def get_epoch(epoch, recording):
filenames = get_filenames(epoch, recording)
epochs = preprocessing.load_epochs([str(f) for f in filenames])
epochs = preprocessing.concatenate_epochs(epochs, None)
epochs = epochs.pick_channels(
[x for x in epochs.ch_names if x.startswith("M")])
epochs._data -= get_baseline(recording)
data_cov = get_cov(epochs, tmin=0, tmax=2)
return data_cov, epochs
def get_baseline(recording):
filenames = get_filenames("stimulus", recording)
print(filenames)
epochs = preprocessing.load_epochs([str(f) for f in filenames])
epochs = preprocessing.concatenate_epochs(epochs, None)
epochs = epochs.pick_channels(
[x for x in epochs.ch_names if x.startswith("M")])
id_time = (-0.5 <= epochs.times) & (epochs.times <= -0.250)
return epochs._data[:, :, id_time].mean(-1)[:, :, np.newaxis]
def get_response_epoch(subject, session, recording):
stimulus = glob(get_filenames(subject, session, recording)[0])
response = glob(get_filenames(subject, session, recording)[1])
stimulus = preprocessing.load_epochs(stimulus)
stimulus = preprocessing.concatenate_epochs(stimulus, None)
stimulus = stimulus.pick_channels(
[x for x in stimulus.ch_names if x.startswith('M')])
response = preprocessing.load_epochs(response)
response = preprocessing.concatenate_epochs(response, None)
response = stimulus.pick_channels(
[x for x in response.ch_names if x.startswith('M')])
id_time = (-0.25 <= stimulus.times) & (stimulus.times <= 0)
means = stimulus._data[:, :, id_time].mean(-1)
stimulus._data = stimulus._data - means[:, :, np.newaxis]
response._data = response._data - means[:, :, np.newaxis]
data_cov = lcmv.get_cov(stimulus, tmin=0, tmax=3)
return data_cov, response
def get_stim_epoch(subject, session, recording):
filenames = glob(get_filenames(subject, session, recording)[0])
epochs = preprocessing.load_epochs(filenames)
epochs = preprocessing.concatenate_epochs(epochs, None)
epochs = epochs.pick_channels(
[x for x in epochs.ch_names if x.startswith('M')])
id_time = (-0.25 <= epochs.times) & (epochs.times <= 0)
means = epochs._data[:, :, id_time].mean(-1)
epochs._data -= means[:, :, np.newaxis]
data_cov = lcmv.get_cov(epochs, tmin=0, tmax=3)
return data_cov, epochs, filenames
def get_ref_head_pos(subject, session, trans, N=-1):
from mne.transforms import apply_trans
data = metadata.get_epoch_filename(subject, session, 0, 'stimulus', 'fif')
data = pymegprepr.load_epochs([data])[0]
cc = head_loc(data.decimate(10))
nasion = np.stack([c[0] for c in cc[:N]]).mean(0)
lpa = np.stack([c[1] for c in cc[:N]]).mean(0)
rpa = np.stack([c[2] for c in cc[:N]]).mean(0)
nasion, lpa, rpa = nasion.mean(-1), lpa.mean(-1), rpa.mean(-1)
return {
'nasion': apply_trans(trans['t_ctf_dev_dev'], np.array(nasion)),
'lpa': apply_trans(trans['t_ctf_dev_dev'], np.array(lpa)),
'rpa': apply_trans(trans['t_ctf_dev_dev'], np.array(rpa))
}
def get_epochs_for_subject(snum, epoch, sessions=None):
from itertools import product
if sessions is None:
sessions = list(range(4))
data = []
meta = get_meta_for_subject(snum, epoch, sessions=sessions)
for session, block in product(ensure_iter(sessions), list(range(5))):
filename = metadata.get_epoch_filename(snum, session, block, epoch,
'fif')
if os.path.isfile(filename):
data.append(filename)
#assert len(data) == len(list(ensure_iter(sessions)))*5
data = pymegprepr.load_epochs(data)
event_ids = reduce(lambda x, y: x + y,
[list(d.event_id.values()) for d in data])
meta = meta.reset_index().set_index('hash')
meta = meta.loc[event_ids, :]
assert len(meta) == sum([d._data.shape[0] for d in data])
return pymegprepr.concatenate_epochs(data, [meta])
def extract_reconstruct_tfr_block(subject,
session,
block,
epoch,
signal_type='BB',
BEM='three_layer',
debug=False,
chunks=50,
njobs=4):
#check if the block exists and recording
subject_int = int(subject[1:])
fname = path + '/filenames_sub%i.pickle' % (subject_int)
f = open(fname, 'rb')
data = pickle.load(f)
df = pd.DataFrame.from_dict(data)
blocks = np.array(
df[df.subject == subject_int][df.session == session].block)
if block in blocks:
recording = df[df.subject == subject_int][df.session == session][
df.block == block].trans_matrix.iloc[0]
else:
print('block does not exist')
sys.exit(0)
if epoch == 'stimulus':
fname = get_filenames_block(subject, session, block, recording)[0][0]
else:
fname = get_filenames_block(subject, session, block, recording)[1][0]
# fname_aux='filter_sub%i_SESS%i_recording%i_epoch%s.pickle'%( subject_int,session,recording,epoch)
# filename = join(path+'/extra', fname_aux)
# f=open(filename,'rb')
# filters=pickle.load(f)
# f.close()
filters = extract_filter(subject,
session_int,
recording,
epoch,
signal_type=signal_type)
fois_h = np.arange(36, 162, 4)
fois_l = np.arange(2, 36, 1)
tfr_params = {
'HF': {
'foi': fois_h,
'cycles': fois_h * 0.25,
'time_bandwidth': 2 + 1,
'n_jobs': njobs,
'est_val': fois_h,
'est_key': 'HF',
'sf': 600,
'decim': 10
},
'LF': {
'foi': fois_l,
'cycles': fois_l * 0.4,
'time_bandwidth': 1 + 1,
'n_jobs': njobs,
'est_val': fois_l,
'est_key': 'LF',
'sf': 600,
'decim': 10
}
}
print(fname)
print('loading data')
epochs = preprocessing.load_epochs([fname])
print('concataneiting data')
epochs = preprocessing.concatenate_epochs(epochs, None)
print('Picking pick_channels')
epochs = epochs.pick_channels(
[x for x in epochs.ch_names if x.startswith('M')])
events = epochs.events[:, 2]
for i in range(0, len(events), chunks):
print('chunk:', i)
filename = lcmvfilename_block(subject,
session,
block,
signal_type,
recording,
epoch,
chunk=i)
if os.path.isfile(filename):
continue
if signal_type == 'BB':
logging.info('Starting reconstruction of BB signal')
M = lcmv.reconstruct_broadband(filters,
epochs.info,
epochs._data[i:i + chunks],
events[i:i + chunks],
epochs.times,
njobs=1)
else:
logging.info('Starting reconstruction of TFR signal')
M = lcmv.reconstruct_tfr(filters,
epochs.info,
epochs._data[i:i + chunks],
events[i:i + chunks],
epochs.times,
est_args=tfr_params[signal_type],
njobs=1)
M.to_hdf(filename, 'epochs')
del M
del epochs
print('done')
def extract_reconstruct_tfr(subject,
session,
recording,
epoch,
signal_type='BB',
BEM='three_layer',
debug=False,
chunks=50,
njobs=4):
if epoch == 'stimulus':
filenames = glob(get_filenames(subject, session, recording)[0])
else:
filenames = glob(get_filenames(subject, session, recording)[1])
subject_int = int(subject[1:])
fname = 'filter_sub%i_SESS%i_recording%i_epoch%s.pickle' % (
subject_int, session, recording, epoch)
filename = join(path + '/extra', fname)
f = open(filename, 'rb')
filters = pickle.load(f)
f.close()
fois_h = np.arange(36, 162, 4)
fois_l = np.arange(2, 36, 1)
tfr_params = {
'HF': {
'foi': fois_h,
'cycles': fois_h * 0.25,
'time_bandwidth': 2 + 1,
'n_jobs': njobs,
'est_val': fois_h,
'est_key': 'HF',
'sf': 600,
'decim': 10
},
'LF': {
'foi': fois_l,
'cycles': fois_l * 0.4,
'time_bandwidth': 1 + 1,
'n_jobs': njobs,
'est_val': fois_l,
'est_key': 'LF',
'sf': 600,
'decim': 10
}
}
print('filters done')
for ifname, fname in enumerate(filenames):
print(fname)
epochs = preprocessing.load_epochs([fname])
epochs = preprocessing.concatenate_epochs(epochs, None)
epochs = epochs.pick_channels(
[x for x in epochs.ch_names if x.startswith('M')])
events = epochs.events[:, 2]
for i in range(0, len(events), chunks):
print('chunk:', i)
filename = lcmvfilename(subject,
session,
signal_type,
recording,
epoch + str(ifname),
chunk=i)
if os.path.isfile(filename):
continue
if signal_type == 'BB':
logging.info('Starting reconstruction of BB signal')
M = lcmv.reconstruct_broadband(filters,
epochs.info,
epochs._data[i:i + chunks],
events[i:i + chunks],
epochs.times,
njobs=1)
else:
logging.info('Starting reconstruction of TFR signal')
M = lcmv.reconstruct_tfr(filters,
epochs.info,
epochs._data[i:i + chunks],
events[i:i + chunks],
epochs.times,
est_args=tfr_params[signal_type],
njobs=1)
M.to_hdf(filename, 'epochs')
del M
del epochs
print('done')
