# Source code for openquake.hazardlib.gsim.bchydro_2016_epistemic

```# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2018 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/>.

import numpy as np
from openquake.hazardlib.gsim.base import CoeffsTable
from openquake.hazardlib.gsim.abrahamson_2015 import (
AbrahamsonEtAl2015SInter, AbrahamsonEtAl2015SInterLow,
AbrahamsonEtAl2015SInterHigh, AbrahamsonEtAl2015SSlab,
AbrahamsonEtAl2015SSlabLow, AbrahamsonEtAl2015SSlabHigh)

[docs]def get_sigma_statistical(mag):
"""
Based on interpretation of the sigma_mu suggested for the GMPE in the
Hanford Site PSHA Project, alongside observation of trends in sigma_mu
for other models. The sigma_mu is determine by interpolation of this
multi-segment linear model dependent only on magnitude (not distance or
period)

<= 5.5 = 0.15
6.5 = 0.1
7.5 = 0.1
8.0 = 0.15
>= 9.0 = 0.3
"""
if mag <= 5.5:
return 0.15
elif (mag > 5.5) and (mag <= 6.5):
return 0.15 - 0.05 * (mag - 5.5)
elif (mag > 6.5) and (mag <= 7.5):
return 0.1
elif (mag > 7.5) and (mag <= 8.0):
return 0.1 + (mag - 7.5) * (0.05 / 0.5)
elif (mag > 8.0) and (mag <= 9.0):
return 0.15 + (mag - 8.0) * 0.15
else:
return 0.3

[docs]class BCHydroSERASInter(AbrahamsonEtAl2015SInter):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events with
theta6 calibrated to Mediterranean data.

Introduces two configurable parameters:

The amount to increase or decrease the theta6 - should be +0.0015 (for
slower attenuation) and -0.0015 (for faster attenuation)

:param float sigma_mu_epsilon:
The number of standard deviations above or below the mean to apply the
statistical uncertainty sigma_mu term.
"""
experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00721467    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00719296    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00712619    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00701600    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00687258    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00671633    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00657867    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00648602    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00643709    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00639138    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00629147    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00609857    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00581454    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00548905    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00520499   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00505022   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00507967   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00529221   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00564790   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00607621   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00647922   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00676355   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00686566   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")

[docs]class BCHydroSERASInterLow(AbrahamsonEtAl2015SInterLow):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the low magnitude
scaling branch.
"""
experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00721467    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00719296    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00712619    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00701600    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00687258    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00671633    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00657867    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00648602    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00643709    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00639138    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00629147    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00609857    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00581454    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00548905    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00520499   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00505022   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00507967   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00529221   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00564790   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00607621   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00647922   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00676355   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00686566   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")

[docs]class BCHydroSERASInterHigh(AbrahamsonEtAl2015SInterHigh):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the high
magnitude scaling branch.
"""

experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return (C['theta2'] + self.CONSTS['theta3'] * (mag - 7.8)) *\
np.log(dists.rrup + self.CONSTS['c4'] * np.exp((mag - 6.) *
self.CONSTS['theta9'])) +\

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00721467    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00719296    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00712619    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00701600    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00687258    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00671633    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00657867    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00648602    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00643709    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00639138    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00629147    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00609857    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00581454    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00548905    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00520499   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00505022   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00507967   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00529221   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00564790   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00607621   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00647922   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00676355   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00686566   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")

[docs]class BCHydroSERASSlab(AbrahamsonEtAl2015SSlab):
"""
SERA Adjustment of the BC Hydro GMPE for subduction in-slab events with
theta6 calibrated to Mediterranean data.

Introduces two configurable parameters:

a6_adjustment - the amount to increase or decrease the theta6 (should be
+0.0015 (for slower attenuation) and -0.0015 (for faster attenuation)

sigma_mu_epsilon - number of standard deviations above or below the mean
to apply the statistical uncertainty sigma_mu term.
"""

experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00278801    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00275821    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00268517    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00261360    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00259240    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00264688    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00277703    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00296427    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00318216    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00340820    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00363798    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00388267    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00415403    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00445479    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00478084   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00513159   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00550694   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00590809   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00634283   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00680074   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00722208   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00752097   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00762908   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")

[docs]class BCHydroSERASSlabLow(AbrahamsonEtAl2015SSlabLow):
"""
SERA Adjustment of the BC Hydro GMPE for subduction in-slab events
with theta6 calibrated to Mediterranean data, for the low magnitude
scaling branch.
"""

experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00278801    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00275821    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00268517    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00261360    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00259240    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00264688    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00277703    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00296427    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00318216    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00340820    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00363798    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00388267    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00415403    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00445479    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00478084   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00513159   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00550694   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00590809   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00634283   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00680074   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00722208   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00752097   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00762908   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")

[docs]class BCHydroSERASSlabHigh(AbrahamsonEtAl2015SSlabHigh):
"""
SERA Adjustment of the BC Hydro GMPE for subduction interface events
with theta6 calibrated to Mediterranean data, for the high magnitude
scaling branch.
"""

experimental = True

super().__init__()
self.sigma_mu_epsilon = sigma_mu_epsilon

[docs]    def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
Returns mean and stddevs applying the statistical uncertainty if
needed
"""
mean, stddevs = super().get_mean_and_stddevs(sites, rup, dists, imt,
stddev_types)
if self.sigma_mu_epsilon:
sigma_mu = get_sigma_statistical(rup.mag)
return mean + (sigma_mu * self.sigma_mu_epsilon), stddevs
else:
return mean, stddevs

def _compute_distance_term(self, C, mag, dists):
"""
Computes the distance scaling term, as contained within equation (1)
"""
return ((C['theta2'] + C['theta14'] + self.CONSTS['theta3'] *
(mag - 7.8)) * np.log(dists.rhypo + self.CONSTS['c4'] *
np.exp((mag - 6.) * self.CONSTS['theta9'])) +
((self.theta6_adj + C['theta6']) * dists.rhypo)) + C["theta10"]

COEFFS = CoeffsTable(sa_damping=5, table="""\
imt          vlin        b   theta1    theta2        theta6    theta7    theta8  theta10  theta11   theta12   theta13   theta14  theta15   theta16      phi     tau   sigma  sigma_ss
pga      865.1000  -1.1860   4.2203   -1.3500   -0.00278801    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0200   865.1000  -1.1860   4.2203   -1.3500   -0.00275821    1.0988   -1.4200   3.1200   0.0130    0.9800   -0.0135   -0.4000   0.9969   -1.0000   0.6000  0.4300  0.7400    0.6000
0.0500  1053.5000  -1.3460   4.5371   -1.4000   -0.00268517    1.2536   -1.6500   3.3700   0.0130    1.2880   -0.0138   -0.4000   1.1030   -1.1800   0.6000  0.4300  0.7400    0.6000
0.0750  1085.7000  -1.4710   5.0733   -1.4500   -0.00261360    1.4175   -1.8000   3.3700   0.0130    1.4830   -0.0142   -0.4000   1.2732   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1000  1032.5000  -1.6240   5.2892   -1.4500   -0.00259240    1.3997   -1.8000   3.3300   0.0130    1.6130   -0.0145   -0.4000   1.3042   -1.3600   0.6000  0.4300  0.7400    0.6000
0.1500   877.6000  -1.9310   5.4563   -1.4500   -0.00264688    1.3582   -1.6900   3.2500   0.0130    1.8820   -0.0153   -0.4000   1.2600   -1.3000   0.6000  0.4300  0.7400    0.6000
0.2000   748.2000  -2.1880   5.2684   -1.4000   -0.00277703    1.1648   -1.4900   3.0300   0.0129    2.0760   -0.0162   -0.3500   1.2230   -1.2500   0.6000  0.4300  0.7400    0.6000
0.2500   654.3000  -2.3810   5.0594   -1.3500   -0.00296427    0.9940   -1.3000   2.8000   0.0129    2.2480   -0.0172   -0.3100   1.1600   -1.1700   0.6000  0.4300  0.7400    0.6000
0.3000   587.1000  -2.5180   4.7945   -1.2800   -0.00318216    0.8821   -1.1800   2.5900   0.0128    2.3480   -0.0183   -0.2800   1.0500   -1.0600   0.6000  0.4300  0.7400    0.6000
0.4000   503.0000  -2.6570   4.4644   -1.1800   -0.00340820    0.7046   -0.9800   2.2000   0.0127    2.4270   -0.0206   -0.2300   0.8000   -0.7800   0.6000  0.4300  0.7400    0.6000
0.5000   456.6000  -2.6690   4.0181   -1.0800   -0.00363798    0.5799   -0.8200   1.9200   0.0125    2.3990   -0.0231   -0.1900   0.6620   -0.6200   0.6000  0.4300  0.7400    0.6000
0.6000   430.3000  -2.5990   3.6055   -0.9900   -0.00388267    0.5021   -0.7000   1.7000   0.0124    2.2730   -0.0256   -0.1600   0.5800   -0.5000   0.6000  0.4300  0.7400    0.6000
0.7500   410.5000  -2.4010   3.2174   -0.9100   -0.00415403    0.3687   -0.5400   1.4200   0.0120    1.9930   -0.0296   -0.1200   0.4800   -0.3400   0.6000  0.4300  0.7400    0.6000
1.0000   400.0000  -1.9550   2.7981   -0.8500   -0.00445479    0.1746   -0.3400   1.1000   0.0114    1.4700   -0.0363   -0.0700   0.3300   -0.1400   0.6000  0.4300  0.7400    0.6000
1.5000   400.0000  -1.0250   2.0123   -0.7700   -0.00478084   -0.0820   -0.0500   0.7000   0.0100    0.4080   -0.0493    0.0000   0.3100    0.0000   0.6000  0.4300  0.7400    0.6000
2.0000   400.0000  -0.2990   1.4128   -0.7100   -0.00513159   -0.2821    0.1200   0.7000   0.0085   -0.4010   -0.0610    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
2.5000   400.0000   0.0000   0.9976   -0.6700   -0.00550694   -0.4108    0.2500   0.7000   0.0069   -0.7230   -0.0711    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
3.0000   400.0000   0.0000   0.6443   -0.6400   -0.00590809   -0.4466    0.3000   0.7000   0.0054   -0.6730   -0.0798    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
4.0000   400.0000   0.0000   0.0657   -0.5800   -0.00634283   -0.4344    0.3000   0.7000   0.0027   -0.6270   -0.0935    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
5.0000   400.0000   0.0000  -0.4624   -0.5400   -0.00680074   -0.4368    0.3000   0.7000   0.0005   -0.5960   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
6.0000   400.0000   0.0000  -0.9809   -0.5000   -0.00722208   -0.4586    0.3000   0.7000  -0.0013   -0.5660   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
7.5000   400.0000   0.0000  -1.6017   -0.4600   -0.00752097   -0.4433    0.3000   0.7000  -0.0033   -0.5280   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
10.0000  400.0000   0.0000  -2.2937   -0.4000   -0.00762908   -0.4828    0.3000   0.7000  -0.0060   -0.5040   -0.0980    0.0000   0.3000    0.0000   0.6000  0.4300  0.7400    0.6000
""")
```