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

Example 21 with Calibration

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

the class BlinkEstimatorTest method estimateBlinking.

private TIntHashSet estimateBlinking(double nBlinks, double tOn, double tOff, int particles, double fixedFraction, boolean timeAtLowerBound, boolean doAssert) {
    SpatialIllumination activationIllumination = new UniformIllumination(100);
    int totalSteps = 100;
    double eAct = totalSteps * 0.3 * activationIllumination.getAveragePhotons();
    ImageModel imageModel = new ActivationEnergyImageModel(eAct, activationIllumination, tOn, 0, tOff, 0, nBlinks);
    imageModel.setRandomGenerator(rand);
    double[] max = new double[] { 256, 256, 32 };
    double[] min = new double[3];
    SpatialDistribution distribution = new UniformDistribution(min, max, rand.nextInt());
    List<CompoundMoleculeModel> compounds = new ArrayList<CompoundMoleculeModel>(1);
    CompoundMoleculeModel c = new CompoundMoleculeModel(1, 0, 0, 0, Arrays.asList(new MoleculeModel(0, 0, 0, 0)));
    c.setDiffusionRate(diffusionRate);
    c.setDiffusionType(DiffusionType.RANDOM_WALK);
    compounds.add(c);
    List<CompoundMoleculeModel> molecules = imageModel.createMolecules(compounds, particles, distribution, false);
    // Activate fluorophores
    List<? extends FluorophoreSequenceModel> fluorophores = imageModel.createFluorophores(molecules, totalSteps);
    totalSteps = checkTotalSteps(totalSteps, fluorophores);
    List<LocalisationModel> localisations = imageModel.createImage(molecules, fixedFraction, totalSteps, photons, 0.5, false);
    //		// Remove localisations to simulate missed counts. 
    //		List<LocalisationModel> newLocalisations = new ArrayList<LocalisationModel>(localisations.size());
    //		boolean[] id = new boolean[fluorophores.size() + 1];
    //		Statistics photonStats = new Statistics();
    //		for (LocalisationModel l : localisations)
    //		{
    //			photonStats.add(l.getIntensity());
    //			// Remove by intensity threshold and optionally at random.
    //			if (l.getIntensity() < minPhotons || rand.nextDouble() < pDelete)
    //				continue;
    //			newLocalisations.add(l);
    //			id[l.getId()] = true;
    //		}
    //		localisations = newLocalisations;
    //		System.out.printf("Photons = %f\n", photonStats.getMean());
    //
    //		List<FluorophoreSequenceModel> newFluorophores = new ArrayList<FluorophoreSequenceModel>(fluorophores.size());
    //		for (FluorophoreSequenceModel f : fluorophores)
    //		{
    //			if (id[f.getId()])
    //				newFluorophores.add(f);
    //		}
    //		fluorophores = newFluorophores;
    MemoryPeakResults results = new MemoryPeakResults();
    results.setCalibration(new Calibration(pixelPitch, 1, msPerFrame));
    for (LocalisationModel l : localisations) {
        // Remove by intensity threshold and optionally at random.
        if (l.getIntensity() < minPhotons || rand.nextDouble() < pDelete)
            continue;
        float[] params = new float[7];
        params[Gaussian2DFunction.X_POSITION] = (float) l.getX();
        params[Gaussian2DFunction.Y_POSITION] = (float) l.getY();
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
        params[Gaussian2DFunction.SIGNAL] = (float) (l.getIntensity());
        results.addf(l.getTime(), 0, 0, 0, 0, 0, params, null);
    }
    // Add random localisations
    for (int i = (int) (localisations.size() * pAdd); i-- > 0; ) {
        float[] params = new float[7];
        params[Gaussian2DFunction.X_POSITION] = (float) (rand.nextDouble() * max[0]);
        params[Gaussian2DFunction.Y_POSITION] = (float) (rand.nextDouble() * max[1]);
