def minimize_sigmas(sigmas, weights, combs):
    """Varies sigmas to minimize gaussian sigma12 - sqrt(sigma1² + sigma2²).

    Parameters
    ----------
    sigmas: numpy array of fitted sigmas of gaussians
    weights: numpy array of weights for the squared sum
    combs: pmt combinations to use for minimization

    Returns
    -------
    opt_sigmas: optimal sigma values for all PMTs

    """

    def make_quality_function(sigmas, weights, combs):
        def quality_function(s):
            sq_sum = 0
            for sigma, comb, weight in zip(sigmas, combs, weights):
                sigma_sqsum = np.sqrt(s[comb[1]]**2 + s[comb[0]]**2)
                sq_sum += ((sigma - sigma_sqsum) * weight)**2
            return sq_sum

        return quality_function

    qfunc = make_quality_function(sigmas, weights, combs)
    s = np.ones(31) * 2.5
    # s = np.random.rand(31)
    bounds = [(0., 5.)] * 31
    opt_sigmas = optimize.minimize(qfunc, s, bounds=bounds)
    return opt_sigmas