def createstimuli(self):
"""Creates the VisionEgg stimuli objects for this Experiment subclass"""
super(ForagingSweeps, self).createstimuli()
# Create instances of the Mask2D class, one for each diameter
if self.static.mask:
print 'Generating masks',
self.nmasksamples = 512 # number of samples in mask, must be power of 2, quality/performance tradeoff
self.masks = {} # init a dictionary
samplesperpix = self.nmasksamples / deg2pix(min(self.static.widthDeg, self.static.heightDeg))
for diameterDeg in toiter(self.dynamic.diameterDeg):
radius = deg2pix(diameterDeg / 2) # in pix
radiusSamples = samplesperpix * radius # in mask samples
self.masks[diameterDeg] = Mask2D(function=self.static.mask,
radius_parameter=radiusSamples, # sigma for gaussian, radius for circle, in units of mask samples
num_samples=(self.nmasksamples, self.nmasksamples)) # size of mask texture data (# of texels)
print '.',
print
else:
self.masks = None
self.nsinsamples = 2048 # number of samples of sine f'n, must be power of 2, quality/performance tradeoff
self.grating = SinGrating2D(position=(self.xorig, self.yorig), # init to orig,
anchor='center',
size=(deg2pix(self.static.heightDeg), deg2pix(self.static.widthDeg)), # VE defines grating ori as direction of motion of grating, but we want it to be the orientation of the grating elements, so add 90 deg (this also makes grating ori def'n correspond to bar ori def'n). This means that width and height have to be swapped
ignore_time=True, # don't use this class' own time f'n
#mask=self.masks.values()[0], # init to a random mask in maskobjects
num_samples=self.nsinsamples,
max_alpha=1.0, # opaque
on=False) # keep it off until first sweep starts
self.gp = self.grating.parameters
self.target = Target2D(anchor='center',
anti_aliasing=False,
color=(self.brightness, self.brightness, self.brightness, 1.0))
self.tp = self.target.parameters
if self.static.syncsq:
self.sync = Target2D(anchor='center',
anti_aliasing=False,
color=(0.0, 0.0, 0.0, 1.0),
position = self.static.syncsqloc,
on = True,
size = (100,100))
self.sp = self.sync.parameters
# Update target stimulus with initial values
self.tp.position = self.x, self.y
self.tp.size = deg2pix(self.terrain.objectwidthDeg),deg2pix(self.terrain.objectwidthDeg)
self.tp.orientation = self.terrain.orientation
self.updateTerrain()
# Set stimuli tuple
if self.static.syncsq:
self.stimuli = (self.background, self.grating, self.target, self.sync) # last entry will be topmost layer in viewport
else:
self.stimuli = (self.background, self.grating, self.target)
def setgamma(self, gamma):
"""Set VisionEgg's gamma parameter"""
vc = VisionEgg.config
if gamma: # could be a single value or a sequence (preferably a tuple)
vc.VISIONEGG_GAMMA_SOURCE = 'invert' # 'invert' in VE means that gamma correction is turned on
if len(toiter(gamma)) == 1: # single value for all 3 guns
vc.VISIONEGG_GAMMA_INVERT_RED = gamma
vc.VISIONEGG_GAMMA_INVERT_GREEN = gamma
vc.VISIONEGG_GAMMA_INVERT_BLUE = gamma
elif len(toiter(gamma)) == 3: # separate value for each gun
vc.VISIONEGG_GAMMA_INVERT_RED = gamma[0]
vc.VISIONEGG_GAMMA_INVERT_GREEN = gamma[1]
vc.VISIONEGG_GAMMA_INVERT_BLUE = gamma[2]
else:
raise ValueError, 'gamma cannot be of length %d' % len(toiter(gamma))
else:
vc.VISIONEGG_GAMMA_SOURCE = 'None' # 'None' in VE means that gamma correction is turned off
vc.VISIONEGG_GAMMA_INVERT_RED = 0 # set them all to 0 too, just to be thorough
vc.VISIONEGG_GAMMA_INVERT_GREEN = 0
vc.VISIONEGG_GAMMA_INVERT_BLUE = 0
def build(self):
"""Builds the SweepTable and the Header for this Experiment, and loads movie frames"""
super(Movie, self).build()
self.load()
assert max(toiter(self.dynamic.framei)) <= self.nframes-1, 'Frame indices exceed movie size of %d frames' % self.nframes
self.header.NVS.data[C.STT_STS] = 16 # enter NVS stimulus code for movies
