mirror of https://github.com/gsi-upm/soil
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
461 lines
148 KiB
Plaintext
461 lines
148 KiB
Plaintext
7 years ago
|
{
|
||
|
|
"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": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDwp9ERkyetJE0l5TFMA
|
||
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8VPWd//HXJ5PJPQFCQFGwYKXWCxE1XqsWxUu9dNUW\nrVbLtQu1XrbVbWXrb1fsttW1Vq3dLvWC1gtWK1q1rl3bVahL1WJARAQValG5SEICIfdMku/vj3Nm\nMiGTZHK/nPfz8ZjHmTlzzpnvYcj7fOd7vud7zDmHiIgEQ8pAF0BERPqPQl9EJEAU+iIiAaLQFxEJ\nEIW+iEiAKPRFRAJEoS8iEiAKfRGRAFHoi4gESOpAFwCgoKDATZw4caCLISIypKxevXqXc25MV9YZ\nFKE/ceJEiouLB7oYIiJDipl91NV11LwjIhIgCn0RkQBR6IuIBIhCX0QkQBT6IiIBklTom9kWM3vH\nzNaaWbE/L9/M/mRmm/zpKH++mdk9ZrbZzNaZ2TF9uQMiIpK8rtT0T3fOTXXOFfmvFwIvO+cmAy/7\nrwHOBSb7j/nA4t4qrIiI9ExP+ulfCEzznz8MrABu9Oc/4rz7ML5hZiPNbJxzbkd7G3rv00ou/M+V\nFOSke4/cNMbkpFOQmx6bNyYnnbzMVMysB0UWEQm2ZEPfAX80Mwfc65y7D9gvGuTOuR1mNtZf9kDg\nk7h1t/rzWoW+mc3H+yXAiAMOZkRWGjsq6nhnWwVl1Q00Nbe9d68B4VAKk/fLYVRWGqOy08jPCnvT\n7DRGZe0zzQ6TnhpK/l9DRGSYSzb0v+Cc2+4H+5/M7L0Olk1UFW+T4P6B4z6AoqIi98jc42PvNTc7\ndtc0sKuqgV1V9ZRW1rOrqp4lK/9OpKmZ/fMyKK9pYOvuGsqrG9hb19huYUZmhRmbm85+eRmM8afR\n1/vlpTM215ufEdbBQUSGv6RC3zm33Z+WmNnvgOOBndFmGzMbB5T4i28FJsStPh7Y3pVCpaQYo3PS\nGZ2TzqHkxuZ/89SDEy7f2NTMntoIu6sbKK9uYHdNA//xh/eINDlO//xYSirr2Lm3nr+VVFFaVU+k\nqe2viFCKEQ4ZUyeMbGlSyk33m5nSWpqectJJS1WnJxEZmjoNfTPLBlKcc5X+87OBHwLPA7OA2/zp\nc/4qzwPXmNkTwAlARUft+b0hNZQSC+SoLx05LuGy0V8RO/fWU1JZR4k/feT1j4g0NdPY5Fi/rYJd\nVQ1U1Sf+BRE9QEw5cAT52WneASo7jdHZaeTnpFOQnUZ+jtfElJ+VRmpIBwkRGRySqenvB/zOP4Ga\nCjzunPsfM3sT+K2ZzQM+Bi7xl38ROA/YDNQAc3q91D0Q/yvicPJi8685Y3KbZWsbmrzmpap6dlXW\nx5qbHnvjIxqbHaEU48PSaoq37Ka8pgHX9gcEAKkpxkH5WbFzD/lZ/kHBPy8xOtub3vzcesKhFJ76\n1kk6YS0ifcJce0nVj4qKitxQH2Wzqdmxp6aBsuoGyqq8Zqay6nru/fPfiDQ5jpuUH2t+ijZBJWpm\nAgiHLOFJ6ehBYlRWGotXbCYcSuG+mUXkZ6fpnIRIAJnZ6rhu9Mmto9AfGM45KusbWx0IfvzfG2ls\nbua8KQd482saYtM9NRF2d/BrIjc9ldE5XlNTfnYaBTlpjM5OZ3ROGo+98RHhUAr3XH40Y3LSGZEZ\nJiVFvyREhjqF/jDX1OzYWxuhvKaBax9fQ6TJMfeUSZRVeU1P3q+MesqrvZ5P5dX1JOj5SmqKxU5U\nF+SkeSesc1tOXv/ylc2khlJ4aM5xjMwM65yEyCCl0JdWmpsde2ojzH5wFZGmZr59+iGx7q+llf65\nCv95WVUDjYmOEMCIzLDf1BRuaXLyz0k8+eYnpIaM22ccxehsr5dTZlripqav3fs6AE8uOKnP9lkk\nSLoT+oPizlnSN1JSjPzsNJ6/9pROl40eIHZV1XPdb96isamZmSdP9M4/VDdQXhOhvLqebXvqWL9t\nL+XVDTQ0NcfWv+iXf4k9z0oLeU1N2emtmpl2VNQRDhkrN+3ym6La792kA4RI31BNX7rFOUdNQxNf\nv/8NGpsd15/1OcqqGthV7f1qKKuqp8xvZoo2OSX6JWEGo7K8HkzRcxIF2Wm8vLGE1JBx/dmHkpeR\nyojMcOyRlxkmHHeg0AFCgkrNOzJoNTc7ZvzqNRqbHDedf1js/IN3LsI7UOyqapl2dJU1QHZaiDz/\nILBtTy2pKcY5R+zvD82RxshoU1R2S9fYvIxULrvvDSC5A4QOJjLYqXlHBq2UFOOZb38h6eUv8Q8Q\nP72kkIraSMujJsLeusZW87btqaUu0swr75V02BU2lGIYkBoyLvnVa4zI9A4OIzPDjMwKMyLLO28x\n0p9fH2kiFErBOZfUdRM6SMhQoNCXQempb52c9LLxYeuco6q+kd3VXi+n3dFur/61EU8VbyXS1Exq\nSgrb9tSyYXsFe2oj1DQ0tbv9Q276Q6smpry4pqYR0QNGZpjy6gbCIePvu6opyEkjJ739UWF1gJCB\notCXIS8+OM2M3IwwuRlhDhqd1WbZ4i27AfjN/BNbza9vbKKiNsKemuijgdv+8B6Nzc38w1EHtv61\nURth6+7a2PN9R4Q9/Y4VAKSn+sOD5KYzJid+/KY0yqrqCYdS+LC0irF5GWSnhXSAkH6h0JdAaS84\n01NDjM0NMTY3IzZvycq/A/DP5xza7vacc1Q3eAeMBY8U09jsWPDFg9lV2RAbwqO00uv19PbWCsr3\nGTb8jJ/9GYDMcIixed4Afy3TDMbkpLOnpoHUUAqflNcwIitMbi/9gtDBJJgU+iLtSCYMzYyc9FRy\n0lPJTvf+nC4+eny7y0cH/Jv90CoiTd4BomSvd2AoqfSm739ayf9V7qJyn5PZp96+HPAuros2KY3K\nSmOkf+J6VFbLSe0X39nByMwwI7LC3vuZYbI6+DXRGR0ghg+FvkgvSSYQowP+ZaV1foCoizRRWlnP\ngkeLaWxyfPO0g6nwh+PYXROhoraB3dURtu6u4d3t3vy6iHftxLeXrmmzvbRQincQ8M9DbCqpIpxi\n/Pi/N8QOHCMzvZPZI7KiB5Qwmd0Y10kHicFLoS8yAJIJw4xwiAn5WeRmhAG4tGhCJ2vAJYtfo7HZ\ncetXp8TOT1TUegeJ6PPo/IbGZmqaHI+98TG1kfZPZKelej2YUlNSmLH4NfIyw+RmpJKX4U8zw62e\n52akUtvQRGrIaPJHo+2ImqT6l0JfZJDrSsClpBhpKcbn98/rdNn4AK2LeOcldtc0xE5k76mJeDcn\nqmngmdXbaGxuJi01hZLKOv5W2sje2giVdY3tDt8BcMhNL5KX0TKMx6jYSLHh2DUU5dUNpKYY739