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Soil Tutorial
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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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%load_ext autoreload
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%autoreload 2
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%pylab inline
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# To display plots in the notebooed_
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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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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 0.02695441246032715 seconds
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INFO:soil.utils:NOT dumping results
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INFO:soil.utils:Finished simulation in 0.03360605239868164 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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'environment_agents':[
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{'agent_type': NewsEnvironmentAgent,
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'state': {
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'event_time': 10
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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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INFO:soil.utils:Using config(s): ExampleSimulation
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INFO:soil.utils:Dumping results to soil_output/ExampleSimulation : False
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 5.869051456451416 seconds
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INFO:soil.utils:NOT dumping results
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INFO:soil.utils:Finished simulation in 6.9609293937683105 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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INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.0
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 1.2258412837982178 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0
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INFO:soil.utils:Finished simulation in 5.597268104553223 seconds
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INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.1
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 1.3026399612426758 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1
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INFO:soil.utils:Finished simulation in 5.534018278121948 seconds
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INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.2
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 1.4764575958251953 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2
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INFO:soil.utils:Finished simulation in 6.170421123504639 seconds
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INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.3
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 1.5429913997650146 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3
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INFO:soil.utils:Finished simulation in 5.936013221740723 seconds
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INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.4
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 1.4097135066986084 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4
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INFO:soil.utils:Finished simulation in 5.732810974121094 seconds
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INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.0
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INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 0.751497745513916 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0
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INFO:soil.utils:Finished simulation in 2.3415369987487793 seconds
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INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.1
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INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1 : True
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INFO:soil.utils:Trial: 0
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INFO:soil.utils: Running
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INFO:soil.utils:Finished trial in 0.8503265380859375 seconds
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INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1
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INFO:soil.utils:Finished simulation in 2.5671920776367188 seconds
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INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.2
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2 : True
|
|
|
INFO:soil.utils:Trial: 0
|
|
|
INFO:soil.utils: Running
|
|
|
INFO:soil.utils:Finished trial in 0.8511502742767334 seconds
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.2
|
|
|
INFO:soil.utils:Finished simulation in 2.55816912651062 seconds
|
|
|
INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.3
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3 : True
|
|
|
INFO:soil.utils:Trial: 0
|
|
|
INFO:soil.utils: Running
|
|
|
INFO:soil.utils:Finished trial in 0.8982968330383301 seconds
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.3
|
|
|
INFO:soil.utils:Finished simulation in 2.6871559619903564 seconds
|
|
|
INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.4
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4 : True
|
|
|
INFO:soil.utils:Trial: 0
|
|
|
INFO:soil.utils: Running
|
|
|
INFO:soil.utils:Finished trial in 0.9563727378845215 seconds
|
|
|
INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.4
|
|
|
INFO:soil.utils:Finished simulation in 2.5253307819366455 seconds
|
|
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|
|
|
|
|
|
The results are conveniently stored in pickle (simulation), csv and
|
|
|
sqlite (history of agent and environment state) and gexf (dynamic
|
|
|
network) format.
