Examples with MemoryPeakResults gdsc.smlm.results.MemoryPeakResults used on opensource projects

Example 66 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class FIRE method cropToRoi.

private MemoryPeakResults cropToRoi(MemoryPeakResults results) {
    if (roiBounds == null)
        return results;
    // Adjust bounds relative to input results image
    //Rectangle2D.Float bounds = results.getDataBounds();
    Rectangle bounds = results.getBounds(true);
    double xscale = roiImageWidth / bounds.width;
    double yscale = roiImageHeight / bounds.height;
    float minX = (float) (bounds.x + roiBounds.x / xscale);
    float maxX = (float) (minX + roiBounds.width / xscale);
    float minY = (float) (bounds.y + (roiBounds.y / yscale));
    float maxY = (float) (minY + roiBounds.height / yscale);
    // Create a new set of results within the bounds
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.begin();
    for (PeakResult peakResult : results.getResults()) {
        float x = peakResult.params[Gaussian2DFunction.X_POSITION];
        float y = peakResult.params[Gaussian2DFunction.Y_POSITION];
        if (x < minX || x > maxX || y < minY || y > maxY)
            continue;
        newResults.add(peakResult);
    }
    newResults.end();
    newResults.copySettings(results);
    newResults.setBounds(new Rectangle((int) minX, (int) minY, (int) (maxX - minX), (int) (maxY - minY)));
    return newResults;
}

Example 67 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class FIRE method runQEstimation.

private void runQEstimation() {
    IJ.showStatus(TITLE + " ...");
    if (!showQEstimationInputDialog())
        return;
    MemoryPeakResults results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        return;
    }
    if (results.getCalibration() == null) {
        IJ.error(TITLE, "The results are not calibrated");
        return;
    }
    results = cropToRoi(results);
    if (results.size() < 2) {
        IJ.error(TITLE, "No results within the crop region");
        return;
    }
    initialise(results, null);
    // We need localisation precision.
    // Build a histogram of the localisation precision.
    // Get the initial mean and SD and plot as a Gaussian.
    PrecisionHistogram histogram = calculatePrecisionHistogram();
    if (histogram == null) {
        IJ.error(TITLE, "No localisation precision available.\n \nPlease choose " + PrecisionMethod.FIXED + " and enter a precision mean and SD.");
        return;
    }
    StoredDataStatistics precision = histogram.precision;
    //String name = results.getName();
    double fourierImageScale = SCALE_VALUES[imageScaleIndex];
    int imageSize = IMAGE_SIZE_VALUES[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 ...");
    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");
    FRC frc = new FRC();
    frc.progress = progress;
    frc.setFourierMethod(fourierMethod);
    frc.setSamplingMethod(samplingMethod);
    frc.setPerimeterSamplingFactor(perimeterSamplingFactor);
    FRCCurve frcCurve = frc.calculateFrcCurve(images.ip1, images.ip2, images.nmPerPixel);
    if (frcCurve == null) {
        IJ.error(TITLE, "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
    double qNorm = (1 / frcCurve.mean1 + 1 / frcCurve.mean2);
    double[] frcnum = new double[frcCurve.getSize()];
    for (int i = 0; i < frcnum.length; i++) {
        FRCCurveResult r = frcCurve.get(i);
        frcnum[i] = qNorm * r.getNumerator() / r.getNumberOfSamples();
    }
    // Compute the spatial frequency and the region for curve fitting
    double[] q = FRC.computeQ(frcCurve, false);
    int low = 0, high = q.length;
    while (high > 0 && q[high - 1] > maxQ) high--;
    while (low < q.length && q[low] < minQ) low++;
    // Require we fit at least 10% of the curve
    if (high - low < q.length * 0.1) {
        IJ.error(TITLE, "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
    boolean sampleDecay = precision != null && FIRE.sampleDecay;
    double[] exp_decay;
    if (sampleDecay) {
        // Random sample of precision values from the distribution is used to 
        // construct the decay curve
        int[] sample = Random.sample(10000, precision.getN(), new Well19937c());
        final double four_pi2 = 4 * Math.PI * Math.PI;
        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;
        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] += FastMath.exp(pre[i] * s2);
        }
        for (int i = 1; i < q.length; i++) hq[i] /= n;
        exp_decay = new double[q.length];
        exp_decay[0] = 1;
        for (int i = 1; i < q.length; i++) {
            double sinc_q = sinc(Math.PI * q[i]);
            exp_decay[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
        exp_decay = computeExpDecay(histogram.mean / images.nmPerPixel, histogram.sigma / images.nmPerPixel, q);
    }
    // Smoothing
    double[] smooth;
    if (loessSmoothing) {
        // Note: This computes the log then smooths it 
        double bandwidth = 0.1;
        int robustness = 0;
        double[] l = new double[exp_decay.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] / exp_decay[i]));
        }
        try {
            LoessInterpolator loess = new LoessInterpolator(bandwidth, robustness);
            smooth = loess.smooth(q, l);
        } catch (Exception e) {
            IJ.error(TITLE, "LOESS smoothing failed");
            return;
        }
    } else {
        // Note: This smooths the curve before computing the log 
        double[] norm = new double[exp_decay.length];
        for (int i = 0; i < norm.length; i++) {
            norm[i] = frcnum[i] / exp_decay[i];
        }
        // Median window of 5 == radius of 2
        MedianWindow mw = new MedianWindow(norm, 2);
        smooth = new double[exp_decay.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. 
    Quadratic curve = new Quadratic();
    SimpleCurveFitter fit = SimpleCurveFitter.create(curve, new double[2]);
    WeightedObservedPoints points = new WeightedObservedPoints();
    for (int i = low; i < high; i++) points.add(q[i], smooth[i]);
    double[] estimate = fit.fit(points.toList());
    double qValue = FastMath.exp(estimate[0]);
    //System.out.printf("Initial q-estimate = %s => %.3f\n", Arrays.toString(estimate), qValue);
    // This could be made an option. Just use for debugging
    boolean debug = false;
    if (debug) {
        // Plot the initial fit and the fit curve
        double[] qScaled = FRC.computeQ(frcCurve, true);
        double[] line = new double[q.length];
        for (int i = 0; i < q.length; i++) line[i] = curve.value(q[i], estimate);
        String title = TITLE + " Initial fit";
        Plot2 plot = new Plot2(title, "Spatial Frequency (nm^-1)", "FRC Numerator");
        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);
        Utils.display(title, plot, Utils.NO_TO_FRONT);
    }
    if (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
            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 ...
        SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10);
        PointValuePair p = null;
        MultiPlateauness f = new MultiPlateauness(frcnum, q, low, high);
        double[] initial = new double[] { histogram.mean / images.nmPerPixel, histogram.sigma / images.nmPerPixel, qValue };
        p = findMin(p, opt, f, scale(initial, 0.1));
        p = findMin(p, opt, f, scale(initial, 0.5));
        p = findMin(p, opt, f, initial);
        p = findMin(p, opt, f, scale(initial, 2));
        p = findMin(p, opt, f, scale(initial, 10));
        if (p != null) {
            double[] point = p.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
                double meanSumOfSquares = (precision.getSumOfSquares() / (images.nmPerPixel * images.nmPerPixel)) / precision.getN();
                histogram.mean = images.nmPerPixel * Math.sqrt(meanSumOfSquares - estimate[1] / (4 * Math.PI * Math.PI));
            }
            exp_decay = 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.
        UnivariateOptimizer o = new BrentOptimizer(1e-3, 1e-6);
        Plateauness f = new Plateauness(frcnum, exp_decay, low, high);
        UnivariatePointValuePair p = null;
        p = findMin(p, o, f, qValue, 0.1);
        p = findMin(p, o, f, qValue, 0.2);
        p = findMin(p, o, f, qValue, 0.333);
        p = findMin(p, o, f, qValue, 0.5);
        // Do some Simplex repeats as well
        SimplexOptimizer opt = new SimplexOptimizer(1e-6, 1e-10);
        p = findMin(p, opt, f, qValue * 0.1);
        p = findMin(p, opt, f, qValue * 0.5);
        p = findMin(p, opt, f, qValue);
        p = findMin(p, opt, f, qValue * 2);
        p = findMin(p, opt, f, qValue * 10);
        if (p != null)
            qValue = p.getPoint();
    }
    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, frcCurve, images.nmPerPixel);
    IJ.showStatus(TITLE + " complete");
}

Example 68 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class FreeFilterResults method run.

/*
	 * (non-)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        // Ask user if they want to show the demo filters
        ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
        gd.enableYesNoCancel();
        gd.hideCancelButton();
        gd.addMessage("No results in memory. Show the demo filters?");
        gd.showDialog();
        if (gd.wasOKed())
            logDemoFilters(TITLE);
        return;
    }
    if (!showDialog())
        return;
    results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Filter results
    Filter filter = Filter.fromXML(filterSettings.freeFilter);
    if (filter != null) {
        MemoryPeakResults newResults = filter.filter(results);
        if (newResults.size() > 0) {
            newResults.setName(results.getName() + " Free Filtered");
            MemoryPeakResults.addResults(newResults);
        }
        IJ.showStatus(String.format("Filtered %d results to %d", results.size(), newResults.size()));
    } else {
        IJ.showStatus("ERROR: Unable to create filter");
    }
}

Example 69 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class FIRE method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    extraOptions = Utils.isExtraOptions();
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    // Require some fit results and selected regions
    int size = MemoryPeakResults.countMemorySize();
    if (size == 0) {
        IJ.error(TITLE, "There are no fitting results in memory");
        return;
    }
    if ("q".equals(arg)) {
        TITLE += " Q estimation";
        runQEstimation();
        return;
    }
    IJ.showStatus(TITLE + " ...");
    if (!showInputDialog())
        return;
    MemoryPeakResults results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        return;
    }
    MemoryPeakResults results2 = ResultsManager.loadInputResults(inputOption2, false);
    results = cropToRoi(results);
    if (results.size() < 2) {
        IJ.error(TITLE, "No results within the crop region");
        return;
    }
    if (results2 != null) {
        results2 = cropToRoi(results2);
        if (results2.size() < 2) {
            IJ.error(TITLE, "No results2 within the crop region");
            return;
        }
    }
    initialise(results, results2);
    if (!showDialog())
        return;
    long start = System.currentTimeMillis();
    // Compute FIRE
    String name = results.getName();
    double fourierImageScale = SCALE_VALUES[imageScaleIndex];
    int imageSize = IMAGE_SIZE_VALUES[imageSizeIndex];
    if (this.results2 != null) {
        name += " vs " + results2.getName();
        FireResult result = calculateFireNumber(fourierMethod, samplingMethod, thresholdMethod, fourierImageScale, imageSize);
        if (result != null) {
            logResult(name, result);
            if (showFRCCurve)
                showFrcCurve(name, result, thresholdMethod);
        }
    } else {
        FireResult result = null;
        int repeats = (randomSplit) ? Math.max(1, FIRE.repeats) : 1;
        if (repeats == 1) {
            result = calculateFireNumber(fourierMethod, samplingMethod, thresholdMethod, fourierImageScale, imageSize);
            if (result != null) {
                logResult(name, result);
                if (showFRCCurve)
                    showFrcCurve(name, result, thresholdMethod);
            }
        } else {
            // Multi-thread this ... 			
            int nThreads = Maths.min(repeats, getThreads());
            ExecutorService executor = Executors.newFixedThreadPool(nThreads);
            TurboList<Future<?>> futures = new TurboList<Future<?>>(repeats);
            TurboList<FIREWorker> workers = new TurboList<FIREWorker>(repeats);
            setProgress(repeats);
            IJ.showProgress(0);
            IJ.showStatus(TITLE + " computing ...");
            for (int i = 1; i <= repeats; i++) {
                FIREWorker w = new FIREWorker(i, fourierImageScale, imageSize);
                workers.add(w);
                futures.add(executor.submit(w));
            }
            // Wait for all to finish
            for (int t = futures.size(); t-- > 0; ) {
                try {
                    // The future .get() method will block until completed
                    futures.get(t).get();
                } catch (Exception e) {
                    // This should not happen. 
                    // Ignore it and allow processing to continue (the number of neighbour samples will just be smaller).  
                    e.printStackTrace();
                }
            }
            IJ.showProgress(1);
            executor.shutdown();
            // Show a combined FRC curve plot of all the smoothed curves if we have multiples.
            LUT valuesLUT = LUTHelper.createLUT(LutColour.FIRE_GLOW);
            @SuppressWarnings("unused") LUT // Black at max value
            noSmoothLUT = LUTHelper.createLUT(LutColour.GRAYS).createInvertedLut();
            LUTHelper.DefaultLUTMapper mapper = new LUTHelper.DefaultLUTMapper(0, repeats);
            FrcCurve curve = new FrcCurve();
            Statistics stats = new Statistics();
            WindowOrganiser wo = new WindowOrganiser();
            boolean oom = false;
            for (int i = 0; i < repeats; i++) {
                FIREWorker w = workers.get(i);
                if (w.oom)
                    oom = true;
                if (w.result == null)
                    continue;
                result = w.result;
                if (!Double.isNaN(result.fireNumber))
                    stats.add(result.fireNumber);
                if (showFRCCurveRepeats) {
                    // Output each FRC curve using a suffix.
                    logResult(w.name, result);
                    wo.add(Utils.display(w.plot.getTitle(), w.plot));
                }
                if (showFRCCurve) {
                    int index = mapper.map(i + 1);
                    //@formatter:off
                    curve.add(name, result, thresholdMethod, LUTHelper.getColour(valuesLUT, index), Color.blue, //LUTHelper.getColour(noSmoothLUT, index)
                    null);
                //@formatter:on
                }
            }
            if (result != null) {
                wo.cascade();
                double mean = stats.getMean();
                logResult(name, result, mean, stats);
                if (showFRCCurve) {
                    curve.addResolution(mean);
                    Plot2 plot = curve.getPlot();
                    Utils.display(plot.getTitle(), plot);
                }
            }
            if (oom) {
                //@formatter:off
                IJ.error(TITLE, "ERROR - Parallel computation out-of-memory.\n \n" + TextUtils.wrap("The number of results will be reduced. " + "Please reduce the size of the Fourier image " + "or change the number of threads " + "using the extra options (hold down the 'Shift' " + "key when running the plugin).", 80));
            //@formatter:on
            }
        }
        // Only do this once
        if (showFRCTimeEvolution && result != null && !Double.isNaN(result.fireNumber))
            showFrcTimeEvolution(name, result.fireNumber, thresholdMethod, nmPerPixel / result.getNmPerPixel(), imageSize);
    }
    IJ.showStatus(TITLE + " complete : " + Utils.timeToString(System.currentTimeMillis() - start));
}

Example 70 with MemoryPeakResults

use of gdsc.smlm.results.MemoryPeakResults in project GDSC-SMLM by aherbert.

the class FilterAnalysis method showDialog.

private boolean showDialog(List<MemoryPeakResults> resultsList, boolean fileInput) {
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addHelp(About.HELP_URL);
    int total = 0;
    int tp = 0;
    for (MemoryPeakResults r : resultsList) {
        total += r.size();
        for (PeakResult p : r.getResults()) if (p.origValue != 0)
            tp++;
    }
    gd.addMessage(String.format("%d files, %d results, %d True-Positives", resultsList.size(), total, tp));
    if (!fileInput) {
        gd.addCheckbox("SNR_filter", snrFilter);
        gd.addNumericField("Min_SNR", minSnr, 0);
        gd.addNumericField("Max_SNR", maxSnr, 0);
        gd.addNumericField("Min_Width", minWidth, 2);
        gd.addNumericField("Max_Width", maxWidth, 2);
        gd.addNumericField("Increment_Width", incWidth, 2);
        gd.addCheckbox("Precision_filter", precisionFilter);
        gd.addNumericField("Min_Precision", minPrecision, 0);
        gd.addNumericField("Max_Precision", maxPrecision, 0);
        gd.addCheckbox("Trace_filter", traceFilter);
        gd.addNumericField("Min_distance", minDistance, 2);
        gd.addNumericField("Max_distance", maxDistance, 2);
        gd.addNumericField("Increment_distance", incDistance, 2);
        gd.addNumericField("Min_time", minTime, 0);
        gd.addNumericField("Max_time", maxTime, 0);
        gd.addNumericField("Increment_time", incTime, 0);
        gd.addCheckbox("Hysteresis_SNR_filter", hysteresisSnrFilter);
        gd.addNumericField("Min_SNR_gap", minSnrGap, 0);
        gd.addNumericField("Max_SNR_gap", maxSnrGap, 0);
        gd.addNumericField("Increment_SNR_gap", incSnrGap, 0);
        gd.addCheckbox("Hysteresis_Precision_filter", hysteresisPrecisionFilter);
        gd.addNumericField("Min_Precision_gap", minPrecisionGap, 0);
        gd.addNumericField("Max_Precision_gap", maxPrecisionGap, 0);
        gd.addNumericField("Increment_Precision_gap", incPrecisionGap, 0);
        gd.addCheckbox("Save_filters", saveFilterSets);
    }
    gd.addCheckbox("Show_table", showResultsTable);
    gd.addSlider("Plot_top_n", 0, 20, plotTopN);
    gd.addCheckbox("Calculate_sensitivity", calculateSensitivity);
    gd.addSlider("Delta", 0.01, 1, delta);
    if (!fileInput) {
        // Re-arrange to 2 columns
        if (gd.getLayout() != null) {
            GridBagLayout grid = (GridBagLayout) gd.getLayout();
            Component splitLabel = (Component) gd.getCheckboxes().get(3);
            int xOffset = 0, yOffset = 0;
            int lastY = -1, rowCount = 0;
            for (Component comp : gd.getComponents()) {
                // Check if this should be the second major column
                if (comp == splitLabel) {
                    xOffset += 2;
                    yOffset -= rowCount;
                    rowCount = 0;
                }
                // Reposition the field
                GridBagConstraints c = grid.getConstraints(comp);
                if (lastY != c.gridy)
                    rowCount++;
                lastY = c.gridy;
                c.gridx = c.gridx + xOffset;
                c.gridy = c.gridy + yOffset;
                c.insets.left = c.insets.left + 10 * xOffset;
                c.insets.top = 0;
                c.insets.bottom = 0;
                grid.setConstraints(comp, c);
            }
            if (IJ.isLinux())
                gd.setBackground(new Color(238, 238, 238));
        }
    }
    gd.showDialog();
    if (gd.wasCanceled() || !readDialog(gd, fileInput))
        return false;
    return true;
}