Compute power spectral density (PSD) using a multi-taper method.
array
, shape=(…, n_times)The data to compute PSD from.
float
The sampling frequency.
float
The lower frequency of interest.
float
The upper frequency of interest.
float
The bandwidth of the multi taper windowing function in Hz.
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
Only use tapers with more than 90% spectral concentration within bandwidth.
Normalization strategy. If “full”, the PSD will be normalized by the
sampling rate as well as the length of the signal (as in
Nitime). Default is 'length'
.
str
The format of the returned psds
array. Can be either 'complex'
or 'power'
. If 'power'
, the power spectral density is returned.
If output='complex'
, the complex fourier coefficients are returned
per taper.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
ndarray
, shape (…, n_freqs) or (…, n_tapers, n_freqs)The power spectral densities. All dimensions up to the last (or the
last two if output='complex'
) will be the same as input.
array
The frequency points in Hz of the PSD.
ndarray
The weights used for averaging across tapers. Only returned if
output='complex'
.
Notes
New in version 0.14.0.