Examples with WindowOrganiser uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser used on opensource projects

Example 21 with WindowOrganiser

use of uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class BenchmarkFit method runFit.

private void runFit() {
    // Initialise the answer.
    answer[Gaussian2DFunction.BACKGROUND] = benchmarkParameters.getBackground();
    answer[Gaussian2DFunction.SIGNAL] = benchmarkParameters.getSignal();
    answer[Gaussian2DFunction.X_POSITION] = benchmarkParameters.x;
    answer[Gaussian2DFunction.Y_POSITION] = benchmarkParameters.y;
    answer[Gaussian2DFunction.Z_POSITION] = benchmarkParameters.z;
    answer[Gaussian2DFunction.X_SD] = benchmarkParameters.sd / benchmarkParameters.pixelPitch;
    answer[Gaussian2DFunction.Y_SD] = benchmarkParameters.sd / benchmarkParameters.pixelPitch;
    // Set up the fit region. Always round down since 0.5 is the centre of the pixel.
    final int x = (int) benchmarkParameters.x;
    final int y = (int) benchmarkParameters.y;
    region = new Rectangle(x - regionSize, y - regionSize, 2 * regionSize + 1, 2 * regionSize + 1);
    if (!new Rectangle(0, 0, imp.getWidth(), imp.getHeight()).contains(region)) {
        // Check if it is incorrect by only 1 pixel
        if (region.width <= imp.getWidth() + 1 && region.height <= imp.getHeight() + 1) {
            ImageJUtils.log("Adjusting region %s to fit within image bounds (%dx%d)", region.toString(), imp.getWidth(), imp.getHeight());
            region = new Rectangle(0, 0, imp.getWidth(), imp.getHeight());
        } else {
            IJ.error(TITLE, "Fit region does not fit within the image");
            return;
        }
    }
    // Adjust the centre & account for 0.5 pixel offset during fitting
    answer[Gaussian2DFunction.X_POSITION] -= (region.x + 0.5);
    answer[Gaussian2DFunction.Y_POSITION] -= (region.y + 0.5);
    // Configure for fitting
    fitConfig.setBackgroundFitting(backgroundFitting);
    fitConfig.setNotSignalFitting(!signalFitting);
    fitConfig.setComputeDeviations(false);
    // Create the camera model
    CameraModel cameraModel = fitConfig.getCameraModel();
    // Crop for speed. Reset origin first so the region is within the model
    cameraModel.setOrigin(0, 0);
    cameraModel = cameraModel.crop(region, false);
    final ImageStack stack = imp.getImageStack();
    final int totalFrames = benchmarkParameters.frames;
    // Create a pool of workers
    final int nThreads = Prefs.getThreads();
    final BlockingQueue<Integer> jobs = new ArrayBlockingQueue<>(nThreads * 2);
    final List<Worker> workers = new LinkedList<>();
    final List<Thread> threads = new LinkedList<>();
    final Ticker ticker = ImageJUtils.createTicker(totalFrames, nThreads, "Fitting frames ...");
    for (int i = 0; i < nThreads; i++) {
        final Worker worker = new Worker(jobs, stack, region, fitConfig, cameraModel, ticker);
        final Thread t = new Thread(worker);
        workers.add(worker);
        threads.add(t);
        t.start();
    }
    // Store all the fitting results
    results = new double[totalFrames * startPoints.length][];
    resultsTime = new long[results.length];
    // Fit the frames
    for (int i = 0; i < totalFrames; i++) {
        // Only fit if there were simulated photons
        if (benchmarkParameters.framePhotons[i] > 0) {
            put(jobs, i);
        }
    }
    // Finish all the worker threads by passing in a null job
    for (int i = 0; i < threads.size(); i++) {
        put(jobs, -1);
    }
    // Wait for all to finish
    for (int i = 0; i < threads.size(); i++) {
        try {
            threads.get(i).join();
        } catch (final InterruptedException ex) {
            Thread.currentThread().interrupt();
            throw new ConcurrentRuntimeException(ex);
        }
    }
    threads.clear();
    if (hasOffsetXy()) {
        ImageJUtils.log(TITLE + ": CoM within start offset = %d / %d (%s%%)", comValid.intValue(), totalFrames, MathUtils.rounded((100.0 * comValid.intValue()) / totalFrames));
    }
    ImageJUtils.finished("Collecting results ...");
    // Collect the results
    Statistics[] stats = null;
    for (int i = 0; i < workers.size(); i++) {
        final Statistics[] next = workers.get(i).stats;
        if (stats == null) {
            stats = next;
            continue;
        }
        for (int j = 0; j < next.length; j++) {
            stats[j].add(next[j]);
        }
    }
    workers.clear();
    Objects.requireNonNull(stats, "No statistics were computed");
    // Show a table of the results
    summariseResults(stats, cameraModel);
    // Optionally show histograms
    if (showHistograms) {
        IJ.showStatus("Calculating histograms ...");
        final WindowOrganiser windowOrganiser = new WindowOrganiser();
        final double[] convert = getConversionFactors();
        final HistogramPlotBuilder builder = new HistogramPlotBuilder(TITLE).setNumberOfBins(histogramBins);
        for (int i = 0; i < NAMES.length; i++) {
            if (displayHistograms[i] && convert[i] != 0) {
                // We will have to convert the values...
                final double[] tmp = ((StoredDataStatistics) stats[i]).getValues();
                for (int j = 0; j < tmp.length; j++) {
                    tmp[j] *= convert[i];
                }
                final StoredDataStatistics tmpStats = StoredDataStatistics.create(tmp);
                builder.setData(tmpStats).setName(NAMES[i]).setPlotLabel(String.format("%s +/- %s", MathUtils.rounded(tmpStats.getMean()), MathUtils.rounded(tmpStats.getStandardDeviation()))).show(windowOrganiser);
            }
        }
        windowOrganiser.tile();
    }
    if (saveRawData) {
        final String dir = ImageJUtils.getDirectory("Data_directory", rawDataDirectory);
        if (dir != null) {
            saveData(stats, dir);
        }
    }
    IJ.showStatus("");
}

