def env_to_mask(self, env, size, stdev): """ desc: Converts an envelope name to a PsychoPy mask. Also returns the appropriate patch size and the smallest power-of-two size __Note:__ Specific to the PsychoPy backend, primarily intended for internal use. Using this function directly will break your experiment when switching backends. arguments: env: An envelope name. size: A size value. returns: A (psychopy_mask, mask_size, power_of_two_size) tuple. """ env = canvas._match_env(env) # Get the smallest power-of-two size i = 2 while size / (i) > 0: i = 2*i s = i # Create a PsychoPy mask if env == u"c": _env = u"circle" _size = size elif env == u"g": _env = u"gauss" _size = 6*stdev elif env == u"r": _env = u"None" _size = size elif env == u"l": _env = np.zeros([s,s]) for x in range(s): for y in range(s): r = np.sqrt((x-s/2)**2+(y-s/2)**2) _env[x,y] = (max(0, (0.5*s-r) / (0.5*s))-0.5)*2 _size = size return (_env, _size, s)
def env_to_mask(self, env, size, stdev): """ desc: Converts an envelope name to a PsychoPy mask. Also returns the appropriate patch size and the smallest power-of-two size __Note:__ Specific to the PsychoPy backend, primarily intended for internal use. Using this function directly will break your experiment when switching backends. arguments: env: An envelope name. size: A size value. returns: A (psychopy_mask, mask_size, power_of_two_size) tuple. """ env = canvas._match_env(env) # Get the smallest power-of-two size i = 2 while size / (i) > 0: i = 2*i s = i # Create a PsychoPy mask if env == u"c": _env = u"circle" _size = size elif env == u"g": _env = u"gauss" _size = 6*stdev elif env == u"r": _env = u"None" _size = size elif env == u"l": _env = np.zeros([s,s]) for x in range(s): for y in range(s): r = np.sqrt((x-s/2)**2+(y-s/2)**2) _env[x,y] = (max(0, (0.5*s-r) / (0.5*s))-0.5)*2 _size = size return (_env, _size, s)
def _mask(self, env, size, stdev):
"""
visible: False
desc:
Generates a PsychoPy mask for Gabor and NoisePatch stimuli.
arguments:
env:
desc: The envelope.
type: str
size:
desc: The stimulus size.
type: int
stdev:
desc: The standard deviation of the mask if the envelope is
gaussian.
type: int
returns:
desc: A PsychoPy mask, which is a numpy array.
type: ndarray
"""
# Get the smallest power-of-two that is larger than or equal to the
# given size
size = int(np.ceil(np.sqrt(size))**2)
# Create a PsychoPy mask
env = canvas._match_env(env)
if env == u'c':
return u'circle', size
if env == u'g':
return u'gauss', 6 * stdev
if env == u'r':
return u'None', size
if env == u'l':
_env = np.zeros([size, size])
for x in range(size):
for y in range(size):
r = np.sqrt((x-size/2)**2+(y-size/2)**2)
_env[x, y] = (max(0, (0.5*size-r) / (0.5*size))-0.5)*2
return _env, size
raise ValueError('Invalid mask')