Agreed too.
Le 5 mars 2014 15:19, "Gael Varoquaux" <notifications@github.com> a écrit :

> In plot_haxby_mass_univariate.py:
>
> > +grid = ImageGrid(plt.figure(), 111, nrows_ncols=(1, 2), direction="row",
> > +                 axes_pad=0.05, add_all=True, label_mode="1",
> > +                 share_all=True, cbar_location="right", cbar_mode="single",
> > +                 cbar_size="7%", cbar_pad="1%")
> > +
> > +# Plot thresholded F-scores map
> > +ax = grid[0]
> > +mask_bonf = np.asarray(mask_img.get_data().copy())
> > +mask_bonf[neg_log_pvals_bonferroni_unmasked < vmin] = 0
> > +p_ma = np.ma.array(neg_log_pvals_bonferroni_unmasked,
> > +                   mask=np.logical_not(mask_bonf))
> > +ax.imshow(np.rot90(mean_fmri[..., picked_slice]), interpolation='nearest',
> > +          cmap=plt.cm.gray)
> > +ax.imshow(np.rot90(p_ma[..., picked_slice]), interpolation='nearest',
> > +          cmap=plt.cm.autumn, vmin=vmin, vmax=vmax)
> > +ax.set_title(r'Negative $\log_{10}$ p-values' + '\n(Analytic F-test + '
>
>  No the permutation-based p-value is not an 'analytic' result. It should
> rather be called 'non-parametric'
> Sounds good with me.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nilearn/nilearn/pull/167/files#r10300366>
> .
>