COVIDpolitics2021.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# COVID politics 2021\n",
    "\n",
    "### Main ideas\n",
    "\n",
    "Did people react differently to COVID-19 depending on their political beliefs?\n",
    "(Does it generate more controversy than other topics controversy?)\n",
    "\n",
    "This notebook intends to visualize if such relationship exists, and if so, quantify it.\n",
    "\n",
    "The study is focused in the US due to two main factors:\n",
    " - Data availability\n",
    " - \"Simplified\" political landscape\n",
    " \n",
    "### Introduction \n",
    " \n",
    "It is impossible to separate now 2020 to the pandemic. Despite the certain feeling that COVID-19 paralyzed the world, the pandemic itself has been far from still. The relentless spread of the virus has taken many forms inside ourselves: what is going on in Wuhan? What is going in Italy? How long will the lockdown last? Are masks effective? Which company is making the vaccine? The urgency and total presence of such topics have modified our discussions and the way we discuss them. Speaking of discussing, could the mental takeover be different depending on your political views?\n",
    "\n",
    "In order to check this, first we will investigate the headlines in the subreddit r/Coronavirus, to discern the main relevant topics that have flooded our conversations during 2020. Then, identifying the clusters of words that are representative from that topic in particular, we can look them up un forums intended to political discussion. In this case, we take advantage of the community based structure of reddit to compare the evolution and impact of those topic in r/republican and r/democrats.\n",
    "\n",
    "\n",
    "## Data sources\n",
    "\n",
    "https://reddit-api.readthedocs.io/en/latest/\n",
    "https://electionlab.mit.edu/data\n",
    "https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36\n",
    "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/42MVDX\n",
    "\n",
    "https://reddit-api.readthedocs.io/en/latest/#comments-search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import datetime as dt\n",
    "import time\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import nltk\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import collections\n",
    "import multiprocessing\n",
    "from itertools import product\n",
    "\n",
    "#Import visualization tools for LDA models\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.corpus import stopwords \n",
    "import string\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "#Import LDA topic modelling plugin from sklearn\n",
    "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "\n",
    "# Importing local functions\n",
    "from COVID_functions import *\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "from colour import Color\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a function that returns the tokenize words given a list of strings. symbols in SYMBOLS are removed. Similarly, the stopwords rom the stopword_list.txt file are also removed. This list will still contain typos and some other strange word formations, but it is a first step. I also remove the super-long words, assuming that they are a porly tokenized word.\n",
    "\n",
    "relevant [link](https://pdf.sciencedirectassets.com/273276/1-s2.0-S0019995800X01489/1-s2.0-S0019995858902298/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJL%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIFD1kIxG5ZJb0fV29phm9L8%2Fddqnfw8tK3cb%2F%2BF80WssAiBaFP%2Baq55%2BvoC7dS9MPlofEJcBlZYmBrNeyvJNeMLv6yq9AwiL%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAMaDDA1OTAwMzU0Njg2NSIM4msGliDTspp48ffVKpEDHUDwg45sCnNaZQGB2Vb5TRSF9N1guyD5HPlAAOGPFQiYWCl4JJT6pgw9HeVzjxFWXkH15OSbvhbXuKKG4icIPhZcob6aJF2W2JZ%2BCHmRtkNcsUbelR3cLfkgmX%2BGUMnrzy2Ggwh7fyl59NIV5cShgCz99Sg5Hy%2FViAbNk2eLjpiFze8UjG6DdOV9r%2FG%2BRxQ2EO8ibb9%2BwHWeApbRp5dCycgKCCZ7vII5ggiRW1idqTB3dwjUF2GrClaEsylaIRwNCxNpQd0VKd2ZEdfvCAUebWkwd0t1pnT8OHJQ0eZXxinb1wPzbXWqfxEGh5uP%2FyC5x1iIlrhuY5%2F6JgPcQzO8YWlXSJ2dUEizJr46XvXkfcmtFtfV8ZHMHJHlA1tksobPo3hHMLNAODwfMKYanw%2BMhlgl3%2Bfs5AdnY6j2X0iVGFoZHv5j4hh38NbTYcB7Xkr3Lw73YzeIZsYNMFb%2Fuoz%2BfEJTKm5mbob6tjeCKTQ3cVw5HUAGIpegRnCyPh0udHy9OV2NiaVnefBuARryZ1RcNZswvaqJgQY67AF7jN%2FrCkgmVdTcsrxXkYkjAPXyfm9yDxtnUgTumN6UK5sX5L9cIlXcpr35zzDnil%2FB0xgxGu1nrVUu%2FyKwi%2F867htTi8mcO0Lnb4IoKQaZYrTxtlCNUFSVfZCBJHC92gO3%2BRLwCqmJVWpMwzlVGpgKqkaR2%2BTUPpgHLQI2nDkArAQNaalQxRUVRVS9JWTahkBhcobN9ZRk0gFvc9%2B%2Bipdr%2FSlhRyFOy9L0sN8RXenYdPjGDVjI86doSDCpQ1Cg3F%2F7KlWwIluSed5SH6UX58%2BUnMX5GEmHSLhj7d65XGFVn0ttEu%2FOqKcR9PUElw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20210209T105847Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY3BMAJPCN%2F20210209%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=96ee09d381778cb253399c5fc1801ed7ed10be9f7ead89579450d498ee843895&hash=f1a328c6a59702212978cb2b420e8d172bbf6f4ae727aef4ae97ec7ee25210e9&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0019995858902298&tid=spdf-5c544ce6-a8b2-4a1d-9b2f-54444592c4a1&sid=da9b492e8f39904a970a303-af62d894b605gxrqb&type=client)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to identify which posts talk about COVID\n",
    "\n",
    "Then, we store the post ID to investigate the comments from those posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>author</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>score</th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>num_crossposts</th>\n",
       "      <th>subreddit_subscribers</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>author_flair_type</th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>date</th>\n",
       "      <th>1dayafter</th>\n",
       "      <th>period</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1579552351</td>\n",
       "      <td>robbye91</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>1</td>\n",
       "      <td>erj61t</td>\n",
       "      <td>When should we start to get worried about the ...</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-20 20:32:31</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-01-21 20:32:31</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1579671329</td>\n",
       "      <td>mcmartian2240</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>1</td>\n",
       "      <td>es7jnl</td>\n",
       "      <td>#plague2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>None</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-22 05:35:29</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2020-01-23 05:35:29</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1579674580</td>\n",
       "      <td>TheLordOfCereals</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>1</td>\n",
       "      <td>es83ce</td>\n",
       "      <td>Trump will fight against corona virus</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>None</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-22 06:29:40</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2020-01-23 06:29:40</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1579760266</td>\n",
       "      <td>blackie6644</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>1</td>\n",
       "      <td>esp4td</td>\n",
       "      <td>how people in Wuhan eat roast bats, bat sushi,...</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>None</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-23 06:17:46</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-01-24 06:17:46</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1579742601</td>\n",
       "      <td>blocketjoe</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>1</td>\n",
       "      <td>eslqng</td>\n",
       "      <td>China Is On Lockdown Due To Corona Virus!</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>None</td>\n",
       "      <td>text</td>\n",
       "      <td>2020-01-23 01:23:21</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-01-24 01:23:21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   created_utc            author    subreddit  score      id  \\\n",
       "0   1579552351          robbye91  Coronavirus      1  erj61t   \n",
       "1   1579671329     mcmartian2240  Coronavirus      1  es7jnl   \n",
       "2   1579674580  TheLordOfCereals  Coronavirus      1  es83ce   \n",
       "3   1579760266       blackie6644  Coronavirus      1  esp4td   \n",
       "4   1579742601        blocketjoe  Coronavirus      1  eslqng   \n",
       "\n",
       "                                               title  num_comments  \\\n",
       "0  When should we start to get worried about the ...            94   \n",
       "1                                        #plague2020             1   \n",
       "2              Trump will fight against corona virus             6   \n",
       "3  how people in Wuhan eat roast bats, bat sushi,...             8   \n",
       "4          China Is On Lockdown Due To Corona Virus!             3   \n",
       "\n",
       "   num_crossposts  subreddit_subscribers author_flair_text author_flair_type  \\\n",
       "0               0                      6              None              text   \n",
       "1               0                     54              None              text   \n",
       "2               0                     56              None              text   \n",
