code/data/usa/add_testing_regimes_to_covidtrackingdotcom_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# local imports\n",
    "import download_latest_covidtrackingdotcom_data as data_loader\n",
    "\n",
    "# flag whether to save this output in the /data/interim/usa folder \n",
    "save_notebook_output = True\n",
    "\n",
    "path_to_int_data = '../../../data/interim/usa'\n",
    "fn_out = 'usa_states_covidtrackingdotcom_int_with_testing_regimes.csv'\n",
    "fp_out = os.path.join(path_to_int_data, fn_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define the functions to programmatically look for testing regime changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def state_data_to_time_series(state_data):\n",
    "    state_data_sorted = state_data.sort_values('date',inplace=False)\n",
    "    \n",
    "    timeseries_state_data = pd.Series(state_data_sorted['cumulative_tests'].values,\n",
    "                    pd.DatetimeIndex([pd.to_datetime(x) for x in state_data_sorted['date'].values]))\n",
    "    \n",
    "    return state_data_sorted, timeseries_state_data\n",
    "\n",
    "def calculate_testing_regimes(state_data, pct_chg_thresh=0.4, abs_chg_thresh=50):\n",
    "    # calculates testing regimes as changing whenever the pct_chg of total tests between previous \n",
    "    # current dates is above pct_chg_thresh\n",
    "    # note pct_chg is consistent with the pandas definition, so multiple by 100 to get the actual \n",
    "    # percentage\n",
    "        \n",
    "    state_data_sorted, timeseries_state_data = state_data_to_time_series(state_data)\n",
    "    \n",
    "    pct_changes_in_testing = timeseries_state_data.pct_change(freq='D').values\n",
    "    abs_changes_in_testing = timeseries_state_data.diff()\n",
    "\n",
    "    testing_regimes = np.zeros(len(pct_changes_in_testing))\n",
    "    regime = 0\n",
    "    \n",
    "    # transition to a new regime if the absolute *and* percent changes exceed the thresholds\n",
    "    for i, (pct_change, abs_change) in enumerate(zip(pct_changes_in_testing, abs_changes_in_testing)):\n",
    "        if pct_change > pct_chg_thresh and abs_change > abs_chg_thresh:\n",
    "            regime += 1\n",
    "        testing_regimes[i] = regime\n",
    "        \n",
    "    # return the pandas indices and the \n",
    "    return testing_regimes, state_data_sorted.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Download the data locally "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# option a: take from local (avoids downloading again):\n",
    "# data_loader.main() # to refresh data\n",
    "states_data = pd.read_csv(os.path.join(fp_out.replace('_with_testing_regimes', '')))\n",
    "\n",
    "# option b: download from covidtracker.com:\n",
    "# don't save locally (you can, but you can also run the python script to refresh the underlying data)\n",
    "#states_data_raw = data_loader.download_and_save_data_raw(save_locally=False)\n",
    "#states_data = data_loader.process_and_save_data_int(states_data_raw, save_locally=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Programmatically add in testing regime changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56 states represented\n"
     ]
    }
   ],
   "source": [
    "# 2. add variable for testing regime\n",
    "states_data['testing_regime'] = 1\n",
    "\n",
    "state_names = np.unique(states_data['adm1_name'])\n",
    "print(len(state_names), \"states represented\")\n",
    "\n",
    "# These factors determine what is programatically considered as a testing regime change candidate\n",
    "pct_change_thresh = 2.5\n",
    "abs_change_thresh = 150\n",
    "\n",
    "for state in state_names:\n",
    "    # find the testing regimes for this state\n",
    "    state_data = states_data[states_data['adm1_name'] == state]\n",
    "    state_testing_regimes, state_data_idxs =  calculate_testing_regimes(state_data, \n",
    "                                                                        pct_chg_thresh=pct_change_thresh,\n",
    "                                                                        abs_chg_thresh=abs_change_thresh)\n",
    "\n",
    "    states_data.loc[state_data_idxs,'testing_regime'] = state_testing_regimes\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Manually inspect results\n",
    "### 3a. print any date/state comboes with testing_regime > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>USA</td>\n",
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       "      <td>47350.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>52874.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3689.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>257.0</td>\n",
       "      <td>5047.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>299.0</td>\n",
       "      <td>5823.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>346.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>402.0</td>\n",
       "      <td>7710.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>480.0</td>\n",
       "      <td>9244.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>602.0</td>\n",
       "      <td>11312.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>719.0</td>\n",
       "      <td>13993.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>806.0</td>\n",
       "      <td>16003.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>887.0</td>\n",
       "      <td>18513.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>21167.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>21065.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>24248.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>28043.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>30892.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>33394.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>34647.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>339 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date adm0_name   adm1_name  cumulative_confirmed_cases  \\\n",
       "897   2020-03-22       USA     Alabama                       138.0   \n",
       "841   2020-03-23       USA     Alabama                       167.0   \n",
       "785   2020-03-24       USA     Alabama                       215.0   \n",
       "729   2020-03-25       USA     Alabama                       283.0   \n",
       "673   2020-03-26       USA     Alabama                       506.0   \n",
       "617   2020-03-27       USA     Alabama                       587.0   \n",
       "561   2020-03-28       USA     Alabama                       696.0   \n",
       "505   2020-03-29       USA     Alabama                       806.0   \n",
       "449   2020-03-30       USA     Alabama                       859.0   \n",
       "393   2020-03-31       USA     Alabama                       981.0   \n",
       "337   2020-04-01       USA     Alabama                      1077.0   \n",
       "281   2020-04-02       USA     Alabama                      1233.0   \n",
       "225   2020-04-03       USA     Alabama                      1432.0   \n",
       "169   2020-04-04       USA     Alabama                      1580.0   \n",
       "113   2020-04-05       USA     Alabama                      1796.0   \n",
       "57    2020-04-06       USA     Alabama                      1968.0   \n",
       "1     2020-04-07       USA     Alabama                      2119.0   \n",
       "620   2020-03-27       USA     Arizona                       736.0   \n",
       "564   2020-03-28       USA     Arizona                       873.0   \n",
       "508   2020-03-29       USA     Arizona                       919.0   \n",
       "452   2020-03-30       USA     Arizona                      1157.0   \n",
       "396   2020-03-31       USA     Arizona                      1289.0   \n",
       "340   2020-04-01       USA     Arizona                      1413.0   \n",
       "284   2020-04-02       USA     Arizona                      1598.0   \n",
       "228   2020-04-03       USA     Arizona                      1769.0   \n",
       "172   2020-04-04       USA     Arizona                      2019.0   \n",
       "116   2020-04-05       USA     Arizona                      2269.0   \n",
       "60    2020-04-06       USA     Arizona                      2456.0   \n",
       "4     2020-04-07       USA     Arizona                      2575.0   \n",
       "1237  2020-03-16       USA  California                       335.0   \n",
       "...          ...       ...         ...                         ...   \n",
       "550   2020-03-29       USA   Tennessee                      1537.0   \n",
       "494   2020-03-30       USA   Tennessee                      1834.0   \n",
       "438   2020-03-31       USA   Tennessee                      2239.0   \n",
       "382   2020-04-01       USA   Tennessee                      2683.0   \n",
       "326   2020-04-02       USA   Tennessee                      2845.0   \n",
       "270   2020-04-03       USA   Tennessee                      3067.0   \n",
       "214   2020-04-04       USA   Tennessee                      3321.0   \n",
       "158   2020-04-05       USA   Tennessee                      3633.0   \n",
       "102   2020-04-06       USA   Tennessee                      3802.0   \n",
       "46    2020-04-07       USA   Tennessee                      4138.0   \n",
       "1112  2020-03-19       USA        Utah                        78.0   \n",
       "1056  2020-03-20       USA        Utah                       112.0   \n",
       "1000  2020-03-21       USA        Utah                       136.0   \n",
       "944   2020-03-22       USA        Utah                       181.0   \n",
       "888   2020-03-23       USA        Utah                       257.0   \n",
       "832   2020-03-24       USA        Utah                       299.0   \n",
       "776   2020-03-25       USA        Utah                       346.0   \n",
       "720   2020-03-26       USA        Utah                       402.0   \n",
       "664   2020-03-27       USA        Utah                       480.0   \n",
       "608   2020-03-28       USA        Utah                       602.0   \n",
       "552   2020-03-29       USA        Utah                       719.0   \n",
