number of movies released in that particular genre and the color of the genre represents the Average gross of that particular genre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we are able to interpret that even though the number of movies produced in the animation genre is less the gross obtained from it is about 80 M USD. \n",
    "Adventure contains a considerably large number of movies and the gross obtained on average is also $80 M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets explore the trend of the current decade 2010-2016"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![png](/projects/movie/2010.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph it is clear that the average gross from the **animation and family** is higher and the number of movies released in the genre are less our production company can use this opportunity and capitalize on it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 3: Common Plots of successful movies?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this we can use field plot_keywords to arrive at the keywords which occur more often in the profitable movies of the particular genre. Based on the keywords frequency we can make a plot out of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "1. [statista](https://www.statista.com/statistics/237749/value-of-the-global-entertainment-and-media-market/)\n",
    "2. [History of Film](https://en.wikipedia.org/wiki/History_of_film)\n",
    "3. [Box office](https://en.wikipedia.org/wiki/Box_office)\n"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}