List of usage examples for org.jfree.data.xy XYDataset getItemCount
public int getItemCount(int series);
From source file:com.newatlanta.bluedragon.XYItemLabelGenerator.java
private static double calculateYTotal(XYDataset dataset, int series) { double total = 0; int numItems = dataset.getItemCount(series); for (int i = 0; i < numItems; i++) total += dataset.getYValue(series, i); return total; }
From source file:org.jfree.data.statistics.Regression.java
/** * Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to * the data using ordinary least squares regression. The result is returned * as a double[], where result[0] --> a, and result[1] --> b. * * @param data the data.//from w ww.ja v a2 s .c o m * @param series the series (zero-based index). * * @return The parameters. */ public static double[] getOLSRegression(XYDataset data, int series) { int n = data.getItemCount(series); if (n < 2) { throw new IllegalArgumentException("Not enough data."); } double sumX = 0; double sumY = 0; double sumXX = 0; double sumXY = 0; for (int i = 0; i < n; i++) { double x = data.getXValue(series, i); double y = data.getYValue(series, i); sumX += x; sumY += y; double xx = x * x; sumXX += xx; double xy = x * y; sumXY += xy; } double sxx = sumXX - (sumX * sumX) / n; double sxy = sumXY - (sumX * sumY) / n; double xbar = sumX / n; double ybar = sumY / n; double[] result = new double[2]; result[1] = sxy / sxx; result[0] = ybar - result[1] * xbar; return result; }
From source file:org.jfree.data.statistics.Regression.java
/** * Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to * the data using a power regression equation. The result is returned as * an array, where double[0] --> a, and double[1] --> b. * * @param data the data.// ww w.j ava 2 s.c o m * @param series the series to fit the regression line against. * * @return The parameters. */ public static double[] getPowerRegression(XYDataset data, int series) { int n = data.getItemCount(series); if (n < 2) { throw new IllegalArgumentException("Not enough data."); } double sumX = 0; double sumY = 0; double sumXX = 0; double sumXY = 0; for (int i = 0; i < n; i++) { double x = Math.log(data.getXValue(series, i)); double y = Math.log(data.getYValue(series, i)); sumX += x; sumY += y; double xx = x * x; sumXX += xx; double xy = x * y; sumXY += xy; } double sxx = sumXX - (sumX * sumX) / n; double sxy = sumXY - (sumX * sumY) / n; double xbar = sumX / n; double ybar = sumY / n; double[] result = new double[2]; result[1] = sxy / sxx; result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar); return result; }
From source file:org.jfree.data.statistics.Regression.java
/** * Returns the parameters 'a0', 'a1', 'a2', ..., 'an' for a polynomial * function of order n, y = a0 + a1 * x + a2 * x^2 + ... + an * x^n, * fitted to the data using a polynomial regression equation. * The result is returned as an array with a length of n + 2, * where double[0] --> a0, double[1] --> a1, .., double[n] --> an. * and double[n + 1] is the correlation coefficient R2 * Reference: J. D. Faires, R. L. Burden, Numerische Methoden (german * edition), pp. 243ff and 327ff.// w w w .j a v a 2 s. c om * * @param dataset the dataset (<code>null</code> not permitted). * @param series the series to fit the regression line against (the series * must have at least order + 1 non-NaN items). * @param order the order of the function (> 0). * * @return The parameters. * * @since 1.0.14 */ public static double[] getPolynomialRegression(XYDataset dataset, int series, int order) { ParamChecks.nullNotPermitted(dataset, "dataset"); int itemCount = dataset.getItemCount(series); if (itemCount < order + 1) { throw new IllegalArgumentException("Not enough data."); } int validItems = 0; double[][] data = new double[2][itemCount]; for (int item = 0; item < itemCount; item++) { double x = dataset.getXValue(series, item); double y = dataset.getYValue(series, item); if (!Double.isNaN(x) && !Double.isNaN(y)) { data[0][validItems] = x; data[1][validItems] = y; validItems++; } } if (validItems < order + 1) { throw new IllegalArgumentException("Not enough data."); } int equations = order + 1; int coefficients = order + 2; double[] result = new double[equations + 1]; double[][] matrix = new double[equations][coefficients]; double sumX = 0.0; double sumY = 0.0; for (int item = 0; item < validItems; item++) { sumX += data[0][item]; sumY += data[1][item]; for (int eq = 0; eq < equations; eq++) { for (int coe = 0; coe < coefficients - 1; coe++) { matrix[eq][coe] += Math.pow(data[0][item], eq + coe); } matrix[eq][coefficients - 1] += data[1][item] * Math.pow(data[0][item], eq); } } double[][] subMatrix = calculateSubMatrix(matrix); for (int eq = 1; eq < equations; eq++) { matrix[eq][0] = 0; for (int coe = 1; coe < coefficients; coe++) { matrix[eq][coe] = subMatrix[eq - 1][coe - 1]; } } for (int eq = equations - 1; eq > -1; eq--) { double value = matrix[eq][coefficients - 1]; for (int coe = eq; coe < coefficients - 1; coe++) { value -= matrix[eq][coe] * result[coe]; } result[eq] = value / matrix[eq][eq]; } double meanY = sumY / validItems; double yObsSquare = 0.0; double yRegSquare = 0.0; for (int item = 0; item < validItems; item++) { double yCalc = 0; for (int eq = 0; eq < equations; eq++) { yCalc += result[eq] * Math.pow(data[0][item], eq); } yRegSquare += Math.pow(yCalc - meanY, 2); yObsSquare += Math.pow(data[1][item] - meanY, 2); } double rSquare = yRegSquare / yObsSquare; result[equations] = rSquare; return result; }
