def test_total_stddev_only(self):
truncation_level = None
numpy.random.seed(37)
realizations = 1000
gmfs = ground_motion_fields(self.rupture, self.sites_total,
[self.imt2],
self.total_stddev_gsim,
truncation_level,
realizations=realizations,
correlation_model=None)
intensity = gmfs[self.imt2]
assert_allclose((intensity[0].mean(), intensity[0].std()),
(self.mean1, self.total1), rtol=4e-2)
assert_allclose((intensity[1].mean(), intensity[1].std()),
(self.mean2, self.total2), rtol=4e-2)
assert_allclose((intensity[2].mean(), intensity[2].std()),
(self.mean3, self.total3), rtol=4e-2)
assert_allclose((intensity[3].mean(), intensity[3].std()),
(self.mean4567, self.total45), rtol=4e-2)
assert_allclose((intensity[4].mean(), intensity[4].std()),
(self.mean4567, self.total45), rtol=4e-2)
assert_allclose((intensity[5].mean(), intensity[5].std()),
(self.mean4567, self.total67), rtol=4e-2)
assert_allclose((intensity[6].mean(), intensity[6].std()),
(self.mean4567, self.total67), rtol=4e-2)
def test_rupture_site_filtering(self):
mean = 10
inter = 2
intra = 3
points = [Point(0, 0), Point(0, 0.05)]
sites = [Site(point, mean, False, inter, intra) for point in points]
self.sites = SiteCollection(sites)
def rupture_site_filter(rupture_sites):
[(rupture, sites)] = rupture_sites
yield rupture, sites.filter(sites.mesh.lats == 0)
self.gsim.expect_same_sitecol = False
numpy.random.seed(37)
cormo = JB2009CorrelationModel(vs30_clustering=False)
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1], self.gsim,
truncation_level=None, realizations=1,
correlation_model=cormo,
rupture_site_filter=rupture_site_filter
)
s1gmf, s2gmf = gmfs[self.imt1]
numpy.testing.assert_array_equal(s2gmf, 0)
numpy.testing.assert_array_almost_equal(s1gmf, 11.1852253)
def test_no_filtering_no_truncation(self):
truncation_level = None
numpy.random.seed(3)
realizations = 2000
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt2], self.gsim,
truncation_level,
realizations=realizations)
intensity = gmfs[self.imt2]
assert_allclose((intensity[0].mean(), intensity[0].std()),
(self.mean1, self.stddev1), rtol=4e-2)
assert_allclose((intensity[1].mean(), intensity[1].std()),
(self.mean2, self.stddev2), rtol=4e-2)
assert_allclose((intensity[2].mean(), intensity[2].std()),
(self.mean3, self.stddev3), rtol=4e-2)
assert_allclose((intensity[3].mean(), intensity[3].std()),
(self.mean4567, self.stddev45), rtol=4e-2)
assert_allclose((intensity[4].mean(), intensity[4].std()),
(self.mean4567, self.stddev45), rtol=4e-2)
assert_allclose((intensity[5].mean(), intensity[5].std()),
(self.mean4567, self.stddev67), rtol=4e-2)
assert_allclose((intensity[6].mean(), intensity[6].std()),
(self.mean4567, self.stddev67), rtol=4e-2)
# sites with zero intra-event stddev, should give exactly the same
# result, since inter-event distribution is sampled only once
assert_array_equal(intensity[5], intensity[6])
self.assertFalse((intensity[3] == intensity[4]).all())
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_no_filtering_no_truncation(self):
truncation_level = None
numpy.random.seed(3)
realizations = 2000
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt2], self.gsim,
truncation_level,
realizations=realizations)
intensity = gmfs[self.imt2]
assert_allclose((intensity[0].mean(), intensity[0].std()),
(self.mean1, self.stddev1), rtol=4e-2)
assert_allclose((intensity[1].mean(), intensity[1].std()),
(self.mean2, self.stddev2), rtol=4e-2)
assert_allclose((intensity[2].mean(), intensity[2].std()),
(self.mean3, self.stddev3), rtol=4e-2)
assert_allclose((intensity[3].mean(), intensity[3].std()),
(self.mean4567, self.stddev45), rtol=4e-2)
assert_allclose((intensity[4].mean(), intensity[4].std()),
(self.mean4567, self.stddev45), rtol=4e-2)
assert_allclose((intensity[5].mean(), intensity[5].std()),
(self.mean4567, self.stddev67), rtol=4e-2)
assert_allclose((intensity[6].mean(), intensity[6].std()),
(self.mean4567, self.stddev67), rtol=4e-2)
# sites with zero intra-event stddev, should give exactly the same
# result, since inter-event distribution is sampled only once
assert_array_equal(intensity[5], intensity[6])
self.assertFalse((intensity[3] == intensity[4]).all())
def test_filter_all_out(self):
def rupture_site_filter(rupture_site):
return []
for truncation_level in (None, 0, 1.3):
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1, self.imt2], self.gsim,
truncation_level=truncation_level,
realizations=123,
rupture_site_filter=rupture_site_filter
)
self.assertEqual(gmfs[self.imt1].shape, (7, 123))
self.assertEqual(gmfs[self.imt2].shape, (7, 123))
assert_array_equal(gmfs[self.imt1], 0)
assert_array_equal(gmfs[self.imt2], 0)
def test_filter_all_out(self):
def rupture_site_filter(rupture_site):
return []
for truncation_level in (None, 0, 1.3):
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1, self.imt2], self.gsim,
truncation_level=truncation_level,
realizations=123,
rupture_site_filter=rupture_site_filter
)
self.assertEqual(gmfs[self.imt1].shape, (7, 123))
self.assertEqual(gmfs[self.imt2].shape, (7, 123))
assert_array_equal(gmfs[self.imt1], 0)
assert_array_equal(gmfs[self.imt2], 0)
def test_no_filtering_zero_truncation(self):
truncation_level = 0
self.gsim.expect_stddevs = False
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt1, self.imt2], self.gsim,
realizations=100,
truncation_level=truncation_level)
for intensity in gmfs[self.imt1], gmfs[self.imt2]:
for i in xrange(7):
self.assertEqual(intensity[i].std(), 0)
self.assertEqual(intensity[0].mean(), self.mean1)
self.assertEqual(intensity[1].mean(), self.mean2)
self.assertEqual(intensity[2].mean(), self.mean3)
self.assertEqual(intensity[3].mean(), self.mean4567)
self.assertEqual(intensity[4].mean(), self.mean4567)
self.assertEqual(intensity[5].mean(), self.mean4567)
self.assertEqual(intensity[6].mean(), self.mean4567)
def test_no_filtering_zero_truncation(self):
truncation_level = 0
self.gsim.expect_stddevs = False
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt1, self.imt2], self.gsim,
realizations=100,
truncation_level=truncation_level)
for intensity in gmfs[self.imt1], gmfs[self.imt2]:
for i in xrange(7):
self.assertEqual(intensity[i].std(), 0)
self.assertEqual(intensity[0].mean(), self.mean1)
self.assertEqual(intensity[1].mean(), self.mean2)
self.assertEqual(intensity[2].mean(), self.mean3)
self.assertEqual(intensity[3].mean(), self.mean4567)
self.assertEqual(intensity[4].mean(), self.mean4567)
self.assertEqual(intensity[5].mean(), self.mean4567)
self.assertEqual(intensity[6].mean(), self.mean4567)
def test_no_filtering_with_truncation(self):
truncation_level = 1.9
numpy.random.seed(11)
realizations = 400
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt1], self.gsim,
realizations=realizations,
truncation_level=truncation_level)
intensity = gmfs[self.imt1]
max_deviation1 = (self.inter1 + self.intra1) * truncation_level
max_deviation2 = (self.inter2 + self.intra2) * truncation_level
max_deviation3 = (self.inter3 + self.intra3) * truncation_level
max_deviation4567 = truncation_level
self.assertLessEqual(intensity[0].max(), self.mean1 + max_deviation1)
self.assertGreaterEqual(intensity[0].min(),
self.mean1 - max_deviation1)
self.assertLessEqual(intensity[1].max(), self.mean2 + max_deviation2)
self.assertGreaterEqual(intensity[1].min(),
self.mean2 - max_deviation2)
self.assertLessEqual(intensity[2].max(), self.mean3 + max_deviation3)
self.assertGreaterEqual(intensity[2].min(),
self.mean3 - max_deviation3)
for i in (3, 4, 5, 6):
self.assertLessEqual(intensity[i].max(),
self.mean4567 + max_deviation4567)
self.assertGreaterEqual(intensity[i].min(),
self.mean4567 - max_deviation4567)
assert_allclose(intensity.mean(axis=1),
[self.mean1, self.mean2, self.mean3] +
[self.mean4567] * 4,
rtol=5e-2)
self.assertLess(intensity[0].std(), self.stddev1)
self.assertLess(intensity[1].std(), self.stddev2)
self.assertLess(intensity[2].std(), self.stddev3)
self.assertLess(intensity[3].std(), self.stddev45)
self.assertLess(intensity[4].std(), self.stddev45)
self.assertLess(intensity[5].std(), self.stddev67)
self.assertLess(intensity[6].std(), self.stddev67)
for i in xrange(7):
self.assertGreater(intensity[i].std(), 0)
def test_no_filtering_with_truncation(self):
truncation_level = 1.9
numpy.random.seed(11)
realizations = 400
gmfs = ground_motion_fields(self.rupture, self.sites,
[self.imt1], self.gsim,
realizations=realizations,
truncation_level=truncation_level)
intensity = gmfs[self.imt1]
max_deviation1 = (self.inter1 + self.intra1) * truncation_level
max_deviation2 = (self.inter2 + self.intra2) * truncation_level
max_deviation3 = (self.inter3 + self.intra3) * truncation_level
max_deviation4567 = truncation_level
self.assertLessEqual(intensity[0].max(), self.mean1 + max_deviation1)
self.assertGreaterEqual(intensity[0].min(),
self.mean1 - max_deviation1)
self.assertLessEqual(intensity[1].max(), self.mean2 + max_deviation2)
self.assertGreaterEqual(intensity[1].min(),
self.mean2 - max_deviation2)
self.assertLessEqual(intensity[2].max(), self.mean3 + max_deviation3)
self.assertGreaterEqual(intensity[2].min(),
self.mean3 - max_deviation3)
for i in (3, 4, 5, 6):
self.assertLessEqual(intensity[i].max(),
self.mean4567 + max_deviation4567)
self.assertGreaterEqual(intensity[i].min(),
self.mean4567 - max_deviation4567)
assert_allclose(intensity.mean(axis=1),
[self.mean1, self.mean2, self.mean3] +
[self.mean4567] * 4,
rtol=5e-2)
self.assertLess(intensity[0].std(), self.stddev1)
self.assertLess(intensity[1].std(), self.stddev2)
self.assertLess(intensity[2].std(), self.stddev3)
self.assertLess(intensity[3].std(), self.stddev45)
self.assertLess(intensity[4].std(), self.stddev45)
self.assertLess(intensity[5].std(), self.stddev67)
self.assertLess(intensity[6].std(), self.stddev67)
for i in xrange(7):
self.assertGreater(intensity[i].std(), 0)
def test_no_truncation(self):
mean = 10
inter = 1e-300
intra = 3
points = [Point(0, 0), Point(0, 0.05), Point(0.06, 0.025),
Point(0, 1.0), Point(-10, -10)]
