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scipy.spatial.transform.RigidTransform.

# as_components

RigidTransform.as_components(self)

  Return the translation and rotation components of the transform, where the rotation is applied first, followed by the translation.

  4x4 rigid transformation matrices are of the form:

  > [R | t] [0 | 1]

  Where `R` is a 3x3 orthonormal rotation matrix and `t` is a 3x1 translation vector `[tx, ty, tz]`. This function returns the rotation corresponding to this rotation matrix `r = Rotation.from_matrix(R)` and the translation vector `t`.

  Take a transform `tf` and a vector `v`. When applying the transform to the vector, the result is the same as if the transform was applied to the vector in the following way: `tf.apply(v) == translation + rotation.apply(v)`

  Returns:
    translation numpy.ndarray, shape (N, 3) or (3,)

      The translation of the transform.

    rotation `Rotation` instance

      The rotation of the transform.

  Examples

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

  Recover the rotation and translation from a transform:

      >>> t = np.array([2, 3, 4])
      >>> r = R.from_matrix([[0, 0, 1],
      ...                    [1, 0, 0],
      ...                    [0, 1, 0]])
      >>> tf = Tf.from_components(t, r)
      >>> tf_t, tf_r = tf.as_components()
      >>> tf_t
      array([2., 3., 4.])
      >>> tf_r.as_matrix()
      array([[0., 0., 1.],
             [1., 0., 0.],
             [0., 1., 0.]])

  The transform applied to a vector is equivalent to the rotation applied to the vector followed by the translation:

      >>> r.apply([1, 0, 0])
      array([0., 1., 0.])
      >>> t + r.apply([1, 0, 0])
      array([2., 4., 4.])
      >>> tf.apply([1, 0, 0])
      array([2., 4., 4.])