Examples with HistogramPlotBuilder uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder used on opensource projects

Example 1 with HistogramPlotBuilder

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder 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 HistogramPlotBuilder

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method showDoubleHistogram.

private double[] showDoubleHistogram(StoredDataStatistics[][] stats, final int index, WindowOrganiser wo, double[][] matchScores) {
    final String xLabel = filterCriteria[index].name;
    LowerLimit lower = filterCriteria[index].lower;
    UpperLimit upper = filterCriteria[index].upper;
    double[] jaccard = null;
    double[] metric = null;
    double maxJaccard = 0;
    if (index <= FILTER_PRECISION && (settings.showFilterScoreHistograms || upper.requiresJaccard || lower.requiresJaccard)) {
        // Jaccard score verses the range of the metric
        for (final double[] d : matchScores) {
            if (!Double.isFinite(d[index])) {
                System.out.printf("Error in fit data [%d]: %s%n", index, d[index]);
            }
        }
        // Do not use Double.compare(double, double) so we get exceptions in the sort for inf/nan
        Arrays.sort(matchScores, (o1, o2) -> {
            if (o1[index] < o2[index]) {
                return -1;
            }
            if (o1[index] > o2[index]) {
                return 1;
            }
            return 0;
        });
        final int scoreIndex = FILTER_PRECISION + 1;
        final int n = results.size();
        double tp = 0;
        double fp = 0;
        jaccard = new double[matchScores.length + 1];
        metric = new double[jaccard.length];
        for (int k = 0; k < matchScores.length; k++) {
            final double score = matchScores[k][scoreIndex];
            tp += score;
            fp += (1 - score);
            jaccard[k + 1] = tp / (fp + n);
            metric[k + 1] = matchScores[k][index];
        }
        metric[0] = metric[1];
        maxJaccard = MathUtils.max(jaccard);
        if (settings.showFilterScoreHistograms) {
            final String title = TITLE + " Jaccard " + xLabel;
            final Plot plot = new Plot(title, xLabel, "Jaccard");
            plot.addPoints(metric, jaccard, Plot.LINE);
            // Remove outliers
            final double[] limitsx = MathUtils.limits(metric);
            final Percentile p = new Percentile();
            final double l = p.evaluate(metric, 25);
            final double u = p.evaluate(metric, 75);
            final double iqr = 1.5 * (u - l);
            limitsx[1] = Math.min(limitsx[1], u + iqr);
            plot.setLimits(limitsx[0], limitsx[1], 0, MathUtils.max(jaccard));
            ImageJUtils.display(title, plot, wo);
        }
    }
    // [0] is all
    // [1] is matches
    // [2] is no match
    final StoredDataStatistics s1 = stats[0][index];
    final StoredDataStatistics s2 = stats[1][index];
    final StoredDataStatistics s3 = stats[2][index];
    if (s1.getN() == 0) {
        return new double[4];
    }
    final DescriptiveStatistics d = s1.getStatistics();
    double median = 0;
    Plot plot = null;
    String title = null;
    if (settings.showFilterScoreHistograms) {
        median = d.getPercentile(50);
        final String label = String.format("n = %d. Median = %s nm", s1.getN(), MathUtils.rounded(median));
        final HistogramPlot histogramPlot = new HistogramPlotBuilder(TITLE, s1, xLabel).setMinBinWidth(filterCriteria[index].minBinWidth).setRemoveOutliersOption((filterCriteria[index].restrictRange) ? 1 : 0).setPlotLabel(label).build();
        final PlotWindow plotWindow = histogramPlot.show(wo);
        if (plotWindow == null) {
            IJ.log("Failed to show the histogram: " + xLabel);
            return new double[4];
        }
        title = plotWindow.getTitle();
        // Reverse engineer the histogram settings
        plot = histogramPlot.getPlot();
        final double[] xvalues = histogramPlot.getPlotXValues();
        final int bins = xvalues.length;
        final double yMin = xvalues[0];
        final double binSize = xvalues[1] - xvalues[0];
        final double yMax = xvalues[0] + (bins - 1) * binSize;
        if (s2.getN() > 0) {
            final double[] values = s2.getValues();
            final double[][] hist = HistogramPlot.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.red);
                plot.addPoints(hist[0], hist[1], Plot.BAR);
                ImageJUtils.display(title, plot);
            }
        }
        if (s3.getN() > 0) {
            final double[] values = s3.getValues();
            final double[][] hist = HistogramPlot.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.blue);
                plot.addPoints(hist[0], hist[1], Plot.BAR);
                ImageJUtils.display(title, plot);
            }
        }
    }
    // Do cumulative histogram
    final double[][] h1 = MathUtils.cumulativeHistogram(s1.getValues(), true);
