def execute(self):
"""
Method responsible for the distribution strategy. It divides
the considered exposure into chunks of homogeneous assets
(i.e. having the same taxonomy).
"""
# create a Gmf output for each realization
self.hcalc = EBHC(self.hc.oqjob)
self.hcalc.source_model_lt = SourceModelLogicTree.from_hc(self.hc)
with db.transaction.commit_on_success(using='job_init'):
# TODO: think about how to remove the need for .delete()
# one should retrieve only the latest realizations
# for a given hazard calculation; alternatively, the
# realizations should be associated to RiskCalculation,
# not to HazardCalculation
models.LtRealization.objects.filter(
lt_model__hazard_calculation=self.hc).delete()
self.hcalc.initialize_realizations()
for rlz in self.hcalc._get_realizations():
output = models.Output.objects.create(
oq_job=self.job,
display_name='GMF rlz-%s' % rlz.id,
output_type='gmf')
models.Gmf.objects.create(output=output, lt_realization=rlz)
self.compute_risk()
class EventBasedFRRiskCalculator(core.EventBasedRiskCalculator):
def pre_execute(self):
"""
Inherited from core.EventBasedRiskCalculator.pre_execute.
Enforces no correlation, both on GMFs and assets.
"""
correl_model = models.get_correl_model(self.job)
assert correl_model is None, correl_model
assert not self.rc.asset_correlation, self.rc.asset_correlation
core.EventBasedRiskCalculator.pre_execute(self)
@EnginePerformanceMonitor.monitor
def execute(self):
"""
Method responsible for the distribution strategy. It divides
the considered exposure into chunks of homogeneous assets
(i.e. having the same taxonomy).
"""
# create a Gmf output for each realization
self.hcalc = EBHC(self.hc.oqjob)
self.hcalc.source_model_lt = SourceModelLogicTree.from_hc(self.hc)
with db.transaction.commit_on_success(using='job_init'):
# TODO: think about how to remove the need for .delete()
# one should retrieve only the latest realizations
# for a given hazard calculation; alternatively, the
# realizations should be associated to RiskCalculation,
# not to HazardCalculation
models.LtRealization.objects.filter(
lt_model__hazard_calculation=self.hc).delete()
self.hcalc.initialize_realizations()
for rlz in self.hcalc._get_realizations():
output = models.Output.objects.create(
oq_job=self.job,
display_name='GMF rlz-%s' % rlz.id,
output_type='gmf')
models.Gmf.objects.create(output=output, lt_realization=rlz)
self.compute_risk()
def compute_risk(self):
"""
Generate the GMFs and optionally the hazard curves too, then
compute the risk.
"""
getter_builders = []
risk_models = []
with self.monitor('associating assets<->sites'):
for risk_model in self.risk_models.itervalues():
logs.LOG.info('associating assets<->sites for taxonomy %s',
risk_model.taxonomy)
try:
with db.transaction.commit_on_success(using='job_init'):
gbuilder = GetterBuilder(self.rc, risk_model.taxonomy)
getter_builders.append(gbuilder)
risk_models.append(risk_model)
except AssetSiteAssociationError as e:
logs.LOG.warn(str(e))
continue
# notice that here the commit is really needed, since
# combine_builders save the loss curve containers on the db
with db.transaction.commit_on_success(using='job_init'):
outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
args = []
# compute the risk by splitting by sites
for sites in split_site_collection(
self.hc.site_collection, self.concurrent_tasks):
args.append((self.job.id, sites, self.rc,
risk_models, getter_builders, outputdict,
self.calculator_parameters))
self.acc = tasks.map_reduce(event_based_fr, args, self.agg_result, {})
