`scipy.spatial.transform._rotation:Rotation.mul`

source: build-install/usr/lib/python3.14/site-packages/scipy/spatial/transform/_rotation.py:1719

Signature

def __mul__(self, other: Rotation) → Rotation

Summary

Compose this rotation with the other.

Extended Summary

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

Parameters

other : `Rotation` instance: Object containing the rotations to be composed with this one. Note that rotation compositions are not commutative, so p * q is generally different from q * p.

Returns

composition : `Rotation` instance: This function supports composition of multiple rotations at a time. Composition follows standard numpy broadcasting rules. The resulting Rotation object will have the shape np.broadcast_shapes(p.shape, q.shape). In dimensions with size > 1, rotations are composed with matching indices. In dimensions with only one rotation, the single rotation is composed with each rotation in the other object.

Examples

from scipy.spatial.transform import Rotation as R
import numpy as np

✓

Composition of two single rotations:

p = R.from_quat([0, 0, 1, 1])
q = R.from_quat([1, 0, 0, 1])
p.as_matrix()
q.as_matrix()
r = p * q
r.as_matrix()

✓

array([[0., 0., 1.],
       [1., 0., 0.],
       [0., 1., 0.]])

Composition of two objects containing equal number of rotations:

p = R.from_quat([[0, 0, 1, 1], [1, 0, 0, 1]])
q = R.from_rotvec([[np.pi/4, 0, 0], [-np.pi/4, 0, np.pi/4]])

✓

p.as_quat()
q.as_quat()

✗

array([[ 0.38268,  0.     ,  0.     ,  0.92388],
       [-0.37282,  0.     ,  0.37282,  0.84971]])

r = p * q

✓

r.as_quat()

✗

array([[ 0.2706 ,  0.2706 ,  0.65328,  0.65328],
       [ 0.33721, -0.26362,  0.26362,  0.86446]])

Broadcasting rules apply:

p = R.from_quat(np.tile(np.array([0, 0, 1, 1]), (5, 1, 1)))
q = R.from_quat(np.tile(np.array([1, 0, 0, 1]), (1, 6, 1)))
p.shape, q.shape
r = p * q
r.shape

✓

(5, 6)

Aliases

scipy.spatial.transform.Rotation.__mul__