notebooks/mobility/Lockdown Data Mobility Processing New District.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "from covid19_abm.dir_manager import get_data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>home_region</th>\n",
       "      <th>region</th>\n",
       "      <th>date2</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [day, home_region, region, date2, count]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_calibrated_home_subscribers_mobility_df = pd.read_csv(\n",
    "    get_data_dir('raw', 'mobility','week_home_vs_day_location_febapr.csv'))\n",
    "\n",
    "full_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df.dropna(axis=0)\n",
    "full_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df[full_calibrated_home_subscribers_mobility_df['home_region'] < 100]\n",
    "\n",
    "full_calibrated_home_subscribers_mobility_df['region'] = full_calibrated_home_subscribers_mobility_df['region'].astype(int).astype(str).map(lambda x: f'd_{x}')\n",
    "full_calibrated_home_subscribers_mobility_df['home_region'] = full_calibrated_home_subscribers_mobility_df['home_region'].astype(int).astype(str).map(lambda x: f'd_{x}')\n",
    "full_calibrated_home_subscribers_mobility_df['day'] = pd.to_datetime(full_calibrated_home_subscribers_mobility_df['date2'])\n",
    "\n",
    "region = sorted(full_calibrated_home_subscribers_mobility_df['region'].unique())\n",
    "home_region = sorted(full_calibrated_home_subscribers_mobility_df['home_region'].unique())\n",
    "days = sorted(full_calibrated_home_subscribers_mobility_df['day'].unique())\n",
    "\n",
    "expanded_idx = [(\n",
    "    day,\n",
    "    h_reg,\n",
    "    reg) for day in days for h_reg in home_region for reg in region]\n",
    "\n",
    "full_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df.set_index(\n",
    "    ['day', 'home_region', 'region']).reindex(expanded_idx).fillna(0)\n",
    "\n",
    "full_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df.reset_index()\n",
    "\n",
    "full_calibrated_home_subscribers_mobility_df[full_calibrated_home_subscribers_mobility_df['home_region'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transition rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-lockdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_normal_date = datetime(2020, 3, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>d_1</th>\n",
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       "      <th>weekday</th>\n",
       "      <th>home_region</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
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       "      <td>0.952643</td>\n",
       "      <td>0.952643</td>\n",
       "      <td>0.952690</td>\n",
       "      <td>0.952690</td>\n",
       "      <td>0.952690</td>\n",
       "      <td>0.952690</td>\n",
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       "      <td>0.953035</td>\n",
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       "      <td>0.995582</td>\n",
       "      <td>0.997295</td>\n",
       "      <td>0.998182</td>\n",
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       "      <td>0.998797</td>\n",
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       "      <td>0.999516</td>\n",
       "      <td>0.999945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.912164</td>\n",
       "      <td>0.912616</td>\n",
       "      <td>0.913231</td>\n",
       "      <td>0.935119</td>\n",
       "      <td>0.942043</td>\n",
       "      <td>0.942656</td>\n",
       "      <td>0.965920</td>\n",
       "      <td>0.965920</td>\n",
       "      <td>0.972148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998710</td>\n",
       "      <td>0.998997</td>\n",
       "      <td>0.999262</td>\n",
       "      <td>0.999262</td>\n",
       "      <td>0.999262</td>\n",
       "      <td>0.999466</td>\n",
       "      <td>0.999466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>d_11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001716</td>\n",
       "      <td>0.900063</td>\n",
       "      <td>0.936411</td>\n",
       "      <td>0.950390</td>\n",
       "      <td>0.983489</td>\n",
       "      <td>0.983489</td>\n",
       "      <td>0.983489</td>\n",
       "      <td>0.983489</td>\n",
       "      <td>0.984168</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>d_12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000889</td>\n",
       "      <td>0.014743</td>\n",
       "      <td>0.963280</td>\n",
       "      <td>0.969570</td>\n",
       "      <td>0.970181</td>\n",
       "      <td>0.970181</td>\n",
       "      <td>0.970181</td>\n",
       "      <td>0.970181</td>\n",
       "      <td>0.971053</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.016048</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>0.021197</td>\n",
       "      <td>0.931744</td>\n",
       "      <td>0.933867</td>\n",
       "      <td>0.933925</td>\n",
       "      <td>0.935037</td>\n",
       "      <td>0.935099</td>\n",
       "      <td>0.944762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998916</td>\n",
       "      <td>0.999060</td>\n",
       "      <td>0.999332</td>\n",
       "      <td>0.999332</td>\n",
       "      <td>0.999332</td>\n",
       "      <td>0.999599</td>\n",
       "      <td>0.999599</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>d_6</th>\n",
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       "      <td>0.000553</td>\n",
       "      <td>0.000553</td>\n",
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       "      <td>0.000749</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.000749</td>\n",
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       "      <td>0.001100</td>\n",
       "      <td>0.002620</td>\n",
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       "      <td>0.045029</td>\n",
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       "      <td>0.987383</td>\n",
       "      <td>0.997199</td>\n",
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       "      <td>0.003827</td>\n",
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       "      <td>0.004163</td>\n",
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       "      <td>0.047831</td>\n",
       "      <td>0.076204</td>\n",
       "      <td>0.084937</td>\n",
       "      <td>0.084937</td>\n",
       "      <td>0.999292</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>d_7</th>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.034994</td>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.035196</td>\n",
       "      <td>0.035245</td>\n",
       "      <td>0.043500</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.981834</td>\n",
       "      <td>0.998665</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>d_8</th>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012964</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.060809</td>\n",
       "      <td>0.990583</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_9</th>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019381</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.026639</td>\n",
       "      <td>0.026639</td>\n",
       "      <td>0.034537</td>\n",
       "      <td>0.050461</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 60 columns</p>\n",
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       "region                    d_1      d_10      d_11      d_12      d_13  \\\n",
       "weekday home_region                                                     \n",
       "0       d_1          0.952601  0.952643  0.952643  0.952643  0.952690   \n",
       "        d_10         0.000111  0.912164  0.912616  0.913231  0.935119   \n",
       "        d_11         0.000000  0.001716  0.900063  0.936411  0.950390   \n",
       "        d_12         0.000000  0.000889  0.014743  0.963280  0.969570   \n",
       "        d_13         0.000185  0.016048  0.018727  0.021197  0.931744   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       d_6          0.000424  0.000553  0.000553  0.000553  0.000749   \n",
       "        d_60         0.003827  0.003827  0.003827  0.003827  0.003827   \n",
       "        d_7          0.000604  0.000808  0.000808  0.000808  0.000980   \n",
       "        d_8          0.000195  0.000195  0.000195  0.000195  0.000195   \n",
       "        d_9          0.000471  0.000471  0.000471  0.000471  0.000471   \n",
       "\n",
       "region                   d_14      d_15      d_16      d_17      d_18  ...  \\\n",
       "weekday home_region                                                    ...   \n",
       "0       d_1          0.952690  0.952690  0.952690  0.952846  0.953035  ...   \n",
       "        d_10         0.942043  0.942656  0.965920  0.965920  0.972148  ...   \n",
       "        d_11         0.983489  0.983489  0.983489  0.983489  0.984168  ...   \n",
       "        d_12         0.970181  0.970181  0.970181  0.970181  0.971053  ...   \n",
       "        d_13         0.933867  0.933925  0.935037  0.935099  0.944762  ...   \n",
       "...                       ...       ...       ...       ...       ...  ...   \n",
       "6       d_6          0.000749  0.000749  0.000749  0.001100  0.002620  ...   \n",
       "        d_60         0.003827  0.003827  0.003827  0.004020  0.004163  ...   \n",
       "        d_7          0.000995  0.000995  0.001045  0.001288  0.002296  ...   \n",
       "        d_8          0.000195  0.000195  0.000195  0.000195  0.001165  ...   \n",
       "        d_9          0.000471  0.000471  0.000471  0.000471  0.001928  ...   \n",
       "\n",
       "region                   d_55      d_56      d_57      d_58      d_59  \\\n",
       "weekday home_region                                                     \n",
       "0       d_1          0.995582  0.997295  0.998182  0.998490  0.998797   \n",
       "        d_10         0.998710  0.998997  0.999262  0.999262  0.999262   \n",
       "        d_11         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        d_12         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        d_13         0.998916  0.999060  0.999332  0.999332  0.999332   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       d_6          0.044532  0.044945  0.045029  0.045029  0.045029   \n",
       "        d_60         0.040840  0.046243  0.047831  0.076204  0.084937   \n",
       "        d_7          0.034407  0.034994  0.035176  0.035196  0.035245   \n",
       "        d_8          0.012964  0.013082  0.013082  0.013082  0.013082   \n",
       "        d_9          0.019381  0.019548  0.019548  0.019548  0.019548   \n",
       "\n",
       "region                    d_6      d_60       d_7       d_8  d_9  \n",
       "weekday home_region                                               \n",
       "0       d_1          0.998930  0.999516  0.999945  1.000000  1.0  \n",
       "        d_10         0.999466  0.999466  1.000000  1.000000  1.0  \n",
       "        d_11         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        d_12         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        d_13         0.999599  0.999599  1.000000  1.000000  1.0  \n",
       "...                       ...       ...       ...       ...  ...  \n",
       "6       d_6          0.972009  0.972009  0.987383  0.997199  1.0  \n",
       "        d_60         0.084937  0.999292  1.000000  1.000000  1.0  \n",
       "        d_7          0.043500  0.043735  0.981834  0.998665  1.0  \n",
       "        d_8          0.029248  0.029248  0.060809  0.990583  1.0  \n",
       "        d_9          0.026639  0.026639  0.034537  0.050461  1.0  \n",
       "\n",
       "[420 rows x 60 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_lockdown_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df[full_calibrated_home_subscribers_mobility_df['day'] <= end_normal_date].copy()\n",
    "\n",
    "pre_lockdown_calibrated_home_subscribers_mobility_df['date'] = pd.to_datetime(pre_lockdown_calibrated_home_subscribers_mobility_df['day'])\n",
    "pre_lockdown_calibrated_home_subscribers_mobility_df['weekday'] = pre_lockdown_calibrated_home_subscribers_mobility_df.date.dt.weekday\n",
    "\n",
    "pre_lockdown_daily_population_subscribers = pre_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum()\n",
    "pre_lockdown_day_src_dst_location_count = pre_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region', 'region'])['count'].sum()\n",
    "\n",
    "pre_lockdown_daily_region_transition_probability = pre_lockdown_day_src_dst_location_count.divide(\n",
    "    pre_lockdown_daily_population_subscribers\n",
    ").groupby(level=['date', 'home_region']).cumsum()\n",
    "\n",
    "pre_lockdown_daily_region_transition_probability = pre_lockdown_daily_region_transition_probability.reset_index()\n",
    "pre_lockdown_daily_region_transition_probability['weekday'] = pre_lockdown_daily_region_transition_probability['date'].dt.weekday\n",
    "pre_lockdown_daily_region_transition_probability.rename(columns={'count': 'prob'}, inplace=True)\n",
    "\n",
    "pre_lockdown_daily_region_transition_probability = pre_lockdown_daily_region_transition_probability.groupby(['weekday', 'home_region', 'region'], sort=True)['prob'].mean()\n",
    "pre_lockdown_daily_region_transition_probability = pre_lockdown_daily_region_transition_probability.unstack('region')\n",
    "\n",
    "pre_lockdown_daily_region_transition_probability.to_csv(\n",
    "    get_data_dir('preprocessed', 'mobility', 'daily_region_transition_probability-new-district-pre-lockdown.csv'))\n",
    "pre_lockdown_daily_region_transition_probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-lockdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_lockdown_date = datetime(2020, 4, 1)\n",
    "end_lockdown_date = datetime(2020, 5, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>d_1</th>\n",
       "      <th>d_10</th>\n",
       "      <th>d_11</th>\n",
       "      <th>d_12</th>\n",
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       "      <th>d_8</th>\n",
       "      <th>d_9</th>\n",
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       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th>home_region</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>d_1</th>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978868</td>\n",
       "      <td>0.978885</td>\n",
       "      <td>0.978885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998895</td>\n",
       "      <td>0.999362</td>\n",
       "      <td>0.999583</td>\n",
       "      <td>0.999683</td>\n",
       "      <td>0.999776</td>\n",
       "      <td>0.999776</td>\n",
       "      <td>0.999936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>d_10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.952490</td>\n",
       "      <td>0.952562</td>\n",
       "      <td>0.952562</td>\n",
       "      <td>0.966524</td>\n",
       "      <td>0.970408</td>\n",
       "      <td>0.970502</td>\n",
       "      <td>0.989808</td>\n",
       "      <td>0.989808</td>\n",
       "      <td>0.994114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>0.999924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.941212</td>\n",
       "      <td>0.968412</td>\n",
       "      <td>0.976766</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>0.997433</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.013281</td>\n",
       "      <td>0.985242</td>\n",
       "      <td>0.987898</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>0.987997</td>\n",
       "      <td>0.988086</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>d_13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010781</td>\n",
       "      <td>0.012729</td>\n",
       "      <td>0.014179</td>\n",
       "      <td>0.967064</td>\n",
       "      <td>0.967866</td>\n",
       "      <td>0.967866</td>\n",
       "      <td>0.968350</td>\n",
       "      <td>0.968350</td>\n",
       "      <td>0.975453</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>d_6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031883</td>\n",
       "      <td>0.031883</td>\n",
       "      <td>0.031883</td>\n",
       "      <td>0.031883</td>\n",
       "      <td>0.031883</td>\n",
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       "      <td>0.979339</td>\n",
       "      <td>0.990447</td>\n",
       "      <td>0.998294</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>d_60</th>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018821</td>\n",
       "      <td>0.019868</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.047256</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>0.054945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_7</th>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>0.017351</td>\n",
       "      <td>0.023025</td>\n",
       "      <td>0.023025</td>\n",
       "      <td>0.987717</td>\n",
       "      <td>0.999570</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_8</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.001620</td>\n",
       "      <td>0.014740</td>\n",
       "      <td>0.014740</td>\n",
       "      <td>0.029914</td>\n",
       "      <td>0.992222</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.005887</td>\n",
       "      <td>0.009966</td>\n",
       "      <td>0.009966</td>\n",
       "      <td>0.011736</td>\n",
       "      <td>0.022515</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 60 columns</p>\n",
       "</div>"
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      "text/plain": [
       "region                    d_1      d_10      d_11      d_12      d_13  \\\n",
       "weekday home_region                                                     \n",
       "0       d_1          0.978868  0.978868  0.978868  0.978868  0.978868   \n",
       "        d_10         0.000000  0.952490  0.952562  0.952562  0.966524   \n",
       "        d_11         0.000000  0.000718  0.941212  0.968412  0.976766   \n",
       "        d_12         0.000000  0.000094  0.013281  0.985242  0.987898   \n",
       "        d_13         0.000000  0.010781  0.012729  0.014179  0.967064   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       d_6          0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "        d_60         0.000501  0.000501  0.000501  0.000501  0.000501   \n",
       "        d_7          0.000051  0.000051  0.000051  0.000051  0.000051   \n",
       "        d_8          0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "        d_9          0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "region                   d_14      d_15      d_16      d_17      d_18  ...  \\\n",
       "weekday home_region                                                    ...   \n",
       "0       d_1          0.978868  0.978868  0.978868  0.978885  0.978885  ...   \n",
       "        d_10         0.970408  0.970502  0.989808  0.989808  0.994114  ...   \n",
       "        d_11         0.997433  0.997433  0.997433  0.997433  0.997433  ...   \n",
       "        d_12         0.987997  0.987997  0.987997  0.987997  0.988086  ...   \n",
       "        d_13         0.967866  0.967866  0.968350  0.968350  0.975453  ...   \n",
       "...                       ...       ...       ...       ...       ...  ...   \n",
       "6       d_6          0.000000  0.000000  0.000000  0.000121  0.000485  ...   \n",
       "        d_60         0.000501  0.000501  0.000501  0.000501  0.000501  ...   \n",
       "        d_7          0.000051  0.000051  0.000051  0.000051  0.000274  ...   \n",
       "        d_8          0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "        d_9          0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "\n",
       "region                   d_55      d_56      d_57      d_58      d_59  \\\n",
       "weekday home_region                                                     \n",
       "0       d_1          0.998895  0.999362  0.999583  0.999683  0.999776   \n",
       "        d_10         0.999924  0.999924  0.999924  0.999924  0.999924   \n",
       "        d_11         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        d_12         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        d_13         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       d_6          0.031883  0.031883  0.031883  0.031883  0.031883   \n",
       "        d_60         0.018821  0.019868  0.020080  0.047256  0.054945   \n",
       "        d_7          0.017351  0.017351  0.017351  0.017351  0.017351   \n",
       "        d_8          0.001620  0.001620  0.001620  0.001620  0.001620   \n",
       "        d_9          0.005887  0.005887  0.005887  0.005887  0.005887   \n",
       "\n",
       "region                    d_6      d_60       d_7       d_8  d_9  \n",
       "weekday home_region                                               \n",
       "0       d_1          0.999776  0.999936  1.000000  1.000000  1.0  \n",
       "        d_10         0.999924  0.999924  1.000000  1.000000  1.0  \n",
       "        d_11         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        d_12         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        d_13         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "...                       ...       ...       ...       ...  ...  \n",
       "6       d_6          0.979339  0.979339  0.990447  0.998294  1.0  \n",
       "        d_60         0.054945  1.000000  1.000000  1.000000  1.0  \n",
       "        d_7          0.023025  0.023025  0.987717  0.999570  1.0  \n",
       "        d_8          0.014740  0.014740  0.029914  0.992222  1.0  \n",
       "        d_9          0.009966  0.009966  0.011736  0.022515  1.0  \n",
       "\n",
       "[420 rows x 60 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_lockdown_calibrated_home_subscribers_mobility_df = full_calibrated_home_subscribers_mobility_df[\n",
    "    (full_calibrated_home_subscribers_mobility_df['day'] >= start_lockdown_date) &\n",
    "    (full_calibrated_home_subscribers_mobility_df['day'] <= end_lockdown_date)\n",
    "].copy()\n",
    "\n",
    "post_lockdown_calibrated_home_subscribers_mobility_df['date'] = pd.to_datetime(post_lockdown_calibrated_home_subscribers_mobility_df['day'])\n",
    "post_lockdown_calibrated_home_subscribers_mobility_df['weekday'] = post_lockdown_calibrated_home_subscribers_mobility_df.date.dt.weekday\n",
    "\n",
    "post_lockdown_daily_population_subscribers = post_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum()\n",
    "post_lockdown_day_src_dst_location_count = post_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region', 'region'])['count'].sum()\n",
    "\n",
    "post_lockdown_daily_region_transition_probability = post_lockdown_day_src_dst_location_count.divide(\n",
    "    post_lockdown_daily_population_subscribers\n",
    ").groupby(level=['date', 'home_region']).cumsum()\n",
    "\n",
    "post_lockdown_daily_region_transition_probability = post_lockdown_daily_region_transition_probability.reset_index()\n",
    "post_lockdown_daily_region_transition_probability['weekday'] = post_lockdown_daily_region_transition_probability['date'].dt.weekday\n",
    "post_lockdown_daily_region_transition_probability.rename(columns={'count': 'prob'}, inplace=True)\n",
    "\n",
    "post_lockdown_daily_region_transition_probability = post_lockdown_daily_region_transition_probability.groupby(['weekday', 'home_region', 'region'], sort=True)['prob'].mean()\n",
    "post_lockdown_daily_region_transition_probability = post_lockdown_daily_region_transition_probability.unstack('region')\n",
    "\n",
    "post_lockdown_daily_region_transition_probability.to_csv(\n",
    "    get_data_dir('preprocessed', 'mobility', 'daily_region_transition_probability-new-district-post-lockdown.csv'))\n",
    "post_lockdown_daily_region_transition_probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compute reduction in inter-district mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_post = post_lockdown_day_src_dst_location_count.divide(\n",
    "    post_lockdown_daily_population_subscribers\n",
    ")  #.groupby(level=['date', 'home_region']).cumsum().reset_index()\n",
    "\n",
    "dd_post = d_post.reset_index().rename(columns={'count': 'prob'})\n",
    "dd_post = dd_post[dd_post['home_region'] == dd_post['region']]\n",
    "dd_post['weekday'] = dd_post['date'].dt.weekday\n",
    "dd_post = dd_post.groupby(['weekday', 'home_region'])['prob'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_pre = pre_lockdown_day_src_dst_location_count.divide(\n",
    "    pre_lockdown_daily_population_subscribers\n",
    ")  #.groupby(level=['date', 'home_region']).cumsum().reset_index()\n",
    "\n",
    "dd_pre = d_pre.reset_index().rename(columns={'count': 'prob'})\n",
    "dd_pre = dd_pre[dd_pre['home_region'] == dd_pre['region']]\n",
    "dd_pre['weekday'] = dd_pre['date'].dt.weekday\n",
    "dd_pre = dd_pre.groupby(['weekday', 'home_region'])['prob'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday  home_region\n",
       "0        d_1            1.027573\n",
       "         d_10           1.044337\n",
       "         d_11           1.046916\n",
       "         d_12           1.024696\n",
       "         d_13           1.046497\n",
       "                          ...   \n",
       "6        d_6            1.022088\n",
       "         d_60           1.033575\n",
       "         d_7            1.028347\n",
       "         d_8            1.034990\n",
       "         d_9            1.029431\n",
       "Name: prob, Length: 420, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dd_post / dd_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate districts to lockdown for different scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_region_transition_rates = pre_lockdown_day_src_dst_location_count.divide(pre_lockdown_daily_population_subscribers)\n",
    "\n",
    "daily_region_transition_rates = daily_region_transition_rates.reset_index()\n",
    "\n",
    "daily_region_transition_rates['weekday'] = daily_region_transition_rates['date'].dt.weekday\n",
    "daily_region_transition_rates.rename(columns={'count': 'prob'}, inplace=True)\n",
    "\n",
    "daily_region_transition_rates = daily_region_transition_rates.groupby(['weekday', 'home_region', 'region'], sort=True)['prob'].mean()\n",
    "daily_region_transition_rates = daily_region_transition_rates.unstack('region')"
   ]
  },
  {
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>region</th>\n",
       "      <th>d_1</th>\n",
       "      <th>d_10</th>\n",
       "      <th>d_11</th>\n",
       "      <th>d_12</th>\n",
       "      <th>d_13</th>\n",
       "      <th>d_14</th>\n",
       "      <th>d_15</th>\n",
       "      <th>d_16</th>\n",
       "      <th>d_17</th>\n",
       "      <th>d_18</th>\n",
       "      <th>...</th>\n",
       "      <th>d_55</th>\n",
       "      <th>d_56</th>\n",
       "      <th>d_57</th>\n",
       "      <th>d_58</th>\n",
       "      <th>d_59</th>\n",
       "      <th>d_6</th>\n",
       "      <th>d_60</th>\n",
       "      <th>d_7</th>\n",
       "      <th>d_8</th>\n",
       "      <th>d_9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d_1</th>\n",
       "      <td>258002.278912</td>\n",
       "      <td>6.326531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>12.312925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.401361</td>\n",
       "      <td>45.034014</td>\n",
       "      <td>...</td>\n",
       "      <td>81.452381</td>\n",
       "      <td>495.377551</td>\n",
       "      <td>223.527211</td>\n",
       "      <td>79.316327</td>\n",
       "      <td>72.806122</td>\n",
       "      <td>28.095238</td>\n",
       "      <td>155.894558</td>\n",
       "      <td>97.258503</td>\n",
       "      <td>10.258503</td>\n",
       "      <td>3.938776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_10</th>\n",
       "      <td>5.704082</td>\n",
       "      <td>52654.982993</td>\n",
       "      <td>17.850340</td>\n",
       "      <td>32.928571</td>\n",
       "      <td>1055.323129</td>\n",
       "      <td>345.163265</td>\n",
       "      <td>28.823129</td>\n",
       "      <td>1437.486395</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>350.795918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.037415</td>\n",
       "      <td>13.867347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.044218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.387755</td>\n",
       "      <td>14482.799320</td>\n",
       "      <td>557.088435</td>\n",
       "      <td>198.741497</td>\n",
       "      <td>464.153061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.962585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.809524</td>\n",
       "      <td>598.836735</td>\n",
       "      <td>45178.493197</td>\n",
       "      <td>243.530612</td>\n",
       "      <td>16.091837</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.578231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.452381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_13</th>\n",
       "      <td>5.078231</td>\n",
       "      <td>1245.061224</td>\n",
       "      <td>191.122449</td>\n",
       "      <td>173.180272</td>\n",
       "      <td>73418.496599</td>\n",
       "      <td>141.639456</td>\n",
       "      <td>7.088435</td>\n",
       "      <td>79.139456</td>\n",
       "      <td>8.149660</td>\n",
       "      <td>702.721088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.421769</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.683673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.659864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>373.397959</td>\n",
       "      <td>536.421769</td>\n",
       "      <td>12.119048</td>\n",
       "      <td>154.438776</td>\n",
       "      <td>50953.639456</td>\n",
       "      <td>360.544218</td>\n",
       "      <td>507.697279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.227891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.530612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.819728</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.244898</td>\n",
       "      <td>370.877551</td>\n",
       "      <td>9357.326531</td>\n",
       "      <td>10.670068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.125850</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1487.030612</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.996599</td>\n",
       "      <td>86.340136</td>\n",
       "      <td>556.700680</td>\n",
       "      <td>7.731293</td>\n",
       "      <td>27546.816327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>176.612245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_17</th>\n",
       "      <td>18.517007</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.846939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32508.108844</td>\n",
       "      <td>36.721088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.285714</td>\n",
       "      <td>14.034014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.156463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.207483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_18</th>\n",
       "      <td>39.459184</td>\n",
       "      <td>355.173469</td>\n",
       "      <td>4.214286</td>\n",
       "      <td>28.119048</td>\n",
       "      <td>821.153061</td>\n",
       "      <td>53.197279</td>\n",
       "      <td>1.183673</td>\n",
       "      <td>193.816327</td>\n",
       "      <td>51.982993</td>\n",
       "      <td>103781.571429</td>\n",
       "      <td>...</td>\n",
       "      <td>4.387755</td>\n",
       "      <td>38.122449</td>\n",
       "      <td>32.013605</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>109.180272</td>\n",
       "      <td>2.132653</td>\n",
       "      <td>115.173469</td>\n",
       "      <td>32.074830</td>\n",
       "      <td>31.782313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_19</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>17.176871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.605442</td>\n",
       "      <td>32.153061</td>\n",
       "      <td>3.996599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.183673</td>\n",
       "      <td>74.527211</td>\n",
       "      <td>643.489796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.544218</td>\n",
       "      <td>4.989796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>512.925170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.258503</td>\n",
       "      <td>18.901361</td>\n",
       "      <td>13.772109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_2</th>\n",
       "      <td>947.047619</td>\n",
       "      <td>965.170068</td>\n",
       "      <td>167.799320</td>\n",
       "      <td>382.241497</td>\n",
       "      <td>3175.972789</td>\n",
       "      <td>514.197279</td>\n",
       "      <td>115.037415</td>\n",
       "      <td>416.789116</td>\n",
       "      <td>859.792517</td>\n",
       "      <td>13352.034014</td>\n",
       "      <td>...</td>\n",
       "      <td>173.870748</td>\n",
       "      <td>778.336735</td>\n",
       "      <td>599.210884</td>\n",
       "      <td>63.921769</td>\n",
       "      <td>133.574830</td>\n",
       "      <td>1123.040816</td>\n",
       "      <td>181.061224</td>\n",
       "      <td>1455.197279</td>\n",
       "      <td>422.472789</td>\n",
       "      <td>398.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_20</th>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.517007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.891156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.925170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.231293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_21</th>\n",
       "      <td>0.870748</td>\n",
       "      <td>20.149660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.986395</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>106.775510</td>\n",
       "      <td>2.404762</td>\n",
       "      <td>1531.840136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>788.578231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.210884</td>\n",
       "      <td>0.840136</td>\n",
       "      <td>0.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.880952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.034014</td>\n",
       "      <td>0.408163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.425170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.908163</td>\n",
       "      <td>0.734694</td>\n",
       "      <td>4.251701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>136.149660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_23</th>\n",
       "      <td>2.265306</td>\n",
       "      <td>15.537415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.676871</td>\n",
       "      <td>41.149660</td>\n",
       "      <td>4.044218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>192.554422</td>\n",
       "      <td>432.717687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.578231</td>\n",
       "      <td>2.537415</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.938776</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>28.027211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.598639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.129252</td>\n",
       "      <td>54.200680</td>\n",
       "      <td>377.707483</td>\n",
       "      <td>293.802721</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.136054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>507.217687</td>\n",
       "      <td>32.738095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>675.306122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.442177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_26</th>\n",
       "      <td>53.527211</td>\n",
       "      <td>39.789116</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>16.377551</td>\n",
       "      <td>77.911565</td>\n",
       "      <td>19.329932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.295918</td>\n",
       "      <td>194.812925</td>\n",
       "      <td>144.387755</td>\n",
       "      <td>...</td>\n",
       "      <td>42.125850</td>\n",
       "      <td>93.234694</td>\n",
       "      <td>131.292517</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>21.707483</td>\n",
       "      <td>32.527211</td>\n",
       "      <td>13.411565</td>\n",
       "      <td>46.149660</td>\n",
       "      <td>3.632653</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_27</th>\n",
       "      <td>7.098639</td>\n",
       "      <td>4.564626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.772109</td>\n",
       "      <td>17.197279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>47.170068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.166667</td>\n",
       "      <td>20.462585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.734694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.612245</td>\n",
       "      <td>0.547619</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_28</th>\n",
       "      <td>89.816327</td>\n",
       "      <td>32.438776</td>\n",
       "      <td>0.595238</td>\n",
       "      <td>8.384354</td>\n",
       "      <td>51.772109</td>\n",
       "      <td>6.156463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.976190</td>\n",
       "      <td>869.421769</td>\n",
       "      <td>88.581633</td>\n",
       "      <td>...</td>\n",
       "      <td>737.993197</td>\n",
       "      <td>210.435374</td>\n",
       "      <td>864.275510</td>\n",
       "      <td>7.183673</td>\n",
       "      <td>46.819728</td>\n",
       "      <td>17.384354</td>\n",
       "      <td>33.687075</td>\n",
       "      <td>39.581633</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.326531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_29</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.319728</td>\n",
       "      <td>...</td>\n",
       "      <td>3.129252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.380952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_3</th>\n",
       "      <td>20.431973</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>667.146259</td>\n",
       "      <td>28.357143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.642857</td>\n",
       "      <td>7.370748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>307.799320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>566.537415</td>\n",
       "      <td>20.829932</td>\n",
       "      <td>3.319728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_30</th>\n",
       "      <td>17.227891</td>\n",
       "      <td>13.935374</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>230.571429</td>\n",
       "      <td>47.629252</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>3.693878</td>\n",
       "      <td>60.972789</td>\n",
       "      <td>...</td>\n",
       "      <td>3.738095</td>\n",
       "      <td>34.319728</td>\n",
       "      <td>38.255102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.921769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.700680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_31</th>\n",
       "      <td>50.275510</td>\n",
       "      <td>72.636054</td>\n",
       "      <td>8.857143</td>\n",
       "      <td>348.030612</td>\n",
       "      <td>1277.064626</td>\n",
       "      <td>43.653061</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>30.210884</td>\n",
       "      <td>50.772109</td>\n",
       "      <td>719.476190</td>\n",
       "      <td>...</td>\n",
       "      <td>5.659864</td>\n",
       "      <td>52.829932</td>\n",
       "      <td>48.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.363946</td>\n",
       "      <td>67.568027</td>\n",
       "      <td>5.843537</td>\n",
       "      <td>77.564626</td>\n",
       "      <td>15.646259</td>\n",
       "      <td>17.414966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_32</th>\n",
       "      <td>19.394558</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.370748</td>\n",
       "      <td>2.336735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.374150</td>\n",
       "      <td>2.431973</td>\n",
       "      <td>0.877551</td>\n",
       "      <td>9.119048</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>12.642857</td>\n",
       "      <td>69.554422</td>\n",
       "      <td>1.020408</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_33</th>\n",
       "      <td>38.802721</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.952381</td>\n",
       "      <td>9.931973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.799320</td>\n",
       "      <td>4.918367</td>\n",
       "      <td>25.554422</td>\n",
       "      <td>7.778912</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>27.901361</td>\n",
       "      <td>63.931973</td>\n",
       "      <td>0.772109</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_34</th>\n",
       "      <td>23.278912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.727891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.020408</td>\n",
       "      <td>9.755102</td>\n",
       "      <td>272.166667</td>\n",
       "      <td>219.925170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>463.802721</td>\n",
       "      <td>6.479592</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_35</th>\n",
       "      <td>29.418367</td>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.445578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>266.500000</td>\n",
       "      <td>19.741497</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.877551</td>\n",
       "      <td>18.261905</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>5.860544</td>\n",
       "      <td>1.976190</td>\n",
       "      <td>13.489796</td>\n",
       "      <td>35.350340</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_36</th>\n",
       "      <td>134.482993</td>\n",
       "      <td>4.435374</td>\n",
       "      <td>0.632653</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.564626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.465986</td>\n",
       "      <td>35.891156</td>\n",
       "      <td>...</td>\n",
       "      <td>16.489796</td>\n",
       "      <td>158.200680</td>\n",
       "      <td>54.255102</td>\n",
       "      <td>80.091837</td>\n",
       "      <td>317.486395</td>\n",
       "      <td>12.884354</td>\n",
       "      <td>212.843537</td>\n",
       "      <td>121.938776</td>\n",
       "      <td>6.843537</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_37</th>\n",
       "      <td>12.391156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.578231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>632.435374</td>\n",
       "      <td>1.693878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.955782</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_38</th>\n",
       "      <td>9.540816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.122449</td>\n",
       "      <td>0.326531</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>1.272109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.527211</td>\n",
       "      <td>13.350340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_39</th>\n",
       "      <td>166.561224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.683673</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_4</th>\n",
       "      <td>3.996599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>83.068027</td>\n",
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       "      <td>38.282313</td>\n",
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       "      <td>18.088435</td>\n",
       "      <td>62.574830</td>\n",
       "      <td>37.017007</td>\n",
