Correlations

class schnell.correlation.NoiseCorrelationBase(ndet)

Noise correlation objects have methods to compute noise PSD correlation matrices.

Do not use the bare class.

get_corrmat(f)

Return covariance matrix as a function of frequency.

Parameters:f – array of N_f frequencies. Returns:array of shape [N_f, N_d, N_d], where N_d is the number of detectors in the network, containing the correlation matrix for each input frequency. Return type:array_like

class schnell.correlation.NoiseCorrelationConstant(corrmat)

This describes constant correlation matrices.

Parameters:corrmat – 2D array providing the constant covariance matrix.

class schnell.correlation.NoiseCorrelationConstantIdentity(ndet)

This describes diagonal correlation matrices.

Parameters:ndet – number of detectors in the network.

class schnell.correlation.NoiseCorrelationConstantR(ndet, r)

This class implements correlation matrices that have the same cross-correlation coefficient for all pairs of different detector, which is also constant in frequency.

Parameters:

• ndet – number of detectors in the network.
• r – pairwise correlation coefficient.

class schnell.correlation.NoiseCorrelationFromFunctions(ndet, psd_auto, psd_cross)

This implements a correlation matrix that has the same auto-correlation PSD for all detectors and the same cross-correlation PSD for all pairs of different detectors.

Parameters:

• ndet – number of detectors in the network.
• psd_auto – function of frequency returning the detector noise auto-correlation.
• psd_cross – function of frequency returning the detector noise cross-correlation.

class schnell.correlation.NoiseCorrelationLISA(det)

This implements the LISA noise correlation matrix.

Parameters:det – LISADetector object.