syncopy.specest.compRoutines.mtmconvol_cF¶
- syncopy.specest.compRoutines.mtmconvol_cF(trl_dat, soi, postselect, equidistant=True, toi=None, foi=None, nTaper=1, tapsmofrq=None, timeAxis=0, keeptapers=True, polyremoval=0, output_fmt='pow', noCompute=False, chunkShape=None, method_kwargs=None)[source]¶
Perform time-frequency analysis on multi-channel time series data using a sliding window FFT
- Parameters
trl_dat (2D
numpy.ndarray
) – Uniformly sampled multi-channel time-seriessoi (list of slices or slice) – Samples of interest; either a single slice encoding begin- to end-samples to perform analysis on (if sliding window centroids are equidistant) or list of slices with each slice corresponding to coverage of a single analysis window (if spacing between windows is not constant)
samplerate (float) – Samplerate of trl_dat in Hz
noverlap (int) – Number of samples covered by two adjacent analysis windows
nperseg (int) – Size of analysis windows (in samples)
equidistant (bool) – If True, spacing of window-centroids is equidistant.
toi (1D
numpy.ndarray
or float or str) – Either time-points to center windows on if toi is anumpy.ndarray
, or percentage of overlap between windows if toi is a scalar or “all” to center windows on all samples in trl_dat. Please refer tofreqanalysis()
for further details. Note: The value of toi has to agree with provided padding and window settings. See Notes for more information.foi (1D
numpy.ndarray
) – Frequencies of interest (Hz) for output. If desired frequencies cannot be matched exactly the closest possible frequencies (respecting data length and padding) are used.nTaper (int) – Number of tapers to use
timeAxis (int) – Index of running time axis in trl_dat (0 or 1)
taper (callable) – Taper function to use, one of
availableTapers
taper_opt (dict) – Additional keyword arguments passed to taper (see above). For further details, please refer to the SciPy docs
keeptapers (bool) – If True, results of Fourier transform are preserved for each taper, otherwise spectrum is averaged across tapers.
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). Detrending is done on each segment! 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 nTaper, keeptapers etc.)
method_kwargs (dict) – Keyword arguments passed to
mtmconvol()
controlling the spectral estimation method
- Returns
spec – Complex or real time-frequency 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 cross-checked.The computational heavy lifting in this code is performed by SciPy’s Short Time Fourier Transform (STFT) implementation
scipy.signal.stft()
.See also
syncopy.freqanalysis
parent metafunction
MultiTaperFFTConvol
ComputationalRoutine
instance that calls this method ascomputeFunction()
scipy.signal.stft
SciPy’s STFT implementation