Examples with PeakResultProcedure uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure used on opensource projects

Example 1 with PeakResultProcedure

use of uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure in project GDSC-SMLM by aherbert.

the class CreateData method setNoise.

/**
 * Sets the noise in the results if missing.
 *
 * @param results the results
 * @param imp the imp
 */
private static void setNoise(MemoryPeakResults results, ImagePlus imp) {
    // Loaded results do not have noise
    if (results.hasNoise()) {
        return;
    }
    IJ.showStatus("Estimating noise ...");
    // Compute noise per frame
    final ImageStack stack = imp.getImageStack();
    final int width = stack.getWidth();
    final int height = stack.getHeight();
    final IJImageSource source = new IJImageSource(imp);
    final float[] noise = new float[source.getFrames() + 1];
    source.setReadHint(ReadHint.SEQUENTIAL);
    source.open();
    for (int slice = 1; slice < noise.length; slice++) {
        final float[] data = source.next();
        // Use the trimmed method as there may be a lot of spots in the frame
        noise[slice] = FitWorker.estimateNoise(data, width, height, NoiseEstimatorMethod.QUICK_RESIDUALS_LEAST_TRIMMED_OF_SQUARES);
    }
    // Statistics stats = Statistics.create(Arrays.copyOfRange(noise, 1, noise.length));
    // System.out.printf("Noise = %.3f +/- %.3f (%d)\n", stats.getMean(),
    // stats.getStandardDeviation(), stats.getN());
    // Convert noise units from counts to the result format
    final TypeConverter<IntensityUnit> c = results.getIntensityConverter(IntensityUnit.COUNT);
    for (int i = 0; i < noise.length; i++) {
        noise[i] = c.convertBack(noise[i]);
    }
    results.forEach((PeakResultProcedure) result -> {
        if (result.getFrame() < noise.length) {
            result.setNoise(noise[result.getFrame()]);
        }
    });
}

Example 2 with PeakResultProcedure

use of uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure in project GDSC-SMLM by aherbert.

the class CreateData method showSummary.

private double showSummary(List<? extends FluorophoreSequenceModel> fluorophores, List<LocalisationModel> localisations) {
    IJ.showStatus("Calculating statistics ...");
    final Statistics[] stats = new Statistics[NAMES.length];
    for (int i = 0; i < stats.length; i++) {
        stats[i] = (settings.getShowHistograms() || alwaysRemoveOutliers[i]) ? new StoredDataStatistics() : new Statistics();
    }
    // Find the largest timepoint
    final ImagePlus outputImp = WindowManager.getImage(benchmarkImageId);
    int frameCount;
    if (outputImp == null) {
        sortLocalisationsByTime(localisations);
        frameCount = localisations.get(localisations.size() - 1).getTime();
    } else {
        frameCount = outputImp.getStackSize();
    }
    final int[] countHistogram = new int[frameCount + 1];
    // Use the localisations that were drawn to create the sampled on/off times
    rebuildNeighbours(localisations);
    // Assume that there is at least one localisation
    final LocalisationModel first = localisations.get(0);
    // The current localisation
    int currentId = first.getId();
    // The last time this localisation was on
    int lastT = first.getTime();
    // Number of blinks
    int blinks = 0;
    // On-time of current pulse
    int currentT = 0;
    double signal = 0;
    final double centreOffset = settings.getSize() * 0.5;
    // Used to convert the sampled times in frames into seconds
    final double framesPerSecond = 1000.0 / settings.getExposureTime();
    // final double gain = new CreateDataSettingsHelper(settings).getTotalGainSafe();
    for (final LocalisationModel l : localisations) {
        final double[] data = l.getData();
        if (data == null) {
            throw new IllegalStateException("No localisation data. This should not happen!");
        }
        final double noise = data[1];
        final double sx = data[2];
        final double sy = data[3];
        final double intensityInPhotons = data[4];
        // Q. What if the noise is zero, i.e. no background photon / read noise?
        // Just ignore it at current. This is only an approximation to the SNR estimate
        // if this is not a Gaussian spot.
        final double snr = Gaussian2DPeakResultHelper.getMeanSignalUsingP05(intensityInPhotons, sx, sy) / noise;
        stats[SIGNAL].add(intensityInPhotons);
        stats[NOISE].add(noise);
        if (noise != 0) {
            stats[SNR].add(snr);
        }
        // if (l.isContinuous())
        if (l.getNext() != null && l.getPrevious() != null) {
            stats[SIGNAL_CONTINUOUS].add(intensityInPhotons);
            if (noise != 0) {
                stats[SNR_CONTINUOUS].add(snr);
            }
        }
        final int id = l.getId();
        // Check if this a new fluorophore
        if (currentId != id) {
            // Add previous fluorophore
            stats[SAMPLED_BLINKS].add(blinks);
            stats[SAMPLED_T_ON].add(currentT / framesPerSecond);
            stats[TOTAL_SIGNAL].add(signal);
            // Reset
            blinks = 0;
            currentT = 1;
            currentId = id;
            signal = intensityInPhotons;
        } else {
            signal += intensityInPhotons;
            // Check if the current fluorophore pulse is broken (i.e. a blink)
            if (l.getTime() - 1 > lastT) {
                blinks++;
                stats[SAMPLED_T_ON].add(currentT / framesPerSecond);
                currentT = 1;
                stats[SAMPLED_T_OFF].add(((l.getTime() - 1) - lastT) / framesPerSecond);
            } else {
                // Continuous on-time
                currentT++;
            }
        }
        lastT = l.getTime();
        countHistogram[lastT]++;
        stats[X].add((l.getX() - centreOffset) * settings.getPixelPitch());
        stats[Y].add((l.getY() - centreOffset) * settings.getPixelPitch());
        stats[Z].add(l.getZ() * settings.getPixelPitch());
    }
    // Final fluorophore
    stats[SAMPLED_BLINKS].add(blinks);
    stats[SAMPLED_T_ON].add(currentT / framesPerSecond);
    stats[TOTAL_SIGNAL].add(signal);
    // Samples per frame
    for (int t = 1; t < countHistogram.length; t++) {
        stats[SAMPLES].add(countHistogram[t]);
    }
    if (fluorophores != null) {
        for (final FluorophoreSequenceModel f : fluorophores) {
            stats[BLINKS].add(f.getNumberOfBlinks());
            // On-time
            for (final double t : f.getOnTimes()) {
                stats[T_ON].add(t);
            }
            // Off-time
            for (final double t : f.getOffTimes()) {
                stats[T_OFF].add(t);
            }
        }
    } else {
        // show no blinks
        stats[BLINKS].add(0);
        stats[T_ON].add(1);
    }
    if (results != null) {
        // Convert depth-of-field to pixels
        final double depth = settings.getDepthOfField() / settings.getPixelPitch();
        try {
            // Get widths
            final WidthResultProcedure wp = new WidthResultProcedure(results, DistanceUnit.PIXEL);
            wp.getW();
            stats[WIDTH].add(wp.wx);
        } catch (final DataException ex) {
            ImageJUtils.log("Unable to compute width: " + ex.getMessage());
        }
        try {
            // Get z depth
            final StandardResultProcedure sp = new StandardResultProcedure(results, DistanceUnit.PIXEL);
            sp.getXyz();
            // Get precision
            final PrecisionResultProcedure pp = new PrecisionResultProcedure(results);
            pp.getPrecision();
            stats[PRECISION].add(pp.precisions);
            for (int i = 0; i < pp.size(); i++) {
                if (Math.abs(sp.z[i]) < depth) {
                    stats[PRECISION_IN_FOCUS].add(pp.precisions[i]);
                }
            }
        } catch (final DataException ex) {
            ImageJUtils.log("Unable to compute LSE precision: " + ex.getMessage());
        }
        // Compute density per frame. Multi-thread for speed
        if (settings.getDensityRadius() > 0) {
            final int threadCount = Prefs.getThreads();
            final Ticker ticker = ImageJUtils.createTicker(results.getLastFrame(), threadCount, "Calculating density ...");
            final ExecutorService threadPool = Executors.newFixedThreadPool(threadCount);
            final List<Future<?>> futures = new LinkedList<>();
            final TFloatArrayList coordsX = new TFloatArrayList();
            final TFloatArrayList coordsY = new TFloatArrayList();
            final Statistics densityStats = stats[DENSITY];
            final float radius = (float) (settings.getDensityRadius() * getHwhm());
            final Rectangle bounds = results.getBounds();
            final double area = (double) bounds.width * bounds.height;
            // Store the density for each result.
            final int[] allDensity = new int[results.size()];
            final FrameCounter counter = results.newFrameCounter();
            results.forEach((PeakResultProcedure) result -> {
                if (counter.advance(result.getFrame())) {
                    counter.increment(runDensityCalculation(threadPool, futures, coordsX, coordsY, densityStats, radius, area, allDensity, counter.getCount(), ticker));
                }
                coordsX.add(result.getXPosition());
                coordsY.add(result.getYPosition());
            });
            runDensityCalculation(threadPool, futures, coordsX, coordsY, densityStats, radius, area, allDensity, counter.getCount(), ticker);
            ConcurrencyUtils.waitForCompletionUnchecked(futures);
            threadPool.shutdown();
            ImageJUtils.finished();
            // Split results into singles (density = 0) and clustered (density > 0)
            final MemoryPeakResults singles = copyMemoryPeakResults("No Density");
            final MemoryPeakResults clustered = copyMemoryPeakResults("Density");
            counter.reset();
            results.forEach((PeakResultProcedure) result -> {
                final int density = allDensity[counter.getAndIncrement()];
                result.setOrigValue(density);
                if (density == 0) {
                    singles.add(result);
                } else {
