def __call__(self, initial_value=None, current_value=None, random_state=None):
random_state = random_state or np.random
axis = variation_values.evaluate(
self._axis, initial_value, current_value, random_state)
angle = variation_values.evaluate(
self._angle, initial_value, current_value, random_state)
sine, cosine = np.sin(angle / 2), np.cos(angle / 2)
return np.array([cosine, axis[0] * sine, axis[1] * sine, axis[2] * sine])
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
first_value = variation_values.evaluate(self._first, initial_value,
current_value, random_state)
second_value = variation_values.evaluate(self._second, initial_value,
current_value, random_state)
return self._op(first_value, second_value)
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
base_value = current_value if self._cumulative else initial_value
return base_value * (variation_values.evaluate(
self._variation, initial_value, current_value, random_state))
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
value = variation_values.evaluate(self._variation, initial_value,
current_value, random_state)
return self._op(value)
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
value = variation_values.evaluate(self._variation, initial_value,
current_value, random_state)
return np.asarray(value)[self._index]
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
h, s, v, alpha = variation_values.evaluate(
(self._h, self._s, self._v, self._alpha), initial_value,
current_value, random_state)
return np.asarray(list(colorsys.hsv_to_rgb(h, s, v)) + [alpha])
def __call__(self,
initial_value=None,
current_value=None,
random_state=None):
return np.asarray(
variation_values.evaluate([self._r, self._g, self._b, self._alpha],
initial_value, current_value,
random_state))
def apply_variations(self, physics, random_state):
for element, variations in self._variations.items():
binding = physics.bind(element)
for attribute_name, variation_info in variations.items():
current_value = getattr(binding, attribute_name)
if variation_info.initial_value is None:
variation_info.initial_value = copy.copy(current_value)
setattr(binding, attribute_name, evaluate(
variation_info.variation, variation_info.initial_value,
current_value, random_state))
def __call__(self, initial_value=None, current_value=None, random_state=None):
random_state = random_state or np.random
q1 = variation_values.evaluate(self._quat, initial_value, current_value,
random_state)
q2 = current_value if self._cumulative else initial_value
return np.array([
q1[0]*q2[0] - q1[1]*q2[1] - q1[2]*q2[2] - q1[3]*q2[3],
q1[0]*q2[1] + q1[1]*q2[0] + q1[2]*q2[3] - q1[3]*q2[2],
q1[0]*q2[2] - q1[1]*q2[3] + q1[2]*q2[0] + q1[3]*q2[1],
q1[0]*q2[3] + q1[1]*q2[2] - q1[2]*q2[1] + q1[3]*q2[0]])
def apply_variations(self, random_state):
"""Applies variations in-place to the specified MJCF element.
Args:
random_state: A `numpy.random.RandomState` instance.
"""
for element, attribute_variations in self._variations.items():
new_values = {}
for attribute_name, variation_info in attribute_variations.items():
current_value = getattr(element, attribute_name)
if variation_info.initial_value is None:
variation_info.initial_value = copy.copy(current_value)
new_values[attribute_name] = evaluate(
variation_info.variation, variation_info.initial_value,
current_value, random_state)
element.set_attributes(**new_values)