scipy.spatial.transform.RigidTransform.

mul#

RigidTransform.__mul__()#

Compose this transform with the other.

If p and q are two transforms, then the composition of ‘q followed by p’ is equivalent to p * q. In terms of transformation matrices, the composition can be expressed as p.as_matrix() @ q.as_matrix().

In terms of translations and rotations, the composition when applied to a vector v is equivalent to p.translation + p.rotation.apply(q.translation) + (p.rotation * q.rotation).apply(v).

This function supports composition of multiple transforms at a time. The following cases are possible:

Either p or q contains a single or length 1 transform. In this case the result contains the result of composing each transform in the other object with the one transform. If both are single transforms, the result is a single transform.

Both p and q contain N transforms. In this case each transform p[i] is composed with the corresponding transform q[i] and the result contains N transforms.

Parameters:

otherRigidTransform instance: Object containing the transforms to be composed with this one.

Returns:

RigidTransform instance: The composed transform.

Examples

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

Compose two transforms:

>>> tf1 = Tf.from_translation([1, 0, 0])
>>> tf2 = Tf.from_translation([0, 1, 0])
>>> tf = tf1 * tf2
>>> tf.translation
array([1., 1., 0.])
>>> tf.single
True

When applied to a vector, the composition of two transforms is applied in right-to-left order.

>>> t1, r1 = [1, 2, 3], R.from_euler('z', 60, degrees=True)
>>> t2, r2 = [0, 1, 0], R.from_euler('x', 30, degrees=True)
>>> tf1 = Tf.from_components(t1, r1)
>>> tf2 = Tf.from_components(t2, r2)
>>> tf = tf1 * tf2
>>> tf.apply([1, 0, 0])
array([0.6339746, 3.3660254, 3.       ])
>>> tf1.apply(tf2.apply([1, 0, 0]))
array([0.6339746, 3.3660254, 3.       ])

When at least one of the transforms is not single, the result is a stack of transforms.

>>> tf1 = Tf.from_translation([1, 0, 0])
>>> tf2 = Tf.from_translation([[0, 2, 0], [0, 0, 3]])
>>> tf = tf1 * tf2
>>> tf.translation
array([[1., 2., 0.],
       [1., 0., 3.]])
>>> tf.single
False
>>> len(tf)
2

__mul__#

mul#