Examples with FractionalAssignment gdsc.core.match.FractionalAssignment used on opensource projects

Example 1 with FractionalAssignment

use of gdsc.core.match.FractionalAssignment in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method getAssignments.

/**
	 * Score the filter using the results list and the configured fail count.
	 *
	 * @param filter
	 *            the filter
	 * @param resultsList
	 *            the results list
	 * @param allAssignments
	 *            all the assignments
	 * @return The score
	 */
private ArrayList<FractionalAssignment[]> getAssignments(DirectFilter filter) {
    final MultiPathFilter multiPathFilter = createMPF(filter, minimalFilter);
    ArrayList<FractionalAssignment[]> allAssignments = new ArrayList<FractionalAssignment[]>(resultsList.length);
    multiPathFilter.fractionScoreSubset(resultsList, failCount, nActual, allAssignments, null, coordinateStore);
    return allAssignments;
}

Example 2 with FractionalAssignment

use of gdsc.core.match.FractionalAssignment in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method scoreAnalysis.

/**
	 * Score analysis.
	 *
	 * @param allAssignments
	 *            The assignments generated from running the filter (or null)
	 * @param filter
	 *            the filter
	 * @return the assignments
	 */
private ArrayList<FractionalAssignment[]> scoreAnalysis(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
    if (!scoreAnalysis)
        return null;
    // Build a histogram of the fitted spots that were available to be scored
    double[] signal = signalFactorStats.getValues();
    double[] distance = distanceStats.getValues();
    double[] limits1;
    if (BenchmarkSpotFit.signalFactor > 0 && upperSignalFactor > 0) {
        double range = BenchmarkSpotFit.signalFactor * upperSignalFactor / 100.0;
        limits1 = new double[] { -range, range };
    } else {
        limits1 = Maths.limits(signal);
        // Prevent the auto-range being too big
        final double bound = 3;
        if (limits1[0] < -bound)
            limits1[0] = -bound;
        if (limits1[1] > bound)
            limits1[1] = bound;
    }
    double[] limits2;
    if (BenchmarkSpotFit.distanceInPixels > 0 && upperMatchDistance > 0) {
        double range = simulationParameters.a * BenchmarkSpotFit.distanceInPixels * upperMatchDistance / 100.0;
        limits2 = new double[] { 0, range };
    } else {
        limits2 = Maths.limits(distance);
    }
    //final int bins = Math.max(10, nActual / 100);
    //final int bins = Utils.getBinsSturges(signal.length);
    final int bins = Utils.getBinsSqrt(signal.length);
    double[][] h1 = Utils.calcHistogram(signal, limits1[0], limits1[1], bins);
    double[][] h2 = Utils.calcHistogram(distance, limits2[0], limits2[1], bins);
    // Run the filter manually to get the results that pass.
    if (allAssignments == null)
        allAssignments = getAssignments(filter);
    double[] signal2 = new double[results.size()];
    double[] distance2 = new double[results.size()];
    int count = 0;
    double sumSignal = 0, sumDistance = 0;
    for (FractionalAssignment[] assignments : allAssignments) {
        if (assignments == null)
            continue;
        for (int i = 0; i < assignments.length; i++) {
            final CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
            sumDistance += distance2[count] = c.d;
            sumSignal += signal2[count] = c.getSignalFactor();
            count++;
        }
    }
    signal2 = Arrays.copyOf(signal2, count);
    distance2 = Arrays.copyOf(distance2, count);
    // Build a histogram using the same limits
    double[][] h1b = Utils.calcHistogram(signal2, limits1[0], limits1[1], bins);
    double[][] h2b = Utils.calcHistogram(distance2, limits2[0], limits2[1], bins);
    // Since the distance and signal factor are computed for all fits (single, multi, doublet)
    // there will be far more of them so we normalise and just plot the histogram profile.
    double s1 = 0, s2 = 0, s1b = 0, s2b = 0;
    for (int i = 0; i < h1b[0].length; i++) {
        s1 += h1[1][i];
        s2 += h2[1][i];
        s1b += h1b[1][i];
        s2b += h2b[1][i];
    }
    for (int i = 0; i < h1b[0].length; i++) {
        h1[1][i] /= s1;
        h2[1][i] /= s2;
        h1b[1][i] /= s1b;
        h2b[1][i] /= s2b;
    }
    // Draw distance histogram first
    String title2 = TITLE + " Distance Histogram";
    Plot2 plot2 = new Plot2(title2, "Distance (nm)", "Frequency");
    plot2.setLimits(limits2[0], limits2[1], 0, Maths.maxDefault(Maths.max(h2[1]), h2b[1]));
    plot2.setColor(Color.black);
    plot2.addLabel(0, 0, String.format("Blue = Fitted (%s); Red = Filtered (%s)", Utils.rounded(distanceStats.getMean()), Utils.rounded(sumDistance / count)));
    plot2.setColor(Color.blue);
    plot2.addPoints(h2[0], h2[1], Plot2.BAR);
    plot2.setColor(Color.red);
    plot2.addPoints(h2b[0], h2b[1], Plot2.BAR);
    PlotWindow pw2 = Utils.display(title2, plot2);
    if (Utils.isNewWindow())
        wo.add(pw2);
    // Draw signal factor histogram
    String title1 = TITLE + " Signal Factor Histogram";
    Plot2 plot1 = new Plot2(title1, "Signal Factor", "Frequency");
    plot1.setLimits(limits1[0], limits1[1], 0, Maths.maxDefault(Maths.max(h1[1]), h1b[1]));
    plot1.setColor(Color.black);
    plot1.addLabel(0, 0, String.format("Blue = Fitted (%s); Red = Filtered (%s)", Utils.rounded(signalFactorStats.getMean()), Utils.rounded(sumSignal / count)));
    plot1.setColor(Color.blue);
    plot1.addPoints(h1[0], h1[1], Plot2.BAR);
    plot1.setColor(Color.red);
    plot1.addPoints(h1b[0], h1b[1], Plot2.BAR);
    PlotWindow pw1 = Utils.display(title1, plot1);
    if (Utils.isNewWindow())
        wo.add(pw1);
    return allAssignments;
}

