def test_scenario_risk_sample_based(self):
# This QA test is a longer-running test of the Scenario Risk
# calculator.
# The vulnerabiilty model has non-zero Coefficients of Variation and
# therefore exercises the 'sample-based' path through the calculator.
# This test is configured to produce 1000 ground motion fields at each
# location of interest (in the test above, only 10 are produced).
# Since we're seeding the random epsilon sampling, we can consistently
# reproduce all result values.
# When these values are compared to the results computed by a similar
# config which takes the 'mean-based' path (with CoVs = 0), we expect
# the following:
# All of the mean values in the 'sample-based' results should be with
# 5%, + or -, of the 'mean-based' results.
# The standard deviation values of the 'sample-based' results should
# simply be greater than those produced with the 'mean-based' method.
# For comparison, mean and stddev values for the region were computed
# with 1000 GMFs using the mean-based approach. These values (rounded
# to 2 decimal places) are:
mb_mean_loss = 1233.26
mb_stddev_loss = 443.63
# Loss map for the mean-based approach:
mb_loss_map = [
dict(asset='a3', pos='15.48 38.25', mean=200.54874638,
stddev=94.2302991022),
dict(asset='a2', pos='15.56 38.17', mean=510.821363253,
stddev=259.964152622),
dict(asset='a1', pos='15.48 38.09', mean=521.885458891,
stddev=244.825980356),
]
# Sanity checks are done. Let's do this.
scen_cfg = helpers.demo_file(
'scenario_risk/config_sample-based_qa.gem')
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.demo_file(
'scenario_risk/computed_output/loss-map-%s.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
loss_map = helpers.loss_map_result_from_file(expected_loss_map_file)
self._verify_loss_map_within_range(sorted(mb_loss_map),
sorted(loss_map), 0.05)
exp_mean_loss, exp_stddev_loss = helpers.mean_stddev_from_result_line(
result)
self.assertAlmostEqual(mb_mean_loss, exp_mean_loss,
delta=mb_mean_loss * 0.05)
self.assertTrue(exp_stddev_loss > mb_stddev_loss)
def test_scenario_risk_insured_losses(self):
# This test exercises the 'mean-based' path through the Scenario Risk
# calculator. There is no random sampling done here so the results are
# 100% predictable.
scen_cfg = helpers.qa_file('scenario_risk_insured_losses/config.gem')
exp_mean_loss = 799.102578
exp_stddev_loss = 382.148808
expected_loss_map = [
dict(asset='a3',
pos='15.48 38.25',
mean=156.750910806,
stddev=100.422061776),
dict(asset='a2',
pos='15.56 38.17',
mean=314.859579324,
stddev=293.976254984),
dict(asset='a1',
pos='15.48 38.09',
mean=327.492087529,
stddev=288.47906994),
]
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.qa_file(
'scenario_risk_insured_losses/computed_output/insured-loss-map%s'
'.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
helpers.verify_loss_map(self, expected_loss_map_file,
expected_loss_map, self.LOSSMAP_PRECISION)
actual_mean, actual_stddev = helpers.mean_stddev_from_result_line(
result)
self.assertAlmostEqual(exp_mean_loss,
actual_mean,
places=self.TOTAL_LOSS_PRECISION)
self.assertAlmostEqual(exp_stddev_loss,
actual_stddev,
places=self.TOTAL_LOSS_PRECISION)
# Cleaning generated results file.
rmtree(QA_OUTPUT_DIR)
def test_scenario_risk(self):
# This test exercises the 'mean-based' path through the Scenario Risk
# calculator. There is no random sampling done here so the results are
# 100% predictable.
scen_cfg = helpers.demo_file('scenario_risk/config.gem')
exp_mean_loss = 1053.09
exp_stddev_loss = 246.62
expected_loss_map = [
dict(asset='a3',
pos='15.48 38.25',
mean=180.717534009275,
stddev=92.2122644809969),
dict(asset='a2',
pos='15.56 38.17',
mean=432.225448142534,
stddev=186.864456949986),
dict(asset='a1',
pos='15.48 38.09',
mean=440.147078317589,
stddev=182.615976701858),
]
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.demo_file(
'scenario_risk/computed_output/loss-map-%s.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
helpers.verify_loss_map(self, expected_loss_map_file,
expected_loss_map, self.LOSSMAP_PRECISION)
actual_mean, actual_stddev = helpers.mean_stddev_from_result_line(
result)
self.assertAlmostEqual(exp_mean_loss,
actual_mean,
places=self.TOTAL_LOSS_PRECISION)
self.assertAlmostEqual(exp_stddev_loss,
actual_stddev,
places=self.TOTAL_LOSS_PRECISION)
def test_scenario_risk_insured_losses(self):
# This test exercises the 'mean-based' path through the Scenario Risk
# calculator. There is no random sampling done here so the results are
# 100% predictable.
scen_cfg = helpers.qa_file('scenario_risk_insured_losses/config.gem')
exp_mean_loss = 799.102578
exp_stddev_loss = 382.148808
expected_loss_map = [
dict(asset='a3', pos='15.48 38.25', mean=156.750910806,
stddev=100.422061776),
dict(asset='a2', pos='15.56 38.17', mean=314.859579324,
stddev=293.976254984),
dict(asset='a1', pos='15.48 38.09', mean=327.492087529,
stddev=288.47906994),
]
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.qa_file(
'scenario_risk_insured_losses/computed_output/insured-loss-map%s'
'.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
helpers.verify_loss_map(self, expected_loss_map_file,
expected_loss_map, self.LOSSMAP_PRECISION)
actual_mean, actual_stddev = helpers.mean_stddev_from_result_line(result)
self.assertAlmostEqual(
exp_mean_loss, actual_mean, places=self.TOTAL_LOSS_PRECISION)
self.assertAlmostEqual(
exp_stddev_loss, actual_stddev, places=self.TOTAL_LOSS_PRECISION)
# Cleaning generated results file.
rmtree(QA_OUTPUT_DIR)
def test_scenario_risk(self):
# This test exercises the 'mean-based' path through the Scenario Risk
# calculator. There is no random sampling done here so the results are
# 100% predictable.
scen_cfg = helpers.demo_file('scenario_risk/config.gem')
exp_mean_loss = 1053.09
exp_stddev_loss = 246.62
expected_loss_map = [
dict(asset='a3', pos='15.48 38.25', mean=180.717534009275,
stddev=92.2122644809969),
dict(asset='a2', pos='15.56 38.17', mean=432.225448142534,
stddev=186.864456949986),
dict(asset='a1', pos='15.48 38.09', mean=440.147078317589,
stddev=182.615976701858),
]
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.demo_file(
'scenario_risk/computed_output/loss-map-%s.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
helpers.verify_loss_map(self, expected_loss_map_file,
expected_loss_map, self.LOSSMAP_PRECISION)
actual_mean, actual_stddev = helpers.mean_stddev_from_result_line(result)
self.assertAlmostEqual(
exp_mean_loss, actual_mean, places=self.TOTAL_LOSS_PRECISION)
self.assertAlmostEqual(
exp_stddev_loss, actual_stddev, places=self.TOTAL_LOSS_PRECISION)
def test_scenario_risk_sample_based(self):
# This QA test is a longer-running test of the Scenario Risk
# calculator.
# The vulnerabiilty model has non-zero Coefficients of Variation and
# therefore exercises the 'sample-based' path through the calculator.
# This test is configured to produce 1000 ground motion fields at each
# location of interest (in the test above, only 10 are produced).
# Since we're seeding the random epsilon sampling, we can consistently
# reproduce all result values.
# When these values are compared to the results computed by a similar
# config which takes the 'mean-based' path (with CoVs = 0), we expect
# the following:
# All of the mean values in the 'sample-based' results should be with
# 5%, + or -, of the 'mean-based' results.
# The standard deviation values of the 'sample-based' results should
# simply be greater than those produced with the 'mean-based' method.
# For comparison, mean and stddev values for the region were computed
# with 1000 GMFs using the mean-based approach. These values (rounded
# to 2 decimal places) are:
mb_mean_loss = 1233.26
mb_stddev_loss = 443.63
# Loss map for the mean-based approach:
mb_loss_map = [
dict(asset='a3',
pos='15.48 38.25',
mean=200.54874638,
stddev=94.2302991022),
dict(asset='a2',
pos='15.56 38.17',
mean=510.821363253,
stddev=259.964152622),
dict(asset='a1',
pos='15.48 38.09',
mean=521.885458891,
stddev=244.825980356),
]
# Sanity checks are done. Let's do this.
scen_cfg = helpers.demo_file(
'scenario_risk/config_sample-based_qa.gem')
result = helpers.run_job(scen_cfg, ['--output-type=xml'],
check_output=True)
job = OqJob.objects.latest('id')
self.assertEqual('succeeded', job.status)
expected_loss_map_file = helpers.demo_file(
'scenario_risk/computed_output/loss-map-%s.xml' % job.id)
self.assertTrue(os.path.exists(expected_loss_map_file))
loss_map = helpers.loss_map_result_from_file(expected_loss_map_file)
self._verify_loss_map_within_range(sorted(mb_loss_map),
sorted(loss_map), 0.05)
exp_mean_loss, exp_stddev_loss = helpers.mean_stddev_from_result_line(
result)
self.assertAlmostEqual(mb_mean_loss,
exp_mean_loss,
delta=mb_mean_loss * 0.05)
self.assertTrue(exp_stddev_loss > mb_stddev_loss)