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = psfWidth;
        // Intensity doesn't matter at the moment for tracing
        params[Gaussian2DFunction.SIGNAL] = (float) (photons);
        results.addf(1 + rand.nextInt(totalSteps), 0, 0, 0, 0, 0, params, null);
    }
    // Get actual simulated stats ...
    Statistics statsNBlinks = new Statistics();
    Statistics statsTOn = new Statistics();
    Statistics statsTOff = new Statistics();
    Statistics statsSampledNBlinks = new Statistics();
    Statistics statsSampledTOn = new Statistics();
    StoredDataStatistics statsSampledTOff = new StoredDataStatistics();
    for (FluorophoreSequenceModel f : fluorophores) {
        statsNBlinks.add(f.getNumberOfBlinks());
        statsTOn.add(f.getOnTimes());
        statsTOff.add(f.getOffTimes());
        int[] on = f.getSampledOnTimes();
        statsSampledNBlinks.add(on.length);
        statsSampledTOn.add(on);
        statsSampledTOff.add(f.getSampledOffTimes());
    }
    System.out.printf("N = %d (%d), N-blinks = %f, tOn = %f, tOff = %f, Fixed = %f\n", fluorophores.size(), localisations.size(), nBlinks, tOn, tOff, fixedFraction);
    System.out.printf("Actual N-blinks = %f (%f), tOn = %f (%f), tOff = %f (%f), 95%% = %f, max = %f\n", statsNBlinks.getMean(), statsSampledNBlinks.getMean(), statsTOn.getMean(), statsSampledTOn.getMean(), statsTOff.getMean(), statsSampledTOff.getMean(), statsSampledTOff.getStatistics().getPercentile(95), statsSampledTOff.getStatistics().getMax());
    System.out.printf("-=-=--=-\n");
    BlinkEstimator be = new BlinkEstimator();
    be.maxDarkTime = (int) (tOff * 10);
    be.msPerFrame = msPerFrame;
    be.relativeDistance = false;
    double d = ImageModel.getRandomMoveDistance(diffusionRate);
    be.searchDistance = (fixedFraction < 1) ? Math.sqrt(2 * d * d) * 3 : 0;
    be.timeAtLowerBound = timeAtLowerBound;
    be.showPlots = false;
    //Assert.assertTrue("Max dark time must exceed the dark time of the data (otherwise no plateau)",
    //		be.maxDarkTime > statsSampledTOff.getStatistics().getMax());
    int nMolecules = fluorophores.size();
    if (usePopulationStatistics) {
        nBlinks = statsNBlinks.getMean();
        tOff = statsTOff.getMean();
    } else {
        nBlinks = statsSampledNBlinks.getMean();
        tOff = statsSampledTOff.getMean();
    }
    // See if any fitting regime gets a correct answer
    TIntHashSet ok = new TIntHashSet();
    for (int nFittedPoints = MIN_FITTED_POINTS; nFittedPoints <= MAX_FITTED_POINTS; nFittedPoints++) {
        be.nFittedPoints = nFittedPoints;
        be.computeBlinkingRate(results, true);
        double moleculesError = DoubleEquality.relativeError(nMolecules, be.getNMolecules());
        double blinksError = DoubleEquality.relativeError(nBlinks, be.getNBlinks());
        double offError = DoubleEquality.relativeError(tOff * msPerFrame, be.getTOff());
        System.out.printf("Error %d: N = %f, blinks = %f, tOff = %f : %f\n", nFittedPoints, moleculesError, blinksError, offError, (moleculesError + blinksError + offError) / 3);
        if (moleculesError < relativeError && blinksError < relativeError && offError < relativeError) {
            ok.add(nFittedPoints);
            System.out.printf("-=-=--=-\n");
            System.out.printf("*** Correct at %d fitted points ***\n", nFittedPoints);
            if (doAssert)
                break;
        }
    //if (!be.isIncreaseNFittedPoints())
    //	break;
    }
    System.out.printf("-=-=--=-\n");
    if (doAssert)
        Assert.assertFalse(ok.isEmpty());
    //Assert.assertEquals("Invalid t-off", tOff * msPerFrame, be.getTOff(), tOff * msPerFrame * relativeError);
    return ok;
}

Example 22 with Calibration

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

the class DiffusionRateTest method aggregateIntoFrames.

private void aggregateIntoFrames(ArrayList<Point> points, boolean addError, double precisionInPixels, RandomGenerator[] random) {
    if (myAggregateSteps < 1)
        return;
    MemoryPeakResults results = new MemoryPeakResults(points.size() / myAggregateSteps);
    Calibration cal = new Calibration(settings.pixelPitch, 1, myAggregateSteps * 1000.0 / settings.stepsPerSecond);
    results.setCalibration(cal);
    results.setName(TITLE + " Aggregated");
    MemoryPeakResults.addResults(results);
    lastSimulatedDataset[1] = results.getName();
    int id = 0;
    int peak = 1;
    int n = 0;
    double cx = 0, cy = 0;
    // Get the mean square distance
    double sum = 0;
    int count = 0;
    PeakResult last = null;
    for (Point result : points) {
        final boolean newId = result.id != id;
        if (n >= myAggregateSteps || newId) {
            if (n != 0) {
                final float[] params = new float[7];
                double[] xyz = new double[] { cx / n, cy / n };
                if (addError)
                    xyz = addError(xyz, precisionInPixels, random);
                params[Gaussian2DFunction.X_POSITION] = (float) xyz[0];
                params[Gaussian2DFunction.Y_POSITION] = (float) xyz[1];
                params[Gaussian2DFunction.SIGNAL] = n;
                params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = 1;
                final float noise = 0.1f;
                PeakResult r = new ExtendedPeakResult(peak, (int) params[Gaussian2DFunction.X_POSITION], (int) params[Gaussian2DFunction.Y_POSITION], n, 0, noise, params, null, peak, id);
                results.add(r);
                if (last != null) {
                    sum += last.distance2(r);
                    count++;
                }
                last = r;
                n = 0;
                cx = cy = 0;
                peak++;
            }
            if (newId) {
                // Increment the frame so that tracing analysis can distinguish traces
                peak++;
                last = null;
                id = result.id;
            }
        }
        n++;
        cx += result.x;
        cy += result.y;
    }
    // Final peak
    if (n != 0) {
        final float[] params = new float[7];
        double[] xyz = new double[] { cx / n, cy / n };
        if (addError)
            xyz = addError(xyz, precisionInPixels, random);
        params[Gaussian2DFunction.X_POSITION] = (float) xyz[0];
        params[Gaussian2DFunction.Y_POSITION] = (float) xyz[1];
        params[Gaussian2DFunction.SIGNAL] = n;
        params[Gaussian2DFunction.X_SD] = params[Gaussian2DFunction.Y_SD] = 1;
        final float noise = 0.1f;
        PeakResult r = new ExtendedPeakResult(peak, (int) params[Gaussian2DFunction.X_POSITION], (int) params[Gaussian2DFunction.Y_POSITION], n, 0, noise, params, null, peak, id);
        results.add(r);
        if (last != null) {
            sum += last.distance2(r);
            count++;
        }
    }
    // MSD in pixels^2 / frame
    double msd = sum / count;
    // Convert to um^2/second
    Utils.log("Aggregated data D=%s um^2/s, Precision=%s nm, N=%d, step=%s s, mean=%s um^2, MSD = %s um^2/s", Utils.rounded(settings.diffusionRate), Utils.rounded(myPrecision), count, Utils.rounded(results.getCalibration().getExposureTime() / 1000), Utils.rounded(msd / conversionFactor), Utils.rounded((msd / conversionFactor) / (results.getCalibration().getExposureTime() / 1000)));
    msdAnalysis(points);
}

Example 23 with Calibration

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

the class BenchmarkFit method showDialog.

