Research_Question_2/Figure-7_Topic-evolution-over-time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "19a16881",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "\n",
    "import re\n",
    "import string\n",
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
    "from nltk.stem.porter import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import datetime as dt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "import matplotlib\n",
    "\n",
    "pd.set_option('max_colwidth', 200)\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00879f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def latexify(fig_width=None, fig_height=None, columns=2):\n",
    "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
    "    Call this before plotting a figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig_width : float, optional, inches\n",
    "    fig_height : float,  optional, inches\n",
    "    columns : {1, 2}\n",
    "    \"\"\"\n",
    "\n",
    "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
    "\n",
    "    # Width and max height in inches for IEEE journals taken from\n",
    "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
    "\n",
    "    assert(columns in [1,2])\n",
    "\n",
    "    if fig_width is None:\n",
    "        fig_width = 3.32 if columns==1 else 6.75 # width in inches\n",
    "\n",
    "    if fig_height is None:\n",
    "        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
    "        fig_height = fig_width*golden_mean # height in inches\n",
    "\n",
    "    MAX_HEIGHT_INCHES = 8.0\n",
    "    if fig_height > MAX_HEIGHT_INCHES:\n",
    "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
    "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
    "        fig_height = MAX_HEIGHT_INCHES\n",
    "\n",
    "    params = {'backend': 'ps',\n",
    "              'text.latex.preamble': [r'\\usepackage{gensymb}'],\n",
    "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
    "              'axes.titlesize': 8,\n",
    "              'font.size': 8, # was 10\n",
    "              'legend.fontsize': 8, # was 10\n",
    "              'xtick.labelsize': 8,\n",
    "              'ytick.labelsize': 8,\n",
    "              'text.usetex': True,\n",
    "              'figure.figsize': [fig_width,fig_height],\n",
    "              'font.family': 'serif'\n",
    "    }\n",
    "\n",
    "    matplotlib.rcParams.update(params)\n",
    "\n",
    "\n",
    "def format_axes(ax,twinx=False,SPINE_COLOR=\"gray\"):\n",
    "    \n",
    "    if twinx:\n",
    "        for spine in ['top']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom', 'right']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "        \n",
    "    else:\n",
    "        for spine in ['top', 'right']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "\n",
    "    for axis in [ax.xaxis, ax.yaxis]:\n",
    "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a383b1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>media</th>\n",
       "      <th>date</th>\n",
       "      <th>url</th>\n",
       "      <th>heading</th>\n",
       "      <th>content</th>\n",
       "      <th>other.author</th>\n",
       "      <th>other.top_image</th>\n",
       "      <th>other.category</th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17025</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>https://www.thehindu.com/news/cities/Delhi/aqi-likely-to-be-very-poor-in-delhi-today/article33136263.ece</td>\n",
       "      <td>AQI likely to be ‘very poor’ in Delhi today</td>\n",
       "      <td>City recorded season’s minimum temperature of 9.4°C\\n\\nThe air quality of Delhi and Noida deteriorated slightly and continued to be in ‘poor’ category on Thursday and Gurugram’s fell to ‘poor’ cat...</td>\n",
       "      <td>StaffReporter</td>\n",
       "      <td>https://www.thehindu.com/static/theme/default/base/img/og-image.jpg</td>\n",
       "      <td>delhi</td>\n",
       "      <td>delhi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>delhi</td>\n",
       "      <td>central delhi</td>\n",
       "      <td>{'air quality': 4.0, 'aqi': 3.0, 'pm2.5': 1.0, 'stubble burning': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11317</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/mumbai/mumbai-beats-delhis-aqi-light-rain-likely-on-fri-sat/articleshow/80159526.cms</td>\n",
       "      <td>Mumbai beats Delhi’s AQI, light rain likely on Fri, Sat</td>\n",
       "      <td>Mumbai: The city’s overall air quality index (AQI) on Thursday was higher compared to Delhi, according to System of Air Quality Weather Forecasting and Research’s (SAFAR) real-time monitoring. Thi...</td>\n",
       "      <td>Vinamrata Borwankar / TNN / Updated: Jan 8, 2021, 14:43 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>maharashtra</td>\n",
       "      <td>mumbai suburban</td>\n",
       "      <td>{'air quality': 3.0, 'aqi': 7.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6725</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/ahmedabad/unsafe-cargo-handling-at-ports-polluting-marine-life/articleshow/64860286.cms</td>\n",
       "      <td>Unsafe cargo handling at ports polluting marine life</td>\n",
       "      <td>AHMEDABAD: State’s nodal maritime agency, Gujarat Maritime Board (GMB) has been found guilty for causing severe air and water pollution at Bedi, Nava Bandar and associated ports in Jamnagar. The G...</td>\n",
       "      <td>Premal Balan / TNN / Updated: Jul 5, 2018, 05:15 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-64862606,width-1070,height-580,imgsize-893782,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>gujarat</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>{'air pollution': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8386</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/delhi/no-relief-in-sight-heatwave-to-continue/articleshow/69604594.cms</td>\n",
       "      <td>It’s 46.2°C in Delhi; no rain in sight</td>\n",
       "      <td>Delhi recorded a maximum of 44.8°C on Friday\\n\\nNEW DELHI: It was another hot day in the city as the maximum temperature reached season’s highest till now — 44.8 degree Celsius at Safdarjung, whic...</td>\n",
       "      <td>TNN / Updated: Jun 1, 2019, 07:17 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-69605923,width-1070,height-580,imgsize-615101,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>delhi</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>delhi</td>\n",
       "      <td>central delhi</td>\n",
       "      <td>{'air quality': 2.0, 'pm10': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11005</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/chandigarh/harder-to-breathe-in-haryana-air-quality-of-six-cities-severe/articleshow/79240376.cms</td>\n",
