def test_dmg_per_asset_node(self):
point1 = writers.Site(-116., 41.)
point2 = writers.Site(-117., 42.)
e1 = writers.ExposureData('asset_1', point1)
e2 = writers.ExposureData('asset_2', point2)
e3 = writers.ExposureData('asset_3', point2)
data = itertools.starmap(DMG_DIST_PER_ASSET, [
(e1, NO_DAMAGE, 1.0, 1.6),
(e1, SLIGHT, 34.8, 18.3),
(e1, MODERATE, 64.2, 19.8),
(e1, EXTENSIVE, 64.3, 19.7),
(e1, COMPLETE, 64.3, 19.7),
(e2, NO_DAMAGE, 1.0, 1.6),
(e2, SLIGHT, 34.8, 18.3),
(e2, MODERATE, 64.2, 19.8),
(e2, EXTENSIVE, 64.3, 19.7),
(e2, COMPLETE, 64.3, 19.7),
(e3, NO_DAMAGE, 1.1, 1.7),
(e3, SLIGHT, 34.9, 18.4),
(e3, MODERATE, 64.2, 19.8),
(e3, EXTENSIVE, 64.3, 19.7),
(e3, COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_per_asset.xml',
dw.to_nrml('dmg_dist_per_asset', data))
def test(self):
result_dir = tempfile.mkdtemp()
aaae = numpy.testing.assert_array_almost_equal
try:
cfg = os.path.join(os.path.dirname(__file__), 'job.ini')
expected_curve_poes = [0.75421006, 0.08098179, 0.00686616]
job = self.run_hazard(cfg)
# Test the poe values of the single curve:
[curve] = models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id)
aaae(expected_curve_poes, curve.poes, decimal=2)
# Test the exports as well:
exported_file = hazard_export.export(
curve.hazard_curve.output.id, result_dir)
check_equal(__file__, 'expected_hazard_curves.xml',
exported_file)
except:
raise
else:
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
try:
cfg = os.path.join(os.path.dirname(__file__), 'job.ini')
expected_curve_poes = [0.632120, 0.54811, 0.15241]
job = self.run_hazard(cfg)
# Test the poe values of the single curve:
[actual_curve] = models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id)
numpy.testing.assert_array_almost_equal(
expected_curve_poes, actual_curve.poes, decimal=3)
# Test the export as well:
exported_file = hazard_export.export(
actual_curve.hazard_curve.output.id, result_dir)
check_equal(__file__, 'expected_hazard_curves.xml',
exported_file)
except:
raise
else:
shutil.rmtree(result_dir)
def test_serialize(self):
# Test data is:
# - 1 gmf collection
# - 3 gmf sets
# for each set:
# - 2 ground motion fields
# for each ground motion field:
# - 2 nodes
# Total nodes: 12
locations = [Location(i * 0.1, i * 0.1) for i in range(12)]
gmf_nodes = [GmfNode(i * 0.2, locations[i]) for i in range(12)]
gmfs = [
Gmf('SA', 0.1, 5.0, gmf_nodes[:2], 'i=1'),
Gmf('SA', 0.2, 5.0, gmf_nodes[2:4], 'i=2'),
Gmf('SA', 0.3, 5.0, gmf_nodes[4:6], 'i=3'),
Gmf('PGA', None, None, gmf_nodes[6:8], 'i=4'),
Gmf('PGA', None, None, gmf_nodes[8:10], 'i=5'),
Gmf('PGA', None, None, gmf_nodes[10:], 'i=6'),
]
gmf_sets = [
GmfSet(gmfs[:2], 50.0, 1),
GmfSet(gmfs[2:4], 40.0, 2),
GmfSet(gmfs[4:], 30.0, 3),
]
gmf_collection = GmfCollection(gmf_sets)
sm_lt_path = 'b1_b2_b3'
gsim_lt_path = 'b1_b7_b15'
writer = writers.EventBasedGMFXMLWriter(
path, sm_lt_path, gsim_lt_path)
writer.serialize(gmf_collection)
check_equal(__file__, 'expected_gmf.xml', path)
def test_serialize(self):
# Test data is:
# - 1 gmf collection
# - 3 gmf sets
# for each set:
# - 2 ground motion fields
# for each ground motion field:
# - 2 nodes
# Total nodes: 12
locations = [Location(i * 0.1, i * 0.1) for i in range(12)]
gmf_nodes = [GmfNode(i * 0.2, locations[i]) for i in range(12)]
gmfs = [
Gmf('SA', 0.1, 5.0, gmf_nodes[:2], 'i=1'),
Gmf('SA', 0.2, 5.0, gmf_nodes[2:4], 'i=2'),
Gmf('SA', 0.3, 5.0, gmf_nodes[4:6], 'i=3'),
Gmf('PGA', None, None, gmf_nodes[6:8], 'i=4'),
Gmf('PGA', None, None, gmf_nodes[8:10], 'i=5'),
Gmf('PGA', None, None, gmf_nodes[10:], 'i=6'),
]
gmf_sets = [
GmfSet(gmfs[:2], 50.0, 1),
GmfSet(gmfs[2:4], 40.0, 2),
GmfSet(gmfs[4:], 30.0, 3),
]
gmf_collection = GmfCollection(gmf_sets)
sm_lt_path = 'b1_b2_b3'
gsim_lt_path = 'b1_b7_b15'
writer = writers.EventBasedGMFXMLWriter(
path, sm_lt_path, gsim_lt_path)
writer.serialize(gmf_collection)
check_equal(__file__, 'expected_gmf.xml', path)
def test_dmg_per_asset_node(self):
point1 = writers.Site(-116., 41.)
point2 = writers.Site(-117., 42.)
e1 = writers.ExposureData('asset_1', point1)
e2 = writers.ExposureData('asset_2', point2)
e3 = writers.ExposureData('asset_3', point2)
data = itertools.starmap(DMG_DIST_PER_ASSET, [
(e1, NO_DAMAGE, 1.0, 1.6),
(e1, SLIGHT, 34.8, 18.3),
(e1, MODERATE, 64.2, 19.8),
(e1, EXTENSIVE, 64.3, 19.7),
(e1, COMPLETE, 64.3, 19.7),
(e2, NO_DAMAGE, 1.0, 1.6),
(e2, SLIGHT, 34.8, 18.3),
(e2, MODERATE, 64.2, 19.8),
(e2, EXTENSIVE, 64.3, 19.7),
(e2, COMPLETE, 64.3, 19.7),
(e3, NO_DAMAGE, 1.1, 1.7),
(e3, SLIGHT, 34.9, 18.4),
(e3, MODERATE, 64.2, 19.8),
(e3, EXTENSIVE, 64.3, 19.7),
(e3, COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_per_asset.xml',
dw.to_nrml('dmg_dist_per_asset', data))
def test_serialize_xml(self):
metadata = dict(
investigation_time=50.0, imt='SA', poe=0.1, sa_period=0.025,
sa_damping=5.0, smlt_path='b1_b2_b4', gsimlt_path='b1_b4_b5'
)
writer = writers.HazardMapXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_hazard_map.xml', path)
def test_export(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(__file__), 'job.ini')
job = self.run_hazard(cfg)
[output] = export.core.get_outputs(job.id, 'gmf_scenario')
exported_file = export.core.export(output.id, result_dir)
check_equal(__file__, 'expected.xml', exported_file)
shutil.rmtree(result_dir)
def test_export(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_1.__file__), 'job.ini')
job = self.run_hazard(cfg)
[output] = export.core.get_outputs(job.id, 'gmf_scenario')
exported_file = export.core.export(output.id, result_dir)
check_equal(case_1.__file__, 'expected.xml', exported_file)
shutil.rmtree(result_dir)
def test_serialize_xml(self):
metadata = dict(
investigation_time=50.0, imt='SA', poe=0.1, sa_period=0.025,
sa_damping=5.0, smlt_path='b1_b2_b4', gsimlt_path='b1_b4_b5'
)
writer = writers.HazardMapXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_hazard_map.xml', path)
def test_serialize_quantile_xml(self):
metadata = dict(
investigation_time=50.0, imt='SA', poe=0.1, sa_period=0.025,
sa_damping=5.0, statistics='quantile', quantile_value=0.85
)
writer = writers.HazardMapXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_quantile.xml', path)
def test_serialize_quantile_xml(self):
metadata = dict(
investigation_time=50.0, imt='SA', poe=0.1, sa_period=0.025,
sa_damping=5.0, statistics='quantile', quantile_value=0.85
)
writer = writers.HazardMapXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_quantile.xml', path)
def test_dmg_total_node(self):
data = itertools.starmap(DMG_DIST_TOTAL, [
(NO_DAMAGE, 1.0, 1.6),
(SLIGHT, 34.8, 18.3),
(MODERATE, 64.2, 19.8),
(EXTENSIVE, 64.3, 19.7),
(COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_total.xml',
dw.to_nrml('dmg_dist_total', data))
def test_serialize(self):
# Just a basic serialization test
metadata = dict(
investigation_time=self.TIME, imt='SA', imls=self.IMLS,
sa_period=0.025, sa_damping=5.0, smlt_path='b1_b2_b4',
gsimlt_path='b1_b4_b5'
)
writer = writers.HazardCurveXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_hazard_curves.xml', path)
def test_serialize_quantile(self):
# Test serialization of qunatile curves.
metadata = dict(
investigation_time=self.TIME, imt='SA', imls=self.IMLS,
sa_period=0.025, sa_damping=5.0, statistics='quantile',
quantile_value=0.15
)
writer = writers.HazardCurveXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_quantile_curves.xml', path)
def test_dmg_total_node(self):
data = itertools.starmap(DMG_DIST_TOTAL, [
(NO_DAMAGE, 1.0, 1.6),
(SLIGHT, 34.8, 18.3),
(MODERATE, 64.2, 19.8),
(EXTENSIVE, 64.3, 19.7),
(COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_total.xml',
dw.to_nrml('dmg_dist_total', data))
def test_serialize_quantile(self):
# Test serialization of qunatile curves.
metadata = dict(
investigation_time=self.TIME, imt='SA', imls=self.IMLS,
sa_period=0.025, sa_damping=5.0, statistics='quantile',
quantile_value=0.15
)
writer = writers.HazardCurveXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_quantile_curves.xml', path)
def test_serialize(self):
# Just a basic serialization test
metadata = dict(
investigation_time=self.TIME, imt='SA', imls=self.IMLS,
sa_period=0.025, sa_damping=5.0, smlt_path='b1_b2_b4',
gsimlt_path='b1_b4_b5'
)
writer = writers.HazardCurveXMLWriter(path, **metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_hazard_curves.xml', path)
def compare_xml_outputs(self, job, expected_fnames):
result_dir = tempfile.mkdtemp()
for output in self.actual_xml_outputs(job):
exported_file = export.core.export(output.id, result_dir)
actual = os.path.basename(exported_file)
for expected in expected_fnames:
if actual.startswith(os.path.basename(expected)[:-4]):
if self.OVERWRITE_EXPECTED:
shutil.copy(exported_file, self._test_path(expected))
else:
check_equal(
self.module.__file__, expected, exported_file)
shutil.rmtree(result_dir)
def test_get_stat_curves_maps(self):
tempdir = tempfile.mkdtemp()
curves, maps = self.builder.get_curves_maps(self.stats)
# expecting arrays of shape (Q1, N) with Q1=3, N=4
actual = os.path.join(tempdir, 'expected_loss_curves.csv')
writers.write_csv(actual, curves, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_curves.csv', actual)
actual = os.path.join(tempdir, 'expected_loss_maps.csv')
writers.write_csv(actual, maps, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_maps.csv', actual)
# remove only if the test pass
shutil.rmtree(tempdir)
def test_dmg_per_taxonomy_node(self):
data = itertools.starmap(DMG_DIST_PER_TAXONOMY, [
('RC', NO_DAMAGE, 1.0, 1.6),
('RC', SLIGHT, 34.8, 18.3),
('RC', MODERATE, 64.2, 19.8),
('RC', EXTENSIVE, 64.3, 19.7),
('RC', COMPLETE, 64.3, 19.7),
('RM', NO_DAMAGE, 1.0, 1.6),
('RM', SLIGHT, 34.8, 18.3),
('RM', MODERATE, 64.2, 19.8),
('RM', EXTENSIVE, 64.3, 19.7),
('RM', COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_per_taxonomy.xml',
dw.to_nrml('dmg_dist_per_taxonomy', data))
def test_dmg_per_taxonomy_node(self):
data = itertools.starmap(DMG_DIST_PER_TAXONOMY, [
('RC', NO_DAMAGE, 1.0, 1.6),
('RC', SLIGHT, 34.8, 18.3),
('RC', MODERATE, 64.2, 19.8),
('RC', EXTENSIVE, 64.3, 19.7),
('RC', COMPLETE, 64.3, 19.7),
('RM', NO_DAMAGE, 1.0, 1.6),
('RM', SLIGHT, 34.8, 18.3),
('RM', MODERATE, 64.2, 19.8),
('RM', EXTENSIVE, 64.3, 19.7),
('RM', COMPLETE, 64.3, 19.7),
])
check_equal(__file__, 'expected_dmg_per_taxonomy.xml',
dw.to_nrml('dmg_dist_per_taxonomy', data))
def test_get_stat_curves_maps(self):
tempdir = tempfile.mkdtemp()
curves, maps = self.builder.get_curves_maps(self.stats)
# expecting arrays of shape (Q1, N) with Q1=3, N=4
actual = os.path.join(tempdir, 'expected_loss_curves.csv')
writers.write_csv(actual, curves, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_curves.csv', actual)
actual = os.path.join(tempdir, 'expected_loss_maps.csv')
writers.write_csv(actual, maps, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_maps.csv', actual)
# remove only if the test pass
shutil.rmtree(tempdir)
def test_get_stat_curves(self):
tempdir = tempfile.mkdtemp()
curves, ins_curves, maps = scientific.get_stat_curves(self.stats)
actual = os.path.join(tempdir, 'expected_loss_curves.csv')
writers.write_csv(actual, curves, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_curves.csv', actual)
actual = os.path.join(tempdir, 'expected_loss_maps.csv')
writers.write_csv(actual, maps, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_maps.csv', actual)
# remove only if the test pass
shutil.rmtree(tempdir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(self.CURRENTDIR, 'job.ini')
job = self.run_hazard(cfg)
curves_PGA = get_mean_curves(job, 'PGA')
actual = write_csv(os.path.join(result_dir, 'PGA.csv'), curves_PGA)
check_equal(case_13.__file__, 'expected/mean-PGA.csv', actual)
curves_SA = get_mean_curves(job, 'SA', 0.2)
actual = write_csv(os.path.join(result_dir, 'SA.csv'), curves_SA)
check_equal(case_13.__file__, 'expected/mean-SA.csv', actual)
shutil.rmtree(result_dir)
def test_collapse_map_node(self):
point1 = writers.Site(-72.2, 18.)
