# plot mag uncert with mag
###############################################################################
fig = plt.figure(2, figsize=(10, 5))
sym = ['^', 'D', 'o']
cols = get_mpl2_colourlist()
for i, reg in enumerate(reg_dict):
plt.plot(reg['mag'],
reg['mag_uncert'],
sym[i],
mec=cols[i],
mfc='none',
mew=1.5,
label=reg['reg'])
# get binned stats
bins = arange(2.5, 6.1, 0.1)
med_uncert, std_uncert, medx, bins, nperbin = get_binned_stats(
bins, allmags, alluncert)
plt.errorbar(bins,
med_uncert,
yerr=std_uncert,
fmt='rs',
label='Binned Median')
plt.legend(loc=1, numpoints=1)
plt.ylim([0, 1])
plt.xlim([2.4, 5])
plt.savefig('ml_uncertainty_vs_ml.png', fmt='png', bbox_inches='tight')
plt.savefig('ml_uncertainty_vs_ml.eps', fmt='eps', bbox_inches='tight')
plt.show()
mrhyps.append(rec['rhyp'])
mamps.append(rec[channel]['swave_spec'][fidx])
mrhyps = array(mrhyps)
mamps = array(mamps)
if len(mrhyps) > 0:
'''
plt.loglog(mrhyps, mamps, '+', c='0.6', lw=0.5, ms=5)
plt.xlim([100, 2250])
plt.ylim([1E-8, 1E-3])
plt.ylabel(str(round(m,1)))
i += 1
'''
# get binned data
logmedamp, stdbin, medx, binstrp, nperbin = get_binned_stats(
bins, log10(mrhyps), log10(mamps))
#plt.loglog(10**medx, 10**logmedamp, 'rs', ms=6.5)
# normalise data @ 630 km
nidx = where((binstrp > 2.79) & (binstrp < 2.81))[0]
if len(nidx) > 0:
print(m, nidx)
namps = log10(mamps) - logmedamp[nidx]
if len(log_norm_amps) == 0:
log_norm_amps = namps
norm_rhyps = mrhyps
else:
log_norm_amps = hstack((log_norm_amps, namps))
norm_rhyps = hstack((norm_rhyps, mrhyps))
# look at stats
plt.plot(rrup, rmmi, '+', c='0.5')
plt.plot([0, xmax],[0, 0], 'k--')
plt.title('Prelim Atten Res')
#plt.xlabel('Rrup')
plt.ylabel('MMI Residual')
plt.xlim([0, xmax])
plt.ylim([-3, 3])
# get stats
bins = arange(5, xmax+1, 10)
medbin, stdbin, medx, binstrp, npb = get_binned_stats(bins, rrup, rmmi)
# plot errors
plt.errorbar(binstrp, medbin, yerr=stdbin, fmt='rs', lw=1.5)
# annotate
didx = where(array(rrup) <= xmax)[0] # comparable with other mod cmp
pltstr = 'Med = ' + str('%0.2f' % median(rmmi[didx])) + '\n' \
'Std = ' + str('%0.2f' % std(rmmi[didx]))
plt.text(280, -2.35, pltstr, fontsize=14, va='center')
y_at_xhinge = 0
xhinge = 100.
def fit_fixed_intercept(c, x):
'''
x = array fo x values
if getcwd().startswith('/nas'):
magdiffcsv = '/nas/active/ops/community_safety/ehp/georisk_earthquake/modelling/sandpits/tallen/NSHA2018/catalogue/matlab/rev_mag_diff.csv'
else:
magdiffcsv = '/Users/trev/Documents/Geoscience_Australia/NSHA2018/catalogue/matlab/rev_mag_diff.csv'
data = loadtxt(magdiffcsv, delimiter=',')
fig = plt.figure(1, figsize=(7, 7))
plt.plot([2, 7], [2, 7], '--', c='0.25', lw=2.0, label='1:1')
plt.plot(data[:, 2], data[:, 3], '+', c='r', label='Data')
# bin data and regress
mbins = arange(1, 6.2, 0.2)
medres, stdres, medx, mbins, nperbin = get_binned_stats(
mbins, data[:, 2], data[:, 3])
#plt.errorbar(mbins, medres, stdres, marker='s', c='orange', mec='orangered', ls='none', ms=8, label='Binned Data')
reg_pars = linregress(mbins, medres)
xplt = array([2.4, 6.5])
yplt = reg_pars[0] * xplt + reg_pars[1]
#plt.plot(xplt, yplt, 'r-', lw=2., zorder=100, label='Linear Fit')
#reg_pars2 = linregress(data[:,2], data[:,3])
# now do ODR
# Define a function (quadratic in our case) to fit the data with.
def linear_func(p, x):
m, c = p
return m * x + c
letters = ['(a)', '(b)', '(c)', '(d)']
plt.figure(i, figsize=(18, 7))
for mi, ma in zip(mmin, mmax):
plt.tick_params(labelsize=15)
midx = where((seamags > mi) & (seamags <= ma))[0]
# get gippsland data
gidx = where((seamags > mi) & (seamags <= ma) & (lares < -37.8))[0]
#plt.suptitle('ML '+str(mi)+'-'+str(ma), fontsize=22)
# get MLM92
plt.subplot(2, 2, 2 * i + 2)
plt.plot(rhypres[midx], mlm92res[midx], '+', color='0.6', zorder=-32)
#plt.plot(rhypres[gidx], mlm92res[gidx], '+', color='seagreen')
medbin, stdbin, meanx, outbins, nperbin = get_binned_stats(
bins, rhypres[midx], mlm92res[midx])
plt.errorbar(outbins,
medbin,
yerr=stdbin,
color='r',
fmt='s',
mec='r',
zorder=1000)
#medbin, stdbin, meanx, outbins, nperbin = get_binned_stats(bins, rhypres[gidx], mlm92res[gidx])
#plt.errorbar(outbins, medbin, yerr=stdbin, color='b', fmt='s', mfc='none', mec='b')
plt.plot([0, 800], [0, 0], 'k--')
plt.xlim([0, 800])
plt.ylim([-0.75, 0.75])
if i == 0:
plt.plot(rrup, mr, '+', c='0.5')
plt.plot([0, xmax],[0, 0], 'k--')
plt.title(titles[i])
#plt.xlabel('Rrup')
plt.ylabel('MMI Residual')
if i == 4 or i == 5:
plt.xlabel('Rrup (km)')
plt.xlim([0, xmax])
plt.ylim([-3, 3])
# get stats
bins = arange(10, 361, 10)
medbin, stdbin, medx, binstrp, npb = get_binned_stats(bins, array(rrup), mr)
# plot errors
plt.errorbar(medx, medbin, yerr=stdbin, fmt='rs', lw=1.5)
plt.savefig('figs/existing_mmi_dist_residuals.png', fmt='png', bbox_inches='tight')
plt.show()
# plt residuals with mag
fig = plt.figure(2, figsize=(14, 10))
# look at stats
for i, mr in enumerate(modres):
plt.subplot(3, 2, i+1)
else:
tmplognormamps = log10(array([ev['specs'][regf_idx]
])) - logsrcamp
# build normalised array
if len(lognormamps) > 0:
lognormamps = hstack((lognormamps, tmplognormamps))
normrhyps = hstack((normrhyps, ev['rhyp']))
else:
lognormamps = tmplognormamps
normrhyps = ev['rhyp']
###############################################################################
# plt normalised data
###############################################################################
plt.figure(1, figsize=(9, 7))
plt.plot(log10(normrhyps), lognormamps, '+', c='0.5', ms=6)
distbins = arange(0.1, 3.1, 0.1)
medres, stdres, medx, bins, nperbin = get_binned_stats(distbins,
log10(normrhyps),
lognormamps)
nidx = where(nperbin > 2)[0]
plt.plot(medx[nidx], medres[nidx], 'rs')
plt.ylabel('Normalised Amplitude')
plt.xlabel('log10 Rhyp')
plt.show()
for mi, ma in zip(mmin, mmax):
i += 1
plt.figure(i, figsize=(18, 10))
plt.tick_params(labelsize=15)
midx = where((seamags > mi) & (seamags <= ma))[0]
# get gippsland data
gidx = where((seamags > mi) & (seamags <= ma) & (lares < -37.8))[0]
#plt.suptitle('ML '+str(mi)+'-'+str(ma), fontsize=22)
# get MLM92
plt.subplot(323)
plt.plot(rhypres[midx], mlm92res[midx], '+', color='0.6')
#plt.plot(rhypres[gidx], mlm92res[gidx], '+', color='seagreen')
medbin, stdbin, meanx, outbins, nperbin = get_binned_stats(
bins, rhypres[midx], mlm92res[midx])
plt.errorbar(outbins, medbin, yerr=stdbin, color='r', fmt='s', mec='r')
medbin, stdbin, meanx, outbins, nperbin = get_binned_stats(
bins, rhypres[gidx], mlm92res[gidx])
#plt.errorbar(outbins, medbin, yerr=stdbin, color='b', fmt='s', mfc='none', mec='b')
plt.plot([0, 800], [0, 0], 'k--')
plt.xlim([0, 800])
plt.ylim([-0.75, 0.75])
plt.title('Michael-Lieba & Malafant (1992)', fontsize=16)
plt.ylabel('Stn ML - Mean ML', fontsize=14)
#plt.xlabel('Hypocentral Distance (km)', fontsize=14)
plt_stats(plt, rhypres[midx], mlm92res[midx])
# get WGW96