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

Example 6 with MultiPathFilter

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

the class BenchmarkFilterAnalysis method createResults.

/**
 * Create peak results.
 *
 * @param filterResults The results from running the filter (or null)
 * @param filter the filter
 */
private MemoryPeakResults createResults(PreprocessedPeakResult[] filterResults, DirectFilter filter, boolean withBorder) {
    if (filterResults == null) {
        final MultiPathFilter multiPathFilter = createMpf(filter, defaultMinimalFilter);
        filterResults = filterResults(multiPathFilter);
    }
    final MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.copySettings(this.results);
    newResults.setName(TITLE);
    if (withBorder) {
        // To produce the same results as the PeakFit plugin we must implement the border
        // functionality used in the FitWorker. This respects the border of the spot filter.
        final FitEngineConfiguration config = new FitEngineConfiguration();
        updateAllConfiguration(config);
        final MaximaSpotFilter spotFilter = config.createSpotFilter();
        final int border = spotFilter.getBorder();
        final Rectangle bounds = getBounds();
        final int borderLimitX = bounds.x + bounds.width - border;
        final int borderLimitY = bounds.y + bounds.height - border;
        for (final PreprocessedPeakResult spot : filterResults) {
            if (spot.getX() > border && spot.getX() < borderLimitX && spot.getY() > border && spot.getY() < borderLimitY) {
                final double[] p = spot.toGaussian2DParameters();
                final float[] params = new float[p.length];
                for (int j = 0; j < p.length; j++) {
                    params[j] = (float) p[j];
                }
                final int frame = spot.getFrame();
                final int origX = (int) p[Gaussian2DFunction.X_POSITION];
                final int origY = (int) p[Gaussian2DFunction.Y_POSITION];
                newResults.add(frame, origX, origY, 0, 0, spot.getNoise(), spot.getMeanSignal(), params, null);
            }
        }
    } else {
        for (final PreprocessedPeakResult spot : filterResults) {
            final double[] p = spot.toGaussian2DParameters();
            final float[] params = new float[p.length];
            for (int j = 0; j < p.length; j++) {
                params[j] = (float) p[j];
            }
            final int frame = spot.getFrame();
            final int origX = (int) p[Gaussian2DFunction.X_POSITION];
            final int origY = (int) p[Gaussian2DFunction.Y_POSITION];
            newResults.add(frame, origX, origY, 0, 0, spot.getNoise(), spot.getMeanSignal(), params, null);
        }
    }
    return newResults;
}

Example 7 with MultiPathFilter

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

the class BenchmarkFilterAnalysis method showOverlay.

/**
 * Show overlay.
 *
 * <ul>
 *
 * <li>Green = TP
 *
 * <li>Red = FP
 *
 * <li>Magenta = FP (Ignored from analysis)
 *
 * <li>Yellow = FN
 *
 * <li>Orange = FN (Outside border)
 *
 * </ul>
 *
 * @param allAssignments The assignments generated from running the filter (or null)
 * @param filter the filter
 * @return The results from running the filter (or null)
 */
@Nullable
@SuppressWarnings("null")
private PreprocessedPeakResult[] showOverlay(ArrayList<FractionalAssignment[]> allAssignments, DirectFilter filter) {
    final 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();
    for (final FractionalAssignment[] assignments : allAssignments) {
        if (assignments == null || assignments.length == 0) {
            continue;
        }
        float[] tx = null;
        float[] ty = null;
        int count = 0;
        if (settings.showTP) {
            tx = new float[assignments.length];
            ty = new float[assignments.length];
        }
        int frame = 0;
        for (int i = 0; i < assignments.length; i++) {
            final CustomFractionalAssignment c = (CustomFractionalAssignment) assignments[i];
            final UniqueIdPeakResult peak = (UniqueIdPeakResult) c.peak;
            final BasePreprocessedPeakResult spot = (BasePreprocessedPeakResult) c.peakResult;
            actual.add(peak.uniqueId);
            predicted.add(spot.getUniqueId());
            frame = spot.getFrame();
            if (settings.showTP) {
                tx[count] = spot.getX();
                ty[count++] = spot.getY();
            }
        }
        if (settings.showTP) {
            SpotFinderPreview.addRoi(frame, o, tx, ty, count, 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 (settings.showFP) {
        final MultiPathFilter multiPathFilter = createMpf(filter, defaultMinimalFilter);
        filterResults = filterResults(multiPathFilter);
        int frame = 0;
        int c1 = 0;
        int c2 = 0;
        for (int i = 0; i < filterResults.length; i++) {
            if (frame != filterResults[i].getFrame()) {
                if (c1 != 0) {
                    SpotFinderPreview.addRoi(frame, o, x, y, c1, Color.red);
                }
                if (c2 != 0) {
                    SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
                }
                c1 = 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 == c1) {
                    x = Arrays.copyOf(x, c1 * 2);
                    y = Arrays.copyOf(y, c1 * 2);
                }
                x[c1] = filterResults[i].getX();
                y[c1++] = filterResults[i].getY();
            }
        }
        if (c1 != 0) {
            SpotFinderPreview.addRoi(frame, o, x, y, c1, Color.red);
        }
        if (c2 != 0) {
            SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.magenta);
        }
    }
    // Do FN (all remaining peaks that have not been matched)
    if (settings.showFN) {
        final boolean checkBorder = (filterResult.analysisBorder != null && filterResult.analysisBorder.x != 0);
        final float border;
        final float xlimit;
        final float ylimit;
        if (checkBorder) {
            final Rectangle lastAnalysisBorder = filterResult.analysisBorder;
            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) {

            @Override
            public boolean execute(int frame, UniqueIdPeakResult[] results) {
                int c1 = 0;
                int 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[c1] = results[i].getXPosition();
                        y[c1++] = results[i].getYPosition();
                    }
                }
                if (c1 != 0) {
                    SpotFinderPreview.addRoi(frame, o, x, y, c1, Color.yellow);
                }
                if (c2 != 0) {
                    SpotFinderPreview.addRoi(frame, o, x2, y2, c2, Color.orange);
                }
                return true;
            }
        });
    }
    imp.setOverlay(o);
    return filterResults;
}

Example 8 with MultiPathFilter

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

the class BenchmarkSpotFit method summariseResults.

private void summariseResults(BenchmarkSpotFitResult spotFitResults, long runTime, final PreprocessedPeakResult[] preprocessedPeakResults, int uniqueIdCount, CandidateData candidateData, TIntObjectHashMap<List<Coordinate>> actualCoordinates) {
    // Summarise the fitting results. N fits, N failures.
    // Optimal match statistics if filtering is perfect (since fitting is not perfect).
    final StoredDataStatistics distanceStats = new StoredDataStatistics();
    final StoredDataStatistics depthStats = new StoredDataStatistics();
    // Get stats for all fitted results and those that match
    // Signal, SNR, Width, xShift, yShift, Precision
    createFilterCriteria();
    final 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.pixelPitch;
    double tp = 0;
    double fp = 0;
    int failCtp = 0;
    int failCfp = 0;
    int ctp = 0;
    int cfp = 0;
    final int[] singleStatus = new int[FitStatus.values().length];
    final int[] multiStatus = new int[singleStatus.length];
    final int[] doubletStatus = new int[singleStatus.length];
    final int[] multiDoubletStatus = new int[singleStatus.length];
    // Easier to materialise the values since we have a lot of non final variables to manipulate
    final TIntObjectHashMap<FilterCandidates> fitResults = spotFitResults.fitResults;
    final int[] frames = new int[fitResults.size()];
    final FilterCandidates[] candidates = new FilterCandidates[fitResults.size()];
    final int[] counter = new int[1];
    fitResults.forEachEntry((frame, candidate) -> {
        frames[counter[0]] = frame;
        candidates[counter[0]] = candidate;
        counter[0]++;
        return true;
    });
    for (final 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;
            }
            final FitMatch fitMatch = (FitMatch) result.match[i];
            distanceStats.add(fitMatch.distance * nmPerPixel);
            depthStats.add(fitMatch.zdepth * nmPerPixel);
        }
    }
    if (tp == 0) {
        IJ.error(TITLE, "No fit results matched the simulation actual results");
        return;
    }
    // Store data for computing correlation
    final double[] i1 = new double[depthStats.getN()];
    final double[] i2 = new double[i1.length];
    final double[] is = new double[i1.length];
    int ci = 0;
    for (final 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;
            }
            final FitMatch fitMatch = (FitMatch) result.match[i];
            final ScoredSpot spot = result.spots[fitMatch.index];
            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
    final ArrayList<MultiPathFitResults> multiPathResults = new ArrayList<>(fitResults.size());
    for (int i = 0; i < frames.length; i++) {
        final int frame = frames[i];
        final MultiPathFitResult[] multiPathFitResults = candidates[i].fitResult;
        final int totalCandidates = candidates[i].spots.length;
        final List<Coordinate> list = actualCoordinates.get(frame);
        final int nActual = (list == null) ? 0 : list.size();
        multiPathResults.add(new MultiPathFitResults(frame, multiPathFitResults, totalCandidates, nActual));
    }
    // Score the results and count the number returned
    final List<FractionalAssignment[]> assignments = new ArrayList<>();
    final TIntHashSet set = new TIntHashSet(uniqueIdCount);
    final FractionScoreStore scoreStore = set::add;
    final MultiPathFitResults[] multiResults = multiPathResults.toArray(new MultiPathFitResults[0]);
    // Filter with no filter
    final MultiPathFilter mpf = new MultiPathFilter(new SignalFilter(0), null, multiFilter.residualsThreshold);
    mpf.fractionScoreSubset(multiResults, NullFailCounter.INSTANCE, this.results.size(), assignments, scoreStore, CoordinateStoreFactory.create(0, 0, imp.getWidth(), imp.getHeight(), config.convertUsingHwhMax(config.getDuplicateDistanceParameter())));
    final double[][] matchScores = new double[set.size()][];
    int count = 0;
    for (int i = 0; i < assignments.size(); i++) {
        final 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) {

        @Override
        public boolean execute(int uniqueId) {
            // This should not be null or something has gone wrong
            final PreprocessedPeakResult r = preprocessedPeakResults[uniqueId];
            if (r == null) {
                throw new IllegalArgumentException("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[count++] = score;
            return true;
        }
    });
    final FitConfiguration fitConfig = config.getFitConfiguration();
    // Debug the reasons the fit failed
    if (singleStatus != null) {
        String name = PeakFit.getSolverName(fitConfig);
        if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera()) {
            name += " Camera";
        }
        IJ.log("Failure counts: " + name);
        printFailures("Single", singleStatus);
        printFailures("Multi", multiStatus);
        printFailures("Doublet", doubletStatus);
        printFailures("Multi doublet", multiDoubletStatus);
    }
    final StringBuilder sb = new StringBuilder(300);
    // Add information about the simulation
    final double signal = simulationParameters.averageSignal;
    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');
    final double density = ((double) n / imp.getStackSize()) / (w * h) / (simulationParameters.pixelPitch * simulationParameters.pixelPitch / 1e6);
    sb.append(MathUtils.rounded(density)).append('\t');
    sb.append(MathUtils.rounded(signal)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.sd)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.pixelPitch)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.depth)).append('\t');
    sb.append(simulationParameters.fixedDepth).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.gain)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.readNoise)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.background)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.noise)).append('\t');
    if (simulationParameters.fullSimulation) {
    // The total signal is spread over frames
    }
    sb.append(MathUtils.rounded(signal / simulationParameters.noise)).append('\t');
    sb.append(MathUtils.rounded(simulationParameters.sd / simulationParameters.pixelPitch)).append('\t');
    sb.append(spotFilter.getDescription());
    // nP and nN is the fractional score of the spot candidates
    addCount(sb, (double) candidateData.countPositive + candidateData.countNegative);
    addCount(sb, candidateData.countPositive);
    addCount(sb, candidateData.countNegative);
    addCount(sb, candidateData.fractionPositive);
    addCount(sb, candidateData.fractionNegative);
    String name = PeakFit.getSolverName(fitConfig);
    if (fitConfig.getFitSolver() == FitSolver.MLE && fitConfig.isModelCamera()) {
        name += " Camera";
    }
    add(sb, name);
    add(sb, config.getFitting());
    spotFitResults.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) / candidateData.countPositive);
    add(sb, (100.0 * cfp) / candidateData.countNegative);
    // 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 match = new FractionClassificationResult(ctp, cfp, 0, simulationParameters.molecules - ctp);
    add(sb, match.getRecall());
    add(sb, match.getPrecision());
    add(sb, match.getF1Score());
    add(sb, match.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);
    match = new FractionClassificationResult(tp, fp, 0, simulationParameters.molecules - tp);
    add(sb, match.getRecall());
    add(sb, match.getPrecision());
    add(sb, match.getF1Score());
    add(sb, match.getJaccard());
    // Do it again but pretend we can perfectly filter all the false positives
    // add(sb, tp);
    match = new FractionClassificationResult(tp, 0, 0, simulationParameters.molecules - tp);
    // Recall is unchanged
    // Precision will be 100%
    add(sb, match.getF1Score());
    add(sb, match.getJaccard());
    // The mean may be subject to extreme outliers so use the median
    double median = distanceStats.getMedian();
    add(sb, median);
    final WindowOrganiser wo = new WindowOrganiser();
    String label = String.format("Recall = %s. n = %d. Median = %s nm. SD = %s nm", MathUtils.rounded(match.getRecall()), distanceStats.getN(), MathUtils.rounded(median), MathUtils.rounded(distanceStats.getStandardDeviation()));
    new HistogramPlotBuilder(TITLE, distanceStats, "Match Distance (nm)").setPlotLabel(label).show(wo);
    median = depthStats.getMedian();
    add(sb, median);
    // Sort by spot intensity and produce correlation
    double[] correlation = null;
    double[] rankCorrelation = null;
    double[] rank = null;
    final FastCorrelator fastCorrelator = new FastCorrelator();
    final ArrayList<Ranking> pc1 = new ArrayList<>();
    final ArrayList<Ranking> pc2 = new ArrayList<>();
    ci = 0;
    if (settings.showCorrelation) {
        final int[] indices = SimpleArrayUtils.natural(i1.length);
        SortUtils.sortData(indices, is, settings.rankByIntensity, true);
        correlation = new double[i1.length];
        rankCorrelation = new double[i1.length];
        rank = new double[i1.length];
        for (final int ci2 : indices) {
            fastCorrelator.add(Math.round(i1[ci2]), Math.round(i2[ci2]));
            pc1.add(new Ranking(i1[ci2], ci));
            pc2.add(new Ranking(i2[ci2], ci));
            correlation[ci] = fastCorrelator.getCorrelation();
            rankCorrelation[ci] = Correlator.correlation(rank(pc1), rank(pc2));
            if (settings.rankByIntensity) {
                rank[ci] = is[0] - is[ci];
            } else {
                rank[ci] = ci;
            }
            ci++;
        }
    } else {
