def get_water(mousename):
wei = (action.WaterAdministration()
& 'subject_nickname="%s"' % mousename).fetch(as_dict=True)
wei = pd.DataFrame(wei)
if not wei.empty:
wei.rename(columns={
'administration_time': 'date_time',
'watertype_name': 'water_type'
},
inplace=True)
wei['date_time'] = pd.to_datetime(wei.date_time)
wei.sort_values('date_time', inplace=True)
wei.reset_index(drop=True, inplace=True)
wei['date'] = wei['date_time'].dt.floor('D')
wei['days'] = wei.date - wei.date[0]
wei['days'] = wei.days.dt.days # convert to number of days from start of the experiment
return wei
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']))
# loading and plotting functions
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.analyses import psychofit as psy # https://github.com/cortex-lab/psychofit
# loading and plotting functions
from behavior_plots import *
from load_mouse_data_datajoint import * # this has all plotting functions
# 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()).fetch(
as_dict=True, order_by=['lab_name', 'subject_nickname']))
if allsubjects.empty:
raise ValueError('DataJoint seems to be down, please try again later')
users = allsubjects['lab_name'].unique()
print(users)
# from guido: make sure max 5 mice are plotted on a single figure
sub_batch_size = 5
# keep track of when each mouse is trained
training_review = pd.DataFrame([])
for lidx, lab in enumerate(users):
