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mvrn_esi_imp_analysis.py
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#%%
'''IMPORTING TOOLS'''
#%%
import os
import numpy as np
import pandas as pd
from glob import glob
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import seaborn as sns
from scipy.stats import norm
#%%
'''ESI VS. IMP ANALYSIS'''
#%%
# establishing the directory from which to retrieve esi vs. imp csv files
root_dir = "/home/data/madlab/mvrn_ESI_study/clean_pilot_subs/"
# compiling list of esi vs. imp subjects
final_esi_imp_subs = [
"101","102","103","105","106","107","109","110","111","112","113",
"114","115","117","118","119","120","121","122","123","124","127",
"128","129","130","131","132","133","134","135","136","137","138",
"139","140","141","142","145","146","147","148","149","151","152",
"153","154","158","159","160","162","163","164","165","168","169",
"170","173","175","180","181","184","185","186","187","194","197",
"200","201","204","210","211","212","213","216","222","246"
]
behav_data = {
'Subject':[],
'PostESI_NegLureFA_Count': [],
'PostESI_NeuLureFA_Count': [],
'PostESI_NegLureCR_Count': [],
'PostESI_NeuLureCR_Count': [],
'PostESI_NegTargHit_Count': [],
'PostESI_NeuTargHit_Count': [],
'PostESI_NegTargMiss_Count': [],
'PostESI_NeuTargMiss_Count': [],
'PostIMP_NegLureFA_Count': [],
'PostIMP_NeuLureFA_Count': [],
'PostIMP_NegLureCR_Count': [],
'PostIMP_NeuLureCR_Count': [],
'PostIMP_NegTargHit_Count': [],
'PostIMP_NeuTargHit_Count': [],
'PostIMP_NegTargMiss_Count': [],
'PostIMP_NeuTargMiss_Count': [],
'NegFoilFA_Count': [],
'NegFoilCR_Count': [],
'NeuFoilFA_Count': [],
'NeuFoilCR_Count': []
}
study_cols = [
'Subject',
'Randomization',
'study_stim1',
'study_stim2'
]
test_cols = [
'Subject',
'Randomization',
'test_run',
'test_stim',
'test_stimtype',
'test_stimsim',
'test_resp'
]
study_run1 = "study_stim1"
study_run2 = "study_stim2"
lurefas = "test_stimsim == 'lure' and test_resp == 'old'"
lurecrs = "test_stimsim == 'lure' and test_resp == 'new'"
targhits = "test_stimsim == 'target' and test_resp == 'old'"
targmiss = "test_stimsim == 'target' and test_resp == 'new'"
foilfas = "test_stimsim == 'foil' and test_resp == 'old'"
foilcrs = "test_stimsim == 'foil' and test_resp == 'new'"
trial_types = [
lurefas,
lurecrs,
targhits,
targmiss,
foilfas,
foilcrs
]
runs = [
study_run1,
study_run2
]
# function to query for FA, CR, TH, TM for each subject
def query_facr_thtm(df, resp_cat):
query = df[df["Subject"] == int(f"{sub}")].query(resp_cat).reset_index(drop=True)
query_stim_list = list(query['test_stim'].str.slice(15, 20))
return(query_stim_list)
# function to query for foils for each subject
# no need to grab the test_stim column cause it's just foils
def query_foils(df, resp_cat):
query = df[df["Subject"] == int(f"{sub}")].query(resp_cat).reset_index(drop=True)
return(query)
# function to match specific test trials to study items in run 1 or 2
def matching(df, run, lista):
length = len(df[df[run].str[15:20].isin(lista)].reset_index(drop=True))
return(length)
# iterating over every subject
for sub in final_esi_imp_subs:
for file in glob(
os.path.join(
root_dir,
f"{sub}*.csv"
)):
study_df = pd.read_csv(
file,
usecols=study_cols,
nrows=86
)
test_df = pd.read_csv(
file,
usecols=test_cols,
nrows=238
)
study_df['Randomization'] = study_df['Randomization'].replace([1, 2], ['ESIthenIMP','IMPthenESI'])
test_df['Randomization'] = test_df['Randomization'].replace([1, 2], ['ESIthenIMP','IMPthenESI'])
test_df['test_stimtype'] = test_df['test_stimtype'].replace([1, 2], ['negative','neutral'])
test_df['test_stimsim'] = test_df['test_stimsim'].replace([1, 2, 4], ['target','lure', 'foil'])
test_df['test_resp'] = test_df['test_resp'].replace([1, 2], ['old','new'])
test_df['test_resp'] = test_df['test_resp'].replace('None', np.nan)
# # adding list of subjects to the dictionary we created above
behav_data['Subject'].append(sub)
negative_list = []
neutral_list = []
# searching for negative/neutral stimuli in each row of the test DF
for index, row in test_df.iterrows():
# print(row)
if "negative" in row["test_stimtype"]:
# adding the filtered rows to a list
negative_list.append(row)
else:
neutral_list.append(row)
# getting DF with only negative stimuli shown at test
neg_stim_pd = pd.DataFrame(negative_list).reset_index(drop=True)
# getting DF with only neutral stimuli shown at test
neu_stim_pd = pd.DataFrame(neutral_list).reset_index(drop=True)
# # testing whether each subject has 117 negative stimuli at test
# total_neg_stim_persub = len(neg_stim_pd[neg_stim_pd["Subject"]==int(f"{sub}")])
# if total_neg_stim_persub != 117:
# print(f"{sub}" + " test stim count: not all is well :/")
for trials in trial_types:
# negative querying for each trial type
if trials == lurefas:
# negative lurefas
neg_lurefas_list = query_facr_thtm(neg_stim_pd,trials)
# neutral lurefas
neu_lurefa_list = query_facr_thtm(neu_stim_pd,trials)
elif trials == lurecrs:
# negative lurecrs
neg_lurecr_list = query_facr_thtm(neg_stim_pd,trials)
# neutral lurecrs
neu_lurecr_list = query_facr_thtm(neu_stim_pd,trials)
elif trials == targhits:
# negative ths
neg_ths_list = query_facr_thtm(neg_stim_pd,trials)
# neutral ths
neu_ths_list = query_facr_thtm(neu_stim_pd,trials)
elif trials == targmiss:
# negative tms
neg_tms_list = query_facr_thtm(neg_stim_pd,trials)
# neutral ths
neu_tms_list = query_facr_thtm(neu_stim_pd,trials)
elif trials == foilfas:
# negative foilfas
neg_foilfa_list = query_foils(neg_stim_pd,trials)
# neutral foilfas
neu_foilfa_list = query_foils(neu_stim_pd,trials)
elif trials == foilcrs:
# negative foilcrs
neg_foilcr_list = query_foils(neg_stim_pd,trials)
# neutral foilcrs
neu_foilcr_list = query_foils(neu_stim_pd,trials)
# matching all the FA/CR Foils
behav_data['NegFoilFA_Count'].append(len(
neg_foilfa_list
))
behav_data['NegFoilCR_Count'].append(len(
neg_foilcr_list
))
# matching all the FA/CR Foils
behav_data['NeuFoilFA_Count'].append(len(
neu_foilfa_list
))
behav_data['NeuFoilCR_Count'].append(len(
neu_foilcr_list
))
if study_df['Randomization'][0] == "ESIthenIMP":
'''
Negative
'''
# matching all the FAs
behav_data['PostESI_NegLureFA_Count'].append(
matching(
study_df,
study_run1,
neg_lurefas_list
))
behav_data['PostIMP_NegLureFA_Count'].append(
matching(
study_df,
study_run2,
neg_lurefas_list
))
# matching all the CRs
behav_data['PostESI_NegLureCR_Count'].append(
matching(
study_df,
study_run1,
neg_lurecr_list
))
behav_data['PostIMP_NegLureCR_Count'].append(
matching(
study_df,
study_run2,
neg_lurecr_list
))
# matching all the THs
behav_data['PostESI_NegTargHit_Count'].append(
matching(
study_df,
study_run1,
neg_ths_list
))
behav_data['PostIMP_NegTargHit_Count'].append(
matching(
study_df,
study_run2,
neg_ths_list
))
# matching all the TMs
behav_data['PostESI_NegTargMiss_Count'].append(
matching(
study_df,
study_run1,
neg_tms_list
))
behav_data['PostIMP_NegTargMiss_Count'].append(
matching(
study_df,
study_run2,
neg_tms_list
))
'''Neutral'''
# matching all the FAs
behav_data['PostESI_NeuLureFA_Count'].append(
matching(
study_df,
study_run1,
neu_lurefa_list
))
behav_data['PostIMP_NeuLureFA_Count'].append(
matching(
study_df,
study_run2,
neu_lurefa_list
))
# matching all the CRs
behav_data['PostESI_NeuLureCR_Count'].append(
matching(
study_df,
study_run1,
neu_lurecr_list
))
behav_data['PostIMP_NeuLureCR_Count'].append(
matching(
study_df,
study_run2,
neu_lurecr_list
))
# matching all the THs
behav_data['PostESI_NeuTargHit_Count'].append(
matching(
study_df,
study_run1,
neu_ths_list
))
behav_data['PostIMP_NeuTargHit_Count'].append(
matching(
study_df,
study_run2,
neu_ths_list
))
# matching all the TMs
behav_data['PostESI_NeuTargMiss_Count'].append(
matching(
study_df,
study_run1,
neu_tms_list
))
behav_data['PostIMP_NeuTargMiss_Count'].append(
matching(
study_df,
study_run2,
neu_tms_list
))
else:
'''
Negative
'''
# matching all the FAs
behav_data['PostESI_NegLureFA_Count'].append(
matching(
study_df,
study_run2,
neg_lurefas_list
))
behav_data['PostIMP_NegLureFA_Count'].append(
matching(
study_df,
study_run1,
neg_lurefas_list
))
# matching all the CRs
behav_data['PostESI_NegLureCR_Count'].append(
matching(
study_df,
study_run2,
neg_lurecr_list
))
behav_data['PostIMP_NegLureCR_Count'].append(
matching(
study_df,
study_run1,
neg_lurecr_list
))
# matching all the THs
behav_data['PostESI_NegTargHit_Count'].append(
matching(
study_df,
study_run2,
neg_ths_list
))
behav_data['PostIMP_NegTargHit_Count'].append(
matching(
study_df,
study_run1,
neg_ths_list
))
# matching all the TMs
behav_data['PostESI_NegTargMiss_Count'].append(
matching(
study_df,
study_run2,
neg_tms_list
))
behav_data['PostIMP_NegTargMiss_Count'].append(
matching(
study_df,
study_run1,
neg_tms_list
))
'''Neutral'''
# matching all the FAs
behav_data['PostESI_NeuLureFA_Count'].append(
matching(
study_df,
study_run2,
neu_lurefa_list
))
behav_data['PostIMP_NeuLureFA_Count'].append(
matching(
study_df,
study_run1,
neu_lurefa_list
))
# matching all the CRs
behav_data['PostESI_NeuLureCR_Count'].append(
matching(
study_df,
study_run2,
neu_lurecr_list
))
behav_data['PostIMP_NeuLureCR_Count'].append(
matching(
study_df,
study_run1,
neu_lurecr_list
))
# matching all the THs
behav_data['PostESI_NeuTargHit_Count'].append(
matching(
study_df,
study_run2,
neu_ths_list
))
behav_data['PostIMP_NeuTargHit_Count'].append(
matching(
study_df,
study_run1,
neu_ths_list
))
# matching all the TMs
behav_data['PostESI_NeuTargMiss_Count'].append(
matching(
study_df,
study_run2,
neu_tms_list
))
behav_data['PostIMP_NeuTargMiss_Count'].append(
matching(
study_df,
study_run1,
neu_tms_list
))
#%%
'''PRINTING DATAFRAME (INCL. CREATING CSV)'''
#%%
behav_data_df = pd.DataFrame(behav_data)
behav_data_df.reset_index(drop=True)
# dropping 109 and 129 due to excessive number of FAd Foils
# behav_data_df = behav_data_df.drop([6, 23])
# getting mean for every column except for the subject column
# behav_data_df.loc['Mean'] = behav_data_df.iloc[:,1:].mean()
behav_data_df.to_csv("/home/mrive301/mvrn_esi_analysis/esi_imp.csv")
behav_data_df
#%%
'''POST ESI VS. IMP LURE FA, LURE CR COUNTS (VALENCE COLLAPSED)'''
#%%
behav_data_df['PostESI_LureFA_Count'] = behav_data_df['PostESI_NegLureFA_Count'] + behav_data_df['PostESI_NeuLureFA_Count']
behav_data_df['PostIMP_LureFA_Count'] = behav_data_df['PostIMP_NegLureFA_Count'] + behav_data_df['PostIMP_NeuLureFA_Count']
behav_data_df['PostESI_LureCR_Count'] = behav_data_df['PostESI_NegLureCR_Count'] + behav_data_df['PostESI_NeuLureCR_Count']
behav_data_df['PostIMP_LureCR_Count'] = behav_data_df['PostIMP_NegLureCR_Count'] + behav_data_df['PostIMP_NeuLureCR_Count']
#%%
'''POST ESI VS. IMP TARG HIT/MISS COUNT (VALENCE COLLAPSED)'''
#%%
behav_data_df['PostESI_TargHit_Count'] = behav_data_df['PostESI_NegTargHit_Count'] + behav_data_df['PostESI_NeuTargHit_Count']
behav_data_df['PostIMP_TargHit_Count'] = behav_data_df['PostIMP_NegTargHit_Count'] + behav_data_df['PostIMP_NeuTargHit_Count']
behav_data_df['PostESI_TargMiss_Count'] = behav_data_df['PostESI_NegTargMiss_Count'] + behav_data_df['PostESI_NeuTargMiss_Count']
behav_data_df['PostIMP_TargMiss_Count'] = behav_data_df['PostIMP_NegTargMiss_Count'] + behav_data_df['PostIMP_NeuTargMiss_Count']
#%%
'''POST ESI VS. IMP FOILS COUNT (VALENCE COLLAPSED)'''
#%%
behav_data_df['FoilFA_Count'] = behav_data_df['NegFoilFA_Count'] + behav_data_df['NeuFoilFA_Count']
behav_data_df['FoilCR_Count'] = behav_data_df['NegFoilCR_Count'] + behav_data_df['NeuFoilCR_Count']
behav_data_df
#%%
'''OFFICIAL POST ESI VS. IMP
LURE FAs, LURE CRs, HITS, MISSES,
FOIL FAs, FOIL CRs, LGI, LDI
(VALENCE COLLAPSED)'''
#%%
# LURE FAs AND LURE CRs
behav_data_df['PostESI_LureFA'] = behav_data_df['PostESI_LureFA_Count']/(behav_data_df['PostESI_LureFA_Count'] + behav_data_df['PostESI_LureCR_Count'])
behav_data_df['PostIMP_LureFA'] = behav_data_df['PostIMP_LureFA_Count']/(behav_data_df['PostIMP_LureFA_Count'] + behav_data_df['PostIMP_LureCR_Count'])
behav_data_df['PostESI_LureCR'] = behav_data_df['PostESI_LureCR_Count']/(behav_data_df['PostESI_LureCR_Count'] + behav_data_df['PostESI_LureFA_Count'])
behav_data_df['PostIMP_LureCR'] = behav_data_df['PostIMP_LureCR_Count']/(behav_data_df['PostIMP_LureCR_Count'] + behav_data_df['PostIMP_LureFA_Count'])
# HITS AND MISSES
behav_data_df["PostESI_TargHit"] = behav_data_df["PostESI_TargHit_Count"]/(behav_data_df["PostESI_TargHit_Count"] + behav_data_df["PostESI_TargMiss_Count"])
behav_data_df["PostIMP_TargHit"] = behav_data_df["PostIMP_TargHit_Count"]/(behav_data_df["PostIMP_TargHit_Count"] + behav_data_df["PostIMP_TargMiss_Count"])
behav_data_df["PostESI_TargMiss"] = behav_data_df["PostESI_TargMiss_Count"]/(behav_data_df["PostESI_TargMiss_Count"] + behav_data_df["PostESI_TargHit_Count"])
behav_data_df["PostIMP_TargMiss"] = behav_data_df["PostIMP_TargMiss_Count"]/(behav_data_df["PostIMP_TargMiss_Count"] + behav_data_df["PostIMP_TargHit_Count"])
# FOILS
behav_data_df['FoilFA'] = behav_data_df['FoilFA_Count']/(behav_data_df['FoilFA_Count'] + behav_data_df['FoilCR_Count'])
behav_data_df['FoilCR'] = behav_data_df['FoilCR_Count']/(behav_data_df['FoilCR_Count'] + behav_data_df['FoilFA_Count'])
# LGI
behav_data_df['PostESI_LGI'] = behav_data_df['PostESI_LureFA'] - behav_data_df['FoilFA']
behav_data_df['PostIMP_LGI'] = behav_data_df['PostIMP_LureFA'] - behav_data_df['FoilFA']
# LDI
behav_data_df['PostESI_LDI'] = behav_data_df['PostESI_LureCR'] - behav_data_df['PostESI_TargMiss']
behav_data_df['PostIMP_LDI'] = behav_data_df['PostIMP_LureCR'] - behav_data_df['PostIMP_TargMiss']
#%%
'''OFFICIAL POST ESI VS. IMP
LURE FAs, LURE CRs, HITS, MISSES,
FOIL FAs, FOIL CRs, LGI, LDI
(FOR EACH VALENCE)'''
#%%
# NEGATIVE LURE FA
behav_data_df['PostESI_NegLureFA'] = behav_data_df['PostESI_NegLureFA_Count']/(behav_data_df['PostESI_NegLureFA_Count'] + behav_data_df['PostESI_NegLureCR_Count'])
behav_data_df['PostIMP_NegLureFA'] = behav_data_df['PostIMP_NegLureFA_Count']/(behav_data_df['PostIMP_NegLureFA_Count'] + behav_data_df['PostIMP_NegLureCR_Count'])
# NEUTRAL LURE FA
behav_data_df['PostESI_NeuLureFA'] = behav_data_df['PostESI_NeuLureFA_Count']/(behav_data_df['PostESI_NeuLureFA_Count'] + behav_data_df['PostESI_NeuLureCR_Count'])
behav_data_df['PostIMP_NeuLureFA'] = behav_data_df['PostIMP_NeuLureFA_Count']/(behav_data_df['PostIMP_NeuLureFA_Count'] + behav_data_df['PostIMP_NeuLureCR_Count'])
# NEGATIVE LURE CR
behav_data_df['PostESI_NegLureCR'] = behav_data_df['PostESI_NegLureCR_Count']/(behav_data_df['PostESI_NegLureCR_Count'] + behav_data_df['PostESI_NegLureFA_Count'])
behav_data_df['PostIMP_NegLureCR'] = behav_data_df['PostIMP_NegLureCR_Count']/(behav_data_df['PostIMP_NegLureCR_Count'] + behav_data_df['PostIMP_NegLureFA_Count'])
# NEUTRAL LURE CR
behav_data_df['PostESI_NeuLureCR'] = behav_data_df['PostESI_NeuLureCR_Count']/(behav_data_df['PostESI_NeuLureCR_Count'] + behav_data_df['PostESI_NeuLureFA_Count'])
behav_data_df['PostIMP_NeuLureCR'] = behav_data_df['PostIMP_NeuLureCR_Count']/(behav_data_df['PostIMP_NeuLureCR_Count'] + behav_data_df['PostIMP_NeuLureFA_Count'])
# TARGET HITS
behav_data_df["PostESI_NegTargHit"] = behav_data_df["PostESI_NegTargHit_Count"]/(behav_data_df["PostESI_NegTargHit_Count"] + behav_data_df["PostESI_NegTargMiss_Count"])
behav_data_df["PostIMP_NegTargHit"] = behav_data_df["PostIMP_NegTargHit_Count"]/(behav_data_df["PostIMP_NegTargHit_Count"] + behav_data_df["PostIMP_NegTargMiss_Count"])
behav_data_df["PostESI_NeuTargHit"] = behav_data_df["PostESI_NeuTargHit_Count"]/(behav_data_df["PostESI_NeuTargHit_Count"] + behav_data_df["PostESI_NeuTargMiss_Count"])
behav_data_df["PostIMP_NeuTargHit"] = behav_data_df["PostIMP_NeuTargHit_Count"]/(behav_data_df["PostIMP_NeuTargHit_Count"] + behav_data_df["PostIMP_NeuTargMiss_Count"])
# TARGET MISSES
behav_data_df["PostESI_NegTargMiss"] = behav_data_df["PostESI_NegTargMiss_Count"]/(behav_data_df["PostESI_NegTargMiss_Count"] + behav_data_df["PostESI_NegTargHit_Count"])
behav_data_df["PostIMP_NegTargMiss"] = behav_data_df["PostIMP_NegTargMiss_Count"]/(behav_data_df["PostIMP_NegTargMiss_Count"] + behav_data_df["PostIMP_NegTargHit_Count"])
behav_data_df["PostESI_NeuTargMiss"] = behav_data_df["PostESI_NeuTargMiss_Count"]/(behav_data_df["PostESI_NeuTargMiss_Count"] + behav_data_df["PostESI_NeuTargHit_Count"])
behav_data_df["PostIMP_NeuTargMiss"] = behav_data_df["PostIMP_NeuTargMiss_Count"]/(behav_data_df["PostIMP_NeuTargMiss_Count"] + behav_data_df["PostIMP_NeuTargHit_Count"])
# FOILS
behav_data_df['NegFoilFA'] = behav_data_df['NegFoilFA_Count']/(behav_data_df['NegFoilFA_Count'] + behav_data_df['NegFoilCR_Count'])
behav_data_df['NeuFoilFA'] = behav_data_df['NeuFoilFA_Count']/(behav_data_df['NeuFoilFA_Count'] + behav_data_df['NeuFoilCR_Count'])
# LGI
behav_data_df['PostESI_NegativeLGI'] = behav_data_df['PostESI_NegLureFA'] - behav_data_df['NegFoilFA']
behav_data_df['PostIMP_NegativeLGI'] = behav_data_df['PostIMP_NegLureFA'] - behav_data_df['NegFoilFA']
behav_data_df['PostESI_NeutralLGI'] = behav_data_df['PostESI_NeuLureFA'] - behav_data_df['NeuFoilFA']
behav_data_df['PostIMP_NeutralLGI'] = behav_data_df['PostIMP_NeuLureFA'] - behav_data_df['NeuFoilFA']
# LDI
behav_data_df['PostESI_NegativeLDI'] = behav_data_df['PostESI_NegLureCR'] - behav_data_df['PostESI_NegTargMiss']
behav_data_df['PostIMP_NegativeLDI'] = behav_data_df['PostIMP_NegLureCR'] - behav_data_df['PostIMP_NegTargMiss']
behav_data_df['PostESI_NeutralLDI'] = behav_data_df['PostESI_NeuLureCR'] - behav_data_df['PostESI_NeuTargMiss']
behav_data_df['PostIMP_NeutralLDI'] = behav_data_df['PostIMP_NeuLureCR'] - behav_data_df['PostIMP_NeuTargMiss']
#%%
'''HRs, FAR, 1/2HRs, 1/2FARs, DPrime
FOR EACH VALENCE <<< NEEDS WORK'''
#%%
# HIT RATES
behav_data_df['PostESI_HRNeg'] = float(len(behav_data_df["PostESI_NegTargHit_Count"]))/(len(behav_data_df["PostESI_NegTargHit_Count"]) + len(behav_data_df["PostESI_NegTargMiss_Count"]))
behav_data_df['PostESI_HRNeu'] = float(len(behav_data_df["PostESI_NeuTargHit_Count"]))/(len(behav_data_df["PostESI_NeuTargHit_Count"]) + len(behav_data_df["PostESI_NeuTargMiss_Count"]))
behav_data_df['PostIMP_HRNeg'] = float(len(behav_data_df["PostIMP_NegTargHit_Count"]))/(len(behav_data_df["PostIMP_NegTargHit_Count"]) + len(behav_data_df["PostIMP_NegTargMiss_Count"]))
behav_data_df['PostIMP_HRNeu'] = float(len(behav_data_df["PostIMP_NeuTargHit_Count"]))/(len(behav_data_df["PostIMP_NeuTargHit_Count"]) + len(behav_data_df["PostIMP_NeuTargMiss_Count"]))
# FA RATE
behav_data_df['PostESI_FARNeg'] = float(len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["PostESI_NegLureFA_Count"]))/ (len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["NegFoilCR_Count"]) +
len(behav_data_df["PostESI_NegLureFA_Count"]) + len(behav_data_df["PostESI_NegLureCR_Count"]))
behav_data_df['PostESI_FARNeu'] = float(len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["PostESI_NeuLureFA_Count"]))/ (len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["NeuFoilCR_Count"]) +
len(behav_data_df["PostESI_NeuLureFA_Count"]) + len(behav_data_df["PostESI_NeuLureCR_Count"]))
behav_data_df['PostIMP_FARNeg'] = float(len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["PostIMP_NegLureFA_Count"]))/ (len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["NegFoilCR_Count"]) +
len(behav_data_df["PostIMP_NegLureFA_Count"]) + len(behav_data_df["PostIMP_NegLureCR_Count"]))
behav_data_df['PostIMP_FARNeu'] = float(len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["PostIMP_NeuLureFA_Count"]))/ (len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["NeuFoilCR_Count"]) +
len(behav_data_df["PostIMP_NeuLureFA_Count"]) + len(behav_data_df["PostIMP_NeuLureCR_Count"]))
# HALF HR
behav_data_df['Half_PostESI_HRNeg'] = 0.5/(len(behav_data_df["PostESI_NegTargHit_Count"])+len(behav_data_df["PostESI_NegTargMiss_Count"]))
behav_data_df['Half_PostESI_HRNeu'] = 0.5/(len(behav_data_df["PostESI_NeuTargHit_Count"])+len(behav_data_df["PostESI_NeuTargMiss_Count"]))
behav_data_df['Half_PostIMP_HRNeg'] = 0.5/(len(behav_data_df["PostIMP_NegTargHit_Count"])+len(behav_data_df["PostIMP_NegTargMiss_Count"]))
behav_data_df['Half_PostIMP_HRNeu'] = 0.5/(len(behav_data_df["PostIMP_NeuTargHit_Count"])+len(behav_data_df["PostIMP_NeuTargMiss_Count"]))
# HALF FAR
behav_data_df['Half_PostESI_FARNeg'] = 0.5/(len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["NegFoilCR_Count"]) +
len(behav_data_df["PostESI_NegLureFA_Count"]) + len(behav_data_df["PostESI_NegLureCR_Count"]))
behav_data_df['Half_PostIMP_FARNeg'] = 0.5/(len(behav_data_df["NegFoilFA_Count"]) + len(behav_data_df["NegFoilCR_Count"]) +
len(behav_data_df["PostIMP_NegLureFA_Count"]) + len(behav_data_df["PostIMP_NegLureCR_Count"]))
behav_data_df['Half_PostESI_FARNeu'] = 0.5/(len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["NeuFoilCR_Count"]) +
len(behav_data_df["PostESI_NeuLureFA_Count"]) + len(behav_data_df["PostESI_NeuLureCR_Count"]))
behav_data_df['Half_PostIMP_FARNeu'] = 0.5/(len(behav_data_df["NeuFoilFA_Count"]) + len(behav_data_df["NeuFoilCR_Count"]) +
len(behav_data_df["PostIMP_NeuLureFA_Count"]) + len(behav_data_df["PostIMP_NeuLureCR_Count"]))
# HALF POST ESI HR
if behav_data_df['PostESI_HRNeg'] == 1: behav_data_df['PostESI_HRNeg'] = 1 - behav_data_df['Half_PostESI_HRNeg']
if behav_data_df['PostESI_HRNeg'] == 0: behav_data_df['PostESI_HRNeg'] = behav_data_df['Half_PostESI_HRNeg']
if behav_data_df['PostESI_HRNeu'] == 1: behav_data_df['PostESI_HRNeu'] = 1 - behav_data_df['Half_PostESI_HRNeu']
if behav_data_df['PostESI_HRNeu'] == 0: behav_data_df['PostESI_HRNeu'] = behav_data_df['Half_PostESI_HRNeu']
# HALF POST IMP HR
if behav_data_df['PostIMP_HRNeg'] == 1: behav_data_df['PostIMP_HRNeg'] = 1 - behav_data_df['Half_PostIMP_HRNeg']
if behav_data_df['PostIMP_HRNeg'] == 0: behav_data_df['PostIMP_HRNeg'] = behav_data_df['Half_PostIMP_HRNeg']
if behav_data_df['PostIMP_HRNeu'] == 1: behav_data_df['PostIMP_HRNeu'] = 1 - behav_data_df['Half_PostIMP_HRNeu']
if behav_data_df['PostIMP_HRNeu'] == 0: behav_data_df['PostIMP_HRNeu'] = behav_data_df['Half_PostIMP_HRNeu']
# HALF POST ESI FAR
if behav_data_df['PostESI_FARNeg'] == 1: behav_data_df['PostESI_FARNeg'] = 1 - behav_data_df['Half_PostESI_FARNeg']
if behav_data_df['PostESI_FARNeg'] == 0: behav_data_df['PostESI_FARNeg'] = behav_data_df['Half_PostESI_FARNeg']
if behav_data_df['PostESI_FARNeu'] == 1: behav_data_df['PostESI_FARNeu'] = 1 - behav_data_df['Half_PostESI_FARNeu']
if behav_data_df['PostESI_FARNeu'] == 0: behav_data_df['PostESI_FARNeu'] = behav_data_df['Half_PostESI_FARNeu']
# HALF POST IMP FAR
if behav_data_df['PostIMP_FARNeg'] == 1: behav_data_df['PostIMP_FARNeg'] = 1 - behav_data_df['Half_PostIMP_FARNeg']
if behav_data_df['PostIMP_FARNeg'] == 0: behav_data_df['PostIMP_FARNeg'] = behav_data_df['Half_PostIMP_FARNeg']
if behav_data_df['PostIMP_FARNeu'] == 1: behav_data_df['PostIMP_FARNeu'] = 1 - behav_data_df['Half_PostIMP_FARNeu']
if behav_data_df['PostIMP_FARNeu'] == 0: behav_data_df['PostIMP_FARNeu'] = behav_data_df['Half_PostIMP_FARNeu']
# dPRIME
behav_data_df['PostESI_dPrimeNeg'] = (norm.ppf(behav_data_df['PostESI_HRNeg']) - norm.ppf(behav_data_df['PostESI_FARNeg']))
behav_data_df['PostESI_dPrimeNeu'] = (norm.ppf(behav_data_df['PostESI_HRNeu']) - norm.ppf(behav_data_df['PostESI_FARNeu']))
behav_data_df['PostIMP_dPrimeNeg'] = (norm.ppf(behav_data_df['PostIMP_HRNeg']) - norm.ppf(behav_data_df['PostIMP_FARNeg']))
behav_data_df['PostIMP_dPrimeNeu'] = (norm.ppf(behav_data_df['PostIMP_HRNeu']) - norm.ppf(behav_data_df['PostIMP_FARNeu']))
behav_data_df
#%%
'''WINSORIZING LGIs, LDIs (WITH AND WITHOUT VALENCE)'''
#%%
from scipy.stats.mstats import winsorize
behav_data_df['PostESI_LGI'] = winsorize(behav_data_df['PostESI_LGI'], limits=[0.05, 0.05])
behav_data_df['PostESI_LGI'] = winsorize(behav_data_df['PostESI_LGI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_LGI'] = winsorize(behav_data_df['PostIMP_LGI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_LGI'] = winsorize(behav_data_df['PostIMP_LGI'], limits=[0.05, 0.05])
behav_data_df['PostESI_LDI'] = winsorize(behav_data_df['PostESI_LDI'], limits=[0.05, 0.05])
behav_data_df['PostESI_LDI'] = winsorize(behav_data_df['PostESI_LDI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_LDI'] = winsorize(behav_data_df['PostIMP_LDI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_LDI'] = winsorize(behav_data_df['PostIMP_LDI'], limits=[0.05, 0.05])
behav_data_df['PostESI_NegativeLGI'] = winsorize(behav_data_df['PostESI_NegativeLGI'], limits=[0.05, 0.05])
behav_data_df['PostESI_NeutralLGI'] = winsorize(behav_data_df['PostESI_NeutralLGI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_NegativeLGI'] = winsorize(behav_data_df['PostIMP_NegativeLGI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_NeutralLGI'] = winsorize(behav_data_df['PostIMP_NeutralLGI'], limits=[0.05, 0.05])
behav_data_df['PostESI_NegativeLDI'] = winsorize(behav_data_df['PostESI_NegativeLDI'], limits=[0.05, 0.05])
behav_data_df['PostESI_NeutralLDI'] = winsorize(behav_data_df['PostESI_NeutralLDI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_NegativeLDI'] = winsorize(behav_data_df['PostIMP_NegativeLDI'], limits=[0.05, 0.05])
behav_data_df['PostIMP_NeutralLDI'] = winsorize(behav_data_df['PostIMP_NeutralLDI'], limits=[0.05, 0.05])
#%%
'''ADDING SEQUENCE MEMORY
**NOTE: MAKE SURE TO CHANGE
INDICES FOR SROPPING SUBS IF
ADDING SEQ MEMO'''
#%%
# Due to issues with audio recordings
# and the randomization of ESI vs. IMP
# subjects, sample size for this analysis
# would be 66.
cols = [
'Subject',
'Seq_Memo'
]
esi_details_dataset = pd.read_csv('/home/data/madlab/mvrn_ESI_study/esi_scored_details/esi_details_scored_3.csv', usecols=cols)
# the file includes all participants, I am isolating those that were
# used for the ESI vs. IMP condition only
# NOTE: the number of participants (i.e., rows) between the
# behav_data_df and the esi_details_df is different because of the
# poor audio quality of some participants' recordings leading to
# further reduction of the pool...to 66.
final_esi_imp_subs = [int(i) for i in final_esi_imp_subs]
esi_details_df = esi_details_dataset.query("Subject in @final_esi_imp_subs")
esi_details_df.reset_index(drop=True, inplace=True)
# Getting new list of subjects based on the esi_details_df
esi_details_ppt_list = esi_details_df['Subject'].tolist()
behav_data_df['Subject'] = pd.to_numeric(behav_data_df['Subject'])
# # converting each subject in the list into a string
# esi_details_ppt_list = [str(element) for element in esi_details_ppt_list]
# Reducing the behav_data_df and then concatenate them to run analyses
behav_data_df = behav_data_df.query("Subject in @esi_details_ppt_list")
behav_data_df.reset_index(drop=True, inplace=True)
# Concatenate both dataframes
behav_data_df = pd.concat([behav_data_df, esi_details_df], axis=1)
# drop duplicate Subject columns
behav_data_df = behav_data_df.loc[:,~behav_data_df.columns.duplicated()].copy()
behav_data_df
#%%
'''RECOGNITION MEMORY
PERFORMANCE'''
#%%
behav_data_df["Neg_Recog_Mem"] = behav_data_df["PostESI_NegTargHit"] - behav_data_df['NegFoilFA']
behav_data_df["Neu_Recog_Mem"] = behav_data_df["PostESI_NeuTargHit"] - behav_data_df['NeuFoilFA']
#%%
'''DROPPING SUBJECTS DUE TO
POOR PERFORMANCE'''
#%%
# Dropping subjects 165 and 246 due to poor memory recognition (less than 0.5)
# NOTE: you need to change indices to 52 and 75 if not including seq memo
# NOTE: if including seq memo then indices change to 42 and 65
behav_data_df = behav_data_df.drop([52, 75])
behav_data_df.reset_index(drop=True, inplace=True)
behav_data_df
#%%
"""""""""""""""""""""""""""""""""
MELTING NEG VS. NEU THs
FOR POST ESI VS. POST IMP
COMPARISON
(NOT INCLUDING SEQ. MEMO.)
"""""""""""""""""""""""""""""""""
#%%
esi_melt = pd.melt(behav_data_df, id_vars=['Subject'], value_vars=[
'PostESI_NegTargHit',
'PostESI_NeuTargHit',
'PostIMP_NegTargHit',
'PostIMP_NeuTargHit'
])
esi_melt.columns = ['Subject', 'Response', 'Value']
valence = []
for j, row in esi_melt.iterrows():
if 'Neg' in row['Response']:
valence.append('Neg')
if 'Neu' in row['Response']:
valence.append('Neu')
esi_melt['Valence'] = valence
randomization = []
for j, row in esi_melt.iterrows():
if 'ESI' in row['Response']:
randomization.append('ESI')
if 'IMP' in row['Response']:
randomization.append('IMP')
esi_melt['Randomization'] = randomization
esi_melt = esi_melt[[
"Subject", "Response", "Valence", "Randomization", "Value"]]
esi_melt.dropna(inplace = True)
esi_melt
#%%
"""""""""""""""""""""""""""""""""
GLM ON NEG VS. NEU THs RESULTS
FOR POST ESI VS. POST IMP
COMPARISON -- INCL FIG
(NOT INCLUDING SEQ. MEMO.)
"""""""""""""""""""""""""""""""""
#%%
import statsmodels.api as sm
import statsmodels.formula.api as smf
# Note: groups must be your repeated measure aka subjects
md = smf.glm(
"Value ~ Valence*Randomization",
esi_melt, groups=esi_melt["Subject"])
mdf = md.fit()
print(mdf.summary())
import statsmodels.api as sm
from scipy import stats
my_pal = {
'PostESI_NegTargHit': "mediumvioletred",
'PostESI_NeuTargHit': "darkgoldenrod",
'PostIMP_NegTargHit': "mediumvioletred",
'PostIMP_NeuTargHit': "darkgoldenrod"
}
axes = sns.swarmplot(
size=5,
palette=my_pal,
linewidth=1.7,
data=behav_data_df[[
'PostESI_NegTargHit',
'PostESI_NeuTargHit',
'PostIMP_NegTargHit',
'PostIMP_NeuTargHit'
]])
sns.boxplot(
data=behav_data_df[[
'PostESI_NegTargHit',
'PostESI_NeuTargHit',
'PostIMP_NegTargHit',
'PostIMP_NeuTargHit'
]],
palette=my_pal,
linewidth = 2,
width = 0.8,
ax=axes
)
axes.set_ylabel(('Target Hits'),
fontsize=15)
axes.tick_params(
axis='y',
labelsize=13,
width=2
)
axes.tick_params(
axis='x',
labelsize=15,
width=2
)
labels = [item.get_text() for item in axes.get_xticklabels()]
labels[0] = 'Post-ESI \n Negative'
labels[1] = 'Post-ESI \n Neutral'
labels[2] = 'Post-IMP \n Negative'
labels[3] = 'Post-IMP \n Neutral'
axes.set_xticklabels(labels)
for location in [
'left',
'bottom'
]:
axes.spines[location].set_linewidth(2)
for location in [
'right',
'top',
]:
axes.spines[location].set_linewidth(0)
#%%
"""
ESI vs IMP CR
"""
'''
Shapiro for Post ESI Negative vs. Neutral
'''
from scipy import stats
postesi_neglurecr = behav_data_df["PostESI_NegLureCR_Count"]
postesi_neulurecr = behav_data_df["PostESI_NeuLureCR_Count"]
print(postesi_neglurecr.mean())
print(postesi_neulurecr.mean())
print('Shapiro for Post ESI NEG CR:', stats.shapiro(postesi_neglurecr))
print('Shapiro for Post ESI NEU CR:', stats.shapiro(postesi_neulurecr))
'''
Plotting Post ESI vs. Post IMP CR
'''
from scipy import stats
import seaborn as sns
test_data = behav_data_df.dropna(how='any', subset=['PostESI_NegLureCR_Count', 'PostESI_NeuLureCR_Count'])
print(stats.ttest_rel(test_data['PostESI_NegLureCR_Count'], test_data['PostESI_NeuLureCR_Count']))