# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2017 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:`ChiouYoungs2014`.
"""
from __future__ import division
import numpy as np
import math
from openquake.hazardlib.gsim.base import GMPE, CoeffsTable
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, PGV, SA
[docs]class ChiouYoungs2014(GMPE):
"""
Implements GMPE developed by Brian S.-J. Chiou and Robert R. Youngs
and published as "Updated of the Chiou and Youngs NGA Model for the
Average Horizontal Component of Peak Ground Motion and Response Spectra"
(2014, Earthquake Spectra).
"""
#: Supported tectonic region type is active shallow crust
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.ACTIVE_SHALLOW_CRUST
#: Supported intensity measure types are spectral acceleration,
#: peak ground velocity and peak ground acceleration
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([
PGA,
PGV,
SA
])
#: Supported intensity measure component is orientation-independent
#: measure :attr:`~openquake.hazardlib.const.IMC.RotD50`,
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.RotD50
#: Supported standard deviation types are inter-event, intra-event
#: and total, see chapter "Variance model".
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([
const.StdDev.TOTAL,
const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT
])
#: Required site parameters are Vs30, Vs30 measured flag
#: and Z1.0.
REQUIRES_SITES_PARAMETERS = set(('vs30', 'vs30measured', 'z1pt0'))
#: Required rupture parameters are magnitude, rake,
#: dip and ztor.
REQUIRES_RUPTURE_PARAMETERS = set(('dip', 'rake', 'mag', 'ztor'))
#: Required distance measures are RRup, Rjb and Rx.
REQUIRES_DISTANCES = set(('rrup', 'rjb', 'rx'))
[docs] def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
"""
# extracting dictionary of coefficients specific to required
# intensity measure type.
C = self.COEFFS[imt]
# intensity on a reference soil is used for both mean
# and stddev calculations.
ln_y_ref = self._get_ln_y_ref(rup, dists, C)
# exp1 and exp2 are parts of eq. 12 and eq. 13,
# calculate it once for both.
exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360))
exp2 = np.exp(C['phi3'] * (1130 - 360))
mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2)
stddevs = self._get_stddevs(sites, rup, C, stddev_types,
ln_y_ref, exp1, exp2)
return mean, stddevs
def _get_mean(self, sites, C, ln_y_ref, exp1, exp2):
"""
Add site effects to an intensity.
Implements eq. 13b.
"""
# we do not support estimating of basin depth and instead
# rely on it being available (since we require it).
# centered_z1pt0
centered_z1pt0 = self._get_centered_z1pt0(sites)
# we consider random variables being zero since we want
# to find the exact mean value.
eta = epsilon = 0.
ln_y = (
# first line of eq. 12
ln_y_ref + eta
# second line
+ C['phi1'] * np.log(sites.vs30 / 1130).clip(-np.inf, 0)
# third line
+ C['phi2'] * (exp1 - exp2)
* np.log((np.exp(ln_y_ref) * np.exp(eta) + C['phi4']) / C['phi4'])
# fourth line
+ C['phi5']
* (1.0 - np.exp(-1. * centered_z1pt0 / C['phi6']))
# fifth line
+ epsilon
)
return ln_y
def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2):
"""
Get standard deviation for a given intensity on reference soil.
Implements equations 13 for inter-event, intra-event
and total standard deviations.
"""
Fmeasured = sites.vs30measured
Finferred = 1 - sites.vs30measured
# eq. 13 to calculate inter-event standard error
mag_test = min(max(rup.mag, 5.0), 6.5) - 5.0
tau = C['tau1'] + (C['tau2'] - C['tau1']) / 1.5 * mag_test
# b and c coeffs from eq. 10
b = C['phi2'] * (exp1 - exp2)
c = C['phi4']
y_ref = np.exp(ln_y_ref)
# eq. 13
NL = b * y_ref / (y_ref + c)
sigma = ((C['sig1'] + (C['sig2'] - C['sig1']) * mag_test / 1.5)
* np.sqrt((C['sig3'] * Finferred + 0.7 * Fmeasured) +
(1. + NL) ** 2.))
ret = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# eq. 13
ret += [np.sqrt(((1 + NL) ** 2) * (tau ** 2) + (sigma ** 2))]
elif stddev_type == const.StdDev.INTRA_EVENT:
ret.append(sigma)
elif stddev_type == const.StdDev.INTER_EVENT:
# this is implied in eq. 21
ret.append(np.abs((1 + NL) * tau))
return ret
def _get_ln_y_ref(self, rup, dists, C):
"""
Get an intensity on a reference soil.
Implements eq. 13a.
"""
# reverse faulting flag
Frv = 1. if 30 <= rup.rake <= 150 else 0.
# normal faulting flag
Fnm = 1. if -120 <= rup.rake <= -60 else 0.
# hanging wall flag
Fhw = np.zeros_like(dists.rx)
idx = np.nonzero(dists.rx >= 0.)
Fhw[idx] = 1.
# a part in eq. 11
mag_test1 = np.cosh(2. * max(rup.mag - 4.5, 0))
# centered DPP
centered_dpp = self._get_centered_cdpp(dists)
# centered_ztor
centered_ztor = self._get_centered_ztor(rup, Frv)
#
dist_taper = np.fmax(1 - (np.fmax(dists.rrup - 40,
np.zeros_like(dists)) / 30.),
np.zeros_like(dists))
dist_taper = dist_taper.astype(np.float64)
ln_y_ref = (
# first part of eq. 11
C['c1']
+ (C['c1a'] + C['c1c'] / mag_test1) * Frv
+ (C['c1b'] + C['c1d'] / mag_test1) * Fnm
+ (C['c7'] + C['c7b'] / mag_test1) * centered_ztor
+ (C['c11'] + C['c11b'] / mag_test1) *
np.cos(math.radians(rup.dip)) ** 2
# second part
+ C['c2'] * (rup.mag - 6)
+ ((C['c2'] - C['c3']) / C['cn'])
* np.log(1 + np.exp(C['cn'] * (C['cm'] - rup.mag)))
# third part
+ C['c4']
* np.log(dists.rrup + C['c5']
* np.cosh(C['c6'] * max(rup.mag - C['chm'], 0)))
+ (C['c4a'] - C['c4'])
* np.log(np.sqrt(dists.rrup ** 2 + C['crb'] ** 2))
# forth part
+ (C['cg1'] + C['cg2'] / (np.cosh(max(rup.mag - C['cg3'], 0))))
* dists.rrup
# fifth part
+ C['c8'] * dist_taper
* min(max(rup.mag - 5.5, 0) / 0.8, 1.0)
* np.exp(-1 * C['c8a'] * (rup.mag - C['c8b']) ** 2) * centered_dpp
# sixth part
+ C['c9'] * Fhw * np.cos(math.radians(rup.dip)) *
(C['c9a'] + (1 - C['c9a']) * np.tanh(dists.rx / C['c9b']))
* (1 - np.sqrt(dists.rjb ** 2 + rup.ztor ** 2)
/ (dists.rrup + 1.0))
)
return ln_y_ref
def _get_centered_z1pt0(self, sites):
"""
Get z1pt0 centered on the Vs30- dependent avarage z1pt0(m)
California and non-Japan regions
"""
#: California and non-Japan regions
mean_z1pt0 = (-7.15 / 4.) * np.log(((sites.vs30) ** 4. + 570.94 ** 4.)
/ (1360 ** 4. + 570.94 ** 4.))
centered_z1pt0 = sites.z1pt0 - np.exp(mean_z1pt0)
return centered_z1pt0
def _get_centered_ztor(self, rup, Frv):
"""
Get ztor centered on the M- dependent avarage ztor(km)
by different fault types.
"""
if Frv == 1:
mean_ztor = max(2.704 - 1.226 * max(rup.mag - 5.849, 0.0), 0.) ** 2
centered_ztor = rup.ztor - mean_ztor
else:
mean_ztor = max(2.673 - 1.136 * max(rup.mag - 4.970, 0.0), 0.) ** 2
centered_ztor = rup.ztor - mean_ztor
return centered_ztor
def _get_centered_cdpp(self, dists):
"""
Get directivity prediction parameter centered on the avgerage
directivity prediction parameter. Here we set the centered_dpp
equals to zero, since the near fault directivity effect prediction is
off in our calculation.
"""
centered_dpp = 0.
return centered_dpp
#: Coefficient tables are constructed from values in tables 1 - 5
COEFFS = CoeffsTable(sa_damping=5, table="""\
IMT c1 c1a c1b c1c c1d cn cm c2 c3 c4 c4a crb c5 chm c6 c7 c7b c8 c8a c8b c9 c9a c9b c11 c11b cg1 cg2 cg3 phi1 phi2 phi3 phi4 phi5 phi6 gjpit gwn phi1jp phi5jp phi6jp tau1 tau2 sig1 sig2 sig3 sig2jp
pga -1.5065 0.165 -0.255 -0.165 0.255 16.0875 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0956 0.4908 0.0352 0.0462 0. 0.2695 0.4833 0.9228 0.1202 6.8607 0. -0.4536 -0.007146 -0.006758 4.2542 -0.521 -0.1417 -0.00701 0.102151 0. 300 1.5817 0.7594 -0.6846 0.459 800. 0.4 0.26 0.4912 0.3762 0.8 0.4528
pgv 2.3549 0.165 -0.0626 -0.165 0.0626 3.3024 5.423 1.06 2.3152 -2.1 -0.5 50 5.8096 3.0514 0.4407 0.0324 0.0097 0.2154 0.2695 5. 0.3079 0.1 6.5 0 -0.3834 -0.001852 -0.007403 4.3439 -0.7936 -0.0699 -0.008444 5.41 0.0202 300. 2.2306 0.335 -0.7966 0.9488 800. 0.3894 0.2578 0.4785 0.3629 0.7504 0.3918
0.01 -1.5065 0.165 -0.255 -0.165 0.255 16.0875 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0956 0.4908 0.0352 0.0462 0. 0.2695 0.4833 0.9228 0.1202 6.8607 0. -0.4536 -0.007146 -0.006758 4.2542 -0.521 -0.1417 -0.00701 0.102151 0. 300 1.5817 0.7594 -0.6846 0.459 800. 0.4 0.26 0.4912 0.3762 0.8 0.4528
0.02 -1.4798 0.165 -0.255 -0.165 0.255 15.7118 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0963 0.4925 0.0352 0.0472 0. 0.2695 1.2144 0.9296 0.1217 6.8697 0. -0.4536 -0.007249 -0.006758 4.2386 -0.5055 -0.1364 -0.007279 0.10836 0. 300 1.574 0.7606 -0.6681 0.458 800. 0.4026 0.2637 0.4904 0.3762 0.8 0.4551
0.03 -1.2972 0.165 -0.255 -0.165 0.255 15.8819 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0974 0.4992 0.0352 0.0533 0. 0.2695 1.6421 0.9396 0.1194 6.9113 0. -0.4536 -0.007869 -0.006758 4.2519 -0.4368 -0.1403 -0.007354 0.119888 0. 300 1.5544 0.7642 -0.6314 0.462 800. 0.4063 0.2689 0.4988 0.3849 0.8 0.4571
0.04 -1.1007 0.165 -0.255 -0.165 0.255 16.4556 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.0988 0.5037 0.0352 0.0596 0. 0.2695 1.9456 0.9661 0.1166 7.0271 0. -0.4536 -0.008316 -0.006758 4.296 -0.3752 -0.1591 -0.006977 0.133641 0. 300 1.5502 0.7676 -0.5855 0.453 800. 0.4095 0.2736 0.5049 0.391 0.8 0.4642
0.05 -0.9292 0.165 -0.255 -0.165 0.255 17.6453 4.9993 1.06 1.9636 -2.1 -0.5 50 6.4551 3.1011 0.5048 0.0352 0.0639 0. 0.2695 2.181 0.9794 0.1176 7.0959 0. -0.4536 -0.008743 -0.006758 4.3578 -0.3469 -0.1862 -0.006467 0.148927 0. 300 1.5391 0.7739 -0.5457 0.436 800. 0.4124 0.2777 0.5096 0.3957 0.8 0.4716
0.075 -0.658 0.165 -0.254 -0.165 0.254 20.1772 5.0031 1.06 1.9636 -2.1 -0.5 50 6.4551 3.1094 0.5048 0.0352 0.063 0. 0.2695 2.6087 1.026 0.1171 7.3298 0. -0.4536 -0.009537 -0.00619 4.5455 -0.3747 -0.2538 -0.005734 0.190596 0. 300 1.4804 0.7956 -0.4685 0.383 800. 0.4179 0.2855 0.5179 0.4043 0.8 0.5022
0.1 -0.5613 0.165 -0.253 -0.165 0.253 19.9992 5.0172 1.06 1.9636 -2.1 -0.5 50 6.8305 3.2381 0.5048 0.0352 0.0532 0. 0.2695 2.9122 1.0177 0.1146 7.2588 0. -0.4536 -0.00983 -0.005332 4.7603 -0.444 -0.2943 -0.005604 0.230662 0. 300 1.4094 0.7932 -0.4985 0.375 800. 0.4219 0.2913 0.5236 0.4104 0.8 0.523
0.12 -0.5342 0.165 -0.252 -0.165 0.252 18.7106 5.0315 1.06 1.9795 -2.1 -0.5 50 7.1333 3.3407 0.5048 0.0352 0.0452 0. 0.2695 3.1045 1.0008 0.1128 7.2372 0. -0.4536 -0.009913 -0.004732 4.8963 -0.4895 -0.3077 -0.005696 0.253169 0. 300 1.3682 0.7768 -0.5603 0.377 800. 0.4244 0.2949 0.527 0.4143 0.8 0.5278
0.15 -0.5462 0.165 -0.25 -0.165 0.25 16.6246 5.0547 1.06 2.0362 -2.1 -0.5 50 7.3621 3.43 0.5045 0.0352 0.0345 0. 0.2695 3.3399 0.9801 0.1106 7.2109 0. -0.4536 -0.009896 -0.003806 5.0644 -0.5477 -0.3113 -0.005845 0.266468 0. 300 1.3241 0.7437 -0.6451 0.379 800. 0.4275 0.2993 0.5308 0.4191 0.8 0.5304
0.17 -0.5858 0.165 -0.248 -0.165 0.248 15.3709 5.0704 1.06 2.0823 -2.1 -0.5 50 7.4365 3.4688 0.5036 0.0352 0.0283 0. 0.2695 3.4719 0.9652 0.115 7.2491 0. -0.4536 -0.009787 -0.00328 5.1371 -0.5922 -0.3062 -0.005959 0.26506 0. 300 1.3071 0.7219 -0.6981 0.38 800. 0.4292 0.3017 0.5328 0.4217 0.8 0.531
0.2 -0.6798 0.165 -0.2449 -0.165 0.2449 13.7012 5.0939 1.06 2.1521 -2.1 -0.5 50 7.4972 3.5146 0.5016 0.0352 0.0202 0. 0.2695 3.6434 0.9459 0.1208 7.2988 0. -0.444 -0.009505 -0.00269 5.188 -0.6693 -0.2927 -0.006141 0.255253 0. 300 1.2931 0.6922 -0.7653 0.384 800. 0.4313 0.3047 0.5351 0.4252 0.8 0.5312
0.25 -0.8663 0.165 -0.2382 -0.165 0.2382 11.2667 5.1315 1.06 2.2574 -2.1 -0.5 50 7.5416 3.5746 0.4971 0.0352 0.009 0. 0.2695 3.8787 0.9196 0.1208 7.3691 0. -0.3539 -0.008918 -0.002128 5.2164 -0.7766 -0.2662 -0.006439 0.231541 0. 300 1.315 0.6579 -0.8469 0.393 800. 0.4341 0.3087 0.5377 0.4299 0.7999 0.5309
