# -*- coding: utf-8 -*-
'''
Generates .csv files for experimental stimuli (composite images). The .csv file contains 4 columns: attentive category name, binary category number,
image 1 used in the composite image and image 2 used in the composite image (strings of file directories to the images).
'''
# Imports
import os
import random
from settings import script_path_init
script_path = script_path_init()
os.chdir(script_path)
from experimentFunctions import createIndices, closeWin
closeWin()
#### Global variables ####
global blockIdx
global imgIdx
subjID_prep = '01'
numRuns = 6 # E.g. 6 runs creates 6*8*50 = 2400 composite images indices (string of path to directories of images)
# Different combinations of attentive versus non-attentive categories. Four in total.
catComb1 = [['male', 'female', 'indoor', 'outdoor'],
['indoor', 'outdoor', 'male', 'female']]
catComb2 = [['male', 'female', 'outdoor', 'indoor'],
import csv
import pandas as pd
from PIL import Image
import random
from random import sample
from pylsl import StreamInfo, StreamOutlet, StreamInlet, resolve_stream, resolve_byprop
from randomizeParticipants import assign_subject, add_avail_feedback_sub, gen_group_fname
from psychopy import visual, core, data, event, monitors, logging
import settings
# Initialize paths and variables
data_path = settings.data_path_init()
subject_path = settings.subject_path_init()
script_path = settings.script_path_init()
os.chdir(subject_path)
###### Global variables (changes over the time course of the experiment) ######
global blockIdx
global imgIdx
blockIdx = 1
imgIdx = 0
###### Data frames for logging ######
df_imgIdxSave = pd.DataFrame(
columns=['attentive_cat', 'binary_cat', 'img1', 'img2'])
# For createIndices function. Columns: