[Mne_analysis] compute_covariance

[Rezvan Farahi]

Hi,
This is probably a dummy question but to assemble the inverse operator in
the older versions of mne_python, one had to first compute the noise
covariance then regularize it manually by assigning mag grad and eeg
regularisation factors. the defaults are 0.1 here
<http://martinos.org/mne/stable/generated/mne.cov.regularize.html?highlight=regularize#mne.cov.regularize>
.
At the moment the automated paradigm computes the noise covariance and
regularizes in the same framework by cross-validation. the defaults of
regularisation factors for empirical approach and fixed diagonal are much
less than the old ones (0.01, 0.01 and 0.0 for grad, mag and eeg
respectively).

I had a look at the new cov.py code to see if can see some scalings that
could explain why the new regularisation uses smaller defaults but seems
that both the old and new versions use the same "regularize" function for
empirical and fixed diagonal.

I was wondering if you could please let me know where am I making a
mistake? and if I'm not making a mistake then why the regularisation
factors are so different in the old and new versions?

Many thanks,
Rezvan
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