Adapt GAMA example to the new GAMATask

gama/PredatorPrey.oms

 
 // Declare the variable
-val numberOfPreys = Val[Int]
-val numberOfPreysAvg = Val[Double]
+val numberOfPreys = Val[Double]
 val nbPreysInit = Val[Int]
-val seed = Val[Int]
+val seed = Val[Long]
 
 // Gama task
 // The first argument is the gaml
 // The second argument of the GamaTask is the gama experiment name
 // The third argument is the number of steps
 val gama = 
-  GamaTask(workDirectory / "predatorPrey.gaml", "prey_predatorExp", maxStep = 10) set (
-    gamaInputs += (nbPreysInit, "nb_preys_init"),
-    gamaSeed := seed,
-    gamaOutputs += ("nb_preys", numberOfPreys)
+  GAMATask(workDirectory / "predatorPrey.gaml", "prey_predatorExp", finalStep = 100, seed = seed) set (
+    inputs += (nbPreysInit mapped "nb_preys_init"),
+    outputs += (numberOfPreys mapped "Number of preys")
   )
 
-// Compute the average among the simulations
-val average = 
-  ScalaTask("val numberOfPreysAvg = numberOfPreys.map(_.toDouble).average") set (
-    inputs += numberOfPreys.toArray,
-    outputs += numberOfPreysAvg,
-    (inputs, outputs) += nbPreysInit
-  )
-
-//Store the results
-val csvHook = CSVHook(workDirectory / "result.csv", nbPreysInit, numberOfPreysAvg)
-
 // Explore and replicate the model
 DirectSampling(
-  evaluation = Replication(gama, seed, 10, aggregation = average hook csvHook),
-  sampling = nbPreysInit in (0 to 200 by 10)
-)
-
+  evaluation =
+    Replication(
+      evaluation = gama, 
+      seed = seed, 
+      replications = 10, 
+      aggregation = Seq(numberOfPreys aggregate average)) hook(workDirectory / "result.csv"),
+  sampling = nbPreysInit in (0 to 200 by 50)
+)