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%matplotlib inline


    

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import os
import mne
import matplotlib.pyplot as plt
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'
subjects_dir = data_path + '/subjects'
os.environ['SUBJECTS_DIR'] = subjects_dir

stc_fname = data_path + '/MEG/sample/sample_audvis-meg'
stc = mne.read_source_estimate(stc_fname)

morph = mne.compute_source_morph(stc, subject_from='sample',
                                 subject_to='fsaverage', spacing=5, subjects_dir=subjects_dir)
new_stc = morph.apply(stc)

subject = 'fsaverage'

# Plot brain in 3D with PySurfer if available
brain = new_stc.plot(subject, hemi='lh', subjects_dir=subjects_dir)
brain.show_view('lateral')

# use peak getter to move vizualization to the time point of the peak
vertno_max, time_idx = new_stc.get_peak(hemi='lh', time_as_index=True)

brain.set_data_time_index(time_idx)

# draw marker at maximum peaking vertex
brain.add_foci(vertno_max, coords_as_verts=True, hemi='lh', color='blue',
               scale_factor=0.6, map_surface='white')

mni_coords = mne.vertex_to_mni(vertno_max, hemis=0, subject=subject,
                               subjects_dir=subjects_dir)
print('The MNI coords are ', mni_coords)

#my_trans = mne.read_trans(?)
#src_pts = apply_trans(trans, some_tgt_pts)

from surfer import utils
utils.coord_to_label(subject, mni_coords[0], label='mycoord',
                     hemi='lh', n_steps=25, map_surface="white")
brain.add_label('mycoord-lh.label', color="darkseagreen", alpha=.8)

# if the new mni_coords are computed
brain.add_foci(mni_coords[0], coords_as_verts=False, hemi='lh', color='red',
               map_surface='white', scale_factor=0.6)