simulation-for-last-year.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle as pkl\n",
    "from itertools import product\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "from datetime import timedelta, datetime\n",
    "from joblib import delayed, Parallel\n",
    "from copy import copy\n",
    "\n",
    "from core import  do_simulation\n",
    "from helpers import Params, T, get_T1_and_T2, R0, plot_total, DATE_FORMAT, total_to_csv, save_bundle, save_to_json, makedir_if_not_there\n",
    "\n",
    "from const import STATE, COLORS, NUM_STATES,  STATES\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.770415654913046"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans_2019 = np.array([3.73, 3.51, 4.3, 4.46, 4.27, 4.34, 4.42, 3.7, 3.45, 4.2, 4.14, 4.13, 4.29, 4.15, 3.49, 3.27, 3.95, 3.81, 3.59, 3.55, 3.09, 2.85, 2.32, 1.99, 2.03, 2.0, 1.89, 1.91, 1.99, 2.17, 2.94, 3.32, 3.56, 3.66, 3.64, 3.16, 3.09, 3.98, 3.82, 4.01, 4.05, 4.27, 3.69, 3.54, 4.34, 4.28, 4.38, 4.62, 4.7, 3.92, 3.83, 4.5, 4.55, 4.51, 4.73, 4.03, 3.9, 4.59, 4.63, 4.65])\n",
    "tran_coef_after_ld = np.array([0.66 ,0.67 ,0.68 ,0.66 ,0.69 ,0.69 ,0.66 ,0.69 ,0.68 ,0.67 ,0.60 ,0.62 ,0.61 ,0.62 ,0.62 ,0.61 ,0.60 ,0.62 ,0.67 ,0.60 ,0.58 ,0.59 ,0.58 ,0.57 ,0.59 ,0.60 ,0.57 ])\n",
    "infection_factor = np.mean(trans_2019) / np.mean(tran_coef_after_ld)\n",
    "infection_factor = np.log(infection_factor)\n",
    "infection_factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_jan27 = pkl.load(\n",
    "    open('output/params_after_lockdown.pkl', 'rb')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_before = pkl.load(\n",
    "    open('output/params_before_lockdown.pkl', 'rb')\n",
    ")\n",
    "\n",
    "params_after = pkl.load(\n",
    "    open('output/params_after_lockdown.pkl', 'rb')\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "p0_time = pkl.load(open('output/p0_time.pkl', 'rb'))\n",
    "p0_time = datetime(year=2018,  month=p0_time.month, day=p0_time.day)\n",
    "bed_info = [\n",
    "    (0, 3000), # 3000),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = copy(params_jan27)\n",
    "params.alpha = infection_factor * params_jan27.alpha\n",
    "params.beta = infection_factor * params_jan27.beta\n",
    "params.initial_num_E = 1\n",
    "params.initial_num_I = 0\n",
    "params.initial_num_M = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "total, delta, increase, trans, stats = do_simulation(360, bed_info, params, verbose=0,  p0_time=p0_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R0_by_stage': {0: (6.001509535275191,\n",
       "   14.945419037023832,\n",
       "   3.6145075623769465)},\n",
       " 'end_time': None,\n",
       " 'peak_time': (127, '05/04/19'),\n",
       " 'when_dO_gt_dI': (128, '06/04/19'),\n",
       " 'when_dO_gt_dE': (126, '04/04/19'),\n",
       " 'turning_time_real': (133, '11/04/19'),\n",
       " 'turning_time_theory': (135, '13/04/19')}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "makedir_if_not_there('output/tbl/last-year/')\n",
    "save_bundle([total, delta, increase, trans], p0_time, 360, f'output/tbl/last-year/')\n",
    "save_to_json(stats, f'output/tbl/last-year/stats.txt')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cloud-user/code/covid-19/helpers.py:287: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subdf['index'] = df.index\n",
      "/home/cloud-user/code/covid-19/helpers.py:288: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subdf['value'] = subdf[state].copy()\n",
      "/home/cloud-user/code/covid-19/helpers.py:290: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  subdf['state'] = state\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['29/11/18' '09/02/19' '22/04/19' '03/07/19' '13/09/19' '24/11/19']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_total(total, p0_time,  360)\n",
    "fig\n",
    "fig.savefig('figs/simulation-last-year.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}