soil/examples/newsspread/NewsSpread.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T16:22:30.732107Z",
     "start_time": "2017-11-08T17:22:30.059855+01:00"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import soil\n",
    "import networkx as nx\n",
    " \n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To display plots in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T16:22:35.580593Z",
     "start_time": "2017-11-08T17:22:35.542745+01:00"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "from soil import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# News Spreading example with SOIL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we three different kinds of models, which we combine in five types of simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T16:22:37.242327Z",
     "start_time": "2017-11-08T17:22:37.087039+01:00"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\r\n",
      "default_state: {}\r\n",
      "load_module: newsspread\r\n",
      "environment_agents: []\r\n",
      "environment_params:\r\n",
      "  prob_neighbor_spread: 0.0\r\n",
      "  prob_tv_spread: 0.01\r\n",
      "interval: 1\r\n",
      "max_time: 30\r\n",
      "name: Sim_all_dumb\r\n",
      "network_agents:\r\n",
      "- agent_class: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_class: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "num_trials: 50\r\n",
      "---\r\n",
      "default_state: {}\r\n",
      "load_module: newsspread\r\n",
      "environment_agents: []\r\n",
      "environment_params:\r\n",
      "  prob_neighbor_spread: 0.0\r\n",
      "  prob_tv_spread: 0.01\r\n",
      "interval: 1\r\n",
      "max_time: 30\r\n",
      "name: Sim_half_herd\r\n",
      "network_agents:\r\n",
      "- agent_class: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_class: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "num_trials: 50\r\n",
      "---\r\n",
      "default_state: {}\r\n",
      "load_module: newsspread\r\n",
      "environment_agents: []\r\n",
      "environment_params:\r\n",
      "  prob_neighbor_spread: 0.0\r\n",
      "  prob_tv_spread: 0.01\r\n",
      "interval: 1\r\n",
      "max_time: 30\r\n",
      "name: Sim_all_herd\r\n",
      "network_agents:\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "num_trials: 50\r\n",
      "---\r\n",
      "default_state: {}\r\n",
      "load_module: newsspread\r\n",
      "environment_agents: []\r\n",
      "environment_params:\r\n",
      "  prob_neighbor_spread: 0.0\r\n",
      "  prob_tv_spread: 0.01\r\n",
      "  prob_neighbor_cure: 0.1\r\n",
      "interval: 1\r\n",
      "max_time: 30\r\n",
      "name: Sim_wise_herd\r\n",
      "network_agents:\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_class: WiseViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "num_trials: 50\r\n",
      "---\r\n",
      "default_state: {}\r\n",
      "load_module: newsspread\r\n",
      "environment_agents: []\r\n",
      "environment_params:\r\n",
      "  prob_neighbor_spread: 0.0\r\n",
      "  prob_tv_spread: 0.01\r\n",
      "  prob_neighbor_cure: 0.1\r\n",
      "interval: 1\r\n",
      "max_time: 30\r\n",
      "name: Sim_all_wise\r\n",
      "network_agents:\r\n",
      "- agent_class: WiseViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_class: WiseViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "network_params:\r\n",
      "  generator: barabasi_albert_graph\r\n",
      "  n: 500\r\n",
      "  m: 5\r\n",
      "num_trials: 50\r\n"
     ]
    }
   ],
   "source": [
    "!cat NewsSpread.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:07:46.781745Z",
     "start_time": "2017-11-08T19:07:41.146659+01:00"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:07:50.696420Z",
     "start_time": "2017-11-08T19:07:50.652496+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>infected</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_step</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.120000</td>\n",
