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soil/notebooks/soil_tutorial.ipynb
J. Fernando Sánchez f8538fe057 Tidying up the repository
2017-07-03 18:40:00 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:44:14.120953Z",
"start_time": "2017-07-02T18:44:14.117152+02:00"
}
},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "center",
"collapsed": true
},
"source": [
"This notebook is an introduction to the soil agent-based social network simulation framework.\n",
"In particular, we will focus on a specific use case: studying the propagation of news in a social network.\n",
"\n",
"The steps we will follow are:\n",
"\n",
"* Modelling the behavior of agents\n",
"* Running the simulation using different configurations\n",
"* Analysing the results of each simulation"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:38:48.052876Z",
"start_time": "2017-07-03T15:38:48.044762+02:00"
}
},
"source": [
"But before that, let's import the soil module and networkx."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
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"start_time": "2017-07-03T16:42:51.185463+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"import soil\n",
"import networkx as nx"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:51.690373Z",
"start_time": "2017-07-03T16:42:51.682644+02:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"# To display plots in the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:41:19.788717Z",
"start_time": "2017-07-03T15:41:19.785448+02:00"
}
},
"source": [
"# Basic concepts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are three main elements in a soil simulation:\n",
" \n",
"* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly\n",
"* Agents. There are two types: 1) network agents, which are linked to a node in the topology, and 2) environment agents, which are freely assigned to the environment.\n",
"* The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n",
"\n",
"Soil is based on ``simpy``, which is an event-based network simulation library.\n",
"Soil provides several abstractions over events to make developing agents easier.\n",
"This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T15:55:12.933978Z",
"start_time": "2017-07-02T17:55:12.930860+02:00"
}
},
"source": [
"# Modeling behaviour"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T13:49:31.269687Z",
"start_time": "2017-07-03T15:49:31.257850+02:00"
}
},
"source": [
"Our first step will be to model how every person in the social network reacts when it comes to news.\n",
"We will follow a very simple model (a finite state machine).\n",
"\n",
"There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral).\n",
"A neutral person may heard about the news either on the TV (with probability **prob_tv_spread**) or through their friends.\n",
"Once a person has heard the news, they will spread it to their friends (with a probability **prob_neighbor_spread**).\n",
"Some users do not have a TV, so they only rely on their friends.\n",
"\n",
"The spreading probabilities will change over time due to different factors.\n",
"We will represent this variance using an environment agent."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network Agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:03:07.171127Z",
"start_time": "2017-07-03T16:03:07.165779+02:00"
}
},
"source": [
"A basic network agent in Soil should inherit from ``soil.agents.BaseAgent``, and define its behaviour in every step of the simulation by implementing a ``run(self)`` method.\n",
"The most important attributes of the agent are:\n",
"\n",
"* ``agent.state``, a dictionary with the state of the agent. ``agent.state['id']`` reflects the state id of the agent. That state id can be used to look for other networks in that specific state. The state can be access via the agent as well. For instance:\n",
"```py\n",
"a = soil.agents.BaseAgent(env=env)\n",
"a['hours_of_sleep'] = 10\n",
"print(a['hours_of_sleep'])\n",
"```\n",
" The state of the agent is stored in every step of the simulation:\n",
" ```py\n",
" print(a['hours_of_sleep', 10]) # hours of sleep before step #10\n",
" print(a[None, 0]) # whole state of the agent before step #0\n",
" ```\n",
"\n",
"* ``agent.env``, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:\n",
" ```py\n",
" a.env.G.nodes() # Get all nodes ids in the topology\n",
" a.env['minimum_hours_of_sleep']\n",
"\n",
" ```\n",
"\n",
"Since our model is a finite state machine, we will be basing it on ``soil.agents.FSM``.\n",
"\n",
"With ``soil.agents.FSM``, we do not need to specify a ``step`` method.\n",
"Instead, we describe every step as a function.\n",
"To change to another state, a function may return the new state.\n",
"If no state is returned, the state remains unchanged.[\n",
"It will consist of two states, ``neutral`` (default) and ``infected``.\n",
"\n",
"Here's the code:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:51.715535Z",
"start_time": "2017-07-03T16:42:51.692301+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"\n",
"class NewsSpread(soil.agents.FSM):\n",
" @soil.agents.default_state\n",
" @soil.agents.state\n",
" def neutral(self):\n",
" r = random.random()\n",
" if self['has_tv'] and r < self.env['prob_tv_spread']:\n",
" return self.infected\n",
" return\n",
" \n",
" @soil.agents.state\n",
" def infected(self):\n",
" prob_infect = self.env['prob_neighbor_spread']\n",
" for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):\n",
" r = random.random()\n",
" if r < prob_infect:\n",
" neighbor.state['id'] = self.infected.id\n",
" return\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T12:22:53.931963Z",
"start_time": "2017-07-02T14:22:53.928340+02:00"
}
},
"source": [
"## Environment agents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Environment agents allow us to control the state of the environment.\n",
"In this case, we will use an environment agent to simulate a very viral event.\n",
"\n",
"When the event happens, the agent will modify the probability of spreading the rumor."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:51.727938Z",
"start_time": "2017-07-03T16:42:51.717828+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"NEIGHBOR_FACTOR = 0.9\n",
"TV_FACTOR = 0.5\n",
"class NewsEnvironmentAgent(soil.agents.BaseAgent):\n",
" def step(self):\n",
" if self.now == self['event_time']:\n",
" self.env['prob_tv_spread'] = 1\n",
" self.env['prob_neighbor_spread'] = 1\n",
" elif self.now > self['event_time']:\n",
" self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR\n",
" self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T11:23:18.052235Z",
"start_time": "2017-07-02T13:23:18.047452+02:00"
}
},
"source": [
"## Testing the agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:14:54.572431Z",
"start_time": "2017-07-02T18:14:54.564095+02:00"
}
},
"source": [
"Feel free to skip this section if this is your first time with soil."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing agents is not easy, and this is not a thorough testing process for agents.\n",
"Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents."
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"First of all, let's check if our network agent has the states we would expect:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:51.816465Z",
"start_time": "2017-07-03T16:42:51.811222+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"data": {
"text/plain": [
"{'infected': <function __main__.NewsSpread.infected>,\n",
" 'neutral': <function __main__.NewsSpread.neutral>}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"NewsSpread.states"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"Now, let's run a simulation on a simple network. It is comprised of three nodes:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:52.106636Z",
"start_time": "2017-07-03T16:42:51.904738+02:00"
},
"cell_style": "split",
"scrolled": false
},
"outputs": [
{
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iOYXZF8E+eXJy6hNJgeuvv54333yTffv2cd111/HQQw+RmZnJ+lGj8LKyU9oeBjoSDF0e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\n/X8ieN7dQkZLXwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd08276d978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G = nx.Graph()\n",
"G.add_edge(0, 1)\n",
"G.add_edge(0, 2)\n",
"G.add_edge(2, 3)\n",
"G.add_node(4)\n",
"pos = nx.spring_layout(G)\n",
"nx.draw_networkx(G, pos, node_color='red')\n",
"nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:53:30.997756Z",
"start_time": "2017-07-03T13:53:30.989609+02:00"
},
"cell_style": "split"
},
"source": [
"Let's run a simple simulation that assigns a NewsSpread agent to all the nodes in that network.\n",
"Notice how node 0 is the only one with a TV."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:52.136477Z",
"start_time": "2017-07-03T16:42:52.108729+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.014928102493286133 seconds\n",
"Finished simulation in 0.015764951705932617 seconds\n"
]
}
],
"source": [
"env_params = {'prob_tv_spread': 0,\n",
" 'prob_neighbor_spread': 0}\n",
"\n",
"MAX_TIME = 100\n",
"EVENT_TIME = 10\n",
"\n",
"sim = soil.simulation.SoilSimulation(topology=G,\n",
" num_trials=1,\n",
" max_time=MAX_TIME,\n",
" environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",
" 'state': {\n",
" 'event_time': EVENT_TIME\n",
" }}],\n",
" network_agents=[{'agent_type': NewsSpread,\n",
" 'weight': 1}],\n",
" states={0: {'has_tv': True}},\n",
" default_state={'has_tv': False},\n",
" environment_params=env_params)\n",
"env = sim.run_simulation()[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"cell_style": "split"
},
"source": [
"Now we can access the results of the simulation and compare them to our expected results"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:52.160856Z",
"start_time": "2017-07-03T16:42:52.138976+02:00"
},
"cell_style": "split",
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"agents = list(env.network_agents)\n",
"\n",
"# Until the event, all agents are neutral\n",
"for t in range(10):\n",
" for a in agents:\n",
" assert a['id', t] == a.neutral.id\n",
"\n",
"# After the event, the node with a TV is infected, the rest are not\n",
"assert agents[0]['id', 11] == NewsSpread.infected.id\n",
"\n",
"for a in agents[1:4]:\n",
" assert a['id', 11] == NewsSpread.neutral.id\n",
"\n",
"# At the end, the agents connected to the infected one will probably be infected, too.\n",
"assert agents[1]['id', MAX_TIME] == NewsSpread.infected.id\n",
"assert agents[2]['id', MAX_TIME] == NewsSpread.infected.id\n",
"\n",
"# But the node with no friends should not be affected\n",
"assert agents[4]['id', MAX_TIME] == NewsSpread.neutral.id\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T16:41:09.110652Z",
"start_time": "2017-07-02T18:41:09.106966+02:00"
},
"cell_style": "split"
},
"source": [
"Lastly, let's see if the probabilities have decreased as expected:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:52.193918Z",
"start_time": "2017-07-03T16:42:52.163476+02:00"
},
"cell_style": "split",
"collapsed": true
},
"outputs": [],
"source": [
"assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4\n",
"assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running the simulation"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:20:28.566944Z",
"start_time": "2017-07-03T13:20:28.561052+02:00"
},
"cell_style": "split"
},
"source": [
"To run a simulation, we need a configuration.\n",
"Soil can load configurations from python dictionaries as well as JSON and YAML files.\n",
"For this demo, we will use a python dictionary:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:52.219072Z",
"start_time": "2017-07-03T16:42:52.196203+02:00"
},
"cell_style": "split",
"collapsed": true
},
"outputs": [],
"source": [
"config = {\n",
" 'name': 'ExampleSimulation',\n",
" 'max_time': 20,\n",
" 'interval': 1,\n",
" 'num_trials': 1,\n",
" 'network_params': {\n",
" 'generator': 'complete_graph',\n",
" 'n': 500,\n",
" },\n",
" 'network_agents': [\n",
" {\n",
" 'agent_type': NewsSpread,\n",
" 'weight': 1,\n",
" 'state': {\n",
" 'has_tv': False\n",
" }\n",
" },\n",
" {\n",
" 'agent_type': NewsSpread,\n",
" 'weight': 2,\n",
" 'state': {\n",
" 'has_tv': True\n",
" }\n",
" }\n",
" ],\n",
" 'states': [ {'has_tv': True} ],\n",
" 'environment_params':{\n",
" 'prob_tv_spread': 0.01,\n",
" 'prob_neighbor_spread': 0.5\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:57:34.219618Z",
"start_time": "2017-07-03T13:57:34.213817+02:00"
},
"cell_style": "split"
},
"source": [
"Let's run our simulation:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:42:55.366288Z",
"start_time": "2017-07-03T16:42:52.295584+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using config(s): ExampleSimulation\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 1.4140360355377197 seconds\n",
"Finished simulation in 2.4056642055511475 seconds\n"
]
}
],
"source": [
"soil.simulation.run_from_config(config, dump=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T12:03:32.183588Z",
"start_time": "2017-07-03T14:03:32.167797+02:00"
},
"cell_style": "split",
"collapsed": true
},
"source": [
"In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research questions.\n",
"\n",
"For instance:\n",
" \n",
"* Does the outcome depend on the structure of our network? We will use different generation algorithms to compare them (Barabasi-Albert and Erdos-Renyi)\n",
"* How does neighbor spreading probability affect my simulation? We will try probability values in the range of [0, 0.4], in intervals of 0.1."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:15.488799Z",
"start_time": "2017-07-03T16:42:55.368021+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.2691483497619629 seconds\n",
"Finished simulation in 0.3650345802307129 seconds\n",
"Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.34261059761047363 seconds\n",
"Finished simulation in 0.44017767906188965 seconds\n",
"Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.34417223930358887 seconds\n",
"Finished simulation in 0.4550771713256836 seconds\n",
"Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.3237779140472412 seconds\n",
"Finished simulation in 0.42307496070861816 seconds\n",
"Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.3507683277130127 seconds\n",
"Finished simulation in 0.45061564445495605 seconds\n",
"Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.19115304946899414 seconds\n",
"Finished simulation in 0.20927715301513672 seconds\n",
"Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.22086191177368164 seconds\n",
