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.
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# OpenQuake is distributed in the hope that it will be useful,
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import numpy

from openquake.hazardlib.stats import compute_stats2
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.rlzi] = 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 weights = [rlz.weight for rlz in self.rlzs_assoc.realizations] if len(weights) > 1: snames, sfuncs = zip(*self.oqparam.risk_stats()) bcr_stats = numpy.zeros((self.N, len(sfuncs)), self.oqparam.loss_dt(bcr_dt)) for lt in bcr_data.dtype.names: bcr_stats[lt] = compute_stats2(bcr_data[lt], sfuncs, weights) self.datastore['bcr-stats'] = bcr_stats