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_from_scale(self): v = Vector3([1, 2, 3]) m = Matrix44.from_scale(v) self.assertTrue(np.array_equal(m, np.diag([1, 2, 3, 1])))
def test_create_from_scale(self): v = Vector3([1,2,3]) m = Matrix44.from_scale(v) self.assertTrue(np.array_equal(m, np.diag([1,2,3,1])))