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 _get_phi(self, imt, mag):
"""
Returns the within-event standard deviation (phi)
"""
phi = get_phi_ss(imt, mag, self.PHI_SS)
if self.ergodic:
C = self.PHI_S2SS[imt]
phi = np.sqrt(phi**2. + C["phi_s2ss"]**2.)
return phi
def _get_phi(self, imt, mag, sites, num_sites):
"""
Returns the within-event standard deviation (phi). If the ergodic
model is chosen the "global" phi_s2s model of Stewart et al. (2019)
is used.
"""
phi = get_phi_ss(imt, mag, self.PHI_SS)
if self.ergodic:
phi_s2s = get_stewart_2019_phis2s(imt, sites.vs30)
phi = np.sqrt(phi**2. + phi_s2s**2.)
return phi
def _get_phi(self, imt, mag, num_sites):
"""
Returns the within-event standard deviation (phi). If the ergodic
model is chosen the "global" phi_s2s model of Stewart et al. (2019)
is used.
"""
phi = get_phi_ss(imt, mag, self.PHI_SS)
if self.ergodic:
phi_s2s = get_phi_s2s(
imt, self.DEFINED_FOR_REFERENCE_VELOCITY + np.zeros(num_sites))
phi = np.sqrt(phi**2. + phi_s2s**2.)
return phi
def get_stddevs(ergodic, tau_model, TAU, PHI_SS, imt, ctx):
"""
Returns the standard deviations for either the ergodic or
non-ergodic models
"""
phi = get_phi_ss(imt, ctx.mag, PHI_SS)
if ergodic:
phi_s2s = get_stewart_2019_phis2s(imt, ctx.vs30)
phi = np.sqrt(phi**2. + phi_s2s**2.)
tau = TAU_EXECUTION[tau_model](imt, ctx.mag, TAU)
sigma = np.sqrt(tau**2. + phi**2.)
return [sigma, tau, phi]