"Finished simulation in 0.2390913963317871 seconds\n",
"Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.21225976943969727 seconds\n",
"Finished simulation in 0.23252630233764648 seconds\n",
"Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.2853121757507324 seconds\n",
"Finished simulation in 0.30568504333496094 seconds\n",
"Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
"Trial: 0\n",
"\tRunning\n",
"Finished trial in 0.21434736251831055 seconds\n",
"Finished simulation in 0.23370599746704102 seconds\n"
]
}
],
"source": [
"network_1 = {\n",
" 'generator': 'erdos_renyi_graph',\n",
" 'n': 500,\n",
" 'p': 0.1\n",
"}\n",
"network_2 = {\n",
" 'generator': 'barabasi_albert_graph',\n",
" 'n': 500,\n",
" 'm': 2\n",
"}\n",
"\n",
"\n",
"for net in [network_1, network_2]:\n",
" for i in range(5):\n",
" prob = i / 10\n",
" config['environment_params']['prob_neighbor_spread'] = prob\n",
" config['network_params'] = net\n",
" config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)\n",
" s = soil.simulation.run_from_config(config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T11:05:18.043194Z",
"start_time": "2017-07-03T13:05:18.034699+02:00"
},
"cell_style": "split"
},
"source": [
"The results are conveniently stored in pickle (simulation), csv (history of agent and environment state) and gexf format."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:15.721720Z",
"start_time": "2017-07-03T16:43:15.490854+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34msoil_output\u001b[00m\n",
"├── \u001b[01;34mSim_prob_0\u001b[00m\n",
"│   ├── Sim_prob_0.dumped.yml\n",
"│   ├── Sim_prob_0.simulation.pickle\n",
"│   ├── Sim_prob_0_trial_0.environment.csv\n",
"│   └── Sim_prob_0_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.0\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.1\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.2\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.3\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.gexf\n",
"├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.4\u001b[00m\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4.simulation.pickle\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.environment.csv\n",
"│   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.0\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.1\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.2\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.gexf\n",
"├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.3\u001b[00m\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3.simulation.pickle\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.environment.csv\n",
"│   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.gexf\n",
"└── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.4\u001b[00m\n",
" ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml\n",
" ├── Spread_erdos_renyi_graph_prob_0.4.simulation.pickle\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv\n",
" └── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf\n",
"\n",
"11 directories, 44 files\n",
"1.8M\tsoil_output/Sim_prob_0\n",
"652K\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
"684K\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
"692K\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
"688K\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
"1.8M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
"1.9M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4\n"
]
}
],
"source": [
"!tree soil_output\n",
"!du -xh soil_output/*"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-02T10:40:14.384177Z",
"start_time": "2017-07-02T12:40:14.381885+02:00"
}
},
"source": [
"# Analysing the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the simulations are over, we can use soil to analyse the results."
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:44:30.978223Z",
"start_time": "2017-07-03T16:44:30.971952+02:00"
}
},
"source": [
"First, let's load the stored results into a pandas dataframe."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:15.987794Z",
"start_time": "2017-07-03T16:43:15.724519+02:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"%pylab inline\n",
"from soil import analysis"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:16.590910Z",
"start_time": "2017-07-03T16:43:15.990320+02:00"
},
"scrolled": true
},
"outputs": [
{
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" <th>21023</th>\n",
" <td>499</td>\n",
" <td>11</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21024</th>\n",
" <td>499</td>\n",
" <td>12</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21025</th>\n",
" <td>499</td>\n",
" <td>12</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21026</th>\n",
" <td>499</td>\n",
" <td>13</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21027</th>\n",
