def test_depths_go_to_zero(self):
# Depth information is zero when a :class:`Site`
# contains only (lon, lat)
s1 = Site(location=Point(10, 20),
vs30=1.2,
vs30measured=True,
z1pt0=3.4,
z2pt5=5.6)
s2 = Site(location=Point(-1.2, -3.4),
vs30=55.4,
vs30measured=False,
z1pt0=66.7,
z2pt5=88.9)
cll = SiteCollection([s1, s2])
cll_sites = list(cll)
exp_s1 = Site(location=Point(10, 20, 0.0),
vs30=1.2,
vs30measured=True,
z1pt0=3.4,
z2pt5=5.6)
exp_s2 = Site(location=Point(-1.2, -3.4, 0.0),
vs30=55.4,
vs30measured=False,
z1pt0=66.7,
z2pt5=88.9)
for i, s in enumerate([exp_s1, exp_s2]):
self.assertEqual(s, cll_sites[i])
# test equality of site collections
sc = SiteCollection([exp_s1, exp_s2])
self.assertEqual(cll, sc)
class JB2009ApplyCorrelationTestCase(unittest.TestCase):
SITECOL = SiteCollection([
Site(Point(2, -40), 1, 1, 1),
Site(Point(2, -40.1), 1, 1, 1),
Site(Point(2, -39.9), 1, 1, 1)
])
def test(self):
numpy.random.seed(13)
cormo = JB2009CorrelationModel(vs30_clustering=False)
intra_residuals_sampled = numpy.random.normal(size=(3, 100000))
intra_residuals_correlated = cormo.apply_correlation(
self.SITECOL, PGA(), intra_residuals_sampled)
inferred_corrcoef = numpy.corrcoef(intra_residuals_correlated)
mean = intra_residuals_correlated.mean()
std = intra_residuals_correlated.std()
self.assertAlmostEqual(mean, 0, delta=0.002)
self.assertAlmostEqual(std, 1, delta=0.002)
actual_corrcoef = cormo._get_correlation_matrix(self.SITECOL, PGA())
numpy.testing.assert_almost_equal(inferred_corrcoef,
actual_corrcoef,
decimal=2)
def test_filtered_sitecol(self):
filtered = self.SITECOL.filtered([0, 2])
numpy.random.seed(13)
cormo = JB2009CorrelationModel(vs30_clustering=False)
intra_residuals_sampled = numpy.random.normal(size=(2, 5))
intra_residuals_correlated = cormo.apply_correlation(
filtered, PGA(), intra_residuals_sampled)
aaae(intra_residuals_correlated,
[[-0.71239066, 0.75376638, -0.04450308, 0.45181234, 1.34510171],
[0.51816327, 1.36481251, 0.86016437, 1.48732124, -1.01860545]],
decimal=6)
def test_simple_fault_surface_vertical(self):
# Create the rupture
mag = 10.0
rake = 90.0
trt = TRT.ACTIVE_SHALLOW_CRUST
hypoc = Point(0.25, 0.0, 5)
pmf = PMF([(0.8, 0), (0.2, 1)])
rup = NPPR(mag, rake, trt, hypoc, self.srfc1, pmf)
# Compute distances
param = 'closest_point'
sites = SiteCollection(
[Site(Point(0.25, -0.1, 0.0)),
Site(Point(-0.1, 0.0, 0.0))])
dsts = get_distances(rup, sites, param)
# Check first point
msg = 'The longitude of the first point is wrong'
self.assertTrue(abs(dsts[0, 0] - 0.25) < 1e-2, msg)
msg = 'The latitude of the first point is wrong'
self.assertTrue(abs(dsts[0][1] - 0.0) < 1e-2, msg)
# Check second point
msg = 'The longitude of the second point is wrong'
self.assertTrue(abs(dsts[1, 0] - 0.0) < 1e-2, msg)
msg = 'The latitude of the second point is wrong'
self.assertTrue(abs(dsts[1][1] - 0.0) < 1e-2, msg)
def example_calc(apply):
sitecol = SiteCollection([
Site(Point(30.0, 30.0), 760., 1.0, 1.0),
Site(Point(30.25, 30.25), 760., 1.0, 1.0),
Site(Point(30.4, 30.4), 760., 1.0, 1.0)
])
mfd_1 = TruncatedGRMFD(4.5, 8.0, 0.1, 4.0, 1.0)
mfd_2 = TruncatedGRMFD(4.5, 7.5, 0.1, 3.5, 1.1)
sources = [
PointSource('001', 'Point1', 'Active Shallow Crust', mfd_1, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5), PMF([(1.0, NodalPlane(0.0, 90.0,
0.0))]),
PMF([(1.0, 10.0)])),
PointSource('002', 'Point2', 'Active Shallow Crust', mfd_2, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5), PMF([(1.0, NodalPlane(0.0, 90.0,
0.0))]),
PMF([(1.0, 10.0)]))
]
imtls = {
'PGA': [0.01, 0.1, 0.2, 0.5, 0.8],
'SA(0.5)': [0.01, 0.1, 0.2, 0.5, 0.8]
}
gsims = {'Active Shallow Crust': AkkarBommer2010()}
return calc_hazard_curves(sources,
sitecol,
imtls,
gsims,
apply=apply,
filter_distance='rrup')
def test_no_correlation_mean_and_intra_respected(self):
mean1 = 10
mean2 = 14
inter = 1e-300
intra1 = 0.2
intra2 = 1.6
p1 = Point(0, 0)
p2 = Point(0, 0.3)
sites = [
Site(p1, mean1, False, inter, intra1),
Site(p2, mean2, False, inter, intra2)
]
self.sites = SiteCollection(sites)
numpy.random.seed(41)
cormo = JB2009CorrelationModel(vs30_clustering=False)
s1_intensity, s2_intensity = ground_motion_fields(
self.rupture,
self.sites,
[self.imt1],
self.gsim,
truncation_level=None,
realizations=6000,
correlation_model=cormo,
)[self.imt1]
self.assertAlmostEqual(s1_intensity.mean(), mean1, delta=1e-3)
self.assertAlmostEqual(s2_intensity.mean(), mean2, delta=1e-3)
self.assertAlmostEqual(s1_intensity.std(), intra1, delta=2e-3)
self.assertAlmostEqual(s2_intensity.std(), intra2, delta=1e-2)
def test_correlation_with_total_stddev(self):
mean1 = 10
mean2 = 14
inter = 1e-300
intra1 = 0.2
intra2 = 1.6
p1 = Point(0, 0)
p2 = Point(0, 0.3)
sites = [
Site(p1, mean1, False, inter, intra1),
Site(p2, mean2, False, inter, intra2)
]
self.sites = SiteCollection(sites)
numpy.random.seed(41)
cormo = JB2009CorrelationModel(vs30_clustering=False)
gsim = FakeGSIMTotalStdDev(self)
gsim.expect_same_sitecol = False
with self.assertRaises(CorrelationButNoInterIntraStdDevs):
ground_motion_fields(self.rupture,
self.sites, [self.imt1],
gsim,
truncation_level=None,
realizations=6000,
correlation_model=cormo)
def test_depths_go_to_zero(self):
# Depth information is meant to be discarded when a site collection is
# created.
s1 = Site(location=Point(10, 20, 30),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6)
s2 = Site(location=Point(-1.2, -3.4, -5.6),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9)
cll = SiteCollection([s1, s2])
cll_sites = list(cll)
exp_s1 = Site(location=Point(10, 20, 0.0),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6)
exp_s2 = Site(location=Point(-1.2, -3.4, 0.0),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9)
for i, s in enumerate([exp_s1, exp_s2]):
self.assertEqual(s, cll_sites[i])
# test equality of site collections
sc = SiteCollection([exp_s1, exp_s2])
self.assertEqual(cll, sc)
# test nonequality of site collections
# (see https://github.com/gem/oq-hazardlib/pull/403)
sc._vs30 = numpy.array([numpy.nan, numpy.nan])
self.assertNotEqual(cll, sc)
def test_from_sites(self):
s1 = Site(location=Point(10, 20, 30),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6, backarc=True)
s2 = Site(location=Point(-1.2, -3.4, -5.6),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9, backarc=False)
cll = SiteCollection([s1, s2])
self.assertTrue((cll.vs30 == [1.2, 55.4]).all())
self.assertTrue((cll.vs30measured == [True, False]).all())
self.assertTrue((cll.z1pt0 == [3.4, 66.7]).all())
self.assertTrue((cll.z2pt5 == [5.6, 88.9]).all())
self.assertTrue((cll.mesh.lons == [10, -1.2]).all())
self.assertTrue((cll.mesh.lats == [20, -3.4]).all())
self.assertTrue((cll.backarc == [True, False]).all())
self.assertIs(cll.mesh.depths, None)
for arr in (cll.vs30, cll.z1pt0, cll.z2pt5):
self.assertIsInstance(arr, numpy.ndarray)
self.assertEqual(arr.flags.writeable, False)
self.assertEqual(arr.dtype, float)
for arr in (cll.vs30measured, cll.backarc):
self.assertIsInstance(arr, numpy.ndarray)
self.assertEqual(arr.flags.writeable, False)
self.assertEqual(arr.dtype, bool)
self.assertEqual(len(cll), 2)
# test __toh5__ and __fromh5__
array, attrs = cll.__toh5__()
newcll = cll.__new__(cll.__class__)
newcll.__fromh5__(array, attrs)
self.assertEqual(newcll, cll)
def test_from_sites(self):
s1 = Site(location=Point(10, 20, 30),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6)
s2 = Site(location=Point(-1.2, -3.4, -5.6),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9)
cll = SiteCollection([s1, s2])
assert_eq(cll.vs30, [1.2, 55.4])
assert_eq(cll.vs30measured, [True, False])
assert_eq(cll.z1pt0, [3.4, 66.7])
assert_eq(cll.z2pt5, [5.6, 88.9])
assert_eq(cll.mesh.lons, [10, -1.2])
assert_eq(cll.mesh.lats, [20, -3.4])
assert_eq(cll.mesh.depths, [30, -5.6])
for arr in (cll.vs30, cll.z1pt0, cll.z2pt5):
self.assertIsInstance(arr, numpy.ndarray)
self.assertEqual(arr.flags.writeable, False)
self.assertEqual(arr.dtype, float)
for arr in (cll.vs30measured,):
self.assertIsInstance(arr, numpy.ndarray)
self.assertEqual(arr.flags.writeable, False)
self.assertEqual(arr.dtype, bool)
self.assertEqual(len(cll), 2)
# test serialization to hdf5
fd, fpath = tempfile.mkstemp(suffix='.hdf5')
os.close(fd)
with hdf5.File(fpath, 'w') as f:
f['folder'] = dict(sitecol=cll, b=[2, 3])
newcll = f['folder/sitecol']
self.assertEqual(newcll, cll)
self.assertEqual(list(f['folder/b']), [2, 3])
os.remove(fpath)
def setUp(self):
"""
"""
mfd_1 = TruncatedGRMFD(4.5, 8.0, 0.1, 4.0, 1.0)
mfd_2 = TruncatedGRMFD(4.5, 7.5, 0.1, 3.5, 1.1)
self.source_model = [
PointSource('001', 'Point1', 'Active Shallow Crust', mfd_1, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5),
PMF([(1.0, NodalPlane(0.0, 90.0, 0.0))]),
PMF([(1.0, 10.0)])),
PointSource('002', 'Point2', 'Active Shallow Crust', mfd_2, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5),
PMF([(1.0, NodalPlane(0.0, 90.0, 0.0))]),
PMF([(1.0, 10.0)]))
]
self.sites = SiteCollection([
Site(Point(30.0, 30.0), 760., True, 1.0, 1.0, 1),
Site(Point(30.25, 30.25), 760., True, 1.0, 1.0, 2),
Site(Point(30.4, 30.4), 760., True, 1.0, 1.0, 2)
])
self.gsims = {'Active Shallow Crust': 'AkkarBommer2010'}
self.imts = ['PGA', 'SA(0.5)']
self.imls = [[0.01, 0.1, 0.2, 0.5, 0.8]]
def reference_psha_calculation_openquake():
"""
Sets up the reference PSHA calculation calling OpenQuake directly. All
subsequent implementations should match this example
"""
# Site model - 3 Sites
site_model = SiteCollection([
Site(Point(30.0, 30.0), 760., True, 1.0, 1.0, 1),
Site(Point(30.25, 30.25), 760., True, 1.0, 1.0, 2),
Site(Point(30.4, 30.4), 760., True, 1.0, 1.0, 2)
])
# Source Model Two Point Sources
mfd_1 = TruncatedGRMFD(4.5, 8.0, 0.1, 4.0, 1.0)
mfd_2 = TruncatedGRMFD(4.5, 7.5, 0.1, 3.5, 1.1)
source_model = [
PointSource('001', 'Point1', 'Active Shallow Crust', mfd_1, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5), PMF([(1.0, NodalPlane(0.0, 90.0,
0.0))]),
PMF([(1.0, 10.0)])),
PointSource('002', 'Point2', 'Active Shallow Crust', mfd_2, 1.0,
WC1994(), 1.0, PoissonTOM(50.0), 0.0, 30.0,
Point(30.0, 30.5), PMF([(1.0, NodalPlane(0.0, 90.0,
0.0))]),
PMF([(1.0, 10.0)]))
]
imts = {
'PGA': [0.01, 0.1, 0.2, 0.5, 0.8],
'SA(0.5)': [0.01, 0.1, 0.2, 0.5, 0.8]
}
# Akkar & Bommer (2010) GMPE
gsims = {'Active Shallow Crust': gsim.akkar_bommer_2010.AkkarBommer2010()}
truncation_level = None
return calc_hazard_curves(source_model, site_model, imts, gsims,
truncation_level)
def test_source_filter_filter_all_out(self):
col = SiteCollection([Site(Point(10, 10), 1, True, 2, 3),
Site(Point(11, 12), 2, True, 2, 3),
Site(Point(13, 14), 1, True, 2, 3)])
for int_dist in (0, 1, 10, 100, 1000):
filtered = self.source.filter_sites_by_distance_to_source(
integration_distance=int_dist, sites=col
)
self.assertIs(filtered, None) # all filtered
def get_target_sites_line(self,
maximum_distance,
spacing,
vs30,
line_azimuth=90.,
origin_point=(0.5, 0.5),
as_log=False,
vs30measured=True,
z1pt0=None,
z2pt5=None,
backarc=False):
"""
Defines the target sites along a line with respect to the rupture
"""
#input_origin_point = deepcopy(origin_point)
azimuth, origin_location, z1pt0, z2pt5 = _setup_site_peripherals(
line_azimuth, origin_point, vs30, z1pt0, z2pt5, self.strike,
self.surface)
self.target_sites = [
Site(origin_location,
vs30,
vs30measured,
z1pt0,
z2pt5,
backarc=backarc)
]
spacings = self._define_line_spacing(maximum_distance, spacing, as_log)
for offset in spacings:
target_loc = origin_location.point_at(offset, 0., azimuth)
# Get Rupture distance
temp_mesh = Mesh(np.array(target_loc.longitude),
np.array(target_loc.latitude),
np.array(target_loc.depth))
distance = self.surface.get_min_distance(temp_mesh)
self.target_sites.append(
Site(target_loc,
vs30,
vs30measured,
z1pt0,
z2pt5,
backarc=backarc))
self.target_sites_config = {
"TYPE": "Line",
"RMAX": maximum_distance,
"SPACING": spacing,
"AZIMUTH": line_azimuth,
"ORIGIN": origin_point,
"AS_LOG": as_log,
"VS30": vs30,
"VS30MEASURED": vs30measured,
"Z1.0": z1pt0,
"Z2.5": z2pt5,
"BACKARC": backarc
}
self.target_sites = SiteCollection(self.target_sites)
return self.target_sites
class JB2009LowerTriangleCorrelationMatrixTestCase(unittest.TestCase):
SITECOL = SiteCollection([Site(Point(2, -40), 1, 1, 1),
Site(Point(2, -40.1), 1, 1, 1),
Site(Point(2, -39.9), 1, 1, 1)])
def test(self):
cormo = JB2009CorrelationModel(vs30_clustering=False)
lt = cormo.get_lower_triangle_correlation_matrix(self.SITECOL, PGA())
aaae(lt, [[1.0, 0.0, 0.0],
[1.97514806e-02, 9.99804920e-01, 0.0],
[1.97514806e-02, 5.42206860e-20, 9.99804920e-01]])
def test(self):
s1 = Site(location=Point(10, 20),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6)
s2 = Site(location=Point(-1.2, -3.4),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9)
cll = SiteCollection([s1, s2])
cll_sites = list(cll)
for i, s in enumerate([s1, s2]):
self.assertEqual(s, cll_sites[i])
class HM2018ApplyCorrelationTestCase(unittest.TestCase):
SITECOL = SiteCollection([
Site(Point(2, -40), 1, 1, 1),
Site(Point(2, -40.1), 1, 1, 1),
Site(Point(2, -39.95), 1, 1, 1)
])
def test_no_uncertainty(self):
numpy.random.seed(1)
Nsim = 100000
imt = SA(period=2.0, damping=5)
stddev_intra = numpy.array([0.5, 0.6, 0.7])
cormo = HM2018CorrelationModel(uncertainty_multiplier=0)
intra_residuals_sampled = numpy.random.multivariate_normal(
