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

Example 26 with PeakResult

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

the class OptimumDistanceResultFilter method finalise.

/*
	 * (non-Javadoc)
	 * 
	 * @see gdsc.smlm.engine.filter.ResultFilter#finalise()
	 */
@Override
public void finalise() {
    // Note that there could be the same result allocated to two target positions
    // so find the unique results
    int[] uniqueIndices = new int[bestIndices.length];
    int unique = 0;
    for (int i = 0; i < bestIndices.length; i++) {
        if (bestFitResults[i] == null)
            continue;
        boolean found = false;
        for (int j = unique; j-- > 0; ) {
            if (bestIndices[uniqueIndices[j]] == bestIndices[i]) {
                found = true;
                break;
            }
        }
        if (!found)
            uniqueIndices[unique++] = i;
    }
    // The fit results and the indices must match so preserve the same order
    filteredCount = unique;
    filteredFitResults = new FitResult[unique];
    filteredIndices = new int[unique];
    for (int i = 0; i < unique; i++) {
        filteredFitResults[i] = bestFitResults[uniqueIndices[i]];
        filteredIndices[i] = bestIndices[uniqueIndices[i]];
    }
    // The peak results can be in any order so use a set to find the unique results
    if (unique > 0) {
        TreeSet<PeakResult> set = new TreeSet<PeakResult>();
        for (PeakResult r : bestPeakResults) {
            if (r != null)
                set.add(r);
        }
        peakResults = new ArrayList<PeakResult>(set.size());
        peakResults.addAll(set);
    } else {
        peakResults = new ArrayList<PeakResult>();
    }
}

