soil/examples/newsspread/NewsSpread.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T16:22:30.732107Z",
     "start_time": "2017-11-08T17:22:30.059855+01:00"
    }
   },
   "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": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T16:22:35.580593Z",
     "start_time": "2017-11-08T17:22:35.542745+01:00"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib 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": 3,
   "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: 300\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: 300\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: 300\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",
      "    state_id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_class: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    state_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: 300\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",
      "    state_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: 300\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",
      "    state_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": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-08T18:07:46.781745Z",
     "start_time": "2017-11-08T19:07:41.146659+01:00"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evodumb \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msoil_output/Sim_all_dumb/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m;\n",
      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:14\u001b[0m, in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m iterable \u001b[38;5;241m=\u001b[39m _read_data(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgroup_trials\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(iterable)\n",
      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:201\u001b[0m, in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    199\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trials)\n\u001b[1;32m    200\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x, trials))\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39magg(aggfunc)\u001b[38;5;241m.\u001b[39mreorder_levels([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m] ,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:425\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    422\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "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": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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": 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69OlUVFQwffp03n33XQCuvPJKbrjhBk4++WQ++MEPsmTJktQ+P/vZzzj++OOpqKjgxhtv\n7PJ7//Vf/5Wf/vSnndZXV1dz4YUXUlFRwUknncTatWsBmD9/PrNnz+b000/ngx/8IHfccUdqn1/9\n6leccMIJTJ48mauvvpp4PL7P/x79rcegN7MyMxvpzxcAZwEbgGXADH+zWcAT/vyT/jL++8875zpd\n0YvI0HbdddexePFiampq2q2//vrrmTlzJmvXruWyyy7jhhtuSL23bds2VqxYwdNPP828efMAePbZ\nZ9m4cSMrV65kzZo1rFq1iuXLl2f8zksuuYTVq1ezadOmdutvvPFGpkyZwtq1a/npT3/KzJkzU+/9\n/e9/55lnnmHlypX88Ic/JBqNsmHDBh5++GH+8pe/sGbNGsLhMIsXL+6rf5o+l00/+rHAIjML450Y\nfuuce9rM1gMPmdmPgVeAhf72C4EHzWwT3pX85/uh3CJykCsuLmbmzJnccccdFBQUpNa/9NJLPPbY\nYwBcccUVfPvb3069d+GFFxIKhTjyyCPZsWMH4AX9s88+y5QpUwCor69n48aNnHbaaZ2+MxwO861v\nfYubbrqJc889N7V+xYoVPProowCceeaZ7N69O3UCOu+888jPzyc/P5/y8nJ27NjB0qVLWbVqFccf\nfzwATU1NlJeX9+U/T5/qMeidc2uBKRnWv4VXX99xfTPwuT4pnYgE2te+9jWmTp3KVVdd1eU26T1O\n8vPzU/PJigLnHN/97ne5+uqrs/rOK664gptuuomjjjqq02dl+t707wyHw8RiMZxzzJo1i5tuuimr\n7xxoGgJBRAZMSUkJl1xyCQsXLkytO/nkk3nooYcAWLx4Maecckq3n3HOOedw3333UV/v9QJ///33\n2blzJwDTp0/n/fffb7d9bm4uX//617n99ttT60477bRU1csLL7xAaWkpxcXFXX7n9OnTWbJkSep7\nqqureeedd7I97ANOQS8iA+qb3/xmu943d9xxB/fffz8VFRU8+OCD/OIXv+h2/7PPPpsvfvGLfOxj\nH+OYY45hxowZ1NXVkUgk2LRpEyUlJZ32mTNnDrFYLLU8f/58KisrqaioYN68eSxatKjTPumOPPJI\nfvzjH3P22WdTUVHBJz/5SbZt29bLIz9wbDC0k06bNs1VVlYOdDFEJM2GDRs44ohMPakPDuvWreO+\n++7jtttuG+ii9IlMv4eZrXLOTetpX13Ri0ggHX300YEJ+f2loBcRCTgFvYhIwCnoRUQCTkEvIhJw\nCnoRkYBT0IuIBJyCXkQGraamJj7xiU8Qj8fZunUrM2bMyLjd6aefTk/34vzgBz/gueee63ablpYW\nzjrrLCZPnszDDz/cq7Ju3ryZX//6173aB7xROZMjcX7+859n48aNvf6MnijoRWTQuu+++7jooosI\nh8Mceuih7YYm7q0f/ehHnHXWWd1u88orrxCNRlmzZg2XXnpprz5/X4M+3bXXXsstt9yyX5+RSTaj\nV4rIEPfDp15n/dbaPv3MIw8t5sbPHNXtNosXL06F5+bNmzn//PNZt24dTU1NXHXVVaxfv54jjjiC\npqamHr/vyiuv5Pzzz2fGjBlMmDCBWbNm8dRTTxGNRnnkkUcoKSnh8ssvp6qqismTJ/Poo4+yd+9e\nvvGNb1BfX09paSkPPPAAY8eOZdOmTVxzzTVUVVURDod55JFHmDdvHhs2bGDy5MnMmjWLG264gXnz\n5vHCCy/Q0tLCddddx9VXX41zjq9+9as8//zzTJw4sd2AaqeeeipXXnklsViMnJy+i2dd0YvIoNTa\n2spbb73FhAkTOr135513MmzYMNauXcv