Time frequency transformer.
Time-frequency transform of times series along the last axis.
float
, shape (n_freqs,)The frequencies.
float
| int
, default 1.0Sampling frequency of the data.
The time-frequency method. ‘morlet’ convolves a Morlet wavelet. ‘multitaper’ uses Morlet wavelets windowed with multiple DPSS multitapers.
float
| array
of float
, default 7.0Number of cycles in the Morlet wavelet. Fixed number or one per frequency.
float
, default None
If None and method=multitaper, will be set to 4.0 (3 tapers). Time x (Full) Bandwidth product. Only applies if method == ‘multitaper’. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1).
True
Use the FFT for convolutions or not.
int
| slice
, default 1To reduce memory usage, decimation factor after time-frequency
decomposition.
If int
, returns tfr[…, ::decim].
If slice
, returns tfr[…, decim].
Note
Decimation may create aliasing artifacts, yet decimation is done after the convolutions.
str
, default ‘complex’‘complex’ : single trial complex.
‘power’ : single trial power.
‘phase’ : single trial phase.
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
.
The number of epochs to process at the same time. The parallelization
is implemented across channels.
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.
Methods
|
Do nothing (for scikit-learn compatibility purposes). |
|
Time-frequency transform of times series along the last axis. |
|
Get parameters for this estimator. |
|
Set the parameters of this estimator. |
|
Time-frequency transform of times series along the last axis. |
Time-frequency transform of times series along the last axis.
array
, shape (n_samples, n_channels, n_freqs, n_times)The time-frequency transform of the data, where n_channels can be zero- or 1-dimensional.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
dict
Parameters.
The object.