        for (int i = 0; i < i1.length; i++) {
            fastCorrelator.add(Math.round(i1[i]), Math.round(i2[i]));
            pc1.add(new Ranking(i1[i], i));
            pc2.add(new Ranking(i2[i], i));
        }
    }
    final double pearsonCorr = fastCorrelator.getCorrelation();
    final double rankedCorr = Correlator.correlation(rank(pc1), rank(pc2));
    // Get the regression
    final 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 (settings.showCorrelation) {
        String title = TITLE + " Intensity";
        Plot plot = new Plot(title, "Candidate", "Spot");
        final double[] limits1 = MathUtils.limits(i1);
        final double[] limits2 = MathUtils.limits(i2);
        plot.setLimits(limits1[0], limits1[1], limits2[0], limits2[1]);
        label = String.format("Correlation=%s; Ranked=%s; Slope=%s", MathUtils.rounded(pearsonCorr), MathUtils.rounded(rankedCorr), MathUtils.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]);
        }
        ImageJUtils.display(title, plot, wo);
        title = TITLE + " Correlation";
        plot = new Plot(title, "Spot Rank", "Correlation");
        final double[] xlimits = MathUtils.limits(rank);
        double[] ylimits = MathUtils.limits(correlation);
        ylimits = MathUtils.limits(ylimits, rankCorrelation);
        plot.setLimits(xlimits[0], xlimits[1], ylimits[0], ylimits[1]);
        plot.setColor(Color.red);
        plot.addPoints(rank, correlation, Plot.LINE);
        plot.setColor(Color.blue);
        plot.addPoints(rank, rankCorrelation, Plot.LINE);
        plot.setColor(Color.black);
        plot.addLabel(0, 0, label);
        ImageJUtils.display(title, plot, wo);
    }
    add(sb, pearsonCorr);
    add(sb, rankedCorr);
    add(sb, slope);
    label = String.format("n = %d. Median = %s nm", depthStats.getN(), MathUtils.rounded(median));
    new HistogramPlotBuilder(TITLE, depthStats, "Match Depth (nm)").setRemoveOutliersOption(1).setPlotLabel(label).show(wo);
    // Plot histograms of the stats on the same window
    final double[] lower = new double[filterCriteria.length];
    final double[] upper = new double[lower.length];
    final double[] min = new double[lower.length];
    final double[] max = new double[lower.length];
    for (int i = 0; i < stats[0].length; i++) {
        final double[] limits = showDoubleHistogram(stats, i, wo, matchScores);
        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;
    final 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
    final double[] increment = new double[lower.length];
    for (int i = 0; i < increment.length; i++) {
        lower[i] = MathUtils.floor(lower[i], interval[i]);
        upper[i] = MathUtils.ceil(upper[i], interval[i]);
        final 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] = MathUtils.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] = MathUtils.round(lower[i]);
        upper[i] = MathUtils.round(upper[i]);
        min[i] = MathUtils.round(min[i]);
        max[i] = MathUtils.round(max[i]);
        increment[i] = MathUtils.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(TextUtils.nanosToString(runTime));
    createTable().append(sb.toString());
    if (settings.saveFilterRange) {
        GUIFilterSettings filterSettings = SettingsManager.readGuiFilterSettings(0);
        String filename = (silent) ? filterSettings.getFilterSetFilename() : ImageJUtils.getFilename("Filter_range_file", filterSettings.getFilterSetFilename());
        if (filename == null) {
            return;
        }
        // Remove extension to store the filename
        filename = FileUtils.replaceExtension(filename, ".xml");
        filterSettings = filterSettings.toBuilder().setFilterSetFilename(filename).build();
        // Create a filter set using the ranges
        final ArrayList<Filter> filters = new ArrayList<>(4);
        // Create the multi-filter using the same precision type as that used during fitting.
        // Currently no support for z-filter as 3D astigmatism fitting is experimental.
        final PrecisionMethod precisionMethod = getPrecisionMethod((DirectFilter) multiFilter.getFilter());
        Function<double[], Filter> generator;
        if (precisionMethod == PrecisionMethod.POISSON_CRLB) {
            generator = parameters -> new MultiFilterCrlb(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
        } else if (precisionMethod == PrecisionMethod.MORTENSEN) {
            generator = parameters -> new MultiFilter(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
        } else {
            // Default
            generator = parameters -> new MultiFilter2(parameters[FILTER_SIGNAL], (float) parameters[FILTER_SNR], parameters[FILTER_MIN_WIDTH], parameters[FILTER_MAX_WIDTH], parameters[FILTER_SHIFT], parameters[FILTER_ESHIFT], parameters[FILTER_PRECISION], 0f, 0f);
        }
        filters.add(generator.apply(lower));
        filters.add(generator.apply(upper));
        filters.add(generator.apply(increment));
        if (saveFilters(filename, filters)) {
            SettingsManager.writeSettings(filterSettings);
        }
        // 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_SNR] = Math.min(max[FILTER_SNR], 10000);
        max[FILTER_PRECISION] = Math.min(max[FILTER_PRECISION], 100);
        // Make the 4-set filters the same as the 3-set filters.
        filters.clear();
        filters.add(generator.apply(min));
        filters.add(generator.apply(lower));
        filters.add(generator.apply(upper));
        filters.add(generator.apply(max));
        saveFilters(FileUtils.replaceExtension(filename, ".4.xml"), filters);
    }
    spotFitResults.min = min;
    spotFitResults.max = max;
}

Example 9 with MultiPathFilter

use of uk.ac.sussex.gdsc.smlm.results.filter.MultiPathFilter 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
 * @return The score
 */
private ArrayList<FractionalAssignment[]> getAssignments(DirectFilter filter) {
    final MultiPathFilter multiPathFilter = createMpf(filter, defaultMinimalFilter);
    final ArrayList<FractionalAssignment[]> allAssignments = new ArrayList<>(fitResultData.resultsList.length);
    multiPathFilter.fractionScoreSubset(fitResultData.resultsList, createFailCounter(settings.failCount), fitResultData.countActual, allAssignments, null, coordinateStore);
    return allAssignments;
}

Example 10 with MultiPathFilter

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

the class FitWorker method run.

/**
 * Locate all the peaks in the image specified by the fit job.
 *
 * <p>WARNING: The FitWorker fits a sub-region of the data for each maxima. It then updates the
 * FitResult parameters with an offset reflecting the position. The initialParameters are not
 * updated with this offset unless configured.
 *
 * @param job The fit job
 */
public void run(FitJob job) {
    final long start = System.nanoTime();
    job.start();
    this.job = job;
    benchmarking = false;
    this.slice = job.slice;
    // Used for debugging
    // if (logger == null) logger = new gdsc.fitting.logging.ConsoleLogger();
    // Crop to the ROI
    cc = new CoordinateConverter(job.bounds);
    // Note if the bounds change for efficient caching.
    newBounds = !cc.dataBounds.equals(lastBounds);
    if (newBounds) {
        lastBounds = cc.dataBounds;
    }
    final int width = cc.dataBounds.width;
    final int height = cc.dataBounds.height;
    borderLimitX = width - border;
    borderLimitY = height - border;
    data = job.data;
    // This is tied to the input data
    dataEstimator = null;
    // relative to the global origin.
    if (isFitCameraCounts) {
        cameraModel.removeBias(cc.dataBounds, data);
    } else {
        cameraModel.removeBiasAndGain(cc.dataBounds, data);
    }
    final FitParameters params = job.getFitParameters();
    this.endT = (params != null) ? params.endT : -1;
    candidates = indentifySpots(job, width, height, params);
    if (candidates.getSize() == 0) {
        finishJob(job, start);
        return;
    }
    fittedBackground = new Statistics();
    // Always get the noise and store it with the results.
    if (params != null && !Float.isNaN(params.noise)) {
        noise = params.noise;
        fitConfig.setNoise(noise);
    } else if (calculateNoise) {
        noise = estimateNoise();
        fitConfig.setNoise(noise);
    }
    // System.out.printf("Slice %d : Noise = %g\n", slice, noise);
    if (logger != null) {
        LoggerUtils.log(logger, Level.INFO, "Slice %d: Noise = %f", slice, noise);
    }
    final ImageExtractor ie = ImageExtractor.wrap(data, width, height);
    double[] region = null;
    final float offsetx = cc.dataBounds.x;
    final float offsety = cc.dataBounds.y;
    if (params != null && params.fitTask == FitTask.MAXIMA_IDENITIFICATION) {
        final float sd0 = (float) xsd;
        final float sd1 = (float) ysd;
        for (int n = 0; n < candidates.getSize(); n++) {
            // Find the background using the perimeter of the data.
            // TODO - Perhaps the Gaussian Fitter should be used to produce the initial estimates but no
            // actual fit done.
            // This would produce coords using the centre-of-mass.
            final Candidate candidate = candidates.get(n);
            int x = candidate.x;
            int y = candidate.y;
            final Rectangle regionBounds = ie.getBoxRegionBounds(x, y, fitting);
            region = ie.crop(regionBounds, region);
            final float b = (float) Gaussian2DFitter.getBackground(region, regionBounds.width, regionBounds.height, 1);
