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Complete this python program:
def banddepth(data, method='MBD'):
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#Here is the Python program you requested.

    """Calculate the band depth for a set of functional curves.
    Band depth is an order statistic for functional data (see `fboxplot`), with
    a higher band depth indicating larger "centrality".  In analog to scalar
    data, the functional curve with highest band depth is called the median
    curve, and the band made up from the first N/2 of N curves is the 50%
    central region.
    Parameters
    data : ndarray
    The vectors of functions to create a functional boxplot from.
    The first axis is the function index, the second axis the one along
    which the function is defined.  So ``data[0, :]`` is the first
    functional curve.
    method : {\MBD\, \BD2\}, optional
    Whether to use the original band depth (with J=2) of [1]_ or the
    modified band depth.  See Notes for details.
    Returns
    depth : ndarray
    Depth values for functional curves.
    Notes
    Functional band depth as an order statistic for functional data was
    proposed in [1]_ and applied to functional boxplots and bagplots in [2]_.
    The method \BD2\ checks for each curve whether it lies completely inside
    bands constructed from two curves.  All permutations of two curves in the
    set of curves are used, and the band depth is normalized to one.  Due to
    the complete curve having to fall within the band, this method yields a lot
    of ties.
    The method \MBD\ is similar to \BD2\, but checks the fraction of the curve
    falling within the bands.  It therefore generates very few ties.
    References
    .. [1] S. Lopez-Pintado and J. Romo, "On the Concept of Depth for
    Functional Data", Journal of the American Statistical Association,
    vol.  104, pp. 718-734, 2009.
    .. [2] Y. Sun and M.G. Genton, "Functional Boxplots", Journal of
    Computational and Graphical Statistics, vol. 20, pp. 1-19, 2011.
    """
    def _band2(x1, x2, curve):
        xb = np.vstack([x1, x2])
        if (np.any((curve < xb.min(axis=0))) or np.any((curve > xb.max(axis=0)))):
            res = 0
        else:
            res = 1
        return res
    def _band_mod(x1, x2, curve):
        xb = np.vstack([x1, x2])
        res = np.logical_and((curve >= xb.min(axis=0)), (curve <= xb.max(axis=0)))
        return (np.sum(res) / float(res.size))
    if (method == 'BD2'):
        band = _band2
    elif (method == 'MBD'):
        band = _band_mod
    else:
        raise ValueError('Unknown    input    value    for    parameter    `method`.')
    num = data.shape[0]
    ix = np.arange(num)
    depth = []
    for ii in range(num):
        res = 0
        for (ix1, ix2) in combinations(ix, 2):
            res += band(data[ix1, :], data[ix2, :], data[ii, :])
        normfactor = ((factorial(num) / 2.0) / factorial((num - 2)))
        depth.append((float(res) / normfactor))
    return np.asarray(depth)