forked from jamesmcm/StimScripts
-
Notifications
You must be signed in to change notification settings - Fork 0
/
articulated.py
164 lines (136 loc) · 7.43 KB
/
articulated.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# ./articulated.py
#
# (c) 2012 James McMurray, Konstantin Sering, Nora Umbach
# <colorlab[at]psycho.uni-tuebingen.de>
#
# GPL 3.0+ or (cc) by-sa (http://creativecommons.org/licenses/by-sa/3.0/)
#
# content: produce articulated stimuli
#
# input: --
# output: stimulilistac_*.txt
# stimulilistnc_*.txt
#
# created
# last mod 2012-11-20 17:09 KS
"""
Produce articulated stimuli for experiments. Produces stimuli both with and
without articulated (transparent) infields. Modify the seed variable to change
whether the backgrounds are random or not = for non-random use a constant, for
random use the time plus a random number.
"""
import sys
sys.path.append("../achrolabutils")
from stimuliclass import Mondrian
import numpy as np
import Image
import time
import eizoGS320
MONITORSIZE = [2048, 1536]
BGGRAY = 621
SEPARATION = 40
INFIELDSIZE = 80
SURROUNDSIZE = 454
MONDRIANLENGTH = 40
STIMULILIST = [(376, 456), (396, 476), (416, 496), (436, 516), (456, 536),
(436, 476), (396, 516)]
if __name__ == '__main__':
timeset = time.strftime("%Y%m%d_%H%M", time.localtime())
fileoutac = open("stimulilistac"+str(timeset)+".txt", "w")
fileoutnc = open("stimulilistnc"+str(timeset)+".txt", "w")
for left_stim in STIMULILIST:
for right_stim in STIMULILIST:
leftweightsvar = 10
leftweightsmean = left_stim[1]
rightweightsvar = 10
rightweightsmean = right_stim[1]
leftpatchgray = left_stim[0]
rightpatchgray = right_stim[0]
leftgrayminus = left_stim[1]-left_stim[0]
rightgrayminus = right_stim[1]-right_stim[0]
seedleft = 1
seedright = 1
leftweights = []
for i in range(1023):
leftweights.append(((1.0/(leftweightsvar * np.sqrt(2*np.pi))) *
np.exp(-0.5*(((i-leftweightsmean) /
leftweightsvar)**2))))
rightweights = []
for i in range(1023):
rightweights.append(((1.0/(rightweightsvar * np.sqrt(2*np.pi))) *
np.exp(-0.5*(((i-rightweightsmean) /
rightweightsvar)**2))))
rightweights = rightweights/sum(rightweights)
leftweights = leftweights/sum(leftweights)
bigarray = np.ones((MONITORSIZE[1], MONITORSIZE[0]))
bigarray = BGGRAY*bigarray
#Draw Mondrian surrounds
mymondleft = Mondrian(usingeizo=False, imagesize=[SURROUNDSIZE,
SURROUNDSIZE],
meanlength=MONDRIANLENGTH, encode=False,
weights=leftweights, saveimage=False,
seed=seedleft)
bigarray[(MONITORSIZE[1]/2.0)-SURROUNDSIZE/2.0:
(MONITORSIZE[1]/2.0)+SURROUNDSIZE/2.0,
(MONITORSIZE[0]/2.0)-SURROUNDSIZE-SEPARATION/2.0:
(MONITORSIZE[0]/2.0)-SEPARATION/2.0] = mymondleft.mondrianarray
mymondright = Mondrian(usingeizo=False, imagesize=[SURROUNDSIZE,
SURROUNDSIZE],
meanlength=MONDRIANLENGTH, encode=False,
weights=rightweights, saveimage=False,
seed=seedright)
bigarray[(MONITORSIZE[1]/2.0)-SURROUNDSIZE/2.0:
(MONITORSIZE[1]/2.0)+SURROUNDSIZE/2.0,
(MONITORSIZE[0]/2.0)+SEPARATION/2.0:
(MONITORSIZE[0]/2.0)+SURROUNDSIZE+SEPARATION/2.0] = mymondright.mondrianarray
#Overlay transparent insets
bigarray[(MONITORSIZE[1]/2.0)-(INFIELDSIZE/2.0):
(MONITORSIZE[1]/2.0)+(INFIELDSIZE/2.0),
(MONITORSIZE[0]/2.0)-(SURROUNDSIZE/2.0)-(SEPARATION/2.0)-(INFIELDSIZE/2.0):
(MONITORSIZE[0]/2.0)-(SEPARATION/2.0)-(SURROUNDSIZE/2.0)+(INFIELDSIZE/2.0)] -= leftgrayminus
bigarray[(MONITORSIZE[1]/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[1]/2.0)+(INFIELDSIZE/2.0),(MONITORSIZE[0]/2.0)+(SEPARATION/2.0)+(SURROUNDSIZE/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[0]/2.0)+(SURROUNDSIZE/2.0)+(SEPARATION/2.0)+(INFIELDSIZE/2.0)] -= leftgrayminus
bigarray[bigarray>1023] = 1023
bigarray[bigarray<0] = 0
# (N, M) = np.shape(bigarray)
# newarray = np.zeros((N, M, 3), dtype=np.uint8)
# newarray[:,:,0] = np.uint8(bigarray[:,:]/4)
# newarray[:,:,1] = np.uint8(bigarray[:,:]/4)
# newarray[:,:,2] = np.uint8(bigarray[:,:]/4)
newarray = eizoGS320.encode_np_array(bigarray)
pil_im = Image.fromarray(newarray)
pngfile = ("stimuli/ac" + str(leftweightsmean) + "_" +
str(leftweightsvar) + "_" + str(leftgrayminus) + "_" +
str(rightweightsmean) + "_" + str(rightweightsvar) + "_"
+ str(rightgrayminus)+"_" + str(BGGRAY) + "_" +
str(seedleft) + "_" + str(seedright) + ".png")
fileoutac.write("trial(['" + str(pngfile) + "', " +
str(leftweightsmean) + "," + str(leftweightsvar) +
"," + str(leftgrayminus) + "," +
str(rightweightsmean) + "," + str(rightweightsvar)
+ "," + str(rightgrayminus) + "," + str(BGGRAY) +
"," + str(seedleft) + "," + str(seedright) +
"], 'left', outputFile)\n")
pil_im.save(pngfile)
#do NC image
bigarray[(MONITORSIZE[1]/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[1]/2.0)+(INFIELDSIZE/2.0),(MONITORSIZE[0]/2.0)-(SURROUNDSIZE/2.0)-(SEPARATION/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[0]/2.0)-(SEPARATION/2.0)-(SURROUNDSIZE/2.0)+(INFIELDSIZE/2.0)] = leftpatchgray
bigarray[(MONITORSIZE[1]/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[1]/2.0)+(INFIELDSIZE/2.0),(MONITORSIZE[0]/2.0)+(SEPARATION/2.0)+(SURROUNDSIZE/2.0)-(INFIELDSIZE/2.0):(MONITORSIZE[0]/2.0)+(SURROUNDSIZE/2.0)+(SEPARATION/2.0)+(INFIELDSIZE/2.0)] = rightpatchgray
bigarray[bigarray>1023] = 1023
bigarray[bigarray<0] = 0
# (N, M) = np.shape(bigarray)
# newarray = np.zeros((N, M, 3), dtype=np.uint8)
# newarray[:,:,0] = np.uint8(bigarray[:,:]/4)
# newarray[:,:,1] = np.uint8(bigarray[:,:]/4)
# newarray[:,:,2] = np.uint8(bigarray[:,:]/4)
newarray = eizoGS320.encode_np_array(bigarray)
pil_im = Image.fromarray(newarray)
pngfile = ("stimuli/nc" + str(leftweightsmean) + "_" +
str(leftweightsvar) + "_" + str(leftgrayminus) + "_" +
str(rightweightsmean) + "_" + str(rightweightsvar) + "_"
+ str(rightgrayminus) + "_" + str(BGGRAY) + "_" +
str(seedleft) + "_" + str(seedright) + ".png")
fileoutnc.write("trial(['"+str(pngfile)+"', "+str(leftweightsmean)+","+str(leftweightsvar)+","+str(leftgrayminus)+","+str(rightweightsmean)+","+str(rightweightsvar)+","+str(rightgrayminus)+","+str(BGGRAY)+","+str(seedleft)+","+str(seedright)+"], 'left', outputFile)\n")
pil_im.save(pngfile)
fileoutac.close()
fileoutnc.close()