Source code for openquake.calculators.classical_bcr
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2018 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 AccumDict
from openquake.hazardlib import stats
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(riskinputs, riskmodel, param, monitor):
"""
Compute and return the average losses for each asset.
:param riskinputs:
:class:`openquake.risklib.riskinput.RiskInput` objects
:param riskmodel:
a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param param:
dictionary of extra parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
"""
R = riskinputs[0].hazard_getter.num_rlzs
result = AccumDict(accum=numpy.zeros((R, 3), F32))
for ri in riskinputs:
for outputs in riskmodel.gen_outputs(ri, monitor):
assets = outputs.assets
for out in outputs:
for asset, (eal_orig, eal_retro, bcr) in zip(assets, out):
aval = asset.value('structural')
result[asset.ordinal][outputs.rlzi] = numpy.array([
eal_orig * aval, eal_retro * aval, bcr])
return result
[docs]@base.calculators.add('classical_bcr')
class ClassicalBCRCalculator(classical_risk.ClassicalRiskCalculator):
"""
Classical BCR Risk calculator
"""
core_task = classical_bcr
[docs] def post_execute(self, result):
# NB: defined only for loss_type = 'structural'
bcr_data = numpy.zeros((self.A, self.R), bcr_dt)
for aid, data in result.items():
bcr_data[aid]['annual_loss_orig'] = data[:, 0]
bcr_data[aid]['annual_loss_retro'] = data[:, 1]
bcr_data[aid]['bcr'] = data[:, 2]
stats.set_rlzs_stats(self.datastore, 'bcr', bcr_data)