import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse

data_path = sample.data_path()
label = 'Aud-lh'
label_fname = data_path + '/MEG/sample/labels/%s.label' % label
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'

snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM"  # use dSPM method (could also be MNE or sLORETA)

# Load data
evoked = mne.read_evokeds(fname_evoked, condition=0, baseline=(None, 0))
inverse_operator = read_inverse_operator(fname_inv)
src = inverse_operator['src']

label = mne.read_label(label_fname)

# Compute inverse solution
pick_ori = "normal"  # Get signed values to see the effect of sign filp
stc = apply_inverse(evoked, inverse_operator, lambda2, method,
                      pick_ori=pick_ori, label=label)

stc_full = apply_inverse(evoked, inverse_operator, lambda2, method,
                         pick_ori=pick_ori)

stc_in_label = stc_full.in_label(label)

# these fails!
mean = stc.extract_label_time_course(label, src, mode='mean')
mean_2 = stc_in_label.extract_label_time_course(label, src, mode='mean')

# it would then be good to be able to put it (or some derived measure) back
stc_label = mne.label_data_to_stc(label, mean, src)