Sea09YCr = []
Pea11r = []
A12r = []
AA13r = []
Bea14r = []
YA15r = []
G90WAr = []
G90SEAr = []
G90WArPGV = []
G90SEArPGV = []
for i, r in enumerate(rrup):
# get ground motion estimates from GMPEs
Tea02imt, C03imt, AB06imt, Sea09imt, Sea09YCimt, Pea11imt, A12imt, Bea14imt , YA15imt, SP16imt \
= scr_gsims(mag, dep, ztor, dip, rake, rrup[i], rjb[i], vs30)
# get one of the NGA-W2 GMMs
# get ground motion estimates from GMPEs
G90WAimt, G90SEAimt, G90INDimt, G90WA_PGVimt, G90SEA_PGVimt, G90IND_PGVimt = gaull1990_gsim(
mag, dep, rrup[i])
if t == 0.0:
#Tea02r.append(Tea02imt['pga'][0])
Tea02r.append(Tea02imt['sa'][0])
#C03r.append(C03imt['pga'][0])
C03r.append(C03imt['sa'][0])
AB06r.append(AB06imt['pga'][0])
Sea09r.append(Sea09imt['pga'][0])
Sea09YCr.append(Sea09YCimt['pga'][0])
Pea11r.append(nan)
A12r.append(A12imt['sa'][0])
Bea14r.append(Bea14imt['pga'][0])
ab03 = []
gea05 = []
mp10 = []
a15 = []
ab06 = []
bea14 = []
sea09 = []
a12 = []
a17 = []
for rr in rrup:
rjb = sqrt(rr**2 - 50**2)
YYea97imt, AB03imt, AB03CISimt, Gea05imt, Zea06imt, Zea06CISimt, MP10imt, Aea15imt \
= inslab_gsims(mplt, dep, 40, 30., 90., rr, rjb, 760)
Tea02imt, C03imt, AB06imt, Sea09imt, Sea09YCimt, Pea11imt, A12imt, Bea14imt, YA15imt, SP16imt \
= scr_gsims(mplt, dep, 40, 30., 90., rr, rjb, 760)
# subduction gmms
ab03.append(interp(Tplt, AB03imt['per'], AB03imt['sa']))
gea05.append(interp(Tplt, Gea05imt['per'], Gea05imt['sa']))
mp10.append(interp(Tplt, MP10imt['per'], MP10imt['sa']))
a15.append(interp(Tplt, Aea15imt['per'], Aea15imt['sa']))
# scr gmms
ab06.append(interp(Tplt, AB06imt['per'], AB06imt['sa']))
sea09.append(interp(Tplt, Sea09imt['per'], Sea09imt['sa']))
a12.append(interp(Tplt, A12imt['per'], A12imt['sa']))
# plt nga-w2
bea14.append(interp(Tplt, Bea14imt['per'], Bea14imt['sa']))
# parse chimney file
csvfile = 'Chimney_RJB_GMPE.csv'
data = loadtxt(csvfile, skiprows=1, delimiter=',')
rjbs = data[:,1]
n_chim = data[:,0]
txt = '#CHIMNEY,RJB,Gea90SEA,Gea90WA,AB06,Sea09NC,Sea09YC,A12,Bea14,Tea19,NGA-E\n'
for chim, rjb in zip(n_chim, rjbs):
print(chim)
rrup = sqrt(rjb**2 + ztor**2)
rhyp = rrup = sqrt(rjb**2 + dep**2) # assume for now as need site locations
Tea02imt, C03imt, AB06imt, AB11imt, Sea09imt_SS14, Sea09YCimt_SS14, Pea11imt, A12imt, A12imt_SS14, Bea14imt \
= scr_gsims(mag, dep, ztor, dip, rake, rrup, rjb, vs30)
Tea19 = tang2019_cam_gsim(mag, dep, rrup, vs30)
G90WAimt, G90SEAimt, G90INDimt, G90WA_PGVimt, G90SEA_PGVimt, G90IND_PGVimt = gaull1990_gsim(mag, dep, rhyp)
# adjust some GMMs using Seyhan & Stewart 2014
#A12imt = adjust_gmm_with_SS14(A12imt, 820., vs30) - corrected in gsim code
#Sea09imt = adjust_gmm_with_SS14(Sea09imt, 865., vs30)
'''
# get mean USGS NGA-E
nga_e_imt = nga_east_mean(mag, dep, dip, rake, rrup, vs30)
#plt.loglog(nga_e_imt['per'], exp(nga_e_imt['sa']),'--' , lw=1.5, color=cs[5]) # for testing amp factors
nga_e_imt_hold = nga_e_imt
def makesubplt(i, fig, plt, sta, sps, mag, dep, ztor, dip, rake, rhyp, vs30):
import matplotlib
matplotlib.rc('xtick', labelsize=8)
matplotlib.rc('ytick', labelsize=8)
rrup = rhyp
rjb = sqrt(rrup**2 - dep**2) # assume point source; i.e. repi = rjb
# get ground motion estimates from GMPEs
Yea97imt, AB03imt, AB03CISimt, Gea05imt, Zea06imt, Zea06CISimt, MP10imt, Aea15imt, Zea16imt \
= inslab_gsims(mag, dep, ztor, dip, rake, rhyp, rjb, vs30)
Tea02imt, C03imt, AB06imt, Sea09imt, Sea09YCimt, Pea11imt, A12imt, Bea14imt \
= scr_gsims(mag, dep, ztor, dip, rake, rrup, rjb, vs30)
A19imt = calc_nac_gmm_spectra(mag, rhyp, dep) # use rrup
#print(exp(A19imt['sa']))
ax = plt.subplot(3, 3, i)
if colTrue == 'True':
'''
plt.loglog(Yea97imt['per'], exp(Yea97imt['sa']), '.-' , lw=1., color=cs[0])
plt.loglog(AB03imt['per'], exp(AB03imt['sa']), '--', lw=1., color=cs[1])
plt.loglog(Gea05imt['per'], exp(Gea05imt['sa']), '-.', lw=1., color=cs[2])
plt.loglog(Zea06imt['per'], exp(Zea06imt['sa']), '-' , lw=1., color=cs[3])
plt.loglog(MP10imt['per'], exp(MP10imt['sa']), '-' , lw=2.5, color=cs[4])
plt.loglog(Aea15imt['per'], exp(Aea15imt['sa']), '-.', lw=2.5, color=cs[5])
plt.loglog(Bea14imt['per'], exp(Bea14imt['sa']), '--', lw=2.5, color=cs[6])
plt.loglog(YA15imt['per'], exp(YA15imt['sa']), ':' , lw=2.5, color=cs[7])
'''
plt.loglog(Yea97imt['per'][:-1],
exp(Yea97imt['sa'][:-1]),
'-',
lw=1.5,
color=cs[0])
plt.loglog(AB03imt['per'][:-1],
exp(AB03imt['sa'][:-1]),
'-',
lw=1.5,
color=cs[1])
plt.loglog(A12imt['per'], exp(A12imt['sa']), '-', lw=1.5, color=cs[2])
plt.loglog(Zea06imt['per'],
exp(Zea06imt['sa']),
'-',
lw=1.5,
color=cs[3])
plt.loglog(AB06imt['per'],
exp(AB06imt['sa']),
'-',
lw=1.5,
color=cs[4])
plt.loglog(Aea15imt['per'],
exp(Aea15imt['sa']),
'-',
lw=1.5,
color=cs[5])
plt.loglog(A19imt['per'], exp(A19imt['sa']), '-', lw=1.5, color=cs[6])
else:
plt.loglog(Yea97imt['per'],
exp(Yea97imt['sa']),
'-',
lw=1.5,
color='0.35')
plt.loglog(AB03imt['per'],
exp(AB03imt['sa']),
'--',
lw=1.5,
color='0.15')
plt.loglog(Gea05imt['per'],
exp(Gea05imt['sa']),
'-.',
lw=1.5,
color='0.15')
plt.loglog(CY08imt['per'],
exp(CY08imt['sa']),
'-',
lw=1.5,
color='0.65')
plt.loglog(Zea06imt['per'],
exp(Zea06imt['sa']),
'--',
lw=1.5,
color='0.65')
plt.loglog(MP10imt['per'],
exp(MP10imt['sa']),
'-.',
lw=1.5,
color='0.65')
plt.loglog(Aea15imt['per'],
exp(Aea15imt['sa']),
'--',
lw=1.5,
color='0.35')
# get recorded process_waves.py psa data
T, geomean, pga, rhyp = get_site_geomean(sta, folder)
plt.loglog(T, geomean, lw=1.5, color='k')
if i >= 7:
plt.xlabel('Period (s)', fontsize=9)
if i == 1 or i == 4 or i == 7:
plt.ylabel('Spectral Acceleration (g)', fontsize=9)
plt.xlim([0.02, 10])
#plt.title(' '.join((sta+'; MW =',str("%0.1f" % mag)+'; Rrup =',str(rrup),'km')), fontsize=9)
plt.title(' '.join((sta + '; Rhyp =', str(rhyp), 'km')), fontsize=9)
plt.grid(which='both', color='0.75')
if i == 1:
plt.legend([
'Yea97', 'AB03', 'A12imt', 'Zea06', 'AB06', 'Aea15', 'A19', 'Data'
],
loc=3,
fontsize=7.)