       "3               0                    162              None              text   \n",
       "4               0                    130              None              text   \n",
       "\n",
       "            TimeStamp        date           1dayafter  period  hour  \n",
       "0 2020-01-20 20:32:31  2020-01-20 2020-01-21 20:32:31       4    20  \n",
       "1 2020-01-22 05:35:29  2020-01-22 2020-01-23 05:35:29       4     5  \n",
       "2 2020-01-22 06:29:40  2020-01-22 2020-01-23 06:29:40       4     6  \n",
       "3 2020-01-23 06:17:46  2020-01-23 2020-01-24 06:17:46       4     6  \n",
       "4 2020-01-23 01:23:21  2020-01-23 2020-01-24 01:23:21       4     1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_folder = '/home/jpre/Documents/DTU/COVIDpolitics2021/COVIDvsPOLITICS/data/posts/submissions/coronavirus/'\n",
    "\n",
    "metadf1_column_titles = ['created_utc',\n",
    "          'author',\n",
    "          'subreddit',\n",
    "          'score',\n",
    "          'id',\n",
    "          'title',\n",
    "          'num_comments',\n",
    "          'num_crossposts',\n",
    "          'subreddit_subscribers',\n",
    "          'author_flair_text',\n",
    "          'author_flair_type']\n",
    "\n",
    "#time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(praw_submission.created_utc))\n",
    "\n",
    "# creating empty elements\n",
    "metadat0 = pd.DataFrame(columns = metadf1_column_titles)\n",
    "meta_file_path = data_folder + 'metadata1.txt'\n",
    "\n",
    "metadat0 = pd.read_csv(meta_file_path, sep='\\t', header = None, names = metadf1_column_titles)\n",
    "metadat0['TimeStamp'] = pd.to_datetime(metadat0['created_utc'], unit = 's')\n",
    "\n",
    "metadat0['date'] = metadat0['TimeStamp'].dt.date\n",
    "metadat0['1dayafter'] = metadat0['TimeStamp'] + pd.DateOffset(1)\n",
    "\n",
    "metadat0['period'] = metadat0['TimeStamp'].astype('int')//(1e9*60*60*24*7)\n",
    "metadat0['period'] = metadat0['TimeStamp'].dt.week\n",
    "\n",
    "dc19 = metadat0[metadat0['TimeStamp'].dt.year.isin([2020])]\n",
    "dc19['hour'] = dc19['TimeStamp'].dt.hour\n",
    "\n",
    "dc19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandemic has occupied almost every aspect of our lives, however there have been different stages of invasion. The pandemic has triggered different discussion topics throughout the year. Every person that has been around has an idea of most of these topics: masks, vaccines, lockdown,... Even though, probably different people would focus on different topics depending on personal experience. In order to have an _objective_ overlook of the different topics, I have used LDA to identify categories\n",
    "\n",
    "(relevant [link](https://towardsdatascience.com/topic-modeling-the-comment-section-from-a-new-york-times-article-e4775261530e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "\n",
    "stopwords_alt = []\n",
    "with open('/home/jpre/Documents/DTU/COVIDpolitics2021/COVIDvsPOLITICS/stopword_list.txt', 'r') as file:\n",
    "    stopwords_alt = file.read().split('\\n')\n",
    "    \n",
    "stopwords_alt = stopwords_alt + ['time', 'state', '10','–',\n",
    "                                 '1','2','3','2020','amid','de','en','day','people','case','cases','florida','live','update','updating']\n",
    "\n",
    "def tokenizer(text):\n",
    "    '''\n",
    "    -covert everything to lowercase\n",
    "    -remove punctuations\n",
    "    -remove stopwords\n",
    "    -stemmer\n",
    "    '''\n",
    "    #All characters in this string will be converted to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    #Removing sentence punctuations\n",
    "    for punctuation_mark in string.punctuation:\n",
    "        text = text.replace(punctuation_mark,'')\n",
    "    \n",
    "    #Creating our list of tokens\n",
    "    list_of_tokens = text.split(' ')\n",
    "    #Creating our cleaned tokens list \n",
    "    cleaned_tokens = []\n",
    "    #Let us use a stemmer\n",
    "    stemmer = SnowballStemmer(language = 'english')\n",
    "    \n",
    "    #Removing Stop Words in our list of tokens and any tokens that happens to be empty strings\n",
    "    for token in list_of_tokens:\n",
    "        if (not token in stopwords_alt) and (token != ''):\n",
    "            #Stem tokens\n",
    "            token_stemmed = stemmer.stem(token)\n",
    "            #appending our finalized cleaned token\n",
    "            cleaned_tokens.append(token_stemmed)\n",
    "    \n",
    "    return cleaned_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
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       "\n",
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       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
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       "   // already loaded: just create the visualization\n",
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       "       new LDAvis(\"#\" + \"ldavis_el65181398507934659368940295578\", ldavis_el65181398507934659368940295578_data);\n",
       "   }(LDAvis);\n",
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       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.2.2/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el65181398507934659368940295578\", ldavis_el65181398507934659368940295578_data);\n",
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       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.2.2/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el65181398507934659368940295578\", ldavis_el65181398507934659368940295578_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "3     -0.171045 -0.009007       1        1  27.804524\n",
       "1      0.426540 -0.029995       2        1  26.309427\n",
       "0     -0.152785 -0.304722       3        1  24.947175\n",
       "2     -0.102710  0.343723       4        1  20.938874, topic_info=           Term         Freq        Total Category  logprob  loglift\n",
       "4   coronavirus  8323.000000  8323.000000  Default  30.0000  30.0000\n",
       "41       vaccin  2360.000000  2360.000000  Default  29.0000  29.0000\n",
       "8       covid19  5911.000000  5911.000000  Default  28.0000  28.0000\n",
       "7         covid  1967.000000  1967.000000  Default  27.0000  27.0000\n",
       "10        death  2195.000000  2195.000000  Default  26.0000  26.0000\n",
       "..          ...          ...          ...      ...      ...      ...\n",
       "14       health     0.297633   896.803957   Topic4 -10.5884  -6.4472\n",
       "1         china     0.297608  1215.321802   Topic4 -10.5885  -6.7512\n",
       "37         test     0.296761  1744.400407   Topic4 -10.5913  -7.1154\n",
       "16       hospit     0.296616  1088.520775   Topic4 -10.5918  -6.6443\n",
       "27      patient     0.296264   815.559016   Topic4 -10.5930  -6.3568\n",
       "\n",
       "[176 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "0         4  0.998216      cdc\n",
       "1         1  0.982456    china\n",
       "1         3  0.017279    china\n",
       "2         3  0.998917    close\n",
       "3         3  0.999330  confirm\n",
       "...     ...       ...      ...\n",
       "43        1  0.998449     warn\n",
       "44        3  0.999269     week\n",
       "45        1  0.998253   worker\n",
       "46        1  0.778624    world\n",
       "46        4  0.221000    world\n",
       "\n",
       "[62 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[4, 2, 1, 3])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I want 15 topics generated\n",
    "num_topics = 4\n",
    "\n",
    "#Instatiate TFIDF Vectorizer\n",
    "tfidf = TfidfVectorizer(tokenizer = tokenizer,\n",
    "                            # min_df = 365,\n",
    "                          min_df = 0.015, \n",
    "                           analyzer='word',\n",
    "                           ngram_range = (1,2))\n",
    "\n",
    "#dc19.comments is referring to the text column from my dataframe containing the comments\n",
    "token_matrix = tfidf.fit_transform(dc19.title)\n",
    "\n",
    "#If you would like to store the matrix to a dataframe, execute the code below\n",
    "matrix_df = pd.DataFrame(token_matrix.toarray(),\n",
    "                        columns = tfidf.get_feature_names())\n",
    "\n",
    "#instatiate LDA model\n",
    "lda_model = LDA(n_components = num_topics, n_jobs = -1)\n",
    "lda_model.fit(token_matrix)\n",
    "\n",
    "#import sys\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "# Let us visualize these topics\n",
    "pyLDAvis.sklearn.prepare(lda_model, token_matrix, tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: ['coronavirus', 'lockdown', 'reopen', 'school']\n",
      "Topic 1: ['china', 'confirm', 'covid19', 'covid19 vaccin', 'hospit', 'mask', 'order']\n",
      "Topic 2: ['covid', 'die', 'face', 'outbreak', 'posit', 'test', 'test posit', 'uk']\n",
      "Topic 3: ['close', 'doctor', 'quarantin', 'spread', 'vaccin']\n",
      "Topic 4: ['number', 'patient', 'report', 'worker', 'world']\n",
      "Topic 5: ['countri', 'death', 'health', 'home', 'million', 'trump']\n",
      "Topic 6: ['cdc', 'infect', 'pandem', 'virus']\n",
      "Topic 7: ['counti', 'news', 'offici', 'record', 'show', 'studi', 'warn', 'week']\n"
     ]
    }
   ],
   "source": [
    "#I'm looking for the top n words for each topic\n",
    "token_names = tfidf.get_feature_names()\n",
    "\n",
    "# Normalized probabilities of finding a word inside the topic N\n",
    "normcomp = lda_model.components_/lda_model.components_.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "dftopic = pd.DataFrame(normcomp).transpose()\n",
    "dftopic.index = token_names\n",
    "\n",
    "sorted_columns = (-lda_model.components_.sum(axis=1)).argsort().tolist()[:num_topics]\n",
    "old_columns = dftopic.columns\n",
    "\n",
    "dftopic = dftopic.rename(columns = {old_columns[i]: sorted_columns[i] for i in range(len(sorted_columns))} )\n",
    "topic_words = dftopic.index.tolist()\n",
    "\n",
    "dftopic['topic'] = dftopic.apply(lambda x : np.argmax(x), axis=1)\n",
    "\n",
    "dftopic.index.tolist()\n",
    "main_contributors = {}\n",
    "for t in range(num_topics):\n",
    "    contributors_i = dftopic[dftopic.topic.isin([int(t)])].index.tolist()\n",
    "    main_contributors[t] = contributors_i\n",
    "    print(f'Topic {t}: {contributors_i}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from wordcloud import WordCloud, get_single_color_func\n",
    "from os import path\n",
    "import os\n",
    "\n",
    "\n",
    "class colormap_color_func(object):\n",
    "    \"\"\"Color func created from matplotlib colormap.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    colormap : string or matplotlib colormap\n",
    "        Colormap to sample from\n",
    "\n",
    "    Example\n",
    "    -------\n",
    "    >>> WordCloud(color_func=colormap_color_func(\"magma\"))\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, colormap):\n",
    "        import matplotlib.pyplot as plt\n",
    "        self.colormap = plt.cm.get_cmap(colormap)\n",
    "\n",
    "    def __call__(self, word, font_size, position, orientation,\n",
    "                 random_state=None, **kwargs):\n",
    "        if random_state is None:\n",
    "            random_state = Random()\n",
    "        r, g, b, _ = np.maximum(0, 255 * np.array(self.colormap(\n",
    "            random_state.uniform(0, 1))))\n",
    "        return \"rgb({:.0f}, {:.0f}, {:.0f})\".format(r, g, b)\n",
    "    \n",
    "\n",
    "# get data directory (using getcwd() is needed to support running example in generated IPython notebook)\n",
    "d = path.dirname(__file__) if \"__file__\" in locals() else os.getcwd()\n",
    "\n",
    "image_folder = '/home/jpre/Documents/DTU/COVIDpolitics2021/COVIDvsPOLITICS/images/'\n",
    "\n",
    "for mci in main_contributors.items():\n",
    "    \n",
    "    topic_n = mci[0]\n",
    "    my_list = mci[1]\n",
    "\n",
    "    word_could_dict=Counter(my_list)\n",
    "\n",
    "    # The Symbola font includes most emoji\n",
    "    font_path = path.join(d, 'fonts', 'GothamBold.ttf')\n",
    "    wordcloud = WordCloud(width = 840, height = 240, background_color='white',\n",
    "                          font_path = font_path, color_func=colormap_color_func(\"magma\"),\n",
    "                          min_font_size =60, max_font_size = 150).generate_from_frequencies(word_could_dict)\n",
    "    \n",
    "    plt.figure(figsize=(12,4))\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis(\"off\")\n",
    "    #plt.show()\n",
    "    plt.savefig(path.join(d,'images',''.join(['wc',str(topic_n),'.png'])), bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I am interested in seeing which words are the main contributors of each topic. So I am assigning to each top word a topic, based on their probabilities. Then, a topic word can only belong to one topic, which is the one with highest probability. In most of the topics, the conflict is minimal given that one probability is way larger than the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:5: DeprecationWarning: invalid escape sequence \\&\n",
      "<>:5: DeprecationWarning: invalid escape sequence \\&\n",
      "<>:5: DeprecationWarning: invalid escape sequence \\&\n",
      "<ipython-input-7-1506f3989f3d>:5: DeprecationWarning: invalid escape sequence \\&\n",
      "  SYMBOLS = '^_@{}()[].,:;+-*•/\\&|<>=~$#!?\"\"“”`%0123456789' + \"''’\"\n"
     ]
    }
   ],
   "source": [
    "def GetWords(sampletext):\n",
    "    \n",
    "    global topic_words\n",
    "    \n",
    "    SYMBOLS = '^_@{}()[].,:;+-*•/\\&|<>=~$#!?\"\"“”`%0123456789' + \"''’\"\n",
    "    \n",
    "    if len(sampletext)>0:\n",
    "        stxt = sampletext[1]\n",
    "        for snt in sampletext[2:]:\n",
    "            stxt += ' ' + snt\n",
    "            \n",
    "        stopwords = []\n",
    "        with open('/home/jpre/Documents/DTU/COVIDpolitics2021/COVIDvsPOLITICS/stopword_list.txt', 'r') as file:\n",
    "            stopwords = file.read().split('\\n')\n",
    "\n",
    "        other_words = ['people', 'gt', 've', 's'] + ['thing', 'things','don', 'back', 'day', 'make','lot','ca']\n",
    "        stopwords = stopwords + other_words\n",
    "\n",
    "        nltk_tokens = tokenizer(stxt)\n",
    "        nltk_tokens = [x for x in nltk_tokens if x not in stopwords]\n",
    "\n",
    "        # Taking out ridiculously long words\n",
    "        nltk_tokens = [x for x in nltk_tokens if len(x) < 30]\n",
    "\n",
    "        nltk_tokens.sort()\n",
    "\n",
    "        ctr = collections.Counter(nltk_tokens)\n",
    "        # We divide by len(sampletext) to take into account the number of comments of that specific week\n",
    "        ctr = {k: v/len(sampletext) for k, v in sorted(ctr.items(), key=lambda item: item[1], reverse=True) if k in topic_words}\n",
    "\n",
    "    else:\n",
    "        ctr = {}\n",
    "    return(ctr)\n",
    "\n",
    "\n",
    "def GetTopWords(sampletext):\n",
    "    \n",
    "    TopWords_dict = GetWords(sampletext)\n",
    "\n",
    "    return(list(TopWords_dict.keys())[:num_topics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>date</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-20 20:32:31</td>\n",
       "      <td>{'coronavirus': 0.21017699115044247, 'virus': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2020-01-27 00:02:59</td>\n",
       "      <td>{'coronavirus': 0.24889673433362755, 'china': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2020-02-03 00:02:39</td>\n",
       "      <td>{'coronavirus': 0.2727272727272727, 'china': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>2020-02-10 00:00:08</td>\n",
       "      <td>{'coronavirus': 0.301464254952627, 'china': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>2020-02-17 00:03:53</td>\n",
       "      <td>{'coronavirus': 0.33359559402045635, 'china': ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   period                date  \\\n",
       "0       4 2020-01-20 20:32:31   \n",
       "1       5 2020-01-27 00:02:59   \n",
       "2       6 2020-02-03 00:02:39   \n",
       "3       7 2020-02-10 00:00:08   \n",
       "4       8 2020-02-17 00:03:53   \n",
       "\n",
       "                                               words  \n",
       "0  {'coronavirus': 0.21017699115044247, 'virus': ...  \n",
       "1  {'coronavirus': 0.24889673433362755, 'china': ...  \n",
       "2  {'coronavirus': 0.2727272727272727, 'china': 0...  \n",
       "3  {'coronavirus': 0.301464254952627, 'china': 0....  \n",
       "4  {'coronavirus': 0.33359559402045635, 'china': ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topwords_weekly = dc19[['period', 'title','TimeStamp']].groupby(['period']).agg(date = ('TimeStamp',min),\n",
    "                                                                                words=('title',GetWords)).reset_index()\n",
    "\n",
    "topwords_weekly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.layouts import gridplot, layout, Spacer, row\n",
    "from bokeh.models.annotations import Title\n",
    "from bokeh.models import ColumnDataSource, Legend, Rect, LegendItem, Span, Arrow, NormalHead, OpenHead, VeeHead\n",
    "from bokeh.models import BoxAnnotation, Label, InvertedTriangle, Text, Triangle, Line, ImageURL, Image\n",
    "from bokeh.models import BasicTicker, ColorBar, LinearColorMapper, PrintfTickFormatter\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.plotting import figure, output_file, show, output_notebook, save, show\n",
    "from bokeh.io import show\n",
    "from bokeh.embed import components\n",
    "from bokeh.core.properties import value\n",
    "\n",
    "from bokeh.palettes import Category20\n",
    "from bokeh.io import output_file, reset_output\n",
    "\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution of the topics\n",
    "\n",
    "Now I have identified the top N words that characterized the different topics. I want to see the evolution of such topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pt0</th>\n",
       "      <th>pt1</th>\n",
       "      <th>pt2</th>\n",
       "      <th>pt3</th>\n",
       "      <th>pt4</th>\n",
       "      <th>pt5</th>\n",
       "      <th>pt6</th>\n",
       "      <th>pt7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.022412</td>\n",
       "      <td>0.149359</td>\n",
       "      <td>0.024824</td>\n",
       "      <td>0.023968</td>\n",
       "      <td>0.032444</td>\n",
       "      <td>0.030914</td>\n",
       "      <td>0.069937</td>\n",
       "      <td>0.026907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.027677</td>\n",
       "      <td>0.176882</td>\n",
       "      <td>0.029808</td>\n",
       "      <td>0.027782</td>\n",
       "      <td>0.040129</td>\n",
       "      <td>0.038098</td>\n",
       "      <td>0.065258</td>\n",
       "      <td>0.039785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.029972</td>\n",
       "      <td>0.193315</td>\n",
       "      <td>0.027588</td>\n",
       "      <td>0.029422</td>\n",
       "      <td>0.040384</td>\n",
       "      <td>0.045120</td>\n",
       "      <td>0.060719</td>\n",
       "      <td>0.045601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.038053</td>\n",
       "      <td>0.213738</td>\n",
       "      <td>0.040196</td>\n",
       "      <td>0.036854</td>\n",
       "      <td>0.047950</td>\n",
       "      <td>0.056681</td>\n",
       "      <td>0.062194</td>\n",
       "      <td>0.056159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.048959</td>\n",
       "      <td>0.237016</td>\n",
       "      <td>0.051865</td>\n",
       "      <td>0.041264</td>\n",
       "      <td>0.052724</td>\n",
       "      <td>0.058509</td>\n",
       "      <td>0.068513</td>\n",
       "      <td>0.068585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pt0       pt1       pt2       pt3       pt4       pt5       pt6  \\\n",
       "period                                                                         \n",
       "4       0.022412  0.149359  0.024824  0.023968  0.032444  0.030914  0.069937   \n",
       "5       0.027677  0.176882  0.029808  0.027782  0.040129  0.038098  0.065258   \n",
       "6       0.029972  0.193315  0.027588  0.029422  0.040384  0.045120  0.060719   \n",
       "7       0.038053  0.213738  0.040196  0.036854  0.047950  0.056681  0.062194   \n",
       "8       0.048959  0.237016  0.051865  0.041264  0.052724  0.058509  0.068513   \n",
       "\n",
       "             pt7  \n",
       "period            \n",
       "4       0.026907  \n",
       "5       0.039785  \n",
       "6       0.045601  \n",
       "7       0.056159  \n",
       "8       0.068585  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def RetProb(title, topic, comments = None):\n",
    "    \n",
    "    global topic_words, n_titles, n_comments\n",
    "    \n",
    "    tokens = tokenizer(title)\n",
    "        \n",
    "    if comments != None:\n",
    "        cr_list = []\n",
    "        \n",
    "        for wi in topic_words:\n",
    "            \n",
    "            if (wi in all_comments) and (wi in all_titles):\n",
    "                freq_in_x = all_comments.count(wi)/n_comments\n",
    "                freq_in_covid = all_titles.count(wi)/n_titles\n",
    "                cr = freq_in_covid/max(freq_in_x, 0.000001)\n",
    "            else:\n",
    "                cr = 0\n",
    "\n",
    "            # Checking if the word is typically related to the subreddit, or it is brought by corona\n",
    "            cr_list.append(cr)\n",
    "    \n",
    "    else:\n",
    "        cr_list = [1 for j in topic_words]\n",
    "    \n",
    "    topic_probabilities = [dftopic.loc[x,topic] for x in tokens if x in topic_words]\n",
    "    \n",
    "    return np.sum([x[0]*x[1] for x in zip(topic_probabilities, cr_list)])\n",
    "\n",
    "prob_topics = []\n",
    "for topic in range(num_topics):\n",
    "    colname = ''.join(['pt', str(topic)])\n",
    "    dc19[colname] = dc19.title.apply(lambda x: RetProb(x, topic))\n",
    "    prob_topics.append(colname)\n",
    "    \n",
    "    \n",
    "\n",
    "datest = dc19[['period'] + prob_topics].groupby('period').agg(np.mean)\n",
    "datest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MostProbTopic(x):\n",
    "\n",
    "    a = [RetProb(x,i) for i in range(num_topics)]\n",
    "    pos_max = a.index(max(a))\n",
    "\n",
    "    return pos_max\n",
    "\n",
    "dc19['MPT'] = dc19.title.apply(MostProbTopic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"97c6a84e-e076-4bcc-abe1-470ea59373a0\" data-root-id=\"1525\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\"title\":null,\"toolbar\":{\"id\":\"1334\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"1316\",\"type\":\"Range1d\"},\"x_scale\":{\"id\":\"1320\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1318\",\"type\":\"Range1d\"},\"y_scale\":{\"id\":\"1322\",\"type\":\"LinearScale\"}},\"id\":\"1314\",\"subtype\":\"Figure\",\"type\":\"Plot\"}],\"root_ids\":[\"1525\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"ffdcdb08-a412-4f30-8e80-1c3fc438246a\",\"roots\":{\"1525\":\"97c6a84e-e076-4bcc-abe1-470ea59373a0\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1525"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Settting the figure object\n",
    "output_notebook()\n",
    "\n",
    "items = [] # items for legend\n",
    "lines ={} # bar-information for plotting\n",
    "days = list(range(106))\n",
    "\n",
    "rngsize = 10\n",
    "TOOLS = \"hover,save,reset\"\n",
    "\n",
    "List_of_plots = []\n",
    "topics_to_plot = prob_topics\n",
    "\n",
    "datest['zero'] = 0\n",
    "\n",
    "for indx,i in enumerate(topics_to_plot):\n",
    "    \n",
    "    max_freq = 1.05*max(datest[i])\n",
    "    \n",
    "    p = figure(plot_width= 280, plot_height=80, title = None, x_range=(4,52) ,y_range = (0,max_freq), tools = '')\n",
    "\n",
    "    source = ColumnDataSource(data=datest.reset_index())\n",
    "    \n",
    "    #glyph2 = ImageURL(url=['test_image.png'], x=30, y=0.2, w=52, h=0.2, global_alpha = 0.1)\n",
    "    #r_image = p.add_glyph(source_or_glyph=glyph2)\n",
    "    \n",
    "    image_bkg = path.join('.','images',''.join(['wc',str(indx),'.png']))\n",
    "    r_image = p.image_url(url=[image_bkg], x=4, y=0, w=52-4, h=max_freq, global_alpha = 0.9, anchor = 'bottom_left')\n",
    "    r_rect = p.rect(x=-10, y = -10, height = 100, width = 200, fill_color = 'skyblue', fill_alpha = 0.99)\n",
    "    r = p.line(y=i, x='period', color='red', source=source, alpha = 1, line_width = 2.5)\n",
    "    glyph = Text(x='x', y='y', text=\"text\", angle=0., text_color=\"pink\", text_alpha = 0.1)\n",
    "    r_text = p.add_glyph(ColumnDataSource(dict(x=[25], y=[0.1], text=[i])), glyph)\n",
    "    \n",
    "    hover1 = HoverTool(tooltips=[('week',  '@period')],renderers = [r], mode = 'vline')\n",
    "    hover2 = HoverTool(tooltips = None, renderers = [r_text])\n",
    "    hover3 = HoverTool(tooltips = [(\"x\", \"$x\"),(\"y\", \"$y\")], renderers = [r_rect])\n",
    "    \n",
    "    \n",
    "    p.tools.append(hover1)\n",
    "    p.tools.append(hover2)\n",
    "    p.tools.append(hover3)\n",
    "\n",
    "    selected_line = Line(line_alpha=0.8, line_color='pink', line_width = 2.5)\n",
    "    selected_rect = Rect(fill_alpha=0.1, fill_color = 'white')\n",
    "    selected_text = Text(text_alpha=1, text_font_style = 'bold')\n",
    "\n",
    "    # I add the selection glyph because I want to fix a particular line for comparing\n",
    "    r.hover_glyph = selected_line\n",
    "    r_text.hover_glyph = selected_text\n",
    "    r_rect.hover_glyph = selected_rect\n",
    "    \n",
    "    # Adjusting plot parameters\n",
    "    #p.x_range.range_padding = 0.05\n",
    "    p.xaxis.axis_label = \"\"\n",
    "    p.yaxis.axis_label = \"\"\n",
    "    p.xaxis.visible = False\n",
    "    p.yaxis.visible = False\n",
    "    p.yaxis.axis_line_width = 1\n",
    "    p.yaxis.ticker = [0,max_freq]\n",
    "    p.grid.visible = False\n",
    "    p.background_fill_color = 'white'\n",
    "    p.background_fill_alpha = 1\n",
    "        \n",
    "    List_of_plots.append(p)\n",
    "    \n",
    "    \n",
    "p_all = gridplot(List_of_plots, ncols=4)\n",
    "\n",
    "show(p_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "mywords = prob_topics\n",
    "\n",
    "# Normalizing by row\n",
    "#dfoot = datest[mywords].div(datest[mywords].sum(axis=1, skipna=True), axis=0)\n",
    "\n",
    "from sklearn import preprocessing\n",
    "\n",
    " # I substract the minimum of the row so all rows start at zero and for\n",
    "x = datest[mywords].transform(lambda x: x-x.min(), axis = 0).values #returns a numpy array\n",
    "#x = datest[mywords].values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "dfoot = pd.DataFrame(x_scaled)\n",
    "\n",
    "\n",
    "footprint = np.transpose(dfoot.pow(2).to_numpy())\n",
    "\n",
    "im = ax.imshow(footprint, cmap = 'Reds')\n",
    "\n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]))\n",
    "ax.set_yticks(np.arange(footprint.shape[0]))\n",
    "\n",
    "#datenames = dfi.reset_index().date.apply(lambda x: x[:-5]).tolist()\n",
    "ax.set_yticklabels(mywords)\n",
    "#ax.set_xticklabels(datenames,rotation=45,ha=\"left\")\n",
    "\n",
    "# Let the horizontal axes labeling appear on top.\n",
    "ax.tick_params(top=True, bottom=False,\n",
    "               labeltop=True, labelbottom=False)\n",
    "\n",
    "for edge, spine in ax.spines.items():\n",
    "    spine.set_visible(False)\n",
    "        \n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(footprint.shape[0]+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=1.5)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Every word has a probability of being in topic i. Then, I tokenize every post in reddit and quantify the probability of each post of belonging to topic i. Finally, I aggregate those probabilities weekly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracking words inside the topics\n",
    "\n",
    "\n",
    "https://github.com/bokeh/bokeh/issues/5594\n",
    "\n",
    "https://docs.bokeh.org/en/latest/docs/user_guide/styling.html#glyphs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outbreak</th>\n",
       "      <th>die</th>\n",
       "      <th>test</th>\n",
       "      <th>uk</th>\n",
       "      <th>face</th>\n",
       "      <th>posit</th>\n",
       "      <th>covid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-20 20:32:31</th>\n",
       "      <td>0.039823</td>\n",
       "      <td>0.011062</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27 00:02:59</th>\n",
       "      <td>0.034422</td>\n",
       "      <td>0.018535</td>\n",
       "      <td>0.014122</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.003530</td>\n",
       "      <td>0.003530</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03 00:02:39</th>\n",
       "      <td>0.027917</td>\n",
       "      <td>0.012169</td>\n",
       "      <td>0.019327</td>\n",
       "      <td>0.008590</td>\n",
       "      <td>0.008590</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10 00:00:08</th>\n",
       "      <td>0.032730</td>\n",
       "      <td>0.017227</td>\n",
       "      <td>0.029285</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.006029</td>\n",
       "      <td>0.011197</td>\n",
       "      <td>0.000861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-17 00:03:53</th>\n",
       "      <td>0.040126</td>\n",
       "      <td>0.014949</td>\n",
       "      <td>0.049567</td>\n",
       "      <td>0.007868</td>\n",
       "      <td>0.009441</td>\n",
       "      <td>0.031471</td>\n",
       "      <td>0.008655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     outbreak       die      test        uk      face  \\\n",
       "date                                                                    \n",
       "2020-01-20 20:32:31  0.039823  0.011062  0.006637  0.006637  0.004425   \n",
       "2020-01-27 00:02:59  0.034422  0.018535  0.014122  0.004413  0.003530   \n",
       "2020-02-03 00:02:39  0.027917  0.012169  0.019327  0.008590  0.008590   \n",
       "2020-02-10 00:00:08  0.032730  0.017227  0.029285  0.025840  0.006029   \n",
       "2020-02-17 00:03:53  0.040126  0.014949  0.049567  0.007868  0.009441   \n",
       "\n",
       "                        posit     covid  \n",
       "date                                     \n",
       "2020-01-20 20:32:31  0.004425  0.000000  \n",
       "2020-01-27 00:02:59  0.003530  0.000000  \n",
       "2020-02-03 00:02:39  0.007874  0.000000  \n",
       "2020-02-10 00:00:08  0.011197  0.000861  \n",
       "2020-02-17 00:03:53  0.031471  0.008655  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_i = 2\n",
    "\n",
    "tp0_test = topwords_weekly\n",
    "tp0_test['tpw0'] = tp0_test.words.apply(lambda x: {k: v for k, v in x.items() if k in main_contributors[topic_i]})\n",
    "\n",
    "tp0_evol = pd.DataFrame(tp0_test.tpw0.tolist()).fillna(0)\n",
    "tp0_evol.index = tp0_test.date\n",
    "tp0_evol.topic = topic_i\n",
    "\n",
    "tp0_evol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2584\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"2584\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"2584\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2584' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"2584\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"2584\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"2584\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2584' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"2584\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"722fdfbe-98b5-4f8e-8f5e-e85df0025885\" data-root-id=\"2585\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"722b5339-b980-48c3-8f76-bd26a1398491\",\"roots\":{\"2585\":\"722fdfbe-98b5-4f8e-8f5e-e85df0025885\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "2585"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reset_output()\n",
    "\n",
    "# Settting the figure object\n",
    "output_notebook()\n",
    "\n",
    "WORDS_IN_TOPIC = tp0_evol.columns.tolist()\n",
    "\n",
    "red = Color(\"tomato\")\n",
    "colors_ = [c.hex for c in list(red.range_to(Color(\"gold\"), len(WORDS_IN_TOPIC)))]\n",
    "\n",
    "nweeks = tp0_evol.shape[0]\n",
    "items = [] # items for legend\n",
    "bar ={} # bar-information for plotting\n",
    "weeks = [x+1 for x in range(nweeks)] # adding 1 to 0:23 for readibiltiy\n",
    "\n",
    "tooltips = [(\"word\", \"$name\")]\n",
    "tools_to_show = 'box_zoom,hover,reset,tap'\n",
    "\n",
    "p = figure(plot_width=1100, plot_height=400, title = 'Evolution of words in topic i',\n",
    "           tools = tools_to_show, tooltips = tooltips, x_axis_type=\"datetime\")\n",
    "\n",
    "dates = tp0_evol.index.to_list()\n",
    "for indx, word_i in enumerate(WORDS_IN_TOPIC):\n",
    "    \n",
    "     # Selecting hour and probabilities for plotting category i\n",
    "    probs = tp0_evol[word_i].to_list()\n",
    "     # adding bar for category i\n",
    "    bar[word_i] = p.line(x = dates, y = probs, muted_color = colors_[indx], muted_alpha = 0.4, muted = True,\n",
    "                         color = colors_[indx], alpha = 1, line_width = 1.5, name = word_i)\n",
    "    \n",
    "    selected_line = Line(line_alpha=1, line_color=colors_[indx], line_width = 2.5)\n",
    "\n",
    "    # I add the selection glyph because I want to fix a particular line for comparing\n",
    "    bar[word_i].hover_glyph = selected_line\n",
    "    bar[word_i].selection_glyph = selected_line\n",
    "        \n",
    "# Adding info of category i for the legend\n",
    "    items.append((word_i, [bar[word_i]]))\n",
    "\n",
    " # Adjusting plot parameters\n",
    "p.x_range.range_padding = 0.05\n",
    "#p.xaxis.axis_label = \"Week of the year\"\n",
    "p.yaxis.axis_label = \"Probability of using the word\"\n",
    " # Adusting legend information\n",
    "legend = Legend(items=items, location=(0, 10))\n",
    "p.add_layout(legend, 'right')\n",
    "p.legend.click_policy=\"hide\"\n",
    "\n",
    "show(p) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Co-occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_message = \"If patients are patient enough but not vaccinated they will need to quarantine\"\n",
    "#wordlist = ['show', 'order', 'patient','quarantin', 'warn', 'counti', 'vaccin']\n",
    "\n",
    "def ReturnCoocMat(t_m, wl):\n",
    "    \n",
    "    # Returns a N-square matrix where N is the number of words of a topic.\n",
    "    # The matrix diagonal is always 1\n",
    "    # The matrix is symetric\n",
    "    \n",
    "    # When the word i is said, how many times j is said.\n",
    "\n",
    "    from collections import Counter\n",
    "    \n",
    "    nwl = len(wl)\n",
    "    mat = np.zeros((nwl,nwl))\n",
    "    \n",
    "    tokens_tm = tokenizer(t_m)\n",
    "    c = Counter(x for x in tokens_tm if x)\n",
    "    # selecting only the topic words\n",
    "    c_topic = Counter({k: 0  for k in wl})\n",
    "    \n",
    "    c_topic.update(c)\n",
    "    \n",
    "    for i,w in enumerate(wl):\n",
    "        if c_topic[w] != 0:\n",
    "            mat[i,i] = 1\n",
    "            for j in range(i+1, nwl):\n",
    "                mat[i,j] = mat[j,i] = c_topic[wl[j]]/c_topic[w]\n",
    "    \n",
    "    return mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating a mean of matrices\n",
    "\n",
    "Nwt = len(WORDS_IN_TOPIC)\n",
    "Mat = np.zeros((Nwt,Nwt))\n",
    "\n",
    "for title in dc19.title[dc19.MPT == topic_i]:\n",
    "    Mat = Mat + ReturnCoocMat(title, WORDS_IN_TOPIC)\n",
    "    \n",
    "Mat = Mat/dc19.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpre/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "Matlog = np.log(Mat)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.imshow(Mat)\n",
    "ax.set_xticks(range(len(WORDS_IN_TOPIC)))\n",
    "ax.set_xticklabels(WORDS_IN_TOPIC)\n",
    "ax.set_yticks(range(len(WORDS_IN_TOPIC)))\n",
    "ax.set_yticklabels(WORDS_IN_TOPIC)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0459284067648734e-16, 1.0459284067648808e-16)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.signal import correlate2d\n",
    "\n",
    "M0 = Mat#/np.linalg.norm(Mat)\n",
    "\n",
    "covid_message = 'face masks prevent covid in the uk'\n",
    "nocovid_message = 'london is the capital of uk'\n",
    "\n",
    "M1 = ReturnCoocMat(covid_message, WORDS_IN_TOPIC)\n",
    "M2 = ReturnCoocMat(nocovid_message, WORDS_IN_TOPIC)\n",
    "\n",
    "#M1 = M1/np.linalg.norm(M1)\n",
    "#M2 = M2/np.linalg.norm(M2)\n",
    "\n",
    "np.linalg.det(correlate2d(M0,M2,mode='same')), np.linalg.det(correlate2d(M0,M1,mode='same'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 1., 0., 1.],\n",
       "       [0., 0., 0., 1., 1., 0., 1.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 1., 0., 1.]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polar coordinates??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpolar = dfoot\n",
