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Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/scipy/spatial/transform/rotation.py

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Fixed database typo and removed unnecessary class identifier.
2020-10-14 14:10:37 +00:00
import re
import warnings
import numpy as np
import scipy.linalg
from scipy._lib._util import check_random_state
from ._rotation_groups import create_group
_AXIS_TO_IND = {'x': 0, 'y': 1, 'z': 2}
def _elementary_basis_vector(axis):
b = np.zeros(3)
b[_AXIS_TO_IND[axis]] = 1
return b
def _compute_euler_from_matrix(matrix, seq, extrinsic=False):
# The algorithm assumes intrinsic frame transformations. The algorithm
# in the paper is formulated for rotation matrices which are transposition
# rotation matrices used within Rotation.
# Adapt the algorithm for our case by
# 1. Instead of transposing our representation, use the transpose of the
# O matrix as defined in the paper, and be careful to swap indices
# 2. Reversing both axis sequence and angles for extrinsic rotations
if extrinsic:
seq = seq[::-1]
if matrix.ndim == 2:
matrix = matrix[None, :, :]
num_rotations = matrix.shape[0]
# Step 0
# Algorithm assumes axes as column vectors, here we use 1D vectors
n1 = _elementary_basis_vector(seq[0])
n2 = _elementary_basis_vector(seq[1])
n3 = _elementary_basis_vector(seq[2])
# Step 2
sl = np.dot(np.cross(n1, n2), n3)
cl = np.dot(n1, n3)
# angle offset is lambda from the paper referenced in [2] from docstring of
# `as_euler` function
offset = np.arctan2(sl, cl)
c = np.vstack((n2, np.cross(n1, n2), n1))
# Step 3
rot = np.array([
[1, 0, 0],
[0, cl, sl],
[0, -sl, cl],
])
res = np.einsum('...ij,...jk->...ik', c, matrix)
matrix_transformed = np.einsum('...ij,...jk->...ik', res, c.T.dot(rot))
# Step 4
angles = np.empty((num_rotations, 3))
# Ensure less than unit norm
positive_unity = matrix_transformed[:, 2, 2] > 1
negative_unity = matrix_transformed[:, 2, 2] < -1
matrix_transformed[positive_unity, 2, 2] = 1
matrix_transformed[negative_unity, 2, 2] = -1
angles[:, 1] = np.arccos(matrix_transformed[:, 2, 2])
# Steps 5, 6
eps = 1e-7
safe1 = (np.abs(angles[:, 1]) >= eps)
safe2 = (np.abs(angles[:, 1] - np.pi) >= eps)
# Step 4 (Completion)
angles[:, 1] += offset
# 5b
safe_mask = np.logical_and(safe1, safe2)
angles[safe_mask, 0] = np.arctan2(matrix_transformed[safe_mask, 0, 2],
-matrix_transformed[safe_mask, 1, 2])
angles[safe_mask, 2] = np.arctan2(matrix_transformed[safe_mask, 2, 0],
matrix_transformed[safe_mask, 2, 1])
if extrinsic:
# For extrinsic, set first angle to zero so that after reversal we
# ensure that third angle is zero
# 6a
angles[~safe_mask, 0] = 0
# 6b
angles[~safe1, 2] = np.arctan2(matrix_transformed[~safe1, 1, 0]
- matrix_transformed[~safe1, 0, 1],
matrix_transformed[~safe1, 0, 0]
+ matrix_transformed[~safe1, 1, 1])
# 6c
angles[~safe2, 2] = -np.arctan2(matrix_transformed[~safe2, 1, 0]
+ matrix_transformed[~safe2, 0, 1],
matrix_transformed[~safe2, 0, 0]
- matrix_transformed[~safe2, 1, 1])
else:
# For instrinsic, set third angle to zero
# 6a
angles[~safe_mask, 2] = 0
# 6b
angles[~safe1, 0] = np.arctan2(matrix_transformed[~safe1, 1, 0]
- matrix_transformed[~safe1, 0, 1],
matrix_transformed[~safe1, 0, 0]
+ matrix_transformed[~safe1, 1, 1])
# 6c
angles[~safe2, 0] = np.arctan2(matrix_transformed[~safe2, 1, 0]
+ matrix_transformed[~safe2, 0, 1],
matrix_transformed[~safe2, 0, 0]
- matrix_transformed[~safe2, 1, 1])
# Step 7
if seq[0] == seq[2]:
# lambda = 0, so we can only ensure angle2 -> [0, pi]
adjust_mask = np.logical_or(angles[:, 1] < 0, angles[:, 1] > np.pi)
else:
# lambda = + or - pi/2, so we can ensure angle2 -> [-pi/2, pi/2]
adjust_mask = np.logical_or(angles[:, 1] < -np.pi / 2,
angles[:, 1] > np.pi / 2)
# Dont adjust gimbal locked angle sequences
adjust_mask = np.logical_and(adjust_mask, safe_mask)
angles[adjust_mask, 0] += np.pi
angles[adjust_mask, 1] = 2 * offset - angles[adjust_mask, 1]
angles[adjust_mask, 2] -= np.pi
angles[angles < -np.pi] += 2 * np.pi
angles[angles > np.pi] -= 2 * np.pi
# Step 8
if not np.all(safe_mask):
warnings.warn("Gimbal lock detected. Setting third angle to zero since"
" it is not possible to uniquely determine all angles.")
# Reverse role of extrinsic and intrinsic rotations, but let third angle be
# zero for gimbal locked cases
if extrinsic:
angles = angles[:, ::-1]
return angles
def _make_elementary_quat(axis, angles):
quat = np.zeros((angles.shape[0], 4))
quat[:, 3] = np.cos(angles / 2)
quat[:, _AXIS_TO_IND[axis]] = np.sin(angles / 2)
return quat
def _compose_quat(p, q):
product = np.empty((max(p.shape[0], q.shape[0]), 4))
product[:, 3] = p[:, 3] * q[:, 3] - np.sum(p[:, :3] * q[:, :3], axis=1)
product[:, :3] = (p[:, None, 3] * q[:, :3] + q[:, None, 3] * p[:, :3] +
np.cross(p[:, :3], q[:, :3]))
return product
def _elementary_quat_compose(seq, angles, intrinsic=False):
result = _make_elementary_quat(seq[0], angles[:, 0])
for idx, axis in enumerate(seq[1:], start=1):
if intrinsic:
result = _compose_quat(
result,
_make_elementary_quat(axis, angles[:, idx]))
else:
result = _compose_quat(
_make_elementary_quat(axis, angles[:, idx]),
result)
return result
class Rotation(object):
"""Rotation in 3 dimensions.
This class provides an interface to initialize from and represent rotations
with:
- Quaternions
- Rotation Matrices
- Rotation Vectors
- 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.
Methods
-------
__len__
from_quat
from_matrix
from_rotvec
from_euler
as_quat
as_matrix
as_rotvec
as_euler
apply
__mul__
inv
magnitude
mean
reduce
create_group
__getitem__
identity
random
align_vectors
See Also
--------
Slerp
Notes
-----
.. versionadded: 1.2.0
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
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]])
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.]])
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.
"""
def __init__(self, quat, normalize=True, copy=True):
self._single = False
quat = np.asarray(quat, dtype=float)
if quat.ndim not in [1, 2] or quat.shape[-1] != 4:
raise ValueError("Expected `quat` to have shape (4,) or (N x 4), "
"got {}.".format(quat.shape))
# If a single quaternion is given, convert it to a 2D 1 x 4 matrix but
# set self._single to True so that we can return appropriate objects
# in the `to_...` methods
if quat.shape == (4,):
quat = quat[None, :]
self._single = True
if normalize:
self._quat = quat.copy()
norms = scipy.linalg.norm(quat, axis=1)
zero_norms = norms == 0
if zero_norms.any():
raise ValueError("Found zero norm quaternions in `quat`.")
# Ensure norm is broadcasted along each column.
self._quat[~zero_norms] /= norms[~zero_norms][:, None]
else:
self._quat = quat.copy() if copy else quat
def __len__(self):
"""Number of rotations contained in this object.
Multiple rotations can be stored in a single instance.
Returns
-------
length : int
Number of rotations stored in object.
"""
return self._quat.shape[0]
@classmethod
def from_quat(cls, quat, normalized=None):
"""Initialize from quaternions.
3D rotations can be represented using unit-norm quaternions [1]_.
Parameters
----------
quat : array_like, shape (N, 4) or (4,)
Each row is a (possibly non-unit norm) quaternion in scalar-last
(x, y, z, w) format. Each quaternion will be normalized to unit
norm.
normalized
Deprecated argument. Has no effect, input `quat` is always
normalized.
.. deprecated:: 1.4.0
Returns
-------
rotation : `Rotation` instance
Object containing the rotations represented by input quaternions.
References
----------
.. [1] https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Initialize a single rotation:
>>> r = R.from_quat([1, 0, 0, 0])
>>> r.as_quat()
array([1., 0., 0., 0.])
>>> r.as_quat().shape
(4,)
Initialize multiple rotations in a single object:
>>> r = R.from_quat([
... [1, 0, 0, 0],
... [0, 0, 0, 1]
... ])
>>> r.as_quat()
array([[1., 0., 0., 0.],
[0., 0., 0., 1.]])
>>> r.as_quat().shape
(2, 4)
It is also possible to have a stack of a single rotation:
>>> r = R.from_quat([[0, 0, 0, 1]])
>>> r.as_quat()
array([[0., 0., 0., 1.]])
>>> r.as_quat().shape
(1, 4)
Quaternions are normalized before initialization.
>>> r = R.from_quat([0, 0, 1, 1])
>>> r.as_quat()
array([0. , 0. , 0.70710678, 0.70710678])
"""
if normalized is not None:
warnings.warn("`normalized` is deprecated in scipy 1.4.0 and "
"will be removed in scipy 1.6.0. The input `quat` "
"is always normalized.", DeprecationWarning)
return cls(quat, normalize=True)
@classmethod
def from_matrix(cls, matrix):
"""Initialize from rotation matrix.
Rotations in 3 dimensions can be represented with 3 x 3 proper
orthogonal matrices [1]_. If the input is not proper orthogonal,
an approximation is created using the method described in [2]_.
Parameters
----------
matrix : array_like, shape (N, 3, 3) or (3, 3)
A single matrix or a stack of matrices, where ``matrix[i]`` is
the i-th matrix.
Returns
-------
rotation : `Rotation` instance
Object containing the rotations represented by the rotation
matrices.
References
----------
.. [1] https://en.wikipedia.org/wiki/Rotation_matrix#In_three_dimensions
.. [2] F. Landis Markley, "Unit Quaternion from Rotation Matrix",
Journal of guidance, control, and dynamics vol. 31.2, pp.
440-442, 2008.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Initialize a single rotation:
>>> r = R.from_matrix([
... [0, -1, 0],
... [1, 0, 0],
... [0, 0, 1]])
>>> r.as_matrix().shape
(3, 3)
Initialize multiple rotations in a single object:
>>> r = R.from_matrix([
... [
... [0, -1, 0],
... [1, 0, 0],
... [0, 0, 1],
... ],
... [
... [1, 0, 0],
... [0, 0, -1],
... [0, 1, 0],
... ]])
>>> r.as_matrix().shape
(2, 3, 3)
If input matrices are not special orthogonal (orthogonal with
determinant equal to +1), then a special orthogonal estimate is stored:
>>> a = np.array([
... [0, -0.5, 0],
... [0.5, 0, 0],
... [0, 0, 0.5]])
>>> np.linalg.det(a)
0.12500000000000003
>>> r = R.from_matrix(a)
>>> matrix = r.as_matrix()
>>> matrix
array([[-0.38461538, -0.92307692, 0. ],
[ 0.92307692, -0.38461538, 0. ],
[ 0. , 0. , 1. ]])
>>> np.linalg.det(matrix)
1.0000000000000002
It is also possible to have a stack containing a single rotation:
>>> r = R.from_matrix([[
... [0, -1, 0],
... [1, 0, 0],
... [0, 0, 1]]])
>>> r.as_matrix()
array([[[ 0., -1., 0.],
[ 1., 0., 0.],
[ 0., 0., 1.]]])
>>> r.as_matrix().shape
(1, 3, 3)
Notes
-----
This function was called from_dcm before.
.. versionadded:: 1.4.0
"""
is_single = False
matrix = np.asarray(matrix, dtype=float)
if matrix.ndim not in [2, 3] or matrix.shape[-2:] != (3, 3):
raise ValueError("Expected `matrix` to have shape (3, 3) or "
"(N, 3, 3), got {}".format(matrix.shape))
# If a single matrix is given, convert it to 3D 1 x 3 x 3 matrix but
# set self._single to True so that we can return appropriate objects in
# the `to_...` methods
if matrix.shape == (3, 3):
matrix = matrix.reshape((1, 3, 3))
is_single = True
num_rotations = matrix.shape[0]
decision_matrix = np.empty((num_rotations, 4))
decision_matrix[:, :3] = matrix.diagonal(axis1=1, axis2=2)
decision_matrix[:, -1] = decision_matrix[:, :3].sum(axis=1)
choices = decision_matrix.argmax(axis=1)
quat = np.empty((num_rotations, 4))
ind = np.nonzero(choices != 3)[0]
i = choices[ind]
j = (i + 1) % 3
k = (j + 1) % 3
quat[ind, i] = 1 - decision_matrix[ind, -1] + 2 * matrix[ind, i, i]
quat[ind, j] = matrix[ind, j, i] + matrix[ind, i, j]
quat[ind, k] = matrix[ind, k, i] + matrix[ind, i, k]
quat[ind, 3] = matrix[ind, k, j] - matrix[ind, j, k]
ind = np.nonzero(choices == 3)[0]
quat[ind, 0] = matrix[ind, 2, 1] - matrix[ind, 1, 2]
quat[ind, 1] = matrix[ind, 0, 2] - matrix[ind, 2, 0]
quat[ind, 2] = matrix[ind, 1, 0] - matrix[ind, 0, 1]
quat[ind, 3] = 1 + decision_matrix[ind, -1]
quat /= np.linalg.norm(quat, axis=1)[:, None]
if is_single:
return cls(quat[0], normalize=False, copy=False)
else:
return cls(quat, normalize=False, copy=False)
@classmethod
@np.deprecate(message="from_dcm is renamed to from_matrix in scipy 1.4.0 "
"and will be removed in scipy 1.6.0")
def from_dcm(cls, dcm):
return cls.from_matrix(dcm)
@classmethod
def from_rotvec(cls, rotvec):
"""Initialize from rotation vectors.
A rotation vector is a 3 dimensional vector which is co-directional to
the axis of rotation and whose norm gives the angle of rotation (in
radians) [1]_.
Parameters
----------
rotvec : array_like, shape (N, 3) or (3,)
A single vector or a stack of vectors, where `rot_vec[i]` gives
the ith rotation vector.
Returns
-------
rotation : `Rotation` instance
Object containing the rotations represented by input rotation
vectors.
References
----------
.. [1] https://en.wikipedia.org/wiki/Axis%E2%80%93angle_representation#Rotation_vector
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Initialize a single rotation:
>>> r = R.from_rotvec(np.pi/2 * np.array([0, 0, 1]))
>>> r.as_rotvec()
array([0. , 0. , 1.57079633])
>>> r.as_rotvec().shape
(3,)
Initialize multiple rotations in one object:
>>> r = R.from_rotvec([
... [0, 0, np.pi/2],
... [np.pi/2, 0, 0]])
>>> r.as_rotvec()
array([[0. , 0. , 1.57079633],
[1.57079633, 0. , 0. ]])
>>> r.as_rotvec().shape
(2, 3)
It is also possible to have a stack of a single rotaton:
>>> r = R.from_rotvec([[0, 0, np.pi/2]])
>>> r.as_rotvec().shape
(1, 3)
"""
is_single = False
rotvec = np.asarray(rotvec, dtype=float)
if rotvec.ndim not in [1, 2] or rotvec.shape[-1] != 3:
raise ValueError("Expected `rot_vec` to have shape (3,) "
"or (N, 3), got {}".format(rotvec.shape))
# If a single vector is given, convert it to a 2D 1 x 3 matrix but
# set self._single to True so that we can return appropriate objects
# in the `as_...` methods
if rotvec.shape == (3,):
rotvec = rotvec[None, :]
is_single = True
num_rotations = rotvec.shape[0]
norms = np.linalg.norm(rotvec, axis=1)
small_angle = (norms <= 1e-3)
large_angle = ~small_angle
scale = np.empty(num_rotations)
scale[small_angle] = (0.5 - norms[small_angle] ** 2 / 48 +
norms[small_angle] ** 4 / 3840)
scale[large_angle] = (np.sin(norms[large_angle] / 2) /
norms[large_angle])
quat = np.empty((num_rotations, 4))
quat[:, :3] = scale[:, None] * rotvec
quat[:, 3] = np.cos(norms / 2)
if is_single:
return cls(quat[0], normalize=False, copy=False)
else:
return cls(quat, normalize=False, copy=False)
@classmethod
def from_euler(cls, seq, angles, degrees=False):
"""Initialize from Euler angles.
Rotations in 3-D can be represented by a sequence of 3
rotations around a sequence of axes. In theory, any three axes spanning
the 3-D Euclidean space are enough. In practice, the axes of rotation are
chosen to be the basis vectors.
The three rotations can either be in a global frame of reference
(extrinsic) or in a body centred frame of reference (intrinsic), which
is attached to, and moves with, the object under rotation [1]_.
Parameters
----------
seq : string
Specifies sequence of axes for rotations. Up to 3 characters
belonging to the set {'X', 'Y', 'Z'} for intrinsic rotations, or
{'x', 'y', 'z'} for extrinsic rotations. Extrinsic and intrinsic
rotations cannot be mixed in one function call.
angles : float or array_like, shape (N,) or (N, [1 or 2 or 3])
Euler angles specified in radians (`degrees` is False) or degrees
(`degrees` is True).
For a single character `seq`, `angles` can be:
- a single value
- array_like with shape (N,), where each `angle[i]`
corresponds to a single rotation
- array_like with shape (N, 1), where each `angle[i, 0]`
corresponds to a single rotation
For 2- and 3-character wide `seq`, `angles` can be:
- array_like with shape (W,) where `W` is the width of
`seq`, which corresponds to a single rotation with `W` axes
- array_like with shape (N, W) where each `angle[i]`
corresponds to a sequence of Euler angles describing a single
rotation
degrees : bool, optional
If True, then the given angles are assumed to be in degrees.
Default is False.
Returns
-------
rotation : `Rotation` instance
Object containing the rotation represented by the sequence of
rotations around given axes with given angles.
References
----------
.. [1] https://en.wikipedia.org/wiki/Euler_angles#Definition_by_intrinsic_rotations
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Initialize a single rotation along a single axis:
>>> r = R.from_euler('x', 90, degrees=True)
>>> r.as_quat().shape
(4,)
Initialize a single rotation with a given axis sequence:
>>> r = R.from_euler('zyx', [90, 45, 30], degrees=True)
>>> r.as_quat().shape
(4,)
Initialize a stack with a single rotation around a single axis:
>>> r = R.from_euler('x', [90], degrees=True)
>>> r.as_quat().shape
(1, 4)
Initialize a stack with a single rotation with an axis sequence:
>>> r = R.from_euler('zyx', [[90, 45, 30]], degrees=True)
>>> r.as_quat().shape
(1, 4)
Initialize multiple elementary rotations in one object:
>>> r = R.from_euler('x', [90, 45, 30], degrees=True)
>>> r.as_quat().shape
(3, 4)
Initialize multiple rotations in one object:
>>> r = R.from_euler('zyx', [[90, 45, 30], [35, 45, 90]], degrees=True)
>>> r.as_quat().shape
(2, 4)
"""
num_axes = len(seq)
if num_axes < 1 or num_axes > 3:
raise ValueError("Expected axis specification to be a non-empty "
"string of upto 3 characters, got {}".format(seq))
intrinsic = (re.match(r'^[XYZ]{1,3}$', seq) is not None)
extrinsic = (re.match(r'^[xyz]{1,3}$', seq) is not None)
if not (intrinsic or extrinsic):
raise ValueError("Expected axes from `seq` to be from ['x', 'y', "
"'z'] or ['X', 'Y', 'Z'], got {}".format(seq))
if any(seq[i] == seq[i+1] for i in range(num_axes - 1)):
raise ValueError("Expected consecutive axes to be different, "
"got {}".format(seq))
seq = seq.lower()
angles = np.asarray(angles, dtype=float)
if degrees:
angles = np.deg2rad(angles)
is_single = False
# Prepare angles to have shape (num_rot, num_axes)
if num_axes == 1:
if angles.ndim == 0:
# (1, 1)
angles = angles.reshape((1, 1))
is_single = True
elif angles.ndim == 1:
# (N, 1)
angles = angles[:, None]
elif angles.ndim == 2 and angles.shape[-1] != 1:
raise ValueError("Expected `angles` parameter to have shape "
"(N, 1), got {}.".format(angles.shape))
elif angles.ndim > 2:
raise ValueError("Expected float, 1D array, or 2D array for "
"parameter `angles` corresponding to `seq`, "
"got shape {}.".format(angles.shape))
else: # 2 or 3 axes
if angles.ndim not in [1, 2] or angles.shape[-1] != num_axes:
raise ValueError("Expected `angles` to be at most "
"2-dimensional with width equal to number "
"of axes specified, got {} for shape".format(
angles.shape))
if angles.ndim == 1:
# (1, num_axes)
angles = angles[None, :]
is_single = True
# By now angles should have shape (num_rot, num_axes)
# sanity check
if angles.ndim != 2 or angles.shape[-1] != num_axes:
raise ValueError("Expected angles to have shape (num_rotations, "
"num_axes), got {}.".format(angles.shape))
quat = _elementary_quat_compose(seq, angles, intrinsic)
if is_single:
return cls(quat[0], normalize=False, copy=False)
else:
return cls(quat, normalize=False, copy=False)
def as_quat(self):
"""Represent as quaternions.
Rotations in 3 dimensions can be represented using unit norm
quaternions [1]_. The mapping from quaternions to rotations is
two-to-one, i.e. quaternions ``q`` and ``-q``, where ``-q`` simply
reverses the sign of each component, represent the same spatial
rotation.
Returns
-------
quat : `numpy.ndarray`, shape (4,) or (N, 4)
Shape depends on shape of inputs used for initialization.
References
----------
.. [1] https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_matrix([[0, -1, 0],
... [1, 0, 0],
... [0, 0, 1]])
>>> r.as_quat()
array([0. , 0. , 0.70710678, 0.70710678])
>>> r.as_quat().shape
(4,)
Represent a stack with a single rotation:
>>> r = R.from_quat([[0, 0, 0, 1]])
>>> r.as_quat().shape
(1, 4)
Represent multiple rotations in a single object:
>>> r = R.from_rotvec([[np.pi, 0, 0], [0, 0, np.pi/2]])
>>> r.as_quat().shape
(2, 4)
"""
if self._single:
return self._quat[0].copy()
else:
return self._quat.copy()
def as_matrix(self):
"""Represent as rotation matrix.
3D rotations can be represented using rotation matrices, which
are 3 x 3 real orthogonal matrices with determinant equal to +1 [1]_.
Returns
-------
matrix : ndarray, shape (3, 3) or (N, 3, 3)
Shape depends on shape of inputs used for initialization.
References
----------
.. [1] https://en.wikipedia.org/wiki/Rotation_matrix#In_three_dimensions
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_rotvec([0, 0, np.pi/2])
>>> 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_matrix().shape
(3, 3)
Represent a stack with a single rotation:
>>> r = R.from_quat([[1, 1, 0, 0]])
>>> r.as_matrix()
array([[[ 0., 1., 0.],
[ 1., 0., 0.],
[ 0., 0., -1.]]])
>>> r.as_matrix().shape
(1, 3, 3)
Represent multiple rotations:
>>> r = R.from_rotvec([[np.pi/2, 0, 0], [0, 0, np.pi/2]])
>>> r.as_matrix()
array([[[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 2.22044605e-16, -1.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 2.22044605e-16]],
[[ 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_matrix().shape
(2, 3, 3)
Notes
-----
This function was called as_dcm before.
.. versionadded:: 1.4.0
"""
x = self._quat[:, 0]
y = self._quat[:, 1]
z = self._quat[:, 2]
w = self._quat[:, 3]
x2 = x * x
y2 = y * y
z2 = z * z
w2 = w * w
xy = x * y
zw = z * w
xz = x * z
yw = y * w
yz = y * z
xw = x * w
num_rotations = len(self)
matrix = np.empty((num_rotations, 3, 3))
matrix[:, 0, 0] = x2 - y2 - z2 + w2
matrix[:, 1, 0] = 2 * (xy + zw)
matrix[:, 2, 0] = 2 * (xz - yw)
matrix[:, 0, 1] = 2 * (xy - zw)
matrix[:, 1, 1] = - x2 + y2 - z2 + w2
matrix[:, 2, 1] = 2 * (yz + xw)
matrix[:, 0, 2] = 2 * (xz + yw)
matrix[:, 1, 2] = 2 * (yz - xw)
matrix[:, 2, 2] = - x2 - y2 + z2 + w2
if self._single:
return matrix[0]
else:
return matrix
@np.deprecate(message="as_dcm is renamed to as_matrix in scipy 1.4.0 "
"and will be removed in scipy 1.6.0")
def as_dcm(self):
return self.as_matrix()
def as_rotvec(self):
"""Represent as rotation vectors.
A rotation vector is a 3 dimensional vector which is co-directional to
the axis of rotation and whose norm gives the angle of rotation (in
radians) [1]_.
Returns
-------
rotvec : ndarray, shape (3,) or (N, 3)
Shape depends on shape of inputs used for initialization.
References
----------
.. [1] https://en.wikipedia.org/wiki/Axis%E2%80%93angle_representation#Rotation_vector
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_euler('z', 90, degrees=True)
>>> r.as_rotvec()
array([0. , 0. , 1.57079633])
>>> r.as_rotvec().shape
(3,)
Represent a stack with a single rotation:
>>> r = R.from_quat([[0, 0, 1, 1]])
>>> r.as_rotvec()
array([[0. , 0. , 1.57079633]])
>>> r.as_rotvec().shape
(1, 3)
Represent multiple rotations in a single object:
>>> r = R.from_quat([[0, 0, 1, 1], [1, 1, 0, 1]])
>>> r.as_rotvec()
array([[0. , 0. , 1.57079633],
[1.35102172, 1.35102172, 0. ]])
>>> r.as_rotvec().shape
(2, 3)
"""
quat = self._quat.copy()
# w > 0 to ensure 0 <= angle <= pi
quat[quat[:, 3] < 0] *= -1
angle = 2 * np.arctan2(np.linalg.norm(quat[:, :3], axis=1), quat[:, 3])
small_angle = (angle <= 1e-3)
large_angle = ~small_angle
num_rotations = len(self)
scale = np.empty(num_rotations)
scale[small_angle] = (2 + angle[small_angle] ** 2 / 12 +
7 * angle[small_angle] ** 4 / 2880)
scale[large_angle] = (angle[large_angle] /
np.sin(angle[large_angle] / 2))
rotvec = scale[:, None] * quat[:, :3]
if self._single:
return rotvec[0]
else:
return rotvec
def as_euler(self, seq, degrees=False):
"""Represent as Euler angles.
Any orientation can be expressed as a composition of 3 elementary
rotations. Once the axis sequence has been chosen, Euler angles define
the angle of rotation around each respective axis [1]_.
The algorithm from [2]_ has been used to calculate Euler angles for the
rotation about a given sequence of axes.
Euler angles suffer from the problem of gimbal lock [3]_, where the
representation loses a degree of freedom and it is not possible to
determine the first and third angles uniquely. In this case,
a warning is raised, and the third angle is set to zero. Note however
that the returned angles still represent the correct rotation.
Parameters
----------
seq : string, length 3
3 characters belonging to the set {'X', 'Y', 'Z'} for intrinsic
rotations, or {'x', 'y', 'z'} for extrinsic rotations [1]_.
Adjacent axes cannot be the same.
Extrinsic and intrinsic rotations cannot be mixed in one function
call.
degrees : boolean, optional
Returned angles are in degrees if this flag is True, else they are
in radians. Default is False.
Returns
-------
angles : ndarray, shape (3,) or (N, 3)
Shape depends on shape of inputs used to initialize object.
The returned angles are in the range:
- First angle belongs to [-180, 180] degrees (both inclusive)
- Third angle belongs to [-180, 180] degrees (both inclusive)
- Second angle belongs to:
- [-90, 90] degrees if all axes are different (like xyz)
- [0, 180] degrees if first and third axes are the same
(like zxz)
References
----------
.. [1] https://en.wikipedia.org/wiki/Euler_angles#Definition_by_intrinsic_rotations
.. [2] Malcolm D. Shuster, F. Landis Markley, "General formula for
extraction the Euler angles", Journal of guidance, control, and
dynamics, vol. 29.1, pp. 215-221. 2006
.. [3] https://en.wikipedia.org/wiki/Gimbal_lock#In_applied_mathematics
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_rotvec([0, 0, np.pi/2])
>>> r.as_euler('zxy', degrees=True)
array([90., 0., 0.])
>>> r.as_euler('zxy', degrees=True).shape
(3,)
Represent a stack of single rotation:
>>> r = R.from_rotvec([[0, 0, np.pi/2]])
>>> r.as_euler('zxy', degrees=True)
array([[90., 0., 0.]])
>>> r.as_euler('zxy', degrees=True).shape
(1, 3)
Represent multiple rotations in a single object:
>>> r = R.from_rotvec([
... [0, 0, np.pi/2],
... [0, -np.pi/3, 0],
... [np.pi/4, 0, 0]])
>>> r.as_euler('zxy', degrees=True)
array([[ 90., 0., 0.],
[ 0., 0., -60.],
[ 0., 45., 0.]])
>>> r.as_euler('zxy', degrees=True).shape
(3, 3)
"""
if len(seq) != 3:
raise ValueError("Expected 3 axes, got {}.".format(seq))
intrinsic = (re.match(r'^[XYZ]{1,3}$', seq) is not None)
extrinsic = (re.match(r'^[xyz]{1,3}$', seq) is not None)
if not (intrinsic or extrinsic):
raise ValueError("Expected axes from `seq` to be from "
"['x', 'y', 'z'] or ['X', 'Y', 'Z'], "
"got {}".format(seq))
if any(seq[i] == seq[i+1] for i in range(2)):
raise ValueError("Expected consecutive axes to be different, "
"got {}".format(seq))
seq = seq.lower()
angles = _compute_euler_from_matrix(self.as_matrix(), seq, extrinsic)
if degrees:
angles = np.rad2deg(angles)
return angles[0] if self._single else angles
def apply(self, vectors, inverse=False):
"""Apply this rotation to a set of vectors.
If the original frame rotates to the final frame by this rotation, then
its application to a vector can be seen in two ways:
- As a projection of vector components expressed in the final frame
to the original frame.
- As the physical rotation of a vector being glued to the original
frame as it rotates. In this case the vector components are
expressed in the original frame before and after the rotation.
In terms of rotation matricies, this application is the same as
``self.as_matrix().dot(vectors)``.
Parameters
----------
vectors : array_like, shape (3,) or (N, 3)
Each `vectors[i]` represents a vector in 3D space. A single vector
can either be specified with shape `(3, )` or `(1, 3)`. The number
of rotations and number of vectors given must follow standard numpy
broadcasting rules: either one of them equals unity or they both
equal each other.
inverse : boolean, optional
If True then the inverse of the rotation(s) is applied to the input
vectors. Default is False.
Returns
-------
rotated_vectors : ndarray, shape (3,) or (N, 3)
Result of applying rotation on input vectors.
Shape depends on the following cases:
- If object contains a single rotation (as opposed to a stack
with a single rotation) and a single vector is specified with
shape ``(3,)``, then `rotated_vectors` has shape ``(3,)``.
- In all other cases, `rotated_vectors` has shape ``(N, 3)``,
where ``N`` is either the number of rotations or vectors.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Single rotation applied on a single vector:
>>> vector = np.array([1, 0, 0])
>>> r = R.from_rotvec([0, 0, np.pi/2])
>>> 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.apply(vector)
array([2.22044605e-16, 1.00000000e+00, 0.00000000e+00])
>>> r.apply(vector).shape
(3,)
Single rotation applied on multiple vectors:
>>> vectors = np.array([
... [1, 0, 0],
... [1, 2, 3]])
>>> r = R.from_rotvec([0, 0, np.pi/4])
>>> r.as_matrix()
array([[ 0.70710678, -0.70710678, 0. ],
[ 0.70710678, 0.70710678, 0. ],
[ 0. , 0. , 1. ]])
>>> r.apply(vectors)
array([[ 0.70710678, 0.70710678, 0. ],
[-0.70710678, 2.12132034, 3. ]])
>>> r.apply(vectors).shape
(2, 3)
Multiple rotations on a single vector:
>>> r = R.from_rotvec([[0, 0, np.pi/4], [np.pi/2, 0, 0]])
>>> vector = np.array([1,2,3])
>>> r.as_matrix()
array([[[ 7.07106781e-01, -7.07106781e-01, 0.00000000e+00],
[ 7.07106781e-01, 7.07106781e-01, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]],
[[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 2.22044605e-16, -1.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 2.22044605e-16]]])
>>> r.apply(vector)
array([[-0.70710678, 2.12132034, 3. ],
[ 1. , -3. , 2. ]])
>>> r.apply(vector).shape
(2, 3)
Multiple rotations on multiple vectors. Each rotation is applied on the
corresponding vector:
>>> r = R.from_euler('zxy', [
... [0, 0, 90],
... [45, 30, 60]], degrees=True)
>>> vectors = [
... [1, 2, 3],
... [1, 0, -1]]
>>> r.apply(vectors)
array([[ 3. , 2. , -1. ],
[-0.09026039, 1.11237244, -0.86860844]])
>>> r.apply(vectors).shape
(2, 3)
It is also possible to apply the inverse rotation:
>>> r = R.from_euler('zxy', [
... [0, 0, 90],
... [45, 30, 60]], degrees=True)
>>> vectors = [
... [1, 2, 3],
... [1, 0, -1]]
>>> r.apply(vectors, inverse=True)
array([[-3. , 2. , 1. ],
[ 1.09533535, -0.8365163 , 0.3169873 ]])
"""
vectors = np.asarray(vectors)
if vectors.ndim > 2 or vectors.shape[-1] != 3:
raise ValueError("Expected input of shape (3,) or (P, 3), "
"got {}.".format(vectors.shape))
single_vector = False
if vectors.shape == (3,):
single_vector = True
vectors = vectors[None, :]
matrix = self.as_matrix()
if self._single:
matrix = matrix[None, :, :]
n_vectors = vectors.shape[0]
n_rotations = len(self)
if n_vectors != 1 and n_rotations != 1 and n_vectors != n_rotations:
raise ValueError("Expected equal numbers of rotations and vectors "
", or a single rotation, or a single vector, got "
"{} rotations and {} vectors.".format(
n_rotations, n_vectors))
if inverse:
result = np.einsum('ikj,ik->ij', matrix, vectors)
else:
result = np.einsum('ijk,ik->ij', matrix, vectors)
if self._single and single_vector:
return result[0]
else:
return result
def __mul__(self, other):
"""Compose this rotation with the other.
If `p` and `q` are two rotations, then the composition of 'q followed
by p' is equivalent to `p * q`. In terms of rotation matrices,
the composition can be expressed as
``p.as_matrix().dot(q.as_matrix())``.
Parameters
----------
other : `Rotation` instance
Object containing the rotations to be composed with this one. Note
that rotation compositions are not commutative, so ``p * q`` is
different from ``q * p``.
Returns
-------
composition : `Rotation` instance
This function supports composition of multiple rotations at a time.
The following cases are possible:
- Either ``p`` or ``q`` contains a single rotation. In this case
`composition` contains the result of composing each rotation in
the other object with the single rotation.
- Both ``p`` and ``q`` contain ``N`` rotations. In this case each
rotation ``p[i]`` is composed with the corresponding rotation
``q[i]`` and `output` contains ``N`` rotations.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Composition of two single rotations:
>>> p = R.from_quat([0, 0, 1, 1])
>>> q = R.from_quat([1, 0, 0, 1])
>>> p.as_matrix()
array([[ 0., -1., 0.],
[ 1., 0., 0.],
[ 0., 0., 1.]])
>>> q.as_matrix()
array([[ 1., 0., 0.],
[ 0., 0., -1.],
[ 0., 1., 0.]])
>>> r = p * q
>>> r.as_matrix()
array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.]])
