soil/examples/NewsSpread.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-11-02T09:48:41.843Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import soil\n",
    "import networkx as nx\n",
    " \n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# To display plots in the notebook\n",
    "%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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 288K\r\n",
      "drwxr-xr-x  7 j users 4.0K May 23 12:48 .\r\n",
      "drwxr-xr-x 15 j users  20K May  7 18:59 ..\r\n",
      "-rw-r--r--  1 j users  451 Oct 17  2017 complete.yml\r\n",
      "drwxr-xr-x  2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
      "drwxr-xr-x  2 j users 4.0K Oct 17  2017 long_running\r\n",
      "-rw-r--r--  1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
      "drwxr-xr-x  4 j users 4.0K May  4 11:23 newsspread\r\n",
      "-rw-r--r--  1 j users 225K May  4 11:23 NewsSpread.ipynb\r\n",
      "drwxr-xr-x  4 j users 4.0K May  4 11:21 rabbits\r\n",
      "-rw-r--r--  1 j users   42 Jul  3  2017 torvalds.edgelist\r\n",
      "-rw-r--r--  1 j users  245 Oct 13  2017 torvalds.yml\r\n",
      "drwxr-xr-x  4 j users 4.0K May  4 11:23 tutorial\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-11-02T09:48:43.440Z"
    }
   },
   "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_type: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_type: 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_type: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_type: DumbViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "  weight: 1\r\n",
      "- agent_type: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: false\r\n",
      "  weight: 1\r\n",
      "- agent_type: 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_type: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_type: 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_type: HerdViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_type: 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_type: WiseViewer\r\n",
      "  state:\r\n",
      "    has_tv: true\r\n",
      "    id: neutral\r\n",
      "  weight: 1\r\n",
      "- agent_type: 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/NewsSpread.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-11-02T09:48:43.879Z"
    }
   },
   "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)",
      "\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m     \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    210\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m                        copy=copy)\n\u001b[0m\u001b[1;32m    213\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2017-11-02T09:48:45.458Z"
    }
   },
   "outputs": [],
   "source": [
    "evodumb['mean'].plot(yerr=evodumb['std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-01T13:26:19.361423Z",
     "start_time": "2017-11-01T14:25:57.017418+01:00"
    }
   },
   "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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-01T13:26:20.461665Z",
     "start_time": "2017-11-01T14:26:19.363815+01:00"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa48c26df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa48c26d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa460c6c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa460d184e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMnznTx/iDYS4dP4kttZ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa46017c0b8>"
      ]
     },
     "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": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-01T13:27:56.226662Z",
     "start_time": "2017-11-01T14:27:55.987887+01:00"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAEXCAYAAADP3/fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF1lGiBOIxZheZB1ZG1g
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa46019e748>"
      ]
     },
     "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": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-01T13:28:05.554284Z",
     "start_time": "2017-11-01T14:28:05.324360+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEXCAYAAADcCLc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXAty//zMp/t7wP95/99
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa45f75d978>"
      ]
     },
     "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": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-01T13:32:12.118102Z",
     "start_time": "2017-11-01T14:32:11.479970+01:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa45f950e10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW7aML774gtOnT9O/f3/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa460d17c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa45f8fd4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj+fNm8eYMWP45ptvcHF
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa460444588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(evohalfherd['std']/m).plot()\n",
    "(evodumb['std']/m).plot()\n",
    "(evowise['std']/m).plot()"
   ]
  },
  {
   "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": "3.6.5"
  },
  "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
}