|
|
|
|
|
|
.. code:: ipython3
|
|
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|
|
!tree soil_output
|
|
|
!du -xh soil_output/*
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|
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|
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|
|
.. parsed-literal::
|
|
|
|
|
|
[01;34msoil_output[00m
|
|
|
├── [01;34mSpread_barabasi_albert_graph_prob_0.0[00m
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml
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|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0.simulation.pickle
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508409808.7944386.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.backup1508428617.9811945.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.environment.csv
|
|
|
│ └── Spread_barabasi_albert_graph_prob_0.0_trial_0.gexf
|
|
|
├── [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
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508409810.9913027.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.backup1508428620.3419535.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.db.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.environment.csv
|
|
|
│ └── Spread_barabasi_albert_graph_prob_0.1_trial_0.gexf
|
|
|
├── [01;34mSpread_barabasi_albert_graph_prob_0.2[00m
|
|
|
│ ├── 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.backup1508409813.2012305.sqlite
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|
|
│ ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.backup1508428622.91827.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409815.5177016.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.backup1508428625.5117545.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409818.1516452.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.backup1508428628.1986933.sqlite
|
|
|
│ ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409781.0791047.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.backup1508428588.625598.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409786.6177793.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.backup1508428594.3783743.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409791.9751768.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.backup1508428600.041021.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409797.606661.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.backup1508428606.2884977.sqlite
|
|
|
│ ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.db.sqlite
|
|
|
│ ├── 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.backup1508409803.4306188.sqlite
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4_trial_0.backup1508428612.3312593.sqlite
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4_trial_0.db.sqlite
|
|
|
├── Spread_erdos_renyi_graph_prob_0.4_trial_0.environment.csv
|
|
|
└── Spread_erdos_renyi_graph_prob_0.4_trial_0.gexf
|
|
|
|
|
|
10 directories, 70 files
|
|
|
2.5M soil_output/Spread_barabasi_albert_graph_prob_0.0
|
|
|
2.5M soil_output/Spread_barabasi_albert_graph_prob_0.1
|
|
|
2.5M soil_output/Spread_barabasi_albert_graph_prob_0.2
|
|
|
2.5M soil_output/Spread_barabasi_albert_graph_prob_0.3
|
|
|
2.5M soil_output/Spread_barabasi_albert_graph_prob_0.4
|
|
|
3.6M soil_output/Spread_erdos_renyi_graph_prob_0.0
|
|
|
3.7M soil_output/Spread_erdos_renyi_graph_prob_0.1
|
|
|
3.7M soil_output/Spread_erdos_renyi_graph_prob_0.2
|
|
|
3.7M soil_output/Spread_erdos_renyi_graph_prob_0.3
|
|
|
3.7M soil_output/Spread_erdos_renyi_graph_prob_0.4
|
|
|
|
|
|
|
|
|
Analysing the results
|
|
|
---------------------
|
|
|
|
|
|
Loading data
|
|
|
~~~~~~~~~~~~
|
|
|
|
|
|
Once the simulations are over, we can use soil to analyse the results.
|
|
|
|
|
|
Soil allows you to load results for specific trials, or for a set of
|
|
|
trials if you specify a pattern. The specific methods are:
|
|
|
|
|
|
- ``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.
|
|
|
- ``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')``
|
|
|
- ``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')``
|
|
|
|
|
|
Let's see it in action by loading the stored results into a pandas
|
|
|
dataframe:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
from soil.analysis import *
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
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'])
|
|
|
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>t_step</th>
|
|
|
<th>key</th>
|
|
|
<th>value</th>
|
|
|
<th>value_type</th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>5</th>
|
|
|
<td>0</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>7</th>
|
|
|
<td>1</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>2</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>3</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>13</th>
|
|
|
<td>4</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>15</th>
|
|
|
<td>5</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>17</th>
|
|
|
<td>6</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>19</th>
|
|
|
<td>7</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21</th>
|
|
|
<td>8</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>23</th>
|
|
|
<td>9</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>25</th>
|
|
|
<td>10</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>27</th>
|
|
|
<td>11</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>29</th>
|
|
|
<td>12</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>31</th>
|
|
|
<td>13</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>33</th>
|
|
|
<td>14</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>35</th>
|
|
|
<td>15</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>37</th>
|
|
|
<td>16</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>39</th>
|
|
|
<td>17</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>41</th>
|
|
|
<td>18</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>43</th>
|
|
|
<td>19</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>45</th>
|
|
|
<td>20</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>47</th>
|
|
|
<td>21</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>49</th>
|
|
|
<td>22</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>51</th>
|
|
|
<td>23</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>53</th>
|
|
|
<td>24</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>55</th>
|
|
|
<td>25</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>57</th>
|
|
|
<td>26</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>59</th>
|
|
|
<td>27</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>61</th>
|
|
|
<td>28</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>63</th>
|
|
|
<td>29</td>
|
|
|
<td>0</td>
|
|
|
<td>id</td>
|
|
|
<td>neutral</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>...</th>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21025</th>
|
|
|
<td>470</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21027</th>
|
|
|
<td>471</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21029</th>
|
|
|
<td>472</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21031</th>
|
|
|
<td>473</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21033</th>
|
|
|
<td>474</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21035</th>
|
|
|
<td>475</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21037</th>
|
|
|
<td>476</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21039</th>
|
|
|
<td>477</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21041</th>
|
|
|
<td>478</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21043</th>
|
|
|
<td>479</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21045</th>
|
|
|
<td>480</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21047</th>
|
|
|
<td>481</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21049</th>
|
|
|
<td>482</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21051</th>
|
|
|
<td>483</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21053</th>
|
|
|
<td>484</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21055</th>
|
|
|
<td>485</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21057</th>
|
|
|
<td>486</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21059</th>
|
|
|
<td>487</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21061</th>
|
|
|
<td>488</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21063</th>
|
|
|
<td>489</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21065</th>
|
|
|
<td>490</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21067</th>
|
|
|
<td>491</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21069</th>
|
|
|
<td>492</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21071</th>
|
|
|
<td>493</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21073</th>
|
|
|
<td>494</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21075</th>
|
|
|
<td>495</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21077</th>
|
|
|
<td>496</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21079</th>
|
|
|
<td>497</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21081</th>
|
|
|
<td>498</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>21083</th>
|
|
|
<td>499</td>
|
|
|
<td>20</td>
|
|
|
<td>id</td>
|
|
|
<td>infected</td>
|
|
|
<td>str</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
<p>10500 rows × 5 columns</p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
env, agents = process(df)
|
|
|
agents
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. 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></th>
|
|
|
<th>id</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>t_step</th>
|
|
|
<th>agent_id</th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th rowspan="30" valign="top">0</th>
|
|
|
<th>0</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>100</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>101</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>102</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>103</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>104</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>105</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>106</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>107</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>108</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>109</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>110</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>111</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>112</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>113</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>114</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>115</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>116</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>117</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>118</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>119</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>120</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>121</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>122</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>123</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>124</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>...</th>
|
|
|
<th>...</th>
|
|
|
<td>...</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th rowspan="30" valign="top">20</th>
|
|
|
<th>72</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>73</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>74</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>75</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>76</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>77</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>78</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>79</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>80</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>81</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>82</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>83</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>84</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>85</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>86</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>87</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>88</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>89</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>90</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>91</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>92</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>93</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>94</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>95</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>96</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>97</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>98</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>99</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
<p>10500 rows × 1 columns</p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
agents.loc[0]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. 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>id</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>agent_id</th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>0</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>100</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>101</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>102</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>103</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>104</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>105</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>106</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>107</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>108</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>109</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>110</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>111</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>112</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>113</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>114</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>115</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>116</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>117</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>118</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>119</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>120</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>121</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>122</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>123</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>124</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>...</th>
|
|
|
<td>...</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>72</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>73</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>74</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>75</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>76</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>77</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>78</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>79</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>80</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>81</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>82</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>83</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>84</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>85</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>86</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>87</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>88</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>89</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>90</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>91</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>92</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>93</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>94</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>95</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>96</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>97</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>98</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>99</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
<p>500 rows × 1 columns</p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
Or, we can perform more complex tasks such as showing the agents that
|
|
|
have changed their state between two simulation steps:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
changed = agents.loc[1]['id'] != agents.loc[0]['id']
|
|
|
agents.loc[0][changed]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. 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>id</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>agent_id</th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>140</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>164</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>170</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>310</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>455</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
To focus on specific agents, we can swap the levels of the index:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
agents1 = agents.swaplevel()
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
agents1.loc['0'].dropna(axis=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></th>
|
|
|
<th>id</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>t_step</th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>0</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>2</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>3</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>4</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>5</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>6</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>7</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>13</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>14</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>15</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>16</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>17</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>18</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>19</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>20</th>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
Plotting data
|
|
|
~~~~~~~~~~~~~
|
|
|
|
|
|
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
|
|
|
|
|
|
plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', get_count, 'id');
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_54_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_54_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
plot_all('soil_output/Spread_barabasi*', get_count, 'id');
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_1.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_2.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_3.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_4.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_5.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_6.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_7.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_8.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_55_9.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
plot_all('soil_output/Spread_erdos*', get_value, 'prob_tv_spread');
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_1.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_2.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_3.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_4.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_5.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_6.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_7.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_8.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_56_9.png
|
|
|
|
|
|
|
|
|
Manually plotting with pandas
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
|
|
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.
|
|
|
|
|
|
For that, we can use native pandas over the results.
|
|
|
|
|
|
Soil provides some convenience methods to simplify common operations:
|
|
|
|
|
|
- ``analysis.split_df`` to separate a history dataframe into
|
|
|
environment and agent parameters.
|
|
|
- ``analysis.get_count`` to get a dataframe with the value counts for
|
|
|
different attributes during the simulation.
|
|
|
- ``analysis.get_value`` to get the evolution of the value of an
|
|
|
attribute during the simulation.
|
|
|
|
|
|
And, as we saw earlier, ``analysis.process`` can turn a dataframe in
|
|
|
canonical form into a dataframe with a column per attribute.
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
p = read_sql('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.db.sqlite')
|
|
|
env, agents = split_df(p);
|
|
|
|
|
|
Let's look at the evolution of agent parameters in the simulation
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
res = agents.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)
|
|
|
res.plot();
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_61_0.png
|
|
|
|
|
|
|
|
|
As we can see, ``event_time`` is cluttering our results,
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
del res['event_time']
|
|
|
res.plot()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
<matplotlib.axes._subplots.AxesSubplot at 0x7fd795b17b38>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_63_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
processed = process_one(agents);
|
|
|
processed
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. 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></th>
|
|
|
<th>event_time</th>
|
|
|
<th>has_tv</th>
|
|
|
<th>id</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>t_step</th>
|
|
|
<th>agent_id</th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
<th></th>
|
|
|
</tr>
|
|
|
</thead>
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th rowspan="30" valign="top">0</th>
|
|
|
<th>0</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>1</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>10</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>100</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>101</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>102</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>103</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>104</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>105</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>106</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>107</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>108</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>109</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>11</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>110</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>111</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>112</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>113</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>114</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>115</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>116</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>117</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>118</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>119</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>12</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>120</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>121</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>122</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>123</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>124</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>neutral</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>...</th>
|
|
|
<th>...</th>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
<td>...</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th rowspan="30" valign="top">20</th>
|
|
|
<th>73</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>74</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>75</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>76</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>77</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>78</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>79</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>8</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>80</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>81</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>82</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>83</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>84</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>85</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>86</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>87</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>88</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>89</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>9</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>90</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>91</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>92</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>93</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>94</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>95</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>96</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>97</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>98</th>
|
|
|
<td>0</td>
|
|
|
<td>False</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>99</th>
|
|
|
<td>0</td>
|
|
|
<td>True</td>
|
|
|
<td>infected</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th>NewsEnvironmentAgent</th>
|
|
|
<td>10</td>
|
|
|
<td>False</td>
|
|
|
<td>0</td>
|
|
|
</tr>
|
|
|
</tbody>
|
|
|
</table>
|
|
|
<p>10521 rows × 3 columns</p>
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
|
|
Which is equivalent to:
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
get_count(agents, 'id', 'has_tv').plot()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
<matplotlib.axes._subplots.AxesSubplot at 0x7fd799c15748>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_66_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
get_value(agents, 'event_time').plot()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
<matplotlib.axes._subplots.AxesSubplot at 0x7fd79a228c88>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_67_1.png
|
|
|
|
|
|
|
|
|
Dealing with bigger data
|
|
|
------------------------
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
from soil import analysis
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
!du -xsh ../rabbits/soil_output/rabbits_example/
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
267M ../rabbits/soil_output/rabbits_example/
|
|
|
|
|
|
|
|
|
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.
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'id')
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_72_0.png
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_72_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.db.sqlite', keys=['id', 'rabbits_alive'])
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
states = analysis.get_count(df, 'id')
|
|
|
states.plot()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
<matplotlib.axes._subplots.AxesSubplot at 0x7fd799b5b2b0>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_74_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
alive = analysis.get_value(df, 'rabbits_alive', 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)
|
|
|
alive.plot()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
<matplotlib.axes._subplots.AxesSubplot at 0x7fd796161cf8>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_75_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
h = alive.join(states);
|
|
|
h.plot();
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
|
|
/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)
|
|
|
warnings.warn(msg, UserWarning)
|
|
|
|
|
|
|
|
|
|
|
|
.. image:: output_76_1.png
|
|
|
|
|
|
|
|
|
.. code:: ipython3
|
|
|
|
|
|
states[[('id','newborn'),('id','fertile'),('id', 'pregnant')]].sum(axis=1).sub(alive['rabbits_alive'], fill_value=0)
|