def _verify_aggregate_curve(self):
job = OqJob.objects.latest("id")
[output] = Output.objects.filter(oq_job=job.id,
output_type="agg_loss_curve")
export_agg_loss_curve(output, OUTPUT_DIR)
filename = "%s/aggregate_loss_curve.xml" % OUTPUT_DIR
root = self._root(filename)
xpath = "//nrml:aggregateLossCurve//nrml:poE"
poes = [float(x) for x in self._get(root, xpath).split()]
expected_poes = [
1.0000000000, 1.0000000000, 0.9999991685, 0.9932621249,
0.9502177204, 0.8646647795, 0.8646752036, 0.6321506245,
0.6321525149
]
self.assertTrue(
numpy.allclose(poes, expected_poes, atol=0.0, rtol=0.05))
xpath = "//nrml:aggregateLossCurve//nrml:loss"
losses = [float(x) for x in self._get(root, xpath).split()]
expected_losses = [
18.5629274028, 55.6887822085, 92.8146370142, 129.9404918199,
167.0663466256, 204.1922014313, 241.3180562370, 278.4439110427,
315.5697658484
]
self.assertTrue(
numpy.allclose(losses, expected_losses, atol=0.0, rtol=0.05))
def _verify_aggregate_curve(self):
job = OqJob.objects.latest("id")
[output] = Output.objects.filter(
oq_job=job.id,
output_type="agg_loss_curve")
export_agg_loss_curve(output, OUTPUT_DIR)
filename = "%s/aggregate_loss_curve.xml" % OUTPUT_DIR
root = self._root(filename)
xpath = "//nrml:aggregateLossCurve//nrml:poE"
poes = [float(x) for x in self._get(root, xpath).split()]
expected_poes = [1.0000000000, 1.0000000000, 0.9999991685,
0.9932621249, 0.9502177204, 0.8646647795,
0.8646752036, 0.6321506245, 0.6321525149]
self.assertTrue(numpy.allclose(
poes, expected_poes, atol=0.0, rtol=0.05))
xpath = "//nrml:aggregateLossCurve//nrml:loss"
losses = [float(x) for x in self._get(root, xpath).split()]
expected_losses = [18.5629274028, 55.6887822085, 92.8146370142,
129.9404918199, 167.0663466256, 204.1922014313,
241.3180562370, 278.4439110427, 315.5697658484]
self.assertTrue(numpy.allclose(
losses, expected_losses, atol=0.0, rtol=0.05))
def test_insured_loss_mean_based(self):
try:
cfg = helpers.qa_file(
"probabilistic_event_based_risk/m_il_config.gem")
self._run_job(cfg)
job_id = OqJob.objects.latest("id").id
[output] = Output.objects.filter(oq_job=job_id,
output_type="agg_loss_curve")
export_agg_loss_curve(output, QA_OUTPUT_DIR)
expected_files = [
"loss_curves-block-#%s-block#0.xml" % job_id,
"loss_curves-loss-block-#%s-block#0.xml" % job_id,
"loss_curves-insured-block=#%s-block#0.xml" % job_id,
"losses_at-0.99.xml",
"aggregate_loss_curve.xml",
"insured_loss_curves-insured-loss-block=#%s-block#0.xml" %
job_id]
self._verify_job_succeeded()
self._verify_outputs(QA_OUTPUT_DIR, expected_files)
expected_poes = {}
expected_losses = {}
expected_poes["a1"] = test_data.EXPECTED_POES_LR_A1_MB_IL
expected_poes["a2"] = test_data.EXPECTED_POES_LR_A2_MB_IL
expected_poes["a3"] = test_data.EXPECTED_POES_LR_A3_MB_IL
expected_losses["a1"] = test_data.EXPECTED_LOSS_RATIOS_A1_MB_IL
expected_losses["a2"] = test_data.EXPECTED_LOSS_RATIOS_A2_MB_IL
expected_losses["a3"] = test_data.EXPECTED_LOSS_RATIOS_A3_MB_IL
self._verify_loss_ratio_curves(expected_poes, expected_losses,
QA_OUTPUT_DIR, 0.05,
"%s/loss_curves-insured-block=#%s-block#0.xml")
expected_poes["a1"] = test_data.EXPECTED_POES_LC_A1_MB_IL
expected_poes["a2"] = test_data.EXPECTED_POES_LC_A2_MB_IL
expected_poes["a3"] = test_data.EXPECTED_POES_LC_A3_MB_IL
expected_losses["a1"] = test_data.EXPECTED_LOSSES_A1_MB_IL
expected_losses["a2"] = test_data.EXPECTED_LOSSES_A2_MB_IL