       "      <td>497.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.938776</td>\n",
       "      <td>495.061224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.468085</td>\n",
       "      <td>492.531915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.333333</td>\n",
       "      <td>489.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.191489</td>\n",
       "      <td>487.808511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.680851</td>\n",
       "      <td>485.319149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17.377778</td>\n",
       "      <td>482.622222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>480.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22.212766</td>\n",
       "      <td>477.787234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24.046512</td>\n",
       "      <td>475.953488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26.809524</td>\n",
       "      <td>473.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29.418605</td>\n",
       "      <td>470.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31.777778</td>\n",
       "      <td>468.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>33.666667</td>\n",
       "      <td>466.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>36.113636</td>\n",
       "      <td>463.886364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>36.976190</td>\n",
       "      <td>463.023810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.113636</td>\n",
       "      <td>459.886364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>41.872340</td>\n",
       "      <td>458.127660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44.550000</td>\n",
       "      <td>455.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45.418605</td>\n",
       "      <td>454.581395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>47.666667</td>\n",
       "      <td>452.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>48.738095</td>\n",
       "      <td>451.261905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>51.731707</td>\n",
       "      <td>448.268293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>54.390244</td>\n",
       "      <td>445.609756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>56.116279</td>\n",
       "      <td>443.883721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>58.404762</td>\n",
       "      <td>441.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>58.976744</td>\n",
       "      <td>441.023256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>61.522727</td>\n",
       "      <td>438.477273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>63.263158</td>\n",
       "      <td>436.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65.090909</td>\n",
       "      <td>434.909091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id            \n",
       "         infected     neutral\n",
       "t_step                       \n",
       "0        2.120000  497.880000\n",
       "1        4.938776  495.061224\n",
       "2        7.468085  492.531915\n",
       "3       10.333333  489.666667\n",
       "4       12.191489  487.808511\n",
       "5       14.680851  485.319149\n",
       "6       17.377778  482.622222\n",
       "7       20.000000  480.000000\n",
       "8       22.212766  477.787234\n",
       "9       24.046512  475.953488\n",
       "10      26.809524  473.190476\n",
       "11      29.418605  470.581395\n",
       "12      31.777778  468.222222\n",
       "13      33.666667  466.333333\n",
       "14      36.113636  463.886364\n",
       "15      36.976190  463.023810\n",
       "16      40.113636  459.886364\n",
       "17      41.872340  458.127660\n",
       "18      44.550000  455.450000\n",
       "19      45.418605  454.581395\n",
       "20      47.666667  452.333333\n",
       "21      48.738095  451.261905\n",
       "22      51.731707  448.268293\n",
       "23      54.390244  445.609756\n",
       "24      56.116279  443.883721\n",
       "25      58.404762  441.595238\n",
       "26      58.976744  441.023256\n",
       "27      61.522727  438.477273\n",
       "28      63.263158  436.736842\n",
       "29      65.090909  434.909091"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evodumb['mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:01.093889Z",
     "start_time": "2017-11-08T19:08:00.861308+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73a065ca20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n33XOjXPOTQA+DzzvnLs
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a0830828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evodumb['mean'].plot(yerr=evodumb['std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:31.598791Z",
     "start_time": "2017-11-08T19:08:02.764487+01:00"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);\n",
    "evohalfherd = analysis.read_data('soil_output/Sim_half_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
    "evoherd = analysis.read_data('soil_output/Sim_all_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
    "evoherdwise = analysis.read_data('soil_output/Sim_wise_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
    "evowise = analysis.read_data('soil_output/Sim_all_wise/', group=True, process=analysis.get_count, keys=['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:37.909477Z",
     "start_time": "2017-11-08T19:08:36.873751+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n33XOjXPOTQA+DzzvnLs
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e630f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8hrTF063BouzRwkdldo
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noLXuUko5gGeAeUA9cLX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1f6d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in [evodumb, evohalfherd, evoherd, evoherdwise, evowise]:\n",
    "    i['mean'].plot(yerr=i['std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:44.132725Z",
     "start_time": "2017-11-08T19:08:43.880341+01:00"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAEXCAYAAADP3/fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH1g53+/hVCDWhcLXBE8
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1d84dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = evodumb['mean']-evohalfherd['mean']\n",
    "m = evodumb['max'].loc[0].sum()\n",
    "diff = diff / m\n",
    "diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
    "          title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 50% herd agents.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:45.320345Z",
     "start_time": "2017-11-08T19:08:45.068706+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEXCAYAAADcCLc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8FdX9//HXJ3uAhC0gS4CAgAoS2cQdUXFr9etSq1RF\nQVutVeliF/q1P0W/Fq1fa9Uu1lqpbQWxYrXq11Yri2hFWRQjm+xLCCA7CSRAkvP740ySm5ub5AaS\n3CS8n49HHrmzn5k5c2Y+M2fOmHMOERERERERaTriYp0AERERERERqUyBmoiIiIiISBOjQE1ERERE\nRKSJUaAmIiIiIiLSxChQExERERERaWIUqImIiIiIiDQxLTpQM7MbzOydRlpWlpk5M0tojOXVBzN7\n3sweaqRlOTPr2xjLqk1d99XRbKeG2saNmbdbCjObY2bfDH7X+/Y7mnxlZueY2Rchw04ws0/NLN/M\nJphZqpm9YWZ7zezl+kx3c2Vm681sdKzTUR0z62lmBWYWH+u01MTMRplZbgyXf5WZbQq21ZBYpaM+\nmdlSMxsV63SUacxzfSw09fVrLmVBfQo93zbQ/CeZ2QsNNf+mJKpAzcyuN7OFQUbbYmb/NLOzGzpx\nR8s5N9U5d1FDzLshLxKqO3E2dMaPlWC9ioKL0n1mtsjMJppZcqzT1lQ1ZN4+FjS17eece985d0JI\nrx8Dc5xzac65p4BrgOOAjs65r8ckkVInzrmNzrk2zrmSWKeliXsMuCvYVp/GOjFljuYc75wb6Jyb\nU89Jirm6XoMcSxfTNWnIssDMzjOz2cFNvPURhmcFww+Y2YrwPG1m3zezrcH0U8quu8wswcymm9me\n4Jo/LWSae83s+/W9Ls1dQz2wqTVQM7MfAE8Ak/EXCj2B3wFX1GdC6ltzerLV0JrJtrjLOZcGdAXu\nAcYAb5mZxTZZx5amkleaSjpiqBewNKx7pXOuuK4z0rasWaTtU9dtpm3sHeF2CM/rIhK9/cAU4EfV\nDH8R+BToCNwLzDCzTgBmdjEwEbgAyAL6AA8E010NOCAD2AfcHkzTG7gc+HX9r0pV5rXo2n+1cs5V\n+we0BQqAr9cwTjI+kMsL/p4AkoNho4Bc/N3hL4EtwJXAV4CVwC7gv0PmNQmYAbwE5AOfAKeEDJ8I\nrAmGLQOuChk2DvgP8Ktgvg8F/T4IGccB3wZWAbuB3wIWDIsHfgnsANYBdwXjJ0RY578CpUBhsH1+\njM/kDrgZ2BjM596QaeJC0r8T+BvQoZptOgrIjdB/DvDNkO7LgMXAHuBDIDtk2HrgJ0AOcBBIAIYE\n2zQ/2MbTgYeqScPxwKwgrTuAqUC7sPn/MJj/3mB+KSHDfxTs7zzglmDb9K1mWZXWK+jXEzgAXBZ0\nPx+a1vBtFKTnR0F69gPP4W8s/DNY33eB9sG4ZfvqtiB9W4B7asjjzwO/B/4dzOs9oFfI8BODYbuA\nL4Brw6b9LfB/wbQfA8eHDH8S2IQvCBcB5wT9u+HzV4eQcYcE+yKRqnn7TGBBsC8WAGeGbZvRYcfZ\nC2Hb4lZ8vp0LpAAvBPt+TzC/42oqK+qQL74FrA621etAt7Dj80788bkupN93gn75wP/g8+a8YJv9\nDUgKxm0PvAlsxx/fbwKZkfJZ6PbDH78FIX+HgedDysDngjyyGV+uxIeUGY8F+2RtkPaIZUbI/ot4\n/BGSn/HHXQlQFKTnReBQkK4C4NZgvFuA5cG6vk3lPBlpWx5NPh0YMu02gnKbupVr0eyf/8GX4/nA\nO0BGyPCxwIZgOfcSlq/DlpUc7JuNQXp/D6SGbmt8+bgVX55X6Xck+TUsDVmh+aG29Yt0DsDftCo7\nd46v4VwwjqrnumiPm7Jl/Tc+L68HbjjSbRlhXeKAnwX77kvgL/jjKhmfnx2+zF5TzbaIWEYGw1KB\nP+Pz03L8sRx6XugGvILPc+uACWHl4N+C9OTjg8XhwbBI5/ioy0VC8mZNy6lm2pqO06/iL7r3Bdtk\nUti0Z+OvBfYEw8dFc3yHzSPiegI/p3K59Jua9g9wCZXLrc9qK1MjpKOQ4BgJ8lAxkB50PwQ8EWX5\ndcRlX6TjsoZ9PQJYGGyLbcDjR1IWADdRUdb9P2oo60KmGQ2sD+vXH3/9lxbS733g28HvacDkkGEX\nAFuD3z8Bbg9+fxv4XfD7DeDsmtIS5TqeTkVe/QwYFTbtz4NpC4G+QG/8tVd+sC9/Q3AdE2HZtZ1r\neuOvdcquDX8bOq8o0hZxvfBlpKPiWuKMIO3v4a+HdgAv1bbtqqxPLRv6EvyBEfHCIxjnQeAjoDPQ\nKVi5/wnJ1MXAffiLy28FG24akIY/+RcBfUIKtMP4aj6J+Au+dUBiMPzr+II3DrgOX7h3DYaNC5Z1\nNz4oSSXyyetNoB0+ENgOXBKSEZcBmcFOfpeaL7rWU/niNysY/9lg2afgD5CTguHfC7ZTJv4E9Qzw\nYrSFQUgGKbvQHIo/6Z2Gv2C8OUhTckj6FgM9gvQk4Q/87wfb9ppgW1cXqPUFLgzS2gmfqZ8IW//5\nwf7ogD9Jlh38l+ALqZOB1sH+dtQhUAv6zwV+EVKY1haofYQ/oXQPts0n+IvjZPzF7/1h++rFIH2D\ngrxQ3UXf8/gDcmQwryepuMhvjT9Jjcfnu6H4g3FgyLS78AV4Aj7gnR4y7xvxd7oS8BdlWwkCmyDN\n3woZ93+B34fk97I0dMAXRmOD+Xwj6O5YTV6dRNVA7S/BuqTi75y9AbTC561hVJwcJwJv1lAe1JQv\nzg+2zdBgO/4amBt2fP47mC41pN/rQDq+vDgIzMTf+WuLP2ZvDsbtCHwtSHca8DLwWjXHT/n2C0t/\nD3zw/pWg+zX8sdoaX8bNp/LJa0UwTQdgNtXf3Knx+KNqfi5Pa/g+C7qvxAcQJwX7/GfAh9VtS44i\nnwbbcgs+f6YE3acdQbkWzf5Zg7+4SA26HwmGDcCf+MqOwcfx5X11x+wT+HzTIVjWG8DDYeelXwTz\nSq2mX53za1gasqh6cRZx/ao5BxTjz6+J+JubB6i42RSeP8ZR9VwX7XFTtqzHg/U8F39uPeFItmWE\ndbkFn1f7AG2AvxMS0FHDuSGKMvIR/EVQe3wezKHihkccPnC4D3/89cHfULk45JgqCrZtPPAw8FFY\nWRZablZbLlZTDo6OZjlh09V2nI7Cn6/igGz8efbKYFhP/HnqG/g80xEYHM15KCwNNZX/c6h6U7Wm\n/TOJsItpaihTI6RlLvC14Pc7+OPn0pBhV0VRfh3VOTrCcVlToDYPGBv8bgOcXteygIqy7mx8vn0M\nf644kkDtKmB5WL/fAL8Ofn8GXBcyLCNIZ0f8TYGXgjS8hL8pdRXwp5rSEWV53h0fhH4Fn5cvDLo7\nhUy7EV92JeDz8zwqyqiR+LxeXaBW27lmXrBdk4LtvI+Ka6Jo0lbdelXaz0G/F/E3FuPw589ag9wq\n61PLhr6BILquYZw1BBc1QffFZZkFn6kLqbgDnRasxGkh4y+ioqCZROWCMg5/gXBONcteDFwR/B4H\nbAwbPo6qJ6+zQ7r/BkwMfs8ipLDAZ/qIF13hB2fYDgqN2ucDY4Lfy4ELQoZ1xR98kS7qRuHv5u0J\n+yum4kLzaYKAOGS6L4BzQ9J3S8iwkfgLUAvp9yHVBGoR0nQl8GnY+t8Y0v0oFUHEFEIuQPAZutqT\nMdUHatOBZ4Pfz1N7oBZ6F/gV4OmQ7rsJDtSQfXViWPqfqyZ9z1M5uGqDv7PYA3/D4P2w8Z+hIih8\nHvhjyLCvACtq2M67CZ4iA98EZgW/DX+yGRmet/EB2vyw+cyj4m7qemoP1PqEDL+FsCe00f7Vki+e\nAx4N246HgayQ4/P8sPk54KyQ7kXAT0K6f0nIDYSwaQcDuyPlMyIEavhCt3z++KD/ICEXn/iLoNnB\n71kEQWjQfRHVB2o1Hn/UPVD7J8GTtaA7Dn8h3yvStuQo8mmwzp+Gr1MwLOpyLcr987OQ7u8A/wp+\n30flY7A1/m59lYsX/LG
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a200d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = evodumb['mean']-evoherd['mean']\n",
    "m = evodumb['max'].loc[0].sum()\n",
    "diff = diff / m\n",
    "diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
    "          title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 100% herd agents.');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:08:46.641058Z",
     "start_time": "2017-11-08T19:08:46.052870+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73a1cd97b8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e6d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXFwcwcHBjBw5kkmTJjF
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e6d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e6de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(evohalfherd['std']/m).plot()\n",
    "(evodumb['std']/m).plot()\n",
    "(evowise['std']/m).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc": {
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    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
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   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
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}