1
0
mirror of https://github.com/gsi-upm/soil synced 2024-11-14 15:32:29 +00:00
soil/examples/newsspread/NewsSpread.ipynb

768 lines
252 KiB
Plaintext
Raw Normal View History

{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T16:22:30.732107Z",
"start_time": "2017-11-08T17:22:30.059855+01:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"import soil\n",
"import networkx as nx\n",
" \n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# To display plots in the notebook"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T16:22:35.580593Z",
"start_time": "2017-11-08T17:22:35.542745+01:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"\n",
"from soil import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# News Spreading example with SOIL"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example we three different kinds of models, which we combine in five types of simulation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T16:22:37.242327Z",
"start_time": "2017-11-08T17:22:37.087039+01:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_dumb\r\n",
"network_agents:\r\n",
"- agent_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.yml"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:07:46.781745Z",
"start_time": "2017-11-08T19:07:41.146659+01:00"
},
"scrolled": true
},
"outputs": [],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:07:50.696420Z",
"start_time": "2017-11-08T19:07:50.652496+01:00"
}
},
"outputs": [
{
"data": {
"text/html": [
"<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": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n33XOjXPOTQA+DzzvnLs
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a0830828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"evodumb['mean'].plot(yerr=evodumb['std'])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:08:31.598791Z",
"start_time": "2017-11-08T19:08:02.764487+01:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);\n",
"evohalfherd = analysis.read_data('soil_output/Sim_half_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evoherd = analysis.read_data('soil_output/Sim_all_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evoherdwise = analysis.read_data('soil_output/Sim_wise_herd/', group=True, process=analysis.get_count, keys=['id'])\n",
"evowise = analysis.read_data('soil_output/Sim_all_wise/', group=True, process=analysis.get_count, keys=['id'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:08:37.909477Z",
"start_time": "2017-11-08T19:08:36.873751+01:00"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n33XOjXPOTQA+DzzvnLs
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e63da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nV51zlwELgPF+tknAs/7
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e630f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2oJu77mIBBmcEM3ghME
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e63a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8hrTF063BouzRwkdldo
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e63cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noLXuUko5gGeAeUA9cLX
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1f6d198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in [evodumb, evohalfherd, evoherd, evoherdwise, evowise]:\n",
" i['mean'].plot(yerr=i['std'])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:08:44.132725Z",
"start_time": "2017-11-08T19:08:43.880341+01:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAEXCAYAAADP3/fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FVX6wPHvm5sKJIQWCDU0qUaaoCgIIiqWRREBG83C\nqsha1hXXVdFlLayisvpzVwSxgKhYFhUXlSKiSIdIr6EGCD2B9JzfH2cSbm7uzb1JICHwfp4nT+6d\nOTNzZubMmXnnnJkrxhiUUkoppZRSSpWdoPLOgFJKKaWUUkqdbzQQU0oppZRSSqkypoGYUkoppZRS\nSpUxDcSUUkoppZRSqoxpIKaUUkoppZRSZUwDMaWUUkoppZQqY+d0ICYid4jI92W0rDgRMSISXBbL\nOx1EZIqIjC2jZRkRaVYWy/KnuPuqNNvpTG3jsizb5woRmS8i9zifT/v2K025EpFuIrLRbVwLEVkp\nIikiMkpEIkTkaxE5JiKfnc58V1QikigiV5V3PnwRkYYikioirvLOS1FEpIeI7C7H5d8sIrucbdW+\nvPJxOonIWhHpUd75yFOW5/rycLavX0WpC4pDRIaKyMIzOP8Kd01dUgEFYiJyu4gscwpSkoh8JyKX\nn+nMlZYxZqox5uozMe8zeRHg68TofiF5LnHWK9256DwuIstFZLSIhJV33s5WZ7Jsnw/Otu1njPnZ\nGNPCbdBfgPnGmEhjzASgP1AbqGGMubVcMqmKxRiz0xhTxRiTU955Ocu9Aox0ttXK8s5MntKc440x\nbYwx809zlspdca9BRGSMiHx0JvNUEZzJusDZxlnO9XneXxO38e2ca6qTzv92buNud67pt7vfOBCR\npiLy67kUOJ4uZ+I63G8gJiKPAq8DL2AvBBoC/wf0PZ0ZOd3Ohyg6UBVkW4w0xkQCscBjwCBglohI\n+Wbr/HK2lJWzJR/lqBGw1uP7JmNMdnFnpNuyaN62T3G3mW5jq4TbwbOsK6WK5xMn0Mv72wYgIqHA\nf4GPgGrA+8B/RSTUOVZfAjoADwFvus1vAvBoWd1EOu/rT2OMzz+gKpAK3FpEmjBsoLbX+XsdCHPG\n9QB2Y+/uHgCSgJuA64BNwGHgr27zGgPMAD4BUoAVwEVu40cDW51x64Cb3cYNBX4BXnPmO9YZttAt\njQH+CGwGjgBvAeKMcwGvAgeB7cBIJ32wl3X+EMgF0pzt8xcgzkk/BNjpzOcpt2mC3PJ/CPgUqO5j\nm/YAdnsZPh+4x+37DcAq4CjwKxDvNi4ReAJIADKAYKC9s01TnG08HRjrIw9NgblOXg8CU4Foj/n/\n2Zn/MWd+4W7jH3f2915guLNtmvlYVoH1coY1BE4CNzjfp7jn1XMbOfl53MnPCWAS9sbBd876/ghU\nc9Lm7av7nPwlAY8VUcanAP8GfnDm9RPQyG18S2fcYWAjMMBj2reAb51pFwNN3ca/AewCjgPLgW7O\n8LrY8lXdLW17Z1+EULhsdwWWOvtiKdDVY9tc5XGcfeSxLe7GltsFQDi24j6ELVtLgdpF1RXFKBf3\nAlucbTUTqOtxfD6IPT63uw17wBmWAvwdWzYXOdvsUyDUSVsN+AZIxh7f3wD1vZUz9+2HPX5T3f6y\ngCludeAkp4zswdYrLrc64xVnn2xz8u61znDbf16PP9zKM/a4ywHSnfx8DGQ6+UoF7nbSDQfWO+s6\nm4Jl0tu2LE05beM27X6cepvi1WuB7J+/Y+vxFOB7oKbb+LuAHc5ynsKjXHssK8zZNzud/P4biHDf\n1tj6cR+2Pi80rCTl1SMPce7lwd/6eTsHYG9K5Z07hxVxLhhK4XNdoMdN3rL+ii3LicAdJd2WXtYl\nCPibs+8OAB9gj6swbHk22Dp7q49t4bWOdMZFYC8uj2CPhb9Q8LxQF/gcW+a2A6M86sFPnfykYIPB\nTs44b+f4gOtF3MpmUcvxMW1Rx+n1wEpnW+wCxnhMezn2WuCoM35oIMe3xzy8rifwDwrWS28WtX+A\naylYb632V6d6yUcazjHilKFsIMr5PhZ4PcD6q8R1n7fjsoh93RlY5myL/cD4ktQFwGBO1XVPU3Rd\nNwbnfO5l3NXONha3YTudfVMbWOS2rU86n/sD7/gqn551DrZuOII9vvq4jS/q3DmUwtfrxT2fFhUP\nFHk9H0DevK4XXo4BQJz1OIC95kkA2vrbfgXWxc+GvhZb8L1uCCfN88BvQAxQC1sJ/N2t0GYDz2Av\nHu/FVojTgEjsyT0daOJWoLKcghCCvaDbDoQ442/FVqxBwEBs5R3rtvGysZF9MLaCHkrhk9M3QDT2\nQj8ZuNYZ90dnZ9bHXjD86KcQJFLw4jbOST/RWfZF2AColTP+YWc71ceegP4DfBzowe528OZdSHZw\ndnwXbKEb4uQpzC1/q4AGTn5CsQf2I8627e9sa1+BWDOgt5PXWtgL9Nc91n+Jsz+qY0+Cf3QrN/uB\ntkBlZ38bihGIOcMXAC+7VZb+ArHfsJVLPWfbrMBe/IZhL26f9dhXHzv5u9ApC74quinYg727M683\nOHURXxl7EhqGLXcdsAd/G7dpD2Mr6GBsQDvdbd53AjWccY9hL2bCnXFzgXvd0v4T+Ld7ZeF8ro6t\nMO5y5nOb872Gj7I6hsKB2AfOukQAI4CvgUrYstWRUye/0cA3RdQHRZWLK51t08HZjv8CFngcnz84\n00W4DZsJRGHriwxgDtAEW5muA4Y4aWsAtzj5jgQ+A77ycfzkbz+P/DfABufXOd+/wh6rlbF13BJg\nhFudscGZpjowD983b4o8/ihcnvPz6rnPnO83YQOEVs4+/xvwq69tSSnKqbMtk7DlM9z53qUE9Vog\n+2crcIGT5/nAS8641tgTX94xOB5b3/s6Zl/HlpvqzrK+Bl70OC+97MwrwsewYpdXjzzEUfjiy+v6\n+TgHZGPPryHYm5cnOXUzybN8DKXwuS7Q4yZvWeOd9bwCe25tUZJt6WVdhmPLahOgCvAFbgEbRZwb\nAqgjX8LeGKuGLYMJnLqhEYQNDJ7BHn9NsBd417gdU+nOtnUBLwK/edRl7vWmz3rRRz14VSDL8ZjO\n33HaA3u+CgLisefZm5xxDbHnqduwZaYG0C6Q85BHHoqq/+dT+KZpUftnDB5BAkXUqV7ysgC4xfn8\nPfb46eM27uYA6q9SnaO9HJdFBWKLgLucz1WAS4pbF3CqrrscW25fwZ4rigrEjjnrsBa4323cI8B3\nHum/cfZTELZBpD5wIzbgroK9bqzh63j0qHOysNf1LuB+7Lkzr3GjqHPnUApfrwd8PnXmUVQ8UOT1\nfAB5K2q95lOw7r0GW89EY4OyVnn5CPTP34a+A9jnJ81WnIsWt0wluhXaNE5FmpHOxujiln45pyqS\nMRSsCIOwFwDdfCx7FdDXbePt9FJQPE9Ol7t9/xQY7Xyei1tlAFzlpxAk4j0Qc7/DuwQY5HxeD/Ry\nGxfr7GxvF209sHfjjnr8ZXPqQvJtnIDXbbqNwBVu+RvuNq67e2Fyhv2Kj0DMS55uAlZ6rP+dbt/H\ncSpImIzbBQa2svF5svUs2G7DpwMTnc9T8B+Iud/F/Rx42+37QzgXfW77qqVH/if5yN8UCgZPVbB3\nRRpgK4CfPdL/h1NB3xTgXbdx1wEbitjOR3BagYF7gLnOZ8GeTLp7lm1sALbEYz6LOHU3NBH/gVgT\nt/HD8WhhDfTPT7mYBIzz2I5ZQJzb8Xmlx/wMcJnb9+XAE27fX8XtBoHHtO2AI97KGV4CMezJIH/+\n2KA+A7eLS+xFzjzn81ycINP5fjW+A7Eijz+KH4h9h9My5nwPwl6oN/K2LSlFOXXWeaXnOjnjAq7X\nAtw/f3P7/gDwP+fzMxQ8Bitj77YXujjBHisnKHhH/FJOtQz2cKZ1b6n1NqzY5dUjH3EUvvjyun5e\npu2BPXcGuw07wKkLO8/yMZTC57qAjhtOBVOV3cZ/ir0TX+xt6WVd5gAPuH1v4V5G8BOIeZmfex2Z\nH1g53+/hVCDWhcLXBE8
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1d84dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = evodumb['mean']-evohalfherd['mean']\n",
"m = evodumb['max'].loc[0].sum()\n",
"diff = diff / m\n",
"diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
" title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 50% herd agents.');"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:08:45.320345Z",
"start_time": "2017-11-08T19:08:45.068706+01:00"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEXCAYAAADcCLc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n30flY7A1/m59lYsX/LG
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a200d2e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = evodumb['mean']-evoherd['mean']\n",
"m = evodumb['max'].loc[0].sum()\n",
"diff = diff / m\n",
"diff.plot(yerr=(evodumb['std']+evoherd['std'])/m,\n",
" title='Comparing the Herd and Dumb behaviours: normalized difference and error in number of agents in each state when using 100% herd agents.');"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-08T18:08:46.641058Z",
"start_time": "2017-11-08T19:08:46.052870+01:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f73a1cd97b8>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nf80VdObMGXbs2EF4ePh
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6d8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXFwcwcHBjBw5kkmTJjF
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6d438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEKCAYAAAAMzhLIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n587NnswuiK5du3L58mV
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6de10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(evohalfherd['std']/m).plot()\n",
"(evodumb['std']/m).plot()\n",
"(evowise['std']/m).plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"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",
"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
}