def METHOD_NAME(C, mag, c1_dists, log_phi_ss, mean_phi_ss):
    """
    Returns the embeded logic tree for single station sigma
    as defined to be used in the Swiss Hazard Model 2014:
    the single station sigma branching levels combines with equal
    weights: the phi_ss reported as function of magnitude
    as proposed by Rodriguez-Marek et al (2013) with the mean
    (mean_phi_ss) single station value;
    the resulted phi_ss is in natural logarithm units
    """
    phi_ss = np.zeros_like(c1_dists)
    if not isinstance(mag, np.ndarray):
        mag = np.full_like(c1_dists, mag)
    phi_ss[mag < C['Mc1']] = c1_dists[mag < C['Mc1']]
    within = (mag >= C['Mc1']) & (mag <= C['Mc2'])
    phi_ss[within] = c1_dists[within] + (C['C2'] - c1_dists[within]) * (
        (mag[within] - C['Mc1']) / (C['Mc2'] - C['Mc1']))
    phi_ss[mag > C['Mc2']] = C['C2']
    return (phi_ss * 0.50 + mean_phi_ss * 0.50) / log_phi_ss