Examples with Region org.apache.commons.math3.geometry.partitioning.Region used on opensource projects

Example 11 with Region

use of org.apache.commons.math3.geometry.partitioning.Region in project GDSC-SMLM by aherbert.

the class FIRE method showInputDialog.

private boolean showInputDialog() {
    ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    gd.addMessage("Compute the resolution using Fourier Ring Correlation");
    gd.addHelp(About.HELP_URL);
    // Build a list of all images with a region ROI
    List<String> titles = new LinkedList<String>();
    if (WindowManager.getWindowCount() > 0) {
        for (int imageID : WindowManager.getIDList()) {
            ImagePlus imp = WindowManager.getImage(imageID);
            if (imp != null && imp.getRoi() != null && imp.getRoi().isArea())
                titles.add(imp.getTitle());
        }
    }
    ResultsManager.addInput(gd, inputOption, InputSource.MEMORY);
    ResultsManager.addInput(gd, "Input2", inputOption2, InputSource.NONE, InputSource.MEMORY);
    if (!titles.isEmpty())
        gd.addCheckbox((titles.size() == 1) ? "Use_ROI" : "Choose_ROI", chooseRoi);
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    inputOption = ResultsManager.getInputSource(gd);
    inputOption2 = ResultsManager.getInputSource(gd);
    if (!titles.isEmpty())
        chooseRoi = gd.getNextBoolean();
    if (!titles.isEmpty() && chooseRoi) {
        if (titles.size() == 1) {
            roiImage = titles.get(0);
            Recorder.recordOption("Image", roiImage);
        } else {
            String[] items = titles.toArray(new String[titles.size()]);
            gd = new ExtendedGenericDialog(TITLE);
            gd.addMessage("Select the source image for the ROI");
            gd.addChoice("Image", items, roiImage);
            gd.showDialog();
            if (gd.wasCanceled())
                return false;
            roiImage = gd.getNextChoice();
        }
        ImagePlus imp = WindowManager.getImage(roiImage);
        roiBounds = imp.getRoi().getBounds();
        roiImageWidth = imp.getWidth();
        roiImageHeight = imp.getHeight();
    } else {
        roiBounds = null;
    }
    return true;
}

Example 12 with Region

use of org.apache.commons.math3.geometry.partitioning.Region in project GDSC-SMLM by aherbert.

the class DensityImage method logDensityResults.

/**
	 * Output a log message of the results including the average density for localisations and the expected average.
	 * 
	 * @param results
	 * @param density
	 * @param radius
	 * @param filtered
	 * @return
	 */
private SummaryStatistics logDensityResults(MemoryPeakResults results, int[] density, float radius, int filtered) {
    float region = (float) (radius * radius * ((useSquareApproximation) ? 4 : Math.PI));
    Rectangle bounds = results.getBounds();
    float area = bounds.width * bounds.height;
    float expected = results.size() * region / area;
    SummaryStatistics summary = new SummaryStatistics();
    for (int i = 0; i < results.size(); i++) {
        summary.addValue(density[i]);
    }
    DensityManager dm = createDensityManager(results);
    // Compute this using the input density scores since the radius is the same.
    final double l = (useSquareApproximation) ? dm.ripleysLFunction(radius) : dm.ripleysLFunction(density, radius);
    String msg = String.format("Density %s : N=%d, %.0fpx : Radius=%s : L(r) - r = %s : E = %s, Obs = %s (%sx)", results.getName(), summary.getN(), area, rounded(radius), rounded(l - radius), rounded(expected), rounded(summary.getMean()), rounded(summary.getMean() / expected));
    if (filterLocalisations)
        msg += String.format(" : Filtered=%d (%s%%)", filtered, rounded(filtered * 100.0 / density.length));
    IJ.log(msg);
    return summary;
}

Example 13 with Region

use of org.apache.commons.math3.geometry.partitioning.Region in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method summariseResults.

private BenchmarkFilterResult summariseResults(TIntObjectHashMap<FilterResult> filterResults, FitEngineConfiguration config, MaximaSpotFilter spotFilter, boolean relativeDistances, boolean batchSummary) {
    BenchmarkFilterResult filterResult = new BenchmarkFilterResult(filterResults, config, spotFilter);
    // Note: 
    // Although we can compute the TP/FP score as each additional spot is added
    // using the RankedScoreCalculator this is not applicable to the PeakFit method.
    // The method relies on all spot candidates being present in order to make a
    // decision to fit the candidate as a multiple. So scoring the filter candidates using
    // for example the top 10 may get a better score than if all candidates were scored
    // and the scores accumulated for the top 10, it is not how the algorithm will use the 
    // candidate set. I.e. It does not use the top 10, then top 20 to refine the fit, etc. 
    // (the method is not iterative) .
    // We require an assessment of how a subset of the scored candidates
    // in ranked order contributes to the overall score, i.e. are the candidates ranked
    // in the correct order, those most contributing to the match to the underlying data 
    // should be higher up and those least contributing will be at the end.
    // TODO We could add some smart filtering of candidates before ranking. This would
    // allow assessment of the candidate set handed to PeakFit. E.g. Threshold the image
    // and only use candidates that are in the foreground region.
    double[][] cumul = histogramFailures(filterResult);
    // Create the overall match score
    final double[] total = new double[3];
    final ArrayList<ScoredSpot> allSpots = new ArrayList<BenchmarkSpotFilter.ScoredSpot>();
    filterResults.forEachValue(new TObjectProcedure<FilterResult>() {

