def test_expand_1d(self):
col = SiteCollection(self.SITES)
col = col.filter(numpy.array([1, 0, 1, 1]))
data_condensed = numpy.array([5, 6, 7])
data_expanded = col.expand(data_condensed, total_sites=4, placeholder=100)
data_expanded_expected = numpy.array([5, 100, 6, 7])
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_expand_no_filtering(self):
col = SiteCollection(self.SITES)
data_condensed = numpy.array([3, 2, 1, 0])
data_expanded = col.expand(data_condensed, total_sites=4,
placeholder=100)
data_expanded_expected = data_condensed
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_expand_no_filtering(self):
col = SiteCollection(self.SITES)
data_condensed = numpy.array([3, 2, 1, 0])
data_expanded = col.expand(data_condensed,
total_sites=4,
placeholder=100)
data_expanded_expected = data_condensed
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_expand_2d(self):
col = SiteCollection(self.SITES)
col.indices = numpy.array([1, 3, 5, 6])
data_condensed = numpy.array([[1, 2, 3], [5, 6, 7], [10, 11, 12], [15, 16, 17]])
data_expanded = col.expand(data_condensed, total_sites=8, placeholder=-1)
data_expanded_expected = numpy.array(
[[-1, -1, -1], [1, 2, 3], [-1, -1, -1], [5, 6, 7], [-1, -1, -1], [10, 11, 12], [15, 16, 17], [-1, -1, -1]]
)
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_expand_1d(self):
col = SiteCollection(self.SITES)
col = col.filter(numpy.array([1, 0, 1, 1]))
data_condensed = numpy.array([5, 6, 7])
data_expanded = col.expand(data_condensed,
total_sites=4,
placeholder=100)
data_expanded_expected = numpy.array([5, 100, 6, 7])
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_expand_2d(self):
col = SiteCollection(self.SITES)
col.indices = numpy.array([1, 3, 5, 6])
data_condensed = numpy.array([[1, 2, 3], [5, 6, 7], [10, 11, 12],
[15, 16, 17]])
data_expanded = col.expand(data_condensed,
total_sites=8,
placeholder=-1)
data_expanded_expected = numpy.array([[-1, -1, -1], [1, 2, 3],
[-1, -1, -1], [5, 6, 7],
[-1, -1, -1], [10, 11, 12],
[15, 16, 17], [-1, -1, -1]])
numpy.testing.assert_array_equal(data_expanded, data_expanded_expected)
def test_double_filter(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.array([True, False, True, True]))
filtered2 = filtered.filter(numpy.array([False, True, False]))
arreq = numpy.testing.assert_array_equal
arreq(filtered2.vs30, [2])
arreq(filtered2.vs30measured, [True])
arreq(filtered2.z1pt0, [9])
arreq(filtered2.z2pt5, [17])
arreq(filtered2.mesh.lons, [0])
arreq(filtered2.mesh.lats, [2])
self.assertIs(filtered2.mesh.depths, None)
arreq(filtered.indices, [0, 2, 3])
arreq(filtered2.indices, [2])
filtered2 = filtered.filter(numpy.array([True, False, True]))
arreq(filtered2.indices, [0, 3])
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)
lt_corma = cormo.get_lower_triangle_correlation_matrix(self.sites,
self.imt1)
s1_intensity, s2_intensity = ground_motion_fields(
self.rupture, self.sites, [self.imt1], self.gsim,
truncation_level=None, realizations=6000,
lt_correlation_matrices={self.imt1: lt_corma}
)[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_double_filter(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.array([True, False, True, True]))
filtered2 = filtered.filter(numpy.array([False, True, False]))
arreq = numpy.testing.assert_array_equal
arreq(filtered2.vs30, [2])
arreq(filtered2.vs30measured, [True])
arreq(filtered2.z1pt0, [9])
arreq(filtered2.z2pt5, [17])
arreq(filtered2.mesh.lons, [0])
arreq(filtered2.mesh.lats, [2])
self.assertIs(filtered2.mesh.depths, None)
arreq(filtered.indices, [0, 2, 3])
arreq(filtered2.indices, [2])
filtered2 = filtered.filter(numpy.array([True, False, True]))
arreq(filtered2.indices, [0, 3])
def setUp(self):
super(PointSourceSourceFilterTestCase, self).setUp()
self.sitecol = SiteCollection(self.SITES)
self.source1 = make_point_source(
mfd=EvenlyDiscretizedMFD(min_mag=5,
bin_width=1,
occurrence_rates=[1]),
rupture_aspect_ratio=1.9,
upper_seismogenic_depth=0,
lower_seismogenic_depth=18.5,
magnitude_scaling_relationship=PeerMSR(),
nodal_plane_distribution=PMF([
(0.5, NodalPlane(strike=1, dip=2, rake=3)),
(0.5, NodalPlane(strike=1, dip=20, rake=3)),
]),
location=Point(2.0, 0.0),
)
self.source2 = make_point_source(
mfd=EvenlyDiscretizedMFD(min_mag=6.5,
bin_width=1,
occurrence_rates=[1]),
rupture_aspect_ratio=0.5,
upper_seismogenic_depth=0,
lower_seismogenic_depth=18.5,
magnitude_scaling_relationship=PeerMSR(),
nodal_plane_distribution=PMF([
(0.5, NodalPlane(strike=1, dip=10, rake=3)),
(0.5, NodalPlane(strike=1, dip=20, rake=3)),
]),
location=Point(2.0, 0.0),
)
def test(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])
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.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)
self.assertIsInstance(cll.vs30measured, numpy.ndarray)
self.assertEqual(cll.vs30measured.flags.writeable, False)
self.assertEqual(cll.vs30measured.dtype, bool)
self.assertEqual(len(cll), 2)
def test_unknown_distance_error(self):
self.gsim_class.REQUIRES_DISTANCES.add('jump height')
err = "FakeGSIM requires unknown distance measure 'jump height'"
sites = SiteCollection([self.site1])
self._assert_value_error(self.gsim.make_contexts,
err,
site_collection=sites,
rupture=self.rupture)
def test_unknown_rupture_param_error(self):
self.gsim_class.REQUIRES_RUPTURE_PARAMETERS.add('stuff')
err = "FakeGSIM requires unknown rupture parameter 'stuff'"
sites = SiteCollection([self.site1])
self._assert_value_error(self.gsim.make_contexts,
err,
site_collection=sites,
rupture=self.rupture)
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)
def test_case_11(self):
hypocenter_probability = (Decimal(1) /
len(test_data.SET1_CASE11_HYPOCENTERS))
hypocenter_pmf = PMF([
(hypocenter_probability, hypocenter)
for hypocenter in test_data.SET1_CASE11_HYPOCENTERS
])
# apart from hypocenter pmf repeats case 10
sources = [
AreaSource(
source_id='area',
name='area',
tectonic_region_type=const.TRT.ACTIVE_SHALLOW_CRUST,
mfd=test_data.SET1_CASE11_MFD,
nodal_plane_distribution=PMF([(1, NodalPlane(0.0, 90.0,
0.0))]),
hypocenter_distribution=hypocenter_pmf,
upper_seismogenic_depth=0.0,
lower_seismogenic_depth=10.0,
magnitude_scaling_relationship=PeerMSR(),
rupture_aspect_ratio=test_data.SET1_RUPTURE_ASPECT_RATIO,
polygon=test_data.SET1_CASE11_SOURCE_POLYGON,
area_discretization=30.0,
rupture_mesh_spacing=10.0)
]
sites = SiteCollection([
test_data.SET1_CASE11_SITE1, test_data.SET1_CASE11_SITE2,
test_data.SET1_CASE11_SITE3, test_data.SET1_CASE11_SITE4
])
gsims = {const.TRT.ACTIVE_SHALLOW_CRUST: SadighEtAl1997()}
truncation_level = 0
time_span = 1.0
imts = {test_data.IMT: test_data.SET1_CASE11_IMLS}
curves = hazard_curves(sources, sites, imts, time_span, gsims,
truncation_level)
s1hc, s2hc, s3hc, s4hc = curves[test_data.IMT]
assert_hazard_curve_is(self,
s1hc,
test_data.SET1_CASE11_SITE1_POES,
tolerance=2e-3)
assert_hazard_curve_is(self,
s2hc,
test_data.SET1_CASE11_SITE2_POES,
tolerance=2e-3)
assert_hazard_curve_is(self,
s3hc,
test_data.SET1_CASE11_SITE3_POES,
tolerance=2e-3)
assert_hazard_curve_is(self,
s4hc,
test_data.SET1_CASE11_SITE4_POES,
tolerance=2e-3)
class JB2009LowerTriangleCorrelationMatrixTestCase(unittest.TestCase):
SITECOL = SiteCollection([
Site(Point(2, -40), 1, True, 1, 1),
Site(Point(2, -40.1), 1, True, 1, 1),
Site(Point(2, -39.9), 1, True, 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_filter(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.array([True, False, True, False]))
self.assertIsInstance(filtered, SiteCollection)
arreq = numpy.testing.assert_array_equal
arreq(filtered.vs30, [1.2, 2])
arreq(filtered.vs30measured, [True, True])
arreq(filtered.z1pt0, [3, 9])
arreq(filtered.z2pt5, [5, 17])
arreq(filtered.mesh.lons, [10, 0])
arreq(filtered.mesh.lats, [20, 2])
self.assertIs(filtered.mesh.depths, None)
filtered = col.filter(numpy.array([False, True, True, True]))
self.assertIsInstance(filtered, SiteCollection)
arreq(filtered.vs30, [55.4, 2, 4])
arreq(filtered.vs30measured, [False, True, False])
arreq(filtered.z1pt0, [6, 9, 22])
arreq(filtered.z2pt5, [8, 17, 11])
arreq(filtered.mesh.lons, [11, 0, 1])
arreq(filtered.mesh.lats, [12, 2, 1])
self.assertIs(filtered.mesh.depths, None)
def test_filter(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.array([True, False, True, False]))
self.assertIsInstance(filtered, SiteCollection)
arreq = numpy.testing.assert_array_equal
arreq(filtered.vs30, [1.2, 2])
arreq(filtered.vs30measured, [True, True])
arreq(filtered.z1pt0, [3, 9])
arreq(filtered.z2pt5, [5, 17])
arreq(filtered.mesh.lons, [10, 0])
arreq(filtered.mesh.lats, [20, 2])
self.assertIs(filtered.mesh.depths, None)
filtered = col.filter(numpy.array([False, True, True, True]))
self.assertIsInstance(filtered, SiteCollection)
arreq(filtered.vs30, [55.4, 2, 4])
arreq(filtered.vs30measured, [False, True, False])
arreq(filtered.z1pt0, [6, 9, 22])
arreq(filtered.z2pt5, [8, 17, 11])
arreq(filtered.mesh.lons, [11, 0, 1])
arreq(filtered.mesh.lats, [12, 2, 1])
self.assertIs(filtered.mesh.depths, None)
def setUp(self):
class FakeSource(SeismicSource):
iter_ruptures = None
get_rupture_enclosing_polygon = None
self.source_class = FakeSource
mfd = EvenlyDiscretizedMFD(min_mag=3,
bin_width=1,
occurrence_rates=[5, 6, 7])
self.source = FakeSource('source_id',
'name',
const.TRT.VOLCANIC,
mfd=mfd,
rupture_mesh_spacing=2,
magnitude_scaling_relationship=PeerMSR(),
