scipy.spatial.transform.RigidTransform.

identity#

classmethod RigidTransform.identity(cls, num=None)#

Initialize an identity transform.

Composition with the identity transform has no effect, and applying the identity transform to a vector has no effect.

Parameters:

numint, optional: Number of identity transforms to generate. If None (default), then a single transform is generated.

Returns:

transformRigidTransform instance: The identity transform.

Examples

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

Creating a single identity transform:

>>> tf = Tf.identity()
>>> tf.as_matrix()
array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]])
>>> tf.single
True

The identity transform can be applied to a vector without effect:

>>> tf.apply([1, 2, 3])
array([1., 2., 3.])

The identity transform when composed with another transform has no effect:

>>> rng = np.random.default_rng()
>>> t = rng.random(3)
>>> r = R.random(rng=rng)
>>> tf = Tf.from_components(t, r)
>>> np.allclose((Tf.identity() * tf).as_matrix(),
...             tf.as_matrix(), atol=1e-12)
True

Multiple identity transforms can be generated at once:

>>> tf = Tf.identity(2)
>>> tf.as_matrix()
array([[[1., 0., 0., 0.],
        [0., 1., 0., 0.],
        [0., 0., 1., 0.],
        [0., 0., 0., 1.]],
       [[1., 0., 0., 0.],
        [0., 1., 0., 0.],
        [0., 0., 1., 0.],
        [0., 0., 0., 1.]]])
>>> tf.single
False
>>> len(tf)
2