def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros((evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
if args.normalize_evoked:
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile, args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'), names=ch_names, conditions=list(events.keys()), dt=dt)
return True
def calc_connections_colors(data, labels, hemis, args):
# stat, conditions, w, threshold=0, threshold_percentile=0, color_map='jet',
# norm_by_percentile=True, norm_percs=(1, 99), symetric_colors=True):
M = data.shape[0]
W = data.shape[2] if args.windows == 0 else args.windows
L = int((M * M + M) / 2 - M)
con_indices = np.zeros((L, 2))
con_values = np.zeros((L, W, len(args.conditions)))
con_names = [None] * L
con_type = np.zeros((L))
for cond in range(len(args.conditions)):
for w in range(W):
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
if W > 1 and data.ndim == 4:
con_values[ind, w, cond] = data[i, j, w, cond]
elif data.ndim > 2:
con_values[ind, w, cond] = data[i, j, cond]
else:
con_values[ind, w, cond] = data[i, j]
if len(args.conditions) > 1:
stat_data = utils.calc_stat_data(con_values, args.stat)
else:
stat_data = np.squeeze(con_values)
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
con_indices[ind, :] = [i, j]
con_names[ind] = '{}-{}'.format(labels[i], labels[j])
con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN
con_indices = con_indices.astype(np.int)
con_names = np.array(con_names)
data_max, data_min = utils.get_data_max_min(stat_data, args.norm_by_percentile, args.norm_percs)
data_minmax = max(map(abs, [data_max, data_min]))
if args.threshold_percentile > 0:
args.threshold = np.percentile(np.abs(stat_data), args.threshold_percentile)
if args.threshold > data_minmax:
raise Exception('threshold > abs(max(data)) ({})'.format(data_minmax))
if args.threshold >= 0:
indices = np.where(np.abs(stat_data) > args.threshold)[0]
# con_colors = con_colors[indices]
con_indices = con_indices[indices]
con_names = con_names[indices]
con_values = con_values[indices]
con_type = con_type[indices]
stat_data = stat_data[indices]
con_values = np.squeeze(con_values)
if args.data_max == 0 and args.data_min == 0:
if args.symetric_colors and np.sign(data_max) != np.sign(data_min):
data_max, data_min = data_minmax, -data_minmax
else:
data_max, data_min = args.data_max, args.data_min
print('data_max: {}, data_min: {}'.format(data_max, data_min))
con_colors = utils.mat_to_colors(stat_data, data_min, data_max, args.color_map)
print(len(con_names))
return con_colors, con_indices, con_names, con_values, con_type, data_max, data_min
def calc_connections_colors(data, labels, hemis, stat, w, threshold=0, threshold_percentile=0, color_map='jet',
norm_by_percentile=True, norm_percs=(1, 99)):
M = data.shape[0]
W = data.shape[2] if w == 0 else w
L = int((M * M + M) / 2 - M)
con_indices = np.zeros((L, 2))
con_values = np.zeros((L, W, 2))
con_names = [None] * L
con_type = np.zeros((L))
axis = data.ndim - 1
coh_stat = utils.calc_stat_data(data, stat, axis=axis)
x = coh_stat.ravel()
data_max, data_min = utils.get_data_max_min(x, norm_by_percentile, norm_percs)
data_minmax = max(map(abs, [data_max, data_min]))
for cond in range(2):
for w in range(W):
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
if W > 1:
con_values[ind, w, cond] = data[i, j, w, cond]
else:
con_values[ind, w, cond] = data[i, j, cond]
stat_data = utils.calc_stat_data(con_values, stat)
con_colors = utils.mat_to_colors(stat_data, -data_minmax, data_minmax, color_map)
for ind, (i, j) in enumerate(utils.lower_rec_indices(M)):
con_indices[ind, :] = [i, j]
con_names[ind] = '{}-{}'.format(labels[i], labels[j])
con_type[ind] = HEMIS_WITHIN if hemis[i] == hemis[j] else HEMIS_BETWEEN
con_indices = con_indices.astype(np.int)
con_names = np.array(con_names)
if threshold_percentile > 0:
threshold = np.percentile(np.abs(stat_data), threshold_percentile)
if threshold > 0:
indices = np.where(np.abs(stat_data) >= threshold)[0]
con_colors = con_colors[indices]
con_indices = con_indices[indices]
con_names = con_names[indices]
con_values = con_values[indices]
con_type = con_type[indices]
print(len(con_names))
return con_colors, con_indices, con_names, con_values, con_type
def save_evoked_to_blender(mri_subject, events, args, evoked=None):
fol = op.join(MMVT_DIR, mri_subject, 'eeg')
utils.make_dir(fol)
if '{cond}' in meg.EVO:
for event_ind, event_id in enumerate(events.keys()):
if evoked is None:
evo = mne.read_evokeds(meg.get_cond_fname(meg.EVO, event_id))
else:
evo = evoked[event_id]
if event_ind == 0:
ch_names = np.array(evo[0].ch_names)
dt = np.diff(evo[0].times[:2])[0]
data = np.zeros(
(evo[0].data.shape[0], evo[0].data.shape[1], 2))
data[:, :, event_ind] = evo[0].data
else:
if evoked is None:
evoked = mne.read_evokeds(meg.EVO)
data = evoked[0].data
data = data[..., np.newaxis]
ch_names = np.array(evoked[0].ch_names)
dt = np.diff(evoked[0].times[:2])[0]
if 'Event' in ch_names:
event_ind = np.where(ch_names == 'Event')[0]
ch_names = np.delete(ch_names, event_ind)
data = np.delete(data, event_ind, 0)
data_max, data_min = utils.get_data_max_min(data, args.norm_by_percentile,
args.norm_percs)
max_abs = utils.get_max_abs(data_max, data_min)
if args.normalize_evoked:
data = data / max_abs
np.save(op.join(fol, 'eeg_data.npy'), data)
np.savez(op.join(fol, 'eeg_data_meta.npz'),
names=ch_names,
conditions=list(events.keys()),
dt=dt,
minmax=(-max_abs, max_abs))
return True
