def export_curves_rlzs(self, aids, key):
"""
:returns: a dictionary key -> record of dtype loss_curve_dt
"""
if 'loss_curves-rlzs' in self.dstore: # classical_risk
data = self.dstore['loss_curves-rlzs'][aids] # shape (A, R)
if key.startswith('rlz-'):
rlzi = int(key[4:])
return {key: data[:, rlzi]}
# else key == 'rlzs', returns all data
return {'rlz-%03d' % rlzi: data[:, rlzi] for rlzi in range(self.R)}
# otherwise event_based
crm = riskinput.read_composite_risk_model(self.dstore)
builder = crm.curve_builder
avalues = self.assetcol.values(aids)
lrgetter = riskinput.LossRatiosGetter(self.dstore, aids)
if key.startswith('rlz-'):
rlzi = int(key[4:])
ratios = lrgetter.get(rlzi)
return {'rlz-%03d' % rlzi: builder.build_curves(avalues, ratios)}
else: # key is 'rlzs', return a dictionary will all realizations
# this may be disabled in the future unless an asset is specified
dic = {}
for rlzi in range(self.R):
dic['rlz-%03d' % rlzi] = builder.build_curves(
avalues, lrgetter.get(rlzi))
return dic
def export_rcurves_rlzs(ekey, dstore):
oq = dstore['oqparam']
riskmodel = riskinput.read_composite_risk_model(dstore)
assetcol = dstore['assetcol']
aref = dstore['asset_refs'].value
kind = ekey[0].split('-')[1] # rlzs or stats
if oq.avg_losses:
acurves = dstore['avg_losses-' + kind]
rcurves = dstore[ekey[0]]
[loss_ratios] = dstore['loss_ratios']
fnames = []
writercls = (risk_writers.LossCurveGeoJSONWriter
if ekey[0] == 'geojson' else
risk_writers.LossCurveXMLWriter)
for writer, (ltype, poe, r, ins) in _gen_writers(
dstore, writercls, ekey[0]):
if ltype not in loss_ratios.dtype.names:
continue # ignore loss type
l = riskmodel.lti[ltype]
poes = rcurves[ltype][:, r, ins]
curves = []
for aid, ass in enumerate(assetcol):
loc = Location(*ass.location)
losses = loss_ratios[ltype] * ass.value(ltype)
# -1 means that the average was not computed
avg = acurves[aid, r, l][ins] if oq.avg_losses else -1
curve = LossCurve(loc, aref[ass.idx], poes[aid],
losses, loss_ratios[ltype], avg, None)
curves.append(curve)
writer.serialize(curves)
fnames.append(writer._dest)
return sorted(fnames)
def load_riskmodel(self):
"""
Read the risk model and set the attribute .riskmodel.
The riskmodel can be empty for hazard calculations.
Save the loss ratios (if any) in the datastore.
"""
logging.info('Reading the risk model if present')
self.riskmodel = readinput.get_risk_model(self.oqparam)
if not self.riskmodel:
parent = self.datastore.parent
if 'fragility' in parent or 'vulnerability' in parent:
self.riskmodel = riskinput.read_composite_risk_model(parent)
return
self.save_params() # re-save oqparam
def load_riskmodel(self):
# to be called before read_exposure
# NB: this is called even if there is no risk model
"""
Read the risk model and set the attribute .riskmodel.
The riskmodel can be empty for hazard calculations.
Save the loss ratios (if any) in the datastore.
"""
logging.info('Reading the risk model if present')
self.riskmodel = readinput.get_risk_model(self.oqparam)
if not self.riskmodel:
parent = self.datastore.parent
if 'fragility' in parent or 'vulnerability' in parent:
self.riskmodel = riskinput.read_composite_risk_model(parent)
return
if self.oqparam.ground_motion_fields and not self.oqparam.imtls:
raise InvalidFile('No intensity_measure_types specified in %s' %
self.oqparam.inputs['job_ini'])
self.save_params() # re-save oqparam
def load_riskmodel(self):
"""
Read the risk model and set the attribute .riskmodel.
The riskmodel can be empty for hazard calculations.
Save the loss ratios (if any) in the datastore.
"""
logging.info('Reading the risk model if present')
self.riskmodel = rm = readinput.get_risk_model(self.oqparam)
if not self.riskmodel:
parent = self.datastore.parent
if 'composite_risk_model' in parent:
self.riskmodel = riskinput.read_composite_risk_model(parent)
return
self.save_params() # re-save oqparam
# save the risk models and loss_ratios in the datastore
self.datastore['composite_risk_model'] = rm
attrs = self.datastore.getitem('composite_risk_model').attrs
attrs['min_iml'] = hdf5.array_of_vstr(sorted(rm.get_min_iml().items()))
self.datastore.set_nbytes('composite_risk_model')
self.datastore.hdf5.flush()