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%matplotlib inline


    

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# Author: Praveen Sripad <pravsripad@gmail.com>

from jumeg.connectivity import get_label_distances
from surfer import Brain
from nilearn import plotting
from mne.datasets import sample

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
subject = 'sample'
parc = 'aparc_sub'

# compute the distances between COM's of the labels
rounded_coms, coords, coms_lh, coms_rh = get_label_distances(subject,
                                                             subjects_dir,
                                                             parc=parc)
# np.save('%s_distances.npy' % subject, rounded_com)

# get maximum distance between ROIs
print('Max distance between ROIs', rounded_coms.ravel().max())

# do plotting using PySurfer
brain = Brain(subject, hemi='both', surf='inflated', subjects_dir=subjects_dir)
brain.add_foci(coms_lh, coords_as_verts=True, hemi='lh')
brain.add_foci(coms_rh, coords_as_verts=True, hemi='rh')
brain.save_montage('%s_%s_coms.png' % (subject, parc),
                   order=['lat', 'ven', 'med'],
                   orientation='h', border_size=15, colorbar='auto',
                   row=-1, col=-1)
brain.close()

# show the label ROIs using Nilearn plotting
fig = plotting.plot_connectome(rounded_coms, coords,
                               edge_threshold='99%', node_color='cornflowerblue',
                               title='%s - label distances' % parc)
fig.savefig('%s_label_distances.png' % parc)