rupture_aspect_ratio=1)
self.sitecol = SiteCollection(self.SITES)
def test_some_values(self):
self.gsim_class.REQUIRES_DISTANCES = set('rjb rx'.split())
self.gsim_class.REQUIRES_RUPTURE_PARAMETERS = set('mag rake'.split())
self.gsim_class.REQUIRES_SITES_PARAMETERS = set('vs30 z1pt0'.split())
sites = SiteCollection([self.site1, self.site2])
sctx, rctx, dctx = self.gsim.make_contexts(sites, self.rupture)
self.assertEqual((rctx.mag, rctx.rake), (123.45, 123.56))
self.assertTrue((sctx.vs30 == (456, 1456)).all())
self.assertTrue((sctx.z1pt0 == (12.1, 112.1)).all())
self.assertTrue((dctx.rx == (4, 5)).all())
self.assertFalse(hasattr(rctx, 'dip'))
self.assertFalse(hasattr(sctx, 'vs30measured'))
self.assertFalse(hasattr(sctx, 'z2pt0'))
self.assertFalse(hasattr(dctx, 'rrup'))
self.assertFalse(hasattr(dctx, 'ztor'))
self.assertEqual(self.fake_surface.call_counts, {
'get_rx_distance': 1,
'get_joyner_boore_distance': 1
})
def test_array_instead_of_matrix(self):
mean = 10
inter = 1e-300
intra = 1
points = [Point(0, 0), Point(0, 0.23)]
sites = [Site(point, mean, False, inter, intra) for point in points]
self.sites = SiteCollection(sites)
numpy.random.seed(43)
cormo = JB2009CorrelationModel(vs30_clustering=False)
corma = cormo.get_correlation_matrix(self.sites, self.imt1)
lt_corma = cormo.get_lower_triangle_correlation_matrix(self.sites,
self.imt1)
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1], self.gsim,
truncation_level=None, realizations=6000,
lt_correlation_matrices={self.imt1: lt_corma.A}
)
sampled_corma = numpy.corrcoef(gmfs[self.imt1])
assert_allclose(corma, sampled_corma, rtol=0, atol=0.02)
def test_all_values(self):
self.gsim_class.REQUIRES_DISTANCES = set(
'rjb rx rrup repi rhypo'.split())
self.gsim_class.REQUIRES_RUPTURE_PARAMETERS = set(
'mag rake dip ztor hypo_depth'.split())
self.gsim_class.REQUIRES_SITES_PARAMETERS = set(
'vs30 vs30measured z1pt0 z2pt5'.split())
sites = SiteCollection([self.site1, self.site2])
sctx, rctx, dctx = self.gsim.make_contexts(sites, self.rupture)
self.assertIsInstance(sctx, SitesContext)
self.assertIsInstance(rctx, RuptureContext)
self.assertIsInstance(dctx, DistancesContext)
self.assertEqual(rctx.mag, 123.45)
self.assertEqual(rctx.rake, 123.56)
self.assertEqual(rctx.dip, 45.4545)
self.assertEqual(rctx.ztor, 30)
self.assertEqual(rctx.hypo_depth, 40)
self.assertTrue((sctx.vs30 == [456, 1456]).all())
self.assertTrue((sctx.vs30measured == [False, True]).all())
self.assertTrue((sctx.z1pt0 == [12.1, 112.1]).all())
self.assertTrue((sctx.z2pt5 == [15.1, 115.1]).all())
self.assertTrue((dctx.rjb == [6, 7]).all())
self.assertTrue((dctx.rx == [4, 5]).all())
self.assertTrue((dctx.rrup == [10, 11]).all())
numpy.testing.assert_almost_equal(dctx.rhypo,
[162.18749272, 802.72247682])
numpy.testing.assert_almost_equal(dctx.repi,
[157.17755181, 801.72524895])
self.assertEqual(
self.fake_surface.call_counts, {
'get_top_edge_depth': 1,
'get_rx_distance': 1,
'get_joyner_boore_distance': 1,
'get_dip': 1,
'get_min_distance': 1
})
class JB2009CorrelationMatrixTestCase(unittest.TestCase):
SITECOL = SiteCollection([
Site(Point(2, -40), 1, True, 1, 1),
