.

    Methods
    -------
    __call__
    derivative
    antiderivative
    integrate
    roots

    See Also
    --------
    Akima1DInterpolator : Akima 1D interpolator.
    PchipInterpolator : PCHIP 1-D monotonic cubic interpolator.
    PPoly : Piecewise polynomial in terms of coefficients and breakpoints.

    Notes
    -----
    Parameters `bc_type` and ``extrapolate`` work independently, i.e. the
    former controls only construction of a spline, and the latter only
    evaluation.

    When a boundary condition is 'not-a-knot' and n = 2, it is replaced by
    a condition that the first derivative is equal to the linear interpolant
    slope. When both boundary conditions are 'not-a-knot' and n = 3, the
    solution is sought as a parabola passing through given points.

    When 'not-a-knot' boundary conditions is applied to both ends, the
    resulting spline will be the same as returned by `splrep` (with ``s=0``)
    and `InterpolatedUnivariateSpline`, but these two methods use a
    representation in B-spline basis.

    .. versionadded:: 0.18.0

    Examples
    --------
    In this example the cubic spline is used to interpolate a sampled sinusoid.
    You can see that the spline continuity property holds for the first and
    second derivatives and violates only for the third derivative.

    >>> import numpy as np
    >>> from scipy.interpolate import CubicSpline
    >>> import matplotlib.pyplot as plt
    >>> x = np.arange(10)
    >>> y = np.sin(x)
    >>> cs = CubicSpline(x, y)
    >>> xs = np.arange(-0.5, 9.6, 0.1)
    >>> fig, ax = plt.subplots(figsize=(6.5, 4))
    >>> ax.plot(x, y, 'o', label='data')
    >>> ax.plot(xs, np.sin(xs), label='true')
    >>> ax.plot(xs, cs(xs), label="S")
    >>> ax.plot(xs, cs(xs, 1), label="S'")
    >>> ax.plot(xs, cs(xs, 2), label="S''")
    >>> ax.plot(xs, cs(xs, 3), label="S'''")
    >>> ax.set_xlim(-0.5, 9.5)
    >>> ax.legend(loc='lower left', ncol=2)
    >>> plt.show()

    In the second example, the unit circle is interpolated with a spline. A
    periodic boundary condition is used. You can see that the first derivative
    values, ds/dx=0, ds/dy=1 at the periodic point (1, 0) are correctly
    computed. Note that a circle cannot be exactly represented by a cubic
    spline. To increase precision, more breakpoints would be required.

    >>> theta = 2 * np.pi * np.linspace(0, 1, 5)
    >>> y = np.c_[np.cos(theta), np.sin(theta)]
    >>> cs = CubicSpline(theta, y, bc_type='periodic')
    >>> print("ds/dx={:.1f} ds/dy={:.1f}".format(cs(0, 1)[0], cs(0, 1)[1]))
    ds/dx=0.0 ds/dy=1.0
    >>> xs = 2 * np.pi * np.linspace(0, 1, 100)
    >>> fig, ax = plt.subplots(figsize=(6.5, 4))
    >>> ax.plot(y[:, 0], y[:, 1], 'o', label='data')
    >>> ax.plot(np.cos(xs), np.sin(xs), label='true')
    >>> ax.plot(cs(xs)[:, 0], cs(xs)[:, 1], label='spline')
    >>> ax.axes.set_aspect('equal')
    >>> ax.legend(loc='center')
    >>> plt.show()

    The third example is the interpolation of a polynomial y = x**3 on the
    interval 0 <= x<= 1. A cubic spline can represent this function exactly.
    To achieve that we need to specify values and first derivatives at
    endpoints of the interval. Note that y' = 3 * x**2 and thus y'(0) = 0 and
    y'(1) = 3.

    >>> cs = CubicSpline([0, 1], [0, 1], bc_type=((1, 0), (1, 3)))
    >>> x = np.linspace(0, 1)
    >>> np.allclose(x**3, cs(x))
    True

    References
    ----------
    .. [1] `Cubic Spline Interpolation
            <https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation>`_
            on Wikiversity.
    .. [2] Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978.
    r