        public boolean execute(FilterResult result) {
            total[0] += result.result.getTP();
            total[1] += result.result.getFP();
            total[2] += result.result.getFN();
            allSpots.addAll(Arrays.asList(result.spots));
            return true;
        }
    });
    double tp = total[0], fp = total[1], fn = total[2];
    FractionClassificationResult allResult = new FractionClassificationResult(tp, fp, 0, fn);
    // The number of actual results
    final double n = (tp + fn);
    StringBuilder sb = new StringBuilder();
    double signal = (simulationParameters.minSignal + simulationParameters.maxSignal) * 0.5;
    // Create the benchmark settings and the fitting settings
    sb.append(imp.getStackSize()).append("\t");
    final int w = lastAnalysisBorder.width;
    final int h = lastAnalysisBorder.height;
    sb.append(w).append("\t");
    sb.append(h).append("\t");
    sb.append(Utils.rounded(n)).append("\t");
    double density = (n / imp.getStackSize()) / (w * h) / (simulationParameters.a * simulationParameters.a / 1e6);
    sb.append(Utils.rounded(density)).append("\t");
    sb.append(Utils.rounded(signal)).append("\t");
    sb.append(Utils.rounded(simulationParameters.s)).append("\t");
    sb.append(Utils.rounded(simulationParameters.a)).append("\t");
    sb.append(Utils.rounded(simulationParameters.depth)).append("\t");
    sb.append(simulationParameters.fixedDepth).append("\t");
    sb.append(Utils.rounded(simulationParameters.gain)).append("\t");
    sb.append(Utils.rounded(simulationParameters.readNoise)).append("\t");
    sb.append(Utils.rounded(simulationParameters.b)).append("\t");
    sb.append(Utils.rounded(simulationParameters.b2)).append("\t");
    // Compute the noise
    double noise = simulationParameters.b2;
    if (simulationParameters.emCCD) {
        // The b2 parameter was computed without application of the EM-CCD noise factor of 2.
        //final double b2 = backgroundVariance + readVariance
        //                = simulationParameters.b + readVariance
        // This should be applied only to the background variance.
        final double readVariance = noise - simulationParameters.b;
        noise = simulationParameters.b * 2 + readVariance;
    }
    sb.append(Utils.rounded(signal / Math.sqrt(noise))).append("\t");
    sb.append(Utils.rounded(simulationParameters.s / simulationParameters.a)).append("\t");
    sb.append(config.getDataFilterType()).append("\t");
    //sb.append(spotFilter.getName()).append("\t");
    sb.append(spotFilter.getSearch()).append("\t");
    sb.append(spotFilter.getBorder()).append("\t");
    sb.append(Utils.rounded(spotFilter.getSpread())).append("\t");
    sb.append(config.getDataFilter(0)).append("\t");
    final double param = config.getSmooth(0);
    final double hwhmMin = config.getHWHMMin();
    if (relativeDistances) {
        sb.append(Utils.rounded(param * hwhmMin)).append("\t");
        sb.append(Utils.rounded(param)).append("\t");
    } else {
        sb.append(Utils.rounded(param)).append("\t");
        sb.append(Utils.rounded(param / hwhmMin)).append("\t");
    }
    sb.append(spotFilter.getDescription()).append("\t");
    sb.append(lastAnalysisBorder.x).append("\t");
    sb.append(MATCHING_METHOD[matchingMethod]).append("\t");
    sb.append(Utils.rounded(lowerMatchDistance)).append("\t");
    sb.append(Utils.rounded(matchDistance)).append("\t");
    sb.append(Utils.rounded(lowerSignalFactor)).append("\t");
    sb.append(Utils.rounded(upperSignalFactor));
    resultPrefix = sb.toString();
    // Add the results
    sb.append("\t");
    // Rank the scored spots by intensity
    Collections.sort(allSpots);
    // Produce Recall, Precision, Jaccard for each cut of the spot candidates
    double[] r = new double[allSpots.size() + 1];
    double[] p = new double[r.length];
    double[] j = new double[r.length];
    double[] c = new double[r.length];
    double[] truePositives = new double[r.length];
    double[] falsePositives = new double[r.length];
    double[] intensity = new double[r.length];
    // Note: fn = n - tp
    tp = fp = 0;
    int i = 1;
    p[0] = 1;
    FastCorrelator corr = new FastCorrelator();
    double lastC = 0;
    double[] i1 = new double[r.length];
    double[] i2 = new double[r.length];
    int ci = 0;
    SimpleRegression regression = new SimpleRegression(false);
    for (ScoredSpot s : allSpots) {
        if (s.match) {
            // Score partial matches as part true-positive and part false-positive.
            // TP can be above 1 if we are allowing multiple matches.
            tp += s.getScore();
            fp += s.antiScore();
            // Just use a rounded intensity for now
            final double spotIntensity = s.getIntensity();
            final long v1 = (long) Math.round(spotIntensity);
            final long v2 = (long) Math.round(s.intensity);
            regression.addData(spotIntensity, s.intensity);
            i1[ci] = spotIntensity;
            i2[ci] = s.intensity;
            ci++;
            corr.add(v1, v2);
            lastC = corr.getCorrelation();
        } else
            fp++;
        r[i] = (double) tp / n;
        p[i] = (double) tp / (tp + fp);
        // (tp+fp+fn) == (fp+n) since tp+fn=n;
        j[i] = (double) tp / (fp + n);
        c[i] = lastC;
        truePositives[i] = tp;
        falsePositives[i] = fp;
        intensity[i] = s.getIntensity();
        i++;
    }
    i1 = Arrays.copyOf(i1, ci);
    i2 = Arrays.copyOf(i2, ci);
    final double slope = regression.getSlope();
    sb.append(Utils.rounded(slope)).append("\t");
    addResult(sb, allResult, c[c.length - 1]);
    // Output the match results when the recall achieves the fraction of the maximum.
    double target = r[r.length - 1];
    if (recallFraction < 100)
        target *= recallFraction / 100.0;
    int fractionIndex = 0;
    while (fractionIndex < r.length && r[fractionIndex] < target) {
        fractionIndex++;
    }
    if (fractionIndex == r.length)
        fractionIndex--;
    addResult(sb, new FractionClassificationResult(truePositives[fractionIndex], falsePositives[fractionIndex], 0, n - truePositives[fractionIndex]), c[fractionIndex]);
    // Output the match results at the maximum jaccard score
    int maxIndex = 0;
    for (int ii = 1; ii < r.length; ii++) {
        if (j[maxIndex] < j[ii])
            maxIndex = ii;
    }
    addResult(sb, new FractionClassificationResult(truePositives[maxIndex], falsePositives[maxIndex], 0, n - truePositives[maxIndex]), c[maxIndex]);
    sb.append(Utils.rounded(time / 1e6));
    // Calculate AUC (Average precision == Area Under Precision-Recall curve)
    final double auc = AUCCalculator.auc(p, r);
    // Compute the AUC using the adjusted precision curve
    // which uses the maximum precision for recall >= r
    final double[] maxp = new double[p.length];
    double max = 0;
    for (int k = maxp.length; k-- > 0; ) {
        if (max < p[k])
            max = p[k];
        maxp[k] = max;
    }
    final double auc2 = AUCCalculator.auc(maxp, r);
    sb.append("\t").append(Utils.rounded(auc));
    sb.append("\t").append(Utils.rounded(auc2));
    // Output the number of fit failures that must be processed to capture fractions of the true positives
    if (cumul[0].length != 0) {
        sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.80)));
        sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.90)));
        sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.95)));
        sb.append("\t").append(Utils.rounded(getFailures(cumul, 0.99)));
        sb.append("\t").append(Utils.rounded(cumul[0][cumul[0].length - 1]));
    } else
        sb.append("\t\t\t\t\t");
    BufferedTextWindow resultsTable = getTable(batchSummary);
    resultsTable.append(sb.toString());
    // Store results
    filterResult.auc = auc;
    filterResult.auc2 = auc2;
    filterResult.r = r;
    filterResult.p = p;
    filterResult.j = j;
    filterResult.c = c;
    filterResult.maxIndex = maxIndex;
    filterResult.fractionIndex = fractionIndex;
    filterResult.cumul = cumul;
    filterResult.slope = slope;
    filterResult.i1 = i1;
    filterResult.i2 = i2;
    filterResult.intensity = intensity;
    filterResult.relativeDistances = relativeDistances;
    filterResult.time = time;
    return filterResult;
}

