Examples with MemoryPeakResults gdsc.smlm.results.MemoryPeakResults used on opensource projects

Example 11 with MemoryPeakResults

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

the class DiffusionRateTest method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (IJ.controlKeyDown()) {
        simpleTest();
        return;
    }
    extraOptions = Utils.isExtraOptions();
    if (!showDialog())
        return;
    lastSimulatedDataset[0] = lastSimulatedDataset[1] = "";
    lastSimulatedPrecision = 0;
    final int totalSteps = (int) Math.ceil(settings.seconds * settings.stepsPerSecond);
    conversionFactor = 1000000.0 / (settings.pixelPitch * settings.pixelPitch);
    // Diffusion rate is um^2/sec. Convert to pixels per simulation frame.
    final double diffusionRateInPixelsPerSecond = settings.diffusionRate * conversionFactor;
    final double diffusionRateInPixelsPerStep = diffusionRateInPixelsPerSecond / settings.stepsPerSecond;
    final double precisionInPixels = myPrecision / settings.pixelPitch;
    final boolean addError = myPrecision != 0;
    Utils.log(TITLE + " : D = %s um^2/sec, Precision = %s nm", Utils.rounded(settings.diffusionRate, 4), Utils.rounded(myPrecision, 4));
    Utils.log("Mean-displacement per dimension = %s nm/sec", Utils.rounded(1e3 * ImageModel.getRandomMoveDistance(settings.diffusionRate), 4));
    if (extraOptions)
        Utils.log("Step size = %s, precision = %s", Utils.rounded(ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep)), Utils.rounded(precisionInPixels));
    // Convert diffusion co-efficient into the standard deviation for the random walk
    final double diffusionSigma = (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) ? // Q. What should this be? At the moment just do 1D diffusion on a random vector
    ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep) : ImageModel.getRandomMoveDistance(diffusionRateInPixelsPerStep);
    Utils.log("Simulation step-size = %s nm", Utils.rounded(settings.pixelPitch * diffusionSigma, 4));
    // Move the molecules and get the diffusion rate
    IJ.showStatus("Simulating ...");
    final long start = System.nanoTime();
    final long seed = System.currentTimeMillis() + System.identityHashCode(this);
    RandomGenerator[] random = new RandomGenerator[3];
    RandomGenerator[] random2 = new RandomGenerator[3];
    for (int i = 0; i < 3; i++) {
        random[i] = new Well19937c(seed + i * 12436);
        random2[i] = new Well19937c(seed + i * 678678 + 3);
    }
    Statistics[] stats2D = new Statistics[totalSteps];
    Statistics[] stats3D = new Statistics[totalSteps];
    StoredDataStatistics jumpDistances2D = new StoredDataStatistics(totalSteps);
    StoredDataStatistics jumpDistances3D = new StoredDataStatistics(totalSteps);
    for (int j = 0; j < totalSteps; j++) {
        stats2D[j] = new Statistics();
        stats3D[j] = new Statistics();
    }
    SphericalDistribution dist = new SphericalDistribution(settings.confinementRadius / settings.pixelPitch);
    Statistics asymptote = new Statistics();
    // Save results to memory
    MemoryPeakResults results = new MemoryPeakResults(totalSteps);
    Calibration cal = new Calibration(settings.pixelPitch, 1, 1000.0 / settings.stepsPerSecond);
    results.setCalibration(cal);
    results.setName(TITLE);
    int peak = 0;
    // Store raw coordinates
    ArrayList<Point> points = new ArrayList<Point>(totalSteps);
    StoredData totalJumpDistances1D = new StoredData(settings.particles);
    StoredData totalJumpDistances2D = new StoredData(settings.particles);
    StoredData totalJumpDistances3D = new StoredData(settings.particles);
    for (int i = 0; i < settings.particles; i++) {
        if (i % 16 == 0) {
            IJ.showProgress(i, settings.particles);
            if (Utils.isInterrupted())
                return;
        }
        // Increment the frame so that tracing analysis can distinguish traces
        peak++;
        double[] origin = new double[3];
        final int id = i + 1;
        MoleculeModel m = new MoleculeModel(id, origin.clone());
        if (addError)
            origin = addError(origin, precisionInPixels, random);
        if (useConfinement) {
            // Note: When using confinement the average displacement should asymptote
            // at the average distance of a point from the centre of a ball. This is 3r/4.
            // See: http://answers.yahoo.com/question/index?qid=20090131162630AAMTUfM
            // The equivalent in 2D is 2r/3. However although we are plotting 2D distance
            // this is a projection of the 3D position onto the plane and so the particles
            // will not be evenly spread (there will be clustering at centre caused by the
            // poles)
            final double[] axis = (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) ? nextVector() : null;
            for (int j = 0; j < totalSteps; j++) {
                double[] xyz = m.getCoordinates();
                double[] originalXyz = xyz.clone();
                for (int n = confinementAttempts; n-- > 0; ) {
                    if (settings.getDiffusionType() == DiffusionType.GRID_WALK)
                        m.walk(diffusionSigma, random);
                    else if (settings.getDiffusionType() == DiffusionType.LINEAR_WALK)
                        m.slide(diffusionSigma, axis, random[0]);
                    else
                        m.move(diffusionSigma, random);
                    if (!dist.isWithin(m.getCoordinates())) {
                        // Reset position
                        for (int k = 0; k < 3; k++) xyz[k] = originalXyz[k];
                    } else {
                        // The move was allowed
                        break;
                    }
                }
                points.add(new Point(id, xyz));
                if (addError)
                    xyz = addError(xyz, precisionInPixels, random2);
                peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
            }
            asymptote.add(distance(m.getCoordinates()));
        } else {
            if (settings.getDiffusionType() == DiffusionType.GRID_WALK) {
                for (int j = 0; j < totalSteps; j++) {
                    m.walk(diffusionSigma, random);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            } else if (settings.getDiffusionType() == DiffusionType.LINEAR_WALK) {
                final double[] axis = nextVector();
                for (int j = 0; j < totalSteps; j++) {
                    m.slide(diffusionSigma, axis, random[0]);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            } else {
                for (int j = 0; j < totalSteps; j++) {
                    m.move(diffusionSigma, random);
                    double[] xyz = m.getCoordinates();
                    points.add(new Point(id, xyz));
                    if (addError)
                        xyz = addError(xyz, precisionInPixels, random2);
                    peak = record(xyz, id, peak, stats2D[j], stats3D[j], jumpDistances2D, jumpDistances3D, origin, results);
                }
            }
        }
        // Debug: record all the particles so they can be analysed
        // System.out.printf("%f %f %f\n", m.getX(), m.getY(), m.getZ());
        final double[] xyz = m.getCoordinates();
        double d2 = 0;
        totalJumpDistances1D.add(d2 = xyz[0] * xyz[0]);
        totalJumpDistances2D.add(d2 += xyz[1] * xyz[1]);
        totalJumpDistances3D.add(d2 += xyz[2] * xyz[2]);
    }
    final double time = (System.nanoTime() - start) / 1000000.0;
    IJ.showProgress(1);
    MemoryPeakResults.addResults(results);
    lastSimulatedDataset[0] = results.getName();
    lastSimulatedPrecision = myPrecision;
    // Convert pixels^2/step to um^2/sec
    final double msd2D = (jumpDistances2D.getMean() / conversionFactor) / (results.getCalibration().getExposureTime() / 1000);
    final double msd3D = (jumpDistances3D.getMean() / conversionFactor) / (results.getCalibration().getExposureTime() / 1000);
    Utils.log("Raw data D=%s um^2/s, Precision = %s nm, N=%d, step=%s s, mean2D=%s um^2, MSD 2D = %s um^2/s, mean3D=%s um^2, MSD 3D = %s um^2/s", Utils.rounded(settings.diffusionRate), Utils.rounded(myPrecision), jumpDistances2D.getN(), Utils.rounded(results.getCalibration().getExposureTime() / 1000), Utils.rounded(jumpDistances2D.getMean() / conversionFactor), Utils.rounded(msd2D), Utils.rounded(jumpDistances3D.getMean() / conversionFactor), Utils.rounded(msd3D));
    aggregateIntoFrames(points, addError, precisionInPixels, random2);
    IJ.showStatus("Analysing results ...");
    if (showDiffusionExample) {
        showExample(totalSteps, diffusionSigma, random);
    }
    // Plot a graph of mean squared distance
    double[] xValues = new double[stats2D.length];
    double[] yValues2D = new double[stats2D.length];
    double[] yValues3D = new double[stats3D.length];
    double[] upper2D = new double[stats2D.length];
    double[] lower2D = new double[stats2D.length];
    double[] upper3D = new double[stats3D.length];
    double[] lower3D = new double[stats3D.length];
    SimpleRegression r2D = new SimpleRegression(false);
    SimpleRegression r3D = new SimpleRegression(false);
    final int firstN = (useConfinement) ? fitN : totalSteps;
    for (int j = 0; j < totalSteps; j++) {
        // Convert steps to seconds
        xValues[j] = (double) (j + 1) / settings.stepsPerSecond;
        // Convert values in pixels^2 to um^2
        final double mean2D = stats2D[j].getMean() / conversionFactor;
        final double mean3D = stats3D[j].getMean() / conversionFactor;
        final double sd2D = stats2D[j].getStandardDeviation() / conversionFactor;
        final double sd3D = stats3D[j].getStandardDeviation() / conversionFactor;
        yValues2D[j] = mean2D;
        yValues3D[j] = mean3D;
        upper2D[j] = mean2D + sd2D;
        lower2D[j] = mean2D - sd2D;
        upper3D[j] = mean3D + sd3D;
        lower3D[j] = mean3D - sd3D;
        if (j < firstN) {
            r2D.addData(xValues[j], yValues2D[j]);
            r3D.addData(xValues[j], yValues3D[j]);
        }
    }
    // TODO - Fit using the equation for 2D confined diffusion:
    // MSD = 4s^2 + R^2 (1 - 0.99e^(-1.84^2 Dt / R^2)
    // s = localisation precision
    // R = confinement radius
    // D = 2D diffusion coefficient
    // t = time
    final PolynomialFunction fitted2D, fitted3D;
    if (r2D.getN() > 0) {
        // Do linear regression to get diffusion rate
        final double[] best2D = new double[] { r2D.getIntercept(), r2D.getSlope() };
        fitted2D = new PolynomialFunction(best2D);
        final double[] best3D = new double[] { r3D.getIntercept(), r3D.getSlope() };
        fitted3D = new PolynomialFunction(best3D);
        // For 2D diffusion: d^2 = 4D
        // where: d^2 = mean-square displacement
        double D = best2D[1] / 4.0;
        String msg = "2D Diffusion rate = " + Utils.rounded(D, 4) + " um^2 / sec (" + Utils.timeToString(time) + ")";
        IJ.showStatus(msg);
        Utils.log(msg);
        D = best3D[1] / 6.0;
        Utils.log("3D Diffusion rate = " + Utils.rounded(D, 4) + " um^2 / sec (" + Utils.timeToString(time) + ")");
    } else {
        fitted2D = fitted3D = null;
    }
    // Create plots
    plotMSD(totalSteps, xValues, yValues2D, lower2D, upper2D, fitted2D, 2);
    plotMSD(totalSteps, xValues, yValues3D, lower3D, upper3D, fitted3D, 3);
    plotJumpDistances(TITLE, jumpDistances2D, 2, 1);
    plotJumpDistances(TITLE, jumpDistances3D, 3, 1);
    if (idCount > 0)
        new WindowOrganiser().tileWindows(idList);
    if (useConfinement)
        Utils.log("3D asymptote distance = %s nm (expected %.2f)", Utils.rounded(asymptote.getMean() * settings.pixelPitch, 4), 3 * settings.confinementRadius / 4);
}

Example 12 with MemoryPeakResults

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

the class DensityImage method plotResults.

/**
	 * Draw an image of the density for each localisation. Optionally filter results below a threshold.
	 * 
	 * @param results
	 * @param density
	 * @param scoreCalculator
	 * @return
	 */
private int plotResults(MemoryPeakResults results, float[] density, ScoreCalculator scoreCalculator) {
    // Filter results using 5x higher than average density of the sample in a 150nm radius:
    // Annibale, et al (2011). Identification of clustering artifacts in photoactivated localization microscopy.
    // Nature Methods, 8, pp527-528
    MemoryPeakResults newResults = null;
    // No filtering
    float densityThreshold = Float.NEGATIVE_INFINITY;
    if (filterLocalisations) {
        densityThreshold = scoreCalculator.getThreshold();
        newResults = new MemoryPeakResults();
        newResults.copySettings(results);
        newResults.setName(results.getName() + " Density Filter");
    }
    // Draw an image - Use error so that a floating point value can be used on a single pixel
    List<PeakResult> peakResults = results.getResults();
    IJImagePeakResults image = ImagePeakResultsFactory.createPeakResultsImage(ResultsImage.ERROR, false, false, results.getName() + " Density", results.getBounds(), results.getNmPerPixel(), results.getGain(), imageScale, 0, (cumulativeImage) ? ResultsMode.ADD : ResultsMode.MAX);
    image.setDisplayFlags(image.getDisplayFlags() | IJImagePeakResults.DISPLAY_NEGATIVES);
    image.setLutName("grays");
    image.begin();
    for (int i = 0; i < density.length; i++) {
        if (density[i] < densityThreshold)
            continue;
        PeakResult r = peakResults.get(i);
        image.add(0, 0, 0, 0, density[i], 0, r.params, null);
        if (newResults != null)
            newResults.add(r);
    }
    image.end();
    // Add to memory
    if (newResults != null && newResults.size() > 0)
        MemoryPeakResults.addResults(newResults);
    return image.size();
}

Example 13 with MemoryPeakResults

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

the class DensityImage method cropWithBorder.

/**
	 * Crop the results to the ROI. Add a border using the sampling radius so that counts do not have to be approximated
	 * (i.e. all spots around the edge of the ROI will have a complete image to sample from). The results are modified
	 * in place.
	 * 
	 * @param results
	 * @param isWithin
	 *            Set to true if the added border is within the original bounds (i.e. no adjustment for missing counts
	 *            is required)
	 * @return
	 */
private MemoryPeakResults cropWithBorder(MemoryPeakResults results, boolean[] isWithin) {
    isWithin[0] = false;
    if (roiBounds == null)
        return results;
    // Adjust bounds relative to input results image:
    // Use the ROI relative to the frame the ROI is drawn on. 
    // Map those fractional coordinates back to the original data bounds.
    Rectangle bounds = results.getBounds();
    double xscale = (double) roiImageWidth / bounds.width;
    double yscale = (double) roiImageHeight / bounds.height;
    // Compute relative to the results bounds (if present)
    scaledRoiMinX = bounds.x + roiBounds.x / xscale;
    scaledRoiMaxX = scaledRoiMinX + roiBounds.width / xscale;
    scaledRoiMinY = bounds.y + roiBounds.y / yscale;
    scaledRoiMaxY = scaledRoiMinY + roiBounds.height / yscale;
    // Allow for the border
    final float minX = (int) (scaledRoiMinX - radius);
    final float maxX = (int) Math.ceil(scaledRoiMaxX + radius);
    final float minY = (int) (scaledRoiMinY - radius);
    final float maxY = (int) Math.ceil(scaledRoiMaxY + radius);
    // Create a new set of results within the bounds
    MemoryPeakResults newResults = new MemoryPeakResults();
    newResults.begin();
    for (PeakResult peakResult : results.getResults()) {
        float x = peakResult.params[Gaussian2DFunction.X_POSITION];
        float y = peakResult.params[Gaussian2DFunction.Y_POSITION];
        if (x < minX || x > maxX || y < minY || y > maxY)
            continue;
        newResults.add(peakResult);
    }
    newResults.end();
    newResults.copySettings(results);
    newResults.setBounds(new Rectangle((int) minX, (int) minY, (int) (maxX - minX), (int) (maxY - minY)));
    isWithin[0] = minX >= bounds.x && minY >= bounds.y && maxX <= (bounds.x + bounds.width) && maxY <= (bounds.y + bounds.height);
    return newResults;
}

Example 14 with MemoryPeakResults

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

the class DrawClusters method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    if (MemoryPeakResults.isMemoryEmpty()) {
        IJ.error(TITLE, "No localisations in memory");
        return;
    }
    if (!showDialog())
        return;
    // Load the results
    MemoryPeakResults results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        return;
    }
    // Get the traces
    Trace[] traces = TraceManager.convert(results);
    if (traces == null || traces.length == 0) {
        IJ.error(TITLE, "No traces could be loaded");
        return;
    }
    // Filter traces to a min size
    int maxFrame = 0;
    int count = 0;
    final int myMaxSize = (maxSize < minSize) ? Integer.MAX_VALUE : maxSize;
    final boolean myDrawLines = (myMaxSize < 2) ? false : drawLines;
    for (int i = 0; i < traces.length; i++) {
        if (expandToSingles)
            traces[i].expandToSingles();
        if (traces[i].size() >= minSize && traces[i].size() <= myMaxSize) {
            traces[count++] = traces[i];
            traces[i].sort();
            if (maxFrame < traces[i].getTail().getFrame())
                maxFrame = traces[i].getTail().getFrame();
        }
    }
    if (count == 0) {
        IJ.error(TITLE, "No traces achieved the size limits");
        return;
    }
    String msg = String.format(TITLE + ": %d / %s (%s)", count, Utils.pleural(traces.length, "trace"), Utils.pleural(results.size(), "localisation"));
    IJ.showStatus(msg);
    //Utils.log(msg);
    Rectangle bounds = results.getBounds(true);
    ImagePlus imp = WindowManager.getImage(title);
    boolean isUseStackPosition = useStackPosition;
    if (imp == null) {
        // Create a default image using 100 pixels as the longest edge
        double maxD = (bounds.width > bounds.height) ? bounds.width : bounds.height;
        int w, h;
        if (maxD == 0) {
            // Note that imageSize can be zero (for auto sizing)
            w = h = (imageSize == 0) ? 20 : imageSize;
        } else {
            // Note that imageSize can be zero (for auto sizing)
            if (imageSize == 0) {
                w = bounds.width;
                h = bounds.height;
            } else {
                w = (int) (imageSize * bounds.width / maxD);
                h = (int) (imageSize * bounds.height / maxD);
            }
        }
        ByteProcessor bp = new ByteProcessor(w, h);
        if (isUseStackPosition) {
            ImageStack stack = new ImageStack(w, h, maxFrame);
            for (int i = 1; i <= maxFrame; i++) // Do not clone as the image is empty
            stack.setPixels(bp.getPixels(), i);
            imp = Utils.display(TITLE, stack);
        } else
            imp = Utils.display(TITLE, bp);
        // Enlarge
        ImageWindow iw = imp.getWindow();
        for (int i = 9; i-- > 0 && iw.getWidth() < 500 && iw.getHeight() < 500; ) {
            iw.getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
        }
    } else {
        // Check if the image has enough frames for all the traces
        if (maxFrame > imp.getNFrames())
            isUseStackPosition = false;
    }
    final float xScale = (float) (imp.getWidth() / bounds.getWidth());
    final float yScale = (float) (imp.getHeight() / bounds.getHeight());
    // Create ROIs and store data to sort them
    Roi[] rois = new Roi[count];
    int[][] frames = (isUseStackPosition) ? new int[count][] : null;
    int[] indices = Utils.newArray(count, 0, 1);
    double[] values = new double[count];
    for (int i = 0; i < count; i++) {
        Trace trace = traces[i];
        int nPoints = trace.size();
        float[] xPoints = new float[nPoints];
        float[] yPoints = new float[nPoints];
        int j = 0;
        if (isUseStackPosition)
            frames[i] = new int[nPoints];
        for (PeakResult result : trace.getPoints()) {
            xPoints[j] = (result.getXPosition() - bounds.x) * xScale;
            yPoints[j] = (result.getYPosition() - bounds.y) * yScale;
            if (isUseStackPosition)
                frames[i][j] = result.getFrame();
            j++;
        }
        Roi roi;
        if (myDrawLines) {
            roi = new PolygonRoi(xPoints, yPoints, nPoints, Roi.POLYLINE);
            if (splineFit)
                ((PolygonRoi) roi).fitSpline();
        } else {
            roi = new PointRoi(xPoints, yPoints, nPoints);
            ((PointRoi) roi).setShowLabels(false);
        }
        rois[i] = roi;
        switch(sort) {
            case 0:
            default:
                break;
            case // Sort by ID
            1:
                values[i] = traces[i].getId();
                break;
            case // Sort by time
            2:
                values[i] = traces[i].getHead().getFrame();
                break;
            case // Sort by size descending
            3:
                values[i] = -traces[i].size();
                break;
            case // Sort by length descending
            4:
                values[i] = -roi.getLength();
                break;
            case // Mean Square Displacement
            5:
                values[i] = -traces[i].getMSD();
                break;
            case // Mean / Frame
            6:
                values[i] = -traces[i].getMeanPerFrame();
                break;
        }
    }
    if (sort > 0)
        Sort.sort(indices, values);
    // Draw the traces as ROIs on an overlay
    Overlay o = new Overlay();
    LUT lut = LUTHelper.createLUT(DrawClusters.lut);
    final double scale = 256.0 / count;
    if (isUseStackPosition) {
        // Add the tracks on the frames containing the results
        final boolean isHyperStack = imp.isDisplayedHyperStack();
        for (int i = 0; i < count; i++) {
            final int index = indices[i];
            final Color c = LUTHelper.getColour(lut, (int) (i * scale));
            final PolygonRoi roi = (PolygonRoi) rois[index];
            roi.setFillColor(c);
            roi.setStrokeColor(c);
            final FloatPolygon fp = roi.getNonSplineFloatPolygon();
            // For each frame in the track, add the ROI track and a point ROI for the current position
            for (int j = 0; j < frames[index].length; j++) {
                addToOverlay(o, (Roi) roi.clone(), isHyperStack, frames[index][j]);
                //PointRoi pointRoi = new PointRoi(pos.x + fp.xpoints[j], pos.y + fp.ypoints[j]);
                PointRoi pointRoi = new PointRoi(fp.xpoints[j], fp.ypoints[j]);
                pointRoi.setPointType(3);
                pointRoi.setFillColor(c);
                pointRoi.setStrokeColor(Color.black);
                addToOverlay(o, pointRoi, isHyperStack, frames[index][j]);
            }
        }
    } else {
        // Add the tracks as a single overlay
        for (int i = 0; i < count; i++) {
            final Roi roi = rois[indices[i]];
            roi.setStrokeColor(new Color(lut.getRGB((int) (i * scale))));
            o.add(roi);
        }
    }
    imp.setOverlay(o);
    IJ.showStatus(msg);
}

Example 15 with MemoryPeakResults

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

the class DriftCalculator method applyDriftCorrection.

private void applyDriftCorrection(MemoryPeakResults results, double[][] drift) {
    ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    gd.addMessage("Apply drift correction to in-memory results?");
    gd.addChoice("Update_method", UPDATE_METHODS, UPDATE_METHODS[updateMethod]);
    // Option to save the drift unless it was loaded from file
    if (method != DRIFT_FILE)
        gd.addCheckbox("Save_drift", saveDrift);
    gd.showDialog();
    if (gd.wasCanceled())
        return;
    updateMethod = gd.getNextChoiceIndex();
    if (method != DRIFT_FILE) {
        saveDrift = gd.getNextBoolean();
        saveDrift(calculatedTimepoints, lastdx, lastdy);
    }
    if (updateMethod == 0)
        return;
    final double[] dx = drift[0];
    final double[] dy = drift[1];
    if (updateMethod == 1) {
        // Update the results in memory
        Utils.log("Applying drift correction to the results set: " + results.getName());
        for (PeakResult r : results) {
            r.params[Gaussian2DFunction.X_POSITION] += dx[r.getFrame()];
            r.params[Gaussian2DFunction.Y_POSITION] += dy[r.getFrame()];
        }
    } else {
        // Create a new set of results
        MemoryPeakResults newResults = new MemoryPeakResults(results.size());
        newResults.copySettings(results);
        newResults.setName(results.getName() + " (Corrected)");
        MemoryPeakResults.addResults(newResults);
        final boolean truncate = updateMethod == 3;
        Utils.log("Creating %sdrift corrected results set: " + newResults.getName(), (truncate) ? "truncated " : "");
        for (PeakResult r : results) {
            if (truncate) {
                if (r.getFrame() < interpolationStart || r.getFrame() > interpolationEnd)
                    continue;
            }
            float[] params = Arrays.copyOf(r.params, r.params.length);
            params[Gaussian2DFunction.X_POSITION] += dx[r.getFrame()];
            params[Gaussian2DFunction.Y_POSITION] += dy[r.getFrame()];
            newResults.addf(r.getFrame(), r.origX, r.origY, r.origValue, r.error, r.noise, params, r.paramsStdDev);
        }
    }
}