from __future__ import print_function
import mne

data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
subject = 'sample'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'

# Read the source space. Normally, you would use mne.read_source_spaces for
# this, but the sample data does not include a '-src.fif' file. Therefore, in
# this example, we take it from the inverse operator.
inverse_operator = mne.minimum_norm.read_inverse_operator(fname_inv)
src = inverse_operator['src']  # get the source space

# Read a label
aparc_label_name = 'bankssts-lh'
label = mne.read_labels_from_annot(subject, parc='aparc',
                                   subjects_dir=subjects_dir,
                                   regexp=aparc_label_name)[0]

# The source space contains a distance matrix that measures the distance (in
# meters) from each vertex to each other vertex.
hemi = 0  # Left hemisphere
dist = src[hemi]['dist']

# Reduce the distance matrix to only the vertices present in the label
label_dist = dist[label.vertices, :][:, label.vertices]

# How big is the label? Here, defined as the maximum distance between two
# vertices in the label.
print('Size of the label:', label_dist.max(), 'm')

# Or in milimeters
print('Size of the label:', label_dist.max() * 1000, 'mm')