Example 27 with PeakResult

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

the class TraceDiffusion method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    jumpDistanceParameters = null;
    extraOptions = Utils.isExtraOptions();
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    ArrayList<MemoryPeakResults> allResults = new ArrayList<MemoryPeakResults>();
    // Option to pick multiple input datasets together using a list box.
    if ("multi".equals(arg)) {
        if (!showMultiDialog(allResults))
            return;
    }
    // This shows the dialog for selecting trace options
    if (!showTraceDialog(allResults))
        return;
    if (// Sense check
    allResults.isEmpty())
        return;
    Utils.log(TITLE + "...");
    // This optionally collects additional datasets then gets the traces:
    // - Trace each single dataset (and store in memory)
    // - Combine trace results held in memory
    Trace[] traces = getTraces(allResults);
    // This still allows a zero entry in the results table.
    if (traces.length > 0)
        if (!showDialog())
            return;
    int count = traces.length;
    double[] fitMSDResult = null;
    int n = 0;
    double[][] jdParams = null;
    if (count > 0) {
        calculatePrecision(traces, allResults.size() > 1);
        //--- MSD Analysis ---
        // Conversion constants
        final double px2ToUm2 = results.getCalibration().getNmPerPixel() * results.getCalibration().getNmPerPixel() / 1e6;
        final double px2ToUm2PerSecond = px2ToUm2 / exposureTime;
        // Get the maximum trace length
        int length = settings.minimumTraceLength;
        if (!settings.truncate) {
            for (Trace trace : traces) {
                if (length < trace.size())
                    length = trace.size();
            }
        }
        // Get the localisation error (4s^2) in um^2
        final double error = (settings.precisionCorrection) ? 4 * precision * precision / 1e6 : 0;
        // Pre-calculate MSD correction factors. This accounts for the fact that the distance moved 
        // in the start/end frames is reduced due to the averaging of the particle location over the 
        // entire frame into a single point. The true MSD may be restored by applying a factor.
        // Note: These are used for the calculation of the diffusion coefficients per molecule and 
        // the MSD passed to the Jump Distance analysis. However the error is not included in the 
        // jump distance analysis so will be subtracted from the fitted D coefficients later.
        final double[] factors;
        if (settings.msdCorrection) {
            factors = new double[length];
            for (int t = 1; t < length; t++) factors[t] = JumpDistanceAnalysis.getConversionfactor(t);
        } else {
            factors = Utils.newArray(length, 0.0, 1.0);
        }
        // Extract the mean-squared distance statistics
        Statistics[] stats = new Statistics[length];
        for (int i = 0; i < stats.length; i++) stats[i] = new Statistics();
        ArrayList<double[]> distances = (saveTraceDistances || displayTraceLength) ? new ArrayList<double[]>(traces.length) : null;
        // Store all the jump distances at the specified interval
        StoredDataStatistics jumpDistances = new StoredDataStatistics();
        final int jumpDistanceInterval = settings.jumpDistance;
        // Compute squared distances
        StoredDataStatistics msdPerMoleculeAllVsAll = new StoredDataStatistics();
        StoredDataStatistics msdPerMoleculeAdjacent = new StoredDataStatistics();
        for (Trace trace : traces) {
            ArrayList<PeakResult> results = trace.getPoints();
            // Sum the MSD and the time
            final int traceLength = (settings.truncate) ? settings.minimumTraceLength : trace.size();
            // Get the mean for each time separation
            double[] sumDistance = new double[traceLength + 1];
            double[] sumTime = new double[sumDistance.length];
            // Do the distances to the origin (saving if necessary)
            {
                final float x = results.get(0).getXPosition();
                final float y = results.get(0).getYPosition();
                if (distances != null) {
                    double[] msd = new double[traceLength - 1];
                    for (int j = 1; j < traceLength; j++) {
                        final int t = j;
                        final double d = distance2(x, y, results.get(j));
                        msd[j - 1] = px2ToUm2 * d;
                        if (t == jumpDistanceInterval)
                            jumpDistances.add(msd[j - 1]);
                        sumDistance[t] += d;
                        sumTime[t] += t;
                    }
                    distances.add(msd);
                } else {
                    for (int j = 1; j < traceLength; j++) {
                        final int t = j;
                        final double d = distance2(x, y, results.get(j));
                        if (t == jumpDistanceInterval)
                            jumpDistances.add(px2ToUm2 * d);
                        sumDistance[t] += d;
                        sumTime[t] += t;
                    }
