Notebook_CORD-19_2_text_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CORD-19 text analysis\n",
    "\n",
    "In this notebook, we perform a topic modelling analysis of CORD-19 publications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# magics and warnings\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "import os, random, codecs, json, pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "seed = 99\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "import nltk, sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 2.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load metadata (follow the other notebooks in the repository to create these)\n",
    "\n",
    "df_meta = pd.read_csv(\"datasets_output/df_pub.csv\",compression=\"gzip\")\n",
    "df_datasource = pd.read_csv(\"datasets_output/sql_tables/datasource.csv\",sep=\"\\t\",header=None,names=['datasource_metadata_id', 'datasource', 'url'])\n",
    "df_pub_datasource = pd.read_csv(\"datasets_output/sql_tables/pub_datasource.csv\",sep=\"\\t\",header=None,names=['pub_id','datasource_metadata_id'])\n",
    "df_cord_meta = pd.read_csv(\"datasets_output/sql_tables/cord19_metadata.csv\",sep=\"\\t\",header=None,names=[ 'cord19_metadata_id', 'source', 'license', 'ms_academic_id',\n",
    "       'who_covidence', 'sha', 'full_text', 'pub_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pub_id</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>publication_month</th>\n",
       "      <th>journal</th>\n",
       "      <th>volume</th>\n",
       "      <th>issue</th>\n",
       "      <th>pages</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmid</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>dimensions_id</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Coronavirus disease 2019 (COVID-19) and simult...</td>\n",
       "      <td>Coronavirus disease 2019 (COVID-19) is a recen...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CardioVascular Journal of Africa</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-4</td>\n",
       "      <td>10-5830-cvja-2020-016</td>\n",
       "      <td>32490506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1128171889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[Ten hot issues of breast cancer under the nov...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Chinese medical journal</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>e002</td>\n",
       "      <td>10.0376/cma.j.issn.0376-2491.2020.0002</td>\n",
       "      <td>32036640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1124777091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Cohesion in Distancing.</td>\n",
       "      <td>In isolation, we are physically apart; in soli...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>The AMA Journal of Ethic</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>e344-345</td>\n",
       "      <td>10.1001/amajethics.2020.344</td>\n",
       "      <td>32345430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1127182341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Salvation in a Time of Plague.</td>\n",
       "      <td>Health workers offer their skills and care to ...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>The AMA Journal of Ethic</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>e441-445</td>\n",
       "      <td>10.1001/amajethics.2020.441</td>\n",
       "      <td>32449663.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1127905371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>What Should Health Science Journalists Do in E...</td>\n",
       "      <td>Journalists have long covered outbreaks of inf...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMA journal of ethics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1001/amajethics.2020.55</td>\n",
       "      <td>31958392.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pub_id                                              title  \\\n",
       "0       0  Coronavirus disease 2019 (COVID-19) and simult...   \n",
       "1       1  [Ten hot issues of breast cancer under the nov...   \n",
       "2       2                            Cohesion in Distancing.   \n",
       "3       3                     Salvation in a Time of Plague.   \n",
       "4       4  What Should Health Science Journalists Do in E...   \n",
       "\n",
       "                                            abstract  publication_year  \\\n",
       "0  Coronavirus disease 2019 (COVID-19) is a recen...            2020.0   \n",
       "1                                                NaN            2020.0   \n",
       "2  In isolation, we are physically apart; in soli...            2020.0   \n",
       "3  Health workers offer their skills and care to ...            2020.0   \n",
       "4  Journalists have long covered outbreaks of inf...            2020.0   \n",
       "\n",
       "   publication_month                           journal volume issue     pages  \\\n",
       "0                5.0  CardioVascular Journal of Africa     31   NaN       1-4   \n",
       "1                2.0           Chinese medical journal    100     0      e002   \n",
       "2                4.0          The AMA Journal of Ethic     22     4  e344-345   \n",
       "3                5.0          The AMA Journal of Ethic     22     5  e441-445   \n",
       "4                1.0             AMA journal of ethics    NaN   NaN       NaN   \n",
       "\n",
       "                                      doi        pmid pmcid   dimensions_id  \\\n",
       "0                   10-5830-cvja-2020-016  32490506.0   NaN  pub.1128171889   \n",
       "1  10.0376/cma.j.issn.0376-2491.2020.0002  32036640.0   NaN  pub.1124777091   \n",
       "2             10.1001/amajethics.2020.344  32345430.0   NaN  pub.1127182341   \n",
       "3             10.1001/amajethics.2020.441  32449663.0   NaN  pub.1127905371   \n",
       "4              10.1001/amajethics.2020.55  31958392.0   NaN             NaN   \n",
       "\n",
       "  arxiv_id                   timestamp  \n",
       "0      NaN  2020-07-02 10:54:27.347501  \n",
       "1      NaN  2020-07-02 10:54:27.347501  \n",
       "2      NaN  2020-07-02 10:54:27.347501  \n",
       "3      NaN  2020-07-02 10:54:27.347501  \n",
       "4      NaN  2020-07-02 10:54:27.347501  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pub_id', 'title', 'abstract', 'publication_year', 'publication_month',\n",
       "       'journal', 'volume', 'issue', 'pages', 'doi', 'pmid', 'pmcid',\n",
       "       'dimensions_id', 'arxiv_id', 'timestamp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datasource_metadata_id</th>\n",
       "      <th>datasource</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>CORD19</td>\n",
       "      <td>https://pages.semanticscholar.org/coronavirus-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dimensions</td>\n",
       "      <td>https://docs.google.com/spreadsheets/d/1-kTZJZ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   datasource_metadata_id  datasource  \\\n",
       "0                       0      CORD19   \n",
       "1                       1  Dimensions   \n",
       "\n",
       "                                                 url  \n",
       "0  https://pages.semanticscholar.org/coronavirus-...  \n",
       "1  https://docs.google.com/spreadsheets/d/1-kTZJZ...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_datasource"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select just CORD-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_meta = df_meta.merge(df_pub_datasource, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")\n",
    "df_meta = df_meta.merge(df_datasource, how=\"inner\", left_on=\"datasource_metadata_id\", right_on=\"datasource_metadata_id\")\n",
    "df_cord19 = df_meta[df_meta.datasource_metadata_id==0]\n",
    "df_cord19 = df_cord19.merge(df_cord_meta, how=\"inner\", left_on=\"pub_id\", right_on=\"pub_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201599, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140351, 25)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cord19.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13233, 25)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# publications without DOI will be missing from the network clustering analysis\n",
    "\n",
    "df_cord19[pd.isnull(df_cord19[\"doi\"])].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31055, 25)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cord19[pd.isnull(df_cord19[\"abstract\"])].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def clean_year(s):\n",
    "    if pd.isna(s):\n",
    "        return np.nan\n",
    "    if not (s>1900):\n",
    "        return np.nan\n",
    "    elif s>2020:\n",
    "        return 2020\n",
    "    return s\n",
    "\n",
    "df_cord19[\"publication_year\"] = df_cord19[\"publication_year\"].apply(clean_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we save a list of DOIs to filter data from Dimensions for the network clustering analysis (see notebook 3)\n",
    "\n",
    "#pickle.dump(df_cord19[pd.notnull(df_cord19[\"doi\"])][\"doi\"].to_list(),open(\"datasets_output/networks/cord-19-dois.pk\",\"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: add a language detection to remove all non-English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate abstracts and titles\n",
    "\n",
    "# note that ~8k articles have no abstract\n",
    "df_cord19[\"title\"] = df_cord19[\"title\"].fillna('')\n",
    "df_cord19[\"abstract\"] = df_cord19[\"abstract\"].fillna('')\n",
    "df_cord19[\"title_abstract\"] = df_cord19.title +\" \"+df_cord19.abstract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Coronavirus disease 2019 (COVID-19) and simultaneous acute anteroseptal and inferior ST-segment elevation myocardial infarction. Coronavirus disease 2019 (COVID-19) is a recently recognised pandemic spreading rapidly from Wuhan, Hubei, to other provinces in China and to many countries around the world. The number of COVID-19-related deaths is steadily increasing. Acute ST-segment elevation myocardial infarction (STEMI) is a disease with high morbidity and mortality rates, and primary percutaneous coronary intervention is usually recommended for the treatment. A patient with diabetes mellitus and hypertension for five years was admitted to the emergency unit with symptoms of fever, cough and dyspnoea. These symptoms were consistent with viral pneumonia and a COVID PCR test was performed, which tested positive three days later. The patient had chest pain on the eighth day of hospitalisation. On electrocardiography, simultaneous acute inferior and anterior STEMI were identified. High levels of stress and increased metabolic demand in these patients may lead to concomitant thrombosis of different coronary arteries, presenting with two different STEMIs.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#texts = df_cord19[(pd.notnull(df_cord19[\"title_abstract\"])) & (df_cord19.publication_year>2019)].title_abstract.tolist()\n",
    "texts = df_cord19[(pd.notnull(df_cord19[\"title_abstract\"]))].title_abstract.tolist()\n",
    "texts[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topic modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import gensim, sklearn\n",
    "import scispacy\n",
    "import spacy\n",
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m spacy download en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nlp = spacy.load('en')\n",
    "nlp = spacy.load(\"en_core_sci_md\")\n",
    "#STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12min 30s, sys: 45.5 s, total: 13min 15s\n",
      "Wall time: 14min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# pre-processing: use lemmas and remove stopwords, add entities\n",
    "\n",
    "processed_docs = list()\n",
    "for doc in nlp.pipe(texts, n_process=6, batch_size=100):\n",
    "\n",
    "    ents = doc.ents  # Named entities\n",
    "\n",
    "    # Lemmatize tokens, remove punctuation and remove stopwords.\n",
    "    doc = [token.lemma_ for token in doc if not token.is_punct and not token.is_stop]# token.is_alpha]\n",
    "\n",
    "    # Remove common words from a stopword list and keep only words of length 3 or more.\n",
    "    #doc = [token for token in doc if token not in STOPWORDS and len(token) > 2]\n",
    "\n",
    "    # Add named entities, but only if they are a compound of more than one word.\n",
    "    doc.extend([str(entity) for entity in ents if len(entity) > 1])\n",
    "                        \n",
    "    processed_docs.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(processed_docs, open(\"datasets_output/texts/processed_docs_cord19_scispacy.pk\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "processed_docs = pickle.load(open(\"datasets_output/texts/processed_docs_cord19_scispacy.pk\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = processed_docs\n",
    "del processed_docs\n",
    "\n",
    "# Add bigrams\n",
    "from gensim.models.phrases import Phrases\n",
    "# Add bigrams to docs (only ones that appear several times or more). A better approach would be to use a chi_sq test.\n",
    "bigram = Phrases(docs, min_count=50)\n",
    "for idx in range(len(docs)):\n",
    "    for token in bigram[docs[idx]]:\n",
    "        if '_' in token:\n",
    "            # Token is a bigram, add to document.\n",
    "            docs[idx].append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rare and common tokens.\n",
    "# Filter out words that occur too frequently or too rarely.\n",
    "max_freq = 0.5\n",
    "min_wordcount = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 65165\n",
      "Number of docs: 140351 (140351)\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary representation of the documents, and filter out frequent and rare words.\n",
    "from gensim.corpora import Dictionary\n",
    "dictionary = Dictionary(docs)\n",
    "dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)\n",
    "\n",
    "# Bag-of-words representation of the documents.\n",
    "corpus = [dictionary.doc2bow(doc) for doc in docs]\n",
    "#MmCorpus.serialize(\"models/corpus.mm\", corpus)\n",
    "\n",
    "print('Number of unique tokens: %d' % len(dictionary))\n",
    "print('Number of docs: %d (%d)' % (len(corpus),len(texts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary.token2id[\"covid-19\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13min 26s, sys: 7.39 s, total: 13min 33s\n",
      "Wall time: 13min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from gensim.models import LdaMulticore, LdaModel\n",
    "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n",
    "model = LdaModel(corpus=corpus, num_topics=params['num_topics'], id2word=dictionary, #workers=6,\n",
    "                passes=params['passes'], random_state=params['random_state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.043*\"respiratory\" + 0.028*\"infection\" + 0.021*\"virus\" + 0.018*\"patient\" + 0.015*\"child\" + 0.013*\"influenza\" + 0.012*\"acute\" + 0.012*\"clinical\" + 0.012*\"viral\" + 0.011*\"pneumonia\"'),\n",
       " (1,\n",
       "  '0.046*\"covid-19\" + 0.029*\"COVID-19\" + 0.023*\"pandemic\" + 0.017*\"health\" + 0.014*\"sars-cov-2\" + 0.012*\"coronavirus\" + 0.010*\"2020\" + 0.010*\"public\" + 0.010*\"care\" + 0.009*\"2019\"'),\n",
       " (2,\n",
       "  '0.025*\"group\" + 0.023*\"=\" + 0.018*\"p\" + 0.013*\"study\" + 0.013*\"<\" + 0.011*\"day\" + 0.010*\"compare\" + 0.009*\"p_=\" + 0.009*\"95\" + 0.008*\"rate\"'),\n",
       " (3,\n",
       "  '0.028*\"virus\" + 0.014*\"sequence\" + 0.012*\"coronavirus\" + 0.011*\"strain\" + 0.011*\"gene\" + 0.009*\"assay\" + 0.009*\"sample\" + 0.008*\"antibody\" + 0.008*\"isolate\" + 0.008*\"detect\"'),\n",
       " (4,\n",
       "  '0.022*\"study\" + 0.011*\"review\" + 0.009*\"include\" + 0.009*\"health\" + 0.007*\"result\" + 0.007*\"datum\" + 0.007*\"report\" + 0.006*\"intervention\" + 0.006*\"search\" + 0.006*\"evidence\"'),\n",
       " (5,\n",
       "  '0.026*\"disease\" + 0.023*\"case\" + 0.016*\"infection\" + 0.016*\"outbreak\" + 0.015*\"SARS\" + 0.011*\"transmission\" + 0.010*\"country\" + 0.010*\"epidemic\" + 0.010*\"control\" + 0.009*\"syndrome\"'),\n",
       " (6,\n",
       "  '0.039*\"patient\" + 0.015*\"treatment\" + 0.013*\"case\" + 0.011*\"ct\" + 0.010*\"aneurysm\" + 0.009*\"clinical\" + 0.007*\"treat\" + 0.007*\"lesion\" + 0.007*\"artery\" + 0.007*\"chest\"'),\n",
       " (7,\n",
       "  '0.026*\"patient\" + 0.019*\"surgery\" + 0.014*\"laparoscopic\" + 0.011*\"surgical\" + 0.010*\"complication\" + 0.010*\"procedure\" + 0.009*\"undergo\" + 0.009*\"postoperative\" + 0.009*\"perform\" + 0.009*\"technique\"'),\n",
       " (8,\n",
       "  '0.018*\"cell\" + 0.017*\"infection\" + 0.012*\"response\" + 0.012*\"expression\" + 0.010*\"mouse\" + 0.010*\"disease\" + 0.009*\"lung\" + 0.009*\"immune\" + 0.008*\"increase\" + 0.008*\"role\"'),\n",
       " (9,\n",
       "  '0.029*\"model\" + 0.016*\"datum\" + 0.009*\"$\" + 0.009*\"number\" + 0.008*\"analysis\" + 0.008*\"time\" + 0.008*\"result\" + 0.007*\"network\" + 0.007*\"different\" + 0.006*\"base\"'),\n",
       " (10,\n",
       "  '0.012*\"method\" + 0.009*\"result\" + 0.008*\"study\" + 0.007*\"device\" + 0.006*\"pressure\" + 0.006*\"test\" + 0.005*\"image\" + 0.005*\"temperature\" + 0.005*\"flow\" + 0.005*\"evaluate\"'),\n",
       " (11,\n",
       "  '0.039*\"protein\" + 0.036*\"virus\" + 0.020*\"rna\" + 0.019*\"viral\" + 0.017*\"cell\" + 0.011*\"replication\" + 0.010*\"coronavirus\" + 0.010*\"activity\" + 0.009*\"antiviral\" + 0.008*\"membrane\"'),\n",
       " (12,\n",
       "  '0.054*\"cell\" + 0.024*\"vaccine\" + 0.020*\"antibody\" + 0.017*\"mouse\" + 0.014*\"response\" + 0.014*\"t\" + 0.014*\"infection\" + 0.009*\"immune\" + 0.009*\"antigen\" + 0.008*\"induce\"'),\n",
       " (13,\n",
       "  '0.013*\"disease\" + 0.011*\"new\" + 0.011*\"drug\" + 0.010*\"development\" + 0.010*\"review\" + 0.009*\"human\" + 0.008*\"research\" + 0.008*\"treatment\" + 0.008*\"potential\" + 0.008*\"approach\"'),\n",
       " (14,\n",
       "  '0.078*\"patient\" + 0.017*\"treatment\" + 0.014*\"clinical\" + 0.013*\"severe\" + 0.013*\"blood\" + 0.013*\"disease\" + 0.011*\"therapy\" + 0.009*\"level\" + 0.009*\"cancer\" + 0.007*\"care\"')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.show_topics(num_words=10, num_topics=params['num_topics'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# printout for LaTeX\n",
    "#for t in model.show_topics(num_words=100, num_topics=params['num_topics'], formatted=False):\n",
    "#    print(\"\\\\item \\\\textbf{Topic #%d}\"%t[0]+\": \"+\"``\"+\"'', ``\".join([w[0] for w in t[1] if len(w[0])>3][:20])+\"''.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el469811185027747362000754445\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el469811185027747362000754445_data = {\"mdsDat\": {\"x\": [-0.15948345817963494, 0.14897670605679894, -0.05565318446845301, 0.04609178979122528, -0.0032670038443019427, 0.28474398529910766, -0.049088939703198047, 0.035232503131644735, -0.09401978954160146, 0.18252960520063777, -0.209449319170029, -0.008835235239489462, -0.20905036306421768, -0.12239443153356033, 0.21366713526507136], \"y\": [0.13560122492668686, 0.08507211171092476, -0.28868426024451616, -0.21778019725165007, -0.121853211159689, 0.06173855548607187, 0.12419544857579215, -0.14858632821715267, -0.15649497520098368, 0.07817664133862906, 0.07219333992111392, 0.025269976927856645, 0.12518937608680855, 0.1174514321129556, 0.1085108649871527], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [10.221784591674805, 8.765304565429688, 7.983642101287842, 7.644094467163086, 7.148164749145508, 6.8864665031433105, 6.814214706420898, 6.801616191864014, 6.541372776031494, 6.345002174377441, 6.110149383544922, 5.276716232299805, 5.146267414093018, 4.338910102844238, 3.976289987564087]}, \"tinfo\": {\"Term\": [\"patient\", \"covid-19\", \"cell\", \"virus\", \"respiratory\", \"protein\", \"COVID-19\", \"infection\", \"=\", \"model\", \"disease\", \"pandemic\", \"case\", \"p\", \"group\", \"viral\", \"rna\", \"antibody\", \"health\", \"coronavirus\", \"treatment\", \"surgery\", \"vaccine\", \"mouse\", \"clinical\", \"response\", \"outbreak\", \"sars-cov-2\", \"sequence\", \"<\", \"=\", \"p\", \"p_=\", \"95\", \"p_<\", \"n_=\", \"ci\", \"95_ci\", \"0.001\", \"0.05\", \"control_group\", \"CI\", \"<_>\", \"95_CI\", \"odd\", \"95_confidence\", \"confidence interval\", \"0.01\", \"control group\", \"odd_ratio\", \"odds ratio\", \".001\", \"logistic_regression\", \"0.0001\", \"interval_ci\", \"degree_c\", \"95%ci\", \"iqr\", \"propofol\", \"korean\", \"vs\", \"vs.\", \"statistically significant\", \"statistically_significant\", \"<\", \"interval\", \"p<0.001\", \"placebo\", \"group\", \"sd\", \"confidence\", \"statistically\", \"score\", \"regression\", \"age\", \"ratio\", \"day\", \"\\u00b1\", \"n\", \"significantly\", \"compare\", \"difference\", \"respectively\", \">\", \"rate\", \"median\", \"study\", \"mean\", \"year\", \"significant\", \"1\", \"high\", \"conclusion\", \"total\", \"result\", \"analysis\", \"control\", \"patient\", \"method\", \"2\", \"cat\", \"spike\", \"ibv\", \"feline\", \"amino\", \"amino_acid\", \"bat\", \"tgev\", \"elisa\", \"canine\", \"rotavirus\", \"chicken\", \"gastroenteritis\", \"enteric\", \"coli\", \"pedv\", \"mab\", \"s_protein\", \"s1\", \"phylogenetic\", \"recombination\", \"spike_protein\", \"orf\", \"cdna\", \"infectious_bronchitis\", \"piglet\", \"transmissible_gastroenteritis\", \"bird\", \"wild-type\", \"polypeptide\", \"sequence\", \"calf\", \"bovine\", \"s\", \"genotype\", \"strain\", \"dog\", \"assay\", \"isolate\", \"porcine\", \"genome\", \"diarrhea\", \"gene\", \"specie\", \"acid\", \"virus\", \"sample\", \"pig\", \"antibody\", \"detect\", \"coronavirus\", \"coronaviruse\", \"detection\", \"analysis\", \"serum\", \"infectious\", \"human\", \"protein\", \"region\", \"study\", \"result\", \"test\", \"show\", \"identify\", \"viral\", \"covid-19\", \"COVID-19\", \"sars-cov-2\", \"2020\", \"covid-19_pandemic\", \"lockdown\", \"coronavirus_disease\", \"distancing\", \"social_distancing\", \"social distancing\", \"2019_covid-19\", \"Wuhan\", \"Pandemic\", \"covid-19_outbreak\", \"coronavirus disease 2019\", \"coronavirus disease\", \"sars-cov-2_infection\", \"2019-ncov\", \"Public\", \"acute respiratory syndrome coronavirus 2\", \"COVID-19_Pandemic\", \"SARS-CoV-2 infection\", \"COVID-19 infection\", \"healthcare_worker\", \"March_2020\", \"healthcare workers\", \"December_2019\", \"Public_Health\", \"Coronavirus_Disease\", \"hcw\", \"2019\", \"health_care\", \"crisis\", \"pandemic\", \"coronavirus_2\", \"emergency\", \"healthcare\", \"worker\", \"staff\", \"public\", \"health\", \"service\", \"care\", \"novel_coronavirus\", \"Health\", \"public_health\", \"coronavirus\", \"medical\", \">\", \"hospital\", \"face\", \"patient\", \"need\", \"response\", \"system\", \"outbreak\", \"disease\", \"novel\", \"graph\", \"veterinary\", \"ct_image\", \"biomedical\", \"secrete\", \"glycosylation\", \"machinery\", \"Drug\", \"apn\", \"article_review\", \"recent_advance\", \"virus-like\", \"biosensor\", \"development_new\", \"virus-like_particle\", \"virtual_screening\", \"persistently\", \"provide_overview\", \"repurposing\", \"crystal_structure\", \"small_molecule\", \"physics\", \"Food\", \"drug_candidate\", \"FDA\", \"agricultural\", \"protein-protein\", \"Organization_declare\", \"veterinarian\", \"cloning\", \"advance\", \"inference\", \"engineering\", \"technology\", \"biology\", \"discovery\", \"drug\", \"review_summarize\", \"automatic\", \"genus\", \"review_focus\", \"promise\", \"discuss\", \"development\", \"antimicrobial\", \"application\", \"medicine\", \"recent\", \"therapeutic\", \"research\", \"new\", \"decade\", \"biological\", \"clinical trials\", \"food\", \"potential\", \"approach\", \"current\", \"review\", \"agent\", \"emerge\", \"focus\", \"pathogen\", \"scientific\", \"understanding\", \"strategy\", \"currently\", \"system\", \"provide\", \"use\", \"human\", \"disease\", \"animal\", \"treatment\", \"develop\", \"target\", \"challenge\", \"include\", \"clinical\", \"need\", \"novel\", \"Middle\", \"South\", \"Hong\", \"Kong\", \"Hong_Kong\", \"Africa\", \"fatality\", \"mers\", \"east\", \"East\", \"Hong Kong\", \"control_measure\", \"Asia\", \"Middle_East\", \"Middle_east\", \"South Korea\", \"America\", \"South_Korea\", \"tb\", \"Iran\", \"control measures\", \"SARS_outbreak\", \"Middle East respiratory syndrome\", \"surveillance_system\", \"traveler\", \"communicable\", \"case_fatality\", \"Republic\", \"South_Africa\", \"South Africa\", \"SARS\", \"2003\", \"covid-19_epidemic\", \"Korea\", \"province\", \"transmission\", \"acute respiratory syndrome\", \"outbreak\", \"surveillance\", \"respiratory_Syndrome\", \"country\", \"United\", \"United_States\", \"States\", \"infectious disease\", \"infectious_disease\", \"respiratory_syndrome\", \"China\", \"United States\", \"epidemic\", \"severe_acute\", \"sars\", \"case\", \"death\", \"disease\", \"contact\", \"syndrome\", \"infectious\", \"spread\", \"control\", \"infection\", \"population\", \"severe\", \"risk\", \"report\", \"public\", \"respiratory\", \"acute\", \"health\", \"rate\", \"influenza\", \"number\", \"virus\", \"mhv\", \"protease\", \"app\", \"hcv\", \"de\", \"subgenomic\", \"host_cell\", \"viral_replication\", \"er\", \"viral replication\", \"h5n1\", \"antiviral_activity\", \"jhm\", \"virus_mhv\", \"proteinase\", \"virus_replication\", \"a59\", \"coronavirus_sars-cov\", \"replicase\", \"antiviral activity\", \"endoplasmic\", \"la\", \"reticulum\", \"subgenomic_mrna\", \"endoplasmic_reticulum\", \"antiviral_drug\", \"en\", \"hbv\", \"sirna\", \"proteolytic\", \"cleavage\", \"replication\", \"fusion\", \"fusion_protein\", \"binding\", \"virion\", \"residue\", \"bind\", \"antiviral\", \"mutant\", \"protein\", \"membrane\", \"rna\", \"domain\", \"entry\", \"synthesis\", \"envelope\", \"inhibitor\", \"sars-cov\", \"translation\", \"hepatitis\", \"mrna\", \"inhibit\", \"structure\", \"viral\", \"virus\", \"glycoprotein\", \"activity\", \"host\", \"cell\", \"interaction\", \"coronavirus\", \"site\", \"receptor\", \"infection\", \"target\", \"cellular\", \"study\", \"rsv\", \"cough\", \"respiratory_tract\", \"rhinovirus\", \"syncytial\", \"respiratory viruses\", \"respiratory_syncytial\", \"pneumoniae\", \"nasopharyngeal\", \"respiratory tract\", \"respiratory syncytial virus\", \"copd\", \"hmpv\", \"tract_infection\", \"parainfluenza\", \"upper_respiratory\", \"low_respiratory\", \"Respiratory\", \"Saudi\", \"hrv\", \"metapneumovirus\", \"Arabia\", \"throat\", \"Saudi Arabia\", \"community-acquired\", \"bronchiolitis\", \"Saudi_Arabia\", \"sputum\", \"parainfluenza_virus\", \"respiratory_failure\", \"acute respiratory distress syndrome\", \"acute_respiratory\", \"exacerbation\", \"child\", \"pneumonia\", \"asthma\", \"respiratory\", \"ards\", \"swab\", \"tract\", \"nasal\", \"mers-cov\", \"influenza\", \"illness\", \"symptom\", \"infection\", \"diagnosis\", \"specimen\", \"acute\", \"positive\", \"viral\", \"virus\", \"clinical\", \"test\", \"common\", \"patient\", \"detect\", \"cause\", \"severe\", \"case\", \"detection\", \"adult\", \"sample\", \"study\", \"pathogen\", \"result\", \"syndrome\", \"human\", \"disease\", \"network\", \"dataset\", \"tracing\", \"contact_tracing\", \"machine_learning\", \"sir\", \"mathematical\", \"spatial\", \"Twitter\", \"time_series\", \"representation\", \"assumption\", \"sentiment\", \"publicly_available\", \"bayesian\", \"modelling\", \"equilibrium\", \"political\", \"price\", \"classifier\", \"Networks\", \"benchmark\", \"Monte\", \"artificial_intelligence\", \"social_network\", \"ecosystem\", \"house\", \"model_predict\", \"genetic_diversity\", \"hierarchical\", \"$\", \"simulation\", \"prediction\", \"paper_present\", \"estimation\", \"machine\", \"dynamic\", \"numerical\", \"algorithm\", \"modeling\", \"model\", \"learning\", \"publicly\", \"propose\", \"reproduction\", \"framework\", \"datum\", \"scenario\", \"paper\", \"social\", \"parameter\", \"number\", \"predict\", \"estimate\", \"base\", \"individual\", \"different\", \"analysis\", \"time\", \"epidemic\", \"policy\", \"state\", \"information\", \"population\", \"approach\", \"result\", \"method\", \"study\", \"find\", \"change\", \"effect\", \"rate\", \"system\", \"systematic\", \"student\", \"mental\", \"systematic_review\", \"systematic review\", \"ai\", \"anxiety\", \"perception\", \"mental_health\", \"perceive\", \"attitude\", \"respondent\", \"pubmed\", \"star\", \"cognitive\", \"notification\", \"journal\", \"mental health\", \"emotion\", \"Systematic\", \"coinfection\", \"Cochrane\", \"rct\", \"emotional\", \"medline\", \"cxr\", \"physical_activity\", \"3c-like\", \"citation\", \"reviewer\", \"meta-analysis\", \"psychological\", \"depression\", \"item\", \"search\", \"publication\", \"questionnaire\", \"sleep\", \"quality\", \"online\", \"participant\", \"web\", \"survey\", \"education\", \"training\", \"literature\", \"practice\", \"review\", \"intervention\", \"study\", \"conduct\", \"publish\", \"article\", \"evidence\", \"database\", \"health\", \"include\", \"research\", \"trial\", \"report\", \"datum\", \"knowledge\", \"result\", \"assess\", \"risk\", \"method\", \"identify\", \"care\", \"use\", \"outcome\", \"conclusion\", \"ace2\", \"macrophage\", \"signaling\", \"innate\", \"cns\", \"nervous\", \"central_nervous\", \"demyelination\", \"epithelium\", \"endothelial\", \"sclerosis\", \"central nervous system\", \"innate_immune\", \"multiple_sclerosis\", \"chemokine\", \"angiotensin\", \"autoimmune\", \"inflammatory_response\", \"angiotensin-converting\", \"signaling_pathway\", \"dc\", \"multiple sclerosis\", \"angiotensin-converting_enzyme\", \"rupture\", \"astrocyte\", \"neuron\", \"demyelinating\", \"hev\", \"inflammatory response\", \"antioxidant\", \"cytokine\", \"rat\", \"epithelial\", \"activation\", \"inflammation\", \"encephalomyelitis\", \"inflammatory\", \"alveolar\", \"expression\", \"brain\", \"t-cell\", \"pathway\", \"immune\", \"activate\", \"lung\", \"pathogenesis\", \"receptor\", \"mouse\", \"response\", \"tissue\", \"cell\", \"induce\", \"mechanism\", \"role\", \"infection\", \"gene\", \"interferon\", \"function\", \"effect\", \"increase\", \"disease\", \"level\", \"study\", \"factor\", \"suggest\", \"cause\", \"system\", \"type\", \"result\", \"surgery\", \"laparoscopic\", \"postoperative\", \"resection\", \"hernia\", \"suture\", \"operative\", \"gastric\", \"surgeon\", \"endoscopic\", \"intraoperative\", \"incision\", \"port\", \"minimally\", \"robotic\", \"anastomosis\", \"minimally_invasive\", \"gastrectomy\", \"perioperative\", \"preoperative\", \"cholecystectomy\", \"colorectal\", \"bariatric\", \"operative time\", \"laparoscopy\", \"postoperative_complication\", \"single-incision\", \"hysterectomy\", \"single-port\", \"inguinal\", \"surgical\", \"abdominal\", \"mesh\", \"repair\", \"pain\", \"patient_undergo\", \"operation\", \"undergo\", \"procedure\", \"complication\", \"technique\", \"perform\", \"patient\", \"invasive\", \"tumor\", \"cancer\", \"open\", \"time\", \"mean\", \"case\", \"result\", \"group\", \"method\", \"approach\", \"conclusion\", \"experience\", \"study\", \"forecast\", \"equation\", \"coil\", \"l\", \"optical\", \"hole\", \"motion\", \"soft\", \"tidal\", \"Device\", \"tendon\", \"laser\", \"velocity\", \"tip\", \"angle\", \"tidal_volume\", \"h2o\", \"weaning\", \"park\", \"electrode\", \"thickness\", \"dressing\", \"burnout\", \"pc\", \"dioxide\", \"pm\", \"reproductive_number\", \"electrical\", \"bifurcation\", \"3D\", \"\\u00b0\", \"temperature\", \"filter\", \"liquid\", \"aerosol\", \"muscle\", \"air\", \"ventilator\", \"pressure\", \"water\", \"sensor\", \"device\", \"implant\", \"heat\", \"tube\", \"flow\", \"gas\", \"measurement\", \"volume\", \"thermal\", \"\\u00ae\", \"emission\", \"skin\", \"performance\", \"image\", \"droplet\", \"concentration\", \"ventilation\", \"method\", \"material\", \"surface\", \"tissue\", \"mask\", \"test\", \"min\", \"result\", \"evaluate\", \"fluid\", \"study\", \"measure\", \"compare\", \"technique\", \"use\", \"system\", \"high\", \"increase\", \"perform\", \"low\", \"time\", \"effect\", \"ct\", \"aneurysm\", \"artery\", \"stroke\", \"occlusion\", \"endovascular\", \"stent\", \"ablation\", \"ischemic\", \"embolization\", \"carotid\", \"opacity\", \"angiography\", \"thrombectomy\", \"consolidation\", \"chest_ct\", \"endovascular_treatment\", \"rfa\", \"recanalization\", \"infarction\", \"compute_tomography\", \"Stroke\", \"angiographic\", \"aortic\", \"acute_ischemic\", \"dialysis\", \"endovascular treatment\", \"hemodialysis\", \"pleural\", \"acute ischemic stroke\", \"venous\", \"intracranial\", \"hemorrhage\", \"chest\", \"vein\", \"tomography\", \"radiofrequency\", \"imaging\", \"catheter\", \"radiographic\", \"lesion\", \"cerebral\", \"follow-up\", \"image\", \"patient\", \"scan\", \"treat\", \"treatment\", \"vessel\", \"treated with\", \"x-ray\", \"case\", \"clinical\", \"outcome\", \"acute\", \"month\", \"complication\", \"report\", \"result\", \"present\", \"finding\", \"diagnosis\", \"therapy\", \"2\", \"diabete\", \"critically_ill\", \"crp\", \"oxygenation\", \"sepsis\", \"import\", \"critically ill\", \"electron microscopy\", \"diabetes\", \"\\u00b0_c\", \"lactate\", \"c-reactive\", \"c-reactive_protein\", \"phage\", \"cardiovascular_disease\", \"septic\", \"y\", \"blood_pressure\", \"nk\", \"diabetic\", \"heart_rate\", \"lung cancer\", 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\"population\", \"population\", \"population\", \"population\", \"population\", \"porcine\", \"porcine\", \"port\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"positive\", \"postoperative\", \"postoperative_complication\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"potential\", \"practice\", \"practice\", \"practice\", \"practice\", \"practice\", \"predict\", \"predict\", \"predict\", \"predict\", \"predict\", \"predict\", \"prediction\", \"prediction\", \"pregnancy\", \"pregnant\", \"pregnant women\", \"pregnant_woman\", \"preoperative\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"present\", \"pressure\", \"pressure\", \"pressure\", \"pressure\", \"pressure\", \"price\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"promise\", \"promise\", \"promise\", \"propofol\", \"propose\", \"propose\", \"propose\", \"propose\", \"propose\", \"propose\", \"propose\", \"propose\", \"propose\", \"protease\", \"protection\", \"protection\", \"protection\", \"protection\", \"protection\", \"protection\", \"protein\", \"protein\", \"protein\", \"protein\", \"protein\", \"protein-protein\", \"proteinase\", \"proteolytic\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide_overview\", \"province\", \"province\", \"province\", \"psychological\", \"psychological\", \"public\", \"public\", \"public\", \"public\", \"public_health\", \"public_health\", \"publication\", \"publication\", \"publicly\", \"publicly\", \"publicly_available\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"publish\", \"pubmed\", \"quality\", \"quality\", \"quality\", \"quality\", \"quality\", \"questionnaire\", \"questionnaire\", \"questionnaire\", \"radiofrequency\", \"radiofrequency\", \"radiographic\", \"radiographic\", \"rat\", \"rat\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"ratio\", \"ratio\", \"ratio\", \"ratio\", \"ratio\", \"ratio\", \"rct\", \"recanalization\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent\", \"recent_advance\", \"receptor\", \"receptor\", \"recombinant\", \"recombinant\", \"recombination\", \"region\", \"region\", \"region\", \"region\", \"region\", \"region\", \"region\", \"region\", \"regression\", \"regression\", \"renal\", \"renal\", \"renal\", \"repair\", \"repair\", \"replicase\", \"replication\", \"replication\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"representation\", \"reproduction\", \"reproduction\", \"reproductive_number\", \"repurposing\", \"research\", \"research\", \"research\", \"research\", \"resection\", \"residue\", \"residue\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respectively\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory\", \"respiratory syncytial virus\", \"respiratory tract\", \"respiratory viruses\", \"respiratory_Syndrome\", \"respiratory_Syndrome\", \"respiratory_failure\", \"respiratory_syncytial\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_syndrome\", \"respiratory_tract\", \"respondent\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"reticulum\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review_focus\", \"review_focus\", \"review_summarize\", \"review_summarize\", \"reviewer\", \"rfa\", \"rhinovirus\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"risk\", \"rna\", \"rna\", \"robotic\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"rotavirus\", \"rsv\", \"rupture\", \"s\", \"s\", \"s1\", \"s_protein\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sars\", \"sars\", \"sars\", \"sars-cov\", \"sars-cov\", \"sars-cov\", \"sars-cov-2\", \"sars-cov-2_infection\", \"saturation\", \"saturation\", \"scan\", \"scan\", \"scan\", \"scenario\", \"scenario\", \"scenario\", \"scientific\", \"scientific\", \"scientific\", \"sclerosis\", \"score\", \"score\", \"score\", \"score\", \"sd\", \"sd\", \"search\", \"search\", \"search\", \"search\", \"search\", \"secrete\", \"sensor\", \"sensor\", \"sensor\", \"sensor\", \"sensor\", \"sentiment\", \"sepsis\", \"septic\", \"sequence\", \"sequence\", \"serum\", \"serum\", \"serum\", \"serum\", \"service\", \"service\", \"service\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe\", \"severe_acute\", \"severe_acute\", \"severe_acute\", \"severe_acute\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"signaling\", \"signaling_pathway\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significant\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"significantly\", \"simulation\", \"simulation\", \"single-incision\", \"single-port\", \"sir\", \"sirna\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"skin\", \"skin\", \"skin\", \"skin\", \"skin\", \"sleep\", \"sleep\", \"small_molecule\", \"social\", \"social\", \"social\", \"social distancing\", \"social_distancing\", \"social_network\", \"soft\", \"spatial\", \"specie\", \"specie\", \"specie\", \"specie\", \"specimen\", \"specimen\", \"specimen\", \"specimen\", \"spike\", \"spike_protein\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"spread\", \"sputum\", \"staff\", \"staff\", \"star\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"statistically\", \"statistically\", \"statistically\", \"statistically\", \"statistically\", \"statistically significant\", \"statistically significant\", \"statistically_significant\", \"statistically_significant\", \"statistically_significant\", \"stem\", \"stem\", \"stem\", \"stem\", \"stem_cell\", \"stent\", \"strain\", \"strain\", \"strain\", \"strain\", \"strain\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"stroke\", \"structure\", \"structure\", \"structure\", \"structure\", \"student\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"subgenomic\", \"subgenomic_mrna\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"suggest\", \"surface\", \"surface\", \"surface\", \"surface\", \"surface\", \"surgeon\", \"surgery\", \"surgical\", \"surgical\", \"surgical\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance\", \"surveillance_system\", \"survey\", \"survey\", \"survey\", \"survey\", \"suture\", \"swab\", \"swab\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"symptom\", \"syncytial\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"syndrome\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"systematic\", \"systematic review\", \"systematic_review\", \"t\", \"t\", \"t-cell\", \"t-cell\", \"t_cell\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"tb\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"tendon\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"tgev\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"thermal\", \"thermal\", \"thickness\", \"throat\", \"thrombectomy\", \"tidal\", \"tidal_volume\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time_series\", \"tip\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"titer\", \"titer\", \"tomography\", \"tomography\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"tracing\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract_infection\", \"training\", \"training\", \"training\", \"training\", \"training\", \"translation\", \"translation\", \"transmissible_gastroenteritis\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"traveler\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treated with\", \"treated with\", \"treated with\", \"treated with\", \"treated with\", \"treated with\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"tube\", \"tube\", \"tumor\", \"tumor\", \"tumor\", \"tumor\", \"tumor\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"undergo\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"upper_respiratory\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"vaccinate\", \"vaccinate\", \"vaccinate\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vector\", \"vector\", \"vector\", \"vector\", \"vector\", \"vein\", \"vein\", \"vein\", \"velocity\", \"venous\", \"venous\", \"venous\", \"ventilation\", \"ventilation\", \"ventilation\", \"ventilator\", \"ventilator\", \"ventilator\", \"vessel\", \"vessel\", \"vessel\", \"vessel\", \"veterinarian\", \"veterinary\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral replication\", \"viral_replication\", \"virion\", \"virion\", \"virtual_screening\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus-like\", \"virus-like_particle\", \"virus_mhv\", \"virus_replication\", \"vitro\", \"vitro\", \"vitro\", \"vivo\", \"vivo\", \"vivo\", \"vivo\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"vs\", \"vs\", \"vs.