Example 22 with WindowOrganiser

use of uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class Fire method run.

@Override
public void run(String arg) {
    extraOptions = ImageJUtils.isExtraOptions();
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    // Require some fit results and selected regions
    final int size = MemoryPeakResults.countMemorySize();
    if (size == 0) {
        IJ.error(pluginTitle, "There are no fitting results in memory");
        return;
    }
    settings = Settings.load();
    settings.save();
    if ("q".equals(arg)) {
        pluginTitle += " Q estimation";
        runQEstimation();
        return;
    }
    IJ.showStatus(pluginTitle + " ...");
    if (!showInputDialog()) {
        return;
    }
    MemoryPeakResults inputResults1 = ResultsManager.loadInputResults(settings.inputOption, false, null, null);
    if (MemoryPeakResults.isEmpty(inputResults1)) {
        IJ.error(pluginTitle, "No results could be loaded");
        return;
    }
    MemoryPeakResults inputResults2 = ResultsManager.loadInputResults(settings.inputOption2, false, null, null);
    inputResults1 = cropToRoi(inputResults1);
    if (inputResults1.size() < 2) {
        IJ.error(pluginTitle, "No results within the crop region");
        return;
    }
    if (inputResults2 != null) {
        inputResults2 = cropToRoi(inputResults2);
        if (inputResults2.size() < 2) {
            IJ.error(pluginTitle, "No results2 within the crop region");
            return;
        }
    }
    initialise(inputResults1, inputResults2);
    if (!showDialog()) {
        return;
    }
    final long start = System.currentTimeMillis();
    // Compute FIRE
    String name = inputResults1.getName();
    final double fourierImageScale = Settings.scaleValues[settings.imageScaleIndex];
    final int imageSize = Settings.imageSizeValues[settings.imageSizeIndex];
    if (this.results2 != null) {
        name += " vs " + this.results2.getName();
        final FireResult result = calculateFireNumber(fourierMethod, samplingMethod, thresholdMethod, fourierImageScale, imageSize);
        if (result != null) {
            logResult(name, result);
            if (settings.showFrcCurve) {
                showFrcCurve(name, result, thresholdMethod);
            }
        }
    } else {
        FireResult result = null;
        final int repeats = (settings.randomSplit) ? Math.max(1, settings.repeats) : 1;
        setProgress(repeats);
        if (repeats == 1) {
            result = calculateFireNumber(fourierMethod, samplingMethod, thresholdMethod, fourierImageScale, imageSize);
            if (result != null) {
                logResult(name, result);
                if (settings.showFrcCurve) {
                    showFrcCurve(name, result, thresholdMethod);
                }
            }
        } else {
            // Multi-thread this ...
            final int nThreads = MathUtils.min(repeats, getThreads());
            final ExecutorService executor = Executors.newFixedThreadPool(nThreads);
            final LocalList<Future<?>> futures = new LocalList<>(repeats);
            final LocalList<FireWorker> workers = new LocalList<>(repeats);
            IJ.showProgress(0);
            IJ.showStatus(pluginTitle + " computing ...");
            for (int i = 1; i <= repeats; i++) {
                final FireWorker w = new FireWorker(i, fourierImageScale, imageSize);
                workers.add(w);
                futures.add(executor.submit(w));
            }
            // Wait for all to finish
            executor.shutdown();
            ConcurrencyUtils.waitForCompletionUnchecked(futures);
            IJ.showProgress(1);
            // Show a combined FRC curve plot of all the smoothed curves if we have multiples.
            final LUT valuesLut = LutHelper.createLut(LutColour.FIRE_GLOW);
            final LutHelper.DefaultLutMapper mapper = new LutHelper.DefaultLutMapper(0, repeats);
            final FrcCurvePlot curve = new FrcCurvePlot();
            final Statistics stats = new Statistics();
            final WindowOrganiser wo = new WindowOrganiser();
            boolean oom = false;
            for (int i = 0; i < repeats; i++) {
                final 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 (settings.showFrcCurveRepeats) {
                    // Output each FRC curve using a suffix.
                    logResult(w.name, result);
                    wo.add(ImageJUtils.display(w.plot.getTitle(), w.plot));
                }
                if (settings.showFrcCurve) {
                    final int index = mapper.map(i + 1);
                    curve.add(name, result, thresholdMethod, LutHelper.getColour(valuesLut, index), Color.blue, null);
                }
            }
            if (result != null) {
                wo.cascade();
                final double mean = stats.getMean();
                logResult(name, result, mean, stats);
                if (settings.showFrcCurve) {
                    curve.addResolution(mean);
                    final Plot plot = curve.getPlot();
                    ImageJUtils.display(plot.getTitle(), plot);
                }
            }
            if (oom) {
                // @formatter:off
                IJ.error(pluginTitle, "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 (settings.showFrcTimeEvolution && result != null && !Double.isNaN(result.fireNumber)) {
            showFrcTimeEvolution(name, result.fireNumber, thresholdMethod, nmPerUnit / result.getNmPerPixel(), imageSize);
        }
    }
    IJ.showStatus(pluginTitle + " complete : " + TextUtils.millisToString(System.currentTimeMillis() - start));
}

Example 23 with WindowOrganiser

use of uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class DriftCalculator method plotDrift.

private static PlotWindow plotDrift(PlotWindow parent, double[][] interpolated, double[][] original, String name, int index) {
    // Create plot
    final double[] xlimits = MathUtils.limits(interpolated[0]);
    double[] ylimits = MathUtils.limits(original[index]);
    ylimits = MathUtils.limits(ylimits, interpolated[index]);
    final Plot plot = new Plot(name, "Frame", "Drift (px)");
    plot.setLimits(xlimits[0], xlimits[1], ylimits[0], ylimits[1]);
    // De-saturated blue
    plot.setColor(new Color(0, 0, 155));
    plot.addPoints(original[0], original[index], Plot.CROSS);
    plot.setColor(java.awt.Color.RED);
    plot.addPoints(interpolated[0], interpolated[index], Plot.LINE);
    final WindowOrganiser wo = new WindowOrganiser();
    final PlotWindow window = ImageJUtils.display(name, plot, wo);
    if (wo.isNotEmpty() && parent != null) {
        final Point location = parent.getLocation();
        location.y += parent.getHeight();
        window.setLocation(location);
    }
    return window;
}

Example 24 with WindowOrganiser

use of uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class PsfEstimator method calculateStatistics.

private boolean calculateStatistics(PeakFit fitter, double[] params, double[] paramsDev) {
    debug("  Fitting PSF");
    swapStatistics();
    // Create the fit engine using the PeakFit plugin
    final FitConfiguration fitConfig = config.getFitConfiguration();
    fitConfig.setInitialPeakStdDev0((float) params[1]);
    try {
        fitConfig.setInitialPeakStdDev1((float) params[2]);
        fitConfig.setInitialAngle((float) Math.toRadians(params[0]));
    } catch (IllegalStateException ex) {
    // Ignore this as the current PSF is not a 2 axis and theta Gaussian PSF
    }
    final ImageStack stack = imp.getImageStack();
    final Rectangle roi = stack.getProcessor(1).getRoi();
    ImageSource source = new IJImageSource(imp);
    // Allow interlaced data by wrapping the image source
    if (interlacedData) {
        source = new InterlacedImageSource(source, dataStart, dataBlock, dataSkip);
    }
    // Allow frame aggregation by wrapping the image source
    if (integrateFrames > 1) {
        source = new AggregatedImageSource(source, integrateFrames);
    }
    fitter.initialiseImage(source, roi, true);
    fitter.addPeakResults(this);
    fitter.initialiseFitting();
    final FitEngine engine = fitter.createFitEngine();
    // Use random slices
    final int[] slices = new int[stack.getSize()];
    for (int i = 0; i < slices.length; i++) {
        slices[i] = i + 1;
    }
    RandomUtils.shuffle(slices, UniformRandomProviders.create());
    IJ.showStatus("Fitting ...");
    // Use multi-threaded code for speed
    int sliceIndex;
    for (sliceIndex = 0; sliceIndex < slices.length; sliceIndex++) {
        final int slice = slices[sliceIndex];
        IJ.showProgress(size(), settings.getNumberOfPeaks());
        final ImageProcessor ip = stack.getProcessor(slice);
        // stack processor does not set the bounds required by ImageConverter
        ip.setRoi(roi);
        final FitJob job = new FitJob(slice, ImageJImageConverter.getData(ip), roi);
        engine.run(job);
        if (sampleSizeReached() || ImageJUtils.isInterrupted()) {
            break;
        }
    }
    if (ImageJUtils.isInterrupted()) {
        IJ.showProgress(1);
        engine.end(true);
        return false;
    }
    // Wait until we have enough results
    while (!sampleSizeReached() && !engine.isQueueEmpty()) {
        IJ.showProgress(size(), settings.getNumberOfPeaks());
        try {
            Thread.sleep(50);
        } catch (final InterruptedException ex) {
            Thread.currentThread().interrupt();
            throw new ConcurrentRuntimeException("Unexpected interruption", ex);
        }
    }
    // End now if we have enough samples
    engine.end(sampleSizeReached());
    ImageJUtils.finished();
    // This count will be an over-estimate given that the provider is ahead of the consumer
    // in this multi-threaded system
    debug("  Processed %d/%d slices (%d peaks)", sliceIndex, slices.length, size());
    setParams(ANGLE, params, paramsDev, sampleNew[ANGLE]);
    setParams(X, params, paramsDev, sampleNew[X]);
    setParams(Y, params, paramsDev, sampleNew[Y]);
    if (settings.getShowHistograms()) {
        final HistogramPlotBuilder builder = new HistogramPlotBuilder(TITLE).setNumberOfBins(settings.getHistogramBins());
        final WindowOrganiser wo = new WindowOrganiser();
        for (int ii = 0; ii < 3; ii++) {
            if (sampleNew[ii].getN() == 0) {
                continue;
            }
            final StoredDataStatistics stats = StoredDataStatistics.create(sampleNew[ii].getValues());
            builder.setData(stats).setName(NAMES[ii]).setPlotLabel("Mean = " + MathUtils.rounded(stats.getMean()) + ". Median = " + MathUtils.rounded(sampleNew[ii].getPercentile(50))).show(wo);
        }
        wo.tile();
    }
    if (size() < 2) {
        log("ERROR: Insufficient number of fitted peaks, terminating ...");
        return false;
    }
    return true;
}

Example 25 with WindowOrganiser

use of uk.ac.sussex.gdsc.core.ij.plugin.WindowOrganiser in project GDSC-SMLM by aherbert.

the class MeanVarianceTest method run.

@Override
public void run(String arg) {
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    settings = Settings.load();
    settings.save();
    String helpKey = "mean-variance-test";
    if (ImageJUtils.isExtraOptions()) {
        final ImagePlus imp = WindowManager.getCurrentImage();
        if (imp.getStackSize() > 1) {
            final GenericDialog gd = new GenericDialog(TITLE);
            gd.addMessage("Perform single image analysis on the current image?");
            gd.addNumericField("Bias", settings.bias, 0);
            gd.addHelp(HelpUrls.getUrl(helpKey));
            gd.showDialog();
            if (gd.wasCanceled()) {
                return;
            }
            singleImage = true;
            settings.bias = Math.abs(gd.getNextNumber());
        } else {
            IJ.error(TITLE, "Single-image mode requires a stack");
            return;
        }
    }
    List<ImageSample> images;
    String inputDirectory = "";
    if (singleImage) {
        IJ.showStatus("Loading images...");
        images = getImages();
        if (images.size() == 0) {
            IJ.error(TITLE, "Not enough images for analysis");
            return;
        }
    } else {
        inputDirectory = IJ.getDirectory("Select image series ...");
        if (inputDirectory == null) {
            return;
        }
        final SeriesOpener series = new SeriesOpener(inputDirectory);
        series.setVariableSize(true);
        if (series.getNumberOfImages() < 3) {
            IJ.error(TITLE, "Not enough images in the selected directory");
            return;
        }
        if (!IJ.showMessageWithCancel(TITLE, String.format("Analyse %d images, first image:\n%s", series.getNumberOfImages(), series.getImageList()[0]))) {
            return;
        }
        IJ.showStatus("Loading images");
        images = getImages(series);
        if (images.size() < 3) {
            IJ.error(TITLE, "Not enough images for analysis");
            return;
        }
        if (images.get(0).exposure != 0) {
            IJ.error(TITLE, "First image in series must have exposure 0 (Bias image)");
            return;
        }
    }
    final boolean emMode = (arg != null && arg.contains("em"));
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addMessage("Set the output options:");
    gd.addCheckbox("Show_table", settings.showTable);
    gd.addCheckbox("Show_charts", settings.showCharts);
    if (emMode) {
        // Ask the user for the camera gain ...
        gd.addMessage("Estimating the EM-gain requires the camera gain without EM readout enabled");
        gd.addNumericField("Camera_gain (Count/e-)", settings.cameraGain, 4);
    }
    if (emMode) {
        helpKey += "-em-ccd";
    }
    gd.addHelp(HelpUrls.getUrl(helpKey));
    gd.showDialog();
    if (gd.wasCanceled()) {
        return;
    }
    settings.showTable = gd.getNextBoolean();
    settings.showCharts = gd.getNextBoolean();
    if (emMode) {
        settings.cameraGain = gd.getNextNumber();
    }
    IJ.showStatus("Computing mean & variance");
    final double nImages = images.size();
    for (int i = 0; i < images.size(); i++) {
        IJ.showStatus(String.format("Computing mean & variance %d/%d", i + 1, images.size()));
        images.get(i).compute(singleImage, i / nImages, (i + 1) / nImages);
    }
    IJ.showProgress(1);
    IJ.showStatus("Computing results");
    // Allow user to input multiple bias images
    int start = 0;
    final Statistics biasStats = new Statistics();
    final Statistics noiseStats = new Statistics();
    final double bias;
    if (singleImage) {
        bias = settings.bias;
    } else {
        while (start < images.size()) {
            final ImageSample sample = images.get(start);
            if (sample.exposure == 0) {
                biasStats.add(sample.means);
                for (final PairSample pair : sample.samples) {
                    noiseStats.add(pair.variance);
                }
                start++;
            } else {
                break;
            }
        }
        bias = biasStats.getMean();
    }
    // Get the mean-variance data
    int total = 0;
    for (int i = start; i < images.size(); i++) {
        total += images.get(i).samples.size();
    }
    if (settings.showTable && total > 2000) {
        gd = new GenericDialog(TITLE);
        gd.addMessage("Table output requires " + total + " entries.\n \nYou may want to disable the table.");
        gd.addCheckbox("Show_table", settings.showTable);
        gd.showDialog();
        if (gd.wasCanceled()) {
            return;
        }
        settings.showTable = gd.getNextBoolean();
    }
    final TextWindow results = (settings.showTable) ? createResultsWindow() : null;
    double[] mean = new double[total];
    double[] variance = new double[mean.length];
    final Statistics gainStats = (singleImage) ? new StoredDataStatistics(total) : new Statistics();
    final WeightedObservedPoints obs = new WeightedObservedPoints();
    for (int i = (singleImage) ? 0 : start, j = 0; i < images.size(); i++) {
        final StringBuilder sb = (settings.showTable) ? new StringBuilder() : null;
        final ImageSample sample = images.get(i);
        for (final PairSample pair : sample.samples) {
            if (j % 16 == 0) {
                IJ.showProgress(j, total);
            }
            mean[j] = pair.getMean();
            variance[j] = pair.variance;
            // Gain is in Count / e
            double gain = variance[j] / (mean[j] - bias);
            gainStats.add(gain);
            obs.add(mean[j], variance[j]);
            if (emMode) {
                gain /= (2 * settings.cameraGain);
            }
            if (sb != null) {
                sb.append(sample.title).append('\t');
                sb.append(sample.exposure).append('\t');
                sb.append(pair.slice1).append('\t');
                sb.append(pair.slice2).append('\t');
                sb.append(IJ.d2s(pair.mean1, 2)).append('\t');
                sb.append(IJ.d2s(pair.mean2, 2)).append('\t');
                sb.append(IJ.d2s(mean[j], 2)).append('\t');
                sb.append(IJ.d2s(variance[j], 2)).append('\t');
                sb.append(MathUtils.rounded(gain, 4)).append("\n");
            }
            j++;
        }
        if (results != null && sb != null) {
            results.append(sb.toString());
        }
    }
    IJ.showProgress(1);
    if (singleImage) {
        StoredDataStatistics stats = (StoredDataStatistics) gainStats;
        ImageJUtils.log(TITLE);
        if (emMode) {
            final double[] values = stats.getValues();
            MathArrays.scaleInPlace(0.5, values);
            stats = StoredDataStatistics.create(values);
        }
        if (settings.showCharts) {
            // Plot the gain over time
            final String title = TITLE + " Gain vs Frame";
            final Plot plot = new Plot(title, "Slice", "Gain");
            plot.addPoints(SimpleArrayUtils.newArray(gainStats.getN(), 1, 1.0), stats.getValues(), Plot.LINE);
            final PlotWindow pw = ImageJUtils.display(title, plot);
            // Show a histogram
            final String label = String.format("Mean = %s, Median = %s", MathUtils.rounded(stats.getMean()), MathUtils.rounded(stats.getMedian()));
            final WindowOrganiser wo = new WindowOrganiser();
            final PlotWindow pw2 = new HistogramPlotBuilder(TITLE, stats, "Gain").setRemoveOutliersOption(1).setPlotLabel(label).show(wo);
            if (wo.isNotEmpty()) {
                final Point point = pw.getLocation();
                point.y += pw.getHeight();
                pw2.setLocation(point);
            }
        }
        ImageJUtils.log("Single-image mode: %s camera", (emMode) ? "EM-CCD" : "Standard");
        final double gain = stats.getMedian();
        if (emMode) {
            final double totalGain = gain;
            final double emGain = totalGain / settings.cameraGain;
            ImageJUtils.log("  Gain = 1 / %s (Count/e-)", MathUtils.rounded(settings.cameraGain, 4));
            ImageJUtils.log("  EM-Gain = %s", MathUtils.rounded(emGain, 4));
            ImageJUtils.log("  Total Gain = %s (Count/e-)", MathUtils.rounded(totalGain, 4));
        } else {
            settings.cameraGain = gain;
