if np.mod(i+1, 10) == 0:
print('Processing data of subject %d of %d' % (i+1,
len(behav['subject_nickname'].unique())))
# Get lab and timezone
lab = behav.loc[behav['subject_nickname'] == nickname, 'institution_code'].unique()[0]
time_zone = behav.loc[behav['subject_nickname'] == nickname, 'time_zone'].unique()[0]
if (time_zone == 'Europe/Lisbon') or (time_zone == 'Europe/London'):
time_zone_number = 0
elif time_zone == 'America/New_York':
time_zone_number = -5
elif time_zone == 'America/Los_Angeles':
time_zone_number = -7
# Fit psychometric curve
left_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 80)])
right_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 20)])
neutral_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 50)])
perf_easy = (behav.loc[behav['subject_nickname'] == nickname, 'correct_easy'].mean()) * 100
fits = pd.DataFrame(data={'perf_easy': perf_easy,
'threshold_l': left_fit['threshold'],
'threshold_r': right_fit['threshold'],
'threshold_n': neutral_fit['threshold'],
'bias_l': left_fit['bias'],
'bias_r': right_fit['bias'],
'bias_n': neutral_fit['bias'],
'time_zone': time_zone_number,
'nickname': nickname, 'lab_number': lab})
for i, nickname in enumerate(behav['subject_nickname'].unique()):
if np.mod(i + 1, 10) == 0:
print('Processing data of subject %d of %d' %
(i + 1, len(behav['subject_nickname'].unique())))
# Get the trials of the sessions around criterion for this subject (first
# 90% + next session)
trials = behav[behav['subject_nickname'] == nickname].reset_index()
# Exclude sessions with less than 4 contrasts
trials['contrast_set'] = trials.session_start_time.map(
trials.groupby(['session_start_time'])['signed_contrast'].unique())
trials = trials.loc[trials['contrast_set'].str.len() > 4]
if len(trials['session_start_time'].unique()) < 3:
continue
# Fit a psychometric function to these trials and get fit results
fit_result = fit_psychfunc(trials)
# Get RT, performance and number of trials
reaction_time = trials['rt'].median() * 1000
perf_easy = trials['correct_easy'].mean() * 100
ntrials_perday = trials.groupby('session_uuid').count()['trial_id'].mean()
# average trials/minute to normalise by session length
trials['session_length'] = (
trials.session_end_time -
trials.session_start_time).astype('timedelta64[m]')
total_session_length = trials.groupby(
'session_uuid')['session_length'].mean().sum()
total_n_trials = trials['trial_id'].count()
# Add results to dataframe