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MASON is a fast discrete-event multiagent simulation library core in Java, designed to be the foundation for large custom-purpose Java simulations, and also to provide more than enough functionality for many lightweight simulation needs. MASON contains both a model library and an optional suite of visualization tools in 2D and 3D.

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/*
  Copyright 2006 by Sean Luke and George Mason University
  Licensed under the Academic Free License version 3.0
  See the file "LICENSE" for more information
*/

package sim.app.schelling;
import sim.engine.*;
import sim.field.grid.*;
import sim.util.*;

public /*strictfp*/ class Schelling extends SimState
    {
    private static final long serialVersionUID = 1;

    public int gridHeight;
    public int gridWidth;
    public int neighborhood = 1;
    public int threshold = 3;
    public double redProbability = 0.3;
    public double blueProbability = 0.3;
    public double emptyProbability = 0.3;
    public double unavailableProbability = 0.1;

    // we presume that no one relies on these DURING a simulation
    public int getGridHeight() { return gridHeight; }
    public void setGridHeight(int val) { if (val > 0) gridHeight = val; }
    public int getGridWidth() { return gridWidth; }
    public void setGridWidth(int val) { if (val > 0) gridWidth = val; }
    public int getNeighborhood() { return neighborhood; }
    public void setNeighborhood(int val) { if (val > 0) neighborhood = val; }
    public int getThreshold() { return threshold; }
    public void setThreshold(int val) { if (val >= 0) threshold = val; }

    // some cutsie-pie probability sliders.  More work than necessary, but it was fun.
    public Object domRedProbability() { return new Interval(0.0,1.0); }
    public double getRedProbability() { return redProbability; }
    public void setRedProbability(double val) 
        { 
        if (val >= 0 && val <= 1.0)
            {
            redProbability = val;
            }
        }
    
    public Object domBlueProbability() { return new Interval(0.0,1.0); }
    public double getBlueProbability() { return blueProbability; }
    public void setBlueProbability(double val) 
        { 
        if (val >= 0 && val <= 1.0)
            {
            blueProbability = val;
            }
        }

    public Object domEmptyProbability() { return new Interval(0.0,1.0); }
    public double getEmptyProbability() { return emptyProbability; }
    public void setEmptyProbability(double val) 
        { 
        if (val >= 0 && val <= 1.0)
            {
            emptyProbability = val;
            }
        }

    public Object domUnavailableProbability() { return new Interval(0.0,1.0); }
    public double getUnavailableProbability() { return unavailableProbability; }
    public void setUnavailableProbability(double val) 
        { 
        if (val >= 0 && val <= 1.0)
            {
            unavailableProbability = val;
            double total = redProbability + blueProbability + emptyProbability; 
            if (total==0.0) total = 1.0;
            redProbability *= (1.0 - unavailableProbability)/total;
            blueProbability *= (1.0 - unavailableProbability)/total;
            emptyProbability *= (1.0 - unavailableProbability)/total;
            }
        }

    public IntGrid2D neighbors;
    public Bag emptySpaces = new Bag();
    public static final  int EMPTY = 0;
    public static final int UNAVAILABLE = 1;
    public static final int RED = 2;
    public static final int BLUE = 3;

    /** Creates a Schelling simulation with the given random number seed. */
    public Schelling(long seed)
        {
        this(seed, 100, 100);
        }
        
    public Schelling(long seed, int width, int height)
        {
        super(seed);
        gridWidth = width; gridHeight = height;
        createGrids();
        }

    protected void createGrids()
        {
        emptySpaces.clear();
        neighbors = new IntGrid2D(gridWidth, gridHeight,0);
        int[][] g = neighbors.field;
        for(int x=0;x