            ImageJUtils.log("  Gain = 1 / %s (Count/e-)", MathUtils.rounded(settings.cameraGain, 4));
        }
    } else {
        IJ.showStatus("Computing fit");
        // Sort
        final int[] indices = rank(mean);
        mean = reorder(mean, indices);
        variance = reorder(variance, indices);
        // Compute optimal coefficients.
        // a - b x
        final double[] init = { 0, 1 / gainStats.getMean() };
        final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(2).withStartPoint(init);
        final double[] best = fitter.fit(obs.toList());
        // Construct the polynomial that best fits the data.
        final PolynomialFunction fitted = new PolynomialFunction(best);
        if (settings.showCharts) {
            // Plot mean verses variance. Gradient is gain in Count/e.
            final String title = TITLE + " results";
            final Plot plot = new Plot(title, "Mean", "Variance");
            final double[] xlimits = MathUtils.limits(mean);
            final double[] ylimits = MathUtils.limits(variance);
            double xrange = (xlimits[1] - xlimits[0]) * 0.05;
            if (xrange == 0) {
                xrange = 0.05;
            }
            double yrange = (ylimits[1] - ylimits[0]) * 0.05;
            if (yrange == 0) {
                yrange = 0.05;
            }
            plot.setLimits(xlimits[0] - xrange, xlimits[1] + xrange, ylimits[0] - yrange, ylimits[1] + yrange);
            plot.setColor(Color.blue);
            plot.addPoints(mean, variance, Plot.CROSS);
            plot.setColor(Color.red);
            plot.addPoints(new double[] { mean[0], mean[mean.length - 1] }, new double[] { fitted.value(mean[0]), fitted.value(mean[mean.length - 1]) }, Plot.LINE);
            ImageJUtils.display(title, plot);
        }
        final double avBiasNoise = Math.sqrt(noiseStats.getMean());
        ImageJUtils.log(TITLE);
        ImageJUtils.log("  Directory = %s", inputDirectory);
        ImageJUtils.log("  Bias = %s +/- %s (Count)", MathUtils.rounded(bias, 4), MathUtils.rounded(avBiasNoise, 4));
        ImageJUtils.log("  Variance = %s + %s * mean", MathUtils.rounded(best[0], 4), MathUtils.rounded(best[1], 4));
        if (emMode) {
            // The gradient is the observed gain of the noise.
            // In an EM-CCD there is a noise factor of 2.
            // Q. Is this true for a correct noise factor calibration:
            // double noiseFactor = (Read Noise EM-CCD) / (Read Noise CCD)
            // Em-gain is the observed gain divided by the noise factor multiplied by camera gain
            final double emGain = best[1] / (2 * settings.cameraGain);
            // Compute total gain
            final double totalGain = emGain * settings.cameraGain;
            final double readNoise = avBiasNoise / settings.cameraGain;
            // Effective noise is standard deviation of the bias image divided by the total gain (in
            // Count/e-)
            final double readNoiseE = avBiasNoise / totalGain;
            ImageJUtils.log("  Read Noise = %s (e-) [%s (Count)]", MathUtils.rounded(readNoise, 4), MathUtils.rounded(avBiasNoise, 4));
            ImageJUtils.log("  Gain = 1 / %s (Count/e-)", MathUtils.rounded(1 / settings.cameraGain, 4));
            ImageJUtils.log("  EM-Gain = %s", MathUtils.rounded(emGain, 4));
            ImageJUtils.log("  Total Gain = %s (Count/e-)", MathUtils.rounded(totalGain, 4));
            ImageJUtils.log("  Effective Read Noise = %s (e-) (Read Noise/Total Gain)", MathUtils.rounded(readNoiseE, 4));
        } else {
            // The gradient is the observed gain of the noise.
            settings.cameraGain = best[1];
            // Noise is standard deviation of the bias image divided by the gain (in Count/e-)
            final double readNoise = avBiasNoise / settings.cameraGain;
            ImageJUtils.log("  Read Noise = %s (e-) [%s (Count)]", MathUtils.rounded(readNoise, 4), MathUtils.rounded(avBiasNoise, 4));
            ImageJUtils.log("  Gain = 1 / %s (Count/e-)", MathUtils.rounded(1 / settings.cameraGain, 4));
        }
    }
    IJ.showStatus("");
}