0.3 -1.0514 0.165 -0.2313 -0.165 0.2313 9.1908 5.167 1.06 2.344 -2.1 -0.5 50 7.56 3.6232 0.4919 0.0352 -0.0004 0. 0.2695 4.0711 0.8829 0.1175 6.8789 0. -0.2688 -0.008251 -0.001812 5.1954 -0.8501 -0.2405 -0.006704 0.207277 0.001 300 1.3514 0.6362 -0.8999 0.408 800. 0.4363 0.3119 0.5395 0.4338 0.7997 0.5307
0.4 -1.3794 0.165 -0.2146 -0.165 0.2146 6.5459 5.2317 1.06 2.4709 -2.1 -0.5 50 7.5735 3.6945 0.4807 0.0352 -0.0155 0. 0.2695 4.3745 0.8302 0.106 6.5334 0. -0.1793 -0.007267 -0.001274 5.0899 -0.9431 -0.1975 -0.007125 0.165464 0.004 300 1.4051 0.6049 -0.9618 0.462 800. 0.4396 0.3165 0.5422 0.4399 0.7988 0.531
0.5 -1.6508 0.165 -0.1972 -0.165 0.1972 5.2305 5.2893 1.06 2.5567 -2.1 -0.5 50 7.5778 3.7401 0.4707 0.0352 -0.0278 0.0991 0.2695 4.6099 0.7884 0.1061 6.526 0. -0.1428 -0.006492 -0.001074 4.7854 -1.0044 -0.1633 -0.007435 0.133828 0.01 300 1.4402 0.5507 -0.9945 0.524 800. 0.4419 0.3199 0.5433 0.4446 0.7966 0.5313
0.75 -2.1511 0.165 -0.162 -0.165 0.162 3.7896 5.4109 1.06 2.6812 -2.1 -0.5 50 7.5808 3.7941 0.4575 0.0352 -0.0477 0.1982 0.2695 5.0376 0.6754 0.1 6.5 0. -0.1138 -0.005147 -0.001115 4.3304 -1.0602 -0.1028 -0.00812 0.085153 0.034 300 1.528 0.3582 -1.0225 0.658 800. 0.4459 0.3255 0.5294 0.4533 0.7792 0.5309
1 -2.5365 0.165 -0.14 -0.165 0.14 3.3024 5.5106 1.06 2.7474 -2.1 -0.5 50 7.5814 3.8144 0.4522 0.0352 -0.0559 0.2154 0.2695 5.3411 0.6196 0.1 6.5 0. -0.1062 -0.004277 -0.001197 4.1667 -1.0941 -0.0699 -0.008444 0.058595 0.067 300 1.6523 0.2003 -1.0002 0.78 800. 0.4484 0.3291 0.5105 0.4594 0.7504 0.5302
1.5 -3.0686 0.165 -0.1184 -0.165 0.1184 2.8498 5.6705 1.06 2.8161 -2.1 -0.5 50 7.5817 3.8284 0.4501 0.0352 -0.063 0.2154 0.2695 5.7688 0.5101 0.1 6.5 0. -0.102 -0.002979 -0.001675 4.0029 -1.1142 -0.0425 -0.007707 0.031787 0.143 300 1.8872 0.0356 -0.9245 0.96 800. 0.4515 0.3335 0.4783 0.468 0.7136 0.5276
2 -3.4148 0.1645 -0.11 -0.1645 0.11 2.5417 5.7981 1.06 2.8514 -2.1 -0.5 50 7.5818 3.833 0.45 0.0352 -0.0665 0.2154 0.2695 6.0723 0.3917 0.1 6.5 0. -0.1009 -0.002301 -0.002349 3.8949 -1.1154 -0.0302 -0.004792 0.019716 0.203 300 2.1348 0. -0.8626 1.11 800. 0.4534 0.3363 0.4681 0.4681 0.7035 0.5167
3 -3.9013 0.1168 -0.104 -0.1168 0.104 2.1488 5.9983 1.06 2.8875 -2.1 -0.5 50 7.5818 3.8361 0.45 0.016 -0.0516 0.2154 0.2695 6.5 0.1244 0.1 6.5 0. -0.1003 -0.001344 -0.003306 3.7928 -1.1081 -0.0129 -0.001828 0.009643 0.277 300 3.5752 0. -0.7882 1.291 800. 0.4558 0.3398 0.4617 0.4617 0.7006 0.4917
