# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import numpy
from openquake.hazardlib.stats import compute_stats
from openquake.risklib import scientific
from openquake.calculators import base
F32 = numpy.float32
[docs]def classical_risk(riskinputs, oqparam, monitor):
"""
Compute and return the average losses for each asset.
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param oqparam:
input parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
"""
crmodel = monitor.read('crmodel')
result = dict(loss_curves=[], stat_curves=[])
weights = [w['default'] for w in oqparam._weights]
statnames, stats = zip(*oqparam._stats)
mon = monitor('getting hazard', measuremem=False)
for ri in riskinputs:
A = len(ri.asset_df)
L = len(crmodel.lti)
R = ri.hazard_getter.num_rlzs
loss_curves = numpy.zeros((R, L, A), object)
avg_losses = numpy.zeros((R, L, A))
with mon:
haz = ri.hazard_getter.get_hazard()
for taxo, asset_df in ri.asset_df.groupby('taxonomy'):
for rlz in range(R):
pcurve = haz.extract(rlz)
out = crmodel.get_output(asset_df, pcurve)
for li, loss_type in enumerate(crmodel.loss_types):
# loss_curves has shape (A, C)
for i, asset in enumerate(asset_df.to_records()):
loss_curves[rlz, li, i] = lc = out[loss_type][i]
aid = asset['ordinal']
avg = scientific.average_loss(lc)
avg_losses[rlz, li, i] = avg
lcurve = (lc['loss'], lc['poe'], avg)
result['loss_curves'].append((li, rlz, aid, lcurve))
# compute statistics
for li, loss_type in enumerate(crmodel.loss_types):
for i, asset in enumerate(ri.asset_df.to_records()):
avg_stats = compute_stats(
avg_losses[:, li, i], stats, weights)
losses = loss_curves[0, li, i]['loss']
all_poes = numpy.array(
[loss_curves[r, li, i]['poe'] for r in range(R)])
poes_stats = compute_stats(all_poes, stats, weights)
result['stat_curves'].append(
(li, asset['ordinal'], losses, poes_stats, avg_stats))
if R == 1: # the realization is the same as the mean
del result['loss_curves']
return result
[docs]@base.calculators.add('classical_risk')
class ClassicalRiskCalculator(base.RiskCalculator):
"""
Classical Risk calculator
"""
core_task = classical_risk
precalc = 'classical'
accept_precalc = ['classical']
[docs] def pre_execute(self):
"""
Associate the assets to the sites and build the riskinputs.
"""
oq = self.oqparam
super().pre_execute()
if '_poes' not in self.datastore: # when building short report
return
full_lt = self.datastore['full_lt']
self.realizations = full_lt.get_realizations()
weights = [rlz.weight for rlz in self.realizations]
stats = list(oq.hazard_stats().items())
oq._stats = stats
oq._weights = weights
self.riskinputs = self.build_riskinputs()
self.A = len(self.assetcol)
self.L = len(self.crmodel.loss_types)
self.S = len(oq.hazard_stats())
[docs] def post_execute(self, result):
"""
Saving loss curves in the datastore.
:param result: aggregated result of the task classical_risk
"""
curve_res = {cp.loss_type: cp.curve_resolution
for cp in self.crmodel.curve_params
if cp.user_provided}
self.loss_curve_dt = scientific.build_loss_curve_dt(
curve_res, insurance_losses=False)
ltypes = self.crmodel.loss_types
# loss curves stats are generated always
stats = list(self.oqparam.hazard_stats())
stat_curves = numpy.zeros((self.A, self.S), self.loss_curve_dt)
avg_losses = numpy.zeros((self.A, self.S, self.L), F32)
for li, a, losses, statpoes, statloss in result['stat_curves']:
stat_curves_lt = stat_curves[ltypes[li]]
for s in range(self.S):
avg_losses[a, s, li] = statloss[s]
base.set_array(stat_curves_lt['poes'][a, s], statpoes[s])
base.set_array(stat_curves_lt['losses'][a, s], losses)
for li, lt in enumerate(ltypes):
self.datastore['avg_losses-stats/' + lt] = avg_losses[:, :, li]
self.datastore.set_shape_descr(
'avg_losses-stats/' + lt,
asset_id=self.assetcol['id'], stat=stats)
self.datastore['loss_curves-stats'] = stat_curves
self.datastore.set_attrs('loss_curves-stats', stat=stats)
if self.R > 1: # individual realizations saved only if many
loss_curves = numpy.zeros((self.A, self.R), self.loss_curve_dt)
avg_losses = numpy.zeros((self.A, self.R, self.L), F32)
for li, r, a, (losses, poes, avg) in result['loss_curves']:
lc = loss_curves[a, r][ltypes[li]]
avg_losses[a, r, li] = avg
base.set_array(lc['losses'], losses)
base.set_array(lc['poes'], poes)
for li, lt in enumerate(ltypes):
self.datastore['avg_losses-rlzs/' + lt] = avg_losses[:, :, li]
self.datastore.set_shape_descr(
'avg_losses-rlzs/' + lt,
asset_id=self.assetcol['id'], rlz=numpy.arange(self.R))
self.datastore['loss_curves-rlzs'] = loss_curves