def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the ruptures according to their weight and
tectonic region type.
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
self.csm_info = self.datastore['csm_info']
self.sm_id = {tuple(sm.path): sm.ordinal
for sm in self.csm_info.source_models}
L = len(oq.imtls.array)
R = len(self.datastore['realizations'])
self.gmdata = {}
self.offset = 0
self.indices = collections.defaultdict(list) # sid -> indices
acc = parallel.Starmap(
self.core_task.__func__, self.gen_args()
).reduce(self.combine_pmaps_and_save_gmfs, {
r: ProbabilityMap(L) for r in range(R)})
save_gmdata(self, R)
if self.indices:
logging.info('Saving gmf_data/indices')
with self.monitor('saving gmf_data/indices', measuremem=True,
autoflush=True):
self.datastore.save_vlen(
'gmf_data/indices',
[numpy.array(self.indices[sid], indices_dt)
for sid in self.sitecol.complete.sids])
return acc
def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the ruptures according to their weight and
tectonic region type.
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
self.sesruptures = []
if self.precalc: # the ruptures are already in memory
for grp_id, sesruptures in self.precalc.result.items():
for sr in sesruptures:
self.sesruptures.append(sr)
else: # read the ruptures from the datastore
for serial in self.datastore['sescollection']:
sr = self.datastore['sescollection/' + serial]
self.sesruptures.append(sr)
self.sesruptures.sort(key=operator.attrgetter('serial'))
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
L = len(oq.imtls.array)
res = parallel.starmap(
self.core_task.__func__, self.gen_args(self.sesruptures)
).submit_all()
acc = functools.reduce(self.combine_pmaps_and_save_gmfs, res, {
rlz.ordinal: ProbabilityMap(L, 1)
for rlz in self.rlzs_assoc.realizations})
self.save_data_transfer(res)
return acc
def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the ruptures according to their weight and
tectonic region type.
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
with self.monitor('reading ruptures', autoflush=True):
ruptures_by_grp = (self.precalc.result if self.precalc else
get_ruptures_by_grp(self.datastore.parent))
self.csm_info = self.datastore['csm_info']
self.sm_id = {
tuple(sm.path): sm.ordinal
for sm in self.csm_info.source_models
}
L = len(oq.imtls.array)
rlzs = self.rlzs_assoc.realizations
res = parallel.Starmap(self.core_task.__func__,
self.gen_args(ruptures_by_grp)).submit_all()
self.gmdata = {}
acc = res.reduce(self.combine_pmaps_and_save_gmfs,
{rlz.ordinal: ProbabilityMap(L, 1)
for rlz in rlzs})
save_gmdata(self, len(rlzs))
return acc
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly)
"""
super(EventBasedRiskCalculator, self).pre_execute()
calc.check_overflow(self)
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly)
"""
super(EventBasedRiskCalculator, self).pre_execute()
calc.check_overflow(self)
if not self.riskmodel: # there is no riskmodel, exit early
self.execute = lambda: None
self.post_execute = lambda result: None
return
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')
acc = parallel.Starmap(self.core_task.__func__,
iterargs).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)
calc.check_overflow(self)
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 pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some empty files in the export directory to store the gmfs
(if any). If there were pre-existing files, they will be erased.
"""
super(EventBasedCalculator, self).pre_execute()
rlzs_by_tr_id = self.rlzs_assoc.get_rlzs_by_grp_id()
num_rlzs = {t: len(rlzs) for t, rlzs in rlzs_by_tr_id.items()}
self.sesruptures = []
for serial in self.datastore['sescollection']:
sr = self.datastore['sescollection/' + serial]
sr.set_weight(num_rlzs, {})
self.sesruptures.append(sr)
self.sesruptures.sort(key=operator.attrgetter('serial'))
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
for rlz in self.rlzs_assoc.realizations:
self.datastore.create_dset(
'gmf_data/%04d' % rlz.ordinal, calc.gmv_dt)
def pre_execute(self):
"""
Read the precomputed ruptures (or compute them on the fly) and
prepare some empty files in the export directory to store the gmfs
(if any). If there were pre-existing files, they will be erased.
"""
super(EventBasedCalculator, self).pre_execute()
rlzs_by_tr_id = self.rlzs_assoc.get_rlzs_by_trt_id()
num_rlzs = {t: len(rlzs) for t, rlzs in rlzs_by_tr_id.items()}
self.sesruptures = []
for serial in self.datastore['sescollection']:
sr = self.datastore['sescollection/' + serial]
sr.set_weight(num_rlzs, {})
self.sesruptures.append(sr)
self.sesruptures.sort(key=operator.attrgetter('serial'))
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
for rlz in self.rlzs_assoc.realizations:
self.datastore.create_dset('gmf_data/%04d' % rlz.ordinal,
calc.gmv_dt)
def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the ruptures according to their weight and
tectonic region type.
"""
oq = self.oqparam
calc.check_overflow(self)
self.csm_info = self.datastore['csm_info']
self.sm_id = {
tuple(sm.path): sm.ordinal
for sm in self.csm_info.source_models
}
L = len(oq.imtls.array)
R = self.datastore['csm_info'].get_num_rlzs()
self.gmdata = {}
self.offset = 0
self.indices = collections.defaultdict(list) # sid -> indices
ires = parallel.Starmap(self.core_task.__func__,
self.gen_args()).submit_all()
if self.precalc and self.precalc.result:
# remove the ruptures in memory to save memory
self.precalc.result.clear()
acc = ires.reduce(self.combine_pmaps_and_save_gmfs,
{r: ProbabilityMap(L)
for r in range(R)})
base.save_gmdata(self, R)
if self.indices:
logging.info('Saving gmf_data/indices')
with self.monitor('saving gmf_data/indices',
measuremem=True,
autoflush=True):
self.datastore.save_vlen('gmf_data/indices', [
numpy.array(self.indices[sid], indices_dt)
for sid in self.sitecol.complete.sids
])
else:
raise RuntimeError('No GMFs were generated, perhaps they were '
'all below the minimum_intensity threshold')
return acc
def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the ruptures according to their weight and
tectonic region type.
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
ruptures_by_grp = (self.precalc.result if self.precalc
else get_ruptures_by_grp(self.datastore.parent))
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
self.sm_id = {sm.path: sm.ordinal
for sm in self.csm.info.source_models}
L = len(oq.imtls.array)
res = parallel.starmap(
self.core_task.__func__, self.gen_args(ruptures_by_grp)
).submit_all()
acc = functools.reduce(self.combine_pmaps_and_save_gmfs, res, {
rlz.ordinal: ProbabilityMap(L, 1)
for rlz in self.rlzs_assoc.realizations})
self.save_data_transfer(res)
return acc