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soil/examples/newsspread/NewsSpread.ipynb

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{
"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": [
{
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" <td>47.666667</td>\n",
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" <td>451.261905</td>\n",
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" <td>51.731707</td>\n",
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"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",
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"22 51.731707 448.268293\n",
"23 54.390244 445.609756\n",
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"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": [
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]
},
"execution_count": 24,
"metadata": {},
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{
"data": {
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"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\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8VPWd//HXZyaXCSQBQhJEwUJbtvUWAfFSq1bFaq12\ntUq1rQoKLWq1bi/blrb7q7TbVtdase5v1/WCSi2tVrTetq1WxPKj2mJARIQqVFGRWyCQ+2Uu398f\n58xkkkySCSQknLyfj8c8zmXOmfkeRt/n5Pv9nu8x5xwiIhJcoYEugIiI9C8FvYhIwCnoRUQCTkEv\nIhJwCnoRkYBT0IuIBJyCXkQk4BT0IiIBp6AXEQm4nIEuAEBpaambMGHCQBdDROSgsmrVql3OubKe\nthsUQT9hwgQqKysHuhgiIgcVM3snm+1UdSMiEnAKehGRgFPQi4gEnIJeRCTgsgp6M9tsZq+Z2Roz\nq/TXlZjZn8xsoz8d5a83M7vDzDaZ2Vozm9qfByAiIt3rzRX9Gc65yc65af7yPGCpc24SsNRfBjgX\nmOS/5gJ39lVhRUSk9/an6uYCYJE/vwi4MG39L53nr8BIMxu7H98jIiL7Idugd8CzZrbKzOb668Y4\n57YB+NNyf/1hwHtp+27x13WtuQbeexn2vguxlqwLLyIiPcv2hqmPO+e2mlk58Ccz+3s321qGdZ0e\nTOufMOYCHDc2BAvPanuzYBQUHgKF5VB0CBSOaZsWjvHWDy+DyEgIqT1ZRKQ7WQW9c26rP91pZr8D\nTgB2mNlY59w2v2pmp7/5FmB82u7jgK0ZPvNu4G6Aacce6fjiAqjbDvU7vFdy/p2XvGk8w5V+KAeG\nlXqhP9yfFpa3zSdfRYfA8HIID4obgUVEDqgek8/MhgMh51ydP3828CPgSWAWcLM/fcLf5UngejN7\nCDgRqElW8XQpdxj80zldv+8cNO+Fuh1Qvx0adkFDlfeq39m2XP2WN402ZjoSP/THeH8tpKb+q/AQ\n+OM8COXC7D+AZfrDRETk4JPNJe4Y4HfmBV8O8Gvn3B/N7GXgt2Y2B3gX+Jy//e+BTwObgEbgqv0u\npZlXnVMwCso/2vP2rQ1e4D88E+KtcMKX/b8QtredLLa/Bg07wSU67//vpRAZ4b9GetOCke3XvfIr\n7y+K825t+8uhYBSEwvt9uCIifcmc61R9fsBNmzbNDcigZom4d0JIVhP98XuQiMHRF3kNxM013l8S\nqfkaaNqbuRoJwEIwbLRfndSh+ujV30A4D/75P73qpcJyyC04sMcrIoFiZqvSurx3aWhXWofCbVU3\n0H31UbpoMyz6DMSjcPa/+9VIadVJyeVtr3rTlpq2fdMbnfOL/dAf47cv+A3Nr/4GQnlw4X+1NTzn\n5PfdcYvIkDK0r+gPlFgL3H+ed2I483teu0L9jrZpQ1Xbcktt5s+IjPAalJPBXzgGCsu8dX+9E8K5\n8LkHYFgJ5I9QbySRISDbK3oF/WATbYIHzvfaFk6f5zc2Jxudd0J9Vds0/S+FdBb22guGjfaCf9jo\ntuXXH/d6H501P63NwX/lF3duY7j/PG961f/251GLyD5Q0A8F0WbvJPDQZZCIwsf/BRp3Q2O1N22q\n9ufTluOt3X9mfnFbA3RkBOxc7/21MOWKtqqlwjF+r6UxkDe8/f46MYgcMKqjHwpyIzByPFyzPLvt\nnYP7PuU1OJ+/oH0jc6eX3wgda4bWelhxW+YeSnmFfvj7N7hV/8NrdF61yK9mKm+rYsqNtN9XJwWR\nA0JBP5SYwZxnerdPMoxnPen9ZVC/vX37Qt2OtuUd67ypi8NTN3T+rPzitJvaymD3Ju+ksPIer1pp\neKnXY2nYaO/V8QY3nRhE9omqbqRv3X+e12314rvT2hMytC80VHlX/4lY158VGemHv99l9f1K796F\n4+e03c9QMNKvakq73yGcq5OCDAmqo5fB7/7zvOqgzz0Ajbu8doQGf5qaT67fDbvfhHiMDEMntZc7\n3GuzCOXAuOPb7mlI3d/QYTkywmsAh+xODDqJyCChOnoZ/NKDsmhMz9snA/byR9vaEJr2Zp5f+1vv\nr4VoE2x9xb+foYuuq6Fcr1rLQvDfJ0NOHuREvHsXwvneNPWKeENthMJQeR8UHwZFY73psJLMQ2fo\nxCADTEEvB4/0oMxNu9Etk61rOu8Ta2m7sa1xlz/vL6/5jde2UDLR2y7W7J0kmvZArNVbjvvTpr3e\ntk9/vf13hvOheCwUHQrFh3rzxYd53xHOgW1rIVLcdVdW0ElB+oWqbkSgdwF7/3leD6aL74G6bVD7\nPtQmp1vbr+tquAyAvKL2wR8ZAe+v9k4K02an9Voa0zc9l3QSCRxV3Yj0Rm/CL33bEYcBXfx/5pzX\nU+lXF3ltC2fM87uu1nrTltr2XVrrt0Nrnde+sOwnmT8zf0TbWEnJ7qyhHPjzz9qqlpJVT+G0Kqic\nfGip86qnardCQUnnk0ZHvT0x6EQyaCnoRfqLGQwf7d1rAHDEZ3reJxmWVzyWNjSGP032YEq+tr/W\n1p112Y+zL9dtR3jTvEIv8JN3T7d7jfKqnEJheOdFbwC+nAJvmlvgnUByh+kZDwcJ/Uoi/W1frnBz\n8mHEOO/VnWQ10qwnvPaDWEtbG0OyTSG57o/zIJGAk65uf8d08q7p6n946zo2Wt9/btffH8rxAj8n\n4u0XzoXfzvQaqFOvQ7w2i6JDIL+ordz7+m8jvaagFxlM9iX4zLyADee2BWkmBSXedNrs7j8v1uo1\nQv/6Uu+eiLN/5DdON3rDbsSavIbqqL8u+d6G/4VEK+zcAP9YlrmXU16hF/j1VV7V0h+/57U/FI5p\nu4u6cIzX9TX9rwW1RewXBb3IwWxf2xa6k5PndXdNjmP0oTOy22/Xpvbf01Ln3Tldty3ttd1rI6hf\n6rVHrHoAog0ZPsy8KqXkEN5Vb3jBv/RHfsN1cdq4TMXt1znXuyfEDYGTiIJeRDLrbZh13D6/yHuV\nfrjztumB2VLfdsd0qi2iKm0I751+I3UMVizIPOZSR6Ec+L/Hp7U5ZGqH8F+JmNdI3dsTRDYGyYlB\nQS8iAyu/0HuVfLDrbZKBeeXT3qNCW2o7915KTlfe64V3+RFem0P1W7DlZa8torshN3440hviO5zr\n3UQXzvGnud6JI5zr/TViIVh8if/0uNK2sZuSr8Jyr5qstw3V/XhSUNCLyIG3r2Fm1nZiKD408zYb\nn/Oml/yy/XrnvJNB+lDejbvhz//hvXfsF7yurfGod0KIR/3lWNv6pj1eu0XdNv+501Xee50L6v0V\nEW3yThK/vrSteil130RyfoQ3H20Ey/H2yYn0/NdF8sSQBQW9iAx+fdEWYdb2nIX0vx5eWexNz/hu\nz5/d8arbOW/YjYZdbQ8JSn+9/oR30qjdCi1/b7uPwsW7/o6fHOI1VHd8MFDHV922nsvrU9CLiOwr\nM+/pbQWjoHRS5/er3vSm6Scf57yr92ToJ6ucnvm+d1KYekXmZ0TUbGmbjzX3qpgKehEZ2vqj51J3\nzLweTXnD21c/rbjdm57y9cz7pYs2w6LPAM9l9ZUKehGR/tJfJ5Fcf4iLLIWy/2QRERk0enFiUNCL\niAScgl5EJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnBZB72Zhc3sFTN72l+eaGZ/M7ONZvaw\nmeX56/P95U3++xP6p+giIpKN3lzR/wuwIW35P4AFzrlJwB5gjr9+DrDHOfdhYIG/nYiIDJCsgt7M\nxgHnAff6ywacCSzxN1kEXOjPX+Av478/3d9eREQGQLZX9LcD3waSj3YZDex1ziVH8d8CHObPHwa8\nB+C/X+Nv346ZzTWzSjOrrKqq2sfii4hIT3oMejM7H9jpnFuVvjrDpi6L99pWOHe3c26ac25aWVlZ\nVoUVEZHey2b0yo8D/2xmnwYiQDHeFf5IM8vxr9rHAVv97bcA44EtZpYDjACq+7zkIiKSlR6v6J1z\n33XOjXPOTQA+DzzvnLs
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e63da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e630f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e63a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"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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\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": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6d8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6d438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f73a1e6de10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(evohalfherd['std']/m).plot()\n",
"(evodumb['std']/m).plot()\n",
"(evowise['std']/m).plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "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
}