def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('classical_psha_based_risk/job.ini'),
get_data_path('simple_fault_demo_hazard/job.ini'))
models.JobStats.objects.create(oq_job=self.job)
self.job.is_running = True
self.job.save()
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('classical_bcr/job.ini'),
get_data_path('simple_fault_demo_hazard/job.ini'))
self.calculator = classical_bcr.ClassicalBCRRiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('event_based_bcr/job.ini'),
get_data_path('event_based_hazard/job.ini'), output_type="gmf")
self.calculator = core.EventBasedBCRRiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
def test_job_from_file(self):
# make a hazard job
haz_cfg = helpers.get_data_path('event_based_hazard/job.ini')
haz_job = engine.job_from_file(haz_cfg, 'test_user')
# make a fake Output
out = models.Output.objects.create(
oq_job=haz_job, display_name='fake', output_type='gmf')
# make a risk job
risk_cfg = helpers.get_data_path('event_based_risk/job.ini')
with mock.patch.object(logs.LOG, 'warn') as warn:
risk_job = engine.job_from_file(
risk_cfg, 'another_user', hazard_output_id=out.id)
# make sure a warning is printed because you are using a hazard
# generated by a different user
self.assertEqual(warn.call_args[0],
('You are using a hazard calculation ran by %s',
'test_user'))
with mock.patch.object(logs.LOG, 'warn') as warn:
risk_job = engine.job_from_file(
risk_cfg, 'test_user', hazard_output_id=out.id,
quantile_loss_curves='0.1 0.2')
# make sure a warning is printed because you are using
# quantile_loss_curves with a single hazard output
self.assertEqual(
warn.call_args[0][0],
'quantile_loss_curves is on, but you passed a single hazard '
'output: the statistics will not be computed')
# make sure the hazard job is associated correctly
self.assertEqual(risk_job.hazard_calculation.id, haz_job.id)
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('classical_psha_based_risk/job.ini'),
get_data_path('simple_fault_demo_hazard/job.ini'))
models.JobStats.objects.create(oq_job=self.job)
self.job.is_running = True
self.job.save()
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('event_based_risk/job.ini'),
get_data_path('event_based_hazard/job.ini'), output_type="gmf")
self.calculator = event_based.EventBasedRiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
self.calculator.pre_execute()
self.job.is_running = True
self.job.status = 'executing'
self.job.save()
def setUp(self):
job, _ = helpers.get_fake_risk_job(
get_data_path('classical_psha_based_risk/job.ini'),
get_data_path('simple_fault_demo_hazard/job.ini')
)
self.compulsory_arguments = dict(
lrem_steps_per_interval=5)
self.other_args = dict(
calculation_mode="classical",
region_constraint=(
'POLYGON((-122.0 38.113, -122.114 38.113, -122.57 38.111, '
'-122.0 38.113))'),
hazard_output=job.risk_calculation.hazard_output)
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('scenario_risk/job.ini'),
get_data_path('scenario_hazard/job.ini'),
output_type="gmf_scenario")
self.calculator = scenario.ScenarioRiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
self.job.is_running = True
self.job.save()
self.calculator.pre_execute()
self.job.status = 'executing'
self.job.save()
def test_export_for_scenario(self):
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path("scenario_hazard/job.ini")
# run the calculation in process to create something to export
os.environ["OQ_NO_DISTRIBUTE"] = "1"
try:
helpers.run_job(cfg)
finally:
del os.environ["OQ_NO_DISTRIBUTE"]
job = models.OqJob.objects.latest("id")
self.assertEqual(job.status, "complete")
outputs = export_core.get_outputs(job.id)
self.assertEqual(1, len(outputs)) # 1 GMF
gmf_outputs = outputs.filter(output_type="gmf_scenario")
self.assertEqual(1, len(gmf_outputs))
exported_file = check_export(gmf_outputs[0].id, target_dir)
# Check the file paths exist, is absolute, and the file isn't
# empty.
self._test_exported_file(exported_file)
# Check for the correct number of GMFs in the file:
tree = etree.parse(exported_file)
self.assertEqual(20, number_of("nrml:gmf", tree))
finally:
shutil.rmtree(target_dir)
def setUpClass(cls):
cfg = helpers.get_data_path('event_based_hazard/job.ini')
job = helpers.get_hazard_job(cfg)
rlz1 = models.LtRealization.objects.create(
hazard_calculation=job.hazard_calculation,
ordinal=1, seed=1, weight=None,
sm_lt_path="test_sm", gsim_lt_path="test_gsim")
rlz2 = models.LtRealization.objects.create(
hazard_calculation=job.hazard_calculation,
ordinal=2, seed=1, weight=None,
sm_lt_path="test_sm", gsim_lt_path="test_gsim_2")
ses_coll1 = models.SESCollection.objects.create(
output=models.Output.objects.create_output(
job, "Test SES Collection 1", "ses"),
lt_realization=rlz1)
ses_coll2 = models.SESCollection.objects.create(
output=models.Output.objects.create_output(
job, "Test SES Collection 2", "ses"),
lt_realization=rlz2)
gmf_data1 = helpers.create_gmf_data_records(job, rlz1, ses_coll1)[0]
points = [(15.3, 38.22), (15.7, 37.22),
(15.4, 38.09), (15.56, 38.1), (15.2, 38.2)]
gmf_data2 = helpers.create_gmf_data_records(
job, rlz2, ses_coll2, points)[0]
cls.gmf_coll1 = gmf_data1.gmf
cls.ruptures1 = tuple(get_tags(gmf_data1))
cls.ruptures2 = tuple(get_tags(gmf_data2))
cls.investigation_time = job.hazard_calculation.investigation_time
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path("classical_psha_based_risk/job.ini"), get_data_path("simple_fault_demo_hazard/job.ini")
)
calculator = base.RiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
calculator.pre_execute()
self.rc = self.job.risk_calculation
common_fake_args = dict(exposure_model=self.rc.exposure_model, taxonomy="test")
asset = models.ExposureData(site=Point(0.5, 0.5), asset_ref="test1", **common_fake_args)
asset.save()
asset = models.ExposureData(site=Point(179.1, 0), asset_ref="test2", **common_fake_args)
asset.save()
def get_hazard_job(self):
hazard_imls = [
0.001, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.55, 0.6, 0.7, 0.8, 0.9, 1.0
]
poes = [
0.039861266979, 0.039861266979, 0.0397287574803, 0.0296134266256,
0.0198273287565, 0.0130622701615, 0.00865538795, 0.00589852059369,
0.00406169858951, 0.00281172717953, 0.00199511741778,
0.00135870597285, 0.000989667841574, 0.000757544444296,
0.000272824002046, 0.0, 0.0, 0.
]
job = helpers.get_job(
helpers.get_data_path("simple_fault_demo_hazard/job.ini"),
intensity_measure_types_and_levels=str({'PGA': hazard_imls}))
models.HazardSite.objects.create(hazard_calculation=job,
location="POINT(1 1)")
models.HazardCurveData.objects.create(
hazard_curve=models.HazardCurve.objects.create(
output=models.Output.objects.create_output(
job, "Test Hazard curve", "hazard_curve"),
investigation_time=50,
imt="PGA",
imls=hazard_imls,
statistics="mean"),
poes=poes,
location="POINT(1 1)")
return job
def get_hazard_job(self):
job = helpers.get_hazard_job(
helpers.get_data_path("simple_fault_demo_hazard/job.ini"))
hazard_curve = [
(0.001, 0.0398612669790014),
(0.01, 0.039861266979001400), (0.05, 0.039728757480298900),
(0.10, 0.029613426625612500), (0.15, 0.019827328756491600),
(0.20, 0.013062270161451900), (0.25, 0.008655387950000430),
(0.30, 0.005898520593689670), (0.35, 0.004061698589511780),
(0.40, 0.002811727179526820), (0.45, 0.001995117417776690),
(0.50, 0.001358705972845710), (0.55, 0.000989667841573727),
(0.60, 0.000757544444296432), (0.70, 0.000272824002045979),
(0.80, 0.00), (0.9, 0.00), (1.0, 0.00)]
models.HazardCurveData.objects.create(
hazard_curve=models.HazardCurve.objects.create(
output=models.Output.objects.create_output(
job, "Test Hazard curve", "hazard_curve"),
investigation_time=50,
imt="PGA", imls=[hz[0] for hz in hazard_curve],
statistics="mean"),
poes=[hz[1] for hz in hazard_curve],
location="POINT(1 1)")
return job
def test_export_for_scenario(self):
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path('scenario_hazard/job.ini')
# run the calculation in process to create something to export
with mock.patch.dict(os.environ, {'OQ_NO_DISTRIBUTE': '1'}):
helpers.run_job(cfg)
job = models.OqJob.objects.latest('id')
self.assertEqual(job.status, 'complete')
outputs = core.get_outputs(job.id)
gmf_outputs = outputs.filter(ds_key='gmfs')
self.assertEqual(1, len(gmf_outputs))
exported_file = check_export(gmf_outputs[0].id, target_dir)
# Check the file paths exist, is absolute, and the file isn't
# empty.
self._test_exported_file(exported_file)
# Check for the correct number of GMFs in the file:
tree = etree.parse(exported_file)
self.assertEqual(20, number_of('nrml:gmf', tree))
finally:
shutil.rmtree(target_dir)
def test(self):
# check that if risk models are provided, then the ``points to
# compute`` and the imls are got from there
username = helpers.default_user()
job = engine.prepare_job(username)
cfg = helpers.get_data_path("classical_job-sd-imt.ini")
params = vars(readini.parse_config(open(cfg)))
del params["hazard_calculation_id"]
del params["hazard_output_id"]
haz_calc = engine.create_calculation(models.HazardCalculation, params)
haz_calc = models.HazardCalculation.objects.get(id=haz_calc.id)
job.hazard_calculation = haz_calc
job.is_running = True
job.save()
calc = get_calculator_class("hazard", job.hazard_calculation.calculation_mode)(job)
calc.parse_risk_models()
self.assertEqual(
[(1.0, -1.0), (0.0, 0.0)], [(point.latitude, point.longitude) for point in haz_calc.points_to_compute()]
)
self.assertEqual(["PGA"], haz_calc.get_imts())
self.assertEqual(3, haz_calc.oqjob.exposuremodel.exposuredata_set.count())
return job
def get_hazard_job(self):
job = helpers.get_job(
helpers.get_data_path("scenario_hazard/job.ini"),
number_of_ground_motion_fields=1000)
fname = self._test_path('gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
return job
def test_job_from_file(self):
# make a hazard job
haz_cfg = helpers.get_data_path('event_based_hazard/job.ini')
haz_job = engine.job_from_file(haz_cfg, 'test_user')
# make a fake Output
out = models.Output.objects.create(
oq_job=haz_job, display_name='fake', output_type='gmf')
# make a risk job
risk_cfg = helpers.get_data_path('event_based_risk/job.ini')
risk_job = engine.job_from_file(risk_cfg, 'test_user',
hazard_output_id=out.id)
# make sure the hazard job is associated correctly
oqjob = risk_job.risk_calculation.hazard_calculation
self.assertEqual(oqjob.id, haz_job.id)
def setUpClass(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
job = helpers.get_job(cfg)
output = models.Output.objects.create(
oq_job=job, display_name='test', output_type='ses')
ses_coll = models.SESCollection.create(
output=output)
self.mesh_lons = numpy.array(
[0.1 * x for x in range(16)]).reshape((4, 4))
self.mesh_lats = numpy.array(
[0.2 * x for x in range(16)]).reshape((4, 4))
self.mesh_depths = numpy.array(
[0.3 * x for x in range(16)]).reshape((4, 4))
sfs = SimpleFaultSurface(
Mesh(self.mesh_lons, self.mesh_lats, self.mesh_depths))
ps = PlanarSurface(
10, 20, 30,
Point(3.9, 2.2, 10), Point(4.90402718, 3.19634248, 10),
Point(5.9, 2.2, 90), Point(4.89746275, 1.20365263, 90))
self.fault_rupture = models.ProbabilisticRupture.objects.create(