4 -4.2466 0.0732 -0.102 -0.0732 0.102 1.8957 6.1552 1.06 2.9058 -2.1 -0.5 50 7.5818 3.8369 0.45 0.0062 -0.0448 0.2154 0.2695 6.8035 0.0086 0.1 6.5 0. -0.1001 -0.001084 -0.003566 3.7443 -1.0603 -0.0016 -0.001523 0.005379 0.309 300 3.8646 0. -0.7195 1.387 800. 0.4574 0.3419 0.4571 0.4571 0.7001 0.4682
5 -4.5143 0.0484 -0.101 -0.0484 0.101 1.7228 6.2856 1.06 2.9169 -2.1 -0.5 50 7.5818 3.8376 0.45 0.0029 -0.0424 0.2154 0.2695 7.0389 0. 0.1 6.5 0. -0.1001 -0.00101 -0.00364 3.709 -0.9872 0. -0.00144 0.003223 0.321 300 3.7292 0. -0.656 1.433 800. 0.4584 0.3435 0.4535 0.4535 0.7 0.4517
7.5 -5.0009 0.022 -0.101 -0.022 0.101 1.5737 6.5428 1.06 2.932 -2.1 -0.5 50 7.5818 3.838 0.45 0.0007 -0.0348 0.2154 0.2695 7.4666 0. 0.1 6.5 0. -0.1 -0.000964 -0.003686 3.6632 -0.8274 0. -0.001369 0.001134 0.329 300 2.3763 0. -0.5202 1.46 800. 0.4601 0.3459 0.4471 0.4471 0.7 0.4167
10 -5.3461 0.0124 -0.1 -0.0124 0.1 1.5265 6.7415 1.06 2.9396 -2.1 -0.5 50 7.5818 3.838 0.45 0.0003 -0.0253 0.2154 0.2695 7.77 0. 0.1 6.5 0. -0.1 -0.00095 -0.0037 3.623 -0.7053 0. -0.001361 0.000515 0.33 300 1.7679 0. -0.4068 1.464 800. 0.4612 0.3474 0.4426 0.4426 0.7 0.3755
""")
[docs]class ChiouYoungs2014PEER(ChiouYoungs2014):
"""
This implements the Chiou & Youngs (2014) GMPE for use with the PEER
tests. In this version the total standard deviation is fixed at 0.65
"""
#: Only the total standars deviation is defined
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([
const.StdDev.TOTAL,
])
#: The PEER tests requires only PGA
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([
PGA,
])
def _get_stddevs(self, sites, rup, C, stddev_types, ln_y_ref, exp1, exp2):
"""
Returns the standard deviation, which is fixed at 0.65 for every site
"""
ret = []
for stddev_type in stddev_types:
assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
if stddev_type == const.StdDev.TOTAL:
# eq. 13
ret.append(0.65 * np.ones_like(sites.vs30))
return ret
[docs]class ChiouYoungs2014NearFaultEffect(ChiouYoungs2014):
"""
This implements the Chiou & Youngs (2014) GMPE include the near fault
effect prediction. In this version, we add the distance measure, rcdpp
for directivity prediction.
"""
#: Required distance measures are RRup, Rjb, Rx, and Rcdpp
REQUIRES_DISTANCES = set(('rrup', 'rjb', 'rx', 'rcdpp'))
def _get_centered_cdpp(self, dists):
"""
Get directivity prediction parameter centered on the avgerage
directivity prediction parameter.
"""
centered_dpp = dists.rcdpp
return centered_dpp