numpy.zeros(3), numpy.diag(stddev_intra ** 2), Nsim).\
transpose(1, 0)
intra_residuals_correlated = cormo.apply_correlation(
self.SITECOL, imt, intra_residuals_sampled, stddev_intra)
inferred_corrcoef = numpy.corrcoef(intra_residuals_correlated)
mean = intra_residuals_correlated.mean(1)
std = intra_residuals_correlated.std(1)
aaae(numpy.squeeze(numpy.asarray(mean)), numpy.zeros(3), 2)
aaae(numpy.squeeze(numpy.asarray(std)), stddev_intra, 2)
actual_corrcoef = cormo._get_correlation_matrix(self.SITECOL, imt)
aaae(inferred_corrcoef, actual_corrcoef, 2)
def test_with_uncertainty(self):
numpy.random.seed(1)
Nsim = 100000
imt = SA(period=3.0, damping=5)
stddev_intra = numpy.array([0.3, 0.6, 0.9])
cormo = HM2018CorrelationModel(uncertainty_multiplier=1)
intra_residuals_sampled = numpy.random.multivariate_normal(
numpy.zeros(3), numpy.diag(stddev_intra ** 2), Nsim).\
transpose(1, 0)
intra_residuals_correlated = cormo.apply_correlation(
self.SITECOL, imt, intra_residuals_sampled, stddev_intra)
inferred_corrcoef = numpy.corrcoef(intra_residuals_correlated)
mean = intra_residuals_correlated.mean(1)
std = intra_residuals_correlated.std(1)
aaae(numpy.squeeze(numpy.asarray(mean)), numpy.zeros(3), 2)
aaae(numpy.squeeze(numpy.asarray(std)), stddev_intra, 2)
aaae(inferred_corrcoef, [[1., 0.3807, 0.5066], [0.3807, 1., 0.3075],
[0.5066, 0.3075, 1.]], 2)
def setUp(self):
self.orig_make_contexts = ContextMaker.make_contexts
ContextMaker.make_contexts = lambda self, sites, rupture: (
FakeSiteContext(sites), rupture, None)
self.truncation_level = 3.4
self.imts = {'PGA': [1, 2, 3], 'PGD': [2, 4]}
self.time_span = 49.2
rup11 = self.FakeRupture(0.23, const.TRT.ACTIVE_SHALLOW_CRUST)
rup12 = self.FakeRupture(0.15, const.TRT.ACTIVE_SHALLOW_CRUST)
rup21 = self.FakeRupture(0.04, const.TRT.VOLCANIC)
self.source1 = self.FakeSource(1, [rup11, rup12], self.time_span,
const.TRT.ACTIVE_SHALLOW_CRUST)
self.source2 = self.FakeSource(2, [rup21], self.time_span,
const.TRT.VOLCANIC)
self.sources = [self.source1, self.source2]
site1 = Site(Point(10, 20), 1, True, 2, 3)
site2 = Site(Point(20, 30), 2, False, 4, 5)
self.sites = SiteCollection([site1, site2])
gsim1 = self.FakeGSIM(
self.truncation_level,
self.imts,
poes={
(site1.location.latitude, rup11, imt.PGA()): [0.1, 0.05, 0.03],
(site2.location.latitude, rup11, imt.PGA()):
[0.11, 0.051, 0.034],
(site1.location.latitude, rup12, imt.PGA()):
[0.12, 0.052, 0.035],
(site2.location.latitude, rup12, imt.PGA()):
[0.13, 0.053, 0.036],
(site1.location.latitude, rup11, imt.PGD()): [0.4, 0.33],
(site2.location.latitude, rup11, imt.PGD()): [0.39, 0.331],
(site1.location.latitude, rup12, imt.PGD()): [0.38, 0.332],
(site2.location.latitude, rup12, imt.PGD()): [0.37, 0.333],
})
gsim2 = self.FakeGSIM(self.truncation_level,
self.imts,
poes={
(site1.location.latitude, rup21, imt.PGA()):
[0.5, 0.3, 0.2],
(site2.location.latitude, rup21, imt.PGA()):
[0.4, 0.2, 0.1],
(site1.location.latitude, rup21, imt.PGD()):
[0.24, 0.08],
(site2.location.latitude, rup21, imt.PGD()):
[0.14, 0.09],
})
self.gsims = {
const.TRT.ACTIVE_SHALLOW_CRUST: gsim1,
const.TRT.VOLCANIC: gsim2
}
def test_mearn_nearfault_distance_taper(self):
rupture = self.make_rupture()
site1 = Site(location=Point(27.9, 41), vs30=1200.,
vs30measured=True, z1pt0=2.36, z2pt5=2.)
site2 = Site(location=Point(28.1, 41), vs30=1200.,
vs30measured=True, z1pt0=2.36, z2pt5=2.)
sites = SiteCollection([site1, site2])
fields = ground_motion_fields(
rupture, sites, [PGV()], ChiouYoungs2014NearFaultEffect(),
truncation_level=0, realizations=1)
gmf = fields[PGV()]
self.assertAlmostEquals(2.27395, gmf[0], delta=1e-4)
self.assertAlmostEquals(3.38409, gmf[1], delta=1e-4)
def test_two_sites(self):
site1 = Site(Point(0, 0), vs30=760., z1pt0=48.0, z2pt5=0.607,
vs30measured=True)
site2 = Site(Point(0, 0.5), vs30=760., z1pt0=48.0, z2pt5=0.607,
vs30measured=True)
sitecol = SiteCollection([site1, site2])
srcfilter = SourceFilter(sitecol, IntegrationDistance.new('200'))
imtls = {"PGA": [.123]}
for period in numpy.arange(.1, .5, .1):
imtls['SA(%.2f)' % period] = [.123]
assert len(imtls) == 5 # 5 periods
gsim_by_trt = {'Stable Continental Crust': ExampleA2021()}
hcurves = calc_hazard_curves(
[asource], srcfilter, DictArray(imtls), gsim_by_trt)
print(hcurves)
def test_get_bounding_boxes(self):
maxdist = IntegrationDistance({'default': [
(3, 30), (4, 40), (5, 100), (6, 200), (7, 300), (8, 400)]})
sitecol = SiteCollection([
Site(location=Point(10, 20, 30),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6, backarc=True),
Site(location=Point(-1.2, -3.4, -5.6),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9, backarc=False)])
srcfilter = SourceFilter(sitecol, maxdist)
bb1, bb2 = srcfilter.get_bounding_boxes(mag=4)
# bounding boxes in the form min_lon, min_lat, max_lon, max_lat
aae(bb1, (9.6171855, 19.640272, 10.3828145, 20.359728))
aae(bb2, (-1.5603623, -3.759728, -0.8396377, -3.040272))
def test_international_date_line(self):
maxdist = IntegrationDistance({'default': [
(3, 30), (4, 40), (5, 100), (6, 200), (7, 300), (8, 400)]})
sitecol = SiteCollection([
Site(location=Point(179, 80),
vs30=1.2, vs30measured=True,
z1pt0=3.4, z2pt5=5.6, backarc=True),
Site(location=Point(-179, 80),
vs30=55.4, vs30measured=False,
z1pt0=66.7, z2pt5=88.9, backarc=False)])
srcfilter = SourceFilter(sitecol, maxdist)
bb1, bb2 = srcfilter.get_bounding_boxes(mag=4)
# bounding boxes in the form min_lon, min_lat, max_lon, max_lat
aae(bb1, (176.928409, 79.640272, 181.071591, 80.359728))
aae(bb2, (-181.071591, 79.640272, -176.928409, 80.359728))
def test(self):
sitecol = SiteCollection([Site(Point(-65.13490, 0.0),
vs30=760., z1pt0=48.0, z2pt5=0.607,
vs30measured=True)])
mfd = ArbitraryMFD([6.0], [0.01604252])
trace = Line([Point(-65.0000, -0.11240), Point(-65.000, 0.11240)])
# 1.0 km Mesh Spacing
mesh_spacing = 1.0
msr = PeerMSR()
sources = [SimpleFaultSource("001", "PEER Fault Set 2.5",
"Active Shallow Crust", mfd,
mesh_spacing, msr, 2.0, PoissonTOM(1.0),
0.0, 12., trace, 90., 0.)]