Example 3 with FractionalAssignment

use of gdsc.core.match.FractionalAssignment in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFit method summariseResults.

private void summariseResults(TIntObjectHashMap<FilterCandidates> filterCandidates, long runTime, final PreprocessedPeakResult[] preprocessedPeakResults, int nUniqueIDs) {
    createTable();
    // Summarise the fitting results. N fits, N failures. 
    // Optimal match statistics if filtering is perfect (since fitting is not perfect).
    StoredDataStatistics distanceStats = new StoredDataStatistics();
    StoredDataStatistics depthStats = new StoredDataStatistics();
    // Get stats for all fitted results and those that match 
    // Signal, SNR, Width, xShift, yShift, Precision
    createFilterCriteria();
    StoredDataStatistics[][] stats = new StoredDataStatistics[3][filterCriteria.length];
    for (int i = 0; i < stats.length; i++) for (int j = 0; j < stats[i].length; j++) stats[i][j] = new StoredDataStatistics();
    final double nmPerPixel = simulationParameters.a;
    double tp = 0, fp = 0;
    int failcTP = 0, failcFP = 0;
    int cTP = 0, cFP = 0;
    int[] singleStatus = null, multiStatus = null, doubletStatus = null, multiDoubletStatus = null;
    singleStatus = new int[FitStatus.values().length];
    multiStatus = new int[singleStatus.length];
    doubletStatus = new int[singleStatus.length];
    multiDoubletStatus = new int[singleStatus.length];
    // Easier to materialise the values since we have a lot of non final variables to manipulate
    final int[] frames = new int[filterCandidates.size()];
    final FilterCandidates[] candidates = new FilterCandidates[filterCandidates.size()];
    final int[] counter = new int[1];
    filterCandidates.forEachEntry(new TIntObjectProcedure<FilterCandidates>() {

        public boolean execute(int a, FilterCandidates b) {
            frames[counter[0]] = a;
            candidates[counter[0]] = b;
            counter[0]++;
            return true;
        }
    });
    for (FilterCandidates result : candidates) {
        // Count the number of fit results that matched (tp) and did not match (fp)
        tp += result.tp;
        fp += result.fp;
        for (int i = 0; i < result.fitResult.length; i++) {
            if (result.spots[i].match)
                cTP++;
            else
                cFP++;
            final MultiPathFitResult fitResult = result.fitResult[i];
            if (singleStatus != null && result.spots[i].match) {
                // Debugging reasons for fit failure
                addStatus(singleStatus, fitResult.getSingleFitResult());
                addStatus(multiStatus, fitResult.getMultiFitResult());
                addStatus(doubletStatus, fitResult.getDoubletFitResult());
                addStatus(multiDoubletStatus, fitResult.getMultiDoubletFitResult());
            }
            if (noMatch(fitResult)) {
                if (result.spots[i].match)
                    failcTP++;
                else
                    failcFP++;
            }
            // We have multi-path results.
            // We want statistics for:
            // [0] all fitted spots
            // [1] fitted spots that match a result
            // [2] fitted spots that do not match a result
            addToStats(fitResult.getSingleFitResult(), stats);
            addToStats(fitResult.getMultiFitResult(), stats);
            addToStats(fitResult.getDoubletFitResult(), stats);
            addToStats(fitResult.getMultiDoubletFitResult(), stats);
        }
        // Statistics on spots that fit an actual result
        for (int i = 0; i < result.match.length; i++) {
            if (!result.match[i].isFitResult())
                // For now just ignore the candidates that matched
                continue;
            FitMatch fitMatch = (FitMatch) result.match[i];
            distanceStats.add(fitMatch.d * nmPerPixel);
            depthStats.add(fitMatch.z * nmPerPixel);
        }
    }
    // Store data for computing correlation
    double[] i1 = new double[depthStats.getN()];
    double[] i2 = new double[i1.length];
    double[] is = new double[i1.length];
    int ci = 0;
    for (FilterCandidates result : candidates) {
        for (int i = 0; i < result.match.length; i++) {
            if (!result.match[i].isFitResult())
                // For now just ignore the candidates that matched
                continue;
            FitMatch fitMatch = (FitMatch) result.match[i];
            ScoredSpot spot = result.spots[fitMatch.i];
            i1[ci] = fitMatch.predictedSignal;
            i2[ci] = fitMatch.actualSignal;
            is[ci] = spot.spot.intensity;
            ci++;
        }
    }
    // We want to compute the Jaccard against the spot metric
    // Filter the results using the multi-path filter
    ArrayList<MultiPathFitResults> multiPathResults = new ArrayList<MultiPathFitResults>(filterCandidates.size());
    for (int i = 0; i < frames.length; i++) {
        int frame = frames[i];
        MultiPathFitResult[] multiPathFitResults = candidates[i].fitResult;
        int totalCandidates = candidates[i].spots.length;
        int nActual = actualCoordinates.get(frame).size();
        multiPathResults.add(new MultiPathFitResults(frame, multiPathFitResults, totalCandidates, nActual));
    }
    // Score the results and count the number returned
    List<FractionalAssignment[]> assignments = new ArrayList<FractionalAssignment[]>();
    final TIntHashSet set = new TIntHashSet(nUniqueIDs);
    FractionScoreStore scoreStore = new FractionScoreStore() {

        public void add(int uniqueId) {
            set.add(uniqueId);
        }
    };
    MultiPathFitResults[] multiResults = multiPathResults.toArray(new MultiPathFitResults[multiPathResults.size()]);
    // Filter with no filter
    MultiPathFilter mpf = new MultiPathFilter(new SignalFilter(0), null, multiFilter.residualsThreshold);
    FractionClassificationResult fractionResult = mpf.fractionScoreSubset(multiResults, Integer.MAX_VALUE, this.results.size(), assignments, scoreStore, CoordinateStoreFactory.create(imp.getWidth(), imp.getHeight(), fitConfig.getDuplicateDistance()));
    double nPredicted = fractionResult.getTP() + fractionResult.getFP();
    final double[][] matchScores = new double[set.size()][];
    int count = 0;
    for (int i = 0; i < assignments.size(); i++) {
        FractionalAssignment[] a = assignments.get(i);
        if (a == null)
            continue;
        for (int j = 0; j < a.length; j++) {
            final PreprocessedPeakResult r = ((PeakFractionalAssignment) a[j]).peakResult;
            set.remove(r.getUniqueId());
            final double precision = Math.sqrt(r.getLocationVariance());
            final double signal = r.getSignal();
            final double snr = r.getSNR();
            final double width = r.getXSDFactor();
            final double xShift = r.getXRelativeShift2();
            final double yShift = r.getYRelativeShift2();
            // Since these two are combined for filtering and the max is what matters.
            final double shift = (xShift > yShift) ? Math.sqrt(xShift) : Math.sqrt(yShift);
            final double eshift = Math.sqrt(xShift + yShift);
            final double[] score = new double[8];
            score[FILTER_SIGNAL] = signal;
            score[FILTER_SNR] = snr;
            score[FILTER_MIN_WIDTH] = width;
            score[FILTER_MAX_WIDTH] = width;
            score[FILTER_SHIFT] = shift;
            score[FILTER_ESHIFT] = eshift;
            score[FILTER_PRECISION] = precision;
            score[FILTER_PRECISION + 1] = a[j].getScore();
            matchScores[count++] = score;
        }
    }
    // Add the rest
    set.forEach(new CustomTIntProcedure(count) {