                    clustered.add(result);
                }
            });
        }
    }
    final StringBuilder sb = new StringBuilder();
    sb.append(datasetNumber).append('\t');
    if (settings.getCameraType() == CameraType.SCMOS) {
        sb.append("sCMOS (").append(settings.getCameraModelName()).append(") ");
        final Rectangle bounds = cameraModel.getBounds();
        sb.append(" ").append(bounds.x).append(",").append(bounds.y);
        final int size = settings.getSize();
        sb.append(" ").append(size).append("x").append(size);
    } else if (CalibrationProtosHelper.isCcdCameraType(settings.getCameraType())) {
        sb.append(CalibrationProtosHelper.getName(settings.getCameraType()));
        final int size = settings.getSize();
        sb.append(" ").append(size).append("x").append(size);
        if (settings.getCameraType() == CameraType.EMCCD) {
            sb.append(" EM=").append(settings.getEmGain());
        }
        sb.append(" CG=").append(settings.getCameraGain());
        sb.append(" RN=").append(settings.getReadNoise());
        sb.append(" B=").append(settings.getBias());
    } else {
        throw new IllegalStateException();
    }
    sb.append(" QE=").append(settings.getQuantumEfficiency()).append('\t');
    sb.append(settings.getPsfModel());
    if (psfModelType == PSF_MODEL_IMAGE) {
        sb.append(" Image").append(settings.getPsfImageName());
    } else if (psfModelType == PSF_MODEL_ASTIGMATISM) {
        sb.append(" model=").append(settings.getAstigmatismModel());
    } else {
        sb.append(" DoF=").append(MathUtils.rounded(settings.getDepthOfFocus()));
        if (settings.getEnterWidth()) {
            sb.append(" SD=").append(MathUtils.rounded(settings.getPsfSd()));
        } else {
            sb.append(" λ=").append(MathUtils.rounded(settings.getWavelength()));
            sb.append(" NA=").append(MathUtils.rounded(settings.getNumericalAperture()));
        }
    }
    sb.append('\t');
    sb.append((fluorophores == null) ? localisations.size() : fluorophores.size()).append('\t');
    sb.append(stats[SAMPLED_BLINKS].getN() + (int) stats[SAMPLED_BLINKS].getSum()).append('\t');
    sb.append(localisations.size()).append('\t');
    sb.append(frameCount).append('\t');
    sb.append(MathUtils.rounded(areaInUm)).append('\t');
    sb.append(MathUtils.rounded(localisations.size() / (areaInUm * frameCount), 4)).append('\t');
    sb.append(MathUtils.rounded(getHwhm(), 4)).append('\t');
    double sd = getPsfSd();
    sb.append(MathUtils.rounded(sd, 4)).append('\t');
    sd *= settings.getPixelPitch();
    final double sa = PsfCalculator.squarePixelAdjustment(sd, settings.getPixelPitch()) / settings.getPixelPitch();
    sb.append(MathUtils.rounded(sa, 4)).append('\t');
    // Width not valid for the Image PSF.
    // Q. Is this true? We can approximate the FHWM for a spot-like image PSF.
    final int nStats = (psfModelType == PSF_MODEL_IMAGE) ? stats.length - 1 : stats.length;
    for (int i = 0; i < nStats; i++) {
        final double centre = (alwaysRemoveOutliers[i]) ? ((StoredDataStatistics) stats[i]).getStatistics().getPercentile(50) : stats[i].getMean();
        sb.append(MathUtils.rounded(centre, 4)).append('\t');
    }
    createSummaryTable().accept(sb.toString());
    // Show histograms
    if (settings.getShowHistograms() && !java.awt.GraphicsEnvironment.isHeadless()) {
        IJ.showStatus("Calculating histograms ...");
        final boolean[] chosenHistograms = getChoosenHistograms();
        final WindowOrganiser wo = new WindowOrganiser();
        final HistogramPlotBuilder builder = new HistogramPlotBuilder(TITLE);
        for (int i = 0; i < NAMES.length; i++) {
            if (chosenHistograms[i]) {
                builder.setData((StoredDataStatistics) stats[i]).setName(NAMES[i]).setIntegerBins(integerDisplay[i]).setRemoveOutliersOption((settings.getRemoveOutliers() || alwaysRemoveOutliers[i]) ? 2 : 0).setNumberOfBins(settings.getHistogramBins()).show(wo);
            }
        }
        wo.tile();
    }
    IJ.showStatus("");
    return stats[SIGNAL].getMean();
}

Example 3 with PeakResultProcedure

use of uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure in project GDSC-SMLM by aherbert.

the class CreateData method loadBenchmarkData.

/**
 * Load benchmark data using an open image and a XYZ text file.
 */
private void loadBenchmarkData() {
    if (!showLoadDialog()) {
        // resetMemory();
        return;
    }
    // Load the image
    final ImagePlus imp = WindowManager.getImage(benchmarkImage);
    if (imp == null) {
        IJ.error(TITLE, "No benchmark image: " + benchmarkImage);
        // resetMemory();
        return;
    }
    // Load the results
    final MemoryPeakResults results = getSimulationResults();
    if (results == null) {
        IJ.error(TITLE, "No benchmark results: " + benchmarkResultsName);
        // resetMemory();
        return;
    }
    results.setName(imp.getTitle() + " (Results)");
    results.setBounds(new Rectangle(0, 0, imp.getWidth(), imp.getHeight()));
    final IJImageSource imageSource = new IJImageSource(imp);
    results.setSource(imageSource);
    // Load the settings as these are used in the dialog
    settings = SettingsManager.readCreateDataSettings(0).toBuilder();
    simulationParameters = showSimulationParametersDialog(imp, results);
    if (simulationParameters != null) {
        // Convert data to allow analysis as if a Gaussian2D PSF
        final boolean isGaussian2D = PsfHelper.isGaussian2D(results.getPsf());
        if (isGaussian2D) {
            Gaussian2DPeakResultHelper.addMeanIntensity(results.getPsf(), results);
        } else if (simulationParameters.sd > 0) {
            final TypeConverter<DistanceUnit> dc = results.getDistanceConverter(DistanceUnit.NM);
            final PSF.Builder psf = PsfProtosHelper.getDefaultPsf(PSFType.ONE_AXIS_GAUSSIAN_2D).toBuilder();
            psf.getParametersBuilder(0).setValue(dc.convertBack(simulationParameters.sd));
            results.setPsf(psf.build());
            // Update all the results. This assumes the results do not have data for a custom PSF,
            // i.e. the parameters only have [t,i,x,y,z]
            final LocalList<PeakResult> newResults = new LocalList<>(results.size());
            final float sd = (float) dc.convertBack(simulationParameters.sd);
            final double meanFactor = Gaussian2DPeakResultHelper.getMeanSignalUsingP05(1, sd, sd);
            results.forEach((PeakResultProcedure) r -> {
                final PeakResult peak = r.resize(PeakResult.STANDARD_PARAMETERS + 1);
                peak.setMeanIntensity((float) (peak.getIntensity() * meanFactor));
                peak.setParameter(PeakResult.STANDARD_PARAMETERS, sd);
                newResults.add(peak);
            });
            results.begin();
            results.addAll(newResults);
            results.end();
        }
        setBackground(results);
        setNoise(results, imp);
        setBenchmarkResults(imp, results);
        IJ.showStatus("Loaded " + TextUtils.pleural(results.size(), "result"));
    } else {
        resetMemory();
    }
}

Example 4 with PeakResultProcedure

use of uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure in project GDSC-SMLM by aherbert.

the class PsfCreator method fitPsf.

/**
 * Fit the new PSF image and show a graph of the amplitude/width.
 *
 * @param psfStack the psf stack
 * @param loess the loess
 * @param cz the cz
 * @param averageRange the average range
 * @param fitCom the fit com
 * @return The width of the PSF in the z-centre
 */
private double fitPsf(ImageStack psfStack, LoessInterpolator loess, int cz, double averageRange, final double[][] fitCom) {
    IJ.showStatus("Fitting final PSF");
    // is not appropriate for a normalised PSF.
    if (fitConfig.getFitSolver() != FitSolver.LVM_LSE) {
        ImageJUtils.log("  " + FitProtosHelper.getName(fitConfig.getFitSolver()) + " is not appropriate for final PSF fitting.");
        ImageJUtils.log("  Switching to Least Square Estimation");
        fitConfig.setFitSolver(FitSolver.LVM_LSE);
        if (settings.getInteractiveMode()) {
            // This assumes the LVM does not need the calibration
            PeakFit.configureFitSolver(config, null, null, 0);
        }
    }
    // Update the box radius since this is used in the fitSpot method.
    boxRadius = psfStack.getWidth() / 2;
    final int x = boxRadius;
    final int y = boxRadius;
    final double shift = fitConfig.getCoordinateShiftFactor();
    // Scale the PSF
    final PSF.Builder localPsf = fitConfig.getPsf().toBuilder();
    for (int i = 0; i < localPsf.getParametersCount(); i++) {
        final PSFParameter param = localPsf.getParameters(i);
        if (param.getUnit() == PSFParameterUnit.DISTANCE) {
            final PSFParameter.Builder b = param.toBuilder();
            b.setValue(b.getValue() * settings.getMagnification());
            localPsf.setParameters(i, b);
        }
    }
    fitConfig.setPsf(localPsf.build());
    // Need to be updated after the widths have been set
    fitConfig.setCoordinateShiftFactor(shift);
    fitConfig.setBackgroundFitting(false);
    // Since the PSF will be normalised remove the camera calibration
    fitConfig.setCameraType(CameraType.CAMERA_TYPE_NA);
    fitConfig.setMinPhotons(0);
    fitConfig.setBias(0);
    fitConfig.setGain(1);
    // No complex filtering so we get a fit. It should be easy to fit anyway.
    fitConfig.setPrecisionThreshold(0);
    fitConfig.setDirectFilter(null);
    // fitConfig.setDisableSimpleFilter(true);
    // Use this for debugging the fit
    // fitConfig.setLog(uk.ac.sussex.gdsc.core.ij.ImageJPluginLoggerHelper.getDefaultLogger());
    final MemoryPeakResults results = fitSpot(psfStack, psfStack.getWidth(), psfStack.getHeight(), x, y);
    if (results.size() < 5) {
        ImageJUtils.log("  Final PSF: Not enough fit results %d", results.size());
        return 0;
    }
    // Get the results for the spot centre and width
    final double[] z = new double[results.size()];
    final double[] xCoord = new double[z.length];
    final double[] yCoord = new double[z.length];
    final double[] sd = new double[z.length];
    final double[] a = new double[z.length];
    // Set limits for the fit
    final float maxWidth = (float) (Math.max(fitConfig.getInitialXSd(), fitConfig.getInitialYSd()) * settings.getMagnification() * 4);
    // PSF is normalised to 1
    final float maxSignal = 2;
    final WidthResultProcedure wp = new WidthResultProcedure(results, DistanceUnit.PIXEL);
    wp.getWxWy();
    final HeightResultProcedure hp = new HeightResultProcedure(results, IntensityUnit.COUNT);
    hp.getH();
    final Counter counter = new Counter();
    final Counter counterOk = new Counter();
    // We have fit the results so they will be in the preferred units
    results.forEach((PeakResultProcedure) peak -> {
        int index = counter.getAndIncrement();
        final float w = Math.max(wp.wx[index], wp.wy[index]);
        if (peak.getIntensity() > maxSignal || w > maxWidth) {
            return;
        }
        index = counterOk.getAndIncrement();
        z[index] = peak.getFrame();
        fitCom[0][peak.getFrame() - 1] = xCoord[index] = peak.getXPosition() - x;
        fitCom[1][peak.getFrame() - 1] = yCoord[index] = peak.getYPosition() - y;
        sd[index] = w;
        a[index] = hp.heights[index];
    });
    // Truncate
    final double[] z2 = Arrays.copyOf(z, counter.getCount());
    final double[] xCoord2 = Arrays.copyOf(xCoord, z2.length);
    final double[] yCoord2 = Arrays.copyOf(yCoord, z2.length);
    final double[] sd2 = Arrays.copyOf(sd, z2.length);
    final double[] a2 = Arrays.copyOf(a, z2.length);
    // Extract the average smoothed range from the individual fits
    final int r = (int) Math.ceil(averageRange / 2);
    int start = 0;
    int stop = z2.length - 1;
    for (int j = 0; j < z2.length; j++) {
        if (z2[j] > cz - r) {
            start = j;
            break;
        }
    }
    for (int j = z2.length; j-- > 0; ) {
        if (z2[j] < cz + r) {
            stop = j;
            break;
        }
    }
    // Extract xy centre coords and smooth
    double[] smoothX = new double[stop - start + 1];
    double[] smoothY = new double[smoothX.length];
    double[] smoothSd = new double[smoothX.length];
    double[] smoothA = new double[smoothX.length];
    final double[] newZ = new double[smoothX.length];
    int smoothCzIndex = 0;
    for (int j = start, k = 0; j <= stop; j++, k++) {
        smoothX[k] = xCoord2[j];
        smoothY[k] = yCoord2[j];
        smoothSd[k] = sd2[j];
        smoothA[k] = a2[j];
        newZ[k] = z2[j];
        if (newZ[k] == cz) {
            smoothCzIndex = k;
        }
    }
    smoothX = loess.smooth(newZ, smoothX);
    smoothY = loess.smooth(newZ, smoothY);
    smoothSd = loess.smooth(newZ, smoothSd);
    smoothA = loess.smooth(newZ, smoothA);
    // Update the widths and positions using the magnification
    final double scale = 1.0 / settings.getMagnification();
    for (int j = 0; j < xCoord2.length; j++) {
        xCoord2[j] *= scale;
        yCoord2[j] *= scale;
        sd2[j] *= scale;
    }
    for (int j = 0; j < smoothX.length; j++) {
        smoothX[j] *= scale;
        smoothY[j] *= scale;
        smoothSd[j] *= scale;
    }
    showPlots(z2, a2, newZ, smoothA, xCoord2, yCoord2, sd2, newZ, smoothX, smoothY, smoothSd, cz);
    // Store the data for replotting
    this.z = z2;
    this.amplitude = a2;
    this.smoothAz = newZ;
    this.smoothA = smoothA;
    this.xCoord = xCoord2;
    this.yCoord = yCoord2;
    this.sd = sd2;
    this.newZ = newZ;
    this.smoothX = smoothX;
    this.smoothY = smoothY;
    this.smoothSd = smoothSd;
    // maximumIndex = findMinimumIndex(smoothSd, maximumIndex - start);
    return smoothSd[smoothCzIndex];
}

