for i in range(len(eids)):
print('\nProcessing session %d of %d' % (i+1, len(eids)))
# Load in data
eid = eids[i]
try:
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(
eid, aligned=True, one=one)
ses_path = one.path_from_eid(eid)
trials = load_trials(eid)
except Exception as error_message:
print(error_message)
continue
# Check data integrity
if check_trials(trials) is False:
continue
# Extract session data
ses_info = one.get_details(eid)
subject = ses_info['subject']
date = ses_info['start_time'][:10]
probes_to_use = probes[i]
# Process per probe
for p, probe in enumerate(probes_to_use):
print('Processing %s (%d of %d)' % (probe, p + 1, len(probes_to_use)))
# Check if data is available for this probe
if probe not in clusters.keys():
continue
from oneibl.one import ONE
one = ONE()
# Settings
INCL_SESSIONS = 'aligned-behavior'
# Query sessions
eids, _ = query_sessions(selection=INCL_SESSIONS)
# Loop over sessions
all_trials = pd.DataFrame()
for i, eid in enumerate(eids):
print(f'Loading trails of session {i+1} of {len(eids)}')
try:
trials = load_trials(eid, invert_stimside=True)
except:
continue
if check_trials(trials):
ses_info = one.get_details(eid)
trials['subject'] = ses_info['subject']
trials['date'] = ses_info['start_time'][:10]
trials = trials.drop(columns=[
'stimOn_times', 'feedback_times', 'contrastLeft', 'contrastRight',
'goCue_times', 'right_choice', 'correct', 'firstMovement_times'
])
all_trials = all_trials.append(
trials[trials['probabilityLeft'] != 0.5])
print(f'Added {len(trials)} trial (total {len(all_trials)})')
print('Saving results..')
all_trials.to_pickle(join(paths()[2], 'Ephys', 'Decoding', 'all_trials.p'))
print('Done')