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plot_synthetic_data_quality_stats.py
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import os
import numpy as np
import random
from tqdm import tqdm
import sys
import joblib
import seaborn as sns
import pandas as pd
from typing import Dict
from typing import List
from typing import Tuple
import tensorflow
import tensorflow as tf
from sp_eyegan.model import eventGAN as eventGAN
from sp_eyegan.model import vae_baseline as vae
from scipy.stats import ttest_ind, ttest_1samp
def vel_to_dva(vel_data, x_start = 0,
y_start = 0):
x_vel = vel_data[:,0]
y_vel = vel_data[:,1]
x_px = []
y_px = []
cur_x_pos = x_start
cur_y_pos = y_start
for i in range(len(x_vel)):
x_px.append(cur_x_pos + x_vel[i])
y_px.append(cur_y_pos + y_vel[i])
cur_x_pos = x_px[-1]
cur_y_pos = y_px[-1]
return np.concatenate([np.expand_dims(np.array(x_px),axis=1),
np.expand_dims(np.array(y_px),axis=1)],axis=1)
def get_fixation_stats(input_data):
real_x_vels = []
real_y_vels = []
real_vels = []
real_dispersions = []
cur_vel_data = input_data
for i in tqdm(np.arange(cur_vel_data.shape[0])):
cur_vels = cur_vel_data[i]
cur_dva = vel_to_dva(cur_vels)
try:
end_id = np.where(np.logical_and(cur_vels[:,0] == 0,
cur_vels[:,1] ==0 ))[0][0]
except:
end_id = len(cur_vels)
if end_id == 0:
continue
cur_vels = cur_vels[0:end_id]
cur_dva = cur_dva[0:end_id]
x_dva = cur_dva[:,0]
y_dva = cur_dva[:,1]
real_x_vels.append(cur_vels[:,0])
real_y_vels.append(cur_vels[:,1])
vels = np.power(np.array(real_x_vels[-1]),2) +\
np.power(np.array(real_y_vels[-1]),2)
vels = np.sqrt(vels)
real_vels.append(vels)
x_amp = np.abs(np.max(x_dva) - np.min(x_dva))
y_amp = np.abs(np.max(y_dva) - np.min(y_dva))
cur_dispersion = x_amp + y_amp
real_dispersions.append(cur_dispersion)
return real_vels, real_x_vels,real_y_vels, real_dispersions
def get_saccade_stats(input_data, max_velocity = 0.5):
real_x_vels = []
real_y_vels = []
real_vels = []
real_amplitudes = []
real_x_accs = []
real_y_accs = []
real_accs = []
cur_vel_data = input_data
for i in tqdm(np.arange(cur_vel_data.shape[0])):
cur_vels = cur_vel_data[i]
cur_dva = vel_to_dva(cur_vels)
try:
end_id = np.where(np.logical_and(cur_vels[:,0] == 0,
cur_vels[:,1] ==0 ))[0][0]
except:
end_id = len(cur_vels)
if end_id == 0:
continue
cur_vels = cur_vels[0:end_id]
cur_dva = cur_dva[0:end_id]
x_dva = cur_dva[:,0]
y_dva = cur_dva[:,1]
real_x_vels.append(cur_vels[:,0])
real_y_vels.append(cur_vels[:,1])
vels = np.power(np.array(real_x_vels[-1]),2) +\
np.power(np.array(real_y_vels[-1]),2)
vels = np.sqrt(vels)
real_vels.append(vels)
x_accs = cur_vels[1:,0] - cur_vels[:-1,0]
y_accs = cur_vels[1:,1] - cur_vels[:-1,0]
accs = np.power(np.array(x_accs),2) +\
np.power(np.array(y_accs),2)
accs = np.sqrt(accs)
real_x_accs.append(x_accs)
real_y_accs.append(y_accs)
real_accs.append(accs)
cur_complete_vels = np.sqrt(np.power(cur_vels[:,0],2) + np.power(cur_vels[:,1],2))
cur_complete_vels[cur_complete_vels > max_velocity] = max_velocity
cur_amplitude = np.sqrt(np.power(x_dva[0] - x_dva[-1],2) + np.power(y_dva[0] - y_dva[-1],2))
real_amplitudes.append(cur_amplitude)
return real_vels, real_x_vels, real_y_vels, real_accs, real_x_accs, real_y_accs, np.array(real_amplitudes)
# calculate the kl divergence
def kl_divergence(p, q):
from math import log2
from math import sqrt
rel_etropies = [p[i] * log2(p[i]/q[i]) for i in range(len(p))]
return np.sum(np.array(rel_etropies,dtype=np.float32))
# calculate the js divergence
def js_divergence(p, q):
m = 0.5 * (p + q)
return 0.5 * kl_divergence(p, m) + 0.5 * kl_divergence(q, m)
def js_divergence_sampling( values_1, values_2,
bins,
epsilon = 0.00001,
iterations = 10,
number_per_iter = 10000,
random_state = 42):
np.random.seed(random_state)
values = []
for iter in range(iterations):
idx_1 = np.random.choice(len(values_1), number_per_iter)
idx_2 = np.random.choice(len(values_2), number_per_iter)
counts_1,_ = np.histogram(values_1[idx_1], bins = bins, density=True)
counts_1 /= np.sum(counts_1)
counts_2,_ = np.histogram(values_2[idx_2], bins = bins, density=True)
counts_2 /= np.sum(counts_2)
values.append(js_divergence(np.array(counts_1) + epsilon,
np.array(counts_2) + epsilon))
return values
def draw_display(dispsize: Tuple[int, int], imagefile=None):
# construct screen (black background)
# dots per inch
img = image.imread(imagefile)
dpi = 100.0
# determine the figure size in inches
figsize = (dispsize[0]/dpi, dispsize[1]/dpi)
# create a figure
fig = pyplot.figure(figsize=figsize, dpi=dpi, frameon=False)
ax = pyplot.Axes(fig, [0, 0, 1, 1])
ax.set_axis_off()
fig.add_axes(ax)
# plot display
ax.axis([0, dispsize[0], 0, dispsize[1]])
ax.imshow(img)
return fig, ax
def pix2deg(pix, screenPX,screenCM,distanceCM, adjust_origin=True):
# Converts pixel screen coordinate to degrees of visual angle
# screenPX is the number of pixels that the monitor has in the horizontal
# axis (for x coord) or vertical axis (for y coord)
# screenCM is the width of the monitor in centimeters
# distanceCM is the distance of the monitor to the retina
# pix: screen coordinate in pixels
# adjust origin: if origin (0,0) of screen coordinates is in the corner of the screen rather than in the center, set to True to center coordinates
pix=np.array(pix)
# center screen coordinates such that 0 is center of the screen:
if adjust_origin:
pix = pix-(screenPX)/2 # pixel coordinates start with (0,0)
# eye-to-screen-distance in pixels of screen
distancePX = distanceCM*(screenPX/screenCM)
return np.arctan2(pix,distancePX) * 180/np.pi # *180/pi wandelt bogenmass in grad
def deg2pix(deg, screenPX, screenCM, distanceCM, adjust_origin = True, offsetCM = 0):
# Converts degrees of visual angle to pixel screen coordinates
# screenPX is the number of pixels that the monitor has in the horizontal
# screenCM is the width of the monitor in centimeters
# distanceCM is the distance of the monitor to the retina
phi = np.arctan2(1,distanceCM)*180/np.pi
pix = deg/(phi/(screenPX/(screenCM)))
if adjust_origin:
