Source code for openquake.calculators.scenario_risk

# -*- 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 logging

import numpy

from openquake.baselib.python3compat import zip, encode
from openquake.baselib.general import AccumDict
from openquake.risklib import scientific
from openquake.calculators import base

F32 = numpy.float32
F64 = numpy.float64  # higher precision to avoid task order dependency
stat_dt = numpy.dtype([('mean', F32), ('stddev', F32)])

[docs]def scenario_risk(riskinput, riskmodel, param, monitor): """ Core function for a scenario computation. :param riskinput: a of :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 dictionary { 'agg': array of shape (E, L, R, 2), 'avg': list of tuples (lt_idx, rlz_idx, asset_idx, statistics) } where E is the number of simulated events, L the number of loss types, R the number of realizations and statistics is an array of shape (n, R, 4), with n the number of assets in the current riskinput object """ E = param['number_of_ground_motion_fields'] L = len(riskmodel.loss_types) R = riskinput.hazard_getter.num_rlzs I = param['insured_losses'] + 1 result = dict(agg=numpy.zeros((E, R, L * I), F32), avg=[], all_losses=AccumDict(accum={})) for outputs in riskmodel.gen_outputs(riskinput, monitor): r = outputs.rlzi assets = outputs.assets for l, losses in enumerate(outputs): if losses is None: # this may happen continue stats = numpy.zeros((len(assets), I), stat_dt) # mean, stddev for a, asset in enumerate(assets): stats['mean'][a] = losses[a].mean() stats['stddev'][a] = losses[a].std(ddof=1) result['avg'].append((l, r, asset.ordinal, stats[a])) agglosses = losses.sum(axis=0) # shape E, I for i in range(I): result['agg'][:, r, l + L * i] += agglosses[:, i] if param['asset_loss_table']: aids = [asset.ordinal for asset in outputs.assets] result['all_losses'][l, r] += AccumDict(zip(aids, losses)) return result
[docs]@base.calculators.add('scenario_risk') class ScenarioRiskCalculator(base.RiskCalculator): """ Run a scenario risk calculation """ core_task = scenario_risk pre_calculator = 'scenario' is_stochastic = True
[docs] def pre_execute(self): """ Compute the GMFs, build the epsilons, the riskinputs, and a dictionary with the unit of measure, used in the export phase. """ if 'gmfs' in self.oqparam.inputs: self.pre_calculator = None base.RiskCalculator.pre_execute(self) eids, self.R = base.get_gmfs(self) self.assetcol = self.datastore['assetcol'] A = len(self.assetcol) E = self.oqparam.number_of_ground_motion_fields'Building the epsilons') if self.oqparam.ignore_covs: eps = numpy.zeros((A, E), numpy.float32) else: eps = self.make_eps(E) self.riskinputs = self.build_riskinputs('gmf', eps, E) self.param['number_of_ground_motion_fields'] = E self.param['insured_losses'] = self.oqparam.insured_losses self.param['asset_loss_table'] = self.oqparam.asset_loss_table
[docs] def post_execute(self, result): """ Compute stats for the aggregated distributions and save the results on the datastore. """ loss_dt = self.oqparam.loss_dt() I = self.oqparam.insured_losses + 1 with self.monitor('saving outputs', autoflush=True): A = len(self.assetcol) # agg losses res = result['agg'] E, R, LI = res.shape L = LI // I mean, std = scientific.mean_std(res) # shape (R, LI) agglosses = numpy.zeros((R, L * I), stat_dt) agglosses['mean'] = F32(mean) agglosses['stddev'] = F32(std) # losses by asset losses_by_asset = numpy.zeros((A, R, L * I), stat_dt) for (l, r, aid, stat) in result['avg']: for i in range(I): losses_by_asset[aid, r, l + L * i] = stat[i] self.datastore['losses_by_asset'] = losses_by_asset self.datastore['agglosses-rlzs'] = agglosses # losses by event self.datastore['losses_by_event'] = res # shape (E, R, LI) # all losses if self.oqparam.asset_loss_table: array = numpy.zeros((A, E, R), loss_dt) for (l, r), losses_by_aid in result['all_losses'].items(): for aid in losses_by_aid: lba = losses_by_aid[aid] # (E, I) for i in range(I): lt = loss_dt.names[l + L * i] array[lt][aid, :, r] = lba[:, i] self.datastore['all_losses-rlzs'] = array tags = [encode(tag) for tag in self.assetcol.tagcol] self.datastore.set_attrs('all_losses-rlzs', tags=tags)