Source code for openquake.calculators.classical_bcr
# -*- 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.
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# 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.calculators import base, classical_risk
F32 = numpy.float32
bcr_dt = numpy.dtype([('annual_loss_orig', F32), ('annual_loss_retro', F32),
('bcr', F32)])
[docs]def classical_bcr(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 = {} # (N, R) -> data
for outputs in riskmodel.gen_outputs(riskinput, monitor):
assets = outputs.assets
for l, out in enumerate(outputs):
loss_type = riskmodel.loss_types[l]
for asset, (eal_orig, eal_retro, bcr) in zip(assets, out):
aval = asset.value(loss_type)
result[asset.ordinal, loss_type, outputs.r] = numpy.array([
(eal_orig * aval, eal_retro * aval, bcr)], bcr_dt)
return result
@base.calculators.add('classical_bcr')
[docs]class ClassicalBCRCalculator(classical_risk.ClassicalRiskCalculator):
"""
Classical BCR Risk calculator
"""
core_task = classical_bcr
[docs] def post_execute(self, result):
bcr_data = numpy.zeros((self.N, self.R), self.oqparam.loss_dt(bcr_dt))
for (aid, lt, r), data in result.items():
bcr_data[lt][aid, r] = data
self.datastore['bcr-rlzs'] = bcr_data