project_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# Read the CSV file into a DataFrame\n",
    "mask_dataset = pd.read_csv('Mask_use_data_ON_2020.csv')\n",
    "OxGRT_dataset = pd.read_csv(\"OxGRT_Index_ON_2020.csv\")\n",
    "\n",
    "mobility_dataset = pd.read_csv(\"Mobility_Data_ON_2020.csv\")\n",
    "hospitality_dataset = pd.read_csv(\"region_hospital_icu_covid_data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_date(date_string): \n",
    "    '''\n",
    "    change the date fomat yyyy-mm-dd to yyyymmdd\n",
    "    '''\n",
    "    return ''.join(filter(str.isdigit, date_string))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OxGRT v.s. Mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      20200101\n",
       "1      20200102\n",
       "2      20200103\n",
       "3      20200104\n",
       "4      20200105\n",
       "         ...   \n",
       "361    20201227\n",
       "362    20201228\n",
       "363    20201229\n",
       "364    20201230\n",
       "365    20201231\n",
       "Name: Date, Length: 366, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OxGRT_dataset['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_dataset['date'] = mask_dataset['date'].apply(convert_date).astype(int)\n",
    "OxGRT_dataset['Date'] = OxGRT_dataset['Date'].astype(int)\n",
    "\n",
    "mask_dataset = mask_dataset.dropna(subset=['mask_use_mean'])\n",
    "OxGRT_dataset = OxGRT_dataset.dropna(subset=['StringencyIndex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>location_id</th>\n",
       "      <th>date</th>\n",
       "      <th>version_name</th>\n",
       "      <th>location_name</th>\n",
       "      <th>mask_use_mean</th>\n",
       "      <th>mask_use_obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43866</td>\n",
       "      <td>20200204</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43866</td>\n",
       "      <td>20200205</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43866</td>\n",
       "      <td>20200206</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43866</td>\n",
       "      <td>20200207</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43866</td>\n",
       "      <td>20200208</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>43866</td>\n",
       "      <td>20201226</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.8585</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>43866</td>\n",
       "      <td>20201227</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.8568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>43866</td>\n",
       "      <td>20201228</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.8553</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>43866</td>\n",
       "      <td>20201229</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.8540</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>43866</td>\n",
       "      <td>20201230</td>\n",
       "      <td>reference</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>0.8528</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>331 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     location_id      date version_name location_name  mask_use_mean  \\\n",
       "0          43866  20200204    reference       Ontario         0.0028   \n",
       "1          43866  20200205    reference       Ontario         0.0044   \n",
       "2          43866  20200206    reference       Ontario         0.0064   \n",
       "3          43866  20200207    reference       Ontario         0.0089   \n",
       "4          43866  20200208    reference       Ontario         0.0117   \n",
       "..           ...       ...          ...           ...            ...   \n",
       "326        43866  20201226    reference       Ontario         0.8585   \n",
       "327        43866  20201227    reference       Ontario         0.8568   \n",
       "328        43866  20201228    reference       Ontario         0.8553   \n",
       "329        43866  20201229    reference       Ontario         0.8540   \n",
       "330        43866  20201230    reference       Ontario         0.8528   \n",
       "\n",
       "     mask_use_obs  \n",
       "0             1.0  \n",
       "1             1.0  \n",
       "2             1.0  \n",
       "3             1.0  \n",
       "4             1.0  \n",
       "..            ...  \n",
       "326           1.0  \n",
       "327           1.0  \n",
       "328           1.0  \n",
       "329           1.0  \n",
       "330           1.0  \n",
       "\n",
       "[331 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(331, 366)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mask_dataset['date']), len(OxGRT_dataset['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to np array with int entries\n",
    "mask_date = np.array(mask_dataset['date'])\n",
    "OxGRT_date = np.array(OxGRT_dataset['Date'])\n",
    "# finding the indexes with identical entries\n",
    "\n",
    "identical_dates = np.intersect1d(mask_date, OxGRT_date)\n",
    "indexes_in_mask_dataset = np.where(np.isin(mask_date, identical_dates))[0]\n",
    "indexes_in_OxGRT_dataset = np.where(np.isin(OxGRT_date, identical_dates))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The starting date: 20200204. \n",
      " The end date: 20201230\n"
     ]
    }
   ],
   "source": [
    "# common dates between valid mask_date \n",
    "print(f'The starting date: {min(identical_dates)}. \\n The end date: {max(identical_dates)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.array(mask_dataset['mask_use_mean'][indexes_in_mask_dataset])\n",
    "OxGRT = np.array(OxGRT_dataset['StringencyIndex'][indexes_in_OxGRT_dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.array_equal(mask_date[indexes_in_mask_dataset], OxGRT_date[indexes_in_OxGRT_dataset])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mask_use_mean')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B4xX4ZG8N1mz5DmlGHZpb3bA6PdCoVVArBSYMTOVkXqIgMdwQEYVBRYMd7+06gSprK7w+wAfA6wNanT40OB0AAKUEqISMrCQ9Jg1K42GUREHifzlERL1MCIHNB+tRc8oFSO3B5kwSAIUEKCWJOwITXSD23BAR9TKzxYEDtS2AENCrlJCErz3kyO0PGe29NkaNCiNzkrgjMNEFYrghIuplTXYXlBKQm6pHQ4sb/ZN1uHpUFiobW7Gv+hS8AshO0mFUfxMuH56FQRlGzrUhugAMN0REvUgIgaMNDhxrdKDNKyPdqMWssf0x53t5DDBEvYRzboiIelFFgx3fVFjQ4vLCoFEiP82AQRnc+ZeoNzHcEBH1on3VNtTYXNAoFWhoccMry0g1aiNdFlFc47AUEVEvEUKg3tYGt9eHRK0aaqUU1uMUiPoqhhsiintCCJgtDjQ73GE9ENJscaDZ6YZGpUSb14dh2Uk8ToEoDBhuiCjumS0ObD3cALdXhkbVPhofjqXWzQ43EnVqzBqbg0O1NkwYmMol3kRhwDk3RBTXhBDYW2VFRYMdBq0SLq8PzQ53WD47xaCBVq2Ew+VFYYYRo/qb2GtDFAbsuSGiuGa2OHC4rgU11jbUWNswPDsRKQZNWD67Y27N6cNhRNT7GG6IKK412V1we30oTDegztaKoZnGsIUMSZLah6EywvJxRPRvDDdEFNccbh8qm1rh8srQqhQwJWg4NEQU5xhuiChuCSFwyumGVqVAXqoekiTBoFFGuiwi6mWcUExEcctsceBIvR0Otw9mixMapYIb6BH1AQw3RBS3Tp9vY9Aowjrfhogih8NSRBS3ON+GqG9iuCGiuMT5NkR9F4eliCgucb4NUd/FcENEcYnzbYj6Lg5LEVFc4nwbor6L4YaI4lKCWgGTXg21UoLHJ5CgZkc1UV/BcENEccnpkWFt9fh7bpweOdIlEVGYMNwQUVwyaJQYkGZAaoIaTU4PV0oR9SHspyWiuCOEgMPtg9cro9npRr8kHVdKEfUhDDdEFHfMFgeONtihUing8QkUZhi4UoqoD2G4IaK40+xww+MTmDAwFVlJOhi1Kq6UIupDGG6IKO6kGDTQqBQ42mCHRqVAikET6ZKIKIwYbogo7gxMS0BhugFGrRKF6QYMTEuIdElEFEYMN0QUd441OlFhccDu8qHC4sCxRmekSyKiMGK4IaK40+xww+2VMSjDCLdXRrPDHemSiCiMGG6IKO5wzg1R38ZN/IgorgghIIRAukEDAWBUThKXgRP1MQw3RBRXzBYHvjhigdsrQ6NSQJIkLgMn6mM4LEVEcYXzbYiI4YaI4grn2xARww0RxRXucUNEDDdEFFe4xw0RMdwQUVzhnBsi4mopIoobQgjYXV7UWdvQ5HAhO0nPOTdEfRB7bogobpgtDhxtsEOlUsDjEyjMMHCPG6I+iOGGiOJGs8MNj09gwsBUZCXpYNSquMcNUR/EcENEcYPLwIkI4JwbIoojHUNQzQ43UgwaDkkR9VHsuSEiIqK4EhXhZtWqVSgoKIBOp0NRURG2bdt21vtdLheWL1+O/Px8aLVaDBo0CK+99lqYqiWiaGW2OLD1cAN2Hm/G1sMNMFsckS6JiCIg4sNS69evx+LFi7Fq1SpceumlePnllzFz5kzs378fAwYM6PI1c+bMQV1dHV599VUMHjwY9fX18Hq9Ya6ciKLN6XvcHG2wt+9xkxHpqogo3CQhhIhkARMnTsT48eOxevVq/7URI0Zg9uzZWLlyZaf7P/nkE9xyyy2oqKhAampqUJ9ps9lgMplgtVqRlJQUdO1EFF0qGuzYerjBfyL4tKEZKMwwRrosIgqRnv7+juiwlNvtRllZGUpKSgKul5SUYPv27V2+ZuPGjSguLsZzzz2H/v37Y+jQofjFL36B1tbWbj/H5XLBZrMFPIgo/vBcKSICIjwsZbFY4PP5kJWVFXA9KysLtbW1Xb6moqICX375JXQ6HT744ANYLBbcc889aGpq6nbezcqVK/Hkk0+GvH4iii4d50q5vTIqLA7kpSaw54aoD4qKCcVnbrIlhOh24y1ZliFJEt58801MmDAB1157LZ5//nmsXbu2296bhx9+GFar1f84ceJEyL8DEUUez5UiIiDCPTfp6elQKpWdemnq6+s79eZ06NevH/r37w+TyeS/NmLECAghcPLkSQwZMqTTa7RaLbRabWiLJ6Kow038iAiIcM+NRqNBUVERSktLA66XlpZi8uTJXb7m0ksvRXV1Nex2u//a4cOHoVAokJub26v1ElF0K0g3YNrQDBTnp2Da0Axu4kfUR0V8WGrp0qV45ZVX8Nprr+HAgQNYsmQJKisrsWjRIgDtQ0oLFy703z9v3jykpaXhjjvuwP79+/HFF1/gwQcfxJ133gm9Xh+pr0FEESaEgNniCNidmOdKEfVNEd/nZu7cuWhsbMSKFStQU1OD0aNHY9OmTcjPzwcA1NTUoLKy0n+/0WhEaWkp7r//fhQXFyMtLQ1z5szB008/HamvQERRoGMDv45l4AA4mZioj4r4PjeRwH1uiOJP2bEm7Dze7N/Arzg/BUUDg9sLi4iiU0zsc0NEFCqcTExEHSI+LEVEFAo8EZyIOjDcEFFckCSpfY4Nz5Ii6vMYbogoLnC1FBF1YLghorjA1VJE1CGoCcV1dXVYsGABcnJyoFKpoFQqAx5EROHGoxeIqENQPTe33347Kisr8dhjj6Ffv37s+iWiiONqKSLqEFS4+fLLL7Ft2zZcfPHFIS6HiCg4XC1FRB2CGpbKy8tDH9z7j4iiFCcTE9Hpggo3L7zwApYtW4Zjx46FuBwiovPXMZl45/FmbD3cALPFEemSiCiCghqWmjt3LpxOJwYNGoSEhASo1eqA55uamkJSHBFRT5w+mfhog719MjH3uyHqs4IKNy+88EKIyyAiCh4nExPR6YIKN7fddluo6yAiChonExPR6S54E7/W1lZ4PJ6Aazxpm4jCiUcvENHpgppQ7HA4cN999yEzMxNGoxEpKSkBDyKicBJCoKLBjrJjTahosHM1J1EfF1S4+eUvf4nPP/8cq1atglarxSuvvIInn3wSOTk5WLduXahrJCI6K66WIqLTBTUs9eGHH2LdunWYPn067rzzTkydOhWDBw9Gfn4+3nzzTcyfPz/UdRIRdYurpYjodEH13DQ1NaGgoABA+/yajqXfU6ZMwRdffBG66oiIeoCrpYjodEH13BQWFuLYsWPIz8/HyJEj8c4772DChAn48MMPkZycHOISiYjOjquliOh0QfXc3HHHHfj2228BAA8//LB/7s2SJUvw4IMPhrRAIqKz4dELRHQmSYRgWUFlZSV27tyJQYMG4aKLLgpFXb3KZrPBZDLBarVy2TpRjKtosGPr4Qa4vTI0KgWmDc1oXxZORHGnp7+/L3ifm7a2NgwYMAADBgy40LciIjpvnExMRGcKaljK5/PhqaeeQv/+/WE0GlFRUQEAeOyxx/Dqq6+GtEAiorPhZGIiOlNQ4ebXv/411q5di+eeew4azX/+IhkzZgxeeeWVkBVHRHQuBekGTBuageL8FEwbmsHJxEQUXLhZt24d1qxZg/nz50OpVPqvjx07FgcPHgxZcURERETnK6g5N1VVVRg8eHCn67IsdzpnioioN3XsTtwxoRgAJxQT9XFB9dyMGjUK27Zt63R9w4YNGDdu3AUXRUTUU6dPKHZ75fYJxUTUpwXVc/PEE09gwYIFqKqqgizLeP/993Ho0CGsW7cOH330UahrJCLqFicUE9GZggo31113HdavX4/f/OY3kCQJjz/+OMaPH48PP/wQV111VahrJCLqFncnJqIzhWQTv1jDTfyIiIhiT9g28bPb7ZBlOeAaAwMRhQOPXiCirgQVbsxmM+677z5s2bIFbW1t/utCCEiSBJ/PF7ICiYi6w5VSRNSVoMLN/PnzAQCvvfYasrKy+C8lIooIHr1ARF0JKtz861//QllZGYYNGxbqeoiIeowrpYioK0GFm+9973s4ceIEww0RRRRXShFRV4IKN6+88goWLVqEqqoqjB49Gmq1OuD5sWPHhqQ4IiIiovMVVLhpaGjA0aNHcccdd/ivSZLECcVEFFacUExEXQkq3Nx5550YN24c/vKXv3BCMRFFDCcUE1FXggo3x48fx8aNG7s8PJOIKFw4oZiIuhJUuLniiivw7bffMtwQUURxQjERdSXos6WWLFmCPXv2YMyYMZ0mFF9//fUhKY6IiIjofAV1tpRCoej+DWNgQjHPliKKDxUN9oAJxdOGZnBCMVEc6+nv7+5TylnIstztI9qDDRHFj9MnFLu9cvuEYiLq84IKNz01ZswYnDhxojc/goj6ME4oJqKuXPCp4Gdz7NgxeDye3vwIIurDOKGYiLrSqz03RES9RQgBs8UREGy45xYRAb3cc0NE1Fu4OzERdYc9N0QUkziZmIi6w3BDRDGJk4mJqDscliKimMTJxETUnQvuuWlra+v2uZdffhlZWVkX+hFEREREPRb0Jn5PPfUU+vfvD6PRiIqKCgDAY489hldffdV/37x582Aw8F9TRBR6HROKdx5vxtbDDTBbHJEuiYiiRFDh5umnn8batWvx3HPPQaP5zzj3mDFj8Morr4SsOCKi7nBCMRF1J6hws27dOqxZswbz58+HUqn0Xx87diwOHjwYsuKIiLrDCcVE1J2gJhRXVVVh8ODBna7LsswdiYmo1wkhIIRAukEDAWBUThInFBORX1A9N6NGjcK2bds6Xd+wYQPGjRt3wUUREZ2N2eLAF0csqLa2odHhhiRJ3J2YiPyC6rl54oknsGDBAlRVVUGWZbz//vs4dOgQ1q1bh48++ijUNRIRBTh9vs3RBnv7fJuMSFdFRNEiqJ6b6667DuvXr8emTZsgSRIef/xxHDhwAB9++CGuuuqqUNdIRBSA822I6GwkIYSIdBHhZrPZYDKZYLVakZSUFOlyiOg8ybKMbUcsONnsRG5KAqYOSYdCwQ3XieJdT39/B/W3wYkTJ3Dy5En/zzt27MDixYuxZs2aYN6OiOi8HGt0osLigN3lQ4XFgWONzkiXRERRJKhwM2/ePGzevBkAUFtbixkzZmDHjh145JFHsGLFipAWSER0Ju5xQ0RnE1S42bt3LyZMmAAAeOeddzBmzBhs374db731FtauXRvK+oiIAgghYHd5UWdtwz+PNUKj5JwbIgoUVLjxeDzQarUAgM8++wzXX389AGD48OGoqakJXXVERGcwWxw42mCHSqWAxydQmGHgHjdEFCDofW5eeuklbNu2DaWlpbjmmmsAANXV1UhLSwtpgUREp2t2uOHxCUwYmIqsJB2MWhX3uCGiAEGFm2effRYvv/wypk+fjltvvRUXXXQRAGDjxo3+4Soiot7AZeBEdC5BbeI3ffp0WCwW2Gw2pKSk+K//5Cc/QUJCQsiKIyI608C0BJxIN/iXgQ9M4985RBQoqHADAEqlMiDYAMDAgQMvtB4iorPqWAbu9sqosDiQl5qAwgxjpMsioigS1LBUQUEBCgsLu32cr1WrVqGgoAA6nQ5FRUVdnlvVlX/84x9QqVS4+OKLz/sziSg2NdldqLW2QgiBWmsrmuyuSJdERFEmqJ6bxYsXB/zs8XhQXl6OTz75BA8++OB5vdf69euxePFirFq1CpdeeilefvllzJw5E/v378eAAQO6fZ3VasXChQtx5ZVXoq6uLpivQUQxyOH24XijE4fr7NCqFHC4fZEuiYiiTEiPX/jTn/6EnTt34vXXX+/xayZOnIjx48dj9erV/msjRozA7NmzsXLlym5fd8stt2DIkCFQKpX461//it27d/f4M3n8AlHs2mluxKcH6pGaoEaT04OSEZkoLuAqTaK+oFePX+jOzJkz8d577/X4frfbjbKyMpSUlARcLykpwfbt27t93euvv46jR4/iiSee6NHnuFwu2Gy2gAcRBUcIgYoGO8qONaGiwY5wH0+XatSin0kHSZLQz6RDqlEb1s8nougX9ITirrz77rtITU3t8f0WiwU+nw9ZWVkB17OyslBbW9vla44cOYJly5Zh27ZtUKl6Vv7KlSvx5JNP9rguIuqe2eLA1sMNcHtlaFTt/z4K54Tejg37mh1upBg03MCPiDoJKtyMGzcuYNMsIQRqa2vR0NCAVatWnff7nbkBlxCiy025fD4f5s2bhyeffBJDhw7t8fs//PDDWLp0qf9nm82GvLy8866TiP4zoTclQeOf0BvOcCNJUvvnZYTtI4koxgQVbmbPnh3ws0KhQEZGBqZPn47hw4f3+H3S09OhVCo79dLU19d36s0BgJaWFuzcuRPl5eW47777AACyLEMIAZVKhU8//RRXXHFFp9dptVr/cRFEdGE4oZeIol1Q4aanc12eeeYZLFq0CMnJyV0+r9FoUFRUhNLSUtxwww3+66WlpfjBD37Q6f6kpCTs2bMn4NqqVavw+eef491330VBQUHPvwQRnTchBE453dCqFMhL1UOSJBg0ykiXRUQUIKRzbs70m9/8BnPmzOk23ADA0qVLsWDBAhQXF2PSpElYs2YNKisrsWjRIgDtQ0pVVVVYt24dFAoFRo8eHfD6zMxM6HS6TteJKPTMFgeO1NvhcPtgtjgxPDuRE3qJKOr0arjpySqKuXPnorGxEStWrEBNTQ1Gjx6NTZs2IT8/HwBQU1ODysrK3iyTiHqoye6C2+tDYboBdbZWDM00ckIvEUWdkO5zc6bExER8++23Qe1a3Ju4zw1RcLYcqsOr28ywu70walS4a2oBpg/rPD+OiKg3RGSfGyKKX0IIHKltwSmnB2qFAl5ZwOr0RLosIqJOGG6IqEcqGuz457FmNDndsLW1hxpFF1s2EBFFGsMNEZ2TLMt4t+wkjtS1QJIAW6sHRq0KI3M4rEtE0Sfk4aa1tdX/v6dOnQq9Xh/qjyCiMBJCYMPOk/h0Xy2anG64PTIMGiUuKUgN6+Z9REQ9FVS4uffee7u87nA4MHPmTP/PmzZtQr9+/YKrjIiiQkWDHR/vqYat1Q29RgkBgZxkPaYPz+xyJ3EiokgLKtx8+umnePTRRwOuORwOXHPNNfD5uFspUbzoGI76rsEBrwBa3T4k6dS4dkwOe22IKGoFtc/Np59+iilTpiAtLQ1LlixBS0sLrr76aqhUKnz88cehrpGIIuSLIw34/EAdTjldUCqVMGpVuHpkNm4uzmWvDRFFraDCTUFBAf7+979j+vTpUCgUePvtt6HVavG3v/0NBgM39CKKB0IIbDlQj2anGwlaNdrcPuQkJ+DG4lwoFFyLQETRK+gdikePHo2PPvoIM2bMwMSJE/HRRx9x8jBRHKlosONIvR2tHhkqpQIJGiW+l5/C4Sgiino9Djfjxo3rshtaq9Wiuroal156qf/arl27QlMdEUWEEAKbD9bjlNMNk16NNrcXhRmJ+GFRfw5HEVHU63G4mT17di+WQUTRpKLBjh3mJjS3eqBTK2HS63Hl8EwMykyMdGlEROfU43DzxBNP9GYdRBQlOnptqk+1QqdSwuXxYVCGkUu/iShmBDUr8MSJEzh58qT/5x07dmDx4sVYs2ZNyAojosjo6LVpcroBCUhO0HDDPiKKKUGFm3nz5mHz5s0AgNraWsyYMQM7duzAI488ghUrVoS0QCIKr73VVtS3tEGpUMDh8nLDPiKKOUGFm71792LChAkAgHfeeQdjxozB9u3b8dZbb2Ht2rWhrI+IwkiWZXx9tBFVza2QhYBaKWFwhpG9NkQUU4IKNx6PB1qtFgDw2Wef4frrrwcADB8+HDU1NaGrjojCpuMMqW8qGtHmldHm9iFRp8aIfonstSGimBJUuBk1ahReeuklbNu2DaWlpbjmmmsAANXV1UhLSwtpgUTU+4QQ2Hq4Hm/vOI5mpwcJGiUUCiAzUYtR/U2RLo+I6LwEFW6effZZvPzyy5g+fTpuvfVWXHTRRQCAjRs3+oeriCg2dASbP33+HcyNTri8PjhcPEOKiGJXUDsUT58+HRaLBTabDSkpKf7rP/nJT5CQkBCy4oio91U02PHWN8fxXb0dLo8XapUKCRolz5AiopgV9PELSqUyINgAwMCBAy+0HqI+SQgBs8WBZocbKQYNCtINYQsVe6utMFuc8AkBSBKUCmBsfxPPkCKimBV0uHn33XfxzjvvoLKyEm63O+A5Hr9A1HNCCHxxuAFbDzVArZKQndR+Rlu4hoMabC64vT5oFBJ8soTcZD3mXTKAw1FEFLOC+mfZiy++iDvuuAOZmZkoLy/HhAkTkJaWhoqKCsycOTPUNRLFtYoGOzburkL5yWacbG5FjbUVzQ73uV8YIpmJWvQz6VGYmYiBaQb84OL+mDaU+9oQUewKqudm1apVWLNmDW699Vb8+c9/xi9/+UsUFhbi8ccfR1NTU6hrJIpbHUcdHK6zw+OTcai2BVqVAikGTdhqGNXfhOKBqbC2emDSq3E5N+wjohgXVLiprKzE5MmTAQB6vR4tLS0AgAULFuCSSy7BH//4x9BVSBTHOo46aHS4oFMroVcrMapfEgrSDWGroTDDiB+Ozw2Y70NEFMuCGpbKzs5GY2MjACA/Px9ff/01AMBsNkMIEbrqiOJcNBx1IEkSCjOMKBrYfn4Ue22IKNYFFW6uuOIKfPjhhwCAu+66C0uWLMFVV12FuXPn4oYbbghpgUTxrMHmgsPthV6tgFrFow6IiEIhqGGpNWvWQJZlAMCiRYuQlpaGbdu24brrrsPPfvazkBZIFK+EEBBCQKtSIkGjQppCw6MOiIhCIKhwo1Ao4Ha7sWvXLtTX10Or1WLGjBkAgE8++QTXXXddSIskikcVDXaYGx0AAKfbi4tyk3nUARFRCAQVbj755BMsWLDAP+/mdJIkwefzXXBhRPHMv0qq1o7kBA3aPD4UpBs4JEVEFAJBzbm57777MGfOHNTU1ECW5YAHgw3RuZktDhyobcGpVg9a2rzQqBTIStJxSIqIKASCCjf19fVYunQpsrKyQl0PUZ/QZHdBKQEZiRp4vD5kJ+kwMicp0mUREcWFoMLNTTfdhC1btoS4FKK+QQiBow0OHGt0wOH2IVGnxsSCNA5JERGFSFBzbv74xz/i5ptvxrZt2zBmzBio1eqA5x944IGQFEcUj4422PHZ/lrU29xITlAhP82AQRnhOyiTiCjeBRVu3nrrLfz973+HXq/Hli1bAv5SliSJ4YaoC0IIHK1vwbObDmLH8WYoFIDd5UVmkg6pRm2kyyMiihtBhZtHH30UK1aswLJly6BQBDWyRdSnyLKMd/55Am/uqMSRWhvcPkCvUUKrkpFj0vHIAyKiEAoq3LjdbsydO5fBhqgHZFnGqs1H8dY3x2Gxu+CTAaUC8PpkZCfpMX0YD6okIgqloNLJbbfdhvXr14e6FqK4I4TAOztP4K1vjqPB7oIMQACQFBLSDBrcMC4Xlw3NiHSZRERxJaieG5/Ph+eeew5///vfMXbs2E4Tip9//vmQFEcU68wWBz7ZU4OWNi8kCfD6AN2/z5CaPykfc4rz2ANKRBRiQYWbPXv2YNy4cQCAvXv3BjzH7nWi/2iyu+DyyVApAUhKaBQCkwrT8NCsERjEE7iJiHpFUOFm8+bNoa6DKO507GfT0uaBSqmASgGM7p+Mh64djsGZiZEuj4gobgUVbojo3MwWBw7W2pCgUWFwphoJahV+cHEOBjHYEBH1Kg72E/WSJrsLTrcPSTo1ZAEMSEvA6NxkDkUREfUy9twQ9YKOISmzxY42rwy9Sonh2Uncz4aIKAwYboh6QceQlCRJyDBqkW7U8ogFIqIw4bAUUS/oGJJK1Kpgd3mRoFHyiAUiojBhzw1RiHFIiogoshhuiEKMQ1JERJHFYSmiEOOQFBFRZLHnhijE7G4vqpqdaHS6oVYoMCw7kUNSRERhxHBDFGJWpwc+AWQl6uDyytCrlRySIiIKI4YbijtCCJgtDjQ73EgxaFCQHt75LgpJgkGrQrJeg1OtbigYbIiIworhhuJORYMdH5RXwdrqgUmvxg3j+of1yIOROUk4VNcCa6sH2aZEjMxJCttnExERww3FoX3VNhysbUGyXo0aaxv2VdvCGm4KM4z44fjcgJ4jIiIKH4YbiitCCNTZ2lBrbYOt1QOlQgKECGsNkiShMMMIZIT1Y4mI6N8YbiiuVDTY8e2JZljsbRACyE1OQJJeHemyiIgojBhuKG4IIbD5YD2O1NmhU6ugVAAmgxpGLf+YExH1Jfxbn+KCEAJbD9fj0321sDjaVyjp1Uqk6NXcQI+IqI9huKG4UNFgx1vfHMexRgdkISAEkJWkxXUX53BCLxFRH8NwQzFPlmW8W3YSe05a4fYJqBQSEnVqXDMqG9OGZnIDPSKiPobhhmLeF0ca8PmBOlhbPRCQoFArMCTTiOnDGWyIiPoiHpxJMU2WZfxveRXqWlzQqZVQQCAzUYd5lwxoX45NRER9DntuKKZtO2LB/mobXB5f+yRijQpXDMvkcBQRUR/GnhuKWUIIlFc2A5JA/5QEaNUKFKQb8MOi/gw2RER9GHtuKGZVNNhxtMEOh8sH+d8b9t1UlBfWoxaIiCj6MNxQTOrYsK+6uRVZSXrYXR5cOjgdNxfnsteGiKiP47AUxSSzxYEDtS2wtnnhlQXSjFqMzEmCQsE/0kREfR1/E1BMarK7oJSAjEQNPF4fspN0GJmTFOmyiIgoCkRFuFm1ahUKCgqg0+lQVFSEbdu2dXvv+++/j6uuugoZGRlISkrCpEmT8Pe//z2M1VI0cLh9sLZ6IAsgUafGxII0Lv0mIiIAURBu1q9fj8WLF2P58uUoLy/H1KlTMXPmTFRWVnZ5/xdffIGrrroKmzZtQllZGS6//HJcd911KC8vD3PlFEkJagWSEzQYkmnEwHQDCtMTONeGiIgAAJIQQkSygIkTJ2L8+PFYvXq1/9qIESMwe/ZsrFy5skfvMWrUKMydOxePP/54j+632WwwmUywWq1ISuJQRizaeqgef9lRCZdXhlalwK0TBmDasMxIl0VERL2op7+/I7payu12o6ysDMuWLQu4XlJSgu3bt/foPWRZRktLC1JTU7u9x+VyweVy+X+22WzBFUxRQQiBU043tCoF8lL1kCQJBo0y0mUREVGUiOiwlMVigc/nQ1ZWVsD1rKws1NbW9ug9fve738HhcGDOnDnd3rNy5UqYTCb/Iy8v74LqpsgyWxw4Um+Hw+2D2eKERqlAqlEb6bKIiChKRHzODYBOcyWEED2aP/GXv/wFv/rVr7B+/XpkZnY/JPHwww/DarX6HydOnLjgmilymuwuuL0+FKYbYNAoMDTTiIJ0Q6TLIiKiKBHRYan09HQolcpOvTT19fWdenPOtH79etx1113YsGEDZsyYcdZ7tVottFr+yz5eONw+VDa1+ufbmBI0nExMRER+Ee250Wg0KCoqQmlpacD10tJSTJ48udvX/eUvf8Htt9+Ot956C7NmzertMimKnD7fZnCmAQPSEjjfhoiIAkT8+IWlS5diwYIFKC4uxqRJk7BmzRpUVlZi0aJFANqHlKqqqrBu3ToA7cFm4cKF+P3vf49LLrnE3+uj1+thMpki9j0oPM6cbzM8O5HzbYiIKEDEw83cuXPR2NiIFStWoKamBqNHj8amTZuQn58PAKipqQnY8+bll1+G1+vFvffei3vvvdd//bbbbsPatWvDXT6F2enzbepsrZxvQ0REnUR8n5tI4D43sYv72xAR9V0xsc8N0fng/jZERNQTUbEUnKgnuL8NERH1BMMNxQzub0NERD3BYSmKGdzfhoiIeoLhhmKGQaPEgDQDUhPUaHJ6ON+GiIi6xGEpiglCCDjcPni9MpqdbvRL0nG+DRERdYnhhmKC2eLA0QY7VCoFPD6BwgwD59sQEVGXGG4oJjTZXaiztSE1QQ21sn0JOOfbEBFRVzjnhmKCw+3D8UYnDtfZoVUp4HD7Il0SERFFKYYbigkJagVM+vZeG49PIEHNTkciIuoaww3FBKdHhrXV418G7vTIkS6JiIiiFMMNRT0eu0BEROeDffsU9XjsAhERnQ+GG4p6zQ43jFoVpg/LRD+TDsOyErkMnIiIusVhKYp6yQlq2F1eVJ1qhUmvxsicJC4DJyKibrHnhoiIiOIKe26oR4QQMFscaHa4kWLQoCDdELbek1NODxJ1alycl4KjDXaccnrC8rlERBSbGG6oR8wWB7YeboDbK0Ojau/wK8wwhuWzUwwaaFQKHG2wQ6NSIMWgCcvnEhFRbGK4oXMSQmBvlRUVDXYMy06E3eVFs8MNZITn8zsmD5/ea0RERNQdhhs6J7PFgcN1LaixtqHG2obh2Ylh7T2RJKm9lyhMYYqIiGIbJxTTOTXZXXB7fShMN8CgUWBoppG9J0REFLXYc0Pn5HD7UNnU6j/6wJSg4VJsIiKKWgw3dE4GjRID0gxITVCjyenh0QdERBTVOCxF55Ri0ECjlFB1qhUapcTVSkREFNUYboiIiCiucFiKzkoIgX3VNlhbPf5l4NxEj4iIohnDDZ1VpJeBExERnS8OS9FZcRk4ERHFGvbc0FlxGTgREcUahhvqlhACp5xuaFUK5KXqIUkSl4ETEVHU47AUdctsceBIvR0Otw9mixMapQKpRm2kyyIiIjorhhvqVrPDDaNWhenDMtHPpMOwrETOtyEioqjHYSnqVnKCGnaXF1WnWmHSqzEyJ4nzbYiIKOqx54aIiIjiCntuqEvcvI+IiGIVww11iZv3ERFRrOKwFHWJm/cREVGsYs8NdYmb9xERUaxiuKEuGTRKDEgzIDVBjSanh5v3ERFRzOCwFHUpxaCBRimh6lQrNEqJ822IiChmMNwQERFRXOGwFHXCZeBERBTLGG6oEy4DJyKiWMZhKeqEy8CJiCiWseeGOuEycCIiimUMNxRACIFTTje0KgXyUvWQJInLwImIKKZwWIoCVDTYseNYE6pOtWJftQ1qhQKpRm2kyyIiIuoxhhsKsK/ahlprG9IMGri9MlIS1JxvQ0REMYXhhvyEEKi3tcHa6gEgITlBjawkHefbEBFRTGG4IT+zxYFmpxtqpQKNDheyknQYmZMU6bKIiIjOCycUk1+T3QWPT8aY/ibU2VoxcWAqCjOMkS6LiIjovDDcEID2IamjDQ58e9IKAEjRa7gEnIiIYhKHpQhA+yqpbyossLV5oJCAJL2aS8CJiCgmMdwQAGBvtRXmRifcXoHqU23wyjKXgBMRUUzisBRBlmV8fbQRJ5uc0KiVkCCQlajjEnAiIopJDDd9mBACR+tb8No2Mz47UAunW4YsgMwkLUb0S+R8GyIiikkMN32QLMvYcrAOb+2oxN4qK045PfDKgE6thAyBzEQtRvU3RbpMIiKioDDchElHL8nmA3XYU22F0+1D/2Q9Lh+RhcuGZEChCN/0p21HLPhd6REcqWuBW26/ppIAnxBI16px7ZgcLgEnIqKYxXDTyzpCzbs7T+DjfXVoaHHB7ZUBCUhQK7G3ugUSJEwblhm2mk42O2F1uqFUSlDKAr5/X0/Rq3FT8QDcXJzLISkiIopZDDe9SJZlvPPPE3hzRyWO1rXA6RX+55QAVEoFWtq8ONnsDGtduSkJMCVoYLG7IABolEB+agLumlqIOcV5Ye1FIiIiCjWGm14iyzJWbT6Kt745DovdBY8c+LwQgNcnI1GnQm5KQlhrmzokHT7fEGz8thpNdjdG5ppw0/hcDMrkJGIiIop9DDe95IsjDfig/CSaHO29Ix2UAIxaBQoyjLgo14TLR2Rh6pD0sNamUChwxch+uGJkv7B+LhERUTgw3PQCWZbxv+VVaLC7IUkSvD4BjQLoZ9Lj4nwTZo/LDfskYiIior6C4SbEhBDYsPMkdh5vRpvbC4UEJKgVmFyYhodmjcCgDCOHfoiIiHoRw02IVTTY8fGearS6PEjSq+HxyRialYiHrh2OwZmJkS6PiIgo7nFcJMT2VdtwqtULtUoJr08g3ajFTUV5GMRgQ0REFBbsuQkhIQTqbW1QSAI5pgTY3W5cOSyb+8YQERGFUVT03KxatQoFBQXQ6XQoKirCtm3bznr/1q1bUVRUBJ1Oh8LCQrz00kthqvTsjjbYsbvyFE45vbC1uVGUn4Ybi3M5cZiIiCiMIv5bd/369Vi8eDGWL1+O8vJyTJ06FTNnzkRlZWWX95vNZlx77bWYOnUqysvL8cgjj+CBBx7Ae++9F+bKAwkh8F7ZSew+2QwZAm0eGSadmscYEBERhVnEw83zzz+Pu+66C3fffTdGjBiBF154AXl5eVi9enWX97/00ksYMGAAXnjhBYwYMQJ333037rzzTvz2t78Nc+WBzBYH9tfY0OqRAUiQFBJ0GgWHo4iIiMIsouHG7XajrKwMJSUlAddLSkqwffv2Ll/z1Vdfdbr/6quvxs6dO+HxeLp8jcvlgs1mC3iEWrPDjcxELTKTdHB7fUhOUOOi3OSQfw4RERGdXUTDjcVigc/nQ1ZWVsD1rKws1NbWdvma2traLu/3er2wWCxdvmblypUwmUz+R15eXmi+wGlSDBoMz07EmP4mjBuQgvkT8nHZ0IyQfw4RERGdXVSsljpz6EYIcdbhnK7u7+p6h4cffhhLly71/2yz2UIecArSDcDwLFycl4IUgwYF6QYOSREREUVARMNNeno6lEplp16a+vr6Tr0zHbKzs7u8X6VSIS0trcvXaLVaaLXa0BTdDUmS2icPs7OGiIgooiI6LKXRaFBUVITS0tKA66WlpZg8eXKXr5k0aVKn+z/99FMUFxdDrVb3Wq1EREQUGyK+Wmrp0qV45ZVX8Nprr+HAgQNYsmQJKisrsWjRIgDtQ0oLFy70379o0SIcP34cS5cuxYEDB/Daa6/h1VdfxS9+8YtIfQUiIiKKIhGfczN37lw0NjZixYoVqKmpwejRo7Fp0ybk5+cDAGpqagL2vCkoKMCmTZuwZMkS/OlPf0JOTg5efPFF3HjjjZH6CkRERBRFJNExG7cPsdlsMJlMsFqtSEpKinQ5RERE1AM9/f0d8WEpIiIiolBiuCEiIqK4wnBDREREcYXhhoiIiOIKww0RERHFFYYbIiIiiisMN0RERBRXGG6IiIgorkR8h+JI6Ni30GazRbgSIiIi6qmO39vn2n+4T4ablpYWAEBeXl6EKyEiIqLz1dLSApPJ1O3zffL4BVmWUV1djcTEREiSdMHvZ7PZkJeXhxMnTvA4h17A9u1dbN/exfbtXWzf3hVt7SuEQEtLC3JycqBQdD+zpk/23CgUCuTm5ob8fZOSkqLi//x4xfbtXWzf3sX27V1s394VTe17th6bDpxQTERERHGF4YaIiIjiCsNNCGi1WjzxxBPQarWRLiUusX17F9u3d7F9exfbt3fFavv2yQnFREREFL/Yc0NERERxheGGiIiI4grDDREREcUVhhsiIiKKKww33Vi5ciW+973vITExEZmZmZg9ezYOHToUcI8QAr/61a+Qk5MDvV6P6dOnY9++fQH3uFwu3H///UhPT4fBYMD111+PkydPhvOrxISVK1dCkiQsXrzYf43te2Gqqqrwox/9CGlpaUhISMDFF1+MsrIy//Ns3+B5vV48+uijKCgogF6vR2FhIVasWAFZlv33sH177osvvsB1112HnJwcSJKEv/71rwHPh6otm5ubsWDBAphMJphMJixYsACnTp3q5W8XeWdrX4/Hg4ceeghjxoyBwWBATk4OFi5ciOrq6oD3iLn2FdSlq6++Wrz++uti7969Yvfu3WLWrFliwIABwm63++955plnRGJionjvvffEnj17xNy5c0W/fv2EzWbz37No0SLRv39/UVpaKnbt2iUuv/xycdFFFwmv1xuJrxWVduzYIQYOHCjGjh0rfv7zn/uvs32D19TUJPLz88Xtt98uvvnmG2E2m8Vnn30mvvvuO/89bN/gPf300yItLU189NFHwmw2iw0bNgij0SheeOEF/z1s357btGmTWL58uXjvvfcEAPHBBx8EPB+qtrzmmmvE6NGjxfbt28X27dvF6NGjxfe///1wfc2IOVv7njp1SsyYMUOsX79eHDx4UHz11Vdi4sSJoqioKOA9Yq19GW56qL6+XgAQW7duFUIIIcuyyM7OFs8884z/nra2NmEymcRLL70khGj/Q6NWq8Xbb7/tv6eqqkooFArxySefhPcLRKmWlhYxZMgQUVpaKqZNm+YPN2zfC/PQQw+JKVOmdPs82/fCzJo1S9x5550B1374wx+KH/3oR0IItu+FOPOXb6jacv/+/QKA+Prrr/33fPXVVwKAOHjwYC9/q+jRVXg8044dOwQAcfz4cSFEbLYvh6V6yGq1AgBSU1MBAGazGbW1tSgpKfHfo9VqMW3aNGzfvh0AUFZWBo/HE3BPTk4ORo8e7b+nr7v33nsxa9YszJgxI+A62/fCbNy4EcXFxbj55puRmZmJcePG4b//+7/9z7N9L8yUKVPwf//3fzh8+DAA4Ntvv8WXX36Ja6+9FgDbN5RC1ZZfffUVTCYTJk6c6L/nkksugclkYnufwWq1QpIkJCcnA4jN9u2TB2eeLyEEli5diilTpmD06NEAgNraWgBAVlZWwL1ZWVk4fvy4/x6NRoOUlJRO93S8vi97++23sWvXLvzzn//s9Bzb98JUVFRg9erVWLp0KR555BHs2LEDDzzwALRaLRYuXMj2vUAPPfQQrFYrhg8fDqVSCZ/Ph1//+te49dZbAfDPbyiFqi1ra2uRmZnZ6f0zMzPZ3qdpa2vDsmXLMG/ePP9BmbHYvgw3PXDffffhX//6F7788stOz0mSFPCzEKLTtTP15J54d+LECfz85z/Hp59+Cp1O1+19bN/gyLKM4uJi/OY3vwEAjBs3Dvv27cPq1auxcOFC/31s3+CsX78eb7zxBt566y2MGjUKu3fvxuLFi5GTk4PbbrvNfx/bN3RC0ZZd3c/2/g+Px4NbbrkFsixj1apV57w/mtuXw1LncP/992Pjxo3YvHkzcnNz/dezs7MBoFMira+v9/8LIzs7G263G83Nzd3e01eVlZWhvr4eRUVFUKlUUKlU2Lp1K1588UWoVCp/+7B9g9OvXz+MHDky4NqIESNQWVkJgH9+L9SDDz6IZcuW4ZZbbsGYMWOwYMECLFmyBCtXrgTA9g2lULVldnY26urqOr1/Q0MD2xvtwWbOnDkwm80oLS3199oAsdm+DDfdEELgvvvuw/vvv4/PP/8cBQUFAc8XFBQgOzsbpaWl/mtutxtbt27F5MmTAQBFRUVQq9UB99TU1GDv3r3+e/qqK6+8Env27MHu3bv9j+LiYsyfPx+7d+9GYWEh2/cCXHrppZ22Ljh8+DDy8/MB8M/vhXI6nVAoAv/6VCqV/qXgbN/QCVVbTpo0CVarFTt27PDf880338Bqtfb59u4INkeOHMFnn32GtLS0gOdjsn3DPoU5RvzsZz8TJpNJbNmyRdTU1PgfTqfTf88zzzwjTCaTeP/998WePXvErbfe2uXyxNzcXPHZZ5+JXbt2iSuuuKJPLvXsidNXSwnB9r0QO3bsECqVSvz6178WR44cEW+++aZISEgQb7zxhv8etm/wbrvtNtG/f3//UvD3339fpKeni1/+8pf+e9i+PdfS0iLKy8tFeXm5ACCef/55UV5e7l+tE6q2vOaaa8TYsWPFV199Jb766isxZsyYPrEU/Gzt6/F4xPXXXy9yc3PF7t27A37fuVwu/3vEWvsy3HQDQJeP119/3X+PLMviiSeeENnZ2UKr1YrLLrtM7NmzJ+B9WltbxX333SdSU1OFXq8X3//+90VlZWWYv01sODPcsH0vzIcffihGjx4ttFqtGD58uFizZk3A82zf4NlsNvHzn/9cDBgwQOh0OlFYWCiWL18e8MuA7dtzmzdv7vLv29tuu00IEbq2bGxsFPPnzxeJiYkiMTFRzJ8/XzQ3N4fpW0bO2drXbDZ3+/tu8+bN/veItfaVhBAifP1ERERERL2Lc26IiIgorjDcEBERUVxhuCEiIqK4wnBDREREcYXhhoiIiOIKww0RERHFFYYbIiIiiisMN0RERH1EU1MT7r//fgwbNgwJCQkYMGAAHnjgAVit1nO+dtWqVSgoKIBOp0NRURG2bdvmf87j8eChhx7CmDFjYDAYkJOTg4ULF6K6uvq86rv99tshSVKnx6hRo87rfRhuiCiqTZ8+HYsXL450GUQxZfr06Vi7dm2n69XV1aiursZvf/tb7NmzB2vXrsUnn3yCu+6666zvt379eixevBjLly9HeXk5pk6dipkzZ/oP43U6ndi1axcee+wx7Nq1C++//z4OHz6M66+//rzq/v3vf4+amhr/48SJE0hNTcXNN998Xu/D4xeIKKqdeSzH2XRsM98XttQnOptp06YFHBd0Nu+8847QaDTC4/F0e8+ECRPEokWLAq4NHz5cLFu2rNvX7NixQwDwnxEmhBCnTp0SP/7xj0VGRoZITEwUl19+udi9e3e37/HBBx8ISZLEsWPHevRdOrDnhoiIqA+zWq1ISkqCSqXq8nm3242ysjKUlJQEXC8pKcH27dvP+r6SJCE5ORkAIITArFmzUFtbi02bNqGsrAzjx4/HlVdeiaampi7f49VXX8WMGTOQn59/Xt+J4YaIoobD4cDChQthNBrRr18//O53vwt4/o033kBxcTESExORnZ2NefPmob6+HgBw7NgxXH755QCAlJQUSJKE22+/HUD7X6rPPfccCgsLodfrcdFFF+Hdd9/1v29zczPmz5+PjIwM6PV6DBkyBK+//np4vjRRBDU2NuKpp57CT3/6027vsVgs8Pl8yMrKCrielZWF2traLl/T1taGZcuWYd68eUhKSgIAbN68GXv27MGGDRtQXFyMIUOG4Le//S2Sk5MD/nvsUFNTg48//hh33333eX8vhhsiihoPPvggNm/ejA8++ACffvoptmzZgrKyMv/zbrcbTz31FL799lv89a9/hdls9geYvLw8vPfeewCAQ4cOoaamBr///e8BAI8++ihef/11rF69Gvv27cOSJUvwox/9CFu3bgUAPPbYY9i/fz8+/vhjHDhwAKtXr0Z6enp4vzzRBfjNb34Do9Hof2zbtg2LFi3qdO10NpsNs2bNwsiRI/HEE0+c8zMkSQr4WQjR6RrQPrn4lltugSzLWLVqlf96WVkZ7HY70tLSAuoym804evRop/dZu3YtkpOTMXv27B62wn903QdFRBRmdrsdr776KtatW4errroKAPDnP/8Zubm5/nvuvPNO//8uLCzEiy++iAkTJsBut8NoNCI1NRUAkJmZ6e8KdzgceP755/H5559j0qRJ/td++eWXePnllzFt2jRUVlZi3LhxKC4uBgAMHDgwDN+YKHQWLVqEOXPm+H+eP38+brzxRvzwhz/0X+vfv7//f7e0tOCaa66B0WjEBx98ALVa3e17p6enQ6lUduqlqa+v79Sb4/F4MGfOHJjNZnz++ef+XhsAkGUZ/fr1w5YtWzp9Rsd/rx2EEHjttdewYMECaDSas373rjDcEFFUOHr0KNxutz+AAEBqaiqGDRvm/7m8vBy/+tWvsHv3bjQ1NUGWZQBAZWUlRo4c2eX77t+/H21tbf7A1MHtdmPcuHEAgJ/97Ge48cYbsWvXLpSUlGD27NmYPHlyqL8iUa9JTU31h3sA0Ov1yMzMxODBgzvda7PZcPXVV0Or1WLjxo3Q6XRnfW+NRoOioiKUlpbihhtu8F8vLS3FD37wA//PHcHmyJEj2Lx5M9LS0gLeZ/z48aitrYVKpTrnPyC2bt2K77777pyruLrDcENEUUEIcdbnHQ4HSkpKUFJSgjfeeAMZGRmorKzE1VdfDbfb3e3rOgLQ3/72t4B/uQKAVqsFAMycORPHjx/H3/72N3z22We48sorce+99+K3v/3tBX4roujS0tKCkpISOJ1OvPHGG7DZbLDZbACAjIwMKJVKAMCVV16JG264Affddx8AYOnSpViwYAGKi4sxadIkrFmzBpWVlVi0aBEAwOv14qabbsKuXbvw0Ucfwefz+Xt6UlNTodFoMGPGDEyaNAmzZ8/Gs88+i2HDhqG6uhqbNm3C7Nmz/T2nQPtE4okTJ2L06NHBfdHzWltFRNRLWlpahFqtFuvXr/dfa2pqEgkJCeLnP/+52LlzpwAgKisr/c//z//8jwAgysvLhRBC/OMf/xAAhMVi8d9js9mEVqsV69at63EtL730kkhMTLzwL0UUId0tBe/YLqGrh9ls9t+Xn58vnnjiiYDX/ulPfxL5+flCo9GI8ePHi61bt/qfM5vN3b7v5s2b/ffZbDZx//33i5ycHKFWq0VeXp6YP39+wH/Xp06dEnq9XqxZsybo78+eGyKKCkajEXfddRcefPBBpKWlISsrC8uXL4dC0b7uYcCAAdBoNPjDH/6ARYsWYe/evXjqqacC3iM/Px+SJOGjjz7CtddeC71ej8TERPziF7/AkiVLIMsypkyZApvNhu3bt8NoNOK2227D448/jqKiIowaNQoulwsfffQRRowYEYlmIAqJrua1AO2b+4lz9JIC7asPz3TPPffgnnvu6fL+gQMH9uh9ExMT8eKLL+LFF1/s9h6TyQSn03nO9zobrpYioqjxX//1X7jssstw/fXXY8aMGZgyZQqKiooAtHeZr127Fhs2bMDIkSPxzDPPdBo26t+/P5588kksW7YMWVlZ/i71p556Co8//jhWrlyJESNG4Oqrr8aHH36IgoICAO1zCh5++GGMHTsWl112GZRKJd5+++3wfnkiChlJ9CRqEREREcUI9twQERFRXGG4ISIiorjCcENERERxheGGiIiI4grDDREREcUVhhsiIiKKKww3REREFFcYboiIiCiuMNwQERFRXGG4ISIiorjCcENERERx5f8Hrale719VCa8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.scatter(mask_date[indexes_in_mask_dataset], mask,s=5, alpha=0.3)\n",
    "\n",
    "plt.xlabel('dates')\n",
    "plt.ylabel('mask_use_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'OxGRT')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(OxGRT_date[indexes_in_OxGRT_dataset], OxGRT, s=5, alpha=0.3)\n",
    "\n",
    "plt.xlabel('dates')\n",
    "plt.ylabel('OxGRT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36212293193720274"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_mask_OxGRT = np.corrcoef(mask, OxGRT)[0,1]\n",
    "r_mask_OxGRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression to compute r_c\n",
    "OxGRT = OxGRT.reshape(-1,1)\n",
    "modelc = LinearRegression().fit(OxGRT, mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.13113301783479658\n"
     ]
    }
   ],
   "source": [
    "print(f\"coefficient of determination: {modelc.score(OxGRT, mask)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.00642185]), 0.13977373201910803)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelc.coef_, modelc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc = modelc.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mobility v.s Hopitality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>oh_region</th>\n",
       "      <th>icu_current_covid</th>\n",
       "      <th>icu_current_covid_vented</th>\n",
       "      <th>hospitalizations</th>\n",
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       "      <th>0</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>51</td>\n",
       "      <td>39</td>\n",
       "      <td>113</td>\n",
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       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>141</td>\n",
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       "      <th>2</th>\n",
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       "      <td>65</td>\n",
       "      <td>57</td>\n",
       "      <td>143</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>73</td>\n",
       "      <td>58</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
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       "      <th>2850</th>\n",
       "      <td>2021-10-21</td>\n",
       "      <td>WEST</td>\n",
       "      <td>52</td>\n",
       "      <td>35</td>\n",
       "      <td>108</td>\n",
       "      <td>63</td>\n",
       "      <td>43</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2851</th>\n",
       "      <td>2021-10-22</td>\n",
       "      <td>WEST</td>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>102</td>\n",
       "      <td>59</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>2021-10-23</td>\n",
       "      <td>WEST</td>\n",
       "      <td>38</td>\n",
       "      <td>28</td>\n",
       "      <td>104</td>\n",
       "      <td>49</td>\n",
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       "      <td>2021-10-24</td>\n",
       "      <td>WEST</td>\n",
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       "      <td>27</td>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
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       "      <td>5</td>\n",
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       "      <td>2021-10-25</td>\n",
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       "      <td>41</td>\n",
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       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>33</td>\n",
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      ],
      "text/plain": [
       "            date oh_region  icu_current_covid  icu_current_covid_vented  \\\n",
       "0     2020-04-02   CENTRAL                 51                        39   \n",
       "1     2020-04-03   CENTRAL                 57                        52   \n",
       "2     2020-04-04   CENTRAL                 65                        57   \n",
       "3     2020-04-05   CENTRAL                 65                        55   \n",
       "4     2020-04-06   CENTRAL                 73                        58   \n",
       "...          ...       ...                ...                       ...   \n",
       "2850  2021-10-21      WEST                 52                        35   \n",
       "2851  2021-10-22      WEST                 48                        27   \n",
       "2852  2021-10-23      WEST                 38                        28   \n",
       "2853  2021-10-24      WEST                 38                        27   \n",
       "2854  2021-10-25      WEST                 41                        26   \n",
       "\n",
       "      hospitalizations  icu_crci_total  icu_crci_total_vented  \\\n",
       "0                  113               0                      0   \n",
       "1                  141               0                      0   \n",
       "2                  143               0                      0   \n",
       "3                  174               0                      0   \n",
       "4                  187               0                      0   \n",
       "...                ...             ...                    ...   \n",
       "2850               108              63                     43   \n",
       "2851               102              59                     35   \n",
       "2852               104              49                     35   \n",
       "2853                70              49                     32   \n",
       "2854                72              52                     33   \n",
       "\n",
       "      icu_former_covid  icu_former_covid_vented  \n",
       "0                    0                        0  \n",
       "1                    0                        0  \n",
       "2                    0                        0  \n",
       "3                    0                        0  \n",
       "4                    0                        0  \n",
       "...                ...                      ...  \n",
       "2850                11                        8  \n",
       "2851                11                        8  \n",
       "2852                11                        7  \n",
       "2853                11                        5  \n",
       "2854                11                        7  \n",
       "\n",
       "[2855 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "hospitality_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert dates type to yyyymmdd as INT\n",
    "mobility_dataset['date'] = mobility_dataset['date'].apply(convert_date).astype(int)\n",
    "hospitality_dataset['date'] = hospitality_dataset['date'].apply(convert_date).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>20211022</td>\n",
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       "      <td>41</td>\n",
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      "text/plain": [
       "         date oh_region  icu_current_covid  icu_current_covid_vented  \\\n",
       "0    20200402   CENTRAL                 51                        39   \n",
       "1    20200403   CENTRAL                 57                        52   \n",
       "2    20200404   CENTRAL                 65                        57   \n",
       "3    20200405   CENTRAL                 65                        55   \n",
       "4    20200406   CENTRAL                 73                        58   \n",
       "..        ...       ...                ...                       ...   \n",
       "566  20211021   CENTRAL                 35                        21   \n",
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       "568  20211023   CENTRAL                 26                        20   \n",
       "569  20211024   CENTRAL                 29                        22   \n",
       "570  20211025   CENTRAL                 29                        21   \n",
       "\n",
       "     hospitalizations  icu_crci_total  icu_crci_total_vented  \\\n",
       "0                 113               0                      0   \n",
       "1                 141               0                      0   \n",
       "2                 143               0                      0   \n",
       "3                 174               0                      0   \n",
       "4                 187               0                      0   \n",
       "..                ...             ...                    ...   \n",
       "566                87              46                     30   \n",
       "567                88              41                     30   \n",
       "568                89              39                     31   \n",
       "569                58              42                     33   \n",
       "570                56              41                     32   \n",
       "\n",
       "     icu_former_covid  icu_former_covid_vented  \n",
       "0                   0                        0  \n",
       "1                   0                        0  \n",
       "2                   0                        0  \n",
       "3                   0                        0  \n",
       "4                   0                        0  \n",
       "..                ...                      ...  \n",
       "566                11                        9  \n",
       "567                13                       10  \n",
       "568                13                       11  \n",
       "569                13                       11  \n",
       "570                12                       11  \n",
       "\n",
       "[571 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospitality_dataset[hospitality_dataset['oh_region'] == 'CENTRAL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Here the hospitality_dataset is on regions in ON, want to sum over regions to construct dataset over the whole ON. \n",
    "'''\n",
    "regions = hospitality_dataset['oh_region'].unique()\n",
    "centrals = hospitality_dataset[hospitality_dataset['oh_region'] == 'CENTRAL']\n",
    "easts = hospitality_dataset[hospitality_dataset['oh_region'] == 'EAST']\n",
    "norths = hospitality_dataset[hospitality_dataset['oh_region'] == 'NORTH']\n",
    "torontos = hospitality_dataset[hospitality_dataset['oh_region'] == 'TORONTO']\n",
    "wests = hospitality_dataset[hospitality_dataset['oh_region'] == 'WEST']\n",
    "check_same_dates = np.array_equal(np.array(centrals['date']), np.array(easts['date'])) and np.array_equal(np.array(centrals['date']), np.array(norths['date'])) and np.array_equal(np.array(centrals['date']), np.array(norths['date'])) and np.array_equal(np.array(centrals['date']), np.array(torontos['date'])) and np.array_equal(np.array(centrals['date']), np.array(wests['date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CENTRAL', 'EAST', 'NORTH', 'TORONTO', 'WEST'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>55</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200406</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>73</td>\n",
