Examples with TcPalmAnalysisSettings uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings used on opensource projects

Example 1 with TcPalmAnalysisSettings

use of uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method runSelection.

/**
 * Run selection of the current clusters.
 *
 * @param work the current work
 */
private void runSelection(Work work) {
    // Support square ROI
    // - Map image ROI bounds to the data
    // - Check if the cluster is inside the rectangle bounds
    final Roi roi = work.roi;
    final TcPalmAnalysisSettings settings = work.settings;
    final Rectangle2D scaledBounds = createScaledBounds(roi);
    final BiPredicate<ClusterData, Rectangle2D> filter = createSelectionFilter(roi, settings);
    clusters = new LocalList<>();
    clusterData.forEach(c -> {
        if (filter.test(c, scaledBounds)) {
            clusters.add(c);
        }
    });
    // Build total activations data
    countData = createCumulativeCountData(clusters, true);
    // Add a table of the clusters.
    final ClusterDataTableModelFrame clustersTable = createGroupsTable();
    clustersTable.getModel().setData(clusters, dataCalibration);
    // Allow a configurable action that accepts the array of ClusterData that is selected.
    clustersTable.selectedAction = clusterSelectedListener;
    // Extract the localisations into a loop view
    final MemoryPeakResults subset = new MemoryPeakResults();
    subset.copySettings(results);
    clusters.forEach(c -> {
        c.results.forEach(peak -> {
            if (scaledBounds.contains(peak.getXPosition(), peak.getYPosition())) {
                subset.add(peak);
            }
        });
    });
    // Clear bounds to force a recompute
    subset.setBounds(null);
    loopImage.setSettings(settings).setResults(subset).update();
}

Example 2 with TcPalmAnalysisSettings

use of uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method analyseRois.

/**
 * Analyses all the ROIs in the ROI manager.
 *
 * @param event the event
 */
private void analyseRois(ActionEvent event) {
    final RoiManager manager = RoiManager.getInstance();
    if (manager == null) {
        IJ.error(TITLE, "ROI manager is not open");
        return;
    }
    final LocalList<Roi> rois = Arrays.stream(manager.getRoisAsArray()).filter(Roi::isArea).collect(LocalCollectors.toLocalList());
    if (rois.isEmpty()) {
        IJ.error(TITLE, "No area ROIs");
        return;
    }
    // Check for overlaps.
    if (!settings.getDisableOverlapCheck() && anyOverlap(rois)) {
        final GenericDialog gd = new GenericDialog(TITLE);
        gd.addMessage(TextUtils.wrap("WARNING - Bounding rectangles of ROIs overlap. You can verify " + "the ROIs on the image using the ROI manager 'Show all' function.", 80));
        gd.setOKLabel("Continue");
        gd.showDialog();
        if (gd.wasCanceled()) {
            return;
        }
    }
    // For each ROI:
    // - Extract the current groups
    // - Build the cumulative count plot
    // - Identify the bursts
    // - Extract ClusterData for each burst
    final TcPalmAnalysisSettings settings = this.settings.build();
    final LocalList<ClusterData> allClusters = rois.parallelStream().map(roi -> {
        final Rectangle2D scaledBounds = createScaledBounds(roi);
        final BiPredicate<ClusterData, Rectangle2D> filter = createSelectionFilter(roi, settings);
        // Filter all cluster groups
        final LocalList<ClusterData> clusters = new LocalList<>();
        clusterData.forEach(c -> {
            if (filter.test(c, scaledBounds)) {
                clusters.add(c);
            }
        });
        // Extract activation bursts
        final CumulativeCountData countData = createCumulativeCountData(clusters, false);
        final LocalList<int[]> bursts = runBurstAnalysis(settings, countData);
        final LocalList<LocalList<PeakResult>> burstLocalisations = createBurstLocalisations(clusters, bursts);
        clusters.clear();
        burstLocalisations.forEach(list -> {
            final ClusterData d = new ClusterData(clusters.size() + 1, list);
            // Save this for analysis
            d.sourceRoi = roi;
            d.getArea();
            clusters.add(d);
        });
        return clusters;
    }).collect(LocalList::new, LocalList::addAll, LocalList::addAll);
    // Reorder
    final Counter count = new Counter();
    allClusters.forEach(c -> c.id = count.incrementAndGet());
    // Display in a table
    final ClusterDataTableModelFrame frame = createAllClustersTable();
    frame.getModel().setData(allClusters, dataCalibration);
    // Allow the results to be repeated
    frame.selectedAction = clusters -> {
        // Expecting a single cluster. No clusters occurs when the table (and selection) is cleared.
        if (clusters.size() == 1) {
            final ClusterData c = clusters.get(0);
            // Push the correct ROI and settings to the analysis action.
            // We do not directly update the ROI or dialog settings as
            // these trigger events that are processed to add work with a delay.
            // Updating them at the end should generate events that are
            // ignored when finally executed as the ROI/settings should be the same.
            addWork(0, c.sourceRoi, settings, () -> {
                // When analysis has finished update the settings and image ROI.
                image.getImagePlus().setRoi(c.sourceRoi);
                darkTimeToleranceTextField.setText(Integer.toString(settings.getDarkTimeTolerance()));
                minClusterSizeTextField.setText(Integer.toString(settings.getMinClusterSize()));
                // When analysis has finished the cluster should be selected in the
                // current clusters table.
                final ClusterDataTableModelFrame currentClusters = currentClustersTable.get();
                if (currentClusters != null) {
                    currentClusters.select(c);
                }
            });
        }
    };
    // Show histogram of cluster size/duration
    reportAnalysis(settings, allClusters, dataCalibration);
    // Save clusters to memory
    final Trace[] traces = allClusters.stream().map(c -> {
        final Trace t = new Trace();
        t.setId(c.id);
        c.results.forEach(t::add);
        return t;
    }).toArray(Trace[]::new);
    TraceMolecules.saveResults(results, traces, "TC PALM");
    IJ.showStatus(TITLE + ": " + TextUtils.pleural(allClusters.size(), "cluster"));
}

Example 3 with TcPalmAnalysisSettings

use of uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method createSelectionFilter.

/**
 * Creates the selection filter to identify all cluster groups using a custom filter.
 *
 * @param roi the roi
 * @param settings the settings
 * @return the bi predicate
 */
private BiPredicate<ClusterData, Rectangle2D> createSelectionFilter(final Roi roi, final TcPalmAnalysisSettings settings) {
    // Filter on time first as this is simple.
    BiPredicate<ClusterData, Rectangle2D> test = null;
    if (settings.getMinFrame() > minT) {
        final int min = settings.getMinFrame();
        test = (c, r) -> c.start >= min;
    }
    if (settings.getMaxFrame() < maxT) {
        final int max = settings.getMaxFrame();
        test = and(test, (c, r) -> c.end <= max);
    }
    // Add square bounds check
    test = and(test, settings.getIntersects() ? ClusterData::intersects : ClusterData::isWithin);
    // -- Check if the cluster is inside the ROI using a CoordinatePredicate
    if (roi.getType() != Roi.RECTANGLE || roi.getCornerDiameter() != 0) {
        final CoordinatePredicate pred = CoordinatePredicateUtils.createContainsPredicate(roi);
        if (settings.getIntersects()) {
            test = and(test, (c, r) -> c.intersects(pred, image::mapX, image::mapY));
        } else {
            test = and(test, (c, r) -> c.isWithin(pred, image::mapX, image::mapY));
        }
    }
    return test;
}

Example 4 with TcPalmAnalysisSettings

use of uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method runAnalysis.

/**
 * Run the analysis of clusters inside the bounds with the provided analysis settings.
 *
 * @param last the last work
 * @param current the current work
 */
private void runAnalysis(Work last, Work current) {
    final TcPalmAnalysisSettings lastSettings = last.settings;
    final TcPalmAnalysisSettings settings = current.settings;
    final boolean selectionChanged = selectionChanged(last, current);
    if (selectionChanged) {
        runSelection(current);
    } else if (loopSettingsChanged(lastSettings, settings)) {
        loopImage.setSettings(settings).update();
    }
    final boolean plotChanged = selectionChanged || plotSettingsChanged(lastSettings, settings);
    if (plotChanged) {
        runPlot(settings);
    }
    final boolean analysisChanged = selectionChanged || analysisSettingsChanged(lastSettings, settings);
    if (analysisChanged) {
        clearClustersTableSelection();
        bursts = runBurstAnalysis(settings, countData);
        runBurstOverlay(createBurstLocalisations(clusters, bursts));
    }
    if (plotChanged || analysisChanged) {
        runBurstPlotSelection(bursts);
    }
}

Example 5 with TcPalmAnalysisSettings

use of uk.ac.sussex.gdsc.smlm.ij.settings.GUIProtos.TcPalmAnalysisSettings in project GDSC-SMLM by aherbert.

the class TcPalmAnalysis method reportAnalysis.

/**
 * Report statistics on the analysis results.
 *
 * @param settings the settings
 * @param clusters the clusters
 * @param calibration the data calibration
 */
private static void reportAnalysis(TcPalmAnalysisSettings settings, LocalList<ClusterData> clusters, DataCalibration calibration) {
    final WindowOrganiser wo = new WindowOrganiser();
    final Consumer<HistogramPlotBuilder> action = builder -> {
    /* noop. */
    };
    plotHistogram(settings.getShowSizeHistogram(), wo, clusters, "Size", c -> c.results.size(), null, action);
    plotHistogram(settings.getShowDurationHistogram(), wo, clusters, "Duration (" + calibration.getTimeUnitName() + ")", c -> calibration.timeConverter.convert(c.getDuration()), null, action);
    plotHistogram(settings.getShowAreaHistogram(), wo, clusters, "Area (" + calibration.getDistanceUnitName() + "^2)", c -> calibration.convertArea(c.getArea()), null, action);
    plotHistogram(settings.getShowDensityHistogram(), wo, clusters, "Density (" + calibration.getDistanceUnitName() + "^-2)", c -> c.results.size() / calibration.convertArea(c.getArea()), Double::isFinite, action);
    wo.tile();
}