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Developing new models
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---------------------
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Introduction
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============
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This notebook is an introduction to the soil agent-based social network
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simulation framework. In particular, we will focus on a specific use
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case: studying the propagation of news in a social network.
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The steps we will follow are:
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- Modelling the behavior of agents
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- Running the simulation using different configurations
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- Analysing the results of each simulation
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But before that, let's import the soil module and networkx.
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.. code:: ipython3
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import soil
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import networkx as nx
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.. code:: ipython3
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%pylab inline
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# To display plots in the notebook
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.. parsed-literal::
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Populating the interactive namespace from numpy and matplotlib
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Basic concepts
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==============
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There are three main elements in a soil simulation:
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- The network topology. A simulation may use an existing NetworkX
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topology, or generate one on the fly
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- Agents. There are two types: 1) network agents, which are linked to a
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node in the topology, and 2) environment agents, which are freely
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assigned to the environment.
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- The environment. It assigns agents to nodes in the network, and
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stores the environment parameters (shared state for all agents).
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Soil is based on ``simpy``, which is an event-based network simulation
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library. Soil provides several abstractions over events to make
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developing agents easier. This means you can use events (timeouts,
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delays) in soil, but for the most part we will assume your models will
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be step-based.
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Modeling behaviour
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==================
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Our first step will be to model how every person in the social network
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reacts when it comes to news. We will follow a very simple model (a
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finite state machine).
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There are two types of people, those who have heard about a newsworthy
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event (infected) or those who have not (neutral). A neutral person may
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heard about the news either on the TV (with probability
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**prob\_tv\_spread**) or through their friends. Once a person has heard
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the news, they will spread it to their friends (with a probability
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**prob\_neighbor\_spread**). Some users do not have a TV, so they only
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rely on their friends.
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The spreading probabilities will change over time due to different
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factors. We will represent this variance using an environment agent.
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Network Agents
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++++++++++++++
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A basic network agent in Soil should inherit from
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``soil.agents.BaseAgent``, and define its behaviour in every step of the
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simulation by implementing a ``run(self)`` method. The most important
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attributes of the agent are:
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- ``agent.state``, a dictionary with the state of the agent.
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``agent.state['id']`` reflects the state id of the agent. That state
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id can be used to look for other networks in that specific state. The
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state can be access via the agent as well. For instance:
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.. code:: py
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a = soil.agents.BaseAgent(env=env)
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a['hours_of_sleep'] = 10
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print(a['hours_of_sleep'])
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The state of the agent is stored in every step of the simulation:
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``py print(a['hours_of_sleep', 10]) # hours of sleep before step #10 print(a[None, 0]) # whole state of the agent before step #0``
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- ``agent.env``, a reference to the environment. Most commonly used to
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get access to the environment parameters and the topology:
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.. code:: py
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a.env.G.nodes() # Get all nodes ids in the topology
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a.env['minimum_hours_of_sleep']
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Since our model is a finite state machine, we will be basing it on
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``soil.agents.FSM``.
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With ``soil.agents.FSM``, we do not need to specify a ``step`` method.
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Instead, we describe every step as a function. To change to another
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state, a function may return the new state. If no state is returned, the
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state remains unchanged.[ It will consist of two states, ``neutral``
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(default) and ``infected``.
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Here's the code:
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.. code:: ipython3
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import random
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class NewsSpread(soil.agents.FSM):
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@soil.agents.default_state
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@soil.agents.state
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def neutral(self):
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r = random.random()
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if self['has_tv'] and r < self.env['prob_tv_spread']:
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return self.infected
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return
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@soil.agents.state
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def infected(self):
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prob_infect = self.env['prob_neighbor_spread']
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for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
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r = random.random()
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if r < prob_infect:
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neighbor.state['id'] = self.infected.id
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return
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Environment agents
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++++++++++++++++++
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Environment agents allow us to control the state of the environment. In
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this case, we will use an environment agent to simulate a very viral
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event.
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When the event happens, the agent will modify the probability of
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spreading the rumor.
