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

Example 1 with HistogramPlot

use of uk.ac.sussex.gdsc.core.ij.HistogramPlot 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 2 with HistogramPlot

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

the class TraceLengthAnalysis method run.

@Override
public void run(String arg) {
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog()) {
        return;
    }
    // Load the results
    MemoryPeakResults results = ResultsManager.loadInputResults(settings.inputOption, false, null, null);
    if (MemoryPeakResults.isEmpty(results)) {
        IJ.error(TITLE, "No results could be loaded");
        return;
    }
    try {
        distanceConverter = results.getDistanceConverter(DistanceUnit.UM);
        timeConverter = results.getTimeConverter(TimeUnit.SECOND);
    } catch (final Exception ex) {
        IJ.error(TITLE, "Cannot convert units to um or seconds: " + ex.getMessage());
        return;
    }
    // Get the localisation error (4s^2) in raw units^2
    double precision = 0;
    try {
        final PrecisionResultProcedure p = new PrecisionResultProcedure(results);
        p.getPrecision();
        // Precision in nm using the median
        precision = new Percentile().evaluate(p.precisions, 50);
        // Convert from nm to um to raw units
        final double rawPrecision = distanceConverter.convertBack(precision / 1e3);
        // Get the localisation error (4s^2) in units^2
        error = 4 * rawPrecision * rawPrecision;
    } catch (final Exception ex) {
        ImageJUtils.log(TITLE + " - Unable to compute precision: " + ex.getMessage());
    }
    // Analyse the track lengths
    results = results.copy();
    results.sort(IdFramePeakResultComparator.INSTANCE);
    // Ensure the first result triggers an id change
    lastid = results.getFirst().getId() - 1;
    results.forEach(this::processTrackLength);
    // For the final track
    store();
    msds = msdList.toArray();
    lengths = lengthList.toArray();
    ids = idList.toArray();
    final int[] limits = MathUtils.limits(lengths);
    h1 = new int[limits[1] + 1];
    h2 = new int[h1.length];
    x1 = SimpleArrayUtils.newArray(h1.length, 0, 1f);
    y1 = new float[x1.length];
    y2 = new float[x1.length];
    // Sort by MSD
    final int[] indices = SimpleArrayUtils.natural(msds.length);
    SortUtils.sortIndices(indices, msds, false);
    final double[] msds2 = msds.clone();
    final int[] lengths2 = lengths.clone();
    final int[] ids2 = ids.clone();
    for (int i = 0; i < indices.length; i++) {
        msds[i] = msds2[indices[i]];
        lengths[i] = lengths2[indices[i]];
        ids[i] = ids2[indices[i]];
    }
    // Interactive analysis
    final NonBlockingExtendedGenericDialog gd = new NonBlockingExtendedGenericDialog(TITLE);
    ImageJUtils.addMessage(gd, "Split traces into fixed or moving using the track diffusion coefficient (D).\n" + "Localisation error has been subtracted from jumps (%s nm).", MathUtils.rounded(precision));
    final Statistics s = Statistics.create(msds);
    final double av = s.getMean();
    final String msg = String.format("Average D per track = %s um^2/s", MathUtils.rounded(av));
    gd.addMessage(msg);
    // Histogram the diffusion coefficients
    final WindowOrganiser wo = new WindowOrganiser();
    final HistogramPlot histogramPlot = new HistogramPlotBuilder("Trace diffusion coefficient", StoredData.create(msds), "D (um^2/s)").setRemoveOutliersOption(1).setPlotLabel(msg).build();
    histogramPlot.show(wo);
    final double[] xvalues = histogramPlot.getPlotXValues();
    final double min = xvalues[0];
    final double max = xvalues[xvalues.length - 1];
    // see if we can build a nice slider range from the histogram limits
    if (max - min < 5) {
        // Because sliders are used when the range is <5 and floating point
        gd.addSlider("D_threshold", min, max, settings.msdThreshold);
    } else {
        gd.addNumericField("D_threshold", settings.msdThreshold, 2, 6, "um^2/s");
    }
    gd.addCheckbox("Normalise", settings.normalise);
    gd.addDialogListener((gd1, event) -> {
        settings.msdThreshold = gd1.getNextNumber();
        settings.normalise = gd1.getNextBoolean();
        update();
        return true;
    });
    if (ImageJUtils.isShowGenericDialog()) {
        draw(wo);
        wo.tile();
    }
    gd.setOKLabel("Save datasets");
    gd.setCancelLabel("Close");
    gd.addHelp(HelpUrls.getUrl("trace-length-analysis"));
    gd.showDialog();
    if (gd.wasCanceled()) {
        return;
    }
    // Sort by ID
    final PeakResult[] list = results.toArray();
    Arrays.sort(list, IdFramePeakResultComparator.INSTANCE);
    createResults(results, "Fixed", 0, lastIndex, list);
    createResults(results, "Moving", lastIndex, msds.length, list);
}

Example 3 with HistogramPlot

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

the class CmosAnalysis method showHistogram.

private static void showHistogram(String name, double[] values, int bins, Statistics stats, WindowOrganiser wo) {
    final DoubleData data = DoubleData.wrap(values);
    final double minWidth = 0;
    final int removeOutliers = 0;
    final int shape = Plot.CIRCLE;
    final String label = String.format("Mean = %s +/- %s", MathUtils.rounded(stats.getMean()), MathUtils.rounded(stats.getStandardDeviation()));
    final HistogramPlot histogramPlot = new HistogramPlotBuilder(TITLE, data, name).setMinBinWidth(minWidth).setRemoveOutliersOption(removeOutliers).setNumberOfBins(bins).setPlotShape(shape).setPlotLabel(label).build();
    histogramPlot.show(wo);
    // Redraw using a log scale. This requires a non-zero y-min
    final Plot plot = histogramPlot.getPlot();
    final double[] limits = plot.getLimits();
    plot.setLimits(limits[0], limits[1], 1, limits[3]);
    plot.setAxisYLog(true);
    plot.updateImage();
}

Example 4 with HistogramPlot

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

the class DiffusionRateTest method plotJumpDistances.

/**
 * Plot a cumulative histogram and standard histogram of the jump distances.
 *
 * @param title the title
 * @param jumpDistances the jump distances
 * @param dimensions the number of dimensions for the jumps
 */
private void plotJumpDistances(String title, DoubleData jumpDistances, int dimensions) {
    // Cumulative histogram
    // --------------------
    final double[] values = jumpDistances.values();
    String title2 = title + " Cumulative Jump Distance " + dimensions + "D";
    final double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(values);
    Plot jdPlot = new Plot(title2, "Distance (um^2)", "Cumulative Probability");
    jdPlot.addPoints(jdHistogram[0], jdHistogram[1], Plot.LINE);
    ImageJUtils.display(title2, jdPlot, windowOrganiser);
    // Plot the expected function
    // This is the Chi-squared distribution: The sum of the squares of k independent
    // standard normal random variables with k = dimensions. It is a special case of
    // the gamma distribution. If the normals have non-unit variance the distribution
    // is scaled.
    // Chi ~ Gamma(k/2, 2) // using the scale parameterisation of the gamma
    // s^2 * Chi ~ Gamma(k/2, 2*s^2)
    // So if s^2 = 2D:
    // 2D * Chi ~ Gamma(k/2, 4D)
    final double estimatedD = pluginSettings.simpleD * pluginSettings.simpleSteps;
    final double max = MathUtils.max(values);
    final double[] x = SimpleArrayUtils.newArray(1000, 0, max / 1000);
    final double k = dimensions / 2.0;
    final double mean = 4 * estimatedD;
    final GammaDistribution dist = new GammaDistribution(null, k, mean);
    final double[] y = new double[x.length];
    for (int i = 0; i < x.length; i++) {
        y[i] = dist.cumulativeProbability(x[i]);
    }
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    ImageJUtils.display(title2, jdPlot);
    // Histogram
    // ---------
    title2 = title + " Jump " + dimensions + "D";
    final HistogramPlot histogramPlot = new HistogramPlotBuilder(title2, jumpDistances, "Distance (um^2)").build();
    // Assume the plot works
    histogramPlot.show(windowOrganiser);
    // Recompute the expected function
    for (int i = 0; i < x.length; i++) {
        y[i] = dist.density(x[i]);
    }
    // Scale to have the same area
    final double[] xvalues = histogramPlot.getPlotXValues();
    if (xvalues.length > 1) {
        final double area1 = jumpDistances.size() * (xvalues[1] - xvalues[0]);
        final double area2 = dist.cumulativeProbability(x[x.length - 1]);
        final double scale = area1 / area2;
        for (int i = 0; i < y.length; i++) {
            y[i] *= scale;
        }
    }
    jdPlot = histogramPlot.getPlot();
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    ImageJUtils.display(histogramPlot.getPlotTitle(), jdPlot);
}