bands = ([.5, 4], [4, 8])
raw_fname = env.data + '/MEG_data/fifs/' + subj + '_rest_LP100_CP3_DS300_raw.fif'
fwd_path = env.data + '/MEG_data/analysis/rest/'
fwd_fname = fwd_path + subj + '_rest_LP100_CP3_DS300_raw-5-fwd.fif'
# preloading makes computing the covariance a lot faster
raw = mne.fiff.Raw(raw_fname, preload=True)
fwd = mne.read_forward_solution(fwd_fname)
epochs = fgs.crop_good_epochs(raw, threshold=3500e-15, fmin=.5, fmax=58)
stcs = ve.localize_epochs(epochs, fwd, reg=0)
labels_folder = os.environ['SUBJECTS_DIR'] + '/' + subj + '/labels/'
label_names = glob.glob(labels_folder + '/*.label')
print 'Reading subject labels...'
labels = [mne.read_label(ln) for ln in label_names]
selected_voxels = ve.find_best_voxels_epochs(stcs, labels, bands, job_num=1)
plis = []
for r, rand_ids in enumerate(epoch_ids):
print '\nRunning random set ' + str(r+1) + '/' + str(num_rand_sets)
rnd_epochs = [stcs[epoch] for epoch in rand_ids]
pli = ve.compute_pli_epochs(rnd_epochs, labels, selected_voxels, bands)
plis.append(pli)
np.savez(env.results + subj + '_rand_epochs', subj=subj, plis=plis, bands=bands, epoch_ids=epoch_ids)
res = np.load(env.results + 'num_clean_epochs_chl.5_lp58_hp.5_visual.npz')
data_mrks = res['markers'][()]
chl_mrks = res['chl_mrks'][()]
seg_len = res['seg_len'][()]
res = env.load(env.results + 'selected_voxels_all_chl.5_lp58_hp.5_visual.npz')
labels = res['labels']
selected_voxels = res['selected_voxels']
good_subjects = list(res['good_nvs']) + list(res['good_adhds'])
plis = {}
for subj in good_subjects:
raw_fname = env.data + '/MEG_data/fifs/' + subj + '_rest_LP58_HP.5_CP3_DS300_raw.fif'
fwd_fname = env.data + '/MEG_data/analysis/rest/' + subj + '_rest_LP100_CP3_DS300_raw-5-fwd.fif'
# preloading makes computing the covariance a lot faster
raw = mne.fiff.Raw(raw_fname, preload=True)
fwd = mne.read_forward_solution(fwd_fname)
_, times = raw[:, :]
mrks = data_mrks[subj] + chl_mrks[subj]
events = fgs.get_good_events(mrks, times, seg_len)
epochs = fgs.crop_clean_epochs(raw, events, seg_len=seg_len)
stcs = ve.localize_epochs(epochs, fwd, reg=0)
plis[subj] = ve.compute_pli_epochs(stcs, labels[subj], selected_voxels[subj], res['bands'])
np.savez(env.results + 'good_plis_all_chl.5_lp58_hp.5_visual.npz', plis=plis)
raw = mne.fiff.Raw(raw_fname, preload=True)
fwd = mne.read_forward_solution(fwd_fname)
_, times = raw[:, :]
mrks = data_mrks[subj] + chl_mrks[subj]
events = fgs.get_good_events(mrks, times, seg_len)
epochs = fgs.crop_clean_epochs(raw, events, seg_len=seg_len)
stcs = ve.localize_epochs(epochs, fwd, reg=0)
rand_plis = np.zeros(
[num_perms,
len(bands),
len(labels[subj]),
len(labels[subj])])
for r in range(num_perms):
print '==================================='
print '======== Permutation ' + str(r + 1) + ' ==========='
print '==================================='
pli = ve.compute_pli_epochs(stcs,
labels[subj],
selected_voxels[subj],
bands,
randomize=True)
for band in range(len(bands)):
rand_plis[r, band, :, :] = pli[band]
np.savez(env.results + 'rand_' + str(num_perms) + '_plis_' + subj + '_' +
str(int(time.time())),
rand_plis=rand_plis)
for subj, voxels in selected_voxels.iteritems():
print '==================================='
print '======= Subject ' + subj + ' ========='
print '==================================='
raw_fname = env.data + '/MEG_data/fifs/' + subj + '_rest_LP58_HP.5_CP3_DS300_raw.fif'
fwd_fname = env.data + '/MEG_data/analysis/rest/' + subj + '_rest_LP100_CP3_DS300_raw-5-fwd.fif'
# preloading makes computing the covariance a lot faster
raw = mne.fiff.Raw(raw_fname, preload=True)
fwd = mne.read_forward_solution(fwd_fname)
_, times = raw[:, :]
mrks = data_mrks[subj] + chl_mrks[subj]
events = fgs.get_good_events(mrks, times, seg_len)
epochs = fgs.crop_clean_epochs(raw, events, seg_len=seg_len)
stcs = ve.localize_epochs(epochs, fwd, reg=0)
rand_plis = np.zeros([num_perms, len(bands), len(labels[subj]), len(labels[subj])])
for r in range(num_perms):
print '==================================='
print '======== Permutation ' + str(r+1) + ' ==========='
print '==================================='
pli = ve.compute_pli_epochs(stcs, labels[subj], selected_voxels[subj], bands, randomize=True)
for band in range(len(bands)):
rand_plis[r, band, :, :] = pli[band]
np.savez(env.results + 'rand_' + str(num_perms) + '_plis_' + subj + '_' + str(int(time.time())), rand_plis=rand_plis)
fwd_path = env.data + '/MEG_data/analysis/rest/'
fwd_fname = fwd_path + subj + '_rest_LP100_CP3_DS300_raw-5-fwd.fif'
# preloading makes computing the covariance a lot faster
raw = mne.fiff.Raw(raw_fname, preload=True)
fwd = mne.read_forward_solution(fwd_fname)
epochs = fgs.crop_good_epochs(raw, threshold=3500e-15, fmin=.5, fmax=58)
stcs = ve.localize_epochs(epochs, fwd, reg=0)
labels_folder = os.environ['SUBJECTS_DIR'] + '/' + subj + '/labels/'
label_names = glob.glob(labels_folder + '/*.label')
print 'Reading subject labels...'
labels = [mne.read_label(ln) for ln in label_names]
selected_voxels = ve.find_best_voxels_epochs(stcs, labels, bands, job_num=1)
plis = []
for r, rand_ids in enumerate(epoch_ids):
print '\nRunning random set ' + str(r + 1) + '/' + str(num_rand_sets)
rnd_epochs = [stcs[epoch] for epoch in rand_ids]
pli = ve.compute_pli_epochs(rnd_epochs, labels, selected_voxels, bands)
plis.append(pli)
np.savez(env.results + subj + '_rand_epochs',
subj=subj,
plis=plis,
bands=bands,
epoch_ids=epoch_ids)
