human
I wrote the below code

    def _hdsd_1D(data, prob):
        'Computes    the    std    error    for    1D    arrays.'
        xsorted = np.sort(data.compressed())
        n = len(xsorted)
        hdsd = np.empty(len(prob), float_)
        if (n < 2):
            hdsd.flat = np.nan
        vv = (np.arange(n) / float((n - 1)))
        betacdf = beta.cdf
        for (i, p) in enumerate(prob):
            _w = betacdf(vv, ((n + 1) * p), ((n + 1) * (1 - p)))
            w = (_w[1:] - _w[:(-1)])
            mx_ = np.fromiter([np.dot(w, xsorted[np.r_[(list(range(0, k)), list(range((k + 1), n)))].astype(int_)]) for k in range(n)], dtype=float_)
            mx_var = ((np.array(mx_.var(), copy=False, ndmin=1) * n) / float((n - 1)))
            hdsd[i] = (float((n - 1)) * np.sqrt((np.diag(mx_var).diagonal() / float(n))))
        return hdsd
    data = ma.array(data, copy=False, dtype=float_)
    p = np.array(prob, copy=False, ndmin=1)
    if (axis is None):
        result = _hdsd_1D(data, p)
    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(_hdsd_1D, axis, data, p)
    return ma.fix_invalid(result, copy=False).ravel()

What's a good function header?

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Here's a good function header?
assistant
#Here's a good function header.

def hdquantiles_sd(data, prob=list([0.25, 0.5, 0.75]), axis=None):