Sparse Fascicle Model

This is a start on an implementation of the model we describe here: 

http://arxiv.org/abs/1411.0721

The main current issue with this is that this will have a local dependency on scikit-learn, because it uses ElasticNet for the fit. How do people feel about that? 
Is that much longer than say, a CSD reconstruction takes?

The bottleneck here is really the fact that we have to run ElasticNet on
each and every voxel. Even with 'warm_start' set to True this takes time.



On Tue, Nov 25, 2014 at 3:47 PM, Stefan van der Walt <
notifications@github.com> wrote:

> In doc/examples/sfm_tracking.py:
>
> > +
> > +"""
> > +
> > +pnm = dpp.peaks_from_model(sf_model, maskdata, sphere,
> > +                            mask=mask,
> > +                            relative_peak_threshold=.5,
> > +                            min_separation_angle=25,
> > +                            return_sh=False)
> > +
> > +
> > +"""
> > +We will use the generalized fractional anisotropy (GFA) as a stopping
> > +criterion, and seed in 10k points
> > +"""
> > +
> > +eu = EuDX(pnm.gfa,
>
> This took more than half an hour on my laptop :/
>
> —
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> <https://github.com/nipy/dipy/pull/460/files#r20906637>.
>