def compute_disagg(sitecol, sources, cmaker, iml2s, trti, bin_edges, oqparam, monitor): # see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation # of the algorithm used """ :param sitecol: a :class:`openquake.hazardlib.site.SiteCollection` instance :param sources: list of hazardlib source objects :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :param iml2s: a list of N arrays of shape (M, P) :param dict trti: tectonic region type index :param bin_egdes: a quintet (mag_edges, dist_edges, lon_edges, lat_edges, eps_edges) :param oqparam: the parameters in the job.ini file :param monitor: monitor of the currently running job :returns: a dictionary of probability arrays, with composite key (sid, rlzi, poe, imt, iml, trti). """ result = {'trti': trti, 'num_ruptures': 0} # all the time is spent in collect_bin_data ruptures = [] for src in sources: ruptures.extend(src.iter_ruptures()) for sid, iml2 in zip(sitecol.sids, iml2s): singlesitecol = sitecol.filtered([sid]) bin_data = disagg.collect_bin_data( ruptures, singlesitecol, cmaker, iml2, oqparam.truncation_level, oqparam.num_epsilon_bins, monitor) if bin_data: # dictionary poe, imt, rlzi -> pne bins = disagg.get_bins(bin_edges, sid) for (poe, imt, rlzi), matrix in disagg.build_disagg_matrix( bin_data, bins, monitor).items(): result[sid, rlzi, poe, imt] = matrix result['num_ruptures'] += len(bin_data.mags) return result # sid, rlzi, poe, imt, iml -> array
def compute_disagg(sitecol, sources, cmaker, iml4, trti, bin_edges, oqparam, monitor): # see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation # of the algorithm used """ :param sitecol: a :class:`openquake.hazardlib.site.SiteCollection` instance :param sources: list of hazardlib source objects :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :param iml4: an array of intensities of shape (N, R, M, P) :param dict trti: tectonic region type index :param bin_egdes: a dictionary site_id -> edges :param oqparam: the parameters in the job.ini file :param monitor: monitor of the currently running job :returns: a dictionary of probability arrays, with composite key (sid, rlzi, poe, imt, iml, trti). """ result = {'trti': trti, 'num_ruptures': 0} # all the time is spent in collect_bin_data ruptures = [] for src in sources: ruptures.extend(src.iter_ruptures()) bin_data = disagg.collect_bin_data( ruptures, sitecol, cmaker, iml4, oqparam.truncation_level, oqparam.num_epsilon_bins, monitor) if bin_data: # dictionary poe, imt, rlzi -> pne for sid in sitecol.sids: for (poe, imt, rlzi), matrix in disagg.build_disagg_matrix( bin_data, bin_edges, sid, monitor).items(): result[sid, rlzi, poe, imt] = matrix result['cache_info'] = monitor.cache_info result['num_ruptures'] = len(bin_data.mags) return result # sid, rlzi, poe, imt, iml -> array
def compute_disagg(src_filter, sources, cmaker, iml4, trti, bin_edges, oqparam, monitor): # see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation # of the algorithm used """ :param src_filter: a :class:`openquake.hazardlib.calc.filter.SourceFilter` instance :param sources: list of hazardlib source objects :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :param iml4: an array of intensities of shape (N, R, M, P) :param dict trti: tectonic region type index :param bin_egdes: a dictionary site_id -> edges :param oqparam: the parameters in the job.ini file :param monitor: monitor of the currently running job :returns: a dictionary of probability arrays, with composite key (sid, rlzi, poe, imt, iml, trti). """ result = {'trti': trti, 'num_ruptures': 0} # all the time is spent in collect_bin_data bin_data = disagg.collect_bin_data( sources, src_filter.sitecol, cmaker, iml4, oqparam.truncation_level, oqparam.num_epsilon_bins, monitor) if bin_data: # dictionary poe, imt, rlzi -> pne for sid in src_filter.sitecol.sids: for (poe, imt, rlzi), matrix in disagg.build_disagg_matrix( bin_data, bin_edges, sid, monitor).items(): result[sid, rlzi, poe, imt] = matrix result['cache_info'] = monitor.cache_info result['num_ruptures'] = len(bin_data.mags) return result # sid, rlzi, poe, imt, iml -> array
def compute_disagg(dstore, idxs, cmaker, iml3, trti, bin_edges, oq, monitor): # see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation # of the algorithm used """ :param dstore a DataStore instance :param idxs: an array of indices to ruptures :param cmaker: a :class:`openquake.hazardlib.gsim.base.ContextMaker` instance :param iml3: an ArrayWrapper of shape (N, P, Z) with an attribute imt :param trti: tectonic region type index :param bin_egdes: a quintet (mag_edges, dist_edges, lon_edges, lat_edges, eps_edges) :param monitor: monitor of the currently running job :returns: a dictionary sid -> 8D-array """ with monitor('reading rupdata', measuremem=True): dstore.open('r') sitecol = dstore['sitecol'] rupdata = {k: dstore['rup/' + k][idxs] for k in dstore['rup']} RuptureContext.temporal_occurrence_model = PoissonTOM( oq.investigation_time) pne_mon = monitor('disaggregate_pne', measuremem=False) mat_mon = monitor('build_disagg_matrix', measuremem=True) gmf_mon = monitor('disagg mean_std', measuremem=False) for sid, iml2 in zip(sitecol.sids, iml3): singlesite = sitecol.filtered([sid]) bins = disagg.get_bins(bin_edges, sid) gsim_by_z = {} for z in range(iml3.shape[-1]): try: gsim = cmaker.gsim_by_rlzi[iml3.rlzs[sid, z]] except KeyError: pass else: gsim_by_z[z] = gsim ctxs = [] ok, = numpy.where( rupdata['rrup_'][:, sid] <= cmaker.maximum_distance(cmaker.trt)) for ridx in ok: # consider only the ruptures close to the site ctx = RuptureContext((par, rupdata[par][ridx]) for par in rupdata if not par.endswith('_')) for par in rupdata: if par.endswith('_'): setattr(ctx, par[:-1], rupdata[par][ridx, [sid]]) ctxs.append(ctx) if not ctxs: continue eps3 = disagg._eps3(cmaker.trunclevel, oq.num_epsilon_bins) matrix = numpy.zeros([len(b) - 1 for b in bins] + list(iml2.shape)) for z, gsim in gsim_by_z.items(): with gmf_mon: ms = disagg.get_mean_stdv(singlesite, ctxs, iml3.imt, gsim) bdata = disagg.disaggregate( ms, ctxs, iml3.imt, iml2[:, z], eps3, pne_mon) if bdata.pnes.sum(): with mat_mon: matrix[..., z] = disagg.build_disagg_matrix(bdata, bins) if matrix.any(): yield {'trti': trti, 'imti': iml3.imti, sid: matrix}