ITD_TWF_Behavior_ECoG_Rat/behavior/behavior/behaviorSummary.py

# -*- coding: utf-8 -*-
"""
Created on Mon May 11 16:55:37 2020

@author: Cecilia
"""


"""
Summerize the behavioral data (the table in TWF manuscript) and 

"""


import os
import pandas as pd
import numpy as np

#%%
## Get the path of the folder where the data csv file sits in. 
## The results will save to this folder.   

#csv_path = r"C:\Users\Cecilia\OneDrive\Python\TWF_behaviour\behaviouralRecalculated\TWF_ITD_dataset_withoutCorrectionTrials.csv"
csv_path = r"C:\Users\USER\OneDrive\Python\TWF_behaviour\behaviouralRecalculated\TWF_ITD_dataset_withoutCorrectionTrials.csv"

folder_path, csv_filename = os.path.split(csv_path)



## Load data file (without correction trials)

dataset = pd.read_csv(csv_path,encoding='utf-8-sig', sep='\s*,\s*', engine='python').copy()

## Get rid of the white spaces in front or at the end of collumns. If not doing so, will raise error when calling get_group() 
dataset['animal'] = dataset['animal'].astype(str).str.strip()
dataset.columns = dataset.columns.str.strip()



## Get the unique condition and animal ID
conditions = list(dataset['condition'].unique())
animals = list(dataset['animal'].unique())



#%% How many probe trials in each condition for each rat
## Extract the offset (ms) == 0 (probe) trials
probe_data = dataset.loc[dataset['offset (ms)'] == 0].copy()


## Group data by condition and animal
grouped_probe_data = probe_data.groupby(['condition','animal'])

## Print out the number of trials in each conditon
for (probe_condition, probe_animal), probe_group in grouped_probe_data:
    print("The total number of probe trials in group {} {} is {}".format(probe_condition, probe_animal, probe_group.shape[0] ))
    
#%% How is the performance in honesty trial in each condition for each rat

## calculate the honesty trial correct rate in each condition
honesty_data = dataset.loc[np.abs(dataset['offset (ms)'])>dataset['jitter (ms)']].copy()

## Group data by condition and animal
grouped_honesty_data = honesty_data.groupby(['condition','animal'])


for (honesty_condition, honesty_animal), honesty_group in grouped_honesty_data:
    # The number of correct trials in each group
    honesty_correct=sum(honesty_group['correct'])
    print('Honesty trials in group {} {} :- Total:{} Correct: {} ({}%)'.format(honesty_condition, honesty_animal, len(honesty_group),honesty_correct,honesty_correct*100/len(honesty_group)))