Examples with MemoryPeakResults uk.ac.sussex.gdsc.smlm.results.MemoryPeakResults used on opensource projects

Example 91 with MemoryPeakResults

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

the class ResultsMatchCalculator method getCoordinates.

/**
 * Build a map between the peak id (time point) and a list of coordinates.
 *
 * @param results the results
 * @param coordinateMethod the coordinate method
 * @param integerCoordinates True if the values should be rounded down to integers
 * @return the coordinates
 */
public static TIntObjectHashMap<List<Coordinate>> getCoordinates(MemoryPeakResults results, CoordinateMethod coordinateMethod, final boolean integerCoordinates) {
    final TIntObjectHashMap<List<Coordinate>> coords = new TIntObjectHashMap<>();
    if (results.size() > 0) {
        // Do not use HashMap directly to build the coords object since there
        // will be many calls to getEntry(). Instead sort the results and use
        // a new list for each time point
        results.sort();
        final int minT = results.getFirstFrame();
        final int maxT = results.getLastFrame();
        // Create lists
        final ArrayList<ArrayList<Coordinate>> tmpCoords = new ArrayList<>(maxT - minT + 1);
        for (int t = minT; t <= maxT; t++) {
            tmpCoords.add(new ArrayList<Coordinate>());
        }
        // Add the results to the lists
        results.forEach((PeakResultProcedure) result -> {
            final float x;
            final float y;
            final float z;
            if (integerCoordinates) {
                x = (int) result.getXPosition();
                y = (int) result.getYPosition();
                z = (int) result.getZPosition();
            } else {
                x = result.getXPosition();
                y = result.getYPosition();
                z = result.getZPosition();
            }
            final int startFrame = getStartFrame(result, coordinateMethod);
            final int endFrame = getEndFrame(result, coordinateMethod);
            for (int t = startFrame - minT, i = endFrame - startFrame + 1; i-- > 0; t++) {
                tmpCoords.get(t).add(new PeakResultPoint(t + minT, x, y, z, result));
            }
        });
        // Put in the map
        for (int t = minT, i = 0; t <= maxT; t++, i++) {
            coords.put(t, tmpCoords.get(i));
        }
    }
    return coords;
}

Example 92 with MemoryPeakResults

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

the class ResultsMatchCalculator method getIds.

private static TIntHashSet getIds(MemoryPeakResults results) {
    final TIntHashSet ids = new TIntHashSet(results.size());
    results.forEach((PeakResultProcedure) result -> ids.add(result.getId()));
    return ids;
}

Example 93 with MemoryPeakResults

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

the class TrackPopulationAnalysis method run.

@Override
public void run(String arg) {
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    settings = Settings.load();
    // Saved by reference so just save now
    settings.save();
    // Read in multiple traced datasets
    // All datasets must have the same pixel pitch and exposure time
    // Get parameters
    // Convert datasets to tracks
    // For each track compute the 4 local track features using the configured window
    // 
    // Optional:
    // Fit a multi-variate Gaussian mixture model to the data
    // (using the configured number of components/populations)
    // Assign each point in the track using the model.
    // Smooth the assignments.
    // 
    // The alternative is to use the localisation category to assign populations.
    // 
    // Plot histograms of each track parameter, coloured by component
    final List<MemoryPeakResults> combinedResults = new LocalList<>();
    if (!showInputDialog(combinedResults)) {
        return;
    }
    final boolean hasCategory = showHasCategoryDialog(combinedResults);
    if (!showDialog(hasCategory)) {
        return;
    }
    ImageJUtils.log(TITLE + "...");
    final List<Trace> tracks = getTracks(combinedResults, settings.window, settings.minTrackLength);
    if (tracks.isEmpty()) {
        IJ.error(TITLE, "No tracks. Please check the input data and min track length setting.");
        return;
    }
    final Calibration cal = combinedResults.get(0).getCalibration();
    final CalibrationReader cr = new CalibrationReader(cal);
    // Use micrometer / second
    final TypeConverter<DistanceUnit> distanceConverter = cr.getDistanceConverter(DistanceUnit.UM);
    final double exposureTime = cr.getExposureTime() / 1000.0;
    final Pair<int[], double[][]> trackData = extractTrackData(tracks, distanceConverter, exposureTime, hasCategory);
    final double[][] data = trackData.getValue();
    // Histogram the raw data.
    final Array2DRowRealMatrix raw = new Array2DRowRealMatrix(data, false);
    final WindowOrganiser wo = new WindowOrganiser();
    // Store the histogram data for plotting the components
    final double[][] columns = new double[FEATURE_NAMES.length][];
    final double[][] limits = new double[FEATURE_NAMES.length][];
    // Get column data
    for (int i = 0; i < FEATURE_NAMES.length; i++) {
        columns[i] = raw.getColumn(i);
        if (i == FEATURE_D) {
            // Plot using a logarithmic scale
            SimpleArrayUtils.apply(columns[i], Math::log10);
        }
        limits[i] = MathUtils.limits(columns[i]);
    }
    // Compute histogram bins
    final int[] bins = new int[FEATURE_NAMES.length];
    if (settings.histogramBins > 0) {
        Arrays.fill(bins, settings.histogramBins);
    } else {
        for (int i = 0; i < FEATURE_NAMES.length; i++) {
            bins[i] = HistogramPlot.getBins(StoredData.create(columns[i]), BinMethod.FD);
        }
        // Use the maximum so all histograms look the same
        Arrays.fill(bins, MathUtils.max(bins));
    }
    // Compute plots
    final Plot[] plots = new Plot[FEATURE_NAMES.length];
    for (int i = 0; i < FEATURE_NAMES.length; i++) {
        final double[][] hist = HistogramPlot.calcHistogram(columns[i], limits[i][0], limits[i][1], bins[i]);
        plots[i] = new Plot(TITLE + " " + FEATURE_NAMES[i], getFeatureLabel(i, i == FEATURE_D), "Frequency");
        plots[i].addPoints(hist[0], hist[1], Plot.BAR);
        ImageJUtils.display(plots[i].getTitle(), plots[i], ImageJUtils.NO_TO_FRONT, wo);
    }
    wo.tile();
    // The component for each data point
    int[] component;
    // The number of components
    int numComponents;
    // Data used to fit the Gaussian mixture model
    double[][] fitData;
    // The fitted model
    MixtureMultivariateGaussianDistribution model;
    if (hasCategory) {
        // Use the category as the component.
        // No fit data and no output model
        fitData = null;
        model = null;
        // The component is stored at the end of the raw track data.
        final int end = data[0].length - 1;
        component = Arrays.stream(data).mapToInt(d -> (int) d[end]).toArray();
        numComponents = MathUtils.max(component) + 1;
        // In the EM algorithm the probability of each data point is computed and normalised to
        // sum to 1. The normalised probabilities are averaged to create the weights.
        // Note the probability of each data point uses the previous weight and the algorithm
        // iterates.
        // This is not a fitted model but the input model so use
        // zero weights to indicate no fitting was performed.
        final double[] weights = new double[numComponents];
        // Remove the trailing component to show the 'model' in a table.
        createModelTable(Arrays.stream(data).map(d -> Arrays.copyOf(d, end)).toArray(double[][]::new), weights, component);
    } else {
        // Multivariate Gaussian mixture EM
        // Provide option to not use the anomalous exponent in the population mix.
        int sortDimension = SORT_DIMENSION;
        if (settings.ignoreAlpha) {
            // Remove index 0. This shifts the sort dimension.
            sortDimension--;
            fitData = Arrays.stream(data).map(d -> Arrays.copyOfRange(d, 1, d.length)).toArray(double[][]::new);
        } else {
            fitData = SimpleArrayUtils.deepCopy(data);
        }
        final MultivariateGaussianMixtureExpectationMaximization mixed = fitGaussianMixture(fitData, sortDimension);
        if (mixed == null) {
            IJ.error(TITLE, "Failed to fit a mixture model");
            return;
        }
        model = sortComponents(mixed.getFittedModel(), sortDimension);
        // For the best model, assign to the most likely population.
        component = assignData(fitData, model);
        // Table of the final model using the original data (i.e. not normalised)
        final double[] weights = model.getWeights();
        numComponents = weights.length;
        createModelTable(data, weights, component);
    }
    // Output coloured histograms of the populations.
    final LUT lut = LutHelper.createLut(settings.lutIndex);
    IntFunction<Color> colourMap;
    if (LutHelper.getColour(lut, 0).equals(Color.BLACK)) {
        colourMap = i -> LutHelper.getNonZeroColour(lut, i, 0, numComponents - 1);
    } else {
        colourMap = i -> LutHelper.getColour(lut, i, 0, numComponents - 1);
    }
    for (int i = 0; i < FEATURE_NAMES.length; i++) {
        // Extract the data for each component
        final double[] col = columns[i];
        final Plot plot = plots[i];
        for (int n = 0; n < numComponents; n++) {
            final StoredData feature = new StoredData();
            for (int j = 0; j < component.length; j++) {
                if (component[j] == n) {
                    feature.add(col[j]);
                }
            }
            if (feature.size() == 0) {
                continue;
            }
            final double[][] hist = HistogramPlot.calcHistogram(feature.values(), limits[i][0], limits[i][1], bins[i]);
            // Colour the points
            plot.setColor(colourMap.apply(n));
            plot.addPoints(hist[0], hist[1], Plot.BAR);
        }
        plot.updateImage();
    }
    createTrackDataTable(tracks, trackData, fitData, model, component, cal, colourMap);
// Analysis.
// Assign the original localisations to their track component.
// Q. What about the start/end not covered by the window?
// Save tracks as a dataset labelled with the sub-track ID?
// Output for the bound component and free components track parameters.
// Compute dwell times.
// Other ...
// Track analysis plugin:
// Extract all continuous segments of the same component.
// Produce MSD plot with error bars.
// Fit using FBM model.
}

Example 94 with MemoryPeakResults

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

the class UpdateResultsBounds method run.

@Override
public void run(String arg) {
    SmlmUsageTracker.recordPlugin(this.getClass(), arg);
    if (!showInputDialog()) {
        return;
    }
    final MemoryPeakResults results = ResultsManager.loadInputResults(settings.inputOption, true, null, null);
    if (MemoryPeakResults.isEmpty(results)) {
        IJ.error(TITLE, "No results could be loaded");
        return;
    }
    if (!showDialog(results)) {
        return;
    }
    IJ.showStatus("Updated " + results.getName());
}

Example 95 with MemoryPeakResults

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

the class ClassificationMatchCalculator method runCompareClassifications.

private void runCompareClassifications(MemoryPeakResults results1, MemoryPeakResults results2) {
    final List<PointPair> allMatches = new LinkedList<>();
    // Optionally exclude results which do not have an id and/or category
    Predicate<PeakResult> test = settings.useId == ClassAnalysis.IGNORE_ZERO ? r -> r.getId() != 0 : null;
    if (settings.useCategory == ClassAnalysis.IGNORE_ZERO) {
        final Predicate<PeakResult> test2 = r -> r.getCategory() != 0;
        test = test == null ? test2 : test.and(test2);
    } else if (test == null) {
        test = r -> true;
    }
    // Divide the results into time points
    final TIntObjectHashMap<List<PeakResultPoint>> coordinates1 = getCoordinates(results1, test);
    final TIntObjectHashMap<List<PeakResultPoint>> coordinates2 = getCoordinates(results2, test);
    // Process each time point
    int n1 = 0;
    int n2 = 0;
    for (final int t : getTimepoints(coordinates1, coordinates2)) {
        final Coordinate[] c1 = getCoordinates(coordinates1, t);
        final Coordinate[] c2 = getCoordinates(coordinates2, t);
        n1 += c1.length;
        n2 += c2.length;
        final List<PointPair> matches = new LinkedList<>();
        MatchCalculator.analyseResults3D(c1, c2, settings.matchDistance, null, null, null, matches);
        allMatches.addAll(matches);
    }
    if (allMatches.isEmpty()) {
        IJ.error(TITLE, "No localisation matches between the two results sets");
        return;
    }
    // Get the unique Ids and Categories in the matches.
    final Mapper ids = getMapper(allMatches, PeakResult::getId, settings.useId);
    final Mapper cats = getMapper(allMatches, PeakResult::getCategory, settings.useCategory);
    // Map id/category to an index = stride * cat + id
    final int stride = ids.size();
    // Any integer is allowed as an index
    if ((long) stride * cats.size() > 1L << 32) {
        IJ.error(TITLE, "Too many combinations of id and category to assigne unique labels");
        return;
    }
    // Extract indices
    final int[] set1 = new int[allMatches.size()];
    final int[] set2 = new int[allMatches.size()];
    int i = 0;
    for (final PointPair r : allMatches) {
        set1[i] = toIndex(stride, ids, cats, ((PeakResultPoint) r.getPoint1()).getPeakResult());
        set2[i] = toIndex(stride, ids, cats, ((PeakResultPoint) r.getPoint2()).getPeakResult());
        i++;
    }
    final Resequencer re = new Resequencer();
    re.setCacheMap(true);
    re.renumber(set1);
    re.renumber(set2);
    // Compare
    final RandIndex r = new RandIndex().compute(set1, set2);
    final TextWindow resultsWindow = ImageJUtils.refresh(resultsWindowRef, () -> new TextWindow(TITLE + " Results", "Results1\tResults2\tID\tCategory\tn1\tc1\tn2\tc2\tMatched\tRand Index\tAdjusted RI", "", 900, 300));
    try (BufferedTextWindow bw = new BufferedTextWindow(resultsWindow)) {
        final StringBuilder sb = new StringBuilder(2048);
        sb.append(results1.getName()).append('\t');
        sb.append(results2.getName()).append('\t');
        sb.append(ANALYSIS_OPTION[settings.useId.ordinal()]).append('\t');
        sb.append(ANALYSIS_OPTION[settings.useCategory.ordinal()]).append('\t');
        sb.append(n1).append('\t');
        sb.append(MathUtils.max(set1) + 1).append('\t');
        sb.append(n2).append('\t');
        sb.append(MathUtils.max(set2) + 1).append('\t');
        sb.append(set1.length).append('\t');
        sb.append(MathUtils.rounded(r.getRandIndex())).append('\t');
        sb.append(MathUtils.rounded(r.getAdjustedRandIndex())).append('\t');
        bw.append(sb.toString());
    }
}