- syncopy.specest.compRoutines.wavelet_cF(trl_dat, preselect, postselect, toi=None, timeAxis=0, polyremoval=0, output='pow', noCompute=False, chunkShape=None, method_kwargs=None)[source]#
This is the middleware for the
wavelet()spectral estimation method.
numpy.ndarray) – Uniformly sampled multi-channel time-series
preselect (slice) – Begin- to end-samples to perform analysis on (trim data to interval). See Notes for details.
postselect (list of slices or list of 1D NumPy arrays) – Actual time-points of interest within interval defined by preselect See Notes for details.
numpy.ndarrayor str) – Either time-points to center wavelets on if toi is a
numpy.ndarray, or “all” to center wavelets on all samples in trl_dat. Please refer to
freqanalysis()for further details.
timeAxis (int) – Index of running time axis in trl_dat (0 or 1)
polyremoval (int) – Order of polynomial used for de-trending data in the time domain prior to spectral analysis. A value of 0 corresponds to subtracting the mean (“de-meaning”),
polyremoval = 1removes linear trends (subtracting the least squares fit of a linear polynomial). If polyremoval is None, no de-trending is performed.
output (str) – Output of spectral estimation; one of
noCompute (bool) – Preprocessing flag. If True, do not perform actual calculation but instead return expected shape and
numpy.dtypeof output array.
chunkShape (None or tuple) – If not None, represents shape of output object spec (respecting provided values of scales, preselect, postselect etc.)
method_kwargs (dict) – Keyword arguments passed to
wavelet()controlling the spectral estimation method
spec – Complex or real time-frequency representation of (padded) input data. Shape is (nTime, 1, len(scales), nChannels), so that the individual spectra per channel can be assessed via spec[:, 1, :, channel].
- Return type
This method is intended to be used as
ComputationalRoutine. Thus, input parameters are presumed to be forwarded from a parent metafunction. Consequently, this function does not perform any error checking and operates under the assumption that all inputs have been externally validated and cross-checked.
For wavelets, data concatenation is performed by first trimming trl_dat to an interval of interest (via preselect), then performing the actual wavelet transform, and subsequently extracting the actually wanted time-points (via postselect).
ComputationalRoutineinstance that calls this method as