# Soil Tutorial¶

## Introduction¶

This notebook is an introduction to the soil agent-based social network simulation framework. In particular, we will focus on a specific use case: studying the propagation of news in a social network.

The steps we will follow are:

- Modelling the behavior of agents
- Running the simulation using different configurations
- Analysing the results of each simulation

But before that, let's import the soil module and networkx.

In [1]:

    import soil
    import networkx as nx

    %load_ext autoreload
    %autoreload 2

    %pylab inline
    # To display plots in the notebooed_

    Populating the interactive namespace from numpy and matplotlib

## Basic concepts¶

There are three main elements in a soil simulation:

- The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly
- Agents. There are two types: 1) network agents, which are linked to a node in the topology, and 2) environment agents, which are freely assigned to the environment.
- The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).

Soil is based on `simpy`, which is an event-based network simulation library. Soil provides several abstractions over events to make developing agents easier. This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based.

## Modeling behaviour¶

Our first step will be to model how every person in the social network reacts when it comes to news. We will follow a very simple model (a finite state machine).

There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral). A neutral person may heard about the news either on the TV (with probability prob_tv_spread) or through their friends. Once a person has heard the news, they will spread it to their friends (with a probability prob_neighbor_spread). Some users do not have a TV, so they only rely on their friends.

The spreading probabilities will change over time due to different factors. We will represent this variance using an environment agent.

### Network Agents¶

A basic network agent in Soil should inherit from `soil.agents.BaseAgent`, and define its behaviour in every step of the simulation by implementing a `run(self)` method. The most important attributes of the agent are:

- `agent.state`, a dictionary with the state of the agent. `agent.state['id']` reflects the state id of the agent. That state id can be used to look for other networks in that specific state. The state can be access via the agent as well. For instance:

      a = soil.agents.BaseAgent(env=env)
      a['hours_of_sleep'] = 10
      print(a['hours_of_sleep'])

  The state of the agent is stored in every step of the simulation:

      print(a['hours_of_sleep', 10]) # hours of sleep before step #10
      print(a[None, 0]) # whole state of the agent before step #0

- `agent.env`, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:

      a.env.G.nodes() # Get all nodes ids in the topology
        a.env['minimum_hours_of_sleep']

Since our model is a finite state machine, we will be basing it on `soil.agents.FSM`.

With `soil.agents.FSM`, we do not need to specify a `step` method. Instead, we describe every step as a function. To change to another state, a function may return the new state. If no state is returned, the state remains unchanged.[ It will consist of two states, `neutral` (default) and `infected`.

Here's the code:

In [2]:

    import random

    class NewsSpread(soil.agents.FSM):
        @soil.agents.default_state
        @soil.agents.state
        def neutral(self):
            r = random.random()
            if self['has_tv'] and r < self.env['prob_tv_spread']:
                    return self.infected
            return

        @soil.agents.state
        def infected(self):
            prob_infect = self.env['prob_neighbor_spread']
            for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
                r = random.random()
                if r < prob_infect:
                    neighbor.state['id'] = self.infected.id
            return

### Environment agents¶

Environment agents allow us to control the state of the environment. In this case, we will use an environment agent to simulate a very viral event.

When the event happens, the agent will modify the probability of spreading the rumor.

In [3]:

    NEIGHBOR_FACTOR = 0.9
    TV_FACTOR = 0.5
    class NewsEnvironmentAgent(soil.agents.BaseAgent):
        def step(self):
            if self.now == self['event_time']:
                self.env['prob_tv_spread'] = 1
                self.env['prob_neighbor_spread'] = 1
            elif self.now > self['event_time']:
                self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR
                self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR

### Testing the agents¶

Feel free to skip this section if this is your first time with soil.

Testing agents is not easy, and this is not a thorough testing process for agents. Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents.

First of all, let's check if our network agent has the states we would expect:

In [4]:

    NewsSpread.states

Out[4]:

    {'infected': <function __main__.NewsSpread.infected>,
     'neutral': <function __main__.NewsSpread.neutral>}

Now, let's run a simulation on a simple network. It is comprised of three nodes:

In [5]:

    G = nx.Graph()
    G.add_edge(0, 1)
    G.add_edge(0, 2)
    G.add_edge(2, 3)
    G.add_node(4)
    pos = nx.spring_layout(G)
    nx.draw_networkx(G, pos, node_color='red')
    nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')

Let's run a simple simulation that assigns a NewsSpread agent to all the nodes in that network. Notice how node 0 is the only one with a TV.

In [6]:

    env_params = {'prob_tv_spread': 0,
                 'prob_neighbor_spread': 0}

    MAX_TIME = 100
    EVENT_TIME = 10

    sim = soil.Simulation(topology=G,
                                         num_trials=1,
                                         max_time=MAX_TIME,
                                         environment_agents=[{'agent_type': NewsEnvironmentAgent,
                                                             'state': {
                                                                 'event_time': EVENT_TIME
                                                             }}],
                                         network_agents=[{'agent_type': NewsSpread,
                                                          'weight': 1}],
                                         states={0: {'has_tv': True}},
                                         default_state={'has_tv': False},
                                         environment_params=env_params)
    env = sim.run_simulation()[0]

    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 0.02695441246032715 seconds
    INFO:soil.utils:NOT dumping results
    INFO:soil.utils:Finished simulation in 0.03360605239868164 seconds

Now we can access the results of the simulation and compare them to our expected results

In [7]:

    agents = list(env.network_agents)

    # Until the event, all agents are neutral
    for t in range(10):
        for a in agents:
            assert a['id', t] == a.neutral.id

    # After the event, the node with a TV is infected, the rest are not
    assert agents[0]['id', 11] == NewsSpread.infected.id

    for a in agents[1:4]:
        assert a['id', 11] == NewsSpread.neutral.id

    # At the end, the agents connected to the infected one will probably be infected, too.
    assert agents[1]['id', MAX_TIME] == NewsSpread.infected.id
    assert agents[2]['id', MAX_TIME] == NewsSpread.infected.id

    # But the node with no friends should not be affected
    assert agents[4]['id', MAX_TIME] == NewsSpread.neutral.id

Lastly, let's see if the probabilities have decreased as expected:

In [8]:

    assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4
    assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6

## Running the simulation¶

To run a simulation, we need a configuration. Soil can load configurations from python dictionaries as well as JSON and YAML files. For this demo, we will use a python dictionary:

In [9]:

    config = {
        'name': 'ExampleSimulation',
        'max_time': 20,
        'interval': 1,
        'num_trials': 1,
        'network_params': {
           'generator': 'complete_graph',
            'n': 500,
        },
        'network_agents': [
            {
                'agent_type': NewsSpread,
                'weight': 1,
                'state': {
                    'has_tv': False
                }
            },
            {
                'agent_type': NewsSpread,
                'weight': 2,
                'state': {
                    'has_tv': True
                }
            }
        ],
        'environment_agents':[
            {'agent_type': NewsEnvironmentAgent,
             'state': {
                 'event_time': 10
             }
            }
        ],
        'states': [ {'has_tv': True} ],
        'environment_params':{
            'prob_tv_spread': 0.01,
            'prob_neighbor_spread': 0.5
        }
    }

Let's run our simulation:

In [10]:

    soil.simulation.run_from_config(config, dump=False)

    INFO:soil.utils:Using config(s): ExampleSimulation
    INFO:soil.utils:Dumping results to soil_output/ExampleSimulation : False
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 5.869051456451416 seconds
    INFO:soil.utils:NOT dumping results
    INFO:soil.utils:Finished simulation in 6.9609293937683105 seconds

In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research questions.

For instance:

- Does the outcome depend on the structure of our network? We will use different generation algorithms to compare them (Barabasi-Albert and Erdos-Renyi)
- How does neighbor spreading probability affect my simulation? We will try probability values in the range of [0, 0.4], in intervals of 0.1.

In [11]:

    network_1 = {
           'generator': 'erdos_renyi_graph',
            'n': 500,
            'p': 0.1
    }
    network_2 = {
           'generator': 'barabasi_albert_graph',
            'n': 500,
            'm': 2
    }

    for net in [network_1, network_2]:
        for i in range(5):
            prob = i / 10
            config['environment_params']['prob_neighbor_spread'] = prob
            config['network_params'] = net
            config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)
            s = soil.simulation.run_from_config(config)

    INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.0
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 1.2258412837982178 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.0
    INFO:soil.utils:Finished simulation in 5.597268104553223 seconds
    INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.1
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 1.3026399612426758 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.1
    INFO:soil.utils:Finished simulation in 5.534018278121948 seconds
    INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.2
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 1.4764575958251953 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.2
    INFO:soil.utils:Finished simulation in 6.170421123504639 seconds
    INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.3
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 1.5429913997650146 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.3
    INFO:soil.utils:Finished simulation in 5.936013221740723 seconds
    INFO:soil.utils:Using config(s): Spread_erdos_renyi_graph_prob_0.4
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 1.4097135066986084 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_erdos_renyi_graph_prob_0.4
    INFO:soil.utils:Finished simulation in 5.732810974121094 seconds
    INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.0
    INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 0.751497745513916 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.0
    INFO:soil.utils:Finished simulation in 2.3415369987487793 seconds
    INFO:soil.utils:Using config(s): Spread_barabasi_albert_graph_prob_0.1
    INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1 : True
    INFO:soil.utils:Trial: 0
    INFO:soil.utils:	Running
    INFO:soil.utils:Finished trial in 0.8503265380859375 seconds
    INFO:soil.utils:Dumping results to soil_output/Spread_barabasi_albert_graph_prob_0.1
    INFO:soil.utils:Finished simulation in 2.5671920776367188 seconds
    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

The results are conveniently stored in pickle (simulation), csv and sqlite (history of agent and environment state) and gexf (dynamic network) format.

In [12]:

    !tree soil_output
    !du -xh soil_output/*

    soil_output
    ├── Spread_barabasi_albert_graph_prob_0.0
    │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml
    │   ├── 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
    ├── Spread_barabasi_albert_graph_prob_0.1
    │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml
    │   ├── 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
    ├── Spread_barabasi_albert_graph_prob_0.2
    │   ├── 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
    │   ├── 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
    ├── Spread_barabasi_albert_graph_prob_0.3
    │   ├── 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
    ├── Spread_barabasi_albert_graph_prob_0.4
    │   ├── 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
    ├── Spread_erdos_renyi_graph_prob_0.0
    │   ├── 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
    ├── Spread_erdos_renyi_graph_prob_0.1
    │   ├── 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
    ├── Spread_erdos_renyi_graph_prob_0.2
    │   ├── 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
    ├── Spread_erdos_renyi_graph_prob_0.3
    │   ├── 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
    └── Spread_erdos_renyi_graph_prob_0.4
        ├── 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:

In [13]:

    from soil.analysis import *

In [14]:

    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

Out[14]:

| agent_id | t_step | key | value | value_type
--- | --- | --- | --- | --- | ---
5 | 0 | 0 | id | neutral | str
7 | 1 | 0 | id | neutral | str
9 | 2 | 0 | id | neutral | str
11 | 3 | 0 | id | neutral | str
13 | 4 | 0 | id | neutral | str
15 | 5 | 0 | id | neutral | str
17 | 6 | 0 | id | neutral | str
19 | 7 | 0 | id | neutral | str
21 | 8 | 0 | id | neutral | str
23 | 9 | 0 | id | neutral | str
25 | 10 | 0 | id | neutral | str
27 | 11 | 0 | id | neutral | str
29 | 12 | 0 | id | neutral | str
31 | 13 | 0 | id | neutral | str
33 | 14 | 0 | id | neutral | str
35 | 15 | 0 | id | neutral | str
37 | 16 | 0 | id | neutral | str
39 | 17 | 0 | id | neutral | str
41 | 18 | 0 | id | neutral | str
43 | 19 | 0 | id | neutral | str
45 | 20 | 0 | id | neutral | str
47 | 21 | 0 | id | neutral | str
49 | 22 | 0 | id | neutral | str
51 | 23 | 0 | id | neutral | str
53 | 24 | 0 | id | neutral | str
55 | 25 | 0 | id | neutral | str
57 | 26 | 0 | id | neutral | str
59 | 27 | 0 | id | neutral | str
61 | 28 | 0 | id | neutral | str
63 | 29 | 0 | id | neutral | str
... | ... | ... | ... | ... | ...
21025 | 470 | 20 | id | infected | str
21027 | 471 | 20 | id | infected | str
21029 | 472 | 20 | id | infected | str
21031 | 473 | 20 | id | infected | str
21033 | 474 | 20 | id | infected | str
21035 | 475 | 20 | id | infected | str
21037 | 476 | 20 | id | infected | str
21039 | 477 | 20 | id | infected | str
21041 | 478 | 20 | id | infected | str
21043 | 479 | 20 | id | infected | str
21045 | 480 | 20 | id | infected | str
21047 | 481 | 20 | id | infected | str
21049 | 482 | 20 | id | infected | str
21051 | 483 | 20 | id | infected | str
21053 | 484 | 20 | id | infected | str
21055 | 485 | 20 | id | infected | str
21057 | 486 | 20 | id | infected | str
21059 | 487 | 20 | id | infected | str
21061 | 488 | 20 | id | infected | str
21063 | 489 | 20 | id | infected | str
21065 | 490 | 20 | id | infected | str
21067 | 491 | 20 | id | infected | str
21069 | 492 | 20 | id | infected | str
21071 | 493 | 20 | id | infected | str
21073 | 494 | 20 | id | infected | str
21075 | 495 | 20 | id | infected | str
21077 | 496 | 20 | id | infected | str
21079 | 497 | 20 | id | infected | str
21081 | 498 | 20 | id | infected | str
21083 | 499 | 20 | id | infected | str

10500 rows × 5 columns

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:

In [15]:

    env, agents = process(df)
    agents

Out[15]:

|  | id
--- | --- | ---
t_step | agent_id |
0 | 0 | neutral
1 | neutral
10 | neutral
100 | neutral
101 | neutral
102 | neutral
103 | neutral
104 | neutral
105 | neutral
106 | neutral
107 | neutral
108 | neutral
109 | neutral
11 | neutral
110 | neutral
111 | neutral
112 | neutral
113 | neutral
114 | neutral
115 | neutral
116 | neutral
117 | neutral
118 | neutral
119 | neutral
12 | neutral
120 | neutral
121 | neutral
122 | neutral
123 | neutral
124 | neutral
... | ... | ...
20 | 72 | infected
73 | infected
74 | infected
75 | infected
76 | infected
77 | infected
78 | infected
79 | infected
8 | infected
80 | infected
81 | infected
82 | infected
83 | infected
84 | infected
85 | infected
86 | infected
87 | infected
88 | infected
89 | infected
9 | infected
90 | infected
91 | infected
92 | infected
93 | infected
94 | infected
95 | infected
96 | infected
97 | infected
98 | infected
99 | infected

10500 rows × 1 columns

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:

In [16]:

    agents.loc[0]

Out[16]:

| id
--- | ---
agent_id |
0 | neutral
1 | neutral
10 | neutral
100 | neutral
101 | neutral
102 | neutral
103 | neutral
104 | neutral
105 | neutral
106 | neutral
107 | neutral
108 | neutral
109 | neutral
11 | neutral
110 | neutral
111 | neutral
112 | neutral
113 | neutral
114 | neutral
115 | neutral
116 | neutral
117 | neutral
118 | neutral
119 | neutral
12 | neutral
120 | neutral
121 | neutral
122 | neutral
123 | neutral
124 | neutral
... | ...
72 | neutral
73 | neutral
74 | neutral
75 | neutral
76 | neutral
77 | neutral
78 | neutral
79 | neutral
8 | neutral
80 | neutral
81 | neutral
82 | neutral
83 | neutral
84 | neutral
85 | neutral
86 | neutral
87 | neutral
88 | neutral
89 | neutral
9 | neutral
90 | neutral
91 | neutral
92 | neutral
93 | neutral
94 | neutral
95 | neutral
96 | neutral
97 | neutral
98 | neutral
99 | neutral

500 rows × 1 columns

Or, we can perform more complex tasks such as showing the agents that have changed their state between two simulation steps:

In [17]:

    changed = agents.loc[1]['id'] != agents.loc[0]['id']
    agents.loc[0][changed]

Out[17]:

| id
--- | ---
agent_id |
140 | neutral
164 | neutral
170 | neutral
310 | neutral
455 | neutral

To focus on specific agents, we can swap the levels of the index:

In [18]:

    agents1 = agents.swaplevel()

In [19]:

    agents1.loc['0'].dropna(axis=1)

