List of usage examples for javafx.scene.chart NumberAxis NumberAxis
public NumberAxis()
From source file:de.ifsr.adam.ImageGenerator.java
/** * Generates a bar chart/*w w w . j av a 2s .c o m*/ * * @param question A question JSONObject of a report * @return */ private BarChart generateBarChart(JSONObject question) { XYChart.Series data; BarChart<Number, String> chart; JSONObject surveyQuestion; JSONObject answerType; JSONObject result; try { surveyQuestion = getSurveyQuestion(question.getString("question")); try { answerType = getAnswerType(surveyQuestion.getString("type")); } catch (NullPointerException e) { log.error("The answerType of " + question + " wasnt generated due to a Nullpointer"); log.debug("", e); return null; } result = question.getJSONObject("result"); data = generateDataBarChart(result, answerType); //Axis CategoryAxis yAxis = new CategoryAxis(); yAxis.setAnimated(false); //Needs to be set, otherwise the labels are not printed to a file in the end. NumberAxis xAxis = new NumberAxis(); xAxis.setLabel("Stimmen"); //TODO: Multi Language support. xAxis.setAnimated(false); //Needs to be set, otherwise the labels are not printed to a file in the end. chart = new BarChart<>(xAxis, yAxis); chart.getData().addAll(data); chart.setTitle(surveyQuestion.getString("text")); chart.setLegendVisible(false); } catch (JSONException e) { log.error(e); return null; } return chart; }
From source file:qupath.lib.gui.panels.survival.KaplanMeierDisplay.java
@SuppressWarnings("unchecked") private void generatePlot() { KaplanMeierDisplay.ScoreData newScoreData = scoreData; // If we have a hierarchy, update the scores with the most recent data if (hierarchy != null) { List<TMACoreObject> cores = PathObjectTools.getTMACoreObjects(hierarchy, false); double[] survival = new double[cores.size()]; boolean[] censored = new boolean[cores.size()]; double[] scores = new double[cores.size()]; // // Optionally sort by scores... helps a bit when debugging e.g. p-values, Hazard ratios etc. // cores.sort((c1, c2) -> Double.compare(c1.getMeasurementList().getMeasurementValue(scoreColumn), c2.getMeasurementList().getMeasurementValue(scoreColumn))); // scoreColumn = "Positive %"; // scoreColumn = "RoughScore"; for (int i = 0; i < cores.size(); i++) { TMACoreObject core = cores.get(i); MeasurementList ml = core.getMeasurementList(); survival[i] = core.getMeasurementList().getMeasurementValue(survivalColumn); double censoredValue = core.getMeasurementList().getMeasurementValue(censoredColumn); boolean hasCensoredValue = !Double.isNaN(censoredValue) && (censoredValue == 0 || censoredValue == 1); censored[i] = censoredValue != 0; if (!hasCensoredValue) { // If we don't have a censored value, ensure we mask out everything else scores[i] = Double.NaN; survival[i] = Double.NaN; } else if (ml.containsNamedMeasurement(scoreColumn)) // Get the score if we can scores[i] = ml.getMeasurementValue(scoreColumn); else { // // Try to compute score if we need to // Map<String, Number> map = ROIMeaningfulMeasurements.getPathClassSummaryMeasurements(core.getChildObjects(), true); // Number value = map.get(scoreColumn); // if (value == null) scores[i] = Double.NaN; // else // scores[i] = value.doubleValue(); }//w ww .ja v a2 s . c om } // Mask out any scores that don't have associated survival data for (int i = 0; i < survival.length; i++) { if (Double.isNaN(survival[i])) scores[i] = Double.NaN; } newScoreData = new ScoreData(scores, survival, censored); } if (newScoreData == null || newScoreData.scores.length == 0) return; // KaplanMeier kmHigh = new KaplanMeier("Above threshold"); // KaplanMeier kmLow = new KaplanMeier("Below threshold"); double[] quartiles = StatisticsHelper.getQuartiles(newScoreData.scores); double q1 = quartiles[0]; double median = quartiles[1]; double q3 = quartiles[2]; double[] thresholds; if (params != null) { Object thresholdMethod = params.getChoiceParameterValue("scoreThresholdMethod"); if (thresholdMethod.equals("Median")) { // panelParams.setNumericParameterValue("scoreThreshold", median); // ((DoubleParameter)params.getParameters().get("scoreThreshold")).setValue(median); // TODO: UPDATE DIALOG! thresholds = new double[] { median }; } else if (thresholdMethod.equals("Tertiles")) { // ((DoubleParameter)params.getParameters().get("scoreThreshold")).setValue(median); // TODO: UPDATE DIALOG! thresholds = StatisticsHelper.getTertiles(newScoreData.scores); } else if (thresholdMethod.equals("Quartiles")) { // ((DoubleParameter)params.getParameters().get("scoreThreshold")).setValue(median); // TODO: UPDATE DIALOG! thresholds = new double[] { q1, median, q3 }; } else if (thresholdMethod.equals("Manual (1)")) { thresholds = new double[] { params.getDoubleParameterValue("threshold1") }; } else if (thresholdMethod.equals("Manual (2)")) { thresholds = new double[] { params.getDoubleParameterValue("threshold1"), params.getDoubleParameterValue("threshold2") }; } else //if (thresholdMethod.equals("Manual (3)")) { thresholds = new double[] { params.getDoubleParameterValue("threshold1"), params.getDoubleParameterValue("threshold2"), params.getDoubleParameterValue("threshold3") }; } else thresholds = new double[] { median }; double minVal = Double.POSITIVE_INFINITY; double maxVal = Double.NEGATIVE_INFINITY; int numNonNaN = 0; for (double d : newScoreData.scores) { if (Double.isNaN(d)) continue; if (d < minVal) minVal = d; if (d > maxVal) maxVal = d; numNonNaN++; } boolean scoresValid = maxVal > minVal; // If not this, we don't have valid scores that we can work with double maxTimePoint = 0; for (double d : newScoreData.survival) { if (Double.isNaN(d)) continue; if (d > maxTimePoint) maxTimePoint = d; } if (panelParams != null && maxTimePoint > ((IntParameter) params.getParameters().get("censorTimePoints")).getUpperBound()) { panelParams.setNumericParameterValueRange("censorTimePoints", 0, Math.ceil(maxTimePoint)); } // Optionally censor at specified time double censorThreshold = params == null ? maxTimePoint : params.getIntParameterValue("censorTimePoints"); // Compute log-rank p-values for *all* possible thresholds // Simultaneously determine the threshold that yields the lowest p-value, // resolving ties in favour of a more even split between high/low numbers of events boolean pValuesChanged = false; if (calculateAllPValues) { if (!(pValues != null && pValueThresholds != null && newScoreData.equals(scoreData) && censorThreshold == lastPValueCensorThreshold)) { Map<Double, Double> mapLogRank = new TreeMap<>(); Set<Double> setObserved = new HashSet<>(); for (int i = 0; i < newScoreData.scores.length; i++) { Double d = newScoreData.scores[i]; boolean observed = !newScoreData.censored[i] && newScoreData.survival[i] < censorThreshold; if (observed) setObserved.add(d); if (mapLogRank.containsKey(d)) continue; List<KaplanMeierData> kmsTemp = splitByThresholds(newScoreData, new double[] { d }, censorThreshold, false); // if (kmsTemp.get(1).nObserved() == 0 || kmsTemp.get(1).nObserved() == 0) // continue; LogRankResult test = LogRankTest.computeLogRankTest(kmsTemp.get(0), kmsTemp.get(1)); double pValue = test.getPValue(); // double pValue = test.hazardRatio < 1 ? test.hazardRatio : 1.0/test.hazardRatio; // Checking usefulness of Hazard ratios... if (!Double.isFinite(pValue)) continue; // if (!Double.isFinite(test.getHazardRatio())) { //// continue; // pValue = Double.NaN; // } mapLogRank.put(d, pValue); } pValueThresholds = new double[mapLogRank.size()]; pValues = new double[mapLogRank.size()]; pValueThresholdsObserved = new boolean[mapLogRank.size()]; int count = 0; for (Entry<Double, Double> entry : mapLogRank.entrySet()) { pValueThresholds[count] = entry.getKey(); pValues[count] = entry.getValue(); if (setObserved.contains(entry.getKey())) pValueThresholdsObserved[count] = true; count++; } // Find the longest 'significant' stretch int maxSigCount = 0; int maxSigInd = -1; int sigCurrent = 0; int[] sigCount = new int[pValues.length]; for (int i = 0; i < pValues.length; i++) { if (pValues[i] < 0.05) { sigCurrent++; sigCount[i] = sigCurrent; if (sigCurrent > maxSigCount) { maxSigCount = sigCurrent; maxSigInd = i; } } else sigCurrent = 0; } if (maxSigCount == 0) { logger.info("No p-values < 0.05"); } else { double minThresh = maxSigInd - maxSigCount < 0 ? pValueThresholds[0] - 0.0000001 : pValueThresholds[maxSigInd - maxSigCount]; double maxThresh = pValueThresholds[maxSigInd]; int nBetween = 0; int nBetweenObserved = 0; for (int i = 0; i < newScoreData.scores.length; i++) { if (newScoreData.scores[i] > minThresh && newScoreData.scores[i] <= maxThresh) { nBetween++; if (newScoreData.survival[i] < censorThreshold && !newScoreData.censored[i]) nBetweenObserved++; } } logger.info("Longest stretch of p-values < 0.05: {} - {} ({} entries, {} observed)", minThresh, maxThresh, nBetween, nBetweenObserved); } pValuesSmoothed = new double[pValues.length]; Arrays.fill(pValuesSmoothed, Double.NaN); int n = (pValues.length / 20) * 2 + 1; logger.info("Smoothing log-rank test p-values by " + n); for (int i = n / 2; i < pValues.length - n / 2; i++) { double sum = 0; for (int k = i - n / 2; k < i - n / 2 + n; k++) { sum += pValues[k]; } pValuesSmoothed[i] = sum / n; } // for (int i = 0; i < pValues.length; i++) { // double sum = 0; // for (int k = Math.max(0, i-n/2); k < Math.min(pValues.length, i-n/2+n); k++) { // sum += pValues[k]; // } // pValuesSmoothed[i] = sum/n; // } // pValues = pValuesSmoothed; lastPValueCensorThreshold = censorThreshold; pValuesChanged = true; } } else { lastPValueCensorThreshold = Double.NaN; pValueThresholds = null; pValues = null; } // if (params != null && !Double.isNaN(bestThreshold) && (params.getChoiceParameterValue("scoreThresholdMethod").equals("Lowest p-value"))) if (params != null && (params.getChoiceParameterValue("scoreThresholdMethod").equals("Lowest p-value"))) { int bestIdx = -1; double bestPValue = Double.POSITIVE_INFINITY; for (int i = pValueThresholds.length / 10; i < pValueThresholds.length * 9 / 10; i++) { if (pValues[i] < bestPValue) { bestIdx = i; bestPValue = pValues[i]; } } thresholds = bestIdx >= 0 ? new double[] { pValueThresholds[bestIdx] } : new double[0]; } else if (params != null && (params.getChoiceParameterValue("scoreThresholdMethod").equals("Lowest smoothed p-value"))) { int bestIdx = -1; double bestPValue = Double.POSITIVE_INFINITY; for (int i = pValueThresholds.length / 10; i < pValueThresholds.length * 9 / 10; i++) { if (pValuesSmoothed[i] < bestPValue) { bestIdx = i; bestPValue = pValuesSmoothed[i]; } } thresholds = bestIdx >= 0 ? new double[] { pValueThresholds[bestIdx] } : new double[0]; } // Split into different curves using the provided thresholds List<KaplanMeierData> kms = splitByThresholds(newScoreData, thresholds, censorThreshold, params != null && "Quartiles".equals(params.getChoiceParameterValue("scoreThresholdMethod"))); // for (KaplanMeier km : kms) // km.censorAtTime(censorThreshold); //// kmHigh.censorAtTime(censorThreshold); //// kmLow.censorAtTime(censorThreshold); // logger.info("High: " + kmHigh.toString()); // logger.info("Low: " + kmLow.toString()); // logger.info("Log rank comparison: {}", LogRankTest.computeLogRankTest(kmLow, kmHigh)); if (plotter == null) { plotter = new KaplanMeierChartWrapper(survivalColumn + " time"); // plotter.setBorder(BorderFactory.createTitledBorder("Survival plot")); // plotter.getCanvas().setWidth(300); // plotter.getCanvas().setHeight(300); } KaplanMeierData[] kmArray = new KaplanMeierData[kms.size()]; plotter.setKaplanMeierCurves(survivalColumn + " time", kms.toArray(kmArray)); tableModel.setSurvivalCurves(thresholds, params != null && params.getChoiceParameterValue("scoreThresholdMethod").equals("Lowest p-value"), kmArray); // Bar width determined using 'Freedman and Diaconis' rule' (but overridden if this gives < 16 bins...) double barWidth = (2 * q3 - q1) * Math.pow(numNonNaN, -1.0 / 3.0); int nBins = 100; if (!Double.isNaN(barWidth)) barWidth = (int) Math.max(16, Math.ceil((maxVal - minVal) / barWidth)); Histogram histogram = scoresValid ? new Histogram(newScoreData.scores, nBins) : null; if (histogramPanel == null) { GridPane paneHistogram = new GridPane(); histogramPanel = new HistogramPanelFX(); histogramPanel.getChart().setAnimated(false); histogramWrapper = new ThresholdedChartWrapper(histogramPanel.getChart()); for (ObservableNumberValue val : threshProperties) histogramWrapper.addThreshold(val, ColorToolsFX.getCachedColor(240, 0, 0, 128)); histogramWrapper.getPane().setPrefHeight(150); paneHistogram.add(histogramWrapper.getPane(), 0, 0); Tooltip.install(histogramPanel.getChart(), new Tooltip("Distribution of scores")); GridPane.setHgrow(histogramWrapper.getPane(), Priority.ALWAYS); GridPane.setVgrow(histogramWrapper.getPane(), Priority.ALWAYS); NumberAxis xAxis = new NumberAxis(); xAxis.setLabel("Score threshold"); NumberAxis yAxis = new NumberAxis(); yAxis.setLowerBound(0); yAxis.setUpperBound(1); yAxis.setTickUnit(0.1); yAxis.setAutoRanging(false); yAxis.setLabel("P-value"); chartPValues = new LineChart<>(xAxis, yAxis); chartPValues.setAnimated(false); chartPValues.setLegendVisible(false); // Make chart so it can be navigated ChartToolsFX.makeChartInteractive(chartPValues, xAxis, yAxis); pValuesChanged = true; Tooltip.install(chartPValues, new Tooltip( "Distribution of p-values (log-rank test) comparing low vs. high for all possible score thresholds")); // chartPValues.getYAxis().setAutoRanging(false); pValuesWrapper = new ThresholdedChartWrapper(chartPValues); for (ObservableNumberValue val : threshProperties) pValuesWrapper.addThreshold(val, ColorToolsFX.getCachedColor(240, 0, 0, 128)); pValuesWrapper.getPane().setPrefHeight(150); paneHistogram.add(pValuesWrapper.getPane(), 0, 1); GridPane.setHgrow(pValuesWrapper.getPane(), Priority.ALWAYS); GridPane.setVgrow(pValuesWrapper.getPane(), Priority.ALWAYS); ContextMenu popup = new ContextMenu(); ChartToolsFX.addChartExportMenu(chartPValues, popup); RadioMenuItem miZoomY1 = new RadioMenuItem("0-1"); miZoomY1.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(1); yAxis.setTickUnit(0.2); }); RadioMenuItem miZoomY05 = new RadioMenuItem("0-0.5"); miZoomY05.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(0.5); yAxis.setTickUnit(0.1); }); RadioMenuItem miZoomY02 = new RadioMenuItem("0-0.2"); miZoomY02.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(0.2); yAxis.setTickUnit(0.05); }); RadioMenuItem miZoomY01 = new RadioMenuItem("0-0.1"); miZoomY01.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(0.1); yAxis.setTickUnit(0.05); }); RadioMenuItem miZoomY005 = new RadioMenuItem("0-0.05"); miZoomY005.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(0.05); yAxis.setTickUnit(0.01); }); RadioMenuItem miZoomY001 = new RadioMenuItem("0-0.01"); miZoomY001.setOnAction(e -> { yAxis.setAutoRanging(false); yAxis.setUpperBound(0.01); yAxis.setTickUnit(0.005); }); ToggleGroup tgZoom = new ToggleGroup(); miZoomY1.setToggleGroup(tgZoom); miZoomY05.setToggleGroup(tgZoom); miZoomY02.setToggleGroup(tgZoom); miZoomY01.setToggleGroup(tgZoom); miZoomY005.setToggleGroup(tgZoom); miZoomY001.setToggleGroup(tgZoom); Menu menuZoomY = new Menu("Set y-axis range"); menuZoomY.getItems().addAll(miZoomY1, miZoomY05, miZoomY02, miZoomY01, miZoomY005, miZoomY001); MenuItem miCopyData = new MenuItem("Copy chart data"); miCopyData.setOnAction(e -> { String dataString = ChartToolsFX.getChartDataAsString(chartPValues); ClipboardContent content = new ClipboardContent(); content.putString(dataString); Clipboard.getSystemClipboard().setContent(content); }); popup.getItems().addAll(miCopyData, menuZoomY); chartPValues.setOnContextMenuRequested(e -> { popup.show(chartPValues, e.getScreenX(), e.getScreenY()); }); for (int col = 0; col < tableModel.getColumnCount(); col++) { TableColumn<Integer, String> column = new TableColumn<>(tableModel.getColumnName(col)); int colNumber = col; column.setCellValueFactory( new Callback<CellDataFeatures<Integer, String>, ObservableValue<String>>() { @Override public ObservableValue<String> call(CellDataFeatures<Integer, String> p) { return new SimpleStringProperty( (String) tableModel.getValueAt(p.getValue(), colNumber)); } }); column.setCellFactory(new Callback<TableColumn<Integer, String>, TableCell<Integer, String>>() { @Override public TableCell<Integer, String> call(TableColumn<Integer, String> param) { TableCell<Integer, String> cell = new TableCell<Integer, String>() { @Override protected void updateItem(String item, boolean empty) { super.updateItem(item, empty); setText(item); setTooltip(new Tooltip(item)); } }; return cell; } }); table.getColumns().add(column); } table.setPrefHeight(250); table.setColumnResizePolicy(TableView.CONSTRAINED_RESIZE_POLICY); table.maxHeightProperty().bind(table.prefHeightProperty()); params = new ParameterList(); // maxTimePoint = 0; // for (TMACoreObject core : hierarchy.getTMAGrid().getTMACoreList()) { // double os = core.getMeasurementList().getMeasurementValue(TMACoreObject.KEY_OVERALL_SURVIVAL); // double rfs = core.getMeasurementList().getMeasurementValue(TMACoreObject.KEY_RECURRENCE_FREE_SURVIVAL); // if (os > maxTimePoint) // maxTimePoint = os; // if (rfs > maxTimePoint) // maxTimePoint = rfs; // } params.addIntParameter("censorTimePoints", "Max censored time", (int) (censorThreshold + 0.5), null, 0, (int) Math.ceil(maxTimePoint), "Latest time point beyond which data will be censored"); // params.addChoiceParameter("scoreThresholdMethod", "Threshold method", "Manual", Arrays.asList("Manual", "Median", "Log-rank test")); if (calculateAllPValues) // Don't include "Lowest smoothed p-value" - it's not an established method and open to misinterpretation... params.addChoiceParameter("scoreThresholdMethod", "Threshold method", "Median", Arrays.asList("Manual (1)", "Manual (2)", "Manual (3)", "Median", "Tertiles", "Quartiles", "Lowest