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

Todo:

- [ ] allow auto separately for mutliple sensors
- [ ] show basic sensor examples / update existing examples
- [ ] consolidate API including default values
- [ ] deal with projs
Will try later

2014-11-28 14:45 GMT+01:00 Alexandre Gramfort <notifications@github.com>:

> In mne/tests/test_cov.py:
>
> > +    assert_array_equal(cov['data'], cov2['data'])  # We know it's sc.
> > +
> > +    cov3 = compute_covariance(epochs, method=[ec, 'factor_analysis'],
> > +                              method_params=mp,
> > +                              return_estimators=True)
> > +    assert_equal(set(cov3), set([ec.__name__, 'factor_analysis']))
> > +
> > +    # invalid prespecified method
> > +    assert_raises(ValueError, compute_covariance, epochs, method='pizza')
> > +
> > +    # invalid callable
> > +    assert_raises(ValueError, compute_covariance, epochs, method=lambda x: x)
> > +
> > +    # invalid scalings
> > +    assert_raises(ValueError, compute_covariance, epochs, method='shrunk',
> > +                  scalings=dict(misc=123))
>
> can you divide time by 2 or 3 by using less channels?
>
> —
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> <https://github.com/mne-tools/mne-python/pull/1671/files#r21031677>.
>