" <td>499</td>\n",
" <td>13</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21028</th>\n",
" <td>499</td>\n",
" <td>14</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21029</th>\n",
" <td>499</td>\n",
" <td>14</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21030</th>\n",
" <td>499</td>\n",
" <td>15</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21031</th>\n",
" <td>499</td>\n",
" <td>15</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21032</th>\n",
" <td>499</td>\n",
" <td>16</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21033</th>\n",
" <td>499</td>\n",
" <td>16</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21034</th>\n",
" <td>499</td>\n",
" <td>17</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21035</th>\n",
" <td>499</td>\n",
" <td>17</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21036</th>\n",
" <td>499</td>\n",
" <td>18</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21037</th>\n",
" <td>499</td>\n",
" <td>18</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21038</th>\n",
" <td>499</td>\n",
" <td>19</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21039</th>\n",
" <td>499</td>\n",
" <td>19</td>\n",
" <td>id</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21040</th>\n",
" <td>499</td>\n",
" <td>20</td>\n",
" <td>has_tv</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21041</th>\n",
" <td>499</td>\n",
" <td>20</td>\n",
" <td>id</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21042 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" agent_id tstep attribute value\n",
"0 env 0 prob_tv_spread 0.01\n",
"1 env 0 prob_neighbor_spread 0.1\n",
"2 env 1 prob_tv_spread 0.01\n",
"3 env 1 prob_neighbor_spread 0.1\n",
"4 env 2 prob_tv_spread 0.01\n",
"5 env 2 prob_neighbor_spread 0.1\n",
"6 env 3 prob_tv_spread 0.01\n",
"7 env 3 prob_neighbor_spread 0.1\n",
"8 env 4 prob_tv_spread 0.01\n",
"9 env 4 prob_neighbor_spread 0.1\n",
"10 env 5 prob_tv_spread 0.01\n",
"11 env 5 prob_neighbor_spread 0.1\n",
"12 env 6 prob_tv_spread 0.01\n",
"13 env 6 prob_neighbor_spread 0.1\n",
"14 env 7 prob_tv_spread 0.01\n",
"15 env 7 prob_neighbor_spread 0.1\n",
"16 env 8 prob_tv_spread 0.01\n",
"17 env 8 prob_neighbor_spread 0.1\n",
"18 env 9 prob_tv_spread 0.01\n",
"19 env 9 prob_neighbor_spread 0.1\n",
"20 env 10 prob_tv_spread 0.01\n",
"21 env 10 prob_neighbor_spread 0.1\n",
"22 env 11 prob_tv_spread 0.01\n",
"23 env 11 prob_neighbor_spread 0.1\n",
"24 env 12 prob_tv_spread 0.01\n",
"25 env 12 prob_neighbor_spread 0.1\n",
"26 env 13 prob_tv_spread 0.01\n",
"27 env 13 prob_neighbor_spread 0.1\n",
"28 env 14 prob_tv_spread 0.01\n",
"29 env 14 prob_neighbor_spread 0.1\n",
"... ... ... ... ...\n",
"21012 499 6 has_tv True\n",
"21013 499 6 id neutral\n",
"21014 499 7 has_tv True\n",
"21015 499 7 id neutral\n",
"21016 499 8 has_tv True\n",
"21017 499 8 id neutral\n",
"21018 499 9 has_tv True\n",
"21019 499 9 id neutral\n",
"21020 499 10 has_tv True\n",
"21021 499 10 id neutral\n",
"21022 499 11 has_tv True\n",
"21023 499 11 id neutral\n",
"21024 499 12 has_tv True\n",
"21025 499 12 id neutral\n",
"21026 499 13 has_tv True\n",
"21027 499 13 id neutral\n",
"21028 499 14 has_tv True\n",
"21029 499 14 id neutral\n",
"21030 499 15 has_tv True\n",
"21031 499 15 id neutral\n",
"21032 499 16 has_tv True\n",
"21033 499 16 id neutral\n",
"21034 499 17 has_tv True\n",
"21035 499 17 id neutral\n",
"21036 499 18 has_tv True\n",
"21037 499 18 id neutral\n",
"21038 499 19 has_tv True\n",
"21039 499 19 id neutral\n",
"21040 499 20 has_tv True\n",
"21041 499 20 id infected\n",
"\n",
"[21042 rows x 4 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config_file, df, config = list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=False))[0]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:17.192030Z",
"start_time": "2017-07-03T16:43:16.601046+02:00"
}
},
"outputs": [
{
"data": {
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" <td>1.0</td>\n",
" <td>163.0</td>\n",
" <td>337.0</td>\n",
" <td>204.0</td>\n",
" <td>296.0</td>\n",
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" <td>163.0</td>\n",
" <td>337.0</td>\n",
" <td>254.0</td>\n",
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" <td>293.0</td>\n",
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" <td>336.0</td>\n",
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" <td>337.0</td>\n",
" <td>365.0</td>\n",
" <td>135.0</td>\n",
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" <td>337.0</td>\n",
" <td>391.0</td>\n",
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" <td>93.0</td>\n",
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],
"text/plain": [
"value 0.01 0.1 False True infected neutral\n",
"tstep \n",
"0 1.0 1.0 163.0 337.0 0.0 500.0\n",
"1 1.0 1.0 163.0 337.0 3.0 497.0\n",
"2 1.0 1.0 163.0 337.0 6.0 494.0\n",