point2 = writers.Site(-72.25, 18.)
e1 = writers.ExposureData('a1', point1)
e2 = writers.ExposureData('a2', point1)
e3 = writers.ExposureData('a3', point1)
e4 = writers.ExposureData('a4', point2)
data = itertools.starmap(COLLAPSE_MAP, [
(e1, 1.0, 1.6),
(e2, 34.8, 18.3),
(e3, 64.2, 19.8),
(e4, 64.3, 19.7),
])
check_equal(__file__, 'expected_collapse_map.xml',
dw.to_nrml('collapse_map', data))
def test_collapse_map_node(self):
point1 = writers.Site(-72.2, 18.)
point2 = writers.Site(-72.25, 18.)
e1 = writers.ExposureData('a1', point1)
e2 = writers.ExposureData('a2', point1)
e3 = writers.ExposureData('a3', point1)
e4 = writers.ExposureData('a4', point2)
data = itertools.starmap(COLLAPSE_MAP, [
(e1, 1.0, 1.6),
(e2, 34.8, 18.3),
(e3, 64.2, 19.8),
(e4, 64.3, 19.7),
])
check_equal(__file__, 'expected_collapse_map.xml',
dw.to_nrml('collapse_map', data))
def test_serialize(self):
# Just a basic serialization test
metadata1 = dict(
investigation_time=50, imt='SA', imls=[0.005, 0.007, 0.0098],
sa_period=0.025, sa_damping=5.0, smlt_path='b1_b2_b4',
gsimlt_path='b1_b4_b5'
)
metadata2 = dict(
investigation_time=30, imt='PGA', imls=[0.05, 0.07, 0.8],
smlt_path='b1_b2_b4', gsimlt_path='b1_b4_b5'
)
writer = writers.MultiHazardCurveXMLWriter(
self.path, [metadata1, metadata2])
writer.serialize([self.data1, self.data2])
check_equal(__file__, 'expected_multicurves.xml', self.path)
def test_serialize(self):
# Just a basic serialization test
metadata1 = dict(
investigation_time=50, imt='SA', imls=[0.005, 0.007, 0.0098],
sa_period=0.025, sa_damping=5.0, smlt_path='b1_b2_b4',
gsimlt_path='b1_b4_b5'
)
metadata2 = dict(
investigation_time=30, imt='PGA', imls=[0.05, 0.07, 0.8],
sa_period=None, sa_damping=None,
smlt_path='b1_b2_b4', gsimlt_path='b1_b4_b5'
)
writer = writers.MultiHazardCurveXMLWriter(
path, [metadata1, metadata2])
writer.serialize([self.data1, self.data2])
check_equal(__file__, 'expected_multicurves.xml', path)
def test(self):
result_dir = tempfile.mkdtemp()
try:
cfg = os.path.join(os.path.dirname(__file__), 'job.ini')
expected_curve_poes_b1_b2 = [0.00995, 0.00076, 9.7E-5, 0.0]
expected_curve_poes_b1_b3 = [0.043, 0.0012, 7.394E-5, 0.0]
job = self.run_hazard(cfg)
# Test the poe values for the two curves:
curve_b1_b2, curve_b1_b3 = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id)\
.order_by('hazard_curve__lt_realization__lt_model__sm_lt_path')
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(
['b1', 'b2'],
curve_b1_b2.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b1', 'b3'],
curve_b1_b3.hazard_curve.lt_realization.sm_lt_path)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1_b2, curve_b1_b2.poes, decimal=4)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1_b3, curve_b1_b3.poes, decimal=4)
# Test the exports as well:
exported_file_b1_b2 = hazard_export.export(
curve_b1_b2.hazard_curve.output.id, result_dir)
check_equal(__file__, 'expected_b1_b2.xml',
exported_file_b1_b2)
exported_file_b1_b3 = hazard_export.export(
curve_b1_b3.hazard_curve.output.id, result_dir)
check_equal(__file__, 'expected_b1_b3.xml',
exported_file_b1_b3)
except:
raise
else:
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_2.__file__), 'job.ini')
expected_curve_poes = [0.0095, 0.00076, 0.000097, 0.0]
job = self.run_hazard(cfg)
# Test the poe values of the single curve:
[actual_curve] = models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id)
numpy.testing.assert_array_almost_equal(expected_curve_poes,
actual_curve.poes,
decimal=3)
# Test the export as well:
exported_file = hazard_export.export(
actual_curve.hazard_curve.output.id, result_dir)
check_equal(case_2.__file__, 'expected_hazard_curves.xml',
exported_file)
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_10.__file__), 'job.ini')
expected_curve_poes_b1_b2 = [0.00995, 0.00076, 9.7E-5, 0.0]
expected_curve_poes_b1_b3 = [0.043, 0.0012, 7.394E-5, 0.0]
job = self.run_hazard(cfg)
# Test the poe values for the two curves:
curve_b1_b2, curve_b1_b3 = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id)\
.order_by('hazard_curve__lt_realization__lt_model__sm_lt_path')
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(['b1', 'b2'],
curve_b1_b2.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(['b1', 'b3'],
curve_b1_b3.hazard_curve.lt_realization.sm_lt_path)
numpy.testing.assert_array_almost_equal(expected_curve_poes_b1_b2,
curve_b1_b2.poes,
decimal=4)
numpy.testing.assert_array_almost_equal(expected_curve_poes_b1_b3,
curve_b1_b3.poes,
decimal=4)
# Test the exports as well:
exported_file_b1_b2 = hazard_export.export(
curve_b1_b2.hazard_curve.output.id, result_dir)
check_equal(case_10.__file__, 'expected_b1_b2.xml',
exported_file_b1_b2)
exported_file_b1_b3 = hazard_export.export(
curve_b1_b3.hazard_curve.output.id, result_dir)
check_equal(case_10.__file__, 'expected_b1_b3.xml',
exported_file_b1_b3)
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
aaae = numpy.testing.assert_array_almost_equal
cfg = os.path.join(os.path.dirname(case_12.__file__), 'job.ini')
expected_curve_poes = [0.75421006, 0.08098179, 0.00686616]
job = self.run_hazard(cfg)
# Test the poe values of the single curve:
[curve] = models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id)
aaae(expected_curve_poes, curve.poes, decimal=2)
# Test the exports as well:
exported_file = hazard_export.export(curve.hazard_curve.output.id,
result_dir)
check_equal(case_12.__file__, 'expected_hazard_curves.xml',
exported_file)
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_6.__file__), 'job.ini')
expected_curve_poes = [0.86466, 0.82460, 0.36525]
job = self.run_hazard(cfg)
# Test the poe values of the single curve:
[actual_curve] = models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id)
numpy.testing.assert_array_almost_equal(
expected_curve_poes, actual_curve.poes, decimal=2)
# Test the export as well:
exported_file = hazard_export.export(
actual_curve.hazard_curve.output.id, result_dir)
check_equal(case_6.__file__, 'expected_hazard_curves.xml',
exported_file)
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_7.__file__), 'job.ini')
expected_curve_poes_b1 = [0.86466, 0.82460, 0.36525]
expected_curve_poes_b2 = [0.63212, 0.61186, 0.25110]
expected_mean_poes = [0.794898, 0.760778, 0.331005]
job = self.run_hazard(cfg)
# Test the poe values for the two curves.
actual_curve_b1, actual_curve_b2 = (
models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id,
hazard_curve__lt_realization__isnull=False).order_by(
'hazard_curve__lt_realization__lt_model__sm_lt_path'))
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(
['b1'], actual_curve_b1.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b2'], actual_curve_b2.hazard_curve.lt_realization.sm_lt_path)
numpy.testing.assert_array_almost_equal(expected_curve_poes_b1,
actual_curve_b1.poes,
decimal=3)
numpy.testing.assert_array_almost_equal(expected_curve_poes_b2,
actual_curve_b2.poes,
decimal=3)
# Test the mean curve:
[mean_curve] = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='mean')
numpy.testing.assert_array_almost_equal(expected_mean_poes,
mean_curve.poes,
decimal=3)
# Test the exports as well:
exported_file_b1 = hazard_export.export(
actual_curve_b1.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_b1.xml', exported_file_b1)
exported_file_b2 = hazard_export.export(
actual_curve_b2.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_b2.xml', exported_file_b2)
# mean:
exported_file_mean = hazard_export.export(
mean_curve.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_mean.xml', exported_file_mean)
shutil.rmtree(result_dir)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_7.__file__), 'job.ini')
expected_curve_poes_b1 = [0.86466, 0.82460, 0.36525]
expected_curve_poes_b2 = [0.63212, 0.61186, 0.25110]
expected_mean_poes = [0.794898, 0.760778, 0.331005]
job = self.run_hazard(cfg)
