Examples with Nullable uk.ac.sussex.gdsc.core.annotation.Nullable used on opensource projects

Example 41 with Nullable

use of uk.ac.sussex.gdsc.core.annotation.Nullable in project gdsc-smlm by aherbert.

the class BenchmarkFilterAnalysis method depthAnalysis.

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

Example 42 with Nullable

use of uk.ac.sussex.gdsc.core.annotation.Nullable in project GDSC-SMLM by aherbert.

the class CameraModelFisherInformationAnalysis method createExponents.

@Nullable
private double[] createExponents() {
    final int n = 1 + Math.max(0, settings.getSubDivisions());
    final double h = 1.0 / n;
    final double minExp = settings.getMinExponent();
    final double maxExp = settings.getMaxExponent();
    final double size = (maxExp - minExp) * n + 1;
    if (size > 100) {
        final ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
        gd.addMessage("Number of exponents is " + Math.ceil(size) + ". OK to continue?");
        gd.showDialog();
        if (gd.wasCanceled()) {
            return null;
        }
    }
    final TDoubleArrayList list = new TDoubleArrayList();
    for (int i = 0; ; i++) {
        final double e = minExp + i * h;
        list.add(e);
        if (e >= settings.getMaxExponent()) {
            break;
        }
    }
    return list.toArray();
}

Example 43 with Nullable

use of uk.ac.sussex.gdsc.core.annotation.Nullable in project GDSC-SMLM by aherbert.

the class BlinkEstimator method fit.

/**
 * Fit the dark time to counts of molecules curve. Only use the first n fitted points.
 *
 * <p>Calculates:<br> N = The number of photoblinking molecules in the sample<br> nBlink = The
 * average number of blinks per flourophore<br> tOff = The off-time
 *
 * @param td The dark time
 * @param ntd The counts of molecules
 * @param numberOfFittedPointsSetting the number of fitted points
 * @param log Write the fitting results to the ImageJ log window
 * @return The fitted parameters [N, nBlink, tOff], or null if no fit was possible
 */
@Nullable
public double[] fit(double[] td, double[] ntd, int numberOfFittedPointsSetting, boolean log) {
    blinkingModel = new BlinkingFunction();
    blinkingModel.setLogging(true);
    for (int i = 0; i < numberOfFittedPointsSetting; i++) {
        blinkingModel.addPoint(td[i], ntd[i]);
    }
    final LevenbergMarquardtOptimizer optimiser = new LevenbergMarquardtOptimizer(INITIAL_STEP_BOUND_FACTOR, COST_RELATIVE_TOLERANCE, PAR_RELATIVE_TOLERANCE, ORTHO_TOLERANCE, THRESHOLD);
    try {
        final double[] obs = blinkingModel.getY();
        // @formatter:off
        final LeastSquaresProblem problem = new LeastSquaresBuilder().maxEvaluations(Integer.MAX_VALUE).maxIterations(1000).start(new double[] { ntd[0], 0.1, td[1] }).target(obs).weight(new DiagonalMatrix(blinkingModel.getWeights())).model(blinkingModel, blinkingModel::jacobian).build();
        // @formatter:on
        blinkingModel.setLogging(false);
        final Optimum optimum = optimiser.optimize(problem);
        final double[] parameters = optimum.getPoint().toArray();
        double mean = 0;
        for (final double d : obs) {
            mean += d;
        }
        mean /= obs.length;
        double ssResiduals = 0;
        double ssTotal = 0;
        for (int i = 0; i < obs.length; i++) {
            ssTotal += (obs[i] - mean) * (obs[i] - mean);
        }
        // This is true if the weights are 1
        ssResiduals = optimum.getResiduals().dotProduct(optimum.getResiduals());
        r2 = 1 - ssResiduals / ssTotal;
        adjustedR2 = getAdjustedCoefficientOfDetermination(ssResiduals, ssTotal, obs.length, parameters.length);
        if (log) {
            ImageJUtils.log("  Fit %d points. R^2 = %s. Adjusted R^2 = %s", obs.length, MathUtils.rounded(r2, 4), MathUtils.rounded(adjustedR2, 4));
            ImageJUtils.log("  N=%s, nBlink=%s, tOff=%s (%s frames)", MathUtils.rounded(parameters[0], 4), MathUtils.rounded(parameters[1], 4), MathUtils.rounded(parameters[2], 4), MathUtils.rounded(parameters[2] / msPerFrame, 4));
        }
        return parameters;
    } catch (final TooManyIterationsException ex) {
        if (log) {
            ImageJUtils.log("  Failed to fit %d points: Too many iterations: (%s)", blinkingModel.size(), ex.getMessage());
        }
    } catch (final ConvergenceException ex) {
        if (log) {
            ImageJUtils.log("  Failed to fit %d points", blinkingModel.size());
        }
    }
    return null;
}

Example 44 with Nullable

use of uk.ac.sussex.gdsc.core.annotation.Nullable in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method scoreAnalysis.

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

Example 45 with Nullable

use of uk.ac.sussex.gdsc.core.annotation.Nullable in project GDSC-SMLM by aherbert.

the class BenchmarkFilterAnalysis method scoreFilters.

@Nullable
private FilterScoreResult[] scoreFilters(FilterSet filterSet, boolean createTextResult) {
    if (filterSet.size() == 0) {
        return null;
    }
    initialiseScoring(filterSet);
    FilterScoreResult[] scoreResults = new FilterScoreResult[filterSet.size()];
    if (scoreResults.length == 1) {
        // No need to multi-thread this
        scoreResults[0] = scoreFilter((DirectFilter) filterSet.getFilters().get(0), defaultMinimalFilter, createTextResult, coordinateStore);
    } else {
        // Multi-thread score all the result
        final int nThreads = getThreads(scoreResults.length);
        final BlockingQueue<ScoreJob> jobs = new ArrayBlockingQueue<>(nThreads * 2);
        final List<Thread> threads = new LinkedList<>();
        final Ticker ticker = ImageJUtils.createTicker(scoreResults.length, nThreads, "Scoring Filters");
        for (int i = 0; i < nThreads; i++) {
            final ScoreWorker worker = new ScoreWorker(jobs, scoreResults, createTextResult, (coordinateStore == null) ? null : coordinateStore.newInstance(), ticker);
            final Thread t = new Thread(worker);
            threads.add(t);
            t.start();
        }
        int index = 0;
        for (final Filter filter : filterSet.getFilters()) {
            if (IJ.escapePressed()) {
                break;
            }
            put(jobs, new ScoreJob((DirectFilter) filter, index++));
        }
        // Finish all the worker threads by passing in a null job
        for (int i = 0; i < threads.size(); i++) {
            put(jobs, new ScoreJob(null, -1));
        }
        // Wait for all to finish
        for (int i = 0; i < threads.size(); i++) {
            try {
                threads.get(i).join();
            } catch (final InterruptedException ex) {
                Logger.getLogger(BenchmarkFilterAnalysis.class.getName()).log(Level.WARNING, "Interrupted!", ex);
                Thread.currentThread().interrupt();
                throw new ConcurrentRuntimeException("Unexpected interruption", ex);
            }
        }
        threads.clear();
        ImageJUtils.finished();
        // In case the threads were interrupted
        if (ImageJUtils.isInterrupted()) {
            scoreResults = null;
        }
    }
    finishScoring();
    return scoreResults;
}