def test_steps(self):
"""
Tests :func:`colour.utilities.array.steps` definition.
"""
np.testing.assert_almost_equal(steps(range(0, 10, 2)), np.array([2]))
np.testing.assert_almost_equal(steps([1, 2, 3, 4, 6, 6.5]),
np.array([0.5, 1, 2]))
def test_steps(self):
"""
Tests :func:`colour.utilities.array.steps` definition.
"""
np.testing.assert_almost_equal(steps(range(0, 10, 2)), np.array([2]))
np.testing.assert_almost_equal(
steps([1, 2, 3, 4, 6, 6.5]),
np.array([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 = np.atleast_1d(value).astype(np.float_)
assert value.ndim == 1, (
'"x" independent variable must have exactly one dimension!')
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 = np.atleast_1d(value).astype(np.float_)
assert value.ndim == 1, (
'"x" independent variable must have exactly one dimension!')
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
