example_multi_environment.json

{
  "metadata": {
    "model_name": "Multi-Environment Ecosystem Model",
    "description": "A model simulating wildlife movement between forest and urban environments with different behaviors in each habitat.",
    "author": "Ross Williams",
    "date_created": "2024-01-15T10:30:00Z",
    "version": "1.0.0",
    "random_seed": 42
  },

  "functions": [
    {
      "name": "calculate_food_availability",
      "description": "Calculates food availability based on vegetation density",
      "language": "python",
      "code": "return vegetation_layer * season_multiplier"
    }
  ],

  "global_variables": [
    {
      "name": "current_season", 
      "type": "string",
      "initial_value": "spring",
      "description": "Current season affecting resource availability"
    },
    {
      "name": "simulation_day",
      "type": "integer", 
      "initial_value": 0,
      "description": "Current day of simulation"
    }
  ],

  "environments": [
    {
      "name": "forest",
      "topology": {
        "type": "grid",
        "description": "200x200 grid representing forest habitat",
        "parameters": {
          "width": 200,
          "height": 200,
          "cell_size": 10
        },
        "boundary_conditions": "torus"
      },
      "spatial_layers": [
        {
          "layer_name": "vegetation_density",
          "layer_type": "raster", 
          "source_file": "data/forest_vegetation.tif",
          "description": "Vegetation density from 0-1 affecting food availability"
        },
        {
          "layer_name": "water_sources",
          "layer_type": "vector",
          "source_file": "data/forest_streams.shp", 
          "description": "Stream and pond locations for water access"
        }
      ],
      "attributes": [
        {
          "name": "temperature",
          "type": "float",
          "initial_value": 15.5,
          "description": "Average temperature in Celsius"
        },
        {
          "name": "canopy_cover",
          "type": "float", 
          "initial_value": 0.8,
          "description": "Proportion of area under tree canopy"
        }
      ],
      "environment_behaviors": [
        {
          "rule_name": "seasonal_growth",
          "description": "Vegetation grows and changes with seasons",
          "effect": "vegetation_density *= seasonal_growth_rate"
        }
      ]
    },
    
    {
      "name": "urban",
      "topology": {
        "type": "network",
        "description": "Road network connecting urban patches",
        "parameters": {
          "node_file": "data/urban_nodes.csv",
          "edge_file": "data/urban_roads.csv"
        }
      },
      "spatial_layers": [
        {
          "layer_name": "building_footprints",
          "layer_type": "vector",
          "source_file": "data/buildings.shp",
          "description": "Building polygons blocking movement"
        },
        {
          "layer_name": "green_spaces", 
          "layer_type": "vector",
          "source_file": "data/parks.shp",
          "description": "Parks and green corridors providing refuge"
        }
      ],
      "attributes": [
        {
          "name": "noise_level",
          "type": "float",
          "initial_value": 65.0,
          "description": "Average noise level in decibels"
        },
        {
          "name": "human_activity",
          "type": "float",
          "initial_value": 0.7,
          "description": "Level of human activity from 0-1"
        }
      ],
      "environment_behaviors": [
        {
          "rule_name": "traffic_cycles",
          "description": "Human activity varies by time of day",
          "effect": "human_activity = calculate_traffic_pattern(time_of_day)"
        }
      ]
    }
  ],

  "agents": [
    {
      "agent_type": "deer",
      "count": 50,
      "attributes": [
        {
          "name": "energy",
          "type": "float",
          "initial_value": 100.0,
          "description": "Current energy level"
        },
        {
          "name": "current_environment",
          "type": "string", 
          "initial_value": "forest",
          "description": "Which environment the agent is currently in"
        },
        {
          "name": "stress_level",
          "type": "float",
          "initial_value": 0.0,
          "description": "Stress from 0-1, higher in urban areas"
        }
      ],
      "behaviors": [
        {
          "behavior_name": "forage",
          "description": "Search for food in current environment",
          "rules": "if current_environment == 'forest': energy += vegetation_density * 5; else: energy += green_space_proximity * 2"
        },
        {
          "behavior_name": "avoid_humans",
          "description": "Move away from high human activity areas", 
          "rules": "if human_activity > 0.5: stress_level += 0.1; move_to_lower_activity_area()"
        },
        {
          "behavior_name": "migrate_environment",
          "description": "Move between forest and urban environments",
          "rules": "if stress_level > 0.8 and current_environment == 'urban': migrate_to('forest')"
        }
      ]
    },

    {
      "agent_type": "bird",
      "count": 200,
      "attributes": [
        {
          "name": "energy",
          "type": "float", 
          "initial_value": 80.0,
          "description": "Current energy level"
        },
        {
          "name": "nesting_site",
          "type": "string",
          "initial_value": "forest",
          "description": "Preferred nesting environment"
        }
      ],
      "behaviors": [
        {
          "behavior_name": "fly_between_environments",
          "description": "Birds can easily move between habitats",
          "rules": "if food_scarcity > 0.6: fly_to_better_habitat()"
        }
      ]
    }
  ],

  "scheduler": {
    "mode": "discrete_time",
    "activation": {
      "type": "staged",
      "stages": [
        {
          "name": "environment_update",
          "agent_types": [],
          "order": "sequential"
        },
        {
          "name": "animal_behaviors", 
          "agent_types": ["deer", "bird"],
          "order": "random"
        }
      ]
    },
    "duration": 365,
    "time_unit": "days",
    "time_step_size": 1,
    "local_schedulers": [
      {
        "scope": "environment",
        "name": "forest_scheduler",
        "activation": {
          "type": "sequential"
        }
      },
      {
        "scope": "environment", 
        "name": "urban_scheduler",
        "activation": {
          "type": "simultaneous"
        }
      }
    ]
  },

  "outputs": {
    "tracked_variables": [
      {
        "variable": "deer.energy",
        "description": "Track deer energy levels across environments",
        "collection_frequency": "each_step"
      },
      {
        "variable": "deer.current_environment",
        "description": "Track which environment deer are in",
        "collection_frequency": "each_step"
      },
      {
        "variable": "forest.vegetation_density",
        "description": "Monitor forest health over time",
        "collection_frequency": "each_step" 
      }
    ],
    "visualizations": [
      {
        "type": "heatmap",
        "data_source": ["deer.current_environment"],
        "description": "Spatial distribution of deer across environments",
        "visual_params": {
          "color_map": "viridis",
          "update_frequency": "daily"
        }
      },
      {
        "type": "timeseries",
        "data_source": ["deer.energy", "deer.stress_level"],
        "description": "Deer health metrics over simulation time"
      }
    ]
  }
}