LTC CMS vs CTP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import pytz\n",
    "\n",
    "from pathlib import Path\n",
    "from math import isnan\n",
    "\n",
    "import matplotlib.ticker as plticker\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "pd.set_option(\"display.precision\", 4)\n",
    "\n",
    "# show all columns by default\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-05-26 14:19:06--  https://www.dropbox.com/s/pfsiodhtzetxbl0/ltc-all-the-states-2021-05-26.zip?dl=1\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 162.125.2.18\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|162.125.2.18|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: /s/dl/pfsiodhtzetxbl0/ltc-all-the-states-2021-05-26.zip [following]\n",
      "--2021-05-26 14:19:06--  https://www.dropbox.com/s/dl/pfsiodhtzetxbl0/ltc-all-the-states-2021-05-26.zip\n",
      "Reusing existing connection to www.dropbox.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com/cd/0/get/BPOhgx3XN6DOuT-kj8NjBSU9fzj8qmMVLEqK08OI3FwgScyhDH4P528YS80NR4zSjfkygFNFn5Pcuqrmkl4VkkIbWxwsyPo1A-w3tGMfoktKn-l1mOy8g9qAkKCPPwBQlRtANuKRrj9UAKm_eG-UZkbP/file?dl=1# [following]\n",
      "--2021-05-26 14:19:07--  https://uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com/cd/0/get/BPOhgx3XN6DOuT-kj8NjBSU9fzj8qmMVLEqK08OI3FwgScyhDH4P528YS80NR4zSjfkygFNFn5Pcuqrmkl4VkkIbWxwsyPo1A-w3tGMfoktKn-l1mOy8g9qAkKCPPwBQlRtANuKRrj9UAKm_eG-UZkbP/file?dl=1\n",
      "Resolving uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com (uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com)... 162.125.2.15\n",
      "Connecting to uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com (uc695134ec738d68f33af3d8d0e5.dl.dropboxusercontent.com)|162.125.2.15|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 51657737 (49M) [application/binary]\n",
      "Saving to: ‘ltc.zip’\n",
      "\n",
      "ltc.zip             100%[===================>]  49.26M  2.19MB/s    in 22s     \n",
      "\n",
      "2021-05-26 14:19:29 (2.29 MB/s) - ‘ltc.zip’ saved [51657737/51657737]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://www.dropbox.com/s/pfsiodhtzetxbl0/ltc-all-the-states-2021-05-26.zip?dl=1 -O ltc.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ltc.zip\n",
      "   creating: ltc-all-the-states/\n",
      "  inflating: ltc-all-the-states/ltc-overview-VT.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-HI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-SC.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-GA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-SC.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-DC.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_deaths_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-KY.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-CO.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MS.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-ME.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-NV.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-TN.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-HI.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-NC.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-OR.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-ID.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-VT.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-WV.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-outbreak_resident_cases_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-NY.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-OR.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-KS.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MI.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-NC.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-CA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-UT.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_cases_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-TX.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-IN.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-IL.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-AR.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MN.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-RI.html  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-outbreak_resident_deaths_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MN.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-ME.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-LA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-OH.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-NC.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-IN.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-OK.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-WI.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-VA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-WY.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-NJ.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-NY.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-MD.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-AR.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-WI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MO.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-outbreak_resident_deaths-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-NJ.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-DE.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_and_staff_cases-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-MI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-OR.html  \n",
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      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-WY.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-KY.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-SC.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-KS.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-GA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-PA.html  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_and_staff_cases_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-DC.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-DE.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-FL.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-NM.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-OK.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-CA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-VA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-FL.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-OH.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-NV.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-RI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-allstates.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-facilities_reporting-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-CT.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-WI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-WV.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-outbreak_resident_cases-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_staff_deaths-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MD.html  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_and_staff_deaths-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-MN.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-DE.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-ME.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-TX.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_and_staff_deaths_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-IL.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-HI.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-AR.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-CA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-UT.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-NY.html  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_cases-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-ND.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-LA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-RI.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-CO.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-IL.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-MO.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-UT.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-PA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-ND.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-IA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-PA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_staff_cases-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-VT.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MD.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-NV.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-ID.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-IA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-OK.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-NJ.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-IN.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-MO.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-CO.html  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-TN.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-LA.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-CT.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-IA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-KY.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-KS.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-cumulative_resident_deaths-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-TN.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-NM.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-DC.csv  \n",
      "  inflating: ltc-all-the-states/ltc-allstates-facilities_reporting_vs_cms_nursing_home_data-2021-05-26.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-CT.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-FL.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-MS.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-ND.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-NM.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-MS.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-WV.html  \n",
      "  inflating: ltc-all-the-states/ltc-overview-GA.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-overview-TX.csv  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-VA.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-WY.html  \n",
      "  inflating: ltc-all-the-states/csvexport-ltc-ctpcmsmerged-ID.csv  \n",
      "  inflating: ltc-all-the-states/ltc-overview-OH.html  \n"
     ]
    }
   ],
   "source": [
    "!unzip ltc.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load CTP data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julia/miniconda3/envs/deep-learning-nlp/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (4,5,12) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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>ctp_facility</th>\n",
       "      <th>ctp_ctp_id</th>\n",
       "      <th>ctp_state_facility_type</th>\n",
       "      <th>ctp_ctp_facility_type</th>\n",
       "      <th>ctp_cms_id</th>\n",
       "      <th>ctp_city</th>\n",
       "      <th>ctp_county</th>\n",
       "      <th>ctp_cms_week</th>\n",
       "      <th>ctp_cume_res_death</th>\n",
       "      <th>ctp_cume_res_probdeath</th>\n",
       "      <th>ctp_cume_res_pos</th>\n",
       "      <th>ctp_cume_res_probpos</th>\n",
       "      <th>ctp_outbrk_status</th>\n",
       "      <th>ctp_outbrk_res_death</th>\n",
       "      <th>ctp_outbrk_res_probdeath</th>\n",
       "      <th>ctp_outbrk_res_pos</th>\n",
       "      <th>ctp_outbrk_res_probpos</th>\n",
       "      <th>ctp_outbrk_resstaff_death</th>\n",
       "      <th>ctp_outbrk_resstaff_probdeath</th>\n",
       "      <th>ctp_outbrk_resstaff_pos</th>\n",
       "      <th>ctp_outbrk_resstaff_probpos</th>\n",
       "      <th>ctp_cume_resstaff_pos</th>\n",
       "      <th>ctp_cume_resstaff_probpos</th>\n",
       "      <th>ctp_cume_resstaff_death</th>\n",
       "      <th>ctp_cume_resstaff_probdeath</th>\n",
       "      <th>ctp_cume_staff_pos</th>\n",
       "      <th>ctp_cume_staff_probpos</th>\n",
       "      <th>ctp_cume_staff_death</th>\n",
       "      <th>ctp_cume_staff_probdeath</th>\n",
       "      <th>order</th>\n",
       "      <th>ctp_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALLAY HEALTH AND REHAB</td>\n",
       "      <td>AR00004</td>\n",
       "      <td>UNCATEGORIZED LTC</td>\n",
       "      <td>Nursing Home</td>\n",
       "      <td>45288</td>\n",
       "      <td>LITTLE ROCK</td>\n",
       "      <td>PULASKI</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AMBERWOOD HEALTH &amp; REHAB</td>\n",
       "      <td>AR00008</td>\n",
       "      <td>UNCATEGORIZED LTC</td>\n",
       "      <td>Nursing Home</td>\n",
       "      <td>45464</td>\n",
       "      <td>BENTON</td>\n",
       "      <td>SALINE</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APPLE CREEK HEALTH &amp; REHABILITATION</td>\n",
       "      <td>AR00010</td>\n",
       "      <td>UNCATEGORIZED LTC</td>\n",
       "      <td>Nursing Home</td>\n",
       "      <td>45465</td>\n",
       "      <td>CENTERTON</td>\n",
       "      <td>BENTON</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASH FLAT HEALTHCARE AND REHABILITATION CENTER</td>\n",
       "      <td>AR00022</td>\n",
       "      <td>UNCATEGORIZED LTC</td>\n",
       "      <td>Nursing Home</td>\n",
       "      <td>45155</td>\n",
       "      <td>ASH FLAT</td>\n",
       "      <td>SHARP</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BELLE VIEW ESTATES REHABILITATION &amp; CARE CENTER</td>\n",
       "      <td>AR00035</td>\n",
       "      <td>UNCATEGORIZED LTC</td>\n",
       "      <td>Nursing Home</td>\n",
       "      <td>45239</td>\n",
       "      <td>MONTICELLO</td>\n",
       "      <td>DREW</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      ctp_facility ctp_ctp_id  \\\n",
       "0                           ALLAY HEALTH AND REHAB    AR00004   \n",
       "1                         AMBERWOOD HEALTH & REHAB    AR00008   \n",
       "2              APPLE CREEK HEALTH & REHABILITATION    AR00010   \n",
       "3    ASH FLAT HEALTHCARE AND REHABILITATION CENTER    AR00022   \n",
       "4  BELLE VIEW ESTATES REHABILITATION & CARE CENTER    AR00035   \n",
       "\n",
       "  ctp_state_facility_type ctp_ctp_facility_type ctp_cms_id     ctp_city  \\\n",
       "0       UNCATEGORIZED LTC          Nursing Home      45288  LITTLE ROCK   \n",
       "1       UNCATEGORIZED LTC          Nursing Home      45464       BENTON   \n",
       "2       UNCATEGORIZED LTC          Nursing Home      45465    CENTERTON   \n",
       "3       UNCATEGORIZED LTC          Nursing Home      45155     ASH FLAT   \n",
       "4       UNCATEGORIZED LTC          Nursing Home      45239   MONTICELLO   \n",
       "\n",
       "  ctp_county ctp_cms_week  ctp_cume_res_death  ctp_cume_res_probdeath  \\\n",
       "0    PULASKI   2020-05-24                 1.0                     NaN   \n",
       "1     SALINE   2020-05-24                 0.0                     NaN   \n",
       "2     BENTON   2020-05-24                 0.0                     NaN   \n",
       "3      SHARP   2020-05-24                 2.0                     NaN   \n",
       "4       DREW   2020-05-24                 0.0                     NaN   \n",
       "\n",
       "   ctp_cume_res_pos  ctp_cume_res_probpos ctp_outbrk_status  \\\n",
       "0               5.0                   NaN               NaN   \n",
       "1               0.0                   NaN               NaN   \n",
       "2               1.0                   NaN               NaN   \n",
       "3              32.0                   NaN               NaN   \n",
       "4               1.0                   NaN               NaN   \n",
       "\n",
       "   ctp_outbrk_res_death  ctp_outbrk_res_probdeath  ctp_outbrk_res_pos  \\\n",
       "0                   NaN                       NaN                 NaN   \n",
       "1                   NaN                       NaN                 NaN   \n",
       "2                   NaN                       NaN                 NaN   \n",
       "3                   NaN                       NaN                 NaN   \n",
       "4                   NaN                       NaN                 NaN   \n",
       "\n",
       "   ctp_outbrk_res_probpos  ctp_outbrk_resstaff_death  \\\n",
       "0                     NaN                        NaN   \n",
       "1                     NaN                        NaN   \n",
       "2                     NaN                        NaN   \n",
       "3                     NaN                        NaN   \n",
       "4                     NaN                        NaN   \n",
       "\n",
       "   ctp_outbrk_resstaff_probdeath  ctp_outbrk_resstaff_pos  \\\n",
       "0                            NaN                      NaN   \n",
       "1                            NaN                      NaN   \n",
       "2                            NaN                      NaN   \n",
       "3                            NaN                      NaN   \n",
       "4                            NaN                      NaN   \n",
       "\n",
       "   ctp_outbrk_resstaff_probpos  ctp_cume_resstaff_pos  \\\n",
       "0                          NaN                    NaN   \n",
       "1                          NaN                    NaN   \n",
       "2                          NaN                    NaN   \n",
       "3                          NaN                    NaN   \n",
       "4                          NaN                    NaN   \n",
       "\n",
       "   ctp_cume_resstaff_probpos  ctp_cume_resstaff_death  \\\n",
       "0                        NaN                      NaN   \n",
       "1                        NaN                      NaN   \n",
       "2                        NaN                      NaN   \n",
       "3                        NaN                      NaN   \n",
       "4                        NaN                      NaN   \n",
       "\n",
       "   ctp_cume_resstaff_probdeath  ctp_cume_staff_pos  ctp_cume_staff_probpos  \\\n",
       "0                          NaN                 5.0                     NaN   \n",
       "1                          NaN                 1.0                     NaN   \n",
       "2                          NaN                 0.0                     NaN   \n",
       "3                          NaN                13.0                     NaN   \n",
       "4                          NaN                 0.0                     NaN   \n",
       "\n",
       "   ctp_cume_staff_death  ctp_cume_staff_probdeath  order ctp_state  \n",
       "0                   NaN                       NaN      0        AR  \n",
       "1                   NaN                       NaN      0        AR  \n",
       "2                   NaN                       NaN      0        AR  \n",
       "3                   NaN                       NaN      0        AR  \n",
       "4                   NaN                       NaN      0        AR  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctp_df = pd.read_csv('ltc-all-the-states/csvexport-ltc-allstates.csv').drop_duplicates()\n",
    "ctp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(759522, 36)\n",
      "(759522, 38)\n"
     ]
    }
   ],
   "source": [
    "# annotate CTP data\n",
    "\n",
    "def annotate_ctp_df(ctp_df):\n",
    "  # cases\n",
    "  ctp_resident_case_cols = ['ctp_cume_res_pos', 'ctp_cume_res_probpos']\n",
    "  ctp_staff_case_cols = ['ctp_cume_staff_pos', 'ctp_cume_staff_probpos']\n",
    "  ctp_resstaff_case_cols = ['ctp_cume_resstaff_pos', 'ctp_cume_resstaff_probpos']\n",
    "\n",
    "  # deaths\n",
    "  ctp_resident_death_cols = ['ctp_cume_res_death', 'ctp_cume_res_probdeath']\n",
    "  ctp_staff_death_cols = ['ctp_cume_staff_death', 'ctp_cume_staff_probdeath']\n",
    "  ctp_resstaff_death_cols = ['ctp_cume_resstaff_death', 'ctp_cume_resstaff_probdeath']\n",
    "\n",
    "  # total case columns\n",
    "  ctp_df['ctp_resident_total_cases'] = ctp_df[ctp_resident_case_cols].sum(axis=1)\n",
    "  ctp_df['ctp_staff_total_cases'] = ctp_df[ctp_staff_case_cols].sum(axis=1)\n",
    "  ctp_df['ctp_resstaff_total_cases'] = ctp_df[ctp_resstaff_case_cols].sum(axis=1)\n",
    "\n",
    "  # total death columns\n",
    "  ctp_df['ctp_resident_total_deaths'] = ctp_df[ctp_resident_death_cols].sum(axis=1)\n",
    "  ctp_df['ctp_resident_and_resstaff_total_deaths'] = ctp_df[\n",
    "    ctp_resident_death_cols + ctp_resstaff_death_cols].sum(axis=1)\n",
    "  ctp_df['ctp_staff_total_deaths'] = ctp_df[ctp_staff_death_cols].sum(axis=1)\n",
    "  ctp_df['ctp_resstaff_total_deaths'] = ctp_df[ctp_resstaff_death_cols].sum(axis=1)\n",
    "\n",
    "print(ctp_df.shape)\n",
    "annotate_ctp_df(ctp_df)\n",
    "print(ctp_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load CMS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download CMS: doing this offline\n",
    "# !wget -O cms.csv https://data.cms.gov/api/views/s2uc-8wxp/rows.csv?accessType=DOWNLOAD&api_foundry=true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/julia/miniconda3/envs/deep-learning-nlp/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (1,82,83) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(782376, 138)\n"
     ]
    }
   ],
   "source": [
    "cms_df = pd.read_csv('cms.csv')\n",
    "print(cms_df.shape)\n",
    "\n",
    "# clean up the cms df column names (copied from Dave's initial notebook)\n",
    "cms_df['week'] = pd.to_datetime(cms_df['Week Ending'])\n",
    "cms_df.sort_values(['Provider State','Federal Provider Number','week'], ascending=True, inplace=True)\n",
    "cms_df = cms_df.reset_index().drop('index', axis=1)\n",
    "cms_df.columns = ['cms_'+i.lower().strip().replace(' ','_').replace('/','_').replace('-','_') for i in cms_df.columns]\n",
    "cms_df['cms_federal_provider_number'] = cms_df['cms_federal_provider_number'].apply(lambda x: str(x).lstrip('0').split('.')[0])\n",
    "\n",
    "# convert cms_week to cms_week_str to match the CTP data types\n",
    "cms_df['cms_week'] = cms_df.cms_week.apply(lambda x: x.strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "slim_cms_df = cms_df[(cms_df['cms_week']>'2020-05-24')][\n",
    "                        ['cms_week','cms_federal_provider_number',\n",
    "                        'cms_provider_name',\n",
    "                        'cms_provider_state',\n",
    "                        'cms_residents_weekly_confirmed_covid_19',\n",
    "                        'cms_residents_total_confirmed_covid_19',\n",
    "                        'cms_residents_weekly_suspected_covid_19',\n",
    "                        'cms_residents_total_suspected_covid_19',\n",
    "                        'cms_residents_weekly_covid_19_deaths',\n",
    "                        'cms_residents_total_covid_19_deaths',\n",
    "                        'cms_residents_weekly_all_deaths',\n",
    "                        'cms_residents_total_all_deaths',\n",
    "                        'cms_staff_weekly_confirmed_covid_19',\n",
    "                        'cms_staff_total_confirmed_covid_19',\n",
    "                        'cms_staff_weekly_suspected_covid_19',\n",
    "                        'cms_staff_total_suspected_covid_19',\n",
    "                        'cms_staff_weekly_covid_19_deaths',\n",
    "                        'cms_staff_total_covid_19_deaths',\n",
    "                        # 'cms_number_of_all_beds',\n",
    "                        # 'cms_total_number_of_occupied_beds',\n",
    "                      ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(767008, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slim_cms_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(767008, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# annotate CMS data\n",
    "def annotate_cms_df(cms_df):\n",
    "    # resident and staff case columns\n",
    "    cms_resident_case_cols = ['cms_residents_total_confirmed_covid_19', 'cms_residents_total_suspected_covid_19']\n",
    "    cms_staff_case_cols = ['cms_staff_total_confirmed_covid_19', 'cms_staff_total_suspected_covid_19']\n",
    "\n",
    "    # total case columns\n",
    "    cms_df['cms_resident_total_cases'] = cms_df[cms_resident_case_cols].sum(axis=1)\n",
    "    cms_df['cms_staff_total_cases'] = cms_df[cms_staff_case_cols].sum(axis=1)\n",
    "\n",
    "annotate_cms_df(slim_cms_df)\n",
    "slim_cms_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge CTP and CMS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner merged shape: (289533, 58)\n",
      "Outer merged shape: (1245086, 58)\n",
      "CMS shape: (767008, 20)\n",
      "CTP shape: (759522, 38)\n"
     ]
    }
   ],
   "source": [
    "cms_ctp_merged = pd.merge(slim_cms_df, ctp_df, how='inner', left_on=['cms_week','cms_federal_provider_number'], right_on=['ctp_cms_week','ctp_cms_id'])\n",
    "cms_ctp_merged_outer = pd.merge(slim_cms_df, ctp_df, how='outer', left_on=['cms_week','cms_federal_provider_number'], right_on=['ctp_cms_week','ctp_cms_id'])\n",
    "print('Inner merged shape: %s' % str(cms_ctp_merged.shape))\n",
    "print('Outer merged shape: %s' % str(cms_ctp_merged_outer.shape))\n",
    "print('CMS shape: %s' % str(slim_cms_df.shape))\n",
    "print('CTP shape: %s' % str(ctp_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(289533, 68)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def annotate_cms_ctp_merged_df(cms_ctp_merged):\n",
    "  # case difference columns\n",
    "  cms_ctp_merged['cms_ctp_resident_cases_diff'] = \\\n",
    "    cms_ctp_merged['cms_resident_total_cases'] - cms_ctp_merged['ctp_resident_total_cases']\n",
    "  cms_ctp_merged['cms_ctp_staff_cases_diff'] = \\\n",
    "    cms_ctp_merged['cms_staff_total_cases'] - cms_ctp_merged['ctp_staff_total_cases']\n",
    "\n",
    "  # deaths difference columns\n",
    "  cms_ctp_merged['cms_ctp_resident_deaths_diff'] = \\\n",
    "    cms_ctp_merged['cms_residents_total_covid_19_deaths'] - cms_ctp_merged['ctp_resident_total_deaths']\n",
    "  cms_ctp_merged['cms_ctp_staff_deaths_diff'] = \\\n",
    "    cms_ctp_merged['cms_staff_total_covid_19_deaths'] - cms_ctp_merged['ctp_staff_total_deaths']\n",
    "  cms_ctp_merged['cms_ctp_resident_and_resstaff_deaths_diff'] = \\\n",
    "    cms_ctp_merged['cms_residents_total_covid_19_deaths'] - cms_ctp_merged['ctp_resident_and_resstaff_total_deaths']\n",
    "\n",
    "  # for each \"diff\" col, compute its absolute value\n",
    "  diff_cols = [x for x in cms_ctp_merged.columns if x.endswith('_diff')]\n",
    "  for diff_col in diff_cols:\n",
    "    cms_ctp_merged[diff_col + '_abs'] = cms_ctp_merged[diff_col].abs()\n",
    "\n",
    "annotate_cms_ctp_merged_df(cms_ctp_merged)\n",
    "cms_ctp_merged.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-07\n"
     ]
    }
   ],
   "source": [
    "LATEST_DATE = cms_ctp_merged.ctp_cms_week.max()\n",
    "print(LATEST_DATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actual analysis bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_facilities_barchart(cms_ctp_merged, state, diffs_to_plot, use_date='latest'):\n",
    "  # create the necessary data, save a file describing matching problems for the latest date\n",
    "  state_cms_ctp_merged_df = cms_ctp_merged.loc[cms_ctp_merged.ctp_state == state].copy()\n",
    "\n",
    "  for (abs_diff_cutoff, y_axis) in diffs_to_plot:\n",
    "    abs_column = y_axis + '_abs'\n",
    "    title = '%s %s: CMS minus CTP (min diff cutoff: %d)' % (state, y_axis[8:].replace('_', ' '), abs_diff_cutoff)\n",
    "\n",
    "    # plot away and save results\n",
    "    plt.figure(figsize=(18,7))\n",
    "\n",
    "    def get_color(row):\n",
    "        if row[y_axis] > 0:\n",
    "            return '#ffad4a'  # orange\n",
    "        else:\n",
    "            return '#585bc1'  # purple\n",
    "\n",
    "    # grab everything as of the given date\n",
    "    if use_date == 'latest':\n",
    "      use_date = state_cms_ctp_merged_df.cms_week.max()\n",
    "    state_cms_ctp_merged_df_most_recent_date = state_cms_ctp_merged_df.loc[\n",
    "        (state_cms_ctp_merged_df.cms_week == use_date)]\n",
    "\n",
    "    high_diffs_df = state_cms_ctp_merged_df_most_recent_date.loc[\n",
    "      state_cms_ctp_merged_df_most_recent_date[abs_column] > abs_diff_cutoff].sort_values(by=[y_axis]).reset_index()\n",
    "    data = high_diffs_df\n",
    "    if data.empty:\n",
    "      print('No data passing the input filters')\n",
    "      return\n",
    "    palette = list(data.apply(get_color, axis=1))\n",
    "\n",
    "    ax = sns.barplot(x=data.index, y=y_axis, data=data, palette=palette)\n",
    "    sns.despine(ax=ax, left=True)\n",
    "    ax.set_xticklabels(rotation=90, labels=data.ctp_facility, fontsize=10)\n",
