mne.time_frequency.tfr.cwt¶

mne.time_frequency.tfr.cwt(X, Ws, use_fft=True, mode=’same’, decim=1)[source]¶

Compute time freq decomposition with continuous wavelet transform.

Parameters:

Parameters:	X : array, shape (n_signals, n_times) The signals. Ws : list of array Wavelets time series. use_fft : bool Use FFT for convolutions. Defaults to True. mode : ‘same’ \| ‘valid’ \| ‘full’ Convention for convolution. ‘full’ is currently not implemented with use_fft=False. Defaults to ‘same’. decim : int \| slice To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim]. Note Decimation may create aliasing artifacts. Defaults to 1.
Returns:	tfr : array, shape (n_signals, n_frequencies, n_times) The time-frequency decompositions.

X : array, shape (n_signals, n_times)

The signals.

Ws : list of array

Wavelets time series.

use_fft : bool

Use FFT for convolutions. Defaults to True.

mode : ‘same’ | ‘valid’ | ‘full’

Convention for convolution. ‘full’ is currently not implemented with use_fft=False. Defaults to ‘same’.

decim : int | slice

To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim].

Note

Decimation may create aliasing artifacts.

Defaults to 1.

Returns:

tfr : array, shape (n_signals, n_frequencies, n_times)

The time-frequency decompositions.