df and
    # pdf rescaler
    quants = np.array(fit_quantiles)
    zbounds = []
    rescale = []
    for b in range(len(shape_range)):
        gam = gammaincinv(
            1 / shape_range[b], np.sign(quants - 1 / 2) * (2 * quants - 1))
        zbounds.append(np.sign(quants - 1 / 2) * gam ** (1 / shape_range[b]))
        rescale.append(shape_range[b] / (2 * gamma(1 / shape_range[b])))

    # determine the quantile-dependent limits for the grid search
    # we can generally skip the tail below the lower qua