Examples with PoissonDistribution org.apache.commons.math3.distribution.PoissonDistribution used on opensource projects

Example 6 with PoissonDistribution

use of org.apache.commons.math3.distribution.PoissonDistribution in project incubator-systemml by apache.

the class PoissonPRNGenerator method setup.

public void setup(double mean, long sd) {
    seed = sd;
    SynchronizedRandomGenerator srg = new SynchronizedRandomGenerator(new Well1024a());
    srg.setSeed(seed);
    _pdist = new PoissonDistribution(srg, _mean, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
}

Example 7 with PoissonDistribution

use of org.apache.commons.math3.distribution.PoissonDistribution in project GDSC-SMLM by aherbert.

the class CreateData method run.

/*
	 * (non-Javadoc)
	 * 
	 * @see ij.plugin.PlugIn#run(java.lang.String)
	 */
public void run(String arg) {
    SMLMUsageTracker.recordPlugin(this.getClass(), arg);
    extraOptions = Utils.isExtraOptions();
    simpleMode = (arg != null && arg.contains("simple"));
    benchmarkMode = (arg != null && arg.contains("benchmark"));
    spotMode = (arg != null && arg.contains("spot"));
    trackMode = (arg != null && arg.contains("track"));
    if ("load".equals(arg)) {
        loadBenchmarkData();
        return;
    }
    // Each localisation is a simulated emission of light from a point in space and time
    List<LocalisationModel> localisations = null;
    // Each localisation set is a collection of localisations that represent all localisations
    // with the same ID that are on in the same image time frame (Note: the simulation
    // can create many localisations per fluorophore per time frame which is useful when 
    // modelling moving particles)
    List<LocalisationModelSet> localisationSets = null;
    // Each fluorophore contains the on and off times when light was emitted 
    List<? extends FluorophoreSequenceModel> fluorophores = null;
    if (simpleMode || benchmarkMode || spotMode) {
        if (!showSimpleDialog())
            return;
        resetMemory();
        // 1 second frames
        settings.exposureTime = 1000;
        areaInUm = settings.size * settings.pixelPitch * settings.size * settings.pixelPitch / 1e6;
        // Number of spots per frame
        int n = 0;
        int[] nextN = null;
        SpatialDistribution dist;
        if (benchmarkMode) {
            // --------------------
            // BENCHMARK SIMULATION
            // --------------------
            // Draw the same point on the image repeatedly
            n = 1;
            dist = createFixedDistribution();
            reportAndSaveFittingLimits(dist);
        } else if (spotMode) {
            // ---------------
            // SPOT SIMULATION
            // ---------------
            // The spot simulation draws 0 or 1 random point per frame. 
            // Ensure we have 50% of the frames with a spot.
            nextN = new int[settings.particles * 2];
            Arrays.fill(nextN, 0, settings.particles, 1);
            Random rand = new Random();
            rand.shuffle(nextN);
            // Only put spots in the central part of the image
            double border = settings.size / 4.0;
            dist = createUniformDistribution(border);
        } else {
            // -----------------
            // SIMPLE SIMULATION
            // -----------------
            // The simple simulation draws n random points per frame to achieve a specified density.
            // No points will appear in multiple frames.
            // Each point has a random number of photons sampled from a range.
            // We can optionally use a mask. Create his first as it updates the areaInUm
            dist = createDistribution();
            // Randomly sample (i.e. not uniform density in all frames)
            if (settings.samplePerFrame) {
                final double mean = areaInUm * settings.density;
                System.out.printf("Mean samples = %f\n", mean);
                if (mean < 0.5) {
                    GenericDialog gd = new GenericDialog(TITLE);
                    gd.addMessage("The mean samples per frame is low: " + Utils.rounded(mean) + "\n \nContinue?");
                    gd.enableYesNoCancel();
                    gd.hideCancelButton();
                    gd.showDialog();
                    if (!gd.wasOKed())
                        return;
                }
                PoissonDistribution poisson = new PoissonDistribution(createRandomGenerator(), mean, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS);
                StoredDataStatistics samples = new StoredDataStatistics(settings.particles);
                while (samples.getSum() < settings.particles) {
                    samples.add(poisson.sample());
                }
                nextN = new int[samples.getN()];
                for (int i = 0; i < nextN.length; i++) nextN[i] = (int) samples.getValue(i);
            } else {
                // Use the density to get the number per frame
                n = (int) FastMath.max(1, Math.round(areaInUm * settings.density));
            }
        }
        RandomGenerator random = null;
        localisations = new ArrayList<LocalisationModel>(settings.particles);
        localisationSets = new ArrayList<LocalisationModelSet>(settings.particles);
        final int minPhotons = (int) settings.photonsPerSecond;
        final int range = (int) settings.photonsPerSecondMaximum - minPhotons + 1;
        if (range > 1)
            random = createRandomGenerator();
        // Add frames at the specified density until the number of particles has been reached
        int id = 0;
        int t = 0;
        while (id < settings.particles) {
            // Allow the number per frame to be specified
            if (nextN != null) {
                if (t >= nextN.length)
                    break;
                n = nextN[t];
            }
            // Simulate random positions in the frame for the specified density
            t++;
            for (int j = 0; j < n; j++) {
                final double[] xyz = dist.next();
                // Ignore within border. We do not want to draw things we cannot fit.
                //if (!distBorder.isWithinXY(xyz))
                //	continue;
                // Simulate random photons
                final int intensity = minPhotons + ((random != null) ? random.nextInt(range) : 0);
                LocalisationModel m = new LocalisationModel(id, t, xyz, intensity, LocalisationModel.CONTINUOUS);
                localisations.add(m);
                // Each localisation can be a separate localisation set
                LocalisationModelSet set = new LocalisationModelSet(id, t);
                set.add(m);
                localisationSets.add(set);
                id++;
            }
        }
    } else {
        if (!showDialog())
            return;
        resetMemory();
        areaInUm = settings.size * settings.pixelPitch * settings.size * settings.pixelPitch / 1e6;
        int totalSteps;
        double correlation = 0;
        ImageModel imageModel;
        if (trackMode) {
            // ----------------
            // TRACK SIMULATION
            // ----------------
            // In track mode we create fixed lifetime fluorophores that do not overlap in time.
            // This is the simplest simulation to test moving molecules.
            settings.seconds = (int) Math.ceil(settings.particles * (settings.exposureTime + settings.tOn) / 1000);
            totalSteps = 0;
            final double simulationStepsPerFrame = (settings.stepsPerSecond * settings.exposureTime) / 1000.0;
            imageModel = new FixedLifetimeImageModel(settings.stepsPerSecond * settings.tOn / 1000.0, simulationStepsPerFrame);
        } else {
            // ---------------
            // FULL SIMULATION
            // ---------------
            // The full simulation draws n random points in space.
            // The same molecule may appear in multiple frames, move and blink.
            //
            // Points are modelled as fluorophores that must be activated and then will 
            // blink and photo-bleach. The molecules may diffuse and this can be simulated 
            // with many steps per image frame. All steps from a frame are collected
            // into a localisation set which can be drawn on the output image.
            SpatialIllumination activationIllumination = createIllumination(settings.pulseRatio, settings.pulseInterval);
            // Generate additional frames so that each frame has the set number of simulation steps
            totalSteps = (int) Math.ceil(settings.seconds * settings.stepsPerSecond);
            // Since we have an exponential decay of activations
            // ensure half of the particles have activated by 30% of the frames.
            double eAct = totalSteps * 0.3 * activationIllumination.getAveragePhotons();
            // Q. Does tOn/tOff change depending on the illumination strength?
            imageModel = new ActivationEnergyImageModel(eAct, activationIllumination, settings.stepsPerSecond * settings.tOn / 1000.0, settings.stepsPerSecond * settings.tOffShort / 1000.0, settings.stepsPerSecond * settings.tOffLong / 1000.0, settings.nBlinksShort, settings.nBlinksLong);
            imageModel.setUseGeometricDistribution(settings.nBlinksGeometricDistribution);
            // Only use the correlation if selected for the distribution
            if (PHOTON_DISTRIBUTION[PHOTON_CORRELATED].equals(settings.photonDistribution))
                correlation = settings.correlation;
        }
        imageModel.setRandomGenerator(createRandomGenerator());
        imageModel.setPhotonBudgetPerFrame(true);
        imageModel.setDiffusion2D(settings.diffuse2D);
        imageModel.setRotation2D(settings.rotate2D);
        IJ.showStatus("Creating molecules ...");
        SpatialDistribution distribution = createDistribution();
        List<CompoundMoleculeModel> compounds = createCompoundMolecules();
        if (compounds == null)
            return;
        List<CompoundMoleculeModel> molecules = imageModel.createMolecules(compounds, settings.particles, distribution, settings.rotateInitialOrientation);
        // Activate fluorophores
        IJ.showStatus("Creating fluorophores ...");
        // Note: molecules list will be converted to compounds containing fluorophores
        fluorophores = imageModel.createFluorophores(molecules, totalSteps);
        if (fluorophores.isEmpty()) {
            IJ.error(TITLE, "No fluorophores created");
            return;
        }
        IJ.showStatus("Creating localisations ...");
        // TODO - Output a molecule Id for each fluorophore if using compound molecules. This allows analysis
        // of the ratio of trimers, dimers, monomers, etc that could be detected.
        totalSteps = checkTotalSteps(totalSteps, fluorophores);
        if (totalSteps == 0)
            return;
        imageModel.setPhotonDistribution(createPhotonDistribution());
        imageModel.setConfinementDistribution(createConfinementDistribution());
        // This should be optimised
        imageModel.setConfinementAttempts(10);
        localisations = imageModel.createImage(molecules, settings.fixedFraction, totalSteps, (double) settings.photonsPerSecond / settings.stepsPerSecond, correlation, settings.rotateDuringSimulation);
        // Re-adjust the fluorophores to the correct time
        if (settings.stepsPerSecond != 1) {
            final double scale = 1.0 / settings.stepsPerSecond;
            for (FluorophoreSequenceModel f : fluorophores) f.adjustTime(scale);
        }
        // Integrate the frames
        localisationSets = combineSimulationSteps(localisations);
        localisationSets = filterToImageBounds(localisationSets);
    }
    datasetNumber++;
    localisations = drawImage(localisationSets);
    if (localisations == null || localisations.isEmpty()) {
        IJ.error(TITLE, "No localisations created");
        return;
    }
    fluorophores = removeFilteredFluorophores(fluorophores, localisations);
    double signalPerFrame = showSummary(fluorophores, localisations);
    if (!benchmarkMode) {
        boolean fullSimulation = (!(simpleMode || spotMode));
        saveSimulationParameters(localisations.size(), fullSimulation, signalPerFrame);
    }
    IJ.showStatus("Saving data ...");
    //convertRelativeToAbsolute(molecules);
    saveFluorophores(fluorophores);
    saveImageResults(results);
    saveLocalisations(localisations);
    // The settings for the filenames may have changed
    SettingsManager.saveSettings(globalSettings);
    IJ.showStatus("Done");
}

Example 8 with PoissonDistribution

use of org.apache.commons.math3.distribution.PoissonDistribution in project GDSC-SMLM by aherbert.

the class GradientCalculatorSpeedTest method mleGradientCalculatorComputesLikelihood.

@Test
public void mleGradientCalculatorComputesLikelihood() {
    //@formatter:off
    NonLinearFunction func = new NonLinearFunction() {

        double u;

        public void initialise(double[] a) {
            u = a[0];
        }

        public int[] gradientIndices() {
            return null;
        }

        public double eval(int x, double[] dyda) {
            return 0;
        }

        public double eval(int x) {
            return u;
        }

        public double eval(int x, double[] dyda, double[] w) {
            return 0;
        }

        public double evalw(int x, double[] w) {
            return 0;
        }

        public boolean canComputeWeights() {
            return false;
        }

        public int getNumberOfGradients() {
            return 0;
        }
    };
    //@formatter:on
    int[] xx = Utils.newArray(100, 0, 1);
    double[] xxx = Utils.newArray(100, 0, 1.0);
    for (double u : new double[] { 0.79, 2.5, 5.32 }) {
        double ll = 0;
        PoissonDistribution pd = new PoissonDistribution(u);
        for (int x : xx) {
            double o = MLEGradientCalculator.likelihood(u, x);
            double e = pd.probability(x);
            Assert.assertEquals("likelihood", e, o, e * 1e-10);
            o = MLEGradientCalculator.logLikelihood(u, x);
            e = pd.logProbability(x);
            Assert.assertEquals("log likelihood", e, o, Math.abs(e) * 1e-10);
            ll += e;
        }
        MLEGradientCalculator gc = new MLEGradientCalculator(1);
        double o = gc.logLikelihood(xxx, new double[] { u }, func);
        Assert.assertEquals("sum log likelihood", ll, o, Math.abs(ll) * 1e-10);
    }
}

Example 9 with PoissonDistribution

use of org.apache.commons.math3.distribution.PoissonDistribution in project GDSC-SMLM by aherbert.

the class PoissonCalculatorTest method cumulativeProbabilityIsOneWithRealDataForCountAbove4.

@Test
public void cumulativeProbabilityIsOneWithRealDataForCountAbove4() {
    for (double mu : photons) {
        // Determine upper limit for a Poisson
        double max = new PoissonDistribution(mu).inverseCumulativeProbability(P_LIMIT);
        // Determine lower limit
        double sd = Math.sqrt(mu);
        double min = (int) Math.max(0, mu - 4 * sd);
        cumulativeProbabilityIsOneWithRealData(mu, min, max, mu >= 4);
    }
}

Example 10 with PoissonDistribution

use of org.apache.commons.math3.distribution.PoissonDistribution in project GDSC-SMLM by aherbert.

the class SCMOSLikelihoodWrapperTest method cumulativeProbabilityIsOneWithRealDataForCountAbove8.

@Test
public void cumulativeProbabilityIsOneWithRealDataForCountAbove8() {
    for (double mu : photons) {
        // Determine upper limit for a Poisson
        double max = new PoissonDistribution(mu).inverseCumulativeProbability(P_LIMIT);
        // Determine lower limit
        double sd = Math.sqrt(mu);
        double min = (int) Math.max(0, mu - 4 * sd);
        // Map to observed values using the gain and offset
        max = max * G + O;
        min = min * G + O;
        cumulativeProbabilityIsOneWithRealData(mu, min, max, mu > 8);
    }
}