gauss = filters.makeGauss(x=np.linspace(-1.0,1.0,size), mean=0, sd=width)/np.sqrt(2*np.pi*width**2)
smthEdge = np.convolve(edge, gauss,'same')
smthEdge = (smthEdge[size:size*2]-smthEdge.min())/(smthEdge.max()-smthEdge.min())#just take the middle section
smthEdge.shape=(1,size)
return np.tile(smthEdge,(size,1))*2-1
#Setting for Trial Handler
stimList = data.importConditions('MOAConflictConds.xlsx')
trials = data.TrialHandler(trialList=stimList, nReps=repeats, extraInfo=info)
trials.data.addDataType('LumEdge')
trials.data.addDataType('Marker')
#trials.data.addDataType('Condition')
#Create stimuli
if info['Flip']=='n':
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5, size=512)*info['Edge Contrast Lum']
lmlms= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5, size=512)*info['Edge Contrast LM']
slms= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(0.5+info['Gap']), size=512)*info['Edge Contrast S']
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(0.5+info['Gap']), size=512)*info['Edge Contrast LM']
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(0.5+info['Gap']), size=512)*info['Edge Contrast S']
if info['Flip']=='y':
lum = makeRevEdgeGauss(width=info['Blur'],center=0.5, size=512)*info['Edge Contrast Lum']
lmlms= makeRevEdgeGauss(width=info['Blur'],center=0.5, size=512)*info['Edge Contrast LM']
slms= makeRevEdgeGauss(width=info['Blur'],center=(0.5-info['Gap']), size=512)*info['Edge Contrast S']
lm= makeRevEdgeGauss(width=info['Blur'],center=(0.5-info['Gap']), size=512)*info['Edge Contrast LM']
s= makeRevEdgeGauss(width=info['Blur'],center=(0.5-info['Gap']), size=512)*info['Edge Contrast S']
tex= colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s)
texlms = colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lmlms, S=slms)
edgePos = 0.0
if DEBUG==False:
monitor = 'heron'
bitsMode = 'Fast'
myMon=monitors.Monitor('heron')
conversionMatrix = myMon.getDKL_RGB(RECOMPUTE=False)
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
#Create stimuli
lum = colorFunctions.makeEdgeGauss(width=blur,center=0.5)*0.3
lm= colorFunctions.makeEdgeGauss(width=blur,center=(0.5+gap))*0.3
s=colorFunctions.makeEdgeGauss(width=0.2,center=0.5)*0.0
tex= colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s)
noiseContrast = 1.0
noise1 = makeFilteredNoise(512, radius=noiseSize)*noiseContrast
lum += noise1
noise2 = makeFilteredNoise(res=512, radius=noiseSize)*noiseContrast
lm += noise2
lumEdge= colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm*0, S=s*0, conversionMatrix = conversionMatrix)
rgEdge = colorFunctions.dklCartToRGB_2d(LUM=lum*0, LM=lm, S=s*0, conversionMatrix = conversionMatrix)
combEdge = colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s*0, conversionMatrix = conversionMatrix)
info["lumEdgePos"] = float(random.randrange(20, 80, 1)) / float(100)
print "edge", info["lumEdgePos"]
# For Loop to Run Through the Trials
for thisDistance in stairs:
trialClock.reset()
print thisDistance
# #Calculate the size of the gap in percentage terms
# gapPix = (misc.deg2pix(info['Gap'], monitor = myMon)*0.0512)
# gapDeg = info['Gap']/2
# Create stimuli
lum = colorFunctions.makeEdgeGauss(width=info["Blur"], center=info["lumEdgePos"]) * 0.3
lm = s = colorFunctions.makeEdgeGauss(width=info["Blur"], center=(info["lumEdgePos"] + info["Gap"])) * 0.3
# s=colorFunctions.makeEdgeGauss(width=info['Blur'],center=(info['lumEdgePos']+info['Gap']))*0.3
tex = colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s)
# noise1 = makeFilteredNoise(512, radius=info['Noise Size'])*info['Noise Contrast']
# lum += noise1
# noise2 = makeFilteredNoise(res=512, radius=info['Noise Size'])*info['Noise Contrast']
# lm += noise2
# noise3 = makeFilteredNoise(res=512, radius=info['Noise Size'])*info['Noise Contrast']
# s += noise3
# lumEdge= colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm*0, S=s*0, conversionMatrix = conversionMatrix)
# rgEdge = colorFunctions.dklCartToRGB_2d(LUM=lum*0, LM=lm, S=s*0, conversionMatrix = conversionMatrix)
sEdge = colorFunctions.dklCartToRGB_2d(LUM=lum * 0, LM=lm * 0, S=s, conversionMatrix=conversionMatrix)
# combEdge = colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s*0, conversionMatrix = conversionMatrix)
nDown=info['nDown'],
extraInfo=thisInfo,
minVal=info['minVal'],
maxVal=info['maxVal']
)
thisStair.extraInfo['condition']=thisCond
stairs.append(thisStair)
for trialN in range(info['nTrials']):
shuffle(stairs)
for thisStair in stairs:
thisContrast=thisStair.next()
#Create stimuli
if thisStair.extraInfo['condition']=='LM':
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
if thisStair.extraInfo['condition']=='S':
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)
if thisStair.extraInfo['condition']=='Lum':
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5)*0
order = random.randint(1.0, 2.0)
rgbEdge = misc.dklCart2rgb(lum, lm, s, conversionMatrix)
#Draw the picture
myWin.flip()
junk = event.waitKeys()
#Jitter position of edges
info['markerPos'] = float(random.randrange(-10, 10, 1))/10
print 'marker', info['markerPos']
#For Loop to Run Through the Trials
for thisDistance in stairs:
trialClock.reset()
print thisDistance
info['lumEdgePos'] = (thisDistance)
#Create stimuli
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=info['lumEdgePos'])*info['Edge Contrast Lum']
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(info['lumEdgePos']+info['Gap']))*info['Edge Contrast LM']
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(info['lumEdgePos']+info['Gap']))*info['Edge Contrast S']
tex= colorFunctions.dklCartToRGB_2d(LUM=lum, LM=lm, S=s)
rand1 = random.randrange(0,99,1)
noise1 = (noise[rand1])*info['Noise Contrast Lum']
lum += noise1
info['LumNoise'].append(rand1)
rand2 = random.randrange(0,99,1)
noise2 = noise[rand2]*info['Noise Contrast LM']
lm += noise2
info['LMNoise'].append(rand2)
rand3 = random.randrange(0,99,1)
noise3 = noise[rand3]*info['Noise Contrast S']
s += noise3
myWin = visual.Window(size=(1024, 768), monitor = 'testMonitor', units = 'deg',
fullscr=False, allowGUI=True, bitsMode=None)
myMon = monitors.Monitor('testMonitor')
conversionMatrix = numpy.asarray([ \
#LUMIN %L-M %L+M-S (note that dkl has to be in cartesian coords first!)
[1.0000, 1.0000, -0.1462],#R
[1.0000, -0.3900, 0.2094],#G
[1.0000, 0.0180, -1.0000]])#B
info = {}
info['Blur'] = 0.1
edgeSize = 10.0
edgeSF = 1/10.0
edgePos = 0.5
lum = colorFunctions.makeEdgeGauss(width=info['Blur'],center=0.5, size=512)
lm= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(0.5), size=512)*0.0
s= colorFunctions.makeEdgeGauss(width=info['Blur'],center=(0.5), size=512)
sphEdge = misc.cart2sph(z = lum, y = s, x = lm)
print len(sphEdge.shape)
#print sphEdge[:,:,0][0]
temp = copy.copy(sphEdge[:,:,1])+1
temp = (temp/numpy.abs(temp))*-15.0
sphEdge[:,:,0] += temp
#print sphEdge[:,:,0][0]