Example 5 with PeakResultProcedure

use of uk.ac.sussex.gdsc.smlm.results.procedures.PeakResultProcedure in project GDSC-SMLM by aherbert.

the class TraceExporter method exportVbSpt.

@SuppressWarnings("resource")
private void exportVbSpt(MemoryPeakResults results) {
    // vbSPT file format:
    // https://sourceforge.net/projects/vbspt/
    // Matlab matrix file (.mat) containing at least one variable that is a cell
    // array where each element, representing a trajectory, is a matrix
    // where the rows define the coordinates in one, two or three dimensions
    // in subsequent timesteps. The number of dimensions to be used for the
    // analysis will be set by the runinputfile.
    // The units are arbitrary but vbSPT starting estimates must be in the same
    // units. Either nm or μm are recommended.
    // 3 columns for n rows of localisations
    // 1. x coordinate (μm)
    // 2. y coordinate (μm)
    // 3. z coordinate (μm)
    // 
    // Note: An extra column is added containing the frame. This allows results to
    // be uniquely identified using frame,x,y,z
    // Count the IDs. Each new result ID will increment the count.
    final FrameCounter idCounter = new FrameCounter(results.getFirst().getId() - 1);
    results.forEach((PeakResultProcedure) result -> {
        if (idCounter.advance(result.getId())) {
            idCounter.increment();
        }
    });
    // Create the cell array as 1xN
    final Cell out = Mat5.newCell(1, idCounter.getCount());
    // This will reset the counter to zero and ensure the current frame does not match
    // in the event of a single track
    idCounter.advanceAndReset(idCounter.currentFrame() + 1);
    final boolean is3d = results.is3D();
    // Write the tracks
    final LocalList<double[]> list = new LocalList<>();
    results.forEach(DistanceUnit.UM, (XyzrResultProcedure) (x, y, z, result) -> {
        if (idCounter.advance(result.getId())) {
            addTrack(out, idCounter.getCount() - 1, list, is3d);
            idCounter.increment();
            list.clear();
        }
        list.add(new double[] { x, y, z, result.getFrame() });
    });
    addTrack(out, idCounter.getCount() - 1, list, is3d);
    try (MatFile matFile = Mat5.newMatFile()) {
        matFile.addArray("tracks", out);
        Mat5.writeToFile(matFile, Paths.get(settings.directory, results.getName() + ".mat").toFile());
    } catch (final IOException ex) {
        handleException(ex);
    }
}