'''
def makesubplt(i, fig, plt, sta, sps, mag, dep, ztor, dip, rake, rhyp, vs30):
import matplotlib
matplotlib.rc('xtick', labelsize=8)
matplotlib.rc('ytick', labelsize=8)
rrup = rhyp # subtract for misc distance to rup
rjb = sqrt(rrup**2 - dep**2) # assume point source; i.e. repi = rjb
# get ground motion estimates from GMPEs
print(mag, dep, ztor, dip, rake, rrup, rjb, vs30)
Tea02imt, C03imt, AB06imt, Sea09imt, Sea09YCimt, Pea11imt, A12imt, Bea14imt \
= scr_gsims(mag, dep, ztor, dip, rake, rrup, rjb, vs30)
Tea19 = tang2019_cam_gsim(mag, dep, rrup, vs30)
# adjust some GMMs using Seyhan & Stewart 2014
A12imt = adjust_gmm_with_SS14(A12imt, 820., vs30)
Sea09imt = adjust_gmm_with_SS14(Sea09imt, 865., vs30)
# get mean USGS NGA-E
nga_e_imt = nga_east_mean(mag, dep, dip, rake, rrup, vs30)
#plt.loglog(nga_e_imt['per'], exp(nga_e_imt['sa']),'--' , lw=1.5, color=cs[5]) # for testing amp factors
nga_e_imt_hold = nga_e_imt
# adjust NGA-E from 3000 -> target
nga_e_imt = adjust_gmm_with_nga_east(nga_e_imt, vs30)
#print(nga_e_imt['per'])
#print(exp(nga_e_imt['sa']) / exp(nga_e_imt_hold['sa']))
ax = plt.subplot(3, 3, i)
syms = ['s', 'x', '+', '1', '2', 'o']
if colTrue == 'True':
plt.loglog(AB06imt['per'], exp(AB06imt['sa']), syms[0], ls='-', lw=1., color=cs[0], \
ms=4, mec=cs[0], mfc='none', mew=1., markevery=5)
plt.loglog(Sea09imt['per'], exp(Sea09imt['sa']), syms[1], ls='-', lw=1., color=cs[1], \
ms=5, mec=cs[1], mfc='none', mew=1., markevery=5)
plt.loglog(A12imt['per'], exp(A12imt['sa']),syms[2], ls='-', lw=1., color=cs[2], \
ms=5, mec=cs[2], mfc='none', mew=1., markevery=4)
plt.loglog(Bea14imt['per'], exp(Bea14imt['sa']),syms[3], ls='-', lw=1., color=cs[3], \
ms=6, mec=cs[3], mfc='none', mew=1., markevery=10)
#plt.loglog(YA15imt['per'], exp(YA15imt['sa']),'-' , lw=1., color=cs[4])
plt.loglog(nga_e_imt['per'], exp(nga_e_imt['sa']),syms[4], ls='-', lw=1., color=cs[5], \
ms=6, mec=cs[5], mfc='none', mew=1., markevery=5)
plt.loglog(Tea19['per'], exp(Tea19['sa']),syms[5], ls='-', lw=1., color=cs[6], \
ms=4, mec=cs[6], mfc='none', mew=1., markevery=5)
# get recorded process_waves.py psa data
T, geomean, pga, rhyp, zcomp = get_site_geomean(sta, folder)
plt.loglog(T, geomean, lw=1.5, color='k')
if i >= 7:
plt.xlabel('Period (s)', fontsize=9)
if i == 1 or i == 4 or i == 7:
plt.ylabel('Spectral Acceleration (g)', fontsize=9)
plt.xlim([0.01, 10])
#plt.title(' '.join((sta+'; MW =',str("%0.1f" % mag)+'; Rrup =',str(rrup),'km')), fontsize=9)
if zcomp == True:
sta += '*'
plt.title(' '.join((sta+'; Rhyp =',str(rhyp),'km; VS30 =', str(int(round(vs30))))), fontsize=8)
plt.grid(which='both', color='0.75')
if i == 1:
#plt.legend(['Yea97', 'AB06','A12imt','Aea16', 'A19 (BS)', 'A19 (NGH)', 'A19 (OB)','Data'],loc=3, fontsize=7.)
plt.legend(['AB06','Sea09', 'A12','Bea14', 'NGA-E', 'Tea20', 'Data'],loc=3, fontsize=9., numpoints=1)
return ax