expected_losses["a3"] = test_data.EXPECTED_LOSSES_A3_MB_IL
self._verify_loss_curves(expected_poes, expected_losses,
QA_OUTPUT_DIR, 0.05,
"%s/insured_loss_curves-insured-loss-block=#%s-block#0.xml")
finally:
rmtree(QA_OUTPUT_DIR)
def test_insured_loss_mean_based(self):
try:
cfg = helpers.qa_file(
"probabilistic_event_based_risk/m_il_config.gem")
self._run_job(cfg)
job_id = OqJob.objects.latest("id").id
[output] = Output.objects.filter(oq_job=job_id,
output_type="agg_loss_curve")
export_agg_loss_curve(output, QA_OUTPUT_DIR)
expected_files = [
"loss_curves-block-#%s-block#0.xml" % job_id,
"loss_curves-loss-block-#%s-block#0.xml" % job_id,
"loss_curves-insured-block=#%s-block#0.xml" % job_id,
"losses_at-0.99.xml", "aggregate_loss_curve.xml",
"insured_loss_curves-insured-loss-block=#%s-block#0.xml" %
job_id
]
self._verify_job_succeeded()
self._verify_outputs(QA_OUTPUT_DIR, expected_files)
expected_poes = {}
expected_losses = {}
expected_poes["a1"] = test_data.EXPECTED_POES_LR_A1_MB_IL
expected_poes["a2"] = test_data.EXPECTED_POES_LR_A2_MB_IL
expected_poes["a3"] = test_data.EXPECTED_POES_LR_A3_MB_IL
expected_losses["a1"] = test_data.EXPECTED_LOSS_RATIOS_A1_MB_IL
expected_losses["a2"] = test_data.EXPECTED_LOSS_RATIOS_A2_MB_IL
expected_losses["a3"] = test_data.EXPECTED_LOSS_RATIOS_A3_MB_IL
self._verify_loss_ratio_curves(
expected_poes, expected_losses, QA_OUTPUT_DIR, 0.05,
"%s/loss_curves-insured-block=#%s-block#0.xml")
expected_poes["a1"] = test_data.EXPECTED_POES_LC_A1_MB_IL
expected_poes["a2"] = test_data.EXPECTED_POES_LC_A2_MB_IL
expected_poes["a3"] = test_data.EXPECTED_POES_LC_A3_MB_IL
expected_losses["a1"] = test_data.EXPECTED_LOSSES_A1_MB_IL
expected_losses["a2"] = test_data.EXPECTED_LOSSES_A2_MB_IL
expected_losses["a3"] = test_data.EXPECTED_LOSSES_A3_MB_IL
self._verify_loss_curves(
expected_poes, expected_losses, QA_OUTPUT_DIR, 0.05,
"%s/insured_loss_curves-insured-loss-block=#%s-block#0.xml")
finally:
rmtree(QA_OUTPUT_DIR)
def _verify_aggregate_curve(self, expected_poes,
expected_losses, output_dir, tol):
job = OqJob.objects.latest("id")
[output] = Output.objects.filter(
oq_job=job.id, output_type="agg_loss_curve")
export_agg_loss_curve(output, output_dir)
filename = "%s/aggregate_loss_curve.xml" % output_dir
root = self._root(filename)
xpath = "//nrml:aggregateLossCurve//nrml:poE"
poes = [float(x) for x in self._get(root, xpath).split()]
self.assertTrue(numpy.allclose(
poes, expected_poes, atol=0.0, rtol=tol))
xpath = "//nrml:aggregateLossCurve//nrml:loss"
losses = [float(x) for x in self._get(root, xpath).split()]
self.assertTrue(numpy.allclose(
losses, expected_losses, atol=0.0, rtol=0.05))
def _verify_aggregate_curve(self, expected_poes, expected_losses,
output_dir, tol):
job = OqJob.objects.latest("id")
[output] = Output.objects.filter(oq_job=job.id,
output_type="agg_loss_curve")
export_agg_loss_curve(output, output_dir)
filename = "%s/aggregate_loss_curve.xml" % output_dir
root = self._root(filename)
xpath = "//nrml:aggregateLossCurve//nrml:poE"
poes = [float(x) for x in self._get(root, xpath).split()]
self.assertTrue(numpy.allclose(poes, expected_poes, atol=0.0,
rtol=tol))
xpath = "//nrml:aggregateLossCurve//nrml:loss"
losses = [float(x) for x in self._get(root, xpath).split()]
self.assertTrue(
numpy.allclose(losses, expected_losses, atol=0.0, rtol=0.05))
