Spatial

distanceBetween(agentA, agentB, distanceFunction)
This function returns the distance between two agents, using a specific distance function. You can pass the current agent's state as one of the agents. The different distance functions are:
​Euclidean (default)
​Manhattan​
​Euclidean Squared​
​Chebyshev​
function behavior(state, context) {
    const { neighborA, neighborB } = context.neighbors();
    
    // Find the closest of 2 neighbors to you
    const distanceToA = hash_stdlib.distanceBetween(state, neighborA);
    const distanceToB = hash_stdlib.distanceBetween(state, neighborB);
    const closest = distanceToB > distanceToA ? "A" : "B";
    
    state.set("closest", closest);
    
    // Check if neighbors are closer to each other than to you
    const neighborDistance = hash_stdlib.distanceBetween(neighborA, neighborB);
    const selfDistance = closest === "A" ? distanceToA : distanceToB;
    
    state.set("closer_to_neighbors", selfDistance < neighborDistance);
}
normalizeVector(vec)
This function returns the unit vector of the vec array. You can use it to normalize an agent's direction vector after it's modified.
function behavior(state, context) {
    const dir = state.get("direction");
    
    // Modify the direction by adding a vector [1, 2, 0]
    dir[0] += 1;
    dir[1] += 2;
    
    // Turn it back into a unit vector
    state.set("direction", hash_stdlib.normalizeVector(dir));
}
randomPosition(topology, z_plane)
This function returns a random integer position within the bounds of the topology. The Topology should be user-defined in globals.json (see Topology). By defaultz_plane is false and the returned position is in a 2D plane. Pass true to return a position in 3D space.
function behavior(state, context) {
    // Move to a random position
    const topology = context.globals().topology;
    const new_pos = hash_stdlib.randomPosition(topology);
    
    state.set("position", new_pos);
}
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Neighbors
Last updated 2 months ago