MoneyModel1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mesa import Agent, Model\n",
    "from mesa.time import RandomActivation\n",
    "import random\n",
    "from mesa.space import MultiGrid\n",
    "import numpy as np\n",
    "from mesa.datacollection import DataCollector\n",
    "from mesa.batchrunner import BatchRunner\n",
    "\n",
    "def compute_gini(model):\n",
    "    agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
    "    x = sorted(agent_wealths)\n",
    "    N = model.num_agents\n",
    "    B = sum(xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))\n",
    "    return (1 + (1/N) - 2*B)\n",
    "\n",
    "class MoneyModel(Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "    def __init__(self, N, width, height):\n",
    "        self.num_agents = N\n",
    "        self.grid = MultiGrid(width, height, True)\n",
    "        self.schedule = RandomActivation(self)\n",
    "        self.running = True\n",
    "        \n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "            \n",
    "            x = random.randrange(self.grid.width)\n",
    "            y = random.randrange(self.grid.height)\n",
    "            self.grid.place_agent(a, (x, y))\n",
    "            \n",
    "        self.datacollector = DataCollector(model_reporters={\"Gini\": compute_gini},agent_reporters={\"Wealth\": lambda a: a.wealth})           \n",
    "\n",
    "    def step(self):\n",
    "        self.datacollector.collect(self)\n",
    "        self.schedule.step()\n",
    "\n",
    "class MoneyAgent(Agent):\n",
    "    \"\"\" An agent with fixed initial wealth.\"\"\"\n",
    "    def __init__(self, unique_id, model):\n",
    "        super().__init__(unique_id, model)\n",
    "        self.wealth = 1\n",
    "\n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(self.pos, moore=True, include_center=False)\n",
    "        new_position = random.choice(possible_steps)\n",
    "        self.model.grid.move_agent(self, new_position)\n",
    "\n",
    "    def give_money(self):\n",
    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
    "        if len(cellmates) > 1:\n",
    "            other = random.choice(cellmates)\n",
    "            other.wealth += 1\n",
    "            self.wealth -= 1\n",
    "\n",
    "    def step(self):\n",
    "        self.move()\n",
    "        if self.wealth > 0:\n",
    "            self.give_money()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "empty_model = MoneyModel(10,10, 10)\n",
    "empty_model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = MoneyModel(10,10, 10)\n",
    "for i in range(10):\n",
    "    model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.,  0.,  0.,  3.,  0.,  0.,  2.,  0.,  0.,  1.]),\n",
       " array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8,  2.1,  2.4,  2.7,  3. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a69ba8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "agent_wealth = [a.wealth for a in model.schedule.agents]\n",
    "plt.hist(agent_wealth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_wealth = []\n",
    "for j in range(100):\n",
    "    # Run the model\n",
    "    model = MoneyModel(10,10,10)\n",
    "    for i in range(10):\n",
    "        model.step()\n",
    "\n",
    "    # Store the results\n",
    "    for agent in model.schedule.agents:\n",
    "        all_wealth.append(agent.wealth)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 432.,  295.,  170.,   60.,   31.,   12.]),\n",
       " array([0, 1, 2, 3, 4, 5, 6]),\n",
       " <a list of 6 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wkr3AYeAkcFdVLXLKRpK0WJlU9iapRU/Vv+09BNxBu+cH/V/k2j4/xyV6O0pCVc17i7lP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\nGe6S1CDDXZIaZLhLUoMMd0lq0P8D6iWeDBC5XkYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2712e8ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_wealth, bins=range(max(all_wealth)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x9a6edccd68>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FxA+Bt0742dcaHt858fdFnJzMLJPYDnEnJzPLNN8mUBsnJzPLeORkZklycjKzJDk5mVmS\nOtxKUBUnJzPLdLGVoApOTmaW8bd1ZpYkrzmZWZK85mRmSfKak5klydM6M0uSk5OZJSmxNSefIW5m\nmVdKXk0UlYbK2wxLWi1pnaTbisLxyMnMMh1O6xpKQ50CbAZWSbopIjY0tJkDXAn8fkRsknRAUb9O\nTmaW6XxaN14aCkDSWGmoDQ1tzgG+GxGbACJia1GnntaZWabDopqUKw11BLC/pNskrZL04aJwejty\nqqKyRIWVMCqrZOKqLuPE4H0Wu61W07qtI/CrkW57nwUsAE4GXgv8TNLPIuLn7V5gZtY6Oe03nF1j\nHp5UfaWwNBTZaGprRLwMvCzpduBYoGVy8rTOzDLbS16TjZeGkrQHWWmoFRPa3AS8S9JMSXsDxwEP\ntQvHIyczy7TYJlCkTGmoiNgg6UfAA2QrV8siYn27fp2czCzTxQ7xotJQ+fNLgUvL9unkZGaZxHaI\nOzmZWSaxUwlKLYhL+nS+5fwBSdfni15mNp2Mlrz6pDA5SToI+BSwICKOIRttLa46MDPrs8SSU9lp\n3UzgtZJ2AnuT3T9jZtPJoK05RcRmSZcBjwPbgFsi4ieVR2Zm/dXhVoKqFCYnSfuR3cR3KPA8sFzS\nORHx7cmtRxoeD+WXmfXWxvzqsQE8bO69wKMR8QyApO8BJwBNktNwD0Mzs+aG2PUf/pW96XbQpnVk\n07lFkl5DNvA7hWy7uplNJ4ltJSiz5nS3pOXAarLcuhpYVnVgZtZnAzitIyKWApNuRTazaWQQk5OZ\n7QYGcM3JzHYHiY2cfJ6TmXWtqPqKpDMl3Z9XX7lb0olFfXrkZGZdKVN9BfhJRKzI278D+DfgyHb9\neuRkZt0ar74SEduBseor