Examples with AiryPsfModel uk.ac.sussex.gdsc.smlm.model.AiryPsfModel used on opensource projects

Example 1 with AiryPsfModel

use of uk.ac.sussex.gdsc.smlm.model.AiryPsfModel in project GDSC-SMLM by aherbert.

the class CreateData method createPsfModel.

private PsfModel createPsfModel(List<LocalisationModelSet> localisationSets) {
    // Allow reuse of the cached model
    if (psfModelCache != null) {
        return psfModelCache;
    }
    if (psfModelType == PSF_MODEL_IMAGE) {
        return createImagePsf(localisationSets);
    }
    if (psfModelType == PSF_MODEL_ASTIGMATISM) {
        astigmatismModel = AstigmatismModelManager.getModel(settings.getAstigmatismModel());
        if (astigmatismModel == null) {
            throw new IllegalArgumentException("Failed to load model: " + settings.getAstigmatismModel());
        }
        // Convert for simulation
        try {
            if (DoubleEquality.relativeError(astigmatismModel.getNmPerPixel(), settings.getPixelPitch()) > 1e-6) {
                final String message = String.format("Astigmatism model '%s' calibration (%s nm) does not match pixel pitch (%s nm)", settings.getAstigmatismModel(), MathUtils.rounded(astigmatismModel.getNmPerPixel()), MathUtils.rounded(settings.getPixelPitch()));
                // Optionally convert
                final GenericDialog gd = new GenericDialog(TITLE);
                gd.addMessage(TextUtils.wrap(message + ". Created data is not suitable for fitting.", 80));
                gd.addMessage(TextUtils.wrap("Click OK to continue anyway (i.e. draw the spot using the " + "correct nm width on the different sized pixels).", 80));
                gd.showDialog();
                if (gd.wasCanceled()) {
                    throw new IllegalArgumentException(message);
                }
                // Convert to nm
                astigmatismModel = AstigmatismModelManager.convert(astigmatismModel, DistanceUnit.NM, DistanceUnit.NM);
                // Reset pixel pitch. This will draw the spot using the correct size on the different size
                // pixels.
                astigmatismModel = astigmatismModel.toBuilder().setNmPerPixel(settings.getPixelPitch()).build();
            }
            // Convert for simulation in pixels
            astigmatismModel = AstigmatismModelManager.convert(astigmatismModel, DistanceUnit.PIXEL, DistanceUnit.PIXEL);
            return new GaussianPsfModel(AstigmatismModelManager.create(astigmatismModel));
        } catch (final ConversionException ex) {
            // Wrap so this can be caught as the same type
            throw new IllegalArgumentException(ex);
        }
    }
    final double d = settings.getDepthOfFocus() / settings.getPixelPitch();
    if (psfModelType == PSF_MODEL_GAUSSIAN) {
        final double sd = getPsfSd();
        final double gamma = 0;
        final HoltzerAstigmatismZModel zModel = HoltzerAstigmatismZModel.create(sd, sd, gamma, d, 0, 0, 0, 0);
        final GaussianPsfModel m = new GaussianPsfModel(zModel);
        // m.setRange(10);
        return m;
    }
    // Default to Airy pattern
    final double width = getPsfSd() / PsfCalculator.AIRY_TO_GAUSSIAN;
    final AiryPsfModel m = new AiryPsfModel(width, width, d);
    m.setRing(2);
    return m;
}

Example 2 with AiryPsfModel

use of uk.ac.sussex.gdsc.smlm.model.AiryPsfModel in project GDSC-SMLM by aherbert.

the class CreateData method reportAndSaveFittingLimits.

/**
 * Output the theoretical limits for fitting a Gaussian and store the benchmark settings.
 *
 * @param dist The distribution
 */
private void reportAndSaveFittingLimits(SpatialDistribution dist) {
    ImageJUtils.log(TITLE + " Benchmark");
    final double a = settings.getPixelPitch();
    final double[] xyz = dist.next().clone();
    final int size = settings.getSize();
    final double offset = size * 0.5;
    for (int i = 0; i < 2; i++) {
        xyz[i] += offset;
    }
    // Get the width for the z-depth by using the PSF Model
    final PsfModel psf = createPsfModel(xyz);
    psfModelCache = psf;
    double sd0;
    double sd1;
    if (psf instanceof GaussianPsfModel) {
        sd0 = ((GaussianPsfModel) psf).getS0(xyz[2]);
        sd1 = ((GaussianPsfModel) psf).getS1(xyz[2]);
    } else if (psf instanceof AiryPsfModel) {
        psf.create3D((double[]) null, size, size, 1, xyz[0], xyz[1], xyz[2], null);
        sd0 = ((AiryPsfModel) psf).getW0() * AiryPattern.FACTOR;
        sd1 = ((AiryPsfModel) psf).getW1() * AiryPattern.FACTOR;
    } else if (psf instanceof ImagePsfModel) {
        psf.create3D((double[]) null, size, size, 1, xyz[0], xyz[1], xyz[2], null);
        sd0 = ((ImagePsfModel) psf).getHwhm0() / Gaussian2DFunction.SD_TO_HWHM_FACTOR;
        sd1 = ((ImagePsfModel) psf).getHwhm1() / Gaussian2DFunction.SD_TO_HWHM_FACTOR;
    } else {
        throw new IllegalStateException("Unknown PSF: " + psf.getClass().getSimpleName());
    }
    final double sd = Gaussian2DPeakResultHelper.getStandardDeviation(sd0, sd1) * a;
    ImageJUtils.log("X = %s nm : %s px", MathUtils.rounded(xyz[0] * a), MathUtils.rounded(xyz[0], 6));
    ImageJUtils.log("Y = %s nm : %s px", MathUtils.rounded(xyz[1] * a), MathUtils.rounded(xyz[1], 6));
    ImageJUtils.log("Z = %s nm : %s px", MathUtils.rounded(xyz[2] * a), MathUtils.rounded(xyz[2], 6));
    ImageJUtils.log("Width (s) = %s nm : %s px", MathUtils.rounded(sd), MathUtils.rounded(sd / a));
    final double sa = PsfCalculator.squarePixelAdjustment(sd, a);
    ImageJUtils.log("Adjusted Width (sa) = %s nm : %s px", MathUtils.rounded(sa), MathUtils.rounded(sa / a));
    ImageJUtils.log("Signal (N) = %s - %s photons", MathUtils.rounded(settings.getPhotonsPerSecond()), MathUtils.rounded(settings.getPhotonsPerSecondMaximum()));
    boolean emCcd;
    double totalGain;
    final double qe = getQuantumEfficiency();
    final double noise = getNoiseEstimateInPhotoelectrons(qe);
    double readNoise;
    if (CalibrationProtosHelper.isCcdCameraType(settings.getCameraType())) {
        final CreateDataSettingsHelper helper = new CreateDataSettingsHelper(settings);
        emCcd = helper.isEmCcd;
        totalGain = helper.getTotalGainSafe();
        // Store read noise in ADUs
        readNoise = settings.getReadNoise() * ((settings.getCameraGain() > 0) ? settings.getCameraGain() : 1);
    } else if (settings.getCameraType() == CameraType.SCMOS) {
        // Assume sCMOS amplification is like a CCD for the precision computation.
        emCcd = false;
        // Not required for sCMOS
        totalGain = 0;
        readNoise = 0;
    } else {
        throw new IllegalStateException("Unknown camera type: " + settings.getCameraType());
    }
    // The precision calculation is dependent on the model. The classic Mortensen formula
    // is for a Gaussian Mask Estimator. Use other equation for MLE. The formula provided
    // for WLSE requires an offset to the background used to stabilise the fitting. This is
    // not implemented (i.e. we used an offset of zero) and in this case the WLSE precision
    // is the same as MLE with the caveat of numerical instability.
    // Apply QE directly to simulated photons to allow computation of bounds
    // with the captured photons...
    final double min = settings.getPhotonsPerSecond() * qe;
    final double max = settings.getPhotonsPerSecondMaximum() * qe;
    final double lowerP = Gaussian2DPeakResultHelper.getPrecision(a, sd, max, noise, emCcd);
