def mean_std(shakemap, site_effects):
gmf_dict.update({'kind': 'Silva&Horspool',
'spatialcorr': 'yes', 'crosscorr': 'yes'})
_, gmfs = to_gmfs(
shakemap, gmf_dict, site_effects, trunclevel=3,
num_gmfs=1000, seed=42, imts=['PGA', 'SA(0.3)', 'SA(1.0)', 'SA(3.0)'])
return gmfs.mean(axis=1), numpy.log(gmfs).std(axis=1)
def test_for_mmi(self):
f1 = 'file://' + os.path.join(CDIR, 'ghorka_grid.xml')
f2 = 'file://' + os.path.join(CDIR, 'ghorka_uncertainty.xml')
uridict = dict(kind='usgs_xml', grid_url=f1, uncertainty_url=f2)
sitecol, shakemap, *_ = get_sitecol_shakemap(uridict.pop('kind'),
uridict, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
_, gmfs = to_gmfs(shakemap, {'kind': 'mmi'}, False,
trunclevel=3, num_gmfs=1000, seed=42, imts=['MMI'])
gmf_by_imt = gmfs.mean(axis=1)
std_by_imt = gmfs.std(axis=1)
aae(gmf_by_imt, [[3.80704848],
[3.89791949],
[3.88040454],
[3.93584243]])
aae(std_by_imt, [[0.71068558],
[0.72233552],
[0.72033749],
[0.69906908]])
def test_matrices(self):
# distance matrix
lons = numpy.array([84., 84., 84., 85.5, 85.5, 85.5, 87., 87., 87.])
lats = numpy.array([26., 27.5, 29., 26., 27.5, 29., 26., 27.5, 29.])
dmatrix = geo.geodetic.distance_matrix(lons, lats)
aae(dmatrix.sum(), 18539.6131407)
# spatial correlation
sca = spatial_correlation_array(dmatrix, imts, 'yes')
aae(sca.sum(), 36.000370229)
# spatial covariance
std = numpy.array([(0.5, 0.52, 0.64, 0.73)] * 9, imt_dt) # 9 sites
scov = spatial_covariance_array([std[n] for n in imt_dt.names], sca)
aae(scov.sum(), 13.166200147)
# cross correlation
ccor = cross_correlation_matrix(imts, 'yes')
aae(ccor.sum(), 10.49124788)
# cholesky decomposition
L = cholesky(scov, ccor)
self.assertEqual(L.shape, (36, 36))
aae(L.sum(), 30.5121263)
# intensity
val = numpy.array([(5.38409665, 3.9383686, 3.55435415, 4.37692394)] *
9, imt_dt)
shakemap = numpy.zeros(9, shakemap_dt) # 9 sites
shakemap['lon'] = lons
shakemap['lat'] = lats
shakemap['vs30'] = numpy.array([301.17] * 9)
shakemap['val'] = val
shakemap['std'] = std
gmf_dict.update({
'kind': 'Silva&Horspool',
'spatialcorr': 'yes',
'crosscorr': 'no'
})
_, gmfs = to_gmfs(shakemap,
gmf_dict,
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.4202056, 0.6426098]) # PGA
gmf_dict.update({
'kind': 'Silva&Horspool',
'spatialcorr': 'yes',
'crosscorr': 'yes'
})
_, gmfs = to_gmfs(shakemap,
gmf_dict,
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5809818, 0.8790579]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.6053580, 0.8245417]) # SA(1.0)
# disable spatial correlation
gmf_dict.update({
'kind': 'Silva&Horspool',
'spatialcorr': 'no',
'crosscorr': 'no'
})
_, gmfs = to_gmfs(shakemap,
gmf_dict,
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.4202077, 0.6426078]) # PGA
_, gmfs = to_gmfs(shakemap, {'kind': 'basic'},
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.4202077, 0.6426078]) # PGA
gmf_dict.update({
'kind': 'Silva&Horspool',
'spatialcorr': 'no',
'crosscorr': 'yes'
})
_, gmfs = to_gmfs(shakemap,
gmf_dict,
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5809846, 0.8790549]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.6053580, 0.8245417]) # SA(1.0)
# set stddev to zero
shakemap['std'] = 0
with self.assertRaises(ValueError) as ctx:
gmf_dict.update({
'kind': 'Silva&Horspool',
'spatialcorr': 'no',
'crosscorr': 'yes'
})
to_gmfs(shakemap,
gmf_dict,
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
self.assertIn('stddev==0 for IMT=PGA', str(ctx.exception))