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.. code:: ipython3
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NEIGHBOR_FACTOR = 0.9
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TV_FACTOR = 0.5
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class NewsEnvironmentAgent(soil.agents.BaseAgent):
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def step(self):
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if self.now == self['event_time']:
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self.env['prob_tv_spread'] = 1
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self.env['prob_neighbor_spread'] = 1
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elif self.now > self['event_time']:
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self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR
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self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR
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Testing the agents
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++++++++++++++++++
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Feel free to skip this section if this is your first time with soil.
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Testing agents is not easy, and this is not a thorough testing process
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for agents. Rather, this section is aimed to show you how to access
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internal pats of soil so you can test your agents.
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First of all, let's check if our network agent has the states we would
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expect:
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.. code:: ipython3
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NewsSpread.states
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.. parsed-literal::
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{'infected': <function __main__.NewsSpread.infected>,
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'neutral': <function __main__.NewsSpread.neutral>}
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Now, let's run a simulation on a simple network. It is comprised of
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three nodes:
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.. code:: ipython3
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G = nx.Graph()
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G.add_edge(0, 1)
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G.add_edge(0, 2)
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G.add_edge(2, 3)
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G.add_node(4)
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pos = nx.spring_layout(G)
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nx.draw_networkx(G, pos, node_color='red')
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nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')
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.. image:: output_21_0.png
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Let's run a simple simulation that assigns a NewsSpread agent to all the
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nodes in that network. Notice how node 0 is the only one with a TV.
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.. code:: ipython3
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env_params = {'prob_tv_spread': 0,
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'prob_neighbor_spread': 0}
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MAX_TIME = 100
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EVENT_TIME = 10
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sim = soil.simulation.SoilSimulation(topology=G,
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num_trials=1,
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max_time=MAX_TIME,
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environment_agents=[{'agent_type': NewsEnvironmentAgent,
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'state': {
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'event_time': EVENT_TIME
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}}],
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network_agents=[{'agent_type': NewsSpread,
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'weight': 1}],
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states={0: {'has_tv': True}},
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default_state={'has_tv': False},
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environment_params=env_params)
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env = sim.run_simulation()[0]
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.. parsed-literal::
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Trial: 0
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Running
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Finished trial in 0.014928102493286133 seconds
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Finished simulation in 0.015764951705932617 seconds
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Now we can access the results of the simulation and compare them to our
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expected results
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.. code:: ipython3
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agents = list(env.network_agents)
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# Until the event, all agents are neutral
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for t in range(10):
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for a in agents:
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assert a['id', t] == a.neutral.id
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# After the event, the node with a TV is infected, the rest are not
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assert agents[0]['id', 11] == NewsSpread.infected.id
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for a in agents[1:4]:
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assert a['id', 11] == NewsSpread.neutral.id
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# At the end, the agents connected to the infected one will probably be infected, too.
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assert agents[1]['id', MAX_TIME] == NewsSpread.infected.id
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assert agents[2]['id', MAX_TIME] == NewsSpread.infected.id
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# But the node with no friends should not be affected
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assert agents[4]['id', MAX_TIME] == NewsSpread.neutral.id
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Lastly, let's see if the probabilities have decreased as expected:
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.. code:: ipython3
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assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4
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assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6
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Running the simulation
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======================
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To run a simulation, we need a configuration. Soil can load
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configurations from python dictionaries as well as JSON and YAML files.
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For this demo, we will use a python dictionary:
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.. code:: ipython3
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config = {
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'name': 'ExampleSimulation',
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'max_time': 20,
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'interval': 1,
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'num_trials': 1,
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'network_params': {
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'generator': 'complete_graph',
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'n': 500,
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},
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'network_agents': [
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{
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'agent_type': NewsSpread,
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'weight': 1,
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'state': {
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'has_tv': False
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}
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},
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{
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'agent_type': NewsSpread,
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'weight': 2,
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'state': {
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'has_tv': True
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}
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}
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],
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'states': [ {'has_tv': True} ],
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'environment_params':{
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'prob_tv_spread': 0.01,
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'prob_neighbor_spread': 0.5
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}
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}
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Let's run our simulation:
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.. code:: ipython3
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soil.simulation.run_from_config(config, dump=False)
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.. parsed-literal::
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Using config(s): ExampleSimulation
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Trial: 0
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Running
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Finished trial in 1.4140360355377197 seconds
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Finished simulation in 2.4056642055511475 seconds
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In real life, you probably want to run several simulations, varying some
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of the parameters so that you can compare and answer your research
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questions.
