# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

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

print(__doc__)

data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name

event_id, tmin, tmax = 1, -0.2, 0.5

# Using the same inverse operator when inspecting single trials Vs. evoked
snr = 3.0  # Standard assumption for average data but using it for single trial
lambda2 = 1.0 / snr ** 2

method = "dSPM"  # use dSPM method (could also be MNE or sLORETA)

# Load data
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw = mne.io.read_raw_fif(fname_raw)
events = mne.read_events(fname_event)

# Set up pick list
include = []

# Add a bad channel
raw.info['bads'] += ['EEG 053']  # bads + 1 more

# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
                       include=include, exclude='bads')
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,
                                                    eog=150e-6))

# Get evoked data (averaging across trials in sensor space)
evoked = epochs.average()

# Compute inverse solution and stcs for each epoch
# Use the same inverse operator as with evoked data (i.e., set nave)
# If you use a different nave, dSPM just scales by a factor sqrt(nave)
stcs = apply_inverse_epochs(epochs, inverse_operator, lambda2, method, label,
                            pick_ori="normal", nave=evoked.nave)

stc_evoked = apply_inverse(evoked, inverse_operator, lambda2, method,
                           pick_ori="normal")

stc_evoked_label = stc_evoked.in_label(label)

# Mean across trials but not across vertices in label
mean_stc = sum(stcs) / len(stcs)

# compute sign flip to avoid signal cancellation when averaging signed values
flip = mne.label_sign_flip(label, inverse_operator['src'])

label_mean = np.mean(mean_stc.data, axis=0)
label_mean_flip = np.mean(flip[:, np.newaxis] * mean_stc.data, axis=0)

# Get inverse solution by inverting evoked data
stc_evoked = apply_inverse(evoked, inverse_operator, lambda2, method,
                           pick_ori="normal")

# apply_inverse() does whole brain, so sub-select label of interest
stc_evoked_label = stc_evoked.in_label(label)

# Average over label (not caring to align polarities here)
label_mean_evoked = np.mean(stc_evoked_label.data, axis=0)

"""
Explanation: Compute MNE-dSPM inverse solution on single epochs
Compute dSPM inverse solution on single trial epochs restricted
to a brain label.
End of explanation
"""


times = 1e3 * stcs[0].times  # times in ms

plt.figure()
h0 = plt.plot(times, mean_stc.data.T, 'k')
h1, = plt.plot(times, label_mean, 'r', linewidth=3)
h2, = plt.plot(times, label_mean_flip, 'g', linewidth=3)
plt.legend((h0[0], h1, h2), ('all dipoles in label', 'mean',
                             'mean with sign flip'))
plt.xlabel('time (ms)')
plt.ylabel('dSPM value')
plt.show()

"""
Explanation: View activation time-series to illustrate the benefit of aligning/flipping
End of explanation
"""


# Single trial
plt.figure()
for k, stc_trial in enumerate(stcs):
    plt.plot(times, np.mean(stc_trial.data, axis=0).T, 'k--',
             label='Single Trials' if k == 0 else '_nolegend_',
             alpha=0.5)

# Single trial inverse then average.. making linewidth large to not be masked
plt.plot(times, label_mean, 'b', linewidth=6,
         label='dSPM first, then average')

# Evoked and then inverse
plt.plot(times, label_mean_evoked, 'r', linewidth=2,
         label='Average first, then dSPM')

plt.xlabel('time (ms)')
plt.ylabel('dSPM value')
plt.legend()
plt.show()

"""
Explanation: Viewing single trial dSPM and average dSPM for unflipped pooling over label
Compare to (1) Inverse (dSPM) then average, (2) Evoked then dSPM
End of explanation
"""


