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)

  • 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.


Time frequency decompositions.

Return type: