def post_execute(self, dummy):
"""
Compute and store average losses from the risk_by_event dataset,
and then loss curves and maps.
"""
oq = self.oqparam
# sanity check on the risk_by_event
alt = self.datastore.read_df('risk_by_event', 'event_id')
K = self.datastore['risk_by_event'].attrs.get('K', 0)
upper_limit = self.E * self.L * (K + 1)
size = len(alt)
assert size <= upper_limit, (size, upper_limit)
if oq.avg_losses:
for r in range(self.R):
self.avg_losses[:, r] *= self.avg_ratio[r]
self.datastore['avg_losses-rlzs'] = self.avg_losses
stats.set_rlzs_stats(self.datastore,
'avg_losses',
asset_id=self.assetcol['id'],
loss_type=oq.loss_names)
# save agg_losses
units = self.datastore['cost_calculator'].get_units(oq.loss_names)
if oq.calculation_mode == 'scenario_risk': # compute agg_losses
alt['rlz_id'] = self.rlzs[alt.index.to_numpy()]
agglosses = numpy.zeros((K + 1, self.R, self.L), F32)
for (agg_id, rlz_id,
loss_id), df in alt.groupby(['agg_id', 'rlz_id', 'loss_id']):
agglosses[agg_id, rlz_id,
loss_id] = (df.loss.sum() * self.avg_ratio[rlz_id])
self.datastore['agg_losses-rlzs'] = agglosses
stats.set_rlzs_stats(self.datastore,
'agg_losses',
agg_id=K,
loss_types=oq.loss_names,
units=units)
logging.info('Total portfolio loss\n' +
views.view('portfolio_loss', self.datastore))
else: # event_based_risk, run post_risk
prc = PostRiskCalculator(oq, self.datastore.calc_id)
if hasattr(self, 'exported'):
prc.exported = self.exported
with prc.datastore:
prc.run(exports='')
if (oq.investigation_time or not oq.avg_losses
or 'agg_losses-rlzs' not in self.datastore):
return
# sanity check on the agg_losses and sum_losses
sumlosses = self.avg_losses.sum(axis=0)
if not numpy.allclose(agglosses[K], sumlosses, rtol=1E-6):
url = ('https://docs.openquake.org/oq-engine/advanced/'
'addition-is-non-associative.html')
logging.warning(
'Due to rounding errors inherent in floating-point arithmetic,'
' agg_losses != sum(avg_losses): %s != %s\nsee %s',
agglosses[K].mean(), sumlosses.mean(), url)
def build_aggcurves(self):
prc = PostRiskCalculator(self.oqparam, self.datastore.calc_id)
prc.assetcol = self.assetcol
if hasattr(self, 'exported'):
prc.exported = self.exported
with prc.datastore:
prc.run(exports='')
# save agg_curves-stats
if self.R > 1 and 'aggcurves' in self.datastore:
save_curve_stats(self.datastore)