    final double[][] h2 = MathUtils.cumulativeHistogram(s2.getValues(), true);
    final double[][] h3 = MathUtils.cumulativeHistogram(s3.getValues(), true);
    if (settings.showFilterScoreHistograms) {
        title = TITLE + " Cumul " + xLabel;
        plot = new Plot(title, xLabel, "Frequency");
        // Find limits
        double[] xlimit = MathUtils.limits(h1[0]);
        xlimit = MathUtils.limits(xlimit, h2[0]);
        xlimit = MathUtils.limits(xlimit, h3[0]);
        // Restrict using the inter-quartile range
        if (filterCriteria[index].restrictRange) {
            final double q1 = d.getPercentile(25);
            final double q2 = d.getPercentile(75);
            final double iqr = (q2 - q1) * 2.5;
            xlimit[0] = MathUtils.max(xlimit[0], median - iqr);
            xlimit[1] = MathUtils.min(xlimit[1], median + iqr);
        }
        plot.setLimits(xlimit[0], xlimit[1], 0, 1.05);
        plot.addPoints(h1[0], h1[1], Plot.LINE);
        plot.setColor(Color.red);
        plot.addPoints(h2[0], h2[1], Plot.LINE);
        plot.setColor(Color.blue);
        plot.addPoints(h3[0], h3[1], Plot.LINE);
    }
    // Determine the maximum difference between the TP and FP
    double maxx1 = 0;
    double maxx2 = 0;
    double max1 = 0;
    double max2 = 0;
    // We cannot compute the delta histogram, or use percentiles
    if (s2.getN() == 0) {
        upper = UpperLimit.ZERO;
        lower = LowerLimit.ZERO;
    }
    final boolean requireLabel = (settings.showFilterScoreHistograms && filterCriteria[index].requireLabel);
    if (requireLabel || upper.requiresDeltaHistogram() || lower.requiresDeltaHistogram()) {
        if (s2.getN() != 0 && s3.getN() != 0) {
            final LinearInterpolator li = new LinearInterpolator();
            final PolynomialSplineFunction f1 = li.interpolate(h2[0], h2[1]);
            final PolynomialSplineFunction f2 = li.interpolate(h3[0], h3[1]);
            for (final double x : h1[0]) {
                if (x < h2[0][0] || x < h3[0][0]) {
                    continue;
                }
                try {
                    final double v1 = f1.value(x);
                    final double v2 = f2.value(x);
                    final double diff = v2 - v1;
                    if (diff > 0) {
                        if (max1 < diff) {
                            max1 = diff;
                            maxx1 = x;
                        }
                    } else if (max2 > diff) {
                        max2 = diff;
                        maxx2 = x;
                    }
                } catch (final OutOfRangeException ex) {
                    // Because we reached the end
                    break;
                }
            }
        }
    }
    if (plot != null) {
        // We use bins=1 on charts where we do not need a label
        if (requireLabel) {
            final String label = String.format("Max+ %s @ %s, Max- %s @ %s", MathUtils.rounded(max1), MathUtils.rounded(maxx1), MathUtils.rounded(max2), MathUtils.rounded(maxx2));
            plot.setColor(Color.black);
            plot.addLabel(0, 0, label);
        }
        ImageJUtils.display(title, plot, wo);
    }
    // Now compute the bounds using the desired limit
    double lowerBound;
    double upperBound;
    switch(lower) {
        case MAX_NEGATIVE_CUMUL_DELTA:
            // Switch to percentiles if we have no delta histogram
            if (maxx2 < 0) {
                lowerBound = maxx2;
                break;
            }
        // fall-through
        case ONE_PERCENT:
            lowerBound = getPercentile(h2, 0.01);
            break;
        case MIN:
            lowerBound = getPercentile(h2, 0.0);
            break;
        case ZERO:
            lowerBound = 0;
            break;
        case HALF_MAX_JACCARD_VALUE:
            lowerBound = getXValue(metric, jaccard, maxJaccard * 0.5);
            break;
        default:
            throw new IllegalStateException("Missing lower limit method");
    }
    switch(upper) {
        case MAX_POSITIVE_CUMUL_DELTA:
            // Switch to percentiles if we have no delta histogram
            if (maxx1 > 0) {
                upperBound = maxx1;
                break;
            }
        // fall-through
        case NINETY_NINE_PERCENT:
            upperBound = getPercentile(h2, 0.99);
            break;
        case NINETY_NINE_NINE_PERCENT:
            upperBound = getPercentile(h2, 0.999);
            break;
        case ZERO:
            upperBound = 0;
            break;
        case MAX_JACCARD2:
            upperBound = getXValue(metric, jaccard, maxJaccard) * 2;
            // System.out.printf("MaxJ = %.4f @ %.3f\n", maxJ, u / 2);
            break;
        default:
            throw new IllegalStateException("Missing upper limit method");
    }
    final double min = getPercentile(h1, 0);
    final double max = getPercentile(h1, 1);
    return new double[] { lowerBound, upperBound, min, max };
}

Example 3 with HistogramPlotBuilder

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder in project GDSC-SMLM by aherbert.

the class PcPalmMolecules method plot.

@Nullable
private static double[][] plot(DoubleData stats, String label, boolean integerBins) {
    if (integerBins) {
        // The histogram is not need for the return statement
        new HistogramPlotBuilder(TITLE, stats, label).setMinBinWidth(1).show();
        return null;
    }
    // Show a cumulative histogram so that the bin size is not relevant
    final double[][] hist = MathUtils.cumulativeHistogram(stats.values(), false);
    // Create the axes
    final double[] xValues = hist[0];
    final double[] yValues = hist[1];
    // Plot
    final String title = TITLE + " " + label;
    final Plot plot = new Plot(title, label, "Frequency");
    plot.addPoints(xValues, yValues, Plot.LINE);
    ImageJUtils.display(title, plot);
    return hist;
}

Example 4 with HistogramPlotBuilder

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder in project GDSC-SMLM by aherbert.

the class SpotFinderPreview method run.

private void run(ImageProcessor ip, MaximaSpotFilter filter) {
    if (refreshing) {
        return;
    }
    currentSlice = imp.getCurrentSlice();
    final Rectangle bounds = ip.getRoi();
    // Crop to the ROI
    FloatProcessor fp = ip.crop().toFloat(0, null);
    float[] data = (float[]) fp.getPixels();
    final int width = fp.getWidth();
    final int height = fp.getHeight();
    // Store the mean bias and gain of the region data.
    // This is used to correctly overlay the filtered data on the original image.
    double bias = 0;
    double gain = 1;
    boolean adjust = false;
    // Set weights
    final CameraModel cameraModel = fitConfig.getCameraModel();
    if (!(cameraModel instanceof FakePerPixelCameraModel)) {
        // This should be done on the normalised data
        final float[] w = cameraModel.getNormalisedWeights(bounds);
        filter.setWeights(w, width, height);
        data = data.clone();
        if (data.length < ip.getPixelCount()) {
            adjust = true;
            bias = MathUtils.sum(cameraModel.getBias(bounds)) / data.length;
            gain = MathUtils.sum(cameraModel.getGain(bounds)) / data.length;
        }
        cameraModel.removeBiasAndGain(bounds, data);
    }
    final Spot[] spots = filter.rank(data, width, height);
    data = filter.getPreprocessedData();
    final int size = spots.length;
    if (topNScrollBar != null) {
        topNScrollBar.setMaximum(size);
        selectScrollBar.setMaximum(size);
    }
    fp = new FloatProcessor(width, height, data);
    final FloatProcessor out = new FloatProcessor(ip.getWidth(), ip.getHeight());
    out.copyBits(ip, 0, 0, Blitter.COPY);
    if (adjust) {
        fp.multiply(gain);
        fp.add(bias);
    }
    out.insert(fp, bounds.x, bounds.y);
    final double min = fp.getMin();
    final double max = fp.getMax();
    out.setMinAndMax(min, max);
    final Overlay o = new Overlay();
    o.add(new ImageRoi(0, 0, out));
    if (label != null) {
        // Get results for frame
        final Coordinate[] actual = ResultsMatchCalculator.getCoordinates(actualCoordinates, imp.getCurrentSlice());
        final Coordinate[] predicted = new Coordinate[size];
        for (int i = 0; i < size; i++) {
            predicted[i] = new BasePoint(spots[i].x + bounds.x, spots[i].y + bounds.y);
        }
        // Compute assignments
        final LocalList<FractionalAssignment> fractionalAssignments = new LocalList<>(3 * predicted.length);
        final double matchDistance = settings.distance * fitConfig.getInitialPeakStdDev();
        final RampedScore score = RampedScore.of(matchDistance, matchDistance * settings.lowerDistance / 100, false);
        final double dmin = matchDistance * matchDistance;
        final int nActual = actual.length;
        final int nPredicted = predicted.length;
        for (int j = 0; j < nPredicted; j++) {
            // Centre in the middle of the pixel
            final float x = predicted[j].getX() + 0.5f;
            final float y = predicted[j].getY() + 0.5f;
            // Any spots that match
            for (int i = 0; i < nActual; i++) {
                final double dx = (x - actual[i].getX());
                final double dy = (y - actual[i].getY());
                final double d2 = dx * dx + dy * dy;
                if (d2 <= dmin) {
                    final double d = Math.sqrt(d2);
                    final double s = score.score(d);
                    if (s == 0) {
                        continue;
                    }
                    double distance = 1 - s;
                    if (distance == 0) {
                        // In the case of a match below the distance thresholds
                        // the distance will be 0. To distinguish between candidates all below
                        // the thresholds just take the closest.
                        // We know d2 is below dmin so we subtract the delta.
                        distance -= (dmin - d2);
                    }
                    // Store the match
                    fractionalAssignments.add(new ImmutableFractionalAssignment(i, j, distance, s));
                }
            }
        }
        final FractionalAssignment[] assignments = fractionalAssignments.toArray(new FractionalAssignment[0]);
        // Compute matches
        final RankedScoreCalculator calc = RankedScoreCalculator.create(assignments, nActual - 1, nPredicted - 1);
        final boolean save = settings.showTP || settings.showFP;
        final double[] calcScore = calc.score(nPredicted, settings.multipleMatches, save);
        final ClassificationResult result = RankedScoreCalculator.toClassificationResult(calcScore, nActual);
        // Compute AUC and max jaccard (and plot)
        final double[][] curve = RankedScoreCalculator.getPrecisionRecallCurve(assignments, nActual, nPredicted);
        final double[] precision = curve[0];
        final double[] recall = curve[1];
        final double[] jaccard = curve[2];
        final double auc = AucCalculator.auc(precision, recall);
        // Show scores
        final String scoreLabel = String.format("Slice=%d, AUC=%s, R=%s, Max J=%s", imp.getCurrentSlice(), MathUtils.rounded(auc), MathUtils.rounded(result.getRecall()), MathUtils.rounded(MathUtils.maxDefault(0, jaccard)));
        setLabel(scoreLabel);
        // Plot
        String title = TITLE + " Performance";
        Plot plot = new Plot(title, "Spot Rank", "");
        final double[] rank = SimpleArrayUtils.newArray(precision.length, 0, 1.0);
        plot.setLimits(0, nPredicted, 0, 1.05);
        plot.setColor(Color.blue);
        plot.addPoints(rank, precision, Plot.LINE);
        plot.setColor(Color.red);
        plot.addPoints(rank, recall, Plot.LINE);
        plot.setColor(Color.black);
        plot.addPoints(rank, jaccard, Plot.LINE);
        plot.setColor(Color.black);
        plot.addLabel(0, 0, scoreLabel);
        final WindowOrganiser windowOrganiser = new WindowOrganiser();
        ImageJUtils.display(title, plot, 0, windowOrganiser);
        title = TITLE + " Precision-Recall";
        plot = new Plot(title, "Recall", "Precision");
        plot.setLimits(0, 1, 0, 1.05);
        plot.setColor(Color.red);
        plot.addPoints(recall, precision, Plot.LINE);
        plot.drawLine(recall[recall.length - 1], precision[recall.length - 1], recall[recall.length - 1], 0);
        plot.setColor(Color.black);
        plot.addLabel(0, 0, scoreLabel);
        ImageJUtils.display(title, plot, 0, windowOrganiser);
        windowOrganiser.tile();
        // Create Rois for TP and FP
        if (save) {
            final double[] matchScore = RankedScoreCalculator.getMatchScore(calc.getScoredAssignments(), nPredicted);
            int matches = 0;
            for (int i = 0; i < matchScore.length; i++) {
                if (matchScore[i] != 0) {
                    matches++;
                }
            }
            if (settings.showTP) {
                final float[] x = new float[matches];
                final float[] y = new float[x.length];
                int count = 0;
                for (int i = 0; i < matchScore.length; i++) {
                    if (matchScore[i] != 0) {
                        final BasePoint p = (BasePoint) predicted[i];
                        x[count] = p.getX() + 0.5f;
                        y[count] = p.getY() + 0.5f;
                        count++;
                    }
                }
                addRoi(0, o, x, y, count, Color.green);
            }
            if (settings.showFP) {