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For instance:
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- Does the outcome depend on the structure of our network? We will use
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different generation algorithms to compare them (Barabasi-Albert and
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Erdos-Renyi)
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- How does neighbor spreading probability affect my simulation? We will
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try probability values in the range of [0, 0.4], in intervals of 0.1.
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.. code:: ipython3
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network_1 = {
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'generator': 'erdos_renyi_graph',
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'n': 500,
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'p': 0.1
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}
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network_2 = {
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'generator': 'barabasi_albert_graph',
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'n': 500,
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'm': 2
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}
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for net in [network_1, network_2]:
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for i in range(5):
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prob = i / 10
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config['environment_params']['prob_neighbor_spread'] = prob
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config['network_params'] = net
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config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)
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s = soil.simulation.run_from_config(config)
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.. parsed-literal::
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Using config(s): Spread_erdos_renyi_graph_prob_0.0
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Trial: 0
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Running
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Finished trial in 0.2691483497619629 seconds
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Finished simulation in 0.3650345802307129 seconds
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Using config(s): Spread_erdos_renyi_graph_prob_0.1
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Trial: 0
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Running
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Finished trial in 0.34261059761047363 seconds
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Finished simulation in 0.44017767906188965 seconds
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Using config(s): Spread_erdos_renyi_graph_prob_0.2
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Trial: 0
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Running
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Finished trial in 0.34417223930358887 seconds
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Finished simulation in 0.4550771713256836 seconds
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Using config(s): Spread_erdos_renyi_graph_prob_0.3
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Trial: 0
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Running
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Finished trial in 0.3237779140472412 seconds
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Finished simulation in 0.42307496070861816 seconds
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Using config(s): Spread_erdos_renyi_graph_prob_0.4
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Trial: 0
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Running
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Finished trial in 0.3507683277130127 seconds
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Finished simulation in 0.45061564445495605 seconds
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Using config(s): Spread_barabasi_albert_graph_prob_0.0
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Trial: 0
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Running
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Finished trial in 0.19115304946899414 seconds
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Finished simulation in 0.20927715301513672 seconds
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Using config(s): Spread_barabasi_albert_graph_prob_0.1
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Trial: 0
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Running
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Finished trial in 0.22086191177368164 seconds
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Finished simulation in 0.2390913963317871 seconds
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Using config(s): Spread_barabasi_albert_graph_prob_0.2
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Trial: 0
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Running
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Finished trial in 0.21225976943969727 seconds
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Finished simulation in 0.23252630233764648 seconds
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Using config(s): Spread_barabasi_albert_graph_prob_0.3
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Trial: 0
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Running
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Finished trial in 0.2853121757507324 seconds
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Finished simulation in 0.30568504333496094 seconds
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Using config(s): Spread_barabasi_albert_graph_prob_0.4
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Trial: 0
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Running
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Finished trial in 0.21434736251831055 seconds
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Finished simulation in 0.23370599746704102 seconds
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The results are conveniently stored in pickle (simulation), csv (history
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of agent and environment state) and gexf format.
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.. code:: ipython3
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!tree soil_output
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!du -xh soil_output/*
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.. parsed-literal::
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[01;34msoil_output[00m
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├── [01;34mSim_prob_0[00m
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│ ├── Sim_prob_0.dumped.yml
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│ ├── Sim_prob_0.simulation.pickle
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│ ├── Sim_prob_0_trial_0.environment.csv
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│ └── Sim_prob_0_trial_0.gexf
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├── [01;34mSpread_barabasi_albert_graph_prob_0.0[00m
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│ ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml
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│ ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle
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│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv
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│ └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf
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├── [01;34mSpread_barabasi_albert_graph_prob_0.1[00m
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│ ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml
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│ ├── Spread_barabasi_albert_graph_prob_0.1.simulation.pickle
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│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv
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│ └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf
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├── [01;34mSpread_barabasi_albert_graph_prob_0.2[00m
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│ ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.2.simulation.pickle
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.environment.csv
|
|
|
│ └── Spread_barabasi_albert_graph_prob_0.2_trial_0.gexf
|
|
|
├── [01;34mSpread_barabasi_albert_graph_prob_0.3[00m
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.3.simulation.pickle
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.environment.csv
|
|
|
│ └── Spread_barabasi_albert_graph_prob_0.3_trial_0.gexf
|
|
|
├── [01;34mSpread_barabasi_albert_graph_prob_0.4[00m
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.4.simulation.pickle
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.environment.csv
|
|
|
│ └── Spread_barabasi_albert_graph_prob_0.4_trial_0.gexf
|
|
|
├── [01;34mSpread_erdos_renyi_graph_prob_0.0[00m
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.0.simulation.pickle
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.environment.csv
|
|
|
│ └── Spread_erdos_renyi_graph_prob_0.0_trial_0.gexf
|
|
|
├── [01;34mSpread_erdos_renyi_graph_prob_0.1[00m
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.1.simulation.pickle
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.environment.csv
|
|
|
│ └── Spread_erdos_renyi_graph_prob_0.1_trial_0.gexf
|
|
|
├── [01;34mSpread_erdos_renyi_graph_prob_0.2[00m
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.2.simulation.pickle
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.environment.csv
|
|
|
│ └── Spread_erdos_renyi_graph_prob_0.2_trial_0.gexf
|
|
|
├── [01;34mSpread_erdos_renyi_graph_prob_0.3[00m
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.3.simulation.pickle
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.environment.csv
|
|
|
│ └── Spread_erdos_renyi_graph_prob_0.3_trial_0.gexf
|
|
|
└── [01;34mSpread_erdos_renyi_graph_prob_0.4[00m
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4.simulation.pickle
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv
|
|
|
└── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf
|
|
|
|
|
|
11 directories, 44 files
|
|
|
1.8M soil_output/Sim_prob_0
|
|
|
652K soil_output/Spread_barabasi_albert_graph_prob_0.0
|
|
|
684K soil_output/Spread_barabasi_albert_graph_prob_0.1
|
|
|
692K soil_output/Spread_barabasi_albert_graph_prob_0.2
|
|
|
692K soil_output/Spread_barabasi_albert_graph_prob_0.3
|
|
|
688K soil_output/Spread_barabasi_albert_graph_prob_0.4
|
|
|
1.8M soil_output/Spread_erdos_renyi_graph_prob_0.0
|
|
|
1.9M soil_output/Spread_erdos_renyi_graph_prob_0.1
|
|
|
1.9M soil_output/Spread_erdos_renyi_graph_prob_0.2
|
|
|
1.9M soil_output/Spread_erdos_renyi_graph_prob_0.3
|
|
|
1.9M soil_output/Spread_erdos_renyi_graph_prob_0.4
|
|
|
|
|
|
|
|
|
Analysing the results
|
|
|
=====================
|
|
|
|
|
|
Once the simulations are over, we can use soil to analyse the results.
|
|
|
|
|
|
First, let's load the stored results into a pandas dataframe.
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
%pylab inline
|
|
|
from soil import analysis
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
Populating the interactive namespace from numpy and matplotlib
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
/usr/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']
|
|
|
`%matplotlib` prevents importing * from pylab and numpy
|
|
|
"\n`%matplotlib` prevents importing * from pylab and numpy"
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
config_file, df, config = list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=False))[0]
|
|
|
df
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. raw:: html
|
|
|
|
|
|
<div>
|
|
|
<style>
|
|
|
.dataframe thead tr:only-child th {
|
|
|
text-align: right;
|
|
|
}
|
|
|
|
|
|
.dataframe thead th {
|
|
|
text-align: left;
|
|
|
}
|
|
|
|
|
|
.dataframe tbody tr th {
|
|
|
vertical-align: top;
|
|
|
}
|
|
|
</style>
|
|
|
<table border="1" class="dataframe">
|
|
|
<thead>
|
|
|
<tr style="text-align: right;">
|
|
|
<th></th>
|
|
|
<th>agent_id</th>
|
|
|
<th>tstep</th>
|
|
|
<th>attribute</th>
|
|
|
<th>value</th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>0</th>
|
|
|
<td>env</td>
|
|
|
<td>0</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>env</td>
|
|
|
<td>0</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>2</th>
|
|
|
<td>env</td>
|
|
|
<td>1</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>3</th>
|
|
|
<td>env</td>
|
|
|
<td>1</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>4</th>
|
|
|
<td>env</td>
|
|
|
<td>2</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>5</th>
|
|
|
<td>env</td>
|
|
|
<td>2</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>6</th>
|
|
|
<td>env</td>
|
|
|
<td>3</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>7</th>
|
|
|
<td>env</td>
|
|
|
<td>3</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>env</td>
|
|
|
<td>4</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>env</td>
|
|
|
<td>4</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>env</td>
|
|
|
<td>5</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>env</td>
|
|
|
<td>5</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>env</td>
|
|
|
<td>6</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>13</th>
|
|
|
<td>env</td>
|
|
|
<td>6</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>14</th>
|
|
|
<td>env</td>
|
|
|
<td>7</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>15</th>
|
|
|
<td>env</td>
|
|
|
<td>7</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>16</th>
|
|
|
<td>env</td>
|
|
|
<td>8</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>17</th>
|
|
|
<td>env</td>
|
|
|
<td>8</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>18</th>
|
|
|
<td>env</td>
|
|
|
<td>9</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>19</th>
|
|
|
<td>env</td>
|
|
|
<td>9</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>20</th>
|
|
|
<td>env</td>
|
|
|
<td>10</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21</th>
|
|
|
<td>env</td>
|
|
|
<td>10</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>22</th>
|
|
|
<td>env</td>
|
|
|
<td>11</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>23</th>
|
|
|
<td>env</td>
|
|
|
<td>11</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>24</th>
|
|
|
<td>env</td>
|
|
|
<td>12</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>25</th>
|
|
|
<td>env</td>
|
|
|
<td>12</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>26</th>
|
|
|
<td>env</td>
|
|
|
<td>13</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>27</th>
|
|
|
<td>env</td>
|
|
|
<td>13</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>28</th>
|
|
|
<td>env</td>
|
|
|
<td>14</td>
|
|
|
<td>prob_tv_spread</td>
|
|
|
<td>0.01</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>29</th>
|
|
|
<td>env</td>
|
|
|
<td>14</td>
|
|
|
<td>prob_neighbor_spread</td>
|
|
|
<td>0.1</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>...</th>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21012</th>
|
|
|
<td>499</td>
|
|
|
<td>6</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21013</th>
|
|
|
<td>499</td>
|
|
|
<td>6</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21014</th>
|
|
|
<td>499</td>
|
|
|
<td>7</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21015</th>
|
|
|
<td>499</td>
|
|
|
<td>7</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21016</th>
|
|
|
<td>499</td>
|
|
|
<td>8</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21017</th>
|
|
|
<td>499</td>
|
|
|
<td>8</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21018</th>
|
|
|
<td>499</td>
|
|
|
<td>9</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21019</th>
|
|
|
<td>499</td>
|
|
|
<td>9</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21020</th>
|
|
|
<td>499</td>
|
|
|
<td>10</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21021</th>
|
|
|
<td>499</td>
|
|
|
<td>10</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21022</th>
|
|
|
<td>499</td>
|
|
|
<td>11</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21023</th>
|
|
|
<td>499</td>
|
|
|
<td>11</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21024</th>
|
|
|
<td>499</td>
|
|
|
<td>12</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21025</th>
|
|
|
<td>499</td>
|
|
|
<td>12</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21026</th>
|
|
|
<td>499</td>
|
|
|
<td>13</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21027</th>
|
|
|
<td>499</td>
|
|
|
<td>13</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21028</th>
|
|
|
<td>499</td>
|
|
|
<td>14</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21029</th>
|
|
|
<td>499</td>
|
|
|
<td>14</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21030</th>
|
|
|
<td>499</td>
|
|
|
<td>15</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21031</th>
|
|
|
<td>499</td>
|
|
|
<td>15</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21032</th>
|
|
|
<td>499</td>
|
|
|
<td>16</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21033</th>
|
|
|
<td>499</td>
|
|
|
<td>16</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21034</th>
|
|
|
<td>499</td>
|
|
|
<td>17</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21035</th>
|
|
|
<td>499</td>
|
|
|
<td>17</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21036</th>
|
|
|
<td>499</td>
|
|
|
<td>18</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21037</th>
|
|
|
<td>499</td>
|
|
|
<td>18</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21038</th>
|
|
|
<td>499</td>
|
|
|
<td>19</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21039</th>
|
|
|
<td>499</td>
|
|
|
<td>19</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21040</th>
|
|
|
<td>499</td>
|
|
|
<td>20</td>
|
|
|
<td>has_tv</td>
|
|
|
<td>True</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21041</th>
|
|
|
<td>499</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
<p>21042 rows × 4 columns</p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
list(analysis.get_data('soil_output/Spread_barabasi*prob_0.1*', process=True))[0][1]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. raw:: html
|
|
|
|
|
|
<div>
|
|
|
<style>
|
|
|
.dataframe thead tr:only-child th {
|
|
|
text-align: right;
|
|
|
}
|
|
|
|
|
|
.dataframe thead th {
|
|
|
text-align: left;
|
|
|
}
|
|
|
|
|
|
.dataframe tbody tr th {
|
|
|
vertical-align: top;
|
|
|
}
|
|
|
</style>
|
|
|
<table border="1" class="dataframe">
|
|
|
<thead>
|
|
|
<tr style="text-align: right;">
|
|
|
<th>value</th>
|
|
|
<th>0.01</th>
|
|
|
<th>0.1</th>
|
|
|
<th>False</th>
|
|
|
<th>True</th>
|
|
|
<th>infected</th>
|
|
|
<th>neutral</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>tstep</th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>0</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>0.0</td>
|
|
|
<td>500.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>3.0</td>
|
|
|
<td>497.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>2</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>6.0</td>
|
|
|
<td>494.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>3</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>12.0</td>
|
|
|
<td>488.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>4</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>23.0</td>
|
|
|
<td>477.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>5</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>36.0</td>
|
|
|
<td>464.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>6</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>53.0</td>
|
|
|
<td>447.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>7</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>79.0</td>
|
|
|
<td>421.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>119.0</td>
|
|
|
<td>381.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>164.0</td>
|
|
|
<td>336.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>204.0</td>
|
|
|
<td>296.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>254.0</td>
|
|
|
<td>246.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>293.0</td>
|
|
|
<td>207.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>13</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>336.0</td>
|
|
|
<td>164.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>14</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>365.0</td>
|
|
|
<td>135.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>15</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>391.0</td>
|
|
|
<td>109.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>16</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>407.0</td>
|
|
|
<td>93.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>17</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>424.0</td>
|
|
|
<td>76.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>18</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>442.0</td>
|
|
|
<td>58.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>19</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>452.0</td>
|
|
|
<td>48.0</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>20</th>
|
|
|
<td>1.0</td>
|
|
|
<td>1.0</td>
|
|
|
<td>163.0</td>
|
|
|
<td>337.0</td>
|
|
|
<td>464.0</td>
|
|
|
<td>36.0</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
If you don't want to work with pandas, you can also use some pre-defined
|
|
|
functions from soil to conveniently plot the results:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
analysis.plot_all('soil_output/Spread_barabasi*', attributes=['id'])
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_44_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_44_1.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_44_2.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_44_3.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_44_4.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
analysis.plot_all('soil_output/Spread_erdos*', attributes=['id'])
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_45_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_45_1.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_45_2.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_45_3.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_45_4.png
|
|
|
|