def spectral(x, sf, f, axis, stype, dcomplex, filt, filtorder, cycle, width,
njobs):
"""Extract spectral informations from data.
Parameters
----------
x : array_like
Array of data
sf : float
Sampling frequency
f : array_like
Frequency vector of shape (N, 2)
axis : int
Axis where the time is located.
stype : string
Spectral informations to extract (use either 'pha' or 'amp')
dcomplex : string
Complex decomposition type. Use either 'hilbert' or 'wavelet'
filt : string
Name of the filter to use (only if dcomplex is 'hilbert'). Use
either 'eegfilt', 'butter' or 'bessel'.
filtorder : int
Order of the filter (only if dcomplex is 'hilbert')
cycle : int
Number of cycles to use for fir1 filtering.
width : int
Width of the wavelet.
njobs : int
Number of jobs to use. If jobs is -1, all of them are going to be
used.
"""
# Filtering + complex decomposition :
if dcomplex is 'hilbert':
# Filt each time series :
nf = range(f.shape[0])
xf = Parallel(n_jobs=njobs)(delayed(filtdata)(
x, sf, f[k, :], axis, filt, cycle, filtorder) for k in nf)
# Use hilbert for the complex decomposition :
xd = hilbert(xf, axis=axis + 1) if stype is not None else np.array(xf)
elif dcomplex is 'wavelet':
f = f.mean(1) # centered frequencies
xd = Parallel(n_jobs=njobs)(delayed(morlet)(
x, sf, k, axis, width) for k in f)
# Extract phase / amplitude :
if stype is 'pha':
return np.angle(np.moveaxis(xd, axis + 1, -1))
elif stype is 'amp':
return np.abs(np.moveaxis(xd, axis + 1, -1))
elif stype is None:
return np.moveaxis(xd, axis + 1, -1)
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