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Add a demo of the Jzy3D library
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‎pom.xml‎

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<artifactId>tablesaw-jsplot</artifactId>
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<version>${tablesaw-jsplot.version}</version>
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</dependency>
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<dependency>
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<groupId>org.jzy3d</groupId>
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<artifactId>jzy3d-core</artifactId>
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</dependency>
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<dependency>
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<groupId>org.jzy3d</groupId>
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<artifactId>jzy3d-native-jogl-awt</artifactId>
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</dependency>
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<!-- Test scope dependencies -->
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<dependency>

‎src/test/java/org/scijava/ui/swing/plot/Jzy3DDemo.java‎

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package org.scijava.ui.swing.plot;
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import org.jzy3d.analysis.AWTAbstractAnalysis;
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import org.jzy3d.chart.Chart;
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import org.jzy3d.chart.factories.AWTChartFactory;
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import org.jzy3d.colors.Color;
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import org.jzy3d.colors.ColorMapper;
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import org.jzy3d.colors.colormaps.ColorMapRainbow;
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import org.jzy3d.maths.Coord3d;
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import org.jzy3d.maths.Range;
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import org.jzy3d.plot3d.builder.Mapper;
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import org.jzy3d.plot3d.builder.SurfaceBuilder;
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import org.jzy3d.plot3d.builder.concrete.OrthonormalGrid;
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import org.jzy3d.plot3d.primitives.Scatter;
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import org.jzy3d.plot3d.primitives.Shape;
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import org.jzy3d.plot3d.rendering.canvas.Quality;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Random;
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public class Jzy3DDemo extends AWTAbstractAnalysis {
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private double[][] data;
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private int gridSize = 50;
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private double bandwidthX;
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private double bandwidthY;
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public Jzy3DDemo() {
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super();
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setFactory(new AWTChartFactory());
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}
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public static void main(String[] args) throws Exception {
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Jzy3DDemo demo = new Jzy3DDemo();
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demo.init();
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demo.getChart().open("Bivariate KDE", 800, 600);
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}
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@Override
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public Chart initializeChart() {
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Quality quality = Quality.Advanced();
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// Initialize the chart
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Chart chart = getFactory().newChart(quality);
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this.chart = chart; // Store the chart in the parent class
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return chart;
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}
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@Override
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public void init() throws Exception {
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// First initialize the chart
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Chart chart = initializeChart();
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// Generate sample data - two clusters
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int n = 1000;
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Random rand = new Random(42);
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data = new double[n][2];
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// Generate two clusters
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for (int i = 0; i < n; i++) {
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if (rand.nextDouble() < 0.6) {
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// First cluster
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data[i][0] = rand.nextGaussian() * 0.5 + 2;
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data[i][1] = rand.nextGaussian() * 0.5 + 2;
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} else {
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// Second cluster
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data[i][0] = rand.nextGaussian() * 0.3 + 4;
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data[i][1] = rand.nextGaussian() * 0.3 + 4;
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}
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}
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// Calculate grid boundaries
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double minX = Double.POSITIVE_INFINITY;
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double maxX = Double.NEGATIVE_INFINITY;
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double minY = Double.POSITIVE_INFINITY;
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double maxY = Double.NEGATIVE_INFINITY;
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for (double[] point : data) {
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minX = Math.min(minX, point[0]);
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maxX = Math.max(maxX, point[0]);
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minY = Math.min(minY, point[1]);
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maxY = Math.max(maxY, point[1]);
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}
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// Add padding
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double padX = (maxX - minX) * 0.1;
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double padY = (maxY - minY) * 0.1;
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minX -= padX;
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maxX += padX;
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minY -= padY;
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maxY += padY;
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// Calculate bandwidth using Silverman's rule
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double sdX = calculateSD(data, 0);
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double sdY = calculateSD(data, 1);
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bandwidthX = 1.06 * sdX * Math.pow(n, -0.2);
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bandwidthY = 1.06 * sdY * Math.pow(n, -0.2);
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// Create KDE mapper
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Mapper mapper = new Mapper() {
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@Override
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public double f(double x, double y) {
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double sum = 0;
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for (double[] point : data) {
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double zx = (x - point[0]) / bandwidthX;
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double zy = (y - point[1]) / bandwidthY;
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sum += Math.exp(-0.5 * (zx * zx + zy * zy)) /
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(2 * Math.PI * bandwidthX * bandwidthY);
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}
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return sum / data.length;
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}
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};
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// Create surface
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Range xRange = new Range((float)minX, (float)maxX);
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Range yRange = new Range((float)minY, (float)maxY);
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OrthonormalGrid grid = new OrthonormalGrid(xRange, gridSize, yRange, gridSize);
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Shape surface = new SurfaceBuilder().orthonormal(grid, mapper);
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// Style the surface
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surface.setColorMapper(new ColorMapper(new ColorMapRainbow(), surface.getBounds().getZmin(), surface.getBounds().getZmax()));
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surface.setWireframeDisplayed(true);
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surface.setWireframeColor(Color.BLACK);
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chart.add(surface);
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// Create scatter plot of original data
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List<Coord3d> points = new ArrayList<>();
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for (double[] point : data) {
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points.add(new Coord3d(point[0], point[1], 0));
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}
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Scatter scatter = new Scatter(points.toArray(new Coord3d[0]), Color.BLACK);
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chart.add(scatter);
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}
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private static double calculateSD(double[][] data, int dimension) {
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double mean = 0;
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for (double[] point : data) {
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mean += point[dimension];
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}
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mean /= data.length;
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double variance = 0;
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for (double[] point : data) {
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double diff = point[dimension] - mean;
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variance += diff * diff;
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}
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variance /= (data.length - 1);
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return Math.sqrt(variance);
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}
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@Override
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public String getName() {
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return "Bivariate KDE Example";
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}
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@Override
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public String getPitch() {
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return "2D Kernel Density Estimation visualization";
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}
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}

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