# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import math

import sys
if sys.version_info >= (3,0):
    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)
    True

    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, which is::

      [                       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
    -----
    We can derive this formula in Sympy using:

    1. Formula giving quaternion corresponding to rotation of theta radians
       about arbitrary axis:
       http://mathworld.wolfram.com/EulerParameters.html
    2. Generated formulae from 1.) for quaternions corresponding to
       theta radians rotations about ``x, y, z`` axes
    3. Apply quaternion multiplication formula -
       http://en.wikipedia.org/wiki/Quaternions#Hamilton_product - to
       formulae from 2.) to give formula for combined rotations.
    '''
    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
    import nibabel.quaternions as nq
    return mat2euler(nq.quat2mat(q))


def euler2angle_axis(z=0, y=0, x=0):
    ''' Return angle, axis 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
    -------
    theta : scalar
       angle of rotation
    vector : array shape (3,)
       axis around which rotation occurs

    Examples
    --------
    >>> theta, vec = euler2angle_axis(0, 1.5, 0)
    >>> print(theta)
    1.5
    >>> np.allclose(vec, [0, 1, 0])
    True
    '''
    # delayed import to avoid cyclic dependencies
    import nibabel.quaternions as nq
    return nq.quat2angle_axis(euler2quat(z, y, x))


def angle_axis2euler(theta, vector, is_normalized=False):
    ''' Convert angle, axis pair to Euler angles

    Parameters
    ----------
    theta : scalar
       angle of rotation
    vector : 3 element sequence
       vector specifying axis for rotation.
    is_normalized : bool, optional
       True if vector is already normalized (has norm of 1).  Default
       False

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

    Examples
    --------
    >>> z, y, x = angle_axis2euler(0, [1, 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
    import nibabel.quaternions as nq
    M = nq.angle_axis2mat(theta, vector, is_normalized)
    return mat2euler(M)