Parameters: |
- x : array
Signal to filter.
- Fs : float
Sampling rate in Hz.
- freqs : float | array of float | None
Frequencies to notch filter in Hz, e.g. np.arange(60, 241, 60).
None can only be used with the mode ‘spectrum_fit’, where an F
test is used to find sinusoidal components.
- filter_length : str | int
Length of the FIR filter to use (if applicable):
- ‘auto’ (default): the filter length is chosen based
on the size of the transition regions (6.6 times the reciprocal
of the shortest transition band for fir_window=’hamming’
and fir_design=”firwin2”, and half that for “firwin”).
- str: 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 for
phase="zero-double" .
- int: specified length in samples. For fir_design=”firwin”,
this should not be used.
- notch_widths : float | array of float | None
Width of the stop band (centred at each freq in freqs) in Hz.
If None, freqs / 200 is used.
- trans_bandwidth : float
Width of the transition band in Hz.
Only used for method='fir' .
- method : str
‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR
forward-backward filtering (via filtfilt). ‘spectrum_fit’ will
use multi-taper estimation of sinusoidal components. If freqs=None
and method=’spectrum_fit’, significant sinusoidal components
are detected using an F test, and noted by logging.
- iir_params : dict | None
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.
- mt_bandwidth : float | None
The bandwidth of the multitaper windowing function in Hz.
Only used in ‘spectrum_fit’ mode.
- p_value : float
p-value to use in F-test thresholding to determine significant
sinusoidal components to remove when method=’spectrum_fit’ and
freqs=None. Note that this will be Bonferroni corrected for the
number of frequencies, so large p-values may be justified.
- picks : array-like of int | None
Indices of channels to filter. If None all channels will be
filtered. Only supported for 2D (n_channels, n_times) and 3D
(n_epochs, n_channels, n_times) data.
- n_jobs : int | str
Number of jobs to run in parallel. Can be ‘cuda’ if scikits.cuda
is installed properly, CUDA is initialized, and method=’fir’.
- copy : bool
If True, a copy of x, filtered, is returned. Otherwise, it operates
on x in place.
- phase : str
Phase of the filter, only used if method='fir' .
By default, a symmetric linear-phase FIR filter is constructed.
If phase='zero' (default), the delay of this filter
is compensated for. If phase=='zero-double' , then this filter
is applied twice, once forward, and once backward. If ‘minimum’,
then a minimum-phase, causal filter will be used.
- fir_window : str
The window to use in FIR design, can be “hamming” (default),
“hann” (default in 0.13), or “blackman”.
- fir_design : str
Can be “firwin” (default) to use scipy.signal.firwin() ,
or “firwin2” to use scipy.signal.firwin2() . “firwin” uses
a time-domain design technique that generally gives improved
attenuation using fewer samples than “firwin2”.
..versionadded:: 0.15
- pad : str
The type of padding to use. Supports all numpy.pad() mode
options. Can also be “reflect_limited” (default), which pads with a
reflected version of each vector mirrored on the first and last
values of the vector, followed by zeros.
Only used for method='fir' .
- verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
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