Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project title: Investigate a Dataset (TMDb_Movies Dataset)\n",
    "\n",
    "## Mohamed Balam\n",
    "## Benghazi, Libya"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    " ## Overview\n",
    "> **At this project I'll analyze and investigate as well as write an observations about the TMDb_Movies Dataset which is supported from Udacity at this link bellow  https://docs.google.com/document/d/e/2PACX-1vTlVmknRRnfy_4eTrjw5hYGaiQim5ctr9naaRd4V9du2B5bxpd8FEH3KtDgp8qVekw7Cj1GLk1IXdZi/pub?embedded=True**\n",
    "\n",
    ">**This dataset had a lot of information, it's nearly to 11 thousand movies collected from The Movie Database (TMDb), it includes a lot of important data to analyze like 'Revenue, Budget, and user rating, etc. So I'll try to ask questions and answer it after analysis process.**\n",
    "\n",
    ">**My questions**\n",
    "\n",
    ">        1.)Which movies had the highest and lowest popularity?\n",
    ">        2.)What is the best number of voters that effects on movies profit?\n",
    ">        3.)Which movies had the highest and lowest earned profit?\n",
    ">        4.)Which genre had the greatest number of movies?\n",
    ">        5.)Who is the top 5 directors that directed the greatest number of movies?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling\n",
    ">Lesson after a lesson, my experience is increasing, so I just followed the analysis process steps that I learned from your course, and filled the knowledge gap by searching about auxiliary functions which helped me to complete this project, so I’ll mention every helping recourse at references section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing packeges\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue      original_title  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810      Jurassic World   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "\n",
       "                        homepage         director             tagline  ...  \\\n",
       "0  http://www.jurassicworld.com/  Colin Trevorrow   The park is open.  ...   \n",
       "1    http://www.madmaxmovie.com/    George Miller  What a Lovely Day.  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('tmdb-movies.csv')\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7930\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show sum of missy data for each column\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show sum of duplicated values\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning dataset\n",
    "\n",
    ">Here I’ll clean the dataset by using some functions to show and delete duplicated data, show and drop zero values as well as fill some of it with Nan, and drop unneeded columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-06-09\n",
       "1   2015-05-13\n",
       "2   2015-03-18\n",
       "3   2015-12-15\n",
       "4   2015-04-01\n",
       "Name: release_date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change \"release_data\" type to datetime\n",
    "df['release_date'] = pd.to_datetime(df['release_date'])\n",
    "df['release_date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Now let’s drop duplicated and null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop dublicated values\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Drop unneeded columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue      original_title  \\\n",
       "0   32.985763  150000000  1513528810      Jurassic World   \n",
       "1   28.419936  150000000   378436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...    George Miller   \n",
       "\n",
       "   runtime                                     genres release_date  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller   2015-05-13   \n",
       "\n",
       "   vote_count  vote_average  release_year  \n",
       "0        5562           6.5          2015  \n",
       "1        6185           7.1          2015  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping all unneeded columns.\n",
    "df.drop(['id', 'imdb_id', 'budget_adj', 'revenue_adj', 'homepage',  'tagline', 'keywords', 'overview','production_companies'], axis=1, inplace=True)\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>1.086500e+04</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.646446</td>\n",
       "      <td>1.462429e+07</td>\n",
       "      <td>3.982690e+07</td>\n",
       "      <td>102.071790</td>\n",
       "      <td>217.399632</td>\n",
       "      <td>5.975012</td>\n",
       "      <td>2001.321859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.000231</td>\n",
       "      <td>3.091428e+07</td>\n",
       "      <td>1.170083e+08</td>\n",
       "      <td>31.382701</td>\n",
       "      <td>575.644627</td>\n",
       "      <td>0.935138</td>\n",
       "      <td>12.813260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.207575</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.383831</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.713857</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity        budget       revenue       runtime    vote_count  \\\n",
       "count  10865.000000  1.086500e+04  1.086500e+04  10865.000000  10865.000000   \n",
       "mean       0.646446  1.462429e+07  3.982690e+07    102.071790    217.399632   \n",
       "std        1.000231  3.091428e+07  1.170083e+08     31.382701    575.644627   \n",
       "min        0.000065  0.000000e+00  0.000000e+00      0.000000     10.000000   \n",
       "25%        0.207575  0.000000e+00  0.000000e+00     90.000000     17.000000   \n",
       "50%        0.383831  0.000000e+00  0.000000e+00     99.000000     38.000000   \n",
       "75%        0.713857  1.500000e+07  2.400000e+07    111.000000    146.000000   \n",
       "max       32.985763  4.250000e+08  2.781506e+09    900.000000   9767.000000   \n",
       "\n",
       "       vote_average  release_year  \n",
       "count  10865.000000  10865.000000  \n",
       "mean       5.975012   2001.321859  \n",
       "std        0.935138     12.813260  \n",
       "min        1.500000   1960.000000  \n",
       "25%        5.400000   1995.000000  \n",
       "50%        6.000000   2006.000000  \n",
       "75%        6.600000   2011.000000  \n",
       "max        9.200000   2015.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(0,None)\n",
    "\n",
    "# referred to the code:\n",
    "# https://stackoverflow.com/questions/22649693/drop-rows-with-all-zeros-in-pandas-data-frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>10731.000000</td>\n",
       "      <td>1.073100e+04</td>\n",
       "      <td>1.073100e+04</td>\n",
       "      <td>10731.000000</td>\n",
       "      <td>10731.000000</td>\n",
       "      <td>10731.000000</td>\n",
       "      <td>10731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.652615</td>\n",
       "      <td>2.184488e+07</td>\n",
       "      <td>5.360318e+07</td>\n",
       "      <td>102.710838</td>\n",
       "      <td>219.812972</td>\n",
       "      <td>5.964710</td>\n",
       "      <td>2001.259622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.004804</td>\n",
       "      <td>3.167713e+07</td>\n",
       "      <td>1.203636e+08</td>\n",
       "      <td>30.058647</td>\n",
       "      <td>578.815324</td>\n",
       "      <td>0.930283</td>\n",
       "      <td>12.820151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.210765</td>\n",
       "      <td>3.250000e+06</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.387081</td>\n",
       "      <td>1.050000e+07</td>\n",
       "      <td>1.456788e+07</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.720889</td>\n",
       "      <td>2.600000e+07</td>\n",
       "      <td>4.848732e+07</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity        budget       revenue       runtime    vote_count  \\\n",
       "count  10731.000000  1.073100e+04  1.073100e+04  10731.000000  10731.000000   \n",
       "mean       0.652615  2.184488e+07  5.360318e+07    102.710838    219.812972   \n",
       "std        1.004804  3.167713e+07  1.203636e+08     30.058647    578.815324   \n",
       "min        0.000188  1.000000e+00  2.000000e+00      3.000000     10.000000   \n",
       "25%        0.210765  3.250000e+06  2.000000e+06     90.000000     17.000000   \n",
       "50%        0.387081  1.050000e+07  1.456788e+07     99.000000     39.000000   \n",
       "75%        0.720889  2.600000e+07  4.848732e+07    112.000000    148.000000   \n",
       "max       32.985763  4.250000e+08  2.781506e+09    900.000000   9767.000000   \n",
       "\n",
       "       vote_average  release_year  \n",
       "count  10731.000000  10731.000000  \n",
       "mean       5.964710   2001.259622  \n",
       "std        0.930283     12.820151  \n",
       "min        1.500000   1960.000000  \n",
       "25%        5.400000   1995.000000  \n",
       "50%        6.000000   2006.000000  \n",
       "75%        6.600000   2011.000000  \n",
       "max        9.200000   2015.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.dropna(how='any',axis=0,inplace=True)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10731, 12)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Creating 'net_profit' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>movie_profit</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>228436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue  movie_profit      original_title  \\\n",
       "0   32.985763  150000000  1513528810    1363528810      Jurassic World   \n",
       "1   28.419936  150000000   378436354     228436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...    George Miller   \n",
       "\n",
       "   runtime                                     genres release_date  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   2015-06-09   \n",
       "1      120  Action|Adventure|Science Fiction|Thriller   2015-05-13   \n",
       "\n",
       "   vote_count  vote_average  release_year  \n",
       "0        5562           6.5          2015  \n",
       "1        6185           7.1          2015  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating 'net_profit' column by substracting values in 'budget' column from those in 'revenue' column.\n",
    "df.insert(3, 'movie_profit', df['revenue'] - df['budget'])\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Budget increase over the years plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a Budget vs Years plot.\n",
    "\n",
    "df.groupby('release_year')['budget'].sum().plot(figsize = (35, 16),xticks=np.arange(1960,2018,3), color='r')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.title('Budget increase over the years', fontsize = 18)\n",
    "plt.xlabel('Year', fontsize = 16)\n",
    "plt.ylabel('Budget', fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze dataset and answering questions\n",
    ">Now after cleaning, and trimming, the dataset is ready now to compute statistics, create visualizations to answer all questions.\n",
    "Before answering any questions, I just did some statistic about “budget increase over the years” so I write this code below that shows me the chart after compiling.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define a function to calculate highest and lowest values in some column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define high_vs_low function to calculate and return the highest and lowest values from dataset\n",
    "def high_vs_low(column_name):\n",
    "    low_value = df[column_name].idxmin()\n",
    "    high_value = df[column_name].idxmax()\n",
    "    high = pd.DataFrame(df.loc[high_value,:])\n",
    "    low = pd.DataFrame(df.loc[low_value,:])\n",
    "    \n",
    "    # Print ing solutions \n",
    "    print(df['original_title'][high_value],\"is highest in \"+ column_name)\n",
    "    print(df['original_title'][low_value],\"is the lowest in \"+ column_name )\n",
    "    return pd.concat([high,low],axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1: Which movies had the highest and lowest popularity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jurassic World is highest in popularity\n",
      "The Hospital is the lowest in popularity\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>9977</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>popularity</td>\n",
       "      <td>32.9858</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>budget</td>\n",
       "      <td>150000000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>revenue</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>28623900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movie_profit</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>28523900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>original_title</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>The Hospital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>George C. Scott|Diana Rigg|Richard Dysart|Barn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>director</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>Arthur Hiller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>runtime</td>\n",
