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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      "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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSIQo9EVEIkShLyISIQp
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOnTowKmnnspFF11U+Zz
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfb3c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl4FFX2+P/37e7sZCUJhLCEXbYQwiaILIKICwqKICqb\nGz/Un87oODLjR0VncR0dcVxRUBFFB1cQEEEYRBEMCGGHAAECIUACIXt6ud8/uhMSSMhCd7o7fV7P\n009XV92qOpV+OFXcrjpXaa0RQgjhGwzuDkAIIUTDkaQvhBA+RJK+EEL4EEn6QgjhQyTpCyGED5Gk\nL4QQPkSSvhBC+BBJ+kII4UMk6QshhA8xuTsAgOjoaJ2QkODuMIQQwqts2rTplNY6pi7reETST0hI\nICUlxd1hCCGEV1FKHarrOtK9I4QQPkSSvhBC+BBJ+kII4UMk6QshhA+RpC+EED5Ekr4QQvgQSfpC\nCOFDJOkLIYQP8YikX5S5u3YN511vf9VWXdpLW8+KwxPaekocntC2ju2nfdCHaR/0aZRtPSWOusRb\nkUc8kStEYzBNZQEwrxG2rU974Zkk6QufIolO+DpJ+sLrSWIWovYk6QuPJIlcCNeo8YdcpVQrpdRq\npdQupdQOpdTDjvmzlFJHlVJbHK/rKqzzF6VUmlJqj1LqGlcegPAe01RWeTIXQrhHba70LcCjWuvN\nSqlQYJNS6gfHsle11i9XbKyU6grcBnQDWgArlVKdtNbW6nZQKv/hEEKIBqG01nVbQalvgP8AVwD5\nVST9vwBorZ9zfP4emKW1Xl/dNru0CNHb/jYYk6GG/3gcT7W/N0+sXbB1aS9tXb7t3cc3AXBZ895e\n0dZT4qhNW43GAmzP+h0L0K5ZT2yAFe14rzhtf9+fvQsb0LLpZdjQjjb25RXbWwAbmozcg9iA5uEJ\n2Bz7tTleGo2u8Dkr7wgaiAltiYbyZbpC27J5J/OPoRU0bRJX3oYq2tuA7IIsNBAREovVsdx63r7L\njiW3OAcNNAmMdPyNLnyV/e3ySnLRQEhA+Hnx6krty467wJyPBgL9Qi5oXzl2KLYUARBgCqJittUV\nPpVNlViKAfA3BZ73/V44XWotZuU9uzdpret072adLrGVUglAL2AD9qT/oFJqMpCC/X8Dp4F44NcK\nq2U45p2/rfuA+wAuiwvmyOki2jYNqUs4wgPsxgzAZW6Oo6HZ0BShKURTiI0CNLv9/TArOE4hpUAp\nGjOaUsfL7JhXqjTHw0KwAGEqB4tjmcWRuK3o8mkLmrNNw7EohVFlOrZl346lwrZLlSOwuGjHxNGa\nDyIm0jFRyy63yDDHRE7NbcObOCbO1Nw2rOzf/VmUBsW5fmcDoFDln3VwIArwoxCDY7n9pS6YtphM\nKCAAMwpF2Z9IVfEqNhhQgBlbpfmGCusZAKPj3d9xsdwEwwXbMjjWKDuOfIu9kyPMdGG6Pbd1e/uz\nlnwUEGbyL59XFQWcLc1nZTXLL6bWSV8p1QT4AviD1vqsUuot4G/YTzx/A/4F3FVNnBf8d0Jr/S7w\nLkBsi3g9IufPLL3zSjo3D60+iLIHQ6Z9V7ug69Je2tar/QuOB0TmTfWethrNaxM/Ia80j7OlZzlb\netY+XXL2gs8/qyPYgPioIAothRSYCyiyFFHkuHqrJDrCMXHqojEYlREdEowCwoP8MSojJoOp8kuZ\n8DP4YTQYycjaggKSW12Bn9EPf6M/fgY//A3+9mnjuemvNr+JAiZd/jhGZcSgDBiVEaPh3HTZ+39W\nP4YCHr36dXsbR7uyaYPBgEmZ7O0NRp74ahygeOmWb1BKYcCAUvZ0alD2aYOyp8mHFo4A4M3b15Qv\nByq1KVv3no/6A/DB1E01fn/Tyr/rmkfac1Xbiu3fmfpbrdu+MnVjrdu+NHVDrdvWVa2SvlLKD3vC\nX6C1/hJAa51VYfkcYInjYwbQqsLqLYFjF9t+U5WHf4CJZxbvYME9/VGquvObEBeyOa6Kd+fsJqc4\nhzPFZzhdcpqc4hxOF5/mdLFjuuQ0RyjBAgxaOKja7RmVkVD/UML8w7Bgv7qLaxJHsCmYYL9gQkwh\nBPsFl38OMgUR4hfCGz/+CQU8c8P8Som4PEkb/fE3+GM0GM8lmQn/q/H4ytq+Pvz1Gtuu3/wuABMv\nm1hj2wUYARgUX/3foqJAx/V2y9CWNbY1Oa79Qv0vchHnoKq9nhWuUGPSV/YM/D6wS2v9SoX5cVrr\nTMfHscB2x/S3wCdKqVew/5DbEbjoKc6AjUeu7sTT3+7g+x3HGdU9rh6HIpzFU26X1GhyS3I5UXii\n6leR/f0UpaDg1sW3VlrfoAxEBEQQGRBJZGAkHSI6kH/mECYUd/Z5hLCAsPLkXv4KCCPYFFx+4VGe\ncK+qOeHOdyTFbtHdnPyXEMJ5anOlfwUwCdimlNrimPdXYKJSKgl71006MB1Aa71DKfU5sBP7b0AP\nXOzOnTJ39G/NJxsO8/fvdjG0cyyBfsa6H43wSlablaP5R0k7k2Z/nU5jB6WUoKu8Io8IiCA2OJbY\n4Fi6RHXhl71f468Vfxj2EpGB9gQfFRBFWEBYebdCmbIkPrnb5AY5NiE8TY1JX2u9jqr76ZdeZJ1/\nAP+oUyBGA0+P7srt721gztoD/P/DO9ZldeEFtOMHy58yfjqX4M+kceDMAYqtxeXt4pvE448iDAOT\n+vyRZsHNypN8THAMAcaAStudttfesziizYiGPBwhvJJH3SA/sEM0o7o15801+xnXpyVx4UHuDklc\nAqvNyt7Te9mUtYlNWZvYSikWBfevuh+AmKAYOkR04NbOt9IhogMdIjrQPqI9IX4h5VfkU7pNcech\nCNHoeETSD6rQlfPE9V34cc8Jnlu6m9kTe7kxKlFXZpuZndk72ZS1iZTjKfx+4nfyzfmA/eo9HAMh\n2sAzo+bQIaIDEYERNWxRCOFsHpH0iT7XldMqKpjpg9vx+o9pTBrQhr4JUefa1faWw/q0b8Rtp8XF\nAnX4YbZ5j1o1s2kb+VFtOVt6lntW3EPqydTyWxkTwhIY1XYUvZv1pndsb+KaxDFt+TQA+jSvxa1m\ntYzBpW09JQ4Xta3t7Yn1ad+Y23pKHPOmpvDBtLrf+eQZSf88M4a2Z9GmDGZ9u4NvHxyE0SC3dHmS\nvaf38t2B71h2cBmZBfYbuEL9QxnbYSy9m/UmuVky0UHRNWzFt80bVft7o1zVVvgmj0z6wf4mZl57\nGQ8v3MLnKUeY2K+1u0Pyecfyj7H04FK+O/AdaWfSMCojA1oMINAYSHhAOPOvm+/uEF1CEq5obDwy\n6QPc2LMFH/96iJe+38N1PeIID/Jjwjv28j2fTR/g5uh8w+ni06xIX8HSg0vZfGIzAL1ie/FE/ycY\nmTCSqMCo8i4bbyGJWfg6j036SimeHt2N0f9Zx2sr9/HU6K7uDsknWGwWcopzyC7K5qrPr8KiLXSI\n6MDDyQ8zKmFUrZ7GbGiSyIWoPY9N+gDd48O5rW8rPlqfzu39W9XYXtSf1WZlefpy3tr6FofOHsLf\n4M/kbpO5ru11dIrsJKUxhGgkPDrpA/xpZGeWpGbyzOKdaK0l+TiZTdtYdXgVb/z+Bvtz99MxsiPt\nw9sTERDBH3v/0W1xydW7EK5R48hZ7ta0SQB/GNGJn/ad4kyh2d3heIxpy6ddUn+61po1R9YwYckE\nHlnzCDZsvDTkJRaNXkRkYKScXIVopDz+Sh9g8oA2fLrxMIdyCgkP9nN3OF5Na836Y+v5z5b/sO3U\nNlqFtuKfg/7JdW2vw2hwbb0juXoXwv28Iun7GQ08dUNXJs/dyPHc4ppXEFX67fhv/Of3/7D5xGbi\nQuJ4ZuAzjG4/Gj+DnEiF8BVekfQBBneKISLYj6NnisgpKCUqxN/dIXmNYksxh/IOcdf3dxETFMNf\n+/+VWzregr9R/oZC+BqvSfoArSKD2FZo5rPfjjBjaHt3h+MVFu9fzM6cnSgUj/V5jPGdxxN43vib\nl0K6bITwLh7/Q25Fwf4
      "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": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEKCAYAAAD6q1UVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3gTPTYLqg6Zm30lZIDI
      "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": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfc79358>"
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17bfc79ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evohalfherd['std'].plot()\n",
    "evodumb['std'].plot()"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "toc_section_display": "block",
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 },
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}