            // Offset the coords to the centre of the pixel. Note the bounds will be added later.
            // Subtract the background to get the amplitude estimate then convert to signal.
            final float amplitude = candidate.intensity - ((relativeIntensity) ? 0 : b);
            final float signal = (float) (amplitude * 2.0 * Math.PI * sd0 * sd1);
            final int index = y * width + x;
            x += offsetx;
            y += offsety;
            final float[] peakParams = new float[1 + Gaussian2DFunction.PARAMETERS_PER_PEAK];
            peakParams[Gaussian2DFunction.BACKGROUND] = b;
            peakParams[Gaussian2DFunction.SIGNAL] = signal;
            peakParams[Gaussian2DFunction.X_POSITION] = x + 0.5f;
            peakParams[Gaussian2DFunction.Y_POSITION] = y + 0.5f;
            // peakParams[Gaussian2DFunction.Z_POSITION] = 0;
            peakParams[Gaussian2DFunction.X_SD] = sd0;
            peakParams[Gaussian2DFunction.Y_SD] = sd1;
            // peakParams[Gaussian2DFunction.ANGLE] = 0;
            final float u = (float) Gaussian2DPeakResultHelper.getMeanSignalUsingP05(signal, sd0, sd1);
            sliceResults.add(createResult(x, y, data[index], 0, noise, u, peakParams, null, n, 0));
        }
    } else {
        initialiseFitting();
        // Smooth the data to provide initial background estimates
        final float[] smoothedData = backgroundSmoothing.process(data, width, height);
        final ImageExtractor ie2 = ImageExtractor.wrap(smoothedData, width, height);
        // Perform the Gaussian fit
        // The SpotFitter is used to create a dynamic MultiPathFitResult object.
        // This is then passed to a multi-path filter. Thus the same fitting decision process
        // is used when benchmarking and when running on actual data.
        // Note: The SpotFitter labels each PreprocessedFitResult using the offset in the FitResult
        // object.
        // The initial params and deviations can then be extracted for the results that pass the
        // filter.
        MultiPathFilter filter;
        final IMultiPathFitResults multiPathResults = this;
        final SelectedResultStore store = this;
        coordinateStore = coordinateStore.resize(cc.dataBounds.x, cc.dataBounds.y, width, height);
        if (params != null && params.fitTask == FitTask.BENCHMARKING) {
            // Run filtering as normal. However in the event that a candidate is missed or some
            // results are not generated we must generate them. This is done in the complete(int)
            // method if we set the benchmarking flag.
            benchmarking = true;
            // Filter using the benchmark filter
            filter = params.benchmarkFilter;
            if (filter == null) {
                // Create a default filter using the standard FitConfiguration to ensure sensible fits
                // are stored as the current slice results.
                // Note the current fit configuration for benchmarking may have minimal filtering settings
                // so we do not use that object.
                final FitConfiguration tmp = new FitConfiguration();
                final double residualsThreshold = 0.4;
                filter = new MultiPathFilter(tmp, createMinimalFilter(PrecisionMethod.POISSON_CRLB), residualsThreshold);
            }
        } else {
            // Filter using the configuration.
            if (this.filter == null) {
                // This can be cached. Q. Clone the config?
                this.filter = new MultiPathFilter(fitConfig, createMinimalFilter(fitConfig.getPrecisionMethod()), config.getResidualsThreshold());
            }
            filter = this.filter;
        }
        // If we are benchmarking then do not generate results dynamically since we will store all
        // results in the fit job.
        dynamicMultiPathFitResult = new DynamicMultiPathFitResult(ie, ie2, !benchmarking);
