Examples with WindowOrganiser ij.plugin.WindowOrganiser used on opensource projects

Example 6 with WindowOrganiser

use of ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class FilterAnalysis method showPlots.

private void showPlots() {
    if (plots.isEmpty())
        return;
    // Display the top N plots
    int[] list = new int[plots.size()];
    int i = 0;
    for (NamedPlot p : plots) {
        Plot2 plot = new Plot2(p.name, p.xAxisName, "Jaccard", p.xValues, p.yValues);
        plot.setLimits(p.xValues[0], p.xValues[p.xValues.length - 1], 0, 1);
        plot.setColor(Color.RED);
        plot.draw();
        plot.setColor(Color.BLUE);
        plot.addPoints(p.xValues, p.yValues, Plot2.CROSS);
        PlotWindow plotWindow = Utils.display(p.name, plot);
        list[i++] = plotWindow.getImagePlus().getID();
    }
    new WindowOrganiser().tileWindows(list);
}

Example 7 with WindowOrganiser

use of ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class BenchmarkFit method run.

private void run() {
    // Initialise the answer. Convert to units of the image (ADUs and pixels)
    answer[Gaussian2DFunction.BACKGROUND] = benchmarkParameters.getBackground() * benchmarkParameters.gain;
    answer[Gaussian2DFunction.SIGNAL] = benchmarkParameters.getSignal() * benchmarkParameters.gain;
    answer[Gaussian2DFunction.X_POSITION] = benchmarkParameters.x;
    answer[Gaussian2DFunction.Y_POSITION] = benchmarkParameters.y;
    answer[Gaussian2DFunction.X_SD] = benchmarkParameters.s / benchmarkParameters.a;
    answer[Gaussian2DFunction.Y_SD] = benchmarkParameters.s / benchmarkParameters.a;
    // Set up the fit region. Always round down since 0.5 is the centre of the pixel.
    int x = (int) benchmarkParameters.x;
    int y = (int) benchmarkParameters.y;
    region = new Rectangle(x - regionSize, y - regionSize, 2 * regionSize + 1, 2 * regionSize + 1);
    if (!new Rectangle(0, 0, imp.getWidth(), imp.getHeight()).contains(region)) {
        // Check if it is incorrect by only 1 pixel
        if (region.width <= imp.getWidth() + 1 && region.height <= imp.getHeight() + 1) {
            Utils.log("Adjusting region %s to fit within image bounds (%dx%d)", region.toString(), imp.getWidth(), imp.getHeight());
            region = new Rectangle(0, 0, imp.getWidth(), imp.getHeight());
        } else {
            IJ.error(TITLE, "Fit region does not fit within the image");
            return;
        }
    }
    // Adjust the centre & account for 0.5 pixel offset during fitting
    x -= region.x;
    y -= region.y;
    answer[Gaussian2DFunction.X_POSITION] -= (region.x + 0.5);
    answer[Gaussian2DFunction.Y_POSITION] -= (region.y + 0.5);
    // Configure for fitting
    fitConfig.setBackgroundFitting(backgroundFitting);
    fitConfig.setNotSignalFitting(!signalFitting);
    fitConfig.setComputeDeviations(false);
    final ImageStack stack = imp.getImageStack();
    // Create a pool of workers
    int nThreads = Prefs.getThreads();
    BlockingQueue<Integer> jobs = new ArrayBlockingQueue<Integer>(nThreads * 2);
    List<Worker> workers = new LinkedList<Worker>();
    List<Thread> threads = new LinkedList<Thread>();
    for (int i = 0; i < nThreads; i++) {
        Worker worker = new Worker(jobs, stack, region, fitConfig);
        Thread t = new Thread(worker);
        workers.add(worker);
        threads.add(t);
        t.start();
    }
    final int totalFrames = benchmarkParameters.frames;
    // Store all the fitting results
    results = new double[totalFrames * getNumberOfStartPoints()][];
    resultsTime = new long[results.length];
    // Fit the frames
    totalProgress = totalFrames;
    stepProgress = Utils.getProgressInterval(totalProgress);
    progress = 0;
    for (int i = 0; i < totalFrames; i++) {
        // Only fit if there were simulated photons
        if (benchmarkParameters.p[i] > 0) {
            put(jobs, i);
        }
    }
    // Finish all the worker threads by passing in a null job
    for (int i = 0; i < threads.size(); i++) {
        put(jobs, -1);
    }
    // Wait for all to finish
    for (int i = 0; i < threads.size(); i++) {
        try {
            threads.get(i).join();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
    threads.clear();
    if (comFitting)
        Utils.log(TITLE + ": CoM within start offset = %d / %d (%s%%)", comValid.intValue(), totalFrames, Utils.rounded((100.0 * comValid.intValue()) / totalFrames));
    IJ.showProgress(1);
    IJ.showStatus("Collecting results ...");
    // Collect the results
    Statistics[] stats = new Statistics[NAMES.length];
    for (int i = 0; i < workers.size(); i++) {
        Statistics[] next = workers.get(i).stats;
        for (int j = 0; j < next.length; j++) {
            if (stats[j] == null)
                stats[j] = next[j];
            else
                stats[j].add(next[j]);
        }
    }
    workers.clear();
    // Show a table of the results
    summariseResults(stats);
    // Optionally show histograms
    if (showHistograms) {
        IJ.showStatus("Calculating histograms ...");
        int[] idList = new int[NAMES.length];
        int count = 0;
        double[] convert = getConversionFactors();
        boolean requireRetile = false;
        for (int i = 0; i < NAMES.length; i++) {
            if (displayHistograms[i] && convert[i] != 0) {
                // We will have to convert the values...
                double[] tmp = ((StoredDataStatistics) stats[i]).getValues();
                for (int j = 0; j < tmp.length; j++) tmp[j] *= convert[i];
                StoredDataStatistics tmpStats = new StoredDataStatistics(tmp);
                idList[count++] = Utils.showHistogram(TITLE, tmpStats, NAMES[i], 0, 0, histogramBins, String.format("%s +/- %s", Utils.rounded(tmpStats.getMean()), Utils.rounded(tmpStats.getStandardDeviation())));
                requireRetile = requireRetile || Utils.isNewWindow();
            }
        }
        if (count > 0 && requireRetile) {
            idList = Arrays.copyOf(idList, count);
            new WindowOrganiser().tileWindows(idList);
        }
    }
    if (saveRawData) {
        String dir = Utils.getDirectory("Data_directory", rawDataDirectory);
        if (dir != null)
            saveData(stats, dir);
    }
    IJ.showStatus("");
}