    "    ax.set_yticklabels(ax.get_yticks(), size=14)\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('')\n",
    "    ax.set_title(title, size=20)\n",
    "    return high_diffs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# state = 'AR'\n",
    "\n",
    "# # each item in the tuple is: (the minimum abs diff to include a facility in the plot, column whose values we're plotting)\n",
    "# diffs_to_plot = (\n",
    "#     [15, 'cms_ctp_resident_deaths_diff'],\n",
    "# )\n",
    "\n",
    "# high_diffs = plot_facilities_barchart(cms_ctp_merged, state, diffs_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what are the most divergent rows in the most recent data, for a particular state and diff column?\n",
    "state = 'CT'\n",
    "diff_column = 'cms_ctp_resident_deaths_diff'\n",
    "abs_diff_cutoff = 15\n",
    "\n",
    "def get_facility_data(cms_ctp_merged_outer, facility_name, state):\n",
    "    data = cms_ctp_merged_outer.loc[(cms_ctp_merged_outer.ctp_facility == facility_name) & (cms_ctp_merged_outer.ctp_state == state)].sort_values(by=['ctp_cms_week'])\n",
    "    data = data[data['ctp_cms_week'].notna()]\n",
    "    return data\n",
    "\n",
    "# same thing but for the outer merged bit so we can see what happened before that\n",
    "def plot_facility_over_time(state, facility_name, ax=None):\n",
    "    data = get_facility_data(cms_ctp_merged_outer, facility_name, state)\n",
    "    if ax is None:\n",
    "        ax = sns.lineplot(x='ctp_cms_week', y='ctp_resident_total_deaths', data=data, color='orange')  # CTP is orange\n",
    "    else:\n",
    "        sns.lineplot(x='ctp_cms_week', y='ctp_resident_total_deaths', ax=ax, data=data, color='orange')  # CTP is orange\n",
    "    sns.lineplot(x='ctp_cms_week', y='cms_residents_total_covid_19_deaths', data=data, ax=ax, color='blue')  # CMS is blue\n",
    "    ax.set_ylabel('Cumulative deaths')\n",
    "    loc = plticker.MultipleLocator(base=11.0) # this locator puts ticks at regular intervals\n",
    "    ax.xaxis.set_major_locator(loc)\n",
    "    ax.set_title(facility_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Are there states that reported way more NH deaths the last week of May/first 2 weeks of June than CMS?\n",
    "# If you track specific facilities that were reporting more deaths in those first 3 weeks, do those discrepancies persist?\n",
    "\n",
    "## Looking at CT first: which facilities drive discrepancy in counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1944x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1944x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what are the most divergent rows in the most recent data, for a particular state and diff column?\n",
    "state = 'CT'\n",
    "diff_column = 'cms_ctp_resident_deaths_diff'\n",
    "abs_diff_cutoff = 15\n",
    "\n",
    "latest_state_data = cms_ctp_merged.loc[\n",
    "    (cms_ctp_merged.ctp_state == state) &\n",
    "    (cms_ctp_merged.ctp_cms_week == LATEST_DATE)]\n",
    "\n",
    "# throw out any CMS IDs that occur more than once (questionable matching)\n",
    "dupes = latest_state_data.cms_federal_provider_number.duplicated(keep=False)\n",
    "latest_state_data = latest_state_data[~dupes]\n",
    "\n",
    "abs_column = diff_column + '_abs'\n",
    "sorted_data = latest_state_data.loc[\n",
    "  latest_state_data[abs_column] > abs_diff_cutoff].sort_values(by=[diff_column]).reset_index()\n",
    "highest_ctp = list(sorted_data[:5].ctp_facility)\n",
    "lowest_ctp = list(sorted_data[-5:].ctp_facility)\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(27, 4))\n",
    "for i, facility_name in enumerate(highest_ctp):\n",
    "    plot_facility_over_time(state, facility_name, ax=axes[i])\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(27, 4))\n",
    "for i, facility_name in enumerate(lowest_ctp):\n",
    "    plot_facility_over_time(state, facility_name, ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AR: which facilities drive discrepancy in counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1944x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1944x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what are the most divergent rows in the most recent data, for a particular state and diff column?\n",
    "state = 'AR'\n",
    "diff_column = 'cms_ctp_resident_deaths_diff'\n",
    "abs_diff_cutoff = 15\n",
    "\n",
    "latest_state_data = cms_ctp_merged.loc[\n",
    "    (cms_ctp_merged.ctp_state == state) &\n",
    "    (cms_ctp_merged.ctp_cms_week == LATEST_DATE)]\n",
    "\n",
    "# throw out any CMS IDs that occur more than once (questionable matching)\n",
    "dupes = latest_state_data.cms_federal_provider_number.duplicated(keep=False)\n",
    "latest_state_data = latest_state_data[~dupes]\n",
    "\n",
    "abs_column = diff_column + '_abs'\n",
    "sorted_data = latest_state_data.loc[\n",
    "  latest_state_data[abs_column] > abs_diff_cutoff].sort_values(by=[diff_column]).reset_index()\n",
    "highest_ctp = list(sorted_data[:5].ctp_facility)\n",
    "lowest_ctp = list(sorted_data[-5:].ctp_facility)\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(27, 4))\n",
    "for i, facility_name in enumerate(highest_ctp):\n",
    "    plot_facility_over_time(state, facility_name, ax=axes[i])\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(27, 4))\n",
    "for i, facility_name in enumerate(lowest_ctp):\n",
    "    plot_facility_over_time(state, facility_name, ax=axes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(314, 58)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_state_data_outer = cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.ctp_state == 'AR') &\n",
    "    ((cms_ctp_merged_outer.ctp_cms_week == LATEST_DATE) | (cms_ctp_merged_outer.cms_week == LATEST_DATE))]\n",
    "latest_state_data_outer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nursing Home     220\n",
       "Uncategorized     94\n",
       "Name: ctp_ctp_facility_type, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_state_data_outer.ctp_ctp_facility_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1763.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_state_data.ctp_resident_total_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1886.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latest_state_data_outer.ctp_resident_total_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1886.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in CTP data: total reported resident deaths\n",
    "ctp_df.loc[\n",
    "    (ctp_df.ctp_state == 'AR') &\n",
    "    (ctp_df.ctp_cms_week == LATEST_DATE)\n",
    "].ctp_resident_total_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1808.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in CTP data: resident deaths in NH vs uncategorized\n",
    "ctp_df.loc[\n",
    "    (ctp_df.ctp_state == 'AR') &\n",
    "    (ctp_df.ctp_cms_week == LATEST_DATE) &\n",
    "    (ctp_df.ctp_ctp_facility_type == 'Nursing Home')\n",
    "].ctp_resident_total_deaths.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctp_df.loc[\n",
    "    (ctp_df.ctp_state == 'AR') &\n",
    "    (ctp_df.ctp_cms_week == LATEST_DATE) &\n",
    "    (ctp_df.ctp_ctp_facility_type == 'Uncategorized')\n",
    "].ctp_resident_total_deaths.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What facilities did CMS report that states did not? From those, how many cases or deaths are not included in what the state reported? We think this is based on how they report based on outbreak definitions. Spot check states to check: IA, KS, MN, NJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CRESTRIDGE CARE CENTER',\n",
       " 'CRYSTAL HEIGHTS CARE CENTER',\n",
       " 'PEARL VALLEY REHABILITATION AND NURSING AT SUTHERL',\n",
       " 'SOUTHFIELD WELLNESS COMMUNITY',\n",
       " 'TRINITY CENTER AT LUTHER PARK'}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the outer merge, where is CMS populated that CTP isn't? check IA first\n",
    "\n",
    "set(cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.cms_provider_state == 'IA') &\n",
    "    (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "    (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].sort_values(\n",
    "    'cms_resident_total_cases', ascending=False).head(5).cms_provider_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IA: ['TRINITY CENTER AT LUTHER PARK', 'SOUTHFIELD WELLNESS COMMUNITY', 'LIVING CENTER WEST', 'WESTBROOK ACRES', 'CRESTRIDGE CARE CENTER']\n",
      "KS: ['BONNER SPRINGS NURSING & REHAB CENTER', 'HALSTEAD HEALTH AND REHABILITATION CENTER', 'CATHOLIC CARE CENTER, INC', 'SANDPIPER HEALTHCARE & REHABILITATION CENTER LLC', 'GOVE COUNTY MEDICAL CENTER LTCU']\n",
      "MN: ['SOUTHVIEW ACRES HEALTHCARE CENTER', 'NORTH RIDGE HEALTH AND REHAB', 'INTERLUDE RESTORATIVE SUITES UNITY', 'LYNGBLOMSTEN CARE CENTER', 'CAMILIA ROSE CARE CENTER LLC']\n",
      "NJ: ['ARISTACARE AT DELAIRE', 'N J VETERANS MEM HOME PARAMUS', 'OAKLAND REHABILITATION AND HEALTHCARE  CENTER', 'SEACREST VILLAGE', 'PARK CRESCENT HEALTHCARE & REHABILITATION CENTER']\n"
     ]
    }
   ],
   "source": [
    "for state in ['IA', 'KS', 'MN', 'NJ']:\n",
    "    print('%s: %s' % (state, list(cms_ctp_merged_outer.loc[\n",
    "        (cms_ctp_merged_outer.cms_provider_state == state) &\n",
    "        (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "        (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].sort_values(\n",
    "        'cms_residents_total_covid_19_deaths', ascending=False).head(5).cms_provider_name)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IA: 247 deaths, 1718 cases\n",
      "KS: 619 deaths, 3939 cases\n",
      "MN: 2341 deaths, 12338 cases\n",
      "NJ: 1330 deaths, 6032 cases\n",
      "CT: 0 deaths, 0 cases\n"
     ]
    }
   ],
   "source": [
    "# unmapped cases and deaths\n",
    "for state in ['IA', 'KS', 'MN', 'NJ']:\n",
    "    print('%s: %d deaths, %d cases' % (\n",
    "        state,\n",
    "        cms_ctp_merged_outer.loc[\n",
    "            (cms_ctp_merged_outer.cms_provider_state == state) &\n",
    "            (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "            (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].cms_residents_total_covid_19_deaths.sum(),\n",
    "        cms_ctp_merged_outer.loc[\n",
    "            (cms_ctp_merged_outer.cms_provider_state == state) &\n",
    "            (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "            (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].cms_resident_total_cases.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 58)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cms_ctp_merged_outer.loc[\n",
    "            (cms_ctp_merged_outer.cms_provider_state == 'CT') &\n",
    "            (pd.isnull(cms_ctp_merged_outer.ctp_cms_week)) &\n",
    "            (cms_ctp_merged_outer.cms_week == '2021-01-31')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-01-31    15692\n",
       "2021-01-24    15687\n",
       "2020-12-20    15684\n",
       "2020-12-27    15676\n",
       "2021-01-03    15674\n",
       "2021-01-10    15670\n",
       "2020-12-13    15668\n",
       "2021-02-14    15652\n",
       "2021-02-21    15646\n",
       "2021-03-07    15627\n",
       "2020-11-29    15626\n",
       "2021-02-28    15622\n",
       "2021-02-07    15620\n",
       "2020-11-22    15606\n",
       "2020-11-15    15593\n",
       "2020-12-06    15591\n",
       "2021-01-17    15588\n",
       "2020-11-08    15585\n",
       "2020-10-25    15575\n",
       "2020-11-01    15573\n",
       "2020-10-18    15563\n",
       "2020-10-11    15554\n",
       "2020-09-27    15550\n",
       "2020-10-04    15543\n",
       "2020-09-20    15525\n",