imtls = {"PGA": [0.001, 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0,
1.25, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0, 6.0, 7.0]}
gmpe = ChiouYoungs2014PEER()
gmpe.mixture_model = {"factors": [0.8, 1.2], "weights": [0.5, 0.5]}
hcm = calc_hazard_curves(sources, sitecol, imtls,
{"Active Shallow Crust": gmpe})
# Match against the benchmark is not exact - but differences in the
# log space should be on the order of less than 0.04%
expected = numpy.array([-4.140470001, -4.140913368, -4.259457496,
-4.724733842, -5.900747959, -7.734816415,
-9.019329629, -10.03864778, -10.90333404,
-11.83885783, -12.65826442, -14.05429951,
-15.22535996, -16.23988897, -17.94685518,
-19.36079032, -20.57460101, -21.64201335])
expected = numpy.around(expected, 5)
hcm_lnpga = numpy.around(numpy.log(hcm["PGA"].flatten()), 5)
perc_diff = 100.0 * ((hcm_lnpga / expected) - 1.0)
numpy.testing.assert_allclose(perc_diff, numpy.zeros(len(perc_diff)),
atol=0.04)
def test_ses_generation_from_parametric_source_with_filtering(self):
# generate stochastic event set (SES) from 2 area sources (area1,
# area2). However, by including a single site co-located with the
# area1 center, and with source site filtering of 100 km (exactly
# the radius of area1), the second source (area2), which is centered
# at 5., 5. (that is about 500 km from center of area1), will be
# excluded. the MFD from the SES will be therefore approximately equal
# to the one of area1 only.
numpy.random.seed(123)
sites = SiteCollection([
Site(location=Point(0., 0.),
vs30=760,
vs30measured=True,
z1pt0=40.,
z2pt5=2.)
])
ses = stochastic_event_set([self.area1, self.area2],
filters.SourceFilter(
sites,
filters.MagDepDistance.new('100')))
rates = self._extract_rates(ses,
time_span=self.time_span,
bins=numpy.arange(5., 6.6, 0.1))
expect_rates = numpy.array(
[r for m, r in self.mfd.get_annual_occurrence_rates()])
numpy.testing.assert_allclose(rates, expect_rates, rtol=0, atol=1e-4)
def test_magnitude_bins(self):
""" Testing build disaggregation matrix """
fname = os.path.join(DATA_PATH, 'data', 'ssm.xml')
converter = sourceconverter.SourceConverter(50., 1., 10, 0.1, 10)
groups = to_python(fname, converter)
sources = []
for g in groups:
sources += g.sources
site = Site(Point(172.63, -43.53),
vs30=250,
vs30measured=False,
z1pt0=330)
imt = SA(3.0)
iml = 0.25612220
gsim_by_trt = {TRT.ACTIVE_SHALLOW_CRUST: Bradley2013()}
truncation_level = 3.0
n_epsilons = 1
mag_bin_width = 0.1
dist_bin_width = 100.
coord_bin_width = 100.
# Compute the disaggregation matrix
edges, mtx = disagg.disaggregation(sources, site, imt, iml,
gsim_by_trt, truncation_level,
n_epsilons, mag_bin_width,
dist_bin_width, coord_bin_width)
tm = disagg.mag_pmf(mtx[:, :, :, :, :, 0])
numpy.testing.assert_array_less(numpy.zeros_like(tm[2:]), tm[2:])
def to_openquake_site(self, missing_vs30=None):
"""
Returns the site as an instance of the :class:
openquake.hazardlib.site.Site
"""
if self.vs30:
vs30 = self.vs30
vs30_measured = self.vs30_measured
else:
vs30 = missing_vs30
vs30_measured = False
if self.z1pt0:
z1pt0 = self.z1pt0
else:
z1pt0 = vs30_to_z1pt0_as08(vs30)
if self.z2pt5:
z2pt5 = self.z2pt5
else:
z2pt5 = z1pt0_to_z2pt5(z1pt0)
location = Point(self.longitude,
self.latitude,
-self.altitude / 1000.) # Elevation from m to km
oq_site = Site(location,
vs30,
z1pt0,
z2pt5,
vs30measured=vs30_measured,
backarc=self.backarc)
setattr(oq_site, "id", self.id)
return oq_site
def test_nankai(self):
# source model for the Nankai region provided by M. Pagani
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
# it has a single group containing 15 mutex sources
[group] = nrml.to_python(source_model)
aae(group.srcs_weights, [
0.0125, 0.0125, 0.0125, 0.0125, 0.1625, 0.1625, 0.0125, 0.0125,
0.025, 0.025, 0.05, 0.05, 0.325, 0.025, 0.1
])
rup_serial = numpy.arange(group.tot_ruptures, dtype=numpy.uint32)
start = 0
