Examples with Min org.apache.commons.math3.stat.descriptive.rank.Min used on opensource projects

Example 36 with Min

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

the class FIRE method calculatePrecisionHistogram.

/**
	 * Calculate a histogram of the precision. The precision can be either stored in the results or calculated using the
	 * Mortensen formula. If the precision method for Q estimation is not fixed then the histogram is fitted with a
	 * Gaussian to create an initial estimate.
	 *
	 * @param precisionMethod
	 *            the precision method
	 * @return The precision histogram
	 */
private PrecisionHistogram calculatePrecisionHistogram() {
    boolean logFitParameters = false;
    String title = results.getName() + " Precision Histogram";
    // Check if the results has the precision already or if it can be computed.
    boolean canUseStored = canUseStoredPrecision(results);
    boolean canCalculatePrecision = canCalculatePrecision(results);
    // Set the method to compute a histogram. Default to the user selected option.
    PrecisionMethod m = null;
    if (canUseStored && precisionMethod == PrecisionMethod.STORED)
        m = precisionMethod;
    else if (canCalculatePrecision && precisionMethod == PrecisionMethod.CALCULATE)
        m = precisionMethod;
    if (m == null) {
        // We only have two choices so if one is available then select it.
        if (canUseStored)
            m = PrecisionMethod.STORED;
        else if (canCalculatePrecision)
            m = PrecisionMethod.CALCULATE;
        // If the user selected a method not available then log a warning
        if (m != null && precisionMethod != PrecisionMethod.FIXED) {
            IJ.log(String.format("%s : Selected precision method '%s' not available, switching to '%s'", TITLE, precisionMethod, m.getName()));
        }
        if (m == null) {
            // This does not matter if the user has provide a fixed input.
            if (precisionMethod == PrecisionMethod.FIXED) {
                PrecisionHistogram histogram = new PrecisionHistogram(title);
                histogram.mean = mean;
                histogram.sigma = sigma;
                return histogram;
            }
            // No precision
            return null;
        }
    }
    // We get here if we can compute precision.
    // Build the histogram 
    StoredDataStatistics precision = new StoredDataStatistics(results.size());
    if (m == PrecisionMethod.STORED) {
        for (PeakResult r : results.getResults()) {
            precision.add(r.getPrecision());
        }
    } else {
        final double nmPerPixel = results.getNmPerPixel();
        final double gain = results.getGain();
        final boolean emCCD = results.isEMCCD();
        for (PeakResult r : results.getResults()) {
            precision.add(r.getPrecision(nmPerPixel, gain, emCCD));
        }
    }
    //System.out.printf("Raw p = %f\n", precision.getMean());
    double yMin = Double.NEGATIVE_INFINITY, yMax = 0;
    // Set the min and max y-values using 1.5 x IQR 
    DescriptiveStatistics stats = precision.getStatistics();
