def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the sources according to their weight and
tectonic region type.
"""
monitor = self.monitor(self.core_task.__name__)
oq = self.oqparam
acc = self.zerodict()
self.nsites = [] # used in agg_dicts
param = dict(imtls=oq.imtls, truncation_level=oq.truncation_level,
filter_distance=oq.filter_distance)
for sm in self.csm.source_models: # one branch at the time
[grp] = sm.src_groups
gsims = self.csm.info.get_gsims(sm.ordinal)
acc = parallel.Starmap.apply(
classical, (grp, self.src_filter, gsims, param, monitor),
weight=operator.attrgetter('weight'),
concurrent_tasks=oq.concurrent_tasks,
).reduce(self.agg_dicts, acc)
ucerf = grp.sources[0].orig
logging.info('Getting background sources from %s', ucerf.source_id)
srcs = ucerf.get_background_sources(self.src_filter)
for src in srcs:
self.csm.infos[src.source_id] = source.SourceInfo(src)
acc = parallel.Starmap.apply(
classical, (srcs, self.src_filter, gsims, param, monitor),
weight=operator.attrgetter('weight'),
concurrent_tasks=oq.concurrent_tasks,
).reduce(self.agg_dicts, acc)
with self.monitor('store source_info', autoflush=True):
self.store_source_info(self.csm.infos, acc)
return acc # {grp_id: pmap}
def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the sources according to their weight and
tectonic region type.
"""
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq = self.oqparam
self.src_filter = SourceFilter(self.sitecol, oq.maximum_distance)
self.nsites = []
acc = AccumDict({
grp_id: ProbabilityMap(len(oq.imtls.array), len(gsims))
for grp_id, gsims in self.gsims_by_grp.items()
})
acc.calc_times = {}
acc.eff_ruptures = AccumDict() # grp_id -> eff_ruptures
acc.bb_dict = {} # just for API compatibility
param = dict(imtls=oq.imtls, truncation_level=oq.truncation_level)
for sm in self.csm.source_models: # one branch at the time
grp_id = sm.ordinal
gsims = self.gsims_by_grp[grp_id]
[[ucerf_source]] = sm.src_groups
ucerf_source.nsites = len(self.sitecol)
self.csm.infos[ucerf_source.source_id] = source.SourceInfo(
ucerf_source)
logging.info('Getting the background point sources')
bckgnd_sources = ucerf_source.get_background_sources(
self.src_filter)
# since there are two kinds of tasks (background and rupture_set)
# we divide the concurrent_tasks parameter by 2;
# notice the "or 1" below, to avoid issues when
# self.oqparam.concurrent_tasks is 0 or 1
ct2 = (self.oqparam.concurrent_tasks // 2) or 1
# parallelize on the background sources, small tasks
args = (bckgnd_sources, self.src_filter, gsims, param, monitor)
bg_res = parallel.Starmap.apply(classical,
args,
name='background_sources_%d' %
grp_id,
concurrent_tasks=ct2)
# parallelize by rupture subsets
rup_sets = numpy.arange(ucerf_source.num_ruptures)
taskname = 'ucerf_classical_%d' % grp_id
acc = parallel.Starmap.apply(
ucerf_classical,
(rup_sets, ucerf_source, self.src_filter, gsims, monitor),
concurrent_tasks=ct2,
name=taskname).reduce(self.agg_dicts, acc)
# compose probabilities from background sources
for pmap in bg_res:
acc[grp_id] |= pmap[grp_id]
with self.monitor('store source_info', autoflush=True):
self.store_source_info(self.csm.infos, acc)
return acc # {grp_id: pmap}
def pre_execute(self):
super().pre_execute()
self.src_filter = UcerfFilter(
self.sitecol, self.oqparam.maximum_distance)
for sm in self.csm.source_models: # one branch at the time
[grp] = sm.src_groups
for src in grp:
self.csm.infos[src.source_id] = source.SourceInfo(src)
grp.tot_ruptures += src.num_ruptures
def pre_execute(self):
super().pre_execute()
self.src_filter = UcerfFilter(self.sitecol,
self.oqparam.maximum_distance)
for sm in self.csm.source_models: # one branch at the time
[grp] = sm.src_groups
ucerf = grp.sources[0].orig
logging.info('Getting background sources from %s', ucerf.source_id)
grp.sources.extend(ucerf.get_background_sources(self.src_filter))
for src in grp:
self.csm.infos[src.source_id] = source.SourceInfo(src)
grp.tot_ruptures += src.num_ruptures
def agg(self, acc, val):
"""
Aggregated the ruptures and the calculation times
"""
for trt_id in val:
ltbrid, dt = val.calc_times[trt_id]
info = source.SourceInfo(
trt_id,
ltbrid,
source_class=UCERFSESControl.__class__.__name__,
weight=1,
sources=1,
filter_time=0,
split_time=0,
calc_time=dt)
self.infos.append(info)
return acc + val