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
_, gmfs = to_gmfs(
shakemap, 'yes', 'no', site_effects=False, trunclevel=3,
num_gmfs=2, seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.3708301, 0.5671011]) # PGA
_, gmfs = to_gmfs(
shakemap, 'yes', 'yes', site_effects=True, trunclevel=3,
num_gmfs=2, seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5127146, 0.7800232]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.4932519, 0.6731384]) # SA(1.0)
# disable spatial correlation
_, gmfs = to_gmfs(
shakemap, 'no', 'no', site_effects=False,
trunclevel=3, num_gmfs=2, seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.370832, 0.5670994]) # PGA
_, gmfs = to_gmfs(
shakemap, 'no', 'yes', site_effects=True,
trunclevel=3, num_gmfs=2, seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5127171, 0.7800206]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.4932519, 0.6731384]) # SA(1.0)
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, 'spatial')
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, 'cross')
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
gmfs = to_gmfs(shakemap,
crosscorr='corr',
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (R, N, E, M)
aae(gmfs[..., 0].sum(axis=1), [[0.3708301, 0.5671011]]) # PGA
gmfs = to_gmfs(shakemap,
crosscorr='cross',
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
aae(gmfs[..., 0].sum(axis=1), [[0.4101717, 0.6240185]]) # PGA
aae(gmfs[..., 2].sum(axis=1), [[0.3946015, 0.5385107]]) # SA(1.0)
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
_, gmfs = to_gmfs(shakemap,
'yes',
'no',
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.3708301, 0.5671011]) # PGA
_, gmfs = to_gmfs(shakemap,
'yes',
'yes',
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5127146, 0.7800232]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.4932519, 0.6731384]) # SA(1.0)
# disable spatial correlation
_, gmfs = to_gmfs(shakemap,
'no',
'no',
site_effects=False,
trunclevel=3,
num_gmfs=2,
seed=42)
# shape (N, E, M)
aae(gmfs[..., 0].sum(axis=0), [0.370832, 0.5670994]) # PGA
_, gmfs = to_gmfs(shakemap,
'no',
'yes',
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
aae(gmfs[..., 0].sum(axis=0), [0.5127171, 0.7800206]) # PGA
aae(gmfs[..., 2].sum(axis=0), [0.4932519, 0.6731384]) # SA(1.0)
# set stddev to zero
shakemap['std'] = 0
with self.assertRaises(ValueError) as ctx:
to_gmfs(shakemap,
'no',
'yes',
site_effects=True,
trunclevel=3,
num_gmfs=2,
seed=42)
self.assertIn('stddev==0 for IMT=PGA', str(ctx.exception))