a\nGfuV0Ru3OdUBIjGFvsgw0t2AywiHyAiH2C8vI+H7az/eA8Dj/9i615NzjtpIE3trG6msi7C3zruO\n4kcvbKCx2XHR1ANjzVG7qxvYUVHHxh17KatuoL6xudW2zrn71djzrLSQ3wwVPV+RxoisMJ+U15Aa\nMp5882O/y2yaN/XPcXTUC6ojQfq1odAXCajeCC0zIystlay0VPYf0XLA+NWKvwHw3bM+1+66tQ1N\n7K5pYL7f6+maMw5p9Stjd1zT1Huf7mVPTYSy6gYAbnz6nYTbTE2x2HUUJZV1pKakcO1v3iLPb3oa\n4TdF5WWmxj0PE2lq7rQZqrsG20FCoS8inepqYCWzfGZaiMy0zFivpwsKD+h0nUt/9RpNDn5+2dTY\ndRJ7466f8M5beI/yTfU0NjWzfltFbJmOmqIAPv+vf/Cv80glNyNMXkYquRle76zo/B0VdaSmGC+9\n+2mri/RG9LCHFHTvF0dXKfRFZEB15YBiZqQajB+VxfhRHS+7b4DGN0XtrWs5YOyti3D3/26iqclx\nXuE4v5mqkco6r8lqR0UdlXXe+Yv4K7cXPLq6zWeGQ0ZeRusrtzeXVBFKMX760nuxXxbRXxv7ngzv\nDwp9ERkyetJE0l5TFMA
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f17bfb3c278>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcFOW97/HPr3tWNpEtomDAyPWIOiKOxhhRIi5xyXEJ\nMSYqq4EQjEk0CzmeGzGvRI0xLiT3eFwgEsXjgmiUY44ahMs1MeKAiAhRiEFFEBAEGWbv/t0/qqan\nZ2N6mH3q+369+lVLP9391DR866mnqp8yd0dERKIh1tEVEBGR9qPQFxGJEIW+iEiEKPRFRCJEoS8i\nEiEKfRGRCFHoi4hEiEJfRCRCFPoiIhGS1dEVABgwYIAPGzaso6shItKlrFy58mN3H9ic13SK0B82\nbBhFRUUdXQ0RkS7FzN5r7mvUvSMiEiEKfRGRCFHoi4hEiEJfRCRCFPoiIhGSUeib2SYze9PMVptZ\nUbiun5m9aGYbwunB4XozszlmttHM1pjZ6LbcABERyVxzWvpfcvdR7l4YLs8Clrj7CGBJuAxwHjAi\nfEwD7mmtyoqISMu05Dr9i4Cx4fx8YBnwk3D9Hzy4D+PfzKyvmQ12962NvdH6rZ9y5u3L6JmbRY+c\nOL1ys+iRm0Wv3Dg9crLomZtFz5x4MM2Nk58dJy87mObnpC2nzcdj1oJNExHpnjINfQdeMDMH7nX3\n+4DPVAe5u281s0Fh2cOAD9JeuzlcVyv0zWwawZEABx16BCMP7cO+8ir2VST46NMySioSFJdXURKu\nay4ziJkxqHduamfQIydOfk4WPVLzNeufev1D4jHje2eNoGdOFr3ysuiVm/bIyyI/O46ZdiYi0nVl\nGvpfdPctYbC/aGZ/30/ZhlKx3t3Xwx3HfQCFhYX+u2823vWfTDqllYnUTqGsMkFpZYKyimBaWpmg\nNG19aUWSR197n6Q7Y0YMoKQieL6kIsGe0ko+2lNaa11pZc1O5QePvdFoPWIGPXOyKK9KEovBkYN6\n0SM7i/yccCcSHm2kdi45cf5rxfvEzbjunP8Vlsmqt8PpkZNFXnZMOxQRaXMZhb67bwmn283sKeBk\nYFt1t42ZDQa2h8U3A0PTXj4E2NKSSsZiFnbtZN4b9b2zRmRcNpl0yqqCI4t95QmKy6rC+WBad37x\nG1tIOAzqnUdJRRW7SyrYsrtmB1JaZ0cCcM0jrze9neHRySEH5TV4VFK9g8jPifPs6i3EYsaMsZ9L\n7XB65GTRIzcsl10zn5cVJ6buLhEBLOh6308Bs55AzN33hvMvAj8HxgE73f1WM5sF9HP3H5vZBcA1\nwPnA54E57n7y/j6jsLDQu9vYO9U7kuojitLKYL6kooqy1Hyi1vwTRR+QSDqnHRkcnZRUJiitqKp1\nVFISLlcl9/+91RUz6NczJ9gxpO1AeoTnSvJz4vQMdzRPrdpMLGZ8Z+znyAvPkeRlx8nLiqXOneRl\nxcnLjjHj4ZXEYsbj07+gIxWRdmZmK9MursnsNRmE/hHAU+FiFvCIu//SzPoDjwOHA+8DX3P3XRb8\nz/8d8GWgBJjs7vtN9O4Y+m2toioZ7Agqa+8U9lVU1XRbVQTdYQ+9solEEsYdPSgoU16V6i4rSduZ\n7Cuvf4SSqZiROokePGKpnUN+TpzcrDivv/8JMYPzjxtMXnV3WNoJ+Py0db/47/XEY/AfV5yY2jHl\nZqkLTCRdm4R+e1Dodx7JpHPZva+QdOe33xydOldSXpWgrDLY0ZSF82WVCe5b/g+SDhePOiw4z1JZ\n81zq3EtlgtLKJP/8uJhkEnrkximtSFBelWxW3arPqfTIjaemPXKyeOejvcRixtijBoZHJMFOp3rn\nU70uNzvG717aSMyMX15ybNhlVnOOpUd2nKx4zVXMX7/3FQAem/6FVv0bi7QWhb50anVDNJH0tJPv\nNTuJkooEN/5xLQmH6acfkTqCKSmvOZLZV5EIr+yq4s3Ne0i4079nbmrnVFbZ/C4wgJx4LHX+5JOS\nCmJm/MshvcmOx8jJipETTlPLaesWv7GFmBlXfeGz5GbFyMmKk5sVIzc7Rm44n5MVIzcrxv9+ei2x\nmPGfV56Yuvw4NyvW4LkX7XykMQp9iaTGQrEqkaSsquaoo6wywfcfXU3SnVnnHR2eeK97zqSmW+yl\nv28nmXSOPewgKhJJKqqSVNaZViSciqoElQk/4K6xdLlZNUco1d1lH3xSQsyMEz97cJ2jmHido5kY\n81/ZRMyMH517FLlpRz0152Fqyk+Y+ypmltHORDuezkmhL9KKmht0X7/3FdydB6ecTHllkvKqJOVV\nQTdWRfV8uP7m59aTdOdbY45IdX+l75xK07rJXv3nTpJJGD6wZ73us7LwvQ+UGfTJy67VDZaXHQt2\nGGkn71/5x8fEzLjohMPIywqezw2neQ1Mf754HTGDuy4/gZx4cHSTmxVPHR2l/3iyOX9n7XxqU+iL\ndENNBV0i6alurSkPriDpcOulBeG5l2BHU3dHUlaVYMHf3iPpcM7IzwTrq2rOyZRWJiivLl+VYMvu\nUpIO2XGjrPLAdzLV4jFLdXftK68iZsaw/j3DE/yxml/cZ8dTJ/3zsmP8cfUWYgbfGnNEaseTV2ea\nG+64rnt8NTEzFlx9SpO/g+mqOxOFvohk7EBb2O5ORSJJWWX60UsitVxWmeSmZ9/CHa4588i0I52w\nS6wqSUUieF1FIsmf3vyIpDuFww6udcRTfalz9Y6opKKKAzhNk1L3SrEeOcHRTI+cOG98sJt4zDh7\n5CH1zsPUPaL57ZINxAxuuujYWkPCVB8h5WfHyY5baifTlkcyCn0R6XIyDTr3mivL/vPKwlo7mvLw\n3E15VTK1E7rrzxtIuvONkw9P+01MFaUVyVrnckorE2zYVkzCnYPysymvfp8WdJulX8K8t7yKmAW/\n4E+/rLl6R1F9NJOXFWfRqs3ELPjRZfWlznlpRz7p52S+8/BKFs08rdmh3ylujC4i0