soil/examples/newsspread/NewsSpread.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:01.378353Z",
     "start_time": "2017-10-19T17:54:00.685043+02:00"
    },
    "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": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:02.678166Z",
     "start_time": "2017-10-19T17:54:02.508949+02:00"
    }
   },
   "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_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: infected\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: infected\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: infected\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.yml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:07.617556Z",
     "start_time": "2017-10-19T17:54:03.481983+02:00"
    }
   },
   "outputs": [],
   "source": [
    "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:07.832237Z",
     "start_time": "2017-10-19T17:54:07.620220+02:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f17bfc78390>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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LJSIDbPz48WzdupXS0tKBLkrgZWRkMH78+F7Zlq7IFZGEwuEwkyZNGuhiSC/T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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfce7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evodumb['mean'].plot(yerr=evodumb['std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:28.017296Z",
     "start_time": "2017-10-19T17:54:07.834047+02:00"
    },
    "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": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:28.963822Z",
     "start_time": "2017-10-19T17:54:28.020292+02:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LJSIDbPz48WzdupXS0tKBLkrgZWRkMH78+F7Zlq7IFZGEwuEwkyZNGuhiSC/T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\n2be7sd4P+qI8IgNFXTYlEPz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2uZpg92TTdX9qWp0VBtn54aMHZOUF7x+LBfP5/SA7D7Lyg2l1mep1RfOA15v9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\nocXJrmBn1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JJp+r93eKMbOpZrbBzJbGLDvMzN4xs5XBn4emssfqquTYJpjZ2pgJBM9LZY81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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vv/wyS5YswWw2M2nSJL755htiYmL47LPPeOKJJ5g7dy533HEHM2fOZOzYsRQX\nF2Oz2Xj++efL1wV49913CQ8P57fffqOkpIQrrriCkSNH8vvvv7Nnzx62bdtGVlYWXbt25a677gLA\nYDDQoUMHtm7dSu/evet8TLUhSb+CqBB/juYW89XvR6tM+kfzj7IpaxMtQlpI+WQhXOzUqVNERFR9\n8bV27dryOvuJiYkkJibWefs333wzAL179+bLL7+8YPmePXvYvn07V199NQBWq5W4uDjy8vI4evQo\nY8eOBSAwMLDK7a9YsYLU1NTy/13k5uayb98+1q5dy8SJE8u7rK66qnJl3tjYWI4dOyZJvyGYjAZG\ndIll8dZjPHF9F/yMlXu/vjvwHQBNg5q6Izwh3OZiV+SuEhQURHFxcbXLL/XCq6xkclm55PNprenW\nrRvr11ceZ+Ps2dr9j0drzeuvv84111xTaf7SpUsvGntxcTFBQUG12kd9SJ/+ecb2akl2QSk/7av8\nwJjWmsX7F9O7WW8puSBEA4iMjMRqtVaZ+CuWQt6+fXv58IYAkydPZuPGS79TvHPnzpw8ebI86ZvN\nZnbs2EFYWBgtW7bk66+/Bux3/BQWFhIaGkpeXl75+tdccw1vvfUWZrMZgL1791JQUMDgwYNZuHAh\nVquVzMxMVq9eXWm/e/furVTr39kk6Z9nSKcYIoP9+HJz5RrcO7N3kn42ndHtRrspMiF8z8iRIyuN\nnFVmxowZ5Ofnk5iYyIsvvki/fudKfKWmphIXF3fBOnXl7+/PokWLePzxx+nZsydJSUn88ssvAMyf\nP5/Zs2eTmJjIwIEDOX78OImJiZhMJnr27Mmrr77KPffcQ9euXUlOTqZ79+5Mnz4di8XC2LFj6dix\nIz169GDGjBkMGTKkfJ9ZWVkEBQU5Jf5qaa3d/urdu7d2t/Fv/6LHv/2L1lrr//tqm+70xFJ9tqi0\nfPlzG57TyR8l69ySXD112VQ9ddlUd4UqRIPYuXOnu0PQmzdv1nfeeWet2+fm5upx48a5MCLXeuWV\nV/R7771X5bKqvg8gRdcx38qVvsNn0weU19IfmxxPicXGsu3HATDbzCw7uIwhrYYQ5l/9aDpCCOfq\n1asXw4YNw2qt3TOeYWFh/Pe//3VxVK4TERHBlClTXLoPSfpV6NUqgrbRIXzl6OJZf2w9OcU50rUj\nhBvcddddGI0eOyyHU02bNg2TybX310jSr4JSijFJ8fx6MJtjZ4pYsn8JEQERDIq/+FidQgjh6STp\nV2Nsr3gnM+uPAAAgAElEQVS0hv9uTuPHIz8yKmEUfkY/d4clhBCXRJJ+NVo3DaZ3m0j+u+s7Sqwl\n3ND+BneHJIQQl0yS/kWM7RVPtlpP8+CWJEbX/Yk/IYTwNJL0L6JvB4Ux+CBN9QApuyCEGziztLIz\n/fvf/6awsOYy7Odr6DLKVZGkfxHrMn9AKc2+/Z3qNJSiEMI5nFla2ZkulvRre3tpWRnlhiZJvxpa\na5YcWEKbkK5k54by8/7s8mXzRs1j3qh5boxOCN/gzNLKQ4cO5fHHH6dfv3506tSJn376CaDaEshr\n1qzhhhvO/Zb34IMP8sEHHzB79myOHTvGsGHDGDZsGABNmjThqaeeon///qxfv55nn32Wvn370r17\nd+67774qy7U3RBnlqkjBtWrsztlN2pk0ZvZ9ghd3+PHV5gyGdLKP5TvhHXstjrKHuYRo9JbNhOPb\nnLvN5j3g2uerXeyK0soWi4WNGzeydOlSnnnmGVauXMn7779fZQnk6jz00EO88sorrF69muho+7ga\nBQUFdO/evXyglq5du/LUU08BMGnSJJYsWcLo0ZWf82mIMspVkSt9h2nLpzFt+bTyz4sPLMbP4McN\n7a/l+sQ4vt+RRUFJw56RhfBlNZVWvvPOO4G6lVauWE45PT0dsJdA/uijj0hKSqJ///5kZ2fXua/d\naDRyyy23lH9evXo1/fv3p0ePHvz444/s2LGjyvXKyig3JLnSr4LFZmHpgaUMbjmY8IBwbu4Vzycb\nDrN8+3Fu6d3S3eEJ0fAuckXuKq4orVxVOWVdTQnkdevWYbOd+y3vYrEEBgaWPzVcXFzM/fffT0pK\nCq1atWLWrFnVruvqMspVkSv9KmzI3EB2cTY3tLP35/VuE0mrqCC+3nK0hjWFEM7SUKWVqyuB3KZN\nG3bu3ElJSQm5ubmsWrWqfJ3zyyhXVBZvdHQ0+fn5F/3x2dVllKsiV/pVWHxgMWH+YQxuORiwX1GM\nTYrnP6vTyDpb/dleCOFcZaWVR4wYUWn+jBkzmDZtGomJiSQlJV1SaeV77rmH9PR0kpOT0VoTExPD\n119/TatWrRg/fjyJiYl07NiRXr16la9z3333ce211xIXF3dBPfyIiAjuvfdeevToQUJCAn379q1y\nvw1SRrkKqqpflRtanz59dEPeY1uVsv78N4a/wdDPh3JDuxt4asBT5csPnipg2Mtr+Ot1l7Fql30g\nY/khVzRmu3btokuXLm6N4ffff+eVV15h/vz5tWp/9uxZ7r77bq+otPnqq68SFhbG3XffXav2VX0f\nSqlNWus+ddmvdO+cZ+XhlRRZihjdvvIv7W2jQ0hqFXHB4CpCCNdpzKWVG6KMclUk6Z9n8f7FxDeJ\nJykm6YJlNyfHs/t4HoWlchePEA2lsZZWbogyylWRpF9BqbWUDZkbuKHdDVXeGXBDYgtMBsWp/FI3\nRCeEEJdOkn4FOcU5aPQFXTtlokL8Gdo5hlP5JVU+YSeEEJ5Okn4F2cXZJEYn0iasTbVtxvZqidmq\nOVssXTxCCO8jSd+h0FxIkaWoxrr5w7vEYjQoTuSVNFBkQgjhPJL0HbKLs1EoRiWMumi7QD8jsaEB\n5BSUcuBkfgNFJ4Rv8tTSyhfTpEkTAE6ePMmoURfPJ+5QY9JXSs1VSp1QSm2vMC9KKfWDUmqf4z3S\nMV8ppWYrpdKUUqlKqdpVQXIzq81KTnEOYf5hRAZG1tg+LjwQpeDt/+1vgOiE8F2eUlq5treMVhQT\nE0NcXBw///yzCyKqv9pc6X8AnH+6mgms0lp3BFY5PgNcC3R0vO4D3nJOmK61KWsTZpuZpkFNa9Xe\nz2ggNjSALzcf5eiZmku6CiHqx5mlldPS0hgxYgQ9e/YkOTmZ/fv3V1s+GSAhIYFnn32WQYMG8d//\n/pf9+/czatQoevfuzZVXXsnu3bsBOHjwIAMGDKBv3748+eSTlfY5ZsyY8nIRnqLGm0S11muVUgnn\nzb4JGOqY/hBYAzzumP+Rtt/a8qtSKkIpFae1znRWwK6wLH0ZBmUgPCC81uvEhQeSnV/KnLUHmHVj\nw9bOEKKhvbDxBXbn7HbqNi+LuozH+z1e7XJnl1a+4447mDlzJmPHjqW4uBibzcaRI0cuuk5gYCDr\n1q0DYPjw4bz99tt07NiRDRs2cP/99/Pjjz/y8MMPM2PGDCZPnswbb7xRaf0+ffrwf//3fzXG1pDq\n26ffrCyRO95jHfPjgYp/xQzHvAsope5TSqUopVJOnjxZzzAundlq5odDPxAREIFR1f4BkACTkZuT\n4/l042FOyo+6QjidM0sr5+XlcfToUcaOHQvYk3lwcHCNMUyYMAGA/Px8fvnlF2699VaSkpKYPn06\nmZn2a9mff/6ZiRMnAvba+RW5o3RyTZz9OFhVtU6rvKFda/0u8C7Ya+84OY5aW5+5ntySXDpEdKjz\nujOGdmDRpgzm/nyQx0dd5oLohPAMF7sidxVnllau7rkak8l00fLJISEhANhsNiIiItiyZUudYnFH\n6eSa1PdKP0spFQfgeD/hmJ8BtKrQriXgWae58yw/uJxQ/1DC/MPqvG7b6BCu6xHH/PWHyC00uyA6\nIXyXM0srh4WF0bJlS77++msASkpKKCwsvGj55PPXb9u2bXldH601W7duBeCKK65g4cKFABf03+/d\nu7f8dwhPUd+k/y1QViloCvBNhfmTHXfxXA7kenJ/frGlmFWHV3F1m6sxqPr9KR4Y1oH8EgsfrU93\namxCiHOllc83Y8YM8vPzSUxM5MUXX6xVaeX58+cze/ZsEhMTGThwIMePH69UPvmOO+6oVD75fAsW\nLOD999+nZ8+edOvWjW++sae91157jTfeeIO+ffuSm5tbaZ3Vq1dz/fXX1/fwXUNrfdEX8CmQCZix\nX8nfDTTFftfOPsd7lKOtAt4A9gPbgD41bV9rTe/evbU7rEhfobt/0F2vP7ZeT102VU9dNrVW641/\n+xc9/u1fyj/fNW+jTnrme51fbHZVqEI0uJ07d7o7BL1582Z955131rp9bm6uHjdunAsjqpsrr7xS\n5+TkOGVbVX0fQIquRY6t+Krx8lZrPVFrHae19tNat9Rav6+1ztZaD9dad3S85zjaaq31A1rr9lrr\nHlprz3haohrLDi6jaWBT+jarepCD2nrgqg6cLjTz6cbDTopMCAHeXVr55MmTPPLII0RG1vzsT0Py\n2Sdy80vzWZuxlpEJIzEaLq1sa3LrSAa0a8qcnw5