Suggestions for Tutorial 01: Linear Models

[sheyma]

Hi,

Tutorial 01: Linear Models works amazing!

Here are two minor suggestions:

• adding brain figures representing t-values and p-values, see the naive example below.

from brainspace.datasets import load_fsa5
from brainspace.plotting import plot_hemispheres
# Load fsaverage5 surfaces
surf_lh, surf_rh = load_fsa5()
grad = np.ones((1, 10242*2))
MASK = np.concatenate((mask,mask),axis=0)
MASK[10242:] = False
grad[0, 0:10242] = slm.t
grad[0, np.where(MASK == False)] = np.nan
plot_hemispheres(surf_lh, surf_rh, array_name=grad, size=(1200, 400), cmap='viridis_r',
                 color_bar=True,  zoom=1.,  interactive=False, embed_nb=True,
                 nan_color = (0.9,0.9,0.9,1))

• adding some figures explaining the models (maybe FixedEffect and MixedEffect), something like this from surfstat earlier:

Cheers!

[sheyma]

actually, this one looks also beautiful: