# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2017 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Module exports :class:`AbrahamsonEtAl2014NSHMPUpper`
:class:`AbrahamsonEtAl2014NSHMPLower`
:class:`BooreEtAl2014NSHMPUpper`
:class:`BooreEtAl2014NSHMPLower`
:class:`CampbellBozorgnia2014NSHMPUpper`
:class:`CampbellBozorgnia2014NSHMPLower`
:class:`ChiouYoungs2014NSHMPUpper`
:class:`ChiouYoungs2014NSHMPLower`
:class:`Idriss2014NSHMPUpper`
:class:`Idriss2014NSHMPLower`
"""
from __future__ import division
import numpy as np
from openquake.hazardlib.gsim.base import _norm_sf, _truncnorm_sf
from openquake.hazardlib import const
# NGA West 2 GMPEs
from openquake.hazardlib.gsim.abrahamson_2014 import AbrahamsonEtAl2014
from openquake.hazardlib.gsim.boore_2014 import BooreEtAl2014
from openquake.hazardlib.gsim.campbell_bozorgnia_2014 import \
CampbellBozorgnia2014
from openquake.hazardlib.gsim.chiou_youngs_2014 import ChiouYoungs2014
from openquake.hazardlib.gsim.idriss_2014 import Idriss2014
[docs]def nga_west2_epistemic_adjustment(magnitude, distance):
"""
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
"""
if magnitude < 6.0:
adjustment = 0.22 * np.ones_like(distance)
adjustment[distance < 10.0] = 0.37
elif magnitude >= 7.0:
adjustment = 0.36 * np.ones_like(distance)
adjustment[distance < 10.0] = 0.40
adjustment[distance >= 30.0] = 0.33
else:
adjustment = 0.23 * np.ones_like(distance)
adjustment[distance < 10.0] = 0.25
return adjustment
DEFAULT_WEIGHTING = [(0.185, -1.), (0.63, 0.), (0.185, 1.)]
[docs]def get_weighted_poes(gsim, sctx, rctx, dctx, imt, imls, truncation_level,
weighting=DEFAULT_WEIGHTING):
"""
This function implements the NGA West 2 GMPE epistemic uncertainty
adjustment factor without re-calculating the actual GMPE each time.
:param gsim:
Instance of the GMPE
:param list weighting:
Weightings as a list of tuples of (weight, number standard deviations
of the epistemic uncertainty adjustment)
"""
if truncation_level is not None and truncation_level < 0:
raise ValueError('truncation level must be zero, positive number '
'or None')
gsim._check_imt(imt)
adjustment = nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup)
adjustment = adjustment.reshape(adjustment.shape + (1, ))
if truncation_level == 0:
# zero truncation mode, just compare imls to mean
imls = gsim.to_distribution_values(imls)
mean, _ = gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt, [])
mean = mean.reshape(mean.shape + (1, ))
output = np.zeros([mean.shape[0], imls.shape[0]])
for (wgt, fct) in weighting:
output += (wgt *
(imls <= (mean + (fct * adjustment))).astype(float))
return output
else:
# use real normal distribution
assert (const.StdDev.TOTAL
in gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES)
imls = gsim.to_distribution_values(imls)
mean, [stddev] = gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt,
[const.StdDev.TOTAL])
mean = mean.reshape(mean.shape + (1, ))
stddev = stddev.reshape(stddev.shape + (1, ))
output = np.zeros([mean.shape[0], imls.shape[0]])
for (wgt, fct) in weighting:
values = (imls - (mean + (fct * adjustment))) / stddev
if truncation_level is None:
output += (wgt * _norm_sf(values))
else:
output += (wgt * _truncnorm_sf(truncation_level, values))
return output
[docs]class AbrahamsonEtAl2014NSHMPUpper(AbrahamsonEtAl2014):
"""
Implements the positive NSHMP adjustment factor for the Abrahamson et al.
(2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(AbrahamsonEtAl2014NSHMPUpper, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class AbrahamsonEtAl2014NSHMPLower(AbrahamsonEtAl2014):
"""
Implements the negative NSHMP adjustment factor for the Abrahamson et al.
(2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(AbrahamsonEtAl2014NSHMPLower, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class AbrahamsonEtAl2014NSHMPMean(AbrahamsonEtAl2014):
"""
Implements the Abrahamson et al (2014) GMPE for application to the
weighted mean case
"""
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level):
"""
Adapts the original `get_poes()` from the :class:
openquake.hazardlib.gsim.base.GMPE to call a function that take the
weighted sum of the PoEs from the epistemic uncertainty adjustment
"""
return get_weighted_poes(self, sctx, rctx, dctx, imt, imls,
truncation_level)
[docs]class BooreEtAl2014NSHMPUpper(BooreEtAl2014):
"""
Implements the positive NSHMP adjustment factor for the Boore et al.
(2014) NGA West 2 GMPE
"""
# Originally Boore et al. (2014) requires only Rjb, but the epistemic
# adjustment factors are given in terms of Rrup, so both are required here
REQUIRES_DISTANCES = set(("rjb", "rrup"))
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(BooreEtAl2014NSHMPUpper, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class BooreEtAl2014NSHMPLower(BooreEtAl2014):
"""
Implements the negative NSHMP adjustment factor for the Boore et al.
(2014) NGA West 2 GMPE
"""
# See similar comment above
REQUIRES_DISTANCES = set(("rjb", "rrup"))
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(BooreEtAl2014NSHMPLower, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class BooreEtAl2014NSHMPMean(BooreEtAl2014):
"""
Implements the Boore et al (2014) GMPE for application to the
weighted mean case
"""
# See similar comment above
REQUIRES_DISTANCES = set(("rjb", "rrup"))
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level):
"""
Adapts the original `get_poes()` from the :class:
openquake.hazardlib.gsim.base.GMPE to call a function that take the
weighted sum of the PoEs from the epistemic uncertainty adjustment
"""
return get_weighted_poes(self, sctx, rctx, dctx, imt, imls,
truncation_level)
[docs]class CampbellBozorgnia2014NSHMPUpper(CampbellBozorgnia2014):
"""
Implements the positive NSHMP adjustment factor for the Campbell and
Bozorgnia (2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(CampbellBozorgnia2014NSHMPUpper, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class CampbellBozorgnia2014NSHMPLower(CampbellBozorgnia2014):
"""
Implements the negative NSHMP adjustment factor for the Campbell and
Bozorgnia (2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(CampbellBozorgnia2014NSHMPLower, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class CampbellBozorgnia2014NSHMPMean(CampbellBozorgnia2014):
"""
Implements the Campbell & Bozorgnia (2014) GMPE for application to the
weighted mean case
"""
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level):
"""
Adapts the original `get_poes()` from the :class:
openquake.hazardlib.gsim.base.GMPE to call a function that take the
weighted sum of the PoEs from the epistemic uncertainty adjustment
"""
return get_weighted_poes(self, sctx, rctx, dctx, imt, imls,
truncation_level)
[docs]class ChiouYoungs2014NSHMPUpper(ChiouYoungs2014):
"""
Implements the positive NSHMP adjustment factor for the Chiou & Youngs
(2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(ChiouYoungs2014NSHMPUpper, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class ChiouYoungs2014NSHMPLower(ChiouYoungs2014):
"""
Implements the negative NSHMP adjustment factor for the Chiou & Youngs
(2014) NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(ChiouYoungs2014NSHMPLower, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class ChiouYoungs2014NSHMPMean(ChiouYoungs2014):
"""
Implements the Chiou & Youngs (2014) GMPE for application to the
weighted mean case
"""
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level):
"""
Adapts the original `get_poes()` from the :class:
openquake.hazardlib.gsim.base.GMPE to call a function that take the
weighted sum of the PoEs from the epistemic uncertainty adjustment
"""
return get_weighted_poes(self, sctx, rctx, dctx, imt, imls,
truncation_level)
[docs]class Idriss2014NSHMPUpper(Idriss2014):
"""
Implements the positive NSHMP adjustment factor for the Idriss (2014)
NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(Idriss2014NSHMPUpper, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean + nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class Idriss2014NSHMPLower(Idriss2014):
"""
Implements the negative NSHMP adjustment factor for the Idriss (2014)
NGA West 2 GMPE
"""
[docs] def get_mean_and_stddevs(self, sctx, rctx, dctx, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# Get original mean and standard deviations
mean, stddevs = super(Idriss2014NSHMPLower, self).\
get_mean_and_stddevs(sctx, rctx, dctx, imt, stddev_types)
# Return mean, increased by the adjustment factor,
# and standard devation
return mean - nga_west2_epistemic_adjustment(rctx.mag, dctx.rrup),\
stddevs
[docs]class Idriss2014NSHMPMean(Idriss2014):
"""
Implements the Idriss (2014) GMPE for application to the
weighted mean case
"""
[docs] def get_poes(self, sctx, rctx, dctx, imt, imls, truncation_level):
"""
Adapts the original `get_poes()` from the :class:
openquake.hazardlib.gsim.base.GMPE to call a function that take the
weighted sum of the PoEs from the epistemic uncertainty adjustment
"""
return get_weighted_poes(self, sctx, rctx, dctx, imt, imls,
truncation_level)