])
for i, nickname in enumerate(np.unique(sessions.fetch('subject_nickname'))):
if np.mod(i + 1, 10) == 0:
print('Loading data of subject %d of %d' %
(i + 1, len(np.unique(sessions.fetch('subject_nickname')))))
# Get only the trials of the 50/50 blocks
trials = (sessions * behavior.TrialSet.Trial
& 'subject_nickname = "%s"' % nickname
& 'trial_stim_prob_left = "0.5"').fetch(format='frame')
trials = trials.reset_index()
# Fit a psychometric function to these trials and get fit results
fit_df = dj2pandas(trials)
fit_result = fit_psychfunc(fit_df)
# Get performance, reaction time and number of trials
reaction_time = trials['rt'].median() * 1000
perf_easy = trials['correct_easy'].mean() * 100
# Get all the trials to get number of trials
trials = (sessions * behavior.TrialSet.Trial
& 'subject_nickname = "%s"' % nickname).fetch(format='frame')
trials = trials.reset_index()
ntrials_perday = trials.groupby('session_uuid').count()['trial_id'].mean()
nsessions = trials.groupby('session_uuid').size().shape[0]
# Add results to dataframe
learned.loc[i, 'mouse'] = nickname
learned.loc[i, 'lab'] = (
(i + 1, len(behav['subject_nickname'].unique())))
# Get lab and timezone
lab = behav.loc[behav['subject_nickname'] == nickname,
'institution_code'].unique()[0]
time_zone = behav.loc[behav['subject_nickname'] == nickname,
'time_zone'].unique()[0]
if (time_zone == 'Europe/Lisbon') or (time_zone == 'Europe/London'):
time_zone_number = 0
elif time_zone == 'America/New_York':
time_zone_number = -5
elif time_zone == 'America/Los_Angeles':
time_zone_number = -7
# Fit psychometric curve
left_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 80)])
right_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 20)])
fits = pd.DataFrame(
data={
'threshold_l': left_fit['threshold'],
'threshold_r': right_fit['threshold'],
'bias_l': left_fit['bias'],
'bias_r': right_fit['bias'],
'lapselow_l': left_fit['lapselow'],
'lapselow_r': right_fit['lapselow'],
'lapsehigh_l': left_fit['lapsehigh'],
'lapsehigh_r': right_fit['lapsehigh'],
'nickname': nickname,
'lab': lab,
'time_zone': time_zone_number
# Create dataframe with behavioral metrics of all mice
learned = pd.DataFrame(columns=['mouse', 'lab', 'perf_easy', 'n_trials',
'threshold', 'bias', 'reaction_time',
'lapse_low', 'lapse_high'])
for i, nickname in enumerate(behav['subject_nickname'].unique()):
if np.mod(i+1, 10) == 0:
print('Processing data of subject %d of %d' % (i+1,
len(behav['subject_nickname'].unique())))
# Get the trials of the sessions around criterion for this subject
trials = behav[behav['subject_nickname'] == nickname]
trials = trials.reset_index()
# Fit a psychometric function to these trials and get fit results
fit_result = fit_psychfunc(trials)
# Get RT, performance and number of trials
reaction_time = trials['rt'].median()*1000
perf_easy = trials['correct_easy'].mean()*100
ntrials_perday = trials.groupby('session_uuid').count()['trial_id'].mean()
# Add results to dataframe
learned.loc[i, 'mouse'] = nickname
learned.loc[i, 'lab'] = trials['institution_short'][0]
learned.loc[i, 'perf_easy'] = perf_easy
learned.loc[i, 'n_trials'] = ntrials_perday
learned.loc[i, 'reaction_time'] = reaction_time
learned.loc[i, 'threshold'] = fit_result.loc[0, 'threshold']
learned.loc[i, 'bias'] = fit_result.loc[0, 'bias']
learned.loc[i, 'lapse_low'] = fit_result.loc[0, 'lapselow']
def run_glm(behav,
example,
correction=True,
bias=False,
cross_validation=True):
for i, nickname in enumerate(np.unique(behav['subject_nickname'])):
if np.mod(i + 1, 10) == 0:
print('Loading data of subject %d of %d' %
(i + 1, len(np.unique(behav['subject_nickname']))))
# Get the trials of the sessions around criterion
trials = behav.loc[behav['subject_nickname'] == nickname].copy()
if bias == True:
neutral_n = fit_psychfunc(
behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 50)])
left_fit = fit_psychfunc(
behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 80)])
right_fit = fit_psychfunc(
behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 20)])
behav.loc[behav['subject_nickname'] == nickname, 'bias_n'] = \
neutral_n.loc[0, 'bias']
behav.loc[behav['subject_nickname'] == nickname, 'bias_r'] = \
right_fit.loc[0, 'bias']
behav.loc[behav['subject_nickname'] == nickname, 'bias_l'] = \
left_fit.loc[0, 'bias']
else:
fit_df = dj2pandas(trials.copy())
fit_result = fit_psychfunc(fit_df)
behav.loc[behav['subject_nickname'] == nickname, 'threshold'] = \
fit_result.loc[0, 'threshold']
## GLM
#make separate datafrme
data = trials[[
'index', 'trial_feedback_type', 'signed_contrast', 'choice',
'probabilityLeft'
]].copy()
# Rewardeded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()),
'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) & (data['trial_feedback_type'] == -1),
'rchoice'] = 0
data.loc[(data['choice'] == -1) & (data['trial_feedback_type'] == 1),
'rchoice'] = -1
data.loc[(data['choice'] == 1) & (data['trial_feedback_type'] == 1),
'rchoice'] = 1
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()),
'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) & (data['trial_feedback_type'] == -1),
'rchoice'] = 0
# Unrewarded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()),
'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) & (data['trial_feedback_type'] == -1),
'uchoice'] = -1
data.loc[(data['choice'] == -1) & (data['trial_feedback_type'] == 1),
'uchoice'] = 0
data.loc[(data['choice'] == 1) & (data['trial_feedback_type'] == 1),
'uchoice'] = 0
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()),
'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) & (data['trial_feedback_type'] == -1),
'uchoice'] = 1
# Apply correction
if correction == True:
data['rchoice+1'] = \
data['rchoice'].shift(periods=-1).to_numpy()
data['uchoice+1'] = \
data['uchoice'].shift(periods=-1).to_numpy()
# Shift rewarded and unrewarded predictors by one
data.loc[:, ['rchoice', 'uchoice']] = \
data[['rchoice', 'uchoice']].shift(periods=1).to_numpy()
# Drop any nan trials
data.dropna(inplace=True)
# Make sensory predictors (no 0 predictor)
contrasts = [25, 100, 12.5, 6.25]
for i in contrasts:
data.loc[(data['signed_contrast'].abs() == i), i] = \
np.sign(data.loc[(data['signed_contrast'].abs() == i),
'signed_contrast'].to_numpy())
data_con = data[i].copy()
data[i] = data_con.fillna(0)
# If contrast missing break
for i in contrasts:
if np.sum(data[i]) == 0:
print('missing contrast')
missing_contrast = True
else:
missing_contrast = False
if missing_contrast == True:
continue
# Make block identity (across predictors right is positive, hence logic below)
if bias == True:
data.loc[(data['probabilityLeft'] == 50), 'block'] = 0
data.loc[(data['probabilityLeft'] == 20), 'block'] = 1
data.loc[(data['probabilityLeft'] == 80), 'block'] = -1
# Make choice in between 0 and 1 -> 1 for right and 0 for left
data.loc[data['choice'] == -1, 'choice'] = 0
# Store index
index = data['index'].copy()
# Create predictor matrix
endog = data['choice'].copy()
exog = data.copy()
exog.drop(columns=[
'trial_feedback_type', 'signed_contrast', 'choice',
'probabilityLeft'
],
inplace=True)
exog = sm.add_constant(exog)
if cross_validation == False:
X_train = exog.copy()
X_test = exog.copy()
y_train = endog.copy()
y_test = endog.copy()
else:
X_train = exog.iloc[:int(len(exog) * 0.70), :].copy()
X_test = exog.iloc[int(len(endog) * 0.70):, :].copy()
y_train = endog.iloc[:int(len(endog) * 0.70)].copy()
y_test = endog.iloc[int(len(endog) * 0.70):].copy()
# Store index
index = X_test['index'].to_numpy()
X_train.drop(columns=['index'], inplace=True)
X_test.drop(columns=['index'], inplace=True)
# Fit model
try:
logit_model = sm.Logit(y_train, X_train)
result = logit_model.fit_regularized()
# print(result.summary2())
# Store model weights
behav.loc[behav['subject_nickname'] == nickname,
'intercept'] = result.params['const'].copy()
behav.loc[behav['subject_nickname'] == nickname,
'rchoice'] = result.params['rchoice'].copy()
behav.loc[behav['subject_nickname'] == nickname,
'uchoice'] = result.params['uchoice'].copy()
mask = result.params.index.get_level_values(0)
behav.loc[behav['subject_nickname'] == nickname,
'25'] = result.params[25].copy()
behav.loc[behav['subject_nickname'] == nickname,
'6'] = result.params.loc[mask == 6.25][0]
behav.loc[behav['subject_nickname'] == nickname,
'100'] = result.params[100].copy()
behav.loc[behav['subject_nickname'] == nickname,
'12'] = result.params.loc[mask == 12.5][0]
if bias == True:
behav.loc[behav['subject_nickname'] == nickname,
'block'] = result.params['block'].copy()
if correction == True:
result.params['rchoice+1'] = result.params['rchoice+1'] * -1
result.params['uchoice+1'] = result.params['uchoice+1'] * -1
behav.loc[behav['subject_nickname'] == nickname,
'rchoice+1'] = result.params['rchoice+1'].copy()
behav.loc[behav['subject_nickname'] == nickname,
'uchoice+1'] = result.params['uchoice+1'].copy()
# Probabilities on test data
prob = result.predict(X_test).to_numpy()
if nickname == example:
example_model = result
# Propagate to storing dataframe
behav.loc[behav['index'].isin(index), 'simulation_prob'] = prob
except:
print('singular matrix')
return behav, example_model
def model_psychometric_history(behav):
select = behav.copy()
select['t-1'] = select['trial_feedback_type'].shift(periods=1).to_numpy()
select.loc[select['choice'] == -1, 'choice'] = 0
select = select.iloc[1:,:]
#select['t-1'].fillna(0, inplace=True)
select['t-1'] = select['t-1'].astype(int)
plot_psychometric(select.loc[select['signed_contrast'],
select.loc[select['probabilityLeft'] ==i, 'signed_contrast'], palette = ['red', 'green'],
ci = 68)
sns.lineplot(data = select_50, hue = 't-1', x = select_50['signed_contrast'],
y = select_50['simulation_prob'], palette = ['red', 'green'], ci = 68)
## Functions
def run_glm(behav, example, correction = True, bias = False, cross_validation = True):
for i, nickname in enumerate(np.unique(behav['subject_nickname'])):
if np.mod(i+1, 10) == 0:
print('Loading data of subject %d of %d' % (i+1, len(
np.unique(behav['subject_nickname']))))
# Get the trials of the sessions around criterion
trials = behav.loc[behav['subject_nickname'] == nickname].copy()
if bias == True:
neutral_n = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 50)])
left_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 80)])
right_fit = fit_psychfunc(behav[(behav['subject_nickname'] == nickname)
& (behav['probabilityLeft'] == 20)])
behav.loc[behav['subject_nickname'] == nickname, 'bias_n'] = \
neutral_n.loc[0, 'bias']
behav.loc[behav['subject_nickname'] == nickname, 'bias_r'] = \
right_fit.loc[0, 'bias']
behav.loc[behav['subject_nickname'] == nickname, 'bias_l'] = \
left_fit.loc[0, 'bias']
else:
fit_df = dj2pandas(trials.copy())
fit_result = fit_psychfunc(fit_df)
behav.loc[behav['subject_nickname'] == nickname, 'threshold'] = \
fit_result.loc[0, 'threshold']
## GLM
#make separate datafrme
data = trials[['index', 'trial_feedback_type',
'signed_contrast', 'choice',
'probabilityLeft']].copy()
#drop trials with odd probabilities of left
data.drop(
data['probabilityLeft'][~data['probabilityLeft'].isin([50,20,80])].index,
inplace=True)
# Rewardeded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()), 'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == -1), 'rchoice'] = 0
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == 1), 'rchoice'] = -1
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == 1), 'rchoice'] = 1
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()) , 'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == -1), 'rchoice'] = 0
# Unrewarded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()), 'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == -1), 'uchoice'] = -1
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == 1), 'uchoice'] = 0
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == 1), 'uchoice'] = 0
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()) , 'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == -1) , 'uchoice'] = 1
# Apply correction
if correction == True:
data['rchoice+1'] = \
data['rchoice'].shift(periods=-1).to_numpy()
data['uchoice+1'] = \
data['uchoice'].shift(periods=-1).to_numpy()
# Shift rewarded and unrewarded predictors by one
data.loc[:, ['rchoice', 'uchoice']] = \
data[['rchoice', 'uchoice']].shift(periods=1).to_numpy()
# Drop any nan trials
data.dropna(inplace=True)
# Make sensory predictors (no 0 predictor)
contrasts = [ 25, 100, 12.5, 6.25]
for i in contrasts:
data.loc[(data['signed_contrast'].abs() == i), i] = \
np.sign(data.loc[(data['signed_contrast'].abs() == i),
'signed_contrast'].to_numpy())
data_con = data[i].copy()
data[i] = data_con.fillna(0)
# If contrast missing break
for i in contrasts:
if np.sum(data[i]) == 0:
print('missing contrast')
missing_contrast = True
else:
missing_contrast = False
if missing_contrast == True:
continue
# Make block identity (across predictors right is positive, hence logic below)
if bias == True:
data.loc[(data['probabilityLeft'] == 50), 'block'] = 0
data.loc[(data['probabilityLeft'] == 20), 'block'] = 1
data.loc[(data['probabilityLeft'] == 80), 'block'] = -1
# Make choice in between 0 and 1 -> 1 for right and 0 for left
data.loc[data['choice'] == -1, 'choice'] = 0
# Store index
index = data['index'].copy()
# Create predictor matrix
endog = data['choice'].copy()
exog = data.copy()
exog.drop(columns=['trial_feedback_type',
'signed_contrast', 'choice',
'probabilityLeft'], inplace=True)
exog = sm.add_constant(exog)
if cross_validation == False:
X_train = exog.copy()
X_test = exog.copy()
y_train = endog.copy()
y_test = endog.copy()
else:
X_train = exog.iloc[:int(len(exog)*0.70),:].copy()
X_test = exog.iloc[int(len(endog)*0.70):,:].copy()
y_train = endog.iloc[:int(len(endog)*0.70)].copy()
y_test = endog.iloc[int(len(endog)*0.70):].copy()
# Store index
index = X_test['index'].to_numpy()
X_train.drop(columns=['index'], inplace=True)
X_test.drop(columns=['index'], inplace=True)
# Fit model
try:
logit_model = sm.Logit(y_train, X_train)
result = logit_model.fit_regularized()
# print(result.summary2())
# Store model weights
behav.loc[behav['subject_nickname'] == nickname, 'intercept'] = result.params['const'].copy()
behav.loc[behav['subject_nickname'] == nickname, 'rchoice'] = result.params['rchoice'].copy()
