mne.compute_rank(inst, rank=None, scalings=None, info=None, tol='auto', proj=True, verbose=None)[source]

Compute the rank of data or noise covariance.

This function will normalize the rows of the data (typically channels or vertices) such that non-zero singular values should be close to one.

instinstance of Raw, Epochs, or Covariance

Raw measurements to compute the rank from or the covariance.

rankNone | dict | ‘info’ | ‘full’

This controls the rank computation that can be read from the measurement info or estimated from the data. See Notes of mne.compute_rank() for details.The default is None.

scalingsdict | None (default None)

Defaults to dict(mag=1e15, grad=1e13, eeg=1e6). These defaults will scale different channel types to comparable values.

infoinstance of Info | None

The measurement info used to compute the covariance. It is only necessary if inst is a Covariance object (since this does not provide

tolfloat | str

Tolerance. See estimate_rank.


If True, all projs in inst and info will be applied or considered when rank=None or rank='info'.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).


Estimated rank of the data for each channel type. To get the total rank, you can use sum(rank.values()).


The rank parameter can be:

None (default)

Rank will be estimated from the data after proper scaling of different channel types.


Rank is inferred from info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 68.


Rank is assumed to be full, i.e. equal to the number of good channels. If a Covariance is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.


This value is used as the MEG rank. For other channel types, rank is taken from info. This is deprecated and will be removed in 0.19, use dict(meg=...) instead.

New in version 0.18.