Examples with CalibrationWriter uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter used on opensource projects

Example 1 with CalibrationWriter

use of uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter in project GDSC-SMLM by aherbert.

the class CreateData method showSimulationParametersDialog.

private SimulationParameters showSimulationParametersDialog(ImagePlus imp, MemoryPeakResults results) {
    final int molecules = results.size();
    // Get the missing parameters from the user
    boolean fullSimulation = false;
    double sd = -1;
    if (!results.convertToPreferredUnits()) {
        IJ.error(TITLE, String.format("Results should be in the preferred units (%s,%s)", UnitHelper.getName(MemoryPeakResults.PREFERRED_DISTANCE_UNIT), UnitHelper.getName(MemoryPeakResults.PREFERRED_INTENSITY_UNIT)));
        return null;
    }
    // Get these from the data
    final RawResultProcedure sp = new RawResultProcedure(results);
    sp.getBixyz();
    final float[] signal = sp.intensity;
    float[] limits = MathUtils.limits(signal);
    final double minSignal = limits[0];
    final double maxSignal = limits[1];
    final double signalPerFrame = MathUtils.sum(signal) / molecules;
    final float[] depths = sp.z;
    limits = MathUtils.limits(depths);
    float depth = Math.max(Math.abs(limits[0]), Math.abs(limits[1]));
    final boolean fixedDepth = Double.compare(limits[0], limits[1]) == 0;
    final CalibrationWriter cal = results.getCalibrationWriter();
    final String iUnits = " " + UnitHelper.getName(cal.getIntensityUnit());
    final String zUnits = " " + UnitHelper.getName(cal.getDistanceUnit());
    // Get this from the user
    double background = -1;
    // Use last simulation parameters for missing settings.
    // This is good if we are re-running the plugin to load data.
    Rectangle lastCameraBounds = null;
    if (simulationParameters != null && simulationParameters.isLoaded()) {
        fullSimulation = simulationParameters.fullSimulation;
        sd = simulationParameters.sd;
        background = simulationParameters.background;
        if (!cal.hasBias()) {
            cal.setBias(simulationParameters.bias);
        }
        if (!cal.hasCountPerPhoton()) {
            cal.setCountPerPhoton(simulationParameters.gain);
        }
        if (!cal.hasQuantumEfficiency()) {
            cal.setQuantumEfficiency(simulationParameters.qe);
        }
        if (!cal.hasReadNoise()) {
            cal.setReadNoise(simulationParameters.readNoise);
        }
        if (!cal.hasCameraType()) {
            cal.setCameraType(simulationParameters.cameraType);
        }
        if (!cal.hasNmPerPixel()) {
            cal.setNmPerPixel(simulationParameters.pixelPitch);
        }
        if (!cal.hasCameraModelName()) {
            cal.setCameraModelName(simulationParameters.cameraModelName);
        }
        lastCameraBounds = simulationParameters.cameraBounds;
    }
    // Show a dialog to confirm settings
    final ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    final StringBuilder sb = new StringBuilder();
    sb.append("Results contain ").append(TextUtils.pleural(molecules, "molecule")).append('\n');
    sb.append("Min signal = ").append(MathUtils.rounded(minSignal)).append(iUnits).append('\n');
    sb.append("Max signal = ").append(MathUtils.rounded(maxSignal)).append(iUnits).append('\n');
    sb.append("Av signal = ").append(MathUtils.rounded(signalPerFrame)).append(iUnits).append('\n');
    if (fixedDepth) {
        sb.append("Fixed depth = ").append(MathUtils.rounded(depth)).append(zUnits).append('\n');
    }
    gd.addMessage(sb.toString());
    gd.addCheckbox("Flourophore_simulation", fullSimulation);
    gd.addNumericField("Gaussian_SD", sd, 3, 8, "nm");
    gd.addNumericField("Pixel_pitch", cal.getNmPerPixel(), 3, 8, "nm");
    gd.addNumericField("Background", background, 3, 8, "photon");
    // Camera type does not need the full simulation settings. Plus the units are different
    // so just re-implement.
    gd.addChoice("Camera_type", SettingsManager.getCameraTypeNames(), CalibrationProtosHelper.getName(cal.getCameraType()), new OptionListener<Integer>() {

        @Override
        public boolean collectOptions(Integer field) {
            cal.setCameraType(SettingsManager.getCameraTypeValues()[field]);
            return collectOptions(false);
        }

        @Override
        public boolean collectOptions() {
            return collectOptions(true);
        }

        private boolean collectOptions(boolean silent) {
            final CameraType cameraType = cal.getCameraType();
            final boolean isCcd = CalibrationProtosHelper.isCcdCameraType(cameraType);
            final ExtendedGenericDialog egd = new ExtendedGenericDialog(TITLE, null);
            if (isCcd) {
                egd.addNumericField("Total_gain", cal.getCountPerPhoton(), 3, 8, "count/photon");
                egd.addNumericField("Quantum_efficiency", cal.getQuantumEfficiency(), 3, 8, "e-/photon");
                egd.addNumericField("Read_noise", cal.getReadNoise(), 3, 8, "count");
                egd.addNumericField("Bias", cal.getBias(), 3, 8, "count");
            } else if (cameraType == CameraType.SCMOS) {
                final String[] models = CameraModelManager.listCameraModels(true);
                egd.addChoice("Camera_model_name", models, cal.getCameraModelName());
                egd.addNumericField("Quantum_efficiency", cal.getQuantumEfficiency(), 2, 6, "electron/photon");
            } else {
                IJ.error("Unsupported camera type " + CalibrationProtosHelper.getName(cameraType));
                return false;
            }
            egd.setSilent(silent);
            egd.showDialog(true, gd);
            if (egd.wasCanceled()) {
                return false;
            }
            if (isCcd) {
                cal.setCountPerPhoton(egd.getNextNumber());
                cal.setQuantumEfficiency(egd.getNextNumber());
                cal.setReadNoise(egd.getNextNumber());
                cal.setBias(egd.getNextNumber());
            } else if (cameraType == CameraType.SCMOS) {
                cal.setCameraModelName(egd.getNextChoice());
                cal.setQuantumEfficiency(Math.abs(egd.getNextNumber()));
            }
            return true;
        }
    });
    if (!fixedDepth) {
        gd.addNumericField("Depth", depth, 3, 8, "pixel");
    }
    gd.addHelp(HelpUrls.getUrl("load-benchmark-data"));
    gd.showDialog();
    if (gd.wasCanceled()) {
        return null;
    }
    fullSimulation = gd.getNextBoolean();
    sd = gd.getNextNumber();
    cal.setNmPerPixel(gd.getNextNumber());
    background = gd.getNextNumber();
    cal.setCameraType(SettingsManager.getCameraTypeValues()[gd.getNextChoiceIndex()]);
    float myDepth = depth;
    if (!fixedDepth) {
        myDepth = (float) gd.getNextNumber();
        if (myDepth < depth) {
            IJ.error(TITLE, String.format("Input depth is smaller than the depth guessed from the data: %f < %f", myDepth, depth));
            return null;
        }
        depth = myDepth;
    }
    gd.collectOptions();
    // Validate settings
    Rectangle modelBounds = null;
    try {
        ParameterUtils.isAboveZero("Gaussian SD", sd);
        ParameterUtils.isAboveZero("Pixel pitch", cal.getNmPerPixel());
        ParameterUtils.isPositive("Background", background);
        ParameterUtils.isAboveZero("Quantum efficiency", cal.getQuantumEfficiency());
        ParameterUtils.isEqualOrBelow("Quantum efficiency", cal.getQuantumEfficiency(), 1);
        if (cal.isCcdCamera()) {
            ParameterUtils.isAboveZero("Total gain", cal.getCountPerPhoton());
            ParameterUtils.isPositive("Read noise", cal.getReadNoise());
            ParameterUtils.isPositive("Bias", cal.getBias());
        } else if (cal.isScmos()) {
            // Load the model
            cameraModel = CameraModelManager.load(cal.getCameraModelName());
            if (cameraModel == null) {
                IJ.error(TITLE, "Unknown camera model for name: " + cal.getCameraModelName());
                return null;
            }
            int ox = 0;
            int oy = 0;
            if (lastCameraBounds != null) {
                ox = lastCameraBounds.x;
                oy = lastCameraBounds.y;
            }
            cameraModel = PeakFit.cropCameraModel(cameraModel, new Rectangle(ox, oy, imp.getWidth(), imp.getHeight()), null, false);
            modelBounds = cameraModel.getBounds();
            final IJImageSource imageSource = (IJImageSource) results.getSource();
            imageSource.setOrigin(modelBounds.x, modelBounds.y);
            cal.clearGlobalCameraSettings();
        } else {
            IJ.error(TITLE, "Unknown camera type: " + cal.getCameraType());
            return null;
        }
    } catch (final IllegalArgumentException ex) {
        IJ.error(TITLE, ex.getMessage());
        return null;
    }
    // Store calibration
    results.setCalibration(cal.getCalibration());
    final double a = cal.getNmPerPixel();
    final double bias = cal.getBias();
    final double gain = cal.getCountPerPhoton();
    final double readNoise = cal.getReadNoise();
    final double qe = cal.getQuantumEfficiency();
    // Note: The calibration will throw an exception if the converter cannot be created.
    // This is OK as the data will be invalid.
    // Convert +/- depth to total depth in nm
    depth = cal.getDistanceConverter(DistanceUnit.NM).convert(depth * 2);
    // Compute total background variance in photons
    final double backgroundVariance = background;
    // Do not add EM-CCD noise factor. The Mortensen formula also includes this factor
    // so this is "double-counting" the EM-CCD.
    // if (emCCD)
    // backgroundVariance *= 2;
    // Read noise is in ADUs. Convert to Photons to get contribution to background variance
    final double readNoiseInPhotons = readNoise / gain;
    // Get the expected value at each pixel in photons. Assuming a Poisson distribution this
    // is equal to the total variance at the pixel.
    final double b2 = backgroundVariance + readNoiseInPhotons * readNoiseInPhotons;
    // Convert values to photons
    final TypeConverter<IntensityUnit> ic = cal.getIntensityConverter(IntensityUnit.PHOTON);
    final SimulationParameters p = new SimulationParameters(molecules, fullSimulation, sd, a, ic.convert(minSignal), ic.convert(maxSignal), ic.convert(signalPerFrame), depth, fixedDepth, bias, gain, qe, readNoise, cal.getCameraType(), cal.getCameraModelName(), modelBounds, background, b2, createPsf(sd / a));
    p.loaded = true;
    return p;
}

Example 2 with CalibrationWriter

use of uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter in project GDSC-SMLM by aherbert.

the class DoubletAnalysis method showDialog.

/**
 * Show dialog.
 *
 * @return true, if successful
 */
private boolean showDialog() {
    ExtendedGenericDialog gd = new ExtendedGenericDialog(TITLE);
    final String helpKey = "doublet-analysis";
    settings = Settings.load();
    config = configRef.get().createCopy();
    final FitConfiguration fitConfig = config.getFitConfiguration();
    final double sa = getSa();
    ImageJUtils.addMessage(gd, "Fits the benchmark image created by CreateData plugin.\nPSF width = %s, adjusted = %s", MathUtils.rounded(simulationParameters.sd / simulationParameters.pixelPitch), MathUtils.rounded(sa));
    // For each new benchmark width, reset the PSF width to the square pixel adjustment
    if (lastId.get() != simulationParameters.id) {
        final double w = sa;
        settings.matchDistance = w * Gaussian2DFunction.SD_TO_HWHM_FACTOR;
        settings.lowerDistance = 0.5 * settings.matchDistance;
        fitConfig.setInitialPeakStdDev(w);
        final CalibrationWriter cal = new CalibrationWriter(fitConfig.getCalibration());
        cal.setNmPerPixel(simulationParameters.pixelPitch);
        cal.setCountPerPhoton(simulationParameters.gain);
        cal.setQuantumEfficiency(simulationParameters.qe);
        cal.setExposureTime(100);
        cal.setReadNoise(simulationParameters.readNoise);
        cal.setBias(simulationParameters.bias);
        cal.setCameraType(simulationParameters.cameraType);
        fitConfig.setCameraModel(CreateData.getCameraModel(simulationParameters));
        fitConfig.setCalibration(cal.getCalibration());
    }
    // Support for using templates
    final String[] templates = ConfigurationTemplate.getTemplateNames(true);
    gd.addChoice("Template", templates, templates[0]);
    // Allow the settings from the benchmark analysis to be used
    gd.addCheckbox("Benchmark_settings", settings.useBenchmarkSettings);
    // Collect options for fitting
    PeakFit.addPsfOptions(gd, fitConfig);
    final PeakFit.SimpleFitEngineConfigurationProvider provider = new PeakFit.SimpleFitEngineConfigurationProvider(config);
    PeakFit.addDataFilterOptions(gd, provider);
    PeakFit.addSearchOptions(gd, provider);
    PeakFit.addBorderOptions(gd, provider);
    PeakFit.addFittingOptions(gd, provider);
    gd.addChoice("Fit_solver", SettingsManager.getFitSolverNames(), fitConfig.getFitSolver().ordinal());
    gd.addSlider("Iteration_increase", 1, 4.5, settings.iterationIncrease);
    gd.addCheckbox("Ignore_with_neighbours", settings.ignoreWithNeighbours);
    gd.addCheckbox("Show_overlay", settings.showOverlay);
    gd.addCheckbox("Show_histograms", settings.showHistograms);
    gd.addCheckbox("Show_results", settings.showResults);
    gd.addCheckbox("Show_Jaccard_Plot", settings.showJaccardPlot);
    gd.addCheckbox("Use_max_residuals", settings.useMaxResiduals);
    gd.addNumericField("Match_distance", settings.matchDistance, 2);
    gd.addNumericField("Lower_distance", settings.lowerDistance, 2);
    gd.addNumericField("Signal_factor", settings.signalFactor, 2);
    gd.addNumericField("Lower_factor", settings.lowerSignalFactor, 2);
    gd.addChoice("Matching", Settings.MATCHING_METHODS, settings.matchingMethod);
    // Add a mouse listener to the config file field
    if (ImageJUtils.isShowGenericDialog()) {
        final Vector<TextField> numerics = gd.getNumericFields();
        final Vector<Choice> choices = gd.getChoices();
        final Iterator<TextField> nu = numerics.iterator();
        final Iterator<Choice> ch = choices.iterator();
        ch.next().addItemListener(this);
        final Checkbox b = (Checkbox) gd.getCheckboxes().get(0);
        b.addItemListener(this);
        textPsf = ch.next();
        textDataFilterType = ch.next();
        textDataFilter = ch.next();
        textSmooth = nu.next();
        textSearch = nu.next();
        textBorder = nu.next();
        textFitting = nu.next();
        textFitSolver = ch.next();
        // Iteration increase
        nu.next();
        textMatchDistance = nu.next();
        textLowerDistance = nu.next();
        textSignalFactor = nu.next();
        textLowerFactor = nu.next();
    }
    gd.addHelp(HelpUrls.getUrl(helpKey));
    gd.showDialog();
    if (gd.wasCanceled()) {
        return false;
    }
    // Ignore the template
    gd.getNextChoice();
    settings.useBenchmarkSettings = gd.getNextBoolean();
    fitConfig.setPsfType(PeakFit.getPsfTypeValues()[gd.getNextChoiceIndex()]);
    config.setDataFilterType(gd.getNextChoiceIndex());
    config.setDataFilter(gd.getNextChoiceIndex(), Math.abs(gd.getNextNumber()), false, 0);
    config.setSearch(gd.getNextNumber());
    config.setBorder(gd.getNextNumber());
    config.setFitting(gd.getNextNumber());
    // Some enum values are not supported
    fitConfig.setFitSolver(SettingsManager.getFitSolverValues()[gd.getNextChoiceIndex()]);
    // Avoid stupidness. Note: We are mostly ignoring the validation result and
    // checking the results for the doublets manually.
    // Realistically we cannot fit lower than this
    fitConfig.setMinPhotons(15);
    // Set the width factors to help establish bounds for bounded fitters
    fitConfig.setMinWidthFactor(1.0 / 10);
    fitConfig.setMaxWidthFactor(10);
    settings.iterationIncrease = gd.getNextNumber();
    settings.ignoreWithNeighbours = gd.getNextBoolean();
    settings.showOverlay = gd.getNextBoolean();
    settings.showHistograms = gd.getNextBoolean();
    settings.showResults = gd.getNextBoolean();
    settings.showJaccardPlot = gd.getNextBoolean();
    settings.useMaxResiduals = gd.getNextBoolean();
    settings.matchDistance = Math.abs(gd.getNextNumber());
    settings.lowerDistance = Math.abs(gd.getNextNumber());
    settings.signalFactor = Math.abs(gd.getNextNumber());
    settings.lowerSignalFactor = Math.abs(gd.getNextNumber());
    settings.matchingMethod = gd.getNextChoiceIndex();
    gd.collectOptions();
    settings.save();
    configRef.set(config);
    if (gd.invalidNumber()) {
        return false;
    }
    if (settings.lowerDistance > settings.matchDistance) {
        settings.lowerDistance = settings.matchDistance;
    }
    if (settings.lowerSignalFactor > settings.signalFactor) {
        settings.lowerSignalFactor = settings.signalFactor;
    }
    if (settings.useBenchmarkSettings && !updateFitConfiguration(config)) {
        return false;
    }
    boolean configure = true;
    if (settings.useBenchmarkSettings) {
        // Only configure the fit solver if not in a macro
        configure = Macro.getOptions() == null;
    }
    if (configure && !PeakFit.configurePsfModel(config)) {
        return false;
    }
    if (configure && !PeakFit.configureFitSolver(config, IJImageSource.getBounds(imp), null, PeakFit.FLAG_NO_SAVE)) {
        return false;
    }
    lastId.set(simulationParameters.id);
    if (settings.showHistograms) {
        gd = new ExtendedGenericDialog(TITLE);
        gd.addMessage("Select the histograms to display");
        for (int i = 0; i < Settings.NAMES.length; i++) {
            gd.addCheckbox(Settings.NAMES[i].replace(' ', '_'), settings.displayHistograms[i]);
        }
        for (int i = 0; i < Settings.NAMES2.length; i++) {
            gd.addCheckbox(Settings.NAMES2[i].replace(' ', '_'), settings.displayHistograms[i + Settings.NAMES.length]);
        }
        gd.addHelp(HelpUrls.getUrl(helpKey));
        gd.showDialog();
        if (gd.wasCanceled()) {
            return false;
        }
        for (int i = 0; i < settings.displayHistograms.length; i++) {
            settings.displayHistograms[i] = gd.getNextBoolean();
        }
    }
    return true;
}

Example 3 with CalibrationWriter

use of uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter in project GDSC-SMLM by aherbert.

the class ImageJAbstractPeakResults method setCalibration.

/**
 * Sets the calibration.
 *
 * @param nmPerPixel the nm per pixel
 * @param gain the gain
 */
public void setCalibration(double nmPerPixel, double gain) {
    final CalibrationWriter cw = getCalibrationWriterSafe();
    cw.setNmPerPixel(nmPerPixel);
    cw.setCountPerPhoton(gain);
    setCalibration(cw.getCalibration());
}

Example 4 with CalibrationWriter

use of uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter in project GDSC-SMLM by aherbert.

the class BlinkEstimatorTest method estimateBlinking.

private TIntHashSet estimateBlinking(UniformRandomProvider rg, double blinkingRate, double ton, double toff, int particles, double fixedFraction, boolean timeAtLowerBound, boolean doAssert) {
    Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MAXIMUM));
    final SpatialIllumination activationIllumination = new UniformIllumination(100);
    int totalSteps = 100;
    final double eAct = totalSteps * 0.3 * activationIllumination.getAveragePhotons();
    final ImageModel imageModel = new ActivationEnergyImageModel(eAct, activationIllumination, ton, 0, toff, 0, blinkingRate, rg);
    final double[] max = new double[] { 256, 256, 32 };
    final double[] min = new double[3];
    final SpatialDistribution distribution = new UniformDistribution(min, max, rg.nextInt());
    final List<CompoundMoleculeModel> compounds = new ArrayList<>(1);
    final 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);
    final List<CompoundMoleculeModel> molecules = imageModel.createMolecules(compounds, particles, distribution, false);
    // Activate fluorophores
    final List<? extends FluorophoreSequenceModel> fluorophores = imageModel.createFluorophores(molecules, totalSteps);
    totalSteps = checkTotalSteps(totalSteps, fluorophores);
    final 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;
    // logger.info("Photons = %f", photonStats.getMean());
    // 
    // List<FluorophoreSequenceModel> newFluorophores = new
    // ArrayList<FluorophoreSequenceModel>(fluorophores.size());
    // for (FluorophoreSequenceModel f : fluorophores)
    // {
    // if (id[f.getId()])
    // newFluorophores.add(f);
    // }
    // fluorophores = newFluorophores;
    final MemoryPeakResults results = new MemoryPeakResults();
    final CalibrationWriter calibration = new CalibrationWriter();
    calibration.setNmPerPixel(pixelPitch);
    calibration.setExposureTime(msPerFrame);
    calibration.setCountPerPhoton(1);
    results.setCalibration(calibration.getCalibration());
    results.setPsf(PsfHelper.create(PSFType.ONE_AXIS_GAUSSIAN_2D));
    final float b = 0;
    float intensity;
    final float z = 0;
    for (final LocalisationModel l : localisations) {
        // Remove by intensity threshold and optionally at random.
        if (l.getIntensity() < minPhotons || rg.nextDouble() < probabilityDelete) {
            continue;
        }
        final int frame = l.getTime();
        intensity = (float) l.getIntensity();
        final float x = (float) l.getX();
        final float y = (float) l.getY();
        final float[] params = Gaussian2DPeakResultHelper.createParams(b, intensity, x, y, z, psfWidth);
        results.add(frame, 0, 0, 0, 0, 0, 0, params, null);
    }
    // Add random localisations
    // Intensity doesn't matter at the moment for tracing
    intensity = (float) photons;
    for (int i = (int) (localisations.size() * probabilityAdd); i-- > 0; ) {
        final int frame = 1 + rg.nextInt(totalSteps);
        final float x = (float) (rg.nextDouble() * max[0]);
        final float y = (float) (rg.nextDouble() * max[1]);
        final float[] params = Gaussian2DPeakResultHelper.createParams(b, intensity, x, y, z, psfWidth);
        results.add(frame, 0, 0, 0, 0, 0, 0, params, null);
    }
    // Get actual simulated stats ...
    final Statistics statsNBlinks = new Statistics();
    final Statistics statsTOn = new Statistics();
    final Statistics statsTOff = new Statistics();
    final Statistics statsSampledNBlinks = new Statistics();
    final Statistics statsSampledTOn = new Statistics();
    final StoredDataStatistics statsSampledTOff = new StoredDataStatistics();
    for (final FluorophoreSequenceModel f : fluorophores) {
        statsNBlinks.add(f.getNumberOfBlinks());
        statsTOn.add(f.getOnTimes());
        statsTOff.add(f.getOffTimes());
        final int[] on = f.getSampledOnTimes();
        statsSampledNBlinks.add(on.length);
        statsSampledTOn.add(on);
        statsSampledTOff.add(f.getSampledOffTimes());
    }
    logger.info(FunctionUtils.getSupplier("N = %d (%d), N-blinks = %f, tOn = %f, tOff = %f, Fixed = %f", fluorophores.size(), localisations.size(), blinkingRate, ton, toff, fixedFraction));
    logger.info(FunctionUtils.getSupplier("Actual N-blinks = %f (%f), tOn = %f (%f), tOff = %f (%f), 95%% = %f, max = %f", statsNBlinks.getMean(), statsSampledNBlinks.getMean(), statsTOn.getMean(), statsSampledTOn.getMean(), statsTOff.getMean(), statsSampledTOff.getMean(), statsSampledTOff.getStatistics().getPercentile(95), statsSampledTOff.getStatistics().getMax()));
    logger.info("-=-=--=-");
    final BlinkEstimator be = new BlinkEstimator();
    be.setMaxDarkTime((int) (toff * 10));
    be.setMsPerFrame(msPerFrame);
    be.setRelativeDistance(false);
    final double d = ImageModel.getRandomMoveDistance(diffusionRate);
    be.setSearchDistance((fixedFraction < 1) ? Math.sqrt(2 * d * d) * 3 : 0);
    be.setTimeAtLowerBound(timeAtLowerBound);
    // Assertions.assertTrue("Max dark time must exceed the dark time of the data (otherwise no
    // plateau)",
    // be.maxDarkTime > statsSampledTOff.getStatistics().getMax());
    final int nMolecules = fluorophores.size();
    if (usePopulationStatistics) {
        blinkingRate = statsNBlinks.getMean();
        toff = statsTOff.getMean();
    } else {
        blinkingRate = statsSampledNBlinks.getMean();
        toff = statsSampledTOff.getMean();
    }
    // See if any fitting regime gets a correct answer
    final TIntHashSet ok = new TIntHashSet();
    for (int numberOfFittedPoints = MIN_FITTED_POINTS; numberOfFittedPoints <= MAX_FITTED_POINTS; numberOfFittedPoints++) {
        be.setNumberOfFittedPoints(numberOfFittedPoints);
        be.computeBlinkingRate(results, true);
        final double moleculesError = DoubleEquality.relativeError(nMolecules, be.getNMolecules());
        final double blinksError = DoubleEquality.relativeError(blinkingRate, be.getNBlinks());
        final double offError = DoubleEquality.relativeError(toff * msPerFrame, be.getTOff());
        logger.info(FunctionUtils.getSupplier("Error %d: N = %f, blinks = %f, tOff = %f : %f", numberOfFittedPoints, moleculesError, blinksError, offError, (moleculesError + blinksError + offError) / 3));
        if (moleculesError < relativeError && blinksError < relativeError && offError < relativeError) {
            ok.add(numberOfFittedPoints);
            logger.info("-=-=--=-");
            logger.info(FunctionUtils.getSupplier("*** Correct at %d fitted points ***", numberOfFittedPoints));
            if (doAssert) {
                break;
            }
        }
    // if (!be.isIncreaseNFittedPoints())
    // break;
    }
    logger.info("-=-=--=-");
    if (doAssert) {
        Assertions.assertFalse(ok.isEmpty());
    }
    // relativeError);
    return ok;
}