Site(Point(2, -40.1), 1, True, 1, 1),
Site(Point(2, -40), 1, True, 1, 1),
Site(Point(2, -39.9), 1, True, 1, 1)
])
def test_no_clustering(self):
cormo = JB2009CorrelationModel(vs30_clustering=False)
imt = SA(period=0.1, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.03823366, 1, 0.03823366],
[0.03823366, 1, 0.03823366, 0.00146181],
[1, 0.03823366, 1, 0.03823366],
[0.03823366, 0.00146181, 0.03823366, 1]])
imt = SA(period=0.95, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.26107857, 1, 0.26107857],
[0.26107857, 1, 0.26107857, 0.06816202],
[1, 0.26107857, 1, 0.26107857],
[0.26107857, 0.06816202, 0.26107857, 1]])
def test_clustered(self):
cormo = JB2009CorrelationModel(vs30_clustering=True)
imt = SA(period=0.001, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.44046654, 1, 0.44046654],
[0.44046654, 1, 0.44046654, 0.19401077],
[1, 0.44046654, 1, 0.44046654],
[0.44046654, 0.19401077, 0.44046654, 1]])
imt = SA(period=0.5, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.36612758, 1, 0.36612758],
[0.36612758, 1, 0.36612758, 0.1340494],
[1, 0.36612758, 1, 0.36612758],
[0.36612758, 0.1340494, 0.36612758, 1]])
def test_period_one_and_above(self):
cormo = JB2009CorrelationModel(vs30_clustering=False)
cormo2 = JB2009CorrelationModel(vs30_clustering=True)
imt = SA(period=1.0, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.2730787, 1, 0.2730787],
[0.2730787, 1, 0.2730787, 0.07457198],
[1, 0.2730787, 1, 0.2730787],
[0.2730787, 0.07457198, 0.2730787, 1]])
corma2 = cormo2.get_correlation_matrix(self.SITECOL, imt)
self.assertTrue((corma == corma2).all())
imt = SA(period=10.0, damping=5)
corma = cormo.get_correlation_matrix(self.SITECOL, imt)
aaae(corma, [[1, 0.56813402, 1, 0.56813402],
[0.56813402, 1, 0.56813402, 0.32277627],
[1, 0.56813402, 1, 0.56813402],
[0.56813402, 0.32277627, 0.56813402, 1]])
corma2 = cormo2.get_correlation_matrix(self.SITECOL, imt)
self.assertTrue((corma == corma2).all())
def test_pga(self):
sa = SA(period=1e-50, damping=5)
pga = PGA()
cormo = JB2009CorrelationModel(vs30_clustering=False)
corma = cormo.get_correlation_matrix(self.SITECOL, sa)
corma2 = cormo.get_correlation_matrix(self.SITECOL, pga)
self.assertTrue((corma == corma2).all())
cormo = JB2009CorrelationModel(vs30_clustering=True)
corma = cormo.get_correlation_matrix(self.SITECOL, sa)
corma2 = cormo.get_correlation_matrix(self.SITECOL, pga)
self.assertTrue((corma == corma2).all())
def test_filter_all_in(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.ones(len(self.SITES), bool))
self.assertIs(filtered, col)
def test_filter_all_out(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.zeros(len(self.SITES), bool))
self.assertIs(filtered, None)
def test_case_5(self):
# only mfd differs from case 2
sources = [
SimpleFaultSource(
source_id='fault1',
name='fault1',
tectonic_region_type=const.TRT.ACTIVE_SHALLOW_CRUST,
mfd=test_data.SET1_CASE5_MFD,
rupture_mesh_spacing=1.0,
magnitude_scaling_relationship=PeerMSR(),
rupture_aspect_ratio=test_data.SET1_RUPTURE_ASPECT_RATIO,
upper_seismogenic_depth=test_data.
SET1_CASE1TO9_UPPER_SEISMOGENIC_DEPTH,
lower_seismogenic_depth=test_data.
SET1_CASE1TO9_LOWER_SEISMOGENIC_DEPTH,
fault_trace=test_data.SET1_CASE1TO9_FAULT_TRACE,
dip=test_data.SET1_CASE1TO9_DIP,
rake=test_data.SET1_CASE1TO9_RAKE)
]
sites = SiteCollection([
test_data.SET1_CASE1TO9_SITE1, test_data.SET1_CASE1TO9_SITE2,
test_data.SET1_CASE1TO9_SITE3, test_data.SET1_CASE1TO9_SITE4,
test_data.SET1_CASE1TO9_SITE5, test_data.SET1_CASE1TO9_SITE6,
test_data.SET1_CASE1TO9_SITE7
])
gsims = {const.TRT.ACTIVE_SHALLOW_CRUST: SadighEtAl1997()}
truncation_level = 0
time_span = 1.0
imts = {test_data.IMT: test_data.SET1_CASE5_IMLS}
curves = hazard_curves(sources, sites, imts, time_span, gsims,
truncation_level)
s1hc, s2hc, s3hc, s4hc, s5hc, s6hc, s7hc = curves[test_data.IMT]
assert_hazard_curve_is(self,
s1hc,
test_data.SET1_CASE5_SITE1_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s2hc,
test_data.SET1_CASE5_SITE2_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s3hc,
test_data.SET1_CASE5_SITE3_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s4hc,
test_data.SET1_CASE5_SITE4_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s5hc,
test_data.SET1_CASE5_SITE5_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s6hc,
test_data.SET1_CASE5_SITE6_POES,
tolerance=1e-3)
assert_hazard_curve_is(self,
s7hc,
test_data.SET1_CASE5_SITE7_POES,
tolerance=1e-3)
def _collect_bins_data(sources, site, imt, iml, gsims, tom, truncation_level,
n_epsilons, source_site_filter, rupture_site_filter):
"""
Extract values of magnitude, distance, closest point, tectonic region
types and PoE distribution.
This method processes the source model (generates ruptures) and collects
all needed parameters to arrays. It also defines tectonic region type
bins sequence.
"""
mags = []
dists = []
lons = []
lats = []
tect_reg_types = []
joint_probs = []
sitecol = SiteCollection([site])
sitemesh = sitecol.mesh
_next_trt_num = 0
trt_nums = {}
sources_sites = ((source, sitecol) for source in sources)
# here we ignore filtered site collection because either it is the same
# as the original one (with one site), or the source/rupture is filtered
# out and doesn't show up in the filter's output
for source, s_sites in source_site_filter(sources_sites):
tect_reg = source.tectonic_region_type
gsim = gsims[tect_reg]
if not tect_reg in trt_nums:
trt_nums[tect_reg] = _next_trt_num
_next_trt_num += 1
tect_reg = trt_nums[tect_reg]
ruptures_sites = ((rupture, s_sites)
for rupture in source.iter_ruptures(tom))
for rupture, r_sites in rupture_site_filter(ruptures_sites):
# extract rupture parameters of interest
mags.append(rupture.mag)
[jb_dist] = rupture.surface.get_joyner_boore_distance(sitemesh)
dists.append(jb_dist)
[closest_point] = rupture.surface.get_closest_points(sitemesh)
lons.append(closest_point.longitude)
lats.append(closest_point.latitude)
tect_reg_types.append(tect_reg)
# compute conditional probability of exceeding iml given
# the current rupture, and different epsilon level, that is
# ``P(IMT >= iml | rup, epsilon_bin)`` for each of epsilon bins
sctx, rctx, dctx = gsim.make_contexts(sitecol, rupture)
[poes_given_rup_eps
] = gsim.disaggregate_poe(sctx, rctx, dctx, imt, iml,
truncation_level, n_epsilons)
# compute the probability of the rupture occurring once,
# that is ``P(rup)``
p_rup = rupture.get_probability_one_occurrence()
# compute joint probability of rupture occurrence and
# iml exceedance for the different epsilon levels
joint_probs.append(poes_given_rup_eps * p_rup)
mags = numpy.array(mags, float)
dists = numpy.array(dists, float)
lons = numpy.array(lons, float)
lats = numpy.array(lats, float)
tect_reg_types = numpy.array(tect_reg_types, int)
joint_probs = numpy.array(joint_probs, float)
trt_bins = [
trt
for (num, trt) in sorted((num, trt) for (trt, num) in trt_nums.items())
]
return mags, dists, lons, lats, joint_probs, tect_reg_types, trt_bins
def setUp(self):
self.mean1 = 1
self.mean2 = 5
self.mean3 = 10
self.mean4567 = 10
self.inter1 = 0.4
self.inter2 = 1
self.inter3 = 1.4
self.inter45 = 1e-300
self.inter67 = 1
self.intra1 = 0.7
self.intra2 = 2
self.intra3 = 0.3
self.intra45 = 1
self.intra67 = 1e-300
self.stddev1 = (self.inter1 ** 2 + self.intra1 ** 2) ** 0.5
self.stddev2 = (self.inter2 ** 2 + self.intra2 ** 2) ** 0.5
self.stddev3 = (self.inter3 ** 2 + self.intra3 ** 2) ** 0.5
self.stddev45 = (self.inter45 ** 2 + self.intra45 ** 2) ** 0.5
self.stddev67 = (self.inter67 ** 2 + self.intra67 ** 2) ** 0.5
p = [Point(0, 0), Point(0, 0.1), Point(0, 0.2), Point(0, 0.3),
Point(0, 0.4), Point(0, 0.5), Point(0, 0.6)]
sites = [Site(p[0], self.mean1, False, self.inter1, self.intra1),
Site(p[1], self.mean2, True, self.inter2, self.intra2),
Site(p[2], self.mean3, False, self.inter3, self.intra3),
Site(p[3], self.mean4567, True, self.inter45, self.intra45),
Site(p[4], self.mean4567, False, self.inter45, self.intra45),
Site(p[5], self.mean4567, True, self.inter67, self.intra67),
Site(p[6], self.mean4567, False, self.inter67, self.intra67)]
self.sites = SiteCollection(sites)
self.rupture = object()
self.imt1 = SA(10, 5)
self.imt2 = PGV()
class FakeGSIM(object):
expect_stddevs = True
expect_same_sitecol = True
def make_contexts(gsim, sites, rupture):
if gsim.expect_same_sitecol:
self.assertIs(sites, self.sites)
else:
self.assertIsNot(sites, self.sites)
self.assertIs(rupture, self.rupture)
return sites.vs30, sites.z1pt0, sites.z2pt5
def get_mean_and_stddevs(gsim, mean, std_inter, std_intra, imt,
stddev_types):
assert imt is self.imt1 or imt is self.imt2
if gsim.expect_stddevs:
self.assertEqual(stddev_types, [const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT])
# + 10 is needed to make sure that to_imt_unit_values()
# is called on the result of gmf calc
return mean + 10, [std_inter, std_intra]
else:
self.assertEqual(stddev_types, [])
return mean + 10, []
def to_imt_unit_values(gsim, intensities):
return intensities - 10.
def rupture_site_filter(rupture_site_gen):
[(rupture, sites)] = rupture_site_gen
assert rupture is self.rupture
assert sites is self.sites
yield rupture, sites.filter(sites.vs30measured)
self.rupture_site_filter = rupture_site_filter
self.gsim = FakeGSIM()
def test_filter_all_out(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.zeros(len(self.SITES), bool))
self.assertIs(filtered, None)
def setUp(self):
super(PointSourceRuptureFilterTestCase, self).setUp()
self.hypocenter = Point(2, 0, 50)
self.sitecol = SiteCollection(self.SITES)
def test_filter_all_in(self):
col = SiteCollection(self.SITES)
filtered = col.filter(numpy.ones(len(self.SITES), bool))
self.assertIs(filtered, col)