def compute_risk(self):
"""
Generate the GMFs and optionally the hazard curves too, then
compute the risk.
"""
getter_builders = []
risk_models = []
with self.monitor('associating assets<->sites'):
for risk_model in self.risk_models.itervalues():
logs.LOG.info('associating assets<->sites for taxonomy %s',
risk_model.taxonomy)
try:
with db.transaction.commit_on_success(using='job_init'):
gbuilder = GetterBuilder(self.rc, risk_model.taxonomy)
getter_builders.append(gbuilder)
risk_models.append(risk_model)
except AssetSiteAssociationError as e:
logs.LOG.warn(str(e))
continue
# notice that here the commit is really needed, since
# combine_builders save the loss curve containers on the db
with db.transaction.commit_on_success(using='job_init'):
outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
args = []
# compute the risk by splitting by sites
for sites in split_site_collection(
self.hc.site_collection, self.concurrent_tasks):
args.append((self.job.id, sites, self.rc,
risk_models, getter_builders, outputdict,
self.calculator_parameters))
self.acc = tasks.map_reduce(event_based_fr, args, self.agg_result, {})
def task_arg_gen(self):
"""
Yields the argument to be submitted to run_subtasks. Tasks with
fewer assets are submitted first.
"""
outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
ct = sorted((counts, taxonomy) for taxonomy, counts
in self.taxonomies_asset_count.iteritems())
for counts, taxonomy in ct:
yield self.job.id, self, taxonomy, counts, outputdict
def execute(self):
"""
Method responsible for the distribution strategy.
"""
self.outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
ct = sorted((counts, taxonomy) for taxonomy, counts
in self.taxonomies_asset_count.iteritems())
self.acc = tasks.apply_reduce(
build_getters, (self.job.id, ct, self),
lambda acc, otm: otm.aggregate_results(self.agg_result, acc),
self.acc, self.concurrent_tasks)
def prepare_risk(self):
"""
Associate assets and sites.
"""
self.outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
# build the initializers hazard -> risk
ct = sorted((counts, taxonomy) for taxonomy, counts
in self.taxonomies_asset_count.iteritems())
tasks.apply_reduce(prepare_risk, (ct, self, self.monitor),
concurrent_tasks=self.concurrent_tasks)
def prepare_risk(self):
"""
Associate assets and sites and for some calculator generate the
epsilons.
"""
self.outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
# build the initializers hazard -> risk
ct = sorted((counts, taxonomy) for taxonomy, counts
in self.taxonomies_asset_count.iteritems())
tasks.apply_reduce(prepare_risk, (self.job.id, ct, self.rc),
concurrent_tasks=self.concurrent_tasks)
def task_arg_gen(self, block_size):
"""
Generator function for creating the arguments for each task.
It is responsible for the distribution strategy. It divides
the considered exposure into chunks of homogeneous assets
(i.e. having the same taxonomy). The chunk size is given by
the `block_size` openquake config parameter
:param int block_size:
The number of work items per task (sources, sites, etc.).
:returns:
An iterator over a list of arguments. Each contains:
1. the job id
2. a getter object needed to get the hazard data
3. the needed risklib calculators
4. the output containers to be populated
5. the specific calculator parameter set
"""
output_containers = writers.combine_builders(
[builder(self) for builder in self.output_builders])
num_tasks = 0
for taxonomy, assets_nr in self.taxonomies_asset_count.items():
asset_offsets = range(0, assets_nr, block_size)
for offset in asset_offsets:
with logs.tracing("getting assets"):
assets = models.ExposureData.objects.get_asset_chunk(
self.rc, taxonomy, offset, block_size)
calculation_units = [
self.calculation_unit(loss_type, assets)
for loss_type in models.loss_types(self.risk_models)
]
num_tasks += 1
yield [
self.job.id, calculation_units, output_containers,
self.calculator_parameters
]
# sanity check to protect against future changes of the distribution
# logic
expected_tasks = self.expected_tasks(block_size)
if num_tasks != expected_tasks:
raise RuntimeError('Expected %d tasks, generated %d!' %
(expected_tasks, num_tasks))
def prepare_risk(self):
"""
Associate assets and sites and for some calculator generate the
epsilons.
"""
self.outputdict = writers.combine_builders(
[ob(self) for ob in self.output_builders])
# build the initializers hazard -> risk
ct = sorted(
(counts, taxonomy)
for taxonomy, counts in self.taxonomies_asset_count.iteritems())
tasks.apply_reduce(prepare_risk, (ct, self, self.monitor),
concurrent_tasks=self.concurrent_tasks)
def task_arg_gen(self, block_size):
"""
Generator function for creating the arguments for each task.
It is responsible for the distribution strategy. It divides
the considered exposure into chunks of homogeneous assets
(i.e. having the same taxonomy). The chunk size is given by
the `block_size` openquake config parameter
:param int block_size:
The number of work items per task (sources, sites, etc.).
:returns:
An iterator over a list of arguments. Each contains:
1. the job id
2. a getter object needed to get the hazard data
3. the needed risklib calculators
4. the output containers to be populated
5. the specific calculator parameter set
"""
output_containers = writers.combine_builders(
[builder(self) for builder in self.output_builders])
num_tasks = 0
for taxonomy, assets_nr in self.taxonomies_asset_count.items():
asset_offsets = range(0, assets_nr, block_size)
for offset in asset_offsets:
with logs.tracing("getting assets"):
assets = models.ExposureData.objects.get_asset_chunk(
self.rc, taxonomy, offset, block_size)
calculation_units = [
self.calculation_unit(loss_type, assets)
for loss_type in models.loss_types(self.risk_models)]
num_tasks += 1
yield [self.job.id,
calculation_units,
output_containers,
self.calculator_parameters]
# sanity check to protect against future changes of the distribution
# logic
expected_tasks = self.expected_tasks(block_size)
if num_tasks != expected_tasks:
raise RuntimeError('Expected %d tasks, generated %d!' % (
expected_tasks, num_tasks))
