''' Module implementing Euler angle rotations and their conversions

See:

* http://en.wikipedia.org/wiki/Rotation_matrix
* http://en.wikipedia.org/wiki/Euler_angles
* http://mathworld.wolfram.com/EulerAngles.html

See also: *Representing Attitude with Euler Angles and Quaternions: A
Reference* (2006) by James Diebel. A cached PDF link last found here:

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5134

Euler's rotation theorem tells us that any rotation in 3D can be
described by 3 angles.  Let's call the 3 angles the *Euler angle vector*
and call the angles in the vector :math:`alpha`, :math:`beta` and
:math:`gamma`.  The vector is [ :math:`alpha`,
:math:`beta`. :math:`gamma` ] and, in this description, the order of the
parameters specifies the order in which the rotations occur (so the
rotation corresponding to :math:`alpha` is applied first).

In order to specify the meaning of an *Euler angle vector* we need to
specify the axes around which each of the rotations corresponding to
:math:`alpha`, :math:`beta` and :math:`gamma` will occur.

There are therefore three axes for the rotations :math:`alpha`,
:math:`beta` and :math:`gamma`; let's call them :math:`i` :math:`j`,
:math:`k`. 

Let us express the rotation :math:`alpha` around axis `i` as a 3 by 3
rotation matrix `A`.  Similarly :math:`beta` around `j` becomes 3 x 3
matrix `B` and :math:`gamma` around `k` becomes matrix `G`.  Then the
whole rotation expressed by the Euler angle vector [ :math:`alpha`,
:math:`beta`. :math:`gamma` ], `R` is given by::

   R = np.dot(G, np.dot(B, A))

See http://mathworld.wolfram.com/EulerAngles.html

The order :math:`G B A` expresses the fact that the rotations are
performed in the order of the vector (:math:`alpha` around axis `i` =
`A` first).

To convert a given Euler angle vector to a meaningful rotation, and a
rotation matrix, we need to define:

* the axes `i`, `j`, `k`
* whether a rotation matrix should be applied on the left of a vector to
  be transformed (vectors are column vectors) or on the right (vectors
  are row vectors).
* whether the rotations move the axes as they are applied (intrinsic
  rotations) - compared the situation where the axes stay fixed and the
  vectors move within the axis frame (extrinsic)
* the handedness of the coordinate system

See: http://en.wikipedia.org/wiki/Rotation_matrix#Ambiguities

We are using the following conventions:

* axes `i`, `j`, `k` are the `z`, `y`, and `x` axes respectively.  Thus
  an Euler angle vector [ :math:`alpha`, :math:`beta`. :math:`gamma` ]
  in our convention implies a :math:`alpha` radian rotation around the
  `z` axis, followed by a :math:`beta` rotation around the `y` axis,
  followed by a :math:`gamma` rotation around the `x` axis.
* the rotation matrix applies on the left, to column vectors on the
  right, so if `R` is the rotation matrix, and `v` is a 3 x N matrix
  with N column vectors, the transformed vector set `vdash` is given by
  ``vdash = np.dot(R, v)``.
* extrinsic rotations - the axes are fixed, and do not move with the
  rotations.
* a right-handed coordinate system

The convention of rotation around ``z``, followed by rotation around
``y``, followed by rotation around ``x``, is known (confusingly) as
"xyz", pitch-roll-yaw, Cardan angles, or Tait-Bryan angles.
'''
from __future__ import absolute_import

import math
from functools import reduce

import numpy as np


_FLOAT_EPS_4 = np.finfo(float).eps * 4.0


def euler2mat(z=0, y=0, x=0):
    ''' Return matrix for rotations around z, y and x axes

    Uses the z, then y, then x convention above

    Parameters
    ----------
    z : scalar
       Rotation angle in radians around z-axis (performed first)
    y : scalar
       Rotation angle in radians around y-axis
    x : scalar
       Rotation angle in radians around x-axis (performed last)

    Returns
    -------
    M : array shape (3,3)
       Rotation matrix giving same rotation as for given angles

    Examples
    --------
    >>> zrot = 1.3 # radians
    >>> yrot = -0.1
    >>> xrot = 0.2
    >>> M = euler2mat(zrot, yrot, xrot)
    >>> M.shape
    (3, 3)

    The output rotation matrix is equal to the composition of the
    individual rotations
    
    >>> M1 = euler2mat(zrot)
    >>> M2 = euler2mat(0, yrot)
    >>> M3 = euler2mat(0, 0, xrot)
    >>> composed_M = np.dot(M3, np.dot(M2, M1))
    >>> np.allclose(M, composed_M)
    True

    You can specify rotations by named arguments
    
    >>> np.all(M3 == euler2mat(x=xrot))
    True
    
    When applying M to a vector, the vector should column vector to the
    right of M.  If the right hand side is a 2D array rather than a
    vector, then each column of the 2D array represents a vector.
    
    >>> vec = np.array([1, 0, 0]).reshape((3,1))
    >>> v2 = np.dot(M, vec)
    >>> vecs = np.array([[1, 0, 0],[0, 1, 0]]).T # giving 3x2 array
    >>> vecs2 = np.dot(M, vecs)

    Rotations are counter-clockwise.

    >>> zred = np.dot(euler2mat(z=np.pi/2), np.eye(3))
    >>> np.allclose(zred, [[0, -1, 0],[1, 0, 0], [0, 0, 1]])
    True
    >>> yred = np.dot(euler2mat(y=np.pi/2), np.eye(3))
    >>> np.allclose(yred, [[0, 0, 1],[0, 1, 0], [-1, 0, 0]])
    True
    >>> xred = np.dot(euler2mat(x=np.pi/2), np.eye(3))
    >>> np.allclose(xred, [[1, 0, 0],[0, 0, -1], [0, 1, 0]])
    True
    
    Notes
    -----
    The direction of rotation is given by the right-hand rule (orient
    the thumb of the right hand along the axis around which the rotation
    occurs, with the end of the thumb at the positive end of the axis;
    curl your fingers; the direction your fingers curl is the direction
    of rotation).  Therefore, the rotations are counterclockwise if
    looking along the axis of rotation from positive to negative.
    '''
    Ms = []
    if z:
        cosz = math.cos(z)
        sinz = math.sin(z)
        Ms.append(np.array(
                [[cosz, -sinz, 0],
                 [sinz, cosz, 0],
                 [0, 0, 1]]))
    if y:
        cosy = math.cos(y)
        siny = math.sin(y)
        Ms.append(np.array(
                [[cosy, 0, siny],
                 [0, 1, 0],
                 [-siny, 0, cosy]]))
    if x:
        cosx = math.cos(x)
        sinx = math.sin(x)
        Ms.append(np.array(
                [[1, 0, 0],
                 [0, cosx, -sinx],
                 [0, sinx, cosx]]))
    if Ms:
        return reduce(np.dot, Ms[::-1])
    return np.eye(3)


def mat2euler(M, cy_thresh=None):
    ''' Discover Euler angle vector from 3x3 matrix

    Uses the conventions above.

    Parameters
    ----------
    M : array-like, shape (3,3)
    cy_thresh : None or scalar, optional
       threshold below which to give up on straightforward arctan for
       estimating x rotation.  If None (default), estimate from
       precision of input.

    Returns
    -------
    z : scalar
    y : scalar
    x : scalar
       Rotations in radians around z, y, x axes, respectively

    Notes
    -----
    If there was no numerical error, the routine could be derived using
    Sympy expression for z then y then x rotation matrix, (see
    ``eulerangles.py`` in ``derivations`` subdirectory)::

      [                       cos(y)*cos(z),                       -cos(y)*sin(z),         sin(y)],
      [cos(x)*sin(z) + cos(z)*sin(x)*sin(y), cos(x)*cos(z) - sin(x)*sin(y)*sin(z), -cos(y)*sin(x)],
      [sin(x)*sin(z) - cos(x)*cos(z)*sin(y), cos(z)*sin(x) + cos(x)*sin(y)*sin(z),  cos(x)*cos(y)]

    with the obvious derivations for z, y, and x

       z = atan2(-r12, r11)
       y = asin(r13)
       x = atan2(-r23, r33)

    Problems arise when cos(y) is close to zero, because both of::

       z = atan2(cos(y)*sin(z), cos(y)*cos(z))
       x = atan2(cos(y)*sin(x), cos(x)*cos(y))

    will be close to atan2(0, 0), and highly unstable.

