Robust Averaging Face Stimuli
Mixed Valence 200 Trial Stimuli Generation
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 10 11:07:36 2024
@author: xdong23
"""
import numpy as np
from scipy import stats
import math
import random
import csv
#generate 100 random mean values
def generate_100_mean(mean = 25, variance = 5):
values = []
while len(values) < 100:
value = np.random.normal(mean, variance)
value = np.clip(value, 1, 50) #must be within range (1, 50)
values.append(value)
return values
#generate two random mean value lists
def generate_mean(mean = 25, variance = 5):
values1 = []
values2 = []
values1 = generate_100_mean()
values2 = generate_100_mean()
tValue, pValue = stats.ttest_ind(values1, values2)
if pValue < 0.05:
return generate_mean()
else:
return values1, values2
#use the generate_mean() function
meanList, meanList2 = generate_mean()
#generate a list of 8 values from a Gaussian distribution
def generate_stimuli(mean, variance, depth = 0):
values = []
while len(values) < 8:
value = np.random.normal(mean, variance)
value = np.clip(value, 1, 50)
value = int(round(value))
if value not in values: #no repeats
values.append(value)
actMean = sum(values) / 8
actVar = math.sqrt(sum([(x - actMean) ** 2 for x in values]) / 8)
diffMean = abs(actMean - mean)
diffVar= abs(actVar - variance)
tolMean = mean * 0.05
tolVar = variance * 0.05
if diffMean <= tolMean and diffVar <= tolVar:
depthCheck = 'checked'
return values, depthCheck
elif depth >= 300:
depthCheck = 'ALERT'
return values, depthCheck
else:
return generate_stimuli(mean, variance, depth + 1)
return values, depthCheck
#input previously generated means into the generate_stimuli() function
X1 = [] #create emotion value lists X1 to X8
X2 = []
X3 = []
X4 = []
X5 = []
X6 = []
X7 = []
X8 = []
emotionList = []
conditionList = []
depthCheckList = []
for i in range(100): #small variance conditoin
emotion_new = []
emotion_new, depth_new = generate_stimuli(meanList[i], 5) #generate emotion list
X1.append(emotion_new[0]) #assign each emotion value to X1 to X8
X2.append(emotion_new[1])
X3.append(emotion_new[2])
X4.append(emotion_new[3])
X5.append(emotion_new[4])
X6.append(emotion_new[5])
X7.append(emotion_new[6])
X8.append(emotion_new[7])
emotionList.append(emotion_new)
conditionList.append('small')
depthCheckList.append(depth_new)
for i in range(100): #large variance condition
emotion_new = []
emotion_new, depth_new = generate_stimuli(meanList2[i], 15)
X1.append(emotion_new[0])
X2.append(emotion_new[1])
X3.append(emotion_new[2])
X4.append(emotion_new[3])
X5.append(emotion_new[4])
X6.append(emotion_new[5])
X7.append(emotion_new[6])
X8.append(emotion_new[7])
emotionList.append(emotion_new)
conditionList.append('large')
depthCheckList.append(depth_new)
meanList.extend(meanList2) #combine mean lists
#create identity list
identity = []
for i in range(200):
identity_new = [] #empty list to temporarily store each eight-identity list
male_numbers = ["M" + str(num) for num in random.sample(range(1, 9), 4)]
female_numbers = ["F" + str(num) for num in random.sample(range(1, 9), 4)]
identity_new.extend(male_numbers + female_numbers)
random.shuffle(identity_new)
identity.append(identity_new)
#randomize the stimuli rows
rows = list(zip(meanList, emotionList, conditionList, X1, X2, X3, X4,
X5, X6, X7, X8, depthCheckList, identity)) #make sure same row stays together
random.shuffle(rows)
meanList, emotionList, conditionList, X1, X2, X3, X4, X5, X6, X7, X8, depthCheckList, identity = zip(*rows)
meanList = list(meanList) #turn tuples to lists
emotionList = list(emotionList)
conditionList = list(conditionList)
X1 = list(X1)
X2 = list(X2)
X3 = list(X3)
X4 = list(X4)
X5 = list(X5)
X6 = list(X6)
X7 = list(X7)
X8 = list(X8)
depthCheckList = list(depthCheckList)
identity = list(identity)
#write csv file
with open('new_stimuli_randomized.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['mean', 'emotion', 'variance', 'X1', 'X2', 'X3',
'X4', 'X5', 'X6', 'X7', 'X8', 'depth', 'identity']) #create column names
for i in range(200):
writer.writerow([meanList[i], emotionList[i], conditionList[i],
X1[i], X2[i], X3[i], X4[i],
X5[i], X6[i], X7[i], X8[i], depthCheckList[i],
identity[i]])
Robust Averaging Stimuli Generation
import csv
import random
#create 150 identity lists
identity = [] #create empty list
for i in range(150): #repeat 150 times
identity_new = [] #create new empty list
male_numbers = ["M" + str(num) for num in random.sample(range(1, 9), 4)] #select 4 M faces
female_numbers = ["F" + str(num) for num in random.sample(range(1, 9), 4)] #select 4 F faces
identity_new.extend(male_numbers + female_numbers) #add selected faces to the new list
random.shuffle(identity_new) #randomly shuffle the new list
identity.append(identity_new) #append the randomly shuffled list to the identity list
#create mean emotion values
mean = [22]*50 + [25]*50 + [28]*50
#cretea emotion variance values
variance = ["small"]*25 + ["large"]*25 #25 repeats of each variance value ("small", "large")
variance = variance * 3 #repeat 3 times (one for each mean value)
#function to create full emotion values
def generate_emotion(mean, variance):
emotions = { #create a dictionary where mean and variance correspond with respective emotion values
(22, "small"): [13, 19, 25, 31]*2,
(22, "large"): [4, 16, 28, 40]*2,
(25, "small"): [16, 22, 28, 34]*2,
(25, "large"): [7, 19, 31, 43]*2,
(28, "small"): [19, 25, 31, 37]*2,
(28, "large"): [10, 22, 34, 46]*2
}
emotion_list = emotions[(mean, variance)] #create list with emotion values
random.shuffle(emotion_list) #randomly shuffle the list
return emotion_list
#create emotion based on mean and variance values for 150 trials
X1 = [] #create emotion value lists X1 to X8
X2 = []
X3 = []
X4 = []
X5 = []
X6 = []
X7 = []
X8 = []
for i in range(150): #repeat 150 times
emotion_new = []
emotion_new = generate_emotion(mean[i], variance[i]) #generate emotion list
X1.append(emotion_new[0]) #assign each emotion value to X1 to X8
X2.append(emotion_new[1])
X3.append(emotion_new[2])
X4.append(emotion_new[3])
X5.append(emotion_new[4])
X6.append(emotion_new[5])
X7.append(emotion_new[6])
X8.append(emotion_new[7])
#write csv file
with open('demo_stimuli.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['identity', 'mean', 'variance', 'X1', 'X2', 'X3', 'X4',
'X5', 'X6', 'X7', 'X8']) #create column names
for i in range(150): #create 150 stimuli
writer.writerow([identity[i], mean[i], variance[i], X1[i], X2[i], X3[i], X4[i],
X5[i], X6[i], X7[i], X8[i]])
Robust Averaging Stimuli Generation (X1:X8 not separated)
import csv
import random
#create 150 identity lists
identity = [] #create empty list
for _ in range(150): #repeat 150 times
identity_new = [] #create new empty list
male_numbers = ["M" + str(num) for num in random.sample(range(1, 9), 4)] #select 4 M faces
female_numbers = ["F" + str(num) for num in random.sample(range(1, 9), 4)] #select 4 F faces
identity_new.extend(male_numbers + female_numbers) #add selected faces to the new list
random.shuffle(identity_new) #randomly shuffle the new list
identity.append(identity_new) #append the randomly shuffled list to the identity list
#create mean emotion values
mean = [22]*50 + [25]*50 + [28]*50
#cretea emotion variance values
variance = ["small"]*25 + ["large"]*25 #25 repeats of each variance value ("small", "large")
variance = variance * 3 #repeat 3 times (one for each mean value)
#function to create full emotion values
def generate_emotion(mean, variance):
emotions = { #create a dictionary where mean and variance correspond with respective emotion values
(22, "small"): [13, 19, 25, 31]*2,
(22, "large"): [4, 16, 28, 40]*2,
(25, "small"): [16, 22, 28, 34]*2,
(25, "large"): [7, 19, 31, 43]*2,
(28, "small"): [19, 25, 31, 37]*2,
(28, "large"): [10, 22, 34, 46]*2
}
emotion_list = emotions[(mean, variance)] #create list with emotion values
random.shuffle(emotion_list) #randomly shuffle the list
return emotion_list
#create emotion based on mean and variance values for 150 trials
emotion = [generate_emotion(mean[i], variance[i]) for i in range(150)]
#write csv file
with open('demo_stimuli.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['identity', 'mean', 'variance', 'emotion']) #create column names
for i in range(150): #create 150 stimuli
writer.writerow([identity[i], mean[i], variance[i], emotion[i]])
Robust Averaging Stimuli Sample 5.1.24
