def test_NoiseStim_defaults_image(self):
noiseType = 'image'
# noiseImage kwarg missing.
with pytest.raises(ValueError):
visual.NoiseStim(win=self.win,
noiseType=noiseType,
size=(32, 32),
units='pix')
def test_NoiseStim_defaults(self):
noiseTypes = ['binary', 'uniform', 'normal', 'white', 'filtered']
for noiseType in noiseTypes:
stim = visual.NoiseStim(win=self.win,
noiseType=noiseType,
size=(32, 32),
units='pix')
stim.updateNoise()
stim.draw()
def test_noiseFiltersAndRaisedCos(self):
numpy.random.seed(1)
win = self.win
size = numpy.array([2.0,2.0])*self.scaleFactor
tres=128
elementsize=4
sf=None
ntype='Binary'
comp='Amplitude'
fileName = os.path.join(utils.TESTS_DATA_PATH, 'testimagegray.jpg')
if win.units in ['pix']:
ftype='Butterworth'
size = numpy.array([128,128])
elif win.units in ['degFlatPos']:
ftype='Gabor'
sf=0.125
elementsize=1
elif win.units in ['degFlat']:
ftype='Isotropic'
sf=0.125
elementsize=1
elif win.units in ['deg']:
ntype='Image'
ftype='Butterworth'
sf=0.125
elif win.units in ['cm']:
ntype='Image'
ftype='Butterworth'
comp='Phase'
sf=0.25
else:
if self.contextName=='stencil':
ntype='White'
ftype='Butterworth'
elif self.contextName=='height':
ntype='Uniform'
ftype='Butterworth'
else:
ntype='Normal'
ftype='Butterworth'
elementsize=1.0/8.0
image = visual.NoiseStim(win=win, name='noise',units=win.units,
noiseImage=fileName, mask='raisedCos',
ori=0, pos=(0, 0), size=size, sf=sf, phase=0,
color=[1,1,1], colorSpace='rgb', opacity=1, blendmode='avg', contrast=0.5,
texRes=tres,
noiseType=ntype, noiseElementSize=elementsize, noiseBaseSf=32.0/size[0],
noiseBW=0.5, noiseBWO=7, noiseFractalPower=-1,noiseFilterLower=4.0/size[0],
noiseFilterUpper=16.0/size[0], noiseFilterOrder=1, noiseOri=45.0,
noiseClip=4.0, imageComponent=comp, filter=ftype, interpolate=False, depth=-1.0)
image.draw()
utils.compareScreenshot('noiseFiltersAndRcos_%s.png' %(self.contextName), win)
win.flip()
str(image)
def generate_noise():
global generated_noise
global inverse_noise
background_noise = visual.NoiseStim(win=mywin,
units='pix',
ori=0,
pos=(0, 0),
size=(1024, 1024),
color=[1, 1, 1],
colorSpace='rgb',
opacity=1,
blendmode='avg',
contrast=1.0,
texRes=512,
noiseType='Binary',
noiseElementSize=noise_size)
background_noise.draw()
generated_noise = mywin.getMovieFrame(buffer='back')
inverse_noise = PIL.ImageOps.invert(generated_noise)
monitor='testMonitor', color=[0,0,0], colorSpace='rgb',
blendMode='avg', useFBO=True,
units='height')
# store frame rate of monitor if we can measure it
expInfo['frameRate'] = win.getActualFrameRate()
if expInfo['frameRate'] != None:
frameDur = 1.0 / round(expInfo['frameRate'])
else:
frameDur = 1.0 / 60.0 # could not measure, so guess
# Initialize components for Routine "trial"
trialClock = core.Clock()
noise = visual.NoiseStim(
win=win, name='noise',
noiseImage=None, mask=None,
ori=0, pos=(0, 0), size=(0.5, 0.5), sf=None, phase=0.0,
color=[1,1,1], colorSpace='rgb', opacity=1, blendmode='avg', contrast=1.0,
texRes=128,
noiseType='Binary', noiseElementSize=0.0625, noiseBaseSf=8.0,
noiseBW=1, noiseBWO=1, noiseFractalPower=0.0,noiseFilterLower=1.0, noiseFilterUpper=8.0, noiseFilterOrder=0.0, noiseClip=3.0, interpolate=False, depth=0.0)
noise.buildNoise()
# Create some handy timers
globalClock = core.Clock() # to track the time since experiment started
routineTimer = core.CountdownTimer() # to track time remaining of each (non-slip) routine
# ------Prepare to start Routine "trial"-------
t = 0
trialClock.reset() # clock
frameN = -1
continueRoutine = True
routineTimer.add(1.000000)
fullscr=True)
grating1 = visual.GratingStim(win=window,
size=(2000, 2000),
sf=0.01,
ori=45,
contrast=.2,
opacity=1)
grating2 = visual.GratingStim(win=window,
size=(2000, 2000),
sf=0.01,
ori=135,
contrast=.2,
opacity=.5)
noise1 = visual.NoiseStim(win=window,
units='pix',
noiseType='white',
size=(1650, 1050),
opacity=.3)
mouse = event.Mouse(visible=False)
timer = core.Clock()
seed = random.randrange(1e6)
rng = random.Random(seed)
print(seed)
#######################
# Feedback Global Settings
abortKey = '9'
correct1 = sound.Sound(500, secs=.1)
correct2 = sound.Sound(1000, secs=.1)
error = sound.Sound(300, secs=.3)
wrongKey = sound.Sound(100, secs=1)
noAudio = False,
filename=cap,
ori=0, pos=(0, 0), opacity=1,
loop=False,
depth=0.0,
)
sound_1 = sound.Sound('A', secs=-1, stereo=True, hamming=True,
name='sound_1')
sound_1.setVolume(1)
noise = visual.NoiseStim(
win=win, name='noise',
noiseImage=None, mask=None,
ori=0, pos=(0, 0), size=(0.6, 0.6), sf=None,
phase=0.0,
color=[1,1,1], colorSpace='rgb', opacity=0.95, blendmode='avg', contrast=1.0,
texRes=128, filter=None,
noiseType='Binary', noiseElementSize=0.0625,
noiseBaseSf=8.0, noiseBW=1,
noiseBWO=30, noiseOri=0.0,
noiseFractalPower=0.0,noiseFilterLower=1.0,
noiseFilterUpper=8.0, noiseFilterOrder=0.0,
noiseClip=3.0, imageComponent='Phase', interpolate=False, depth=-2.0)
noise.buildNoise()
sound_2 = sound.Sound('C', secs=-1, stereo=True, hamming=True,
name='sound_2')
sound_2.setVolume(1)
noise_2 = visual.NoiseStim(
win=win, name='noise_2',
noiseImage=None, mask=None,
ori=0, pos=(0, 0), size=(0.6, 0.6), sf=None,
phase=0.0,
languageStyle='LTR',
depth=0.0);
# Initialize components for Routine "Video_Tuning"
Video_TuningClock = core.Clock()
accuracy = 0
target_accuracy = .5
noise_level = 0
minimum_iterations = 10
margin_of_error = .05
noise = visual.NoiseStim(
win,
noiseType="white",
noiseElementSize=.1,
opacity=.5,
size=(2,2)
)
video_stimulus = visual.MovieStim3(
win=win, name='video_stimulus',units='norm',
noAudio = True,
filename='GA.mov',
ori=0, pos=(0, 0), opacity=1,
size=(2, 2),
depth=-1.0,
)
prompt = visual.TextStim(win=win, name='prompt',
text='What was said:\nBA, DA, or GA?\nPress B, D, or G.',
font='Arial',
pos=(0, 0), height=0.1, wrapWidth=None, ori=0,
win = visual.Window([100, 110], units='pix', color=bg_scale)
stim_A = visual.TextStim(win,
text='A',
units='pix',
height=120,
color=flk_scale)
stim_A.draw()
win.flip()
win._getFrame().save('stimuli/set1-' + 'AH-0.00' + '.png', size=(100, 110))
win.close()
######################
win = visual.Window([800, 150], units='pix', color=bg_scale)
stim_A = visual.NoiseStim(win,
name='noise',
units='pix',
noiseType='Normal',
color=flk_scale)
stim_A.draw()
win.flip()
win._getFrame().save('stimuli/set1-' + 'AH-0.00' + '.png', size=(100, 110))
win.close()
win = visual.Window([100, 110], units='pix', color=bg_scale)
stim_H = visual.TextStim(win,
text='H',
