Examples with Line org.apache.commons.math3.geometry.euclidean.twod.Line used on opensource projects

Example 36 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project GDSC-SMLM by aherbert.

the class SpotInspector method run.

@Override
public void run(String arg) {
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog()) {
        return;
    }
    // Load the results
    results = ResultsManager.loadInputResults(settings.inputOption, false, DistanceUnit.PIXEL, null);
    if (MemoryPeakResults.isEmpty(results)) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Check if the original image is open
    ImageSource source = results.getSource();
    if (source == null) {
        IJ.error(TITLE, "Unknown original source image");
        return;
    }
    source = source.getOriginal();
    source.setReadHint(ReadHint.NONSEQUENTIAL);
    if (!source.open()) {
        IJ.error(TITLE, "Cannot open original source image: " + source.toString());
        return;
    }
    final float stdDevMax = getStandardDeviation(results);
    if (stdDevMax < 0) {
        // TODO - Add dialog to get the initial peak width
        IJ.error(TITLE, "Fitting configuration (for initial peak width) is not available");
        return;
    }
    // Rank spots
    rankedResults = new ArrayList<>(results.size());
    // Data for the sorting
    final PrecisionResultProcedure pp;
    if (settings.sortOrderIndex == 1) {
        pp = new PrecisionResultProcedure(results);
        pp.getPrecision();
    } else {
        pp = null;
    }
    // Build procedures to get:
    // Shift = position in pixels - originXY
    final StandardResultProcedure sp;
    if (settings.sortOrderIndex == 9) {
        sp = new StandardResultProcedure(results, DistanceUnit.PIXEL);
        sp.getXyr();
    } else {
        sp = null;
    }
    // SD = gaussian widths only for Gaussian PSFs
    final WidthResultProcedure wp;
    if (settings.sortOrderIndex >= 6 && settings.sortOrderIndex <= 8) {
        wp = new WidthResultProcedure(results, DistanceUnit.PIXEL);
        wp.getWxWy();
    } else {
        wp = null;
    }
    // Amplitude for Gaussian PSFs
    final HeightResultProcedure hp;
    if (settings.sortOrderIndex == 2) {
        hp = new HeightResultProcedure(results, IntensityUnit.PHOTON);
        hp.getH();
    } else {
        hp = null;
    }
    final Counter c = new Counter();
    results.forEach((PeakResultProcedure) result -> {
        final float[] score = getScore(result, c.getAndIncrement(), pp, sp, wp, hp, stdDevMax);
        rankedResults.add(new PeakResultRank(result, score[0], score[1]));
    });
    Collections.sort(rankedResults, PeakResultRank::compare);
    // Prepare results table
    final ImageJTablePeakResults table = new ImageJTablePeakResults(false, results.getName(), true);
    table.copySettings(results);
    table.setTableTitle(TITLE);
    table.setAddCounter(true);
    table.setShowZ(results.is3D());
    // TODO - Add to settings
    table.setShowFittingData(true);
    table.setShowNoiseData(true);
    if (settings.showCalibratedValues) {
        table.setDistanceUnit(DistanceUnit.NM);
        table.setIntensityUnit(IntensityUnit.PHOTON);
    } else {
        table.setDistanceUnit(DistanceUnit.PIXEL);
        table.setIntensityUnit(IntensityUnit.COUNT);
    }
    table.begin();
    // Add a mouse listener to jump to the frame for the clicked line
    textPanel = table.getResultsWindow().getTextPanel();
    // We must ignore old instances of this class from the mouse listeners
    id = currentId.incrementAndGet();
    textPanel.addMouseListener(new MouseAdapter() {

        @Override
        public void mouseClicked(MouseEvent event) {
            SpotInspector.this.mouseClicked(event);
        }
    });
    // Add results to the table
    int count = 0;
    for (final PeakResultRank rank : rankedResults) {
        rank.rank = count++;
        table.add(rank.peakResult);
    }
    table.end();
    if (settings.plotScore || settings.plotHistogram) {
        // Get values for the plots
        float[] xValues = null;
        float[] yValues = null;
        double yMin;
        double yMax;
        int spotNumber = 0;
        xValues = new float[rankedResults.size()];
        yValues = new float[xValues.length];
        for (final PeakResultRank rank : rankedResults) {
            xValues[spotNumber] = spotNumber + 1;
            yValues[spotNumber++] = recoverScore(rank.score);
        }
        // Set the min and max y-values using 1.5 x IQR
        final DescriptiveStatistics stats = new DescriptiveStatistics();
        for (final float v : yValues) {
            stats.addValue(v);
        }
        if (settings.removeOutliers) {
            final double lower = stats.getPercentile(25);
            final double upper = stats.getPercentile(75);
            final double iqr = upper - lower;
            yMin = Math.max(lower - iqr, stats.getMin());
            yMax = Math.min(upper + iqr, stats.getMax());
            IJ.log(String.format("Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax));
        } else {
            yMin = stats.getMin();
            yMax = stats.getMax();
            IJ.log(String.format("Data range: %f - %f", yMin, yMax));
        }
        plotScore(xValues, yValues, yMin, yMax);
        plotHistogram(yValues);
    }
    // Extract spots into a stack
    final int w = source.getWidth();
    final int h = source.getHeight();
    final int size = 2 * settings.radius + 1;
    final ImageStack spots = new ImageStack(size, size, rankedResults.size());
    // To assist the extraction of data from the image source, process them in time order to allow
    // frame caching. Then set the appropriate slice in the result stack
    Collections.sort(rankedResults, (o1, o2) -> Integer.compare(o1.peakResult.getFrame(), o2.peakResult.getFrame()));
    for (final PeakResultRank rank : rankedResults) {
        final PeakResult r = rank.peakResult;
        // Extract image
        // Note that the coordinates are relative to the middle of the pixel (0.5 offset)
        // so do not round but simply convert to int
        final int x = (int) (r.getXPosition());
        final int y = (int) (r.getYPosition());
        // Extract a region but crop to the image bounds
        int minX = x - settings.radius;
        int minY = y - settings.radius;
        final int maxX = Math.min(x + settings.radius + 1, w);
        final int maxY = Math.min(y + settings.radius + 1, h);
        int padX = 0;
        int padY = 0;
        if (minX < 0) {
            padX = -minX;
            minX = 0;
        }
        if (minY < 0) {
            padY = -minY;
            minY = 0;
        }
        final int sizeX = maxX - minX;
        final int sizeY = maxY - minY;
        float[] data = source.get(r.getFrame(), new Rectangle(minX, minY, sizeX, sizeY));
        // Prevent errors with missing data
        if (data == null) {
            data = new float[sizeX * sizeY];
        }
        ImageProcessor spotIp = new FloatProcessor(sizeX, sizeY, data, null);
        // Pad if necessary, i.e. the crop is too small for the stack
        if (padX > 0 || padY > 0 || sizeX < size || sizeY < size) {
            final ImageProcessor spotIp2 = spotIp.createProcessor(size, size);
            spotIp2.insert(spotIp, padX, padY);
            spotIp = spotIp2;
        }
        final int slice = rank.rank + 1;
        spots.setPixels(spotIp.getPixels(), slice);
        spots.setSliceLabel(MathUtils.rounded(rank.originalScore), slice);
    }
    source.close();
    // Reset to the rank order
    Collections.sort(rankedResults, PeakResultRank::compare);
    final ImagePlus imp = ImageJUtils.display(TITLE, spots);
    imp.setRoi((PointRoi) null);
    // Make bigger
    for (int i = 10; i-- > 0; ) {
        imp.getWindow().getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
    }
}

Example 37 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project GDSC-SMLM by aherbert.

the class Fire method runQEstimation.

@SuppressWarnings("null")
private void runQEstimation() {
    IJ.showStatus(pluginTitle + " ...");
    if (!showQEstimationInputDialog()) {
        return;
    }
    MemoryPeakResults inputResults = ResultsManager.loadInputResults(settings.inputOption, false, null, null);
    if (MemoryPeakResults.isEmpty(inputResults)) {
        IJ.error(pluginTitle, "No results could be loaded");
        return;
    }
    if (inputResults.getCalibration() == null) {
        IJ.error(pluginTitle, "The results are not calibrated");
        return;
    }
    inputResults = cropToRoi(inputResults);
    if (inputResults.size() < 2) {
        IJ.error(pluginTitle, "No results within the crop region");
        return;
    }
    initialise(inputResults, null);
    // We need localisation precision.
    // Build a histogram of the localisation precision.
    // Get the initial mean and SD and plot as a Gaussian.
    final PrecisionHistogram histogram = calculatePrecisionHistogram();
    if (histogram == null) {
        IJ.error(pluginTitle, "No localisation precision available.\n \nPlease choose " + PrecisionMethod.FIXED + " and enter a precision mean and SD.");
        return;
    }
    final StoredDataStatistics precision = histogram.precision;
    final double fourierImageScale = Settings.scaleValues[settings.imageScaleIndex];
    final int imageSize = Settings.imageSizeValues[settings.imageSizeIndex];
    // Create the image and compute the numerator of FRC.
    // Do not use the signal so results.size() is the number of localisations.
    IJ.showStatus("Computing FRC curve ...");
    final FireImages images = createImages(fourierImageScale, imageSize, false);
    // DEBUGGING - Save the two images to disk. Load the images into the Matlab
    // code that calculates the Q-estimation and make this plugin match the functionality.
    // IJ.save(new ImagePlus("i1", images.ip1), "/scratch/i1.tif");
    // IJ.save(new ImagePlus("i2", images.ip2), "/scratch/i2.tif");
    final Frc frc = new Frc();
    frc.setTrackProgress(progress);
    frc.setFourierMethod(fourierMethod);
    frc.setSamplingMethod(samplingMethod);
    frc.setPerimeterSamplingFactor(settings.perimeterSamplingFactor);
    final FrcCurve frcCurve = frc.calculateFrcCurve(images.ip1, images.ip2, images.nmPerPixel);
    if (frcCurve == null) {
        IJ.error(pluginTitle, "Failed to compute FRC curve");
        return;
    }
    IJ.showStatus("Running Q-estimation ...");
    // Note:
    // The method implemented here is based on Matlab code provided by Bernd Rieger.
    // The idea is to compute the spurious correlation component of the FRC Numerator
    // using an initial estimate of distribution of the localisation precision (assumed
    // to be Gaussian). This component is the contribution of repeat localisations of
    // the same molecule to the numerator and is modelled as an exponential decay
    // (exp_decay). The component is scaled by the Q-value which
    // is the average number of times a molecule is seen in addition to the first time.
    // At large spatial frequencies the scaled component should match the numerator,
    // i.e. at high resolution (low FIRE number) the numerator is made up of repeat
    // localisations of the same molecule and not actual structure in the image.
    // The best fit is where the numerator equals the scaled component, i.e. num / (q*exp_decay) ==
    // 1.
    // The FRC Numerator is plotted and Q can be determined by
    // adjusting Q and the precision mean and SD to maximise the cost function.
    // This can be done interactively by the user with the effect on the FRC curve
    // dynamically updated and displayed.
    // Compute the scaled FRC numerator
    final double qNorm = (1 / frcCurve.mean1 + 1 / frcCurve.mean2);
    final double[] frcnum = new double[frcCurve.getSize()];
    for (int i = 0; i < frcnum.length; i++) {
        final FrcCurveResult r = frcCurve.get(i);
        frcnum[i] = qNorm * r.getNumerator() / r.getNumberOfSamples();
    }
    // Compute the spatial frequency and the region for curve fitting
    final double[] q = Frc.computeQ(frcCurve, false);
    int low = 0;
    int high = q.length;
    while (high > 0 && q[high - 1] > settings.maxQ) {
        high--;
    }
    while (low < q.length && q[low] < settings.minQ) {
        low++;
    }
    // Require we fit at least 10% of the curve
    if (high - low < q.length * 0.1) {
        IJ.error(pluginTitle, "Not enough points for Q estimation");
        return;
    }
    // Obtain initial estimate of Q plateau height and decay.
    // This can be done by fitting the precision histogram and then fixing the mean and sigma.
    // Or it can be done by allowing the precision to be sampled and the mean and sigma
    // become parameters for fitting.
    // Check if we can sample precision values
    final boolean sampleDecay = precision != null && settings.sampleDecay;
    double[] expDecay;
    if (sampleDecay) {
        // Random sample of precision values from the distribution is used to
        // construct the decay curve
        final int[] sample = RandomUtils.sample(10000, precision.getN(), UniformRandomProviders.create());
        final double four_pi2 = 4 * Math.PI * Math.PI;
        final double[] pre = new double[q.length];
        for (int i = 1; i < q.length; i++) {
            pre[i] = -four_pi2 * q[i] * q[i];
        }
        // Sample
        final int n = sample.length;
        final double[] hq = new double[n];
        for (int j = 0; j < n; j++) {
            // Scale to SR pixels
            double s2 = precision.getValue(sample[j]) / images.nmPerPixel;
            s2 *= s2;
            for (int i = 1; i < q.length; i++) {
                hq[i] += StdMath.exp(pre[i] * s2);
            }
        }
        for (int i = 1; i < q.length; i++) {
            hq[i] /= n;
        }
        expDecay = new double[q.length];
        expDecay[0] = 1;
        for (int i = 1; i < q.length; i++) {
            final double sinc_q = sinc(Math.PI * q[i]);
            expDecay[i] = sinc_q * sinc_q * hq[i];
        }
    } else {
        // Note: The sigma mean and std should be in the units of super-resolution
        // pixels so scale to SR pixels
        expDecay = computeExpDecay(histogram.mean / images.nmPerPixel, histogram.sigma / images.nmPerPixel, q);
    }
    // Smoothing
    double[] smooth;
    if (settings.loessSmoothing) {
        // Note: This computes the log then smooths it
        final double bandwidth = 0.1;
        final int robustness = 0;
        final double[] l = new double[expDecay.length];
        for (int i = 0; i < l.length; i++) {
            // Original Matlab code computes the log for each array.
            // This is equivalent to a single log on the fraction of the two.
            // Perhaps the two log method is more numerically stable.
            // l[i] = Math.log(Math.abs(frcnum[i])) - Math.log(exp_decay[i]);
            l[i] = Math.log(Math.abs(frcnum[i] / expDecay[i]));
        }
        try {
            final LoessInterpolator loess = new LoessInterpolator(bandwidth, robustness);
            smooth = loess.smooth(q, l);
        } catch (final Exception ex) {
            IJ.error(pluginTitle, "LOESS smoothing failed");
            return;
        }
    } else {
        // Note: This smooths the curve before computing the log
        final double[] norm = new double[expDecay.length];
        for (int i = 0; i < norm.length; i++) {
            norm[i] = frcnum[i] / expDecay[i];
        }
        // Median window of 5 == radius of 2
        final DoubleMedianWindow mw = DoubleMedianWindow.wrap(norm, 2);
        smooth = new double[expDecay.length];
        for (int i = 0; i < norm.length; i++) {
            smooth[i] = Math.log(Math.abs(mw.getMedian()));
            mw.increment();
        }
    }
    // Fit with quadratic to find the initial guess.
    // Note: example Matlab code frc_Qcorrection7.m identifies regions of the
    // smoothed log curve with low derivative and only fits those. The fit is
    // used for the final estimate. Fitting a subset with low derivative is not
    // implemented here since the initial estimate is subsequently optimised
    // to maximise a cost function.
    final Quadratic curve = new Quadratic();
    final SimpleCurveFitter fit = SimpleCurveFitter.create(curve, new double[2]);
    final WeightedObservedPoints points = new WeightedObservedPoints();
    for (int i = low; i < high; i++) {
        points.add(q[i], smooth[i]);
    }
    final double[] estimate = fit.fit(points.toList());
    double qvalue = StdMath.exp(estimate[0]);
    // This could be made an option. Just use for debugging
    final boolean debug = false;
    if (debug) {
        // Plot the initial fit and the fit curve
        final double[] qScaled = Frc.computeQ(frcCurve, true);
        final double[] line = new double[q.length];
        for (int i = 0; i < q.length; i++) {
            line[i] = curve.value(q[i], estimate);
        }
        final String title = pluginTitle + " Initial fit";
        final Plot plot = new Plot(title, "Spatial Frequency (nm^-1)", "FRC Numerator");
        final String label = String.format("Q = %.3f", qvalue);
        plot.addPoints(qScaled, smooth, Plot.LINE);
        plot.setColor(Color.red);
        plot.addPoints(qScaled, line, Plot.LINE);
        plot.setColor(Color.black);
        plot.addLabel(0, 0, label);
        ImageJUtils.display(title, plot, ImageJUtils.NO_TO_FRONT);
    }
    if (settings.fitPrecision) {
        // Q - Should this be optional?
        if (sampleDecay) {
            // If a sample of the precision was used to construct the data for the initial fit
            // then update the estimate using the fit result since it will be a better start point.
            histogram.sigma = precision.getStandardDeviation();
            // Normalise sum-of-squares to the SR pixel size
            final double meanSumOfSquares = (precision.getSumOfSquares() / (images.nmPerPixel * images.nmPerPixel)) / precision.getN();
            histogram.mean = images.nmPerPixel * Math.sqrt(meanSumOfSquares - estimate[1] / (4 * Math.PI * Math.PI));
        }
        // Do a multivariate fit ...
        final SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10);
        PointValuePair pair = null;
        final MultiPlateauness f = new MultiPlateauness(frcnum, q, low, high);
        final double[] initial = new double[] { histogram.mean / images.nmPerPixel, histogram.sigma / images.nmPerPixel, qvalue };
        pair = findMin(pair, opt, f, scale(initial, 0.1));
        pair = findMin(pair, opt, f, scale(initial, 0.5));
        pair = findMin(pair, opt, f, initial);
        pair = findMin(pair, opt, f, scale(initial, 2));
        pair = findMin(pair, opt, f, scale(initial, 10));
        if (pair != null) {
            final double[] point = pair.getPointRef();
            histogram.mean = point[0] * images.nmPerPixel;
            histogram.sigma = point[1] * images.nmPerPixel;
            qvalue = point[2];
        }
    } else {
        // If so then this should be optional.
        if (sampleDecay) {
            if (precisionMethod != PrecisionMethod.FIXED) {
                histogram.sigma = precision.getStandardDeviation();
                // Normalise sum-of-squares to the SR pixel size
                final double meanSumOfSquares = (precision.getSumOfSquares() / (images.nmPerPixel * images.nmPerPixel)) / precision.getN();
                histogram.mean = images.nmPerPixel * Math.sqrt(meanSumOfSquares - estimate[1] / (4 * Math.PI * Math.PI));
            }
            expDecay = computeExpDecay(histogram.mean / images.nmPerPixel, histogram.sigma / images.nmPerPixel, q);
        }
        // Estimate spurious component by promoting plateauness.
        // The Matlab code used random initial points for a Simplex optimiser.
        // A Brent line search should be pretty deterministic so do simple repeats.
        // However it will proceed downhill so if the initial point is wrong then
        // it will find a sub-optimal result.
        final UnivariateOptimizer o = new BrentOptimizer(1e-3, 1e-6);
        final Plateauness f = new Plateauness(frcnum, expDecay, low, high);
        UnivariatePointValuePair result = null;
        result = findMin(result, o, f, qvalue, 0.1);
        result = findMin(result, o, f, qvalue, 0.2);
        result = findMin(result, o, f, qvalue, 0.333);
        result = findMin(result, o, f, qvalue, 0.5);
        // Do some Simplex repeats as well
        final SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10);
        result = findMin(result, opt, f, qvalue * 0.1);
        result = findMin(result, opt, f, qvalue * 0.5);
        result = findMin(result, opt, f, qvalue);
        result = findMin(result, opt, f, qvalue * 2);
        result = findMin(result, opt, f, qvalue * 10);
        if (result != null) {
            qvalue = result.getPoint();
        }
    }
    final QPlot qplot = new QPlot(frcCurve, qvalue, low, high);
    // Interactive dialog to estimate Q (blinking events per flourophore) using
    // sliders for the mean and standard deviation of the localisation precision.
    showQEstimationDialog(histogram, qplot, images.nmPerPixel);
    IJ.showStatus(pluginTitle + " complete");
}

Example 38 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project GDSC-SMLM by aherbert.

the class PsfDrift method showHwhm.

private void showHwhm() {
    // Build a list of suitable images
    final List<String> titles = createImageList(false);
    if (titles.isEmpty()) {
        IJ.error(TITLE, "No suitable PSF images");
        return;
    }
    final GenericDialog gd = new GenericDialog(TITLE);
    gd.addMessage("Approximate the volume of the PSF as a Gaussian and\n" + "compute the equivalent Gaussian width.");
    settings = Settings.load();
    gd.addChoice("PSF", titles.toArray(new String[0]), settings.title);
    gd.addCheckbox("Use_offset", settings.useOffset);
    gd.addSlider("Smoothing", 0, 0.5, settings.smoothing);
    gd.addHelp(HelpUrls.getUrl("psf-hwhm"));
    gd.showDialog();
    if (gd.wasCanceled()) {
        return;
    }
    settings.title = gd.getNextChoice();
    settings.useOffset = gd.getNextBoolean();
    settings.smoothing = gd.getNextNumber();
    settings.save();
    imp = WindowManager.getImage(settings.title);
    if (imp == null) {
        IJ.error(TITLE, "No PSF image for image: " + settings.title);
        return;
    }
    psfSettings = getPsfSettings(imp);
    if (psfSettings == null) {
        IJ.error(TITLE, "No PSF settings for image: " + settings.title);
        return;
    }
    final int size = imp.getStackSize();
    final ImagePsfModel psf = createImagePsf(1, size, 1);
    final double[] w0 = psf.getAllHwhm0();
    final double[] w1 = psf.getAllHwhm1();
    // Get current centre
    final int centre = psfSettings.getCentreImage();
    // Extract valid values (some can be NaN)
    double[] sw0 = new double[w0.length];
    double[] sw1 = new double[w1.length];
    final TDoubleArrayList s0 = new TDoubleArrayList(w0.length);
    final TDoubleArrayList s1 = new TDoubleArrayList(w0.length);
    int c0 = 0;
    int c1 = 0;
    for (int i = 0; i < w0.length; i++) {
        if (Double.isFinite(w0[i])) {
            s0.add(i + 1);
            sw0[c0++] = w0[i];
        }
        if (Double.isFinite(w1[i])) {
            s1.add(i + 1);
            sw1[c1++] = w1[i];
        }
    }
    if (c0 == 0 && c1 == 0) {
        IJ.error(TITLE, "No computed HWHM for image: " + settings.title);
        return;
    }
    double[] slice0 = s0.toArray();
    sw0 = Arrays.copyOf(sw0, c0);
    double[] slice1 = s1.toArray();
    sw1 = Arrays.copyOf(sw1, c1);
    // Smooth
    if (settings.smoothing > 0) {
        final LoessInterpolator loess = new LoessInterpolator(settings.smoothing, 1);
        sw0 = loess.smooth(slice0, sw0);
        sw1 = loess.smooth(slice1, sw1);
    }
    final TDoubleArrayList minWx = new TDoubleArrayList();
    final TDoubleArrayList minWy = new TDoubleArrayList();
    for (int i = 0; i < w0.length; i++) {
        double weight = 0;
        if (Double.isFinite(w0[i])) {
            if (Double.isFinite(w1[i])) {
                weight = w0[i] * w1[i];
            } else {
                weight = w0[i] * w0[i];
            }
        } else if (Double.isFinite(w1[i])) {
            weight = w1[i] * w1[i];
        }
        if (weight != 0) {
            minWx.add(i + 1);
            minWy.add(Math.sqrt(weight));
        }
    }
    // Smooth the combined line
    final double[] cx = minWx.toArray();
    double[] cy = minWy.toArray();
    if (settings.smoothing > 0) {
        final LoessInterpolator loess = new LoessInterpolator(settings.smoothing, 1);
        cy = loess.smooth(cx, cy);
    }
    final int newCentre = SimpleArrayUtils.findMinIndex(cy);
    // Convert to FWHM
    final double fwhm = psfSettings.getFwhm();
    // Widths are in pixels
    final String title = TITLE + " HWHM";
    final Plot plot = new Plot(title, "Slice", "HWHM (px)");
    double[] limits = MathUtils.limits(sw0);
    limits = MathUtils.limits(limits, sw1);
    final double maxY = limits[1] * 1.05;
    plot.setLimits(1, size, 0, maxY);
    plot.setColor(Color.red);
    plot.addPoints(slice0, sw0, Plot.LINE);
    plot.setColor(Color.blue);
    plot.addPoints(slice1, sw1, Plot.LINE);
    plot.setColor(Color.magenta);
    plot.addPoints(cx, cy, Plot.LINE);
    plot.setColor(Color.black);
    plot.addLabel(0, 0, "X=red; Y=blue, Combined=Magenta");
    final PlotWindow pw = ImageJUtils.display(title, plot);
    // Show a non-blocking dialog to allow the centre to be updated ...
    // Add a label and dynamically update when the centre is moved.
    final NonBlockingExtendedGenericDialog gd2 = new NonBlockingExtendedGenericDialog(TITLE);
    final double scale = psfSettings.getPixelSize();
    // @formatter:off
    ImageJUtils.addMessage(gd2, "Update the PSF information?\n \n" + "Current z-centre = %d, FHWM = %s px (%s nm)\n", centre, MathUtils.rounded(fwhm), MathUtils.rounded(fwhm * scale));
    // @formatter:on
    gd2.addSlider("z-centre", cx[0], cx[cx.length - 1], newCentre);
    final TextField tf = gd2.getLastTextField();
    gd2.addMessage("");
    gd2.addAndGetButton("Reset", event -> tf.setText(Integer.toString(newCentre)));
    final Label label = gd2.getLastLabel();
    gd2.addCheckbox("Update_centre", settings.updateCentre);
    gd2.addCheckbox("Update_HWHM", settings.updateHwhm);
    gd2.enableYesNoCancel();
    gd2.hideCancelButton();
    final UpdateDialogListener dl = new UpdateDialogListener(cx, cy, maxY, newCentre, scale, pw, label);
    gd2.addDialogListener(dl);
    gd.addHelp(HelpUrls.getUrl("psf-hwhm"));
    gd2.showDialog();
    if (gd2.wasOKed() && (settings.updateCentre || settings.updateHwhm)) {
        final ImagePSF.Builder b = psfSettings.toBuilder();
        if (settings.updateCentre) {
            b.setCentreImage(dl.centre);
        }
        if (settings.updateHwhm) {
            b.setFwhm(dl.getFwhm());
        }
        imp.setProperty("Info", ImagePsfHelper.toString(b));
    }
}

Example 39 with Line

use of org.apache.commons.math3.geometry.euclidean.twod.Line in project recordinality by cscotta.

the class RecordinalityTest method buildRun.

private Callable<Result> buildRun(final int kSize, final int numRuns, final List<String> lines) {
    return new Callable<Result>() {

        public Result call() throws Exception {
            long start = System.currentTimeMillis();
            final double[] results = new double[numRuns];
            for (int i = 0; i < numRuns; i++) {
                Recordinality rec = new Recordinality(kSize);
                for (String line : lines) rec.observe(line);
                results[i] = rec.estimateCardinality();
            }
            double mean = new Mean().evaluate(results);
            double stdDev = new StandardDeviation().evaluate(results);
            double stdError = stdDev / 3193;
            long runTime = System.currentTimeMillis() - start;
            return new Result(kSize, mean, stdError, runTime);
        }
    };
}