mne.time_frequency.tfr_array_stockwell#
- mne.time_frequency.tfr_array_stockwell(data, sfreq, fmin=None, fmax=None, n_fft=None, width=1.0, decim=1, return_itc=False, n_jobs=None)[source]#
Compute power and intertrial coherence using Stockwell (S) transform.
Same computation as
tfr_stockwell
, but operates onNumPy arrays
instead ofEpochs
objects.See [1][2][3][4] for more information.
- Parameters:
- data
ndarray
, shape (n_epochs, n_channels, n_times) The signal to transform.
- sfreq
float
The sampling frequency.
- fmin
None
,float
The minimum frequency to include. If None defaults to the minimum fft frequency greater than zero.
- fmax
None
,float
The maximum frequency to include. If None defaults to the maximum fft.
- n_fft
int
|None
The length of the windows used for FFT. If None, it defaults to the next power of 2 larger than the signal length.
- width
float
The width of the Gaussian window. If < 1, increased temporal resolution, if > 1, increased frequency resolution. Defaults to 1. (classical S-Transform).
- decim
int
The decimation factor on the time axis. To reduce memory usage.
- return_itc
bool
Return intertrial coherence (ITC) as well as averaged power.
- n_jobs
int
|None
The number of jobs to run in parallel. If
-1
, it is set to the number of CPU cores. Requires thejoblib
package.None
(default) is a marker for ‘unset’ that will be interpreted asn_jobs=1
(sequential execution) unless the call is performed under ajoblib.parallel_backend()
context manager that sets another value forn_jobs
.
- data
- Returns:
See also
References