Examples with Cluster uk.ac.sussex.gdsc.core.clustering.Cluster used on opensource projects

Example 1 with Cluster

use of uk.ac.sussex.gdsc.core.clustering.Cluster in project GDSC-SMLM by aherbert.

the class PcPalmClusters method doClustering.

/**
 * Extract the results from the PCPALM molecules using the area ROI and then do clustering to
 * obtain the histogram of molecules per cluster.
 *
 * @return the histogram data
 */
private HistogramData doClustering() {
    // Perform clustering analysis to generate the histogram of cluster sizes
    final PcPalmAnalysis analysis = new PcPalmAnalysis();
    final List<Molecule> molecules = analysis.cropToRoi(WindowManager.getCurrentImage(), moleculesResults);
    if (molecules.size() < 2) {
        error("No results within the crop region");
        return null;
    }
    ImageJUtils.log("Using %d molecules (Density = %s um^-2) @ %s nm", molecules.size(), MathUtils.rounded(molecules.size() / analysis.croppedArea), MathUtils.rounded(settings.distance));
    final long s1 = System.nanoTime();
    final ClusteringEngine engine = new ClusteringEngine(1, clusteringAlgorithm, SimpleImageJTrackProgress.getInstance());
    if (settings.multiThread) {
        engine.setThreadCount(Prefs.getThreads());
    }
    engine.setTracker(SimpleImageJTrackProgress.getInstance());
    IJ.showStatus("Clustering ...");
    final List<Cluster> clusters = engine.findClusters(convertToPoint(molecules), settings.distance);
    IJ.showStatus("");
    if (clusters == null) {
        ImageJUtils.log("Aborted");
        return null;
    }
    numberOfMolecules = molecules.size();
    ImageJUtils.log("Finished : %d total clusters (%s ms)", clusters.size(), MathUtils.rounded((System.nanoTime() - s1) / 1e6));
    // Save cluster centroids to a results set in memory. Then they can be plotted.
    final MemoryPeakResults results = new MemoryPeakResults(clusters.size());
    results.setName(TITLE);
    // Set an arbitrary calibration so that the lifetime of the results is stored in the exposure
    // time
    // The results will be handled as a single mega-frame containing all localisation.
    results.setCalibration(CalibrationHelper.create(100, 1, moleculesResults.seconds * 1000));
    int id = 0;
    for (final Cluster c : clusters) {
        results.add(new ExtendedPeakResult((float) c.getX(), (float) c.getY(), c.getSize(), ++id));
    }
    MemoryPeakResults.addResults(results);
    // Get the data for fitting
    final float[] values = new float[clusters.size()];
    for (int i = 0; i < values.length; i++) {
        values[i] = clusters.get(i).getSize();
    }
    final float yMax = (int) Math.ceil(MathUtils.max(values));
    final int nBins = (int) (yMax + 1);
    final float[][] hist = HistogramPlot.calcHistogram(values, 0, yMax, nBins);
    final HistogramData histogramData = (settings.calibrateHistogram) ? new HistogramData(hist, settings.frames, settings.area, Settings.UNITS[settings.units]) : new HistogramData(hist);
    saveHistogram(histogramData);
    return histogramData;
}

Example 2 with Cluster

use of uk.ac.sussex.gdsc.core.clustering.Cluster in project GDSC-SMLM by aherbert.

the class TraceMolecules method convertToTraces.

/**
 * Convert the clusters from the clustering engine into traces composed of the original list of
 * peak results.
 *
 * @param results the results
 * @param clusters the clusters
 * @return the traces
 */
public static Trace[] convertToTraces(MemoryPeakResults results, List<Cluster> clusters) {
    final Trace[] traces = new Trace[clusters.size()];
    int index = 0;
    for (final Cluster cluster : clusters) {
        final Trace trace = new Trace();
        trace.setId(index + 1);
        for (ClusterPoint point = cluster.getHeadClusterPoint(); point != null; point = point.getNext()) {
            // The point Id was the position in the original results array
            trace.add(results.get(point.getId()));
        }
        trace.sort();
        traces[index++] = trace;
    }
    return traces;
}

Example 3 with Cluster

use of uk.ac.sussex.gdsc.core.clustering.Cluster in project GDSC-SMLM by aherbert.

the class DarkTimeAnalysis method analyse.

private void analyse(MemoryPeakResults results) {
    // Find min and max time frames
    results.sort();
    final int min = results.getFirstFrame();
    final int max = results.getLastFrame();
    // Trace results:
    // TODO - The search distance could have units to avoid assuming the results are in pixels
    final double d = settings.searchDistance / results.getCalibrationReader().getNmPerPixel();
    int range = max - min + 1;
    if (settings.maxDarkTime > 0) {
        range = Math.max(1, (int) Math.round(settings.maxDarkTime * 1000 / msPerFrame));
    }
    final TrackProgress tracker = SimpleImageJTrackProgress.getInstance();
    tracker.status("Analysing ...");
    tracker.log("Analysing (d=%s nm (%s px) t=%s s (%d frames)) ...", MathUtils.rounded(settings.searchDistance), MathUtils.rounded(d), MathUtils.rounded(range * msPerFrame / 1000.0), range);
    Trace[] traces;
    if (settings.method == 0) {
        final TraceManager tm = new TraceManager(results);
        tm.setTracker(tracker);
        tm.traceMolecules(d, range);
        traces = tm.getTraces();
    } else {
        final ClusteringEngine engine = new ClusteringEngine(Prefs.getThreads(), algorithms[settings.method - 1], tracker);
        final List<Cluster> clusters = engine.findClusters(TraceMolecules.convertToClusterPoints(results), d, range);
        traces = TraceMolecules.convertToTraces(results, clusters);
    }
    tracker.status("Computing histogram ...");
    // Build dark-time histogram
    final int[] times = new int[range];
    final StoredData stats = new StoredData();
    for (final Trace trace : traces) {
        if (trace.getBlinks() > 1) {
            for (final int t : trace.getOffTimes()) {
                times[t]++;
            }
            stats.add(trace.getOffTimes());
        }
    }
    plotDarkTimeHistogram(stats);
    // Cumulative histogram
    for (int i = 1; i < times.length; i++) {
        times[i] += times[i - 1];
    }
    final int total = times[times.length - 1];
    // Plot dark-time up to 100%
    double[] x = new double[range];
    double[] y = new double[range];
    int truncate = 0;
    for (int i = 0; i < x.length; i++) {
        x[i] = i * msPerFrame;
        if (times[i] == total) {
            // Final value at 100%
            y[i] = 100.0;
            truncate = i + 1;
            break;
        }
        y[i] = (100.0 * times[i]) / total;
    }
    if (truncate > 0) {
        x = Arrays.copyOf(x, truncate);
        y = Arrays.copyOf(y, truncate);
    }
    final String title = "Cumulative Dark-time";
    final Plot plot = new Plot(title, "Time (ms)", "Percentile");
    plot.addPoints(x, y, Plot.LINE);
    ImageJUtils.display(title, plot);
    // Report percentile
    for (int i = 0; i < y.length; i++) {
        if (y[i] >= settings.percentile) {
            ImageJUtils.log("Dark-time Percentile %.1f @ %s ms = %s s", settings.percentile, MathUtils.rounded(x[i]), MathUtils.rounded(x[i] / 1000));
            break;
        }
    }
    tracker.status("");
}

Example 4 with Cluster

use of uk.ac.sussex.gdsc.core.clustering.Cluster in project GDSC-SMLM by aherbert.

the class PcPalmMolecules method performDistanceAnalysis.

private void performDistanceAnalysis(double[][] intraHist, int p99) {
    // We want to know the fraction of distances between molecules at the 99th percentile
    // that are intra- rather than inter-molecule.
    // Do single linkage clustering of closest pair at this distance and count the number of
    // links that are inter and intra.
    // Convert molecules for clustering
    final ArrayList<ClusterPoint> points = new ArrayList<>(settings.molecules.size());
    for (final Molecule m : settings.molecules) {
        // Precision was used to store the molecule ID
        points.add(ClusterPoint.newClusterPoint((int) m.precision, m.x, m.y, m.photons));
    }
    final ClusteringEngine engine = new ClusteringEngine(Prefs.getThreads(), ClusteringAlgorithm.PARTICLE_SINGLE_LINKAGE, SimpleImageJTrackProgress.getInstance());
    IJ.showStatus("Clustering to check inter-molecule distances");
    engine.setTrackJoins(true);
    final List<Cluster> clusters = engine.findClusters(points, intraHist[0][p99]);
    IJ.showStatus("");
    if (clusters != null) {
        final double[] intraIdDistances = engine.getIntraIdDistances();
        final double[] interIdDistances = engine.getInterIdDistances();
        final int all = interIdDistances.length + intraIdDistances.length;
        log("  * Fraction of inter-molecule particle linkage @ %s nm = %s %%", MathUtils.rounded(intraHist[0][p99], 4), (all > 0) ? MathUtils.rounded(100.0 * interIdDistances.length / all, 4) : "0");
        // Show a double cumulative histogram plot
        final double[][] intraIdHist = MathUtils.cumulativeHistogram(intraIdDistances, false);
        final double[][] interIdHist = MathUtils.cumulativeHistogram(interIdDistances, false);
        // Plot
        final String title = TITLE + " molecule linkage distance";
        final Plot plot = new Plot(title, "Distance", "Frequency");
        plot.addPoints(intraIdHist[0], intraIdHist[1], Plot.LINE);
        double max = (intraIdHist[1].length > 0) ? intraIdHist[1][intraIdHist[1].length - 1] : 0;
        if (interIdHist[1].length > 0) {
            max = Math.max(max, interIdHist[1][interIdHist[1].length - 1]);
        }
        plot.setLimits(0, intraIdHist[0][intraIdHist[0].length - 1], 0, max);
        plot.setColor(Color.blue);
        plot.addPoints(interIdHist[0], interIdHist[1], Plot.LINE);
        plot.setColor(Color.black);
        plot.addLegend("Intra-molecule\nInter-molecule");
        ImageJUtils.display(title, plot);
    } else {
        log("Aborted clustering to check inter-molecule distances");
    }
}