# -*- 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 logging
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
"""
ins = param['insured_losses']
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, insured_curves) in enumerate(outputs):
for i, asset in enumerate(outputs.assets):
aid = asset.ordinal
avg = scientific.average_loss(loss_curves[i])
outputs.average_losses[l].append(avg)
lcurve = (loss_curves[i, 0], loss_curves[i, 1], avg)
if ins:
lcurve += (
insured_curves[i, 0], insured_curves[i, 1],
scientific.average_loss(insured_curves[i]))
else:
lcurve += (None, None, None)
result['loss_curves'].append((l, r, aid, lcurve))
# compute statistics
R = riskinput.hazard_getter.num_rlzs
if R > 1 and param['stats']:
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)
# out is index by the loss type index l and out[l]
# is a pair loss_curves, insured_loss_curves
# loss_curves[i, 0] are the i-th losses,
# loss_curves[i, 1] are the i-th poes
losses = out[l][0][i, 0]
poes_stats = compute_stats(
numpy.array([out[l][0][i, 1] for out in outs]),
stats, weights)
result['stat_curves'].append(
(l, asset.ordinal, losses, poes_stats, avg_stats))
return result
@base.calculators.add('classical_risk')
[docs]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['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['realizations']['weight']
self.R = len(weights)
with self.monitor('build riskinputs', measuremem=True, autoflush=True):
self.riskinputs = self.build_riskinputs('poe')
self.param = dict(insured_losses=oq.insured_losses,
stats=oq.risk_stats(), weights=weights)
self.N = len(self.assetcol)
self.L = len(self.riskmodel.loss_types)
self.I = oq.insured_losses
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
"""
loss_ratios = {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(
loss_ratios, self.I)
ltypes = self.riskmodel.loss_types
loss_curves = numpy.zeros((self.N, self.R), self.loss_curve_dt)
for l, r, aid, lcurve in result['loss_curves']:
loss_curves_lt = loss_curves[ltypes[l]]
for i, name in enumerate(loss_curves_lt.dtype.names):
if name.startswith('avg'):
loss_curves_lt[name][aid, r] = lcurve[i]
else: # 'losses', 'poes'
base.set_array(loss_curves_lt[name][aid, r], lcurve[i])
self.datastore['loss_curves-rlzs'] = loss_curves
self.datastore.set_nbytes('loss_curves-rlzs')
# loss curves stats
if self.R > 1:
stats = [encode(n) for (n, f) in self.oqparam.risk_stats()]
stat_curves = numpy.zeros((self.N, self.S), self.loss_curve_dt)
for l, aid, losses, statpoes, statloss in result['stat_curves']:
stat_curves_lt = stat_curves[ltypes[l]]
for s in range(self.S):
stat_curves_lt['avg'][aid, s] = statloss[s]
base.set_array(stat_curves_lt['poes'][aid, s], statpoes[s])
base.set_array(stat_curves_lt['losses'][aid, s], losses)
self.datastore['loss_curves-stats'] = stat_curves
self.datastore.set_attrs(
'loss_curves-stats', nbytes=stat_curves.nbytes, stats=stats)