sites = [Site(point, mean, False, inter, intra) for point in points]
self.sites = SiteCollection(sites)
numpy.random.seed(23)
cormo = JB2009CorrelationModel(vs30_clustering=False)
corma = cormo._get_correlation_matrix(self.sites, self.imt1)
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1], self.gsim,
truncation_level=None, realizations=6000,
correlation_model=cormo
)
sampled_corma = numpy.corrcoef(gmfs[self.imt1])
assert_allclose(corma, sampled_corma, rtol=0, atol=0.02)
def test_filtered_no_truncation(self):
numpy.random.seed(17)
realizations = 50
self.gsim.expect_same_sitecol = False
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1, self.imt2],
self.gsim, truncation_level=None,
realizations=realizations,
rupture_site_filter=self.rupture_site_filter
)
for imt in [self.imt1, self.imt2]:
intensity = gmfs[imt]
self.assertEqual(intensity.shape, (7, realizations))
assert_array_equal(
intensity[(1 - self.sites.vs30measured).nonzero()], 0
)
self.assertFalse(
(intensity[self.sites.vs30measured.nonzero()] == 0).any()
)
def test_filtered_no_truncation(self):
numpy.random.seed(17)
realizations = 50
self.gsim.expect_same_sitecol = False
gmfs = ground_motion_fields(
self.rupture, self.sites, [self.imt1, self.imt2],
self.gsim, truncation_level=None,
realizations=realizations,
rupture_site_filter=self.rupture_site_filter
)
for imt in [self.imt1, self.imt2]:
intensity = gmfs[imt]
self.assertEqual(intensity.shape, (7, realizations))
assert_array_equal(
intensity[(1 - self.sites.vs30measured).nonzero()], 0
)
self.assertFalse(
(intensity[self.sites.vs30measured.nonzero()] == 0).any()
)
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 ses_and_gmfs(job_id, src_ids, lt_rlz_id, task_seed):
"""
Celery task for the stochastic event set calculator.
Samples logic trees and calls the stochastic event set calculator.
Once stochastic event sets are calculated, results will be saved to the
database. See :class:`openquake.db.models.SESCollection`.
Optionally (specified in the job configuration using the
`ground_motion_fields` parameter), GMFs can be computed from each rupture
in each stochastic event set. GMFs are also saved to the database.
Once all of this work is complete, a signal will be sent via AMQP to let
the control noe know that the work is complete. (If there is any work left
to be dispatched, this signal will indicate to the control node that more
work can be enqueued.)
:param int job_id:
ID of the currently running job.
:param src_ids:
List of ids of parsed source models from which we will generate
stochastic event sets/ruptures.
:param lt_rlz_id:
Id of logic tree realization model to calculate for.
:param int task_seed:
Value for seeding numpy/scipy in the computation of stochastic event
sets and ground motion fields.
"""
logs.LOG.debug(
("> starting `stochastic_event_sets` task: job_id=%s, " "lt_realization_id=%s") % (job_id, lt_rlz_id)
)
numpy.random.seed(task_seed)
hc = models.HazardCalculation.objects.get(oqjob=job_id)
cmplt_lt_ses = None
if hc.complete_logic_tree_ses:
cmplt_lt_ses = models.SES.objects.get(ses_collection__output__oq_job=job_id, complete_logic_tree_ses=True)
cmplt_lt_gmf = None
if hc.complete_logic_tree_gmf:
cmplt_lt_gmf = models.GmfSet.objects.get(gmf_collection__output__oq_job=job_id, complete_logic_tree_gmf=True)
if hc.ground_motion_fields:
# For ground motion field calculation, we need the points of interest
# for the calculation.
points_to_compute = hc.points_to_compute()
lt_rlz = models.LtRealization.objects.get(id=lt_rlz_id)
ltp = logictree.LogicTreeProcessor(hc.id)
apply_uncertainties = ltp.parse_source_model_logictree_path(lt_rlz.sm_lt_path)
gsims = ltp.parse_gmpe_logictree_path(lt_rlz.gsim_lt_path)
sources = list(
haz_general.gen_sources(
src_ids, apply_uncertainties, hc.rupture_mesh_spacing, hc.width_of_mfd_bin, hc.area_source_discretization
)
)
logs.LOG.debug("> creating site collection")
site_coll = haz_general.get_site_collection(hc)
logs.LOG.debug("< done creating site collection")
if hc.ground_motion_fields:
imts = [haz_general.imt_to_nhlib(x) for x in hc.intensity_measure_types]
correl_model = None
if hc.ground_motion_correlation_model is not None:
correl_model = _get_correl_model(hc)
# Compute stochastic event sets
# For each rupture generated, we can optionally calculate a GMF
for ses_rlz_n in xrange(1, hc.ses_per_logic_tree_path + 1):
logs.LOG.debug("> computing stochastic event set %s of %s" % (ses_rlz_n, hc.ses_per_logic_tree_path))
# This is the container for all ruptures for this stochastic event set
# (specified by `ordinal` and the logic tree realization).
# NOTE: Many tasks can contribute ruptures to this SES.
ses = models.SES.objects.get(ses_collection__lt_realization=lt_rlz, ordinal=ses_rlz_n)
sources_sites = ((src, site_coll) for src in sources)
ssd_filter = filters.source_site_distance_filter(hc.maximum_distance)
# Get the filtered sources, ignore the site collection:
filtered_sources = (src for src, _ in ssd_filter(sources_sites))
# Calculate stochastic event sets:
logs.LOG.debug("> computing stochastic event sets")
if hc.ground_motion_fields:
logs.LOG.debug("> computing also ground motion fields")
# This will be the "container" for all computed ground motion field
# results for this stochastic event set.
gmf_set = models.GmfSet.objects.get(gmf_collection__lt_realization=lt_rlz, ses_ordinal=ses_rlz_n)
ses_poissonian = stochastic.stochastic_event_set_poissonian(filtered_sources, hc.investigation_time)
logs.LOG.debug("> looping over ruptures")
rupture_ctr = 0
for rupture in ses_poissonian:
# Prepare and save SES ruptures to the db:
logs.LOG.debug("> saving SES rupture to DB")
_save_ses_rupture(ses, rupture, cmplt_lt_ses)
logs.LOG.debug("> done saving SES rupture to DB")
# Compute ground motion fields (if requested)
logs.LOG.debug("compute ground motion fields? %s" % hc.ground_motion_fields)
if hc.ground_motion_fields:
# Compute and save ground motion fields
gmf_calc_kwargs = {
"rupture": rupture,
"sites": site_coll,
"imts": imts,
"gsim": gsims[rupture.tectonic_region_type],
"truncation_level": hc.truncation_level,
"realizations": DEFAULT_GMF_REALIZATIONS,
"correlation_model": correl_model,
"rupture_site_filter": filters.rupture_site_distance_filter(hc.maximum_distance),
}
logs.LOG.debug("> computing ground motion fields")
gmf_dict = gmf_calc.ground_motion_fields(**gmf_calc_kwargs)
logs.LOG.debug("< done computing ground motion fields")
logs.LOG.debug("> saving GMF results to DB")
_save_gmf_nodes(gmf_set, gmf_dict, points_to_compute, cmplt_lt_gmf)
logs.LOG.debug("< done saving GMF results to DB")
rupture_ctr += 1
logs.LOG.debug("< Done looping over ruptures")
logs.LOG.debug(
"%s ruptures computed for SES realization %s of %s" % (rupture_ctr, ses_rlz_n, hc.ses_per_logic_tree_path)
)
logs.LOG.debug("< done computing stochastic event set %s of %s" % (ses_rlz_n, hc.ses_per_logic_tree_path))
logs.LOG.debug("< task complete, signalling completion")
haz_general.signal_task_complete(job_id, len(src_ids))
