Analyzing the accuracy of Fandango movie ratings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing the accuracy of Fandango's movie ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In October 2015, a data journalist named Walt Hickey from FiveThirtyEight analyzed [movie ratings data](https://fivethirtyeight.com/features/fandango-movies-ratings/) and found strong evidence to suggest that Fandango's rating system was biased, generally displaying higher ratings than what the movie actually received.\n",
    "\n",
    "Fandango's officials replied that the biased rounding off was caused by a bug in their system rather than being intentional, and they promised to fix the bug as soon as possible. In this project, we'll compare Fandango's data from Hickey's analysis and from movies released in 2016 & 2017 to determine whether there has been any change in Fandango's rating system.\n",
    "\n",
    "Both datasets are available on Github:\n",
    "- [Hickey's data](https://github.com/fivethirtyeight/data/tree/master/fandango)\n",
    "- [2016 & 2017 data](https://github.com/mircealex/Movie_ratings_2016_17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data and selecting what we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# reading in Hickey's data\n",
    "fandango = pd.read_csv('fandango_score_comparison.csv')\n",
    "# reading in the data for movies released in 2016 and 2017\n",
    "ratings1617 = pd.read_csv('movie_ratings_16_17.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading the data into dataframes and exploring the data, we know that the Fandango data has 146 rows and 22 columns consisting of ratings information from various sources. \n",
    "\n",
    "The data on movie ratings from 2016 and 2017 has 214 rows and 15 columns with ratings info from similar sources. However, the data is stored in columns with different names.\n",
    "\n",
    "Both sources have a combination of strings, integers and floats, and do not have any null values.\n",
    "\n",
    "Now we'll select the columns that offer information about Fandango's ratings in separate variables so we have just the data we're interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fandango_subset = fandango[['FILM','Fandango_Stars', 'Fandango_Ratingvalue', 'Fandango_votes', 'Fandango_Difference']].copy()\n",
    "ratings_subset = ratings1617[['movie', 'year', 'fandango']].copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fandango dataset contains every film that has a Rotten Tomatoes rating, a RT User rating, a Metacritic score, a Metacritic User score, and IMDb score, and at least 30 fan reviews on Fandango as of  24 August 2015.\n",
    "\n",
    "The other ratings data set contains movie ratings data for 214 of the most popular movies (with a significant number of votes, but the exact number is unspecified) released in 2016 and 2017, and is accurate as of 22 March 2017. \n",
    "\n",
    "The data come from the periods of time that we need - before and after Hickey's analysis, respectively. However, the sampling is not random and the samples were created based on the authors' purpose. They are also unlikely to be representative of the population of movie reviews on Fandango, as they represent subsets of movies released and reviewed at specific points in time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the goal of this analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the data is not representative of what we were planning to analyse - Fandango's movie rating system - we will change the goal of our analysis by placing some limitations on it.\n",
    "\n",
    "We'll focus on determining whether there has been any change in Fandango's rating system for movies with a significant number of votes/reviews by comparing those released in 2015 with those released in 2016 and 2017. This should still be a relatively good proxy for movies released before and after Hickey's analysis.\n",
    "\n",
    "We'll use the minimum of 30 fan ratings in Hickey's dataset as the minimum to be considered a \"significant number\". However, as the 2nd dataset doesn't provide info about how many fan reviews each movie has, we'll select a random sample of 12 movies. If at least 9 of them have 30 or more fan reviews then we'll proceed to use the data as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>year</th>\n",
       "      <th>fandango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Mechanic: Resurrection</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Warcraft</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Max Steel</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Me Before You</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Fantastic Beasts and Where to Find Them</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Cell</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Genius</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Sully</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Hologram for the King</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Mr. Church</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Crouching Tiger, Hidden Dragon: Sword of Destiny</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                movie  year  fandango\n",
       "108                            Mechanic: Resurrection  2016       4.0\n",
       "206                                          Warcraft  2016       4.0\n",
       "106                                         Max Steel  2016       3.5\n",
       "107                                     Me Before You  2016       4.5\n",
       "51            Fantastic Beasts and Where to Find Them  2016       4.5\n",
       "33                                               Cell  2016       3.0\n",
       "59                                             Genius  2016       3.5\n",
       "152                                             Sully  2016       4.5\n",
       "4                             A Hologram for the King  2016       3.0\n",
       "31                         Captain America: Civil War  2016       4.5\n",
       "118                                        Mr. Church  2016       4.5\n",
       "39   Crouching Tiger, Hidden Dragon: Sword of Destiny  2016       4.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_subset.sample(12, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of 2 Feb 2022, the number of fan ratings for each of the movies is as follows.\n",
    "\n",
    "| Movie | Fan ratings | \n",
    "| --- | --- | \n",
    "| Mechanic: Resurrection | 25,672 | \n",
    "| Warcraft | 31,814 |\n",
    "| Max Steel | 6,874|\n",
    "| Me Before You | 30,827 |\n",
    "| Fantastic Beasts and Where to Find Them | 88,321 |\n",
    "| Cell| 180,919 |\n",
    "| Genius | 12 |\n",
    "| Sully | 48,723 |\n",
    "| A Hologram for the King | 10,266 |\n",
    "| Captain America: Civil War | 185,188 |\n",
    "| Mr. Church | 4,714 |\n",
    "| Crouching Tiger, Hidden Dragon: Sword of Destiny | 12,119 |\n",
    "\n",
    "11 out of 12 have more than 30 fan ratings, so we should be able to proceed with the data that we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>RottenTomatoes</th>\n",
       "      <th>RottenTomatoes_User</th>\n",
       "      <th>Metacritic</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>RT_norm</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>RT_norm_round</th>\n",
       "      <th>RT_user_norm_round</th>\n",
       "      <th>Metacritic_norm_round</th>\n",
       "      <th>Metacritic_user_norm_round</th>\n",
       "      <th>IMDB_norm_round</th>\n",
       "      <th>Metacritic_user_vote_count</th>\n",
       "      <th>IMDB_user_vote_count</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "    </tr>\n",
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       "<p>0 rows × 22 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [FILM, RottenTomatoes, RottenTomatoes_User, Metacritic, Metacritic_User, IMDB, Fandango_Stars, Fandango_Ratingvalue, RT_norm, RT_user_norm, Metacritic_norm, Metacritic_user_nom, IMDB_norm, RT_norm_round, RT_user_norm_round, Metacritic_norm_round, Metacritic_user_norm_round, IMDB_norm_round, Metacritic_user_vote_count, IMDB_user_vote_count, Fandango_votes, Fandango_Difference]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "fandango[fandango['Fandango_votes']<30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have also confirmed that there are no movies <30 fan ratings in the 2015 dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolating movies from 2015, 2016 and 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FILM</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_votes</th>\n",
       "      <th>Fandango_Difference</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14846</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12640</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                             FILM  Fandango_Stars  Fandango_Ratingvalue  \\\n",
       "0  Avengers: Age of Ultron (2015)             5.0                   4.5   \n",
       "1               Cinderella (2015)             5.0                   4.5   \n",
       "\n",
       "   Fandango_votes  Fandango_Difference  year  \n",
       "0           14846                  0.5  2015  \n",
       "1           12640                  0.5  2015  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting years from the FILM column in Hickey's data\n",
    "fandango_subset['year'] = fandango_subset['FILM'].str[-5:-1]\n",
    "fandango_subset.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015    129\n",
      "2014     17\n",
      "Name: year, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(129, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only selecting films released in 2015\n",
    "print(fandango_subset['year'].value_counts())\n",
    "fandango_2015 = fandango_subset[fandango_subset['year']=='2015']\n",
    "fandango_2015.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We retained 129 out of 146 movies as 17 of them were released in 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016    191\n",
      "2017     23\n",
      "Name: year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking years of release for 2nd dataset\n",
    "print(ratings_subset['year'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are only movies released in 2016 and 2017, we don't need to filter the data further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the distributions of movie ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start with a simple with making a high-level comparison between the shapes of the distributions of movie ratings for both samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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WyxTLBJhQueQyWC5bCNvIV/j/rBbNBEqKmrpkPJN5Vi8Ik2z6BoAePXpg165d\ngmNjxozh/92xY0et1wkh5FlnvuMnSK5dFBwTP0mCxf++Q8Wcz5umUKRJmWSNmhBCXkRMdjrMTuzR\n+ZrZn2cgun+9kUtETAEFakIIMRFmMbvAKBTVvm5+eFsjloaYCgrUhBBiCmSVMLtwQnAor9PLgrT4\n2kUwhbSw04uGAjUhhJgA8a0rYEoK+TRnY4eUIRFQ+LTgjzEcC8nlM01QOtKUKFATQogJkPwVJ0jL\nX+oLTmIGeY/+guPi+PONWSxiAkx21PeLJisrC59//jmePn0KkUiE0aNHY/z48SgsLMQnn3yCjIwM\neHl54euvv4atrS0KCgqwcOFC3LlzB8OHD8eHH37IXysqKgq5ubmwsLAAwzBYsWIFHB0dm/DdEUJq\nxHEQa4z0lnepavaWd+sNi50/88fF928AlRWAuUWjFpE0HQrUJkIsFuO9995DUFAQSktLMXXqVISG\nhuLgwYPo3r07pk6dio0bN2LDhg2YM2cOLCwsEBUVhQcPHuDBgwda1/vyyy/RunXrJngnhJC6YrLS\nIHqq2s+AM7eAom1n4PETcB4+YJ3dIXpatZcBI6uEOOkWFO1Cmqq4pJFRoNbhlQOG3dzjzEj3WvO4\nurrC1dUVAGBtbQ1/f39kZ2fj3LlzWLNmDQBg2LBhiIqKwpw5c2BpaYng4GA8efJE5/VYljXcGyCE\nGJX4/g1BWhHYQVVjZhgo2neFKPZ3Vf5bVylQv0Coj9oEpaenIzExER06dMDTp0/h4uICoCqY67uV\n5xdffIEpU6bgl19+MWZRCSEGIE7UDNQdhWmNoCy+E2/0MhHTQTVqE1NaWoqFCxfi/fffh7W1NRiG\nqfM1Fi9eDFdXV5SVleGjjz7C77//jiFDhhihtIQQQ9CsUbNBGoG6bRdBWvQ4CZBVAmbmRi8baXpU\nozYhcrkcCxcuxNChQ9GnTx8AgLOzM/Ly8gAAubm5cHZ2rvU6yiZ0KysrDBo0CLdu3TJeoQkhDcIU\n5kOUmcKnOZEIipZtBXk4J1ewzqouNEYugyjlYaOVkTQtqlHroE+fsjEsXrwYAQEBmDBhAn+sV69e\nOHToECIjI3H48GGtXcQAgOM4/t8KhQJFRUVwdHSEXC7H+fPnERoa2ijlJ4TUnejRPUGa9WsFWGrv\nBMi2aMMPKAMA8cM7YFu0MXr5SNOjQG0irl27hmPHjqFly5aYPHkyGIbBrFmzMHXqVPzzn//EwYMH\n4eXlhSWd3fSuAAAgAElEQVRLlvDnjBo1CqWlpZDL5Th37hxWrFgBT09PzJs3DwqFAizLonv37hg9\nenQTvjNCSE1Ej4U7UbH+umdrKFq0heTKOdV5D+8atVzEdFCgNhGdOnXCxYsXdb62atUqncf379+v\n8/imTZsMVi5CiHGJNQK1wj9QZz7N2rP44W2jlYmYFuqjJoSQJiR6nCRIs366A7UioDU4tcGlTGYq\nUFFm1LIR00CBmhBCmkpJEUQ56XySE4nA+rbQndfSGpy7N59kOA6itMfGLiExARSoCSGkiYhShKsK\nsl5+NS4NyvoIg7jm+eT5RIGaEEKaiDj1kSDN+rWqMb9mbVuUSlO0XgQmO5gsLi4Oy5YtA8uyGDly\nJCIjIwWvS6VSfPbZZ8jNzQXLsoiIiMDw4cObqLSEEFJ3TLqw6Zr1Cagxv0KzRq0R6MnzySQDNcuy\nWLp0KVatWgU3NzdERkaiT58+8Pf35/Ps3LkTQUFB+OGHHyCVSjF27FgMHjwYEolJviVCCNEiSksW\npFnv5jXm16xRi1MeABwH1GMFQ/LsMMmoduvWLfj6+sLLywsAMHDgQJw9e1YQqF1cXPhdo0pKSuDg\n4PBMB2lDbnMpl8uxdOlSXL16FWKxGO+88w769u0ruF95eTk+/vhjpKWlQSwWo1evXpg1axYAQCaT\n4fPPP8f169fh5uaGJUuWwNPTEwAwf/583Lx5E507d8Z3333HX+/tt99GWVkZOI5Dfn4+2rdvj2++\n+aYRnhwhzy6RZo3a27/G/Jy7NzhzCzCVFQAApqgATMFTcI4uxioiMQEmGdlycnLg4eHBp93d3bWW\nwRw9ejRmz56NoUOHoqysDF999VVjF9OgDLnN5fr16+Hs7Ixdu3YBAAoKCnTec8qUKQgJCYFcLses\nWbMQFxeHHj16YP/+/bC3t8eyZcuQnJyMFStW8M93ypQpKC8vx969ewXX+umnn/h/L1y4kF8ClRBS\njSIpRIWqTXY4MzNwbp41nyMSg23mD7Haamai1EdQUKB+rplkoNbHhg0bEBgYiOjoaKSmpmLOnDn4\n9ddfYW2tvfSeLomJidW+1uXLtwxVTABAwr9+rjVPfn4+GIbhy+Xm5oaEhAScOHECixYtQmJiItq1\na4cvv/wSgwYNAlC1lvfTp08hlUoF72fv3r349ttvBceys7W37rSzs+PzeHp64ubNm3B1dcXRo0fx\n+uuvAwD8/Pxw8eJFPp+DgwPS09NRUlKi8xmWlpbi0qVLiIiIqPEZN5Qxr11fplgmwDTLZYplAhq3\nXDZP7iNILV3u5IHEh9p9zppl8rN3hQtUgTov4RJyLByMVcxqmdrfMDBQ9/zz54FJBmo3NzdkZmby\n6ezsbLi5uQnyXL9+HW+++SYAwMfHB97e3khOTka7du30ukdj/lFru1diYqIgT3p6OtLT0zFo0CCs\nXLkS3bp1418rKSkR5L137x7y8/P5Y8XFxZBIJDh+/Dji4+Ph4+ODBQsWwMnJqdr7FxUV4caNG4iK\nioKXlxdKSkrQrVs3SKVStG7dGo6OjnB3d4eDgwOf38bGRuf7OnLkCMLCwtCxY0et1wxF83mZAlMs\nE2Ca5TLFMgGNXy5J6h1hOiBI6/66ymTWNhi49gef9pCXw7GRn6ep/g2fVyY5Patdu3ZITU1FRkYG\nZDIZYmJitDaj8Pf3x+XLlwEAeXl5ePLkCZo1a9YUxTWohm5zKZfLkZ2djU6dOmHTpk3o0KEDli9f\nXm1+hUKBRYsWYcKECfyYAE3qm37UJiYmBgMHDqxTmQl5EWn1Tzfz1+s81stXeJ2MJ4YqEjFRJlmj\nFovFWLBgAebOnQuO4zBy5EgEBARgz549YBgGr732GqZNm4YvvvgCkyZNAgDMnTuXr/E9q2ra5tLF\nxUWvbS4dHR1hZWXFDx7r378/Dh48CI7jMGXKFDAMg169euHtt98GACxZsgR+fn4YP348fw13d3dk\nZWXBwsICCoWCH6xXG6lUitu3b2Pp0qX1fQSEvDA0VxWrbcQ3n8/LT5BmMo0fqPMrWOx7VIqkQjmc\nLURoz4hB9enGY5KBGgB69OjBD4ZSGjNmDP9vR0dHLFu2zCj3Lt54xijXrY0htrkEgJ49e+LKlSvo\n1q0b/vzzTwQEBIBhGGzZskWQLzo6GiUlJVi0aJHgeK9evXD48GGMGTMGJ0+eFDS9K++nq5Z98uRJ\n9OzZE2ZmZnV+74S8aDRrwvoGas7VE5xYAkYhr7pOQT5QUgTY2Bm8jABwO1+GhZekKKxU/T9/ELbI\ntizB1CAbo9yTCJlk0/eLSLnN5ZUrVzB58mRMmTIFcXFxmDp1Ki5fvoyxY8fiypUrgoVfRo0ahR9+\n+AFHjhzBiBEjkJycDACYM2cOfv75Z0RERODo0aOYP3++1v2ys7OxYcMGPHr0iL/fgQMHAAAjR46E\nVCrF+++/j23btmH27Nn8eW+//TY++eQTXLlyBSNGjMClS5f4106cOEHN3oToo7JCsLc0xzCCdbxr\nJJaA9fARHBJlphiydLy0EjkWxAmDtNL6uyWISSk3yn2JkMnWqF80htzm0tPTE2vXrq3xfu7u7oIg\nq87c3Bxff/21zgEj6tOwNEVHR9d4T0JIFVF2uiDNubgDZuZ6n895+QLpyarrZTwB21K/gbT6YjkO\nS+ILUSKvfozKjzeL0NXNDC6WYoPemwhRjZoQQhoZk5UqSGvWkGvDemoOKDN8jfp0WgVu5csFx8I9\nzGGuFjWKZRx+Syo1+L2JEAVqQghpZKKsNEGaq2ug9hYOKDN007ec5fC/eyWCYz08zPHVSw6Y0cZW\ncPzg4zJIK1iD3p8IUaAmhJBGJso0bI2aSTfsyO8z6RVILVHwaREDzG5vWzXrJsAKjhJVYK5QAMeo\nr9qoKFATQkgjY7KFNWrWo25rQGhO0RJlpwEKeTW5625/cpkgPdjXEj62VUOazMUMBjhXCF4/mlJW\np/UWSN1QoCaEkEYm0uqjruNiTTZ2YO1Vqw0ychmY3MwaTtDfw0I5bjyVCY6NbylcmjnMQQaR2lpM\nj4oUSCo03A8FIkSBmhBCGlNFOURPc/gkx4jAueleFbAmnJEGlB1+LKxNd3YxQ3M74QQhBwmHl9yF\no9QvZFYa5P5EGwVqQghpRKKchk3NUtJaSlSj37s+FByH0+nCZu0R/lY68/bytBCkL2RV6MxHGo4C\nNSGENCImU7N/um4DyfjzNGvUBhj5fT1PhqdqI7itJQx6agRkpTAP4Y+Le1I5cssVOvOShqFATQgh\njUirf9qzvoFaeJ7m3Oz6OJUmHL3d09MCFmLdGwO5WIrR1lHYJH4pi5q/jYECNSGENCLNQM3VdSDZ\n3wxdo5azHM5qNHv3a6a7Nq3Uw0P4ekIuBWpjoEBNCCGNiMlq2NQsJc7NCxyj+goXPc0BKspqOKNm\n1/NkKJSppljZmzHo6lZz33lnV+EGPH/lyWialhFQoCaEkEakPTWrfk3fMDMH5+qpce20ajLXTnMw\nWLinBcxEupu9ldo4msFCbZnv3HIW6aXUT21oFKgJIaSxVJRDlJ/LJ+s7NUtJq5+6niO/OY7Tml4V\n7lH7SHRzMYP2TsJa9bU8WTW5SX1RoCaEkEaiNTXL1QOQ1H//dkP1Uz8pVghqwmYioJu7flPGOrkI\n81GgNjwK1IQQ0ki0+qf13YO6Gpyn5r7U9atRX8gUNnt3djGHtUS/8BDsIvyhcSefArWhmex+1HFx\ncVi2bBlYlsXIkSMRGRkpeH3Lli04evQoGIaBXC5HcnIyYmJiYGdn10QlJoSQmmntQ93AQG2oGvUF\njWlV4Z76L8DS2lECBoByCNmTYgWKZSxszageaCgmGahZlsXSpUuxatUquLm5ITIyEn369IG/vz+f\nZ/LkyZg8eTIAIDY2Ftu2baMgTQgxaZqBmnWv34hv/nwD1KgLKlnc0ljbW3PaVU2sJSI0txMjuUjV\ndH5fKkdILSPGif5M8ifPrVu34OvrCy8vL0gkEgwcOBBnz56tNn9MTAwGDhzYiCUkhJC6YwwcqDln\nd3BmqqZnpqQQKC6o0zUuZVVAfTfplvYSeFqLq82vS1tHjeZvKTV/G5JJBuqcnBx4eHjwaXd3d+Tk\n5OjMW15ejri4OPTr16+xikcIIfUi0tjesqFN3xCJtKZ31bVWHafR7N1Dj9HemtporFB2N5920jIk\nkwzUdREbG4vOnTtTszchxLQp5GDysgSHWPf6T81S0tpFqw791HKWw+VszUCtf7O3UhsnqlEbk0n2\nUbu5uSEzU7W3anZ2Ntzc3HTmPX78eL2avRMTE+tdPmMwtfIoUbn0Z4plAkyzXKZYJsC45TLPz0F7\nhaofV2Zjj8SU2hcoqa1MXuY2UF/2RHr7OjI8WupVpnslYpTIbfm0rZiFJDcZiXm1n6teLpYDJIw9\n5FzVAim55Sz+vJ0ER7PGW6UsMDCw0e7V2EwyULdr1w6pqanIyMiAq6srYmJi8OWXX2rlKy4uRnx8\nPL744os638OU/qiJiYkmVR4lKpf+TLFMgGmWyxTLBBi/XOKbwr5jkZdfrffTp0ySjGDgwu982rWy\nBLZ6vo+Tt4oBlPLpcC9rtA7yrP6EGsoVmPUUd9SavFkXXwTWo3ZOtJlkoBaLxViwYAHmzp0LjuMw\ncuRIBAQEYM+ePWAYBq+99hoA4MyZMwgLC4OlpWUTl5gQQmrGZBt2DjV/nQasTnYxWzh/WnPryrpo\nZS8RBOqkQjlCKVAbhEkGagDo0aMHdu3aJTg2ZswYQXr48OEYPnx4YxaLEELqRXtqlqECtUYfdVYq\nwLKAqOYhSBmlCsGUKhEDdNdzNTJdWtgLw8nDQhpQZijP/GAyQgh5FmhumNHgEd9Kdg7grFX9zExl\nBRi19cSrc1FjE46Ozmawa8AiJS01AvUDCtQGQ4GaEEIagdYc6npub6l9YaZeK5Rd0piWFdqA2jSg\nXaN+UqxApYK2vDQECtSEEGJsHKe1IUdDFzsRXKuO/dTlcg7xucJAHdbA/mRbMxE8rFQhheWAx8VU\nqzYECtSEEGJkTMFTMBXlfJqztAbsHAx2/brWqBPyKlGpthyZh5UIAXZ1W41MF63m7wIK1IZAgZoQ\nQoxMe+lQb4BhDHZ9rV20smquUWvulhXmYQHGAOVp6UADyoyBAjUhhBiZoXfN0qRdo64+UCs4Ducz\nhIG6PsuG6qJZo06iQG0QFKgJIcTIDL1rlibNgWlMTjog1x0kbz6VIb9SNcjLRsIgxNUwgVpzQBmN\n/DYMCtSEEGJkxlrshGdpDdbRVXU/lgWTm6Ez67l07dq0udgwzfDNbMQwV4sqBZUcpBVs9ScQvVCg\nJoQQIzN20zega29q7QFlHMfhnEazd28vw60eJmYY+NkKa9U08rvhKFATQoiRaW5vabA51Gq0d9HS\n7qe+K5Ujp1xVw7UUAy+5G3aZz+Yao8cfq61+RuqHAjUhhBhTWQmYItWGHJxYAs5Z926ADaFPjTom\ntVyQfsndApYSw40+B4DmmjXqIqpRNxQFakIIMSKtZm83L0DU8DnLmjRHfmsuelKp4HBSI1D3a2b4\nTTP8NGvU1PTdYBSoCSHEiIw+kEx53Vpq1HFZFSiUqUZ725sxCDfC7lb+dpo1amr6bigK1IQQYkTG\n2jVLE+fmBU5txyxRfi5Qrtpr+vcUYW26v4+lwUZ7q2tmI4ZI7bI55SxKZDTyuyEoUBNCiBGJMoQ1\nW87Dp5qcDSQxq2pWV7/33zt2pRbLtTbhGOxraZRimIkYNLMRNn8/KaZadUOY7H7UhBDyPNBsgma9\nfKvJqe1BmRi7EgpxO1+GCgWHZjZi9PKywCAfS9jo2JKS9fQVbKcpykwF2zwQux6WQX0fqyAHCYIc\n6vD1X1wI85hdEN/5C5yVNZz82wOtWlW7DKq/rQQpasE5uUiOtk5m+t+PCFCgJoQQI9IK1J61B2o5\ny2HlzWLsS7YFoGqyzi5jkZArw8Z7JXinnS0G+1oK1uhmPTR30UpBbrkCv6eUCY6Pa2mt99reTFYq\nrP77AUR5Wfwx/2sXIct5goq3FuocGNfcTozYTFWaatQNQ03fhBBiLMWFwqlZZmbgXNxrPEXBcfji\naiH2JZdVm6egksN//yrCF1cLUSpX9f9qrfmd8QTr75agQi1OulqK0Ndbz0Fk5aWw+m6hIEgrmV04\nDrNDv+o8TWvRE5qi1SAmG6jj4uIwbtw4vP7669i4caPOPFevXsXkyZMxYcIEREVFNXIJCSGkZlq1\nafdmtU7N2nivRGv1sOqcTq9A1Ll8JP8dCNlm/oLXZckPcPSJcBDZlEBrSET61abNd/5c405c5gc2\ngcnRXqpUc9GTZBr53SAm2fTNsiyWLl2KVatWwc3NDZGRkejTpw/8/f35PMXFxfjmm2+wYsUKuLu7\nQyqVNl2BCSFEB81AzXn51Zg/IbcSm+6XCo55WInwXrAdPK3FOJlWju1JpYK9pB8XK/DOuad4P9ge\nA31bCM41z3wCESsHK6r6qve1FWNYcyu9ys5kpcLs9AHBMUXzQIiy08GUlVTlkclgfmQbKiLfE+TT\nrFFnlipQoeBgYYRR5i8Ck6xR37p1C76+vvDy8oJEIsHAgQNx9uxZQZ6jR4+iX79+cHevakZydHRs\niqISQki1NJfx1OxDVidnOfxwo0hwzE7MYkVPJ4R5WMDfToIZbWyx/hVntNCosZYrgCUJhVh4U4Fy\nB9XmHBJOAf8i1eCyOR1s9a9N790ARqGqCbNuXij7ZAUqJs0R5JPE/g4UFwqOWUkYeFipwgsLIK2E\natX1ZZKBOicnBx4eHnza3d0dOTk5gjxPnjxBYWEhoqKiEBkZiSNHjjR2MQkhpEaijCeCdE0jvg8k\nl2k1Eb/drBTuVsKg7GMrwepezhikY3rVxaxKXLES3iOw4DEAYJS/FUL1Xde7UArJ5dOCQ5VjpgMW\nlpCHv4pKe2f+OCOr1MoLVNXe1aXSCmX1ZpJN3/pQKBS4e/cuVq9ejbKyMsyYMQMdO3aEr69+Ux8S\nExONXMK6MbXyKFG59GeKZQJMs1ymWCbA8OVq8/iB4Ev2sZxBqY57yFhg0wM7qNedwh0q0cZGUW2Z\nXrcBPL3M8GumFWScqpac6NAcPTMT+HRgwRM8tpFhsGUBEhMzdV1Ki9ulE7BVq02XO3vgjqsf8HdZ\nPLr0gvfZ/fzr8pMHkOjbTnANO7klANUPg4TH2fAq0a/vvT4CAwONdu2mZpKB2s3NDZmZqg9UdnY2\n3NyEi9i7u7vD0dERFhYWsLCwQJcuXZCYmKh3oDalP2piYqJJlUeJyqU/UywTYJrlMsUyAUYoF8vC\nUpotONSsew/A1kEr6/7kMkjlqmZvCzHwQagXnqY8rLFMQQD6FMqx7HoRbj6VAQCSHIT94GEVKRja\nx7tOm29YbfqPIM0MGIXAoNZ8+rE0TBCobVMfIMjdBZyDqqbdUVyK0/nFfLrM0hGBgfZ6l4GomGTT\nd7t27ZCamoqMjAzIZDLExMSgd+/egjx9+vTBtWvXoFAoUF5ejlu3bgkGmxFCSFNi8rLAyGR8mrO1\n1xmkWY7DjgfCAWQjm1vBxVK/jTta2Euw4mVHLA1zwBBfS1Q2Ew4oa1XwpE5BWvQ4EeInD1TlZkSQ\nhw8U5Kl0dIXCP0hwTHzjT0HaR6vpm/qo68ska9RisRgLFizA3LlzwXEcRo4ciYCAAOzZswcMw+C1\n116Dv78/wsLCMGnSJIjFYowePRotWrSo/eKEENIItAaSVbPQSXyuTDDQykwEjG9lXad7MQyD7u4W\n6O5uAXTsAO6ABIyiqk9YJM0DCqWAvX4DbiWxRwVpRcfu4JxctfIpOoVBnHyfT4uvX4S85yA+7asx\n8julhPqo68skAzUA9OjRA7t27RIcGzNmjCA9efJkTJ48uTGLRQghehGlJwvS1QXqAxoLm/TxsoCr\nnrVpnSQSsD4BED9W9W2LH92DolNo7efKZTCLOy44JOs1RHfW4FCY79+kuu2NP1GhkAPiqrDibiWC\nmaiq/x2oWqSlsJKFvblJNuSaNHpihBBiBKLUR4I06xOglSevXIHzmcIBViP99ZvnXBO2RRthWR7e\n0es88V9xYNSmWnE2dlB0Ca/2Hpytqs+ZKS2GKOWh6lqM9uYcKdT8XS8UqAkhxAj0CdTHUyvAqu2W\n4W8nRkfnhm9eoWjRVpAWP7qr13lmsb8L0rKw/oCZue7MIjEUrTsJ73P/uiDtayNstE2l5u96oUBN\nCCGGxrIQpWkGau0xNKfShMt7DvOz0nuzjBpvr1GjFj+8A3BcNbmrMNI8iK9fEhyTV9PsraQI6ii8\nz/0bgrTmXGqqUdcPBWpCCDEwJjcTTIUqCHM2duAcXQR5nhTLcb9AVcNkAPRrpueCJLVgvZuDs1At\niMIUFehck1ud5MJxMKxqbVKFTwBYjZHdmhRBwYK06P51wQ8CCtSGQYGaEEIMTKvZu1mA1t7NJ1OF\ntekurmZ6T8mqvQBisAGtBYc0m6UFOA6S88LR3vJeQ6rdb1qJ9WsFzlz1g0BUkA8mW7VkqY9GH3Uq\nLSNaLxSoCSHEwESpDwVphcZmGRzH4WSacBBZ/2baS4I2hKJ1Z0FafDuhmpyA6NE9iNOSVeUTiSDv\nMaD2m0gkULTU7A+/x/9bc4pWaokcbC1N8EQbBWpCCDEwnTVqNUmFckHtUsIAvb0M0+ytpGirEajv\nJFTbT605iEzRqYdglbGaaNbcRWrTwhzMGdiZqWrlFQogp4wFqRuTnUdNCCGNorgA5vs3od2lMzAD\nBzawAypHTgHbvP7LiWrWqDVHfP+RWSlIv+RuDjsDzy9WtGwHzsyMXx1N9DQbTHYaOM0dvCrKIIk7\nITgk6zVY7/toPieR2iIoDMPAx1aMO/mqvviUEgU8rA3UxP+CoBo1IeSFxWSnw/qzf8A8ZjcsCvIg\nKngKyZVzsPpiFiRxJ+t30YoyiNI1ds3SCNQXNOZO9zRwbRoAYG4BRaBwVLbk+mWtbJJLp/n9pQGA\ntXeColOY3rdR+Gv0hSffFw4o05yiRbto1RkFakLIi6myApY//AuiXO0dpRi5DBY/fQXxrat1vqzo\ncRIYTtW8y3r4ADZ2fDq7TKE12jtM3+0n60jRobsgLfnzrFYes9MHBWl5r8GARP+53Jy7NzgrGz7N\nlBYLRphrrvmdQgPK6owCNSHkhWR2dAfEGk3U6hiWhcVPXwMlRdXm0UVzcRGFRh/uHxq16fZOZnC2\nNM5Xsbx7H0FadP86GGmeKv3oXtUcazWyPsPrdhORCGzzVsJDj1XN3340RavBjPLpkMupaYMQYrqY\nwnyYH/5NcEzaujMqJs8DpzYlSSTNhcW26DpdW6Q26hkA2ADh4iMXNPqnX/asZuUvA+DcvaForpoL\nzXAcJJdO8WnzA5sF+eXtu4HzaFbn+2g1fz9SBWqtKVrU9F1nRgnUQ4cOxQ8//IBHjx7VnpkQQhqZ\nWcxuMOWqrSU5G3s8GfEmZK+OgWx4hCCvJPZ3wRrWtREnCwO1eo26WMYiIVcYqMM9jdPsrSR/SVir\nNju+F2AVECXfhyT+vOA12aCx9bqH1oCyJ6qR3800+qgzS1lUKmiKVl0YJVDPmjUL169fx8SJEzF9\n+nTs378fpaWltZ9ICCHGJpdDcu6I4FDlyMlQWFZtLVk5aipY7+b8awzHwXzXOv2uXSSFKCOFT3IM\nI2gWvpxdCblajPK1EaO5nXEn38h7Dgan1ucsykmH2e/bYbF+qSCfIqA1FMF67LClA+vXUpAWqc3J\ntpIwcLdShRoWQHopNX/XhVEC9ejRo/HLL7/gt99+Q+fOnbFmzRoMHToUixcvxrVr14xxS0II0Yv4\nrziICp7yac7SGrJX1PplzcxRMe5twTmSvy5ApLGOtc5ra+Rh/VoBlqq9pTVHe79s5No0AHCOLpCH\nvyo4ZrHjJ8E2mABQOWZ6rSuRVYf19AUnVjVxi57mCPr2NZu/qZ+6bow6mCwgIADz5s3DoUOHEBUV\nhWPHjuEf//gHxo0bh927d4NlaeI7IaRxmf2hsVRmjwGCYAoAii7hULTqIDhmvm9DrdcW3xVWRBSt\nVWthy1kOF7M1m72N1z+trnLMdHAa71Gd7KW+9a5NAwAkZmA9hPtti9If8//WWqGM+qnrxKiBWiaT\nISYmBu+++y6WL1+ODh064NNPP8Xw4cOxfv16LFq0yJi3J4QQofJSiG/8KTgk6zNUOx/DoGLcTMEh\nya2rVZtO1EB8T/i6+jKe1/JkKJap2r0dzBm0N8CWlvrgnFxR8eYHgoFyfBl9WqBi2vsNvofmXHH1\n1dm0atQ0RatOjNI5cvfuXRw8eBDHjh2DRCLB0KFD8eGHH6J5c1W/T8+ePTFt2rRqrxEXF4dly5aB\nZVmMHDkSkZGRgtfj4+Px4YcfolmzqhGKr7zyCmbMmGGMt0MIeU6Ib1wGI1PVallXT7AaI5b519p0\nhrxtF0juqNbINt+7AeX/t0z3xUuKIHqSJDikaK1acESz2buHhwXEBtjSUl/ysP7gJOaw+G0VRLmZ\n4MQSyEP7oSJitmCed32xzfwFafV+apqi1TBGCdTTpk1DaGgo/vnPf6J3796QSLRv4+3tjVdffVXH\n2QDLsli6dClWrVoFNzc3REZGok+fPvD39xfk69KlC7777jtjvAVCyHNIciVWkJZ37VVjv2zl6GmC\nQC25HQ/RvetgWwdr5ZX8FSdY6EThEwDYOQKo2oTjj6zG75/WpOjWC6Vde4IpzAdnaQVYWBns2lo1\narX9uLUWPaGm7zoxSqDeu3cvvLy8asxjZWWFTz/9VOdrt27dgq+vL3+NgQMH4uzZs1qBmqNdWAgh\n+mJZSDSaveVde9V8SptOkLcLgeR2PH/MfJ/uWrUk4Q9BWtE5nP/3w0IFMktVQdxcBHRza5z+aS0M\no/eGG3WhXaNWBWpPazHMRIDs70cgreRQVMkafH3z55VRnlJUVBSkUqnW8aKiIowePbrW83NycuDh\n4azkpMgAACAASURBVMGn3d3dkZOTo5Xvxo0biIiIwHvvvYeHD/Wf50gIefGIniSBKSnk05y1DdjA\n9rWeVzlK2O2mrFULM1VAfEO4jrY8pCf/b83VyLq6mcNK0njN3o2Bc/cGZ6Y2DawgHyiqigNihkEz\njX7qJ9T8rTejBOqMjAydI7orKyt1Btz6aNOmDQ4ePIitW7di3LhxWLBggUGuSwh5PonVasUAoGjT\nBRDVvouTslatTnMEuCThApjyMtU5ji6C7R9Nodnb6MQSsF5+gkOiNLWR3xoLn1Dzt/4M2vR9+vRp\n/t9//PEHbG1t+bRCocCff/5Za5M4ALi5uSEzU7VQfnZ2Ntzc3AR5rK1VUw3Cw8Px3//+FwUFBXBw\ncNCrrImJibVnakSmVh4lKpf+TLFMgGmWqynK1PLPc1APjxnufsjVKEd15bLp1h9BaoFecjseGccP\nofjvgWitDm+DpVr+vFbBSH3wAACQL2NwT2ovuJ5naRoSE/XrujPFvx+gu1z+ts5wwgM+nXvtCvLE\nVd/VtjILQO0pXUvJQcuKCs1L1FtgYP23JTV1Bg3UCxcuBFC1B+mXX34pvJFEAi8vL8yfP7/W67Rr\n1w6pqanIyMiAq6srYmJitK6Xl5cHFxcXAFV92gD0DtKAaf1RExMTTao8SlQu/ZlimQDTLFeTlElW\nCZvUB4JDzq8MhpNaDbDGcgUGQn7lpKCvuuXx31C6eB1Ej+7D+rFw2VCb0VMQ6NsCALA/uQyAavGP\ntk4SdGsr3MSiOqb49wOqL5d5YDvgtmocgCdbAee/8wVblOH3PNVzKDF3QGCg/t/ZLzKDBupLly4B\nAEaNGoWNGzfC0dGxXtcRi8VYsGAB5s6dC47jMHLkSAQEBGDPnj1gGAavvfYaTp06hd27d0MikcDC\nwgJfffWVId8KIeQ5InpwG0ylqvbGOrmC8/St4Qxtla+9KQjUoqw0WP33fTBPhd15ijadwP4dpAEd\ne08/j83ef2O9NBY9yVQtp+qnsejJE2r61ptRRn3v37+/wdfo0aMHdu3aJTg2ZswY/t/jxo3DuHHj\nGnwfQsjzT5x4U5BWtA2p83KZbFBHyPqNgtkp1feb+MEdrXwVr6vWcyiVs4jX3ITD4zkO1Bo/ftTX\nPffVmKKVVqKAguMadS75s8pggXrr1q0YO3YsLCwssHXr1hrzRkRE1Pg6IYQYkjjpliCtCOpQTc6a\nVUx4B6J71yBWW8xDnazPcLBBqjnWf2ZX8lOSAMDbWgR/u9oHsD2rNAM1k5MOyGWAxAz25iI4mDMo\nqKzqm5exQFYpC2+b5/d5GIrBAvWOHTswbNgwWFhYYMeOHdXmYxiGAjUhpPFwHMQPbgsOsS1rn5al\nk4UVyhd8C8tvP4I4VTglVB4ciorJcwXHtPeetgDzPNcgrazBOrpCJM0FADAsCyY7Hdzfu5H52kpQ\n8FTGZ08pllOg1oPBArV6c7chmr4JIcQQmKw0MEUFfJqztAbr41/v63FOrij7fA3MzhyqmvIlFkPe\nuUfVDlVq071kLIfzGv3Txt572hSwXr58oAaq+qkVfwdqP1sxbqoF6ifFCoR6aF2CaDDuRqhq5HK5\nzqVECSHEmMRJGv3TLdroNX+6RmbmkL06BrJXx1Sb5UpOJUrkwk04OjbSJhxNifP0BdSWXRVlpEC5\ntIkvbXdZL0ZZ8GT79u04deoUn168eDF69eqFsWPH4vHjxzWcSQghhqXZP822qmezdx2dSRfWpnt7\nWUAieo6bvf9W08hvze0uU0po5Lc+jBaolVOz4uPjcfLkSXzxxRcICgrC8uXLjXFLQgjRSZQk7J9W\nNEKgrlRwOJ8hDNR9vS2ryf18YT01VifLrH7kN9Wo9WOUtuicnBx++8nz58+jf//+ePXVV9GqVSu8\n/fbbxrglIYRoKysV7IsMAIqW7Yx+W81mbydzBsEuz3+zN6Bdo2bUpmh524ghYgD270eTW86iVM7C\nWkKbc9TEKE/HxsYGT58+BVC1CEr37t0BVK1OVllZWdOphBBiMOLke4KtJ1kvX8DWvoYzDONEWrkg\n3dvb8oVo9gYAztVDuDlHkRQoqVqRzEzEwMuaatV1ZZQadWhoKJYsWYLWrVsjNTUVPXr0AAA8fPgQ\n3t7exrglIYRoET0WrketCGhr9HsWyVjEajV7P/+jvXkiMVi3ZhCnJ6sOZaWBbdEGQFXzd1qJKjin\nFCvQ2vHFaG2oL6PUqBcsWIDg4GBIpVL85z//4dfgvnfvHgYOHGiMWxJCiBZR8n1BmvU3/rrZp9Iq\nBIuceFmLXphmbyXOo5kgLcpK4//tpzXymwaU1cYoNWpbW1ud205S/zQhpDGJNWvUzYOMfs+jKWWC\n9CBfK4ie50VOdGA1AjWTrQrUmiO/aV/q2hl1YnNOTg7y8/O19qZu06aNMW9LCCFARZlgIBMAsM31\n27WqvhILZLiTL6whDvJ9MUZ7q2PdhV2cIkGgpj7qujJKoL537x4+/fRTPH78GBwn3HOVYRhcvHjR\nGLclhBCeKOWhcCCZhw9gZWPUe+56IKxNh7iaaQ2eehFw7tU3fWsG6tQSOViOe+FaHerCKIF6yZIl\n8PDwwCeffAJXV9fne21bQohJEmv0TyuM3D+dV67ASY3R3mNbWBv1nqaqpqZvZwsRbCQMP32tXFE1\nTcvd6sX7QaMvowTqR48eYfPmzWjevLkxLk8IIbXSHPHNGrl/es+jMqhNnYa3tRhhHuZGvaep4lzc\nwYnFYBRVzdqignygrBSwsgbDMPC1FeOuVNVF8KRYQYG6BkYZ9d2qVSvk5eUZ49KEEKKXxhzxnV/B\nYvdDYbP32BYv3iAynlgCztVLcEi9n9pPY0DZ4yIa+V0TowTqqKgorFy5EpcvX0ZeXh4KCgoE/xFC\niFHJKiFK01iRrLnxAvXm+yUoVwhXIhviZ2W0+z0LNAeUMdnp/L819+ROpkBdI6M0fc+ZMwcAMHfu\nXEH/NMdxNJiMEGJ0otRHfLMrALCuHoCtg1HulVggw75Hwtp0RJANrCQvaG36b6xHM+CGKi3KTuN3\n0fK3E4aeR0U08rsmRgnU0dHRDb5GXFwcli1bBpZlMXLkSERGRurMd/v2bcyYMQNLlixB3759G3xf\nQsizr7H6pysUHP6bUAT1CageViKMaP5i16aBmhc9CbDTbvpWVuSItv9v777jo6jWx49/ZneTbAoh\nnQQSSAg19F4F9PpDLFRBBIFcUUEQLCA2LFiwIVxFAVFE4FoQEVEsiF+lCAQIINIhdAIJ6QRI3d35\n/ZGbJZMNZANJdpM879fLl8zMmZ0ns+WZc+bMORWSqNu3b39T+1ssFmbNmsW8efMIDAwkOjqa3r17\nEx4eblPuo48+omvXrjd1PCFE9WLT47sCmr1VVeU/ey9xLFPbbPtEq1q46SXhWIo9oqUUSdR1PHQY\n9QU9vgEu5auk5VrwN0qHspJU2JQlx44dY9asWTzxxBOkpKQAsGHDBo4cOVLqvgcOHCAsLIyQkBAM\nBgN9+/Zl48aNNuVWrFjBbbfdhp+fX7nHL4Soumxq1OHlW6POt6jM2XuJtWe1j2PdWteN7sE1aFzv\n67Ad9OTqPWqdotBAmr/tViGJetu2bfz73/8mKSmJnTt3kptbMED9uXPn+PTTT0vdPzk5mTp16liX\ng4KCSE5OtimzceNGhg4dajOoihCiBjOZ0J09plllKacadZbJwvpzOTy6KZ01p7VJOsxTz9Q2tcrl\nONWBGhiCWqQpW5eWBHlXJyspfp9aOpRdW4U0fS9cuJAnn3ySoUOH0qdPH+v69u3b8+WXX5bLMebM\nmWPttAaUOVnHxcWVXqgSOVs8hSQu+zljTOCccVVkTMakeJrn51uX871qczQ5DZLTSt33yNE4TmTr\nOZxlICFXR6ZJR64FclWFSyaFS+aS6za19BYeqXOJhFOlH6OsnPH9A/viivL2w+3i1Ud1z+7cRk5g\nQU3bK9cVuHov/5/4NNqYzxV/Cbs1blzxE644SoUk6uPHj9O9e3eb9d7e3mRmZpa6f2BgIImJidbl\npKQkAgMDNWUOHTrE9OnTUVWVixcvsnXrVgwGA7169bIrRmd6U+Pi4pwqnkISl/2cMSZwzrgqOibD\nheOaZSWyeanHM6sqS2JP83umF4lZluuWLS7QqGNWNz/CawWXOdbSOOP7B/bHpa/XAIok6nCjAfP/\n9kvxzuW7pKuP66YrnjRuHFr+wVYDFZKovb29SU5Otpl7+siRIwQFBZW6f1RUFPHx8SQkJBAQEMC6\ndet44403NGVWr15t/fdrr73GLbfcYneSFkJUX7pTZbs/nZhl5o3dmexP8wDKlqT/Vc+NJ1rVwtu1\nwrr7VGlqUD04uNu6fP1HtKTn97VUSKK+4447mDt3Lm+++SaKomAymdi9ezcffPAB/fv3L3V/vV7P\ntGnTmDx5MqqqMmDAACIiIli1ahWKojB48GBNeXljhRCF9Kft7/F9ItPEtJgMUnPtS9A6Bep56ukc\n6MrdDdxp6F2hExBWebZjfl/tUFbHXYdRr1gHirliUknJsRAoQ4naqJBP2YQJE3j11VcZOHAgqqpy\n//33Y7FY6NevHw8++KBdr9GtWzdWrlypWTdkyJASy7700ks3HbMQohqwWNCdKdaR7Bo16oQsM1O2\nppORp+3f4qKDW0LcaOPvSoiHDk+DDneDgpeLgp+bDoNOKgb2Kv6IVtFnqXWKQngt7Zjfpy6ZJFGX\noEIStcFg4PXXX+fRRx/l8OHDWCwWmjZtSv369SvicEIIAYByIR4l5+ooYaqnN6qf7e22LJOFF7Zn\n2CTpLkGuTG1TSyaIKCfXG/QECpq/tYnaTKfS747WOOWeqHNycvjvf//L+vXrOXfuHIqiULduXf71\nr3/xwAMPYDTWvEnUhRCVQ1/s/rQ5vAmUcGvsg72XbZ7b7eOby8tdAmvuRBoVwBKknZhDSUkEUz4Y\nXICS71MLW+WaqE0mExMnTuTIkSN069aNHj16oKoqJ0+e5LPPPiMmJoaPP/4Yg0Hu6wghyp/udOkz\nZm1NzOW3eO0z0L1D3BhR+6Ik6fLm5o7Fxx9dRkHPb0W1oKRcQA0u6N0d4a1tuTiRKYm6JOWaMVev\nXk18fDzLli0jMjJSs+348eNMmDCB1atXM3To0PI8rBBCACWN8a1N1FkmC7P/uaRZF1FLz/PtvDl7\nMqnC46uJ1KB6kHH1ES1d0jnM/0vUjYp1xjuRacJkUaUfQDHl+kzBH3/8wYMPPmiTpAEiIyOJjo7m\njz/+KM9DCiFEAVUtYYxvbUeyr49laXp46xR4vp03xho+01VFKt7zu+h9aj83Hb6uV899ngXir8hQ\nosWVa6I+efIkHTt2vOb2zp07c+LEifI8pBBCAAX3P5Wsy9Zl1eiBWmS86aRsMyuOZ2n2GdHIgyY+\nLpUWY01k+4jW1UStKAqNamvPf9xFaf4urlwTdWZmJr6+vtfc7uvry6VLl665XQghbpSuWG3a0qAx\n6K7+xC0+fIXcIpU1XzcdDzT2qKzwaiz1Oo9oATSqrW3+PiaJ2ka5JmqLxYJef+3HGhRFwWIp28g/\nQghhD32x+9NFBzo5d8XEumIzXT3UzBMPg4woVtGu1/QN0LhYoo67mI/QKtfOZKqq8vLLL+Pq6lri\n9ry8vPI8nBBCWNnUqIsMdLL8WJZmcNAGXnr6hcmjopXBpuk7OQHMJtAXpJ/iifrYRRlKtLhyTdR3\n3313qWXuuuuu8jykEEKAql6zx3dqjtlm3ugHGntKz+LK4u6JxdsXXWY6AIrZhJKWjBpY8Ix1PU+9\nZijRzHyV5ByLDDpTRLkm6pdffrk8X04IIeyiZKRaEwGA6uqGJSQMgBXHs8kvUp0O9tBxWz23yg6x\nRlOD6kGR90d34Rzm/yVqnaLQyNvA/vSrTd5xF02SqIuQGzRCiCrPZqCTsIagN3Al38KPp7I120ZE\nekhtupLZNH9fiNcsS4ey65NELYSo8mymtvzf89O/xeeQbb46nrevm45+9d0rNTZReoey4olaOpRp\nSaIWQlR5+hOHNMvmBo1RVZUfTmpr04PC3XHTS226spU2OUfxRH1UatQakqiFEFWbqqI7cVizytKw\nOX+n5HP68tUHp/UK3NNAeno7gk2NOkmbqBvWMuBSJBslZVtIyZERygpJohZCVGlKSiK6SxnWZdXN\niCU0nNXF7k33CnHD3ygdlByh+LzUStJ5sFxNxK56xaZWfShdatWFJFELIao0/fGDmmVLeFOScmFz\nYq5m/aAIuTftMJ61UL28rYuKKR8lLVlTJKrYUK4H0+U+dSGnTdQxMTEMGzaMe++9l6VLl9ps37Rp\nEyNHjmTUqFGMGTOG2NhYB0QphHA03fFi96cjm7PmdDaWq33IiKilp7WfjOntSKV1KGvuq31/Dkmi\ntnLKiaEtFguzZs1i3rx5BAYGEh0dTe/evQkPD7eW6dy5M7169QLg2LFjPPPMM6xatcpBEQshHEVf\n7P50XkQzfjqtHeBkUISHjHTlYJY6oeiLXFQpSeegRQfrclSxRH04w4RZVdHL++acNeoDBw4QFhZG\nSEgIBoOBvn37snHjRk0Zo/Fqp5CsrCx8fHwqO0whhKOZTDbPUG/xiiS9yFSWHgaF/xcqA5w4WvH7\n1MVr1CEeOmoXmfIyx6xyKlM6lIGT1qiTk5OpU6eOdTkoKIgDBw7YlNuwYQPz588nNTWVuXPnVmaI\nQggnoDt7HCX/6hwCFh9/lqd6Alc7It0RZpTJN5yA7SNa2kFPFEWhua8L2y5cfT8PZeQTWdsp01Sl\nqtKf3j59+rBixQpmz57NK6+84uhwhBCVTFfs+elL9Zuxr1hv4UHh0onMGdiOTnbOpkzxDmUH0uQ+\nNThpjTowMJDExETrclJSEoGBgdcs37ZtW8xmMxkZGXY3gcfFxZVeqBI5WzyFJC77OWNM4JxxlVdM\nDXZtpeiT0X+5apNBMw8TeYkniUvELtX5XJW3ssalz86jdZFl5cI54o4eAeVqfdEnWw94WZd3Jl4h\nLu6CXa/fuHHj0gtVUU6ZqKOiooiPjychIYGAgADWrVvHG2+8oSkTHx9PaGgoAIcPF3QmKct9amd6\nU+Pi4pwqnkISl/2cMSZwzrjKLSZVxeP8Cc2qn7xba5ZHRPnRuG7dyo2rHDljTHDjcametVCuXAJA\nZ8qnSYAvql+QdXuYSWXu2WRM/+uxn5yvp3Zowxo/QYdTJmq9Xs+0adOYPHkyqqoyYMAAIiIiWLVq\nFYqiMHjwYP78809++eUXXFxcMBqNzJw509FhCyEqkZKcgC49xbpsMriyp3ZD63KAUUePYOlE5kws\nQfXQn7zaS1934RzmIonaaFBo5uvC/iJN3ntS8ukbJonaKXXr1o2VK1dq1g0ZMsT67zFjxjBmzJjK\nDksI4ST0R/dqlg8FNMGku/qT1r+Bu8yS5WQsdbSJWrlwDpq305Rp618sUafm0TesZg/9WqU7kwkh\nai79EW2ijvFtdnWbjOvtlNQ6oZrl4o9oAbQNcNUs/5MqHcokUQshqiT9kX80y38HNLf+W8b1dk6l\nTc4B0MLXBUORhpBzV8wkZdfs56klUQshqhwlOUFTGzMpevb5Xe3cNFAeyXJKNo9oJcbblHE3KDQr\n9pjWnpSaXauWRC2EqHL0+7Vj+//j35QcQ0FTd3gtPW38ZVxvZ2Q73nc8WCw25doGaN+/2ORcmzI1\niSRqIUSVY9i7Q7McU6eN9d+DZVxv51XLRzuLVn4eSqrtc9Kdg7T3qXck5WFRVZtyNYUkaiFE1WIy\noT+4W7MqJrgtALVdFfrV8B7Czs4SUl+zrEs4Y1MmytcFzyI3qi/mqRzJqLnzU0uiFkJUKbpjB1By\nsqzLKW61OVq7AVBQm3bTS23amdmTqA06hU7FatXbLtTc5m9J1EKIKsWwZ6tmeXudNqiKDje9jOtd\nFdiTqAG6FEvU25PySixXE0iiFkJUHaqKIVY75e3m4PYA9Atzx8dNftKcXfFErSScLbFc8fvUhzNM\nNfYxLflUCyGqDN2po+hSrs6wkaNzYXNIewwKjGjk4cDIhL3srVH7G/U099UOnrkpoWY2f0uiFkJU\nGYYd6zXLW4PbkW0wck8Dd4I9ZICTqkANDEbVX03Auotp8L+JOorrE6LtGLjhvCRqIYRwXiYThi2/\naVb9Ua8rLjp4oLHUpqsMvQG1+PPUiSU3f/eqq51UZX9aPsk1sPlbErUQokrQ/xOD7mK6dfmywZ2N\ndTsyONydwBo+DWJVY2/zd4iHnqY+2ubvmlirlkQthKgSXP74QbP8W1gPjB7ujG7q6aCIxI2yTdQl\n16gBbi1Wq157Nge1hg1+IolaCOH0dCcPYziwU7Puh/DbeKS5F7Vc5GesqrG3Rg1we6hRk6iOZ5qI\nu1izBj+RT7gQwumZVi7VLO8KaI6uUXPurC+jkFVFZUnUAUa9zeAnv57NqZC4nJUkaiGEU8vZGYP3\n/hjNuq+aD+b5drXQyZjeVZIlJEyzrFw4B6Zr15KLX5D9Hp9DtqnmNH9LohZCOK3LqWmweLZm3V6/\nxnS6vSf1PA3X2Es4PQ8vLLX9rIuK2YRSwtzUhbrXccPb9epF2eV8ld/OZldoiM7EaRN1TEwMw4YN\n495772Xp0qU229euXcvIkSMZOXIkjzzyCMeOHXNAlEKIipKYlM7Ft54n4EqKdZ0ZhW13TuSecHkc\nq6qz1AvXLOvOnbxmWVe9woAG2uFhV57IrjEzajnlJanFYmHWrFnMmzePwMBAoqOj6d27N+Hh4dYy\n9erV45NPPsHLy4uYmBjefPNNFi9e7LighRB2U1IS0SWcQbmYXvBcrasrGN1RjR6owLHd+wn+8xvq\nZKVq9vuj9QCG3d7WMUGLcmUJbQhFZkHTnz2JuVOfa5YfFOHO8mNZFLZ4x18xs+1CHt2D3a65T3Xh\nlIn6wIEDhIWFERISAkDfvn3ZuHGjJlG3atXK+u+WLVuSlJRU2WEKIcoiLxeXjT/TbO23uKckXLdo\nmxLWHQlpQbvHJuGik/vS1YElrKFmWRd/4rrlA4x6bq3nxu/xV5+j/vpYFt3quFb7+cedsuk7OTmZ\nOnXqWJeDgoJITk6+ZvkffviB7t27V0ZoQogboDt2AI8Xx+L2xdxSk3RJTtSNIuD5t3A3ulRAdMIR\nLPUiNMulJWqAoQ21tzz2peWzM7n6z6rllIm6LHbu3MmaNWuYNGmSo0MRQpRAH7sB9zefQHfh2p2F\nriVf0bO32xACZ/wHt9reFRCdcBRLaDhqkZqwknQecq/fQaypj4vN9JeLD1+p9gOgOGXTd2BgIImJ\nV2fISUpKIjAw0KZcXFwcb731Fh988AHe3mX7EsfFxd10nOXJ2eIpJHHZzxljAsfG5btvGw1+XIxS\n7IfUrNNzxLchZ90DUQCjORd3Uw4ephwMFhNJ7v6cD2lCUKd2+AT5cezMtZ+zLU/O+B46Y0xQPnFF\n+QTgll7QWqqoKvHbNpNdN/y6+9zuoWc7XtblQxkmVv59imHtI66zV9XmlIk6KiqK+Ph4EhISCAgI\nYN26dbzxxhuaMomJiTz33HPMmDGD0NDQMh+jcePG5RXuTYuLi3OqeApJXPZzxpjAsXHpju7D/ael\nNkn698b/4r0mw0k1+pS4X7sAF8Y08eQuf5dKvffojO+hM8YE5ReXPqIppF+9rRmumDCV8rqNgQ05\nGWxJvNrk/UO6N8NuOhrn5ZSJWq/XM23aNCZPnoyqqgwYMICIiAhWrVqFoigMHjyYzz77jIsXL/Lu\nu++iqioGg4ElS5Y4OnQhBKCkp2Cc+xKK+eogFhadjjc7TmR1WC+b8joKZkq6L9KDKF+5D11TWEIj\nYPdm67I996kBxjb1IiYxDcv/luOvVO8ZtZwyUQN069aNlStXatYNGTLE+u/p06czffr0yg5LCFEa\nVcVt8Sx0lzI0q1/qOInfwnpq1ukVuKeBO/dHehDiKTNg1TSW0LL1/C4UWdvA3Q2MrDldM4YSddpE\nLYSomgx//Yph73bNuk+b3WuTpLsGufJYSy/CvORnqKYyl/ERraIeaubF+vO5XM6v3h3JoBr0+hZC\nOA8lIxW3r+Zp1v3t34xPo67eQXRRVJ5uU4u3utSWJF3DqXXqobpcvdWhu5iOkpF6nT2u8nHTMbZZ\nzZjiVBK1EKLcuK5egpJ9xbqcrXfj1Y4TsSgFPzW1XRWm1r/CPQ3cq/0gFcIOegOW0EjNKt2pI3bv\nPqCBO01qV/+LPUnUQohyoSSexbDxZ826+S3uJ94rGABfV4UPe/oS6VG9O/6IsrFENNUs604etXtf\ng05hWtta6Kv5NZ8kaiFEuXBbuQjFYrEun/EMZkXkHQB4uSjM6uZDfWnqFsWYw5tolvVlqFEDNK7t\nwohG1XuSFknUQoibpjt+CEPsRs26BS3ux6wzoANmdKhNo9ry2JWwZVOjPmV/jbrQ6CbV+161JGoh\nxM1RVVxXLNSsOujTkP8L7QrAuChPOhYb9lGIQpa64doOZRmpKOkp19nDlls1b/uWRC2EuCn6fTsw\nHN6jWfdhqwdQFR1d67gyPLJ6N0uKm2QwYKnfSLPqRmrV1ZkkaiHEjbNYcP32E82qbUGtiQ1qhber\nwrQ2taR3tyiVOVzb/F3W+9TVnSRqIcQNM2z7A/2Z45p1H7YcCcDTrWvhb5TRxkTpLMU6lOlOSqIu\nShK1EOLG5Ofh+t1nmlVrw3pwxLchPYJd6VXX6KDARFVjKVaj1p04DNV86sqykEQthLghLuvXoEu5\nOh2tSdGzIGo4BgUmRHldZ08htCz1GqC6Xb2w013KQLmB+curK0nUQoiyy76C64/LNKu+a/j/OOcV\nzL0NPQiV56VFWegNmBu10K6K2+egYJyPJGohRJm5/roC5dJF6/IVg5FFze7Fx1VhdBPp5S3KztK4\npWZZf1QSdSFJ1EKIMlEupuGy9hvNui8b30O6sTYPNfPCy0V+VkTZmZu00izr4/Y7KBLnI98oHlSW\nfQAAIABJREFUIUSZuK76HCX36jzAaW7efNG4P5HeBu5qIB3IxI0xN4xCVa6mJF3CGcjMuM4eNYck\naiGE3XTxJ2wm3vis2b1kubgzqaUXenlmWtwodw+bgU/kPnUBSdRCCLu5Ll+Aol6deOO0VwgrG/4/\nbgl2o12ADBMqbo5N8/fB3Q6KxLk4baKOiYlh2LBh3HvvvSxdutRm++nTp3nooYfo2bMnX375pQMi\nFKJm0f+zHcO+WM26D1qNRmcw8GiL6j0pgqgc5hYdNMuGAzsdFIlzccpEbbFYmDVrFnPnzuWbb75h\n3bp1nDp1SlOmdu3aPP3004waNcoxQQpRk+Tm4PbfDzSrYgNbsCmkA0MbelDPUx7HEjfP3Kwtqv7q\naHa6hLMoqRccGJFzcMpEfeDAAcLCwggJCcFgMNC3b182btROoefj40Pz5s3R62WIQiEqmuv3n6NL\nPm9dtqDwfusx+LrpGNVYHscS5cTdA0tklGaVfr/Uqp0yUScnJ1OnTh3rclBQEMnJyQ6MSIiaS3fi\nMC5rv9Ws+yayH0d8IniouRee8jiWKEemFh01y3pp/qbGtlfFxcU5OgQNZ4unkMRlP2eMCW4uLl1O\nFs0WvaHpQJbgEcCCFvcT6mamcW48N/Ly1fFcVRRnjAkqLi6P2nUoOvK38s924g4fAv3101Xjxo0r\nJB5n4JSJOjAwkMTEq2MIJyUlERgYWK7HcKY3NS4uzqniKSRx2c8ZY4KbjMtiwfjRKxgytK1Zb7d9\nmCwXd2Z28KFpQEjlxlSBnDEuZ4wJKjiuhhGoK+ejXM4EwJCTRTPzFczNOpayY/XllG1WUVFRxMfH\nk5CQQH5+PuvWraNXr16ODkuImkNVcf16HoZdf2lWr2jYly0h7ekVIo9jiQqiN2Bq10OzyrDzr2sU\nrhmcskat1+uZNm0akydPRlVVBgwYQEREBKtWrUJRFAYPHkxqairR0dFkZWWh0+lYvnw533zzDR4e\n0rFFiJtiseD6zce4rvtOs/pI7XDebz0GFx08KrNjiQpk6ngLLn/9al3W794Mo58AnVPWLSucUyZq\ngG7durFy5UrNuiFDhlj/7e/vz08//VTZYQlRrSkX03Bb/B6GPVs165ONvkztNo08vSsjG3pQ11Oe\nthAVxxzVAdXojpKTDYAuIxXdsQNYig2IUlPUzMsTIYSGkpqEy+qleDwfbZOkLxvcebzH8yR6BhJg\n1DFKZscSFc3VDVObrppVLlvXOSgYx3PaGrUQ4iblZuOecBp9ZiJK1mWU7CuQk42Slwu5OSh5uSiX\nL6I7dxJdwtkSXyLF6MPjPV4gziccgAlRXngY5PpeVDxT19tx2b7eumzY9ie5Ix4Dt5o38YskaiGq\nC1VFd+Iwhp0b0e+PRXf2BM1U9YZf7nBAY6Z1fIIEzyAAWvu5cFs9t/KKVojrMrfugqW2L7qL6QAo\n2Vcw7NyEqUdfB0dW+SRRC1HVmU0YtvyOy7qV6M8ev+mXUz28WN9xKM/734FZV3AvWgdMbuWFIrNj\nicpiMGDqcQeuvyy3rnLZsEYStRCiatHv/Au3FQvRXYi/6dcyR0Zh6nwre1v/P57dmUfRuvjQSHca\n13a56WMIURb5t9ypSdT6o/sKOpU1auHAqCqfJGohqiAlNQm3Lz7AsHvLNcuoikKeTwD60AjUWrVR\n3T3B6IHqZgQ3I6qrG7i5Y6lTD0u9cHD3JMek8tamNE2SruuhY2xTeRxLVD61bgNMUe0xFJnu0vXn\nr8l54g0HRlX5JFELUcXoYzdi/Ozdgs5hxaiKDnObrpi6346pRQfiEpLKNILUvAOXOHPZrFn3dBtv\njAZp8haOkX/3SE2iNuzejC7+JJbQCAdGVbkkUQtRVeTn4frNQlx//85mk6rTYbrlTvIGjEYNCC6y\nJcnul99wPoc1p3M06+6ub6R9oIxAJhzH3KID5gZN0J8+al3n+vV8cp5+F2pInwl5zkKIKkBJTsB9\n5uMlJmlzoxZkv7aI3LHTiiVp+529bGLWnkuadaGeeh5rKU3ewsEUhbwBozWrDPtj0f8T46CAKp/U\nqIVwcvpdf2Fc9A5K1mXNelVvIO++8eT3vfemhla8lGfh+e0XuWK6emfaoMDLHbzlmWnhFMwdemJq\n1hbD4T3WdW5L3yercSvwrOXAyCqHfAuFcFamfFy//Aj3uS/ZJGlLQDDZ0z8kv9+wm0rSeWaVV3Ze\nJP6K9r70+CgvmvhIL2/hJBSFvAcmoSpXP+u6tCSMn78HNzFWQFUhiVoIJ6RciMf9jUm4rltps83U\nvgdZr32KJbL5TR3DZFF5dddFdqfka9bfEWZkaEP3m3ptIcqbpX6jggvTIgyxG3H9foljAqpE0vQt\nhJMxxPyB25LZKDlZmvWqXk/efY+Sf8fQm+5Ek2tWmbk7ky2JeZr1Lf1cmNq6lgxsIpxS3tCH0R/8\nW9ux7IelqK6u5N/zgAMjq1hSoxbCWVy+iNvCNzF+/LpNkrb417na1H2TSTQzz8K0mAw2JeRq1od5\n6Xm9U21c9ZKkhZMyuJAz8WXUYvel3b791EEBVQ5J1EI4mqpi2PYHHs9FlzhDkKnDLf9r6o666UMd\nSMvnkY1p7E3TNnfX9dAzp5sPvm7ykyCcmxocSvaUt1Fda87kHNL0LYQD6Y4dwO2bj9Ef3WezTXVx\nIXfEY5huG3jTtegsk4VlR7L49kQW5mJ9bxp46Xm3qw+B7jLHtKgaLI1akDPlLYxzX0TJsh34p7qR\nRC1EZVNV9Pt34rJ2BYb9sSUWMYdGkDt+Opb6jW7qULkWWHUyi6/iskjJsdhsb+3nwhuda+PtKjVp\nUbWYm7cj68V5uL//Arqk844Op0I5baKOiYlhzpw5WCwWBgwYQHR0tE2Z9957j5iYGIxGI6+88gpN\nmjRxQKRC2EFV0cWfxLD9Twzb/kSXXPIPi2pwIW/AaPLvHgGGG3s8yqKqHM0w8fu5HH47XYvL5ssl\nlhva0J1xzb3knrSostR64WS98RmuKz5xdCgVyikTtcViYdasWcybN4/AwECio6Pp3bs34eHh1jJb\nt27l3LlzfPfdd+zfv5+3336bxYsXOy5oIQqpKkpmOrr4k+jOnUR37CD6w3vQXUy77m6mNl3JvX8C\nat0GZTiUSnquyolLJk5mmjiYns/fKXlk5BW2b9vWlIPcdTzZqhbdg2VuaVENuLmTN/oJR0dRoZwy\nUR84cICwsDBCQkIA6Nu3Lxs3btQk6o0bN3LXXXcB0LJlSy5fvkxqair+/v6OCFlcz+WL6A/tgcI5\nmf73P8U6UEHheu122/UF//dNTMSQdLLIQAdl29/Kzv2V4vtbzJCbg5KbA7nZKLk5+CUkkZufjTEz\nDbdLaRjytT2qryc5LIo9/4omKbwVllxQT2aBChbAokKOSSXbrJJd5P/puRZSc8yk5FjIs23RLpGL\nDoY19GB0E0/cZZINIaoMp0zUycnJ1KlTx7ocFBTEgQMHrlsmMDCQ5ORkSdROSJdwFvePXim31wsv\nt1cqP/bXgQvk6lzYWLcjXzW6m/3+TeASsK/kJuqb5WlQGBjuzr0N3fE3SocxIaoap0zUNU1ZpiGs\nTOUVl6VxSy4v3VAur1WddP/ffzVNdf+8lydnjAmcN67qyim7egYGBpKYmGhdTkpKIjAw0KbMhQsX\nrltGCCGEqOqcMlFHRUURHx9PQkIC+fn5rFu3jl69emnK9OrVi19++QWAffv2UatWLWn2FkIIUe04\nZdO3Xq9n2rRpTJ48GVVVGTBgABEREaxatQpFURg8eDA9evRg69atDBkyBKPRyMsvv+zosIUQQohy\np2RkZFT/OcKEEEKIKsopm76FEEIIUUAStRBCCOHEJFELIYQQTswpO5PdLGcdJ7y0uHbv3s3TTz9N\nvXr1AOjTpw8PPfRQhcb0+uuvs2XLFvz8/Pjqq69KLOOIc1VaXI44VxcuXGDGjBmkpaWh0+kYNGgQ\nw4cPtylXmefLnpgcca7y8vIYP348+fn55Ofn07t3byZOnGhTrrI/W/bE5YjzBQVDJ0dHRxMUFMTs\n2bNttjtqboPrxeWIczVw4EA8PT3R6XQYDAaWLFliU6a6zQNR7RK1s44Tbk9cAO3atSvxS1pR+vfv\nz/Dhw5kxY0aJ2x01pnppcUHlnyu9Xs9TTz1FkyZNyMrKYsyYMXTp0sWhny17YoLKP1eurq4sWLAA\no9GI2WzmkUce4Z9//qFNmzbWMo74bNkTF1T++QJYvnw5ERERXLliO22jI+c2uF5cUPnnSlEUPv74\nY7y9vUvcXh3ngah2Td9Fxwk3GAzWccKLutY44Y6OCwomWahMbdu2pVatWtfc7ohzZU9cUPnnKiAg\nwHpl7uHhQXh4OElJSZoylX2+7IkJKv9cARiNRgDy8/OxWCw276ejPlulxQWVf74uXLjA1q1bGThw\nYInbHXWuSosLKv9cqaqKxXLtAe4dda4qUrVL1CWNE56cnHzdMoXjhDs6LigYvOWBBx7gqaee4sSJ\nExUakz0cca7s5chzdf78eeLi4mjZsqVmvSPP17ViAsecK4vFwqhRo7jzzjtp3749DRs21Gx31Lkq\nLS6o/PP1n//8h8cffxxFKXmyFEedq9Ligso/V4qiMHnyZKKjo1m9erXNdmf+zbpR1a7puypr1qwZ\na9aswWg0snXrVqZNm8Z3333n6LCckiPPVVZWFs899xxTpkzBw8OjUo5ZmuvF5KhzpdPp+OKLL7h8\n+TKPP/44u3fvpn379hV+3JuNq7LP1+bNm/H396dJkybs2rXLIa0fJbEnLkd8thYtWkRAQADp6elM\nmjSJ8PBw2rZtW6HHdLRqV6N21nHC7YnLw8PD2izXvXt3TCYTFy9erNC4SuOsY6o76lyZTCaee+45\n7rrrLnr37m2z3RHnq7SYHP258vLyokePHhw6dEiz3tGfrWvFVdnna+/evWzatIlBgwbx4osvsmvX\nLl55RTvbnCPOlT1xOeKzFRAQAICvry99+vSxmVnR0Z+rilDtErWzjhNuT1xF76MUfvhq165doXFB\nwT2fa13FO3JM9evF5ahz9frrrxMREcH9999f4nZHnK/SYnLEucrIyODy5YJpO3Nycti+fbvNjEuO\nOFf2xFXZ52vixImsWbOG1atXM3PmTDp27Mirr76qKeOIc2VPXJV9rnJycsjKygIgOzub7du3ExkZ\nqSlTHeeBqHZN3846Trg9cf3555989913GAwG3NzcmDlzZoXH9eKLL7J7924uXrxI//79GTduHPn5\n+Q4fU720uBxxrv755x9+++03IiMjGTVqFIqiMHHiRBISEhx2vuyJyRHnKiUlhVdffdXa8eeuu+6i\nc+fODv8e2hOXI85XSRx9ruyJq7LPVWpqKs888wyKomA2m+nXrx9du3Z12nNVXmSsbyGEEMKJVbum\nbyGEEKI6kUQthBBCODFJ1EIIIYQTk0QthBBCODFJ1EIIIYQTk0QthBBCODFJ1EJUQxMmTOC9995z\ndBhCiHIgz1EL4SCvvfYaP//8M4qioCgK/v7+dOzYkUmTJlmHSSzNTz/9xHvvvceGDRs06y9duoTB\nYMDd3b0CIhdCVCapUQvhQJ07d+bXX3/lxx9/5OWXX2bXrl02wzSWpqSZjWrVqiVJWohqQhK1EA7k\n6uqKr68vgYGBdO7cmdtvv539+/dbt3/11VeMHDmS3r17c8899zBz5kzrWNW7d+/m9ddfJzs7my5d\nutC1a1cWLVoE2DZ9Dxw4kMWLF/PWW29x6623cs899/DFF19oYjlz5gzjx4/nlltuYfjw4cTExNCn\nTx9+/vlna5lFixYxcOBAevbsyZ133lnmiwohRNlVu7G+haiqzp07R0xMDM2bN7eu0+v1TJ06lXr1\n6pGQkMB7773He++9x4wZM2jdujVTpkxhwYIFfP/996iqet1pN5cvX864ceMYM2YMW7ZsYfbs2bRt\n25aWLVuiqirTpk0jMDCQzz//nJycHObMmUN+fr51/z///JMvv/ySN998k4YNG5Kenq65qBBCVAxJ\n1EI40NatW+nTpw9ms5m8vDx69OihmUpw+PDh1n8HBwczadIknnnmGWbMmIHBYMDT0xNFUfD19S31\nWF26dGHo0KEA3HfffXzzzTfExsbSsmVLtm3bxtmzZ5k3b571/viTTz7JI488Yt0/MTHRWvPX6/XU\nqVOHZs2aldepEEJcgyRqIRyoffv2vPDCC+Tk5PDDDz+wZs0a0tPTrVMFxsbGsmzZMk6ePMmVK1cw\nm83k5+eTkpJid4ezQo0aNdIsBwYGkp6eDhQ0ewcGBmpeMyoqCp3u6t2xf/3rX3zzzTcMHDiQrl27\n0q1bN3r16oWLi8uN/vlCCDvIPWohHMhoNFKvXj0iIyOZMmUKzZo1Y/bs2UBBDXbKlClERETw9ttv\ns2zZMl566SUATCZTmY9lMGivyxVFwWKx2L1/nTp1WLlyJS+88AJeXl7MnTuXMWPGkJOTU+ZYhBD2\nk0QthBN55JFHiI2N5dChQxw6dAiTycRTTz1Fy5YtCQsLIykpSVPexcUFs9l808dt0KABycnJpKSk\nWNcdPHjQJpG7uLjQvXt3nnzyST7//HNOnDjB3r17b/r4Qohrk0QthBNp3749TZo04b///S9hYWGo\nqspXX33F+fPn+e233/jmm2805UNCQsjLy2PHjh1kZGTccO22S5cu1K9fn1dffZW4uDj27dvHBx98\ngMFgsD7+9dNPP/HDDz9w/Phxzp8/z5o1a3BxcSEsLOym/24hxLVJohbCyTzwwAOsX78ed3d3pkyZ\nwtdff83999/PmjVreOKJJzRlW7duzZAhQ3jxxRfp16+fzSNXhUp61rr49lmzZpGfn8/YsWN5/fXX\nefDBB4GCR8ig4NnsH3/8kfHjxzNy5Eg2bNjAu+++S0hISDn81UKIa5GRyYQQJTp69CijR49m2bJl\nNG3a1NHhCFFjSa9vIQQAGzZswN3dnbCwMM6fP88HH3xA06ZNJUkL4WCSqIUQAGRlZfHRRx+RlJSE\nt7c3HTp04Mknn3R0WELUeNL0LYQQQjgx6UwmhBBCODFJ1EIIIYQTs7lH/dlnn5GYmOiIWIQQQghB\nwdj+Dz30EFBCok5MTOTw4cOVHpQQQgghbEnTtxBCCOHEJFELIYQQTkwStRA1SFRUFHPmzHF0GFVK\ns2bNePPNNyv0GIqisHTpUvz9/Uvc3qNHD6ZOnVqhMRQ1duxY7rnnHrvKjh8/nkGDBlVwRDWb3QOe\n1K9fnyFDhtCkSROgYHjBVatWcebMGU25OXPm4O3tjdlsxmKxcO7cObZs2cL69ettXnPw4MEMGjSI\nGTNmcPLkSQD69+9P//79AdDr9ej1evLy8gBISUnhhRdesO4/e/Zs8vLyeP75521eu23btgwaNIh6\n9eqRl5fH3r17WbFihXX+3cGDBxMUFMTChQs1+y1dupSnn36a5ORkXnjhBSIjIzWzEx08eJD333/f\nGmufPn2oVasWV65cIS4ujvnz5/PWW29Zv3Curq6YzWbra/z444/89NNP9p52UUSTJk0YPnw49erV\nw2KxcP78eb744gtOnTrl6NBuWs+ePXn44Yetn3WAv/76i//+97/lfixVlaETilMUhSVLlpCbm2td\nl5+fz2OPPcbhw4c1vzs3YuDAgdx9992A7e/ahQsXePnll6+7/5YtW9iyZUup8U+ZMoXU1NRS4wkK\nCmLWrFmavxdg4cKF7Nq1i8WLF5f6GvYoa1wAjz76KJ07dyY/Px8o+N3/+++/+emnn+yedOb9999n\nwYIFHDly5IZjr2jjx4/nwoULrF69utSydiXq5s2bM2LECFasWMEHH3wAFFyZjx07luXLl9t0Pps9\nezaHDh3CaDTSrFkzRo8eTWRkJIsWLdKU69GjB/Hx8fTs2dOaqNesWcOaNWuAgh+v3r17M3PmTJuY\nmjZtisFgwGg0Eh4ervmx7tSpEw8//DCLFy9m165deHh4cN999/HSSy8xffp0srOz7fmzUVWVJUuW\n8Ndff9ls69mzJ927d+ett94iJSUFb29v2rVrB6C5cHj++efZvHlzia8h7Gc0GpkyZQqLFy9mx44d\nGAwGmjZtekPzMpdGURSHJLO4uLgSP+ui8jz33HOkpaWV++v+8MMP/PDDDwD07t3b+ttRqLRJU65H\nUZQb3n/cuHE3fFx73Ehcqqry448/snr1avR6PfXr12fkyJG0bduWGTNmVMh33tmVmqj1ej1Dhgzh\n3Xff5Z577mHcuHEYDAY2bdrEO++8w5NPPsnbb7+t+WErfHNycnLYs2cPFy9eZMaMGfzyyy+cP38e\nKEi07u7uLFq0iMcff5wvv/yyTJPY33LLLcTGxuLq6sott9yiSdQjRoxg9erVbN++HYDMzEwWLVrE\nm2++Sb9+/fj+++/tPs61PmgRERHs27fPOn9vZmYmGzduLNNrCPsFBwejqio7duwAwGQyceDAAU2Z\ngQMH0rt3b1xcXNi3bx/Lli0jJyeHZs2a8eijj2qGw5wzZw6LFi3i4MGDDB48mNDQUPLy8mjXrh1f\nffUVf/31F/fccw+9e/emVq1aJCYm8v7775Oenk5ISAijR48mPDyczMxMVq1aZY2rTZs23H///fj7\n+5OVlcXatWtZu3btTf3t7dq1Y8iQIQQFBXHlyhU2btxo/dEvrBl98sknDBs2DIPBwK+//srPP/8M\nFMwfPXbsWNq2bUt6erpNrWzAgAH06tULb29vUlJS+Pbbb/n777+BgoTSo0cPTp06Ra9evbh8+TJL\nlixh//79AAQGBjJ+/HjCwsI4duwYSUlJuLq68umnnwLQoUMHhg4dio+PD6dOnWLp0qXXfPSzadOm\nDB8+nLp165Kdnc23337L1q1bcXd3Jzo6mlatWpGTk8P69eutLVKF8Z05c4aePXuSmZnJwoULCQ0N\nZfDgwej1er766itiYmLsPtclfVejoqJ4+OGHmTJlCv379ycsLIz58+dbt0dHR5OXl8fXX3+Nu7s7\nDzzwAK1bt8ZsNvPXX3+xatUqu4/funVr7rrrLjw9Pdm6dat1NrSiyb2wlrps2TL69euHqqpcunQJ\nwPpb/MknnzB06FC+/PJL9u3bBxT8ln/00Ue88cYb1trqtRSv7fXv35++fftiNpv5/vvvGTt2rKaW\n7OXlxdNPP02TJk04e/Ys8+fPJzU1lenTp2viKqyx28tsNnPy5Elmz57NrFmzuOWWW1i/fj1BQUE8\n9NBD1qlg9+7dy9KlS8nJyWHChAn4+voybdo0LBYL3333Hb/99huTJ0+mSZMmGAwGzpw5w5IlS0hI\nSLDGP27cOJo2bcq5c+c4ePAgjRs35q233rJ+x6Kjo61xvfjii6xfv976ferduzd33nkn3t7eHD9+\nnMWLF1tbb0ePHk2XLl0wGAykpKQwb948oqKi6Nq1KxaLhbvuuov9+/czd+7ca56HUhN148aN2b59\nOx06dCAiIoIXXniBy5cv4+PjQ3Z2NrGxsTRu3JijR49e8zVOnjxJWloaTZs2tSbqnj17smvXLg4f\nPmz9gbT3DXRxcaFTp0689957uLm5MWHCBGuiDw4Oxt/f3/rDWVRsbCwtWrQoU6K+luPHjzNq1Cgy\nMjI4ePAgp0+flibFCpSYmIjFYmHcuHHExMRw/PhxsrKyrNt79epFjx49mDlzJpcuXeLRRx8lOjra\nemujtPemXbt2fPjhhyxcuBCDwcCdd95J165deffdd0lKSiI0NJTc3FxcXV155plnWLlyJe+++y71\n69fn2Wef5ezZsyQkJPDQQw/x4YcfEhcXh7u7O4GBgdZjfPzxx8yePZu4uLgy/e3Z2dksWLCA8+fP\nExoayvPPP8+pU6f4559/rGUaNWrE1KlTCQ0N5eWXXyY2NpakpCSGDh2Kn58fTz31FB4eHkybNk3z\n2gkJCbz22mtkZmbStWtXJkyYwNSpU60//I0aNWLTpk08+uij3H777Tz00EM89dRTADz22GMcOHCA\nN998k0aNGvH0009bv3d169Zl/PjxzJkzhyNHjnDPPffw1FNP8dxzz9m8FwEBAUydOpVPPvnE2gLm\n5+cHwL///W8MBgNPPfUUtWvX5plnntFccDRq1Ij169fzxRdfcN999zFp0iR27tzJ1KlTadGiBRMn\nTmTnzp2lJqbSFMa8bds2+vfvj4uLC/n5+SiKQqdOnZg1axYAEyZMIDk5mSlTpuDu7m69jWZvi1rr\n1q158cUX8fLy4vXXX2f37t0cPHiwxLLt2rXj5ZdfJj8/H7PZzJIlS3j22WetLQLBwcH06NHDmqjb\ntWtHcnIy586dIygoyO6/vW3bttx+++3MnDmT1NRUHn74YZsy3bp149133+XMmTNMnDiRoUOHsnDh\nQt544w2buG5ETk4OBw8epEmTJqxfvx5FUVi9ejWHDx/G09OTJ598kkGDBrF8+XIWLFhA06ZNmT9/\nviYv7d69m4ULF2I2mxk5ciSPPvoor7zyCgAPPvgg2dnZPPbYYwQHB/PMM89Yk3hpOnbsyJ133smc\nOXNITk5m4MCBTJw4kZkzZ9KmTRsaNmzI1KlTyc3NJSQkhCtXrvDHH3/QqFEju5u+S+1M5uPjQ2Ji\nIvXr12fHjh1cvnwZgIyMDKDgB7TwS3U96enpeHl5AQWJtnPnztYvdWxsLD179iz1NQp16tSJ7Oxs\n4uLirLWqtm3bAgVz5haNr6iMjAzrdnuNGTOGBQsW8PHHH7NgwQKGDBkCwNatW1m2bBktW7bkhRde\n4KOPPrLegxLlLycnh9dffx2LxcLYsWOZN28eTz75pPX97NatG2vXriU1NZW8vDxWrFhBly5d7G7N\nOHbsmLUmaTKZ6N27N99++y1JSUkAxMfHk5WVRdu2bUlJSbEmijNnzhAbG0vnzp2t+9arVw+j0Uh2\ndramD8ejjz563STdqFEjzWetYcOGABw+fNh6gRsfH8+2bdto1qyZZt9Vq1ZhNps5ffq0YVZ9AAAN\nAElEQVQ0586do379+gB07tyZ1atXk5OTQ1paGv/3f/+n2S82NpbMzEygIAmlpKQQERFh3Z6UlMTm\nzZsB2Lx5M35+fnh6ehIYGEhYWBjff/89FouFo0ePsmfPHut+Xbp0Yffu3Rw+fBhVVVmzZg0eHh40\natTI5u/u0aMH//zzDzt37kRVVa5cucLZs2fR6XR07tyZb775hry8PJKTk1m7di09evSw7nvhwgVr\njXnbtm34+fnx/fffYzab2bt3L4DmYqk0M2fOtJ7/ESNG2GxPTk4mPj6eDh06ANCqVSsuX77M6dOn\n8fX1JSoqiq+++gqTycSlS5f47bff6Natm93H//HHH8nNzSU1NZXDhw9b38eS/PDDD2RnZ2uagot+\n3rdu3Uq7du1wcXEBCs5z8RaVBQsWaD5zJSXwzp07s3HjRhITE8nPzy+xorNjxw5rZWXr1q02cZdH\nq2LRHHLhwgUOHTqEqqpcvnyZtWvX2nwnih9zy5Yt5OXlWVsFwsPDcXFxQafT0b59e1auXInJZCI+\nPv66/QGKu+2221izZg1JSUnWZvuGDRtSu3ZtzGYz7u7u1KtXDyi4MC78vpVFqTXqjIwMQkNDOXny\nJH369GH37t2kp6fj5+dHWloawcHBNh3KSuLr62tN8p06ddI0Xe7YsYPnn38eLy8va5nr6dmzJ7Gx\nsUDBle6uXbvo2bMnu3fvttYEfHx8bDov+Pj4WE+S2WxGr9drtut0Ouu2QsuWLWPTpk0lxrFt2za2\nbduGoih06NCBiRMncvr0aWvToChfiYmJ1n4OwcHBTJgwgVGjRrFgwQJ8fX2ttyGgoAOKXq/H29vb\nrtcu/lnx9/e3JumiAgICiIyMZMGCBUDBj4FOp7Mms7lz5zJo0CCGDx/OmTNnWLFiBcePH7crhmPH\njpV4j7pRo0YMGzaM0NBQ9Ho9BoOBbdu2acoUfu4B8vLycHNzAwo+80VrMkXPERTcQrrjjjvw9/dH\nURTc3Nw0F7MXL160/ruw45HRaMTHx4fLly9rkkRaWpr1h7T4+1G43dfX1+bv8/PzK/Fce3t7o9Pp\nNO9NSkqK5jWK/ujl5eVhsVg0LS35+fkYjUab176WF154odSaX0xMDF27dmXbtm1069bNeqHg7++P\ni4sLH330EXA1USQnJ9t9/KJ/T25u7nVjLy3OtLQ0Tpw4QadOndizZw+tWrXi888/15SZMGFCqTH5\n+vpqOmWVdNzin5OynHN7Fc0h3t7ejB49miZNmuDm5oZOp7tuAlQUheHDh9OxY0frZxQKKnaqqqLT\n6Wy+J4UXyqUJCAggOjqaUaNGWY9lsVjw8/Nj//79/Pnnn/z73//Gz8+P2NhYli9fbtOJrzSlJuq4\nuDiGDBnC3LlzCQoK4sUXX8TFxYUNGzawdu1aOnXqxB9//HHd14iIiNC82T169MBoNPLBBx9YP8x6\nvZ5u3brx+++/X/e1Cq9aGzZsSNeuXYGCGrqLiwuenp4kJiaSlpZGly5d+OWXXzT7durUyfqlSk1N\ntdbCCwUFBWE2m633Fuylqio7d+7k7NmzhIaGSqKuBImJifz111/ceuutQMHVdkBAgHV7QEAAZrOZ\nzMxM/Pz8rIkLCr5IpbWspKSkUKdOHWtNtlBaWhqHDh2yNnUWd+rUKd5//30URaFv375MmjTJ2lR8\noyZOnMjPP//Mu+++i9lsZvTo0Zq/53oyMjLw9/fnwoULAJpzFBgYSHR0NG+99Zb1YsLex5AyMjLw\n8vJCr9dbL2z9/PysPZnT09OpU6eOZh8/P78Sv1tpaWmEhYXZrM/MzMRisWgumgICAsr8/SwLe2p+\n27dvZ/jw4fj4+NC+fXteeukloOA3JTc3167kV1k2b95s/b09fPjwDdXmMjIyNK2m13qErCIZjUai\noqL47rvvALj//vvJz8/nueeeIzs7m44dO2paQIrfXunZsyetWrXizTfftF5Qzps3D0VRyMjIsCbW\nwouqon9jYVItvN0BULt2bev21NRUVq5cWeLtVoB169axbt06atWqxeOPP06/fv2sfUzsVWrTt9ls\nZtWqVTzzzDMcPXqUp59+mieeeIIjR47wzDPP8N13313z/p/RaKRt27Y89thjbNmyhfPnz+Pr60uL\nFi2YPXs2L774ItOnT2f69On8/PPPdjV/9+zZk4SEBKZNm2bdt/C+VWET09dff82AAQPo2rUrBoOB\n2rVr8/DDD+Pi4mJt+tu7dy9169ale/fu6HQ6PD09GTZsGLGxsXbda+7Zsydt2rSx/mC2bt2aevXq\n2V17EmUTHBxMv379rLUpPz8/unbtam1K3rZtG/369SMgIAA3NzeGDRvGtm3bUFWVhIQEXFxcaN26\nNTqdjkGDBmEwXP8addOmTdx7773WpsDQ0FA8PDz4+++/CQkJsX5u9Ho9ERERhISEWC82jUYjqqqS\nk5NTLv0WjEYjV65cwWw2ExkZab1AtceOHTvo378/7u7u+Pv7c/vtt1u3ubm5WTsiKYpCnz59CAkJ\nset1k5OTOXv2rLXTVpMmTTQXvtu3b6d9+/Y0bdoUnU7H3XffTXZ2NseOHbN5rS1bttCqVSs6dOiA\noih4eXkRFhaGxWJhx44d3Hfffbi5uREYGMgdd9xRpmbJonr37n3NC6yyyMzM5OjRo4wbN46EhATr\nRUR6ejqHDx9mxIgR1hplUFCQ9ZHWilTYBFy8mT82NpbIyEhuv/12a6tPWW3fvp3evXsTHByMq6sr\nAwcOvKm4Cp8Zb9y4can7F36/nnrqKTIzM63vvdFoJDc3l5ycHPz8/Ljrrrs0+128eFHTjG80GjGZ\nTFy5csX6+1DIYrGwe/duhgwZgouLC6GhoZrbKxcvXiQjI4Pu3bujKAq33nqr5oL3zz//ZODAgdbv\njoeHBx07dgQKKqkREREoikJeXh4mk8n6m1A8xuux6/Gsw4cP8+mnn1o7a6iqSlxcHJ999hlnz561\nKT9lyhTMZjOqqnLu3Dl++eUX/vzzTwC6d+/OqVOnbDpI/Pbbb/Tr14+6deva1GKK6tGjB7///rum\nqQ8KTlbPnj35v//7P3bs2EF+fj4DBw60JuiTJ0/yxhtvWB/NunTpErNmzWLEiBGMHj2avLw89uzZ\nw/LlyzWvO2bMGGuTBhTcY3jllVfIzs6mf//+jB8/3to89/nnn5e5o5CwT05ODpGRkdx55524u7uT\nlZXF33//bX2/Nm7ciI+PD9OnT8fFxYW9e/dan0HOyclhyZIlPPzwwyiKws8//1xqs+Gvv/6KXq/n\n2WefxcvLi4SEBN5//30yMjJ45513eOCBB3jggQeAgvvUX331FVDw+Rw9ejQ6nY7ExERrEznAJ598\nwqxZs8r8GVmyZAkjRozgwQcf5NChQ2zfvh1XV9drli96cbBq1SoefPBB/vOf/5CWlsZff/3Fv/71\nL6Dgfvfvv//Oq6++itlsZvPmzWW60Jw/fz7jxo1j/vz5HDt2jG3btlmf3Dh//jwLFy7kwQcfpHbt\n2pw+fZo5c+ZYY5s2bRoHDhzgl19+ISUlhTlz5jBixAjGjRvHlStX+Pbbbzl79ixLly4lOjqaOXPm\nkJubq+lpa4+i58LPz++6nV7LIiYmhocffpgvv/xSs37BggUMHz6ct99+G6PRSFJSkvVx04q2atUq\nHnvsMQwGA4sWLWLXrl3k5eWxe/duOnToUGJn3U8++USzvGLFCpt+DHv27OGPP/5g+vTpmM1mfvzx\nR7p162Z3B73icZ0+fZqsrKwSc0eh/v37c+edd6IoCikpKezevZuffvrJesxVq1Yxbtw4Fi5cSGJi\nIjExMZqL0DVr1jBq1ChGjhzJ999/z8aNG2nZsiUffvghly5dYtWqVfTp08dafsmSJYwbN44PP/yQ\n8+fPs2nTJs0F1uLFi4mOjmb48OFs2LBBc8FZ+PTR5MmT8fPzIysri3379rFz5048PT0ZMWIEgYGB\n5Ofn888///Drr78CsGHDBiZNmsT8+fM5ePCg9ZZJSZSMjAzNJf/MmTOr3aQcLVq0YPLkybzzzjvW\n57WFEOVr8uTJnD59mh9//NHRoZTo2Wef5fPPPy/xfnh1NnjwYPz9/W3GsbhRoaGhvPbaa4wdO/aG\n9u/ZsydBQUFlemytspX0rHtla9asmfXxthoxhOiBAwdYsGAB4eHhjg5FiGojIiLC2gTYpk0b2rZt\nW6ZnZCvbO++8U+OStJeXF7169SpxZMiy6NChA3q9Hk9PT+67776bep83b97s1EnaGdk9hGhVV/SZ\nUyHEzfP19eXxxx/H09OTtLQ0PvvsM86dO+fosMT/3HbbbYwYMYKNGzfedN+Z22+/nXHjxmE2mzl4\n8CDLli0rpyiFPWpE07cQQghRldS4pm8hhBCiqpJELYQQQjgxm3vUwcHBjohDCCGEEP9TNBfb3KMW\nQgghhPOQpm8hhBDCiUmiFkIIIZzY/we+owCJnk/W6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9992a048d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "fandango_2015['Fandango_Stars'].plot.kde(label='2015',legend=True)\n",
