data_operations.overlap_save#

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Summary#

Convolution with the overlap-save method. Look at the modes in chunkedfft to understand what transients are captured at the edges Coordinates assumed dimensionless, as in DFT Note: Overlap-save is only justified when the window is strictly compact and contained within wl

Signature#

def overlap_save(chunked_data_f, window_f, fftsize, wl)

Input variables#
Name	Type	Default	Description
`chunked_data_f`			n_chunk x nrfft(fftsize) array of rFFTs of data chunks
`window_f`			Complex array of length nrfft(fftsize) RFFT of {window with support within wl, padded to fftsize in the time domain}
`fftsize`			Size of FFT for each sub-chunk
`wl`			Length of window (time domain)

Output variables#

Return annotation	Docstring type	Description
`None`		Array of length nchunk x chunksize with convolved data

Docstring#

Convolution with the overlap-save method. Look at the modes in chunkedfft
to understand what transients are captured at the edges
Coordinates assumed dimensionless, as in DFT
Note: Overlap-save is only justified when the window is strictly compact
and contained within wl
:param chunked_data_f: n_chunk x nrfft(fftsize) array of rFFTs of data chunks
:param window_f: Complex array of length nrfft(fftsize) RFFT of {window with
                 support within wl, padded to fftsize in the time domain}
:param fftsize: Size of FFT for each sub-chunk
:param wl: Length of window (time domain)
:return: Array of length nchunk x chunksize with convolved data