        // dynamicMultiPathFitResult = new DynamicMultiPathFitResult(ie, false);
        // The local background computation is only required for the precision method.
        // Also compute it when benchmarking.
        localBackground = benchmarking || fitConfig.getPrecisionMethodValue() == PrecisionMethod.MORTENSEN_LOCAL_BACKGROUND_VALUE;
        // Debug where the fit config may be different between benchmarking and fitting
        if (slice == -1) {
            fitConfig.initialise(1, 1, 1);
            final String newLine = System.lineSeparator();
            final String tmpdir = System.getProperty("java.io.tmpdir");
            try (BufferedWriter writer = Files.newBufferedWriter(Paths.get(tmpdir, String.format("config.%d.txt", slice)))) {
                JsonFormat.printer().appendTo(config.getFitEngineSettings(), writer);
            } catch (final IOException ex) {
                logger.log(Level.SEVERE, "Unable to write message", ex);
            }
            FileUtils.save(Paths.get(tmpdir, String.format("filter.%d.xml", slice)).toString(), filter.toXml());
            // filter.setDebugFile(String.format("/tmp/fitWorker.%b.txt", benchmarking));
            final StringBuilder sb = new StringBuilder();
            sb.append((benchmarking) ? ((uk.ac.sussex.gdsc.smlm.results.filter.Filter) filter.getFilter()).toXml() : fitConfig.getSmartFilterString()).append(newLine);
            sb.append(((uk.ac.sussex.gdsc.smlm.results.filter.Filter) filter.getMinimalFilter()).toXml()).append(newLine);
            sb.append(filter.residualsThreshold).append(newLine);
            sb.append(config.getFailuresLimit()).append(newLine);
            sb.append(config.getDuplicateDistance()).append(":");
            sb.append(config.getDuplicateDistanceAbsolute()).append(newLine);
            if (spotFilter != null) {
                sb.append(spotFilter.getDescription()).append(newLine);
            }
            sb.append("MaxCandidate = ").append(candidates.getSize()).append(newLine);
            for (int i = 0, len = candidates.getLength(); i < len; i++) {
                TextUtils.formatTo(sb, "Fit %d [%d,%d = %.1f]%n", i, candidates.get(i).x, candidates.get(i).y, candidates.get(i).intensity);
            }
            FileUtils.save(Paths.get(tmpdir, String.format("candidates.%d.xml", slice)).toString(), sb.toString());
        }
        FailCounter failCounter = config.getFailCounter();
        if (!benchmarking && params != null && params.pass != null) {
            // We want to store the pass/fail for consecutive candidates
            params.pass = new boolean[candidates.getLength()];
            failCounter = new RecordingFailCounter(params.pass, failCounter);
            filter.select(multiPathResults, failCounter, true, store, coordinateStore);
        } else {
            filter.select(multiPathResults, failCounter, true, store, coordinateStore);
        }
        // Note: We go deeper into the candidate list than max candidate
        // for any candidate where we have a good fit result as an estimate.
        // Q. Should this only be for benchmarking?
        // if (benchmarking)
        // System.out.printf("Slice %d: %d + %d\n", slice, dynamicMultiPathFitResult.extra,
        // candidates.getSize());
        // Create the slice results
        final CandidateList fitted = gridManager.getFittedCandidates();
        sliceResults.ensureCapacity(fitted.getSize());
        for (int i = 0; i < fitted.getSize(); i++) {
            if (fitted.get(i).fit) {
                sliceResults.push(createResult(offsetx, offsety, fitted.get(i)));
            }
        }
        if (logger != null) {
            LoggerUtils.log(logger, Level.INFO, "Slice %d: %d / %d = %s", slice, success, candidates.getSize(), TextUtils.pleural(fitted.getSize(), "result"));
        }
    }
    this.results.addAll(sliceResults);
    finishJob(job, start);
}