def psy_by_mouse(unique_signed_contrasts):
mice = pd.DataFrame.from_dict(
(subject.Subject()) * (subject.SubjectLab()) *
(behavior_analysis.SessionTrainingStatus()
& 'training_status="trained" '))
psy_df = pd.DataFrame(columns=unique_signed_contrasts)
for row, mouse in enumerate(mice.subject_nickname.unique()):
key = ((subject.Subject() & 'subject_nickname = "{}"'.format(mouse)) *
(behavior.TrialSet() & 'n_trials > 100') *
(subject.SubjectLab()) *
(behavior_analysis.SessionTrainingStatus()
& 'training_status="trained" ')).fetch('KEY')
choice, cont_left, cont_right = (behavior.TrialSet.Trial & key).fetch(
'trial_response_choice', 'trial_stim_contrast_left',
'trial_stim_contrast_right')
signed_contrasts = cont_right - cont_left
right_choices = choice == 'CCW'
total_trials = []
right_trials = []
for cont in unique_signed_contrasts:
matching = (signed_contrasts == cont)
total_trials.append(np.sum(matching))
right_trials.append(np.sum(right_choices[matching]))
prop_right_trials = np.divide(right_trials, total_trials)
psy_df.loc[row, :] = prop_right_trials
return psy_df
# ================================= #
# GRAB ALL DATA FROM DATAJOINT
# ================================= #
sess = (acquisition.Session & 'task_protocol LIKE "%biased%"')
s = pd.DataFrame.from_dict(sess.fetch(as_dict=True))
labs = list(s['session_lab'].unique())
labs.append('zadorlab')
print(labs)
# hack to get around SQL limit
for lidx, lab in enumerate(labs):
print(lab)
b = (behavior.TrialSet.Trial & (subject.SubjectLab() & 'lab_name="%s"'%lab)) \
* sess.proj('session_uuid','task_protocol') \
* subject.SubjectLab.proj('lab_name') \
* subject.Subject() & 'subject_line IS NULL OR subject_line="C57BL/6J"'
#* subject.Subject() & 'subject_birth_date between "2018-09-01" and "2019-02-01"' & 'subject_line IS NULL OR subject_line="C57BL/6J"'
bdat = pd.DataFrame(
b.fetch(order_by='subject_nickname, session_start_time, trial_id'))
print(bdat['subject_nickname'].unique())
if lidx == 0:
behav = bdat.copy()
else:
behav = behav.append(bdat.copy(), sort=False, ignore_index=True)
behav = dj2pandas(behav)
#requires Alex glm module
import matplotlib.pyplot as plt
import pandas as pd
## CONNECT TO datajoint
import datajoint as dj
dj.config['database.host'] = 'datajoint.internationalbrainlab.org'
from ibl_pipeline.analyses import behavior as behavior_analysis
from ibl_pipeline import reference, subject, behavior
from alexfigs_datajoint_functions import * # this has all plotting functions
import seaborn as sns
from glm import *
key = ((subject.Subject() & 'sex!="U"') *
(behavior.TrialSet() & 'n_trials > 100') * (subject.SubjectLab()) *
(behavior_analysis.SessionTrainingStatus()
& 'training_status="ready for ephys" ')).fetch('KEY')
trials_ibl = pd.DataFrame.from_dict(
(subject.Subject() * behavior.TrialSet.Trial & key).fetch(as_dict=True))
trials_ibl['signed_contrasts'] = trials_ibl[
'trial_stim_contrast_right'] - trials_ibl['trial_stim_contrast_left']
##Rename for GLM function
trials_ibl = trials_ibl.rename(index=str,
columns={
"session_start_time": "ses",
"subject_uuid": "mouse_name",
"trial_feedback_type": "feedbackType",
"trial_response_choice": "choice"
})
#Set path for data
"""
You will need to modify the paths below if running this code on your own computer
"""
path_training = '/Users/alex/Documents/Postdoc/GLM_behavior_paper/Data_by_animal/training/'
path_biased = '/Users/alex/Documents/Postdoc/GLM_behavior_paper/Data_by_animal/biased/'
path_model_analysis = '/Users/alex/Documents/PYTHON/trialhistory_frund/analysis.py'
path_model2array = '/Users/alex/Documents/PYTHON/analysis/paper-behavior/model2array.py'
#Import data from all the animals in both stages of training
use_subjects = subject.Subject * subject.SubjectLab * subject.SubjectProject & 'subject_project="ibl_neuropixel_brainwide_01"'
sess = (acquisition.Session & (behavior.TrialSet.Trial() & 'ABS(trial_stim_contrast_left-0)<0.0001' \
& 'ABS(trial_stim_contrast_right-0)<0.0001') & 'task_protocol like "%trainingChoiceWorld%"') \
* use_subjects
b = (behavior.TrialSet.Trial & sess) * subject.Subject() * subject.SubjectLab()
bdat = pd.DataFrame(
b.fetch(order_by='subject_nickname, session_start_time, trial_id'))
behav = dj2pandas(bdat)
#Import data from biased choice world
sess = (acquisition.Session & (behavior.TrialSet.Trial() & 'ABS(trial_stim_contrast_left-0)<0.0001' \
& 'ABS(trial_stim_contrast_right-0)<0.0001') & 'task_protocol like "%biasedChoiceWorld%"') * use_subjects
b = (behavior.TrialSet.Trial & sess) * subject.Subject() * subject.SubjectLab()
bdat = pd.DataFrame(
b.fetch(order_by='subject_nickname, session_start_time, trial_id'))
behav_biased = dj2pandas(bdat)
#Store lab identities for later
mousenlabid = behav[['subject_uuid', 'lab_name']]
mousenlabid = mousenlabid.drop_duplicates()
sns.set_style("darkgrid")
sns.set_context(context="poster")
# import wrappers etc
from ibl_pipeline import reference, subject, action, acquisition, data, behavior
from ibl_pipeline.utils import psychofit as psy
from dj_tools import *
figpath = os.path.join(os.path.expanduser('~'), 'Data/Figures_IBL')
# ================================= #
## GET DATA
# ================================= #
b = (behavior.TrialSet) \
* (acquisition.Session.proj(session_date='DATE(session_start_time)') & 'session_start_time > "2019-03-01"') \
* (subject.Subject() & 'subject_birth_date between "2018-09-01" and "2019-02-01"') * subject.SubjectLab() \
* action.Weighing.proj(weighing_date='DATE(weighing_time)')
bdat = pd.DataFrame(b.fetch(order_by='subject_nickname, session_start_time'))
print(bdat['subject_nickname'].unique())
bdat['date_march'] = pd.to_timedelta(pd.to_datetime(
bdat.session_date)).dt.total_seconds().astype(int)
g = sns.lmplot(x="date_march",
y="n_trials",
hue="sex",
col="lab_name",
units="subject_nickname",
col_wrap=4,
lowess=True,
data=bdat)
# import wrappers etc
from ibl_pipeline import reference, subject, action, acquisition, data, behavior
from ibl_pipeline.utils import psychofit as psy
from ibl_pipeline.analyses import behavior as behavioral_analyses
from dj_tools import *
figpath = os.path.join(os.path.expanduser('~'), 'Data/Figures_IBL')
# ================================= #
# GRAB ALL DATA FROM DATAJOINT
# ================================= #
sess = ((acquisition.Session & 'task_protocol LIKE "%trainingchoice%"') * \
(behavioral_analyses.SessionTrainingStatus() & 'training_status="trained"')) * \
subject.SubjectLab()
s = pd.DataFrame.from_dict(sess.fetch(as_dict=True))
labs = list(s['lab_name'].unique())
print(labs)
# hack to get around SQL limit
for lidx, lab in enumerate(labs):
print(lab)
b = (behavior.TrialSet.Trial & (subject.SubjectLab() & 'lab_name="%s"'%lab)) \
* sess.proj('session_uuid','task_protocol') \
* subject.SubjectLab.proj('lab_name') \
* subject.Subject() & 'subject_birth_date between "2018-09-01" and "2019-02-01"' & 'subject_line IS NULL OR subject_line="C57BL/6J"'
bdat = pd.DataFrame(b.fetch(order_by='subject_nickname, session_start_time, trial_id'))