pix += (screenPX/2)
if offsetCM != 0:
offsetPX = offsetCM*(screenPX/screenCM)
pix += offsetPX
return pix
# params
GPU = 0
data_dir = 'data/'
plot_dir = 'plots/'
result_dir = 'results/'
model_name = 'GAN'
real_name = 'real'
gauss_name = 'Gauss'
# load data
column_dict = joblib.load(data_dir + 'column_dict.joblib')
fixation_matrix = np.load(data_dir + 'fixation_matrix_gazebase_vd_text.npy')
saccade_matrix = np.load(data_dir + 'saccade_matrix_gazebase_vd_text.npy')
fix_window_size = fixation_matrix.shape[1]
sac_window_size = saccade_matrix.shape[1]
gen_kernel_sizes_fixation = [fix_window_size,8,4,2]
gen_kernel_sizes_saccade = [sac_window_size,8,4,2]
# params for NN
random_size = 32
gen_filter_sizes = [16,8,4,2]
channels = 2
relu_in_last = False
batch_size = 256
dis_kernel_sizes = [8,16,32]
dis_fiter_sizes = [32,64,128]
dis_dropout = 0.3
sample_size = 1000
max_velocity = 0.5
seed = 42
np.random.seed(seed)
model_config_fixation = {'gen_kernel_sizes':gen_kernel_sizes_fixation,
'gen_filter_sizes':gen_filter_sizes,
'dis_kernel_sizes':dis_kernel_sizes,
'dis_fiter_sizes':dis_fiter_sizes,
'dis_dropout':dis_dropout,
'window_size':fix_window_size,
'channels':channels,
'batch_size':batch_size,
'random_size':random_size,
'relu_in_last':relu_in_last,
}
model_config_saccade = {'gen_kernel_sizes':gen_kernel_sizes_saccade,
'gen_filter_sizes':gen_filter_sizes,
'dis_kernel_sizes':dis_kernel_sizes,
'dis_fiter_sizes':dis_fiter_sizes,
'dis_dropout':dis_dropout,
'window_size':sac_window_size,
'channels':channels,
'batch_size':batch_size,
'random_size':random_size,
'relu_in_last':relu_in_last,
}
fixation_path = 'event_model/fixation_model_text'
saccade_path = 'event_model/saccade_model_text'
flag_train_on_gpu = True
if flag_train_on_gpu:
import tensorflow as tf
# select graphic card
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPU)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
config = tf.compat.v1.ConfigProto(log_device_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 1.
config.gpu_options.allow_growth = True
tf_session = tf.compat.v1.Session(config=config)
else:
import tensorflow as tf
# select graphic card
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
print('shape of fixation_matrix: ' + str(fixation_matrix.shape))
print('shape of saccade_matrix: ' + str(saccade_matrix.shape))
## Data for statitical baseline
### select model for baseline method (statistical method)
gen_data_statitical = pd.read_csv('data/statistical_baseline_fix_data.txt',sep=';',header = None)
gen_data_statitical.columns = ['vel','type','frame']
gen_data_statitical.head()
fixation_index = 0
saccade_index = 1
velocities = np.array(gen_data_statitical['vel'])
event_type = np.array(gen_data_statitical['type'])
print('number fix points: ' + str(np.sum(event_type == fixation_index)))
print('number sac points: ' + str(np.sum(event_type == saccade_index)))
fix_vels_statistical = []
sac_vels_statistical = []
sac_acc_statistical = []
# transfrom °/s to °/ms
scaling_factor = 1000.
prev_event = -1
cur_fixation = []
cur_saccade = []
for i in tqdm(np.arange(len(velocities))):
cur_event = event_type[i]
cur_vel = velocities[i]
if cur_event == prev_event:
if cur_event == fixation_index:
cur_fixation.append(cur_vel/scaling_factor)
elif cur_event == saccade_index:
cur_saccade.append(cur_vel/scaling_factor)
else:
if prev_event == fixation_index and len(cur_fixation) > 0:
fix_vels_statistical.append(np.array(cur_fixation))
elif prev_event == saccade_index and len(cur_saccade) > 0:
sac_vels_statistical.append(np.array(cur_saccade))
if len(cur_saccade) > 2:
sac_acc_statistical.append(np.array(cur_saccade)[1:] - np.array(cur_saccade)[0:-1])
if cur_event == fixation_index:
cur_fixation = [cur_vel/scaling_factor]
cur_saccade = []
elif cur_event == saccade_index:
cur_saccade = [cur_vel/scaling_factor]
cur_fixation = []
prev_event = cur_event
## Evaluate Fixations
# EyeSyn
from scipy.io import loadmat
eye_syn_fixation_data = loadmat('data/eyeSyn_velocities.mat')
eye_syn_fixation_data = eye_syn_fixation_data['out_data']
tf.keras.backend.clear_session()
fix_model = eventGAN.eventGAN(model_config_fixation)
fix_model.load_model(fixation_path)
noise = tf.random.normal([sample_size, random_size], seed = seed)
gen_fixations = np.array(fix_model.generator(noise, training=False),dtype=np.float32)
print('shape of gen_fixations: ' + str(gen_fixations.shape))
#pd.DataFrame(gen_fixations.flatten()).describe()
tf.keras.backend.clear_session()
vae_model = vae.VAE(vae.get_vae_encoder(64, 2, 2), vae.get_vae_decoder(2, 2))
vae_model.load_model('event_model/vae_fixation_10')
noise = tf.random.normal([sample_size * 2, 2], seed = seed)
vae_fixations = np.array(vae_model.decoder(noise, training=False),dtype=np.float32)
vae_fixations = np.concatenate([vae_fixations[0:sample_size],
vae_fixations[sample_size:]],axis=1)
vae_fixations = vae_fixations[:,0:100,:] -0.5
print('shape of gen_fixations: ' + str(vae_fixations.shape))
#pd.DataFrame(vae_fixations.flatten()).describe()
rand_ids = np.random.permutation(np.arange(fixation_matrix.shape[0]))
rand_ids_1 = rand_ids[0:sample_size]
rand_ids_2 = rand_ids[sample_size:sample_size+sample_size]
orig_fixations = fixation_matrix[rand_ids_1,:,4:6]
orig_fixations[orig_fixations > max_velocity] = max_velocity
orig_fixations[orig_fixations < -max_velocity] = -max_velocity
orig_fixations_2 = fixation_matrix[rand_ids_2,:,4:6]
orig_fixations_2[orig_fixations_2 > max_velocity] = max_velocity
orig_fixations_2[orig_fixations_2 < -max_velocity] = -max_velocity
real_vels, real_x_vels, real_y_vels, real_dispersions = get_fixation_stats(orig_fixations)
real_vels_2, real_x_vels_2, real_y_vels_2, real_dispersions_2 = get_fixation_stats(orig_fixations_2)
fake_vels, fake_x_vels, fake_y_vels, fake_dispersions = get_fixation_stats(gen_fixations)
eyesyn_vels, eyesyn_x_vels, eyesyn_y_vels, eyesyn_dispersions = get_fixation_stats(eye_syn_fixation_data)
vae_vels, vae_x_vels, vae_y_vels, vae_dispersions = get_fixation_stats(vae_fixations)
x_vel_mean = np.nanmean([item for sublist in real_x_vels for item in sublist])
x_vel_std = np.nanstd([item for sublist in real_x_vels for item in sublist])
y_vel_mean = np.nanmean([item for sublist in real_y_vels for item in sublist])
y_vel_std = np.nanstd([item for sublist in real_y_vels for item in sublist])
gauss_fixations = np.concatenate([np.random.normal(x_vel_mean,x_vel_std,(sample_size,fix_window_size,1)),
np.random.normal(y_vel_mean,y_vel_std,(sample_size,fix_window_size,1))],axis=2)
gauss_vels, gauss_x_vels, gauss_y_vels, gauss_dispersions = get_fixation_stats(gauss_fixations)
x_vel_mean = np.nanmean([item for sublist in real_x_vels for item in sublist])
x_vel_std = np.nanstd([item for sublist in real_x_vels for item in sublist])
#print(x_vel_mean, x_vel_std)
y_vel_mean = np.nanmean([item for sublist in real_y_vels for item in sublist])
y_vel_std = np.nanstd([item for sublist in real_y_vels for item in sublist])
#print(y_vel_mean, y_vel_std)
## velocity
real = np.array([item for sublist in real_vels for item in sublist])
real_2 = np.array([item for sublist in real_vels_2 for item in sublist])
fake = np.array([item for sublist in fake_vels for item in sublist])
gauss = np.array([item for sublist in gauss_vels for item in sublist])
eyesyn = np.array([item for sublist in eyesyn_vels for item in sublist])
stat = np.array([item for sublist in fix_vels_statistical for item in sublist])
f_vae = np.array([item for sublist in vae_vels for item in sublist])
min_val = np.min(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
number_per_iter = 10000
iterations = 10
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
gauss_counts,_ = np.histogram(gauss, bins = bins, density=True)
gauss_counts /= np.sum(gauss_counts)
eyesyn_counts,_ = np.histogram(eyesyn, bins = bins, density=True)
eyesyn_counts /= np.sum(eyesyn_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_gauss = js_divergence(np.array(real_counts) + epsilon,
np.array(gauss_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_stat = js_divergence(np.array(real_counts) + epsilon,
np.array(stat_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Fixation velocity one fold')
print('JS(real || stat): ' + str(np.round(js_divergence_vel_stat,decimals = 3)))
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || EyeSyn): ' + str(np.round(js_divergence_vel_syneye,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
#print('JS(real || Gauss): ' + str(np.round(js_divergence_vel_gauss,decimals=3)))
print('JS(real || real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
stat_vals = js_divergence_sampling( real, stat, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
eyesyn_vals = js_divergence_sampling( real, eyesyn, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [stat_vals, vae_vals, eyesyn_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('Fixation velocity ' + str(iterations) + ' folds')
print('JS(real || stat): ' + prefixes[0] + str(np.round(np.mean(stat_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(stat_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || VAE): ' + prefixes[1] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || EyeSyn): ' + prefixes[2] + str(np.round(np.mean(eyesyn_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(eyesyn_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || GAN): ' + prefixes[3] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[3])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# mean velocity
real = np.array([np.mean(a) for a in real_vels])
real_2 = np.array([np.mean(a) for a in real_vels_2])
fake = np.array([np.mean(a) for a in fake_vels])
gauss = np.array([np.mean(a) for a in gauss_vels])
eyesyn = np.array([np.mean(a) for a in eyesyn_vels])
f_vae = np.array([np.mean(a) for a in vae_vels])
min_val = np.min(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
number_per_iter = 500
iterations = 10
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
gauss_counts,_ = np.histogram(gauss, bins = bins, density=True)
gauss_counts /= np.sum(gauss_counts)
eyesyn_counts,_ = np.histogram(eyesyn, bins = bins, density=True)
eyesyn_counts /= np.sum(eyesyn_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_gauss = js_divergence(np.array(real_counts) + epsilon,
np.array(gauss_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_stat = js_divergence(np.array(real_counts) + epsilon,
np.array(stat_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Fixation mean velocity one fold')
print('JS(real || stat): ' + str(np.round(js_divergence_vel_stat,decimals = 3)))
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || EyeSyn): ' + str(np.round(js_divergence_vel_syneye,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
print('JS(real || real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
stat_vals = js_divergence_sampling( real, stat, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
eyesyn_vals = js_divergence_sampling( real, eyesyn, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [stat_vals, vae_vals, eyesyn_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('Fixation mean velocity ' + str(iterations) + ' folds')
print('JS(real || stat): ' + prefixes[0] + str(np.round(np.mean(stat_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(stat_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || VAE): ' + prefixes[1] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || EyeSyn): ' + prefixes[2] + str(np.round(np.mean(eyesyn_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(eyesyn_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || GAN): ' + prefixes[3] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[3])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# dispersion
real = np.array(real_dispersions)
real_2 = np.array(real_dispersions_2)
fake = np.array(fake_dispersions)
gauss = np.array(gauss_dispersions)
eyesyn = np.array(eyesyn_dispersions)
f_vae = np.array(vae_dispersions)
min_val = np.min(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(f_vae))
num_bins = 100
epsilon = 0.00001
number_per_iter = 500
iterations = 10
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
gauss_counts,_ = np.histogram(gauss, bins = bins, density=True)
gauss_counts /= np.sum(gauss_counts)
eyesyn_counts,_ = np.histogram(eyesyn, bins = bins, density=True)
eyesyn_counts /= np.sum(eyesyn_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_gauss = js_divergence(np.array(real_counts) + epsilon,
np.array(gauss_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Fixation dispersion one fold')
print('JS(real || stat): -')
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || EyeSyn): ' + str(np.round(js_divergence_vel_syneye,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
print('JS(real || real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
eyesyn_vals = js_divergence_sampling( real, eyesyn, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [vae_vals, eyesyn_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('Fixation velocity ' + str(iterations) + ' folds')
print('JS(real || stat): -')
print('JS(real || VAE): ' + prefixes[0] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || EyeSyn): ' + prefixes[1] + str(np.round(np.mean(eyesyn_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(eyesyn_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || GAN): ' + prefixes[2] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# Saccade
tf.keras.backend.clear_session()
sac_model = eventGAN.eventGAN(model_config_saccade)
sac_model.load_model(saccade_path)
noise = tf.random.normal([sample_size, random_size], seed = seed)
gen_saccades = np.array(sac_model.generator(noise, training=False),dtype=np.float32)
tf.keras.backend.clear_session()
vae_model = vae.VAE(vae.get_vae_encoder(64, 2, 2), vae.get_vae_decoder(2, 2))
vae_model.load_model('event_model/vae_saccade_10')
noise = tf.random.normal([sample_size, 2], seed = seed)
vae_saccades = np.array(vae_model.decoder(noise, training=False),dtype=np.float32)
vae_saccades = vae_saccades[:,0:30,:] -0.5
rand_ids = np.random.permutation(np.arange(saccade_matrix.shape[0]))
rand_ids_1 = rand_ids[0:sample_size]
rand_ids_2 = rand_ids[sample_size:sample_size+sample_size]
rand_ids = np.arange(saccade_matrix.shape[0])
rand_ids = np.random.permutation(rand_ids)[0:sample_size]
orig_saccades = saccade_matrix[rand_ids_1,:,4:6]
orig_saccades[orig_saccades > max_velocity] = max_velocity
orig_saccades[orig_saccades < -max_velocity] = -max_velocity
orig_saccades_2 = saccade_matrix[rand_ids_2,:,4:6]
orig_saccades_2[orig_saccades_2 > max_velocity] = max_velocity
orig_saccades_2[orig_saccades_2 < -max_velocity] = -max_velocity
real_vels_sac, real_x_vels_sac, real_y_vels_sac, real_accs_sac, real_x_accs_sac, real_y_accs_sac, real_amplitudes_sac = get_saccade_stats(orig_saccades)
real_vels_sac_2, real_x_vels_sac_2, real_y_vels_sac_2, real_accs_sac_2, real_x_accs_sac_2, real_y_accs_sac_2, real_amplitudes_sac_2 = get_saccade_stats(orig_saccades_2)
fake_vels_sac, fake_x_vels_sac, fake_y_vels_sac, fake_accs_sac, fake_real_x_accs_sac, fake_y_accs_sac, fake_amplitudes_sac = get_saccade_stats(gen_saccades)
vae_vels_sac, vae_x_vels_sac, vae_y_vels_sac, vae_accs_sac, vae_x_accs_sac, vae_y_accs_sac, vae_amplitudes_sac = get_saccade_stats(vae_saccades)
x_vel_mean_sac = np.nanmean([item for sublist in real_x_vels_sac for item in sublist])
x_vel_std_sac = np.nanstd([item for sublist in real_x_vels_sac for item in sublist])
y_vel_mean_sac = np.nanmean([item for sublist in real_y_vels_sac for item in sublist])
y_vel_std_sac = np.nanstd([item for sublist in real_y_vels_sac for item in sublist])
gauss_saccades = np.concatenate([np.random.normal(x_vel_mean_sac,x_vel_std_sac,(sample_size,sac_window_size,1)),
np.random.normal(y_vel_mean_sac,y_vel_std_sac,(sample_size,sac_window_size,1))],axis=2)
gauss_vels_sac, gauss_x_vels_sac, gauss_y_vels_sac, gauss_accs_sac, gauss_real_x_accs_sac, gauss_y_accs_sac, gauss_amplitudes_sac = get_saccade_stats(gauss_saccades)
# peak velocity
real = np.array([np.max(a) for a in real_vels_sac])
real_2 = np.array([np.max(a) for a in real_vels_sac_2])
fake = np.array([np.max(a) for a in fake_vels_sac])
gauss = np.array([np.max(a) for a in gauss_vels_sac])
stat = np.array([np.max(a) for a in sac_vels_statistical])
f_vae = np.array([np.max(a) for a in vae_vels_sac])
min_val = np.min(list(real) + list(fake) + list(real_2) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(real_2) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
number_per_iter = 500
iterations = 10
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_stat = js_divergence(np.array(real_counts) + epsilon,
np.array(stat_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Saccade peak velocity one fold')
print('JS(real || stat): ' + str(np.round(js_divergence_vel_stat,decimals = 3)))
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
print('JS(real || Real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
print('Saccade peak velocity ' + str(iterations) + ' folds')
stat_vals = js_divergence_sampling( real, stat, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [stat_vals, vae_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('JS(real || stat): ' + prefixes[0] + str(np.round(np.mean(stat_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(stat_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || VAE): ' + prefixes[1] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || GAN): ' + prefixes[2] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# mean velocity
real = np.array([np.mean(a) for a in real_vels_sac])
real_2 = np.array([np.mean(a) for a in real_vels_sac_2])
fake = np.array([np.mean(a) for a in fake_vels_sac])
gauss = np.array([np.mean(a) for a in gauss_vels_sac])
stat = np.array([np.mean(a) for a in sac_vels_statistical])
f_vae = np.array([np.mean(a) for a in vae_vels_sac])
min_val = np.min(list(real) + list(fake) + list(real_2) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(real_2) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
number_per_iter = 500
iterations = 10
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_stat = js_divergence(np.array(real_counts) + epsilon,
np.array(stat_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Saccade mean velocity one fold')
print('JS(real || stat): ' + str(np.round(js_divergence_vel_stat,decimals = 3)))
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
print('JS(real || Real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
print('Saccade mean velocity ' + str(iterations) + ' folds')
stat_vals = js_divergence_sampling( real, stat, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [stat_vals, vae_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('JS(real || stat): ' + prefixes[0] + str(np.round(np.mean(stat_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(stat_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || VAE): ' + prefixes[1] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || GAN): ' + prefixes[2] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# peak acceleration
real = np.array([np.max(a) for a in real_accs_sac])
real_2 = np.array([np.mean(a) for a in real_accs_sac])
fake = np.array([np.max(a) for a in fake_accs_sac])
gauss = np.array([np.max(a) for a in gauss_accs_sac])
stat = np.array([np.max(a) for a in sac_acc_statistical])
f_vae = np.array([np.max(a) for a in vae_accs_sac])
min_val = np.min(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
gauss_counts,_ = np.histogram(gauss, bins = bins, density=True)
gauss_counts /= np.sum(gauss_counts)
