def METHOD_NAME(value_2g=1, value_2g_sigma_low=0, value_2g_sigma_high=0, weight_2g = 0.5,
                            value_pf=1, value_pf_sigma_low=0, value_pf_sigma_high=0, weight_pf = 0.5,
                            nsamples = 10000):
    '''
    Compute the combined value from two-Gaussian and polyfit MCMC results.
    Parameters
    ----------
    value_2g: float
        TwoGaussian value mean
    value_2g_sigma_low: float
        TwoGaussian value std for samples < value_2g
    value_2g_sigma_high: float
        TwoGaussian value std for samples > value_2g
    weight_2g: float
        The ratio of samples to be drawn from the TwoGaussian distribution, typically 1/chi2_2g
    value_pf: float
        Polyfit value mean
    value_pf_sigma_low: float
        Polyfit value std for samples < value_pf
    value_pf_sigma_high: float
        Polyfit value std for samples > value_pf
    weight_pf: float
        The ratio of samples to be drawn from the Polyfit distribution, typically 1/chi2_pf
    nsamples: int
        Number of samples for the combined distribution.   
    Returns
    -------
    value_mean: float
        The mean calculated from the combined distributions.
    value_sigma_low: float
        The standard deviation of the samples < mean
    value_sigma_high: float
        The standard deviation of the samples > mean 
    '''
    # from 2g and pf to combined values
    # print('2g fit: P = %.5f + %.5f - %.5f' % (value_2g, value_2g_sigma_high, value_2g_sigma_low))
    # print('pf fit: P = %.5f + %.5f - %.5f' % (value_pf, value_pf_sigma_high, value_pf_sigma_low))
    if ~np.isnan(weight_2g) and ~np.isnan(weight_pf):
        wratio = weight_2g/weight_pf
        nsamples_2g = int(wratio*nsamples/(1+wratio))
        nsamples_pf = int(nsamples/(1+wratio))
        # print('values check: w_2g = %.6f, w_pf=%.6f, N_2g = %i, N_pf = %i'
        #  % (weight_2g, weight_pf, nsamples_2g, nsamples_pf))
        samples_2g = sample_skewed_gaussian(value_2g, value_2g_sigma_low, value_2g_sigma_high, size=nsamples_2g)
        samples_pf = sample_skewed_gaussian(value_pf, value_pf_sigma_low, value_pf_sigma_high, size=nsamples_pf)
        combined_result = combine_dists_from_hist(samples_2g, samples_pf, bins=1000, plot=False)
        return combined_result['mean'], combined_result['sigma_low'], combined_result['sigma_high']
    elif np.isnan(weight_2g) and ~np.isnan(weight_pf):
        return value_pf, value_pf_sigma_low, value_pf_sigma_high
    elif ~np.isnan(weight_2g) and np.isnan(weight_pf):
        return value_2g, value_2g_sigma_low, value_2g_sigma_high
    else:
        return np.nan, np.nan, np.na