Examples with NullArgumentException org.apache.commons.math3.exception.NullArgumentException used on opensource projects

Example 1 with NullArgumentException

use of org.apache.commons.math3.exception.NullArgumentException in project GDSC-SMLM by aherbert.

the class CreateData method createPhotonDistribution.

/**
	 * @return A photon distribution loaded from a file of floating-point values with the specified population mean.
	 */
private RealDistribution createPhotonDistribution() {
    if (PHOTON_DISTRIBUTION[PHOTON_CUSTOM].equals(settings.photonDistribution)) {
        // Get the distribution file
        String filename = Utils.getFilename("Photon_distribution", settings.photonDistributionFile);
        if (filename != null) {
            settings.photonDistributionFile = filename;
            try {
                InputStream is = new FileInputStream(new File(settings.photonDistributionFile));
                BufferedReader in = new BufferedReader(new UnicodeReader(is, null));
                StoredDataStatistics stats = new StoredDataStatistics();
                try {
                    String str = null;
                    double val = 0.0d;
                    while ((str = in.readLine()) != null) {
                        val = Double.parseDouble(str);
                        stats.add(val);
                    }
                } finally {
                    in.close();
                }
                if (stats.getSum() > 0) {
                    // Update the statistics to the desired mean.
                    double scale = (double) settings.photonsPerSecond / stats.getMean();
                    double[] values = stats.getValues();
                    for (int i = 0; i < values.length; i++) values[i] *= scale;
                    // TODO - Investigate the limits of this distribution. 
                    // How far above and below the input data will values be generated.
                    // Create the distribution using the recommended number of bins
                    final int binCount = stats.getN() / 10;
                    EmpiricalDistribution dist = new EmpiricalDistribution(binCount, createRandomGenerator());
                    dist.load(values);
                    return dist;
                }
            } catch (IOException e) {
            // Ignore
            } catch (NullArgumentException e) {
            // Ignore 
            } catch (NumberFormatException e) {
            // Ignore
            }
        }
        Utils.log("Failed to load custom photon distribution from file: %s. Default to fixed.", settings.photonDistributionFile);
    } else if (PHOTON_DISTRIBUTION[PHOTON_UNIFORM].equals(settings.photonDistribution)) {
        if (settings.photonsPerSecond < settings.photonsPerSecondMaximum) {
            UniformRealDistribution dist = new UniformRealDistribution(createRandomGenerator(), settings.photonsPerSecond, settings.photonsPerSecondMaximum);
            return dist;
        }
    } else if (PHOTON_DISTRIBUTION[PHOTON_GAMMA].equals(settings.photonDistribution)) {
        final double scaleParameter = settings.photonsPerSecond / settings.photonShape;
        GammaDistribution dist = new GammaDistribution(createRandomGenerator(), settings.photonShape, scaleParameter, ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
        return dist;
    } else if (PHOTON_DISTRIBUTION[PHOTON_CORRELATED].equals(settings.photonDistribution)) {
        // No distribution required
        return null;
    }
    settings.photonDistribution = PHOTON_DISTRIBUTION[PHOTON_FIXED];
    return null;
}

Example 2 with NullArgumentException

use of org.apache.commons.math3.exception.NullArgumentException in project FSensor by KalebKE.

the class RotationKalmanFilter method correct.

/**
 * Correct the current state estimate with an actual measurement.
 *
 * @param z
 *            the measurement vector
 * @throws NullArgumentException
 *             if the measurement vector is {@code null}
 * @throws DimensionMismatchException
 *             if the dimension of the measurement vector does not fit
 * @throws SingularMatrixException
 *             if the covariance matrix could not be inverted
 */
public void correct(final RealVector z) throws NullArgumentException, DimensionMismatchException, SingularMatrixException {
    // sanity checks
    MathUtils.checkNotNull(z);
    if (z.getDimension() != measurementMatrix.getRowDimension()) {
        throw new DimensionMismatchException(z.getDimension(), measurementMatrix.getRowDimension());
    }
    // S = H * P(k) * H' + R
    RealMatrix s = measurementMatrix.multiply(errorCovariance).multiply(measurementMatrixT).add(measurementModel.getMeasurementNoise());
    // Inn = z(k) - H * xHat(k)-
    RealVector innovation = z.subtract(measurementMatrix.operate(stateEstimation));
    // calculate gain matrix
    // K(k) = P(k)- * H' * (H * P(k)- * H' + R)^-1
    // K(k) = P(k)- * H' * S^-1
    // instead of calculating the inverse of S we can rearrange the formula,
    // and then solve the linear equation A x X = B with A = S', X = K' and
    // B = (H * P)'
    // K(k) * S = P(k)- * H'
    // S' * K(k)' = H * P(k)-'
    RealMatrix kalmanGain = new CholeskyDecomposition(s).getSolver().solve(measurementMatrix.multiply(errorCovariance.transpose())).transpose();
    // update estimate with measurement z(k)
    // xHat(k) = xHat(k)- + K * Inn
    stateEstimation = stateEstimation.add(kalmanGain.operate(innovation));
    // update covariance of prediction error
    // P(k) = (I - K * H) * P(k)-
    RealMatrix identity = MatrixUtils.createRealIdentityMatrix(kalmanGain.getRowDimension());
    errorCovariance = identity.subtract(kalmanGain.multiply(measurementMatrix)).multiply(errorCovariance);
}

Example 3 with NullArgumentException

use of org.apache.commons.math3.exception.NullArgumentException in project GDSC-SMLM by aherbert.

the class CreateData method createPhotonDistribution.

/**
 * Creates the photon distribution.
 *
 * @return A photon distribution loaded from a file of floating-point values with the specified
 *         population mean.
 */
private RealDistribution createPhotonDistribution() {
    if (PHOTON_DISTRIBUTION[PHOTON_CUSTOM].equals(settings.getPhotonDistribution())) {
        // Get the distribution file
        final String filename = ImageJUtils.getFilename("Photon_distribution", settings.getPhotonDistributionFile());
        if (filename != null) {
            settings.setPhotonDistributionFile(filename);
            try (BufferedReader in = new BufferedReader(new UnicodeReader(new FileInputStream(new File(settings.getPhotonDistributionFile())), null))) {
                final StoredDataStatistics stats = new StoredDataStatistics();
                String str = in.readLine();
                double val = 0.0d;
                while (str != null) {
                    val = Double.parseDouble(str);
                    stats.add(val);
                    str = in.readLine();
                }
                if (stats.getSum() > 0) {
                    // Update the statistics to the desired mean.
                    final double scale = settings.getPhotonsPerSecond() / stats.getMean();
                    final double[] values = stats.getValues();
                    for (int i = 0; i < values.length; i++) {
                        values[i] *= scale;
                    }
                    // TODO - Investigate the limits of this distribution.
                    // How far above and below the input data will values be generated.
                    // Create the distribution using the recommended number of bins
                    final int binCount = stats.getN() / 10;
                    final EmpiricalDistribution dist = new EmpiricalDistribution(binCount, new RandomGeneratorAdapter(createRandomGenerator()));
                    dist.load(values);
                    return dist;
                }
            } catch (final IOException | NullArgumentException | NumberFormatException ex) {
            // Ignore
            }
        }
        ImageJUtils.log("Failed to load custom photon distribution from file: %s. Default to fixed.", settings.getPhotonDistributionFile());
    } else if (PHOTON_DISTRIBUTION[PHOTON_UNIFORM].equals(settings.getPhotonDistribution())) {
        if (settings.getPhotonsPerSecond() < settings.getPhotonsPerSecondMaximum()) {
            return new UniformRealDistribution(new RandomGeneratorAdapter(createRandomGenerator()), settings.getPhotonsPerSecond(), settings.getPhotonsPerSecondMaximum());
        }
    } else if (PHOTON_DISTRIBUTION[PHOTON_GAMMA].equals(settings.getPhotonDistribution())) {
        final double scaleParameter = settings.getPhotonsPerSecond() / settings.getPhotonShape();
        return new GammaDistribution(new RandomGeneratorAdapter(createRandomGenerator()), settings.getPhotonShape(), scaleParameter, ExponentialDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    } else if (PHOTON_DISTRIBUTION[PHOTON_CORRELATED].equals(settings.getPhotonDistribution())) {
        // No distribution required
        return null;
    }
    settings.setPhotonDistribution(PHOTON_DISTRIBUTION[PHOTON_FIXED]);
    return null;
}