for i, src in enumerate(group):
src.id = i
nr = src.num_ruptures
src.serial = rup_serial[start:start + nr]
start += nr
group.samples = 1
lonlat = 135.68, 35.68
site = Site(geo.Point(*lonlat), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
param = dict(ses_per_logic_tree_path=10, seed=42)
gsims = [SiMidorikawa1999SInter()]
dic = sample_ruptures(group, s_filter, gsims, param)
self.assertEqual(dic['num_ruptures'], 19) # total ruptures
self.assertEqual(dic['num_events'], 16)
self.assertEqual(len(dic['eb_ruptures']), 8)
self.assertEqual(len(dic['calc_times']), 15) # mutex sources
# test export
mesh = numpy.array([lonlat], [('lon', float), ('lat', float)])
ebr = dic['eb_ruptures'][0]
ebr.export(mesh)
def get_regular_site_collection(limits, vs30, z1pt0=100.0, z2pt5=1.0):
"""
Gets a collection of sites from a regularly spaced grid of points
:param list limits:
Limits of mesh as [lower_long, upper_long, long_spc, lower_lat,
upper_lat, lat_spc]
:param float vs30:
Vs30 values
:param float z1pt0:
Depth (m) to the 1 km/s interface
:param float z2pt5:
Depth (km) to the 2.5 km/s interface
"""
xgrd = np.arange(limits[0], limits[1] + limits[2], limits[2])
ygrd = np.arange(limits[3], limits[4] + limits[5], limits[5])
gx, gy = np.meshgrid(xgrd, ygrd)
nx, ny = np.shape(gx)
ngp = nx * ny
gx = np.reshape(gx, [ngp, 1]).flatten()
gy = np.reshape(gy, [ngp, 1]).flatten()
depths = np.zeros(ngp, dtype=float)
return SiteCollection([
Site(Point(gx[i], gy[i]), vs30, True, z1pt0, z2pt5, i)
for i in range(0, ngp)
])
def point_at_distance(self,
model,
distance,
vs30,
line_azimuth=90.,
origin_point=(0.5, 0.),
vs30measured=True,
z1pt0=None,
z2pt5=None,
backarc=False):
"""
Generates a point at a given hypocentral distance
"""
azimuth, origin_point, z1pt0, z2pt5 = _setup_site_peripherals(
line_azimuth, origin_point, vs30, z1pt0, z2pt5, model.strike,
model.surface)
xdist = sqrt(distance**2. - model.hypocentre.depth**2.)
return SiteCollection([
Site(model.hypocentre.point_at(xdist, -model.hypocentre.depth,
azimuth),
vs30,
vs30measured,
z1pt0,
z2pt5,
backarc=backarc)
])
def point_at_distance(self,
model,
distance,
vs30,
line_azimuth=90.,
origin_point=(0.5, 0.),
vs30measured=True,
z1pt0=None,
z2pt5=None,
backarc=False):
"""
Generates a site given a specified rupture distance from the
rupture surface
"""
azimuth, origin_location, z1pt0, z2pt5 = _setup_site_peripherals(
line_azimuth, origin_point, vs30, z1pt0, z2pt5, model.strike,
model.surface)
selected_point = _rup_to_point(distance, model.surface,
origin_location, azimuth, 'rjb')
target_sites = SiteCollection([
Site(selected_point,
vs30,
vs30measured,
z1pt0,
z2pt5,
backarc=backarc)
])
return target_sites
def test_nankai(self):
# source model for the Nankai region provided by M. Pagani
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
# it has a single group containing 15 mutex sources
[group] = nrml.to_python(source_model)
aae([src.mutex_weight for src in group],
[0.0125, 0.0125, 0.0125, 0.0125, 0.1625, 0.1625, 0.0125, 0.0125,
0.025, 0.025, 0.05, 0.05, 0.325, 0.025, 0.1])
seed = 42
rup_serial = numpy.arange(seed, seed + group.tot_ruptures,
dtype=numpy.uint32)
start = 0
for i, src in enumerate(group):
src.id = i
nr = src.num_ruptures
src.serial = rup_serial[start:start + nr]
start += nr
lonlat = 135.68, 35.68
site = Site(geo.Point(*lonlat), 800, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
param = dict(ses_per_logic_tree_path=10, filter_distance='rjb',
gsims=[SiMidorikawa1999SInter()])
dic = sum(sample_ruptures(group, param, s_filter), {})
self.assertEqual(len(dic['rup_array']), 5)
self.assertEqual(len(dic['calc_times']), 15) # mutex sources
# test no filtering 1
ruptures = list(stochastic_event_set(group))
self.assertEqual(len(ruptures), 19)
# test no filtering 2
ruptures = sum(sample_ruptures(group, param), {})['rup_array']
self.assertEqual(len(ruptures), 5)