Out[19]:

| id
--- | ---
t_step |
0 | neutral
1 | neutral
2 | neutral
3 | neutral
4 | neutral
5 | neutral
6 | neutral
7 | neutral
8 | neutral
9 | neutral
10 | neutral
11 | infected
12 | infected
13 | infected
14 | infected
15 | infected
16 | infected
17 | infected
18 | infected
19 | infected
20 | infected

### 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:

In [20]:

    plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', get_count, 'id');

In [21]:

    plot_all('soil_output/Spread_barabasi*', get_count, 'id');

In [22]:

    plot_all('soil_output/Spread_erdos*', get_value, 'prob_tv_spread');

### 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.

In [23]:

    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

In [24]:

    res = agents.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)
    res.plot();

As we can see, `event_time` is cluttering our results,

In [25]:

    del res['event_time']
    res.plot()

Out[25]:

    <matplotlib.axes._subplots.AxesSubplot at 0x7fd795b17b38>

In [28]:

    processed = process_one(agents);
    processed

Out[28]:

|  | event_time | has_tv | id
--- | --- | --- | --- | ---
t_step | agent_id |  |  |
0 | 0 | 0 | True | neutral
1 | 0 | False | neutral
10 | 0 | True | neutral
100 | 0 | True | neutral
101 | 0 | True | neutral
102 | 0 | False | neutral
103 | 0 | True | neutral
104 | 0 | True | neutral
105 | 0 | False | neutral
106 | 0 | False | neutral
107 | 0 | True | neutral
108 | 0 | True | neutral
109 | 0 | False | neutral
11 | 0 | True | neutral
110 | 0 | False | neutral
111 | 0 | False | neutral
112 | 0 | True | neutral
113 | 0 | True | neutral
114 | 0 | True | neutral
115 | 0 | True | neutral
116 | 0 | False | neutral
117 | 0 | True | neutral
118 | 0 | True | neutral
119 | 0 | False | neutral
12 | 0 | False | neutral
120 | 0 | False | neutral
121 | 0 | True | neutral
122 | 0 | True | neutral
123 | 0 | True | neutral
124 | 0 | False | neutral
... | ... | ... | ... | ...
20 | 73 | 0 | True | infected
74 | 0 | True | infected
75 | 0 | True | infected
76 | 0 | True | infected
77 | 0 | True | infected
78 | 0 | True | infected
79 | 0 | False | infected
8 | 0 | False | infected
80 | 0 | True | infected
81 | 0 | False | infected
82 | 0 | False | infected
83 | 0 | True | infected
84 | 0 | False | infected
85 | 0 | True | infected
86 | 0 | True | infected
87 | 0 | True | infected
88 | 0 | False | infected
89 | 0 | False | infected
9 | 0 | True | infected
90 | 0 | True | infected
91 | 0 | True | infected
92 | 0 | True | infected
93 | 0 | False | infected
94 | 0 | True | infected
95 | 0 | True | infected
96 | 0 | True | infected
97 | 0 | True | infected
98 | 0 | False | infected
99 | 0 | True | infected
NewsEnvironmentAgent | 10 | False | 0

10521 rows × 3 columns

Which is equivalent to:

In [30]:

    get_count(agents, 'id', 'has_tv').plot()

Out[30]:

    <matplotlib.axes._subplots.AxesSubplot at 0x7fd799c15748>

In [31]:

    get_value(agents, 'event_time').plot()

Out[31]:

    <matplotlib.axes._subplots.AxesSubplot at 0x7fd79a228c88>

## Dealing with bigger data¶

In [32]:

    from soil import analysis

In [33]:

    !du -xsh ../rabbits/soil_output/rabbits_example/

    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.

In [34]:

    p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'id')

In [39]:

    df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.db.sqlite', keys=['id', 'rabbits_alive'])

In [40]:

    states = analysis.get_count(df, 'id')
    states.plot()

Out[40]:

    <matplotlib.axes._subplots.AxesSubplot at 0x7fd799b5b2b0>

In [41]:

    alive = analysis.get_value(df, 'rabbits_alive', 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)
    alive.plot()

Out[41]:

    <matplotlib.axes._subplots.AxesSubplot at 0x7fd796161cf8>

In [44]:

    h = alive.join(states);
    h.plot();

    /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)

In [ ]:

    states[[('id','newborn'),('id','fertile'),('id', 'pregnant')]].sum(axis=1).sub(alive['rabbits_alive'], fill_value=0)