mne.viz.plot_cov#

mne.viz.plot_cov(cov, info, exclude=(), colorbar=True, proj=False, show_svd=True, show=True, verbose=None)[source]#

Plot Covariance data.

Parameters:
covinstance of Covariance

The covariance matrix.

infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

excludelist of str | str

List of channels to exclude. If empty do not exclude any channel. If ‘bads’, exclude info[‘bads’].

colorbarbool

Show colorbar or not.

projbool

Apply projections or not.

show_svdbool

Plot also singular values of the noise covariance for each sensor type. We show square roots ie. standard deviations.

showbool

Show figure if True.

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.

Returns:
fig_covinstance of matplotlib.figure.Figure

The covariance plot.

fig_svdinstance of matplotlib.figure.Figure | None

The SVD spectra plot of the covariance.

See also

mne.compute_rank

Notes

For each channel type, the rank is estimated using mne.compute_rank().

Changed in version 0.19: Approximate ranks for each channel type are shown with red dashed lines.

Examples using mne.viz.plot_cov#

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE