mne.time_frequency.cwt_morlet

mne.time_frequency.cwt_morlet(*args, **kwargs)

Warning

DEPRECATED: This function will be removed in mne 0.14; use mne.time_frequency.tfr_morlet() with average=False instead.

Compute time freq decomposition with Morlet wavelets

This function operates directly on numpy arrays. Consider using tfr_morlet to process Epochs or Evoked instances.
Parameters:

X : array, shape (n_signals, n_times)

Signals (one per line)

sfreq
: float

Sampling frequency.

freqs
: array

Array of frequencies of interest

use_fft
: bool

Compute convolution with FFT or temoral convolution.

n_cycles: float | array of float

Number of cycles. Fixed number or one per frequency.

zero_mean
: bool

Make sure the wavelets have a mean of zero.

decim
: int | slice

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

Defaults to 1.

Returns:

tfr : 3D array

Time Frequency Decompositions (n_signals x n_frequencies x n_times)

See also

tfr.cwt
Compute time-frequency decomposition with user-provided wavelets