scipy.spatial.transform.RigidTransform.
from_translation#
- classmethod RigidTransform.from_translation(cls, translation)#
- 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- tis 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:
- transformRigidTransforminstance
 
- transform
 - 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