Commit f3c60521 authored by Hélène Arduin's avatar Hélène Arduin
Browse files

[Doc] update Tutorials pages (after css modif)

parent 7fb5aae9
......@@ -50,6 +50,7 @@ Not all examples are listed here, for a full list look at the GitHub @aa("reposi
@img(src := Resource.img.guiGuide.market.file, width := "95%")
@h2{Contribute your example/tutorial!}
You can contribute to the market place either by making a pull request on the @aa("central OpenMOLE market place", href := shared.link.repo.market), or by pushing your example workflow on your own git repository and post it to the @aa("user mailing list", href:=shared.link.forum).
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......@@ -9,7 +9,9 @@
OpenMOLE is a model exploration tool.
It offers several methods to perform experiments on a model, in order to better understand its behavior and capabilities.
@br
The composition of a full exploration experiment is achieved by writing a script in the @a("OpenMOLE language", href := language.file), which is an extension of the Scala language.
Such a script is called a @i{workflow} and should contain:
@ul
......@@ -119,12 +121,11 @@ Once the model execution is over, and since we asked in the workflow to @code{di
@h2{To go further}
In the @a("next tutorial", href := exploreTuto.file), you will learn how to prepare and run a more complex experiment, in order to explore your model with OpenMOLE.
@h2{Get help}
@h3{Get help}
To get help you are more than welcomed to contact the OpenMOLE community on our @aa("forum", href := shared.link.forum) and post your questions.
You can also reach us via our @aa("RocketChat", href := shared.link.chat).
@h3{Next tutorial}
In the @a("next tutorial", href := exploreTuto.file), you will learn how to prepare and run a more complex experiment, in order to explore your model with OpenMOLE.
\ No newline at end of file
......@@ -58,8 +58,6 @@ A visual representation of this model looks like:
@img(src := Resource.img.example.ants.file)
@br
In this tutorial we use a headless version (@aa("see NetLogo task documentation", href := DocumentationPages.netLogo.file)) of the model.
This modified version is available @aa("here", href := Resource.script.antsNLogo.file).
......@@ -80,8 +78,6 @@ Each source is positioned at different distances from the Ant colony.
@img(src := Resource.img.example.antNumbers.file)
@br
It can be interesting to search for the @b{best combination of the two parameters} @code{evaporation-rate} and @code{diffusion-rate} which minimises the eating time of each food source.
To build our fitness function, we modify the NetLogo Ants source code to store, for each food source, the first ticks indicating that this food source is empty.
......@@ -119,7 +115,7 @@ $model
// Define the hooks to collect the results
val displayHook = DisplayHook(food1, food2, food3)
//Definie the environment
//Define the environment
val env = LocalEnvironment(5)
// Start a workflow with 1 task
......@@ -150,7 +146,7 @@ Notice how the @code{evaluation} parameter of the @code{SteadyStateEvolution} me
// Define the fitness evaluation
// Define the parallelism level
// Terminate after 10000 evaluations
// Define a hook to save the Pareto frontier
// Define a hook to save the Pareto front
NSGA2Evolution(
// Define the inputs and their respective variation bounds.
// Define the objectives to minimize.
......@@ -167,7 +163,7 @@ NSGA2Evolution(
@h2{Scale up}
If you use distributed computing, it might be a good idea to opt for an Island model.
If you use distributed computing, it might be a good idea to opt for an island model (see @aa("this page", href := island.file)) for more details on the island distribution scheme.
Islands are better suited to exploit distributed computing resources than classical generational genetic algorithms.
See how the end of the script changes to implement islands in the workflow.
Here we compute 2,000 islands in parallel, each running during 10 minutes on the European grid:
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......@@ -16,8 +16,6 @@ val burned = Val[Double]
Typical sensitivity analysis (in a simulation experiment context) is the study of how the variations of an input affects the output(s) of a model.
@br
@img(src := Resource.img.method.sensitivityAnim.file, width := "80%")
@basicButton("Run", classIs(btn, btn_danger))(id := shared.sensitivity.button, stylesheet.svgRunButton(10))
......@@ -52,9 +50,7 @@ We will perform sensitivity analysis to make this change of regime appear.
@br
The Fire model integration has been covered in the @aa("NetLogo page of the Model section", href:= DocumentationPages.netLogo.file), so we take it from here.
The former script was already performing a sensitivity analysis, by varying @code{density} from 20 to 80 by step of 10, with 10 replications for each (@code{mySeed} has 10 different values for each value of @code{density}).
@br
In our case, the quantum of 10 percent is quite coarsed, so we make it 1 percent:
@hl.openmole("""
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