Simulate indoor

Indoor environments (bidimensional spaces with obstacles) can be generated from images by leveraging ImageEnvironment, which loads the map as raster image from file, interpreting the black pixels as obstacles (wall-like areas not accessible to nodes). Color of pixels that represents obstacles can be set to every color with a constructor’s parameter, black is default.

By default, each pixel is considered as a 1x1 block. As a consequence, a 1200x600 image with a vertical line of black pixels at coordinate 500 will be interpreted as a single obstacle of size 1x600 starting at coordinate (500, 0). It is possible to scale up or down the size of the environment by acting on the zoom parameter of ImageEnvironment, as well as changing the initial coordinates.

Examples

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Direct reference to the image

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Search for the image in the file system via Kotlin

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incarnation: protelis
  variables:
    zoom: &zoom
      formula: 0.1 # Must be a valid Groovy snippet
    image_name: { formula: "'chiaravalle.png'" }
    image_path: &image_path
      language: kotlin # Pick whatever JSR223 language you like and add it to the classpath
      formula: | # The following is pure Kotlin code. Other variables can be referenced!
        import java.io.File
        fun File.findImage(): String? = walkTopDown().find { image_name in it.name }?.absolutePath
        fun File.findImageRecursively(): String = findImage() ?: File(this, "..").findImageRecursively()
        File(".").findImageRecursively()
      timeout: 5000
    # Linear free variable
    walking_speed: &walk-speed { default: 1.4, min: 1, max: 2, step: 0.1 }
    seed: &seed { default: 0, min: 0, max: 99, step: 1 } # 100 samples
    scenario_seed: &scenario_seed { formula: (seed + 31) * seed } # Variable-dependent
    people_count: &people_count
      type: GeometricVariable # A variable scanning a space with geometric segmentation
      parameters: [300, 50, 500, 9] # default 300, minimum 50, maximum 100, 9 samples
  seeds: { simulation: *seed, scenario: *scenario_seed}
  export:
      type: CSVExporter
      parameters:
        fileNameRoot: "snippet-variables-export"
      data:
        - time
        - molecule: "default_module:default_program"
          aggregators: [ mean, max, min, variance, median ] # From Apache's UnivariateStatistic
          value-filter: onlyfinite # discards NaN and Infinity
  environment: { type: ImageEnvironment, parameters: [*image_path, *zoom] }
  network-model: { type: ObstaclesBreakConnection, parameters: [50] }
  deployments:
    type: Rectangle
    parameters: [*people_count, 62, 15, 95, 200]
    programs:
      - time-distribution: 1
        program: >
          import protelis:coord:spreading
          let source = [110, 325]
          let vector = self.getCoordinates() - source
          let distance = hypot(vector.get(0), vector.get(1))
          distanceTo(distance < 50)        
      - program: send
      - { type: Event, time-distribution: 1, actions: { type: LevyWalk, parameters: [*walk-speed] } }