scipy.spatial.transform.RigidTransform.

from_components#

classmethod RigidTransform.from_components(cls, translation, rotation)#

Initialize a rigid transform from translation and rotation components.

When creating a rigid transform from a translation and rotation, the translation is applied after the rotation, such that tf = Tf.from_components(translation, rotation) is equivalent to tf = Tf.from_translation(translation) * Tf.from_rotation(rotation).

When applying a transform to a vector v, the result is the same as if the transform was applied to the vector in the following way: tf.apply(v) == translation + rotation.apply(v)

Parameters:

translationarray_like, shape (N, 3) or (3,): A single translation vector or a stack of translation vectors.
rotationRotation instance: A single rotation or a stack of rotations.

Returns:

RigidTransform: If rotation is single and translation is shape (3,), then a single transform is returned. Otherwise, a stack of transforms is returned.

Examples

>>> from scipy.spatial.transform import RigidTransform as Tf
>>> from scipy.spatial.transform import Rotation as R
>>> import numpy as np

Creating from a single rotation and translation:

>>> t = np.array([2, 3, 4])
>>> r = R.from_euler("ZYX", [90, 30, 0], degrees=True)
>>> r.as_matrix()
array([[ 0.       , -1.,  0.        ],
       [ 0.8660254,  0.,  0.5       ],
       [-0.5      ,  0.,  0.8660254 ]])
>>> tf = Tf.from_components(t, r)
>>> tf.rotation.as_matrix()
array([[ 0.       , -1.,  0.        ],
       [ 0.8660254,  0.,  0.5       ],
       [-0.5      ,  0.,  0.8660254 ]])
>>> tf.translation
array([2., 3., 4.])
>>> tf.single
True

When applying a transform to a vector v, the result is the same as if the transform was applied to the vector in the following way: tf.apply(v) == translation + rotation.apply(v)

>>> r.apply([1, 0, 0])
array([0.       , 0.8660254, -0.5     ])
>>> t + r.apply([1, 0, 0])
array([2.       , 3.8660254,  3.5     ])
>>> tf.apply([1, 0, 0])
array([2.       , 3.8660254,  3.5     ])