dWMVi1PfPvU\njN/3kVffB+DbZ3yuybIN7XjcPRX+6edjvv/o6yTd+fcLRqa6zErTzseU1Vn3/FsfkQx/wV9WGfzy\n/+PiitTRTFlVot4lzLMWvZnxdjaXQl9EuqXm9M83VNbMUq1yyE6tX3ztmGbVY+P2YgDmTTppv+XS\nfyPzu2+Orn9+pc5vZe79v+/yXrNqElDoi4i0oUx3PrGYsXBG5kcyz6w+sCHNdLtEEZEu6ECvNFLo\ni4hEiEJfRCRCFPoiIhGi0BcRiRCFvohIhCj0RUQiRKEvIhIhCn0RkQhR6IuIRIhCX0QkQhT6IiIR\notAXEYkQhb6ISIQo9EVEIkShLyISIQp9EZEIUeiLiESIQl9EJEIU+iIiEaLQFxGJkIxD38ziZva6\nmS0Ol4eb2atmtsHMHjOznHB9bri8MXx+WNtUXUREmqs5Lf3vAevTln8F3OnuI4BPgKnh+qnAJ+5+\nJHBnWE5ERDqBjELfzIYAFwAPhMsGnAksDIvMBy4O5y8KlwmfHxeWFxGRDpZpS/8u4MdAMlzuD+x2\n96pweTNwWDh/GPABQPj8nrB8LWY2zcyKzKxox44dB1h9ERFpjiZD38wuBLa7+8r01Q0U9Qyeq1nh\nfp+7F7p74cCBAzOqrIiItExWBmW+CPyrmZ0P5AF9CFr+fc0sK2zNDwG2hOU3A0OBzWaWBRwE7Gr1\nmouISLM12dJ395+6+xB3HwZcDrzk7lcAS4HxYbGJwB/D+WfCZcLnX3L3ei19ERFpfy25Tv8nwHVm\ntpGgz35uuH4u0D9cfx0wq2VVFBGR1pJJ906Kuy8DloXz7wInN1CmDPhaK9RNRERamX6RKyISIQp9\nEZEIUeiLiESIQl9EJEIU+iIiEaLQFxGJEIW+iEiEKPRFRCJEoS8iEiEKfRGRCFHoi4hEiEJfRCRC\nFPoiIhGi0BcRiRCFvohIhCj0RUQiRKEvIhIhCn0RkQhR6IuIRIhCX0QkQhT6IiIRotAXEYkQhb6I\nSIQo9EVEIkShLyISIQp
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f17bfb3c2e8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO1a4c9Zzzz0XfRw7zXL
|
||
|
|
"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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\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": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEKCAYAAAALoA6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6ykvL2fmzJlMnz6d9ev
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f17bfc79358>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEKCAYAAAALoA6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ9O2bVumTJlS6bYFBQW
|
||
|
|
"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",
|
||
|
|
"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
|
||
|
|
}
|