    The ``cy`` fix for numerical instability below is from: *Graphics
    Gems IV*, Paul Heckbert (editor), Academic Press, 1994, ISBN:
    0123361559.  Specifically it comes from EulerAngles.c by Ken
    Shoemake, and deals with the case where cos(y) is close to zero:

    See: http://www.graphicsgems.org/

    The code appears to be licensed (from the website) as "can be used
    without restrictions".
    '''
    M = np.asarray(M)
    if cy_thresh is None:
        try:
            cy_thresh = np.finfo(M.dtype).eps * 4
        except ValueError:
            cy_thresh = _FLOAT_EPS_4
    r11, r12, r13, r21, r22, r23, r31, r32, r33 = M.flat
    # cy: sqrt((cos(y)*cos(z))**2 + (cos(x)*cos(y))**2)
    cy = math.sqrt(r33*r33 + r23*r23)
    if cy > cy_thresh: # cos(y) not close to zero, standard form
        z = math.atan2(-r12,  r11) # atan2(cos(y)*sin(z), cos(y)*cos(z))
        y = math.atan2(r13,  cy) # atan2(sin(y), cy)
        x = math.atan2(-r23, r33) # atan2(cos(y)*sin(x), cos(x)*cos(y))
    else: # cos(y) (close to) zero, so x -> 0.0 (see above)
        # so r21 -> sin(z), r22 -> cos(z) and
        z = math.atan2(r21,  r22) 
        y = math.atan2(r13,  cy) # atan2(sin(y), cy)
        x = 0.0
    return z, y, x

    
def euler2quat(z=0, y=0, x=0):
    ''' Return quaternion corresponding to these Euler angles

    Uses the z, then y, then x convention above

    Parameters
    ----------
    z : scalar
       Rotation angle in radians around z-axis (performed first)
    y : scalar
       Rotation angle in radians around y-axis
    x : scalar
       Rotation angle in radians around x-axis (performed last)

    Returns
    -------
    quat : array shape (4,)
       Quaternion in w, x, y z (real, then vector) format

    Notes
    -----
    Formula from Sympy - see ``eulerangles.py`` in ``derivations``
    subdirectory
    '''
    z = z/2.0
    y = y/2.0
    x = x/2.0
    cz = math.cos(z)
    sz = math.sin(z)
    cy = math.cos(y)
    sy = math.sin(y)
    cx = math.cos(x)
    sx = math.sin(x)
    return np.array([
             cx*cy*cz - sx*sy*sz,
             cx*sy*sz + cy*cz*sx,
             cx*cz*sy - sx*cy*sz,
             cx*cy*sz + sx*cz*sy])


def quat2euler(q):
    ''' Return Euler angles corresponding to quaternion `q`

    Parameters
    ----------
    q : 4 element sequence
       w, x, y, z of quaternion

    Returns
    -------
    z : scalar
       Rotation angle in radians around z-axis (performed first)
    y : scalar
       Rotation angle in radians around y-axis
    x : scalar
       Rotation angle in radians around x-axis (performed last)

    Notes
    -----
    It's possible to reduce the amount of calculation a little, by
    combining parts of the ``quat2mat`` and ``mat2euler`` functions, but
    the reduction in computation is small, and the code repetition is
    large.
    '''
    # delayed import to avoid cyclic dependencies
    from . import quaternions as nq
    return mat2euler(nq.quat2mat(q))


def euler2axangle(z=0, y=0, x=0):
    ''' Return axis, angle corresponding to these Euler angles

    Uses the z, then y, then x convention above

    Parameters
    ----------
    z : scalar
       Rotation angle in radians around z-axis (performed first)
    y : scalar
       Rotation angle in radians around y-axis
    x : scalar
       Rotation angle in radians around x-axis (performed last)

    Returns
    -------
    vector : array shape (3,)
       axis around which rotation occurs
    theta : scalar
       angle of rotation

    Examples
    --------
    >>> vec, theta = euler2axangle(0, 1.5, 0)
    >>> np.allclose(vec, [0, 1, 0])
    True
    >>> print(theta)
    1.5
    '''
    # delayed import to avoid cyclic dependencies
    from . import quaternions as nq
    return nq.quat2axangle(euler2quat(z, y, x))

    
def axangle2euler(vector, theta):
    ''' Convert axis, angle pair to Euler angles

    Parameters
    ----------
    vector : 3 element sequence
       vector specifying axis for rotation.
    theta : scalar
       angle of rotation

    Returns
    -------
    z : scalar
    y : scalar
    x : scalar
       Rotations in radians around z, y, x axes, respectively

    Examples
    --------
    >>> z, y, x = axangle2euler([1, 0, 0], 0)
    >>> np.allclose((z, y, x), 0)
    True
    
    Notes
    -----
    It's possible to reduce the amount of calculation a little, by
    combining parts of the ``angle_axis2mat`` and ``mat2euler``
    functions, but the reduction in computation is small, and the code
    repetition is large.
    '''
    # delayed import to avoid cyclic dependencies
    from . import quaternions as nq
    M = nq.axangle2rmat(vector, theta)
    return mat2euler(M)
