examples/example_101.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenABM-Covid19 - Example 101\n",
    "\n",
    "This shows a very simple example of getting the model, changing some parameters and then running the simulation.\n",
    "\n",
    "The first step is to get the baseline parameters. We then reduce the size of the population to 10,000 people so that the simulation will run quickly in this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import example_utils as utils\n",
    "params = utils.get_baseline_parameters()\n",
    "params.set_param( \"n_total\", 10000 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to get the simulation and then to run it for 10 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = utils.get_simulation( params )\n",
    "sim.steps( 10 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now look at the time-series of the number of people of people infected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   time  total_infected\n",
      "0     1               5\n",
      "1     2               5\n",
      "2     3               7\n",
      "3     4               9\n",
      "4     5               9\n",
      "5     6              11\n",
      "6     7              13\n",
      "7     8              15\n",
      "8     9              18\n",
      "9    10              22\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "timeseries = pd.DataFrame( sim.results )\n",
    "print( timeseries.loc[:,{\"time\",\"total_infected\"}])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can now be run for a further 100 days and the plot the time-sereies of the total number of people infected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1227bf710>"
      ]
     },
     "execution_count": 4,
     "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": [
    "sim.steps( 100 )\n",
    "timeseries = pd.DataFrame( sim.results )\n",
    "timeseries.plot( x = \"time\", y = \"total_infected\" )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}