assert len(choice[trial_start:trial_end]) == len(ses_data)
assert np.array_equal(choice[trial_start:trial_end],ses_data['choices'].to_numpy(),equal_nan=True)==True
ses_data.loc[ses_data['choices']]
if trial_end<0:
filling_length_end = abs(trial_end)
else:
filling_length_end = len(choice)-trial_end
filling_length_start = trial_start
end_filler=np.empty(filling_length_end)
end_filler[:] = np.nan
start_filler=np.empty(filling_length_start)
start_filler[:] = np.nan
assert (np.isnan(np.nanmean(ses_data['Qwater'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['Qlaser'].to_numpy()))==False)
assert (np.isnan(np.nanmean(ses_data['QRstay'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['QLstay'].to_numpy()))==False)
np.save(alf+'/'+prefix+'_choice_prediction.npy', np.concatenate([start_filler,ses_data['predicted_choice'].to_numpy(),end_filler]))
np.save(alf+'/'+prefix+'_water.npy', np.concatenate([start_filler,ses_data['Qwater'].to_numpy(),end_filler]))
np.save(alf+'/'+prefix+'_laser.npy', np.concatenate([start_filler,ses_data['Qlaser'].to_numpy(),end_filler]))
np.save(alf+'/'+prefix+'_QLstay.npy', np.concatenate([start_filler,ses_data['QLstay'].to_numpy(),end_filler]))
np.save(alf+'/'+prefix+'_QRstay.npy', np.concatenate([start_filler,ses_data['QRstay'].to_numpy(),end_filler]))
ROOT_FOLDER = '/Volumes/witten/Alex/Data/Subjects'
psy=mc.load_data(trial_start=0,trial_end=-150)
standata = mc.make_stan_data(psy)
reinforce_params = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/REINFORCE_laserdecaywin_mixedstay/output/summary.csv')
reinforce_data = pd.DataFrame()
reinforce_data = mc.reinforce_model_alphalaserdecay_mixed_perseveration(standata,saved_params=reinforce_params)
assert len(psy) == len(reinforce_data)
prefix='REINFORCE_mixedstay_alphalaserdecay'
save_q_values(ROOT_FOLDER,psy,reinforce_data,prefix, trial_start=0,trial_end=-150)
ses_bias_data['first_opto_block']=['L']
else:
ses_bias_data['first_opto_block']=['R']
bias_data = pd.concat([bias_data,ses_bias_data])
sns.barplot(data=bias_data,x='first_opto_block', y='bias', ci=68,palette='gray')
sns.swarmplot(data=bias_data,x='first_opto_block', y='bias', color='k')
plt.ylabel('Bias Coefficient')
plt.show()
sns.barplot(data=bias_data,x='first_opto_block', y='right_choices', ci=68, palette='gray')
sns.swarmplot(data=bias_data,x='first_opto_block', y='right_choices', color='k')
plt.ylabel('Fraction of right choices')
plt.show()
return bias_data
psy=load_data()
standata = make_stan_data(psy)
model_standard_75 = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/standard_75/output/summary.csv')
model_standard_75150 = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/standard_75150/output/summary.csv')
model_standard_rest = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/standard_rest/output/summary.csv')
model_reinforce_75 = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/reinforce_75/output/summary.csv')
model_reinforce_75150 = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/reinforce_75150/output/summary.csv')
model_reinforce_rest = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/investigation_laserdecay/reinforce_rest/output/summary.csv')
compare_params_3(model_standard_75,model_standard_75150,model_standard_rest,standata)
compare_params_2(model_reinforce_75,model_reinforce_rest,standata)
saved_params1=model_standard_75
saved_params2=model_standard_75150
saved_params3=model_standard_rest
ses_data.loc[ses_data['choices']]
filler=np.empty(150)
filler[:] = np.nan
assert (np.isnan(np.nanmean(ses_data['QL'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['QR'].to_numpy()))==False)
assert (np.isnan(np.nanmean(ses_data['Qwater'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['Qlaser'].to_numpy()))==False)
assert (np.isnan(np.nanmean(ses_data['QRstay'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['QLstay'].to_numpy()))==False)
assert (np.isnan(np.nanmean(ses_data['QRreward'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['QLreward'].to_numpy()))==False)
assert (np.isnan(np.nanmean(ses_data['QRlaser'].to_numpy()))==False) & (np.isnan(np.nanmean(ses_data['QLlaser'].to_numpy()))==False)
np.save(alf+'/'+prefix+'_choice_prediction.npy', np.concatenate([ses_data['predicted_choice'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_water.npy', np.concatenate([ses_data['Qwater'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_laser.npy', np.concatenate([ses_data['Qlaser'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QLstay.npy', np.concatenate([ses_data['QLstay'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QRstay.npy', np.concatenate([ses_data['QRstay'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QLreward.npy', np.concatenate([ses_data['QLreward'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QRreward.npy', np.concatenate([ses_data['QRreward'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QLlaser.npy', np.concatenate([ses_data['QLlaser'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QRlaser.npy', np.concatenate([ses_data['QRlaser'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QL.npy', np.concatenate([ses_data['QL'].to_numpy(),filler]))
np.save(alf+'/'+prefix+'_QR.npy', np.concatenate([ses_data['QR'].to_numpy(),filler]))
print(alf)
ROOT_FOLDER = '/Volumes/witten/Alex/Data/Subjects'
psy=mc.load_data()
standata = mc.make_stan_data(psy)
qlearning_params = pd.read_csv('/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/standard_alphalaserdecay/output/summary.csv')
qlearning_data = pd.DataFrame()
qlearning_data = mc.q_learning_model_alphalaserdecay(standata,saved_params=qlearning_params)
assert len(psy) == len(qlearning_data)
prefix='QLearning_alphalaserdecay'
save_q_values(ROOT_FOLDER,psy,qlearning_data,prefix)
axis=0)))
WAIC_laserdecay_mixedperseveration = -2 * (
np.sum(np.log(REINFORCE_laserdecaywinloss_mixedstay.mean(axis=0)))
- np.sum(
np.var(np.log(REINFORCE_laserdecaywinloss_mixedstay), axis=0)))
WAIC_laserdecay_mixedperseveration_noloss = -2 * (np.sum(
np.log(REINFORCE_laserdecaywinloss_mixedstay_noloss.mean(axis=0))
) - np.sum(
np.var(np.log(REINFORCE_laserdecaywinloss_mixedstay_noloss),
axis=0)))
WAIC_standard_laserdecay = -2 * (
np.sum(np.log(standard_laserdecay.mean(axis=0))) -
np.sum(np.var(np.log(standard_laserdecay), axis=0)))
except:
# 1
standata = mc.make_stan_data(mc.load_data(trial_end=-1))
path = '/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/standard_reduced_stay'
model = mc.q_learning_model_reduced_stay
WAIC_reducedstay = lppd_from_chain(path,
model,
standata,
save=True,
save_name='qreducedstay')
# 2
path = '/Volumes/witten/Alex/Data/ephys_bandit/stan_fits/standard_reduced'
model = mc.q_learning_model_reduced
WAIC_reduced = lppd_from_chain(path,
model,
standata,
save=True,
save_name='qreduced')