def _compute_bcr(self, block_id):
"""
Calculate and store in the kvs the benefit-cost ratio data for block.
A value is stored with key :func:`openquake.kvs.tokens.bcr_block_key`.
See :func:`openquake.risk.job.general.compute_bcr_for_block` for result
data structure spec.
"""
result = defaultdict(list)
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
vulnerability_model_original = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
vulnerability_model_retrofitted = (
vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id, retrofitted=True))
assets_getter = lambda site: general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
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()}
bcr = api.bcr(api.probabilistic_event_based(
vulnerability_model_original, loss_histogram_bins, seed,
correlation_type), api.probabilistic_event_based(
vulnerability_model_retrofitted, loss_histogram_bins, seed,
correlation_type), float(self.job_ctxt.params["INTEREST_RATE"]),
float(self.job_ctxt.params["ASSET_LIFE_EXPECTANCY"]))
for asset_output in api.compute_on_sites(
block.sites, assets_getter, hazard_getter, bcr):
asset = asset_output.asset
result[(asset.site.x, asset.site.y)].append(({
"bcr": asset_output.bcr,
"eal_original": asset_output.eal_original,
"eal_retrofitted": asset_output.eal_retrofitted},
asset.asset_ref))
bcr_block_key = kvs.tokens.bcr_block_key(
self.job_ctxt.job_id, block_id)
result = result.items()
kvs.set_value_json_encoded(bcr_block_key, result)
LOGGER.debug("bcr result for block %s: %r", block_id, result)
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
