Examples with PSF uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF used on opensource projects

Example 1 with PSF

use of uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF 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 2 with PSF

use of uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method showDialog.

private boolean showDialog() {
    final ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    settings = Settings.load();
    config = settings.config;
    final StringBuilder sb = new StringBuilder();
    sb.append("Finds spots in the benchmark image created by CreateData plugin.\n");
    final double s = simulationParameters.sd / simulationParameters.pixelPitch;
    final double sa = getSa() / simulationParameters.pixelPitch;
    sb.append("PSF width = ").append(MathUtils.rounded(s)).append(" px (sa = ").append(MathUtils.rounded(sa)).append(" px). HWHM = ").append(MathUtils.rounded(s * Gaussian2DFunction.SD_TO_HWHM_FACTOR)).append(" px\n");
    sb.append("Simulation depth = ").append(MathUtils.rounded(simulationParameters.depth)).append(" nm");
    if (simulationParameters.fixedDepth) {
        sb.append(" (fixed)");
    }
    sb.append("\n \nConfigure the spot filter:");
    gd.addMessage(sb.toString());
    if (batchMode) {
        // Support enumeration of single/difference spot filters
        gd.addCheckbox("Mean", settings.batchMean);
        gd.addCheckbox("Gaussian", settings.batchGaussian);
        gd.addCheckbox("Circular", settings.batchCircular);
        gd.addCheckbox("Median", settings.batchMedian);
        // For difference filters we set the smoothing for the second filter
        // using only one distance
        gd.addMessage("Difference filter settings:");
        gd.addCheckbox("Difference_filter", settings.differenceFilter);
        gd.addSlider("Difference_smoothing", 1.5, 5, settings.differenceSmooth);
        gd.addMessage("Local maxima search settings:");
        gd.addSlider("Min_search_width", 1, 4, settings.minSearch);
        gd.addSlider("Max_search_width", 1, 4, settings.maxSearch);
        gd.addCheckbox("Filter_relative_distances (to HWHM)", settings.filterRelativeDistances);
    } else {
        gd.addChoice("Spot_filter_type", SettingsManager.getDataFilterTypeNames(), config.getDataFilterType().ordinal());
        gd.addChoice("Spot_filter", SettingsManager.getDataFilterMethodNames(), config.getDataFilterMethod(0).ordinal());
        gd.addCheckbox("Filter_relative_distances (to HWHM)", !config.getDataFilterParameterAbsolute(0));
        gd.addSlider("Smoothing", 0, 2.5, config.getDataFilterParameterValue(0));
        gd.addSlider("Search_width", 1, 4, settings.search);
    }
    gd.addSlider("Border", 0, 5, settings.border);
    gd.addMessage("Scoring options:");
    gd.addCheckbox("Score_relative_distances (to HWHM)", settings.scoreRelativeDistances);
    gd.addSlider("Analysis_border", 0, 5, settings.analysisBorder);
    gd.addCheckbox("Hard_border", settings.hardBorder);
    gd.addChoice("Matching_method", Settings.MATCHING_METHOD, settings.matchingMethod);
    gd.addSlider("Match_distance", 0.5, 3.5, settings.upperDistance);
    gd.addSlider("Lower_distance", 0, 3.5, settings.lowerDistance);
    gd.addSlider("Signal_factor", 0, 3.5, settings.upperSignalFactor);
    gd.addSlider("Lower_factor", 0, 3.5, settings.lowerSignalFactor);
    gd.addSlider("Recall_fraction", 50, 100, settings.recallFraction);
    if (!batchMode) {
        gd.addCheckbox("Show_plots", settings.showPlot);
        gd.addCheckbox("Plot_rank_by_intensity", settings.rankByIntensity);
        gd.addCheckbox("Show_failures_plots", settings.showFailuresPlot);
        gd.addCheckbox("Show_TP", settings.showTP);
        gd.addCheckbox("Show_FP", settings.showFP);
        gd.addCheckbox("Show_FN", settings.showFN);
    }
    if (extraOptions) {
        gd.addCheckbox("Debug", settings.debug);
    }
    if (batchMode) {
        gd.addHelp(HelpUrls.getUrl("filter-spot-data-batch"));
    } else {
        gd.addHelp(HelpUrls.getUrl("filter-spot-data"));
    }
    gd.showDialog();
    if (gd.wasCanceled()) {
        return false;
    }
    // Here we use PSF stored in the results if supported (i.e. a Gaussian).
    // The results are likely to come from the CreateData simulation.
    final PSF psf = results.getPsf();
    if (PsfHelper.isGaussian2D(psf)) {
        config.getFitConfiguration().setPsf(results.getPsf());
    } else {
        config.getFitConfiguration().setInitialPeakStdDev(s);
    }
    if (batchMode) {
        settings.batchMean = gd.getNextBoolean();
        settings.batchGaussian = gd.getNextBoolean();
        settings.batchCircular = gd.getNextBoolean();
        settings.batchMedian = gd.getNextBoolean();
        if (!(settings.batchMean || settings.batchGaussian || settings.batchCircular || settings.batchMedian)) {
            return false;
        }
        settings.differenceFilter = gd.getNextBoolean();
        settings.differenceSmooth = gd.getNextNumber();
        settings.minSearch = (int) gd.getNextNumber();
        settings.maxSearch = (int) gd.getNextNumber();
        settings.filterRelativeDistances = gd.getNextBoolean();
    } else {
        config.setDataFilterType(SettingsManager.getDataFilterTypeValues()[gd.getNextChoiceIndex()]);
        final int filterIndex = gd.getNextChoiceIndex();
        settings.filterRelativeDistances = gd.getNextBoolean();
        final double smoothing = Math.abs(gd.getNextNumber());
        config.setDataFilter(SettingsManager.getDataFilterMethodValues()[filterIndex], MathUtils.roundUsingDecimalPlaces(smoothing, 3), !settings.filterRelativeDistances, 0);
        settings.search = gd.getNextNumber();
    }
    settings.border = gd.getNextNumber();
    settings.scoreRelativeDistances = gd.getNextBoolean();
    settings.analysisBorder = Math.abs(gd.getNextNumber());
    settings.hardBorder = gd.getNextBoolean();
    settings.matchingMethod = gd.getNextChoiceIndex();
    settings.upperDistance = Math.abs(gd.getNextNumber());
    settings.lowerDistance = Math.abs(gd.getNextNumber());
    settings.upperSignalFactor = Math.abs(gd.getNextNumber());
    settings.lowerSignalFactor = Math.abs(gd.getNextNumber());
    settings.recallFraction = Math.abs(gd.getNextNumber());
    if (!batchMode) {
        settings.showPlot = gd.getNextBoolean();
        settings.rankByIntensity = gd.getNextBoolean();
        settings.showFailuresPlot = gd.getNextBoolean();
        settings.showTP = gd.getNextBoolean();
        settings.showFP = gd.getNextBoolean();
        settings.showFN = gd.getNextBoolean();
    }
    if (extraOptions) {
        settings.debug = gd.getNextBoolean();
    }
    settings.save();
    if (gd.invalidNumber()) {
        return false;
    }
    if (settings.lowerDistance > settings.upperDistance) {
        settings.lowerDistance = settings.upperDistance;
    }
    if (settings.lowerSignalFactor > settings.upperSignalFactor) {
        settings.lowerSignalFactor = settings.upperSignalFactor;
    }
    // Set border here so that the results are consistent with single-filter mode.
    config.setBorder(MathUtils.roundUsingDecimalPlaces(settings.border, 3), !settings.filterRelativeDistances);
    if (!batchMode) {
        // Single filter ...
        config.setSearch(MathUtils.roundUsingDecimalPlaces(settings.search, 3), !settings.filterRelativeDistances);
        // Allow more complicated filters to be configured
        if (!PeakFit.configureDataFilter(config, PeakFit.FLAG_NO_SAVE)) {
            return false;
        }
    }
    int analysisBorder;
    if (settings.scoreRelativeDistances) {
        // Convert distance to PSF standard deviation units
        final double hwhmMax = config.getHwhmMax();
        matchDistance = settings.upperDistance * hwhmMax;
        lowerMatchDistance = settings.lowerDistance * hwhmMax;
        analysisBorder = (int) (settings.analysisBorder * hwhmMax);
    } else {
        matchDistance = settings.upperDistance;
        lowerMatchDistance = settings.lowerDistance;
        analysisBorder = (int) settings.analysisBorder;
    }
    if (analysisBorder > 0) {
        border = new Rectangle(analysisBorder, analysisBorder, imp.getWidth() - 2 * analysisBorder, imp.getHeight() - 2 * analysisBorder);
    } else {
        border = new Rectangle(imp.getWidth(), imp.getHeight());
    }
    return true;
}

