WIP: add option for automated regularization to mne.cov.compute_covariance

Todo:

- [x] show basic sensor examples / update existing examples
- [x] consolidate API including default values
- [x] deal with projs
Teon yes you saw it already it's the sensor space whitening example 

> On 18 Dec 2014, at 16:50, Teon <notifications@github.com> wrote:
> 
> In examples/inverse/plot_covariance_whitening_dspm.py:
> 
> > @@ -0,0 +1,146 @@
> > +"""
> > +===================================================
> > +Demonstrate impact of whitening on source estimates
> > +===================================================
> > +
> > +This example computes dSPM source estimates for the SPM faces data and compares
> > +proper regularization with insufficient regularization based on the methods
> > +described in [1]. The example demonstrates that insufficient regularization can
> > +lead to overestimation of source amplitudes.
> > +
> > +References
> > +----------
> > +[1] Engemann D. and Gramfort A. Automated model selection in covariance
> > +estimation and spatial whitening of MEG and EEG signals. (in press.)
> > +NeuroImage.
> is there an easy example? I would just expect something like the user run auto and return either all the tests or just the best cov and the standard cov that we have been using, and then have a plot showing the contrast of two on whitened data to show the difference it makes.
> 
> —
> Reply to this email directly or view it on GitHub.
>