def compute_hazard(sources, src_filter, rlzs_by_gsim, param, monitor): """ :param sources: a list with a single UCERF source :param src_filter: a SourceFilter instance :param rlzs_by_gsim: a dictionary gsim -> rlzs :param param: extra parameters :param monitor: a Monitor instance :returns: an AccumDict grp_id -> EBRuptures """ [src] = sources res = AccumDict() res.calc_times = [] serial = 1 sampl_mon = monitor('sampling ruptures', measuremem=True) filt_mon = monitor('filtering ruptures', measuremem=False) res.trt = DEFAULT_TRT ebruptures = [] background_sids = src.get_background_sids(src_filter) sitecol = src_filter.sitecol cmaker = ContextMaker(rlzs_by_gsim, src_filter.integration_distance) for sample in range(param['samples']): for ses_idx, ses_seed in param['ses_seeds']: seed = sample * TWO16 + ses_seed with sampl_mon: rups, n_occs = generate_event_set(src, background_sids, src_filter, seed) with filt_mon: for rup, n_occ in zip(rups, n_occs): rup.serial = serial try: rup.sctx, rup.dctx = cmaker.make_contexts(sitecol, rup) indices = rup.sctx.sids except FarAwayRupture: continue events = [] for _ in range(n_occ): events.append((0, src.src_group_id, ses_idx, sample)) if events: evs = numpy.array(events, stochastic.event_dt) ebruptures.append(EBRupture(rup, src.id, indices, evs)) serial += 1 res.num_events = len(stochastic.set_eids(ebruptures)) res['ruptures'] = {src.src_group_id: ebruptures} if param['save_ruptures']: res.ruptures_by_grp = {src.src_group_id: ebruptures} else: res.events_by_grp = { src.src_group_id: event_based.get_events(ebruptures) } res.eff_ruptures = {src.src_group_id: src.num_ruptures} if param.get('gmf'): getter = getters.GmfGetter(rlzs_by_gsim, ebruptures, sitecol, param['oqparam'], param['min_iml'], param['samples']) res.update(getter.compute_gmfs_curves(monitor)) return res
def compute_ruptures(sources, src_filter, gsims, param, monitor): """ :param sources: a list with a single UCERF source :param src_filter: a SourceFilter instance :param gsims: a list of GSIMs :param param: extra parameters :param monitor: a Monitor instance :returns: an AccumDict grp_id -> EBRuptures """ [src] = sources res = AccumDict() res.calc_times = [] serial = 1 sampl_mon = monitor('sampling ruptures', measuremem=True) filt_mon = monitor('filtering ruptures', measuremem=False) res.trt = DEFAULT_TRT ebruptures = [] background_sids = src.get_background_sids(src_filter) sitecol = src_filter.sitecol cmaker = ContextMaker(gsims, src_filter.integration_distance) for sample in range(param['samples']): for ses_idx, ses_seed in param['ses_seeds']: seed = sample * TWO16 + ses_seed with sampl_mon: rups, n_occs = generate_event_set(src, background_sids, src_filter, seed) with filt_mon: for rup, n_occ in zip(rups, n_occs): rup.serial = serial rup.seed = seed try: rup.sctx, rup.dctx = cmaker.make_contexts(sitecol, rup) indices = rup.sctx.sids except FarAwayRupture: continue events = [] for _ in range(n_occ): events.append((0, src.src_group_id, ses_idx, sample)) if events: evs = numpy.array(events, stochastic.event_dt) ebruptures.append(EBRupture(rup, indices, evs)) serial += 1 res.num_events = len(stochastic.set_eids(ebruptures)) res[src.src_group_id] = ebruptures if not param['save_ruptures']: res.events_by_grp = { grp_id: event_based.get_events(res[grp_id]) for grp_id in res } res.eff_ruptures = {src.src_group_id: src.num_ruptures} return res
def pt_src_are(self, pt_src, gsim, weight, lnSA, monitor): """ Returns the vector-valued Annual Rate of Exceedance for one single point-source :param pt_src: single instance of class "openquake.hazardlib.source.area.PointSource" :param gsim: tuple, containing (only one?) instance of Openquake GSIM class :param: weight, weight to be multiplied to ARE estimate :param lnSA: list, natural logarithm of acceleration values for each spectral period. Note : Values should be ordered in the same order than self.periods """ annual_rate = 0 # Loop over ruptures: # i.e. one rupture for each combination of (mag, nodal plane, hypocentral depth): for r in pt_src.iter_ruptures(): # NOTE: IF ACCOUNTING FOR "pointsource_distance" IN THE INI FILE, ONE SHOULD USE THE # "point_ruptures()" METHOD BELOW: # Loop over ruptures, one rupture for each magnitude ( neglect floating and combination on # nodal plane and hypocentral depth): ## for r in pt_src.point_ruptures(): # Note: Seismicity rate evenly distributed over all point sources # Seismicity rate also accounts for FMD (i.e. decreasing for # increasing magnitude value) # Filter the site collection with respect to the rupture and prepare context objects: context_maker = ContextMaker(r.tectonic_region_type, gsim) site_ctx, dist_ctx = context_maker.make_contexts(self.sites, r) rup_ctx = RuptureContext() rup_ctx.mag = r.mag rup_ctx.rake = r.rake assert len(gsim)==1 annual_rate += r.occurrence_rate * weight * self.gm_poe(gsim[0], dist_ctx, rup_ctx, site_ctx, lnSA) return annual_rate