for evaluation of the spline and its derivatives.
The number of dimensions N must be smaller than 11.

The number of coefficients in the `c` array is ``k+1`` less than the number
of knots, ``len(t)``. This is in contrast with `splrep`, which zero-pads
the array of coefficients to have the same length as the array of knots.
These additional coefficients are ignored by evaluation routines, `splev`
and `BSpline`.

References
----------
.. [1] P. Dierckx, "Algorithms for smoothing data with periodic and
    parametric splines, Computer Graphics and Image Processing",
    20 (1982) 171-184.
.. [2] P. Dierckx, "Algorithms for smoothing data with periodic and
    parametric splines", report tw55, Dept. Computer Science,
    K.U.Leuven, 1981.
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs on
    Numerical Analysis, Oxford University Press, 1993.

Examples
--------
Generate a discretization of a limacon curve in the polar coordinates:

>>> import numpy as np
>>> phi = np.linspace(0, 2.*np.pi, 40)
>>> r = 0.5 + np.cos(phi)         # polar coords
>>> x, y = r * np.cos(phi), r * np.sin(phi)    # convert to cartesian

And interpolate:

>>> from scipy.interpolate import splprep, splev
>>> tck, u = splprep([x, y], s=0)
>>> new_points = splev(u, tck)

Notice that (i) we force interpolation by using ``s=0``,
(ii) the parameterization, ``u``, is generated automatically.
Now plot the result:

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ax.plot(x, y, 'ro')
>>> ax.plot(new_points[0], new_points[1], 'r-')
>>> plt.show()

)