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# %matplotlib inline

#
# # Extracting the time series of activations in a label
#
#
# We first apply a dSPM inverse operator to get signed activations
# in a label (with positive and negative values) and we then
# compare different strategies to average the times series
# in a label. We compare a simple average, with an averaging
# using the dipoles normal (flip mode) and then a PCA,
# also using a sign flip.
#
#

# +
# Author: <NAME> <<EMAIL>>
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

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

print(__doc__)

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'

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']

# 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 = mne.read_label(label_fname)

stc_label = stc.in_label(label)
mean = stc.extract_label_time_course(label, src, mode='mean')
mean_flip = stc.extract_label_time_course(label, src, mode='mean_flip')
pca = stc.extract_label_time_course(label, src, mode='pca_flip')

print("Number of vertices : %d" % len(stc_label.data))

# View source activations
plt.figure()
plt.plot(1e3 * stc_label.times, stc_label.data.T, 'k', linewidth=0.5)
h0, = plt.plot(1e3 * stc_label.times, mean.T, 'r', linewidth=3)
h1, = plt.plot(1e3 * stc_label.times, mean_flip.T, 'g', linewidth=3)
h2, = plt.plot(1e3 * stc_label.times, pca.T, 'b', linewidth=3)
plt.legend([h0, h1, h2], ['mean', 'mean flip', 'PCA flip'])
plt.xlabel('Time (ms)')
plt.ylabel('Source amplitude')
plt.title('Activations in Label : %s' % label)
plt.show()

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