def test_create(self):
v = Vector3()
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
v = Vector3([1.,2.,3.])
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
self.assertEqual(v.shape, self._shape)
v = Vector3(Vector3())
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
v4 = [1., 2., 3., 4.]
result = vector3.create_from_vector4(v4)
v, w = result
np.testing.assert_almost_equal(v, [1.,2.,3.], decimal=5)
np.testing.assert_almost_equal(w, 4., decimal=5)
v4 = Vector4([1., 2., 3., 4.])
result = vector3.create_from_vector4(v4)
v, w = result
np.testing.assert_almost_equal(v, [1.,2.,3.], decimal=5)
np.testing.assert_almost_equal(w, 4., decimal=5)
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3.from_matrix44_translation(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
def test_create(self):
v = Vector3()
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
v = Vector3([1.,2.,3.])
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
self.assertEqual(v.shape, self._shape)
v = Vector3(Vector3())
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
v4 = [1., 2., 3., 4.]
result = vector3.create_from_vector4(v4)
v, w = result
np.testing.assert_almost_equal(v, [1.,2.,3.], decimal=5)
np.testing.assert_almost_equal(w, 4., decimal=5)
v4 = Vector4([1., 2., 3., 4.])
result = vector3.create_from_vector4(v4)
v, w = result
np.testing.assert_almost_equal(v, [1.,2.,3.], decimal=5)
np.testing.assert_almost_equal(w, 4., decimal=5)
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3.from_matrix44_translation(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
def test_decompose(self):
# define expectations for multiple cases
testsets = [
(Vector3([1, 1, 2],
dtype='f4'), Quaternion.from_y_rotation(np.pi,
dtype='f4'),
Vector3([10, 0, -5], dtype='f4'),
Matrix44([
[-1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, -2, 0],
[10, 0, -5, 1],
],
dtype='f4')),
(Vector3([-1, 3, .5], dtype='f4'),
Quaternion.from_axis_rotation(Vector3([.75, .75, 0],
dtype='f4').normalized,
np.pi,
dtype='f4').normalized,
Vector3([1, -1, 1], dtype='f4'),
Matrix44([
[0, -1, 0, 0],
[3, 0, 0, 0],
[0, 0, -.5, 0],
[1, -1, 1, 1],
],
dtype='f4')),
]
for expected_scale, expected_rotation, expected_translation, expected_model in testsets:
# compose model matrix using original inputs
s = Matrix44.from_scale(expected_scale, dtype='f4')
r = Matrix44.from_quaternion(expected_rotation, dtype='f4')
t = Matrix44.from_translation(expected_translation, dtype='f4')
m = t * r * s
# check that it's the same as the expected matrix
np.testing.assert_almost_equal(np.array(m),
np.array(expected_model))
self.assertTrue(m.dtype == expected_model.dtype)
self.assertTrue(isinstance(m, expected_model.__class__))
# decompose this matrix and recompose the model matrix from the decomposition
ds, dr, dt = m.decompose()
ds = Matrix44.from_scale(ds, dtype='f4')
dr = Matrix44.from_quaternion(dr, dtype='f4')
dt = Matrix44.from_translation(dt, dtype='f4')
dm = dt * dr * ds
# check that it's the same as the original matrix
np.testing.assert_almost_equal(np.array(m), np.array(dm))
self.assertTrue(m.dtype == dm.dtype)
self.assertTrue(isinstance(dm, m.__class__))
def test_decompose(self):
# define expectations for multiple cases
testsets = [
(
Vector3([1, 1, 2], dtype='f4'),
Quaternion.from_y_rotation(np.pi, dtype='f4'),
Vector3([10, 0, -5], dtype='f4'),
Matrix44([
[-1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, -2, 0],
[10, 0, -5, 1],
], dtype='f4')
),
(
Vector3([-1, 3, .5], dtype='f4'),
Quaternion.from_axis_rotation(Vector3([.75, .75, 0], dtype='f4').normalized, np.pi, dtype='f4').normalized,
Vector3([1, -1, 1], dtype='f4'),
Matrix44([
[0, -1, 0, 0],
[3, 0, 0, 0],
[0, 0, -.5, 0],
[1, -1, 1, 1],
], dtype='f4')
),
]
for expected_scale, expected_rotation, expected_translation, expected_model in testsets:
# compose model matrix using original inputs
s = Matrix44.from_scale(expected_scale, dtype='f4')
r = Matrix44.from_quaternion(expected_rotation, dtype='f4')
t = Matrix44.from_translation(expected_translation, dtype='f4')
m = t * r * s
# check that it's the same as the expected matrix
np.testing.assert_almost_equal(np.array(m), np.array(expected_model))
self.assertTrue(m.dtype == expected_model.dtype)
self.assertTrue(isinstance(m, expected_model.__class__))
# decompose this matrix and recompose the model matrix from the decomposition
ds, dr, dt = m.decompose()
ds = Matrix44.from_scale(ds, dtype='f4')
dr = Matrix44.from_quaternion(dr, dtype='f4')
dt = Matrix44.from_translation(dt, dtype='f4')
dm = dt * dr * ds
# check that it's the same as the original matrix
np.testing.assert_almost_equal(np.array(m), np.array(dm))
self.assertTrue(m.dtype == dm.dtype)
self.assertTrue(isinstance(dm, m.__class__))
def test_create(self):
v = Vector3()
self.assertTrue(np.array_equal(v, [0., 0., 0.]))
self.assertEqual(v.shape, self._shape)
v = Vector3([1., 2., 3.])
self.assertTrue(np.array_equal(v, [1., 2., 3.]))
self.assertEqual(v.shape, self._shape)
v = Vector3(Vector3())
self.assertTrue(np.array_equal(v, [0., 0., 0.]))
self.assertEqual(v.shape, self._shape)
m = Matrix44.from_translation([1., 2., 3.])
v = Vector3.from_matrix44_translation(m)
self.assertTrue(np.array_equal(v, [1., 2., 3.]))
m = Matrix44.from_translation([1., 2., 3.])
v = Vector3(m)
self.assertTrue(np.array_equal(v, [1., 2., 3.]))
def test_create(self):
v = Vector3()
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
v = Vector3([1.,2.,3.])
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
self.assertEqual(v.shape, self._shape)
v = Vector3(Vector3())
self.assertTrue(np.array_equal(v, [0.,0.,0.]))
self.assertEqual(v.shape, self._shape)
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3.from_matrix44_translation(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
m = Matrix44.from_translation([1.,2.,3.])
v = Vector3(m)
self.assertTrue(np.array_equal(v, [1.,2.,3.]))
