Examples with GenericDialog ij.gui.GenericDialog used on opensource projects

Example 26 with GenericDialog

use of ij.gui.GenericDialog in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method showDialog.

private boolean showDialog() {
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addHelp(About.HELP_URL);
    StringBuilder sb = new StringBuilder();
    sb.append("Finds spots in the benchmark image created by CreateData plugin.\n");
    final double sa = getSa() / simulationParameters.a;
    sb.append("PSF width = ").append(Utils.rounded(simulationParameters.s / simulationParameters.a)).append(" px (sa = ").append(Utils.rounded(sa)).append(" px). HWHM = ").append(Utils.rounded(sa * Gaussian2DFunction.SD_TO_HWHM_FACTOR)).append(" px\n");
    sb.append("Simulation depth = ").append(Utils.rounded(simulationParameters.depth)).append(" nm");
    if (simulationParameters.fixedDepth)
        sb.append(" (fixed)");
    sb.append("\n \nConfigure the spot filter:");
    gd.addMessage(sb.toString());
    if (batchMode) {
        // Support enumeration of single spot filters
        gd.addCheckbox("Mean", batchMean);
        gd.addCheckbox("Gaussian", batchGaussian);
        gd.addCheckbox("Circular", batchCircular);
        gd.addCheckbox("Median", batchMedian);
        gd.addSlider("Min_search_width", 1, 4, minSearch);
        gd.addSlider("Max_search_width", 1, 4, maxSearch);
        gd.addCheckbox("Filter_relative_distances (to HWHM)", filterRelativeDistances);
    } else {
        String[] filterTypes = SettingsManager.getNames((Object[]) DataFilterType.values());
        gd.addChoice("Spot_filter_type", filterTypes, filterTypes[config.getDataFilterType().ordinal()]);
        String[] filterNames = SettingsManager.getNames((Object[]) DataFilter.values());
        gd.addChoice("Spot_filter", filterNames, filterNames[config.getDataFilter(0).ordinal()]);
        gd.addCheckbox("Filter_relative_distances (to HWHM)", filterRelativeDistances);
        gd.addSlider("Smoothing", 0, 2.5, config.getSmooth(0));
        gd.addSlider("Search_width", 1, 4, search);
    }
    gd.addSlider("Border", 0, 5, border);
    gd.addCheckbox("Hard_border", hardBorder);
    gd.addMessage("Scoring options:");
    gd.addCheckbox("Score_relative_distances (to HWHM)", scoreRelativeDistances);
    gd.addSlider("Analysis_border", 0, 5, sAnalysisBorder);
    gd.addChoice("Matching_method", MATCHING_METHOD, MATCHING_METHOD[matchingMethod]);
    gd.addSlider("Match_distance", 0.5, 3.5, upperDistance);
    gd.addSlider("Lower_distance", 0, 3.5, lowerDistance);
    gd.addSlider("Signal_factor", 0, 3.5, upperSignalFactor);
    gd.addSlider("Lower_factor", 0, 3.5, lowerSignalFactor);
    gd.addSlider("Recall_fraction", 50, 100, recallFraction);
    if (!batchMode) {
        gd.addCheckbox("Show_plots", showPlot);
        gd.addCheckbox("Plot_rank_by_intensity", rankByIntensity);
        gd.addCheckbox("Show_failures_plots", showFailuresPlot);
        gd.addCheckbox("Show_TP", showTP);
        gd.addCheckbox("Show_FP", showFP);
        gd.addCheckbox("Show_FN", showFN);
    }
    if (extraOptions)
        gd.addCheckbox("Debug", sDebug);
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    fitConfig.setInitialPeakStdDev(Maths.round(sa));
    if (batchMode) {
        batchMean = gd.getNextBoolean();
        batchGaussian = gd.getNextBoolean();
        batchCircular = gd.getNextBoolean();
        batchMedian = gd.getNextBoolean();
        if (!(batchMean || batchGaussian || batchCircular || batchMedian))
            return false;
        minSearch = gd.getNextNumber();
        maxSearch = gd.getNextNumber();
        filterRelativeDistances = gd.getNextBoolean();
    } else {
        config.setDataFilterType(gd.getNextChoiceIndex());
        config.setDataFilter(gd.getNextChoiceIndex(), Maths.round(Math.abs(gd.getNextNumber()), 0.001), 0);
        filterRelativeDistances = gd.getNextBoolean();
        search = gd.getNextNumber();
    }
    border = gd.getNextNumber();
    hardBorder = gd.getNextBoolean();
    scoreRelativeDistances = gd.getNextBoolean();
    sAnalysisBorder = Math.abs(gd.getNextNumber());
    matchingMethod = gd.getNextChoiceIndex();
    upperDistance = Math.abs(gd.getNextNumber());
    lowerDistance = Math.abs(gd.getNextNumber());
    upperSignalFactor = Math.abs(gd.getNextNumber());
    lowerSignalFactor = Math.abs(gd.getNextNumber());
    recallFraction = Math.abs(gd.getNextNumber());
    if (!batchMode) {
        showPlot = gd.getNextBoolean();
        rankByIntensity = gd.getNextBoolean();
        showFailuresPlot = gd.getNextBoolean();
        showTP = gd.getNextBoolean();
        showFP = gd.getNextBoolean();
        showFN = gd.getNextBoolean();
    }
    if (extraOptions)
        debug = sDebug = gd.getNextBoolean();
    if (gd.invalidNumber())
        return false;
    if (lowerDistance > upperDistance)
        lowerDistance = upperDistance;
    if (lowerSignalFactor > upperSignalFactor)
        lowerSignalFactor = upperSignalFactor;
    if (batchMode) {
        // Clear the cached results if the setting changed
        Settings settings = new Settings(simulationParameters.id, filterRelativeDistances, //search, maxSearch, // Ignore search distance for smart caching 
        border, scoreRelativeDistances, sAnalysisBorder, hardBorder, matchingMethod, upperDistance, lowerDistance, upperSignalFactor, lowerSignalFactor, recallFraction);
        if (!settings.equals(batchSettings)) {
            cachedBatchResults.clear();
        }
        batchSettings = settings;
        // relative (if requested) so that the results are consistent with single-filter mode.
        if (filterRelativeDistances) {
            final double hwhmMax = config.getHWHMMax();
            config.setBorder(Maths.round(border * hwhmMax, 0.001));
        } else {
            config.setBorder(Maths.round(border, 0.001));
        }
    } else {
        config.setSearch(Maths.round(search, 0.001));
        config.setBorder(Maths.round(border, 0.001));
        // Single filter ...
        // Allow more complicated filters to be configured
        GlobalSettings settings = new GlobalSettings();
        settings.setFitEngineConfiguration(config);
        if (!PeakFit.configureDataFilter(settings, null, false))
            return false;
    }
    int analysisBorder;
    if (scoreRelativeDistances) {
        // Convert distance to PSF standard deviation units
        final double hwhmMax = config.getHWHMMax();
        matchDistance = upperDistance * hwhmMax;
        lowerMatchDistance = lowerDistance * hwhmMax;
        analysisBorder = (int) (sAnalysisBorder * hwhmMax);
    } else {
        matchDistance = upperDistance;
        lowerMatchDistance = lowerDistance;
        analysisBorder = (int) (sAnalysisBorder);
    }
    if (analysisBorder > 0) {
        lastAnalysisBorder = new Rectangle(analysisBorder, analysisBorder, imp.getWidth() - 2 * analysisBorder, imp.getHeight() - 2 * analysisBorder);
    } else {
        lastAnalysisBorder = new Rectangle(imp.getWidth(), imp.getHeight());
    }
    return true;
}

Example 27 with GenericDialog

use of ij.gui.GenericDialog in project GDSC-SMLM by aherbert.

the class BenchmarkSpotFilter method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    extraOptions = Utils.isExtraOptions();
    batchMode = "batch".equals(arg);
    simulationParameters = CreateData.simulationParameters;
    if (simulationParameters == null) {
        IJ.error(TITLE, "No benchmark spot parameters in memory");
        return;
    }
    imp = CreateData.getImage();
    if (imp == null) {
        IJ.error(TITLE, "No benchmark image");
        return;
    }
    results = CreateData.getResults();
    if (results == null) {
        IJ.error(TITLE, "No benchmark results in memory");
        return;
    }
    if (!showDialog())
        return;
    // Clear old results to free memory
    if (filterResult != null) {
        filterResult.filterResults.clear();
        filterResult.filterResults = null;
        filterResult = null;
    }
    // For graphs
    windowOrganiser = new WindowOrganiser();
    if (batchMode) {
        // Batch mode to test enumeration of filters
        final double sd = simulationParameters.s / simulationParameters.a;
        final int limit = (int) Math.floor(3 * sd);
        double[] searchParam = getRange(minSearch, maxSearch, 1);
        // Continuous parameters
        double[] pEmpty = new double[0];
        double[] mParam = (batchMean) ? getRange(limit, 0.05) : pEmpty;
        double[] gParam = (batchGaussian) ? getRange(limit, 0.05) : pEmpty;
        // Less continuous parameters
        double[] cParam = (batchCircular) ? getRange(limit, 0.5) : pEmpty;
        // Discrete parameters
        double[] medParam = (batchMedian) ? getRange(limit, 1) : pEmpty;
        setupProgress(imp.getImageStackSize() * searchParam.length * (mParam.length + gParam.length + cParam.length + medParam.length), "Frame");
        ArrayList<BatchResult[]> batchResults = new ArrayList<BatchResult[]>(cachedBatchResults.size());
        config.setDataFilterType(DataFilterType.SINGLE);
        for (double search : searchParam) {
            // Run all, store the results for plotting.
            // Allow re-use of these if they are cached to allow quick reanalysis of results.
            config.setSearch(search);
            if (batchMean)
                batchResults.add(addToCache(DataFilter.MEAN, mParam, search));
            if (batchGaussian)
                batchResults.add(addToCache(DataFilter.GAUSSIAN, gParam, search));
            if (batchCircular)
                batchResults.add(addToCache(DataFilter.CIRCULAR_MEAN, cParam, search));
            if (batchMean)
                batchResults.add(addToCache(DataFilter.MEDIAN, medParam, search));
        }
        IJ.showProgress(-1);
        IJ.showStatus("");
        if (Utils.isInterrupted())
            return;
        // Analysis options
        GenericDialog gd = new GenericDialog(TITLE);
        gd.addMessage("Choose performance plots:");
        for (int i = 0; i < batchPlot.length; i++) gd.addCheckbox(batchPlotNames[i], batchPlot[i]);
        gd.addChoice("Selection", SELECTION, SELECTION[selection]);
        gd.addCheckbox("Show_plots", showPlot);
        gd.addCheckbox("Plot_rank_by_intensity", rankByIntensity);
        gd.addCheckbox("Show_failures_plots", showFailuresPlot);
        gd.addCheckbox("Show_TP", showTP);
        gd.addCheckbox("Show_FP", showFP);
        gd.addCheckbox("Show_FN", showFN);
        gd.showDialog();
        if (gd.wasCanceled())
            return;
        for (int i = 0; i < batchPlot.length; i++) batchPlot[i] = gd.getNextBoolean();
        selection = gd.getNextChoiceIndex();
        showPlot = gd.getNextBoolean();
        rankByIntensity = gd.getNextBoolean();
        showFailuresPlot = gd.getNextBoolean();
        showTP = gd.getNextBoolean();
        showFP = gd.getNextBoolean();
        showFN = gd.getNextBoolean();
        // Plot charts			
        for (int i = 0; i < batchPlot.length; i++) plot(i, batchResults);
        // Store in global singleton
        filterResult = analyse(batchResults);
    } else {
        // Single filter mode
        setupProgress(imp.getImageStackSize(), "Frame");
        filterResult = run(config, filterRelativeDistances);
    }
    IJ.showProgress(-1);
    IJ.showStatus("");
    getTable(false).flush();
    if (filterResult == null)
        return;
    // Store a clone of the config
    filterResult.config = filterResult.config.clone();
    // Debugging the matches
    if (debug)
        addSpotsToMemory(filterResult.filterResults);
    if (showFailuresPlot)
        showFailuresPlot(filterResult);
    if (showPlot)
        showPlot(filterResult);
    if (isShowOverlay())
        showOverlay(imp, filterResult);
    windowOrganiser.tile();
}

Example 28 with GenericDialog

use of ij.gui.GenericDialog in project GDSC-SMLM by aherbert.

the class PCPALMClusters method showDialog.

private boolean showDialog() {
    if (PCPALMMolecules.molecules == null || PCPALMMolecules.molecules.size() < 2) {
        Utils.log(TITLE + " defaulting to File mode");
        fileInput = true;
        // Ensure this gets recorded
        Recorder.recordOption("Method", "File");
    } else {
        GenericDialog gd = new GenericDialog(TITLE);
        String[] items = { "Clustering", "File" };
        gd.addMessage("Fit a Binomial distribution to a histogram of cluster sizes.\n \nSelect the method to generate the histogram:");
        gd.addChoice("Method", items, items[runMode]);
        gd.showDialog();
        if (gd.wasCanceled())
            return false;
        runMode = gd.getNextChoiceIndex();
        fileInput = (runMode == 1);
    }
    if (fileInput) {
        if ((histogramFile = Utils.getFilename("Histogram_file", histogramFile)) == null)
            return false;
    }
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addHelp(About.HELP_URL);
    // Check if the molecules have weights
    boolean haveWeights = false;
    if (!fileInput) {
        haveWeights = checkForWeights();
        gd.addMessage("Find clusters using centroid-linkage clustering.");
        gd.addNumericField("Distance (nm)", distance, 0);
        String[] names = SettingsManager.getNames((Object[]) ClusteringAlgorithm.values());
        gd.addChoice("Algorithm", names, names[sClusteringAlgorithm.ordinal()]);
        gd.addCheckbox("Multi_thread", multiThread);
        if (haveWeights)
            gd.addCheckbox("Weighted_clustering", sWeightedClustering);
    }
    gd.addSlider("Min_N", 1, 10, minN);
    gd.addSlider("Max_N", 0, 10, maxN);
    gd.addCheckbox("Show_cumulative_histogram", showCumulativeHistogram);
    gd.addCheckbox("Maximum_likelihood", maximumLikelihood);
    if (!fileInput) {
        gd.addCheckbox("Save_histogram", saveHistogram);
        gd.addMessage("Histogram calibration (optional)");
        gd.addCheckbox("Calibrate_histogram", calibrateHistogram);
        gd.addNumericField("Frames", frames, 0);
        gd.addNumericField("Area", area, 2);
        gd.addChoice("Units", UNITS, units);
    }
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    if (!fileInput) {
        distance = gd.getNextNumber();
        clusteringAlgorithm = sClusteringAlgorithm = ClusteringAlgorithm.values()[gd.getNextChoiceIndex()];
        multiThread = gd.getNextBoolean();
        if (haveWeights)
            weightedClustering = sWeightedClustering = gd.getNextBoolean();
    }
    minN = (int) Math.abs(gd.getNextNumber());
    maxN = (int) Math.abs(gd.getNextNumber());
    showCumulativeHistogram = gd.getNextBoolean();
    maximumLikelihood = gd.getNextBoolean();
    if (!fileInput) {
        saveHistogram = gd.getNextBoolean();
        calibrateHistogram = gd.getNextBoolean();
        frames = (int) Math.abs(gd.getNextNumber());
        area = Math.abs(gd.getNextNumber());
        units = gd.getNextChoice();
    }
    // Check arguments
    try {
        Parameters.isAboveZero("Min N", minN);
        if (!fileInput) {
            Parameters.isAboveZero("Distance", distance);
            Parameters.isAboveZero("Frames", frames);
            Parameters.isAboveZero("Area", area);
        }
    } catch (IllegalArgumentException ex) {
        error(ex.getMessage());
        return false;
    }
    return true;
}

Example 29 with GenericDialog

use of ij.gui.GenericDialog in project GDSC-SMLM by aherbert.

the class PCPALMClusters method loadNoiseHistogram.

/**
	 * If the histogram is calibrated then ask the user if they wish to subtract a calibrated noise histogram.
	 * <p>
	 * Loads a noise histogram from a user selected file and check the units match those provided
	 * 
	 * @param histogramData
	 * @return The histogram (or null)
	 */
private HistogramData loadNoiseHistogram(HistogramData histogramData) {
    if (!histogramData.isCalibrated())
        return null;
    GenericDialog gd = new GenericDialog(TITLE);
    gd.enableYesNoCancel();
    gd.hideCancelButton();
    gd.addMessage("The histogram is calibrated.\n \nDo you want to subtract a noise histogram before fitting?");
    boolean allowSave = new File(histogramData.filename).exists();
    if (allowSave)
        gd.addCheckbox("Auto_save noise-subtracted histogram", sAutoSave);
    // If this is a macro then the dialog will not have Yes or No pressed.
    // Add a checkbox that can be read from the macro arguments by ImageJ.
    String macroOption = "subtract";
    if (IJ.isMacro())
        gd.addCheckbox(macroOption, true);
    gd.showDialog();
    if (!gd.wasOKed())
        return null;
    if (allowSave)
        autoSave = sAutoSave = gd.getNextBoolean();
    if (IJ.isMacro()) {
        // If the macro option flag is not found then the arguments do not want this to run 
        if (!gd.getNextBoolean())
            return null;
    } else {
        // Ensure that the 'Yes' result is recorded for macros to detect 
        Recorder.recordOption(macroOption);
    }
    noiseFile = Utils.getFilename("Noise_file", noiseFile);
    if (noiseFile != null) {
        HistogramData data = loadHistogram(noiseFile);
        // Check the data is calibrated with the same units
        if (data.isCalibrated() && data.units.equalsIgnoreCase(histogramData.units))
            return data;
    }
    return null;
}

Example 30 with GenericDialog

use of ij.gui.GenericDialog in project GDSC-SMLM by aherbert.

the class PCPALMFitting method getCorrelationResults.

private boolean getCorrelationResults() {
    // Option to:
    // - load a correlation curve
    // - use previous results (if available)
    // - select a set of analysis results (if available)
    String[] options = new String[] { INPUT_FROM_FILE, "", "" };
    int count = 1;
    if (previous_gr != null)
        options[count++] = INPUT_PREVIOUS;
    if (!PCPALMAnalysis.results.isEmpty())
        options[count++] = INPUT_ANALYSIS;
    options = Arrays.copyOf(options, count);
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addMessage("Select the source for the correlation curve");
    gd.addChoice("Input", options, inputOption);
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    inputOption = gd.getNextChoice();
    if (inputOption.equals(INPUT_PREVIOUS)) {
        // In the case of a macro the previous results may be null
        if (previous_gr == null)
            return false;
        gr = previous_gr;
        peakDensity = previous_peakDensity;
        spatialDomain = previous_spatialDomain;
        return true;
    } else if (inputOption.equals(INPUT_FROM_FILE)) {
        return loadCorrelationCurve();
    }
    // Fill the results list with analysis results from PCPALM Analysis
    ArrayList<CorrelationResult> results = new ArrayList<CorrelationResult>();
    if (!selectAnalysisResults(results))
        return false;
    // We have some results. Convert them to the format used for fitting.
    header();
    log("Computing combined pair correlation curve (%d datasets)", results.size());
    spatialDomain = results.get(0).spatialDomain;
    // Get average peak density
    peakDensity = 0;
    int size = 0;
    for (CorrelationResult r : results) {
        peakDensity += r.peakDensity;
        size = FastMath.max(size, r.gr[0].length);
    }
    peakDensity /= results.size();
    // Combine all selected g(r) curves
    gr = combineCurves(results, size);
    return true;
}