# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2017 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.baselib.general import groupby, AccumDict
from openquake.baselib.python3compat import encode
from openquake.hazardlib.stats import compute_stats
from openquake.risklib import scientific
from openquake.commonlib import readinput, source
from openquake.calculators import base
F32 = numpy.float32
[docs]def classical_risk(riskinput, riskmodel, param, monitor):
"""
Compute and return the average losses for each asset.
:param riskinput:
a :class:`openquake.risklib.riskinput.RiskInput` object
:param riskmodel:
a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param param:
dictionary of extra parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
"""
result = dict(loss_curves=[], stat_curves=[])
all_outputs = list(riskmodel.gen_outputs(riskinput, monitor))
for outputs in all_outputs:
r = outputs.rlzi
outputs.average_losses = AccumDict(accum=[]) # l -> array
for l, loss_curves in enumerate(outputs):
# loss_curves has shape (C, N, 2)
for i, asset in enumerate(outputs.assets):
aid = asset.ordinal
avg = scientific.average_loss(loss_curves[:, i].T)
outputs.average_losses[l].append(avg)
lcurve = (loss_curves[:, i, 0], loss_curves[:, i, 1], avg)
result['loss_curves'].append((l, r, aid, lcurve))
# compute statistics
R = riskinput.hazard_getter.num_rlzs
w = param['weights']
statnames, stats = zip(*param['stats'])
l_idxs = range(len(riskmodel.lti))
for assets, outs in groupby(
all_outputs, lambda o: tuple(o.assets)).items():
weights = [w[out.rlzi] for out in outs]
out = outs[0]
for l in l_idxs:
for i, asset in enumerate(assets):
avgs = numpy.array([r.average_losses[l][i] for r in outs])
avg_stats = compute_stats(avgs, stats, weights)
# is a pair loss_curves, insured_loss_curves
# out[l][:, i, 0] are the i-th losses
# out[l][:, i, 1] are the i-th poes
losses = out[l][:, i, 0]
poes_stats = compute_stats(
numpy.array([out[l][:, i, 1] for out in outs]),
stats, weights)
result['stat_curves'].append(
(l, 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
"""
pre_calculator = 'classical'
core_task = classical_risk
[docs] def pre_execute(self):
"""
Associate the assets to the sites and build the riskinputs.
"""
oq = self.oqparam
if oq.insured_losses:
raise ValueError(
'insured_losses are not supported for classical_risk')
if 'hazard_curves' in oq.inputs: # read hazard from file
haz_sitecol, pmap = readinput.get_pmap(oq)
self.datastore['poes/grp-00'] = pmap
self.save_params()
self.read_exposure() # define .assets_by_site
self.load_riskmodel()
self.sitecol, self.assetcol = self.assoc_assets_sites(haz_sitecol)
self.datastore['assetcol'] = self.assetcol
self.datastore['csm_info'] = fake = source.CompositionInfo.fake()
self.rlzs_assoc = fake.get_rlzs_assoc()
self.before_export() # save 'realizations' dataset
else: # compute hazard or read it from the datastore
super(ClassicalRiskCalculator, self).pre_execute()
if 'poes' not in self.datastore: # when building short report
return
weights = self.datastore['csm_info'].rlzs['weight']
self.R = len(weights)
with self.monitor('build riskinputs', measuremem=True, autoflush=True):
self.riskinputs = self.build_riskinputs('poe')
self.param = dict(stats=oq.risk_stats(), weights=weights)
self.A = len(self.assetcol)
self.L = len(self.riskmodel.loss_types)
self.I = oq.insured_losses + 1
self.S = len(oq.risk_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.riskmodel.curve_params
if cp.user_provided}
self.loss_curve_dt = scientific.build_loss_curve_dt(
curve_res, self.oqparam.insured_losses)
ltypes = self.riskmodel.loss_types
# loss curves stats are generated always
stats = [encode(n) for (n, f) in self.oqparam.risk_stats()]
stat_curves = numpy.zeros((self.A, self.S), self.loss_curve_dt)
avg_losses = numpy.zeros((self.A, self.R, self.L * self.I), F32)
for l, a, losses, statpoes, statloss in result['stat_curves']:
stat_curves_lt = stat_curves[ltypes[l]]
for s in range(self.S):
avg_losses[a, s, l] = statloss[s]
base.set_array(stat_curves_lt['poes'][a, s], statpoes[s])
base.set_array(stat_curves_lt['losses'][a, s], losses)
self.datastore['avg_losses-stats'] = avg_losses
self.datastore.set_attrs('avg_losses-stats', stats=stats)
self.datastore['loss_curves-stats'] = stat_curves
self.datastore.set_attrs('loss_curves-stats', stats=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 * self.I), F32)
for l, r, a, (losses, poes, avg) in result['loss_curves']:
lc = loss_curves[a, r][ltypes[l]]
avg_losses[a, r, l] = avg
base.set_array(lc['losses'], losses)
base.set_array(lc['poes'], poes)
self.datastore['avg_losses-rlzs'] = avg_losses
self.datastore['loss_curves-rlzs'] = loss_curves