       "496   2020-03-30       USA        Utah                       806.0   \n",
       "440   2020-03-31       USA        Utah                       887.0   \n",
       "384   2020-04-01       USA        Utah                      1012.0   \n",
       "328   2020-04-02       USA        Utah                      1074.0   \n",
       "272   2020-04-03       USA        Utah                      1246.0   \n",
       "216   2020-04-04       USA        Utah                      1428.0   \n",
       "160   2020-04-05       USA        Utah                      1605.0   \n",
       "104   2020-04-06       USA        Utah                      1675.0   \n",
       "48    2020-04-07       USA        Utah                      1738.0   \n",
       "\n",
       "      cumulative_tests  cumulative_deaths  testing_regime  \n",
       "897             1602.0                0.0             1.0  \n",
       "841             1832.0                0.0             1.0  \n",
       "785             2321.0                0.0             1.0  \n",
       "729             2812.0                0.0             1.0  \n",
       "673             4099.0                1.0             1.0  \n",
       "617             4771.0                3.0             1.0  \n",
       "561             4880.0                3.0             1.0  \n",
       "505             4990.0                4.0             1.0  \n",
       "449             6553.0                6.0             1.0  \n",
       "393             7279.0               13.0             1.0  \n",
       "337             7774.0               26.0             1.0  \n",
       "281             8736.0               32.0             1.0  \n",
       "225             9619.0               35.0             1.0  \n",
       "169            10853.0               43.0             1.0  \n",
       "113            13078.0               45.0             1.0  \n",
       "57             14765.0               50.0             1.0  \n",
       "1              14916.0               56.0             1.0  \n",
       "620             8191.0               13.0             1.0  \n",
       "564             8328.0               15.0             1.0  \n",
       "508            13872.0               17.0             1.0  \n",
       "452            16759.0               20.0             1.0  \n",
       "396            19371.0               24.0             1.0  \n",
       "340            21058.0               29.0             1.0  \n",
       "284            22709.0               32.0             1.0  \n",
       "228            24673.0               41.0             1.0  \n",
       "172            27160.0               52.0             1.0  \n",
       "116            27410.0               64.0             1.0  \n",
       "60             32534.0               65.0             1.0  \n",
       "4              33375.0               73.0             1.0  \n",
       "1237            8316.0                6.0             1.0  \n",
       "...                ...                ...             ...  \n",
       "550            20574.0                7.0             1.0  \n",
       "494            23304.0               13.0             1.0  \n",
       "438            27360.0               23.0             1.0  \n",
       "382            32452.0               24.0             1.0  \n",
       "326            34611.0               32.0             1.0  \n",
       "270            37839.0               37.0             1.0  \n",
       "214            41391.0               43.0             1.0  \n",
       "158            45300.0               44.0             1.0  \n",
       "102            47350.0               65.0             1.0  \n",
       "46             52874.0               72.0             1.0  \n",
       "1112            1526.0                0.0             1.0  \n",
       "1056            2147.0                0.0             1.0  \n",
       "1000            2560.0                0.0             1.0  \n",
       "944             3689.0                1.0             1.0  \n",
       "888             5047.0                1.0             1.0  \n",
       "832             5823.0                1.0             1.0  \n",
       "776             6837.0                1.0             1.0  \n",
       "720             7710.0                1.0             1.0  \n",
       "664             9244.0                2.0             1.0  \n",
       "608            11312.0                2.0             1.0  \n",
       "552            13993.0                2.0             1.0  \n",
       "496            16003.0                4.0             1.0  \n",
       "440            18513.0                5.0             1.0  \n",
       "384            21167.0                7.0             1.0  \n",
       "328            21065.0                7.0             1.0  \n",
       "272            24248.0                7.0             1.0  \n",
       "216            28043.0                8.0             1.0  \n",
       "160            30892.0                8.0             1.0  \n",
       "104            33394.0               13.0             1.0  \n",
       "48             34647.0               13.0             1.0  \n",
       "\n",
       "[339 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check on any places where we made a testing regime change -- we might want to manually change \n",
    "# anything that looks inconsistent\n",
    "entries_with_regime_change = states_data[states_data['testing_regime'] >0].sort_values(['adm1_name','date'],inplace=False)\n",
    "states_with_regime_change = np.unique(entries_with_regime_change['adm1_name'])\n",
    "\n",
    "entries_with_regime_change\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b plot the states that have regime changes, + the states that "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'date')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# note: we plot these b/c missing we don't want to miss a testing regime change in these states \n",
    "high_instances_states = []#['WA',\"CA\",\"NY\",'FL',\"MA\"]\n",
    "\n",
    "states_to_plot = np.union1d(high_instances_states,states_with_regime_change)\n",
    "for state in states_to_plot:\n",
    "#for state in state_names:\n",
    "\n",
    "    state_data = states_data[states_data['adm1_name'] == state]\n",
    "\n",
    "    _, ts = state_data_to_time_series(state_data)\n",
    "\n",
    "\n",
    "    ts.plot(label=state)\n",
    "    \n",
    "    plt.legend(loc=(1,0))\n",
    "    \n",
    "    plt.yscale('log')\n",
    "    \n",
    "plt.ylim(1e2)\n",
    "plt.ylabel('# tests over time')\n",
    "plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3c.  spot check and make any nessecary changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "2020-03-04        NaN\n",
      "2020-03-05        NaN\n",
      "2020-03-06        NaN\n",
      "2020-03-07        NaN\n",
      "2020-03-08        NaN\n",
      "2020-03-09        NaN\n",
      "2020-03-10        NaN\n",
      "2020-03-11        NaN\n",
      "2020-03-12        NaN\n",
      "2020-03-13      215.0\n",
      "2020-03-14      490.0\n",
      "2020-03-15      490.0\n",
      "2020-03-16      516.0\n",
      "2020-03-17     1759.0\n",
      "2020-03-18     2271.0\n",
      "2020-03-19     3132.0\n",
      "2020-03-20     4091.0\n",
      "2020-03-21     5277.0\n",
      "2020-03-22     6105.0\n",
      "2020-03-23     8922.0\n",
      "2020-03-24    13749.0\n",
      "2020-03-25    19794.0\n",
      "2020-03-26    23621.0\n",
      "2020-03-27    29371.0\n",
      "2020-03-28    35049.0\n",
      "2020-03-29    39066.0\n",
      "2020-03-30    42793.0\n",
      "2020-03-31    46935.0\n",
      "2020-04-01    51738.0\n",
      "2020-04-02    56608.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'date')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make any refinements before changing\n",
    "\n",
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for state in ['Massachusetts']:\n",
    "#for state in state_names:\n",
    "\n",
    "    state_data = states_data[states_data['adm1_name'] == state]\n",
    "\n",
    "    _, ts = state_data_to_time_series(state_data)\n",
    "    print(ts)\n",
    "\n",
    "    ts.plot(label=state)\n",
    "    \n",
    "    plt.legend()\n",
    "    \n",
    "    plt.yscale('log')\n",
    "    \n",
    "plt.ylabel('log (tests @ time t) - log (tests @ time t-1)')\n",
    "plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Make any changes to the testing regime designations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for now i have none, but this will likeley change as ore data comes in\n",
    "\n",
    "#for state in []:\n",
    "#    states_data.loc[states_data['adm1_name'] == state, 'testing_regime'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>adm0_name</th>\n",
       "      <th>adm1_name</th>\n",
       "      <th>adm_level</th>\n",
       "      <th>cumulative_confirmed_cases</th>\n",
       "      <th>cumulative_tests</th>\n",
       "      <th>cumulative_deaths</th>\n",
       "      <th>testing_regime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2321.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2812.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>506.0</td>\n",
       "      <td>4099.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>587.0</td>\n",
       "      <td>4771.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>696.0</td>\n",
       "      <td>4880.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>806.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>859.0</td>\n",
       "      <td>6553.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>981.0</td>\n",
       "      <td>7279.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>7774.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>8736.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>736.0</td>\n",
       "      <td>8191.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>873.0</td>\n",
       "      <td>8328.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>919.0</td>\n",
       "      <td>13872.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>16759.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>19371.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>21058.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>1</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>22709.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>335.0</td>\n",
       "      <td>8316.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>483.0</td>\n",
       "      <td>8464.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>611.0</td>\n",
       "      <td>8592.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>924.0</td>\n",
       "      <td>9711.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>11487.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>12528.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>12840.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>2102.0</td>\n",
       "      <td>15554.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>2355.0</td>\n",
       "      <td>18276.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>3006.0</td>\n",
       "      <td>20386.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>29539.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1</td>\n",
       "      <td>2902.0</td>\n",
       "      <td>34918.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>371.0</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>505.0</td>\n",
       "      <td>3777.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>615.0</td>\n",
       "      <td>3887.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>667.0</td>\n",
       "      <td>11184.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>784.0</td>\n",
       "      <td>11796.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>957.0</td>\n",
       "      <td>14909.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>16091.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>18338.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>20574.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>23304.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>27360.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>2683.0</td>\n",
       "      <td>32452.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>1</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>34611.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3689.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>257.0</td>\n",
       "      <td>5047.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>299.0</td>\n",
       "      <td>5823.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>346.0</td>\n",
       "      <td>6837.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>402.0</td>\n",
       "      <td>7710.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>480.0</td>\n",
       "      <td>9244.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>602.0</td>\n",
       "      <td>11312.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>719.0</td>\n",
       "      <td>13993.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>806.0</td>\n",
       "      <td>16003.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>887.0</td>\n",
       "      <td>18513.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>21167.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>Utah</td>\n",
       "      <td>1</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>21065.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date adm0_name   adm1_name  adm_level  cumulative_confirmed_cases  \\\n",
       "617  2020-03-22       USA     Alabama          1                       138.0   \n",
       "561  2020-03-23       USA     Alabama          1                       167.0   \n",
       "505  2020-03-24       USA     Alabama          1                       215.0   \n",
       "449  2020-03-25       USA     Alabama          1                       283.0   \n",
       "393  2020-03-26       USA     Alabama          1                       506.0   \n",
       "337  2020-03-27       USA     Alabama          1                       587.0   \n",
       "281  2020-03-28       USA     Alabama          1                       696.0   \n",
       "225  2020-03-29       USA     Alabama          1                       806.0   \n",
       "169  2020-03-30       USA     Alabama          1                       859.0   \n",
       "113  2020-03-31       USA     Alabama          1                       981.0   \n",
       "57   2020-04-01       USA     Alabama          1                      1077.0   \n",
       "1    2020-04-02       USA     Alabama          1                      1233.0   \n",
       "340  2020-03-27       USA     Arizona          1                       736.0   \n",
       "284  2020-03-28       USA     Arizona          1                       873.0   \n",
       "228  2020-03-29       USA     Arizona          1                       919.0   \n",
       "172  2020-03-30       USA     Arizona          1                      1157.0   \n",
       "116  2020-03-31       USA     Arizona          1                      1289.0   \n",
       "60   2020-04-01       USA     Arizona          1                      1413.0   \n",
       "4    2020-04-02       USA     Arizona          1                      1598.0   \n",
       "957  2020-03-16       USA  California          1                       335.0   \n",
       "901  2020-03-17       USA  California          1                       483.0   \n",
       "845  2020-03-18       USA  California          1                       611.0   \n",
       "789  2020-03-19       USA  California          1                       924.0   \n",
       "733  2020-03-20       USA  California          1                      1063.0   \n",
       "677  2020-03-21       USA  California          1                      1279.0   \n",
       "621  2020-03-22       USA  California          1                      1536.0   \n",
       "565  2020-03-23       USA  California          1                      1733.0   \n",
       "509  2020-03-24       USA  California          1                      2102.0   \n",
       "453  2020-03-25       USA  California          1                      2355.0   \n",
       "397  2020-03-26       USA  California          1                      3006.0   \n",
       "..          ...       ...         ...        ...                         ...   \n",
       "94   2020-04-01       USA        Ohio          1                      2547.0   \n",
       "38   2020-04-02       USA        Ohio          1                      2902.0   \n",
       "718  2020-03-21       USA   Tennessee          1                       371.0   \n",
       "662  2020-03-22       USA   Tennessee          1                       505.0   \n",
       "606  2020-03-23       USA   Tennessee          1                       615.0   \n",
       "550  2020-03-24       USA   Tennessee          1                       667.0   \n",
       "494  2020-03-25       USA   Tennessee          1                       784.0   \n",
       "438  2020-03-26       USA   Tennessee          1                       957.0   \n",
       "382  2020-03-27       USA   Tennessee          1                      1203.0   \n",
       "326  2020-03-28       USA   Tennessee          1                      1373.0   \n",
       "270  2020-03-29       USA   Tennessee          1                      1537.0   \n",
       "214  2020-03-30       USA   Tennessee          1                      1834.0   \n",
       "158  2020-03-31       USA   Tennessee          1                      2239.0   \n",
       "102  2020-04-01       USA   Tennessee          1                      2683.0   \n",
       "46   2020-04-02       USA   Tennessee          1                      2845.0   \n",
       "832  2020-03-19       USA        Utah          1                        78.0   \n",
       "776  2020-03-20       USA        Utah          1                       112.0   \n",
       "720  2020-03-21       USA        Utah          1                       136.0   \n",
       "664  2020-03-22       USA        Utah          1                       181.0   \n",
       "608  2020-03-23       USA        Utah          1                       257.0   \n",
       "552  2020-03-24       USA        Utah          1                       299.0   \n",
       "496  2020-03-25       USA        Utah          1                       346.0   \n",
       "440  2020-03-26       USA        Utah          1                       402.0   \n",
       "384  2020-03-27       USA        Utah          1                       480.0   \n",
       "328  2020-03-28       USA        Utah          1                       602.0   \n",
       "272  2020-03-29       USA        Utah          1                       719.0   \n",
       "216  2020-03-30       USA        Utah          1                       806.0   \n",
       "160  2020-03-31       USA        Utah          1                       887.0   \n",
       "104  2020-04-01       USA        Utah          1                      1012.0   \n",
       "48   2020-04-02       USA        Utah          1                      1074.0   \n",
       "\n",
       "     cumulative_tests  cumulative_deaths  testing_regime  \n",
       "617            1602.0                0.0             1.0  \n",
       "561            1832.0                0.0             1.0  \n",
       "505            2321.0                0.0             1.0  \n",
       "449            2812.0                0.0             1.0  \n",
       "393            4099.0                1.0             1.0  \n",
       "337            4771.0                3.0             1.0  \n",
       "281            4880.0                3.0             1.0  \n",
       "225            4990.0                4.0             1.0  \n",
       "169            6553.0                6.0             1.0  \n",
       "113            7279.0               13.0             1.0  \n",
       "57             7774.0               26.0             1.0  \n",
       "1              8736.0               32.0             1.0  \n",
       "340            8191.0               13.0             1.0  \n",
       "284            8328.0               15.0             1.0  \n",
       "228           13872.0               17.0             1.0  \n",
       "172           16759.0               20.0             1.0  \n",
       "116           19371.0               24.0             1.0  \n",
       "60            21058.0               29.0             1.0  \n",
       "4             22709.0               32.0             1.0  \n",
       "957            8316.0                6.0             1.0  \n",
       "901            8464.0               11.0             1.0  \n",
       "845            8592.0               13.0             1.0  \n",
       "789            9711.0               18.0             1.0  \n",
       "733           11487.0               20.0             1.0  \n",
       "677           12528.0               24.0             1.0  \n",
       "621           12840.0               27.0             1.0  \n",
       "565           14300.0               27.0             1.0  \n",
       "509           15554.0               40.0             1.0  \n",
       "453           18276.0               53.0             1.0  \n",
       "397           20386.0               65.0             1.0  \n",
       "..                ...                ...             ...  \n",
       "94            29539.0               65.0             1.0  \n",
       "38            34918.0               81.0             1.0  \n",
       "718            3643.0                NaN             1.0  \n",
       "662            3777.0                NaN             1.0  \n",
       "606            3887.0                2.0             1.0  \n",
       "550           11184.0                2.0             1.0  \n",
       "494           11796.0                3.0             1.0  \n",
       "438           14909.0                3.0             1.0  \n",
       "382           16091.0                6.0             1.0  \n",
       "326           18338.0                6.0             1.0  \n",
       "270           20574.0                7.0             1.0  \n",
       "214           23304.0               13.0             1.0  \n",
       "158           27360.0               23.0             1.0  \n",
       "102           32452.0               24.0             1.0  \n",
       "46            34611.0               32.0             1.0  \n",
       "832            1526.0                0.0             1.0  \n",
       "776            2147.0                0.0             1.0  \n",
       "720            2560.0                0.0             1.0  \n",
       "664            3689.0                1.0             1.0  \n",
       "608            5047.0                1.0             1.0  \n",
       "552            5823.0                1.0             1.0  \n",
       "496            6837.0                1.0             1.0  \n",
       "440            7710.0                1.0             1.0  \n",
       "384            9244.0                2.0             1.0  \n",
       "328           11312.0                2.0             1.0  \n",
       "272           13993.0                2.0             1.0  \n",
       "216           16003.0                4.0             1.0  \n",
       "160           18513.0                5.0             1.0  \n",
       "104           21167.0                7.0             1.0  \n",
       "48            21065.0                7.0             1.0  \n",
       "\n",
       "[243 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the changes\n",
    "\n",
    "entries_with_regime_change = states_data[states_data['testing_regime'] >0].sort_values(['adm1_name','date'],inplace=False)\n",
    "states_with_regime_change = np.unique(entries_with_regime_change['adm1_name'])\n",
    "\n",
    "entries_with_regime_change.sort_values(['adm1_name','date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'date')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "states_to_plot = states_with_regime_change\n",
    "for state in states_to_plot:\n",
    "#for state in state_names:\n",
    "\n",
    "    state_data = states_data[states_data['adm1_name'] == state]\n",
    "\n",
    "    _, ts = state_data_to_time_series(state_data)\n",
    "\n",
    "\n",
    "    ts.plot(label=state)\n",
    "    \n",
    "    plt.legend(loc=(1,0))\n",
    "    \n",
    "    plt.yscale('log')\n",
    "    \n",
    "plt.ylabel('# tests over time')\n",
    "plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Save data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing csv with testing regime changes to ../../../data/interim/usa/usa_states_covidtrackingdotcom_int_with_testing_regimes.csv\n"
     ]
    }
   ],
   "source": [
    "if save_notebook_output:\n",
    "    print('writing csv with testing regime changes to {0}'.format(fp_out))\n",
    "    states_data.to_csv(fp_out, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'date')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# note: we plot states_to_plot b/c missing we don't want to miss a testing regime change in these states \n",
    "\n",
    "states_to_plot = ['Washington',\"North Carolina\",\"New York\"]\n",
    "for state in states_to_plot:\n",
    "#for state in state_names:\n",
    "\n",
    "    state_data = states_data[states_data['adm1_name'] == state]\n",
    "\n",
    "    _, ts = state_data_to_time_series(state_data)\n",
    "\n",
    "\n",
    "    ts.plot(label=state)\n",
    "    \n",
    "    plt.legend(loc=(1,0))\n",
    "    \n",
    "    plt.yscale('log')\n",
    "    \n",
    "plt.ylabel('# tests over time')\n",
    "plt.xlabel('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}