                }
            }
            if (settings.internalDistances) {
                // Do the internal distances
                for (int i = 1; i < traceLength; i++) {
                    final float x = results.get(i).getXPosition();
                    final float y = results.get(i).getYPosition();
                    for (int j = i + 1; j < traceLength; j++) {
                        final int t = j - i;
                        final double d = distance2(x, y, results.get(j));
                        if (t == jumpDistanceInterval)
                            jumpDistances.add(px2ToUm2 * d);
                        sumDistance[t] += d;
                        sumTime[t] += t;
                    }
                }
                // Add the average distance per time separation to the population
                for (int t = 1; t < traceLength; t++) {
                    // Note: (traceLength - t) == count
                    stats[t].add(sumDistance[t] / (traceLength - t));
                }
            } else {
                // Add the distance per time separation to the population
                for (int t = 1; t < traceLength; t++) {
                    stats[t].add(sumDistance[t]);
                }
            }
            // Fix this for the precision and MSD adjustment.
            // It may be necessary to:
            // - sum the raw distances for each time interval (this is sumDistance[t])
            // - subtract the precision error
            // - apply correction factor for the n-frames to get actual MSD
            // - sum the actual MSD
            double sumD = 0, sumD_adjacent = Math.max(0, sumDistance[1] - error) * factors[1];
            double sumT = 0, sumT_adjacent = sumTime[1];
            for (int t = 1; t < traceLength; t++) {
                sumD += Math.max(0, sumDistance[t] - error) * factors[t];
                sumT += sumTime[t];
            }
            // Calculate the average displacement for the trace (do not simply use the largest 
            // time separation since this will miss moving molecules that end up at the origin)
            msdPerMoleculeAllVsAll.add(px2ToUm2PerSecond * sumD / sumT);
            msdPerMoleculeAdjacent.add(px2ToUm2PerSecond * sumD_adjacent / sumT_adjacent);
        }
        StoredDataStatistics dPerMoleculeAllVsAll = null;
        StoredDataStatistics dPerMoleculeAdjacent = null;
        if (saveTraceDistances || (settings.showHistograms && displayDHistogram)) {
            dPerMoleculeAllVsAll = calculateDiffusionCoefficient(msdPerMoleculeAllVsAll);
            dPerMoleculeAdjacent = calculateDiffusionCoefficient(msdPerMoleculeAdjacent);
        }
        if (saveTraceDistances) {
            saveTraceDistances(traces.length, distances, msdPerMoleculeAllVsAll, msdPerMoleculeAdjacent, dPerMoleculeAllVsAll, dPerMoleculeAdjacent);
        }
        if (displayTraceLength) {
            StoredDataStatistics lengths = calculateTraceLengths(distances);
            showHistogram(lengths, "Trace length (um)");
        }
        if (displayTraceSize) {
            StoredDataStatistics sizes = calculateTraceSizes(traces);
            showHistogram(sizes, "Trace size", true);
        }
        // Plot the per-trace histogram of MSD and D
        if (settings.showHistograms) {
            if (displayMSDHistogram) {
                showHistogram(msdPerMoleculeAllVsAll, "MSD/Molecule (all-vs-all)");
                showHistogram(msdPerMoleculeAdjacent, "MSD/Molecule (adjacent)");
            }
            if (displayDHistogram) {
                showHistogram(dPerMoleculeAllVsAll, "D/Molecule (all-vs-all)");
                showHistogram(dPerMoleculeAdjacent, "D/Molecule (adjacent)");
            }
        }
        // Calculate the mean squared distance (MSD)
        double[] x = new double[stats.length];
        double[] y = new double[x.length];
        double[] sd = new double[x.length];
        // Intercept is the 4s^2 (in um^2)
        y[0] = 4 * precision * precision / 1e6;
        for (int i = 1; i < stats.length; i++) {
            x[i] = i * exposureTime;
            y[i] = stats[i].getMean() * px2ToUm2;
            //sd[i] = stats[i].getStandardDeviation() * px2ToUm2;
            sd[i] = stats[i].getStandardError() * px2ToUm2;
        }
        String title = TITLE + " MSD";
        Plot2 plot = plotMSD(x, y, sd, title);
        // Fit the MSD using a linear fit
        fitMSDResult = fitMSD(x, y, title, plot);
        // Jump Distance analysis
        if (saveRawData)
            saveStatistics(jumpDistances, "Jump Distance", "Distance (um^2)", false);
        // Calculate the cumulative jump-distance histogram
        double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(jumpDistances.getValues());
        // Always show the jump distance histogram
        jdTitle = TITLE + " Jump Distance";
        jdPlot = new Plot2(jdTitle, "Distance (um^2)", "Cumulative Probability", jdHistogram[0], jdHistogram[1]);
        display(jdTitle, jdPlot);
        // Fit Jump Distance cumulative probability
        n = jumpDistances.getN();
        jumpDistanceParameters = jdParams = fitJumpDistance(jumpDistances, jdHistogram);
    }
    summarise(traces, fitMSDResult, n, jdParams);
}