       "      <td>42.085034</td>\n",
       "      <td>25.863946</td>\n",
       "      <td>4.102041</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>22.833333</td>\n",
       "      <td>5.336735</td>\n",
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       "      <td>1.530612</td>\n",
       "      <td>182.374150</td>\n",
       "      <td>10.908163</td>\n",
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       "    <tr>\n",
       "      <th>d_5</th>\n",
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       "      <td>1.727891</td>\n",
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       "    <tr>\n",
       "      <th>d_50</th>\n",
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       "    <tr>\n",
       "      <th>d_51</th>\n",
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       "      <th>d_52</th>\n",
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       "      <th>d_53</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>113317.666667</td>\n",
       "      <td>1112.350340</td>\n",
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       "      <td>1277.622449</td>\n",
       "      <td>1035.023810</td>\n",
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       "    <tr>\n",
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       "      <td>36.765306</td>\n",
       "      <td>1260.299320</td>\n",
       "      <td>106.442177</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>39043.462585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>421.292517</td>\n",
       "      <td>5.438776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_6</th>\n",
       "      <td>14.472789</td>\n",
       "      <td>2.619048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.884354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.367347</td>\n",
       "      <td>22.908163</td>\n",
       "      <td>89.996599</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.751701</td>\n",
       "      <td>2.687075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73893.612245</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1240.727891</td>\n",
       "      <td>740.714286</td>\n",
       "      <td>199.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_60</th>\n",
       "      <td>139.387755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.112245</td>\n",
       "      <td>5.829932</td>\n",
       "      <td>...</td>\n",
       "      <td>8.860544</td>\n",
       "      <td>198.418367</td>\n",
       "      <td>58.823129</td>\n",
       "      <td>1245.132653</td>\n",
       "      <td>393.673469</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>43298.231293</td>\n",
       "      <td>26.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_7</th>\n",
       "      <td>74.731293</td>\n",
       "      <td>15.860544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.408163</td>\n",
       "      <td>25.482993</td>\n",
       "      <td>1.486395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.585034</td>\n",
       "      <td>34.173469</td>\n",
       "      <td>130.806122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346939</td>\n",
       "      <td>60.272109</td>\n",
       "      <td>23.367347</td>\n",
       "      <td>1.214286</td>\n",
       "      <td>4.574830</td>\n",
       "      <td>1195.500000</td>\n",
       "      <td>23.544218</td>\n",
       "      <td>168813.448980</td>\n",
       "      <td>2598.445578</td>\n",
       "      <td>212.710884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_8</th>\n",
       "      <td>4.054422</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.755102</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.309524</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>743.506803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1688.853741</td>\n",
       "      <td>50712.615646</td>\n",
       "      <td>528.414966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_9</th>\n",
       "      <td>3.530612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.173469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>225.880952</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>236.479592</td>\n",
       "      <td>494.469388</td>\n",
       "      <td>33752.465986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                 d_1          d_10          d_11          d_12  \\\n",
       "home_region                                                            \n",
       "d_1          258002.278912      6.326531      0.000000      0.428571   \n",
       "d_10              5.704082  52654.982993     17.850340     32.928571   \n",
       "d_11              0.000000     20.387755  14482.799320    557.088435   \n",
       "d_12              0.000000     32.809524    598.836735  45178.493197   \n",
       "d_13              5.078231   1245.061224    191.122449    173.180272   \n",
       "d_14              0.000000    373.397959    536.421769     12.119048   \n",
       "d_15              0.000000     38.819728      0.000000      0.000000   \n",
       "d_16              0.000000   1487.030612      0.476190      0.996599   \n",
       "d_17             18.517007      0.785714      0.000000      0.000000   \n",
       "d_18             39.459184    355.173469      4.214286     28.119048   \n",
       "d_19             14.000000     17.176871      0.000000      1.605442   \n",
       "d_2             947.047619    965.170068    167.799320    382.241497   \n",
       "d_20              0.380952      0.000000      0.000000      0.000000   \n",
       "d_21              0.870748     20.149660      0.000000      0.000000   \n",
       "d_22              0.000000      0.880952      0.000000      0.000000   \n",
       "d_23              2.265306     15.537415      0.000000      4.676871   \n",
       "d_24              0.000000     34.598639      0.000000      0.000000   \n",
       "d_25              0.000000      0.000000      0.000000      0.000000   \n",
       "d_26             53.527211     39.789116      0.809524     16.377551   \n",
       "d_27              7.098639      4.564626      0.000000      4.772109   \n",
       "d_28             89.816327     32.438776      0.595238      8.384354   \n",
       "d_29              0.500000      0.000000      0.000000      0.367347   \n",
       "d_3              20.431973      0.000000      0.000000      0.000000   \n",
       "d_30             17.227891     13.935374      0.428571    230.571429   \n",
       "d_31             50.275510     72.636054      8.857143    348.030612   \n",
       "d_32             19.394558      0.000000      0.000000      0.000000   \n",
       "d_33             38.802721      0.000000      0.387755      0.000000   \n",
       "d_34             23.278912      0.000000      0.000000      0.000000   \n",
       "d_35             29.418367      0.404762      0.000000      0.000000   \n",
       "d_36            134.482993      4.435374      0.632653      0.000000   \n",
       "d_37             12.391156      0.000000      0.000000      0.000000   \n",
       "d_38              9.540816      0.000000      0.000000      0.000000   \n",
       "d_39            166.561224      0.000000      0.000000      0.000000   \n",
       "d_4               3.996599      0.000000      0.000000      0.000000   \n",
       "d_40            617.789116      0.000000      0.000000      0.000000   \n",
       "d_41            552.704082      0.000000      0.000000      0.000000   \n",
       "d_42            304.408163      0.000000      0.000000      0.000000   \n",
       "d_43            208.017007      0.000000      0.000000      0.000000   \n",
       "d_44            304.411565      0.000000      0.000000      0.000000   \n",
       "d_45           3099.659864      0.000000      0.000000      0.000000   \n",
       "d_46            242.935374      0.000000      0.000000      0.000000   \n",
       "d_47            328.942177      0.000000      0.000000      0.000000   \n",
       "d_48            537.017007      0.000000      0.000000      0.000000   \n",
       "d_49            514.704082      0.000000      0.000000      0.000000   \n",
       "d_5              15.241497      0.452381      0.000000      0.000000   \n",
       "d_50            281.605442      0.000000      0.000000      0.000000   \n",
       "d_51            387.989796      0.000000      0.000000      0.000000   \n",
       "d_52            938.829932      0.000000      0.000000      0.000000   \n",
       "d_53             21.530612      0.000000      0.000000      0.000000   \n",
       "d_54             19.537415      0.000000      0.000000      0.000000   \n",
       "d_55             68.778912      0.000000      0.000000      0.000000   \n",
       "d_56            381.023810      3.877551      0.000000      0.000000   \n",
       "d_57            172.034014     10.517007      0.000000      0.000000   \n",
       "d_58             57.993197      0.000000      0.000000      0.000000   \n",
       "d_59             66.639456      0.000000      0.000000      0.000000   \n",
       "d_6              14.472789      2.619048      0.000000      0.000000   \n",
       "d_60            139.387755      0.000000      0.000000      0.000000   \n",
       "d_7              74.731293     15.860544      0.000000      1.408163   \n",
       "d_8               4.054422      0.000000      0.000000      0.000000   \n",
       "d_9               3.530612      0.000000      0.000000      0.000000   \n",
       "\n",
       "region               d_13          d_14         d_15          d_16  \\\n",
       "home_region                                                          \n",
       "d_1             12.312925      0.000000     0.000000      0.000000   \n",
       "d_10          1055.323129    345.163265    28.823129   1437.486395   \n",
       "d_11           198.741497    464.153061     0.000000      0.000000   \n",
       "d_12           243.530612     16.091837     0.000000      0.000000   \n",
       "d_13         73418.496599    141.639456     7.088435     79.139456   \n",
       "d_14           154.438776  50953.639456   360.544218    507.697279   \n",
       "d_15             9.244898    370.877551  9357.326531     10.670068   \n",
       "d_16            86.340136    556.700680     7.731293  27546.816327   \n",
       "d_17             2.846939      0.000000     0.000000      0.000000   \n",
       "d_18           821.153061     53.197279     1.183673    193.816327   \n",
       "d_19            32.153061      3.996599     0.000000      1.183673   \n",
       "d_2           3175.972789    514.197279   115.037415    416.789116   \n",
       "d_20             2.517007      0.000000     0.000000      0.000000   \n",
       "d_21            21.986395      1.285714     0.000000    106.775510   \n",
       "d_22             3.034014      0.408163     0.000000      0.000000   \n",
       "d_23            41.149660      4.044218     0.000000      0.000000   \n",
       "d_24            11.129252     54.200680   377.707483    293.802721   \n",
       "d_25             0.404762      0.000000     0.000000      0.000000   \n",
       "d_26            77.911565     19.329932     0.000000     12.295918   \n",
       "d_27            17.197279      0.000000     0.000000      0.380952   \n",
       "d_28            51.772109      6.156463     0.000000      7.976190   \n",
       "d_29             0.000000      0.000000     0.000000      0.000000   \n",
       "d_3              0.000000      0.000000     0.000000      0.000000   \n",
       "d_30            47.629252      0.500000     0.000000      0.761905   \n",
       "d_31          1277.064626     43.653061     0.428571     30.210884   \n",
       "d_32             0.000000      0.000000     0.000000      0.000000   \n",
       "d_33             0.000000      0.000000     0.000000      0.000000   \n",
       "d_34             0.000000      0.000000     0.000000      0.000000   \n",
       "d_35             1.445578      0.000000     0.000000      0.000000   \n",
       "d_36             5.564626      0.000000     0.000000      0.000000   \n",
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       "d_38             0.000000      0.000000     0.000000      0.000000   \n",
       "d_39             0.000000      0.000000     0.000000      0.000000   \n",
       "d_4              0.000000      0.000000     0.000000      0.000000   \n",
       "d_40             0.000000      0.000000     0.000000      0.000000   \n",
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       "d_46             1.414966      0.000000     0.000000      0.000000   \n",
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       "d_48             0.000000      0.000000     0.000000      0.000000   \n",
       "d_49             0.000000      0.000000     0.000000      0.000000   \n",
       "d_5              0.000000      0.000000     0.000000      0.000000   \n",
       "d_50             0.000000      0.000000     0.000000      0.000000   \n",
       "d_51             0.000000      0.000000     0.000000      0.000000   \n",
       "d_52             0.000000      0.000000     0.000000      0.000000   \n",
       "d_53             0.000000      0.000000     0.000000      0.000000   \n",
       "d_54             0.000000      0.000000     0.000000      0.000000   \n",
       "d_55             0.000000      0.000000     0.000000      0.000000   \n",
       "d_56            10.489796      0.000000     0.000000      0.000000   \n",
       "d_57            18.231293      0.000000     0.000000      1.023810   \n",
       "d_58             0.000000      0.000000     0.000000      0.000000   \n",
       "d_59             0.000000      0.000000     0.000000      0.000000   \n",
       "d_6             11.884354      0.000000     0.000000      0.367347   \n",
       "d_60             0.000000      0.000000     0.000000      0.000000   \n",
       "d_7             25.482993      1.486395     0.000000      1.585034   \n",
       "d_8              0.000000      0.000000     0.000000      0.000000   \n",
       "d_9              0.000000      0.000000     0.000000      0.000000   \n",
       "\n",
       "region               d_17           d_18  ...          d_55           d_56  \\\n",
       "home_region                               ...                                \n",
       "d_1             44.401361      45.034014  ...     81.452381     495.377551   \n",
       "d_10             2.285714     350.795918  ...      0.000000       9.037415   \n",
       "d_11             0.000000       6.962585  ...      0.000000       0.000000   \n",
       "d_12             0.000000      29.578231  ...      0.000000       0.000000   \n",
       "d_13             8.149660     702.721088  ...      0.000000       7.421769   \n",
       "d_14             0.000000      51.227891  ...      0.000000       1.530612   \n",
       "d_15             0.000000       3.125850  ...      0.000000       0.000000   \n",
       "d_16             0.000000     176.612245  ...      0.000000       0.000000   \n",
       "d_17         32508.108844      36.721088  ...      0.000000      39.285714   \n",
       "d_18            51.982993  103781.571429  ...      4.387755      38.122449   \n",
       "d_19            74.527211     643.489796  ...      0.000000      12.544218   \n",
       "d_2            859.792517   13352.034014  ...    173.870748     778.336735   \n",
       "d_20             0.000000      49.891156  ...      0.000000       0.380952   \n",
       "d_21             2.404762    1531.840136  ...      0.000000       0.523810   \n",
       "d_22             0.000000      82.425170  ...      0.000000       0.000000   \n",
       "d_23           192.554422     432.717687  ...      0.000000       4.578231   \n",
       "d_24             0.000000      68.136054  ...      0.000000       0.000000   \n",
       "d_25           507.217687      32.738095  ...      0.000000       0.000000   \n",
       "d_26           194.812925     144.387755  ...     42.125850      93.234694   \n",
       "d_27             1.238095      47.170068  ...      0.000000      21.166667   \n",
       "d_28           869.421769      88.581633  ...    737.993197     210.435374   \n",
       "d_29             0.000000       6.319728  ...      3.129252       0.000000   \n",
       "d_3            667.146259      28.357143  ...      0.000000      25.642857   \n",
       "d_30             3.693878      60.972789  ...      3.738095      34.319728   \n",
       "d_31            50.772109     719.476190  ...      5.659864      52.829932   \n",
       "d_32             4.370748       2.336735  ...      0.000000      19.374150   \n",
       "d_33            14.952381       9.931973  ...      0.000000      31.799320   \n",
       "d_34             0.727891       0.000000  ...      0.000000      39.020408   \n",
       "d_35           266.500000      19.741497  ...      0.000000      60.877551   \n",
       "d_36            65.465986      35.891156  ...     16.489796     158.200680   \n",
       "d_37             0.000000       0.000000  ...      0.000000      10.578231   \n",
       "d_38             0.380952       0.000000  ...      0.000000      10.122449   \n",
       "d_39             0.000000       0.000000  ...      9.683673       0.428571   \n",
       "d_4              0.000000       8.074830  ...      0.000000      11.938776   \n",
       "d_40             0.000000       0.000000  ...      0.809524       5.591837   \n",
       "d_41             0.000000       5.693878  ...      0.000000      51.241497   \n",
       "d_42             0.000000       0.000000  ...      0.000000       0.000000   \n",
       "d_43             0.000000       0.000000  ...     83.068027      63.503401   \n",
       "d_44             0.000000       0.000000  ...      0.000000       0.000000   \n",
       "d_45             0.000000       0.000000  ...      0.000000      38.282313   \n",
       "d_46            30.731293      24.241497  ...     18.088435      62.574830   \n",
       "d_47             0.000000       0.000000  ...      0.000000       0.000000   \n",
       "d_48             0.000000       0.000000  ...      0.000000      22.833333   \n",
       "d_49             0.000000       0.000000  ...      1.530612     182.374150   \n",
       "d_5              0.000000      11.744898  ...      0.000000      12.408163   \n",
       "d_50             0.000000       0.000000  ...      0.000000       8.666667   \n",
       "d_51             0.000000       0.000000  ...      0.000000       0.000000   \n",
       "d_52             0.000000       0.000000  ...      1.969388      15.197279   \n",
       "d_53           269.462585       3.829932  ...      1.704082     383.911565   \n",
       "d_54             0.000000       0.000000  ...    962.510204      62.496599   \n",
       "d_55             0.000000       5.710884  ...  48937.197279     136.336735   \n",
       "d_56            56.829932      38.156463  ...    132.707483  113317.666667   \n",
       "d_57            27.384354      40.741497  ...   1277.622449    1035.023810   \n",
       "d_58             0.000000       0.000000  ...      0.387755      51.717687   \n",
       "d_59             5.489796       2.010204  ...     36.765306    1260.299320   \n",
       "d_6             22.908163      89.996599  ...      0.000000       8.751701   \n",
       "d_60             5.112245       5.829932  ...      8.860544     198.418367   \n",
       "d_7             34.173469     130.806122  ...      0.346939      60.272109   \n",
       "d_8              0.000000      31.755102  ...      0.000000       1.309524   \n",
       "d_9              0.000000      33.173469  ...      0.000000       0.833333   \n",
       "\n",
       "region               d_57          d_58          d_59           d_6  \\\n",
       "home_region                                                           \n",
       "d_1            223.527211     79.316327     72.806122     28.095238   \n",
       "d_10            13.867347      0.000000      0.000000      8.714286   \n",
       "d_11             0.000000      0.000000      0.000000      0.000000   \n",
       "d_12             0.000000      0.000000      0.000000      0.000000   \n",
       "d_13            12.500000      0.000000      0.000000     18.683673   \n",
       "d_14             0.000000      0.000000      0.000000      0.000000   \n",
       "d_15             0.000000      0.000000      0.000000      0.000000   \n",
       "d_16             0.000000      0.000000      0.000000      0.000000   \n",
       "d_17            14.034014      0.000000      0.000000     21.156463   \n",
       "d_18            32.013605      0.000000      0.346939    109.180272   \n",
       "d_19             4.989796      0.000000      0.000000    512.925170   \n",
       "d_2            599.210884     63.921769    133.574830   1123.040816   \n",
       "d_20             0.000000      0.000000      0.000000      0.428571   \n",
       "d_21             0.000000      0.000000      0.000000    788.578231   \n",
       "d_22             0.000000      0.000000      0.000000     84.908163   \n",
       "d_23             2.537415      0.000000      0.000000     24.938776   \n",
       "d_24             0.000000      0.000000      0.000000      0.000000   \n",
       "d_25             0.000000      0.000000      0.000000    675.306122   \n",
       "d_26           131.292517      0.326531     21.707483     32.527211   \n",
       "d_27            20.462585      0.000000      0.000000      8.734694   \n",
       "d_28           864.275510      7.183673     46.819728     17.384354   \n",
       "d_29             1.380952      0.000000      0.000000      0.000000   \n",
       "d_3              7.370748      0.000000      0.000000    307.799320   \n",
       "d_30            38.255102      0.000000      0.000000      9.921769   \n",
       "d_31            48.142857      0.000000      5.363946     67.568027   \n",
       "d_32             2.431973      0.877551      9.119048      0.809524   \n",
       "d_33             4.918367     25.554422      7.778912      0.785714   \n",
       "d_34             9.755102    272.166667    219.925170      0.000000   \n",
       "d_35            18.261905      0.346939      5.860544      1.976190   \n",
       "d_36            54.255102     80.091837    317.486395     12.884354   \n",
       "d_37             0.000000    632.435374      1.693878      0.000000   \n",
       "d_38             0.326531      0.346939      1.272109      0.000000   \n",
       "d_39             0.000000      0.000000      0.000000      0.000000   \n",
       "d_4              0.000000      0.000000      0.000000     21.772109   \n",
       "d_40             5.918367      0.000000      0.000000      0.000000   \n",
       "d_41            22.666667      0.000000      0.000000      0.000000   \n",
       "d_42             0.000000      0.000000      0.000000      0.000000   \n",
       "d_43           371.251701      0.000000      0.000000      0.000000   \n",
       "d_44             0.000000      0.000000      0.000000      0.000000   \n",
       "d_45            11.945578      0.000000      0.000000      0.476190   \n",
       "d_46            37.017007     42.085034     25.863946      4.102041   \n",
       "d_47             0.000000      0.000000      0.000000      0.000000   \n",
       "d_48             5.336735     61.377551      1.023810      0.000000   \n",
       "d_49            10.908163     69.149660     20.401361      0.000000   \n",
       "d_5              1.795918      1.074830      0.000000     28.000000   \n",
       "d_50             0.775510      0.000000      0.000000      0.000000   \n",
       "d_51             0.000000      0.000000      0.000000      0.000000   \n",
       "d_52             3.078231      1.000000      3.380952      0.000000   \n",
       "d_53           101.414966      0.380952    205.833333      0.000000   \n",
       "d_54           216.384354      0.326531      7.129252      0.387755   \n",
       "d_55          1027.078231      0.367347     30.442177      0.000000   \n",
       "d_56          1112.350340     53.853741   1254.476190     19.755102   \n",
       "d_57         96319.581633     15.431973    108.931973      9.275510   \n",
       "d_58            14.993197  27866.040816     30.738095      0.000000   \n",
       "d_59           106.442177     35.000000  39043.462585      0.000000   \n",
       "d_6              2.687075      0.000000      0.000000  73893.612245   \n",
       "d_60            58.823129   1245.132653    393.673469      0.346939   \n",
       "d_7             23.367347      1.214286      4.574830   1195.500000   \n",
       "d_8              0.428571      0.000000      0.000000    743.506803   \n",
       "d_9              0.000000      0.000000      0.000000    225.880952   \n",
       "\n",
       "region               d_60            d_7           d_8           d_9  \n",
       "home_region                                                           \n",
       "d_1            155.894558      97.258503     10.258503      3.938776  \n",
       "d_10             0.000000      20.044218      0.000000      0.000000  \n",
       "d_11             0.000000       0.000000      0.000000      0.000000  \n",
       "d_12             0.000000       0.452381      0.000000      0.000000  \n",
       "d_13             0.000000      21.659864      0.000000      0.380952  \n",
       "d_14             0.000000       0.326531      0.000000      0.000000  \n",
       "d_15             0.000000       0.000000      0.000000      0.000000  \n",
       "d_16             0.000000       0.500000      0.000000      0.000000  \n",
       "d_17             0.000000      26.207483      0.000000      0.000000  \n",
       "d_18             2.132653     115.173469     32.074830     31.782313  \n",
       "d_19             0.000000     101.258503     18.901361     13.772109  \n",
       "d_2            181.061224    1455.197279    422.472789    398.489796  \n",
       "d_20             0.000000       4.925170      0.000000     22.231293  \n",
       "d_21             0.000000      42.210884      0.840136      0.523810  \n",
       "d_22             0.734694       4.251701      0.000000    136.149660  \n",
       "d_23             0.326531      28.027211      0.000000      0.000000  \n",
       "d_24             0.000000       2.000000      0.000000      0.000000  \n",
       "d_25             0.000000      25.442177      0.000000      0.000000  \n",
       "d_26            13.411565      46.149660      3.632653      1.714286  \n",
       "d_27             0.000000      51.612245      0.547619      0.000000  \n",
       "d_28            33.687075      39.581633      0.785714      0.326531  \n",
       "d_29             0.000000       0.000000      0.000000      0.000000  \n",
       "d_3              0.000000     566.537415     20.829932      3.319728  \n",
       "d_30             0.000000      27.700680      0.000000      0.000000  \n",
       "d_31             5.843537      77.564626     15.646259     17.414966  \n",
       "d_32            12.642857      69.554422      1.020408      0.000000  \n",
       "d_33            27.901361      63.931973      0.772109      0.000000  \n",
       "d_34           463.802721       6.479592      0.000000      0.000000  \n",
       "d_35            13.489796      35.350340      0.326531      0.690476  \n",
       "d_36           212.843537     121.938776      6.843537      0.000000  \n",
       "d_37            39.955782       0.326531      0.000000      0.000000  \n",
       "d_38             1.527211      13.350340      0.000000      0.000000  \n",
       "d_39             0.000000       0.000000      0.000000      0.000000  \n",
       "d_4              0.448980    1607.714286     41.914966      3.540816  \n",
       "d_40             0.000000       0.000000      0.000000      0.000000  \n",
       "d_41             8.479592       3.588435      0.000000      0.000000  \n",
       "d_42             0.000000       0.000000      0.000000      0.000000  \n",
       "d_43             0.000000       0.000000      0.000000      0.000000  \n",
       "d_44             0.000000       0.000000      0.000000      0.000000  \n",
       "d_45             1.166667       1.530612      0.000000      0.000000  \n",
       "d_46            39.996599      62.700680      0.346939      0.000000  \n",
       "d_47             0.000000       0.000000      0.000000      0.000000  \n",
       "d_48            31.540816       0.000000      0.000000      0.000000  \n",
       "d_49           166.863946       0.000000      0.000000      0.000000  \n",
       "d_5              1.727891     304.748299     26.207483      2.772109  \n",
       "d_50             0.000000       0.000000      0.000000      0.000000  \n",
       "d_51             0.000000       0.000000      0.000000      0.000000  \n",
       "d_52            10.455782       0.326531      0.000000      0.000000  \n",
       "d_53            12.687075       3.204082      0.000000      0.000000  \n",
       "d_54             0.346939       0.000000      0.000000      0.000000  \n",
       "d_55             5.527211       1.204082      0.000000      0.000000  \n",
       "d_56           168.897959      63.064626      2.503401      0.404762  \n",
       "d_57            51.353741      33.744898      0.346939      0.000000  \n",
       "d_58          1340.023810       0.993197      0.000000      0.000000  \n",
       "d_59           421.292517       5.438776      0.000000      0.000000  \n",
       "d_6              0.000000    1240.727891    740.714286    199.642857  \n",
       "d_60         43298.231293      26.428571      0.000000      0.000000  \n",
       "d_7             23.544218  168813.448980   2598.445578    212.710884  \n",
       "d_8              0.000000    1688.853741  50712.615646    528.414966  \n",
       "d_9              0.000000     236.479592    494.469388  33752.465986  \n",
       "\n",
       "[60 rows x 60 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_day_src_dst_location_count = pre_lockdown_day_src_dst_location_count.reset_index()\n",
    "\n",
    "daily_day_src_dst_location_count['weekday'] = daily_day_src_dst_location_count['date'].dt.weekday\n",
    "daily_day_src_dst_location_count.rename(columns={'count': 'prob'}, inplace=True)\n",
    "\n",
    "daily_day_src_dst_location_count = daily_day_src_dst_location_count.groupby(['weekday', 'home_region', 'region'], sort=True)['prob'].mean()\n",
    "daily_day_src_dst_location_count = daily_day_src_dst_location_count.unstack('region')\n",
    "\n",
    "daily_day_src_dst_location_count.groupby(level=['home_region']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greatest number of outbound movements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = daily_day_src_dst_location_count.groupby(level=['home_region']).mean().values\n",
    "np.fill_diagonal(values, 0)\n",
    "largest_outbound_sorted_index = values.sum(axis=1).argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['d_2', 'd_31', 'd_18', 'd_1', 'd_36', 'd_7', 'd_26', 'd_23',\n",
       "       'd_28', 'd_19', 'd_13', 'd_56', 'd_57', 'd_21', 'd_6', 'd_10',\n",
       "       'd_34', 'd_30', 'd_45', 'd_3', 'd_17', 'd_60', 'd_35', 'd_8',\n",
       "       'd_55', 'd_16', 'd_33', 'd_59', 'd_4', 'd_32', 'd_27', 'd_25',\n",
       "       'd_38', 'd_58', 'd_22', 'd_14', 'd_53', 'd_5', 'd_37', 'd_24',\n",
       "       'd_52', 'd_46', 'd_54', 'd_12', 'd_49', 'd_20', 'd_9', 'd_11',\n",
       "       'd_41', 'd_48', 'd_47', 'd_44', 'd_40', 'd_29', 'd_51', 'd_15',\n",
       "       'd_43', 'd_50', 'd_42', 'd_39'], dtype='<U4')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outbound_districts = np.array(region)[largest_outbound_sorted_index]\n",
    "outbound_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greatest number of inbound movements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "largest_inbound_sorted_index = values.sum(axis=0).argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['d_2', 'd_31', 'd_18', 'd_1', 'd_36', 'd_23', 'd_26', 'd_7',\n",
       "       'd_13', 'd_19', 'd_28', 'd_21', 'd_6', 'd_56', 'd_57', 'd_45',\n",
       "       'd_10', 'd_34', 'd_8', 'd_30', 'd_17', 'd_35', 'd_3', 'd_55',\n",
       "       'd_60', 'd_46', 'd_27', 'd_16', 'd_4', 'd_32', 'd_59', 'd_38',\n",
       "       'd_25', 'd_58', 'd_33', 'd_22', 'd_14', 'd_53', 'd_24', 'd_5',\n",
       "       'd_54', 'd_52', 'd_37', 'd_49', 'd_20', 'd_12', 'd_9', 'd_11',\n",
       "       'd_48', 'd_44', 'd_41', 'd_47', 'd_50', 'd_29', 'd_15', 'd_40',\n",
       "       'd_43', 'd_51', 'd_42', 'd_39'], dtype='<U4')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inbound_districts = np.array(region)[largest_inbound_sorted_index]\n",
    "inbound_districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined inbound and outbound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>index</th>\n",
       "      <th>outbound</th>\n",
       "      <th>inbound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>d_2</td>\n",
       "      <td>d_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>d_31</td>\n",
       "      <td>d_31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>d_18</td>\n",
       "      <td>d_18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>d_1</td>\n",
       "      <td>d_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>d_36</td>\n",
       "      <td>d_36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index outbound inbound\n",
       "0      0      d_2     d_2\n",
       "1      1     d_31    d_31\n",
       "2      2     d_18    d_18\n",
       "3      3      d_1     d_1\n",
       "4      4     d_36    d_36"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inbound_outbound_df = pd.DataFrame({'outbound': outbound_districts, 'inbound': inbound_districts}).reset_index()\n",
    "inbound_outbound_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d_2     0.0\n",
       "d_31    1.0\n",
       "d_18    2.0\n",
       "d_1     3.0\n",
       "d_36    4.0\n",
       "d_7     6.0\n",
       "d_26    6.0\n",
       "d_23    6.0\n",
       "d_28    9.0\n",
       "d_13    9.0\n",
       "Name: index, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_dist = ((inbound_outbound_df.set_index('outbound')['index'] + inbound_outbound_df.set_index('inbound')['index']) / 2).sort_values().head(10)\n",
    "top_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d_2', 'd_31', 'd_18', 'd_1', 'd_36', 'd_7', 'd_26', 'd_23', 'd_28',\n",
       "       'd_13'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_dist.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sh = gpd.read_file(\n",
    "    get_data_dir('raw', 'shapefiles', 'new_districts', 'ZWE_adm2.shp'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inbound_outbound_df['inbound_disttrict'] = inbound_outbound_df['inbound'].str.lstrip('d_').astype(int)\n",
    "inbound_outbound_df['outbound_disttrict'] = inbound_outbound_df['outbound'].str.lstrip('d_').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "inbound_outbound_df['inbound_district'] = inbound_outbound_df['inbound_disttrict'].map(sh.set_index('ID_2')['NAME_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "inbound_outbound_df['outbound_district'] = inbound_outbound_df['outbound_disttrict'].map(sh.set_index('ID_2')['NAME_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "inbound_outbound_df[['inbound_district', 'outbound_district']].to_excel(\n",
    "    get_data_dir('preprocessed', 'mobility', 'Most mobile districts.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find seed districts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_new_df = pd.read_csv(\n",
    "    get_data_dir('raw', 'district_relation.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_new_df['new_dist_id'] = old_new_df['NEW_DIST_ID_2'].map(lambda x: f'd_{x}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Unnamed: 0</th>\n",
       "      <th>CNTRY_NAME</th>\n",
       "      <th>ADMIN_NAME</th>\n",
       "      <th>CNTRY_CODE</th>\n",
       "      <th>IPUM2012</th>\n",
       "      <th>DIST2012</th>\n",
       "      <th>PARENT</th>\n",
       "      <th>NEW_DIST_ID_2</th>\n",
       "      <th>new_dist_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Bulawayo Urban</td>\n",
       "      <td>716</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "   Unnamed: 0 CNTRY_NAME      ADMIN_NAME  CNTRY_CODE  IPUM2012  DIST2012  \\\n",
       "0           1   Zimbabwe  Bulawayo Urban         716        21        21   \n",
       "\n",
       "   PARENT  NEW_DIST_ID_2 new_dist_id  \n",
       "0       0              1         w_1  "
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 26,
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   "outputs": [
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       "    Unnamed: 0 CNTRY_NAME    ADMIN_NAME  CNTRY_CODE  IPUM2012  DIST2012  \\\n",
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    "old_new_df[old_new_df['ADMIN_NAME'] == 'Harare Urban']"
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   "execution_count": 27,
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   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>716</td>\n",
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       "      <td>716</td>\n",
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       "      <td>716</td>\n",
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       "      <td>Mudzi</td>\n",
       "      <td>716</td>\n",
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       "      <td>716</td>\n",
       "      <td>3006</td>\n",
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       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Mutoko</td>\n",
       "      <td>716</td>\n",
       "      <td>3007</td>\n",
       "      <td>307</td>\n",
       "      <td>3</td>\n",
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       "      <th>15</th>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Seke</td>\n",
       "      <td>716</td>\n",
       "      <td>3008</td>\n",
       "      <td>308</td>\n",
       "      <td>3</td>\n",
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       "      <th>16</th>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Uzumba Maramba Pfungwe</td>\n",
       "      <td>716</td>\n",
       "      <td>3009</td>\n",
       "      <td>309</td>\n",
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       "      <th>17</th>\n",
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       "      <td>Hwedza</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>Chegutu</td>\n",
       "      <td>716</td>\n",
       "      <td>4001</td>\n",
       "      <td>401</td>\n",
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       "      <th>19</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Hurungwe, Karoi</td>\n",
       "      <td>716</td>\n",
       "      <td>4002</td>\n",
       "      <td>402</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Mhondoro-Ngezi</td>\n",
       "      <td>716</td>\n",
       "      <td>4003</td>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>w_28</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Kariba Urban</td>\n",
       "      <td>716</td>\n",
       "      <td>4024</td>\n",
       "      <td>424</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>w_29</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Buhera</td>\n",
       "      <td>716</td>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
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       "      <td>w_3</td>\n",
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       "      <th>23</th>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chinhoyi</td>\n",
       "      <td>716</td>\n",
       "      <td>4021</td>\n",
       "      <td>421</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>w_30</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zvimba</td>\n",
       "      <td>716</td>\n",
       "      <td>4006</td>\n",
       "      <td>406</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>w_31</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Bikita</td>\n",
       "      <td>716</td>\n",
       "      <td>8001</td>\n",
       "      <td>801</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>w_32</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chiredzi Town</td>\n",
       "      <td>716</td>\n",
       "      <td>8022</td>\n",
       "      <td>822</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chivi</td>\n",
       "      <td>716</td>\n",
       "      <td>8003</td>\n",
       "      <td>803</td>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gutu</td>\n",
       "      <td>716</td>\n",
       "      <td>8004</td>\n",
       "      <td>804</td>\n",
       "      <td>8</td>\n",
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       "      <td>w_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Masvingo Rural</td>\n",
       "      <td>716</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25</td>\n",
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       "      <td>Mwenezi</td>\n",
       "      <td>716</td>\n",
       "      <td>8006</td>\n",
       "      <td>806</td>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>w_37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Zaka</td>\n",
       "      <td>716</td>\n",
       "      <td>8007</td>\n",
       "      <td>807</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>w_38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Binga</td>\n",
       "      <td>716</td>\n",
       "      <td>5001</td>\n",
       "      <td>501</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>w_39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chimanimani</td>\n",
       "      <td>716</td>\n",
       "      <td>1002</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>w_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>BUBI</td>\n",
       "      <td>716</td>\n",
       "      <td>5002</td>\n",
       "      <td>502</td>\n",
       "      <td>5</td>\n",
       "      <td>40</td>\n",
       "      <td>w_40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Victoria Falls</td>\n",
       "      <td>716</td>\n",
       "      <td>5022</td>\n",
       "      <td>522</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>w_41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Lupane</td>\n",
       "      <td>716</td>\n",
       "      <td>5004</td>\n",
       "      <td>504</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>w_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Nkayi</td>\n",
       "      <td>716</td>\n",
       "      <td>5005</td>\n",
       "      <td>505</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>w_43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Tsholotsho</td>\n",
       "      <td>716</td>\n",
       "      <td>5006</td>\n",
       "      <td>506</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>w_44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Umguza</td>\n",
       "      <td>716</td>\n",
       "      <td>5007</td>\n",
       "      <td>507</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>w_45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Beitbridge Rural</td>\n",
       "      <td>716</td>\n",
       "      <td>6001</td>\n",
       "      <td>601</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>w_46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Bulilima</td>\n",
       "      <td>716</td>\n",
       "      <td>6002</td>\n",
       "      <td>602</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>w_47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gwanda Rural</td>\n",
       "      <td>716</td>\n",
       "      <td>6004</td>\n",
       "      <td>604</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "      <td>w_48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Insiza</td>\n",