       "2020-09-06    15516\n",
       "2020-09-13    15504\n",
       "2020-08-30    15464\n",
       "2020-08-23    15444\n",
       "2020-08-02    15427\n",
       "2020-08-09    15426\n",
       "2020-08-16    15426\n",
       "2020-07-26    15423\n",
       "2020-07-12    15420\n",
       "2020-07-19    15420\n",
       "2020-06-28    15417\n",
       "2020-07-05    15412\n",
       "2020-06-21    15394\n",
       "2020-06-14    15386\n",
       "2020-06-07    15385\n",
       "2020-05-31    15382\n",
       "2021-03-14    15323\n",
       "2021-03-28    15322\n",
       "2021-03-21    15321\n",
       "2021-04-04    15319\n",
       "2021-04-11    15318\n",
       "2021-04-18    15317\n",
       "2021-04-25    15317\n",
       "2021-05-09    15312\n",
       "2021-05-02    15312\n",
       "Name: cms_week, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cms_ctp_merged_outer.cms_week.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AR: 58 deaths, 324 cases\n",
      "CA: 237 deaths, 1709 cases\n",
      "CO: 1755 deaths, 8841 cases\n",
      "CT: 0 deaths, 0 cases\n",
      "DC: 95 deaths, 707 cases\n",
      "DE: 70 deaths, 540 cases\n",
      "FL: 4752 deaths, 37696 cases\n",
      "GA: 106 deaths, 493 cases\n",
      "HI: 17 deaths, 242 cases\n",
      "IA: 247 deaths, 1718 cases\n",
      "ID: 27 deaths, 233 cases\n",
      "IL: 1746 deaths, 10552 cases\n",
      "IN: 99 deaths, 668 cases\n",
      "KS: 619 deaths, 3939 cases\n",
      "KY: 77 deaths, 693 cases\n",
      "LA: 74 deaths, 481 cases\n",
      "MA: 4479 deaths, 20899 cases\n",
      "MD: 2103 deaths, 12071 cases\n",
      "ME: 85 deaths, 908 cases\n",
      "MI: 3701 deaths, 18157 cases\n",
      "MN: 2341 deaths, 12338 cases\n",
      "MO: 2980 deaths, 21683 cases\n",
      "MS: 1158 deaths, 8114 cases\n",
      "NC: 1148 deaths, 7971 cases\n",
      "ND: 7 deaths, 45 cases\n",
      "NJ: 1330 deaths, 6032 cases\n",
      "NM: 63 deaths, 109 cases\n",
      "NV: 41 deaths, 327 cases\n",
      "NY: 21 deaths, 429 cases\n",
      "OH: 1579 deaths, 10015 cases\n",
      "OK: 132 deaths, 649 cases\n",
      "OR: 97 deaths, 407 cases\n",
      "PA: 9899 deaths, 53063 cases\n",
      "RI: 37 deaths, 161 cases\n",
      "SC: 78 deaths, 469 cases\n",
      "TN: 647 deaths, 4200 cases\n",
      "TX: 183 deaths, 1410 cases\n",
      "UT: 174 deaths, 1469 cases\n",
      "VA: 2618 deaths, 17763 cases\n",
      "VT: 82 deaths, 611 cases\n",
      "WI: 319 deaths, 2002 cases\n",
      "WV: 1 deaths, 2 cases\n",
      "WY: 23 deaths, 87 cases\n"
     ]
    }
   ],
   "source": [
    "# do this for all states?\n",
    "\n",
    "all_states = sorted(set(cms_ctp_merged_outer.loc[~pd.isnull(cms_ctp_merged_outer.ctp_ctp_id)].ctp_state))\n",
    "\n",
    "# unmapped cases and deaths\n",
    "for state in all_states:\n",
    "    print('%s: %d deaths, %d cases' % (\n",
    "        state,\n",
    "        cms_ctp_merged_outer.loc[\n",
    "            (cms_ctp_merged_outer.cms_provider_state == state) &\n",
    "            (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "            (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].cms_residents_total_covid_19_deaths.sum(),\n",
    "        cms_ctp_merged_outer.loc[\n",
    "            (cms_ctp_merged_outer.cms_provider_state == state) &\n",
    "            (cms_ctp_merged_outer.cms_week == LATEST_DATE) &\n",
    "            (pd.isnull(cms_ctp_merged_outer.ctp_cms_week))].cms_resident_total_cases.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary stats\n",
    "\n",
    "How many individual facilities had a COVID case?\n",
    "How many individual facilities had a COVID death?\n",
    "How many individual CMS facilities had a COVID case?\n",
    "How many individual CMS facilities had a COVID death?\n",
    "How many states do not have facility level data?\n",
    "If filled in with CMS data, what is the total number of cases and deaths in the US?\n",
    "How many states report higher CMS NH data than the state reported information?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21201"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many CTP facilities had a covid case?\n",
    "len(set(cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.ctp_resident_total_cases > 0) |\n",
    "    (cms_ctp_merged_outer.ctp_staff_total_cases > 0) |\n",
    "    (cms_ctp_merged_outer.ctp_resstaff_total_cases > 0)\n",
    "].ctp_ctp_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16430"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many CTP facilities had a covid death?\n",
    "len(set(cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.ctp_resident_total_deaths > 0) |\n",
    "    (cms_ctp_merged_outer.ctp_staff_total_deaths > 0) |\n",
    "    (cms_ctp_merged_outer.ctp_resstaff_total_deaths > 0)\n",
    "].ctp_ctp_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15220"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many individual CMS facilities had a COVID case?\n",
    "len(set(cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.cms_resident_total_cases > 0) |\n",
    "    (cms_ctp_merged_outer.cms_staff_total_cases > 0)\n",
    "].cms_federal_provider_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12623"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many individual CMS facilities had a COVID death?\n",
    "len(set(cms_ctp_merged_outer.loc[\n",
    "    (cms_ctp_merged_outer.cms_residents_total_covid_19_deaths > 0) |\n",
    "    (cms_ctp_merged_outer.cms_staff_total_covid_19_deaths > 0)\n",
    "].cms_federal_provider_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many states have facility-level data? tells us how many don't\n",
    "len(set(cms_ctp_merged_outer.loc[~pd.isnull(cms_ctp_merged_outer.ctp_ctp_id)].ctp_state))"
   ]
  }
 ],
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