LiFE

This is just to let you know that I am working on an implementation of the Linear Fascicle Evaluation algorithm, described in our recent paper: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3098.html

There might be an opportunity to speed things up, using cython. In particular, I am thinking about ways to speed up both sl_signal and voxel2fiber, and any suggestions on that are most welcome. 

On Mon, Oct 13, 2014 at 10:05 AM, Eleftherios Garyfallidis <
notifications@github.com> wrote:

> In dipy/tracking/life.py:
>
> > +"""
> > +import numpy as np
> > +import scipy.sparse as sps
> > +import scipy.linalg as la
> > +
> > +from dipy.reconst.base import ReconstModel, ReconstFit
> > +from dipy.core.onetime import ResetMixin, auto_attr
> > +import dipy.core.sphere as dps
> > +from dipy.tracking.utils import unique_rows, move_streamlines, transform_sl
> > +import dipy.reconst.dti as dti
> > +import dipy.sims.voxel as sims
> > +from dipy.tracking.vox2track import _voxel2fiber
> > +import dipy.data as dpd
> > +
> > +
> > +def gradient(f, *varargs):
>
> This looks very similar to what numpy.gradient does. Why you are not
> calling numpy.gradient?
>
>
It's actually exactly `np.gradient` from version 1.8. Sadly, they changed
the behavior of this function in 1.9, and this is the behavior I want. See
discussion here:

http://mail.scipy.org/pipermail/numpy-discussion/2014-October/071334.html




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