       "      <td>Harder to breathe in Haryana: Air quality of six cities ‘severe’</td>\n",
       "      <td>Harder to breathe in Haryana: Air quality of six cities ‘severe’\\nKARNAL: The air quality index (AQI) of Haryana’s in six cities again reached ‘severe’ category a day after Diwali on Sunday. Track...</td>\n",
       "      <td>TNN /  Nov 16, 2020, 08:12 IST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NewsCity Newschandigarh NewsHarder to breathe in Haryana: Air quality of six cities ‘severe’</td>\n",
       "      <td>chandigarh</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>chandigarh</td>\n",
       "      <td>chandigarh</td>\n",
       "      <td>{'air quality': 3.0, 'aqi': 2.0}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       media        date  \\\n",
       "17025  Hindu  2020-11-20   \n",
       "11317    TOI  2021-01-08   \n",
       "6725     TOI  2018-07-05   \n",
       "8386     TOI  2019-06-01   \n",
       "11005    TOI  2020-11-16   \n",
       "\n",
       "                                                                                                                                              url  \\\n",
       "17025                                    https://www.thehindu.com/news/cities/Delhi/aqi-likely-to-be-very-poor-in-delhi-today/article33136263.ece   \n",
       "11317               http://timesofindia.indiatimes.com//city/mumbai/mumbai-beats-delhis-aqi-light-rain-likely-on-fri-sat/articleshow/80159526.cms   \n",
       "6725             http://timesofindia.indiatimes.com//city/ahmedabad/unsafe-cargo-handling-at-ports-polluting-marine-life/articleshow/64860286.cms   \n",
       "8386                              http://timesofindia.indiatimes.com//city/delhi/no-relief-in-sight-heatwave-to-continue/articleshow/69604594.cms   \n",
       "11005  http://timesofindia.indiatimes.com//city/chandigarh/harder-to-breathe-in-haryana-air-quality-of-six-cities-severe/articleshow/79240376.cms   \n",
       "\n",
       "                                                                heading  \\\n",
       "17025                       AQI likely to be ‘very poor’ in Delhi today   \n",
       "11317           Mumbai beats Delhi’s AQI, light rain likely on Fri, Sat   \n",
       "6725               Unsafe cargo handling at ports polluting marine life   \n",
       "8386                             It’s 46.2°C in Delhi; no rain in sight   \n",
       "11005  Harder to breathe in Haryana: Air quality of six cities ‘severe’   \n",
       "\n",
       "                                                                                                                                                                                                       content  \\\n",
       "17025  City recorded season’s minimum temperature of 9.4°C\\n\\nThe air quality of Delhi and Noida deteriorated slightly and continued to be in ‘poor’ category on Thursday and Gurugram’s fell to ‘poor’ cat...   \n",
       "11317  Mumbai: The city’s overall air quality index (AQI) on Thursday was higher compared to Delhi, according to System of Air Quality Weather Forecasting and Research’s (SAFAR) real-time monitoring. Thi...   \n",
       "6725   AHMEDABAD: State’s nodal maritime agency, Gujarat Maritime Board (GMB) has been found guilty for causing severe air and water pollution at Bedi, Nava Bandar and associated ports in Jamnagar. The G...   \n",
       "8386   Delhi recorded a maximum of 44.8°C on Friday\\n\\nNEW DELHI: It was another hot day in the city as the maximum temperature reached season’s highest till now — 44.8 degree Celsius at Safdarjung, whic...   \n",
       "11005  Harder to breathe in Haryana: Air quality of six cities ‘severe’\\nKARNAL: The air quality index (AQI) of Haryana’s in six cities again reached ‘severe’ category a day after Diwali on Sunday. Track...   \n",
       "\n",
       "                                                      other.author  \\\n",
       "17025                                                StaffReporter   \n",
       "11317  Vinamrata Borwankar / TNN / Updated: Jan 8, 2021, 14:43 IST   \n",
       "6725          Premal Balan / TNN / Updated: Jul 5, 2018, 05:15 IST   \n",
       "8386                         TNN / Updated: Jun 1, 2019, 07:17 IST   \n",
       "11005                               TNN /  Nov 16, 2020, 08:12 IST   \n",
       "\n",
       "                                                                                                                                other.top_image  \\\n",
       "17025                                                                       https://www.thehindu.com/static/theme/default/base/img/og-image.jpg   \n",
       "11317  https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg   \n",
       "6725   https://static.toiimg.com/thumb/msid-64862606,width-1070,height-580,imgsize-893782,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg   \n",
       "8386   https://static.toiimg.com/thumb/msid-69605923,width-1070,height-580,imgsize-615101,resizemode-75,overlay-toi_sw,pt-32,y_pad-40/photo.jpg   \n",
       "11005                                                                                                                                       NaN   \n",
       "\n",
       "                                                                                     other.category  \\\n",
       "17025                                                                                         delhi   \n",
       "11317                                                                                           NaN   \n",
       "6725                                                                                            NaN   \n",
       "8386                                                                                            NaN   \n",
       "11005  NewsCity Newschandigarh NewsHarder to breathe in Haryana: Air quality of six cities ‘severe’   \n",
       "\n",
       "             city    year        state         district  \\\n",
       "17025       delhi     NaN        delhi    central delhi   \n",
       "11317      mumbai  2021.0  maharashtra  mumbai suburban   \n",
       "6725    ahmedabad  2018.0      gujarat        ahmedabad   \n",
       "8386        delhi  2019.0        delhi    central delhi   \n",
       "11005  chandigarh  2020.0   chandigarh       chandigarh   \n",
       "\n",
       "                                                                      matches  \n",
       "17025  {'air quality': 4.0, 'aqi': 3.0, 'pm2.5': 1.0, 'stubble burning': 1.0}  \n",
