code/world_pop_transition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling and Simulation in Python\n",
    "\n",
    "Project 1 example\n",
    "\n",
    "Copyright 2018 Allen Downey\n",
    "\n",
    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Jupyter so figures appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Configure Jupyter to display the assigned value after an assignment\n",
    "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
    "\n",
    "# import functions from the modsim library\n",
    "from modsim import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_html\n",
    "\n",
    "filename = 'data/World_population_estimates.html'\n",
    "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
    "table2 = tables[2]\n",
    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
    "                  'thomlinson', 'durand', 'clark']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(census, un, timeseries, title):\n",
    "    \"\"\"Plot the estimates and the model.\n",
    "    \n",
    "    census: TimeSeries of population estimates\n",
    "    un: TimeSeries of population estimates\n",
    "    timeseries: TimeSeries of simulation results\n",
    "    title: string\n",
    "    \"\"\"\n",
    "    plot(census, ':', label='US Census')\n",
    "    plot(un, '--', label='UN DESA')\n",
    "    if len(timeseries):\n",
    "        plot(timeseries, color='gray', label='model')\n",
    "    \n",
    "    decorate(xlabel='Year', \n",
    "             ylabel='World population (billion)',\n",
    "             title=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "un = table2.un / 1e9\n",
    "census = table2.census / 1e9\n",
    "empty = TimeSeries()\n",
    "plot_results(census, un, empty, 'World population estimates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.278814327"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half = get_first_value(census) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>1.278814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1.278814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "young    1.278814\n",
       "old      1.278814\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init = State(young=half, old=half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>birth_rate1</th>\n",
       "      <td>0.0555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birth_rate2</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mature_rate</th>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>death_rate</th>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_0</th>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_end</th>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>init</th>\n",
       "      <td>young    1.278814\n",
       "old      1.278814\n",
       "dtype: flo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "birth_rate1                                            0.0555556\n",
       "birth_rate2                                                 0.04\n",
       "mature_rate                                                0.025\n",
       "death_rate                                                 0.025\n",
       "t_0                                                         1950\n",
       "t_end                                                       2016\n",
       "init           young    1.278814\n",
       "old      1.278814\n",
       "dtype: flo...\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = System(birth_rate1 = 1/18,\n",
    "                birth_rate2 = 1/25,\n",
    "                mature_rate = 1/40,\n",
    "                death_rate = 1/40,\n",
    "                t_0 = 1950,\n",
    "                t_end = 2016,\n",
    "                init=init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_func1(state, t, system):\n",
    "    if(t >= 1970):\n",
    "        system.birth_rate1 = system.birth_rate2\n",
    "    \n",
    "    births = system.birth_rate1 * state.young\n",
    "        \n",
    "    maturings = system.mature_rate * state.young\n",
    "    deaths = system.death_rate * state.old\n",
    "    \n",
    "    young = state.young + births - maturings\n",
    "    old = state.old + maturings - deaths\n",
    "    \n",
    "    return State(young=young, old=old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>1.317889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1.278814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "young    1.317889\n",
       "old      1.278814\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = update_func1(init, system.t_0, system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>1.358158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>1.279791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "young    1.358158\n",
       "old      1.279791\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = update_func1(state, system.t_0, system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_simulation(system, update_func):\n",
    "    \"\"\"Simulate the system using any update function.\n",
    "    \n",
    "    init: initial State object\n",
    "    system: System object\n",
    "    update_func: function that computes the population next year\n",
    "    \n",
    "    returns: TimeSeries\n",
    "    \"\"\"\n",
    "    results = TimeSeries()\n",
    "    \n",
    "    state = system.init\n",
    "    results[system.t_0] = state.young + state.old\n",
    "    \n",
    "    for t in linrange(system.t_0, system.t_end):\n",
    "        state = update_func(state, t, system)\n",
    "        results[t+1] = state.young + state.old\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = run_simulation(system, update_func1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(census, un, results, 'World population estimates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}