def test_bcr_risk_export(self):
# Tests that outputs of a risk classical calculation are
# exported
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.demo_file('classical_bcr/job.ini')
# run the calculation to create something to export
retcode = helpers.run_risk_job_sp(cfg, self.hazard_id,
silence=True)
self.assertEqual(0, retcode)
job = models.OqJob.objects.latest('id')
outputs = export_core.get_outputs(job.id)
expected_outputs = 1 # 1 bcr distribution
self.assertEqual(expected_outputs, len(outputs))
# Export the loss curves:
distribution = outputs.filter(output_type='bcr_distribution')[0]
rc_files = risk.export(distribution.id, target_dir)
self.assertEqual(1, len(rc_files))
for f in rc_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
def test_classical_risk_export(self):
# Tests that outputs of a risk classical calculation are exported
target_dir = tempfile.mkdtemp()
try:
cfg = helpers.demo_file('classical_psha_based_risk/job.ini')
# run the calculation to create something to export
retcode = helpers.run_risk_job_sp(cfg, self.hazard_id,
silence=True)
self.assertEqual(0, retcode)
job = models.OqJob.objects.latest('id')
outputs = export_core.get_outputs(job.id)
expected_outputs = 4 # 1 loss curve set + 3 loss curve map set
self.assertEqual(expected_outputs, len(outputs))
# Export the loss curves:
curves = outputs.filter(output_type='loss_curve')
rc_files = []
for curve in curves:
rc_files.extend(risk.export(curve.id, target_dir))
self.assertEqual(1, len(rc_files))
for f in rc_files:
self._test_exported_file(f)
# Test loss map export as well.
maps = outputs.filter(output_type='loss_map')
lm_files = sum(
[risk.export(loss_map.id, target_dir)
for loss_map in maps], [])
self.assertEqual(3, len(lm_files))
for f in lm_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)
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_hazard_job(haz_cfg)
# Run the risk on all outputs produced by the haz calc:
risk_job = helpers.run_risk_job(
risk_cfg, hazard_calculation_id=haz_job.hazard_calculation.id
)
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(risk.export(o.id, target_dir, 'xml'))
for o in event_loss_curve_outputs:
loss_curve_files.append(risk.export(o.id, target_dir, 'xml'))
agg_loss_curve_files = []
for o in agg_loss_curve_outputs:
agg_loss_curve_files.append(
risk.export(o.id, target_dir, 'xml')
)
event_loss_table_files = []
for o in event_loss_tables:
event_loss_table_files.append(
risk.export(o.id, target_dir, 'xml')
)
loss_map_files = []
for o in loss_map_outputs:
loss_map_files.append(risk.export(o.id, target_dir, 'xml'))
self.assertEqual(38, 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 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_hazard_job(haz_cfg)
# Run the risk on all outputs produced by the haz calc:
risk_job = helpers.run_risk_job(
risk_cfg, hazard_calculation_id=haz_job.hazard_calculation.id
)
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(risk.export(o.id, target_dir, 'xml'))
loss_map_files = []
for o in loss_map_outputs:
loss_map_files.append(risk.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 test_event_based_risk_export(self):
# Tests that outputs of a risk classical calculation are exported
target_dir = tempfile.mkdtemp()
try:
# use get_risk_job to create a fake GmfCollection
job, _ = helpers.get_risk_job('event_based_risk/job.ini',
'event_based_hazard/job.ini',
'gmf')
cfg = helpers.demo_file('event_based_risk/job.ini')
# run the calculation to create something to export
# at the moment, only gmf for a specific realization are
# supported as hazard input
retcode = helpers.run_risk_job_sp(
cfg, silence=True,
hazard_id=job.risk_calculation.hazard_output.id)
self.assertEqual(0, retcode)
job = models.OqJob.objects.latest('id')
outputs = export_core.get_outputs(job.id)
# 1 loss curve set + 3 loss curve map set + 1 insured + 1 aggregate
expected_outputs = 6
self.assertEqual(expected_outputs, len(outputs))
# Export the loss curves...
curves = outputs.filter(output_type='loss_curve')
rc_files = []
for curve in curves:
rc_files.extend(risk.export(curve.id, target_dir))
self.assertEqual(1, len(rc_files))
for f in rc_files:
self._test_exported_file(f)
# ... loss map ...
maps = outputs.filter(output_type='loss_map')
lm_files = sum(
[risk.export(loss_map.id, target_dir)
for loss_map in maps], [])
self.assertEqual(3, len(lm_files))
for f in lm_files:
self._test_exported_file(f)
# ... aggregate losses...
maps = outputs.filter(output_type='agg_loss_curve')
lm_files = sum(
[risk.export(loss_map.id, target_dir)
for loss_map in maps], [])
self.assertEqual(1, len(lm_files))
for f in lm_files:
self._test_exported_file(f)
# and insured losses.
maps = outputs.filter(output_type='ins_loss_curve')
lm_files = sum(
[risk.export(loss_map.id, target_dir)
for loss_map in maps], [])
self.assertEqual(1, len(lm_files))
for f in lm_files:
self._test_exported_file(f)
finally:
shutil.rmtree(target_dir)