Examples with UniformDistribution gdsc.smlm.model.UniformDistribution used on opensource projects

Example 1 with UniformDistribution

use of gdsc.smlm.model.UniformDistribution in project GDSC-SMLM by aherbert.

the class PCPALMMolecules method runSimulation.

private void runSimulation(boolean resultsAvailable) {
    if (resultsAvailable && !showSimulationDialog())
        return;
    startLog();
    log("Simulation parameters");
    if (blinkingDistribution == 3) {
        log("  - Clusters = %d", nMolecules);
        log("  - Simulation size = %s um", Utils.rounded(simulationSize, 4));
        log("  - Molecules/cluster = %s", Utils.rounded(blinkingRate, 4));
        log("  - Blinking distribution = %s", BLINKING_DISTRIBUTION[blinkingDistribution]);
        log("  - p-Value = %s", Utils.rounded(p, 4));
    } else {
        log("  - Molecules = %d", nMolecules);
        log("  - Simulation size = %s um", Utils.rounded(simulationSize, 4));
        log("  - Blinking rate = %s", Utils.rounded(blinkingRate, 4));
        log("  - Blinking distribution = %s", BLINKING_DISTRIBUTION[blinkingDistribution]);
    }
    log("  - Average precision = %s nm", Utils.rounded(sigmaS, 4));
    log("  - Clusters simulation = " + CLUSTER_SIMULATION[clusterSimulation]);
    if (clusterSimulation > 0) {
        log("  - Cluster number = %s +/- %s", Utils.rounded(clusterNumber, 4), Utils.rounded(clusterNumberSD, 4));
        log("  - Cluster radius = %s nm", Utils.rounded(clusterRadius, 4));
    }
    final double nmPerPixel = 100;
    double width = simulationSize * 1000.0;
    // Allow a border of 3 x sigma for +/- precision
    //if (blinkingRate > 1)
    width -= 3 * sigmaS;
    RandomGenerator randomGenerator = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this));
    RandomDataGenerator dataGenerator = new RandomDataGenerator(randomGenerator);
    UniformDistribution dist = new UniformDistribution(null, new double[] { width, width, 0 }, randomGenerator.nextInt());
    molecules = new ArrayList<Molecule>(nMolecules);
    // Create some dummy results since the calibration is required for later analysis
    results = new MemoryPeakResults();
    results.setCalibration(new gdsc.smlm.results.Calibration(nmPerPixel, 1, 100));
    results.setSource(new NullSource("Molecule Simulation"));
    results.begin();
    int count = 0;
    // Generate a sequence of coordinates
    ArrayList<double[]> xyz = new ArrayList<double[]>((int) (nMolecules * 1.1));
    Statistics statsRadius = new Statistics();
    Statistics statsSize = new Statistics();
    String maskTitle = TITLE + " Cluster Mask";
    ByteProcessor bp = null;
    double maskScale = 0;
    if (clusterSimulation > 0) {
        // Simulate clusters.
        // Note: In the Veatch et al. paper (Plos 1, e31457) correlation functions are built using circles
        // with small radii of 4-8 Arbitrary Units (AU) or large radii of 10-30 AU. A fluctuations model is
        // created at T = 1.075 Tc. It is not clear exactly how the particles are distributed.
        // It may be that a mask is created first using the model. The particles are placed on the mask using
        // a specified density. This simulation produces a figure to show either a damped cosine function
        // (circles) or an exponential (fluctuations). The number of particles in each circle may be randomly
        // determined just by density. The figure does not discuss the derivation of the cluster size 
        // statistic.
        // 
        // If this plugin simulation is run with a uniform distribution and blinking rate of 1 then the damped
        // cosine function is reproduced. The curve crosses g(r)=1 at a value equivalent to the average
        // distance to the centre-of-mass of each drawn cluster, not the input cluster radius parameter (which 
        // is a hard upper limit on the distance to centre).
        final int maskSize = lowResolutionImageSize;
        int[] mask = null;
        // scale is in nm/pixel
        maskScale = width / maskSize;
        ArrayList<double[]> clusterCentres = new ArrayList<double[]>();
        int totalSteps = 1 + (int) Math.ceil(nMolecules / clusterNumber);
        if (clusterSimulation == 2 || clusterSimulation == 3) {
            // Clusters are non-overlapping circles
            // Ensure the circles do not overlap by using an exclusion mask that accumulates 
            // out-of-bounds pixels by drawing the last cluster (plus some border) on an image. When no
            // more pixels are available then stop generating molecules.
            // This is done by cumulatively filling a mask and using the MaskDistribution to select 
            // a new point. This may be slow but it works.
            // TODO - Allow clusters of different sizes...
            mask = new int[maskSize * maskSize];
            Arrays.fill(mask, 255);
            MaskDistribution maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
            double[] centre;
            IJ.showStatus("Computing clusters mask");
            int roiRadius = (int) Math.round((clusterRadius * 2) / maskScale);
            if (clusterSimulation == 3) {
                // Generate a mask of circles then sample from that.
                // If we want to fill the mask completely then adjust the total steps to be the number of 
                // circles that can fit inside the mask.
                totalSteps = (int) (maskSize * maskSize / (Math.PI * Math.pow(clusterRadius / maskScale, 2)));
            }
            while ((centre = maskDistribution.next()) != null && clusterCentres.size() < totalSteps) {
                IJ.showProgress(clusterCentres.size(), totalSteps);
                // The mask returns the coordinates with the centre of the image at 0,0
                centre[0] += width / 2;
                centre[1] += width / 2;
                clusterCentres.add(centre);
                // Fill in the mask around the centre to exclude any more circles that could overlap
                double cx = centre[0] / maskScale;
                double cy = centre[1] / maskScale;
                fillMask(mask, maskSize, (int) cx, (int) cy, roiRadius, 0);
                //Utils.display("Mask", new ColorProcessor(maskSize, maskSize, mask));
                try {
                    maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
                } catch (IllegalArgumentException e) {
                    // This can happen when there are no more non-zero pixels
                    log("WARNING: No more room for clusters on the mask area (created %d of estimated %d)", clusterCentres.size(), totalSteps);
                    break;
                }
            }
            IJ.showProgress(1);
            IJ.showStatus("");
        } else {
            // Pick centres randomly from the distribution 
            while (clusterCentres.size() < totalSteps) clusterCentres.add(dist.next());
        }
        if (showClusterMask || clusterSimulation == 3) {
            // Show the mask for the clusters
            if (mask == null)
                mask = new int[maskSize * maskSize];
            else
                Arrays.fill(mask, 0);
            int roiRadius = (int) Math.round((clusterRadius) / maskScale);
            for (double[] c : clusterCentres) {
                double cx = c[0] / maskScale;
                double cy = c[1] / maskScale;
                fillMask(mask, maskSize, (int) cx, (int) cy, roiRadius, 1);
            }
            if (clusterSimulation == 3) {
                // We have the mask. Now pick points at random from the mask.
                MaskDistribution maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
                // Allocate each molecule position to a parent circle so defining clusters.
                int[][] clusters = new int[clusterCentres.size()][];
                int[] clusterSize = new int[clusters.length];
                for (int i = 0; i < nMolecules; i++) {
                    double[] centre = maskDistribution.next();
                    // The mask returns the coordinates with the centre of the image at 0,0
                    centre[0] += width / 2;
                    centre[1] += width / 2;
                    xyz.add(centre);
                    // Output statistics on cluster size and number.
                    // TODO - Finding the closest cluster could be done better than an all-vs-all comparison
                    double max = distance2(centre, clusterCentres.get(0));
                    int cluster = 0;
                    for (int j = 1; j < clusterCentres.size(); j++) {
                        double d2 = distance2(centre, clusterCentres.get(j));
                        if (d2 < max) {
                            max = d2;
                            cluster = j;
                        }
                    }
                    // Assign point i to cluster
                    centre[2] = cluster;
                    if (clusterSize[cluster] == 0) {
                        clusters[cluster] = new int[10];
                    }
                    if (clusters[cluster].length <= clusterSize[cluster]) {
                        clusters[cluster] = Arrays.copyOf(clusters[cluster], (int) (clusters[cluster].length * 1.5));
                    }
                    clusters[cluster][clusterSize[cluster]++] = i;
                }
                // Generate real cluster size statistics
                for (int j = 0; j < clusterSize.length; j++) {
                    final int size = clusterSize[j];
                    if (size == 0)
                        continue;
                    statsSize.add(size);
                    if (size == 1) {
                        statsRadius.add(0);
                        continue;
                    }
                    // Find centre of cluster and add the distance to each point
                    double[] com = new double[2];
                    for (int n = 0; n < size; n++) {
                        double[] xy = xyz.get(clusters[j][n]);
                        for (int k = 0; k < 2; k++) com[k] += xy[k];
                    }
                    for (int k = 0; k < 2; k++) com[k] /= size;
                    for (int n = 0; n < size; n++) {
                        double dx = xyz.get(clusters[j][n])[0] - com[0];
                        double dy = xyz.get(clusters[j][n])[1] - com[1];
                        statsRadius.add(Math.sqrt(dx * dx + dy * dy));
                    }
                }
            }
            if (showClusterMask) {
                bp = new ByteProcessor(maskSize, maskSize);
                for (int i = 0; i < mask.length; i++) if (mask[i] != 0)
                    bp.set(i, 128);
                Utils.display(maskTitle, bp);
            }
        }
        // Use the simulated cluster centres to create clusters of the desired size
        if (clusterSimulation == 1 || clusterSimulation == 2) {
            for (double[] clusterCentre : clusterCentres) {
                int clusterN = (int) Math.round((clusterNumberSD > 0) ? dataGenerator.nextGaussian(clusterNumber, clusterNumberSD) : clusterNumber);
                if (clusterN < 1)
                    continue;
                //double[] clusterCentre = dist.next();
                if (clusterN == 1) {
                    // No need for a cluster around a point
                    xyz.add(clusterCentre);
                    statsRadius.add(0);
                    statsSize.add(1);
                } else {
                    // Generate N random points within a circle of the chosen cluster radius.
                    // Locate the centre-of-mass and the average distance to the centre.
                    double[] com = new double[3];
                    int j = 0;
                    while (j < clusterN) {
                        // Generate a random point within a circle uniformly
                        // http://stackoverflow.com/questions/5837572/generate-a-random-point-within-a-circle-uniformly
                        double t = 2.0 * Math.PI * randomGenerator.nextDouble();
                        double u = randomGenerator.nextDouble() + randomGenerator.nextDouble();
                        double r = clusterRadius * ((u > 1) ? 2 - u : u);
                        double x = r * Math.cos(t);
                        double y = r * Math.sin(t);
                        double[] xy = new double[] { clusterCentre[0] + x, clusterCentre[1] + y };
                        xyz.add(xy);
                        for (int k = 0; k < 2; k++) com[k] += xy[k];
                        j++;
                    }
                    // Add the distance of the points from the centre of the cluster.
                    // Note this does not account for the movement due to precision.
                    statsSize.add(j);
                    if (j == 1) {
                        statsRadius.add(0);
                    } else {
                        for (int k = 0; k < 2; k++) com[k] /= j;
                        while (j > 0) {
                            double dx = xyz.get(xyz.size() - j)[0] - com[0];
                            double dy = xyz.get(xyz.size() - j)[1] - com[1];
                            statsRadius.add(Math.sqrt(dx * dx + dy * dy));
                            j--;
                        }
                    }
                }
            }
        }
    } else {
        // Random distribution
        for (int i = 0; i < nMolecules; i++) xyz.add(dist.next());
    }
    // The Gaussian sigma should be applied so the overall distance from the centre
    // ( sqrt(x^2+y^2) ) has a standard deviation of sigmaS?
    final double sigma1D = sigmaS / Math.sqrt(2);
    // Show optional histograms
    StoredDataStatistics intraDistances = null;
    StoredData blinks = null;
    if (showHistograms) {
        int capacity = (int) (xyz.size() * blinkingRate);
        intraDistances = new StoredDataStatistics(capacity);
        blinks = new StoredData(capacity);
    }
    Statistics statsSigma = new Statistics();
    for (int i = 0; i < xyz.size(); i++) {
        int nOccurrences = getBlinks(dataGenerator, blinkingRate);
        if (showHistograms)
            blinks.add(nOccurrences);
        final int size = molecules.size();
        // Get coordinates in nm
        final double[] moleculeXyz = xyz.get(i);
        if (bp != null && nOccurrences > 0) {
            bp.putPixel((int) Math.round(moleculeXyz[0] / maskScale), (int) Math.round(moleculeXyz[1] / maskScale), 255);
        }
        while (nOccurrences-- > 0) {
            final double[] localisationXy = Arrays.copyOf(moleculeXyz, 2);
            // Add random precision
            if (sigma1D > 0) {
                final double dx = dataGenerator.nextGaussian(0, sigma1D);
                final double dy = dataGenerator.nextGaussian(0, sigma1D);
                localisationXy[0] += dx;
                localisationXy[1] += dy;
                if (!dist.isWithinXY(localisationXy))
                    continue;
                // Calculate mean-squared displacement
                statsSigma.add(dx * dx + dy * dy);
            }
            final double x = localisationXy[0];
            final double y = localisationXy[1];
            molecules.add(new Molecule(x, y, i, 1));
            // Store in pixels
            float[] params = new float[7];
            params[Gaussian2DFunction.X_POSITION] = (float) (x / nmPerPixel);
            params[Gaussian2DFunction.Y_POSITION] = (float) (y / nmPerPixel);
            results.addf(i + 1, (int) x, (int) y, 0, 0, 0, params, null);
        }
        if (molecules.size() > size) {
            count++;
            if (showHistograms) {
                int newCount = molecules.size() - size;
                if (newCount == 1) {
                    //intraDistances.add(0);
                    continue;
                }
                // Get the distance matrix between these molecules
                double[][] matrix = new double[newCount][newCount];
                for (int ii = size, x = 0; ii < molecules.size(); ii++, x++) {
                    for (int jj = size + 1, y = 1; jj < molecules.size(); jj++, y++) {
                        final double d2 = molecules.get(ii).distance2(molecules.get(jj));
                        matrix[x][y] = matrix[y][x] = d2;
                    }
                }
                // Get the maximum distance for particle linkage clustering of this molecule
                double max = 0;
                for (int x = 0; x < newCount; x++) {
                    // Compare to all-other molecules and get the minimum distance 
                    // needed to join at least one
                    double linkDistance = Double.POSITIVE_INFINITY;
                    for (int y = 0; y < newCount; y++) {
                        if (x == y)
                            continue;
                        if (matrix[x][y] < linkDistance)
                            linkDistance = matrix[x][y];
                    }
                    // Check if this is larger 
                    if (max < linkDistance)
                        max = linkDistance;
                }
                intraDistances.add(Math.sqrt(max));
            }
        }
    }
    results.end();
    if (bp != null)
        Utils.display(maskTitle, bp);
    // Used for debugging
    //System.out.printf("  * Molecules = %d (%d activated)\n", xyz.size(), count);
    //if (clusterSimulation > 0)
    //	System.out.printf("  * Cluster number = %s +/- %s. Radius = %s +/- %s\n",
    //			Utils.rounded(statsSize.getMean(), 4), Utils.rounded(statsSize.getStandardDeviation(), 4),
    //			Utils.rounded(statsRadius.getMean(), 4), Utils.rounded(statsRadius.getStandardDeviation(), 4));
    log("Simulation results");
    log("  * Molecules = %d (%d activated)", xyz.size(), count);
    log("  * Blinking rate = %s", Utils.rounded((double) molecules.size() / xyz.size(), 4));
    log("  * Precision (Mean-displacement) = %s nm", (statsSigma.getN() > 0) ? Utils.rounded(Math.sqrt(statsSigma.getMean()), 4) : "0");
    if (showHistograms) {
        if (intraDistances.getN() == 0) {
            log("  * Mean Intra-Molecule particle linkage distance = 0 nm");
            log("  * Fraction of inter-molecule particle linkage @ 0 nm = 0 %%");
        } else {
            plot(blinks, "Blinks/Molecule", true);
            double[][] intraHist = plot(intraDistances, "Intra-molecule particle linkage distance", false);
            // Determine 95th and 99th percentile
            int p99 = intraHist[0].length - 1;
            double limit1 = 0.99 * intraHist[1][p99];
            double limit2 = 0.95 * intraHist[1][p99];
            while (intraHist[1][p99] > limit1 && p99 > 0) p99--;
            int p95 = p99;
            while (intraHist[1][p95] > limit2 && p95 > 0) p95--;
            log("  * Mean Intra-Molecule particle linkage distance = %s nm (95%% = %s, 99%% = %s, 100%% = %s)", Utils.rounded(intraDistances.getMean(), 4), Utils.rounded(intraHist[0][p95], 4), Utils.rounded(intraHist[0][p99], 4), Utils.rounded(intraHist[0][intraHist[0].length - 1], 4));
            if (distanceAnalysis) {
                performDistanceAnalysis(intraHist, p99);
            }
        }
    }
    if (clusterSimulation > 0) {
        log("  * Cluster number = %s +/- %s", Utils.rounded(statsSize.getMean(), 4), Utils.rounded(statsSize.getStandardDeviation(), 4));
        log("  * Cluster radius = %s +/- %s nm (mean distance to centre-of-mass)", Utils.rounded(statsRadius.getMean(), 4), Utils.rounded(statsRadius.getStandardDeviation(), 4));
    }
}

Example 2 with UniformDistribution

use of gdsc.smlm.model.UniformDistribution in project GDSC-SMLM by aherbert.

the class CreateData method createUniformDistribution.

/**
	 * Create distribution within an XY border
	 * 
	 * @param border
	 * @return
	 */
private UniformDistribution createUniformDistribution(double border) {
    double depth = (settings.fixedDepth) ? settings.depth / settings.pixelPitch : settings.depth / (2 * settings.pixelPitch);
    // Ensure the focal plane is in the middle of the zDepth
    double[] max = new double[] { settings.size / 2 - border, settings.size / 2 - border, depth };
    double[] min = new double[3];
    for (int i = 0; i < 3; i++) min[i] = -max[i];
    if (settings.fixedDepth)
        min[2] = max[2];
    // Try using different distributions:
    final RandomGenerator rand1 = createRandomGenerator();
    if (settings.distribution.equals(DISTRIBUTION[UNIFORM_HALTON])) {
        return new UniformDistribution(min, max, rand1.nextInt());
    }
    if (settings.distribution.equals(DISTRIBUTION[UNIFORM_SOBOL])) {
        SobolSequenceGenerator rvg = new SobolSequenceGenerator(3);
        rvg.skipTo(rand1.nextInt());
        return new UniformDistribution(min, max, rvg);
    }
    // Create a distribution using random generators for each dimension 
    UniformDistribution distribution = new UniformDistribution(min, max, this);
    return distribution;
}

Example 3 with UniformDistribution

use of gdsc.smlm.model.UniformDistribution 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;
}