def _get_phi(ergodic, PHI_SS, PHI_S2SS, phi_ss_quantile, phi_model, imt, mag):
"""
Returns the within-event standard deviation (phi)
"""
phi = get_phi_ss(imt, mag, PHI_SS)
# check if phi adjustment (2.6.6) is needed
# -> if phi < 0.4 in middle branch and T is below 0.5 s
# -> preserve separation between high and low phi values
if imt.period < 0.5 and phi_ss_quantile == 0.5:
phi = max(phi, 0.4)
elif imt.period < 0.5 and phi_ss_quantile != 0.5:
# compute phi at quantile 0.5 and take the maximum comp to 0.4
PHI_SS_0p5 = get_phi_ss_at_quantile_ACME(
PHI_SETUP[phi_model], 0.5)
phi_0p5 = get_phi_ss(imt, mag, PHI_SS_0p5)
# make adjustment if needed
phi = 0.4 + phi - phi_0p5 if phi_0p5 < 0.4 else phi
if ergodic:
C = PHI_S2SS[imt]
phi = np.sqrt(phi ** 2. + C["phi_s2ss"] ** 2.)
return phi
def _setup_standard_deviations(gsim):
# setup tau
gsim.TAU = get_tau_at_quantile(TAU_SETUP[gsim.tau_model]["MEAN"],
TAU_SETUP[gsim.tau_model]["STD"],
gsim.tau_quantile)
# setup phi
PHI_SETUP['global_linear'] = gsim.COEFFS_PHI_SS_GLOBAL_LINEAR
gsim.PHI_SS = get_phi_ss_at_quantile_ACME(PHI_SETUP[gsim.phi_model],
gsim.phi_ss_quantile)
# if required setup phis2ss
if gsim.ergodic:
if gsim.phi_s2ss_model == 'cena':
gsim.PHI_S2SS = get_phi_s2ss_at_quantile(
PHI_S2SS_MODEL[gsim.phi_s2ss_model], gsim.phi_s2ss_quantile)
elif gsim.phi_s2ss_model == 'brb':
gsim.PHI_S2SS = gsim.COEFFS_PHI_S2SS_BRB
else:
opts = "'cena', 'brb', or 'None'"
raise ValueError('phi_s2ss_model can be {}'.format(opts))