        public boolean execute(int uniqueId) {
            // This should not be null or something has gone wrong
            PreprocessedPeakResult r = preprocessedPeakResults[uniqueId];
            if (r == null)
                throw new RuntimeException("Missing result: " + uniqueId);
            final double precision = Math.sqrt(r.getLocationVariance());
            final double signal = r.getSignal();
            final double snr = r.getSNR();
            final double width = r.getXSDFactor();
            final double xShift = r.getXRelativeShift2();
            final double yShift = r.getYRelativeShift2();
            // Since these two are combined for filtering and the max is what matters.
            final double shift = (xShift > yShift) ? Math.sqrt(xShift) : Math.sqrt(yShift);
            final double eshift = Math.sqrt(xShift + yShift);
            final double[] score = new double[8];
            score[FILTER_SIGNAL] = signal;
            score[FILTER_SNR] = snr;
            score[FILTER_MIN_WIDTH] = width;
            score[FILTER_MAX_WIDTH] = width;
            score[FILTER_SHIFT] = shift;
            score[FILTER_ESHIFT] = eshift;
            score[FILTER_PRECISION] = precision;
            matchScores[c++] = score;
            return true;
        }
    });
    // Debug the reasons the fit failed
    if (singleStatus != null) {
        String name = PeakFit.getSolverName(fitConfig);
        if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera())
            name += " Camera";
        System.out.println("Failure counts: " + name);
        printFailures("Single", singleStatus);
        printFailures("Multi", multiStatus);
        printFailures("Doublet", doubletStatus);
        printFailures("Multi doublet", multiDoubletStatus);
    }
    StringBuilder sb = new StringBuilder(300);
    // Add information about the simulation
    //(simulationParameters.minSignal + simulationParameters.maxSignal) * 0.5;
    final double signal = simulationParameters.signalPerFrame;
    final int n = results.size();
    sb.append(imp.getStackSize()).append("\t");
    final int w = imp.getWidth();
    final int h = imp.getHeight();
    sb.append(w).append("\t");
    sb.append(h).append("\t");
    sb.append(n).append("\t");
    double density = ((double) n / imp.getStackSize()) / (w * h) / (simulationParameters.a * simulationParameters.a / 1e6);
    sb.append(Utils.rounded(density)).append("\t");
    sb.append(Utils.rounded(signal)).append("\t");
    sb.append(Utils.rounded(simulationParameters.s)).append("\t");
    sb.append(Utils.rounded(simulationParameters.a)).append("\t");
    sb.append(Utils.rounded(simulationParameters.depth)).append("\t");
    sb.append(simulationParameters.fixedDepth).append("\t");
    sb.append(Utils.rounded(simulationParameters.gain)).append("\t");
    sb.append(Utils.rounded(simulationParameters.readNoise)).append("\t");
    sb.append(Utils.rounded(simulationParameters.b)).append("\t");
    sb.append(Utils.rounded(simulationParameters.b2)).append("\t");
    // Compute the noise
    double noise = simulationParameters.b2;
    if (simulationParameters.emCCD) {
        // The b2 parameter was computed without application of the EM-CCD noise factor of 2.
        //final double b2 = backgroundVariance + readVariance
        //                = simulationParameters.b + readVariance
        // This should be applied only to the background variance.
        final double readVariance = noise - simulationParameters.b;
        noise = simulationParameters.b * 2 + readVariance;
    }
    if (simulationParameters.fullSimulation) {
    // The total signal is spread over frames
    }
    sb.append(Utils.rounded(signal / Math.sqrt(noise))).append("\t");
    sb.append(Utils.rounded(simulationParameters.s / simulationParameters.a)).append("\t");
    sb.append(spotFilter.getDescription());
    // nP and nN is the fractional score of the spot candidates 
    addCount(sb, nP + nN);
    addCount(sb, nP);
    addCount(sb, nN);
    addCount(sb, fP);
    addCount(sb, fN);
    String name = PeakFit.getSolverName(fitConfig);
    if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera())
        name += " Camera";
    add(sb, name);
    add(sb, config.getFitting());
    resultPrefix = sb.toString();
    // Q. Should I add other fit configuration here?
    // The fraction of positive and negative candidates that were included
    add(sb, (100.0 * cTP) / nP);
    add(sb, (100.0 * cFP) / nN);
    // Score the fitting results compared to the original simulation.
    // Score the candidate selection:
    add(sb, cTP + cFP);
    add(sb, cTP);
    add(sb, cFP);
    // TP are all candidates that can be matched to a spot
    // FP are all candidates that cannot be matched to a spot
    // FN = The number of missed spots
    FractionClassificationResult m = new FractionClassificationResult(cTP, cFP, 0, simulationParameters.molecules - cTP);
    add(sb, m.getRecall());
    add(sb, m.getPrecision());
    add(sb, m.getF1Score());
    add(sb, m.getJaccard());
    // Score the fitting results:
    add(sb, failcTP);
    add(sb, failcFP);
    // TP are all fit results that can be matched to a spot
    // FP are all fit results that cannot be matched to a spot
    // FN = The number of missed spots
    add(sb, tp);
    add(sb, fp);
    m = new FractionClassificationResult(tp, fp, 0, simulationParameters.molecules - tp);
    add(sb, m.getRecall());
    add(sb, m.getPrecision());
    add(sb, m.getF1Score());
    add(sb, m.getJaccard());
    // Do it again but pretend we can perfectly filter all the false positives
    //add(sb, tp);
    m = new FractionClassificationResult(tp, 0, 0, simulationParameters.molecules - tp);
    // Recall is unchanged
    // Precision will be 100%
    add(sb, m.getF1Score());
    add(sb, m.getJaccard());
    // The mean may be subject to extreme outliers so use the median
    double median = distanceStats.getMedian();
    add(sb, median);
    WindowOrganiser wo = new WindowOrganiser();
    String label = String.format("Recall = %s. n = %d. Median = %s nm. SD = %s nm", Utils.rounded(m.getRecall()), distanceStats.getN(), Utils.rounded(median), Utils.rounded(distanceStats.getStandardDeviation()));
    int id = Utils.showHistogram(TITLE, distanceStats, "Match Distance (nm)", 0, 0, 0, label);
    if (Utils.isNewWindow())
        wo.add(id);
    median = depthStats.getMedian();
    add(sb, median);
    // Sort by spot intensity and produce correlation
    int[] indices = Utils.newArray(i1.length, 0, 1);
    if (showCorrelation)
        Sort.sort(indices, is, rankByIntensity);
    double[] r = (showCorrelation) ? new double[i1.length] : null;
    double[] sr = (showCorrelation) ? new double[i1.length] : null;
    double[] rank = (showCorrelation) ? new double[i1.length] : null;
    ci = 0;
    FastCorrelator fastCorrelator = new FastCorrelator();
    ArrayList<Ranking> pc1 = new ArrayList<Ranking>();
    ArrayList<Ranking> pc2 = new ArrayList<Ranking>();
    for (int ci2 : indices) {
        fastCorrelator.add((long) Math.round(i1[ci2]), (long) Math.round(i2[ci2]));
        pc1.add(new Ranking(i1[ci2], ci));
        pc2.add(new Ranking(i2[ci2], ci));
        if (showCorrelation) {
            r[ci] = fastCorrelator.getCorrelation();
            sr[ci] = Correlator.correlation(rank(pc1), rank(pc2));
            if (rankByIntensity)
                rank[ci] = is[0] - is[ci];
            else
                rank[ci] = ci;
        }
        ci++;
    }
    final double pearsonCorr = fastCorrelator.getCorrelation();
    final double rankedCorr = Correlator.correlation(rank(pc1), rank(pc2));
    // Get the regression
    SimpleRegression regression = new SimpleRegression(false);
    for (int i = 0; i < pc1.size(); i++) regression.addData(pc1.get(i).value, pc2.get(i).value);
    //final double intercept = regression.getIntercept();
    final double slope = regression.getSlope();
    if (showCorrelation) {
        String title = TITLE + " Intensity";
        Plot plot = new Plot(title, "Candidate", "Spot");
        double[] limits1 = Maths.limits(i1);
        double[] limits2 = Maths.limits(i2);
        plot.setLimits(limits1[0], limits1[1], limits2[0], limits2[1]);
        label = String.format("Correlation=%s; Ranked=%s; Slope=%s", Utils.rounded(pearsonCorr), Utils.rounded(rankedCorr), Utils.rounded(slope));
        plot.addLabel(0, 0, label);
        plot.setColor(Color.red);
        plot.addPoints(i1, i2, Plot.DOT);
        if (slope > 1)
            plot.drawLine(limits1[0], limits1[0] * slope, limits1[1], limits1[1] * slope);
        else
            plot.drawLine(limits2[0] / slope, limits2[0], limits2[1] / slope, limits2[1]);
        PlotWindow pw = Utils.display(title, plot);
        if (Utils.isNewWindow())
            wo.add(pw);
        title = TITLE + " Correlation";
        plot = new Plot(title, "Spot Rank", "Correlation");
        double[] xlimits = Maths.limits(rank);
        double[] ylimits = Maths.limits(r);
        ylimits = Maths.limits(ylimits, sr);
        plot.setLimits(xlimits[0], xlimits[1], ylimits[0], ylimits[1]);
        plot.setColor(Color.red);
        plot.addPoints(rank, r, Plot.LINE);
        plot.setColor(Color.blue);
        plot.addPoints(rank, sr, Plot.LINE);
        plot.setColor(Color.black);
        plot.addLabel(0, 0, label);
        pw = Utils.display(title, plot);
        if (Utils.isNewWindow())
            wo.add(pw);
    }
    add(sb, pearsonCorr);
    add(sb, rankedCorr);
    add(sb, slope);
    label = String.format("n = %d. Median = %s nm", depthStats.getN(), Utils.rounded(median));
    id = Utils.showHistogram(TITLE, depthStats, "Match Depth (nm)", 0, 1, 0, label);
    if (Utils.isNewWindow())
        wo.add(id);
    // Plot histograms of the stats on the same window
    double[] lower = new double[filterCriteria.length];
    double[] upper = new double[lower.length];
    min = new double[lower.length];
    max = new double[lower.length];
    for (int i = 0; i < stats[0].length; i++) {
        double[] limits = showDoubleHistogram(stats, i, wo, matchScores, nPredicted);
        lower[i] = limits[0];
        upper[i] = limits[1];
        min[i] = limits[2];
        max[i] = limits[3];
    }
    // Reconfigure some of the range limits
    // Make this a bit bigger
    upper[FILTER_SIGNAL] *= 2;
    // Make this a bit bigger
    upper[FILTER_SNR] *= 2;
    double factor = 0.25;
    if (lower[FILTER_MIN_WIDTH] != 0)
        // (assuming lower is less than 1)
        upper[FILTER_MIN_WIDTH] = 1 - Math.max(0, factor * (1 - lower[FILTER_MIN_WIDTH]));