3v/99Vq1a1evPLy0tZfXq1Vx77bXceuutlJeXc++993Lq\nqaeyZs0aDj/8cL761a+yZMkSVq1axezZs/n+978PwGWXXcZ1113Hq6++yosvvsjYsWO5+eabU/t+\n/etfZ+HChYwYMYKXX36Zl19+mXvuuYe3336b3/3ud7zxxhu89tpr3HPPPbz44oupMoVCIT784Q/z\n6quv7vO/Wya6oheRHvV05d0fdu3axciRIzO+t3z58tQDSSoqKqioqOj151900UUAHHfccanx79O9\n8cYbrFu3jk9+8pMAxONxxo4dS11dHe+//z6f/exnAYhEIhk//9lnn2Xt2rWp6qaamho2btzI8uXL\n+cIXvpCqjjrzzDPb7VdeXs7WrVs57rjjen1MXVHQi8igVFBQQHNzc5fvp49Tvy+S48wnx5jvyDnH\nUUcdxUsvvdRufW1tdlVYzjn+8z//k3POOafd+t///vfdlr25ubndg1j6gqpuRGRQGjVqFPF4PGPY\np48fv27dutQzXgFmzpzJypUr9/v7P/KRj1BVVZUK+mg0yuuvv05xcTHjxo3j8ccfB7yeOo2NjRQV\nFVFXV5fa/5xzzuHOO+8kGo0C8Oabb9LQ0MBpp53GQw89RDweZ9u2bSxbtqzd97755pvtHorSFxT0\nIjJonX322axYsaLT+muvvZb6+noqKiq45ZZbOOGEtofdrV27lrFjx+73d+fl5bFkyRK+853vcOyx\nxzJ58uRUffqDDz7IHXfcQUVFBSeffDLbt2+noqKCnJwcjj32WBYsWMCXvvQljjzySKZOncrRRx/N\n1VdfTSwW47Of/SyTJk3imGOO4dprr+UTn/hE6jt37NhBQUFBn5Q/ncajF5GMBsN49K+88gq33XYb\nDz74YFbb19bWMmfOHB555JF+Lln/WLBgAcXFxcyZM6fTexqPXkQCacqUKZxxxhnE4/Gsti8uLj5o\nQx5g5MiRzJo1q88/V42xIjKozZ49e6CLcMB095D0/aErehGRgFPQi4gEnIJeRCTgFPQiIgGnoBeR\nQasvhynuS7fffjuNjY293u9ADEmciYJeRAatvhymuC91F/TZdgXtryGJM1H3ShHp2R/mwfbX+vYz\nDzkGzr252036cpji008/nRNPPJFly5axd+9eFi5cyKmnnko8Hs84nPALL7zArbfeytNPPw3A9ddf\nz7Rp06itrWXr1q2cccYZlJaWsmzZMgoLC/nGN77BM888w89//nOef/55nnrqKZqamjj55JO56667\nOo1v019DEmeiK3oRGZT6Y5jiWCzGypUruf322/nhD38I0OVwwl254YYbOPTQQ1m2bFlqnJqGhgaO\nPvpo/va3v3HKKadw/fXX8/LLL6dOSsmTRbr+GpI4E13Ri0jPerjy7g/9MUxx+tDEmzdvBroeTjgv\nLy/rsobDYS6++OLU8rJly7jllltobGykurqao446is985jOd9uuPIYkz6THozWw88EvgECAB3O2c\n+4WZlQAPAxOAzcAlzrk95v198gvg00AjcKVzbnX/FF9Egqo/hinONDRxV8MJr1ixgkQikVruriyR\nSIRwOJza7itf+QqVlZWMHz+e+fPnd7lvfwxJnEk2VTcx4JvOuSOAk4DrzOxIYB6w1Dk3CVjqLwOc\nC0zyX3OBO/u81CISeAdqmOKuhhP+wAc+wPr162lpaaGmpoalS5em9uk4JHG6ZHlLS0upr6/vtgG5\nP4YkzqTHK3rn3DZgmz9fZ2YbgMOAC4DT/c0WAS8A3/HX/9J5w2L+1cxGmtlY/3NERLKWHKa440O9\nr732Wq666ioqKiqYPHnyfg1T/KUvfYnNmzczdepUnHOUlZXx+OOPM378eC655BIqKiqYNGkSU6ZM\nSe0zd+5czj33XMaOHdtpPPmRI0fy5S9/mWOOOYYJEyZw/PHHZ/ze/hqSOCPnXNYvvGqad4FiYG+H\n9/b406eBU9LWLwWmZfisuUAlUHn44Yc7ERlc1q9fP9BFcKtXr3aXX3551tvX1NS4GTNm9GOJ+s5t\nt93m7r333qy3z/R7AJUui+zOuteNmRUCjwJfc8519yytTBVnnQa9d87d7Zyb5pybVlZWlm0xRGQI\nCfIwxf01JHEmWQW9meXihfxi51zyKbo7zGys//5YYKe/fgswPm33ccDWvimuiAw1s2fPTjV0BslV\nV13V7/3nk3oMer8XzUJgg3PutrS3ngSSp6NZwBNp62ea5ySgxql+XuSg5AbBE+hk/3+HbE4nHweu\nAF4zszX+uu8BNwO/NbM5ePX2n/Pf+z1e18pNeN0r+2ckfRHpV5FIhN27dzN69Oh96soofcM5x+7d\nu4lEIvv8Gdn0ullB5np3gOkZtnfAdftcIhEZFMaNG8eWLVuoqqoa6KIMeZFIhHHjxu3z/rozVkQy\nys3NZeLEiQNdDOkDGutGRCTgFPQiIgGnoBcRCTgFvYhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBJyC\nXkQk4BT0IiIBp6AXEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CLiAScgl5EJOAU9CIiAaegFxEJ\nOAW9iEjAKehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiIgGnoBcRCTgFvYhIwCnoRUQCTkEv\nIhJwPQa9md1nZjvNbF3auhIz+5OZbfSno/z1ZmZ3mNkmM1trZlP7s/AiItKzbK7oHwA+1WHdPGCp\nc24SsNRfBjgXmOS/5gJ39k0xRURkX/UY9M655UB1h9UXAIv8+UXAhWnrf+k8fwVGmtnYviqsiIj0\n3r7W0Y9xzm0D8Kfl/vrDgPfSttvir+vEzOaaWaWZVVZVVe1jMUREpCd93RhrGda5TBs65+52zk1z\nzk0rKyvr42KIiEjSvgb9jmSVjD/d6a/fAoxP224csHXfiyciIvtrX4P+SWCWPz8LeCJt/Uy/981J\nQE2yikdERAZGTk8bmNlvgNOBUjPbAtwI3Az81szmAO8Cn/M3/z3waWAT0Ahc1Q9lFhGRXugx6J1z\nX+jirekZtnXAdftbKBER6Tu6M1ZEJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8ichC69K6Xst5W\nQS8iMghcetdLvQrv3uixH72IiOybZHA/fPXHetzWOUfcObbsaaS2KUZtc5Tapii1zTF/Gm23fv22\n2qzLoaAXkSGtN2Hc07aJhKO2Ocqu+hZ21beyu6GVWDzB//z5H9T5QV3XHKWu2QvsuuaYN98Upa4l\nBsAp/7Gsy+8vys+huCCXokjvoltBLyKB0pvgbo7GaY0lSDjHpp31xBIJYnFHNJ4glvCncUcskSAa\nd+xuaCWRcPzPn//Bbj/Md9W3sNufVje0Ekt0HrD35j/8nXDIKIrkUBzxgrooksPhJcMo8pf/tH47\n4ZBx/ZmTKI7kUlzgbTuiIJfiSC6FkRzCobYBgi+96yXWdfqmzBT0IjLoZXMlXd/qXRk3tsaIxR1/\neG0b1Y2t7Glopbohyp7GVqobWtumDa00tMZTn3HWbX/Oujw3/+HvRHJDlBbmU1qYz6EjIxxz2AhG\nF+ZRWpifmt78hw3khEIs/vKJFOSGMcs0krtng18Vc8m08V1us6/MG55mYE2bNs1VVlYOdDFE5ADp\nKridc9Q2xaiqb6GqrsWvAmnhnuVvEUs4Pvah0X51R7StyqM5Rn1rjO6ibHhemFHD8ygZnseoYd40\n+Xqk8j1CZvzLWZPIDYfICRm54ZA3HzZyw0ZOyJuf9+haQmb8Zu5JDMvr+Tq5N39d7AszW+Wcm9bT\ndrqiF5GMehtS3W0fjSeoqmthR20zO2qb2V7bTDSW4DtL1lLlh/muOq8qpDWeyPj5OSHjlXf3pqo9\nxpcMS1WDFEdyUlUg9654m5yQcdslkykZnsfIYblEcsNdlnv5m94T7i6YnPFheO0kwz2bkIf+C/je\nUtCLDBH7e3XpnKMllqAlmqApGqc5Gm833dPYSjSW4Pbn3mRHbVuo76htYXdDS8Yr7mVv7KS0MJ+y\nonwmlRdRWpRHmb9cmja95sFKzCyrsv/ulfcBOPLQ4qyOqzf/HoMluHtLQS9yEOtteDvn9QrZ2xBN\nq7/26q29uusoe/zltVtqiCccU370LM3RBM2xeLfVI0m3P7eR0sI8xhRHGFMcoWLciNT8mOJ8yosi\n/OCJdeSEjN9ec3JW5e6ubrujgzWM+5OCXmQQ6Sq4G1pi7Eqrt66qa6GqvpW3dzUQTziueXAVLbE4\nLbEErbFE2rRt3Z7GVpyDivnPZvzucMj8+utcRg7LI5IbIhwyzj16LAV5YSI5ISJ5YSI5YSK5YQry\nQt68v+64vANjAAALP0lEQVTH/7ue3LDxyDUnkxvu/l7Mnt7vSOG9fxT0Iv