eelbrain.cwt_morlet
- eelbrain.cwt_morlet(y, frequencies, use_fft=True, n_cycles=3.0, zero_mean=True, output='magnitude', decim=1)
Time frequency decomposition with Morlet wavelets (mne-python)
- Parameters
y (
NDVar
) – Input signal.frequencies (
Union
[Sequence
[float
],float
]) – Frequencies of interest. For a scalar, the output will not contain a frequency dimension.use_fft (
bool
) – Compute convolution with FFT or temporal convolution.n_cycles (
Union
[float
,Sequence
[float
]]) – Number of cycles. Fixed number or one per frequency.zero_mean (
bool
) – Make sure the wavelets are zero mean.output (
Literal
[‘complex’, ‘power’, ‘phase’, ‘magnitude’]) – Format of the data in the returned NDVar. Default is the complex wavelet transform.decim (
int
) – Decimate the time axis by this factor.
- Returns
Time frequency decompositions.
- Return type
tfr