    if (upper[FILTER_MIN_WIDTH] != 0)
        // (assuming upper is more than 1)
        lower[FILTER_MAX_WIDTH] = 1 + Math.max(0, factor * (upper[FILTER_MAX_WIDTH] - 1));
    // Round the ranges
    final double[] interval = new double[stats[0].length];
    interval[FILTER_SIGNAL] = SignalFilter.DEFAULT_INCREMENT;
    interval[FILTER_SNR] = SNRFilter.DEFAULT_INCREMENT;
    interval[FILTER_MIN_WIDTH] = WidthFilter2.DEFAULT_MIN_INCREMENT;
    interval[FILTER_MAX_WIDTH] = WidthFilter.DEFAULT_INCREMENT;
    interval[FILTER_SHIFT] = ShiftFilter.DEFAULT_INCREMENT;
    interval[FILTER_ESHIFT] = EShiftFilter.DEFAULT_INCREMENT;
    interval[FILTER_PRECISION] = PrecisionFilter.DEFAULT_INCREMENT;
    interval[FILTER_ITERATIONS] = 0.1;
    interval[FILTER_EVALUATIONS] = 0.1;
    // Create a range increment
    double[] increment = new double[lower.length];
    for (int i = 0; i < increment.length; i++) {
        lower[i] = Maths.floor(lower[i], interval[i]);
        upper[i] = Maths.ceil(upper[i], interval[i]);
        double range = upper[i] - lower[i];
        // Allow clipping if the range is small compared to the min increment
        double multiples = range / interval[i];
        // Use 8 multiples for the equivalent of +/- 4 steps around the centre
        if (multiples < 8) {
            multiples = Math.ceil(multiples);
        } else
            multiples = 8;
        increment[i] = Maths.ceil(range / multiples, interval[i]);
        if (i == FILTER_MIN_WIDTH)
            // Requires clipping based on the upper limit
            lower[i] = upper[i] - increment[i] * multiples;
        else
            upper[i] = lower[i] + increment[i] * multiples;
    }
    for (int i = 0; i < stats[0].length; i++) {
        lower[i] = Maths.round(lower[i]);
        upper[i] = Maths.round(upper[i]);
        min[i] = Maths.round(min[i]);
        max[i] = Maths.round(max[i]);
        increment[i] = Maths.round(increment[i]);
        sb.append("\t").append(min[i]).append(':').append(lower[i]).append('-').append(upper[i]).append(':').append(max[i]);
    }
    // Disable some filters
    increment[FILTER_SIGNAL] = Double.POSITIVE_INFINITY;
    //increment[FILTER_SHIFT] = Double.POSITIVE_INFINITY;
    increment[FILTER_ESHIFT] = Double.POSITIVE_INFINITY;
    wo.tile();
    sb.append("\t").append(Utils.timeToString(runTime / 1000000.0));
    summaryTable.append(sb.toString());
    if (saveFilterRange) {
        GlobalSettings gs = SettingsManager.loadSettings();
        FilterSettings filterSettings = gs.getFilterSettings();
        String filename = (silent) ? filterSettings.filterSetFilename : Utils.getFilename("Filter_range_file", filterSettings.filterSetFilename);
        if (filename == null)
            return;
        // Remove extension to store the filename
        filename = Utils.replaceExtension(filename, ".xml");
        filterSettings.filterSetFilename = filename;
        // Create a filter set using the ranges
        ArrayList<Filter> filters = new ArrayList<Filter>(3);
        filters.add(new MultiFilter2(lower[0], (float) lower[1], lower[2], lower[3], lower[4], lower[5], lower[6]));
        filters.add(new MultiFilter2(upper[0], (float) upper[1], upper[2], upper[3], upper[4], upper[5], upper[6]));
        filters.add(new MultiFilter2(increment[0], (float) increment[1], increment[2], increment[3], increment[4], increment[5], increment[6]));
        if (saveFilters(filename, filters))
            SettingsManager.saveSettings(gs);
        // Create a filter set using the min/max and the initial bounds.
        // Set sensible limits
        min[FILTER_SIGNAL] = Math.max(min[FILTER_SIGNAL], 30);
        max[FILTER_PRECISION] = Math.min(max[FILTER_PRECISION], 100);
        // Commented this out so that the 4-set filters are the same as the 3-set filters.
        // The difference leads to differences when optimising.
        //			// Use half the initial bounds (hoping this is a good starting guess for the optimum)
        //			final boolean[] limitToLower = new boolean[min.length];
        //			limitToLower[FILTER_SIGNAL] = true;
        //			limitToLower[FILTER_SNR] = true;
        //			limitToLower[FILTER_MIN_WIDTH] = true;
        //			limitToLower[FILTER_MAX_WIDTH] = false;
        //			limitToLower[FILTER_SHIFT] = false;
        //			limitToLower[FILTER_ESHIFT] = false;
        //			limitToLower[FILTER_PRECISION] = true;
        //			for (int i = 0; i < limitToLower.length; i++)
        //			{
        //				final double range = (upper[i] - lower[i]) / 2;
        //				if (limitToLower[i])
        //					upper[i] = lower[i] + range;
        //				else
        //					lower[i] = upper[i] - range;
        //			}
        filters = new ArrayList<Filter>(4);
        filters.add(new MultiFilter2(min[0], (float) min[1], min[2], min[3], min[4], min[5], min[6]));
        filters.add(new MultiFilter2(lower[0], (float) lower[1], lower[2], lower[3], lower[4], lower[5], lower[6]));
        filters.add(new MultiFilter2(upper[0], (float) upper[1], upper[2], upper[3], upper[4], upper[5], upper[6]));
        filters.add(new MultiFilter2(max[0], (float) max[1], max[2], max[3], max[4], max[5], max[6]));
        saveFilters(Utils.replaceExtension(filename, ".4.xml"), filters);
    }
}

