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soil/examples/NewsSpread.ipynb

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Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
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{
"cells": [
{
"cell_type": "code",
All tests pass
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"execution_count": 1,
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
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"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:41.843Z"
},
"scrolled": false
},
All tests pass
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
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"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",
All tests pass
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"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 288K\r\n",
"drwxr-xr-x 7 j users 4.0K May 23 12:48 .\r\n",
"drwxr-xr-x 15 j users 20K May 7 18:59 ..\r\n",
"-rw-r--r-- 1 j users 451 Oct 17 2017 complete.yml\r\n",
"drwxr-xr-x 2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
"drwxr-xr-x 2 j users 4.0K Oct 17 2017 long_running\r\n",
"-rw-r--r-- 1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 newsspread\r\n",
"-rw-r--r-- 1 j users 225K May 4 11:23 NewsSpread.ipynb\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:21 rabbits\r\n",
"-rw-r--r-- 1 j users 42 Jul 3 2017 torvalds.edgelist\r\n",
"-rw-r--r-- 1 j users 245 Oct 13 2017 torvalds.yml\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 tutorial\r\n"
]
}
],
"source": [
"!ls "
]
},
{
"cell_type": "code",
"execution_count": 9,
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
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"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.440Z"
}
},
All tests pass
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"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"
]
}
],
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
2018-05-04 08:01:49 +00:00
"source": [
All tests pass
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"!cat newsspread/NewsSpread.yml"
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
2018-05-04 08:01:49 +00:00
]
},
{
"cell_type": "code",
All tests pass
2018-12-04 08:54:29 +00:00
"execution_count": 10,
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
2018-05-04 08:01:49 +00:00
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.879Z"
}
},
All tests pass
2018-12-04 08:54:29 +00:00
"outputs": [
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m copy=copy)\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
]
}
],
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
2018-05-04 08:01:49 +00:00
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:45.458Z"
}
},
"outputs": [],
"source": [
"evodumb['mean'].plot(yerr=evodumb['std'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:26:19.361423Z",
"start_time": "2017-11-01T14:25:57.017418+01:00"
}
},
"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": "2017-11-01T13:26:20.461665Z",