                final float[] x = new float[nPredicted - matches];
                final float[] y = new float[x.length];
                int count = 0;
                for (int i = 0; i < matchScore.length; i++) {
                    if (matchScore[i] == 0) {
                        final BasePoint p = (BasePoint) predicted[i];
                        x[count] = p.getX() + 0.5f;
                        y[count] = p.getY() + 0.5f;
                        count++;
                    }
                }
                addRoi(0, o, x, y, count, Color.red);
            }
        }
    } else {
        final WindowOrganiser wo = new WindowOrganiser();
        // Option to show the number of neighbours within a set pixel box radius
        final int[] count = spotFilterHelper.countNeighbours(spots, width, height, settings.neighbourRadius);
        // Show as histogram the totals...
        new HistogramPlotBuilder(TITLE, StoredData.create(count), "Neighbours").setIntegerBins(true).setPlotLabel("Radius = " + settings.neighbourRadius).show(wo);
        // TODO - Draw n=0, n=1 on the image overlay
        final LUT lut = LutHelper.createLut(LutColour.FIRE_LIGHT);
        // These are copied by the ROI
        final float[] x = new float[1];
        final float[] y = new float[1];
        // Plot the intensity
        final double[] intensity = new double[size];
        final double[] rank = SimpleArrayUtils.newArray(size, 1, 1.0);
        final int top = (settings.topN > 0) ? settings.topN : size;
        final int size_1 = size - 1;
        for (int i = 0; i < size; i++) {
            intensity[i] = spots[i].intensity;
            if (i < top) {
                x[0] = spots[i].x + bounds.x + 0.5f;
                y[0] = spots[i].y + bounds.y + 0.5f;
                final Color c = LutHelper.getColour(lut, size_1 - i, size);
                addRoi(0, o, x, y, 1, c, 2, 1);
            }
        }
        final String title = TITLE + " Intensity";
        final Plot plot = new Plot(title, "Rank", "Intensity");
        plot.setColor(Color.blue);
        plot.addPoints(rank, intensity, Plot.LINE);
        if (settings.topN > 0 && settings.topN < size) {
            plot.setColor(Color.magenta);
            plot.drawLine(settings.topN, 0, settings.topN, intensity[settings.topN - 1]);
        }
        if (settings.select > 0 && settings.select < size) {
            plot.setColor(Color.yellow);
            final int index = settings.select - 1;
            final double in = intensity[index];
            plot.drawLine(settings.select, 0, settings.select, in);
            x[0] = spots[index].x + bounds.x + 0.5f;
            y[0] = spots[index].y + bounds.y + 0.5f;
            final Color c = LutHelper.getColour(lut, size_1 - settings.select, size);
            addRoi(0, o, x, y, 1, c, 3, 3);
            plot.setColor(Color.black);
            plot.addLabel(0, 0, "Selected spot intensity = " + MathUtils.rounded(in));
        }
        ImageJUtils.display(title, plot, 0, wo);
        wo.tile();
    }
    imp.setOverlay(o);
}

Example 5 with HistogramPlotBuilder

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot.HistogramPlotBuilder in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method plotHistogram.

/**
 * Plot a histogram of the extracted statistic.
 *
 * @param show set to true to show the histogram
 * @param wo the window organiser
 * @param clusters the clusters
 * @param name the name
 * @param function the function to extract the plotted statistic
 * @param predicate the predicate to filter the stream of data
 * @param action the action to use on the histogram plot (to set non-standard options)
 */
private static void plotHistogram(boolean show, WindowOrganiser wo, LocalList<ClusterData> clusters, String name, ToDoubleFunction<ClusterData> function, DoublePredicate predicate, Consumer<HistogramPlotBuilder> action) {
    if (!show) {
        return;
    }
    final StoredData data = new StoredData(clusters.size());
    DoubleStream stream = clusters.stream().mapToDouble(function);
    if (predicate != null) {
        stream = stream.filter(predicate);
    }
    stream.forEach(data::add);
    final HistogramPlotBuilder builder = new HistogramPlotBuilder(TITLE, data, name);
    action.accept(builder);
    builder.show(wo);
}