def median_cihs(data, alpha=0.05, axis=None):
    """
    Computes the alpha-level confidence interval for the median of the data.

    Uses the Hettmasperger-Sheather method.

    Parameters
    ----------
    data : array_like
        Input data. Masked values are discarded. The input should be 1D only,
        or `axis` should be set to None.
    alpha : float, optional
        Confidence level of the intervals.
    axis : int or None, optional
        Axis along which to compute the quantiles. If None, use a flattened
        array.

    Returns
    -------
    median_cihs :
        Alpha level confidence interval.

    """
    def _cihs_1D(data, alpha):
        data = np.sort(data.compressed())
        n = len(data)
        alpha = min(alpha, 1-alpha)
        k = int(binom._ppf(alpha/2., n, 0.5))
        gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
        if gk < 1-alpha:
            k -= 1
            gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
        gkk = binom.cdf(n-k-1,n,0.5) - binom.cdf(k,n,0.5)
        I = (gk - 1 + alpha)/(gk - gkk)
        lambd = (n-k) * I / float(k + (n-2*k)*I)
        lims = (lambd*data[k] + (1-lambd)*data[k-1],
                lambd*data[n-k-1] + (1-lambd)*data[n-k])
        return lims
    data = ma.rray(data, copy=False)
    # Computes quantiles along axis (or globally)
    if (axis is None):
        result = _cihs_1D(data.compressed(), alpha)
    else:
        if data.ndim > 2:
            raise ValueError("Array 'data' must be at most two dimensional, "
                             "but got data.ndim = %d" % data.ndim)
        result = ma.apply_along_axis(_cihs_1D, axis, data, alpha)

    return result
dataset/ETHPy150Open scipy/scipy/scipy/stats/mstats_extras.py/median_cihs/original