0sGd4PzT2JllgideNL\nXbN3M0x6/+r3qhuJ+cGdrLuuqmuhMa13SJIZhM0Ih4y3dtWTnxMmPydEfm6IokiOt5wbIi/srXtu\n/Q5CZsw+ZSKjhuUxanhuqmFy1PA8ivJz2l05J8v97xcendVxFuZ7cZJNiCu4Dyz1uhFh/26aSSQc\n1Y2tbK9pZluN19C4vaaJ7TUtbK9tYtU7e4jFHSGzLhsa0+WEjImlwzvVU3vTPMqK8ikrzKdkeB6X\n3fu3fS63HPzU60aGtGxCLVmdsau+hZqmKPGE46lXt6ZVfXSoCoknaInGecuvLplx54ts9xsco/H2\nF0w5IaO8KJ9DRkQYlhsmJxLi4uPG+T1Gcin2b5wpLkguez1Grrp/ZdaNjj0d3/5sK8GioJeDRnfh\nHYsnqG2OUdPkjQOytzFKPJHg/r+8TXVDK7vqW6lu8O5g3N3Qyu76FmqbY50+56u/eSXjd4eMVFVI\nQ0uMsHl9rY+fUMIhIyIcUhzhkBERxvrzowvzU3cxJsv9nU99tMdj7E2jo0i2VHUjAyZTcCcSjpom\nr0dIdUMru+vb7mR88KXNxBKOKYePSgV6bVOUmqZouzscOwoZqZtjRg/Pp6Qwj9LheZQM9+5gHD08\njzuWbiQcMhZcOpm8nFCqvtubD5GTVu+sKhAZLFR1IwMiUwgme4x4DYttY4O8vauBWDzBpXe9lHZ7\nuleFkknIICcU4r3qRooLchlfMiw1DsiIAq8aZESBN//zZ98gHAqxaPYJjCjIbTdGSCYPvLgZgElj\nino8RgW8HGwU9NKtrq5em6NxdtW3sLPO7+pX582/vauBaDzBZ//7L/7djq00RTNfbYdD3u3lCeeY\nWDqc4z5Qwmi/B8jo4e1vUy8Znses+1ZmLEsmdy9/C/Cu5LOh8JYgU9XNEJMpuJuj8U5jXdf442Df\n9ed/EIsnOGHiaD/Mm6mqy1y/DaTGCZn6gZGpAZ+8Vx6lfm+RUr/HyBULs+8xIiKdZVt1o6APgGR4\nPzjnRPY2etUf1Q2t7G1spbqxlb3+8p7GVp7fsJNYIkF5cSQV7K2x7rv8hQzGlwxL3ZpeVpRPeVHb\nfFlhhPJiL7wv70V3PxHZP6qjP4il32BT3xJLjXm9K3lXZIfl17fWEo0n+Kd/+0OXnzksL8yoYXm0\nxhPkhIwjxha3G/O6uMDv8tehzvu6X60mFOqf7n4icmAo6A+Q9CqTxtYYu+paqapvTt3Knqzn3lXf\nwrqtNUTjjo/+nz/SkuFq2wxKhnnjXZcW5VGYn0Nu2LjsxA8wcngeJcPyGDUsl1H+kKzpo/cly/Ff\nX5yaVbkfuTa7sUhEZPBS0PeBeMKxtzHZP7uV3cn+2vUt7PL7bCevuo/6wR8zdgVMhndZUT5hMyL5\nYS6aelhbHXfyrki/fjunl2OFJOmKW2ToUdCnmXHni8QSCW66qKJdg2TbvD9tivG3t3cTizuG54ep\nbmglU4/AZHiPLszDDIbn53DB5ENTt7AnGyfLi9qHd/Kq+/vnHXkgD19EAirwjbHesKwxdtY2s9Pv\nNbKztsWf98bMrqprYWdtc7c33UDbg3mLC3LZsqeRnJDxqaPHUurfdDO6MHkDjjcdNSyv092RuqIW\nkb4S6MbYWDzBnsYouxu8ftq7G/wnrvs35eyub2VXQyt/31ZLazyRcQztgtww5cX5jCmKcOShxZz+\nkTKeW7+DnLDx7XM+2qlRsijS80033VHAi8hA6ZegN7NPAb8AwsC9zrmbs9mvoSWWugEn/co7vf+2\n96T2zH+F5ISs3RW110gZYs4pEyn3H3jgTfMp7DAkK8CNnzlqP49cRGTw6fOgN7Mw8F/AJ4EtwMtm\n9qRzbn1X+6x7v6bLRsrcsHn9t4sjjC8ZRnVDK7lh45pPfMirKvGrTEoL8yiO5BLaj6tuEZEg6o8r\n+hOATc65twDM7CHgAqDLoA+FjEuOH+9dcRflp66+y4ryGVmg8BYR2R/9EfSHAe+lLW8BTuy4kZnN\nBeYCHH744ao2ERHpJ/vWGbt7mS6/O1WqO+fuds5Nc85NKysr64diiIgI9E/QbwHGpy2PA7b2w/eI\niEgW+iPoXwYmmdlEM8sDPg882Q/fIyIiWejzOnrnXMzMrgeeweteeZ9z7vW+/h4REclOv/Sjd879\nHvh9f3y2iIj0Tn9U3YiIyCCioBcRCTgFvYhIwA2K0SvNrA54Y6DLcQCUArsGuhAHwFA4zqFwjKDj\nHOw+4Jzr8UakwTJ65RvZDLV5sDOzSh1nMAyFYwQdZ1Co6kZEJOAU9CIiATdYgv7ugS7AAaLjDI6h\ncIyg4wyEQdEYKyIi/WewXNGLiEg/UdCLiATcgAe9mX3KzN4ws01mNm+gy9MfzGyzmb1mZmvMrHKg\ny9NXzOw+M9tpZuvS1pWY2Z/MbKM/HTWQZewLXRznfDN73/9N15jZpweyjPvLzMab2TIz22Bmr5vZ\nv/jrA/V7dnOcgfo9OxrQOnr/+bJvkvZ8WeAL3T1f9mBkZpuBac65g/GGjC6Z2WlAPfBL59zR/rpb\ngGrn3M3+iXuUc+47A1nO/dXFcc4H6p1ztw5k2fqKmY0FxjrnVptZEbAKuBC4kgD9nt0c5yUE6Pfs\naKCv6FPPl3XOtQLJ58vKQcA5txyo7rD6AmCRP78I73+ig1oXxxkozrltzrnV/nwdsAHvsaCB+j27\nOc5AG+igz/R82SD+ozvgWTNb5T8rN8jGOOe2gfc/FVA+wOXpT9eb2Vq/auegrtJIZ2YTgCnA3wjw\n79nhOCGgvycMfNBn9XzZAPi4c24qcC5wnV8VIAe3O4EPAZOBbcDPB7Y4fcPMCoFHga8552oHujz9\nJcNxBvL3TBrooB8Sz5d1zm31pzuB3+FVWQXVDr8eNFkfunOAy9MvnHM7nHNx51wCuIcA/KZmlosX\nfoudc4/5qwP3e2Y6ziD+nukGOugD/3xZMxvuN/pgZsOBs4F13e91UHsSmOXPzwKeGMCy9Jtk+Pk+\ny0H+m5qZAQuBDc6529LeCtTv2dVxBu337GjA74z1uzHdTtvzZX8yoAXqY2b2QbyrePBGC/11UI7R\nzH4DnI43xOsO4EbgceC3wOHAu8DnnHMHdUNmF8d5Ot6f+Q7YDFydrMs+GJnZKcD/A14DEv7q7+HV\nXwfm9+zmOL9AgH7PjgY86EVEpH8NdNWNiIj0MwW9iEjAKehFRAJOQS8iEnAKehGRgFPQy5BgZiPN\n7Cv7sN/3+qM8IgeSulfKkOCPa/J0cvTJXuxX75wr7JdCiRwguqKXoeJm4EP+WOM/6/immY01s+X+\n++vM7FQzuxko8Nct9re73MxW+uvu8ofaxszqzeznZrbazJaaWdmBPTyRrumKXoaEnq7ozeybQMQ5\n9xM/vIc55+rSr+jN7AjgFuAi51zUzP4b+Ktz7pdm5oDLnXOLzewHQLlz7voDcWwiPckZ6AKIDBIv\nA/f5A1497pxbk2Gb6cBxwMvekCkU0DbIVwJ42J//FfBYp71FBoiqbkRIPVzkNOB94EEzm5lhMwMW\nOecm+6+POOfmd/WR/VRUkV5T0MtQUQcUdfWmmX0A2OmcuwdvdMOp/ltR/yofYCkww8zK/X1K/P3A\n+39phj//RWBFH5dfZJ+p6kaGBOfcbjP7i/+A7z84577VYZPTgW+ZWRTv+bDJK/q7gbVmtto5d5mZ\n/Rve08JCQBS4DngHaACOMrNVQA1waf8flUh21Bgr0gfUDVMGM1XdiIgEnK7oZUgxs2OABzusbnHO\nnTgQ5RE5EBT0IiIBp6obEZGAU9CLiAScgl5EJOAU9CIiAaegFxEJuP8Pj290SDnIisQAAAAASUVO\nRK5CYII=\n",
      "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": {
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bKBER6Tu6M1ZEJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8ichC69K6Xst5W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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YEZGAU6AXEQk4BXoRkYBToBcRCTgFehGRgFOgFxEJOAV6EZEB4OLfv8nFv3+z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\n/wcp5D+/6eaXZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e630f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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I5OtMUBpjiIkyTM9JZXpO6iGPNzS1sGd/rf8L4A/vO4XkdlfU8umWMmobm/1tY7weRqYl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\nn3zyCVlZWdx///3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lC/RP/uDsJPXGOBe58PFEOzXP0yfAxEucQxjTJ8A7d4InCq5/pfe2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SZv3XX3/N2rVrOeOMMwDrLlPZ2dkxD3m8ZMkS1qxZE/kVUVlZSVFREe+99x6X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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e63cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NoIFea/094HsAZon+21rr65RSLwJXYrS8uRF4xdzkz+b8++b6t6R+XojxZ7hKxPG2eONl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\nAAAAAElFTkSuQmCC\n",
      "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": 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z+PBhduzYEehqK6VUmdBATCmllFI+PfbYYwXenjhhwgTee+894uPj+fDDD3nj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\npBoziEmSJElSjf1/jS69m/Nq6awAAAAASUVORK5CYII=\n",
      "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": 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MTOT73/8+TzzxRHm/kSNHllddnDNnDhkZGaSnp1e7zAsuuIAZM2aUL2fXrl1s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\n",
      "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": {
      "image/png": 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FDu9cGdTh4MyZbLKzTpOblUlO9ikyDu4h+LsZtHIcJ27A3xgwdGKV7NuYymKB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\nGzxTGIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e6d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+5W/TBKHd4EesLuU8nl616D38aeBpq0tRNZzedqqqvYwzqXQ8vISYOpfTpGVb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\nSimlAA0EpZRSdv8Pp8WopGa5RzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73a1e6d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cK5/1Z+LEicyZM4fly5cXuYzyTGxaLOP2jeNG3A2+bf8tz7k8V2B6IwMjBnkM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\n0I3S/ZT+E8t2LuOe+t3okvIjDJxWaYqbVwZv/ud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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": "venv-soil",
   "language": "python",
   "name": "venv-soil"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "30px",
    "width": "252px"
   },
   "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,
 "nbformat_minor": 2
}