double yValue = levelValues[i]; series.add(timeMillisSpent, timeMillisSpent, timeMillisSpent, yValue, (yValue > 0.0) ? 0.0 : yValue, (yValue > 0.0) ? yValue : 0.0);
double yValue = levelValues[i]; series.add(timeMillisSpent, timeMillisSpent, timeMillisSpent, yValue, (yValue > 0.0) ? 0.0 : yValue, (yValue > 0.0) ? yValue : 0.0);
double yValue = mutationCount; series.add(timeMillisSpent, timeMillisSpent, timeMillisSpent, yValue, (yValue > 0.0) ? 0.0 : yValue, (yValue > 0.0) ? yValue : 0.0);
/** * Adds a data item to the series and sends a {@link SeriesChangeEvent} to * all registered listeners. * * @param x the x-value. * @param xLow the lower bound of the x-interval. * @param xHigh the upper bound of the x-interval. * @param y the y-value. * @param yLow the lower bound of the y-interval. * @param yHigh the upper bound of the y-interval. */ public void add(double x, double xLow, double xHigh, double y, double yLow, double yHigh) { add(new XYIntervalDataItem(x, xLow, xHigh, y, yLow, yHigh), true); }
double maxY = getChartValue(sbXpDataSet.toString(), "maxY", doc); series.add(X,X-3,X+3,minY,minY,maxY);
private XYIntervalSeries computeAcceptableDeviationData(double lowerBound, double upperBound) { final Function2D identityFunction = new Function2D() { @Override public double getValue(double x) { return x; } }; final XYSeries identity = DatasetUtilities.sampleFunction2DToSeries(identityFunction, lowerBound, upperBound, 100, "1:1 line"); final XYIntervalSeries xyIntervalSeries = new XYIntervalSeries(identity.getKey()); final List<XYDataItem> items = identity.getItems(); for (XYDataItem item : items) { final double x = item.getXValue(); final double y = item.getYValue(); if (scatterPlotModel.showAcceptableDeviation) { final double acceptableDeviation = scatterPlotModel.acceptableDeviationInterval; final double xOff = acceptableDeviation * x / 100; final double yOff = acceptableDeviation * y / 100; xyIntervalSeries.add(x, x - xOff, x + xOff, y, y - yOff, y + yOff); } else { xyIntervalSeries.add(x, x, x, y, y, y); } } return xyIntervalSeries; }
private XYIntervalSeries computeAcceptableDeviationData(double lowerBound, double upperBound) { final XYSeries identity = DatasetUtilities.sampleFunction2DToSeries(x -> x, lowerBound, upperBound, 100, "1:1 line"); final XYIntervalSeries xyIntervalSeries = new XYIntervalSeries(identity.getKey()); for (int i = 0; i < identity.getItemCount(); i++) { XYDataItem item = identity.getDataItem(i); final double x = item.getXValue(); final double y = item.getYValue(); if (scatterPlotModel.showAcceptableDeviation) { final double acceptableDeviation = scatterPlotModel.acceptableDeviationInterval; final double xOff = acceptableDeviation * x / 100; final double yOff = acceptableDeviation * y / 100; xyIntervalSeries.add(x, x - xOff, x + xOff, y, y - yOff, y + yOff); } else { xyIntervalSeries.add(x, x, x, y, y, y); } } return xyIntervalSeries; }
if (population[i] != 0) { pop.add(midValue(i), leftValue(i), rightValue(i), population[i], prev[i], prev[i] + population[i]); prev[i] = prev[i] + population[i]; if (population[i] != 0) { pop.add(midValue(i), leftValue(i), rightValue(i), population[i], prev[i], prev[i] + population[i]); prev[i] = prev[i] + population[i];
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()); final List<XYDataItem> regressionDataItems = regressionData.getItems(); for (XYDataItem item : regressionDataItems) { 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 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 { Dialogs.showInformation("Unable to compute regression line.\n" + "At least 2 values are needed to compute regression coefficients."); return null; } }
final float rasterSigma = computedData.rasterSigma; final float correlativeData = computedData.correlativeData; scatterValues.add(correlativeData, correlativeData, correlativeData, rasterMean, rasterMean - rasterSigma, rasterMean + rasterSigma);
final float rasterSigma = computedData.rasterSigma; final float correlativeData = computedData.correlativeData; scatterValues.add(correlativeData, correlativeData, correlativeData, rasterMean, rasterMean - rasterSigma, rasterMean + rasterSigma);