Example 8 with WindowOrganiser

use of ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    extraOptions = Utils.isExtraOptions();
    batchMode = "batch".equals(arg);
    simulationParameters = CreateData.simulationParameters;
    if (simulationParameters == null) {
        IJ.error(TITLE, "No benchmark spot parameters in memory");
        return;
    }
    imp = CreateData.getImage();
    if (imp == null) {
        IJ.error(TITLE, "No benchmark image");
        return;
    }
    results = CreateData.getResults();
    if (results == null) {
        IJ.error(TITLE, "No benchmark results in memory");
        return;
    }
    if (!showDialog())
        return;
    // Clear old results to free memory
    if (filterResult != null) {
        filterResult.filterResults.clear();
        filterResult.filterResults = null;
        filterResult = null;
    }
    // For graphs
    windowOrganiser = new WindowOrganiser();
    if (batchMode) {
        // Batch mode to test enumeration of filters
        final double sd = simulationParameters.s / simulationParameters.a;
        final int limit = (int) Math.floor(3 * sd);
        double[] searchParam = getRange(minSearch, maxSearch, 1);
        // Continuous parameters
        double[] pEmpty = new double[0];
        double[] mParam = (batchMean) ? getRange(limit, 0.05) : pEmpty;
        double[] gParam = (batchGaussian) ? getRange(limit, 0.05) : pEmpty;
        // Less continuous parameters
        double[] cParam = (batchCircular) ? getRange(limit, 0.5) : pEmpty;
        // Discrete parameters
        double[] medParam = (batchMedian) ? getRange(limit, 1) : pEmpty;
        setupProgress(imp.getImageStackSize() * searchParam.length * (mParam.length + gParam.length + cParam.length + medParam.length), "Frame");
        ArrayList<BatchResult[]> batchResults = new ArrayList<BatchResult[]>(cachedBatchResults.size());
        config.setDataFilterType(DataFilterType.SINGLE);
        for (double search : searchParam) {
            // Run all, store the results for plotting.
            // Allow re-use of these if they are cached to allow quick reanalysis of results.
            config.setSearch(search);
            if (batchMean)
                batchResults.add(addToCache(DataFilter.MEAN, mParam, search));
            if (batchGaussian)
                batchResults.add(addToCache(DataFilter.GAUSSIAN, gParam, search));
            if (batchCircular)
                batchResults.add(addToCache(DataFilter.CIRCULAR_MEAN, cParam, search));
            if (batchMean)
                batchResults.add(addToCache(DataFilter.MEDIAN, medParam, search));
        }
        IJ.showProgress(-1);
        IJ.showStatus("");
        if (Utils.isInterrupted())
            return;
        // Analysis options
        GenericDialog gd = new GenericDialog(TITLE);
        gd.addMessage("Choose performance plots:");
        for (int i = 0; i < batchPlot.length; i++) gd.addCheckbox(batchPlotNames[i], batchPlot[i]);
        gd.addChoice("Selection", SELECTION, SELECTION[selection]);
        gd.addCheckbox("Show_plots", showPlot);
        gd.addCheckbox("Plot_rank_by_intensity", rankByIntensity);
        gd.addCheckbox("Show_failures_plots", showFailuresPlot);
        gd.addCheckbox("Show_TP", showTP);
        gd.addCheckbox("Show_FP", showFP);
        gd.addCheckbox("Show_FN", showFN);
        gd.showDialog();
        if (gd.wasCanceled())
            return;
        for (int i = 0; i < batchPlot.length; i++) batchPlot[i] = gd.getNextBoolean();
        selection = gd.getNextChoiceIndex();
        showPlot = gd.getNextBoolean();
        rankByIntensity = gd.getNextBoolean();
        showFailuresPlot = gd.getNextBoolean();
        showTP = gd.getNextBoolean();
        showFP = gd.getNextBoolean();
        showFN = gd.getNextBoolean();
        // Plot charts			
        for (int i = 0; i < batchPlot.length; i++) plot(i, batchResults);
        // Store in global singleton
        filterResult = analyse(batchResults);
    } else {
        // Single filter mode
        setupProgress(imp.getImageStackSize(), "Frame");
        filterResult = run(config, filterRelativeDistances);
    }
    IJ.showProgress(-1);
    IJ.showStatus("");
    getTable(false).flush();
    if (filterResult == null)
        return;
    // Store a clone of the config
    filterResult.config = filterResult.config.clone();
    // Debugging the matches
    if (debug)
        addSpotsToMemory(filterResult.filterResults);
    if (showFailuresPlot)
        showFailuresPlot(filterResult);
    if (showPlot)
        showPlot(filterResult);
    if (isShowOverlay())
        showOverlay(imp, filterResult);
    windowOrganiser.tile();
}

Example 9 with WindowOrganiser

use of ij.plugin.WindowOrganiser 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 10 with WindowOrganiser

use of ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class PSFDrift method computeDrift.