       "11317                                        {'air quality': 3.0, 'aqi': 7.0}  \n",
       "6725                                                   {'air pollution': 1.0}  \n",
       "8386                                        {'air quality': 2.0, 'pm10': 1.0}  \n",
       "11005                                        {'air quality': 3.0, 'aqi': 2.0}  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_articles = pd.read_csv(\"../dataset/News_articles_dataset/News_articles_dataset.csv.gz\")\n",
    "df_articles.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c35357fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17374"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_articles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc28fa6e",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "      <th>date</th>\n",
       "      <th>heading</th>\n",
       "      <th>freq</th>\n",
       "      <th>url</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.8940</td>\n",
       "      <td>degrees, temperature, celsius, rain, minimum, record, maximum, delhi, wind, likely</td>\n",
       "      <td>mumbai celebrate cool years mumbai city witness lowest temperature season year mercury dip degrees celsius early thursdaythe city wake smogcovered morning ready goodbye officials say expect cold s...</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Mumbai celebrates a cool New Year's Eve at 13.4फ्C</td>\n",
       "      <td>1</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/mumbai/Mumbai-celebrates-a-cool-New-Years-Eve-at-13-4C/articleshow/5401190.cms</td>\n",
       "      <td>mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.2531</td>\n",
       "      <td>fuel, power, diesel, emissions, emission, vehicles, petrol, cars, norms, reduce</td>\n",
       "      <td>dense lead trip power line lucknow power corporation limit keep finger cross saturday state meteorological department indicate heavy foggy condition western region statethe warn department tizzy c...</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>Dense fog leads to tripping of power lines</td>\n",
       "      <td>1</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/lucknow/Dense-fog-leads-to-tripping-of-power-lines/articleshow/5403987.cms</td>\n",
       "      <td>lucknow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.2555</td>\n",
       "      <td>health, respiratory, people, cause, patients, asthma, lung, diseases, case, increase</td>\n",
       "      <td>city high level smogforming ozone chennai dangerous level groundlevel ozone cause respiratory ailments envelop chennai city like indian metros blissfully unaware absence monitor study take ramacha...</td>\n",
       "      <td>2010-01-09</td>\n",
       "      <td>City has high levels of smog-forming ozone</td>\n",
       "      <td>1</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/chennai/City-has-high-levels-of-smog-forming-ozone/articleshow/5425554.cms</td>\n",
       "      <td>chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.5919</td>\n",
       "      <td>police, team, take, work, hospital, case, die, india, death, tell</td>\n",
       "      <td>woman kid hospital inhale smoke jaipur woman kid admit city hospital condition faint smoke emanate traditional room heater incident occur shankar vihar colony gonae road sunday morning condition s...</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>Woman, four kids in hospital after inhaling smoke</td>\n",
       "      <td>1</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/jaipur/Woman-four-kids-in-hospital-after-inhaling-smoke/articleshow/5431588.cms</td>\n",
       "      <td>jaipur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.4770</td>\n",
       "      <td>level, particulate, city, matter, increase, record, metre, cubic, quality, high</td>\n",
       "      <td>high bhogi study chennai time read bhogi pollutants envelop city study central  control board indicate normal days get worse neighbourhoods consider greenadyar traditionally area comparatively lo...</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>Pollution high on Bhogi eve: Study</td>\n",
       "      <td>1</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/chennai/Pollution-high-on-Bhogi-eve-Study/articleshow/5438915.cms</td>\n",
       "      <td>chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17395</th>\n",
       "      <td>17395</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.3792</td>\n",
       "      <td>water, mine, coal, cause, smoke, chemical, port, chemicals, metal, tobacco</td>\n",
       "      <td>naval dockyard team repair oxygen plant nellore srikalahasti oxygen supply get boost state pandemicteams naval dockyard visakhapatnam achieve major breakthrough repair major oxygen plant nellore s...</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>Naval Dockyard teams repair two oxygen plants in Nellore and Srikalahasti</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.thehindu.com/news/national/andhra-pradesh/naval-dockyard-teams-repair-two-oxygen-plants-in-nellore-and-srikalahasthi/article34572309.ece</td>\n",
       "      <td>visakhapatnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17396</th>\n",
       "      <td>17396</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.4754</td>\n",
       "      <td>project, government, crore, state, plan, development, fund, meet, propose, land</td>\n",
       "      <td>green relook silverline implementation kerala paristhithi aikya vedi forum urge chief minister pinarayi vijayan withdraw hurriedly execute semihigh speed railway project silverlinethe government f...</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>Greens call for a relook into SilverLine implementation</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.thehindu.com/news/national/kerala/greens-call-for-a-relook-into-silverline-implementation/article34634767.ece</td>\n",
       "      <td>thiruvananthapuram</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17397</th>\n",
       "      <td>17397</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.4006</td>\n",
       "      <td>company, build, market, design, technology, mask, instal, launch, help, work</td>\n",
       "      <td>cftris ozonebased disinfection combat droplet infections csircentral food technological research institute cftri mysuru come ozonebased disinfection combat transmission droplet infections like ozo...</td>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>CFTRI’s ozone-based air disinfection system to combat droplet infections</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.thehindu.com/news/national/karnataka/cftris-ozone-based-air-disinfection-system-to-combat-droplet-infections/article34640165.ece</td>\n",
       "      <td>mysuru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17398</th>\n",
       "      <td>17398</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>court, plant, order, bench, state, report, tribunal, control, direct, issue</td>\n",
       "      <td>stay green ntpcs karimnagar plant environmental clearance hold seven months bench call fresh appraisalthe southern bench national green tribunal abeyance environment clearance grant thermal power ...</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>NGT stays green nod for NTPC’s Karimnagar plant</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.thehindu.com/news/national/ngt-stays-green-nod-for-ntpcs-karimnagar-plant/article34669935.ece</td>\n",
       "      <td>chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17399</th>\n",
       "      <td>17399</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.4365</td>\n",
       "      <td>plant, tree, green, forest, water, city, park, cover, bird, area</td>\n",
       "      <td>create forest stop global warm say minister minister local selfgovernments excise govindan call conscious effort create forest meet challenge global little forest challenge launch district panchay...</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>Create forests to stop global warming, says Minister</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.thehindu.com/news/national/kerala/create-forests-to-stop-global-warming-says-minister/article34684152.ece</td>\n",
       "      <td>kannur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17400 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "0                0            11.0              0.8940   \n",
       "1                1             2.0              0.2531   \n",
       "2                2            13.0              0.2555   \n",
       "3                3            10.0              0.5919   \n",
       "4                4            23.0              0.4770   \n",
       "...            ...             ...                 ...   \n",
       "17395        17395             4.0              0.3792   \n",
       "17396        17396            20.0              0.4754   \n",
       "17397        17397            24.0              0.4006   \n",
       "17398        17398             3.0              0.7572   \n",
       "17399        17399             6.0              0.4365   \n",
       "\n",
       "                                                                                   Keywords  \\\n",
       "0        degrees, temperature, celsius, rain, minimum, record, maximum, delhi, wind, likely   \n",
       "1           fuel, power, diesel, emissions, emission, vehicles, petrol, cars, norms, reduce   \n",
       "2      health, respiratory, people, cause, patients, asthma, lung, diseases, case, increase   \n",
       "3                         police, team, take, work, hospital, case, die, india, death, tell   \n",
       "4           level, particulate, city, matter, increase, record, metre, cubic, quality, high   \n",
       "...                                                                                     ...   \n",
       "17395            water, mine, coal, cause, smoke, chemical, port, chemicals, metal, tobacco   \n",
       "17396       project, government, crore, state, plan, development, fund, meet, propose, land   \n",
       "17397          company, build, market, design, technology, mask, instal, launch, help, work   \n",
       "17398           court, plant, order, bench, state, report, tribunal, control, direct, issue   \n",
       "17399                      plant, tree, green, forest, water, city, park, cover, bird, area   \n",
       "\n",
       "                                                                                                                                                                                                          Text  \\\n",
       "0      mumbai celebrate cool years mumbai city witness lowest temperature season year mercury dip degrees celsius early thursdaythe city wake smogcovered morning ready goodbye officials say expect cold s...   \n",
       "1      dense lead trip power line lucknow power corporation limit keep finger cross saturday state meteorological department indicate heavy foggy condition western region statethe warn department tizzy c...   \n",
       "2      city high level smogforming ozone chennai dangerous level groundlevel ozone cause respiratory ailments envelop chennai city like indian metros blissfully unaware absence monitor study take ramacha...   \n",
       "3      woman kid hospital inhale smoke jaipur woman kid admit city hospital condition faint smoke emanate traditional room heater incident occur shankar vihar colony gonae road sunday morning condition s...   \n",
       "4       high bhogi study chennai time read bhogi pollutants envelop city study central  control board indicate normal days get worse neighbourhoods consider greenadyar traditionally area comparatively lo...   \n",
       "...                                                                                                                                                                                                        ...   \n",
       "17395  naval dockyard team repair oxygen plant nellore srikalahasti oxygen supply get boost state pandemicteams naval dockyard visakhapatnam achieve major breakthrough repair major oxygen plant nellore s...   \n",
       "17396  green relook silverline implementation kerala paristhithi aikya vedi forum urge chief minister pinarayi vijayan withdraw hurriedly execute semihigh speed railway project silverlinethe government f...   \n",
       "17397  cftris ozonebased disinfection combat droplet infections csircentral food technological research institute cftri mysuru come ozonebased disinfection combat transmission droplet infections like ozo...   \n",
       "17398  stay green ntpcs karimnagar plant environmental clearance hold seven months bench call fresh appraisalthe southern bench national green tribunal abeyance environment clearance grant thermal power ...   \n",
       "17399  create forest stop global warm say minister minister local selfgovernments excise govindan call conscious effort create forest meet challenge global little forest challenge launch district panchay...   \n",
       "\n",
       "            date  \\\n",
       "0     2010-01-01   \n",
       "1     2010-01-02   \n",
       "2     2010-01-09   \n",
       "3     2010-01-11   \n",
       "4     2010-01-13   \n",
       "...          ...   \n",
       "17395 2021-05-16   \n",
       "17396 2021-05-24   \n",
       "17397 2021-05-25   \n",
       "17398 2021-05-28   \n",
       "17399 2021-05-30   \n",
       "\n",
       "                                                                         heading  \\\n",
       "0                             Mumbai celebrates a cool New Year's Eve at 13.4फ्C   \n",
       "1                                     Dense fog leads to tripping of power lines   \n",
       "2                                     City has high levels of smog-forming ozone   \n",
       "3                              Woman, four kids in hospital after inhaling smoke   \n",
       "4                                             Pollution high on Bhogi eve: Study   \n",
       "...                                                                          ...   \n",
       "17395  Naval Dockyard teams repair two oxygen plants in Nellore and Srikalahasti   \n",
       "17396                   Greens call for a relook into SilverLine implementation    \n",
       "17397  CFTRI’s ozone-based air disinfection system to combat droplet infections    \n",
       "17398                            NGT stays green nod for NTPC’s Karimnagar plant   \n",
       "17399                      Create forests to stop global warming, says Minister    \n",
       "\n",
       "       freq  \\\n",
       "0         1   \n",
       "1         1   \n",
       "2         1   \n",
       "3         1   \n",
       "4         1   \n",
       "...     ...   \n",
       "17395     1   \n",
       "17396     1   \n",
       "17397     1   \n",
       "17398     1   \n",
       "17399     1   \n",
       "\n",
       "                                                                                                                                                        url  \\\n",
       "0                                   http://timesofindia.indiatimes.com//city/mumbai/Mumbai-celebrates-a-cool-New-Years-Eve-at-13-4C/articleshow/5401190.cms   \n",
       "1                                       http://timesofindia.indiatimes.com//city/lucknow/Dense-fog-leads-to-tripping-of-power-lines/articleshow/5403987.cms   \n",
       "2                                       http://timesofindia.indiatimes.com//city/chennai/City-has-high-levels-of-smog-forming-ozone/articleshow/5425554.cms   \n",
       "3                                  http://timesofindia.indiatimes.com//city/jaipur/Woman-four-kids-in-hospital-after-inhaling-smoke/articleshow/5431588.cms   \n",
       "4                                                http://timesofindia.indiatimes.com//city/chennai/Pollution-high-on-Bhogi-eve-Study/articleshow/5438915.cms   \n",
       "...                                                                                                                                                     ...   \n",
       "17395  https://www.thehindu.com/news/national/andhra-pradesh/naval-dockyard-teams-repair-two-oxygen-plants-in-nellore-and-srikalahasthi/article34572309.ece   \n",
       "17396                             https://www.thehindu.com/news/national/kerala/greens-call-for-a-relook-into-silverline-implementation/article34634767.ece   \n",
       "17397          https://www.thehindu.com/news/national/karnataka/cftris-ozone-based-air-disinfection-system-to-combat-droplet-infections/article34640165.ece   \n",
       "17398                                             https://www.thehindu.com/news/national/ngt-stays-green-nod-for-ntpcs-karimnagar-plant/article34669935.ece   \n",
       "17399                                 https://www.thehindu.com/news/national/kerala/create-forests-to-stop-global-warming-says-minister/article34684152.ece   \n",
       "\n",
       "                     city  \n",
       "0                  mumbai  \n",
       "1                 lucknow  \n",
       "2                 chennai  \n",
       "3                  jaipur  \n",
       "4                 chennai  \n",
       "...                   ...  \n",
       "17395       visakhapatnam  \n",
       "17396  thiruvananthapuram  \n",
       "17397              mysuru  \n",
       "17398             chennai  \n",
       "17399              kannur  \n",
       "\n",
       "[17400 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "path = \"LDA/files/\"\n",
    "Path(path).mkdir(exist_ok=True,parents=True)\n",
    "\n",
    "final = pd.read_csv(\"LDA/dominant_topic.csv.gz\",index_col=0)\n",
    "final['freq'] = 1 \n",
    "final[\"date\"] = pd.to_datetime(final[\"date\"])\n",
    "temp = final.merge(df_articles,left_on = \"heading\",right_on = \"heading\",how=\"left\")\n",
    "final[\"url\"] = temp[\"url\"]\n",
    "final[\"city\"] = temp[\"city\"]\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e5d12847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(0, 0.04962283), (11, 0.893961), (18, 0.050840937)],\n",
       " [(2, 0.25312424),\n",
       "  (8, 0.06554247),\n",
       "  (10, 0.094449624),\n",
       "  (11, 0.23136246),\n",
       "  (12, 0.131322),\n",
       "  (16, 0.13094117),\n",
       "  (23, 0.08658438)],\n",
       " [(5, 0.24445632),\n",
       "  (9, 0.05622677),\n",
       "  (13, 0.25555125),\n",
       "  (22, 0.22276776),\n",
       "  (23, 0.21620393)],\n",
       " [(2, 0.013209456),\n",
       "  (10, 0.5919308),\n",
       "  (13, 0.23401205),\n",
       "  (18, 0.05172585),\n",
       "  (19, 0.039709322),\n",
       "  (23, 0.062740676)],\n",
       " [(1, 0.022339605),\n",
       "  (2, 0.012554037),\n",
       "  (5, 0.32332495),\n",
       "  (11, 0.03081114),\n",
       "  (15, 0.043442372),\n",
       "  (19, 0.08605452),\n",
       "  (23, 0.4770502)],\n",
       " [(0, 0.21804069),\n",
       "  (1, 0.087345846),\n",
       "  (10, 0.073918276),\n",
       "  (11, 0.15147771),\n",
       "  (18, 0.11992742),\n",
       "  (19, 0.039568592),\n",
       "  (23, 0.30535278)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "corpus_list = pickle.load(open(\"LDA/LDA-25-200-alldata.pickle\",\"rb\"))\n",
    "corpus_list[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d5c938f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert corpus into list of probabilities\n",
    "n_topic = 25\n",
    "probs = []\n",
    "for doc in corpus_list:\n",
    "    temp = [np.nan]*n_topic\n",
    "    for topic in doc:\n",
    "        topic_no, prob = topic\n",
    "        temp[topic_no] = prob\n",
    "    probs.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83e0bee4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7513 - articles\n",
      "0 - articles having more than 1 dominant topics\n"
     ]
    },
    {
     "data": {
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       "<p>17400 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  \\\n",