ses_collection=ses_coll, magnitude=5, rake=0, surface=sfs,
is_from_fault_source=True, is_multi_surface=False)
self.source_rupture = models.ProbabilisticRupture.objects.create(
ses_collection=ses_coll, magnitude=5, rake=0, surface=ps,
is_from_fault_source=False, is_multi_surface=False)
def get_hazard_job(self):
job = helpers.get_job(
helpers.get_data_path("scenario_hazard/job.ini"))
fname = os.path.join(os.path.dirname(case_1.__file__),
'gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
return job
def test_export_for_scenario(self):
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path('scenario_hazard/job.ini')
# run the calculation in process to create something to export
with mock.patch.dict(os.environ, {'OQ_NO_DISTRIBUTE': '1'}):
helpers.run_job(cfg)
job = models.OqJob.objects.latest('id')
self.assertEqual(job.status, 'complete')
outputs = core.get_outputs(job.id)
self.assertEqual(2, len(outputs)) # 1 GMF, 1 SES
gmf_outputs = outputs.filter(output_type='gmf_scenario')
self.assertEqual(1, len(gmf_outputs))
exported_file = check_export(gmf_outputs[0].id, target_dir)
# Check the file paths exist, is absolute, and the file isn't
# empty.
self._test_exported_file(exported_file)
# Check for the correct number of GMFs in the file:
tree = etree.parse(exported_file)
self.assertEqual(20, number_of('nrml:gmf', tree))
finally:
shutil.rmtree(target_dir)
def test_validate_warns(self):
# Test that `validate` raises warnings if unnecessary parameters are
# specified for a given calculation.
# For example, `ses_per_logic_tree_path` is an event-based hazard
# param; if this param is specified for a classical hazard job, a
# warning should be raised.
cfg_file = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
job = engine.prepare_job()
params = engine.parse_config(open(cfg_file, 'r'))
# Add a few superfluous parameters:
params['ses_per_logic_tree_path'] = 5
params['ground_motion_correlation_model'] = 'JB2009'
calculation = engine.create_calculation(
models.HazardCalculation, params)
job.hazard_calculation = calculation
job.save()
with warnings.catch_warnings(record=True) as w:
validation.validate(job, 'hazard', params, ['xml'])
expected_warnings = [
"Unknown parameter '%s' for calculation mode 'classical'."
" Ignoring." % x for x in ('ses_per_logic_tree_path',
'ground_motion_correlation_model')
]
actual_warnings = [m.message.message for m in w]
self.assertEqual(sorted(expected_warnings), sorted(actual_warnings))
def test_store_site_model(self):
# Setup
site_model = helpers.get_data_path("site_model.xml")
exp_site_model = [
dict(lon=-122.5, lat=37.5, vs30=800.0, vs30_type="measured", z1pt0=100.0, z2pt5=5.0),
dict(lon=-122.6, lat=37.6, vs30=801.0, vs30_type="measured", z1pt0=101.0, z2pt5=5.1),
dict(lon=-122.7, lat=37.7, vs30=802.0, vs30_type="measured", z1pt0=102.0, z2pt5=5.2),
dict(lon=-122.8, lat=37.8, vs30=803.0, vs30_type="measured", z1pt0=103.0, z2pt5=5.3),
dict(lon=-122.9, lat=37.9, vs30=804.0, vs30_type="measured", z1pt0=104.0, z2pt5=5.4),
]
job = models.OqJob.objects.create(user_name="openquake")
ids = general.store_site_model(job, site_model)
actual_site_model = models.SiteModel.objects.filter(job=job).order_by("id")
for i, exp in enumerate(exp_site_model):
act = actual_site_model[i]
self.assertAlmostEqual(exp["lon"], act.location.x)
self.assertAlmostEqual(exp["lat"], act.location.y)
self.assertAlmostEqual(exp["vs30"], act.vs30)
self.assertEqual(exp["vs30_type"], act.vs30_type)
self.assertAlmostEqual(exp["z1pt0"], act.z1pt0)
self.assertAlmostEqual(exp["z2pt5"], act.z2pt5)
# last, check that the `store_site_model` function returns all of the
# newly-inserted records
for i, s in enumerate(ids):
self.assertEqual(s, actual_site_model[i].id)
def setUp(self):
self.cfg = helpers.get_data_path('event_based_hazard/job_2.ini')
self.job = helpers.get_job(self.cfg, username=getpass.getuser())
self.calc = core.EventBasedHazardCalculator(self.job)
hc = self.job.hazard_calculation
hc._site_collection = make_site_coll(0, 0, n=5)
models.JobStats.objects.create(oq_job=self.job)
def test_initialize_site_model(self):
# we need a slightly different config file for this test
cfg = helpers.get_data_path(
'simple_fault_demo_hazard/job_with_site_model.ini')
self.job = helpers.get_hazard_job(cfg)
self.calc = core.ClassicalHazardCalculator(self.job)
self.calc.initialize_site_model()
# If the site model isn't valid for the calculation geometry, a
# `RuntimeError` should be raised here
# Okay, it's all good. Now check the count of the site model records.
sm_nodes = models.SiteModel.objects.filter(job=self.job)
self.assertEqual(2601, len(sm_nodes))
num_pts_to_compute = len(
self.job.hazard_calculation.points_to_compute())
hazard_site = models.HazardSite.objects.filter(
hazard_calculation=self.job.hazard_calculation)
