Xval

This revives #235. Now that all the other elements are in place, I think that this is ready for review and in principle ready to be merged upstream (once it's been reviewed and fixed up, of course!). I am excited about this PR - evaluation of DWI models is something that I have been thinking a lot about in the past couple of years, since I started working on DWI. 

This PR also demonstrates the power and elegance of the uniform `Model`/`Fit` API we have in place. GGT!

For now, this PR only implements cross-validation for CSD and for the tensor model, but future work could extend this to other models, by implementing their `predict` methods. 

Also : a lot of PEP8 fixes in the CSD module and elsewhere :-) 
Is this clearer?


On Fri, Jun 27, 2014 at 10:02 AM, Stefan van der Walt <
notifications@github.com> wrote:

> In doc/examples/kfold_xval.py:
>
> > +cso_vox = data[30, 76, 38]
> > +
> > +"""
> > +
> > +We initialize each kind of model:
> > +
> > +"""
> > +
> > +dti_model = dti.TensorModel(gtab)
> > +response, ratio = csd.auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
> > +csd_model = csd.ConstrainedSphericalDeconvModel(gtab, response)
> > +
> > +"""
> > +
> > +Next, we perform cross-validation for each kind of model, with each one of the
> > +voxel's data.
>
> I wasn't sure what it meant "each one of the voxel's data"?
>
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> <https://github.com/nipy/dipy/pull/335/files#r14303149>.
>