mth5.processing.spectre.prewhitening

This module has methods for pre-whitening time series to reduce spectral leakage before FFT.

Functions

`apply_prewhitening`(→ xarray.Dataset)	Applies pre-whitening to time series to avoid spectral leakage when FFT is applied.
`apply_recoloring`(→ xarray.Dataset)	Inverts the pre-whitening operation in frequency domain. Modifies the input xarray in-place.

Module Contents

mth5.processing.spectre.prewhitening.apply_prewhitening(prewhitening_type: Literal['first difference', ''], run_xrds_input: xarray.Dataset) → xarray.Dataset[source]

Applies pre-whitening to time series to avoid spectral leakage when FFT is applied.

Parameters:

prewhitening_type (Literal["first difference", ]) – Placeholder to allow for multiple methods of pre-whitening. Currently only “first difference” is supported.
run_xrds_input (xr.Dataset) – Time series to be pre-whitened (can be multivariate).

Returns:

run_xrds – pre-whitened time series

Return type:

xr..Dataset

mth5.processing.spectre.prewhitening.apply_recoloring(prewhitening_type: Literal['first difference'], stft_obj: xarray.Dataset) → xarray.Dataset[source]

Inverts the pre-whitening operation in frequency domain. Modifies the input xarray in-place.

Parameters:

prewhitening_type (Literal["first difference", ]) – Placeholder to allow for multiple methods of pre-whitening. Currently only “first difference” is supported.
stft_obj (xarray.core.dataset.Dataset) – Time series of Fourier coefficients to be recoloured

Returns:

stft_obj – Recolored time series of Fourier coefficients.

Return type:

xarray.core.dataset.Dataset