Source code for openquake.calculators.classical_damage

# -*- 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
# 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 <>.

import numpy

from openquake.baselib.general import AccumDict
from openquake.hazardlib.stats import compute_stats2
from openquake.calculators import base, classical_risk

[docs]def classical_damage(riskinput, riskmodel, param, monitor): """ Core function for a classical damage computation. :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 :returns: a nested dictionary rlz_idx -> asset -> <damage array> """ R = riskinput.hazard_getter.num_rlzs result = {i: AccumDict() for i in range(R)} for outputs in riskmodel.gen_outputs(riskinput, monitor): for l, out in enumerate(outputs): ordinals = [a.ordinal for a in outputs.assets] result[outputs.rlzi] += dict(zip(ordinals, out)) return result
[docs]@base.calculators.add('classical_damage') class ClassicalDamageCalculator(classical_risk.ClassicalRiskCalculator): """ Scenario damage calculator """ core_task = classical_damage
[docs] def post_execute(self, result): """ Export the result in CSV format. :param result: a dictionary asset -> fractions per damage state """ damages_dt = numpy.dtype([(ds, numpy.float32) for ds in self.riskmodel.damage_states]) damages = numpy.zeros((self.A, self.R), damages_dt) for r in result: for aid, fractions in result[r].items(): damages[aid, r] = tuple(fractions) self.datastore['damages-rlzs'] = damages weights = [rlz.weight for rlz in self.rlzs_assoc.realizations] if len(weights) > 1: # compute stats snames, sfuncs = zip(*self.oqparam.risk_stats()) dmg_stats = compute_stats2(damages, sfuncs, weights) self.datastore['damages-stats'] = dmg_stats