eyesyn_counts,_ = np.histogram(eyesyn, bins = bins, density=True)
eyesyn_counts /= np.sum(eyesyn_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)
vae_counts /= np.sum(vae_counts)
js_divergence_vel_fake = js_divergence(np.array(real_counts) + epsilon,
np.array(fake_counts) + epsilon)
js_divergence_vel_gauss = js_divergence(np.array(real_counts) + epsilon,
np.array(gauss_counts) + epsilon)
js_divergence_vel_real = js_divergence(np.array(real_counts) + epsilon,
np.array(real_counts_2) + epsilon)
js_divergence_vel_syneye = js_divergence(np.array(real_counts) + epsilon,
np.array(eyesyn_counts) + epsilon)
js_divergence_vel_stat = js_divergence(np.array(real_counts) + epsilon,
np.array(stat_counts) + epsilon)
js_divergence_vel_vae = js_divergence(np.array(real_counts) + epsilon,
np.array(vae_counts) + epsilon)
print('Saccade peak acceleration one fold')
print('JS(real || stat): ' + str(np.round(js_divergence_vel_stat,decimals = 3)))
print('JS(real || VAE): ' + str(np.round(js_divergence_vel_vae,decimals = 3)))
print('JS(real || GAN): ' + str(np.round(js_divergence_vel_fake,decimals = 3)))
print('JS(real || Real): ' + str(np.round(js_divergence_vel_real,decimals = 3)))
print('Saccade peak acceleration ' + str(iterations) + ' folds')
stat_vals = js_divergence_sampling( real, stat, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
vae_vals = js_divergence_sampling( real, f_vae, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
gan_vals = js_divergence_sampling( real, fake, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
real_vals = js_divergence_sampling( real, real_2, bins, epsilon = epsilon,
iterations = iterations, number_per_iter = number_per_iter)
model_vals = [stat_vals, vae_vals, gan_vals]
model_means = [np.mean(a) for a in model_vals]
prefixes = ['' for a in range(len(model_means))]
suffixes = ['' for a in range(len(model_means))]
arg_sort = np.argsort(model_means)
prefixes[arg_sort[0]] = '\\textbf{'
suffixes[arg_sort[0]] = '}'
tt_test_pvalue = ttest_ind(model_vals[arg_sort[0]],model_vals[arg_sort[1]],alternative='two-sided').pvalue
if tt_test_pvalue <= 0.05:
suffixes[arg_sort[0]] += '$^*$'
print('JS(real || stat): ' + prefixes[0] + str(np.round(np.mean(stat_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(stat_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[0])
print('JS(real || VAE): ' + prefixes[1] + str(np.round(np.mean(vae_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(vae_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[1])
print('JS(real || GAN): ' + prefixes[2] + str(np.round(np.mean(gan_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(gan_vals)/np.sqrt(iterations),decimals = 3)) + suffixes[2])
print('JS(real || Real): ' + str(np.round(np.mean(real_vals),decimals = 3)) + ' $\\pm$ ' + str(np.round(np.std(real_vals)/np.sqrt(iterations),decimals = 3)))
# mean acceleration
real = np.array([np.mean(a) for a in real_accs_sac])
real_2 = np.array([np.mean(a) for a in real_accs_sac])
fake = np.array([np.mean(a) for a in fake_accs_sac])
gauss = np.array([np.mean(a) for a in gauss_accs_sac])
stat = np.array([np.mean(a) for a in sac_acc_statistical])
f_vae = np.array([np.mean(a) for a in vae_accs_sac])
min_val = np.min(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
max_val = np.max(list(real) + list(fake) + list(gauss) + list(real_2) + list(eyesyn) + list(stat) + list(f_vae))
num_bins = 100
epsilon = 0.00001
bins = np.linspace(min_val,max_val,num_bins)
real_counts,_ = np.histogram(real, bins = bins, density=True)
real_counts /= np.sum(real_counts)
real_counts_2,_ = np.histogram(real_2, bins = bins, density=True)
real_counts_2 /= np.sum(real_counts_2)
fake_counts,_ = np.histogram(fake, bins = bins, density=True)
fake_counts /= np.sum(fake_counts)
gauss_counts,_ = np.histogram(gauss, bins = bins, density=True)
gauss_counts /= np.sum(gauss_counts)
eyesyn_counts,_ = np.histogram(eyesyn, bins = bins, density=True)
eyesyn_counts /= np.sum(eyesyn_counts)
stat_counts,_ = np.histogram(stat, bins = bins, density=True)
stat_counts /= np.sum(stat_counts)
vae_counts,_ = np.histogram(f_vae, bins = bins, density=True)