mne.filter.resample(x, up=1.0, down=1.0, npad=100, axis=-1, window='boxcar', n_jobs=None, pad='reflect_limited', *, verbose=None)[source]#

Resample an array.

Operates along the last dimension of the array.


Signal to resample.


Factor to upsample by.


Factor to downsample by.

npadint | str

Amount to pad the start and end of the data. Can also be "auto" to use a padding that will result in a power-of-two size (can be much faster).


Axis along which to resample (default is the last axis).

windowstr | tuple

Frequency-domain window to use in resampling. See scipy.signal.resample().

n_jobsint | str

Number of jobs to run in parallel. Can be 'cuda' if cupy is installed properly.


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. The default is 'reflect_limited'.

New in v0.15.

verbosebool | 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.


The x array resampled.


This uses (hopefully) intelligent edge padding and frequency-domain windowing improve scipy.signal.resample’s resampling method, which we have adapted for our use here. Choices of npad and window have important consequences, and the default choices should work well for most natural signals.

Resampling arguments are broken into “up” and “down” components for future compatibility in case we decide to use an upfirdn implementation. The current implementation is functionally equivalent to passing up=up/down and down=1.

Examples using mne.filter.resample#

Spectro-temporal receptive field (STRF) estimation on continuous data

Spectro-temporal receptive field (STRF) estimation on continuous data

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction