eelbrain.cwt_morlet
- eelbrain.cwt_morlet(y, frequencies, use_fft=True, n_cycles=3.0, zero_mean=True, output='magnitude', decim=1, n_jobs=1)
Time frequency decomposition with Morlet wavelets (mne-python)
- Parameters:
y (NDVar) – Input signal.
frequencies (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 (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.
n_jobs (int) – Nomber of parallel threads.
- Returns:
Time frequency decompositions.
- Return type: