{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example implements the first model from \"Modeling civil violence: An agent-based computational approach,\" by Joshua Epstein.  The paper (pdf) can be found [here](http://www.uvm.edu/~pdodds/files/papers/others/2002/epstein2002a.pdf).\n",
    "\n",
    "The model consists of two types of agents: \"Citizens\" (called \"Agents\" in the paper) and \"Cops.\"  Agents decide whether or not to rebel by weighing their unhappiness ('grievance') against the risk of rebelling, which they estimate by comparing the local ratio of rebels to cops.  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from civil_violence.agent import Citizen, Cop",
    "from civil_violence.model import CivilViolenceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = CivilViolenceModel(height=40, \n",
    "                           width=40, \n",
    "                           citizen_density=.7, \n",
    "                           cop_density=.074, \n",
    "                           citizen_vision=7, \n",
    "                           cop_vision=7, \n",
    "                           legitimacy=.8, \n",
    "                           max_jail_term=1000, \n",
    "                           max_iters=1000) # cap the number of steps the model takes\n",
    "model.run_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model's data collector counts the number of citizens who are Active (in rebellion), Jailed, or Quiescent after each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_out = model.dc.get_model_vars_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAEZCAYAAACHJRySAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEFBmZ40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1063e1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = model_out.plot()\n",
    "ax.set_title('Citizen Condition Over Time')\n",
    "ax.set_xlabel('Step')\n",
    "ax.set_ylabel('Number of Citizens')\n",
    "_ = ax.legend(bbox_to_anchor=(1.35, 1.025))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
