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ohiosonia/Exploratory_Analysis

Created April 7, 2015 00:04
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Exploratory_Analysis
{
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"signature": "sha256:cb85482cc790c4be7ef7bcf4fe5550c3f141609d316736fb1377fe4ca94ea64b"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"##Import Modules and input matplotlib graphs\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"from pandas.tools.plotting import scatter_matrix"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##Import data\n",
"names = ['imdbID', 'title', 'year', 'score', 'votes', 'runtime', 'genres']\n",
"data = pd.read_csv('http://bit.ly/cs109_imdb', delimiter='\\t', names=names).dropna()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##Look at the first ten rows\n",
"data[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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",
" <tr>\n",
" <th>5</th>\n",
" <td> tt0109830</td>\n",
" <td> Forrest Gump (1994)</td>\n",
" <td> 1994</td>\n",
" <td> 8.7</td>\n",
" <td> 368994</td>\n",
" <td> 142 mins.</td>\n",
" <td> Comedy|Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> tt0169547</td>\n",
" <td> American Beauty (1999)</td>\n",
" <td> 1999</td>\n",
" <td> 8.6</td>\n",
" <td> 338332</td>\n",
" <td> 122 mins.</td>\n",
" <td> Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> tt0499549</td>\n",
" <td> Avatar (2009)</td>\n",
" <td> 2009</td>\n",
" <td> 8.1</td>\n",
" <td> 336855</td>\n",
" <td> 162 mins.</td>\n",
" <td> Action|Adventure|Fantasy|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> tt0108052</td>\n",
" <td> Schindler's List (1993)</td>\n",
" <td> 1993</td>\n",
" <td> 8.9</td>\n",
" <td> 325888</td>\n",
" <td> 195 mins.</td>\n",
" <td> Biography|Drama|History|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> tt0080684</td>\n",
" <td> Star Wars: Episode V - The Empire Strikes Back...</td>\n",
" <td> 1980</td>\n",
" <td> 8.8</td>\n",
" <td> 320105</td>\n",
" <td> 124 mins.</td>\n",
" <td> Action|Adventure|Family|Sci-Fi</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" imdbID title year score \\\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 \n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 \n",
"2 tt0137523 Fight Club (1999) 1999 8.8 \n",
"3 tt0133093 The Matrix (1999) 1999 8.7 \n",
"4 tt1375666 Inception (2010) 2010 8.9 \n",
"5 tt0109830 Forrest Gump (1994) 1994 8.7 \n",
"6 tt0169547 American Beauty (1999) 1999 8.6 \n",
"7 tt0499549 Avatar (2009) 2009 8.1 \n",
"8 tt0108052 Schindler's List (1993) 1993 8.9 \n",
"9 tt0080684 Star Wars: Episode V - The Empire Strikes Back... 1980 8.8 \n",
"\n",
" votes runtime genres \n",
"0 619479 142 mins. Crime|Drama \n",
"1 490065 154 mins. Crime|Thriller \n",
"2 458173 139 mins. Drama|Mystery|Thriller \n",
"3 448114 136 mins. Action|Adventure|Sci-Fi \n",
"4 385149 148 mins. Action|Adventure|Sci-Fi|Thriller \n",
"5 368994 142 mins. Comedy|Drama|Romance \n",
"6 338332 122 mins. Drama \n",
"7 336855 162 mins. Action|Adventure|Fantasy|Sci-Fi \n",
"8 325888 195 mins. Biography|Drama|History|War \n",
"9 320105 124 mins. Action|Adventure|Family|Sci-Fi "
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Remove minutes\n",
"clean_runtime = [float(r.split(' ')[0]) for r in data.runtime]\n",
"data['runtime'] = clean_runtime\n",
"data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" imdbID title year score votes runtime \\\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 \n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 \n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 \n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 \n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 \n",
"\n",
" genres \n",
"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 "
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Confirm runtime no longer contains minutes\n",
"data[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" imdbID title year score votes runtime \\\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 \n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 \n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 \n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 \n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 \n",
"\n",
" genres \n",
"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 "
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#make a column for each genre\n",
"genres = set()\n",
"for m in data.genres:\n",
" genres.update(g for g in m.split('|'))\n",
"genres = sorted(genres)\n",
"\n",
"for genre in genres:\n",
" data[genre] = [genre in movie.split('|') for movie in data.genres]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>...</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>...</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>...</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>...</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>...</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>...</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",
"<p>5 rows \u00d7 31 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" imdbID title year score votes runtime \\\n",
"0 tt0111161 The Shawshank Redemption (1994) 1994 9.2 619479 142 \n",
"1 tt0110912 Pulp Fiction (1994) 1994 9.0 490065 154 \n",
"2 tt0137523 Fight Club (1999) 1999 8.8 458173 139 \n",
"3 tt0133093 The Matrix (1999) 1999 8.7 448114 136 \n",
"4 tt1375666 Inception (2010) 2010 8.9 385149 148 \n",
"\n",
" genres Action Adult Adventure ... Musical \\\n",
"0 Crime|Drama False False False ... False \n",
"1 Crime|Thriller False False False ... False \n",
"2 Drama|Mystery|Thriller False False False ... False \n",
"3 Action|Adventure|Sci-Fi True False True ... False \n",
"4 Action|Adventure|Sci-Fi|Thriller True False True ... False \n",
"\n",
" Mystery News Reality-TV Romance Sci-Fi Sport Thriller War Western \n",
"0 False False False False False False False False False \n",
"1 False False False False False False True False False \n",
"2 True False False False False False True False False \n",
"3 False False False False True False False False False \n",
"4 False False False False True False True False False \n",
"\n",
"[5 rows x 31 columns]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Remove the year from the title\n",
"data['title'] = [t[0:-7] for t in data.title]\n",
"data.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>...</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>...</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>...</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>...</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>...</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>...</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",
"<p>5 rows \u00d7 31 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" imdbID title year score votes runtime \\\n",
"0 tt0111161 The Shawshank Redemption 1994 9.2 619479 142 \n",
"1 tt0110912 Pulp Fiction 1994 9.0 490065 154 \n",
"2 tt0137523 Fight Club 1999 8.8 458173 139 \n",
"3 tt0133093 The Matrix 1999 8.7 448114 136 \n",
"4 tt1375666 Inception 2010 8.9 385149 148 \n",
"\n",
" genres Action Adult Adventure ... Musical \\\n",
"0 Crime|Drama False False False ... False \n",
"1 Crime|Thriller False False False ... False \n",
"2 Drama|Mystery|Thriller False False False ... False \n",
"3 Action|Adventure|Sci-Fi True False True ... False \n",
"4 Action|Adventure|Sci-Fi|Thriller True False True ... False \n",
"\n",
" Mystery News Reality-TV Romance Sci-Fi Sport Thriller War Western \n",
"0 False False False False False False False False False \n",
"1 False False False False False False True False False \n",
"2 True False False False False False True False False \n",
"3 False False False False True False False False False \n",
"4 False False False False True False True False False \n",
"\n",
"[5 rows x 31 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## Begin exploration of data here\n",
"# Get summary stats for the table\n",
"data.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>score</th>\n",
" <th>votes</th>\n",
" <th>runtime</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>...</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>count</th>\n",
" <td> 9999.000000</td>\n",
" <td> 9999.000000</td>\n",
" <td> 9999.000000</td>\n",
" <td> 9999.000000</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td>...</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</td>\n",
" <td> 9999</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",
" <td> 0.1891189</td>\n",
" <td> 0.00090009</td>\n",
" <td> 0.1313131</td>\n",
" <td> 0.03140314</td>\n",
" <td> 0.03940394</td>\n",
" <td> 0.3922392</td>\n",
" <td>...</td>\n",
" <td> 0.0260026</td>\n",
" <td> 0.1009101</td>\n",
" <td> 0.00010001</td>\n",
" <td> 0.00010001</td>\n",
" <td> 0.2441244</td>\n",
" <td> 0.08970897</td>\n",
" <td> 0.02880288</td>\n",
" <td> 0.2832283</td>\n",
" <td> 0.05120512</td>\n",
" <td> 0.02350235</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",
" <td> 0.3916226</td>\n",
" <td> 0.02998949</td>\n",
" <td> 0.3377594</td>\n",
" <td> 0.1744134</td>\n",
" <td> 0.1945638</td>\n",
" <td> 0.488274</td>\n",
" <td>...</td>\n",
" <td> 0.1591509</td>\n",
" <td> 0.3012247</td>\n",
" <td> 0.0100005</td>\n",
" <td> 0.0100005</td>\n",
" <td> 0.4295883</td>\n",
" <td> 0.2857787</td>\n",
" <td> 0.1672605</td>\n",
" <td> 0.4505889</td>\n",
" <td> 0.2204269</td>\n",
" <td> 0.1515001</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",
" <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>...</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>25%</th>\n",
" <td> 1986.000000</td>\n",
" <td> 5.700000</td>\n",
" <td> 2334.500000</td>\n",
" <td> 93.000000</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 1998.000000</td>\n",
" <td> 6.600000</td>\n",
" <td> 4981.000000</td>\n",
" <td> 102.000000</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 2005.000000</td>\n",
" <td> 7.200000</td>\n",
" <td> 15278.500000</td>\n",
" <td> 115.000000</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </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",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td>...</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" <td> True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows \u00d7 28 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" year score votes runtime Action \\\n",
"count 9999.000000 9999.000000 9999.000000 9999.000000 9999 \n",
"mean 1993.471447 6.385989 16605.462946 103.580358 0.1891189 \n",
"std 14.830049 1.189965 34564.883945 26.629310 0.3916226 \n",
"min 1950.000000 1.500000 1356.000000 0.000000 False \n",
"25% 1986.000000 5.700000 2334.500000 93.000000 0 \n",
"50% 1998.000000 6.600000 4981.000000 102.000000 0 \n",
"75% 2005.000000 7.200000 15278.500000 115.000000 0 \n",
"max 2011.000000 9.200000 619479.000000 450.000000 True \n",
"\n",
" Adult Adventure Animation Biography Comedy ... \\\n",
"count 9999 9999 9999 9999 9999 ... \n",
"mean 0.00090009 0.1313131 0.03140314 0.03940394 0.3922392 ... \n",
"std 0.02998949 0.3377594 0.1744134 0.1945638 0.488274 ... \n",
"min False False False False False ... \n",
"25% 0 0 0 0 0 ... \n",
"50% 0 0 0 0 0 ... \n",
"75% 0 0 0 0 1 ... \n",
"max True True True True True ... \n",
"\n",
" Musical Mystery News Reality-TV Romance Sci-Fi \\\n",
"count 9999 9999 9999 9999 9999 9999 \n",
"mean 0.0260026 0.1009101 0.00010001 0.00010001 0.2441244 0.08970897 \n",
"std 0.1591509 0.3012247 0.0100005 0.0100005 0.4295883 0.2857787 \n",
"min False False False False False False \n",
"25% 0 0 0 0 0 0 \n",
"50% 0 0 0 0 0 0 \n",
"75% 0 0 0 0 0 0 \n",
"max True True True True True True \n",
"\n",
" Sport Thriller War Western \n",
"count 9999 9999 9999 9999 \n",
"mean 0.02880288 0.2832283 0.05120512 0.02350235 \n",
"std 0.1672605 0.4505889 0.2204269 0.1515001 \n",
"min False False False False \n",
"25% 0 0 0 0 \n",
"50% 0 0 0 0 \n",
"75% 0 1 0 0 \n",
"max True True True True \n",
"\n",
"[8 rows x 28 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#How many movies have a run time of 102?\n",
"print len(data[data.runtime == 102])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"212\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Lets plot a histogram\n",
"with plt.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 4}):\n",
" plt.xlabel('Release Year')\n",
" plt.ylabel('Total Movies')\n",
" plt.title('Total Movies by Year') \n",
" a = plt.hist(data['year'], color= \"lightseagreen\", bins=5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x116919510>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# What happens when the bins change?\n",
"with plt.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 4}):\n",
" plt.xlabel('Release Year')\n",
" plt.ylabel('Total Movies')\n",
" plt.title('Total Movies by Year') \n",
" a = plt.hist(data['year'], color= \"lightseagreen\", bins=np.arange(1950, 2013))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x116919250>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Try plotting a histogram with votes\n",
"with plt.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 4}):\n",
" plt.xlabel('Vote Score')\n",
" plt.ylabel('Total Votes')\n",
" plt.title('Total Number of Votes') \n",
" a = plt.hist(data['score'], color= \"lightseagreen\", bins= 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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NnA9hmSXpt8Ufpxh1d76kdwN/S/Htf2/gnIh4QtIhFKOorgTOpfh9h0kUewy7\nUfwGxUN16/5b4CjgF8Dr03KTI+LJmu38lmIY7fMi4gVJ51MM+S1gdeoDOQL4FnAdMD0F0GbAdOAF\n0t5PRFxUyptkVsMBYmZmWdwHYmZmWRwgZmaWxQFiZmZZHCBmZpbFAWJmZlkcIGZmlsUBYmZmWRwg\nZmaW5f8AOMEOdlACDywAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x115584510>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Try plotting run time\n",
"with plt.rc_context(rc={'font.family': 'serif', 'font.weight': 'bold', 'font.size': 4}):\n",
" plt.xlabel('Runtime')\n",
" plt.ylabel('Total Movies')\n",
" plt.title('Total Number of Votes') \n",
" a = plt.hist(data['runtime'], color= \"lightseagreen\", bins= 5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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sljSpzbrJLcZkZmYFaOYur3MAJI0je0Hk5yPi4CbbnwDMA15AdofYBcCcVL4uLbMamAjM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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x116caf9d0>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Are there movies with a runtime of 0?\n",
"print len(data[data.runtime == 0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"282\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ... Interesting, let's keep it for now. If runtime becomes critical to our analysis, we will drop it."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Let's plot some variables against each other\n",
"plt.scatter(data.year, data.score, color='k', alpha=.08)\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"IMDB Rating\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.text.Text at 0x116cff310>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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uFtpdLpeFfEEbGxuo1+vMF0QiEeh0OnaHLRaLsFqtLBnTJhoIBNh5gUht6mu5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\nPugR0Sp65TCguBEv3UTs9qMg3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"text": [
"<matplotlib.figure.Figure at 0x116919090>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# What do you see?\n",
"\n",
"#More recent movies have lower ratings"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Another variable\n",
"plt.scatter(data.votes, data.score, alpha=.2, color='k')\n",
"plt.xlabel(\"Number of Votes\")\n",
"plt.ylabel(\"IMDB Rating\")\n",
"plt.xscale('log')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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pf/755wC2PONEIgFZlmGxWKDRaOBwOLC0tMTcc7fbRSwWQzweRzabhdVq5bTG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\nMzU1pUpF0+k0U1NTHD9+nPn5eaUX5Pf7V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"text": [
"<matplotlib.figure.Figure at 0x116f58f90>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# low-score movies with lots of votes\n",
"data[(data.votes > 50000) & (data.score < 5)][['title', 'year', 'score', 'votes', 'genres']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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",
" <tr>\n",
" <th>507</th>\n",
" <td> Godzilla</td>\n",
" <td> 1998</td>\n",
" <td> 4.9</td>\n",
" <td> 68564</td>\n",
" <td> Action|Horror|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>601</th>\n",
" <td> Wild Wild West</td>\n",
" <td> 1999</td>\n",
" <td> 4.3</td>\n",
" <td> 61595</td>\n",
" <td> Action|Western|Comedy|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>619</th>\n",
" <td> Eclipse</td>\n",
" <td> 2010</td>\n",
" <td> 4.7</td>\n",
" <td> 59205</td>\n",
" <td> Adventure|Drama|Fantasy|Romance|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>696</th>\n",
" <td> Epic Movie</td>\n",
" <td> 2007</td>\n",
" <td> 2.2</td>\n",
" <td> 55070</td>\n",
" <td> Adventure|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>697</th>\n",
" <td> Meet the Spartans</td>\n",
" <td> 2008</td>\n",
" <td> 2.5</td>\n",
" <td> 54864</td>\n",
" <td> Comedy|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>720</th>\n",
" <td> Scary Movie 2</td>\n",
" <td> 2001</td>\n",
" <td> 4.8</td>\n",
" <td> 51638</td>\n",
" <td> Comedy|Horror</td>\n",
" </tr>\n",
" <tr>\n",
" <th>734</th>\n",
" <td> 10,000 BC</td>\n",
" <td> 2008</td>\n",
" <td> 4.9</td>\n",
" <td> 53002</td>\n",
" <td> Adventure|Fantasy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
" title year score votes \\\n",
"317 New Moon 2009 4.5 90457 \n",
"334 Batman & Robin 1997 3.5 91875 \n",
"507 Godzilla 1998 4.9 68564 \n",
"601 Wild Wild West 1999 4.3 61595 \n",
"619 Eclipse 2010 4.7 59205 \n",
"696 Epic Movie 2007 2.2 55070 \n",
"697 Meet the Spartans 2008 2.5 54864 \n",
"720 Scary Movie 2 2001 4.8 51638 \n",
"734 10,000 BC 2008 4.9 53002 \n",
"\n",
" genres \n",
"317 Adventure|Drama|Fantasy|Romance \n",
"334 Action|Crime|Fantasy|Sci-Fi \n",
"507 Action|Horror|Sci-Fi|Thriller \n",
"601 Action|Western|Comedy|Sci-Fi \n",
"619 Adventure|Drama|Fantasy|Romance|Thriller \n",
"696 Adventure|Comedy \n",
"697 Comedy|War \n",
"720 Comedy|Horror \n",
"734 Adventure|Fantasy "
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# low-score movies with lots of votes\n",
"data[(data.votes > 90000) & (data.score < 5)][['title', 'year', 'score', 'votes', 'genres']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" title year score 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"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The lowest rated movies\n",
"data[data.score == data.score.min()][['title', 'year', 'score', 'votes', 'genres']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
" title year score votes \\\n",
"1982 Manos: The Hands of Fate 1966 1.5 20927 \n",
"2793 Superbabies: Baby Geniuses 2 2004 1.5 13196 \n",
"3746 Daniel the Wizard 2004 1.5 8271 \n",
"5158 Ben & Arthur 2002 1.5 4675 \n",
"5993 Night Train to Mundo Fine 1966 1.5 3542 \n",
"6257 Monster a-Go Go 1965 1.5 3255 \n",
"6726 Dream Well 2009 1.5 2848 \n",
"\n",
" genres \n",
"1982 Horror \n",
"2793 Comedy|Family \n",
"3746 Comedy|Crime|Family|Fantasy|Horror \n",
"5158 Drama|Romance \n",
"5993 Action|Adventure|Crime|War \n",
"6257 Sci-Fi|Horror \n",
"6726 Comedy|Romance|Sport "
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The highest rated movies\n",
"data[data.score == data.score.max()][['title', 'year', 'score', 'votes', 'genres']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>score</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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" title year score votes genres\n",
"0 The Shawshank Redemption 1994 9.2 619479 Crime|Drama\n",
"26 The Godfather 1972 9.2 474189 Crime|Drama"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#sum over rows by default\n",
"genre_count = np.sort(data[genres].sum())[::-1]\n",
"pd.DataFrame({'Genre Count': genre_count})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
" 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"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#axis=1 sums over columns instead\n",
"genre_count = data[genres].sum(axis=1) \n",
"print \"Average movie has %0.2f genres\" % genre_count.mean()\n",
"genre_count.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Average movie has 2.75 genres\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"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"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"decade = (data.year // 10) * 10\n",
"\n",
"tyd = data[['title', 'year']]\n",
"tyd['decade'] = decade\n",
"\n",
"tyd.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:4: 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"
]
},
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
" 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"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#mean score for all movies in each decade\n",
"decade_mean = data.groupby(decade).score.mean()\n",
"decade_mean.name = 'Decade Mean'\n",
"print decade_mean\n",
"\n",
"plt.plot(decade_mean.index, decade_mean.values, 'o-',\n",
" color='r', lw=3, label='Decade Average')\n",
"plt.scatter(data.year, data.score, alpha=.04, color='k')\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Score\")\n",
"plt.legend(frameon=False)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"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"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"<matplotlib.legend.Legend at 0x116fa4490>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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RiUSYN263G/39/ZicnOR1sWPHDoRCIW4jFArB4XAwb8hRhMbu9XphMpmYNz09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\n",
"text": [
"<matplotlib.figure.Figure at 0x117542450>"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for year, subset in data.groupby('year'):\n",
" print year, subset[subset.score == subset.score.max()].title.values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1950 ['Sunset Blvd.']\n",
"1951 ['Strangers on a Train']\n",
"1952 [\"Singin' in the Rain\"]\n",
"1953 ['The Wages of Fear' 'Tokyo Story']\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' 'Lawrence of Arabia']\n",
"1963 ['The Great Escape' 'High and Low']\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' 'Army of Shadows']\n",
"1970 ['Patton' 'The Conformist' 'Le Cercle Rouge']\n",
"1971 ['A Clockwork Orange']\n",
"1972 ['The Godfather']\n",
"1973 ['The Sting' 'Scenes from a Marriage']\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' \"Don't Let Them Shoot the Kite\"]\n",
"1990 ['Goodfellas']"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\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' 'Se7en']\n",
"1996 ['Fargo' 'The Bandit']\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' 'The Lives of Others']\n",
"2007 ['Like Stars on Earth']\n",
"2008 ['The Dark Knight']\n",
"2009 ['Inglourious Basterds']\n",
"2010 ['Inception']\n",
"2011 ['A Separation']\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"matrix = pd.scatter_matrix(data.loc[:,'score':'votes'], figsize=(10,5), diagonal='kde')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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0NUVQVydRBQ0pPb4Nu90+oAKDQEK9ajPU7dOMbEJNtMWhuk2CupvNDtxpeN+u\nA24JOL454Pi4C2CjRnPe7N2rmuS+/fbwCzNOmqSKEl55BdasCbY1oUor8GLA2obystnpDolG0R0W\nNW9f4ca2eFRLjnSU6JuKqhL1oQoQbKjihZeBlSgBV4MZWo2JaSQrK5zKymak9BnHe/D5mhEiHJsN\n0tNnsmiRFavVwuTJk1iwIIvCwiKef/4kjY0n8HpnMXv2LDo7i7BYDuD3q+vU1DTjcrURE5NJfHwc\ny5erxn3vvddIYuJFzJunJkH4fD6sViterxePx3NWT5T2Wmk050aoibYmVI07dGfmdiGlvFMI8X1g\nHcrL1mQcV2OcF5gM0sX999/ftc7LyyMvL2+QzdZo+qe+Hj7zGXj0UcjJCbY158cdd6hGu1q0nQkn\n3b8pn0WFQh0oQZYAtBhrO8rj5kSFSTtRgu04KmBQg1khCjOA8ah8tx1ALpCOxfJn/P6LUSHVdiCK\nzs6dnDhxNYmJS7j88r9QUrKf2top+HxzsNkKSEsbz+LFdtzuJfh8NpYsUbMzd+/eQ1vbXIToJCPD\nZeSs2SktzSE2tpz4+DgSExO44QY/CxfO7zGYPSfHFF1fwG63d83mLCmpxmY7uyeqL69VqFdthrp9\nmpFNqIm2rcBXgdeAZaikDQCEEA4pZQeqkZEl4PhlQojXUF0q9/d10UDRptEEAynhy19WLTO++MX+\njw9Vrr4avv51KCyEBQuCbU0oIukOFkjj4UGJtGaU182CEmRm6w+rsd+OmpLQhvKctRv73caxFuP1\nblTlKKhQ6kTMIgevt539+z+gvt7HwoXjmDAhjbVrx9DU5MXni6Cmpox//rMUKQ8zYcIC3O543npr\nLa+//lcaG604nePIyJiHzdbzT4MQoqtIobcIO5Mo8/v9Xa0/zsWLNhgTB4bSg6cnImiCSUiJNill\noRDCLYR4HyiUUu4UQjwhpbwL+KUQYibqZ+vDxinPoapHv4mqHvX2fWWNJrg88wycPAmvvRZsSz4e\nVqvKbXvmGXj22WBbE4r4UFWd5tqCumVBt0Azx1WtAv6KyoObgPKmNaN6qI1DCbww43pbgGnAClTh\nwlr8/oWovLYtgJukJBft7VdSXx9GY+P7VFZmMmtWMldf3ciWLR9SWjqZtra5HDu2B6s1HSEOsXt3\nEq+91kRJyUV4vWUIocK1S5dehtfrpaiomNmzP43NZuvhXTsbpieqoKCwa7bomXK/hsprpfPONCOV\nkBJtAIGx9rfHAAAgAElEQVRtPozXdxnPd/RxbAtq1otGE7Ls3at6sH3wAYSF9X98qHPrrTBrFjz8\nMMTE9H/86EKghJa5Nj1kZnsPc5/VWIejqkJPobI9ko1z2lEh03ZUsUEn3dMUQAg70IIqXo0lKSmS\nq66awzvvjMHtduPz2XG5TnLgQDOTJydx6aWLKSuLxuPpAJrx+VpobW2hs7OTlpbj+P1uQGKz2bBa\nrT0KCAK9Vr3py6MV6IlSgu3sxy9dehkbN25m48bNOq9No+mHkBNtGs1Iwu2GG2+En/98eFWKno2U\nFFixAl58UU1y0ATiB94LWFvozuiwojxsYahb78soL1wL3blt+1Eet3K6RV41KgQ6GXgTSEPKaVgs\nPqR8EYigtXUc48ensmLFCd5+u4impjh8vpO43VbeeEPyla/MJCHhdSorLcAcoMIIlTqIiJhLdHQl\nUMKECQuYN28OHo+nq1WHz+djz54kY0i78lqZ4sv0pvW1z+v1csMN8ezduw+vN6lre28P2FB4xXTe\nmWakokWbRjOE/Od/qirRL3852JYMLl/7mppJescdoTEfNXRwAp8y1s8Zz4Hu1cC1FSXsOo3tnahb\nciIqNNqA8rTFGs+HcDiS6OxcgZSn8PurgSwgkdbWUv72t0MsWhSDxzMPVZelivFbW8t4661SGhqu\nNK5/ANUDrp3du9ux22txOJJITo5kzpxLcTqdbNy4mYcf3k1paTuxseUkJDSSmprZZbkptCoqJHCw\nz30Ac+dWUVycTHGxKkq4UOi8M81IRYs2jWaIeO89+MtfYPfukSdsli6Fzk7YuhUuuSTY1oQSbuBQ\nwNr0tpn/AVgCngUq3GlDtQFxoypILaiWHz6UcOsEYnE667j11lt48cWdNDXZ6W4N4gI6qKxsobS0\nHqv1iPEeU1FtQLyEhYXR0aHsio7eQ1tbLFLOJzraQ1xcO3FxFjIy/MyYUcXixZ/mqaeeo67uCFIm\nIaWfsLASMjPjyM1d3ePTJicnkZUlyM5OO6d8t+619oppNOdCSE1EGAr0RARNMGhpgXnz4KmnVMXl\nSOShh+DAAfjtb4NtSXDoeyKCE7jJePVSwB4/3cLNiRJkZk5bKyoUmgXU0x1OdaO8bg6ghqioSaxe\nPYXNm10cP+7G728DDqPCpkewWCbicCTg89Xh883Dat1KbKyXpKQFVFfvpaZmHkL4mTOnmOPHp9DW\n1sa4cc3cdtuVWK3WrjDn3LlVFBUlUllZhcPxAeXlSTQ1pTFlSmfX7NCzVWcG7svNvbjHcHidr6bR\n9M9wmoig0YwI/vM/lTdqpAo2UK1LZs6EX/4SoqODbU2oECjOzLXZqsP0rpkeNlDtQMyWII2oRrqx\nqPCmROW52YFUOjo6KCrajcsVjt9v7g9DFS+UARKvtwavtwkpO4iMtJOYOIm2NjcdHa1YLOHY7Ras\nVhtCCCwWJ9HR4eTkLMLr9fLuuxupr28kLMwPJJKWlsrcuZfx7rt+qqoaOH78BDt27OwSX2cKP/be\nd7YwpW6uq9GcG5b+D9FoNOfCe+/B3/8Ov/hFsC0ZWlJSIC9PTUjQmEhUMcF+uicgmJ4zs+eaOSmh\nFSW6zDFWdpRXrQkl7OqM/fuACjweF0VFjdTU1BrXq0d57fYDrfj9dXg8x5EyAfiQ5uZGjh4toqam\nHYdjLOnp73HppQe58sorGD8+jfj4aObPjyQ392IAGhosVFVFUloaTVZWDWvWpHH33XewfLkkMnIn\nLS021q2TZ6wkPR/M/LcXXigf1OtqNCMVLdo0mkHE5VItMX79a4gbBUPVbrtt9IZH+8YBXGQ8HChR\nFUl389xIuickqLYd6jZsQVWJmjNIO1CCz4Vq+WGKvkxjXYUKrfpRkxaygIWo4gOPcY4Dt3s6bW0N\nNDdnImUGfv88tm+PwWJpISxsAidPJrJlyzZsNhvx8WNwOmOxWsPIzl7AypVXEBERQU7OIiZNWkRE\nxBQsFusQf38ajeZs6PCoRjOIPPAAXH75yA6LBnLVVfDVr0JJCWRm9n/8yKcd1a7DXFtRYsuLEmtm\nxagpftwo8eU1HgIVGjWb8Lag2n2cRAm5KJTYq0Q14vWjhsivRIm1SKAZu70Di2UsYWEZ+Hz/xOst\npb6+Cr/fQ2zsXJKTTzFmTAzJybMAlXu2bNlOJk+uYNWqa1i69LIerTu+9a1c9u7dR1bW3I9dLNA7\n5w26c940Gs3Z0aJNoxkk9uyBF15Qz6MFmw2+9CXlbXv00WBbEwrYUILKXPvobqRroadAM2+/4Six\n1oLylu0x1itRgiwcyEaFSffRnRuXCxwFJhAWthOPJx0pG3E4Ern++gxmzZrBoUOlbNq0mFOnwOVy\n0t5eS2PjHuLiZnP11UlkZ0/sam5bUpIGpOF0OnvMEAVYsyaNe+/9zqB8Qz37sm3TrTk0mnNAh0c1\nmkFAStWz7Cc/geTkYFtzYfn3f4c//lG1ANH4UR6xDrpni5qhTR9KbHlRQi7WOMccbwVK3NmNfXF0\nCzSrcU1zPqnHuL4L8BEeHoPNlgbEEBbmZNasGXzve99i5sxpWK0urNZwLBY7drsThyP5jGFOv99L\nQUEh+fkbcLvdVFSUUFFRgtfb94RAj8dDfv4G8vM34PF4aGtr48EHH+XBBx+lra3tfL9EjUZzBkLO\n0yaEeAz1s/KjwJFWQogfA1caL++TUm4QQqwBvo+amrxdSvn9C22vRgNKtLjdai7naGPqVBUafest\n+Ld/C7Y1wcYK5BjrIuO1WWRgQRUfmLloHaiQaQzKOxePanwbDcwANhrnjUENif8UFssxYB9+/xXA\neuBynM6t2O0T8PlcQDzh4XbmzZvDxo2bKSpKZMKEuUyZ8gEpKclMnjyXMGOWWuBc0J7zQhMpLi4n\nM7PcsOnM9J5mUFBQyDPPmGLt6T69c7ovm0Zz/oSUaBNCLAQipZSXCyGeEkIsklLuNHb/Xkr5gBAi\nFngL2ID6ifqwlFKnQmuCRns7/PCH8OqraqD6aOTWW1WIVIs2c95o4NqcgmDmspkBjk6U182D8qrZ\nUXlwEpUXZ6Vb5Kkh9FI2Y7XGY7EIhEjCZkshOXkOQkTT1DQWi6WCpKQ4du/ew6FDpezd6yc+fgrf\n+tY3+OQnV3ZZmZ+/gZKS7rmgZ5sXGkjvFh390d9s0o+LbhmiGW2ElGgDFgP5xno9sATYCSClPGZs\nN8urTO4RQnwReEBKueEC2anRdPH447B4MSxZEmxLgsf116uxVmVlMGFCsK0JJm66b2Fu1C3WjBub\n6w66w57RqP5srUASSqCZs0ibUeOmPMa1apHSgtdbB/yLsDA/DscrREenMm1aFC7XZkAwZ85cXnnl\nMCdOtCFlBy7XB7z6aikAK1YsBcDr9TJ3blVXYUFg0cEttyRhs9lwu+N5770tPT5db89ab6+ZKix4\nGoC7775jSOaKns0enR+nGemEmmhTw/IUTcDsPo65H3jGWL8ppfy9EGIskC+EyO5r/MH999/ftc7L\nyyMvL28QTdaMZmpq4JFH1Din0UxEBHz+8/D738N99wXbmmASyemzRx19HCfo7s8mjfM66W4B0oHy\n0NWhcuH8wHSUqPsQuJTOzsN0dk6jqSmRsrJD2O1ZhIdPpr6+gOrq2XR0dGC17qahIYm1az0cP74J\nm03d8l98sRpIJjvb1mfRwcqVV5Cfv6Frpqh5Xm96e83sdvugFSxoNJrTCTXR1oRK8ACVidsYuFMI\ncR0wRkr5MoCUssl4rhVCHARSUPltPQgUbRrNYPLTn8KNN8K0acG2JPh86Uvq8cMfjrxZqwPHQ7dn\nzYMKf5qFCOYEAwsqFNpJ92QEa8BxflSO2xxUqw8L6tb4V2AJNls4qi6gFWhEykhcrnIiItpJSorF\n4/EQHt5IeLiFqVMdVFVF0NSUFmDXwDjbnFCv14vX6yU/f8NZw5JDnb+m8+M0o42Qmj0qhFgAfFVK\n+TUhxP8Cz5s5bUKIecCjwKeklJ3GtmgpZYsQIhzYAuRIKX29rqlnj2qGhLIymD8f9u2DpKRgWxN8\npIQZM1RRxuLFwbZm6Ol79qgN1QAXoISeo6ugu6muDSXqrKj5opUogZeK8qiVAhFADTAeuBg4TkbG\nMcaPT+P99yNQv1G3ooTd9Vgs9URHH8Buv5LOzr2kp4/hpz+9CZvNRlFRMVlZc7vCo73zwM41N6wv\nz5xGoxkchs3sUSlloRDCLYR4HyiUUu4UQjwhpbwLeAiV9PGuEKJRSnkd8C0hxFWoO+ODvQWbRjOU\nPPSQSsDXgk0hBHzhC/CHP4wO0dY3FmCWsd5vPJuiTaJuuWYuW4uxzWVstwIzUWLNAlgRwoqUfpSn\nTRU4lJWdAiagRB9YLDH4/U5A4nBE09HhR4go4uLS2L379KaBgSFNs2UH6ER+jWY4EFKetqFAe9o0\nQ0F5OcyZo7xso60v29k4ehRycuDUKQgL6//44UzfnjYncI3x6u/Gs5/uytEwYz0L1RhXoMRXOGrw\neyUqvy0cNZ4qAiXurNjtJ5DyU0hZC5RjsbiJi6vD5ZpLe/tJYmJa+eQnl7B3bydSRpKdLTh6NIP9\n+5uRsoLZs1O4997lPbxi5+sx01WbGs3QMWw8bRrNcOHhh2HNGi3YejN5MsyaBWvXwqpVwbYmGEig\nNmAtML1m6tlsuGuOoIpE5bWZfdw8qHBojbG/HmjF4ZhObGwYDQ0O/H4fDoeNqVNnsHBhJO+8MxYh\nZpKefpLw8HASElJJTMzAYvmIhoZjSBnLYDOYbTs0Gs3AGTJPmxAiApggpTwwJG8wcDu0p00zqFRW\nqmaye/dCamqwrQk9nntOibY33gi2JUPLmXPabjZe/R9KkJlizSxAEKiK0lTgGGqeaBvKu2ZOQXCj\nKkjjUKHUUi66aC7HjnlobZVERPhISfkE112nBNn27TtJTk6kre0i6uqamTy5FJcrh+rqGtLTjzBt\nWgYLF85nxYqlPbxiZ/OYaW+aRhMczuZpGxLRJoS4FngYcEgpJxkFBg9IKa8d9Dfr3xYt2jSDyne/\nq6YfPPlksC0JTZqaYOJEFSqNP3tD/WFN36ItCrjNePXcaef0ZBxwCtU4twlVMG9FFc3nAAWoaQht\nQCQORydO5yW43SlYrbuJjBzP7NkprFyZQHFxMhUVlUA9qamZzJ1bRXGxcgOfb6GALjbQaIJDMMKj\n96Ma5W6ErgKDjCF6L43mglFTozr/794dbEtCl9hYuOoqeOUVNY91dOFGVXSaawvK2xaGqg51GK/N\nWaQ+unuzmdMR2lFetijjOo3AfJzO44wZ00Jraxjh4Qm43YeorbXR3h5Befle6usbWLhQ8rnPJZGX\nt5pNm7ZQVFSM15uEx+M5rUo0N/ditmzZBmhPmkYzXBgq0eaRUjaKns2a/Gc6WKMZLvziF/C5z8H4\n8cG2JLT54hfh//2/0SjarMBcY73LeDZ7sjnonniQjAqTxgGTUGHSMGAiSrQdNY69BIvlAyZPPs5P\nfnIfUVFR7N27j5KS/bz77iKqq60cPXqchoYkqqqiOHbMis1mIyIiApvNRnFxMsXF1dhsalpA4ASB\ngoKnu7xxfU0T0D3QNJrQY6hE214hxM2ATQgxDbgL1cZboxm21NfDr38NBQXBtiT0WbkS/v3f4dCh\n0dZ42IKq+DTXXtRt1iylNXu0NQNpxjY/yuPmROWveVHtPpKBGOx2O9HRMTidTpYvz8Nms7Fp02ak\n9AApVFfXEBs7hcrKDurrT+F2u1m7Np9XXnmD4mI1e9TrPfe+NLrYQKMJPYZKtH0DuA/l4/8T8C7w\n30P0XhrNBeHJJ+Ezn4FJk4JtSehjt8MNN8D//R+MroEkbuCtgHWY8SxQt9s649mBaqA7A9iLGgRz\nHBU2bQLsjB9fzsmTh+noSGXXrgS+850nOHDgEPn5ksOH59Pevg2bbQfh4XeSkrKHxkbBmDFz2b17\nD+vWSXbtcuH1lqP6wC0AenrPcnNX9wiPajSa0GfQRZtQ5VNvSymXAj8Y7OtrNMGguRl+9Sv44INg\nWzJ8uPFG+PKX4cc/Hk1jrSIBs9eJWYjQO1fMiiousAPVKM+cCyXwZgM7gFiqqzuQcjGqke77tLVZ\nu67Q0uKioyMWjyeL0tJt3HjjAjweFeq0WqtoaDhMR0cKEIcQjV2zQ3t7z7QnTaMZXgy6aJNSeoUQ\nfiFEnJSysf8zNJrQ5+mnYcUKmD492JYMHxYvhvZ2VbSRlRVsay4UnaicNHPtQYkxsxebAyXSGlEC\nz4vKbztpbO8wjovC4bDj87nweiux2apZvnwlt9/+JTo7f8PJk+vw+VJwOuexYEELs2fPAvaRlTWX\nvLzVdHY+zgsvVCCE4PrrZw/Ik6ZbfGg0oc9QhUdbgWIhxDpjDSCNcVRnRQjxGJANfCSlvCdg+4+B\nK42X90kpNwghooGXUHXxz0op/ziYH0KjAWhrg8ceg/Xrg23J8EII+Pzn4eWXR5No8wI7A9ZhdDfO\nDUMJuTjUMPiDxutoVLuPeMLDj5Oc7CQjI56xYxN45x0PnZ2dJCdn4fN9gt/85vds2mSno+N6EhMP\nkpfXyqpV1/Lyy/VAMtnZqgjhoouy2bRpEwAXXZQ9IAEWWKTQV2GCRqMJPpYhuu4bwI+Af6LuYAXG\n46wIIRYCkVLKy4EwIcSigN2/l1JeAlwN/NjYdjtKtF0O3CaE0D8NNYPOb34Dl1yixlZpzo0bblCt\nP0ZPq0QbcInxMH8Tm0PjrcbDg/otaxYljAOyEMJLYuJEHn30Qb7znbtobGzC4QgjJmYqsbFxABw6\ndIS6uiOAj6ioaDIyJlFYWMSePVvYs2cPO3bsxOPxYLPZSE3NJDl5JkVFxeTnb8Dj8QzJJzbnlw7l\ne2g0GsWQeNqklC8IIRyAGUzaL1WpU38sBvKN9XpgCcbPVinlMWN7Jypb1zz+61JKvxCiCDVtufjj\nfwKNRtHWpgbDv/VW/8dqTmf+fFWU8K9/qZmko4MtvV53osKjVpRXzQyPBt7KSpDycmpqSvnrX9dy\n4sRUjhxZSHT0QS67rJHrr7+JvXv3UVi4iPj4OlJTP8DtnsG6dZKGhuOcOBGGlMWsWzeHnJzNXQUH\nBQWFFBUlUFxcTn/es/Nt8aE9dBrNhWNIRJsQIg/4PaocCmCiEOJLUsp/9nNqHKqkClQJ1ew+jrkf\neCbg+OaA4+PO02SNpk+efFJ52bKzg23J8EQI5W17+eXRJNoCEcbDbPkhMVt6qPw2t/G6E9MT9+GH\nW/F4TiFlFlFRUVitKiDi8/moqTlMSsp0srIuC5iCIHA4opEyGYtFFSsEFhwowdY/usWHRhP6DFVO\n2y+AlebcUSHEdOBlYGE/5zWhat9BJXn0KGQQQlwHjJFSvhxwfCxqunIM0NDXRe8P6DmQl5dHXl7e\nwD+JZtTS0ACPPAJbejtONOfE5z+v+rY98ghYhiohI2TwoipDzbVACTUbKhzaivqdaXrekoETqKa6\nb9LR4aC0dBEORyszZ27E45nN+vUp7Nz5N+LiYmhqsjF58g7uvPNBtm8vwOtNwuvNpLCwiKNH7Uyd\nGkdu7sVd1lyIBrm6Ca9Gc+EYKtFmCxwUL6U8aLQC6Y+twFeB14BlwPPmDiHEPOBO4FO9jl8mhHgN\nmA/s7+ui94+uRlGaQeKhh2DVKpgxI9iWDG8yM9UM0g8+gMtG/N/0CFTaLai5or0xB8ebjXTtqN+d\nDiAevz8caMLvzyQx8SSdndNpblbVqI2NzTQ1pXH0aBzbtxf08Io5nU4OHJhASQls2bKta9+F8J5p\nD51Gc+EYKtFWIIR4DngR9VPzZrpLqs6IMaPULYR4HyiUUu4UQjxhVJ0+BCQB7wohmqSUq1CNkF4C\nvomqHvUO0efRjDKOH1cFCIWFwbZkZGCGSEe+aOugu+VHB93hUYEaS+VBCTSnsc2FEnr1QAxCOIiI\naGDSpAgWL16E1eph/PgTgAo1Hz/uIjl5Vr9W6PYdGs3IRMghKOsSQjiBrwOXGps2A09JKTsG/c36\nt0UOxWfUjGz+7d9Um4of/SjYlowMjhxRuYHl5WC19n/8cEAIQe97i6q/WmK82kp3GNQUbT6Upy0W\nmIYKl47FYqknPDyKsWM9TJiwnGnTTtLWlkNVVS11dUdoakpj8uQ2rrxyLNnZC04TYr1FWmBxwJo1\nadoTptEMI4x7S58tyYfK02YFfimlfNQwwMy61WhCnnXrlIftxReDbcnIYcoUSEmBDz8c6d42AYwN\nWNtQIs1Ct2jzofLdPKhwqR0hIDLSSVRUfFcxgd/vp76+nurqI/j9sYAgO3tBnwJMhyg1mtHBUIm2\nDaicNJfxOgI1f/SSIXo/jWZQaGuDb3wDfvlLcDqDbc3IYvVq+MtfRrpo86AEmbm2ogoRPKhK0cmo\nQoWxwDHAhxDN+HwzaG6uJCIiA4ulGJfrCqKidiKlH58vk6ioj1i+fPmAE/11cYBGMzIZqlouh5TS\nFGxIKVtQwk2jCWl++EPV3uPTnw62JSOP1avhjTdGeqNdO8p75g9Ym5gFCGFd67AwO07nTOz2VMLC\nxhAdHUtCwkRstjCmTZtCQsJUIiISSE/Pxmq1snHj5h4NbM/U2Nb0vK1ceYXOZ9NoRhBD5WlrE0Jk\nSykLAIzJBu39nKPRBJX334dXX1WzMjWDz5w5YLPBrl2wYEGwrRkqvHSHR02Pmw3lbXMBu4AEVDtK\nH2Fhmfh8+7Dbj5GVlcSyZRNZsCALp9NJbu5qZs/exJ///Bbl5eWsXXsxRUUnCWxgqxvbajSji6ES\nbXcDrwohKozXKcANQ/ReGs3Hpq4O1qyBZ56BhIRgWzMyEaLb2zZyRZuT7laTgfF1c4xVGErMTQPa\ncLlqgQyEmM+BA1sYP34CS5Y4u8SX0+nkxImplJREIeVhbLYwYPwF+zQajSa0GCrRNhlYAKQDq4Ec\nesYJNJqQweOBz30Orr9eh0WHmuuug9tvh//+72BbMlS0oUYvm2vzFutHDWzxowoRIlCVowIhwrFY\nXISHR512Na/XS13dEaTsIDnZzbJlfrxeL/n5G1i69DKdu6bRjDKGSrT9SEr5qhAiFlgKPAI8jZoV\nqtGEFPfco4oO/ud/gm3JyGfxYqivh4MHYfr0/o8fftjobq77PEqkRaE8bV4gHlWQcAqLZQFpaW10\ndnYQGdnGLbdcQW5u2mniKz4+AyEa+exnY1m4cD4vvlht7Nnclbem0WhGB0NViOAznq8BfiOl/Dvd\nw/c0mpDh6adh40b4059GTv+wUMZiUVMm/vKXYFsyVFgwZ4iqtd147p5BardHkJiYRlrabMaMmURy\n8lQiIyewdesOduzY2aOgwGazkZaWyuzZs8jJWYTNNlS/szUazXBgqO4Ap4QQvwZWAP9jNNsd8VMH\nNcOLjRvhgQfUbNGYmP6P1wwOq1erKt3vfS/YlgwFHajuRuYalIctGRUudeHxhOF2j2fBgmIuuWQx\nr79eyqFDJZSUuNi1qwT4FTk5iwCMOaLbgMDw57mHQ/WEBI1mZDBUou1zwFXAw1LKRiFEKvDdgZwo\nhHgMyAY+klLeE7D934H7gA+klF8wtq0Bvg9UANullN8f1E+hGbEcOQI33ggvvQRTpwbbmtHFJz4B\nhw/DyZMwfsTl1EcCZmLkcwHbW1FTEFqAaIRwkZd3BdnZC3jjDRd+fzRSnqKzs4bS0mOUlKQZ5207\nLfx5PuFQXWWq0YwMhsT7JaVslVL+WUp5yHhdIaXM7+88IcRCIFJKeTkQZrQKMfkrynPX461QwnCp\nFmyagdLcrAoOfvxjuEL/7brg2O1wzTXw5pvBtmQo6ASqjEcn3RMQJCq3zcOECbNZtEjQ2dmJ2+1m\n9eooZsxoJT09kVWrJrBq1TXBM1+j0YQ0oZYgsRgwxd161BC/nQBSyjohRHQf59wjhPgi8ICUcsOF\nMVMzXPH54KabIC8P7rgj2NaMXlatgqeeUtMnRh6BI5YtqL5t9UAYl19+CVddtYR//MPL737XSUbG\nFr773TyWLFE1WmbI0+kc3IpQXWWq0YwMQi3PLA4VPwBoMl6fjTellHOB64FHhBB9DljVaEzuvVeN\nqnr88WBbMrpZuRK2b4eGhmBbMhRI4wGqZ5sNVYTgQAgLr7/+Jvv2vYXPp/qN22y2LiFl5p31N83g\nTJMQzoSekKDRjAxCzdPWRHdnyligsdf+HgNwpJRNxnOtEOIgqolvRa9zuP/++7vWeXl55OXlDZrB\nmuHDH/6gGrtu365CdJrgERkJS5fC22/DLbcE25rBxEL3LcwCNBuPCcAR9u5dTENDDkKcZN68zXz3\nuz9i6dLLzjnnTOeoaTSjk1ATbVuBrwKvoQbOP99rfw9PmhAiWkrZIoQIR7UYr6YPAkWbZnSybRv8\nx3/Apk164kGocN11Kq9tZIk2AYwJWIM5b9Ruj8RiseL3t2Gx1BEfr45bt24jRUXF+P0JWCyhdkvW\naDShhJAhNr1ZCPFLYCFQKKW8WwjxhJTyLiHENcD3gCmoCtLPCiH+C1WlagEekVK+3sf1ZKh9Rs2F\n5dQpyMmBZ59VCfCa0KC2FqZMgcpKCA8PtjXnjhCC3vcWlaFhetqagWhUIYIDqzWCGTNSOHpUzSdN\nT48lKSkHi6WJ5OSZZGXVkJ29YEAtOXQLD41m5GLcW/pM9wo50TbYaNE2unG7VYuJz3wGfvCDYFuj\n6U1envKADkcx3bdoiwJuM16ZLT8EKhBQhdWaghCqx0xEhIv09IkkJIwhNTWTNWvSdJhTo9GcVbSF\nWiGCRjNoSKkqRNPTVQGCJvRYtWqktf7w9VpL1G02GQhDyjCgnPBwLxMmjGPBAkleXjSZmeV4vd4B\nFRVoNJrRi/a0aUYsTzwBv/0tfPihSnzXhB5Hj6p5pBUVw2+MWN+eNisw0Xh1AtVsdyJQC6Rjs3US\nEXEJOTllfOITuezZk0RVVS1Qr71tGo0G0J42zShk0yb42c+UF0cLttBl8mRIS1PCemQQhuoBvsJY\n20broggAACAASURBVIB2VPeiVrzeZqTsICUlGetwU6kajSbo6FIlzYijqgpuvhn++EclCjShjRki\nvWxE9HyVKA8bgB9VOepDedwygKO0tRWya9dUPJ5EsrJqyMqaB/Ts16bRaDR9oUWbZkTh86kWErfe\nCit6Dz3ThCSrVsH118Mjj8Dwb4/tBUqMtQ9IBSqBcFRgoxMpYykr2wdMZM6cRFasWKqrPzUazYDQ\n4VHNiOJnPwOPR80V1QwPsrKU2N6zJ9iWDAY24FPGwwqYPb9P4XTuxenMxO+vo6lpLiUl7/Lcc5t5\n5511wTRYo9EMI7Ro04wYNm5U8yxfemn4JbWPZoTobrQ7/LEBDuNhes/aCAtLY9q0i4iLSwBmA4n4\n/R4qKqJ48/+zd+fhUVZn48e/JxlCQhbCFjZZRBBLgCggoAYbFKkidd+KbeVtra3aam3VV6uvUpfX\nn0tt1VZtsa7UV611wzWyCcgmWwgB2UGWLJBA9mWW8/vjPM/kmSEMCcxkJuH+XNdc88yzzZmIyT33\nOec+H3wcrcYKIdoYCdpEu1BcbLpFX3vNDGwXbUv7Kf3hBtZYjwZM4FZCcvIgKivX4vXOAdah1FZc\nrkTS0nrh9fqavYaoEOLEJiU/RJvn9cKFF8L48fDww9FujTgWHg/07g2rVkH//kc/PxY0VfIjIaEb\nbred5j0IdAImApuAScAS4uJOYcSIvVx++VR27dpNVdUoXK5EKfchhACk5Ido52QcW9vncplVEdp6\ntq1jxwRgmPUwa45CGVCKKf1xiLg4Lz179uCss8Zx1VWX4XIltkrb3G43ubnzJKsnRBsWc0GbUurP\nSqmF1hqkzv0/U0ptV0q94diXqpSarZRarJT6Seu3VkTbggWN49hcMhe6TWsPXaQNDW5MkFYG1AMD\ngSpgLPAhkE1KynKSky/g1Vf3ATB9eh9+/OMMPB5PRAOq+fMX8eqr+3j11X3+dUuFEG1LTAVtSqlR\nQLLW+lwgQSk1xnH4Q0zFSqdfAG8C5wI3KqVk3vwJRMaxtS8XXAArV0JpabRbcuwaGuqA84DzMVm2\nCqAzJnDrBnSloaETBw/uBUxttsmTz8PlcjFrVokEVEKIkGItNzEOyLW25wBnASsBtNalSqnUJs6/\nVWvtU0rlAacB+a3VWBE9dj226dNh8uRot0aEQ6dOcP758Mkn8NOfRrs1x2Oe9VwPFAOJJCUVUlvb\nGVjIwIFZTJ7cjaysxuyax+OJeKtM4d5Fjm0hRFsTa0FbOrDd2i7HzI0/2vkVjvPTI9QuEWMeewwa\nGmDGjGi3RIST3UXatoO2idbzViANqCU5+SJ69fIyZEgVt976X1x00QX+7kqAH/84g+nTTbo4UgFV\nhw4dZKKDEG1crAVt5ZjfcmD6FA4FHQ+eBlpunbffuu5gUzed4fjLnpOTQ05OzvG3VETN/Pnwt7+Z\nmYYyjq19mToVbrsNampM5q1tsid9xWF+ZfloaKhG68bRG263m1Wr1lBYWErPnqf6u0ndbre/e3Ti\nxAmyUoIQIkCs/clbCvwS+DdmUMgrQceDp8AuBc5XSv0bOB34tqmbzpB0TLtRVGS6RV9/XcaxtUfd\nusGYMfDZZ2Zpq7ame/fuHDjwuvWqBhhMXJwXpUooLk6guPh0CgvfpKBgI3l5PQBNVlYpEydeDxCQ\nfYNFkhkTQgSIqYkIWus1QJ1SaiHg0VqvVEo9C6CUmgq8QWOQBvAScD2wEPin1jryA0NE1Hi9MG0a\n3HijrCvank2bBv/6V7RbcWwSEjoAXaxHEtAPlyudjh070qFDBko1LtURFxdH7969GD36DMmoCSGa\nRYrrijbjgQfg668hN1eWqWrPDh2CAQNgxw7o2jXarTmyporrulxd8Xqvt169DHQiMfFsMjK8ZGf3\npGPHjlx22VQmTcph8eJlQGA3qHSPCiFCFdeNte5RIZqUmwv//CesXi0BW3uXnm5mBL/7Ltx0U7Rb\n0zJebwNmoXhlPTRxcadRW7uf4cOHce+9v/ef21TXp0wWEEKEIpk2EfO2boVzzoF33oHvfz/arRGt\n4cMP4U9/goULo92SI2sq05aW1ofKyo7Wq/2kpqbRv/8UunUbwu9/P4LExER/eQ+XyyXZNCHEYSTT\nJtqs8nK45BL44x8lYDuRXHQR/PznsHMnDBwY7dY0X2JiRyorp1qvXqG6ujNjxyZw3XVn4vF4ePXV\nfRQWFgFl9O49DJlsIIRoiZiaiCCEk9cLP/oRTJwIv/pVtFsjWlNCAlx7rVntou2Jtx4d8Pk87N69\nh7q6OvLy8vH5ZK6UEOLYSfeoiElawy23wJYtpvyD9CCdeNatgylTTLYtFuvxNdU92r37QEpL7e7R\nYiCZ7t2n0bt3IcOGTSYraz9ZWSMA6R4VQjQtVPeoZNpETLrvPrMO5fvvS8B2oho50swi/eSTaLek\n+UwQN9R6+HC5OhMXl+A/7vV6ycszK+1JwCaEaKkY/P4qTmRaw8MPm6WMFi6E1ODVZsUJ5Ve/ghde\ngEsvjXZLmufgwXLMmqMAbgYP7sSYMSX4fB0pKXmXt97KoKLiJE45ZYF/FQSQUh9CiOaRoE3EDJ8P\nfvc7s0zVvHnQvXu0WySi7eqrzb+JrVth8OBot+boTH3v72HKfXyNx+Nmz56hbNhwkPr6eqCOhITv\ngF4B18lKCEKI5pDuURETqqrMpIOVK+Grr6BXr6NfI9q/xERTq+3pp6PdkuZJT+8ClGDGs8FJJ52E\n1+uhvr4SrQ/Sr18SU6cmctddOS1eGN7tdpObO4/c3Hm43e7wN14IEfNiLtOmlPozMBpYrbX+rWN/\nH2AW0BF4QGs9Vyk1HbgHKASWa63viUKTxXHasAGuuQbGjoUvv4SkpGi3SMSS22+H004zK2LEejDf\np09PDh2qAiA+PoVu3S4lOXk1hw4BjOCaa7ry3/99x2HdnyaAW+TYPpxk44QQMRW0KaVGAcla63OV\nUs8rpcZorVdah+8B7gPWAR8DcwENPKm1/md0WiyOR0MD/L//B889Z55/9jNQTc6XESeyjAy4/nr4\ny1/Mv5NYFhcXD6QAGq/Xy6ZNC7jqqhG43f0AGDu2T0DA1tRYNjuj5twnhBAQY0EbMA7ItbbnAGcB\ndtA2XGu9FEApVamUsoeo/1Yp9VPgj1rrea3aWnFMvF54801TMHfYMLM0Vb9+0W6ViGW//z2MHg13\n3x3b65F27NgRsLNhmh076gCYPr0PEJhFc7vdPPXUs+TmltKz56nY2bMjZdSak40TQrRvsRa0pQPb\nre1yINNxzLniZLl17gda69eUUt2BXKXUaCnKFrvKy+GNN+CvfzWTDGbONIVzhTiagQPNpIT//V94\n6qlot+bI8vIKgJ9gJiKsprr6GxISzmmyK3P+/EXk5mq2b08CNgP9Q95b1iUVQsRa0FYOpFnbnYFD\njmM+x3YacFBrXQWgtT6glNqMmZJVGHzTGTNm+LdzcnLIyckJa6PFkWkN33wD//gH/Oc/8IMfwIsv\nmiWppCtUtMSMGZCZCbfeCiefHO3WNK1z51RKS/dYr+Lo0gWGDh3Cp5/mNllMt2fPDKCEyZM7+bNn\nR8qoSVkQIUSsBW1LgV8C/wbOB15xHFunlBoP5ANpWusqpVSa1rpCKZUEDMFM2zqMM2gTraO0FGbN\ngpdegtpas47kt99Cz57Rbploq3r1gt/8Bu65B95+O9qtaVrPnj0oLXVjhtsqhg69lOee20xTa402\nBmcnBQRhR8qoyUQEIURMBW1a6zVKqTql1EJgjdZ6pVLqWa31bcATwOtAEvCAdclvlVIXYkqXPKa1\n9kan5QJMnbW5c02g9sUXMHWqmWRw7rkQJ8VlRBjcfTeMGAGffmqWuIpN9giNeCorD5CQ0K/Jf//S\n3SmEaClZe1Qct+++g1deMY+uXU1Wbdo06NIl2i0T7dGcOebf2Pr10V0xo6m1RwcOHMauXeOtV58y\nYMBYbrwxm1GjTj/utUale1SIE0OotUclaBPHpL4ePvoI/vlPM2btuuvMH9JRo6LdMnEiuPFG8Hjg\n1Vej14amgrb+/Yeye3dXTLZtM8OHX8jjj0/H5XLh8XgCzpUF44UQTQkVtMVU96iIfWvXmj+Ub75p\nBoXfeKNZ1F0K4orW9MwzcOaZ5t/i9OnRbk2jqqoaYJL1ahMjRybh8XiYNauEwsIioAzoSlNj3IQQ\n4mgkaBNHVVxsBn6/8gqUlcENN8DSpXDKKdFumThRJSfDO++YkjFDh8JZZ0W7RYZZMD7BeuUmP7+e\n4cM3AjIDRwhx/CRoE03avh0+/BDeew/y882kgiefhPPOk0kFIjYMHw6vvQaXXw4LF8Kpp0a7RZCW\nlkZFxXzrlaaiwkNtbS0jRhRz3XXD/N2k69atJz6+mOzsK6LaXiFE2yJ/fgX19abb8+WXTVfTwIFw\n9tlQUGDKKxQVmfIdkyZJwCZiy5QpZmmr884z/16jLSHBBXSyHgmUlJTy/vsbyM/vSWJiIlOmTCYx\nMZENG/qQn9+TxYuXRbnFQoi2RDJtJ5jiYsjLC3xs3QqDBkFWlinPcc89pstJit+KtmD6dEhIgPPP\nNwWczzkn2i0aYT1vxOXqhlLmm47H4yE3dx6rVq3B5+tGXNzhv35lhqgQIhQJ2topt9sUsw0O0Boa\nTHCWlWWyE3fcYSYUJCZGu8VCHDu7xMwVV8Af/gC33RadLx01NXXAFuuVm4EDd3HllZdw5pkZALz6\n6j58vh5kZe1n9OgzDltDVAroCiFCkaCtjXK74cABKCkxjz17YPNm2LLFPG/dCv37w8iRJkC77Tbz\nfNJJkkET7dNFF8GyZXDNNTB7NrzwAgwZ0rptqKurB0Zi1h5dSWVlKps29eOssxp/1cbFxTF69BkS\nkAkhWkyCthjhdjcGYMXFsH+/eRw4YB72tr2/ogK6dYOMDPPo3dsMxL76avN86qlmhp0QJ5KTTzYz\nm597zswonTbNdPf36dM67z9mTBYrVtiZtgSSklLZt6+AFSv2MWrU6Vx3XVcKCjbi8WTgdrsP6/48\n0rqjQggBUly3ST4fFBbCrl0mUKqoMI/qanPM5zMLoTufnY+m9nm9UFNj7uF8HDpkgrTKSuje3azN\naQdiPXqYfd27B25nZJiVB2RSgBBHVlRkZjy/8oqZ/XzTTWa8W7gyzU0V173vvj/y2GNvobUmLi6d\nfv0y6Nx5BF26eOjTZwQjRhSTn2/Kf0yf3keybUKIw7Sp4rpKqT8Do4HVWuvfOvb3AWYBHYEHtNZz\nlVKpwJtAF+DvWus3Wvp+hYWwZg2sXm2e160zyzJ16QIDBpgAKS3NPJKTIT7e/NKPizPPSpl9cXHg\ncjUecz7sfcnJhz86dzaBmgRhQoRXr17wpz/BvffC6683rqJw6aXmcdZZEO5x/ikpKaSlXUxVlaZj\nx3zS0tLo2rULpqiuEEIcn5jKtCmlRgG/0lrfpJR6HnhZa73SOvYs8H/AOuBjrfVEpdTvgELgbWA+\nMElr7Q66Z5OZtmuvha++Mr/EzzjDPEaNMuO+Bg6UCv9CtDdam9I2H35olmDbssWsqnDOOTBmDAwb\nZmZRx8c3735NZdpqamp48slnWLJkOePGjWHMmFG4XOa7scvlIjt7vL/Mh8wOFUI0pc2sPaqUuhnY\nr7V+Vyl1BdBXa/2cdWye1vo8a/sj4HrgJeBWrfUBK6ibqbXOD7pnk0HbwoUmOOvXTwbmC3EiOnTI\nTFxYtMgEcxs2mKEKgwebSTz9+sGIEXDLLU1f31TQJoQQx6stdY+mA9ut7XIg03HM+f233Do3HagI\n2tcs55577I0UQrR96elw4YXmYauqMrOv9+yB3buj1zYhhGhKrAVt5UCatd0ZOOQ45nNsp1nHyq3z\n9lv7DjZ1UyWpNCHEMbr11iMfk98tQojWFGtB21Lgl8C/gfOBVxzH1imlxgP5QJrWulIptRQ4Xyn1\nb+B04NumbipdGEKIlmjOygTSPSqEiIRQXwZjKmjTWq9RStUppRYCa7TWK5VSz2qtbwOeAF4HkoAH\nrEtewswe/Q1m9qgnKg0XQrQrsjKBECIWRbTIhFLqp0qpOUqpeUqpPkqpu5RSi5RSs5RSLuuc65VS\nXyulZiulUq0yHzOAM5VS84DHrdt1wZT78AHF1r5UwC4huzeSn0UIIYQQIpoiNntUKdUX+KPW+kbr\ndQbwitb6YqXU3ZgJBx8Cc4Ec4Cqgv9b6KStY+yFmIsJPtda/Vkq9h8moaeB5rfVlTZUBaaIdLS6u\nK4Q4sUn3qBAiWqI1e/QHQLxSag6wAfgcWGAdm4Mp2VEA5GutfdZ5M5VSSUCt1roaWKGU8mfatNZ7\nAZRS9izR4Vrrpda+SitTVxnBzySEOAF06NBBukSFEDEnkt2jPYEOWutJQA1mlqddnqOCw0t2NLUP\nGkt9ONuqgo5BC0t+CCGEEEK0JZHMtB0CFlrb84AxgL1agbNkR1qIfQBe69nZD+ELeravb7Lkx4wZ\nM/zbOTk55OTkNPtDCCFES73+OvzgB2aJOiGECJdIBm1LgF9Y22cAu4FrgSeBSZjyHpuB4UqpOHuf\n1rpGKZWklErGjGkrsO5RZo2T05jADg4vA1LVVEOcQZsQQkRSTQ3ccAPccw889li0WyOEaE8iFrRp\nrfOUUrVKqfmY4rdPA72VUouAXcDTWmuPUmomsAizovI06/JHgS+BWuAGa9+DmDVGNWCXu2yqDIgQ\nQkTN1q3mefv20OcJIURLxdTao5Egs0eFEJFwpNmjH38MP/oRjBwJX38dhYYJIdq0ULNHI1qnTQgh\nTjT79sHpp8P+/dFuiRCivZGgTQghwujAAcjMhJKSaLdECNHeSNAmhBBhdOAAnHKKmZBQXx/t1ggh\n2hMJ2oQQIoxKS6F7d/M4cCDarRFCtCcStAkhThhut5vc3Hnk5s7D7XYf/YJjUF4O6emQkQHFxUc/\nXwghmiuSddqEECKmzJ+/iFdf3We9WhSRparKy6FzZ+jWDQ42We5bCCGOjWTahBAijOygLS0NKiqO\nfr4QQjSXZNqEECeMiRMnYGp529vhJ0GbECJSJGgTQpwwOnToEJEuUafrr4cePSRoE0KEnwRtQggR\nRg89ZJ7T0kzWTQghwkXGtAkhRARIpk0IEW4StAkhRARI0CaECDcJ2oQQIgI6d5agTQgRXhK0CSFE\nBEimTQgRbhEL2pRSA5VSxUqp+Uqpz619dymlFimlZimlXNa+65VSXyulZiulUq195ymlliil5iml\n+lr7hiulFluPEda+PtY5Xyulzo/UZxFCiJaSiQhCiHCLdKYtV2s9UWt9oVIqA8jRWk8A1gGXKaU6\nAL8EJgBvWNsA9wMXAPcA91r7HgKuBa4BHrb23QPcB0y2rhFCiJiQnAzV1dFuhRCiPYl00DZRKbVQ\nKfVbYDSwwNo/BzgLGAzka6199j6lVBJQq7Wu1lqvADKta7porfdqrfcB6da+4VrrpVrraqDSztQJ\nIUS0JSdDVVW0WyGEaE8iWadtHzAEaAA+BFKBEutYBSbwSre2j7QPIN56dgaYKugYQLl1bWV4mi+E\nEMdOMm1CiHCLWNCmtW6wt5VSH2MCsb7WrjTgECbQSguxD8Br39Kxzxf0bF/f5PLMM2bM8G/n5OSQ\nk5PT7M8hhGif3G438+c3LmnVoUOHsN5fgjYhRLhFLGhTSqVore3OgXOA54BpwJPAJGApsBkYrpSK\ns/dprWuUUklKqWRM12iBdY8ya1KCxgR2AOuUUuOBfCDN8X4BnEGbEEIAzJ+/iFdf3We9WhT25a1S\nUiRoE0KEVyS7RycopR4G6oGFWusV1vi2RcAu4GmttUcpNROzgnMZJqgDeBT4EqgFbrD2PQi8jQna\nbrX2PQG8DiQBD0TwswghRIskJJjnhobGbSGEOB5Ka330s9owpZRu759RCNFyobpHm9N1qpTiaL9b\n0tNhxw7o0iWMDRdCtGvW7xbV1DFZMF4IcULq0KHDEbtEw9V1ao9rk6BNCBEOsiKCEEJEiExGEEKE\nk2TahBAiyMSJEzBDbe3tYyO12oQQ4SRBmxCiXTuW0h6huk5bQjJtQohwkqBNCNGuffnlfJ58cgEA\nHo+HKVMmt9p7S9AmhAgnGdMmhGhX3G43ubnzyM2dh9vtJi8vn+3bk9i+PYm8vPxWbYsEbUKIcJJM\nmxCiXQme+ZmVNYJBg9YBkJU1olXbIgV2hRDhJEGbEKJdu+CCibhc5lfd8UwqOBaSaRNChJMEbUKI\ndiV45me4JhUcCwnahBDhJEGbEKJdCQ7SIr0wfCgStAkhwkmCNiFEuxbpheFDSU6G/ftb7e2EEO2c\nzB4VQogIkeK6QohwkkybEKJdC9fqBsdCukeFEOEU8UybUuoOpdQia/supdQipdQspZTL2ne9Uupr\npdRspVSqte88pdQSpdQ8pVRfa99wpdRi6zHC2tfHOudrpdT5kf4sQoi2xx7jNnnyea06ng0kaBNC\nhFdEgzalVEcgC9BKqR5AjtZ6ArAOuEwp1QH4JTABeMPaBrgfuAC4B7jX2vcQcC1wDfCwte8e4D5g\nsnWNEELEDKnTJoQIp0hn2n4OvAYoYAywwNo/BzgLGAzka6199j6lVBJQq7Wu1lqvADKta7porfdq\nrfcB6da+4VrrpVrraqDSztQJIUQskEybECKcIha0WVm072ut51u70oEKa7vCen20fQDxTbRVBR0D\nKKcxmBNCiKiToE0IEU6RnIjwE+BNx+ty4CRrOw04ZO1LC7EPwGs9a8c+X9Czff3BphoyY8YM/3ZO\nTg45OTnN+wRCCHEcJGgTQoRTJIO2U4HTlVK/wnRxjgHGAk8Ck4ClwGZguFIqzt6nta5RSiUppZKt\n6wqs+5VZkxI0JrADWKeUGg/kA2la6yYn1zuDNiHEiau1C+1K0CaECKeIBW1a63vsbaXUQq31Q0qp\nu62ZpLuAp7XWHqXUTMx8/DJgmnXJo8CXQC1wg7XvQeBtTNB2q7XvCeB1IAl4IFKfRQjRdoQKzFq7\n0K7UaRNChFOr1GnTWp9rPT+BCbScx2YBs4L2zQXmBu3LB7KD9u0FpNSHEMIvmisgBJNMmxAinKS4\nrhDihNHahXYTEkBraGgw20IIcTwkaBNCtCvZ2eNZteoFa/uKgGPBi8lHmlKNtdokaBNCHC9Ze1QI\n0a4sXryM/Pye5Of3ZPHiZbjdbnJz55GbOw+3293q7ZFxbUKIcJFMmxCiXYv2GDdZFUEIES5HDdqU\nUtnAWq11lVLqJ8AZwDNa610Rb50QQrRQ8Lg1eyZptKSmSqZNCBEezcm0vQCMVEplAb8DXsKU2fh+\nJBsmhBDHInjcWmtPPgiWkiJBmxAiPJoTtHm01lopdRnwN631S0qpn0e6YUIIEQ6tPfkgmARtQohw\naU7QVqmU+gPwY2CCUioeiGwZcSGEaIHWXumgJSRoE0KES3OCtmuBHwE/01oXKaX6Y5aiEkKI49KS\nYKu5Kx14PPNxuVzNumdrkKBNCBEuRw3atNaFSqn3gMHWrgPABxFtlRDihNCSmZ3NPTcvL5/8/J7N\numdrSEmBysqoNkEI0U40Z/boTcAvgK7AKcBJmMkJsnyUECImOCcb1NV1JTd3MQAeT0bAedHoRpVM\nmxAiXJrTPXorMBZYBqC13qyUygh9iRBCHF1LZnaGOtc52eDTT3Mx3zENZ6Dm8XiYNavEOtI6WbiU\nFDh4MOJvI4Q4ATQnaKvXWtcrpQBQSrkAHdFWCSFOCKFmdjaVFbPPramp4amnngXg9ttvplOnTv7r\nXC4XvXv38m87u1VHjCgGetKaUlNh9+5WfUshRDvVnKDtK6XUfUAnpdQFwC3A7Mg2Swhxogs1hu2Z\nZ17gxRdrrFcvcO+9v/cfC1VcNytrBKNHuxznRZ50jwohwqU5Qdt/AzcC+cAvgU8xBXZDUkplAv8A\nvECB1vpmpdRdwCXALmC61tqjlLoeEwiWAdO01pVKqfOAR4A64Cda671KqeHAi9btb9Za5yul+gCz\ngI7AA1rruc3+5EKIE0JwENfas0klaBNChEtzgrbfaK2fwQRgACilbgeeOcp1m7TW51jnv6yUOhPI\n0VpPUErdDVymlPoQEwhOAK6ytp8C7gcuADKBe4FfAw9hyo9o4HngMuAe4D5gHfAxIEGbEO1EqDFs\nt99+M2Y+lL3dqKkMnRTXFUK0B80J2qZzeID2X03sC6C19jheJgFnAgus13OA64ECIF9r7VNKzQFm\nKqWSgFqtdTWwQin1uHVNF631XgClVLq1b7jWeqm1r1Iplaq1lsn1QrQDoca7derUKaBLNJZJ0CaE\nCJcjBm1KqR8B04CTlVLOMWypQGlzbq6UugR4FFgFHMR0lQJUAOnWoyLEPoB46znOeeugYwDl1rUS\ntAnRDhxreY7s7PGsWvWCtX1F2O/fUhK0CSHCJVSmbQlQCPTAdFnaqoC85txca/0R8JFS6lmgGlPj\nDSANOIQJtNJC7IPGQM85Y9UX9Gxf3+TE+hkzZvi3c3JyyMnJaU7zhRBR1JLCu06LFy/zF9ddvHjZ\nEa871vu3lARtQohwOWLQprXehZkwMF4p1QvTvamBjUFdn01SSiVorRusl3YW7fuYJbAmAUuBzcBw\npVScvU9rXaOUSlJKJWPGtBVY9yhTSvW12lBu7VunlBqPmSSRprVu8lejM2gTQjRfJLJRsbxOaCSk\npsqKCEKI8GjOigjXYAKtr6xdf1VK3aW1/vdRLr1QKfU7TFfmDuBBoJdSahEmGHzamj06EzPauAzT\nHQumS/VLoBa4wdr3IPA2Jmi71dr3BPA6ZszcA0f7LEKIlolENirUPZ0BXXb2eKya3kycOCFksOc8\nNm7caFatWmPd48jdoy0p7Hs8JNMmhAiX5kxEuB84U2tdAqCU6oGZpRkyaLO7RoN2P2E9nOfNwpTt\ncO6bS9BMUK11PpAdtG8vspyWEO3GZ599yf33vwnAI4/UccklU/zHcnPnHTHYcwaCq1ataVb3pn7P\nGAAAIABJREFUaKiJDuGUlAT19eD1Qnz80c8XQogjaU7QpoD9jtelNE4EEEK0Y8eajQqVFQt1zw8+\n+Jht2/r6t51BWygej4fCwg0ADBuWHnAs2t2xSkFyMlRXQ1ra0c8XQogjaU7Q9jnwhVLqTUywdi3w\nWURbJYSICceajQrVBRrqnoMGDSQlpdy/7XT0ANKsNzpy5DDGjk30n9daEw5CsbtIJWgTQhyP5gRt\nxZjuy9Ot13/XWr8fuSYJIU5Uv/vdr4mPb7porlNw9sy53mhiYmJUi+k2JSVFJiMIIY5fc4K2FEwx\n3YOYiQBLItoiIUSbd6zdqqGK5gaOW3vBP24NFoV8P+ex7Ozx5ObO8+9vra7Szp2houLo5wkhRChH\nDdq01jOAGUqpLOAaYKFSao/WWiYACCGa1FqD/Jvzfs5joSYzRFJ6Ohw61CpvJYRox5qTabOVAEWY\niQg9ItMcIcSJLNSkgXHjRvPWW+8C8ItfPMTq1ev850V7ssHRSNAmhAiH5tRpuwWTYcvAlPm4UWu9\nIdINE0KceJxdoB7PfFwu8ytq4sQJ/OUvz/Pee2ZhlAEDZvLgg/f6r2tuBq21arMF69xZgjYhxPFr\nTqatH/BbrfXaSDdGCHFic5buWL16Hxs29LGOLGLJkuVUVZ0CwJIly4/p/q3dbWtLT4fy8qOfJ4QQ\noTRnTNu9RztHCCHCp6v17AvYe/bZ41i+vMC/7RStDFpzSfeoECIcWjKmTQjRDsXSeDBn6Y5RozIY\nO7axezQ7ezwJCaYcyC233HjYLNBYK/PhlJ4O334b7VYIIdo6CdqEOMHFQvFZW3DGzBlAdujQwV8O\n5NNPc3nyyQWA6VKdMmVyaze1RWRMmxAiHOKi3QAhhGip1avXUlBQREFBEatXx/5wWxnTJoQIB8m0\nCRGDWrPLsrXHgzk/W3b2eBYvXuZ/71CzR50/g4aGBqqry/3bsU7GtAkhwiFiQZtSahzwNGY08Tda\n698ppe4CLgF2AdO11h6l1PXALUAZME1rXamUOg94BKgDfqK13quUGg68aN3+Zq11vlKqD2aJrY7A\nA1rruZH6PEK0ptbssmzt8WChVjZwzh5dtmwnH3xgBoI98khdwOLxO3bsoqHB5d+OdRK0CSHCIZLd\nozuBiVrrCUCGUupcIMd6vQ64TCnVAfglMAF4w9oGuB+4ALgHsGevPoRZrP4a4GFr3z3AfcBk6xoh\nRAxwu93k5s4jN3ceNTU1/m232x3yOo/HQ1mZoqxM8fXXy9m2rS/btvXlgw8+DjgvLi6e+Pi+xMf3\nJS4uPpIfJSxkTJsQIhwilmnTWhc7XrqBTGCB9XoOcD1QAORrrX1KqTnATKVUElCrta4GViilHreu\n6aK13guglEq39g3XWi+19lUqpVK11rIss2jzYr2ExdE0d53Q7OwrArpHH3/8zxQV1QPQvTukpJhf\nUYMGDQy4/5VXXsLatV9b25NCdifHwuxYybQJIcIh4mPalFIjMcteHaKx8FIFkG49KkLsA7C/Rjuz\ngiroGEC5da0EbaLNi/USFsejpqbGvxzVqFEjDzuudRkA48ePYcAA893vN7+5O+CcSZNyKCjY6N8O\n1Z18pGOtGcylpkJDA9TVQWJixN5GCNHORTRoU0p1BZ4DrgbGACdZh9IwQVy5tX2kfQBe61k79vmC\nnu3rDzbVjhkzZvi3c3JyyMnJadHnEOJEdayBTahs2i9/eTtvv50KwO7dP8PjGQ6YrtGRI4fTq9cO\nABISEqirOxuA5ctXBQRiixcv82fv7Hu3VGuOG1QKevSA/fuhX7+IvY0Qop2L5EQEF2aSwJ1a6xKl\n1ErMhIMngUnAUmAzMFwpFWfv01rXKKWSlFLJmC7VAuuWZUqpvpjgzZ48v04pNR7IB9K01lVNtcUZ\ntAkhmu9YAxtnpjB4HJvP58Xj+Q6A0tJSSkuTAMjLyycrawTdup1inen2T0rweDKoqanhmWdMcd3M\nzO8F3DM7ezyrVr1gbV8RcCxWupolaBNCHK9IZtrs7NoTSikwEwoWKqUWYWaPPm3NHp2J+Y1aBkyz\nrn0U+BKoBW6w9j0IvI0J2m619j0BvA4kAQ9E8LMI0a60Ztfgl1/ODyiEe+mlF7NwoZnoPXnyqXz6\nqQkKMzN/aF1RZj2n0rikFTz55DM8/fRiAG6/vZbp08/2t3/+/EUBmTdncHmkrubWDuYyMqCkJOJv\nI4RoxyI5EeH/gP8L2r0ME2g5z5uFycg5980F5gbtyweyg/btBc4PU5OFaPOaG4yFyqAF11Ez/9se\ne2BjF8K1t8eOHcP48WcCkJKyz59Zc7lc1NXVsWuXycJ5vafRu3c//7HFi5dSWdkfgKVLV/DQQ40T\nxp2lQjyejGa1q7XHDdqZNiGEOFZSXFeINiZUYNbc7sxQQU7gPZYdU2DjbKPX60Wp3v5jzq7MzMzh\nbNhgMmsul4uPPvqEvXvNMNZt23YwYkSSdc0VvPFGT1wu87pXr54crmsT+2KHZNqEEMdLgjYh2pjw\nDaCPXJDjbOOwYfEMG9YFMLNFP/98Dv/4xxcAPPpoP0aMMDNEs7Ov4F//escf4BUWbiI/35T/yMxc\nwNVXX05e3mwArr768oD3cy40Dxy2mHwskEybEOJ4SdAmRDvS3HFaziDHXiqqpfdwTgy4/fab6dSp\nk/+YM5N33XXZjB2b6L/f1KlXs3v3CACefPJZ0tPPBSArazGXXjqFRYveAcDn87Fpk8m6vffeh8yc\n+VcSrXoZ2dnjAwIzZ5s9Hk+rzQptiYwM2LIl2q0QQrRlErQJEYNCdYGGCqqONE4r+H6BJTnGH5aZ\nak6g88wzL/DiizXWqxe4/fab/UHcaacNwc7kuVyugPtlZPRAqT0AaO1j+/bA2aMDBpxunTkfn89U\n9fF6fQHtys2dd1hg5jwWiyTTJoQ4XhK0CRGDjrZwekuzR8EzOKdMmXzEAMiejel8v+Z4+um/8uyz\nZq3QKVM207u3CSiDM3mXXDKFOXM+A+DCC09j+XITtGVljbDOMGPc+vTpRXKy6VYdPLhbsz9rrJT4\nCNa7N+zbd/TzhBDiSCRoEyLGrV69lrlzzRpIdsDVUnl5+QEZrVD3CDVmrry8nDvuMMsBP/ro/wBv\nAqZ79Ne/vpPq6h4AaF3O9Ol9ABg3bjSPPfYn/3mbNm3B6/UAkJSUyL33jgJMgPXZZ19SVmYWPDnn\nnAEMGGDGtJ1xRlZAG48l2wjRXdKqf3/YvbvV3k4I0Q5J0CaEJRbWqLQ5g5KlS3ezYUMDYAK4Ywna\nsrJGMGjQOv92qLIe9v6m3HHHvf7VDOBhXn75ef+xyy6byqpVX1vbU/z7n3vu78ycWW+9eoHa2lrK\ny83groaGAQEBVkHBRg4dMuPWdu/eS9eujV2sTsGBWThKnURajx5QUQG1tZCU1GpvK4RoRyRoE8IS\nzT/owZxByYoVK4GOx3W/nJxs8vLy/duhynqEGu/mXM3A53OuNgcXXXSBf6KAczJAQsI2amvNqghe\n72ksW/YNDQ1mhuiSJcsD7m+CywUADBkyCLfbZOuCg7ZgR+tOjgVxcWY1hO++g6FDo90aIURbJEGb\nEFESKjvkPDZy5HCGDTMrAYwadfrhN2qGlqzVGWrA/8UX/4DPPzddohdf/INmvffJJw+kd2+zwtzI\nkcOZP38h9lLCZWVlPPbYHMAEehdcMNEfcGVnjw9Ys7S58vLy/Z81OPiO9ni3/v0laBNCHDsJ2oSw\ntPYf9FCZPeexH/84g3vvnRTWdjX3s9bV1bF+/efW9jQ2bNhEXV08ABs2bAo497PPvuT++01Ad//9\nV/jrr512WiYLFmwGTMYsO3s8y5cfAKBbtxq2bWsca5eTk82qVWsAE7Q1d2xaYMmPDPLzm65i29qr\nIAQbMMAEbUIIcSwkaBPCEu0/6EcSXDIjFGfttF/84gZmznwNgFtuudF/ztECP2dAtGZNHsXFAwBY\nt249mzdvparKrKCwYcO3ARMM/vOfj9i8OR2Af/zjFbxeMxu0traW0lIzq9TjGcbo0aMYNMgUyT3r\nrLEcONC49ujTT/+V554zAZfX+1fuv//uJtsYHPA6P1NOTjYu1/EtvRUp/fvDrl3RboUQoq2SoE2I\nMGrJZIZQ2S7nUk/jxv0wYNwXcMT3cNZOW7DgRr79djQAXu/fGTt2jP+8UFk+Z8bs9NOTSEw0633G\nx8dTVFSM12sybStWbOOLL0zx24aGZ/D5vHi9BwE4cKCUgwdNBs3lWkFx8amACfyGDTuNykrz3nV1\n6ZSUmOJl9fV1bN++k6oqM9Fh+/adR/zZejyegGPhWHqrNQweDB9/HO1WCCHaKgnahAijlkxmCJXZ\nc45Be/75lwLGaAHNeg+fz0dt7Q4ANm+ODygbEmpg/wcffMy2bX0ByMoq41e/Misd3H77zaxfv4G4\nONO1WVNTS0WF22rvUs46aywJCSaNNGTIKaxcuRKArl27UF9vFoz3ervxwQez2b27GoB//evfHDhw\nKQAvvfQ6t956EytXmizc1KkXBQSrwV3GdkmRo814jSWZmfDEE9FuhRCirZKgTYgjiJUSIF6vN2Bx\n91AB1y233MiWLaaO2nnn/YgHHjDdhD5fVUCdtjvvvI0jZfkGDOhHhw4FAPTvPyTg2KmnDiY11RS8\n7dlzPwcPmmCyV69a4uPj6dChFoCiov2UlJjJBvv21QHp/nsUFhbj840EQKlduFz11j16MWlSDgUF\nGwHTLewMTs3SWEXW64xmTzCIlf+OYCYgbNkCHg8cZUKsEEIcJqK/NpRZ+fkT4HtAstbap5S6C7gE\n2AVM11p7lFLXA7dgSqFP01pXKqXOAx4B6oCfaK33KqWGAy9at79Za52vlOoDzMLURHhAaz03kp9J\nnDiOpQTIsU5mCDWwvq6uK3PnbvCf6+w6zc6+IuA+X3+9nB07TIA0Z84CvN6TAFCqhvT0OgAyM0cH\nXFNYWMjll18PwMcfv8PIkZl07WqCo507v2Px4oHWmS9wxhlZ9O9vMmEXXzyJqqrtAFx++TVs2PAt\nHTua9Ux3715DTU0Pa3s/HTuartn4+Hiys8/im29KAZg+fRrFxaZ79M9/fpwFCxaTm2uONTTkUVhY\nCZhg1SizXnsO6zI+klgq5dKpE/TtC9u2yQxSIUTLRfq7XhlwHvA+gFIqA8jRWk9QSt0NXKaU+hD4\nJTABuMrafgq4H7gAyATuBX4NPARci6kX8DxwGXAPcB+wDvgYkKBNtKrggOtYqvE3FVg4y244F3d3\ndp0uWLA4oCaZc+WDk07y0bmzmeHZv/8Q8vLs1crPCVjWavfu37Jtm5mdOnXqNVx11WV4vd0BOHBg\nFzDQ306Xy0W3bqcAkJjo9q8TmpiYyLBhp9GpkymuW15eCZhxZ5WVdfTta4KtkSPP4eyzx7JkiZkY\n8fvfv0T37t3991+9ei0FBSZg7NOnmNLSBODwLt1169azYUMf/88LmtdlHAuGDYP16yVoE0K0XESD\nNq11PVCvlAJQwBhggXV4DnA9UADkW1m4OcBMpVQSUKu1rgZWKKUet67porXeC6CUsvtbhmutl1r7\nKpVSqVrrykh+LnFiaG7WrKl1PZsSKuNjuv4au0CdgjNrzjprwTXJMjO/R+fOJhPWt29vFi/OA8yg\n/qIiEwCtW7ee2tpaliz5AoC0tApgLQA+n8e6h5mI8POf/5Rt28y4uNtvv5kvvpjDrl1mLaba2nRK\nSwusNg9jw4ZvqakZDkBFxSJgEACVlfkUF68HoK7uAl588WXWrDErHbz44ssBM0S9Xi8NDWb1h6Ki\nQ5SXjwPMSgmZmd+jtPSgdV4qzRXt2mzBzjwTli+HK6+MdkuEEG1Na4+q6AxUWNsVmIEu6UfZBxBv\nPcc59qmgYwDl1rUStLVQLI37iRXNLQHSknU9Q+va5N7gwrijRo3kT396FoABA6aRm2sCLnu8m50J\n++abBezbZ4Kb9evzKS83//vU1g7g448/o6EhzbquBNgDQHb2D613bToo+s9/PmTnThMIzp7tY+tW\n8ytk+fJviI+Pp7JyPgBer9t/jdvdQGnpWAD++c/X6d27J+XlZuzb1q3bAu4fHx9Px46mxEifPkn4\nfI2Lya9evZaiIjP+raGhgcTEJQCMG/cYnTp14ljWIo2Gs8+GBx+MdiuEEG1RawZtGhNUnWS9TgMO\nWfvSQuwD8DruYfMFPdvXHwx+4xkzZvi3c3JyyMnJOYbmt2+xMu6nLQaPwet6HkmosWgej4fSUjv4\nGhby/X772//myy9NkFVV9Q/27UsGoK5uPCkpKf6u1H37vLjd5rtPcXEZbvdlAKxYsYqqqhrgLAAO\nHVoFDAbg009z6dKlC1u3msDsb3/bwcaNphvS6/0rGzduRutzAdi69XUaGr4PwCeffMHQoUOorbW/\nV8Vh/jeGuLh47P8tu3fvRt++fQDTVdu372kBn23kyOH07GkyhZdf/kNSUlIA829h9eq1mGGysGzZ\nN+Tnm4zkoEF/5/77747JReKbMnYsrFkD9fXQ8fhWJxNCnGBaM2hTwErMhIMngUnAUmAzMFwpFWfv\n01rXKKWSlFLJmDFtBdY9ypRSfWkMAAHWKaXGA/lAmta6KviNnUGbiG2RCB6b+0c7+Dy7PU1d5zz3\nnHPGBazreSRz5izg//7PVPvPzFzAJZc0Lqq+bt36gCK2kyblBBTJbVwl4ApuvfUOtDY/l7y8t9H6\nJwB8/PHn/PnPj/HWW+8CJhDU2owi0FqjlAkKMzJOZuTITJ5+2qx0kJKSQlWVvTzWXJYuXUFd3XgA\nNmx4nwMHzLi49eu9TJ36AzZtMiU/kpM7U1LS37quyGqjPVArHztbl5qaSlWVCQL79buYrVu309Bg\nJhts3br9iD+v4KLCzoAOoLraTHQIrucWLFa+kNhSU824tqVLQb4/CiFaItKzR13A50CW9XwfsFAp\ntQgze/Rpa/boTEzfRhkwzbr8UeBLoBa4wdr3IPA2Jmi71dr3BPA6kAQ8EMnP057F2rifcGruH+3g\n86BxcHvwIuTOArRXXrmcTZv6Aab70nl/Z3D37rvvs2mTSRq/996HAYFZQ0MDWhdaV3Xm6af/yrPP\nmlUENm26j4aGCf77jxs3hh07TNJ5wIA+bN1quiR7976YJ574C7Nmmffr1cuDGZEAiYkdsbNdffue\nxe7d+4Au1mvYsmW19Vkuobj4AAkJJmNWU1MDmMzaihWrefnlF0hMNG0+ePBHPPOMCcYuvngyX3+9\njG3bTLvi4uLw+cws1fLytYDJmC1duoLdu/dghrfCihUrAmaBFhRspLzcZPYKCjYGBLYej8dflPfC\nC7PYscPUhJs69RramqlTYfZsCdqEEC0T6YkIHkz2zGkFJtBynjcLU7bDuW8uQTNBtdb5QHbQvr3A\n+WFq8gkrVsb9RDp4DC4V4cyeBU8GcM5WDB7w7yxAu3jxUjye4NIUhnOSQlxcIUqZ7kCtqwICswsu\nUPTubbr+Ro4cznvvfegf97V7dwk+X2O7/vd/H2T5cjP2bNCgU9mwwWSttm/fyTffrMLtNu9x8OBi\n4uLMmLHa2jp8vslWm74iLk4BZwJQVvYBJtFtgq0//ekRiorM7M5Nm7qza5ddNiRwrdHTTx9J3747\nARg9+gxcrnhWrjRZuNTUbhQV/cs6UwPDrc/yLVoDmOtqauoDAmXnRIrMTHt8nfHRR5+wb58p9Ltq\n1ToGDDBds4mJiYQSi19IfvhDuPZaeOopUOro5wshBEhxXRFjjjV4DNUFGriYuOeIWbe6ujp27iyw\ntsdz4YWT/GPQMjO/R35+mf9cZwHa7t27UVBw0H8PZ1DonKRw9tk9SE83Y98uvvhXfPDBbPbvNzNB\nd+7si5lsDXAOJ588kKQkE4x17VrMokVmdYGqqjE88MCjfPedGfdWVPQVdjZtw4ZKOnVKAg5YPxMP\nPp/JppkVCUwXblpaKhMmjKegwLxWSuH1mgDoyy/nM3LkcJYvNwu8X3nlBF5/PReAe+65kyeffIan\nn14MwBln+CgtNcFefn4B48aN5cwzzedbs6aexqT5K4D5bMnJyXTunMbOnfXWzy49IFB2ZtOCf5Ze\nrw+fzx7CqgPKoIQSK19InM44A+rqYONG01UqhBDNIUGbaBdCdYE6/2jbQUBTnJmcjz76hJSUFH92\nLTMTRowoBsy4sqqqKrp2NUGbz+dj167vAPjgg9l4PN/3t8M5SSEuLo66OpOJ27hxEwUF32JnoL79\ndgXl5ab9S5YM4txzsznjDHPd5s2LKS427fr731+moaGBhgYzkL+hoQq7q7G8fBtnnjma/HzTFlNq\nx4wZ83h82HN2evXKICtrBOnpJmjr27cPJSXvAzBw4Nn84Q8zKC835UHeeONdfL4swNSE27NnLxUV\nplsyP7+GqqpTrDZu5a67bveP7Vu/3gUs9H9un8/cb9iw09i3rxB7grfJ8DXOmv3ww08pLDRdzTNn\nvkqPHnbX5yL69z+JuLivADjzzGzOO69xGSunWJt40BSl4Ec/gtdeg8cfP/r5QggBErSJKDjWP6rh\n+GMcqqtMa4WZ52K6L+vq6li/3gzWX7nyNBYtMt1wWVmLyc8v8NcM+/bbUurrTa/97t0bKS0119TV\nTWPSpBx/ILNunY+qKlM4dtu2JDyeemA5AGVlZYApi/Huux9w772/5913PwDgwAEv0A2A0tK9lJUd\nBBKsVnsxi4GY4KikpAQ7IKqrqwH6Wtul2NVxSkr2M3v2Zxw4YNYiranZBpwNwJYt29i3r8T/2u3e\nCJixduvXV1JZWYcdaJaVfQ6Ymmpbt+qA0iQDBvTnwAF7xmhXSkp2WG0cSXn5IezsYGVlMXFxwbNm\nm+7u3LOnEKVGAbB3b2GT50DsTTw4kp/9DCZOhEcegRiMK4UQMUiCtgg41tmKsZgRiIRj/aMa6rpw\njFu69NIpLFr0jrV9TcCMzqVLV7B9uxkDZro8d1Ff/z0AlFpK586NtRu+M4ku1qzJw+VyOZZl2oPX\nawKZ+vq+HDhQAdilP17G7tbs1CmJv/zlef7zH5PR6tvXC2wF4NRThzN79hc0Vs7xAt8AMGLEeLZv\n30ZgZZwc63k7duAHNezevRev10yKqKyswM6KHTrUldTUZMrKnOPXTIbu4MEqUlI6ARut/W7AjJ/L\nz99AVVUVS5e+DsCBA1uwZ5Lu378LMMFWUVEJWvuwJ39XV9dQXt7487ryyktYs8aMafvFL6b7C/tm\nZ1/BihUr6dzZnjQOjz02Bwhd0DiWnXYaDBkC778P17S9uRRCiCiQoC0CjnW2YqxmBGKFc6JAXV3X\nI649Gap0h8fjYdasEuvMRWRnj/fP4Bw6dEjAskyAf0ZnRkYPiotNIJOZOY3y8nI8HlNa4/zzL6F7\nd9PFt3ZtdyoqTLfpli0diY+P9y/LpNR27OBl06YtJCV1xA6WOnTogNudarXjJObMmU9FxRarzTU0\nBj37SUpKpK6ucXwdnAyYUiGmS9QEUgkJ39HQYEqFnHRSb4qL+1nblWzevBWwAzMvcDUA5eUfkJqa\n4rh3HGAWd6+sXGbVXLNrq23DlFU0JUX+/e/32bnTBKguV53/mFJxaG1G25sJEAq7DGNDQxH19SZ4\n3LFjJ3/4w53+n70Z02bul5W1mN/97tfEx9uzbVNYuNAsk5WXl88FF0z0/zfOzh4PmLGCsTLx4Eju\nugv+53/g6qtlQoIQ4ugkaBOt7viyYiY4WrMmjwUL7BmbZl1KOwBeseKvzJ17yH/M4/E4ynNkAv38\nd3PO4JwyZTO9e5v2uFwuTj11MHFxJpvTq9colizZ6L/n8uWr8HozAVi5Mo/77rsLgPff/witTcZs\n69btDBjQn/Jys4RTjx4uTKADqak9OPnkAezaZcoKdupUQnm5CdKKivqxceNmfxtrag4CJuO0a1cc\nbrcHsGeyFuLMWg0efDI7dpixcOnpaZSU7ARg8ODvUVRksogTJjzE++/PpnGBkXrsidr19Q1ANWAX\nCf4OO8vXp09PKiurMeUWwWTazP179+7LkiXL/G0xgabJKiYnJ1FdbT7b6NFT+PTTzwE7kI3D7V5r\n/XyuCBh/+NhjfwpYaeKcc8axZYv5+V144SQGDTLj9bKyRgR9AVrWZr4ATZ0K998Pn34KF18c7dYI\nIWKdBG1BIj1uynn/SGcEYrX7NdRsvlBtdrlc/hmDu3YtYft2UyIjLy+f0aPP8J+3ffvOgGNbtmzz\nl+fYvn0X06eP89//rbfe9RdpdbsPBCyNdMcd91JZae77/vsfs2uXyQC9996HVFRUYHf/fffdF/zk\nJ/cAUFLyLfbqAnl5BbjdbhoaTNBTVKSxF1/X2se2bZsxpQmhvHw/djfkzp3aGreWbH0iL6YMIdTU\nHGoiI2Pu0bVrOvv3l2AHRCUlFYDp3l2w4EvATIJ4+OEnqK4+5Li/xl41rkuXNKqr67D/XZoxa2YC\nRlLSECoqqrDru8E+7Czfjh1bSEhIoHH5Kzf22LTKyiLsMWxLl35DVVUdYP47VlSsBoYAkJs7lz/+\n8T5/5vOUU06mc+cvAVP+4ze/uZM33zTBa21tLZMnm7F1OTnZAeuxtiVKwQMPwB/+AD/4ARxlIqwQ\n4gQnvyKChKPLMlRQ0poZgbbY/Rrc5okTJzQZ5NbVTWXHDtNVmpU1IiBQPnRoEvPnvwbA0KE/p6Gh\nwV+e46SThjhWFxjPpEk5vP/+U9Y9h/DxxybgGTDgeQYNGkhKiskcVVXVoLW94Ho+N930X6xe/RAA\n1dVJ7N9fY7W5ElM3GtxuL6tXrwNMkVmvdylmaVwzkP7QoXLsUhgmODL337evCFPi0GMdc45Rg5SU\nZCori61XbuzJAHv2FFFdXQHYqxTU0Dgzc4///nv27CFw9Tf7PlBUVIUJEntY+xtXlFu/fgMuVzyN\nywK7sQNG8PkXejfisScsmJ+HGU+3adNWEhI6ABscbTSB2MGDSQGZz4kT11FcbLJpdXXWzNQ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7zyE8DwuP1aH0VFRTp16o06deqNWlRUpHv37tXx4yfo+PETNC8vT8866zw966zzdMOGDdq372Dt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\nm9tMKA4AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x116bafad0>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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