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priyankamandikal/imdb_datawrangling_exploration.ipynb

Created November 2, 2015 15:49
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Creating some plots from the imdb top 10000 dataset and finding some interesting properties.
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imdb_datawrangling_exploration.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IMDb Data Wrangling and Exploration\n",
"## Task: Use the IMDb top 10,000 movies data for cleaning and exploration\n",
"Data is available at http://bit.ly/cs109_imdb."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"#tell pandas to display wide tables as pretty HTML tables\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"\n",
"def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
" \"\"\"\n",
"Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
" \n",
" The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
" \"\"\"\n",
" ax = axes or plt.gca()\n",
" ax.spines['top'].set_visible(top)\n",
" ax.spines['right'].set_visible(right)\n",
" ax.spines['left'].set_visible(left)\n",
" ax.spines['bottom'].set_visible(bottom)\n",
" \n",
" #turn off all ticks\n",
" ax.yaxis.set_ticks_position('none')\n",
" ax.xaxis.set_ticks_position('none')\n",
" \n",
" #now re-enable visibles\n",
" if top:\n",
" ax.xaxis.tick_top()\n",
" if bottom:\n",
" ax.xaxis.tick_bottom()\n",
" if left:\n",
" ax.yaxis.tick_left()\n",
" if right:\n",
" ax.yaxis.tick_right()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tt0111161\tThe Shawshank Redemption (1994)\t1994\t 9.2\t619479\t142 mins.\tCrime|Drama\r\n",
"tt0110912\tPulp Fiction (1994)\t1994\t 9.0\t490065\t154 mins.\tCrime|Thriller\r\n",
"tt0137523\tFight Club (1999)\t1999\t 8.8\t458173\t139 mins.\tDrama|Mystery|Thriller\r\n",
"tt0133093\tThe Matrix (1999)\t1999\t 8.7\t448114\t136 mins.\tAction|Adventure|Sci-Fi\r\n",
"tt1375666\tInception (2010)\t2010\t 8.9\t385149\t148 mins.\tAction|Adventure|Sci-Fi|Thriller\r\n",
"tt0109830\tForrest Gump (1994)\t1994\t 8.7\t368994\t142 mins.\tComedy|Drama|Romance\r\n",
"tt0169547\tAmerican Beauty (1999)\t1999\t 8.6\t338332\t122 mins.\tDrama\r\n",
"tt0499549\tAvatar (2009)\t2009\t 8.1\t336855\t162 mins.\tAction|Adventure|Fantasy|Sci-Fi\r\n",
"tt0108052\tSchindler's List (1993)\t1993\t 8.9\t325888\t195 mins.\tBiography|Drama|History|War\r\n",
"tt0080684\tStar Wars: Episode V - The Empire Strikes Back (1980)\t1980\t 8.8\t320105\t124 mins.\tAction|Adventure|Family|Sci-Fi\r\n"
]
}
],
"source": [
"!head imdb_top_10000.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Build a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows: 9999\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imdbID</th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>runtime</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>tt0111161</td>\n",
" <td>The Shawshank Redemption (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>142 mins.</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tt0110912</td>\n",
" <td>Pulp Fiction (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.0</td>\n",
" <td>490065</td>\n",
" <td>154 mins.</td>\n",
" <td>Crime|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tt0137523</td>\n",
" <td>Fight Club (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.8</td>\n",
" <td>458173</td>\n",
" <td>139 mins.</td>\n",
" <td>Drama|Mystery|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>tt0133093</td>\n",
" <td>The Matrix (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.7</td>\n",
" <td>448114</td>\n",
" <td>136 mins.</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>tt1375666</td>\n",
" <td>Inception (2010)</td>\n",
" <td>2010</td>\n",
" <td>8.9</td>\n",
" <td>385149</td>\n",
" <td>148 mins.</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imdbID title year rating votes runtime genres\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 mins. Crime|Drama\n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 mins. Crime|Thriller\n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 mins. Drama|Mystery|Thriller\n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 mins. Action|Adventure|Sci-Fi\n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 mins. Action|Adventure|Sci-Fi|Thriller"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names = ['imdbID', 'title', 'year', 'rating', 'votes','runtime', 'genres']\n",
"data = pd.read_csv('imdb_top_10000.txt', delimiter='\\t', names=names).dropna()\n",
"print \"Number of rows: %i\" % data.shape[0]\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Clean the dataframe\n",
"\n",
"### a. Fixing runtime \n",
"remove unnecessary details (mins) and convert to float value"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"142\n"
]
}
],
"source": [
"dirty = '142 mins.'\n",
"clean = int(dirty.split(' ')[0])\n",
"#can also do: \n",
"#clean = int(dirty[0:-6])\n",
"print clean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Applying this for all the rows:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imdbID</th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>runtime</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>tt0111161</td>\n",
" <td>The Shawshank Redemption (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>142</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tt0110912</td>\n",
" <td>Pulp Fiction (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.0</td>\n",
" <td>490065</td>\n",
" <td>154</td>\n",
" <td>Crime|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tt0137523</td>\n",
" <td>Fight Club (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.8</td>\n",
" <td>458173</td>\n",
" <td>139</td>\n",
" <td>Drama|Mystery|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>tt0133093</td>\n",
" <td>The Matrix (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.7</td>\n",
" <td>448114</td>\n",
" <td>136</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>tt1375666</td>\n",
" <td>Inception (2010)</td>\n",
" <td>2010</td>\n",
" <td>8.9</td>\n",
" <td>385149</td>\n",
" <td>148</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imdbID title year rating votes runtime genres\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 Crime|Drama\n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 Crime|Thriller\n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 Drama|Mystery|Thriller\n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 Action|Adventure|Sci-Fi\n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 Action|Adventure|Sci-Fi|Thriller"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_runtime = [float(r.split(' ')[0]) for r in data.runtime]\n",
"data.runtime = clean_runtime # or data['runtime'] = clean_runtime\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Fixing genres\n",
"We can split up the genres column into many columns (one for each genre) and assign a boolean value to each"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imdbID</th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>runtime</th>\n",
" <th>genres</th>\n",
" <th>Action</th>\n",
" <th>Adult</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Biography</th>\n",
" <th>Comedy</th>\n",
" <th>Crime</th>\n",
" <th>Drama</th>\n",
" <th>Family</th>\n",
" <th>Fantasy</th>\n",
" <th>Film-Noir</th>\n",
" <th>History</th>\n",
" <th>Horror</th>\n",
" <th>Music</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>News</th>\n",
" <th>Reality-TV</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Sport</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>tt0111161</td>\n",
" <td>The Shawshank Redemption (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>142</td>\n",
" <td>Crime|Drama</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tt0110912</td>\n",
" <td>Pulp Fiction (1994)</td>\n",
" <td>1994</td>\n",
" <td>9.0</td>\n",
" <td>490065</td>\n",
" <td>154</td>\n",
" <td>Crime|Thriller</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tt0137523</td>\n",
" <td>Fight Club (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.8</td>\n",
" <td>458173</td>\n",
" <td>139</td>\n",
" <td>Drama|Mystery|Thriller</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>tt0133093</td>\n",
" <td>The Matrix (1999)</td>\n",
" <td>1999</td>\n",
" <td>8.7</td>\n",
" <td>448114</td>\n",
" <td>136</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>tt1375666</td>\n",
" <td>Inception (2010)</td>\n",
" <td>2010</td>\n",
" <td>8.9</td>\n",
" <td>385149</td>\n",
" <td>148</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imdbID title year rating votes runtime genres Action Adult Adventure Animation Biography Comedy Crime Drama Family Fantasy Film-Noir History Horror Music Musical Mystery News Reality-TV Romance Sci-Fi Sport Thriller War Western\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 Crime|Drama False False False False False False True True False False False False False False False False False False False False False False False False\n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 Crime|Thriller False False False False False False True False False False False False False False False False False False False False False True False False\n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 Drama|Mystery|Thriller False False False False False False False True False False False False False False False True False False False False False True False False\n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 Action|Adventure|Sci-Fi True False True False False False False False False False False False False False False False False False False True False False False False\n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 Action|Adventure|Sci-Fi|Thriller True False True False False False False False False False False False False False False False False False False True False True False False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#determine the unique genres. Use 'set' for thi\n",
"genres = set()\n",
"for m in data.genres:\n",
" genres.update(m for m in m.split('|'))\n",
"genres = sorted(genres)\n",
"\n",
"#make a column for each genre\n",
"#assign a boolean value to these columns for each movie in the db\n",
"#Action column gets filled for all movies, followed by Adult and so on\n",
"for genre in genres:\n",
" data[genre] = [genre in m.split('|') for m in data.genres]\n",
" \n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### c. Removing year from the title"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>imdbID</th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>runtime</th>\n",
" <th>genres</th>\n",
" <th>Action</th>\n",
" <th>Adult</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Biography</th>\n",
" <th>Comedy</th>\n",
" <th>Crime</th>\n",
" <th>Drama</th>\n",
" <th>Family</th>\n",
" <th>Fantasy</th>\n",
" <th>Film-Noir</th>\n",
" <th>History</th>\n",
" <th>Horror</th>\n",
" <th>Music</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>News</th>\n",
" <th>Reality-TV</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Sport</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>tt0111161</td>\n",
" <td>The Shawshank Redemption</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>142</td>\n",
" <td>Crime|Drama</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tt0110912</td>\n",
" <td>Pulp Fiction</td>\n",
" <td>1994</td>\n",
" <td>9.0</td>\n",
" <td>490065</td>\n",
" <td>154</td>\n",
" <td>Crime|Thriller</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tt0137523</td>\n",
" <td>Fight Club</td>\n",
" <td>1999</td>\n",
" <td>8.8</td>\n",
" <td>458173</td>\n",
" <td>139</td>\n",
" <td>Drama|Mystery|Thriller</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>tt0133093</td>\n",
" <td>The Matrix</td>\n",
" <td>1999</td>\n",
" <td>8.7</td>\n",
" <td>448114</td>\n",
" <td>136</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>tt1375666</td>\n",
" <td>Inception</td>\n",
" <td>2010</td>\n",
" <td>8.9</td>\n",
" <td>385149</td>\n",
" <td>148</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" imdbID title year rating votes runtime genres Action Adult Adventure Animation Biography Comedy Crime Drama Family Fantasy Film-Noir History Horror Music Musical Mystery News Reality-TV Romance Sci-Fi Sport Thriller War Western\n",
"0 tt0111161 The Shawshank Redemption 1994 9.2 619479 142 Crime|Drama False False False False False False True True False False False False False False False False False False False False False False False False\n",
"1 tt0110912 Pulp Fiction 1994 9.0 490065 154 Crime|Thriller False False False False False False True False False False False False False False False False False False False False False True False False\n",
"2 tt0137523 Fight Club 1999 8.8 458173 139 Drama|Mystery|Thriller False False False False False False False True False False False False False False False True False False False False False True False False\n",
"3 tt0133093 The Matrix 1999 8.7 448114 136 Action|Adventure|Sci-Fi True False True False False False False False False False False False False False False False False False False True False False False False\n",
"4 tt1375666 Inception 2010 8.9 385149 148 Action|Adventure|Sci-Fi|Thriller True False True False False False False False False False False False False False False False False False False True False True False False"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.title = [t[0:-7] for t in data.title]\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the data in the right form, we can move on to exploring some properties of this data.\n",
"\n",
"## 3. Explore global properties\n",
"\n",
"We need to look at what questions we want to answer. Some possibilities are:<br/>\n",
"-> What are the most popular genres?<br/>\n",
"-> Is there any correlation between rating and number of votes?<br/>\n",
"-> How does the rating vary by year, genre?<br/>\n",
"-> What are the best movies year-wise?\n",
"\n",
"We 'll now go about finding answers to these questions.\n",
"\n",
"### Call describe on relevant columns\n",
"We can get a rough overview of the data by calling the describe method."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>runtime</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>9999.000000</td>\n",
" <td>9999.000000</td>\n",
" <td>9999.000000</td>\n",
" <td>9999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1993.471447</td>\n",
" <td>6.385989</td>\n",
" <td>16605.462946</td>\n",
" <td>103.580358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>14.830049</td>\n",
" <td>1.189965</td>\n",
" <td>34564.883945</td>\n",
" <td>26.629310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1950.000000</td>\n",
" <td>1.500000</td>\n",
" <td>1356.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1986.000000</td>\n",
" <td>5.700000</td>\n",
" <td>2334.500000</td>\n",
" <td>93.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1998.000000</td>\n",
" <td>6.600000</td>\n",
" <td>4981.000000</td>\n",
" <td>102.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2005.000000</td>\n",
" <td>7.200000</td>\n",
" <td>15278.500000</td>\n",
" <td>115.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2011.000000</td>\n",
" <td>9.200000</td>\n",
" <td>619479.000000</td>\n",
" <td>450.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year rating votes runtime\n",
"count 9999.000000 9999.000000 9999.000000 9999.000000\n",
"mean 1993.471447 6.385989 16605.462946 103.580358\n",
"std 14.830049 1.189965 34564.883945 26.629310\n",
"min 1950.000000 1.500000 1356.000000 0.000000\n",
"25% 1986.000000 5.700000 2334.500000 93.000000\n",
"50% 1998.000000 6.600000 4981.000000 102.000000\n",
"75% 2005.000000 7.200000 15278.500000 115.000000\n",
"max 2011.000000 9.200000 619479.000000 450.000000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['year', 'rating', 'votes', 'runtime']].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see some basic properties of the attributes. \n",
"One may note that some properties don't make sense. For e.g. the min runtime is 0.0 which may shows the quality of data mined. So we may want to see the number of records having that behavior, and flag them so that those values aren't taken for calculating mean, std dev, etc."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"282\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/priyanka/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"#take the subset of the df having runtime=0 and find the number of records in that subset\n",
"print len(data[data.runtime==0])\n",
"\n",
"#Flagging the bad values\n",
"data.runtime[data.runtime==0] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we get"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 9717.000000\n",
"mean 106.586395\n",
"std 20.230330\n",
"min 45.000000\n",
"25% 93.000000\n",
"50% 103.000000\n",
"75% 115.000000\n",
"max 450.000000\n",
"Name: runtime, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['runtime'].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make some basic plots\n",
"We can make use of matplotlib for this purpose."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2011\n",
"1950\n"
]
}
],
"source": [
"#You can find the max and min ranges for relevant axes. For example\n",
"print max(data.year)\n",
"print min(data.year)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a9fb590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plotting a histogram of the number of movies per year\n",
"#arange returns evenly spaced values within a given interval\n",
"plt.hist(data.year, bins=np.arange(1950,2015),color='#cccccc') \n",
"plt.xlabel('Release Year')\n",
"remove_border()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Useful links<br/>\n",
"http://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fcebc0f58d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data.rating, bins=20, color='#cccccc')\n",
"plt.xlabel('Rating')\n",
"remove_border()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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lxqmqer8x3FR0GLgUeAuwD7i8p1qeBy6Y0fffgP/ctT8C3LXKNfwScAWwf6EaGB6ct68b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\nTg/tZkft+sQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a6a3f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data.runtime.dropna(), bins=50,color='#cccccc')\n",
"plt.xlabel('Runtime')\n",
"remove_border()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"450.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We know the x axis goes till 450 because\n",
"max(data.runtime)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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cO4oWF78b0X0W6L9D2sSBQceER48esXcU0P9OtXNTr9clvgBIgLM2LY7owQbs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\nKU8C+AUAfygM3mSPU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"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a455710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#we can use scatter plots to represent correlations\n",
"plt.scatter(data.year, data.rating, lw=0, alpha=0.08, color='k')\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"IMDB Rating\")\n",
"remove_border()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems to suggest that earlier movies had better ratings compared to later ones.<br/>\n",
"Is this a selection bias?<br/>\n",
"One possible explanation could be that IMDB came into existence only in 2005 and people are more likely to have seen and rated good movies from the 50's to 80's."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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T6TSWl5dlcMlms7h79y4A4OXLl9jb25N9DdlsFolEQl7Al5aWYFmWHNo0HA5l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\nhoiIVExJQ0REKvb/8dfIOTovvAAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a5f3f10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(data.votes, data.rating, alpha=0.08, color='k')\n",
"plt.xlabel('Votes')\n",
"plt.ylabel('Year')\n",
"remove_border()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clearly we need to change our x-axis in this case.<br/>\n",
"We can choose a log scale for the purpose."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NBuFwGLOzs0gkEmg2m9DpdBgOh6wEIruJbDbLAod6vQ6lUsmlLJ1Oh7fffhv9\nfh+Hh4col8vQ6/UQRZHtJ9RqNWdyxBl1Oh1oNJoxCa6UozhrLDgrAzipL0nODr7EqygZzQH4EwAz\nAKoA/k9BEP4zURT/N+nnfvSjH/HPN2/exM2bN5+4bZLH0QyuVquhVCrx7Bk4eiAikQjC4TAUCgVU\nKhXMZjPW1tbQ6XQgiiK/MMCXRJvVaoXFYoHD4eDGmtFoxJ2ls7Oz6Ha7uHr1KqLRKKLRKA+6JEuk\n0gwRue12G41GA61WC61Wi5VLfr8fVqsVv/zlL8fOj4g7g8EAs9nMstLBYABBEBAKhWAymeDz+bjU\nRYO7TqeDWq1Gv9/nTEev18NsNrNKh8pHtE26HkajEZOTk1hYWGDyloJuvV6H3+9na4Af/OAH8Pl8\n6HQ6TNp7PB6WZTocDj6fcrmMubk5ti4wmUyYnp5GtVpFu93G1tYWBEHgoASAA4LP54PJZMLe3h72\n9vbQbDZZf97pdOD1ejEcDpkDoNIgDeI009Zqtbhy5QoCgQAsFguWl5eZKJ6ZmcGvfvUrHB4ecolv\nZmYGV65cGTNxm5yc5AAxHA5hs9lQr9cxPT2NQCCAUCgEl8uFhYUF2Gw2iKKIZrOJmZkZlMtlvkdO\np5NLfzTDpWtN3kIk7c3lcnwPSJEEgLkhj8eDSqWCarWKTqeDfr/P5VTgaOBst9sAjrgD6t4lkcVx\nIQSVTHu9HoxGI0wmE0KhEBwOB1qtFubm5jg7rdfrTMST0ZzT6cTMzAyMRiMHaKvVygKBVqvFZVed\n7mj1M5fLNUbevyh8ndc8/qrxKkpGbwP4RBTFIgAIgvB/A/gtAKcGhCdBp9NBEISx2UG1WsXW1hbu\n3LkDhUKBXq+HarWKQCAAt9uNR48eIZPJQKFQYHZ2FrVaDfF4nLXT/X6fDb0ajQaMRiM8Hg8ODw9Z\nZ04zvlu3bkGtVkOv1+Mf/uEfsLe3h0KhwLN0rVYLk8mEWq0Gj8fDs7ZkMol+v49qtYrDw0N+yVwu\nFxKJBIxGI0tIHQ4HqtUql6coeyH9vlqtxq1bt2AymTj4KJVKnmVR6YS6n9vtNtediVNJJpOYnJzE\n3t4eisUiy2kVCgXcbjfX1vf397m0QJlDu93G9evXMTMzw6RdKpXC1tYWisUihsMhFhYWYDab0Wg0\nsLOzw7M9KmMEg0F4PB6kUilsbm4imUzyTH55eRnz8/OIRqPc2To9PY1Op4NSqYRGowFBEDhDI/28\nwWBgwpvywCsbAAAgAElEQVTq1aIoolarsSoqn8+zdTOV78jvRhAERCIRxGIxlr1SxkJyyUwmA61W\nC6PRCJVKxSW1cDiMg4MDLpPYbDYu5wyHQ2QymbFuYKPRyM9vMBhEu91GPB5HPp9HNpuFTqfD0tIS\nfuu3fgtGo5EnHZVKhQN4LpeDy+WCxWJBMplELBZDqVTizE2tVnMmTfetXC7js88+41KQWq2G2WzG\n9evX4fP5EIlExngA4koKhQJLkAVB4CBEz7darYbBYECn08H8/Dw8Hg/u3buHXC7Hkw7qODaZTDyT\np+OgpS7P01X8tBmAHAhOxqsICNsA/q0gCHoAHQDfA3D7eTZI6eXS0hKUSiVarRbu3bsHtVqN0WiE\naDSKt956i2dxVObR6/VcGsjn8+j3+zxDoQGVHBVpFk4Dilqthkql4jTW5/NBrVZjZ2eHjbd6vR6G\nwyHUajUsFgvXgYk4VKvVTMjSLJAyB6fTyTPK5eVlpNNpLncUi0U4HA6sra1hY2ODB1bKCgDwDKtc\nLkOr1UKlUvE5UOMYWRRIm4muXr2Kfr+PZrOJxcVFqFQqOJ1OfP/732f9fa/Xg9/v5yajS5cuwWQy\nMaFHjUPkwWQ2m3nW3263WcNOwcpiseDChQtoNptwuVyw2+0wGAwsozQajVhdXcXVq1fR6/V4dh+N\nRjEajdi+go55OBxicnKSs6hSqYR4PI5CoYB8Po+lpSUMBgMYjUY0m02YTCb4/X40Gg1YrVY2T0ul\nUhwsiGTu9/uo1WqoVCro9/ucrVy6dAnFYhETExM4PDxEIpFAJpPhsuD9+/fx/vvvw+1244MPPkC/\n38f9+/exsrKCqakp9Pt9LqfR/TOZTLh16xaKxSLfu3q9Do/Hg7W1NXz22Wfo9XpIp9N8L4bDIQeD\n0WiECxcuIBqNwmAwcGZDhDAA9sOi9Qc8Hg+mp6ehVqvhcDiQyWTQ6XQQDAZZhUXCARJxXLhwAd1u\nF2+//TYuXbqER48eIR6PY3JyElarFYFAANevX4fNZsONGzeQy+VweHiIVCrF0tKJiQksLi5iYmIC\nqVSKszSSuz5NIxlJSeUM4NnxKjiEDUEQ/lcA/4wj2ennAP7n591uJpNhxUosFkMqleKsoVqt4uOP\nP+YsoVarQaFQsGaeaqyj0YgNwrLZLDeFUV04kUjA6/WiWCyi2+1iNBpx40wsFsNoNOL66nA45Hpr\nrVbjFzqRSLBCRRRF/hzxDr1eD7u7u2g2m0z0fvjhh8jlcrhz5w4ODg7QaDTgdDoRiURYDy71nAG+\nNMgjEp3SeErPKTNIJpP84tVqNdy9e5ezhXK5zMQhSSW1Wi0KhQKTkdScRDM+u92OX/7yl9jd3UWp\nVOK6e7fb5ZoxNUsRYQ8cEa5arRbFYhG5XA6tVosXX6GaOnWDdzodxONxxGIxtFotKBQKtuxYX1+H\nSqXCwsICgsEgfD4fJicn4XK5uJwBHNWWR6MRBySVSoVut4v9/X3s7u4ik8mg3W5zOUuv17M1Qj6f\nRzwe50xlcnISbrcbjUYDarUag8EAWq0W/X4f0WgUg8EAh4eH0Ov1mJubg0ajwc9+9jPcu3cP9+7d\n41IVqcVcLhf6/T5mZmZweHiIaDTKjV50nDabjYOatER6eHiIg4MD5rACgQAEQeDBt9vtcu2fslNq\nRmu329Dr9fj4449ZYUVNadTbQ2R0Pp9HOp3mMtzq6iqAo54b4s3K5TIEQcDFixfHvJrO6qr3er3Y\n39/nkhkt0HMeny2ZSH52vJJOZVEU/70oiiuiKF4SRfG/EEWx/+RvPRlUKmi32/B4PFAqlfzAtVot\n7mKV1vWpYUav10OhUMBoNGI0GnFD1Wg04mBBpQDKDnq9HhO0JPmkWv1wOOTtUW1ZpVKNEXPU4Ws0\nGmGxWDgbIQ+Y0WjEg1w2m+V9UAmMCGDqBKWaOaX97XYbKpWKFTSCIECv13PJgNRFdF5Wq5VXlFIq\nlWg0GixlTKVSSCaTTJZS6YDOFTjS/z948IDLZaPRCI1Gg8tvlE2RJHQ0GrE6SRAEGI1GGI1G6PV6\nGI1GPi6tVovRaIRcLgdRFFmlYzKZIAgCCoUCd0R3u12uZRMPRDbPKpUKDocDWq0WjUYDJpMJOp0O\njUaDCW8inOv1OqtzqCxDjXWNRoOPjbKEfD4Pr9fL93tiYoKDA5GmzWaTB9NYLAatVovhcIhkMolK\npYJms8lBlCyz9Xo9k+10vcnnijLNXq/HpSwqD+p0OhYbENkvCAKGwyFbRReLReYMpJMECtilUgmR\nSAT5fJ5LbMCXGa7T6eSyJS02Q++h1+uFWq3mjBQAy2pP66p/1sVqTiKSz+oKl3E63qhOZRpM0uk0\nNBoNAoEAEokE5ubm8Nlnn6FUKiGfz7O/DAB84xvfYN02EapkMEf2BAqFAhaLBU6nk9PjVquFXC6H\nTCbDJR+yX/B4PBiNRvD7/TAajdzeT/V4GnyXl5dRq9WYDN/Z2YHD4cCnn36KdrsNrVbLQU2r1cLp\ndLJ3DS0WHgqFOEMgbyBBELhL02Qy4eDgAJ988gmUSiW8Xi9Go9FYSWU0GsHlcsFms6Hf73M9l/op\ngKNSBHX1hkIhJhipFKNSqXhwVCqVMJvNEAQBo9EIXq8XdrsdqVQKKpUKlUoFBoOBOZPr168zZ0Kz\n+xs3bsBisbA75nA4RKVSwdra2lgTFw24w+GQvXCAL5u0LBYLvF4vJiYmIIoiB/rhcAi73c5BcGVl\nBT6fD9FoFHa7nQOeyWQaa0hbXl7Gzs4O9yBQg5nb7eayUjAYhMPhYHuR0WjEEw6v18uzcOpGHw6H\nHEg8Hg+XTfr9PiwWCy5duoRKpYJUKsVd4QC4YUutVuMHP/gB7t+/j9FohHq9zoo6u92Ob3/723j4\n8CF6vR5nLNRfQWonpVLJ8lPKOOg6URbg8/kwMTEBn8/HRnTAkfzXbDaPWXQDR4GKTAcrlcpY5zSt\nc0AZhLQ/R7pA1Iu2b5dxNt6IgECa842NDdy6dQvr6+ssMyXpYKlUwnA45EGBiOJ6vY7PP/+cB7da\nrYbRaATgaKYjlaaqVCpEo1GePdLsiiSAw+GQJXgul4v9fHQ6HROglCbX63WUy2Ue7EiVodfr0Wg0\n0Gw2eUZIhnc02zeZTDx4kO9Op9PhWR7JMbVaLTweD896qazkdDpRqVTw4MEDrk8rlUpYLBbWehPp\nTYOE1WqFSqXCwcEB0uk0QqEQms0mDg8P2WaCpJLlchkHBwdMKmazWVQqFb6ORAwbjUZ0Oh0mZofD\nIYLBIBYXF6HRaGC321njPxwOkUql0O12sbS0xI1epB6anp5GNBplywoyaZuamkI2m8Unn3yCn//8\n56jX65iamsL09DSMRiPK5TJUKhWX/ARBYAUZ1eypAzuTySAej0OtViOTyaDZbMLhcGBubg7D4RCf\nfvopK390Oh3Lhskry2q1IhKJYDQa8eSEVFBEDPf7fRQKBZZlUmaiUqkwMTHB92lycpI7e4PBIBQK\nBa5cuYJCocAcjl6vx+rqKpaWllAul/Ho0SPObBKJBMuSR6MR8wI0QTAYDGyMRxkwSUatViuTzKlU\nijO169ev48aNG2zRXS6XkcvlsLm5yf0+Ho+HB3uLxQK32z1mQPcsslBZSvri8EYEBJJHlstlbG9v\nc7ot9dGhGSvN3slL3WAwoFAooFqtshkWzfzcbjfPIOn3lIrSrIpmUtTgRJp1GpitVis0Gg2Wl5fR\n6XTQaDSQzWbZX4fISQA8aNMMvdvtMr9Bg/KNGzcwMTHB9epf/epXaDQarLuvVqu8oAhpzX0+H4LB\nIMrlMprNJjuCDgYDzlr6/T4EQeBOUFLpUKlpOBxyTweZopFx2WAwwMTEBHK5HPc0pNNpeL1eOJ1O\n7O3tsUz0zp07UCqVsFqtrPChMlez2YQgCGyBbLVaYTabuUFOoVDg8PAQRqMRwWCQBxO9Xg+9Xo8H\nDx6wb1OlUoHdbsfk5CSSySQ2NjbQarWY9/F6vbxOQKPR4NXFer0elpeXYbfbOUjcunUL3W6XtfCk\nunI6nXC5XFAoFFyiIUWR3W5HtVrlz33++ecQBIG5CSL27XY724DMzs4ik8mwjPPw8BAajYZJXhpA\nSUUUjUa59JJIJBAKhfDDH/4QH374Ifb39xGPxzE9PQ2tVovvfOc7MBgMmJ2dRaFQQCqV4h4bq9WK\n4XAIv9/P7wT1xMzNzXHZiVRzJPE1m83Q6XTw+/2o1+vY2trC0tISZwpkS06mfxR0yK4aOJn0fRZS\nWCaSXwzeiIAAAL/+9a/x4x//GJFIZMx0i5YwpPIFAK5tqlQqrqGTbp56B2gbpVKJfweASxNEOA8G\nA94ufa5arWI0GkGhUDCPkEgkuLTU6/WYoKSXRalUcomoVCox2UqNT0SAGwwGtoAgUpdq/TTzpKYg\nmn0SwUZulHRspIIi9RNJGamOTc15wFGwopmrTqeDXq+Hx+Phmnw8Hkc4HMZoNIJGo0GtVmOFSqfT\nwd7eHmw2G6rVKpPYo9GIyVeyheh2uygWi1wrT6fTY82G1EextbWFVqsFpVKJTqfDJa3RaASr1cqN\ncF6vF4IgMAlKvRO//vWv8dlnnyGRSHCJi85zYWEBKpUK6XQah4eH2N/f5/KNRqNh8QER5GSdQbN7\nsl+m86TP0TMmiiKKxSIT4iqVCpOTk7wNk8mEYrGIZrPJM22pgEClUmF1dRWTk5O4du0aWq0W9vf3\nsb29Da/XC4fDgVwuh/39fWSzWSwsLMBoNMLlcqHb7WJvbw/ZbJZ5LuK96vU6K32kMu52u809MPF4\nHPF4nEtJOp0Ok5OTj72PVGYCwP5U6XQa7XYbs7OzWFxcPPN9fpZBXQ4Ez483IiCQLxEpSI5jMBhA\nFEUe1KkUA3w54NJgTiQykV1SJRCpjaT1arKIAMCSVLJpINUREao0ONDg3W63eUCgshHVTCnI0HZH\noxGTynTc3W4XJpOJZ28KhYIHWDpeQRB4cKeZP5ngUVYjCAJ3GzcaDe6/IL28SqXi8x4Oh9Bqtaw4\nor+l02lYLBa2HqcBhawXiOylYEz7XFlZgSiKSKVSTH7ToE6+RtTZKwgC9wvQPkgCSj0TVJO32+0w\nm838M/ETdC1plivV4judTkxPTzNvRIGKgoBUNED1cWpkpOYvtVrNzxldH8q8crkcP2etVouDMQWp\nYrEIr9cLr9fLfBYJBlKpFEajET8/NMOPRCJc+tFoNGxDTRkFWVO/++677NdEqinivahjmYQE9NxS\n3wJZSuv1ei5lEgenUCgwGAzgcDiwtLTE/kdkEU+lQACcLRcKhRNXF5Tx6vFGBAQAPFBoNBq2jCAy\nmMgz6isAwPYLNEBQHZ0UKXa7HWq1mhVFxCeQR3upVOIZJ2UJ9DlS89CAQ3a91GVKDU6Hh4cAwDN1\nh8PBC6DTYCw1OCMvGlEU4fV6IYoi3G43yw/7/T42NjaQyWS4B4A0/ZQh0UCn0Wh4dk+E9Te/+U3c\nuXOH6/3k50O9BHSepBu/du0aLl68CJ/Phx//+Mc8C45EIrwGQKVSYUUWLRbv9Xp58fg/+qM/wv37\n97G5uQkA3MdB7qI0+2y325xRqNVqxGIxWCwWDgDUcU6KrYmJCbYzN5vNWFpagtPp5L4EURRRqVSg\nUCjYfXViYgIulwvNZpPtJIg8JaXU3NwcBxDC5OQkD+wqlQrFYhELCwtwu91j6yLkcjlWce3u7rJM\nmAQI09PTWF5exsrKCnfDk9x2f3+fsy3KUMhvip5TABzEqOxD6yNPTU1hcXERfr8fOp0O6XSarS2I\n7PZ4PJicnMR3vvMdRCIRnpwEg0GIosieU+12m8njK1eu4IMPPhhbIY/+r9FocPXqVZhMJg5c57GC\nl/Hy8UYEBJ/Ph9XVVfz85z/nARo4etGIZJQOiMCRMqLZbHJJhAhnWhOABhpSa1D2AIC7QmmWSKB9\nkIkbmZJR8KHSFB0D+SpRIxvNvqgEQvskySB56dOaypOTk+zlT+WcbDbLhHe320W1WuVzoyY5GkCb\nzSbS6TSXx3Z3dzE9Pc1Gc8CXDW69Xg+ZTIZ/n8lkYDabMT8/z0EmkUhwEADA/AdwlOWQ7Qb1DAQC\nAXz00UdMjpIUmGSYALiPgfgWsqegYEXqKPKmIk+hzc1NVnZJswuPx4NQKIRkMskafEEQMD8/j0aj\nwctf5vN5mEwm9Ho9tokwm808MydhgtSJlCSd5PNkMpm41p9MJrlJje4FGcTpdDrMzs5y13I0GkWx\nWMTu7i52dnY4MxVFkZ8rsvag60SBkFCtVtmmW7q+wszMDL75zW/ir//6r7lcScugkhT37t27vCwo\n2Wcnk0lsbm5yMEylUrwoFFl1S+23t7e32ZdramqKOSzpGtIyXj+8EQGh0+lgcXERN2/eZDO3breL\n4XDIgy3ZTJAOn9rvqfuYyi0AmEAmwzMqAVGtn1abIhAfQbMy6oAmGwWXy4UrV64gmUwin8/zizcx\nMYGdnR0+DtL404BNSyDSanDkPUNkK60qpVKpUCqV2Nef9PC0DCKVNJRKJXw+H4bDIXw+H3MOVMLJ\n5XLcLet0Ovnn1dVVFAoF3Lp1i2eoNBOORqMwGo2w2+0IhUKsdqKeECqJUWMX2WuTe+lnn32G5eVl\nzMzMoFgsspqGSmNkOEhqLK1WC0EQEAgEcPnyZVy8eBELCws82N6+fRtbW1vMRVD2RZbhGo0GMzMz\nfA2ojHXlyhWsr69zoKlUKlAqlVhaWoJOp2P/Kfo+1ffNZjPi8TjbiwwGAwQCAT7PfD4Pu90OvV6P\n+/fvs4ePXq9HIBDAD3/4Q4RCISaIAeDevXu8RCnd/+XlZS7rOBwORKNRhMNhLi1RmZMCZK/XQzAY\nxLVr1wAcZV5WqxU2mw1+vx/z8/Nwu91sD0HcxcTEBB48eIBvf/vbTKJTqcpsNuPKlSssdqBS1f37\n96HT6TA/P49EIoHd3V2Uy2UuN5XLZfzO7/wOcw6nBYMXuWiNvADOs+GNCAiEarXKsz4psSxdGEa6\nvOBxSAOE1FCN1EqngTKLarXKclcA2N/f57LV4eEhRqMRWxoTP0FEKtWTafZIskf6P/2n0WjYV+fO\nnTt8vBS4Op0O2w5Q0CMimxQfUqfJVqvFzXoAsLe3B4vFgtFohHg8DqVSie3tbSgUCoTDYe7MpgZA\n8lsiw8B4PM4SXTo2UhBRNkZLNZKpH/VaFAoFpNNplMtl5gisViv3cpCU1mAwIJfLIZ1OI51O4yc/\n+QlbYxQKBeRyubHVxYg7oCU/M5kMK9GIU/npT3/KXJPD4YAgCOzfn8vlmFg1GAxcyovH48wlGY1G\nXLx4EcPhELlcDsViERsbG8wxUTc1ZXPUfOd2u5n0Bo4yoo8//ph7YSiYlUolXsRJoVCgUCgwMU2K\nNyqxzczMwOVywWQy4d69e/jbv/1bJBIJaDQaLh998skn/NwYjUYEAoGxHgfgKIgkEglEo1EA4LIk\nuf3SehXVahVOp5MbGok7ki75SlndaXiRncZy1/Kz441YU5lmnvTy0eB5HMQjHMdxYvhZQQM4NYpJ\nlTGpVArZbJbLVjQ40aBJQYAGJQDcyQuASWca9KmmTl3HFAQpqLTb7TFvHFLS6HQ65jLIvoNKUxQU\npAGGAgmR2FIZKh1jKpUaI1iJJKcZNUlbSd8ubTaiwEhkJpVDgC8H8n6/zxwRAHZp7ff7vPYxGcDR\ncdK9puyEZt/SOnapVEKtVuOsRmqCSDJfIpSJRCcxADnGSq95t9uF0Wjk3hXiklqtFttokw0EBR4K\nECRVPjg4YL6FrLKpO55M5ChQ+3w+7i2h7JTOy2g0wmw248GDB7xSYDqdxv379zl4Up2fmjiJgyEb\nikQiwb+jiYbRaEQoFGJpM+2XVmkjvoaaHC0WyxjZfBJeZKex3LX8fHhjMoTJyUn2riEVSbvd5sFo\nMBjA5XLxDJcGQxoQaYAluwEa/GigOwtkFUEENQ0GNMAREUoyVKrfUwMTlYusVit3+pKSSVquor4G\naviiQZ3WIqBVsmg2RktwHhwccOqcTqcxMTGBWq2GVqvFhB8AtrKYnZ3l5jlSPhGpbTab0Wq1eIU0\ng8EAl8sFv9/PzUcUTKampniQoLUU3nnnHSSTSej1elbBkPEflW+IiKalK6lLnLxx3nrrLR5Y2+02\nN2/RAEQKJuomvn79OhPDJKv1eDwctOi+Uee21+vlQWxpaYl7I0idZbVaufRHx28wGLC2toZr164h\nk8lge3sb+/v70Ol0CAQCLDUmPyGv14vV1VWWGJOPUaFQgNPphNPpZDEC3WtaTpKIZFoNTRRFbiCb\nnJzE0tISrly5cuKzOhwO2XmVeCir1Yr333+fyzk2mw2ZTIa5kV6vB6fTiffee48H152dHTQaDSbd\nA4EAotEoFhcX2axwYmLizMxAHqhfP7wxAeEv/uIv8JOf/ITrqoR2u81SP1qNi2Z1VCIiVY9SqeR6\nKc3OT8o0joM+Jw0iJPmk4EScg7S2STNj0syT+oVquhSI6LtEkh8fmAHwgEs8hFqtxqeffgqHwwGf\nz4dqtYqDgwOUy2XE43F4vV5YrVYoFAomSalRrdlssmW11EzN4/Egm81yhlAoFBCJRNi+mpr8KHiR\nlxI1201MTGB+fh79fh+RSAT3799na2atVotms8kmbpT5UOlre3sbwNH6yI8ePeKSnlTfrtPpxkpM\n5CBLJTaSJ1erVSgUCuYlqFxH16BYLOLevXtwu90wm83Q6/VsuBcKhTAzM4N+vw+Hw4Fer8f3lNZh\njkajuH379tiaBmQUp9VqEQqFYDQasb+/j83NTe6EJgUQNbBRVkg9J3a7He+++y5arRbbatOEgSTI\nRHpTuSYQCCAYDDLBOxwOsb29zQIFkuTSegik/qL7EA6HYTAY8O6773JZkDyQfD4fd8xTsxxlX+Rr\nlEwmkUwmHyvdHC/rvKhOY7lr+fnwRgSEnZ0d/OM//iO/4MdBM0YqxxBZSzVPqWSRBlnKKsiGl0pB\nxzMGGszInI6CDG2bFoShWS/NVOkhlUpWgS9LQxQYpPuiDIOWvZyZmeGOWgoU1WqVyyS1Wo1LG2S5\nEAgE2E7hrbfegsvlwoULF9iVlWrkNLjSEokTExPweDy4desWl8CoBJRMJmGz2fDd734X6XQaDx48\nGFuxbWpqCmtra+j1ejAYDFhcXOTmJiqfUF2aXuhGowG/38/ZGi1rSY6opHyiJUZnZ2chCAImJiYQ\nCoV4jV7KtgaDASqVClt/EIFNpUa1Wo2FhQUolUq2ynA6nSwAoIAHABMTE7h06RIajQb++Z//GbFY\njMs6qVSKS2gkYCBL6pWVFZb35vN53L59m/2fqB9CqVTyIkvkOEueS7R2By2JSteYMDk5CZ1Oh+3t\nbTidTvj9fni9XvzxH/8xarUaYrEY26VnMhmo1WrOqFOpFJxOJxsmbmxsQKlUYnFxkTPSra0tAGAe\njgLm8vIyZxfSydjDhw/5ZwpI1Al/vKyzsrLyxE7jpyWK5a7lZ8cbERCAI/8c8is6jtFoNGYNLSWM\n6fckiaT6P83uKRicBpIa0naOl5foJZfO+AHwTPusctTxv1HWkkqlkMlk2B201WpxJkQBK5fLYTQa\njTUu6fV6OJ1OlEolJJNJxONxVkfRrH44HHIPRTKZZOLXbDbDZrOxPr5cLvNCMSqVChsbG3j33XeR\nz+c5E6EyyfT0NO7du8f9G9SRSx3WZAsu5TRoMXnKFkjaSjr7dDrNDrZUgqNGMJvNBovFwhxOIpFA\nu91m+ahGo0GlUmGugmTF1JBHHcG5XI6PhZRGJLmkwB4Oh7G9vY12u80zc1ociHgRjUaDeDzOZn2k\n/6fMgJ4TmoDQtUqlUuj1etxtbbVasbW1hfX1dTb9I5sVKksWi0UkEgmUy2W+DtPT02yXrVarEY/H\ncffuXfT7fTx8+BBarZY7lK9du4ZHjx7h7/7u7zizWl1dZYM/KUh2KlUO0f+lgSGTyXDfBi2CdBLO\nGrzPSxTLgeDZ8EaQyl6vFy6Xi2WOzwLqACWZIwWBpyWZT/ocBZXj/Qpnfec8x9vpdFCr1cYcPGkQ\nlK7HQI1otMoVKVeITKXZKamNqIxG5SwATOCSdz6RvVRmIf18JBJhW2gqoZHiplQqsdEbEdbSYyUS\nmTIGKuORrp8yDIfDwWU28hCq1WosfSR/H3KRpa5dWuiItkNLawJHDVY0yFDjIK0bTDYLyWSS/5ZK\npZBOpzlQEXdEfQjSBiyShVJWQsQ0ZYWkHqNGQfKZor9LVTt0f+g5pf4KqaLH7XazEWOhUAAAlgJT\ngxw9E6TM63a7yOVyiMfjnAmIosg2JuRMS9c2GAyyt9JpK5LRgkqU5dCKdHSM0uM9awCXieKXhzcm\nQ/j+97/Pi5fQzJZqw8QLPAlEzJH6QjqYU7PYaaB1YqWlJWqUIlKYpLDHeQnKWKSrUFEpQao0IlDg\noxkx8RbEZdBMkM6FVh1Tq9WsjxcEgT2DSL1is9mgVCrhdDqZ08hkMjzTJ9mltONbEARWDul0Ol7p\nSzrjJQdN4EtLZOkAQKWGiYkJJoOvXLmCe/fuceZDi+9cu3YNoiji9u3bXB6jnpFvfOMbWFlZAQC2\nz+52u/irv/orVCoVWCwWXr6R+gFKpRKWlpbwve99D2q1GmtrawiHwzwgk4qGZvtEcJPhHg3GROaT\nlTWVclQqFZf2KMsi91m/3w+Px8MWD2SwF4/HWc5rtVqxurqK+fl5XLp0CbFYDHNzczCbzdzPsba2\nxkZ9n376KVt3k5cSZXkejwe/93u/xxLg4XCIWCzGjZNmsxlWq5UDLZkZhkIheL1eXL169bGB+Dhh\nLC3rSO2wpcoyWofc4/FwmVDG64GXHhAEQbgA4P+Q/CoE4N+Kovhnz7pNKmVEIhFeSIZw1iB+HNSk\nQx45p0HatUygGSvNcmnWLC0X0bHQPo7zESQ9lS7KI90PzQppgR/py0lBjzIkOhZSWdHgSZ5GUpko\nSZAbv9YAACAASURBVFbT6TTzDyaTiWeHg8EA1WqV/YDo2Gh/NGulv5PNQrfbRaFQQL1eZxURZQ1S\nDod8gAqFAkwmExQKBR48eIBqtTrmo9/v93Hv3j0+hmKxyE62Pp8P7XYbe3t7HACpa7Zer2NjY4Nt\nGmw2G/cSdDodfPrppzg8PMS3vvUtWCwW7O3toVwuMydD/Q4qlQqBQICtO6gR0G63I5PJMKdCK/dR\nFkR9DHq9Ho8ePQJwVCKKRqNcn6dMw+fzwe/3syMvLQkbi8UAgJVcRPpT/wbZT6ytrY0Fy3Q6jYcP\nH7Ipotlsxvvvv498Po+NjQ10u100m02sr69jZmaG11eQWqWTHQuVhk4r35z0e5vNxnbYAHitafpZ\nul7yaWUgmSh+eXgVS2juAHgLAARBUABIAvgPz7PNTCbDRN55AoAU0kH+SWUnqp1SsxJlAWRXQCud\n0YAnDRS0fSon0fGSPQHp7fv9PnvkU48A2SSTJUOj0WCfJmlvABHl5JsPHJF6oiiycZvU956yGgBc\nqiFOhCSjtKKYzWaDyWTC3Nwccy30+6WlJbYS12q1yOVyY46fFGioPEKDIEkt6Tr5/X5UKhWo1Wq4\nXC7W4hNBT1JPs9nMM3jyY6L1mx88eMDWzmq1Gh6PB81mkzuh6V6Q9UetVsOjR484y7Hb7djb22OC\nmSTMtHLb6uoq9wHMzs7i7//+75lruHfvHhwOB2deFNiJZKZSD5WO6vU63G43K76+853vwGKxoNfr\n8ZKwxKV4PB5ekAf40tpjNBohkUjg6tWrmJ2dxSeffIJYLIZIJMLmf+12G5ubm/jDP/xD/Mmf/An+\n/M//HHa7ndekoIa/ubk5rK6uIpFI4PLlyzCbzcy70JrZNOPP5/PMCSQSCX6GpL/3eDywWCxsuwIc\nTaAODg4QCoXGPn/aQC8TxS8Hr7pk9D0AYVEU48+7oQcPHqBUKp1JAJ8F6feetA1pFywAroEDYCdT\nAg20UiL7pO2Tm2Y2m+WaOZWMCORiSfV2kqeetD0qs3S73bH1mmkQp0yDiFXiIahBi4Ii1bHJnpos\nun0+35iFgVKpRDgcZstqctIkbyHaB9XxSd5Jzqzlcpm7dqlmLG2GIoK/2WwiHo/z7FupVKJYLEKh\nUKBYLMJgMODw8JAVOLR+wP7+PiqVCtfFp6enOcsoFAoYjUbY39+HXq/H1NQUG+jRoj8kVaZmtjt3\n7sDlciEQCDCJTgaIZJpH95QmKvl8fswtl35PCzlJZZu0aEwul0O9XkcymUQ2m4XX68XDhw9Z/STt\nACcuDQDC4TA+//xz9pcij6tut4t4PI6NjQ3s7OywlxU5pdJ19nq9bCnucrmYDC+Xy6jValhYWBhb\n2CYWi+Hg4ADA0TrIPp+PAxldQ1qJTvq980AOBF89XjWp/C8A/O/Pu5FOp8PS0mcllV8UTtv/0/Qz\nAOBZqHSdBSkoIyFSV1omkkLaz0BeS9ISD5nu0WAiDQJE6tKx0Lak1hq0/i7xEQC4VEJKISqNSZsA\n6W8UnOjfNECSAy3wZTDSaDRwOp08wJJpIHUHU+Dodrvs30RZT6lU4vWI6T5QqUK6zjDxMNQpTVYP\nZrOZrxftk5q5yJiQFp4BjuxTSF5Jg6terx9bXInWSQa+XO/ZYDDA4/GwEdzc3BwCgQA0Gg3zSlSC\npJ4LCqrUES2KIuv+CTQRoWfFZDIhGo1id3cXfr+fO6mpgcxoNAIAu/RSgyIFYyofkQULZSr1ep27\n4ik40vfK5fKYOyxljXNzc5xpyGWg1wOvLEMQBEED4D8B8F+f9Pcf/ehH/PPNmzdx8+bNM7c3NTWF\nfD7PEkPgy5eBXvZTjmOMIH0W5Q/N1slmmhZqEUWRiVxS3ZDmXOqZT53NSqWSrSWokY6W9KQZJ0kM\npQGQZuNk5DcYDHgBGxqwiPS22WzsfeP1epFIJHhQJbULALhcLhiNRu4BiEQi3B+hVqsRCoV4XWki\nU0kRRL0b7XYbPp+P9f8AuMxTLBa5E5ZI3vfffx+iKGJ/f59/r1AosLi4iPfeew+DwQDr6+u4d+8e\n183JsJCyCwBjdhlqtZo7s6Wz8XfeeQepVAoWi4Ubu6jMRmsikKVDt9tFMBjE8vIywuEwXC4XZznf\n/OY3UalUEAgEUCgUmMCtVCpIJBI4PDyEy+VikcPq6iov2kTd2H6/H9euXcNbb70Fj8cztr7wO++8\ng7m5OTx69AiZTAY+nw97e3ucUZC9NfVINBoNJonpmtB18fl8XKenDOOdd95BNpvFzZs3OZhTh/fF\nixfR7XYxMzMD8Qu7cArOtPazVEIqXQt5cnKSuQIpfD4fu69K+xbkYPB64FWWjD4EcEcUxfxJf5QG\nhCdhZmYG1WqVu5TPUzaSBoBnlYFS4CHDNCnI7IwgtbOm71JGAICdVEn3TdkA8OUaz9LmNyq50GAt\n7bimBrR8Ps9EMM0u9Xo9a/opeNHLTgqUSqXCnbMkQ6VZfDab5WY+IndpKUtq1iOn1L29PZ65Uyct\nEcm0UI1Op8P6+jq71VL/htVqZfO75eVlVg4Rd0L8BJn10TWg5j+Hw8EW141GgwPp+vo64vE4l9Is\nFgsvvSlVGQFHs9dgMMh+Pfl8nrt319bW8Itf/IJ9f2w2G27dusVlK+py7vf7LFeljIQCeLlcxu7u\nLpRKJdtN0wIygiAgHA5jb2+PZ+vpdJqDpsfj4QV3ACCVSmEwGODg4ICDztzcHD9D7XYbDx8+xP7+\nPtLpNBwOB9566y0kEgmEw2Ho9XpcuHCB12EGjhrMqHRFpHYymUQul+PjJdKX7LBp7QtyewUw9jeC\nHAheL7zKgPCfAvjxi9jQzs4O+8w8K4fwLJDKL0nyeVJQoXo2aeZ9Ph87R6ZSqbGV0KgebzQamRRt\nNptsmUwpO3kykZrHZDKNEbfHDdik5SKdTsezb6/Xy9bgJHE0mUxsvwwckfa0QhYAlqBSSWNzcxMW\ni4UXtVlaWsLy8jI3zfn9fuzs7GBvb48HDVKwGI1Gbhaj8gcALoFQUGg2m9ja2kIwGMTly5cRj8e5\nbEXXkpoIadZKq3iRxTfZP4uiiEgkwiZ45A7rcDigVqvZGZeI4YsXL+Ltt99GPp/nhWYEQYDFYkGl\nUsHMzAwmJiag0WiwsbHBxDuZylEGSHYZCwsL0Ol0yOVymJ+fR6/X44Y9yuhIrkklMrfbzesUkz2H\ndPEhyv6oK10QBMzMzEClUuHatWtMyJdKJXz00Ufwer2stqIO5ampKe7aX1lZ4Z6SZDIJjUbD1tfA\nUU8GZaV2ux1Xr159jPQlIpikwNK/0bkd/52MV4tXEhAEQTDiiFD+Vy9qmzSDeZk4rmg6K8OgQVmr\n1fJMlRZ1p1q8QqHgJSdpICfSmGbrtCA62S4fJ4qpfEQliuMd0jR7pgGKSlFU8tnc3EQoFIJKpeL1\ndDOZDJrNJg9stL14PM7aeRq8qcP417/+Nc/GlUolq6MqlQoKhQJnAESSS8+DsiJS52xvb3NZLpFI\nYDQasYU1dVabTCYIgsCDFKmB8vk8/67T6XBQpayG9kGd6nR+ZGntcDh40fu9vT0cHBwgnU7j4OCA\nVWUXLlzA7/7u7yIej2N3d5ctJ0h6StYkdC+pXEMTBQrs5HhaLBaZAA+Hw0gkEojFYvy8UDZIVt60\nXjJ1S2cyGRQKhf+fvTeLjSu/08W+Imvf94VVRVaxuFOk1JLdrd4M9fSdsWczYCQZDJDBDRJgXi6y\nIHkKECTwRd4SIECCIEAmQWYwycVcJIO5DwbGY4/d43G3u2VL6pYocSeLZG2sfV9YVSRPHujvp0O2\n1K3e2zL/QKMlsVh16rD4X75VpKJarRZzc3Po9XrIZrPI5XLSzke/Sa1WE9+FTqdDuVzGysqKdDuP\nj4+LMS2VSmFtbQ2dTkfkvcCTJ/anTfaXEdVfz/GVMLCKonQURfEqivK5zOCU933dh6IosFgsiEQi\nMvnQxETSlxMfJ3PgsWJIrVginEHCV+3yVYfrXbwvnGw5gTMxlItbo9FApVIReIlf4/OoFxea5+g2\nZuzHYDCQ5jQqiLiTJxlODFm9QHHx4GLAeGXCWi6XS8hNqqLU7+figqwoZ/WV6qpK+iD4d3WYIRdn\nvgfuvIfDocRNdLtdFAoFkQTX63Xs7e1hZ2cHqVQKfr8fo6Oj4hBme546AoVSU3I7AIS74Xuo1Wqo\n1+vyWVDLgBmbQaLbYDBIRtDx8bHAjezQ7vf7ODw8RKVSgc1mQzgcls/R5OSkxFZwRCIRub/qak7g\njFtiexuv95NCrZfO46/v+Kplp5/bWFxcxOrq6jP5ED4pecxdKxu7+EvAOASmmVLGedEd7Xa7YbVa\nodVqsbS0JC1njKvO5XJIpVLnIg2I4at/4bxeL8bHx1EsFqXtiicHPo7BcIyYULumufvjAsMAPE42\n3LGT6CSUxbwhTviMCefj1cUq5DnIk3ARnJubg9/vh8PhEFcuM6ZomKM2nxAQg+iKxSLGxsbgcDgE\nxjIYDDLpms1mjI+Pw+fziTyT0ArVPVRN0ZHLFj0uEDqdTmA0mvVsNht8Ph88Hg9cLpckqPJ0QMUX\nfQ7dbhfRaBS1Wg2Hh4fS5024igtMIpEQp3I8HpeTwvT0tKSqNhoNgRkTiYSEDvIUSGUV7wE7DFwu\nF+r1uoQAUr1D+BE4S0B1OByYnp6WePTZ2Vk5YS8vLyOZTIqvIxwOIx6PS4hduVzG4uKiXPenlZFe\njq/feC4WhGAwiOXlZfzgBz84Fxj2tPFJdzRqw5u6OpP9wurnU3c0c5TLZdmt/ehHPxK1kMvlgl6v\nl3A6Gt24w+fOmxOvRqMRuGV9fV1UQYSH6FzlRK6WbxJ+4gkEgNRCAji34242m7hz544kdXa73XMJ\nrBeJbXUYIK+fHoTBYACfz4dWq4VSqSSwBBfU6elp5HI5Ib45YTPcjTLLRqMhOUbqTB+eUPL5vPQN\nsPSGprl2uy0x2zwJqGWujORggB0X05OTE6TTaRgMBvzjP/4jSqWSTNgk5I1GIwaDAQqFAgaDAX78\n4x8jk8mgWq3i8PBQVGOc3JkpxdC9/f19BAIBhMNhlEolVKtVbG1toVQqCR8SDocFWqvVahKn3Wg0\nxFRXLpclzK7T6cjvQbFYxMnJidxTnspisRjS6TTS6Q9bgLLZrDiwmUOUSCSEDGY/8qftSL50Hn99\nx3OxINTrdVitVnzjG9/AvXv3PhOXQM05OwX4/GpcmxPjkxYWNnJxkuTOXb2g6PV60WcHg0GRabJH\nmY5amphcLhdCoZB0HthsNuzv78NoNCIYDKJYLMLj8UhMhBpCAiB4NWOeAYgMlK/JCZ0TF+OcR0ZG\nZOHiLt5sNovu3OVyoVwuyy84275KpZL0+xIqGgwGmJ2dhVarRTQahdfrhcFgwE9+8hMx4w2HQ0Qi\nEZTLZcmGInxEOWY4HMbExATK5TKSyaSktfZ6PekbYOQDTywsMarVavB6vVCUsy6KxcVFjI2NweVy\nodfr4e7du0in0/Jzdjgc6Ha7UmTD+2G1WiWi22w24/DwEBMTE3j06BF0Oh1CoRCq1SrC4bCE473x\nxhsYDAb45S9/CavVKt0EPEHwpMmfV7FYFGMeF8pCoSCkczKZxNTUFE5OTrC1tSXvV6PRIBKJIBAI\noNFoyIkvm81iampKeAEm4cbjcVGZkczW6/W4ceMGAMh9Bx7nEL355puSQ/RJJ3N1lhFwSSp/ncZz\nsSAAZ7v1dDotHoRPOzgBEQ4g6XpRvfS0U4baxax+TK/XO+deBXCu6pERBDSDMdQNgMQxq3f9jH4m\nzszXZaYSJ3jeG/VgXDQXK/ocuNvnDlEdyaEuvFG7hGu1Go6OjqSWkSQlr8FkMklU9WAwwNraGmZn\nZ2GxWLC5uYlisSgSSp4cqARi/lGr1ZKaUF6H2WxGu91GvV6XnmiS84wWocS30+nIAl+r1WThGgwG\nKJVKSCQSmJyclLC3UqkkxHKpVBLZLZVcjPdg/IJer0exWMT6+rrwJYTR1CqdO3fuwGg0Ynd3V8IA\nd3Z2kMvlcHBwIAms7XYbpVJJ6jUPDg4AnC3iJMkZBWE0GiV+4vj4GL1eT8je09NTCRfkZ2Zraws7\nOzvSWT06Oiptg1NTU7Ig5PN5MZANBgOMj49/LkTwJZn89R5ftVP5cxnESDnBfh6Dk/GnkbE+jeDm\nzp3/pwGN0AVJVZ4O2E9MFRAxbr5PnkA4YXECZ/8B4ykuXgMhC+CxdFbdA8ETjfpERMKb/Iaa/Cac\nQa8DAPk3NdzECb5areLo6AiVSgVHR0fCzxC68fl8smtXY/vqulD2Hns8HlkIuJvmomUwGBAIBGQC\nt1gscjrjNbH/YG9vD+12W34m9Hbwvag9HgDkXpAXIHxltVpl10zTmzrK4fj4WOK7uZgTqqpUKhIu\nRw+BVqsVyIyLI+8fNwL9fl+iswFIHpPBYJDroUGS74vkPPOwOOiDAR4nlJZKJUkS5vg0RPAlmfz1\nH8/NCWFubk7copzIONS5PFwwiG+rcXZ+HycF7qyI7wNPPhlwQlDHG6ijszlBsEayWCxKzDRxaEJN\nJDs5WbRaLQlWo/pFrZqh+oa9t3QB06BHgxrwWClCiSa9E+QK9Hr9OZkqr9vhcEBRFLRaLYldoGGs\nXC7DYrGg0WiIyWxkZEQCzaamprCxsYFMJiMYfiAQgM1mE/+Dz+fD6ekpvF4vFhYWkEgkoNPp8Npr\nryGZTIoxC8A5jf7o6Cjsdjv8fr84vhmHcXR0hFgsJvWR7XYbfr8f77//Ph4+fChqIk7cXq9X/CH8\nvDAeHIDAMMFgUJze/X4fOzs7orzR6XSYn5+HTqfD1NSUdBsQ0jo6OpKaTMZp8/0zzDAQCCAQCMg9\nZJFPt9sVmSgzodhLfe3aNUxOTmJzc1M+H1qtFhMTE5iZmcH8/DwAYH19Haurq/K+qVYjce50OjE5\nOSmnGA462fln4Hyc9cXBx/L/T+tUvhxfv/FcLAhGoxGHh4cCVTwJ3qH7Vq27v+hS5o6akj7m41Bl\nQz05d+V8He6U1ZJPqpHUcsZOpyNNacBZ4T3VOnwdTrYs/KFWvlwuyyTGa1A7XblbpOmM0RFWq1XK\n7MkjqHkM4OyXm/ERdL2qs2t4f1imwuc3mUxCeiuKIrCVOho7nU7j4OBAFmr6FHZ2dqDRaJBOp7G7\nuyuE96NHjxAMBuF2uzE7O4tyuSz5PCcnJ5K0enp6Kq5fRVEQCoUAQOoqCb0UCgWJuaY/QB0uR3iQ\niwg7oXni6na7QjAXCgUYjUbE43GR1iq/jnTQarVSGmO1WoWcbjQa2NnZEbNhvV5Ht9tFJpORVNlU\nKgWj0YjT01O899578Hq9SCQS4kwGIDwMTxQMtfP7/dBoNFhZWcHh4aE8nka5RCIhpxOKD/R6PSYm\nJuSzzx6Qbrcr/cfkhBhMl81mkUqlBNpk/MTFQUhoZWUFzWYTHo8HiUQCL7/88iWZ/BswnosFIZ/P\ni3nJarWKxZ56ae7eOVFRbqg2dJnNZollPj4+6xcOhUIy6RDO8Xg8GBsbE2cpd0+cQGhmIsZLNQZP\nC2qohhM7pY/qLgTgTHMeiUSkD5eKGb4v5vJwF+pwOERiSQydmTa1Wk0iFIAz74a6AIa5Pdvb2/jg\ngw8AQN43d7KBQAAAJKaCuf3j4+NIJpOyKBwfH4uzudVqyX2hpJMZNjMzM8jlcoKnE04CzhaparWK\ncrkMu92OcDgsUlSv14tKpSIl9yy/8fv9UBRFVDnlchlmsxm1Wg1Wq1Ugn/HxcYHNfD4f4vG4XG8i\nkUAkEkE6nRZyvNlswmaz4ejoCIFAQE5io6Oj55rKuFPX6/U4PDyUk6XVapVCenYeUIFEtQ+jzbe3\nt6HT6XDz5k3s7OxgYmJCYDyqwiwWC3Z3dxGNRrGwsIDt7W1Uq1UEg0GJyLh165YU+TCOw2w245vf\n/CYGgwHm5ubOEcXJZPIcRLS4uCgxIdwAdDod+P1+gTO5AVHDPrxfjMK22WzY3d1FPB6H0+n8xDHW\nl27mL3c8FwsCADHe0FEL4Kn45MV/p1OWO32SczxxABAFC01B/X5fMurVeHOn00G1WhWlDif5i6cW\nSkrVXAClptSh9/t9yaahFJI7QMJLlKE2Gg15DuLNw+FQJlZGYPPE0ev1RKfPAL2dnR0cHBxIEJz6\n5MJTDxVGJI/7/T52d3cF2uC973a7KJVKMJlMaLVasjM9PT2VmHLCJDy5dLtdwbRJqnP3zryndruN\nlZUVMZzxMf1+H9FoFAaDQWKc8/k8ms2m3Buz2SxOcP7M6/U6ksmkZBkRjuJJAYAEBtLpy8me94Gl\nQuVyGdFoFIqiSJZRv9+XRffo6AidTgdms1la0SYnJwGcKdnu3r2LUqmE7e1tDAYD2XjQk6LT6RCL\nxUQS6nA4BKrSarVoNptotVoiGhgZGcHa2hoePnyIra0tKatZXl7+kHLoSRAQT5sclUpFkmJdLhcW\nFxfPkcQ2m+2Jv2+VSgXr6+tSufmsRPIlAf3lj+eGVHY6necclJ90EC7hBE9iUT24i+ckQIJR3XXA\nv6sdsU8bXAy44yJxy5MKd5+Ui5LM5Wtzl0tpJYvm+UvMnb2aiOb38j8ay9gNADwu8OH1W61WUc3w\nORi5QEybj+Uip3ZKc8Ki/JOTkVarlZpFqrosFouc4Gjkc7vdcorjddjtdoHjuNBw0WHscrvdlsBB\nLrTquG1eEw18xPl5HZz4fD4fLBaLkNtqfogBfTSLkfil+opQGydTxorwZ8LqUMpgKTVm1Wej0ZA8\nI/IGPKkEAgHY7XYsLy9LKqvL5UI4HBbYj41tXDgZ632xwvRJHccX/13d1wB8mCRutVqSwkr4jN/H\nPz8rkXxJQH8147k5IVy7dg3pdFqKSLijVaeJqiOpOZlTb88POnNZmCnDSVuj0SAYDErOPSc9RisQ\n3ydRS3iAWfacIKkKIWSk1WoF+nA4HFheXpY4Au7s7XY7Tk5OUC6X5YRCp3OpVBJ82e/3Y3p6GgcH\nB6LosVgsCAaDMJvNsoPsdrtwu91YWlqS43s+nxc5ptlslgiEkZERTExMiIRxbm4Ou7u7cp9JcPN+\n8CRFV6zH4wFwdqJxOBwwm80Ih8PnXLuzs7PY3d2V3XC/38dwOMT09DS+/e1vS/a+Xq/H1tYW7t69\nK3r7k5MTBINBnJycIBKJQKfTSfIo8/zZQKfX67GwsAAAODg4gM1mk0UjGAxKoF0oFMLx8THMZjO8\nXi/sdjvcbjc2NjaQTqdRKpXk/qjJYi4k/BrDDJleS96G2L7BYMDi4iJeeeUVPHjwQAILObHOz8+j\n0+kIVAicOZL1ej1mZ2flsx8MBjE/Py+8AfuWmZs1HA7hcDjkZ034S70oqPuPSQJf7D4mdAQ8nVTm\nLn5xcVE4JXU/w+X4eo/nYkFgcTnTNLljJ9RxEa6hxJK8glpqR8ybX+POV6PRyKTJkwIzfrh7v9iK\nxrA1s9kskzahIsJI3MlStsidMElL4vgmk0m+l5JV7kp5zd1uF3t7ezJJsfrx+PgYY2NjUjV6enqK\nQqEghjbmCTWbTZlAeIJgVDXVV3TLlkolietgbSgVStwZUzev5kj0ej0sFgsCgQCuXLmCn/70p0in\n01Jiw130yMgIWq0WHj16hNnZ2XOdEdVqFa1WC+12W56X3cJcpHjP1A1mLpcLBwcHotxidad6otNo\nNDg8PBTXsEajgdvthsfjkQRTxkZwUiR06PF4EAwGZXdNo9nJyQmazaa4wSkIoIO70+ngtddew507\nd7CxsSGf0UajAbfbLe5rh8OBZDKJ4XCId999F4eHh7DZbLhy5Qrm5uYwMjIin/+lpSWJqKbUmCeh\nd95551zMNvBheAbAh+Cai4SwOuKa/3bx5MHfw09KJF8S0F/NeC4WhHw+j2KxKJOwenyUj4DH84sw\nk/okQchF7Tam5h44rzCiv4ALA3fJ3B1SyqqW8PG5uAhRxUFvgqIogvdTnkjXKtVFVDGpVSTc0dts\nNknz5KRKiIkErtVqFSkj3yuJcUoSAcjiw66D4+NjGAwGTE1NodfroVaryQ6X+T57e3uyaNJFzQWG\nkRMMtuN7pyKn1+thb28Pk5OTgtd7PB7pXW6322g2m4hGo0ilUucC6ejfSCQScm/dbjfu378vO3cA\nuHnzppzu2F2wu7t7TmpZr9fRarUQj8cF3ovH4+egRQb2+f1+LCwsYHp6Gjs7O6jVapIt1e12MTU1\nBbfbjUajgatXrwrpHYlEcPXqVVlQyAO88soraLVa2N7ehsfjOafa4ufu7t27yOVyeOONN0SCzNOI\nz+eT+zcYDARCq9VqyGQycmpQwzPqbmR+jW1uT4u45rh48rj4mE8yqV/2KH/547lYEICz4nViuc8y\nqNr4qK9zR6ceF3mBi4QxiUhi8iMjIzJxU9/OHZs6uI4LC3e7w+FQCmmIQ3s8Hmg0GthsNlFzEMvm\nCYOnA8JSjUZDro94v1pxRWy50+mcSzblyaZQKEj4HN8bazPViyZw5ogmIawoZ81nTCpVO565GGUy\nGfFhnJycyM6dHAzzm9jRvLm5iXa7LfeTEzujHo6OjuRnSmf42tqacAXcaXOYTCbcvXsXOp1OuqHr\n9TpqtRp6vd45xzlhJ7VBjqdPmrZY0vSrX/1KTk38GiPCyXcpioJUKoWTkxNks1m8++67qFQqMBqN\nWFhYEDIdOBMqEFLa2toSGMxqtSKfz6Pb7Yq67vXXX0etVsOjR4+wt7cnRPLs7Cz6/f4TS5w+yXha\nxPXHEcCfdkK/XAi+3PFckMpOpxMTExPSMPV1GlwwOHkQVnlaFhL5A7VxjKoYTpLMwSGHYTAYhIzl\nBE31CaEtEsEko7kL50THa1R3FQMQ1RIVPZysufDy5MSdMid/xmqr1VNUcvF51HATd5aU//KaI5HI\nhxY8LmJckI6OjuDz+eD1es+d6kgCM+eI+D2hN56u+HPiCUvtyeCpLxAISEUkeYZvfetbWFpaeu3U\npwAAIABJREFUkqA7SpOp8uKizwgNcjP0TdDJ3Ww2RTnEwLp4PI7l5WUAEJhSHd+h1Wol2sLhcMDj\n8WBzcxNbW1sS0QFAiGSbzYZEIiEcmTqU7iJ5HIlEEIlE5O8fB9dcEsDPz/iqCnKcAP5PAIsAFAD/\niaIotz/Lc77++uvY2NhAq9USMlg9CCNwUiUBzFA14vkXv4+TMycIToRUBamLYy7CU5zM2fvLyYw5\nPGqzHCMbxsfHMTo6isPDQ5lcOVHHYjGMj4/DZDIhnU5Dp9MhEomg0WggGo0il8uh3++jWq1KeUyz\n2US/3xfOQf3eI5GILDrlchmVSkX4BJvNJrg5m7442XJR4iITj8fh9/uxuroqO9BisSgZQzwZWCwW\nIS5PTk6E6CRprtPpJMLcYDAgHA7jz/7sz4QkT6VSooTq9XoIhUKYnZ2Fy+XCt7/9bWxubmJlZQWn\np6dIpVISZ8IFp1KpCLmu0WgQjUbFqUuYjaqt4XAo3EwwGMStW7ck74djenoawFmc9DvvvIPDw0PU\n63Wcnp4iFAqJ1HRsbEycxg6HAy+99JJ0IzebTYnVjkajCAaD+N73vodoNCoaf3oMeBKgP8VkMqFa\nrcJms8lnaXx8HB6PB1tbW7BYLPD7/TCbzYhGowCAGzduyOR+kVRWQz8ALuGa38LxVUFG/zOAv1cU\n5d/XaDRaAJbP8mRGoxFbW1v44IMPzmXjqAdNaNy9chAqYCSDWksPPLbqq4tC1NlBaofyxfGkcDvu\nVtW7fzU/QXUQ/+PkzUIcxkNUKhVotVrU63UEg0Gsra0hk8kIrGEwGM7JFdvt9rku4pGREVSrVUnu\n5EmGqitGVRBf53UBEF6CkzwNcYqiCNzCxZGLJSEjQlEulwsmkwn1el3I6rW1NRSLRUlMDQQCSCaT\nAIC1tTXxIdCjQK/GzMwMms2mBLql02l5n+w+4E6dSp7j42NxECeTSfk3un8JQ42MjKBcLuODDz5A\nLpfDL3/5SxQKBYyOjiKRSODatWvweDwIhULI5/OSHktICcA5yEpRFCSTSZRKJfkZUznW6XQwOzuL\nYrGId999V9JWAYiSzWaziSlwdnYWKysr+OCDD6AoCq5fvy5KI97bWq2GcDiM9fV1iaOwWCwC6ajh\nnWeBfp72+3dJAD8f40tfEDQajQPA64qi/EcAoCjKMYDPBGzm83k8evQIg8EAbrdb0jfV3QW/fm3p\nBlAP7s4ZjZBOp0Uyx90tJxTKUzmB04BGZzQAOYUAEDMXoZJqtSqvTyUPHdZU8FBvTpWQ2jFMmand\nbhfoRa/Xo1aryeufnp5ibGwMw+EQXq8Xw+EQ6+vrApvw+UhcWywWhEIhqWH0+/3I5/Ow2+3nop3p\n/A0EAtjf38dLL70Ev98v2f5zc3PizqaT1ePxyMnL4/FgOBzC6XTi2rVr8Pl8ODg4gEajEXUUI5td\nLhdisRiKxSK8Xi+Ojo5ESVOv1yXUzmKxIBwOC7kci8VQr9cxMTEBvV6PyclJzM7OwmAwYH19He++\n+y4GgwG2t7dFmtlqtYSE50nS4/GIaYxfLxaLqFQqUr7DRfPb3/423njjDfh8PthsNqnoZMkNPRAm\nk0nMgb1eD16vV4pqmIdktVqxt7cnUSMsB5qfn0csFkM0GoVer8fMzIxsCvhzv3Lliji1b9y4IeTy\ncDjEysoKLBYLBoOBcAt2u10IY55GLkI//NrHjUsC+PkYX8UJIQ6gpNFo/hLAVQD3APwXiqI8neF9\nhnF4eCgBa4Q9Lg6qUC4O4rZP2umrd+/AeYXRxVOIGjfl61BGSdWLmvTm7p+TDV/bZDLBZDJhMBiI\nRJYcAklqLiYWiwXValWczJQW7u7uQqvVSuIm3chqExnJYpK3lUoF1WoVBwcH5wxfR0dHyOfzsNls\n0Gg0It1st9tIJBIinez3+6L2uVjh2Wq1JLuJu+7p6WnxPuRyOekB5vVRFlutVpHL5YRcr9VqKBQK\nshAlk0lMTk7i1Vdfxe7uLmq1mqihgsGgkN0PHjyQP5Ps5j1R8woABOrz+/2o1WpSulMul0XiajQa\nkc/n4fV6MTc3JwU6AHDnzh30ej0RFvh8PrnPPGkFg0FYrVZJdb0YAqeu/+Spr9FoiCmN8Bl5AY5S\nqSTaf+YyZbNZUY59UeNyIfjNH18FqawFcB3A/6YoynUAHQD/9Wd5QqPRKC1a3AFTVvihF1c1hqkH\nVTdPG5zc1IvAJyGwCbF83HMSCyapSv5APVHRlEbfAiEs+ikIf/T7fVEi8XX4fIR7vF6v5PyrFyDy\nI51OR56/0WhIT/Lo6CgKhQIymYxcB70PvE5Ofiy2Z84RCVIulpy82fFMnoKFLwAEVhkOh+L54CJJ\n2TF31fRsaDQaeL1eWUTozG632+e6HthDwHvscDhgsVjketUQFyNDeB1UYq2srEgibqFQQDabFaUY\nF+NKpSInDP6M6vW6TPyDwQBerxeBQECkpz6fDyaTCe12Wybcfr8Po9EoJTiscnU6nfB6vRgMBiI/\npbQ0Ho9L7MmVK1dkYVDDO09zLF8c6s6Py/F8ja/ihJABkFEU5c6v//63eMKC8P3vf1/+fOvWLdy6\ndesjn3R8fBwWiwXlcvmp3gMqeLhr+qihhpeeNPFzMn+WqAzyBRcjudWdAozD0Ov10jTGPB3CGMD5\nWAxGHPCXVl3mQy6FsBAXEsZEUF2i1+sxNTUlaaI8tVAKSo6EhLQ6B4iYu9vtxtTUlGD1XCAuJsWq\n7x3/zvx/7rx5HwAgnU6jWCxKjIV6QePJjaS+yWTC/v4+LBYLpqenpS7SbDajWCwK71AoFERKy5gH\nl8slDXaMtMjn8xJNoXbwMlYjnU5jOBxiY2MDOzs78Pv9sNlsMJvNyOfzqNVqQqgTPiSPw7IdGhqb\nzSZqtRpisRj0ej2uXLmCGzdu4J//+Z+RTqdRrVbl5whAElJnZmbEH1Aul8+5hxnlDQCBQADj4+OI\nx+OYm5tDMBh8amjcx0E/l/lCz/f40hcERVHyGo0mrdFoZhRF2QLwLwCsXnycekH4uMEPLknAJ0E/\nah8AJ/qPmsyZZkllESduTkbA48wf8gsXB3e1lEGqs4U4oblcLiFAuQixVrPRaMBkMgmPYDabATyG\npnhNnCjVkyWAc13MAERRBUBiPdQtW5StEm7jBERZKsP9OKmps310Op0QmgcHBzKBmc3mc5M2CVaH\nwwG73S58SKVSEQiMJx2dTicBgVwoLBaL+DP4HgEI3+LxeERRZbVa4XQ6odPpsLa2JgYznl54AmCZ\nDJvP6N3gZK1WinGS5f0+OjoS02G1WpW4EZfLJeGChHQoPaWRrdVqyc+Uybn1eh3lclkWSibLAmcQ\nWiwWg8vlQqvVks/BYDCQRYuxF+r4C56yJicnEQwGz/3OPGk87WufhWO4HL8Z46tSGf1nAP6NRqPR\nA9gF8B9/1iecmZmB1+sViIS7cnV4HH+x1HCDepAQNJlMcLlckjLJ5+PjTSaTEMycWOnO5STNX3wS\n09SgU7JJWSgnBJfLhXg8jlgshu9+97v4wQ9+gFqtBpPJJNn3oVBIYqzV4WwOh0Pim5vNpuDODodD\n1DrkFphSarVaBXpglpPP54PD4UCv14PT6ZSuBRa1c8dNyMBms8FqtSIWi+Hll1/G5OQkOp0O3nrr\nLfT7fWSz2XP4POOUXS4Xrl+/jitXrohCZ2RkBA6HA+12G9lsFjqdTjwG6tBC9gaQLAXOVDMTExOS\nwBmPx89l7tB7oNPpZLHiaTESieDatWvyGdjZ2cHm5iZ6vZ7AY3R8ezweRCIRHB8fy8K0ubl57gTH\nRS4ej0tn89LSEjY2NrCysoJSqQSr1QqLxQKXyyWTabvd/hC+HwwGUa/X5e/D4RDxeFx+zhzq99rv\n95FIJM4ph9TwzpOcxOrTwmXc9G/3+MgF4deS0FVFUWY/6nGfdCiK8gDANz+v56PEjzEB6uIa7vDZ\nEtXr9dBut58IGREX7na70mqmlpbyNNBsNs/tDrljBs5+Kf1+/zmcmxMXUz+5wz0+Puvs5U6yVCpB\nr9fj7/7u77C7uysSTIPBAKfTKTs06taBx5wI3yN3vZyknE4ngsEgtra2UCqVUKvVzpnMLBYL9vf3\nZQFl2xZPEureZ55sLBaLEKuVSgXdbhcWiwW/+MUv4HQ6kc/ncXBwIDHUzLyhbPTo6Aj3799HvV7H\n8vIy9vb2ZPKlGofQFABxXvMkw+sBIKcio9EouT8kVPf399Fut2Gz2c4tHoQVPR4Ppqam0Ol0BJri\n6zGimz3P/Fz1ej2Ew2HY7Xa0Wi3o9Xp0Oh2kUilYLBaJvDaZTHC73QIz+Xw+9Ho9HB4eQlEU+Hw+\nzMzMCKfh8Xjg9/sRCAQQiUTEYcw+DRri2D5H3iAcDstjhsMh/H4/dnd3n1lOqv53CgkuPkb9e3Yp\nL32+x0cuCIqiHGs0mg2NRjOhKMrBl3VRn3TU63Wsr6+fC6UDIFEBxM8ZMKfuo+UgRs2dI/FeqnRO\nTk7g9XplQrPZbIIxA493VEajEePj46jVarDb7cjn8yKH5e6NWHar1RLjGElVYtw0q5HgZKw1c4gI\ni/Fx3N0xBtxkMuHKlStipKK2n1ENnNzpy9DpdMIvWCwWWCwWNJtNxONxkaiqM4Tu3bsn4XVMYq1U\nKohEIuKBYLwCYZO5uTlsbW0JDFKv1yXawWw249q1a9jb20MwGITdbkehUJDQvZ2dHcHSeR9MJhPm\n5+dhNBphsViwtLQEg8GA7e1tdLtdWWh5YvL7/bh16xbK5TLcbreoe+iO3tzcRDAYFF+Dw+E4p/ji\nqcnr9Uqr2SuvvIK3334btVoN0WgUjUYDXq8XMzMzCIVC0q6mKAquXLkiSq0bN25gcXFRXosJsSSH\ngcd4Pk9kR0dHWF9fRzgchl6vl1Yy8gWbm5ty4iScw98P5hNd/Bon936/L2otbk6eBAddykuf7/Es\nkJEbwKpGo/kVzhRBAKAoivLdL+6yPt1g2Yp6MDCNX1fv+NWDO27GEPDrF4/s5AvoC+DulSeITqeD\nn//85+dcyqOjo3KqYN8ud52NRkNOF5QsUv6oVhYxAI4nDi56LHkhJ0H4x2w2Y2VlRWSRhEB4OiKx\ny/dDRRNLXFgPyYA0RjqzBrLT6Qif0O/3sbKygn6/L+maPMXwxFGr1ZDL5WQBrNVqmJ6eRrVaxf7+\nPg4PD4VcZccxHd2Hh4eyA2ZjGK+3UqmI4a5Wq0Gr1WJ7e1vuezweF7VPqVRCp9OBw+FApVLB1tYW\nAIhjnaeCZrOJUqmEg4MD4XGMRiOmpqawtLQkJLHZbMbm5ua5Ih6W/iiKIoshf44k9YGzU0gmk8G9\ne/dEbhsMBsXPoN7df/DBB9jd3UWn00Eul8P09LQolvjZ5b3X6XQSs5FKpbC9vY1CoSCNe16vV6C1\nTzsuF4LndzzLgvDffuFX8RkHy8GfJjUFHktESRA/aagdrU8ahFXUOTfq6Gf19xESAh7DGpzITk9P\n4XK5UKvVPkRMXyTFyUnQE8DH8v/0IxAjJxTEBYXEMiWh6o5kppUyawc4i+SgqxqAwGEsOen3+7DZ\nbAiHwyJJ5X0jVg9AMHYSvVarFfV6XR7PUxGJZrp2+TNi/Ei32xXpJ9VLRqMR0WhU2t648FYqFeFt\nnE6ntN4xtZUnOrPZjFQqhePjY7jdbiSTSXi9XjHRMRhwMBjg6OhIdt1cMCKRCNxuN1KplLi9FUVB\nuVyGyWSCx+MR2S4A2XUzfI+nG51OJ9cBnG1elpaWAEB256y/5D0FzjYpPL35/X4UCgUAkD6IWq0m\nyiNmGhHyUvMUagjIYDAgkUicg4wuJ/7fvvGxC4KiKD/7Eq7jM48XXngB8/PzWFlZkYmFWDiJZaZ0\nulwuOU0wkweAZN+os+sBnNOqm81m2Gw2CdPrdDpSAk9ymX0ClG+yxHxmZgbhcBgvvPCCFOKsr69L\nRESz2RRMmnAQFwEqdiiR5CmGihburKl+UffrhkIhMU7RWU2+xOfz4ejoCHa7HTabDc1mE1tbWxIc\nZzQaMTs7K85dVjm+8sorODg4EPfu4eGhtHkpioLJyUkUi0VUq1VMTk6i1+shl8vB5XIJ7PLd735X\neBU6aMlHOBwOcUfzdKTVanHz5k185zvfQSQSwaNHj/DjH/8YALC3tydS2Xa7Db/fD4PBgImJCSGo\nqbx57bXXcPv2bcHmG40GYrEY4vE4KpUK2u02KpWKKK5YlDM9PY0//dM/lYL6UCiEnZ0d2Gw2HB8f\n4+HDh/D7/ZiYmIBWq5X7Rmno/Py8QD97e3sSTaLmgz5uTE9PY3Z2Fvl8HmNjYwAgUCgXbACIRqNY\nW1sDAHkP4XAY8Xj83KJwEQK6JJV/u8fHfgI1Gs3LAP4XAPMADABGAbQVRfniLI+fYrBti4mb3EWz\nWYsTFTsHSDgzMMxgMEg3sfo5ONRxFBqNRpyzhCGYlMlTgrqbgeYskpkHBwc4ODiQHSSVLFQKccLm\nSUGj0Ug3LyEj5uLQ5cz6RovFAqfTiVQqhXa7jWKxKBNkNptFJpORnaLL5ZJrPz09hdfrFc6Fi9PI\nyAiSySRyuRySySRsNhuq1ars/AnzUBpLYxTb3RwOB7LZrEhHU6mUnFz4mGKxiG63ey4uxOv1wmKx\nIJ/PI5/P4/j4WIroX3vtNSFDe70eUqmU/OxIqh8eHgpc9P7772Nzc1MC7RgFcXBwgHa7DZ/Ph3K5\njM3NTRQKBTkJ8OdHnuT69evSmVAqlfBP//RPePToEfL5vEiIufDp9Xq88847WFtbQyQSwZtvvonx\n8XEJ6Nvf35fPWr1eh9VqldpU9e7c6XQikUjIKaHb7WJzcxOVSgXlchnLy8sSscGdPstrIpEINjc3\nBcZj+97Timwu/vly/PaNZ4GM/lcAfwrg/wXwDQD/EsDnqjr6rKNer+PevXsSS8wYCK1WKyQe5XZM\n7uQOmvWGAORIz5IbtXLIbDZL6BldtEzS5PMeHR3B5XJJnITX6xUIY2FhAYqioFAoCO9AFYvBYMD0\n9LTk+JTLZZmY2MxlMpkQjUZRrVaFgNZozroR6vU6HA6HtJo5nU4JkaO8lC5VZuYEg0EEAgGJT263\n23A6ndL6dXBwpiEgZGM0GlEul2GxWGQhZX2ow+HAwsICWq0WlpaW4PF4sL29DeBsgsnlcucMWEaj\nES6XCxsbG5idnZX8IZ4cKHENhUISqWEwGKR0nhOiTqfD1atX0Wq1MDExgU6ng2azKcY0lghxceUO\nPJlM4o//+I/x+7//+3jw4AE0Gg1+9KMfoVgsijmO8Nv4+Di8Xi9MJhO8Xu+5TKDd3V0YjUYEAgG0\nWi28+uqrEtbHOBTGT+/u7kp2EDcXiqIIJxEKheB2u5FIJD4UYfHyyy8jHo+jWCzi7t27ACBZTuFw\nWB5/kez1+/2YnZ3F2NiYEPFPev6PG5enht+e8Uw+BEVRtjUazaiiKCcA/lKj0dzHZ4yb+CIGQ8PU\nHgO1GYyTA01BVNsw9oGyzYvGNk4orVZLlEFqSIkBZnRAMy+IxOpwOEQymTxXUMLTDJUwW1tbUiWp\nLorRaDQCCezu7gouDzw2l9Gxq05kZb0k7wO/jzJQ8gaUUna7XZE/8p4VCgWJ1OYg6dxoNJBOp8XL\nwJC6ZrMpMBQAOXEYjUYJfGNmEmWbyWRSoqCZ5kleRKPRSIwDT2Nvv/023G43AEjHMSOtTSYTDg4O\nkEwm5RTFCA3+DBuNBn74wx/iD/7gD+D1eiU6Qx2PQWUa/71Wq+H27dvY2dlBLBZDMBgU3oMnQ6bm\nOhyOc2KGJ43t7W3kcjkAECiSktaLE3YqlcLDhw+RyWSwtbWFaDQqC7s6t+hJUlFKUgGc8z0867h0\nJv92jWfJMupoNBoDgAcajeZ/0Gg0/xWAr1ULjdPpxJUrV0QWSGkmd9csjKG6iBOhGt4h8fukwVYu\ndcXkRQKYz8tSGrXclTEO5CLU+UUkFwkTcTHhc/J1SBZzZwk87mpgZzO7in0+nySVjo6OSkm8VqsV\n053D4cDk5KTk5rjdbgSDQdlNko9QE948GVFSyn+n25lEdD6fFwiKp5tQKCT8ARvHCAkVi0XJU2IN\np8ViEQ6D7trR0VHhbgCIJ8Tv98PtdgtZS4iEEBzvPe+l3W5HtVpFNpuFzWaDzWbD9PQ0Jicn4XK5\n5H5GIhFMTk5KzAgnfPo1qLpyOBwIhUJwOByIRCKIx+Ny4mNnBXfmPp9PMpz4M6IX5aIDGXisRqrV\narBYLLBarahWqxgMBohEIuc2Jp93Mc1l8c1v33iWE8K/xNnC8Z8C+C8BRAD8e1/kRX2acfPmTbTb\nbYyNjaFWq+Gdd96BXq/HxMSEFIxwh63RnDVueTweiSSm8oUBZoREOIF+73vfw71794Q8HhkZETiK\nv+THx8dwOp2C2VssFtk1c+dJCIbuab/fj263K0UnXKi4kLE1LBAIiKwwnU7DbDZjdvYMuVtYWIDd\nbodOpxN/BA1ozDoKBoPIZDJwu92Ix+MIBoOYnZ2VWkoWr4yNjWFzcxPD4RA//OEPZXfc7/cxPj4u\nyh6dTodcLifFLyzV8Xq9AM52o1NTUzCbzQiFQjCbzZiZmcHExIT0CQSDQTgcjnPl8yyUcbvdmJub\nQywWw/T0NN5//33k83m51z6fD1NTUzg8PBQVEABcu3YNf/mXf4l0Oo1erye4ucfjQaPRQLPZhMvl\nQrvdRj6fRzwex+LiIhYXF/HGG2/gvffeQ7vdls9OLBbDw4cPcXBwIDt6m82GeDyOmZkZOREaDAbM\nzMzI4gWcTaj1eh1Op1OMhX6/X05FwNlpLpvNIhaLyb3j9z5pTE9Pw+FwYGlpCcFgEKurq6KMopIJ\neAzvsOUNwBO//kWMS4jpN3c8i8poX6PRmAEEFUX5/hd/SZ9uGI1GLC8vY2NjA2+//TYqlQoGgwHy\n+TysVisODw+lXIbKFnYTqAtraEKj5JEQy9bWFqrVKlKplJCBAMS4xsm+UqmIlt1gMMjOkZMzW8lG\nR0dhs9kE4iKcwRhrpp0SpwcgGLo6MoPwzOjoKCqVihi9Tk9PRRLLwLV2u42trS3s7OxgYmICuVxO\n3LzRaBQTExPyvBsbG7KLJrTT7XYxPT2NkZERZDKZc6FzwBlkt7u7i9HRUWQyGWxvb8Pn80nPMjXy\nhEdMJhN8Ph/8fj+2trbklJTNZmVhiMfj2NnZkbC+nZ0dKMpZH3G9XkehUEC/34fH48Hs7Cxu374t\nHgKeZHQ6ncBftVoN29vbsFgsWF1dxd27d/Haa6/hxo0bWF1dxcbGhtyPmZkZxGIxiesmia3T6bC8\nvIxwOIyHDx+em9xHRkYEVuHPm59FtaRzfn4eu7u70Ov1eOGFF2Qx8Pl8Qlrz72rHMl+DsdqDwQB3\n7txBs9mEyWRCrVZDMBgUeEftLCbvwef9OPjn0ziTLyGm3+zxLCqj7wL4H3GmMIppNJoXAPzrr6Mx\nze/3yw7dZrOh3+/LboXEKHfR/OWhmYu7VIfDcY6MBs4IvJ2dHcnbUcc4zMzMyHO5XC7s7e0BODMt\n0fC1vLx8jtwk2ciTit1uh9/vRzqdlrYtZtVEIhH4/X6ZOJlh1O/30el0cPXqVQyHQ1QqFZRKJRwf\nH4uKiu+FclqW7iiKglwuJ1wEydbt7W2B3La2tqQPgNJJvV4vOvmf//znSCQSCIVCSKVSGBkZQSgU\nwvb2NlqtlrSgZbNZtNttIb37/T5eeuklRKNRKYN5/fXX4fP5UCgUxGzFMh2tVov9/X0Mh0MkEgmR\nUlYqFezu7gpMSDfx9va2SGQ7nQ70ej3cbjdKpRI0Go3s1I+OjtBqtVAsFvHee+/h9PQU5XJZHO2U\n0NbrdTHKLSwsnMtYYm4RM7BqtRoymYxEfedyOckXUruAM5kMFhYWMD8/D+Cx3JOT7fvvvw8A4hhe\nXFzEm2++iXq9jmQyKammXJRtNhuMRiOKxSIKhQJcLpc4jdVuZy4GwLMH030SZ/Jl+N1v/ngWyOj7\nAF4C8E8AoCjKBxqNZvKLvKhPO1KpFO7evSttaTRf0XNAqWmr1ZJOAUI9xOWZpcPJkioRABJQR8MS\n3cbE9RmTQIcvjVGlUglutxvNZhOtVkuup9FoyG6fpirm+fA0w56Cvb09IaEJOZGodbvdyGQyUjPJ\nPHyqi6hKstvtEslhMBiEMFbHeP/iF78QVzFd33QqK4oiu/d6vS68TKVSEUKXuH65XJbIBr4vdibs\n7OxgMBjAZDJhe3tbvBfsBej3+xgOh3A4HHjnnXewv7+PXq8nJjq+ZrPZRK/Xg9lsRr/fl/gMRkAQ\nruMph1AQn5/vs9/vi8S20+nA6XSi1WpJxlKxWMTa2pp0MttsNuh0OiF2ufMHgEePHuHdd98VkvvF\nF188V2DDaGwAUmSv3rUfHR3JicPlcp1LJ3U6necirjnUr/+k8Vkn5MsJ/bdnPAupPFQUpX7h355s\n5f0Kh3pH5nK5ZAfPrBp1yQwACX97UlQ2zUKc0NTlNsT4ie8zNoDksbozgC1XVDHxGtQuZHX4nsVi\nwejoqCiPjEajxD/QCc2JkwsDm+IIQ/F5Gauh3u2yqpPXaTQaJbepUqnAaDQK3MKTAglju92O+fl5\nkeoGg0HodDpUq1WMjY0hHA4L7+J0OkXdQ26DC4/P5xPo6+DgQOpCgcfkPXA2ybGnmFEe+XxefoZH\nR0fyXlh9Su6BXQcTExNC+rKvgImtLLzRarVyAmMtKcUATqcTtVoN2WxWhAA8hbGAhlEQwJnajBWZ\nOp0OnU5HNhOJRAIA5CTJk4I6YyiTyaBcLsvz1Wo1uWbgwwU2JKtdLhf0er1wTfQiXPQXPEv5zWcZ\nX8ZrXI4vdjz1hKDRaH4I4F8BeKTRaP5DAFqNRjMN4D8H8O6XdH2faLDU5Fvf+paQv5QSHhwcyE6V\nHAJ348R4qarxer0YGxsTk1Ov14PFYpF46H6/j0wmI9/P72FAHI1ONGQxOtnhcAgRyIw9WRDAAAAg\nAElEQVQkq9WKkZERRKNRqaNkVaa6apGSSxbYWCwWeDweuFwuTE5OYmdnB8FgUH4BA4GA7KpPTk7Q\narXwwgsvyOLAFq7j42Ps7e0hl8vJSYBKHOYGXb9+HVevXpXUVHYrA2eyTxqjut2u9C0/ePBACNzR\n0VFEo1FYLBacnp7C7XbDaDRKNhHvQ6PRQDgcllNXKBRCvV4X+K/RaCAYDAqswwWKBjgACIfDsNls\nePHFF2G32/GjH/1I5KelUgnRaBSFQkGun99Lgn92dlb6igHIAkBF0NHREaLRKJxOp4QVEpIBgL//\n+7/H/v4+gDP4MRaLYWFhAU6nU37mT9rlq4e63OYiBv8kCIevr3YaP8lr8GUE012G3/1mj4+CjP4v\nAD8C8P8AWARwBOBvfv1v//0Xf2mfbPz1X/813n77bWQyGQk8I0ZPZQwxfWbqqIvVCYnQFVwulwVr\nppdgbGwMV65cgVarlQA47lAzmYwUyLMsJp/Po9VqSSy32+1GvV4/Z0wj1s/4ZcYPFAoFgVUuxj8T\nqzUajXA4HDg4OJCMHJ4uPB4PMpmMTE52ux27u7vY39+H0WiUshTmKjGYbzAYiObfZrNhamoKXq8X\nOzs7KBQKyOfzyGaz6PV6mJ2dlQRTdjbkcjnxATBwLRAIIBQKATjLdWLQGola/kdDms/nQzgcxoMH\nD+Q1ecLZ2dnB9evXMTY2Jhk+oVAIrVYLm5ubSKVSMJvNSKfT+M53vgO/349Hjx7JPRgMBuJJiMVi\nMnkVi0Wsr69LZIjb7Ua1WpWIcwBCrGcyGdTrdcRiMYF/SOS6XC6sra2h0+kgkUhgfn5eJme6h4mz\nEzK6+PeHDx/KSaJYLD4xhvri3z8q4vqjvveLGJcLwW/ueOqCoCjK//frU8J/B+A7AP5vPIaK/hWA\n/+mLv7xnG/v7+/jggw9k0uAvsdFolCjpQCAAjUYjqh5WIer1ethstnMnBH6gCemMjIwgHo9jfn4e\nV65ckd0u+YHt7W3JuTcYDKjX65ibm8OLL76ISqWC1dVVga2oN+eul56Ao6MjybZvt9sIhUKyCBiN\nRlGacMImTOL1eiWWmgVBdrtdTjI0cJGzACDvV6fTnZNK3r59G5VKBRMTE6jVakKk0lNBM1ogEIBW\nqxV+gnDRzs6OKLbY1wxAspy8Xi9CoRAODw8Ri8UkW4j1mVRd5fN5uFwukfEWi0Vxc/P1rFarYPrd\nbldUNrzPuVwO9+7dw/T0NF588UVRLwFn0k0AiMVieOGFF3B0dIQHDx7IPanVauInWV5ehsFgkLyp\nUqmEbreLXC6HwWCAQqEg6bKtVguBQAB/9Ed/JGQvFxwOv98vXQbc0TNbiJ87l8slsNKzErMfR+he\nSkEvx7OMjyOVhziLvNYDsOJz4g40Gs0+gCaAE5xxFC9+1ue8ffs2MpmMEMXEpYnnkrzlJEsXsTqz\nhvAFj+s8Hai5gUKhICmVJpNJ5KQkcJktVCwWRW1CspMTAHecDN6juWt7exvFYlH4BKvVKtJJEt3s\nUqCfYn9/X2ojvV6vGMoIE/E9pVIpdLtdIW7NZrNEKiQSCamuzGazcuIoFArSDUyHdrvdRiAQAHC2\nyLCHwm63S9cBiWT6E2q1mkhig8GgxIU0Gg1Uq1XxZpRKJXlfp6eniEajGAwGaLVa0h89OjqK27dv\ny2mPZT3smVabCxlvwc8Efz6EbPx+P4rFIsrlskSFO51OyVw6OjqC2+3G9PS0kPS7u7uoVquC7wcC\nAYEcgbPJfHx8HDab7UOf0Ys7eOBs4s7n8wDOThms8/w8x6UU9HI86/goDuE7ODsF/ADAdUVRup/j\n6yoAbimKUv08nqzZbAq+rnb4khSmOoUnAsZUqDuSOSkzzoIuZmbaDAYDmewajYbECbOwHoDEWfM1\nqHTiBKmGqHhdLpcLXq8X1WoVu7u7QvaSNHW73bBaredSWUmuMuyOBizuqO12O5rNpqhSSHqTI6Dr\nlZPfwcEBotEo/H6/9BowOqLX60nGP+OyeVIJBAIYGRkROI49CzRAUXtvtVqlLY2O3263C4PBICQw\noZjR0VG43W7ZoY+OjooEle+DeVKpVAqFQgHxeBzhcBjZbFbEBJz4HA4HVlfPKrv9fj/29/cFEmJk\nNQAJQSQsF4lEpCSoUCjIqWJlZQU6nQ5+vx9msxlms1mCAEkGU8WlJlUv7uDVZLI6WgKAdCIDz07M\nPs0zcCkFvRyfZHzUCeG/AfAfKIqy+gW99ucafxEIBMR3MDIyInrzkZEReDwe6QIwmUxy7OeEzlMA\nj/7EclutlpwirFarwE4sjA8EAhJZMTExgZ/85CeS3d9oNCSOwWazibOZ5CTJ4Lm5OVitVuTzefzV\nX/2V5O83Gg184xvfwOTkpLh96aj+27/9W3Q6HeEywuEwgsEgFEWB1+vF1NQUBoOBpLi2Wi1pCOt2\nu7h//z58Pp9EZXBXGggEMD4+Lj4LSjAZNcG/U+0UiUTEYc0gOgCi/nG5XLKQpVIpuN1uhMNhiYC4\nefMmrFYrhsMhqtUq3nrrLTQaDZjNZmi1WvzO7/wOgLOdPpvT+L61Wi2CwSCOj4+xsLAAi8WC8fFx\nvPrqq6LQWl9fR7vdFvhpYmICZrMZPp9Pwt44PB7PuUmVp0qbzSZeEABYXl4WZ7Jer8fY2Bj29vak\n0Q54rCh62qTLjgm+/sU6V/UO/pNM3JeE7uX4rOOjFoRvKRf1mJ/fUAD8RKPRnAD43xVF+T8+y5Mt\nLy9jaWlJYpD7/T6q1arkzJOsPT4+RjqdFiUNd5NcDBiqRgikWCzi8PAQg8FAjD+cSA0GA/b29mRC\nrdVqOD09lYpNp9Mp+PZgMMDo6Cja7fa5WOtIJCKZ9ixfPzw8lCygzc1NNJtNXL9+HbFYDKlUCvfv\n35fGMfY3WywW6R7Y2trCnTt3MD09LaTp2NgYIpEICoUCGo2GLJ6MoubJhKeNZrN5riaU0kqmqFKz\nT/c3T0Onp6dySorFYpicnITJZMLt27dRr9flcTabTZzaVqtV4hUmJyexurqKVqslKajVavWce5i4\n+vr6uiSNskVsfn5e4jzee+89/OxnP0MymcTh4SFsNhvS6TR8Pp/AX5FIBHNzc0LGM+SPJrxarSbC\nhF/84hcAzshn1mSy2rTb7aLb7QqxzMY14DFEw8VGTRgbDAYJ+WPcyNLS0meazJ9EOF/2IF+OZx0f\nRSp/UYsBALyqKMqhRqPxAfhHjUazoSjK25/2yY6OjvAnf/InaLVakk9P2IMTvdlsFniHuLLRaMTM\nzIyoi65cuSJQ0fHxMfL5PCqVivgHTk9P4fP5cHJyIhWF/X4fkUhE4o05ITG/p16vY3t7WxzO7XYb\n8XgcVqsVLpdLIIujoyPE43EYjUYcHh7KrphJoVarVVJPb9y4gWQyCYvFgpdfflkcvoyoZrJpo9EQ\nJdLExARCoRAODg7kBGKz2dBut7G2toaRkRGp8KQMk4XvCwsLiEQiiEaj+Id/+AdotVo4HA7cvXtX\nMPx6vQ6j0Qi9Xi8hb1arVXoH4vG4eEUsFgvMZjMGgwGq1SqWlpZkJ85qR/Iv9BN0u12JyZ6enpb7\nEovF0O128c1vflPuPTu2qRjq9/vw+/3S82C1WuH3+2VhDofD4gznTj+RSIhJbDAYiFkMgEiLGWVN\nXiSRSMBoNApEBZyHaPx+/znCmB0X3/zmN+Xxagnrs07cH/f4y5PD5XjW8Uzx15/3UBTl8Nf/L2k0\nmn8H4EUA5xaE73//+/LnW7du4datWx/5nNT2Hx0didKFYWmEGwgNEY/WaDSoVqvisK1Wq5iYmMDY\n2JgoeeiMVfMAdMiSDCyVStLn2263MT8/j42NDdlNdjodjI2NwWq1Cjmaz+eRyWTw85//XEhpqp+y\n2SzK5bIsDOFwGPv7+9jf30en00G1WpXwtmw2K6oV8hPdbherq6u4ffu2TIBWqxWJRALBYFCMZrwW\nnqa63e45pRUnrYODA2SzWfzN3/wNtre3odPpBPphN0KpVMLo6Ciq1SoKhQIODw+xtraGbDaLvb09\nMeSVy2U0Gg1JMw0EAh+CbmgGAyCuYQbq9Xo9LCwsCGnLsp5cLgeTyfQhwrTX60l5EPuUCQUCEMUX\nX4/uYKqAarUaCoUCstksjEYjPB7Ph9zJAASm+rhxkTBWQ03AGQGs5g8+jgB+VsL4ciG4HM8yvvQF\n4ddBeaOKorQ0Go0FwO8B+NcXH6deED5uUA3k9XqRyWRkomLUA4laGrGYkcNYiJGRESl0r9frmJmZ\nQTQaRTKZFEOYTqeD2+2WfCBmI7EfgBMoi1EYYw1AMHe1i5q/9PV6HfV6XQxklUpFlFGcbCnPjEQi\nSCaTEhUxMjIipx5m9/P56ZjtdDryOEpGacZiDg/17iRJSUBTTcSTTS6XE/K4UCggkUhID/Ho6Cgc\nDgeazaZ0JNAIyDA29jLTu0HIR70rJ3TC1y4UCpKkarFYhKSORCIol8vo9XqIRCJSPsM8q/n5eSnW\nCYVCsmjOz8+jWq1K9wIVVKwhrdVqmJ6ePmfyYmKqoijiCubzPamD+GkQzUX45qIPQU0m898/igC+\nJIwvx+c9vooTQgDAv/s1AakF8G8URfnxZ31Sj8eDmzdvwuVyIZfLIRgM4uTkBNvb20JEsh1sf39f\n4hBarRYcDocQvfPz83jxxRdx48YNTExM4N69e1KenkgkJLoilUoJqbyysgKj0SgGr+vXrwvWz3TV\nhYUFSe/kTpkNX2oz1OjoqDRyAWfwxMTEBPr9PiwWizhwK5UK9vb2ZCGMRqN47bXXYLfbsb29jbfe\negvr6+tyMmJ72uLiIqxWK2ZnZxEIBLC6uoqpqSncv38foVAIfr8fGo0G3/ve92C327G5uYn9/X2s\nrq7i5OREcHObzYZr165hcnISg8EADx48EFK+2WzC6/VKJEcikcDExAQ8Hg86nY6Y9EKhEJaWlsTr\nYDQasbKygtHRUXESh8NhkRAHg0HY7XYkEglcv35doCGa+ZgxBZx1bLNLmhyBTqeTyG816U4/CFU+\n6l12MBiE2WyGw+GAwWDAiy++eG6Cp/tY7Qz+KIjmSR3GXFwoUf68ZaeX43I86/jSFwRFUfYAXPs8\nn1PdO8tOga2tLZyenqLVaqFcLks1ptvtlp2YRqOB3++Hw+EQSWW/30c6nUaz2USlUsH777+PXC4n\nuUDhcFhgBqpjhsMhOp2OmMUePXokpwuqi6hKyefzsNlsYm5KpVLSB202mxGPx6VQZ3R0FNPT05ie\nnsbKygrS6bTs6jmJ1Go17O3todFoQKPRYHp6Gh6PB0tLS9jZ2QHwOLDPZDLh4cOHIjcdDAbY2dmR\nSspsNguLxYJgMIh79+7hzTffxPz8vHQEM16C0trd3V2sr68L9EQ5LtNbmQ1E/ub09BQ7OzsCYw2H\nQ/z+7/++TKZ/8Rd/IW5zpsSWy2XpVJ6YmMCf//mfy+ODwSAGgwEePnwosdh7e3vnzGCEqEgOb2xs\nYG9vT+I0JiYmoNfrkc1m4XK5zpG6RqMRg8EAP/vZz8RxbbPZ8PLLLwP4aLjmo3bp/Jr6+6k6uuh8\n/rjnuSSML8fnOb4SDuGLGOydpWM0n8/L7o2SwkAgIO1WjFJYXFyEoijQ6/UoFApSfp5MJtFut0Ul\nRCiEkEE0GkU4HMb9+/dlV08l0vHxsXQgJBIJxONxaLVaTE1NSQAaw9tYFkOzmNF41l725ptvwm63\nY2lpCYPBQHoNGM0dj8cxOzuLdDot15xKpSTY7ZVXXkGpVMJLL72EQqEgMs1yuSzQGrP13W638CnU\n+W9vb2NsbAwvvfQSYrEY7HY7YrGYQFHdblcmr1qtJi5xr9cLv9+PcDiM69evS5MZ47STySQcDgfs\ndrtUngJnbvM7d+6gXq9L8Xw6ncbBwQEMBgOcTqcE76njIOgHIKeQyWQE4qMpjwbCavXM9sKfo1ar\nRa1Ww9zcnJgR1YsJI0PoLh8dHcX6+jrm5+clCJDjk8I1arin3+9jY2NDiotIUKtPHU8jji8J48vx\neY7nZkEAzgxqm5ub2NvbEylgsVgUPJ9dw8yGVxQFDx48EAXOL3/5S1EOEeOmXJKQw+joqEg9mcfD\nHT4193QEEwJQFAVmsxmKosBisaBSqQi2T5iDv/CUbUYiEZycnODtt9+W6kKr1QqHwyH9BwBw//59\n7OzsiNlsaWkJrVYLkUgEe3t70Ov10vm8u7uLdDqNdDoNv98vEyJ5CqayUm20v78PjUaD9fV1uWZ1\nFj8XNsI1x8fHKJfLKBQKSKfTSCaTeP311yXm+vT0FIVCAQaDAYVCAZ1OB//23/5b/N7v/R5CoZB0\nFNdqNYkIZ2k9cNZD/atf/QoGgwE3btyQHTlPX4PBQBYsLpwejwfNZhO1Wg2lUkkwfxrkAMi1AGe7\n9pmZGflM0a0NnE3c4XD4c//cMh8KOPPTqEMKeU0fRRxfLgSX4/MazxJ//RsxOPmazWbJ0KEz2Gw2\nC45MvJ4LA3X329vbov+nMoVqJMYknJ6eotvtCuHKWAkmlobDYdy4cQOjo6PisGVUNpM72V/g8/nE\n78DTBaGgUCgkbt6TkxN4PB5otVqZ7Gw2m3QzsF+XTuxer4dMJiN6ei44VMEwZZSk+NjYmJDSoVAI\nTqcTg8FAIqF5kuCi0ul0YLVaxWfBPCW/3w+TySRSWUZtpFIpibmmUa1er0sX8sjIiHgKJicnodfr\nxRtis9mkWKfRaMDpdGJ8fFxipSkdjkQigv9TUko1kNlsFjUT+QiPxwO32y2dyVwMntRpzOfjmJyc\nhNPp/MxRz/z+fr+PbreLSCQikJE68vqy1/hyfJnjuTohAGfw0Msvv4zp6Wk0m01Uq1WBCgaDgcQW\nc/dLg5pOp4PVapWJjG5nxiewjjIWi+Gll14Sxytbr4bDIRYXF7G8vIz5+Xkkk0nodDo8evQIwWAQ\niUQCBwcHku+j0+kwNTWFYDAosI/f74fX68Xi4iLi8Tjef/99KfOJRqPSI2y1WpHNZqEoiqiGms2m\n1DeSQB8bG5P/9Ho99vf3JUiPPolYLIabN29ifX1dJKD5fB5TU1Ow2WyiLKIaiUmuHo8HAOS5gbOd\n7g9/+EPU63XpgiApS5LY7/ejUqkgk8l8CBL5wz/8Q1y/fh0ffPCBNM9xcfH5fIhGo9IzwAWd+v5X\nX331XLkMBzuimWU1GAwwNzcniyNwvqHs4ggGg7h165a08N28eVOulzwJjYifdIyPj0uwHfkr/vvl\nuBxfxXhuFgR1IujOzg663S7a7TYymYyEoxE3Bs5+6RhStru7K5LRXq8nmUYkcNWZRTqdDrdv35YO\nX6fTKe1Y2WwWw+FQThcMaPP5fLBYLIjFYvjpT3+KZDIJrVaLmZkZ/O7v/q4U3BQKBfELZLNZpNNp\n7O3t4ejoCJOTk4jFYrh69SpKpZIsaD6fD4qiyATHvuONjQ0YDAa8+uqruHr1KoAzs10ul8PJyYn0\nQjNcbWNjA7/61a+g0+ng8XikNCeXy8kO9erVq1hYWBA4zG63Y2xsTHbKXq8XW1tb0lrn8Xjg8/kw\nNzcn95rxH3t7e9ja2kIkEsGVK1dQqVRQq9XQbDbh8/mQy+WQzWYxOjqKmZkZ3LhxA/V6Haurq2g2\nm9LoRo4CeNw/rCZqV1dXhXCenp6Wcnr1UH/PRZnoYDAQOMfr9Z6Lmc7n8ygWi9DpdEJIf9LJXB2J\n/VHFNpfE8eX4MsZzsyAAZ7vPGzduQKvVSkGLx+NBMBhErVZDNBoVN+v169exvr6O27dvS2cAoyzs\ndjtsNhsWFxfxyiuvwOVy4f79+ygWi8hms2g0GnC5XDAajdK3ywasvb09DIdDiXr2eDyYm5sDANy5\ncweDwUDiE2q1moTHLS8vA4Dg5vfu3cPp6Smmp6fR6XTw6quvyo5Sr9eLs5YR23S/vvPOO5Imqo6U\nIDHMxY0VmA6HA++//75kLvHk4fP5pIfAZrOJOY9fY7wD3bnAmV7/5s2bAtnxBPLCCy+IaZDKqJdf\nflnCAs1ms/QZK4qCubk5OZVFIhF4vV6BqDqdDorFIrRaLTKZDAqFAubm5s51FdPtu7a2hlqtBo/H\ng8FgALPZ/KE4auDpxCyTaVkAxNMTg+loWGNseSaT+RCx/Cyu448jhr8I4vgyDvtyPGk8NwsCd239\nfh9vvfUWtra2xEnMtqrj42OYTCb4/X5Uq1Vks1n5Ho1Gg3a7LcQkH9fpdBAKhbC3t4e3334bjUYD\niqKgWCwik8lIxo/T6cTW1hYKhYI83+zsLCYnJzE2NiY7R5qyiA3fvXsXlUpFTFbD4RDvvfceSqWS\nQFZjY2MolUrSFZ3P55FKpeD1ejEcDrG/vw9FUWAymURZdXR0JEUuGo0Gb7zxBvr9Pu7fvy/qoqmp\nKdTrdSFxy+UyyuUy9vf3ZVdMIpx8RTKZRKvVEi4DODNUcaLMZrPodDrSGWEwGCSaHHis4qHb9/D/\nb+/cYxvNzvP+HF1IihQ5pCiRksiRKCmalTI7k5nReu0Z192x1/bGjmMHhpHYbeqgaAs0bYGkRdra\nQQtsChRI0T9apGhSBEVTO22TFE3cJuvGbro3724nuzuz45nZHWkuFGckUqKoGyWKFC/SnP4hve8c\nfvp4040SdX7AYCWS3/kOydW5vM95n3d2Fq+99ho7htLA/+DBA8zMzEAIUVSghjQBI8ZaxcaBn0JG\npSg3MNJ18XicazRTCKwctdhOVxqY93Pg1nbYmlI0hKisCm+pVAqxWIwzfekED53bp9UlCbNut5tP\nsVCIiMJKwNbJktnZWayurvIqmYrMb2xssOOoOmBvbm5idXUVq6uryOfz7Ff0Uz/1UxgaGuKJyev1\nshvr/Pw8Zmdn0dLSgsXFRWxubvK1NAhS+1S2M51O48GDB0gmkyz6trS0FE0Mfr8fm5ubuHHjBu7d\nu8elMcm+WwiBs2fPwm63c8EZSpKi47qbm5twuVw4ffo0+wEBW7uZyclJtohOJBKce7C0tMSfp2r1\nnEgkeLCmVTtldJPF+MLCAmeG0+mlQCCAoaEh+P1+3sEFg0HWcFTDODUTmMRmj8eDYDC4K+GX/j8A\nthIFyTWW2iTra7X9oyoGH9V+aY4GDbNDINbW1vjYJmX8er1euN1utLe3Y319nVdETqcTzz33HHp7\ne7G8vIx4PM4eQi0tLRgdHeVSiuRqeurUKWxubqKjo4NdLElUDIVCXIN3bW2NC7eTJYbP58O3v/1t\nvPrqq1hbW8Pc3BySyST6+/vZmM7v92N+fh5erxenT59GMpnElStX0NHRwat2YEs89/l82NjYYO2i\ntbUVQ0NDeO655/Duu+/iwYMHOwq1eL1eOJ1OdHR0YHR0lL2XBgYGAGwZ201PTwMAXnjhBdYU2tvb\ni3QSAJzslU6nkclslcugxL/e3l586lOfAgDcunWL75/P53H+/Hk+0vvhhx9yjeONjQ309vaiv7+/\naBdw5swZ/qxJQAaelo4sVauYQi0UqioVyikXPlHbCIfDAMDurJRhbda+RnMcaYgJgVZyr776Kpvc\npVIpdHV14ezZs/B6vbh16xbu3LkDYMs/iMzZyEbC5/Nhc3OTC8U7HA44HA7cvn0bP/rRj/Do0SOs\nra3B7XZjZGQEAwMD8Pl8nPNAeQv9/f1YW1tDZ2cn7HY7Zmdn2eiNRM2mpibcuXOHyy/SCpMqqPX3\n97NT6+nTpzkRbGhoiOv6AmArCKrQRm6a09PTnFCXTCZx+vRpjI2NAQDC4TAeP37MJ7Da2towOjqK\nUCiEc+fO4fHjx5zc1tXVxd7+7733Httip9Npdle1Wq24f/8+F8YhK2cqnNPX18dZ5ORnFIvF2Ok0\nHA5jdXUVmUyG/Yo2NjaQzWbZolrVKcwGXmOtYqMwbHy9WYYwXVeqDrGZDxGdLDKbCI6qGHxU+6U5\nGjTEhABsbeXJq5+MzZ5//nl86Utf4m0y2UGrcXUAbAn9zDPPoLe3l4XnDz74ADdv3kQqlUJ7ezts\nNhtOnz6NL3/5y+ju7kZHRwefPCJo9QoA9+/f59MyiUQCbW1taG1tZSO5gYEBzmq+cOEChBBYXFzE\n2NgY50aEQiEOV1y8eBHAVnY1ha1yuRxeeumlIlO0R48esZFbZ2cnxsbGEAqFAIDzISgsRklcgUAA\nn/vc51hgp7P6LpcLkUiEB8VYLMZZ1mRZ0dPTwxMHubparVZMTk7CYrFwFvmtW7d40rh37x7fx2az\nIZPJsIvpxMQEC8UWi8VUCDaiHuGk4kjqyt8slJPL5TA5OYnBwcGq6hebibvV7C5KPb8b9kMM1tnN\nmlI0zIQwPT2NRCKBGzduYHZ2Fuvr6wiHw5ifn8fg4CBnvlI4J5PJ4J133sH6+joWFxcRj8fR2dnJ\nJ4aogtfdu3cxPT3N9RDIhmFubg6jo6NYX19nDWJxcRE//vGPEY/H2ZxueXmZrarT6TSLkeokQnUT\npqamsLq6ivv376OlpYVXyXQCCQAfe6QdxurqKoaHh9Hd3Y3Hjx9jYmKCawHY7XacOXMGPT09nKNA\noi0JsMCW4ErGcuoJmo6ODj4Sq2bPUjb2zMwMW3V0dXXB6/VylvaDBw+wtraGyclJjI6Owuv1YmJi\ngq3DyYqaQlinTp1icVrN2jWrTWyGcdW/tLS047jpfointWQQG1+/V/ZTDNYTgcaMhhGV8/k8Ojo6\nOFwkhOCjh8lkEqFQiMVUqmO8uLiITCbDPkbk5UPGdDabDXa7nWsitLW1ccxYSslWEvl8nquqkYMp\n5SLQeX/KBianUVrNkicRncGncpOpVAptbW2IRqN8Np/uHY1Gsby8zHWAaWIAnk406XSawzfAU/GQ\nrKHp/hSSAcBtpFIphMNh/lzJkTWbzSIYDKK/v5/9n3p6etDc3Mx2Ge3t7RzGIsbHxxGJRCClRKFQ\nwPLyMtrb24tqP5M4XC5rt9z3r676x8fH+XNLpVKYm5srymwmoZgst0l7qCV8cq9RmWsAACAASURB\nVNjirBaDNYdBw+wQAGB0dBQ/8RM/gaamJjQ3NwMAexiRlkDWyzMzM3j8+DGfzCGvII/Hg5WVFfbp\nOXv2LFsfUyZzV1cXOjs7kcvlMDAwgJ6eHlitVszMzGBiYoL7Y7fb0d/fj9HRUTZb8/v9uHTpEi5c\nuMADNJmoAVsnd6amprCxsVEk/hqTqQiv14tAIMDZ0clkkquT0WkmI2QNDRSHU2jwbW9vx/j4OA84\n7e3tuHjxIlteA8C7776LR48eobW1FYVCAQMDA3j++edx9+5dNo8jKwuq3dzZ2cn9CYVCLGRfvHiR\njwYDT7N2yUJjN9B9zY6omllQEzRpqL/T6zSaRqchJgR11Xfx4kWkUinMzMzwUcz5+Xm88cYb/PrJ\nyUl2+Hzy5AkikQivamdnZ3lln06n0dfXh1AoxOEbsrqg8prLy8twu90YHh5GR0cH/H4/UqkUIpEI\nkskk+xy53e6io4lGC+SNjQ0IIeBwONDR0YFsNotEIoFnn322aDJQM7LpqCVVQsvn81heXub7ZzIZ\nRCIROBwOvPjii0XhJjpVRQ6lDocDQggkEgksLi6ira2NP8OxsTHOSCYhdXR0FBsbG5x9PDIywuaC\n5KVEZTxJoAe2jvaSIE+lKKlNVRymkFY4HK7qDD8JpVQkKJVKIZPJwOFwcClQYwaw+rNZOKaSvfVh\nirNaDNYcBuJgSyfvDiHErko6Z7NZ3L9/H2+//TaXVEyn0/B4PJifn0dLSwtXOxsbG8PCwgJcLhdu\n3rzJg+vc3ByamprQ1dXFsXOyfnjw4AEKhQL7DwHgfIShoSEu9pJMJvHDH/6wKBP3pZdeKorDJ5NJ\nttumOHk+n0coFMLMzEzRuXeqMww8XdHSP5vNxgNqNpvl5Klr167x6h0AvvKVr8Bms7FPUKFQwPXr\n19HV1YX+/n7EYjF88pOfxNraGt59912MjIzwaaavfe1rbOhm/LypD9lsFjdv3uQTRIVCgfMvaDcS\nDAZx/vz5It8fM3E2mUzi7t27/BoK7ZidGDIeH6UwISUHAsCFCxd4J6beU21DrYMMgE9GqZw9e7bs\n/Q8DvWPRVEDs5eK67RCEEM0ArgOISil/dj/a/O53v4vXX38dExMTEEIgGAyiubkZfr8fKysrXNPY\narXiww8/xOrqKgYGBpDL5XiyoLKV0WiU7aDpXPytW7cQi8Wwvr6Ozc1NNDc3I5vNwm638wmVS5cu\nwe12Y3x8HPfv3+cC71euXOGTPteuXcMPfvAD3L9/H9lsFqFQCP39/ejs7ITX6y1bZ5eOSVIWNhVS\nAYBXX32Vi8tks1l0dHRgdXUVKysrePPNN9Ha2sraANlMA1seR6lUik3eUqkUFhcXTcNKKvT41NQU\n23ZQtbSHDx/i4cOHKBQKaGtr47ZCodCO8JdxJe7z+XgHBIBdZ8nrh1bqpVb1ZHCn1kc+qOzcwx6Y\n9USgOUjqKSr/CoC7APZli0IFVtbW1jhTeGFhAfl8nn1xyALb6XRyJTPKVvZ6vWhra8OZM2fQ2dnJ\nInFnZyeklJiZmWFrC7oH+fdTghadLKLwEq3ys9ksV11LJpO4ffs2m7NlMhmEw2EkEglOpFNP1qh1\ndnO5HJ/bpwEzl8uxmR2dENrY2MD6+jrXI3Y4HEin0+xfRNnG5ESaSqXQ09PDJS/7+vpYUDYWajFC\nYqfVamXjumQyiXQ6zbuKubk5ZLNZU3vpSmIpidrG582uowMFlJ1MojQ9X+oeasgRAIex9mJvrdEc\nR+qyQxBCBAF8EcC/BPCP9rt9i8WCpqYmdHd3IxgMIhgM4tSpU+jt7cXjx4/R09OD2dlZzgUYGRlh\nAzNgy4RufHyci9F3dHQAANsv04kj2iWMjIxwjQAarDo7O5HNZnkyIuj0EdVioNKU5IdE1bLUFSyt\n3Ok0UzX09fXxqnxhYYEnH7vdzsdAz5w5g3w+j0ePHuHMmTNsOT0yMsI20WZitnFAJyiDl94vmfeR\n1bff76+q77TrstvtEEIUWV+o0ISr7qbK1UcuhzGrWX0M2Gl6Z3xMo2kE6hUy+jcA/jEA1341GAqF\n8LGPfQx//ud/DmBrkKAKXuT/b7FYkM1mce3aNayurqKnpwd+v59FWRJcr1+/jvv376O5uRldXV1Y\nWlpCa2sr7xq6u7vR3t7OQiydqNnY2MA777yDRCLBFhhtbW3o6+vD6OgoEokEexY9fvyYE+Xa2tow\nNzeHN998E/39/UWhkampKbb0llJyaU4a8Oi1ABCLxRCNRrG5uQmHw4FIJMI+TCsrK1wkx2Kx4Pz5\n85yhSw6qNGBbrVYEg0HTyUC1fgae1jWmtmi35HA4eIfm9XohhMDy8nJRzWJgp1iaz+dx48aNohwC\nytAGnq7U6XMh7efcuXNFOydjfeRqBFn6fug1ZJdh9v7V12g0jcKhTwhCiC8BSEgpbwohru5n29/8\n5jfR09ODdDqN2dlZFn5bWlrgdruRTqdhs9nw5MkTuN1u9Pb2orW1lYuchMNhRKNRzjGgFerS0hL6\n+/tx/vx5dHV17RjUstksr64jkQgePnyI7u5uTnS7cOECLBYL12lubm5GKBTivIVAIIC+vj7MzMyw\nbjE/Pw+Xy4X5+Xl4PB4OfVAWNfnoAE9Xqt/4xjcQj8dZNI5EIrBarXC5XFhYWEAoFEJLSwsfTVWt\nF7LZ7A7xmt6bKhzTap3i+2QoR7kM4XAYuVyOs77z+Tz7IJXKOqYTRwBw8+ZNRKNRfr+0o1EFdAoX\n0Y6EKsbNzMzwY9QuYcxkNmIWgjKzst5LHWWN5qhTjx3CFQBfFkJ8EYANgEsI8V0p5TfVF7388sv8\n89WrV3H16tWqGu/t7UUul8P09DRmZ2extLSE5uZmpNNpOBwOzM7OYmZmhj2BqFJaPp9HJBLhegd0\njj+dTqO5uRktLS1c26C7u7toNUkOq/l8Hg8fPuTHXS4XF6xZWlpCPB7nZLNMJoPm5mYuz0mr25aW\nFvh8vh0rT1ohAygqh6lCu5ePPvoI0WiUawhbrVY0NzdzFvH8/DzefPNNXL58me9jNqipR1QLhQLv\nTsysn+l61YaaJlTKPKaB2ngP9XO8fv06JicnYbfb0dfXh2Qyiddff51Pi507d65ooKfd071797C+\nvs5Z2+Xuo1f2Go05hz4hSCl/HcCvA4AQ4gUAv2acDIDiCaFaKPxAq9i2tjY0Nzdztu7m5iafMtrc\n3MTi4iKfj6faBqlUCisrK2htbYXT6SxKbJJScvEXNTM2Go2yIJzP57neMtVwdrvdbFkNbNVEcLlc\nsFqtvFOh8/y0k3E6nSxs0i4BeBoiqrQqdTgc8Pl8WFtbg8/n452OlBK9vb0oFAqmBV0INSOaqoZJ\nKeHxeHhypclGzaKuBTNfIcrFSKfTSKfTyOVy2NjYAICiIjT0Pc/NzXHNhmw2i7m5uaqsqI3vu5pz\n/joXQNPoHIXEtH1LhKBTNIODg1hcXOQBnFbdmUwGUkqEQiGubUBF5fP5PAYHB+HxeLjYO5V6XF9f\nh9/vR09PT8mVpd/v50F7YGAAQgjkcjle2SeTSbasBrbKMVosFni93qK6v2poRa3bSyGZUkdAVShs\nMjg4yLkNNpsN77//PiebUWjKmJlbCsr8JZ3BLGRFJ4loB2G0o7ZarRUF2Y6ODnR0dHD2s5SSDfVU\nKASUy+V4B0JZ24ODg0V5B0bU9632pxrTN20Mp2lk6johSCnfBPDmfrR17do1U4vmzc1N+P1+PHr0\nCJlMhp1LA4EA2tvbMTExwWLtvXv3uD6uy+XC0tISxsfHMTU1BbvdjgsXLmBwcBB9fX1FmbEkelqt\nVoyOjvLgNTg4iFgshnA4jFQqBZfLhUKhgFQqhUQiAafTiY2NDba9WFlZ4axhq9XKwi1BOQfVZu0C\nYAdVYGsSWlhYYOFZCMHZwsY21Yzohw8foqmpCevr6ywMG+PwFJK5d+8ev4+hoSEEAoEiwZiSveie\n6uc4OjpaVHvZ6XRiaWmJPZmGh4eLVv5utxtDQ0PIZDIsJJMj640bN4o+M7oPfabhcNjU+rqaQV5P\nBJpG5SjsEPZMMpnE+Pg4FhcXsbKygocPH8Ln8/HZehoI3W43D7gjIyPo6elh76FQKIRIJIJAIMAr\nT1rhnzp1CsCW+dvt27fx5MkTdHd3F8Wy1SIp5Mljs9nYbZVW67lcjk/mZDIZruCWz+d5cJ6fn8fC\nwgLm5ua4faqZQGJzKRsGoFikVbNtKVzU1dXFxeFJwDYTUKkYjd1uL1rtG4VhCsnkcjkW6QOBAO90\nfD4fkskkJ4wBT8M2Zr5CtGoPh8NFwjFlHKuox0XpmlQqhWg0ypP7/Pw8zp49y98reSVNTExgYGAA\nLpcL0WgULperbM6FRtPoNMSEAGxZT9+5cwcLCwtIJpPs8w9sxZ5nZmawsbHBVcU6OzuL6vUCxXV3\n4/E4ZmZmMD09jZWVFdjtdkxMTGBpaQnvvfceBgYG8OKLLwJAUUxZ/Z1OyqiDKekZlA08PT3Njqo+\nnw8DAwNYXFxkbyWbzQav18vXx+Nx5HI5Fm6NuwZVPDVaR8fjcZ6sKKPYDGMbNLlUi7F+MdWfnpyc\n5OxhFePkZtQjKHO71MpcvSYej+Pu3bssTHs8niLhXP1+KdQkhGCtKBgMasFZc2JpCPtrOsuvrugp\n65ZM7KxWK548eYJUKgWn08magsPh4BARWSHTKpKKvQDAkydPONGstbUV0WiUbZaJaDRa9DvdiyCr\nabvdjtXVVS6Y8+TJE1itVqTTad4pOBwOzg9QE60INVO5VPauen96T36/n7UKCk2p4mi5NoDyYisZ\ny9FRVHWCpOeoz5UEWbPs4WpCNaQP0GEB1UKb2qS61MFgEAD4UAAVydG20pqTSsPsEM6ePcu7ADKw\n+/jHPw6n04m3334b3d3dyOVyWFpagt/vR0tLC4LBINs5A8WVtug8/eXLl7G+vo6Ojg7cuXOHdx1m\n5PN5SCmLVse02lTFy8985jPweDxYW1vD+vo6AHBJz2eeeYattVVRmEIZ1C8VVSQlmwfqA+UWqDWB\nSXAmYdhsoFWzgI0un0ZhmMJLPp+Pq7mpnyeh1iIulQug/kxtlhK+zQZuj8cDn8+HQCAAYEvgV/uv\n5iNQ9TYAJbOodVay5iTREBOCzWbD0NAQHj16xLHgvr4+HgguXrzI1ciogMr9+/d5R2AmLFIsnwYK\n1SSNav1SURdVrAS2irxQKMdoOa1m0MZiMT6K6nA4MDQ0hFAohKamJm6Twi10nfEYqmoTPTU1hamp\nKUxPT8PhcOD5559HIpHY8Z5o9W42KNtsNuTzeY73q/WMgZ3n+en9l/pdFY6B4lrEKmoGNOkbHo8H\nHR0dpjWPS2VMZzIZtgWhLGfjYK7abDudzqKDAOpOROcuaE4aDWd/nUwmizJagaeWynNzc/iLv/gL\nTE1NsbtpIBDAuXPnilbURktm2jW43W6O4ff39xdZWd+9e5fbyOVyvArOZrP44IMPigRVn89XlN8g\nhNjhGxSPx3Hr1q2i8M3g4CAuXbpU9H7pvqlUCm+99RZ8Ph/Hxj/96U+zfbaZzbTZ6pesoNUdAl1v\n9j7pNWa/0+dJn5G6GzHqBHTPiYkJxGIxXuEXCgWEQiEOW9Guhl5Pn2sgEEAsFmNPqlIidKn3b7br\nMVpim9lfH2f07qchOZ721wcBZeqqvxNutxvT09N48OABYrEYi7U08JRqr1RBmaWlJVy+fJlfpw6C\n5QRQEoxTqRS35fF4igYuspOOxWKm4q/atvGsPwDTKmHGayutfo3tUp9KCcOlmJqawoMHDzjb2efz\n7fA/KlU3eXFxEbFYDLFYDE1NTbwrKiWGq303+w4qFbw5Sejdj8aMhhCVS5FMJvlUTTabxcLCApfB\nTKfTsFqt7A1EQie9lv5L2brpdJptquln9XhpOQGUBn3SGGjwX1paQjqd5nsZhWGPxwMhBPL5PGff\nqv1T7+t0OvHss8+yGB0MBvkYq3GwK2U5Te0a3wu9BtgKbZEwTE6yhPF3p9OJhYUFznamzOLV1dUi\nLYSEaxK91Tbos5qbm2On14WFBUgpkcvl+CSReijA7DuoZLNtpBpRW/3MjhO1fhaak0ND7RBUrl27\nxiLq0NAQvF4vIpEIbDYbent7IaXk7F8iFovtKNSikkqlWLQmS2zCLINVXYUNDw9jbGwM165d491G\noVBAIBBAIpHgUIzT6SzyAzp9+jSuXLkCt9uNRCLBYQxa1an3HRsbK5qk1P9WQi3C09XVxaEe9Tin\n6lGkCsPG961+blQ1rRK0QlVDTOPj44jFYjxper1ezM3NYWpqim1BhoaGMDw8zDrJfoZBymUl6xW2\nphFpyB1CMpksSsiigcXv9/NJHMpeVY+JqqtWVQT1eDxobW1FZ2cnXC4X10kwO35Z7vimzWYrynug\nE0tqARiyjCbI70jtE/2sDn5q9i4Vpil3bl9d/apFeNT7lLreGI4x3ot+p2xnj8cDi8WCYDAIv99f\ncjWv/uvu7sbQ0BA8Hg/XYXa73bzbWF9fZ81InXRKve/dHmM1a++4r7B3+1loGp+G2yGQwVk6neZY\neqFQwNraGrxeLwYHB5HJZDAyMgKv18uiZKniM7RKJMvodDrNHkSl7m9EPSZKVcqIYDCIRCJR9Bq/\n3899oRoONpvNVLRV71nuj1q1sbbZbEXZzACKJgQzyKPIWN6zEpTtbOxfudU8PW4sWpPNbtVtBsDC\neS3U4kPU6IKr9mTSmNFQE8LU1BTXFY5EImhubsapU6fYVO173/se21RPTEzg85//PEZGRtg/h4q4\nqEdGgadHMelYq9PpxMc//vGKYYSurq6i46a0MwGennv3er1Ip9M8wFEYKxqNYnFxkV1R8/m8afGX\nakIXxmOvdJSzVJEb44oxkUiULDxT6ftQ+2Y88WPWhtn7Ub8H8i4ifyPSHKod1HbTbzOfp0ZwPT2O\nfdYcLA0zIWSzWUxOTiIajaJQKMBqtaKtrQ0+nw+5XA5PnjzhnUNHRweHG8hRk7Jrc7kcAoFA0eBF\n4nJrayufSpJSFiVMJZPJovDT/Pw8vF4vWlpaMDg4yFmwZ8+e5QQuaht4uoug47aBQAD5fB6tra1Y\nXV3lI5V00sblciGZTFZV1EW1sSZhd2BgYEeRm0AgwEd21eupGA1l/5oVuTH7Pkr1rdzOoNL78fl8\nGBsbw9jYWFGIaa8renVnV00RnGpW2I2+y9A0Hg0zIZhhs9lgt9t5sKXYs2obARSHYJaXlzE5OVlU\nxlLFaMMMPD2S+f7777Pldjweh9PpRCKRQDAYxPnz54v6ZcTsLL/ZvcgF9e7du9zfao6ALi4uYnV1\nFYVCAU6nEwMDA0XPU35FqfetispTU1M4c+ZMxXuasRcx1izBz+1271ngLef/VI5yA70WnTXHkYYR\nlW02GwYHBxEMBtHa2gq/34/h4WEMDAwgGAzi1KlTOH36NDo7O9Hc3Ay3241nn32Ww0PAzsQqEgtV\ncRRAUQEWM/E4mUxidnaWnT+j0SiHi8yEaFXgo6Obqi+Q0WeJ+micREq1r57b7+joQGdnZ1H7hPF9\n0/VOp7NoN5FKpSqKqGbCJbVNGMXYcmKnutMBnhbLMdsl1SLw7sa7qdY2j5vorDm5NNQOwefz4Qtf\n+AJnFavhnJWVFYyMjCCVSrHA/IlPfALAU38bOrJpJt4aBU6aDOgPPZ/Pw+l0snEeDVxer5ftEdSV\nvFlhFqPoa6xxTGEuEsJzuRx7A5U6DUN9/9jHPsY7AKrhoA626qksI319fXySpxpB2SgKq/0v9fka\nP2e6ph6c5IQ1zcnm0CcEIYQNW0VxrAAsAP6XlPLbe223VDhBzUdwOBzo6+tDZ2fnDs8as2xkow+O\nOuiqIYGpqSnOPna5XOjp6UGhUODz88bJwCycoNZoLhVioNcsLy8X+f2srq6WLFhD7ZF3D/2uvr6S\nSEo7pGpE1ErZwOV8ktTXmT1GBXvoOyZfpL0IvAchEDeK6Kw5edSjpnJWCPFpKWVGCNEC4G0hxF+R\nUr692zbNwgnhcBhPnjwpWvmm02kWTuk6ACy0FgoFrK+vs1umlBLJZHKHyGqsBUztko9OIBDY4fqp\nXm8MJ1gsFkSjUfYkSqVSRUKmWQbz3NwcF/MxCp9m9yAxGzAfcCutzKsVUdX7GovOZLNZLnEKFB+p\nrQaznVS1faum3d1ef1htajQHTV1CRlLKzPaPFgDNAJb2s/3FxUXk83kUCgUsLCwUxdDn5+cxNTXF\nq8zh4WEulkKr+kAggGQyySt8KppCq18qUKOu+ikUE4/Hy4rSRuLxOJ8iisfjbIcNAFevXi15T2MR\nmmqoNDDt9XkVVYQ2Fp2ptd/GPlS7q6i13f1GTwSa40ZdRGUhRJMQ4scA5gC8LqW8u5f2VNGX/IL8\nfj+8Xi9OnTrFSV7BYBCpVKpoJ0EW1JSB7HQ6YbfbkU6n+Tjm/Px8kXipirkUjzcKvoC5mKgKp/R6\nunc4HOZTQNFoFPF4vOI9AfMQTz0yUem+NHmpnx+t6nWGrEZzdKnXDuEJgAtCiFMAfiiEuCqlfEN9\nzcsvv8w/X716FVevXi3bpppRfOvWLVgsFuRyOQwMDHCBGZvNhg8++GDHtX6/H36/nwfbwcFBXuWr\nqAM+FZmhdtXCOpUwq3ns9/vR1dWFQCDA5/3V+5oJyMaCMupjpUIWtZyN3805emMBGrPnDyqUUqm/\nOi9AoylPXU8ZSSlXhBDfB/AcgDfU59QJoVpsNhsikQgmJiYwPT2Nzc1N9Pf3I51Os8hsFCYpM1gt\nHEOZu6oouLq6yteRjTNlPVM2bTViolk2MxVquXjxIoephoaG0N3djUgkgvfff5+L8qgFZoyZysZs\nY2O4qpaz8Xs5R68WoDH7HA5iQK7UX50XoNFUph6njDoBbEgpk0KINgCfA/Ab+9E2mdo5nU5YLBbM\nz8+jra2Nz6z7fL6inUQ2m2XRk1a1NNjSSpdW3uFwmLN1qSYAsDOTlVb/pY6BlhN7VbdSt9vNfaTj\nrBRWoj4bxe3JyUkEAgEO06j9qCYLuFw/zV5rFg4j9mMnUO2KvlJ/a3nvGs1Jph47hB4A3xFCNGFL\nw/h9KeWr+30TqohmRiKRYM8jp9OJ/v5+XjHS6lE9ikqmbt3d3bBYLKYZxMDuV6HqwEQTEmU/RyIR\nLpJDYrnVakUwGNxhIUHPWywWFAqFoszjauwmasGshOV+Fp3RK3qN5vA5dFFZSnlHSnlJSnlBSnle\nSvmv96ttt9uNoaEhdiMdGhqCw+HYkVlMnkfAVpGamzdvsgU1CcjqMdZMJoN8Pl9W0K0mO7VaUZXa\nooIxQgik02kWy2kHQG0QLpcLFosF+Xy+yFLb7LXlBN1K/aT+kXhMBXP2KyN3v4vZaDFbo6mOhspU\nBoDLly9jdHSUfzdmLasUCgVsbGwAQJH1dTabRS6X46Orra2tGBwcNBV0je2Wy8IFigVlVYwudZyy\nu7sbHo8Hvb29mJmZKTrNZGyPniMTOyO1hHGO2zn6SqE6n893rN6PRlMPGm5CALDD8IyEX+Cp59H1\n69cxMzODzc1NFnY3NjbQ0dGBWCyGR48e4fbt28jlcvD5fBgdHd2R3atSTRauMcwCoCjj+Ny5cyxQ\nq+I0neNvamoqysa+efNmkYhM19AuxszOutYs3lKP071IS6HQ1H4MtrVm+pYLL+nQk0ZTPQ05IVDI\nR80HUGPoPp8PV65cwTPPPIOZmRk4HA4EAgEIIbhWr5QSLpcLPp8Pdrud7a5VjL5GahauMcvZGGbJ\n5/PIZDJIJBIIhUKcXU2CsbpCp+t9Pl+RIV04HDa11gbKF6DZD6h/Rr+laqimX+VW/GobQGm76sMQ\nk/VRVk0j0XATAomxdBKIMnvVmsFU0N3r9WJlZQWAeYintbUVdrudV9rURjwe37Gyp4GYspVLZekS\ni4uLHH+nU1GqYEw7BaM1s9Hh1MhBH+8sda9qqbagT7VHSGuxq95v9O5D02g0jP01UCzGejweFjuN\nNYPJ4li1gKaBmKynqRKXxWKBx+NBZ2cnUqkUcrkc5ubmEI1GuYoZxeurydK1Wq2w2+1s0e33+5FK\npZDP54sEY6N/kdpvABWzlY8i1YjFlV5Ti131QYrJ2uJa04g03A6BoOLsJAR/9NFHRc/Tas4s5EH1\nByjsQI9/9NFHWFtbQyaTgRnlsnRVYXNoaIgL3ABbg5rFYimqcVwK47HOgwxZ1CMcQoJ+LX5H5Y66\nHjdxnNChKE09aKgJgVaExjrGtVgkVxIoP/zwQ6ysrHBFM/VIK2CepWtmba2+JhgMAoBp/yr1+6AG\njIMIh1QSi+meFG4z1raupo1S991vdtOPatGhKE29EFTD9yghhJC77Vc2m2W/IjqG+ZM/+ZOc2QuY\n1/Kl/969e7dodaruFF555ZWi46mf+cxn0N3dXVH0NO5Ozp49a7q6N+tfpWOpu6FchjE9X6rP+3l/\n4/tU76l+b9W2cZBU+n+nmmzqal93kJ+9puERe7m4oXYIBA3o1Yi76lHQXC6HTCbDYrRaZ5iEZTVL\nuVR+A7BzkC/3mlK/H8RKsZoM44OmmsHNarWWfd1hDpCViv7s5XqN5ijRUKIyUNmCWcV4FDSTycDh\ncGB5eZkzfWlyyefzHNoBtnYOpVavZv0hqg0tHIRoWW2GcT0ye49qNvFev4f9zrrWaA6Shtwh9PX1\nsf11NUKtit/vBwAMDg5ifHwcq6ur3MYnPvEJDuEYq6ABpUXeaqyo9xoCKRdu2k279RBjj4oAXG9B\n96h8DpqTR0NOCLRFz2QyyGQypuIkUD7jNhKJ4Pbt22w7/eKLL8Jms7FAHIvFeCVXjf10uXCQ2XW1\niJZmIQky53v48CGklBgeHsa5c+dqyjCux2BU7wHQ7LOsR83men8OmpNJQ4rKpcTJSqKt+tgrr7wC\n4KnH0Ve/+tUdx1epcA2FlSYnJzlzGADXWjAKwmofybZavU4VEasp+mIU7TbRGgAADQtJREFUIQOB\nAMbHxxGJRBCLxfixkZERXLp0aUcbRyFzt96rcupDtQcAdtN2Ldcfhc9DcyzRonI5SJwstYo2E/vU\nGG8pq2tgS7ROJpOwWCyw2+07npuamuJSnORTVCu1DghUozkSiWBxcXFf2qyW3Yqnx0F03etnVsv1\nx+Hz0DQmDSsqE8awDv2s1kimx2giIBttggRktW3aGZDmkMlkOMuZdg5U/YyymdVVH7Wz14xjY5+A\nrcQ6v98Pi8WCtrY2OJ1Ozrw+qMlgt+LrUcr4PQqC7lH6PDQnj4bbIZhZHRv/oHK5XMU/MtVGWxWQ\nSfBT6yd7PB7k83lcuHCh6H7kfAoAa2trSCaT7K2kZjUbw1m1hgvU6m4UIiLb7MHBQQ5ZlRK0NU/R\ngq7mJFOPEpqnAXwXgA+ABPC7Usrf2o+2S221VWGPzt9TPWQzi2ii1LFSGlypTcp1CIfDRZnIdLQz\nHo9jYWEBiUQCQ0NDuHz5csm+7iZcUEqgDgaDPAFV8znthb2IpweV8btb6nn/o/h5aE4Ohy4qCyG6\nAXRLKX8shGgHcAPAz0kpx5XX1CwqZ7NZXLt2Dfl8no3rjBmeyWTSNBMZeDrIl1o5q7WOjY8b21SF\nyHg8jtdee61Ii/jsZz/LK3ljP2jXYWyr3Ps2CqGUXV0qg/qws5Brve4gBdjjsjM6Lv3UHDmOl6gs\npYwDiG//vCaEGAfQC2C87IUV+Mu//Et8//vfRzqdhs/nw/PPP19kXAds/XEZTdMSiQQ7oZY6Nnrt\n2jUeqGmFX65N9Tm3211WmAbAGdHAlt5gtqqvhf20udjNvfdyXa27l1pef5zEWj0RaOpBXUVlIUQI\nwEUA7+6lnWQyWRSvTyQSWFtb2/E6o2io2mLncjmEw2EemEnMSyaTRav2cDjMuwWzNo1bfDOBmvIi\n6L7AlrhsrHVQrXFbLULoURBOS1GroFrL67VYq9FUpm6i8na46H8A+BUp5Y7R++WXX+afr169iqtX\nr1Zs89SpU3z8U602RqiCMw0GVOOABuG1tTUOO5XCmLtQSYhUBWoKiZiJ08CWGGwM+VQKH9QqhFaq\nRqbRaE4mdZkQhBCtAP4YwH+RUv5Ps9eoE0Il3G43RkdHkUqlOLPYWNO4lPBKuQIAEI1GEYlEAADP\nPvssxsbGYLPZMDQ0xIO2w+FALBbjTGVVuK7Ux6mpKW6nXEayqlNUG+aodmA/ymGTWgXVWl6vxVqN\npjL1EJUFgO8AWJRS/sMSr9lVpnIymWSfoWoyg/P5PCYnJzEwMIB8Po/33nsPHo8Hra2tAICXXnoJ\noVCIQ0cATMVg9V61ZBSXy0imXUitInM5jJ8DAFy6dKmk+Fzu/eyWato9DFGZ0JOCpsE4XqIygE8C\n+EUAt4UQN7cf+7aU8gd7bbga91Fibm4Oy8vLiMViXEYzmUyiUChgdnYW6+vrALbCK7SKNoaRSAw2\nFnTZa0YyreKp7b2KzEZUW/DOzk6cOXOm6PmD2kXs925nN68vlbWu0WjqICpLKd+WUjZJKS9IKS9u\n/9vzZFAOY2ZwMBhEOp2GxWLhn6WU6OnpwcbGBpaXl9HW1gYA+PDDD9kKW63fqxa5L2clbdYHoHTI\nQhU/dyMyV/ocnE4nTwYejwepVKpszeL9El+Piqh7VPqh0RxFGi5TuRSq8AoAH3zwQZEx3eDgIFwu\nF2ZmZgAAbW1tKBQKKBQKfE0ul0MgEGAxWD3ZVAo1nGEm5pYKd9AkYCYyl7tHNZ/DwsICgJ11n48j\nR/m8/lHum0ZjxomZEIDiP0whBOsBdBSUnE2Hh4fx+PFjLC8vw+VysY12IpHA5OQkCoVC0eRSykq6\nksW1WejCZrOxtkF9KxcKqzX8YbPZdtR8NmogByG+HkS7uwn9HJa4rMNSmuNIw9lfVwOJq2oOgCoO\nZ7NZ3Lx5k5+jXIX5+Xnk83k8evQIgUAA586dAwBTv6BKFtfqySWCEumMfStXg3m3WcfV2Gobn9+P\nFe9+rZr3mnF9kKt3XRdZU0eOnah8ZFBrL1P4qKuri0M78XicPY8ov2FxcZFXfj6fDxaLBZOTk3zt\nfq0EjeGc/V5xVpPwdhD3PyqD4lHph0ZzlGg4++tqUAVedSUOPLXJVsVXv98Pp9OJlpYWZLNZ+P1+\ndHR0YHl5Gfl8vujaai2u3W63qchsJj6r/VLvc1hZx0dRiD3KGddHuW8aTTlO7A6hVKaw+rxRfB0b\nG4Pf7+ffVV3ADKOIbDzNo/ZBHTCMmcfl7BiMVt8HhXHi3E8Oqu5zPUVdbaOtOY6c2AkB2GljDRSv\n5oziKwnP9FgwGAQA02tLCcbGx43Xm2U+m/WRajsbrzsIEokEH62l6m/7NcjtNRRVqh9HQdTVE4Hm\nuHEiRWUzSq0mqxFXqxV7AeyoyQzA1Dq7XB+N7VS6bi/Ukt28l7aJ/XgfWtTVnGC0qLwflPPAqfTY\nYQ00xgnosGmEvAWNRlOaEykqHzSlREXj48FgkMNO6ut22/5BcJD3Oqi2tair0ewOHTI6QKoNQ+1H\nlbGD5qDP7R9E2zpTWHMC2VPISE8IGo1G0zjsaULQISONRqPRANATgkaj0Wi20ROCRqPRaADUaUIQ\nQvwnIcScEOJOPe6v0Wg0mp3Ua4fwewB+uk731hwwb7zxRr27oNkl+rs73gghru7l+rpMCFLKtwAs\n1+PemoNHDyrHF/3dHXuu7uVirSGUYT//OHbbVi3XVfPacq/ZzXNHdQDZ734d5+/vuH13wNH4/g7z\nuyv3/GF+f3pCKIOeECo/d1QHlaMwoNR6nZ4QnnIUvr+TOCHULTFNCBEC8GdSynMmz+msNI1Go9kF\nUspdJ6cdSXO7vbwhjUaj0eyOeh07/QMA/w/AGSHEtBDib9ajHxqNRqN5ypH0MtJoNBrN4aNFZY1G\no9EA0BOCRqPRaLY5FhOCEGJECPE7Qoj/LoT4W/Xuj6Y2hBAOIcT7QoifqXdfNLUhhLgqhHhr++/v\nhXr3R1MbYot/KYT4LSHENyu9/lhMCFLKCSnlLwP4OoCX6t0fTc38EwB/VO9OaHbFEwApAFYA0Tr3\nRVM7PwcgACCPKr6/YzEhAIAQ4mcBfB/AH9a7L5rqEUJ8DsBdAPP17otmV7wlpfwigG8B+I16d0ZT\nM2cAvCOl/DUAv1zpxXWbEEo5ngohfloIMSGEeCCE+Kf0uJTyz6SUXwDwS4feWU0RNX53LwD4BIC/\nBuDvCCF0jkmdqeX7U0oXJrG1S9DUmRr//qLY+u6Ard1e+bbrmKn8KQBrAL5L2cpCiGYA9wB8FkAM\nwPsAvgHAB+CrAGwAxqWU/7YundYAqO27k1KObz//SwDmpZT/uz691hA1/u2NYCtM6wbw21LKH9Wl\n0xqmxu/vEYB/ByCDrbHzd8q1XbdMZSnlW9v2FSrPA3gopXwEAEKIPwTwFSnlbwJ481A7qClJLd8d\ngPHta75ziF3UlGEXf3vfO9QOasqyi+/vb1fb9lHTEAIAppXfo9uPaY4++rs73ujv73izL9/fUZsQ\ndNr08UV/d8cb/f0db/bl+ztqE0IMwGnl99PQR92OC/q7O97o7+94sy/f31GbEK4DGBZChIQQFgC/\nAOBP69wnTXXo7+54o7+/482+fH/1PHa6w/FUSrkB4B8A+CG2zq7/EZ1S0Rwd9Hd3vNHf3/HmIL8/\n7Xaq0Wg0GgBHL2Sk0Wg0mjqhJwSNRqPRANATgkaj0Wi20ROCRqPRaADoCUGj0Wg02+gJQaPRaDQA\n9ISg0Wg0mm30hKA5sQghXhNCfN7w2K8KIX67xOt//XB6ptHUBz0haE4yf4CtsqwqvwDgv5V4/bcP\ntjsaTX3RE4LmJPPHAH5GCNECANse870AgkKI20KIO0KI39x+7jcBtAkhbgohfn/7sV8UQry7/dh/\nEEI0CSGahRD/efva20KIX63PW9NoaqduBXI0mnojpVwSQrwH4IvYMgL7OoD/C+BfAbiErdKD/0cI\n8RUp5beEEH9fSnkRAIQQowB+HsAVKeWmEOLfA/jrAD4C0KtUsjp16G9Mo9kleoegOemoYaOvA3gM\n4HUp5aKUchPAfwXwV02uexHAGIDrQoib278PAJgEMCiE+C0hxEsAVg/6DWg0+4WeEDQnnT8F8KIQ\n4iKANgA/BiCU5wVKFx/5jpTy4va/ESnlv5BSJgGcB/AGgL8L4D8eXNc1mv1FTwiaE42Ucg3A6wB+\nD1ti8nsAXhBCeLcLl38dT+t5F0hvAPAqgK8JIboAQAjRIYToE0J4AbRIKf8EwD/HVuhJozkWaA1B\no9kKG/0JgJ+XUsaFEN/C1iQhALwipfyz7df9LoDbQogbUsq/IYT4Z9jSGJoAFAD8PQBZAL+3/RgA\nfOtQ34lGswd0PQSNRqPRANAhI41Go9FsoycEjUaj0QDQE4JGo9FottETgkaj0WgA6AlBo9FoNNvo\nCUGj0Wg0APSEoNFoNJpt9ISg0Wg0GgDA/wfKfxsDURJ5KgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a2c0d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(data.votes, data.rating, lw=0,alpha=0.2, color='k')\n",
"plt.xlabel('Votes')\n",
"plt.ylabel('Year')\n",
"plt.xscale('log')\n",
"remove_border()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we tend to think is that movies with a lot of votes tend to be more popular and hence have a higher rating. But in this plot, we do see some outliers which have a lot of votes but a very ppor rating. So we can infer that these movies were probably highly hated by the viewers. Let's pull out these movies from the data."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>317</th>\n",
" <td>New Moon</td>\n",
" <td>2009</td>\n",
" <td>4.5</td>\n",
" <td>90457</td>\n",
" <td>Adventure|Drama|Fantasy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>334</th>\n",
" <td>Batman &amp; Robin</td>\n",
" <td>1997</td>\n",
" <td>3.5</td>\n",
" <td>91875</td>\n",
" <td>Action|Crime|Fantasy|Sci-Fi</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year rating votes genres\n",
"317 New Moon 2009 4.5 90457 Adventure|Drama|Fantasy|Romance\n",
"334 Batman & Robin 1997 3.5 91875 Action|Crime|Fantasy|Sci-Fi"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#low rating movies with lots of votes\n",
"data[(data.votes>90000) & (data.rating<5)][['title','year','rating','votes','genres']]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>Manos: The Hands of Fate</td>\n",
" <td>1966</td>\n",
" <td>1.5</td>\n",
" <td>20927</td>\n",
" <td>Horror</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2793</th>\n",
" <td>Superbabies: Baby Geniuses 2</td>\n",
" <td>2004</td>\n",
" <td>1.5</td>\n",
" <td>13196</td>\n",
" <td>Comedy|Family</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3746</th>\n",
" <td>Daniel the Wizard</td>\n",
" <td>2004</td>\n",
" <td>1.5</td>\n",
" <td>8271</td>\n",
" <td>Comedy|Crime|Family|Fantasy|Horror</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5158</th>\n",
" <td>Ben &amp; Arthur</td>\n",
" <td>2002</td>\n",
" <td>1.5</td>\n",
" <td>4675</td>\n",
" <td>Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5993</th>\n",
" <td>Night Train to Mundo Fine</td>\n",
" <td>1966</td>\n",
" <td>1.5</td>\n",
" <td>3542</td>\n",
" <td>Action|Adventure|Crime|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6257</th>\n",
" <td>Monster a-Go Go</td>\n",
" <td>1965</td>\n",
" <td>1.5</td>\n",
" <td>3255</td>\n",
" <td>Sci-Fi|Horror</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6726</th>\n",
" <td>Dream Well</td>\n",
" <td>2009</td>\n",
" <td>1.5</td>\n",
" <td>2848</td>\n",
" <td>Comedy|Romance|Sport</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year rating votes genres\n",
"1982 Manos: The Hands of Fate 1966 1.5 20927 Horror\n",
"2793 Superbabies: Baby Geniuses 2 2004 1.5 13196 Comedy|Family\n",
"3746 Daniel the Wizard 2004 1.5 8271 Comedy|Crime|Family|Fantasy|Horror\n",
"5158 Ben & Arthur 2002 1.5 4675 Drama|Romance\n",
"5993 Night Train to Mundo Fine 1966 1.5 3542 Action|Adventure|Crime|War\n",
"6257 Monster a-Go Go 1965 1.5 3255 Sci-Fi|Horror\n",
"6726 Dream Well 2009 1.5 2848 Comedy|Romance|Sport"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The lowest rated movies\n",
"data[data.rating==min(data.rating)][['title','year','rating','votes','genres']]\n",
"# or data[data.rating==data.rating.min()][['title','year','rating','votes','genres']]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The Shawshank Redemption</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>The Godfather</td>\n",
" <td>1972</td>\n",
" <td>9.2</td>\n",
" <td>474189</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year rating votes genres\n",
"0 The Shawshank Redemption 1994 9.2 619479 Crime|Drama\n",
"26 The Godfather 1972 9.2 474189 Crime|Drama"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The highest rated movies\n",
"data[data.rating==data.rating.max()][['title', 'year', 'rating', 'votes', 'genres']]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>rating</th>\n",
" <th>votes</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The Shawshank Redemption</td>\n",
" <td>1994</td>\n",
" <td>9.2</td>\n",
" <td>619479</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>The Godfather</td>\n",
" <td>1972</td>\n",
" <td>9.2</td>\n",
" <td>474189</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3386</th>\n",
" <td>Outrageous Class</td>\n",
" <td>1975</td>\n",
" <td>9.0</td>\n",
" <td>9823</td>\n",
" <td>Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>The Godfather: Part II</td>\n",
" <td>1974</td>\n",
" <td>9.0</td>\n",
" <td>291169</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>The Good, the Bad and the Ugly</td>\n",
" <td>1966</td>\n",
" <td>9.0</td>\n",
" <td>195238</td>\n",
" <td>Western</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pulp Fiction</td>\n",
" <td>1994</td>\n",
" <td>9.0</td>\n",
" <td>490065</td>\n",
" <td>Crime|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>The Dark Knight</td>\n",
" <td>2008</td>\n",
" <td>8.9</td>\n",
" <td>555122</td>\n",
" <td>Action|Crime|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>12 Angry Men</td>\n",
" <td>1957</td>\n",
" <td>8.9</td>\n",
" <td>148155</td>\n",
" <td>Drama|Mystery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>One Flew Over the Cuckoo's Nest</td>\n",
" <td>1975</td>\n",
" <td>8.9</td>\n",
" <td>255503</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Inception</td>\n",
" <td>2010</td>\n",
" <td>8.9</td>\n",
" <td>385149</td>\n",
" <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year rating votes genres\n",
"0 The Shawshank Redemption 1994 9.2 619479 Crime|Drama\n",
"26 The Godfather 1972 9.2 474189 Crime|Drama\n",
"3386 Outrageous Class 1975 9.0 9823 Comedy|Drama\n",
"37 The Godfather: Part II 1974 9.0 291169 Crime|Drama\n",
"85 The Good, the Bad and the Ugly 1966 9.0 195238 Western\n",
"1 Pulp Fiction 1994 9.0 490065 Crime|Thriller\n",
"25 The Dark Knight 2008 8.9 555122 Action|Crime|Drama|Thriller\n",
"143 12 Angry Men 1957 8.9 148155 Drama|Mystery\n",
"44 One Flew Over the Cuckoo's Nest 1975 8.9 255503 Drama\n",
"4 Inception 2010 8.9 385149 Action|Adventure|Sci-Fi|Thriller"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The top 10 highest rated movies\n",
"data.sort('rating', ascending=False)[['title', 'year', 'rating', 'votes', 'genres']].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that record 3386 appears number 3 on this list, although it's featured as the 3386th best movies in IMDB. <br/>This is because it's vote count is low.\n",
"### Run aggregation functions like sum over several rows or columns\n",
"What genres are more frequent?"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1891"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.Action.sum()\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Action 1891\n",
"Adult 9\n",
"Adventure 1313\n",
"Animation 314\n",
"Biography 394\n",
"Comedy 3922\n",
"Crime 1867\n",
"Drama 5697\n",
"Family 754\n",
"Fantasy 916\n",
"Film-Noir 40\n",
"History 358\n",
"Horror 1215\n",
"Music 371\n",
"Musical 260\n",
"Mystery 1009\n",
"News 1\n",
"Reality-TV 1\n",
"Romance 2441\n",
"Sci-Fi 897\n",
"Sport 288\n",
"Thriller 2832\n",
"War 512\n",
"Western 235\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#we can do the above for all genres\n",
"#sum sums over rows by default\n",
"data[genres].sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Genre Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>314</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Genre Count\n",
"0 5697\n",
"1 3922\n",
"2 2832\n",
"3 2441\n",
"4 1891\n",
"5 1867\n",
"6 1313\n",
"7 1215\n",
"8 1009\n",
"9 916\n",
"10 897\n",
"11 754\n",
"12 512\n",
"13 394\n",
"14 371\n",
"15 358\n",
"16 314\n",
"17 288\n",
"18 260\n",
"19 235\n",
"20 40\n",
"21 9\n",
"22 1\n",
"23 1"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre_count = np.sort(data[genres].sum())[::-1] #sort in ascending order\n",
"pd.DataFrame({'Genre Count': genre_count})\n",
"#need to find a way to print the genre name :("
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also note the difference between the following two statements."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 Crime|Drama\n",
"1 Crime|Thriller\n",
"2 Drama|Mystery|Thriller\n",
"3 Action|Adventure|Sci-Fi\n",
"4 Action|Adventure|Sci-Fi|Thriller\n",
"Name: genres, dtype: object"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.genres.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Action</th>\n",
" <th>Adult</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Biography</th>\n",
" <th>Comedy</th>\n",
" <th>Crime</th>\n",
" <th>Drama</th>\n",
" <th>Family</th>\n",
" <th>Fantasy</th>\n",
" <th>Film-Noir</th>\n",
" <th>History</th>\n",
" <th>Horror</th>\n",
" <th>Music</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>News</th>\n",
" <th>Reality-TV</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Sport</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Action Adult Adventure Animation Biography Comedy Crime Drama Family Fantasy Film-Noir History Horror Music Musical Mystery News Reality-TV Romance Sci-Fi Sport Thriller War Western\n",
"0 False False False False False False True True False False False False False False False False False False False False False False False False\n",
"1 False False False False False False True False False False False False False False False False False False False False False True False False\n",
"2 False False False False False False False True False False False False False False False True False False False False False True False False\n",
"3 True False True False False False False False False False False False False False False False False False False True False False False False\n",
"4 True False True False False False False False False False False False False False False False False False False True False True False False"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[genres].head() #because genres was the unique set that we had created"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many genres does a movie have on an average?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"On an average, a movie has %0.2f genres 2.75397539754\n"
]
},
{
"data": {
"text/plain": [
"count 9999.000000\n",
"mean 2.753975\n",
"std 1.168910\n",
"min 1.000000\n",
"25% 2.000000\n",
"50% 3.000000\n",
"75% 3.000000\n",
"max 8.000000\n",
"dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#axis=1 sums over columns instead\n",
"genre_count = data[genres].sum(axis=1)\n",
"print \"On an average, a movie has %0.2f genres\",genre_count.mean()\n",
"genre_count.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Explore Group Properties\n",
"Let's split up movies by decade"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/priyanka/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>year</th>\n",
" <th>decade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>The Shawshank Redemption</td>\n",
" <td>1994</td>\n",
" <td>1990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pulp Fiction</td>\n",
" <td>1994</td>\n",
" <td>1990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Fight Club</td>\n",
" <td>1999</td>\n",
" <td>1990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>The Matrix</td>\n",
" <td>1999</td>\n",
" <td>1990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Inception</td>\n",
" <td>2010</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title year decade\n",
"0 The Shawshank Redemption 1994 1990\n",
"1 Pulp Fiction 1994 1990\n",
"2 Fight Club 1999 1990\n",
"3 The Matrix 1999 1990\n",
"4 Inception 2010 2010"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"decade = (data.year//10)*10\n",
"dec = data[['title','year']]\n",
"dec['decade'] = decade\n",
"dec.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"year\n",
"1950 7.244522\n",
"1960 7.062367\n",
"1970 6.842297\n",
"1980 6.248693\n",
"1990 6.199316\n",
"2000 6.277858\n",
"2010 6.344552\n",
"Name: Decade Mean, dtype: float64\n"
]
}
],
"source": [
"#use groupby to get group properties\n",
"#gather mean rating for each decade\n",
"\n",
"decade_mean = data.groupby(decade).rating.mean()\n",
"decade_mean.name = 'Decade Mean'\n",
"print decade_mean"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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T7/kIvd3FYhHFYlFe65MF/b0SFsEzDYvFoqiRPB7PgqeGhcaNPv5tNptcGOh+YGn9eybe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\nsLCow/8P4XyVrxoZDEkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a0afa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plot the decade values against their corresponding mean rating\n",
"plt.plot(decade_mean.index, decade_mean.values,'o-', color='r', lw=2, label='Decade Average')\n",
"plt.scatter(data.year, data.rating, alpha=0.04, lw=0, color='k')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Rating')\n",
"plt.legend(frameon='False')\n",
"remove_border()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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o7Hk56JwDySTs4KMzDAONVovl0U83PZbNYiCQm5JwYZWEpqq9yUxRoLguTMNA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\n1MK7WwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fce9a092690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#we can go one step further, and compute the scatter in each year as well\n",
"#standard deviation is a measure of how much the members of a group differ from the mean value for the group\n",
"grouped_scores = data.groupby(decade).rating\n",
"\n",
"mean = grouped_scores.mean() #each decade has a mean value\n",
"std = grouped_scores.std() # each decade has a std dev value\n",
"\n",
"plt.plot(decade_mean.index, decade_mean.values, 'o-',\n",
" color='r', lw=3, label='Decade Average')\n",
"plt.fill_between(decade_mean.index, (decade_mean + std).values,\n",
" (decade_mean - std).values, color='r', alpha=.2)\n",
"plt.scatter(data.year, data.rating, alpha=.04, lw=0, color='k')\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Score\")\n",
"plt.legend(frameon=False)\n",
"remove_border()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also iterate over a GroupBy object. Each iteration yields two variables: one of the distinct values of the group key, and the subset of the dataframe where the key equals that value. To find the most popular movie each year:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1950 Sunset Blvd.\n",
"1951 Strangers on a Train\n",
"1952 Singin' in the Rain\n",
"1953 The Wages of Fear\n",
"1954 Seven Samurai\n",
"1955 Diabolique\n",
"1956 The Killing\n",
"1957 12 Angry Men\n",
"1958 Vertigo\n",
"1959 North by Northwest\n",
"1960 Psycho\n",
"1961 Yojimbo\n",
"1962 To Kill a Mockingbird\n",
"1963 The Great Escape\n",
"1964 Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb\n",
"1965 For a Few Dollars More\n",
"1966 The Good, the Bad and the Ugly\n",
"1967 Cool Hand Luke\n",
"1968 Once Upon a Time in the West\n",
"1969 Butch Cassidy and the Sundance Kid\n",
"1970 Patton\n",
"1971 A Clockwork Orange\n",
"1972 The Godfather\n",
"1973 The Sting\n",
"1974 The Godfather: Part II\n",
"1975 Outrageous Class\n",
"1976 Tosun Pasa\n",
"1977 Star Wars: Episode IV - A New Hope\n",
"1978 The Girl with the Red Scarf\n",
"1979 Apocalypse Now\n",
"1980 Star Wars: Episode V - The Empire Strikes Back\n",
"1981 Raiders of the Lost Ark\n",
"1982 The Marathon Family\n",
"1983 Star Wars: Episode VI - Return of the Jedi\n",
"1984 Balkan Spy\n",
"1985 The Broken Landlord\n",
"1986 Aliens\n",
"1987 Mr. Muhsin\n",
"1988 Cinema Paradiso\n",
"1989 Indiana Jones and the Last Crusade\n",
"1990 Goodfellas\n",
"1991 The Silence of the Lambs\n",
"1992 Reservoir Dogs\n",
"1993 Schindler's List\n",
"1994 The Shawshank Redemption\n",
"1995 The Usual Suspects\n",
"1996 Fargo\n",
"1997 Life Is Beautiful\n",
"1998 American History X\n",
"1999 Fight Club\n",
"2000 Memento\n",
"2001 The Lord of the Rings: The Fellowship of the Ring\n",
"2002 City of God\n",
"2003 The Lord of the Rings: The Return of the King\n",
"2004 Eternal Sunshine of the Spotless Mind\n",
"2005 My Father and My Son\n",
"2006 The Departed\n",
"2007 Like Stars on Earth\n",
"2008 The Dark Knight\n",
"2009 Inglourious Basterds\n",
"2010 Inception\n",
"2011 A Separation\n"
]
}
],
"source": [
"for year, subset in data.groupby(data.year):\n",
" print year, subset[subset.rating==subset.rating.max()].title.values[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Small multiples\n",
"Let's split up the movies by genre, and look at how their release year/runtime/IMDB score vary. The distribution for all movies is shown as a grey background.\n",
"\n",
"This isn't a standard groupby, so we can't use the groupby method here. A manual loop is needed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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