Examples with PeakResult gdsc.smlm.results.PeakResult used on opensource projects

Example 56 with PeakResult

use of gdsc.smlm.results.PeakResult in project GDSC-SMLM by aherbert.

the class SummariseResults method addSummary.

private void addSummary(StringBuilder sb, MemoryPeakResults result) {
    DescriptiveStatistics[] stats = new DescriptiveStatistics[2];
    char[] suffix = new char[2];
    for (int i = 0; i < stats.length; i++) {
        stats[i] = new DescriptiveStatistics();
        suffix[i] = 0;
    }
    if (result.hasNullResults()) {
        IJ.log("Null results in dataset: " + result.getName());
        if (removeNullResults == UNKNOWN) {
            GenericDialog gd = new GenericDialog(TITLE);
            gd.addMessage("There are invalid results in memory.\n \nClean these results?");
            gd.enableYesNoCancel();
            gd.hideCancelButton();
            gd.showDialog();
            removeNullResults = (gd.wasOKed()) ? YES : NO;
        }
        if (removeNullResults == NO)
            result = result.copy();
        result.removeNullResults();
    }
    final int size = result.size();
    if (size > 0) {
        // Check if we can use the stored precision
        if (result.hasStoredPrecision()) {
            suffix[0] = '*';
            for (PeakResult peakResult : result.getResults()) {
                stats[0].addValue(peakResult.getPrecision());
            }
        } else if (result.isCalibratedForPrecision()) {
            final double nmPerPixel = result.getNmPerPixel();
            final double gain = result.getGain();
            final boolean emCCD = result.isEMCCD();
            for (PeakResult peakResult : result.getResults()) {
                stats[0].addValue(peakResult.getPrecision(nmPerPixel, gain, emCCD));
            }
        }
        // SNR requires noise
        if (result.getHead().noise > 0) {
            for (PeakResult peakResult : result.getResults()) {
                stats[1].addValue(peakResult.getSignal() / peakResult.noise);
            }
        }
    }
    sb.append(result.getName());
    sb.append("\t").append(result.size());
    int maxT = getMaxT(result);
    sb.append("\t").append(maxT);
    final double exposureTime = (result.getCalibration() != null) ? result.getCalibration().getExposureTime() : 0;
    sb.append("\t").append(Utils.timeToString(maxT * exposureTime));
    if (size > 0) {
        boolean includeDeviations = result.getHead().paramsStdDev != null;
        long memorySize = MemoryPeakResults.estimateMemorySize(size, includeDeviations);
        String memory = MemoryPeakResults.memorySizeString(memorySize);
        sb.append("\t").append(memory);
    } else {
        sb.append("\t-");
    }
    Rectangle bounds = result.getBounds(true);
    sb.append(String.format("\t%d,%d,%d,%d\t%s\t%s\t%s", bounds.x, bounds.y, bounds.x + bounds.width, bounds.y + bounds.height, Utils.rounded(result.getNmPerPixel(), 4), Utils.rounded(result.getGain(), 4), Utils.rounded(exposureTime, 4)));
    for (int i = 0; i < stats.length; i++) {
        if (Double.isNaN(stats[i].getMean())) {
            sb.append("\t-\t-\t-\t-");
        } else {
            sb.append("\t").append(IJ.d2s(stats[i].getMean(), 3));
            if (suffix[i] != 0)
                sb.append(suffix[i]);
            sb.append("\t").append(IJ.d2s(stats[i].getPercentile(50), 3));
            sb.append("\t").append(IJ.d2s(stats[i].getMin(), 3));
            sb.append("\t").append(IJ.d2s(stats[i].getMax(), 3));
        }
    }
    sb.append("\n");
}

Example 57 with PeakResult

use of gdsc.smlm.results.PeakResult in project GDSC-SMLM by aherbert.

the class SpotAnalysis method saveSpot.

private void saveSpot() {
    if (onFrames.isEmpty() || !updated)
        return;
    createResultsWindow();
    id++;
    double signal = 0;
    Trace trace = null;
    float psfWidth = Float.parseFloat(widthTextField.getText());
    float cx = areaBounds.x + areaBounds.width / 2.0f;
    float cy = areaBounds.y + areaBounds.height / 2.0f;
    for (Spot s : onFrames) {
        // Get the signal again since the background may have changed.
        final double spotSignal = getSignal(s.frame);
        signal += spotSignal;
        //signal += s.signal;
        float[] params = new float[7];
        params[Gaussian2DFunction.X_POSITION] = cx;
        params[Gaussian2DFunction.Y_POSITION] = cy;
        params[Gaussian2DFunction.SIGNAL] = (float) (spotSignal);
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
        PeakResult result = new PeakResult(s.frame, (int) cx, (int) cy, 0, 0, 0, params, null);
        if (trace == null)
            trace = new Trace(result);
        else
            trace.add(result);
    }
    Statistics tOn = new Statistics(trace.getOnTimes());
    Statistics tOff = new Statistics(trace.getOffTimes());
    resultsWindow.append(String.format("%d\t%.1f\t%.1f\t%s\t%s\t%s\t%d\t%s\t%s\t%s", id, cx, cy, Utils.rounded(signal, 4), Utils.rounded(tOn.getSum() * msPerFrame, 3), Utils.rounded(tOff.getSum() * msPerFrame, 3), trace.getNBlinks() - 1, Utils.rounded(tOn.getMean() * msPerFrame, 3), Utils.rounded(tOff.getMean() * msPerFrame, 3), imp.getTitle()));
    // Save the individual on/off times for use in creating a histogram
    traces.put(id, trace);
    updated = false;
//clearSelectedFrames();
}

Example 58 with PeakResult

use of gdsc.smlm.results.PeakResult in project GDSC-SMLM by aherbert.

the class PeakFit method runMaximaFitting.

/**
	 * Load the selected results from memory. All multiple frame results are added directly to the results. All single
	 * frame
	 * results are added to a list of candidate maxima per frame and fitted using the configured parameters.
	 */
private void runMaximaFitting() {
    MemoryPeakResults results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        log("No results for maxima fitting");
        return;
    }
    // No check for imageSource since this has been check in the calling method
    int totalFrames = source.getFrames();
    // Store the indices of each new time-frame
    results.sort();
    List<PeakResult> candidateMaxima = results.getResults();
    // Use the FitEngine to allow multi-threading.
    FitEngine engine = createFitEngine(getNumberOfThreads(totalFrames));
    final int step = Utils.getProgressInterval(totalFrames);
    runTime = System.nanoTime();
    boolean shutdown = false;
    int slice = candidateMaxima.get(0).getFrame();
    ArrayList<PeakResult> sliceCandidates = new ArrayList<PeakResult>();
    Iterator<PeakResult> iter = candidateMaxima.iterator();
    while (iter.hasNext()) {
        PeakResult r = iter.next();
        if (slice != r.getFrame()) {
            if (escapePressed()) {
                shutdown = true;
                break;
            }
            if (slice % step == 0) {
                if (Utils.showStatus("Slice: " + slice + " / " + totalFrames))
                    IJ.showProgress(slice, totalFrames);
            }
            // Process results
            if (!processResults(engine, sliceCandidates, slice))
                break;
            sliceCandidates.clear();
        }
        slice = r.getFrame();
        sliceCandidates.add(r);
    }
    // Process final results
    if (!shutdown)
        processResults(engine, sliceCandidates, slice);
    engine.end(shutdown);
    time = engine.getTime();
    runTime = System.nanoTime() - runTime;
    showResults();
    source.close();
}

Example 59 with PeakResult

use of gdsc.smlm.results.PeakResult in project GDSC-SMLM by aherbert.

the class Filter method fractionScoreSubset.

/**
	 * Filter the results and return the performance score. Allows benchmarking the filter by marking the results as
	 * true or false.
	 * <p>
	 * Input PeakResults must be allocated a score for true positive, false positive, true negative and false negative
	 * (accessed via the object property get methods). The filter is run and results that pass accumulate scores for
	 * true positive and false positive, otherwise the scores are accumulated for true negative and false negative. The
	 * simplest scoring scheme is to mark valid results as tp=fn=1 and fp=tn=0 and invalid results the opposite.
	 * <p>
	 * The number of consecutive rejections are counted per frame. When the configured number of failures is reached all
	 * remaining results for the frame are rejected. This assumes the results are ordered by the frame.
	 * <p>
	 * Note that this method is to be used to score a subset that was generated using
	 * {@link #filterSubset(MemoryPeakResults, int)} since the number of consecutive failures before each peak are
	 * expected to be stored in the origX property.
	 * 
	 * @param resultsList
	 *            a list of results to analyse
	 * @param failures
	 *            the number of failures to allow per frame before all peaks are rejected
	 * @param tn
	 *            The initial true negatives (used when the results have been pre-filtered)
	 * @param fn
	 *            The initial false negatives (used when the results have been pre-filtered)
	 * @param n
	 *            The initial negatives (used when the results have been pre-filtered)
	 * @return the score
	 */
public FractionClassificationResult fractionScoreSubset(List<MemoryPeakResults> resultsList, int failures, double tn, double fn, int n) {
    int p = 0;
    double fp = 0;
    double tp = 0;
    for (MemoryPeakResults peakResults : resultsList) {
        setup(peakResults);
        int frame = -1;
        int failCount = 0;
        for (PeakResult peak : peakResults.getResults()) {
            // Reset fail count for new frames
            if (frame != peak.getFrame()) {
                frame = peak.getFrame();
                failCount = 0;
            }
            failCount += peak.origX;
            // Reject all peaks if we have exceeded the fail count
            final boolean isPositive;
            if (failCount > failures) {
                isPositive = false;
            } else {
                // Otherwise assess the peak
                isPositive = accept(peak);
            }
            if (isPositive) {
                failCount = 0;
            } else {
                failCount++;
            }
            if (isPositive) {
                p++;
                tp += peak.getTruePositiveScore();
                fp += peak.getFalsePositiveScore();
            } else {
                fn += peak.getFalseNegativeScore();
                tn += peak.getTrueNegativeScore();
            }
        }
        n += peakResults.size();
        end();
    }
    n -= p;
    return new FractionClassificationResult(tp, fp, tn, fn, p, n);
}

Example 60 with PeakResult

use of gdsc.smlm.results.PeakResult in project GDSC-SMLM by aherbert.

the class Filter method filter.

/**
	 * Filter the results
	 * 
	 * @param results
	 * @return the filtered results
	 */
public MemoryPeakResults filter(MemoryPeakResults results) {
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.copySettings(results);
    setup(results);
    for (PeakResult peak : results.getResults()) {
        if (accept(peak))
            newResults.add(peak);
    }
    end();
    return newResults;
}