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.read_exposure() # define .assets_by_site
self.load_riskmodel()
self.sitecol, self.assets_by_site = self.assoc_assets_sites(
haz_sitecol)
curves_by_trt_gsim = {(0, 'FromFile'): haz_curves}
self.rlzs_assoc = logictree.trivial_rlzs_assoc()
self.save_mesh()
else: # compute hazard
super(ClassicalRiskCalculator, self).pre_execute()
logging.info('Preparing the risk input')
curves_by_trt_gsim = {}
for dset in self.datastore['curves_by_sm'].values():
for key, curves in dset.items():
trt_id, gsim = key.split('-')
curves_by_trt_gsim[int(trt_id), gsim] = curves.value
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
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
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.rlzs_assoc = logictree.trivial_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 dset in self.datastore['curves_by_sm'].values():
for key, curves in dset.items():
trt_id, gsim = key.split('-')
curves_by_trt_gsim[int(trt_id), gsim] = curves.value
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
def pre_execute(self):
if 'gmfs' in self.oqparam.inputs:
self.pre_calculator = None
base.RiskCalculator.pre_execute(self)
self.monitor.consequence_models = riskmodels.get_risk_models(
self.oqparam, 'consequence')
if 'gmfs' in self.oqparam.inputs:
self.rlzs_assoc = logictree.trivial_rlzs_assoc()
self.etags, gmfs = base.get_gmfs(self.datastore)
self.riskinputs = self.build_riskinputs(gmfs)
self.monitor.taxonomies = sorted(self.taxonomies)
def pre_execute(self):
"""
Compute the GMFs, build the epsilons, the riskinputs, and a dictionary
with the unit of measure, used in the export phase.
"""
if 'gmfs' in self.oqparam.inputs:
self.pre_calculator = None
base.RiskCalculator.pre_execute(self)
logging.info('Building the epsilons')
epsilon_matrix = self.make_eps(
self.oqparam.number_of_ground_motion_fields)
if 'gmfs' in self.oqparam.inputs:
self.rlzs_assoc = logictree.trivial_rlzs_assoc()
self.etags, gmfs = base.get_gmfs(self.datastore)
self.riskinputs = self.build_riskinputs(gmfs, epsilon_matrix)
def pre_execute(self):
"""
Compute the GMFs, build the epsilons, the riskinputs, and a dictionary
with the unit of measure, used in the export phase.
"""
if 'gmfs' in self.oqparam.inputs:
self.pre_calculator = None
base.RiskCalculator.pre_execute(self)
logging.info('Building the epsilons')
epsilon_matrix = self.make_eps(
self.oqparam.number_of_ground_motion_fields)
if 'gmfs' in self.oqparam.inputs:
self.rlzs_assoc = logictree.trivial_rlzs_assoc()
self.etags, gmfs = base.get_gmfs(self.datastore)
self.riskinputs = self.build_riskinputs(gmfs, epsilon_matrix)
def get_gmfs(calc):
"""
:param calc: a ScenarioDamage or ScenarioRisk calculator
:returns: a dictionary of gmfs
"""
if 'gmfs' in calc.oqparam.inputs: # from file
logging.info('Reading gmfs from file')
sitecol, calc.tags, gmfs_by_imt = readinput.get_gmfs(calc.oqparam)
calc.save_params() # save number_of_ground_motion_fields and sites
# reduce the gmfs matrices to the filtered sites
for imt in calc.oqparam.imtls:
gmfs_by_imt[imt] = gmfs_by_imt[imt][sitecol.indices]
logging.info('Preparing the risk input')
calc.rlzs_assoc = logictree.trivial_rlzs_assoc()
return sitecol, {(0, 'FromFile'): gmfs_by_imt}
# else from rupture
gmf = calc.datastore['gmfs/col00'].value
# NB: if the hazard site collection has N sites, the hazard
# filtered site collection for the nonzero GMFs has N' <= N sites
# whereas the risk site collection associated to the assets
# has N'' <= N' sites
if calc.datastore.parent:
haz_sitecol = calc.datastore.parent['sitecol'] # N' values
else:
haz_sitecol = calc.sitecol
risk_indices = set(calc.sitecol.indices) # N'' values
N = len(haz_sitecol.complete)
imt_dt = numpy.dtype([(imt, float) for imt in calc.oqparam.imtls])
gmf_by_idx = general.groupby(gmf, lambda row: row['idx'])
R = len(gmf_by_idx)
# build a matrix N x R for each GSIM realization
gmfs = {(trt_id, gsim): numpy.zeros((N, R), imt_dt)
for trt_id, gsim in calc.rlzs_assoc}
for rupid, rows in sorted(gmf_by_idx.items()):
assert len(haz_sitecol.indices) == len(rows), (len(
haz_sitecol.indices), len(rows))
for sid, gmv in zip(haz_sitecol.indices, rows):
if sid in risk_indices:
for trt_id, gsim in gmfs:
gmfs[trt_id, gsim][sid, rupid] = gmv[gsim]
return haz_sitecol, gmfs