Examples with Mean org.apache.commons.math3.stat.descriptive.moment.Mean used on opensource projects

Example 56 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project GDSC-SMLM by aherbert.

the class DiffusionRateTest method plotJumpDistances.

/**
	 * Plot a cumulative histogram and standard histogram of the jump distances.
	 *
	 * @param title
	 *            the title
	 * @param jumpDistances
	 *            the jump distances
	 * @param dimensions
	 *            the number of dimensions for the jumps
	 * @param steps
	 *            the steps
	 */
private void plotJumpDistances(String title, DoubleData jumpDistances, int dimensions) {
    // Cumulative histogram
    // --------------------
    double[] values = jumpDistances.values();
    String title2 = title + " Cumulative Jump Distance " + dimensions + "D";
    double[][] jdHistogram = JumpDistanceAnalysis.cumulativeHistogram(values);
    Plot2 jdPlot = new Plot2(title2, "Distance (um^2)", "Cumulative Probability", jdHistogram[0], jdHistogram[1]);
    PlotWindow pw2 = Utils.display(title2, jdPlot);
    if (Utils.isNewWindow())
        idList[idCount++] = pw2.getImagePlus().getID();
    // Plot the expected function
    // This is the Chi-squared distribution: The sum of the squares of k independent
    // standard normal random variables with k = dimensions. It is a special case of
    // the gamma distribution. If the normals have non-unit variance the distribution 
    // is scaled.
    // Chi       ~ Gamma(k/2, 2)      // using the scale parameterisation of the gamma
    // s^2 * Chi ~ Gamma(k/2, 2*s^2)
    // So if s^2 = 2D:
    // 2D * Chi  ~ Gamma(k/2, 4D)
    double estimatedD = simpleD * simpleSteps;
    double max = Maths.max(values);
    double[] x = Utils.newArray(1000, 0, max / 1000);
    double k = dimensions / 2.0;
    double mean = 4 * estimatedD;
    GammaDistribution dist = new GammaDistribution(k, mean);
    double[] y = new double[x.length];
    for (int i = 0; i < x.length; i++) y[i] = dist.cumulativeProbability(x[i]);
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(title2, jdPlot);
    // Histogram
    // ---------
    title2 = title + " Jump " + dimensions + "D";
    int plotId = Utils.showHistogram(title2, jumpDistances, "Distance (um^2)", 0, 0, Math.max(20, values.length / 1000));
    if (Utils.isNewWindow())
        idList[idCount++] = plotId;
    // Recompute the expected function
    for (int i = 0; i < x.length; i++) y[i] = dist.density(x[i]);
    // Scale to have the same area
    if (Utils.xValues.length > 1) {
        final double area1 = jumpDistances.size() * (Utils.xValues[1] - Utils.xValues[0]);
        final double area2 = dist.cumulativeProbability(x[x.length - 1]);
        final double scaleFactor = area1 / area2;
        for (int i = 0; i < y.length; i++) y[i] *= scaleFactor;
    }
    jdPlot = Utils.plot;
    jdPlot.setColor(Color.red);
    jdPlot.addPoints(x, y, Plot.LINE);
    Utils.display(WindowManager.getImage(plotId).getTitle(), jdPlot);
}

Example 57 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean 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 58 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project GDSC-SMLM by aherbert.

