def gen_args(self, ruptures_by_grp):
"""
:param ruptures_by_grp: a dictionary of EBRupture objects
:yields: the arguments for compute_gmfs_and_curves
"""
oq = self.oqparam
monitor = self.monitor(self.core_task.__name__)
imts = list(oq.imtls)
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
correl_model = oq.get_correl_model()
try:
csm_info = self.csm.info
except AttributeError: # no csm
csm_info = self.datastore['csm_info']
samples_by_grp = csm_info.get_samples_by_grp()
for grp_id in ruptures_by_grp:
ruptures = ruptures_by_grp[grp_id]
if not ruptures:
continue
rlzs_by_gsim = self.rlzs_assoc.get_rlzs_by_gsim(grp_id)
for block in block_splitter(ruptures, oq.ruptures_per_block):
samples = samples_by_grp[grp_id]
getter = GmfGetter(rlzs_by_gsim, block, self.sitecol, imts,
min_iml, oq.truncation_level, correl_model,
samples)
yield getter, oq, monitor
def gen_args(self, ebruptures):
"""
:param ebruptures: a list of EBRupture objects to be split
:yields: the arguments for compute_gmfs_and_curves
"""
oq = self.oqparam
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq
imts = list(oq.imtls)
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
grp_trt = {sg.id: sg.trt for sm in self.csm.info.source_models
for sg in sm.src_groups}
rlzs_by_grp = self.rlzs_assoc.get_rlzs_by_grp_id()
correl_model = oq.get_correl_model()
for block in split_in_blocks(
ebruptures, oq.concurrent_tasks or 1,
key=operator.attrgetter('grp_id')):
grp_id = block[0].grp_id
trt = grp_trt[grp_id]
gsims = [dic[trt] for dic in self.rlzs_assoc.gsim_by_trt]
samples = self.rlzs_assoc.samples[grp_id]
getter = GmfGetter(gsims, block, self.sitecol,
imts, min_iml, oq.truncation_level,
correl_model, samples)
yield getter, rlzs_by_grp[grp_id], monitor
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
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq
min_iml = calc.fix_minimum_intensity(
oq.minimum_intensity, oq.imtls)
acc = parallel.apply_reduce(
self.core_task.__func__,
(self.sesruptures, self.sitecol, oq.imtls, self.rlzs_assoc,
min_iml, monitor),
concurrent_tasks=self.oqparam.concurrent_tasks,
agg=self.combine_curves_and_save_gmfs,
acc=ProbabilityMap(),
key=operator.attrgetter('grp_id'),
weight=operator.attrgetter('weight'))
if oq.ground_motion_fields:
self.datastore.set_nbytes('gmf_data')
return acc
def _calc_gmfs(dstore, serial, eid):
oq = dstore['oqparam']
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
csm_info = dstore['csm_info']
rlzs_assoc = csm_info.get_rlzs_assoc()
rlzs = rlzs_assoc.realizations
sitecol = dstore['sitecol'].complete
N = len(sitecol.complete)
rup = dstore['sescollection/' + serial]
correl_model = oq.get_correl_model()
realizations = rlzs_assoc.get_rlzs_by_grp_id()[rup.grp_id]
gmf_dt = numpy.dtype([('%03d' % rlz.ordinal, F64) for rlz in realizations])
for sm in csm_info.source_models:
for sg in sm.src_groups:
if sg.id == rup.grp_id:
break
gsims = [dic[sg.trt] for dic in rlzs_assoc.gsim_by_trt]
getter = GmfGetter(gsims, [rup], sitecol,
oq.imtls, min_iml, oq.truncation_level,
correl_model, rlzs_assoc.samples[sg.id])
array_by_imt = {imt: numpy.zeros(N, gmf_dt) for imt in oq.imtls}
for rlzname in gmf_dt.names:
rlz = rlzs[int(rlzname)]
for sid, gmvdict in zip(getter.sids, getter(rlz)):
if len(gmvdict):
for imt in oq.imtls:
array_by_imt[imt][rlzname][sid] = gmvdict[imt]['gmv']
return sorted(array_by_imt.items())
def gen_args(self, ruptures_by_grp):
"""
:param ruptures_by_grp: a dictionary of EBRupture objects
:yields: the arguments for compute_gmfs_and_curves
"""
oq = self.oqparam
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq
imts = list(oq.imtls)
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
self.grp_trt = self.csm.info.grp_trt()
rlzs_by_grp = self.rlzs_assoc.get_rlzs_by_grp_id()
correl_model = oq.get_correl_model()
for grp_id in ruptures_by_grp:
ruptures = ruptures_by_grp[grp_id]
if not ruptures:
continue
for block in split_in_blocks(ruptures, oq.concurrent_tasks or 1):
trt = self.grp_trt[grp_id]
gsims = [dic[trt] for dic in self.rlzs_assoc.gsim_by_trt]
samples = self.rlzs_assoc.samples[grp_id]
getter = GmfGetter(gsims, block, self.sitecol,
imts, min_iml, oq.truncation_level,
correl_model, samples)
yield getter, rlzs_by_grp[grp_id], monitor
def gen_args(self):
"""
Yield the arguments required by build_starmap, i.e. the
source models, the asset collection, the riskmodel and others.
"""
oq = self.oqparam
correl_model = oq.get_correl_model()
if not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.get_min_iml()
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
if min_iml.sum() == 0:
logging.warn('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
self.csm.init_serials()
imts = list(oq.imtls)
for sm_id in range(len(self.csm.source_models)):
ssm = self.csm.get_model(sm_id)
monitor = self.monitor.new(
avg_losses=oq.avg_losses,
ses_per_logic_tree_path=oq.ses_per_logic_tree_path,
maximum_distance=oq.maximum_distance,
samples=ssm.source_models[0].samples,
seed=ssm.source_model_lt.seed)
yield (ssm, self.sitecol, self.assetcol, self.riskmodel,
imts, oq.truncation_level, correl_model, min_iml, monitor)
def init(self):
"""
Set the random seed passed to the SourceManager and the
minimum_intensity dictionary.
"""
oq = self.oqparam
self.random_seed = oq.random_seed
self.rlzs_assoc = self.datastore['csm_info'].get_rlzs_assoc()
self.min_iml = calc.fix_minimum_intensity(
oq.minimum_intensity, oq.imtls)
def init(self):
"""
Set the random seed passed to the SourceManager and the
minimum_intensity dictionary.
"""
oq = self.oqparam
self.min_iml = calc.fix_minimum_intensity(oq.minimum_intensity,
oq.imtls)
self.rupser = calc.RuptureSerializer(self.datastore)
self.csm_info = self.datastore['csm_info']
def init(self):
"""
Set the random seed passed to the SourceManager and the
minimum_intensity dictionary.
"""
oq = self.oqparam
self.random_seed = oq.random_seed
self.rlzs_assoc = self.datastore['csm_info'].get_rlzs_assoc()
self.min_iml = calc.fix_minimum_intensity(oq.minimum_intensity,
oq.imtls)
self.rup_data = {}
def get_min_iml(self, oq):
# set the minimum_intensity
if hasattr(self, 'riskmodel') and not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.get_min_iml()
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
if min_iml.sum() == 0:
logging.warn('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
return min_iml
def export_gmf_scenario_csv(ekey, dstore):
what = ekey[0].split('/')
if len(what) == 1:
raise ValueError('Missing "/rup-\d+"')
oq = dstore['oqparam']
csm_info = dstore['csm_info']
rlzs_assoc = csm_info.get_rlzs_assoc()
samples = csm_info.get_samples_by_grp()
imts = list(oq.imtls)
mo = re.match('rup-(\d+)$', what[1])
if mo is None:
raise ValueError("Invalid format: %r does not match 'rup-(\d+)$'" %
what[1])
rup_id = int(mo.group(1))
grp_ids = sorted(int(grp[4:]) for grp in dstore['ruptures'])
events = dstore['events']
ruptures = list(calc._get_ruptures(dstore, events, grp_ids, rup_id))
if not ruptures:
logging.warn('There is no rupture %d', rup_id)
return []
[ebr] = ruptures
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(ebr.grp_id)
samples = samples[ebr.grp_id]
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
correl_model = oq.get_correl_model()
sitecol = dstore['sitecol'].complete
getter = GmfGetter(rlzs_by_gsim, ruptures, sitecol, imts, min_iml,
oq.truncation_level, correl_model, samples)
getter.init()
hazardr = getter.get_hazard()
rlzs = rlzs_assoc.realizations
fields = ['eid-%03d' % eid for eid in getter.eids]
dt = numpy.dtype([(f, F32) for f in fields])
mesh = numpy.zeros(len(ebr.sids), [('lon', F64), ('lat', F64)])
mesh['lon'] = sitecol.lons[ebr.sids]
mesh['lat'] = sitecol.lats[ebr.sids]