\", \"vs.\", \"water\", \"water\", \"weaning\", \"web\", \"web\", \"wild-type\", \"worker\", \"worker\", \"worker\", \"x-ray\", \"x-ray\", \"x-ray\", \"x-ray\", \"y\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"\\u00ae\", \"\\u00ae\", \"\\u00ae\", \"\\u00ae\", \"\\u00b0\", \"\\u00b0\", \"\\u00b0_c\", \"\\u00b1\", \"\\u00b1\", \"\\u00b1\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 4, 2, 14, 6, 12, 1, 10, 5, 9, 8, 11, 7, 15, 13]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el469811185027747362000754445\", ldavis_el469811185027747362000754445_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el469811185027747362000754445\", ldavis_el469811185027747362000754445_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el469811185027747362000754445\", ldavis_el469811185027747362000754445_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot topics (NOTE: the IDs here do not match those from the model)\n",
    "data =  pyLDAvis.gensim.prepare(model, corpus, dictionary)\n",
    "pyLDAvis.display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics over time\n",
    "\n",
    "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n",
    "publication_years = df_local.publication_year.tolist()\n",
    "dois = df_local.doi.tolist()\n",
    "dims = df_local.dimensions_id.tolist()\n",
    "topics = np.zeros((len(docs),params['num_topics']))\n",
    "\n",
    "for n,doc_topics in enumerate(model.get_document_topics(corpus)):\n",
    "    for t in doc_topics:\n",
    "        topics[n][t[0]] = t[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.12924378, 0.19328743, 0.        , 0.        , 0.        ,\n",
       "       0.17505603, 0.37349966, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.12393145])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.12924466),\n",
       " (1, 0.19328845),\n",
       " (5, 0.17505251),\n",
       " (6, 0.37350014),\n",
       " (14, 0.123932675)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_document_topics(corpus[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics = pd.DataFrame(topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics[\"year\"] = publication_years\n",
    "df_topics[\"doi\"] = dois\n",
    "df_topics[\"dim_id\"] = dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_which_year = 1980\n",
    "\n",
    "grouped = df_topics.groupby('year')\n",
    "#grouped = df_topics[df_topics.isin({'doi': clusters[3]})[\"doi\"]].groupby('year')\n",
    "df_grouped = grouped.aggregate(np.mean)\n",
    "df_grouped = df_grouped[df_grouped.index >= from_which_year]\n",
    "#df_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "plt.pcolor(df_grouped.to_numpy(), norm=None, cmap='Blues')\n",
    "plt.yticks(np.arange(df_grouped.to_numpy().shape[0])+0.5, [int(x) for x in df_grouped.index.values], fontsize=15)\n",
    "plt.xticks(np.arange(df_grouped.to_numpy().shape[1])+0.5, [\"Topic #\"+str(n) for n in range(model.num_topics)], rotation = 45, fontsize=15)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.savefig(\"figures/topic_model_all_time_15.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Macrotopic analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate topic distances\n",
    "from scipy.spatial.distance import cosine, euclidean, jensenshannon\n",
    "\n",
    "topics_distances = np.zeros((params['num_topics'],params['num_topics']))\n",
    "topics_terms = model.state.get_lambda()\n",
    "\n",
    "for k in range(params['num_topics']):\n",
    "    for m in range(params['num_topics'])[k+1:]:\n",
    "        d = jensenshannon(topics_terms[k,:],topics_terms[m,:])\n",
    "        topics_distances[k][m] = d\n",
    "        topics_distances[m][k] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From: https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py\n",
    "\n",
    "from scipy.cluster.hierarchy import dendrogram\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "def plot_dendrogram(model, **kwargs):\n",
    "    # Create linkage matrix and then plot the dendrogram\n",
    "\n",
    "    # create the counts of samples under each node\n",
    "    counts = np.zeros(model.children_.shape[0])\n",
    "    n_samples = len(model.labels_)\n",
    "    for i, merge in enumerate(model.children_):\n",
    "        current_count = 0\n",
    "        for child_idx in merge:\n",
    "            if child_idx < n_samples:\n",
    "                current_count += 1  # leaf node\n",
    "            else:\n",
    "                current_count += counts[child_idx - n_samples]\n",
    "        counts[i] = current_count\n",
    "\n",
    "    linkage_matrix = np.column_stack([model.children_, model.distances_,\n",
    "                                      counts]).astype(float)\n",
    "\n",
    "    # Plot the corresponding dendrogram\n",
    "    dendrogram(linkage_matrix, **kwargs)\n",
    "\n",
    "# setting distance_threshold=0 ensures we compute the full tree.\n",
    "model_hc = AgglomerativeClustering(distance_threshold=0, n_clusters=None)\n",
    "\n",
    "model_hc = model_hc.fit(topics_distances)\n",
    "#plt.title('Hierarchical Clustering Dendrogram', fontsize=15)\n",
    "# plot the top three levels of the dendrogram\n",
    "plot_dendrogram(model_hc, truncate_mode='level', p=10)\n",
    "plt.xlabel(\"Topic #\", fontsize=15)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.savefig(\"figures/tm_dendrogram.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define macro topics\n",
    "\n",
    "topic_labels = {\n",
    "    \"Clinical medicine\": [2,6,7,14],\n",
    "    \"Coronavirus outbreaks\": [1,5],\n",
    "    \"Epidemics\": [9,10],\n",
    "    \"Immunology\": [8,12],\n",
    "    \"Molecular biology\": [3,11],\n",
    "    \"Public health\": [0,4,13]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get number of papers per macro topic over time\n",
    "\n",
    "is_selected_topic = {t:[0 for _ in range(len(docs))] for t in topic_labels.keys()}\n",
    "topic_to_label = dict()\n",
    "for k,v in topic_labels.items():\n",
    "    for vv in v:\n",
    "        topic_to_label[vv] = k\n",
    "for d in range(len(docs)):\n",
    "    d_topics = model.get_document_topics(corpus[d])\n",
    "    for x in d_topics:\n",
    "        is_selected_topic[topic_to_label[x[0]]][d] += x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot trends\n",
    "\n",
    "df_topic_intensity = pd.DataFrame.from_dict(is_selected_topic)\n",
    "df_topic_intensity[\"year\"] = publication_years\n",
    "df_topic_intensity[\"doi\"] = dois\n",
    "df_topic_intensity[\"dim_id\"] = dims "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_which_year = 1980\n",
    "\n",
    "grouped_ti = df_topic_intensity.groupby('year')\n",
    "df_grouped_ti = grouped_ti.aggregate(np.sum)\n",
    "df_grouped_ti = df_grouped_ti[df_grouped_ti.index >= from_which_year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "for k in topic_labels.keys():\n",
    "    plt.plot(df_grouped_ti[k].values, label=\"%s\"%k, linewidth=3)\n",
    "plt.xticks(np.arange(df_grouped_ti.shape[0]), [int(x) for x in df_grouped_ti.index.values], rotation=45)\n",
    "plt.legend(loc=\"upper left\",fontsize=15)\n",
    "plt.ylabel(\"Topic intensity\",fontsize=18)\n",
    "plt.xlabel(\"Publication year\",fontsize=18)\n",
    "plt.tight_layout()  # fixes margins\n",
    "#plt.savefig(\"figures/topic_model_trends_mean.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Network cluster to topic analysis - 1\n",
    "\n",
    "Use the extended citation clustering and characterize with topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "clustering_solution_df = pd.read_csv(\"datasets_input/paper_CORD19_supporting_materials/clustering_01072020.csv\", sep=\";\",header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104508, 3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_solution_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>id</th>\n",
       "      <th>clusters_0</th>\n",
       "      <th>clusters_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pub.1027207099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pub.1027223267</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pub.1027236442</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pub.1027256915</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pub.1027274375</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  clusters_0  clusters_1\n",
       "0  pub.1027207099           0           0\n",
       "1  pub.1027223267          12          10\n",
       "2  pub.1027236442          33          25\n",
       "3  pub.1027256915          30          23\n",
       "4  pub.1027274375          23          19"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering_solution_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusters_0 contains smaller clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9fc940d0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(clustering_solution_df.clusters_0.values, kde=False, norm_hist=False, bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusters_1 contains larger clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba089cb10>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(clustering_solution_df.clusters_1.values, kde=False, norm_hist=False, bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "which_clustering = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sub = clustering_solution_df[clustering_solution_df[\"clusters_%d\"%which_clustering].isin(clustering_solution_df[\"clusters_%d\"%which_clustering].value_counts()[:10].index.tolist())]\n",
    "b = sns.countplot(y=\"clusters_%d\"%which_clustering, data=df_sub, order=df_sub[\"clusters_%d\"%which_clustering].value_counts().index, color='lightblue')\n",
    "#b.axes.set_title(\"Title\",fontsize=50)\n",
    "b.set_xlabel(\"Size\",fontsize=15)\n",
    "b.set_ylabel(\"Cluster\",fontsize=15)\n",
    "b.tick_params(labelsize=12)\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"figures/clustering_size_%d.pdf\"%which_clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# characterize a cluster by Macro topics\n",
    "\n",
    "cluster_topic_join_df = clustering_solution_df.merge(df_topic_intensity, how=\"left\", left_on=\"id\", right_on=\"dim_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "which_cluster = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sub = cluster_topic_join_df[cluster_topic_join_df[\"clusters_%d\"%which_clustering] == which_cluster][[mt for mt in topic_labels.keys()]].mean()\n",
    "b = sns.barplot(y=df_sub.index,x=df_sub.values)\n",
    "#b.axes.set_title(\"Title\",fontsize=50)\n",
    "b.set_xlabel(\"Proportion\",fontsize=15)\n",
    "b.set_ylabel(\"General topic\",fontsize=15)\n",
    "#b.set_title(\"Clustering\")\n",
    "b.tick_params(labelsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figures/clustering_size_%d_cluster%d.pdf\"%(which_clustering,which_cluster))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Number of topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "\n",
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics.\n",
    "    From: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    params = {'passes': 3, 'random_state': seed}\n",
    "    for num_topics in range(start, limit, step):\n",
    "        m = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary,# workers=6,\n",
    "                passes=params['passes'], random_state=params['random_state'])\n",
    "        model_list.append(m)\n",
    "        coherencemodel = CoherenceModel(model=m, texts=docs, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can take a long time to run\n",
    "limit=50; start=5; step=5;\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=dictionary, corpus=corpus, texts=docs, start=start, limit=limit, step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show graph\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Journal to topics\n",
