# scipy.spatial.transform.Rotation

class scipy.spatial.transform.Rotation

  Rotation in 3 dimensions.

  This class provides an interface to initialize from and represent rotations with:

  - Quaternions

  - Rotation Matrices

  - Rotation Vectors

  - Modified Rodrigues Parameters

  - Euler Angles

  The following operations on rotations are supported:

  - Application on vectors

  - Rotation Composition

  - Rotation Inversion

  - Rotation Indexing

  Indexing within a rotation is supported since multiple rotation transforms can be stored within a single `Rotation` instance.

  To create `Rotation` objects use `from_...` methods (see examples below). `Rotation(...)` is not supposed to be instantiated directly.

  See also

  `Slerp`

  Notes

  New in version 1.2.0.

  Examples

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

  A `Rotation` instance can be initialized in any of the above formats and converted to any of the others. The underlying object is independent of the representation used for initialization.

  Consider a counter-clockwise rotation of 90 degrees about the z-axis. This corresponds to the following quaternion (in scalar-last format):

      >>> r = R.from_quat([0, 0, np.sin(np.pi/4), np.cos(np.pi/4)])

  The rotation can be expressed in any of the other formats:

      >>> r.as_matrix()
      array([[ 2.22044605e-16, -1.00000000e+00,  0.00000000e+00],
      [ 1.00000000e+00,  2.22044605e-16,  0.00000000e+00],
      [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])
      >>> r.as_rotvec()
      array([0.        , 0.        , 1.57079633])
      >>> r.as_euler('zyx', degrees=True)
      array([90.,  0.,  0.])

  The same rotation can be initialized using a rotation matrix:

      >>> r = R.from_matrix([[0, -1, 0],
      ...                    [1, 0, 0],
      ...                    [0, 0, 1]])

  Representation in other formats:

      >>> r.as_quat()
      array([0.        , 0.        , 0.70710678, 0.70710678])
      >>> r.as_rotvec()
      array([0.        , 0.        , 1.57079633])
      >>> r.as_euler('zyx', degrees=True)
      array([90.,  0.,  0.])

  The rotation vector corresponding to this rotation is given by:

      >>> r = R.from_rotvec(np.pi/2 * np.array([0, 0, 1]))

  Representation in other formats:

      >>> r.as_quat()
      array([0.        , 0.        , 0.70710678, 0.70710678])
      >>> r.as_matrix()
      array([[ 2.22044605e-16, -1.00000000e+00,  0.00000000e+00],
             [ 1.00000000e+00,  2.22044605e-16,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])
      >>> r.as_euler('zyx', degrees=True)
      array([90.,  0.,  0.])

  The `from_euler` method is quite flexible in the range of input formats it supports. Here we initialize a single rotation about a single axis:

      >>> r = R.from_euler('z', 90, degrees=True)

  Again, the object is representation independent and can be converted to any other format:

      >>> r.as_quat()
      array([0.        , 0.        , 0.70710678, 0.70710678])
      >>> r.as_matrix()
      array([[ 2.22044605e-16, -1.00000000e+00,  0.00000000e+00],
             [ 1.00000000e+00,  2.22044605e-16,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])
      >>> r.as_rotvec()
      array([0.        , 0.        , 1.57079633])

  It is also possible to initialize multiple rotations in a single instance using any of the `from_...` functions. Here we initialize a stack of 3 rotations using the `from_euler` method:

      >>> r = R.from_euler('zyx', [
      ... [90, 0, 0],
      ... [0, 45, 0],
      ... [45, 60, 30]], degrees=True)

  The other representations also now return a stack of 3 rotations. For example:

      >>> r.as_quat()
      array([[0.        , 0.        , 0.70710678, 0.70710678],
             [0.        , 0.38268343, 0.        , 0.92387953],
             [0.39190384, 0.36042341, 0.43967974, 0.72331741]])

  Applying the above rotations onto a vector:

      >>> v = [1, 2, 3]
      >>> r.apply(v)
      array([[-2.        ,  1.        ,  3.        ],
             [ 2.82842712,  2.        ,  1.41421356],
             [ 2.24452282,  0.78093109,  2.89002836]])

  A `Rotation` instance can be indexed and sliced as if it were a single 1D array or list:

      >>> r.as_quat()
      array([[0.        , 0.        , 0.70710678, 0.70710678],
             [0.        , 0.38268343, 0.        , 0.92387953],
             [0.39190384, 0.36042341, 0.43967974, 0.72331741]])
      >>> p = r[0]
      >>> p.as_matrix()
      array([[ 2.22044605e-16, -1.00000000e+00,  0.00000000e+00],
             [ 1.00000000e+00,  2.22044605e-16,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])
      >>> q = r[1:3]
      >>> q.as_quat()
      array([[0.        , 0.38268343, 0.        , 0.92387953],
             [0.39190384, 0.36042341, 0.43967974, 0.72331741]])

  In fact it can be converted to numpy.array:

      >>> r_array = np.asarray(r)
      >>> r_array.shape
      (3,)
      >>> r_array[0].as_matrix()
      array([[ 2.22044605e-16, -1.00000000e+00,  0.00000000e+00],
             [ 1.00000000e+00,  2.22044605e-16,  0.00000000e+00],
             [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])

  Multiple rotations can be composed using the `*` operator:

      >>> r1 = R.from_euler('z', 90, degrees=True)
      >>> r2 = R.from_rotvec([np.pi/4, 0, 0])
      >>> v = [1, 2, 3]
      >>> r2.apply(r1.apply(v))
      array([-2.        , -1.41421356,  2.82842712])
      >>> r3 = r2 * r1 # Note the order
      >>> r3.apply(v)
      array([-2.        , -1.41421356,  2.82842712])

  Finally, it is also possible to invert rotations:

      >>> r1 = R.from_euler('z', [90, 45], degrees=True)
      >>> r2 = r1.inv()
      >>> r2.as_euler('zyx', degrees=True)
      array([[-90.,   0.,   0.],
             [-45.,   0.,   0.]])

  The following function can be used to plot rotations with Matplotlib by showing how they transform the standard x, y, z coordinate axes:

      >>> import matplotlib.pyplot as plt

      >>> def plot_rotated_axes(ax, r, name=None, offset=(0, 0, 0), scale=1):
      ...     colors = ("#FF6666", "#005533", "#1199EE")  # Colorblind-safe RGB
      ...     loc = np.array([offset, offset])
      ...     for i, (axis, c) in enumerate(zip((ax.xaxis, ax.yaxis, ax.zaxis),
      ...                                       colors)):
      ...         axlabel = axis.axis_name
      ...         axis.set_label_text(axlabel)
      ...         axis.label.set_color(c)
      ...         axis.line.set_color(c)
      ...         axis.set_tick_params(colors=c)
      ...         line = np.zeros((2, 3))
      ...         line[1, i] = scale
      ...         line_rot = r.apply(line)
      ...         line_plot = line_rot + loc
      ...         ax.plot(line_plot[:, 0], line_plot[:, 1], line_plot[:, 2], c)
      ...         text_loc = line[1]*1.2
      ...         text_loc_rot = r.apply(text_loc)
      ...         text_plot = text_loc_rot + loc[0]
      ...         ax.text(*text_plot, axlabel.upper(), color=c,
      ...                 va="center", ha="center")
      ...     ax.text(*offset, name, color="k", va="center", ha="center",
      ...             bbox={"fc": "w", "alpha": 0.8, "boxstyle": "circle"})

  Create three rotations - the identity and two Euler rotations using intrinsic and extrinsic conventions:

      >>> r0 = R.identity()
      >>> r1 = R.from_euler("ZYX", [90, -30, 0], degrees=True)  # intrinsic
      >>> r2 = R.from_euler("zyx", [90, -30, 0], degrees=True)  # extrinsic

  Add all three rotations to a single plot:

      >>> ax = plt.figure().add_subplot(projection="3d", proj_type="ortho")
      >>> plot_rotated_axes(ax, r0, name="r0", offset=(0, 0, 0))
      >>> plot_rotated_axes(ax, r1, name="r1", offset=(3, 0, 0))
      >>> plot_rotated_axes(ax, r2, name="r2", offset=(6, 0, 0))
      >>> _ = ax.annotate(
      ...     "r0: Identity Rotation\n"
      ...     "r1: Intrinsic Euler Rotation (ZYX)\n"
      ...     "r2: Extrinsic Euler Rotation (zyx)",
      ...     xy=(0.6, 0.7), xycoords="axes fraction", ha="left"
      ... )
      >>> ax.set(xlim=(-1.25, 7.25), ylim=(-1.25, 1.25), zlim=(-1.25, 1.25))
      >>> ax.set(xticks=range(-1, 8), yticks=[-1, 0, 1], zticks=[-1, 0, 1])
      >>> ax.set_aspect("equal", adjustable="box")
      >>> ax.figure.set_size_inches(6, 5)
      >>> plt.tight_layout()

  Show the plot:

      >>> plt.show()

  These examples serve as an overview into the `Rotation` class and highlight major functionalities. For more thorough examples of the range of input and output formats supported, consult the individual method’s examples.

  Attributes:
    `single`

      Whether this instance represents a single rotation.

  Methods

  `__len__` | Number of rotations contained in this object.
  `from_quat`(type cls, quat) | Initialize from quaternions.
  `from_matrix`(type cls, matrix) | Initialize from rotation matrix.
  `from_rotvec`(type cls, rotvec[, degrees]) | Initialize from rotation vectors.
  `from_mrp`(type cls, mrp) | Initialize from Modified Rodrigues Parameters (MRPs).
  `from_euler`(type cls, seq, angles[, degrees]) | Initialize from Euler angles.
  `as_quat`(self[, canonical]) | Represent as quaternions.
  `as_matrix`(self) | Represent as rotation matrix.
  `as_rotvec`(self[, degrees]) | Represent as rotation vectors.
  `as_mrp`(self) | Represent as Modified Rodrigues Parameters (MRPs).
  `as_euler`(self, seq[, degrees]) | Represent as Euler angles.
  `concatenate`(type cls, rotations) | Concatenate a sequence of `Rotation` objects.
  `apply`(self, vectors[, inverse]) | Apply this rotation to a set of vectors.
  `__mul__` | Compose this rotation with the other.
  `inv`(self) | Invert this rotation.
  `magnitude`(self) | Get the magnitude(s) of the rotation(s).
  `mean`(self[, weights]) | Get the mean of the rotations.
  `reduce`(self[, left, right, return_indices]) | Reduce this rotation with the provided rotation groups.
  `create_group`(type cls, group[, axis]) | Create a 3D rotation group.
  `__getitem__` | Extract rotation(s) at given index(es) from object.
  `identity`(type cls[, num]) | Get identity rotation(s).
  `random`(type cls[, num, random_state]) | Generate uniformly distributed rotations.
  `align_vectors`(type cls, a, b[, weights, ...]) | Estimate a rotation to optimally align two sets of vectors.