pgv 6.75 330 -2.02 2400 4.5 5.975 -0.919 0.275 -0.1 -0.41 2.366 0.0 -0.094 2.36 0 -0.1 0.25 0.22 0.3 -0.0005 0.28 0.15 0.09 0.07 -0.0001 0.0005 -0.0037 -0.1462 0.377 0.212 0.157 0 0.095 -0.038 0.065 0.662 0.51 0.38 0.38 0.66 0.51 0.58 0.5300
0.01 6.75 660 -1.47 2.4 4.5 0.587 -0.790 0.275 -0.1 -0.41 2.154 0.0 -0.015 1.735 0 -0.1 0.6 -0.3 1.1 -0.0072 0.1 0.05 0 -0.05 -0.0015 0.0025 -0.0034 -0.1503 0.265 0.337 0.188 0 0.088 -0.196 0.044 0.754 0.52 0.47 0.36 0.741 0.501 0.54 0.6300
0.02 6.75 680 -1.46 2.4 4.5 0.598 -0.790 0.275 -0.1 -0.41 2.146 0.0 -0.015 1.718 0 -0.1 0.6 -0.3 1.1 -0.0073 0.1 0.05 0 -0.05 -0.0015 0.0024 -0.0033 -0.1479 0.255 0.328 0.184 0 0.088 -0.194 0.061 0.76 0.52 0.47 0.36 0.747 0.501 0.54 0.6300
0.03 6.75 770 -1.39 2.4 4.5 0.602 -0.790 0.275 -0.1 -0.41 2.157 0.0 -0.015 1.615 0 -0.1 0.6 -0.3 1.1 -0.0075 0.1 0.05 0 -0.05 -0.0016 0.0023 -0.0034 -0.1447 0.249 0.32 0.18 0 0.093 -0.175 0.162 0.781 0.52 0.47 0.36 0.769 0.501 0.55 0.6300
0.05 6.75 915 -1.22 2.4 4.5 0.707 -0.790 0.275 -0.1 -0.41 2.085 0.0 -0.015 1.358 0 -0.1 0.6 -0.3 1.1 -0.008 0.1 0.05 0 -0.05 -0.002 0.0027 -0.0033 -0.1326 0.202 0.289 0.167 0 0.133 -0.09 0.451 0.81 0.53 0.47 0.36 0.798 0.512 0.56 0.6500
0.075 6.75 960 -1.15 2.4 4.5 0.973 -0.790 0.275 -0.1 -0.41 2.029 0.0 -0.015 1.258 0 -0.1 0.6 -0.3 1.1 -0.0089 0.1 0.05 0 -0.05 -0.0027 0.0032 -0.0029 -0.1353 0.126 0.275 0.173 0 0.186 0.09 0.506 0.81 0.54 0.47 0.36 0.798 0.522 0.57 0.6900
0.1 6.75 910 -1.23 2.4 4.5 1.169 -0.790 0.275 -0.1 -0.41 2.041 0.0 -0.015 1.31 0 -0.1 0.6 -0.3 1.1 -0.0095 0.1 0.05 0 -0.05 -0.0033 0.0036 -0.0025 -0.1128 0.022 0.256 0.189 0 0.16 0.006 0.335 0.81 0.55 0.47 0.36 0.795 0.527 0.57 0.7000
0.15 6.75 740 -1.59 2.4 4.5 1.442 -0.790 0.275 -0.1 -0.41 2.121 0.0 -0.022 1.66 0 -0.1 0.6 -0.3 1.1 -0.0095 0.1 0.05 0 -0.05 -0.0035 0.0033 -0.0025 0.0383 -0.136 0.162 0.108 0 0.068 -0.156 -0.084 0.801 0.56 0.47 0.36 0.773 0.519 0.58 0.7000
0.2 6.75 590 -2.01 2.4 4.5 1.637 -0.790 0.275 -0.1 -0.41 2.224 0.0 -0.03 2.22 0 -0.1 0.6 -0.3 1.1 -0.0086 0.1 0.05 0 -0.03 -0.0033 0.0027 -0.0031 0.0775 -0.078 0.224 0.115 0 0.048 -0.274 -0.178 0.789 0.565 0.47 0.36 0.753 0.514 0.59 0.7000
0.25 6.75 495 -2.41 2.4 4.5 1.701 -0.790 0.275 -0.1 -0.41 2.312 0.0 -0.038 2.77 0 -0.1 0.6 -0.24 1.1 -0.0074 0.1 0.05 0 0 -0.0029 0.0024 -0.0036 0.0741 0.037 0.248 0.122 0 0.055 -0.248 -0.187 0.77 0.57 0.47 0.36 0.729 0.513 0.61 0.7000
0.3 6.75 430 -2.76 2.4 4.5 1.712 -0.790 0.275 -0.1 -0.41 2.338 0.0 -0.045 3.25 0 -0.1 0.6 -0.19 1.03 -0.0064 0.1 0.05 0.03 0.03 -0.0027 0.002 -0.0039 0.2548 -0.091 0.203 0.096 0 0.073 -0.203 -0.159 0.74 0.58 0.47 0.36 0.693 0.519 0.63 0.7000
0.4 6.75 360 -3.28 2.4 4.5 1.662 -0.790 0.275 -0.1 -0.41 2.469 0.0 -0.055 3.99 0 -0.1 0.58 -0.11 0.92 -0.0043 0.1 0.07 0.06 0.06 -0.0023 0.001 -0.0048 0.2136 0.129 0.232 0.123 0 0.143 -0.154 -0.023 0.699 0.59 0.47 0.36 0.644 0.524 0.66 0.7000
0.5 6.75 340 -3.6 2.4 4.5 1.571 -0.790 0.275 -0.1 -0.41 2.559 0.0 -0.065 4.45 0 -0.1 0.56 -0.04 0.84 -0.0032 0.1 0.1 0.1 0.09 -0.002 0.0008 -0.005 0.1542 0.31 0.252 0.134 0 0.16 -0.159 -0.029 0.676 0.6 0.47 0.36 0.616 0.532 0.69 0.7000
0.75 6.75 330 -3.8 2.4 4.5 1.299 -0.790 0.275 -0.1 -0.41 2.682 0.0 -0.095 4.75 0 -0.1 0.53 0.07 0.68 -0.0025 0.14 0.14 0.14 0.13 -0.001 0.0007 -0.0041 0.0787 0.505 0.208 0.129 0 0.158 -0.141 0.061 0.631 0.615 0.47 0.36 0.566 0.548 0.73 0.6900
1 6.75 330 -3.5 2.4 4.5 1.043 -0.790 0.275 -0.1 -0.41 2.763 0.0 -0.11 4.3 0 -0.1 0.5 0.15 0.57 -0.0025 0.17 0.17 0.17 0.14 -0.0005 0.0007 -0.0032 0.0476 0.358 0.208 0.152 0 0.145 -0.144 0.062 0.609 0.63 0.47 0.36 0.541 0.565 0.77 0.6800
1.5 6.75 330 -2.4 2.4 4.5 0.665 -0.790 0.275 -0.1 -0.41 2.836 0.0 -0.124 2.6 0 -0.1 0.42 0.27 0.42 -0.0022 0.22 0.21 0.2 0.16 -0.0004 0.0006 -0.002 -0.0163 0.131 0.108 0.118 0 0.131 -0.126 0.037 0.578 0.64 0.47 0.36 0.506 0.576 0.8 0.6600
2 6.75 330 -1 2.4 4.5 0.329 -0.790 0.275 -0.1 -0.41 2.897 0.0 -0.138 0.55 0 -0.1 0.35 0.35 0.31 -0.0019 0.26 0.25 0.22 0.16 -0.0002 0.0003 -0.0017 -0.1203 0.123 0.068 0.119 0 0.083 -0.075 -0.143 0.555 0.65 0.47 0.36 0.48 0.587 0.8 0.6200
3 6.82 330 0 2.4 4.5 -0.060 -0.790 0.275 -0.1 -0.41 2.906 0.0 -0.172 -0.95 0 -0.1 0.2 0.46 0.16 -0.0015 0.34 0.3 0.23 0.16 0 0 -0.002 -0.2719 0.109 -0.023 0.093 0 0.07 -0.021 -0.028 0.548 0.64 0.47 0.36 0.472 0.576 0.8 0.5500
4 6.92 330 0 2.4 4.5 -0.299 -0.790 0.275 -0.1 -0.41 2.889 0.0 -0.197 -0.95 0 -0.1 0 0.54 0.05 -0.001 0.41 0.32 0.23 0.14 0 0 -0.002 -0.2958 0.135 0.028 0.084 0 0.101 0.072 -0.097 0.527 0.63 0.47 0.36 0.447 0.565 0.76 0.5200
5 7 330 0 2.4 4.5 -0.562 -0.765 0.275 -0.1 -0.41 2.898 0.0 -0.218 -0.93 0 -0.1 0 0.61 -0.04 -0.001 0.51 0.32 0.22 0.13 0 0 -0.002 -0.2718 0.189 0.031 0.058 0 0.095 0.205 0.015 0.505 0.63 0.47 0.36 0.425 0.568 0.72 0.5000
6 7.06 330 0 2.4 4.5 -0.875 -0.711 0.275 -0.1 -0.41 2.896 0.0 -0.235 -0.91 0 -0.2 0 0.65 -0.11 -0.001 0.55 0.32 0.2 0.1 0 0 -0.002 -0.2517 0.215 0.024 0.065 0 0.133 0.285 0.104 0.477 0.63 0.47 0.36 0.395 0.571 0.7 0.5000
7.5 7.15 330 0 2.4 4.5 -1.303 -0.634 0.275 -0.1 -0.41 2.870 0.0 -0.255 -0.87 0 -0.2 0 0.72 -0.19 -0.001 0.49 0.28 0.17 0.09 0 0 -0.002 -0.14 0.15 -0.07 0 0 0.151 0.329 0.299 0.457 0.63 0.47 0.36 0.378 0.575 0.67 0.5000
10 7.25 330 0 2.4 4.5 -1.928 -0.529 0.275 -0.1 -0.41 2.843 0.0 -0.285 -0.8 0 -0.2 0 0.8 -0.3 -0.001 0.42 0.22 0.14 0.08 0 0 -0.002 -0.0216 0.092 -0.159 -0.05 0 0.124 0.301 0.243 0.429 0.63 0.47 0.36 0.359 0.585 0.64 0.5000
""")
for region in 'CHN JPN TWN'.split():
add_alias('AbrahamsonEtAl2014Reg' + region,
AbrahamsonEtAl2014,
region=region)
0.02 -4.548 0.976 0.549 -1.488 -0.453 -2.699 0.215 6.936 0 -0.27 0.768 -0.344 0.95 0.4 -0.1454 -0.081 0.1059 0.0427 0.00786 -0.0052 -0.0018 0.0036 0.166 0.244 1.467 -0.711 -0.339 -0.263 865 0.7 0.508 0.474 0.375
0.03 -4.05 0.931 0.628 -1.494 -0.464 -2.772 0.216 7.235 0 -0.315 0.766 -0.297 1.056 0.394 -0.1957 -0.091 0.1175 0.041 0.00815 -0.0052 -0.002 0.0033 0.167 0.246 1.467 -0.713 -0.338 -0.259 908 0.722 0.536 0.529 0.416
0.05 -3.435 0.887 0.674 -1.388 -0.552 -2.76 0.202 8.334 0 -0.329 0.764 -0.363 1.316 0.422 -0.187 -0.29 0.1238 0.0408 0.00783 -0.0062 -0.0026 0.0039 0.173 0.251 1.449 -0.701 -0.338 -0.263 1054 0.751 0.584 0.576 0.468
0.075 -3.435 0.902 0.726 -1.469 -0.543 -2.575 0.177 8.761 0 -0.29 0.795 -0.427 1.758 0.336 -0.095 -0.261 0.1088 0.0516 0.00726 -0.0072 -0.0021 0.0048 0.198 0.26 1.435 -0.695 -0.347 -0.219 1086 0.74 0.578 0.523 0.427
0.1 -3.93 0.993 0.698 -1.572 -0.47 -2.461 0.166 9.049 0 -0.203 0.842 -0.429 1.411 0.314 -0.0999 -0.091 0.0918 0.0559 0.00644 -0.0072 -0.0018 0.005 0.174 0.259 1.449 -0.708 -0.391 -0.201 1032 0.723 0.57 0.461 0.39
0.15 -5.505 1.267 0.51 -1.669 -0.452 -2.349 0.164 8.633 0 -0.203 0.736 -0.421 1.227 0.289 0.0017 -0.092 0.072 0.0447 0.00745 -0.0066 -0.0018 0.0048 0.198 0.254 1.461 -0.715 -0.449 -0.099 878 0.731 0.536 0.391 0.343
0.2 -6.28 1.366 0.447 -1.75 -0.435 -2.335 0.175 8.742 0 -0.203 0.801 -0.429 0.987 0.29 0.0402 -0.081 0.0602 0.0485 0.00789 -0.0056 -0.0022 0.0041 0.204 0.237 1.484 -0.721 -0.393 -0.198 748 0.701 0.51 0.363 0.308
0.25 -6.789 1.458 0.274 -1.711 -0.41 -2.332 0.183 8.4 0 -0.203 0.715 -0.438 0.577 0.303 0.0468 0.011 0.05 0.0416 0.00629 -0.0049 -0.0025 0.0034 0.185 0.206 1.581 -0.787 -0.339 -0.21 654 0.687 0.507 0.355 0.288
0.3 -7.4 1.528 0.193 -1.77 -0.305 -2.297 0.19 7.643 0 -0.203 0.708 -0.421 0.279 0.336 0.0255 0.092 0.0382 0.0438 0.00524 -0.0046 -0.0027 0.0031 0.164 0.21 1.586 -0.795 -0.447 -0.121 587 0.668 0.514 0.355 0.265
0.4 -8.75 1.739 -0.02 -1.594 -0.446 -2.219 0.185 7.059 0 -0.203 0.683 -0.401 0.358 0.358 0.0606 0.122 0.0264 0.0307 0.00522 -0.0037 -0.0024 0.0024 0.16 0.226 1.544 -0.77 -0.525 -0.086 503 0.628 0.521 0.36 0.28
0.5 -9.74 1.872 -0.121 -1.577 -0.489 -2.205 0.191 6.375 0 -0.203 0.704 -0.417 0.229 0.432 0.0904 0.287 0.0163 0.0287 0.00539 -0.0031 -0.0025 0.0021 0.184 0.217 1.554 -0.77 -0.407 -0.281 457 0.606 0.526 0.376 0.284
0.75 -11.05 2.021 -0.042 -1.757 -0.53 -2.143 0.188 5.166 0.016 -0.203 0.602 -0.49 0.574 0.459 0.1776 0.292 -0.0016 0.0277 0.00501 -0.0021 -0.0025 0.002 0.216 0.154 1.626 -0.78 -0.371 -0.285 410 0.568 0.536 0.416 0.322
1 -12.184 2.18 -0.069 -1.707 -0.624 -2.092 0.176 5.642 0.032 -0.115 0.394 -0.539 0.98 0.442 0.2389 0.316 -0.0072 0.0277 0.00506 -0.0012 -0.0023 0.0012 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.536 0.55 0.472 0.311
1.5 -13.451 2.27 0.047 -1.621 -0.686 -1.913 0.144 5.963 0.128 -0.005 0.328 -0.611 0.819 0.52 0.2758 0.45 -0.0262 0.0293 0.00353 -0.0004 -0.0013 0.0004 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.511 0.559 0.507 0.329
2 -13.7 2.271 0.149 -1.512 -0.84 -1.882 0.126 7.584 0.255 0.12 0.112 -0.63 0.044 0.566 0.3051 0.424 -0.0408 0.0221 0.0022 0 -0.0004 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.507 0.571 0.539 0.345
3 -13.9 2.15 0.368 -1.315 -0.89 -1.789 0.105 8.645 0.284 0.17 0.011 -0.562 -0.396 0.562 0.3482 0.3 -0.0512 0.0321 -0.00137 0 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.474 0.557 0.515 0.335
4 -14.59387 2.132 0.726 -1.506 -0.885 -1.78139 0.10009 10.20357 0.26112 0.17 0 -0.53663 0.00115 0.51499 0.35267 0.25726 -0.0567 0.02249 0.00053 0 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.466 0.566 0.553 0.331
5 -15.63449 2.116 1.027 -1.721 -0.878 -1.68982 0.098 8.38571 0.28229 0.17747 0 -0.44173 -0.59234 0.51133 0.30443 0.17039 -0.04288 0.02372 0.00233 0 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.43 0.568 0.578 0.294
7.5 -17.12864 2.223 0.169 -0.756 -1.077 -1.72135 0.125 5.77927 0.38692 0.38278 0 -0.3428 -1.13827 0.57479 0.16789 0.21872 -0.0308 0.0171 -0.00298 0 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.386 0.527 0.6 0.379
10 -17.65672 2.132 0.367 -0.8 -1.282 -1.948 0.163 4.13478 0.32216 0.33417 0 -0.19908 -0.32493 0.32431 0.16858 0.12681 0.00668 -0.00165 0.00092 0 0 0 0.596 0.117 1.616 -0.733 -0.128 -0.756 400 0.395 0.481 0.495 0.442
""")
add_alias('BozorgniaCampbell2016HighQ', BozorgniaCampbell2016, sgn=1)
add_alias('BozorgniaCampbell2016LowQ', BozorgniaCampbell2016, sgn=-1)
add_alias('BozorgniaCampbell2016AveQJapanSite', BozorgniaCampbell2016, SJ=1)
add_alias('BozorgniaCampbell2016HighQJapanSite', BozorgniaCampbell2016,
SJ=1, sgn=+1)
add_alias('BozorgniaCampbell2016LowQJapanSite', BozorgniaCampbell2016,
SJ=1, sgn=-1)
# populate gsim_aliases for the NGA East GMPEs
lines = '''\
Boore2015NGAEastA04 BOORE_A04_J15
Boore2015NGAEastAB14 BOORE_AB14_J15
Boore2015NGAEastAB95 BOORE_AB95_J15
Boore2015NGAEastBCA10D BOORE_BCA10D_J15
Boore2015NGAEastBS11 BOORE_BS11_J15
Boore2015NGAEastSGD02 BOORE_SGD02_J15
DarraghEtAl2015NGAEast1CCSP DARRAGH_1CCSP
DarraghEtAl2015NGAEast1CVSP DARRAGH_1CVSP
DarraghEtAl2015NGAEast2CCSP DARRAGH_2CCSP
DarraghEtAl2015NGAEast2CVSP DARRAGH_2CVSP
YenierAtkinson2015NGAEast YENIER_ATKINSON
PezeschkEtAl2015NGAEastM1SS PEZESCHK_M1SS
PezeschkEtAl2015NGAEastM2ES PEZESCHK_M2ES
Frankel2015NGAEast FRANKEL_J15
ShahjoueiPezeschk2015NGAEast SHAHJOUEI_PEZESCHK
AlNomanCramer2015NGAEast ALNOMAN_CRAMER
Graizer2015NGAEast GRAIZER
HassaniAtkinson2015NGAEast HASSANI_ATKINSON
HollenbackEtAl2015NGAEastGP PEER_GP
HollenbackEtAl2015NGAEastEX PEER_EX
'''.splitlines()
for line in lines:
alias, key = line.split()
add_alias(alias, NGAEastGMPE,
gmpe_table=f"NGAEast_{key}.hdf5")
add_alias(alias + 'TotalSigma', NGAEastGMPETotalSigma,
gmpe_table=f"NGAEast_{key}.hdf5")
"SA_S": 7.25,
"TW_W": 7.7,
"TW_E": 7.7,
"default": 7.6
}
class ParkerEtAl2020SSlabB(ParkerEtAl2020SSlab):
"""
For Cascadia and Japan where basins are defined (also require z2pt5).
"""
REQUIRES_SITES_PARAMETERS = {'vs30', 'z2pt5'}
add_alias('ParkerEtAl2020SInterAleutian',
ParkerEtAl2020SInter,
region="AK",
saturation_region="Aleutian")
add_alias('ParkerEtAl2020SInterAlaska', ParkerEtAl2020SInter, region="AK")
add_alias('ParkerEtAl2020SInterCAMN',
ParkerEtAl2020SInter,
region="CAM",
saturation_region="CAM_N")
add_alias('ParkerEtAl2020SInterCAMS',
ParkerEtAl2020SInter,
region="CAM",
saturation_region="CAM_S")
add_alias('ParkerEtAl2020SInterSAN',
ParkerEtAl2020SInter,
region="SA",
saturation_region="SA_N")
add_alias('ParkerEtAl2020SInterSAS',
0.600 -0.66 -0.49 -0.03
0.750 -0.69 -0.47 -0.00
0.850 -0.69 -0.46 -0.00
1.000 -0.70 -0.44 -0.00
1.500 -0.72 -0.40 -0.00
2.000 -0.73 -0.38 -0.00
3.000 -0.74 -0.34 -0.00
4.000 -0.75 -0.31 -0.00
5.000 -0.75 -0.291 -0.00
7.500 -0.692 -0.247 -0.00
10.00 -0.650 -0.215 -0.00
""")
add_alias("AtkinsonBoore2006MblgAB1987bar140NSHMP2008",
AtkinsonBoore2006,
mag_eq="Mblg87",
scale_fac=0.)
add_alias("AtkinsonBoore2006MblgJ1996bar140NSHMP2008",
AtkinsonBoore2006,
mag_eq="Mblg96",
scale_fac=0.)
add_alias("AtkinsonBoore2006Mwbar140NSHMP2008",
AtkinsonBoore2006,
mag_eq="Mw",
scale_fac=0.)
add_alias("AtkinsonBoore2006MblgAB1987bar200NSHMP2008",
AtkinsonBoore2006,
mag_eq="Mblg87",
scale_fac=0.5146)
add_alias("AtkinsonBoore2006MblgJ1996bar200NSHMP2008",
AtkinsonBoore2006,
- different constant
- different magnitude scaling coefficent
- different geometrical spreading coefficient
- no magnitude break adjustment at long periods
- different depth scaling and adjustment to break point
- different depth centering and break point
- different default magnitude break point
"""
#: Supported tectonic region type is subduction in-slab
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.SUBDUCTION_INTRASLAB
# For the aliases use the verbose form of the region name
REGION_ALIASES = {
"GLO": "",
"USA-AK": "Alaska",
"CAS": "Cascadia",
"CAM": "CentralAmericaMexico",
"JPN": "Japan",
"NZL": "NewZealand",
"SAM": "SouthAmerica",
"TWN": "Taiwan",
}
for region in SUPPORTED_REGIONS[1:]:
add_alias("KuehnEtAl2021SInter" + REGION_ALIASES[region],
KuehnEtAl2020SInter, region=region)
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)
def compute(self, ctx, 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)
ctx.adjustment = nga_west2_epistemic_adjustment(ctx.mag, ctx.rrup)
mean[:] += self.sgn * ctx.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)
2.0 1.230
5.0 1.148
10.0 1.072
""")
# populating `gsim_aliases` so that the engine can associate a string
# to a specific gsim; for instance the string "NBCC2015_AA13_offshore_high"
# is associated to the gsim (in TOML representation)
# [NBCC2015_AA13]
# REQUIRES_DISTANCES = ["rhypo"]
# DEFINED_FOR_TECTONIC_REGION_TYPE = "Offshore"
# gmpe_table = "Woffshore_high_clC.hdf5"
arguments = [
['stablecrust', 'rhypo', 'Stable Crust', 'ENA_%s_clC'],
['activecrust', 'rhypo', 'Active Crust', 'Wcrust_%s_clC'],
['activecrustFRjb', 'rjb', 'Active Crust Fault', 'WcrustFRjb_%s_clC'],
['inslab30', 'rhypo', 'Subduction Inslab 30', 'WinslabD30_%s_clC'],
['inslab50', 'rhypo', 'Subduction Inslab 50', 'WinslabD50_%s_clC'],
['interface', 'rrup', 'Subduction Interface', 'WinterfaceCombo_%sclC'],
['offshore', 'rhypo', 'Offshore', 'Woffshore_%s_clC']
]
for key, dist, trt, hdf5 in arguments:
for kind in ('low', 'med', 'high'):
name = f"NBCC2015_AA13_{key}_" + ("central" if kind == "med" else kind)
add_alias(name,
NBCC2015_AA13,
REQUIRES_DISTANCES=[dist],
DEFINED_FOR_TECTONIC_REGION_TYPE=trt,
gmpe_table=f"{hdf5}.hdf5" % kind)
NGAEastUSGSSeedB_ab14 B_ab14
NGAEastUSGSSeedHA15 HA15
NGAEastUSGSSeedPEER_EX PEER_EX
NGAEastUSGSSeedPEER_GP PEER_GP
NGAEastUSGSSeedGraizer16 Graizer16
NGAEastUSGSSeedGraizer17 Graizer17
NGAEastUSGSSeedFrankel Frankel
NGAEastUSGSSeedYA15 YA15
NGAEastUSGSSeedPZCT15_M1SS PZCT15_M1SS
NGAEastUSGSSeedPZCT15_M2ES PZCT15_M2ES
NGAEastUSGSSammons1 usgs_1
NGAEastUSGSSammons2 usgs_2
NGAEastUSGSSammons3 usgs_3
NGAEastUSGSSammons4 usgs_4
NGAEastUSGSSammons5 usgs_5
NGAEastUSGSSammons6 usgs_6
NGAEastUSGSSammons7 usgs_7
NGAEastUSGSSammons8 usgs_8
NGAEastUSGSSammons9 usgs_9
NGAEastUSGSSammons10 usgs_10
NGAEastUSGSSammons11 usgs_11
NGAEastUSGSSammons12 usgs_12
NGAEastUSGSSammons13 usgs_13
NGAEastUSGSSammons14 usgs_14
NGAEastUSGSSammons15 usgs_15
NGAEastUSGSSammons16 usgs_16
NGAEastUSGSSammons17 usgs_17'''.splitlines()
for line in lines:
alias, key = line.split()
add_alias(alias, NGAEastUSGSGMPE, gmpe_table=f"nga_east_{key}.hdf5")
3.000 -1.463000 -1.496000 -1.330000 -1.453000 2.268000 -0.056800 0.883600 6.200000 -1.168000 0.052610 -0.000704 0.002870 -0.000350 9.490000 -0.645000 921.230000 -0.005430 -0.002025 0.52179 0.39886 0.51070 0.63690
3.200 -1.581000 -1.619000 -1.425000 -1.569000 2.303000 -0.048700 0.938200 6.200000 -1.160000 0.050690 -0.000639 0.002822 -0.000200 9.510000 -0.641000 904.140000 -0.004790 -0.001836 0.52305 0.39689 0.50642 0.63678
3.400 -1.689000 -1.731000 -1.509000 -1.677000 2.333000 -0.039880 0.981900 6.200000 -1.156000 0.050610 -0.000577 0.002752 -0.000120 9.480000 -0.630000 888.700000 -0.004230 -0.001710 0.52408 0.39438 0.50215 0.63691
3.500 -1.740000 -1.784000 -1.553000 -1.730000 2.347000 -0.035940 1.001000 6.200000 -1.154000 0.050710 -0.000547 0.002710 -0.000100 9.470000 -0.628000 881.420000 -0.003970 -0.001662 0.52485 0.39179 0.49792 0.63691
3.600 -1.792000 -1.836000 -1.601000 -1.782000 2.361000 -0.032380 1.020000 6.200000 -1.151000 0.050570 -0.000519 0.002663 -0.000095 9.480000 -0.623000 874.260000 -0.003730 -0.001625 0.52561 0.38959 0.49358 0.63656
3.800 -1.894000 -1.937000 -1.700000 -1.890000 2.385000 -0.026020 1.055000 6.200000 -1.144000 0.050080 -0.000464 0.002559 0.000000 9.490000 -0.611000 860.430000 -0.003300 -0.001568 0.52656 0.38729 0.48900 0.63664
4.000 -1.999000 -2.039000 -1.806000 -1.999000 2.413000 -0.020680 1.088000 6.200000 -1.133000 0.048750 -0.000412 0.002443 0.000000 9.510000 -0.600000 847.560000 -0.002930 -0.001530 0.52701 0.38482 0.48369 0.63725
4.200 -2.103000 -2.141000 -1.910000 -2.106000 2.438000 -0.015490 1.116000 6.200000 -1.121000 0.047640 -0.000362 0.002318 0.000000 9.520000 -0.590000 835.890000 -0.002570 -0.001504 0.52914 0.38235 0.47779 0.63808
4.400 -2.203000 -2.241000 -2.011000 -2.208000 2.462000 -0.011090 1.142000 6.200000 -1.110000 0.046810 -0.000315 0.002189 0.000000 9.540000 -0.578000 825.460000 -0.002240 -0.001485 0.53211 0.38034 0.47172 0.63903
4.600 -2.305000 -2.344000 -2.109000 -2.309000 2.476000 -0.008411 1.164000 6.200000 -1.101000 0.046890 -0.000270 0.002059 0.000000 9.540000 -0.563000 816.480000 -0.001920 -0.001470 0.53349 0.37980 0.46516 0.63997