4yJiW8PTfYCdRZ165GRmuY5XxJtVX1k4sZGks4AvAW8A3l/UqZOTmeVa\nrYjfnl/diYjvA9+X9C7gYuB97dqnn5yqKDeV07qopuN11XQLoInVwHqlshJcVfVLdTFX+HeukpiP\n7tUWxFYjp+Pza8zfT2xQpvrKuIi4Q9JhkvYfuy2umfSTk5n1yUudvnC8+grwFNl5b2c3NpB0eET8\nIn+8ANijXWICJyczG9fZmlOZ6ivAH0n6CPAqWRb806J+nZzMLNf5Lsyi6isR8Y/AP06lTycnM8ul\ndf+Kk5OZ5dK6f8XJycxyHjmZWZI6/rauEk5OZpbztM7MkuRpnZklySMnM0uSR05mliSPnMwsSR45\nmVmSvJXAzJLkkZOZJSmtNSefIW5mue0lr8mKqq/kba6Q9IikNZLmF0VTU3LaWM/bdurukboj6MDG\nugPowMa6A5iijXUH0GOjJa9dNVRfORV4O3C2pLdNaHM6cHhEvAX4BHBVUTROTmWsGqk7gg5srDuA\nDmysO4Ap2lh3AD3W8cipsPpK/vw6gIi4C5gj6cB20XhaZ2a5zkZONK++MvG0+4ltNjVpswsviJtZ\nLq2tBIroTQUSSRWVMjGzIhGhbl4vaSNwaMnmWyLijQ2vXQQsiYjT8udfyEKKf2hocxVwW0TcmD/f\nALwnIra0epOejZy6/XDMrD4RMdTFywurrwArgPOAG/Nk9ly7xASe1plZl8pUX4mImyWdIennwIvA\nuUX99mxaZ2bWS339tq7MRq2USJor6VZJD0paK+n8umMqQ9IMSfdJWlF3LGVImiPp3yU9lH/Wx9Ud\nUxFJn5a0TtIDkq6XtEfdMU03fUtOZTZqJWgU+ExEvJ2sHvN5AxAzwAXA+rqDmILLgZsj4kjgWOCh\nmuNpS9JBwKeABRFxDNnyyOJ6o5p++jlyKrNRKykR8XRErMkfv0D2P03bvRl1kzQXOAP4Rt2xlCFp\nX+DdEXENQESMRsSvaw6rjJnAayXNAvYGNtccz7TTz+RUZqNWsiQNAfOBu+qNpNCXgc8Bg7KY+CZg\nq6Rr8qnoMkl71R1UOxGxGbgMeJxsM+FzEfGTeqOafrxDvARJ+wDLgQvyEVSSJL2fbA/KGkD5lbpZ\nwALgyohYAGwDvlBvSO1J2o9s1H8ocBCwj6Rz6o1q+ulnctoEzGt4Pjf/WdLyYfty4FsRcVPd8RQ4\nEThT0qPAd4CTJF1Xc0xFngSeiIh78ufLyZJVyt4LPBoRz0TEDuB7wAk1xzTt9DM5jW/Uyr/ZWEy2\nMSt13wTWR8TldQdSJCIujIh5EXEY2ed7a0R8pO642sk34j0h6Yj8R6eQ/mL+48AiSa+RJLKYk17E\nH0R924TZaqNWv96/E5JOBD4ErJW0mmwd58KI+GG9kU075wPXS5oNPEqJDXp1ioi7JS0HVpPdpr8a\nWFZvVNOPN2GaWZK8IG5mSXJyMrMkOTmZWZKcnMwsSU5OZpYkJyczS5KTk5klycnJzJL0/zLxOO50\nKgY6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a6ed87358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = MoneyModel(50, 10, 10)\n",
    "for i in range(20):\n",
    "    model.step()\n",
    "    \n",
    "agent_counts = np.zeros((model.grid.width, model.grid.height))\n",
    "for cell in model.grid.coord_iter():\n",
    "    cell_content, x, y = cell\n",
    "    agent_count = len(cell_content)\n",
    "    agent_counts[x][y] = agent_count\n",
    "plt.imshow(agent_counts, interpolation='nearest')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a6f01a9e8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wSeOWLSIikONl9yIikl05G9hMZ8GQNJyZLTezV8xsnpnNjbqeuDOzqWa23szm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\nS2+WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a6f037e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = MoneyModel(50, 10, 10)\n",
    "for i in range(20):\n",
    "    model.step()\n",
    "gini = model.datacollector.get_model_vars_dataframe()\n",
    "gini.plot()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = MoneyModel(50, 10, 10)\n",
    "for i in range(20):\n",
    "    model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Wealth\n",
       "Step AgentID        \n",
       "0    0             1\n",
       "     1             1\n",
       "     2             1\n",
       "     3             1\n",
       "     4             1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_wealth = model.datacollector.get_agent_vars_dataframe()\n",
    "agent_wealth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a6f43e208>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1q6qU+yTdWO2Rd1JEnI6It2pwjcW7bL+p6TU1wfYHJC1XP6VZnCi+KyLepsFPJp+u6thz\nmlaIj30hELrF9owGAf7ViPhO0+tpg4h4XtJDkq5uei0N2SXpg1UX/F+S3mv7Kw2vqTER8Wz1/beS\nvqVBPX1O0wrxw5L+xvaltv9C0sckdfqIs9jDWHG3pCcj4o6mF9Ik26+1va26vVXSP0rq5AHeiLg1\nIl4fEZdpkBUHI+Kfm15XE2yfX/2kKtuvkvQ+SY8Pe81UQjwi/iRp5UKgJyR9rcsXAtm+V9L/SLrc\n9i9tf6LpNTXB9i5J10q6sjp96lHbXd37/CtJD9le1OC4wAMR8d8NrwnN2y7pUHWs5EeS9kfEgWEv\n4GIfACgYBzYBoGCEOAAUjBAHgIIR4gBQMEIcAApGiANAwQhxACgYIQ4ABfsz+/W/us5vHUEAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a6f4482e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "end_wealth = agent_wealth.xs(14, level=\"Step\")[\"Wealth\"]\n",
    "end_wealth.hist(bins=range(agent_wealth.Wealth.max()+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a6f4771d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EeI2V0aqNlmyjK6MSZ1NzdLGuixPiNVZGqzbCYXRltGpjyWt0sR4zrE2Is7HZXdFVHpfp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\nIjIYoNsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a6f4483c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "one_agent_wealth = agent_wealth.xs(14, level=\"AgentID\")\n",
    "one_agent_wealth.Wealth.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████| 245/245 [02:31<00:00,  1.44s/it]\n"
     ]
    }
   ],
   "source": [
    "parameters = {\"width\": 10,\n",
    "              \"height\": 10,\n",
    "              \"N\": range(10, 500, 10)}\n",
    "\n",
    "batch_run = BatchRunner(MoneyModel,\n",
    "                        parameters,\n",
    "                        iterations=5,\n",
    "                        max_steps=100,\n",
    "                        model_reporters={\"Gini\": compute_gini})\n",
    "batch_run.run_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9a740f54a8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NlF3PrdOu9w6koV5l1czsrN0wY2vU7KBJ7GrXfFSVJMkOird1JRCcz0/58PB4\nooBvtzQ+Wo1EoM9oxm+Z1jeb9OGvFRQMTzfQDhFI8mA3kxufdJ9ZtYiboZH3NPdSgDSLuFO3x9ay\nQCLQh6R1W7hnX/HV27YTeddJWu9JjrNTreGSfZ1unfZSgDSJrZXrZJkV1QtIBPqcrCq+cFmNPPyV\n7of3v//mtlakSY+xFdMutJpOCEM3iFFSkthambTQry+Vj0Ii0MdkWfGFy2x8Mroj3qkRmEmPsR2C\nmCW9khLcS/TzC2SikAj0MVlXfKV104pAowOFsiTNMSaZXrpUZidbw93WI+kXVkZUl88dpJ5A/N8q\nRFcyNTXFa68tUHxlM8Bxzp5dZGpqqmy9iYkJ9u27l0JhO2NjWykUtrNv371MTEyUrXfgwCEmJzdy\nww0fZnJyIwcOHGrKjttuu5XFxZM89tj9LC6eZOfOmxs/2DokPcYS7m8A3wv+x5d57bXXxpbRahYW\nFsjlpoCrgiVXMTQ0ycLCQkfs6ReKz8erwGeA+4HPYPa3Vc+NCNGsimT1h3oCVaQZbFWrZdtsq7Nb\n3CeDkP3Sjbb2Gt1yv7YD5A7qf7IY8ZlFRkyn3SdJ6GTmTyMMUmXVbnrhfs2CLETAiuV0HjPzbrGl\nW1laWmJyciPLy0couhGOUyhsZ3HxZE23RqPbNWLfwsICU1NTHXGztOs4s6TT56wfGORzaGa4uzVT\nhmICPUSjfuS0PvVGaDTmkCXtOM6s6XRsotfphvuu10nUEzCzHcDvUhSNfe5+T8Q608AngSFgyd23\nJ902WE89gTo029JtVYup21rgg9wyHCS67b7rBFn0BNYk2Mkq4FPAe4CXgDkz+5y7nwytsx74T8CN\n7v6imb0p6bYiOaWW7q5d2xkamuTs2cVULd2JiYmqdbOoMEs9lOXl6h5KJx7GqOMU/Ue33Xe9ShJ3\n0DbglLsvuvtZ4CBwU8U6/wT4jLu/CODu30yxrUjBzp03Z5aamVVXOmk6qxBZovsuG5KIwEXA86Hv\nLwTLwlwBXGhmR8xszsx+LsW2IsTS0hJzc3MsLS3FrpOFH3lpaYldu25nefkIr776OMvLR9i16/aa\n+61lT6/54kXvo/suG+q6g1KUsxV4NzAC/IWZ/UXaQvbu3Xv+8/T0NNPT0xmZ1xscOHCIXbtuJ5cr\ntnD27bu3ZYOwsu5K79x5M9df/2754kVbGbT7bmZmhpmZmUzLrBsYNrPrgL3uviP4vodibuo9oXXu\nAvLu/q8vmZQAAAAJrklEQVSD778PfAF4sd62oTIGOjDc7iCXgmpC9D7tShGdAy43s0kzywG3AA9X\nrPM54J1mttrM1gI/Aswn3FbQ/mkE1JUWQkACd5C7nzOz3cAjrKR5zpvZbcWf/QF3P2lmhylGaM4B\nD7j7CYCobVt1ML1MeZCr2DJvdZBr0LrSQohqNGK4iyjFBMLpn62cmE0I0dtk4Q6SCHQZGugkhEiK\nRECkQgIjRH+huYN6iCT5/61Ec6wIIaJQT6ANtDP/PwqlgwrRn6gn0ANkOTK3UfQWKyFEHBKBFhB2\n/XRDBaw5VoQQcUgEMqbS9/7EE0+yvPzXhCvg7373ubZWwBoYJoSIQzGBDInyvefz7+Lcudc5e3YI\nmAIWGBo6y4svPtf2SljZQUL0F215n4BITtSkbKtXv5nVqwucPfsosABMUSi8tyNznmuefSFEJXIH\nZUiU7/3cudO88cbzwMvAtcDL5/3x7U4b7XSaqhCi+5AIZEiU7/3Tn74v0h//2GNfamvevsYJCCGi\nUEygBUT53sPLAE0bLYRoGsUEupQo33t42dzcXFvfjap3sQoh4pA7qAO0O29f4wSEEHFIBDpAu/P2\nNU5ACBGHYgIdpN15+xonIER/oamkhRBigNEEckIIIZpCIiCEEAOMREAIIQYYiYAQQgwwEgEhhBhg\nJAJCCDHAJBIBM9thZifN7Fkzuyvi93eZ2XfM7Ing79dCvy2Y2VNmdszMZrM0XgghRHPUnTvIzFYB\nnwLeA7wEzJnZ59z9ZMWqf+buPxlRxBvAtLt/u2lrhRBCZEqSnsA24JS7L7r7WeAgcFPEenEDFizh\nfoQQQrSZJJXzRcDzoe8vBMsq+VEze9LM/tTMNoeWO/Comc2Z2a1N2CqEECJjsppK+nHgEnf/ezN7\nH/AnwBXBb+9w95fNbIKiGMy7+9GoQvbu3Xv+8/T0NNPT0xmZJ4QQvc/MzAwzMzOZlll37iAzuw7Y\n6+47gu97AHf3e2ps83XgGnf/VsXyu4G/c/ffidhGcwcJIUQK2jV30BxwuZlNmlkOuAV4uMKQDaHP\n2yiKy7fMbK2ZjQbLR4Abga80Y3C/o/cACyHaSV0RcPdzwG7gEeCrwEF3nzez28zsQ8FqP2NmXzGz\nY8DvAjcHyzcAR4PlXwY+7+6PZH4UfYLeAyyEaDeaSrpL0HuAhRBp0VTSfUTpPcBFAYDwe4CFEKJV\nSAS6BL0HWAjRCSQCXYLeAyyE6ASKCXSQqHf+6j3AQoik6B3DPcyBA4fYtet2crmiG2jfvnvZufPm\n+hsKIUSARKBHUSaQECILlB3UoygTSAjRLUgEOoAygYQQ3YJEoAMoE0gI0S0oJtBBlAkkhGgGBYaF\nEGKAUWBYCCFEU0gEhBBigJEICCHEACMREEKIAUYiIIQQA4xEQAghBhiJgBBCDDASASGEGGAkAkII\nMcBIBIQQYoCRCAghxACTSATMbIeZnTSzZ83srojf32Vm3zGzJ4K/X0u6rRBCiM5RVwTMbBXwKeC9\nwA8BO81sY8Sqf+buW4O/30i5bU8zMzPTaROaQvZ3FtnfWXrd/mZJ0hPYBpxy90V3PwscBG6KWC9q\nJruk2/Y0vX4Tyf7OIvs7S6/b3yxJROAi4PnQ9xeCZZX8qJk9aWZ/amabU24rhBCiA6zJqJzHgUvc\n/e/N7H3AnwBXZFS2EEKIFlH3pTJmdh2w1913BN/3AO7u99TY5uvANRSFING2ZqY3ygghREqafalM\nkp7AHHC5mU0CLwO3ADvDK5jZBnd/Jfi8jaK4fMvM6m5botkDEUIIkZ66IuDu58xsN/AIxRjCPnef\nN7Pbij/7A8DPmNkvAWeBZeDmWtu26FiEEEKkpGveMSyEEKL9tH3EsJn9jJl9xczOmdnWit8+aman\nzGzezG4MLd9qZseDAWe/226ba9ELg+HMbJ+ZvWJmx0PLLjCzR8zsGTM7bGbrQ79FXodOYGYXm9mX\nzOyrZva0md0RLO8V+4fN7C/N7Fhg/93B8p6wv4SZrQoGgj4cfO8Z+81swcyeCq7BbLCsl+xfb2b/\nPbDnq2b2I5na7+5t/QPeBvwg8CVga2j5JuAYRRfVFPA1VnoqfwlcG3z+X8B72213zLGsCuycBIaA\nJ4GNnbYrws53Am8HjoeW3QP8avD5LuDfBZ83x12HDtn+FuDtwedR4BlgY6/YH9i0Nvi/GvgyxfEz\nPWN/YNc/B/4r8HAv3T+BTc8BF1Qs6yX79wM/H3xeA6zP0v629wTc/Rl3P0X14LKbgIPu/rq7LwCn\ngG1m9hZgnbvPBev9F+Cn2mZwbXpiMJy7HwW+XbH4JuDB4PODrJzTnyTiOrTDzijc/Rvu/mTw+Qww\nD1xMj9gP4O5/H3wcpvhwOj1kv5ldDPwY8PuhxT1jP8W6prKu6wn7zWwM+Ifu/gcAgV2vkqH93TSB\nXOXAsheDZRdRHGRWopsGnPXyYLg3e5DR5e7fAN4cLI+7Dh3HzKYo9mi+DGzoFfsDV8ox4BvAo0GD\npmfsBz4J/EuK4lWil+x34FEzmzOzXwyW9Yr9lwLfNLM/CNxxD5jZWjK0P6vBYmWY2aPAhvAiihfi\nX7n751uxT9E0XZ0hYGajwP8A7nT3MxHjSrrWfnd/A9gStOo+a2Y/RLW9XWm/mf0j4BV3f9LMpmus\n2pX2B7zD3V82swngETN7hh45/xTr6K3AP3P3vzKzTwJ7yND+loiAu9/QwGYvAm8Nfb84WBa3vBt4\nEbgk9L2bbKvHK6XxHYHL7XSwvOvOt5mtoSgAf+junwsW94z9Jdz9b81sBthB79j/DuAnzezHgAKw\nzsz+EPhGj9iPu78c/F8ysz+h6B7plfP/AvC8u/9V8P0zFEUgM/s77Q4KxwUeBm4xs5yZXQpcDswG\nXZ1XzWybmRnwT4HPRZTVCc4PhjOzHMXBcA932KY4jOrz/cHg8wdYOaeR16FdRsbwaeCEu/9eaFlP\n2G9mbyplbphZAbiBYlyjJ+x394+5+yXu/gMU7+8vufvPAZ+nB+w3s7VBLxIzGwFuBJ6md87/K8Dz\nZlaahuc9wFfJ0v4ORLp/iqLPapniKOIvhH77KMVo9jxwY2j5NRQv3Cng99ptc53j2UExY+UUsKfT\n9sTY+BDwEvA94P8BPw9cADwW2P4IMF7vOnTI9ncA5yhmXh0DngjO+YU9Yv8PBzY/CRyn6BKlV+yv\nOJZ3sZId1BP2U/Spl+6dp0vPaK/YH9hzNcUG55PAH1PMDsrMfg0WE0KIAabT7iAhhBAdRCIghBAD\njERACCEGGImAEEIMMBIBIYQYYCQCQggxwEgEhBBigJEICCHEAPP/AUuf4v2C0r8KAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a71dcfe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_data = batch_run.get_model_vars_dataframe()\n",
    "run_data.head()\n",
    "plt.scatter(run_data.N, run_data.Gini)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}