MovieLens/02-Augment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Augment the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us augment the items(movie) data with more fields from IMDB and / or TMDB data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json, glob\n",
    "import requests\n",
    "import re\n",
    "import tmdb\n",
    "from importlib import reload\n",
    "from utils import create_directory, update_progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Movie Features from TMDB\n",
    "\n",
    "- We will use the \"links.csv\" which contains:\n",
    "    - movie_id\n",
    "    - imdb_id\n",
    "    - tmdb_id\n",
    "- Source: Scraper + Manual Enhancement\n",
    "     - Bootstrapped using the scraper to get the IMDB title link\n",
    "     - Enhanced through search on TMDB database to get complete list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "links = pd.read_csv(\"links.csv\", dtype={\"tmdb_id\": 'Int64' })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>tmdb_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>tt0113189</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>tt0113101</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>tt0113161</td>\n",
       "      <td>8012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>tt0112722</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id    imdb_id  tmdb_id\n",
       "0         1  tt0114709      862\n",
       "1         2  tt0113189      710\n",
       "2         3  tt0113101        5\n",
       "3         4  tt0113161     8012\n",
       "4         5  tt0112722     1710"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1682, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Movie Feature from TMDB "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adult': False,\n",
       " 'backdrop_path': '/dji4Fm0gCDVb9DQQMRvAI8YNnTz.jpg',\n",
       " 'belongs_to_collection': {'id': 10194,\n",
       "  'name': 'Toy Story Collection',\n",
       "  'poster_path': '/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg',\n",
       "  'backdrop_path': '/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg'},\n",
       " 'budget': 30000000,\n",
       " 'genres': [{'id': 16, 'name': 'Animation'},\n",
       "  {'id': 35, 'name': 'Comedy'},\n",
       "  {'id': 10751, 'name': 'Family'}],\n",
       " 'homepage': 'http://toystory.disney.com/toy-story',\n",
       " 'id': 862,\n",
       " 'imdb_id': 'tt0114709',\n",
       " 'original_language': 'en',\n",
       " 'original_title': 'Toy Story',\n",
       " 'overview': \"Led by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.\",\n",
       " 'popularity': 29.3,\n",
       " 'poster_path': '/rhIRbceoE9lR4veEXuwCC2wARtG.jpg',\n",
       " 'production_companies': [{'id': 3,\n",
       "   'logo_path': '/1TjvGVDMYsj6JBxOAkUHpPEwLf7.png',\n",
       "   'name': 'Pixar',\n",
       "   'origin_country': 'US'}],\n",
       " 'production_countries': [{'iso_3166_1': 'US',\n",
       "   'name': 'United States of America'}],\n",
       " 'release_date': '1995-10-30',\n",
       " 'revenue': 373554033,\n",
       " 'runtime': 81,\n",
       " 'spoken_languages': [{'iso_639_1': 'en', 'name': 'English'}],\n",
       " 'status': 'Released',\n",
       " 'tagline': '',\n",
       " 'title': 'Toy Story',\n",
       " 'video': False,\n",
       " 'vote_average': 7.9,\n",
       " 'vote_count': 10895}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_id = links.tmdb_id[0]\n",
    "tmdb.movie(sample_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmdb_ids = links.tmdb_id.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_movie_features(tmdb_ids):\n",
    "    num = len(tmdb_ids)\n",
    "    tick = 0\n",
    "    features = []\n",
    "    for i in tmdb_ids:\n",
    "        feature = tmdb.movie(i)\n",
    "        if feature != False: \n",
    "            features.append(feature)\n",
    "        \n",
    "        tick = tick + 1\n",
    "        update_progress(tick / num)\n",
    "    \n",
    "    update_progress(1)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_movie_features(features):\n",
    "    outfile = \"data/features.json\"\n",
    "    with open(outfile, 'w') as fout:\n",
    "            json.dump(features, fout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment and Run this to download movie features from tmdb\n",
    "\n",
    "#features = get_movie_features(tmdb_ids)\n",
    "#save_movie_features(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Item Features Augment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.read_json(\"data/features.json\")\n",
    "#df_features = pd.DataFrame.from_dict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the non NA links\n",
    "links_full = links[~links.tmdb_id.isna()][[\"movie_id\", \"tmdb_id\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_item_features = pd.merge(left=df_features, right=links_full, left_on=\"id\", right_on=\"tmdb_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>backdrop_path</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>tmdb_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>/dji4Fm0gCDVb9DQQMRvAI8YNnTz.jpg</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td></td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10878</td>\n",
       "      <td>1</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>/dA9I0Vd9OZzRQ2GyGcsFXdKGMz3.jpg</td>\n",
       "      <td>{'id': 645, 'name': 'James Bond Collection', '...</td>\n",
       "      <td>58000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 28, '...</td>\n",
       "      <td>http://www.mgm.com/view/movie/757/Goldeneye/</td>\n",
       "      <td>710</td>\n",
       "      <td>tt0113189</td>\n",
       "      <td>en</td>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>...</td>\n",
       "      <td>130.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>No limits. No fears. No substitutes.</td>\n",
       "      <td>GoldenEye</td>\n",
       "      <td>False</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2037</td>\n",
       "      <td>2</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>/3EqYpbGCE9S5GddU2K4cYzP5UmI.jpg</td>\n",
       "      <td>None</td>\n",
       "      <td>4000000</td>\n",
       "      <td>[{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>tt0113101</td>\n",
       "      <td>en</td>\n",
       "      <td>Four Rooms</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Twelve outrageous guests. Four scandalous requ...</td>\n",
       "      <td>Four Rooms</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1251</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>/g1BfxcnplYEveGqS1ttfhdRBgbR.jpg</td>\n",
       "      <td>{'id': 91698, 'name': 'Chili Palmer Collection...</td>\n",
       "      <td>30250000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 53, 'nam...</td>\n",
       "      <td>None</td>\n",
       "      <td>8012</td>\n",
       "      <td>tt0113161</td>\n",
       "      <td>en</td>\n",
       "      <td>Get Shorty</td>\n",
       "      <td>...</td>\n",
       "      <td>105.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The mob is tough, but it’s nothing like show b...</td>\n",
       "      <td>Get Shorty</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>501</td>\n",
       "      <td>4</td>\n",
       "      <td>8012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>/gE1DHa82NZVb9B2Lx2cLNz43Iwd.jpg</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 18, 'name': 'Drama'}, {'id': 53, 'name...</td>\n",
       "      <td>None</td>\n",
       "      <td>1710</td>\n",
       "      <td>tt0112722</td>\n",
       "      <td>en</td>\n",
       "      <td>Copycat</td>\n",
       "      <td>...</td>\n",
       "      <td>124.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>One man is copying the most notorious killers ...</td>\n",
       "      <td>Copycat</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>424</td>\n",
       "      <td>5</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                     backdrop_path  \\\n",
       "0  False  /dji4Fm0gCDVb9DQQMRvAI8YNnTz.jpg   \n",
       "1  False  /dA9I0Vd9OZzRQ2GyGcsFXdKGMz3.jpg   \n",
       "2  False  /3EqYpbGCE9S5GddU2K4cYzP5UmI.jpg   \n",
       "3  False  /g1BfxcnplYEveGqS1ttfhdRBgbR.jpg   \n",
       "4  False  /gE1DHa82NZVb9B2Lx2cLNz43Iwd.jpg   \n",
       "\n",
       "                               belongs_to_collection    budget  \\\n",
       "0  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  {'id': 645, 'name': 'James Bond Collection', '...  58000000   \n",
       "2                                               None   4000000   \n",
       "3  {'id': 91698, 'name': 'Chili Palmer Collection...  30250000   \n",
       "4                                               None         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 28, '...   \n",
       "2  [{'id': 80, 'name': 'Crime'}, {'id': 35, 'name...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 53, 'nam...   \n",
       "4  [{'id': 18, 'name': 'Drama'}, {'id': 53, 'name...   \n",
       "\n",
       "                                       homepage    id    imdb_id  \\\n",
       "0          http://toystory.disney.com/toy-story   862  tt0114709   \n",
       "1  http://www.mgm.com/view/movie/757/Goldeneye/   710  tt0113189   \n",
       "2                                          None     5  tt0113101   \n",
       "3                                          None  8012  tt0113161   \n",
       "4                                          None  1710  tt0112722   \n",
       "\n",
       "  original_language original_title  ... runtime  \\\n",
       "0                en      Toy Story  ...    81.0   \n",
       "1                en      GoldenEye  ...   130.0   \n",
       "2                en     Four Rooms  ...    98.0   \n",
       "3                en     Get Shorty  ...   105.0   \n",
       "4                en        Copycat  ...   124.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "2           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "3           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "\n",
       "                                             tagline       title  video  \\\n",
       "0                                                      Toy Story  False   \n",
       "1               No limits. No fears. No substitutes.   GoldenEye  False   \n",
       "2  Twelve outrageous guests. Four scandalous requ...  Four Rooms  False   \n",
       "3  The mob is tough, but it’s nothing like show b...  Get Shorty  False   \n",
       "4  One man is copying the most notorious killers ...     Copycat  False   \n",
       "\n",
       "   vote_average  vote_count movie_id tmdb_id  \n",
       "0           7.9       10878        1     862  \n",
       "1           6.8        2037        2     710  \n",
       "2           6.1        1251        3       5  \n",
       "3           6.5         501        4    8012  \n",
       "4           6.5         424        5    1710  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_item_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_item_features.to_csv(\"data/item_features.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Movie Posters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already exists /Users/amitkaps/Documents/github/recommendation/MovieLens/data/posters\n"
     ]
    }
   ],
   "source": [
    "# Create Poster Directory\n",
    "create_directory(\"/data/posters\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poster(movie_id, df):\n",
    "    \"\"\"\n",
    "    Gets the movie poster from the movie_id\n",
    "    \n",
    "    poster_path (string): path for the poster\n",
    "    df (pd.Dataframe): pandas dataframe with movie id and poster_path\n",
    "    \"\"\"\n",
    "    directory_path = \"data/posters/\"    \n",
    "    poster_path = df[df.movie_id == movie_id].poster_path.values[0]\n",
    "    if poster_path == None:\n",
    "        poster_path = \"\"\n",
    "    \n",
    "    save_path = directory_path + str(movie_id) + \".jpg\"\n",
    "    \n",
    "    base_url = \"https://image.tmdb.org/t/p/w92\"\n",
    "    url = base_url + poster_path   \n",
    "    \n",
    "    response = requests.request(\"GET\", url, stream=True)\n",
    "    if response.status_code == 200:\n",
    "        with open(save_path, 'wb') as out_file:\n",
    "            response.raw.decode_content = True \n",
    "            shutil.copyfileobj(response.raw, out_file)\n",
    "        return True\n",
    "    else: \n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_item_features[df_item_features.poster_path.isna()][[\"movie_id\", \"poster_path\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poster(1, df_item_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image \n",
    "Image(filename='data/posters/1.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_posters(df):\n",
    "    \"\"\"\n",
    "    Get all posters from the dataframe and save with movie_id\n",
    "    \"\"\"\n",
    "    id_list = df.movie_id.tolist()\n",
    "    num = len(id_list)\n",
    "    tick = 0\n",
    "    \n",
    "    for i in id_list:\n",
    "        df[\"image\"] = poster(i, df)\n",
    "        tick = tick + 1\n",
    "        update_progress(tick / num)\n",
    "    \n",
    "    update_progress(1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment and run this to get posters\n",
    "# get_all_posters(df_item_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}