       "      <td>716</td>\n",
       "      <td>6005</td>\n",
       "      <td>605</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "      <td>w_49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chipinge Urban</td>\n",
       "      <td>716</td>\n",
       "      <td>1023</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>w_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Mangwe Rural, Mangwe Urban (Plumtree)</td>\n",
       "      <td>716</td>\n",
       "      <td>6003</td>\n",
       "      <td>603</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>w_50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Matobo</td>\n",
       "      <td>716</td>\n",
       "      <td>6006</td>\n",
       "      <td>606</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>w_51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Umzingwane</td>\n",
       "      <td>716</td>\n",
       "      <td>6007</td>\n",
       "      <td>607</td>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "      <td>w_52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Chirumanzu</td>\n",
       "      <td>716</td>\n",
       "      <td>7001</td>\n",
       "      <td>701</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>w_53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gokwe North</td>\n",
       "      <td>716</td>\n",
       "      <td>7002</td>\n",
       "      <td>702</td>\n",
       "      <td>7</td>\n",
       "      <td>54</td>\n",
       "      <td>w_54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  Unnamed: 0 CNTRY_NAME                             ADMIN_NAME  \\\n",
       "0       0           1   Zimbabwe                         Bulawayo Urban   \n",
       "1       8           9   Zimbabwe                          Bindura Rural   \n",
       "2       9          10   Zimbabwe                             Muzarabani   \n",
       "3      59          60   Zimbabwe                                 Guruve   \n",
       "4      10          11   Zimbabwe                         Mazowe, Mvurwi   \n",
       "5      11          12   Zimbabwe                              Mt Darwin   \n",
       "6      12          13   Zimbabwe                               Rushinga   \n",
       "7      13          14   Zimbabwe                                 Shamva   \n",
       "8      54          55   Zimbabwe                               Chikomba   \n",
       "9      62          63   Zimbabwe                                   Ruwa   \n",
       "10     64          65   Zimbabwe                        Marondera Urban   \n",
       "11     55          56   Zimbabwe                            Chitungwiza   \n",
       "12     49          50   Zimbabwe                                  Mudzi   \n",
       "13     50          51   Zimbabwe                                Murehwa   \n",
       "14     51          52   Zimbabwe                                 Mutoko   \n",
       "15     52          53   Zimbabwe                                   Seke   \n",
       "16     53          54   Zimbabwe                 Uzumba Maramba Pfungwe   \n",
       "17     47          48   Zimbabwe                                 Hwedza   \n",
       "18     14          15   Zimbabwe                                Chegutu   \n",
       "19     15          16   Zimbabwe                        Hurungwe, Karoi   \n",
       "20     85          86   Zimbabwe                         Mhondoro-Ngezi   \n",
       "21     65          66   Zimbabwe                           Kariba Urban   \n",
       "22      1           2   Zimbabwe                                 Buhera   \n",
       "23     66          67   Zimbabwe                               Chinhoyi   \n",
       "24     19          20   Zimbabwe                                 Zvimba   \n",
       "25     20          21   Zimbabwe                                 Bikita   \n",
       "26     79          80   Zimbabwe                          Chiredzi Town   \n",
       "27     22          23   Zimbabwe                                  Chivi   \n",
       "28     23          24   Zimbabwe                                   Gutu   \n",
       "29     24          25   Zimbabwe                         Masvingo Rural   \n",
       "30     25          26   Zimbabwe                                Mwenezi   \n",
       "31     26          27   Zimbabwe                                   Zaka   \n",
       "32     40          41   Zimbabwe                                  Binga   \n",
       "33      2           3   Zimbabwe                            Chimanimani   \n",
       "34     41          42   Zimbabwe                                   BUBI   \n",
       "35     70          71   Zimbabwe                         Victoria Falls   \n",
       "36     42          43   Zimbabwe                                 Lupane   \n",
       "37     43          44   Zimbabwe                                  Nkayi   \n",
       "38     44          45   Zimbabwe                             Tsholotsho   \n",
       "39     45          46   Zimbabwe                                 Umguza   \n",
       "40     27          28   Zimbabwe                       Beitbridge Rural   \n",
       "41     57          58   Zimbabwe                               Bulilima   \n",
       "42     28          29   Zimbabwe                           Gwanda Rural   \n",
       "43     29          30   Zimbabwe                                 Insiza   \n",
       "44     83          84   Zimbabwe                         Chipinge Urban   \n",
       "45     58          59   Zimbabwe  Mangwe Rural, Mangwe Urban (Plumtree)   \n",
       "46     30          31   Zimbabwe                                 Matobo   \n",
       "47     31          32   Zimbabwe                             Umzingwane   \n",
       "48     32          33   Zimbabwe                             Chirumanzu   \n",
       "49     38          39   Zimbabwe                            Gokwe North   \n",
       "\n",
       "    CNTRY_CODE  IPUM2012  DIST2012  PARENT  NEW_DIST_ID_2 new_dist_id  \n",
       "0          716        21        21       0              1         w_1  \n",
       "1          716      2001       201       2             10        w_10  \n",
       "2          716      2002       202       2             11        w_11  \n",
       "3          716      2003       203       2             12        w_12  \n",
       "4          716      2004       204       2             13        w_13  \n",
       "5          716      2005       205       2             14        w_14  \n",
       "6          716      2006       206       2             15        w_15  \n",
       "7          716      2007       207       2             16        w_16  \n",
       "8          716      3001       301       3             17        w_17  \n",
       "9          716      3023       323       3             18        w_18  \n",
       "10         716      3021       321       3             19        w_19  \n",
       "11         716      9022       922       9              2         w_2  \n",
       "12         716      3005       305       3             20        w_20  \n",
       "13         716      3006       306       3             21        w_21  \n",
       "14         716      3007       307       3             22        w_22  \n",
       "15         716      3008       308       3             23        w_23  \n",
       "16         716      3009       309       3             24        w_24  \n",
       "17         716      3003       303       3             25        w_25  \n",
       "18         716      4001       401       4             26        w_26  \n",
       "19         716      4002       402       4             27        w_27  \n",
       "20         716      4003       403       4             28        w_28  \n",
       "21         716      4024       424       4             29        w_29  \n",
       "22         716      1001       101       1              3         w_3  \n",
       "23         716      4021       421       4             30        w_30  \n",
       "24         716      4006       406       4             31        w_31  \n",
       "25         716      8001       801       8             32        w_32  \n",
       "26         716      8022       822       8             33        w_33  \n",
       "27         716      8003       803       8             34        w_34  \n",
       "28         716      8004       804       8             35        w_35  \n",
       "29         716      8005       805       8             36        w_36  \n",
       "30         716      8006       806       8             37        w_37  \n",
       "31         716      8007       807       8             38        w_38  \n",
       "32         716      5001       501       5             39        w_39  \n",
       "33         716      1002       102       1              4         w_4  \n",
       "34         716      5002       502       5             40        w_40  \n",
       "35         716      5022       522       5             41        w_41  \n",
       "36         716      5004       504       5             42        w_42  \n",
       "37         716      5005       505       5             43        w_43  \n",
       "38         716      5006       506       5             44        w_44  \n",
       "39         716      5007       507       5             45        w_45  \n",
       "40         716      6001       601       6             46        w_46  \n",
       "41         716      6002       602       6             47        w_47  \n",
       "42         716      6004       604       6             48        w_48  \n",
       "43         716      6005       605       6             49        w_49  \n",
       "44         716      1023       123       1              5         w_5  \n",
       "45         716      6003       603       6             50        w_50  \n",
       "46         716      6006       606       6             51        w_51  \n",
       "47         716      6007       607       6             52        w_52  \n",
       "48         716      7001       701       7             53        w_53  \n",
       "49         716      7002       702       7             54        w_54  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_new_df.sort_values('new_dist_id').drop_duplicates('new_dist_id').reset_index().head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze pre- and post-lockdown subscriber counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_lockdown_subs = post_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum().reset_index().groupby('home_region')['count'].mean()\n",
    "pre_lockdown_subs = pre_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum().reset_index().groupby('home_region')['count'].mean()\n",
    "\n",
    "sub_diff = (\n",
    "    (post_lockdown_subs - pre_lockdown_subs) / pre_lockdown_subs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    60.000000\n",
       "mean      0.002664\n",
       "std       0.069888\n",
       "min      -0.196475\n",
       "25%      -0.038342\n",
       "50%       0.026602\n",
       "75%       0.053646\n",
       "max       0.099094\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_diff.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.05870281231527186"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_lockdown_subs.sum() / pre_lockdown_subs.sum() - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = post_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum().reset_index().groupby('home_region')['count'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e27b21350>"
      ]
     },
     "execution_count": 46,
     "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": [
    "pre_lockdown_calibrated_home_subscribers_mobility_df.groupby(['date', 'home_region'])['count'].sum().reset_index().groupby('home_region')['count'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday  home_region  region\n",
       "0        w_1          w_1       259622.571429\n",
       "                      w_10          11.285714\n",
       "                      w_11           0.000000\n",
       "                      w_12           0.000000\n",
       "                      w_13          12.714286\n",
       "                                    ...      \n",
       "6        w_9          w_6          248.500000\n",
       "                      w_60           0.000000\n",
       "                      w_7          277.000000\n",
       "                      w_8          565.166667\n",
       "                      w_9        33314.000000\n",
       "Name: count, Length: 25200, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_lockdown_calibrated_home_subscribers_mobility_df.groupby(['weekday', 'home_region', 'region'])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekday  home_region  region\n",
       "0        w_1          w_1       218722.00\n",
       "                      w_10           0.00\n",
       "                      w_11           0.00\n",
       "                      w_12           0.00\n",
       "                      w_13           0.00\n",
       "                                  ...    \n",
       "6        w_9          w_6          148.00\n",
       "                      w_60           0.00\n",
       "                      w_7           64.50\n",
       "                      w_8          394.00\n",
       "                      w_9        35702.25\n",
       "Name: count, Length: 25200, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_lockdown_calibrated_home_subscribers_mobility_df.groupby(['weekday', 'home_region', 'region'])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>day</th>\n",
       "      <th>home_region</th>\n",
       "      <th>region</th>\n",
       "      <th>date2</th>\n",
       "      <th>count</th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>w_1</td>\n",
       "      <td>w_1</td>\n",
       "      <td>03feb2020</td>\n",
       "      <td>263449.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>w_1</td>\n",
       "      <td>w_10</td>\n",
       "      <td>03feb2020</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>w_1</td>\n",
       "      <td>w_11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>w_1</td>\n",
       "      <td>w_12</td>\n",
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       "              day home_region region      date2     count       date  weekday\n",
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       "\n",
       "[169200 rows x 7 columns]"
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     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pre_lockdown_calibrated_home_subscribers_mobility_df"
