scipy.spatial.transform.RigidTransform.

from_rotation#

static RigidTransform.from_rotation(rotation)[source]#

Initialize from a rotation, without a translation.

When applying this transform to a vector v, the result is the same as if the rotation was applied to the vector. Tf.from_rotation(r).apply(v) == r.apply(v)

Parameters:

rotationRotation instance: A single rotation or a stack of rotations.

Returns:

transformRigidTransform instance

Notes

Array API Standard Support

from_rotation has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	✅
JAX	✅	✅
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

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

Creating a transform from a single rotation:

>>> r = R.from_euler("ZYX", [90, 30, 0], degrees=True)
>>> r.apply([1, 0, 0])
array([0.       , 0.8660254, -0.5     ])
>>> tf = Tf.from_rotation(r)
>>> tf.apply([1, 0, 0])
array([0.       , 0.8660254, -0.5     ])
>>> tf.single
True

The upper 3x3 submatrix of the transformation matrix is the rotation matrix:

>>> np.allclose(tf.as_matrix()[:3, :3], r.as_matrix(), atol=1e-12)
True

Creating multiple transforms from a stack of rotations:

>>> r = R.from_euler("ZYX", [[90, 30, 0], [45, 30, 60]], degrees=True)
>>> r.apply([1, 0, 0])
array([[0.        , 0.8660254 , -0.5       ],
       [0.61237244, 0.61237244, -0.5       ]])
>>> tf = Tf.from_rotation(r)
>>> tf.apply([1, 0, 0])
array([[0.        , 0.8660254 , -0.5       ],
       [0.61237244, 0.61237244, -0.5       ]])
>>> tf.single
False
>>> len(tf)
2