terpolation results are meaningless.

    Note that, since NaN is unsortable, `xp` also cannot contain NaNs.

    A simple check for `xp` being strictly increasing is::

        np.all(np.diff(xp) > 0)

    Examples
    --------
    >>> xp = [1, 2, 3]
    >>> fp = [3, 2, 0]
    >>> np.interp(2.5, xp, fp)
    1.0
    >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
    array([3.  , 3.  , 2.5 , 0.56, 0.  ])
    >>> UNDEF = -99.0
    >>> np.interp(3.14, xp, fp, right=UNDEF)
    -99.0

    Plot an interpolant to the sine function:

    >>> x = np.linspace(0, 2*np.pi, 10)
    >>> y = np.sin(x)
    >>> xvals = np.linspace(0, 2*np.pi, 50)
    >>> yinterp = np.interp(xvals, x, y)
    >>> import matplotlib.pyplot as plt
    >>> plt.plot(x, y, 'o')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.plot(xvals, yinterp, '-x')
    [<matplotlib.lines.Line2D object at 0x...>]
    >>> plt.show()

    Interpolation with periodic x-coordinates:

    >>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
    >>> xp = [190, -190, 350, -350]
    >>> fp = [5, 10, 3, 4]
    >>> np.interp(x, xp, fp, period=360)
    array([7.5 , 5.  , 8.75, 6.25, 3.  , 3.25, 3.5 , 3.75])

    Complex interpolation:

    >>> x = [1.5, 4.0]
    >>> xp = [2,3,5]
    >>> fp = [1.0j, 0, 2+3j]
    >>> np.interp(x, xp, fp)
    array([0.+1.j , 1.+1.5j])

    Nr