sns.color_palette('colorblind', as_cmap=True)[3])
# Query session list
eids, probes = query_sessions(selection=INCL_SESSIONS)
results_df = pd.DataFrame()
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
from my_functions import paths, query_sessions, check_trials, load_trials
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'))
ATLAS, int(PRE_TIME * 1000), int(POST_TIME * 1000)))))
else:
decoding_result = pd.DataFrame(
columns=['subject', 'date', 'eid', 'probe', 'region'])
# Loop over subjects
for i, subject in enumerate(np.unique(subjects)):
print('\nStarting subject %s [%d of %d]\n' %
(subject, i + 1, len(np.unique(subjects))))
# Generate stimulus vectors for all sessions of this subject
stimuli_arr, actions_arr, stim_sides_arr, session_uuids = [], [], [], []
for j, eid in enumerate(eids[subjects == subject]):
try:
# Load in trials vectors
trials = load_trials(eid, invert_stimside=True, one=one)
incl_trials = get_incl_trials(trials, TARGET, EXCL_5050, MIN_RT)
stimuli_arr.append(trials['signed_contrast'][incl_trials].values)
actions_arr.append(trials['choice'][incl_trials].values)
stim_sides_arr.append(trials['stim_side'][incl_trials].values)
session_uuids.append(eid)
except:
print(f'Could not load trials for {eid}')
print(f'\nLoaded data from {len(session_uuids)} sessions')
if len(session_uuids) == 0:
continue
# Get maximum number of trials across sessions
max_len = np.array([len(stimuli_arr[k])
for k in range(len(stimuli_arr))]).max()
model = exp_prev_action(
join(SAVE_PATH, 'Behavior', 'exp_smoothing_model_fits/'), all_eids,
SUBJECT, np.array([np.array(None)] * all_eids.shape[0]),
np.array([np.array(None)] * all_eids.shape[0]),
np.array([np.array(None)] * all_eids.shape[0]))
elif TARGET == 'prior-stimside':
model = exp_stimside(
join(SAVE_PATH, 'Behavior', 'exp_smoothing_model_fits/'), all_eids,
SUBJECT, np.array([np.array(None)] * all_eids.shape[0]),
np.array([np.array(None)] * all_eids.shape[0]),
np.array([np.array(None)] * all_eids.shape[0]))
model.load_or_train(nb_steps=2000, remove_old=False)
params = model.get_parameters(parameter_type='posterior_mean')
# Get priors per trial
trials = load_trials(EID, invert_stimside=True)
priors = model.compute_signal(signal='prior',
act=np.array(trials['choice']),
stim=np.array(trials['signed_contrast']),
side=np.array(trials['stim_side']),
parameter_type='posterior_mean')['prior']
# Get clusters in this brain region
spikes, clusters, channels = bbone.load_spike_sorting_with_channel(
EID, aligned=True, one=one)
# Get list of neurons that pass QC
if INCL_NEURONS == 'pass-QC':
clusters_pass = np.where(clusters[PROBE]['metrics']['label'] == 1)[0]
elif INCL_NEURONS == 'all':
clusters_pass = np.arange(clusters[PROBE]['metrics'].shape[0])