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       "4       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "17395   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "17396   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "17397   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "17398   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "17399   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "       18  19  20  21  22  23  24  \n",
       "0       0   0   0   0   0   0   0  \n",
       "1       0   0   0   0   0   0   0  \n",
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       "3       0   0   0   0   0   0   0  \n",
       "4       0   0   0   0   0   0   0  \n",
       "...    ..  ..  ..  ..  ..  ..  ..  \n",
       "17395   0   0   0   0   0   0   0  \n",
       "17396   0   0   0   0   0   0   0  \n",
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       "17398   0   0   0   0   0   0   0  \n",
       "17399   0   0   0   0   0   0   0  \n",
       "\n",
       "[17400 rows x 25 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_probs = pd.DataFrame(probs)\n",
    "#df_probs = df_probs.apply(lambda x: x == x.max(), axis=1).astype(int) #select maximum probability\n",
    "df_probs = (df_probs>=0.5).astype(int) # or select only >0.5 probability\n",
    "print(f\"{len(df_probs[df_probs.apply(lambda x:sum(x)>0,axis=1)])} - articles\")\n",
    "print(f\"{len(df_probs[df_probs.apply(lambda x:sum(x)>=2,axis=1)])} - articles having more than 1 dominant topics\")\n",
    "df_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a802abb",
   "metadata": {},
   "outputs": [
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       "      <th>2021-05-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0    1         2         3    4         5         6    7   \\\n",
       "date                                                                          \n",
       "2010-01-01  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-11  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-21  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-31  0.000000  0.0  0.142857  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-02-10  0.000000  0.0  0.000000  0.000000  0.0  0.333333  0.000000  0.0   \n",
       "...              ...  ...       ...       ...  ...       ...       ...  ...   \n",
       "2021-04-13  0.100000  0.0  0.000000  0.000000  0.0  0.100000  0.000000  0.0   \n",
       "2021-04-23  0.333333  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2021-05-03  0.000000  0.0  0.000000  0.100000  0.0  0.000000  0.200000  0.0   \n",
       "2021-05-13  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2021-05-23  0.000000  0.0  0.000000  0.285714  0.0  0.000000  0.142857  0.0   \n",
       "\n",
       "             8         9         10        11   12        13        14   15  \\\n",
       "date                                                                          \n",
       "2010-01-01  0.0  0.166667  0.000000  0.166667  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-11  0.0  0.000000  0.200000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-21  0.0  0.400000  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-31  0.0  0.000000  0.285714  0.000000  0.0  0.142857  0.000000  0.0   \n",
       "2010-02-10  0.0  0.166667  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "...         ...       ...       ...       ...  ...       ...       ...  ...   \n",
       "2021-04-13  0.1  0.200000  0.000000  0.200000  0.0  0.000000  0.000000  0.0   \n",
       "2021-04-23  0.0  0.000000  0.166667  0.333333  0.0  0.000000  0.166667  0.0   \n",
       "2021-05-03  0.0  0.000000  0.000000  0.400000  0.0  0.100000  0.000000  0.0   \n",
       "2021-05-13  0.0  0.000000  0.000000  0.555556  0.0  0.111111  0.000000  0.0   \n",
       "2021-05-23  0.0  0.000000  0.000000  0.285714  0.0  0.142857  0.000000  0.0   \n",
       "\n",
       "                  16        17        18        19        20   21        22  \\\n",
       "date                                                                          \n",
       "2010-01-01  0.000000  0.000000  0.166667  0.000000  0.000000  0.0  0.000000   \n",
       "2010-01-11  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.000000   \n",
       "2010-01-21  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.000000   \n",
       "2010-01-31  0.000000  0.285714  0.000000  0.000000  0.000000  0.0  0.000000   \n",
       "2010-02-10  0.000000  0.000000  0.000000  0.000000  0.166667  0.0  0.000000   \n",
       "...              ...       ...       ...       ...       ...  ...       ...   \n",
       "2021-04-13  0.000000  0.100000  0.000000  0.000000  0.200000  0.0  0.300000   \n",
       "2021-04-23  0.000000  0.000000  0.000000  0.000000  0.166667  0.0  0.166667   \n",
       "2021-05-03  0.300000  0.000000  0.000000  0.000000  0.000000  0.0  0.000000   \n",
       "2021-05-13  0.111111  0.000000  0.000000  0.111111  0.000000  0.0  0.000000   \n",
       "2021-05-23  0.000000  0.000000  0.000000  0.000000  0.000000  0.0  0.000000   \n",
       "\n",
       "                  23        24  \n",
       "date                            \n",
       "2010-01-01  0.000000  0.000000  \n",
       "2010-01-11  0.000000  0.000000  \n",
       "2010-01-21  0.000000  0.000000  \n",
       "2010-01-31  0.000000  0.000000  \n",
       "2010-02-10  0.000000  0.000000  \n",
       "...              ...       ...  \n",
       "2021-04-13  0.000000  0.000000  \n",
       "2021-04-23  0.000000  0.000000  \n",
       "2021-05-03  0.000000  0.000000  \n",
       "2021-05-13  0.111111  0.111111  \n",
       "2021-05-23  0.142857  0.000000  \n",
       "\n",
       "[417 rows x 25 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_topics_prob =  df_probs.merge(final,left_index=True,right_index=True)\n",
    "df_top_topics = df_all_topics_prob.groupby(\"date\").sum()[range(25)]\n",
    "df_top_topics.index = pd.to_datetime(df_top_topics.index)\n",
    "df_top_topics = df_top_topics.resample(\"10D\").mean()\n",
    "df_top_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a2d24df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filterd = df_all_topics_prob[df_all_topics_prob.apply(lambda x:sum(x[range(25)])!=0,axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98bc43c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
