scipy.spatial.transform.RigidTransform.

getitem#

RigidTransform.__getitem__(indexer)[source]#

Extract transform(s) at given index(es) from this object.

Creates a new RigidTransform instance containing a subset of transforms stored in this object.

Parameters:

indexerint or slice or array_like: Specifies which transform(s) to extract. A single indexer must be specified, i.e. as if indexing a 1 dimensional array or list.

Returns:

transformRigidTransform instance

Contains

a single transform, if indexer is a single index
a stack of transform(s), if indexer is a slice, or an index array.

Raises:

TypeError: If the transform is a single transform.

Notes

Array API Standard Support

__getitem__ 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	⚠️ no JIT	⚠️ no JIT
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> from scipy.spatial.transform import RigidTransform as Tf
>>> t = [[0, 0, 0], [1, 0, 0], [2, 0, 0]]  # 3 translations
>>> tf = Tf.from_translation(t)

A single index returns a single transform:

>>> tf[0].as_matrix()
array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]])

A slice returns a stack of transforms:

>>> tf[1:3].translation
array([[1., 0., 0.],
       [2., 0., 0.]])

An index array returns a stack of transforms:

>>> tf[[0, 2]].translation
array([[0., 0., 0.],
       [2., 0., 0.]])

__getitem__#

getitem#