Example usage for org.jfree.data.xy XYDataset getYValue

List of usage examples for org.jfree.data.xy XYDataset getYValue

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Introduction

In this page you can find the example usage for org.jfree.data.xy XYDataset getYValue.

Prototype

public double getYValue(int series, int item);

Source Link

Document

Returns the y-value (as a double primitive) for an item within a series.

Usage

From source file:ChartUsingJava.CombinedXYPlotDemo1.java

/**
 * Creates an overlaid chart.//from   w w  w .j  a  v  a  2  s.  c om
 *
 * @return The chart.
 */
private static JFreeChart createCombinedChart() {

    // create plot ...
    IntervalXYDataset data1 = createDataset1();
    XYItemRenderer renderer1 = new XYLineAndShapeRenderer(true, false);
    renderer1.setBaseToolTipGenerator(
            new StandardXYToolTipGenerator(StandardXYToolTipGenerator.DEFAULT_TOOL_TIP_FORMAT,
                    new SimpleDateFormat("d-MMM-yyyy"), new DecimalFormat("0.00")));
    renderer1.setSeriesStroke(0, new BasicStroke(4.0f, BasicStroke.CAP_ROUND, BasicStroke.JOIN_BEVEL));
    renderer1.setSeriesPaint(0, Color.blue);

    DateAxis domainAxis = new DateAxis("Year");
    domainAxis.setLowerMargin(0.0);
    domainAxis.setUpperMargin(0.02);
    ValueAxis rangeAxis = new NumberAxis("$billion");
    XYPlot plot1 = new XYPlot(data1, null, rangeAxis, renderer1);
    plot1.setBackgroundPaint(Color.lightGray);
    plot1.setDomainGridlinePaint(Color.white);
    plot1.setRangeGridlinePaint(Color.white);

    // add a second dataset and renderer...
    IntervalXYDataset data2 = createDataset2();
    XYBarRenderer renderer2 = new XYBarRenderer() {
        public Paint getItemPaint(int series, int item) {
            XYDataset dataset = getPlot().getDataset();
            if (dataset.getYValue(series, item) >= 0.0) {
                return Color.red;
            } else {
                return Color.green;
            }
        }
    };
    renderer2.setSeriesPaint(0, Color.red);
    renderer2.setDrawBarOutline(false);
    renderer2.setBaseToolTipGenerator(
            new StandardXYToolTipGenerator(StandardXYToolTipGenerator.DEFAULT_TOOL_TIP_FORMAT,
                    new SimpleDateFormat("d-MMM-yyyy"), new DecimalFormat("0.00")));

    XYPlot plot2 = new XYPlot(data2, null, new NumberAxis("$billion"), renderer2);
    plot2.setBackgroundPaint(Color.lightGray);
    plot2.setDomainGridlinePaint(Color.white);
    plot2.setRangeGridlinePaint(Color.white);

    CombinedXYPlot cplot = new CombinedXYPlot(domainAxis, rangeAxis);
    cplot.add(plot1, 3);
    cplot.add(plot2, 2);
    cplot.setGap(8.0);
    cplot.setDomainGridlinePaint(Color.white);
    cplot.setDomainGridlinesVisible(true);

    // return a new chart containing the overlaid plot...
    JFreeChart chart = new JFreeChart("CombinedXYPlotDemo1", JFreeChart.DEFAULT_TITLE_FONT, cplot, false);
    chart.setBackgroundPaint(Color.white);
    LegendTitle legend = new LegendTitle(cplot);
    chart.addSubtitle(legend);
    return chart;
}

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.fhcrc.cpl.viewer.gui.SpectrumChartFactory.java

protected static XYPlot createXYPlot(XYDataset dataset, Color[] colors) {
    // break up into two datasets, one for bars one for lines
    //LinkedList lines =new LinkedList();
    //LinkedList bars = new LinkedList();
    XYDataset seriesLines = new XYSeriesCollection();
    XYDataset seriesBars = new XYSeriesCollection();
    ((XYSeriesCollection) seriesBars).setIntervalWidth(0.0);

    if (dataset instanceof XYSeriesCollection) {
        while (0 < dataset.getSeriesCount()) {
            XYSeries s = ((XYSeriesCollection) dataset).getSeries(0);
            ((XYSeriesCollection) dataset).removeSeries(0);
            Comparable key = s.getKey();
            boolean lines = false;
            if (key instanceof String)
                lines = ((String) key).startsWith("-");
            if (lines)
                ((XYSeriesCollection) seriesLines).addSeries(s);
            else/*from  w w w.j a v  a 2s.  co  m*/
                ((XYSeriesCollection) seriesBars).addSeries(s);
        }
    } else {
        seriesBars = dataset;
    }

    NumberAxis axisDomain = new NumberAxis();
    axisDomain.setAutoRange(true);
    axisDomain.setAutoRangeIncludesZero(false);
    //      axisDomain.setRange(400.0, 1600.0);
    // NOTE: zooming in too far kills the chart, prevent this
    axisDomain.addChangeListener(new AxisChangeListener() {
        public void axisChanged(AxisChangeEvent event) {
            NumberAxis axis = (NumberAxis) event.getSource();
            Range range = axis.getRange();
            if (range.getLength() < 2.0) {
                //_log.info("AxisChangeListener " + range.getLength() + " " + range.toString());
                double middle = range.getLowerBound() + range.getLength() / 2.0;
                axis.setRange(new Range(middle - 1.1, middle + 1.1));
            }
        }
    });

    NumberAxis axisRange = new NumberAxis();
    axisRange.setAutoRange(true);
    axisRange.setAutoRangeIncludesZero(true);

    XYToolTipGenerator toolTipGenerator = new XYToolTipGenerator() {
        public String generateToolTip(XYDataset xyDataset, int s, int i) {
            double X = Math.round(xyDataset.getXValue(s, i) * 1000.0) / 1000.0;
            double Y = Math.round(xyDataset.getYValue(s, i) * 1000.0) / 1000.0;
            return "(" + X + ", " + Y + ")";
        }
    };

    XYBarRenderer barRenderer = new XYBarRenderer();
    //dhmay adding 2009/09/14.  As of jfree 1.0.13, shadows on by default        
    barRenderer.setShadowVisible(false);

    //dhmay adding for jfreechart 1.0.6 upgrade.  If this isn't here, we get a
    //nullPointerException in XYBarRenderer.drawItemLabel
    barRenderer.setBaseItemLabelGenerator(new NullLabelGenerator());

    barRenderer.setSeriesItemLabelsVisible(0, true);
    barRenderer.setBaseToolTipGenerator(toolTipGenerator);

    XYLineAndShapeRenderer lineRenderer = new XYLineAndShapeRenderer();
    lineRenderer.setBaseToolTipGenerator(toolTipGenerator);

    XYPlot xy = new XYPlot(null, axisDomain, axisRange, null);

    int ds = 0;
    if (seriesLines.getSeriesCount() > 0) {
        xy.setDataset(ds, seriesLines);
        xy.setRenderer(ds, lineRenderer);
        xy.mapDatasetToRangeAxis(ds, 0);
        ds++;
        for (int i = 0; i < seriesLines.getSeriesCount(); i++) {
            Comparable key = ((XYSeriesCollection) seriesLines).getSeriesKey(i);
            boolean lines = false;
            if (key instanceof String)
                lines = ((String) key).startsWith("-");
            lineRenderer.setSeriesLinesVisible(i, lines);
            lineRenderer.setSeriesShapesVisible(i, !lines);
        }
    }
    if (seriesBars.getSeriesCount() > 0) {
        xy.setDataset(ds, seriesBars);
        xy.setRenderer(ds, barRenderer);
        xy.mapDatasetToRangeAxis(ds, 0);
        ds++;
    }

    setColors(xy, colors);

    return xy;
}

From source file:net.sf.maltcms.common.charts.api.overlay.AbstractChartOverlay.java

/**
 *
 * @param entity/*from  w  w w  . j  a  v  a 2 s  .  c o  m*/
 * @param chartPanel
 * @param dataset
 * @param seriesIndex
 * @param itemIndex
 * @return
 */
public static Shape toView(Shape entity, ChartPanel chartPanel, Dataset dataset, int seriesIndex,
        int itemIndex) {
    if (dataset instanceof XYDataset) {
        XYDataset xyds = (XYDataset) dataset;
        double x1 = xyds.getXValue(seriesIndex, itemIndex);
        double y1 = xyds.getYValue(seriesIndex, itemIndex);
        AffineTransform toPosition = getModelToViewTransformXY(chartPanel, x1, y1);
        toPosition.concatenate(
                getTranslateInstance(-entity.getBounds2D().getCenterX(), -entity.getBounds2D().getCenterY()));
        return toPosition.createTransformedShape(entity);
    } else if (dataset instanceof CategoryDataset) {
        CategoryDataset cds = (CategoryDataset) dataset;
        double y1 = cds.getValue(seriesIndex, itemIndex).doubleValue();
        AffineTransform toPosition = getModelToViewTransformCategory(chartPanel, itemIndex, y1);
        toPosition.concatenate(
                getTranslateInstance(-entity.getBounds2D().getCenterX(), -entity.getBounds2D().getCenterY()));
        return toPosition.createTransformedShape(entity);
    }
    throw new IllegalArgumentException("Unsupported dataset type: " + dataset.getClass());
}

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] --&gt; a, and result[1] --&gt; b.
 *
 * @param data  the data.//www . j  av a 2  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] --&gt; a, and double[1] --&gt; b.
 *
 * @param data  the data.//  www  .  j  a  va2  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: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;/* ww  w .  j a  v  a  2 s  .  c o m*/
    }

    return lowerBound;
}

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] --&gt; a0, double[1] --&gt; a1, .., double[n] --&gt; 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 o  m*/
 *
 * @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 (&gt; 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:de.mpg.mpi_inf.bioinf.netanalyzer.ui.charts.JFreeChartConn.java

/**
 * Extracts the visualized data from a given chart instance.
 * <p>//from ww  w. java2s .c  om
 * 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:net.sf.mzmine.chartbasics.HistogramChartFactory.java

/**
 * Performs Gaussian fit on XYSeries//from w  w w . j a  v  a2 s.c o 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());
}