This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``interval``).
Now run the simulation with the command line tool:
..code:: bash
soil example.yml
Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
..code::
soil_output
└── MyExampleSimulation
├── MyExampleSimulation.dumped.yml
├── MyExampleSimulation.simulation.pickle
├── MyExampleSimulation_trial_0.db.sqlite
├── MyExampleSimulation_trial_1.db.sqlite
└── MyExampleSimulation_trial_2.db.sqlite
You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
Network
=======
The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
Loading a network
#################
To load an existing network, specify its path in the configuration:
..code:: yaml
---
network_params:
path: /tmp/mynetwork.gexf
Soil will try to guess what networkx method to use to read the file based on its extension.
However, we only test using ``gexf`` files.
For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
..code:: yaml
---
topology:
nodes:
- id: First
- id: Second
links:
- source: First
target: Second
Generating a random network
###########################
To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
That means both global parameters, such as the probability of disease outbreak.
But it also means other data, such as a map, or a network topology that connects multiple agents.
As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
Last but not least, an environment controls when and how its agents will be executed.
By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
A configuration may specify the initial value of the environment parameters:
In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
When and how agent steps are executed in a simulation depends entirely on the ``environment``.
Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a
When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
For instance, you may want to run a simulation with different agent distributions.
This can be done in Soil using **templates**.
A template is a configuration where some of the values are specified with a variable.
e.g., ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
There are two types of variables, depending on how their values are decided:
* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
Here is an example with a single fixed variable and two bounded variable:
