Epidem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import model\n",
    "\n",
    "import numpy as np, pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">population</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">cured</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>P0</th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P5</th>\n",
       "      <th>P6</th>\n",
       "      <th>P7</th>\n",
       "      <th>P8</th>\n",
       "      <th>P9</th>\n",
       "      <th>...</th>\n",
       "      <th>P90</th>\n",
       "      <th>P91</th>\n",
       "      <th>P92</th>\n",
       "      <th>P93</th>\n",
       "      <th>P94</th>\n",
       "      <th>P95</th>\n",
       "      <th>P96</th>\n",
       "      <th>P97</th>\n",
       "      <th>P98</th>\n",
       "      <th>P99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>7</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>6</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  population                                 ... cured                      \\\n",
       "          P0  P1  P2 P3 P4  P5 P6 P7  P8 P9  ...   P90 P91 P92 P93 P94 P95   \n",
       "0         12  10   9  8  5  13  5  7  19  6  ...     0   0   0   0   0   0   \n",
       "1          7  10   9  7  4  16  5  6  15  5  ...     0   0   0   0   0   0   \n",
       "2          7  10   9  2  3  16  6  4  15  6  ...     0   0   0   0   0   0   \n",
       "3          6   9  12  1  3  16  6  1  17  4  ...     0   0   0   0   0   0   \n",
       "4          6   9  15  1  3  16  5  2  21  4  ...     0   0   0   0   0   0   \n",
       "\n",
       "                   \n",
       "  P96 P97 P98 P99  \n",
       "0   0   0   0   0  \n",
       "1   0   0   0   0  \n",
       "2   0   0   0   0  \n",
       "3   0   0   0   0  \n",
       "4   0   0   0   0  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>cured</th>\n",
       "      <th>immune</th>\n",
       "      <th>infected</th>\n",
       "      <th>population</th>\n",
       "      <th>susceptible</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>1000</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>1000</td>\n",
       "      <td>974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>999</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>497</td>\n",
       "      <td>497</td>\n",
       "      <td>992</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cured  immune  infected  population  susceptible\n",
       "0      0       1         1        1000          999\n",
       "1      0      18        18        1000          982\n",
       "2      0      26        26        1000          974\n",
       "3      0     179       179         999          820\n",
       "4      0     497       497         992          495"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "world = model.World(randomize=True)\n",
    "\n",
    "populations = {}\n",
    "for i in range(100):\n",
    "    world.evolve()\n",
    "    \n",
    "    s = world.census()\n",
    "    populations[i] = s\n",
    "\n",
    "populations = pd.concat(populations)\n",
    "\n",
    "df = populations.copy()\n",
    "df= df.stack().unstack(1).unstack(1)\n",
    "\n",
    "agg = df.stack(0).sum(axis=1).unstack()\n",
    "\n",
    "display(df.head())\n",
    "display(agg.head())\n",
    "\n",
    "plt.style.use('classic')\n",
    "plt.rc('font', **{'family': 'Arial', 'weight': 'normal', 'size': 12})\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.spines['left'].set_visible(True)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.grid(True,axis='both',linestyle=':')\n",
    "\n",
    "for c in agg.columns:\n",
    "    ax.plot(agg[c])\n",
    "\n",
    "ax.legend(loc='best', fontsize=12, frameon=False, ncol=2)\n",
    "plt.show()"
   ]
  }
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