    double lower = stats.getPercentile(25);
    double upper = stats.getPercentile(75);
    if (Double.isNaN(lower) || Double.isNaN(upper)) {
        if (logFitParameters)
            Utils.log("Error computing IQR: %f - %f", lower, upper);
    } else {
        double iqr = upper - lower;
        yMin = FastMath.max(lower - iqr, stats.getMin());
        yMax = FastMath.min(upper + iqr, stats.getMax());
        if (logFitParameters)
            Utils.log("  Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax);
    }
    if (yMin == Double.NEGATIVE_INFINITY) {
        int n = 5;
        yMin = Math.max(stats.getMin(), stats.getMean() - n * stats.getStandardDeviation());
        yMax = Math.min(stats.getMax(), stats.getMean() + n * stats.getStandardDeviation());
        if (logFitParameters)
            Utils.log("  Data range: %f - %f. Plotting mean +/- %dxSD: %f - %f", stats.getMin(), stats.getMax(), n, yMin, yMax);
    }
    // Get the data within the range
    double[] data = precision.getValues();
    precision = new StoredDataStatistics(data.length);
    for (double d : data) {
        if (d < yMin || d > yMax)
            continue;
        precision.add(d);
    }
    int histogramBins = Utils.getBins(precision, Utils.BinMethod.SCOTT);
    float[][] hist = Utils.calcHistogram(precision.getFloatValues(), yMin, yMax, histogramBins);
    PrecisionHistogram histogram = new PrecisionHistogram(hist, precision, title);
    if (precisionMethod == PrecisionMethod.FIXED) {
        histogram.mean = mean;
        histogram.sigma = sigma;
        return histogram;
    }
    // Fitting of the histogram to produce the initial estimate
    // Extract non-zero data
    float[] x = Arrays.copyOf(hist[0], hist[0].length);
    float[] y = hist[1];
    int count = 0;
    float dx = (x[1] - x[0]) * 0.5f;
    for (int i = 0; i < y.length; i++) if (y[i] > 0) {
        x[count] = x[i] + dx;
        y[count] = y[i];
        count++;
    }
    x = Arrays.copyOf(x, count);
    y = Arrays.copyOf(y, count);
    // Sense check to fitted data. Get mean and SD of histogram
    double[] stats2 = Utils.getHistogramStatistics(x, y);
    if (logFitParameters)
        Utils.log("  Initial Statistics: %f +/- %f", stats2[0], stats2[1]);
    histogram.mean = stats2[0];
    histogram.sigma = stats2[1];
    // Standard Gaussian fit
    double[] parameters = fitGaussian(x, y);
    if (parameters == null) {
        Utils.log("  Failed to fit initial Gaussian");
        return histogram;
    }
    double newMean = parameters[1];
    double error = Math.abs(stats2[0] - newMean) / stats2[1];
    if (error > 3) {
        Utils.log("  Failed to fit Gaussian: %f standard deviations from histogram mean", error);
        return histogram;
    }
    if (newMean < yMin || newMean > yMax) {
        Utils.log("  Failed to fit Gaussian: %f outside data range %f - %f", newMean, yMin, yMax);
        return histogram;
    }
    if (logFitParameters)
        Utils.log("  Initial Gaussian: %f @ %f +/- %f", parameters[0], parameters[1], parameters[2]);
    histogram.mean = parameters[1];
    histogram.sigma = parameters[2];
    return histogram;
}

