human
Here's a good function header given the following:
I wrote the below code

    def _pearsonr(x, brack):
        osm_uniform = _calc_uniform_order_statistic_medians(len(x))
        xvals = distributions.norm.ppf(osm_uniform)
        def _eval_pearsonr(lmbda, xvals, samps):
            y = boxcox(samps, lmbda)
            yvals = np.sort(y)
            (r, prob) = stats.pearsonr(xvals, yvals)
            return (1 - r)
        return optimize.brent(_eval_pearsonr, brack=brack, args=(xvals, x))
    def _mle(x, brack):
        def _eval_mle(lmb, data):
            return (- boxcox_llf(lmb, data))
        return optimize.brent(_eval_mle, brack=brack, args=(x,))
    def _all(x, brack):
        maxlog = np.zeros(2, dtype=float)
        maxlog[0] = _pearsonr(x, brack)
        maxlog[1] = _mle(x, brack)
        return maxlog
    methods = {'pearsonr': _pearsonr, 'mle': _mle, 'all': _all}
    if (method not in methods.keys()):
        raise ValueError(('Method    %s    not    recognized.' % method))
    optimfunc = methods[method]
    return optimfunc(x, brack)

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

def boxcox_normmax(x, brack=((-2.0), 2.0), method='pearsonr'):