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       "      <th>2010-01-01</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>2010-01-11</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2010-01-21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.72</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2010-01-31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th>2021-04-13</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-23</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.151515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>2021-05-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.252525</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061728</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.02849</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.10101</td>\n",
       "      <td>0.37037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.129870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.12987</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0    1         2         3    4         5         6    7   \\\n",
       "date                                                                          \n",
       "2010-01-01  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-11  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-21  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-31  0.000000  0.0  0.178571  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-02-10  0.000000  0.0  0.000000  0.000000  0.0  0.666667  0.000000  0.0   \n",
       "...              ...  ...       ...       ...  ...       ...       ...  ...   \n",
       "2021-04-13  0.125000  0.0  0.000000  0.000000  0.0  0.200000  0.000000  0.0   \n",
       "2021-04-23  0.416667  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2021-05-03  0.000000  0.0  0.000000  0.125000  0.0  0.000000  0.400000  0.0   \n",
       "2021-05-13  0.000000  0.0  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2021-05-23  0.000000  0.0  0.000000  0.357143  0.0  0.000000  0.285714  0.0   \n",
       "\n",
       "                  8     9         10        11   12        13        14   15  \\\n",
       "date                                                                           \n",
       "2010-01-01  0.000000  0.30  0.000000  0.075758  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-11  0.000000  0.00  0.125000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-21  0.000000  0.72  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "2010-01-31  0.000000  0.00  0.178571  0.000000  0.0  0.079365  0.000000  0.0   \n",
       "2010-02-10  0.000000  0.30  0.000000  0.000000  0.0  0.000000  0.000000  0.0   \n",
       "...              ...   ...       ...       ...  ...       ...       ...  ...   \n",
       "2021-04-13  0.033333  0.36  0.000000  0.090909  0.0  0.000000  0.000000  0.0   \n",
       "2021-04-23  0.000000  0.00  0.104167  0.151515  0.0  0.000000  0.208333  0.0   \n",
       "2021-05-03  0.000000  0.00  0.000000  0.181818  0.0  0.055556  0.000000  0.0   \n",
       "2021-05-13  0.000000  0.00  0.000000  0.252525  0.0  0.061728  0.000000  0.0   \n",
       "2021-05-23  0.000000  0.00  0.000000  0.129870  0.0  0.079365  0.000000  0.0   \n",
       "\n",
       "                  16        17        18       19        20   21        22  \\\n",
       "date                                                                         \n",
       "2010-01-01  0.000000  0.000000  0.185185  0.00000  0.000000  0.0  0.000000   \n",
       "2010-01-11  0.000000  0.000000  0.000000  0.00000  0.000000  0.0  0.000000   \n",
       "2010-01-21  0.000000  0.000000  0.000000  0.00000  0.000000  0.0  0.000000   \n",
       "2010-01-31  0.000000  0.197802  0.000000  0.00000  0.000000  0.0  0.000000   \n",
       "2010-02-10  0.000000  0.000000  0.000000  0.00000  0.333333  0.0  0.000000   \n",
       "...              ...       ...       ...      ...       ...  ...       ...   \n",
       "2021-04-13  0.000000  0.069231  0.000000  0.00000  0.400000  0.0  0.250000   \n",
       "2021-04-23  0.000000  0.000000  0.000000  0.00000  0.333333  0.0  0.138889   \n",
       "2021-05-03  0.136364  0.000000  0.000000  0.00000  0.000000  0.0  0.000000   \n",
       "2021-05-13  0.050505  0.000000  0.000000  0.02849  0.000000  0.0  0.000000   \n",
       "2021-05-23  0.000000  0.000000  0.000000  0.00000  0.000000  0.0  0.000000   \n",
       "\n",
       "                 23       24  \n",
       "date                          \n",
       "2010-01-01  0.00000  0.00000  \n",
       "2010-01-11  0.00000  0.00000  \n",
       "2010-01-21  0.00000  0.00000  \n",
       "2010-01-31  0.00000  0.00000  \n",
       "2010-02-10  0.00000  0.00000  \n",
       "...             ...      ...  \n",
       "2021-04-13  0.00000  0.00000  \n",
       "2021-04-23  0.00000  0.00000  \n",
       "2021-05-03  0.00000  0.00000  \n",
       "2021-05-13  0.10101  0.37037  \n",
       "2021-05-23  0.12987  0.00000  \n",
       "\n",
       "[417 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# df = df_cross.copy()\n",
    "df = df_top_topics\n",
    "df = df.replace(np.nan,0)\n",
    "x = df.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df = pd.DataFrame(x_scaled)\n",
    "df.index = df_top_topics.index\n",
    "df_norm = df.copy()\n",
    "df_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a70f65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.rcParams[\"figure.figsize\"] = (4.5,3)\n",
    "fig, ax = plt.subplots()\n",
    "latexify(columns=2,fig_height=3.8,fig_width=4)\n",
    "\n",
    "date_range = pd.date_range(datetime(2015,1,1),datetime(2021,5,31))\n",
    "selected_topics = [19,1,16,21,13,2,8,11,9,17]\n",
    "selected_topic_labels = {\n",
    "    19: \"Poor air\\nquality\",\n",
    "    1: \"Diwali\",\n",
    "    16: \"Stubble\\nburning\",\n",
    "    21: \"Construction\",\n",
    "    13: \"Health effects\",\n",
    "    2: \"Vehicular\\nemissions\", #have to check\n",
    "    8: \"Delhi\\nGovernment\",\n",
    "    11: \"Weather\",\n",
    "    9: \"Climate\\nChange\",\n",
    "    17: \"Students\"\n",
    "}\n",
    "\n",
    "#choose columns of seletced topics only\n",
    "df = df_norm[df_norm.index.isin(date_range)][selected_topics]\n",
    "cols = df.columns\n",
    "fac = 0.85\n",
    "x = pd.to_datetime(df.index)\n",
    "for ind, col_name in enumerate(cols):\n",
    "    plt.plot(x, df[col_name]+ind/fac)\n",
    "    plt.fill_between(x, df[col_name]+ind/fac, ind/fac )\n",
    "\n",
    "topics = final.drop_duplicates(subset=\"Dominant_Topic\").nsmallest(30,\"Dominant_Topic\")[[\"Dominant_Topic\",\"Keywords\"]]\n",
    "topics = topics[topics[\"Dominant_Topic\"].isin(selected_topics)]\n",
    "\n",
    "\n",
    "#set y-labels\n",
    "labels = [\" \".join(each.split(\",\")[:3]) for each in topics[\"Keywords\"]]\n",
    "labels = [selected_topic_labels[each] for each in selected_topics]\n",
    "plt.yticks(np.arange(0, len(cols)*1.0/fac, 1/fac), labels)\n",
