def event_based_bcr(job_id, hazard, seed,
vulnerability_function, vulnerability_function_retrofitted,
output_containers, time_span, tses,
loss_curve_resolution, asset_correlation,
asset_life_expectancy, interest_rate):
"""
Celery task for the BCR risk calculator based on the event based
calculator.
Instantiates risklib calculators, computes bcr
and stores results to db in a single transaction.
:param int job_id:
ID of the currently running job.
:param dict hazard:
A dictionary mapping IDs of
:class:`openquake.engine.db.models.Output` (with output_type set
to 'gmf_collection') to a tuple where the first element is a list
of list (one for each asset) with the ground motion values used by the
calculation, and the second element is the corresponding weight.
:param output_containers: A dictionary mapping hazard Output ID to
a tuple with only the ID of the
:class:`openquake.engine.db.models.BCRDistribution` output container
used to store the computed bcr distribution
:param float time_span:
Time Span of the hazard calculation.
:param float tses:
Time of the Stochastic Event Set.
:param int loss_curve_resolution:
Resolution of the computed loss curves (number of points).
:param int seed:
Seed used to generate random values.
:param float asset_correlation:
asset correlation (0 uncorrelated, 1 perfectly correlated).
:param float interest_rate
The interest rate used in the Cost Benefit Analysis.
:param float asset_life_expectancy
The life expectancy used for every asset.
"""
for hazard_output_id, hazard_data in hazard.items():
hazard_getter, _ = hazard_data
(bcr_distribution_id,) = output_containers[hazard_output_id]
# FIXME(lp). We should not pass the exact same seed for
# different hazard
calc_original = api.ProbabilisticEventBased(
vulnerability_function, curve_resolution=loss_curve_resolution,
time_span=time_span, tses=tses,
seed=seed, correlation=asset_correlation)
calc_retrofitted = api.ProbabilisticEventBased(
vulnerability_function_retrofitted,
curve_resolution=loss_curve_resolution,
time_span=time_span, tses=tses,
seed=seed, correlation=asset_correlation)
with logs.tracing('getting hazard'):
assets, gmvs_ruptures, missings = hazard_getter()
if len(assets):
ground_motion_values = numpy.array(gmvs_ruptures)[:, 0]
else:
# we are relying on the fact that if all the
# hazard_getter in this task will either return some
# results or they all return an empty result set.
logs.LOG.info("Exit from task as no asset could be processed")
base.signal_task_complete(job_id=job_id,
num_items=len(missings))
return
with logs.tracing('computing risk'):
_, original_loss_curves = calc_original(ground_motion_values)
_, retrofitted_loss_curves = calc_retrofitted(ground_motion_values)
eal_original = [
scientific.mean_loss(*original_loss_curves[i].xy)
for i in range(len(assets))]
eal_retrofitted = [
scientific.mean_loss(*retrofitted_loss_curves[i].xy)
for i in range(len(assets))]
bcr_results = [
scientific.bcr(
eal_original[i], eal_retrofitted[i],
interest_rate, asset_life_expectancy,
asset.value, asset.retrofitting_cost)
for i, asset in enumerate(assets)]
with logs.tracing('writing results'):
with transaction.commit_on_success(using='reslt_writer'):
for i, asset in enumerate(assets):
general.write_bcr_distribution(
bcr_distribution_id, asset,
eal_original[i], eal_retrofitted[i], bcr_results[i])
base.signal_task_complete(job_id=job_id,
num_items=len(assets) + len(missings))
def classical_bcr(
job_id,
hazard,
vulnerability_function,
vulnerability_function_retrofitted,
output_containers,
lrem_steps_per_interval,
asset_life_expectancy,
interest_rate,
):
"""
Celery task for the BCR risk calculator based on the classical
calculator.
Instantiates risklib calculators, computes BCR and stores the
results to db in a single transaction.
:param int job_id:
ID of the currently running job
:param dict hazard:
A dictionary mapping IDs of
:class:`openquake.engine.db.models.Output` (with output_type set
to 'hazard_curve') to a tuple where the first element is an instance of
:class:`..hazard_getters.HazardCurveGetter, and the second element is the
corresponding weight.
:param output_containers: A dictionary mapping hazard Output ID to
a tuple with only the ID of the
:class:`openquake.engine.db.models.BCRDistribution` output container
used to store the computed bcr distribution
:param int lrem_steps_per_interval
Steps per interval used to compute the Loss Ratio Exceedance matrix
:param float interest_rate
The interest rate used in the Cost Benefit Analysis
:param float asset_life_expectancy
The life expectancy used for every asset
"""
calc_original = api.Classical(vulnerability_function, lrem_steps_per_interval)
calc_retrofitted = api.Classical(vulnerability_function_retrofitted, lrem_steps_per_interval)
for hazard_output_id, hazard_data in hazard.items():
hazard_getter, _ = hazard_data
(bcr_distribution_id,) = output_containers[hazard_output_id]
with logs.tracing("getting hazard"):
assets, hazard_curves, missings = hazard_getter()
with logs.tracing("computing original losses"):
original_loss_curves = calc_original(hazard_curves)
retrofitted_loss_curves = calc_retrofitted(hazard_curves)
eal_original = [scientific.mean_loss(*original_loss_curves[i].xy) for i in range(len(assets))]
eal_retrofitted = [scientific.mean_loss(*retrofitted_loss_curves[i].xy) for i in range(len(assets))]
bcr_results = [
scientific.bcr(
eal_original[i],
eal_retrofitted[i],
interest_rate,
asset_life_expectancy,
asset.value,
asset.retrofitting_cost,
)
for i, asset in enumerate(assets)
]
with logs.tracing("writing results"):
with transaction.commit_on_success(using="reslt_writer"):
for i, asset in enumerate(assets):
general.write_bcr_distribution(
bcr_distribution_id, asset, eal_original[i], eal_retrofitted[i], bcr_results[i]
)
base.signal_task_complete(job_id=job_id, num_items=len(assets) + len(missings))