private boolean showDialog() {
    GenericDialog gd = new GenericDialog(TITLE);
    gd.addHelp(About.HELP_URL);
    final double sa = getSa();
    gd.addMessage(String.format("Fits the benchmark image created by CreateData plugin.\nPSF width = %s, adjusted = %s", Utils.rounded(benchmarkParameters.s / benchmarkParameters.a), Utils.rounded(sa)));
    // For each new benchmark width, reset the PSF width to the square pixel adjustment
    if (lastS != benchmarkParameters.s) {
        lastS = benchmarkParameters.s;
        psfWidth = sa;
    }
    final String filename = SettingsManager.getSettingsFilename();
    GlobalSettings settings = SettingsManager.loadSettings(filename);
    fitConfig = settings.getFitEngineConfiguration().getFitConfiguration();
    fitConfig.setNmPerPixel(benchmarkParameters.a);
    gd.addSlider("Region_size", 2, 20, regionSize);
    gd.addNumericField("PSF_width", psfWidth, 3);
    String[] solverNames = SettingsManager.getNames((Object[]) FitSolver.values());
    gd.addChoice("Fit_solver", solverNames, solverNames[fitConfig.getFitSolver().ordinal()]);
    String[] functionNames = SettingsManager.getNames((Object[]) FitFunction.values());
    gd.addChoice("Fit_function", functionNames, functionNames[fitConfig.getFitFunction().ordinal()]);
    gd.addCheckbox("Offset_fit", offsetFitting);
    gd.addNumericField("Start_offset", startOffset, 3);
    gd.addCheckbox("Include_CoM_fit", comFitting);
    gd.addCheckbox("Background_fitting", backgroundFitting);
    gd.addMessage("Signal fitting can be disabled for " + FitFunction.FIXED.toString() + " function");
    gd.addCheckbox("Signal_fitting", signalFitting);
    gd.addCheckbox("Show_histograms", showHistograms);
    gd.addCheckbox("Save_raw_data", saveRawData);
    gd.showDialog();
    if (gd.wasCanceled())
        return false;
    regionSize = (int) Math.abs(gd.getNextNumber());
    psfWidth = Math.abs(gd.getNextNumber());
    fitConfig.setFitSolver(gd.getNextChoiceIndex());
    fitConfig.setFitFunction(gd.getNextChoiceIndex());
    offsetFitting = gd.getNextBoolean();
    startOffset = Math.abs(gd.getNextNumber());
    comFitting = gd.getNextBoolean();
    backgroundFitting = gd.getNextBoolean();
    signalFitting = gd.getNextBoolean();
    showHistograms = gd.getNextBoolean();
    saveRawData = gd.getNextBoolean();
    if (!comFitting && !offsetFitting) {
        IJ.error(TITLE, "No initial fitting positions");
        return false;
    }
    if (regionSize < 1)
        regionSize = 1;
    if (gd.invalidNumber())
        return false;
    // Initialise the correct calibration
    Calibration calibration = settings.getCalibration();
    calibration.setNmPerPixel(benchmarkParameters.a);
    calibration.setGain(benchmarkParameters.gain);
    calibration.setAmplification(benchmarkParameters.amplification);
    calibration.setBias(benchmarkParameters.bias);
    calibration.setEmCCD(benchmarkParameters.emCCD);
    calibration.setReadNoise(benchmarkParameters.readNoise);
    calibration.setExposureTime(1000);
    if (!PeakFit.configureFitSolver(settings, filename, false))
        return false;
    if (showHistograms) {
        gd = new GenericDialog(TITLE);
        gd.addMessage("Select the histograms to display");
        gd.addNumericField("Histogram_bins", histogramBins, 0);
        double[] convert = getConversionFactors();
        for (int i = 0; i < displayHistograms.length; i++) if (convert[i] != 0)
            gd.addCheckbox(NAMES[i].replace(' ', '_'), displayHistograms[i]);
        gd.showDialog();
        if (gd.wasCanceled())
            return false;
        histogramBins = (int) Math.abs(gd.getNextNumber());
        for (int i = 0; i < displayHistograms.length; i++) if (convert[i] != 0)
            displayHistograms[i] = gd.getNextBoolean();
    }
    return true;
}

