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scipy.spatial.transform.RigidTransform.

# as_matrix

RigidTransform.as_matrix()

  Return a copy of the matrix representation of the transform.

  4x4 rigid transformation matrices are of the form:

      [       tx]
      [   R   ty]
      [       tz]
      [ 0 0 0  1]

  where `R` is a 3x3 orthonormal rotation matrix and `t = [tx, ty, tz]` is a 3x1 translation vector.

  Returns:
    matrix numpy.ndarray, shape (…, 4, 4)

      Transformation matrices with the same leading dimensions as the transform.

  Notes

  Array API Standard Support

  `as_matrix` 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
      >>> from scipy.spatial.transform import Rotation as R
      >>> import numpy as np

  A transformation matrix is a 4x4 matrix formed from a 3x3 rotation matrix and a 3x1 translation vector:

      >>> 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.as_matrix()
      array([[ 0., 0., 1., 2.],
             [ 1., 0., 0., 3.],
             [ 0., 1., 0., 4.],
             [ 0., 0., 0., 1.]])

      >>> Tf.identity(2).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.]]])