Filter a subset of channels.
ndarray
, shape (…, n_times)The data to filter.
float
The sample frequency in Hz.
float
| None
For FIR filters, the lower pass-band edge; for IIR filters, the lower cutoff frequency. If None the data are only low-passed.
float
| None
For FIR filters, the upper pass-band edge; for IIR filters, the upper cutoff frequency. If None the data are only high-passed.
list
| slice
| None
Channels to include. Slices and lists of integers will be interpreted as channel indices.
None (default) will pick all channels. Note that channels in info['bads']
will be included if their indices are explicitly provided.
Currently this is only supported for 2D (n_channels, n_times) and
3D (n_epochs, n_channels, n_times) arrays.
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.
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) to use a multiple of l_freq
:
min(max(l_freq * 0.25, 2), l_freq)
Only used for method='fir'
.
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'
.
int
| str
Number of jobs to run in parallel. Can be ‘cuda’ if cupy
is installed properly and method=’fir’.
str
‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt).
dict
| None
Dictionary of parameters to use for IIR filtering.
If iir_params is None and method=”iir”, 4th order Butterworth will be used.
For more information, see mne.filter.construct_iir_filter()
.
If True, a copy of x, filtered, is returned. Otherwise, it operates on x in place.
str
Phase of the filter, only used if method='fir'
.
Symmetric linear-phase FIR filters are constructed, and if phase='zero'
(default), the delay of this filter is compensated for, making it
non-causal. If phase='zero-double'
,
then this filter is applied twice, once forward, and once backward
(also making it non-causal). If 'minimum'
, then a minimum-phase filter
will be constricted and applied, which is causal but has weaker stop-band
suppression.
New in version 0.13.
str
The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.
New in version 0.15.
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”.
New in version 0.15.
str
The type of padding to use. Supports all numpy.pad()
mode
options. Can also be "reflect_limited"
, 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'
.
The default is 'reflect_limited'
.
New in version 0.15.
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.
ndarray
, shape (…, n_times)The filtered data.
Notes
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
filter to the channels selected by picks
.
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 and h_freq is None
: high-pass filter
l_freq is None and h_freq is not None
: low-pass filter
Note
If n_jobs > 1, more memory is required as
len(picks) * n_times
additional time points need to
be temporaily stored in memory.
For more information, see the tutorials
Background information on filtering and Filtering and resampling data and
mne.filter.create_filter()
.
mne.filter.filter_data
#Background information on filtering