7_Word Clouds.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Clouds\n",
    "We propose to implement a wordcloud video, where we compute one word cloud per time point, and then we create a video from all the different word clouds, that will illustrate the evolution of the word set over time.\n",
    "\n",
    "We also implement a smoothing on the data so that the wordcloud is a little bit more constant. If the time series have to many up and downs the resulting word cloud will make no sense. We tried to do the word cloud per month, but it was too variable. We choose here to measure the counts per year, and then smooth the time series (rolling mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "            -a  -la  -le  -on  -—  aarau  aarberg  aarbourg  aarg  aarhus  \\\n",
       "1798-01-01 NaN  NaN  NaN  NaN NaN    NaN      NaN       NaN   NaN     NaN   \n",
       "1799-01-01 NaN  NaN  NaN  NaN NaN    NaN      NaN       NaN   NaN     NaN   \n",
       "1804-01-01 NaN  NaN  NaN  NaN NaN    NaN      NaN       NaN   NaN     NaN   \n",
       "1805-01-01 NaN  NaN  NaN  NaN NaN    NaN      NaN       NaN   NaN     NaN   \n",
       "1806-01-01 NaN  NaN  NaN  NaN NaN    NaN      NaN       NaN   NaN     NaN   \n",
       "\n",
       "           ...   œil  œillet  œquo  œrlikon  œuf  œur-  œuvre  ——  ———  „„„  \n",
       "1798-01-01 ...   NaN     NaN   NaN      NaN  NaN   NaN    NaN NaN  NaN  NaN  \n",
       "1799-01-01 ...   NaN     NaN   NaN      NaN  NaN   NaN    NaN NaN  NaN  NaN  \n",
       "1804-01-01 ...   NaN     NaN   NaN      NaN  NaN   NaN    NaN NaN  NaN  NaN  \n",
       "1805-01-01 ...   NaN     NaN   NaN      NaN  NaN   NaN    NaN NaN  NaN  NaN  \n",
       "1806-01-01 ...   NaN     NaN   NaN      NaN  NaN   NaN    NaN NaN  NaN  NaN  \n",
       "\n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordYear = pd.read_csv('Data/3kPluswordPerYear.csv',index_col=0)\n",
    "wordYear = wordYear.rolling(window=24,center=True).mean()\n",
    "wordYear.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "words = wordYear.columns.astype(str)\n",
    "numWords = wordYear.loc[wordYear.index[101]].values\n",
    "numWords2 = wordYear.loc[wordYear.index[101]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first generate two word clouds from two consecutive years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/thomas/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "listWords = []\n",
    "listWords2 = []\n",
    "for counter,i in enumerate(words):\n",
    "    listWords.append(str(str(i)+' ')*numWords[counter])\n",
    "    listWords2.append(str(str(i)+' ')*numWords2[counter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = ' '.join(listWords)\n",
    "test2 = ' '.join(listWords2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordcloud = WordCloud().generate(test)\n",
    "wordcloud2 = WordCloud().generate(test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first show the two word clouds that were computed separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "execution_count": 14,
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H/6MLz6eqMJM/vPXhe5cx+naVqTVK6CwStTsD/KOghGlvZnLZz3kA+F2CF48q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112822da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(wordcloud)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(wordcloud2)\n",
    "plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What can be seen here is that the same word does not have the same color, and the same words appear at different places on the word cloud. Therefore we have to keep the position of the words in the first word cloud and add them to the other word cloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(wordcloud2.layout_)):\n",
    "    word = wordcloud2.layout_[i][0][0]\n",
    "    for j in range(len(wordcloud.layout_)):\n",
    "        otherWord = wordcloud.layout_[j][0][0]\n",
    "        if otherWord == word:\n",
    "            wordcloud2.layout_[i] = list(wordcloud2.layout_[i])\n",
    "            wordcloud2.layout_[i][2:] = list(wordcloud.layout_[j][2:])\n",
    "            wordcloud2.layout_[i] = tuple(wordcloud2.layout_[i])\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have done that we can look at the two onsecutive word clouds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 399.5, 199.5, -0.5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MQdOvB/pTJwTs2HrekoSINJfig77/TAkLdpV6cZclB66dJUrizJMNgKC5mVXSIE3cuvZb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\nFSpUqDiPoCp9FSpUqDiP8H8fQXPFmOsV4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11623a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(wordcloud)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(wordcloud2)\n",
    "plt.axis(\"off\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that we succeeded to keep the position and the color of each word between the two word cloud. Bun unfortunately the top 200 words in two succesive years change too much, therefore we have too many words that overlap and the cloud becomes not clear at all.\n",
    "\n",
    "Based on this result we see that a word cloud video will not be very interesting as everything will overlap with each other,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To solve this problem we will try to take the n most frequent words, choose a subset that is always present, and then put their word count. This would be not completely representative of the reality, but could be a nice proxy that would not be too noisy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordYear = pd.read_csv('Data/3kPluswordPerYear.csv',index_col=0)\n",
    "wordYear.index = pd.to_datetime(wordYear.index)\n",
    "yearIndex = wordYear.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "setMostFrequent = []\n",
    "\n",
    "for i in range(10,len(yearIndex)):\n",
    "    rankYear = wordYear.loc[wordYear.index[i]]\\\n",
    "    .sort_values(ascending=False)\n",
    "    wordRank = rankYear.index.values[0:844]\n",
    "    numRank = np.arange(1,len(wordRank)+1)\n",
    "    rankDF = pd.DataFrame(data=numRank,\n",
    "                          index=wordRank, \n",
    "                          columns=[wordYear.index[i]])\n",
    "    setMostFrequent.append(rankDF)\n",
    "    \n",
    "dfMostFrequent = pd.concat(setMostFrequent,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at how many words are always in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 187)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1812-01-01 00:00:00</th>\n",
       "      <th>1813-01-01 00:00:00</th>\n",
       "      <th>1814-01-01 00:00:00</th>\n",
       "      <th>1815-01-01 00:00:00</th>\n",
       "      <th>1816-01-01 00:00:00</th>\n",
       "      <th>1817-01-01 00:00:00</th>\n",
       "      <th>1818-01-01 00:00:00</th>\n",
       "      <th>1819-01-01 00:00:00</th>\n",
       "      <th>1820-01-01 00:00:00</th>\n",
       "      <th>1821-01-01 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>1989-01-01 00:00:00</th>\n",
       "      <th>1990-01-01 00:00:00</th>\n",
       "      <th>1991-01-01 00:00:00</th>\n",
       "      <th>1992-01-01 00:00:00</th>\n",
       "      <th>1993-01-01 00:00:00</th>\n",
       "      <th>1994-01-01 00:00:00</th>\n",
       "      <th>1995-01-01 00:00:00</th>\n",
       "      <th>1996-01-01 00:00:00</th>\n",
       "      <th>1997-01-01 00:00:00</th>\n",
       "      <th>1998-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>actuelle</th>\n",
       "      <td>308.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>153.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>affairer</th>\n",
       "      <td>365.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ancien</th>\n",
       "      <td>753.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>...</td>\n",
       "      <td>361.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anner</th>\n",
       "      <td>159.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>années</th>\n",
       "      <td>487.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>477.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>...</td>\n",
       "      <td>102.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 187 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1812-01-01  1813-01-01  1814-01-01  1815-01-01  1816-01-01  \\\n",
       "actuelle       308.0       264.0       350.0       347.0       282.0   \n",
       "affairer       365.0       369.0       233.0       283.0       432.0   \n",
       "ancien         753.0       616.0       235.0       368.0       205.0   \n",
       "anner          159.0       279.0       248.0       226.0       175.0   \n",
       "années         487.0       589.0       453.0       525.0       390.0   \n",
       "\n",
       "          1817-01-01  1818-01-01  1819-01-01  1820-01-01  1821-01-01  \\\n",
       "actuelle       277.0       345.0       342.0       390.0       327.0   \n",
       "affairer       342.0       221.0       215.0       158.0       121.0   \n",
       "ancien         261.0       290.0       251.0       361.0       438.0   \n",
       "anner           96.0       160.0       149.0       258.0       315.0   \n",
       "années         346.0       381.0       351.0       477.0       516.0   \n",
       "\n",
       "             ...      1989-01-01  1990-01-01  1991-01-01  1992-01-01  \\\n",
       "actuelle     ...           153.0       167.0       170.0       165.0   \n",
       "affairer     ...            87.0        80.0        82.0       121.0   \n",
       "ancien       ...           361.0       371.0       373.0       384.0   \n",
       "anner        ...            31.0        30.0        25.0        25.0   \n",
       "années       ...           102.0       109.0       104.0        97.0   \n",
       "\n",
       "          1993-01-01  1994-01-01  1995-01-01  1996-01-01  1997-01-01  \\\n",
       "actuelle       200.0       262.0       280.0       306.0       309.0   \n",
       "affairer       132.0       133.0       139.0       146.0       149.0   \n",
       "ancien         411.0       405.0       408.0       400.0       415.0   \n",
       "anner           30.0        34.0        38.0        44.0        48.0   \n",
       "années          81.0        86.0        78.0        89.0        89.0   \n",
       "\n",
       "          1998-01-01  \n",
       "actuelle       471.0  \n",
       "affairer       151.0  \n",
       "ancien         459.0  \n",
       "anner           81.0  \n",
       "années          97.0  \n",
       "\n",
       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordAlways = dfMostFrequent.dropna(axis=0)\n",
    "print(wordAlways.shape)\n",
    "wordAlways.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we select the time serie of these words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197, 200)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordMostFrequ = wordAlways.index.values\n",
    "dfWithMostF = wordYear[wordMostFrequ]\n",
    "dfWithMostF.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will implement the word cloud that was done with the method described above.\n",
    "\n",
    "We start by generating the first word cloud:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x1192ee5f8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = dfWithMostF.columns.astype(str)\n",
    "numWords = dfWithMostF.loc[dfWithMostF.index[0]].values\n",
    "\n",
    "listWords = []\n",
    "for counter,i in enumerate(words):\n",
    "    listWords.append(str(str(i)+' ')*numWords[counter])\n",
    "\n",
    "test = ' '.join(listWords)\n",
    "wordcloud = WordCloud().generate(test)\n",
    "wordcloud.to_file('Images/yearo_0.png')\n",
    "#plt.imsave('Images/year_0.png',wordcloud.to_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we generate each wordcloud based on the corrdinates of the first one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "for k in np.arange(1,len(dfWithMostF)):\n",
    "    numWordsi = dfWithMostF.loc[dfWithMostF.index[k]].values\n",
    "    listWordsi = []\n",
    "    \n",
    "    for counter,i in enumerate(words):\n",
    "        listWordsi.append(str(str(i)+' ')*numWordsi[counter])\n",
    "\n",
    "    testi = ' '.join(listWordsi)\n",
    "    wordcloudi = WordCloud().generate(testi)\n",
    "    \n",
    "    for i in range(len(wordcloudi.layout_)):\n",
    "        wordi = wordcloudi.layout_[i][0][0]\n",
    "        for j in range(len(wordcloud.layout_)):\n",
    "            otherWord = wordcloud.layout_[j][0][0]\n",
    "            if otherWord == wordi:\n",
    "                wordcloudi.layout_[i] = list(wordcloudi.layout_[i])\n",
    "                wordcloudi.layout_[i][2:] = list(wordcloud.layout_[j][2:])\n",
    "                wordcloudi.layout_[i] = tuple(wordcloudi.layout_[i])\n",
    "                break\n",
    "    \n",
    "    wordcloudi.to_file('Images/yearo_'+str(dfWithMostF.index[k])+'.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we try to generate a gif."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import imageio\n",
    "import os\n",
    "images = []\n",
    "for filename in os.listdir('Images/'):\n",
    "    if filename.endswith('.png'):\n",
    "        images.append(imageio.imread('Images/'+filename))\n",
    "imageio.mimsave('movie.gif', images, **{ 'duration': 100 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"movie.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url='movie.gif')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that it's still very noisy but it's fun to look at!\n",
    "\n",
    "As the gif is static on the GiHub notebook please look at the **movie.gif** file to view the gif."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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