def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = Block.from_kvs(self.job_ctxt.job_id, block_id)
vulnerability_model = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
steps = self.job_ctxt.oq_job_profile.lrem_steps_per_interval
assets_getter = lambda site: BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
hazard_getter = lambda site: (
self._get_db_curve(hazard_input_site(self.job_ctxt, site)))
calculator = api.conditional_losses(
conditional_loss_poes(self.job_ctxt.params),
api.classical(vulnerability_model, steps=steps))
for asset_output in api.compute_on_sites(block.sites,
assets_getter, hazard_getter, calculator):
location = asset_output.asset.site
point = self.job_ctxt.region.grid.point_at(
shapes.Site(location.x, location.y))
loss_key = kvs.tokens.loss_curve_key(
self.job_ctxt.job_id, point.row,
point.column, asset_output.asset.asset_ref)
kvs.get_client().set(loss_key, asset_output.loss_curve.to_json())
loss_ratio_key = kvs.tokens.loss_ratio_key(self.job_ctxt.job_id,
point.row, point.column, asset_output.asset.asset_ref)
kvs.get_client().set(loss_ratio_key,
asset_output.loss_ratio_curve.to_json())
for poe, loss in asset_output.conditional_losses.items():
key = kvs.tokens.loss_key(
self.job_ctxt.job_id, point.row, point.column,
asset_output.asset.asset_ref, poe)
kvs.get_client().set(key, loss)
def _compute_loss(self, block_id):
"""Compute risk for a block of sites, that means:
* loss ratio curves
* loss curves
* conditional losses
* (partial) aggregate loss curve
"""
self.vulnerability_model = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
seed, correlation_type = self._get_correlation_type()
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
loss_histogram_bins = self.job_ctxt.oq_job_profile.loss_histogram_bins
def hazard_getter(site):
gmvs = self._get_gmvs_at(general.hazard_input_site(
self.job_ctxt, site))
return {"IMLs": gmvs, "TSES": self._tses(),
"TimeSpan": self._time_span()}
assets_getter = lambda site: general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
probabilistic_event_based_calculator = api.probabilistic_event_based(
self.vulnerability_model, loss_histogram_bins,
seed, correlation_type)
calculator = api.conditional_losses(general.conditional_loss_poes(
self.job_ctxt.params), probabilistic_event_based_calculator)
if self.job_ctxt.params.get("INSURED_LOSSES"):
calculator = api.insured_curves(self.vulnerability_model,
loss_histogram_bins, seed, correlation_type,
api.insured_losses(calculator))
for asset_output in api.compute_on_sites(block.sites,
assets_getter, hazard_getter, calculator):
location = asset_output.asset.site
point = self.job_ctxt.region.grid.point_at(
shapes.Site(location.x, location.y))
self._loss_ratio_curve_on_kvs(
point.column, point.row, asset_output.loss_ratio_curve,
asset_output.asset)
self._loss_curve_on_kvs(
point.column, point.row, asset_output.loss_curve,
asset_output.asset)
for poe, loss in asset_output.conditional_losses.items():
key = kvs.tokens.loss_key(
self.job_ctxt.job_id, point.row, point.column,
asset_output.asset.asset_ref, poe)
kvs.get_client().set(key, loss)
if self.job_ctxt.params.get("INSURED_LOSSES"):
self._insured_loss_curve_on_kvs(
point.column, point.row,
asset_output.insured_loss_curve, asset_output.asset)
self._insured_loss_ratio_curve_on_kvs(
point.column, point.row,
asset_output.insured_loss_ratio_curve, asset_output.asset)
return probabilistic_event_based_calculator.aggregate_losses
