mne.decoding.TemporalFilter¶
- class mne.decoding.TemporalFilter(l_freq=None, h_freq=None, sfreq=1.0, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, method='fir', iir_params=None, fir_window='hamming', fir_design='firwin', verbose=None)[source]¶
Estimator to filter data array along the last dimension.
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop filter to the channels.
l_freq and h_freq are the frequencies below which and above which, respectively, to filter out of the data. Thus the uses are:
l_freq < h_freq: band-pass filter
l_freq > h_freq: band-stop filter
l_freq is not None, h_freq is None: low-pass filter
l_freq is None, h_freq is not None: high-pass filter
- Parameters
- l_freq
float|None Low cut-off frequency in Hz. If None the data are only low-passed.
- h_freq
float|None High cut-off frequency in Hz. If None the data are only high-passed.
- sfreq
float, default 1.0 Sampling frequency in Hz.
- filter_length
str|int, default ‘auto’ Length of the FIR filter to use (if applicable):
int: specified length in samples.
‘auto’ (default in 0.14): the filter length is chosen based on the size of the transition regions (7 times the reciprocal of the shortest transition band).
str: (default in 0.13 is “10s”) a human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if
phase="zero", or the shortest power-of-two length at least that duration forphase="zero-double".
- l_trans_bandwidth
float|str Width of the transition band at the low cut-off frequency in Hz (high pass or cutoff 1 in bandpass). Can be “auto” (default in 0.14) to use a multiple of
l_freq:min(max(l_freq * 0.25, 2), l_freq)
Only used for
method='fir'.- h_trans_bandwidth
float|str Width of the transition band at the high cut-off frequency in Hz (low pass or cutoff 2 in bandpass). Can be “auto” (default in 0.14) to use a multiple of
h_freq:min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq)
Only used for
method='fir'.- n_jobs
int|str, default 1 Number of jobs to run in parallel. Can be ‘cuda’ if
cupyis installed properly and method=’fir’.- method
str, default ‘fir’ ‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt).
- iir_params
dict|None, defaultNone Dictionary of parameters to use for IIR filtering. See mne.filter.construct_iir_filter for details. If iir_params is None and method=”iir”, 4th order Butterworth will be used.
- fir_window
str, default ‘hamming’ The window to use in FIR design, can be “hamming”, “hann”, or “blackman”.
- fir_design
str Can be “firwin” (default) to use
scipy.signal.firwin(), or “firwin2” to usescipy.signal.firwin2(). “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.New in version 0.15.
- verbosebool |
str|int|None Control verbosity of the logging output. If
None, use the default verbosity level. See the logging documentation andmne.verbose()for details. Should only be passed as a keyword argument.
- l_freq
Methods
__hash__(/)Return hash(self).
fit(X[, y])Do nothing (for scikit-learn compatibility purposes).
fit_transform(X[, y])Fit to data, then transform it.
transform(X)Filter data along the last dimension.
- fit(X, y=None)[source]¶
Do nothing (for scikit-learn compatibility purposes).
- Parameters
- Returns
- selfinstance of
TemporalFilter The modified instance.
- selfinstance of