Example 4 with FractionalAssignment

use of gdsc.core.match.FractionalAssignment in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method showOverlay.

/**
	 * Show overlay.
	 *
	 * @param allAssignments
	 *            The assignments generated from running the filter (or null)
	 * @param filter
	 *            the filter
	 * @return The results from running the filter (or null)
	 */
private PreprocessedPeakResult[] showOverlay(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
    ImagePlus imp = CreateData.getImage();
    if (imp == null)
        return null;
    // Run the filter manually to get the results that pass.
    if (allAssignments == null)
        allAssignments = getAssignments(filter);
    final Overlay o = new Overlay();
    // Do TP
    final TIntHashSet actual = new TIntHashSet();
    final TIntHashSet predicted = new TIntHashSet();
    //int tp = 0, fp = 0, fn = 0;
    for (FractionalAssignment[] assignments : allAssignments) {
        if (assignments == null || assignments.length == 0)
            continue;
        float[] tx = null, ty = null;
        int t = 0;
        //tp += assignments.length;
        if (showTP) {
            tx = new float[assignments.length];
            ty = new float[assignments.length];
        }
        int frame = 0;
        for (int i = 0; i < assignments.length; i++) {
            CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
            IdPeakResult peak = (IdPeakResult) c.peak;
            BasePreprocessedPeakResult spot = (BasePreprocessedPeakResult) c.peakResult;
            actual.add(peak.uniqueId);
            predicted.add(spot.getUniqueId());
            frame = spot.getFrame();
            if (showTP) {
                tx[t] = spot.getX();
                ty[t++] = spot.getY();
            }
        }
        if (showTP)
            SpotFinderPreview.addRoi(frame, o, tx, ty, t, Color.green);
    }
    float[] x = new float[10];
    float[] y = new float[x.length];
    float[] x2 = new float[10];
    float[] y2 = new float[x2.length];
    // Do FP (all remaining results that are not a TP)
    PreprocessedPeakResult[] filterResults = null;
    if (showFP) {
        final MultiPathFilter multiPathFilter = createMPF(filter, minimalFilter);
        //multiPathFilter.setDebugFile("/tmp/filter.txt");
        filterResults = filterResults(multiPathFilter);
        int frame = 0;
        int c = 0;
        int c2 = 0;
        for (int i = 0; i < filterResults.length; i++) {
            if (frame != filterResults[i].getFrame()) {
                if (c != 0)
                    SpotFinderPreview.addRoi(frame, o, x, y, c, Color.red);
                if (c2 != 0)
                    SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
                c = c2 = 0;
            }
            frame = filterResults[i].getFrame();
            if (predicted.contains(filterResults[i].getUniqueId()))
                continue;
            if (filterResults[i].ignore()) {
                if (x2.length == c2) {
                    x2 = Arrays.copyOf(x2, c2 * 2);
                    y2 = Arrays.copyOf(y2, c2 * 2);
                }
                x2[c2] = filterResults[i].getX();
                y2[c2++] = filterResults[i].getY();
            } else {
                if (x.length == c) {
                    x = Arrays.copyOf(x, c * 2);
                    y = Arrays.copyOf(y, c * 2);
                }
                x[c] = filterResults[i].getX();
                y[c++] = filterResults[i].getY();
            }
        }
        //fp += c;
        if (c != 0)
            SpotFinderPreview.addRoi(frame, o, x, y, c, Color.red);
        if (c2 != 0)
            SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
    }
    // Do TN (all remaining peaks that have not been matched)
    if (showFN) {
        final boolean checkBorder = (BenchmarkSpotFilter.lastAnalysisBorder != null && BenchmarkSpotFilter.lastAnalysisBorder.x != 0);
        final float border, xlimit, ylimit;
        if (checkBorder) {
            final Rectangle lastAnalysisBorder = BenchmarkSpotFilter.lastAnalysisBorder;
            border = lastAnalysisBorder.x;
            xlimit = lastAnalysisBorder.x + lastAnalysisBorder.width;
            ylimit = lastAnalysisBorder.y + lastAnalysisBorder.height;
        } else
            border = xlimit = ylimit = 0;
        // Add the results to the lists
        actualCoordinates.forEachEntry(new CustomTIntObjectProcedure(x, y, x2, y2) {

            public boolean execute(int frame, IdPeakResult[] results) {
                int c = 0, c2 = 0;
                if (x.length <= results.length) {
                    x = new float[results.length];
                    y = new float[results.length];
                }
                if (x2.length <= results.length) {
                    x2 = new float[results.length];
                    y2 = new float[results.length];
                }
                for (int i = 0; i < results.length; i++) {
                    // Ignore those that were matched by TP
                    if (actual.contains(results[i].uniqueId))
                        continue;
                    if (checkBorder && outsideBorder(results[i], border, xlimit, ylimit)) {
                        x2[c2] = results[i].getXPosition();
                        y2[c2++] = results[i].getYPosition();
                    } else {
                        x[c] = results[i].getXPosition();
                        y[c++] = results[i].getYPosition();
                    }
                }
                //fn += c;
                if (c != 0)
                    SpotFinderPreview.addRoi(frame, o, x, y, c, Color.yellow);
                if (c2 != 0)
                    SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.orange);
                return true;
            }
        });
    }
    //System.out.printf("TP=%d, FP=%d, FN=%d, N=%d (%d)\n", tp, fp, fn, tp + fn, results.size());
    imp.setOverlay(o);
    return filterResults;
}

Example 5 with FractionalAssignment

use of gdsc.core.match.FractionalAssignment in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method depthAnalysis.