Example 5 with CalibrationWriter

use of uk.ac.sussex.gdsc.smlm.data.config.CalibrationWriter in project GDSC-SMLM by aherbert.

the class FitEngineConfiguration method configureOutputUnits.

/**
 * Configure the output units from fitting using the current calibration and fit solver settings.
 *
 * <p>This method should be called before the calibration is passed to any object that will handle
 * the fitting output.
 *
 * <p>It will update the calibration units and the precision method to match that used by
 * precision filter.
 */
public void configureOutputUnits() {
    final FitConfiguration fitConfig = getFitConfiguration();
    // If there is no calibration then the writer will just have the defaults
    final CalibrationWriter calibration = fitConfig.getCalibrationWriterReference();
    // Fitting is always done pixels and radians
    calibration.setDistanceUnit(DistanceUnit.PIXEL);
    calibration.setAngleUnit(AngleUnit.RADIAN);
    // Most fitters fit in photons unless we have no calibration.
    IntensityUnit intensityUnit = IntensityUnit.PHOTON;
    if (fitConfig.isFitCameraCounts()) {
        intensityUnit = IntensityUnit.COUNT;
    }
    calibration.setIntensityUnit(intensityUnit);
    // This initialises the calibration precision method
    fitConfig.getFilterPrecisionMethod();
}