    "dpolar['degree'] =[2*np.pi*x/52 for x in dpolar.index.tolist()]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n",
    "ax.plot(dpolar['degree'], dpolar[0])\n",
    "ax.plot(dpolar['degree'], dpolar[1])\n",
    "ax.plot(dpolar['degree'], dpolar[2])\n",
    "ax.plot(dpolar['degree'], dpolar[3])\n",
    "ax.plot(dpolar['degree'], dpolar[4])\n",
    "ax.plot(dpolar['degree'], dpolar[5])\n",
    "ax.plot(dpolar['degree'], dpolar[6])\n",
    "ax.plot(dpolar['degree'], dpolar[7])\n",
    "ax.set_rmax(1.1)\n",
    "#ax.set_rticks([0.5, 1, 1.5, 2])  # Less radial ticks\n",
    "#ax.set_rlabel_position(-22.5)  # Move radial labels away from plotted line\n",
    "ax.grid(False)\n",
    "\n",
    "ax.set_title(\"A line plot on a polar axis\", va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are different subreddits reacting similarly to the evolution of the pandemic??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "subreddit = 'republican'\n",
    "\n",
    "dat1_sample = ReadSubreddit(subreddit)\n",
    "comments_dat1 = dat1_sample.comments.tolist()\n",
    "\n",
    "titles_covid = dc19.title.tolist()\n",
    "\n",
    "all_titles = ' '.join(titles_covid)\n",
    "all_comments = ' '.join(comments_dat1)\n",
    "\n",
    "n_comments = len(comments_dat1)\n",
    "n_titles = len(titles_covid)\n",
    "\n",
    "# import sys\n",
    "# !{sys.executable} -m pip install pyLDAvis\n",
    "\n",
    "\n",
    "try:\n",
    "    cpus = multiprocessing.cpu_count()\n",
    "except NotImplementedError:\n",
    "    cpus = 2   # arbitrary default\n",
    "\n",
    "\n",
    "pool = multiprocessing.Pool(processes=cpus)\n",
    "matrix_list = pool.starmap(RetProb, product(comments_dat1,range(num_topics)))\n",
    "\n",
    "matrix_probs = np.array(matrix_list)\n",
    "shape = (n_comments, num_topics)\n",
    "matrix_df = pd.DataFrame(matrix_probs.reshape(shape))\n",
    "matrix_df.columns = prob_topics\n",
    "\n",
    "dat1_sample = pd.concat([dat1_sample.reset_index(), matrix_df.reset_index()], axis=1)\n",
    "\n",
    "test2 = dat1_sample[['period'] + prob_topics + ['comments']].groupby(['period']).agg(pt0 = ('pt0', np.mean),\n",
    "                                                                      pt1 = ('pt1', np.mean),\n",
    "                                                                      pt2 = ('pt2', np.mean),\n",
    "                                                                      pt3 = ('pt3', np.mean),\n",
    "                                                                      pt4 = ('pt4', np.mean),\n",
    "                                                                      pt5 = ('pt5', np.mean),\n",
    "                                                                      pt6 = ('pt6', np.mean),\n",
    "                                                                      pt7 = ('pt7', np.mean),\n",
    "                                                                      words=('comments',GetTopWords)).reset_index()\n",
    "\n",
    "words2 = pd.DataFrame(test2.words.tolist(), columns=[''.join(['w',str(x)]) for x in range(num_topics)])\n",
    "test2 = pd.concat([test2[['period'] + prob_topics],words2], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/46882618/add-tooltips-to-bokeh-stacked-bar-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pt0</th>\n",
       "      <th>pt1</th>\n",
       "      <th>pt2</th>\n",
       "      <th>pt3</th>\n",
       "      <th>pt4</th>\n",
       "      <th>pt5</th>\n",
       "      <th>pt6</th>\n",
       "      <th>pt7</th>\n",
       "      <th>zero</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.144159</td>\n",
       "      <td>0.010376</td>\n",
       "      <td>0.015226</td>\n",
       "      <td>0.011944</td>\n",
       "      <td>0.020332</td>\n",
       "      <td>0.028926</td>\n",
       "      <td>0.033421</td>\n",
       "      <td>0.084758</td>\n",
       "      <td>0</td>\n",
       "      <td>0.483322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.170901</td>\n",
       "      <td>0.012610</td>\n",
       "      <td>0.020151</td>\n",
       "      <td>0.013568</td>\n",
       "      <td>0.025417</td>\n",
       "      <td>0.038167</td>\n",
       "      <td>0.043956</td>\n",
       "      <td>0.081779</td>\n",
       "      <td>0</td>\n",
       "      <td>0.604152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.185539</td>\n",
       "      <td>0.009253</td>\n",
       "      <td>0.022210</td>\n",
       "      <td>0.015383</td>\n",
       "      <td>0.029431</td>\n",
       "      <td>0.041260</td>\n",
       "      <td>0.050630</td>\n",
       "      <td>0.074373</td>\n",
       "      <td>0</td>\n",
       "      <td>0.724983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.204821</td>\n",
       "      <td>0.029310</td>\n",
       "      <td>0.030701</td>\n",
       "      <td>0.016457</td>\n",
       "      <td>0.038583</td>\n",
       "      <td>0.038400</td>\n",
       "      <td>0.062618</td>\n",
       "      <td>0.080945</td>\n",
       "      <td>0</td>\n",
       "      <td>0.845813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.225653</td>\n",
       "      <td>0.045857</td>\n",
       "      <td>0.034819</td>\n",
       "      <td>0.022813</td>\n",
       "      <td>0.051884</td>\n",
       "      <td>0.045963</td>\n",
       "      <td>0.071335</td>\n",
       "      <td>0.080196</td>\n",
       "      <td>0</td>\n",
       "      <td>0.966644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pt0       pt1       pt2       pt3       pt4       pt5       pt6  \\\n",
       "period                                                                         \n",
       "4       0.144159  0.010376  0.015226  0.011944  0.020332  0.028926  0.033421   \n",
       "5       0.170901  0.012610  0.020151  0.013568  0.025417  0.038167  0.043956   \n",
       "6       0.185539  0.009253  0.022210  0.015383  0.029431  0.041260  0.050630   \n",
       "7       0.204821  0.029310  0.030701  0.016457  0.038583  0.038400  0.062618   \n",
       "8       0.225653  0.045857  0.034819  0.022813  0.051884  0.045963  0.071335   \n",
       "\n",
       "             pt7  zero    degree  \n",
       "period                            \n",
       "4       0.084758     0  0.483322  \n",
       "5       0.081779     0  0.604152  \n",
       "6       0.074373     0  0.724983  \n",
       "7       0.080945     0  0.845813  \n",
       "8       0.080196     0  0.966644  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sr = test2\n",
    "data_sr = data_sr.set_index('period')\n",
    "data_sr = data_sr[prob_topics]\n",
    "data_sr.head()\n",
    "datest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"42749\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"42749\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"42749\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '42749' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"42749\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"42749\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"42749\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '42749' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"42749\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"b0f523cc-e6c1-4e58-adc3-4459fb8931d3\" data-root-id=\"43041\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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s/oesJ3pYynT8/cW0VdP+TP6sjLWb9eJc/f3/Lw751gD/NcW4YzrV7PxJ8ghdpVYE/ms0Ex8lwdD+PwarWqtd4P2Ff1RDuHH0/u2XDucq3cj9nzSRfq9iTP4ViUy/68n0/3YIUZZIUjD9HCrgl1/iBP8s0vlGMuXY/ETSf5xx2Zj90ri7BP3GFPwOQnoySFX4/3RmqoEWEiT+j4Z8ACOSKP4XOwnYJc4I/IfQBNiI0ej+aQ+LdA614PxMBFeanFGM/pupfPe8QhD80zrDtjUyJP9zFqyMyEoA/H3QOQNO7gD+DaI/4VCaaP5+P3hXaPmY/Rwch/GBRdz95OYRyEpOAP30boTebw3k/nwOTe1Mhdj8=\",\"dtype\":\"float64\",\"shape\":[52]},\"pt2\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"pt3\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"pt4\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"pt5\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"pt6\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"pt7\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[52]},\"zero\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]},\"selected\":{\"id\":\"43036\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"43035\",\"type\":\"UnionRenderers\"}},\"id\":\"42974\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"period\"},\"y\":{\"field\":\"pt7\"}},\"id\":\"42977\",\"type\":\"Line\"},{\"attributes\":{\"background_fill_color\":{\"value\":\"white\"},\"below\":[{\"id\":\"42760\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"42764\",\"type\":\"Grid\"},{\"id\":\"42769\",\"type\":\"Grid\"}],\"left\":[{\"id\":\"42765\",\"type\":\"LinearAxis\"}],\"plot_height\":80,\"plot_width\":280,\"renderers\":[{\"id\":\"42775\",\"type\":\"GlyphRenderer\"}],\"title\":null,\"toolbar\":{\"id\":\"42770\",\"type\":\"Toolbar\"},\"toolbar_location\":null,\"x_range\":{\"id\":\"42752\",\"type\":\"Range1d\"},\"x_scale\":{\"id\":\"42756\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"42754\",\"type\":\"Range1d\"},\"y_scale\":{\"id\":\"42758\",\"type\":\"LinearScale\"}},\"id\":\"42750\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null,\"end\":52.5,\"start\":-0.5},\"id\":\"42926\",\"type\":\"Range1d\"},{\"attributes\":{},\"id\":\"43035\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"42992\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"line_color\":\"red\",\"line_width\":2,\"x\":{\"field\":\"period\"},\"y\":{\"field\":\"pt3\"}},\"id\":\"42860\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"43034\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"source\":{\"id\":\"42945\",\"type\":\"ColumnDataSource\"}},\"id\":\"42950\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"43000\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"43039\",\"type\":\"ProxyToolbar\"},{\"attributes\":{\"data_source\":{\"id\":\"42771\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"42773\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"42774\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"42776\",\"type\":\"CDSView\"}},\"id\":\"42775\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"ticks\":[0,0.03278333310119214]},\"id\":\"42777\",\"type\":\"FixedTicker\"}],\"root_ids\":[\"43041\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"46a7ce1b-0e34-4454-8041-7e7d7717f410\",\"roots\":{\"43041\":\"b0f523cc-e6c1-4e58-adc3-4459fb8931d3\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "43041"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Settting the figure object\n",
    "output_notebook()\n",
    "\n",
    "items = [] # items for legend\n",
    "lines ={} # bar-information for plotting\n",
    "days = list(range(106))\n",
    "\n",
    "rngsize = 10\n",
    "TOOLS = \"hover,save,reset\"\n",
    "\n",
    "List_of_plots = []\n",
    "topics_to_plot = prob_topics\n",
    "\n",
    "data_sr['zero'] = 0\n",
    "for indx,i in enumerate(topics_to_plot):\n",
    "    \n",
    "    max_freq = 1.05*max(data_sr[i])\n",
    "    \n",
    "    p = figure(plot_width= 280, plot_height=80, title = None, x_range=(-0.5,52.5) ,y_range = (0,max_freq),tools = \"\")\n",
    "\n",
    "    source = ColumnDataSource(data=data_sr.reset_index())\n",
    "    \n",
    "    r = p.line(y=i, x='period', color='red', source=source, alpha = 1, line_width = 2)\n",
    "\n",
    "    # Adjusting plot parameters\n",
    "    #p.x_range.range_padding = 0.05\n",
    "    p.xaxis.axis_label = \"\"\n",
    "    p.yaxis.axis_label = \"\"\n",
    "    #p.yaxis.visible = False\n",
    "    p.xaxis.visible = False\n",
    "    p.yaxis.visible = True\n",
    "    p.yaxis.axis_line_width = 1\n",
    "    p.yaxis.ticker = [0,max_freq]\n",
    "    p.grid.visible = False\n",
    "    p.background_fill_color = 'white'\n",
    "    p.background_fill_alpha = 1 \n",
    "        \n",
    "    List_of_plots.append(p)\n",
    "    \n",
    "    \n",
    "p_all = gridplot(List_of_plots, ncols=4)\n",
    "\n",
    "show(p_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning all the code between chunks, to get the streamlined version to generate the plots above: given a subreddit and a topic, how does it look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"1c44f3d3-74d5-43a4-8b8b-f3460fd33717\" data-root-id=\"18432\"></div>\n"
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       "  var render_items = [{\"docid\":\"b1ae327f-ab3e-45e0-84c3-ffb5d879fcea\",\"roots\":{\"18432\":\"1c44f3d3-74d5-43a4-8b8b-f3460fd33717\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "18432"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_i = 4\n",
    "\n",
    "df0 = ReadSubreddit('republican')\n",
    "\n",
    "comments_ = df0.comments.tolist()\n",
    "\n",
    "all_titles = ' '.join(titles_covid)\n",
    "all_comments = ' '.join(comments_)\n",
    "\n",
    "n_comments = len(comments_)\n",
    "n_titles = len(titles_covid)\n",
    "\n",
    "\n",
    "try:\n",
    "    cpus = multiprocessing.cpu_count()\n",
    "except NotImplementedError:\n",
    "    cpus = 2   # arbitrary default\n",
    "\n",
    "\n",
    "pool = multiprocessing.Pool(processes=cpus)\n",
    "matrix_list = pool.starmap(RetProb, product(comments_,range(num_topics)))\n",
    "\n",
    "matrix_probs = np.array(matrix_list)\n",
    "shape = (n_comments, num_topics)\n",
    "matrix_df = pd.DataFrame(matrix_probs.reshape(shape))\n",
    "matrix_df.columns = prob_topics\n",
    "\n",
    "df0 = pd.concat([df0.reset_index(), matrix_df.reset_index()], axis=1)\n",
    "\n",
    "\n",
    "topww = df0[['period', 'comments','TimeStamp']].groupby(['period']).agg(date = ('TimeStamp',min), words=('comments',GetWords)).reset_index()\n",
    "topww['tpw0'] = topww.words.apply(lambda x: {k: v for k, v in x.items() if k in main_contributors[topic_i]})\n",
    "\n",
    "dfi = pd.DataFrame(topww.tpw0.tolist()).fillna(0)\n",
    "dfi.index = topww.date\n",
    "dfi.topic = topic_i\n",
    "\n",
    "\n",
    "WORDS_IN_TOPIC = dfi.columns.tolist()\n",
    "\n",
    "red = Color(\"tomato\")\n",
    "colors_ = [c.hex for c in list(red.range_to(Color(\"seagreen\"), len(WORDS_IN_TOPIC)))]\n",
    "\n",
    "nweeks = dfi.shape[0]\n",
    "items = [] # items for legend\n",
    "bar ={} # bar-information for plotting\n",
    "weeks = [x+1 for x in range(nweeks)] # adding 1 to 0:23 for readibiltiy\n",
    "\n",
    "tooltips = [(\"word\", \"$name\")]\n",
    "tools_to_show = 'box_zoom,hover,reset,tap'\n",
    "\n",
    "p = figure(plot_width=1100, plot_height=400, title = 'Evolution of words in topic i',\n",
    "           tools = tools_to_show, tooltips = tooltips, x_axis_type=\"datetime\")\n",
    "\n",
    "dates = dfi.index.to_list()\n",
    "for indx, word_i in enumerate(WORDS_IN_TOPIC):\n",
    "    \n",
    "     # Selecting hour and probabilities for plotting category i\n",
    "    probs = dfi[word_i].to_list()\n",
    "     # adding bar for category i\n",
    "    bar[word_i] = p.line(x = dates, y = probs, muted_color = colors_[indx], muted_alpha = 0.4, muted = True,\n",
    "                         color = colors_[indx], alpha = 1, line_width = 1.5, name = word_i)\n",
    "    \n",
    "    selected_line = Line(line_alpha=1, line_color=colors_[indx], line_width = 2.5)\n",
    "\n",
    "    # I add the selection glyph because I want to fix a particular line for comparing\n",
    "    bar[word_i].hover_glyph = selected_line\n",
    "    bar[word_i].selection_glyph = selected_line\n",
    "        \n",
    "# Adding info of category i for the legend\n",
    "    items.append((word_i, [bar[word_i]]))\n",
    "\n",
    " # Adjusting plot parameters\n",
    "p.x_range.range_padding = 0.05\n",
    "#p.xaxis.axis_label = \"Week of the year\"\n",
    "p.yaxis.axis_label = \"Probability of using the word\"\n",
    " # Adusting legend information\n",
    "legend = Legend(items=items, location=(0, 10))\n",
    "p.add_layout(legend, 'right')\n",
    "p.legend.click_policy=\"hide\"\n",
    "\n",
    "show(p) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " __TO DO:__ Need to check what happens with the column with two words (test posit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "mywords = main_contributors[topic_i]\n",
    "\n",
    "# Normalizing by row\n",
    "dfoot = dfi[mywords].div(dfi[mywords].sum(axis=1, skipna=True), axis=0)\n",
    "footprint = np.transpose(dfoot.to_numpy())\n",
    "\n",
    "im = ax.imshow(footprint, cmap = 'Reds')\n",
    "\n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]))\n",
    "ax.set_yticks(np.arange(footprint.shape[0]))\n",
    "\n",
    "#datenames = dfi.reset_index().date.apply(lambda x: x[:-5]).tolist()\n",
    "ax.set_yticklabels(mywords)\n",
    "#ax.set_xticklabels(datenames,rotation=45,ha=\"left\")\n",
    "\n",
    "# Let the horizontal axes labeling appear on top.\n",
    "ax.tick_params(top=True, bottom=False,\n",
    "               labeltop=True, labelbottom=False)\n",
    "\n",
    "for edge, spine in ax.spines.items():\n",
    "    spine.set_visible(False)\n",
    "        \n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(footprint.shape[0]+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=1.5)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corona footprint and other trials..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TEST to show the most relevant words of the week for each topic.\n",
    "### Does not work completely yet.\n",
    "\n",
    "\n",
    "# test2['week_str'] = test2['period'].astype(str)\n",
    "\n",
    "# topwords = [''.join(['w',str(x)]) for x in range(num_topics)]\n",
    "# weeks = test2['week_str'].tolist()\n",
    "\n",
    "# red = Color(\"tomato\")\n",
    "# colors_ = [c.hex for c in list(red.range_to(Color(\"gold\"), num_topics))]\n",
    "\n",
    "# data1 = deepcopy(test2)\n",
    "\n",
    "# #create cumulative sum over years for plotting using vbar\n",
    "# for i in range(1,len(topics)):\n",
    "#     test2[prob_topics[i]] = [sum(x) for x in zip(test2[prob_topics[i]], test2[prob_topics[i-1]])]\n",
    "\n",
    "# p = figure(x_range=weeks,plot_width= 1050, plot_height=300, title=subreddit, toolbar_location=None, tools=\"\")\n",
    "\n",
    "\n",
    "# #create bars for each years\n",
    "# rx = []\n",
    "# for i in range(num_topics):\n",
    "#     if i==0:\n",
    "#         rx.append(p.vbar(x=weeks, top=test2[prob_topics[i]], bottom=[0]*len(weeks), width=0.9, color=colors_[i]))\n",
    "#         #rx[i].data_source.add(data1[topics[i]], \"count\") #add a column in data source for just the count\n",
    "#         rx[i].data_source.add(data1[''.join(['w',str(i)])], \"count\") #add a column in data source for just the count\n",
    "#     else:\n",
    "#         rx.append(p.vbar(x=weeks, top=test2[prob_topics[i]], bottom=test2[prob_topics[i-1]], width=0.9, color=colors_[i]))\n",
    "#         #rx[i].data_source.add(data1[topics[i]], \"count\") #add a column in data source for just the count\n",
    "#         rx[i].data_source.add(data1[''.join(['w',str(i)])], \"count\") #add a column in data source for just the count\n",
    "\n",
    "# #add hover tool for each bar chart\n",
    "# for i in range(len(topics)):\n",
    "#     p.add_tools(HoverTool(tooltips=[(str(topwords[i]), \"@count\")], renderers=[rx[i]]))\n",
    "\n",
    "    \n",
    "\n",
    "#  # Adjusting plot parameters\n",
    "# p.grid.visible = False\n",
    "# p.outline_line_color = \"white\"\n",
    "# p.background_fill_color = 'white'\n",
    "# p.background_fill_alpha = 1\n",
    "\n",
    "# # Creating legend\n",
    "    \n",
    "# p.min_border_left = 110\n",
    "# p.min_border_left = 110\n",
    "# p.min_border_top = 50\n",
    "# p.min_border_bottom = 20\n",
    "\n",
    "# # Configuring axis\n",
    "# p.yaxis.visible = True\n",
    "# p.yaxis.axis_line_width = 2\n",
    "# p.yaxis.major_tick_line_width = 2\n",
    "# p.yaxis.axis_label = None\n",
    "# p.yaxis.ticker = [0,0.06,0.12]\n",
    "# p.yaxis.major_label_overrides = dict(zip([0,1],['0','100']))\n",
    "# p.yaxis.major_label_text_font_size = '18px'\n",
    "# p.yaxis.axis_label_text_color = 'black'\n",
    "\n",
    "# p.xaxis.visible = True\n",
    "# p.xaxis.axis_line_width = 2\n",
    "# p.xaxis.major_tick_line_width = 2\n",
    "# p.xaxis.major_label_text_font_size = '18px'\n",
    "# p.xaxis.major_label_text_baseline = 'bottom'\n",
    "# p.xaxis.major_label_standoff = 10\n",
    "# p.xaxis.major_label_text_color = 'black'\n",
    "\n",
    "# ################################################################\n",
    "# title_axis_y = 'Distribution of categories'\n",
    "# title_y1 = Label(x=-21.5, y=0.45, text=title_axis_y,\n",
    "#                 render_mode='css', text_color = 'black', text_align = 'left',\n",
    "#                text_font_size = '18px', angle = 90, angle_units = 'deg')\n",
    "# p.add_layout(title_y1)\n",
    "# title_axis_y2 = 'in reddit main page (%)'\n",
    "# title_y2 = Label(x=-18.35, y=0.42, text=title_axis_y2,\n",
    "#                 render_mode='css', text_color = 'black', text_align = 'left',\n",
    "#                text_font_size = '18px', angle = 90, angle_units = 'deg')\n",
    "# p.add_layout(title_y2)\n",
    "\n",
    "# ################################################################    \n",
    "\n",
    "# #output_notebook()\n",
    "# show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "p = figure(plot_width= 1050, plot_height=300, title = None, x_range=(-0.5,52.5), y_range=(0,0.9), tools = \"\")\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "source_week = ColumnDataSource(data=datest)\n",
    "\n",
    "# categorical palette generator: http://vrl.cs.brown.edu/color\n",
    "colors_topics = [\"#69ef7b\", \"#1d7583\", \"#96ccfe\", \"#6c41cb\", \"#9693f7\", \"#424175\", \"#61cab8\", \"#167b2b\"]\n",
    "\n",
    "\n",
    "r = p.vbar_stack(prob_topics, x='period', width = 0.8, color=colors_topics, source=source_week, alpha = 1, legend_label = prob_topics)\n",
    "\n",
    " # Adjusting plot parameters\n",
    "p.grid.visible = False\n",
    "p.outline_line_color = \"white\"\n",
    "p.background_fill_color = 'white'\n",
    "p.background_fill_alpha = 1\n",
    "\n",
    "# Creating legend\n",
    "    \n",
    "p.min_border_left = 110\n",
    "p.min_border_left = 110\n",
    "p.min_border_top = 50\n",
    "p.min_border_bottom = 20\n",
    "\n",
    "# Configuring axis\n",
    "p.yaxis.visible = True\n",
    "p.yaxis.axis_line_width = 2\n",
    "p.yaxis.major_tick_line_width = 2\n",
    "p.yaxis.axis_label = None\n",
    "p.yaxis.ticker = [0,0.15,0.3]\n",
    "p.yaxis.major_label_overrides = dict(zip([0,1],['0','100']))\n",
    "p.yaxis.major_label_text_font_size = '18px'\n",
    "p.yaxis.axis_label_text_color = 'black'\n",
    "\n",
    "p.xaxis.visible = True\n",
    "p.xaxis.axis_line_width = 2\n",
    "p.xaxis.major_tick_line_width = 2\n",
    "p.xaxis.major_label_text_font_size = '18px'\n",
    "p.xaxis.major_label_text_baseline = 'bottom'\n",
    "p.xaxis.major_label_standoff = 10\n",
    "p.xaxis.major_label_text_color = 'black'\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "\n",
    "################################################################\n",
    "title_axis_y = 'Distribution of categories'\n",
    "title_y1 = Label(x=-21.5, y=0.45, text=title_axis_y,\n",
    "                render_mode='css', text_color = 'black', text_align = 'left',\n",
    "               text_font_size = '18px', angle = 90, angle_units = 'deg')\n",
    "p.add_layout(title_y1)\n",
    "title_axis_y2 = 'in reddit main page (%)'\n",
    "title_y2 = Label(x=-18.35, y=0.42, text=title_axis_y2,\n",
    "                render_mode='css', text_color = 'black', text_align = 'left',\n",
    "               text_font_size = '18px', angle = 90, angle_units = 'deg')\n",
    "p.add_layout(title_y2)\n",
    "\n",
    "################################################################\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat2 = pd.DataFrame(list(topwords_weekly['words']))\n",
    "dat2['date'] = topwords_weekly['date'].dt.strftime('%d/%m/%Y')\n",
    "dat2 = dat2.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat3 = dat2.transpose()\n",
    "\n",
    "dat3\n",
    "dat3['nfreqs'] = dat3.sum(axis = 1, skipna = True).astype('int').tolist()\n",
    "dat3['percent'] = dat3['nfreqs']/ncomments\n",
    "\n",
    "# Getting the words that appeared in 2% of comments\n",
    "freqwords = dat3.loc[dat3['percent'] > 0.02].index.tolist()\n",
    "\n",
    "# Getting the top 20 words\n",
    "super_freqwords = dat3.sort_values('nfreqs',ascending = False).head(20).index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that the word _virus_ lost steam, as the word covid crawled its way in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat2[['covid','virus','masks']].plot(figsize=(16,7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "mywords = ['covid', 'china','virus', 'cases', 'mask','vaccine','health','government','trump','work','death']\n",
    "\n",
    "# Normalizing by row\n",
    "dfoot = dat2[mywords].div(dat2[mywords].sum(axis=1, skipna=True), axis=0)\n",
    "footprint = np.transpose(dfoot.to_numpy())\n",
    "\n",
    "\n",
    "im = ax.imshow(footprint, cmap = 'Reds')\n",
    "\n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]))\n",
    "ax.set_yticks(np.arange(footprint.shape[0]))\n",
    "\n",
    "datenames = dat2.reset_index().date.apply(lambda x: x[:-5]).tolist()\n",
    "ax.set_yticklabels(mywords)\n",
    "ax.set_xticklabels(datenames,rotation=45,ha=\"left\")\n",
    "\n",
    "# Let the horizontal axes labeling appear on top.\n",
    "ax.tick_params(top=True, bottom=False,\n",
    "               labeltop=True, labelbottom=False)\n",
    "\n",
    "for edge, spine in ax.spines.items():\n",
    "    spine.set_visible(False)\n",
    "        \n",
    "# We want to show all ticks...\n",
    "ax.set_xticks(np.arange(footprint.shape[1]+1)-.5, minor=True)\n",
    "ax.set_yticks(np.arange(footprint.shape[0]+1)-.5, minor=True)\n",
    "ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=1.5)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is time how these words appeared in other subreddits with a political edge...\n",
    "\n",
    "So I will read all comments in the following subreddit: Republican, Democrats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33762, 22)\n",
      "(22415, 22)\n",
      "(151700, 22)\n",
      "(128914, 22)\n"
     ]
    }
   ],
   "source": [
    "dat_rep = ReadSubreddit('republican')\n",
    "dat_dem = ReadSubreddit('democrats')\n",
    "dat_lib = ReadSubreddit('libertarian')\n",
    "dat_cons = ReadSubreddit('conservative')\n",
    "\n",
    "print(dat_rep.shape)\n",
    "print(dat_dem.shape)\n",
    "print(dat_lib.shape)\n",
    "print(dat_cons.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_rep = dat_rep[['period', 'comments','TimeStamp']].reset_index().groupby(['period']).agg(date = ('TimeStamp',min), words=('comments',GetWords)).reset_index()\n",
    "tw_dem = dat_dem[['period', 'comments','TimeStamp']].reset_index().groupby(['period']).agg(date = ('TimeStamp',min), words=('comments',GetWords)).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "relwords = ['china','covid','mask','vaccine']\n",
    "\n",
    "df4 = pd.DataFrame(index=tw_rep.index)\n",
    "for w in relwords:\n",
    "   df4[w] = tw_rep.words.apply(lambda x: x[w]/len(x)*100 if w in x.keys() else 0)\n",
    "\n",
    "df4['subreddit'] = 'republican'\n",
    "#df4['week'] = tw_rep.index\n",
    "\n",
    "df5 = pd.DataFrame(index=tw_dem.index)\n",
    "for w in relwords:\n",
    "   df5[w] = tw_dem.words.apply(lambda x: x[w]/len(x)*100 if w in x.keys() else 0)\n",
    "\n",
    "df5['subreddit'] = 'democrat'\n",
    "#df5['week'] = tw_dem.index\n",
    "\n",
    "df45 = pd.concat([df4, df5])"
   ]
  }
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