Source code for openquake.risklib.riskinput

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2015-2020 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 import hdf5
from openquake.baselib.general import group_array, AccumDict
from openquake.risklib import scientific

U32 = numpy.uint32
F32 = numpy.float32

[docs]def get_assets_by_taxo(assets, tempname=None): """ :param assets: an array of assets :param tempname: hdf5 file where the epsilons are (or None) :returns: assets_by_taxo with attributes eps and idxs """ assets_by_taxo = AccumDict(group_array(assets, 'taxonomy')) assets_by_taxo.assets = assets assets_by_taxo.idxs = numpy.argsort(numpy.concatenate([ a['ordinal'] for a in assets_by_taxo.values()])) assets_by_taxo.eps = {} if tempname is None: # no epsilons return assets_by_taxo # otherwise read the epsilons and group them by taxonomy with hdf5.File(tempname, 'r') as h5: dset = h5['epsilon_matrix'] for taxo, assets in assets_by_taxo.items(): lst = [dset[aid] for aid in assets['ordinal']] assets_by_taxo.eps[taxo] = numpy.array(lst) return assets_by_taxo
[docs]def get_output(crmodel, assets_by_taxo, haz, rlzi=None): """ :param assets_by_taxo: a dictionary taxonomy index -> assets on a site :param haz: an array or a dictionary of hazard on that site :param rlzi: if given, a realization index :returns: an ArrayWrapper loss_type -> array of shape (A, ...) """ if hasattr(haz, 'array'): # classical eids = [] data = [haz.array[crmodel.imtls(imt), 0] for imt in crmodel.imtls] elif isinstance(haz, numpy.ndarray): # NB: in GMF-based calculations the order in which # the gmfs are stored is random since it depends on # which hazard task ends first; here we reorder # the gmfs by event ID; this is convenient in # general and mandatory for the case of # VulnerabilityFunctionWithPMF, otherwise the # sample method would receive the means in random # order and produce random results even if the # seed is set correctly; very tricky indeed! (MS) haz.sort(order='eid') eids = haz['eid'] data = haz['gmv'] # shape (E, M) elif haz == 0: # no hazard for this site (event based) eids = numpy.arange(1) data = [] else: raise ValueError('Unexpected haz=%s' % haz) dic = dict(eids=eids, assets=assets_by_taxo.assets, loss_types=crmodel.loss_types) if rlzi is not None: dic['rlzi'] = rlzi for l, lt in enumerate(crmodel.loss_types): ls = [] for taxonomy, assets_ in assets_by_taxo.items(): if len(assets_by_taxo.eps): epsilons = assets_by_taxo.eps[taxonomy][:, eids] else: # no CoVs epsilons = () arrays = [] rmodels, weights = crmodel.get_rmodels_weights(taxonomy) for rm in rmodels: if len(data) == 0: dat = [0] elif len(eids): # gmfs dat = data[:, rm.imti[lt]] else: # hcurves dat = data[rm.imti[lt]] arrays.append(rm(lt, assets_, dat, eids, epsilons)) res = arrays[0] if len(arrays) == 1 else numpy.average( arrays, weights=weights, axis=0) ls.append(res) arr = numpy.concatenate(ls) dic[lt] = arr[assets_by_taxo.idxs] if len(arr) else arr return hdf5.ArrayWrapper((), dic)
[docs]class RiskInput(object): """ Contains all the assets and hazard values associated to a given imt and site. :param hazard_getter: a callable returning the hazard data for a given realization :param assets_by_site: array of assets, one per site """ def __init__(self, sid, hazard_getter, assets): self.sid = sid self.hazard_getter = hazard_getter self.assets = assets self.weight = len(assets) aids = [] for asset in self.assets: aids.append(asset['ordinal']) self.aids = numpy.array(aids, numpy.uint32)
[docs] def gen_outputs(self, crmodel, monitor, tempname=None, haz=None): """ Group the assets per taxonomy and compute the outputs by using the underlying riskmodels. Yield one output per realization. :param crmodel: a CompositeRiskModel instance :param monitor: a monitor object used to measure the performance """ self.monitor = monitor hazard_getter = self.hazard_getter [sid] = hazard_getter.sids if haz is None: haz = hazard_getter.get_hazard() if isinstance(haz, dict): # scenario, event_based items = haz.items() else: # list of length R, classical items = enumerate(haz) with monitor('computing risk', measuremem=False): # this approach is slow for event_based_risk since a lot of # small arrays are passed (one per realization) instead of # a long array with all realizations; ebrisk does the right # thing since it calls get_output directly assets_by_taxo = get_assets_by_taxo(self.assets, tempname) for rlzi, haz_by_rlzi in items: out = get_output(crmodel, assets_by_taxo, haz_by_rlzi, rlzi) yield out
def __repr__(self): return '<%s sid=%s, %d asset(s)>' % ( self.__class__.__name__, self.sid, len(self.aids))
# used in scenario_risk
[docs]def make_eps(asset_array, num_samples, seed, correlation): """ :param asset_array: an array of assets :param int num_samples: the number of ruptures :param int seed: a random seed :param float correlation: the correlation coefficient :returns: epsilons matrix of shape (num_assets, num_samples) """ assets_by_taxo = group_array(asset_array, 'taxonomy') eps = numpy.zeros((len(asset_array), num_samples), numpy.float32) for taxonomy, assets in assets_by_taxo.items(): shape = (len(assets), num_samples)'Building %s epsilons for taxonomy %s', shape, taxonomy) zeros = numpy.zeros(shape) epsilons = scientific.make_epsilons(zeros, seed, correlation) for asset, epsrow in zip(assets, epsilons): eps[asset['ordinal']] = epsrow return eps
[docs]def cache_epsilons(dstore, oq, assetcol, crmodel, E): """ Do nothing if there are no coefficients of variation of ignore_covs is set. Otherwise, generate an epsilon matrix of shape (A, E) and save it in the cache file, by returning the path to it. """ if oq.ignore_covs or not crmodel.covs or 'LN' not in crmodel.distributions: return A = len(assetcol)'Storing the epsilon matrix in %s', dstore.tempname) if oq.calculation_mode == 'scenario_risk': eps = make_eps(assetcol.array, E, oq.master_seed, oq.asset_correlation) else: # event based if oq.asset_correlation: numpy.random.seed(oq.master_seed) eps = numpy.array([numpy.random.normal(size=E)] * A) else: seeds = oq.master_seed + numpy.arange(E) eps = numpy.zeros((A, E), F32) for i, seed in enumerate(seeds): numpy.random.seed(seed) eps[:, i] = numpy.random.normal(size=A) with hdf5.File(dstore.tempname, 'w') as cache: cache['sitecol'] = dstore['sitecol'] cache['epsilon_matrix'] = eps return dstore.tempname
[docs]def str2rsi(key): """ Convert a string of the form 'rlz-XXXX/sid-YYYY/ZZZ' into a triple (XXXX, YYYY, ZZZ) """ rlzi, sid, imt = key.split('/') return int(rlzi[4:]), int(sid[4:]), imt
[docs]def rsi2str(rlzi, sid, imt): """ Convert a triple (XXXX, YYYY, ZZZ) into a string of the form 'rlz-XXXX/sid-YYYY/ZZZ' """ return 'rlz-%04d/sid-%04d/%s' % (rlzi, sid, imt)