def test_steps(self):
"""
Tests :func:`colour.algebra.common.steps` definition.
"""
self.assertTupleEqual(steps(range(0, 10, 2)), (2, ))
self.assertTupleEqual(tuple(sorted(steps([1, 2, 3, 4, 6, 6.5]))),
(0.5, 1, 2))
def test_steps(self):
"""
Tests :func:`colour.algebra.common.steps` definition.
"""
self.assertTupleEqual(steps(range(0, 10, 2)), (2,))
self.assertTupleEqual(
tuple(sorted(steps([1, 2, 3, 4, 6, 6.5]))),
(0.5, 1, 2))
def x(self, value):
"""
Setter for **self.__x** private attribute.
Parameters
----------
value : array_like
Attribute value.
"""
if value is not None:
value = to_ndarray(value)
assert value.ndim == 1, (
'"x" independent variable must have exactly one dimension!')
assert is_uniform(value), (
'"x" independent variable is not uniform!')
if not issubclass(value.dtype.type, np.inexact):
value = value.astype(np.float_)
value_steps = steps(value)[0]
xp1 = value[0] - value_steps * 2
xp2 = value[0] - value_steps
xp3 = value[-1] + value_steps
xp4 = value[-1] + value_steps * 2
self.__xp = np.concatenate(((xp1, xp2), value, (xp3, xp4)))
self.__x = value
def x(self, value):
"""
Setter for **self.__x** private attribute.
Parameters
----------
value : array_like
Attribute value.
"""
if value is not None:
value = to_ndarray(value)
assert value.ndim == 1, (
'"x" independent variable must have exactly one dimension!')
assert is_uniform(value), (
'"x" independent variable is not uniform!')
if not issubclass(value.dtype.type, np.inexact):
value = value.astype(np.float_)
value_steps = steps(value)[0]
xp1 = value[0] - value_steps * 2
xp2 = value[0] - value_steps
xp3 = value[-1] + value_steps
xp4 = value[-1] + value_steps * 2
self.__xp = np.concatenate(((xp1, xp2), value, (xp3, xp4)))
self.__x = value
