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                  34      # VP                  ^ 8X  d0   \%        V V\         P&                  ! V4      4      P)                  4       # \%        V VV4      # )a  
Geometric spherical linear interpolation.

The interpolation occurs along a unit-radius
great circle arc in arbitrary dimensional space.

Parameters
----------
start : (n_dimensions, ) array-like
    Single n-dimensional input coordinate in a 1-D array-like
    object. `n` must be greater than 1.
end : (n_dimensions, ) array-like
    Single n-dimensional input coordinate in a 1-D array-like
    object. `n` must be greater than 1.
t : float or (n_points,) 1D array-like
    A float or 1D array-like of doubles representing interpolation
    parameters. A common approach is to generate the array
    with ``np.linspace(0, 1, n_pts)`` for linearly spaced points.
    Ascending, descending, and scrambled orders are permitted.

    .. versionchanged:: 1.17.0
        Extrapolation is permitted, allowing values below `0`
        and above `1`.
tol : float
    The absolute tolerance for determining if the start and end
    coordinates are antipodes.

Returns
-------
result : (t.size, D)
    An array of doubles containing the interpolated
    spherical path and including start and
    end when 0 and 1 t are used. The
    interpolated values should correspond to the
    same sort order provided in the t array. The result
    may be 1-dimensional if ``t`` is a float.

Raises
------
ValueError
    If ``start`` or ``end`` are not on the
    unit n-sphere, or for a variety of degenerate conditions.

See Also
--------
scipy.spatial.transform.Slerp : 3-D Slerp that works with quaternions

Notes
-----
The implementation is based on the mathematical formula provided in [1]_,
and the first known presentation of this algorithm, derived from study of
4-D geometry, is credited to Glenn Davis in a footnote of the original
quaternion Slerp publication by Ken Shoemake [2]_.

.. versionadded:: 1.5.0

References
----------
.. [1] https://en.wikipedia.org/wiki/Slerp#Geometric_Slerp
.. [2] Ken Shoemake (1985) Animating rotation with quaternion curves.
       ACM SIGGRAPH Computer Graphics, 19(3): 245-254.

Examples
--------
Interpolate four linearly-spaced values on the circumference of
a circle spanning 90 degrees:

>>> import numpy as np
>>> from scipy.spatial import geometric_slerp
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> start = np.array([1, 0])
>>> end = np.array([0, 1])
>>> t_vals = np.linspace(0, 1, 4)
>>> result = geometric_slerp(start,
...                          end,
...                          t_vals)

The interpolated results should be at 30 degree intervals
recognizable on the unit circle:

>>> ax.scatter(result[...,0], result[...,1], c='k')
>>> circle = plt.Circle((0, 0), 1, color='grey')
>>> ax.add_artist(circle)
>>> ax.set_aspect('equal')
>>> plt.show()

Attempting to interpolate between antipodes on a circle is
ambiguous because there are two possible paths, and on a
sphere there are infinite possible paths on the geodesic surface.
Nonetheless, one of the ambiguous paths is returned along
with a warning:

>>> import warnings
>>> opposite_pole = np.array([-1, 0])
>>> with warnings.catch_warnings():
...     warnings.simplefilter("ignore", UserWarning)
...     geometric_slerp(start,
...                     opposite_pole,
...                     t_vals)
array([[ 1.00000000e+00,  0.00000000e+00],
       [ 5.00000000e-01,  8.66025404e-01],
       [-5.00000000e-01,  8.66025404e-01],
       [-1.00000000e+00,  1.22464680e-16]])

Extend the original example to a sphere and plot interpolation
points in 3D:

>>> from mpl_toolkits.mplot3d import proj3d
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')

Plot the unit sphere for reference (optional):

>>> u = np.linspace(0, 2 * np.pi, 100)
>>> v = np.linspace(0, np.pi, 100)
>>> x = np.outer(np.cos(u), np.sin(v))
>>> y = np.outer(np.sin(u), np.sin(v))
>>> z = np.outer(np.ones(np.size(u)), np.cos(v))
>>> ax.plot_surface(x, y, z, color='y', alpha=0.1)

Interpolating over a larger number of points
may provide the appearance of a smooth curve on
the surface of the sphere, which is also useful
for discretized integration calculations on a
sphere surface:

>>> start = np.array([1, 0, 0])
>>> end = np.array([0, 0, 1])
>>> t_vals = np.linspace(0, 1, 200)
>>> result = geometric_slerp(start,
...                          end,
...                          t_vals)
>>> ax.plot(result[...,0],
...         result[...,1],
...         result[...,2],
...         c='k')
>>> plt.show()

It is also possible to perform extrapolations outside
the interpolation interval by using interpolation parameter
values below 0 or above 1. For example, the above example
may be adjusted to extrapolate to an antipodal position:

>>> fig = plt.figure()
>>> ax = fig.add_subplot(111, projection='3d')
>>> ax.plot_surface(x, y, z, color='y', alpha=0.1)
>>> start = np.array([1, 0, 0])
>>> end = np.array([0, 0, 1])
>>> t_vals = np.linspace(0, 2, 400)
>>> result = geometric_slerp(start,
...                          end,
...                          t_vals)
>>> ax.plot(result[...,0], result[...,1], result[...,2], c='k')
>>> plt.show()
)dtypez:The interpolation parameter value must be one dimensional.z1Start and end coordinates must be one-dimensionalz;The dimensions of start and end must match (have same size)zLThe start and end coordinates must both be in at least two-dimensional spaceg      ?g&.>)rtolatolz(start and end are not on a unit n-sphereztol must be a floatg       @z_start and end are antipodes using the specified tolerance; this may cause ambiguous slerp paths)
stacklevel)r   asarrayfloat64ndim
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isinstancer#   fabsr   warningswarnemptyr   
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