# Test the poe values for the two curves.
actual_curve_b1, actual_curve_b2 = (
models.HazardCurveData.objects
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__lt_realization__isnull=False)
.order_by('hazard_curve__lt_realization__lt_model__sm_lt_path')
)
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(
['b1'], actual_curve_b1.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b2'], actual_curve_b2.hazard_curve.lt_realization.sm_lt_path)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1, actual_curve_b1.poes, decimal=3)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b2, actual_curve_b2.poes, decimal=3)
# Test the mean curve:
[mean_curve] = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='mean')
numpy.testing.assert_array_almost_equal(
expected_mean_poes, mean_curve.poes, decimal=3)
# Test the exports as well:
exported_file_b1 = hazard_export.export(
actual_curve_b1.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_b1.xml', exported_file_b1)
exported_file_b2 = hazard_export.export(
actual_curve_b2.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_b2.xml', exported_file_b2)
# mean:
exported_file_mean = hazard_export.export(
mean_curve.hazard_curve.output.id, result_dir)
check_equal(case_7.__file__, 'expected_mean.xml', exported_file_mean)
shutil.rmtree(result_dir)
def test_get_stat_curves(self):
curves, ins_curves, maps = scientific.get_stat_curves(self.stats)
actual = os.path.join(self.tempdir, 'expected_loss_curves.csv')
writers.write_csv(actual, curves, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_curves.csv', actual)
actual = os.path.join(self.tempdir, 'expected_ins_curves.csv')
writers.write_csv(actual, ins_curves, fmt='%05.2f')
tests.check_equal(__file__, 'expected_ins_curves.csv', actual)
actual = os.path.join(self.tempdir, 'expected_loss_maps.csv')
writers.write_csv(actual, maps, fmt='%05.2f')
tests.check_equal(__file__, 'expected_loss_maps.csv', actual)
def test(self):
result_dir = tempfile.mkdtemp()
cfg = os.path.join(os.path.dirname(case_8.__file__), 'job.ini')
expected_curve_poes_b1_b2 = [0.095163, 0.012362, 0.002262, 0.0]
expected_curve_poes_b1_b3 = [0.009950, 0.00076, 9.99995E-6, 0.0]
expected_curve_poes_b1_b4 = [0.0009995, 4.5489E-5, 4.07365E-6, 0.0]
job = self.run_hazard(cfg)
# Test the poe values for the three curves:
curve_b1_b2, curve_b1_b3, curve_b1_b4 = (
models.HazardCurveData.objects
.filter(hazard_curve__output__oq_job=job.id)
.order_by('hazard_curve__lt_realization__lt_model__sm_lt_path')
)
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(
['b1', 'b2'],
curve_b1_b2.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b1', 'b3'],
curve_b1_b3.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b1', 'b4'],
curve_b1_b4.hazard_curve.lt_realization.sm_lt_path)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1_b2, curve_b1_b2.poes, decimal=3)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1_b3, curve_b1_b3.poes, decimal=3)
numpy.testing.assert_array_almost_equal(
expected_curve_poes_b1_b4, curve_b1_b4.poes, decimal=3)
# Test the exports as well:
exported_file_b1_b2 = hazard_export.export(
curve_b1_b2.hazard_curve.output.id, result_dir)
check_equal(case_8.__file__, 'expected_b1_b2.xml', exported_file_b1_b2)
exported_file_b1_b3 = hazard_export.export(
curve_b1_b3.hazard_curve.output.id, result_dir)
check_equal(case_8.__file__, 'expected_b1_b3.xml', exported_file_b1_b3)
exported_file_b1_b4 = hazard_export.export(
curve_b1_b4.hazard_curve.output.id, result_dir)
check_equal(case_8.__file__, 'expected_b1_b4.xml', exported_file_b1_b4)
shutil.rmtree(result_dir)
def test_serialize(self):
writer = writers.DisaggXMLWriter(path, **self.metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_disagg.xml', path)
def test(self):
current = case_11.__file__
result_dir = tempfile.mkdtemp()
aaae = numpy.testing.assert_array_almost_equal
cfg = os.path.join(os.path.dirname(current), 'job.ini')
expected_curve_poes_b1_b2 = [0.0055, 0.00042, 5.77E-5, 0.0]
expected_curve_poes_b1_b3 = [0.00995, 0.00076, 9.7E-5, 0.0]
expected_curve_poes_b1_b4 = [0.018, 0.0013, 0.00014, 0.0]
expected_mean_poes = [0.01067, 0.0008, 9.774E-5, 0.0]
expected_q0_1_poes = [0.0055, 0.00042, 5.77E-5, 0.0]
expected_q0_9_poes = [0.013975, 0.00103, 0.0001185, 0.0]
job = self.run_hazard(cfg)
# Test the poe values for the two curves:
curve_b1_b2, curve_b1_b3, curve_b1_b4 = (
models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id,
hazard_curve__lt_realization__isnull=False).order_by(
'hazard_curve__lt_realization__lt_model__sm_lt_path'))
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(['b1', 'b2'],
curve_b1_b2.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(['b1', 'b3'],
curve_b1_b3.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(['b1', 'b4'],
curve_b1_b4.hazard_curve.lt_realization.sm_lt_path)
aaae(expected_curve_poes_b1_b2, curve_b1_b2.poes, decimal=4)
aaae(expected_curve_poes_b1_b3, curve_b1_b3.poes, decimal=4)
aaae(expected_curve_poes_b1_b4, curve_b1_b4.poes, decimal=4)
# Test the mean curve:
[mean_curve] = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='mean')
aaae(expected_mean_poes, mean_curve.poes, decimal=4)
# Test the quantile curves:
quantile_0_1_curve, quantile_0_9_curve = \
models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='quantile'
).order_by('hazard_curve__quantile')
aaae(expected_q0_1_poes, quantile_0_1_curve.poes, decimal=4)
aaae(expected_q0_9_poes, quantile_0_9_curve.poes, decimal=4)
# Test the exports as well:
exported_file_b1_b2 = hazard_export.export(
curve_b1_b2.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b2.xml', exported_file_b1_b2)
exported_file_b1_b3 = hazard_export.export(
curve_b1_b3.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b3.xml', exported_file_b1_b3)
exported_file_b1_b4 = hazard_export.export(
curve_b1_b4.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b4.xml', exported_file_b1_b4)
exported_file_mean = hazard_export.export(
mean_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_mean.xml', exported_file_mean)
q01_file = hazard_export.export(
quantile_0_1_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_quantile_0_1.xml', q01_file)
q09_file = hazard_export.export(
quantile_0_9_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_quantile_0_9.xml', q09_file)
shutil.rmtree(result_dir)
def test_serialize(self):
pr1 = ProbabilisticRupture(
1,
5.5, 1.0, 40.0, 10.0, 'Active Shallow Crust',
False, False,
top_left_corner=(1.1, 1.01, 10.0),
top_right_corner=(2.1, 2.01, 20.0),
bottom_right_corner=(3.1, 3.01, 30.0),
bottom_left_corner=(4.1, 4.01, 40.0))
pr2 = ProbabilisticRupture(
2,
6.5, 0.0, 41.0, 0.0, 'Active Shallow Crust',
True, False,
lons=[[5.1, 6.1],
[7.1, 8.1],
],
lats=[[5.01, 6.01],
[7.01, 8.01],
],
depths=[[10.5, 10.6],
[10.7, 10.8],
])
ses1 = SES(1, 50.0, [SESRupture(pr1, 1), SESRupture(pr2, 1)])
pr3 = ProbabilisticRupture(
3,
5.4, 2.0, 42.0, 12.0, 'Stable Shallow Crust',
False, False,
top_left_corner=(1.1, 1.01, 10.0),
top_right_corner=(2.1, 2.01, 20.0),
bottom_left_corner=(4.1, 4.01, 40.0),
bottom_right_corner=(3.1, 3.01, 30.0))
pr4 = ProbabilisticRupture(
4,
6.4, 3.0, 43.0, 13.0, 'Stable Shallow Crust',
True, False,
lons=[
[5.2, 6.2],
[7.2, 8.2],
],
lats=[
[5.02, 6.02],
[7.02, 8.02],
],
depths=[
[10.1, 10.2],
[10.3, 10.4],
])
pr5 = ProbabilisticRupture(
5,
7.4, 4.0, 44.0, 14.0, 'Stable Shallow Crust',
False, True,
lons=[-1.0, 1.0, -1.0, 1.0, 0.0, 1.1, 0.9, 2.0],
lats=[1.0, 1.0, -1.0, -1.0, 1.1, 2.0, 0.0, 0.9],
depths=[21.0, 21.0, 59.0, 59.0, 20.0, 20.0, 80.0, 80.0])
ses2 = SES(2, 40.0, [SESRupture(pr3, 1), SESRupture(pr4, 1),
SESRupture(pr5, 1)])
sm_lt_path = 'b8_b9_b10'
_, self.path = tempfile.mkstemp()
writer = writers.SESXMLWriter(self.path, sm_lt_path)
writer.serialize([ses1, ses2])
check_equal(__file__, 'expected_ses_collection.xml', self.path)
def test_serialize(self):
pr1 = ProbabilisticRupture(
1,
5.5, 1.0, 40.0, 10.0, 'Active Shallow Crust',
False, False,
top_left_corner=(1.1, 1.01, 10.0),
top_right_corner=(2.1, 2.01, 20.0),
bottom_right_corner=(3.1, 3.01, 30.0),
bottom_left_corner=(4.1, 4.01, 40.0))
pr2 = ProbabilisticRupture(
2,
6.5, 0.0, 41.0, 0.0, 'Active Shallow Crust',
True, False,
lons=[[5.1, 6.1],
[7.1, 8.1],
],
lats=[[5.01, 6.01],
[7.01, 8.01],
],
depths=[[10.5, 10.6],
[10.7, 10.8],
])
ses1 = SES(1, 50.0, [SESRupture(pr1, 1), SESRupture(pr2, 1)])
pr3 = ProbabilisticRupture(
3,
5.4, 2.0, 42.0, 12.0, 'Stable Shallow Crust',
False, False,
top_left_corner=(1.1, 1.01, 10.0),
top_right_corner=(2.1, 2.01, 20.0),
bottom_left_corner=(4.1, 4.01, 40.0),
bottom_right_corner=(3.1, 3.01, 30.0))
pr4 = ProbabilisticRupture(
4,
6.4, 3.0, 43.0, 13.0, 'Stable Shallow Crust',
True, False,
lons=[
[5.2, 6.2],
[7.2, 8.2],
],
lats=[
[5.02, 6.02],
[7.02, 8.02],
],
depths=[
[10.1, 10.2],
[10.3, 10.4],
])
pr5 = ProbabilisticRupture(
5,
7.4, 4.0, 44.0, 14.0, 'Stable Shallow Crust',
False, True,
lons=[-1.0, 1.0, -1.0, 1.0, 0.0, 1.1, 0.9, 2.0],
lats=[1.0, 1.0, -1.0, -1.0, 1.1, 2.0, 0.0, 0.9],
depths=[21.0, 21.0, 59.0, 59.0, 20.0, 20.0, 80.0, 80.0])
ses2 = SES(2, 40.0, [SESRupture(pr3, 1), SESRupture(pr4, 1),
SESRupture(pr5, 1)])
sm_lt_path = 'b8_b9_b10'
writer = writers.SESXMLWriter(path, sm_lt_path)
writer.serialize([ses1, ses2])
check_equal(__file__, 'expected_ses_collection.xml', path)
def test(self):
current = case_11.__file__
result_dir = tempfile.mkdtemp()
aaae = numpy.testing.assert_array_almost_equal
cfg = os.path.join(os.path.dirname(current), 'job.ini')
expected_curve_poes_b1_b2 = [0.0055, 0.00042, 5.77E-5, 0.0]
expected_curve_poes_b1_b3 = [0.00995, 0.00076, 9.7E-5, 0.0]
expected_curve_poes_b1_b4 = [0.018, 0.0013, 0.00014, 0.0]
expected_mean_poes = [0.01067, 0.0008, 9.774E-5, 0.0]
expected_q0_1_poes = [0.0055, 0.00042, 5.77E-5, 0.0]
expected_q0_9_poes = [0.013975, 0.00103, 0.0001185, 0.0]
job = self.run_hazard(cfg)
# Test the poe values for the two curves:
curve_b1_b2, curve_b1_b3, curve_b1_b4 = (
models.HazardCurveData.objects
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__lt_realization__isnull=False)
.order_by(
'hazard_curve__lt_realization__lt_model__sm_lt_path'))
# Sanity check, to make sure we have the curves ordered correctly:
self.assertEqual(
['b1', 'b2'],
curve_b1_b2.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b1', 'b3'],
curve_b1_b3.hazard_curve.lt_realization.sm_lt_path)
self.assertEqual(
['b1', 'b4'],
curve_b1_b4.hazard_curve.lt_realization.sm_lt_path)
aaae(expected_curve_poes_b1_b2, curve_b1_b2.poes, decimal=4)
aaae(expected_curve_poes_b1_b3, curve_b1_b3.poes, decimal=4)
aaae(expected_curve_poes_b1_b4, curve_b1_b4.poes, decimal=4)
# Test the mean curve:
[mean_curve] = models.HazardCurveData.objects\
.filter(hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='mean')
aaae(expected_mean_poes, mean_curve.poes, decimal=4)
# Test the quantile curves:
quantile_0_1_curve, quantile_0_9_curve = \
models.HazardCurveData.objects.filter(
hazard_curve__output__oq_job=job.id,
hazard_curve__statistics='quantile'
).order_by('hazard_curve__quantile')
aaae(expected_q0_1_poes, quantile_0_1_curve.poes, decimal=4)
aaae(expected_q0_9_poes, quantile_0_9_curve.poes, decimal=4)
# Test the exports as well:
exported_file_b1_b2 = hazard_export.export(
curve_b1_b2.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b2.xml', exported_file_b1_b2)
exported_file_b1_b3 = hazard_export.export(
curve_b1_b3.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b3.xml', exported_file_b1_b3)
exported_file_b1_b4 = hazard_export.export(
curve_b1_b4.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_b1_b4.xml', exported_file_b1_b4)
exported_file_mean = hazard_export.export(
mean_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_mean.xml', exported_file_mean)
q01_file = hazard_export.export(
quantile_0_1_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_quantile_0_1.xml', q01_file)
q09_file = hazard_export.export(
quantile_0_9_curve.hazard_curve.output.id, result_dir)
check_equal(current, 'expected_quantile_0_9.xml', q09_file)
shutil.rmtree(result_dir)
def test_serialize(self):
writer = writers.DisaggXMLWriter(path, **self.metadata)
writer.serialize(self.data)
check_equal(__file__, 'expected_disagg.xml', path)