writer = writers.CsvWriter(fmt='%.5f')
for rlzi in range(len(rlzs)):
hazard = hazardr[rlzi]
for imti, imt in enumerate(imts):
gmfs = numpy.zeros(len(ebr.sids), dt)
for s, sid in enumerate(ebr.sids):
for rec in hazard[sid]:
event = 'eid-%03d' % rec['eid']
gmfs[s][event] = rec['gmv'][imti]
dest = dstore.build_fname('gmf',
'rup-%s-rlz-%s-%s' % (rup_id, rlzi, imt),
'csv')
data = util.compose_arrays(mesh, gmfs)
writer.save(data, dest)
return writer.getsaved()
def export_gmf_scenario_csv(ekey, dstore):
what = ekey[0].split('/')
if len(what) == 1:
raise ValueError('Missing "/rup-\d+"')
oq = dstore['oqparam']
csm_info = dstore['csm_info']
rlzs_assoc = csm_info.get_rlzs_assoc()
samples = csm_info.get_samples_by_grp()
num_ruptures = len(dstore['ruptures'])
imts = list(oq.imtls)
mo = re.match('rup-(\d+)$', what[1])
if mo is None:
raise ValueError("Invalid format: %r does not match 'rup-(\d+)$'" %
what[1])
ridx = int(mo.group(1))
assert 0 <= ridx < num_ruptures, ridx
ruptures = list(RuptureGetter(dstore, slice(ridx, ridx + 1)))
[ebr] = ruptures
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(ebr.grp_id)
samples = samples[ebr.grp_id]
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
sitecol = dstore['sitecol'].complete
getter = GmfGetter(rlzs_by_gsim, ruptures, sitecol, oq, min_iml, samples)
getter.init()
sids = getter.computers[0].sids
hazardr = getter.get_hazard()
rlzs = rlzs_assoc.realizations
fields = ['eid-%03d' % eid for eid in getter.eids]
dt = numpy.dtype([(f, F32) for f in fields])
mesh = numpy.zeros(len(sids), [('lon', F64), ('lat', F64)])
mesh['lon'] = sitecol.lons[sids]
mesh['lat'] = sitecol.lats[sids]
writer = writers.CsvWriter(fmt='%.5f')
for rlzi in range(len(rlzs)):
hazard = hazardr[rlzi]
for imti, imt in enumerate(imts):
gmfs = numpy.zeros(len(sids), dt)
for s, sid in enumerate(sids):
for rec in hazard[sid]:
event = 'eid-%03d' % rec['eid']
gmfs[s][event] = rec['gmv'][imti]
dest = dstore.build_fname(
'gmf', 'rup-%s-rlz-%s-%s' % (ebr.serial, rlzi, imt), 'csv')
data = util.compose_arrays(mesh, gmfs)
writer.save(data, dest)
return writer.getsaved()
def _get_gmfs(dstore, serial, eid):
oq = dstore['oqparam']
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
sitecol = dstore['sitecol'].complete
N = len(sitecol.complete)
rup = dstore['sescollection/' + serial]
correl_model = readinput.get_correl_model(oq)
gsims = rlzs_assoc.gsims_by_trt_id[rup.trt_id]
rlzs = [
rlz for gsim in map(str, gsims) for rlz in rlzs_assoc[rup.trt_id, gsim]
]
gmf_dt = numpy.dtype([('%03d' % rlz.ordinal, F32) for rlz in rlzs])
gmfadict = create(calc.GmfColl, [rup], sitecol, oq.imtls, rlzs_assoc,
oq.truncation_level, correl_model, min_iml).by_rlzi()
for imti, imt in enumerate(oq.imtls):
gmfa = numpy.zeros(N, gmf_dt)
for rlzname in gmf_dt.names:
rlzi = int(rlzname)
gmvs = get_array(gmfadict[rlzi], eid=eid, imti=imti)['gmv']
gmfa[rlzname][rup.indices] = gmvs
yield gmfa, imt
def _get_gmfs(dstore, serial, eid):
oq = dstore['oqparam']
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
rlzs_assoc = dstore['csm_info'].get_rlzs_assoc()
sitecol = dstore['sitecol'].complete
N = len(sitecol.complete)
rup = dstore['sescollection/' + serial]
correl_model = readinput.get_correl_model(oq)
gsims = rlzs_assoc.gsims_by_grp_id[rup.grp_id]
rlzs = [rlz for gsim in map(str, gsims)
for rlz in rlzs_assoc[rup.grp_id, gsim]]
gmf_dt = numpy.dtype([('%03d' % rlz.ordinal, F32) for rlz in rlzs])
gmfadict = create(calc.GmfColl,
[rup], sitecol, oq.imtls, rlzs_assoc,
oq.truncation_level, correl_model, min_iml).by_rlzi()
for imti, imt in enumerate(oq.imtls):
gmfa = numpy.zeros(N, gmf_dt)
for rlzname in gmf_dt.names:
rlzi = int(rlzname)
gmvs = get_array(gmfadict[rlzi], eid=eid, imti=imti)['gmv']
gmfa[rlzname][rup.indices] = gmvs
yield gmfa, imt
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
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
acc = parallel.apply_reduce(
self.core_task.__func__, (self.sesruptures, self.sitecol, oq.imtls,
self.rlzs_assoc, min_iml, monitor),
concurrent_tasks=self.oqparam.concurrent_tasks,
agg=self.combine_curves_and_save_gmfs,
acc=ProbabilityMap(),
key=operator.attrgetter('trt_id'),
weight=operator.attrgetter('weight'))
if oq.ground_motion_fields:
self.datastore.set_nbytes('gmf_data')
return acc
def execute(self):
"""
Run the event_based_risk calculator and aggregate the results
"""
oq = self.oqparam
correl_model = oq.get_correl_model()
self.N = len(self.assetcol)
self.E = sum(len(v) for v in self.datastore['events'].values())
logging.info('Populating the risk inputs')
all_ruptures = []
preprecalc = getattr(self.precalc, 'precalc', None)
if preprecalc: # the ruptures are already in memory
for grp_id, sesruptures in preprecalc.result.items():
for sr in sesruptures:
all_ruptures.append(sr)
else: # read the ruptures from the datastore
for serial in self.datastore['sescollection']:
rup = self.datastore['sescollection/' + serial]
all_ruptures.append(rup)
all_ruptures.sort(key=operator.attrgetter('serial'))
if not self.riskmodel.covs:
# do not generate epsilons
eps = None
else:
eps = riskinput.make_eps(
self.assets_by_site, self.E, oq.master_seed,
oq.asset_correlation)
logging.info('Generated %s epsilons', eps.shape)
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.C = self.oqparam.loss_curve_resolution
self.L = L = len(loss_types)
self.R = R = len(self.rlzs_assoc.realizations)
self.I = self.oqparam.insured_losses
# ugly: attaching attributes needed in the task function
mon = self.monitor
mon.num_assets = self.count_assets()
mon.avg_losses = self.oqparam.avg_losses
mon.asset_loss_table = self.oqparam.asset_loss_table
mon.insured_losses = self.I
mon.ses_ratio = (
oq.risk_investigation_time or oq.investigation_time) / (
oq.investigation_time * oq.ses_per_logic_tree_path)
self.N = N = len(self.assetcol)
self.E = sum(len(v) for v in self.datastore['events'].values())
# average losses, stored in a composite array of shape N, R
self.avg_losses = numpy.zeros((N, R), oq.loss_dt())
self.ass_loss_table = square(L, R, lambda: None)
self.agg_loss_table = square(L, R, lambda: None)
self.ela_dt, self.elt_dt = mon.ela_dt, mon.elt_dt = build_el_dtypes(
self.I)
for (l, r) in itertools.product(range(L), range(R)):
lt = loss_types[l]
if self.oqparam.asset_loss_table:
self.ass_loss_table[l, r] = self.datastore.create_dset(
'ass_loss_table/rlz-%03d/%s' % (r, lt), self.ela_dt)
self.agg_loss_table[l, r] = self.datastore.create_dset(
'agg_loss_table/rlz-%03d/%s' % (r, lt), self.elt_dt)
self.saved = collections.Counter() # nbytes per HDF5 key
self.ass_bytes = 0
self.agg_bytes = 0
self.gmfbytes = 0
rlz_ids = getattr(self.oqparam, 'rlz_ids', ())
if rlz_ids:
self.rlzs_assoc = self.rlzs_assoc.extract(rlz_ids)
if not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.get_min_iml()
min_iml = calc.fix_minimum_intensity(
oq.minimum_intensity, oq.imtls)
if min_iml.sum() == 0:
logging.warn('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
csm_info = self.datastore['csm_info']
grp_trt = {sg.id: sg.trt for sm in csm_info.source_models
for sg in sm.src_groups}
with self.monitor('building riskinputs', autoflush=True):
riskinputs = self.riskmodel.build_inputs_from_ruptures(
grp_trt, list(oq.imtls), self.sitecol.complete, all_ruptures,
oq.truncation_level, correl_model, min_iml, eps,
oq.concurrent_tasks or 1)
# NB: I am using generators so that the tasks are submitted one at
# the time, without keeping all of the arguments in memory
res = starmap(
self.core_task.__func__,
((riskinput, self.riskmodel, self.rlzs_assoc,
self.assetcol, self.monitor.new('task'))
for riskinput in riskinputs)).submit_all()
acc = functools.reduce(self.agg, res, AccumDict())
self.save_data_transfer(res)
return acc
def compute_ruptures_gmfs_curves(
source_models, sitecol, rlzs_assoc, monitor):
"""
Returns the ruptures as a TRT set
:param source_models:
A list of UCERF source models, one per branch
:param sitecol:
Site collection :class:`openquake.hazardlib.site.SiteCollection`
:param rlzs_assoc:
Instance of :class:`openquake.commonlib.source.RlzsAssoc`
:param monitor:
Instance of :class:`openquake.baselib.performance.Monitor`
:returns:
Dictionary of rupture instances associated to a TRT ID
"""
oq = monitor.oqparam
correl_model = oq.get_correl_model()
imts = list(oq.imtls)
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
integration_distance = oq.maximum_distance[DEFAULT_TRT]
res = AccumDict()
res.calc_times = AccumDict()
serial = 1
event_mon = monitor('sampling ruptures', measuremem=False)
res['ruptures'] = rupdic = AccumDict()
rupdic.num_events = 0
rupdic.trt = DEFAULT_TRT
rlzs_by_grp = rlzs_assoc.get_rlzs_by_grp_id()
for grp_id, source_model in enumerate(source_models):
[grp] = source_model.src_groups # one source group per source model
[ucerf] = grp # one source per source group
t0 = time.time()
# set the seed before calling generate_event_set
numpy.random.seed(oq.random_seed + grp_id)
ses_ruptures = []
eid = 0
ucerf.idx_set = ucerf.build_idx_set()
background_sids = ucerf.get_background_sids(
sitecol, integration_distance)
for ses_idx in range(1, oq.ses_per_logic_tree_path + 1):
with event_mon:
rups, n_occs = ucerf.generate_event_set(background_sids)
for i, rup in enumerate(rups):
rup.seed = oq.random_seed # to think
rrup = rup.surface.get_min_distance(sitecol.mesh)
r_sites = sitecol.filter(rrup <= integration_distance)
if r_sites is None:
continue
indices = r_sites.indices
events = []
for j in range(n_occs[i]):
events.append((eid, ses_idx, j, 0)) # 0 is the sampling ID
eid += 1
if events:
ses_ruptures.append(
event_based.EBRupture(
rup, indices,
numpy.array(events, event_based.event_dt),
ucerf.source_id, grp_id, serial))
serial += 1
rupdic.num_events += len(events)
res['ruptures'][grp_id] = ses_ruptures
gsims = [dic[DEFAULT_TRT] for dic in rlzs_assoc.gsim_by_trt]
gg = riskinput.GmfGetter(gsims, ses_ruptures, sitecol,
imts, min_iml, oq.truncation_level,
correl_model, rlzs_assoc.samples[grp_id])
rlzs = rlzs_by_grp[grp_id]
res.update(event_based.compute_gmfs_and_curves(gg, rlzs, monitor))
res.calc_times[grp_id] = (ucerf.source_id, len(sitecol),
time.time() - t0)
return res
def execute(self):
"""
Run the event_based_risk calculator and aggregate the results
"""
oq = self.oqparam
correl_model = readinput.get_correl_model(oq)
self.N = len(self.assetcol)
self.E = len(self.etags)
logging.info('Populating the risk inputs')
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()}
num_assets = {
sid: len(self.assets_by_site[sid])
for sid in self.sitecol.sids
}
all_ruptures = []
for serial in self.datastore['sescollection']:
rup = self.datastore['sescollection/' + serial]
rup.set_weight(num_rlzs, num_assets)
all_ruptures.append(rup)
all_ruptures.sort(key=operator.attrgetter('serial'))
if not self.riskmodel.covs:
# do not generate epsilons
eps = None
else:
eps = riskinput.make_eps(self.assets_by_site, self.E,
oq.master_seed, oq.asset_correlation)
logging.info('Generated %s epsilons', eps.shape)
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.C = self.oqparam.loss_curve_resolution
self.L = L = len(loss_types)
self.R = R = len(self.rlzs_assoc.realizations)
self.I = self.oqparam.insured_losses
# ugly: attaching attributes needed in the task function
mon = self.monitor
mon.num_assets = self.count_assets()
mon.avg_losses = self.oqparam.avg_losses
mon.asset_loss_table = self.oqparam.asset_loss_table
mon.insured_losses = self.I
mon.ses_ratio = (oq.risk_investigation_time or
oq.investigation_time) / (oq.investigation_time *
oq.ses_per_logic_tree_path)
self.N = N = len(self.assetcol)
self.E = len(self.datastore['etags'])
# average losses, stored in a composite array of shape N, R
multi_avg_dt = self.riskmodel.loss_type_dt(insured=self.I)
self.avg_losses = numpy.zeros((N, R), multi_avg_dt)
self.ass_loss_table = square(L, R, lambda: None)
self.agg_loss_table = square(L, R, lambda: None)
self.ela_dt, self.elt_dt = mon.ela_dt, mon.elt_dt = build_el_dtypes(
self.I)
for (l, r) in itertools.product(range(L), range(R)):
lt = loss_types[l]
if self.oqparam.asset_loss_table:
self.ass_loss_table[l, r] = self.datastore.create_dset(
'ass_loss_table/rlz-%03d/%s' % (r, lt), self.ela_dt)
self.agg_loss_table[l, r] = self.datastore.create_dset(
'agg_loss_table/rlz-%03d/%s' % (r, lt), self.elt_dt)
self.saved = collections.Counter() # nbytes per HDF5 key
self.ass_bytes = 0
self.agg_bytes = 0
self.gmfbytes = 0
rlz_ids = getattr(self.oqparam, 'rlz_ids', ())
if rlz_ids:
self.rlzs_assoc = self.rlzs_assoc.extract(rlz_ids)
if not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.get_min_iml()
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, oq.imtls)
if min_iml.sum() == 0:
logging.warn('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
with self.monitor('building riskinputs', autoflush=True):
riskinputs = self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, oq.truncation_level,
correl_model, min_iml, eps, oq.concurrent_tasks or 1)
# NB: I am using generators so that the tasks are submitted one at
# the time, without keeping all of the arguments in memory
tm = starmap(self.core_task.__func__,
((riskinput, self.riskmodel, self.rlzs_assoc,
self.assetcol, self.monitor.new('task'))
for riskinput in riskinputs))
res = tm.reduce(agg=self.agg)
self.save_data_transfer(tm)
return res
def execute(self):
"""
