def test_numpyFilterMask(self):
"""if the mask is passed in as a numpy array it goes through a different
set of rules when turned into a texture. But the outcome should be as above
"""
win = self.win
from psychopy import filters
gaussMask = filters.makeMask(128, 'gauss')
size = numpy.array([2.0,2.0])*self.scaleFactor
fileName = os.path.join(utils.TESTS_DATA_PATH, 'testimage.jpg')
image = visual.ImageStim(win, image=fileName, mask=gaussMask,
size=size, flipHoriz=True, flipVert=True)
image.draw()
utils.compareScreenshot('imageAndGauss_%s.png' %(self.contextName), win)
win.flip()
def test_numpyFilterMask(self):
"""if the mask is passed in as a numpy array it goes through a different
set of rules when turned into a texture. But the outcome should be as above
"""
win = self.win
from psychopy import filters
gaussMask = filters.makeMask(128, 'gauss')
size = numpy.array([2.0,2.0])*self.scaleFactor
fileName = os.path.join(utils.TESTS_DATA_PATH, 'testimage.jpg')
image = visual.ImageStim(win, image=fileName, mask=gaussMask,
size=size, flipHoriz=True, flipVert=True)
image.draw()
utils.compareScreenshot('imageAndGauss_%s.png' %(self.contextName), win)
win.flip()
For this they should be square arrays, with size in powers of two (e.g. 64,128,256,512) A 256x256 array can then be given color efficiently using the normal stimulus methods. A 256x256x3 array has its color defined by the array (obviously). This demo creates a radial array as a patch stimulus, using helper functions from psychopy.filters and then creates a second sub-stimulus created from a section of the original. Both are masked simply by circles. """ win = visual.Window([800,600], units='pix') #generate the radial textures cycles=6 res=512 radius = filters.makeRadialMatrix(res) radialTexture = np.sin(radius*2*np.pi*cycles) mainMask = filters.makeMask(res) #select the upper left quadrant of our radial stimulus radialTexture_sub = radialTexture[256:,0:256] #and create an appropriate mask for it subMask = filters.makeMask(res, radius=0.5, center=[-0,0]) bigStim = visual.GratingStim(win, tex=radialTexture, mask=mainMask, color=[1,1,1], size=512, sf=1.0/512, interpolate=True) #draw the quadrant stimulus centred in the top left quadrant of the 'base' stimulus (so they're aligned) subStim = visual.GratingStim(win, tex=radialTexture_sub, pos=(-128,128), mask=subMask, color=[1,1,1], size=256, sf=1.0/256, interpolate=True, autoLog=False)#this stim changes too much for autologging to be useful bigStim.draw() subStim.draw()
For this they should be square arrays, with size in powers of two (e.g. 64,128,256,512)
A 256x256 array can then be given color efficiently using the normal stimulus methods.
A 256x256x3 array has its color defined by the array (obviously).
This demo creates a radial array as a patch stimulus, using helper functions from
psychopy.filters and then creates a second sub-stimulus created from a section of
the original. Both are masked simply by circles.
"""
win = visual.Window([800, 600], units='pix')
#generate the radial textures
cycles = 6
res = 512
radius = filters.makeRadialMatrix(res)
radialTexture = np.sin(radius * 2 * np.pi * cycles)
mainMask = filters.makeMask(res)
#select the upper left quadrant of our radial stimulus
radialTexture_sub = radialTexture[256:, 0:256]
#and create an appropriate mask for it
subMask = filters.makeMask(res, radius=0.5, center=[-0, 0])
bigStim = visual.GratingStim(win,
tex=radialTexture,
mask=mainMask,
color=[1, 1, 1],
size=512,
sf=1.0 / 512,
interpolate=True)
#draw the quadrant stimulus centred in the top left quadrant of the 'base' stimulus (so they're aligned)
subStim = visual.GratingStim(
def makeFilteredNoise(res, radius, shape='gauss'):
noise = np.random.random([res, res])
kernel = filters.makeMask(res, shape='gauss', radius=radius)
filteredNoise = filters.conv2d(kernel, noise)
filteredNoise = ((filteredNoise-filteredNoise.min())/(filteredNoise.max()-filteredNoise.min())*2-1)
return filteredNoise
# print i
# print sample(range(nImages),5)
factorsList = data.createFactorialTrialList(factors={'change_type': change_types, 'trial_probe_ecc':ecc_list, 'trial_probe_angle':angle_list})
#print 'factorsList: %g'%len(factorsList)
#mainTrials=mainTrials+factorsList
shuffle(mainTrials)
trials = data.TrialHandler(nReps=1.0, method='random', extraInfo=expInfo, trialList=factorsList)
thisExp.addLoop(trials)
print 'Total trials: %g'%trials.nTotal
aperture=visual.Rect(win=win,width=1, height=1, fillColor='red', units='norm')
gaussTexture = filters.makeMask(196, shape='gauss')
invGaussTexture = -gaussTexture #make opaq->transp and vice versa
def replicate(a, xy, se, n):
rptIdx = np.ones(a.shape[0 if xy == 'X' else 1], dtype=int)
rptIdx[0 if se == 'start' else -1] = n + 1
return np.repeat(a, rptIdx, axis=0 if xy == 'X' else 1)
#print np.shape(invGaussTexture)
invGaussTexture = replicate(invGaussTexture,'X','start',30)
invGaussTexture = replicate(invGaussTexture,'X','end',30)
invGaussTexture = replicate(invGaussTexture,'Y','start',30)
invGaussTexture = replicate(invGaussTexture,'Y','end',30)
"show_probe_on_target": [0, 1, 1, 1],
}
)
# print 'factorsList: %g'%len(factorsList)
# mainTrials=mainTrials+factorsList
# shuffle(mainTrials)
trials = data.TrialHandler(nReps=1, method="random", extraInfo=expInfo, trialList=factorsList)
thisExp.addLoop(trials)
print "Total trials: %g" % trials.nTotal
aperture = visual.Rect(win=win, width=1, height=1, fillColor="red", units="norm")
gaussTexture = filters.makeMask(196, shape="gauss")
invGaussTexture = -gaussTexture # make opaq->transp and vice versa
def replicate(a, xy, se, n):
rptIdx = np.ones(a.shape[0 if xy == "X" else 1], dtype=int)
rptIdx[0 if se == "start" else -1] = n + 1
return np.repeat(a, rptIdx, axis=0 if xy == "X" else 1)
# print np.shape(invGaussTexture)
invGaussTexture = replicate(invGaussTexture, "X", "start", 30)
invGaussTexture = replicate(invGaussTexture, "X", "end", 30)
invGaussTexture = replicate(invGaussTexture, "Y", "start", 30)