    "ratings_subset['fandango'].plot.kde(label='2016-2017',legend=True)\n",
    "plt.xlabel('Ratings',size=14)\n",
    "plt.ylabel('Density',size=14)\n",
    "plt.xlim(0,5)\n",
    "plt.xticks(np.arange(0,5.1,0.5))\n",
    "plt.title('Comparing distributions of movie ratings', y=1.1,size=20)\n",
    "plt.text(-0.7, -0.2, '©DATAQUEST' + ' '*20 + 'Sources: Fandango.com, FiveThirtyEight, Dataquest',\n",
    "        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "       size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both distributions are skewed left, but the 2015 ratings are slightly highger. The 2015 rating with the highest density if 2.5, while ratings of 4.0 have the highest density for the movies released in 2016 & 2017. This suggests that Fandango may have corrected the rounding logic that it attributed to an error after Hickey's analysis was published, though it is impossible to verify this as the actual ratings have been removed from the HTML code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing frequency distribution tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll do more granular analysis by looking at the frequency distribution tables of the 2 samples. We'll do this by comparing frequencies in percentages, as both samples contain different numbers of movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0     8.527132\n",
       "3.5    17.829457\n",
       "4.0    28.682171\n",
       "4.5    37.984496\n",
       "5.0     6.976744\n",
       "Name: Fandango_Stars, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fandango_2015['Fandango_Stars'].value_counts(normalize = True).sort_index() * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5     2.803738\n",
       "3.0     8.411215\n",
       "3.5    23.364486\n",
       "4.0    38.317757\n",
       "4.5    26.635514\n",
       "5.0     0.467290\n",
       "Name: fandango, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_subset['fandango'].value_counts(normalize = True).sort_index() * 100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the frequency distribution tables, we can see that most movies are rated between 4-4.5 stars in both samples (about 66.6% in 2015 compared to about 64.9% in 2016-2017). However, the distributions also differ in the following ways:\n",
    "\n",
    "- Ratings in 2016-2017 have a wider distribution, with 2.8% of ratings below 3 stars compared to none below 3 stars in 2015\n",
    "- More movies were given lower ratings in 2016-2017 - about 8% more movies are rated 3 stars and below\n",
    "- A much smaller percentage of movies in 2016-2017 are rated 5 stars, about 0.5% compared to about 7% in 2015\n",
    "- Ratings of 4.5 were also more popular in 2015 — there were approximately 13% more movies rated with a 4.5 in 2015 compared to 2016.\n",
    "\n",
    "About 6% more movies were given ratings of 3.5-4 stars in 2016-2017. As these are still high ratings, it suggests that despite the change in distributions, overall movie ratings are still high.\n",
    "\n",
    "These observations are similar to what we observe in the kernel density plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Looking at summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We confirmed with the two tables before that there is indeed a clear difference between the two distributions. However, the direction of the difference is not as clear as it was on the kernel density plots. As such, we'll take each distribution of movie ratings and compute its mean, median, and mode, and then compare these statistics to determine what they tell about the direction of the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# computing the mean, median and mode for 2015 data\n",
    "mean_2015 = fandango_2015['Fandango_Stars'].mean()\n",
    "median_2015 = fandango_2015['Fandango_Stars'].median()\n",
    "mode_2015 = fandango_2015['Fandango_Stars'].mode()[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# computing the mean, median and mode for 2016-2017 data\n",
    "mean_aft = ratings_subset['fandango'].mean()\n",
    "median_aft = ratings_subset['fandango'].median()\n",
    "mode_aft = ratings_subset['fandango'].mode()[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Storing both sets of summary statistics in dataframes\n",
    "stats = ['mean','median','mode']\n",
    "stats_2015 = [mean_2015, median_2015, mode_2015]\n",
    "stats_aft = [mean_aft, median_aft, mode_aft]\n",
    "\n",
    "summary = pd.DataFrame(\n",
    "    {'2016-17': stats_aft\n",
    "     ,'2015': stats_2015,\n",
    "    })\n",
    "summary.index = ['mean', 'median', 'mode']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "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>2015</th>\n",
       "      <th>2016-17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.085271</td>\n",
       "      <td>3.89486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2015  2016-17\n",
       "mean    4.085271  3.89486\n",
       "median  4.000000  4.00000\n",
       "mode    4.500000  4.00000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+2muvvYbdu3fD2tq6VidsPu3tt9/GyZMncfnyZVy4cAGCIKBFixaYOHEiAgIC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\nACl2nAcOhZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9992753588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "summary.plot.bar(legend=True,figsize=(8,5))\n",
    "plt.ylabel('Stars',size=14)\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylim(0,5)\n",
    "plt.yticks(np.arange(0,5.1,0.5))\n",
    "plt.legend(loc='upper center')\n",
    "plt.title('Comparing summary statistics: 2015 vs 2016-17',y=1.1,size=20)\n",
    "plt.text(-0.8, -0.6, '©DATAQUEST' + ' '*54 + 'Fandango.com, FiveThirtyEight, Dataquest',\n",
    "        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "       size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the values in the summary dataframe and at the grouped bar plot, ratings for popular movies in 2015 were slightly higher. Mean ratings were about 0.2 stars higher, while the most common rating was 4.5 stars compared to 4 stars in 2016-2017. However, the median rating was the same in both samples, supporting the observation that ratings in 2015 are only slightly higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on our analysis, it is clear that popular movies were rated slightly higher in 2015 compared to 2016 and part of 2017. It is likely that this was due to Fandango fixing the biased rating system after Hickey's analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}