       "      <td>58</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>20211021</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>87</td>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20211022</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>88</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20211023</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>89</td>\n",
       "      <td>39</td>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>20211024</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>20211025</td>\n",
       "      <td>CENTRAL</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>56</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>571 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date oh_region  icu_current_covid  icu_current_covid_vented  \\\n",
       "0    20200402   CENTRAL                 51                        39   \n",
       "1    20200403   CENTRAL                 57                        52   \n",
       "2    20200404   CENTRAL                 65                        57   \n",
       "3    20200405   CENTRAL                 65                        55   \n",
       "4    20200406   CENTRAL                 73                        58   \n",
       "..        ...       ...                ...                       ...   \n",
       "566  20211021   CENTRAL                 35                        21   \n",
       "567  20211022   CENTRAL                 28                        20   \n",
       "568  20211023   CENTRAL                 26                        20   \n",
       "569  20211024   CENTRAL                 29                        22   \n",
       "570  20211025   CENTRAL                 29                        21   \n",
       "\n",
       "     hospitalizations  icu_crci_total  icu_crci_total_vented  \\\n",
       "0                 113               0                      0   \n",
       "1                 141               0                      0   \n",
       "2                 143               0                      0   \n",
       "3                 174               0                      0   \n",
       "4                 187               0                      0   \n",
       "..                ...             ...                    ...   \n",
       "566                87              46                     30   \n",
       "567                88              41                     30   \n",
       "568                89              39                     31   \n",
       "569                58              42                     33   \n",
       "570                56              41                     32   \n",
       "\n",
       "     icu_former_covid  icu_former_covid_vented  \n",
       "0                   0                        0  \n",
       "1                   0                        0  \n",
       "2                   0                        0  \n",
       "3                   0                        0  \n",
       "4                   0                        0  \n",
       "..                ...                      ...  \n",
       "566                11                        9  \n",
       "567                13                       10  \n",
       "568                13                       11  \n",
       "569                13                       11  \n",
       "570                12                       11  \n",
       "\n",
       "[571 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert check_same_dates == True, print('dates in regions are not the same')\n",
    "centrals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['icu_current_covid',\n",
       " 'icu_current_covid_vented',\n",
       " 'hospitalizations',\n",
       " 'icu_crci_total',\n",
       " 'icu_crci_total_vented',\n",
       " 'icu_former_covid',\n",
       " 'icu_former_covid_vented']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = centrals.columns.tolist()[2:]\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset['oh_region'] = 'ALL'\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n",
      "/var/folders/7_/yd2c8wld3sggx9gtmv9ydwm80000gn/T/ipykernel_31692/3694095269.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>oh_region</th>\n",
       "      <th>icu_current_covid</th>\n",
       "      <th>icu_current_covid_vented</th>\n",
       "      <th>hospitalizations</th>\n",
       "      <th>icu_crci_total</th>\n",
       "      <th>icu_crci_total_vented</th>\n",
       "      <th>icu_former_covid</th>\n",
       "      <th>icu_former_covid_vented</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200402</td>\n",
       "      <td>ALL</td>\n",
       "      <td>167</td>\n",
       "      <td>112</td>\n",
       "      <td>407</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200403</td>\n",
       "      <td>ALL</td>\n",
       "      <td>194</td>\n",
       "      <td>140</td>\n",
       "      <td>446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200404</td>\n",
       "      <td>ALL</td>\n",
       "      <td>190</td>\n",
       "      <td>149</td>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200405</td>\n",
       "      <td>ALL</td>\n",
       "      <td>200</td>\n",
       "      <td>154</td>\n",
       "      <td>508</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200406</td>\n",
       "      <td>ALL</td>\n",
       "      <td>216</td>\n",
       "      <td>160</td>\n",
       "      <td>576</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>20211021</td>\n",
       "      <td>ALL</td>\n",
       "      <td>135</td>\n",
       "      <td>86</td>\n",
       "      <td>274</td>\n",
       "      <td>161</td>\n",
       "      <td>107</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20211022</td>\n",
       "      <td>ALL</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>261</td>\n",
       "      <td>149</td>\n",
       "      <td>94</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>20211023</td>\n",
       "      <td>ALL</td>\n",
       "      <td>107</td>\n",
       "      <td>73</td>\n",
       "      <td>269</td>\n",
       "      <td>136</td>\n",
       "      <td>96</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>20211024</td>\n",
       "      <td>ALL</td>\n",
       "      <td>107</td>\n",
       "      <td>76</td>\n",
       "      <td>164</td>\n",
       "      <td>137</td>\n",
       "      <td>97</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>20211025</td>\n",
       "      <td>ALL</td>\n",
       "      <td>109</td>\n",
       "      <td>71</td>\n",
       "      <td>163</td>\n",
       "      <td>138</td>\n",
       "      <td>94</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>571 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date oh_region  icu_current_covid  icu_current_covid_vented  \\\n",
       "0    20200402       ALL                167                       112   \n",
       "1    20200403       ALL                194                       140   \n",
       "2    20200404       ALL                190                       149   \n",
       "3    20200405       ALL                200                       154   \n",
       "4    20200406       ALL                216                       160   \n",
       "..        ...       ...                ...                       ...   \n",
       "566  20211021       ALL                135                        86   \n",
       "567  20211022       ALL                121                        72   \n",
       "568  20211023       ALL                107                        73   \n",
       "569  20211024       ALL                107                        76   \n",
       "570  20211025       ALL                109                        71   \n",
       "\n",
       "     hospitalizations  icu_crci_total  icu_crci_total_vented  \\\n",
       "0                 407               0                      0   \n",
       "1                 446               0                      0   \n",
       "2                 475               0                      0   \n",
       "3                 508               0                      0   \n",
       "4                 576               0                      0   \n",
       "..                ...             ...                    ...   \n",
       "566               274             161                    107   \n",
       "567               261             149                     94   \n",
       "568               269             136                     96   \n",
       "569               164             137                     97   \n",
       "570               163             138                     94   \n",
       "\n",
       "     icu_former_covid  icu_former_covid_vented  \n",
       "0                   0                        0  \n",
       "1                   0                        0  \n",
       "2                   0                        0  \n",
       "3                   0                        0  \n",
       "4                   0                        0  \n",
       "..                ...                      ...  \n",
       "566                26                       21  \n",
       "567                28                       22  \n",
       "568                29                       23  \n",
       "569                30                       21  \n",
       "570                29                       23  \n",
       "\n",
       "[571 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct total_hospitality_dataset, summed over all regions\n",
    "total_hospitality_dataset = pd.DataFrame(columns=[])\n",
    "\n",
    "total_hospitality_dataset = centrals[['date']]\n",
    "total_hospitality_dataset['oh_region'] = 'ALL'\n",
    "#total_hospitality_dataset['icu_current_covid'] = np.array(centrals['icu_current_covid']) + np.array(easts['icu_current_covid'].tolist())\n",
    "for name in col_names: \n",
    "    total_hospitality_dataset[name] = np.array(centrals[name]) + np.array(easts[name]) + np.array(norths[name]) + np.array(torontos[name]) + np.array(wests[name]) \n",
    "\n",
    "total_hospitality_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_hospitality_dates = np.array(total_hospitality_dataset['date'])\n",
    "mobility_dates = np.array(mobility_dataset['date'])\n",
    "\n",
    "identical_dates = np.intersect1d(total_hospitality_dates, mobility_dates)\n",
    "indexes_in_total_hospitality_dates = np.where(np.isin(total_hospitality_dates, identical_dates))[0]\n",
    "indexes_in_mobility_dates = np.where(np.isin(mobility_dates, identical_dates))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.array_equal(total_hospitality_dates[indexes_in_total_hospitality_dates], mobility_dates[indexes_in_mobility_dates])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "hospitalization = np.array(total_hospitality_dataset['hospitalizations'][indexes_in_total_hospitality_dates])\n",
    "mobility = np.array(mobility_dataset['workplaces_percent_change_from_baseline'][indexes_in_mobility_dates])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'hospitalizations')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(total_hospitality_dates[indexes_in_total_hospitality_dates], hospitalization,s=5, alpha=0.3)\n",
    "\n",
    "plt.xlabel('dates')\n",
    "plt.ylabel('hospitalizations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mobility')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mobility_dates[indexes_in_mobility_dates], mobility,s=5, alpha=0.3)\n",
    "\n",
    "plt.xlabel('dates')\n",
    "plt.ylabel('mobility')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3494340419388468"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_hospitalization_mobility = np.corrcoef(mobility, hospitalization)[0,1]\n",
    "r_hospitalization_mobility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear regression to compute r_i\n",
    "hospitalization = hospitalization.reshape(-1,1)\n",
    "modeli = LinearRegression().fit(hospitalization, mobility)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12210414966571959"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeli.score(hospitalization, mobility)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.01854903]), -29.33900549575587)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeli.coef_, modeli.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = modeli.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}