    "\n",
    "Using the Author topic model in gensim to map topics to journals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, we select data for the top journals\n",
    "\n",
    "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n",
    "journals = df_local.journal.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "how_many = 20\n",
    "\n",
    "journal_names = df_local.journal.value_counts().index.to_list()\n",
    "journal2doc = {j:[] for j in list(set(journal_names))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n,j in enumerate(journal_names):\n",
    "    journal2doc[j].append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 57 s, sys: 550 ms, total: 57.6 s\n",
      "Wall time: 40.8 s\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import AuthorTopicModel\n",
    "\n",
    "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n",
    "%time model_j = AuthorTopicModel(corpus=corpus, num_topics=params['num_topics'], id2word=dictionary.id2token, \\\n",
    "                author2doc=journal2doc, chunksize=2000, passes=params['passes'], eval_every=5, \\\n",
    "                iterations=100, random_state=params['random_state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.013*\"disease\" + 0.007*\"health\" + 0.005*\"model\" + 0.005*\"patient\" + 0.005*\"study\" + 0.004*\"datum\" + 0.004*\"risk\" + 0.004*\"SARS\" + 0.004*\"protein\" + 0.004*\"case\"'),\n",
       " (1,\n",
       "  '0.040*\"cell\" + 0.022*\"virus\" + 0.009*\"mouse\" + 0.009*\"infection\" + 0.008*\"viral\" + 0.006*\"abstract\" + 0.006*\"culture\" + 0.006*\"response\" + 0.005*\"rat\" + 0.005*\"t\"'),\n",
       " (2,\n",
       "  '0.019*\"virus\" + 0.018*\"cell\" + 0.018*\"rna\" + 0.014*\"protein\" + 0.007*\"abstract\" + 0.007*\"viral\" + 0.006*\"mrna\" + 0.005*\"sequence\" + 0.005*\"coronavirus\" + 0.005*\"mouse\"'),\n",
       " (3,\n",
       "  '0.010*\"fipv\" + 0.009*\"feline\" + 0.009*\"study\" + 0.008*\"volume\" + 0.006*\"infectious\" + 0.006*\"human\" + 0.005*\"content\" + 0.005*\"index\" + 0.005*\"group\" + 0.005*\"chapter\"'),\n",
       " (4,\n",
       "  '0.011*\"antibody\" + 0.009*\"bovine\" + 0.009*\"disease\" + 0.007*\"canine\" + 0.007*\"monoclonal\" + 0.007*\"dog\" + 0.007*\"infection\" + 0.006*\"use\" + 0.005*\"monoclonal_antibody\" + 0.005*\"bcv\"'),\n",
       " (5,\n",
       "  '0.009*\"strain\" + 0.007*\"sequence\" + 0.006*\"p\" + 0.006*\"study\" + 0.005*\"China\" + 0.005*\"identify\" + 0.005*\"coronavirus\" + 0.005*\"=\" + 0.005*\"analysis\" + 0.004*\"high\"'),\n",
       " (6,\n",
       "  '0.020*\"strain\" + 0.011*\"virus\" + 0.011*\"tgev\" + 0.011*\"gene\" + 0.009*\"isolate\" + 0.008*\"protein\" + 0.008*\"sequence\" + 0.007*\"acid\" + 0.007*\"amino\" + 0.007*\"porcine\"'),\n",
       " (7,\n",
       "  '0.029*\"virus\" + 0.009*\"detection\" + 0.008*\"viral\" + 0.007*\"assay\" + 0.007*\"sample\" + 0.006*\"human\" + 0.006*\"detect\" + 0.005*\"study\" + 0.005*\"method\" + 0.005*\"pcr\"'),\n",
       " (8,\n",
       "  '0.025*\"sequence\" + 0.019*\"gene\" + 0.018*\"rna\" + 0.010*\"probe\" + 0.008*\"region\" + 0.008*\"genome\" + 0.006*\"protein\" + 0.006*\"nucleotide\" + 0.005*\"mrna\" + 0.005*\"analysis\"'),\n",
       " (9,\n",
       "  '0.021*\"antibody\" + 0.020*\"virus\" + 0.013*\"infection\" + 0.011*\"serum\" + 0.010*\"mouse\" + 0.010*\"elisa\" + 0.007*\"day\" + 0.007*\"test\" + 0.006*\"coronavirus\" + 0.005*\"antigen\"'),\n",
       " (10,\n",
       "  '0.023*\"patient\" + 0.011*\"respiratory\" + 0.008*\"acute\" + 0.007*\"infection\" + 0.007*\"clinical\" + 0.006*\"lung\" + 0.005*\"pneumonia\" + 0.005*\"treatment\" + 0.005*\"severe\" + 0.004*\"study\"'),\n",
       " (11,\n",
       "  '0.018*\"infection\" + 0.012*\"respiratory\" + 0.011*\"calf\" + 0.008*\"virus\" + 0.007*\"disease\" + 0.006*\"clinical\" + 0.006*\"study\" + 0.006*\"viral\" + 0.006*\"rotavirus\" + 0.006*\"influenza\"'),\n",
       " (12,\n",
       "  '0.014*\"activity\" + 0.012*\"study\" + 0.008*\"compound\" + 0.006*\"extract\" + 0.005*\"antiviral\" + 0.005*\"protease\" + 0.005*\"inhibitor\" + 0.004*\"inhibition\" + 0.004*\"structure\" + 0.004*\"active\"'),\n",
       " (13,\n",
       "  '0.032*\"protein\" + 0.009*\"membrane\" + 0.008*\"virus\" + 0.007*\"vaccine\" + 0.007*\"recombinant\" + 0.007*\"glycoprotein\" + 0.006*\"chapter\" + 0.005*\"antibody\" + 0.005*\"cell\" + 0.004*\"expression\"'),\n",
       " (14,\n",
       "  '0.028*\"virus\" + 0.007*\"ibv\" + 0.006*\"strain\" + 0.006*\"group\" + 0.006*\"infectious\" + 0.005*\"study\" + 0.005*\"isolate\" + 0.005*\"supplementary\" + 0.005*\"pig\" + 0.005*\"influenza\"')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_j.show_topics(num_words=10, num_topics=params['num_topics'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.26377487722112225), (5, 0.7256559932323484)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_j[\"PLoS ONE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics over time\n",
    "\n",
    "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n",
    "journal_names_reduced = [j for j in reversed(df_local.journal.value_counts()[:how_many].index.to_list())]\n",
    "topics = np.zeros((len(journal_names_reduced),model_j.num_topics))\n",
    "\n",
    "for n,j in enumerate(journal_names_reduced):\n",
    "    for t in model_j[j]:\n",
    "        topics[n][t[0]] = t[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.pcolor(topics, norm=None, cmap='Blues')\n",
    "plt.yticks(np.arange(how_many)+0.5, journal_names_reduced)\n",
    "plt.xticks(np.arange(model_j.num_topics)+0.5, [\"Topic #\"+str(n) for n in range(model_j.num_topics)], rotation = 45)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.savefig(\"figures/topic_model_top_journals.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: match topics here with topics above, or directly work with the topics above for all journals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlated topic model\n",
    "\n",
    "With tomotopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\tLog-likelihood: -7.952682493160052\n",
      "Iteration: 25\tLog-likelihood: -7.580369381135443\n",
      "Iteration: 50\tLog-likelihood: -7.18383278109214\n",
      "Iteration: 75\tLog-likelihood: -6.8724311307285975\n",
      "Iteration: 100\tLog-likelihood: -6.692760464720625\n",
      "Iteration: 125\tLog-likelihood: -6.606490762439231\n",
      "Iteration: 150\tLog-likelihood: -6.559329422748022\n",
      "Iteration: 175\tLog-likelihood: -6.525913741406331\n",
      "Iteration: 200\tLog-likelihood: -6.511598970528629\n",
      "Iteration: 225\tLog-likelihood: -6.505459009571211\n",
      "Iteration: 250\tLog-likelihood: -6.502068255244552\n",
      "Iteration: 275\tLog-likelihood: -6.496780764407905\n"
     ]
    }
   ],
   "source": [
    "import tomotopy as tp\n",
    "\n",
    "params = {'num_topics': 15,'passes': 10, 'random_state': seed}\n",
    "mdl = tp.CTModel(k=params['num_topics'],min_cf=min_wordcount,min_df=2)\n",
    "for doc in docs:\n",
    "    mdl.add_doc(doc)\n",
    "\n",
    "for i in range(0, 300, 25):\n",
    "    mdl.train(25)\n",
    "    print('Iteration: {}\\tLog-likelihood: {}'.format(i, mdl.ll_per_word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 words of topic #0\n",
      "[('virus', 0.15144172310829163), ('infection', 0.08410266786813736), ('viral', 0.06533856689929962), ('human', 0.049547962844371796), ('host', 0.02498638443648815), ('role', 0.022717703133821487), ('type', 0.019872356206178665)]\n",
      "Top 10 words of topic #1\n",
      "[('disease', 0.04943253844976425), ('model', 0.026093825697898865), ('system', 0.024129800498485565), ('factor', 0.019617542624473572), ('development', 0.01689882203936577), ('change', 0.01245930790901184), ('important', 0.011822817847132683)]\n",
      "Top 10 words of topic #2\n",
      "[('influenza', 0.04502635821700096), ('day', 0.021252159029245377), ('child', 0.02024831809103489), ('p', 0.015896260738372803), ('pandemic', 0.013804404996335506), ('pneumonia', 0.013598648831248283), ('age', 0.013355482369661331)]\n",
      "Top 10 words of topic #3\n",
      "[('strain', 0.024857645854353905), ('effect', 0.022751107811927795), ('increase', 0.02177148498594761), ('level', 0.021649908274412155), ('suggest', 0.02157275564968586), ('different', 0.020464543253183365), ('demonstrate', 0.016295887529850006)]\n",
      "Top 10 words of topic #4\n",
      "[('cell', 0.11398348212242126), ('mouse', 0.03412766009569168), ('expression', 0.029636189341545105), ('receptor', 0.01797463186085224), ('express', 0.013930563814938068), ('recombinant', 0.011429169215261936), ('peptide', 0.011347822844982147)]\n",
      "Top 10 words of topic #5\n",
      "[('include', 0.027735957875847816), ('pathogen', 0.02240864373743534), ('provide', 0.021822474896907806), ('method', 0.020167546346783638), ('datum', 0.02010633982717991), ('potential', 0.019039934501051903), ('develop', 0.01900462433695793)]\n",
      "Top 10 words of topic #6\n",
      "[('outbreak', 0.023721741512417793), ('case', 0.01785966381430626), ('transmission', 0.01758626103401184), ('rate', 0.017519250512123108), ('epidemic', 0.01723780669271946), ('population', 0.016224607825279236), ('year', 0.012640886008739471)]\n",
      "Top 10 words of topic #7\n",
      "[('patient', 0.06932062655687332), ('=', 0.021272484213113785), ('case', 0.01882254146039486), ('China', 0.01798456348478794), ('care', 0.014087202958762646), ('risk', 0.014038448221981525), ('hospital', 0.012971930205821991)]\n",
      "Top 10 words of topic #8\n",
      "[('health', 0.03204141557216644), ('review', 0.01774297095835209), ('disease', 0.016567977145314217), ('public', 0.015138133428990841), ('approach', 0.01414287555962801), ('emerge', 0.013692193664610386), ('research', 0.01278009731322527)]\n",
      "Top 10 words of topic #9\n",
      "[('rna', 0.04992866888642311), ('replication', 0.02843376062810421), ('de', 0.01850462332367897), ('membrane', 0.017154159024357796), ('mrna', 0.01145005039870739), ('la', 0.009639783762395382), ('ibv', 0.008643011562526226)]\n",
      "Top 10 words of topic #10\n",
      "[('vaccine', 0.03438764810562134), ('antibody', 0.03125428780913353), ('immune', 0.025321871042251587), ('response', 0.02489408105611801), ('antiviral', 0.021026959642767906), ('induce', 0.019006991758942604), ('t', 0.014950057491660118)]\n",
      "Top 10 words of topic #11\n",
      "[('respiratory', 0.06812440603971481), ('acute', 0.029167180880904198), ('sample', 0.02680761180818081), ('detect', 0.02650444209575653), ('severe', 0.026381459087133408), ('detection', 0.02252606302499771), ('syndrome', 0.01964309625327587)]\n",
      "Top 10 words of topic #12\n",
      "[('study', 0.04021310433745384), ('high', 0.032309725880622864), ('cause', 0.025514258071780205), ('result', 0.025407861918210983), ('group', 0.022798845544457436), ('clinical', 0.02183203026652336), ('associate', 0.020879091694951057)]\n",
      "Top 10 words of topic #13\n",
      "[('protein', 0.08548136800527573), ('gene', 0.03870169818401337), ('sequence', 0.028373470529913902), ('genome', 0.018009956926107407), ('structure', 0.016525188460946083), ('domain', 0.013685944490134716), ('sars-cov', 0.013197355903685093)]\n",
      "Top 10 words of topic #14\n",
      "[('analysis', 0.029071737080812454), ('identify', 0.02810748480260372), ('study', 0.026773754507303238), ('result', 0.023038409650325775), ('find', 0.019809970632195473), ('coronavirus', 0.019659025594592094), ('show', 0.018780386075377464)]\n"
     ]
    }
   ],
   "source": [
    "for k in range(mdl.k):\n",
    "    print('Top 10 words of topic #{}'.format(k))\n",
    "    print(mdl.get_topic_words(k, top_n=7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics over time\n",
    "\n",
    "df_local = df_cord19[pd.notnull(df_cord19[\"title_abstract\"])]\n",
    "publication_years = df_local.publication_year.tolist()\n",
    "dois = df_local.doi.tolist()\n",
    "topics_corr = np.zeros((len(docs),params['num_topics']))\n",
    "\n",
    "for n,doc in enumerate(mdl.docs):\n",
    "    for m,t in enumerate(doc.get_topic_dist()):\n",
    "        topics_corr[n][m] = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics_corr = pd.DataFrame(topics_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics_corr[\"year\"] = publication_years\n",
    "df_topics_corr[\"doi\"] = dois"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_which_year = 1980\n",
    "\n",
    "grouped_corr = df_topics_corr.groupby('year')\n",
    "df_grouped_corr = grouped_corr.aggregate(np.mean)\n",
    "df_grouped_corr = df_grouped_corr[df_grouped_corr.index >= from_which_year]\n",
    "#df_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kyEcef+TIEa1Zs2Zc++7du7V27VqlpaUpLy9Pvb29H9kPhQUAAAAAAAyG4K8AMW6b/FSI4eFhbd26VRs2bFBra6sKCgqUl5enU6dOjTvW6/WqtrZW99xzj0ZGRsbsq66u1r//+7+rurpaLS0tWrp0qR588MGPvDaFBQAAAAAA5rjDhw9rZGREd999tyIiInTzzTcrPj5e9fX1446tqqrSs88+q8LCwnH7nnnmGd1///365Cc/KZPJpLKyMv3TP/3TR17b780bQ5kpzChz+PytjUQY58aNOi6UcY7ckORCeYIdQIAYFsDzGRkx/3OUpKGzC+NVe9bjDXYIs+7dUyP+D5oHDFoY780Rz/x/b4bPkXXbL1Rk2MLI0zv/P2YlLYzv7uELIMdQ9cc//nFc26JFi7Ro0SLf4+PHj2vlypVjjomLi9OxY8fGnXv77berpKREr7/++pj206dPq6OjQ/39/br11lv1pz/9SXa7XQ899NBHxjenCwsAAAAAAMyIEF4V4s477xy3a9u2bSouLvY9Pn36tCIjI8ccY7FYNDg4OO7cSy+99LyX6+/vlyTV1tbqBz/4gT72sY/pa1/7mu6//349/fTTE4ZJYQEAAAAAgBBWXV2tpUuXjmk7d7SCJEVFRenMmTNj2gYHBxUVFTXp65hMJknSvffeq8suu0yStH37dt1www06deqULrroovOetzDGXwEAAAAAMEctXbpUn/jEJ8Zsf1tYWLlypTo7O8e0dXR0KD4+ftLXWbJkiRYvXuwbuSBJo6Ojfs+jsAAAAAAAQNBXgJhgm6RrrrlGXq9Xe/fu1cjIiF588UX99re/ldPpnNKPISsrS7t371ZPT49Onz6tf/mXf9H1118/4WgFaZKFhebmZmVlZSk1NVVOp1P79++X9MH8C5fLpbS0NK1du1Z1dXXjzvV4PCoqKtLevXt9bbt371ZKSsqYbdWqVfr6178+pYQBAAAAAMAH0xh++MMfqqGhQXa7Xbt379aTTz6pJUuWaPfu3br55psn1c/27du1YcMGbd68Wdddd528Xq927tz5kef4vcdCb2+viouLtWvXLq1fv15Hjx7Vli1btGzZMh04cEBGo1FNTU06ceKE8vLytHz5ctntdklSd3e3ysvLdejQIa1evdrXZ0FBgQoKCnyPDx06pK997WsqKiqaVKIAAAAAAGCsK6+8Us8+++y49r/9G/xD11xzjdra2sa0RURE6L777tN999036ev6HbHQ09OjjIwMOZ1OGY1GJSUlyW63q6WlRQ0NDSopKZHFYlFCQoKys7NVU1MjSXK73dq0aZNWrVqllJSUCft///339dWvflWPPPLIuJtRAAAAAAAQGIa/rgwRKtscWUrZ74gFm80mm83me9zX16e2tjbZbDYZDAbFxsb69sXFxamxsVGSZDabVV9fr+joaG3evHnC/p944gmlpaXp+uuvn34WAAAAAAAgKKa03OTAwIAKCwuVnJysxMREmUwmGc5Z59NisWhoaOiDjsPDFR0d/ZH9nTx5UrW1tb57NgAAAAAAgLll0reY7OzsVE5OjmJiYlRVVSWr1arh4WF5vV7fMVNdI/OFF17QqlWrtGrVqqlFDQAAAADATAr26g8XuCpEME0qytbWVuXk5MjhcKiqqkpms1krVqyQ1+tVd3e377iprpH56quvTvrOlAAAAAAAIPT4LSx0dXUpPz9fLpdLpaWlvqkPVqtVDodDFRUVcrvdam9vV21trTIzMyd1YY/HoyNHjig1NfXCMgAAAAAAAEHj9x4L1dXVcrvdqqysVGVlpa89NzdX5eXl2rFjh9LT02UymVRQUKB169ZN6sJ9fX06ffq0LrnkkulHDwAAAADATDDoLysxhJAQC2cifgsLZWVlKisrm3D/ucWGiezbt29c25IlS/Tb3/7W77kAAAAAACB0zY07QQAAAAAAgJA0peUmAQAAAACYl0JxFYZQi2cCcyNKAAAAAAAQkigsAAAAAACAaZvTUyGMBilsjtwlczpM4Quj7hNmnMdP4jkWxrO5MEQukPfm0Ign2CEExgL4CPJ6vcEOISBGzi6M16xnATyfA8OjwQ4hICLCFsbvk1C7yf5sWQjPZ7hxnudoMITeCzbU4pnAPH9lAAAAAACA2URhAQAAAAAATNucngoBAAAAAMCMYFWIaZsbUQIAAAAAgJA0qcJCc3OzsrKylJqaKqfTqf3790uS+vv75XK5lJaWprVr16qurm7cuR6PR0VFRdq7d++Y9rfeektf/OIXlZKSouuvv17V1dUXng0AAAAAAAgov1Mhent7VVxcrF27dmn9+vU6evSotmzZomXLlunAgQMyGo1qamrSiRMnlJeXp+XLl8tut0uSuru7VV5erkOHDmn16tW+PoeGhnTvvfcqOztbP/rRj3Ts2DHdeeedSkhIUGpq6uxlCwAAAADA+TAVYtr8RtnT06OMjAw5nU4ZjUYlJSXJbrerpaVFDQ0NKikpkcViUUJCgrKzs1VTUyNJcrvd2rRpk1atWqWUlJQxfTY2Nuqiiy5SYWGhwsLClJCQoNraWsXFxc1OlgAAAAAAYFb4LSzYbDaVl5f7Hvf19amtrU2XXHKJDAaDYmNjffvi4uJ07NgxSZLZbFZ9fb22b9+uiIiIMX0ePXpUV1xxhR566CGtWbNGGzZs0G9+8xstXrx4htICAAAAAACBMKVxFQMDAyosLFRycrISExNlMplkMBh8+y0Wi4aGhiRJ4eHhio6OPm8/77//vn7605/qM5/5jH7xi1/o4Ycf1je+8Q21tbVdQCoAAAAAAEyTQZLBEGJbsH8okzPpwkJnZ6dycnIUExOjqqoqWa1WDQ8Py+v1+o4ZHBxUVFSU375MJpOuvPJKZWdnKyIiQtdcc40cDodeffXV6WUBAAAAAACCYlKFhdbWVuXk5MjhcKiqqkpms1krVqyQ1+tVd3e377iOjg7Fx8f77S8uLk7vv//+mLazZ89OMXQAAAAAABBsfgsLXV1dys/Pl8vlUmlpqW/qg9VqlcPhUEVFhdxut9rb21VbW6vMzEy/F73xxhvV19enPXv26OzZszp8+LAaGxu1YcOGC88IAAAAAIApM/51ZYhQ2aZ294Kg8bvcZHV1tdxutyorK1VZWelrz83NVXl5uXbs2KH09HSZTCYVFBRo3bp1fi96ySWX6JlnntHOnTu1Z88eLV68WA8//LCSkpIuLBsAAAAAABBQfgsLZWVlKisrm3D/ucWGiezbt29c26c//Wk988wzfs8FAAAAAAChy29hAQAAAACAee/DlRhCSajFM4G5MWEDAAAAAACEJAoLAAAAAABg2pgKAQAAAACAbyWGEBJq8UxgThcWjAaDjHNkzsl0hM2N19AF83q9wQ4hIAzz+LV6roWQ5gJ5yS4Y8/n3yIfOenjRzicRxvn/BeHM2bPBDiEgzvILZV6Z/+/MhZEjpofXBgAAAAAAmLY5PWIBAAAAAIAZwaoQ08aIBQAAAAAAMG0UFgAAAAAAwLRNqrDQ3NysrKwspaamyul0av/+/ZKk/v5+uVwupaWlae3ataqrqxt3rsfjUVFRkfbu3Tum/fvf/76uuuoqpaSk+LbXX3/9wjMCAAAAAGCKDAZDSG5zgd97LPT29qq4uFi7du3S+vXrdfToUW3ZskXLli3TgQMHZDQa1dTUpBMnTigvL0/Lly+X3W6XJHV3d6u8vFyHDh3S6tWrx/T7P//zP3rwwQd1xx13zE5mAAAAAABg1vkdsdDT06OMjAw5nU4ZjUYlJSXJbrerpaVFDQ0NKikpkcViUUJCgrKzs1VTUyNJcrvd2rRpk1atWqWUlJRx/f7mN79RQkLCzGcEAAAAAAACxu+IBZvNJpvN5nvc19entrY22Ww2GQwGxcbG+vbFxcWpsbFRkmQ2m1VfX6/o6Ght3rx5TJ/vvPOO3n33Xe3Zs0dvvPGGFi9erLy8PG3atGmG0gIAAAAAYPI+WBQitKYehFg4E5rScpMDAwMqLCxUcnKyEhMTZTKZxvzgLRaLhoaGPug4PFzR0dHn7efkyZOy2+36whe+oO9///v69a9/rcLCQkVHR+v666+ffjYAAAAAACCgJr0qRGdnp3JychQTE6OqqipZrVYNDw/L6/X6jhkcHFRUVJTfvlatWqV9+/bp2muvVUREhOx2u2655Ra9/PLL08sCAAAAAAAExaQKC62trcrJyZHD4VBVVZXMZrNWrFghr9er7u5u33EdHR2Kj4/3219bW9u4VSJGRkZkNpunFj0AAAAAADPBEKLbHOC3sNDV1aX8/Hy5XC6Vlpb6pj5YrVY5HA5VVFTI7Xarvb1dtbW1yszM9HvRyMhIVVRU6Oc//7k8Ho9++ctf6oUXXtA//MM/XHhGAAAAAAAgYPzeY6G6ulput1uVlZWqrKz0tefm5qq8vFw7duxQenq6TCaTCgoKtG7dOr8Xveqqq/Td735Xjz76qO677z5ddtll+va3v62kpKQLywYAAAAAAASU38JCWVmZysrKJtx/brFhIvv27RvXtmHDBm3YsMHvuQAAAAAAzDaDwRCCq0KEVjwTmfTNGwEAAAAAAP4WhQUAAAAAADBtfqdCAAAAAAAw3xkUglMh5siyEIxYAAAAAAAA0zanRyycGfVoaNQT7DBmjdcb7AgCY4GkqVHP/H2tnmto5GywQ5h1F5nDgh1CQHgXyIeQOXz+19gviowIdggBMXp2YbxmRxbA75Mw49z4H7oLFbFA/o9v+Oz8f81K0tkF8HvTs2C+uWOq5nRhAQAAAACAmcCqENO3MMqkAAAAAABgVlBYAAAAAAAA0zapwkJzc7OysrKUmpoqp9Op/fv3S5L6+/vlcrmUlpamtWvXqq6ubty5Ho9HRUVF2rt373n7fvvtt5WWlqbXX399+lkAAAAAAHABPpwKEWrbXOD3Hgu9vb0qLi7Wrl27tH79eh09elRbtmzRsmXLdODAARmNRjU1NenEiRPKy8vT8uXLZbfbJUnd3d0qLy/XoUOHtHr16nF9nz17Vg888IBOnz4985kBAAAAAIBZ53fEQk9PjzIyMuR0OmU0GpWUlCS73a6WlhY1NDSopKREFotFCQkJys7OVk1NjSTJ7XZr06ZNWrVqlVJSUs7b9w9+8ANdeeWVslqtM5sVAAAAAAAICL+FBZvNpvLyct/jvr4+tbW16ZJLLpHBYFBsbKxvX1xcnI4dOyZJMpvNqq+v1/bt2xURMX6ZqzfeeEMvvfSSvvrVr85AGgAAAAAAXABDiG5zwJSWmxwYGFBhYaGSk5OVmJgok8k0Zs6HxWLR0NDQBx2Hhys6Ovq8/bjdbn31q1/Vd7/7XVkslgsIHwAAAAAABNOkV4Xo7OxUTk6OYmJiVFVVJavVquHhYXm9Xt8xg4ODioqK8tvXI488ooyMDCUlJU0vagAAAAAAEBImVVhobW1VTk6OHA6HqqqqZDabtWLFCnm9XnV3d/uO6+joUHx8vN/+6uvr9aMf/Ug2m002m00DAwMqKCjQnj17pp8JAAAAAADTFQIrQIxbEWK+rArR1dWl/Px8ffnLX9bmzZt97VarVQ6HQxUVFfrWt76lt99+W7W1tfr2t7/t96JHjhwZ89hms+nJJ5/UNddcM40UAAAAAABAsPgtLFRXV8vtdquyslKVlZW+9tzcXJWXl2vHjh1KT0+XyWRSQUGB1q1bN6sBAwAAAACA0OG3sFBWVqaysrIJ959bbJjIvn37PnJ/W1ub3z4AAAAAAJgtvukHISTU4pnIpG/eCAAAAAAA8LcoLAAAAAAAgGnzOxUCAAAAAID5jqkQ08eIBQAAAAAAMG0UFgAAAAAAwLTN6akQZ71ejXq8wQ5j1szn3M41MuoJdggBMXx2YeQ5Mjr/X7cL5b0ZHWkKdggBERE+/2vscdHmYIeAGXR65GywQ5h1HzcvjM+f7lOngx1CQPx5aDjYIQTEQviuN99zNBhCb+pBiIUzofn/bQoAAAAAAMwaCgsAAAAAAGDa5vRUCAAAAAAAZswcmXoQaiY1YqG5uVlZWVlKTU2V0+nU/v37JUn9/f1yuVxKS0vT2rVrVVdXN+5cj8ejoqIi7d27d0x7XV2dbrjhBqWkpCgjI0MHDx688GwAAIRz9I0AACAASURBVAAAAEBA+R2x0Nvbq+LiYu3atUvr16/X0aNHtWXLFi1btkwHDhyQ0WhUU1OTTpw4oby8PC1fvlx2u12S1N3drfLych06dEirV6/29XnkyBF961vf0t69e5WUlKRDhw5p69atOnTokGJiYmYvWwAAAAAAMKP8FhZ6enqUkZEhp9MpSUpKSpLdbldLS4saGhr0wgsvyGKxKCEhQdnZ2aqpqZHdbpfb7damTZt02223qb+/f0yfSUlJeu2112S1WjUyMqI///nPslqtMpkWxh2AAQAAAAChxWAwhOCqEKEVz0T8FhZsNptsNpvvcV9fn9ra2mSz2WQwGBQbG+vbFxcXp8bGRkmS2WxWfX29oqOjtXnz5nH9Wq1WvfXWW9q4caM8Ho++/vWva9GiRTOQEgAAAAAACJQp3bxxYGBAhYWFSk5OVmJiokwm05gKisVi0dDQ0Acdh4crOjr6I/v75Cc/qTfffFOHDx/W1q1bdfnll+vaa6+dRhoAAAAAACAYJr3cZGdnp3JychQTE6OqqipZrVYNDw/L6/X6jhkcHFRUVNSkLx4REaGIiAh99rOfldPp9I12AAAAAAAgkD6cChFq21wwqcJCa2urcnJy5HA4VFVVJbPZrBUrVsjr9aq7u9t3XEdHh+Lj4/3295Of/EQFBQVj2oaHh/Wxj31siuEDAAAAAIBg8ltY6OrqUn5+vlwul0pLS30VE6vVKofDoYqKCrndbrW3t6u2tlaZmZl+L3r11Vfr9ddf10svvSSPx6PGxkY1NTVp48aNF54RAAAAAAAIGL/3WKiurpbb7VZlZaUqKyt97bm5uSovL9eOHTuUnp4uk8mkgoICrVu3zu9Fly9frieffFLf/e539bWvfU2xsbH613/9V8XFxV1YNgAAAAAAIKD8FhbKyspUVlY24f5ziw0T2bdv37i2a6+9Vs8//7zfcwEAAAAAmG2heE+DUItnIpO+eSMAAAAAAMDforAAAAAAAACmze9UCAAAAAAA5j3DX7ZQEmrxTIARCwAAAAAAYNrm9IiFMINB4cY5UsKZhv6hs8EOISCM8/g5PJd72BPsEAJi1DP/8wxbGC9ZLTJFBDuEgPB4vcEOYdYtjgwLdggBMXJ2/n/+SNKod/7naYkwBTuEgBj1zP/PH0maI/eeA3AB5nRhAQAAAACAmcCqENPHVAgAAAAAADBtFBYAAAAAAMC0UVgAAAAAAOAvUyFCaZvqTUra29t122236eqrr9bGjRt15MiRjzz+yJEjWrNmzYT7H3nkEW3evNnvdSdVWGhublZWVpZSU1PldDq1f/9+SVJ/f79cLpfS0tK0du1a1dXVjTvX4/GoqKhIe/fuHdP+wgsv6POf/7xSU1OVkZGhgwcPTiYUAAAAAADwN4aHh7V161Zt2LBBra2tKigoUF5enk6dOjXuWK/Xq9raWt1zzz0aGRk5b3+HDh3Ss88+O6lr+y0s9Pb2qri4WIWFhWpra1NFRYUqKyv12muv6eGHH5bRaFRTU5P27NmjiooKHT582Hdud3e3CgoK9Morr4zp88iRI/rGN76hRx55RP/5n/+p+++/X/fff79+97vfTSpoAAAAAADwV4cPH9bIyIjuvvtuRURE6Oabb1Z8fLzq6+vHHVtVVaVnn31WhYWF5+3r5MmT+uY3v6k77rhjUtf2uypET0+PMjIy5HQ6JUlJSUmy2+1qaWlRQ0ODXnjhBVksFiUkJCg7O1s1NTWy2+1yu93atGmTbrvtNvX394/p8w9/+IO++MUvKi0tTZK0bt06XX755XrzzTd1xRVXTCpwAAAAAABmynSmHsy2D1eF+OMf/zhu36JFi7Ro0SLf4+PHj2vlypVjjomLi9OxY8fGnXv77berpKREr7/++nmv+8///M/6x3/8R7nd7vOe/7f8FhZsNptsNpvvcV9fn9ra2mSz2WQwGBQbGzsm6MbGRkmS2WxWfX29oqOjx83JuOmmm3TTTTf5Hv/+97/X8ePHlZCQ4DdgAAAAAAAWkjvvvHNc27Zt21RcXOx7fPr0aUVGRo45xmKxaHBwcNy5l1566YTXqq6ulsFgUHZ29rhbGkzEb2HhXAMDAyosLFRycrISExNlMpnGrKtpsVg0NDT0Qcfh4YqOjvbb5x//+EcVFBRo06ZNSkxMnEo4AAAAAADMe9XV1Vq6dOmYtnNHK0hSVFSUzpw5M6ZtcHBQUVFRk77OW2+9paeeekr/9m//NqX4Jl1Y6Ozs1NatWxUfH6/vfe97euuttzQ8PCyv1+srLkw16DfeeEPFxcW64YYb9OCDD04pcAAAAAAAZopBITgVQh/Es3TpUn3iE5/4yGNXrlw5boRBR0eHbr311klf7+WXX9bJkyd1ww03SPrghpCjo6Oy2Wxqa2ub8LxJrQrR2tqqnJwcORwOVVVVyWw2a8WKFfJ6veru7h4TdHx8/KQCfumll/SlL31J+fn5+vrXvy6jkZUvAQAAAACYjmuuuUZer1d79+7VyMiIXnzxRf32t7/13S9xMgoLC/XGG2+ora1NbW1t2r59u9LS0j6yqCBNorDQ1dWl/Px8uVwulZaW+kYnWK1WORwOVVRUyO12q729XbW1tcrMzPQb7K9//Ws98MADevTRR/WFL3xhkikCAAAAAIDzMZlM+uEPf6iGhgbZ7Xbt3r1bTz75pJYsWaLdu3fr5ptvnrVr+50KUV1dLbfbrcrKSlVWVvrac3NzVV5erh07dig9PV0mk0kFBQVat26d34s+9dRTGhkZ0f333z+m/Stf+cp5b0oBAAAAAMCsMvxlCyVTjOfKK6/Us88+O669oKBABQUF49qvueaajxyNcPfdd+vuu+/2e12/hYWysjKVlZVNuP/cYsNE9u3bN+bxE0884fccAAAAAAAQ+rixAQAAAAAAmLYpLTcJAAAAAMB8ZDAo9FaFCK1wJsSIBQAAAAAAMG0UFgAAAAAAwLTN6akQHq9XHq832GHMGuNcGfdygebxUziG1UQdb74YPrswXrSDo2eDHUJARITN//dmx8kzwQ4hID4dvTDemwvh+0FkRFiwQwiIj5kigh1CQIx6FsZ7M9w4/3+fzPccDQZDyM09MIRYPBOZ368MAAAAAAAwqygsAAAAAACAaZvTUyEAAAAAAJgJTIWYPkYsAAAAAACAaZtUYaG5uVlZWVlKTU2V0+nU/v37JUn9/f1yuVxKS0vT2rVrVVdXN+5cj8ejoqIi7d2797x9v/TSS9q0adP0MwAAAAAAAEHjdypEb2+viouLtWvXLq1fv15Hjx7Vli1btGzZMh04cEBGo1FNTU06ceKE8vLytHz5ctntdklSd3e3ysvLdejQIa1evXpMv8PDw/rRj36kxx9/XFdeeeXsZAcAAAAAwGQY/rKFklCLZwJ+Ryz09PQoIyNDTqdTRqNRSUlJstvtamlpUUNDg0pKSmSxWJSQkKDs7GzV1NRIktxutzZt2qRVq1YpJSVlXL9lZWX61a9+pXvuuWfmswIAAAAAAAHhd8SCzWaTzWbzPe7r61NbW5tsNpsMBoNiY2N9++Li4tTY2ChJMpvNqq+vV3R0tDZv3jyu3wceeECXXnqpamtrZyANAAAAAAAQDFNaFWJgYECFhYVKTk5WYmKiTCbTmLtUWiwWDQ0NfdBxeLiio6Mn7OvSSy+dZsgAAAAAAMwsVoWYvkmvCtHZ2amcnBzFxMSoqqpKVqtVw8PD8nq9vmMGBwcVFRU1K4ECAAAAAIDQM6nCQmtrq3JycuRwOFRVVSWz2awVK1bI6/Wqu7vbd1xHR4fi4+NnLVgAAAAAABBa/BYWurq6lJ+fL5fLpdLSUt9QDKvVKofDoYqKCrndbrW3t6u2tlaZmZmzHjQAAAAAADPJYDCE5DYX+L3HQnV1tdxutyorK1VZWelrz83NVXl5uXbs2KH09HSZTCYVFBRo3bp1sxowAAAAAAAIHX4LC2VlZSorK5tw/7nFhons27dvwn3Z2dnKzs722wcAAAAAAAg9U1oVAgAAAACA+SkUpx6EWjznN+lVIQAAAAAAAP4WhQUAAAAAADBtTIUAAAAAACx4BoNBCrWpEKEWzwTmdGHBEhGmqIiwYIcxay4ye4IdQkC8N3Qm2CEERLhxYQwQigib/3la5/Hnzrk8Xm+wQwiIoeGzwQ5h1n3MvDBesyNnF8bvzciw+f98jowujOdyYHgk2CEERIzFHOwQAsKwAJ7OufEnLoJh/v8FAAAAAAAAZs2cHrEAAAAAAMCMMCj0hmWEWjwTYMQCAAAAAACYNgoLAAAAAABg2iZVWGhublZWVpZSU1PldDq1f/9+SVJ/f79cLpfS0tK0du1a1dXVjTvX4/GoqKhIe/fuHdP+q1/9Shs3btTVV1+t22+/XV1dXReeDQAAAAAA02AwGEJymwv8FhZ6e3tVXFyswsJCtbW1qaKiQpWVlXrttdf08MMPy2g0qqmpSXv27FFFRYUOHz7sO7e7u1sFBQV65ZVXxvR58uRJFRUVadu2bWptbZXD4VBeXp48noVxB2AAAAAAAOYLv4WFnp4eZWRkyOl0ymg0KikpSXa7XS0tLWpoaFBJSYksFosSEhKUnZ2tmpoaSZLb7damTZu0atUqpaSkjOnz4MGDuuKKK3TjjTcqIiJCW7Zs0fDwsFpaWmYnSwAAAAAAMCv8FhZsNpvKy8t9j/v6+tTW1qZLLrlEBoNBsbGxvn1xcXE6duyYJMlsNqu+vl7bt29XRETEmD6PHz+ulStXjmm7/PLLfecCAAAAABBIBkMoTocI9k9lcqZ088aBgQEVFhYqOTlZiYmJMplMY+Z8WCwWDQ0NSZLCw8MVHR193n5Onz4ti8Uypi0yMlKDg4NTjR8AAAAAAATRpAsLnZ2dysnJUUxMjKqqqmS1WjU8PCyv1+s7ZnBwUFFRUX77OrcA8aGhoaFJnQsAAAAAAELHpAoLra2tysnJkcPhUFVVlcxms1asWCGv16vu7m7fcR0dHYqPj/fbX3x8vDo7O8e0TfZcAAAAAABm2gdTIUJvmwv8Fha6urqUn58vl8ul0tJS39QHq9Uqh8OhiooKud1utbe3q7a2VpmZmX4v6nQ61d7ervr6eo2MjOipp56S0WiU3W6/8IwAAAAAAEDA+C0sVFdXy+12q7KyUikpKb7t0UcfVXl5uYxGo9LT03XvvfeqoKBA69at83vR6Oho7d69W3v27JHdbldDQ4N2794tk8k0I0kBAAAAAIDACPd3QFlZmcrKyibcX1lZ6fci+/btG9e2evVqPf/8837PBQAAAABgthlCce5BqMUzgSmtCgEAAAAAAHAuCgsAAAAAAGDa/E6FAAAAAABgvjMYJIXazINQi2cCjFgAAAAAAADTNqdHLBgNBhnnyM0spuOsJ9gRBMal1shghxAQ7w+PBDuEgPB6gx3B7Hv/zGiwQwiIBfBUSpKMxvn7e+RDv+k9FewQAmLdJ6ODHUJAzOfvPh8661kYn0AfM0UEO4SAiDAujP/LNIXN/zwXynOJqZvThQUAAAAAAGaCQSG4KsQcmQtByQkAAAAAAEwbhQUAAAAAADBtTIUAAAAAACAEZ0J4QyyeiUxqxEJzc7OysrKUmpoqp9Op/fv3S5L6+/vlcrmUlpamtWvXqq6uzneO1+vVY489pjVr1shms2nnzp0aHf3rDc/27dun9evXKy0tTVu2bNHbb789w6kBAAAAAIDZ5nfEQm9vr4qLi7Vr1y6tX79eR48e1ZYtW7Rs2TIdOHBARqNRTU1NOnHihPLy8rR8+XLZ7XbV1NTo4MGDOnDggEwmk4qKirR7925t27ZN9fX1euyxx7R7926lpqbqueee05e+9CW9+OKLMpvNgcgbAAAAAADMAL8jFnp6epSRkSGn0ymj0aikpCTZ7Xa1tLSooaFBJSUlslgsSkhIUHZ2tmpqaiRJzz//vO666y4tXbpUS5YsUXFxsW/fyy+/rJycHNntdoWHhys3N1cRERFqaWmZ3WwBAAAAADgPo9EQkttc4HfEgs1mk81m8z3u6+tTW1ubbDabDAaDYmNjffvi4uLU2NgoSTp+/LhWrlw5Zt8777yjvr4+eTweWSyWMdcJCwvTiRMnLjAdAAAAAAAQSFNaFWJgYECFhYVKTk5WYmKiTCaTDOfc3cJisWhoaEiSdPr06THFg8jISEnS0NCQbrzxRj333HP67//+b42MjOi5555TR0eH71wAAAAAADA3THpViM7OTm3dulXx8fH63ve+p7feekvDw8Pyer2+4sLg4KCioqIkjS0ySPL9OyoqSjfffLP+7//+T9u3b9fp06e1YcMGXXvttVq0aNFM5gYAAAAAwKQYQnBVCIVaPBOY1IiF1tZW5eTkyOFwqKqqSmazWStWrJDX61V3d7fvuI6ODsXHx0uS4uPj1dnZOWbfxRdfrEWLFumdd95Renq6Dh48qObmZpWVlel3v/udrrrqqhlODwAAAAAAzCa/hYWuri7l5+fL5XKptLTUNzrBarXK4XCooqJCbrdb7e3tqq2tVWZmpiQpMzNTTz/9tHp6enTy5Ek9/vjjuuWWWyRJLS0tysvL07vvvqvBwUE99thjuuSSS/SZz3xmFlMFAAAAAAAzze9UiOrqarndblVWVqqystLXnpubq/Lycu3YsUPp6ekymUwqKCjQunXrJEl33HGH3nvvPd1+++0aGhrSTTfdpJKSEkkfFB2OHTumW265RSMjI1qzZo1+8IMfjLlfAwAAAAAAgWIwGELvb9JQi2cCfgsLZWVlKisrm3D/ucWGcxmNRrlcLrlcrnH7DAaD7r//ft1///1TCBUAAAAAAISaKa0KAQAAAAAAcK5JrwoBAAAAAMB8xaoQ08eIBQAAAAAAMG0UFgAAAAAAwLTN6akQljCjosLDgh3GrBk+6wl2CAFhMc3f5/Bcp0fOBjuEgDjr8QY7hFlnDl8YNdn5/Pl6rjDjHBljeAH63MPBDgEzaMQz/78fWEwRwQ4hIBbCcylJQ6ML4zsQ5j5WhZi+hfHtGAAAAAAAzAoKCwAAAAAAYNrm9FQIAAAAAABmRuhNhfDOkWUhGLEAAAAAAACmbVKFhebmZmVlZSk1NVVOp1P79++XJPX398vlciktLU1r165VXV2d7xyv16vHHntMa9askc1m086dOzU6OurbX1dXJ4fDobS0NGVnZ6utrW2GUwMAAAAAALPN71SI3t5eFRcXa9euXVq/fr2OHj2qLVu2aNmyZTpw4ICMRqOampp04sQJ5eXlafny5bLb7aqpqdHBgwd14MABmUwmFRUVaffu3dq2bZva29v1ne98R9XV1briiiv0zDPPqKioSC0tLTIaGUQBAAAAAAgsgyH0FmEItXgm4vev+J6eHmVkZMjpdMpoNCopKUl2u10tLS1qaGhQSUmJLBaLEhISlJ2drZqaGknS888/r7vuuktLly7VkiVLVFxc7Nv3+9//Xp6/LK/j9XplNBoVGRk5i2kCAAAAAIDZ4HfEgs1mk81m8z3u6+tTW1ubbDabDAaDYmNjffvi4uLU2NgoSTp+/LhWrlw5Zt8777yjvr4+XXfddbriiiu0ceNGhYWFyWw266mnnmK0AgAAAAAAc8yU/pIfGBhQYWGhkpOTlZiYKJPJNOaumRaLRUNDQ5Kk06dPy2Kx+PZ9OCJhaGhIZ86c0eWXX67nnntOb7zxhr7yla+oqKhI77777kzkBAAAAADAlBgMhpDc5oJJFxY6OzuVk5OjmJgYVVVVyWq1anh4WF6v13fM4OCgoqKiJI0tMkjy/TsqKkpPPPGELr74YiUnJ8tkMunOO+/UsmXL9NOf/nSm8gIAAAAAAAEwqcJCa2urcnJy5HA4VFVVJbPZrBUrVsjr9aq7u9t3XEdHh+Lj4yVJ8fHx6uzsHLPv4osv1qJFi9Tb26vh4eEx1wgPD1d4uN+ZGQAAAAAAIIT4LSx0dXUpPz9fLpdLpaWlvqEYVqtVDodDFRUVcrvdam9vV21trTIzMyVJmZmZevrpp9XT06OTJ0/q8ccf1y233CJJ+tznPqe6ujq9+eab8ng8ev755/W73/1O119//exlCgAAAADABD5cFSLUtrnA7xCB6upqud1uVVZWqrKy0teem5ur8vJy7dixQ+np6TKZTCooKNC6deskSXfccYfee+893X777RoaGtJNN92kkpISSVJOTo4GBgZUWlqqP//5z4qPj9eePXt02WWXzVKaAAAAAABgNvgtLJSVlamsrGzC/ecWG85lNBrlcrnkcrnOuz8vL095eXmTDBMAAAAAAIQibmoAAAAAAFjwPph6EFpzD0IsnAlNablJAAAAAACAc1FYAAAAAAAA08ZUCAAAAADAgheKqzCEWjwTmdOFBYPBIONc+UlPwyUXRQQ7hIAYGjkb7BACYkmkKdghBERE2Px9T37I4/UGO4SA+KP7TLBDCIjlHxsNdgizLjIiLNghBMSp4fn/XErS30XO/+8HA0ML47mMCl8Y780I48IYJD3i8QQ7hFm3AL7mYZoWxrscAAAAAADMijk9YgEAAAAAgJlgMBhCcFWI0IpnIoxYAAAAAAAA00ZhAQAAAAAATNukCgvNzc3KyspSamqqnE6n9u/fL0nq7++Xy+VSWlqa1q5dq7q6Ot85Xq9Xjz32mNasWSObzaadO3dqdPSDG/E89NBDSklJGbN96lOf0u7du2chRQAAAAAAPtqHq0KE2jYX+L3HQm9vr4qLi7Vr1y6tX79eR48e1ZYtW7Rs2TIdOHBARqNRTU1NOnHihPLy8rR8+XLZ7XbV1NTo4MGDOnDggEwmk4qKirR7925t27ZN5eXlKi8v913jxz/+saqrq/WFL3xhVpMFAAAAAAAzy++IhZ6eHmVkZMjpdMpoNCopKUl2u10tLS1qaGhQSUmJLBaLEhISlJ2drZqaGknS888/r7vuuktLly7VkiVLVFxc7Nt3rq6uLj366KOqqKjQRRddNPMZAgAAAACAWeN3xILNZpPNZvM97uvrU1tbm2w2mwwGg2JjY3374uLi1NjYKEk6fvy4Vq5cOWbfO++8o76+Pi1evNjX/p3vfEc5OTlatWrVTOQDAAAAAMA0hN6qEFKoxXN+U1pucmBgQIWFhUpOTlZiYqJMJtOYH7zFYtHQ0JAk6fTp07JYLL59kZGRkuTbL0nHjh3TL3/5S73yyisXlAQAAAAAAAiOSa8K0dnZqZycHMXExKiqqkpWq1XDw8Pyer2+YwYHBxUVFSVpbJFB+mtB4cP9klRXV6f169crJibmghMBAAAAAGAha29v12233aarr75aGzdu1JEjRz7y+CNHjmjNmjVj2t577z1t375da9as0d///d/rgQce0Pvvv/+R/UyqsNDa2qqcnBw5HA5VVVXJbDZrxYoV8nq96u7u9h3X0dGh+Ph4SVJ8fLw6OzvH7Lv44ou1aNEiX9urr76qm2++eTIhAAAAAAAwa4K9+sOFrgoxPDysrVu3asOGDWptbVVBQYHy8vJ06tSpccd6vV7V1tbqnnvu0cjIyJh9Dz74oMLCwvTqq6/q5ZdfVn9//5jFF87Hb2Ghq6tL+fn5crlcKi0t9U19sFqtcjgcqqiokNvtVnt7u2pra5WZmSlJyszM1NNPP62enh6dPHlSjz/+uG655RZfvydPntTbb7+t1NRU/z8hAAAAAAAwocOHD2tkZER33323IiIidPPNNys+Pl719fXjjq2qqtKzzz6rwsLCMe0ej0dGo1Hbtm3T/8/evUdFeZ77///MIIMDZuL2nIPVCk3DNmoiFIqHmHCoVg2mphKEWJNgFRIZqtZY2uVOMYmHGqBirTY21tS6FWyCzYFVtUlXo5ZlpM2u2FapX/Cnsk0wIEGBkQHm94c7E6lBjnMA3i/XsxZz3888z3XJAMPFffD395fFYtG8efP0l7/85Zb3bnONhd27d6u2tlZZWVnKyspytickJGjNmjXKyMhQZGSkTCaTkpOTNW3aNEnS/PnzVVlZqfj4eNlsNs2YMUNpaWnO51+4cEEmk6nFQo4AAAAAAKCljz766KY2i8XSYkbAv2+gIF3fRKGkpOSm58bHxystLU3Hjh1r0W40GvXzn/+8Rdsf/vAHBQcH3zK+NgsL6enpSk9Pb7X/xmLDvwdktVpltVq/sH/8+PEqLi5u6/YAAAAAALicweB9u0J8Fk9iYuJNfUuXLlVqaqrzcV1dnXPThM+YzWbV19ff9Nzhw4e36/6vvvqq3n33Xe3Zs+eW53VoVwgAAAAAAOBeu3fv1ogRI1q03ThaQbq+UcK1a9datN24wUJH2O12vfDCC/rjH/+o11577aaREP+OwgIAAAAAAF5sxIgRuvvuu295TmBgoHbu3NmirbS0VI8++miH7nX16lWlpKSotrZWv/3tb9s1uqHd200CAAAAANBbeXr3h67uChEeHi6Hw6GdO3fKbrfrnXfe0enTpxUTE9Oh/4fly5erublZu3fvbveUCUYsAAAAAADQw5lMJm3fvl3PP/+8Nm3apLvvvltbtmzRoEGDtG3bNr311lt65513bnmNU6dO6U9/+pNMJpMmTZrkbL/tttv0/vvvt/q8Hl1Y8O/XTwG+PTqFW/p/l+s8HYJb+Bi9a4EUV6lvbPR0CG7RFz6f/9Hf5OkQ3OLiVZunQ3CLq/be/7V5xWZv+6ReYGB/X0+H4Bb9TT6eDsHlPuoj339q+8D3H0ka6Nc3fm42ezoAN+gLOfZ099xzzxcutJicnKzk5OSb2sPDw1VUVOR8fO+99+r06dMdvm/v/a0cAAAAAIB28uZdIbwdaywAAAAAAIBOo7AAAAAAAAA6rV2FhaNHj2ru3LmaOHGiYmJitHfvXklSTU2NrFarQkJCNHXqVL3++uvO5zgcDmVnZysiIkKhoaFau3atGm+YY/7Xv/5Vjz32mB544AFNnz5dv//977s5NQAAAAAA2sfTuz90dVcInE0z+AAAIABJREFUT2pzjYWLFy8qNTVVGzZsUFRUlE6ePKlFixbprrvuUn5+voxGo44cOaKzZ88qKSlJI0eOVFhYmHJzc3Xo0CHl5+fLZDLp2Wef1bZt27R06VJVVFRoyZIl+uEPf6hHH31Uf/7zn5WcnKzx48frzjvvdEfeAAAAAACgG7Q5YqG8vFyzZ89WTEyMjEajxo8fr7CwMBUWFurAgQNKS0uT2WxWcHCw5s2bp9zcXEnS/v37tXDhQo0YMUKDBg1Sampqi77Q0FB961vfksFg0OTJk7Vv3z5ZLBbXZgsAAAAAALpVm4WF0NBQrVmzxvm4urpaRUVFGjZsmAwGg0aPHu3sGzNmjEpKSiRJZ86cUWBgYIu+iooKVVdX6+9//7vuuusuLVu2TOHh4Xr00Uf1ySefaMCAAd2YGgAAAAAA7fPZrhDedvQEHVq88cqVK0pJSdGECRM0duxYmUymFomazWbZbNf3Ha6rq5PZbHb29e/fX5Jks9n06aefat++fYqNjdWRI0e0ePFiLV26VOfOneuOnAAAAAAAgJu0u7BQVlamuLg4DRkyRDk5OQoICFBDQ4McDofznPr6evn7+0tqWWSQ5PzY399fJpNJU6dO1cMPPyxfX1/NnDlT//mf/6n333+/u/ICAAAAAABu0K7CwvHjxxUXF6fo6Gjl5OTIz89Po0aNksPh0IULF5znlZaWKigoSJIUFBSksrKyFn1Dhw6VxWLRmDFj9Omnn7a4R1NTU3fkAwAAAAAA3KjNwsK5c+e0ZMkSWa1WrVixwjn1ISAgQNHR0crMzFRtba1OnTrlnN4gSbGxsdqxY4fKy8tVVVWlzZs3a86cOZKkRx99VB9++KF+97vfqbm5WQUFBTp9+rSioqJcmCoAAAAAAF/M02sp9OQ1FtrcbnL37t2qra1VVlaWsrKynO0JCQlas2aNMjIyFBkZKZPJpOTkZE2bNk2SNH/+fFVWVio+Pl42m00zZsxQWlqaJOnee+/V9u3btXHjRmVkZOjOO+/U5s2bdccdd7goTQAAAAAA4AptFhbS09OVnp7eav+NxYYbGY1GWa1WWa3WL+yPiIjQG2+80c4wAQAAAACAN2qzsAAAAAAAQF/QQ2YeeJ0ObTcJAAAAAABwIwoLAAAAAACg05gKAQAAAADo87xxFwZvi6c1jFgAAAAAAACd1qNHLPgYDepn7BkVnM4YPsDk6RDcwtenb9S3/Pv16C+3dmtqdng6BJdzOHp/jpIUYPLxdAhu0dgHXrP2xmZPh+AWTX3ka7O5D7xmz1+p83QIbjEioL+nQ3CL+sYmT4fgFj495C/LXWHsAzmic/rGbzoAAAAAANyCweB9u0J4Wzyt6Rt/KgYAAAAAAC5BYQEAAAAAAHRauwoLR48e1dy5czVx4kTFxMRo7969kqSamhpZrVaFhIRo6tSpev31153PcTgcys7OVkREhEJDQ7V27Vo1NjY6+5977jmNGzdODzzwgPM4f/58N6cHAAAAAEDbrk+FMHjZ4en/lfZpc42FixcvKjU1VRs2bFBUVJROnjypRYsW6a677lJ+fr6MRqOOHDmis2fPKikpSSNHjlRYWJhyc3N16NAh5efny2Qy6dlnn9W2bdu0dOlSSdI//vEPbdmyRQ8++KDLkwQAAAAAAK7R5oiF8vJyzZ49WzExMTIajRo/frzCwsJUWFioAwcOKC0tTWazWcHBwZo3b55yc3MlSfv379fChQs1YsQIDRo0SKmpqc4+m82m0tJSBQcHuzY7AAAAAADgUm2OWAgNDVVoaKjzcXV1tYqKihQaGiqDwaDRo0c7+8aMGaP33ntPknTmzBkFBga26KuoqFB1dbXKysrk4+Oj1atX63/+5390xx13yGq16uGHH+7G1AAAAAAAaB92hei8Di3eeOXKFaWkpGjChAkaO3asTCaTDDdkajabZbPZJEl1dXUym83Ovv79r+/Ta7PZVFtbq9DQUD377LM6fPiwkpOT9b3vfU+nTp3qjpwAAAAAAICbtLuwUFZWpri4OA0ZMkQ5OTkKCAhQQ0ODHA6H85z6+nr5+/tLallkkOT82N/fX1OmTNGvfvUrjRs3Tr6+vpo+fbq+/vWv69133+2uvAAAAAAAgBu0q7Bw/PhxxcXFKTo6Wjk5OfLz89OoUaPkcDh04cIF53mlpaUKCgqSJAUFBamsrKxF39ChQ2WxWPTee+8pPz+/xT3sdrv8/Py6IycAAAAAADrEaDB45dETtFlYOHfunJYsWSKr1aoVK1Y4pz4EBAQoOjpamZmZqq2t1alTp7Rv3z7FxsZKkmJjY7Vjxw6Vl5erqqpKmzdv1pw5cyRJzc3Neumll3TixAk1NTXprbfe0ocffqiZM2e6MFUAAAAAANDd2ly8cffu3aqtrVVWVpaysrKc7QkJCVqzZo0yMjIUGRkpk8mk5ORkTZs2TZI0f/58VVZWKj4+XjabTTNmzFBaWpokKTo6WsuWLdPy5cv1ySefaMyYMdq2bZvuvPNOF6UJAAAAAABcoc3CQnp6utLT01vtv7HYcCOj0Sir1Sqr1fqF/YmJiUpMTGxnmAAAAAAAuA67QnReh3aFAAAAAAAAuBGFBQAAAAAA0GltToUAAAAAAKDXMxicmxV4DW+LpxWMWAAAAAAAAJ3Wo0csBPj6aIBvj07hlm4z9d7cbtTPp2dU4bpqhH9/T4fgFgMDTJ4OweUMn/aN1+w/P673dAhuUVnb6OkQXK6yqm98LmsbmjwdglsMv733/zx593SVp0Nwi8SJfWNHtKDBAzwdgltcrLF5OgSXu9avb/x+go7jlQEAAAAA6POMkoxe9vejnjLFoKfECQAAAAAAvBCFBQAAAAAA0GlMhQAAAAAA9HkGL9wVwtviaU27RiwcPXpUc+fO1cSJExUTE6O9e/dKkmpqamS1WhUSEqKpU6fq9ddfdz7H4XAoOztbERERCg0N1dq1a9XYePMCWX/72980duxYXbhwoZtSAgAAAAAA7tLmiIWLFy8qNTVVGzZsUFRUlE6ePKlFixbprrvuUn5+voxGo44cOaKzZ88qKSlJI0eOVFhYmHJzc3Xo0CHl5+fLZDLp2Wef1bZt27R06VLntWtra/Xcc899YcEBAAAAAAB4vzZHLJSXl2v27NmKiYmR0WjU+PHjFRYWpsLCQh04cEBpaWkym80KDg7WvHnzlJubK0nav3+/Fi5cqBEjRmjQoEFKTU119n3mxRdf1De+8Q3XZAYAAAAAQDsZDN559ARtFhZCQ0O1Zs0a5+Pq6moVFRVp2LBhMhgMGj16tLNvzJgxKikpkSSdOXNGgYGBLfoqKipUXV0tSfr973+vc+fOKSkpqbtyAQAAAAAAbtahxRuvXLmilJQUTZgwQWPHjpXJZGqxmITZbJbNZpMk1dXVyWw2O/v69+8vSbLZbPr444/1k5/8RL/+9a9lNLIxBQAAAAAAPVW7f6svKytTXFychgwZopycHAUEBKihoUEOh8N5Tn19vfz9/SW1LDJIcn5sNpv13HPPyWq16u677+6uPAAAAAAA6DSDl/7rCdpVWDh+/Lji4uIUHR2tnJwc+fn5adSoUXI4HC12cygtLVVQUJAkKSgoSGVlZS36hg4dqtraWv31r3/Viy++qNDQUD388MOSpNjYWL311lvdmRsAAAAAAHCxNqdCnDt3TkuWLNGyZcu0YMECZ3tAQICio6OVmZmpl156SefPn9e+ffu0bt06SdcLBTt27FBERITMZrM2b96sOXPm6M4771RxcbHzOjU1Nfra176mN998kxEMAAAAAAD0MG0WFnbv3q3a2lplZWUpKyvL2Z6QkKA1a9YoIyNDkZGRMplMSk5O1rRp0yRJ8+fPV2VlpeLj42Wz2TRjxgylpaW5LhMAAAAAADrJaLh+eBNvi6c1bRYW0tPTlZ6e3mr/jcWGGxmNRlmtVlmt1lte32Kx6PTp022FAQAAAAAAvBBbMgAAAAAAgE7r0HaTAAAAAAD0SgaDDAYvm3vgbfG0ghELAAAAAACg0ygsAAAAAACATuvRUyF8DAb59JRlMjuhoanZ0yG4hcPh6Qjcw9+vR3+5tVtv/pr8TH+fvlGTvXqtydMhuMWXB/l5OgSXO/uvjzwdgltY/EI8HYJbmE0+ng7B5Y795YKnQ3CLZyNGezoEtxjQR94DDfTz9XQILmfv5Z9Lg8H7Zh54Wzyt6RvvjgEAAAAAgEtQWAAAAAAAAJ3Wu8eyAAAAAADQDkaDQUYvm3vgbfG0hhELAAAAAACg09pVWDh69Kjmzp2riRMnKiYmRnv37pUk1dTUyGq1KiQkRFOnTtXrr7/ufI7D4VB2drYiIiIUGhqqtWvXqrGx0dn/yiuv6KGHHtIDDzygb3/72yoqKurm1AAAAAAAgKu1ORXi4sWLSk1N1YYNGxQVFaWTJ09q0aJFuuuuu5Sfny+j0agjR47o7NmzSkpK0siRIxUWFqbc3FwdOnRI+fn5MplMevbZZ7Vt2zYtXbpUv//977V79279+te/1pe+9CXt3btXzzzzjAoLC+Xj0/tXOgYAAAAAeBd2hei8NkcslJeXa/bs2YqJiZHRaNT48eMVFhamwsJCHThwQGlpaTKbzQoODta8efOUm5srSdq/f78WLlyoESNGaNCgQUpNTXX2TZ8+XQUFBRo1apSuXbumTz/9VLfffruMRmZmAAAAAADQk7Q5YiE0NFShoaHOx9XV1SoqKlJoaKgMBoNGjx7t7BszZozee+89SdKZM2cUGBjYoq+iokLV1dUaOHCgAgIC9P7772vJkiXy8fHRT3/6Uxl6SjkGAAAAAABI6uCuEFeuXFFKSoomTJigsWPHymQytSgGmM1m2Ww2SVJdXZ3MZrOzr3///pLk7Jekr3/96zpx4oQKCgq0bNky7d+/v0UxAgAAAAAAdzDI4HV/7DbIu+JpTbvnHpSVlSkuLk5DhgxRTk6OAgIC1NDQIIfD4Tynvr5e/v7+kloWGaTPCwqf9UuSyWSSr6+v5syZo/vuu0/vv/9+lxMCAAAAAADu067CwvHjxxUXF6fo6Gjl5OTIz89Po0aNksPh0IULF5znlZaWKigoSJIUFBSksrKyFn1Dhw6VxWLR9u3b9fzzz7e4R0NDg2677bbuyAkAAAAAALhJm4WFc+fOacmSJbJarVqxYoVzaEhAQICio6OVmZmp2tpanTp1Svv27VNsbKwkKTY2Vjt27FB5ebmqqqq0efNmzZkzR5IUEhKi3/3udyoqKlJjY6Nyc3N18eJFRUZGujBVAAAAAAC+2Ge7Qnjb0RO0ucbC7t27VVtbq6ysLGVlZTnbExIStGbNGmVkZCgyMlImk0nJycmaNm2aJGn+/PmqrKxUfHy8bDabZsyYobS0NEnSxIkT9eKLL+pHP/qRKisrFRwcrF/96lcaNGiQi9IEAAAAAACu0GZhIT09Xenp6a3231hsuJHRaJTVapXVav3C/tmzZ2v27NntDBMAAAAAAHijDu0KAQAAAABAb2QwSEYvm3vgZeG0qt27QgAAAAAAAPw7CgsAAAAAAKDTmAoBAAAAAOjzDP93eBNvi6c1Pbqw8M+qGl308fd0GC7Tz8iAkt7kUGmFp0Nwi7NV1zwdgss9+cBdng7BLb7/4Jc9HYJb/Ie/ydMhuJzf0r7x82Tb8fOeDsEtvht6t6dDcLlN3w3zdAhucfxitadDcAtbY5OnQ3CLyw0Nng7B5S7X1Hk6BHipvvFOAwAAAAAAuESPHrEAAAAAAEB3MBgMMnjZNgzeFk9rGLEAAAAAAAA6jcICAAAAAADotHYVFo4ePaq5c+dq4sSJiomJ0d69eyVJNTU1slqtCgkJ0dSpU/X66687n+NwOJSdna2IiAiFhoZq7dq1amxsdPa/9tprioqKUkhIiL797W+rqKiom1MDAAAAAKB9jAbvPHqCNtdYuHjxolJTU7VhwwZFRUXp5MmTWrRoke666y7l5+fLaDTqyJEjOnv2rJKSkjRy5EiFhYUpNzdXhw4dUn5+vkwmk5599llt27ZNS5cu1cGDB/XLX/5Sv/rVrzRmzBjl5+dryZIlOnTokAYNGuSOvAEAAAAAQDdoc8RCeXm5Zs+erZiYGBmNRo0fP15hYWEqLCzUgQMHlJaWJrPZrODgYM2bN0+5ubmSpP3792vhwoUaMWKEBg0apNTUVGffpUuXlJycrKCgIBmNRj322GPy8fHR6dOnXZstAAAAAADoVm2OWAgNDVVoaKjzcXV1tYqKihQaGiqDwaDRo0c7+8aMGaP33ntPknTmzBkFBga26KuoqFB1dbUSExNb3OP48eOqq6vTV77yla7mAwAAAABAh7ErROd1aPHGK1euKCUlRRMmTNDYsWNlMplaJGo2m2Wz2SRJdXV1MpvNzr7+/ftLkrP/MyUlJVq2bJnS0tI0ZMiQTicCAAAAAADcr92FhbKyMsXFxWnIkCHKyclRQECAGhoa5HA4nOfU19fL399fUssig/R5QeGzfkn64x//qMTERH3nO9/Rd7/73S4nAwAAAAAA3KtdhYXjx48rLi5O0dHRysnJkZ+fn0aNGiWHw6ELFy44zystLVVQUJAkKSgoSGVlZS36hg4dKovFIun6rhDLly/Xiy++qMWLF3dnTgAAAAAAdIjB4J1HT9BmYeHcuXNasmSJrFarVqxY4Zz6EBAQoOjoaGVmZqq2tlanTp3Svn37FBsbK0mKjY3Vjh07VF5erqqqKm3evFlz5syRJBUUFCg7O1s7d+7U9OnTXZgeAAAAAABwpTYXb9y9e7dqa2uVlZWlrKwsZ3tCQoLWrFmjjIwMRUZGymQyKTk5WdOmTZMkzZ8/X5WVlYqPj5fNZtOMGTOUlpYmSdq+fbsaGhr05JNPtrhXVlaWHn744W5MDwAAAAAAuFKbhYX09HSlp6e32n9jseFGRqNRVqtVVqv1pr78/PwOhAgAAAAAgGuxK0TndWhXCAAAAAAAgBtRWAAAAAAAAJ1GYQEAAAAA0OcZJBkN3nV0dCLEqVOn9Pjjj+v+++/XI488ohMnTtzy/BMnTigiIqJFW0NDg1avXq2wsDB9/etf1y9+8Ys270thAQAAAACAHq6hoUHPPPOMvvnNb+r48eNKTk5WUlKSrl69etO5DodD+/bt09NPPy273d6ib/PmzSorK9OhQ4f029/+Vvn5+dq/f/8t793m4o3e7Fpjs2yNzZ4Ow2X8fXvGQh1ddc3eez+HN7rW6PB0CG7RUxaY6Yqaa/a2T+oFbvf39XQIbtHft/fX2CcO/w9Ph+AWpy/VezoEtzhfU+fpEFzOv1+PfouKf1Npu+bpENzC16f3/zzx6QPv83qyDz74QHa73bn74qxZs/Sb3/xGBQUFiouLa3FuTk6O/vSnPyklJUVbt25t0Zefn6/169fr9ttv1+23366kpCTt3btXjz76aKv35rs2AAAAAKDP8+ZdIT766KOb+iwWiywWi/PxmTNnFBgY2OKcMWPGqKSk5KbnxsfHKy0tTceOHWvRXlNTo0uXLikoKMjZ9uUvf/kLr3EjCgsAAAAAAHixxMTEm9qWLl2q1NRU5+O6ujr179+/xTlms1n19TeP6Bs+fPgX3qeu7vqouBuvYzabZbPZbhkfhQUAAAAAALzY7t27NWLEiBZtN45WkCR/f39du9Zy6lF9fb38/f3bfR+z2SxJLa7Tnmv0/olAAAAAAAC0weClhySNGDFCd999d4vj3wsLgYGBKisra9FWWlraYlpDW26//XYNHTpUpaWlzraysrI2r9GuwsLRo0c1d+5cTZw4UTExMdq7d6+k6/MvrFarQkJCNHXqVL3++uvO5zgcDmVnZysiIkKhoaFau3atGhsbb7r2zp079cwzz7QrSQAAAAAAcLPw8HA5HA7t3LlTdrtd77zzjk6fPq2YmJgOXSc2NlZbtmxRVVWVLly4oFdffVWxsbG3fE6bhYWLFy8qNTVVKSkpKioqUmZmprKysnT48GH9+Mc/ltFo1JEjR/TKK68oMzNTH3zwgSQpNzdXhw4dUn5+vg4ePKji4mJt27bNed3a2lpt2LBB69ev71CSAAAAAACgJZPJpO3bt+vAgQMKCwvTtm3btGXLFg0aNEjbtm3TrFmz2nWdtLQ0feUrX9Hs2bP17W9/W9OnT9f8+fNv+Zw211goLy/X7NmznVWO8ePHKywsTIWFhTpw4IDefvttmc1mBQcHa968ecrNzVVYWJj279+vhQsXOueBpKamatWqVVq6dKkkacmSJRo0aJAef/xxXbp0qV0JAgAAAADgCkaDQUYv2xWio/Hcc8892rNnz03tycnJSk5Ovqk9PDxcRUVFLdr8/Pz0/PPP6/nnn29/nG2dEBoaqjVr1jgfV1dXq6ioSMOGDZPBYNDo0aOdfTduZfHvW12MGTNGFRUVqq6uliRlZmYqJydHQ4YMaXewAAAAAADAu3Ro8cYrV64oJSVFEyZM0NixY2UymVrs83njNhR1dXXOFSWlz7er+Ky/te0tAAAAAABAz9HuwkJZWZni4uI0ZMgQ5eTkKCAgQA0NDXI4HM5zbtyG4t/3uvzs445sdQEAAAAAgDsYDN559ATtKiwcP35ccXFxio6OVk5Ojvz8/DRq1Cg5HA5duHDBed6NW1kEBQW12OqitLRUQ4cOvWlLDAAAAAAA0HO1WVg4d+6clixZIqvVqhUrVjinPgQEBCg6OlqZmZmqra3VqVOntG/fPuc2FLGxsdqxY4fKy8tVVVWlzZs3a86cOa7NBgAAAAAAuFWbu0Ls3r1btbW1ysrKUlZWlrM9ISFBa9asUUZGhiIjI2UymZScnKxp06ZJkubPn6/KykrFx8fLZrNpxowZSktLc10mAAAAAAB0ksFgaLGGoDfwtnha02ZhIT09Xenp6a3231hsuJHRaJTVapXVar3l9VNTU9sKAQAAAAAAeKkO7QoBAAAAAABwozZHLAAAAAAA0Ot54y4M3hZPKxixAAAAAAAAOo3CAgAAAAAA6LQePRXi4yuNumqyezoMl3ngDj9Ph+AWt5l79Muw3W7r3zfqeP6m3v+6NRn7xufSx9hDxt51kc3e7OkQXM7fz8fTIbjF6f+94ukQ3CJ6zCBPh+ByJmPfeM2OGtjf0yG4xZ//vxpPh+AWwwb4ejoEl7tabfN0CC5lNBhk9LK5EN4WT2v6xrtjAAAAAADgEhQWAAAAAABAp/WNMegAAAAAANyCwQt3hfC2eFrDiAUAAAAAANBp7SosHD16VHPnztXEiRMVExOjvXv3SpJqampktVoVEhKiqVOn6vXXX3c+x+FwKDs7WxEREQoNDdXatWvV2Njo7C8oKFBMTIzuv/9+ffe731VlZWU3pwYAAAAAAFytzcLCxYsXlZqaqpSUFBUVFSkzM1NZWVk6fPiwfvzjH8toNOrIkSN65ZVXlJmZqQ8++ECSlJubq0OHDik/P18HDx5UcXGxtm3bJkk6c+aMfvSjH2ndunU6duyYRo0apWXLlrk2UwAAAAAAWmGQZDAYvOvw9H9KO7VZWCgvL9fs2bMVExMjo9Go8ePHKywsTIWFhTpw4IDS0tJkNpsVHBysefPmKTc3V5K0f/9+LVy4UCNGjNCgQYOUmprq7HvzzTcVGRmp0NBQ+fn5acWKFfrrX/+qs2fPujRZAAAAAADQvdosLISGhmrNmjXOx9XV1SoqKtKwYcNkMBg0evRoZ9+YMWNUUlIi6fqohMDAwBZ9FRUVqq6uvqnPbDbrjjvucD4XAAAAAAD0DB3aFeLKlStKSUnRhAkTNHbsWJlMJhluWKbSbDbLZrNJkurq6mQ2m519/fv3lyTZbLab+j7rr6+v73QiAAAAAAB0llHet7uBt8XTmnbHWVZWpri4OA0ZMkQ5OTkKCAhQQ0ODHA6H85z6+nr5+/tLallkkOT82N/f/6a+z/oDAgK6lAwAAAAAAHCvdhUWjh8/rri4OEVHRysnJ0d+fn4aNWqUHA6HLly44DyvtLRUQUFBkqSgoCCVlZW16Bs6dKgsFstNffX19bp48WKL6REAAAAAAMD7tVlYOHfunJYsWSKr1aoVK1Y4pz4EBAQoOjpamZmZqq2t1alTp7Rv3z7FxsZKkmJjY7Vjxw6Vl5erqqpKmzdv1pw5cyRJs2fP1rvvvqtjx46poaFBmZmZCg4O1pe//GUXpgoAAAAAwBfz+A4QrRw9QZtrLOzevVu1tbXKyspSVlaWsz0hIUFr1qxRRkaGIiMjZTKZlJycrGnTpkmS5s+fr8rKSsXHx8tms2nGjBlKS0uTJH31q1/V2rVr9fzzz+vjjz/WhAkTtGnTJhelCAAAAAAAXKXNwkJ6errS09Nb7b+x2HAjo9Eoq9Uqq9X6hf3Tp0/X9OnT2xkmAAAAAADwRh3aFQIAAAAAgN7IYJCMXjbzoIfMhOgxu1cAAAAAAAAvRGEBAAAAAAB0GlMhAAAAAAB9ntELp0J4Wzyt6dGFhaED+slym6+nw3CZ+sYmT4fgFvamZk+H4BY1tr7x+TT79v6BUNXX7J4OwS1GGv09HYJb9O8Dr1l/k4+nQ3CLi5euejoEtxg+oL+nQ3C5iqvXPB2CWxh7yuTpLmroI+/1HHJ4OgSX6ws5onN6/7spAAAAAADgMj16xAIAAAAAAN3BYDDI4GUjibwtntYwYgEAAAAAAHQahQUAAAAAANBpHSosnDhxQhEREc7HVVVVWrFihcLDwzVlyhRlZ2erqenzBep27dqlqKgohYSEaNGiRTp//ryz75///KcSExM1ceJEzZgxQ2+99VY3pAMAAAAAQMd9tiuEtx09QbsKCw6HQ/v27dPTTz8tu/3z1dBXrVqlyspKFRQU6O2331ZxcbFycnIkSQUFBcrOzta6det07NgxRUZG6qmnntK1a9d09epVffe739WECRNUWFion/3sZ8rMzNTRo0