4.800 -2.407000 -2.450000 -2.206000 -2.408000 2.483000 -0.007098 1.181000 6.200000 -1.092000 0.048030 -0.000227 0.001931 0.000000 9.530000 -0.544000 809.050000 -0.001610 -0.001457 0.53685 0.38183 0.45784 0.64043
5.000 -2.512000 -2.558000 -2.306000 -2.506000 2.488000 -0.005876 1.192000 6.200000 -1.085000 0.050140 -0.000185 0.001810 0.000000 9.500000 -0.522000 802.670000 -0.001350 -0.001446 0.53961 0.38549 0.44984 0.63998
5.500 -2.776000 -2.831000 -2.564000 -2.753000 2.496000 -0.001251 1.204000 6.200000 -1.065000 0.057980 -0.000088 0.001546 0.000000 9.360000 -0.476000 790.210000 -0.000823 -0.001417 0.53757 0.38679 0.44248 0.63865
6.000 -3.053000 -3.120000 -2.836000 -3.010000 2.492000 -0.000063 1.206000 6.200000 -1.043000 0.067320 0.000000 0.001337 0.000000 9.100000 -0.437000 782.060000 -0.000456 -0.001396 0.53492 0.38948 0.43556 0.63641
6.500 -3.325000 -3.406000 -3.103000 -3.256000 2.467000 0.000000 1.194000 6.200000 -1.030000 0.080820 0.000000 0.001176 0.000000 8.690000 -0.400000 776.360000 -0.000215 -0.001383 0.53281 0.39253 0.42732 0.63347
7.000 -3.586000 -3.678000 -3.373000 -3.493000 2.426000 0.000000 1.177000 6.200000 -1.024000 0.096680 0.000000 0.001057 0.000000 8.290000 -0.366000 772.820000 -0.000070 -0.001375 0.52963 0.39481 0.41907 0.62962
7.500 -3.835000 -3.936000 -3.636000 -3.720000 2.384000 0.000000 1.162000 6.200000 -1.019000 0.111600 0.000000 0.000975 0.000000 7.810000 -0.338000 770.950000 -0.000007 -0.001371 0.52521 0.39851 0.41093 0.62800
8.000 -4.068000 -4.175000 -3.894000 -3.932000 2.344000 0.000000 1.152000 6.200000 -1.016000 0.124700 0.000000 0.000923 0.000000 7.370000 -0.317000 770.100000 0.000000 -0.001368 0.50744 0.40286 0.40174 0.62596
8.500 -4.292000 -4.402000 -4.142000 -4.137000 2.308000 0.000000 1.138000 6.200000 -1.012000 0.137700 0.000000 0.000895 0.000000 6.800000 -0.303000 768.530000 0.000000 -0.001368 0.49611 0.41629 0.39884 0.61769
9.000 -4.500000 -4.609000 -4.392000 -4.330000 2.258000 0.000000 1.142000 6.200000 -1.013000 0.150200 0.000000 0.000885 0.000000 6.430000 -0.292000 768.710000 0.000000 -0.001366 0.49300 0.41771 0.38229 0.60829
9.500 -4.689000 -4.800000 -4.605000 -4.509000 2.221000 0.000000 1.158000 6.200000 -1.013000 0.157900 0.000000 0.000888 0.000000 6.130000 -0.284000 771.660000 0.000000 -0.001363 0.48988 0.41800 0.37895 0.59272
10.00 -4.870000 -4.989000 -4.795000 -4.668000 2.187000 0.000000 1.158000 6.200000 -1.015000 0.160100 0.000000 0.000896 0.000000 5.930000 -0.276700 775.000000 0.000000 -0.001360 0.48361 0.41840 0.37531 0.58136
""")
add_alias('StewartEtAl2016RegCHN', StewartEtAl2016, region="CHN")
add_alias('StewartEtAl2016RegJPN', StewartEtAl2016, region="JPN")
add_alias('StewartEtAl2016NoSOF', StewartEtAl2016, sof=0)
add_alias('StewartEtAl2016CHNNoSOF', StewartEtAl2016, region="CHN", sof=0)
add_alias('StewartEtAl2016JPNNoSOF', StewartEtAl2016, region="JPN", sof=0)