p-value")); // params.addChoiceParameter("scoreThresholdMethod", "Threshold method", "Median", Arrays.asList("Manual (1)", "Manual (2)", "Manual (3)", "Median", "Tertiles", "Quartiles", "Lowest p-value", "Lowest smoothed p-value")); else params.addChoiceParameter("scoreThresholdMethod", "Threshold method", "Median", Arrays.asList("Manual (1)", "Manual (2)", "Manual (3)", "Median", "Tertiles", "Quartiles")); params.addDoubleParameter("threshold1", "Threshold 1", thresholds.length > 0 ? thresholds[0] : (minVal + maxVal) / 2, null, "Threshold to distinguish between patient groups"); params.addDoubleParameter("threshold2", "Threshold 2", thresholds.length > 1 ? thresholds[1] : (minVal + maxVal) / 2, null, "Threshold to distinguish between patient groups"); params.addDoubleParameter("threshold3", "Threshold 3", thresholds.length > 2 ? thresholds[2] : (minVal + maxVal) / 2, null, "Threshold to distinguish between patient groups"); params.addBooleanParameter("showAtRisk", "Show at risk", plotter.getShowAtRisk(), "Show number of patients at risk below the plot"); params.addBooleanParameter("showTicks", "Show censored ticks", plotter.getShowCensoredTicks(), "Show ticks to indicate censored data"); params.addBooleanParameter("showKey", "Show key", plotter.getShowKey(), "Show key indicating display of each curve"); // params.addBooleanParameter("useColor", "Use color", plotter.getUseColor(), "Show each curve in a different color"); // params.addBooleanParameter("useStrokes", "Use strokes", plotter.getUseStrokes(), "Show each curve with a differed line stroke"); // Hide threshold parameters if threshold can't be used if (!scoresValid) { // params.setHiddenParameters(true, "scoreThresholdMethod", "scoreThreshold"); histogramPanel.getChart().setVisible(false); } panelParams = new ParameterPanelFX(params); panelParams.addParameterChangeListener(this); updateThresholdsEnabled(); for (int i = 0; i < threshProperties.length; i++) { String p = "threshold" + (i + 1); threshProperties[i].addListener((v, o, n) -> { if (interactiveThresholds()) { // Need to do a decent double check with tolerance to text field value changing while typing if (!GeneralTools.almostTheSame(params.getDoubleParameterValue(p), n.doubleValue(), 0.0001)) panelParams.setNumericParameterValue(p, n); } }); } BorderPane paneBottom = new BorderPane(); TitledPane paneOptions = new TitledPane("Options", panelParams.getPane()); // paneOptions.setCollapsible(false); Pane paneCanvas = new StackPane(); paneCanvas.getChildren().add(plotter.getCanvas()); GridPane paneLeft = new GridPane(); paneLeft.add(paneOptions, 0, 0); paneLeft.add(table, 0, 1); GridPane.setHgrow(paneOptions, Priority.ALWAYS); GridPane.setHgrow(table, Priority.ALWAYS); paneBottom.setLeft(paneLeft); paneBottom.setCenter(paneHistogram); paneMain.setCenter(paneCanvas); paneMain.setBottom(paneBottom); paneMain.setPadding(new Insets(10, 10, 10, 10)); } else if (thresholds.length > 0) { // Ensure the sliders/text fields are set sensibly if (!GeneralTools.almostTheSame(thresholds[0], params.getDoubleParameterValue("threshold1"), 0.0001)) { panelParams.setNumericParameterValue("threshold1", thresholds[0]); } if (thresholds.length > 1 && !GeneralTools.almostTheSame(thresholds[1], params.getDoubleParameterValue("threshold2"), 0.0001)) { panelParams.setNumericParameterValue("threshold2", thresholds[1]); } if (thresholds.length > 2 && !GeneralTools.almostTheSame(thresholds[2], params.getDoubleParameterValue("threshold3"), 0.0001)) { panelParams.setNumericParameterValue("threshold3", thresholds[2]); } } if (histogram != null) { histogramPanel.getHistogramData() .setAll(HistogramPanelFX.createHistogramData(histogram, false, (Color) null)); histogramPanel.getChart().getXAxis().setLabel(scoreColumn); histogramPanel.getChart().getYAxis().setLabel("Count"); ChartToolsFX.addChartExportMenu(histogramPanel.getChart(), null); // histogramWrapper.setVerticalLines(thresholds, ColorToolsFX.getCachedColor(240, 0, 0, 128)); // Deal with threshold adjustment // histogramWrapper.getThresholds().addListener((Observable o) -> generatePlot()); } if (pValues != null) { // TODO: Raise earlier where p-value calculation is if (pValuesChanged) { ObservableList<XYChart.Data<Number, Number>> data = FXCollections.observableArrayList(); for (int i = 0; i < pValueThresholds.length; i++) { double pValue = pValues[i]; if (Double.isNaN(pValue)) continue; data.add(new XYChart.Data<>(pValueThresholds[i], pValue, pValueThresholdsObserved[i])); } ObservableList<XYChart.Data<Number, Number>> dataSmoothed = null; if (pValuesSmoothed != null) { dataSmoothed = FXCollections.observableArrayList(); for (int i = 0; i < pValueThresholds.length; i++) { double pValueSmoothed = pValuesSmoothed[i]; if (Double.isNaN(pValueSmoothed)) continue; dataSmoothed.add(new XYChart.Data<>(pValueThresholds[i], pValueSmoothed)); } } // Don't bother showing the smoothed data... it tends to get in the way... // if (dataSmoothed != null) // chartPValues.getData().setAll(new XYChart.Series<>("P-values", data), new XYChart.Series<>("Smoothed P-values", dataSmoothed)); // else chartPValues.getData().setAll(new XYChart.Series<>("P-values", data)); // Add line to show 0.05 significance threshold if (pValueThresholds.length > 1) { Data<Number, Number> sigData1 = new Data<>(pValueThresholds[0], 0.05); Data<Number, Number> sigData2 = new Data<>(pValueThresholds[pValueThresholds.length - 1], 0.05); XYChart.Series<Number, Number> dataSignificant = new XYChart.Series<>("Signficance 0.05", FXCollections.observableArrayList(sigData1, sigData2)); chartPValues.getData().add(dataSignificant); sigData1.getNode().setVisible(false); sigData2.getNode().setVisible(false); } // chartPValues.getData().get(0).getNode().setVisible(true); // pValuesWrapper.clearThresholds(); for (XYChart.Data<Number, Number> dataPoint : data) { if (!Boolean.TRUE.equals(dataPoint.getExtraValue())) dataPoint.getNode().setVisible(false); } // if (dataSmoothed != null) { // for (XYChart.Data<Number, Number> dataPoint : dataSmoothed) { // dataPoint.getNode().setVisible(false); // } // chartPValues.getData().get(1).getNode().setOpacity(0.5); // } // int count = 0; // for (int i = 0; i < pValueThresholds.length; i++) { // double pValue = pValues[i]; // if (Double.isNaN(pValue)) // continue; // boolean observed = pValueThresholdsObserved[i]; //// if (observed) //// pValuesWrapper.addThreshold(new ReadOnlyDoubleWrapper(pValueThresholds[i]), Color.rgb(0, 0, 0, 0.05)); // // if (!observed) { //// StackPane pane = (StackPane)data.get(count).getNode(); //// pane.setEffect(new DropShadow()); // data.get(count).getNode().setVisible(false); // } // count++; // } } for (int i = 0; i < threshProperties.length; i++) { if (i < thresholds.length) threshProperties[i].set(thresholds[i]); else threshProperties[i].set(Double.NaN); } boolean isInteractive = interactiveThresholds(); histogramWrapper.setIsInteractive(isInteractive); pValuesWrapper.setIsInteractive(isInteractive); chartPValues.setVisible(true); } // else // chartPValues.setVisible(false); // Store values for next time scoreData = newScoreData; }