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_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
seed, correlation_type = self._get_correlation_type()
job_id = self.job_ctxt.job_id
def hazard_getter(site):
point = self.job_ctxt.region.grid.point_at(site)
gmvs = self._get_gmvs_at(general.hazard_input_site(
self.job_ctxt, site))
gmf = {"IMLs": gmvs, "TSES": self._tses(),
"TimeSpan": self._time_span()}
return point, gmf
def on_asset_complete(asset, point, loss_ratio_curve,
loss_curve, loss_conditionals,
insured_curve, insured_loss_ratio_curve):
self._loss_ratio_curve_on_kvs(
point.column, point.row, loss_ratio_curve, asset)
self._loss_curve_on_kvs(
point.column, point.row, loss_curve, asset)
for loss_poe, loss_conditional in loss_conditionals.items():
key = kvs.tokens.loss_key(job_id,
point.row, point.column,
asset.asset_ref, loss_poe)
kvs.get_client().set(key, loss_conditional)
if self.job_ctxt.params.get("INSURED_LOSSES"):
self._insured_loss_curve_on_kvs(
point.column, point.row, insured_curve, asset)
self._insured_loss_ratio_curve_on_kvs(
point.column, point.row, insured_loss_ratio_curve, asset)
losses = event_based.compute(
block.sites,
lambda site: general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site),
self.vulnerability_curves,
hazard_getter,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
general.conditional_loss_poes(self.job_ctxt.params),
self.job_ctxt.params.get("INSURED_LOSSES"),
seed, correlation_type,
on_asset_complete)
return losses
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
hazard_curve = self._get_db_curve(
general.hazard_input_site(self.job_ctxt, site))
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
loss_ratio_curve = self.compute_loss_ratio_curve(
point, asset, hazard_curve, vuln_curves)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point, loss_ratio_curve, asset)
for poe in conditional_loss_poes(self.job_ctxt.params):
compute_conditional_loss(
self.job_ctxt.job_id, point.column,
point.row, loss_curve, asset, poe)
return True
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = general.Block.from_kvs(self.calc_proxy.job_id, block_id)
vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.calc_proxy.job_id)
for point in block.grid(self.calc_proxy.region):
hazard_curve = self._get_db_curve(point.site)
asset_key = kvs.tokens.asset_key(self.calc_proxy.job_id,
point.row, point.column)
for asset in kvs.get_list_json_decoded(asset_key):
LOGGER.debug("processing asset %s" % (asset))
loss_ratio_curve = self.compute_loss_ratio_curve(
point, asset, hazard_curve, vuln_curves)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(point,
loss_ratio_curve, asset)
for loss_poe in conditional_loss_poes(
self.calc_proxy.params):
compute_conditional_loss(
self.calc_proxy.job_id, point.column,
point.row, loss_curve, asset, loss_poe)
return True
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
hazard_curve = self._get_db_curve(
general.hazard_input_site(self.job_ctxt, site))
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
loss_ratio_curve = self.compute_loss_ratio_curve(
point, asset, hazard_curve, vuln_curves)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point, loss_ratio_curve, asset)
for poe in conditional_loss_poes(self.job_ctxt.params):
compute_conditional_loss(self.job_ctxt.job_id,
point.column, point.row,
loss_curve, asset, poe)
return True
def _compute_loss(self, block_id):
"""
Calculate and store in the kvs the loss data.
"""
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
for point in block.grid(self.job_ctxt.region):
hazard_curve = self._get_db_curve(point.site)
assets = self.assets_for_cell(self.job_ctxt.job_id, point.site)
for asset in assets:
LOGGER.debug("processing asset %s" % asset)
loss_ratio_curve = self.compute_loss_ratio_curve(
point, asset, hazard_curve, vuln_curves)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point, loss_ratio_curve, asset)
for poe in conditional_loss_poes(self.job_ctxt.params):
compute_conditional_loss(
self.job_ctxt.job_id, point.column,
point.row, loss_curve, asset, poe)
return True
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.slice_gmfs(block_id)
self.vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
key = kvs.tokens.gmf_set_key(
self.job_ctxt.job_id, point.column, point.row)
gmf = kvs.get_value_json_decoded(key)
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self.compute_loss_ratios(asset, gmf)
loss_ratio_curve = self.compute_loss_ratio_curve(
point.column, point.row, asset, gmf, loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point.column, point.row, loss_ratio_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.job_ctxt.params):
general.compute_conditional_loss(
self.job_ctxt.job_id, point.column,
point.row, loss_curve, asset, loss_poe)
return aggregate_curve.losses
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.slice_gmfs(block_id)
self.vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
key = kvs.tokens.gmf_set_key(self.job_ctxt.job_id, point.column,
point.row)
gmf = kvs.get_value_json_decoded(key)
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self.compute_loss_ratios(asset, gmf)
loss_ratio_curve = self.compute_loss_ratio_curve(
point.column, point.row, asset, gmf, loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point.column, point.row, loss_ratio_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.job_ctxt.params):
general.compute_conditional_loss(
self.job_ctxt.job_id, point.column, point.row,
loss_curve, asset, loss_poe)
return aggregate_curve.losses
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.vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
gmvs = self._get_gmvs_at(general.hazard_input_site(
self.job_ctxt, site))
gmf = {"IMLs": gmvs, "TSES": self._tses(),
"TimeSpan": self._time_span()}
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self.compute_loss_ratios(asset, gmf)
loss_ratio_curve = self.compute_loss_ratio_curve(
point.column, point.row, asset, gmf, loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point.column, point.row, loss_ratio_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.job_ctxt.params):
general.compute_conditional_loss(
self.job_ctxt.job_id, point.column,
point.row, loss_curve, asset, loss_poe)
return aggregate_curve.losses
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.slice_gmfs(block_id)
self.vuln_curves = vulnerability.load_vuln_model_from_kvs(
self.calc_proxy.job_id)
block = general.Block.from_kvs(self.calc_proxy.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for point in block.grid(self.calc_proxy.region):
key = kvs.tokens.gmf_set_key(self.calc_proxy.job_id, point.column,
point.row)
gmf_slice = kvs.get_value_json_decoded(key)
asset_key = kvs.tokens.asset_key(
self.calc_proxy.job_id, point.row, point.column)
for asset in kvs.get_list_json_decoded(asset_key):
LOGGER.debug("Processing asset %s" % (asset))
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self.compute_loss_ratios(asset, gmf_slice)
loss_ratio_curve = self.compute_loss_ratio_curve(
point.column, point.row, asset, gmf_slice, loss_ratios)
aggregate_curve.append(loss_ratios * asset["assetValue"])
if loss_ratio_curve:
loss_curve = self.compute_loss_curve(
point.column, point.row, loss_ratio_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.calc_proxy.params):
general.compute_conditional_loss(
self.calc_proxy.job_id, point.column,
point.row, loss_curve, asset, loss_poe)
return aggregate_curve.losses
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_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
gmf = self._load_ground_motion_field(site)
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self._compute_loss_ratios(asset, gmf)
loss_ratio_curve = self._compute_loss_ratio_curve(
asset, gmf, loss_ratios)
self._loss_ratio_curve_on_kvs(
point.column, point.row, loss_ratio_curve, asset)
losses = loss_ratios * asset.value
aggregate_curve.append(losses)
if loss_ratio_curve:
loss_curve = self._compute_loss_curve(
loss_ratio_curve, asset)
self._loss_curve_on_kvs(point.column, point.row,
loss_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.job_ctxt.params):
general.compute_conditional_loss(
self.job_ctxt.job_id, point.column,
point.row, loss_curve, asset, loss_poe)
if self.job_ctxt.params.get("INSURED_LOSSES"):
insured_losses = general.compute_insured_losses(
asset, losses)
insured_loss_ratio_curve = (
self._compute_insured_loss_ratio_curve(
insured_losses, asset, gmf))
self._insured_loss_ratio_curve_on_kvs(point.column,
point.row, insured_loss_ratio_curve, asset)
insured_loss_curve = self._compute_loss_curve(
insured_loss_ratio_curve, asset)
self._insured_loss_curve_on_kvs(point.column,
point.row, insured_loss_curve, asset)
return aggregate_curve.losses
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_curves = vulnerability.load_vuln_model_from_kvs(
self.job_ctxt.job_id)
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
for site in block.sites:
point = self.job_ctxt.region.grid.point_at(site)
gmf = self._load_ground_motion_field(site)
assets = general.BaseRiskCalculator.assets_at(
self.job_ctxt.job_id, site)
for asset in assets:
# loss ratios, used both to produce the curve
# and to aggregate the losses
loss_ratios = self._compute_loss_ratios(asset, gmf)
loss_ratio_curve = self._compute_loss_ratio_curve(
asset, gmf, loss_ratios)
self._loss_ratio_curve_on_kvs(point.column, point.row,
loss_ratio_curve, asset)
losses = loss_ratios * asset.value
aggregate_curve.append(losses)
if loss_ratio_curve:
loss_curve = self._compute_loss_curve(
loss_ratio_curve, asset)
self._loss_curve_on_kvs(point.column, point.row,
loss_curve, asset)
for loss_poe in general.conditional_loss_poes(
self.job_ctxt.params):
general.compute_conditional_loss(
self.job_ctxt.job_id, point.column, point.row,
loss_curve, asset, loss_poe)
if self.job_ctxt.params.get("INSURED_LOSSES"):
insured_losses = general.compute_insured_losses(
asset, losses)
insured_loss_ratio_curve = (
self._compute_insured_loss_ratio_curve(
insured_losses, asset, gmf))
self._insured_loss_ratio_curve_on_kvs(
point.column, point.row, insured_loss_ratio_curve,
asset)
insured_loss_curve = self._compute_loss_curve(
insured_loss_ratio_curve, asset)
self._insured_loss_curve_on_kvs(
point.column, point.row, insured_loss_curve, asset)
return aggregate_curve.losses
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