behav.loc[behav['subject_nickname'] == nickname, 'uchoice'] = result.params['uchoice'].copy()
mask = result.params.index.get_level_values(0)
behav.loc[behav['subject_nickname'] == nickname, '25'] = result.params[25].copy()
behav.loc[behav['subject_nickname'] == nickname, '6'] = result.params.loc[mask == 6.25][0]
behav.loc[behav['subject_nickname'] == nickname, '100'] = result.params[100].copy()
behav.loc[behav['subject_nickname'] == nickname, '12'] = result.params.loc[mask == 12.5][0]
if bias == True:
behav.loc[behav['subject_nickname'] == nickname, 'block'] = result.params['block'].copy()
if correction == True:
behav.loc[behav['subject_nickname'] == nickname, 'rchoice+1'] = result.params['rchoice+1'].copy()
behav.loc[behav['subject_nickname'] == nickname, 'uchoice+1'] = result.params['uchoice+1'].copy()
# Probabilities on test data
prob = result.predict(X_test).to_numpy()
if nickname == example:
example_model = result
# Propagate to storing dataframe
behav.loc[behav['index'].isin(index), 'simulation_prob'] = prob
except:
print('singular matrix')
return behav, example_model
def data_2_X_test (behav, correction = True, bias = True):
data = behav[['index','trial_feedback_type',
'signed_contrast', 'choice',
'probabilityLeft']].copy()
#drop trials with odd probabilities of left
data.drop(
data['probabilityLeft'][~data['probabilityLeft'].isin([50,20,80])].index,
inplace=True)
# Rewardeded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()), 'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == -1), 'rchoice'] = 0
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == 1), 'rchoice'] = -1
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == 1), 'rchoice'] = 1
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()) , 'rchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == -1), 'rchoice'] = 0
# Unrewarded choices:
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()), 'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == -1), 'uchoice'] = -1
data.loc[(data['choice'] == -1) &
(data['trial_feedback_type'] == 1), 'uchoice'] = 0
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == 1), 'uchoice'] = 0
data.loc[(data['choice'] == 0) &
(data['trial_feedback_type'].isnull()) , 'uchoice'] = 0 # NoGo trials
data.loc[(data['choice'] == 1) &
(data['trial_feedback_type'] == -1) , 'uchoice'] = 1
# Apply correction
if correction == True:
data['rchoice+1'] = \
data['rchoice'].shift(periods=-1).to_numpy()
data['uchoice+1'] = \
data['uchoice'].shift(periods=-1).to_numpy()
# Shift rewarded and unrewarded predictors by one
data.loc[:, ['rchoice', 'uchoice']] = \
data[['rchoice', 'uchoice']].shift(periods=1).to_numpy()
# Drop any nan trials
data.dropna(inplace=True)
# Make sensory predictors (no 0 predictor)
contrasts = [ 25, 100, 12.5, 6.25]
for i in contrasts:
data.loc[(data['signed_contrast'].abs() == i), i] = \
np.sign(data.loc[(data['signed_contrast'].abs() == i),
'signed_contrast'].to_numpy())
data_con = data[i].copy()
data[i] = data_con.fillna(0)
# Make block identity (across predictors right is positive, hence logic below)
if bias == True:
data.loc[(data['probabilityLeft'] == 50), 'block'] = 0
data.loc[(data['probabilityLeft'] == 20), 'block'] = 1
data.loc[(data['probabilityLeft'] == 80), 'block'] = -1
# Make choice in between 0 and 1 -> 1 for right and 0 for left
data.loc[data['choice'] == -1, 'choice'] = 0
index = data['index'].copy()
# Create predictor matrix
endog = data['choice'].copy()
exog = data.copy()
exog.drop(columns=['index', 'trial_feedback_type',
'signed_contrast', 'choice',
'probabilityLeft'], inplace=True)
exog = sm.add_constant(exog)
return exog, index
def plot_psychometric(x, y, col, point = False, mark = 'o', al =1):
# summary stats - average psychfunc over observers
df = pd.DataFrame({'signed_contrast': x, 'choice': y,
'choice2': y})
df2 = df.groupby(['signed_contrast']).agg(
{'choice2': 'count', 'choice': 'mean'}).reset_index()
df2.rename(columns={"choice2": "ntrials",
"choice": "fraction"}, inplace=True)
df2 = df2.groupby(['signed_contrast']).mean().reset_index()
df2 = df2[['signed_contrast', 'ntrials', 'fraction']]
# fit psychfunc
pars, L = psy.mle_fit_psycho(df2.transpose().values, # extract the data from the df
P_model='erf_psycho_2gammas',
parstart=np.array(
[df2['signed_contrast'].mean(), 20., 0.05, 0.05]),
parmin=np.array(
[df2['signed_contrast'].min(), 5, 0., 0.]),
parmax=np.array([df2['signed_contrast'].max(), 100., 1, 1]))
# plot psychfunc
g = sns.lineplot(np.arange(-29, 29),
psy.erf_psycho_2gammas(pars, np.arange(-29, 29)), color = col,
alpha = al)
# plot psychfunc: -100, +100
sns.lineplot(np.arange(-37, -32),
psy.erf_psycho_2gammas(pars, np.arange(-103, -98)), color = col,
alpha = al)
sns.lineplot(np.arange(32, 37),
psy.erf_psycho_2gammas(pars, np.arange(98, 103)), color = col,
alpha = al)
# now break the x-axis
# if 100 in df.signed_contrast.values and not 50 in
# df.signed_contrast.values:
df['signed_contrast'] = df['signed_contrast'].replace(-100, -35)
df['signed_contrast'] = df['signed_contrast'].replace(100, 35)
if point == True:
sns.lineplot(df['signed_contrast'], df['choice'], err_style="bars",
linewidth=0, linestyle='None', mew=0.5,
marker=mark, ci=68, color = col, alpha = al)
g.set_xticks([-35, -25, -12.5, 0, 12.5, 25, 35])
g.set_xticklabels(['-100', '-25', '-12.5', '0', '12.5', '25', '100'],
size='small', rotation=45)
g.set_xlim([-40, 40])
g.set_ylim([0, 1])
g.set_yticks([0, 0.25, 0.5, 0.75, 1])
g.set_yticklabels(['0', '25', '50', '75', '100'])
# FUNCTION UNDER DEVELOPMENT
def updating:
select = behav.copy()
select['signed_contrast-1'] = select['signed_contrast'].shift(periods=1).to_numpy()
select['signed_contrast+1'] = select['signed_contrast'].shift(periods=-1).to_numpy()
select['t-1'] = select['trial_feedback_type'].shift(periods=1).to_numpy()
select['t+1'] = select['trial_feedback_type'].shift(periods=-1).to_numpy()
select = select.iloc[1:-1,:] # First and last trial will have nan for history
select['t-1'] = select['t-1'].astype(int)
select['simulation_prob'] = select['simulation_prob']*100
#select = select.loc[select['signed_contrast-1'] >= 0]
for mouse in select['subject_nickname'].unique():
for c in select['signed_contrast-1'].unique():
for r in select['t-1'].unique():
sub_select = select.loc[(select['signed_contrast-1'] == c) &
(select['t-1'] == r) & (select['subject_nickname'] == mouse)]
fit_result = fit_psychfunc(sub_select)
select.loc[select['subject_nickname'] == nickname, 'updating'] = \
fit_result['bias'][0]
for c in select['signed_contrast+1'].unique():
for r in select['t+1'].unique():
sub_select = select.loc[(select['signed_contrast+1'] == c) &
(select['t+1'] == r) & (select['subject_nickname'] == mouse)]
fit_result = fit_psychfunc(sub_select)
select.loc[select['subject_nickname'] == nickname, 'updating_correction'] = \
fit_result['bias'][0]
sns.lineplot(data = select, hue = 't-1', x = select['signed_contrast-1'],
y = select['simulation_prob'], ci = 68)
sns.lineplot(data = select, hue = 't-1', x = select['signed_contrast-1'],
y = select[select['probabilityLeft'] == 80],'choice'] -
select.loc[select['probabilityLeft'] == 20 ,'choice'])
