Final_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Microsoft new movie studio: Oldies but Goodies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Movie studios image](https://support.musicgateway.com/wp-content/uploads/2021/07/best-movie-studios-ever-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview:\n",
    "\n",
    "Microsoft is trying to break into a robust industry with no prior experience. There are numerous factors and moving parts which must be taken into consideration when making a movie, and the sheer number of forks in the road can create a barrier to entry. Where does one even start? The findings of this project offer a clear, logical path to entry into the movie industry. Each recommendation follows that the previous is accepted, and delves deeper to offer a highly specific path. This project analyzes the most capital efficient and least risky movies to make, and provides a streamlined approach to implement our major findings. Data analysis of multiple movie databases shows that reboots of blockbusters from the 80’s and 90’s are by far the best method to approach this business problem. To take it a step further, this project analyzes specific genres within this class of reboots. Finally, individualized intellectual property acquisitions are recommended on a budgetary basis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Problem\n",
    "\n",
    "Microsoft sees all the big companies creating original video content and they want to get in on the fun. They have decided to create a new movie studio, but they don’t know anything about creating movies. We are charged with exploring what types of films are currently doing the best at the box office. We must then translate those findings into actionable insights that the head of Microsoft's new movie studio can use to help decide what type of films to create."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data understanding\n",
    "\n",
    "#### rotten_tomatoes_movies.csv\n",
    "* **Source** : This RottenTomatoes dataset comes from [Kaggle](https://www.kaggle.com/stefanoleone992/rotten-tomatoes-movies-and-critic-reviews-dataset?select=rotten_tomatoes_critic_reviews.csv)\n",
    "* **Contents** : Data about movies including their synopsis, rating, genre, director, writer, release data in theatre, release data on DVD, Box office rental profit, length of movie and studio of production.\n",
    "\n",
    "#### tmdb.movies.csv\n",
    "* **Source** : This dataset is provided to us by Flatiron school.A compressed version of the data file is available in the project repo in the folder \"zippedData\". \n",
    "* **Contents** : Data about movies' domestic and international gross profit along with the name of studio production and release year\n",
    "\n",
    "#### tn.movie_budgets.csv\n",
    "* **Source** : [The Number](https://www.the-numbers.com/) dataset is provided to us by Flatiron school.A compressed version of the data file is available in the project repo in the folder \"zippedData\".\n",
    "* **Contents** : Data about movies' production budget along with release date, domestic gross and worldwide gross\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we import the `sqlite3`, `pandas` and `numpy`, which will be used to for reading and exploring our data. We are also importing `seaborn` and `matplotlib` modules, which we will use for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run this cell without changes\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_chars(ds,char):\n",
    "    \"\"\"\n",
    "    remove characters from the data series\n",
    "    -\n",
    "    ds: data series, lists. Element in data series or list must be a string.\n",
    "    char: character to be removed. Accepts lists\n",
    "    \"\"\"\n",
    "    for char in char:\n",
    "        idx = 0\n",
    "        for row in ds:\n",
    "            if char in row:\n",
    "                ds[idx] = row.replace(char,\"\")\n",
    "            idx +=1\n",
    "    return ds\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rotten Tomatoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>production_company</th>\n",
       "      <th>tomatometer_status</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Always trouble-prone, the life of teenager Per...</td>\n",
       "      <td>Though it may seem like just another Harry Pot...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action &amp; Adventure, Comedy, Drama, Science Fic...</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>Craig Titley, Chris Columbus, Rick Riordan</td>\n",
       "      <td>Logan Lerman, Brandon T. Jackson, Alexandra Da...</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>...</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>49.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>254421.0</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0878835</td>\n",
       "      <td>Please Give</td>\n",
       "      <td>Kate (Catherine Keener) and her husband Alex (...</td>\n",
       "      <td>Nicole Holofcener's newest might seem slight i...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Catherine Keener, Amanda Peet, Oliver Platt, R...</td>\n",
       "      <td>2010-04-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Sony Pictures Classics</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>87.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11574.0</td>\n",
       "      <td>44</td>\n",
       "      <td>123</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/10</td>\n",
       "      <td>10</td>\n",
       "      <td>A successful, middle-aged Hollywood songwriter...</td>\n",
       "      <td>Blake Edwards' bawdy comedy may not score a pe...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy, Romance</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Dudley Moore, Bo Derek, Julie Andrews, Robert ...</td>\n",
       "      <td>1979-10-05</td>\n",
       "      <td>...</td>\n",
       "      <td>Waner Bros.</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14684.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  rotten_tomatoes_link                                        movie_title  \\\n",
       "0            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "1            m/0878835                                        Please Give   \n",
       "2                 m/10                                                 10   \n",
       "\n",
       "                                          movie_info  \\\n",
       "0  Always trouble-prone, the life of teenager Per...   \n",
       "1  Kate (Catherine Keener) and her husband Alex (...   \n",
       "2  A successful, middle-aged Hollywood songwriter...   \n",
       "\n",
       "                                   critics_consensus content_rating  \\\n",
       "0  Though it may seem like just another Harry Pot...             PG   \n",
       "1  Nicole Holofcener's newest might seem slight i...              R   \n",
       "2  Blake Edwards' bawdy comedy may not score a pe...              R   \n",
       "\n",
       "                                              genres          directors  \\\n",
       "0  Action & Adventure, Comedy, Drama, Science Fic...     Chris Columbus   \n",
       "1                                             Comedy  Nicole Holofcener   \n",
       "2                                    Comedy, Romance      Blake Edwards   \n",
       "\n",
       "                                      authors  \\\n",
       "0  Craig Titley, Chris Columbus, Rick Riordan   \n",
       "1                           Nicole Holofcener   \n",
       "2                               Blake Edwards   \n",
       "\n",
       "                                              actors original_release_date  \\\n",
       "0  Logan Lerman, Brandon T. Jackson, Alexandra Da...            2010-02-12   \n",
       "1  Catherine Keener, Amanda Peet, Oliver Platt, R...            2010-04-30   \n",
       "2  Dudley Moore, Bo Derek, Julie Andrews, Robert ...            1979-10-05   \n",
       "\n",
       "   ...      production_company  tomatometer_status tomatometer_rating  \\\n",
       "0  ...        20th Century Fox              Rotten               49.0   \n",
       "1  ...  Sony Pictures Classics     Certified-Fresh               87.0   \n",
       "2  ...             Waner Bros.               Fresh               67.0   \n",
       "\n",
       "  tomatometer_count  audience_status  audience_rating audience_count  \\\n",
       "0             149.0          Spilled             53.0       254421.0   \n",
       "1             142.0          Upright             64.0        11574.0   \n",
       "2              24.0          Spilled             53.0        14684.0   \n",
       "\n",
       "   tomatometer_top_critics_count  tomatometer_fresh_critics_count  \\\n",
       "0                             43                               73   \n",
       "1                             44                              123   \n",
       "2                              2                               16   \n",
       "\n",
       "   tomatometer_rotten_critics_count  \n",
       "0                                76  \n",
       "1                                19  \n",
       "2                                 8  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load Rotten Tomatoes data using pandas \n",
    "rt_movies_df = pd.read_csv('./zippedData/rotten_tomatoes_movies.csv')\n",
    "rt_movies_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The number (budgets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$330,600,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>$317,000,000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       Avatar   \n",
       "1   2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3   4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4   5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0      $425,000,000   $760,507,625  $2,776,345,279  \n",
       "1      $410,600,000   $241,063,875  $1,045,663,875  \n",
       "2      $350,000,000    $42,762,350    $149,762,350  \n",
       "3      $330,600,000   $459,005,868  $1,403,013,963  \n",
       "4      $317,000,000   $620,181,382  $1,316,721,747  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load The number data using pandas \n",
    "movie_budget_df = pd.read_csv('./zippedData/tn.movie_budgets.csv.gz')\n",
    "movie_budget_df.head()\n",
    "# movie_budget_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TMDB movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Unnamed: 0</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12, 14, 10751</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14, 12, 16, 10751</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids     id original_language  \\\n",
       "0           0      12, 14, 10751  12444                en   \n",
       "1           1  14, 12, 16, 10751  10191                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "\n",
       "                                          title  vote_average  vote_count  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788  \n",
       "1                      How to Train Your Dragon           7.7        7610  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load Rotten Tomatoes data using pandas \n",
    "tmdb_movies_df = pd.read_csv('./zippedData/tmdb.movies.csv.gz')\n",
    "\n",
    "#Remove unwanted characters \n",
    "remove_chars(tmdb_movies_df['genre_ids'],[\"[\",\"]\"]) #genre_ids now is a string. Function turns it into a list.\n",
    "\n",
    "tmdb_movies_df.head(2)\n",
    "# tmdb_movies_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26517 entries, 0 to 26516\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         26517 non-null  int64  \n",
      " 1   genre_ids          26517 non-null  object \n",
      " 2   id                 26517 non-null  int64  \n",
      " 3   original_language  26517 non-null  object \n",
      " 4   original_title     26517 non-null  object \n",
      " 5   popularity         26517 non-null  float64\n",
      " 6   release_date       26517 non-null  object \n",
      " 7   title              26517 non-null  object \n",
      " 8   vote_average       26517 non-null  float64\n",
      " 9   vote_count         26517 non-null  int64  \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#Look at data info\n",
    "tmdb_movies_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Why Reboots?\n",
    "\n",
    "We want to show from the data:\n",
    "- List of the top 50 grossing movies of the 80s and 90s\n",
    "- How many of these top 50 movies of the 80s and 90s have a remake?\n",
    "- Compare the average grossing of the remakes of these movies with the average grossing of the non-remake movies.\n",
    "\n",
    "* Talk in details which data set we use in for this analysis\n",
    "* what are the limitations of the data set for this anlysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 50 grossing movies of the 80s and 90s using The Numbers data\n",
    "\n",
    "Let's start with cleaning and prepping The Numbers data to be use to answer our questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the release date column to datetime format and extract the year to put in a separate column\n",
    "movie_budget_df['release_date'] = pd.to_datetime(movie_budget_df['release_date'])\n",
    "movie_budget_df['Year'] = movie_budget_df['release_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id release_date                                        movie  \\\n",
       "0   1   2009-12-18                                       Avatar   \n",
       "1   2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  Year  \n",
       "0      $425,000,000   $760,507,625  $2,776,345,279  2009  \n",
       "1      $410,600,000   $241,063,875  $1,045,663,875  2011  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check\n",
    "movie_budget_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the funtion defined above, remove '$ and ,' from production budget, domestic gross and worldwide gross\n",
    "\n",
    "#Remove unwanted characters \n",
    "strings_to_remove = ['$',',']\n",
    "\n",
    "remove_chars(movie_budget_df['production_budget'],strings_to_remove)\n",
    "remove_chars(movie_budget_df['domestic_gross'],strings_to_remove)\n",
    "remove_chars(movie_budget_df['worldwide_gross'],strings_to_remove)\n",
    "\n",
    "#Convert column dtype to float and divide by a million. \n",
    "movie_budget_df['production_budget'] = movie_budget_df['production_budget'].astype(float)/ 1000000\n",
    "movie_budget_df['domestic_gross'] = movie_budget_df['domestic_gross'].astype(float)/ 1000000\n",
    "movie_budget_df['worldwide_gross'] = movie_budget_df['worldwide_gross'].astype(float)/ 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>425.0</td>\n",
       "      <td>760.507625</td>\n",
       "      <td>2776.345279</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410.6</td>\n",
       "      <td>241.063875</td>\n",
       "      <td>1045.663875</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350.0</td>\n",
       "      <td>42.762350</td>\n",
       "      <td>149.762350</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330.6</td>\n",
       "      <td>459.005868</td>\n",
       "      <td>1403.013963</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317.0</td>\n",
       "      <td>620.181382</td>\n",
       "      <td>1316.721747</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id release_date                                        movie  \\\n",
       "0   1   2009-12-18                                       Avatar   \n",
       "1   2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   2019-06-07                                 Dark Phoenix   \n",
       "3   4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4   5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "   production_budget  domestic_gross  worldwide_gross  Year  \n",
       "0              425.0      760.507625      2776.345279  2009  \n",
       "1              410.6      241.063875      1045.663875  2011  \n",
       "2              350.0       42.762350       149.762350  2019  \n",
       "3              330.6      459.005868      1403.013963  2015  \n",
       "4              317.0      620.181382      1316.721747  2017  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check\n",
    "movie_budget_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>id</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5782.000000</td>\n",
       "      <td>5782.000000</td>\n",
       "      <td>5782.000000</td>\n",
       "      <td>5782.000000</td>\n",
       "      <td>5782.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.372363</td>\n",
       "      <td>31.587757</td>\n",
       "      <td>41.873327</td>\n",
       "      <td>91.487461</td>\n",
       "      <td>2003.967139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.821076</td>\n",
       "      <td>41.812077</td>\n",
       "      <td>68.240597</td>\n",
       "      <td>174.719969</td>\n",
       "      <td>12.724386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1915.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.429534</td>\n",
       "      <td>4.125415</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.225945</td>\n",
       "      <td>27.984448</td>\n",
       "      <td>2007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>52.348662</td>\n",
       "      <td>97.645837</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>936.662225</td>\n",
       "      <td>2776.345279</td>\n",
       "      <td>2020.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  production_budget  domestic_gross  worldwide_gross  \\\n",
       "count  5782.000000        5782.000000     5782.000000      5782.000000   \n",
       "mean     50.372363          31.587757       41.873327        91.487461   \n",
       "std      28.821076          41.812077       68.240597       174.719969   \n",
       "min       1.000000           0.001100        0.000000         0.000000   \n",
       "25%      25.000000           5.000000        1.429534         4.125415   \n",
       "50%      50.000000          17.000000       17.225945        27.984448   \n",
       "75%      75.000000          40.000000       52.348662        97.645837   \n",
       "max     100.000000         425.000000      936.662225      2776.345279   \n",
       "\n",
       "              Year  \n",
       "count  5782.000000  \n",
       "mean   2003.967139  \n",
       "std      12.724386  \n",
       "min    1915.000000  \n",
       "25%    2000.000000  \n",
       "50%    2007.000000  \n",
       "75%    2012.000000  \n",
       "max    2020.000000  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change the release_date into datetime format.\n",
    "movie_budget_df['release_date'] = pd.to_datetime(movie_budget_df['release_date'])\n",
    "movie_budget_df['Year'] = movie_budget_df['release_date'].dt.year\n",
    "movie_budget_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1997-12-19</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>200.0</td>\n",
       "      <td>659.363944</td>\n",
       "      <td>2208.208395</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>7</td>\n",
       "      <td>1999-06-30</td>\n",
       "      <td>Wild Wild West</td>\n",
       "      <td>175.0</td>\n",
       "      <td>113.805681</td>\n",
       "      <td>221.229335</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>10</td>\n",
       "      <td>1995-07-28</td>\n",
       "      <td>Waterworld</td>\n",
       "      <td>175.0</td>\n",
       "      <td>88.246220</td>\n",
       "      <td>264.246220</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>100</td>\n",
       "      <td>1999-06-16</td>\n",
       "      <td>Tarzan</td>\n",
       "      <td>145.0</td>\n",
       "      <td>171.091819</td>\n",
       "      <td>448.191819</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>8</td>\n",
       "      <td>1998-07-01</td>\n",
       "      <td>Armageddon</td>\n",
       "      <td>140.0</td>\n",
       "      <td>201.578182</td>\n",
       "      <td>554.600000</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date           movie  production_budget  domestic_gross  \\\n",
       "42    43   1997-12-19         Titanic              200.0      659.363944   \n",
       "106    7   1999-06-30  Wild Wild West              175.0      113.805681   \n",
       "109   10   1995-07-28      Waterworld              175.0       88.246220   \n",
       "199  100   1999-06-16          Tarzan              145.0      171.091819   \n",
       "207    8   1998-07-01      Armageddon              140.0      201.578182   \n",
       "\n",
       "     worldwide_gross  Year  \n",
       "42       2208.208395  1997  \n",
       "106       221.229335  1999  \n",
       "109       264.246220  1995  \n",
       "199       448.191819  1999  \n",
       "207       554.600000  1998  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subset df to only includes only entries for movies between 1980 and 2000\n",
    "decades_df = movie_budget_df[(movie_budget_df['Year'] >= 1980) & (movie_budget_df['Year'] <=2000)]\n",
    "decades_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1997-12-19</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>200.0</td>\n",
       "      <td>659.363944</td>\n",
       "      <td>2208.208395</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>25</td>\n",
       "      <td>1993-06-11</td>\n",
       "      <td>Jurassic Park</td>\n",
       "      <td>63.0</td>\n",
       "      <td>395.708305</td>\n",
       "      <td>1038.812584</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>4</td>\n",
       "      <td>1999-05-19</td>\n",
       "      <td>Star Wars Ep. I: The Phantom Menace</td>\n",
       "      <td>115.0</td>\n",
       "      <td>474.544677</td>\n",
       "      <td>1027.044677</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>9</td>\n",
       "      <td>1994-06-15</td>\n",
       "      <td>The Lion King</td>\n",
       "      <td>79.3</td>\n",
       "      <td>421.785283</td>\n",
       "      <td>986.214868</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>27</td>\n",
       "      <td>1996-07-02</td>\n",
       "      <td>Independence Day</td>\n",
       "      <td>75.0</td>\n",
       "      <td>306.169255</td>\n",
       "      <td>817.400878</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id release_date                                movie  production_budget  \\\n",
       "42   43   1997-12-19                              Titanic              200.0   \n",
       "824  25   1993-06-11                        Jurassic Park               63.0   \n",
       "303   4   1999-05-19  Star Wars Ep. I: The Phantom Menace              115.0   \n",
       "608   9   1994-06-15                        The Lion King               79.3   \n",
       "626  27   1996-07-02                     Independence Day               75.0   \n",
       "\n",
       "     domestic_gross  worldwide_gross  Year  \n",
       "42       659.363944      2208.208395  1997  \n",
       "824      395.708305      1038.812584  1993  \n",
       "303      474.544677      1027.044677  1999  \n",
       "608      421.785283       986.214868  1994  \n",
       "626      306.169255       817.400878  1996  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the top 50 grossing movies of 80s and 90s.\n",
    "decades_top50_df = decades_df.sort_values(by='worldwide_gross',ascending=False).head(50)\n",
    "#Assign the names to a list\n",
    "decades_top50_list= list(decades_top50_df['movie'])\n",
    "decades_top50_df.head() #List of the top 50 grossing movies of the 80s and 90s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1997-12-19</td>\n",
       "      <td>Titanic</td>\n",
       "      <td>200.0</td>\n",
       "      <td>659.363944</td>\n",
       "      <td>2208.208395</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>25</td>\n",
       "      <td>1993-06-11</td>\n",
       "      <td>Jurassic Park</td>\n",
       "      <td>63.0</td>\n",
       "      <td>395.708305</td>\n",
       "      <td>1038.812584</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>4</td>\n",
       "      <td>1999-05-19</td>\n",
       "      <td>Star Wars Ep. I: The Phantom Menace</td>\n",
       "      <td>115.0</td>\n",
       "      <td>474.544677</td>\n",
       "      <td>1027.044677</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>9</td>\n",
       "      <td>1994-06-15</td>\n",
       "      <td>The Lion King</td>\n",
       "      <td>79.3</td>\n",
       "      <td>421.785283</td>\n",
       "      <td>986.214868</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>27</td>\n",
       "      <td>1996-07-02</td>\n",
       "      <td>Independence Day</td>\n",
       "      <td>75.0</td>\n",
       "      <td>306.169255</td>\n",
       "      <td>817.400878</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3525</th>\n",
       "      <td>26</td>\n",
       "      <td>1982-06-11</td>\n",
       "      <td>ET: The Extra-Terrestrial</td>\n",
       "      <td>10.5</td>\n",
       "      <td>435.110554</td>\n",
       "      <td>792.965326</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>82</td>\n",
       "      <td>1994-07-06</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>55.0</td>\n",
       "      <td>330.151138</td>\n",
       "      <td>679.850637</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>75</td>\n",
       "      <td>1999-08-06</td>\n",
       "      <td>The Sixth Sense</td>\n",
       "      <td>40.0</td>\n",
       "      <td>293.506292</td>\n",
       "      <td>672.806292</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>31</td>\n",
       "      <td>1997-05-22</td>\n",
       "      <td>The Lost World: Jurassic Park</td>\n",
       "      <td>75.0</td>\n",
       "      <td>229.086679</td>\n",
       "      <td>618.638999</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2485</th>\n",
       "      <td>86</td>\n",
       "      <td>1991-11-13</td>\n",
       "      <td>Beauty and the Beast</td>\n",
       "      <td>20.0</td>\n",
       "      <td>376.057266</td>\n",
       "      <td>608.431132</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                                movie  production_budget  \\\n",
       "42    43   1997-12-19                              Titanic              200.0   \n",
       "824   25   1993-06-11                        Jurassic Park               63.0   \n",
       "303    4   1999-05-19  Star Wars Ep. I: The Phantom Menace              115.0   \n",
       "608    9   1994-06-15                        The Lion King               79.3   \n",
       "626   27   1996-07-02                     Independence Day               75.0   \n",
       "3525  26   1982-06-11            ET: The Extra-Terrestrial               10.5   \n",
       "981   82   1994-07-06                         Forrest Gump               55.0   \n",
       "1374  75   1999-08-06                      The Sixth Sense               40.0   \n",
       "630   31   1997-05-22        The Lost World: Jurassic Park               75.0   \n",
       "2485  86   1991-11-13                 Beauty and the Beast               20.0   \n",
       "\n",
       "      domestic_gross  worldwide_gross  Year  \n",
       "42        659.363944      2208.208395  1997  \n",
       "824       395.708305      1038.812584  1993  \n",
       "303       474.544677      1027.044677  1999  \n",
       "608       421.785283       986.214868  1994  \n",
       "626       306.169255       817.400878  1996  \n",
       "3525      435.110554       792.965326  1982  \n",
       "981       330.151138       679.850637  1994  \n",
       "1374      293.506292       672.806292  1999  \n",
       "630       229.086679       618.638999  1997  \n",
       "2485      376.057266       608.431132  1991  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 10 high grossing movies from 80s and 90s\n",
    "decades_top50_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the list of the top 50 grossing movies of the 80s and 90s, let's find out how many of them are rebooted.\n",
    "\n",
    "#### How many of these movies are rebooted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    5706\n",
       "True       76\n",
       "Name: Reboot, dtype: int64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a column in the movie_budget data frame called 'Reboot' for which we have Boolean values\n",
    "# True if it is a remake of the top grossing 80s or 90s movie\n",
    "\n",
    "check_reboot_df = movie_budget_df\n",
    "check_reboot_df['Reboot'] = False\n",
    "\n",
    "index=[]\n",
    "for decade_movie in decades_top50_list:\n",
    "        if len(check_reboot_df.loc[check_reboot_df['movie'].str.contains(decade_movie, case = False, regex=True)])>1:\n",
    "            index=list(check_reboot_df.loc[check_reboot_df['movie'].str.contains(decade_movie, case = False, regex=True)].index)\n",
    "            for indice in index:\n",
    "                check_reboot_df['Reboot'][indice] =True\n",
    "\n",
    "check_reboot_df['Reboot'].value_counts() #76 remakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    5700\n",
       "True       82\n",
       "Name: Reboot, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We see the data needs some hard-coding for remake movies whose titles are not identical as the original movies\n",
    "\n",
    "\n",
    "franchise=[\"Terminator 2: Judgment Day\",\"Star Wars Ep. V: The Empire Strikes Back\",\n",
    "           \"Star Wars Ep. VI: Return of the Jedi\",\"Star Wars Ep. I: The Phantom Menace\", \"Jurassic\", \n",
    "           \"Indiana Jones and the Last Crusade\", \"Raiders of the Lost Ark\"]\n",
    "for movie in franchise:\n",
    "    index=check_reboot_df.loc[check_reboot_df['movie'] == movie].index\n",
    "    check_reboot_df['Reboot'][index] = True\n",
    "    \n",
    "check_reboot_df['Reboot'].value_counts() #82 remakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "      <th>Reboot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>425.0000</td>\n",
       "      <td>760.507625</td>\n",
       "      <td>2776.345279</td>\n",
       "      <td>2009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410.6000</td>\n",
       "      <td>241.063875</td>\n",
       "      <td>1045.663875</td>\n",
       "      <td>2011</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350.0000</td>\n",
       "      <td>42.762350</td>\n",
       "      <td>149.762350</td>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330.6000</td>\n",
       "      <td>459.005868</td>\n",
       "      <td>1403.013963</td>\n",
       "      <td>2015</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317.0000</td>\n",
       "      <td>620.181382</td>\n",
       "      <td>1316.721747</td>\n",
       "      <td>2017</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5777</th>\n",
       "      <td>78</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>Red 11</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>79</td>\n",
       "      <td>1999-04-02</td>\n",
       "      <td>Following</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.048482</td>\n",
       "      <td>0.240495</td>\n",
       "      <td>1999</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>80</td>\n",
       "      <td>2005-07-13</td>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>2005</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2015</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>82</td>\n",
       "      <td>2005-08-05</td>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.181041</td>\n",
       "      <td>0.181041</td>\n",
       "      <td>2005</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5782 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5777  78   2018-12-31                                       Red 11   \n",
       "5778  79   1999-04-02                                    Following   \n",
       "5779  80   2005-07-13                Return to the Land of Wonders   \n",
       "5780  81   2015-09-29                         A Plague So Pleasant   \n",
       "5781  82   2005-08-05                            My Date With Drew   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  Year  Reboot  \n",
       "0              425.0000      760.507625      2776.345279  2009   False  \n",
       "1              410.6000      241.063875      1045.663875  2011   False  \n",
       "2              350.0000       42.762350       149.762350  2019   False  \n",
       "3              330.6000      459.005868      1403.013963  2015   False  \n",
       "4              317.0000      620.181382      1316.721747  2017   False  \n",
       "...                 ...             ...              ...   ...     ...  \n",
       "5777             0.0070        0.000000         0.000000  2018   False  \n",
       "5778             0.0060        0.048482         0.240495  1999   False  \n",
       "5779             0.0050        0.001338         0.001338  2005   False  \n",
       "5780             0.0014        0.000000         0.000000  2015   False  \n",
       "5781             0.0011        0.181041         0.181041  2005   False  \n",
       "\n",
       "[5782 rows x 8 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_reboot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     28\n",
       "False    22\n",
       "Name: Reboot, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge check_reboot_df with decades_top50_df to see how many out of top grossing 80s and 90s movies have remakes\n",
    "decades_merge_df=decades_top50_df.merge(check_reboot_df, how = 'left', on = 'movie')\n",
    "decades_merge_df_cleaned = decades_merge_df.drop(['release_date_x', 'id_y', 'release_date_y', \n",
    "                                                  'production_budget_y', 'domestic_gross_y', 'worldwide_gross_y', \n",
    "                                                  'Year_y'], axis = 1)\n",
    "decades_merge_df_cleaned = decades_merge_df_cleaned.drop_duplicates(subset=None, keep='first', inplace=False)\n",
    "\n",
    "Reboot_graph = decades_merge_df_cleaned['Reboot'].value_counts() \n",
    "Reboot_graph\n",
    "\n",
    "#28 out of 50 top grossing movies from 80s and 90s have remakes! \n",
    "#~56% of the movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "#plot for how many 80s and 90s movie has a remake already\n",
    "plt.figure(figsize=(15,5))\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.barplot(['Rebooted','Not-Rebooted'], Reboot_graph.values)\n",
    "\n",
    "ax.set_title(\"Top 50 movies of the 80s and 90s movies\", y =1.1, fontsize = 15)\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Number of movies\", fontsize = 15)\n",
    "#ax.set_xticklabels([\"Not Rebooted\",\"Rebooted\"]\n",
    "plt.text(0,29,28, fontsize = 13)\n",
    "plt.text(1,23,22, fontsize = 13)\n",
    "\n",
    "#plt.text(32, Reboot_graph.index[0], '32')\n",
    "#ax.legend()\n",
    "sns.despine()\n",
    "plt.savefig('./Images/Reboot_decades.jpeg',bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So.. Out of the top 50 grossing film from the 80s and 90s, 28 of them were rebooted! Cool. \n",
    "\n",
    "####  Now, how much the 20th century reboots are making versus other 20th century original movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset df for movies which have a reboot and were were released after 2000\n",
    "rebooted_2000_df= movie_budget_df[(movie_budget_df['Reboot']==True) & (movie_budget_df['Year']>2000)]\n",
    "rebooted_2000_df = rebooted_2000_df.groupby(by='Year').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset df for movies which don't have a reboot and were released after 2000\n",
    "non_rebooted_2000_df= movie_budget_df[(movie_budget_df['Reboot']==False) & (movie_budget_df['Year']>2000)]\n",
    "non_rebooted_2000_df=non_rebooted_2000_df.sample(100, random_state=11)\n",
    "non_rebooted_2000_df = non_rebooted_2000_df.groupby(by='Year').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing the mean worldwide gross for remade movies vs original movies after the year 2000\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.lineplot(rebooted_2000_df.index, rebooted_2000_df['worldwide_gross'], linestyle=\"-\", label='Rebooted movies', marker='.')\n",
    "sns.lineplot(non_rebooted_2000_df.index, non_rebooted_2000_df['worldwide_gross'], linestyle=\"--\", label='Original movies', marker='.')\n",
    "#ax.plot(scaley=False)\n",
    "ax.set_title(\"Worldwide gross for\\n Reboot vs. Original movies\\n\", fontsize =18)\n",
    "ax.set_xlabel(\"Year\", fontsize=15)\n",
    "ax.set_ylabel(\"Worldwide gross in $MM\", fontsize = 15)\n",
    "ax.legend()\n",
    "ax.set_xticklabels(['2002','2005','2007','2010','2012','2015','2017', '2019'])\n",
    "plt.setp(ax.get_legend().get_texts(), fontsize='15') # for legend text\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('./Images/Reboots.png')\n",
    "plt.savefig('./Images/Reboots.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data reiterates that more movies from these 2 decades have been rebooted, than not.\n",
    "Comparative fiscal data for reboots vs. non-reboots allows us to conclude that hit films, specifically from these 2 decades, are the most profitable to reboot.\n",
    "\n",
    "Additionally, these reboots happen to be even more profitable than original films. So, now.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. What to reboot?\n",
    "\n",
    "- List out the genres and net revenue for each movie\n",
    "- Calculate the average popularity and net income for each genre\n",
    "- Compare the genre popularity and net revenue and conclude they have a similar pattern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding Popularity and Net Revenue by genre using TMDB data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dictionary of genre name and its associated genre ID. This information is sourced from tmdb.\n",
    "tmdb_genre= {28: 'Action',\n",
    " 12: 'Adventure',\n",
    " 16: 'Animation',\n",
    " 35: 'Comedy',\n",
    " 80: 'Crime',\n",
    " 99: 'Documentary',\n",
    " 18: 'Drama',\n",
    " 10751: 'Family',\n",
    " 14: 'Fantasy',\n",
    " 36: 'History',\n",
    " 27: 'Horror',\n",
    " 10402: 'Music',\n",
    " 9648: 'Mystery',\n",
    " 10749: 'Romance',\n",
    " 878: 'Science Fiction',\n",
    " 10770: 'TV Movie',\n",
    " 53: 'Thriller',\n",
    " 10752: 'War',\n",
    " 37: 'Western',\n",
    " 101: 'Unknown'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make a list out of the genres\n",
    "tmdb_genre_list = list(tmdb_genre.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate net_revenue and make a new column\n",
    "movie_budget_df['net_revenue'] = movie_budget_df['worldwide_gross']-movie_budget_df['production_budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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>Unnamed: 0</th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12, 14, 10751</td>\n",
       "      <td>12444</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>14, 12, 16, 10751</td>\n",
       "      <td>10191</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12, 28, 878</td>\n",
       "      <td>10138</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>16, 35, 10751</td>\n",
       "      <td>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>28, 878, 12</td>\n",
       "      <td>27205</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids     id  \\\n",
       "0           0      12, 14, 10751  12444   \n",
       "1           1  14, 12, 16, 10751  10191   \n",
       "2           2        12, 28, 878  10138   \n",
       "3           3      16, 35, 10751    862   \n",
       "4           4        28, 878, 12  27205   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                    Iron Man 2      28.515   2010-05-07   \n",
       "3                                     Toy Story      28.005   1995-11-22   \n",
       "4                                     Inception      27.920   2010-07-16   \n",
       "\n",
       "                                          title  vote_average  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7  \n",
       "1                      How to Train Your Dragon           7.7  \n",
       "2                                    Iron Man 2           6.8  \n",
       "3                                     Toy Story           7.9  \n",
       "4                                     Inception           8.3  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop irrelevant columns \n",
    "tmdb_movies_df = tmdb_movies_df.drop(['original_language','vote_count'], axis = 1)\n",
    "tmdb_movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   id                 5782 non-null   int64         \n",
      " 1   release_date       5782 non-null   datetime64[ns]\n",
      " 2   movie              5782 non-null   object        \n",
      " 3   production_budget  5782 non-null   float64       \n",
      " 4   domestic_gross     5782 non-null   float64       \n",
      " 5   worldwide_gross    5782 non-null   float64       \n",
      " 6   Year               5782 non-null   int64         \n",
      " 7   Reboot             5782 non-null   bool          \n",
      " 8   net_revenue        5782 non-null   float64       \n",
      "dtypes: bool(1), datetime64[ns](1), float64(4), int64(2), object(1)\n",
      "memory usage: 367.1+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_budget_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10191</td>\n",
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       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>329.870992</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>15</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
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       "      <td>621.156389</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
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       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>37</td>\n",
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       "      <td>Toy Story</td>\n",
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       "      <td>191.796233</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>334.545516</td>\n",
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       "      <th>3</th>\n",
       "      <td>2473</td>\n",
       "      <td>16, 35, 10751</td>\n",
       "      <td>862</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>37</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>30.0</td>\n",
       "      <td>191.796233</td>\n",
       "      <td>364.545516</td>\n",
       "      <td>1995</td>\n",
       "      <td>True</td>\n",
       "      <td>334.545516</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>28, 878, 12</td>\n",
       "      <td>27205</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>38</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>160.0</td>\n",
       "      <td>292.576195</td>\n",
       "      <td>835.524642</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>675.524642</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids   id_x            original_title  popularity  \\\n",
       "0           1  14, 12, 16, 10751  10191  How to Train Your Dragon      28.734   \n",
       "1           2        12, 28, 878  10138                Iron Man 2      28.515   \n",
       "2           3      16, 35, 10751    862                 Toy Story      28.005   \n",
       "3        2473      16, 35, 10751    862                 Toy Story      28.005   \n",
       "4           4        28, 878, 12  27205                 Inception      27.920   \n",
       "\n",
       "  release_date_x                     title  vote_average  id_y release_date_y  \\\n",
       "0     2010-03-26  How to Train Your Dragon           7.7    30     2010-03-26   \n",
       "1     2010-05-07                Iron Man 2           6.8    15     2010-05-07   \n",
       "2     1995-11-22                 Toy Story           7.9    37     1995-11-22   \n",
       "3     1995-11-22                 Toy Story           7.9    37     1995-11-22   \n",
       "4     2010-07-16                 Inception           8.3    38     2010-07-16   \n",
       "\n",
       "                      movie  production_budget  domestic_gross  \\\n",
       "0  How to Train Your Dragon              165.0      217.581232   \n",
       "1                Iron Man 2              170.0      312.433331   \n",
       "2                 Toy Story               30.0      191.796233   \n",
       "3                 Toy Story               30.0      191.796233   \n",
       "4                 Inception              160.0      292.576195   \n",
       "\n",
       "   worldwide_gross  Year  Reboot  net_revenue  \n",
       "0       494.870992  2010   False   329.870992  \n",
       "1       621.156389  2010   False   451.156389  \n",
       "2       364.545516  1995    True   334.545516  \n",
       "3       364.545516  1995    True   334.545516  \n",
       "4       835.524642  2010   False   675.524642  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge TMDB data frame with The numbers data frame\n",
    "\n",
    "tmdb_tn_df = tmdb_movies_df.merge(movie_budget_df,how ='inner',left_on ='original_title',right_on=\"movie\")\n",
    "tmdb_tn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace field that's entirely space (or empty) with '101', which is the genre key for genre \"Unknown\"\n",
    "tmdb_tn_df['genre_ids'] = tmdb_tn_df['genre_ids'].str.replace(r'^\\s*$','101')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert genre id to genre name using the tmdb genre dictionary\n",
    "genre_names_lists=[]\n",
    "idx = 0\n",
    "for genre_list in tmdb_tn_df['genre_ids']:\n",
    "    genre_list = genre_list.split(', ')\n",
    "    genre_names=[]\n",
    "    genre_names = [tmdb_genre[int(genre_id)] for genre_id in genre_list]\n",
    "    genre_names_lists.append(genre_names)\n",
    "tmdb_tn_df['genre_names'] = genre_names_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>[Fantasy, Adventure, Animation, Family]</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Iron Man 2</td>\n",
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       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>170.0</td>\n",
       "      <td>312.433331</td>\n",
       "      <td>621.156389</td>\n",
       "      <td>2010</td>\n",
       "      <td>False</td>\n",
       "      <td>451.156389</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids   id_x            original_title  popularity  \\\n",
       "0           1  14, 12, 16, 10751  10191  How to Train Your Dragon      28.734   \n",
       "1           2        12, 28, 878  10138                Iron Man 2      28.515   \n",
       "\n",
       "  release_date_x                     title  vote_average  id_y release_date_y  \\\n",
       "0     2010-03-26  How to Train Your Dragon           7.7    30     2010-03-26   \n",
       "1     2010-05-07                Iron Man 2           6.8    15     2010-05-07   \n",
       "\n",
       "                      movie  production_budget  domestic_gross  \\\n",
       "0  How to Train Your Dragon              165.0      217.581232   \n",
       "1                Iron Man 2              170.0      312.433331   \n",
       "\n",
       "   worldwide_gross  Year  Reboot  net_revenue  \\\n",
       "0       494.870992  2010   False   329.870992   \n",
       "1       621.156389  2010   False   451.156389   \n",
       "\n",
       "                               genre_names  \n",
       "0  [Fantasy, Adventure, Animation, Family]  \n",
       "1     [Adventure, Action, Science Fiction]  "
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check\n",
    "tmdb_tn_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create columns with genre names with boolean values. True if movie falls under that genre.\n",
    "for genre in tmdb_genre_list:\n",
    "    tmdb_tn_df[genre]=[True if genre in movie else False for movie in tmdb_tn_df['genre_names']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>id_y</th>\n",
       "      <th>release_date_y</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Science Fiction</th>\n",
       "      <th>TV Movie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Unknown</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14, 12, 16, 10751</td>\n",
       "      <td>10191</td>\n",
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       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>12, 28, 878</td>\n",
       "      <td>10138</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>15</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          genre_ids   id_x            original_title  popularity  \\\n",
       "0           1  14, 12, 16, 10751  10191  How to Train Your Dragon      28.734   \n",
       "1           2        12, 28, 878  10138                Iron Man 2      28.515   \n",
       "\n",
       "  release_date_x                     title  vote_average  id_y release_date_y  \\\n",
       "0     2010-03-26  How to Train Your Dragon           7.7    30     2010-03-26   \n",
       "1     2010-05-07                Iron Man 2           6.8    15     2010-05-07   \n",
       "\n",
       "   ... Horror  Music  Mystery  Romance  Science Fiction  TV Movie  Thriller  \\\n",
       "0  ...  False  False    False    False            False     False     False   \n",
       "1  ...  False  False    False    False             True     False     False   \n",
       "\n",
       "     War  Western  Unknown  \n",
       "0  False    False    False  \n",
       "1  False    False    False  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "tmdb_tn_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're ready to calculate the average popularity and net revenue by genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_popularity ={} \n",
    "genre_net_revenue = {}\n",
    "\n",
    "#Calculating average worldwide gross:\n",
    "for genre in tmdb_genre_list: #loop through genre in tmdb genre list \n",
    "    n = 0 #number of movies \n",
    "    popularity_sum = 0 #popularity summation\n",
    "    revenue_sum = 0 #revenue summation\n",
    "    idx =0  #keep track of row index\n",
    "    for row in tmdb_tn_df[genre]: #go down row by row in every genre column\n",
    "        if (row==True): #if True(meaning movie is in that genre)\n",
    "            popularity_sum += (tmdb_tn_df['popularity'][idx]) #grab the popularity of that movie and add it to the total sum\n",
    "            revenue_sum += (tmdb_tn_df['net_revenue'][idx]) #grab the net revenue of that movie and add it to the total sum\n",
    "            n+=1 #add one to count of movie\n",
    "        idx +=1 #keep track of row index\n",
    "    genre_popularity[genre] = popularity_sum/n #calculate average popularity of the genre\n",
    "    genre_net_revenue[genre] = revenue_sum/n #calculate average popularity of the genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform the popularity by genre and net revenue by genre dictionary into a dataframe\n",
    "genre_popularity_ds = pd.DataFrame.from_dict(genre_popularity, orient = 'index')\n",
    "genre_net_revenue_ds = pd.DataFrame.from_dict(genre_net_revenue, orient = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ensure the 2 dataframes are similarly sorted for plotting. \n",
    "genre_popularity_ds = genre_popularity_ds.sort_values(by= 0, ascending = False)\n",
    "genre_net_revenue_ds = genre_net_revenue_ds.reindex(index=genre_popularity_ds.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the matplotlib figure\n",
    "fig, ax = plt.subplots(nrows=2,ncols=1,figsize=(20,10), sharex=True)\n",
    "sns.set_theme(style=\"white\")\n",
    "\n",
    "# Plot the total crashes\n",
    "sns.set_color_codes(\"dark\")\n",
    "sns.barplot(x=genre_popularity_ds.index, y=genre_popularity_ds[0],\n",
    "            color=\"b\", ax =ax[0])\n",
    "ax[0].set_ylabel ('Popularity',fontsize = 15)\n",
    "sns.set_color_codes(\"pastel\")\n",
    "sns.barplot(x=genre_net_revenue_ds.index, y=genre_net_revenue_ds[0],\n",
    "            color=\"b\", ax =ax[1])\n",
    "ax[1].set_ylabel ('Net Revenue in $MM',fontsize = 15)\n",
    "ax[1].set_xticklabels(ax[1].get_xticklabels(),rotation = 30, fontsize =15)\n",
    "fig.suptitle(\"Genre Popularity\",fontsize = 20)\n",
    "sns.despine()\n",
    "plt.savefig('Images/genre.png')\n",
    "plt.savefig('Images/genre.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After deciding that rebooting hit films from the 80’s and 90’s is the way to go, one must ascertain the next step. Which genre? Through analyzing popularity and ROI for specific genres, our findings show that adventure, fantasy, scifi, action, family, and animation are the most popular genres.\n",
    "\n",
    "Exploring the popularity of genres gives our key stakeholders a direction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. How to reboot?\n",
    "\n",
    "Since Microsoft has no prior movie industry experience, and, therefore, no intellectual property of movies, they must acquire IP. Which studios should Microsoft pay for their IP? This wil depend on Microsoft's budget\n",
    "\n",
    "- Categorize high, medium, low budget\n",
    "- Create a list of top 20 studios\n",
    "- Calculate the average net revenue and average production budget for each studio\n",
    "- Also classify genres in each studio and sort that by number of movies in each genre\n",
    "- Compare average revenue, average production budget and number of movies by genre for each studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classify High, Medium, Low Budget Tiers\n",
    "\n",
    "Determine budget tiers from production budget of movies beyond 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter for movies released 2000 and beyong\n",
    "\n",
    "movies_2000s = movie_budget_df[movie_budget_df['Year']>2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4198.000000\n",
       "mean       34.658616\n",
       "std        46.064139\n",
       "min         0.001100\n",
       "25%         5.000000\n",
       "50%        18.000000\n",
       "75%        42.000000\n",
       "max       425.000000\n",
       "Name: production_budget, dtype: float64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the stats of the production budget for those movies\n",
    "movies_2000s['production_budget'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **High Budget**: Over 43 USD Million (above the 75th percentile)\n",
    "* **Medium Budget**: Between 18 to 43 USD Million (between the median and 75th percentile)\n",
    "* **Low Budget**: Between 5 to 18 USD Million (between the 25th percentile and the median)\n",
    "\n",
    "A budget tier system gives Microsoft a clear path to follow in IP acquisition, based on their budgetary preferences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 20 studios from Rotten Tomatoes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop missing data from relevant columns and reset the index.\n",
    "rt_movies_df_cleaned = rt_movies_df.dropna(subset = ['production_company','audience_rating'])\n",
    "rt_movies_df_cleaned = rt_movies_df_cleaned.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16972 entries, 0 to 16971\n",
      "Data columns (total 22 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   rotten_tomatoes_link              16972 non-null  object \n",
      " 1   movie_title                       16972 non-null  object \n",
      " 2   movie_info                        16767 non-null  object \n",
      " 3   critics_consensus                 9020 non-null   object \n",
      " 4   content_rating                    16972 non-null  object \n",
      " 5   genres                            16957 non-null  object \n",
      " 6   directors                         16823 non-null  object \n",
      " 7   authors                           15665 non-null  object \n",
      " 8   actors                            16695 non-null  object \n",
      " 9   original_release_date             16069 non-null  object \n",
      " 10  streaming_release_date            16696 non-null  object \n",
      " 11  runtime                           16762 non-null  float64\n",
      " 12  production_company                16972 non-null  object \n",
      " 13  tomatometer_status                16964 non-null  object \n",
      " 14  tomatometer_rating                16964 non-null  float64\n",
      " 15  tomatometer_count                 16964 non-null  float64\n",
      " 16  audience_status                   16833 non-null  object \n",
      " 17  audience_rating                   16972 non-null  float64\n",
      " 18  audience_count                    16971 non-null  float64\n",
      " 19  tomatometer_top_critics_count     16972 non-null  int64  \n",
      " 20  tomatometer_fresh_critics_count   16972 non-null  int64  \n",
      " 21  tomatometer_rotten_critics_count  16972 non-null  int64  \n",
      "dtypes: float64(5), int64(3), object(14)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#Sanity check\n",
    "rt_movies_df_cleaned.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create studio dictionary to classify subsidiaries into the same conglomerate company\n",
    "studio_dict={\"Sony\": \"Sony Pictures Entertainment\", \"Warner\":\"Warner Brothers\",\"Fox\":\"21st Century Fox\"\n",
    "             , \"Paramount\":\"Paramount Pictures\",'MGM':'MGM', 'HBO':\"HBO\",\"Lion\":\"Lions Gate\",\"Miramax\":\"Miramax\",\n",
    "             \"Disney\": \"Walt Disney\",\"WARNER\":\"Warner Brothers\", \"Buena\":\"Buena Vista\",\"MCA\":\"Universal Pictures\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16972, 22)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sanity check\n",
    "rt_movies_df_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#classify subsidiaries into the same conglomerate company\n",
    "\n",
    "idx = 0\n",
    "for company in rt_movies_df_cleaned['production_company']:\n",
    "    for key,value in studio_dict.items():\n",
    "        if key in company:\n",
    "#             print (company, value)\n",
    "#             print (\"Before: \", rt_movies_df_cleaned['production_company'][idx]) ## uncomment this line for sanity check, and error solving\n",
    "            rt_movies_df_cleaned['production_company'][idx] = value\n",
    "#             print(\"After: \", rt_movies_df_cleaned['production_company'][idx]) ##uncomment this line for sanity check, and error solving\n",
    "    idx +=1     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge rotten tomatoes and TN to get the production budget by studio\n",
    "\n",
    "rt_movies_tn = rt_movies_df_cleaned.merge(movie_budget_df,how = 'inner',\n",
    "                                         left_on = 'movie_title',right_on='movie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Warner Brothers                501\n",
       "21st Century Fox               458\n",
       "Sony Pictures Entertainment    416\n",
       "Paramount Pictures             404\n",
       "Universal Pictures             354\n",
       "Name: production_company, dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the top 20 studios and their movie counts.\n",
    "top_20_studios = list(rt_movies_tn['production_company'].value_counts().index)[0:20]\n",
    "top_20_studios_counts = rt_movies_tn['production_company'].value_counts()[0:20]\n",
    "top_20_studios_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>Year</th>\n",
       "      <th>Reboot</th>\n",
       "      <th>net_revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4746</th>\n",
       "      <td>m/zootopia</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>From the largest elephant to the smallest shre...</td>\n",
       "      <td>The brilliantly well-rounded Zootopia offers a...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action &amp; Adventure, Animation, Comedy</td>\n",
       "      <td>Byron Howard, Rich Moore, Jared Bush</td>\n",
       "      <td>Jared Bush, Phil Johnston</td>\n",
       "      <td>J.K. Simmons, Kristen Bell, Octavia Spencer, A...</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>57</td>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>Zootopia</td>\n",
       "      <td>150.0</td>\n",
       "      <td>341.268248</td>\n",
       "      <td>1019.429616</td>\n",
       "      <td>2016</td>\n",
       "      <td>False</td>\n",
       "      <td>869.429616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4747</th>\n",
       "      <td>m/zulu</td>\n",
       "      <td>Zulu</td>\n",
       "      <td>In 1879, the Zulu nation hands colonial Britis...</td>\n",
       "      <td>Zulu patiently establishes a cast of colorful ...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Classics, Drama</td>\n",
       "      <td>Cy Endfield, Cyril Endfield</td>\n",
       "      <td>Cy Endfield, John Prebble</td>\n",
       "      <td>Stanley Baker, Jack Hawkins, Ulla Jacobsson, J...</td>\n",
       "      <td>1964-06-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>Zulu</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.844228</td>\n",
       "      <td>2013</td>\n",
       "      <td>False</td>\n",
       "      <td>-14.155772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rotten_tomatoes_link movie_title  \\\n",
       "4746           m/zootopia    Zootopia   \n",
       "4747               m/zulu        Zulu   \n",
       "\n",
       "                                             movie_info  \\\n",
       "4746  From the largest elephant to the smallest shre...   \n",
       "4747  In 1879, the Zulu nation hands colonial Britis...   \n",
       "\n",
       "                                      critics_consensus content_rating  \\\n",
       "4746  The brilliantly well-rounded Zootopia offers a...             PG   \n",
       "4747  Zulu patiently establishes a cast of colorful ...             PG   \n",
       "\n",
       "                                     genres  \\\n",
       "4746  Action & Adventure, Animation, Comedy   \n",
       "4747                        Classics, Drama   \n",
       "\n",
       "                                 directors                    authors  \\\n",
       "4746  Byron Howard, Rich Moore, Jared Bush  Jared Bush, Phil Johnston   \n",
       "4747           Cy Endfield, Cyril Endfield  Cy Endfield, John Prebble   \n",
       "\n",
       "                                                 actors original_release_date  \\\n",
       "4746  J.K. Simmons, Kristen Bell, Octavia Spencer, A...            2016-03-04   \n",
       "4747  Stanley Baker, Jack Hawkins, Ulla Jacobsson, J...            1964-06-17   \n",
       "\n",
       "      ... tomatometer_rotten_critics_count  id release_date     movie  \\\n",
       "4746  ...                                7  57   2016-03-04  Zootopia   \n",
       "4747  ...                                1  82   2013-12-31      Zulu   \n",
       "\n",
       "      production_budget  domestic_gross worldwide_gross  Year  Reboot  \\\n",
       "4746              150.0      341.268248     1019.429616  2016   False   \n",
       "4747               16.0        0.000000        1.844228  2013   False   \n",
       "\n",
       "      net_revenue  \n",
       "4746   869.429616  \n",
       "4747   -14.155772  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter dataframe to just movies produced by the top 20 studios\n",
    "rt_movies_top_20_studios = rt_movies_tn[rt_movies_tn['production_company'].isin(top_20_studios)]\n",
    "\n",
    "#Sanity check\n",
    "rt_movies_top_20_studios.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the top 20 studios by movie counts, let's calculate their net revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "production_company\n",
       "Walt Disney                    292.991816\n",
       "Buena Vista                    106.392687\n",
       "Universal Pictures             105.814782\n",
       "Warner Brothers                 93.896417\n",
       "21st Century Fox                91.804085\n",
       "Paramount Pictures              91.251037\n",
       "New Line Cinema                 70.509552\n",
       "Columbia Pictures               70.499169\n",
       "Sony Pictures Entertainment     65.462263\n",
       "Summit Entertainment            64.317540\n",
       "Lions Gate                      49.663584\n",
       "Touchstone Pictures             44.629749\n",
       "The Weinstein Company           40.866310\n",
       "United Artists                  40.231349\n",
       "Miramax                         36.821240\n",
       "Focus Features                  35.349500\n",
       "MGM                             31.523421\n",
       "Open Road Films                 12.757290\n",
       "IFC Films                        9.751333\n",
       "Magnolia Pictures                0.436888\n",
       "Name: net_revenue, dtype: float64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate net revenue by studio\n",
    "net_revenue_by_studio = rt_movies_top_20_studios.groupby('production_company').mean()['net_revenue']\n",
    "net_revenue_by_studio = net_revenue_by_studio.sort_values(ascending = False)\n",
    "\n",
    "#ensure top 20 studio movie counts DF is similarly sorted as net revenue for plotting purposes\n",
    "top_20_studios_counts = top_20_studios_counts.reindex(index=net_revenue_by_studio.index)\n",
    "net_revenue_by_studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "production_company\n",
       "Walt Disney                    108.598276\n",
       "Buena Vista                     50.859409\n",
       "Universal Pictures              46.936638\n",
       "Warner Brothers                 54.467144\n",
       "21st Century Fox                38.776144\n",
       "Paramount Pictures              46.927908\n",
       "New Line Cinema                 36.155814\n",
       "Columbia Pictures               43.439812\n",
       "Sony Pictures Entertainment     37.158791\n",
       "Summit Entertainment            42.448837\n",
       "Lions Gate                      27.032894\n",
       "Touchstone Pictures             33.894939\n",
       "The Weinstein Company           18.821875\n",
       "United Artists                  14.518980\n",
       "Miramax                         20.546828\n",
       "Focus Features                  20.415278\n",
       "MGM                             27.329233\n",
       "Open Road Films                 23.303226\n",
       "IFC Films                        9.123587\n",
       "Magnolia Pictures                7.922000\n",
       "Name: production_budget, dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate production budget by studio\n",
    "average_budget_by_studio = rt_movies_top_20_studios.groupby('production_company').mean()['production_budget']\n",
    "\n",
    "#ensure top 20 studio movie counts DF is similarly sorted as net revenue for plotting purposes\n",
    "average_budget_by_studio = average_budget_by_studio.reindex(index=net_revenue_by_studio.index)\n",
    "average_budget_by_studio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we calculated the average net revenue and production budget by studio. \n",
    "\n",
    "#### Rotten Tomatoes movies genre by studio\n",
    "\n",
    "Now we will find the top 3 movie genres produced by each top 20 studios using Rotten Tomatoes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_movies_top_20_studios['genres'] = rt_movies_top_20_studios['genres'].str.replace(' &',',')\n",
    "rt_movies_top_20_studios['genres'] = rt_movies_top_20_studios['genres'].str.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create columns with genre names with boolean values. True if movie falls under that genre\n",
    "for genre in tmdb_genre_list:\n",
    "    rt_movies_top_20_studios[genre]=[True if genre in movie else False for movie in rt_movies_top_20_studios['genres']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find the sum of movies in each genre by studio\n",
    "studio_genre_sum = rt_movies_top_20_studios.groupby('production_company').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['runtime',\n",
       " 'tomatometer_rating',\n",
       " 'tomatometer_count',\n",
       " 'audience_rating',\n",
       " 'audience_count',\n",
       " 'tomatometer_top_critics_count',\n",
       " 'tomatometer_fresh_critics_count',\n",
       " 'tomatometer_rotten_critics_count',\n",
       " 'id',\n",
       " 'production_budget',\n",
       " 'domestic_gross',\n",
       " 'worldwide_gross',\n",
       " 'Year',\n",
       " 'Reboot',\n",
       " 'net_revenue']"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make a list of irrelevant columns to drop\n",
    "column_to_drop = list(studio_genre_sum.columns)\n",
    "column_to_drop = column_to_drop[0:15]\n",
    "column_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>History</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Science Fiction</th>\n",
       "      <th>TV Movie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_company</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>21st Century Fox</th>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Vista</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia Pictures</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focus Features</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFC Films</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lions Gate</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGM</th>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnolia Pictures</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miramax</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Line Cinema</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Road Films</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paramount Pictures</th>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sony Pictures Entertainment</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Summit Entertainment</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Weinstein Company</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Touchstone Pictures</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Artists</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal Pictures</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walt Disney</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warner Brothers</th>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Action  Adventure  Animation  Comedy  Crime  \\\n",
       "production_company                                                         \n",
       "21st Century Fox                147          0         15     139      0   \n",
       "Buena Vista                      50          0         15      38      0   \n",
       "Columbia Pictures                42          0          1      39      0   \n",
       "Focus Features                   13          0          0      24      0   \n",
       "IFC Films                         3          0          0      13      0   \n",
       "Lions Gate                       51          0          3      36      0   \n",
       "MGM                              66          0          1      37      0   \n",
       "Magnolia Pictures                 5          0          0       8      0   \n",
       "Miramax                          20          0          0      48      0   \n",
       "New Line Cinema                  35          0          0      33      0   \n",
       "Open Road Films                  10          0          2       5      0   \n",
       "Paramount Pictures              142          0         12      94      0   \n",
       "Sony Pictures Entertainment     128          0         14     101      0   \n",
       "Summit Entertainment             20          0          0       8      0   \n",
       "The Weinstein Company             4          0          0      12      0   \n",
       "Touchstone Pictures               7          0          0      15      0   \n",
       "United Artists                   20          0          0       5      0   \n",
       "Universal Pictures              122          0          7     103      0   \n",
       "Walt Disney                      60          0         23      21      0   \n",
       "Warner Brothers                 204          0         13     110      0   \n",
       "\n",
       "                             Documentary  Drama  Family  Fantasy  History  \\\n",
       "production_company                                                          \n",
       "21st Century Fox                       0     91       0        0        0   \n",
       "Buena Vista                            1     16       0        0        0   \n",
       "Columbia Pictures                      0     27       0        0        0   \n",
       "Focus Features                         0     29       0        0        0   \n",
       "IFC Films                              3     18       0        0        0   \n",
       "Lions Gate                             3     49       0        0        0   \n",
       "MGM                                    1     29       0        0        0   \n",
       "Magnolia Pictures                      5     17       0        0        0   \n",
       "Miramax                                0     38       0        0        0   \n",
       "New Line Cinema                        0     22       0        0        0   \n",
       "Open Road Films                        0     13       0        0        0   \n",
       "Paramount Pictures                     7     75       0        0        0   \n",
       "Sony Pictures Entertainment            9    104       0        0        0   \n",
       "Summit Entertainment                   0     10       0        0        0   \n",
       "The Weinstein Company                  1     24       0        0        0   \n",
       "Touchstone Pictures                    0      9       0        0        0   \n",
       "United Artists                         0      5       0        0        0   \n",
       "Universal Pictures                     2     68       0        0        0   \n",
       "Walt Disney                            2      7       0        0        0   \n",
       "Warner Brothers                        2    121       0        0        0   \n",
       "\n",
       "                             Horror  Music  Mystery  Romance  Science Fiction  \\\n",
       "production_company                                                              \n",
       "21st Century Fox                 11      0       12        0                3   \n",
       "Buena Vista                       3      0        2        0                0   \n",
       "Columbia Pictures                 3      0        0        0                0   \n",
       "Focus Features                    1      0        2        1                0   \n",
       "IFC Films                         1      0        2        0                0   \n",
       "Lions Gate                       18      0        5        0                0   \n",
       "MGM                              14      0        2        0                1   \n",
       "Magnolia Pictures                 4      0        2        0                0   \n",
       "Miramax                           6      0        0        0                0   \n",
       "New Line Cinema                  15      0        0        0                1   \n",
       "Open Road Films                   1      0        0        0                0   \n",
       "Paramount Pictures               27      0        6        0                1   \n",
       "Sony Pictures Entertainment      20      0        6        0                1   \n",
       "Summit Entertainment              4      0        1        0                0   \n",
       "The Weinstein Company             5      0        1        0                0   \n",
       "Touchstone Pictures               0      0        0        0                0   \n",
       "United Artists                    3      0        1        0                0   \n",
       "Universal Pictures               26      0        6        0                2   \n",
       "Walt Disney                       0      0        0        0                2   \n",
       "Warner Brothers                  21      0        9        0                0   \n",
       "\n",
       "                             TV Movie  Thriller  War  Western  Unknown  \n",
       "production_company                                                      \n",
       "21st Century Fox                    0         0    0        0        0  \n",
       "Buena Vista                         0         0    0        0        0  \n",
       "Columbia Pictures                   0         0    0        0        0  \n",
       "Focus Features                      0         0    0        0        0  \n",
       "IFC Films                           0         0    0        0        0  \n",
       "Lions Gate                          0         0    0        0        0  \n",
       "MGM                                 0         0    0        0        0  \n",
       "Magnolia Pictures                   0         0    0        0        0  \n",
       "Miramax                             0         0    0        0        0  \n",
       "New Line Cinema                     0         0    0        0        0  \n",
       "Open Road Films                     0         0    0        0        0  \n",
       "Paramount Pictures                  0         0    0        0        0  \n",
       "Sony Pictures Entertainment         0         0    0        0        0  \n",
       "Summit Entertainment                0         0    0        0        0  \n",
       "The Weinstein Company               0         0    0        1        0  \n",
       "Touchstone Pictures                 0         0    0        0        0  \n",
       "United Artists                      0         0    0        0        0  \n",
       "Universal Pictures                  0         0    0        0        0  \n",
       "Walt Disney                         0         0    0        0        0  \n",
       "Warner Brothers                     0         0    0        0        0  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop irrelevant columns\n",
    "studio_genre_sum_drop = studio_genre_sum.drop(column_to_drop,axis = 1)\n",
    "#Sanity check\n",
    "studio_genre_sum_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>production_company</th>\n",
       "      <th>21st Century Fox</th>\n",
       "      <th>Buena Vista</th>\n",
       "      <th>Columbia Pictures</th>\n",
       "      <th>Focus Features</th>\n",
       "      <th>IFC Films</th>\n",
       "      <th>Lions Gate</th>\n",
       "      <th>MGM</th>\n",
       "      <th>Magnolia Pictures</th>\n",
       "      <th>Miramax</th>\n",
       "      <th>New Line Cinema</th>\n",
       "      <th>Open Road Films</th>\n",
       "      <th>Paramount Pictures</th>\n",
       "      <th>Sony Pictures Entertainment</th>\n",
       "      <th>Summit Entertainment</th>\n",
       "      <th>The Weinstein Company</th>\n",
       "      <th>Touchstone Pictures</th>\n",
       "      <th>United Artists</th>\n",
       "      <th>Universal Pictures</th>\n",
       "      <th>Walt Disney</th>\n",
       "      <th>Warner Brothers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>147</td>\n",
       "      <td>50</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>142</td>\n",
       "      <td>128</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>122</td>\n",
       "      <td>60</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>139</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>94</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>103</td>\n",
       "      <td>21</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Documentary</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>38</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>75</td>\n",
       "      <td>104</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>7</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Music</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romance</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Science Fiction</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV Movie</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>War</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "production_company  21st Century Fox  Buena Vista  Columbia Pictures  \\\n",
       "Action                           147           50                 42   \n",
       "Adventure                          0            0                  0   \n",
       "Animation                         15           15                  1   \n",
       "Comedy                           139           38                 39   \n",
       "Crime                              0            0                  0   \n",
       "Documentary                        0            1                  0   \n",
       "Drama                             91           16                 27   \n",
       "Family                             0            0                  0   \n",
       "Fantasy                            0            0                  0   \n",
       "History                            0            0                  0   \n",
       "Horror                            11            3                  3   \n",
       "Music                              0            0                  0   \n",
       "Mystery                           12            2                  0   \n",
       "Romance                            0            0                  0   \n",
       "Science Fiction                    3            0                  0   \n",
       "TV Movie                           0            0                  0   \n",
       "Thriller                           0            0                  0   \n",
       "War                                0            0                  0   \n",
       "Western                            0            0                  0   \n",
       "Unknown                            0            0                  0   \n",
       "\n",
       "production_company  Focus Features  IFC Films  Lions Gate  MGM  \\\n",
       "Action                          13          3          51   66   \n",
       "Adventure                        0          0           0    0   \n",
       "Animation                        0          0           3    1   \n",
       "Comedy                          24         13          36   37   \n",
       "Crime                            0          0           0    0   \n",
       "Documentary                      0          3           3    1   \n",
       "Drama                           29         18          49   29   \n",
       "Family                           0          0           0    0   \n",
       "Fantasy                          0          0           0    0   \n",
       "History                          0          0           0    0   \n",
       "Horror                           1          1          18   14   \n",
       "Music                            0          0           0    0   \n",
       "Mystery                          2          2           5    2   \n",
       "Romance                          1          0           0    0   \n",
       "Science Fiction                  0          0           0    1   \n",
       "TV Movie                         0          0           0    0   \n",
       "Thriller                         0          0           0    0   \n",
       "War                              0          0           0    0   \n",
       "Western                          0          0           0    0   \n",
       "Unknown                          0          0           0    0   \n",
       "\n",
       "production_company  Magnolia Pictures  Miramax  New Line Cinema  \\\n",
       "Action                              5       20               35   \n",
       "Adventure                           0        0                0   \n",
       "Animation                           0        0                0   \n",
       "Comedy                              8       48               33   \n",
       "Crime                               0        0                0   \n",
       "Documentary                         5        0                0   \n",
       "Drama                              17       38               22   \n",
       "Family                              0        0                0   \n",
       "Fantasy                             0        0                0   \n",
       "History                             0        0                0   \n",
       "Horror                              4        6               15   \n",
       "Music                               0        0                0   \n",
       "Mystery                             2        0                0   \n",
       "Romance                             0        0                0   \n",
       "Science Fiction                     0        0                1   \n",
       "TV Movie                            0        0                0   \n",
       "Thriller                            0        0                0   \n",
       "War                                 0        0                0   \n",
       "Western                             0        0                0   \n",
       "Unknown                             0        0                0   \n",
       "\n",
       "production_company  Open Road Films  Paramount Pictures  \\\n",
       "Action                           10                 142   \n",
       "Adventure                         0                   0   \n",
       "Animation                         2                  12   \n",
       "Comedy                            5                  94   \n",
       "Crime                             0                   0   \n",
       "Documentary                       0                   7   \n",
       "Drama                            13                  75   \n",
       "Family                            0                   0   \n",
       "Fantasy                           0                   0   \n",
       "History                           0                   0   \n",
       "Horror                            1                  27   \n",
       "Music                             0                   0   \n",
       "Mystery                           0                   6   \n",
       "Romance                           0                   0   \n",
       "Science Fiction                   0                   1   \n",
       "TV Movie                          0                   0   \n",
       "Thriller                          0                   0   \n",
       "War                               0                   0   \n",
       "Western                           0                   0   \n",
       "Unknown                           0                   0   \n",
       "\n",
       "production_company  Sony Pictures Entertainment  Summit Entertainment  \\\n",
       "Action                                      128                    20   \n",
       "Adventure                                     0                     0   \n",
       "Animation                                    14                     0   \n",
       "Comedy                                      101                     8   \n",
       "Crime                                         0                     0   \n",
       "Documentary                                   9                     0   \n",
       "Drama                                       104                    10   \n",
       "Family                                        0                     0   \n",
       "Fantasy                                       0                     0   \n",
       "History                                       0                     0   \n",
       "Horror                                       20                     4   \n",
       "Music                                         0                     0   \n",
       "Mystery                                       6                     1   \n",
       "Romance                                       0                     0   \n",
       "Science Fiction                               1                     0   \n",
       "TV Movie                                      0                     0   \n",
       "Thriller                                      0                     0   \n",
       "War                                           0                     0   \n",
       "Western                                       0                     0   \n",
       "Unknown                                       0                     0   \n",
       "\n",
       "production_company  The Weinstein Company  Touchstone Pictures  \\\n",
       "Action                                  4                    7   \n",
       "Adventure                               0                    0   \n",
       "Animation                               0                    0   \n",
       "Comedy                                 12                   15   \n",
       "Crime                                   0                    0   \n",
       "Documentary                             1                    0   \n",
       "Drama                                  24                    9   \n",
       "Family                                  0                    0   \n",
       "Fantasy                                 0                    0   \n",
       "History                                 0                    0   \n",
       "Horror                                  5                    0   \n",
       "Music                                   0                    0   \n",
       "Mystery                                 1                    0   \n",
       "Romance                                 0                    0   \n",
       "Science Fiction                         0                    0   \n",
       "TV Movie                                0                    0   \n",
       "Thriller                                0                    0   \n",
       "War                                     0                    0   \n",
       "Western                                 1                    0   \n",
       "Unknown                                 0                    0   \n",
       "\n",
       "production_company  United Artists  Universal Pictures  Walt Disney  \\\n",
       "Action                          20                 122           60   \n",
       "Adventure                        0                   0            0   \n",
       "Animation                        0                   7           23   \n",
       "Comedy                           5                 103           21   \n",
       "Crime                            0                   0            0   \n",
       "Documentary                      0                   2            2   \n",
       "Drama                            5                  68            7   \n",
       "Family                           0                   0            0   \n",
       "Fantasy                          0                   0            0   \n",
       "History                          0                   0            0   \n",
       "Horror                           3                  26            0   \n",
       "Music                            0                   0            0   \n",
       "Mystery                          1                   6            0   \n",
       "Romance                          0                   0            0   \n",
       "Science Fiction                  0                   2            2   \n",
       "TV Movie                         0                   0            0   \n",
       "Thriller                         0                   0            0   \n",
       "War                              0                   0            0   \n",
       "Western                          0                   0            0   \n",
       "Unknown                          0                   0            0   \n",
       "\n",
       "production_company  Warner Brothers  \n",
       "Action                          204  \n",
       "Adventure                         0  \n",
       "Animation                        13  \n",
       "Comedy                          110  \n",
       "Crime                             0  \n",
       "Documentary                       2  \n",
       "Drama                           121  \n",
       "Family                            0  \n",
       "Fantasy                           0  \n",
       "History                           0  \n",
       "Horror                           21  \n",
       "Music                             0  \n",
       "Mystery                           9  \n",
       "Romance                           0  \n",
       "Science Fiction                   0  \n",
       "TV Movie                          0  \n",
       "Thriller                          0  \n",
       "War                               0  \n",
       "Western                           0  \n",
       "Unknown                           0  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Transpose data frame for easier anlysis in finding top 3 genres for each studio\n",
    "studio_genre_sum_drop = studio_genre_sum_drop.transpose()\n",
    "studio_genre_sum_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a nested dictionary populated with studios as outer keys, genre as inner keys and movie count as values\n",
    "genre_studio = {}\n",
    "for studio in top_20_studios: # loop through each studios\n",
    "    studio_value = studio_genre_sum_drop[studio].sort_values(ascending = False) \n",
    "    genre_studio[studio]={studio_value.index[0] : studio_value.values[0],studio_value.index[1] : studio_value.values[1],\n",
    "                  studio_value.index[2] : studio_value.values[2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Horror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Warner Brothers</th>\n",
       "      <td>204.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21st Century Fox</th>\n",
       "      <td>147.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Sony Pictures Entertainment</th>\n",
       "      <td>128.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paramount Pictures</th>\n",
       "      <td>142.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Universal Pictures</th>\n",
       "      <td>122.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGM</th>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lions Gate</th>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Vista</th>\n",
       "      <td>50.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walt Disney</th>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miramax</th>\n",
       "      <td>20.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia Pictures</th>\n",
       "      <td>42.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Line Cinema</th>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focus Features</th>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnolia Pictures</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Artists</th>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Weinstein Company</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IFC Films</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Summit Entertainment</th>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Road Films</th>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Touchstone Pictures</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Action  Drama  Comedy  Animation  Horror\n",
       "Warner Brothers               204.0  121.0   110.0        NaN     NaN\n",
       "21st Century Fox              147.0   91.0   139.0        NaN     NaN\n",
       "Sony Pictures Entertainment   128.0  104.0   101.0        NaN     NaN\n",
       "Paramount Pictures            142.0   75.0    94.0        NaN     NaN\n",
       "Universal Pictures            122.0   68.0   103.0        NaN     NaN\n",
       "MGM                            66.0   29.0    37.0        NaN     NaN\n",
       "Lions Gate                     51.0   49.0    36.0        NaN     NaN\n",
       "Buena Vista                    50.0   16.0    38.0        NaN     NaN\n",
       "Walt Disney                    60.0    NaN    21.0       23.0     NaN\n",
       "Miramax                        20.0   38.0    48.0        NaN     NaN\n",
       "Columbia Pictures              42.0   27.0    39.0        NaN     NaN\n",
       "New Line Cinema                35.0   22.0    33.0        NaN     NaN\n",
       "Focus Features                 13.0   29.0    24.0        NaN     NaN\n",
       "Magnolia Pictures               5.0   17.0     8.0        NaN     NaN\n",
       "United Artists                 20.0    5.0     5.0        NaN     NaN\n",
       "The Weinstein Company           NaN   24.0    12.0        NaN     5.0\n",
       "IFC Films                       3.0   18.0    13.0        NaN     NaN\n",
       "Summit Entertainment           20.0   10.0     8.0        NaN     NaN\n",
       "Open Road Films                10.0   13.0     5.0        NaN     NaN\n",
       "Touchstone Pictures             7.0    9.0    15.0        NaN     NaN"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform it into a dataframe and transpose\n",
    "genre_studio = pd.DataFrame(genre_studio)\n",
    "genre_studio = genre_studio.transpose()\n",
    "genre_studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ensure top genres by studio is sorted similarly to net revenue by studio data frame\n",
    "genre_studio = genre_studio.reindex(index=net_revenue_by_studio.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AZXLsscfmtddey5QpU7LRRhtV7S8GALAkVkoBALBM1lprrbzzzjvZZpttcuWVV1Z9Qx8AwJLYUwoAAACAgrNSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAF1yhLqYULF2bGjBlZuHBhfUcBAAAA4Gs0ylLqvffeS9euXfPee+/VdxQAAAAAvkajLKUAAAAAWL4ppQAAAAAouIKXUn/4wx/Sq1ev7LnnnrnhhhuSJBMnTkyfPn3SrVu3XHLJJVVzJ0+enH79+qV79+459dRT7REFAAAA0EgUtJR66qmn8sQTT2TMmDEZOXJkbrrpprz66qsZOnRorrzyytxzzz2ZNGlSJkyYkCQ54YQTctppp2X8+PGprKzMiBEjChkXAAAAgDpS0FJqhx12yI033piSkpJ88MEHKS8vz6effpoNNtgg7dq1S0lJSfr06ZNx48Zl5syZmTdvXjp27Jgk6devX8aNG1fIuAAAAADUkYK/vldaWpo//vGP2XPPPdOpU6e8//77admyZdX5Vq1aZdasWYuNt2zZMrNmzSp0XAAAAADqQL1sdH7MMcfk8ccfz7vvvpupU6emqKio6lxlZWWKiopSUVHxteMAAAAANHwFLaXefPPNTJ48OUnSvHnzdOvWLU8++WRmz55dNWf27Nlp1apVWrduvcj4nDlz0qpVq0LGBQAAAKCOFLSUmjFjRoYNG5b58+dn/vz5efDBB9O/f/9MmTIl06ZNS3l5ecaOHZvOnTunTZs2adasWZ555pkkyejRo9O5c+dCxgUAAACgjpQU8mZdunTJiy++mL59+6a4uDjdunXLnnvumTXXXDODBw9OWVlZunTpkh49eiRJLrzwwgwbNixz587NVlttlYMPPriQcQEAAACoI0WVlZWV9R2its2YMSNdu3bNgw8+mLZt29Z3HAAAAAC+ol42Oq9P88oW1HeEGmloeQEAAACqo6Cv7y0PVmpWmjU2Pba+Y1TbR69fWt8RAAAAAGrdCrdSCgAAAID6p5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIIrKfQNL7/88tx7771Jki5duuTEE0/MKaeckmeeeSbNmzdPkgwaNCg//vGPM3ny5Jx66qn5/PPPs/322+eMM85ISUnBIwMAAABQywra8EycODGPPfZY7rrrrhQVFeWwww7L/fffn0mTJuWvf/1rWrVqtcj8E044IWeffXY6duyYoUOHZsSIERkwYEAhIwMAAABQBwr6+l7Lli1z8sknp2nTpiktLc3GG2+cd955J++8806GDh2aPn365I9//GMqKioyc+bMzJs3Lx07dkyS9OvXL+PGjStkXAAAAADqSEFXSrVv377q56lTp+bee+/NzTffnKeeeiqnn356vvOd7+SII47IHXfckfbt26dly5ZV81u2bJlZs2YVMi4AAAAAdaReNjp/4403cuihh+bEE0/MRhttlCuuuCKtWrVK8+bNM3DgwEyYMCEVFRUpKiqq+kxlZeUixwAAAAA0XAUvpZ555pn8/Oc/z/HHH5999tknr732WsaPH191vrKyMiUlJWndunVmz55dNT5nzpzF9pwCAAAAoGEqaCn17rvv5uijj86FF16YPffcM8l/Sqhzzz03n3zySRYsWJDbbrstP/7xj9OmTZs0a9YszzzzTJJk9OjR6dy5cyHjAgAAAFBHCrqn1HXXXZeysrIMHz68aqx///755S9/mQMOOCALFy5Mt27d0rt37yTJhRdemGHDhmXu3LnZaqutcvDBBxcyLgAAAAB1pKiysrKyvkPUthkzZqRr16558MEH07Zt28XOr7HpsYUP9S199Pql9R0BAAAAoNbVy0bnAAAAAKzYlFIAAAAAFNxS95Taeuutq32xSZMmLVMYAAAAAFYMSy2lysvLkyTbb7999thjj6y66qp1HgoAAACAxm2ppdQjjzyScePG5d57781FF12UnXfeOb169UrXrl2zyiqrFCIjAAAAAI3MUveUatmyZQYOHJhbbrkl999/fzp16pRbbrklu+yySwYPHpx77rkn8+bNK0RWAAAAABqJGm10vs466+RnP/tZbr311tx7773ZbrvtctNNN2XnnXfOkCFD6iojAAAAAI3Mt/72vTXXXDPrrbde1ltvvSTJ448/XmuhAAAAAGjclrqn1P/64osv8vDDD2f8+PF55JFH0qxZs3Tt2jV/+MMf0qlTp7rKCAAAAEAjs9RSau7cuXnooYcyfvz4/POf/8xKK62UPfbYI5dddlk6deqU4uLiQuQEAAAAoBFZaim18847p7KyMp06dcppp52WnXbaqaqImjNnziJz11lnnbpJCQAAAECjstRSav78+UmSRx55JI8++ujXzqmsrExRUVEmT55cu+kAAAAAaJSWWkrdeOONhcgBAAAAwApkqaXUDjvsUIgcAAAAAKxAllpKXXXVVdW+2JFHHrlMYQAAAABYMSy1lLr00kvTpEmTtG7deonzioqKlFIAAAAAVMtSS6n9998/DzzwQJKkZ8+e6dWrV7baaqs6DwYAAABA49VkaRPOPPPMPPbYYznnnHPy2Wef5bDDDku3bt1y6aWX5vXXXy9ERgAAAAAamaWulEqSJk2apFOnTunUqVNOP/30TJw4MePGjcvAgQOz1lprpVevXunZs2c23njjus4LAAAAQCOw1JVSX1VcXJwf/ehHOeecczJx4sQceOCBueGGG9K7d++6yAcAAABAI1StlVJf9e9//zvjxo3L+PHj8+9//zvbbLNNevToUdvZAAAAAGikql1Kvfrqq1VF1NSpU9OhQ4fsu+++6d69e9Zdd926zAgAAABAI7PUUuqiiy7Kfffdl+nTp6dDhw7p379/unfvntatWxciHwAAAACN0FJLqWuuuSalpaXZZZdd0rp16/z73//Ov//976+de9ZZZ9V6QAAAAAAan6WWUj/4wQ+SJPPmzcvUqVO/cV5RUVGthQIAAACgcVtqKXXTTTcVIgcAAAAAK5Bqb3ReXl6e4uLiquM5c+bkhRdeyGqrrZaOHTumpORbfZEfAAAAACugJkub8PTTT6d79+656667qsbGjRuXPfbYI0cffXQOOuig7LPPPnnnnXfqNCgAAAAAjccSS6nPPvssRx11VLbaaqtsv/32VWO//e1vs99+++X555/PAw88kDXXXDNnnnlmQQIDAAAA0PAt8Z27ESNGpLS0NP3798/s2bMze/bsPPvss/nyyy/TuXPnvPTSS0mS3r175/zzz8+//vWvtGnTJuutt15BwgMAAADQMC2xlHrllVdSVFSUp556qmrs3nvvTfv27fPiiy9WjX300Uf54osv8uSTT2bHHXdcYil1+eWX5957702SdOnSJSeeeGImTpyY8847L2VlZenZs2eGDBmSJJk8eXJOPfXUfP7559l+++1zxhln2LsKAAAAoBFY4ut7xx57bD7++OP84Ac/yKBBg9K9e/e8++67OfTQQzNo0KAMGjQoRx99dMrLy7PJJptk0KBB+cEPfvCN15s4cWIee+yx3HXXXRk1alRefvnljB07NkOHDs2VV16Ze+65J5MmTcqECROSJCeccEJOO+20jB8/PpWVlRkxYkTtPj0AAAAA9WKJpVS7du0ycODA/PznP0/nzp2zzz77ZIsttsiee+6ZJHniiSfSrVu33HHHHTnmmGOWerOWLVvm5JNPTtOmTVNaWpqNN944U6dOzQYbbJB27dqlpKQkffr0ybhx4zJz5szMmzcvHTt2TJL069cv48aNW/YnBgAAAKDeLfVduJNOOildu3bNSy+9lLXXXjvdu3dPkyb/6bLWXHPN7Lzzztl3332zzTbbLPVm7du3r/p56tSpuffee3PQQQelZcuWVeOtWrXKrFmz8v777y8y3rJly8yaNatGDwcAAADA8qlaGzRtv/32Vd++97823XTTnHHGGTW+6RtvvJEjjjgiJ554YoqLizN16tSqc5WVlSkqKkpFRUWKiooWGwcAAACg4Vvi63t14ZlnnsnPf/7zHH/88dlnn33SunXrzJ49u+r87Nmz06pVq8XG58yZk1atWhU6LgAAAAB1oKCl1Lvvvpujjz46F154YdW+VB06dMiUKVMybdq0lJeXZ+zYsencuXPatGmTZs2a5ZlnnkmSjB49Op07dy5kXAAAAADqSLVe36st1113XcrKyjJ8+PCqsf79+2f48OEZPHhwysrK0qVLl/To0SNJcuGFF2bYsGGZO3duttpqqxx88MGFjAsAAABAHSmqrKysrO8QtW3GjBnp2rVrHnzwwbRt23ax82tsemzhQ31LH71+aX1HAAAAAKh1NVopVVFRkb///e95/vnns2DBgny1zzrrrLNqNRwAAAAAjVONSqlzzz03N998czbbbLN85zvfWeScb8YDAAAAoLpqVErdf//9GTZsWA488MC6ygMAAADACqBG3743d+7c/PCHP6yrLAAAAACsIGpUSnXt2jXjxo2rqywAAAAArCBq9Ppe69atc8UVV+Shhx7Kd7/73TRt2nSR8zY6BwAAAKA6alRKPffcc+nQoUOS5J133qmTQAAAAAA0fjUqpW666aa6ygEAAADACmSppdSzzz6bDh06pLi4OM8+++w3zisqKsq2225bq+EAAAAAaJyWWkoNGDAg//znP7PWWmtlwIABKSoqSmVl5WLzioqKMnny5DoJCQAAAEDjstRS6sEHH8yaa65Z9TMAAAAALKulllJt2rT52p8BAAAA4NtqUt8BAAAAAFjxKKUAAAAAKDilFAAAAAAF961LqYULF9ZmDgAAAABWIDUupUaNGpUePXqkY8eOmT59ek4//fRcccUVdZENAAAAgEaqRqXUqFGjcu6556Zv374pLi5Okmy++ea55pprcs0119RJQAAAAAAanxqVUtdff31++9vf5sgjj0yTJv/56AEHHJCzzjorI0aMqJOAAAAAADQ+NSqlpk2blo4dOy423rFjx8yaNau2MgEAAADQyNWolFp33XXz6quvLjb++OOPZ9111621UAAAAAA0biU1mXzooYfmd7/7XWbPnp3Kyso89dRTufPOO/PnP/85xx13XF1lBAAAAKCRqVEptf/++2fhwoW5+uqrM2/evJx66qlZZ511ctJJJ6V///51lREAAACARqZGpVSSDBgwIAMGDMiHH36Ypk2bpkWLFnWRCwAAAIBGrEal1N13373E83369FmmMAAAAACsGGpUSp1wwglfO96sWbO0bt1aKQUAAABAtdSolPrqN++Vl5dn6tSp+d3vfpef/vSntRoMAAAAgMarybJ8uLi4OBtvvHFOPvnk/OEPf6itTAAAAAA0cstUSv1XcXFx3n///dq4FAAAAAArgGXe6Hzu3LkZMWJEttlmm1oLBQAAAEDjtswbnZeUlGTbbbfN7373u2pdY+7cuenfv3+uuuqqtG3bNqecckqeeeaZNG/ePEkyaNCg/PjHP87kyZNz6qmn5vPPP8/222+fM844IyUlNYoLAAAAwHJqmTY6r6kXXnghw4YNy9SpU6vGJk2alL/+9a9p1arVInNPOOGEnH322enYsWOGDh2aESNGZMCAAct0fwAAAACWD7Wyp1R1jRgxIqeffnpVAfXll1/mnXfeydChQ9OnT5/88Y9/TEVFRWbOnJl58+alY8eOSZJ+/fpl3LhxhYwKAAAAQB2q0UqpN998M+ecc06ee+65LFiwYLHzkyZNWuLnzznnnEWO58yZk5122imnn356vvOd7+SII47IHXfckfbt26dly5ZV81q2bJlZs2bVJCoAAAAAy7EalVJnnnlm3nnnnRx77LFZddVVl/nm7dq1yxVXXFF1PHDgwIwaNSobb7xxioqKqsYrKysXOQYAAACgYatRKfXSSy/lhhtuSIcOHWrl5q+99lqmTp2a7t27J/lP+VRSUpLWrVtn9uzZVfPmzJmz2J5TAAAAADRcNdpTavXVV0/Tpk1r7eaVlZU599xz88knn2TBggW57bbb8uMf/zht2rRJs2bN8swzzyRJRo8enc6dO9fafQEAAACoXzUqpY488sice+65mTJlSioqKpb55ptvvnl++ctf5oADDsiee+6ZLbbYIr17906SXHjhhTnvvPPSo0ePfPHFFzn44IOX+X4AAAAALB+KKisrK6s7uWfPnpk+fXrKy8tTVFSUJk0W7bSWttF5ocyYMSNdu3bNgw8+mLZt2y52fo1Njy18qG/po9cvre8IAAAAALWuRntK/fKXv6yrHAAAAACsQGpUSu2zzz51lQMAAACAFUiN9pRKkn/961857LDDsvvuu2fmzJm57LLLMmrUqDqIBgAAAEBjVaNSasKECTnssMOy7rrrZs6cOamoqEhRUVFOPfXUjBw5sq4yAgAAANDI1KiUuvzyy3PiiSfmrLPOSnFxcZJk0KBBOemkk3L99dfXSUAAAAAAGp8alVL//ve/07lz58XGd9ttt0yfPr3WQgEAAADQuNWolFpjjTW+tnyaNGlS1l577VoLBQAAAEDjVqNSav/9988ZZ5yRCRMmJEnefvvt3HHHHTnrrLN8Mx8AAAAA1VZSk8lHHHFEPvvsswwePDjz58/PL37xi5SUlOSQQw7J0UcfXVcZAQAAAGhkalRKFRUV5YQTTsjRRx+dN998M6Wlpfnud7+blVZaqa7yAQAAANAI1aiU2m233dK3b9/07ds33/ve9+oqEwAAAACNXI32lBowYED+8Y9/pEePHunfv39uvfXWfPrpp3WVDQAAAIBGqkal1OGHH55Ro0ZlzJgx2WGHHXLNNdfkhz/8YX7961/nH//4R11lBAAAAKCRqVEp9V/t27fPcccdlwceeCAnnnhiHnvssRx11FG1nQ0AAACARqpGe0r91+TJkzN27Njcc889+fjjj7P77runb9++tRwNAAAAgMaqRqXU5Zdfnr///e+ZOnVqtt122xx11FHp2bNnWrRoUVf5AAAAAGiEalRK3XXXXdl7772zzz77pF27dnWVCQAAAIBGrkal1IMPPlj188KFC1NS8q3e/gMAAABgBVfjjc5HjRqVHj16pGPHjpk+fXpOP/30XHHFFXWRDQAAAIBGqkal1KhRo3Luueemb9++KS4uTpJsvvnmueaaa3LNNdfUSUAAAAAAGp8alVLXX399fvvb3+bII49Mkyb/+egBBxyQs846KyNGjKiTgAAAAAA0PjUqpaZNm5aOHTsuNt6xY8fMmjWrtjIBAAAA0MjVqJRad9118+qrry42/vjjj2fdddettVAAAAAANG41+vq8Qw89NL/73e8ye/bsVFZW5qmnnsqdd96ZP//5zznuuOPqKiMAAAAAjUyNSqn9998/CxcuzNVXX5158+bl1FNPzTrrrJOTTjop/fv3r6uMAAAAADQyNSqlkmTAgAEZMGBAPvzwwzRt2jQtWrRIklRWVqaoqKjWAwIAAADQ+FR7T6nXX389b731VtXxmmuuWVVIvfbaa/npT39a++kAAAAAaJSWulJq+vTpOeqoo/Lvf/87SbL11lvn6quvzpprrpkFCxbk8ssvz3XXXZfVVlutzsMCAAAA0DgsdaXUeeedl88//zzDhw/PRRddlHnz5uWCCy7I+++/n5/85Ce5+uqr07t37/z9738vRF6WYF7ZgvqOUGMNMTMAAACw7Ja6UurZZ5/N+eefny5duiRJ2rdvn4MOOihvvfVWPvnkk1x33XXZZZdd6jwoS7dSs9Kssemx9R2jRj56/dJqz51XtiArNSutuzB1oCFmBgAAgEJYain16aefZvPNN686bt++fT7//PMkyahRo2r82t7cuXPTv3//XHXVVWnbtm0mTpyY8847L2VlZenZs2eGDBmSJJk8eXJOPfXUfP7559l+++1zxhlnpKSkxvuy04g09tINAAAAViRLfX2voqJisTKotLQ0J5xwQo0LqRdeeCEHHHBApk6dmiSZN29ehg4dmiuvvDL33HNPJk2alAkTJiRJTjjhhJx22mkZP358KisrM2LEiBrdCwAAAIDlV7W/fe+r1llnnRp/ZsSIETn99NPTqlWrJMmLL76YDTbYIO3atUtJSUn69OmTcePGZebMmZk3b146duyYJOnXr1/GjRv3baMCAAAAsJxZ6vtwRUVFKSoqWmzs2zjnnHMWOX7//ffTsmXLquNWrVpl1qxZi423bNkys2bN+lb3BAAAAGD5s9RSqrKyMp07d15kbOHChenZs+dicydNmlSjm1dUVCxScFVWVqaoqOgbxwEAAABoHJZaSp133nl1dvPWrVtn9uzZVcezZ89Oq1atFhufM2dO1St/AAAAADR8Sy2l9tlnnzq7eYcOHTJlypRMmzYtbdu2zdixY7PvvvumTZs2adasWZ555plst912GT169GKrtQAAAABouJZaStWlZs2aZfjw4Rk8eHDKysrSpUuX9OjRI0ly4YUXZtiwYZk7d2622mqrHHzwwfUZFQAAAIBaVC+l1EMPPVT1c6dOnTJmzJjF5my++ea54447ChkL6tW8sgVZqVlpfceokYaYGQAAgOVDva6UAv6flZqVZo1Nj63vGDXy0euX1ncEAAAAGqgmNZn8zjvvpLKycrHx8vLyGn/zHrBimVe2oL4j1EhN8zb25wMAAKhtNVop1bVr1/zzn//Mmmuuucj4u+++mwMPPDAvvPBCrYYDGo+GthKspqvAGvvzAQAA1LalllIjR47M6NGjkySVlZU5+uijU1q66B4ys2bNSsuWLesmIQAAAACNzlJLqT322CPPP/98Kisr89RTT6VNmzZZaaWVqs4XFRVlyy23TL9+/eo0KAAAAACNx1JLqdVWWy1nnXVWkqR169Y59NBDs/LKK9d5MAAAAAAarxptdD5o0KA0bdo099xzTy677LJ8/PHHeeqpp/Lhhx/WVT4AAAAAGqEabXT+/vvv52c/+1lmzZqVefPmpW/fvrnhhhvy4osv5i9/+Us22WSTusoJAAAAQCNSo5VSw4cPT/v27fPEE0+kWbNmSZILLrggW2+9dc4///w6CQgAAABA41OjUurJJ5/MUUcdlaZNm1aNtWjRIscff3yef/752s4GAAAAQCNVo1Jq3rx5KS0tXWx8/vz5qaysrLVQAAAAADRuNSqldtlll1xzzTWLFFCfffZZLr744uy44461Hg4AAACAxqlGG50PHTo0AwcOzI9+9KOUlZVl0KBBmTFjRtZYY43ccMMNdZURAAAAgEamRqVU69atM2bMmIwdOzaTJ09OaWlpNtlkk+y1115VG58DAAAAwNLUqJRKkubNm+cnP/lJXWQBAAAAYAWx1FLqt7/9bbUuVFRUlDPPPHOZAwEAAADQ+C21lJo6deoSz8+YMSPvvvtuSkpKlFIAAAAAVMtSS6mbbrrpa8cXLlyYq666Ks8991y22GKLnHvuubUeDoD6N69sQVZqVlrfMWqkJpkb+/MBAMDyqsZ7SiXJK6+8klNOOSVTpkzJUUcdlSOOOCLFxcW1nQ2A5cBKzUqzxqbH1neMGvno9UurPbexPx8AACyvalRKzZ8/P5dffnmuu+66bLXVVrnzzjuzySab1FU2AAAAABqpapdSzz//fE499dTMnDkzxx13XA455JA0adKkLrMBAAAA0EgttZQqKyvLxRdfnL/+9a/Zdttt86c//Snrr79+IbIBAMuoIe4/1RAzAwBQc0stpfbaa6+8/fbbadeuXXbZZZfcc8893zj3yCOPrNVwAMCyaex7ZjW0Aquh5QUAqEtLLaUWLFiQddddNwsXLsztt9/+jfOKioqUUgBAQTW00s0m9QAA/89SS6mHHnqoEDkAAAAAWIHYqRwAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKLilfvteoQwcODAffvhhSkr+E+nMM8/M559/nvPOOy9lZWXp2bNnhgwZUs8pAQAAAKgNy0UpVVlZmalTp+Yf//hHVSk1b9689OjRIzfddFPWXXfdHHHEEZkwYUK6dOlSz2kBAAAAWFbLRSn11ltvJUkOPfTQfPzxx9l///2z6aabZoMNNki7du2SJH369Mm4ceOUUgAAAACNwHKxp9Snn36aTp065Yorrsif//zn3HrrrXnnnXfSsmXLqjmtWrXKrFmz6jElAEBhzStbUN8RaqSh5QUA6tdysVJq2223zbbbblt1vN9+++WPf/xjtttuu6qxysrKFBUV1Uc8AIB6sVKz0qyx6bH1HaPaPnr90vqOAAA0IMvFSqmnn346jz/+eNVxZWVl2rRpk9mzZ1eNzZ49O61ataqPeAAAAADUsuWilPrss8/y+9//PmVlZZk7d27uuuuuHHfccZkyZUqmTZuW8vLyjB07Np07d67vqAAAAADUguXi9b3ddtstL7zwQvr27ZuKiooMGDAg2267bYYPH57BgwenrKwsXbp0SY8ePeo7KgAAtWBe2YKs1Ky0vmPUSEPMDADLs+WilEqSY489Nscee+wiY506dcqYMWPqJxAAAHWmoe2XldgzCwBq23Lx+h4AAAAAKxalFAAA1LJ5ZQvqO0KNNcTMADRsy83rewAA0Fg09tcTG+L+Wg0xM0Bjp5QCAABqpLGXbgAUhtf3AAAA/kdDe5WxoeUF+C8rpQAAAP5HQ1sJZhUY0FBZKQUAALACaWgrqxpaXqD6rJQCAABYgVgJBiwvrJQCAAAAoOCUUgAAADQKDfFVv4aYGWqL1/cAAABoFBraq4mJ1xNZsVkpBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAADQAMwrW1DfEWqsIWamcErqOwAAAACwdCs1K80amx5b3zFq5KPXL63vCCzHrJQCAAAA6l1DXFXVEDMvT6yUAgAAAOqdlWArHiulAAAAACg4pRQAAAAABaeUAgAAAKDglvtS6u67706vXr3SrVu33HzzzfUdBwAAAIBasFxvdD5r1qxccsklufPOO9O0adP0798/O+64YzbZZJP6jgYAAADAMliuS6mJEydmp512yuqrr54k6d69e8aNG5dBgwYt8XPl5eVJkvfee+9rzzfJF7Wasy7NmDGjRvMb0rMlnu+rPN/yo6bPljTu52tIz5Z4vq/yfMsPf7YsqiE9W+L5vsrzLT/82bKohvRsief7qsb+fI1F69atU1Ky7JVSUWVlZWUt5KkTV199db744osMGTIkSXL77bfnxRdfzFlnnbXEzz399NM58MADCxERAAAAYIXy4IMPpm3btst8neV6pVRFRUWKioqqjisrKxc5/iZbb711br755rRs2TLFxcV1GREAAABghdK6detauc5yXUq1bt06Tz/9dNXx7Nmz06pVq6V+bqWVVsr2229fl9EAAAAAWAbL9bfv7bzzznn88cfz4Ycf5ssvv8x9992Xzp0713csAAAAAJbRcr1Sap111smQIUNy8MEHZ8GCBdlvv/2yzTbb1HcsAAAAAJbRcr3ROQAAAACN03L9+h4AAAAAjZNSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUXKMspRYuXJgZM2Zk4cKF9R0FAAAAgK/RKEup9957L127ds17771X31EAAAAA+BqNspQCAAAAYPmmlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOBWuFJq/oLy+o5QIw0tLwAAAEB1lNR3gEJrWlqcAac9XN8xqu2WM3et7wgAAAAAtW6FWykFAAAAQP1TSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAouIKXUnPnzk3v3r0zY8aMJMnEiRPTp0+fdOvWLZdccknVvMmTJ6dfv37p3r17Tj311CxcuLDQUQEAAACoIwUtpV544YUccMABmTp1apJk3rx5GTp0aK688srcc889mTRpUiZMmJAkOeGEE3Laaadl/PjxqayszIgRIwoZFQAAAIA6VNBSasSIETn99NPTqlWrJMmLL76YDTbYIO3atUtJSUn69OmTcePGZebMmZk3b146duyYJOnXr1/GjRtXyKgAAAAA1KGSQt7snHPOWeT4/fffT8uWLauOW7VqlVmzZi023rJly8yaNatgOQEAAACoW/W60XlFRUWKioqqjisrK1NUVPSN4wAAAAA0DvVaSrVu3TqzZ8+uOp49e3ZatWq12PicOXOqXvkDAAAAoOGr11KqQ4cOmTJlSqZNm5by8vKMHTs2nTt3Tps2bdKsWbM888wzSZLRo0enc+fO9RkVAAAAgFpU0D2lvqpZs2YZPnx4Bg8enLKysnTp0iU9evRIklx44YUZNmxY5s6dm6222ioHH3xwfUYFAAAAoBbVSyn10EMPVf3cqVOnjBkzZrE5m2++ee64445CxgIAAACgQOr19T0AAAAAVkxKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABRcyZJOHnroodW+0PXXX7/MYQAAAABYMSyxlJo4cWKaNGmS73//+2nXrl2hMgEAAADQyC2xlLrooosybty4PProoykrK0uvXr3Ss2fPtG7dulD5AAAAAGiEllhK7bnnntlzzz3zxRdf5KGHHsq9996bP/zhD9liiy3Sq1ev9OjRIy1btixUVgAAAAAaiSWWUv+18sorp3fv3undu3fmzp2bBx54IOPHj8/FF1+crbfeOr169coBBxxQ11kBAAAAaCSqVUr9rxYtWqRv377p3LlzRo0alSuuuCJPP/20UgoAAACAaqtRKTV79uzcd999GT9+fJ555pm0bds2AwYMSI8ePeoqHwAAAACN0FJLqffeey/jx4/PuHHj8vzzz2f99ddP9+7dc/LJJ2fLLbcsREYAAAAAGpklllI//elP8+KLL2b99ddPjx49cvrpp2fzzTcvVDYAAAAAGqklllIvvPBCSkpKUllZmXHjxmXcuHHfOHf8+PG1Hg4AAACAxmmJpdSgQYMKlQMAAACAFYhSCgAAAICCW2Ip9eyzz1b7Qt///veXOQwAAAAAK4YlllIDBgxIUVFR1XFlZeXXzisqKsrkyZNrNxkAAAAAjdYSS6kNN9wwU6dOzXbbbZdevXpll112SUnJEj8CAAAAAEu1xIbp3nvvzWuvvZZ77703N954Y/7whz/kxz/+cfbcc8/suOOOadKkSaFyAgAAANCILHXZ02abbZbNNtssxx57bCZPnpx77703p59+eubOnZtu3bqlV69e2WGHHQqRFQAAAIBGokbv4m2xxRbZYostctxxx+WVV17JuHHjctRRR6V58+Z59NFH6yojAAAAAI3Mt9og6l//+lfGjx+f++67L2VlZdl+++1rOxcAAAAAjVi1SqmKioo8+eSTGT9+fO6///589tln2WWXXXL88cena9euadGiRV3nBAAAAKARWWIp9eijj2b8+PF58MEH88UXX+SHP/xhTjrppOy+++6KKAAAAAC+tSWWUocffnhKS0vTqVOn7L777llllVWSJP/4xz8Wm9unT5+6SQgAAABAo7PU1/cWLFiQRx55JI888sg3zikqKlJKAQAAAFBtSyylXn311ULlAAAAAGAF0uTbfvCzzz5LeXl5bWYBAAAAYAWxxFLqww8/zGmnnZYJEyZUjb3++uvp169fdthhh3Ts2DGnnXZa5s2bV+dBAQAAAGg8vvH1vYULF+aggw5KeXl5evbsWTV2zDHHZK211srVV1+dOXPm5I9//GOaNm2aYcOGFSw0AAAAAA3bN5ZSzz77bD755JOMHz8+LVq0SJI89dRTmTFjRm666aa0bNkySdKmTZscf/zxy1xKjR49Ov/3f/+XJOncuXNOOumkTJw4Meedd17KysrSs2fPDBkyZJnuAQAAAMDy4RtLqTPPPDPz58/PUUcdlcrKyiTJlClTstpqq+X444+vmjd//vzMmTMnAwcOzL777pu+ffvWOMSXX36Zc845J+PGjcuqq66aAw44IA899FDOPPPM3HTTTVl33XVzxBFHZMKECenSpUvNnxIAAACA5co3llLDhw/Pz3/+81x88cVZe+2188UXX6R3794ZOHBgDjnkkKp5t956a959993cdNNN3zpEeXl5Kioq8uWXX2bllVfOwoUL06JFi2ywwQZp165dkqRPnz4ZN26cUgoAAACgEfjGUmrrrbfO5ptvnr59+2annXbKSy+9lAULFuQnP/lJkuTf//53rrvuutx999056qijlilEixYt8utf/zo9e/ZM8+bN84Mf/CDvv/9+1SuCSdKqVavMmjVrme4DAAAAwPJhid++d/311+eAAw7I3Llzs8MOO+Tmm2+u2l9q9uzZef755/Ob3/xmmUupV199NSNHjsw//vGPPProo2nSpEmmTp2aoqKiqjmVlZWLHAMAAADQcH3jSqkkadq0aY4++uivPdepU6fce++9tRLiscceS6dOnbLWWmslSfr165frrrsuxcXFVXNmz56dVq1a1cr9AAAAAKhfS1wpVSibb755Jk6cmC+++CKVlZV56KGH0qFDh0yZMiXTpk1LeXl5xo4dm86dO9d3VAAAAABqwRJXShXKD3/4w7zyyivp169fSktL873vfS+DBw/OLrvsksGDB6esrCxdunRJjx496jsqAAAAALVguSilkuSXv/xlfvnLXy4y1qlTp4wZM6aeEgEAAABQV6r9+t4777yTysrKxcbLy8szadKkWg0FAAAAQONW7VKqa9eu+eijjxYbf/fdd3PggQfWaigAAAAAGrclvr43cuTIjB49OklSWVmZo48+OqWlpYvMmTVrVlq2bFl3CQEAAABodJZYSu2xxx55/vnnU1lZmaeeeipt2rTJSiutVHW+qKgoW265Zfr161fnQQEAAABoPJZYSq222mo566yzkiStW7fOoYcempVXXrkgwQAAAABovKq9p9SgQYPStGnT3HPPPbnsssvy8ccf56mnnsqHH35Yl/kAAAAAaISWuFLqf73//vv52c9+llmzZmXevHnp27dvbrjhhrz44ov5y1/+kk022aQucwIAAADQiFR7pdTw4cPTvn37PPHEE2nWrFmS5IILLsjWW2+d888/v84CAgAAAND4VLuUevLJJ3PUUUeladOmVWMtWrTI8ccfn+eff74usgEAAADQSFW7lJo3b15KS0sXG58/f34qKytrNRQAAAAAjVu1S6lddtkl11xzzSIF1GeffZaLL744O+64Y52EAwAAAKBxqvZG50OHDs3AgQPzox/9KGVlZRk0aFBmzJiRNdZYIzfccENdZgQAAACgkal2KdW6deuMGTMmY8eOzeTJk1NaWppNNtkke+21V9XG5wAAAABQHdUupZKkefPm+clPflJXWQAAAABYQVS7lNp9991TVFS02HhRUVFKS0vTunXr7L333unbt29t5gMAAACgEar2Ruf9+vXLu+++m6222ioHH3xwDj744HTo0CHvvPNOtttuu6y//vo588wzc+utt9ZlXgAAAAAagWqvlHr66adz3HHH5bDDDltkvEOHDpkwYUKuv/76bL/99rnqqqvSv3//Wg8KAAAAQONR7ZVSzz33XH784x8vNr7rrrvm6aefTpJst912mT59eu2lAwAAAKBRqnYp1bp16zz22GOLjT/22GNp2bJlkuSdd97J6quvXmvhAAAAAGicqv363pFHHpnf/va3eemll9KxY8dUVFTkxRdfzNixY/Pb3/42b7/9dk499dR069atLvMCAAAA0AhUu5TaZ599suqqq+bPf/5z7rvvvpSUlGSzzTbLFVdckS5duuTpp59O9+7dM2jQoLrMCwAAAEAjUO1SKkm6du2arl27fu257bffPttvv32thAIAAACgcVtiKTVq1KhqX6hv377LGAUAAACAFcUSS6lTTz11keOKiopUVlZmlVVWSWlpaT7++OMUFxenZcuWSikAAAAAqm2JpdTLL79c9fOoUaPy17/+Neeff3423njjJMmMGTNy8sknZ/fdd6/blAAAAAA0Kk2qO/Giiy7K7373u6pCKknatm2bU089Nddcc02dhAMAAACgcap2KfXll1+moqJisfG5c+emvLy8VkMBAAAA0LhVu5Tq0qVLhg0blmeffTZlZWWZN29eHn/88QwbNiw9evSoy4wAAAAANDJL3FPqf5122mk55phjMmDAgBQVFSVJKisr07Vr1wwdOrTOAgIAAADQ+FS7lFpttdXyl7/8JW+++WbeeOONFBUVZYsttsj6669fl/kAAAAAaISqXUrNmjUrSdKiRYtsu+22i42vs846tRwNAAAAgMaq2qVUly5dql7b+zqTJ0+ulUAAAAAANH7VLqVuvPHGRY7Ly8szZcqU/PnPf87JJ59c68EAAAAAaLyqXUrtsMMOi4116tQpbdu2zeWXX57dd999mYI89NBDufzyy/Pll19ml112ybBhwzJx4sScd955KSsrS8+ePTNkyJBlugcAAAAAy4cmy3qBDTfcMK+++uoyXWP69Ok5/fTTc+WVV2bMmDF55ZVXMmHChAwdOjRXXnll7rnnnkyaNCkTJkxY1rgAAAAALAdqvNH5/5o7d26uvvrqtG3bdplC3H///enVq1dat26dJLnkkksybdq0bLDBBmnXrl2SpE+fPhk3bly6dOmyTPcCAAAAoP4t00bnlZWVWXnllXPBBRcsU4hp06altLQ0Rx55ZN59993suuuuad++fVq2bFk1p1WrVl9bjAEAAADQ8Hzrjc6TpLS0NJtuumlWWWWVZQpRXl6ep59+OjfddFNWXnnl/OpXv8pKK620SAlWWVm5xG//AwAAAKDhWKaNzmvL2muvnU6dOmXNNddMkuyxxx4ZN25ciouLq+bMnj07rVq1qrMMAAAAABTOUjc6nzNnTm699dbcdttt+fDDD5Mk1113XXbeeefstNNOGT58eBYuXLhMIXbbbbc89thj+fTTT1NeXp5HH300PXr0yJQpUzJt2rSUl5dn7Nix6dy58zLdBwAAAIDlwxJXSj3//PM57LDDUlFRkdLS0lxyySU5/PDDc+mll6Zv376pqKjI3/72t6y++uo58sgjv3WIDh065LDDDsuAAQOyYMGC7LLLLjnggAOy0UYbZfDgwSkrK0uXLl3So0ePb30PAAAAAJYfSyylLrzwwvTu3TvDhg1LSUlJrrvuulx44YU55ZRTcvDBBydJtttuu1xzzTXLVEolyX777Zf99ttvkbFOnTplzJgxy3RdAAAAAJY/S3x975VXXsnAgQNTUvKf7urggw9OUVHRIvtL7bzzzpk5c2bdpgQAAACgUVliKfXFF19ktdVWqzouLS1Ns2bNFvm2vZKSkixYsKDuEgIAAADQ6Cx1o/OioqJC5AAAAABgBbLEPaWS5KWXXsqqq666yNjLL7+c2bNnJ0k++eSTukkGAAAAQKO11FLqV7/6VSorKxcZO/bYYxc5tpoKAAAAgJpYYin14IMPFioHrPDmLyhP09Li+o5RIw0xMwAAAMuHJZZSbdq0KVQOWOE1LS3OgNMeru8YNXLLmbvWdwQAAAAaqKVudA4AAAAAtU0pBQAAAEDBKaUAAAAAKDilFAAAAAAFt8SNzv9XRUVF/v73v+f555/PggULUllZucj5s846q9bDAQAAANA4VbuUOvfcc3PzzTdns802y3e+851FzhUVFdV6MAAAAAAar2qXUvfff3+GDRuWAw88sC7zsAzmLyhP09Li+o5RIw0xMwAAALDsql1KzZ07Nz/84Q/rMgvLqGlpcQac9nB9x6iRW87ctb4jAAAAAPWg2hudd+3aNePGjavLLAAAAACsIKq9Uqp169a54oor8tBDD+W73/1umjZtush5G50DAAAAUF3VLqWee+65dOjQIUnyzjvvLHLORucAAAAA1ES1S6mbbrqpLnMAAAAAsAKpdimV/GeF1M0335w33ngjJSUlad++ffbff/+0adOmrvIBAAAA0AhVe6PzyZMnp0+fPrnnnnvSvHnzFBcXZ/To0dlrr73y6quv1mVGAAAAABqZaq+UOv/889O5c+f8/ve/T2lpaZJkwYIFOfnkk3PBBRfkuuuuq7OQkCTzF5SnaWlxfceokYaYmW+nof29bmh5AQCAxqfapdTzzz+f22+/vaqQSpLS0tIcccQR6d+/f52Eg//VtLQ4A057uL5j1MgtZ+5a3xEokIb2v0//2wQAAOpbtV/fW3XVVfP5558vNj537tyUlNRoayoAAAAAVnDVLqV23XXXnHnmmXn77berxqZOnZpzzjknXbp0qZNwAAAAADRO1V7idNxxx+WQQw5J9+7ds8YaayRJPvroo3To0CGnnHJKnQUEAAAAoPGpdim1+uqrZ+TIkXn00UfzxhtvpFmzZtlkk03SqVOnuswHAAAAQCNUo82gmjRpki5dunhdDwAAAIBlssRSauutt84jjzySNddcM1tttVWKioq+ce6kSZNqPRwAAAAAjdMSS6mzzjorLVq0SJKcffbZBQkEAIU0f0F5mpYW13eMGmmImQEA4KuWWErts88+VT8XFRWlV69eadq06SJzvvjii4wYMaJu0gFAHWtaWpwBpz1c3zFq5JYzd63vCAAAsMyaVHfiKaeckrlz5y42/tZbb+Wiiy6q1VAAANUxf0F5fUeokYaWFwCgLi1xpdRf/vKXDB8+PElSWVmZXXbZ5Wvnbb/99rWfDABgKRraSjer3AAA/p8lllIHHXRQ1lxzzVRUVOSkk07KsGHD8p3vfKfqfFFRUVZZZZXsuOOOtRbo/PPPz0cffZThw4dn4sSJOe+881JWVpaePXtmyJAhtXYfAAAAAOrPEkup4uLi9OnTJ0my7rrrZtttt82nn36atdZaK0ny7LPP5nvf+15KS0trJczjjz+eu+66K7vuumvmzZuXoUOH5qabbsq6666bI444IhMmTEiXLl1q5V4AAAAA1J9q7ynVsmXL9OjRI9dee23V2KBBg9KnT59Mnz59mYN8/PHHueSSS3LkkUcmSV588cVssMEGadeuXUpKStKnT5+MGzdume8DAAAAQP2rdil19tlnZ6uttsoRRxxRNXbfffelffv2Offcc5c5yGmnnZYhQ4Zk1VVXTZK8//77admyZdX5Vq1aZdasWct8HwAAAADqX7VLqeeeey7HHXdcVl999aqxFi1a5Nhjj83TTz+9TCFuv/32rLvuuunUqVPVWEVFRYqKiqqOKysrFzkGAAAAoOFa4p5S/6t58+Z5//33893vfneR8Y8++ihNmlS72/pa99xzT2bPnp299947n3zySb744ovMnDkzxcXFVXNmz56dVq1aLdN9gPozf0F5mpYWL33icqKh5QUAAGhoql1KdevWLb/73e9y5pln5nvf+16S5KWXXsoZZ5yRPfbYY5lC3HDDDVU/33nnnXnqqadyxhlnpFu3bpk2bVratm2bsWPHZt99912m+wD1x9e2AwAA8L+qXUr95je/ya9//escdNBBi7xGt/vuu+eUU06p9WDNmjXL8OHDM3jw4JSVlaVLly7p0aNHrd8HAAAAgMKrdim1yiqr5Nprr81bb72V119/PaWlpdl4440Xe51vWfXr1y/9+vVLknTq1Cljxoyp1esDAAAAUP+qXUr910YbbZSNNtqoLrIAALWsIe6P1hAzAwBQc9UupbbaaqslfvvdpEmTaiUQAFB7Gtp+bok93QAAVhTVLqXOOuusRUqphQsXZurUqRk1alROPPHEOgkHQP1riKtWGmJmAABY0VS7lPrvPk9ftdVWW+WOO+7I3nvvXWuhAFh+WGkDAADUhSbLeoEOHTrkmWeeqY0sAAAAAKwglqmUKisryy233JK11167tvIAAAAAsAJYpo3Oy8vLU1RUlN/97ne1nQsAAACARuxbb3SeJKWlpenQoUPatWtX68EAAGi8GuIXEjTEzACwPFvmjc4BAKCmfIkCALDEUuryyy+v9oUGDRq0zGEAAAAAWDEssZQaM2bMIsfTp09Ps2bNsv7666e0tDRTp05NWVlZttlmG6UUAAAAANW2xFLqvvvuq/r5mmuuyVNPPZULLrggq6++epJk7ty5GTp0aNq0aVOnIQEAAABoXJpUd+K1116bE088saqQSpIWLVrkmGOOye23314X2QAAAABopKpdSiXJnDlzFhubPn16mjZtWmuBAAAAAGj8qv3te3vuuWeGDh2a4447LltuuWWS5Nlnn80f/vCH/OQnP6mzgAAAAAA0PtUupU4++eTMmzcvp5xySsrLy1NZWZmmTZumf//++fWvf12XGQEAAABoZKpdSjVt2jTnnntuhg4dmilTpqSoqCgbbbRRVl555brMBwAAAEAjVO1SKkneeeed3HzzzXnjjTdSUlKS9u3bZ//99/ftewAAAADUSLU3Op88eXL69OmTe+65J82bN09xcXFGjx6dvfbaK6+99lpdZgQAAACgkan2Sqnzzz8/nTt3zu9///uUlpYmSRYsWJCTTz45v//973PdddfVWUgAAAAAGpdqr5R6/vnnc9RRR1UVUklSWlqaI444Is8991ydhAMAAIDqmr+gvL4j1FhDzAy1pdorpVZdddV8/vnni43PnTs3JSU12poKAIBqmL+gPE1Li+s7RrU1tLxA49O0tDgDTnu4vmPUyC1n7lrfEaDeVLtN2nXXXXPmmWfm0ksvzfrrr58kmTp1as4555x06dKlzgICAKyoGtq/XPkXKwCgJqpdSh133HE55JBD0r1796yxxhpJko8++igdOnTIKaecUmcBAQAAAGh8ql1KlZeXZ+TIkXn00UfzxhtvpFmzZtlkk03SqVOnuswHAAAAQCNU7VJq3333zWWXXZYuXbp4XQ8AAACAZVLtb9+rrKxM06ZN6zILAAAAACuIGq2UOuyww9KvX7+0bds2K6200iLn+/TpU+vhAAAAAGicql1KXXnllUmSq6++erFzRUVFSikAAAAAqq3apdSrr75alzkAAACAJZi/oDxNS4vrO0aNNMTMFM5SS6kvvvgiTzzxRJo1a5aOHTtmlVVWKUQuAAAA4H80LS3OgNMeru8YNXLLmbvWdwSWY0sspV599dUcdthhmTNnTpKkVatWufzyy7PNNtsUJBwAAAAAjdMSv33voosuSrt27XLrrbdmxIgR2XDDDXPGGWcUKhsAAAAAjdQSV0o9//zzufHGG7PFFlskSc4+++x07949X3zxRVZeeeVaDXL55Zfn3nvvTZJ06dIlJ554YiZOnJjzzjsvZWVl6dmzZ4YMGVKr9wQAAACgfixxpdTnn3+etddeu+q4Xbt2KS4uzscff1yrISZOnJjHHnssd911V0aNGpWXX345Y8eOzdChQ3PllVfmnnvuyaRJkzJhwoRavS8AAAAA9WOJpVRFRUWaNFl0SklJScrLy2s1RMuWLXPyySenadOmKS0tzcYbb5ypU6dmgw02SLt27VJSUpI+ffpk3LhxtXpfAACAr5q/oHb/faeu1TRvY38+oOFY6rfvFUL79u2rfp46dWruvffeHHTQQWnZsmXVeKtWrTJr1qz6iAcAAKxAGto3nNX0280a+/MBDcdSS6kbb7wxzZs3rzouLy/PLbfcktVWW22ReUceeeQyh3njjTdyxBFH5MQTT0xxcXGmTp1ada6ysjJFRUXLfA8AAAAA6t8SS6n11lsvd9999yJja6+9dsaPH7/IWFFR0TKXUs8880yOOeaYDB06NHvuuWeeeuqpzJ49u+r87Nmz06pVq2W6BwAAFML8BeVpWlpc3zFqpCFmBqBhW2Ip9dBDDxUkxLvvvpujjz46l1xySTp16pQk6dChQ6ZMmZJp06albdu2GTt2bPbdd9+C5AEAgGXR0F6PSrwiBUDhLRd7Sl133XUpKyvL8OHDq8b69++f4cOHZ/DgwSkrK0uXLl3So0ePekwJAAAAQG1ZLkqpYcOGZdiwYV97bsyYMQVOAwAAAEBda1LfAQAAAABY8SilAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAqJH5C8rrO0KNNcTMAI1dSX0HAAAAGpampcUZcNrD9R2jRm45c9f6jgDAV1gpBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAABQ7+YvKK/vCDXWEDMvT0rqOwAAAABA09LiDDjt4fqOUSO3nLlrfUdo0KyUAgAAAKhjDW1VVSHyWikFAAAAUMca2kqwQqwCs1IKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEt96XU3XffnV69eqVbt265+eab6zsOAAAAALWgpL4DLMmsWbNyySWX5M4770zTpk3Tv3//7Ljjjtlkk03qOxoAAAAAy2C5LqUmTpyYnXbaKauvvnqSpHv37hk3blwGDRq0xM+Vl5cnSd57772vPV/2+ZxazVmXZsyYUaP5DenZEs/3VZ5v+VHTZ0sa9/M1pGdLPN9Xeb7lhz9bFtWQni3xfF/l+ZYf/mxZVEN6tsTzfZXnW34s6dlat26dkpJlr5SKKisrK5f5KnXk6quvzhdffJEhQ4YkSW6//fa8+OKLOeuss5b4uaeffjoHHnhgISICAAAArFAefPDBtG3bdpmvs1yvlKqoqEhRUVHVcWVl5SLH32TrrbfOzTffnJYtW6a4uLguIwIAAACsUFq3bl0r11muS6nWrVvn6aefrjqePXt2WrVqtdTPrbTSStl+++3rMhoAAAAAy2C5/va9nXfeOY8//ng+/PDDfPnll7nvvvvSuXPn+o4FAAAAwDJarldKrbPOOhkyZEgOPvjgLFiwIPvtt1+22Wab+o4FAAAAwDJarjc6BwAAAKBxWq5f3wMAAACgcVJKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKTikFAAAAQMEppQAAAAAoOKUUAAAAAAWnlAIAAACg4JRSAAAAABScUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAAAAKDilFAAAAAAFp5QCAAAAoOCUUgAAAAAUnFIKAAAAgIJTSgEAAABQcEopAAAAAApOKQUAAABAwSmlAAAAACg4pRQAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCa5Sl1MKFCzNjxowsXLiwvqMAAAAA8DUaZSn13nvvpWvXrnnvvffqOwoAAAAAX6NRllIAAAAALN+UUgAAAAAUnFIKAAAAgIJTSgEAAABQcCX1HQAAAACgpioqKjJjxox8/vnn9R2l0SktLU2rVq2y6qqr1ul9lFIAAABAgzNnzpwUFRVls802S5MmXgSrLZWVlfnyyy8zc+bMJKnTYsrfNQAAAKDB+fjjj7POOusopGpZUVFRVl555bRp0ybvv/9+nd7L3zkAAACgwSkvL09paWl9x2i0mjdvngULFtTpPZRSAAAAQINUVFRU3xEarUL8bpVSAAAAQIM3f0F5g7ouNjqHBmN++YI0LS7c0tRC3w8AAGBZNC0tTp/jR9f6de++aO8azX/99dfTp0+f/PGPf0z37t2/cd706dPzpz/9Keeee25eeuml3HrrrTnnnHOWNW6DopSCBqJpcWn2v+1XBbvfiJ/+qWD3AgAAaCxGjhyZHj165LbbbltiKfXOO+9k+vTpSZLvfe97+d73vleoiMsNr+8BAAAA1IIFCxbk7rvvzrHHHpuXX345b7/9dpJk4sSJ2WuvvdKnT58cccQRmTt3bs4+++xMmjQpZ5xxRp588skMHDgwSTJlypQMHDgwffr0yU9/+tO8+OKLSZKTTz45Z599dg444IDsvvvuGTlyZL09Z21RSgEAAADUggkTJmS99dbLhhtumD322CO33XZb5s+fn9/85jc5//zzc/fdd2fTTTfNXXfdlWHDhmXrrbfO6aefvsg1TjjhhAwcODB33313TjnllPz617/O/PnzkyTvvfdebrnllvzpT3/K73//+/p4xFqllAIAAACoBSNHjkzv3r2TJL169cqdd96ZV199Neuss0622GKLJMnxxx9ftSrqqz7//PO8/fbb6datW5KkY8eOWW211fLWW28lSXbZZZcUFRVl0003zccff1z3D1TH7CkFAAAAsIw++OCDPProo3n55Zdz4403prKyMp9++mkeeeSRFBUVVc377LPP8vnnn3/tNSorK792rLz8P98A2KxZsyRZ5HoNmVIKAAAAYBmNHj06O+20U6699tqqscsuuyyPPPJIPvjgg/z73//OJptsUnX+Rz/6URYuXLjINVq0aJG2bdvmvvvuS7du3fL8889nzpw5ad++fUGfpVCUUgAAAECDN39Bee6+aO86uW7T0uKlzrvrrrsyZMiQRcYOPPDAXHvttbnmmmty4oknZsGCBVl//fXz+9//PvPnz89nn32WE044Ifvtt1/VZy644IL87ne/y2WXXZbS0tJcdtlladq0aa0/1/KgqPLr1oY1cDNmzEjXrl3z4IMPpm3btvUdB2rN/rf9qmD3GvHTPxXsXgAAADU1efLkqn2aqBt1/Tu20TkAAAAABaeUAgAAAKDglFIAAAAAFJxSCgAAAICCU0oBAAAAUHBKKQAAAAAKrqS+AwAAAAAsq4qF89OkpGm9XXfGjBnp0aNHNt544yTJvHnz8v3vfz/HH3981l577VrP1RjUSyl1/vnn56OPPsrw4cMzceLEnHfeeSkrK0vPnj0zZMiQJMnkyZNz6qmn5vPPP8/222+fM844IyUlOjQAAABgcU1Kmuatc/at9etudOrIas9t1apVRo8enSSprKzMxRdfnGOOOSa33HJLredqDAre8jz++OO56667suuuu2bevHkZOnRobrrppqy77ro54ogjMmHChHTp0iUnnHBCzj777HTs2DFDhw7NiBEjMmDAgELHBQAAAKixoqKiDB48OLvssktuvPHGjBkzJhUVFWnfvn2OO+64DB06NJ999lnef//97LPPPvn1r3+dO++8Mw8//HA+/vjjvP/+++nfv39mzpyZJ554IquvvnquvfbaNGvWLJdcckkef/zxfPLJJ2nVqlUuueSSBrkaq6B7Sn388ce55JJLcuSRRyZJXnzxxWywwQZp165dSkpK0qdPn4wbNy4zZ87MvHnz0rFjxyRJv379Mm7cuEJGBQAAAFgmTZs2zQYbbJC11147U6dOzV/+8pecf/75GTt2bHr37p0RI0bk7rvvzl/+8pd8+OGHSZKXXnopV155Za677rqcd9556dy5c+6+++4kyaOPPppp06blrbfeyq233prx48dn3XXXzZgxY+rzMb+1gq6UOu200zJkyJC8++67SZL3338/LVu2rDrfqlWrzJo1a7Hxli1bZtasWYWMCgAAALDMioqKstJKK2XDDTfMd77znSTJL37xizzxxBO57rrr8sYbb2TBggX58ssvkyTf//7306JFi7Ro0SJJ0qlTpyRJmzZt8umnn2aDDTbISSedlNtvvz1TpkzJ888/n/XXX79+Hm4ZFWyl1O23355111236peZJBUVFSkqKqo6rqysTFFR0TeOAwAAADQU8+fPz5QpU/LBBx9kpZVWqhofPnx4brrppqy33nr51a9+lTXWWCOVlZVJktLS0kWu8dX9tSdNmpRf/OIXqaioSPfu3bPHHntUfbahKdhKqXvuuSezZ8/O3nvvnU8++SRffPFFZs6cmeLi4qo5s2fPTqtWrdK6devMnj27anzOnDlp1apVoaICAAAALJOKiopcdtll6dChw2Irmf75z3/mjDPOyPe///08/PDDmTVrVioqKqp13X/961/ZYYcdcsABB+Sjjz7Kww8/nG7dutXFI9S5gpVSN9xwQ9XPd955Z5566qmcccYZ6datW6ZNm5a2bdtm7Nix2XfffdOmTZs0a9YszzzzTLbbbruMHj06nTt3LlRUAAAAoIGpWDi/Rt+UV5PrNilpWq2577//fvbee+//fK6iIltssUUuvvjivPrqq4vMO+KII3LiiSdmpZVWSuvWrbP11ltnxowZ1bpHr169MmjQoPTp0ydJavTZ5U3Bv33vfzVr1izDhw/P4MGDU1ZWli5duqRHjx5JkgsvvDDDhg3L3Llzs9VWW+Xggw+uz6gAAADAcqy6xVFdXbdt27aZNGnS157bcccds+OOO1Yd9+7dO7179/7auf369av6+bXXXqv6efjw4VU/33777dXKtLyrl1KqX79+Vb/kTp06fe0u8ZtvvnnuuOOOQkcDAAAAoAAKttE5AAAAAPyXUgoAAACAglNKAQAAAFBwSikAAAAACk4pBQAAAEDBKaUAAACABm9++YIGdV2SkvoOAJD85w/6psWljfZ+AABA3WpaXJr9b/tVrV93xE//VK15c+fOzUUXXZR//etfKS4uzqqrrpqTTz45W221Va1n+qoZM2bk4IMPzkMPPVTn96pNNSqlpk2bluuvvz5vvfVWLrzwwjzwwAPZeOONs9NOO9VVPmAFUVf/APkm1f0HCwAAwNJUVFTk8MMPz4477phRo0alpKQkTzzxRA4//PD8/e9/zxprrFHfEZdL1X5974UXXsjee++d6dOn57nnnsv8+fPz73//O7/4xS/yj3/8oy4zAgAAACy3nnzyybz77rs55phjUlLyn/U/O+20U84777xUVFTkqquuSq9evdKnT58MHz485eXlmTFjRvbee+8MGTIkffr0yUknnZRbb701P/3pT9OjR4+8+eabSZIXX3wxBxxwQPbZZ58ceuihmT59epLklVdeyT777JN99tknV1xxRZL/rNbacccdM3fu3CT/WUHVq1eveviNVE+1S6kLL7wwhx9+eK6//vqUlv7nlZfTTz89hx12WC677LI6CwgAAACwPHvllVey+eabp0mTRWuWLl26ZNKkSXnooYcycuTI3HXXXZk2bVpuvfXWJMlrr72Www8/PKNHj86zzz6bmTNn5rbbbkvv3r1z2223Zf78+Rk2bFguuuii3HXXXTnkkEPy29/+Nkly0kkn5Te/+U3uuuuutG3bNknSokWL7Lrrrhk3blySZNSoUenbt2/hfhE1VO1S6pVXXknv3r0XG99vv/3y1ltv1WooAAAAgIaiSZMmadas2deee+KJJ7LnnnumefPmKSkpyb777pvHH388SbL22mtnyy23TJMmTdK6det06tQpSbLeeuvl008/zdSpUzN9+vT86le/yt57750LL7ww06dPz4cffpj3338/u+yyS5KkX79+Vffbd999M3r06CTJ2LFjs/fee9floy+Tau8p1bx583zwwQfZYIMNFhmfMmVKWrRoUevBAAAAABqCrbfeOrfccksqKytTVFRUNX7xxRfn8ccfzz777LPI/IULFyZJmjZtush4cXHxIscVFRVp27ZtVclUXl6eOXPmpKioKJWVlV/7uR/84Ad5//33c99996Vt27ZZZ511auch60C1V0r17t075513Xt58880UFRWlrKwsjz/+eM4666z06NGjLjMCAAAALLe23377rLXWWrn88stTXl6eJHn00Udz55135mc/+1n+/ve/Z968eVm4cGFGjhxZ7S+M22ijjfLJJ5/k6aefTpKMHDkyv/nNb7LGGmtkvfXWy8MPP5zkPyui/quoqCh9+/bN2WefvcgKquVRtVdKHXfccTnxxBOz5557Jkn69OmTJOnZs2eOP/74ukkHAAAAUA3zyxfUybdszy9fkKbFpUucU1RUlCuvvDLnnXdeevfunZKSkqyxxhr5v//7v2y55ZZ59913s++++2bhwoX54Q9/mIMOOijvvffeUu/dtGnT/OEPf8g555yTsrKytGjRIueff36S5IILLsgpp5ySSy+9NB07dlzkc3vuuWeuv/767LHHHt/6uQuhqPJ/13stweTJk7P55ptn+vTpeeWVV1JaWpr27dtn/fXXr+uMNTZjxox07do1Dz74YNVmX9AY7H/brwp2r7r4w3xpGvvzAQAAtWfy5MnZYost6jvGcqeioiJ/+9vfMmXKlAwbNmyZrlXXv+Nqr5T6xS9+kauuuirbbLPNcllEAQAAAKzoBg0alHfffTfXXXddfUdZqmqXUquuumrmz59fl1kAAAAAWAZXXnllfUeotmqXUrvttlsOP/zw7L777mnXrl1WWmmlRc4feeSRtR4OAAAAgMap2qXU+PHjs8Yaa+S5557Lc889t8i5oqIipRQAAAAA1VbtUuqhhx6qyxwAAAAArECqXUr910cffZQFCxbkq1/at84669RaKAAAAAAatybVnfjMM8+ke/fu2XnnndOlS5fsuuuu2XXXXat+BgAAAKgvFXX05Ww1ve7rr7+ezTbbLOPHj1/q3FNPPTUvvfTSt41W5Y9//GOefvrpWr1mIVR7pdTvf//7rL766jnhhBOy6qqr1mUmAAAAgBpp0rRp/rn3vrV+3V1Gj6zR/JEjR6ZHjx657bbb0r179yXOPeecc5YlWpV//etf2XHHHWv1moVQ7VLq9ddfz9/+9rdsvvnmdZkHAAAAoEFasGBB7r777tx8883p379/3n777ay//vrZfffds9dee+Wxxx7Ll19+mfPPPz9bb711Bg4cmEGDBiVJrrrqqpSWlmbGjBnZfffds/LKK+eBBx5Ikvzf//1f1l577fz1r3/N6NGj8+WXX6a0tDQXXXRRXnzxxUyaNCnDhg3L5ZdfnrPPPjuDBg3KjjvumKuuuipjxoxJcXFxdtlll5xwwgl59913M2jQoLRv3z6TJ0/OWmutlT/84Q9ZffXVC/77qvbre61bt84XX3xRl1kAAAAAGqwJEyZkvfXWy4Ybbpg99tgjt912W9W51VdfPXfccUf69++fq6++erHPvvDCCznjjDMycuTI3HzzzVlzzTVz5513ZrPNNsvf//73zJ07Nw888EBuuummjB07Nrvuumtuvvnm9O3bN1tvvXXOPvvsbLbZZotkeeihhzJy5MjcddddmTZtWm699dYkyauvvppDDjkkY8eOzaqrrpq777677n85X6PapdTxxx+fs88+O4899limT5+eWbNmLfIXAAAAwIps5MiR6d27d5KkV69eufPOOzP//9+T6kc/+lGSpH379vn4448X++ymm26addddN82bN88aa6yRTp06JUnWW2+9fPrpp2nRokUuuuii/P3vf89FF12Uf/zjH0tcPPTEE09kzz33TPPmzVNSUpJ99903jz/+eJJkrbXWypZbblmV55NPPqm130FNVPv1veOOOy4LFizIYYcdlqKioqrxysrKFBUVZfLkyXUSEAAAAGB598EHH+TRRx/Nyy+/nBtvvDGVlZX59NNPc//99ydJmjVrliSLdCr/q7S0dJHj4uLiRY7ffffdDBw4MAcddFA6d+6ctddee4ldTEVFxWJjCxcuXCTLf/NUVlZW4wlrX7VLqWuvvbYucwAAAAA0WKNHj85OO+20SH9y2WWXVb0yt6xeeumlbLDBBvn5z3+eefPm5Y9//GNat26d5D8FVnl5+SLzd9ppp/zpT3/KT3/605SUlGTkyJHZaaedaiVLbal2KbXDDjtU/bxw4cKUlFT7owAAAAB1qmL+/Bp/U151r9ukadOlzrvrrrsyZMiQRcYOPPDAXHvttfn/2LvrsCjWtw/gX9oWCxOxwS5EPSpYBxVEsRO7G1sRCwW7uxUTW2xFxQAVAxNBJcQAaemF3ef9w3fngJ6fB5X2+7mucx2Z3Z3nnt2ZZ2bueaJAgQK/HUezZs1w6NAhmJmZQQiBRo0a4fXr1wC+dg2cN28eli5dKr2/VatW8PLyQrdu3ZCcnIzmzZujf//+CAoK+u1Y0ouK+Ik2WqdOncKWLVvw/v17XLhwATt27ICOjg7Gjh2bkTH+tPfv36NNmzZwcXFBuXLlsjoconTT88joTCvLqdfmTCtLKbdvH1F2JJMnQVNN47/fmEPLIyIiotzLy8sL1atXz+owcrWM/o7T3Nzp1KlTsLe3x5AhQ7B589ebOQMDAyxduhSampoYPnx4hgVJREREGUNTTYMJYSIiIiLKEmmefW/Xrl2wtbXFqFGjoKr69WN9+vSBnZ0dnJycMixAIiIiIiIiIiLKfdKclAoICEC9evW+W16vXj0EBwenZ0xERERERERERJTLpbn7XunSpfHq1Svo6uqmWu7u7o7SpUune2BElJosWZap3V5kyTJoqv/3YH5EREREREREvyLNSakhQ4Zg/vz5CAkJgRAC9+/fx4kTJ7Bnzx5Mnjw5I2MkIgCa6prwXdwt08qrZJP+s1YQERERERERKaU5KdWzZ08kJydj69atSEhIgI2NDUqWLIkZM2agd+/eGRkjERERERERERHlMmlOSgFA37590bdvX4SHh0NTUxMFChTIqLiIiIiIiIiIiNIsOUkOdQ21HLNeSmNSKjo6GgULFgQAnD17FsnJydJrVatWRc2aNTMmOiIiIiIiIiKiNFDXUMPCKWfTfb1zV3ZM0/vev3+PAQMG4Nq1a6mW6+vrw9vbO93jyg3+Mym1ceNGbN26FRcvXkSZMmVga2sLmUwGABBCoGjRorhw4YKUtCIiIiIiIiIiIvovP0xKnT17Fjt27MDChQtRsmRJafnFixehq6uL4OBgdOnSBYcOHcKIESMyPFgiIiIiIiIiopxGoVDA3t4e7u7uUFFRQadOnTBixAjcu3cPy5cvh0KhQNWqVVGuXDl4enri06dP6N+/P5o2bYq5c+ciMjIS+fLlg42NDerUqYOZM2ciMjISAQEBmDZtGlq3bp3Vm/hLfpiUOnz4MCZMmABLS0tpmYqKivTvkiVLYtiwYTh//jyTUkREPyCTJ0FTTSPXlkdERERERMDnz5/RuXPn75YfOnQInz59wpkzZyCTyWBlZYVq1aohb9688Pf3x/Xr11GwYEGsX78eMpkM58+fBwB0794dI0aMgKmpKTw9PTFx4kRcunQJAKCtrY0tW7Zk6valtx8mpV69egU7O7tUy4QQqf42MTHBhg0b0j8yIqJcRFNNAz2PjM608px6bc60soiIiIiI6CsdHR2cPn061TJ9fX3cu3cPXbp0gZqaGvLmzQsLCwu4u7ujdevWqFixYqohkerUqQMAiI2Nxbt372BqagoAqFevHgoXLgxfX99U78vJfpiUksvl0NTUTLXs0qVLKF68uPS3pqZmqtZTRERERERERET0D4VCkepvIQTkcjkAIE+ePKleU/79baOg//pcTqT6oxfLlCkDLy+vVMt0dHSgqvrPx548eQI9Pb00F7h27VqYmZnB3Nwcu3fvBgC4ubnBwsICpqamWL16tfReLy8vdO3aFe3atYONjU2qWf+IiIiIiIiIiHKCJk2a4NSpU5DL5YiPj4ezszMaN278w88UKFAA5cqVw+XLlwEAnp6eCA0NRdWqVTMj5Ezxw5ZSbdu2xcaNG9GiRQtoaWl993pCQgK2bt0KCwuLNBV2//593L17F2fOnEFycjLMzMzQtGlTzJ49G46OjihdujRGjhwJV1dXmJiYYNq0aVi0aBHq1auH2bNnw8nJCX379v21LSUiIiIiIiKiXCs5SY65KztmyHrVNdR+ax29evWCv78/OnfujKSkJFhYWODvv//GvXv3fvi55cuXY/78+Vi/fj00NDSwfv3673q05WQ/TEoNGzYMZ8+eRadOnTBhwgQ0btwYRYoUQVRUFB48eIANGzYgOTkZVlZWaSrMyMgI+/btg7q6OoKDgyGXy/Hlyxfo6elBV1cXAGBhYYGLFy+iSpUqSEhIQL169QAAXbt2xbp165iUIiIiIiIiIqLv/G7i6HfXW65cOVy7du275d7e3gCAOXPmfPda48aNU7WYGj9+fKrXK1euDEdHx+8+t2TJkjTFlN39MClVsGBBHDhwADY2NpgyZUqqsaOEEGjatCmWLFmCvHnzprlADQ0NrFu3Drt27UL79u3x+fNnlChRQnpdR0cHwcHB3y0vUaIEgoODf2bbiIiIiIiIiIgom/phUgoASpUqhZ07d+Ldu3fw8PBAWFgYtLW10aBBA1SpUuWXCp0wYQKGDx+OUaNGwd/f/7tkl4qKChQKxb8uJyIiIiIiIiKinO8/k1JK5cuXR/ny5X+rsLdv30Imk6F69erImzcvTE1NcfHiRaip/dMULiQkBDo6OihVqhRCQkKk5aGhodDR0fmt8omIiIiIiIgo92ADlozz7YyBGeGHs++lt/fv32POnDmQyWSQyWRwcXFB79694efnh4CAAMjlcpw9exbGxsYoW7YstLS08PDhQwDA6dOnYWxsnJnhEhEREREREVE2lSdPHoSFhUEIkdWh5CpCCMhkMnz48AH58+fP0LLS3FIqPZiYmODp06ewtLSEmpoaTE1NYW5ujqJFi2L8+PFITEyEiYkJ2rdvDwBYsWIF5syZg5iYGNSsWRMDBgzIzHApB5LJk6CpppFryyMiIiIiIqKvypUrh/fv36fqZUXpQ11dHYULF0bx4sUztpwMXfu/GD9+/HejyTdt2hRnzpz57r0GBgY4duxYZoVGuYCmmgZ6HhmdaeU59dqcaWURERERERHRPzQ0NFCxYsWsDoN+Q5q77127dg1yuTwjYyEiIiIiIiIioj9EmltKTZkyBfny5UPHjh3RpUsXGBgYZGRcRERERERERESUi6W5pdSdO3cwdepU+Pj4oGvXrrC0tMSePXsQHh6ekfEREREREREREVEulOakVL58+dClSxfs3r0b169fh4WFBS5evIiWLVtizJgxuHr1Krv3ERERERERERFRmqQ5KZVS/vz5oa2tDW1tbQBAYGAg5s+fD1NTUzx+/Dg94yMiIiIiIiIiolwozWNKJScn48aNGzhz5gxcXV2RP39+dOzYERMnTkT16tWRnJyMefPmYcqUKbh27VpGxkxERERERERERDlcmpNSzZo1Q1xcHFq0aIGVK1eiZcuWUFf/5+Pq6upo0aIFXF1dMyRQIiIiIiIiIiLKPdKclBo9ejQ6deqEokWL/s/3tG7dGu3atUuXwIiIiIiIiIiIKPdKc1Jq0KBBAAB3d3e8fv0ampqaqFKlCgwNDaX3aGpqpnuARERERERERESU+6Q5KRUSEoKhQ4fCx8cHRYsWhUKhQGRkJBo1aoS1a9f+sAUVERERERERERFRSmmefc/W1haFChWCi4sL3NzccPfuXZw/fx7x8fGws7PLyBiJiIiIiIiIiCiXSXNLKXd3dxw5cgRly5aVllWqVAlz587FwIEDMyQ4IiIiIiIiIiLKndLcUqpYsWL48uXLd8tlMhkKFSqUrkEREREREREREVHu9sOkVHBwsPTfoEGDYGNjgzt37iA2NhYJCQl49OgR5s2bB2tr68yKl4iIiIiIiIiIcoEfdt8zMTGBioqK9LcQAkOHDv1u2axZs2BpaZlhQRIRERERERERUe7yw6TUvn37MisOIiIiIiIiIiL6g/wwKWVkZJRZcRARERERERER0R8kzQOdExERERERERERpRcmpYiIiIiIiIiIKNMxKUVERERERERERJmOSSkiIiIiIiIiIsp0aU5KyWQybNq0CQEBAQCAhQsXon79+hg0aBDCwsIyLEAiIiIiIiIiIsp90pyUWrZsGQ4cOIC4uDjcuHEDTk5OGDt2LJKSkuDg4JCRMRIRERERERERUS6jntY3Xrx4EatWrUL16tWxf/9+NG7cGMOGDUOzZs0waNCgDAyRiIiIiIiIiIhymzS3lIqOjoaenh4A4M6dO2jevDkAoECBApDJZBkTHRERERERERER5UppbilVqVIl3Lp1Czo6OggKCoKxsTEA4NixY6hSpUqGBUhERERERERERLlPmpNSEydOxPjx45GcnAwzMzNUrlwZS5YswYEDB7Bp06aMjJGIiIiIiIiIiHKZNCelWrZsiZs3byIoKAjVq1cHAHTs2BF9+vSRuvUREf0qWbIMTr02Z2p5muqamVYeERERERERpZbmpFSbNm1w/PhxKSEFALVq1cLnz5/RtGlTuLu7Z0iARPRn0FTXhO/ibplWXiWb45lWFhEREREREX3vh0kpV1dXPHv2DADw4cMHbNu2Dfny5Uv1Hn9/f8jl8oyLkIiIiIiIiIiIcp0fJqXKlSsHe3t7CCEAAJcuXYKampr0uoqKCvLnz485c+ZkbJRERESUIdh1loiIiIiyyg+TUpUrV8alS5cAAFZWVtiwYQMKFy6cKYERERFRxmPXWSIiIiLKKmkeU8rR0TEj4yAiIiIiIiIioj9ImpNSvr6+WLRoER4/foykpKTvXn/+/Hm6BkZERERERERERLlXmpNSCxYswMePHzFp0iQUKlQoI2MiIiIiIiIiIqJcLs1JqWfPnmH37t2oW7duRsZDRERERERERER/ANW0vlFbWxuampwth4iIiIiIiIiIfl+ak1KjRo2Cvb09/Pz8oFAoMjImIiIiIiIiIiLK5dLcfW/37t0IDAyEmZkZVFRUoKqaOp/Fgc6JiP43WbIMTr02Z2p5mups3UpERERERNlXmpNSI0aMyMg4iIhyNU11Tfgu7pZp5VWyOZ5pZREREREREf2KNCelunTpkpFxEBERERERERHRHyTNSSlbW9sfvm5nZ/fbwRARERERERER0Z8hzUkpf3//VH/L5XK8e/cOMTExMDc3T++4iIiIiIiIiIgoF0tzUsrR0fG7ZUIILFiwAAULFkzXoIiIiIiIiIiIKHdT/e+3/G8qKioYPHgwjh07lubPbNiwAebm5jA3N8eyZcsAAG5ubrCwsICpqSlWr14tvdfLywtdu3ZFu3btYGNjg+Tk5N8Jl4iIiIiIiIiIsok0t5T6XwIDAyGTydL0Xjc3N9y+fRsnT56EiooKhg0bhrNnz2LFihVwdHRE6dKlMXLkSLi6usLExATTpk3DokWLUK9ePcyePRtOTk7o27fv74ZMlCMpkmSZOqOaIkkGVQ3NTCuPiIiIiIiI/iy/NdB5TEwMbt26hTZt2qRpHSVKlMDMmTOhqfn1Rrdy5crw9/eHnp4edHV1AQAWFha4ePEiqlSpgoSEBNSrVw8A0LVrV6xbt45JKfpjqWpowmLK6Uwrz3ll50wri4iIiIiIiP48vzzQOQBoampi4MCBGDx4cJrWUbVq1VTru3DhAvr3748SJUpIy3V0dBAcHIzPnz+nWl6iRAkEBwenNVwiIiIiIiIiIsrGfmug81/1+vVrjBw5EtOnT4eamlqqhJcQAioqKlAoFFBRUfluORERERERERER5Xw/NabUx48fceDAAbx+/Rrq6uqoWrUqevbsibJly6Z5HQ8fPsSECRMwe/ZsmJub4/79+wgJCZFeDwkJgY6ODkqVKpVqeWhoKHR0dH4mXCIiIiIiIiIiyqbSPPuel5cXLCwscP78eeTNmxdqamo4ffo0OnXqBG9v7zSt49OnTxg7dixWrFgBc3NzAEDdunXh5+eHgIAAyOVynD17FsbGxihbtiy0tLTw8OFDAMDp06dhbGz8C5tIRERERERERETZTZpbSi1duhTGxsZYtmwZNDQ0AABJSUmYOXMmli1bhp07d/7nOnbu3InExEQsWbJEWta7d28sWbIE48ePR2JiIkxMTNC+fXsAwIoVKzBnzhzExMSgZs2aGDBgwM9uHxERERERERERZUNpTkp5enri6NGjUkIKADQ0NDBy5Ej07t07TeuYM2cO5syZ86+vnTlz5rtlBgYGOHbsWFpDJIIsWQanXpsztTxNdc1MK4+IiIiIiIgot0hzUqpQoUKIjY39bnlMTAzU1X9qaCqiDKOprgnfxd0yrbxKNsczrSwiIiIiIiKi3CTNY0q1bNkSCxcuxLt376Rl/v7+WLx4MUxMTDIkOCIiIiIiIiIiyp3S3MRp8uTJGDx4MNq1a4ciRYoAACIiIlC3bl3MmjUrwwIkIiIiIiIiIqLcJ81JKW1tbRw/fhy3bt3C69evoaWlhSpVqqBp06YZGR8REREREREREeVCaUpKJSQkQEtLC6qqqjAxMYGJiQnevn2LsmXLZnR8RERERERERESUC/3nmFJnz55Fq1at8Pz581TL7e3tYWxsjEuXLmVYcERERERERERElDv9MCnl7u6O6dOno3379t+1ipo7dy7atWuHyZMn48GDBxkaJBERERERERER5S4/7L63detWDB48GNOmTfvuNT09PdjZ2UFFRQWbN2/Gzp07MyxIIiIiIiIiIiLKXX7YUsrLywtdu3b94Qp69+6Nly9fpmtQRERERERERESUu/0wKSWXy6GmpvbDFRQoUAAymSxdgyIiIiIiIiIiotzth0mpKlWq4P79+z9cwb1796Crq5uuQRERERERERERUe72w6RU165dsXbtWrx+/fpfX/fx8cHq1avRqVOnDAmOiIiIiIiIiIhypx8OdN6zZ09cv34dXbp0QZs2bVCvXj0ULFgQUVFRePLkCa5du4YmTZpg4MCBmRUvERERERERERHlAj9MSgHApk2b4OjoCCcnJ1y6dAkAoKKigho1asDGxga9evWCquoPG1wRERERERERERGl8p9JKRUVFQwYMAADBgyATCZDVFQUtLW1oaGhkRnxERERERERERFRLvSfSamUNDU1UaJEiYyKhYiIiIiIiIiI/hDsd0dERERERERERJmOSSkiIiIiIiIiIsp0TEoREREREREREVGm++mkVGhoKO7du4eEhASEhYVlRExERERERERERJTLpTkpJZPJMHv2bDRv3hyDBw9GSEgI5s6di4EDByI6OjojYyQiIiIiIiIiolwmzbPvbdiwAc+ePcPBgwcxdOhQAMCwYcMwc+ZMLF++HAsXLsywIIko91MkyVDJ5nimlqeqoZlp5REREREREVFqaU5KXbhwAYsWLUKDBg2kZfXr14ednR0mT57MpBQR/RZVDU1YTDmdaeU5r+ycaWURERERERHR99Lcfe/z588oU6bMd8uLFy/O7ntERERERERERPRT0txSqnr16nBxccGgQYNSLXdycoKBgUF6x0VElKuweyIREREREVFqaU5KTZ06FcOGDYOnpyeSk5Oxfft2vH37Fk+ePMG2bdsyMkYiohyP3ROJiIiIiIhSS3P3PUNDQxw6dAgaGhrQ09PDs2fPUKZMGZw4cQJ//fVXRsZIRERERERERES5TJpbSgFfu/AtX748o2IhIiIiop8gkydBU00j15ZHREREudsPk1K2traYOXMm8ufPD1tb2x+uyM7OLl0DIyIiIqIf01TTQM8jozOtPKdemzOtLCIiIsr9fpiU8vf3h1wul/79v6ioqKRrUEREREREREQZga1MibKPHyalHB0d//XfRERERERERDkRW5kSZR9pHlOqdevW6Ny5MywtLaGnp5eRMREREVEmUSTJUMnmeKaWp6qhmWnlEREREVH2leakVJ8+fXDu3Dls2bIFdevWhaWlJczMzFCoUKGMjI+IiIgykKqGJiymnM608pxXds60soiIiIgoe1NN6xuHDx+OU6dO4cyZMzAyMsL27dvRvHlzTJw4EdevX8/IGImIiIiIiIiIKJdJc1JKqWrVqpg8eTKuXr2K6dOn4/bt2xgzZkxGxEZERERERERERLlUmrvvKXl5eeHs2bM4f/48IiMj0bp1a1haWmZAaEQ/j2OjEBEREREREeUMaU5KbdiwAefOnYO/vz/q16+PMWPGoEOHDihQoEBGxkf0Uzg2ChEREREREVHOkOak1MmTJ9G5c2d06dIFurq6GRkTERERERERERHlcmlOSrm4uAAAYmJi8PTpU2hoaEBXV5ctpYiIiIiIiIiI6KelOSmlUChgb2+Pw4cPQy6XQwgBTU1N9OzZE7Nnz4aq6k+PmU5ERERERERERH+oNCelNm3aBGdnZ9jY2KBRo0aQy+V48OAB1q9fj+LFi2PUqFEZGScREREREREREeUiaU5KHT9+HPPnz0eHDh2kZfr6+ihatChWrlzJpBQREREREREREaVZmvvcRUREoEaNGt8tr1GjBoKDg9M1KCIiouxEJk/K1eUREREREWWFNLeUqly5MlxcXDBkyJBUy69cuYIKFSqkucCYmBj07t0bW7ZsQbly5eDm5gYHBwckJiaiQ4cOsLa2BgB4eXnBxsYGsbGxMDQ0xIIFC6CunuZwiYiI0o2mmgZ6HhmdaeU59dqcaWUREdHPk8mToKmmkWvLIyLKLGnO8owZMwYTJkyAl5cX6tevDwB4+PAhLl68iKVLl6ZpHU+ePMGcOXPg7+8PAEhISMDs2bPh6OiI0qVLY+TIkXB1dYWJiQmmTZuGRYsWoV69epg9ezacnJzQt2/fn9/Cn6SQyaCqqZnh5WRVeTyBEhERERH9Hj6sICJKH2lOSrVp0warV6/G9u3bceXKFWhpaaFKlSrYunUrmjdvnqZ1ODk5Yd68eZg+fToA4OnTp9DT04Ouri4AwMLCAhcvXkSVKlWQkJCAevXqAQC6du2KdevWZUpSSlVTE3c6d8vwcpSanT6eaWUBPIESERERERERUfbwU/3hTE1NYWpq+suFLV68ONXfnz9/RokSJaS/dXR0EBwc/N3yEiVKcNwqIiIiom/IkmWZ+gBIliyDpnrmtfAmIiKi3O2HSSlnZ+c0r8jCwuKnC1coFFBRUZH+FkJARUXlfy4nIiIion9oqmvCd3HmtfCuZJO5LbyJiIgod/thUmratGlSMkgI8T/fp6Ki8ktJqVKlSiEkJET6OyQkBDo6Ot8tDw0NhY6Ozk+vn4iIiIiIiIiIsqcfJqWaNWuG+/fvo27dujAzM0P79u1RtGjRdCu8bt268PPzQ0BAAMqVK4ezZ8+iW7duKFu2LLS0tPDw4UM0bNgQp0+fhrGxcbqVS0RERERERH8mdn0myj5+mJTauXMnoqKicPnyZWmWvQYNGsDMzAympqYoXLjwbxWupaWFJUuWYPz48UhMTISJiQnat28PAFixYgXmzJmDmJgY1KxZEwMGDPitsoiIiIiIiIjY9Zko+/jPgc4LFy6MHj16oEePHggPD8eVK1dw7tw52NnZoXHjxjAzM0Pbtm1RsGDBNBd67do16d9NmzbFmTNnvnuPgYEBjh07luZ1EhFR1pHJk6CpppFryyMiIiIiovT3U7PvFS1aFL169UKvXr0QHh6O06dPY9GiRZg3bx6ePn2aUTESEVE2p6mmgZ5HRmdaeZnZ5J6IiIiIiDLGTyWlACA6OhouLi64ePEi3NzcULhwYbRr1y4jYiMiIiIiIiIiolwqTUmpyMhIXLlyBZcvX4a7uzuKFi0KU1NT7Nq1Cw0bNpRm6CMiIiIiIiIiIkqLHyalDh8+jEuXLsHDwwPFixeHqakpRo0ahYYNG2ZWfERERERERERElAv9MCk1f/58aGho4K+//kL9+vWhoqICDw8PeHh4fPfeUaNGZViQmUkuk6HZ6cybHUEuk0FNk9ODEhEREREREdGf5YdJqTJlygAA3rx5gzdv3vzP96moqOSapJSapiYH6yUiIiIiIiIiymA/TEpdu3Yts+IgIiIiIiIiIqI/iGpWB0BERERERERERH8eJqWIiIiIiIiIiCjTMSlFRERERERERESZ7odjShEREREgS5Zl6sQUsmQZNNU5MysRERER5W5MShEREf0HTXVN+C7ulmnlVbI5nmllERERERFlFSal/jB82k9ERERERD8ikydBU00j15anSJJl6gMgRZIMqhq8JyL6N0xK/WH4tJ+IiIiIiH5EU00DPY+MzrTyMvOhOQCoamjCYsrpTCvPeWXnTCuLKKfhQOdERERERERERJTp2FLqG+zeRkRERERERESU8ZiU+oamOptyEhERERHR/8YH2URE6YNJKSIi+m28OCcioj8Jx2klIkofTEoREdFv48U5ERERERH9LA50TkREREREREREmY4tpf4wiiRZprYwUCTJoKrBLjZEREQZged1IiIiysmYlPrDqGpwIHciIqLcgud1IiIiysnYfY+IiIiIiIiIiDIdk1JERERERERERJTpmJQiIiIiIiIiIqJMxzGliIiI/gMHkyaijJCcJIe6hlquLY9yLlmyDE69NmdqeZrqPO8R/YmYlCIiIvoPHEyaiDKCuoYaFk45m2nlzV3ZMdPKopxNU10Tvou7ZVp5mfngh4iyF3bfIyIiolxLJk/K1eURERER5WRsKUVERL+N3dsou9JU00DPI6MzrbzM7O5COV9SkjxTWy8lJcmhwe57RESUjTApRUREv43d24iIfp6GhhqTpkREP0EmT4KmmkauLe9PxKQUEREREWVLvPkgIqKU2AI692FSioiIiHItziCVs/HmI+diQpGI6Of9iXUnk1JERESUa3EGKaKswYQiUdb5ExMbucWfWHcyKUVERERERESUS/yJiQ3KuZiUIiIiIiLKArm5e2lu3jYiIko/TEoREREREWWB3Ny9NDdvG1F2x6RwzvUn/nZMShEREVGupUiSZerNqiJJBlUNXpgT5fZjL7dvH+VsuTkpnNuTNrn5t/tfmJQiIiKiXEtVQxMWU05nWnnOKztnWllE2VluP/Zy+/YRZVd/YtImt2NSioiIiIiypdz+RJyIiOhPx6QUEREREWVLfCJORESUuzEpRURERERERBKOmUVEmYVJKSIiIiIiIpJwzKycjUlFykmYlCIiIiIiIiLKJZhUpJyESSkiIiIiypb4tJ+IiCh3y/ZJKWdnZ2zevBnJyckYOHAg+vXrl9UhEREREVEm4NN+IiL6k/yJD2OydVIqODgYq1evxokTJ6CpqYnevXujcePGqFKlSlaHRkRERERERESZKLcnbf7EhzHZOinl5uaGJk2aQFtbGwDQrl07XLx4EePGjfvh5+RyOQAgKCjol8pNigv/pc/9ivfv32daWUrcvvST2duXm7cN4PalJ25f+svN25ebtw3g9qUnbl/6C46RZVpZmqxb0hW3L/1w+9Jfbt6+YYuvZFpZO2z+zrSylHLKb1eqVCmoq/9+SklFCCF+ey0ZZOvWrYiLi4O1tTUA4OjRo3j69Cns7Ox++LkHDx6wmx8RERERERERUQZwcXFBuXLlfns92bqllEKhgIqKivS3ECLV3/9LrVq1cODAAZQoUQJqamoZGSIRERERERER0R+lVKlS6bKebJ2UKlWqFB48eCD9HRISAh0dnf/8XJ48eWBoaJiRoRERERERERER0W9QzeoAfuSvv/6Cu7s7wsPDER8fj8uXL8PY2DirwyIiIiIiIiIiot+UrVtKlSxZEtbW1hgwYACSkpLQvXt31KlTJ6vDIiIiIiIiIiKi35StBzonIiIiIiIiIqLcKVt33yMiIiIiIiIiotyJSSkiIiIiIiIiIsp0TEoREREREREREVGmY1KKiIiIiIiIiIgyHZNS2ZhCocjqECiTcd4BIiL6WXK5PKtDIPopWXm9w+trIvoVvE/LOExKZWOqqrn75+FFwT+UNxQqKirSspxc8f0pv21O/o1+R27b7ty+vzJh8fNiY2Ph6OiIsLCwrA7lPz19+hSXL1/O6jAoDXJb3fk7Ul7vZBaFQgEhRK6/vv7TZKdz3Nu3b7M6hBwvu9aTcrk8S+qtnEYI8Uu/IWvlbEb5IyoUCshkMjg4OCAiIiKLo0pfyhvAlBcF2bUCyixqamoAgAsXLuDgwYOIiYnJ0RWf8re9fv06Ll68iOjo6CyOKH0p92Hlb+Tt7Z2V4WSalMlT5TGbk4/d3H6Dovxt1NTUIIRAcHBwFkeUcyQlJSEmJgZ79+6Fq6trVofzQzKZDGfPnsX+/fvx+vXrTCv3324Ec3J9kNFS3tBkp5vozJJy31AoFAgICMCyZcu+ey0jJCUlAfh6baKiooL79+9jxYoVePHiRYaWmx5S3hcAgJubG+vyb6ipqSEmJgaenp5ZFkNSUhJGjRqFsWPH4tOnT1kWR072bw/osxPlfrZ161Y4OTlldTjZkvI8p6KiAplM9lOfzZ1X4jmY8kBUVVWFpqYmXr16le0viH+GQqGQbgBPnToFW1tbvHjxQrpg+FN8m0UODQ3FmDFjcOHCBfj6+uLq1atZGN2vSdnaJDQ0FJMmTYKjoyMeP36MixcvZmFk6S9lEuPWrVuYPHkykpOTszCizKFMnh47dgyLFi1CYGBgFkf0e5Q3KA8ePMDSpUvx4MGDrA4pXSnPJ0ePHkWfPn2wYMECvHr1Koujyr5SJgoKFy4MX19fbN++HS9fvvzu9ayWMpbatWvj3bt3WLVqVaaeS5X1wYEDB3DgwAF8/PiRSakfUFNTQ0JCAlavXo2VK1fCz88vq0PKVMr6SPkgQFtbG9euXUNQUFCG3YTK5XJcvnxZSj4pFAqsWbMGq1evRr58+XD16tVsv88qvxvl/7dt2wYfH5+sDCnb2blzJwYNGoRNmzbh8uXLWXI9JoSAjo4OEhMT4eHhgbi4uEyPIadTnlNOnDiBTZs2wcPDI0vj+fac7+npiV69ekEmk+Hx48d49+5dFkWWfampqUn1rL29/U9dVzMplcWUT+qVHj9+jNWrV0sXwSYmJtn+hPlfwsPDcfr0aQBfT6rBwcGwtrbGlStXUKhQIaxYsQLPnj3L4igzT8ossjKR4+vrCyEE1q1bBwsLCxgYGGTqE+/0oKqqipiYGJw/fx7Pnj2Dqqoqdu3aBUtLSxgaGub4ylt5chJCID4+HgsXLkRAQABCQkLQrl07qKur57rE1LfJ05iYGIwaNQoPHz5E0aJF8erVq2z7ROt/Sbk98fHxWLp0KZYvXw5NTU3Y2NjgyZMnWRjd7xFCfNcV0cXFBVevXsXevXvRv39/aGtrZ01w2VjKFmXA15aPMpkM3bp1Q69evZCQkJDq9aykrIfU1NTw5csXPHr0CBERERg3bhxq1aoFVVXVDOuO6uvri2vXrklx+Pr6om/fvggICEBERATmz5+PW7duZUjZOdG3v8P79+/Ru3dvaGhoQEdHBzExMVkUWeb59qbO0dERR48eRXBwMAoUKIDmzZtnaBdZNTU1+Pv74/Tp0zh27BgCAgLw8OFD7N27F2ZmZmjSpAnu3r2b7Vpzf/u9HTlyBHZ2dnj79i0+f/6MGjVqAMj9Xc+/9W/nuHfv3uHly5fYs2cPJk+ejOrVq2fKA4SoqCh4enpK8WhqakJfXx8qKipwc3PL8de8mSHl76lQKBAVFYUJEybgxo0bKFWqFHx8fLK0Vb7ynK9s8ePm5obu3btj/PjxaN++PT5+/JjjH87+rm+Px+DgYIwePRrq6upo2rTpT30/6ukdHKVdym4jAQEBKF68OEqXLo34+HgsW7YM8+fPR2JiIvz9/QGkbmWUk8hkMpw7dw5FihRBrVq18P79e6iqqmLjxo24dOkSLly4AHd3d5QvXx4lSpTI6nAznLKS2759O0JCQmBsbIyqVasiKCgIf//9N6pXr46QkBBERUVh0aJFaNCgQRZH/O+U+6Py/+fPn8e5c+egq6sLS0tLfPjwAe3atYO+vj5CQ0Px7t07HDp0CLq6ulkd+k+Ry+VQU1OTfjdlMjE2NhZbtmyBp6cn6tWrBwBQV889Vapyu4GvzdI1NDTw6dMnqKurw8HBAXfu3EFycjLu3LmDGjVqoEiRIlkc8Y8p99OUSbQPHz4gMDAQjo6O+PTpE44fPw5nZ2fo6uqiaNGiWRjtr1EmuyMiIvD582fo6+tDS0sLwcHBOHLkCN6/f48TJ06gSpUq6Nu3LwoVKpTVIWcLyn0iMDAQtra2iIqKgpqaGvbs2QNtbW1s27YNFy9eRPv27bP8PKw8Jm/cuIElS5ZAW1sb1atXx9y5c+Hn54e9e/di8uTJGXIujYmJwYEDB1C8eHFUrVoV79+/R6tWrTB8+HBMmDABGhoaqFChAoQQOS5Znd5S7icymQyamprw8/ODiYkJOnfujGfPniEgIABBQUFo2bIlNDQ0sjji9KXcB5T7q6enJ6pXrw49PT1cv34dN27cwPr16/H27VupdV96HVsKhUKqC4GvLQk3b94MLy8vdO/eHcWKFYOZmRnKli2L4sWL48mTJ+jduzcGDBgATU3N3y7/d3ybIPf390eFChVQvnx5uLi4YMeOHZDL5fDx8UHTpk1z5D3B71D+rr6+vggMDESNGjUQGRmJsLAwzJ49G3nz5kVkZCTU1dXRr18//PXXXxkSR2RkJPr06YOgoCBMnToV/fr1AwA0adIEUVFRePr0KVxcXFCuXDkUKFAgQ2LI6ZR1hIqKCuLi4pAvXz4IIZA/f34MGTIEUVFRSEhIwMWLF9GhQ4dMO6ekvPYNDg7GrFmzULJkSXTq1AkmJiYYPnw4Tp06hdq1a8PDwwO1atXCyJEjUa1atUyJLztJWWdHRkZCW1sbMpkMkZGR6NSpE54/fw4NDQ0cO3YMFhYW0NLS+uH6cs8dVA6koqKCL1++YMmSJXj79i1Kly6NZs2aYerUqThw4ADWr1+PSpUq4enTp0hMTPzPHzM7SXlQFy5cGDExMZgxYwbmzZsHXV1dmJmZ4dKlS3j//j3GjRuHkydPokaNGmjdunUWR57+lBcZygo1IiICEyZMQK1atdC0aVOsWrUKffv2xYEDBxAfH4+CBQtCU1MTU6ZMybZPwVJWRMr/X7x4EV++fMGoUaOgra2NlStXSieYokWLYty4cQgODs4xSam3b9+icuXK0n589uxZnDlzBlWqVEGDBg2wdOlSxMXFYeTIkTh58iTKlSsHfX19tG7dOkdfKKa8mVAoFFi1apWU4Bg6dChiYmLQoUMHGBgYIDY2FgkJCWjZsiWGDBmS1aH/K+W+qvxNLl26hHfv3qFLly5QKBQwNzeHh4cHPD09YWtri40bN6JJkyYwNjbO8huUtEhZ1wLA2rVr4erqCiMjI3z58gVjx46FiYkJPn36hJo1ayI6OhqXLl1CmzZt/uiklPJ7U9bPx44dg6enJ8zNzdGjRw+MHTsWs2fPxrp161CjRg3cu3cP9erVQ8GCBZE/f/5Mi1PZWlFVVVV6qjxkyBAUKFAAe/fuRWxsLPbt24c9e/Zg4MCBGDFiBF68eIGyZcuiQoUKv53sSLl/FS9eHH5+fpg8eTI2btyI8PBwHDt2DM7OzujevTv69OmDY8eOITIyEnXr1k2Pzc9xlPWnqqoqPn78CHt7e+jo6KBSpUowNjaGk5MThg0bhmbNmiEoKAgJCQmIiYlBly5dsjr0dKW83nn79i0WLFiAmJgYxMfHY8+ePTA2NsbQoUPh6OgIdXV1uLm5oV69er993vy2rn/58iWSkpKQN29ejBkzBt7e3oiLi8Py5cuRlJSE5ORkFCpUCDt37oSKikq2qO+V39ujR4+waNEiAICxsTEmTZqEpk2b4tmzZzh58iTs7e3Ru3dv6Ovrw9DQMCtDznAp6yCFQiGd4zp16oSFCxdi48aNGDx4MD5//owqVapALpfD3d09Q1rgKe/FtLW1YWVlhX379mHfvn0oVaoU6tevj/j4eKiqqmLSpEmws7ND48aNc/3v87OUv6dyX9++fTtOnTqFXr16oUaNGihTpgz69u0LY2NjaGlp4eTJk4iNjUX37t0z9IFHygfQycnJePbsGby9vdG8eXMUKVIEa9euxdq1a3H79m2EhYWhRIkSCAgIwPr161GuXLkMiSm7SnmeCwkJwdq1axEREYFGjRqhadOmaNCgAUaOHImGDRtCQ0MDrq6uyJ8/Pzp06PDD9ebcO6cc6N+akx4+fBi6uro4cuQI8uTJg/379+PTp08YPHgw2rZtC09PTyQmJkIul+eYbnzJycmpnvLkzZsXtWvXRpUqVZA3b17UrFkT+vr62L9/P1q1aoWuXbsiJCQEp06dypXjnSifBHh5eeHGjRvw8/NDgwYNMGXKFJw9exY6OjqoWbMmFAoFTpw4geXLl8Pa2hoJCQnZNoGjqqoKPz8/TJkyBTt27IC7uzvmzJmD5ORkeHt7Izk5GTo6Orh58yYcHR0xe/ZsJCcno3z58lkd+n/y9vbGqFGjsHfvXqlJ/+nTp7F//34MHjwYpUuXxt69e3H8+HHky5cPZmZm6NWrF8qWLQt7e3scPnw4R3bjU978Kk/4iYmJGD58ONTV1TFz5kxs27ZN+u/06dNYvXo1tm3bhpo1a6JixYpZHP2/+7a1gqOjI/bv348nT55g4sSJqFq1Klq1aoWTJ0+iSpUqaNeuHfLly4cDBw7kmAFwlXVtXFwcnj9/jujoaJw4cQKVK1eGq6srXr16BWtra1haWqJjx46oX78+tLS0ssUNWFZSfm9RUVFQUVFBnjx58OrVK+TJkwcAsHLlSly7dg1eXl6wsLDAly9fYGVlJXWtzwwpL/w+ffqEkJAQqKmpoWXLlnjw4AFKliyJ8uXL46+//sKzZ88QFhaGIUOGYNu2bVi5ciUSExN/q2zgn+9J+RTUyMgIJUqUgLa2NipVqgQ9PT1MmjQJAwYMgIaGBpycnFCyZMl02f6c5NvJL4KCgjB58mR06NABo0aNwqJFi+Dl5YU1a9bg1KlTmDt3LlatWgVdXV3Url07K0NPNymvcYUQOHfuHNavX49OnTrhxIkTqFGjBpYvXw4AWLJkCfLnzw8fHx/pfPmr17gKhQLbtm2T6uy4uDjMnTsXNjY2OHv2LD5+/Ijhw4cjKioKR48ehRACbm5usLa2xoIFC3DlyhWpO1xW+PbewM3NDStXroSdnR0WLFiAQ4cO4ebNm5DJZMiXLx/69OmDefPm4ePHj7Czs8v13UCVddDr168RFRWFpKQkHDx4EHXq1EF4eDj8/f1hYmKCEiVKQEdHByEhIbh+/ToqVaqUbjG8fv0a48ePx/z587F69WpERESgXbt2aNWqFUqXLo2PHz9i+fLlqFWrFlxdXaGjo4PmzZvj+PHj+Pz5c7rFkdOlvGZ58+YNzpw5g8jISMyePRu3b99GQEAARo8ejdu3b2POnDmwt7fHpEmTpM9nRELq8ePHePv2rRTX3bt30bNnTyxYsADnz59Hjx49YGFhAUNDQ6xZswYKhQLjxo3Dxo0bMWXKFOjq6kJTUzPH3KP/jm/PczExMViwYAEaNmwIa2trnD17FkeOHMGMGTNw4cIFzJ07F/PmzUOzZs3StH62lMoE317cvXnzBoUKFYKOjg5iY2ORnJyMQYMGoUiRIti4cSO+fPkCAOjQoQNq1KiBgQMHIiYmBvny5cuybfgvkZGROHXqFAYNGgR1dXW8efMGDg4OiI2NhampKaZPn45Lly5h9+7daNCgAYKCglCkSBH4+vpi06ZNqFatGqytrbPtze3vSEpKwr59+3DlyhWMGDEC+fPnh6OjI1xcXGBlZYVevXrh9OnTuHfvHoyNjXHhwgVUqVIFvXr1yurQpZuib1tj3Lt3Dw4ODhg+fDi0tLQwbNgw3LlzB0ZGRrhw4QL09fVRoEABVKtWDT4+PjAwMMCAAQOycEv+W0xMDFatWoUXL17AwsIC/fv3l167c+cOevfujaZNm6JJkyYoXbo0duzYgW7duuHJkydo2LAhLC0tUb9+fejp6WXhVvw65UnG09MTDx8+RN26dVG/fn2YmZnB3t4edevWhbGxMdTU1LBixQpERkYiNDQU6urq6Nu3bxZH/+9UVVURFxeHRYsWIT4+HkIIODo6AgA6deqE06dPo3LlyoiOjkaJEiWwcuVKaGlpYejQoahfv34WR/+/pWz2/uHDB8ybNw8dOnRAUlISXr9+jblz50p1a9GiRfH06VOcO3cOjx8/hlwux8yZM3Psfpperl69is2bN6NevXqIi4vD4sWL8eDBA3z+/BmfP3+Gjo4OxowZg5EjR+LmzZuwsbGBTCZDqVKlMi1G5ew1ixYtgo+PD+rWrYvixYtj+PDh2Lt3L06cOIGuXbuiRo0aePnyJTZt2gR7e3vo6+ujTJkyv1xuymSum5sbHBwcUKhQIXTr1g1LlizBxo0bsWbNGixevBgNGzbE1q1b8fTpU9y+fRt16tSBtrb2H9eFT/l9nT59Gp8+fULt2rWl7grTp09Hr1690KBBA6ioqGDcuHGoUKECPD09UbNmzRz/lP3brnqvXr1CtWrVULRoUXz8+BGxsbEAABsbG/Ts2RPu7u5o2rQpevToIc0cOWHChF/aX5Td21q0aCGNJeTv7w+ZTIZjx47hzZs3yJ8/P5KTkzFy5Ehs3LgRTZs2Rfny5dGhQwfExMTAxsYmS7veK7+3jx8/okyZMoiPj0fz5s1RoEABnDx5EjVr1oSzszMaN24MhUIBT09PzJgxA4aGhpg2bVqWxZ2RUtZBr1+/xqJFi1C3bl3o6enh1atXmDRpEuRyOQ4dOoTo6GhpXKlTp04hKioKixcvRvXq1dMljnXr1uHq1avo27cvdHV14e7ujocPH6Jt27Zo0qQJnj9/jqZNm8LT0xPbtm1D9erV8ebNG3Tv3h2zZ8+WxiT8U/n5+SEmJga1a9eGiooK3r9/j6VLlyIkJATBwcGwsbFBs2bN4OvrC09PT+jp6eHDhw9wdXVFQkICwsPDsXLlynSPKzAwECVKlMChQ4dQuXJlVK5cGTNmzMC7d++wcuVKREZGSue2Zs2awdzcHCtWrMCDBw8wZ84cuLu7Y+7cuahTp066x5bdpHxABnztceDt7Y1WrVpBRUUFFStWhJ2dHWrVqoVhw4bh/fv3uHXrFk6fPo2kpCRUrVo1bYkpQZkmKChIjBgxQlhYWIiuXbuK8PBwsX79emFqairu3r0rhBAiLCxMNGvWTEREREifmzp1qnj58mUWRf1jCoVCODg4CA8PD/Hq1SuhUChEUlKSsLa2Fnfv3hUxMTHizp07QqFQiI8fP4oZM2YIJycnERYWJvbt2ycsLS2Fo6NjVm9Ghrl//75ITEwU5ubmYt68eUKIr7/xrFmzxOrVq6X3DR48WDx58iRrgvyBoKCgf12+d+9e4ebmJu7duyd69+4tFi1aJOLj40VERISwsrISp0+fFomJiUIIIeRyeWaG/EvCw8PFiBEjxIABA0RsbKy0/NGjR0IIIdavXy/WrFkjFAqFEEKIZ8+eiZEjR4rAwEAxf/58cezYMSHEP9uaE7b5WzKZTBw8eFCYm5sLDw8PERgYKOrUqSO6dOkizp07J4QQ4tChQ2LZsmUiNDRUXLhwQZw5cyaLo05N+b0rf6ebN2+KxYsXi9WrVwsXFxdhYmIibYuzs7Po2bOn+PDhg1izZo3o1auXWLBggbTfZndyuVx4eXmlqlu8vb2FqampWLt2rfS+8ePHCzc3NyGEEG/evMmKULNccnJyqr/fvHkjRo8eLV6/fi3evn0r9PX1hZubm3B1dRVTp04VN27ckN7br18/ERQUlCnH9r+t+9ixY2LDhg1CCCFsbW1Fhw4dRGhoqLh06ZIwMjKS3ufq6iqOHz/+3bamVWBgoHBzcxMRERFSHMePHxf9+vUTb968Ec7OzsLQ0FB8+PBBeHt7i8GDB4tHjx6JpKQk8fTpU7Fx40Zx9erVXyo7N4iIiBCrVq0SnTt3FpGRkcLFxUXUr19fTJw4UXh4eAghhBg3bpy4dOmSePfunbh48aJ4/Phx1gadzi5evCjat28vBg8eLAYPHiyEEGLDhg1i2bJl4t27d0IIIbZt2yYsLS1FQkKCEEKIuLg4MX78eBEXF/fT5T18+FAsWrRIhISEiOjoaDFz5kzh5OQkzpw5I6ZNmybMzMzE5MmTxaBBg0Tz5s1FbGysdM29e/fuLDtPKxQK6RwlhBBXr14VHTt2FN26dRNubm4iMjJShIaGis2bN4vLly+LT58+CSMjI7F161axdetWsW/fPiGTyVKtIzdKTEwUHz9+FFOnThW9evUSQgiRlJQk/vrrL7F7927pfdbW1uLAgQNCCCHtV+nl/fv3YuzYsanuyZSCg4NFdHS0sLOzE6tWrRLx8fFi1apVQl9fX7omTEpKStd4cpL4+HgREhIi9u/fLzp27CgSExPFgwcPxMiRI8W6deuEEEJMnz5dWFlZCSGEiIqKEjY2NmLfvn3i6dOn4uTJk+LkyZPpHldsbKyIjo4W/fv3F0IIcfLkSTFnzhzx9u1b4eLiImrWrCldBy5btkwsW7ZMfPz4USgUCrFhwwYxc+bMdI8pp4iPjxe2trZiyJAhwt3dXSQlJYn27duLjh07Ck9PTyGEEIcPHxZLliwR8fHx4sqVK9J9VFqw+14G+bY57qlTp7BhwwY0a9YMZ86cQeXKlWFjYwMrKyvky5cPHz9+xLt377B+/Xp06NABBQsWBAAcP34cjx49yrbN4VVUVODv748PHz6gcOHCmDJlCtTV1eHl5YWbN2/CwcEBLi4uGD16NF6/fo3OnTtjx44dmDhxIv7++28cO3YsVYuU3OT169dYuHAhXr9+jYkTJ0qzNGhra6Nr1644efIkFi5ciB49eqBIkSLQ09PLVs0/T548CTMzMwDAgwcPMGjQIDg6OiI8PBxCCAwZMgQHDx7ErFmzYGNjg9mzZyM6OhotW7ZEQECAtJ6cML5SkSJFULt2bTRq1AjJyclwc3NDjx49cPDgQQBAuXLl8OXLF1y8eBEA4OXlhQIFCqBcuXKYPn06unXrBgDfjbOVXSn3M2VT3DNnzkBDQ0Parrp166JcuXLo0KEDypUrl2o/MDIyQrFixdC+fXtYWFhk2TZ8K+WTVeUTdzc3N9y7dw9t27ZF69atMWLECJw+fRoKhQIdO3aEQqHAwYMHMXHiROzYsQNz587Nlt3avh1b7sWLF5g+fToMDAxQqVIl5M2bFzKZDIULF0b79u3x4sULxMTEYM+ePfj48SOqVKkCAKhcuXJWhJ9lRIpWygkJCdIYI8+ePUO1atVw7do1TJs2DXZ2dtKYP2XLlsX169elOmz//v0oWbJkphzbynW/evVK6pITEBCA9+/fY8qUKfj8+TM2bdqEL1++wNTUFKVLl4aNjQ0AoHnz5ujatetPzxCYlJSEFStWYPDgwdi/fz9mzpwp1XOxsbFSK8jnz5+jTJky2LFjB6pVq4b27dtj4sSJmD59OmrWrIkxY8agTZs26fVVZGvK/UoIgejoaLi4uCAxMREhISEoWLAgChcujNatW6N06dIwNDSEoaEh4uPjERcXh8qVK0NXVxft2rWTJsjIib69xn306BEOHTqENWvWwMHBAW5ubrhy5QosLS3x5csX3L17FwAwfPhwVKxYEVFRUYiPj8f8+fOhrq6OvHnz/nTZDRo0wKtXr2BpaYn79++jdu3auHv3LqpWrYqRI0dizpw5GD16NHbu3AkjIyO8ePECw4YNw65duzBo0KAsOU+nHIT9xYsXePDgATw9PbFhwwb06tULixYtgoqKCt68eQMPDw8YGRkhMjISOjo6AABLS0tYWVlBQ0MjV7dEfP78Ofr37w8tLS00b94cwNfrLnV1dfTu3RuXL1/G0aNHsXbtWnh5eaFFixYAkC5j7/r4+GDYsGF4+vQpnj9/jqioKOTPn1867l1dXaWB8WUyGTp16gRPT088e/YM1tbWmDt3rjRBUW6a/OZnfPjwAefPn4efnx/69euHfPny4cyZMyhWrBhUVFTw8eNHAIC9vT2ePHmCW7duoVChQjA0NMSjR4+gra0NS0tLWFpaAvj34W9+xaZNmzB+/HgIIbBv3z7cvXsXlpaWKFCgAJydndG6dWtUq1YNmzdvBgCYmZkhKCgIt2/fhhACAwcOhIODQ7rEkt2lvO5MTk7Gvn37oKGhgcTEROTNmxe1atWS9v8qVaqgbt26SEpKwtmzZ2FkZIQ8efKgbdu2Uq+DtIyRrCKy011wLpHy5ujly5fQ19fHjRs3sGXLFvTo0QM9e/YEANSpUwcHDx5EQkICzp07B29vb+jr62P69OnSCfrt27coU6bMT52wM1N8fDwWLFiAjh07omLFipgzZw769++P0qVL4/z58yhXrhzKly+PCxcuQFdXFyNGjMDNmzehq6ubK7rqyWQynDp1CmZmZihQoACCgoLw6tUrtGzZEnFxcThy5AiePn2K1atXY+zYsWjcuDF69eoFLS0tvHnzBv7+/tDQ0ICJiUlWbwqArzcojo6O0qDVf//9N/T19VGoUCFUrFgRgYGBUFdXR69evTBv3jxYW1ujcePGePz4MTZs2ICdO3dm8Raknfj/5qjK4/XDhw9YtWqV9JtMmDBBmrklMjIS169fx86dO6Grq4uPHz/CxsYGRkZG360vO0tISJDGzEnJwMAAO3bsQLly5bB+/XoYGxujc+fOiIyMhJWVFapVqwY/Pz/o6elhwYIF2XaAbD8/P6xcuVJqiv33339j5syZaNOmDTp06ICwsDAsW7YMBgYGGDFiBN68eYOwsDA0btw4q0P/V8HBwdDR0ZH2K+UsiG/fvoW9vT369OmDkiVLYvny5Zg9ezYMDAwQHx+PefPmIT4+HomJiZg9ezYqVKiQtRuSyb49Fq9cuYJ58+bB0NAQrVu3Rp06dWBmZpZq1rhhw4ahW7duqFixIl6+fInOnTtLCZ5vuy+np5TrVnYhvn79OmrXro2hQ4fC29sbO3fuxMiRI9G1a1cEBARg3rx52LFjB969e4e3b9/i77///qWyY2JiMHfuXOTPnx82NjbIkycP3r59C01NTejq6uLNmzfIkycPjh49imbNmiF//vzo0aMH1q1bh1atWuH69eto1KgRChcunJ5fSbalnEUvpZcvX6Jfv344duwYwsPD4ejoCHNzc7Rr1w737t3D9u3boaqqiuDgYLRo0QJTp07NoujTR8rr25iYGCQnJ0NbWxsnT55EcnIyateujYsXLyI+Ph7Hjx/HgwcPcODAAXh6emLIkCGpulQpFAqpe//Pio6OxvXr1+Hn54dTp05h165dqFixIsaPH4/atWujT58+ePjwIaKjo+Hv74/bt29j48aNKF68eLp9F78qISEBR44cwcGDB1G+fHl4e3vj5s2bAIABAwagdevWqFixIo4ePQqFQoGgoCAMGzZMejiUm7x9+xbJycnQ19cHAPj6+kpjQfXo0QOdOnWCqakp9u7dCzU1NUyZMgXA1wf9z549gxACo0aNkpJ26eH27dsYNmwYpk6diqCgIMTHx2Px4sWQy+UIDAzE7NmzMXHiRGzduhUGBgaYOnUq1q5di6ioKMyfPz/d4siJbt68iW3btmHw4MEwNjZGUFAQvnz5gsTERNja2mLPnj3w9PTE5cuX0atXLxgaGmLPnj3YsGEDHjx4AAAIDw9P99mPz549i4MHD6Js2bKYOnWq1NDDwMAA+/fvR548ebBv3z706NEDRYsWhaWlJdzc3FCwYEFs2LABpUqVQpcuXTLsOiA7+bfrneDgYMyZMwcmJiZo27YtJk+ejMmTJ8PQ0BDh4eGYMWMGVFVV4e/vj7///hsTJ078tUlW0q1N1x/O19dXODk5ifj4eCGEEPfu3RNdu3YVo0ePlrpW2NnZic2bN4sPHz4IIYRwdHQUjRo1ktYRHh4u/Tu7NflUKBQiNjZWzJo1Szx69ChVF4HFixcLW1tbIYQQ586dE1ZWViI8PFwkJSWJ+/fvCyGEmDx5sti+fXuWxJ7RJk6cKFasWCHu3bsnbty4IRo3bixCQ0OFEEL4+/uL0aNHiytXrognT54IKyurbNlNL6XBgweLefPmiYCAAOHj4yP09fXFhQsXhBBCuLm5iTlz5oiTJ0+Kc+fOiVatWokZM2YICwsLsWvXLiGEyNFNys+dOyeGDh0qnj17Ji1Lua8HBARI+3ROIpPJxJUrV8T169eFEEJ8/vxZnDp1SgQEBAghhDhy5Iho1aqVEEKI7du3i8WLFwtfX1/pvU+ePPmpJriZ4dtuSk+ePBHdunUTFy9eFE+ePBH6+vrizZs3Yt++fcLW1lZ4eXkJIYQ4ffq0GDJkiIiJicmKsNPs8+fPYuTIkVK3ga1bt0r1rEwmE87OzmLAgAEiMTFRLFy4UKxatUo6h8jlchEdHZ1VoWeplF1y7t+/L44ePSp27twp3rx5I1xdXUXv3r3Fu3fvhLW1tZg/f74Q4ms3z169ekn1dlbEfPLkSbF//36xc+dOIYQQNjY2YuvWreLEiRPC3t5erFq1SgghhIODg5gwYUK6dD3y8vISffv2/W75w4cPpe4Vhw8fFrNmzRJCCHHjxg3Rpk0bsXnz5hzTzTU9xMbGCicnJ6lbp4+Pj9i9e7d0vNna2opZs2aJiIgIsX37dmFrayvVL9HR0cLDw+N/doXPqY4cOSKMjY3FyJEjxYoVK0R0dLSIj48Xq1evFidPnhSJiYnCyMhILFmyRPj4+IgrV66k2md/5hpXJpOl+syNGzfEqFGjhJ2dnRDi6/e/bNkyoVAohIeHhxgyZIjw8PAQTk5OYuLEicLBwSFVt/zM9O1xGhYWJiZPnixGjhwpYmJiRHh4uBg5cqTU/ezBgweiffv2ws/PT7x69UocOnRI2v7cJiYmRhw8eFDs3r1bfPjwQZw9e1bMnDlTuLu7CyG+3keZmpqKwMBAcePGDTFt2rRUXat/tavyt77tTnnv3j3RoUMHsW7dOrFx40ZhYGAg3bclJydL7927d69YsGCBEEKIyMjIdIklp/Lz8xPW1taif//+4tKlS0KIr8ftjh07xOjRo4VcLheTJk0Sq1atEiEhIWLt2rXC3t5e+nz//v3Fu3fvvhuC4XclJiaKVatWiWbNmkndqIUQUndiR0dH0a5dOyGEEJs3bxaLFi0SQggxYcIE6dyYW4+/byUlJYl169aJV69eCSG+DnPg4OAghPhaj926dUv069dPhIWFiY0bNwpbW1vpmikhIUG8e/dOBAYGSuv7ld8we/cxyUG+fPmCZs2aQVNTE0lJSdixYwdmzZqFTZs2SVP9du7cGa9fv5Zm7+nfvz+qV6+OgIAAKBQKFClSRJoBK7s1+VRRUUG+fPkghMD+/fuxZcsW6bW2bdtCLpcjPj4ejRs3RoUKFeDo6AgfHx8sW7YMFhYW0NfXx7Bhw7JwC9KPQqFI1c3ur7/+wvbt23Hw4EGYmJigYcOG2LFjBwCgdOnSqFmzJg4ePIgaNWpAV1cXwcHBWRX6v/p2e8zNzXH48GGsWLECVatWhZGREa5duwYAqF69OqpXr467d++iZcuWOHDgAJo3b46dO3di8ODBADJmdoz0IL5pFPrgwQOpZVdSUhIAoFWrVihVqhTu37+P0NBQAKm765QvXx6NGjUCkH7NiTODhoYGPnz4gFu3buHIkSNwd3fHhQsX4O3tDQDo2bMntLS0sG/fPnTt2hWxsbG4cuUKAKBEiRKoU6dOthn4W9kE+NsnOX5+fhgxYgTy5s2LZcuWoX///ihZsiR69OiBpKQkuLq6Ii4uDu3atcOmTZuQP3/+rAj/Pyn3qxIlSqBYsWIwNzfHmjVr0KpVK9y5cwevXr2ChoYGmjZtCh0dHRw9ehQjRozAlStX4OPjA+DrPlugQIGs3Iwso6qqiqCgIOzZswcrV67EsWPHcP36dZQoUQJGRkYwNDTE3r17MX/+fHz69AlDhw7Fxo0bMXToUBQrVkxaz7f1RXr5t3po+PDhcHV1xc6dO/Hp0ycAX+thPz8/5MmTB506dYK3tzesrKzw+fNnzJ8/P126Hj179kx6Ip2cnIz4+HjMmDEDy5cvx8aNG3H16lVoaWnh8+fPmDBhAjZv3owFCxZg1KhR2bKba0bR0NCATCbDhQsXcODAAXh4eMDLy0vqkjZ9+nRpWfPmzREfH4+jR48CAAoUKABDQ8NsOwTDfxFCpOp24e/vD1tbWzx58gRHjx7FggULcOPGDbi4uODSpUt4/PgxLC0tcfPmTdSqVQuxsbHQ09ND27ZtU+2z/3WNq1Ao4OfnBysrK2zbti3VZ+7evYuIiAh06tQJANCvXz88efIEjx8/hqGhIUqUKIFz587B0tISy5cvx8yZM7NsoiDlNl+/fh13795FkSJF0Lx5c8TGxsLPzw9FihRBp06dcPnyZYSGhqJhw4aoVKkSbt68CX19ffTu3fvXWhxkY8pzXP78+aGuro6tW7di5cqVKF++PAoXLozHjx8jNjYWRkZGqFatGjZt2oS6detCR0cH9+7d+27yqN9x9epVdO3aFadPn5aW1a9fHyVKlMDr16+RL18+GBgYwNbWFqGhoVBTU4OKigoOHz4MZ2dntGvXDgD+mNai/+b+/fuwsLBArVq14OjoCFNTUyQkJCA2Nhbt2rVDiRIlcPz4ccycORNnz55FREQEGjdujICAAFy6dAkA4OjoCF1d3e+GYPhdmpqa0nWUgYEBZDIZZs6ciblz5+LLly/o378/ZDIZjhw5gm7duuHLly84evQoli5diu7duwP4M7pgKhQKqKuro2DBgpgzZw7Wrl2LggUL4tSpU7h9+zZUVVVRu3Zt1KlTBzt27MDgwYPh7e2NW7duITk5GVpaWtDV1UW5cuWke8pf+Q2ZlPpFSUlJOHToEK5evYrAwEDUrVsXoaGhWLx4MV6/fo3y5ctj/fr1mDJlCg4fPizNWlW+fHm4urri7du3AIC9e/dCT08v1YGYHW/qlRclCxYswKBBg3D06FEcO3YMkZGRUFNTQ2xsLPLmzQttbW106tQJV69ehYaGBvbv348jR45gxIgRWbwF6SM5ORmqqqpQUVHBw4cP8fLlSxQsWBD9+/eXulhOmTIFV65cwcuXL6GpqYn8+fPjy5cvuHr1KhYsWPDLXS0ygrIpvoqKCoKCggAApUqVQvv27aUZcxwcHHDmzBn4+/tDW1sbtWrVQnJyMq5cuYLSpUujY8eOKFGiRFZuxg8pL6qVx5VMJpOWK28cNDQ0IJfLkTdvXvz999/w8fHB06dPAfzvk2N2b8b7bdJMV1cXBw8exPXr19GpUyc0aNAAHh4eePXqFQBg7NixWL16NQoUKIAGDRqgfPnyWRH2/xQREQHgn4v8Bw8eoH///tiwYQP8/PygoqKCCRMm4OzZs5g+fTrmzJmDSZMmwcfHBy1atJAuZLW0tNJl3In0pjyRK/crIQRKliyJ5ORkVK1aFVWrVkXr1q2xadMmAECxYsWgra2NvXv3QlVVFTY2Nqhbt25WbkKW+LdxCoYPHw4XFxccPnwYs2bNgo6ODp4+fYo8efLA3Nwc3t7e8PT0xJYtWzB//nwcPnz4u3o5I87Dcrk81Xo9PT2xefNmVKhQAatXr8bcuXNx4sQJAEDTpk1RpkwZ3L17FwUKFMDmzZuxdu1arFq1CkWKFPmtOJTHgq6uLu7evYvk5GSoq6sjMjISRkZGOHToEOzs7ODg4CCNYdOoUSMcPnw4zVM753Qp60/l+eHUqVN49+4d+vbti0qVKuHp06fw8/NDgQIF0LJlSyxduhQlSpRAs2bNfqlLWnaknHEpODgYSUlJKF68OF6/fo34+HgUK1YMJUuWxNChQ3HhwgUYGhrC19cXw4cPx9q1azFx4kQsXLjwlxKYqqqqKFiwoDQ23vbt26UHuj179kTZsmXh4+ODuLg46Ovro0GDBtizZ490M9mpUydoaGhkSUIn5b7z/v17jB8/HuvXr8fx48cxY8YMdO7cGaVLl8azZ8+QkJCAZs2aoWTJkli3bh0AYM2aNdl+tuJf8e0DJSEEypQpg/Lly6NUqVKoXbs2mjZtiqCgILi5uQH4+jD/1KlT+PjxI/r06YNp06ala92cnJwMLy8vrF69Gu7u7ggNDYWGhgZMTU1hZGSE169fS+PqjR49GosWLcKQIUNw5coVzJkzJ9t2/c8MyvNIw4YNUaZMGWk4i8OHD6Nv3764desWypQpg0aNGuHOnTvQ1NSEpaUlli1bBn19fVhZWaUauiS9H/Qq16ecIW7ixIno3r07ypUrh61bt0rDUDg4OGDJkiUoWrQoGjZsiIIFCyJPnjxSg5LseE+eXpTfkfK6Wk9PD4GBgXj27Bl0dHQwYsQIbNmyRRqztHLlyrh58yYCAgIwcOBAFC5c+LuknfKe8lcwKfULTp06hZ49e+LJkyc4duwYpkyZggsXLqBAgQJISkrCs2fPMGTIELRo0QLm5uaYOHEiTE1NcePGDXTv3h21atVKdcOXlsG/Mtu3T8eUO6yGhgZq166NBQsW4NWrV7C3t0fDhg3x5s0b+Pr6Qk1NDVWrVoWVlRXy588PLS2tLHtClR7kcjkSEhKkwWTV1dWlKYRtbW2lp6Y2NjZ48OABrl69ikqVKqFt27ZYvXo1+vXrh7t372Lp0qVo3759tsm4p6yIoqKiMGXKFFhbW2PatGmIiYnBmjVr8PjxY1y7dg1ly5ZFr169pLEwqlevjkmTJqFz585ZuQlpokxGKS+q9+3bh4MHDyImJgaNGjWSBkoF/rlQMjExgaamJt6+fYv4+PisDP+XfHvhd+vWLTg7OyNv3ryYNm0aKlSogI8fP8LCwgLh4eF4/PgxACBPnjzQ0tLC5cuX0a1bN7Rv3z7LtkFJ2XLU1dUVx48fh1wuR0REBHbt2oVLly7B3NwcycnJcHBwQJs2baCnpwdTU1PUqVMH7969g0KhQOXKlWFmZoZRo0Zly7pIOaC28kR+79499OvXD8uWLUPNmjUxb948HD58GAqFAlOmTIG3tzeOHj0Kd3d3JCcno3v37lAoFGjWrNm/jheWm6Uc3+bChQu4fv06AGDy5Ml49uwZAKBu3booXbo03N3d8eHDBxgYGKBly5bw9fUF8DUxA2Rsy0flutXU1BASEoI9e/bAw8MDdevWRd26deHv74+YmBi0bNkS1apVw4IFCwAAHTp0kB5Uqaio/NI4G9+2hAX+uciuW7cu9PX1pem2S5YsKU3aEBYWBisrKwBf60Tlv3O7lL8VABw9ehQHDhxAvXr1MGXKFGhpaeHTp08wMTFBXFwcXF1dAQBlypRBfHw8IiIiYGlpiSZNmmTZNvyulNd+QgisW7cOXbp0ga2tLfz8/DB+/HgkJiZKD64sLS3h5+eHggULYvfu3Rg0aBCcnZ2lqdJ/9diKiYmBiYkJrK2tERgYiPnz58PPzw8VK1ZEo0aN4OnpiTdv3gD4mjBo0qQJSpYsCUNDwyxp2ZuyBU9iYiKCg4Nx/fp1NG7cGCdOnEDVqlXh6uqK58+fo0uXLvD09MTz589RuHBh9OjRQxqsO7tcJ6Y3ZV195coVdO7cGYsXL4a2tjaWLVuGL1++4Pz582jatCmKFSsGZ2dn7N27F5cvX8bw4cNRrlw5lC1bNt0TBKampujXrx/U1dXh7e2N7du3A/h6XjY0NES9evUQEBCAESNGYOnSpWjRogX69u2LnTt3/pEPgQAgKCgIycnJUFFRQVJSEtTU1DB8+HBMnToVVlZWuHnzJuzt7WFhYQFVVVXUq1cPurq62LlzJ0aPHo0iRYpAQ0NDumZJr5ZvDx48wJMnT6QH7Mr16erqSpMYWVlZYdy4cdDU1IRcLodCoUDjxo1RuXJlHD16FD179swW176ZQaFQSN/RwYMHcfToUURFRWHFihUICwtDQkIChgwZgri4OBw+fBjA13GkDQwM8PTpU5iZmaFVq1bpGhMHOv8JoaGhsLa2hlwux6JFi1CpUiUkJCTgypUrsLW1xeXLl3Hjxg08f/4cVlZWKFeuHC5duoR69eph6dKlaNu2rXTBl519O/CqQqFAoUKFUt0AAF9bnIwZMwZVq1aFn58fxowZI12E5Da3bt1C8+bNoaKigsePH2Pz5s1Sk3Ll97J3715cuXIFe/bsQXR0NHx9ffHmzRv06tUri6P/x78NYLd48WJoampi2rRpOHz4MNzc3NC7d298/vwZ69atw7Vr1yCEgKGhIQ4fPoyqVatmUfRp9+2+evDgQZw6dQrGxsb48OEDAGDChAlYuHAh5s+fj5IlS0oJEFVVVYSGhmaLAVHTSgiBhIQE7Ny5ExYWFihfvjzi4+Nhb2+PZ8+eoU6dOlBVVZVmGtPX18eQIUNw/vx5uLi4wMvLC3p6erC2tka1atWyenO+Ex8fDy0tLXz48AHJyckwMzPDjBkzMGjQIHz48AErV67EX3/9hbJly8LBwQF6enp49+4dzMzMMHLkyKwO/38KDg7GtGnTsHPnTmhoaODmzZvSpAhaWlpQV1dH06ZN0bVrV3Tv3h19+/bF1atXcf36dTx69AiTJ0/OVi0vM4Ofnx/evn0LY2NjaGpqwsfHBwcPHoSXlxc0NTXRuHFjjBs3Dj179oSRkRGmTp2KV69eYf369TAxMUGPHj2QnJycKS0otm3bhsaNG0s3Ly4uLti8eTP09fVx6dIlbNy4EYUKFcKhQ4egr6+Pfv36ITAwEH///TeuXr2KcuXK/Vb5KZvQpxzAPKX79+9j6NChmDlzJho1agS5XI7ly5cjKSkJixcvznYtJjPKp0+f4OzsjA4dOkBXVxefP3/G5MmToa2tDU1NTbRs2RLVq1eXBqcePHgwnJ2dcfbsWbx9+xbNmjXD6NGjUapUqazelHRz4cIFyGQyhIaGwtLSEs7Ozrh79y62bNkCa2trlC5dGt27d8e9e/dw584drF+/PlXS4GcmCAgMDPxu30xMTES3bt0wd+5cGBkZYfbs2fDw8MDkyZPRoUMH2NjYoHz58tLgxFnlyJEj0NHRkW7QLl68iG3btsHa2hpnzpyBlZUVvL29ERERAblcDjc3Nzg6OmLWrFkoVaoUhg8fni0fmKSHlNdiygTns2fPpNas3t7e2Lt3L3bu3Ing4GBYW1sjPDwct2/fxuXLlzFs2DA0bdo0Q2P09vbGuHHjYG9vj/3798PIyAhfvnxBdHQ0JkyYgMmTJ6Nx48YYMGBArm41kxYPHjyAk5MT2rdvj9atW6f6fZUP/latWgUg9fF/9epV3LlzB1OmTEn3oQV8fX2xatUqhIeHo3jx4khOTsasWbOgq6srnQOjo6Oxa9cuqKqqonfv3ihRooTU+EJNTe1fJ7HIbZQPGwYPHoxFixZBV1cXERERsLW1RXx8PFq3bo2nT59i6dKlGDx4MGrUqIFp06bh5s2b2LdvH96/f48KFSrAzs4u43rH/PQoVH+wt2/fiqlTp6Ya0Fk58OLAgQOFra2tiI+PF3Z2dmLr1q3C1dVVTJo0SfTu3VscP3481bpywmDQq1atEmZmZmLIkCHi8+fPqV5TDkYXHh4uHB0dRc2aNcXz58+zIswMcfLkSdGvXz9hZ2cnXFxchBBCtGrVSgQFBYkLFy6I6dOni+TkZJGYmCiSkpLE9u3bhUKhEOPHjxfNmzcXZ86cyeIt+DF3d3fRs2dP8eHDBzFy5Ehp8MjY2Fhx8uRJMXPmTCGEED179hQNGzYUrq6u0iD+OYlcLhe7du2SBr0W4uuAfEOGDBH79+8XrVq1EqdPn/7h57M7mUwm4uLihBBCeHt7S4N/vnv3TvTr1++79zs7O4uZM2eK27dvi1evXolPnz6Jy5cvZ2rM/+X06dOib9++YsaMGWLKlClCiK+D0A8aNEjExsaKJUuWCGtrayHE18EZr169Kvr37y/CwsJEXFyccHFxESEhIVm5Cf+TQqEQp06dEj4+PtIy5cCgR48eFUuXLk31/uTkZOHh4SHat28v7O3thY+Pzx81yPS33NzchJ+fn1AoFOLjx4+iVatWYvXq1UIIIVxcXMScOXOEu7u78PX1FbVr15YGoz548KB4+vRpqnVl1PGtXK+Hh4d0bO7bt0/069dPOta2bt0qBg8eLCIiIoSTk5OYNm2aePv2rRRrWFhYulwnxMTEiJkzZ4ouXbqIOXPmpPoOlOu/ePGimDNnjrC2thY9e/b87nolNwsJCRGJiYlCLpeLoKAgER8fL5KSkoS7u7sYMWLEd+8/cuSIWLRokTh37pzw9vYWQUFB4sGDB1kQecZ59uyZWLRokbCyshIdO3aUJlj49OmTGDdunDh9+rR4+/at6Nixoxg5cqSYOHGi8Pb2/uXyrl27JkxMTERUVJS0THkeW7RokVi5cqXw8vIS3bt3F926dRPNmjUTu3btElu2bBG7du3KskkdvLy8xPTp08X+/fvFly9fhBBCnD17VnTt2lXs2bNHCPH1/Pz06VNhb28vPnz4IC5fviwMDAzE3r17RUBAgPj06VOWxJ5RlHVKykHIlfVhdHS0mDp1qggLCxPPnj0TkydPFu3atRMnTpwQwcHBYu7cuaJnz57i8OHDmR63vb29mDlzpoiJiRELFy4U48aNE5MnTxZCCPH48eM/fiDzlL/nqlWrxOrVq6VJWJQDgT948ECYmppKg4gL8c/+8O39Q3qde2NiYsTIkSPF3r17hRBf97GAgIBUE0soY3BzcxMLFiwQJ0+eTJeyc5KwsDDpOwkLC5Mm4nj37p0YM2bMd4O5+/j4iFatWombN2+Kt2/firCwsO8m6cqIPAa776WBMrtYsWJF/P3333j+/DkePnwoTScPAHPmzIGrqyvkcjkaNmwIb29vFChQAMuXL8ehQ4fQtWvXVOvKTtn2b5v3y2QyTJw4EbGxsTh37hwiIyOlzLeSMjNepEgR9O/fH3fv3kXNmjUzNe6M4OnpiXHjxuHs2bMYN24cNDQ0sGfPHoSHh6Nx48ZwcHBAo0aN8PTpU3z8+BGamppQV1eHq6urNFX3gQMHYGFhkdWbAgB49eoVFixYgCNHjiAuLg4AsGrVKsycORNjxoxBmTJloKurK03Zmi9fPsjlcml/WLduHfbv3w9jY+Ns3zVI2UVAoVAgLi4O27dvh5OTE/r164e8efNKA8xraWlhwYIFKFKkCBISEhAeHv4/BzVOj8GEM9LRo0fRvXt3zJ07F+vXr0e1atUwduxYXL16FeHh4ShYsCB8fHwghEBiYiL27NmD2rVro3Llyli8eDEePnyIUqVKZYvWNkIIxMfHY+bMmTh16hQmT56MQYMGoXLlyoiMjJTGVXJycsLYsWPx5MkTeHh4QF1dHfXr10fZsmVx4sQJ5M2bF61bt86WLd3Onz+Prl274uTJk7C3t8fSpUshhMCECRPg6ekJIQRCQkKk98fHx8PMzAw1atTAkCFDkJSUBB0dnVz/RO9bKbv/NG3aFOHh4di8eTPU1NTw119/SVNJGxkZoUyZMrh+/ToqVKgAY2NjzJgxAwDQp08f1K5dO9V6M+L4lsvl0noNDQ2xePFi7N27F+bm5ihQoIA0ucCIESMQGRkpde9RU1OTBs7u06cPihYt+tPXCf82FMCNGzego6ODEydOoFOnTtDT00NCQoIUKwC0a9cOdnZ2WLBgAQ4ePChdr+RmMpkMK1aswIABAzBhwgQcOnQIhQoVkp4MK7tgff78GcDXacr37duHypUro2zZsli3bh2Cg4NRsmRJNGzYMIu35td927UuJCQEPXr0gIaGBvbt24fevXtDTU0Nr169QqlSpdC5c2ccPHgQxYsXR9u2bVG8eHEsW7YM1apV++mhKFKO+aKrq4vjx49Lr6mpqUnj6jk5OWHq1Kno168fjh07hkWLFsHd3R1t27bF4MGDs2xSh9DQUJw+fRpRUVFYs2YNli9fjr///hsVK1bE58+fIZfLoaGhgQ0bNgCAVDdZWVmhQoUK0lhKuYmyS6eyhYyjoyOmTZuGkydPQlNTEz169MDHjx9x6dIlDBgwAH369MG2bduQN29ejBo1Cubm5rC0tMz0uEeMGIEnT57g5cuXsLW1RalSpRAZGQm5XI569er90QOZA//8npcuXZLGZPTw8ADwdVgXhUKBhg0bon79+lIXSOCfe13l/YOyjvjdc6+yx8O9e/cQFxcnjcGWN29elC9fPtXEEsoyGzdujKJFi8LT0xNhYWG/VX5OsmbNGvTv3x+zZ8/Ghg0bULhwYZibm+PAgQOIjY1FXFwcEhMTkZiYCACYO3cuVFVVMWLECKxYsQKPHj1C0aJFv+uOnRF5jOx9x5WFPn78KA04mnIQ8jp16kBPTw8XL14E8HVkf4VCgaSkJOjp6SExMRFGRkZo06YN9PX1pb7h3w4mll2I/++upKKigpiYGMhkMiQlJaFkyZLo3bs3XFxcULlyZbi5ueH+/fv/cz25Yaanly9fon///qhXrx527NiBJk2aoG/fvtDS0sKTJ0+waNEiXL16FZGRkejSpQuWL18OJycnzJ8/HxoaGihdujSKFSuWLbo7xMbGYsmSJbCxsYGenh7Onj0LW1tbAICxsTGCg4OlgVhLliyJhw8f4ty5cwC+Ns9VVuglS5bM1gO2xsXFSTd5ampqiI6OhqqqqtQU/uXLl4iNjcXEiRNhZ2cnfa5s2bIwMzNDt27dpEGy/1diKjtyd3fHqFGjcOPGDSxcuBAWFhbYu3cvXr9+jcaNG+P27duIi4tD0aJF4ePjAxUVFWhpaeH69esIDQ3F0KFD4ejoKA3gmR2oqKggODgYqqqq2L59Oxo2bAgDAwOMGjUK2traqFq1Kho1aoQnT54gMjISQ4YMwdq1a6FQKFC0aFFMnz49287w+eHDB3Tr1g0XLlzAvHnzsGfPHvTr1w8vX77Ep0+fYGtri/nz56NLly549eoVzp07ByEEAgICUL16deTLl0/qxvKnXRyLFIO/KycIAYAXL17Ay8sLY8aMQUhICNzd3VGgQAE0btwYYWFhOHbsGNasWZPquM+o+FL+X01NDTExMdJYbR06dMCaNWtQpEgRtGnTBhEREdIkCmPHjsXy5cuRN29ejB079reOR+W5HPiaiLp69Sri4uLw4cMH+Pn5YejQoThz5gzGjh2LBQsWIDIyUro+UcZesGDBbD+BQ3pQPqxISkrCzp070blzZyxZsgQymQyVKlXCy5cv8fnzZ9SsWRM3btwAABQtWhTnz59HuXLlMGjQIJw6dUoaAygnU1NTg0KhwPnz5+Hn54cSJUpgwIABUoJUOZPW/fv3ERcXh8aNG8PAwAD+/v7o0qULnjx5Ig2W/7PXt2pqapDL5Thy5Ag0NDSwZcsWaYw98f/dbsqWLQsNDQ2cPXsWlpaWEEKgZcuW2LZtGypXrpzu38ePiP/v4q+kqamJypUrQwiBEiVKYM+ePYiPj0f79u0RHR0tXTMPGDAAHh4eaNeuHUqVKoXZs2fD2Ng4U2PPaHfu3EHv3r1x8uRJAF8ng7K2tsbr168xbNgwHDlyBLa2tjAyMsLu3btRsWJF1K1bF/Hx8QgNDcXx48dRunRpDBgwIEsmIilWrBj69u2L6dOnAwBsbGywc+fOP6I+/DcixbjCQgjI5XJs3boVjo6O6NevH9TU1ODu7i6Nzai8x508eTKcnZ3h5eX1r+v93Xvgu3fvolu3bjhw4AAASNd/KZOhQggkJydj8eLFiImJkeoZVVVVdO7cGWPHjk01025udfLkSfTp0wfh4eFwcnJCt27d8PTpU9y9excLFy7Eli1bYGBggJiYGNy4cUM67uLj45GQkIAePXrg8OHD0iyEShl5TGSvDEk28Pr1a4SHhyM+Ph4+Pj44deqUdNMLfJ2ZzNDQEFFRUVJiSlVVFZ8+fUKpUqVQpEgRFCtWDGZmZqmmHM+uFZsyGTVt2jSMGTMGq1atQv78+dG3b1+Ehobi8ePHWLhwIYyNjeHg4IDAwMCsDjnDlCpVCr1790ZMTIy0TFdXFzKZDMWKFYOamhomTpyIadOmYcSIEejSpQuePXuGEiVKYNu2bdlmRq/g4GAMGzYMz58/x/HjxzFo0CBMmzYNSUlJiI+Ph6GhIVq1agV7e3sAQLdu3WBoaIgLFy6gR48eUFdXx5gxY7J4K9JG+VQL+FqRjh8/Hjt27AAAtGnTBlpaWtJ3IJPJpNn2kpKSAAAWFhaIjo7+blas7Cw8PByDBw+GiYkJNm7ciLp168LY2BgtW7bE1atX0bt3b2ng5Dp16sDFxQWLFi3CrFmzAHxt8amiopItT8qurq7ShAnK30h5sfP69WtUqVIFlStXxoEDB9CvXz9ER0fjxYsXAJClY4r8l0+fPiEqKgpjx45FvXr1AAC1a9eGTCZDfHw8+vXrh7CwMFy5cgVz587F9evXMWzYMMyePRtt2rQBkP0eaGSk2NhYJCcnA/h6jnr58iUGDx6M2bNnY9SoUahRowbq1auH27dvA/h607d+/XoAQIMGDdC6dWsYGRlBXV0dJUuWzNDJRJTnC2X9sXv3bnTr1g2rV6/G+vXr0axZMxgZGWHhwoWwtLSEiooK7t69C5lMhjZt2sDa2hpFixb97fGjVFRUEB4ejqVLl2L16tU4duwYxo0bhxEjRsDc3BwdO3ZEz549MWLECGhoaKR6UpxT6r70cPfuXcydOxczZszArFmzULp0aXTo0AGVK1eWpjYPDAxEcnIyDAwMcPToUWnSkmrVqqFgwYIAkO1bDqfVjRs3YGZmhnPnzmHlypVwcHDAjBkz4Ofnh3v37qFEiRJo3rw57t27B09PT2nK8Dp16qB8+fKYPHkyGjRokKbBub89DhUKBaZPn4579+5hzpw5qFatGrZs2ZLqPQ0bNkSePHnw+vVrAFm3r169ehVdu3bF6dOnpWX169dH8eLF8erVK+jo6KB27dqYN28e2rZtiwIFCuDhw4f48uULmjVrhjVr1uD48eOYMGFClsSfUQICAjBt2jRs2LABw4YNkyZECA0NRbFixTB9+nQ4OTkhT5486NSpE4CvM97dvHkTU6dOhYeHBw4cOIBBgwZl4VZ81bNnTwwbNgwKhSJbTkCVWZTXw6qqqkhISICKigrU1NTw+PFjjBo1Ci1atMDUqVNRpEgRaZZEDQ0NqSX3oUOHUL169XSNKTAwEDNnzsSaNWswYsQIKXkYExODsLAwfPz4UXqvcvB1Ly8vPHr0CEDqQc+z82zh6SUwMBAbNmxAkyZNsHDhQhQoUACtW7dGnjx58O7dOxgbG6N48eLYvn075s+fj5MnT2LBggUYMWIEQkNDoaenBzU1NeTNm/dfJ0vJKH/OVW4a7dmzB7a2tqhQoQJcXV2xdOlShIeHA4B0kVyrVi3UqFEDDx48QGBgIJYsWYLVq1fDzMwsVYuL7NjyImXmG/h6E7h+/XpUqFABixcvxqlTp3D48GFUqFABBw4cQJ06dZAnTx7kyZMHMplM6gKVGyifyCkVLVoU5ubm8PT0hLe3N+7evYuePXtCT08PlSpVAvB1qnE/Pz/s2LEDrVq1wrx58zB27NhsNVuKlpYWTExMpJtfd3d3qTXBhAkTcPfuXSxbtgzXrl2Dj48PihQpgo4dO2LZsmXYvHkz7Ozssk2C7d+krCANDQ2RmJgIExMTPHr0CD179sSpU6ekJ961a9fG06dPERgYCFtbWymBpampiYiICCxatAgVK1bMtknjb8nlchQtWhRdunSRbsiBr8dzTEwM6tati7x588LCwgIXL15E3bp1MXHiRKnF2969e7N18qZcuXLInz8/AgICoKGhASEE1NXVERsbi5kzZyJPnjxo3LixNNj10aNHv+uSlR1Vr14dvXr1SnXD5ezsjHz58kkPLxwcHDBnzhwYGhpixYoVGD58OI4ePZptugJnllevXmH//v1SF7OgoCAsW7YMffr0waZNm+Dv74+dO3eiT58+CAsLw/3792FmZobw8HAp6WxmZgY9PT1pnRmV0Ltw4YKU3Ae+zsz7/PlzXLp0Ce3bt4eLiwtu3LiBJUuW4OjRowgLC8Nff/2Fly9fwsfHBwDQo0ePX6p/vu129erVK8yZMwdhYWE4ffo0tmzZgufPn+P06dNo164d1NXVUapUKTx+/Bjv379HmTJlfm/jc6gmTZqgUaNGUjIb+NoCT0dHB9WqVUOVKlVQpUoVvHr1ClWrVsXSpUtRsmRJWFlZYeHChTl2QOqnT59i9OjRcHBwwN69ewF8vXm7fPky7OzssHHjRkyaNAk+Pj64du0a5s2bJ50vzc3N0bBhQ6llkrq6eqqud2lpKZ9yQOTExESpJYa6ujqGDx+OSpUqYfPmzXjx4gXu3bsnJZ+CgoLw999/Z/lDlOTkZHh5eWH16tVwd3dHaGgoNDQ0YGpqikaNGuHx48fo2bMnrly5gpcvX8LU1BQ+Pj54+fIlAKB8+fK5okdBSl5eXhgwYADy58+PQ4cOoW3btkhKSkJwcDAKFy6Mc+fOoXPnzqhUqRL27NkDIQTmz5+PBQsWoGLFiqhZsyZ27tyZbSZX0dTURL9+/aCqqvpHPQT6lvJ8tGHDBgwYMAC7d+/Ghw8foK+vL9Wb1atXh0KhwJ07d+Dp6Qngn1kj07t3RXx8PPr374+YmBgcPnwY7dq1g0wmw+PHj2FpaYn4+Hhcv34dUVFRAL7+ju/fv4e2tjYaNWqUrrHkFCVLlsTIkSPx+vVr6X5JLpcjOjpaOvevXLkS69evR9myZbFq1SoYGBiga9eu2L17d6q6StmbKjP8uUddCikv7saPHw9/f39cv34dgwcPRsuWLaV+lsoDrlChQmjYsCECAgJgaWmJvHnz4sSJE2jZsiWAf57kZLenjykvCpSx3b59Gx4eHmjRogV0dXXh4OCAFStWAAAiIiLw8OFDdOrUCUlJSdi1axcMDQ2zLP705O3tjZ49e37X8qtatWpo0qQJrKyssG3bNkydOlXKMitbbuzatUvqW5tdklEpn+poa2ujSZMmCA0NhZmZGXbs2IH58+fj4MGD0NPTw/79+6GpqYkBAwZgxIgR0ueKFCmSLcfhSUm5D6uoqEAmkyEqKgrFihWDXC5HgwYNYGZmBl1dXaxevRoA0KJFC/j5+WHnzp1o2bKl1CUXAPLnz4/Fixdj/PjxWbU5aaJMiiclJUnHr52dHW7evImnT5/C1dUV3bp1g46OjrRfduvWDREREbhy5QoqVKiA4cOHY+DAgVm2Dd/69imk8qRZtGhR5M+fX0q4KespVVVVFChQADKZDNWrV8f8+fNRuXLlbDm20r89jMifPz9at26NxMRE2NnZYciQIXjx4gUWLlyIUqVKQQiB5s2bQ09PT2qW3qRJkxyTLE0PyvNw5cqVce/ePcyePRvnzp3DixcvUK1aNRgaGuLQoUP4+++/cfz4cURGRqJNmza4du0agoODsXHjxkwbi0QZa4cOHfDw4UP069cPL168QEJCAvT19fHs2TP4+vqiXr16uH79OgoWLIgBAwZg6NChaN26NSZMmIBatWr9cvkpp3J2dXWVumNXq1YNsbGxUpeKuXPnYuXKlQC+PpyYPHkyIiMjsX79euTNm/c3v4WcR/m7TZo0CQcPHkRgYKCUjGncuLHU9d7S0hJ+fn549OgRKlWqhL59++bIacKFEEhKSsKKFSswf/58tGvXDg0aNMCHDx8QFBQEIQTevHkjdX2pUqUKunTpglOnTsHS0hIfPnyQxnkaMmRIqnFa0lI3KR/kAl/r8KioKMyYMQMLFizAxo0bkZCQgHfv3iEmJgZJSUkoUKAAjIyM4ODgIF1316lTB9OnT8/yhymmpqbo168f1NXV4e3tLY2bExAQACMjI9SpUwchISH4+++/MWLECNSrVw/jx49HkyZNsjTujFSgQAGYmZmhSpUqAL6Ocdm5c2c8fvwY+fLlQ+/evVG1alWp9dS5c+dQr149aGtrY8KECRg8eHBWhk//79trluDgYEyYMAHR0dGYMmUKTp8+DXd3d2hrayMsLAzXr18H8HX8poSEBAQFBQFI/3te5WzmefPmxeDBg6VxcU+ePInu3btLY1qNGjUKjx8/xty5c/HmzRscP34cY8eORfXq1aGlpZUtG4hkNE1NTTRr1gxaWlrYu3cvjhw5gj59+kg9KxQKBSpVqoTmzZtjwoQJKFy4MHr16iWd57596JVp0n3o9BxEoVCkmgFAOYPH7t27RZ8+fYQQQuzatUvY2dkJPz8/IYSQZttLTEwUbm5u4v3799LnU85OkF29fv1ajBo1Sqxdu1acO3dOfPnyRYwfP16cOXNGJCQkCCGE6NWrl1i1apWQyWTCyclJXL9+PWuDTkcpf6MpU6aI+fPnf/eeN2/eCCsrK3HhwgUhxNffPLvOwpZye1LOhJCYmCicnJxEjx49Us3u4u3tLbp06SLCwsKEEF9nhMppYmNjxYIFC8S4ceOEh4eHEEIIBwcHaTa2Z8+eiXr16okbN26IVatWidmzZ4tHjx5Jn88JM18qPXr06LsZoJQzr+3du1fo6+uLMWPGiMePH0uvK+uoFy9eiNevX2darGmR8jiSyWTStqT8TbZs2SLGjBmTaoaU+fPnC2tr62z/2/3bOUC5zYmJieLo0aOiQYMGqepUuVye6rzyJ0r5u75//17Y2dmJWrVqSft1VFSU2L9/v9i9e7cIDQ0V/fv3F4MHDxZxcXFiz5490sxXQmTejJmJiYli48aNokePHqJFixZCiK/HXkhIiFiyZIl4+fKlcHNzE61atRJLliwRQghx4MCBXy7Px8cn1THh6ekpfQ8DBgwQS5cuFU+ePBFTp04V58+fl/bFlOVn1SxlWUEul/9rfaFcZmNjIxo1aiRsbW1TzS6s/N4ePHiQK76vDx8+iPHjx0vXd0L8U88kJSWJdevWiV27dkmzLzk5OQl7e3shxNdroZTf4c/Uv4GBgeLAgQPStUZYWJiwsrIS+/fvF7GxsUJfX184OzuL5cuXi1GjRgkvLy8hhBAbNmwQzZs3/63Z/DLKq1evRNu2bcX9+/fFhAkTxP79+8WmTZvE0qVLRVxcnBgzZow4deqU2Lx5c1aHmmmuX78uRo4cKdq3by8mTZqUaoau0NBQYW5uLqZOnSp69eolhg8fnuqeibJeymsWZR3w7t07MW7cOBETEyNcXFxEly5dhJ2dnbh69apwcnISHTt2FAMHDhT9+/eXjtuMcPfuXaGvr5/q3rRZs2bC2to61TW9EEIEBAQIW1tbMWPGDDFs2LDvZorLrb7NYwjxzzVQcnKyuHLlijAyMhIjR46UZiIW4p/7hOTkZPHhw4fMC/g/qAjx56UQAwMDERwcLLX6ef36NVasWAF1dXUMHDgQBgYGGDFiBHr16oVmzZphw4YN0jgVBQoU+K5Zp3IAtezWMupbDx48wNKlSzFq1CgULlwY/fv3x6VLl3Dt2jUEBgaiW7duqFmzJnx9fdG9e3fcvn07xzZV/1bKVmJKwcHB6Nu3L5YuXZqqBZhMJsP58+fh7OyMNWvWSGNIZGfOzs64cuUKVq5cCQ0NDQBfuyQcO3YMBQsWlMaIOnHiBM6dO4dly5ZleXP4tPj2d3vw4AH27t2L6tWrQ0NDQxoHQ1NTE8OGDcPatWthYGCAEydO4PLly9DR0cHs2bNz3PgfKbdbOc5Xv3790LFjx1Tva9++PYYMGYKePXtCJpNBXV09RzQ73717N65evYrq1avDwsICderUkerP8PBw3L59GytWrJC6OhkZGWHq1Kk54ndUNjEvX748TE1NU70WHByMVatWoU6dOtJAy8rj9U8XHByMHTt24O3bt7C3t4e9vT2aNGmCzp07IzAwUBrY39fXF9u2bcPbt2+xZcuWTKnHlMej8v8nT57EtWvXULJkSYwZMwaTJk1CmzZtMHDgQBw/fhzHjx/HwYMHcezYMVy9ehUtWrSQZjH7FUIIPH/+HPnz55fGhFu6dKk0MPDHjx9hbW2NCRMm4MWLFwgJCUGXLl1Qo0YNvH37Fv7+/tLYZH8C8f+DZANfW7IUK1ZM6o6gPObCw8PRo0cPbN26FVWqVEk1jkpu4ujoCA8PD6xbtw7x8fGpWsh9/vwZrq6uuH//PgoXLowWLVpg7dq16N69O/r27Qu5XC4NhJ7W7+Xp06cICQlB/fr1sXr1ahQtWhQNGjRAiRIlcPv2bVSvXh0HDhxAvnz5YGNjg6JFi2LZsmX4+PEjfH19YWhoiFGjRqVqlZWdODg44MuXL5gzZw5WrVqFz58/Q1NTEytXrsTDhw+hr6+f67rpAZD2BSXlPhEZGQlHR0fp2gz42kJOOQ5RaGgo3r9/j7CwsD+qDspJYmNjsX79eqioqOCvv/5C6dKloa6uDj8/P/j6+qJp06ZYuHAhGjdujAEDBiAxMRH+/v7466+/pHWkrHPT06hRo5A/f36sXLkSN2/exIwZM+Du7g4g9X6mJJPJsmUL+oyQ8pj09/dHdHT0d8NZREZGYu3atShUqBCsra2/O88pf7efqeMzVFZmxLLKrl27RPv27YUQQpw9e1b07dtXXL16VaxYsUIMHTpUvHnzRri6uop27doJmUwmzp49K/r06SNGjBghgoKCsjj6n+fm5iaEEOLo0aPi2rVrwsXFRfTp00c4ODiIuLg4ERMTI6ZPny527NghIiMjhRC594m9q6urGDhwoHB0dBTR0dFi9+7dYtCgQd+1cPj48aMYM2aMcHZ2zqJI/13KrLhCoRAymUzY2toKS0tL8ebNG2m50qVLl8SsWbPE3r17xZgxY8TQoUMz9MlGevm37H9ISIhYuHChMDc3l5ZNmzZNrFu3TiQmJopdu3aJVq1aidmzZwshRKonw9m1pdu3vo0zOjpadO7cWRgZGUnHZsqWNbdu3RJ169aVnnBlN//2O65fv176jaytrUWPHj2Er6/vd58NDg4WT548Ef7+/pkS6684fPiwcHFxkf4+fvy46N69u7C3txfNmzeXWrYoj0m5XC5u3rwpOnbs+K/b/Kf4txZldnZ2omvXrtITztu3bwsrKyvx4sULIYQQpqamYuLEiaJZs2bi7NmzqT6bkS3o/q3usLS0FP369ZP+dnV1FW3bthVCfD3fTps2TXTs2FEMGTLkt1srptw2Dw8P6djp0qWLePr0qfTajh07xPDhw0VcXJwYPny4cHJyyrXn8bSIiooS1tbWon///mL58uVix44d0mvK/W/jxo3CysoqV35Pyv3G2dlZNG/e/LvXlS0SfX19hbe3t1i2bJkYP368uHXr1i+XKZfLxcOHD0VgYKAQQohRo0YJAwMDcevWLfHq1SvRqFEjMXToUOHq6iqEEGLx4sVi48aNQoiv11wp9+fsKjQ0VHTo0EHcv39fCCHEokWLxJAhQ3JET4lf8W3dmrLlvZKHh4ewtbUVJ06c+NfPUPZx9uxZ6Z5QiK8tcPv06SNWrlwpDh48KEaOHCk2bdokoqKiRP/+/aVjdciQIWLMmDHi3r17qdaX0ft9eHi4qF69unTfMnHiRDF37lwhRM65rk9PcXFxqVrax8XFiSVLlog2bdqILl26iO3btwshUh+Djx8/Fv369RN37tzJ7HB/WjZIi2WOa9euSf/u0qULSpUqBX9/f0RERKBNmzYwNjbGly9fEB0djZs3b6JBgwaoWbMm7OzsYG5uDhsbG6xevTrbPr1R+rYfqK+vL5YvX44HDx5ACIHRo0fjypUrsLGxwcyZMzFq1Ci8f/8ef/31F4QQUoY5N2SaU45bI5PJsGrVKuzevRvm5uZ48uQJtm3bhkGDBiEiIgLOzs6pPluyZEnY2dl91zIlK4n/z2grx2aIiIiAhoYGKlasiODg4H+dHrlu3brQ1NTEnj17YG5ujh07dqT7IIQZQbmdfn5+mD9/vjSAcYcOHVCqVClcunQJANC/f388fPgQjx49wuDBg9GrVy/paZyyL3m2eQLwH0SKKd2vXLmCdevWISYmBvv370fbtm2l2cVUVFSgrq4ujUNUqVIlODk5ZWXoqYj/b3yrUCik3zE2NlYauFpNTQ2NGjWCg4MDPn36hOnTp//rWGbKMbJSDlidXSi3sVatWmjQoIG0/M6dO9KsXuXKlcOePXsQFRWVamysWrVqYcCAATmiBWZGUT7dO3jwIE6ePAl/f38MHz4cOjo68PX1RVJSEpo1awZdXV04Ozvj6tWrWLx4MUxNTXHhwgWYm5sD+Od8l5GtlFVVVfHhwwcsW7YMJ0+eRHh4OFasWIHHjx8jOjoaAGBsbIwaNWpg2rRp8Pf3x4wZMzBjxgzs3LlTGm/lZ7i5uUnXLCnPY8o68c2bN2jRooU0rg0AtG3bFnK5HHnz5pVm3MsN5/G0+Pa6Ry6X49ixY2jYsCEcHR0RGBiIy5cvS5ObKPeXMWPGICwsTJrZLSdTfgfKukm5jXXr1kX58uVx+PBhAJDqYR0dHbi6uiIhIQHVqlXD5MmTsW7dOjRv3hzA9+P+paVsVVVVNGjQAL6+vrCxscGkSZNgYWGByMhI6Ovro169eqhatSqMjY0BAB8+fICRkREAoHTp0jli0opixYqhb9++0uxfNjY22LlzZ64b/+/b/ejUqVPo3bs3Fi5ciCdPnqR6T40aNVCzZk3cvXsXnz59SjXhE2UPd+7cQf/+/XHw4EEs+r/27jygxvR9/Pj7VNptpbKUkCWilOwSI7uxpaRsTfYsU/Y1FBEJjZEt+57I1thlXxKyU0LRgog2rb8/+p1nxHy+n5nPoOJ+/TMzdc6Z5+k8y/1c931dl5cX8+bNAwoaCVSoUAF3d3f69evHoEGDiI+P58CBA+jr63P9+nWcnZ0pV64cHh4e0vkq97WP+/Lly/PLL79I3aPHjBnDxYsXuX37NgoKCj/UcZaUlMTr16+Jjo6WfhYQEEBGRgbHjx+nQ4cOrF+/nri4uEJjopo1a9K8eXPi4+OLYrP/keL/pPYF7Ny5k1GjRrF7924SEhJQUVHB2NiYqKgoLC0tadu2LevXr6ddu3bY2tqyf/9+rl69yrBhw8jIyCArKwsTExPU1dWLrvjX/yExMVGqsK+oqMjLly85ceIEUHCj7927Nxs3bqRLly5UrlyZbt26YWJiQnR0NEpKSlSvXp0ePXowZMiQ76L46ccDJPkFKyUlhdu3b7N+/XpMTU158eIFDx484MaNG7i7u7Nu3Trevn0rfYaCgkKRF9b8lPwis3jxYoYNG4a/vz/79u2jR48eVK1alU2bNgGFB5N6enoMGzaMkydP0qVLlyLZ7r8jJyenUNH5/Px89u/fz5QpU2jZsiWRkZFMmjQJmUxGq1atOHPmDKmpqZiammJgYEBoaCi5ubkMHz6cn376SfqckpCO8fGD9atXr/D29mb79u08e/aM0aNHk5aWxuDBgwkLC+PJkyfScSAvvL9jxw6cnJyKbPs/9mlh29zcXObNm4eLi4s0CIqJicHf359KlSqxfft29PT0sLe3lwrbFnfZ2dnSd2BiYsKmTZvw8PAACoJUKioq/Pbbb4wYMQJVVVXWr19f6P3ly5fHzs6u2DcV+JLi4+OlDj0AL168YMCAAdy6dQtNTU26detGTk4OZmZm3Lt3j5iYGKAgaPDhwweCgoKoVasWXbp0oXTp0tI58zUGxEeOHOHZs2dAwbl58uRJhg4diqGhIU+ePGHcuHFUrVqVdu3aSd87wIwZM3j79q3UfEH+cP9P5eXl8ejRIwIDA6XW1vL9rVu3Lu3bt5far9+6dYtNmzYRFhbGtGnTMDc3Bwq6kn4vqff/l/z8fGncA/Dw4UOpCPzjx4+JiYlh5MiRKCkpsXz5cnJycsjKykJBQUG6VgUFBWFiYlKUu/GvfHoufDpGLV++PJ07d2bNmjW8ePFCSoHetGkTZmZm1KpVq9D75eOHv3PflL9W/l75eVOqVCkeP35M6dKl6dChAydPniQ6Opp58+bx8OFDRo4cSc+ePdHW1sbMzOxf7X9RsLe3Z8iQIeTl5f2j4F1J8vFD7aVLlzhw4ACBgYEMHjxYSpmWv0ZdXV36Hi9duvTZ+4WiExcXh6urK2vXrmXAgAFs3bqVwYMHEx4eTm5uLq9fv0ZHR0dqqFOzZk2eP38uNT14/fo1Tk5O+Pr6oqurWyRBoAkTJhAfH8/hw4cxMjKiRYsWUgOGH+E4y8/PZ/v27SxduhQFBQVsbW1ZsGABUHCd7tq1K3/88QfZ2dnUqVOHlStXFnq/pqYmw4YNw9bWtig2/x8p3k9r/4I8mAQFN+VKlSrx5s0baUZDUVFR6lgTGRnJ+fPnad26NTVr1uTDhw/cuHGDKlWqsGjRokKzjcVpNiQ3N5dly5YxYMAAFi5ciKenJ1AwMJs6dSoJCQmoqalhbW2NgoICJ0+eZNasWSxatAhXV1fc3d1p0aLFdzOb+ukAac+ePYwcOZLjx49TtmxZ+vTpw82bNzly5AhDhgzBwMCAnTt30rp1a3R1dQkPDy/Kzf9MXl7eZzeAbdu2kZOTw86dO8nKymLz5s0kJSUxcuRItm/fTnp6ulQDQq64t/7evXs3ffv2ZcGCBTg6OnLv3j1kMhlpaWmMGjUKHR0d7t69i4WFBWZmZjRu3BhFRUVpddDkyZOZNGmS9L2XtJkTRUVFcnJyePPmDRs2bODy5csEBgayePFiKlasyMGDB6levTpdunRhxowZ0ox3cVzVuGjRIi5cuADAlStX8Pf3R0lJCV9fXyIiIli9ejUDBgxAS0tLqkdw+vTpEnEd2rFjB4mJiZQqVYqsrCwpcNKpUyeCgoJ48+YNzs7OXL58maysLKytrWnWrBmbN2/m3LlzJe64/JKuXbtGaGgo9+7dIzU1lfj4eGxtbZkxYwYRERGFup69efOG69evk56eTpUqVZgwYQIBAQGULVsWoFAQ4mtISEhg/vz53LlzB0VFRR49esTcuXNp0qQJYWFhNGzYEEVFRTw9PTl27JjUIltHR4fff/+dESNG/Kv/v3yQWalSJWmiQT7wVlNTo23btnz48IGIiAh+//133rx5w/r163FwcGD06NH/budLGJlMhkwmIzY2FldXV8aOHcv06dO5f/8+hoaGnD9/HgcHB3x9fYmLi8PX15esrCzy8vKkzrkldSIuNTUV+HO8c+7cOQYMGMDcuXMLzaRramrSv39/rK2tGTt2LJMnT8be3p7IyEhGjx79WQfhvxOMkk8gyV8bERGBvb0948aNIzAwEGNjY1q3bs3y5cuxsbFBQ0ODM2fO8PLlS+bOncvYsWNZunQpc+fOLZE19ZSVlXFyckJBQaHYT3r9Ex9PKuXm5rJnzx727duHhoYGL1++lGrk+fv7s3z58kITubVr12bMmDH06tWrCLZc+CtZWVls27aNW7dusX79ejp27Ehubi5xcXE4OjqiqKhI9erViYuLk1a/6ejokJeXR5kyZWjSpAlz587FxsYG+HP1e1GYOnUq7u7uQMEE0McTQt8zeQ2oWrVqoaamxpUrV6Tsl6ioKMaNG4empiYXLlzA3t6eoUOHsmfPHg4ePCjFQADpOlvcx6Hfz9X0I4mJibi5uUnL3zt06ECNGjWoXr06+fn5LF26lPbt27N3716goBBYzZo18fT0xNPTExcXF9zc3KRihcVxJuTEiRPY2NiQmZlJcHAwvr6+HDt2jDdv3tCyZUusrKwICAgAClbL1K9fn127dmFpacnWrVvp3bs3GzZswMXFpYj35MuQF5uHgu9r7ty5hIWF0bNnT3Jzc1FWVqZDhw6sXLmSGjVqSAP7GzducPDgQQICAqQLb3EgTzmTyWQkJCRIA9DU1FRycnKYOXMmcXFxzJs3Dy0tLRo1akStWrWYM2cO8PcGlsXBwoULpQLt8+bNY+TIkeTn55OVlUVkZCSLFi0iICAADw8PBg0axJQpU6hSpQq1atWSAs/q6upoaGhI52lJmzlJTEykT58+pKam0rRpUxQUFAgLCwPA0dGR8+fPc/fuXVxdXdHV1SU6OpqcnJxieXOZOHEi1atX5/3799y7d49Tp05haWlJlSpV8PPzY9myZdSsWZMOHTqwZMkSHBwc+OOPP+jXr1+x/t7evHnDtWvXCAwMJCwsTEpjWLp0KQYGBjg6OjJ+/HgAQkNDadu2LQcPHuTu3bsMGTIES0vLYr1/X8PHKzbatm3L8ePHcXJy4vnz51y7do0lS5bg4uKChoYGhw8fZv/+/aSlpdG4cWPu3LkjpcbJV/x8rVS9/Pz8QttqamrK5cuXmTlzJgCvXr1izJgxeHl5MW3aNIYPH87y5cvR1NTE3t5eKu4LfLEHbG1tbTp37szp06d5/vw5CgoK0krCatWq0bVrV5YsWUKlSpUYN24cGzZs4Oeff/4i/+/i7tOVQGfOnGHixIl07dqVkJAQUlNTCQ8Pp1y5cjRv3lwK0AQFBaGjo/OXzWpKms2bN3P8+HGgYEywdOlSgoODGTJkCG/fvmXPnj3ExcUBf45f5ROS1tbWuLm5sWzZMqpVq/aP7yNxcXG0b99eCkgEBQWxYsUKZsyYwZw5czhw4AAPHz6kbdu2JCcnc/HiRQYPHkxkZCSenp4oKipSt25dqlWr9sX+HsKX8XGAMiMjg9TUVM6ePYuuri7t27cnLi6OevXq0bBhQ65cucKLFy+AP0sP6OvrF9WmC39BWVmZ7t27Y2pqysWLFzl48CCdOnVi/fr1REVFER8fj5mZGZ06dWL37t2MHz+ebt26YWRkVKgcyD9ZPfm19OjRg0mTJhV61vuefboC1tLSkho1ahAeHk5WVhaTJk1i3LhxQEE2WNWqVdHV1eXy5cuYm5tTrly5v5zoLe7jUKX//pKSR09Pj59++onjx4+Tl5dHly5daNSoEcrKykyYMIGhQ4dSr149tLW1efz4MR06dODixYtERkaybt06KW1LHhgobidAfn4+sbGxaGpqMnnyZAAuXrxIuXLlOHz4MK1atWL69OnY2dkRERGBhYUF6urqZGdnExISgpOT03fXBUNRUZHXr1/j6+uLsbEx6urq9OvXj7S0NN68ecOWLVswNTVFT0+P+/fvc+PGDaKjo/Hz8ytWNZY+PuZSU1Px8fHh+vXr6OvrM336dGQyGRcvXsTe3h5PT0/i4uJwd3fH39+fsWPHlqjaGMnJyTx58oQFCxagpaVFfn4+VlZW0u8bN25MVFQUCxYsoFy5ckRFRfH69WvKlCmDra3tZ+kpxe08/W9iY2PR1dVFT0+PypUrs3nzZkaNGoWNjQ2nT5+mZcuWtGjRgpCQEHbs2IGXlxcLFy4sNjPLb9++pVy5coV+pqSkhL+/PykpKaxYsYJbt26RmJhISkoKtWvXplOnTri5uREQEICtrS1xcXGYmpoWzQ78F/J6ZIqKipQtWxZHR0eWLl3K3bt3+f3333n79i0hISF4e3sze/ZsGjVqRExMDL179yYgIIA3b94wZ84c6tatW9S78k3JO8LIB1PJycmkp6fTvn177ty5Q5UqVejXrx/r1q1j9OjRUppbeHg4nTt3pnv37nTp0gUNDY1Cn/s1VkfJr7eKioq8f/8eBQUFVFVV+fnnn7l9+zZQEKRKTExk9OjR1K5dmxMnTnD37l0UFRWlwNWXJpPJaNq0KRcvXmTdunXMmjULFRUVoKA2iLa2Ns7OzigqKn61rkfFjXw/Pz6utLS0UFJSIjo6mvLly6OiosLPP//MmTNnaNu2Ld26deP333/n6NGj1K5dm2nTphXxXvw78nOrV69eaGpqEh8fT3JyMidOnOCnn37C2tqa8uXLs3XrVm7evEmlSpWkldMymYzq1atTvXp16fP+ab3FnJwc9PX16dKlCx4eHixbtoy4uDhiYmKoU6cOKioqWFlZcfjwYYYOHUqHDh3w9fVl+/btzJkzhzJlynyNP4vwP5Knv8qPgZycHLp27crChQtp2LAhTZs2JTY2lgMHDjBmzBiioqLQ19cnLi6OEydOSNekH+H6U1JVr16d5s2b4+rqSuPGjVm1ahUxMTEcOHAAFxcX2rZty+DBg7GysuL8+fMMHTr0s2ei4jK2/uWXX4p6E74Z+X1u165dpKSkYGpqSocOHXjy5Al79uzBxcVFmiQ1MTHh6NGjbNu2jTZt2rBmzZoS2wFUll8cp9u/gNzcXI4fP86CBQsICgpi06ZN6OvrS4Ga8+fPs379+kKFHT9+r3yVSnH16tUrFi9ejJ6eHmlpaVy5coWff/6Z8PBwMjIymD9/PkeOHOHcuXPSSqHJkydL9QNKuk/b0969e5dRo0Zhb2/PqFGjWL16NX/88QfVq1enbNmypKSkkJSUxMSJE1m7di21a9cuVqkOnw4Oo6Ki8Pf3p27duowYMQJnZ2esra2pUKECV65cwcjIiEGDBrFy5UoePXqEj4/PZ8vwi7u4uDh++eUXNmzYQOXKlQsVJT916hRmZmYsWrSIlJQUSpcuTXh4OCNHjsTOzk76jJJSxPzT4zUiIoKtW7diY2ND586diY+Pp3///vj5+ZGfn8/OnTuxsLCgT58+PH/+nKSkJKleTHHw4cMH7O3tGTx4ML169Sq0f4mJiTg7O+Pj48Pr1685fPgwdnZ2WFpakpWVhYWFBX/88UeJmVVNTk4mOzsbPT09lixZwuHDh6VVCtHR0Sxfvpxp06Zx8OBB1qxZw6VLl/4yYPe9S0xMREtLSwqaRkdHM2vWLFRVVWnXrh2Ojo5MmTKFypUrM3bsWNatW8e+ffvo1q0bV65cQUlJCT8/P9TU1L56i+LExMRCTUvWr1/Pzp07KV++PJMmTcLc3BxnZ2datWpF//79+f333/njjz+wsbHh5MmTDBgwAEdHx69+/YmMjGTp0qV4eHiQl5eHq6srqqqq+Pr6FgoufM9SUlIoW7as9Lc+e/Ysv/32G1paWpibm9O6dWvOnDnDvXv38PPzAwpW4Obl5eHi4oKWlhapqakl+nz8q+NsxYoVPHjwgKlTp3Lq1CkOHDjAxo0bUVZWZvXq1SQlJdGrV69/XS8rLCyM5ORkevXqJbVbT09Pp0mTJuzatQtdXV3mz59Po0aNcHJyIjk5GXd3d3r27ImZmRlXr16lR48eUgBDKH6io6NJSEigZcuWrF69moMHD7J//35yc3M5deoUhw4dwtHRkbNnz3L+/Hny8vKYOHGilIIvFL1Px5gfe/HiBfPmzaNly5Y4OjpKPz9z5gweHh6Ym5vj5eUlTfR+GqwUvr6PJ5fy8/N5+/YtM2bMICcnh44dO7JkyRJmzpzJq1evePToEU5OTrx48YIxY8YQGRlJTEwMOTk50jN+SXk2+lTJ2+K/SVFRkY4dO9KxY0e2b98OFHSPALCwsGDEiBE0b95cKvooJ58ZL84BKShY3t+hQwdp3/bv38/QnQNWEAAAP0hJREFUoUOZMWMGFSpU4Pz587i4uDBmzBi6dOnC2rVrv5uA1Mc1ReTddAwMDFBQUJCWKzo7OxMcHMzChQuZNWsWVlZWtG7dGlNTU3x9fYtVQAr+nIk4d+4cI0aM4Nq1a0RFRUmr9saMGcPZs2epWLEi7dq14/Lly/Tv35+IiAgmTJhQ7ANSHxcxl0tOTsbIyIj79+8DSB1bZDIZQUFBHD16lHnz5jFq1ChMTEzYsWNHoYAUFJ8ZnP9GUVGR7Oxsjhw5QmxsLPXq1aNq1arcv3+fxMREKlWqRKdOnfD19ZVSjSMiIqS6OsUpIAUFnQ0HDx4s1bz5uI6Znp4effv2xdvbG2trazQ1NTl37hwJCQkoKytz5cqVYhuQ+jRVe/369fTr14/Ro0dz584devTogYmJiZQarqOjQ3p6Ounp6bi4uDB8+HCAEv0A/E8lJyczdepU/Pz8SE5OJicnBy8vLzZt2oSzszN2dnZcvnyZsLAwXFxcuHLlCk+fPsXFxYUJEyaQm5tLz549WbVqFerq6oU6FX5pJ0+epFu3buzfvx8o6P61cOFCHj58yP79+7GwsGDjxo28fPlSqtOnpKTE6NGjmTdvHjo6OqxatUoa2H/t60/dunVp0qQJHTt2xMHBgT59+hAcHPzDBKRu374tFW1VUFDgzp07rFixgilTpjBu3Dji4uLYvXs35ubm5OTkcPjwYQDatWsnpaUpKSmV+PNRfpxduXKFrVu38uLFC3755RdSU1O5ceMGLVu2pGrVqmzYsAGAn3/+mefPnxMfH/+v07xTU1Px8/OTAlLylPmRI0cyZcoUKlSoQMuWLbl+/ToxMTFoaWlhZWVFTEwMVapUwd7eXgSkiiF52vLSpUsZOXIkoaGhuLi4MGzYMBITEwkJCUFRUZFq1aoRFxfHgQMHcHd3Z/Hixezdu1cEpIoJ+fktfya6ceMGKSkpwJ8pYHp6evTq1YvQ0FAp5TInJ4fWrVuze/dulixZUiggVRIaBH1PPq3VJZPJSElJQU1NjZUrV9K7d2+mT5/OhQsXMDQ0pFy5cgQHB2NtbY2VlRX379+nevXq1KpVq0R1HP8rJXOr/4Fx48ZRvnx50tLSSE5OlpbklypVihUrVmBpaVno9SXli5TJZDRr1ozu3bsXKtRpYGBAamqqNAtsYWGBg4NDUW3mF/Vxi9o7d+4wYMAAfH198ff358mTJ1IR6NzcXEqVKsXGjRuZOnUqTk5O7N27V0oNKy7pT5/Wxli9ejUzZ86UVgN169aNmzdvkpqaioWFBbVq1SIkJARTU1OWLl3KwoULWbNmTbEvZP7w4UP27t0rFYaWq1evHkpKSty8eZPExETgz5oGmpqa1KhRA0VFRRo0aMCgQYPQ1dUlNze3WNZS+tSn23j06FHs7e05efIkw4YNY8+ePdSuXZsPHz5w9uxZAHr37s3ly5eJiIige/fuzJo1q1h10Po0YNO9e3e0tLT47bffgD9n1/Ly8nBwcCA3N5fg4GD69OlDXl6etC/FaZ8+9vHM4KlTpzhz5gxJSUkcPnwYS0tLKUBhY2PD2rVrSUhI4Pr162RmZkrHrbOzc1Huwje3bt06XFxcpNUSenp6KCkpERcXx40bN7CxsaFVq1a0aNGCgwcPYmhoiKWlJVOmTMHNzY3GjRszatQoqR7S1+pu++DBA8aOHcuGDRuYMmUKQ4cOBQpW4Tx8+JDs7GyUlZVxdXUlNTWVsLAwLC0tadWqFc2aNWPlypVYWloyePBgqSj7t1CqVCm6du3KpEmTOHPmzA+TviC/fpYpU4a3b98yefJkLly4QHJyMpUqVcLc3BxjY2Ps7OzIzc0lNTWV1q1bExgYSE5ODpaWlkybNg1dXd0i3pP/TWpqKrdu3QIK/hYfPnxg0qRJ/P7776ipqTFy5EgiIiKws7MjNDSUnJwcunTpwsmTJ4mJiaFSpUrMmDEDGxubfz3B2rVrV8zNzVmyZAnw5xjZ1dWVd+/ecfz4cdq3b0/ZsmXZtm0bgFSXtbg3r/iRfHr/lslkvH79mlu3brF582a8vLxYtmwZAB4eHsyZM4dXr15x48YN6tevj4WFBXl5eT9MQLykkJ/fFy9epFu3bixcuFCaHJMHqhQVFbGwsCjUmU0+ZpFPfH+tmo3Cfye/pv72229s3bqVp0+fEh8fT2JiIgoKCmRlZdG5c2du3bpFRkYGzZo14+3bt8TGxrJixYpC6ZYlPaBYcrf8b1JTU6NPnz7Ur1+fp0+fFqpGD8WziPnfpa6uTvfu3bl9+zbR0dHExcUxatQo8vLyvptVUR+Tp3SkpqayevVqJkyYwLRp0wgJCeHw4cM0b96cOnXqSIOn7t27M2zYMAYNGsTGjRuLTe2oj7sEZmZm8vDhQwBq1KjBy5cvqVu3LgoKCjRu3BiZTMahQ4cAGDx4MAkJCbx79w5lZWWqVKlSZPvwd8j3s3z58iQlJTFz5kypY15OTg5KSkrY2dnx5MkTVqxYQWJiIsnJyXh6evLy5cvPBj/yFXLF/aYp75bxsVOnTjF06FCpS2ZkZCRQsNImLCyMDRs2sHbtWgYOHEitWrXQ09P7bBVnUfu4jXpOTg6KioqMHDmSvXv3kpSUJH03CgoKlCpVipEjR7Jt2zaMjY1xd3cvtvVEPi6Qn5iYyMyZM1mxYgVr167l8uXLKCoqMmTIEJ4/f86dO3do1KgRSkpKDBo0iKCgINzc3DAwMCjivfi2kpOTadOmDTdu3GDdunW4ubmhoKDAhQsXyM3NxdPTk0ePHhETE4Ompib169endOnSbN68mV9//ZXhw4czefLkQrOz8HXqRp06dQoXFxdq167Npk2baNWqFe/fvyckJIR69epha2tLdnY2d+/ele6poaGhxMTEMH36dJYuXcrYsWO/+Hb9XQYGBvzyyy8/xGoTecdZ+fVTRUWFM2fOEBERgaGhITk5OZQpU4ZXr14BUL9+fe7fv0/p0qVp3bo1tra2f9m1tqSJjo7Gx8eHV69e8f79e96+fYuBgQEbNmwgPT2d9+/fk5KSQufOnYGCFdY1atTA3NxcWpUsHx98ib/FyJEjOXr0KI8fP0ZJSUlqvNKnTx8uXLhAmTJlaNasmTTJW9L//t+bjydcrl69SkREBK9fv+b58+eoqqqioqJCVlYWmpqabNu2DSsrK5ycnHBzc2Pfvn04OzvTs2fPEv2w+734tDFHRkYGmzdvZuPGjcyfP5/t27eTmZnJ+vXrC71PS0tLClKnpaVJP5d/p8Wps/z3Tj6BK3fr1i08PT15+PAhT548Yfz48TRv3pynT59y+vRpKbhvbGyMhoYGTZs2ZebMmdK4syTHMT71Q1xhVFRU6Nmzp7Q8/2Ml/SJbt25dmjZtSu/evXFzc6Ndu3asXbu22Acs/q6PT97IyEjmzJlDWloaMpmMy5cv8+uvv9K9e3dGjBhBamoqY8aMYdu2bcTGxlK+fHlq1apFhw4dingvChw5coRNmzZJx9yWLVvo3r07Hh4e7Nixg5YtW9K2bVu8vb2Bgu/WzMyMU6dO8ezZMypWrMjKlSsxNDQsyt34rz7t1FG6dGlevXpFfHw8lSpVKvS71q1b4+rqSnx8PF5eXjg7O6Ompsbq1as/m+Uu7sGoj4ONCQkJ7Nu3j+joaNLT03n27BkNGzYkPz8fS0tLypcvT0REBA4ODrRr146zZ8/SsmVLpk2bVmxWvn36YBEdHS2tBJLPsllaWtKiRQsWLVokvW7JkiXY2dlhbGzMrl27iv33Jg+2vXz5Eh8fH5KSkggKCsLHx4f3798THh6OtrY2HTt25MCBA7x7945Ro0Yxe/Zs/P39P7un/Ai0tLRo0KABNWrUQEtLi9u3bzNixAj8/f1JTExER0dHqiEFBe3CTUxMSE5OJiMjgzZt2lCxYsVv0jFTVVWVtm3b0qxZMwA2bdrEoEGDpKYQJiYmVK1aVUr96t69O6qqqsTFxVGqVClatmz51bZNKExeyzM8PJz169eTlZXF6tWr6dChA2fPnsXCwoKEhARCQkJ49+4djx8/plSpUpQvXx49PT369euHsrJysb/m/JWPg2lmZmZkZmbSqlUrLly4QGxsLFu2bMHBwYHbt2+ze/dutLW1OX78OAMHDiQ0NBSA8ePH07p160Kf+yX+FsbGxnTq1ImFCxcCSEV0X716RePGjYGClMmOHTt+sf+n8O/Exsbi7e1NcnKylA40ZcoUvL292bNnDyNGjCA7O5uYmBhiYmKkB9+IiAiuXLnCuHHjWLp0KVu2bPmmq0OF/+zjZg+pqalSipeysjIJCQlSx9rJkyezY8cOEhISCr3f0tKSuXPnftZERPg25AFFmUyGTCbj7du3JCQk4O7uTm5uLsuXL2f69OloamqyZ88eFixYgL+/P76+vgwbNoxnz55Rp04dZDIZ6urq0v2ipMcxPvb97MnfULNmzc8ilCWdfHm/m5sbW7duxdbWtqg36V+LjIyUHhDkJy9AQkICGhoapKSk8ObNGy5fvszixYsZO3Ysy5YtY8+ePRgbG0sz8MXte5bJZFL78K1bt3L16lWOHDlC27Zt2b17N3fu3GHcuHFcuHCBqKgoNDQ0qFevHlZWVtIgsLjXjoI/L5B//PEHXl5eHDt2jMWLF2Nvb8+NGzdITk4utOqmdu3arFy5Em9vb9auXcuECRMoVarUV0vl+dI+vTE8fPiQwYMHc/z4ccaPH09ubi6lS5dm37590rHcokULkpOTUVVVpWfPnqxbt45u3boV2T78Ffm23rt3j6ysLCpXrkzlypWJiooq9LoRI0Zw584dfHx8aN++PVevXmXRokXo6ekV25vlxzNL0dHRtGvXDh0dHczNzcnMzCQqKoqKFStia2srpTTIUxLT0tJo1qwZzZs3L6rNLxZ+/fVXDhw4gKurK15eXrRt25bt27dLQdUZM2bw8OFDQkJCKFWqFB07dmTixImF0s2/xvGRmZkJ/JmO0KxZM6pXr86mTZvo0aMHkZGR+Pj4MGHCBAAMDQ1p3LgxT58+5ciRI0BBYNXa2vqLb5vwJ/l18+PrfFZWFj4+PixevJiUlBR27dpF/fr1qVevHhcuXCAzMxM3Nzfi4uIYO3Ys7u7u9O3bt1D78pJIXgNEJpORlJREcnIyzZs3p0KFCpibm2NpaUn9+vUxMzNjwYIFaGtrExISQnZ2NpaWlvj4+GBgYICSktJXG/fIu/uuXr2asLAwRo8ezdOnT6lfvz7wfT0YlWS5ubksW7aMcePGoaGhIaVn3bt3jw8fPhAcHMy8efMwMzPjwYMHWFtbs2rVKjZt2sTy5cuJioqiQYMGKCkpoa2tXcR7I3xMPiYLDAzE3t4ePz8/Fi9eTJs2bWjatCl37twhMzOT5s2bU69ePRYvXlzo/fLnh+9pZU1JIg8ovn//ngMHDuDs7IympiZ9+vQhOTmZly9fAgWp0atXr6ZBgwYsXboUbW1tfv75Z7Zs2VLonPweg/8/3F3k4yDH98LAwIDBgwd/F/n7eXl55OTkYGpqSlZWFsuWLWPdunVAQbG+ixcvUrt2bRo0aICRkRFpaWmkpKTw4MEDatasCRQ8PGpraxf59/zq1SvmzJkjDbpbtGiBqakp169fp1SpUjRp0oRbt26RkJBApUqVOHfuHLq6unTs2JGpU6cCBbP4Tk5O0sCiuMnPzycpKYmzZ89KqbFZWVksXLiQPXv20KJFC6KiotDU1KRevXokJiYSHh4OFA6wKSkpoampiY6OjjRjXFKWE8uPs0OHDjFixAj279/PvHnz+O233zA1NWXx4sXMmTOHXbt2sWXLFnbv3o2Pjw/W1tbFfh8jIiIYM2YMgYGB5ObmoqqqWqhDCBSkiXTp0oV9+/YxadIktm/fTo0aNYpys/9SZGSkdC1RUFAgKSmJvLw8jIyMUFNTIyAggJ49e1KtWjUuXrwIFKTMJiUlSQXd/f39adSoUZHtQ1H4TwNYIyMjbG1tiYiIYMeOHfTt27fQ73Nycpg3b55US04eWP+aA+LTp08TFhYG/JmOIJPJaNOmDSoqKpiYmLB48WLpXgEFdaUqVKhA69atqVatGsAPkSpX1NLT04HCaSMpKSncuXOHLVu2YGdnR/v27Xnw4AENGzakRo0arFu3joSEBKZPn8706dMJCQkpdsH8/4WCggLJycnMmjULV1dXjhw5gru7O927d2f+/PlkZWUxcOBADhw4wK5du5g2bRr379+nbt26AFStWrVQzc2vQVVVlUWLFpGXl8emTZuwtrZm/fr1P1zqcnF2+vRprK2tycrKIiAgoFDa8Y0bN6hYsaL03/369WPv3r3079+fQYMGER8fT3p6OoGBgSW2Ftv3Ji8v77P75fHjx4mOjmb//v00adKEwMBA3r17h5mZGc+fP5fGLuPHj0dNTe2zkjUgAsjf0qeTBEFBQfTq1YvIyEgUFBTYsGGD1LU0MjKS3NxcGjduTJ06dZg9e7b0fP+1624WF+LIFIoF+YmmoKCAhYUFjx8/xsfHBxsbGw4ePEhwcDDGxsbUrFmTa9euMWDAALS0tJg/fz4DBgzAxsYGGxubIt6Lwg4fPsz27dtZt24dkZGRqKqqUrFiRVJSUrCxsaFt27aEhobi6OhI79692bNnD0FBQTg5OdGvXz+g+NdmkMlkhIWFsWDBAqmWhbKyMsnJyTg5OaGnp4exsTF79+7F3NycKlWqcOHCBX777be/7MgHf6ZwFFfyYt5y0dHRUv2hpk2bEh4ezvXr14GCGY9Lly7x9u1b/P39effuHWfPnmX27Nl07969qHbhMx/f6HJycti/fz+JiYnUqFEDNTU1Dh06xIMHD4iNjeXSpUtA4WNz+PDhXLhwgfbt23/zbf+7srOzWb16NQkJCYSFhbFkyRLu3r0LgJ+fH/7+/kDBypqoqCiuXr2Kqqoq06ZNk1YD/GjBio+7uLx79+6z3zs5OaGrq8uxY8ekn505c4YBAwawbt06unTpwq+//lroPV96QJyfn8/27duldIZLly6xdu3aQteXGjVq0KRJE9TV1bl27RpQEDxfsmQJAwYMQEFBATs7O+rUqfNFt034a3PnzmXYsGEAhISEMGjQIPbt20deXh4VK1akTZs2zJo1izVr1jBw4EAiIyNp164d0dHRhIWFlfi6mX/1YOHt7Y2+vj6LFy+WgvqjR4/mwYMH3Lx5E2trazw9PXn79i06Ojrs3r1bCqLCt5k1b9SoESNGjGDNmjWfdcIVil5GRgYfPnxg4sSJ6OrqcunSJWbNmkVcXBwNGzbk4MGD0muNjIzQ1NTk1atXNG/enIkTJzJlypRiOwH6o5HfexUUFEhMTJTquD1+/Jhq1aoxc+ZMtm3bxrp161BTU8PGxgZNTU3OnDlDcnIy+vr6eHp6fheLFUoi+XPCx9fl7OxsIiIipDS9OXPmEBERQXx8PH369OHgwYNSd0QPDw8mTpxY6PPg+6/9VfxzgYTvmvzCKz/Rnj17RtWqVSlVqhR37tzBxcUFT09PqZi5mZkZOTk56OnpMWLECHr27En58uWLzcPiw4cP0dPTo2zZspiYmKCvr4+GhgYbNmxg0qRJlC9fnvDwcNq0acPWrVuJjY2lZs2a3Lhxg6pVq1KuXDm0tbXp3bs3ULyXZ8pXtZ08eZK3b99y4sQJ9PT0UFFRwcLCgqlTp9K4cWMqVapESEgIz58/p1evXuzdu5e0tLRCs3YlRW5urlTQW16sPSIiAn9/f5YvX461tTUymYyEhAQePHhAnTp1sLe3Z/To0Rw7dowGDRoU9S78Jfn59/z5c3R0dDh58iSRkZG4uLgwcOBADh06RHR0NAA3b97E3t6+UBfL4tLR8lPy7wsKHqi6du3K8uXL8fDw4MCBA9y9e5dq1apRt25d2rZty9y5c/Hx8eHs2bNcv34dc3PzHzqNS0FBgejoaAICAsjMzGTkyJHUrVtXui6VK1cOZ2dn1qxZg7GxMcuWLSMxMREXFxfatGkjfc7XbFH8/v17KlasiKamJlWrVmX27NkYGxvTp08f4M9joHXr1jx79oxr165x//599u/fT4MGDdi4cSPly5f/Ktsm/EleT0NJSYlJkybRvHlzPDw8yM7OxsrKikuXLpGUlMTChQt5+fIlCgoKaGtr4+fnx9u3b+ncuTPLly8vsfVQ5KUj5CkcALdv36Z06dJUqlQJJSUlZDIZe/bsITc3l6CgIPr164ebmxtz5sxBR0cHX19f2rVrJ33mx9e3b0mstCieOnfuTGhoKNOnT0ddXZ2IiAh++eUX9PX10dfXx8TEhPHjx2NjY8O5c+dQVFSUUl/Fd1q8KCgokJ2dzZIlSzh27BiGhobY2dlRvnx55s6dy4IFC+jatStQkIWxefNm2rZtS25ubqHA4te89wr/mTwrKyoqij/++INOnTpJiyrkDSrq1q1Ls2bNCAwMxNvbm+3bt3Pr1i309fWlNL2PU7t/BCIoJRSJ2NhYDAwMpItlREQECxYsIDs7m59//plevXphbW2Nr68vixcvxsTEhHHjxhEYGIhMJqNp06YAxSqw8fbtWzw8POjQoQMDBgygUaNGWFhYULlyZXJycpgzZw7u7u5MnjyZ9PR0FBUVKV26NL1790ZLS4uZM2cWmw6B/8mlS5e4f/8+rVu3Rl9fn3fv3lG7dm369u3Lhg0bsLCwoFGjRvTt25eePXuSlpaGlpYWZmZm3L59mypVquDq6lpiL7DyB4DVq1cTERFBr169sLOz49ixY9y+fRtra2tatmzJrl27OH/+PLVr12bgwIGoqqpKDyXFYYAgD/7KXbhwgeXLl1OxYkWaNGlCjx49ePPmDRMnTsTV1RUrKyvMzMwIDg7mwYMHpKamlogHefn3Jb/eDB8+XCpy3a1bN44cOUKNGjWwtLSkS5cuuLu74+rqyvDhw6UHxR/Jpw+5cXFxzJw5kyFDhpCZmfmX6e/dunWT6jW5u7vTv39/6Xcfz/Z+SfJZSEVFRcqUKUPLli2ZP38+pqameHh4EBkZSVxcHOXKlZP2R09PjwYNGrBq1So0NDTw8PCgXr16X3S7hL8mPw6UlJTIyMhATU2NKVOmSMHhWrVqcfz4cUJDQ7l06RL5+fns2LGDSpUqcf36dalgfkkNSD148AB1dXUp1S0hIQFPT0/i4uJIT09n1apVdOrUiaNHj1KtWjXKli2LhoYGgYGB/Pbbb8hkMsqWLVvoYbMkpbgL386oUaPo168fPXv2ZM+ePcCf59/8+fM5deoUJ06coEaNGsybN6+It1aQu3DhAvr6+ujo6KCmpsbVq1cJDg5GU1OT48ePExYWhr+/P9OnT0dbW5u0tDQSExM5efIkJiYmGBoa/mVtveIw3vwRfPjwgR07dtCmTRupU6yfnx8XL16kTp06LFu2DHt7ewYPHsyuXbuwsrJCUVERZWVl7t+/T3h4OGvWrPmsS/WP9v39WCNuoViIi4ujffv2XLp0iXLlyhEUFERoaCgzZswACpYtmpiYSC3HDx48SLdu3Zg2bRonT54stGS9OClXrhyTJk1i165dBAQEMHr0aKpXr05+fj6DBg3i3r17nDlzhgoVKvDs2TN69eqFqakpL168KHaph5/68OEDPj4+XL9+HQsLC7y9venduzedOnWiU6dO1K1bl0uXLnHo0CGqVatGhQoVpIuspqYmcXFxzJ07F/hz9VdJmMH5NJCUmJjItGnT0NfXx9ramh07dlC6dGnc3NwYO3YsvXv3platWtSsWZOrV6/y8OFD6tSpg5OTE1A8Vr4lJiZia2vLxo0bqVevHtHR0ezYsYPFixfz8uVLxo8fT79+/Rg6dCjXrl3D39+fcuXK4eLiwqJFi4iJiSkRASkoGOj5+/ujrq5O7dq16dixI8OGDcPb25utW7dy6tQpQkNDefLkCffv38fZ2RkVFZViFez+luQPua9fv0ZbW5vnz5+jrq5OuXLluHnzJk+ePOHatWv89NNPVK5cmfz8fJSUlPDz80NLS4vSpUsDfwa3vtb5LV9t8ubNG7KysqQVmufPn8fb25vr168TFhaGgYEBZcuWla411tbW1KhRo1BNKeHrkx8HK1eu5OrVq9SoUYMZM2YQGBjIpUuXqFWrFvXq1SMqKoqjR48yZMgQGjZsSEZGBlu2bCm2KzH/ruvXr1O9enUMDAw4ffo0hw4dwtTUlBUrVrBw4UJ8fHwICAigefPmvH//HhUVFVasWCF19vyrDsLF4V4iFD/GxsY4ODiQlJQEIK3ozs/PR0tLC1tbW7p3717iz6nvxblz51i9ejVqamq8e/eOsmXL0rt3b+rXr09ERIRUS8ja2pqzZ88SFhZGQEAAW7duZe/evWhrazNhwoQfbgKtuDl79ize3t7IZDIcHByIjo7m+fPnBAcHc/XqVby8vDh8+LCUeTB//nwqVarE2bNnadiwIceOHaNhw4bAn10Wf0TF+4lQ+O7k5OSgr69Ply5d8PDwAAqCVDExMdSpUwdTU1OsrKw4fPgwmpqadOjQgcDAQLKysqhYsSKOjo60aNGiiPfiPzM3N2fIkCHs2LGDmzdvkp6ezsOHDwEYM2aMlPP99OlTVFRUqFevXrEPSAHEx8cTFRVFcHAwM2bMwNTUlPv37yOTyaRiq7/88gtRUVFSPaXGjRvj4uKCjY0N+/btw9TUtNBnFveAFBQM/BUUFHj06BGnT5/m5s2b6OrqMmfOHKCg01dQUBBGRkY0b94cPz8/ANq3b8+YMWOKXY2a/Px89PT06NWrF6tWrQKQCmEeOnQILy8vfvnlF2xtbUlNTWXGjBl06NCB8+fP8/LlSwwMDD5rOV4cxMXFSV2hXr9+DcDTp09Zs2YNbm5uzJ8/Hw0NDQICArC0tERJSYlDhw4xbtw4dHR0CAoKonnz5kyePPmHCkh92o320qVLODk5sXTpUsaPHy+t9AwICCA9PZ3k5GSuXbvGlStXgD8fjA0NDSldurTUUfNrrOD4tODr+vXrcXBwYPTo0dy5c4cePXrw+vVrwsLCcHBw4PHjx4SHh/PgwQNpO1VUVERA6huRH1fyf3p5eXHr1i1mzpyJvr4+ABMnTmTJkiUAVK5cmXr16knd55ydnRk1alSJfXiWN+yAguYr6enpnDx5kho1apCQkEBaWhpQ0L79+fPnHDt2jJCQEObOnYutrS15eXk4OjoW5S4IJdSQIUN48OABp0+fRklJiZycnEIPuSX1nPreXLlyhWXLluHi4sKqVatYunQpHTt2ZPbs2eTm5uLg4EBqaqrUUdbe3p7Tp09jZGSEp6cnixcv5rfffqNOnTrFvv7s96506dIYGRkRGxvLnTt30NTURF9fn8uXL3Pu3DkcHR3Jycnh2bNnrFixAhMTE2JiYvjtt98wMjKiWrVqUmDxRw1IgQhKCd9AWFgYe/fuBf58sPDy8uLEiRPcvXuX/v3707BhQ4KCgoCCjldPnz4lPDwcc3Nz+vXr99nDU3FmZGSEu7s7x48fR1lZmdDQUDIzM6lSpQp9+/Zlzpw5WFpaFvVm/iP5+fkYGhqSkJAAFDxAREVFSb/Py8tDV1eXDh06sGPHDp4+fYqysjJWVlb07NkTKDldI+TbmZ+fT05ODhs3bmTWrFlkZGRgbm5Op06dOHjwIKmpqYwYMYJ3794RHBzM+PHjuXPnDklJSWhpaWFoaFjEe1LgyJEjeHp6cvv2belm5+bmxv379zl9+jRQkE5y9+5dNm7cSP/+/Zk9ezZHjx5FTU2NAQMGcPHiRXR0dIpwL/5afn4+S5cuZfTo0aSlpbFp0yY2b95MXl4e79+/JyMjgyZNmqCnp0ffvn3R1dUlIiKCoUOH4uHhgbq6OiNGjGDHjh2FarX8CORFOGUyGVlZWbx584YNGzYwceJE3NzcOHToEMHBwVKdLVdXV8aOHUt2dvZ/7K74tWZrP16teOrUKc6cOUNSUhKHDx/G0tKS7du3o6KiQqdOndi6dSu1a9emadOmLF++nFOnTokW2EVAJpMRExNDREQEAGlpafTv35/q1aszePBgAGxsbGjRogWTJ08GCiZ1pk+fLjUWKKk+rgMiL1D89u1bfv31V6pWrUqnTp1IT0/nwYMHALi4uLB06VLatWvH/PnzWb9+PVOnTkVVVVUcu8I/pq2tTb9+/aTJM7GKpniRP8tcvnyZNm3aYG1tTW5urjRh2L59e+bMmYOTkxN37txh27ZtJCYmEhQUhJWVlRRUrFKlCsBnBbWFrys+Ph4/Pz8iIyOln1WvXh1FRUVSUlKIiIhAQ0MDd3d3goODadOmDb169SIuLg5fX1+SkpIwMDDAyMgIX19fDh06VOxLt3wrIiglfHWpqan4+fmRlZWFsrIyWVlZqKurM3LkSKZMmUKFChVo2bIl169fJyYmBi0tLaysrIiJiaFKlSrY2dmhoqJSoi66vXv3pmHDhiQnJ5OcnMydO3ek3/Xt21cqYldSGBgYMGzYMCpUqAAUtB42Nzf/7HX9+/endu3aUhrPx4p7/Qv54P/jVvKZmZkEBwdTo0YNOnfujI6ODlWrViUwMJB+/frRvHlzPnz4wObNm8nMzCQkJKTYtVPOzs5m69atDBkyBD8/P86fP4+amhojR45k1apVaGlpUa9ePapUqUJaWhpPnjyROu9Bwd9DXV29iPfir50+fZqYmBh27tyJm5sbAQEBODo6oqCgQE5ODrVr1+b27dsA6OjokJaWhrKyMq1ateLXX38tUcHuL0V+nMuDPMuWLeP3338nLCyMqlWrcufOHYYMGcLkyZNp06YNHz58YPbs2QwdOhQ7OzsaNmz42arHr00mk5GYmMjMmTNZsWKF1O1SUVGRIUOG8Pz5c27duoWlpSVqamqsXLkSe3t71q5dy4gRI4r9ted7deHCBQYNGgQUBL7T09Ol3x04cAB3d3dmzZrFsWPHSE1NlYp+l0QJCQlSO3YFBQUeP37MhAkTWLhwIQcOHKBXr140bNiQ5cuX06NHD3JycqROpj179qRevXqkpKSgoaFB5cqVpZVWJWFFsVD82NnZMWTIkEIr9oTiQf4sExERIY2VP/6O+vXrR3JyMhkZGfTo0YO9e/cye/ZsMjMzcXFx+ezzxDXi2woNDWXVqlXMmjWL2NhYPnz4gK6uLt27d0dPT4+oqCgiIyOJj4/n5s2bqKiosHnzZipUqMDUqVOldHVFRUUMDAzYvn37Xz5P/YjEkSx8dV27dsXc3Fxapi+/gLq6uvLu3TuOHz9O+/btKVu2LNu2bQMKZg7d3NxKdDvTdu3a0atXL9TV1SlbtmxRb86/oqSkhL6+PkpKSqSmphIbG0vHjh2BghlweQqYTCZj8uTJJa6t8MeD/wsXLuDg4MDSpUt5/fo17u7uJCQk8PjxY6DgoV5ZWZkDBw7g4uJC3bp1Wb9+PXp6esVyRrJr167Y29tjZGSEgYEBs2fPxs3NjebNm5OWlsb169cZMGAAUJBKMmbMGBwdHaX89uIqPz+fw4cP06xZM1RUVPjw4QOlSpWSgoI6OjooKSkREhJCbGwsz54948WLF9KKr/79+6Omplaigt1fgvw4f/XqFR4eHpw/f55ff/2VZs2asW3bNm7fvs3mzZtxdnbG39+frVu3Mn/+fMaPHy8FN7+2T1dVxsTEsHjxYpKSkggKCsLHx4f3798THh6OtrY2HTt25ODBg6Snp+Pg4CB1uSyOq/u+Nx+vLIWC6+fJkycBcHJywsDAgKCgIPr378/mzZulCZq4uDgsLCzQ09Pj0qVLaGpqFs0OfCHnzp1j2rRpQEHqsIeHB127dsXa2prff/+dAwcOMHfuXAICAsjPz6dZs2ZERERIqbCLFi2ievXq0uf9SB2XhC9PWVkZJycncRwVY02bNuXatWtkZWWhpKREdnY2ABkZGWRkZKCoqIitrS1Vq1bF3NycuXPnUq5cuRKTdfC9cnBwwMbGhjdv3vDHH39ImUCpqan06NEDfX19rl27xvPnz+nbty8TJkzg0aNHzJkzh7Zt2wKgqqqKk5OTtGpYKCCCUsI3MXLkSI4ePcrjx4+lwAZAnz59uHDhAmXKlKFZs2ZSWtv3MLOTl5eHqakphw8f/q7qmDx79gwjIyMqV67MihUrmDx5Mi9evCj0mpKQchAXF8eWLVuIiYmRbvIrVqxg5cqVDBgwgMzMTHx9fbG2tkZTU5PTp0+TnZ2Nrq4ujo6OnD59mv79+zNt2rRitzrqYzKZDEdHR+Lj4/npp5/Yt28fpUuXxs/Pj9jYWHx9fVFVVWXSpEnMmzePAwcO0KNHj6Le7P9KJpPx/v17EhMTAQoFsK9evcrly5dxcHBAVVWVuXPn4urqSt++fWncuHFRbXKRkV9P8/LySEhIwMPDgwoVKqCsrEzp0qV5//49FStWpFu3bqSlpaGurs7NmzeJiorC2toadXV1jI2NqVChArm5uV/t+iw/D+Urm3bt2sWpU6eoXr06pqamZGZmEhUVRcWKFbG1tWXZsmVAwSAxJyeH1NRUmjdvjrW19VfZPuFPGRkZwOcrYM+fP8+ZM2ekWore3t54enrSpk0bLCwsCAwMxMHBgfDwcGmAXlInn06dOiX9e9euXTEyMiIyMpLXr19To0YNtLS0WLNmDR06dKBp06ZUq1YNOzs7Ro8eTadOnejYsSNmZmbSZ5SE+6YgCF9Go0aNyM/PZ9++fcCftb5yc3OxsLBAWVkZmUxGr169OHLkCA8fPpS6zgpFR11dHXt7exQVFalfvz779+/n4sWL5Ofnc/r0afr27cvjx4958uQJzs7OrF+/ngULFqClpVXoGi+CxZ+T5X8PT/9CieDj40N0dLRUYBlg9uzZNG3alM6dO5eIbmwCBAYG4u/vT4MGDahYsSLu7u4lqjh0bm4uK1as4NixY1StWpXMzExMTU0ZN24cI0aMYPHixSQmJrJ48WJSUlJwcnLCyMgILy8vxo8fX2KX2Xp7e/P06VMCAgIASElJYc2aNWzZsoXx48dLq6VKkk2bNnHu3Dk8PT3R09OT2s0fPXoUPz8/Dh48iKKiInFxcVJx5R9FVlYW6enplCtXDvizCxMUdGhavXo1hoaGBAQE0KxZM3r06EFmZiZubm7IZDISEhJwcnLC1tb2q2+rfBgiH6RlZGQwa9YsXr9+TXJyMu3bt2fw4MH4+PhQs2ZNKWjco0cPnJycGDhwIB8+fEBFReWrb+uPLjY2ljVr1tCyZUs6duxIXFwcS5YsQUtLi1q1atGhQwe8vb1p1qwZ7du3p3Tp0nTv3p1GjRrh4eFBYmIi8fHxxX4l5n8TEhLC5MmTmTFjBm3atEFXV5fly5djamqKrq4uv/76KwYGBkyfPh1jY2O8vb1p0qQJVlZWuLi4sHbtWnG8CsIPLDc3lx07drB27VqcnZ1p2bIlJ06cICQkBFdXV7p06SK99vjx4yWiKdKPIj8/n7Fjx9KgQQOsra1Zv349mpqaZGZm4uXlxe7du6XmXfLXi3Ts/078dYRvZuzYsTx69EjqkjV69GiePn0qFTUVJ2vJkJ+fLwVxfHx8qFixYomZ4T19+jTW1tbk5OSwbds2VqxYgb29PadOnSIzM5PevXsTGxvL/v37cXR05KeffmLHjh0YGhpSp06dz1aElSTDhg0jLi6OsLAwAMqWLcuECRM4d+5ciQxIAbRq1YrSpUtLab9qampAwSq43r17SzOKP1pA6v379+zcuZNbt24BBSlVq1ev5smTJwDMnz+f6dOnY2hoSO3atblz5w5RUVGoqqqyfPlyPDw82L179zcJSAFSwfWnT58yfPhwlixZgp6eHoGBgfj4+PD7779LKU9RUVFcvXoVVVVVpk2bJt0/xAP+15Wbm8uSJUsYOXIktWrVomPHjjx+/JhRo0ZhYmJC9+7dCQgI4Nq1a9SvX58bN25IKc+NGjXi5MmTvH37Fj09vRIbkMrMzJRS1bW1talYsSKpqals2LCB7Oxs1NXVefHiBVWrVqVVq1a0aNECY2NjXr9+TUREBIaGhigrK7N582ZxvArCD05RUREnJyfGjh1LVFQUS5cu5f79+6xdu1YKSMnH1iIgVbzIZDJGjRrFjh07KFeuHK6ursTExEjp6XZ2doVqb8o7eQv/N7FSSvimrl27xtWrV7l69SqdOnXCzs6uqDdJ+IfS0tLQ0NCQ/rskrXALDQ3Fw8NDquMBEB4ezrp161iwYAGamppMmTIFc3NzHB0d8ff3JygoiIEDB+Ls7Fxi9vM/2bp1K2vXri2UdlLSXbx4UVpxaWFhwR9//EFmZibTpk2jdu3aRb1539TH5+Lq1au5ePEiFhYW1KxZk9DQULp160b79u2RyWR07doVOzs7evbsiZeXF8bGxp/Vi8rNzf0mqQK5ubkEBQVx9+5dqlevzuvXr7lw4QL+/v5UrlyZcePGoaSkhK+vL9OmTaNatWr88ssvxbKG2/do//79bNy4kbdv32JoaEhgYCAA+/bt4+nTp4wbNw6AEydOsH37dubOncumTZt49OgRT548YfDgwfTq1atE1426f/8+fn5+dO/ena5duwIwYsQIunbtyqNHj0hJScHR0ZEpU6YQHBzMrVu3mDNnDnp6ejx48AAHBweGDh1Kfn4+Mpnsm51bgiCUDO/evaNMmTJA4e64QvG1cOFCqXP1hw8fpIkX4X8jglJCkShJgQzhr5XUQfXYsWOpUqUKkydP5vXr13h7e6Ours6MGTNQVlbG3d0dFRUVNDQ0uH37NrNnz/5u2rVmZWWxe/du+vXr910NeCIjI7lx4wb37t3DwsLihwx2f3o+7tu3jzlz5uDo6MjEiRNZu3YtSUlJ9O7dG2NjY6mAeUREBBcuXMDQ0FDquPgthYeH07BhQ2xtbalWrRrLli3j8ePH7NixA21tbYYPH05mZiYNGzYkODiY0qVLo6enV2LrEJU0qampLF68mD59+mBiYkKPHj0YO3YsNjY2bNq0iZCQEPbs2SOlh7Zr1w4vLy+aNm1KeHg4GhoamJiYFPVufBEbNmzg3r17NGnSBFtbWykFVp5e2qdPH0JDQ3FxccHS0pI3b97w9OlTqlSpIoruC4Lwt4jno5IjKSkJT09PPD090dTURElJqcQ+GxUHIiglCMIP5d69e4wePZq2bdty69YtmjVrhpubm/T7uLg41q1bR4UKFXB1dS3CLRX+KfkqhB+FvFuPvEBqamoq/v7+qKqq0qRJExITE7l37x79+vVDQ0ODRYsWUa9ePbp27cqBAwc4cuQIs2bNkrrVfW2fDtYeP36Mm5sbs2bNIiMjg8DAQDw9PalYsSJHjhzh1KlT9OvXDwsLC/bt24eFhQVVq1b9Jtsq/LUtW7YQHBxMcHAwUNBldtSoUdja2vL69WtmzZrFzJkzS1Sdwf9Gfl358OEDly5dYubMmezcuZO9e/eiqanJwIEDiYyM5Ny5cwQGBuLt7U379u0LfUZubq7ohCYIgiAI/4EIxQqC8EOpW7cuXbp0Yd++fWzatEkKSMm7iunr6zNjxgwRkCqBfqQHvvj4eDw9PXn79i1Q0BVz6NChKCoqUqZMGWJjY2nfvj2pqamcPn0aPT09unXrxpMnT7C1tSU9PZ1t27Z99YBUYmIi9+/fJysrC0VFRZKTkzlx4gQAlSpVws7Ojg0bNtCqVSvKlClDaGgoCgoKWFhYoKmpyeXLlwHo2bOnCEgVIXltE3t7e9TU1Fi7di0AU6ZMYcuWLUyePJnBgwdjbGxc4gNSn7Zcl19XlJWVsba2xtbWlu3bt6OoqEhISAgApqamDB8+HCsrK6m23ccUFRV/qOuTIAjCj+LTe4bwvxErpQRB+OG8fv2awYMHM2vWLBo3bkxWVpZIBxJKhMjISHR1daUH/4SEBHR0dIiMjGTp0qVs3Lix0OsPHz7MmTNnMDIywszMjPr16/Pu3Tvp/V9rqXlubi7+/v4cPXoUPT09KlasyJQpU7h//z5jxoxh//79VKxYkdjYWBYtWkSHDh2oWbMmXl5eTJgwgYYNG/6QXRNLgrNnz0odhsqUKUN8fDwPHz7E2Nj4u6qncePGDQwNDSlfvjzwZwfLnJwcdu3aRWxsLCdOnMDb25tGjRoV8dYKgiAIQsklVkoJgvDD0dbWxsHBgYkTJwKIgJRQ7MXGxuLu7s60adOYO3cuU6dOBWDIkCGcOnWKlJQUKlWqJHU8e/fuHW5ubjRo0IBmzZpx6dIlVFRUUFdXlzpm5ufnf5WAVFZWFnPmzOHVq1fs2rWLdevWYWtri5KSEk2bNsXKyopVq1YBoKenR4MGDdi5cyfVq1enVq1axMXFAT9e18SSwsrKijp16uDh4QEUrHiztrb+bgJS58+f5+eff2bnzp14eHhw+vRp8vPzUVJSkv5pa2uLubk5z549kzryyYm5XkEQBEH4Z0TrGkEQfkjyYtiiy4lQXOXn55Ofn4+XlxcPHjygffv2LFmyhPv37zNw4EAePXpEv379OHfuHDY2NigqKnLx4kVq1KhBmTJlePPmDQoKCvTs2ZOePXsW+uyvWUj13bt3PHz4kB07dgDw9OlT1NXVuXPnDubm5kyZMgUHBwciIiKwsLBAXV2dDx8+EBoayowZM0SR0BLAzc2Ne/fuFfVm/Ct/VYMuNTWVgwcP4uvrS7Vq1fjpp5+oVq0alpaWaGpqSq9XUVGhQ4cO7N+//7Mun+JeIgiCIAj/jEjfEwRBEIRiKikpidatW7N3717q1q0r/Xz06NF06dKFn376iZEjR9K3b1/y8vI4ceIE2dnZREdHY2VlxcSJE6Ugz7fsCtOvXz90dXXJyMggNTUVFRUVbty4QbNmzZg6dSonTpzgzJkz5ObmoqyszOTJk6lVq9Y32Tbhx5aTk8PJkydRVlamTZs2pKWl8eDBA+rWrUtGRgaLFi0iKyuLJ0+e0KtXL7p06UJycjJGRkb/MeD0ozVZEARBEIQvSayUEgRBEIRiKDc3F11dXezt7Vm8eDHr1q0DIDw8nDdv3lCzZk1UVVXp2bMnoaGhjBw5ktatW3Pp0iUMDAyoU6dOoc/7liuQ5s+fz/bt28nMzKRdu3ZoaGhQuXJl1qxZw9mzZ3F2dsbMzIyHDx/i4ODwzbZL+LEdPnyY/Px8bty4gZqaGgkJCaxdu5by5ctTqVIlvLy8ePnyJdra2qxbt45y5crh4+NDeno6s2fP/o+fKwJSgiAIgvC/EyulBEEQBKGYyMvLQ0FBQep2Jk+zMzMzw9PTk1u3bnHt2jWGDh1K586dpfc5OztjbW3N4MGDpZ/J0/++ZqrePzV16lR69epFkyZNinpThB/IpUuXWLlyJerq6ixYsIAHDx6wf/9+nj59yoIFC6hSpQo//fQTnp6ePHz4kIcPH2Jubk6tWrXw9vZm2LBh2NjYFPVuCIIgCMJ3SayUEgRBEIRiQh5Ays7ORkVFReoMOWPGDCZNmsSUKVOYPn269Prs7GxKlSrF/PnzqVSpUqHPKupaabdv3+bYsWM0btwYLS0tfH19UVBQwNDQsMi2SfixPH/+HD8/P44cOULjxo1ZuXIlABYWFty5c4fbt2+TlpYGwMiRI1m5ciVLlixBT0+PEydOcOTIEQYNGiQCUoIgCILwFRWf6VNBEARB+AHJV0XJLVy4ED8/PwBKlSoFFBTmNzQ0pHTp0gBkZmYW+r08IPXpZxUlIyMjVFVV2bZtG1OnTqV9+/asWbPmu+nSJhRvycnJeHh4YGJiQlBQEPHx8cTExACgpKREq1atsLCw4OrVq0DBOZabm8vWrVvp0qULXl5erFu3jq5duwKiq54gCIIgfC0ifU8QBEEQvrFPCyM/f/6cKlWqALBr1y5kMhl2dnbk5+eTm5uLkpISly9fZtCgQURGRqKsrFxUm/6PpaamoqqqipKSWJwtfFvp6emoq6sDBXXOYmNjpdVSUHCu3b9/ny5dumBpacmVK1cICwtjwoQJQMFqw2/ZIEAQBEEQfkRipZQgCIIgfEN5eXmFAlJv3ryhR48e7Nmzh4yMDBQVFblw4YL0eyUlJfLy8mjatCkdOnQgMjKyKDb7f6apqSkCUkKRUFdXl1YPDh06lKdPnxIWFib93srKCplMxqlTp8jMzKRJkyZMnDixUOqrCEgJgiAIwtclVkoJgiAIQhFYvXo1SkpKdO7cmVevXhESEkJaWhru7u5MnToVb29vdHR0AMRqDUH4ArZu3crevXsJCgqSfnb69Gm0tLQwNTWVVjDKGw4IgiAIgvD1iaCUIAiCIHxFn6bq3b9/nwMHDvD48WOqVatGWFgY+/bto1SpUgwfPhxlZWVycnJYuHAhZcqU+axY+aefJwjC35OVlcUvv/xC8+bNcXV1LerNEQRBEAQBkb4nCIIgCF9Nbm5uoQBSbGws7u7uvH79mpUrVzJ58mRq1qzJwoULkclkLFiwgBYtWnD+/Hni4uKQyWSfFVgWASlB+N8oKyvj4uJCcnJyUW+KIAiCIAj/nwhKCYIgCMIXJg8kKSoqkpGRwfr16wkPD6dy5coMHDiQ5ORkXrx4AcCoUaM4d+4cUVFRaGlp4ejoyODBg7l16xYgglCC8CW1bduWmTNnFvVmCIIgCILw/4mglCAIgiB8Ie/evQP+DCQdP34ce3t7Hj58yJ49exg8eDDdunXjw4cPREREkJ2djbGxMU2bNmX+/PnS5zx58gQjI6Mi2QdB+BHIC6ALgiAIglC0RDscQRAEQfiXLl68yO+//07ZsmUxMTGhb9++aGlpcf36dSZOnEjr1q0B6NmzJ0eOHGHAgAEEBwdTr149atSowYQJE3j79i1QEMjKysqiatWqRbhHgvB9E4XMBUEQBKF4EEEpQRAEQfgf5eXlsWzZMi5cuICrqytaWlpERkZK6XtnzpyhTp060uvd3d3x8vLi6NGjbNy4kWvXrlGtWjXKlClDmTJlALCxscHGxqZI9kcQBEEQBEEQviURlBIEQRCE/1FKSgoxMTHs3r1b+pmBgYH0787Ozuzfv5/u3bsDUK5cORo1agTA/PnzC71WEARBEARBEH40IiglCIIgCP+j0qVLc/fuXQIDA1FTU+Py5cvk5uZy9epVevXqRadOnVBTU2Py5MmYmZmxc+dOfv75ZwD09fWBgqLoopi5IAiCIAiC8COS5X/aa1oQBEEQhL/t+PHjbNy4kYSEBH7++Wd0dHQoXbo0Fy5coEGDBnTs2JETJ05w8+ZN7OzsMDMzK+pNFgRBEARBEIRiQQSlBEEQBOFfyszMRFVVtdDP5syZQ5s2bbC2ti708/z8fPLz80WhZUEQBEEQBOGHJ0bEgiAIgvAvJSQkcPjwYZKSkgAICAjg7t27VKxYsdDr8vLykMlkIiAlCIIgCIIgCIiaUoIgCILwr2loaBAWFsbOnTtJTk6mVq1a+Pn5Ubly5UKvE8EoQRAEQRAEQfiTSN8TBEEQhC8gMzOTuLg4srOzqVu3LlCwMkoEogRBEARBEAThr4mglCAIgiB8YaJulCAIgiAIgiD8dyIoJQiCIAiCIAiCIAiCIHxzYgpXEARBEARBEARBEARB+OZEUEoQBEEQBEEQBEEQBEH45kRQShAEQRAEQRAEQRAEQfjmRFBKEARBEARBEARBEARB+OZEUEoQBEEQBEEQBEEQBEH45kRQShAEQRAEQRAEQRAEQfjm/h/bsfvlha+5MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar plot of net revenue, average production costs, top 3 genres bt studio\n",
    "sns.set_theme(style=\"white\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "fig, ax = plt.subplots(nrows=3,ncols=1,figsize=(20,15), sharex=True)\n",
    "\n",
    "# Plot the Net Revenue\n",
    "sns.set_color_codes(\"dark\")\n",
    "sns.barplot(x=net_revenue_by_studio.index, y=net_revenue_by_studio.values,\n",
    "            color=\"b\", ax =ax[0])\n",
    "ax[0].set_ylabel ('Net Revenue in $MM',fontsize = 15)\n",
    "\n",
    "#Plot production budget\n",
    "sns.set_color_codes(\"muted\")\n",
    "sns.barplot(x=average_budget_by_studio.index, y=average_budget_by_studio.values,\n",
    "            color=\"b\", ax =ax[1])\n",
    "ax[1].set_ylabel ('Production Budget $MM',fontsize = 15)\n",
    "\n",
    "#Plot Top 3 genre by studio\n",
    "genre_studio.plot(kind='bar', stacked=True,ax=ax[2])\n",
    "ax[2].set_ylabel ('Movie Counts by Genre',fontsize = 15)\n",
    "\n",
    "ax[0].set(xlabel = None)\n",
    "ax[1].set(xlabel = None)\n",
    "ax[2].set(xlabel = None)\n",
    "ax[2].set_xticklabels(ax[2].get_xticklabels(),rotation = 30)\n",
    "fig.suptitle(\"Genre Popularity\",fontsize = 20)\n",
    "sns.despine()\n",
    "plt.savefig('Images/studio.png')\n",
    "plt.savefig('Images/studio.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Microsoft has no prior movie industry experience, and, therefore, no intellectual property of movies, they must acquire IP.\n",
    "\n",
    "A budget tier system gives Microsoft a clear path to follow in IP acquisition, based on their budgetary preferences.\n",
    "\n",
    "The tiers are classified into High, Medium and Low. These figures were acquired through statistical analysis of production costs of all movies in the last 20 years.\n",
    "\n",
    "Through further data analysis, we conclude that Microsoft should acquire their intellectual property from the following movie studios: Walt Disney, Buena Vista, Universal Pictures, Warner Brothers, 21st Century Fox, Paramount Pictures.\n",
    "\n",
    "Fiscal data analysis points us in the direction of specific movie studios, based on their associated value.\n",
    "\n",
    "The more valuable a movie studio, the more expensive their IP is.\n",
    "\n",
    "Furthermore, the most produced genres in our recommended studios allign with our genre popularity finding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Our recommendations follow a chronological pipeline of actionable applications of our data findings. The key stakeholder will be able to apply our recommendations to a real life business problem. \n",
    "\n",
    "The first recommendation is to reboot blockbuster films from the 80’s and 90’s. Our data reiterates that more movies from these 2 decades have been rebooted, than not. Comparative fiscal data for reboots vs. non-reboots allows us to conclude that hit films, specifically from these 2 decades, are the most profitable to reboot. Additionally, these reboots happen to be even more profitable than original films. \n",
    "\n",
    "Once the first recommendation is accepted, the second recommendation provides the next step. After deciding that rebooting hit films from the 80’s and 90’s is the way to go, one must ascertain the next step. Which genre? Through analyzing popularity and ROI for specific genres, our findings show that adventure, fantasy, scifi, action, family, and animation are the most popular genres. Exploring the popularity of genres gives our key stakeholders a direction. \n",
    "\n",
    "The third recommendation gets even more specific. Since Microsoft has no prior movie industry experience, and, therefore, no intellectual property of movies, they must acquire IP. Once the genre has been decided, where would the stakeholder acquire their intellectual property necessary for a reboot that they don’t own the rights to? Data analysis points us in the direction of specific movie studios, based on their associated value. The more valuable a movie studio, the more expensive their IP is. A budget tier system gives Microsoft a clear path to follow, based on their preferences. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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