mne.filter.filter_data

mne.filter.filter_data(data, sfreq, l_freq, h_freq, picks=None, filter_length=’auto’, l_trans_bandwidth=’auto’, h_trans_bandwidth=’auto’, n_jobs=1, method=’fir’, iir_params=None, copy=True, phase=’zero’, fir_window=’hamming’, verbose=None)[source]

Filter a subset of channels.

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.

Parameters:

data : ndarray, shape (…, n_times)

The data to filter.

sfreq : float

The sample frequency in Hz.

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.

picks : array-like of int | None

Indices of channels to filter. If None all channels will be filtered. Currently this is only supported for 2D (n_channels, n_times) and 3D (n_epochs, n_channels, n_times) arrays.

filter_length : str | int

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 (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’).
  • 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 for phase="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

Number of jobs to run in parallel. Can be ‘cuda’ if scikits.cuda is installed properly, CUDA is initialized, and method=’fir’.

method : str

‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt).

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.

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.

New in version 0.13.

fir_window : str

The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.

New in version 0.13.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.
Returns:

data : ndarray, shape (…, n_times)

The filtered data.

See also

construct_iir_filter, create_filter, mne.io.Raw.filter, notch_filter, resample

Notes

For more information, see the tutorials Background information on filtering and Filtering and resampling data, and mne.filter.create_filter().

Examples using mne.filter.filter_data