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
def _insert_virtual_tables(self):
# insert virtual modules tables in order
virtuals = Graph(behavior.TrialSet()).get_table_list(virtual_only=True) + \
Graph(ephys.DefaultCluster()).get_table_list(virtual_only=True) + \
Graph(behavior_analyses.BehavioralSummaryByDate()).get_table_list(virtual_only=True)
for itable, vtable in enumerate(virtuals[::-1]):
table_key = dict(full_table_name=vtable['full_table_name'])
if Table & table_key:
dj.Table._update(self & table_key, 'table_order', itable)
else:
self.insert1(dict(full_table_name=vtable['full_table_name'],
table_class=vtable['table'],
table_order_category='virtual',
table_order=itable,
table_label='virtual'),
skip_duplicates=True)
#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"
})
from define_paths import fig_path
from behavior_plots import *
from load_mouse_data_datajoint import * # this has all plotting functions
import psychofit as psy # https://github.com/cortex-lab/psychofit
# folder to save plots, from DataJoint
path = '/Snapshot_DataJoint_shortcut/'
# ============================================= #
# START BIG OVERVIEW PLOT
# ============================================= #
subjects = pd.DataFrame.from_dict(
((subject.Subject() - subject.Death() & 'sex!="U"')
& action.Weighing() & action.WaterAdministration()
& behavior.TrialSet()).fetch(as_dict=True,
order_by=['lab_name', 'subject_nickname']))
users = subjects['lab_name'].unique()
print(users)
for lidx, lab in enumerate(users):
subjects = pd.DataFrame.from_dict(
((subject.Subject() - subject.Death() & 'subject_nickname="IBL_47"'
& 'sex!="U"' & 'lab_name="%s"' % lab)
& action.Weighing() & action.WaterAdministration()
& behavior.TrialSet()).fetch(as_dict=True,
order_by=['subject_nickname']))
# group by batches: mice that were born on the same day
batches = subjects.subject_birth_date.unique()
from ibl_pipeline import reference, subject, action, acquisition, data, behavior
# loading and plotting functions
from behavior_plots import *
from load_mouse_data_datajoint import * # this has all plotting functions
import psychofit as psy # https://github.com/cortex-lab/psychofit
# folder to save plots, from DataJoint
path = '/Figures_DataJoint_shortcuts/'
# ============================================= #
# START BIG OVERVIEW PLOT
# ============================================= #
allsubjects = pd.DataFrame.from_dict(((subject.Subject() - subject.Death()) & 'sex!="U"'
& action.Weighing() & action.WaterAdministration() & behavior.TrialSet()
).fetch(as_dict=True, order_by=['lab_name', 'subject_nickname']))
users = allsubjects['lab_name'].unique()
print(users)
# from guido: make sure max 5 mice are plotted on a single figure
sub_batch_size = 5
for lidx, lab in enumerate(users):
# take mice from this lab only
subjects = pd.DataFrame.from_dict(((subject.Subject() - subject.Death() & 'sex!="U"' & 'lab_name="%s"'%lab)
& action.Weighing() & action.WaterAdministration() & behavior.TrialSet()
).fetch(as_dict=True, order_by=['subject_nickname']))