Plug your GAMA model

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Preliminary remark 🔗

GAMA is a modelling and simulation development environment for building spatially explicit agent-based simulations. OpenMOLE supports GAMA model natively through the GAMATask.
The GAMATask uses the Singularity container system. You should install Singularity on your system otherwise you won't be able to use it.

The GAMATask 🔗

GAMA by example 🔗

You can provide your .gaml file to the GAMATask to run your model and explore it with OpenMOLE. The example below illustrates an exploration of the predator-prey model of the GAMA model library using a direct sampling:

// Declare the variables
val numberOfPreys = Val[Double]
val nbPreysInit = Val[Int]
val mySeed = Val[Long]

// Gama task
// The first argument is the project directory
// The second argument is the relative path of the gaml file in the project directory
// The second argument is the Gama experiment name
// The third argument is the number of steps
val gama =
  GAMATask(project = workDirectory / "predator", gaml = "predatorPrey.gaml", experiment = "prey_predatorExp", finalStep = 100, seed = mySeed) set (
    inputs += (nbPreysInit mapped "nb_preys_init"),
    outputs += (numberOfPreys mapped "Number of preys")
  )

// Explore and replicate the model
DirectSampling(
  evaluation =
    Replication(
      evaluation = gama,
      seed = mySeed,
      sample = 10,
      aggregation = Seq(numberOfPreys aggregate average)) hook(workDirectory / "result.csv"),
  sampling = nbPreysInit in (0 to 200 by 50)
)

Task arguments 🔗

The GAMA task uses the following arguments:
  • project the location of your GAMA project directory, mandatory, for instance project = workDirectory / "gamaproject"
  • gaml the relative path of your .gaml file in your work directory, mandatory, for instance gaml = "model/model.gaml"
  • experiment the name of your experiment as implemented in the .gaml file, mandatory
  • finalStep the last simulation step of you simulation, mandatory
  • frameRate the frame rate to sample you simulation dynamics, in this case the outputs should be arrays, optional
  • seed the OpenMOLE variable used to set the GAMA random number generator seed, optional the seed is randomly drawn if not set
  • containerImage the label of a container image or a container file containing GAMA headless, optional, the default value is "gamaplatform/gama:1.8.1"
  • memory the memory allocated to the gama headless, optional, for example memory = 3000 megabytes
  • install some command to run on the host system to initialise the container, for instance Seq("apt update", "apt install mylib"), optional
  • clearContainerCache to remove existing GAMA container in OpenMOLE cache, download the image from the docker hub, and recreate it locally when set to true, optional