the class MeanVarianceTest method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (Utils.isExtraOptions()) {
        ImagePlus imp = WindowManager.getCurrentImage();
        if (imp.getStackSize() > 1) {
            GenericDialog gd = new GenericDialog(TITLE);
            gd.addMessage("Perform single image analysis on the current image?");
            gd.addNumericField("Bias", _bias, 0);
            gd.showDialog();
            if (gd.wasCanceled())
                return;
            singleImage = true;
            _bias = Math.abs(gd.getNextNumber());
        } else {
            IJ.error(TITLE, "Single-image mode requires a stack");
            return;
        }
    }
    List<ImageSample> images;
    String inputDirectory = "";
    if (singleImage) {
        IJ.showStatus("Loading images...");
        images = getImages();
        if (images.size() == 0) {
            IJ.error(TITLE, "Not enough images for analysis");
            return;
        }
    } else {
        inputDirectory = IJ.getDirectory("Select image series ...");
        if (inputDirectory == null)
            return;
        SeriesOpener series = new SeriesOpener(inputDirectory, false, 0);
        series.setVariableSize(true);
        if (series.getNumberOfImages() < 3) {
            IJ.error(TITLE, "Not enough images in the selected directory");
            return;
        }
        if (!IJ.showMessageWithCancel(TITLE, String.format("Analyse %d images, first image:\n%s", series.getNumberOfImages(), series.getImageList()[0]))) {
            return;
        }
        IJ.showStatus("Loading images");
        images = getImages(series);
        if (images.size() < 3) {
            IJ.error(TITLE, "Not enough images for analysis");
            return;
        }
        if (images.get(0).exposure != 0) {
            IJ.error(TITLE, "First image in series must have exposure 0 (Bias image)");
            return;
        }
    }
    boolean emMode = (arg != null && arg.contains("em"));
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addMessage("Set the output options:");
    gd.addCheckbox("Show_table", showTable);
    gd.addCheckbox("Show_charts", showCharts);
    if (emMode) {
        // Ask the user for the camera gain ...
        gd.addMessage("Estimating the EM-gain requires the camera gain without EM readout enabled");
        gd.addNumericField("Camera_gain (ADU/e-)", cameraGain, 4);
    }
    gd.showDialog();
    if (gd.wasCanceled())
        return;
    showTable = gd.getNextBoolean();
    showCharts = gd.getNextBoolean();
    if (emMode) {
        cameraGain = gd.getNextNumber();
    }
    IJ.showStatus("Computing mean & variance");
    final double nImages = images.size();
    for (int i = 0; i < images.size(); i++) {
        IJ.showStatus(String.format("Computing mean & variance %d/%d", i + 1, images.size()));
        images.get(i).compute(singleImage, i / nImages, (i + 1) / nImages);
    }
    IJ.showProgress(1);
    IJ.showStatus("Computing results");
    // Allow user to input multiple bias images
    int start = 0;
    Statistics biasStats = new Statistics();
    Statistics noiseStats = new Statistics();
    final double bias;
    if (singleImage) {
        bias = _bias;
    } else {
        while (start < images.size()) {
            ImageSample sample = images.get(start);
            if (sample.exposure == 0) {
                biasStats.add(sample.means);
                for (PairSample pair : sample.samples) {
                    noiseStats.add(pair.variance);
                }
                start++;
            } else
                break;
        }
        bias = biasStats.getMean();
    }
    // Get the mean-variance data
    int total = 0;
    for (int i = start; i < images.size(); i++) total += images.get(i).samples.size();
    if (showTable && total > 2000) {
        gd = new GenericDialog(TITLE);
        gd.addMessage("Table output requires " + total + " entries.\n \nYou may want to disable the table.");
        gd.addCheckbox("Show_table", showTable);
        gd.showDialog();
        if (gd.wasCanceled())
            return;
        showTable = gd.getNextBoolean();
    }
    TextWindow results = (showTable) ? createResultsWindow() : null;
    double[] mean = new double[total];
    double[] variance = new double[mean.length];
    Statistics gainStats = (singleImage) ? new StoredDataStatistics(total) : new Statistics();
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = (singleImage) ? 0 : start, j = 0; i < images.size(); i++) {
        StringBuilder sb = (showTable) ? new StringBuilder() : null;
        ImageSample sample = images.get(i);
        for (PairSample pair : sample.samples) {
            if (j % 16 == 0)
                IJ.showProgress(j, total);
            mean[j] = pair.getMean();
            variance[j] = pair.variance;
            // Gain is in ADU / e
            double gain = variance[j] / (mean[j] - bias);
            gainStats.add(gain);
            obs.add(mean[j], variance[j]);
            if (emMode) {
                gain /= (2 * cameraGain);
            }
            if (showTable) {
                sb.append(sample.title).append("\t");
                sb.append(sample.exposure).append("\t");
