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Code Issues Releases Wiki Activity
73c90887e8
soil/examples/tutorial/soil_tutorial.ipynb
J. Fernando Sánchez a7c51742f6 Improved docs 
Fixed several bugs
Added convenience methods in soil.analysis
2017-10-19 18:06:33 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T12:41:48.007238Z",
"start_time": "2017-10-19T14:41:47.980725+02:00"
}
},
"source": [
"# Soil Tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {
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"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": {
"end_time": "2017-10-19T15:56:07.621891Z",
"start_time": "2017-10-19T17:56:06.963273+02:00"
}
},
"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",
"%pylab inline\n",
"# To display plots in the notebooed_"
]
},
{
"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": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:08.659268Z",
"start_time": "2017-10-19T17:56:08.606261+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": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:09.116995Z",
"start_time": "2017-10-19T17:56:09.081481+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": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:11.367368Z",
"start_time": "2017-10-19T17:56:11.344245+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"data": {
"text/plain": [
"{'infected': <function __main__.NewsSpread.infected>,\n",
" 'neutral': <function __main__.NewsSpread.neutral>}"
]
},
"execution_count": 4,
"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": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:12.472479Z",
"start_time": "2017-10-19T17:56:12.206162+02:00"
},
"cell_style": "split",
"scrolled": false
},
"outputs": [
{
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aGs4666yoSxERCZ3CP4sXXnhBwS8io5bCP4uXXnqJ2bNnR12GiEhZKPyz2Ldvnz7ZKyKj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\n/EVEYuj/A/nJqW3BkOC1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a7d5860>"
]
},
"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": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:12.719473Z",
"start_time": "2017-10-19T17:56:12.653974+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.02695441246032715 seconds\n",
"INFO:soil.utils:NOT dumping results\n",
"INFO:soil.utils:Finished simulation in 0.03360605239868164 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": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:15.004439Z",
"start_time": "2017-10-19T17:56:14.904160+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": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:17.382019Z",
"start_time": "2017-10-19T17:56:17.340851+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": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:19.477080Z",
"start_time": "2017-10-19T17:56:19.386840+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",
" 'environment_agents':[\n",
" {'agent_type': NewsEnvironmentAgent,\n",
" 'state': {\n",
" 'event_time': 10\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": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:56:28.208300Z",
"start_time": "2017-10-19T17:56:20.515069+02:00"
},
"cell_style": "split"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Using config(s): ExampleSimulation\n",
"INFO:soil.utils:Dumping results to soil_output/ExampleSimulation : False\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 5.869051456451416 seconds\n",
"INFO:soil.utils:NOT dumping results\n",
"INFO:soil.utils:Finished simulation in 6.9609293937683105 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": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:10.690166Z",
"start_time": "2017-10-19T17:56:28.210104+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.0\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.2258412837982178 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0\n",
"INFO:soil.utils:Finished simulation in 5.597268104553223 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.1\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.3026399612426758 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1\n",
"INFO:soil.utils:Finished simulation in 5.534018278121948 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.2\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.4764575958251953 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2\n",
"INFO:soil.utils:Finished simulation in 6.170421123504639 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.3\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.5429913997650146 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3\n",
"INFO:soil.utils:Finished simulation in 5.936013221740723 seconds\n",
"INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.4\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 1.4097135066986084 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4\n",
"INFO:soil.utils:Finished simulation in 5.732810974121094 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.0\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.751497745513916 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0\n",
"INFO:soil.utils:Finished simulation in 2.3415369987487793 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.1\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8503265380859375 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1\n",
"INFO:soil.utils:Finished simulation in 2.5671920776367188 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.2\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8511502742767334 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2\n",
"INFO:soil.utils:Finished simulation in 2.55816912651062 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.3\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.8982968330383301 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3\n",
"INFO:soil.utils:Finished simulation in 2.6871559619903564 seconds\n",
"INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.4\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4 : True\n",
"INFO:soil.utils:Trial: 0\n",
"INFO:soil.utils:\tRunning\n",
"INFO:soil.utils:Finished trial in 0.9563727378845215 seconds\n",
"INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4\n",
"INFO:soil.utils:Finished simulation in 2.5253307819366455 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 and sqlite (history of agent and environment state) and gexf (dynamic network) format."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:28.534861Z",
"start_time": "2017-10-19T17:57:28.258984+02:00"
},
"cell_style": "split",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34msoil_output\u001b[00m\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.backup1508409808.7944386.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508428617.9811945.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite\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.backup1508409810.9913027.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508428620.3419535.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.db.sqlite\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.backup1508409813.2012305.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508428622.91827.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.db.sqlite\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.backup1508409815.5177016.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508428625.5117545.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.db.sqlite\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.backup1508409818.1516452.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508428628.1986933.sqlite\n",
"│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.db.sqlite\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.backup1508409781.0791047.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508428588.625598.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.db.sqlite\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.backup1508409786.6177793.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508428594.3783743.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.db.sqlite\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.backup1508409791.9751768.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508428600.041021.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.db.sqlite\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.backup1508409797.606661.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508428606.2884977.sqlite\n",
"│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.db.sqlite\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.backup1508409803.4306188.sqlite\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508428612.3312593.sqlite\n",
" ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.db.sqlite\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",
"10 directories, 70 files\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\n",
"2.5M\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\n",
"3.6M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\n",
"3.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\n",
"3.7M\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": [
"### Loading data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the simulations are over, we can use soil to analyse the results.\n",
"\n",
"Soil allows you to load results for specific trials, or for a set of trials if you specify a pattern. The specific methods are:\n",
"\n",
"* `analysis.read_data(<directory pattern>)` to load all the results from a directory. e.g. `read_data('my_simulation/')`. For each trial it finds in each folder matching the pattern, it will return the dumped configuration for the simulation, the results of the trial, and the configuration itself. By default, it will try to load data from the sqlite database. \n",
"* `analysis.read_csv(<csv_file>)` to load all the results from a CSV file. e.g. `read_csv('my_simulation/my_simulation_trial0.environment.csv')`\n",
"* `analysis.read_sql(<sqlite_file>)` to load all the results from a sqlite database . e.g. `read_sql('my_simulation/my_simulation_trial0.db.sqlite')`"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-03T14:44:30.978223Z",
"start_time": "2017-07-03T16:44:30.971952+02:00"
}
},
"source": [
"Let's see it in action by loading the stored results into a pandas dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:43.662893Z",
"start_time": "2017-10-19T17:57:43.632252+02:00"
},
"cell_style": "center",
"collapsed": true
},
"outputs": [],
"source": [
"from soil.analysis import *"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:44.101253Z",
"start_time": "2017-10-19T17:57:44.039710+02:00"
},
"scrolled": true
},
"outputs": [
{
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"</div>"
],
"text/plain": [
" agent_id t_step key value value_type\n",
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"... ... ... .. ... ...\n",
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"21081 498 20 id infected str\n",
"21083 499 20 id infected str\n",
"\n",
"[10500 rows x 5 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = read_csv('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv', keys=['id'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Soil can also process the data for us and return a dataframe with as many columns as there are attributes in the environment and the agent states:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:45.777794Z",
"start_time": "2017-10-19T17:57:45.698020+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">20</th>\n",
" <th>72</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10500 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" id\n",
"t_step agent_id \n",
"0 0 neutral\n",
" 1 neutral\n",
" 10 neutral\n",
" 100 neutral\n",
" 101 neutral\n",
" 102 neutral\n",
" 103 neutral\n",
" 104 neutral\n",
" 105 neutral\n",
" 106 neutral\n",
" 107 neutral\n",
" 108 neutral\n",
" 109 neutral\n",
" 11 neutral\n",
" 110 neutral\n",
" 111 neutral\n",
" 112 neutral\n",
" 113 neutral\n",
" 114 neutral\n",
" 115 neutral\n",
" 116 neutral\n",
" 117 neutral\n",
" 118 neutral\n",
" 119 neutral\n",
" 12 neutral\n",
" 120 neutral\n",
" 121 neutral\n",
" 122 neutral\n",
" 123 neutral\n",
" 124 neutral\n",
"... ...\n",
"20 72 infected\n",
" 73 infected\n",
" 74 infected\n",
" 75 infected\n",
" 76 infected\n",
" 77 infected\n",
" 78 infected\n",
" 79 infected\n",
" 8 infected\n",
" 80 infected\n",
" 81 infected\n",
" 82 infected\n",
" 83 infected\n",
" 84 infected\n",
" 85 infected\n",
" 86 infected\n",
" 87 infected\n",
" 88 infected\n",
" 89 infected\n",
" 9 infected\n",
" 90 infected\n",
" 91 infected\n",
" 92 infected\n",
" 93 infected\n",
" 94 infected\n",
" 95 infected\n",
" 96 infected\n",
" 97 infected\n",
" 98 infected\n",
" 99 infected\n",
"\n",
"[10500 rows x 1 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"env, agents = process(df)\n",
"agents"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-18T14:01:00.669671Z",
"start_time": "2017-10-18T16:01:00.635624+02:00"
}
},
"source": [
"The index of the results are the simulation step and the agent_id. Hence, we can access the state of the simulation at a given step: "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:47.132212Z",
"start_time": "2017-10-19T17:57:47.084737+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>73</th>\n",
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" <tr>\n",
" <th>74</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>neutral</td>\n",
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" <tr>\n",
" <th>77</th>\n",
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" <tr>\n",
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" <th>87</th>\n",
" <td>neutral</td>\n",
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" <tr>\n",
" <th>88</th>\n",
" <td>neutral</td>\n",
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" <tr>\n",
" <th>89</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>neutral</td>\n",
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" <tr>\n",
" <th>90</th>\n",
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" <tr>\n",
" <th>98</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>500 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" id\n",
"agent_id \n",
"0 neutral\n",
"1 neutral\n",
"10 neutral\n",
"100 neutral\n",
"101 neutral\n",
"102 neutral\n",
"103 neutral\n",
"104 neutral\n",
"105 neutral\n",
"106 neutral\n",
"107 neutral\n",
"108 neutral\n",
"109 neutral\n",
"11 neutral\n",
"110 neutral\n",
"111 neutral\n",
"112 neutral\n",
"113 neutral\n",
"114 neutral\n",
"115 neutral\n",
"116 neutral\n",
"117 neutral\n",
"118 neutral\n",
"119 neutral\n",
"12 neutral\n",
"120 neutral\n",
"121 neutral\n",
"122 neutral\n",
"123 neutral\n",
"124 neutral\n",
"... ...\n",
"72 neutral\n",
"73 neutral\n",
"74 neutral\n",
"75 neutral\n",
"76 neutral\n",
"77 neutral\n",
"78 neutral\n",
"79 neutral\n",
"8 neutral\n",
"80 neutral\n",
"81 neutral\n",
"82 neutral\n",
"83 neutral\n",
"84 neutral\n",
"85 neutral\n",
"86 neutral\n",
"87 neutral\n",
"88 neutral\n",
"89 neutral\n",
"9 neutral\n",
"90 neutral\n",
"91 neutral\n",
"92 neutral\n",
"93 neutral\n",
"94 neutral\n",
"95 neutral\n",
"96 neutral\n",
"97 neutral\n",
"98 neutral\n",
"99 neutral\n",
"\n",
"[500 rows x 1 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agents.loc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, we can perform more complex tasks such as showing the agents that have changed their state between two simulation steps:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:48.168805Z",
"start_time": "2017-10-19T17:57:48.113961+02:00"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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],
"text/plain": [
" id\n",
"agent_id \n",
"140 neutral\n",
"164 neutral\n",
"170 neutral\n",
"310 neutral\n",
"455 neutral"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"changed = agents.loc[1]['id'] != agents.loc[0]['id']\n",
"agents.loc[0][changed]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To focus on specific agents, we can swap the levels of the index:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:49.046261Z",
"start_time": "2017-10-19T17:57:49.019721+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"agents1 = agents.swaplevel()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:49.459500Z",
"start_time": "2017-10-19T17:57:49.420016+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>2</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>infected</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id\n",
"t_step \n",
"0 neutral\n",
"1 neutral\n",
"2 neutral\n",
"3 neutral\n",
"4 neutral\n",
"5 neutral\n",
"6 neutral\n",
"7 neutral\n",
"8 neutral\n",
"9 neutral\n",
"10 neutral\n",
"11 infected\n",
"12 infected\n",
"13 infected\n",
"14 infected\n",
"15 infected\n",
"16 infected\n",
"17 infected\n",
"18 infected\n",
"19 infected\n",
"20 infected"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agents1.loc['0'].dropna(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T10:35:40.140920Z",
"start_time": "2017-10-19T12:35:40.106265+02:00"
}
},
"source": [
"### Plotting data"
]
},
{
"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": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:52.271094Z",
"start_time": "2017-10-19T17:57:51.102434+02:00"
}
},
"outputs": [
{
"data": {