"3 1.0 1.0 163.0 337.0 12.0 488.0\n",
"4 1.0 1.0 163.0 337.0 23.0 477.0\n",
"5 1.0 1.0 163.0 337.0 36.0 464.0\n",
"6 1.0 1.0 163.0 337.0 53.0 447.0\n",
"7 1.0 1.0 163.0 337.0 79.0 421.0\n",
"8 1.0 1.0 163.0 337.0 119.0 381.0\n",
"9 1.0 1.0 163.0 337.0 164.0 336.0\n",
"10 1.0 1.0 163.0 337.0 204.0 296.0\n",
"11 1.0 1.0 163.0 337.0 254.0 246.0\n",
"12 1.0 1.0 163.0 337.0 293.0 207.0\n",
"13 1.0 1.0 163.0 337.0 336.0 164.0\n",
"14 1.0 1.0 163.0 337.0 365.0 135.0\n",
"15 1.0 1.0 163.0 337.0 391.0 109.0\n",
"16 1.0 1.0 163.0 337.0 407.0 93.0\n",
"17 1.0 1.0 163.0 337.0 424.0 76.0\n",
"18 1.0 1.0 163.0 337.0 442.0 58.0\n",
"19 1.0 1.0 163.0 337.0 452.0 48.0\n",
"20 1.0 1.0 163.0 337.0 464.0 36.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=True))[0][1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you don't want to work with pandas, you can also use some pre-defined functions from soil to conveniently plot the results:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:20.937324Z",
"start_time": "2017-07-03T16:43:17.193845+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
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WLCEvL4+lS5dy8803j9jemjVr2LhxIytWrGDt2rUsWrQoq/uTisPuhtrM7gf+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\nIhJxCgQiIhGnQCAiEnH/H+k3EVtiaGJ+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd082247198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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6GDMXqtRyOqoC5eYanvtuM7Ni4nikT1Me6tPU6ZAuWVxcHFWqVCEyMlL7PCqE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd082126630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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rvEPaItjxA2ybC+umWePiWkOrIVZSSL4IIuOdiV2pc3CsoJjtmXlsPZDL0p3ZLEnNZn+O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\nFFLnWUcQVPDnMTzGTgouySEq8fRkEdX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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd082126b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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IgOgcWAUwEclxuaT/uOmRFghCwyAxw3r0nwRv3QT/uQP2rIFz/miVNFTAamg05BZXsmZn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\n3SwTSPv1RqxSnzvtHR/+cLtdhfV8K9U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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd08206ef98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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0tVGlIsSnnx6rx0tLS2u2aKe6uvro51//+tf8+te/PmGa8ePHs2zZsuAH6TIt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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd081e8d860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis.plot_all('soil_output/Spread_barabasi*', attributes=['id'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:43:49.238790Z",
"start_time": "2017-07-03T16:43:20.939175+02:00"
}
},
"outputs": [
{
"data": {
"image/png": 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MosK2rKIw2+/qCu0o69NeG3qmG/4Grz0atcnkw4gxULcP1O0dvcamTFfV5WcfUnQKBCK7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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd081c96978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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V3s2h3v63is2VH60+ApExaOfOnfz85z8f1nuzUXI6GiWCYar3ZwwNlp8uPwYO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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd081c99f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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S8BIiUurilQgSdf2qGlKJQERKWfwSQZ+rhjS8hIiUtvglgq2N0LUjr9W3d+wg2dapqiER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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd081b4e7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Tdb2JRGJgevHixWzZsoVVq1ZRX1/PjBkz2LVr1wh+i/2X9xyBmR1qZsvM7Dkz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\nhPdhAd1fv7EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd0822186a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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c1puZXWdmq83sCTM7otRvopRi8ejbQGvLmJ1jCdRhLFKTck0XPX36dFauXAnA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\nIjLK/X+WBgkuYDfsgAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd0814fb898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analysis.plot_all('soil_output/Spread_erdos*', attributes=['id'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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},
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},
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},
"toc": {
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"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "31px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": true,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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