"start_time": "2017-11-01T14:26:19.363815+01:00"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nn6+qD1MfjtAccSwpKqa
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa48c26df98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntYRDabz8Tql/QPHe604
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa48c26d668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460c6c320>"
]
},
"metadata": {},
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},
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460d184e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd8HNW5+P/Ps0Va9W5btmzJKrhgCzdMDc2Eni+Q0Dsh\nMZfAj+SSArlJaK/cQAgBQi6hfIPBOCSmhACXHyQEU023jTE2brIs27JlVatrte18/5jRSrIleyWr\nep/367WvmZ09M3vGaz3nzJmZZ8QYg1JKqejgGO4KKKWUGjoa9JVSKopo0FdKqSiiQV8ppaKIBn2l\nlIoiGvSVUiqKaNBXSqkookFfKaWiiAZ9pZSKIq7hrgBAZmamycvLG+5qKKXUqLJy5coaY0xWX9YZ\nEUE/Ly+PFStWDHc1lFJqVBGRbX1dR4d3lFIqimjQV0qpKKJBXymloogGfaWUiiIa9JVSKopEFPRF\npExEvhKR1SKywl6WLiL/FpHN9jTNXi4i8rCIlIjIGhGZM5g7oJRSKnJ96emfbIyZZYyZZ7+/DVhm\njCkCltnvAc4EiuzXQuDRgaqsUkqpg3MwwzvnAovt+cXAeV2WP2MsnwCpIpK9vw1tqmzipr+u4rH3\ntvDB5mrqWnwHUS2llFK9ifTmLAO8KSIGeNwY8wQw1hhTAWCMqRCRMXbZCcCOLuuW28squm5QRBZi\nHQmQND6fL7bX89qaziLjUzxMH5/C4eOTmTHBmmaneBCRfuymUkopiDzoH2eM2WUH9n+LyIb9lO0p\nKu/z9HW74XgCYN68eebD206hvtXHul2NrNvVwLpdjazd2cCyDZV0PLvd5RDiY5x8e04ORWMTOWxs\nEkVjEkmNj4lwN5RSKrpFFPSNMbvsaZWI/AOYD1SKSLbdy88Gquzi5cDELqvnALsi+Z7U+BiOK8zk\nuMLM8LJWX4D1FU2s29XAH5eV0OoL8PyKHbT6guEymYmxFI1J5LCxiRTaDcF9/9yA2+ngueuPieSr\nlVIqKogx+3TCuxcQSQAcxpgme/7fwN3AAqDWGHOviNwGpBtjfiYiZwM3AWcBRwEPG2Pm7+875s2b\nZ/qSeycUMuxqaGNzVTMllc1srmpiU2UzJVXNNLcHwuVcDmH+5HSmjktmanYS08YlUzQ2EY/bGfF3\nKaXUSCUiK7tcXBORSHr6Y4F/2GPpLuCvxph/isjnwPMich2wHbjQLv86VsAvAVqBa/tSoUg4HEJO\nWjw5afGcPGVMeLkxht2NXjZXNvPLl9fS5gvS4gvy18+24fWHrHUFJmcmWA3BuCSmZlvTHz+/GhHR\nIwOl1CHtgD39odDXnn5fBUOG7XWtbKhoZP3uJjZUNLJhdxPb61rDZZwixMU4OX/2BKZlW0cGU8cl\nER8zIhKRKqXUPvrT04+KoN+b5vYAmyqb2FDRxENvbaLNPk/QZA8RiUBeRgLT7KGhqdnJTMtO4pbn\n9KhAKTX8NOgPAGMM5Xva+LqikfUVjWyoaGL97ka21XY5KnAI8W4n584ezxR7mOiwsUmkxLmHseZK\nqWijQX8QNbcH2Li7kfUVTTy8bDOtviBC51EBQHaKhynjkqzXWGt656vrcOhRgVJqEAzWiVwFJMa6\nmJubztzcdP73S+sK1KULj2ZXg5dNu5vYsLuJjbutcwUfltTgD3Y2ph63g+uXrKBwTCJFY5IoHJNI\nQVYicTF6FZFSamhpT38Q+IMhympa2LC7iXteX0+bP0h6Qgxlta0EQ9a/twjkpMVRmJVI0dgkCrMS\nefqjMuJiHPz9huOGeQ+UUqOB9vRHCLfTQdHYJIrGJvGXT6xHWD53/TH4AiG21bZY9xdUNYenH26p\nxRcIhdc/5p5lFI1N4rAx9l3HYxMpHJNIkqfznMHFj38c3q5SSkVKg/4g6xqUY1ydjUFXwZChfE8r\n339mBW2+IEfmpbOpqom/fFobvr8ArHxERWOTOGxsItVN7cTFOGluD5AYqz+jUioyGi1GAKdDyM1I\nIC0+hrR4eODiWUBnY7CpsplNlU1srrTuPP64tPPIYMYd/2JCapx9viDRPiqwzht0XE2kRwVKqQ4a\n9EeQvYNyR2OQm5HAN6ePDS8Phgzf/tOHtPqCnDd7Apsrm9hc1cwnpbW0dxkmGpscS9GYJMpqW4hz\nO1lRVkeRXlqqVFTToD8KOR2Cx+3E43Zy48mF4eXBkGHnnjY2V1mNwObKZkqqmqhuaidk4ILHrB7/\nuGQPh42zzxnYl5cWjU0M332sRwZKHbo06I9SPQVkp0OYlBHPpIx4FkzrPDK46LGP8AVC/PDUw9hY\n2cSm3U1sqmpiySfdjwwmpscxZWwS2+taiXM7+aq8gYIxCZqKQqlDiF6yGcU6chJt3G2dL9hY2WSf\nO2ju9gCErucMCjvOG2QlkRLv1qMCpYaRXrKp+sTpECZnJjA5M4EzZowLL7/wsY9o94f4wckF1hBR\ntTVUtPc5g8zEWHyBIHFuJ898XEah3ShkJcb2+IQzbSCUGn4a9NU+HHbG0TNmZHPGjM7lHecMSqqb\n7PMFzby+toKaZh+3v7IuXC4lzh0+KigMHx0kYYzRx10qNcx0eEcdlIsf/xhjDA9fOse+4ayp241n\nXR9y7xCIczs57fBx4VQUhWMSyc2Ix+107LNd0KMCpfZHh3fUkOsalMeleDi+KLPb57XN7ZRUWUNE\nDy/bTJsvyCeltfzji53hMm6ndWlqYVbnkUFLe0CfcKbUINCgrwZVRmIsGYmxHJWfwaurrUR1z11/\nDM3tAbbYRwMl1dZ0U2UT/15fGc5PBHDcvW93GyYqHJNIYVYiaQkx4TJ6VKBU5DToq2GRGOviiImp\nHDExtdtyXyBEWW0LP3h2FV5fkDm5aZRUNfPp1u4pKTISYiiwh4gqGrzEuZ1UNLQxLtmz3/MG2kCo\naKdBXw2ZSAJtjMvBYWOTyEiIgQR4+NLZAIRChp31bZRUN3ceIVQ188baCupb/QAcc8/bJMW6KBxr\np6QYkxSen5AapyeRlUJP5KpRzhjDdx79iDZfkMuOmhS+E3lzVRM1zZ0nkRNinBSOSWRnfRtxbie/\nOmc6BWMSmZS+70nkDnpUoEY6PZGroo6I4HY6cMc5uPKYvG6f1bX4wlcUdTQEDW1+app9LFyyEgCX\nQ5iUHk9+ViIFWQkUZCWSn5VAflZin+uijYQaDTToq1GvtyCbnhDD/MnpzJ+cHl528eMfEwiG+OU5\n0ymtbmFLdTOl1S2U1jTz/qZqfMHO8wYuO8fRLc+tZmJ6PJPS48nNsKZZST3fgKbUSKdBX0Udl9PB\n7ElpzJ6U1m15RyrrjsbgifdLafPbl5iu3knXkVCP28HENKsR6GgQ9rT68Lic+AIhYlw9Dxl10KMC\nNVw06Kuosr8g2zWV9clTx/DvryvD67QHguzc08a2ulZ21LWyvbaV7XXW66MttbT6guHtTP3VG4xP\njWNyZgJ5GQnkZsRb85kJTEyLP2CDsDdtINRA0qCvVARiXU7ysxJ7HOs3xlDb4uOqJz/F6w9xTnE2\nZbWtlNW28PLqnTR5A+GyDoEJaXE0tPqJi3Gy5OOy8J3JOmSkhoIGfaV6EWnPWkTITIwlyeMmyQO3\nnDYl/Jkxhj2tfrbWtLCttoWymha21rby9vpKqpva+VWXnEVJHhcFWZ3pKQqyEigck9innEV6VKAO\nRIO+UoNIREhPiCE9IYa5uZ3nEPbOWbSl2nqVVDWzvKSav68q79wGEOt28L3Fn5OXYQ0TdQwXZSd7\ncDj6d3SgDUR00qCv1ADpa/AUEcaleHrMWdTo9VsnlKuauf/NjXj9Qcr3tLG8pKbbncmxLge5GfHh\nxqCq0YvH7aSq0avDRapHGvSVGgYHaiCSPW5mTUxl1sRUnl+xI7xOKGSobPKytaaFshrrvMHWGuv1\n7qZqfPbzDub/ZhmJsS4mZyaQn5VgTxPJt48SEmL79qevRwWHDg36So0iDoeQnRJHdkocxxZ0/ywY\nMnznTx/iDYS4dP4kttZ
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa46017c0b8>"
]
},
"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": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:27:56.226662Z",
"start_time": "2017-11-01T14:27:55.987887+01:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2IAAAEXCAYAAADP3/fJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF1lGiBOIxZheZB1ZG1g
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa46019e748>"
]
},
"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": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:28:05.554284Z",
"start_time": "2017-11-01T14:28:05.324360+01:00"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa45f75d978>"
]
},
"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": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-11-01T13:32:12.118102Z",
"start_time": "2017-11-01T14:32:11.479970+01:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa45f950e10>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460d17c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa45f8fd4e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa460444588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(evohalfherd['std']/m).plot()\n",
"(evodumb['std']/m).plot()\n",
"(evowise['std']/m).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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",
All tests pass
2018-12-04 08:54:29 +00:00
"version": "3.6.5"
Added history class Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed.
2018-05-04 08:01:49 +00:00
},
"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
}
Reference in New Issue Copy Permalink