Example 37 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min 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 38 with Min

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

the class BenchmarkSpotFit method showDoubleHistogram.

private double[] showDoubleHistogram(StoredDataStatistics[][] stats, final int i, WindowOrganiser wo, double[][] matchScores, double nPredicted) {
    String xLabel = filterCriteria[i].name;
    LowerLimit lower = filterCriteria[i].lower;
    UpperLimit upper = filterCriteria[i].upper;
    double[] j = null;
    double[] metric = null;
    double maxJ = 0;
    if (i <= FILTER_PRECISION && (showFilterScoreHistograms || upper.requiresJaccard || lower.requiresJaccard)) {
        // Jaccard score verses the range of the metric
        Arrays.sort(matchScores, new Comparator<double[]>() {

            public int compare(double[] o1, double[] o2) {
                if (o1[i] < o2[i])
                    return -1;
                if (o1[i] > o2[i])
                    return 1;
                return 0;
            }
        });
        final int scoreIndex = FILTER_PRECISION + 1;
        int n = results.size();
        double tp = 0;
        double fp = 0;
        j = new double[matchScores.length + 1];
        metric = new double[j.length];
        for (int k = 0; k < matchScores.length; k++) {
            final double score = matchScores[k][scoreIndex];
            tp += score;
            fp += (1 - score);
            j[k + 1] = tp / (fp + n);
            metric[k + 1] = matchScores[k][i];
        }
        metric[0] = metric[1];
        maxJ = Maths.max(j);
        if (showFilterScoreHistograms) {
            String title = TITLE + " Jaccard " + xLabel;
            Plot plot = new Plot(title, xLabel, "Jaccard", metric, j);
            // Remove outliers
            double[] limitsx = Maths.limits(metric);
            Percentile p = new Percentile();
            double l = p.evaluate(metric, 25);
            double u = p.evaluate(metric, 75);
            double iqr = 1.5 * (u - l);
            limitsx[1] = Math.min(limitsx[1], u + iqr);
            plot.setLimits(limitsx[0], limitsx[1], 0, Maths.max(j));
            PlotWindow pw = Utils.display(title, plot);
            if (Utils.isNewWindow())
                wo.add(pw);
        }
    }
    // [0] is all
    // [1] is matches
    // [2] is no match
    StoredDataStatistics s1 = stats[0][i];
    StoredDataStatistics s2 = stats[1][i];
    StoredDataStatistics s3 = stats[2][i];
    if (s1.getN() == 0)
        return new double[4];
    DescriptiveStatistics d = s1.getStatistics();
    double median = 0;
    Plot2 plot = null;
    String title = null;
    if (showFilterScoreHistograms) {
        median = d.getPercentile(50);
        String label = String.format("n = %d. Median = %s nm", s1.getN(), Utils.rounded(median));
        int id = Utils.showHistogram(TITLE, s1, xLabel, filterCriteria[i].minBinWidth, (filterCriteria[i].restrictRange) ? 1 : 0, 0, label);
        if (id == 0) {
            IJ.log("Failed to show the histogram: " + xLabel);
            return new double[4];
        }
        if (Utils.isNewWindow())
            wo.add(id);
        title = WindowManager.getImage(id).getTitle();
        // Reverse engineer the histogram settings
        plot = Utils.plot;
        double[] xValues = Utils.xValues;
        int bins = xValues.length;
        double yMin = xValues[0];
        double binSize = xValues[1] - xValues[0];
        double yMax = xValues[0] + (bins - 1) * binSize;
        if (s2.getN() > 0) {
            double[] values = s2.getValues();
            double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.red);
                plot.addPoints(hist[0], hist[1], Plot2.BAR);
                Utils.display(title, plot);
            }
        }
        if (s3.getN() > 0) {
            double[] values = s3.getValues();
            double[][] hist = Utils.calcHistogram(values, yMin, yMax, bins);
            if (hist[0].length > 0) {
                plot.setColor(Color.blue);
                plot.addPoints(hist[0], hist[1], Plot2.BAR);
                Utils.display(title, plot);
            }
        }
    }
    // Do cumulative histogram
    double[][] h1 = Maths.cumulativeHistogram(s1.getValues(), true);
    double[][] h2 = Maths.cumulativeHistogram(s2.getValues(), true);
    double[][] h3 = Maths.cumulativeHistogram(s3.getValues(), true);
    if (showFilterScoreHistograms) {
        title = TITLE + " Cumul " + xLabel;
        plot = new Plot2(title, xLabel, "Frequency");
        // Find limits
        double[] xlimit = Maths.limits(h1[0]);
        xlimit = Maths.limits(xlimit, h2[0]);
        xlimit = Maths.limits(xlimit, h3[0]);
        // Restrict using the inter-quartile range 
        if (filterCriteria[i].restrictRange) {
            double q1 = d.getPercentile(25);
            double q2 = d.getPercentile(75);
            double iqr = (q2 - q1) * 2.5;
            xlimit[0] = Maths.max(xlimit[0], median - iqr);
            xlimit[1] = Maths.min(xlimit[1], median + iqr);
        }
        plot.setLimits(xlimit[0], xlimit[1], 0, 1.05);
        plot.addPoints(h1[0], h1[1], Plot.LINE);
        plot.setColor(Color.red);
        plot.addPoints(h2[0], h2[1], Plot.LINE);
        plot.setColor(Color.blue);
        plot.addPoints(h3[0], h3[1], Plot.LINE);
    }
    // Determine the maximum difference between the TP and FP
    double maxx1 = 0;
    double maxx2 = 0;
    double max1 = 0;
    double max2 = 0;
    // We cannot compute the delta histogram, or use percentiles
    if (s2.getN() == 0) {
        upper = UpperLimit.ZERO;
        lower = LowerLimit.ZERO;
    }
    final boolean requireLabel = (showFilterScoreHistograms && filterCriteria[i].requireLabel);
    if (requireLabel || upper.requiresDeltaHistogram() || lower.requiresDeltaHistogram()) {
        if (s2.getN() != 0 && s3.getN() != 0) {
            LinearInterpolator li = new LinearInterpolator();
            PolynomialSplineFunction f1 = li.interpolate(h2[0], h2[1]);
            PolynomialSplineFunction f2 = li.interpolate(h3[0], h3[1]);
            for (double x : h1[0]) {
                if (x < h2[0][0] || x < h3[0][0])
                    continue;
                try {
                    double v1 = f1.value(x);
                    double v2 = f2.value(x);
                    double diff = v2 - v1;
                    if (diff > 0) {
                        if (max1 < diff) {
                            max1 = diff;
                            maxx1 = x;
                        }
                    } else {
                        if (max2 > diff) {
                            max2 = diff;
                            maxx2 = x;
                        }
                    }
                } catch (OutOfRangeException e) {
                    // Because we reached the end
                    break;
                }
            }
        } else {
            // Switch to percentiles if we have no delta histogram
            if (upper.requiresDeltaHistogram())
                upper = UpperLimit.NINETY_NINE_PERCENT;
            if (lower.requiresDeltaHistogram())
                lower = LowerLimit.ONE_PERCENT;
        }
    //			System.out.printf("Bounds %s : %s, pos %s, neg %s, %s\n", xLabel, Utils.rounded(getPercentile(h2, 0.01)),
    //					Utils.rounded(maxx1), Utils.rounded(maxx2), Utils.rounded(getPercentile(h1, 0.99)));
    }
    if (showFilterScoreHistograms) {
        // We use bins=1 on charts where we do not need a label
        if (requireLabel) {
            String label = String.format("Max+ %s @ %s, Max- %s @ %s", Utils.rounded(max1), Utils.rounded(maxx1), Utils.rounded(max2), Utils.rounded(maxx2));
            plot.setColor(Color.black);
            plot.addLabel(0, 0, label);
        }
        PlotWindow pw = Utils.display(title, plot);
        if (Utils.isNewWindow())
            wo.add(pw.getImagePlus().getID());
    }
    // Now compute the bounds using the desired limit
    double l, u;
    switch(lower) {
        case ONE_PERCENT:
            l = getPercentile(h2, 0.01);
            break;
        case MAX_NEGATIVE_CUMUL_DELTA:
            l = maxx2;
            break;
        case ZERO:
            l = 0;
            break;
        case HALF_MAX_JACCARD_VALUE:
            l = getValue(metric, j, maxJ * 0.5);
            break;
        default:
            throw new RuntimeException("Missing lower limit method");
    }
    switch(upper) {
        case MAX_POSITIVE_CUMUL_DELTA:
            u = maxx1;
            break;
        case NINETY_NINE_PERCENT:
            u = getPercentile(h2, 0.99);
            break;
        case NINETY_NINE_NINE_PERCENT:
            u = getPercentile(h2, 0.999);
            break;
        case ZERO:
            u = 0;
            break;
        case MAX_JACCARD2:
            u = getValue(metric, j, maxJ) * 2;
            //System.out.printf("MaxJ = %.4f @ %.3f\n", maxJ, u / 2);
            break;
        default:
            throw new RuntimeException("Missing upper limit method");
    }
    double min = getPercentile(h1, 0);
    double max = getPercentile(h1, 1);
    return new double[] { l, u, min, max };
}

Example 39 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min 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 40 with Min

use of org.apache.commons.math3.stat.descriptive.rank.Min 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;
}