List of usage examples for org.jfree.data.xy XYIntervalSeries XYIntervalSeries
public XYIntervalSeries(Comparable key, boolean autoSort, boolean allowDuplicateXValues)
From source file:weka.classifiers.timeseries.eval.graph.JFreeChartDriver.java
protected JFreeChart getPredictedTargetsChart(TSForecaster forecaster, ErrorModule preds, List<String> targetNames, int stepNumber, int instanceNumOffset, Instances data) { if (forecaster instanceof TSLagUser && data != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (lagMaker.getAdjustForTrends() && !lagMaker.isUsingAnArtificialTimeIndex()) { // fill in any missing time stamps only data = new Instances(data); data = weka.classifiers.timeseries.core.Utils.replaceMissing(data, null, lagMaker.getTimeStampField(), true, lagMaker.getPeriodicity(), lagMaker.getSkipEntries()); }//from w w w. java2s . co m } // set up a collection of predicted and actual series XYIntervalSeriesCollection xyDataset = new XYIntervalSeriesCollection(); for (String target : targetNames) { XYIntervalSeries targetSeries = new XYIntervalSeries(target + "-actual", false, false); xyDataset.addSeries(targetSeries); targetSeries = new XYIntervalSeries(target + "-predicted", false, false); xyDataset.addSeries(targetSeries); } ValueAxis timeAxis = null; NumberAxis valueAxis = new NumberAxis(""); valueAxis.setAutoRangeIncludesZero(false); int timeIndex = -1; boolean timeAxisIsDate = false; if (forecaster instanceof TSLagUser && data != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (!lagMaker.isUsingAnArtificialTimeIndex() && lagMaker.getAdjustForTrends()) { String timeName = lagMaker.getTimeStampField(); if (data.attribute(timeName).isDate()) { timeAxis = new DateAxis(""); timeAxisIsDate = true; timeIndex = data.attribute(timeName).index(); } } } if (timeAxis == null) { timeAxis = new NumberAxis(""); ((NumberAxis) timeAxis).setAutoRangeIncludesZero(false); } // now populate the series boolean hasConfidenceIntervals = false; for (int i = 0; i < targetNames.size(); i++) { String targetName = targetNames.get(i); List<NumericPrediction> predsForI = preds.getPredictionsForTarget(targetName); int predIndex = xyDataset.indexOf(targetName + "-predicted"); int actualIndex = xyDataset.indexOf(targetName + "-actual"); XYIntervalSeries predSeries = xyDataset.getSeries(predIndex); XYIntervalSeries actualSeries = xyDataset.getSeries(actualIndex); for (int j = 0; j < predsForI.size(); j++) { double x = Utils.missingValue(); if (timeAxisIsDate) { if (instanceNumOffset + j + stepNumber - 1 < data.numInstances()) { x = data.instance(instanceNumOffset + j + stepNumber - 1).value(timeIndex); } } else { x = instanceNumOffset + j + stepNumber; } double yPredicted = predsForI.get(j).predicted(); double yHigh = yPredicted; double yLow = yPredicted; double[][] conf = predsForI.get(j).predictionIntervals(); if (conf.length > 0) { yLow = conf[0][0]; yHigh = conf[0][1]; hasConfidenceIntervals = true; } if (!Utils.isMissingValue(x) && !Utils.isMissingValue(yPredicted)) { if (predSeries != null) { predSeries.add(x, x, x, yPredicted, yLow, yHigh); } // System.err.println("* " + yPredicted + " " + x); } double yActual = predsForI.get(j).actual(); if (!Utils.isMissingValue(x) && !Utils.isMissingValue(yActual)) { if (actualSeries != null) { actualSeries.add(x, x, x, yActual, yActual, yActual); } } } } // set up the chart String title = "" + stepNumber + " step-ahead predictions for: "; for (String s : targetNames) { title += s + ","; } title = title.substring(0, title.lastIndexOf(",")); /* * String algoSpec = forecaster.getAlgorithmName(); title += " (" + algoSpec * + ")"; */ if (forecaster instanceof WekaForecaster && hasConfidenceIntervals) { double confPerc = ((WekaForecaster) forecaster).getConfidenceLevel() * 100.0; title += " [" + Utils.doubleToString(confPerc, 0) + "% conf. intervals]"; } XYErrorRenderer renderer = new XYErrorRenderer(); renderer.setBaseLinesVisible(true); renderer.setDrawXError(false); renderer.setDrawYError(true); // renderer.setShapesFilled(true); XYPlot plot = new XYPlot(xyDataset, timeAxis, valueAxis, renderer); JFreeChart chart = new JFreeChart(title, JFreeChart.DEFAULT_TITLE_FONT, plot, true); chart.setBackgroundPaint(java.awt.Color.white); TextTitle chartTitle = chart.getTitle(); String fontName = chartTitle.getFont().getFontName(); java.awt.Font newFont = new java.awt.Font(fontName, java.awt.Font.PLAIN, 12); chartTitle.setFont(newFont); return chart; }
From source file:org.jfree.data.xy.XYIntervalSeriesTest.java
/** * A check for the indexOf() method for an unsorted series. *//*from ww w .ja v a2s . co m*/ @Test public void testIndexOf2() { XYIntervalSeries s1 = new XYIntervalSeries("Series 1", false, true); s1.add(1.0, 1.0, 1.0, 2.0, 1.9, 2.1); s1.add(3.0, 3.0, 3.0, 3.0, 2.9, 3.1); s1.add(2.0, 2.0, 2.0, 2.0, 1.9, 2.1); assertEquals(0, s1.indexOf(new Double(1.0))); assertEquals(1, s1.indexOf(new Double(3.0))); assertEquals(2, s1.indexOf(new Double(2.0))); }
From source file:org.jfree.data.xy.XYIntervalSeriesTest.java
/** * Some checks for the add() method for an UNSORTED series. *//*from w w w. j a v a 2 s. c o m*/ @Test public void testAdd() { XYIntervalSeries series = new XYIntervalSeries("Series", false, true); series.add(5.0, 5.50, 5.50, 5.50, 5.50, 5.50); series.add(5.1, 5.51, 5.51, 5.51, 5.51, 5.51); series.add(6.0, 6.6, 6.6, 6.6, 6.6, 6.6); series.add(3.0, 3.3, 3.3, 3.3, 3.3, 3.3); series.add(4.0, 4.4, 4.4, 4.4, 4.4, 4.4); series.add(2.0, 2.2, 2.2, 2.2, 2.2, 2.2); series.add(1.0, 1.1, 1.1, 1.1, 1.1, 1.1); assertEquals(5.5, series.getYValue(0), EPSILON); assertEquals(5.51, series.getYValue(1), EPSILON); assertEquals(6.6, series.getYValue(2), EPSILON); assertEquals(3.3, series.getYValue(3), EPSILON); assertEquals(4.4, series.getYValue(4), EPSILON); assertEquals(2.2, series.getYValue(5), EPSILON); assertEquals(1.1, series.getYValue(6), EPSILON); }
From source file:weka.classifiers.timeseries.eval.graph.JFreeChartDriver.java
protected JFreeChart getPredictedStepsChart(TSForecaster forecaster, List<ErrorModule> preds, String targetName, List<Integer> stepsToPlot, int instanceNumOffset, Instances data) { if (forecaster instanceof TSLagUser && data != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (lagMaker.getAdjustForTrends() && !lagMaker.isUsingAnArtificialTimeIndex()) { // fill in any missing time stamps only data = new Instances(data); data = weka.classifiers.timeseries.core.Utils.replaceMissing(data, null, lagMaker.getTimeStampField(), true, lagMaker.getPeriodicity(), lagMaker.getSkipEntries()); }/* w w w . ja va2s . co m*/ } // set up a collection of predicted series XYIntervalSeriesCollection xyDataset = new XYIntervalSeriesCollection(); XYIntervalSeries targetSeries = new XYIntervalSeries(targetName, false, false); xyDataset.addSeries(targetSeries); // for (int i = 0; i < preds.size(); i++) { for (int z = 0; z < stepsToPlot.size(); z++) { int i = stepsToPlot.get(z); i--; // ignore out of range steps if (i < 0 || i >= preds.size()) { continue; } String step = "-steps"; if (i == 0) { step = "-step"; } targetSeries = new XYIntervalSeries(targetName + "_" + (i + 1) + step + "-ahead", false, false); xyDataset.addSeries(targetSeries); } ValueAxis timeAxis = null; NumberAxis valueAxis = new NumberAxis(""); valueAxis.setAutoRangeIncludesZero(false); int timeIndex = -1; boolean timeAxisIsDate = false; if (forecaster instanceof TSLagUser && data != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (!lagMaker.isUsingAnArtificialTimeIndex() && lagMaker.getAdjustForTrends()) { String timeName = lagMaker.getTimeStampField(); if (data.attribute(timeName).isDate()) { timeAxis = new DateAxis(""); timeAxisIsDate = true; timeIndex = data.attribute(timeName).index(); } } } if (timeAxis == null) { timeAxis = new NumberAxis(""); ((NumberAxis) timeAxis).setAutoRangeIncludesZero(false); } // now populate the series // for (int i = 0; i < preds.size(); i++) { boolean doneActual = false; boolean hasConfidenceIntervals = false; for (int z = 0; z < stepsToPlot.size(); z++) { int i = stepsToPlot.get(z); i--; // ignore out of range steps if (i < 0 || i >= preds.size()) { continue; } ErrorModule predsForStepI = preds.get(i); List<NumericPrediction> predsForTargetAtI = predsForStepI.getPredictionsForTarget(targetName); String step = "-steps"; if (i == 0) { step = "-step"; } int predIndex = xyDataset.indexOf(targetName + "_" + (i + 1) + step + "-ahead"); XYIntervalSeries predSeries = xyDataset.getSeries(predIndex); XYIntervalSeries actualSeries = null; if (!doneActual) { int actualIndex = xyDataset.indexOf(targetName); actualSeries = xyDataset.getSeries(actualIndex); } for (int j = 0; j < predsForTargetAtI.size(); j++) { double x = Utils.missingValue(); if (timeAxisIsDate) { if (instanceNumOffset + j + i < data.numInstances()) { x = data.instance(instanceNumOffset + j + i).value(timeIndex); } } else { x = instanceNumOffset + j + i; } double yPredicted = predsForTargetAtI.get(j).predicted(); double yHigh = yPredicted; double yLow = yPredicted; double[][] conf = predsForTargetAtI.get(j).predictionIntervals(); if (conf.length > 0) { yLow = conf[0][0]; yHigh = conf[0][1]; hasConfidenceIntervals = true; } if (!Utils.isMissingValue(x) && !Utils.isMissingValue(yPredicted)) { if (predSeries != null) { predSeries.add(x, x, x, yPredicted, yLow, yHigh); } // System.err.println("* " + yPredicted + " " + x); } if (!doneActual && actualSeries != null) { double yActual = predsForTargetAtI.get(j).actual(); if (!Utils.isMissingValue(x) && !Utils.isMissingValue(yActual)) { actualSeries.add(x, x, x, yActual, yActual, yActual); } } } if (actualSeries != null) { doneActual = true; } } // set up the chart String title = ""; for (int i : stepsToPlot) { title += i + ","; } title = title.substring(0, title.lastIndexOf(",")); title += " step-ahead predictions for " + targetName; /* * String algoSpec = forecaster.getAlgorithmName(); title += " (" + algoSpec * + ")"; */ if (forecaster instanceof WekaForecaster && hasConfidenceIntervals) { double confPerc = ((WekaForecaster) forecaster).getConfidenceLevel() * 100.0; title += " [" + Utils.doubleToString(confPerc, 0) + "% conf. intervals]"; } XYErrorRenderer renderer = new XYErrorRenderer(); renderer.setBaseLinesVisible(true); // renderer.setShapesFilled(true); XYPlot plot = new XYPlot(xyDataset, timeAxis, valueAxis, renderer); JFreeChart chart = new JFreeChart(title, JFreeChart.DEFAULT_TITLE_FONT, plot, true); chart.setBackgroundPaint(java.awt.Color.white); TextTitle chartTitle = chart.getTitle(); String fontName = chartTitle.getFont().getFontName(); java.awt.Font newFont = new java.awt.Font(fontName, java.awt.Font.PLAIN, 12); chartTitle.setFont(newFont); return chart; }
From source file:msi.gama.outputs.layers.charts.ChartJFreeChartOutputScatter.java
@Override protected void createNewSerie(final IScope scope, final String serieid) { final ChartDataSeries dataserie = chartdataset.getDataSeries(scope, serieid); final XYIntervalSeries serie = new XYIntervalSeries(dataserie.getSerieLegend(scope), false, true); final XYPlot plot = (XYPlot) this.chart.getPlot(); final XYIntervalSeriesCollection firstdataset = (XYIntervalSeriesCollection) plot.getDataset(); if (!IdPosition.containsKey(serieid)) { if (firstdataset.getSeriesCount() == 0) { firstdataset.addSeries(serie); plot.setDataset(0, firstdataset); } else {//from ww w .j av a 2 s . c om final XYIntervalSeriesCollection newdataset = new XYIntervalSeriesCollection(); newdataset.addSeries(serie); jfreedataset.add(newdataset); plot.setDataset(jfreedataset.size() - 1, newdataset); } plot.setRenderer(jfreedataset.size() - 1, (XYItemRenderer) getOrCreateRenderer(scope, serieid)); IdPosition.put(serieid, jfreedataset.size() - 1); // DEBUG.LOG("new serie"+serieid+" at // "+IdPosition.get(serieid)+" fdsize "+plot.getSeriesCount()+" jfds // "+jfreedataset.size()+" datasc "+plot.getDatasetCount()); // TODO Auto-generated method stub } }
From source file:weka.classifiers.timeseries.eval.graph.JFreeChartDriver.java
protected JFreeChart getFutureForecastChart(TSForecaster forecaster, List<List<NumericPrediction>> preds, List<String> targetNames, Instances history) { if (forecaster instanceof TSLagUser && history != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (lagMaker.getAdjustForTrends() && !lagMaker.isUsingAnArtificialTimeIndex()) { // fill in any missing time stamps only history = new Instances(history); history = weka.classifiers.timeseries.core.Utils.replaceMissing(history, null, lagMaker.getTimeStampField(), true, lagMaker.getPeriodicity(), lagMaker.getSkipEntries()); }/*www. j ava 2s . com*/ } // set up a collection of series XYIntervalSeriesCollection xyDataset = new XYIntervalSeriesCollection(); if (history != null) { // add actual historical data values for (String targetName : targetNames) { XYIntervalSeries targetSeries = new XYIntervalSeries(targetName, false, false); xyDataset.addSeries(targetSeries); } } // add predicted series for (String targetName : targetNames) { XYIntervalSeries targetSeries = new XYIntervalSeries(targetName + "-predicted", false, false); xyDataset.addSeries(targetSeries); } ValueAxis timeAxis = null; NumberAxis valueAxis = new NumberAxis(""); valueAxis.setAutoRangeIncludesZero(false); int timeIndex = -1; boolean timeAxisIsDate = false; double artificialTimeStart = 0; double lastRealTimeValue = Utils.missingValue(); if (forecaster instanceof TSLagUser && history != null) { TSLagMaker lagMaker = ((TSLagUser) forecaster).getTSLagMaker(); if (!lagMaker.isUsingAnArtificialTimeIndex() && lagMaker.getAdjustForTrends()) { String timeName = lagMaker.getTimeStampField(); if (history.attribute(timeName).isDate()) { timeAxis = new DateAxis(""); timeAxisIsDate = true; timeIndex = history.attribute(timeName).index(); } } else { try { artificialTimeStart = (history != null) ? 1 : lagMaker.getArtificialTimeStartValue() + 1; } catch (Exception ex) { } } } if (timeAxis == null) { timeAxis = new NumberAxis(""); ((NumberAxis) timeAxis).setAutoRangeIncludesZero(false); } boolean hasConfidenceIntervals = false; // now populate the series if (history != null) { // do the actuals first for (int i = 0; i < history.numInstances(); i++) { Instance current = history.instance(i); for (String targetName : targetNames) { int dataIndex = history.attribute(targetName.trim()).index(); if (dataIndex >= 0) { XYIntervalSeries actualSeries = null; int actualIndex = xyDataset.indexOf(targetName); actualSeries = xyDataset.getSeries(actualIndex); double x = Utils.missingValue(); if (timeAxisIsDate) { x = current.value(timeIndex); if (!Utils.isMissingValue(x)) { lastRealTimeValue = x; } } else { x = artificialTimeStart; } double y = Utils.missingValue(); y = current.value(dataIndex); if (!Utils.isMissingValue(x) && !Utils.isMissingValue(y)) { if (actualSeries != null) { actualSeries.add(x, x, x, y, y, y); } } } } if (!timeAxisIsDate) { artificialTimeStart++; } } } // now do the futures List<String> forecasterTargets = AbstractForecaster.stringToList(forecaster.getFieldsToForecast()); // loop over the steps for (int j = 0; j < preds.size(); j++) { List<NumericPrediction> predsForStepJ = preds.get(j); // advance the real time index (if appropriate) if (timeAxisIsDate) { lastRealTimeValue = ((TSLagUser) forecaster).getTSLagMaker() .advanceSuppliedTimeValue(lastRealTimeValue); } for (String targetName : targetNames) { // look up this requested target in the list that the forecaster // has predicted int predIndex = forecasterTargets.indexOf(targetName.trim()); if (predIndex >= 0) { NumericPrediction predsForTargetAtStepJ = predsForStepJ.get(predIndex); XYIntervalSeries predSeries = null; int datasetIndex = xyDataset.indexOf(targetName + "-predicted"); predSeries = xyDataset.getSeries(datasetIndex); if (predSeries != null) { double y = predsForTargetAtStepJ.predicted(); double x = Utils.missingValue(); double yHigh = y; double yLow = y; double[][] conf = predsForTargetAtStepJ.predictionIntervals(); if (conf.length > 0) { yLow = conf[0][0]; yHigh = conf[0][1]; hasConfidenceIntervals = true; } if (!timeAxisIsDate) { x = artificialTimeStart; } else { x = lastRealTimeValue; } if (!Utils.isMissingValue(x) && !Utils.isMissingValue(y)) { predSeries.add(x, x, x, y, yLow, yHigh); } } } } // advance the artificial time index (if appropriate) if (!timeAxisIsDate) { artificialTimeStart++; } } String title = "Future forecast for: "; for (String s : targetNames) { title += s + ","; } title = title.substring(0, title.lastIndexOf(",")); /* * String algoSpec = forecaster.getAlgorithmName(); title += " (" + algoSpec * + ")"; */ if (forecaster instanceof WekaForecaster && hasConfidenceIntervals) { double confPerc = ((WekaForecaster) forecaster).getConfidenceLevel() * 100.0; title += " [" + Utils.doubleToString(confPerc, 0) + "% conf. intervals]"; } XYErrorRenderer renderer = new XYErrorRenderer(); // renderer.setShapesFilled(true); XYPlot plot = new XYPlot(xyDataset, timeAxis, valueAxis, renderer); // renderer = (XYErrorRenderer)plot.getRenderer(); if (history != null) { for (String targetName : targetNames) { XYIntervalSeries predSeries = null; int predIndex = xyDataset.indexOf(targetName + "-predicted"); predSeries = xyDataset.getSeries(predIndex); XYIntervalSeries actualSeries = null; int actualIndex = xyDataset.indexOf(targetName); actualSeries = xyDataset.getSeries(actualIndex); if (actualSeries != null && predSeries != null) { // match the color of the actual series java.awt.Paint actualPaint = renderer.lookupSeriesPaint(actualIndex); renderer.setSeriesPaint(predIndex, actualPaint); // now set the line style to dashed BasicStroke dashed = new BasicStroke(1.5f, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER, 10.0f, new float[] { 5.0f }, 0.0f); renderer.setSeriesStroke(predIndex, dashed); } } } renderer.setBaseLinesVisible(true); renderer.setDrawXError(false); renderer.setDrawYError(true); JFreeChart chart = new JFreeChart(title, JFreeChart.DEFAULT_TITLE_FONT, plot, true); chart.setBackgroundPaint(java.awt.Color.white); TextTitle chartTitle = chart.getTitle(); String fontName = chartTitle.getFont().getFontName(); java.awt.Font newFont = new java.awt.Font(fontName, java.awt.Font.PLAIN, 12); chartTitle.setFont(newFont); return chart; }