    final double upperP = Gaussian2DPeakResultHelper.getPrecision(a, sd, min, noise, emCcd);
    final double lowerMlP = Gaussian2DPeakResultHelper.getMLPrecision(a, sd, max, noise, emCcd);
    final double upperMlP = Gaussian2DPeakResultHelper.getMLPrecision(a, sd, min, noise, emCcd);
    final double lowerN = getPrecisionN(a, sd, min, MathUtils.pow2(noise), emCcd);
    final double upperN = getPrecisionN(a, sd, max, MathUtils.pow2(noise), emCcd);
    if (settings.getCameraType() == CameraType.SCMOS) {
        ImageJUtils.log("sCMOS camera background estimate uses an average read noise");
    }
    ImageJUtils.log("Effective background noise = %s photo-electron " + "[includes read noise and background photons]", MathUtils.rounded(noise));
    ImageJUtils.log("Localisation precision (LSE): %s - %s nm : %s - %s px", MathUtils.rounded(lowerP), MathUtils.rounded(upperP), MathUtils.rounded(lowerP / a), MathUtils.rounded(upperP / a));
    ImageJUtils.log("Localisation precision (MLE): %s - %s nm : %s - %s px", MathUtils.rounded(lowerMlP), MathUtils.rounded(upperMlP), MathUtils.rounded(lowerMlP / a), MathUtils.rounded(upperMlP / a));
    ImageJUtils.log("Signal precision: %s - %s photo-electrons", MathUtils.rounded(lowerN), MathUtils.rounded(upperN));
    // Wrap to a function
    final PsfModelGradient1Function f = new PsfModelGradient1Function(psf, size, size);
    // Set parameters
    final double[] params = new double[5];
    // No background when computing the SNR
    // params[0] = settings.getBackground() * qe;
    params[1] = min;
    System.arraycopy(xyz, 0, params, 2, 3);
    // Compute SNR using mean signal at 50%. Assume the region covers the entire PSF.
    final double[] v = new StandardValueProcedure().getValues(f, params);
    final double u = FunctionHelper.getMeanValue(v, 0.5);
    final double u0 = MathUtils.max(v);
    // Store the benchmark settings when not using variable photons
    if (Double.compare(min, max) == 0) {
        ImageJUtils.log("50%% PSF SNR : %s : Peak SNR : %s", MathUtils.rounded(u / noise), MathUtils.rounded(u0 / noise));
        // Compute the true CRLB using the fisher information
        createLikelihoodFunction();
        // Compute Fisher information
        final UnivariateLikelihoodFisherInformationCalculator c = new UnivariateLikelihoodFisherInformationCalculator(f, fiFunction);
        // Get limits: Include background in the params
        params[0] = settings.getBackground() * qe;
        final FisherInformationMatrix m = c.compute(params);
        // Report and store the limits
        final double[] crlb = m.crlbSqrt();
        if (crlb != null) {
            ImageJUtils.log("Localisation precision (CRLB): B=%s, I=%s photons", MathUtils.rounded(crlb[0]), MathUtils.rounded(crlb[1]));
            ImageJUtils.log("Localisation precision (CRLB): X=%s, Y=%s, Z=%s nm : %s,%s,%s px", MathUtils.rounded(crlb[2] * a), MathUtils.rounded(crlb[3] * a), MathUtils.rounded(crlb[4] * a), MathUtils.rounded(crlb[2]), MathUtils.rounded(crlb[3]), MathUtils.rounded(crlb[4]));
        }
        benchmarkParameters = new BenchmarkParameters(settings.getParticles(), sd, a, settings.getPhotonsPerSecond(), xyz[0], xyz[1], xyz[2], settings.getBias(), totalGain, qe, readNoise, settings.getCameraType(), settings.getCameraModelName(), cameraModel.getBounds(), settings.getBackground(), noise, lowerN, lowerP, lowerMlP, createPsf(sd / a), crlb);
    } else {
        // SNR will just scale
        final double scale = max / min;
        ImageJUtils.log("50%% PSF SNR : %s - %s : Peak SNR : %s - %s", MathUtils.rounded(u / noise), MathUtils.rounded(scale * u / noise), MathUtils.rounded(u0 / noise), MathUtils.rounded(scale * u0 / noise));
        ImageJUtils.log("Warning: Benchmark settings are only stored in memory when the number of photons is " + "fixed. Min %s != Max %s", MathUtils.rounded(settings.getPhotonsPerSecond()), MathUtils.rounded(settings.getPhotonsPerSecondMaximum()));
    }
}