Examples with ExtendedPeakResult uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult used on opensource projects

Example 1 with ExtendedPeakResult

use of uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult in project GDSC-SMLM by aherbert.

the class CreateData method savePulses.

/**
 * Create a set of results that represent the molecule continuous on-times (pulses).
 *
 * @param localisations the localisations
 * @param results the results
 */
@SuppressWarnings("null")
private void savePulses(List<LocalisationModel> localisations, MemoryPeakResults results) {
    sortLocalisationsByIdThenTime(localisations);
    final MemoryPeakResults traceResults = copyMemoryPeakResults("Pulses");
    LocalisationModel start = null;
    int currentId = -1;
    int count = 0;
    float[] params = Gaussian2DPeakResultHelper.createTwoAxisParams(0, 0, 0, 0, 0, 0, 0);
    final int isx = Gaussian2DPeakResultHelper.INDEX_SX;
    final int isy = Gaussian2DPeakResultHelper.INDEX_SY;
    double noise = 0;
    int lastT = -1;
    for (final LocalisationModel localisation : localisations) {
        if (currentId != localisation.getId() || lastT + 1 != localisation.getTime()) {
            if (count > 0) {
                params[PeakResult.BACKGROUND] /= count;
                params[PeakResult.X] /= count;
                params[PeakResult.Y] /= count;
                params[isx] /= count;
                params[isy] /= count;
                final ExtendedPeakResult p = new ExtendedPeakResult(start.getTime(), (int) Math.round(start.getX()), (int) Math.round(start.getY()), 0, 0, (float) (Math.sqrt(noise)), 0, params, null, lastT, currentId);
                // if (p.getPrecision(107, 1) > 2000)
                // {
                // System.out.printf("Weird precision = %g (%d)\n", p.getPrecision(107, 1), n);
                // }
                traceResults.add(p);
            }
            start = localisation;
            currentId = localisation.getId();
            count = 0;
            params = new float[7];
            noise = 0;
        }
        final double[] data = localisation.getData();
        params[PeakResult.BACKGROUND] += data[0];
        params[PeakResult.X] += localisation.getX();
        params[PeakResult.Y] += localisation.getY();
        params[PeakResult.INTENSITY] += localisation.getIntensity();
        noise += data[1] * data[1];
        params[isx] += data[2];
        params[isy] += data[3];
        count++;
        lastT = localisation.getTime();
    }
    // Final pulse
    if (count > 0) {
        params[PeakResult.BACKGROUND] /= count;
        params[PeakResult.X] /= count;
        params[PeakResult.Y] /= count;
        params[isx] /= count;
        params[isy] /= count;
        traceResults.add(new ExtendedPeakResult(start.getTime(), (int) Math.round(start.getX()), (int) Math.round(start.getY()), 0, 0, (float) (Math.sqrt(noise)), 0, params, null, lastT, currentId));
    }
    traceResults.end();
}

Example 2 with ExtendedPeakResult

use of uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult 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 3 with ExtendedPeakResult

use of uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method aggregateIntoFrames.

