Source code for openquake.hazardlib.gsim.nshmp_2014

# -*- coding: utf-8 -*-
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"""
Module exports :class:`AtkinsonMacias2009NSHMP2014` and :class:`NSHMP2014`
"""
import numpy as np
from openquake.hazardlib import const
from openquake.hazardlib.gsim import base


[docs]def nga_west2_epistemic_adjustment(mag, dist): """ Applies the "average" adjustment factor for epistemic uncertainty as defined in Table 17 of Petersen et al., (2014):: | R < 10. | 10.0 <= R < 30.0 | R >= 30.0 ----------------------------------------------------------- M < 6.0 | 0.37 | 0.22 | 0.22 6 <= M <7.0 | 0.25 | 0.23 | 0.23 M >= 7.0 | 0.40 | 0.36 | 0.33 """ adjustment = 0.23 * np.ones_like(dist) adjustment[(mag < 6.) & (dist < 10.)] = .37 adjustment[(mag < 6.) & (dist >= 10.)] = .22 adjustment[(mag >= 6.) & (mag < 7.) & (dist < 10.)] = .25 adjustment[(mag >= 7.) & (dist < 10.)] = .40 adjustment[(mag >= 7.) & (dist >= 10.) & (dist < 30.)] = .36 adjustment[(mag >= 7.) & (dist >= 30.)] = .33 return adjustment
[docs]class NSHMP2014(base.GMPE): """ Implements the NSHMP adjustment factors for the NGA West GMPEs. Requires two parameters `gmpe_name` (one of Idriss2014, ChiouYoungs2014, CampbellBozorgnia2014, BooreEtAl2014, AbrahamsonEtAl2014) and `sgn` (one of -1, 0, +1). """ DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = () DEFINED_FOR_INTENSITY_MEASURE_TYPES = () DEFINED_FOR_STANDARD_DEVIATION_TYPES = {const.StdDev.TOTAL} DEFINED_FOR_TECTONIC_REGION_TYPE = () REQUIRES_DISTANCES = () REQUIRES_RUPTURE_PARAMETERS = () REQUIRES_SITES_PARAMETERS = () def __init__(self, **kwargs): self.gmpe_name = kwargs['gmpe_name'] self.sgn = kwargs['sgn'] if self.sgn == 0: # default weighting self.weights_signs = [(0.185, -1.), (0.63, 0.), (0.185, 1.)] cls = base.registry[self.gmpe_name] for name in vars(cls): if name.startswith(('DEFINED_FOR', 'REQUIRES_')): setattr(self, name, getattr(cls, name)) # the gsim requires only Rjb, but the epistemic adjustment factors # are given in terms of Rrup, so both are required in the subclass self.REQUIRES_DISTANCES = frozenset(self.REQUIRES_DISTANCES | {'rrup'}) self.gsim = cls() # underlying gsim super().__init__(**kwargs)
[docs] def compute(self, ctx: np.recarray, imts, mean, sig, tau, phi): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.compute>` for spec of input and result values. """ self.gsim.compute(ctx, imts, mean, sig, tau, phi) self.adjustment = nga_west2_epistemic_adjustment(ctx.mag, ctx.rrup) mean[:] += self.sgn * self.adjustment
# populate gsim_aliases # for instance "AbrahamsonEtAl2014NSHMPMean" is associated to the TOML string # [NSHMP2014] # gmpe_name = "AbrahamsonEtAl2014" # sgn = 0 SUFFIX = {0: 'Mean', -1: 'Lower', 1: 'Upper'} for name in ('Idriss2014', 'ChiouYoungs2014', 'CampbellBozorgnia2014', 'BooreEtAl2014', 'AbrahamsonEtAl2014'): for sgn in (1, -1, 0): a = name + 'NSHMP' + SUFFIX[sgn] base.add_alias(a, NSHMP2014, gmpe_name=name, sgn=sgn)