as_dataframe=False,
force_cutoff=True)
trial_fields = ('trial_stim_contrast_left', 'trial_stim_contrast_right',
'trial_response_time', 'trial_stim_prob_left',
'trial_feedback_type', 'trial_stim_on_time', 'trial_response_choice')
# query trial data for sessions and subject name and lab info
trials = use_sessions.proj('task_protocol') * behavior.TrialSet.Trial.proj(*trial_fields)
subject_info = subject.Subject.proj('subject_nickname') * \
(subject.SubjectLab * reference.Lab).proj('institution_short')
# Fetch, join and sort data as a pandas DataFrame
behav = dj2pandas(trials.fetch(format='frame')
.join(subject_info.fetch(format='frame'))
.sort_values(by=['institution_short', 'subject_nickname',
'session_start_time', 'trial_id'])
.reset_index())
behav['institution_code'] = behav.institution_short.map(institution_map)
# split the two types of task protocols (remove the pybpod version number)
behav.drop(behav[behav['signed_contrast'].isin([50., -50])].index, inplace=True)
df_basic = behav.groupby(['institution_code', 'subject_nickname', 'signed_contrast']).agg(
{'choice2': 'mean'}).reset_index().copy()
# query sessions - FULL TASK
use_sessions, _ = query_sessions_around_criterion(criterion='ephys',
days_from_criterion=[2, 0],
force_cutoff=True)
use_sessions = use_sessions & 'task_protocol LIKE "%biased%"' # only get biased sessions
session_keys = (use_sessions
& 'task_protocol LIKE "%biased%"').fetch('KEY')
ses = ((use_sessions & 'task_protocol LIKE "%biased%"') * subject.Subject *
subject.SubjectLab * reference.Lab *
(behavior.TrialSet.Trial & session_keys))
ses = ses.proj(
'institution_short', 'subject_nickname', 'task_protocol',
'trial_stim_contrast_left', 'trial_stim_contrast_right',
'trial_response_choice', 'task_protocol', 'trial_stim_prob_left',
'trial_feedback_type', 'trial_response_time', 'trial_stim_on_time',
'time_zone').fetch(
order_by=
'institution_short, subject_nickname,session_start_time, trial_id',
format='frame').reset_index()
behav = dj2pandas(ses)
behav['institution_code'] = behav.institution_short.map(
institution_map()[0])
else:
behav = load_csv('Fig4.csv')
biased_fits = pd.DataFrame()
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 lab and timezone
lab = behav.loc[behav['subject_nickname'] == nickname,
'institution_code'].unique()[0]
if QUERY is True:
from paper_behavior_functions import query_sessions_around_criterion
from ibl_pipeline import reference, subject, behavior
use_sessions, _ = query_sessions_around_criterion(criterion='biased',
days_from_criterion=[1, 3])
use_sessions = use_sessions & 'task_protocol LIKE "%biased%"' # only get biased sessions
b = (use_sessions * subject.Subject * subject.SubjectLab * reference.Lab
* behavior.TrialSet.Trial)
b2 = b.proj('institution_short', 'subject_nickname', 'task_protocol',
'trial_stim_contrast_left', 'trial_stim_contrast_right', 'trial_response_choice',
'task_protocol', 'trial_stim_prob_left', 'trial_feedback_type',
'trial_response_time', 'trial_stim_on_time', 'time_zone')
bdat = b2.fetch(order_by='institution_short, subject_nickname, session_start_time, trial_id',
format='frame').reset_index()
behav = dj2pandas(bdat)
behav['institution_code'] = behav.institution_short.map(institution_map()[0])
else:
behav = load_csv('Fig4.csv')
biased_fits = pd.DataFrame()
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 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
behav['training_day'] = behav.training_day - \
behav.training_day.min() + 1 # start at session 1
for didx, day in enumerate(days):
# get data for today
print(day)
thisdate = behav[behav.training_day ==
day]['session_date'].dt.strftime('%Y-%m-%d').item()
b = (subject.Subject & 'subject_nickname = "%s"' % EXAMPLE_MOUSE) \
* (subject.SubjectLab & 'lab_name="%s"' % lab) \
* (acquisition.Session.proj(session_date='date(session_start_time)') &
'session_date = "%s"' % thisdate) \
* behavior.TrialSet.Trial() \
* endcriteria.SessionEndCriteriaImplemented()
behavtmp = dj2pandas(b.fetch(format='frame').reset_index())
behavtmp['trial_start_time'] = behavtmp.trial_start_time / 60 # in minutes
# unclear how this can be empty - but if it happens, skip
if behavtmp.empty:
continue
# PSYCHOMETRIC FUNCTIONS
fig, ax = plt.subplots(1, 1, figsize=(FIGURE_WIDTH/5, FIGURE_HEIGHT*0.9))
plot_psychometric(behavtmp.signed_contrast,
behavtmp.choice_right,
behavtmp.trial_id,
ax=ax, color='k')
ax.set(xlabel="\u0394 Contrast (%)")
if didx == 0: