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.
"""
job_ctxt = self.job_ctxt
points = list(general.Block.from_kvs(
job_ctxt.job_id, block_id).grid(job_ctxt.region))
hazard_curves = dict((point.site, self._get_db_curve(point.site))
for point in points)
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on hazard curve"
job_profile = self.job_ctxt.oq_job_profile
hazard_curve = hazard_curves[point.site]
loss_ratio_curve = compute_loss_ratio_curve(
vuln_function, hazard_curve,
job_profile.lrem_steps_per_interval)
return compute_loss_curve(loss_ratio_curve, asset.value)
bcr = general.compute_bcr_for_block(job_ctxt.job_id, points,
get_loss_curve, float(job_ctxt.params['INTEREST_RATE']),
float(job_ctxt.params['ASSET_LIFE_EXPECTANCY'])
)
bcr_block_key = kvs.tokens.bcr_block_key(job_ctxt.job_id, block_id)
kvs.set_value_json_encoded(bcr_block_key, bcr)
LOGGER.debug('bcr result for block %s: %r', block_id, bcr)
return True
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.
"""
job_ctxt = self.job_ctxt
points = list(
general.Block.from_kvs(job_ctxt.job_id,
block_id).grid(job_ctxt.region))
hazard_curves = dict(
(point.site, self._get_db_curve(point.site)) for point in points)
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on hazard curve"
job_profile = self.job_ctxt.oq_job_profile
hazard_curve = hazard_curves[point.site]
loss_ratio_curve = compute_loss_ratio_curve(
vuln_function, hazard_curve,
job_profile.lrem_steps_per_interval)
return compute_loss_curve(loss_ratio_curve, asset.value)
bcr = general.compute_bcr_for_block(
job_ctxt.job_id, points, get_loss_curve,
float(job_ctxt.params['INTEREST_RATE']),
float(job_ctxt.params['ASSET_LIFE_EXPECTANCY']))
bcr_block_key = kvs.tokens.bcr_block_key(job_ctxt.job_id, block_id)
kvs.set_value_json_encoded(bcr_block_key, bcr)
LOGGER.debug('bcr result for block %s: %r', block_id, bcr)
return True
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.
"""
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
def get_loss_curve(site, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmvs = self._get_gmvs_at(
general.hazard_input_site(self.job_ctxt, site))
gmf_slice = {
"IMLs": gmvs,
"TSES": self._tses(),
"TimeSpan": self._time_span()
}
loss_ratios = general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function,
gmf_slice,
epsilon_provider,
asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
result = general.compute_bcr_for_block(
self.job_ctxt, block.sites, get_loss_curve,
float(self.job_ctxt.params['INTEREST_RATE']),
float(self.job_ctxt.params['ASSET_LIFE_EXPECTANCY']))
bcr_block_key = kvs.tokens.bcr_block_key(self.job_ctxt.job_id,
block_id)
kvs.set_value_json_encoded(bcr_block_key, result)
LOGGER.debug('bcr result for block %s: %r', block_id, result)
return aggregate_curve.losses
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.
"""
self.slice_gmfs(block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
points = list(
general.Block.from_kvs(self.job_ctxt.job_id,
block_id).grid(self.job_ctxt.region))
gmf_slices = dict(
(point.site,
kvs.get_value_json_decoded(
kvs.tokens.gmf_set_key(self.job_ctxt.job_id, point.column,
point.row))) for point in points)
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmf_slice = gmf_slices[point.site]
loss_ratios = general.compute_loss_ratios(vuln_function, gmf_slice,
epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function,
gmf_slice,
epsilon_provider,
asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
result = general.compute_bcr_for_block(
self.job_ctxt.job_id, points, get_loss_curve,
float(self.job_ctxt.params['INTEREST_RATE']),
float(self.job_ctxt.params['ASSET_LIFE_EXPECTANCY']))
bcr_block_key = kvs.tokens.bcr_block_key(self.job_ctxt.job_id,
block_id)
kvs.set_value_json_encoded(bcr_block_key, result)
LOGGER.debug('bcr result for block %s: %r', block_id, result)
return aggregate_curve.losses
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.
"""
self.slice_gmfs(block_id)
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
points = list(general.Block.from_kvs(
self.job_ctxt.job_id, block_id).grid(self.job_ctxt.region))
gmf_slices = dict(
(point.site, kvs.get_value_json_decoded(
kvs.tokens.gmf_set_key(self.job_ctxt.job_id, point.column,
point.row)
))
for point in points
)
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
def get_loss_curve(point, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmf_slice = gmf_slices[point.site]
loss_ratios = general.compute_loss_ratios(
vuln_function, gmf_slice, epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
result = general.compute_bcr_for_block(self.job_ctxt.job_id, points,
get_loss_curve, float(self.job_ctxt.params['INTEREST_RATE']),
float(self.job_ctxt.params['ASSET_LIFE_EXPECTANCY'])
)
bcr_block_key = kvs.tokens.bcr_block_key(self.job_ctxt.job_id,
block_id)
kvs.set_value_json_encoded(bcr_block_key, result)
LOGGER.debug('bcr result for block %s: %r', block_id, result)
return aggregate_curve.losses
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.
"""
# aggregate the losses for this block
aggregate_curve = general.AggregateLossCurve()
block = general.Block.from_kvs(self.job_ctxt.job_id, block_id)
epsilon_provider = general.EpsilonProvider(self.job_ctxt.params)
def get_loss_curve(site, vuln_function, asset):
"Compute loss curve basing on GMF data"
gmvs = self._get_gmvs_at(general.hazard_input_site(
self.job_ctxt, site))
gmf_slice = {"IMLs": gmvs, "TSES": self._tses(),
"TimeSpan": self._time_span()}
loss_ratios = general.compute_loss_ratios(
vuln_function, gmf_slice, epsilon_provider, asset)
loss_ratio_curve = general.compute_loss_ratio_curve(
vuln_function, gmf_slice, epsilon_provider, asset,
self.job_ctxt.oq_job_profile.loss_histogram_bins,
loss_ratios=loss_ratios)
aggregate_curve.append(loss_ratios * asset.value)
return loss_ratio_curve.rescale_abscissae(asset.value)
result = general.compute_bcr_for_block(self.job_ctxt, block.sites,
get_loss_curve, float(self.job_ctxt.params['INTEREST_RATE']),
float(self.job_ctxt.params['ASSET_LIFE_EXPECTANCY']))
bcr_block_key = kvs.tokens.bcr_block_key(
self.job_ctxt.job_id, block_id)
kvs.set_value_json_encoded(bcr_block_key, result)
LOGGER.debug('bcr result for block %s: %r', block_id, result)
return aggregate_curve.losses
