def event_info(experiment, data, window):
"""
"""
try:
subject = experiment.active_subject
if not subject:
return ""
raw = subject.get_raw()
stim_ch = find_stim_channel(subject.get_raw())
if not stim_ch:
return ""
events = Events(raw, stim_ch=stim_ch).events
if events is None:
return ""
bins = np.bincount(events[:, 2])
event_counts = dict()
for event_id in set(events[:, 2]):
event_counts[event_id] = bins[event_id]
events_string = ''
for key, value in event_counts.items():
events_string += 'Trigger %s, %s events\n' % (str(key), str(value))
return events_string
except Exception as exc:
return ""
def run(self):
try:
subject = self.experiment.active_subject
if not subject:
return ""
raw = subject.get_raw()
stim_ch = find_stim_channel(raw)
if not stim_ch:
return ""
events = find_events(raw, stim_ch=stim_ch)
if events is None:
return ""
bins = np.bincount(events[:,2])
event_counts = dict()
for event_id in set(events[:, 2]):
event_counts[event_id] = bins[event_id]
events_string = ""
for key, value in event_counts.items():
events_string += 'Trigger %s, %s events\n' % (str(key), str(value))
return events_string
except Exception as exc:
return ""
def find_event_times(raw, event_id, mask):
stim_ch = find_stim_channel(raw)
sfreq = raw.info['sfreq']
events = Events(raw, stim_ch, mask, event_id).events
times = [(event[0] - raw.first_samp) / sfreq for event in events]
return times
def test_find_stim_channel():
sample_folder = mne.datasets.sample.data_path()
sample_fname = os.path.join(sample_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_fname, preload=True)
stim_channel = find_stim_channel(raw)
assert (stim_channel == 'STI 014')
def run(self):
subject = self.experiment.active_subject
raw = subject.get_raw()
if not raw:
return
old_bads = raw.info['bads'].copy()
old_annotations = raw.annotations.copy()
# find events
stim_ch = find_stim_channel(raw)
if not stim_ch:
events = None
else:
events = find_events(raw, stim_ch=stim_ch)
def handle_close(event):
bads_changed = (sorted(raw.info['bads']) != sorted(old_bads))
annotations_changed = False
if len(raw.annotations) != len(old_annotations):
annotations_changed = True
elif not np.allclose(raw.annotations.onset, old_annotations.onset):
annotations_changed = True
params = {}
params['bads'] = raw.info['bads']
params['annotations'] = list(raw.annotations)
params['bads_changed'] = bads_changed
params['annotations_changed'] = annotations_changed
try:
self.handler(subject, params)
except Exception as exc:
exc_messagebox(self.window, exc)
fig = raw.plot(events=events, show=False)
fig.canvas.mpl_connect('close_event', handle_close)
plt.show()
def plot(experiment, data, window):
"""
"""
subject = experiment.active_subject
raw = subject.get_raw()
if not raw:
return
old_bads = raw.info['bads'].copy()
old_annotations = raw.annotations.copy()
def handle_close(event):
bads_changed = (sorted(raw.info['bads']) != sorted(old_bads))
annotations_changed = False
if len(raw.annotations) != len(old_annotations):
annotations_changed = True
elif not np.allclose(raw.annotations.onset, old_annotations.onset):
annotations_changed = True
if bads_changed:
logging.getLogger('ui_logger').info('Bads changed!')
if annotations_changed:
logging.getLogger('ui_logger').info('Annotations changed!')
if bads_changed or annotations_changed:
subject.save()
window.initialize_ui()
# find events
stim_ch = find_stim_channel(raw)
if not stim_ch:
events = None
else:
events = Events(raw, stim_ch=stim_ch).events
fig = raw.plot(events=events, show=False)
fig.canvas.mpl_connect('close_event', handle_close)
plt.show()
def create_epochs_from_events(params, subject):
"""
"""
raw = subject.get_raw()
params = copy.deepcopy(params)
event_params = params['events']
reject_params = params['reject']
# convert reject params from human readable units to standard units
for key in reject_params:
reject_params[key] /= get_scaling(key)
# remove params that don't match with the channel types present in raw
if mne.pick_types(raw.info, meg='grad', eeg=False).size == 0:
reject_params.pop('grad', None)
if mne.pick_types(raw.info, meg='mag', eeg=False).size == 0:
reject_params.pop('mag', None)
if mne.pick_types(raw.info, meg=False, eeg=True).size == 0:
reject_params.pop('eeg', None)
events = []
category = {}
stim_channel = find_stim_channel(raw)
# event_id should not matter after epochs are created,
# so we just add placeholders as 1, 2, 3...
if len(event_params) > 0:
for idx, item in enumerate(event_params):
event_id = item['event_id']
mask = item['mask']
category_id = ('id_' + str(event_id) + '_mask_' + str(mask))
new_events = Events(raw, stim_channel, mask, event_id).events
if len(new_events) == 0:
logging.warning('No events found with setting ' +
str(category_id))
continue
category[category_id] = idx + 1
new_events[:, 2] = idx + 1
events.extend([event for event in new_events])
if len(events) == 0:
raise ValueError('No events found.')
# prepare parameters for pick_types
if params['mag'] and params['grad']:
meg = True
elif params['mag']:
meg = 'mag'
elif params['grad']:
meg = 'grad'
else:
meg = False
eeg = params['eeg']
# find all proper picks, dont exclude bads
picks = mne.pick_types(raw.info, meg=meg, eeg=eeg, exclude=[])
if len(picks) == 0:
raise ValueError('You should select channel types to go on with')
mne_epochs = mne.Epochs(raw,
np.array(events),
category,
params['tmin'],
params['tmax'],
baseline=(params['bstart'], params['bend']),
picks=picks,
reject=reject_params)
if len(mne_epochs.get_data()) == 0:
raise ValueError('Could not find any data. Perhaps the ' +
'rejection thresholds are too strict...')
epochs_directory = subject.epochs_directory
epochs = Epochs(params['collection_name'],
epochs_directory,
params,
content=mne_epochs)
epochs.save_content()
subject.add(epochs, 'epochs')