DiagonalSpectralDensity

class typed_lisa_toolkit.types.DiagonalSpectralDensity

Represent a SDM for a frequency domain stationary noise model with no inter-channel correlations.

See also

SpectralDensity

Note

To construct a DiagonalSpectralDensity, use the make_sdm() factory function.

classmethod from_fd_noise(fd_noise: _StationaryFDNoise, frequencies: Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]], channel_names: tuple[str, ...])

Create a SpectralDensity instance from a frequency domain noise model and a frequency grid.

get_kernel(backend: str | None = None) → Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]

Return the inverse of the spectral density matrix.

The inverse SDM is returned as an array of shape (n_freqs, n_channels, n_channels).

Note

We denote the inverse SDM as \(S_n^{-1}\).

get_whitening_matrix(kind: Literal['cholesky'] | None = None) → Array[Any, ModuleType] | ndarray[tuple[Any, ...], dtype[Any]]

Return whitening matrix \(W\) with shape (n_freqs, n_channels, n_channels).

The whitening matrix represents a linear transformation that yields unit variance white noise when applied to noise that follows this model. This is useful in detecting deviations from the model.

Note

\(W\) satisfies \(S_n^{-1} = W^\top W\).

to_subband(f_interval: tuple[float, float]) → Self

Return a new SpectralDensity instance with the frequency grid restricted to the given subband.

property is_diagonal: Literal[True]

Return True.