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FS2c3rZ3dtHT09L/v7KG1w97b5S0d3bR0drO7pZOtu1tp6eihoaWDrp6+rOLJyQqc06gq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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1c60f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FS2c3rZ3dtHT09L/v7KG1w97b5S0d3bR0drO7pZOtu1tp6eihoaWDrp6+rOLJyQqc06gq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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1c62b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', get_count, 'id');"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:57:57.982007Z",
"start_time": "2017-10-19T17:57:52.273160+02:00"
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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FS2c3rZ3dtHT09L/v7KG1w97b5S0d3bR0drO7pZOtu1tp6eihoaWDrp6+rOLJyQqc06gq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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a0b4940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FS2c3rZ3dtHT09L/v7KG1w97b5S0d3bR0drO7pZOtu1tp6eihoaWDrp6+rOLJyQqc06gq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\niGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a0b4518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gQUyYMAGA06dPM3PmTPr160eVKlWYPn06y5cvZ8KECQwf7vn9KAcPHuT5559n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\nefbKZmw+cIKP5231yjyVUn/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BMeaMiAwDpmJdPvqJMWaNt5ejlFLKO3xyH4ExZgowxRfz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799958f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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gQUyYMAGA06dPM3PmTPr160eVKlWYPn06y5cvZ8KECQwf7vn9KAcPHuT5559n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\nefbKZmw+cIKP5231yjyVUn/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BMeaMiAwDpmJdPvqJMWaNt5ejlFLKO3xyH4ExZgowxRfz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d1a518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7+PbbbwGYPHkyX375JTt27ACgtra2S1cUgUwTgQ9kJseceOeQMcf7IFBKtYmM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\nWFJYSnWddlajlPIuTQSesuF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1wCeuDnJvscsJnwO2GGPmu5hmcGvdhYhMxNqPh3wXJYhI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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799764be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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7+PbbbwGYPHkyX375JTt27ACgtra2S1cUgUwTgQ9kJseceOeQMcf7IFBKtYmM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\nWFJYSnWddlajlPIuTQSesuF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1wCeuDnJvscsJnwO2GGPmu5hmcGvdhYhMxNqPh3wXJYhI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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79941eba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FFVecNu9tt93Gm2++yTvvvMPLL7982vSYmBgmTZrExx9/zNy5c7nlllsAeOqp\np+jZsyfr16+nqamJmJiY05aNiIigqamp+bPrtk1jDJdddhlvv/22r75y0NAmJoLYyXaHPFwV7C6A\n1EEQrxXtqmuIiori/fff5/XXX+ett97iiiuu4LnnnqO+vh6Abdu2UVVlNcY4Y8YMnn76aQCGDh0K\nwL59+7j00kub1zdt2jReeeUVFi9e3JxMysvLycjIICwsjDfeeIPGxsbT4sjKymLdunU0NTWxd+9e\nVq5cCcC4ceMoKChg+/btAFRXV3foiiKYaSIIYkN6J5IQHcHylvUEjQ2wZ7nWD6gup1u3bnz00UfN\nv9yHDBlCXl4ew4YN495776WhwXp0qWfPnpx33nnceeedzcseOHDglCKiyy+/nEWLFjFlyhSioqIA\nuP/++3nttdcYN24c27Zto1u30ztyGj9+PNnZ2QwfPpwf/ehH5OXlAZCWlsarr77KLbfcQm5uLuPG\njWPLli3+3B0BI8FwQ8+YMWPM6tWrnQ4jKN35ykp2H6nm8x9OOjly3xp44RK44SUYfmOryyp1NjZv\n3sx553l6ZMh51dXVDB8+nLVr15KUlATA7373O/r168c111zjcHT+4envISJrjDFjznbdekUQ5PJz\nerCztIqSCrfHy4sKrHdtaE6FoE8//ZTBgwfz3e9+tzkJAMyePbvLJgF/08riIJefnQLAyl1HuCq3\ntzVydwGknAMJvRyMTClnTJkyhT179jgdRpeiVwRBblifJOKiwk8+T9DUCLuXaf2AUspnNBEEucjw\nMEb3737yzqFDG6G2XBuaU0r5jCaCTmBcTg+2HarkSFWdW/2AXhEopXxDE0EncLKeoMyqH0juD0mZ\nDkellOoqNBF0ArmZycREhrFix2ErEejdQipEnDhxgokTJ9LY2Mj+/fu58UbPt0tPmjSJQN6C/vTT\nT1NdXd3h5WbMmNHcZMa0adMoLCz0dWhnRBNBJxAVEUZev+4c3LEOThzVZiVUyHj55Ze5/vrrCQ8P\np3fv3s0nUae1lQg8Pa3syX333cevf/1rX4Z1xjQRdBL52T1IL1tlfdD6ARUi3nzzTa699lrg1M5j\nTpw4wbRp08jNzeXmm2/mxIk2+u2wTZo0iYceeoixY8cycOBAFi+22utqbGz02Nz1woULueqqq5qX\nnz17Nq+++irPPPMM+/fvZ/LkyUyePBmA+Ph4HnnkEfLz81m2bBmPPvoo559/PsOGDWPWrFl4enD3\n4osv5tMv5oUdAAAeI0lEQVRPP21+WtpJ+hxBJ5Gfk0LZF5s5EZdBbHJ/p8NRIeaXf/+aTfuP+3Sd\nQ3on8vOrh7Y6va6ujp07d5KVlXXatOeee464uDg2bNjAhg0bmpuBaE9DQwMrV65k3rx5/PKXv+TT\nTz/lpZdeam7uura2lvHjx3P55Ze3uo4HH3yQJ598kgULFpCamgpAVVUVw4YN49FHH7W+25AhPPLI\nI4DVQN5HH33E1Vdffcp6wsLCOPfcc1m/fj2jR4/2Kn5/0SuCTmJkZhL5YVvZHjMCtB9ZFQIOHz5M\ncnKyx2mLFi1i+vTpAOTm5pKbm+vVOq+//noARo8eTVFREWB1NvP6668zcuRI8vPzKSsr63DZfXh4\nODfccEPz5wULFpCfn8/w4cP5/PPP+frrrz0ul56ezv79+z1OCyS9IugkYsp3ECPlvF07kOFOB6NC\nTlu/3P0lNja2uQloT86kQ/fo6GjAOnG7imRaa+56yZIlHpuj9iQmJobw8PDm+e6//35Wr15N3759\n+cUvftHqsjU1NcTGxnb4e/iaXhF0FkVW/8R/O9qf4zX1DgejlP91796dxsZGjyfRCRMm8OabbwKw\nceNGNmzY0Dzt9ttvb2462hutNXfdv39/Nm3aRG1tLeXl5Xz22WfNyyQkJFBRUeFxfa54U1NTqays\nbLOCe9u2bc3NaDtJrwg6i90F1MWms7OmF2uKjjJ5cLrTESnld5dffjlLlixhypQpp4y/7777uPPO\nO8nNzWXkyJGMHXuys8QNGzaQkZHh9TbuvvtuioqKyMvLwxhDWloa77//Pn379uXb3/42ubm5DBgw\ngFGjTnbUOGvWLK688koyMjJYsGDBKetLTk7mnnvuYfjw4WRlZXH++ed73O6hQ4eIjY3tUKz+os1Q\ndwbGwP8NpqHfhZy3/ibuuiib/7gyOJsHVl1HMDRD/eWXX/Lkk0/yxhtveDX/8ePHmTlzJn/5y1/8\nHNnZe+qpp0hMTGTmzJleze/PZqj1iqAzOLITKg8SkX0RuWXJrXdor1QXM2rUKCZPnkxjY2NzGXxb\nEhMTO0USAOvK4bbbbnM6DEDrCDoHu36ArIvIz07hq33lVNU6f++xUoFw1113eZUEOps777zzlB7V\nnKSJoDPYXQDd0iB1IPk5PWhsMqzZfdTpqJRSXYQmgmBnjNXiaP8LQYTR/bsTHiaeO7RXSqkzoIkg\n2B3bDceLm/sfiI+OYFifJK0nUEr5jCaCYOeh/4Fx2SmsLz7GiTrvGrdSSqm2aCIIdrsLIDYF0k7e\nNpafk0J9o+HLPVpPoLo2XzZD/cgjj/Dpp5+2OU9tbS1Tpkxh5MiRzJ07t0OxFhUV8dZbb3VoGQiO\npqk1EQS7oiVW/UDYyT/VmKwUwgSW79LiIdW1+bIZ6kcfffS0B9Na+vLLL6mvr2fdunXcfPPNHVr/\nmSYCd041Ta2JIJiVF1t1BC06okmMiWRo7yRW7NQKY9W1+bIZavdf3llZWfz85z8nLy+P4cOHs2XL\nFkpKSpg+fTrr1q1j5MiR7NixgzVr1jBx4kRGjx7NFVdcwYEDBwDYvn07U6ZMYcSIEeTl5bFjxw4e\nfvhhFi9ezMiRI3nqqadabd7aGMPs2bMZMmQI3/zmNykpKWmO0ammqYPjJlblmat+wENHNPnZKby+\nfDc19Y3ERHa9e6xVkPnnw3DwK9+us9dwuPLxVif7oxlqd6mpqaxdu5Y//OEPPPHEE7z44ou8+OKL\nPPHEE3z00UfU19dz22238cEHH5CWlsbcuXP56U9/yssvv8ytt97Kww8/zHXXXUdNTQ1NTU08/vjj\nzcsCzJkzx2Pz1l9++SVbt27lq6++4tChQwwZMoS77roLcK5pak0EwWz3EohJgp6nN0qVn9ODF5fs\nYt3eY4zL6eFAcEr5V3vNUD/44INAx5qhdufeJPVf//rX06Zv3bqVjRs3ctlllwFWBzYZGRlUVFSw\nb98+rrvuOsBqedST+fPns2HDhuarkPLycgoLC1m0aBG33HJLc3HXJZdccspyrqapNREoS1EB9LsQ\nwk7/xT82KwURWLHziCYC5X9t/HL3F380Q+3OU5PU7owxDB06lGXLlp0y/vhx7zroaa1563nz5rUZ\nuxNNU7dbRyAifUVkgYhsFpGvReR79vgUEflERArt9+72eBGRZ0Rku4hsEJGOX7MpqDgIR3a02i1l\nUlwkg3sl6oNlqssKVDPUrRk0aBClpaXNiaC+vp6vv/6axMREMjMzef/99wHrTqPq6urTmqZurXnr\nCRMm8M4779DY2MiBAwdOa73UiaapvaksbgB+aIw5DxgHPCAiQ4CHgc+MMQOAz+zPAFcCA+zXLOA5\nn0cdClztC7XRUX1+dgpr9xylrqGp1XmU6sxczVC3dN9991FZWUlubi6//vWvz6oZ6tZERUXx7rvv\n8tBDDzFixAhGjhzJ0qVLAXjjjTd45plnyM3N5cILL+TgwYPk5uYSERHBiBEjeOqpp7j77rsZMmQI\neXl5DBs2jHvvvZeGhgauu+46BgwYwPDhw7nvvvuYOHFi8zYda5raGNOhF/ABcBmwFciwx2UAW+3h\n54Fb3OZvnq+11+jRo41q4e/fN+a/+xjTUN/qLP/8ar/p/9BHZtWusgAGpkLFpk2bnA7BrF271kyf\nPt3r+cvLy82NN97ox4j868knnzQvvviix2me/h7AatPBc7inV4duHxWRLGAUsALoaYw5YCeTA4Cr\np5Q+wF63xYrtcS3XNUtEVovI6tLS0o6EERqKlkC/cRDeejXO2GyrbmCFPk+guij3Zqi90ZmaofYk\nOTmZO+64I+Db9ToRiEg88B7wfWNMW7UlnmpBTuv9xhgzxxgzxhgzJi0tzdswQkNlCRze1mr9gEtK\ntygG9oxnuT5PoLqwrtoMtSdONU3tVSIQkUisJPCmMcZ1n9UhEcmwp2cArqciioG+botnAvt9E26I\n2O16fuCitucD8rN7sGb3UeobtZ5AKXVmvLlrSICXgM3GmCfdJn0IuK5h7sCqO3CNv92+e2gcUO4q\nQlJeKiqAyG7Qe2S7s+bnpFBd18jGfeUBCEyFGhMEXdkq//8dvLkiGA/cBlwiIuvs11TgceAyESnE\nqjx23Wg8D9gJbAdeAO73fdhd3O4C6DsWwiPbnXVsdgqg9QTK92JiYigrK9Nk4DBjDGVlZa0+uOYL\n7RZGGWOW4LncH+BSD/Mb4IGzjCt0VZVBySYYdr1Xs6cnxJCT1o0VO8v4zsRz/BycCiWZmZkUFxej\nN3M4LyYmhszMTL+tX58sDjZ7rPuUvakfcMnP7sFH6/fT2GQIDzu7py2VcomMjCQ7O9vpMFQAaOuj\nwaaoACJioI/3D2SPy0mhoraBTfu9e/RdKaXcaSIINruXQOb5EBHt9SL5zc8T6G2kSqmO00QQTE4c\nhYMbT+t/oD29kmLo3yOO5dqPsVLqDGgiCCZ7lgOmzfaFWpOfncKqoiM0NekdHkqpjtFEEEyKlkB4\nFGSO6fCi+dk9KD9Rz5aDFe3PrJRSbjQRBJNdX1j1A5Edb4s8P8f1PIHWEyilOkYTQbA4vt/qCnDA\nZWe0eGb3OPokx7JC6wmUUh2kiSBYbP/Ueh9w+RmvIj8nhZVFR/RJUKVUh2giCBbbPobEPpA+5IxX\nMS67B0eq6igsqfRhYEqprk4TQTBoqIOdC62rgbPoh9VVT7Bsh9YTKKW8p4kgGOxZBnWVZ1UsBNAv\nJY4B6fHMXbVXi4eUUl7TRBAMCudbt41mTzir1YgId1+czaYDx1mqVwVKKS9pIggGhfOth8ii4896\nVdeO7ENqfDTPL9rpg8CUUqFAE4HTjuyyuqUceIVPVhcTGc6d47NYtK2UzQe0ETqlVPs0ETjNB7eN\ntnRrfj/iosJ5YbFeFSil2qeJwGmF8yElB3r4rlOZ5Lgovj2mLx+u28+B8hM+W69SqmvSROCk+hOw\na5FPrwZcZl6UTZMxvFpQ5PN1K6W6Fk0ETtq1GBpq/JII+qbEMXV4Bm+t2ENFTb3P16+U6jo0ETip\ncD5Exp1Rs9PemDUhh4raBt5Zudcv61dKdQ2aCJxiDBR+DNkTITLGL5vIzUxmXE4KLxfsor6xyS/b\nUEp1fpoInHK4EI7tOePWRr01a0IOB8pr+GjDfr9uRynVeWkicErhx9a7H+oH3E0amM6A9Hie/2Kn\nNjuhlPJIE4FTCudbLY0m9/XrZsLChHsm5LDlYAVLth/267aUUp2TJgIn1ByH3cv8Xizkcu3I3qQn\nRDNHm51QSnmgicAJu76Apnq/Fwu5REeEM2N8FosLD7NpvzY7oZQ6lSYCJ2z7GKKToG9+wDZ569j+\n2uyEUsojTQSBZgwUfgLnTIbwyIBtNikukmnn9+Pv6/ez/5g2O6GUOkkTQaAd/AoqDwasWMjdXRdl\nYYBXCnYFfNtKqeCliSDQCudb7+dOCfimM7vH8c3hGby9ci/HtdkJpZSt3UQgIi+LSImIbHQblyIi\nn4hIof3e3R4vIvKMiGwXkQ0ikufP4Dulwk8gYyQk9HRk87Mm5FBZ28DbK/Y4sn2lVPDx5orgVeAb\nLcY9DHxmjBkAfGZ/BrgSGGC/ZgHP+SbMLqL6CBSv9FknNGdiWJ8kLjynB68UFFHXoM1OKKW8SATG\nmEXAkRajrwVes4dfA77lNv51Y1kOJItIhq+C7fR2fA6myZH6AXf3TMjh4PEa/r5em51QSp15HUFP\nY8wBAPs93R7fB3Bv6rLYHqfAqh+I6wG9RzkaxqSBaQzqmcALi7XZCaWU7yuLxcM4j2caEZklIqtF\nZHVpaamPwwhCTY1Wt5TnToGwcEdDERHuvjibLQcrWFSozU4oFerONBEcchX52O8l9vhiwL3xnEzA\nY/mDMWaOMWaMMWZMWlraGYbRiexbC9VljhcLuVw7sg89E6OZs2iH06EopRx2pongQ+AOe/gO4AO3\n8bfbdw+NA8pdRUghr3A+SBicc4nTkQAQFRHGjAuzKdhexsZ95U6Ho5RykDe3j74NLAMGiUixiMwE\nHgcuE5FC4DL7M8A8YCewHXgBuN8vUXdGhfMhcyzEpTgdSbN/y+9HN212QqmQF9HeDMaYW1qZdKmH\neQ3wwNkG1eVUHIID6+CSnzkdySmSYiO5ZWw/XllaxE++MZg+ybFOh6SUcoA+WRwI2z+x3oOkfsDd\nnRdlA/DyEm12QqlQpYkgEArnQ0IG9BrudCSn6ZMcy9W5Gbyzcg/lJ7TZCaVCkSYCf2ushx0LrE5o\nxNPdtc67Z0IOVXWNvKXNTigVkjQR+NveFVB7PCiLhVyG9k7ionNTeaVglzY7oVQI0kTgb4XzISwS\nciY5HUmb7pmQQ0lFLR+s2+d0KEqpANNE4G/b5kP/CyE6welI2jRhQCqDe2mzE0qFIk0E/nRsD5Ru\nDupiIRcR4Z6Lc9h2qJKF20KgyQ+lVDNNBP5UGLy3jXpy9Yje9EqMYc4X+oCZUqFEE4E/FX4Cyf0h\ndYDTkXglKiKMO8dnsWxnGR9t0CaqlQoVmgj8pb4Gdn1hdUITpLeNejJ9XH/y+iXz3be/5I1lRU6H\no5QKAE0E/rJ7CdRXd5piIZdu0RG8efc4Lh3ck5998DW/+XiLVh4r1cVpIvCXwk8gIgayLnI6kg6L\njQrnj9PzuGVsX36/YAc/eXcD9Y36fIFSXVW7jc6pM1Q4H7InQGTnbMgtIjyM/7luOOkJMfz2s0IO\nV9by+1vziIvSQ0aprkavCPzh8HY4srPTFQu1JCL8+2UD+Z/rhvPFtlJueWEFZZW1ToellPIxTQT+\nUDjfeh9wmbNx+Mi/5ffjj9NHs+XAcW784zL2Hql2OiSllA9pIvCHwvmQOgi6Zzkdic9cPrQXb96d\nz5GqOq5/bqn2aqZUF6KJwNdqK2F3QZe5GnA3JiuFd79zAZFhwrQ5yynYrh3fK9UVaCLwtV2LoLGu\n09cPtGZAzwTeu/9C+iTHMuOVlXy4Xh88U6qz00Tga4UfQ1QC9LvA6Uj8JiMplj9/5wJG9evOg29/\nyYva57FSnZomAl8yxnp+4JxJEBHldDR+lRQbyet3jeXKYb147B+b+Z95m2lq0gfPlOqMNBH4Uskm\nOL6vyxYLtRQTGc7v/i2P2y/oz5xFO/nBn9dpxzZKdUL6dJAvuW4bPbfrVRS3JjxM+OU1Q+mZGMNv\nPt5KWVUdz00fTXy0HlpKdRZ6ReBL2+ZDr1xIzHA6koASER6YfC6/uTGXpTvKmDZnGaUV+uCZUp2F\nJgJfOXHU6p84RIqFPLlpTF9evH0MO0qquPK3i/nPv33Fx18fpLK2wenQlFJt0Ot3X6g5DoueANMY\n0okAYPLgdObeO45nP9/OB1/u460Ve4gMF8b0T2HioDQmDUpjUM8EpBM1za1UVyfB0MTwmDFjzOrV\nq50Oo+OO7YUVf4Q1r0FdBQy8Eqa9CWHhTkcWFOoamli9+whfbCvli62lbDlYAUCvxBgmDkxj4qA0\nxp+bSlJspMORKtU5icgaY8yYs16PJoIzsG8tLPsdfP2+9XnodXDBA9Anz9m4gtzB8hq+2FbCF9tK\nWVx4mIqaBsLDhNH9ujNxUBoTB6YxJCORsDC9WlDKG5oIAq2pCbb9y0oAuwsgOhHybof870ByX6ej\n63QaGpv4cu8xFm61EsPGfccBSI2Pbr5auPCcHqTGRzscqVLBSxNBoNRVw/q3YNkf4MgOSOprnfzz\nboeYRKej6zJKKmpYvO0wC7eVsriwlGPV9QD0TYllRGYyI/tar6G9k4iN0qI3pUATgf9VHIJVL8Cq\nl+DEEeg9Ci6YDUO+BeFax+5PjU2G9cXHWF10hPV7y1m39xj7jp0ArOcWBvVMYGS/ZEZmJjOyXzLn\npMUTrsVJKgT5KhHoGa2lQ5tg+e9hw5+hsR4GTYULZ1ttB+mdLgERHibk9etOXr/uzeNKKmpYv7ec\n9XuPsb74GH9fv5+3VuwBoFtUOMMzkxjZtzsj+1rvvZJinApfqU7HL4lARL4B/BYIB140xjzuj+2c\nMWOgoRbqqqCu0no/tgdWzoEdn0FELIy6DcbdD6nnOh2tAtITYrhsSAyXDekJQFOTYVdZFev2WIlh\n3d5jvLRkJ/WN1hVuz8RoRmQm0ysphrioCLpFhRMX3eI9KoJu0dZ7fHQEcdHhxEWGExGuj9eo0OLz\noiERCQe2AZcBxcAq4BZjzKbWlvG6aMgYqK2wHt5qfh2x3msr3U7sruGqU0/27sNNHh5y6pYOY2fB\nmLugW48z3APKKTX1jWw+cJx1e4+xfu8xNuwr52hVHVV1jR1qAyk6Ioxu0RHERYUTHx1Banw0aQnW\nK91+T4t3fY4hMTZCn4tQjgjmoqGxwHZjzE4AEXkHuBZoNRFQexy+erfFCd5+VR859bNpbGPTAlHx\nENXN7RUPcamQ3N8ajo4/dZprOCYJ+o+HCL1LpbOKiQxnVL/ujHIrUnKpb2yiuq6RqtoGqusaqKpt\npKqugWrXe/O0U8dX1DRwuLKWoqIqSipqPSaUqPAw0hKiSbUTRHriyUSRlhBNcmykJgoV1PyRCPoA\ne90+FwP5bS5RtgPem3nyc1QCxHaH2GTrPbG3/bk7xKWcHHa9YpKtO3giYiFML+vV6SLDw0iKDTur\nh9eMMVTUNlByvJbSilpKK633kooa63NFLcVHq1m39yhlVXUEwX0YSnnFH4nA00+f0/4lRGQWMAvg\nnL694IEvTp78w/VJUxV8RITEmEgSYyI5Nz2+zXnrG5s4UlVHaUUt5SfqAxShCjUX/co36/FHIigG\n3J+wygRO68/QGDMHmANWHQFpA/0QilLOiAwPo2diDD0T9e4lFfz8UY6yChggItkiEgVMAz70w3aU\nUkr5gM+vCIwxDSIyG/gY6/bRl40xX/t6O0oppXzDL88RGGPmAfP8sW6llFK+pbfYKKVUiNNEoJRS\nIU4TgVJKhThNBEopFeKCohlqEakAtjodhxdSgcNOB+EFjdN3OkOMoHH6WmeJc5AxJuFsVxIszVBv\n9UXDSf4mIqs1Tt/pDHF2hhhB4/S1zhSnL9ajRUNKKRXiNBEopVSIC5ZEMMfpALykcfpWZ4izM8QI\nGqevhVScQVFZrJRSyjnBckWglFLKIZoIlFIqxAU0EYjIN0Rkq4hsF5GHPUyPFpG59vQVIpIVyPjs\nGPqKyAIR2SwiX4vI9zzMM0lEykVknf16JNBx2nEUichXdgyn3UYmlmfs/blBRPICHN8gt320TkSO\ni8j3W8zj2L4UkZdFpERENrqNSxGRT0Sk0H4/vd9La7477HkKReSOAMf4GxHZYv9N/yYiya0s2+bx\nEYA4fyEi+9z+tlNbWbbN80IA4pzrFmORiKxrZdlA7k+P5yG/HZ/GmIC8sJqk3gHkAFHAemBIi3nu\nB/5oD08D5gYqPrcYMoA8ezgB2OYhzknAR4GOzUOsRUBqG9OnAv/E6jVuHLDCwVjDgYNA/2DZl8AE\nIA/Y6Dbu18DD9vDDwK88LJcC7LTfu9vD3QMY4+VAhD38K08xenN8BCDOXwA/8uK4aPO84O84W0z/\nP+CRINifHs9D/jo+A3lF0NypvTGmDnB1au/uWuA1e/hd4FIJcK/fxpgDxpi19nAFsBmrH+bO6Frg\ndWNZDiSLSIZDsVwK7DDG7HZo+6cxxiwCjrQY7X4MvgZ8y8OiVwCfGGOOGGOOAp8A3whUjMaY+caY\nBvvjcqxeAB3Vyr70hjfnBZ9pK077XPNt4G1/bd9bbZyH/HJ8BjIReOrUvuUJtnke+0AvB3oEJDoP\n7KKpUcAKD5MvEJH1IvJPERka0MBOMsB8EVkjVh/QLXmzzwNlGq3/gwXDvnTpaYw5ANY/I5DuYZ5g\n2q93YV31edLe8REIs+0irJdbKcYIpn15MXDIGFPYynRH9meL85Bfjs9AJgJvOrX3quP7QBCReOA9\n4PvGmOMtJq/FKuIYATwLvB/o+GzjjTF5wJXAAyIyocX0oNifYnVZeg3wFw+Tg2VfdkSw7NefAg3A\nm63M0t7x4W/PAecAI4EDWMUuLQXFvrTdQttXAwHfn+2ch1pdzMO4NvdpIBOBN53aN88jIhFAEmd2\nuXlWRCQSa+e/aYz5a8vpxpjjxphKe3geECkiqQEOE2PMfvu9BPgb1mW2O2/2eSBcCaw1xhxqOSFY\n9qWbQ67iM/u9xMM8ju9XuwLwKuBWYxcMt+TF8eFXxphDxphGY0wT8EIr23d8X0Lz+eZ6YG5r8wR6\nf7ZyHvLL8RnIROBNp/YfAq4a7huBz1s7yP3FLid8CdhsjHmylXl6ueouRGQs1n4sC1yUICLdRCTB\nNYxVgbixxWwfAreLZRxQ7rqsDLBWf2kFw75swf0YvAP4wMM8HwOXi0h3u7jjcntcQIjIN4CHgGuM\nMdWtzOPN8eFXLeqjrmtl+96cFwJhCrDFGFPsaWKg92cb5yH/HJ+BqAF3q82eilX7vQP4qT3uUawD\nGiAGq/hgO7ASyAlkfHYMF2FdRm0A1tmvqcB3gO/Y88wGvsa6w2E5cKEDcebY219vx+Lan+5xCvB7\ne39/BYxxIM44rBN7ktu4oNiXWMnpAFCP9StqJlad1GdAof2eYs87BnjRbdm77ON0O3BngGPcjlUG\n7Do+XXfa9QbmtXV8BDjON+zjbgPWCSyjZZz259POC4GM0x7/quuYdJvXyf3Z2nnIL8enNjGhlFIh\nTp8sVkqpEKeJQCmlQpwmAqWUCnGaCJRSKsRpIlAhQUSSReT+M1juP/0Rj1LBRO8aUiHBfkz/I2PM\nsA4uV2mMifdLUEoFCb0iUKHiceAcuwnh37ScKCIZIrLInr5RRC4WkceBWHvcm/Z800VkpT3ueREJ\nt8dXisj/ichaEflMRNIC+/WUOnN6RaBCQntXBCLyQyDGGPPf9sk9zhhT4X5FICLnYTUDfL0xpl5E\n/gAsN8a8LiIGmG6MeVOsPhXSjTGzA/HdlDpbEU4HoFSQWAW8bLfv8r4xxlPnJJcCo4FVdqsYsZxs\n66WJk+3U/Ak4rY0qpYKVFg0pRXM79ROAfcAbInK7h9kEeM0YM9J+DTLG/KK1VfopVKV8ThOBChUV\nWD09eSQi/YESY8wLWI19ubr1rLevEsBq2+VGEUm3l0mxlwPrf+lGe/jfgCU+jl8pv9GiIRUSjDFl\nIlIgVl+1/zTG/LjFLJOAH4tIPVAJuK4I5gAbRGStMeZWEfl/WJ2ThGE1XPYAsBuoAoaKyBqsDpVu\n9v+3Uso3tLJYKR/Q20xVZ6ZFQ0opFeL0ikCFFBEZjtVOvrtaY0y+E/EoFQw0ESilVIjToiGllApx\nmgiUUirEaSJQSqkQp4lAKaVCnCYCpZQKcZoIlFIqxP1/yRiaKS5hmlIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798e3eeb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FFVecNu9tt93Gm2++yTvvvMPLL7982vSYmBgmTZrExx9/zNy5c7nlllsAeOqp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798ea15c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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y7bff8sorr7Bz504mTJjAa6+9BkBhYSHLli1j8ODBp21n5MiRzJkzB4Dy8nI+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\nz44yb4zZb4xZY78+AfyANQ6zPxoKvGE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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798d70898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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y7bff8sorr7Bz504mTJjAa6+9BkBhYSHLli1j8ODBp21n5MiRzJkzB4Dy8nI+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\nK4YxWoQtWWgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd798d81e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_barabasi*', get_count, 'id');"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:58:26.903783Z",
"start_time": "2017-10-19T17:57:57.983957+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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1wFFdlXHOtZnZNqASaOhqo/tWlHD7tz7Xs2hFRJIkLzeH4YOKGD6o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\nACn5i4gMQP8fEeHAAqu6cxAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799674080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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iT9K6X9m7KfmLJGjbzlbaO1xKh3OOJTfHOPmwGl5YsZmgmn4kSZT8RRIUCA/t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\nACn5i4gMQP8fEeHAAqu6cxAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7996d2eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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9xPNGYGq8Ms65DjPbDlQDTfE2emBVGbd94WO9i1ZEJEUK8vOoHVJC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\nhJT8RUQGof8Puw/Kf+5ObzkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799841390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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9xPNGYGq8Ms65DjPbDlQDTfE2emBVGbd94WO9i1ZEJEUK8vOoHVJC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\nhJT8RUQGof8Puw/Kf+5ObzkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7998504a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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30t7h0jqcczz5ecaZR9fx3MqtBNX0Iymi5C+SpEBkaIcM1/zBa/oJt3Xw1+Wb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\n6nkDcEJXZZxzbWa2A6gGGrv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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7997c82b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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30t7h0jqcczz5ecaZR9fx3MqtBNX0Iymi5C+SpEBkaIcM1/zBa/oJt3Xw1+Wb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\n6nkDcEJXZZxzbWa2A6gGGrv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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7997c83c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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J0LadzbS2uZQO5