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 make(self, key):
# get nickname
subject_nickname = (subject.Subject() & key).fetch1('subject_nickname')
key['subject_nickname'] = subject_nickname
subject_number = [
int(s) for s in re.findall(r'\d+', subject_nickname)
][0]
# load data file
data_file = pd.read_csv('~/Downloads/sfn_data_ibl.csv')
# grab only the row that contains what we need
thisdat = data_file.loc[data_file.subject_nickname ==
subject_number, :]
if not thisdat.empty:
# now insert
if not thisdat.sex.isnull().values.any():
key['sex'] = thisdat.sex.item()
if not thisdat.age.isnull().values.any():
key['age'] = int(thisdat.age.item())
if not thisdat.knowledge.isnull().values.any():
key['task_knowledge'] = thisdat.knowledge.item()
if not thisdat.AQ.isnull().values.any():
key['aq_score'] = thisdat.AQ.item()
print(key)
self.insert1(key)
def make(self, key):
# grab all trials for this subject
trials = (subject.Subject() & key) * behavior.TrialSet.Trial()
stim_left, stim_right, resp, feedback = trials.fetch(
'trial_stim_contrast_left', 'trial_stim_contrast_right',
'trial_response_choice', 'trial_feedback_type')
# # TODO: retrieve days to add dayLength
# # convert to psytrack format
# D = {'y':pd.DataFrame(resp)[0].replace({'CCW': 2, 'No Go': np.nan, 'CW': 1}).values}
# D.update({'inputs':{'contrast_left':stim_left, 'contrast_right':stim_right}})
# # TODO: specify the weights correctly
# weights = {'bias' : 1, # a special key
# 'contrast_left' : 1, # use only the first column of s1 from inputs
# 'contrast_right' : 1} # use only the first column of s2 from inputs
# hyper= {'sigInit' : 2**4.,
# 'sigma' : [2**-4.]*len(weights), # Each weight will have it't own sigma, but all initialized the same
# 'sigDay' : None} # Not necessary to specify as None, but keeps things general
# optList = ['sigma']
# # FIT THE ACTUAL MODEL
hyp, evd, wMode, hess = hyperOpt(D, hyper, weights, optList)
# TODO: make sure to save the primary key for each trial to link it back up later
glm_weights, transition_matrix, posterior_probs = run_zoe_func(
some_inputs_from_dj)
# glm_weights is num_of_states x 3 array; transition matrix is NumStates x NumStates array
# posterior_probs is array of size NumTrials x NumStates
glmhmm_result = dict(**key,
glm_weights=glm_weights,
transition_matrix=transition_matrix)
self.insert1(glmhmm_result)
session_start_times, trial_ids = trials.fetch('session_start_time',
'trial_id')
trials_results = [
dict(**key,
session_start_time=session_start_time,
trial_id=trial_id,
posterior_probs=prob)
for prob, session_start_time, trial_id in zip(
posterior_probs, session_start_times, trial_ids)
]
self.Trial.insert(trials_results)
class Trial(dj.Part):
definition = """
# ================================= #
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)
# ================================= #
# choice history
# ================================================================== #
# COMPARE DIFFERENT CONTRAST SETS
# ================================================================== #
labs = ['churchlandlab', 'mainenlab']
# hack to get around SQL limit
for lidx, lab in enumerate(labs):
print(lab)
if 'churchlandlab' in lab:
# old mice, latest sessions (to check different contrast sets)
b = (behavior.TrialSet.Trial & (subject.SubjectLab() & 'lab_name="%s"'%lab)) \
* (acquisition.Session() & 'session_start_time > "2019-03-01"' & 'task_protocol LIKE "%biased%"') \
* (subject.Subject() & 'subject_birth_date < "2018-08-01"') * subject.SubjectLab.proj('lab_name')
elif 'mainenlab' in lab:
b = (behavior.TrialSet.Trial & (subject.SubjectLab() & 'lab_name="%s"'%lab)) \
* (acquisition.Session() & 'session_start_time > "2019-04-01"' & 'task_protocol LIKE "%biased%"') \
* (subject.Subject() & 'subject_nickname in ("ZM_1369", "ZM_1371", "ZM_1372")') * subject.SubjectLab.proj('lab_name')
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)
# recode
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"
})
from scipy import stats
## CONNECT TO datajoint
import datajoint as dj
dj.config['database.host'] = 'datajoint.internationalbrainlab.org'
import datajoint as dj
from ibl_pipeline.analyses import behavior as behavior_analysis
from ibl_pipeline import reference, subject, action, acquisition, data, behavior
from ibl_pipeline.analyses import behavior as behavior_analysis
from load_mouse_data_datajoint import * # this has all plotting functions
#Collect all alyx data
allsubjects = pd.DataFrame.from_dict((subject.Subject() * subject.SubjectLab & 'sex!="U"' & 'subject_birth_date>"2018-10-15"' ).fetch(as_dict=True, order_by=[ 'subject_nickname']))
allsubjects = pd.DataFrame.from_dict(
((subject.Subject - subject.Death) * subject.SubjectLab & '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')
#Drop double entries
allsubjects = allsubjects.drop_duplicates('subject_nickname')
#Drop transgenics
allsubjects.loc[(allsubjects['subject_line'] == 'C57BL/6J') |(allsubjects['subject_line'] == None)]
# 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
#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="ticks", context="paper", font_scale=1.4)
# 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 *
# import training_criteria_schemas as criteria_urai
figpath = os.path.join(os.path.expanduser('~'), 'Data/Figures_IBL')
# ================================= #
# GRAB ALL DATA FROM DATAJOINT
# ================================= #
use_subjects = subject.Subject(
) & 'subject_birth_date between "2018-09-01" and "2019-02-01"' & 'subject_line IS NULL OR subject_line="C57BL/6J"'
# criterion = criteria_urai.SessionTrainingStatus_v1()
criterion = behavioral_analyses.SessionTrainingStatus()
sess = ((acquisition.Session & 'task_protocol LIKE "%biased%"') * \
(criterion & 'training_status="ready for ephys"')) \
* subject.SubjectLab * use_subjects
s = pd.DataFrame.from_dict(sess.fetch(as_dict=True))
labs = list(s['lab_name'].unique())
labs = ['zadorlab']
print(labs)
# hack to get around SQL limit
for lidx, lab in enumerate(labs):
print(lab)
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 datajoint as dj
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']))
from ibl_pipeline.analyses import behavior as behavior_analysis
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):
# 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
# ============================================= #
# all mice that are alive, without those with undefined sex (i.e. example mice)
# restrict to animals that have trial data, weights and water logged
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']))
print(subjects['subject_nickname'].unique())
for i, mouse in enumerate(subjects['subject_nickname']):
# get all this mouse's data
print(mouse)
weight_water, baseline = get_water_weight(mouse)
behav = get_behavior(mouse)
if weight_water.empty or behav.empty:
continue
# ============================================= #
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'))
print(bdat['subject_nickname'].unique())
if lidx == 0:
behav = bdat.copy()
else:
behav = behav.append(bdat.copy(), sort=False, ignore_index=True)
# ================================= #
# for now, manually add the cortexlab matlab animals
# ================================= #
ucl_mice = ['KS001', 'MW003', 'MW001', 'MW002', 'LEW008', 'LEW009', 'LEW010']
ucl_trained_dates = ['2019-02-25', '2018-12-10', '2019-02-11', '2019-01-14', '2018-10-04', '2018-10-04', 'LEW010']
from scipy import stats
## CONNECT TO datajoint
import datajoint as dj
dj.config['database.host'] = 'datajoint.internationalbrainlab.org'
import datajoint as dj
from ibl_pipeline.analyses import behavior as behavior_analysis
from ibl_pipeline import reference, subject, action, acquisition, data, behavior
from ibl_pipeline.analyses import behavior as behavior_analysis
from load_mouse_data_datajoint import * # this has all plotting functions
#Collect all alyx data
allsubjects = pd.DataFrame.from_dict(
(subject.Subject() * subject.SubjectLab & 'sex!="U"'
& 'subject_birth_date>"2018-10-15"').fetch(as_dict=True,
order_by=['subject_nickname']))
allsubjects = pd.DataFrame.from_dict(
((subject.Subject - subject.Death) * subject.SubjectLab & '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')
#Drop double entries
allsubjects = allsubjects.drop_duplicates('subject_nickname')
#Drop transgenics
allsubjects.loc[(allsubjects['subject_line'] == 'C57BL/6J') |
(allsubjects['subject_line'] == None)]
