def gen_args(self, monitor):
"""
Generate a task for each branch
"""
oq = self.oqparam
allargs = [] # it is better to return a list; if there is single
# branch then `parallel.Starmap` will run the task in core
rlzs_by_gsim = self.csm.info.get_rlzs_by_gsim_grp()
ufilter = UcerfFilter(self.sitecol, self.oqparam.maximum_distance)
for sm_id in range(len(self.csm.source_models)):
ssm = self.csm.get_model(sm_id)
[sm] = ssm.source_models
srcs = ssm.get_sources()
for ses_idx in range(1, oq.ses_per_logic_tree_path + 1):
ses_seeds = [(ses_idx, oq.ses_seed + ses_idx)]
param = dict(ses_seeds=ses_seeds,
samples=sm.samples,
oqparam=oq,
save_ruptures=oq.save_ruptures,
filter_distance=oq.filter_distance,
gmf=oq.ground_motion_fields,
min_iml=self.get_min_iml(oq))
allargs.append(
(srcs, ufilter, rlzs_by_gsim[sm_id], param, monitor))
return allargs
def gen_args(self):
"""
Yield the arguments required by build_ruptures, i.e. the
source models, the asset collection, the riskmodel and others.
"""
oq = self.oqparam
self.L = len(self.riskmodel.lti)
self.I = oq.insured_losses + 1
min_iml = self.get_min_iml(oq)
elt_dt = numpy.dtype([('eid', U64), ('rlzi', U16),
('loss', (F32, (self.L, )))])
monitor = self.monitor('compute_losses')
src_filter = UcerfFilter(self.sitecol.complete, oq.maximum_distance)
for sm in self.csm.source_models:
if sm.samples > 1:
logging.warn('Sampling in ucerf_risk is untested')
ssm = self.csm.get_model(sm.ordinal)
for ses_idx in range(1, oq.ses_per_logic_tree_path + 1):
param = dict(ses_seeds=[(ses_idx, oq.ses_seed + ses_idx)],
samples=sm.samples,
assetcol=self.assetcol,
save_ruptures=False,
ses_ratio=oq.ses_ratio,
avg_losses=oq.avg_losses,
elt_dt=elt_dt,
min_iml=min_iml,
oqparam=oq,
insured_losses=oq.insured_losses)
yield ssm, src_filter, param, self.riskmodel, monitor
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 src_filter(self):
"""
:returns: a SourceFilter/UcerfFilter
"""
oq = self.oqparam
self.hdf5cache = self.datastore.hdf5cache()
sitecol = self.sitecol.complete if self.sitecol else None
if 'ucerf' in oq.calculation_mode:
return UcerfFilter(sitecol, oq.maximum_distance, self.hdf5cache)
return SourceFilter(sitecol, oq.maximum_distance, self.hdf5cache)
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 src_filter(self):
"""
:returns: a SourceFilter/UcerfFilter
"""
oq = self.oqparam
if getattr(self, 'sitecol', None):
sitecol = self.sitecol.complete
else: # can happen to the ruptures-only calculator
sitecol = None
if oq.is_ucerf():
return UcerfFilter(sitecol, oq.maximum_distance)
return SourceFilter(sitecol, oq.maximum_distance)
def src_filter(self, filename=None):
"""
:returns: a SourceFilter/UcerfFilter
"""
oq = self.oqparam
if getattr(self, 'sitecol', None):
sitecol = self.sitecol.complete
else: # can happen to the ruptures-only calculator
sitecol = None
filename = None
if 'ucerf' in oq.calculation_mode:
return UcerfFilter(sitecol, oq.maximum_distance, filename)
return SourceFilter(sitecol, oq.maximum_distance, filename)
def from_sources(self, param):
"""
Generate a task for each branch
"""
oq = self.oqparam
allargs = [] # it is better to return a list; if there is single
# branch then `parallel.Starmap` will run the task in core
rlzs_by_gsim = self.csm.info.get_rlzs_by_gsim_grp()
ufilter = UcerfFilter(self.sitecol, self.oqparam.maximum_distance)
for sm_id in range(len(self.csm.source_models)):
ssm = self.csm.get_model(sm_id)
[sm] = ssm.source_models
srcs = ssm.get_sources()
for ses_idx in range(oq.ses_per_logic_tree_path):
param = param.copy()
param['ses_seeds'] = [(ses_idx, oq.ses_seed + ses_idx + 1)]
allargs.append((srcs, ufilter, rlzs_by_gsim[sm_id], param))
return allargs
def pre_execute(self):
"""
parse the logic tree and source model input
"""
logging.warn('%s is still experimental', self.__class__.__name__)
oq = self.oqparam
self.read_risk_data() # read the site collection
self.csm = readinput.get_composite_source_model(oq)
self.csm.src_filter = UcerfFilter(self.sitecol, oq.maximum_distance)
logging.info('Found %d source model logic tree branches',
len(self.csm.source_models))
self.datastore['sitecol'] = self.sitecol
self.datastore['csm_info'] = self.csm_info = self.csm.info
self.rlzs_assoc = self.csm_info.get_rlzs_assoc()
self.infos = []
self.eid = collections.Counter() # sm_id -> event_id
self.sm_by_grp = self.csm_info.get_sm_by_grp()
if not self.oqparam.imtls:
raise ValueError('Missing intensity_measure_types!')
self.rupser = calc.RuptureSerializer(self.datastore)
self.precomputed_gmfs = False
def filter_csm(self):
return UcerfFilter(
self.sitecol, self.oqparam.maximum_distance), self.csm