private void aggregateIntoFrames(ArrayList<Point> points, boolean addError, double precisionInPixels, NormalizedGaussianSampler gauss) {
    if (myAggregateSteps < 1) {
        return;
    }
    final MemoryPeakResults results = new MemoryPeakResults(points.size() / myAggregateSteps);
    results.setCalibration(CalibrationHelper.create(settings.getPixelPitch(), 1, myAggregateSteps * 1000.0 / settings.getStepsPerSecond()));
    results.setName(TITLE + " Aggregated");
    results.setPsf(PsfHelper.create(PSFType.CUSTOM));
    MemoryPeakResults.addResults(results);
    simulation.dataset[1] = results.getName();
    int id = 0;
    int peak = 1;
    int number = 0;
    double cx = 0;
    double cy = 0;
    // Get the mean square distance
    double sum = 0;
    int count = 0;
    PeakResult last = null;
    for (final Point result : points) {
        final boolean newId = result.id != id;
        if (number >= myAggregateSteps || newId) {
            if (number != 0) {
                double[] xyz = new double[] { cx / number, cy / number };
                if (addError) {
                    xyz = addError(xyz, precisionInPixels, gauss);
                }
                final float[] params = PeakResult.createParams(0, number, (float) xyz[0], (float) xyz[1], 0);
                final float noise = 0.1f;
                final PeakResult r = new ExtendedPeakResult(peak, (int) params[PeakResult.X], (int) params[PeakResult.Y], number, 0, noise, 0, params, null, peak, id);
                results.add(r);
                if (last != null) {
                    sum += last.distance2(r);
                    count++;
                }
                last = r;
                number = 0;
                cx = cy = 0;
                peak++;
            }
            if (newId) {
                // Increment the frame so that tracing analysis can distinguish traces
                peak++;
                last = null;
                id = result.id;
            }
        }
        number++;
        cx += result.x;
        cy += result.y;
    }
    // Final peak
    if (number != 0) {
        double[] xyz = new double[] { cx / number, cy / number };
        if (addError) {
            xyz = addError(xyz, precisionInPixels, gauss);
        }
        final float[] params = PeakResult.createParams(0, number, (float) xyz[0], (float) xyz[1], 0);
        final float noise = 0.1f;
        final PeakResult r = new ExtendedPeakResult(peak, (int) params[PeakResult.X], (int) params[PeakResult.Y], number, 0, noise, 0, params, null, peak, id);
        results.add(r);
        if (last != null) {
            sum += last.distance2(r);
            count++;
        }
    }
    // MSD in pixels^2 / frame
    final double msd = sum / count;
    // Convert to um^2/second
    ImageJUtils.log("Aggregated data D=%s um^2/s, Precision=%s nm, N=%d, step=%s s, mean=%s um^2, " + "MSD = %s um^2/s", MathUtils.rounded(settings.getDiffusionRate()), MathUtils.rounded(myPrecision), count, MathUtils.rounded(results.getCalibrationReader().getExposureTime() / 1000), MathUtils.rounded(msd / conversionFactor), MathUtils.rounded((msd / conversionFactor) / (results.getCalibrationReader().getExposureTime() / 1000)));
    msdAnalysis(points);
}

Example 4 with ExtendedPeakResult

use of uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method record.

private static int record(double[] xyz, int id, int peak, Statistics stats2D, Statistics stats3D, StoredDataStatistics jumpDistances2D, StoredDataStatistics jumpDistances3D, double[] origin, MemoryPeakResults results) {
    final double dx = xyz[0] - origin[0];
    final double dy = xyz[1] - origin[1];
    final double dz = xyz[2] - origin[2];
    final double jump2D = dx * dx + dy * dy;
    jumpDistances2D.add(jump2D);
    jumpDistances3D.add(jump2D + dz * dz);
    for (int i = 0; i < 3; i++) {
        origin[i] = xyz[i];
    }
    final double d2 = squared2D(xyz);
    stats2D.add(d2);
    stats3D.add(d2 + xyz[2] * xyz[2]);
    final float[] params = PeakResult.createParams(0, 10f, (float) xyz[0], (float) xyz[1], (float) xyz[2]);
    final float noise = 0.1f;
    results.add(new ExtendedPeakResult(peak, (int) params[PeakResult.X], (int) params[PeakResult.Y], 10, 0, noise, 0, params, null, peak, id));
    return ++peak;
}

Example 5 with ExtendedPeakResult

use of uk.ac.sussex.gdsc.smlm.results.ExtendedPeakResult in project GDSC-SMLM by aherbert.

the class FitWorker method createResult.

private PeakResult createResult(int origX, int origY, float origValue, double error, float noise, float meanIntensity, float[] params, float[] paramDevs, int id, double precision) {
    // Convert to a variable PSF parameter PeakResult
    params = Gaussian2DPeakResultHelper.createParams(psfType, params);
    if (paramDevs != null) {
        paramDevs = Gaussian2DPeakResultHelper.createParams(psfType, paramDevs);
        // Convert variances to standard deviations
        for (int i = 0; i < paramDevs.length; i++) {
            paramDevs[i] = (float) Math.sqrt(paramDevs[i]);
        }
    }
    if (precision > 0) {
        final AttributePeakResult r = new AttributePeakResult(slice, origX, origY, origValue, error, noise, meanIntensity, params, paramDevs);
        r.setId(id);
        r.setPrecision(precision);
        if (endT >= 0 && slice != endT) {
            r.setEndFrame(endT);
        }
        return r;
    }
    if (endT >= 0 && slice != endT) {
        return new ExtendedPeakResult(slice, origX, origY, origValue, error, noise, meanIntensity, params, paramDevs, endT, id);
    }
    return new IdPeakResult(slice, origX, origY, origValue, error, noise, meanIntensity, params, paramDevs, id);
}