Example 14 with Region

use of org.apache.commons.math3.geometry.partitioning.Region in project GDSC-SMLM by aherbert.

the class PSFDrift method computeDrift.

private void computeDrift() {
    // Create a grid of XY offset positions between 0-1 for PSF insert
    final double[] grid = new double[gridSize];
    for (int i = 0; i < grid.length; i++) grid[i] = (double) i / gridSize;
    // Configure fitting region
    final int w = 2 * regionSize + 1;
    centrePixel = w / 2;
    // Check region size using the image PSF
    double newPsfWidth = (double) imp.getWidth() / scale;
    if (Math.ceil(newPsfWidth) > w)
        Utils.log(TITLE + ": Fitted region size (%d) is smaller than the scaled PSF (%.1f)", w, newPsfWidth);
    // Create robust PSF fitting settings
    final double a = psfSettings.nmPerPixel * scale;
    final double sa = PSFCalculator.squarePixelAdjustment(psfSettings.nmPerPixel * (psfSettings.fwhm / Gaussian2DFunction.SD_TO_FWHM_FACTOR), a);
    fitConfig.setInitialPeakStdDev(sa / a);
    fitConfig.setBackgroundFitting(backgroundFitting);
    fitConfig.setNotSignalFitting(false);
    fitConfig.setComputeDeviations(false);
    fitConfig.setDisableSimpleFilter(true);
    // Create the PSF over the desired z-depth
    int depth = (int) Math.round(zDepth / psfSettings.nmPerSlice);
    int startSlice = psfSettings.zCentre - depth;
    int endSlice = psfSettings.zCentre + depth;
    int nSlices = imp.getStackSize();
    startSlice = (startSlice < 1) ? 1 : (startSlice > nSlices) ? nSlices : startSlice;
    endSlice = (endSlice < 1) ? 1 : (endSlice > nSlices) ? nSlices : endSlice;
    ImagePSFModel psf = createImagePSF(startSlice, endSlice);
    int minz = startSlice - psfSettings.zCentre;
    int maxz = endSlice - psfSettings.zCentre;
    final int nZ = maxz - minz + 1;
    final int gridSize2 = grid.length * grid.length;
    total = nZ * gridSize2;
    // Store all the fitting results
    int nStartPoints = getNumberOfStartPoints();
    results = new double[total * nStartPoints][];
    // TODO - Add ability to iterate this, adjusting the current offset in the PSF
    // each iteration
    // Create a pool of workers
    int nThreads = Prefs.getThreads();
    BlockingQueue<Job> jobs = new ArrayBlockingQueue<Job>(nThreads * 2);
    List<Worker> workers = new LinkedList<Worker>();
    List<Thread> threads = new LinkedList<Thread>();
    for (int i = 0; i < nThreads; i++) {
        Worker worker = new Worker(jobs, psf, w, fitConfig);
        Thread t = new Thread(worker);
        workers.add(worker);
        threads.add(t);
        t.start();
    }
    // Fit 
    Utils.showStatus("Fitting ...");
    final int step = Utils.getProgressInterval(total);
    outer: for (int z = minz, i = 0; z <= maxz; z++) {
        for (int x = 0; x < grid.length; x++) for (int y = 0; y < grid.length; y++, i++) {
            if (IJ.escapePressed()) {
                break outer;
            }
            put(jobs, new Job(z, grid[x], grid[y], i));
            if (i % step == 0) {
                IJ.showProgress(i, total);
            }
        }
    }
    // If escaped pressed then do not need to stop the workers, just return
    if (Utils.isInterrupted()) {
        IJ.showProgress(1);
        return;
    }
    // Finish all the worker threads by passing in a null job
    for (int i = 0; i < threads.size(); i++) {
        put(jobs, new Job());
    }
    // Wait for all to finish
    for (int i = 0; i < threads.size(); i++) {
        try {
            threads.get(i).join();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
    threads.clear();
    IJ.showProgress(1);
    IJ.showStatus("");
    // Plot the average and SE for the drift curve
    // Plot the recall
    double[] zPosition = new double[nZ];
    double[] avX = new double[nZ];
    double[] seX = new double[nZ];
    double[] avY = new double[nZ];
    double[] seY = new double[nZ];
    double[] recall = new double[nZ];
    for (int z = minz, i = 0; z <= maxz; z++, i++) {
        Statistics statsX = new Statistics();
        Statistics statsY = new Statistics();
        for (int s = 0; s < nStartPoints; s++) {
            int resultPosition = i * gridSize2 + s * total;
            final int endResultPosition = resultPosition + gridSize2;
            while (resultPosition < endResultPosition) {
                if (results[resultPosition] != null) {
                    statsX.add(results[resultPosition][0]);
                    statsY.add(results[resultPosition][1]);
                }
                resultPosition++;
            }
        }
        zPosition[i] = z * psfSettings.nmPerSlice;
        avX[i] = statsX.getMean();
        seX[i] = statsX.getStandardError();
        avY[i] = statsY.getMean();
        seY[i] = statsY.getStandardError();
        recall[i] = (double) statsX.getN() / (nStartPoints * gridSize2);
    }
    // Find the range from the z-centre above the recall limit 
    int centre = 0;
    for (int slice = startSlice, i = 0; slice <= endSlice; slice++, i++) {
        if (slice == psfSettings.zCentre) {
            centre = i;
            break;
        }
    }
    if (recall[centre] < recallLimit)
        return;
    int start = centre, end = centre;
    for (int i = centre; i-- > 0; ) {
        if (recall[i] < recallLimit)
            break;
        start = i;
    }
    for (int i = centre; ++i < recall.length; ) {
        if (recall[i] < recallLimit)
            break;
        end = i;
    }
    int iterations = 1;
    LoessInterpolator loess = null;
    if (smoothing > 0)
        loess = new LoessInterpolator(smoothing, iterations);
    double[][] smoothx = displayPlot("Drift X", "X (nm)", zPosition, avX, seX, loess, start, end);
    double[][] smoothy = displayPlot("Drift Y", "Y (nm)", zPosition, avY, seY, loess, start, end);
    displayPlot("Recall", "Recall", zPosition, recall, null, null, start, end);
    WindowOrganiser wo = new WindowOrganiser();
    wo.tileWindows(idList);
    // Ask the user if they would like to store them in the image
    GenericDialog gd = new GenericDialog(TITLE);
    gd.enableYesNoCancel();
    gd.hideCancelButton();
    startSlice = psfSettings.zCentre - (centre - start);
    endSlice = psfSettings.zCentre + (end - centre);
    gd.addMessage(String.format("Save the drift to the PSF?\n \nSlices %d (%s nm) - %d (%s nm) above recall limit", startSlice, Utils.rounded(zPosition[start]), endSlice, Utils.rounded(zPosition[end])));
    gd.addMessage("Optionally average the end points to set drift outside the limits.\n(Select zero to ignore)");
    gd.addSlider("Number_of_points", 0, 10, positionsToAverage);
    gd.showDialog();
    if (gd.wasOKed()) {
        positionsToAverage = Math.abs((int) gd.getNextNumber());
        ArrayList<PSFOffset> offset = new ArrayList<PSFOffset>();
        final double pitch = psfSettings.nmPerPixel;
        int j = 0, jj = 0;
        for (int i = start, slice = startSlice; i <= end; slice++, i++) {
            j = findCentre(zPosition[i], smoothx, j);
            if (j == -1) {
                Utils.log("Failed to find the offset for depth %.2f", zPosition[i]);
                continue;
            }
            // The offset should store the difference to the centre in pixels so divide by the pixel pitch
            double cx = smoothx[1][j] / pitch;
            double cy = smoothy[1][j] / pitch;
            jj = findOffset(slice, jj);
            if (jj != -1) {
                cx += psfSettings.offset[jj].cx;
                cy += psfSettings.offset[jj].cy;
            }
            offset.add(new PSFOffset(slice, cx, cy));
        }
        addMissingOffsets(startSlice, endSlice, nSlices, offset);
        psfSettings.offset = offset.toArray(new PSFOffset[offset.size()]);
        psfSettings.addNote(TITLE, String.format("Solver=%s, Region=%d", PeakFit.getSolverName(fitConfig), regionSize));
        imp.setProperty("Info", XmlUtils.toXML(psfSettings));
    }
}

Example 15 with Region

use of org.apache.commons.math3.geometry.partitioning.Region in project GDSC-SMLM by aherbert.

the class SpotInspector method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog())
        return;
    // Load the results
    results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Check if the original image is open
    ImageSource source = results.getSource();
    if (source == null) {
        IJ.error(TITLE, "Unknown original source image");
        return;
    }
    source = source.getOriginal();
    if (!source.open()) {
        IJ.error(TITLE, "Cannot open original source image: " + source.toString());
        return;
    }
    final float stdDevMax = getStandardDeviation(results);
    if (stdDevMax < 0) {
        // TODO - Add dialog to get the initial peak width
        IJ.error(TITLE, "Fitting configuration (for initial peak width) is not available");
        return;
    }
    // Rank spots
    rankedResults = new ArrayList<PeakResultRank>(results.size());
    final double a = results.getNmPerPixel();
    final double gain = results.getGain();
    final boolean emCCD = results.isEMCCD();
    for (PeakResult r : results.getResults()) {
        float[] score = getScore(r, a, gain, emCCD, stdDevMax);
        rankedResults.add(new PeakResultRank(r, score[0], score[1]));
    }
    Collections.sort(rankedResults);
    // Prepare results table. Get bias if necessary
    if (showCalibratedValues) {
        // Get a bias if required
        Calibration calibration = results.getCalibration();
        if (calibration.getBias() == 0) {
            ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
            gd.addMessage("Calibrated results requires a camera bias");
            gd.addNumericField("Camera_bias (ADUs)", calibration.getBias(), 2);
            gd.showDialog();
            if (!gd.wasCanceled()) {
                calibration.setBias(Math.abs(gd.getNextNumber()));
            }
        }
    }
    IJTablePeakResults table = new IJTablePeakResults(false, results.getName(), true);
    table.copySettings(results);
    table.setTableTitle(TITLE);
    table.setAddCounter(true);
    table.setShowCalibratedValues(showCalibratedValues);
    table.begin();
    // Add a mouse listener to jump to the frame for the clicked line
    textPanel = table.getResultsWindow().getTextPanel();
    // We must ignore old instances of this class from the mouse listeners
    id = ++currentId;
    textPanel.addMouseListener(this);
    // Add results to the table
    int n = 0;
    for (PeakResultRank rank : rankedResults) {
        rank.rank = n++;
        PeakResult r = rank.peakResult;
        table.add(r.getFrame(), r.origX, r.origY, r.origValue, r.error, r.noise, r.params, r.paramsStdDev);
    }
    table.end();
    if (plotScore || plotHistogram) {
        // Get values for the plots
        float[] xValues = null, yValues = null;
        double yMin, yMax;
        int spotNumber = 0;
        xValues = new float[rankedResults.size()];
        yValues = new float[xValues.length];
        for (PeakResultRank rank : rankedResults) {
            xValues[spotNumber] = spotNumber + 1;
            yValues[spotNumber++] = recoverScore(rank.score);
        }
        // Set the min and max y-values using 1.5 x IQR 
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (float v : yValues) stats.addValue(v);
        if (removeOutliers) {
            double lower = stats.getPercentile(25);
            double upper = stats.getPercentile(75);
            double iqr = upper - lower;
            yMin = FastMath.max(lower - iqr, stats.getMin());
            yMax = FastMath.min(upper + iqr, stats.getMax());
            IJ.log(String.format("Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax));
        } else {
            yMin = stats.getMin();
            yMax = stats.getMax();
            IJ.log(String.format("Data range: %f - %f", yMin, yMax));
        }
        plotScore(xValues, yValues, yMin, yMax);
        plotHistogram(yValues, yMin, yMax);
    }
    // Extract spots into a stack
    final int w = source.getWidth();
    final int h = source.getHeight();
    final int size = 2 * radius + 1;
    ImageStack spots = new ImageStack(size, size, rankedResults.size());
    // To assist the extraction of data from the image source, process them in time order to allow 
    // frame caching. Then set the appropriate slice in the result stack
    Collections.sort(rankedResults, new Comparator<PeakResultRank>() {

        public int compare(PeakResultRank o1, PeakResultRank o2) {
            if (o1.peakResult.getFrame() < o2.peakResult.getFrame())
                return -1;
            if (o1.peakResult.getFrame() > o2.peakResult.getFrame())
                return 1;
            return 0;
        }
    });
    for (PeakResultRank rank : rankedResults) {
        PeakResult r = rank.peakResult;
        // Extract image
        // Note that the coordinates are relative to the middle of the pixel (0.5 offset)
        // so do not round but simply convert to int
        final int x = (int) (r.params[Gaussian2DFunction.X_POSITION]);
        final int y = (int) (r.params[Gaussian2DFunction.Y_POSITION]);
        // Extract a region but crop to the image bounds
        int minX = x - radius;
        int minY = y - radius;
        int maxX = FastMath.min(x + radius + 1, w);
        int maxY = FastMath.min(y + radius + 1, h);
        int padX = 0, padY = 0;
        if (minX < 0) {
            padX = -minX;
            minX = 0;
        }
        if (minY < 0) {
            padY = -minY;
            minY = 0;
        }
        int sizeX = maxX - minX;
        int sizeY = maxY - minY;
        float[] data = source.get(r.getFrame(), new Rectangle(minX, minY, sizeX, sizeY));
        // Prevent errors with missing data
        if (data == null)
            data = new float[sizeX * sizeY];
        ImageProcessor spotIp = new FloatProcessor(sizeX, sizeY, data, null);
        // Pad if necessary, i.e. the crop is too small for the stack
        if (padX > 0 || padY > 0 || sizeX < size || sizeY < size) {
            ImageProcessor spotIp2 = spotIp.createProcessor(size, size);
            spotIp2.insert(spotIp, padX, padY);
            spotIp = spotIp2;
        }
        int slice = rank.rank + 1;
        spots.setPixels(spotIp.getPixels(), slice);
        spots.setSliceLabel(Utils.rounded(rank.originalScore), slice);
    }
    source.close();
    ImagePlus imp = Utils.display(TITLE, spots);
    imp.setRoi((PointRoi) null);
    // Make bigger		
    for (int i = 10; i-- > 0; ) imp.getWindow().getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
}