protected boolean useLogarithmicProblemScale(List<XYSeries> seriesList) { NavigableSet<Double> xValueSet = new TreeSet<>(); int xValueListSize = 0; for (XYSeries series : seriesList) { for (XYDataItem dataItem : (List<XYDataItem>) series.getItems()) { xValueSet.add(dataItem.getXValue()); xValueListSize++; } } if (xValueListSize < LOG_SCALE_MIN_DATASETS_COUNT) { return false; } // If 60% of the points are in 20% of the value space, use a logarithmic scale double threshold = 0.2 * (xValueSet.last() - xValueSet.first()); int belowThresholdCount = xValueSet.headSet(threshold).size(); return belowThresholdCount >= (0.6 * xValueSet.size()); }
/** * Returns a string representing this instance, primarily for debugging * use. * * @return A string. */ @Override public String toString() { return "[" + getXValue() + ", " + getYValue() + "]"; }
/** * Returns a string representing this instance, primarily for debugging * use. * * @return A string. */ public String toString() { return "[" + getXValue() + ", " + getYValue() + "]"; }
boolean itemContributesToXBounds = false; boolean itemContributesToYBounds = false; double x = item.getXValue(); if (!Double.isNaN(x)) { if (x <= this.minX || x >= this.maxX) {
boolean itemContributesToXBounds = false; boolean itemContributesToYBounds = false; double x = item.getXValue(); if (!Double.isNaN(x)) { if (x <= this.minX || x >= this.maxX) {
/** * Updates the cached values for the minimum and maximum data values. * * @param item the item added (<code>null</code> not permitted). * * @since 1.0.13 */ private void updateBoundsForAddedItem(XYDataItem item) { double x = item.getXValue(); this.minX = minIgnoreNaN(this.minX, x); this.maxX = maxIgnoreNaN(this.maxX, x); if (item.getY() != null) { double y = item.getYValue(); this.minY = minIgnoreNaN(this.minY, y); this.maxY = maxIgnoreNaN(this.maxY, y); } }
/** * Updates the cached values for the minimum and maximum data values. * * @param item the item added ({@code null} not permitted). * * @since 1.0.13 */ private void updateBoundsForAddedItem(XYDataItem item) { double x = item.getXValue(); this.minX = minIgnoreNaN(this.minX, x); this.maxX = maxIgnoreNaN(this.maxX, x); if (item.getY() != null) { double y = item.getYValue(); this.minY = minIgnoreNaN(this.minY, y); this.maxY = maxIgnoreNaN(this.maxY, y); } }
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 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; }
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 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; } }