mne.equalize_channels

mne.equalize_channels(instances, copy=True, verbose=None)

Equalize channel picks and ordering across multiple MNE-Python objects.

First, all channels that are not common to each object are dropped. Then, using the first object in the list as a template, the channels of each object are re-ordered to match the template. The end result is that all given objects define the same channels, in the same order.

Parameters

instanceslist

A list of MNE-Python objects to equalize the channels for. Objects can be of type Raw, Epochs, Evoked, AverageTFR, Forward, Covariance, CrossSpectralDensity or Info.

copybool

When dropping and/or re-ordering channels, an object will be copied when this parameter is set to True. When set to False (the default) the dropping and re-ordering of channels happens in-place.

New in version 0.20.0.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

equalized_instanceslist

A list of MNE-Python objects that have the same channels defined in the same order.

Notes

This function operates inplace.

mne.concatenate_raws mne.grand_average