units='pix',
height=120,
color=flk_scale)
stim_H.draw()
win.flip()
timer = core.Clock()
seed = random.randrange(1e6)
rng = random.Random(seed)
correct1=sound.Sound(500,secs=.1)
correct2=sound.Sound(1000,secs=.1)
error=sound.Sound(300,secs=.2)
####################################
## TRIAL SET UP #################
####################################
blank=visual.TextStim(win=window,text="")
grate= visual.GratingStim(win=window,sf=.05,size=256,mask="gauss",
ori=1,opacity=.3)
noise=visual.NoiseStim(win=window,size=(256,256),noiseType="uniform",
noiseElementSize=2,opacity=0)
def doTrial(grateOri,noiseOp):
if grateOri<0:
side=0
else:
side=1
noise.opacity=noiseOp
grate.ori=grateOri
blank.draw()
window.flip()
core.wait(.2)
grate.draw()
def __init__(self, sub_val, n_trial, mode='uniform', atten_task=False):
# subject name/id
self.sub_val = sub_val
self.time_stmp = datetime.now().strftime("%d_%m_%Y_%H_%M_")
# create condition sequence / record file for each subject
self.data_dir = os.path.join('.', 'Neural', self.sub_val)
self.record_path = os.path.join(self.data_dir, self.sub_val + '.json')
if os.path.exists(self.record_path):
with open(self.record_path, 'r') as file_handle:
self.sub_record = json.load(file_handle)
else:
os.mkdir(self.data_dir)
cond_seq = list(range(3)) * self.SEN_NUM
np.random.shuffle(cond_seq)
self.sub_record = {
'Cond_Seq': cond_seq,
'Cond_Ctr': 0,
'0': 0,
'1': 0,
'2': 0
}
self._save_json()
print('create subject file at ' + self.record_path)
# will be used for recording response
self.resp_flag = True
self.increment = 0
# parameter for the experiment
self.n_trial = n_trial
self.mode = mode
self.atten_task = atten_task
self.show_center = True
self.line_len = self.DEFAULT_LEN
self.stim_dur = self.DEFAULT_DUR
self.delay = self.DEFAULT_DELAY
self.blank = self.DEFAULT_BLANK
# read in stim sequence
with open(self.STIM_SEQ_PATH, 'r') as seq_file:
stim_seq = seq_file.read().replace('\n', ' ').split()
stim_seq = list(map(int, stim_seq))
self.stim_seq = np.array(stim_seq).reshape(
(self.SEN_NUM, self.SEQ_LEN * 2))
# initialize window, message
# monitor = 'rm_413' for psychophysics and 'sc_3t' for imaging session
self.win = visual.Window(size=(1920, 1080),
fullscr=True,
allowGUI=True,
screen=1,
monitor='sc_3t',
units='deg',
winType=window_backend)
# initialize stimulus
self.target = visual.GratingStim(self.win,
sf=0.50,
size=10.0,
mask='raisedCos',
maskParams={'fringeWidth': 0.25},
contrast=0.10)
self.surround = visual.GratingStim(self.win,
sf=0.50,
size=18.0,
mask='raisedCos',
contrast=0.10)
self.noise = visual.NoiseStim(self.win,
units='pix',
mask='raisedCos',
size=1024,
contrast=0.10,
noiseClip=3.0,
noiseType='Filtered',
texRes=1024,
noiseElementSize=4,
noiseFractalPower=0,
noiseFilterLower=7.5 / 1024.0,
noiseFilterUpper=12.5 / 1024.0,
noiseFilterOrder=3.0)
self.fixation = visual.GratingStim(self.win,
color=0.5,
colorSpace='rgb',
tex=None,
mask='raisedCos',
size=0.25)
self.feedback = visual.Line(self.win,
start=(0.0, -self.line_len),
end=(0.0, self.line_len),
lineWidth=5.0,
lineColor='black',
size=1,
contrast=0.80)
self.prob = visual.GratingStim(self.win,
sf=0.5,
size=[2.0, 5.0],
mask='gauss',
contrast=1.0)
# data recorder
self.record = DataRecord()
return
