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reversal_patternanalysis.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 28 16:10:11 2020
Pattern Analysis
@author: eacru
"""
import pandas as pd
import numpy as np
#%%
# load file
pattern_data = pd.read_csv("C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\OFC_IT_reversal_events_processed.csv")
experiment = 'IT'
#combine winlose stayshift into one column
pattern_data['PatternComplete'] = pattern_data['WinLoseStayShift'] + pattern_data['StayShift']
#%%
#Standardize subject IDs -- only for nonspecific and dual. IT and PT are standardized
def StandardizeSubjectID(dataset):
subject_list = list(set(dataset.subject))
for subject in range(len(subject_list)):
dataset.loc[dataset.subject == subject_list[subject], 'Subject'] = subject_list[subject][-2:]
return(dataset)
#%%
# for nonspecific and dual experiments -- run StandardizeSubjectID
pattern_data = StandardizeSubjectID(pattern_data)
#%%
#for IT and PT -- just duplicate subject column for other functions
pattern_data['Subject'] = pattern_data.subject
#%%
# add labels for what each date is: training, baseline, cno, or veh -- will need to be manually entered
def LabelSessionType(dataset,baseline,test1,test2,group1):
dataset['Session']=0
mask1 = (dataset.date == test1)
dataset['mask1'] = mask1
mask2 = (dataset['date'] == test2)
dataset['mask2'] = mask2
mask3 = (dataset['date'].isin(baseline))
dataset['mask3'] = mask3
for index in range(len(dataset)):
print("Running row " + str(index) + " out of " + str(len(dataset) - 1))
row_number = dataset.index[index]
if dataset.mask2.iloc[index] == True:
if dataset.Subject.isin(group1).iloc[index]:
dataset['Session'][row_number] = 'CNO'
else:
dataset['Session'][row_number] = 'Vehicle'
elif dataset.mask1.iloc[index] == True:
if dataset.Subject.isin(group1).iloc[index]:
dataset['Session'][row_number] = "Vehicle"
else:
dataset['Session'][row_number] = "CNO"
elif dataset.mask3.iloc[index] == True:
dataset['Session'][row_number] = 'Baseline'
else:
dataset['Session'][row_number] = 'Training'
dataset = dataset.drop(columns = ['mask1', 'mask2', 'mask3'])
return(dataset)
#%%
pattern_data_1 = LabelSessionType(pattern_data,list(set(['11/07/18','11/08/18', '11/09/18'])),'11/10/18','11/13/18',pattern_data.Subject.loc[(pattern_data.box == 2) | (pattern_data.box == 4) | (pattern_data.box == 6) |(pattern_data.box == 8) |(pattern_data.box == 10)].unique() )
pattern_data_1.to_csv(f'C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\reversal_pattern_prepped.csv')
#%%
#Analysis 1 get trials to first reversal
#input is dataframe
def getTrialstoFirstReversal(data):
#filter data to just be the presses:
just_presses = data.loc[data.event_type_raw.isin([1.0,2.0])]
#select only reversal number zero
data_zero = just_presses[just_presses.ReversalNumber == 0]
#group by subject and date
#get count of number of 1 and 2's (presses)
trialsToReversal = data_zero.groupby([data_zero.Subject, data_zero.date, data_zero.Session])['event_type_raw'].sum()
return trialsToReversal
trialsToReversal = getTrialstoFirstReversal(pattern_data_1)
trialsToReversal.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\trialsToReversal_{experiment}_reversal_events_processed.csv")
#output is list group of counts for each subject in each session
#%%
# Analysis 2: Percent of win-stay, lose-stay behavior after first reversal
def getWinLoseStayShiftCountsAfterReversal(data):
#since the pattern is matched to the press, just take the press indicators
just_presses = data.loc[data.event_type_raw.isin([1.0,2.0])]
# exclude the block before the first reversal
data_nonzero = just_presses[just_presses.ReversalNumber != 0]
#group by subject, date, and reversal block
winLoseStayShift_counts = data_nonzero.groupby([data_nonzero.Subject, data_nonzero.date, data_nonzero.ReversalNumber, data_nonzero.Session])['PatternComplete'].value_counts()
winLoseStayShift_frequencies = data_nonzero.groupby([data_nonzero.Subject, data_nonzero.date, data_nonzero.ReversalNumber, data_nonzero.Session])['PatternComplete'].value_counts(normalize = True)
winLoseStayShift_aggregate = data_nonzero.groupby([data_nonzero.Subject, data_nonzero.date, data_nonzero.Session])['PatternComplete'].value_counts(normalize = True)
# get counts for WinStay, LoseStay, WinShift, LoseShift
return winLoseStayShift_counts, winLoseStayShift_frequencies, winLoseStayShift_aggregate
winLoseStayShift_counts, winLoseStayShift_frequencies, winLoseStayShift_aggregate = getWinLoseStayShiftCountsAfterReversal(pattern_data_1)
winLoseStayShift_counts.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\winLoseStayShiftafterreversal_counts_{experiment}_reversal_events_processed.csv")
winLoseStayShift_frequencies.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\winLoseStayShiftafterreversal_frequencies_{experiment}_reversal_events_processed.csv")
winLoseStayShift_aggregate.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\winLoseStayShiftafterreversal_aggfrequencies_{experiment}_reversal_events_processed.csv")
#%%
# Analysis 3: Percent win-stay, lose-stay behavior before first reversal
def getWinLoseStayShiftCountsBeforeReversal(data):
just_presses= data.loc[data.event_type_raw.isin([1.0,2.0])]
# take only the trials before the first reversal occurred
data_zero = just_presses[just_presses.ReversalNumber == 0]
#group by subject and date
#get winstaylosestaywinshiftloseshift counts raw and frequencies
winLoseStayShift_counts = data_zero.groupby([data_zero.Subject, data_zero.date, data_zero.Session])['PatternComplete'].value_counts()
winLoseStayShift_frequencies = data_zero.groupby([data_zero.Subject, data_zero.date, data_zero.Session])['PatternComplete'].value_counts(normalize = True)
# get counts for WinStay, LoseStay, WinShift, LoseShift
return winLoseStayShift_counts, winLoseStayShift_frequencies
winLoseStayShift_counts, winLoseStayShift_frequencies = getWinLoseStayShiftCountsBeforeReversal(pattern_data_1)
winLoseStayShift_counts.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\winLoseStayShiftbeforereversal_counts_{experiment}_reversal_events_processed.csv")
winLoseStayShift_frequencies.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\winLoseStayShiftbeforereversal_frequencies_{experiment}_reversal_events_processed.csv")
#%%
# Analysis 4: Get perseverative responses after reversal
def getRelevantWinLossIndices(data):
#exlcude blocks before the first reversal and focus on reward or loss (5 or 33)
just_results = data[data.ReversalNumber != 0].loc[data.event_type_raw.isin([5.0, 33.0])]
just_wins = data[data.ReversalNumber != 0].loc[data.event_type_raw.isin([5.0])]
just_losses = data[data.ReversalNumber != 0].loc[data.event_type_raw.isin([33.0])]
# get index of wins, but only for the first win for each reversal block for each session for each subject and collect the indices in a list
idx_wins = just_wins.groupby([just_wins.Subject, just_wins.date, just_wins.ReversalNumber])['event_type_raw'].idxmax().tolist()
# get all of the losses and collect in a list
idx_losses = just_results[just_results.event_type_raw == 33.0].index.values.tolist()
# using indices, select in dataframe where these losses and wins occurred
idx_results = idx_wins + idx_losses
test_df = just_results.loc[idx_results]
# output should be dataframe with relevant wins and losses
return test_df
#%%
#using dataframe with selected indices, make function with data input and look up row in each reversal number where the first win occurred
def getRowForWin(df, index):
return df.loc[(df['Subject'] == df.Subject.iloc[index]) &
(df['experiment'] == df.experiment.iloc[index]) &
(df['date'] == df.date.iloc[index]) &
(df['event_type_raw'] == 5) &
(df['ReversalNumber'] == df.ReversalNumber.iloc[index])]
#%%
#input is dataframe filtered to be all losses and no other event types, as well as list of indexes where a first win occurs in each reversal
#output should be either dataframe or list of indices of all losses with indexes lower than first win for each Reversal block number
#for each Reversal number
def addPreservativeResponses(df):
new_df = pd.DataFrame(columns = df.columns)
for i in range(len(df)):
print("Running row " + str(i) + " out of " + str(len(df) - 1))
row_number = df.index[i]
winning_row_df = getRowForWin(df, i)
if((df.event_type_raw.iloc[i]) != 5 and len(winning_row_df) > 0 and row_number < winning_row_df.index[0]):
new_df.loc[i] = df.iloc[i]
else:
print('after first win')
return new_df
#%%
test_df = getRelevantWinLossIndices(pattern_data_1)
megatest = addPreservativeResponses(test_df)
megatestcounts = megatest.groupby(['Subject','date','ReversalNumber', 'Session'])['event_type_raw'].count()
megatestagg =megatest.groupby(['Subject', 'date', 'Session'])['event_type_raw'].count()
#%%
#everything to csv
megatestcounts.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\PreservativeResponses_{experiment}_reversal_events_processed.csv")
megatestagg.to_csv(f"C:\\Users\\eacru\\OneDrive\\Documents\\Ferguson lab data\\Probability discounting\\compfiledFiles\\Analysis output files\\{experiment}\\PreservativeResponsesagg_{experiment}_reversal_events_processed.csv")