Source code for openquake.calculators.classical_bcr

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2023 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
# GNU Affero General Public License for more details.
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# along with OpenQuake. If not, see <>.

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, param, monitor): """ Compute and return the average losses for each asset. :param riskinputs: :class:`openquake.risklib.riskinput.RiskInput` objects :param param: dictionary of extra parameters :param monitor: :class:`openquake.baselib.performance.Monitor` instance """ R = riskinputs[0].hazard_getter.R result = AccumDict(accum=numpy.zeros((R, 3), F32)) crmodel ='crmodel') mon = monitor('getting hazard', measuremem=False) for ri in riskinputs: with mon: haz = ri.hazard_getter.get_hazard() for taxo, assets in ri.asset_df.groupby('taxonomy'): for rlz in range(R): hcurve = haz[:, rlz] out = crmodel.get_output(assets, hcurve) for asset, (eal_orig, eal_retro, bcr) in zip( assets.to_records(), out['structural']): aval = asset['value-structural'] result[asset['ordinal']][rlz] = numpy.array([ eal_orig * aval, eal_retro * aval, bcr]) return {'bcr_data': result}
[docs]@base.calculators.add('classical_bcr') class ClassicalBCRCalculator(classical_risk.ClassicalRiskCalculator): """ Classical BCR Risk calculator """ core_task = classical_bcr accept_precalc = ['classical']
[docs] def pre_execute(self): super().pre_execute() for asset_ref, retrofitted in zip(self.assetcol['id'], self.assetcol.array['retrofitted']): if numpy.isnan(retrofitted): raise ValueError('The asset %s has no retrofitted value!' % asset_ref.decode('utf8'))
[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['bcr_data'].items(): bcr_data[aid]['annual_loss_orig'] = data[:, 0] bcr_data[aid]['annual_loss_retro'] = data[:, 1] bcr_data[aid]['bcr'] = data[:, 2] self.datastore['bcr-rlzs'] = bcr_data stats.set_rlzs_stats( self.datastore, 'bcr-rlzs', assets=self.assetcol['id'])