def HAZARD_MAP_QUANTILE_DATA():
return [
(Site(-121.7, 37.6),
{'IML': 1.9266716959669603,
'IMT': 'PGA',
'investigationTimeSpan': '50.0',
'poE': 0.01,
'statistics': 'quantile',
'quantileValue': 0.2,
'vs30': 760.0}),
(Site(-121.8, 38.0),
{'IML': 1.9352164637194078,
'IMT': 'PGA',
'investigationTimeSpan': '50.0',
'poE': 0.01,
'statistics': 'quantile',
'quantileValue': 0.2,
'vs30': 760.0}),
(Site(-122.1, 37.8),
{'IML': 1.9459475420737888,
'IMT': 'PGA',
'investigationTimeSpan': '50.0',
'poE': 0.01,
'statistics': 'quantile',
'quantileValue': 0.2,
'vs30': 760.0}),
(Site(-121.9, 37.7),
{'IML': 1.9566716959669603,
'IMT': 'PGA',
'investigationTimeSpan': '50.0',
'poE': 0.01,
'statistics': 'quantile',
'quantileValue': 0.2,
'vs30': 760.0})]
def GMF_DATA():
return {
Site(-117, 40): {'groundMotion': 0.0},
Site(-116, 40): {'groundMotion': 0.1},
Site(-116, 41): {'groundMotion': 0.2},
Site(-117, 41): {'groundMotion': 0.3},
}
def test_compute_uhs_task_pi_failure_counter(self):
# Same as the previous test, except that we want to make sure task
# failure counters are properly incremented if a task fails.
cmpt_uhs = '%s.%s' % (self.UHS_CORE_MODULE, 'compute_uhs')
with helpers.patch(cmpt_uhs) as compute_mock:
# We want to force a failure to occur in the task:
compute_mock.side_effect = RuntimeError('Mock exception')
get_counter = lambda: stats.pk_get(self.job_id, "nhzrd_failed")
# The counter should start out empty:
self.assertEqual(0, get_counter())
self.assertRaises(RuntimeError,
compute_uhs_task,
self.job_id,
0,
site=Site(0.0, 0.0))
self.assertEqual(1, get_counter())
# Create two more failures:
self.assertRaises(RuntimeError,
compute_uhs_task,
self.job_id,
0,
site=Site(0.0, 0.0))
self.assertRaises(RuntimeError,
compute_uhs_task,
self.job_id,
0,
site=Site(0.0, 0.0))
self.assertEqual(3, get_counter())
def test_serialize_scenario(self):
"""
All the records for scenario loss maps are inserted correctly.
"""
output = self.writer.output
# Call the function under test.
data = SAMPLE_SCENARIO_LOSS_MAP_DATA
self.writer.serialize(data)
# Output record
self.assertEqual(1, len(self.job.output_set.all()))
output = self.job.output_set.get()
self.assertTrue(output.db_backed)
self.assertTrue(output.path is None)
self.assertEqual(self.display_name, output.display_name)
self.assertEqual("loss_map", output.output_type)
# LossMap record
self.assertEqual(1, len(output.lossmap_set.all()))
metadata = output.lossmap_set.get()
self.assertEqual(SCENARIO_LOSS_MAP_METADATA['scenario'],
metadata.scenario)
self.assertEqual(SCENARIO_LOSS_MAP_METADATA['endBranchLabel'],
metadata.end_branch_label)
self.assertEqual(SCENARIO_LOSS_MAP_METADATA['lossCategory'],
metadata.category)
self.assertEqual(SCENARIO_LOSS_MAP_METADATA['unit'], metadata.unit)
self.assertEqual(None, metadata.poe)
# LossMapData records
self.assertEqual(3, len(metadata.lossmapdata_set.all()))
[data_a, data_b, data_c] = sorted(metadata.lossmapdata_set.all(),
key=lambda d: d.id)
self.assertEqual(SITE_A, Site(*data_a.location.coords))
self.assertEqual(SAMPLE_SCENARIO_LOSS_MAP_DATA[1][1][0][1]['assetID'],
data_a.asset_ref)
# self.assertEqual(self.asset_a_1.asset_ref, data_a.asset_ref)
self.assertEqual(SITE_A_SCENARIO_LOSS_ONE['mean_loss'], data_a.value)
self.assertEqual(SITE_A_SCENARIO_LOSS_ONE['stddev_loss'],
data_a.std_dev)
self.assertEqual(SITE_A, Site(*data_b.location.coords))
self.assertEqual(SAMPLE_SCENARIO_LOSS_MAP_DATA[1][1][1][1]['assetID'],
data_b.asset_ref)
self.assertEqual(SITE_A_SCENARIO_LOSS_TWO['mean_loss'], data_b.value)
self.assertEqual(SITE_A_SCENARIO_LOSS_TWO['stddev_loss'],
data_b.std_dev)
self.assertEqual(SITE_B, Site(*data_c.location.coords))
self.assertEqual(SAMPLE_SCENARIO_LOSS_MAP_DATA[2][1][0][1]['assetID'],
data_c.asset_ref)
self.assertEqual(SITE_B_SCENARIO_LOSS_ONE['mean_loss'], data_c.value)
self.assertEqual(SITE_B_SCENARIO_LOSS_ONE['stddev_loss'],
data_c.std_dev)
def setUp(self):
path = os.path.join(helpers.SCHEMA_EXAMPLES_DIR, "LCB-exposure.yaml")
inputs = [("exposure", path)]
self.job = self.setup_classic_job(inputs=inputs)
[input] = models.inputs4job(self.job.id,
input_type="exposure",
path=path)
owner = models.OqUser.objects.get(user_name="openquake")
emdl = input.model()
if not emdl:
emdl = models.ExposureModel(owner=owner,
input=input,
description="LCB exposure model",
category="LCB cars",
stco_unit="peanuts",
stco_type="aggregated")
emdl.save()
asset_data = [
(Site(-118.077721, 33.852034), {
u'stco': 5.07,
u'asset_ref': u'a5625',
u'taxonomy': u'HAZUS_RM1L_LC'
}),
(Site(-118.077721, 33.852034), {
u'stco': 5.63,
u'asset_ref': u'a5629',
u'taxonomy': u'HAZUS_URML_LC'
}),
(Site(-118.077721, 33.852034), {
u'stco': 11.26,
u'asset_ref': u'a5630',
u'taxonomy': u'HAZUS_URML_LS'
}),
(Site(-118.077721, 33.852034), {
u'stco': 5.5,
u'asset_ref': u'a5636',
u'taxonomy': u'HAZUS_C3L_MC'
}),
]
for idx, (site, adata) in enumerate(asset_data):
location = GEOSGeometry(site.point.to_wkt())
asset = models.ExposureData(exposure_model=emdl,
site=location,
**adata)
asset.save()
RISK_LOSS_CURVE_DATA[idx][1][1] = asset
output_path = self.generate_output_path(self.job)
self.display_name = os.path.basename(output_path)
self.writer = LossCurveDBWriter(output_path, self.job.id)
self.reader = LossCurveDBReader()
def test_site_keys(self):
"""Verify _sites_to_gmf_keys"""
params = {
'REGION_VERTEX': '40,-117, 42,-117, 42,-116, 40,-116',
'REGION_GRID_SPACING': '1.0'
}
the_job = helpers.create_job(params, job_id=self.job.id)
calculator = EventBasedRiskCalculator(the_job)
keys = calculator._sites_to_gmf_keys([Site(-117, 40), Site(-116, 42)])
self.assertEqual(["0!0", "2!1"], keys)
def test_read_curve(self):
"""Verify _get_db_curve."""
the_job = helpers.create_job({}, job_id=self.job.id)
calculator = ClassicalRiskCalculator(the_job)
curve1 = calculator._get_db_curve(Site(-122.2, 37.5))
self.assertEqual(list(curve1.abscissae),
[0.005, 0.007, 0.0098, 0.0137])
self.assertEqual(list(curve1.ordinates), [0.354, 0.114, 0.023, 0.002])
curve2 = calculator._get_db_curve(Site(-122.1, 37.5))
self.assertEqual(list(curve2.abscissae),
[0.005, 0.007, 0.0098, 0.0137])
self.assertEqual(list(curve2.ordinates), [0.454, 0.214, 0.123, 0.102])
def test_read_gmfs(self):
"""Verify _get_db_gmfs."""
params = {
'REGION_VERTEX': '40,-117, 42,-117, 42,-116, 40,-116',
'REGION_GRID_SPACING': '1.0'
}
the_job = helpers.create_job(params, job_id=self.job.id)
calculator = EventBasedRiskCalculator(the_job)
self.assertEqual(3, len(calculator._gmf_db_list(self.job.id)))
# only the keys in gmfs are used
gmfs = calculator._get_db_gmfs([], self.job.id)
self.assertEqual({}, gmfs)
# only the keys in gmfs are used
sites = [
Site(lon, lat) for lon in xrange(-117, -115)
for lat in xrange(40, 43)
]
gmfs = calculator._get_db_gmfs(sites, self.job.id)
# avoid rounding errors
for k, v in gmfs.items():
gmfs[k] = [round(i, 1) for i in v]
self.assertEqual(
{
'0!0': [0.1, 0.5, 0.0],
'0!1': [0.2, 0.6, 0.0],
'1!0': [0.4, 0.8, 1.3],
'1!1': [0.3, 0.7, 1.2],
'2!0': [0.0, 0.0, 1.0],
'2!1': [0.0, 0.0, 1.1],
}, gmfs)
def test_uhs(self):
# Kick off the engine and run the UHS demo job.
# When that's done, query the database and check the UHS results.
exp_results = self._load_expected_results()
exp_site = Site(0.0, 0.0) # This calculation operates on a single site
run_job(self.UHS_DEMO_CONFIG)
job = OqJob.objects.latest('id')
uh_spectra = UhSpectra.objects.get(output__oq_job=job.id)
self.assertEqual(1, uh_spectra.realizations)
for poe, data in exp_results.items():
uh_spectrum = UhSpectrum.objects.get(poe=poe,
uh_spectra=uh_spectra.id)
uh_spectrum_data = UhSpectrumData.objects.get(
uh_spectrum=uh_spectrum.id)
self.assertTrue(
numpy.allclose(data['sa_values'], uh_spectrum_data.sa_values))
self.assertTrue(numpy.allclose(data['periods'],
uh_spectra.periods))
self.assertEqual(0, uh_spectrum_data.realization)
self.assertEqual(exp_site.point.to_wkt(),
uh_spectrum_data.location.wkt)
def setUp(self):
inputs = [("fragility", ""), ("exposure", "")]
self.job = self.setup_classic_job(inputs=inputs)
kvs.mark_job_as_current(self.job.id)
kvs.cache_gc(self.job.id)
self.site = Site(1.0, 1.0)
block = Block(self.job.id, BLOCK_ID, [self.site])
block.to_kvs()
# this region contains a single site, that is exactly
# a site with longitude == 1.0 and latitude == 1.0
params = {"REGION_VERTEX": "1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0",
"REGION_GRID_SPACING": "0.5", "BASE_PATH": ".",
"OUTPUT_DIR": "."}
self.job_ctxt = JobContext(params, self.job.id, oq_job=self.job)
self.em = self._store_em()
self._store_gmvs([0.40, 0.30, 0.45, 0.35, 0.40])
self.calculator = ScenarioDamageRiskCalculator(self.job_ctxt)
# just stubbing out some preprocessing stuff...
ScenarioDamageRiskCalculator.store_exposure_assets = lambda self: None
ScenarioDamageRiskCalculator.store_fragility_model = lambda self: None
ScenarioDamageRiskCalculator.partition = lambda self: None
def test_get_gmvs_at(self):
params = {
"REGION_VERTEX": "40,-117.5, 42,-117.5, 42,-116, 40,-116",
"REGION_GRID_SPACING": "1.0"
}
the_job = helpers.create_job(params, job_id=self.job.id)
calculator = core.EventBasedRiskCalculator(the_job)
self.assertEqual([0.1, 0.5, 1.0],
calculator._get_gmvs_at(Site(-117, 40)))
self.assertEqual([0.2, 0.6, 1.1],
calculator._get_gmvs_at(Site(-117, 41)))
self.assertEqual([], calculator._get_gmvs_at(Site(-117.5, 40)))
def test_compute_uhs_with_site_model(self):
the_job = helpers.prepare_job_context(
helpers.demo_file('uhs/config_with_site_model.gem'))
the_job.to_kvs()
site = Site(0, 0)
helpers.store_hazard_logic_trees(the_job)
get_sm_patch = helpers.patch(
'openquake.calculators.hazard.general.get_site_model')
get_closest_patch = helpers.patch(
'openquake.calculators.hazard.general.get_closest_site_model_data')
compute_patch = helpers.patch(
'openquake.calculators.hazard.uhs.core._compute_uhs')
get_sm_mock = get_sm_patch.start()
get_closest_mock = get_closest_patch.start()
compute_mock = compute_patch.start()
get_closest_mock.return_value = SiteModel(
vs30=800, vs30_type='measured', z1pt0=100, z2pt5=200)
try:
compute_uhs(the_job, site)
self.assertEqual(1, get_sm_mock.call_count)
self.assertEqual(1, get_closest_mock.call_count)
self.assertEqual(1, compute_mock.call_count)
finally:
get_sm_patch.stop()
get_closest_patch.stop()
compute_patch.stop()
def test_compute_uhs_task_pi(self):
# Test that progress indicators are working properly for
# `compute_uhs_task`.
# Mock out the two 'heavy' functions called by this task;
# we don't need to do these and we don't want to waste the cycles.
cmpt_uhs = '%s.%s' % (self.UHS_CORE_MODULE, 'compute_uhs')
write_uhs_data = '%s.%s' % (self.UHS_CORE_MODULE,
'write_uhs_spectrum_data')
with helpers.patch(cmpt_uhs):
with helpers.patch(write_uhs_data):
get_counter = lambda: stats.get_counter(
self.job_id, 'h', 'compute_uhs_task', 'i')
# First, check that the counter for `compute_uhs_task` is
# `None`:
self.assertIsNone(get_counter())
realization = 0
site = Site(0.0, 0.0)
# execute the task as a plain old function
compute_uhs_task(self.job_id, realization, site)
self.assertEqual(1, get_counter())
compute_uhs_task(self.job_id, realization, site)
self.assertEqual(2, get_counter())
def test_write_uhs_spectrum_data(self):
# Test `write_uhs_spectrum_data`.
# To start with, we need to write the 'container' records for the UHS
# results:
write_uh_spectra(self.job_ctxt)
uhs_results = [] # The results we want to write to HDF5
uhs_result = java.jvm().JClass('org.gem.calc.UHSResult')
# Build up a result list that we can pass to the function under test:
for poe, uhs in self.UHS_RESULTS:
uhs_results.append(uhs_result(poe, list_to_jdouble_array(uhs)))
realization = 0
test_site = Site(0.0, 0.0)
# Call the function under test
write_uhs_spectrum_data(
self.job_ctxt, realization, test_site, uhs_results)
uhs_data = UhSpectrumData.objects.filter(
uh_spectrum__uh_spectra__output__oq_job=(
self.job.id))
self.assertEqual(len(self.UHS_RESULTS), len(uhs_data))
self.assertTrue(all([x.realization == 0 for x in uhs_data]))
uhs_results_dict = dict(self.UHS_RESULTS) # keyed by PoE
for uhs_datum in uhs_data:
self.assertTrue(
numpy.allclose(uhs_results_dict[uhs_datum.uh_spectrum.poe],
uhs_datum.sa_values))
self.assertEqual(test_site.point.to_wkt(), uhs_datum.location.wkt)
def test_serialize(self):
"""serialize() inserts the output and the gmf_data records."""
# This job has no outputs before calling the function under test.
self.assertEqual(0, len(self.job.output_set.all()))
# Call the function under test.
self.writer.serialize(GMF_DATA())
# Reload job row.
self.job = models.OqJob.objects.get(id=self.job.id)
# After calling the function under test we see the expected output.
self.assertEqual(1, len(self.job.output_set.all()))
# After calling the function under test we see the expected map data.
output = self.job.output_set.get()
self.assertEqual(0, len(output.hazardcurve_set.all()))
self.assertEqual(0, len(output.lossmap_set.all()))
self.assertEqual(4, len(output.gmfdata_set.all()))
# read data from the DB and check that it's equal to the original data
inserted_data = []
for gmfd in output.gmfdata_set.all():
location = gmfd.location
inserted_data.append((Site(location.x, location.y),
{'groundMotion': gmfd.ground_motion}))
self.assertEquals(self.normalize(GMF_DATA().items()),
self.normalize(inserted_data))
def HAZARD_CURVE_DATA():
return [
(Site(-122.2, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'endBranchLabel': '1'
}),
(Site(-122.1, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.454, 0.214, 0.123, 0.102],
'IMT': 'PGA',
'endBranchLabel': '1'
}),
]
def GMF_DATA(r1, r2):
data = {}
for lon in xrange(-179, -179 + r1):
for lat in xrange(-90, + r2):
data[Site(lon, lat)] = {'groundMotion': 0.0}
return data
def MEAN_CURVE_DATA():
return [
(Site(-122.2, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'statistics': 'mean'
}),
(Site(-122.1, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.454, 0.214, 0.123, 0.102],
'IMT': 'PGA',
'statistics': 'mean'
}),
]
def QUANTILE_CURVE_DATA():
return [
(Site(-122.2, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'statistics': 'quantile',
'quantileValue': 0.25
}),
(Site(-122.1, 37.5), {
'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.454, 0.214, 0.123, 0.102],
'IMT': 'PGA',
'statistics': 'quantile',
'quantileValue': 0.25
}),
]
def setUp(self):
self.job = self.setup_classic_job()
gmfs = [{
Site(-117, 40): {
"groundMotion": 0.1
},
Site(-117, 41): {
"groundMotion": 0.2
},
}, {
Site(-117, 40): {
"groundMotion": 0.5
},
Site(-117, 41): {
"groundMotion": 0.6
},
}, {
Site(-117, 40): {
"groundMotion": 1.0
},
Site(-117, 41): {
"groundMotion": 1.1
},
}]
for gmf in gmfs:
output_path = self.generate_output_path(self.job)
hcw = GmfDBWriter(output_path, self.job.id)
hcw.serialize(gmf)
def HAZARD_CURVE_DATA(branches, r1, r2):
data = []
poes = imls = [0.1] * 20
for lon in xrange(-179, -179 + r1):
for lat in xrange(-90, + r2):
for branch in branches:
data.append((Site(lon, lat),
{'investigationTimeSpan': '50.0',
'IMLValues': imls,
'PoEValues': poes,
'IMT': 'PGA',
'endBranchLabel': branch}))
data.append((Site(lon, lat),
{'investigationTimeSpan': '50.0',
'IMLValues': imls,
'PoEValues': poes,
'IMT': 'PGA',
'statistics': 'mean'}))
return data
def LOSS_MAP_DATA(assets, r1, r2):
data = [{'scenario': True}]
for lon in xrange(-179, -179 + r1):
for lat in xrange(-90, + r2):
data.append((Site(lon, lat), []))
for asset in assets:
data[-1][-1].append(({'mean_loss': 120000.0,
'stddev_loss': 2000.0},
{'assetID': asset}))
return data
def HAZARD_MAP_DATA(r1, r2):
data = []
for lon in xrange(-179, -179 + r1):
for lat in xrange(-90, + r2):
data.append((Site(lon, lat),
{'IML': 1.9266716959669603,
'IMT': 'PGA',
'investigationTimeSpan': '50.0',
'poE': 0.01,
'statistics': 'mean',
'vs30': 760.0}))
return data
def test_compute_uhs_task_calls_compute_and_write(self):
# The celery task `compute_uhs_task` basically just calls a few other
# functions to do the calculation and write results. Those functions
# have their own test coverage; in this test, we just want to make
# sure they get called.
cmpt_uhs = '%s.%s' % (self.UHS_CORE_MODULE, 'compute_uhs')
write_uhs_data = '%s.%s' % (self.UHS_CORE_MODULE,
'write_uhs_spectrum_data')
with helpers.patch(cmpt_uhs) as compute_mock:
with helpers.patch(write_uhs_data) as write_mock:
# Call the function under test as a normal function, not a
# @task:
compute_uhs_task(self.job_id, 0, Site(0.0, 0.0))
self.assertEqual(1, compute_mock.call_count)
self.assertEqual(1, write_mock.call_count)
def LOSS_CURVE_DATA(r1, r2):
data = []
poes = imls = [0.1] * 20
for lon in xrange(-179, -179 + r1):
for lat in xrange(-90, + r2):
data.append((Site(lon, lat),
(Curve(zip(imls, poes)),
{'assetValue': 5.07,
'assetID': 'a5625',
'listDescription': 'Collection of exposure values',
'structureCategory': 'RM1L',
'lon': -118.077721,
'taxonomy': 'HAZUS_RM1L_LC',
'listID': 'LA01',
'assetValueUnit': 'EUR',
'lat': 33.852034})))
return data
def test_serialize(self):
"""serialize() inserts the output and the hazard_map_data records."""
# This job has no outputs before calling the function under test.
self.assertEqual(0, len(self.job.output_set.all()))
for hcd in HAZARD_CURVE_DATA():
output_path = self.generate_output_path(self.job,
output_type="hazard_curve")
writer = HazardCurveDBWriter(output_path, self.job.id)
# Call the function under test.
writer.serialize(hcd)
# After calling the function under test we see the expected output.
self.job = models.OqJob.objects.get(id=self.job.id)
self.assertEqual(4, len(self.job.output_set.all()))
# After calling the function under test we see the expected map data.
hcs = models.HazardCurve.objects.filter(output__oq_job=self.job)
self.assertEqual(4, len(hcs))
# read data from the DB and check that it's equal to the original data
ebl2index = {"1_1": 0, "1_2": 1, "2": 2}
for hc in hcs:
indb = []
ebl = hc.end_branch_label
hdidx = 3 if ebl is None else ebl2index[ebl]
for hcd in hc.hazardcurvedata_set.all():
location = hcd.location
node = (Site(location.x, location.y),
{'PoEValues': hcd.poes})
if hc.end_branch_label:
node[1]['endBranchLabel'] = hc.end_branch_label
else:
node[1]['statistics'] = hc.statistic_type
if hc.quantile is not None:
node[1]['quantileValue'] = hc.quantile
indb.append(node)
self.assertEquals(self.normalize(HAZARD_CURVE_DATA()[hdidx]),
self.normalize(indb))
def test_serialize(self):
"""All the records are inserted correctly."""
output = self.writer.output
# Call the function under test.
self.writer.serialize(RISK_LOSS_CURVE_DATA)
# output record
self.assertEqual(1, len(self.job.output_set.all()))
output = self.job.output_set.get()
self.assertTrue(output.db_backed)
self.assertTrue(output.path is None)
self.assertEqual(self.display_name, output.display_name)
self.assertEqual("loss_curve", output.output_type)
# loss curve record
self.assertEqual(1, len(output.losscurve_set.all()))
loss_curve = output.losscurve_set.get()
self.assertEqual(loss_curve.unit, "peanuts")
self.assertEqual(loss_curve.end_branch_label, None)
self.assertEqual(loss_curve.category, "LCB cars")
# loss curve data records
self.assertEqual(4, len(loss_curve.losscurvedata_set.all()))
inserted_data = []
for lcd in loss_curve.losscurvedata_set.all():
loc = lcd.location
data = (Site(loc.x, loc.y), (Curve(zip(lcd.losses, lcd.poes)), {
u'assetID': lcd.asset_ref
}))
inserted_data.append(data)
self.assertEquals(self.normalize(RISK_LOSS_CURVE_DATA),
self.normalize(inserted_data))
def test_compute_uhs(self):
# Test the :function:`openquake.hazard.uhs.core.compute_uhs`
# function. This function makes use of the Java `UHSCalculator` and
# performs the main UHS computation.
# The results of the computation are a sequence of Java `UHSResult`
# objects.
the_job = helpers.job_from_file(UHS_DEMO_CONFIG_FILE)
site = Site(0.0, 0.0)
helpers.store_hazard_logic_trees(the_job)
uhs_results = compute_uhs(the_job, site)
for i, result in enumerate(uhs_results):
poe = result.getPoe()
uhs = result.getUhs()
self.assertEquals(self.UHS_RESULTS[i][0], poe)
self.assertTrue(numpy.allclose(self.UHS_RESULTS[i][1],
[x.value for x in uhs]))
def test_serialize(self):
"""serialize() inserts the output and the hazard_map_data records."""
# This job has no outputs before calling the function under test.
self.assertEqual(0, len(self.job.output_set.all()))
# Call the function under test.
self.writer.serialize(HAZARD_CURVE_DATA())
# After calling the function under test we see the expected output.
self.job = models.OqJob.objects.get(id=self.job.id)
self.assertEqual(1, len(self.job.output_set.all()))
# After calling the function under test we see the expected map data.
output = self.job.output_set.get()
self.assertEqual(4, len(output.hazardcurve_set.all()))
self.assertEqual(0, len(output.lossmap_set.all()))
# read data from the DB and check that it's equal to the original data
inserted_data = []
for hc in output.hazardcurve_set.all():
for hcd in hc.hazardcurvedata_set.all():
location = hcd.location
node = (Site(location.x, location.y),
{'PoEValues': hcd.poes})
if hc.end_branch_label:
node[1]['endBranchLabel'] = hc.end_branch_label
else:
node[1]['statistics'] = hc.statistic_type
if hc.quantile is not None:
node[1]['quantileValue'] = hc.quantile
inserted_data.append(node)
self.assertEquals(self.normalize(HAZARD_CURVE_DATA()),
self.normalize(inserted_data))
def HAZARD_CURVE_DATA():
return [
[(Site(-122.2, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'endBranchLabel': '1_1'}),
(Site(-122.0, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'endBranchLabel': '1_1'})],
[(Site(-122.1, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'endBranchLabel': '1_2'})],
[(Site(-121.9, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'endBranchLabel': '2'})],
[(Site(-122.0, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'quantileValue': 0.6,
'statistics': 'quantile'}),
(Site(-122.1, 37.5),
{'investigationTimeSpan': '50.0',
'IMLValues': [0.778, 1.09, 1.52, 2.13],
'PoEValues': [0.354, 0.114, 0.023, 0.002],
'IMT': 'PGA',
'quantileValue': 0.6,
'statistics': 'quantile'})],
]