ddkyUAAAAAAHCJdhUWcnJytGfPHqWkpDiBJ5xGAAAgAElEQVTb6uvrdfjwYaWnp2vw4MEaOHCg0tLSlJeXJ4fDoYMHDyouLk5hYWHq16+fEhIS5Ovrq8LCQv3lL39RU1OTvv/978vPz09BQUFKSEhQbm6uyxIFAAAAAADdr12Fhfj4eL3xxhu67777nG3Nzdf3o/X3/3yfc6PRqKqqKtXU1Ki5uVlms7nFdXx8fHT27Fk5HA75+fnJaDTe1AcAAAAAgLsZDN559ATtKiwMHz78praAgABNnjxZGzduVHV1tS5fvqwtW7ZIkmw2m6ZPn668vDwVFxfLbrcrLy9PpaWlstlsCgkJkd1u1yuvvKKGhgadOXNGubm5unbtWvdmBwAAAAAAXKpLu0Js3LhRvr6+mjlzphYsWKDo6GhJksVi0axZs7R48WItX75cDz30kEpKSjRp0iRZLBbddttt2r59u95//31NnTpVGRkZevTRR2WxWLolKQAAAAAA4B79uvLkiooKZWRkaMCAAZKkw4cPKzAwUGazWRUVFYqMjNTChQslSU1NTYqMjJTValVDQ4Oampr0m9/8xnmtzMxMjR07tivhAAAAAAAAN+vSiIX169dr06ZNamxs1Pnz5/Xyyy8rMTFRklRYWKikpCRdunRJ9fX1ys7O1rBhwzRu3Dg1NTVp4cKFOnDggJqbm3Xs2DHl5eUpPj6+W5ICAAAAAKAjDAaDjF52GHrIIgtdGrHwwgsvaPXq1QoLC5PFYtETTzzhLCzExsaqpKREc+bMkd1uV0REhLZu3SqDwSCz2azNmzdr/fr1+sEPfqAvfelL+slPfqJ77723W5ICAAAAAADu0aHCQnh4uIqKipyPR44cqZ07d37huQaDQStXrtTKlSu/sH/y5Ml66623OnJ7AAAAAADgZbo0YgEAAAAAgN7AqC6uFeAC3hZPa3pKnAAAAAAAwAtRWAAAAAAAAJ3GVAgAAAAAQJ9nkORtmzB4WTit6tGFhYYmh641NXs6DJcZ6u/n6RDcorHJ4ekQ3KKuofe+Vm904sIVT4fgclNHDvZ0CG5xzd43XrO2PpCnv5+Pp0Nwi3mTvuTpENzidGXv/z7bz9ve2btIVX2Dp0NwixG3mTwdglv079f7B4PbfXp/jugcXhkAAAAAAKDTevSIBQAAAAAAuoPRYJDRy0ZMeVs8rWHEAgAAAAAA6LQOFRZOnDihiIgI5+OqqiqtWLFC4eHhmjJlirKzs9XU1OTs37Vrl6KiohQSEqJFixbp/Pnzzr7S0lJ95zvfUWhoqKZNm6atW7d2QzoAAAAAAMCd2lVYcDgc2rdvn55++mnZ7XZn+6pVq1RZWamCggK9/fbbKi4uVk5OjiSpoKBA2dnZWrdunY4dO6bIyEg99dRTunbtmiTp+9//viZNmqQPPvhAr732mnbt2qV3333XBSkCAAAAAHBrBoN3Hj1BuwoLOTk52rNnj1JSUpxt9fX1Onz4sNLT0zV48GANHDhQaWlpysvLk8Ph0MGDBxUXF6ewsDD169dPCQkJ8vX1VWFhoSSprKxM0vWihcFgkMFgkJ9f39gFAQAAAACA3qJdhYX4+Hi98cYbuu+++5xtzc3Xt+fy9/f//GJGo6qqqlRTU6Pm5maZzeYW1/Hx8dHZs2clSc8884w2b96scePG6Rvf+IZmz56tKVOmdDUfAAAAAADgRu0qLAwfPvymtoCAAE2ePFkbN25UdXW1Ll++rC1btkiSbDabpk+frry8PBUXF8tutysvL0+lpaWy2WySJIPBoFWrVunDDz/U7373Ox06dEj79u3rxtQAAAAAAGgfg0EyetnRq6ZCtGbjxo3y9fXVzJkztWDBAkVHR0uSLBaLZs2apcWLF2v58uV66KGHVFJSokmTJslisai4uFg7d+7Ud77zHfn5+enee+9VUlKS9uzZ0y1JAQAAAAAA9+jXlSdXVFQoIyNDAwYMkCQdPnxYgYGBMpvNqqioUGRkpBYuXChJampqUmRkpKxWqz766CPZ7Xbn+gqS1K9fP/Xr16VwAAAAAACAm3VpxML69eu1adMmNTY26vz583r55ZeVmJgoSSosLFRSUpIuXbqk+vp6ZWdna9iwYRo3bpwmTpyo5uZm5eTkqLGxUWfPntWOHTs0a9asbkkKAAAAAICOMBoMXnn0BF0aIvDCCy9o9erVCgsLk8Vi0RNPPOEsLMTGxqqkpERz5syR3W5XRESEtm7dKoPBoMGDB2v79u36yU9+ol27dslisejxxx/XggULuiUpAAAAAADgHh0qLISHh6uoqMj5eOTIkdq5c+cXnmswGLRy5UqtXLnyC/vvv/9+/fd//3dHbg8AAAAAALwMixoAAAAAAPo8gxfuwuBt8bSmS2ssAAAAAACAvo3CAgAAAAAA6DSmQgAAAAAA+jyj4frhTbwtntYwYgEAAAAAAHRajx6x0M9H8vXpISWcTrhsa/B0CG5x9+3+ng7BLfr79o063v9+UuvpEFzO5NM3PpcD+vt4OgS3qKi55ukQXO42c4/+cd9uF2v6xs/N4CEBng7B5Ub/R+/PUZKuXLzs6RDcotnh8HQIbjHYv/d/r/Ux9433Bui43v/qBwAAAACgDYb/++dNvC2e1vSNP7sBAAAAAACXoLAAAAAAAAA6rUOFhRMnTigiIsL5uKqqSitWrFB4eLimTJmi7OxsNTU1Oft37dqlqKgohYSEaNGiRTp//rwkqaioSA888ECLY+zYsZo+fXo3pQUAAAAAQPsZDJ/vDOEth6FnzIRoX2HB4XBo3759evrpp2W3253tq1atUmVlpQoKCvT222+ruLhYOTk5kqSCggJlZ2dr3bp1OnbsmCIjI/XUU0/p2rVrCg0N1Ycffug8CgoKNHDgQK1evdo1WQIAAAAAAJdoV2EhJydHe/bsUUpKirOtvr5ehw8fVnp6ugYPHqyBAwcqLS1NeXl5cjgcOnjwoOLi4hQWFqZ+/fopISFBvr6+KiwsvOn6P/zhDxUbG6spU6Z0X2YAAAAAAMDl2rUrRHx8vNLS0nTs2DFnW3NzsyTJ3//zrQKNRqOqqqpUU1Oj5uZmmc3mFtfx8fHR2bNnW7T94Q9/0JkzZ7R169bO5gAAAAAAQJcYdX36gTfpKYsitivO4cOH39QWEBCgyZMna+PGjaqurtbly5e1ZcsWSZLNZtP06dOVl5en4uJi2e125eXlqbS0VDabrcV1tm7dqsWLF6t///7dkA4AAAAAAHCnLhVANm7cKF9fX82cOVMLFixQdHS0JMlisWjWrFlavHixli9froceekglJSWaNGmSLBaL8/mnTp3SmTNn9K1vfatrWQAAAAAAAI9o11SI1lRUVCgjI0MDBgyQJB0+fFiBgYEym82qqKhQZGSkFi5cKElqampSZGSkrFar8/nvvvuuHnzwQefzAQAAAADwBIPBIIOXbcPgbfG0pksjFtavX69NmzapsbFR58+f18svv6zExERJUmFhoZKSknTp0iXV19crOztbw4YN07hx45zP/9vf/qaJEyd2LQMAAAAAAOAxXRqx8MILL2j16tUKCwuTxWLRE0884SwsxMbGqqSkRHPmzJHdbldERIS2bt3aouJSXl6uYcOGdS0DAAAAAADgMR0qLISHh6uoqMj5eOTIkdq5c+cXnmswGLRy5UqtXLmy1eu98847Hbk9AAAAAAAuYTR44a4QXhZPa3rK7hUAAAAAAMALUVgAAAAAAACd1qU1FgAAAAAA6A0MhuuHN/G2eFrDiAUAAAAAANBpPXrEgr+vjwJMPp4Ow2WaPR2Am/j0lBVJuujKtSZPh+AWX77D4ukQXK7J4fB0CG5hUN/42jT16/019upau6dDcIv+feBzKUl1jb3/54lPT/kTXRfd5tuj34q3W8XVvvE9aKC5938+a/rI+1l0XO9/9QMAAAAA0AaDQTJ6WWHTy8JpVd8o7QMAAAAAAJegsAAAAAAAADqNqRAAAAAAgD7PaLh+eBNvi6c1HRqxcOLECUVERDgfV1VVacWKFQoPD9eUKVOUnZ2tpqbPF/TYtWuXoqKiFBISokWLFun8+fPOvqtXr+oHP/iBwsLCFB4erv/6r/+S3d43FnYBAAAAAKC7nTp1So8//rjuv/9+PfLIIzpx4kSHz6uoqNAzzzyjsLAwTZ48WS+99FKbv6u3q7DgcDi0b98+Pf300y0uuGrVKlVWVqqgoEBvv/22iouLlZOTI0kqKChQdna21q1bp2PHjikyMlJPPfWUrl27Jkn64Q9/qJqaGr333nsqKCjQyZMn9eqrr7YnHAAAAAAAcIOGhgY988wz+uY3v6njx48rOTlZSUlJunr1aofOe/HFFzV06FAdOXJE+/fv19GjR7Vnz55b3rtdhYWcnBzt2bNHKSkpzrb6+nodPnxY6enpGjx4sAYOHKi0tDTl5eXJ4XDo4MGDiouLU1hYmPr166eEhAT5+vqqsLBQFRUVeu+99/Tiiy9qwIABGjx4sH7+85/rkUce6ej/HQAAAAAAXWYweOfRXh988IHsdruefPJJ+fr6atasWQoKClJBQUGHzisrK2txvtFoVP/+/W9573YVFuLj4/XGG2/ovvvuc7Y1NzdLkvz9/VvcsKqqSjU1NWpubpbZbG5xHR8fH509e1b//Oc/dccdd+jNN99UVFSUpk2bpt27d2v48OHtCQcAAAAAgD7jo48+0oULF1ocNTU1Lc45c+aMAgMDW7SNGTNGJSUlHTpv0aJF2r9/v+6//35NmTJFY8aM0bx5824ZX7sKC1/0C39AQIAmT56sjRs3qrq6WpcvX9aWLVskSTabTdOnT1deXp6Ki4tlt9uVl5en0tJS2Ww2VVdXq7y8XP/617/05ptv6je/+Y3ee+89/fKXv2xPOAAAAAAA9BmJiYmKiopqcbz22mstzqmrq7tpZIHZbFZ9fX2HznM4HEpKSlJRUZH+8Ic/6Pz589q0adMt4+vSrhAbN27USy+9pJkzZ2rQoEF68skn9cc//lEWi0WzZs3SJ598ouXLl6uurk7f/OY3NWnSJFksFplMJjU1NekHP/iBAgICFBAQoCeffFJ79uxRcnJyV0ICAAAAAKDDjDLIKO/ahuGzeHbv3q0RI0a06LNYLC0e+/v7O9c0/Ex9fX2LWQZtnVdRUaHVq1frgw8+kNlslr+/v773ve/pueee0/e+971W4+xSYaGiokIZGRkaMGCAJOnw4cMKDAyU2WxWRUWFIiMjtXDhQklSU1OTIiMjZbVa5efnJ0mqqanRbbfd5uwHAAAAAAAtjRgxQnffffctzwkMDNTOnTtbtJWWlurRRx9t93mffPKJGhsb1djY6Ozz8fFRv363Lh10aLvJf7d+/Xpt2rRJjY2NOn/+vF5++WUlJiZKkgoLC5WUlKRLly6pvr5e2dnZGjZsmMaNG6evfvWruu+++7Ru3TrV1dWpvLxcO3fu1OzZs7sSDgAAAAAAfVJ4eLgcDod27twpu92ud955R6dPn1ZMTEy7zwsKCtIdd9yhtWvX6tq1a6qoqNDPfvYzzZo165b37lJh4YUXXtC//vUvhYWFacGCBXrkkUechYXY2FjFxMRozpw5evDBB3Xu3Dlt3bpVhv9b1nL79u3y8/NTdHS0HnvsMUVFRTlHNwAAAAAA4E6e3v2hq7tCmEwmbd++XQcOHFBYWJi2bdumLVu2aNCgQdq2bZuzOHCr8z7ru3Tpkh588EE99thjuv/++7VixYpb3rtDUyHCw8NVVFTkfDxy5MibhlB8xmAwaOXKlVq5cuUX9g8aNEiZmZkduT0AAAAAAGjFPffcoz179tzUnpyc3GI9w9bOk65PlejoxgpdGrEAAAAAAAD6ti4t3ggAAAAAQG9gkGT0rk0hvGyPitYxYgEAAAAAAHQahQUAAAAAANBpPXoqxOD+Jg0y+3k6DJcZ0r/35nYjH28bb+QitQ3Nng7BLT6psXk6BHST2muNbZ/UCwy/vb+nQ3C5/62u93QIbnHywqeeDsEtvna3xdMhuFyzw9MRuIevsW/8ja++ocnTIbjFpat2T4fgcrW1vfu9gdFgkLEj2zC4gbfF05q+8d0MAAAAAAC4BIUFAAAAAADQaT16KgQAAAAAAN3BYLh+eBNvi6c1jFgAAAAAAACd1qHCwokTJxQREeF8XFVVpRUrVig8PFxTpkxRdna2mpo+X5xl165dioqKUkhIiBYtWqTz5887+woLCxUcHKwHHnjAeWzZsqUbUgIAAAAAAO7SrqkQDodDv/3tb7Vhw4YW7atWrZLdbldBQYF8fHy0fPly5eTkaNmyZSooKFB2dra2bdumiRMnKi8vT0899ZTeeecd+fn56R//+IdmzJih7OxslyQGAAAAAEB7sStE57VrxEJOTo727NmjlJQUZ1t9fb0OHz6s9PR0DR48WAMHDlRaWpry8vLkcDh08OBBxcXFKSwsTP369VNCQoJ8fX1VWFgoSfr73/+u4OBg12QFAAAAAADcol2Fhfj4eL3xxhu67777nG3Nzc2SJH9//88vZjSqqqpKNTU1am5ultlsbnEdHx8fnT17VtL1wsKf//xnPfzww3rooYe0YcMGNTQ0dDUfAAAAAADgRu0qLAwfPvymtoCAAE2ePFkbN25UdXW1Ll++7FwjwWazafr06crLy1NxcbHsdrvy8vJUWloqm82mxsZGjRgxQjExMSooKNBrr72mwsJC/fSnP+3e7AAAAAAAaIfPdoXwtqMn6NKuEBs3bpSvr69mzpypBQsWKDo6WpJksVg0a9YsLV68WMuXL9dDDz2kkpISTZo0SRaLRf369dNrr72mxMREmc1mjRo1SsnJyTp48GC3JAUAAAAAANyjXYs3tqaiokIZGRkaMGCAJOnw4cMKDAyU2WxWRUWFIiMjtXDhQklSU1OTIiMjZbVa9fHHH2vnzp1atmyZTCaTJMlut8vPz6+L6QAAAAAAAHfq0oiF9evXa9OmTWpsbNT58+f18ssvKzExUdL17SSTkpJ06dIl1dfXKzs7W8OGDdO4ceM0cOBAvfXWW9q6dasaGxt19uxZbd26VXPnzu2WpAAAAAAA6AiDrv+C7E1HD5kJ0bURCy+88IJWr16tsLAwWSwWPfHEE87CQmxsrEpKSjRnzhzZ7XZFRERo69atMhgM8vPz0/bt27V27VqFh4fLbDYrLi5OTz31VLckBQAAAAAA3KNDhYXw8HAVFRU5H48cOVI7d+78wnMNBoNWrlyplStXfmF/cHCwdu3a1ZHbAwAAAAAAL9OlEQsAAAAAAPQGBoNBBi/bhsHb4mlNl9ZYAAAAAAAAfRuFBQAAAAAA0GlMhQAAAAAA9HkGed8uDN4WT2t6dGHh49prsl21eToMl6m1N3o6BLcYe7fF0yG4hcPh8HQIblF88qKnQ3C55un3ejoEtxjQv0f/iGg3Yx8Yu+dv6hufy/vuvt3TIbjFmao6T4fgcl8ZNMDTIaAb3dbf19MhuMUIS+/Ps6ahb/w8Qcf1gbdTAAAAAADAVSg5AQAAAAD6PKPBIKOX7cLgbfG0hhELAAAAAACg0ygsAAAAAACATutQYeHEiROKiIhwPq6qqtKKFSsUHh6uKVOmKDs7W01NTc7+Xbt2KSoqSiEhIVq0aJHOnz9/0zXtdrvmzp2rzZs3dyENAAAAAAA6z+ClR0/QrsKCw+HQvn379PTTT8tutzvbV61apcrKShUUFOjtt99WcXGxcnJyJEkFBQXKzs7WunXrdOzYMUVGRuqpp57StWvXWlz7pz/9qf75z392Y0oAAAAAAMBd2lVYyMnJ0Z49e5SSkuJsq6+v1+HDh5Wenq7Bgwdr4MCBSktLU15enhwOhw4ePKi4uDiFhYWpX79+SkhIkK+vrwoLC53XOHbsmP785z9rypQp3Z8ZAAAAAABwuXYVFuLj4/XGG2/ovvvuc7Y1NzdLkvz9/T+/mNGoqqoq1dTUqLm5WWazucV1fHx8dPbsWUnSp59+qtWrV2vDhg3y9e39e74CAAAAALyXweCdR0/QrsLC8OHDb2oLCAjQ5MmTtXHjRlVXV+vy5cvasmWLJMlms2n69OnKy8tTcXGx7Ha78vLyVFpaKpvNJkl6/vnnlZCQoHvuuacb0wEAAAAAAO7UpV0hNm7cKF9fX82cOVMLFixQdHS0JMlisWjWrFlavHixli9froceekglJSWaNGmSLBaL3njjDV2+fFkLFy7sliQAAAAAAIBn9OvKkysqKpSRkaEBAwZIkg4fPqzAwECZzWZVVFQoMjLSWTxoampSZGSkrFarNm3apBMnTuhrX/uapM/Xazh58qR+8YtfdDElAAAAAAA6yiCD18098LZ4vliXCgvr16/XV77yFa1atUoXL17Uyy+/rMTERElSYWGhtmzZot27d2vAgAHasmWLhg0bpnHjxunVV19tcZ1nnnlGwcHBSk1N7Uo4AAAAAADAzbpUWHjhhRe0evVqhYWFyWKx6IknnnAWFmJjY1VSUqI5c+bIbrcrIiJCW7du9cIKEAAAAAAA6KwOFRbCw8NVVFTkfDxy5Ejt3Pn/s3f3YXHVd/7/XzPAwACOVEwwvxhjAtG61W9UlFmEuiTiWolgbzZIQ4gKqU3ESivJheleqZvSrlSoGBqSbjRb3NTSTsSmFdmoTXtZtqbY8TKBdBuTGJNSjYUyIMQMMDDz+yM6KRuJAwMzAzwfXOeP8zln5rw+Gu7efG7qPvZeg8Gg9evXa/369Z/4vlu3bh1LDAAAAAAAJpRRfi5COAlCLc9opkpOAAAAAAAQgigsAAAAAACAcfNrjQUAAAAAAKYDgyH0doUItTyjYcQCAAAAAAAYtyk9YuFvp4fUf8oV7BiT5tMXXRDsCAHxt1ODwY4QEHMspmBHCIirP3NJsCNMOo/HE+wIAdHvcgc7QkCYwqd/P7tODQQ7QkCYTTPj7yWfjo8JdoRJNzg8/T8vJWl4hnw/cQ4OBTtCQPQ4p/SvVj451T8zPjcxdtP/Xz8AAAAAAJ/A8OERSkItz2hmRmkfAAAAAABMCgoLAAAAAABg3JgKAQAAAACY8QyG0NuFIcTijGpMIxZaW1uVmprqPXc4HCotLZXValV6erqqq6s1PDzsvb5z507dcsstSk5O1urVq9Xe3u699sYbbyg3N1fXX3+9br75ZtXW1s6YBdEAAAAAAJgufCoseDwe7dq1S4WFhXK5zu7CUFZWpq6uLjU1NamxsVFtbW2qqamRJDU1Nam6ulqPPvqoWlpatHTpUt17770aGBjQ4OCg1qxZoy996Ut6/fXX9cwzz6i+vl4vv/zy5PQSAAAAAABMCp8KCzU1Naqvr9fatWu9bU6nU83NzdqwYYPi4+MVFxenkpIS2Ww2eTwevfTSS8rNzVVKSorCw8O1YsUKRUREaN++fTKZTNq7d6/uuusuSVJ3d7eGh4d14YUXTk4vAQAAAAA4D2OIHlOBT2ss5OXlqaSkRC0tLd42t/vMHqbR0dHeNqPRKIfDod7eXrndbpnN5hHvExYWpuPHj0uSYmNjJUmf/exn1dnZqTvuuEM33nijX50BAAAAAACB5VMBJCEh4Zy2mJgYpaWlqbKyUj09Peru7lZtba0kqb+/X7fddptsNpva2trkcrlks9l07Ngx9ff3j3ifvXv36sUXX9TBgwe90ygAAAAAAMDU4NfIisrKSkVERCgrK0sFBQXKzMyUJFksFi1btkz33XefHnroIWVkZOjw4cO66aabZLFYRrxHZGSkLr/8cq1evVp79+71Jw4AAAAAAONjMMgQYsdU2RbCr+0mOzo6tGnTJu+0hubmZiUmJspsNqujo0NLly7V3XffLUkaHh7W0qVL9eCDD+r48eP6yle+ol/84hfeqRSDg4PnFB0AAAAAAEBo82vEQkVFhTZv3qyhoSG1t7erqqpK+fn5kqR9+/apqKhInZ2dcjqdqq6u1uzZs3XNNdfosssuk8lk0hNPPCGXy6WjR49qx44dWr58+YR0CgAAAAAABIZfIxbKy8u1ceNGpaSkyGKxaOXKld7CQk5Ojg4fPqw777xTLpdLqamp2rZtm3dIx7Zt2/Ttb39bqampuuiii7R69Wp9/vOfn5BOAQAAAAAwFoYPj1ASanlGM6bCgtVqld1u957PmzdPdXV1H3uvwWDQ+vXrtX79+o+9ftlll+mpp54ay+MBAAAAAECImSrbYgIAAAAAgBDk11QIAAAAAACmg1DchCHU8oyGEQsAAAAAAGDcKCwAAAAAAIBxm9JTIeZaTIqLiwx2jEkz5PYEO0JATJHRPX67yDylP918NutCc7AjTLrwsJlRkx0YGg52hID4c9dgsCNMuvf6+oMdISCMM+QbSu+gK9gRJt1cTf/vJZIUHREW7AgBcYll+v68/veiI6b/zwfD4dO7j0YZZAyx305CLc9opve/DAAAAAAAMKkoLAAAAAAAgHGbGWOzAQAAAAA4D3aFGD9GLAAAAAAAgHEbU2GhtbVVqamp3nOHw6HS0lJZrValp6erurpaw8NnF/vauXOnbrnlFiUnJ2v16tVqb2/3Xjt48KDy8/N1ww03KCMjQ1u2bJHHMzMWKwQAAAAAYLrwqbDg8Xi0a9cuFRYWyuU6uxJxWVmZurq61NTUpMbGRrW1tammpkaS1NTUpOrqaj366KNqaWnR0qVLde+992pgYEBOp1Nf/epXdfvtt6ulpUV1dXX6+c9/LpvNNjm9BAAAAADgPAwh+jEV+FRYqKmpUX19vdauXettczqdam5u1oYNGxQfH6+4uDiVlJTIZrPJ4/HopZdeUm5urlJSUhQeHq4VK1YoIiJC+/bt08mTJ3Xttddq5cqVCgsL0+WXX67MzEy9/vrrk9ZRAAAAAAAw8XwqLOTl5em5557T1Vdf7W1zu92SpOjo6LNvZjTK4XCot7dXbrdbZvPIPYjDwsJ0/PhxLVy4ULW1td72wcFB/fa3v9U//MM/+NUZAAAAAAAQWD4VFhISEs5pi4mJUZt+RGwAACAASURBVFpamiorK9XT06Pu7m5vsaC/v1+33XabbDab2tra5HK5ZLPZdOzYMfX39494n8HBQZWWlspsNisvL28CugQAAAAAAALFr10hKisrFRERoaysLBUUFCgzM1OSZLFYtGzZMt1333166KGHlJGRocOHD+umm26SxWLxvr6zs1OrVq1SV1eXfvSjHykqKsq/3gAAAAAAMA4fbTcZasdUEO7Pizs6OrRp0ybFxsZKkpqbm5WYmCiz2ayOjg4tXbpUd999tyRpeHhYS5cu1YMPPihJOnr0qIqKivSP//iPKi8vl8lk8rMrAAAAAAAg0PwasVBRUaHNmzdraGhI7e3tqqqqUn5+viRp3759KioqUmdnp5xOp6qrqzV79mxdc801ev/991VYWKisrCx973vfo6gAAAAAAMAU5deIhfLycm3cuFEpKSmyWCxauXKlt7CQk5Ojw4cP684775TL5VJqaqq2bdsmg8GgX/ziF/rrX/+q+vp6/fSnP/W+35IlS/T444/71yMAAAAAAMbIIIOMIba941TZbnJMhQWr1Sq73e49nzdvnurq6j72XoPBoPXr12v9+vXnXFu1apVWrVo1tqQAAAAAACDk+DUVAgAAAAAAzGx+TYUAAAAAAGA6CMVdGEItz2gYsQAAAAAAAMaNwgIAAAAAABg3pkKEsGGPJ9gRAmJw2B3sCAHxzvuDwY4QEGHGKTJeyw8fDA4FO0JAmE1hwY4QENGm6f+tMDJsZvy/vPTCyGBHCIiZ8NPBTPi8lKRuR1+wIwTEBZEz42+Zp13T/2da59D07qNBoTf1IMTijGpmfJYDAAAAAIBJQWEBAAAAAACM28wYZwYAAAAAwHkYPvwIJaGWZzSMWAAAAAAAAOM2psJCa2urUlNTvecOh0OlpaWyWq1KT09XdXW1hoeHvdd37typW265RcnJyVq9erXa29vPec/29nbdeOON6u3t9aMbAAAAAAAgGHwqLHg8Hu3atUuFhYVyuVze9rKyMnV1dampqUmNjY1qa2tTTU2NJKmpqUnV1dV69NFH1dLSoqVLl+r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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "plt.pcolor(df_grouped_corr.to_numpy(), norm=None, cmap='Blues')\n",
    "plt.yticks(np.arange(df_grouped_corr.to_numpy().shape[0])+0.5, [int(x) for x in df_grouped_corr.index.values])\n",
    "plt.xticks(np.arange(df_grouped_corr.to_numpy().shape[1])+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)], rotation = 45)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "#plt.savefig(\"figures/corr_topic_model_all_time_15.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topic correlations\n",
    "\n",
    "topics_correlations = np.zeros((mdl.k,mdl.k))\n",
    "\n",
    "for k in range(mdl.k):\n",
    "    for m,c in enumerate(mdl.get_correlations(k)):\n",
    "        topics_correlations[k][m] = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.pcolor(topics_correlations, norm=None, cmap='RdBu_r')\n",
    "plt.yticks(np.arange(mdl.k)+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)])\n",
    "plt.xticks(np.arange(mdl.k)+0.5, [\"Topic #\"+str(n) for n in range(mdl.k)], rotation = 45)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "#plt.savefig(\"figures/corr_topic_model_correlations_15.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Language modelling, semantic spaces and clustering\n",
    "\n",
    "Future work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "covid",
   "language": "python",
   "name": "covid"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}