From source file:playground.benjamin.scenarios.zurich.analysis.charts.BkChartWriter.java
public static void writeChartDataToFile(String filename, JFreeChart chart) { filename += ".txt"; try {/*from w w w . ja v a 2 s .c o m*/ BufferedWriter writer = IOUtils.getBufferedWriter(filename); try { /*read "try" as if (plot instanceof XYPlot)*/ XYPlot xy = chart.getXYPlot(); String yAxisLabel = xy.getRangeAxis().getLabel(); String xAxisLabel = ""; if (xy.getDomainAxis() != null) { xAxisLabel = xy.getDomainAxis().getLabel(); } String header = xAxisLabel + "\t " + yAxisLabel; writer.write(header); writer.newLine(); for (int i = 0; i < xy.getDatasetCount(); i++) { XYDataset xyds = xy.getDataset(i); for (int seriesIndex = 0; seriesIndex < xyds.getSeriesCount(); seriesIndex++) { writer.newLine(); writer.write("Series " + "'" + xyds.getSeriesKey(seriesIndex).toString()); writer.write("'"); writer.newLine(); int items = xyds.getItemCount(seriesIndex); for (int itemsIndex = 0; itemsIndex < items; itemsIndex++) { Number xValue = xyds.getX(seriesIndex, itemsIndex); Number yValue = xyds.getY(seriesIndex, itemsIndex); writer.write(xValue.toString()); writer.write("\t"); writer.write(yValue.toString()); writer.newLine(); } } } System.out.println("Table written to : " + filename + "\n" + "=================================================="); } catch (ClassCastException e) { //else instanceof CategoryPlot log.info("caught class cast exception, trying to write CategoryPlot"); CategoryPlot cp = chart.getCategoryPlot(); String header = "CategoryRowKey \t CategoryColumnKey \t CategoryRowIndex \t CategoryColumnIndex \t Value"; writer.write(header); writer.newLine(); for (int i = 0; i < cp.getDatasetCount(); i++) { CategoryDataset cpds = cp.getDataset(i); for (int rowIndex = 0; rowIndex < cpds.getRowCount(); rowIndex++) { for (int columnIndex = 0; columnIndex < cpds.getColumnCount(); columnIndex++) { Number value = cpds.getValue(rowIndex, columnIndex); writer.write(cpds.getRowKey(rowIndex).toString()); writer.write("\t"); writer.write(cpds.getColumnKey(columnIndex).toString()); writer.write("\t"); writer.write(Integer.toString(rowIndex)); writer.write("\t"); writer.write(Integer.toString(columnIndex)); writer.write("\t"); writer.write(value.toString()); writer.newLine(); } } } } writer.close(); } catch (FileNotFoundException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } }
From source file:playground.dgrether.analysis.charts.utils.DgChartWriter.java
public static void writeChartDataToFile(String filename, JFreeChart chart) { filename += ".txt"; try {/* ww w. ja v a 2 s . c om*/ BufferedWriter writer = IOUtils.getBufferedWriter(filename); try { /*read "try" as if (plot instanceof XYPlot)*/ XYPlot xy = chart.getXYPlot(); String yAxisLabel = xy.getRangeAxis().getLabel(); String xAxisLabel = ""; if (xy.getDomainAxis() != null) { xAxisLabel = xy.getDomainAxis().getLabel(); } String header = "#" + xAxisLabel + "\t " + yAxisLabel; writer.write(header); writer.newLine(); //write the header writer.write("#"); for (int i = 0; i < xy.getDatasetCount(); i++) { XYDataset xyds = xy.getDataset(i); int seriesIndex = 0; int maxItems = 0; int seriesCount = xyds.getSeriesCount(); while (seriesIndex < seriesCount) { writer.write("Series " + xyds.getSeriesKey(seriesIndex).toString()); if (seriesIndex < seriesCount - 1) { writer.write("\t \t"); } if (xyds.getItemCount(seriesIndex) > maxItems) { maxItems = xyds.getItemCount(seriesIndex); } seriesIndex++; } writer.newLine(); //write the data Number xValue, yValue = null; for (int itemsIndex = 0; itemsIndex < maxItems; itemsIndex++) { for (int seriesIdx = 0; seriesIdx < seriesCount; seriesIdx++) { if (seriesIdx < xyds.getSeriesCount() && itemsIndex < xyds.getItemCount(seriesIdx)) { xValue = xyds.getX(seriesIdx, itemsIndex); yValue = xyds.getY(seriesIdx, itemsIndex); if (xValue != null && yValue != null) { writer.write(xValue.toString()); writer.write("\t"); writer.write(yValue.toString()); if (seriesIdx < seriesCount - 1) { writer.write("\t"); } } } } writer.newLine(); } } } catch (ClassCastException e) { //else instanceof CategoryPlot log.info("Due to a caught class cast exception, it should be a CategoryPlot"); CategoryPlot cp = chart.getCategoryPlot(); String header = "# CategoryRowKey \t CategoryColumnKey \t CategoryRowIndex \t CategoryColumnIndex \t Value"; writer.write(header); writer.newLine(); for (int i = 0; i < cp.getDatasetCount(); i++) { CategoryDataset cpds = cp.getDataset(i); for (int rowIndex = 0; rowIndex < cpds.getRowCount(); rowIndex++) { for (int columnIndex = 0; columnIndex < cpds.getColumnCount(); columnIndex++) { Number value = cpds.getValue(rowIndex, columnIndex); writer.write(cpds.getRowKey(rowIndex).toString()); writer.write("\t"); writer.write(cpds.getColumnKey(columnIndex).toString()); writer.write("\t"); writer.write(Integer.toString(rowIndex)); writer.write("\t"); writer.write(Integer.toString(columnIndex)); writer.write("\t"); writer.write(value.toString()); writer.newLine(); } } } } writer.close(); log.info("Chart data written to: " + filename); } catch (FileNotFoundException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } }
From source file:de.mpg.mpi_inf.bioinf.netanalyzer.ui.charts.JFreeChartConn.java
/** * Extracts the visualized data from a given chart instance. * <p>//from w w w . j av a2 s . co m * This methods extracts the default data series from the default dataset of the given chart. * </p> * * @param aChart * Chart to extract the data from. * @return Visualized data in the form of an array of points. */ public static Point2D.Double[] extractData(JFreeChart aChart) { XYDataset dataColl = aChart.getXYPlot().getDataset(); final int n = dataColl.getItemCount(0); Point2D.Double[] dataPoints = new Point2D.Double[n]; for (int i = 0; i < n; ++i) { dataPoints[i] = new Point2D.Double(dataColl.getXValue(0, i), dataColl.getYValue(0, i)); } return dataPoints; }
From source file:de.mpg.mpi_inf.bioinf.netanalyzer.ui.charts.JFreeChartConn.java
private static double logLowerBound(XYDataset aDataset, boolean isDomainAxis) { double lowerBound = 0.0; for (int i = 0; i < aDataset.getItemCount(0); i++) { double tmp = isDomainAxis ? aDataset.getXValue(0, i) : aDataset.getYValue(0, i); if (lowerBound == 0.0 || tmp < lowerBound) lowerBound = tmp;//from w ww.j a v a 2 s. c o m } return lowerBound; }
From source file:net.sf.mzmine.chartbasics.HistogramChartFactory.java
/** * Performs Gaussian fit on XYSeries//from ww w.ja va 2 s . co m * * @param data the data * @param series the series index * @param gMin lower bound of Gaussian fit * @param gMax upper bound of Gaussian fit * @param sigDigits number of significant digits * @return double[] {normFactor, mean, sigma} as a result of * GaussianCurveFitter.create().fit(obs.toList()) */ public static double[] gaussianFit(XYDataset data, int series, double gMin, double gMax) { // gaussian fit WeightedObservedPoints obs = new WeightedObservedPoints(); for (int i = 0; i < data.getItemCount(series); i++) { double x = data.getXValue(series, i); if (x >= gMin && x <= gMax) obs.add(x, data.getYValue(series, i)); } return fitter.fit(obs.toList()); }
From source file:org.jfree.data.time.MovingAverage.java
/** * Creates a new {@link XYSeries} containing the moving averages of one * series in the <code>source</code> dataset. * * @param source the source dataset./* w w w . j a va2 s. c om*/ * @param series the series index (zero based). * @param name the name for the new series. * @param period the averaging period. * @param skip the length of the initial skip period. * * @return The dataset. */ public static XYSeries createMovingAverage(XYDataset source, int series, String name, double period, double skip) { ParamChecks.nullNotPermitted(source, "source"); if (period < Double.MIN_VALUE) { throw new IllegalArgumentException("period must be positive."); } if (skip < 0.0) { throw new IllegalArgumentException("skip must be >= 0.0."); } XYSeries result = new XYSeries(name); if (source.getItemCount(series) > 0) { // if the initial averaging period is to be excluded, then // calculate the lowest x-value to have an average calculated... double first = source.getXValue(series, 0) + skip; for (int i = source.getItemCount(series) - 1; i >= 0; i--) { // get the current data item... double x = source.getXValue(series, i); if (x >= first) { // work out the average for the earlier values... int n = 0; double sum = 0.0; double limit = x - period; int offset = 0; boolean finished = false; while (!finished) { if ((i - offset) >= 0) { double xx = source.getXValue(series, i - offset); Number yy = source.getY(series, i - offset); if (xx > limit) { if (yy != null) { sum = sum + yy.doubleValue(); n = n + 1; } } else { finished = true; } } else { finished = true; } offset = offset + 1; } if (n > 0) { result.add(x, sum / n); } else { result.add(x, null); } } } } return result; }