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   "execution_count": 85,
   "metadata": {},
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       "              day home_region region      date2     count       date  weekday\n",
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   "source": [
    "post_lockdown_calibrated_home_subscribers_mobility_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
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       "      <td>0.003827</td>\n",
       "      <td>0.004020</td>\n",
       "      <td>0.004163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040840</td>\n",
       "      <td>0.046243</td>\n",
       "      <td>0.047831</td>\n",
       "      <td>0.076204</td>\n",
       "      <td>0.084937</td>\n",
       "      <td>0.084937</td>\n",
       "      <td>0.999292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w_7</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.000995</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034407</td>\n",
       "      <td>0.034994</td>\n",
       "      <td>0.035176</td>\n",
       "      <td>0.035196</td>\n",
       "      <td>0.035245</td>\n",
       "      <td>0.043500</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.981834</td>\n",
       "      <td>0.998665</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w_8</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.001165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012964</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.013082</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.060809</td>\n",
       "      <td>0.990583</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>w_9</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019381</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.026639</td>\n",
       "      <td>0.026639</td>\n",
       "      <td>0.034537</td>\n",
       "      <td>0.050461</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "region                    w_1      w_10      w_11      w_12      w_13  \\\n",
       "weekday home_region                                                     \n",
       "0       w_1          0.952601  0.952643  0.952643  0.952643  0.952690   \n",
       "        w_10         0.000111  0.912164  0.912616  0.913231  0.935119   \n",
       "        w_11         0.000000  0.001716  0.900063  0.936411  0.950390   \n",
       "        w_12         0.000000  0.000889  0.014743  0.963280  0.969570   \n",
       "        w_13         0.000185  0.016048  0.018727  0.021197  0.931744   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       w_6          0.000424  0.000553  0.000553  0.000553  0.000749   \n",
       "        w_60         0.003827  0.003827  0.003827  0.003827  0.003827   \n",
       "        w_7          0.000604  0.000808  0.000808  0.000808  0.000980   \n",
       "        w_8          0.000195  0.000195  0.000195  0.000195  0.000195   \n",
       "        w_9          0.000471  0.000471  0.000471  0.000471  0.000471   \n",
       "\n",
       "region                   w_14      w_15      w_16      w_17      w_18  ...  \\\n",
       "weekday home_region                                                    ...   \n",
       "0       w_1          0.952690  0.952690  0.952690  0.952846  0.953035  ...   \n",
       "        w_10         0.942043  0.942656  0.965920  0.965920  0.972148  ...   \n",
       "        w_11         0.983489  0.983489  0.983489  0.983489  0.984168  ...   \n",
       "        w_12         0.970181  0.970181  0.970181  0.970181  0.971053  ...   \n",
       "        w_13         0.933867  0.933925  0.935037  0.935099  0.944762  ...   \n",
       "...                       ...       ...       ...       ...       ...  ...   \n",
       "6       w_6          0.000749  0.000749  0.000749  0.001100  0.002620  ...   \n",
       "        w_60         0.003827  0.003827  0.003827  0.004020  0.004163  ...   \n",
       "        w_7          0.000995  0.000995  0.001045  0.001288  0.002296  ...   \n",
       "        w_8          0.000195  0.000195  0.000195  0.000195  0.001165  ...   \n",
       "        w_9          0.000471  0.000471  0.000471  0.000471  0.001928  ...   \n",
       "\n",
       "region                   w_55      w_56      w_57      w_58      w_59  \\\n",
       "weekday home_region                                                     \n",
       "0       w_1          0.995582  0.997295  0.998182  0.998490  0.998797   \n",
       "        w_10         0.998710  0.998997  0.999262  0.999262  0.999262   \n",
       "        w_11         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        w_12         1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "        w_13         0.998916  0.999060  0.999332  0.999332  0.999332   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "6       w_6          0.044532  0.044945  0.045029  0.045029  0.045029   \n",
       "        w_60         0.040840  0.046243  0.047831  0.076204  0.084937   \n",
       "        w_7          0.034407  0.034994  0.035176  0.035196  0.035245   \n",
       "        w_8          0.012964  0.013082  0.013082  0.013082  0.013082   \n",
       "        w_9          0.019381  0.019548  0.019548  0.019548  0.019548   \n",
       "\n",
       "region                    w_6      w_60       w_7       w_8  w_9  \n",
       "weekday home_region                                               \n",
       "0       w_1          0.998930  0.999516  0.999945  1.000000  1.0  \n",
       "        w_10         0.999466  0.999466  1.000000  1.000000  1.0  \n",
       "        w_11         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        w_12         1.000000  1.000000  1.000000  1.000000  1.0  \n",
       "        w_13         0.999599  0.999599  1.000000  1.000000  1.0  \n",
       "...                       ...       ...       ...       ...  ...  \n",
       "6       w_6          0.972009  0.972009  0.987383  0.997199  1.0  \n",
       "        w_60         0.084937  0.999292  1.000000  1.000000  1.0  \n",
       "        w_7          0.043500  0.043735  0.981834  0.998665  1.0  \n",
       "        w_8          0.029248  0.029248  0.060809  0.990583  1.0  \n",
       "        w_9          0.026639  0.026639  0.034537  0.050461  1.0  \n",
       "\n",
       "[420 rows x 60 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_lockdown_daily_region_transition_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95384743, 0.9175776 , 0.90561874, 0.96713908, 0.93757666,\n",
       "       0.96957809, 0.95372824, 0.96138031, 0.89315545, 0.83906965,\n",
       "       0.91319829, 0.95812863, 0.93044896, 0.95859965, 0.98637798,\n",
       "       0.97182457, 0.99867426, 0.96567518, 0.95639563, 0.96815308,\n",
       "       0.9729292 , 0.99185798, 0.94562326, 0.97850835, 0.99651129,\n",
       "       0.94527324, 0.96538904, 0.88678946, 0.95740796, 0.9730564 ,\n",
       "       0.97370615, 0.99792811, 0.98181177, 0.93761492, 0.97023864,\n",
       "       0.99389132, 0.98978179, 0.96543724, 0.95033937, 0.97503572,\n",
       "       0.98401418, 0.97437268, 0.98345431, 0.94365953, 0.99512389,\n",
       "       0.98689602, 0.99921629, 0.99835115, 0.96997531, 0.96275057,\n",
       "       0.97530087, 0.97645239, 0.99759866, 0.95189301, 0.98954831,\n",
       "       0.97107254, 0.99918607, 0.98310624, 0.98978915, 1.        ])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.diag(pre_lockdown_daily_region_transition_probability.loc[0]) + np.diag(pre_lockdown_daily_region_transition_probability.loc[1])) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95260146, 0.91216435, 0.90006275, 0.96327955, 0.93174366,\n",
       "       0.96779743, 0.94813166, 0.95995971, 0.89259106, 0.83432499,\n",
       "       0.9066875 , 0.95575969, 0.92142953, 0.95433775, 0.98541907,\n",
       "       0.97140032, 0.99824495, 0.96776732, 0.95547789, 0.96465198,\n",
       "       0.97122781, 0.99069945, 0.94189666, 0.97717223, 0.99577464,\n",
       "       0.94222335, 0.96158013, 0.89294494, 0.95856635, 0.97056054,\n",
       "       0.97537049, 0.99768907, 0.98047197, 0.93999492, 0.96735876,\n",
       "       0.99266064, 0.98853611, 0.96548562, 0.9488375 , 0.97117727,\n",
       "       0.98129387, 0.97307624, 0.98219521, 0.938897  , 0.99440798,\n",
       "       0.98589046, 0.99894065, 0.99789422, 0.96896401, 0.95924681,\n",
       "       0.97186422, 0.97500445, 0.99738466, 0.9555304 , 0.98899619,\n",
       "       0.96984592, 0.99912642, 0.98198858, 0.98844565, 1.        ])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(pre_lockdown_daily_region_transition_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97886775, 0.95249028, 0.94121232, 0.98524211, 0.96706381,\n",
       "       0.9831196 , 0.96136441, 0.97230212, 0.9284363 , 0.91333768,\n",
       "       0.93593655, 0.97421563, 0.9506026 , 0.96865521, 0.9885466 ,\n",
       "       0.97849578, 1.        , 0.96822518, 0.96685794, 0.98001873,\n",
       "       0.98370817, 0.99543281, 0.96138   , 0.98458314, 0.99928889,\n",
       "       0.95778937, 0.97364145, 0.92257047, 0.97144513, 0.98038857,\n",
       "       0.97834923, 1.        , 0.98929007, 0.95618854, 0.97797933,\n",
       "       0.9987635 , 0.99298429, 0.97361365, 0.95087876, 0.98045171,\n",
       "       0.99357762, 0.98234279, 0.98576736, 0.9539339 , 0.99829378,\n",
       "       0.9865713 , 1.        , 1.        , 0.98185915, 0.9746045 ,\n",
       "       0.98377662, 0.98537201, 0.99943221, 0.96449478, 0.9922637 ,\n",
       "       0.98278878, 1.        , 0.99024947, 0.99275901, 1.        ])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(post_lockdown_daily_region_transition_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.78867750e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        6.37248641e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 9.52490279e-01, 7.17334385e-05, ...,\n",
       "        7.62167785e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 7.18326894e-04, 9.40493992e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [1.00148466e-04, 3.29321598e-05, 0.00000000e+00, ...,\n",
       "        9.67739032e-01, 9.27717769e-03, 4.73351519e-04],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        1.67397004e-02, 9.60775697e-01, 7.24099359e-03],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        2.05056738e-03, 9.67849686e-03, 9.78527184e-01]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(post_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.81543330e-01, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        2.09654710e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 9.55595439e-01, 8.54209002e-05, ...,\n",
       "        7.64292265e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 9.23432792e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [6.49369038e-05, 3.26221700e-05, 0.00000000e+00, ...,\n",
       "        9.72253210e-01, 8.19391813e-03, 4.37832881e-04],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        1.52664123e-02, 9.64911160e-01, 7.93417541e-03],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        1.54781354e-03, 8.68112350e-03, 9.82220719e-01]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(post_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.02621735, 1.03771199, 1.02622983, 1.01854773, 1.03891184,\n",
       "       1.02087227, 1.02290393, 1.03735869, 1.03585097, 1.08965962,\n",
       "       1.03324967, 1.03013781, 1.02445384, 1.0406608 , 1.02660076,\n",
       "       1.06700174, 1.02348402, 1.02906007, 1.04313194, 1.01603901,\n",
       "       1.03766049, 1.01802766, 1.02330465, 1.02446711, 1.07773556,\n",
       "       1.02433409, 1.01649551, 1.04366835, 1.0274174 , 1.02400112,\n",
       "       1.02118928, 1.0244449 , 1.01473734, 1.02357977, 1.0379566 ,\n",
       "       1.0168393 , 1.02684391, 1.02309424, 1.02607631, 1.0813054 ,\n",
       "       1.03398159, 1.02376036, 1.0196291 , 1.03695839, 1.01359001,\n",
       "       1.02702276, 1.01924516, 1.05190263, 1.04417487, 1.02213118,\n",
       "       1.02117115, 1.03110093, 1.02672291, 1.02784615, 1.03744043,\n",
       "       1.02196007, 1.03095007, 1.02338173, 1.03011789, 1.02479599])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(np.diff(post_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60).mean(axis=0)) / np.diag(np.diff(pre_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9795566 , 0.95324053, 0.93274562, 0.97504914, 0.9558135 ,\n",
       "       0.97073389, 0.93265699, 0.92580276, 0.93037543, 0.90830597,\n",
       "       0.93297047, 0.96199297, 0.94349419, 0.91649658, 0.95136453,\n",
       "       0.88389884, 0.92694036, 0.91149546, 0.95017521, 0.97536909,\n",
       "       0.96031853, 0.97309358, 0.94423484, 0.96651306, 0.88679082,\n",
       "       0.95793441, 0.97257978, 0.92341969, 0.94890002, 0.94960994,\n",
       "       0.94699835, 0.94197449, 0.98896858, 0.95349369, 0.96840405,\n",
       "       0.9944525 , 0.97979456, 0.96728479, 0.94604352, 0.88469766,\n",
       "       0.98613362, 0.94294946, 0.97904767, 0.94015384, 0.98396223,\n",
       "       0.96636682, 0.96892287, 0.92295539, 0.94139775, 0.96363379,\n",
       "       0.95544882, 0.97987252, 0.96874365, 0.9350208 , 0.9600987 ,\n",
       "       0.95301972, 0.95126326, 0.97020371, 0.96422273, 0.98029979])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(np.diff(post_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.95453132, 0.91859836, 0.90890519, 0.95729352, 0.92001405,\n",
       "       0.95088672, 0.91177379, 0.89246156, 0.89817498, 0.83356854,\n",
       "       0.90294775, 0.93384881, 0.92097288, 0.88068713, 0.92671325,\n",
       "       0.82839494, 0.90567155, 0.88575535, 0.91088689, 0.95997209,\n",
       "       0.92546506, 0.95586162, 0.92273092, 0.94343005, 0.82282784,\n",
       "       0.93517771, 0.95679693, 0.8847827 , 0.92357791, 0.92735244,\n",
       "       0.9273485 , 0.91949747, 0.97460548, 0.93152847, 0.93299089,\n",
       "       0.97798393, 0.95418063, 0.94545033, 0.92200114, 0.81817557,\n",
       "       0.95372455, 0.92106463, 0.96019981, 0.90664568, 0.97076947,\n",
       "       0.94094003, 0.95062789, 0.87741523, 0.90157097, 0.9427692 ,\n",
       "       0.93564024, 0.95031678, 0.94352979, 0.90968945, 0.92544947,\n",
       "       0.93254105, 0.92270546, 0.94803696, 0.93603144, 0.95658043])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(np.diff(pre_lockdown_daily_region_transition_probability, prepend=0).reshape(7, 60, 60).mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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