mne.compute_rank(inst, rank=None, scalings=None, info=None, tol='auto', proj=True, tol_kind='absolute', 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 | ‘auto’

Tolerance for singular values to consider non-zero in calculating the rank. The singular values are calculated in this method such that independent data are expected to have singular value around one. Can be ‘auto’ to use the same thresholding as scipy.linalg.orth().


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


Can be: “absolute” (default) or “relative”. Only used if tol is a float, because when tol is a string the mode is implicitly relative. After applying the chosen scale factors / normalization to the data, the singular values are computed, and the rank is then taken as:

  • 'absolute'

    The number of singular values s greater than tol. This mode can fail if your data do not adhere to typical data scalings.

  • 'relative'

    The number of singular values s greater than tol * s.max(). This mode can fail if you have one or more large components in the data (e.g., artifacts).

New in version 0.21.0.

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.


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.

New in version 0.18.

Examples using mne.compute_rank