syncopy.specest.wavelet.wavelet¶

syncopy.specest.wavelet.
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)[source]¶ Perform timefrequency analysis on multichannel time series data using a wavelet transform
 Parameters
trl_dat (2D
numpy.ndarray
) – Uniformly sampled multichannel timeseriespreselect (slice) – Begin to endsamples to perform analysis on (trim data to interval). See Notes for details.
postselect (list of slices or list of 1D NumPy arrays) – Actual timepoints of interest within interval defined by preselect See Notes for details.
padbegin (int) – Number of samples to prepend to trl_dat
padend (int) – Number of samples to append to trl_dat
samplerate (float) – Samplerate of trl_dat in Hz
toi (1D
numpy.ndarray
or str) – Either timepoints to center wavelets on if toi is anumpy.ndarray
, or “all” to center wavelets on all samples in trl_dat. Please refer tofreqanalysis()
for further details. Note: The value of toi has to agree with provided padding values. See Notes for more information.scales (1D
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
availableWavelets
polyremoval (int) – FIXME: Not implemented yet Order of polynomial used for detrending. A value of 0 corresponds to subtracting the mean (“demeaning”),
polyremoval = 1
removes linear trends (subtracting the least squares fit of a linear function),polyremoval = N
for N > 1 subtracts a polynomial of order N (N = 2
quadratic,N = 3
cubic etc.). If polyremoval is None, no detrending 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.)
 Returns
spec – Complex or real timefrequency representation of (padded) input data.
 Return type
Notes
This method is intended to be used as
computeFunction()
inside aComputationalRoutine
. 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 crosschecked.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 timepoints (via postselect).
See also
syncopy.freqanalysis()
parent metafunction
WaveletTransform()
ComputationalRoutine
instance that calls this method ascomputeFunction()