private void computeChartDataIfPossible() {
// need to do this later: all GUI events must be processed first in order to get the correct
// state
SwingUtilities.invokeLater(
() -> {
if (scatterPlotModel.pointDataSource != null
&& scatterPlotModel.dataField != null
&& scatterPlotModel.pointDataSource.getFeatureCollection() != null
&& scatterPlotModel.pointDataSource.getFeatureCollection().features() != null
&& scatterPlotModel.pointDataSource.getFeatureCollection().features().hasNext()
&& scatterPlotModel.pointDataSource.getFeatureCollection().features().next() != null
&& scatterPlotModel
.pointDataSource
.getFeatureCollection()
.features()
.next()
.getAttribute(scatterPlotModel.dataField.getLocalName())
!= null
&& getRaster() != null) {
compute(scatterPlotModel.useRoiMask ? scatterPlotModel.roiMask : null);
} else {
scatterpointsDataset.removeAllSeries();
acceptableDeviationDataset.removeAllSeries();
regressionDataset.removeAllSeries();
getPlot().removeAnnotation(r2Annotation);
computedDatas = null;
}
});
}
@Override
public void populate(XYPlot plot, AROTraceData analysis) {
if (analysis != null) {
gpsData.removeAllSeries();
// create the GPS dataset...
Map<GpsState, XYIntervalSeries> seriesMap =
new EnumMap<GpsState, XYIntervalSeries>(GpsState.class);
for (GpsState eventType : GpsState.values()) {
XYIntervalSeries series = new XYIntervalSeries(eventType);
seriesMap.put(eventType, series);
gpsData.addSeries(series);
}
Iterator<GpsInfo> iter =
analysis.getAnalyzerResult().getTraceresult().getGpsInfos().iterator();
if (iter.hasNext()) {
while (iter.hasNext()) {
GpsInfo gpsEvent = iter.next();
if (gpsEvent.getGpsState() != GpsState.GPS_DISABLED) {
seriesMap
.get(gpsEvent.getGpsState())
.add(
gpsEvent.getBeginTimeStamp(),
gpsEvent.getBeginTimeStamp(),
gpsEvent.getEndTimeStamp(),
0.5,
0,
1);
}
}
}
XYItemRenderer renderer = plot.getRenderer();
renderer.setSeriesPaint(gpsData.indexOf(GpsState.GPS_STANDBY), Color.YELLOW);
renderer.setSeriesPaint(gpsData.indexOf(GpsState.GPS_ACTIVE), new Color(34, 177, 76));
// Assign ToolTip to renderer
renderer.setBaseToolTipGenerator(
new XYToolTipGenerator() {
@Override
public String generateToolTip(XYDataset dataset, int series, int item) {
GpsState eventType = (GpsState) gpsData.getSeries(series).getKey();
return MessageFormat.format(
ResourceBundleHelper.getMessageString("gps.tooltip"),
dataset.getX(series, item),
ResourceBundleHelper.getEnumString(eventType));
}
});
}
plot.setDataset(gpsData);
}
private void finishScalingUpdate(
AxisRangeControl axisRangeControl, ValueAxis newAxis, ValueAxis oldAxis) {
if (axisRangeControl.isAutoMinMax()) {
newAxis.setAutoRange(false);
acceptableDeviationDataset.removeAllSeries();
regressionDataset.removeAllSeries();
getPlot().removeAnnotation(r2Annotation);
newAxis.setAutoRange(true);
axisRangeControl.adjustComponents(newAxis, 3);
newAxis.setAutoRange(false);
computeRegressionAndAcceptableDeviationData();
} else {
newAxis.setAutoRange(false);
newAxis.setRange(oldAxis.getRange());
}
}
private void computeRegressionAndAcceptableDeviationData() {
acceptableDeviationDataset.removeAllSeries();
regressionDataset.removeAllSeries();
getPlot().removeAnnotation(r2Annotation);
if (computedDatas != null) {
final ValueAxis domainAxis = getPlot().getDomainAxis();
final double min = domainAxis.getLowerBound();
final double max = domainAxis.getUpperBound();
acceptableDeviationDataset.addSeries(computeAcceptableDeviationData(min, max));
if (scatterPlotModel.showRegressionLine) {
final XYIntervalSeries series = computeRegressionData(min, max);
if (series != null) {
regressionDataset.addSeries(series);
computeCoefficientOfDetermination();
}
}
}
}
@Override
protected String getDataAsText() {
if (scatterpointsDataset.getItemCount(0) > 0) {
final ScatterPlotTableModel scatterPlotTableModel;
scatterPlotTableModel =
new ScatterPlotTableModel(getRasterName(), getCorrelativeDataName(), computedDatas);
return scatterPlotTableModel.toCVS();
}
return "";
}
private void computeCoefficientOfDetermination() {
int numberOfItems = scatterpointsDataset.getSeries(0).getItemCount();
double arithmeticMeanOfX = 0; // arithmetic mean of X
double arithmeticMeanOfY = 0; // arithmetic mean of Y
double varX = 0; // variance of X
double varY = 0; // variance of Y
double coVarXY = 0; // covariance of X and Y;
// compute arithmetic means
for (int i = 0; i < numberOfItems; i++) {
arithmeticMeanOfX += scatterpointsDataset.getXValue(0, i);
arithmeticMeanOfY += scatterpointsDataset.getYValue(0, i);
}
arithmeticMeanOfX /= numberOfItems;
arithmeticMeanOfY /= numberOfItems;
// compute variances and covariance
for (int i = 0; i < numberOfItems; i++) {
varX += Math.pow(scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX, 2);
varY += Math.pow(scatterpointsDataset.getYValue(0, i) - arithmeticMeanOfY, 2);
coVarXY +=
(scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX)
* (scatterpointsDataset.getYValue(0, i) - arithmeticMeanOfY);
}
// computation of coefficient of determination
double r2 = Math.pow(coVarXY, 2) / (varX * varY);
r2 = MathUtils.round(r2, Math.pow(10.0, 5));
final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0);
final double intercept = coefficients[0];
final double slope = coefficients[1];
final String linearEquation;
if (intercept >= 0) {
linearEquation = "y = " + (float) slope + "x + " + (float) intercept;
} else {
linearEquation = "y = " + (float) slope + "x - " + Math.abs((float) intercept);
}
TextTitle tt = new TextTitle(linearEquation + "\nR² = " + r2);
tt.setTextAlignment(HorizontalAlignment.RIGHT);
tt.setFont(chart.getLegend().getItemFont());
tt.setBackgroundPaint(new Color(200, 200, 255, 100));
tt.setFrame(new BlockBorder(Color.white));
tt.setPosition(RectangleEdge.BOTTOM);
r2Annotation = new XYTitleAnnotation(0.98, 0.02, tt, RectangleAnchor.BOTTOM_RIGHT);
r2Annotation.setMaxWidth(0.48);
getPlot().addAnnotation(r2Annotation);
}
private XYIntervalSeries computeRegressionData(double xStart, double xEnd) {
if (scatterpointsDataset.getItemCount(0) > 1) {
final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0);
final Function2D curve = new LineFunction2D(coefficients[0], coefficients[1]);
final XYSeries regressionData =
DatasetUtilities.sampleFunction2DToSeries(curve, xStart, xEnd, 100, "regression line");
final XYIntervalSeries xyIntervalRegression = new XYIntervalSeries(regressionData.getKey());
for (int i = 0; i < regressionData.getItemCount(); i++) {
XYDataItem item = regressionData.getDataItem(i);
final double x = item.getXValue();
final double y = item.getYValue();
xyIntervalRegression.add(x, x, x, y, y, y);
}
return xyIntervalRegression;
} else {
JOptionPane.showMessageDialog(
this,
"Unable to compute regression line.\n"
+ "At least 2 values are needed to compute regression coefficients.");
return null;
}
}
private void createUI() {
final XYPlot plot = getPlot();
plot.setAxisOffset(new RectangleInsets(5, 5, 5, 5));
plot.setNoDataMessage(NO_DATA_MESSAGE);
int confidenceDSIndex = 0;
int regressionDSIndex = 1;
int scatterpointsDSIndex = 2;
plot.setDataset(confidenceDSIndex, acceptableDeviationDataset);
plot.setDataset(regressionDSIndex, regressionDataset);
plot.setDataset(scatterpointsDSIndex, scatterpointsDataset);
plot.addAnnotation(r2Annotation);
final DeviationRenderer identityRenderer = new DeviationRenderer(true, false);
identityRenderer.setSeriesPaint(0, StatisticChartStyling.SAMPLE_DATA_PAINT);
identityRenderer.setSeriesFillPaint(0, StatisticChartStyling.SAMPLE_DATA_FILL_PAINT);
plot.setRenderer(confidenceDSIndex, identityRenderer);
final DeviationRenderer regressionRenderer = new DeviationRenderer(true, false);
regressionRenderer.setSeriesPaint(0, StatisticChartStyling.REGRESSION_DATA_PAINT);
regressionRenderer.setSeriesFillPaint(0, StatisticChartStyling.REGRESSION_DATA_FILL_PAINT);