QYqn7QxxCiOp5a2nlmJgYxowZ4+4wLuCzSX/1\nkdWUWEu4ru11dV73s+kDLhg168GrOpB1toQvNskgK6Lx0FJN1iM483vw2aS/7OAy4kLiSIy5+P29\ntTWwfVN6torg7f/tx2K11byCEB4uMDCQ7OxsSfxuprUmOzubwMBAp2zPJwdGP1N8hvXH1jOp66R6\n37VzPqUUDw7rwL0fpbAkNZMxvezPpE14Zz0g4+kK79OyZUsyMjJw58OTwi4wMJCWLVs6ZVs+mfRX\nHl6JRVu4tu21Tt3u8Mti6dwslDdWp3FjzxYYDLV/eEQIT+Pn50fbtm3dHYZwMp/s3ll2cBkJYQlc\nFnXuKdp5o+Yxb9S8S9quwaC4f1h79p3I54ddWZcaphBCOJ3PJf2ThSf57fhvXNv22jo9xl1b1/eI\no03TYN5YnSZ9oUIIj+NzSX/FoRVodI2DpdSXyWhgxpD2pGbksi7tlEv2IYQQ9eVzSX/pwaV0juxM\nu4h2LtvH2OR4mocF8p8f01y2DyGEqA+fSvoZeRmknkx1+g+45wswGbl3cDs2HMwhr1gKsQkhPIdP\nJf3l6csBGNXW9eNWTuzXiqgQf46dqb40rBBCNDTfSvoHl5MYk0h8kyrHdXGqYH8Td12RwJkiMwUl\nFpfvTwghasNnkv6BMwfYc3pPvcou1NekAQkYlZJxdIUQHsNnkv6y9GUoFCPbjGywfYYH+REXEcjp\nQjM/7JT79oUQ7ucTSV9rzfKDy+nbvC8xwTENuu+48ECC/Iw8+fV2+VFXCOF2PpH0d+fsJv1susvv\n2qmKQSnaRYeQlVfMS9/vafD9CyFERY269s605dMA6BHdA5MyMaL1CLfE0STQxNSBCXzwSzo3JbWg\nd5sot8QhhBCN/kpfa83y9OUMjB9IRGCE2+L408jOtAgPYuYX22SgFSGE2zT6pJ9vziezINNlZRdq\nKyTAxN/HdGffiXzeXnPArbEIIXxXo0/6p4tPE2AM4KrWV7k7FIZdFsvoni14Y3UaaSfy3B2OEMIH\nNeqkr7UmpySHwS0HE+IX4u5wAHjqhq4E+Rv5y5fbsNmkCqcQomE16qSfV5qHxeb8wVIuRUxoAP93\nfRd+Sz/NJxsPuzscIYSPadRJP6c4B4MycGX8le4OpZJxvVtyRYemvLBsN8dzz9XmmfDO+vLhFYUQ\nwhUabdI3W82cLjlNZEAkgSbnDChcH59NH3DB+LhKKf4xpgelVhtPf7vdTZEJIXxRo036q4+sxqqt\nRAZGujuUKiVEh/CHEZ34fkcWy7dnujscIYSPaLRJf+Gehfgb/An3D3d3KNW658q2dI0L46lvdpBb\nJCUahBCu1yiTftrpNH47/hsxwTEuGQfXWfyMBl64JZFT+SW8sHy3u8MRQviARpn0y67yo4Oi3R1K\njXq0DOeuK9ryyYbDnJWCbEIIF2t0ST+/NJ/F+xczqu0o/Ax+7g6nVh4Z2YmWkUEcPFWATcu9+0II\n17mkpK+USldKbVNKbVFKpTjmRSmlflBK7XO8N+gvqd/u/5ZCSyETL5vYkLu9JMH+Jv4xtgfFZhvH\nZMAVIYQLOeNKf5jWOklr3cfxeSawSmvdEVjl+NwgtNYs3LOQHtE96B7dvaF26xRDOsUQ3cSfo2eK\nWbZN7uYRQriGK7p3bgI+dEx/CIxxwT6qtOH4Bg7mHuS2y25rqF06VULTEJoEmHh44RZ+STvl7nCE\nEI3QpSZ9DaxQSm1SSt3nmNdMa50J4HiPrWpFpdR9SqkUpVTKyZMnLzEMu093fUpkQCTXJFzjlO01\nNKNB0blZExKig7n3oxS2ZeS6OyQhRCNzqUn/Cq11MnAt8IBSanBtV9Rav6u17qO17hMTc+lDGGbm\nZ7ImYw03d7yZAGMAAPNGzWPeqHmXvO2GZDIa+Oiu/kQE+zN13kYOnipwd0hCiEbkkpK+1vqY4/0E\n8BXQD8hSSsUBON5PXGqQtfH53s8BGN95fEPszqWahwfy0d390MCk9zeQdba4xnWEEKI26p30lVIh\nSqnQsmlgJLAd+BaY4mg2BfjmUoOsSYm1hC/2fsGQlkNo0aSFq3fXINrHNOGDaX05XVDK5Pc3klso\n9/ALIS7dpVzpNwPWKaW2AhuB77TWy4HngauVUvuAqx2fXWpF+gpOl5z2qts0ayOxZQTvTOrDwVMF\n3P3hbxSVnhtmUSpyCiHqo95JX2t9QGvd0/HqprX+h2N+ttZ6uNa6o+M9x3nhVm3h7oUkhCVwedzl\nrt5VgxvUMZpXJySx6fBpHvxkM2arzd0hCSG8mNc/kbvj1A5ST6Vy22W3eXSdndqoqgwzwPWJcTx7\nU3dW7T7B41+kyohbQoh6M7k7gEv16e5PCTIFcWP7G90diktNurwNOfmlvLpyL9FNAtwdjhDCS3l1\n0j9TfIZlB5cxpsMYQv1D3R2Oyz00vAPZBSW8u/YArSKDaBER5O6QhBBexquT/pdpX1JqK/XaJ3Dr\nSinFrNHdyCkoZUlqJn5Gr++dE0I0MK/NGlablc/3fE6fZn3oGNnR3eE0GINB8cr4JMKDTBw4VcDL\n3+/BIj/uCiFqyWuT/k9Hf+Jo/tFGd5tmbfibDHSMDSWmSQD/WZ3GhHd/JeN0obvDEkJ4Aa9N+gt3\nLyQ2KJZhrYe5OxS3MBoU7WJCmD2xF3uO53Hdaz9JdU4hRI28MukfOnuIn4/9zK2db/WagVJc5cae\nLVj60JW0jWnCjAWb+etX2yg2Wyu1kQe5hBBlvDLpL9y9EJPBxLhO49wdikdo3TSYRf/fAKYPaccn\nGw5z43/WsTcrz91hCSE8kFfdvTNt+TSs2kra6TSubnO1V4yB21D8jAb+cm0XrmgfzSOfb2H06+t4\nanRXbu/X2t2hCSE8iNdd6ecU5ZBnzvPJH3BrY3CnGJY9PJh+baN44qvtPPDJZrm7RwhRzquu9LXW\nnCg6QefIziTFJLk7HLeqqlxDmZjQAD6c1o85Px3gpe/3YFCKDrEhDRidEMJTedWVfr45nyJLERMv\nm+j1dXZczWBQTB/SnkUzBqIU7MzM48+LtnIkR27tFMKXeU3St2kbGXkZ+Bn8uK7dde4Ox2sktYqg\ne3wYzcMC+HrLMa761xqe+GobmblF7g5NCOEGXpP0F+9fTIGlgPgm8QSZpOZMXZgMBto0DWHtY8OY\n0LcVn6ccYchLa3hm8Q5O5F04Kpfc4ilE4+UVSb/AXMC/N/+bEL8QmgY2dXc4Xqt5eCB/H9ODHx8d\nytikeD5af4jBL67muWW7yCkodXd4QogG4BVJ/53UdzhVdIpWoa2kL98JWkUF88K4RFY+MoRru8fx\n7toDXPnCj/xrxR5yi2RYRiEaM4+/e+fQ2UPM3zmfm9rfxN8H/d3d4TQqbaNDeHVCEvcPbc+/V+7j\n9R/T+PCXdMIC/YgNk5r9QjRGSmv3j8LUp08fnZKSUuWyB1c9SEpWCkvGLpGHsVxsx7FcXv1hHyt3\nZaGAoZ1juCkpnqu7NiMkwOOvD4TwOUqpTVrrPnVZx6P/Ja87uo7/ZfyPR3o/Igm/AXRrEc57U/ow\n+vWfOJVfyp7jefzhsy0E+Rm5umszxvRqwZUdYyrV8S/7wfdizw0IITyHxyZ9s9XMCxtfoE1YG+7s\ncqe7w/Epwf4mWkeZ+PTey0k5dJqvtxxl6bZMvt16jMhgP65PjOOmpHh6t450d6hCiDry2KT/ye5P\nSD+bzhvD38DP6NuVNN3FYFD0axtFv7ZRzBrdjZ/2neTrLcdYtCmDj389THxEEDatiQz2o9Riw9/k\nFfcFCOHTPDLpnyo6xdtb32ZQ/CAGtxzs7nAE9oFbhndpxvAuzSgosbBi53G+2XKMNXtOkplbTI9Z\n35PcOpJ+baPo3zaKXq0jCfI3VtqGdAUJ4X4emfRf//11ii3F/Lnvn90dik+qKSmHBJgY26slY3u1\n5OY3fyav2MKVHWPYmJ7N6z/u4zUNfkZFYsuI8v8p9GkjXUFCeAKPS/o7snfw1b6vmNx1Mm3D27o7\nHFEDP6OBqBB/nhrdFYCzxWY2HTrNhgM5bDyYzZy1B3hrzX4MCgL9jDQJMDF/fTqdmoXSuXkoEcH+\n7j0AIXyMRyV9rTXPb3ieyMBIpvec7u5wRD2EBfoxrHMswzrHAlBUauX3w6fZcDCHuT8fJLuglCe/\n2VHePjY0gM7NQ+kYG0rn5k3o1CyUjs1CufuD3wDpChLC2Twq6X938Du2nNzCswOfJdQ/1N3hiFqo\nKSkH+RsZ2CGagR2i+fVANlprXnOM67svK589WXnszcrjk42HKDafq/sfYDIQ6Gfgsf9upXl4IM3C\nAmkeZn9vFh5AdEgABoP96Wz5rUCI2vOYpF9oLuTVlFfp1rQbN3W4yd3hCBdRShEXHkRceBBDHf8b\nALDZNBmni8pPAnPXHaTYbGXtvpOczCvBdt4zhCaDIjY0gGbhgaSfKsDfZOC1lfuIauJP0xB/oiq8\nIoP9McoJQgjAg57InfzuZOZsm8P8a+eTFOvbA6SIysnZatOcyi/heG4xx88Wk+V4Hc8tIetsMSmH\ncjBbNdbzzwwOSkFEkB9RIf6cOFuCyai4umszwoP8CAv0IzzYr3w6LMgxHWQiPMiPye9vLI9DCE9T\nnydyXZb0lVKjgNcAI/Ce1vr56tomJidqvz/6MTJhJM9d+ZxL4hGNV9kJ4uN7+nO6oJTsgtLy95zy\n9xJyCkr5ad8pLFZNWJCJs0UWiszWi27boMCgFC0iggj2NxISYLK//I0E+5sICbC/Nwkw8t+UDJSC\nB4Z1wN9kIMBkwN9kwM9owN9ony6b/+jnW1FKMWdyH/yMCpPRgMmgMBkURoOSwoKiVjwm6SuljMBe\n4GogA/gNmKi13llV+2adm+k2T7VhydglxAbHVtVECKc4v3unxGLlbJGFs8Vmcovsr7OOV26RmY9/\nPYxNawa2b0pBqZWCEgsFpVYKSywUllopKLVQWGKl1MnjEPsZFSaDAZNRUVRqRSl7aWz7ycOIv1GV\nn0TOnVCMrN9/CqUUI7s2w2hQGJT9JGIyKAwGhVGde//vpiMoYNKANhgNBowKjEYDRnWufaV3pcpP\ngqrCuzpvvsL+uWx+WRtFhXmUrVvVekCFbSjOtQHOHYeyTxvOmzY62hoN5+2zEZ5IPSnpDwBmaa2v\ncXz+C4DWusrL+KC2Qfr1b1/nnh73OD0WIRpCqcXGxHfXY9Mwe2IvSiw2Si02Sq2Od4sNs9Vmn2+1\n8e8f9qK15q5BbTFbNRabzf5u1VhtNsw2jcVqn7d0WyZaw6CO0ZRazm3DfN72S602jp4uQqMJC/TD\nqu1dXjabLp+22vQFv4/4GvsJpuKJCCxWDcp+A0HFk1bFthVPZGUlyKNC/Cu1BzAYzp3UDEqVj1LX\nIsI++JOqFMu5TwrIOG1v2yqqrG2F5eedsw7nFLLrb9d6TNIfB4zSWt/j+DwJ6K+1frBCm/uA+wBC\nEkJ6Z6dlE2CUcr5CuJrW9sRfdhKoeEKoNM9atsyG1QYWmw2tQWuwaY3G8a61Y579s83xWWvQ2PdV\n1kajsdlAV4hDO7Z1fnvKt195nxXj1/rciaxs3xWX2c6Lg/P2ZX8/9xl9bp+2im0qHS+OE6c+F1eF\n9lSK1T5d/rev9EVUnLR/WL8/G4DL2zVFV7EcxzbLVn9vSl+PqbJZ1f+jKh+v1u8C7wIk907WkvCF\naBhKKXtXjqHxdXf4mvem1H0dV1XIygBaVfjcEjhWbRBKCnUJIURDcFW2/Q3oqJRqq5TyB24DvnXR\nvoQQQtSSS7p3tNYWpdSDwPfYb9mcq7XeUcNqQgghXMxlT+RqrZcCS121fSGEEHUnnelCCOFDJOkL\nIYQPkaQvhBA+RJK+EEL4EI+osqmUygP2uDsOF4oGTrk7CBeS4/NejfnYoPEfX2etdZ0GH/GUevp7\n6voosTdRSqXI8Xmvxnx8jfnYwDeOr67rSPeOEEL4EEn6QgjhQzwl6b/r7gBcTI7PuzXm42vMxwZy\nfBfwiB9yhRBCNAxPudIXQgjRACTpCyGED3F70ldKjVJK7VFKpSmlZro7HmdTSqUrpbYppbbU5/Yq\nT6OUmquUOqGU2l5hXpRS6gel1D7He6Q7Y6yvao5tllLqqOP726KUus6dMV4KpVQrpdRqpdQupdQO\npdTDjvmN5fur7vi8/jtUSgUqpTYqpbY6ju0Zx/y2SqkNju/uM0cp+4tvy519+nUdQN0bKaXSgT5a\n60bxgIhSajCQD3ykte7umPcikKO1ft5x4o7UWj/uzjjro5pjmwXka61fdmdszqCUigPitNablVKh\nwCZgDDCVxvH9VXd84/Hy71DZB9MN0VrnK6X8gHXAw8AjwJda64VKqbeBrVrrty62LXdf6fcD0rTW\nB7TWpcBC4CY3xyQuQmu9Fsg5b/ZNwIeO6Q+x/0PzOtUcW6Ohtc7UWm92TOcBu4B4Gs/3V93xeT1t\nl+/46Od4aeAqYJFjfq2+O3cn/XjgSIXPGTSSL6kCDaxQSm1yDAbfGDXTWmeC/R8eEOvmeJztQaVU\nqqP7xyu7Ps6nlEoAegEbaITf33nHB43gO1RKGZVSW4ATwA/AfuCM1triaFKr/OnupF/jAOqNwBVa\n62TgWuABRxeC8B5vAe2BJCAT+Jd7w7l0SqkmwBfAH7TWZ90dj7NVcXyN4jvUWlu11knYxxzvB3Sp\nqllN23F30q/TAOreSGt9zPF+AvgK+5fV2GQ5+lPL+lVPuDkep9FaZzn+sdmAOXj59+foD/4CWKC1\n/tIxu9F8f1UdX2P7DrXWZ4A1wOVAhFKqrIZarfKnu5N+ox5AXSkV4vhBCaVUCDAS2H7xtbzSt8AU\nx/QU4Bs3xuJUZcnQYSxe/P05fgx8H9iltX6lwqJG8f1Vd3yN4TtUSsUopSIc00HACOy/WawGxjma\n1eq7c/sTuY7bp/7NuQHU/+HWgJxIKdUO+9U92CuafuLtx6eU+hQYir1kbRbwNPA18DnQGjgM3Kq1\n9rofRKs5tqHYuwU0kA5ML+v/9jZKqUHAT8A2wOaY/Vfs/d6N4fur7vgm4uXfoVIqEfsPtUbsF+uf\na62fdeSYhUAU8Dtwp9a65KLbcnfSF0II0XDc3b0jhBCiAUnSF0IIHyJJXwghfIgkfSGE8CGS9IUQ\nwodI0hc+QSkVoZS6vx7r/dUV8QjhLnLLpvAJjlosS8qqZ9ZhvXytdROXBCWEG8iVvvAVzwPtHfXU\nXzp/oVIqTim11rF8u1LqSqXU80CQY94CR7s7HXXNtyil3nGUB0cpla+U+pdSarNSapVSKqZhD0+I\n2pErfeETarrSV0o9CgRqrf/hSOTBWuu8ilf6SqkuwIvAzVprs1LqTeBXrfVHSimN/WnIBUqpp4BY\nrfWDDXFsQtSFqeYmQviE34C5joJdX2utt1TRZjjQG/jNXuaFIM4VJ7MBnzmmPwa+vGBtITyAdO8I\nQfkAKoOBo8B8pdTkKpop4EOtdZLj1VlrPau6TbooVCEuiSR94SvygNDqFiql2gAntNZzsFdqTHYs\nMjuu/gFWAeOUUrGOdaIc64H931JZtcPbsQ9nJ4THke4d4RO01tlKqZ+VfdDzZVrrx85rMhR4TCll\nxj5ObtmV/rtAqlJqs9b6DqXU/2EfCc0AmIEHgENAAdBNKbUJyAUmuP6ohKg7+SFXCCeQWzuFt5Du\nHSGE8CFypS98ilKqBzD/vNklWuv+7ohHiIYmSV8IIXyIdO8IIYQPkaQvhBA+RJK+EEL4EEn6Qgjh\nQyajjSUAAAAMSURBVCTpCyGED/l/Oz1/0C1SjDUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bf15a438>"
      ]
     },
     "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": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:29.248966Z",
     "start_time": "2017-10-19T17:54:28.966025+02:00"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dWQaYxJrD4sMcJ4SIjkSGWsqcd2BuRE4UQoi+GPCB71rrj4GPnfmdjtp5ejTj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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bf9f5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = evodumb['mean']-evohalfherd['mean']\n",
    "diff.plot(yerr=evodumb['std']+evohalfherd['std']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-19T15:54:29.688734Z",
     "start_time": "2017-10-19T17:54:29.251456+02:00"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f17bf608518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3bgke+1YyoDSZBKU56NqTzb0fIWzkROJCehldohDCYE15CmUXEG2DWlq9ivIy\n9vx3OXrX50QU/soQVUmW6sjWkMl0uWoCvfrHIrEthDhHPolpByorykl6/2H6n/4P0RRzFn92Bt1M\n2/h7CI8dRRcZ3xZC1EEC3GDabGb7/KkknP2GRL9rcY8ZT8SVtxAvy5oJIRohAW6wLUueJeHsN2zq\nOoVhD7xhdDlCCAciv5sbaPvqTxma9v9I9r2a+KmvGl2OEMLBSIAbJH3nRsJ/eZR09z5EPLRE5iwR\nQjSbBLgBTh8/jP+XEyhQ/gTcvwJPb1+jSxJCOCAJcBsrLsyjYOHteOtSSu9cTGAnmTdGCNEyEuA2\nVGUycWD+eMJMhzg48i3CBsQbXZIQwoFJgNvQtvceJrrkVxL7P8nga+40uhwhhIOTALeRLV+8SsKp\nz9gSeDvxdz1ldDlCCCcgAW4Duzd8RWzK8+z0HELs9HeMLkcI4SQkwK0sc18yPdY+yFHXbvSc8Tlu\n7m2MLkkI4SQkwK0o9/Rx3JbdRQVt8Jz0BX5tA4wuSQjhROSj9M1kqqygpLiQ8pJCykuKKC8torK0\nkMqyEkxlhVRVlGAuL8ZcXkz7Q/+mhzmXzJs/J7yHrFEphLAsCfBGnDl5hMzkHzEd2khwbjJh5gz8\nm7hvuXYnJf5lYuNGWbVGIUTrJAF+kZNH0ji6/Ud0xi90zkumm84iECjRHqR7DWRT4CiUVztUGx9c\n2njj5umDaxsf3Lx8cff0oY2XHx5ePnh6++Hl60+sp7fRpySEcFKtPsBPHTtI5pavUUd+ISR/O53J\nphNQgA+HvCM53mUcARHXEDZwGJEyxasQwo60ygDPytjPkY2f0S7je/qZUukI5NCWTN/BZHadStDA\nUYT2jyPKrVX+eIQQDqLVJNSx9BSO/rqMwCPf0ceURhcg3bUXm0Nn0jn+drqHR9NBVr4RQjgQpw7w\nzH3JZG1aRvCxH+hVdZgQ4IBbXzb3+iPdrhxP75796W10kUII0UJOGeDJ339EwNZXCDUfpQewzz2C\nzT0fo8dVv6evPM4nhHASThfgR9N2ErHpcU64dmFLv9mEDb+Lfl3DjC5LCCEszqkCvMpkonjZdPxV\nG/zu/5qwLj2MLkkIIazGqe7abVv6PP1MqaTFPE2ghLcQwsk5TYBn7t9BdNq/2O59BbE3TTO6HCGE\nsDqnCPAqk4myL6ZRqjzoNvFdlDwOKIRoBZwi6bYteZZw037S456RNSaFEK2Gwwd4ZmoS0Qfns93n\nKmJvuN/TJvqeAAANKklEQVTocoQQwmYcOsBNlRWUr3iQEuVJtwnvyNCJEKJVaTTxlFLdlFLrlFKp\nSqk9Sqk/2qKwpti25Fn6mg5waMgcAjt1M7ocIYSwqaY8B24CHtNaJyul/IAkpdSPWuu9Vq6tQRmp\nicQeeodkvxHEjJ1qZClCCGGIRnvgWusTWuvkmq8LgVSgq7ULa4ipsoLKFQ9SrLzpMWG+DJ0IIVql\nZiWfUioUiAa2WKOYpkpcPIc+pjQODX2ODh1DjCxFCCEM0+QAV0r5AiuAR7TWBXW8P00plaiUSszO\nzrZkjRc4vGcLMYffIcl3JLE3TLFaO0IIYe+aFOBKKXeqw3ux1nplXdtorRdoreO01nFBQUGWrLFW\nZUU5VStnUKh86TnpHau0IYQQjqIpT6Eo4AMgVWv9mvVLql/i4qfpXXWQI8P+TvugzkaWIoQQhmtK\nD/xKYAIwSim1o+bPDVau6xIHd28mNuM9Ev2uJXrMJFs3L4QQdqfRxwi11hsBZYNa6q/BbMb01SwK\nlB+9J80zshQhhLAbDvH8XdqOnwk37edg/4doF9jJ6HKEEMIuOESA5/38LiXag/5jZK4TIYQ4x+4D\nvDA/l4G5a0gJuA7/dh2MLkcIIeyG3Qf43h/ex1uV0264LNIghBDns+sA12YzgfuXcNC1J32iRhhd\njhBC2BW7DvC0HRvoVXWYM+F3y3wnQghxEbtOxbyfF1CiPYgYc5/RpQghhN2x2wAvyMupuXl5PX5t\nA4wuRwgh7I7dBniq3LwUQogG2WWAa7OZoP1LSHftRZ+o4UaXI4QQdskuA/xA8np6mjPICR8vNy+F\nEKIedpmO+Rvfk5uXQgjRCLsL8IK8HAae/UluXgohRCPsLsDP3bxsP2K60aUIIYRds6sAv+DmZbR8\n8lIIIRpiVwFee/Oy391GlyKEEHbPrgL83M3LAXLzUgghGmU3AV6Ql8Ogs2tI6TAaX//2RpcjhBB2\nz24CPPWH9/BSFXLzUgghmsguAlybzQTv/0w+eSmEEM1gFwG+P3kdYeYMcvrdY3QpQgjhMOwiwAs3\nvkex9mTAmKlGlyKEEA7D8ADPP3um+pOXcvNSCCGaxfAA31dz87LD1XLzUgghmsPQANdmM8EHlpLm\n2pveg68yshQhhHA4hgb4/qS1hJkzONtfbl4KIURzGRrghb+8T7H2JGL0FCPLEEIIh2RYgJcWF8rN\nSyGEuAyGBXh60lq8VAWeg24xqgQhhHBohgV40f61mLQLvWKvM6oEIYRwaI0GuFJqoVLqtFIqxZIN\ntz+1hYPufWX4RAghWqgpPfCPgN9YstHiwjx6VR4gNzjekocVQohWpdEA11pvAHIt2ejBxDW4qyp8\n+11jycMKIUSrYrExcKXUNKVUolIqMTs7u8Ftiw+so0K70lvGv4UQosUsFuBa6wVa6zitdVxQUFCD\n23bI3srBNv3w8vGzVPNCCNHq2PwplIK8HHpVppHfMcHWTQshhFOxeYAfSvoRV6Xx7S/j30IIcTma\n8hjhZ8AmIFwpdUwpdVkrDpcdWEe5dqd3zKjLOYwQQrR6bo1toLUeb8kGg85sJd0jggFePpY8rBBC\ntDo2HULJzzlFmOkwBZ2H2bJZIYRwSjYN8IOJq3FRmnYRMnwihBCXy6YBXpm+nhLtQa+oq23ZrBBC\nOCWbBnhwzjYOeg6gjYenLZsVQginZLMAzz19nDBzJkVdZPxbCCEswWYBfjhxNQDtB1xrqyaFEMKp\n2SzATQfXU6w96RUpixcLIYQl2CzAO5/dRrrXINzbeNiqSSGEcGo2CfDsrAy6m49TGnKlLZoTQohW\nwSYBnpn0AwAdBsr0sUIIYSk2CXDzoQ0U4EPPgfIEihBCWIpNArxLXiIHvQfj6tbo1CtCCCGayOoB\nfvJoOiH6JOUhV1i7KSGEaFWsHuBHa8a/gwZdb+2mhBCiVbH+EErGz5zFj7CIIVZvSgghWhOrB3hI\nXiKHfaJwcXW1dlNCCNGqWDXAsw7vozPZVHaT57+FEMLSrBrgx5K/B6DjYBn/FkIIS7NqgLtk/kwO\nbekRHmPNZoQQolWyWoBrs5nuBclk+MWgXGw67bgQQrQKVkvWYwd3E0wupu4y+6AQQliD1QI8a8eP\nAHSJkvFvIYSwBqsFuNuRjZwmgJBeg6zVhBBCtGpWC/Aehds54i/j30IIYS1WSdeKshICycPcY7g1\nDi+EEAIrBXhlaQEAXaPHWOPwQgghsFKAq/IiThJEl9BwaxxeCCEEVgpwD3MJR9vFyfi3EEJYkVUS\n1pUqCJXxbyGEsKYmBbhS6jdKqf1KqXSl1Oym7NMtVsa/hRDCmhoNcKWUK/A2MBaIAMYrpSIa2qcC\ndzp1622ZCoUQQtSpKT3woUC61vqQ1roCWAr8tqEdKt18LFGbEEKIBjQlwLsCR8/7/ljNa/VSHr6X\nU5MQQogmaEqAqzpe05dspNQ0pVSiUiqxuPySt4UQQlhYUwL8GNDtvO9DgKyLN9JaL9Bax2mt44KC\ngy1VnxBCiHo0JcC3AX2UUmFKqTbAXcC/rVuWEEKIxrg1toHW2qSUehj4AXAFFmqt91i9MiGEEA1q\nNMABtNbfAt9auRYhhBDNIJ91F0IIByUBLoQQDkoCXAghHJQEuBBCOCilteU/dKOUKgT2W/zA9iEQ\nOGN0EVYk5+fY5PwcV7jW2q85OzTpKZQW2K+1jrPSsQ2llEp01nMDOT9HJ+fnuJRSic3dR4ZQhBDC\nQUmACyGEg7JWgC+w0nHtgTOfG8j5OTo5P8fV7HOzyk1MIYQQ1idDKEII4aAkwIUQwkFZNMBbsvix\nI1FKZSildiuldrTkkR97o5RaqJQ6rZRKOe+1AKXUj0qptJq/2xtZ4+Wo5/zmKKWO11zDHUqpG4ys\nsaWUUt2UUuuUUqlKqT1KqT/WvO4U16+B83OW6+eplNqqlNpZc37P1rweppTaUnP9ltVM4V3/cSw1\nBl6z+PEB4HqqF4HYBozXWu+1SAN2QCmVAcRprZ3igwRKqRFAEfCx1npgzWtzgVyt9Us1/wm311o/\naWSdLVXP+c0BirTWrxhZ2+VSSnUGOmutk5VSfkAS8DtgMk5w/Ro4v3E4x/VTgI/Wukgp5Q5sBP4I\nPAqs1FovVUq9A+zUWs+v7ziW7IE3e/FjYSyt9QYg96KXfwssqvl6EdX/aBxSPefnFLTWJ7TWyTVf\nFwKpVK9V6xTXr4Hzcwq6WlHNt+41fzQwClhe83qj18+SAd7sxY8dkAZWK6WSlFLTjC7GSjpqrU9A\n9T8iwBnXx3tYKbWrZojFIYcYzqeUCgWigS044fW76PzASa6fUspVKbUDOA38CBwE8rTWpppNGs1Q\nSwZ4kxY/dnBXaq1jgLHAzJpf0YVjmQ/0AqKAE8CrxpZzeZRSvsAK4BGtdYHR9VhaHefnNNdPa12l\ntY6iep3hoUD/ujZr6BiWDPAmLX7syLTWWTV/nwa+pPqH7mxO1Yw/nhuHPG1wPRaltT5V8w/HDLyH\nA1/DmrHTFcBirfXKmped5vrVdX7OdP3O0VrnAeuBBKCdUurcHFWNZqglA9ypFz9WSvnU3ExBKeUD\njAZSGt7LIf0bmFTz9STgKwNrsbhz4VbjVhz0GtbcBPsASNVav3beW05x/eo7Pye6fkFKqXY1X3sB\n11E9zr8OuKNms0avn0U/iVnzSM8b/G/x4xcsdnCDKaV6Ut3rhupZHJc4+vkppT4DRlI9Recp4Blg\nFfA50B04AtyptXbIG4H1nN9Iqn/91kAGMP3cmLEjUUpdBfwM7AbMNS//hepxYoe/fg2c33ic4/pF\nUn2T0pXqjvTnWuvnanJmKRAAbAfu1VqX13sc+Si9EEI4JvkkphBCOCgJcCGEcFAS4EII4aAkwIUQ\nwkFJgAshhIOSABcORynVTin1UAv2+4s16hHCKPIYoXA4NXNjfHNuhsFm7Fektfa1SlFCGEB64MIR\nvQT0qpkP+uWL31RKdVZKbah5P0UpNVwp9RLgVfPa4prt7q2Zk3mHUurdmimRUUoVKaVeVUolK6V+\nUkoF2fb0hGga6YELh9NYD1wp9RjgqbV+oSaUvbXWhef3wJVS/YG5wG1a60ql1Dxgs9b6Y6WUpvoT\ncIuVUk8DwVrrh21xbkI0h1vjmwjhcLYBC2smQ1qltd5RxzbXArHAtuppN/DifxM/mYFlNV9/Cqy8\nZG8h7IAMoQinU7OQwwjgOPCJUmpiHZspYJHWOqrmT7jWek59h7RSqUJcFglw4YgKAb/63lRK9QBO\na63fo3pGu5iatypreuUAPwF3KKWCa/YJqNkPqv9dnJsR7m6ql7sSwu7IEIpwOFrrHKXUL6p6seLv\ntNZPXLTJSOAJpVQl1WtinuuBLwB2KaWStdb3KKX+RvUKSy5AJTATyASKgQFKqSQgH/i99c9KiOaT\nm5hCXEQeNxSOQoZQhBDCQUkPXDgspdQg4JOLXi7XWscbUY8QtiYBLoQQDkqGUIQQwkFJgAshhIOS\nABdCCAclAS6EEA5KAlwIIRzU/wfsq3ynW1QhuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfc79358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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zrbV3Obc0z2UdDpLmTCVm/Yvk+DRl5/D5xPW51NVliYibOW+AW2ufBJ4EKL8C\nf1zhfeHyjx0lbfq9xOcuZG1IfyLum0kr3UUiIhdA94HXoqzMNIo+HkW/0u0sbz+B+LtfwMfX19Vl\niYibqlaAW2uXAEucUomHW/P9x3T86TEc+JA2+F0GXDHC1SWJiJvTFbiTlRQXsfL9xxmw+0MyfCOp\nN/pjoiL0UXcRuXgKcCc6sC+LPe+NYkDhGpKa3EjU/W8TFFzP1WWJiIdQgDvJL+tWUH/uSCLtEZKi\n/0L/4RNdXZKIeBgFuBOs//kb2n13L8dNCFk3f0H/Ppe4uiQR8UAK8BqW8t2H9Pj5Mfb6tiTwnvlE\nto10dUki4qEU4DUo8bOXiV3/HJn+XWnxwBc0Cmvp6pJExIMpwGuAdThY8cF/MWDHdNaGxNP54dl6\nqruIOJ0C/CKVlpSQ/OY4BhyYz8qG1xL98L/wDwh0dVki4gUU4BehIP8YG/5+B/HHlrI8/C4S7v8b\nxsfH1WWJiJdQgF+gI4cPsPOfw+hblMqKzo8xYNRTri5JRLyMAvwC5OzeTu6MX9OlZAfJ/f5Kwk0P\nuLokEfFCCvBqyspMw2fWzYQ7DpN+xTvEDr7F1SWJiJdSgFfDpuRFNFtwNwZL1q8/I6rvYFeXJCJe\nTO+4VVHS3Gl0+OpWCkwwR0cuoIvCW0RcTFfg51FUWEDK9AnEH5jPuqC+tL3/f/QBHRGpExTg55Cz\ndwfZM24nvngDK1qOInbcNPz8A1xdlogIoACv1KbkRTReMI729hir+r9Cwq/uc3VJIiKnUICfRdLc\naUSn/oUcn6bsueVL+vVOcHVJIiJnUICf5Gzz3a003y0idZQCvNzJ893Lw+8i7t7XNN8tInWaApwz\n57sHaL5bRNyAVwf48bxc1s79K/22vE2OT1P2jviKfr3iXV2WiEiVeGWAFxUWkPL5a3Ta+BYDOExK\nvUvoOO59WjVt4erSRESqzKsCvLSkhNUL3qL12teJt/vZENCbnCHvEBM/1NWliYhUm1cEuHU4WPP9\nRzRJfJk4x04yfCNZN+iv9LpsmPp3i4jb8ugAtw4Haf+ZT9DS54gpzWS7TxtWJ7xOzNAxCm4RcXse\nG+Abk76n9Idn6F20jj00I6nPs/S9YQLtdWugiHgIjwvwvTsy2PPpo8QcW0YOjUjsNpnoYb8lPCjE\n1aWJiNQojwnw4qJCVn36HFGZb9EQy/IOD9JnxJPE6+nwIuKhPCLA0xO/I+i735Pg2E5KvYG0vH0a\nA9p3dXX7NNqNAAALdUlEQVRZIiJO5dYBfih7DxmzJtH/8DfspRlrLnmTmKtHurosEZFa4ZYB7igt\nJXn+G3RZN5UYm8/yVmPoM+pZWmq6RES8iNsF+Ja0RIq+eJT+xRvYENCbkOHTGNA91tVliYjUOrcJ\n8Lwjh0ib9SSxez/lqKnPyujniL3pId3PLSJeyy0CfOv6RALnjCbB7iOx6U10GzWVOPUtEREvV+cD\nPGXhTLr+NIljJoT06z4jPv4aV5ckIlIn1NkAtw4HKz58kgHb32Kzfxca3zub7q0iXF2WiEidcd4A\nN8a0Bf4FtAQcwHRr7evOLOp4Xi4b37qLAXlLWdlwKL0f+ICg4HrOHFJExO1U5Qq8BHjMWrvaGNMA\nWGWM+d5au8EZBe3Zvonj/7qdPiXbWNH5d8SPfEpvVIqInMV5A9xauwfYU/71UWNMOtAaqPEA37Di\nW1p+ez/1KGH94HdJuGJETQ8hIuIxqjUHboyJAGKAxLO8Nh4YD9CuXbtqF5I051Wi1z3LXt+WcMfH\nRHWJrvY+RES8SZUD3BhTH5gLPGqtPXL669ba6cB0gNjYWFvV/RYXFbJ6+gPE58wjNTiO9hM+oWHj\nsKpuLiLitaoU4MYYf8rCe5a1dl5NDX4oew+737md+KK1rGg5irj73sDXr87eGCMiUqdU5S4UA8wA\n0q21r9bUwCXFRRx58yoiS/eR3O9FEm56sKZ2LSLiFapye8clwGjgSmPMmvJf11/swJlr/0N7Rxap\nMVOIVXiLiFRbVe5CWQaYmh74UNr3AHQaOLymdy0i4hVcdoN16O6f+cW3A02at3ZVCSIibs0lAV5w\nPI/Iwg1khyW4YngREY/gkgDPSP6RQFNMcNcrXTG8iIhHcEmA5238gWLrS2TcUFcMLyLiEVwS4E33\nryAzoCv1GjRyxfAiIh6h1gM891AOnYozONxyYG0PLSLiUWo9wLes/BZfY2nY46raHlpExKPUeoAX\nZSzmuA0ksu8VtT20iIhHqfUAb3Egkczg3gQEBtX20CIiHqVWAzxn93YiHDs53krz3yIiF6tWA3xb\n8r8BaBql2wdFRC5WrQa4Y8v/kks9OvYaUJvDioh4pFoLcOtw0O7wSn6p11c9v0VEakCtBfiuLRto\nSTbF7S6rrSFFRDxa7QV4yrcAhMdcU1tDioh4tFoLcP/tS9lPE9pGRtXWkCIiHq1WAtxRWkqHvNVs\nbxiH8XFZC3IREY9SK2m6dX0ijTkKHS6vjeFERLxCrQR4dupCANrHXfSjNEVEpFytBHhw1jJ2+LSm\neesOtTGciIhXcHqAFxUW0Dk/lT1N4p09lIiIV3F6gP+y5n8JMYUEdB7s7KFERLyK0wP88PofcFhD\nx9hrnT2UiIhXcXqAN9y7nF/8OtGwaQtnDyUi4lWcGuDH83KJLNxATvMEZw4jIuKVnBrgmck/EGBK\nqd9tiDOHERHxSs69At/4I0XWj8jYq505jIiIV3JqgIdlryAjsDvB9Ro4cxgREa/ktADPPbCPjiVb\nOBJ+ibOGEBHxak4L8F9W/hsfY2nc8ypnDSEi4tWcFuDFGYs5ZoPoFD3IWUOIiHg1pwV4q4NJZIZE\n4R8Q6KwhRES8mlMCvLiokLZ2N/ltLnXG7kVEBCcFeNHxXACaRenxaSIizuKcKZTCPA4RSocecU7Z\nvYiIOCnAA0uPsaV+X3x8fZ2xexERwUkB7kcJJe1194mIiDNVKcCNMdcaYzYZYzKNMZOrsk2bfmof\nKyLiTOcNcGOML/AP4DqgB3CnMabHubYpxp9WEd1rpkIRETmrqlyB9wcyrbVbrLVFwCfAr8+1QZFv\nCManVh63KSLitaqSsq2BnSd9n1W+rFImSM2rREScrSoBbs6yzJ6xkjHjjTHJxpjkY4VnvCwiIjWs\nKgGeBbQ96fs2wO7TV7LWTrfWxlprY5s1b15T9YmISCWqEuArgc7GmA7GmADgDuBL55YlIiLn43e+\nFay1JcaYR4DvAF/gPWvteqdXJiIi53TeAAew1n4DfOPkWkREpBp0r5+IiJtSgIuIuCkFuIiIm1KA\ni4i4KWNtzX/oxhhzFNhU4zuuG8KAHFcX4UQ6Pvem43NfXa211foYe5XuQrkAm6y1sU7at0sZY5I9\n9dhAx+fudHzuyxiTXN1tNIUiIuKmFOAiIm7KWQE+3Un7rQs8+dhAx+fudHzuq9rH5pQ3MUVExPk0\nhSIi4qYU4CIibqpGA/xCHn7sTowx24wx64wxay7klp+6xhjznjFmvzEm7aRlTYwx3xtjMsp/b+zK\nGi9GJcc3xRizq/wcrjHGXO/KGi+UMaatMWaxMSbdGLPeGPPb8uUecf7OcXyecv6CjDFJxpi15cf3\nTPnyDsaYxPLz92l5C+/K91NTc+DlDz/eDFxN2UMgVgJ3Wms31MgAdYAxZhsQa631iA8SGGMGAXnA\nv6y1vcqXvQQctNa+WP6PcGNr7X+5ss4LVcnxTQHyrLVTXVnbxTLGhAPh1trVxpgGwCpgGDAWDzh/\n5zi+2/CM82eAetbaPGOMP7AM+C0wCZhnrf3EGPMWsNZa+2Zl+6nJK/BqP/xYXMtauxQ4eNriXwMf\nln/9IWU/NG6pkuPzCNbaPdba1eVfHwXSKXtWrUecv3Mcn0ewZfLKv/Uv/2WBK4E55cvPe/5qMsCr\n/fBjN2SBhcaYVcaY8a4uxklaWGv3QNkPEeCJz8d7xBiTWj7F4pZTDCczxkQAMUAiHnj+Tjs+8JDz\nZ4zxNcasAfYD3wO/AIettSXlq5w3Q2sywKv08GM3d4m1ti9wHfBw+X/Rxb28CXQCooE9wCuuLefi\nGGPqA3OBR621R1xdT007y/F5zPmz1pZaa6Mpe85wf6D72VY71z5qMsCr9PBjd2at3V3++37gc8r+\n0D3NvvL5xxPzkPtdXE+NstbuK//BcQDv4MbnsHzudC4wy1o7r3yxx5y/sx2fJ52/E6y1h4ElQALQ\nyBhzokfVeTO0JgPcox9+bIypV/5mCsaYesBQIO3cW7mlL4G7y7++G/jChbXUuBPhVm44bnoOy98E\nmwGkW2tfPekljzh/lR2fB52/ZsaYRuVfBwNXUTbPvxgYUb7aec9fjX4Ss/yWnmn8/8OPn6uxnbuY\nMaYjZVfdUNbF8WN3Pz5jzP8Agylr0bkPeBqYD3wGtAN2ALdaa93yjcBKjm8wZf/9tsA2YMKJOWN3\nYoy5FPgPsA5wlC/+A2XzxG5//s5xfHfiGecvirI3KX0pu5D+zFr75/Kc+QRoAqQAd1lrCyvdjz5K\nLyLinvRJTBERN6UAFxFxUwpwERE3pQAXEXFTCnARETelABe3Y4xpZIx56AK2+4Mz6hFxFd1GKG6n\nvDfGghMdBquxXZ61tr5TihJxAV2Bizt6EehU3g/65dNfNMaEG2OWlr+eZoy5zBjzIhBcvmxW+Xp3\nlfdkXmOMebu8JTLGmDxjzCvGmNXGmB+NMc1q9/BEqkZX4OJ2zncFbox5DAiy1j5XHsoh1tqjJ1+B\nG2O6Ay8BN1tri40x/wRWWGv/ZYyxlH0CbpYx5imgubX2kdo4NpHq8Dv/KiJuZyXwXnkzpPnW2jVn\nWWcI0A9YWdZ2g2D+v/GTA/i0/OuZwLwzthapAzSFIh6n/EEOg4BdwEfGmDFnWc0AH1pro8t/dbXW\nTqlsl04qVeSiKMDFHR0FGlT2ojGmPbDfWvsOZR3t+pa/VFx+VQ7wIzDCGNO8fJsm5dtB2c/FiY5w\nIyl73JVInaMpFHE71toDxpifTNnDiv9trf39aasMBn5vjCmm7JmYJ67ApwOpxpjV1tpRxpj/puwJ\nSz5AMfAwsB04BvQ0xqwCcoHbnX9UItWnNzFFTqPbDcVdaApFRMRN6Qpc3JYxpjfw0WmLC6218a6o\nR6S2KcBFRNyUplBERNyUAlxExE0pwEVE3JQCXETETSnARUTc1P8BFstpdoNudu0AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfc79ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evohalfherd['std'].plot()\n",
    "evodumb['std'].plot()"
   ]
  }
 ],
 "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": "3.6.2"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
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 },
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}