def execute(self):
if self.oqparam.hazard_calculation_id:
def saving_sources_by_task(allargs, dstore):
return allargs
else:
from openquake.calculators.classical import saving_sources_by_task
self.gmdata = {}
self.offset = 0
self.gmf_size = 0
self.indices = collections.defaultdict(list) # sid, idx -> indices
acc = self.zerodict()
with self.monitor('managing sources', autoflush=True):
allargs = self.gen_args(self.monitor('classical'))
iterargs = saving_sources_by_task(allargs, self.datastore)
if isinstance(allargs, list):
# there is a trick here: if the arguments are known
# (a list, not an iterator), keep them as a list
# then the Starmap will understand the case of a single
# argument tuple and it will run in core the task
iterargs = list(iterargs)
if self.oqparam.ground_motion_fields is False:
logging.info('Generating ruptures only')
ires = parallel.Starmap(self.core_task.__func__,
iterargs).submit_all()
acc = ires.reduce(self.agg_dicts, acc)
if self.oqparam.hazard_calculation_id is None:
with self.monitor('store source_info', autoflush=True):
self.store_source_info(self.csm.infos, acc)
self.check_overflow() # check the number of events
base.save_gmdata(self, self.R)
if self.indices:
N = len(self.sitecol.complete)
logging.info('Saving gmf_data/indices')
with self.monitor('saving gmf_data/indices',
measuremem=True,
autoflush=True):
dset = self.datastore.create_dset('gmf_data/indices',
hdf5.vuint32,
shape=(N, 2),
fillvalue=None)
for sid in self.sitecol.complete.sids:
dset[sid, 0] = self.indices[sid, 0]
dset[sid, 1] = self.indices[sid, 1]
elif (self.oqparam.ground_motion_fields
and 'ucerf' not in self.oqparam.calculation_mode):
raise RuntimeError('No GMFs were generated, perhaps they were '
'all below the minimum_intensity threshold')
return acc
def execute(self):
with self.monitor('managing sources', autoflush=True):
allargs = self.gen_args(self.csm, self.monitor('classical'))
iterargs = saving_sources_by_task(allargs, self.datastore)
if isinstance(allargs, list):
# there is a trick here: if the arguments are known
# (a list, not an iterator), keep them as a list
# then the Starmap will understand the case of a single
# argument tuple and it will run in core the task
iterargs = list(iterargs)
acc = parallel.Starmap(self.core_task.__func__, iterargs).reduce(
self.agg_dicts, self.zerodict())
with self.monitor('store source_info', autoflush=True):
self.store_source_info(self.csm.infos, acc)
return acc