/**
	 * Depth analysis.
	 *
	 * @param allAssignments
	 *            The assignments generated from running the filter (or null)
	 * @param filter
	 *            the filter
	 * @return the assignments
	 */
private ArrayList<FractionalAssignment[]> depthAnalysis(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
    if (!depthRecallAnalysis || simulationParameters.fixedDepth)
        return null;
    // Build a histogram of the number of spots at different depths
    final double[] depths = depthStats.getValues();
    double[] limits = Maths.limits(depths);
    //final int bins = Math.max(10, nActual / 100);
    //final int bins = Utils.getBinsSturges(depths.length);
    final int bins = Utils.getBinsSqrt(depths.length);
    double[][] h1 = Utils.calcHistogram(depths, limits[0], limits[1], bins);
    double[][] h2 = Utils.calcHistogram(depthFitStats.getValues(), limits[0], limits[1], bins);
    // manually to get the results that pass.
    if (allAssignments == null)
        allAssignments = getAssignments(filter);
    double[] depths2 = new double[results.size()];
    int count = 0;
    for (FractionalAssignment[] assignments : allAssignments) {
        if (assignments == null)
            continue;
        for (int i = 0; i < assignments.length; i++) {
            final CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
            depths2[count++] = c.peak.error;
        }
    }
    depths2 = Arrays.copyOf(depths2, count);
    // Build a histogram using the same limits
    double[][] h3 = Utils.calcHistogram(depths2, limits[0], limits[1], bins);
    // Convert pixel depth to nm
    for (int i = 0; i < h1[0].length; i++) h1[0][i] *= simulationParameters.a;
    limits[0] *= simulationParameters.a;
    limits[1] *= simulationParameters.a;
    // Produce a histogram of the number of spots at each depth
    String title1 = TITLE + " Depth Histogram";
    Plot2 plot1 = new Plot2(title1, "Depth (nm)", "Frequency");
    plot1.setLimits(limits[0], limits[1], 0, Maths.max(h1[1]));
    plot1.setColor(Color.black);
    plot1.addPoints(h1[0], h1[1], Plot2.BAR);
    plot1.addLabel(0, 0, "Black = Spots; Blue = Fitted; Red = Filtered");
    plot1.setColor(Color.blue);
    plot1.addPoints(h1[0], h2[1], Plot2.BAR);
    plot1.setColor(Color.red);
    plot1.addPoints(h1[0], h3[1], Plot2.BAR);
    plot1.setColor(Color.magenta);
    PlotWindow pw1 = Utils.display(title1, plot1);
    if (Utils.isNewWindow())
        wo.add(pw1);
    // Interpolate
    final double halfBinWidth = (h1[0][1] - h1[0][0]) * 0.5;
    // Remove final value of the histogram as this is at the upper limit of the range (i.e. count zero)
    h1[0] = Arrays.copyOf(h1[0], h1[0].length - 1);
    h1[1] = Arrays.copyOf(h1[1], h1[0].length);
    h2[1] = Arrays.copyOf(h2[1], h1[0].length);
    h3[1] = Arrays.copyOf(h3[1], h1[0].length);
    // TODO : Fix the smoothing since LOESS sometimes does not work.
    // Perhaps allow configuration of the number of histogram bins and the smoothing bandwidth.
    // Use minimum of 3 points for smoothing
    // Ensure we use at least x% of data
    double bandwidth = Math.max(3.0 / h1[0].length, 0.15);
    LoessInterpolator loess = new LoessInterpolator(bandwidth, 1);
    PolynomialSplineFunction spline1 = loess.interpolate(h1[0], h1[1]);
    PolynomialSplineFunction spline2 = loess.interpolate(h1[0], h2[1]);
    PolynomialSplineFunction spline3 = loess.interpolate(h1[0], h3[1]);
    // Use a second interpolator in case the LOESS fails
    LinearInterpolator lin = new LinearInterpolator();
    PolynomialSplineFunction spline1b = lin.interpolate(h1[0], h1[1]);
    PolynomialSplineFunction spline2b = lin.interpolate(h1[0], h2[1]);
    PolynomialSplineFunction spline3b = lin.interpolate(h1[0], h3[1]);
    // Increase the number of points to show a smooth curve
    double[] points = new double[bins * 5];
    limits = Maths.limits(h1[0]);
    final double interval = (limits[1] - limits[0]) / (points.length - 1);
    double[] v = new double[points.length];
    double[] v2 = new double[points.length];
    double[] v3 = new double[points.length];
    for (int i = 0; i < points.length - 1; i++) {
        points[i] = limits[0] + i * interval;
        v[i] = getSplineValue(spline1, spline1b, points[i]);
        v2[i] = getSplineValue(spline2, spline2b, points[i]);
        v3[i] = getSplineValue(spline3, spline3b, points[i]);
        points[i] += halfBinWidth;
    }
    // Final point on the limit of the spline range
    int ii = points.length - 1;
    v[ii] = getSplineValue(spline1, spline1b, limits[1]);
    v2[ii] = getSplineValue(spline2, spline2b, limits[1]);
    v3[ii] = getSplineValue(spline3, spline3b, limits[1]);
    points[ii] = limits[1] + halfBinWidth;
    // Calculate recall
    for (int i = 0; i < v.length; i++) {
        v2[i] = v2[i] / v[i];
        v3[i] = v3[i] / v[i];
    }
    final double halfSummaryDepth = summaryDepth * 0.5;
    String title2 = TITLE + " Depth Histogram (normalised)";
    Plot2 plot2 = new Plot2(title2, "Depth (nm)", "Recall");
    plot2.setLimits(limits[0] + halfBinWidth, limits[1] + halfBinWidth, 0, Maths.min(1, Maths.max(v2)));
    plot2.setColor(Color.black);
    plot2.addLabel(0, 0, "Blue = Fitted; Red = Filtered");
    plot2.setColor(Color.blue);
    plot2.addPoints(points, v2, Plot2.LINE);
    plot2.setColor(Color.red);
    plot2.addPoints(points, v3, Plot2.LINE);
    plot2.setColor(Color.magenta);
    if (-halfSummaryDepth - halfBinWidth >= limits[0]) {
        plot2.drawLine(-halfSummaryDepth, 0, -halfSummaryDepth, getSplineValue(spline3, spline3b, -halfSummaryDepth - halfBinWidth) / getSplineValue(spline1, spline1b, -halfSummaryDepth - halfBinWidth));
    }
    if (halfSummaryDepth - halfBinWidth <= limits[1]) {
        plot2.drawLine(halfSummaryDepth, 0, halfSummaryDepth, getSplineValue(spline3, spline3b, halfSummaryDepth - halfBinWidth) / getSplineValue(spline1, spline1b, halfSummaryDepth - halfBinWidth));
    }
    PlotWindow pw2 = Utils.display(title2, plot2);
    if (Utils.isNewWindow())
        wo.add(pw2);
    return allAssignments;
}