Run the event_based_risk calculator and aggregate the results
"""
oq = self.oqparam
correl_model = readinput.get_correl_model(oq)
self.N = len(self.assetcol)
self.E = len(self.etags)
logging.info('Populating the risk inputs')
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()}
num_assets = {sid: len(self.assets_by_site[sid])
for sid in self.sitecol.sids}
all_ruptures = []
for serial in self.datastore['sescollection']:
rup = self.datastore['sescollection/' + serial]
rup.set_weight(num_rlzs, num_assets)
all_ruptures.append(rup)
all_ruptures.sort(key=operator.attrgetter('serial'))
if not self.riskmodel.covs:
# do not generate epsilons
eps = None
else:
eps = riskinput.make_eps(
self.assets_by_site, self.E, oq.master_seed,
oq.asset_correlation)
logging.info('Generated %s epsilons', eps.shape)
# preparing empty datasets
loss_types = self.riskmodel.loss_types
self.C = self.oqparam.loss_curve_resolution
self.L = L = len(loss_types)
self.R = R = len(self.rlzs_assoc.realizations)
self.I = self.oqparam.insured_losses
# ugly: attaching attributes needed in the task function
mon = self.monitor
mon.num_assets = self.count_assets()
mon.avg_losses = self.oqparam.avg_losses
mon.asset_loss_table = self.oqparam.asset_loss_table
mon.insured_losses = self.I
mon.ses_ratio = (
oq.risk_investigation_time or oq.investigation_time) / (
oq.investigation_time * oq.ses_per_logic_tree_path)
self.N = N = len(self.assetcol)
self.E = len(self.datastore['etags'])
# average losses, stored in a composite array of shape N, R
multi_avg_dt = self.riskmodel.loss_type_dt(insured=self.I)
self.avg_losses = numpy.zeros((N, R), multi_avg_dt)
self.ass_loss_table = square(L, R, lambda: None)
self.agg_loss_table = square(L, R, lambda: None)
self.ela_dt, self.elt_dt = mon.ela_dt, mon.elt_dt = build_el_dtypes(
self.I)
for (l, r) in itertools.product(range(L), range(R)):
lt = loss_types[l]
if self.oqparam.asset_loss_table:
self.ass_loss_table[l, r] = self.datastore.create_dset(
'ass_loss_table/rlz-%03d/%s' % (r, lt), self.ela_dt)
self.agg_loss_table[l, r] = self.datastore.create_dset(
'agg_loss_table/rlz-%03d/%s' % (r, lt), self.elt_dt)
self.saved = collections.Counter() # nbytes per HDF5 key
self.ass_bytes = 0
self.agg_bytes = 0
self.gmfbytes = 0
rlz_ids = getattr(self.oqparam, 'rlz_ids', ())
if rlz_ids:
self.rlzs_assoc = self.rlzs_assoc.extract(rlz_ids)
if not oq.minimum_intensity:
# infer it from the risk models if not directly set in job.ini
oq.minimum_intensity = self.riskmodel.get_min_iml()
min_iml = calc.fix_minimum_intensity(
oq.minimum_intensity, oq.imtls)
if min_iml.sum() == 0:
logging.warn('The GMFs are not filtered: '
'you may want to set a minimum_intensity')
else:
logging.info('minimum_intensity=%s', oq.minimum_intensity)
with self.monitor('building riskinputs', autoflush=True):
riskinputs = self.riskmodel.build_inputs_from_ruptures(
self.sitecol.complete, all_ruptures, oq.truncation_level,
correl_model, min_iml, eps, oq.concurrent_tasks or 1)
# NB: I am using generators so that the tasks are submitted one at
# the time, without keeping all of the arguments in memory
tm = starmap(
self.core_task.__func__,
((riskinput, self.riskmodel, self.rlzs_assoc,
self.assetcol, self.monitor.new('task'))
for riskinput in riskinputs))
return tm.reduce(agg=self.agg, posthook=self.save_data_transfer)