Examples with Ticker uk.ac.sussex.gdsc.core.logging.Ticker used on opensource projects

Example 1 with Ticker

use of uk.ac.sussex.gdsc.core.logging.Ticker 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 Ticker

use of uk.ac.sussex.gdsc.core.logging.Ticker in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method getSimulationCoordinates.

/**
 * Gets the coordinates for the current simulation. This extract all the results in memory into a
 * list per frame and is cached for the simulation Id.
 *
 * @return the coordinates
 */
private TIntObjectHashMap<PsfSpot[]> getSimulationCoordinates() {
    Pair<Integer, TIntObjectHashMap<PsfSpot[]>> coords = coordinateCache.get();
    if (coords.getKey() != simulationParameters.id) {
        // Always use float coordinates.
        // The Worker adds a pixel offset for the spot coordinates.
        final TIntObjectHashMap<List<Coordinate>> coordinates = ResultsMatchCalculator.getCoordinates(results, false);
        // Spot PSFs may overlap so we must determine the amount of signal overlap and amplitude
        // effect for each spot...
        final int nThreads = Prefs.getThreads();
        final BlockingQueue<Integer> jobs = new ArrayBlockingQueue<>(nThreads * 2);
        final List<OverlapWorker> workers = new LinkedList<>();
        final List<Thread> threads = new LinkedList<>();
        final Ticker overlapTicker = ImageJUtils.createTicker(coordinates.size(), nThreads, "Computing PSF overlap ...");
        for (int i = 0; i < nThreads; i++) {
            final OverlapWorker worker = new OverlapWorker(jobs, coordinates, overlapTicker);
            final Thread t = new Thread(worker);
            workers.add(worker);
            threads.add(t);
            t.start();
        }
        // Process the frames
        coordinates.forEachKey(value -> {
            put(jobs, value);
            return true;
        });
        // Finish all the worker threads by passing in a null job
        for (int i = 0; i < threads.size(); i++) {
            put(jobs, -1);
        }
        // Wait for all to finish
        final TIntObjectHashMap<PsfSpot[]> actualCoordinates = new TIntObjectHashMap<>();
        for (int i = 0; i < threads.size(); i++) {
            try {
                threads.get(i).join();
            } catch (final InterruptedException ex) {
                Thread.currentThread().interrupt();
                throw new ConcurrentRuntimeException("Unexpected interrupt", ex);
            }
            actualCoordinates.putAll(workers.get(i).coordinates);
        }
        threads.clear();
        // For testing
        final SimpleRegression regression = new SimpleRegression(false);
        for (final PsfSpot[] spots : actualCoordinates.valueCollection()) {
            for (final PsfSpot spot : spots) {
                regression.addData(spot.getAmplitude(), calculator.getAmplitude(spot.getPeakResult().getParameters()));
            }
        }
        ImageJUtils.log("PixelAmplitude vs Amplitude = %f, slope=%f, n=%d", regression.getR(), regression.getSlope(), regression.getN());
        ImageJUtils.finished();
        coords = Pair.of(simulationParameters.id, actualCoordinates);
        coordinateCache.set(coords);
    }
    return coords.getRight();
}

Example 3 with Ticker

use of uk.ac.sussex.gdsc.core.logging.Ticker in project GDSC-SMLM by aherbert.

the class DoubletAnalysis method runFitting.

private void runFitting() {
    referenceResults.set(null);
    final ImageStack stack = imp.getImageStack();
    // Get the coordinates per frame
    final TIntObjectHashMap<List<Coordinate>> actualCoordinates = ResultsMatchCalculator.getCoordinates(results, false);
    final long[] sumCount = new long[1];
    actualCoordinates.forEachValue(list -> {
        sumCount[0] += list.size();
        return true;
    });
    final double density = 1e6 * sumCount[0] / (simulationParameters.pixelPitch * simulationParameters.pixelPitch * results.getBounds().getWidth() * results.getBounds().getHeight() * actualCoordinates.size());
    // Create a pool of workers
    final int nThreads = Prefs.getThreads();
    final BlockingQueue<Integer> jobs = new ArrayBlockingQueue<>(nThreads * 2);
    final Ticker ticker = ImageJUtils.createTicker(actualCoordinates.size(), nThreads, "Computing results ...");
    final List<Worker> workers = new LinkedList<>();
    final List<Thread> threads = new LinkedList<>();
    final Overlay overlay = (settings.showOverlay) ? new Overlay() : null;
    for (int i = 0; i < nThreads; i++) {
        final Worker worker = new Worker(jobs, stack, actualCoordinates, config, overlay, ticker);
        final Thread t = new Thread(worker);
        workers.add(worker);
        threads.add(t);
        t.start();
    }
    // Fit the frames
    final long startTime = System.nanoTime();
    actualCoordinates.forEachKey(frame -> {
        put(jobs, frame);
        return true;
    });
    // Finish all the worker threads by passing in a null job
    for (int i = 0; i < threads.size(); i++) {
        put(jobs, -1);
    }
    // Wait for all to finish
    for (int i = 0; i < threads.size(); i++) {
        try {
            threads.get(i).join();
        } catch (final InterruptedException ex) {
            Thread.currentThread().interrupt();
            throw new ConcurrentRuntimeException(ex);
        }
    }
    threads.clear();
    ImageJUtils.finished("Collecting results ...");
    final long runTime = System.nanoTime() - startTime;
    // Collect the results
    int cic = 0;
    int daic = 0;
    int dbic = 0;
    ArrayList<DoubletResult> results = null;
    int maxH = 0;
    int maxH2 = 0;
    int maxH3 = 0;
    for (final Worker worker : workers) {
        if (results == null) {
            results = worker.results;
        } else {
            results.addAll(worker.results);
        }
        cic += worker.cic;
        daic += worker.daic;
        dbic += worker.dbic;
        maxH = MathUtils.max(maxH, worker.spotHistogram.length);
        for (int k = 0; k < 3; k++) {
            maxH2 = MathUtils.max(maxH2, worker.neighbourHistogram[k].length);
            maxH3 = MathUtils.max(maxH3, worker.almostNeighbourHistogram[k].length);
        }
    }
    if (cic > 0) {
        ImageJUtils.log("Difference AIC %d, BIC %d, Total %d", daic, dbic, cic);
    }
    if (settings.showHistograms) {
        final double[] spotHistogram = new double[maxH];
        final double[] resultHistogram = new double[maxH];
        final double[][] neighbourHistogram = new double[3][maxH2];
        final double[][] almostNeighbourHistogram = new double[3][maxH3];
        for (final Worker worker : workers) {
            final int[] h1a = worker.spotHistogram;
            final int[] h1b = worker.resultHistogram;
            for (int j = 0; j < h1a.length; j++) {
                spotHistogram[j] += h1a[j];
                resultHistogram[j] += h1b[j];
            }
            final int[][] h2 = worker.neighbourHistogram;
            final int[][] h3 = worker.almostNeighbourHistogram;
            for (int k = 0; k < 3; k++) {
                for (int j = 0; j < h2[k].length; j++) {
                    neighbourHistogram[k][j] += h2[k][j];
                }
                for (int j = 0; j < h3[k].length; j++) {
                    almostNeighbourHistogram[k][j] += h3[k][j];
                }
            }
        }
        showHistogram(0, spotHistogram);
        showHistogram(1, resultHistogram);
        showHistogram(2, neighbourHistogram[0]);
        showHistogram(3, neighbourHistogram[1]);
        showHistogram(4, neighbourHistogram[2]);
        showHistogram(5, almostNeighbourHistogram[0]);
        showHistogram(6, almostNeighbourHistogram[1]);
        showHistogram(7, almostNeighbourHistogram[2]);
    }
    workers.clear();
    if (overlay != null) {
        imp.setOverlay(overlay);
    }
    MemoryUtils.runGarbageCollector();
    Collections.sort(results, DoubletResult::compare);
    summariseResults(results, density, runTime);
    windowOrganiser.tile();
    IJ.showStatus("");
}

Example 4 with Ticker

use of uk.ac.sussex.gdsc.core.logging.Ticker in project GDSC-SMLM by aherbert.

the class SeriesImageSource method initialise.

/**
 * Initialise the TIFF image sizes and data structures.
 */
private void initialise() {
    if (imageData == null) {
        trackProgress.status("Reading images sizes");
        final Ticker ticker = Ticker.createStarted(trackProgress, images.size(), false);
        // All images are TIFF. Get the size of each and count the total frames.
        imageData = new ImageData[images.size()];
        imageSize = new int[images.size()];
        final String[] names = new String[images.size()];
        frames = 0;
        int ok = 0;
        // We can guess for sequential read
        final boolean estimate = getReadHint() == ReadHint.SEQUENTIAL;
        boolean exact = true;
        for (int i = 0; i < names.length; i++) {
            final String path = images.get(i);
            final File file = new File(path);
            // Get the size of each file so we can determine if
            // they can fit into memory. We only use pre-loading for
            // sequential reading if all images fit into memory.
            final long size = getSize(file);
            try (SeekableStream ss = createSeekableStream(path)) {
                final FastTiffDecoder td = FastTiffDecoder.create(ss, path);
                NumberOfImages numberOfImages;
                if (estimate) {
                    numberOfImages = td.getNumberOfImages(() -> size);
                } else {
                    numberOfImages = td.getNumberOfImages();
                }
                if (numberOfImages.isExact()) {
                    trackProgress.log("%s : images=%d (%d bytes)", path, numberOfImages.getImageCount(), size);
                } else {
                    trackProgress.log("%s : images=%d (approx) (%d bytes)", path, numberOfImages.getImageCount(), size);
                }
                if (estimate) {
                    // Track if this is exact
                    exact = exact && numberOfImages.isExact();
                } else if (numberOfImages.getImageCount() <= 0) {
                    // using the cumulative size array so remove the image.
                    continue;
                }
                frames += numberOfImages.getImageCount();
                imageSize[ok] = frames;
                imageData[ok] = new ImageData(size);
                names[ok] = path;
                ok++;
            } catch (final Throwable ex) {
                if (estimate) {
                    // This is an untested method so log the error
                    ex.printStackTrace();
                }
            }
            ticker.tick();
        }
        trackProgress.status("");
        ticker.stop();
        if (ok < images.size()) {
            imageSize = Arrays.copyOf(imageSize, ok);
            imageData = Arrays.copyOf(imageData, ok);
            images.clear();
            images.addAll(Arrays.asList(names));
        }
        // No support for non-sequential access
        if (!exact) {
            imageSize = null;
        }
    }
}

Example 5 with Ticker

use of uk.ac.sussex.gdsc.core.logging.Ticker in project GDSC-SMLM by aherbert.

the class MedianFilter method run.

@Override
public void run(ImageProcessor ip) {
    final long start = System.nanoTime();
    // final ImageJTrackProgress trackProgress = SimpleImageJTrackProgress.getInstance();
    final ImageStack stack = imp.getImageStack();
    final int width = stack.getWidth();
    final int height = stack.getHeight();
    final float[][] imageStack = new float[stack.getSize()][];
    final float[] mean = new float[imageStack.length];
    // Get the mean for each frame and normalise the data using the mean
    final int threadCount = Prefs.getThreads();
    final ExecutorService threadPool = Executors.newFixedThreadPool(threadCount);
    List<Future<?>> futures = new LinkedList<>();
    Ticker ticker = ImageJUtils.createTicker(stack.getSize(), threadCount);
    IJ.showStatus("Calculating means...");
    for (int n = 1; n <= stack.getSize(); n++) {
        futures.add(threadPool.submit(new ImageNormaliser(stack, imageStack, mean, n, ticker)));
    }
    // Finish processing data
    ConcurrencyUtils.waitForCompletionUnchecked(futures);
    futures = new LinkedList<>();
    final int size = width * height;
    ticker = ImageJUtils.createTicker(size, threadCount);
    IJ.showStatus("Calculating medians...");
    for (int i = 0; i < size; i += settings.blockSize) {
        futures.add(threadPool.submit(new ImageGenerator(imageStack, mean, i, Math.min(i + settings.blockSize, size), ticker, settings)));
    }
    // Finish processing data
    ConcurrencyUtils.waitForCompletionUnchecked(futures);
    if (ImageJUtils.isInterrupted()) {
        threadPool.shutdown();
        return;
    }
    if (settings.subtract) {
        IJ.showStatus("Subtracting medians...");
        ticker = ImageJUtils.createTicker(stack.getSize(), threadCount);
        for (int n = 1; n <= stack.getSize(); n++) {
            futures.add(threadPool.submit(new ImageFilter(stack, imageStack, n, ticker, settings.bias)));
        }
        // Finish processing data
        ConcurrencyUtils.waitForCompletionUnchecked(futures);
    }
    threadPool.shutdown();
    // Update the image
    final ImageStack outputStack = new ImageStack(stack.getWidth(), stack.getHeight(), stack.getSize());
    for (int n = 1; n <= stack.getSize(); n++) {
        outputStack.setPixels(imageStack[n - 1], n);
    }
    imp.setStack(outputStack);
    imp.updateAndDraw();
    IJ.showTime(imp, TimeUnit.NANOSECONDS.toMillis(start), "Completed");
    final long nanoseconds = System.nanoTime() - start;
    ImageJUtils.log(TITLE + " : Radius %d, Interval %d, Block size %d = %s, %s / frame", settings.radius, settings.interval, settings.blockSize, TextUtils.millisToString(nanoseconds), TextUtils.nanosToString(Math.round(nanoseconds / (double) imp.getStackSize())));
}