sage corresponding to the integer flag, ier.

    See Also
    --------
    splrep, splev, sproot, spalde, splint,
    bisplrep, bisplev
    UnivariateSpline, BivariateSpline
    BSpline
    make_interp_spline

    Notes
    -----
    See `splev` 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()

    )