# The site model is good. Now test that `hazard_site` was computed.
# For now, just test the length.
self.assertEqual(num_pts_to_compute, len(hazard_site))
def get_hazard_job(self):
hazard_imls = [0.001, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4,
0.45, 0.5, 0.55, 0.6, 0.7, 0.8, 0.9, 1.0]
poes = [0.039861266979, 0.039861266979, 0.0397287574803,
0.0296134266256, 0.0198273287565, 0.0130622701615,
0.00865538795, 0.00589852059369, 0.00406169858951,
0.00281172717953, 0.00199511741778, 0.00135870597285,
0.000989667841574, 0.000757544444296, 0.000272824002046,
0.0, 0.0, 0.]
job = helpers.get_job(
helpers.get_data_path("simple_fault_demo_hazard/job.ini"),
intensity_measure_types_and_levels=str({'PGA': hazard_imls}))
models.HazardSite.objects.create(
hazard_calculation=job, location="POINT(1 1)")
models.HazardCurveData.objects.create(
hazard_curve=models.HazardCurve.objects.create(
output=models.Output.objects.create_output(
job, "Test Hazard curve", "hazard_curve"),
investigation_time=50,
imt="PGA", imls=hazard_imls,
statistics="mean"),
poes=poes,
location="POINT(1 1)")
return job
def setUpClass(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
job = helpers.get_job(cfg)
output = models.Output.objects.create(oq_job=job,
display_name='test',
output_type='ses')
ses_coll = models.SESCollection.create(output=output)
self.mesh_lons = numpy.array([0.1 * x for x in range(16)]).reshape(
(4, 4))
self.mesh_lats = numpy.array([0.2 * x for x in range(16)]).reshape(
(4, 4))
self.mesh_depths = numpy.array([0.3 * x for x in range(16)]).reshape(
(4, 4))
sfs = SimpleFaultSurface(
Mesh(self.mesh_lons, self.mesh_lats, self.mesh_depths))
ps = PlanarSurface(10, 20, 30, Point(3.9, 2.2, 10),
Point(4.90402718, 3.19634248, 10),
Point(5.9, 2.2, 90),
Point(4.89746275, 1.20365263, 90))
self.fault_rupture = models.ProbabilisticRupture.objects.create(
ses_collection=ses_coll,
magnitude=5,
rake=0,
surface=sfs,
is_from_fault_source=True,
is_multi_surface=False)
self.source_rupture = models.ProbabilisticRupture.objects.create(
ses_collection=ses_coll,
magnitude=5,
rake=0,
surface=ps,
is_from_fault_source=False,
is_multi_surface=False)
def setUpClass(cls):
cfg = helpers.get_data_path("calculators/hazard/classical/haz_map_test_job.ini")
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
hc = job.hazard_calculation
cls.calc = get_calculator_class("hazard", hc.calculation_mode)(job)
cls.calc.initialize_site_model()
assert len(hc.site_collection) == 2, len(hc.site_collection)
def setUp(self):
cfg = helpers.get_data_path("simple_fault_demo_hazard/job.ini")
self.job = helpers.get_job(cfg, username="******")
for i in range(0, random.randint(1, 10)):
lt_model = models.LtSourceModel.objects.create(
hazard_calculation=self.job.hazard_calculation, ordinal=i, sm_lt_path=[i]
)
models.LtRealization(lt_model=lt_model, ordinal=i, weight=1 / (i + 1), gsim_lt_path=[i]).save()
def setUpClass(cls):
cfg = helpers.get_data_path(
'calculators/hazard/classical/haz_map_test_job.ini')
job = helpers.get_hazard_job(cfg)
hc = job.hazard_calculation
cls.calc = get_calculator_class('hazard', hc.calculation_mode)(job)
cls.calc.initialize_site_model()
assert len(hc.site_collection) == 2, len(hc.site_collection)
def setUp(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
self.job = helpers.get_hazard_job(cfg, username="******")
for i in range(0, random.randint(1, 10)):
models.LtRealization(
hazard_calculation=self.job.hazard_calculation,
ordinal=i, seed=None, weight=1 / (i + 1), sm_lt_path=[i],
gsim_lt_path=[i]).save()
def setUpClass(cls):
cfg = helpers.get_data_path(
'calculators/hazard/classical/haz_map_test_job.ini')
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
cls.calc = calculators(job)
cls.calc.initialize_site_collection()
num_sites = len(cls.calc.site_collection)
assert num_sites == 2, num_sites
def get_hazard_job(self):
job = helpers.get_job(
helpers.get_data_path("event_based_hazard/job.ini"),
region_grid_spacing='0', ses_per_logic_tree_path='1')
job.save()
helpers.create_gmf_from_csv(job, self._test_path('gmf.csv'))
return job
def setUpClass(cls):
cfg = helpers.get_data_path(
'calculators/hazard/classical/haz_map_test_job.ini')
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
cls.calc = calculators(job)
cls.calc.initialize_site_collection()
num_sites = len(cls.calc.site_collection)
assert num_sites == 2, num_sites
def test(self):
cfg = helpers.get_data_path('classical_job.ini')
with mock.patch.dict(os.environ, {'OQ_NO_DISTRIBUTE': '1'}), \
