Ugh. I just discovered that the test was bogus. I wasn't really testing the
algorithm at all. See this commit:

https://github.com/arokem/dipy/commit/4fd41ba4adac9b6ffbb729a67588121cbf0873d7

I was basically just rerunning the fit on the same data again and again.
Now that I've fixed it, the test is failing and I don't understand at all
why. I am going to dig into this more tomorrow and, if need be, on Sunday.
Have to go catch a train now.




On Thu, Dec 5, 2013 at 3:38 PM, Eleftherios Garyfallidis <
notifications@github.com> wrote:

> In doc/examples/restore_dti.py:
>
> > +ax.hist(np.ravel(fa2), color='b', histtype='step', label='WLS')
> > +ax.hist(np.ravel(fa3), color='r', histtype='step', label='RESTORE')
> > +ax.hist(np.ravel(fa1), color='g', histtype='step', label='Original')
> > +ax.set_xlabel('Fractional Anisotropy')
> > +ax.set_ylabel('Count')
> > +plt.legend()
> > +fig_hist.savefig('dti_fa_distributions.png')
> > +
> > +"""
> > +
> > +.. figure:: dti_fa_distributions.png
> > +   :align: center
> > +
> > +
> > +This demonstrates that RESTORE can recover a distribution of FA that more
> > +closely resembles the baseline distribution of the noiseless signal.
>
> Cool, let me know how it goes.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nipy/dipy/pull/275/files#r8149345>
> .
>