dep = []
ax = plt.subplot(2, 2, i + 1)
for sd in stdict:
res.append(interp(log(T), log(gmmT), sd['lnRes']))
dep.append(sd['dep'])
plt.semilogx(dep, res, 'o', c='dodgerblue', mec='blue', ms=6)
plt.semilogx([5, 1000], [0, 0], 'k--', lw=1.)
deps, dep_terms = return_dep_terms(T)
plt.semilogx(deps, dep_terms, '-', c='darkorange', lw=2.5)
if i >= 2:
plt.xlabel('Depth (km)', fontsize=16)
if i == 0 or i == 2:
plt.ylabel('ln Residual', fontsize=16)
plt.xlim([5, 750])
plt.ylim([-4, 4])
pertxt = ' '.join(('T =', str(T), 's'))
xpos = get_log_xy_locs([5, 750], 0.04)
ypos = (8 * 0.92) - 4
plt.text(xpos, ypos, pertxt, ha='left', va='top', fontsize=16, bbox=props)
plt.savefig('figures/simple_nac_depth_dependence.png',
fmt='png',
bbox_inches='tight')
plt.savefig('figures/fig_7.eps', fmt='eps', bbox_inches='tight')
plt.show()
###############################################################################
# make plot 1
###############################################################################
ax = plt.subplot(2, 1, 1)
for pd, c in zip(pltDict, cs):
plt.loglog(pd['poe_probs_annual'],
pd['imls'],
c=c,
lw=2.0,
label=pd['place'])
plt.semilogy([yhaz2, yhaz2], [1E-4, 10], 'k--')
plt.semilogy([yhaz10, yhaz10], [1E-4, 10], 'k--')
yoff = get_log_xy_locs([1e-5, .1], .015)
plt.text(yhaz10 + yoff,
1.1E-4,
'1/475 AEP',
rotation=90.,
va='bottom',
ha='right',
fontsize=14)
yoff = get_log_xy_locs([1e-5, .1], .005)
plt.text(yhaz2 + yoff / 5.,
1.1E-4,
'1/2475 AEP',
rotation=90.,
va='bottom',
ha='right',
fontsize=14)
lw=2.0,
color='gold',
label='Somerville et al. (2009; Yilgarn Craton)')
plt.loglog(rjb, exp(A12r), '-', lw=2.0, color=cs[5], label='Allen (2012)')
plt.loglog(rjb,
exp(Bea14r),
'-',
lw=2.0,
color=cs[6],
label='Boore et al. (2014)')
plt.xlabel(r'$\mathregular{R_{JB}}$ (km)', fontsize=18)
plt.ylabel('PGA (g)', fontsize=18)
plt.xlim([1, 500])
if t < 0.5:
plt.ylim([1E-3, 2])
elif t > 1.0:
plt.ylim([1E-5, 0.01])
else:
plt.ylim([1E-4, 0.1])
#plt.title(titles[j])
xtxt = get_log_xy_locs(ax.get_xlim(), 0.95)
ytxt = get_log_xy_locs(ax.get_ylim(), 0.95)
#plt.text(xtxt, ytxt, titles[j], size=18, horizontalalignment='right', verticalalignment='top', weight='normal')
plt.grid(which='both', color='0.5')
plt.legend(loc=3, numpoints=1, fontsize=13.25)
plt.savefig('nsha18_atten.png', format='png', dpi=800, bbox_inches='tight')
plt.show()
# plt haz curves
if pltTrue == True:
hx = plt.semilogy(imls,
sdx['poe_probs_annual'],
'--',
color=cs[p * 2 + 1],
lw=2.0,
label=label_place + ' (NF)')
###############################################################################
# make plot pretty
###############################################################################
plt.semilogy([0, 2.5], [yhaz2, yhaz2], 'k--')
plt.semilogy([0, 2.5], [yhaz10, yhaz10], 'k--')
yoff = get_log_xy_locs([1e-4, .1], .015)
plt.text(0.245,
yhaz10 + yoff,
'1/475 AEP',
va='bottom',
ha='right',
fontsize=16)
yoff = get_log_xy_locs([1e-4, .1], .005)
plt.text(0.245,
yhaz2 + yoff / 5.,
'1/2475 AEP',
va='bottom',
ha='right',
fontsize=16)
plt.legend()
def makesubplt(i, fig, plt, sta, sps, mag, dep, ztor, dip, rake, rhyp, vs30,
datestr):
import matplotlib
matplotlib.rc('xtick', labelsize=12)
matplotlib.rc('ytick', labelsize=12)
props = dict(boxstyle='square', facecolor='w', alpha=1)
#dates = ['1995-12-25', '1995-12-25', '2019-06-24', '2019-06-24']
rrup = rhyp
rjb = sqrt(rrup**2 - dep**2) # assume point source; i.e. repi = rjb
A19imt_BS = calc_nac_gmm_spectra(mag, rhyp, dep, vs30, 'BS') # use rrup
nga_e_imt = nga_east_mean(mag, dep, dip, rake, rrup, vs30)
Yea97imt, AB03imt, AB03CISimt, Gea05imt, Zea06imt, Zea06imt, MP10imt, Aea16imt, Zea16imt, Kea20imt \
= inslab_gsims(mag, dep, ztor, dip, rake, rrup, rjb, vs30)
# adjust NGA-E from 3000 -> target
nga_e_imt = adjust_gmm_with_nga_east(nga_e_imt, vs30)
ax = plt.subplot(2, 2, i)
if colTrue == 'True':
plt.loglog(nga_e_imt['per'],
exp(nga_e_imt['sa']),
'--',
lw=1.5,
color=cs[0])
plt.loglog(Kea20imt['per'],
exp(Kea20imt['sa']),
'-.',
lw=2.,
color=cs[1])
plt.loglog(A19imt_BS['per'],
exp(A19imt_BS['sa']),
'-',
lw=1.5,
color=cs[2])
# 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 >= 3:
plt.xlabel('Period (s)', fontsize=14)
if i == 1 or i == 3 or i == 7:
plt.ylabel('Spectral Acceleration (g)', fontsize=14)
plt.xlim([0.02, 10])
plt.ylim([1e-4, .1])
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([
'Goulet et al (2017)', 'Kuehn et al (2020)', 'Present Study',
'Geometric Mean'
],
loc=4,
fontsize=10)
txtbox = datestr+'\nStation: AU.'+sta+'\n'+'$\mathregular{M_W}$: '+str(mag) \
+'\n$\mathregular{R_{hyp}}$: '+str(int(round(rhyp)))+' km'\
+'\n$\mathregular{h_z}$: '+str(int(round(dep)))+' km'\
+'\n'+'$\mathregular{V_{S30}}$: '+ str(int(round(vs30))) +' m/s'
print(txtbox)
xtxt = get_log_xy_locs(ax.get_xlim(), 0.03)
ytxt = get_log_xy_locs(ax.get_ylim(), 0.03)
lspace = (2, 2, 1, 1)
plt.text(xtxt,
ytxt,
txtbox,
size=11,
ha='left',
va='bottom',
weight='normal') #, linespacing=lspace) #, bbox=props)
# add letter
ylims = ax.get_ylim()
plt.text(0.017,
ylims[1] * 1.25,
letters[i - 1],
va='bottom',
ha='right',
fontsize=17)
idx = where(Tplt == T)[0]
if len(idx) > 0:
ax = plt.subplot(2, 2, i + 1)
plt.plot(mag, res, 'o', c='dodgerblue', mec='blue', ms=6)
plt.plot([5, 1000], [0, 0], 'k--', lw=1.)
if i >= 2:
plt.xlabel('Moment Magnitude', fontsize=16)
if i == 0 or i == 2:
plt.ylabel('Between-Event Terms\n(ln Residual)', fontsize=16)
plt.xlim([5, 7.7])
plt.ylim([-3, 3])
pertxt = ' '.join(('T =', str(T), 's'))
xpos = get_log_xy_locs([5, 7.7], 0.96)
ypos = (6 * 0.92) - 3
plt.text(xpos,
ypos,
pertxt,
ha='right',
va='top',
fontsize=16,
bbox=props)
i += 1
# get data for plotting later
if T == 0.5:
tau05 = res
taumag = mag
#plt.xlim([datetime(2012,6,15), datetime(2012,12,31)])
# take b-val at ML 0.9 and get a-value
mc = 1.0
m_upper = mc + 1. # only include well behaved data
a_val = fit_a_value(cum_mag, bvals[10], mrng, mc, m_upper)
N = 10**(a_val - bvals[10] * mrng)
ax1.semilogy(mrng[10:], N[10:], 'r-', lw=2.)
ax1.set_xlim([-0.2, 4.5])
ax1.set_ylim([0.5, 1000])
# label b-value
abtxt = 'a-value = ' + str('%0.2f' % a_val) + '\nb-value = ' + str(
'%0.2f' % bvals[10])
xtxt = 1. #ax1.get_xlim()[1] * 0.96
ytxt = get_log_xy_locs(ax1.get_ylim(), 0.96)
props = dict(boxstyle='round', facecolor='w', alpha=1)
tb = ax1.text(xtxt,
ytxt,
abtxt,
size=18,
ha='left',
va='top',
weight='normal',
bbox=props)
xdiff = diff(ax1.get_xlim())[0]
xtxt = ax1.get_xlim()[0] + xdiff * 0.02
ytxt = get_log_xy_locs(ax1.get_ylim(), 0.98)
ax1.text(xtxt,
ytxt,
plt.xlim([0.1, 40])
# do odr for completeness
start = [0.5, -2.]
om, oc = odr_lin_reg(log10(vsrl[idx]), log10(rat_vdsrl[idx]), start)
plt_vsrl = arange(0.2, 37.1, 0.1)
plt_rat = 10**(om * log10(plt_vsrl) + oc)
plt.semilogy(plt_vsrl,
plt_rat,
'-',
c='k',
lw=1.5,
label='Present Study (ODR)')
plt.ylim([0.1, 1])
xpos = get_log_xy_locs([0.1, 40], 0.02)
ypos = get_log_xy_locs([0.1, 1], 0.98)
# set y ticks
ticks = array([0.1, 0.2, 0.5, 1.0])
ticklabels = [str(x) for x in ticks]
ax.set_yticks(ticks)
ax.set_yticklabels(ticklabels)
txt = plt.text(xpos, ypos, '(c)', va='top', ha='left', fontsize=18)
txt.set_path_effects([PathEffects.withStroke(linewidth=5, foreground='w')])
print('\nRatio coeffs')
print('odr2', om, oc)
##############################################################################
ticks = [0.005, 0.01, 0.02, 0.05, 0.1, 0.2]
# reset ticks
if i == 7 and i == 0:
if altPlaces == True:
plt.xlim([0.01, 0.3])
ticks = [0.01, 0.02, 0.05, 0.1, 0.2]
else:
plt.xlim([0.003, 0.2])
else:
plt.xlim([0.003, 0.2])
# set letters
if altPlaces == False:
xplt = get_log_xy_locs(ax.get_xlim(), 0.02)
txt = plt.text(xplt,
0.98,
letters[i],
fontsize=18,
va='top',
ha='left')
else:
xplt = get_log_xy_locs(ax.get_xlim(), 0.98)
txt = plt.text(xplt,
0.98,
letters[i],
fontsize=18,
va='top',
ha='right')
txt.set_path_effects(