scipy.spatial.transform.RigidTransform.

from_translation#

static RigidTransform.from_translation(translation)[source]#

Initialize from a translation numpy array, without a rotation.

When applying this transform to a vector v, the result is the same as if the translation and vector were added together. If t is the displacement vector of the translation, then:

Tf.from_translation(t).apply(v) == t + v

Parameters:

translationarray_like, shape (N, 3) or (3,): A single translation vector or a stack of translation vectors.

Returns:

transformRigidTransform instance

Notes

Array API Standard Support

from_translation 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
>>> import numpy as np

Creating a transform from a single translation vector:

>>> t = np.array([2, 3, 4])
>>> t + np.array([1, 0, 0])
array([3, 3, 4])
>>> tf = Tf.from_translation(t)
>>> tf.apply([1, 0, 0])
array([3., 3., 4.])
>>> tf.single
True

The top 3x1 points in the rightmost column of the transformation matrix is the translation vector:

>>> tf.as_matrix()
array([[1., 0., 0., 2.],
       [0., 1., 0., 3.],
       [0., 0., 1., 4.],
       [0., 0., 0., 1.]])
>>> np.allclose(tf.as_matrix()[:3, 3], t)
True

Creating multiple transforms from a stack of translation vectors:

>>> t = np.array([[2, 3, 4], [1, 0, 0]])
>>> t + np.array([1, 0, 0])
array([[3, 3, 4],
       [2, 0, 0]])
>>> tf = Tf.from_translation(t)
>>> tf.apply([1, 0, 0])
array([[3., 3., 4.],
       [2., 0., 0.]])
>>> np.allclose(tf.as_matrix()[:, :3, 3], t)
True
>>> tf.single
False
>>> len(tf)
2