    "\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "# Removing spine\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Remove x-axis label\n",
    "ax.set_xlabel('Day')\n",
    "\n",
    "\n",
    "plt.tick_params(\n",
    "    labelsize = 8,\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=True, # labels along the bottom edge are off\n",
    "    left=False\n",
    ")\n",
    "\n",
    " \n",
    "\n",
    "# x_line_annotation = dt.datetime(2016, 4, 1)\n",
    "# x_text_annotation = dt.datetime(2016, 4, 10)\n",
    "# ax.axvline(x=x_line_annotation, linestyle='dashed', alpha=0.5,  color='#000000')\n",
    "# ax.text(x=x_text_annotation, y=len(cols)*1.0/fac + 0.3, s='Odd Even \\nAnnounced', alpha=0.7, color='#000000')\n",
    "fig = plt.gcf()\n",
    "\n",
    "\n",
    "# years = mdates.YearLocator()   # every year\n",
    "# months = mdates.MonthLocator()  # every month\n",
    "# years_fmt = mdates.DateFormatter('%b-%Y')\n",
    "# ax.format_xdata = mdates.DateFormatter('%M')\n",
    "# ax.xaxis.set_major_locator(years)\n",
    "# ax.xaxis.set_major_formatter(years_fmt)\n",
    "# ax.xaxis.set_minor_locator(months)\n",
    "\n",
    "# fig.autofmt_xdate()\n",
    "\n",
    "\n",
    "#set which month to show\n",
    "ax.set_xlim(datetime(2014,11,1), datetime(2021,11,1))\n",
    "\n",
    "\n",
    "#set format of date\n",
    "date_form = DateFormatter(\"%b'%y\")\n",
    "#ax = fig.axes[0]\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "\n",
    "#make interval of n months\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=12))\n",
    "ax.tick_params(axis=\"x\",rotation=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "events = {\"World Ozone Day\" : [17,[datetime(2015, 9, 16)]],\n",
    "        \"World Enviornment Day\" : [17,[datetime(2019,6,5)]],\n",
    "          \"Odd-even scheme\\nimplemented in Delhi\":[8,[datetime(2016,1,1),datetime(2016,4,4),datetime(2017,11,13),datetime(2019,11,4)]],\n",
    "         \"Ban on registration\\nof luxury diesel vehicles/\\nSUVs in Delhi-NCR\":[2,[datetime(2015,12,16)]],\n",
    "          \"Supreme court banned\\nBS-III vehicles\\nin India\":[2,[datetime(2017,3,28)]],\n",
    "          \"Ban on Construction\\nin Delhi-NCR\":[21,[datetime(2019,11,4),datetime(2018,11,1)]],\n",
    "          \n",
    "          #\"Coldest day since\\n 15 years in Delhi\":[11,[datetime(2021,1,1)]],\n",
    "          #\"Mumbai beats Delhi's AQI\":[11,[datetime(2021,1,7)]],\n",
    "         \"Diwali\":[1,[datetime(2015,11,11),datetime(2016,10,30),datetime(2017,10,19),datetime(2018,11,7),datetime(2019,10,27),datetime(2020,11,14)]]}\n",
    "\n",
    "asci = 65\n",
    "plts = []\n",
    "\n",
    "#\"#5e6ef9\",\"#ff28fb\",\n",
    "colors = [\"red\",\"black\",\"gray\",\"blue\",\"#edc900\",\"green\",\"purple\"]\n",
    "for event,color in zip(events,colors):\n",
    "    col,dates = events[event]\n",
    "    y = selected_topics.index(col)/fac\n",
    "    text = chr(asci)\n",
    "    \n",
    "    for each_date in dates:\n",
    "        x_line_smog = each_date\n",
    "        x_text_smog = x_line_smog + dt.timedelta(days=10)\n",
    "        plt1 = ax.plot(x_line_smog,y+0.3,\"o\",color=color,label= f\"{text}: {event}\",markersize=2)\n",
    "        if \"15 years\" in event:\n",
    "            #print(\"coldest\")\n",
    "            ax.text(x=x_text_smog, y= y+0.2, s = text, alpha=1, color='#000000',label=\"some\")\n",
    "        elif \"Mumbai\" in event:\n",
    "            #print(\"mumbai\")\n",
    "            ax.text(x=x_text_smog, y= y+0.6, s = text, alpha=1, color='#000000',label=\"some\")\n",
    "        else:\n",
    "            ax.text(x=x_text_smog, y= y+ 0.4, s = text, alpha=1, color='#000000',label=\"some\")\n",
    "        \n",
    "    plts.append(plt1[0])\n",
    "\n",
    "    asci+=1\n",
    "\n",
    "labs = [pl.get_label() for pl in plts]\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.legend(plts,labs,bbox_to_anchor=(0.95,0.9));\n",
    "\n",
    "#,handlelength=0, handletextpad=0,markerscale=0\n",
    "# leg = ax.get_legend()\n",
    "# for it in leg.legendHandles:\n",
    "#     print(it.set_color('red'))\n",
    "plt.draw()\n",
    "ticks = ax.get_xticklabels()\n",
    "ticks[0] = \"\"\n",
    "ticks[-1] = \"\"\n",
    "#print(ticks)\n",
    "ax.set_xticklabels(ticks);\n",
    "\n",
    "# for item in leg.legendHandles:\n",
    "#     item.set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30de4315",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"../figures/fig6_topic_evolution_with_time.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6305c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "events = {\"World Ozone\\nDay\" : [17,[datetime(2015, 9, 16)]],\n",
    "        \"World Enviorn-\\nment\\nDay\" : [17,[datetime(2019,6,5)]],\n",
    "          \"Odd-even\\nimplemented\\nin Delhi\":[8,[datetime(2016,1,1),datetime(2016,4,4),datetime(2017,11,13),datetime(2019,11,4)]],\n",
    "         \"Ban on registration\\nof luxury diesel\\nvehicle/SUVs\\nin Delhi-NCR\":[2,[datetime(2015,12,16)]],\n",
    "          \"Supreme Court\\nbanned BS-III\\nvehicles in India\":[2,[datetime(2017,3,28)]],\n",
    "          \"Ban on Construction\\nin Delhi-NCR\":[21,[datetime(2019,11,4),datetime(2018,11,1)]],\n",
    "          \n",
    "          #\"Coldest day since\\n 15 years in Delhi\":[11,[datetime(2021,1,1)]],\n",
    "          #\"Mumbai beats Delhi's AQI\":[11,[datetime(2021,1,7)]],\n",
    "         \"Diwali\":[1,[datetime(2015,11,11),datetime(2016,10,30),datetime(2017,10,19),datetime(2018,11,7),datetime(2019,10,27),datetime(2020,11,14)]]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e01594f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "World Ozone\n",
      " Day\n",
      "\n",
      "World Envio\n",
      "rn- ment Da\n",
      "y\n",
      "\n",
      "Odd-even im\n",
      "plemented i\n",
      "n Delhi\n",
      "\n",
      "Ban on regi\n",
      "stration of\n",
      " luxury die\n",
      "sel vehicle\n",
      "/SUVs in De\n",
      "lhi-NCR\n",
      "\n",
      "Supreme Cou\n",
      "rt banned B\n",
      "S-III vehic\n",
      "les in Indi\n",
      "a\n",
      "\n",
      "Ban on Cons\n",
      "truction in\n",
      " Delhi-NCR\n",
      "\n",
      "Diwali\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for event in events:\n",
    "    temp_event = event\n",
    "    temp_event = temp_event.replace(\"\\n\",\" \")\n",
    "    temp = \"\"\n",
    "    for i in range (0,len(temp_event),11):\n",
    "        temp+= temp_event[i:i+11] + \"\\n\"\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4001e798",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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