def post_execute(self, curves_by_trt_id):
"""
Collect the hazard curves by realization and export them.
:param curves_by_trt_id:
a dictionary trt_id -> hazard curves
"""
nsites = len(self.sitecol)
imtls = self.oqparam.imtls
curves_by_trt_gsim = {}
with self.monitor('saving probability maps', autoflush=True):
for trt_id in curves_by_trt_id:
key = 'poes/%04d' % trt_id
self.datastore[key] = curves_by_trt_id[trt_id]
self.datastore.set_attrs(
key, trt=self.csm.info.get_trt(trt_id))
gsims = self.rlzs_assoc.gsims_by_trt_id[trt_id]
for i, gsim in enumerate(gsims):
curves_by_trt_gsim[trt_id, gsim] = (
curves_by_trt_id[trt_id].extract(i))
self.datastore.set_nbytes('poes')
with self.monitor('combine curves_by_rlz', autoflush=True):
curves_by_rlz = self.rlzs_assoc.combine_curves(curves_by_trt_gsim)
self.save_curves({rlz: array_of_curves(curves, nsites, imtls)
for rlz, curves in curves_by_rlz.items()})
def post_execute(self, curves_by_trt_id):
"""
Collect the hazard curves by realization and export them.
:param curves_by_trt_id:
a dictionary trt_id -> hazard curves
"""
nsites = len(self.sitecol)
imtls = self.oqparam.imtls
curves_by_trt_gsim = {}
with self.monitor('saving probability maps', autoflush=True):
for trt_id in curves_by_trt_id:
key = 'poes/%04d' % trt_id
self.datastore[key] = curves_by_trt_id[trt_id]
self.datastore.set_attrs(key,
trt=self.csm.info.get_trt(trt_id))
gsims = self.rlzs_assoc.gsims_by_trt_id[trt_id]
for i, gsim in enumerate(gsims):
curves_by_trt_gsim[trt_id, gsim] = (
curves_by_trt_id[trt_id].extract(i))
self.datastore.set_nbytes('poes')
with self.monitor('combine curves_by_rlz', autoflush=True):
curves_by_rlz = self.rlzs_assoc.combine_curves(curves_by_trt_gsim)
self.save_curves({
rlz: array_of_curves(curves, nsites, imtls)
for rlz, curves in curves_by_rlz.items()
})
def post_execute(self, result):
"""
:param result:
a dictionary (src_group_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
dic = {}
for rlzi in result:
dic[rlzs[rlzi]] = array_of_curves(
result[rlzi], len(self.sitecol), oq.imtls)
self.save_curves(dic)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run()
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(
self.cl.mean_curves[imt], self.mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (trt_model_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
dic = {}
for rlzi in result:
dic[rlzs[rlzi]] = array_of_curves(result[rlzi],
len(self.sitecol), oq.imtls)
self.save_curves(dic)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(hazard_calculation_id=self.datastore.calc_id)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(self.cl.mean_curves[imt],
self.mean_curves[imt])
logging.warn(
'Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d', imt,
rdiff * 100, index)
def post_execute(self, pmap_by_grp_gsim):
"""
Combine the curves and store them
"""
nsites = len(self.sitecol)
imtls = self.oqparam.imtls
with self.monitor('combine curves_by_rlz', autoflush=True):
curves_by_rlz = self.rlzs_assoc.combine_curves(pmap_by_grp_gsim)
self.save_curves({rlz: array_of_curves(curves, nsites, imtls)
for rlz, curves in curves_by_rlz.items()})
def pre_execute(self):
"""
Associate the assets to the sites and build the riskinputs.
"""
if 'hazard_curves' in self.oqparam.inputs: # read hazard from file
haz_sitecol, haz_curves = readinput.get_hcurves(self.oqparam)
self.save_params()
self.read_exposure() # define .assets_by_site
self.load_riskmodel()
self.assetcol = riskinput.AssetCollection(
self.assets_by_site, self.cost_calculator,
self.oqparam.time_event)
self.sitecol, self.assets_by_site = self.assoc_assets_sites(
haz_sitecol)
curves_by_trt_gsim = {(0, 'FromFile'): haz_curves}
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
self.save_mesh()
else: # compute hazard or read it from the datastore
super(ClassicalRiskCalculator, self).pre_execute()
logging.info('Preparing the risk input')
curves_by_trt_gsim = {}
for key in self.datastore['poes']:
pmap = self.datastore['poes/' + key]
trt_id = int(key)
gsims = self.rlzs_assoc.gsims_by_trt_id[trt_id]
for i, gsim in enumerate(gsims):
curves_by_trt_gsim[trt_id, gsim] = array_of_curves(
pmap, len(self.sitecol), self.oqparam.imtls, i)
self.riskinputs = self.build_riskinputs(curves_by_trt_gsim)
self.monitor.oqparam = self.oqparam
self.N = sum(len(assets) for assets in self.assets_by_site)
self.L = len(self.riskmodel.loss_types)
self.R = len(self.rlzs_assoc.realizations)
self.I = self.oqparam.insured_losses
self.Q1 = len(self.oqparam.quantile_loss_curves) + 1