xxLbo5x8qHVvLRsM0E1/UiSKPmLJCgQHtohzTV/8Jp+Qi1t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\nIsl/OLA24nktMLmjMs65FjP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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799a9cf98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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J0LadzbS2uZQO5xxLbo5x8qHVvLRsM0E1/UiSKPmLJCgQHtohzTV/8Jp+Qi1t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\nIsl/OLA24nktMLmjMs65FjP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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799a9c588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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M0dDUQmOotf13Y6iVhlALjU0tNIRaaQy10NDUyo7wcr/spu1NNIZa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\nRKQPUvIXEemDlPxFRPqg/w8q2vnGLNBQ+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c4a7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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+4DX9hFra+PuSDWnft+ydlPxFEhQe2iGVwzl35Ih9BlE9oEi9fiRplPxFEtR+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\nRKQPUvIXEemDlPxFRPqg/w8q2vnGLNBQ+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d51438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_all('soil_output/Spread_erdos*', get_value, 'prob_tv_spread');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Manually plotting with pandas"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:00:37.003972Z",
"start_time": "2017-10-19T13:00:36.983128+02:00"
}
},
"source": [
"Although the simplest way to visualize the results of a simulation is to use the built-in methods in the analysis module, sometimes the setup is more complicated and we need to explore the data a little further.\n",
"\n",
"For that, we can use native pandas over the results.\n",
"\n",
"Soil provides some convenience methods to simplify common operations:\n",
"\n",
"* `analysis.split_df` to separate a history dataframe into environment and agent parameters.\n",
"* `analysis.get_count` to get a dataframe with the value counts for different attributes during the simulation.\n",
"* `analysis.get_value` to get the evolution of the value of an attribute during the simulation.\n",
"\n",
"And, as we saw earlier, `analysis.process` can turn a dataframe in canonical form into a dataframe with a column per attribute.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:15.791793Z",
"start_time": "2017-10-19T17:59:15.604960+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"p = read_sql('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite')\n",
"env, agents = split_df(p);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the evolution of agent parameters in the simulation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:17.153282Z",
"start_time": "2017-10-19T17:59:16.830872+02:00"
}
},
"outputs": [
{
"data": {
"image/png": 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QVsCdVdvZYSWV+rp6Jl80GZ/Px3f7vuO268LX/Lnm4mvYvHFzp8W16IlF1NXW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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c4f908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res = agents.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)\n",
"res.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:10:36.086913Z",
"start_time": "2017-10-19T13:10:36.058547+02:00"
}
},
"source": [
"As we can see, `event_time` is cluttering our results, "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:18.418348Z",
"start_time": "2017-10-19T17:59:18.143443+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd795b17b38>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YsYINGzYwZ84c7r77bgCuu+465s+fzxdffMHHH39MSUkJDzzwQHDfn/zkJ10O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\nqggqHK0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd796e1ff60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"del res['event_time']\n",
"res.plot()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:42.750011Z",
"start_time": "2017-10-19T17:59:42.649353+02:00"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>event_time</th>\n",
" <th>has_tv</th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>t_step</th>\n",
" <th>agent_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">0</th>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>neutral</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">20</th>\n",
" <th>73</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" <td>infected</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NewsEnvironmentAgent</th>\n",
" <td>10</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10521 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" event_time has_tv id\n",
"t_step agent_id \n",
"0 0 0 True neutral\n",
" 1 0 False neutral\n",
" 10 0 True neutral\n",
" 100 0 True neutral\n",
" 101 0 True neutral\n",
" 102 0 False neutral\n",
" 103 0 True neutral\n",
" 104 0 True neutral\n",
" 105 0 False neutral\n",
" 106 0 False neutral\n",
" 107 0 True neutral\n",
" 108 0 True neutral\n",
" 109 0 False neutral\n",
" 11 0 True neutral\n",
" 110 0 False neutral\n",
" 111 0 False neutral\n",
" 112 0 True neutral\n",
" 113 0 True neutral\n",
" 114 0 True neutral\n",
" 115 0 True neutral\n",
" 116 0 False neutral\n",
" 117 0 True neutral\n",
" 118 0 True neutral\n",
" 119 0 False neutral\n",
" 12 0 False neutral\n",
" 120 0 False neutral\n",
" 121 0 True neutral\n",
" 122 0 True neutral\n",
" 123 0 True neutral\n",
" 124 0 False neutral\n",
"... ... ... ...\n",
"20 73 0 True infected\n",
" 74 0 True infected\n",
" 75 0 True infected\n",
" 76 0 True infected\n",
" 77 0 True infected\n",
" 78 0 True infected\n",
" 79 0 False infected\n",
" 8 0 False infected\n",
" 80 0 True infected\n",
" 81 0 False infected\n",
" 82 0 False infected\n",
" 83 0 True infected\n",
" 84 0 False infected\n",
" 85 0 True infected\n",