mock.patch('openquake.engine.logs.LOG.warn') as warn:
# using a small maximum distance of 1 km, so that no sources
# are found, and checking that no realizations are generated
helpers.run_job(cfg, maximum_distance=1)
self.assertEqual(warn.call_args[0][0],
'No realizations for hazard_calculation_id=%d')
def get_hazard_job(self):
job = helpers.get_job(
helpers.get_data_path("scenario_hazard/job.ini"))
fname = self._test_path('gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
# this is needed to make happy the GetterBuilder
job.hazard_calculation.number_of_ground_motion_fields = 1000
job.hazard_calculation.save()
return job
def test_check_limits_event_based(self):
# this is a based on a demo with 2 realizations, 5 ses,
# 2 imt and 121 sites
cfg = helpers.get_data_path('event_based_hazard/job.ini')
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
calc = calculators(job)
input_weight, output_weight = calc.pre_execute()
self.assertEqual(input_weight, 2705.5)
self.assertAlmostEqual(output_weight, 1210.0)
def get_hazard_job(self):
job = helpers.get_job(
helpers.get_data_path("event_based_hazard/job.ini"),
region_grid_spacing='0',
ses_per_logic_tree_path='1')
job.save()
helpers.create_gmf_from_csv(job, self._test_path('gmf.csv'))
return job
def setUp(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
self.job = helpers.get_job(cfg, username="******")
for i in range(0, random.randint(1, 10)):
lt_model = models.LtSourceModel.objects.create(
hazard_calculation=self.job, ordinal=i, sm_lt_path=[i])
models.LtRealization(lt_model=lt_model,
ordinal=i,
weight=1 / (i + 1),
gsim_lt_path=[i]).save()
def test(self):
cfg = helpers.get_data_path('bad_gsim/job.ini')
job = helpers.get_job(cfg, username=getpass.getuser())
calc = core.EventBasedHazardCalculator(job)
with self.assertRaises(ValueError) as ctxt:
calc.initialize_site_collection()
calc.initialize_sources()
errmsg = str(ctxt.exception)
assert errmsg.startswith(
"Found in 'source_model.xml' a tectonic region type "
"'Active Shallow Crust' inconsistent with the ones"), errmsg
def setUp(self):
self.job, _ = helpers.get_fake_risk_job(
get_data_path('classical_psha_based_risk/job.ini'),
get_data_path('simple_fault_demo_hazard/job.ini'))
calculator = base.RiskCalculator(self.job)
models.JobStats.objects.create(oq_job=self.job)
calculator.pre_execute()
common_fake_args = dict(exposure_model=self.job.exposure_model,
taxonomy="test")
asset = models.ExposureData(site=Point(0.5, 0.5),
asset_ref="test1",
**common_fake_args)
asset.save()
asset = models.ExposureData(site=Point(179.1, 0),
asset_ref="test2",
**common_fake_args)
asset.save()
def test(self):
cfg = helpers.get_data_path('event_based_hazard/job.ini')
job_id, oq = actions.job_from_file(cfg, 'test_user')
with tempfile.NamedTemporaryFile() as temp:
with self.assertRaises(ZeroDivisionError), mock.patch(
'openquake.engine.engine._do_run_calc',
lambda *args: 1 / 0):
engine.run_calc(job_id, oq, 'info', temp.name, exports=[])
logged = open(temp.name).read()
# make sure the real error has been logged
self.assertIn('integer division or modulo by zero', logged)
def test(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
with mock.patch('openquake.engine.logs.LOG.warn') as warn:
helpers.run_job(cfg,
number_of_logic_tree_samples=1,
quantile_hazard_curves='0.1 0.2',
hazard_maps='',
uniform_hazard_spectra='')
msg = warn.call_args[0][0]
self.assertEqual(
msg, 'There is only one realization, the configuration'
' parameter quantile_hazard_curves should not be set')
def test(self):
# The bug can be reproduced with any hazard calculation profile which
# the following parameters set:
#
# * number_of_logic_tree_samples = 1
# * mean_hazard_curves = false
# * quantile_hazard_curves =
# * poes = at least one PoE
cfg = helpers.get_data_path(
'calculators/hazard/classical/haz_map_1rlz_no_stats.ini')
job = helpers.run_job(cfg).job
self.assertEqual(job.status, 'complete')
def test_job_from_file(self):
# make a hazard job
haz_cfg = helpers.get_data_path('event_based_hazard/job.ini')
haz_job = engine.job_from_file(haz_cfg, 'test_user')
# make a fake Output
out = models.Output.objects.create(oq_job=haz_job,
display_name='fake',
output_type='gmf')
# make a risk job
risk_cfg = helpers.get_data_path('event_based_risk/job.ini')
with mock.patch.object(logs.LOG, 'warn') as warn:
risk_job = engine.job_from_file(risk_cfg,
'another_user',
hazard_output_id=out.id)
# make sure a warning is printed because you are using a hazard
# generated by a different user
self.assertEqual(
warn.call_args[0],
('You are using a hazard calculation ran by %s', 'test_user'))
with mock.patch.object(logs.LOG, 'warn') as warn:
risk_job = engine.job_from_file(risk_cfg,
'test_user',
hazard_output_id=out.id,
quantile_loss_curves='0.1 0.2')
# make sure a warning is printed because you are using
# quantile_loss_curves with a single hazard output
self.assertEqual(
warn.call_args[0][0],
'quantile_loss_curves is on, but you passed a single hazard '
'output: the statistics will not be computed')
# make sure the hazard job is associated correctly
self.assertEqual(risk_job.hazard_calculation.id, haz_job.id)
def test(self):
# check that if risk models are provided, then the sites
# and the imls are got from there
cfg = helpers.get_data_path('classical_job-sd-imt.ini')
job = engine.job_from_file(cfg, helpers.default_user())
job.is_running = True
job.save()
calc = calculators(job)
calc.parse_risk_model()
self.assertEqual(['PGA'], list(calc.oqparam.imtls))
self.assertEqual(3, calc.job.exposuremodel.exposuredata_set.count())
return job
def test_check_limits_classical(self):
# this is a based on a demo with 3 realizations, 2 sites and 4 rlzs
cfg = helpers.get_data_path(
'calculators/hazard/classical/haz_map_test_job.ini')
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
calc = calculators(job)
input_weight, output_weight = calc.pre_execute()
self.assertEqual(input_weight, 225)
self.assertEqual(output_weight, 24)
calc.max_input_weight = 1
with self.assertRaises(general.InputWeightLimit):
calc.check_limits(input_weight, output_weight)
calc.max_input_weight = 1000
calc.max_output_weight = 1
with self.assertRaises(general.OutputWeightLimit):
calc.check_limits(input_weight, output_weight)
def test_disagg_hazard_export(self):
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path('disaggregation/job.ini')
# run the calculation in process to create something to export
os.environ['OQ_NO_DISTRIBUTE'] = '1'
try:
helpers.run_job(cfg)
finally:
del os.environ['OQ_NO_DISTRIBUTE']
job = models.OqJob.objects.latest('id')
self.assertEqual(job.status, 'complete')
outputs = core.get_outputs(job.id)
# Test curve export:
curves = outputs.filter(output_type='hazard_curve')
self.assertEqual(4, len(curves))
curve_files = []
for curve in curves:
curve_files.append(check_export(curve.id, target_dir))
self.assertEqual(4, len(curve_files))
for f in curve_files:
self._test_exported_file(f)
# Test disagg matrix export:
matrices = outputs.filter(output_type='disagg_matrix')
self.assertEqual(8, len(matrices))
disagg_files = []
for matrix in matrices:
disagg_files.append(check_export(matrix.id, target_dir))
self.assertEqual(8, len(disagg_files))
for f in disagg_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
def test_site_collection_and_ses_collection(self):
cfg = helpers.get_data_path('scenario_hazard/job.ini')
job = helpers.get_job(cfg, username=getpass.getuser())
models.JobStats.objects.create(oq_job=job)
calc = scen_core.ScenarioHazardCalculator(job)
calc.initialize_site_collection()
site_coll = calc.site_collection
# all of the parameters should be the same:
self.assertTrue((site_coll.vs30 == 760).all())
self.assertTrue((site_coll.vs30measured).all())
self.assertTrue((site_coll.z1pt0 == 100).all())
self.assertTrue((site_coll.z2pt5 == 5).all())
# test SESCollection
calc.create_ruptures()
ses_coll = models.SESCollection.objects.get(output__oq_job=job,
output__output_type='ses')
expected_tags = [
'scenario-0000000000',
'scenario-0000000001',
'scenario-0000000002',
'scenario-0000000003',
'scenario-0000000004',
'scenario-0000000005',
'scenario-0000000006',
'scenario-0000000007',
'scenario-0000000008',
'scenario-0000000009',
]
expected_seeds = [
511025145, 1168723362, 794472670, 1296908407, 1343724121,
140722153, 28278046, 1798451159, 556958504, 503221907
]
for ses in ses_coll: # there is a single ses
self.assertEqual(ses.ordinal, 1)
for ses_rup, tag, seed in zip(ses, expected_tags, expected_seeds):
self.assertEqual(ses_rup.ses_id, 1)
self.assertEqual(ses_rup.tag, tag)
self.assertEqual(ses_rup.seed, seed)
def test_get_site_collection_with_site_model(self):
cfg = helpers.get_data_path(
'simple_fault_demo_hazard/job_with_site_model.ini')
job = helpers.get_job(cfg)
models.JobStats.objects.create(oq_job=job)
calc = cls_core.ClassicalHazardCalculator(job)
# Bootstrap the `hazard_site` table:
calc.initialize_site_collection()
calc.initialize_sources()
site_coll = calc.site_collection
# Since we're using a pretty big site model, it's a bit excessive to
# check each and every value.
# Instead, we'll just test that the lenth of each site collection attr
# is equal to the number of points of interest in the calculation.
expected_len = len(site_coll)
self.assertEqual(expected_len, len(site_coll.vs30))
self.assertEqual(expected_len, len(site_coll.vs30measured))
self.assertEqual(expected_len, len(site_coll.z1pt0))
self.assertEqual(expected_len, len(site_coll.z2pt5))
def get_hazard_job(self):
job = helpers.get_job(helpers.get_data_path("scenario_hazard/job.ini"))
fname = self._test_path('gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
return job
def test_classical_risk_export(self):
target_dir = tempfile.mkdtemp()
try:
haz_cfg = helpers.get_data_path(
'end-to-end-hazard-risk/job_haz_classical.ini')
risk_cfg = helpers.get_data_path(
'end-to-end-hazard-risk/job_risk_classical.ini')
haz_job = helpers.run_job(haz_cfg).job
# Run the risk on all outputs produced by the haz calc:
risk_job = helpers.run_job(risk_cfg,
hazard_calculation_id=haz_job.id).job
risk_outputs = models.Output.objects.filter(oq_job=risk_job)
loss_curve_outputs = risk_outputs.filter(output_type='loss_curve')
loss_map_outputs = risk_outputs.filter(output_type='loss_map')
# 16 logic tree realizations + 1 mean + 2 quantiles = 19
# + 19 insured loss curves
self.assertEqual(38, loss_curve_outputs.count())
# make sure the mean and quantile curve sets got created correctly
loss_curves = models.LossCurve.objects.filter(
output__oq_job=risk_job, insured=False)
# sanity check
self.assertEqual(19, loss_curves.count())
insured_curves = models.LossCurve.objects.filter(
output__oq_job=risk_job, insured=True)
# sanity check
self.assertEqual(19, insured_curves.count())
# mean
self.assertEqual(1, loss_curves.filter(statistics='mean').count())
# quantiles
self.assertEqual(2,
loss_curves.filter(statistics='quantile').count())
# mean
self.assertEqual(1,
insured_curves.filter(statistics='mean').count())
# quantiles
self.assertEqual(
2,
insured_curves.filter(statistics='quantile').count())
# 16 logic tree realizations = 16 loss map + 1 mean loss
# map + 2 quantile loss map
self.assertEqual(19, loss_map_outputs.count())
# 19 loss fractions
loss_fraction_outputs = risk_outputs.filter(
output_type="loss_fraction")
self.assertEqual(19, loss_fraction_outputs.count())
# Now try to export everything, just to do a "smoketest" of the
# exporter code:
loss_curve_files = []
for o in loss_curve_outputs:
loss_curve_files.append(core.export(o.id, target_dir, 'xml'))
loss_map_files = []
for o in loss_map_outputs:
loss_map_files.append(core.export(o.id, target_dir, 'xml'))
self.assertEqual(38, len(loss_curve_files))
self.assertEqual(19, len(loss_map_files))
for f in loss_curve_files:
self._test_exported_file(f)
for f in loss_map_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
def _setup_a_new_calculator(self):
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
job = helpers.get_job(cfg, username=getpass.getuser())
calc = core.ClassicalHazardCalculator(job)
return job, calc
def test_export_for_event_based(self):
# Run an event-based hazard calculation to compute SESs and GMFs
# Call the exporters for both SES and GMF results and verify that
# files were created
# Since the XML writers (in `openquake.commonlib`) are concerned
# with correctly generating the XML, we don't test that here
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path('event_based_hazard/job.ini')
# run the calculation in process to create something to export
with mock.patch.dict(os.environ, {'OQ_NO_DISTRIBUTE': '1'}):
job = helpers.run_job(cfg,
maximum_distance=1,
ses_per_logic_tree_path=1,
number_of_logic_tree_samples=1).job
self.assertEqual(job.status, 'complete')
# 1 SES + 1 GMF + 1 hazard_curve_multi + 2 hazard_curve +
# 4 hazard maps (with poes 0.1, 0.2 and IMT PGA, SA(0.1))
outputs = core.get_outputs(job.id)
self.assertEqual(9, len(outputs))
# SESs
ses_outputs = outputs.filter(output_type='ses')
self.assertEqual(1, len(ses_outputs))
exported_files = []
for ses_output in ses_outputs:
out_file = check_export(ses_output.id, target_dir)
exported_files.append(out_file)
self.assertEqual(1, len(exported_files))
for f in exported_files:
self._test_exported_file(f)
# GMFs
gmf_outputs = outputs.filter(output_type='gmf')
self.assertEqual(1, len(gmf_outputs))
exported_files = []
for gmf_output in gmf_outputs:
out_file = check_export(gmf_output.id, target_dir)
exported_files.append(out_file)
self.assertEqual(1, len(exported_files))
# Check the file paths exist, are absolute, and the files aren't
# empty.
for f in exported_files:
self._test_exported_file(f)
# check the exact values of the GMFs
[gmfset1] = gmf_outputs[0].gmf
self.check_file_content('expected_gmfset_1.txt', str(gmfset1))
# Hazard curves
haz_curves = outputs.filter(output_type='hazard_curve')
self.assertEqual(2, haz_curves.count())
for curve in haz_curves:
exported_file = check_export(curve.id, target_dir)
self._test_exported_file(exported_file)
# Hazard maps
haz_maps = outputs.filter(output_type='hazard_map')
self.assertEqual(4, haz_maps.count())
for hmap in haz_maps:
exported_file = check_export(hmap.id, target_dir)
self._test_exported_file(exported_file)
finally:
shutil.rmtree(target_dir)
def test_classical_hazard_export(self):
# Run a hazard calculation to compute some curves and maps
# Call the exporter and verify that files were created
# Since the hazard curve XML writer is concerned with correctly
# generating XML, we won't test that here.
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.get_data_path('simple_fault_demo_hazard/job.ini')
# run the calculation to create something to export
helpers.run_job(cfg)
job = models.OqJob.objects.latest('id')
self.assertEqual(job.status, 'complete')
outputs = core.get_outputs(job.id)
# 10 hazard curves, 20 maps, 10 uhs, 5 multi curves
expected_outputs = 45
self.assertEqual(expected_outputs, outputs.count())
# Number of curves:
# (2 imts * 2 realizations)
# + (2 imts * (1 mean + 2 quantiles)
# = 10
curves = outputs.filter(output_type='hazard_curve')
self.assertEqual(10, curves.count())
# Number of multi-curves
# (2 realizations + 1 mean + 2 quantiles)
multi_curves = outputs.filter(output_type="hazard_curve_multi")
self.assertEqual(5, multi_curves.count())
# Number of maps:
# (2 poes * 2 imts * 2 realizations)
# + (2 poes * 2 imts * (1 mean + 2 quantiles))
# = 20
# Number of UHS:
maps = outputs.filter(output_type='hazard_map')
self.assertEqual(20, maps.count())
# Number of UHS:
# (20 maps_PGA_SA / 2 poes)
# = 10
uhs = outputs.filter(output_type='uh_spectra')
self.assertEqual(10, uhs.count())
# Test hazard curve export:
hc_files = []
for curve in curves:
hc_files.append(check_export(curve.id, target_dir))
self.assertEqual(10, len(hc_files))
# Test multi hazard curve export:
hc_files = []
for curve in multi_curves:
hc_files.append(check_export(curve.id, target_dir))
self.assertEqual(5, len(hc_files))
for f in hc_files:
self._test_exported_file(f)
# Test hazard map export:
hm_files = []
for haz_map in maps:
hm_files.append(check_export(haz_map.id, target_dir))
self.assertEqual(20, len(hm_files))
for f in hm_files:
self._test_exported_file(f)
# Test UHS export:
uhs_files = []
for u in uhs:
uhs_files.append(check_export(u.id, target_dir))
for f in uhs_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
def test(self):
cfg = helpers.get_data_path('classical_job.ini')
with mock.patch.dict(os.environ, {'OQ_NO_DISTRIBUTE': '1'}):
with self.assertRaises(RuntimeError):
helpers.run_job(cfg, maximum_distance=1)
def get_hazard_job(self):
job = helpers.get_job(helpers.get_data_path("scenario_hazard/job.ini"),
number_of_ground_motion_fields=1000)
fname = self._test_path('gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
return job
def test_event_based_risk_export(self):
target_dir = tempfile.mkdtemp()
try:
haz_cfg = helpers.get_data_path(
'end-to-end-hazard-risk/job_haz_event_based.ini')
risk_cfg = helpers.get_data_path(
'end-to-end-hazard-risk/job_risk_event_based.ini')
haz_job = helpers.run_job(haz_cfg).job
# Run the risk on all outputs produced by the haz calc:
risk_job = helpers.run_job(risk_cfg,
hazard_calculation_id=haz_job.id).job
risk_outputs = models.Output.objects.filter(oq_job=risk_job)
agg_loss_curve_outputs = risk_outputs.filter(
output_type='agg_loss_curve')
loss_curve_outputs = risk_outputs.filter(output_type='loss_curve')
loss_map_outputs = risk_outputs.filter(output_type='loss_map')
# (1 mean + 2 quantiles) * 2 (as there also insured curves)
self.assertEqual(6, loss_curve_outputs.count())
# 16 rlzs + 16 (due to insured curves)
event_loss_curve_outputs = risk_outputs.filter(
output_type='event_loss_curve')
self.assertEqual(32, event_loss_curve_outputs.count())
self.assertEqual(16, agg_loss_curve_outputs.count())
# make sure the mean and quantile curve sets got created correctly
loss_curves = models.LossCurve.objects.filter(
output__oq_job=risk_job)
# sanity check (16 aggregate loss curve + 38 loss curves)
self.assertEqual(54, loss_curves.count())
# mean
self.assertEqual(2, loss_curves.filter(statistics='mean').count())
# quantiles
self.assertEqual(4,
loss_curves.filter(statistics='quantile').count())
# 16 logic tree realizations = 16 loss map + 1 mean loss
# map + 2 quantile loss map
self.assertEqual(19, loss_map_outputs.count())
# 16 event loss table (1 per rlz)
event_loss_tables = risk_outputs.filter(output_type="event_loss")
self.assertEqual(16, event_loss_tables.count())
# 32 loss fractions
loss_fraction_outputs = risk_outputs.filter(
output_type="loss_fraction")
self.assertEqual(32, loss_fraction_outputs.count())
# Now try to export everything, just to do a "smoketest" of the
# exporter code:
loss_curve_files = []
for o in loss_curve_outputs:
loss_curve_files.append(core.export(o.id, target_dir, 'xml'))
for o in loss_fraction_outputs:
loss_curve_files.append(core.export(o.id, target_dir, 'xml'))
for o in event_loss_curve_outputs:
loss_curve_files.append(core.export(o.id, target_dir, 'xml'))
agg_loss_curve_files = []
for o in agg_loss_curve_outputs:
agg_loss_curve_files.append(
core.export(o.id, target_dir, 'xml'))
event_loss_table_files = []
for o in event_loss_tables:
event_loss_table_files.append(
core.export(o.id, target_dir, 'csv'))
loss_map_files = []
for o in loss_map_outputs:
loss_map_files.append(core.export(o.id, target_dir, 'xml'))
self.assertEqual(70, len(loss_curve_files))
self.assertEqual(16, len(agg_loss_curve_files))
self.assertEqual(16, len(event_loss_table_files))
self.assertEqual(19, len(loss_map_files))
for f in loss_curve_files:
self._test_exported_file(f)
for f in loss_map_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
def get_hazard_job(self):
job = helpers.get_job(helpers.get_data_path("scenario_hazard/job.ini"))
fname = os.path.join(os.path.dirname(case_1.__file__),
'gmf_scenario.csv')
helpers.create_gmf_from_csv(job, fname, 'gmf_scenario')
return job
def setUpClass(cls):
cls.hazard_cfg = helpers.get_data_path(
'simple_fault_demo_hazard/job.ini')
cls.risk_cfg = helpers.get_data_path(
'classical_psha_based_risk/job.ini')
def _setup_a_new_calculator(self):
cfg = helpers.get_data_path('disaggregation/job.ini')
job = helpers.get_job(cfg, username=getpass.getuser())
calc = core.DisaggHazardCalculator(job)
return job, calc