syncopy.specest.compRoutines.wavelet_cF

syncopy.specest.compRoutines.wavelet_cF(trl_dat, preselect, postselect, toi=None, timeAxis=0, polyremoval=0, output_fmt='pow', noCompute=False, chunkShape=None, method_kwargs=None)[source]

This is the middleware for the wavelet() spectral estimation method.

Parameters
  • trl_dat (2D 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.

  • toi (1D numpy.ndarray or 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 = 1 removes linear trends (subtracting the least squares fit of a linear polynomial). If polyremoval is None, no de-trending is performed.

  • output_fmt (str) – Output of spectral estimation; one of availableOutputs

  • noCompute (bool) – Preprocessing flag. If True, do not perform actual calculation but instead return expected shape and numpy.dtype of 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

Returns

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

numpy.ndarray

Notes

This method is intended to be used as computeFunction() inside a 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).

See also

syncopy.freqanalysis

parent metafunction

WaveletTransform

ComputationalRoutine instance that calls this method as computeFunction()