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ThreeD_step2

RoiArthurB edited this page Sep 11, 2023 · 9 revisions

2. Moving Cells

This second step model adds the moving3D skill to the cell agents and simply makes the cell agents move by defining a reflex that will call the action move. We will also add additional visual information to the display.

Formulation

  • Redefining the shape of the world with a 3D Shape.
  • Attaching new skills (moving3D) to cell agents.
  • Modify cell aspect.
  • Add a graphics layer.

3D tutorial: moving cells in a 3D space.

Model Definition

Global Section

Global variable

We use a new global variable called environment_size to define the size of our 3D environment. In the global section, we define the new variable:

int environment_size <-100;

Then we redefine the shape of the world (by default the shape of the world is a 100x100 square) as a cube that will have the size defined by the environment_size variable. To do so we change the shape of the world in the global section:

geometry shape <- cube(environment_size);	

Model initialization

When we created the cell agents, we want to place them randomly in the 3D environment. To do so we set the location with a random value for x, y and z between 0 and environment_size.

create cell number: nb_cells { 
  location <- {rnd(environment_size), rnd(environment_size), rnd(environment_size)};       
}

Moving3D skills

In the previous example, we only created cell agents that did not have any behavior. In this step we want to make them move. To do so we add a moving3D skill to the cell species.

More information on built-in skills proposed by GAMA can be found here.

species cell skills: [moving3D]{
...  	
}

Then we define a new reflex for the species cell that consists in calling the action move bundled in moving3D skill.

reflex move {
    do move;
}	                    

Finally we modify a bit the aspect of the sphere to set its size according to the environment_size global variable previously defined.

aspect default {
    draw sphere(environment_size*0.01) color: #blue;   
}

Experiment

The experiment is the same as the previous one except that we will display the bounds of the environment by using a graphics layer.

graphics "env" {
    draw cube(environment_size) color: #black wireframe: true;	
}

Output

output {
    display View1 type:opengl{
        graphics "env"{
            draw cube(environment_size) color: #black wireframe: true;	
        }
        species cell;  
    }
}

Complete Model

https://github.com/gama-platform/gama/blob/GAMA_1.9.2/msi.gama.models/models/Tutorials/3D/models/Model%2002.gaml
  1. What's new (Changelog)
  1. Installation and Launching
    1. Installation
    2. Launching GAMA
    3. Updating GAMA
    4. Installing Plugins
  2. Workspace, Projects and Models
    1. Navigating in the Workspace
    2. Changing Workspace
    3. Importing Models
  3. Editing Models
    1. GAML Editor (Generalities)
    2. GAML Editor Tools
    3. Validation of Models
  4. Running Experiments
    1. Launching Experiments
    2. Experiments User interface
    3. Controls of experiments
    4. Parameters view
    5. Inspectors and monitors
    6. Displays
    7. Batch Specific UI
    8. Errors View
  5. Running Headless
    1. Headless Batch
    2. Headless Server
    3. Headless Legacy
  6. Preferences
  7. Troubleshooting
  1. Introduction
    1. Start with GAML
    2. Organization of a Model
    3. Basic programming concepts in GAML
  2. Manipulate basic Species
  3. Global Species
    1. Regular Species
    2. Defining Actions and Behaviors
    3. Interaction between Agents
    4. Attaching Skills
    5. Inheritance
  4. Defining Advanced Species
    1. Grid Species
    2. Graph Species
    3. Mirror Species
    4. Multi-Level Architecture
  5. Defining GUI Experiment
    1. Defining Parameters
    2. Defining Displays Generalities
    3. Defining 3D Displays
    4. Defining Charts
    5. Defining Monitors and Inspectors
    6. Defining Export files
    7. Defining User Interaction
  6. Exploring Models
    1. Run Several Simulations
    2. Batch Experiments
    3. Exploration Methods
  7. Optimizing Model Section
    1. Runtime Concepts
    2. Optimizing Models
  8. Multi-Paradigm Modeling
    1. Control Architecture
    2. Defining Differential Equations
  1. Manipulate OSM Data
  2. Diffusion
  3. Using Database
  4. Using FIPA ACL
  5. Using BDI with BEN
  6. Using Driving Skill
  7. Manipulate dates
  8. Manipulate lights
  9. Using comodel
  10. Save and restore Simulations
  11. Using network
  12. Headless mode
  13. Using Headless
  14. Writing Unit Tests
  15. Ensure model's reproducibility
  16. Going further with extensions
    1. Calling R
    2. Using Graphical Editor
    3. Using Git from GAMA
  1. Built-in Species
  2. Built-in Skills
  3. Built-in Architecture
  4. Statements
  5. Data Type
  6. File Type
  7. Expressions
    1. Literals
    2. Units and Constants
    3. Pseudo Variables
    4. Variables And Attributes
    5. Operators [A-A]
    6. Operators [B-C]
    7. Operators [D-H]
    8. Operators [I-M]
    9. Operators [N-R]
    10. Operators [S-Z]
  8. Exhaustive list of GAMA Keywords
  1. Installing the GIT version
  2. Developing Extensions
    1. Developing Plugins
    2. Developing Skills
    3. Developing Statements
    4. Developing Operators
    5. Developing Types
    6. Developing Species
    7. Developing Control Architectures
    8. Index of annotations
  3. Introduction to GAMA Java API
    1. Architecture of GAMA
    2. IScope
  4. Using GAMA flags
  5. Creating a release of GAMA
  6. Documentation generation

  1. Predator Prey
  2. Road Traffic
  3. 3D Tutorial
  4. Incremental Model
  5. Luneray's flu
  6. BDI Agents

  1. Team
  2. Projects using GAMA
  3. Scientific References
  4. Training Sessions

Resources

  1. Videos
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  3. Code Examples
  4. Pedagogical materials
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