Example 3 with PSF

use of uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF in project GDSC-SMLM by aherbert.

the class CreateData method createPsf.

private PSF createPsf(double psfSd) {
    if (psf == null) {
        if (psfModelType == PSF_MODEL_ASTIGMATISM) {
            // Note: the astigmatismModel may not yet be created so create if necessary.
            // This is used to store the best PSF to use to fit the data.
            AstigmatismModel astigmatismModel = this.astigmatismModel;
            if (astigmatismModel == null) {
                astigmatismModel = AstigmatismModelManager.getModel(settings.getAstigmatismModel());
            }
            if (astigmatismModel == null) {
                throw new IllegalArgumentException("Failed to load model: " + settings.getAstigmatismModel());
            }
            // Assume conversion for simulation
            try {
                if (DoubleEquality.relativeError(astigmatismModel.getNmPerPixel(), settings.getPixelPitch()) > 1e-6) {
                    // Convert to nm
                    astigmatismModel = AstigmatismModelManager.convert(astigmatismModel, DistanceUnit.NM, DistanceUnit.NM);
                    // Reset pixel pitch. This will draw the spot using the correct size on the different
                    // size pixels.
                    astigmatismModel = astigmatismModel.toBuilder().setNmPerPixel(settings.getPixelPitch()).build();
                }
                // Convert for simulation in pixels
                astigmatismModel = AstigmatismModelManager.convert(astigmatismModel, DistanceUnit.PIXEL, DistanceUnit.PIXEL);
            } catch (final ConversionException ex) {
                // Wrap so this can be caught as the same type
                throw new IllegalArgumentException(ex);
            }
            psf = PsfProtosHelper.createPsf(astigmatismModel, DistanceUnit.PIXEL, DistanceUnit.PIXEL);
            psf = psf.toBuilder().setModelName(settings.getAstigmatismModel()).build();
        } else {
            PSF.Builder psfBuilder;
            // Set the PSF as a Gaussian using the width at z=0.
            // In future this could be improved for other PSFs.
            psfBuilder = PsfProtosHelper.defaultOneAxisGaussian2DPSF.toBuilder();
            psfBuilder.getParametersBuilder(PsfHelper.INDEX_SX).setValue(psfSd);
            psf = psfBuilder.build();
        }
    }
    return psf;
}

Example 4 with PSF

use of uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF in project GDSC-SMLM by aherbert.

the class SettingsManager method readFitEngineConfiguration.

/**
 * Read the FitEngineConfiguration from the settings file in the settings directory. This loads
 * the current Calibration, PSF and FitEngineSettings.
 *
 * @param flags the flags
 * @return the FitEngineConfiguration
 */
public static FitEngineConfiguration readFitEngineConfiguration(int flags) {
    final FitEngineSettings fitEngineSettings = readFitEngineSettings(flags);
    final Calibration calibration = readCalibration(flags);
    final PSF psf = readPsf(flags);
    return new FitEngineConfiguration(fitEngineSettings, calibration, psf);
}

Example 5 with PSF

use of uk.ac.sussex.gdsc.smlm.data.config.PSFProtos.PSF in project GDSC-SMLM by aherbert.

the class ImageJImagePeakResultsTest method checkCanAddUsingDifferentMethods.

private void checkCanAddUsingDifferentMethods(RandomSeed seed, int displayFlags) {
    final UniformRandomProvider rand = RngUtils.create(seed.getSeed());
    displayFlags |= ImageJImagePeakResults.DISPLAY_SIGNAL;
    final ImageJImagePeakResults[] r = new ImageJImagePeakResults[8];
    final PSF psf = PsfHelper.create(PSFType.ONE_AXIS_GAUSSIAN_2D);
    for (int i = 0; i < r.length; i++) {
        r[i] = new ImageJImagePeakResults(title + i, bounds, 1);
        r[i].setDisplayFlags(displayFlags);
        r[i].setPsf(psf);
        begin(r[i]);
    }
    final int size = 20;
    final int[] t = new int[size];
    final float[] x = new float[size];
    final float[] y = new float[size];
    final float[] v = new float[size];
    for (int i = 0; i < size; i++) {
        t[i] = i;
        x[i] = (rand.nextFloat() * bounds.width);
        y[i] = (rand.nextFloat() * bounds.height);
        v[i] = (rand.nextFloat());
        addPeakResult(r[0], x[i], y[i], v[i]);
        addPeakResult(r[1], t[i], x[i], y[i], v[i]);
        addValue(r[2], x[i], y[i], v[i]);
        addValue(r[3], t[i], x[i], y[i], v[i]);
    }
    addPeakResults(r[4], x, y, v);
    addPeakResults(r[5], t, x, y, v);
    addValues(r[6], x, y, v);
    addValues(r[7], t, x, y, v);
    final float[][] image = new float[r.length][];
    for (int i = 0; i < r.length; i++) {
        r[i].end();
        image[i] = getImage(r[i]);
        logger.log(TestLogUtils.getRecord(Level.FINE, "[%d] = %s", i, Arrays.toString(image[i])));
    }
    // Test single value adds
    float[] expecteds = image[0];
    IntArrayFormatSupplier msg = new IntArrayFormatSupplier("Single add image %d", 1);
    for (int i = 1; i < 4; i++) {
        final float[] actuals = image[i];
        Assertions.assertArrayEquals(expecteds, actuals, msg.set(0, i));
    }
    // Test multi value adds
    expecteds = image[4];
    msg = new IntArrayFormatSupplier("Multi add image %d", 1);
    for (int i = 5; i < image.length; i++) {
        final float[] actuals = image[i];
        Assertions.assertArrayEquals(expecteds, actuals, msg.set(0, i));
    }
    // Test they are roughly the same (differences occur due to floating point summation
    Assertions.assertArrayEquals(expecteds, image[0], 1e-5f, "Single != Multi");
}