Parameters:  x : array
Fs : float
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 humanreadable 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 poweroftwo length at least that duration for
phase="zerodouble" .
 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 overlapadd FIR filtering, ‘iir’ will use IIR
forwardbackward filtering (via filtfilt). ‘spectrum_fit’ will
use multitaper 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
pvalue to use in Ftest 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 pvalues may be justified.
picks : arraylike 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 linearphase FIR filter is constructed.
If phase='zero' (default), the delay of this filter
is compensated for. If phase=='zerodouble' , then this filter
is applied twice, once forward, and once backward. If ‘minimum’,
then a minimumphase, 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 in 0.16) to use
scipy.signal.firwin() , or “firwin2” (default in 0.15 and
before) to use scipy.signal.firwin2() . “firwin” uses a
timedomain 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
