Examples with SeriesOpener gdsc.smlm.ij.utils.SeriesOpener used on opensource projects

Example 1 with SeriesOpener

use of gdsc.smlm.ij.utils.SeriesOpener in project GDSC-SMLM by aherbert.

the class PeakFit method setup.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.filter.PlugInFilter#setup(java.lang.String, ij.ImagePlus)
	 */
public int setup(String arg, ImagePlus imp) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    plugin_flags = FLAGS;
    extraOptions = Utils.isExtraOptions();
    maximaIdentification = (arg != null && arg.contains("spot"));
    fitMaxima = (arg != null && arg.contains("maxima"));
    simpleFit = (arg != null && arg.contains("simple"));
    boolean runSeries = (arg != null && arg.contains("series"));
    ImageSource imageSource = null;
    if (fitMaxima) {
        imp = null;
        // The image source will be found from the peak results.
        if (!showMaximaDialog())
            return DONE;
        MemoryPeakResults results = ResultsManager.loadInputResults(inputOption, false);
        if (results == null || results.size() == 0) {
            IJ.error(TITLE, "No results could be loaded");
            return DONE;
        }
        // Check for single frame
        singleFrame = results.getHead().getFrame();
        for (PeakResult result : results.getResults()) {
            if (singleFrame != result.getFrame()) {
                singleFrame = 0;
                break;
            }
        }
        imageSource = results.getSource();
        plugin_flags |= NO_IMAGE_REQUIRED;
    } else if (runSeries) {
        imp = null;
        // Select input folder
        String inputDirectory;
        inputDirectory = IJ.getDirectory("Select image series ...");
        //inputDirectory = getInputDirectory("Select image series ...");
        if (inputDirectory == null)
            return DONE;
        // Load input series ...
        SeriesOpener series;
        if (extraOptions)
            series = new SeriesOpener(inputDirectory, true, numberOfThreads);
        else
            series = new SeriesOpener(inputDirectory);
        if (series.getNumberOfImages() == 0) {
            IJ.error(TITLE, "No images in the selected directory:\n" + inputDirectory);
            return DONE;
        }
        SeriesImageSource seriesImageSource = new SeriesImageSource(getName(series.getImageList()), series);
        seriesImageSource.setLogProgress(true);
        if (extraOptions) {
            numberOfThreads = Math.max(1, series.getNumberOfThreads());
            seriesImageSource.setNumberOfThreads(numberOfThreads);
        }
        imageSource = seriesImageSource;
        plugin_flags |= NO_IMAGE_REQUIRED;
    } else {
        if (imp == null) {
            IJ.noImage();
            return DONE;
        }
        // Check it is not a previous result
        if (imp.getTitle().endsWith(IJImagePeakResults.IMAGE_SUFFIX)) {
            IJImageSource tmpImageSource = null;
            // Check the image to see if it has an image source XML structure in the info property
            Object o = imp.getProperty("Info");
            Pattern pattern = Pattern.compile("Source: (<.*IJImageSource>.*<.*IJImageSource>)", Pattern.DOTALL);
            Matcher match = pattern.matcher((o == null) ? "" : o.toString());
            if (match.find()) {
                ImageSource source = ImageSource.fromXML(match.group(1));
                if (source instanceof IJImageSource) {
                    tmpImageSource = (IJImageSource) source;
                    if (!tmpImageSource.open()) {
                        tmpImageSource = null;
                    } else {
                        imp = WindowManager.getImage(tmpImageSource.getName());
                    }
                }
            }
            if (tmpImageSource == null) {
                // Look for a parent using the title
                String parentTitle = imp.getTitle().substring(0, imp.getTitle().length() - IJImagePeakResults.IMAGE_SUFFIX.length() - 1);
                ImagePlus parentImp = WindowManager.getImage(parentTitle);
                if (parentImp != null) {
                    tmpImageSource = new IJImageSource(parentImp);
                    imp = parentImp;
                }
            }
            String message = "The selected image may be a previous fit result";
            if (tmpImageSource != null) {
                // are missing
                if (!Utils.isNullOrEmpty(tmpImageSource.getName()))
                    message += " of: \n \n" + tmpImageSource.getName();
                message += " \n \nFit the parent?";
            } else
                message += " \n \nDo you want to continue?";
            YesNoCancelDialog d = new YesNoCancelDialog(null, TITLE, message);
            if (tmpImageSource == null) {
                if (!d.yesPressed())
                    return DONE;
            } else {
                if (d.yesPressed())
                    imageSource = tmpImageSource;
                if (d.cancelPressed())
                    return DONE;
            }
        }
        if (imageSource == null)
            imageSource = new IJImageSource(imp);
    }
    time = -1;
    if (!initialiseImage(imageSource, getBounds(imp), false)) {
        IJ.error(TITLE, "Failed to initialise the source image: " + imageSource.getName());
        return DONE;
    }
    int flags = showDialog(imp);
    if ((flags & DONE) == 0) {
        // Repeat so that we pass in the selected option for ignoring the bounds.
        // This should not be necessary since it is set within the readDialog method.
        //if (ignoreBoundsForNoise)
        //	initialiseImage(imageSource, bounds, ignoreBoundsForNoise);
        initialiseFitting();
    }
    return flags;
}

Example 2 with SeriesOpener

use of gdsc.smlm.ij.utils.SeriesOpener in project GDSC-SMLM by aherbert.

the class MeanVarianceTest method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (Utils.isExtraOptions()) {
        ImagePlus imp = WindowManager.getCurrentImage();
        if (imp.getStackSize() > 1) {
            GenericDialog gd = new GenericDialog(TITLE);
            gd.addMessage("Perform single image analysis on the current image?");
            gd.addNumericField("Bias", _bias, 0);
            gd.showDialog();
            if (gd.wasCanceled())
                return;
            singleImage = true;
            _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;
        SeriesOpener series = new SeriesOpener(inputDirectory, false, 0);
        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;
        }
    }
    boolean emMode = (arg != null && arg.contains("em"));
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addMessage("Set the output options:");
    gd.addCheckbox("Show_table", showTable);
    gd.addCheckbox("Show_charts", 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 (ADU/e-)", cameraGain, 4);
    }
    gd.showDialog();
    if (gd.wasCanceled())
        return;
    showTable = gd.getNextBoolean();
    showCharts = gd.getNextBoolean();
    if (emMode) {
        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;
    Statistics biasStats = new Statistics();
    Statistics noiseStats = new Statistics();
    final double bias;
    if (singleImage) {
        bias = _bias;
    } else {
        while (start < images.size()) {
            ImageSample sample = images.get(start);
            if (sample.exposure == 0) {
                biasStats.add(sample.means);
                for (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 (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", showTable);
        gd.showDialog();
        if (gd.wasCanceled())
            return;
        showTable = gd.getNextBoolean();
    }
    TextWindow results = (showTable) ? createResultsWindow() : null;
    double[] mean = new double[total];
    double[] variance = new double[mean.length];
    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++) {
        StringBuilder sb = (showTable) ? new StringBuilder() : null;
        ImageSample sample = images.get(i);
        for (PairSample pair : sample.samples) {
            if (j % 16 == 0)
                IJ.showProgress(j, total);
            mean[j] = pair.getMean();
            variance[j] = pair.variance;
            // Gain is in ADU / e
            double gain = variance[j] / (mean[j] - bias);
            gainStats.add(gain);
            obs.add(mean[j], variance[j]);
            if (emMode) {
                gain /= (2 * cameraGain);
            }
            if (showTable) {
                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(Utils.rounded(gain, 4)).append("\n");
            }
            j++;
        }
        if (showTable)
            results.append(sb.toString());
    }
    IJ.showProgress(1);
    if (singleImage) {
        StoredDataStatistics stats = (StoredDataStatistics) gainStats;
        Utils.log(TITLE);
        if (emMode) {
            double[] values = stats.getValues();
            MathArrays.scaleInPlace(0.5, values);
            stats = new StoredDataStatistics(values);
        }
        if (showCharts) {
            // Plot the gain over time
            String title = TITLE + " Gain vs Frame";
            Plot2 plot = new Plot2(title, "Slice", "Gain", Utils.newArray(gainStats.getN(), 1, 1.0), stats.getValues());
            PlotWindow pw = Utils.display(title, plot);
            // Show a histogram
            String label = String.format("Mean = %s, Median = %s", Utils.rounded(stats.getMean()), Utils.rounded(stats.getMedian()));
            int id = Utils.showHistogram(TITLE, stats, "Gain", 0, 1, 100, true, label);
            if (Utils.isNewWindow()) {
                Point point = pw.getLocation();
                point.x = pw.getLocation().x;
                point.y += pw.getHeight();
                WindowManager.getImage(id).getWindow().setLocation(point);
            }
        }
        Utils.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 / cameraGain;
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
            Utils.log("  EM-Gain = %s", Utils.rounded(emGain, 4));
            Utils.log("  Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
        } else {
            cameraGain = gain;
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(cameraGain, 4));
        }
    } else {
        IJ.showStatus("Computing fit");
        // Sort
        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 (showCharts) {
            // Plot mean verses variance. Gradient is gain in ADU/e.
            String title = TITLE + " results";
            Plot2 plot = new Plot2(title, "Mean", "Variance");
            double[] xlimits = Maths.limits(mean);
            double[] ylimits = Maths.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, Plot2.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]) }, Plot2.LINE);
            Utils.display(title, plot);
        }
        final double avBiasNoise = Math.sqrt(noiseStats.getMean());
        Utils.log(TITLE);
        Utils.log("  Directory = %s", inputDirectory);
        Utils.log("  Bias = %s +/- %s (ADU)", Utils.rounded(bias, 4), Utils.rounded(avBiasNoise, 4));
        Utils.log("  Variance = %s + %s * mean", Utils.rounded(best[0], 4), Utils.rounded(best[1], 4));
        if (emMode) {
            final double emGain = best[1] / (2 * cameraGain);
            // Noise is standard deviation of the bias image divided by the total gain (in ADU/e-)
            final double totalGain = emGain * cameraGain;
            Utils.log("  Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(avBiasNoise / totalGain, 4), Utils.rounded(avBiasNoise, 4));
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
            Utils.log("  EM-Gain = %s", Utils.rounded(emGain, 4));
            Utils.log("  Total Gain = %s (ADU/e-)", Utils.rounded(totalGain, 4));
        } else {
            // Noise is standard deviation of the bias image divided by the gain (in ADU/e-)
            cameraGain = best[1];
            final double readNoise = avBiasNoise / cameraGain;
            Utils.log("  Read Noise = %s (e-) [%s (ADU)]", Utils.rounded(readNoise, 4), Utils.rounded(readNoise * cameraGain, 4));
            Utils.log("  Gain = 1 / %s (ADU/e-)", Utils.rounded(1 / cameraGain, 4));
        }
    }
    IJ.showStatus("");
}