Source code for openquake.calculators.scenario_damage

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2019 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.risklib import scientific
from openquake.calculators import base

U16 = numpy.uint16
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64


[docs]def scenario_damage(riskinputs, crmodel, param, monitor): """ Core function for a damage computation. :param riskinputs: :class:`openquake.risklib.riskinput.RiskInput` objects :param crmodel: a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance :param monitor: :class:`openquake.baselib.performance.Monitor` instance :param param: dictionary of extra parameters :returns: a dictionary {'d_asset': [(l, r, a, mean-stddev), ...], 'd_event': damage array of shape R, L, E, D, 'c_asset': [(l, r, a, mean-stddev), ...], 'c_event': damage array of shape R, L, E} `d_asset` and `d_tag` are related to the damage distributions whereas `c_asset` and `c_tag` are the consequence distributions. If there is no consequence model `c_asset` is an empty list and `c_tag` is a zero-valued array. """ L = len(crmodel.loss_types) D = len(crmodel.damage_states) E = param['number_of_ground_motion_fields'] R = riskinputs[0].hazard_getter.num_rlzs result = dict(d_asset=[], d_event=numpy.zeros((E, R, L, D), F64), c_asset=[], c_event=numpy.zeros((E, R, L), F64)) for ri in riskinputs: for out in ri.gen_outputs(crmodel, monitor): r = out.rlzi for l, loss_type in enumerate(crmodel.loss_types): for asset, fractions in zip(ri.assets, out[loss_type]): dmg = fractions[:, :D] * asset['number'] # shape (E, D) result['d_event'][:, r, l] += dmg result['d_asset'].append( (l, r, asset['ordinal'], scientific.mean_std(dmg))) if crmodel.has('consequence'): csq = fractions[:, D] * asset['value-' + loss_type] result['c_asset'].append( (l, r, asset['ordinal'], scientific.mean_std(csq))) result['c_event'][:, r, l] += csq return result
[docs]@base.calculators.add('scenario_damage') class ScenarioDamageCalculator(base.RiskCalculator): """ Scenario damage calculator """ core_task = scenario_damage is_stochastic = True precalc = 'scenario' accept_precalc = ['scenario']
[docs] def pre_execute(self): super().pre_execute() F = self.oqparam.number_of_ground_motion_fields self.param['number_of_ground_motion_fields'] = F self.riskinputs = self.build_riskinputs('gmf') self.param['tags'] = list(self.assetcol.tagcol)
[docs] def post_execute(self, result): """ Compute stats for the aggregated distributions and save the results on the datastore. """ if not result: self.collapsed() return dstates = self.crmodel.damage_states ltypes = self.crmodel.loss_types L = len(ltypes) R = len(self.rlzs_assoc.realizations) D = len(dstates) N = len(self.assetcol) F = self.oqparam.number_of_ground_motion_fields # damage distributions dt_list = [] mean_std_dt = numpy.dtype([('mean', (F32, D)), ('stddev', (F32, D))]) for ltype in ltypes: dt_list.append((ltype, mean_std_dt)) d_asset = numpy.zeros((N, R, L, 2, D), F32) for (l, r, a, stat) in result['d_asset']: d_asset[a, r, l] = stat self.datastore['dmg_by_asset'] = d_asset dmg_dt = [(ds, F32) for ds in self.crmodel.damage_states] d_event = numpy.zeros((F, R, L), dmg_dt) for d, ds in enumerate(self.crmodel.damage_states): d_event[ds] = result['d_event'][:, :, :, d] self.datastore['dmg_by_event'] = d_event # consequence distributions if result['c_asset']: dtlist = [('event_id', U64), ('rlzi', U16), ('loss', (F32, (L,)))] stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)]) c_asset = numpy.zeros((N, R, L), stat_dt) for (l, r, a, stat) in result['c_asset']: c_asset[a, r, l] = stat self.datastore['losses_by_asset'] = c_asset self.datastore['losses_by_event'] = numpy.fromiter( ((eid + rlzi * F, rlzi, F32(result['c_event'][eid, rlzi])) for rlzi in range(R) for eid in range(F)), dtlist)