       "      <td>124</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genres</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Mystery|Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>release_date</td>\n",
       "      <td>2015-06-09 00:00:00</td>\n",
       "      <td>1971-12-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vote_count</td>\n",
       "      <td>5562</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vote_average</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>release_year</td>\n",
       "      <td>2015</td>\n",
       "      <td>1971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             0     \\\n",
       "popularity                                                32.9858   \n",
       "budget                                                  150000000   \n",
       "revenue                                                1513528810   \n",
       "movie_profit                                           1363528810   \n",
       "original_title                                     Jurassic World   \n",
       "cast            Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "director                                          Colin Trevorrow   \n",
       "runtime                                                       124   \n",
       "genres                  Action|Adventure|Science Fiction|Thriller   \n",
       "release_date                                  2015-06-09 00:00:00   \n",
       "vote_count                                                   5562   \n",
       "vote_average                                                  6.5   \n",
       "release_year                                                 2015   \n",
       "\n",
       "                                                             9977  \n",
       "popularity                                               0.000188  \n",
       "budget                                                     100000  \n",
       "revenue                                                  28623900  \n",
       "movie_profit                                             28523900  \n",
       "original_title                                       The Hospital  \n",
       "cast            George C. Scott|Diana Rigg|Richard Dysart|Barn...  \n",
       "director                                            Arthur Hiller  \n",
       "runtime                                                       103  \n",
       "genres                                       Mystery|Comedy|Drama  \n",
       "release_date                                  1971-12-14 00:00:00  \n",
       "vote_count                                                     10  \n",
       "vote_average                                                  6.4  \n",
       "release_year                                                 1971  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_vs_low('popularity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">After calling this function, we found that the most popular movie is “Jurassic World”, and the most unpopular movie is “The Hospital”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2: What is the best number of voters that effects on movies profit?\n",
    ">Here we need to use groupby function to calculate the solution, and after that making a plot depending on groupby solution, and now let’s show the answer:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit average')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AJEAh0RIp2JPx8PMPK92XrkRZwKTRyAAAAACABCgkWiIFezKyntXd7XdXoixg0mhkAAAAAEACFBItkaSTDj5J9TX1xEsQWTQyAAAAACABCo2WzKifoRPnn0gjA5FFIwMAAAAAEqDQaIkU7MlYu3Wtuvu7y10WMGk0MgAAAAAgAQqNlkjBnoyMZ3Tv5nvLXRYwaTQyAAAAACABCo2WSNIpC05RrdUSL0Ek0cgAAAAAgATIT2QU0shobmjWCQedQCMDkUQjAwAAAAASIOOZgvZj5K1oW6E/PvdH7RrcVcaqgMmjkQEAAAAACZDJZgqaxshbsWiFBrODum/zfWWsCpg8GhkAAAAAkABZzxa06DPv1IWnqsZqiJcgcmhkAAAAAEACTDZa0tLYouPnHk8jA5FDIwMAAAAAEmCy0RIp2JPxhy1/UN9QX5mqAiaPRgYAAAAAJMBkoyVSsCejP9OvP2z5Q5mqAiaPRgYAAAAAJMBkoyWSdNrC02Qy4iWIFBoZAAAAAJAAU4mWzJ4+W8cceAyNDEQKjQwAAAAASICpREukYE/GfZvv00BmoAxVAZNHIwMAAAAAEmAq0RIp2JPRO9SrPz73xzJUBUwejQwAAAAASICMTz5aIkmnt50uScRLEBk0MgAAAAAgAaYaLZkzY46OfMWRNDIQGTQyAAAAACABprLsM29F2wrd036PBjODJa4KmDwaGQAAAACQAFPdkSEFezJ2Du7UQ9seKnFVwOTRyAAAAACABJhqtERiTwaihUYGAAAAACRAMdGSuc1ztWT/JTQyEAk0MgAAAAAgAYqJlkjBnoy72+9WJpspYVXA5NHIAAAAAIAEKCZaIgV7Mrr6u/Tw8w+XsCpg8mhkAAAAAEACFBMtkYKJDIk9GQgfjQwAAAAASIBioyXzW+br0NmH0shA6GhkAAAAAEACFBstkYKpjDWpNcp6tkRVAZNHIwMAAAAAEqDYaIkU7Mno6OvQYy88VqKqgMmjkQEAAAAACVBstERiTwaigUYGAAAAACRAKaIlba1tapvVRiMDoaKRAQAAAAAJUIpoiRTES1anVsvdS1AVMHk0MgAAAAAgAUoRLZGCeMlLu17SEy8+UYKqgMmjkQEAAAAACVCKaInEngyEj0YGAAAAACRAqaIlh8w+RPNnzqeRgdDQyAAAAACABMh6tiTREjPTikUrtGrTKvZkIBQVaWSY2QIz+72ZrTezx83s70a5xszsa2b2jJk9ambLKlEbAAAAACRBxjMliZZIQbzkhZ0v6KmXnyrJ4wGTUamJjCFJf+/uSyWdJOlvzeyIva45R9JhubdLJX2zQrUBAAAAQNUrVbREYk8GwlWRRoa7b3P3h3Lvd0taL2n+XpedJ+kHHrhfUquZzatEfQAAAABQ7UoVLZGkw/c/XAc2HUgjA6Go+I4MM1sk6XhJf9jrrvmSNo/4/Rbt2+wAAAAAAExBKaMl7MlAmKySn3Rm1ixplaTL3f3mve67VdIX3f3u3O/vlPRJd1+713WXKoieyMyWL1sW3VUa69ev19KlS8MuY1xRr5H6ikN9xYl6fVL0a6S+4lBfcaJenxT9GqmvONRXvKjXGLf61v3FOjV1NGnxnxaX5PG3t23X5mM266g7j1Ljrsai64sa6ive2rVr17r7CSV/YHevyJukekm/kfR/xrj/25LeNeL3T0qaN95jzpgxw6Ns+fLlYZcwoajXSH3Fob7iRL0+9+jXSH3Fob7iRL0+9+jXSH3Fob7iRb3GuNW3+KuL/aKbLyrZ4697YZ1rpfzqh66e0sfH7fWLmqjX5+4u6UEvQ3+hUqeWmKSrJa1396+McdkvJF2cO73kJElpd99WifoAAAAAoNqVMloiSUe84gjNmTGHPRmouLoKPc9rJV0k6TEzezh322clLZQkd/+WpF9JepOkZyTtkvRXFaoNAAAAAKpeJptRTQl/lm1mOr3tdK3aRCMDlVWRRoYHey9sgmtc0t9Woh4AAAAASJqsZ0s6kSFJZ7SdoZvX36xUZ0ptrW0lfWxgLBU/tQQAAAAAUHkZz6jGSvst4IpFKySJeAkqikYGAAAAACRAJptRrZV2IuOoA47SftP3I16CiqKRAQAAAAAJUI5oSY3V6LSFpzGRgYqikQEAAAAACVCOaIkkrWhboWc7ntVzXc+V/LGB0dDIAAAAAIAEKEe0RGJPBiqPRgYAAAAAJEA5oiWSdOyBx2pW4yz2ZKBiaGQAAAAAQAKUK1pSW1OrUxeeykQGKoZGBgAAAAAkQLmiJVKwJ+PJl5/U8z3Pl+XxgZFoZAAAAABAApQrWiLt3pOxOrW6LI8PjEQjAwAAAACqnLvL5WWJlkjSsnnL1NzQzJ4MVASNDAAAAACochnPSFLZoiV1NXV67YLXsicDFUEjAwAAAACqXNazklS2aIkU7Ml4/MXH9dKul8r2HIBEIwMAAAAAql4mG0xklCtaIrEnA5VDIwMAACTWPe33qLOvM+wyAKDsyh0tkaQTDjpB0+umsycDZUcjAwAAJFLPQI9WXLNCVz90ddilAEDZVSJa0lDboFMWnMKeDJQdjQwAAJBIqc6UMp5R90B32KUAQNlVIloiBXsyHn3hUXX0dpT1eZBsNDIAAEAipdIpSdJAZiDkSgCg/IYnMsoYLZGCPRku15r2NWV9HiQbjQwAAJBIqc6gkTGYGQy5EgAov+EdGWWMlkjSifNPVGNtI3syUFY0MgAAQCK1p9slSYNZGhkAql+loiXT6qbppINPYk8GyopGBgAASCSiJQCSpFLREinYk/Gn5/+kdF+67M+FZKKRAQAAEinfyCBaAiAJKhUtkYI9GVnP6p7N95T9uZBMNDIAAEAi5XdkDGSZyABQ/SoVLZGkkw4+SfU19ezJQNnQyAAAAIkzmBnU1u6tw+8DQLWrZLRkRv0MnTj/RPZkoGxoZAAAgMTZ0rVFLpfEsk8AyVDJaIkU7Ml4cOuD6hnoqcjzIVloZAAAgMTJ78eQWPYJIBkqGS2Rgj0ZGc/o3s33VuT5kCw0MgAAQOLkj15tndZKtARAIlQyWiJJpyw4RbVWy54MlAWNDAAAkDj5RZ+Hzj6UiQwAiZCPllRqIqO5oVknHHQCezJQFjQyAABA4qTSKc1tnquZjTPZkQEgEfLRkkrtyJCCPRkPPPeAdg3uqthzIhloZAAAgMRJpVNaOGuh6mvqiZYASIRKR0ukYE/GYHZQ92+5v2LPiWSgkQEAABKnPd2utlltaqhtIFoCIBEqHS2RpFMXnqoaq2FPBkqORgYAAEgUdx9uZNTX1hMtAZAIYURLWhpbdPzc49mTgZKjkQEAABJl+87t6hvqU1trm+pr6pnIAJAIYURLpGBPxv1b7lffUF9FnxfVjUYGAABIlFQ6OLFk4ayFaqhtYEcGgEQII1oiBXsy+jP9euC5Byr6vKhuNDIAAECitKfbJWk4WsJEBoAkCCNaIkmnLTxNJmNPBkqKRgYAAEiUVGcwkdHW2qaGmgZ2ZABIhLCiJbOnz9YxBx7DngyUFI0MAACQKKl0Si2NLWqd1hos+yRaAiABwoqWSMGejHs338sEHEqGRgYAAEiUVDqlhbMWShLLPgEkRljREinYk9E71KsHtz5Y8edGdaKRAQAAEiV/9KqkYNkn0RIACRBWtESSTm87XZLYk4GSoZEBAAASJdWZGm5ksOwTQFKEGS2ZM2OOjnzFkezJQMnQyAAAAInR3d+tjr6O4WhJQ22Dsp4d/kklAFSrMKMlUrAn457N92goOxTK86O60MgAAACJMXz0amtuIqOmXpJY+Amg6oUZLZGCPRk9Az16aNtDoTw/qguNDAAAkBipdO7o1RHREknESwBUvTCjJRJ7MlBaNDIAAEBipDpzjYzW3cs+JbHwE0DVCztaMrd5rpbsv4Q9GSgJGhkAACAxUumU6mvqNbd5rqTd0RImMgBUu7CjJVKwJ2NN+5rhpgowVTQyAABAYrSn27Vg1oLh0erhiQx2ZACocmFHS6RgT0ZXf5ceeeGR0GpAdaCRAQAAEiOV3n30qrR7RwbREgDVbngiI6RoiRRMZEjsyUDxaGQAAJBQA5kBnfvjc/XI88n5yViqMzW8H0MiWgIgOYZ3ZIQYLZnfMl+Hzj6UPRkoGo0MAAASanN6s3751C+1OrU67FIqYiAzoK3dW7WwZeHwbURLACRFFKIl0u49GfkJEWAqaGQAAJBQPQM9kqSdgztDrqQynut6Ti7fcyKD41cBJEQUoiVSsCdjR+8Ordu+LtQ6EG80MgAASKjugW5J0s6BZDQyUunc0asjdmRw/CqApIhCtERiTwZKg0YGAAAJlbSJjFRn0MhYOGt3tCS/I4NoCYBqF5VoSVtrm9pmtbEnA0WhkQEAQEJ19ydzImPBrAXDt+UnMoiWAKh2UYmWSEG8ZHVqtVwedimIKRoZAAAkVNImMtrT7ZrbPFfT6qYN38bxqwCSIh8tCXsiQwriJS/uelF9zX1hl4KYCv+zGAAAhGJ4R0ZCGhmpdGqP/RgSx68CSI58tCTsHRnS7j0ZPfv3hFwJ4opGBgAACTU8kZGUaElnao/9GBLHrwJIjihFSw6ZfYjmz5yv7v27wy4FMUUjAwCAhBrekZGAiQx3V3u6fd+JDI5fBZAQUYqWmJlWLFqh7v275c6eDExe+J/FAAAgFPmJjF2Du0KupPy279yu/ky/2lr3bGRw/CqApIhStEQK4iVD04b09I6nwy4FMUQjAwCAhBrekZGAaEn+xJKxdmQQLQFQ7fLRkihMZEi792Ss2sQxrJi8aHwWAwCAikvSqSWpzqCRsfeODKIlAJIik83IZDKzsEuRJB2+/+Gq66vTqhSNDEwejQwAABIqSRMZ7el2SSJaAiCxMp6JxKLPPDPTzJdnalVqFXsyMGk0MgAASKhETWSkU2ppbFHrtNY9buf4VQBJkfVsZPZj5DW/3KwtXVu0sXNj2KUgZmhkAACQUPlTS4ayQ1X/jXwqve/RqxLHrwJIjkw2E5n9GHkzX54piT0ZmLxofSYDAICKyU9kSNUfL0l1pvZZ9Cnt3pFBtARAtYtatESSpvVM05wZc9iTgUmjkQEAQEL1DPSorqZOUvXHS9rT7aM2MmqsRjVWU/UTKQAQxWiJyXR62+k0MjBpFWlkmNn3zGy7ma0b4/4zzCxtZg/n3j5fiboAAEiy7oFuHdh0oKTqnsjo7u9WR1/HPos+8xpqG4iWAKh6UYyWSMExrJs6Nw0vZQYKUanP5GsknT3BNWvc/bjc279UoCYAABJrKDukvqE+Hdica2RU8URGKj360at59TX1TGQAqHpRjJZIQSNDYk8GJqcijQx3Xy1pRyWeCwAATCy/H2Nu81xJ1T2RkeoMGhmjRUuk3EQGOzIAVLkoRksk6egDj9bsabOJl2BSojRbdLKZPWJmvzazI8MuBgCAapZvZAxHS6p4IiM/rjxWtKS+tp5oCYCqF9VoSY3V6LS202hkYFLM3SvzRGaLJP3S3Y8a5b4WSVl37zGzN0m6yt0PG+NxLpV0ae795cuWLStf0UVav369li5dGnYZ44p6jdRXHOorTtTrk6JfI/UVp5z19Tb36onXPaG5T8/V84c9r0MePESzt82OTH2lkK9vy6u2aPuh23X8rcfLZPtc9+iZj6rlxRYtemRRaDVGFfUVh/qKF/Ua41TfpmM3qesVXTrmjmNCrmq3fH0vHPKCthy5RUf/9mg19DeEXdawOP39RtXatWvXuvsJJX9gdy/oTdIhkj4r6Ru53y+RdOQkPn6RpHUFXrtJ0pyJrpsxY4ZH2fLly8MuYUJRr5H6ikN9xYl6fe7Rr5H6ilPO+h7Y8oBrpfyr933VtVL+/Ye/P+nHiMvrd+FNF/ohVx0y5nWHXnWov+d/3lOpsvYQl9cwqqivOFGvzz36Ncapvvf99H3edmVbeMWMIl/fg8896Fop/9GjPwq5oj3F6e83qiQ96AX2DCbzVtBskZmdJekRSSdJuih38xxJXypFM8XM5pqZ5d4/UUHk5eVSPDYAANhX90C3pGTsyBjr6NW8+lqWfQKofhmPZrREko6be5xaGluIl6BgdQVed4Wk8939NjPryN32kKSCch1m9mNJZ0iaY2ZbJP2TpHpJcvdvSXqnpAXC5MMAACAASURBVI+Y2ZCkXkkX5ro3AACgDIZ3ZCTh1JLOlM469Kwx72fZJ4AkyGSjeWqJJNXW1OrUhafSyEDBCm1kHOrut+Xed0ly914zqy/kg939XRPc/3VJXy+wFgAAUKTu/mAiY3jZZ5VOZAxkBrS1e+v4Exk1LPsEUP2iempJ3oq2FfrV07/SCz0vDDfZgbEUOlu02cz2WNJpZscq2GUBAABiJj+R0TqtVY21jVU7kbGla4tcroWzFo55TUNtA9ESAFUvytESKWhkSNLq1OqQK0EcFPqZ/DVJN5vZeyXVmtk7JP1Q0pVlqwwAAJRNfkdGc0OzmhqaqnYiY/jo1Ql2ZBAtAVDtsp6NbLREkpbNW6am+ibiJShIQdESd/9ObhnnpyTVSvpnSV9192vLWRwAACiP/ERGU0OTmuqbqnYiI9WZkiS1tY4fLekd6q1USQAQikw2E+loSX1tvV678LU0MlCQgmeL3P2/3P1od29296Pc/bvlLAwAAJRPd3+3muqbVGM1ampo0q7BXWGXVBapdNDIOLjl4DGvaahtYEcGgKoX9WiJFMRL1m1fp5d2vRR2KYi4Qo9fXTjGG1tYAACIoZ6BHs1snClJVT+RMbd5rqbVTRvzGo5fBZAEUY+WSLv3ZKxJrQm5EkRdoS25TZI2jvK21cx2mdn3zKylPCUCAIBS6x7oVnNDsyRpRv2M6t2R0dU+7n4MieNXASRDJhv9iYxXz3+1ptdNJ16CCRX6mfxBSaslnSXp8Nyvd0n6G0lvk3SUpC+VoT4AAFAGPQM9mtmQm8hoqO6JjPH2Y0gcvwogGTIe7R0ZUtBYPnnByTQyMKFCGxmflHS+u//O3Z91999Jepekj7v7byRdKOlN5SoSAACU1siJjKb66jy1xOVqT7drYcvYR69KREsAJEMcoiVSEC955PlH1NHbEXYpiLBCGxlzJe29zntX7na5+wZJs0pYFwAAKKM9dmRU6UTGUOOQ+jP9E05kNNQQLQFQ/eIQLZGCRobLdXf73WGXgggr9DN5jaRrzKzNzGrMbJGk7+Zul5kdLen5slQIAABKrru/+icyBqYHUxYT7chgIgNAEsQhWiJJrzn4NWqsbSRegnFNZkfGfgoWfA5KelbSK3K3S5JJ+lDJqwMAAGXRM9Cj5voRjYwqnMgYbmRMNJHB8asAEiAu0ZJpddP0moNfQyMD4yqokeHuL7j76yUtlHSypIXu/np3fyF3/6Puflf5ygQAAKXUPdC9R7Skb6hPmWwm5KpKq396vyRp4awJdmTU1BMtAVD14hItkYJ4yUPbHlJXf1fYpSCiJvWZ7O5b3P0Bd3+uXAUBAIDycvdgImNEtESSdg3uCrOskhuYPqCWxha1Tmsd9zqiJQCSIC7REiloZGQ9q3va7wm7FERUQY0MM5tuZpeb2f1m9qyZbci/lbtAAABQWr1Dvcp6do/jVyVVXbxkYMbAhPsxpCBaMpQdkrtXoCoACEdcoiWSdPKCk1VfU0+8BGMqdCLjSknnSbpW0oGSviypX9L3ylQXAAAok56BHknaZyKj2hZ+DkwfmDBWIgXREkllj5d87s7P6ZYnbynrcwDAWOIULZlRP0Ovnv9qGhkYU6GfyedKequ7f0PSUO7Xd0h6XdkqAwAAZdHd3y1Je+zIkKpwImN64RMZksq68DPrWX3pvi/p5j/fXLbnAIDxxClaIgXxkge3Plh1TXaURqGNjGZ3z8dIBsyswd2fkPTqMtUFAADKJAkTGV39Xco0ZCY8sUQKdmRI5Z3I2Nq9VQOZgeHXHgAqLU7REiloZAxlh3Tv5nvDLgURVGgjY6OZLc29/2dJHzCzCyWly1MWAAAol+6B3ERGFe/IaE+3S1JBExn5aEk5F35u6Ah+HkQjA0BY4hQtkaRTFpyiWqslXoJR1RV43RcVHL26XtJlkn4qqVHSR8pUFwAAKJMkTGSkOlOSJj56VapMtGRjx0ZJ1fUaA4iXuEVLZjbO1PKDltPIwKgmbMmZmUn6vaTbJcndb5c0W1Kru3+3vOUBAIBSS8KOjFQ6aGRMJlpSzomMjZ1BI4OJDABhiVu0RAriJQ8894B6B3vDLgURU8hskUlKjbzW3QfdvXq+2gEAIEGSMJHRnm6XZUxzm+dOeO3wREYZd2TkoyXV1CwCEC9xi5ZIQSNjIDOg+7fcH3YpiJgJP5PdPStpg4IpDADABL794Ld16vdODbsMYEz5RkY178hIpVNq6Gso6Iv2SuzIYCIDQNjiFi2RpFMXnqoaqyFegn0UuiPjy5J+ZGYrFUxnZPN3uPvWMtQFALH14Vs/HHYJwLjyyz73nsjYNbgrtJpKLdWZUkNvQ0HXVmJHBss+AYQt69nYNTJmTZul4+YeRyMD+yi0kZHfhfF6SZ5733Lvx+u/BgAAEq5noEf1NfVqrGuUFHwjX2u1VRUtSaVTathVWCOj3Mev9g31aWv31uHX2N0VrCADgMqJY7RECuIl33zwm+ob6tO0umlhl4OIKPQzefGIt0Nyb/n3AQBAjHT3dw9PY0iSmampoalqoiUDmQFt695W8ERGuaMl+RNUlsxZooxn1J/pL8vzAMB4Mp6J3bJPSXrL4W9R31CfLr3lUmU9O/EHIBEKamS4e2qst3IXCAAASqtnsGf4xJK8GfUzqmYiY0vXFrk8MtGSfKzkqAOOklRdS1UBxEccoyWS9BeL/0JfeN0XdO2j1+pjv/6Y3H3iD0LVK6iRYWa1ZvZ/zexpM0vnbnujmREEBwAgZvaeyJCCPRnVMpGRn4AoeCKjzMev5hd9HnPAMZLYkwEgHHGNlkjSZ0/7rD5xyif0jT9+Q//4+38MuxxEQKGfyZdJequkT2n3joynJf11OYoCAADl0zPQM3xiSV41RUva0+2SVPCOjHIfv7qxY6Om1U3TofsdKqm6TocBEB9xjZZIQQTy/535//ShZR/S5Wsu17/f8+9hl4SQFbrs892STnb3bWaWX/y5UdKislQFAADKpntgjImMKok8pNK5iYy+ye3IKFu0pHODFrUuGm4eMZEBIAxZz8Z2IkMKmhnffPM31dXfpU/e8Um1TmvVh5Z/KOyyEJJCGxlNkrbvdVuDpL7SlgMAAMqtZ6BHBzQdsMdtTQ1NVfMNdqozpbnNc1WTLewL9rJHSzo26pDZh6ipITjmtlpeZwDxkslmYrkjY6Tamlr94G0/UPdAt/76l3+tlsYWXXDUBWGXhRAU2pJbK+mv9rrt3ZIeKG05AACg3MbckVFFExlts9oKvr7s0ZLOjVrcunj4Na+W1xlAvGQ9G9toyUgNtQ268fwbdVrbaXrvT9+rW5+6NeySEIJCGxn/IOkKM7tD0gwzu0XSvyrYmQEAAGIkCTsy2loLb2SU8/jVjt4OdfZ16pDZhww3MpjIABCGjMd32efeZtTP0C3vukXHHnis3nnjO7Vq06qwS0KFFXr86jpJR0j6taTvSlot6Th3/3MZawMAAGVQzTsysp4NGhlTmcgow46M/Ikli1sXq6k+iJZUS8MIQHy4e2yPXx1LS2OLbnvvbVrculjn/vhcPbj1wbBLQgUVevzqdHff7u5fdvf/5e7/7u4vlLs4AABQWkPZIfUN9e07kVElx69u37ld/Zl+LZy1sOCPye/IKEe0ZGNHrpExezETGQBC47mDJ6shWjLSnBlzdPtFt2v/Gfvr7B+erSdefCLsklAhhc4WvWBm3zGzk8paDQAAKKv8N9H7TGQ0BBMZ7j7ah8VGqjM4sWQyExnljJZs6NggKTeRwbJPACHJZDOSVDXRkpHmt8zXHRfdofraep117VnDDWRUt0I/k8+TVC/pdjNbb2afNLO5ZawLAACUQf6b6JmN+05kuFx9Q/E+kKw93S5Jk9qRUe5oyX7T99OsabPUUNug+pr6qojwAIiXjAeNjGqKlox06H6H6vaLblfvYK/OvPZMbe3eGnZJKLNCd2T83t3fL2mepC9JeouklJn9vIy1AQCAEuvu75Y0+kSGFP/9Dal0MJExlWhJOSYy8ieW5DU3NDORAaDisp6VVH3RkpGOOuAo3fbe27R953a94do36OVdL4ddEspoUrNF7t7j7ldL+jtJdyhoaAAAUFJPvvQkX4CUyfBExig7MqT4Hw2a6kyppbFFrdNaC/6YWquVycqyI2NDxwYdMvuQ4d9X0+kwAOKjmqMlI504/0T94sJf6Jkdz+ic684Zbt6j+hT8mWxm+5vZ35nZw5LultQh6Y1lqwwAkFhv/tGbddnqy8Iuoyp1D1T3REZ71+ROLJEkM1N9bX3JoyVZz2pT5yYmMgCErtqjJSO9bvHrdOP5N+qhbQ/prde/Vb2DvWGXhDIo9NSSmyU9J+lCSd+UdJC7v9fd7yhncQCAZEr3p7Wjd0fYZVSl8XZkSNUxkTGZ/Rh59TX1JY+WbOvepoHMgBbP3t3IaKpvopEBoOKSEC0Z6dwl5+oHb/uBVm1apQtuuqAsO5AQrkInMp6VdLy7n+zu33b3dDmLAgAkWyabif3SyahKwo6MhS2F78fIa6htKHm0JH9iychoSXNDc+xfYwDxk5RoyUjvPvrd+sabvqFbnrpF7//5+4ebOagOdYVc5O6fKHchAADkDWWH1J/pD7uMqjTRjoxdg7sqXlOpdPV3qbOvc2oTGbWln8jY2BkcAbh3tGRbz7aSPg8ATCRJ0ZKRPvLqjyjdn9Zn7vyMWhpa9J9v/k+ZWdhloQQKamRY8Lf9QUmvl/QKScN/++7+F+UpDQCQVBnPqH+IRkY5TLgjI8bRkuGjVye5I0PKTWSUePR4Q8cGmWyPE1SaGppi/RoDiKekRUtG+vSpn1a6L60r7rlCrdNa9cUzvxh2SSiBQmeLLpd0maTNkk6StFbSEZIeLlNdAIAEG8oOES0pk/xERr5xkTe8IyPGsYdUZ3D06pR3ZGRLP5FxcMvBaqxrHL6tuZ5lnwAqL4nRkpH+9fX/qg8v/7CuuOcKXXH3FWGXgxIoaCJD0rslvdHdHzGzD7r7J8zsfyR9soy1AQASimhJ+XT3d6upvmmfL2arYSIjlQ4aGSMnIApVjomMjR0b91j0KQWvM40MAJWW1GhJnpnpG2/+hroGuvSZOz+j1mmt+vAJHw67LBSh0EbGfu7+SO79jJnVuvv9Zva6chUGAEgmd1fWs0xklEnPQM8+sRJJmlE/Q1L8JzIaahs0t3nupD+2vra+LMs+zzzkzD1uyy/7dHdy2gAqJsnRkrwaq9E1512jrv4u/c2tf6OWxha9++h3h10WpqjQ2aLnzCz/440Nks4xs5MkcY4NAKCk8j81YkdGeXQPdO9z9Ko0opER44mM9q52LWhZMKXR6VIfv9o/1K+t3Vv3OLFEChoZQ9mhki8WBYDxJD1akldfW6+fvPMnOmPRGbr4pxfrlidvCbskTFGhn8nflLQ89/6Vkn4m6R5JXytHUQCA5Mp/scVERnmMNZFRYzWaXjc99hMZU4mVSKWPlqTSKbl8jxNLpOrYRQIgfpIeLRlpev10/fzCn2vZvGU6/8bz9fuNvw+7JExBQY0Md/+au/809/6PJS2SdKS7f6GMtQEAEmgoOyRJ7Mgok+6B7n2OXs2L+4kaqXRqSos+pdIfv7qhY4Mk7bMjI99EYk8GgEoiWrKnmY0z9ev3/Fqv3O+Veuv1b9UDzz0QdkmYpCnNFrn7Fnf/c6mLAQBUTnd/t8645gw9/fLTYZeyh/xPjap1ImNDxwa1fbVt+ISNShtrIkMKpgXiOikwkBnQtu5tUzp6VcpNZJRwR8bGjo2StE+0JL9UlUYGgEoiWrKv/Wfsr99e9Fsd0HSAzrnuHK3bvi7skjAJfCYDQEJt6tykValVenDrg2GXsofhiYwq3ZGxbvs6tafb9dTLT4Xy/N39o+/IkHITGTFtZGzp2iKXT7mRUV9TX9JoycbOjWqsbdxn8Wi+iRTnyRcA8UO0ZHQHzTxId1x0h6bVTdNZ156lZ3c8G3ZJKBCNDABIKJdLil6Eo9qjJem+tCRp1+CuUJ6/Z6BHzfXjTGTE9Bvs/ITLVHdklCNasqh10T4//SRaAiAMREvGtnj2Yt1+0e0azAzqzGvP1HNdz4VdEgpAIwMAEso918iI2ORDfvx1KDs0/H41SfcHjYzeod5Qnn+sU0ukeE9kpNJBI2OqOzJKHi3p3LhPrERi2SeAcBAtGd8RrzhCt733Nr2862Wdde1ZemnXS2GXhAkU9JlsZp8b4/bPlLYcAECl5CcyonYMZH4iQ6rOqYz8REbvYOUbGe4+8Y6MmE5ktKfbJUkLWhZM6eNLffzqxo6N+5xYIjGRASAcREsmdsJBJ+iWd92ijZ0bdfYPz1ZXf1fYJWEchbbkPjXG7Z8oVSEAgMrKj5lGrVmQ/2JLqs6Fn519nZLCiZb0DfUp69nxTy2J6aRAqjOlec3z1FjXOKWPL+Xxq519nero69jnxBKJZZ8AwpH/N5+JjPGtWLRCN51/kx554RGd++Nzla3Jhl0SxjDuZ7KZHWRmB0mqMbN5+d/n3lZIitZXvwBQYR/6xYd0+7O3h13GlEQ1WrLHREbEaiuFMKMl3QPdklSVExmpdGrK+zGk3LLPEkVLxjqxRGLZJ4Bw5KMl7MiY2JsPf7Oufdu1WpNao2dPeDZyk6sITNSS2yJps6TpI97fnHv/DklXlbU6AIgwd9d3//Rd/ezPPwu7lCmJarRk5F6MapzIyDcywpjIyE8BjLkjI8bHr6bSqSnvx5BKu+xzY2fQyCBaAiAqhpd9Ei0pyIVHXahvveVb6jqwSxf/9OKq3NkVdxM1MhZLOlRSt6RDRry1SZrp7leUtzwAiK78N9nberaFXMnUDE9kRCxawo6M8unun2AioyGeExlZz2pzevOUj16VShst2dCxQdLoExkNtQ2qq6mLbcMIQDzlY5tESwp36fJLNf+J+brh8Rv0kVs/Mvx1E6Khbrw73T2Ve7e1ArUAQKzkowFxbWQM78iIWHwjKdGSUCcyxtqRUd+kweygBjODqq+tr2RpRdm+c7v6M/1FR0tKNpHRsVGzp83WrGmzRr2/uaGZiQwAFUW0ZGrmPjtX7//r9+vyNZdrVuMs/dtZ/yYzC7ssaJxGhpmd7+435t5/91jXufuPylEYAETd8ERGdzwbGZGNllT5ss/hiYwo7sho2H00aGttfH6GkerMHb1a7ERGqXZkdG4cddFnXpx3kQCIJ6IlU3fZ6y5TZ1+nvnTflzR7+mx99rTPhl0SNP5Exj9JujH3/uVjXOOSaGQASKR8NGBbzza5e+w69ERLwhHmss9CdmRIwSLK1mkxamSkc42MIndkDGYGS/Lf8oaODTr6wKPHvL+5oVk9g0xkAKgcoiVTZ2b62jlfU7o/rc/97nNqaWzR/zrxf4VdVuKN18h4Xf4ddx/7xwoAkFD5b0QHMgPq6OvQftP3C7miyclPZEStWVDtyz7DPH61kB0ZUji1FaM93S6puImM+pp6uVwZz6jOxk3ejivrWW3q3KS3LnnrmNcQLQFQaURLilNjNfrv8/5b3f3d+uivP6qWxhZdfOzFYZeVaOO15J7Nv2Nmd1SgFgCIlZHLGuMYL8mPmUYtWlLNOzIGM4PDTYIwln0WsiNDUuwWUaY6U2ppbBlzJ0UhGmobJKnohZ/burepP9M/6okleXFdqgogvoiWFK+upk7Xv/N6vX7x6/WBn38gtqfWVYvxGhn9Zpb/8eKJlSgGAOJkZDTg+Z7n97k/6tuth6MlEWsWVPOOjK7+ruH3Q5nIKHRHRsy+yU6lU0VNY0gaXm5abGMvf/TqaCeW5I02kdHV36X7Nt9X1HMDwFiIlpTGtLpp+tmFP9MJB52gC266QHduuDPskhJrvM/k6yWlzOwpSTPM7KnR3gp5EjP7npltN7N1Y9xvZvY1M3vGzB41s2VT+LMAQEWN/CY7jieXRDVaUs07MvL7MaTwdmTU19Srsa5x1PtjO5GRThW1H0MaMZFR5MLPjR1BI2PCZZ97vcbfWfsdnX7N6bGL9QCIB6IlpdPc0KxfvedXWrL/Ep13/Xm6f8v9YZeUSGM2Mtz9o5LeIOlfJA0qWPg52lshrpF09jj3nyPpsNzbpZK+WeDjAkBo4h4tyU9kRDlaUm0TGfkTS2qtNrQdGWNNY0jSjPoZkuI3kdGebi9+IqOmNBMZGzo2yGTj1jPaREZHX4eGskN6ceeLRT0/4uNXT/9KB37pQG3q3BR2KUgAoiWltd/0/fTbi36ruc1zdc515+jRFx4Nu6TEGXe2yN3vc/cfSvq8u39/tLdCnsTdV0vaMc4l50n6gQful9RqZvMK/lMAQAhG/kR9tImM/MRDVOW/qIlctGTEss+o1Vas/ETGgc0HhrMjY7BnzBNLpD2PX42Lrv4udfZ1auGshUU9Tql2ZGzs3Kj5LfPHnHqRRm9k5BtbL+6ikZEEvYO9+ttf/a2279yu6x69LuxykABES0pvbvNc3XHxHWqqb9Ibrn2Dnn756bBLSpSCPpPd/d/NrNnMLjCzf8j9OvaPdCZvvqTNI36/JXcbAERW/hvR+pp6oiUlVM3RkvyJJfOa54USLZloImPk8atxkerMHb1aoh0ZRUdLOjeOu+hTykVL9nqN842Ml3a9VNTzIx7+7Z5/06bOTTpo5kG64fEbwi4HCUC0pDwWtS7SHRffoYxndOa1Z2pzevPEH4SSsEKW0ZnZkZJul5SRtEnSIkm1kt7g7qPuvRjlMRZJ+qW7HzXKfbdK+qK73537/Z2SPunua0e59lIF8ROZ2fJly6K7TmP9+vVaunRp2GWMK+o1Ul9xqK84E9X3YtuLaj+mXY09jarvq9eS+5ZIktaeG/yva9kty2SyUGscT9ecLj198tOa3jVdR6w6osSVBaZSX8fcDm149QZJ0kHrD9K8Z8o3oFfpz8GXD35Zm47fpFkvzFLP7B4d95vjxr2+1PU9/ZqnlanP6FV3v2rU+4fqhvTIOY/o4McP1oEbDpzw8aLw33D6gLSeec0zWrJmiZo792zSTKa+HQft0MblG3XE74/Q9J7pU67n0TMf1cyXZmrxw2M3M7Ydtk1bX7VVx//yeD35xJNaunSpNh63UTsW7NCihxZp/+f2n/Lzl1oU/o7HE8f6+qf36/HXPa7W51vVvKNZm4/eXPTnXSnri5qo1xiX+vL//hx1x1Fq7B17YqzS4vL6TWTXrF168uQnVd9fryX3LFH9QH0Fqov+6ydJa9euXevuJ5T8gd19wjcFTYx/0u7Gh0n6R0l3FvLxuY9ZJGndGPd9W9K7Rvz+SUnzJnrMGTNmeJQtX7487BImFPUaqa841Fecier78r1fdq2Uv/HaN/rh/3H48O1aKddK+VBmqNwlFvUa3v7s7a6V8iX/saSEFe1pKvXdsO6G4dfw87/7fBmq2q3Sn4NX3X+Va6X8kp9f4o2XNU54fanrO+XqU/z133/9mPcPDA24Vsr/5a5/KejxovDf8Dce+IZrpXxr19Z97ptMfTc/cbNrpfzhbQ9PuZa+wT63leYrf79y3OuuvO9K10r5jl07hmt8xw3vcK2Uf+Xer0z5+cshCn/H44ljfX95/V/6jMtn+Ob0Zt/Wvc1r/rmm7P+vG0vUXz/36NcYl/qufuhq10r5po5NIVe0p7i8foVYvWm1T//CdD/+W8d7Z29nGavaLeqvn7u7pAe9wJ7BZN4KDUkdL+lfc4Uo9+sVksb/UVLhfiHp4tzpJSdJSrt7/Oa0ASRKPlqyuHVxLJd9Du/IiFh8Y+SOjGpd9jm3ea76M/17/FkroWdg/B0Z9bX1qq+pj9WOjFRnSg21DTqweeIJkvGU4vjV9nS7XD7uiSXS7uNvR+7JIFqSDLc9c5t+9uef6R9P/0cd3HKw5jbP1Yq2Fbr+8esjf2Q34o1oSfmd1naabr7gZq3bvk5v+fFbOIWqzAptZKQVTFSMtEhSVyEfbGY/lnSfpCVmtsXMLjGzD5vZh3OX/ErSBknPSPqOpL8psC4ACE3vUG9wOkFrm7oHuvfJvEd92Wf+i+aoLdSs5h0Z6f60ptdN16zGWZIq36iZaEeGFCz8jNWOjHRKC1oWFL3ArhTHr27oCCJRE+3IoJGRTP1D/frYrz+mw/Y7TB8/6ePDt1941IV66uWn9MgLj4RYHaodp5ZUxtmvPFvXvf063bv5Xr39hrdH7mS4alLov/rfl3SrmX3AzF5nZh+QdIuCY1Un5O7vcvd57l7v7ge7+9Xu/i13/1bufnf3v3X3Q939aHd/cEp/GgCooL6hPk2vn655zcEOh+d7no/VT9TyjZao/SNb7cevtk5r1fT6IAtf6Z/W9Az0aGbD2BMZUm4RZYwmMtrT7WprLW7Rp7T7+NViTi3Z2LlRknTI7EPGvW54qeqI1zn/PqeWVK+v3v9VPb3jaX3tnK/tcarN25e+XbVWqxvWsfQT5cOpJZVz/pHn67/e8l/6zbO/0Xtufk/Fpy+TotDP5Msl/bekT0m6Nffr93O3A0Ai9Q72anrddM2bGTQytvVsG/5CIQ6GJzIiNvUw8outqNVWrHR/WrOmzdKM+hmSVPGTS7oHCpzIiFEjI5VOFX30qlSaaMnGjo1qrG0c/n/CWJjISJ4tXVt02erLdN6S83T2K8/e4745M+bozEPOJF6CsiJaUlmXLLtEX3nDV3TTEzfp0lsu5b/tMpiwkWFmtZKWS/qyuy9x9xm5X7/o7kMTfTwAVKveod49JjK2dW8bHt2UFPl/tIZ3ZEQ0WtJU31R1ExmdfZ2a1ThL0+uCiYz8npVKGMoOqW+or7CJjJhESwYyA9rWva3oo1elEkVLOjeorbVtwp94NjXse8wtjYzq9onbP6Gh7JCufOOVo95/4VEXalPnJv1x6x8rXBmSgmhJ5X385I/r86d/Xt97+Hv6+9/+feS/LoybCRsZ7p6R9HtJxR2sDgBVpneoV9Pqpmlu81xJwUTGyEZG1OWjJYPZlaSMmwAAIABJREFUwUj945r/qVFTQ1PkmizFyk9khBEtyf/0v5omMjanN8vlJWlk5KMlxU5kTBQrkcafyCBaUn3u2nSXrl93vT596qfHXAT7l6/6SzXUNuj6dddXuDokBdGScKw8Y6U+duLHdOX9V+qy1ZeFXU5VKfQz+QlJxX+VAABVJB8t2X/G/qqrqdtnIiPqRjYvorQnIz+RMaN+RvVFS/rSmtUYTrSk4EZGjCYy2tPtklSSHRnDExlF7siYaNGnNH4jY0fvDvLUVcTN9dFff1SLWhfpU6/91JjXtU5r1dmvPFs/efwnsfp3BPFBtCQcZqYrz75S7zv2ffqnu/5JV91/VdglVY1CGxnXSvqZmb3HzE41s1Pyb+UsDkDy/PmlP+v5nufDLqMg+WWfNVajuc1zgx0ZI74BifypJSPqi1LDIP9To2qMlqT703tESyo5kdHd3y1J4x6/KsVrIiOVTklSSXdkTDVaku5La0fvjoIaGXsv+3R37RrcpZkNM5X1rDr7OqdUA6Jn+6LtWrd9na5845XDk1hjueDIC/Rc93O6p/2eClWHJCFaEp4aq9F33/pdve1Vb9P//s3/1jUPXxN2SVWh0EbGVyUdo6ChsVrS3bm3NWWqC0BCvf2Gt2vlXSvDLqMgvUO9w9+QzmueF7toychaoziRUZXRkr69ln1WcEdGNU5kpDqDRsaClgVFP1ax0ZJCTyyR9p3IGMgMKOvZ4ckS4iXV4YWeF7R1yVa98dA36rwl5014/VuXvFXT66brhsc5vQSlR7QkXHU1dfrxO36ssw45S5f84hLdvP7msEuKvYI+k929Zow3WnoASqpnoEfp/nTYZRSkd7B3+Cds82bOi92yz5H1RalhkG9kNDc0V9VExmBmUL1DvaEdv9o9kJvIqKLjV1PplOY1z9vjKMupKjZasrEjaGSMtQNh7+eqtdrhhlH+8yA/WcLCz+rw6Ts/La91XXX2VTKzCa9vbmjWmw9/s2584sY9jqEGSoFoSfga6xr10wt+qpMOPkkX3nShfvvsb8MuKdYKObXklWb2NjOb+F9mAChSxjORmg4YT37ZpxTPiYzIRkuyu6MlUaqrWPkG3R6nlkRxR0ZDU0UbLMVoT7eXZD+GVPzxqxs6NkgqbCLDzNTc0Dz8d5J/vfNLS1/cGd+JDHfXrU/dWlVNyKm4b/N9uubha3TAswdoyZwlBX/cBUdeoO07t2vVplVlrA5JlP/6hImMcDU1NOnWd9+qI15xhN52w9uIkhVh3M9kM3u7pPXS/2fvzOObqPP//5ocbZr0AnpwWKEgIlBAwAt1ERVR8cQLPHA9UITfLqvrruu67i5e67W63/VAFG9Q8ZbDA2ERERW5LNACbaEHlF7QM2nTpMl8fn+kn+kkmSQzyUyO9vN8PHwIbTrzSTIt/bzm9Xq98SmA/RzHzYzKqhgMRp+FJ3xEZXvRpNPV6RUtOd5xPKF+eY/3ss8UY0pCvZ6hoL0HsYqWyO7IMHqEjEQQ5apaq1TpxwAiH79a0VKBTFMmMk2Zsh5vSbIEFDIS2ZGx5fAWXP7B5VhdsjrWS4kZbt6N3339OwxOG4xBZYMUfe3MkTORmpTK4iUM1WHRkvgh05SJdbesw5C0Ibjs/ctQWFcY6yUlJKGu5IcBPAQgDcA/u//MYDAYmuHmE8iR0SXqyEjz/LJaa6sVPh/vZZ/ijapvtMTqsMbsjqCbuKHn9DAZTHEVeYmU1k6RIyPOx68C0RVZwoEnvMeRocLoVSDyjozy5nJZRZ+U1KRUIcJDr4O8DE/XRyILGatKVgHwTF/pq7y+63Xsqt2F52Y8B71bmY3fbDTjylFX4tP9nyaMqM9IDNy8mxV9xhG5qbnYcOsGpCenY8byGShtLI31khKOUEJGPoDnCCHtAJ4HcJL2S2IwGH0ZnvCJI2S4ejoyBqYOBAAcbTsayyUpIli0ZMWeFbjg3QuEu/jRxMW7YNAZYNKbepUjQ4iWmGITLVHSkQEg7nsyGtob4HQ71RMy6NSScDsyWipkxUooUtGSASkDYDFaErbskxAiCBni0bJ9icaORjy08SGcN/Q8zB47O6xjzBk7B032Jmwo36Dy6hh9GZ7wzI0RZ5yYcSLWz10PAJj+7nRhpDhDHqGuZj0hnlt2hJAuAEnaL4nBYPRlEqojo8u7IwMAjloTSMgIEi1p72oHT/iYdFS4eTf0Oj2SDcm9qyND5Mgw6o0w6AwxcWRQx0Ug6OfjfXIJnViiVrSEOjLCiZbwhEdlS6UiR4a4VJVeB2ajGdmW7IR1ZBw4fgAHmw4CQExE0Hjg4Y0Po7WzFS9e+qKsgk8pZoyYgYzkDKwsXqny6hh9GTdxs6LPOGRU1ih8O/dbtDnacNHyi1Bvq4/1khIGQ4jPJ3EcJ46TmHz+DkLIv9RfFoPB6KvwhA87ox5NCCHeHRnd0ZLqtmqvx8QzXo4MnwgHLdyk/48m1JGRrE/uXdESkSMD8Gxaoz1+1WK0hLwjR/s74t2RUdXqETLUKvvkOA4GnSEsIbXOVodOV6esiSWU1KRUwXkhFjKyzFkJK2RQN4ae0/dJR8au2l14deerWHTmIozLHRf2cZINybhm9DX4dP+n6HR1CoI5gxEJPOFZtCROOXXgqfjypi8xY8UMXLziYmy6bZPsvqW+TChHxlYAF4n++8Xn79M1XR2DwehzJEpHhtPtBAERoiW5llxw4KLiyLA6rKoUMXp1ZPg4H+jnYjECUIiWGExwuB1xLwjJhToy6C8nKYaU6I5fdVhD9mMAomhJgjgy1IqWAJ7Cz3CiJXT0qpJoiSXJ4jd+lQoZiRotWV2yGpMHTUa2JVuIMvUVeMLjd1/9DtmWbCyetjji480eOxttjjZ8c/CbyBfHYMDz+xWLlsQv55x4Dj6f/Tn2HduHy96/LO7/DY4Hgl7NhJBphJDzg/x3QbQWymAw+gaJ0pFBuw2oI8OoNyLLnKV5R0a7sx3pT6XjL+v/EvGxxAKBnyOju92c/j+a0LLPZEMygPiaqBIJdGpJenI6AM9UlqiOX+2yhZxYAoiiJXHuyDjcehgZyRmCw0UNjDpjWNdbRYtHyFBa9unbkWE2mpFtTsxoSb2tHlurt+KqUVd5Pbe+wvLdy/Fz9c94evrTqtxJvSD/AgxIGcCmlzBUg0VL4p8ZI2bgg2s/wNbqrZj14axe5UrVAibLMRiMuCJRxq/SSIDY8puenI42R5vwd7WnlpQ1liH1Sc8d9bcK34r4eOL1+W7e4sWRAaBXFH4SQtDqaIXFaIFB50l1mo3m6JZ99jZHhoqjVylJ+qSwom3lzeUAlMVcUo3SQkaiRkvWlq4FAcGVo65EWlJanxIyWjtb8cCGB3DWCWfh1gm3qnJMo96I68Zch9Ulq+P+e5GRGLBoSWJw7Zhr8caVb2B9+Xrc9NlNMfk9LFFgQgaDwYgrEqXsk26uabQE8Mxm17LfY9muZcKf1dgAezky3PHTkSGUfeqTJdeWaBxtOwrLvyxYX77eyz0Q7WiJzWkLObEESBxHRlVrlWr9GBSj3hhetKSlAkPShijqMrAkSZd9ZpmzYHPaEk7AW1WyCkMzhmJ87nikJqX2qWjJ4k2Lcaz9GF669CVVrfuzx85GR1cHviz7UrVjMvouLFqSONx26m34v4v/D5/t/wx3rblLlThxb4RdzQwGI65I1GgJ4BEytFTOxcWQamxyvDoyAkRLYuLIIL3LkVHSWAK7y46ihiJkJPcIGdEu+7Q6e5cj40jrEeSl56l6TKPOCCcfRrSkuUJR0SfgiZY43U4QjghCRooxBdnmbABIKFdGR1cH1pevx5WjrgTHcUhL7juOjKKGIry47UXcPfluTB48WdVjTx06FQNTB7J4CUMVWLQksfjDWX/AI9MewduFb+O+b+7rNX1hasKEDAaDETcQQhJmagndgIodGXqd3uturtr/6IhdGGqo8/EaLXHz3h0ZiZ4RrbPVCX/2cmQYY+DI6CUdGZ2uTjR3NmNI2hBVjxtu2Wd5c7mifgygRzBy693o6OqAyWCCjtMhy5wFADjWnjiFn+sPrUenqxNXjboKgEek6QvjVwkh+P3Xv0eGKQNPXPCE6sfX6/S4fsz1+LL0S6/YIoMRDixaknj8ferfcd9Z9+GFbS9g8abFsV5O3MGEDAaDETfQjXU8OzIONh1ER1dHQEeGliKM2pteWdGSGJR9+nZkJHq0xEvI8HVkRLsjw9g7HBm11loAPWOP1cKoV1726XQ7Ud1WrWhiCQDBHcMbeHR0dQhjb7MtiefIWF2yGhnJGZg6dCoA9JmOjK/KvsKmyk14/PzHMcA8QJNzzCmYA4fbgdUlqzU5PqPv4CYsWpJocByH52Y8hztOvQOPbn4Uz//8fKyXFFewq5nBYMQNdPMcr0IGIQSTXp2EV7a/Iln2qed8HBkql32qvekVry+uoiW8y6sjI9GjJWIhQzzNICYdGTIcGSaDCRy4uHZk1Nq6hYxUdYWMcMo+D7ceBgFR7MgIJGRQR0aiCBlu3o01pWswc+RMGPVGAOgzHRkvbX8Jg9MGY96keZqd46wTzkJeeh5WFq3U7ByMvgHtn2IkFhzH4bUrXsN1Y67D/d/ejzd2vRHrJcUNTMhgMBhxA40zxOvUEofbAavTijZHW8CyTy03/mpvesXxlEDRkpiUfRI3DDpD74yWJHuXfUarI4MQIrsjg+M4TxFlHDsyaqw1AIDBaYNVPa5Rp7zsk04sUdqRQSM8br0bHS5/IeNYR2JES345+guOdRzDlaOuFD5Gx6/25kz3oaZD+ObgN7h70t2CgKMFOk6HG8begG8PfYtme7Nm52H0bvYf24+t1VsVFRIz4ge9To8Vs1bg4hEX4+61d+Pj4o9jvaS4gAkZDAYjbhA2z8Qdkw20FIQQTH93OtaUrPHaUEtFS/Q6vabRErU3vXKiJWz8auQE6siIZrSk09UJnvCyppYAnnhJXDsy4ihaUtFcAQCqRUv6mfpBx+kSxpGx6sAqGHQGXHrSpcLH0pLSwBM+qtGpaLN0x1LoOT3umnyX5ueaUzAHXXwXPj/wuebnYvQu3LwbdcPrMPHViWiyN+HfF/071ktihEmyIRmfzf4MZ+edjZs/uxnfHPwm1kuKOUzIYDAYcYO4jyFeCj+dbif+V/E/bK/Z7rXZlyr71HG6qJV9qkGwaIlYVIo2QtlnLxm/GtCREcWyT2rzl+PIALxHg8YjtbZaGHQGwb2gFuFESypaKpCkT1LsDqFdJLzeW8jQ6/Ton9I/YYSM1aWrMW3YNC+Rjl5nvbUnw95lx5uFb2LW6Fmqu4KkmDxoMob3G87iJQxFlDWWYerbU3F07FFcctIlKF5YjMtOvizWy2JEgNloxtob16IgpwDXfHgNfqj6IdZLiilMyGAwGHFDsKhDrKCbGqfbKTgDCIggKvh1ZCRo2afv6x3rjoy+4sigTgmtoRtKOR0ZQLcjI86jJQNTB6peXGfUKXdklDeXY2jGUMVroZt9OrWEChmAJ16SCNGS0sZSHDh+QJhWQqHXWW8VMj7e9zGa7E1YeNrCqJyP4zjMGTsHGys2oqG9ISrnZCQuPOHxwi8vYMLSCdh3bB+G7RqGz2d/jtzU3FgvjaECGaYMfHPLNzgx40Rc/sHl6MiIXtdWvMGEDAaDETeI4yTx0pNBNzVOt9PLtSB0ZBh6R0dGwKklMerI0Ot6x/hVF+/yurPu25EBREeooaMwe5MjQ4s74eGMX61oqVAcKwECR0sAINucnRCODDpJ44qTr/D6OH1uvXUE65LtS3BK1imYNmxa1M45u2A23MSNT/d9GrVzMhKPiuYKXPjuhfjDN3/AtGHTULSgCAOODgDHcbFeGkNFciw5WD93PTJNmSg7swwHjh+I9ZJiAhMyGIw45Jw3z8GUN6YEfczDGx/GPWvvidKKokNcOjK6NzVd7i6vDadUtESv03ZqieqOjO71mQwmPyGDvhcu3oXGjkZVzxuK3uTIONZ+zOs68HVkAOp3n0ghODKUdGTEuSND7YklgKcjQ6mrqry5XPHEEqCn7NM3WgJ4HBmJIGSsKlmFUweeiqGZQ70+Tq+z3ujI2FmzE78c/QULT1sY1c3huJxxOCXrFHxY/GHUzslIHAgheHXHqxi/dDx21uzE61e8ji9v+hJD0ofEemkMjcjLyMOGuRsAAkx/dzoqWypjvaSow4QMBkMjItn8/XTkJ2yt3hr0MTtqdmBHzY6wzxGPiPsY4kXI8HJkuP3LPmmPA+BxZKgtXojRquzTZDAFjJZsrtqMrGez8Pl+bUrmGtobvKIXQPf41V7SkeH73LzGr3aLYNHoyQinIyOao2GVUmvVxpGhNFrS5mhDk71J8cQSQBQtMXRHSww+0ZL2+I6WOFwO/HTkJ1wy4hK/zwmOjF44gvWVHa/AbDTj1gm3RvW8NF6yuWqzMLWHwQCAI61HcMl7l+CeL+/BmUPOxN4Fe3HnpDuZC6MPMHLASIzcOhLtXe24aPlFfr9z9HaYkMFgaMBPR35C1rNZmlpAXbwrJkWMWiJ2ZMRT2ScAOHnvaIm9yw6TweT1i4Ke03Y+u1ZlnyaDyS++Qa+t4mPFADyFflowf+18/PaL33qfm3d7OTISOVri+0uFVLREzvu679g+YTpGOITVkRGn0RKHy4FGe6Mmjgyl0ZJwJ5YAHhFUx+kkHRk0WhLP40vLm8vBEx5jc8b6fS6aZZ8EBPesvQe/VP+i+bma7c14f+/7uGXcLV7uqmgxu2A2CAgbvcgA4LkZ8Xbh2yh4pQBbDm/ByzNfxrdzv/VzSDF6N2arGV/f/DVqrbWYsXwGmuxNsV5S1GBCBoOhAdQpsalyk2bncPGuqJQERpO4jJZIlX0Sgk5Xp1c/BgC/sj+1NyFq92/Q1ztYtKRfSj8A0OzucK21Fs32Zq+P0WgJ7chI5GiJn5ARRrSEEIKZ783EvevuDXsdijsy4jhaQl9TtUevAsrHr5Y3lwNAWNESjuOQmpQK3sCj3dkuRE0AjyPDTdxodbQqPm60KGsqAwCM7D/S73NUMItGR4Yr2YVXd76K5XuWa36ud3a/A7vLjgWnL9D8XFKcknUKJuROYPESBmqttbhy5ZW4fdXtmJA7AXvu2YOFpy9UvQCZkRicdcJZ+GLOFyhpLMHM92b2ylifFOxqZzA0gG5gtbT19UYhQ1wsGS9CRrBoibgfA/B0ZCQSQaMl3e8FHRGpVV7f5rT5OYuEss9eFC3pZ/IIQr7jV4HQ0ZLd9btR1VqFo21Hw16H4o6MOC77pLZ6Tco+dcrGr1a0eBwZ4URLAI+w5Da44XA7/DoyAO0ERDUoa+wWMgb4CxnRdGQ4zJ6fD3vq92h6Hp7wWLJ9Cc7OOxunDjxV03MFY/bY2fi5+mdUtVTFbA2M2EEIwQd7P8DYJWOxoXwDnp/xPDbdtgkj+o+I9dIYMWb68OlYee1K7KjZgatXXp3QN4HkwoQMBkMDqGVfS2XcTdxBJ0rwhNe0r0ELvKIlcTK1hK7Dd2qJ3WUP7cjQ+PWP9DXyjZaUHC/B3zf+HYQQr7JPAMIoyKKGIlWdIVan1e941JGh1+lh0BkS+h/jOlsd0pPTBSdGenK68DnBkREiWkInQ0QyjlNxR0a3IyMeow21tloA0KzsU4mIWtFcgYzkDEGoUorFaIEryXP9e0VLLNkAtBMQ1aCsqQz9U/qjf0p/v89RwSwaHRnOFM/7tad+j6bX68aKjShrKovayNVAzC6YDQD4qPijmK6DEX0a2htw/cfX46bPbsLJA05G4fxC3DflPubCYAjMGj0Lb171Jv5X8T/M+WSOppP04gF25TMYGiA4MhA7R8Yf1/0RB888qNn5tUCtss+WzpaQZalyETsyfKeW+DkyNO7I8CXSDb7YkeFwO/DFgS/w+A+Pw+q0Cu+FIGS0H0NZYxnGvTIOf93w18gWLsLmtPkJcm7eLbyWyfrkxO7IaK/DwNSBMOqMSE1K9XLtUCEslCNDEDLaj4W9UbM5bTDqjEJcJxRmoxlu4o4bZ5SYWqtHyNCq7FOJQFjeUo78fvlhu+9Sk1LRlew5n6QjIwLxSmvKmsokYyWAp2vEoDNExZHhNHuu0VZHK460HdHsPEu2L0GWOQvXjblOs3PIYXi/4Th98OlYWbwyputgRJdP932KgiUFWFO6Bk9e+CS23LEFo7JGxXpZjDjk1gm34sVLX8SqklW4Y9Udvc69LYYJGQyGBkTDCRFKyKhqrRLuVCUKanVkXLziYkx5Y4oqP7zFHRk04kBAYHfZhTJKSrTvikRa/klfHyoW0Nfczfe4fejztzqtwp3wX46qV6pnc9oCOjIAj8iS6I6MXEsukvRJXrESoCdaEqwjw2lyYmftTuRYcmB32cOOe1gdVtluDKBnNGg8xktqrDXQc3rBtaAmSXqF0ZLmirCKPimWpACODHMCODIayyRjJYAnVpmWlBadaElKj9CpVbykuq0aq0pWYd7EebLFQC2ZPXY2dtXuEuI9jN5Lk70JN316E677+DrkZeRh59078eC5Dwr/RjIYUvzujN/h8fMfx/I9y7Ho60Vx6a5UAyZkMBgaEI2ODDfvDrpRd/NuEC6xfnCJ78xHMrVk29FtANQp26Sb+y53l9eGWqrs07cjQ81/OKTuEkfsyIB3RwZ9zcUimVhkoB9TS7Bxup1wup1+HRku3iW8lsmG5ITvyBiYOhBGvdFr9CrQs3EN5shozfWUPd463jPqMdzOBFuXTfbEEqCnGyUeCz9rbbUYmDpQE+FQSbSEEIKKloqwij4pqUmpcCX7CxnUkRGvQkZHVweOtB3Byf1PDviY1KTU6ERLzE6cPMCzDq2EjNd2vgZCCOafNl+T4yvlhrE3AAAr/ezlrC1di7FLxuLjfR/jkWmPYOudW1GQUxDrZTEShId+8xD+NOVPeHn7y/j7d3+P9XI0gQkZDIYG0A2i1tGSYONXecInnJCh9tQSNZwxXmWfPuNXfaMlWjoypDa7oaZdhIIKLSnGFDjcDkEsEV9bYgGFCk1qPU96t9bXkeEm7l7jyKi31QvREt9xjXLGr7bktmBEvxE4b9h5AMKPGvQ2R4YWE0sAjyODJ3zQ/iFKna0Ona7OiIUM3uD5uScWMsxGM0wGU9yWfR5qOgRAuuiTkpYcJUeG2YHxueORn5mviZDhdDuxbNcyXHbyZRiWOUz144dDXkYezsk7hwkZvZTWzlbcvup2XPHBFcg2Z2PbvG34x3n/gFFvjPXSGAkEx3F45qJnMG/iPDzxwxN49sdnY70k1WG+JAZDA+JhaombuEF0iSVkqNWRQVElWuL2j5YAns3nQMNAr8f6dmSoWbIkKWREGC2hQg+NlogdGb7REqCnuE+t6Sx0k+O7aXTxLhg4Q8/aEtSRYe+yo9XRioGpAzF16FQ/B0+o8as2pw3WLCvuHHUnciw5ADxlb+Fgc9qUCRlx7sjQakNp1Hk2Cl18V8jrnE4siShaYuwZuSoWMjiOQ7Y5G8ft8enICDZ6lZKalKr5+FVCCJwpTgzLGIYud5cmQsYXB75Ana0u5iWfvswpmIPff/17FDcUY2zO2Fgvh6ES3x76FneuvhM11ho8dO5D+Md5/4iLOBMjMeE4DksvXwqr04oHNjyATFMm7pp8V6yXpRrMkcFgaEA0HBluEjxawhMeGp5eE9SeWqJmtESq7DNUR8Y7u98J65z2LjvuWHWHMLoTkBYyInUq0NdbiJZ0v+bia0v8PtD1qOXIoJscP0cG7xY2kXSiSiJS314PABiYOhD/nvFvPHbBY16fDzV+df2h9SB6gitHXSkIGeHeobc6rbJHrwIJ4MjQYGIJ4HFkAPJ+/lQ0RzZ6FfCeIiMWMgBPvCReoyXBRq9SUpNSNXdkNLQ3gOgJhmYOxfjc8ShpLFHdwbVk+xLkZ+bj4pMuVvW4kXLdmOug43TMldFLsDqsuGftPbh4xcVITUrFz3f+jCcufIKJGIyI0ev0eHfWu5g5cibmr52PD4t6z88MJmQwGBoQLUdGMPtzInZkxGO0xKvss3tDTQjxdGT4Ti3xuYNb2VIZ1jmLjxXjrcK38F3Fd8LHpDaUakVL6NQS+pqLoyVikaHZ3gxAveksgaIl4rLPZENywkZLqPAzMHWg5OeNOiP0nD6gs2Z16WronXqck3eOUP7Y1x0ZTrcTxzuOayZkUOu2nI6e8uZyAIjIHRLIkQF4hIx4jZaUNZUhx5LjNU7Yl7SkNM07MujP2GGZwzA+dzx4wmPfsX2qHb+4oRjfV32PBactiLsRlwNTB2LasGn4sPjDXlvk11fYVLkJ45eOx2s7X8P9U+7Hrrt34YwhZ8R6WYxeRJI+CR9f/zF+M/Q3uOXzW/Bl6ZexXpIqxNdPZQajlxCtjoxQjoxEEzLEwowqQobKjgzfaIlvVMD3F902R1tY56QChfjrtXBkiMs+5URL6GuhdkeGb9eLm/iMX03QaEkoIYPjOKQYUyTfWzfvxtrStchoyIBRb4QlyYIUQ0pEHRmKyj7j1JFRb/O4XLQYvQr0REvk/PypaKnA4LTBfs4sJQRzZGRbsuPXkRFk9ColGo4MXyEDULfw85UdryBZn4zbJ96u2jHVZPbY2ShtLEVhXWGsl8IIg46uDiz6ehHOf+d86Dk9Nt++Gf+e8W+/myQMhhqYjWasuXENJuROwHUfX4fvK7+P9ZIihgkZDIYGxMXUEqKdI2PZzmW4ZMUlqh9XbUeG2h0ZvtESv6klPk6FVkdrWOekG1vx10vdGbe77CAg+Mv6v6CqpUrxecSOjEBTS8QWe7WFDHq3treOXw0lZACewk8pZ83W6q043nEcGfU9BaE5lpywhQyb04ZUY+I7MmqsNQCgadknIDNaEuHEEiBEtCQlvqMlwWIlQLcjQ+OOjKpWz8+9oRlDMaLfCKQYUlQTMqwOK97d/S5mF8wWpsjEG9eMvgZ6Ts/iJQnIT0d+woRjNDHOAAAgAElEQVSlE/Dithfxu9N/h9337Ma5J54b62Uxejnpyen45pZvkJ+Zjys+uAI7anbEekkRwYQMBkMD4sWRodXpC+sKsbV6q+rHFd+Zj2T8KkWrqSUEBHZX6I6M1s4IhQzR1weaWuKwOPDMT8/gr//7q+LziDsyCIhwDq+pJaL3gTojtC77FHdkJBuSI+rIONh0EOe+ea4Qi4kmdbY6cOCEWIgUZqMZHS7/93Z1yWoYdAZkNPQIGdmW7LCjJVZn73Bk1NpqAWjoyNDLd2SUN5dH1I8B9LzOgHS0pNXRqoqoqyY2pw21ttqgo1eB6Dky9E490pLToNfpUZBToJqQ8d7e92B1WuOu5FNMljkLF424iMVLEohOVyf+/O2fce6b58LFu7Dx1o14ceaLXj8LGAwtyTJnYf3c9RhgHoBLVlyiahwv2jAhg8HQEM07MoKMX9WyI4MnvKoTOcTHpcRjtKTT7XEGdLm7wBPevyNDJUcG7UyQEy2hk2k+LP4QpY2lis4jjpYA3sICfS/E7zMVFKLZkRGpI+PX2l/x45EfUdRQFP5Cw6TOVocB5gFBR+alGKUdGatLV2PasGnQu3pe62xzdlidCS7ehU5XZ6/oyBAcGVqXfYYQUrvcXahuq8bwzPAnlgChoyUA0NjRGNE51EZO0SfgGb9qd9k1+beCUtlSieSOnjLE8bnjsbt+d8Q/+wkhWLJ9CSYNmhT3XQWzx85GZUslth3dFuulMEKw/eh2THp1Ev79879x16S7sOeePTg///xYL4vRBxmSPgTr566HUW/ERcsvEsqrEw0mZDD6BJGWIiolGndGZI1fTTAhQ/WODBXLPrv4LmEjT4WGUB0ZETsyxNESqbJPlx28znMN8ITHU1ueUnQecbQE8BYWhI4MtzxHhsPlUBzlobZzX0HOq+wzwo4Mep2GG8mIhDpbXdBYCeDZvPqWfZY2luLA8QO48uQrvT4ebrSEihFKppbQTXXcOTKstdBxOmGKi9oI41dDREsOtx4GT/jIHRndgpGe0wvnptA4Q7zFS+SMXgV6RBotxbDKlkok2ZOEv4/PHY/jHceFiUHh8uORH7G3YS8WnrZQ0xsSanD1KVcjSZ/E4iVxjNPtxMMbH8aUN6agzdGGb27+Bq9e8aoilxyDoTYn9T8J6+euh73LjunLp8fEuRopTMhg9Hoqmitg/pcZy3Yui9o56YZOy5ZzWeNXddqIKm7i1tyREY/jV+mGmgpjvo4MPyFDxY4M+jHx5qHT1Qmi9zzHgpwCLN+zXNGkFF9HBt1wBIqWBOvIMD1hwoK1C2SfG5B2ZPCEBwERXB+ROjIEISMG0x/q2+tDChkpBv+yzzUlawAAV4y6wuvj2WZPtETpdU27SJQ4MvQ6PUwGU1w6MnItuarFm3yRGy2hE0vU6sgwG81+G2YqZMRChAsGdWSc1P+koI+jwplW8RJCCKpaq5DU4S1kAJEXfi7ZvgQZyRmYUzAnouNEg0xTJi456RJ8VPyRKr1QDHUprCvE6ctOxxM/PIG5E+aiaGFR3I3yZfRdCnIK8M0t32Du+LnINGXGejmKYUIGo9dzsOkgAET1bkW0OjJCjV8F1Cm89IUnvMfxobJIEs9lnzzhhU1dIEeG7+YqXEeG1NQSeu7TBp/m9TjqyHjo3IfAgcPTW56WfR76+iTrPdZscfmmkrJPeh28tus12ecGvDc49Hz0uvVyZETQkUEFmbh2ZPg4xlaXrsb43PF+Yz2zLdnodHUqdknQ11np3T+L0RJ/jgxbrWb9GID8aElFi8eGO7yfOtES31gJAKFbJR4dGYPTBofM9NPnptUI1uMdx9HR1YFke0+0ZFzOOACRCRn1tnp8su8T3HbqbQnTWzBn7BwctR7Fj4d/jPVSGN10ubvw2PeP4fRlp6OhvQGr56zGW1e9lZCbRUbv5owhZ2DxtMVx7z6TggkZjF4PvcOmZU7Xl2hMLZFV9gltnjfdHKotkogjBvESLRGvg/5CTh0Coco+g3WYBCNY2efkQZOFj9lddqEj46T+J+H2U2/Hm4Vv4mjbUVnnIYSAAyds3iSjJVJlnz4dGeG+zuINDr1O6WtGRSGTwaRKtCTcksxwIYR4hAxLCEeGz/jVxo5GbDm8xS9WAkCIUyh9LjTCo8SRAXiKKKW6WWJJjbVGs4klgPzxq+XN5TDqjBGLKnSjLCVkCI6MGLiJgiFn9CrQc71p5cig7jOxI2OAeQCGpA2JSMh449c30MV3YcFpyhxmseSKUVcgxZCClUUrY70UBoDihmJMeWMK/rHpH7h+zPUoWlDk57BjMBiRw4QMRq+H3tlVYwoG4Nm8U1txKAI5Mly8K6IsGhUQQnVkiP/vCyEE9bbwcsRaiSRe0RI1ppao4BgRr4M6JARHRoiyTyC8iAzdPPqWfZqNZlw/9nrhY52uTvD6bleFIRkPnvsg3Lwbz/38nKzzEBBwHIdkg+eOJnV9uIlbuG6kyj59BZtwBS3xBoeeh/5fcGQYktWJlkTZkdHmaEOnq1NWtETckfFV2VfgCY8rR/kLGfQOvdKNreDIUNCRAcSvI0Orok9A/vjVipYKDM0cGnHEJZgjY4B5AIA4dGQ0yhMyqANIqxGsUkIG4ImXhCtkuHk3lu5YigvzL8SorFGRLjFqpCal4rKTL8Mn+z+J6k0bhjdu3o1nfnwGk16bhKrWKnxy/Sd4/9r3he9lBoOhLkzIYPQqbv38Vvx363+9PkbvsKn1j/szPz6DES+MQHFDccDHhLpDfc/ae9D/mf5h90DQ5xKJI2NT5SYMeX6I7Lv3So4dLvFY9unlyOj+hZwKDaHKPgFvMUIudGPrW/ZpNppxYsaJIP8kyDZnw97V48gwGUzI75eP84adhy2Ht8g6D3Vk0GiJ2JERLFriK9ioIWTQ957+n54jWZ8cUblsrDoy6mx1ACArWiJ2PawuXY1BqYMwefBkv8fSKRZKRZlwOjIAj1sgnjoyutxdONZ+TNNoCXXwhYyWNFdEHCsBeso+pYQMg86AfqZ+cSVktHS24FjHsZATSwDtHRlVrVUA4BUtATxCxr5j+8L69/XLsi9xpO0IFp4evyNXAzFn7Bw0tDdgU+WmWC+lT1LaWIpz3zoXf9nwF1x+8uUoXliMa8dcG+tlMRi9GiZkMHoVy/csx73r7vX6mODIUKE8EgC+r/oegKe1PhChoiXv7X3Ps6YwXQe+NnwpfDeGvtRYa+AmbjR3KneGCMcOMzoRiHgcvyq+buiGMFDZp9Td2XCEDHG0hD6Hjq4OYdNDz93p7hQ6MqgYYTFaZF9XPOHBcT3REir8BIqWCEKGTh0hQypa4uvIoPGdcF0Z9HlE25EhV8hIMXiPX/35yM+4cPiFkqJYuNGSaHdkvLTtJU0s7vXt9SAgmjoylERLIi36BII7MgBPvCSeyj5p0efJA04O+VjqANKqI6OypRKZpkyvEcWAR8jo4rtQ0lii+JhLti/B4LTBko6oeGfmyJlITUrFh0Vsekk04QmP+vx6TFg6ASXHS/DeNe/hk+s/0WyyEoPB6IEJGYxej9w7bHKhm7Zg/Rd0Q6jV1BKxOBFosy4VDRBDX49wNvuaOTJEwoga75caHR5SjgzqmAjVkQGEN7mEChldfJfQD0EdGRSTweTnyAA817tc0Y6AQMfphGgJJZAjg67FrwskSOlsMLwcGT7XKxVL6NrCLfyMtSMjNzU36OPE41dtThuOWo/ilAGnSD423GhJJB0Z4Tgylu5Yitd2Kit+lUOttRYAolP2GeR7yOqwotHeqIqQYTKYAIKApZLZluy4cmTIHb0KRKcjw7cQFwh/csnBpoNYd2gd5k+eLwipiUSKMQVXjboKn+7/VLPXnOFNeXM5zn/nfFQXVOPC/AtRtLAIN427KSFLExmMRIQJGYxeD/2FRK1NN934BxMpBLEjxNSScKea+I6rDLaGQM+bbtDDiV+EEknCRXVHhhrREr5nHfR41B3gN7VEoiMjkmgJ0FP4STsyKLRbQdyRAXg2YnJFIN9oCUU8flX8HgeaWqJmRwY9r3hqCRC+I4Me93jHcU1GEQdCtiOju+yTECJMWAp0t9uSZIHZaFZ8hz7aHRlOtxO1tlrFXxeKGmsNAGhb9ilj/KpaE0sAjyCuc+mCOjLiSshoLAMHDiP6jwj52Gh0ZEgJGaMGjIJRZ1QsZCzdsRQGnQHzJs1TaYXRZ+HpC9HS2YI/rvtjrJfSqyGE4JXtr2D8K+NRWFeIoYVDsebGNZqKrAwGwx8mZDASmqe3PI3CusKgj6FiQTjREhfvwsMbH/Yq5gwmUhBC8ND/HsLu+t2ex4RQ5cPdaIs3l4HiHaHiH+KxokqJRtlnPEZLKIGiJZKOjDBGsIo7E6ijo6Orw+uubYoxBZ2uTn9Hhs4o+7WjZZ/0LjTFzbulp5a4pKeWhB0tEW1w6Pl8oyWRTh2iX9fFd4XljgmXOlsdDDoD+qf0D/o4uoF1uB2ybPvZ5mzlU0u6rf1KR0majeawHBlOt1MQHdSEiiOxHr9Ky57z+0XuyAAAvUsfWMhIibNoSVMZ8jLy/NxoUtAonBbuAEIIqlqrMDRjqN/njHojxmSPUSRk2LvseKvwLcw6ZVZCb0bPzjsbfzr7T1i2axnWlKyJ9XJ6JYdbD2PGihlY+NVCnJ13NooWFCHrSBZzYTAYMYAJGYyE5sH/PYiJr04M+hgqFoQTVfh8/+d44ocncP+39wsfo5s2qU2r3WXHk1uexNrStYrPpQSxOBGuIyOSaInvplMt6HGT9EmqdJqoXfZJCVT2qXZHhvjr253S0RLakUE3YEad/GgJT3iPIyNItERKXFKrI8PmtAn2c8GR4VP2Sf8fbh+L+OuiOYK1vr0euZbckPEyeg11dHWgtLEUgGeUbiCyLdlhOTIsRoviqFu4jowuvgttjjbVi0JrrDXgwGmaPZfTkVHR7HFkqBEtAYATik/AojMWSX6ORkui6SYKhtzRq4Dn54TZaNZEyGiyN8HmtEk6MgDlk0s+Kv4ITfamhCz59OWx8x/DhNwJmLdmXtTHTvdmCCF4Y9cbKFhSgJ+P/Iylly3FulvWIS8jL9ZLYzD6LEzIYMQl7+99H3vr96pyLPoLYDibbtoJIP6lNpiQ4evSCDc6Ego50ZJQ8Y9IoiX0nOF0Izz5w5NYf2h90OOaDKb4cWRICGBad2TYu+zISM7wfL0oWuJV9mnocWQYdUbh3Ea9UVG0RMfpgkZLxATqyAhHyCCEwOa0IdOUCcD/eqWODCqahNvDIb7+o9mTUWerCxkrAXpcPfYuO0qbSnFC+glBnRM5lhzljgyHVXE/BhB+Rwb93lU7XlJrrUVuaq6m/QVCp1IQMbCipQLpyekh3TZy6V/bX3JKDeCJljjdzrjpPJA7epWSmpSqSdknHb0aTMg4aj2Kxo5GWcdbsmMJRmeNxnlDz1NphbEj2ZCMFdesQGtnK+atnhc3IlgiU2OtweUfXI55a+Zh0qBJ2LtgL+afNp+5MBiMGMOEDEZccvNnN2P80vGqHjOcO/xSxZ7Bijx9/1EL9I8cFTg6ujrwj+/+oXjTrqQjI9AGkL4e0S77fGjjQ5ixYobk5+hmVi0hQ84Ge8AzAzDrw1kBPy+1DqEjw3dqiUodGR1dHcImmAohkmWf3R0ZYkFFiSMjULREPLVETKCOjHDcEp2uTriJWxAyfDsyqIARqSPDS8iIokVfrpBB31PqyAi1Scw2ZysWZGxdNsUTSwCPI8PhdigWkeh1ona8pNZWq+nEEkB+tCQ/Mz8qm5gscxaA6E/dkaKxoxHNnc2yRq9SUpNSNRFh5AgZALC3IfQNkR01O7Dt6DYsPH1hr9mYFuQU4MkLn8Sa0jV4fdfrsV5OwkIIwYo9KzB2yVh8V/Ed/nvJf7HxtxtVi5UxGIzIYEIGo9cjHimp+Gslij2DTS2R68iga3pqy1N4bPNjWLpjqaJ1eXVkBNhkhIp/CNGSOCz7TDGkRK3ss8nehC8OfBHw813uroDvoyxHRhgdGXaXXdgEUyHEr+zTmCJMLRFHQ5Q6MkJFS8SE6shQ4kCimxtfIaPPOTK6oyV2lx2ljaUhx1pmmz3REiUCZCSODACK4yVaCRk11hrN+wtkRUtaKlQp+pQDnVQTD4WfdGKJnNGrlLSkNE0cGVWtVQAg2ZEBKJtc8sr2V2AxWjB3/Fz1FhgH/OGsP+CC/Atw37r7hCJhhnzqbfW45qNrMPfzuRidNRqF9xRi0ZmLNJtGx2AwlMO+Gxl9hubOZvzp2z8p+hqpGEmwaIkvoe7uUNu20k27eFMXdkdGjBwZwaDPy2QwqTJ+VQ1LrdPtlLT6J+uT/a4BqWsiYkdGiGgJr/N2ZCjpF+EJD47zn1riJm7Z0ZLZn8zGGcvOABD6ehfjK2T4Cm+qdWTwbmFzGq272m7ejYb2BkWOjOq2ajTZm0JuEnMsOeh0dSq6y21z2hRPLAF6yhqVxkuEaIlV5WhJNB0ZAb6HCCGoaK5QrR8jFNSREQ9CBu1wURot0cqRkZ6cLvz88CXXkotsc3ZIIaPZ3oz3i97HLeNvQYYpQ/V1xhIdp8PbV70No96IuZ/PVf3f697Mx8UfY+ySsfi67Gs8M/0Z/HD7D4oEPAaDER2YkMHo9Yg3s8/9/JyirxVEC4QpZAS4Q00/rsbUklAdGYE2gBGNXw0xESVcVO/IUPDclu1cJik6ON1OLwGB4hsrAdTryOjo6kCuJVf4ekKIdNmnq9uRIRIilE4t0XE6yWiJ1HVFIzXiss+Pij/CkbYj8p9cN4EcGfTaoo6MSMcnu3gXLEkWpCalRs2R0WhvhJu4hfcwGPQ62l3nmXQU0pFh8dyhVyLKWJ3Rc2S4ebdw7ajpyHDxLtTb6jUdvQqEHr9a314Pu8seNWu5EC2JopsoEGWNZdBxOkXPPS05TZPxq3T0asD4JsfJKvx8u/BtdLo6seC0BaqvMR7Iy8jDkplLsLV6K5784clYLyfuOd5xHHM+mYMbPrkB+f3ysWv+Lvz5nD9LFnkzGIzYw4QMBiMIUqKFkrv8Icevdh9LaSmokvGroaIl8TR+Ve2ODCXv1d1r75YcV9fFd0luAqXGD0q930odGYQQT9mnKQNmoxltjjY43A4QEO/xq9SR4duRoTeCgMiKYgSLlkh9fagIiZLrmNrNM5O1L/s06AyesaUd0Wnwr7fVAwByU2UIGd3REjqyWU60BFC2sbU5w+/IAJQ5MsTft2qWfTa0N4CAaB4t0XE66Dl9QEcYnVgStWiJJb6iJcMyh/kJn8HQ0pERqB+DMj53PIoaigL+7OAJj1d2vIJz8s7BhIETVF9jvHDjuBtxY8GNeOT7R7D96PZYLyduWXVgFQqWFOCz/Z/hsfMfw893/owx2WNivSwGgxEEJmQwEha5m9RIRnBKChndx5M6v++5Qm3swl2bKuNXVYiWOFwOtHS2KP76UMc1GUyqjF9VKtJQx4GYQNES39GrgDJHxqwPZ4F7xP/6oKJFiiEFGckZaO1sFTaSUh0ZvI737sjojlHIiebQsk+DzuC19kBTSyjhjlsVE9CRobDs85fqXyTfN4qLd0HP6T1jS6N0V5s+t/Tk9JCPpe9pYV0h9Jw+ZGSBjh5VMrnE6rAi1RgdR4ZYyFDTkUGPpXW0BOjumQnw86eiRd3Rq6FIS0qDUWeMi7JPJaNXKVp0ZBBCUNVaFbAfgzI+dzzsLjsONR+S/Pz/yv+HsqayXjFyNRQvz3wZg9IG4ZbPb1F9LHKi02xvxq2f34qrP7waA1MHYvtd2/Hw1Ic1nY7EYDDUgQkZjIRFrggQSU9CsI4MOZu5UI6MYMWhwVAULQlwNyqiaEn3sd8qfAv5/80PupFUQiyjJYD05r/LLe3IkBstCeTIEBeMrju4Dj8d+QmAJ1YCeDa46cnpaHO2eX2MYjKYQEDAG/wdGXTdoaCODABed1kDRUuErwvwuqrZkSHHkWF1WHHOm+fgkU2PBDyPm7gFR0a0NoP0+0HKteMLvY7KmsqQ3y9feP8CEU60JFqOjCZ7E6rbqoW/qylk0L4NrR0ZQPB4VnlzOYDA0zLUhuM4ZFuyY+7IIIQoHr0KaOPIaOlsQZujTZYjAwhc+LlkxxJkm7Nx7ehrVV1fPNIvpR/eufodlDaW4oH1D8R6OXHDNwe/QcErBXh/7/v4+9S/Y9td23q1O4fB6G0wIYPBCELEQkYIR4bcaQ++Gwkl41dDTi2JwJFxuPUwWjpbUGerC/r4fcf24euyr0MeV1z2GUzIIER+dEIJUpt/p9spLWRIODKk3kc5U0se2PAAntziyS/TySDJhmRkmDyODCpk+JZ9AoDb6PbryADkOTJ4wgvXtvgYbt4d9PUN9LoqipZ05+ZpwZ5vR4acss9WRyvcxI0Ve1cE/D4QoiVRdGTYXXYA0teIL1Sc4gkvq0yORkvkOjIIIVHpyGhztOGMZWfgqpVXAfA8dzWjJYIjQ+OODKC7MDdItGRQ6iBJIVMrssxZMRcyGtobYHVaFY1eBXqEDDWKlymhRq9SxmSPgY7TCf0zYo60HsHqktWYN2meX7Sut3JB/gW476z7sGTHEln/Hvdm2hxtuGv1Xbj0vUuRacrE1nlb8ej5jyqKTTEYjNjDhAxGwhKraAn9mNRxfdcUsiNDxtq2HN6C1CdTse7gOuFj4k1mTMavdh+bbthCbaqe//l5zF87P+RxvaIlQTbiL29/GSe/FHrTp4ojg++SLPuUutvu68jgwMnqyGjtbBUEDCrgJOuTPdESR6uwkfR1ZACAy+jym1oCyHRkdEdLAHj9Mh8qWhLJ9xTFz5ERoCOD/l/qOqfHqG6rxveV30uehwoZOeYcxWNLw0WRI0MkdpzcP/Q1bUmywGw0yxZlOl2d4Amv6dQSQgjuWnMXDjUfEmz8QzOHos3Rptrd+FpbLThwsgpUI8WoD+zIqGipiFrRJyXLnBXzaAkdvRpOtMTFu4SJR2pAhYxQ0RKTwYRRA0ZhT4O/I+O1na+BEIL5k0P/u9Sb+NeF/0JBTgHuWH1HzMWxWLGxYiPGvTIObxa+ib+c8xfsvHsnTht8WqyXxWAwwiBqQgbHcZdwHFfCcdxBjuMelPj8bRzHHeM4rrD7v3nRWhsjMVFjMxWKYGWfajgyhLLPIILHlsNbAADfVX4nfEyJIyPk1JIIHBl0wxZKyHDxLll3deWWfZY2lqK8uTzk2jV1ZMiIlmSaMoWpI8Foc7QJz5f+P0mf5ImWOHqiJV5ln0aRI0PckRFi6oKYsKMlgRwZEURL6DUtjF+lHRnd/5cS5MSb5OV7lkuex8W7oNd5OjKcbqfqeX0p7F0egU+OkCEWp+SO91MSk6HPV0tHxtIdS/FR8UcoyCkQPkbvlqs1grXWWotsS3bI6I0aBHNklDeXR60fg5Jtjn20pKzRI2QoHUFJrzs14yVVrVUA5MV7pCaXON1OLNu1DJeffDmGZgYXQ3obJoMJK2atQJO9CXevuTsqwm680O5sx++++h0ufPdCmAwmbLl9C56a/pSsn9MMBiM+iYqQwXGcHsDLAC4FMAbAjRzHSVUBf0gIObX7v9ejsTZG7yceOzLox+WIMVLxk1BCBiFEOHaoss9wihup4ECFDDqlIRAERFaPBl1LiiEl6Eac/lIcarMe6rn5XhtSm5eA41eloiU+73eGKQMu3hX0uRNCAgoZQcs+u8/v15ERRtkn4B0tkXpdxdefGmWfVqcVOk4nbHR8yz6Fjowg0RJ6HeRn5uOTfZ8IAoIY2pFBBROlU2TCQYiWyIgfJOmThNdW7iYxx5IjO1pCXyOtOjI60jtw37r7cOlJl+LfF/1b+Di9W65WvKTGVhOVfgzA8z0kJWp2ubtwpO1I1CaWUOIhWlLaWAqDzqB440+vOzVHsFa2VCI1KRX9U/qHfOz43PGobKn0ivh9vv9z1LfX94mSTykmDJyAx89/HJ8f+Bzv7H4n1suJClsOb8GEpROwZPsS3Hvmvfh1/q+Ykjcl1stiMBgREi1HxhkADhJCygkhTgArAVwVpXMzeimxnloSiSODrl3O+FUp14Z4Uye1wROvTYtoCT0+3TjK2VR1ujpDvmfijoxg0Qh6h1jKriw+R6jn5vvaSW3gu9xdXgICRY4jIyPZ0/8QaHIJ4Hld3MQtLWSYPNGSQGWfFK+ODAVln14dGYbgQob4TnjAsk8FHRk2pw2pSal+0RElZZ90kz5/8nxYnVasLlnt9xgaLaGvHX0ttUQQD2TEOTiOE64l2Y4MiwJHhiNyR0ag16zN0YbyyeXIMmfh3VnvYnT2aOFz9G65WoWftdbaqEwsAQJHSw63HgZP+Kg7MrLMWWiyN6k+7loJZU1lGN5vuOJJDlo4MujoVTkOMFr4WdRQJHxsyY4lGN5vOGaMmKHamhKNP075I84beh4Wfb0IjhT1Yj/xhr3LjvvX3Y+pb00FT3hsum0T/nPJfyT/TWcwGIlHtISMIQCOiP5e3f0xX67lOG4Px3GfcByXF52lMRKRxo5G3Ln6Ts3PE8yRcfGKi/0er1QUkDO1hB4zkCNj1EujcP3H13t9jZfQEaBDQ43xq3KjJYQQ8IQP+Yu4uCPDTQIXTtJfiqWcDuLnHuq5+a4nULTEZDAJzgCKnI4MOS4A+jkqLPlGS2xOmxAP8Cr7FAkpYTsyAkRLJIUMndHr66RQGi0RCxnhlH3S6+Dyky/HCeknSMZLQgkZjhSH6vZq+p7KdUGYjWakGFIwJF3qn0V/ss3yi0uViCq+JOmTYNAZJKMlhBDcveZuOMwOrLxuJbLMWTgh/QTBKaS2kFFjrYmakBEoWiKMXo1yRwYteG2yN0X1vGLCGb0K9Fx3aka6KlsqQ/ZjUHwnlxQ1FGFz1WYsOG2B5JSpvoJep8c7V78DjuNQMalCVnl2ovFL9S+Y+OpEPL/1edxz2j3Ys2APpg6dGutlMRgMFeGikY/jOO56AETbgOUAACAASURBVBcTQuZ1/30ugDMIIb8XPWYAABshxMFx3D0AbiCEXCBxrLsB3N3958mTJk3SfP3hsn//fowePTr0A2NIvK5x5xU7AQDmZ8yS6ztccBjH8nt+kZ+8ZrLX19G/A0B7RjsOTD3g91g51I2ow9ExR5F7MBcn7D8BAFA8rRidaZ2S6+P1PH6d+avw97yiPORU5Pgd99dLfwVv4NG/uj+aTmjye5z4edSOrEXNKTUYWDoQQ0o8G52WgS04dPohr2OKnxev4/HrZZ515O/MR/8afwtuyZQS2LJsGPnTSKQ3pst+TcSvgd6phzvJjf7V/ZH/a+Bf7ismVqDphCac+vWpKLy0UFiv7/VXe1ItakbXYFDJINSOqsXELydCx/v/sknXPm79OCR1ereMi9+DMZvGIMUa2N7v1rtROLNQ+HvuoVycsO8E4e/79u+D/QE7BpUMQt1JdSD6np+XWVVZGLrH+5fp43nHUXVqlfD3jLoMtA5sxSmbT4Gl1TueQt/jsRvHoviCYpisJozdNBbW/laUnlOKkT+PhD3djuqx1RiybwiOjjnq9Xzp4wAgpzwHecUe7ZdeG6O/Hw1zW/C7TpUTKtGW3YbxG8bjwLkH0N7Ps2EdcGQAGvMavR5L32sAyDmUg7x9eV7PAwA4N4dJX3n/TA70M6Z8Ujk6MjowYscI7Ju2D8N3DEe/2n5oGtyEiskVGPPdGKTYUtCR3oH95+3H8O3D0a+un/frfeJxVE2owrj149AwrAH1I+oxfv14GJ09okvZmWVwGV0YUjIEZWeVYdSWUUht9twl7rR0ovj8Yoz8Wfn3QDCqx1Tj2NBjmPj1RFmP3zN9DwxdBoz53j9xKfX6VY+uRkN+AyZ+NTGkC6Y1uxUHzzro9byVUHhJIQYcGSBcX5RjQ4/h8PjDMH5vxPi28cLH903dB3uGHSN/GomDZx5ETkWO8LMzXAgIdl2+CwPLen4GyiWcf+P2/2Y/DA4DRm7z3rgfO/EYDk84jHEbxiHJrt50g1Br9P2eiDb79u+D448OZB3O8rsOQmHrZ0PJuSUYuXUk0o+p8z1WeHEh+h/tjxOLTgQQ/PUjINh9yW70O9oPQ/cOxeGCwzh+4nGMXz8ehi5l7pJwidffswCg8YRGVE6sxOD9gzHoYHSEwnBQ8hryOh61J9ei7qQ6GDuNGFY4DOnH1fv5Hun6YgFbX2Sw9UXOzp07dxJC1G/VJYRo/h+AKQDWif7+VwB/DfJ4PYDWUMc1m80knpk8eXKslxCSeF0jFoNgMQKub+HahcJjsBh+Xydmx9Edko+Vw782/4tgMciD6x8UPnbyiycHXF+7s93rXC9sfUHyuOYnzASLQW785EbJx4nX+simRwgWg/x949+Fz39c/LHXeXyfl81hEz6+YvcKyTWc/cbZBItBNhzaIP8F6WbUi6MIFkN4Hhe+c2HQx9/86c0Ei0HqbfVe6/V9/R77/jGCxSBP/fAUwWKQ1s5WyeNNfnUywWKQ0uOlfp9rtjcL59hTtyfousSPxWKQRV8t8vr8xNMnEiwG+dfmf5GMJzOCPpYQQt7c9abXY279/Fav13h33W5S3lROCOl5j3fW7CRYDDL8v8MJIYRsOLSBYDHI5srN5I1dbxAsBrn363sJFoM0dTQJ59pWvU04xgPfPiB8/MvSLwkWg/xS/UvQ504IIbd9cRvJez6PEELI1LemCsej16X4v5xnc4Q/3/v1vcIxxI9JeTzF7xyBvodnvjeTTH51MtnXsI9gMcgHez8ghBDy3p73CBaDlBwvIYQQsrd+L8FikI+KPvI7xn9+/o/wutDH+X4vXfTuRWTK61PID1U/ECwGWX9ovd9r9frO10O+VsHY17CPjH5pNNl/bD8hhJB5q+aRQf8eJPvrT3/tdDL3s7mSn5N6/Z7Z8gzBYpC2zraQx35/z/sEi0H21u+VvR4xg58bTO5cdafXx36t/ZUkP5ZMLl5+MZk0eZLX52Z/PJtgMciWqi0k///yyU2f3hTWecXUtNUQLAZ5edvLir82nH/jprw+hVz07kV+H39w/YPE+KiRuNwuxccMRqg10p8Jmyo2qXpeuYw7Z1zYr//uut0Ei0E+Kf5ElbXQn9nP/vis8LFQr99v3vwNOfuNs0lbZxtJ/Vcq+e3nv1VlLXKJ19+zCCGE53nS7+5+xPCogeys2Rnr5QRE7mu4s2YnKVhSQLAY5I4v7iAt9haNV+Yhnt9jQtj6IoWtL3IA7CAaaAzR8tVtBzCS47h8juOSAMwB4BVm5jhOLAVfCWB/lNbG6OWo3pERxMUU7HOSjw+z7DOUDTRUhwYgipZ0r8HqsGL/MXnfdr5ln3KLB0MVfoqjJeI1+hKsI0MciwjVY+IXLfGxkxPO89oY9Ua/+fJS0RK/sk+fjowJSydg+AveRYFCtMQtHS0BegoTpaaWAN79FjQCImdqibgjQ/z8pF5XcbREjbJP2R0ZQaIltITSkmRBQU4BTh14ql+8JFi0hE7UiNSyf/+392P/8f3YWr0VANDmbBPeOzmsmrMKL1z6guzHZ1s8UQM533cbKzYiLSktrFgA4IkziaMlVocVN3x8AwaYB2D5rOV+jpBRA0YB8FxPg9MGqzK1hF7/USv71BsDRktOzDhR6G2JFvT9jlXhp8Pi+XkQzjWkdkdGVYv8iSWUCbkTsLd+L97d/S5sTlufLfmUguM4nLjnRORYcnDLZ7dIFiYnAl3uLizetBhnvn4mGjsasfbGtXjjqjeQYcqI9dIYDIaGREXIIIS4APwOwDp4BIqPCCHFHMc9ynHcld0PW8RxXDHHcbsBLAJwWzTWxkhMlGTxlYoLYoJ1ZMg6dwihYmXRSgAhOjIkyj7ldk0Eeyzd6NLHvrTtJUx5Q16Lt+/klpAdGd2vQ6BfkmxOG7hHOGETSjfmgTbjwToyxF8T6vUP1ZFBdJ6vT9InCRt9ei2o1ZFByxgDTS0BPBs5Paf3EhPE5/fqyFBQ9kmI/Kklsso+FXxfWh1WpCWn+Y1XFcavct7jVwOVfYrfm7nj52J7zXaUHC8RHhNUyLBFLmSsP7QeXx/8GgBwpNVTBdXmUCZkDEobJFwrcsixeGJooQo/ecJjTekaXDryUi+xSwmWJIsgGBFCMH/tfBxqPoQPrv1A2GCLoSNY05LTMChtkCodGfQYUSv71EmXfVa0VER9YgngKfsEQr/fWtFp8fycVTp6FVBfyKhsqQQA2R0ZgKcnw+q04vEfHsfkQZNx+uDTVVlLb8HQZcDbV72N/cf348END8Z6OYrZW78XZ75+Jh75/hHMKZiDooVFuOzky2K9LAaDEQWi1nRECPmKEHIyIWQEIeSJ7o/9gxCyuvvPfyWEjCWETCCEnE8IORD8iAxGeLy/933Zj/UVMr6v/B6Hmg8F+xIv5IooQaeWhCj7lEK86Qs5taR7jS2dLWh1tMpas++m8ljHsaACDz1mIEcGvct2sOkgdJxO2FQHEjLoxsrhCu7ICPVcfF8bJ+99PipkGHU9jgz6PkhtDOUKGeJ10c9JCRmCI8NaC0uSxUsoEI9/9ZpaonT8qsTzCbvsM5KpJd2OC3ptyR2/Kp7GcWPBjdBxOqzYs0L4WDAho85WByB8IcPNu3H/t/cjPzMfA1IG4EhbeEKGUmj5Y6jCz1+qf0F9ez2uGhX+kDCz0Sw4MpbtWoYPij7AY+c/FrA075rR12D93PU4JesUDE4drIqQQV0d0XJkJOmTJIXA8ubyqE8sAYABKQMAxNaRkaxPRl6G8g52tcs+q1qVOzJo4WedrQ4LTlugSHDtK1w04iL8/ozf44VtL2D9ofWxXo4sXLwLT/7wJCa/NhlHrUfx2Q2fYfms5bLG8jIYjN5B361sZiQUlS2V4B7hsKt2FwBlGybfu8c3f3azsHEOha+QMe2dabLPK3XucKCbRvEmOVBchCIWFUJOLQHBsp3LsPXoVlnH9j0+/XtjR2OAR/e8DoGEDPE5dZyux1UQYDNO7+6FipYocWQk65P9HRlcjyODxkvoWiWjJQgQLen0Hr8q3twFEzKoLbbWVus3Li7g1JIwHRle0ZJugUg8qUWOI0MJNqcNqcZU4Rx+jgydDEdGl7eQMShtEKYPn44Ve1cI16ibuKHX6YM7MjrDEzLeLnwbexv24qnpT2FY5jBByGjtbNVWyJAZLfniwBcw6AyYOXJm2OeyGD2OjN11u7Ho60WYMWIGHjw38F1bvU6P6cOnA/AID1anNeK78fR9yk3Njeg4cpEav2pz2nC843jUJ5YAHpExPTk9ZkJGZ2onRvQfEdaUD5PBBB2nU9WRYTaaBZeKHMbmjAUHDhnJGbhx3I2qrKM38vT0pzE6azRuW3VbTCfkyOHA8QM4581z8NDGh3D1KVejaEERZo2eFetlMRiMKMOEDEbM+WDvB+Ae4VDeXB7wMV+WfgkAeGPXG6qcM9CdfkKI10ZHKloSDN8NXqA7175OAlnjVxVES8TCQKhoCSEEd6+9G5urNss6NiAdr5GT17e7eqIl4tdGvEnVc3phUy31PjndTkHgCBUtUdKRMSR9iH9Hhs67IyNQnIPie50Y9UakGFL8HBn7j/d0kdA7lVLjV6kQYnPa/IQM8fmlOjLkODLEHRliVwcViMTihvjPAR0ZSqIlTk+0xG/8KlHmyBCPpAU88ZLKlkr8ePhH4bhadGTYnDY8/N3DmHLCFFw/5nrkZeR5RUu0zGYLjowQUYNVJaswbdg0RbEVXyxJFhzrOIYbPrkB/VP6Y/ms5bJ/Hg5K80RBIu3JqLHWINuc7ddToxVS41crmj2jV2MRLQE88ZJYRUscFkfYHSscxyEtKU2I0EVKZUslhmUOU/SzJjUpFZecdAn+fPaf/X6OMnpIMaZgxTUr0NDegHvW3qP6WGo1cPNuPP/z85j46kQcbDqIldeuxEfXfyQZc2MwGL0fJmQwYs7KYk9PxN76vbK/RquOjK/KvkLOsznCHXShaFNlK6oc1wNFquxTjY4MIVriI77IETKk1h9MyKDvgXgTGeh4Ok4XVMigsRJA3WhJWlJayI4M8WZf/Gfx2sVw4JBhyhDKPin7ju0T/ix2ZBBCJKMlAPw27F5ChjhaosSRIY6WdB8jxZDitQbhuCqWffKER7uzHalJqX6OC9+yT1+hQ4xvtAQAZp0yCxajBW8Xvi18nUFnEF4vtaIlz/74LOpsdXhuxnPgOA556Xne0ZIk7RwZliQLzEZz0GjJgeMHUNJYgqtHXR3ZuYwWlDeX42DTQXxw7QdCP4ccaBQk0nhJra1WEEWigVRHBhXaYxEtATxCRiwcGTzh4TCHL2QAHiHB15Hx6PeP4vZVt+PAcWUp4sqWSkX9GJSvbv4Kf5v6N8Vf19eYNGgSHp32KD7e9zHe2/terJfjxcGmg5j2zjTc/+39mDFiBooXFmN2wexYL4vBYMQQJmT0Qd4ufFsozIoHlAgNaljag1HZUon2rnZhY0PPF46lVvz1oQjakSFR9hlyagmvYGoJUS5kiDey/Uz9AIQQMkKUfYqPp+N0Pa4Cic24eIKCWmWfH173oeSkAp7zrIt2ZIidD3KmlnAch/TkdEGsoBs78XQYsVvDxbu8RASz0Sw4EnzvJIoFH/FagolAvkhFS1KMAYQMOWWfMiNf9i47CIhXR4bgyOi+duWWffoKGZYkC24edzM+KPoAzfZmQcjQcTqkGFIEIYMQIkQWgsWixDTZm9Dl7sLRtqN49qdnccPYGzAlz1OQm5eehzZHG1o7WzXvyAA8hZ8NHYG/51YdWAUAuHLUlQEfIwcqoD0y7RGcN+w8RV9Lr3f6OodLjbUmakWfQPfUEp+fPRUtHkdGLKIlgMeFEwsh40jrERA9wcgB4QsZaclpfh0Zy3Ytw9uFb2PMy2Nw06c3eYm7wahqrVLUj8FQzgPnPIBzTzwX/++r/yc7hqslPOHRMKwBE5Z6ps+8c/U7+GL2FxiYOjDWS2MwGDGGCRl9DBfvwu2rbsc5b54T03UMem4Q8v/r/QthMNeD3wYxgo6MQB8Deiz1dEOrOFpC5EVLfJEVLVFS9qkgWuJ7d12WI0O0qRySPgQAUN9eH/LrvKIlCBAt0QWPlojv7IXsyJDpyEjSJ0nehfV1ZCiNltBcNnVk0E0h3RQB3iV4TrfTS0TgOE6IKIhHr1Jo4We40RKpsk85jgw50ZI6W52kYwboec5pSWl+ZZ9Kxq9KCRkAsOD0BbC77Hi78G24ebeXGESFjFZHq0cI4+U5MqwOK0a9NApnvXEWFn2zCG7ixlMXPiV8nhYhHjh+AAREcyEj25wd1JGxqmQVJg2aFFZBo5jrx16P+866D38996+Kv5aKDxE7Mqy1USv6BIAknXS0JC0pTSjejDZZ5qyQ5a5aUNZUBiC80asUX0eGi3eh1lqL+ZPn489n/xmrS1ajYEkBZn8yG0UNRQGP0+ZoQ5O9iQkZGqPX6fHu1e+CJzx++8VvQ9440ZLKlkpctPwiHBl3BL858TcoWliEWyfcygpbGQwGACZk9DnoBqTeFnrTqSV1trqIXCFaRUvoBo7ejVMqZPidW6OyT6XRkpe3vYxTl57q9VpEEi0RHz/HkgM9p8eR1iOY/clsya6TUNES8TmVREvUcmQYdAbJu7BBoyUyppb4OjIo4tdB/LkuvstPRKAbYqlsNy38DLfsU6ojI8WYIggQXgKJAkeGm3ejYEkBnv7xacnH0U1NalKEZZ8BhIxTB56Ks/POxis7XoHT7RREEbGQQXsbTO0mtHe1BxRdKG/++iaOdxxHcUMxPtv/GRadscjr7nxeukcwKD5WDACadmQAnsLPQC6oOlsdtlZvjThWAgDTh0/H8xc/L7wPSsg0ZcJkMAlCxsGmg1iwdkHA0l8p3Lwbdba6qDsy/KIlLeXI75cfsw1UrBwZZY3dQkYkjowkb0dGva0ebuLGxIET8fRFT6Py3kr85Zy/4KuyrzDulXG4/uPrsad+j99xqDuACRnak98vHy9c8gK+r/oe/9n6n6ifnxBPCfm4V8Zh29FtOHH3ifj65q9xQvoJUV8Lg8GIX5iQ0ccQ/xLW7mxH7chaWRvXaBDNYqlRL42S/DjdzITryPBF1fGr4mhJiI4Nr2gJ70ZZUxl21+/2ujPqK9ZQlAoZSfokZFuysa1mGz4q/gjfVXwX8DmIN/Di10Z8PHHZp5SrwMuREaIjQ27Zp0FngFHnHy0Rl332M/XzGusmZ2qJ0JHR3bki9TqIS/DEjgwqHNDCTykhg64h7PGrUtESQ0rYZZ+Uo9ajaLQ3Ymv1VsnPi4WMZEMykvRJaLY3A1Be9iklZADAwtMWoqypDBUtFZJCBu3HMFk9r2FzZ3PA5+Pm3Xhh2ws4O+9sbLljC+ZPno+Hpz7s9RjqfKB3lKMRLQlU/rimZA0ICK46Jfyxq2rAcRwGpQ4SoiUbKzZi6c6l+OLAF7KPcbzjONzEHV1HhsT41Yrmipj1YwAeR4bdZQ8oBmtFWVMZdC5dRK+/ryODdsnQTWmWOQtPTn8SlX+oxN9+8zesO7gOE5ZOwDUfXoPCukLh6+jNj3A6MhjKue3U2zDrlFn428a/SQpLWlHdVo2Z78/E3WvvxumDT8feBXuRfTibuTAYDIYfTMjooxAQ/HPTP1FzSg2W714e6+WExHfjFGm0JBB0A+cbu4ilI0ONsk8qbOyu3+1ZFyHCRtf3F3alZZ9GnRE5lhxhY+ibhabnAwJ3ZAQavyrpyFDSkSEzWmLQGSQ3L+Lxq/93yf9h+aye7xVZZZ+cJ1ri68gQR2zEn6NCBu10AHru7PuWfQI90ZKwx6+KoiVUKLEkWSIu+zzYdBAAAtrEqXiTlpwGHafDiRknoqrVc7eVvif0+YfjyACA68ZcJ4xolHRkdG+uU6ye1zBYvGRN6RqUN5fjvrPuw2mDT8PSy5f6OS4Gpw2GjtMJjoxoRUukrvFVJaswLHMYxuWM03QNchicNlgQUOl19c7ud2R/PX2fYln2SQhBRUtFzCaWABCu5WjHS0obS5Hcnhz2v4GAv5BR3VYNAH6xpwHmAXj8gsdRdW8V/jH1H9hYsRETX52Iq1ZehZ01O4WfEcyRER04jsNrV7yG/in9cfNnNytyUoUDIQTv7n4XBUsKsLlqM1669CVsuHUDe78ZDEZAmJDRxxD/0ks3E1I9A7EgHtR2wZERwK0QCqmYhhxXRtCODImyT6UdGfR50Ltb4q/3vXOv1JFB3Qq0MDHYmD3xBt5rvVp1ZIQQkuh5hWhJIEeGzojBaYMxNLPnTqDcjoz05HShI0NK0PGKlrg90RKxaBAsWiI4MsLtyBA5MuZOmIv3r3kf6cnp4Zd9dh/rUNMhAJ47r26DtAABQBAh8jPzhbutbt4tCA9A4KklhBBh8okUyYZkzJs4DwAkOzJotESOkPGfrf/B0IyhuPqUwFENg86AQamDUNwQPSHD4Xb4CYc2pw0byjfg6lFXx8XP1MFpg4XXmv5c/fbQtzjadlTW11MRJOqODNH3T0N7Azq6OmLqyKDjJaMdLylrKkNyu79oqwTf8at0THGgmEC/lH545PxHUHlvJRaftxibqzbjtGWn4dHvH4XJYFI0OYcRGVnmLLx55ZsoaijC3/6n3dSXOlsdrv7wavz2i99iXO447L5nN/7fGf8vIgGNwWD0fthPiASnqKEIU9+aqthuKt5cK3E3xBq6Vs07MnyiJeHGXv628W+4b919IR8nK1oSpiPDTdzC36kjQ/xLeliODJHwYNAZ4HQ70WjvFjKkHBlS0RJx2WeA8auSU0s0GL8qREuCODJ8kTu1JCM5A1aHFTzhpaMlTqvgrKCODPH5gkVLpDoylEwtEXdkDEwdiBvH3QiDziC8rgHLPkN0ZBxqPiR8zJ7mL175ChnDMocJBah0ygglULTE7uqZfBKI+afNBwdOEGHMRrPg6Kmz1cFkMCGpw/McAwkZu2p3YXPVZiw6c5HXuqTIy8jDUatng07fN62gmznfO/TrDq6Dw+2IeayEMih1kCBGiH+urtizQtbXUxEk2h0ZYhGaXpvx4MiIppDh4l0oby6Hqd3/Z50SpBwZZqNZmHgViExTJv457Z+o/EMlHjv/Mbh4FybkTogLga4vcenIS7HwtIV4fuvz2FixUdVjE0Kwsmglxi4Zi28PfYvnZzyPTb/dhJP6n6TqeRgMRu+ECRkR4nA5/Gzj0eT+b+/HD4d/wA9VP8h6vHgDojT2EGnzfCC0HqkaTrTE15ERyRpf2vZS2F8LSJd9Khm/+v/ZO+8wKcpsjb9fh+mePMPMAMOQcxhAQKKCKBjwohhWZXUFs9ewu+rqvbKuad1Vd3HXtK5hdxVFWdNVcQ0oYkZFyTnOEIc0gcmhw3f/6P5qqqqrqqtzDZzf8/DQXfF0VddUf2+95xwtR4Z8wK7lyGhsazT8XiscGTYnvt//vfRea71wqSXqYp9i0BzOkdHibcGH2z/EH77+Q/vnkX22SIt96nUtkbsRBKaKfQYdGRxcEbc6taQgI9AJwUjI0BpES11L7KFFOSNNLRE4bA7pO6HryAjTtWRn9U5kp2UDAJpzQs+5vGsJEBAyxFNvH2/vMgK0H1P1d14thmjRO683Fl28CDedfBOA0NSSrlld4WgLHFc9IePxHx5HVloWrh11re5+BKLgJ5AER0bwCb26TsbibYvRKb0TTu15akL3b5Zu2d1Q31aPhrYG6foaVzIOC9YtMCUQi/tOMlstql1N5TWpbb0KyFJLdOqiJII9x/bA6/fG7shwZaPR0yjdN/bV7UP3nO6mBYlcdy5+N+V32H/Hfiy9cmlMsRDRMf+s+RhYMBBz35uLYy3H4rLNo41Hcenbl+Ln//dzDOg0AGtuXIPbJ94eVWFhgiBOTEjIiJEpC6Yg99HEPnlLBJEOzD/e8TFK/lqCD7Z/kKCIInOGJMpFotd+NZZCpGbSU0y1X40gtSSkRkbwafaOqh1obGtUDNjVg3ev34ueT/Q0/F4ramSoBvmGjgyvdrFP+aBbXuzTqEZGhjMDrb5WvLn5TTz09UPSMYmm2Ked2Q2LfZp1ZGjWyAjWUqhrrdMUdOpb66VBipaQIQbEWhZbEYM8FhuzwcZsEaeWCOSCiVwgSbPJin2G+fuxq2YXTul5CrLSskw7MoD2gZM8BtH5RS00mBEyAGB26WwM6zwMQKiQUZxVDIcnsC+RGiWnor4Cr298HdeOutZUF5KkChkZASFD3rnE6/fig+0fYObAmWHdI8lCpIQcrD8oXefXj74eWyu34qeKn8Kuf7DhIArSCzSFw0ShdoSJTkypzNUX5zuZjgzRejVWIUNco8JNt79uv+JaMUuGMwPZruyYYiGiI8OZgVcvfBWHGg7hlo9uiXl77255F8P+Pgzvb3sfj0x7BN9e8y0GFw6OQ6QEQZxIkJARIz8e+DGl+490gB3tgHxlxUoAwIr9K6JaP95o2ez1iCa1RF3s06zwo7WvWB0n0RT7lAsNPr9P8Tk2HNmgGOSqt+X1ew3rBchjApQpB0CYGhk6jgx5PFrtV9t8bVKcDW0NsDEb8tx5aPG2wOv3os3XJrUJjGdqiZ8FPqfp1BKNriViQFvbUit9F+Sfl4OjIL1Amt7mVzkyggNoLYeFSC1RD/K0PosWeo4MgZ4jw0gg4pxjZ/VODOg0AKWdSyMSMnYf2w2v3xvyRG5QwSBsq9qmuQ2tIqh6qGtkFGcXw+a1wc7smt/5Z358Bj6/D78a/ytT25cXL0z0gEsrteSbPd+gpqUGswZZI60EaC/SWVFfAY/fA4fNgUuHXYp0RzoWrF0Qdv2K+oqkFvoEEFJsuPxYObpmdY3ovhNvct25sDN7Uot9ir+psaaWCOeVELmFI4PoWIwtGYv7ptyHRRsW4fWNr0e1jermavzinV/gojcvQvec7lh1wyrcferdlhFeCYLoWJCQcZwQTc6oVhHJcNuPtPhlokhUrrJe63UsmgAAIABJREFUsc9Et4Y1rJGhcZ4iab8qUkuE4LD20FplaolBjYx3tryDf63+l2Y8AvUPEKOuJXq1XOT7tNvs7ekRwQG/6w8ujPvHOACBp3qZzky4HW60+lqldUWHDLmQcbTpqGH9mJCuJQbFPtWY6VpiYzYpNcQoVccotUQM9uWpKQKtriUANAuXaiGvkSGQp3XI45CfZ93UEjAcbTqKhrYG9Mvvh+Gdh6M5pzlk+frWejhtTkmAkQsZ6mKfADC4cDC2Vm5VTDPryJCjbr9anFUMBob89PwQIaPJ04TnVj2HCwZfYPrvjXjKnOnMTPgPc5FaIndkLN62GC67C2f1Oyuh+44E4cioqK+Ax+eB0+ZEjisHFw25CP/e+O+wnRAONhxMaqFPACHtn8tqylJa6BMI/C0pyChIuiMjOy0bjtbYvsvyv2FevxcH6w+SkNFBmTd5HiZ0n4CbPrxJKtpqlg+3f4jSv5fijU1v4IHTHsCK61agtHNpgiIlCOJEgISMDk6kT/u1ljeTpiEGO1YRMsJVspYPnKKqkaFKLbl72d3YXrU90jBNYya1RP6ZI00t8XM/uud0R64rF4s2LMKnuz6V5ht1LXlxzYt4csWTivlqEcWMI0N8BnltCPl5kYspWo4MAFhzaA2A9pabLrsLLd4Wad0NRzaErHPZ25dh/D/Hh8Sj/qxS1xJ1sc84pJZIjozWWl0BoDBdP7VEGgR4QoUMqWuJPUpHRpjUEhEHA1MKGQZdS0Tr1f6d+qO0cyl8aT4cbjysWE7dNrVrVle47C6UHysPODJYqCNjX90+RfpHNEJGpjMTTZ4mtHhbUNNSI9Vd6JTeCdUtSiFj4bqFqG6uxu0TwhfrFQhHRqLTSoCAKJPhzJBqJnDOsXjbYpzZ78yIjkmikVJLGg4GOvIERcqrTroKx1qO4f1t7xuuX1FfkdRCn4CsRkbwGio/Vp7S+hiCoowiVDYnV8gYUDAg5lRO4U6qb63HoYZD8HFfVKklROpx2BxYeOFCeHweXL34alO/CWtbanHN4msw898zUZBRgBXXrcD9U+/XrD1FEAQRCSRkHCdE80MjkgG+GKAlujCnWcK21QzjWNDDqP3qvV/cG3VcIsc6Uvzc3+7IiDK15MW1L6LF2wK7zY7i7GJ8s/cb3PDBDdJ8I0eGj/tCnpiqf7iof4wYFfvUc0fIxRQzNTIy0zLhcrjQ6m13ZGgJGUC7U0OLkNQSPUeGxg8uLXFDS5BS1MjQ+X4YOTKELVvXkeFHSCpGmj3NVNcSs6klNmYz7cgQrVf7deonPW1Tn4MGj1LIsDEbeuX1CjgyeKgjY8aAGXDYHLj8ncsVKUZA5I6MNl+b1PpTDJAL0gsUjgw/9+OJFU9gdPHoiIpmisFZMoQMIJBeIoSM9YfXY/ex3ZZKKwECxWrdDreUWiK+U6f3Ph3dc7rj5XUv667r534cajiUdEeGPLXE4/NgX+0+9M1LXccSQWFGYVJTS7ZXbceATgNi3o7ckbG/bj8A/darhPXp36k/Hj/7cSwrX4anVjxluOzSXUsx/NnheHndy5h36jysvH4lRhePTlKkBEEc75CQ0cGJpUZGJKklRo6MNze9qTnISiThPrc8znjUyABiKzDa76l+UaWnPPrto5rFPsN1LVGfp2Xly2BjNpze+/SQZY0cGXWtdSFChnrfZlJLBLpChsqRoX4iKkc8zXc73FKNDEA7tSQcYbuWGLRf1bpu9LqWAIGnUnqIYp8enyei1JJxJeOQUxk6aDabWhLOkSFiZ0zbkfHD/h9CtrmrZhcYGPrk9dEVMupb60NqSPTO6y3VyFB/p0YXj8Zz//UcPt31KW5fEnBIRCtkAO3Coqi90Cm9k0LI+GTnJ9hauRW3T7g9orS9LlldpNSJZFCUUSSllizethgMDOcNPC8p+zYLYwzdsrspUkuAgPg2Z8QcLNm5RGqxqqaqqQpevzfpjgx5asm+un3wcZ8lHBmFGYVJSy1p87Vh97HdCRMy5PVkiI7HdaOvw/mDzsfdn92t+bCgoa0BN31wE8569SxkODPw3TXf4eFpDye1aC9BEMc/JGQcJ8TSVz2S1BL1IHbtobW47O3LcOMHN4ass6piFe769K6wA/hIBvhiABXWkSGLM5bUEvm64dJZwmE0wNY7B5+VfabZKcbLwzgyVOfJ6/fCxmyYf+Z8TOk1xTAuuZBR21Ib3pERJrVk3mfzsGTnEgDKYp/y866ukSEGzrqODGcmXHYXWn2t0rnaVb0rpCNLOMIV+zSqkaGFZtcSV2jXEjWi2Kd4AmxWyLhixBUYsCJ0oKHlLtFCq0aGXEQQsatTS4RT6M5P7wzZ5s7qneiR2wMuhwudMzvD0eoIdWSoUksAoHdub8mRodV+79rR1+I3E3+Dv/30Nzzz4zMxCRm7agKuEUVqiUzIePyHx1GcVYxLh11qettA4PyX5JSY6nASDzpndpae0C/ethgTe0xEl6wuSdl3JHTL7oaDDQfh8XsU7qa5J82Fn/vx6vpXNdcTrVeT7siQtX+WWq+muEYGEEwtSZKQUV5TDj/3Y0BB7EKGvNinqKtAjoyODWMM/zjvH8h15+IX7/xCcrMCwFe7v8KIZ0fg+VXP4zcTf4M1N67B+O76KZ4EQRDRQkLGCYZ8YB7JAF/krKsHsWLQurd2b8g6E/81EY99/5jpgaWRGKOeJx8QauXaijhfWvMSXlzzoqn9A8apJf/e+G/T29Hctq9Vd57eZ/9i9xfYVxf44adOLTESoNTnqa61DnZmR2ZaZkjhQqPUkhZvS8SpJY2eRmmZmuYaPLr8UWlQLa+RoYhB1bUE0E+P0HNkcHBsqdwSkyODgytFsKAjw2zhRq2uJZlpmWBggRoZUaSWiIG6UTcYNVr1PrTQSi2R16fIc+cFPofakcE53t36LpbvW45O6Z2k6Ywx7KrZhX75/aRp6XXpUtqPQFPIyOuNo01HUdtSq3u8/zT9T5g5cCZ+veTXWLxtMYAohYxg+ot40t8pvZNUf2PjkY1YWrYUt467VdOJE44bRt+AS4dGJoBES1FmEY42HcXe2r1YfXC15dJKBMVZxaiorwj5bg8sGIhJPSZhwboFmiLfwYaAUyPZXUvk7VfLjwWEjEQVmI6EwoxCVDVXJaVWlWi9mghHRoYzA/nu/Ji3S6SWzpmd8a/z/4V1h9fhvi/uQ5OnCbctuQ1TX54KG7Ph66u/xmNnPSZ11yIIgog3JGR0cGKpWaGVsqCHWOaJFU/gtAWnmdq/WMdomfWH15uKVY3Y5oguI+DxezDlpSmKJ1WiRsQ171+DhesXmt6uGASri32ajsvAXSJ/YqEmElcMEHBcGFk0tWqEiPVtqsteq/2qoMXbEiI+qLetNegUT8t/qvhJMV3hyDAo9glAs4sIEOxaoqqRIZ7ubTi8ISZHBqBqjWrjcNqcph1PandDnjsPNmZDjivHsGuJlFriD00tEZ/t/EHnm4oBMO/ICJdaIgkZKkdGq68V//vZ/2JY0TBcO+paaTpDoNhn/079pWnp9enYdGST4lqqb6sPESCEdX9Xza6QYp8Cu82ORRctwrDOw/Dprk/BwCL6kSx3ZNiYTWph2im9E+rb6uHxefDED08g3ZGOG8eEuszMMG/yPFw/5vqo1o0UkVoiCmZaVcjolt0NB+uDjgyVu2nuyLnYfHQzVh1cFbJeyhwZshoZZTVlcNgclnAQFGUWwc/9qGmuSfi+ROvVuDgyZMU+RevVWFykhHWYOXAmbhh9A+Z/Nx+lfy/FkyuexK1jb8W6/14XUX0hgiCIaCAh4ziBgeGL8i8wdcFUw/oJWgPtSAfRX+/52nAbZTVl4Jyb6nQy8rmRYfethfgcdmbHoYZD+GbvN3h5bXvRuGifWEmpJRqODC1qupr/QWnkyDCD/Ief1+81fFqsFbckZKgG20Y1MoTjQT5NK7VEXbxLDNrVNRRMtV8NDmKdttCaFYDSkdHqa4XH58GggkFwO9zYeGQj2vzRCRnyp7ACv80f0VN5cY6cNicWXbQIk3tNBhCoNWHUtUSeWqIWMgoyClD9P9X4/em/Nx1HLI4MLSFDXexzyc4l2Fm9E/PPnK8YmNa11qGyqVLhyHDXu9HoacSeY3ukaQ1tDZLdXCBasO6s3mnogMl2ZeM/P/8PumR2QVZaVkQpX/IaGZ0zO0spLMJVsq1qG15d/yrmjJwjuWSsTOfMzmjztWHh+oUYXDgYgwoHpTokTbpld0N9Wz2qm6tDHFyXDrsUbocbC9YuCFlP1M4QKUDJQi5qlh8rR8/cnprpTslGCJ7JSC/ZUb0Dee486W9TLKgdGdSx5PjiL2f/BQMLBsLP/Vg2ZxmePvdpZKZlpjosgiBOAEjISDI3/udGjHh2hGLa5+Wf47mVz0W1PfnA6PJ3LsdXe76SqthrLi9PLYmgNoXZwcJXu79Cv6f64eV1L0sDpETYYLVaksqJWsgIuia0in1qsa/UfB/1SJwCWqhTS4wG2Fpilq6QESa1BFC6SdTbdtqd+HLul4ppIg1ixYEViulmupaESy2R18gQQovb4cbQoqHYcCQGR4Zdw5HBeERChog9w5mBnw//uTQ9152r27UkzZ4mPbHUEjIAID89P6IBu9muJUY1Mhw2hzTwV6eWtPnaML3vdJzT/xyFwCbcOwpHRl3AMSGvk6GXWgIEzkm4QWPP3J74fO7neHGW+bQxQOnIkA+OhZDxyLePoNXXitsm3BbRdlNFUUYRAODHAz9a1o0BtKfw7K3dG/LdznPn4cLBF2LRhkUhrrWK+grku/M1Wx0nEkVqSU25JdJKgHYhw+geHy92VO/AwIKBcXFOOGwOuB3uQI2MoCODOH7ISsvC6htXY/svt+OMPmekOhyCIE4gSMhIIpVNlXhh9Qsh+eLTXpmGmz68SXOd2pZaKXfbCMaYNOg2O+CJJLVEb5tqMUQMVn468JM0qIlFyDjc97Dm+pIjQzbgiaSrhx6RppYwrqrdYZBG0+pthZ/78UX5F6HbMXEO5NsOK2RopJaIYxWpI0P+PxB6TBw2R0gHivq2enDOsWK/UsjQE9LkYoqIUy+1RAyCXfb21BKHzYFu2d1Q2VQZc2qJfH1u4xH1ute7TnJcObpdS3JcOYp9awkZkRKP1JIcV470Wp1awsAw/8z5YIxpOrr6dZLVyGgICBnyv3taQkaXzC7SgNVMTZKhRUPxs6E/C7ucHCFkNLQ1KDphCCHj3xv+jRn9Z2Bw4eCItpsqijKLpNcXDL4ghZEYI1JD9tbu1SycO3fkXNS01IQUNj7YcDDpaSVAaGqJFQp9Au3CVVIcGVU74lIfQ5CVloVjLcdwsP4gCRnHIRnOjJjvWwRBEJFCQkYSKZpfFH4hFZ0f64zC+YW68+UDQzHANEoViSa1ZPex3WFrWYjBkBRDBKKC1IlEI7b9w/Zj8dbFuuvo5dLHK7UkXOzMb/5pVauvFX/78W8445XQJxZm0nsUA3+/J+RHwyVvXSK9jiS1xKhriRBE5EKGWiTRGpjUt9ZjV80uVDWHF+EAbUeGVjtUn9+HFm8LMp2ZimKfDpsDLrtLEgLk6H1HAB1Hhk9ZIyOi1JLgeVSLA7muXN2uJdlp2YonwHERMmIp9hkUkvLd+dJrtSPjzkl34qSuJ0nz1MhTS+xeO3rl9pJETp/fhyZPU0hqCWNMsrEbnbNYEEIGAE0hg4Pj9gm3J2TfiUDU+Oia1RXjSsalOBp9hBjR5mvTFAan952ObtndsGDdAsX0gw0Hk17oE2h3ZNS01OBo01HLCBnJSi1p8bZgb+3euAoZ2WnZ2Fm9Ez7uo9QSgiAIIi6QkGEhfrvstyHTTHf8QLsjo6alvW5Dq7cVMxfNxOajm0PWMZta0ufJPnh+1fOmltVK+TArKugJGlq1JcQ29SzoWo4EvW0o9uVVtl8Ntx21I8OIVm+rVEAtZDsROjKaPc0hT7Tf3vy29Dqi1BIDR4a0P1nBz3BdS4BArQRRH8OMLVyrRoZWekSjpxFA4Omey9HeflXUuBBChlxcMdq/2K/dZjcs9mkWcWzV4kCOKwfHWo5pOnZyXDnSwMlKjoxcd66mI+MvZ/0Ffz7zz9Ly6u9T58zOIQ6d0s6lkpAhP4dqRF612S4xkaIQMmQDZCGglHYuxfS+0xOy70QgntCfN/C8mNtDJxL5sda6nuw2O+aMmIOPd3yMQw2HpOkV9RWpcWQEY9xetR2ANTqWALLUksbEppbsqt4FDh6XQp+CrLQsbKncAoBarxIEQRDxwbq/fE5AHvn2kajX5eCobq4GAFz85sXS9O/2fYcPd3yIWz66RVpOvg5gbhCtuU/OQ8QHrfQWM6KCfF01Wj/Q5cU+BfLBoxnxRD3Y55xLookYQMfbkRELcoGn2RsqZMiJqNinQY0MgVFqiaYjo60eP+z/AZnOTAwtGqoZo6muJarYRO2NzDSlI8NpdyqEDHmhMaPuLl6/FzZmg43ZtB0ZUdbIUNMjpwf21u7VPC/ZrmzYbXbYmC1+QoZJR4ZRjYwcV450fcmLfapFBrV7Ql4fQzC883BsrdwKj88jdbTR+v6KackQMuQ1Mrpld0PvvN64b8p9HaqbQo/cHrht/G24Y+IdqQ7FkFxXLtIdgRQjve/23JPmwsd9eG39awACf+8O1h9UOGeShfhbIIQM0VEn1aQ705HpzEy4IyOerVcFWWlZkkjVI5ccGQRBEETskJDRQdh9bDfKa8pNuSh2Ve8KmfbTgZ80lowN2+9tOP3l0xXTtNJb/NyPqqYqXPHOFdJAVAuxrvqptZbNXEot0XNk+H2G+wJCBRb5AF5KLYnQkWF0fozcNWas9PLj0uRpMhQyNGtkMO0aGUbtVwWK1BJ/+Par9a31WHFgBcaWjDXlyDBb7FOIdaJGhp/70eJtkWpctPpaA0KGs13IMBIFRFqKfLkQR0YENTLEIFg9GB5QMACtvlbNJ6k5rhwA7V1akurIMOhaoqiRIUstUZ9v9Xt5WomgtHMpPH4Ptldtl65LtWsDgHTeEtUhQi+1JN2ZjvJfl+OSYZdorWZZbMyGx8953PI1PRhjkitD73oaXDgY40vGY8G6BeCco6q5Ch6/JyWODHH9bavaBgCWSS0BAq6MRBf7jGfrVYH8eidHBkEQBBEPSMjoIPR5sg/6PtUX/1j9D8358sGIfKAqBlTCzi0faIvXZuozqHlqxVPK/atqZKhTS/74zR+xaMMi3fjl66ox7ciQDR63Vm5FzqM5hp9BPWCXOyak1JJwjowIU0v0MDNwUzgyPM2KwbpAHMNY2q/+8uNfhqxr6MjQGJgcbTqKtYfWYkLJBN2n6/LPo0gtsem3XxU1N0SNDCBQuNHBAjUufH4f2nxtCpHHZTd2ZIj4pNSSODgy1NeUcCnopZYA7cVN4yFkmOlawjnHoYZDIW3yFI4MUSNDllqiFt3U519PyAACxYCNHBmpSi0hEo8QJIxSta466SpsPLIRaw6tkVqvpsSREYxxW+U2ZKVlSSkdVqAwozApjozCjEKp/XI8ENd7hjMD+e78uG2XIAiCOHEhIaOD8f3+7xXvNYt3Glij39/2vvT6tQ2vhV1ejydXPKk5XcSjSC3x+6T3RikfYl31Z9Ia5IdzZIhcXCPUIoVcaDBdIyOC1BKzKTZ6mHFkiEFiLO1XtYi02OdXe76C1+/FhO4TTNWYMOvIED/gRY0MIPCZnXYnHDYHvH5vxKklkpAh61QgiLTYp15qiVa6hRBYRNHLNHsamj3N8HFfUlJL1h5ai7KaMswcMFMxXYgwOWnROTJKckpC9jW4cDDszB5eyBCOjAQV+xTfE0CZWkIkHiFkGH23Lxt2GVx2FxasXYCK+grFeslExFjfVo8+eX0slW5UlFmUFCFjYMHAuG5T/J3rntPdUseTIAiC6LiQkNHB4JxjWdkyaTAg0Pph4PF5FAPQNl8b5rw3J2S5Jk8TDjccjigOPRfHW5vfAhDqyBDL+7kfb2x8Q3Nds46MdYfW4Y1NgW3EMuBRD8jlA1jTXUsiaL9qRsQxQl0jQ931AWivIaElmpjtWqJFuParapbvXQ4AGN99vKmn64r2q0y//apoRZyZlikJAX7ul1JLhKNB7lYx2r+P+0IdGbJ9+m3+iIp96nUt6Z7TPcQZIgbyckeGcE4lI7Xk9Y2vw2Fz4KIhFymmi78t8hoZckeG+niqj0+uKzdkXy6HCwMLBmLj0Y2obwumlmh8fxPtyADaXRmpeNJ/IiOOt1GqVn56PmYNnoVFGxZhT+2ewHopcM7IY7RKfQxBslJL4lkfA2j/e0cdSwiCIIh4QUKGBeGco+SvJViwdkHIvH11+zB94XTMfW9uYFmNgbMYTJ360qmY8doMabreoPzGD25E17/E9nRS7POnikAtDnWxT/Gecy51sxCoC4WqP5N64H3S8yfhu33fAdB3ZJhJlzFKLZGKfcbRkWEkVhgJIFrLNHmaQlICAEiDxFhSS7Ro9oR2LRHHWGtg4vF70Cu3F7pmddUduCiKfZpsvypSS7LSshS1Nxw2h+TI8Pg8iqf94nu/rGwZzn3tXMWx0XJkxNJ+VS+1xMZsIZ0PRM54woQMA0cG5xxvbHoDZ/Y9EwUZBYp5da11AJRdS4yKfarf57pDhQwgkF6y4fAGc46MBNXIAAJCRq4rF+nO9ITtgwjFTGoJAFw18ipUNVdJaYipEJzk11/fPGt0LBEUpic2taSxrREH6g/EXciQOzIIgiAIIh6QkGFBvH4vKuorcP1/rg+ZJwYB6naqWjUyfjzwo2KZWFMbFPsLYw2Vz5d3RhDFGbXg4DjaeBTOh5Q/dOWuC7UYIB+Um20nK/D5fahqqpLW00wtiWPXErNtaPUQcXp8Hnj9Xs2BoOTI0IhbDA7Vg8SIU0uC2xYpG+qBiThf47uPB2Du6bpWjQyt1BLJkeHMVKSMyIUMdWqJ+N5/s/cbfLzzY2mgLvZr5MjgLLJin0YtMNXpJeKHvfjfaXdK13dcUksMBKoVB1ZgT+0ezC6dHTJPHB9FjQxZaon6+6M+v3p59aWdS1FWUya5v1LRtQQICBlUHyP5mEktAYAz+52JrlldsbJiJfLceSkRnOR/06zmyCjKLEJDW4PufTRWdlbvBBDfQp9A+7VNQgZBEAQRL0jIsCAf7/w47DJGg3Y9kcHsQLrN14avdn+FjUc2oralVnMZ8WNHD6MaGXptSP3cj5UVKzW3peewiCW15EjjEZT8tQTvbn0XgKrYp8muJTav+UvIyHUhzueK/Ss0u87I12/2BtwRWgNBMQiNtyNDK7VE/NjXeyI/oWSCYjk18u+w2fareo4Mp80Jp90Jr9+LJk+TIm1BiCTCVSJPy9LsWhKDI8NI4FM/4dRyZMRNyAjjyHhj4xtIs6dh1qBZIfPq2tqFDKlGhlFqiT18agkQEDI4OH6sCAisRl1LoilAbJYMZwbVx0gBUmpJGEeGw+bAlSOuVKyTbOTfabWTKtWIwqOJcmUkovUqQKklBEEQRPwhIcOCzHo9MLjQaoMpfuCLQa1msU+dQYBZx8Jdn96FqS9PxfBnh2P8P8ebWkc9gJMPlm/84EZsrdoKQFvIEJ/Fz/2mBtVydFNLTBQT21e3D62+Vqw+uBqAqkaGSUeGza+8hIyO8XMrnwsrJs15bw7u+fwezXli22JArtW1RKSWaAkwwq0Rrv2qFlrFPsVgW28gO6H7BMV8o8G5/LwLccqwa4msRgbQ7sgAAh1Teuf1luZJQkZQAJK35dVMLVG3X42gRoaUWqLx/VM7MrRqZMRLyDDqWuLnfry5+U3M6D9DMw1kYveJAIARXUa018iIoNinXmrJ8M7DAUBKLdP6/gonjZm6LdHyy3G/xM0n35yw7RPaSKklJhxOc0fOVayTbOzMLt1HrdR6FQCKMooAJFDICLZe1SpQHAtCuCRHBkEQBBEvEuffJaIi0vQIgXzgpDeIN5tasvHoRun1tqpt0cUjE1O+2vOV9HrFgRW6XSQ455qDaqPBvyLtxESdCTkiTaH8WDkAZWqJ2RoZkTw4Xla+DGsPrdWcJ2Kva63D/rr9hss0eZoAAOnOdNiYTXF8xCBd65h9s/cbALF3LRHb1kstyXPnwWlzYlTxKADtA910R7ruAFV+3sX31yi1JMOZoVkjQyAffAgxKpwjQ3wORdeSOLVfBQxSS1ztXUvE50tkasm3e79FRX2FZloJANwy9hZcMPgCdM/pjgN1BwCEab9qotgnEHiy7Xa4UVZTBpfdpTmgFeJGomzzAHDDmBsStm1CH7OpJQAwrPMwXDj4QowpHpPosDRhjEk1euSiqBVIhiOja1ZXTcdULAh3TbxTVgiCIIgTFxIyOihSq1INZ4Zenn6sNRoiQS+GD3d8iP8a8F+KafKOJlqDarmYsK9un2JeLEUBxdP9spoyAO2pJW6H23TXEs4iE0+OtRzT3o7MbbG3dq/xMkFnQYYzI0TIEKklRnFHVezT217sU2xbz5FxweALcHrv0yWhIcMR6BKR7kxHbWt7qpKi2KfsvMu3H9K1pLlK+txyQcxpdyoG1L3yekmvQxwZbWEcGbGklhgoWyFChiq1xGmLY40MWyDNhnMeImy+vvF1pDvSMXPgTM11GWPSU1NxfdmYTfd8qx0ZcoFJjt1mx9CioVh9cLXuIEk4MvTSz4iOS44rB7MGzcKkHpNMLf/OZe8kOCJj0uxpyHfnaxZVTiVCyDjamJjOJTuq49+xBADO7n82Nt28Ke5tXQmCIIgTFxIyLEY4V4Ge20K+HgPDtspQJ0W4Qbl8/Uj5cveXeGXdK9J7o6KHRjUytAbV8oH60rKlinnyp8ORxi2efgshQzyJz3RmwuP3wM/92HBkg+E2IhUywtHsbVYM9hX7UjsyHOmwMzu8aHcz3Prxrejfqb+haCXOjZ3Z4eO+iNuv/vbz3wJoH2zL6yhwcFx10lXomdtTWl6kUKQsRvTMAAAgAElEQVQ79Iv2yc+7+H7oOTLE9vRSSwCETS1p9jTjox0fhS/2GcfUkh65PRRtUbOcgc8hnBlxrZERFBu8fq9CePD6vXh789s4b9B5mjVW1EjnljGc3O1k/Hn6nzG191TNfQmM0rpKO5di9cHVuvtOhiODSA2MMbw3+71Uh2Eap81puUKfQKDYJ5DY1BI9kTMWbMyGoUVD475dgiAI4sSFamRYjHCpJXqtS9XrlT5bGrKuWUeGmfoSWoiWsOG2oR6kKGpkaDgy5HGrB7aKriURppYcbDgIIFD0s6GtQUotyUrLgsfnwSc7P9Fd94HTHgg8WVJ9zHAx6M3n4PD5jUUFdY0M4UxQ88P+HwxTYuTtTYHIU0s+L/8cgMyRERzoy5/ey5GEB1VK0d2f3Q2/zR8Sg9iXVo2MmpYaabCrl1risDkUufXiWMhTS97c9CZ+9tbPsKtmV3tNDi1HRpSpJVo4bA4suGCB9D7R7VeBULfNF+Vf4GjTUVw27DJT25FqZARTS+465a4Qx4VcQNp/u3ZalEDUydAVMoQjw0uODCK1uBwuy9XHAIB8dz4YGI42xd+RUddah8ONhxPiyCAIgiCIeENCRpworynHmBfGJLS/uxz1gFj+3sZsmrUmzNbIiEfHAKMBnVH71VhqZJhdR/Dt3m+l1+U15ZITICstK2y6BUfgSX28HBmc87BPofVqZKg5UH/AVGqJGIBG0rVEflzVqQbiXOgJGepz8uzKZ1FXFEiFkZ93MYjV6lri535NYcRhc0hx9MjpoRhca6WWHKgP1H5oaGswdGT4bf6I2q8KAU/vGrp8+OXS62l9puHsfmdLrUDlnydejgz18Xt94+vITsvGjP4zTG1H7sjQ3ZfMsVKSU2K4vdLOAYFV3lVGjji35MggUs0z5z6DeafOS3UYIdhtdnRK75SQ3xqi0CfVsSAIgiA6AiRkxIk/L/8zVh9cjTc3vQkA2Fu7F5unbMbhhsMAzBfxjNRVIK0n275usU+TqSXxwEjI0HMA6KWWGMWtVyPDTCcO0WYOCKSXyB0Zbb42pDv1UyE453DYHBELGUbfA3kdCqN11TUy1FTUV5hKLRGD1Ei6lhxqOCRNU6eW6DkyxFN2rfPYmB9wIHj8HmnwL/aVZk+Dj/sUXUbk21O3XxVxiLSS5dcsx+zS2SHtV+tb66Xrss3Xptl+9Z5l92DJziVR18gw42qa2nsqlvxiibR9uUshknQWLcSxueCNC3DfF/dhyc4lONJ4BO9sfQezBs8y/G7LEefUSNxU18gwQggZ4VJLqEYGkWouGnIRhncZnuowNCnKLEqMkJGg1qsEQRAEkQioRkaCePKHJ9Gc24yF6xfizkl36i635uAaxftIu5bI0zIEeoMOMy6FU188Fcv3LY8oBi2MhIx1h9cp3ovPbCa1RI2ia4ns2M1bZu5JmtvhRou3BWU1ZZLFPystCxX1FWFTLuw2e2hqSZjzZ5RaIgba4dZV1MjQEHIq6ivMpZbYzKeWCPFkz7E90rSQ1JIwjgwtkaoxr1GKIdedi2Mtx6RBrNjOOa+do7k9vRoZotDnpB6TpDoY8s/Q0NaAw42hQoZwMTR7m/Gn5X9C2bEycBZZjYxIhEj1uZO7FGJ1ZFw85GJsOboFy/ctxx+/+aPiGpo9TLtbiRbi2Bhdz5E4VkqyS5Dryg2bWkKODILQpzCjMCGpJcKR0a9Tv7hvmyAIgiDiDQkZcUI8gRUDWfV79QCn1dsKl8OFR5c/qphudiCkHjCbqU9hJrUkHiIGYDzw0eOWj27BRUMuCpluKGTYtNuvmnEZAEDP3J441HAIZTVlGFI0BEB7aonRU2EODjuzhzgyonXUAPqOjOZ7mpH+x3TTNTIO1B8w5ciQ0g8iSC2Rd1QRQoKUWhKmRkZjW2PIdhvzGuHnfnj9XuS6AkKG2NfPh/8cC9cvxHf7vlOso1cjQwgOvXN7K6b7uR+ctwtF9W31kpDR6m0NSS3ZV7sPPu7D9qrtgC0yUUG6/qNIz4qnkNElqwuePvdpAAHh5scDP+K7fd+hurkaZ/U7y/R2zKSWROLIYIzhkWmPKLrKyKFinwQRnsKMQuys3hn37e6o3oHuOd2R4cyI+7YJgiAIIt6QkBEn1AMX8V6rPSoAuP/oBr8/+kGveruicKVWLIJkppZEMpCTD5I2HdkUMt/P/boCgXzQHM3ny3PnIcOZgbJjZeib3xdAe7FPo4KDnHPYbUohw+PzoPgvxRHHILan58hQf5fC1cg40njEcCCoTi0xg9jentpQR4aUWhJ0ZKi/65KQ4QkVMvxOP7ZVboPH70Gn9E5Abfu++ub3xdZbt2Lue3MVHXGkLiiy9AinPTS1BGgXW5q9zUpHhkZqiRBkyo4FuthsPrpZMT0SoimYKxxBQOxChpystCyc0ecMnNHnjIjXlbqwxCm1BABuGnuT7jwq9kkQ4SnKKAop/B0PEtV6lSAIgiASAdXIiDNisKl2ZJhF66m1FmYGqmrCpZaI1o/xIBJHhlyA0PpcRk4SeWqJ2WKmcnJdueib3xflNeVSh4ystCxUN1ejvq1edz0OHjKAM9PG1Gh7Wo6Ml2a9FPJdktfI0Ct2WlFfoXsOYhIyZKklXbO6IistS3JGCEeG+jzoPWUX7Vi/3P1lILXElau53DPnPqMYhIvtyeOXF/uUP+0X7RN3Ve/SdGTIhQwbs8HGbFI7XnmtDrPE4sgRXUwi3Weicdgcpot9xoo4t2NLxsZtmwRxvFGYUYjKpsqIf1+EY0fVjkA3LoIgCILoAJCQESfUP/RDHBkaAxxheZdz2ye3mdpfRX2F7rx9dfs0p4cb6D/+/eOm9m2GhesXml52adlS6bXWYN5IgJEPZqN1ZPTN64vyY+XSwDXTmQkOjqsXX224rp3ZUV9Uj56P9wwbpxm0HBlzRs7RdWS4HW5dsWJf3b6wQkYkA1AptaSuPbXkmlHXYMstW6RBd7/8QF61WlzRq4cwossIpDWm4cMdH8LjD9TIkO9Lvv6yOcskoUO+vSGFgXQgh82B03ufjnmnzsOkHpOk+YMKBgEAtlVtk75b1c3VqG6uBhC4JuTfIafNifKacsX+O2JqSTyxM3tcHRlGOO1OrLphFd697N24bZMgjjcKMwrh9XtR21obt23WNNegqrmKHBkEQRBEh4FSSxJESI0MjScn9t+HPk2XW+jjjbDT6/Hwtw/HbV9rDq0Jv5AGWoN5PYHAZXcpBlGrD62OeH/CkdHibcHu2t2wM7ui9oIePr9PciAI4chsXQ4tONd2ZNiYDX7ml5YBAsdIiBi6QkbtPtiZHV6ExhQvR4bb4Ub3nO7S+8WzF+PL3V+iKLNIsa6ekOF2uJF3OA/LypfB6/dKqRV6aQVuhxu1rbVS+gEADCkagi2VW9DsaUauOxcPT1N+h0UbwW2V26TvlnBcCOTHIc2eFuLEiUTwEdsqzCjUXabiDu1irFZ2ZMSr2KcZRhePjuv2COJ4oygj8De2sqkSee68uGxT6lhCrVcJgiCIDgI5MuKM+onsX3/4K2758JaYLOfxQl4Q1KpoDeb1nBHfXP2Nwgnz3tb3It5frjtXqo2xtXIrXA6XqYGZx+8JcR5Ek9oiqGuti6hGhijGFosjIxIhQ8Qmr5Gh/vxFmUW4ZNglIesaCRm5h3PR4m2Rin0C+q03XQ5XyPZGdR0FALoV/LPSslCSXYINRzZIx2/3sd2KZRSOjOC5F98J+TQzlOSU4O/n/h3/+fl/dJcpzi5WCEACyzoybPa4FfskCCJ2hFB6tDF+nUu2V20HQK1XCYIgiI4DCRlBOOeoaqoKu4zadi5QDzaFdb2yqRJ/X/n3OEaqJJJ0hmMtxwzn69VbSCZ6rgYtR0u4J8VmyHPnSYPWLUcDaRJmnsB7/d6QAZw8taVffj9Tzg7BbZ/cJn1P5ANaINTd0+xtlupLaLVfBQIOCr156q4lZmjxtqC2pRZ1rXUh2wmH3EEhx+1wI6s6S6qLIC92qbc8oBQy7px0J343+Xe4bvR1uusNKhyEtYfWSu/V3zF1agkA9O/UX3O+GW4aexNKckoiWgewtiPDKLUknjUyCIIIj3C9VTZVxm2bO6p2wMZsChGXIAiCIKwMCRlBnlzxJArnF2JX9S7dZZ5d+Sz6PtUXPx34KWSeGGz+esmvAQAvrH5BMT/eRbkAYOG6hREJGeEcA1pdJayC1lN6p90Zs5BRmFGIXnm9wMBQ21oLl91lquOEx+cJEQrkA+SuWV3RI6dHRLF8Xv45AODkbicrpkfjyDCaF21qiboui55QokaILoCydoTL4YLNb0OXrC6B93YXeuX2wt/P1Rb+RAcSIXwAAXHjoTMe0nV9AIE6GeJpoxZajoweOT3QMzdQ+yRZAl+iupbEip2RI4MgrIRwZMRVyKjegZ65PSXnG0EQBEFYHRIygny440MAofnzcr7d+y2A9lxSOc+vel56reUqSERqyZz35hjGqyaZ7VfjjVY3E6ctvJAhBr96FGYUIs2ehs6ZnQPLO1yK46T3JFqdWnK08Sgue/sy6T0HR4/cyIQMwYILFijeazoygq1H5Z9f/WRcbwAebbFPeYtf9b6NkA+C5S4Q4bAQ58hhc2D3bbt123NqOTLMMKhgkHT9dUrvFDJfy5HRI6cH+uQFOp6YFWxiRe7EiXfdiVgI68iwUKwEcSIgpZbopNRFA7VeJQiCIDoaJGSoMBIcPtj+AQDtAZy89aaW+yIRjgwAqGo2ToeR4/F7EhJDON782Zsxb+OBLx8ImWYmteT+0+7Hab1O050vfhCKJ+Bp9jSFEKX3dMrr9yoGuFMWTMHyfcsVy2jVQTCDXqHIcI4MkcIhBIxwqSVmn6TbmA0t3hYcajikuZ1IkDsN3Ha3Io5wA2IhZOilqugxqHCQ9FoIVnI0HRm5PaTWrfWt+m1444k8tSRWp1E8sdvshvGQI4MgkkumMxNuhztujgzOOXZUkZBBEARBdCys82vZ4izeuljqZhBukDHr9VnJCKnDEI8n2loijNPulApE6jGs8zDD8yVEAzEYc9ldihQcPUeHx+9RDOC02uF2z45OyNCqrcHAFF1LpBoZMteFSLkQ+dM2ZgNkmUfnDzq/fTrCP2kXZDgz0OprlT6jqJIfTcqFXMgQIpEkZIRxiGgV+zTDwIKB0mtR7V+OumsJAPTM7YmeOYHUkgP1ByLaX7Soa6NYBYfNQaklBGEhGGMozCiMmyOjsqkSta21ir+VBEEQBGF1SMgIw5ajW3DHJ3fggjcukKYxMLyy7hW8s+UdzXU+3vlxyLSMhzMSFmMiiUd9gETVGHDYHGHTN84fdL4pIUM8iVenlujVKvD6vYrP9eXcL0OWiSa1JM2ephkvY8y0I0MM1u3MDsbbB6BvX/K2Yh2HzWEq5UkIJOU15Uh3pEu1HKJxDcjFCiHYiGMfbkAsOTKckTkyeuX2kgQp4ciQx66XWiLOfSxtdSPBqrnpdmanYp8EYTEKMwrj5sig1qsEQRBER4QepYVh6N+HhkyrbKrEzR/dnIJokk9mWqaiU0U0JKrGgNPmNCyo2S+/HwDjAbeomSAGYyGODJ3BpcfnkepUXDzkYowqHqWYzzmPKrVEb8Aod2ToChlajgwZQjCINLVE7Kv8WDmKs4sxqcckvLr+VWk/kSBPHxHigjj24VJLxPKROjLsNjv6d+qPTUc3SUJGl8wuUs0PvdQS8b1NlpBhVciRQRDWoyijKH5CRlVQyKDUEoIgCKIDccI4MlYfXI3altq4bOtEETGA9kFstNx9yt0JdWQYiQVigKUlZEzrMw3zz5wvPXUXy4bUyLC7sPbGtSHrXzH8Culz6bUNNarNoYdeZxm5I6OhraG9HoZMJApxZNiUjgxBpEKG2O7uY7tRnFWMF2a+gB2/3BGxoACoamQ4VDUywjzZj7ZGBtBeJ0MIGcXZxdK8gvQC6bXT5kSn9E7IcGZIcXXkIrnxIFyNDDNdfgiCiC+FGYU42hif1JLtVdthZ3b0zusdl+0RBEEQRDI4IYQMzjnGvDAG5y46V3cZM7UCTkQitfGrmdZ3muEgKBahxGl3GroCjISM8SXjceekOxXbAkJTS1wOF7pmdVWsu/L6lbhwyIXS9vUG4tmubFw36rpIPpJuO125I6PR04gsZ0BEkAsDwl1Skl0CQN+JIrZj1ikjvgO7j+1G16yuSHemo3+n/qbWVSM/RiE1MhLkyAACnUuAdiGjW3Y3ad4pPU+RXqfZ0ySXz4TuEwAAp/WOXJA6njBbS4UgiOQR79SSPvl9qAMRQRAE0aE4IYQM8ZT7u33fhV12/eH1AIBVFasiam16vBJr3r7b4TYcMIdrj2pEuK4lRkLGbyf/VvFenloiL3jmsrtCnjiLzyMcGWI/ItUEaO8wEukPQz0hw+P3YP538wEoHRnydqIPn/Ew7j7lbsybPE8RnxpxPZh1ZAjhoNnbjOKs4jBLG6Mo9ilSSxJcIwMIiBJuhxt98/sCALpltQsZ40rGSa/vmXwPHp3+KADg1J6nYuSSkVKR1BMVO7OT64IgLEZRRhFqW2vh8cXejYxarxIEQRAdkRMiuVkMDtVPFb/d+y0GFQxCUWYRalpqAAB3Lb0LE7pPwOSXJic9Tiuy+ejmmNZ32V1x+aGlhdbAtyS7ROoyIQQHLSFDnZ4gTy25aexN8Pg9uP2T2zWLfarbm4p13Q53SD2RaAshVtxRgdpWZSqUj/uwrXIb2nxtkrggFzLy0/PxyPRHwDlHpjMz8Lk1anmK1BmzQoZ8H/KUjGiQCztSsU9RIyPMscp2ZSMrLSuqmivnDTwPR+48gkZPI9wON07qepI0T+4KmtZ3mmI9h+eE+BNpCNXAIAjrIQpVVzZVxvR3WbRendJzSrxCIwiCIIikcGI4MnRy3Ce/NBmnLQjYxldWrFRMJ+JDOEeGnqNi7si5htu9fPjl0gDrr2f9VZp+Vr+zpNdivmgXaoQ8tcTGbJjRf4b0Xi2AqR0Z4n+ttqnRWnWLs4sxuHBwyPTXNrwGoN2V0MndLjKIOBhjKMkp0T3u4nowW7tEIWTE6MjQ6loizlM4geK2Cbfho8s/imq/jDFku7LRNasr6ufVY3z38QBgWCw2FSy5Yglev/j1VIehwG4jRwZBWA25kBELhxoOodHTSK1XCYIgiA7HCSFkCEcGB8eysmUA2tNMtlRuSVlcJwJuh9twwNzibdGcHk58OLvf2dLr2yfeLr3OTsuWXosB8lMznlLURNBCnloCtBfw1Ep9UaeUSKkljvSQZeVPs6f3nW4YgxkWbVgEAJqODPm+umV3022bGWlqifxcqOuFmOWVC17Bx1d8rEwtUdXICNcdpGtWV0zuFbvIKC8S+9ez/xpm6eRydv+zcVnpZakOQ0G4FC6CIJKPqA8Vq5BBrVcJgiCIjspx/+t0W+U2vLf1Pen99IXT8auPf4VTXgwU+GNgWFWxKlXhWZ5Ii1WqcTvchoOgsSVjNac3tjUablcUrFTTJauLNLgXgkOeOw+PTnvUcHtCuBAD7WxXQBBxOQxqZGiklqjjEwLJg1MfxP+e8r+K7Xx25WeGMWmxq2YXgPbUmPz0/JC4AODSoZdi1qBZKNlSErKNSFNL5OJQtBbmK0deiXP6n6OdWhKclsw2p50zO4Pfz/GzoT9L2j47KnqCGEEQqUM4Mo42xda5hFqvEgRBEB2V417IGPzMYPzi3V8opj3949PSaw6OPy3/U7LD6jD8YsQvwi9kQLjUknP6nYOyX4UWVW30KIWMTGemwtmhVxQz3ZGO0s6lAJQDdfmAX4sRXUYAANp8bdL+GJimI0OIHepaGfJinwL5IF3doUVdjyEStBwZ8uNz09ib8Mdpf0TRntCuLpEKGfL2sgkp9mlLvpBBmCfblR1zG2aCIOKLaLMdiyOj1duK7/d/jzR7Gnrm9oxXaARBEASRFI5rIWPNwTWmlntr81sJjiR6rh99fUzr3zr21oiWV6eBaA3OIyFcagkHR5/8Plh65VLF9CZPk+J916yuUkoEoC9kuBwu9M/vL70WhEtVEcUft1VtA9BeU0GrRoZwEhg5MtTpGx6fRzEYfOuSwHfusTMfUywXDlHTQaqRIRcyTBbBNBIyxFM+OcKdYmM2zfmRYFQjI1FFYYnYeHrG03h+5vOpDoMgCBnib3+kQsbhhsN4ac1LuPjNi1E4vxD/WvMvjC4eHVURZYIgCIJIJceVkLHn2B60elul96NfGJ3CaCJH5O3LuXz45abXX3jhQuy7fZ9i2vyz5uOlWS+Z3kbN/9Yo3mvVfQCAQQWDpNf3TblPd3tuh9vUIF1dP0LtyCjOLsackXOk91yrHUdwfyL9Qb7fcELGyC4jAQD76/a37zOrGJ3cnaSBvHwfcrSEDPE9FAN3j98jORD65feTUhqE40XuejBi1qBZAMI7MowQQoZY/rRep0nzVly3AndMuAMAkOvKBdDuwuiS2SXmH7tGXUs8fhIyrEjvvN7o16lfqsMgCEKG0+5EnjsPRxuNU0s451hzcA0e+uohjP/neHT9S1dc8/41+GH/D7i89HK8P/t9LJuzLElREwRBEET8SFpfPcbYOQCeBGAH8E/O+aOq+S4ArwAYA6AKwGWc891mt9/kaULvJ3sDABp/2yilCHQkvrrqK1z/n+txeu/Tce8X9wLQLjapR0F6QYgY4na4cdVJV+HqxVeb2oa6HoSeCCFvX/rg6Q+iJKcEN35wI4DAwGf3sd3S+kOLhuKuSXehrrUOz69SPtnVq3XR0NageF+cVYx/nvdP1LfW492t74Y4MsaVjMOPB35EuiNdGnjXt9ZL8+VCxl2T7grZX/ec7vj1+F9jdulsadrHV3yMXHduSAtWtZAhaoDIRZ9WX1DI0Kj/0CWri/RaODfEPvp36h8Sm5yrR12Nz8o/kwaWsToyqv+nGhnODLj/GPhMffP74i9n/wWPTH8Ebb42fFH+BUYVjwIQe+tVQJlaIorLXVZ6GZ5b9ZxCULE61JKUIIhUU5RRhMrmUEdGk6cJy8qW4YPtH+CDHR+gor4CDAzjSsbhodMfwsyBMzGyy0jqRkQQBEF0aJLya5wxZgfwDIAzAewH8BNj7H3O+WbZYtcCqOGc92eMzQbwJwCG5fs54zj1xVOxfN9yxfTMhzN11rA2ffP7YtmcZfh277fStGi6RNw+4XY8/sPjuvOn9JqCr/d8rTlPr9WoGvXT/8k927tJLJ69GCOfCzgcGGNw2p3485l/xrtb3sXzq54HA5McFXopIupinxO7T4TT7sSZfc/Eu1vfRb985RNi0ZVE7siobq5WzL/6pKtx89ibcXK3k0M/N2N44pwnFNP65PfRjE04CNQizI1jbsS7W98FEDiXgLIjx+DCwXj4jIcVzhLx+e3Mju+u+S7sk+/RxaOx5Zb2TjvRODLknVn0aoek2dOQZk/DeYPOQ11rHYDY62MA7cduwawFUuxTe08Fv19b0LIiK69fGXX3luOVNHsaeuf1TnUYBHFCUZhRKDky9tbuxYfbP8QHOz7A5+Wfo8Xbgqy0LJzd72zMHDgTM/rPUIjoBEEQBNHRSdZjxXEAdnLOywCAMfY6gFkA5ELGLAAPBF+/DeBvjDHG9R7ZA2jObg4RMY4H5GKC3mDaiN+f/nvUt9bjsbMe05z/9IynMbRoKBgY0n+bDk96u6Vf3WFEPTh+5txncMtHt4QsN6RoiPRab8ArUgccNof0+twB52oue++Ue3Hp25dK70WL1f8++b9xSs9TpOKcgmZPM4CAkCHcF1XNVYrP9eKsFzX3ZYaeuT2xt3YvgHbXijgG4it6dv+z8dHlH+HcRe2f6ZpR12BlxUrcf9r9YIxh3uR5iu0KIcPGbJjYY6Lu/l+54BWM7DoyZHq+OyBEjO02Vvfp2qsXvopeeb2k9zePvRn1bfX4zaTfGH/oIMKBEo/B+5UjrsTC9QsNP6vVGdNtTKpDsBxNv20KvxBBEHGlMKMQy/ctx8jnRmL94fUAAiL6jWNuxMyBMzG552RFrSiCIAiCOJ5IVo2MEgDy4g37g9M0l+GcewHUAigw2qirKT43aL1CdreMvUV6PaXXlJj3o5c28LvJv1O8FwPSsd20W5PqMazzMACB+gn/OP8fyHXnai7HwOCwOWC32VG6rDRk32/87A3pvdyRseSKJZKAoOXUqLyrEq9f/DqKMotw75R7MbhwsGL+1N5TkeHMwJ2T7gQAnD/ofCltAQD+NuNv0utLhl2i+ZSeMRYiYgDAjP4zAACDCgdpOjJi5bbxt4VME0KGvAipmCYEigxnBhZcsABFmaHdQwDA5/cp1tPjypFXan5up92Jz+d8jg8v/1B33StGXIFTe54qvXc5XLjvtPsUKTJ/OP0PmNZHu4uK0+ZEYUYhhhQO0ZwfCWf2OxP8fo6BBQNj3hZhHew2OxULJIgkM6RwCGpbapHvzsf8M+djyy1bsPOXO/HEOU9get/pJGIQBEEQxzXMwPAQv50wdgmAsznn1wXfXwlgHOf8l7JlNgWX2R98vyu4TJVqWzcAuCH4esyo0aPQUNCA+oJ6HBx0UFou41gGmvLanxJ22t8J1d1DB7Z2jx0nLTkJVSVV2D16t2LeSR+fhLUz1gIARv9nNFaftzrsZy39rBQbp29Ufn4fA7dzlGwuwYGhB0LWGfMf5RPehrwGbJu8DZk1mRj87WDsH7If1SXV6L2uN5iP4cCQA2js1Ihe63phz8g9SGtKw/Blww3jWnXeKgCAq9GFoV8Ohc0fGDhv3roZzXc1S8uN+nAUbH6btPzwpcOx4cwNUpz1BfXYPmk7sqqy0FDQoBl/OGq61qBsbBlyD+Wi/0/t4g4Hl46x2Oaq81YBHBjzgfE+ODh8Th8cHgf8Nj/W/NeaqGIzwufwwe5tH6xVDKzAwUEHUbytGN22B1Jb6grrsGPiDmQfzcbAH8IP1r1pXqw7ex0K9hag93/SnkQAABjgSURBVLrecYtVsGXLFgwZErsA4UnzwO6xw8bjq33GK75EYfX4AOvHSPHFBsUXG1aPD4g+Rg4Obuew+RL7TMrqx5Diiw2rxwdYP0aKLzYovtiwenwAsGrVqlWc89Dc/ljhnCf8H4CJAD6RvZ8HYJ5qmU8ATAy+dgCoRFBo0fuXkZHB1Xy0/SP+3pb3pPd+v5/7/D7OOed4ANK/A3UHeFl1Ga9qqpKWfeirh6T5FXUVnHPOfzrwE5+/fD7nnPMb3r+BF/ypgI94doRiW/J/6v3gAfDZb8/meAB87cG1uuvIqW+t58WPFfNlZctC5nHO+T3L7uF4APzbPd9yPAA+5905msvJ0dvX6DGjFbG0elsVy++v3a9Yd1nZMo4HwKcumKq7zXD83+b/43gA/ILXLwgb54CzBvCy6rKI9zH6+dH8n6v+GfF6kfDglw9yPAB+7+f3StOW7lrK8QD4GS+fYXo7W49ulY57vBkzZkxCthsvKL7YsXqMFF9sUHyxYfX4OLd+jBRfbFB8sWP1GCm+2KD4YsPq8XHOOYCVPAEaQ7JSS34CMIAx1ocxlgZgNoD3Vcu8D2Bu8PXPAHwe/OARMWPADMwaPEt6zxiTbPui5SW/n6Nbdjf0ye+jKJZ4x8Q7pNciPeHkbidLqRDPn/c8Kv+nEmtvXIuJ3QM5/uNLxmP9f69XxOC914tHpj2Cgj2BzJix3caC388xsutIfH3V15jQfQIA4LWLXsOh3xwK+QxZaVmo+E0FzuhzhuZnfHDqg9h08yac0vMUbL55M16Y+YKpYzOpx6SQaaIex1n9zsKK61ZIXSVEPQR1yoNIhbAzOx6c+qBujEaI06ouLAoA31z9DRZeuFB6n1OVE1WdkFU3rMK1o6+NeL1IUKeRAO2fKZKv7qDCQSGdUQiCIAiCIAiCIAhtklLsk3PuZYzdioDrwg7gRc75JsbY7xFQaN4H8C8ACxljOwFUIyB2xJW3LnnLcH6GM8PUdhhj+O7a73Tn22123H3q3Xj8hUDnEPmgdnKvybh06KX4Yf8PKO1cGlUVcbvNjqFFQwEoi2waUfU/Vbqf7+hdR5HjylEMpuVFKOUMKhwEALh8+OW4ZtQ1EccOANP6TsPAgoF4YOoDIfNO7Xmqop6DlZFqZPhDa2SIriwEQRAEQRAEQRBEfElW1xJwzj8C8JFq2n2y1y0ALklWPHqsumEVDjccjus21YPa2ybchguHXJjUdoVy54mawozCkGlCyFB3wuiZ2xOeez1SW9FoyHPnYdut26Je3ypoOTJEUc/SolLNdQiCIAiCIAiCIIjYSJqQ0VEYXTw6qvWWXrkUKytWKqal16cDCO1WwhhLqogRDcJFotVNIxYR43hCtKaVCxmlnUvxzdXfYFzJuFSFRRAEQRAEQRAEcVxDI9I4Mb3vdEzvO10xrWBfAd7825sdclA7tGgovtn7DdLsafj+2u8161mc6Gg5MgB0mNQYgiAIgiAIgiCIjggJGQmEgXVIEQMAFs9ejNUHVyPHlSMVJyWUSDUyuC/MkgRBEARBEARBEES8SFbXEqKDkZ+ej2l9p6U6DEuj58ggCIIgCIIgCIIgEgcJGQQRJXZbaI0MgiAIgiAIgiAIIrGQkEEQUUKODIIgCIIgCIIgiORDQgZBRAkJGQRBEARBEARBEMmHhAyCiBKp2Kefin0SBEEQBEEQBEEkCxIyCCJK7IxqZBAEQRAEQRAEQSQbEjIIIkqk1BKQkEEQBEEQBEEQBJEsSMggiCihGhkEQRAEQRAEQRDJh4QMgogSqpFBEARBEARBEASRfEjIIIgosduoRgZBEARBEARBEESyISGDIKKEUksIgiAIgiAIgiCSDwkZBBElJGQQBEEQBEEQBEEkHxIyCCJKpBoZnGpkEARBEARBEARBJAsSMggiSuwsUCODin0SBEEQBEEQBEEkDxIyCCJKKLWEIAiCIAiCIAgi+ZCQQRBRQl1LCIIgCIIgCIIgkg8JGQQRJdP7Tkf+gXw8NeOpVIdCEARBEARBEARxwkBCBkFEidvhRt/VfdE3v2+qQyEIgiAIgiAIgjhhICGDIAiCIAiCIAiCIIgOAwkZBEEQBEEQBEEQBEF0GEjIIAiCIAiCIAiCIAiiw0BCBkEQBEEQBEEQBEEQHQYSMgiCIAiCIAiCIAiC6DCQkEEQBEEQBEEQBEEQRIeBhAyCIAiCIAiCIAiCIDoMJGQQBEEQBEEQBEEQBNFhICGDIAiCIAiCIAiCIIgOAwkZBEEQBEEQBEEQBEF0GEjIIAiCIAiCIAiCIAiiw0BCBkEQBEEQBEEQBEEQHQYSMgiCIAiCIAiCIAiC6DCQkEEQBEEQBEEQBEEQRIeBhAyCIAiCIAiCIAiCIDoMJGQQBEEQBEEQBEEQBNFhICGDIAiCIAiCIAiCIIgOAwkZBEEQBEEQBEEQBEF0GBjnPNUxRA1jrB7AtlTHYUAhgMpUBxEGq8dI8cUGxRcbVo8PsH6MFF9sUHyxYfX4AOvHSPHFBsUXO1aPkeKLDYovNqweHwAM4pxnx3ujjnhvMMls45yfnOog9GCMrbRyfID1Y6T4YoPiiw2rxwdYP0aKLzYovtiwenyA9WOk+GKD4osdq8dI8cUGxRcbVo8PCMSYiO1SaglBEARBEARBEARBEB0GEjIIgiAIgiAIgiAIgugwdHQh44VUBxAGq8cHWD9Gii82KL7YsHp8gPVjpPhig+KLDavHB1g/RoovNii+2LF6jBRfbFB8sWH1+IAExdihi30SBEEQBEEQBEEQBHFi0dEdGQRBEARBEARBEARBnEB0WCGDMXYOY2wbY2wnY+zuFMZhZ4ytYYx9EHzfhzG2gjG2gzH2BmMsLTjdFXy/Mzi/d5Liu50xtokxtpEx9m/GmDuVMTLGXmSMHWGMbZRNm88Y28oYW88Ye5cxliebNy8YzzbG2Nmy6Qk5/1rxBaf/Mri/TYyxP6cwvh6MsS8YY1uCsfw6OL0TY2xp8JwuZYzlB6czxthTwTjWM8ZGy7Y1N7j8DsbY3ETGJ5t/J2OMM8YKUxGfUYyMsZMYYz8wxtYyxlYyxsalIsbgNfojY2xdML4Hg9NfC36nNga/p06LxccYY39kjG0PHttfpSI+2bZj/tvMdK7vRMQnm/40Y6xB9t4S8THGpjHGVgevj28ZY/1THN9uxtgGcb0Gp1npXhISX3C6Ve4leYyxt4PHawtjbCKzyH1ELz7ZPCvcR7SOnyXuIcHtDgrGIf7VMcZus8o1ohdfcJ5VrpG4/H7WizsR8cnmpfw+onP8LHMfYYz9OhjbJtl3zxLXh158wekpuz6Y9hgubvcNxtgYFrhv7gyuy8IGxTnvcP8A2AHsAtAXQBqAdQCGpiiWOwAsAvBB8P2bAGYHXz8H4Kbg65sBPBd8PRvAG0mIrQRAOYB0WWxXpTJGAFMAjAawUTbtLACO4Os/AfhT8PXQ4Ll1AegTPOf2RJ5/nfhOB/AZAFfwfecUxlcMYHTwdTaA7cE4/gzg7uD0u2XH8FwAHwNgACYAWBGc3glAWfD//ODr/ETFF3zfA8AnAPYAKExFfGGO4acAZsji+jJFx5AByAq+dgJYEdzvucF5DMC/0X7dWiW+qwG8AsCmuk6Sfo6D24/pbzN0ru9ExRecdjKAhQAaZNMsEV/wOhkii2lBiuPbjeDfEdk0K91LtOKz0r3kZQDXBV+nAciDRe4jevEFX1vlPqJ1/CxxD9GI1Q7gEIBeVrpGdOKzxDWCOP1+1os7UfEFX6f8PmJw/CxxHwFQCmAjgAwAjuB3boBVrg+D+FJ6fUB7jBS3+waAHwFMDK7zMYJ/T43+dVRHxjgAOznnZZzzNgCvA5iV7CAYY90B/BeAfwbfMwBnAHg7uMjLAC4Ivp4VfI/g/GmmlKbYcQBIZ4w5ELggDqYyRs751wCqVdM+5Zx7g29/ANBdFs/rnPNWznk5gJ0InPuEnX+t+ADcBOBRznlrcJkjKYzvIOd8dfB1PYAtCNww5OdOfU5f4QF+AJDHGCsGcDaApZzzas55DYClAM5JYHwA8DiA/wHAZaskNb4wMXIAOcHFcgFUpCLG4H7EkxRn8B/nnH8UnMcR+GMvv05SHh8C18nvOef+4HLy6ySp5zhOf5v1ru+4xxecZgcwH4FrRI4l4oPx9ZHU+PSw0r1EB0vcSxhjOQj8IP1XMI42zvkxWOQ+YhAfYIH7iEF8lriHaDANwC7O+R6LXiNSfLDINRIkHr+fE/l3UB1fhZXuI1rxwTr3kSEAfuCcNwWvh68AXGih60MzPqT4+uDaY6S43DeC83I4598Hf+e+ItuWLh1VyCgBsE/2fj/aB0vJ5AkE/lj4g+8LAByTXQTyuKSYg/Nrg8snDM75AQCPAdiLwB/gWgCrrBSjBtcgoMIp4gkiYk32+R8IYHLQ7vYVY2ysFeILWu9GIfBEvAvn/CAQGKgD6JzqGOXxMcbOB3CAc75OtZiVjuFtAOYzxvYhcN3MS1WMLGDrXwvgCAJ/8FfI5jkBXAlgicXi6wfgMhawVH/MGBuQqvgQn7/NyYwPAG4F8L64jmVYJb7rAHzEGNuPwPfv0RTGBwR+EH/KGFvFGLtBY36q7yVa8VnlXtIXwFEAL7FA+tA/GWOZsM59RDM+C91H9I6fZe4hKmYj4OJTk+prRCs+S1wjcfz9nLT4OOefwiL3EYP4rHIf2QhgCmOsgDGWgYB7oIdqmVReH3rxWeL6UBGv+0ZJ8HVEsXZUIUPLJcA1piUuAMZmAjjCOV8ln6yxKDcxLyEE85RmIWAz6gYgE8AMgzhSelwZY/cA8AJ4LUw8yY7TgYD9aQKAuwC8GVSKUxYfYywLwP8BuI1zXme0qE4sCY1RHh8C5/QeAPdZJT5A8xjeBOB2znkPALcj+LQtFTFyzn2c85MQeBowjjFWKpv9dwBfc86/sVh8LgAtnPOTAfwDwIupiC+Of5uTFh9jrBuASwA8rbVKquMLcjuAcznn3QG8BOCvqYhPximc89EI3NNuYYxNETMsci/Ris8q9xIHAvbgZznnowA0ImAJ1sMK8T0A69xH9I6fZe4hAhao4XA+gLdU061wjWjFZ4lrJI6/n5MWH2NsDqxzH9GK7xewyH2Ec74FgdSRpQg8FFqHwPUg4k/p9WEQnyWuD5NEGlNUsXZUIWM/lMpZd7Tbk5LFKQDOZ4ztRsCqcwYCT7HygjYqdVxSzMH5uQi158Sb6QDKOedHOeceAO8AmGSxGBHc31wAMwFcEbQUKeJRxZrs878fwDtBe9SPCDylLExVfMEn8v8H4DXO+TvByYeDtiwE/xd2s6THqBFfPwRuZuuC10t3AKsZY11TEZ9OjAAwF4FrBAj8qBL2xZR9D4N25S8RtBszxu4HUIRA/QKBVeLbj8AxBYB3AYxIUXzx+tuczPg2AegPYGdwegZjbKdV4mOMfQhgpMwZ9AYC95JUxAcA4JxXBP8/gsD3TRRWtMS9RCc+q9xL9gPYLzufbyMwMLfKfUQvPqvcR/Tis9w9BIHB92rO+WExwSrXiE58VrlG4vX7OZnxPQjr3Ee04jsFFrqPcM7/xTkfzTmfEtzXjuD+LXF96MRnletDTrzuG/vRnspjPlYeY0GSVPxDQJEqQ+CmJoqXDEthPFPRXhDtLSgLAd0cfH0LlIVs3kxCXOMR+IGcgYDS9TKAX6Y6RgC9oSwUcw6AzQCKVMsNg7J4TRkChWsSev414vtvBHL/gYCta1/weCY9vuB+XwHwhGr6/7d3/7FX1XUcx58vMQkHxI9IM5SkBmnOuaC1GrlvsOWitVm0ZsUEUWktV8t+2CQZ1fplMNamrUWJP/PHoqyN2Jo/2NigFMOvYIlD+SIuxCQg5GsS+u6Pz+cbH073fvl+4X6596zXYzvj3M85n899n8/duZ9z35zz+f6IoyfbuTGvf5SjJ9t5JJePI03ENDYv24FxQxVfZZ8ejkzSdlLjO0Yf/hXoyuuzgMfa1IcTODKx3QhgHWlQvQpYT548q9i/U+L7AbAgl3cBj7brMy5i7eI4v5tpcn4PVXyV8nKStrbHR/pOewmYksuvBFa1Kz7S/46OKtbXk8aRjhhL+omvk8aSdcDUvL6ENIZ0xDjSLL7K9h7aO4406r+OGEMqcd4DXFG87ohzpJ/4OuIcoUXXz83iHqr4Kvu0bRzpp/86aRzpmyjzHOCpfA52zPnRJL62nx/872+klo0bwKN5X+W6s48ZTys6ux0L6Xmhp0mzsS5qcyxdHLlYnkyaiG8b6Quvb2bZN+bX2/L2yScptm/lE2ALaRbj4e2MkfQc5C7g36Ts25X5/XYCj+flp8X+i/JnvJVi9tqh+vybxHcacGfuwz8DM9sY3wzSrVZPFP01m/Qs4YOkjO2DxZeCgJtzHJuB6UVbC3Lfb6O4kBiK+Cr79HDkAvSkxneMPpxBega2mzRnxrQ29eGFwKYc3xZgcS4/nGPoi3lxh8U3BlidY9hA+p+XtnzGRftdnMB3M03O76GIr1JeXoB2RHykicY25/NjbV8c7Ygvf5bdeXmS/B1Lh4wl/cTXSWPJRcDGfB7fT7qg7IhxpFl8le09tHccadR/HTGGFG2fDuwB3lSUdcQ50k98nXSOtOT6uVncQxFfZXtbx5Em/ddJ48g6UtKiG5jVgedHo/jaen7Q+DdSy8YN0l/c2ZLr3AToWDEpVzQzMzMzMzMz63h1nSPDzMzMzMzMzP4POZFhZmZmZmZmZrXhRIaZmZmZmZmZ1YYTGWZmZmZmZmZWG05kmJmZmZmZmVltOJFhZmZm/ZLUJelwu+MAkDRP0vOSXpY0p93xmJmZ2cnnRIaZmVlNSForKSRdXCnfJml+m8I6aSSdCvwEWBgRIyNi1RC8x1pJ32x1u2ZmZtY6TmSYmZnVyx5gqSS1O5ATIekNx1HtTOB04IkWh9NSx3lsZmZmNkBOZJiZmdXLCmAi8OlGGxs9BiJpiaQHitch6RpJGyUdlLRe0kRJX5a0U9IeSd9t0PY8STsk/UPSrZJGFtvGS/pFrv93SfdJOqPY3iNpsaSHJR0EGj4WImmOpG5J+/O/H8/l7we25t225kdLhlfqXiDpkKQJRZkkbZd0eRHn7ZJ2SXpB0m2SxuVtNwEfBG7I7W8t2rla0pYc1yZJH67070OSlkraDfxO0mmSfibpRUn/lPS0pE82OmYzMzMbHCcyzMzM6uUgsBj4XvWH/CDNBS4FJgD/Ah4CxgLvAGYCX5X0gWL/YcDHgAuB84ApwDJIyQLgfiCAC4BJwAHgl5X3vBq4FhgJ/LYaUE5W3AV8AxgPXA/cLel9EbEBeHfedWp+tOTVsn5EbAEeBz5bFHfltn6VX9+Vj/P8fBxvBu7I9a8B1gHfye1PzXEtBK7L7Y4FFgG/lvTO4n0uBnYBZ5OSNPOB9wLnRcRoYBbwl+oxm5mZ2eA5kWFmZlY/K0mJgi+dQBvLIuL5iOgl/cg/E1gSEYciohvoJv0QL10XEfsjYjcpmTJP0inAtLx8IW/vBb4OzJQ0sai/IiI2RfJKg5iuAFZFxJqIOBwRq4HfAAsGcVwrcztlm/dGRK+ks4BLgGsjYm9E7CUlVmZLems/bX4R+HZEdEfE6xHxe+Bh4LJin+ciYlnuv17gEClhc76kUyNiZ0Q4kWFmZtYCTmSYmZnVTES8RkoUXC9p/HE2s6tY7wVejIjXK2WjKnV2FOs9wHDSHQ3n5vXdkvZJ2gc8Q7rT45xKnf6cDTxbKXsmlw/U3cAUSe+RNIp0d8QtRfsA2yvtl9saORe4ue/Y8vF9CHhbsU9Ppc6dwM+B5cAeSdU7OMzMzOw4OZFhZmZWQxGxBniEdGdE6WVgWOWxk7Na9LaTivW3A68CL5ESHAeBcRExplhGRMT6ok6ZKGlkJylpUJqcywckIvaRHnOZD3yKdKfEhqL9vtjL9sttjWLcASyoHNvIiPh8sc9R9fIdJT+MiOmkfuvlSELFzMzMToATGWZmZvX1NWAhaZ6LPltJyYyrJJ0iaQbQqkkmvy9ptKS3AEuAO/JdHBtJc1P8uO8OEUkTJF3WvKmGbgXmSLpE0jBJHwE+QXpcZDBWAp8h9c1/60bE34A/AMskjZE0ljTPx5qI6LtD5QWgeufEcmCJpIvy5KEjJM2Q9K5mAUiaKWla/gsmr5ASPYeb7W9mZmYD50SGmZlZTeW5LO4BRhdlB0jzQnwF2E+aR+O2Frzda8BqYDMpWfIsaX4JcjLjUtJ1xWOSDgB/Ik20OWD57o15wFJgL3AjMDci/jjIWB8g3QExDbi9sm0uaX6Rp/KyD7i82L4cmJ4fIXkyx7Uix7Iyx/UccAPQ359ZPYM0iehe0mM8k4DPDfI4zMzMrAFFRLtjMDMzMzMzMzMbEN+RYWZmZmZmZma14USGmZmZmZmZmdWGExlmZmZmZmZmVhtOZJiZmZmZmZlZbTiRYWZmZmZmZma14USGmZmZmZmZmdWGExlmZmZmZmZmVhtOZJiZmZmZmZlZbTiRYWZmZmZmZma18R8gNlirtjgVAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the group by vote_count and find the mean of movie_profit and make plot for this calculating.\n",
    "df.groupby('vote_count')['movie_profit'].mean().plot(figsize = (18,8),xticks=np.arange(0,10300,400),color='g')\n",
    "plt.grid(True,color='k')\n",
    "plt.title(\"Voters vs. Profit\",fontsize = 14)\n",
    "plt.xlabel('Number of voters',fontsize = 13)\n",
    "plt.ylabel('Profit average',fontsize = 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From this plot you can find that there is a relation between voting and movie profits, to prove that look at the plot again you will show the profit was increased by increasing of number of voters, specially when voters number equal to 8400 voters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3: Which movies had the highest and lowest earned profit?\n",
    ">To answer this question, you need to use the high_vs_low function again, which is defined at Q1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avatar is highest in movie_profit\n",
      "The Warrior's Way is the lowest in movie_profit\n"
     ]
    },
    {
     "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>1386</th>\n",
       "      <th>2244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>popularity</td>\n",
       "      <td>9.43277</td>\n",
       "      <td>0.25054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>budget</td>\n",
       "      <td>237000000</td>\n",
       "      <td>425000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>revenue</td>\n",
       "      <td>2781505847</td>\n",
       "      <td>11087569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>movie_profit</td>\n",
       "      <td>2544505847</td>\n",
       "      <td>-413912431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>original_title</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>The Warrior's Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cast</td>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>director</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sngmoo Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>runtime</td>\n",
       "      <td>162</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>genres</td>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>release_date</td>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vote_count</td>\n",
       "      <td>8458</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>vote_average</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>release_year</td>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             1386  \\\n",
       "popularity                                                9.43277   \n",
       "budget                                                  237000000   \n",
       "revenue                                                2781505847   \n",
       "movie_profit                                           2544505847   \n",
       "original_title                                             Avatar   \n",
       "cast            Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                            James Cameron   \n",
       "runtime                                                       162   \n",
       "genres                   Action|Adventure|Fantasy|Science Fiction   \n",
       "release_date                                  2009-12-10 00:00:00   \n",
       "vote_count                                                   8458   \n",
       "vote_average                                                  7.1   \n",
       "release_year                                                 2009   \n",
       "\n",
       "                                                             2244  \n",
       "popularity                                                0.25054  \n",
       "budget                                                  425000000  \n",
       "revenue                                                  11087569  \n",
       "movie_profit                                           -413912431  \n",
       "original_title                                  The Warrior's Way  \n",
       "cast            Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...  \n",
       "director                                               Sngmoo Lee  \n",
       "runtime                                                       100  \n",
       "genres                  Adventure|Fantasy|Action|Western|Thriller  \n",
       "release_date                                  2010-12-02 00:00:00  \n",
       "vote_count                                                     74  \n",
       "vote_average                                                  6.4  \n",
       "release_year                                                 2010  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_vs_low('movie_profit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">After calling this function, we found that the highest movie in profit  is “Avatar”, and the lowest movie in profit is “The Warrior's Way”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q4: Which genre had the greatest number of movies?\n",
    ">Here I answered this question after searching about auxiliary functions and ideas, so I found some ideas and similar functions which is helped me to write this:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Movies in each genres')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fuction that calculates the number of movies in each genre\n",
    "def movies_count_per_genre(column_name):\n",
    "    splited_plot = df[column_name].str.cat(sep = '|')\n",
    "    data= pd.Series(splited_plot.split('|'))\n",
    "    sol = data.value_counts(ascending=False)\n",
    "    return sol\n",
    "\n",
    "#Calling the function and drawing the chart.\n",
    "per_genre_count = movies_count_per_genre('genres')\n",
    "per_genre_count.plot(kind= 'bar',figsize = (14,6),fontsize=13,color='r')\n",
    "plt.title(\"The most genre that containing movies\",fontsize=15)\n",
    "plt.xlabel('names of genres',fontsize=13)\n",
    "plt.ylabel(\"Number of Movies in each genres\",fontsize= 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From this chart you can find that the genre that had the greatest number of movies is “Drama”, and the genre that had the lowest number of movies is “TV Movie”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q5: Who is the top 5 directors that directed the greatest number of movies?\n",
    ">To answer this question, you need to call the previous function to calculate the number of movies for each director and then make a plot to show the top 5 directors who were had the greatest number of directed movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of movies')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calling movies_count_per_genre function to calculate and answer this question\n",
    "top_directors = movies_count_per_genre('director')\n",
    "top_directors.iloc[:5].plot.bar(figsize=(14,6),fontsize=12,color = 'c')\n",
    "\n",
    "plt.title(\"The 5 directors who directed the most movies\",fontsize=15)\n",
    "plt.xlabel('Directors',fontsize=13)\n",
    "plt.ylabel(\"Number of movies\",fontsize= 13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From this chart you can find the top 5 directors, and the director who had the greatest number of movies is “Woody Allen”, and the director who had the fifth greatest number of movies is “Steven Soderbergh”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:\n",
    ">After completing this project, I gained new knowledge about movies, like, budget is increasing year after a year, number of voters is one of important things that effect on movies profit, and the genre that had the greatest number of movies is “Drama”.\n",
    "# Limitations:\n",
    ">I used TMDb dataset and I found some limitations like, there is a lot of zero values, the data isn’t updated because we are in 2020 and last statistics on this dataset is 2018, to prove this I made an analysis for the most movie had a profit and I saw “Avatar” is the answer, and we all know that is old statistic because “Avengers: Endgame” get the most profit in 2019.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    ">1.\tAuxiliary functions to calculate some statistics like high_vs_low:\n",
    "https://www.geeksforgeeks.org/\n",
    ">2.\tDebugging and find some solutions for pandas:\n",
    "https://stackoverflow.com/questions/tagged/pandas\n",
    ">3.\tSome helped tutorials for plots from:\n",
    "https://matplotlib.org/tutorials/index.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}