                sb.append(pair.slice1).append("\t");
                sb.append(pair.slice2).append("\t");
                sb.append(IJ.d2s(pair.mean1, 2)).append("\t");
                sb.append(IJ.d2s(pair.mean2, 2)).append("\t");
                sb.append(IJ.d2s(mean[j], 2)).append("\t");
                sb.append(IJ.d2s(variance[j], 2)).append("\t");
                sb.append(Utils.rounded(gain, 4)).append("\n");
            }
            j++;
        }
        if (showTable)
            results.append(sb.toString());
    }
    IJ.showProgress(1);
    if (singleImage) {
        StoredDataStatistics stats = (StoredDataStatistics) gainStats;
        Utils.log(TITLE);
        if (emMode) {
            double[] values = stats.getValues();
            MathArrays.scaleInPlace(0.5, values);
            stats = new StoredDataStatistics(values);
        }
        if (showCharts) {
            // Plot the gain over time
            String title = TITLE + " Gain vs Frame";
            Plot2 plot = new Plot2(title, "Slice", "Gain", Utils.newArray(gainStats.getN(), 1, 1.0), stats.getValues());
            PlotWindow pw = Utils.display(title, plot);
            // Show a histogram
            String label = String.format("Mean = %s, Median = %s", Utils.rounded(stats.getMean()), Utils.rounded(stats.getMedian()));
            int id = Utils.showHistogram(TITLE, stats, "Gain", 0, 1, 100, true, label);
            if (Utils.isNewWindow()) {
                Point point = pw.getLocation();
                point.x = pw.getLocation().x;
                point.y += pw.getHeight();
                WindowManager.getImage(id).getWindow().setLocation(point);
            }
        }
        Utils.log("Single-image mode: %s camera", (emMode) ? "EM-CCD" : "Standard");
        final double gain = stats.getMedian();
        if (emMode) {
            final double totalGain = gain;
            final double emGain = totalGain / cameraGain;
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
            Utils.log("  EM-Gain = %s", Utils.rounded(emGain, 4));
            Utils.log("  Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
        } else {
            cameraGain = gain;
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
        }
    } else {
        IJ.showStatus("Computing fit");
        // Sort
        int[] indices = rank(mean);
        mean = reorder(mean, indices);
        variance = reorder(variance, indices);
        // Compute optimal coefficients.
        // a - b x
        final double[] init = { 0, 1 / gainStats.getMean() };
        final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2).withStartPoint(init);
        final double[] best = fitter.fit(obs.toList());
        // Construct the polynomial that best fits the data.
        final PolynomialFunction fitted = new PolynomialFunction(best);
        if (showCharts) {
            // Plot mean verses variance. Gradient is gain in ADU/e.
            String title = TITLE + " results";
            Plot2 plot = new Plot2(title, "Mean", "Variance");
            double[] xlimits = Maths.limits(mean);
            double[] ylimits = Maths.limits(variance);
            double xrange = (xlimits[1] - xlimits[0]) * 0.05;
            if (xrange == 0)
                xrange = 0.05;
            double yrange = (ylimits[1] - ylimits[0]) * 0.05;
            if (yrange == 0)
                yrange = 0.05;
            plot.setLimits(xlimits[0] - xrange, xlimits[1] + xrange, ylimits[0] - yrange, ylimits[1] + yrange);
            plot.setColor(Color.blue);
            plot.addPoints(mean, variance, Plot2.CROSS);
            plot.setColor(Color.red);
            plot.addPoints(new double[] { mean[0], mean[mean.length - 1] }, new double[] { fitted.value(mean[0]), fitted.value(mean[mean.length - 1]) }, Plot2.LINE);
            Utils.display(title, plot);
        }
        final double avBiasNoise = Math.sqrt(noiseStats.getMean());
        Utils.log(TITLE);
        Utils.log("  Directory = %s", inputDirectory);
        Utils.log("  Bias = %s +/- %s (ADU)", Utils.rounded(bias, 4), Utils.rounded(avBiasNoise, 4));
        Utils.log("  Variance = %s + %s * mean", Utils.rounded(best[0], 4), Utils.rounded(best[1], 4));
        if (emMode) {
            final double emGain = best[1] / (2 * cameraGain);
            // Noise is standard deviation of the bias image divided by the total gain (in ADU/e-)
            final double totalGain = emGain * cameraGain;
            Utils.log("  Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(avBiasNoise / totalGain, 4), Utils.rounded(avBiasNoise, 4));
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
            Utils.log("  EM-Gain = %s", Utils.rounded(emGain, 4));
            Utils.log("  Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
        } else {
            // Noise is standard deviation of the bias image divided by the gain (in ADU/e-)
            cameraGain = best[1];
            final double readNoise = avBiasNoise / cameraGain;
            Utils.log("  Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(readNoise, 4), Utils.rounded(readNoise * cameraGain, 4));
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
        }
    }
    IJ.showStatus("");
}

Example 59 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project lucene-solr by apache.

the class EmpiricalDistributionEvaluator method evaluate.

public Tuple evaluate(Tuple tuple) throws IOException {
    if (subEvaluators.size() != 1) {
        throw new IOException("Empirical dist expects 1 column as a parameters");
    }
    StreamEvaluator colEval1 = subEvaluators.get(0);
    List<Number> numbers1 = (List<Number>) colEval1.evaluate(tuple);
    double[] column1 = new double[numbers1.size()];
    for (int i = 0; i < numbers1.size(); i++) {
        column1[i] = numbers1.get(i).doubleValue();
    }
    Arrays.sort(column1);
    EmpiricalDistribution empiricalDistribution = new EmpiricalDistribution();
    empiricalDistribution.load(column1);
    Map map = new HashMap();
    StatisticalSummary statisticalSummary = empiricalDistribution.getSampleStats();
    map.put("max", statisticalSummary.getMax());
    map.put("mean", statisticalSummary.getMean());
    map.put("min", statisticalSummary.getMin());
    map.put("stdev", statisticalSummary.getStandardDeviation());
    map.put("sum", statisticalSummary.getSum());
    map.put("N", statisticalSummary.getN());
    map.put("var", statisticalSummary.getVariance());
    return new EmpiricalDistributionTuple(empiricalDistribution, column1, map);
}

Example 60 with Mean

use of org.apache.commons.math3.stat.descriptive.moment.Mean in project lucene-solr by apache.

the class HistogramEvaluator method evaluate.

public List<Map> evaluate(Tuple tuple) throws IOException {
    StreamEvaluator colEval1 = subEvaluators.get(0);
    List<Number> numbers1 = (List<Number>) colEval1.evaluate(tuple);
    double[] column1 = new double[numbers1.size()];
    for (int i = 0; i < numbers1.size(); i++) {
        column1[i] = numbers1.get(i).doubleValue();
    }
    int bins = 10;
    if (subEvaluators.size() == 2) {
        StreamEvaluator binsEval = subEvaluators.get(1);
        Number binsNum = (Number) binsEval.evaluate(tuple);
        bins = binsNum.intValue();
    }
    EmpiricalDistribution empiricalDistribution = new EmpiricalDistribution(bins);
    empiricalDistribution.load(column1);
    List<Map> binList = new ArrayList();
    List<SummaryStatistics> summaries = empiricalDistribution.getBinStats();
    for (SummaryStatistics statisticalSummary : summaries) {
        Map map = new HashMap();
        map.put("max", statisticalSummary.getMax());
        map.put("mean", statisticalSummary.getMean());
        map.put("min", statisticalSummary.getMin());
        map.put("stdev", statisticalSummary.getStandardDeviation());
        map.put("sum", statisticalSummary.getSum());
        map.put("N", statisticalSummary.getN());
        map.put("var", statisticalSummary.getVariance());
        binList.add(map);
    }
    return binList;
}