Example 24 with Calibration

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

the class SpotInspector 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
    results = ResultsManager.loadInputResults(inputOption, false);
    if (results == null || results.size() == 0) {
        IJ.error(TITLE, "No results could be loaded");
        IJ.showStatus("");
        return;
    }
    // Check if the original image is open
    ImageSource source = results.getSource();
    if (source == null) {
        IJ.error(TITLE, "Unknown original source image");
        return;
    }
    source = source.getOriginal();
    if (!source.open()) {
        IJ.error(TITLE, "Cannot open original source image: " + source.toString());
        return;
    }
    final float stdDevMax = getStandardDeviation(results);
    if (stdDevMax < 0) {
        // TODO - Add dialog to get the initial peak width
        IJ.error(TITLE, "Fitting configuration (for initial peak width) is not available");
        return;
    }
    // Rank spots
    rankedResults = new ArrayList<PeakResultRank>(results.size());
    final double a = results.getNmPerPixel();
    final double gain = results.getGain();
    final boolean emCCD = results.isEMCCD();
    for (PeakResult r : results.getResults()) {
        float[] score = getScore(r, a, gain, emCCD, stdDevMax);
        rankedResults.add(new PeakResultRank(r, score[0], score[1]));
    }
    Collections.sort(rankedResults);
    // Prepare results table. Get bias if necessary
    if (showCalibratedValues) {
        // Get a bias if required
        Calibration calibration = results.getCalibration();
        if (calibration.getBias() == 0) {
            ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
            gd.addMessage("Calibrated results requires a camera bias");
            gd.addNumericField("Camera_bias (ADUs)", calibration.getBias(), 2);
            gd.showDialog();
            if (!gd.wasCanceled()) {
                calibration.setBias(Math.abs(gd.getNextNumber()));
            }
        }
    }
    IJTablePeakResults table = new IJTablePeakResults(false, results.getName(), true);
    table.copySettings(results);
    table.setTableTitle(TITLE);
    table.setAddCounter(true);
    table.setShowCalibratedValues(showCalibratedValues);
    table.begin();
    // Add a mouse listener to jump to the frame for the clicked line
    textPanel = table.getResultsWindow().getTextPanel();
    // We must ignore old instances of this class from the mouse listeners
    id = ++currentId;
    textPanel.addMouseListener(this);
    // Add results to the table
    int n = 0;
    for (PeakResultRank rank : rankedResults) {
        rank.rank = n++;
        PeakResult r = rank.peakResult;
        table.add(r.getFrame(), r.origX, r.origY, r.origValue, r.error, r.noise, r.params, r.paramsStdDev);
    }
    table.end();
    if (plotScore || plotHistogram) {
        // Get values for the plots
        float[] xValues = null, yValues = null;
        double yMin, yMax;
        int spotNumber = 0;
        xValues = new float[rankedResults.size()];
        yValues = new float[xValues.length];
        for (PeakResultRank rank : rankedResults) {
            xValues[spotNumber] = spotNumber + 1;
            yValues[spotNumber++] = recoverScore(rank.score);
        }
        // Set the min and max y-values using 1.5 x IQR 
        DescriptiveStatistics stats = new DescriptiveStatistics();
        for (float v : yValues) stats.addValue(v);
        if (removeOutliers) {
            double lower = stats.getPercentile(25);
            double upper = stats.getPercentile(75);
            double iqr = upper - lower;
            yMin = FastMath.max(lower - iqr, stats.getMin());
            yMax = FastMath.min(upper + iqr, stats.getMax());
            IJ.log(String.format("Data range: %f - %f. Plotting 1.5x IQR: %f - %f", stats.getMin(), stats.getMax(), yMin, yMax));
        } else {
            yMin = stats.getMin();
            yMax = stats.getMax();
            IJ.log(String.format("Data range: %f - %f", yMin, yMax));
        }
        plotScore(xValues, yValues, yMin, yMax);
        plotHistogram(yValues, yMin, yMax);
    }
    // Extract spots into a stack
    final int w = source.getWidth();
    final int h = source.getHeight();
    final int size = 2 * radius + 1;
    ImageStack spots = new ImageStack(size, size, rankedResults.size());
    // To assist the extraction of data from the image source, process them in time order to allow 
    // frame caching. Then set the appropriate slice in the result stack
    Collections.sort(rankedResults, new Comparator<PeakResultRank>() {

        public int compare(PeakResultRank o1, PeakResultRank o2) {
            if (o1.peakResult.getFrame() < o2.peakResult.getFrame())
                return -1;
            if (o1.peakResult.getFrame() > o2.peakResult.getFrame())
                return 1;
            return 0;
        }
    });
    for (PeakResultRank rank : rankedResults) {
        PeakResult r = rank.peakResult;
        // Extract image
        // Note that the coordinates are relative to the middle of the pixel (0.5 offset)
        // so do not round but simply convert to int
        final int x = (int) (r.params[Gaussian2DFunction.X_POSITION]);
        final int y = (int) (r.params[Gaussian2DFunction.Y_POSITION]);
        // Extract a region but crop to the image bounds
        int minX = x - radius;
        int minY = y - radius;
        int maxX = FastMath.min(x + radius + 1, w);
        int maxY = FastMath.min(y + radius + 1, h);
        int padX = 0, padY = 0;
        if (minX < 0) {
            padX = -minX;
            minX = 0;
        }
        if (minY < 0) {
            padY = -minY;
            minY = 0;
        }
        int sizeX = maxX - minX;
        int sizeY = maxY - minY;
        float[] data = source.get(r.getFrame(), new Rectangle(minX, minY, sizeX, sizeY));
        // Prevent errors with missing data
        if (data == null)
            data = new float[sizeX * sizeY];
        ImageProcessor spotIp = new FloatProcessor(sizeX, sizeY, data, null);
        // Pad if necessary, i.e. the crop is too small for the stack
        if (padX > 0 || padY > 0 || sizeX < size || sizeY < size) {
            ImageProcessor spotIp2 = spotIp.createProcessor(size, size);
            spotIp2.insert(spotIp, padX, padY);
            spotIp = spotIp2;
        }
        int slice = rank.rank + 1;
        spots.setPixels(spotIp.getPixels(), slice);
        spots.setSliceLabel(Utils.rounded(rank.originalScore), slice);
    }
    source.close();
    ImagePlus imp = Utils.display(TITLE, spots);
    imp.setRoi((PointRoi) null);
    // Make bigger		
    for (int i = 10; i-- > 0; ) imp.getWindow().getCanvas().zoomIn(imp.getWidth() / 2, imp.getHeight() / 2);
}