Example 28 with PeakResult

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

the class SpotAnalysis method saveTracesToFile.

private void saveTracesToFile(ArrayList<TraceResult> traceResults) {
    String resultsDirectory = IJ.getDirectory(TITLE);
    if (resultsDirectory == null)
        return;
    // Save the traces to a single file. 
    // Also save the blinks and on/off times into data files for histogram analysis
    BufferedWriter[] files = new BufferedWriter[5];
    try {
        files[0] = openBufferedWriter(resultsDirectory + "traces.txt", String.format("#ms/frame = %s\n#Id\tcx\tcy\tsignal\tn-Blinks\tStart\tStop\t...", Utils.rounded(msPerFrame, 3)));
        files[1] = openBufferedWriter(resultsDirectory + "tOn.txt", "");
        files[2] = openBufferedWriter(resultsDirectory + "tOff.txt", "");
        files[3] = openBufferedWriter(resultsDirectory + "blinks.txt", "");
        files[4] = openBufferedWriter(resultsDirectory + "signal.txt", "");
        for (TraceResult traceResult : traceResults) {
            StringBuffer sb = new StringBuffer();
            sb.append(traceResult.spot.frame).append("\t");
            sb.append(traceResult.trace.getHead().getXPosition()).append("\t");
            sb.append(traceResult.trace.getHead().getYPosition()).append("\t");
            sb.append(traceResult.spot.signal).append("\t");
            int nBlinks = traceResult.trace.getNBlinks() - 1;
            sb.append(nBlinks);
            int[] on = traceResult.trace.getOnTimes();
            int[] off = traceResult.trace.getOffTimes();
            int t = traceResult.trace.getHead().getFrame();
            for (int i = 0; i < on.length; i++) {
                writeLine(files[1], Double.toString(msPerFrame * on[i]));
                sb.append("\t").append(t).append("\t").append(t + on[i] - 1);
                if (off != null && i < off.length) {
                    writeLine(files[2], Double.toString(msPerFrame * off[i]));
                    t += on[i] + off[i];
                }
            }
            writeLine(files[0], sb.toString());
            writeLine(files[3], Integer.toString(nBlinks));
            writeLine(files[4], String.format("# Id=%d, Blinks=%d, Signal=%f", traceResult.spot.frame, nBlinks, traceResult.spot.signal));
            for (PeakResult r : traceResult.trace.getPoints()) writeLine(files[4], String.format("%d %f", r.getFrame(), r.getSignal()));
        }
    } catch (Exception e) {
        // Q. Add better handling of errors?
        e.printStackTrace();
        IJ.log("Failed to save traces to results directory: " + resultsDirectory);
    } finally {
        for (BufferedWriter tracesFile : files) {
            if (tracesFile != null) {
                try {
                    tracesFile.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }
}

Example 29 with PeakResult

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

the class SpotAnalysis method addCandidateFrames.

private void addCandidateFrames(String title) {
    for (MemoryPeakResults r : MemoryPeakResults.getAllResults()) {
        if (r.getSource() instanceof IJImageSource && r.getSource().getName().equals(title)) {
            float minx = areaBounds.x;
            float maxx = minx + areaBounds.width;
            float miny = areaBounds.y;
            float maxy = miny + areaBounds.height;
            for (PeakResult p : r.getResults()) {
                if (p.getXPosition() >= minx && p.getXPosition() <= maxx && p.getYPosition() >= miny && p.getYPosition() <= maxy) {
                    candidateFrames.add(p.getFrame());
                }
            }
        }
    }
}

Example 30 with PeakResult

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

the class TraceDiffusion method calculatePrecision.

/**
	 * Calculate the average precision of localisation in the traces
	 * 
	 * @param traces
	 * @param multi
	 */
private void calculatePrecision(Trace[] traces, boolean multi) {
    // Check the diffusion simulation for a precision
    if (DiffusionRateTest.isSimulated(results.getName()) && !multi) {
        precision = DiffusionRateTest.lastSimulatedPrecision;
    } else {
        // Get the average precision of the localisations
        precision = 0;
        final double nmPerPixel = results.getNmPerPixel();
        final double gain = results.getGain();
        final boolean emCCD = results.isEMCCD();
        int n = 0;
        for (Trace trace : traces) {
            for (PeakResult r : trace.getPoints()) precision += r.getPrecision(nmPerPixel, gain, emCCD);
            n += trace.size();
        }
        precision /= n;
    }
    if (precision > 100) {
        ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
        gd.addMessage("The average precision of the traced results is " + Utils.rounded(precision, 4) + " nm.\nPlease verify the precision.");
        gd.addSlider("Precision (nm)", 5, 100, precision);
        gd.showDialog();
        if (!(gd.wasCanceled() || gd.invalidNumber())) {
            precision = Math.abs(gd.getNextNumber());
        }
    }
}