" 86 0 True infected\n",
" 87 0 True infected\n",
" 88 0 False infected\n",
" 89 0 False infected\n",
" 9 0 True infected\n",
" 90 0 True infected\n",
" 91 0 True infected\n",
" 92 0 True infected\n",
" 93 0 False infected\n",
" 94 0 True infected\n",
" 95 0 True infected\n",
" 96 0 True infected\n",
" 97 0 True infected\n",
" 98 0 False infected\n",
" 99 0 True infected\n",
" NewsEnvironmentAgent 10 False 0\n",
"\n",
"[10521 rows x 3 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"processed = process_one(agents);\n",
"processed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which is equivalent to:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:51.165806Z",
"start_time": "2017-10-19T17:59:50.886780+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd799c15748>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YsYINGzYwZ84c7r77bgCuu+465s+fzxdffMHHH39MSUkJDzzwQHDfn/zkJ10O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\nqggqHK0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799d3fbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_count(agents, 'id', 'has_tv').plot()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T15:59:55.203641Z",
"start_time": "2017-10-19T17:59:54.950046+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd79a228c88>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a1fe208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"get_value(agents, 'event_time').plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dealing with bigger data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:18.148006Z",
"start_time": "2017-10-19T18:00:18.117654+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"from soil import analysis"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:18.636440Z",
"start_time": "2017-10-19T18:00:18.504421+02:00"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"267M\t../rabbits/soil_output/rabbits_example/\r\n"
]
}
],
"source": [
"!du -xsh ../rabbits/soil_output/rabbits_example/"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T11:22:22.301765Z",
"start_time": "2017-10-19T13:22:22.281986+02:00"
}
},
"source": [
"If we tried to load the entire history, we would probably run out of memory. Hence, it is recommended that you also specify the attributes you are interested in."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:25.080582Z",
"start_time": "2017-10-19T18:00:19.594165+02:00"
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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p378/eXl5HD16lG+//ZZ58+aRk5NDt27dqud1uP3225k3bx5XXXUVPXv25KKLLgJgxowZ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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799c54ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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p378/eXl5HD16lG+//ZZ58+aRk5NDt27dqud1uP3225k3bx5XXXUVPXv25KKLLgJgxowZ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\n0SbFISIi2qQ4REREmxSHiIho8y9ffsz3DjLrngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799aa66d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'id')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:38.434367Z",
"start_time": "2017-10-19T18:00:33.645762+02:00"
},
"collapsed": true
},
"outputs": [],
"source": [
"df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.db.sqlite', keys=['id', 'rabbits_alive'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:39.160418Z",
"start_time": "2017-10-19T18:00:38.436153+02:00"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd799b5b2b0>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WYQUA3aN0y0EppZSlNjn07NTOk4OIdBKR90Rkl4jsFJFxIhIjIitEJN16jrb6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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79a2f8940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"states = analysis.get_count(df, 'id')\n",
"states.plot()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:39.515032Z",
"start_time": "2017-10-19T18:00:39.162240+02:00"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd796161cf8>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OAZ8DcPcSM/sBsDro9313rzvI/QXCZ0SlAX8MfkREpJ61H4TfNqcMb//bgtbXbDi4+4LT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\npAGFg4iINPD/AfaLVEq6LAp5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd79593ef98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"alive = analysis.get_value(df, 'rabbits_alive', 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)\n",
"alive.plot()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:00:58.815038Z",
"start_time": "2017-10-19T18:00:58.566807+02:00"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jfernando/.local/lib/python3.6/site-packages/pandas/core/reshape/merge.py:551: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"image/png": 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mQgkh2s++c9l4ONkxNaBv3Q2pEWDrAP7tWzKjNjlzaGfVJbuvu+66Ou0PPvgg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\naDCRkVBE2rl80s7lk5lUhNmksbWzYfC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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd799b45588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h = alive.join(states);\n",
"h.plot();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-19T16:01:01.195253Z",
"start_time": "2017-10-19T18:01:01.142907+02:00"
},
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"states[[('id','newborn'),('id','fertile'),('id', 'pregnant')]].sum(axis=1).sub(alive['rabbits_alive'], fill_value=0)"
]
}
],
"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": "31px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_position": {
"height": "867px",
"left": "0px",
"right": "1670px",
"top": "106px",
"width": "250px"
},
"toc_section_display": "block",
"toc_window_display": true,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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