private void computeDrift() {
    // Create a grid of XY offset positions between 0-1 for PSF insert
    final double[] grid = new double[gridSize];
    for (int i = 0; i < grid.length; i++) grid[i] = (double) i / gridSize;
    // Configure fitting region
    final int w = 2 * regionSize + 1;
    centrePixel = w / 2;
    // Check region size using the image PSF
    double newPsfWidth = (double) imp.getWidth() / scale;
    if (Math.ceil(newPsfWidth) > w)
        Utils.log(TITLE + ": Fitted region size (%d) is smaller than the scaled PSF (%.1f)", w, newPsfWidth);
    // Create robust PSF fitting settings
    final double a = psfSettings.nmPerPixel * scale;
    final double sa = PSFCalculator.squarePixelAdjustment(psfSettings.nmPerPixel * (psfSettings.fwhm / Gaussian2DFunction.SD_TO_FWHM_FACTOR), a);
    fitConfig.setInitialPeakStdDev(sa / a);
    fitConfig.setBackgroundFitting(backgroundFitting);
    fitConfig.setNotSignalFitting(false);
    fitConfig.setComputeDeviations(false);
    fitConfig.setDisableSimpleFilter(true);
    // Create the PSF over the desired z-depth
    int depth = (int) Math.round(zDepth / psfSettings.nmPerSlice);
    int startSlice = psfSettings.zCentre - depth;
    int endSlice = psfSettings.zCentre + depth;
    int nSlices = imp.getStackSize();
    startSlice = (startSlice < 1) ? 1 : (startSlice > nSlices) ? nSlices : startSlice;
    endSlice = (endSlice < 1) ? 1 : (endSlice > nSlices) ? nSlices : endSlice;
    ImagePSFModel psf = createImagePSF(startSlice, endSlice);
    int minz = startSlice - psfSettings.zCentre;
    int maxz = endSlice - psfSettings.zCentre;
    final int nZ = maxz - minz + 1;
    final int gridSize2 = grid.length * grid.length;
    total = nZ * gridSize2;
    // Store all the fitting results
    int nStartPoints = getNumberOfStartPoints();
    results = new double[total * nStartPoints][];
    // TODO - Add ability to iterate this, adjusting the current offset in the PSF
    // each iteration
    // Create a pool of workers
    int nThreads = Prefs.getThreads();
    BlockingQueue<Job> jobs = new ArrayBlockingQueue<Job>(nThreads * 2);
    List<Worker> workers = new LinkedList<Worker>();
    List<Thread> threads = new LinkedList<Thread>();
    for (int i = 0; i < nThreads; i++) {
        Worker worker = new Worker(jobs, psf, w, fitConfig);
        Thread t = new Thread(worker);
        workers.add(worker);
        threads.add(t);
        t.start();
    }
    // Fit 
    Utils.showStatus("Fitting ...");
    final int step = Utils.getProgressInterval(total);
    outer: for (int z = minz, i = 0; z <= maxz; z++) {
        for (int x = 0; x < grid.length; x++) for (int y = 0; y < grid.length; y++, i++) {
            if (IJ.escapePressed()) {
                break outer;
            }
            put(jobs, new Job(z, grid[x], grid[y], i));
            if (i % step == 0) {
                IJ.showProgress(i, total);
            }
        }
    }
    // If escaped pressed then do not need to stop the workers, just return
    if (Utils.isInterrupted()) {
        IJ.showProgress(1);
        return;
    }
    // Finish all the worker threads by passing in a null job
    for (int i = 0; i < threads.size(); i++) {
        put(jobs, new Job());
    }
    // Wait for all to finish
    for (int i = 0; i < threads.size(); i++) {
        try {
            threads.get(i).join();
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }
    threads.clear();
    IJ.showProgress(1);
    IJ.showStatus("");
    // Plot the average and SE for the drift curve
    // Plot the recall
    double[] zPosition = new double[nZ];
    double[] avX = new double[nZ];
    double[] seX = new double[nZ];
    double[] avY = new double[nZ];
    double[] seY = new double[nZ];
    double[] recall = new double[nZ];
    for (int z = minz, i = 0; z <= maxz; z++, i++) {
        Statistics statsX = new Statistics();
        Statistics statsY = new Statistics();
        for (int s = 0; s < nStartPoints; s++) {
            int resultPosition = i * gridSize2 + s * total;
            final int endResultPosition = resultPosition + gridSize2;
            while (resultPosition < endResultPosition) {
                if (results[resultPosition] != null) {
                    statsX.add(results[resultPosition][0]);
                    statsY.add(results[resultPosition][1]);
                }
                resultPosition++;
            }
        }
        zPosition[i] = z * psfSettings.nmPerSlice;
        avX[i] = statsX.getMean();
        seX[i] = statsX.getStandardError();
        avY[i] = statsY.getMean();
        seY[i] = statsY.getStandardError();
        recall[i] = (double) statsX.getN() / (nStartPoints * gridSize2);
    }
    // Find the range from the z-centre above the recall limit 
    int centre = 0;
    for (int slice = startSlice, i = 0; slice <= endSlice; slice++, i++) {
        if (slice == psfSettings.zCentre) {
            centre = i;
            break;
        }
    }
    if (recall[centre] < recallLimit)
        return;
    int start = centre, end = centre;
    for (int i = centre; i-- > 0; ) {
        if (recall[i] < recallLimit)
            break;
        start = i;
    }
    for (int i = centre; ++i < recall.length; ) {
        if (recall[i] < recallLimit)
            break;
        end = i;
    }
    int iterations = 1;
    LoessInterpolator loess = null;
    if (smoothing > 0)
        loess = new LoessInterpolator(smoothing, iterations);
    double[][] smoothx = displayPlot("Drift X", "X (nm)", zPosition, avX, seX, loess, start, end);
    double[][] smoothy = displayPlot("Drift Y", "Y (nm)", zPosition, avY, seY, loess, start, end);
    displayPlot("Recall", "Recall", zPosition, recall, null, null, start, end);
    WindowOrganiser wo = new WindowOrganiser();
    wo.tileWindows(idList);
    // Ask the user if they would like to store them in the image
    GenericDialog gd = new GenericDialog(TITLE);
    gd.enableYesNoCancel();
    gd.hideCancelButton();
    startSlice = psfSettings.zCentre - (centre - start);
    endSlice = psfSettings.zCentre + (end - centre);
    gd.addMessage(String.format("Save the drift to the PSF?\n \nSlices %d (%s nm) - %d (%s nm) above recall limit", startSlice, Utils.rounded(zPosition[start]), endSlice, Utils.rounded(zPosition[end])));
    gd.addMessage("Optionally average the end points to set drift outside the limits.\n(Select zero to ignore)");
    gd.addSlider("Number_of_points", 0, 10, positionsToAverage);
    gd.showDialog();
    if (gd.wasOKed()) {
        positionsToAverage = Math.abs((int) gd.getNextNumber());
        ArrayList<PSFOffset> offset = new ArrayList<PSFOffset>();
        final double pitch = psfSettings.nmPerPixel;
        int j = 0, jj = 0;
        for (int i = start, slice = startSlice; i <= end; slice++, i++) {
            j = findCentre(zPosition[i], smoothx, j);
            if (j == -1) {
                Utils.log("Failed to find the offset for depth %.2f", zPosition[i]);
                continue;
            }
            // The offset should store the difference to the centre in pixels so divide by the pixel pitch
            double cx = smoothx[1][j] / pitch;
            double cy = smoothy[1][j] / pitch;
            jj = findOffset(slice, jj);
            if (jj != -1) {
                cx += psfSettings.offset[jj].cx;
                cy += psfSettings.offset[jj].cy;
            }
            offset.add(new PSFOffset(slice, cx, cy));
        }
        addMissingOffsets(startSlice, endSlice, nSlices, offset);
        psfSettings.offset = offset.toArray(new PSFOffset[offset.size()]);
        psfSettings.addNote(TITLE, String.format("Solver=%s, Region=%d", PeakFit.getSolverName(fitConfig), regionSize));
        imp.setProperty("Info", XmlUtils.toXML(psfSettings));
    }
}