scipy.spatial.transform.Rotation.

concatenate#

static Rotation.concatenate(rotations)[source]#

Concatenate a sequence of Rotation objects into a single object.

This is useful if you want to, for example, take the mean of a set of rotations and need to pack them into a single object to do so.

Parameters:

rotationssequence of Rotation objects: The rotations to concatenate. If a single Rotation object is passed in, a copy is returned.

Returns:

concatenatedRotation instance: The concatenated rotations.

Notes

Added in version 1.8.0.

concatenate 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 Rotation as R
>>> r1 = R.from_rotvec([0, 0, 1])
>>> r2 = R.from_rotvec([0, 0, 2])
>>> rc = R.concatenate([r1, r2])
>>> rc.as_rotvec()
array([[0., 0., 1.],
       [0., 0., 2.]])
>>> rc.mean().as_rotvec()
array([0., 0., 1.5])

Concatenation of a split rotation recovers the original object.

>>> rs = [r for r in rc]
>>> R.concatenate(rs).as_rotvec()
array([[0., 0., 1.],
       [0., 0., 2.]])

Note that it may be simpler to create the desired rotations by passing in a single list of the data during initialization, rather then by concatenating:

>>> R.from_rotvec([[0, 0, 1], [0, 0, 2]]).as_rotvec()
array([[0., 0., 1.],
       [0., 0., 2.]])