def analyze_task(subject, args, hcp_params):
flags = {}
events = dict(face=1, tools=2)
baseline = (-0.5, 0)
all_events = collect_events(hcp_params)
files_exist = all([
op.isfile(meg.get_cond_fname(meg.EPO, event))
and op.isfile(meg.get_cond_fname(meg.EVO, event))
for event in events.keys()
])
if not files_exist:
epochs_hcp, evokeds_from_epochs_hcp = using_preprocessed_epochs(
all_events, events, baseline, hcp_params)
for event in events.keys():
epochs_hcp[event].save(meg.get_cond_fname(meg.EPO, event))
mne.write_evokeds(meg.get_cond_fname(meg.EVO, event),
evokeds_from_epochs_hcp[event])
flags = meg.calc_fwd_inv_wrapper(subject, args, events, flags)
flags, stcs_conds, _ = meg.calc_stc_per_condition_wrapper(
subject, events, args.inverse_method, args, flags)
flags = meg.calc_labels_avg_per_condition_wrapper(subject, events,
args.atlas,
args.inverse_method,
stcs_conds, args, flags)
def calc_evoked(indices, epochs_fname, overwrite_epochs=False, overwrite_evoked=False):
epochs = mne.read_epochs(epochs_fname, preload=False)
print(epochs.events.shape)
for event_name, event_indices in indices.items():
evoked_event_fname = meg.get_cond_fname(meg.EVO, event_name)
epochs_event_fname = meg.get_cond_fname(meg.EPO, event_name)
if not op.isfile(epochs_event_fname) or overwrite_epochs:
print('Saving {} epochs to {}, events num: {}'.format(event_name, epochs_event_fname, len(event_indices)))
event_epochs = epochs[event_indices]
event_epochs.save(epochs_event_fname)
if not op.isfile(evoked_event_fname) or overwrite_evoked:
print('Saving {} evoked to {}'.format(event_name, evoked_event_fname))
mne.write_evokeds(evoked_event_fname, event_epochs.average())
def calc_evoked(indices, epochs_fname, overwrite_epochs=False, overwrite_evoked=False):
epochs = mne.read_epochs(epochs_fname, preload=False)
print(epochs.events.shape)
for event_name, event_indices in indices.items():
evoked_event_fname = meg.get_cond_fname(meg.EVO, event_name)
epochs_event_fname = meg.get_cond_fname(meg.EPO, event_name)
if not op.isfile(epochs_event_fname) or overwrite_epochs:
print('Saving {} epochs to {}, events num: {}'.format(event_name, epochs_event_fname, len(event_indices)))
event_epochs = epochs[event_indices]
event_epochs.save(epochs_event_fname)
if not op.isfile(evoked_event_fname) or overwrite_evoked:
print('Saving {} evoked to {}'.format(event_name, evoked_event_fname))
mne.write_evokeds(evoked_event_fname, event_epochs.average())
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
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 plot_evoked(indices):
for event_name in indices.keys():
evoked_fname = meg.get_cond_fname(meg.EVO, event_name)
print('Reading {}'.format(event_name))
evoked = mne.read_evokeds(evoked_fname)[0]
evoked.plot()
def plot_evoked(indices):
for event_name in indices.keys():
evoked_fname = meg.get_cond_fname(meg.EVO, event_name)
print('Reading {}'.format(event_name))
evoked = mne.read_evokeds(evoked_fname)[0]
evoked.plot()
evoked_hcp.data.ravel()[::10] * 1e15,
linestyle='None',
marker='o',
alpha=0.1,
mec='orange',
color='orange')
plt.annotate("r=%0.3f" % r1, xy=(-300, 250))
plt.ylabel('evoked from scratch with MNE-HCP')
plt.xlabel('evoked from HCP evoked file')
plt.show()
if __name__ == '__main__':
tmin, tmax = -1.5, 2.5
decim = 4
events_id = dict(face=1, tools=2)
baseline = (-0.5, 0)
all_events = collect_events()
# all_epochs, evokeds = reprocess_the_data_from_scratch(all_events, event_id, tmin, tmax, baseline, decim)
all_epochs_hcp, evokeds_from_epochs_hcp = using_preprocessed_epochs(
all_events, events_id)
# evoked_hcp = official_ERF()
all_epochs_hcp.save(meg.EPO)
for event, evoked in zip(events_id.keys(), evokeds_from_epochs_hcp):
mne.write_evokeds(meg.get_cond_fname(meg.EVO, event), evoked)
mne.write_evokeds(meg.EVO, evokeds_from_epochs_hcp)
print('Finish!')