plot.setRenderer(regressionDSIndex, regressionRenderer);
final XYErrorRenderer scatterPointsRenderer = new XYErrorRenderer();
scatterPointsRenderer.setDrawXError(true);
scatterPointsRenderer.setErrorStroke(new BasicStroke(1));
scatterPointsRenderer.setErrorPaint(StatisticChartStyling.CORRELATIVE_POINT_OUTLINE_PAINT);
scatterPointsRenderer.setSeriesShape(0, StatisticChartStyling.CORRELATIVE_POINT_SHAPE);
scatterPointsRenderer.setSeriesOutlinePaint(
0, StatisticChartStyling.CORRELATIVE_POINT_OUTLINE_PAINT);
scatterPointsRenderer.setSeriesFillPaint(0, StatisticChartStyling.CORRELATIVE_POINT_FILL_PAINT);
scatterPointsRenderer.setSeriesLinesVisible(0, false);
scatterPointsRenderer.setSeriesShapesVisible(0, true);
scatterPointsRenderer.setSeriesOutlineStroke(0, new BasicStroke(1.0f));
scatterPointsRenderer.setSeriesToolTipGenerator(
0,
(dataset, series, item) -> {
final XYIntervalSeriesCollection collection = (XYIntervalSeriesCollection) dataset;
final Comparable key = collection.getSeriesKey(series);
final double xValue = collection.getXValue(series, item);
final double endYValue = collection.getEndYValue(series, item);
final double yValue = collection.getYValue(series, item);
return String.format(
"%s: mean = %6.2f, sigma = %6.2f | %s: value = %6.2f",
getRasterName(), yValue, endYValue - yValue, key, xValue);
});
plot.setRenderer(scatterpointsDSIndex, scatterPointsRenderer);
final boolean autoRangeIncludesZero = false;
final boolean xLog = scatterPlotModel.xAxisLogScaled;
final boolean yLog = scatterPlotModel.yAxisLogScaled;
plot.setDomainAxis(
StatisticChartStyling.updateScalingOfAxis(
xLog, plot.getDomainAxis(), autoRangeIncludesZero));
plot.setRangeAxis(
StatisticChartStyling.updateScalingOfAxis(
yLog, plot.getRangeAxis(), autoRangeIncludesZero));
createUI(createChartPanel(chart), createInputParameterPanel(), bindingContext);
plot.getDomainAxis().addChangeListener(domainAxisChangeListener);
scatterPlotDisplay.setMouseWheelEnabled(true);
scatterPlotDisplay.setMouseZoomable(true);
}
private void updateDataSet() {
if (!isInitialized) {
return;
}
dataset.removeAllSeries();
double dx = 0.5 * dataSourceConfig.boxSize;
if (profileData != null) {
final float[] sampleValues = profileData.getSampleValues();
final float[] sampleSigmas = profileData.getSampleSigmas();
XYIntervalSeries series =
new XYIntervalSeries(
getRaster() != null ? getRaster().getName() : DEFAULT_SAMPLE_DATASET_NAME);
for (int x = 0; x < sampleValues.length; x++) {
final float y = sampleValues[x];
final float dy = sampleSigmas[x];
series.add(x, x - dx, x + dx, y, y - dy, y + dy);
}
dataset.addSeries(series);
if (dataSourceConfig.useCorrelativeData
&& dataSourceConfig.pointDataSource != null
&& dataSourceConfig.dataField != null) {
XYIntervalSeries corrSeries =
new XYIntervalSeries(
getCorrelativeDataLabel(
dataSourceConfig.pointDataSource, dataSourceConfig.dataField));
int[] shapeVertexIndexes = profileData.getShapeVertexIndexes();
SimpleFeature[] simpleFeatures =
dataSourceConfig.pointDataSource.getFeatureCollection().toArray(new SimpleFeature[0]);
if (shapeVertexIndexes.length == simpleFeatures.length) {
int fieldIndex =
getAttributeIndex(dataSourceConfig.pointDataSource, dataSourceConfig.dataField);
if (fieldIndex != -1) {
for (int i = 0; i < simpleFeatures.length; i++) {
Number attribute = (Number) simpleFeatures[i].getAttribute(fieldIndex);
if (attribute != null) {
final double x = shapeVertexIndexes[i];
final double y = attribute.doubleValue();
corrSeries.add(x, x, x, y, y, y);
}
}
dataset.addSeries(corrSeries);
}
} else {
System.out.println("Weird things happened:");
System.out.println(" shapeVertexIndexes.length = " + shapeVertexIndexes.length);
System.out.println(" simpleFeatures.length = " + simpleFeatures.length);
}
}
profilePlotDisplay.restoreAutoBounds();
xAxisRangeControl
.getBindingContext()
.setComponentsEnabled(
PROPERTY_NAME_MARK_SEGMENTS, profileData.getShapeVertices().length > 2);
}
}