def main():
(raw_data_path, intermediate_data_path,
processed_data_path, figure_path) = cf.path_config()
(unique_subjects, unique_sessions, unique_reward_codes) = md.extract_subjects_sessions(raw_data_path,
reward_task=1)
start_time = time.time()
trial_end = 2000
for subj_id in unique_subjects:
for session_n in unique_sessions:
print('z-scoring baseline corrected & lowpass filtered data for subject {}'.format(subj_id) +
' session {}'.format(session_n))
_, _, reward_code = ep.find_data_files(subj_id=subj_id,
session_n=session_n, reward_task=1, lum_task=0,
raw_data_path=raw_data_path)
reward_samples = ep.read_hdf5('samples', subj_id, session_n,
processed_data_path, reward_code=reward_code, id_str='corr')
reward_messages = ep.read_hdf5('messages', subj_id, session_n,
processed_data_path, reward_code=reward_code, id_str='corr')
reward_events = ep.read_hdf5('events', subj_id, session_n,
processed_data_path, reward_code=reward_code, id_str='corr')
reward_samples = z.zscore(reward_samples)
plotting_reward_samples = reward_samples.loc[reward_samples.trial_sample <= trial_end]
## TODO: get reasonable y limits for all data within a subject and use that for plotting
fig,figname = vz.visualize(plotting_reward_samples.trial_sample,
plotting_reward_samples.z_pupil_diameter,
subj_id, session_n, reward_code, id_str='zscored')
vz.save(fig, figname)
hdf = ep.save_hdf5(reward_samples, reward_events, reward_messages,
subj_id, session_n, processed_data_path,
reward_code=reward_code, id_str='zscored')
print('z-scored data saved')
end_time = time.time()
time_elapsed = end_time - start_time
print('time elapsed: ', time_elapsed)
def main():
(raw_data_path, intermediate_data_path, processed_data_path,
figure_path) = cf.path_config()
(unique_subjects, unique_sessions,
unique_reward_codes) = md.extract_subjects_sessions(raw_data_path,
reward_task=1)
start_time = time.time()
for subj_id in unique_subjects:
for session_n in unique_sessions:
print('bandpass filtering data for subject {}'.format(subj_id) +
' session {}'.format(session_n))
_, _, reward_code = ep.find_data_files(subj_id=subj_id,
session_n=session_n,
reward_task=1,
lum_task=0,
raw_data_path=raw_data_path)
reward_samples = ep.read_hdf5('samples',
subj_id,
session_n,
processed_data_path,
reward_code=reward_code,
id_str='clean')
reward_messages = ep.read_hdf5('messages',
subj_id,
session_n,
processed_data_path,
reward_code=reward_code,
id_str='clean')
reward_events = ep.read_hdf5('events',
subj_id,
session_n,
processed_data_path,
reward_code=reward_code,
id_str='clean')
reward_samples = bp.high_bandpass_filter(reward_samples)
reward_samples = bp.low_bandpass_filter(reward_samples)
lp_fig, lp_figname = vz.visualize(
reward_samples.trial_sample,
reward_samples.lowpass_pupil_diameter,
subj_id,
session_n,
reward_code,
id_str='lowpass')
vz.save(lp_fig, lp_figname)
hp_fig, hp_figname = vz.visualize(
reward_samples.trial_sample,
reward_samples.highpass_pupil_diameter,
subj_id,
session_n,
reward_code,
id_str='highpass')
vz.save(hp_fig, hp_figname)
hdf = ep.save_hdf5(reward_samples,
reward_events,
reward_messages,
subj_id,
session_n,
processed_data_path,
reward_code=reward_code,
id_str='bandpass')
end_time = time.time()
time_elapsed = end_time - start_time
print('time elapsed: ', time_elapsed)