Composition of two objects containing equal number of rotations:
>>> p = R.from_quat([[0, 0, 1, 1], [1, 0, 0, 1]])
>>> q = R.from_rotvec([[np.pi/4, 0, 0], [-np.pi/4, 0, np.pi/4]])
>>> p.as_quat()
array([[0. , 0. , 0.70710678, 0.70710678],
[0.70710678, 0. , 0. , 0.70710678]])
>>> q.as_quat()
array([[ 0.38268343, 0. , 0. , 0.92387953],
[-0.37282173, 0. , 0.37282173, 0.84971049]])
>>> r = p * q
>>> r.as_quat()
array([[ 0.27059805, 0.27059805, 0.65328148, 0.65328148],
[ 0.33721128, -0.26362477, 0.26362477, 0.86446082]])
"""
if not(len(self) == 1 or len(other) == 1 or len(self) == len(other)):
raise ValueError("Expected equal number of rotations in both "
"or a single rotation in either object, "
"got {} rotations in first and {} rotations in "
"second object.".format(
len(self), len(other)))
result = _compose_quat(self._quat, other._quat)
if self._single and other._single:
result = result[0]
return self.__class__(result, normalize=True, copy=False)
def inv(self):
"""Invert this rotation.
Composition of a rotation with its inverse results in an identity
transformation.
Returns
-------
inverse : `Rotation` instance
Object containing inverse of the rotations in the current instance.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Inverting a single rotation:
>>> p = R.from_euler('z', 45, degrees=True)
>>> q = p.inv()
>>> q.as_euler('zyx', degrees=True)
array([-45., 0., 0.])
Inverting multiple rotations:
>>> p = R.from_rotvec([[0, 0, np.pi/3], [-np.pi/4, 0, 0]])
>>> q = p.inv()
>>> q.as_rotvec()
array([[-0. , -0. , -1.04719755],
[ 0.78539816, -0. , -0. ]])
"""
quat = self._quat.copy()
quat[:, -1] *= -1
if self._single:
quat = quat[0]
return self.__class__(quat, normalize=False, copy=False)
def magnitude(self):
"""Get the magnitude(s) of the rotation(s).
Returns
-------
magnitude : ndarray or float
Angle(s) in radians, float if object contains a single rotation
and ndarray if object contains multiple rotations.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
>>> r = R.from_quat(np.eye(4))
>>> r.magnitude()
array([3.14159265, 3.14159265, 3.14159265, 0. ])
Magnitude of a single rotation:
>>> r[0].magnitude()
3.141592653589793
"""
quat = self._quat.reshape((len(self), 4))
s = np.linalg.norm(quat[:, :3], axis=1)
c = np.abs(quat[:, 3])
angles = 2 * np.arctan2(s, c)
if self._single:
return angles[0]
else:
return angles
def mean(self, weights=None):
"""Get the mean of the rotations.
Parameters
----------
weights : array_like shape (N,), optional
Weights describing the relative importance of the rotations. If
None (default), then all values in `weights` are assumed to be
equal.
Returns
-------
mean : `Rotation` instance
Object containing the mean of the rotations in the current
instance.
Notes
-----
The mean used is the chordal L2 mean (also called the projected or
induced arithmetic mean). If ``p`` is a set of rotations with mean
``m``, then ``m`` is the rotation which minimizes
``(weights[:, None, None] * (p.as_matrix() - m.as_matrix())**2).sum()``.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
>>> r = R.from_euler('zyx', [[0, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1]], degrees=True)
>>> r.mean().as_euler('zyx', degrees=True)
array([0.24945696, 0.25054542, 0.24945696])
"""
if weights is None:
weights = np.ones(len(self))
else:
weights = np.asarray(weights)
if weights.ndim != 1:
raise ValueError("Expected `weights` to be 1 dimensional, got "
"shape {}.".format(weights.shape))
if weights.shape[0] != len(self):
raise ValueError("Expected `weights` to have number of values "
"equal to number of rotations, got "
"{} values and {} rotations.".format(
weights.shape[0], len(self)))
if np.any(weights < 0):
raise ValueError("`weights` must be non-negative.")
K = np.dot(weights * self._quat.T, self._quat)
l, v = np.linalg.eigh(K)
return self.__class__(v[:, -1], normalize=False)
def reduce(self, left=None, right=None, return_indices=False):
"""Reduce this rotation with the provided rotation groups.
Reduction of a rotation ``p`` is a transformation of the form
``q = l * p * r``, where ``l`` and ``r`` are chosen from `left` and
`right` respectively, such that rotation ``q`` has the smallest
magnitude.
If `left` and `right` are rotation groups representing symmetries of
two objects rotated by ``p``, then ``q`` is the rotation of the
smallest magnitude to align these objects considering their symmetries.
Parameters
----------
left : `Rotation` instance, optional
Object containing the left rotation(s). Default value (None)
corresponds to the identity rotation.
right : `Rotation` instance, optional
Object containing the right rotation(s). Default value (None)
corresponds to the identity rotation.
return_indices : bool, optional
Whether to return the indices of the rotations from `left` and
`right` used for reduction.
Returns
-------
reduced : `Rotation` instance
Object containing reduced rotations.
left_best, right_best: integer ndarray
Indices of elements from `left` and `right` used for reduction.
"""
if left is None and right is None:
reduced = self.__class__(self._quat, normalize=False, copy=True)
if return_indices:
return reduced, None, None
else:
return reduced
elif right is None:
right = Rotation.identity()
elif left is None:
left = Rotation.identity()
# Levi-Civita tensor for triple product computations
e = np.zeros((3, 3, 3))
e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1
e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1
# We want to calculate the real components of q = l * p * r. It can
# be shown that:
# qs = ls * ps * rs - ls * dot(pv, rv) - ps * dot(lv, rv)
# - rs * dot(lv, pv) - dot(cross(lv, pv), rv)
# where ls and lv denote the scalar and vector components of l.
def split_rotation(R):
q = np.atleast_2d(R.as_quat())
return q[:, -1], q[:, :-1]
p = self
ps, pv = split_rotation(p)
ls, lv = split_rotation(left)
rs, rv = split_rotation(right)
qs = np.abs(np.einsum('i,j,k', ls, ps, rs) -
np.einsum('i,jx,kx', ls, pv, rv) -
np.einsum('ix,j,kx', lv, ps, rv) -
np.einsum('ix,jx,k', lv, pv, rs) -
np.einsum('xyz,ix,jy,kz', e, lv, pv, rv))
qs = np.reshape(np.rollaxis(qs, 1), (qs.shape[1], -1))
# Find best indices from scalar components
max_ind = np.argmax(np.reshape(qs, (len(qs), -1)), axis=1)
left_best = max_ind // len(right)
right_best = max_ind % len(right)
# Reduce the rotation using the best indices
reduced = left[left_best] * p * right[right_best]
if self._single:
reduced = reduced[0]
left_best = left_best[0]
right_best = right_best[0]
if return_indices:
if left is None:
left_best = None
if right is None:
right_best = None
return reduced, left_best, right_best
else:
return reduced
@classmethod
def create_group(cls, group, axis='Z'):
"""Create a 3D rotation group.
Parameters
----------
group : string
The name of the group. Must be one of 'I', 'O', 'T', 'Dn', 'Cn',
where `n` is a positive integer. The groups are:
* I: Icosahedral group
* O: Octahedral group
* T: Tetrahedral group
* D: Dicyclic group
* C: Cyclic group
axis : integer
The cyclic rotation axis. Must be one of ['X', 'Y', 'Z'] (or
lowercase). Default is 'Z'. Ignored for groups 'I', 'O', and 'T'.
Returns
-------
rotation : `Rotation` instance
Object containing the elements of the rotation group.
Notes
-----
This method generates rotation groups only. The full 3-dimensional
point groups [PointGroups]_ also contain reflections.
References
----------
.. [PointGroups] `Point groups
<https://en.wikipedia.org/wiki/Point_groups_in_three_dimensions>`_
on Wikipedia.
"""
return create_group(cls, group, axis=axis)
def __getitem__(self, indexer):
"""Extract rotation(s) at given index(es) from object.
Create a new `Rotation` instance containing a subset of rotations
stored in this object.
Parameters
----------
indexer : index, slice, or index array
Specifies which rotation(s) to extract. A single indexer must be
specified, i.e. as if indexing a 1 dimensional array or list.
Returns
-------
rotation : `Rotation` instance
Contains
- a single rotation, if `indexer` is a single index
- a stack of rotation(s), if `indexer` is a slice, or and index
array.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
>>> r = R.from_quat([
... [1, 1, 0, 0],
... [0, 1, 0, 1],
... [1, 1, -1, 0]])
>>> r.as_quat()
array([[ 0.70710678, 0.70710678, 0. , 0. ],
[ 0. , 0.70710678, 0. , 0.70710678],
[ 0.57735027, 0.57735027, -0.57735027, 0. ]])
Indexing using a single index:
>>> p = r[0]
>>> p.as_quat()
array([0.70710678, 0.70710678, 0. , 0. ])
Array slicing:
>>> q = r[1:3]
>>> q.as_quat()
array([[ 0. , 0.70710678, 0. , 0.70710678],
[ 0.57735027, 0.57735027, -0.57735027, 0. ]])
"""
return self.__class__(self._quat[indexer], normalize=False)
@classmethod
def identity(cls, num=None):
"""Get identity rotation(s).
Composition with the identity rotation has no effect.
Parameters
----------
num : int or None, optional
Number of identity rotations to generate. If None (default), then a
single rotation is generated.
Returns
-------
identity : Rotation object
The identity rotation.
"""
if num is None:
q = [0, 0, 0, 1]
else:
q = np.zeros((num, 4))
q[:, 3] = 1
return cls(q, normalize=False)
@classmethod
def random(cls, num=None, random_state=None):
"""Generate uniformly distributed rotations.
Parameters
----------
num : int or None, optional
Number of random rotations to generate. If None (default), then a
single rotation is generated.
random_state : int, RandomState instance or None, optional
Accepts an integer as a seed for the random generator or a
RandomState object. If None (default), uses global `numpy.random`
random state.
Returns
-------
random_rotation : `Rotation` instance
Contains a single rotation if `num` is None. Otherwise contains a
stack of `num` rotations.
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Sample a single rotation:
>>> R.random(random_state=1234).as_euler('zxy', degrees=True)
array([-110.5976185 , 55.32758512, 76.3289269 ])
Sample a stack of rotations:
>>> R.random(5, random_state=1234).as_euler('zxy', degrees=True)
array([[-110.5976185 , 55.32758512, 76.3289269 ],
[ -91.59132005, -14.3629884 , -93.91933182],
[ 25.23835501, 45.02035145, -121.67867086],
[ -51.51414184, -15.29022692, -172.46870023],
[ -81.63376847, -27.39521579, 2.60408416]])
"""
random_state = check_random_state(random_state)
if num is None:
sample = random_state.normal(size=4)
else:
sample = random_state.normal(size=(num, 4))
return cls(sample)
@classmethod
@np.deprecate(message="match_vectors is deprecated in favor of "
"align_vectors in scipy 1.4.0 and will be removed "
"in scipy 1.6.0")
def match_vectors(cls, a, b, weights=None, normalized=False):
"""Deprecated in favor of `align_vectors`."""
a = np.asarray(a)
if a.ndim != 2 or a.shape[-1] != 3:
raise ValueError("Expected input `a` to have shape (N, 3), "
"got {}".format(a.shape))
b = np.asarray(b)
if b.ndim != 2 or b.shape[-1] != 3:
raise ValueError("Expected input `b` to have shape (N, 3), "
"got {}.".format(b.shape))
if a.shape != b.shape:
raise ValueError("Expected inputs `a` and `b` to have same shapes"
", got {} and {} respectively.".format(
a.shape, b.shape))
if b.shape[0] == 1:
raise ValueError("Rotation cannot be estimated using a single "
"vector.")
if weights is None:
weights = np.ones(b.shape[0])
else:
weights = np.asarray(weights)
if weights.ndim != 1:
raise ValueError("Expected `weights` to be 1 dimensional, got "
"shape {}.".format(weights.shape))
if weights.shape[0] != b.shape[0]:
raise ValueError("Expected `weights` to have number of values "
"equal to number of input vectors, got "
"{} values and {} vectors.".format(
weights.shape[0], b.shape[0]))
weights = weights / np.sum(weights)
if not normalized:
a = a / scipy.linalg.norm(a, axis=1)[:, None]
b = b / scipy.linalg.norm(b, axis=1)[:, None]
B = np.einsum('ji,jk->ik', weights[:, None] * a, b)
u, s, vh = np.linalg.svd(B)
# Correct improper rotation if necessary (as in Kabsch algorithm)
if np.linalg.det(u @ vh) < 0:
s[-1] = -s[-1]
u[:, -1] = -u[:, -1]
C = np.dot(u, vh)
zeta = (s[0]+s[1]) * (s[1]+s[2]) * (s[2]+s[0])
if np.abs(zeta) <= 1e-16:
raise ValueError("Three component error vector has infinite "
"covariance. It is impossible to determine the "
"rotation uniquely.")
kappa = s[0]*s[1] + s[1]*s[2] + s[2]*s[0]
sensitivity = ((kappa * np.eye(3) + np.dot(B, B.T)) /
(zeta * a.shape[0]))
return cls.from_matrix(C), sensitivity
@classmethod
def align_vectors(cls, a, b, weights=None, return_sensitivity=False):
"""Estimate a rotation to optimally align two sets of vectors.
Find a rotation between frames A and B which best aligns a set of
vectors `a` and `b` observed in these frames. The following loss
function is minimized to solve for the rotation matrix
:math:`C`:
.. math::
L(C) = \\frac{1}{2} \\sum_{i = 1}^{n} w_i \\lVert \\mathbf{a}_i -
C \\mathbf{b}_i \\rVert^2 ,
where :math:`w_i`'s are the `weights` corresponding to each vector.
The rotation is estimated with Kabsch algorithm [1]_.
Parameters
----------
a : array_like, shape (N, 3)
Vector components observed in initial frame A. Each row of `a`
denotes a vector.
b : array_like, shape (N, 3)
Vector components observed in another frame B. Each row of `b`
denotes a vector.
weights : array_like shape (N,), optional
Weights describing the relative importance of the vector
observations. If None (default), then all values in `weights` are
assumed to be 1.
return_sensitivity : bool, optional
Whether to return the sensitivity matrix. See Notes for details.
Default is False.
Returns
-------
estimated_rotation : `Rotation` instance
Best estimate of the rotation that transforms `b` to `a`.
rmsd : float
Root mean square distance (weighted) between the given set of
vectors after alignment. It is equal to ``sqrt(2 * minimum_loss)``,
where ``minimum_loss`` is the loss function evaluated for the
found optimal rotation.
sensitivity_matrix : ndarray, shape (3, 3)
Sensitivity matrix of the estimated rotation estimate as explained
in Notes. Returned only when `return_sensitivity` is True.
Notes
-----
This method can also compute the sensitivity of the estimated rotation
to small perturbations of the vector measurements. Specifically we
consider the rotation estimate error as a small rotation vector of
frame A. The sensitivity matrix is proportional to the covariance of
this rotation vector assuming that the vectors in `a` was measured with
errors significantly less than their lengths. To get the true
covariance matrix, the returned sensitivity matrix must be multiplied
by harmonic mean [3]_ of variance in each observation. Note that
`weights` are supposed to be inversely proportional to the observation
variances to get consistent results. For example, if all vectors are
measured with the same accuracy of 0.01 (`weights` must be all equal),
then you should multiple the sensitivity matrix by 0.01**2 to get the
covariance.
Refer to [2]_ for more rigorous discussion of the covariance
estimation.
References
----------
.. [1] https://en.wikipedia.org/wiki/Kabsch_algorithm
.. [2] F. Landis Markley,
"Attitude determination using vector observations: a fast
optimal matrix algorithm", Journal of Astronautical Sciences,
Vol. 41, No.2, 1993, pp. 261-280.
.. [3] https://en.wikipedia.org/wiki/Harmonic_mean
"""
a = np.asarray(a)
if a.ndim != 2 or a.shape[-1] != 3:
raise ValueError("Expected input `a` to have shape (N, 3), "
"got {}".format(a.shape))
b = np.asarray(b)
if b.ndim != 2 or b.shape[-1] != 3:
raise ValueError("Expected input `b` to have shape (N, 3), "
"got {}.".format(b.shape))
if a.shape != b.shape:
raise ValueError("Expected inputs `a` and `b` to have same shapes"
", got {} and {} respectively.".format(
a.shape, b.shape))
if weights is None:
weights = np.ones(len(b))
else:
weights = np.asarray(weights)
if weights.ndim != 1:
raise ValueError("Expected `weights` to be 1 dimensional, got "
"shape {}.".format(weights.shape))
if weights.shape[0] != b.shape[0]:
raise ValueError("Expected `weights` to have number of values "
"equal to number of input vectors, got "
"{} values and {} vectors.".format(
weights.shape[0], b.shape[0]))
B = np.einsum('ji,jk->ik', weights[:, None] * a, b)
u, s, vh = np.linalg.svd(B)
# Correct improper rotation if necessary (as in Kabsch algorithm)
if np.linalg.det(u @ vh) < 0:
s[-1] = -s[-1]
u[:, -1] = -u[:, -1]
C = np.dot(u, vh)
if s[1] + s[2] < 1e-16 * s[0]:
warnings.warn("Optimal rotation is not uniquely or poorly defined "
"for the given sets of vectors.")
rmsd = np.sqrt(max(
np.sum(weights * np.sum(b ** 2 + a ** 2, axis=1)) - 2 * np.sum(s),
0))
if return_sensitivity:
zeta = (s[0] + s[1]) * (s[1] + s[2]) * (s[2] + s[0])
kappa = s[0] * s[1] + s[1] * s[2] + s[2] * s[0]
with np.errstate(divide='ignore', invalid='ignore'):
sensitivity = np.mean(weights) / zeta * (
kappa * np.eye(3) + np.dot(B, B.T))
return cls.from_matrix(C), rmsd, sensitivity
else:
return cls.from_matrix(C), rmsd
class Slerp(object):
"""Spherical Linear Interpolation of Rotations.
The interpolation between consecutive rotations is performed as a rotation
around a fixed axis with a constant angular velocity [1]_. This ensures
that the interpolated rotations follow the shortest path between initial
and final orientations.
Parameters
----------
times : array_like, shape (N,)
Times of the known rotations. At least 2 times must be specified.
rotations : `Rotation` instance
Rotations to perform the interpolation between. Must contain N
rotations.
Methods
-------
__call__
See Also
--------
Rotation
Notes
-----
.. versionadded:: 1.2.0
References
----------
.. [1] https://en.wikipedia.org/wiki/Slerp#Quaternion_Slerp
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
>>> from scipy.spatial.transform import Slerp
Setup the fixed keyframe rotations and times:
>>> key_rots = R.random(5, random_state=2342345)
>>> key_times = [0, 1, 2, 3, 4]
Create the interpolator object:
>>> slerp = Slerp(key_times, key_rots)
Interpolate the rotations at the given times:
>>> times = [0, 0.5, 0.25, 1, 1.5, 2, 2.75, 3, 3.25, 3.60, 4]
>>> interp_rots = slerp(times)
The keyframe rotations expressed as Euler angles:
>>> key_rots.as_euler('xyz', degrees=True)
array([[ 14.31443779, -27.50095894, -3.7275787 ],
[ -1.79924227, -24.69421529, 164.57701743],
[146.15020772, 43.22849451, -31.34891088],
[ 46.39959442, 11.62126073, -45.99719267],
[-88.94647804, -49.64400082, -65.80546984]])
The interpolated rotations expressed as Euler angles. These agree with the
keyframe rotations at both endpoints of the range of keyframe times.
>>> interp_rots.as_euler('xyz', degrees=True)
array([[ 14.31443779, -27.50095894, -3.7275787 ],
[ 4.74588574, -32.44683966, 81.25139984],
[ 10.71094749, -31.56690154, 38.06896408],
[ -1.79924227, -24.69421529, 164.57701743],
[ 11.72796022, 51.64207311, -171.7374683 ],
[ 146.15020772, 43.22849451, -31.34891088],
[ 68.10921869, 20.67625074, -48.74886034],
[ 46.39959442, 11.62126073, -45.99719267],
[ 12.35552615, 4.21525086, -64.89288124],
[ -30.08117143, -19.90769513, -78.98121326],
[ -88.94647804, -49.64400082, -65.80546984]])
"""
def __init__(self, times, rotations):
if len(rotations) == 1:
raise ValueError("`rotations` must contain at least 2 rotations.")
times = np.asarray(times)
if times.ndim != 1:
raise ValueError("Expected times to be specified in a 1 "
"dimensional array, got {} "
"dimensions.".format(times.ndim))
if times.shape[0] != len(rotations):
raise ValueError("Expected number of rotations to be equal to "
"number of timestamps given, got {} rotations "
"and {} timestamps.".format(
len(rotations), times.shape[0]))
self.times = times
self.timedelta = np.diff(times)
if np.any(self.timedelta <= 0):
raise ValueError("Times must be in strictly increasing order.")
self.rotations = rotations[:-1]
self.rotvecs = (self.rotations.inv() * rotations[1:]).as_rotvec()
def __call__(self, times):
"""Interpolate rotations.
Compute the interpolated rotations at the given `times`.
Parameters
----------
times : array_like
Times to compute the interpolations at. Can be a scalar or
1-dimensional.
Returns
-------
interpolated_rotation : `Rotation` instance
Object containing the rotations computed at given `times`.
"""
# Clearly differentiate from self.times property
compute_times = np.asarray(times)
if compute_times.ndim > 1:
raise ValueError("`times` must be at most 1-dimensional.")
single_time = compute_times.ndim == 0
compute_times = np.atleast_1d(compute_times)
# side = 'left' (default) excludes t_min.
ind = np.searchsorted(self.times, compute_times) - 1
# Include t_min. Without this step, index for t_min equals -1
ind[compute_times == self.times[0]] = 0
if np.any(np.logical_or(ind < 0, ind > len(self.rotations) - 1)):
raise ValueError("Interpolation times must be within the range "
"[{}, {}], both inclusive.".format(
self.times[0], self.times[-1]))
alpha = (compute_times - self.times[ind]) / self.timedelta[ind]
result = (self.rotations[ind] *
Rotation.from_rotvec(self.rotvecs[ind] * alpha[:, None]))
if single_time:
result = result[0]
return result
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