wavelet(trl_dat, preselect, postselect, padbegin, padend, samplerate=None, toi=None, scales=None, timeAxis=0, wav=None, polyremoval=None, output_fmt='pow', noCompute=False, chunkShape=None)¶
Perform time-frequency analysis on multi-channel time series data using a wavelet transform
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.
padbegin (int) – Number of samples to pre-pend to trl_dat
padend (int) – Number of samples to append to trl_dat
samplerate (float) – Samplerate of trl_dat in Hz
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. Note: The value of toi has to agree with provided padding values. See Notes for more information.
numpy.ndarray) – Set of scales to use in wavelet transform.
timeAxis (int) – Index of running time axis in trl_dat (0 or 1)
wav (callable) – Wavelet function to use, one of
polyremoval (int) – FIXME: Not implemented yet Order of polynomial used for de-trending. A value of 0 corresponds to subtracting the mean (“de-meaning”),
polyremoval = 1removes linear trends (subtracting the least squares fit of a linear function),
polyremoval = Nfor N > 1 subtracts a polynomial of order N (
N = 2quadratic,
N = 3cubic etc.). If polyremoval is None, no de-trending is performed.
spec – Complex or real time-frequency representation of (padded) input data.
- 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).