Source code for openquake.calculators.event_based

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2015-2022 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 time
import os.path
import logging
import operator
import numpy
import pandas

from openquake.baselib import hdf5, parallel
from openquake.baselib.general import AccumDict, copyobj, humansize
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.stats import geom_avg_std, compute_pmap_stats
from openquake.hazardlib.calc.stochastic import sample_ruptures
from openquake.hazardlib.gsim.base import ContextMaker, FarAwayRupture
from openquake.hazardlib.calc.filters import nofilter, getdefault, SourceFilter
from openquake.hazardlib.calc.gmf import GmfComputer
from openquake.hazardlib import InvalidFile
from openquake.hazardlib.calc.stochastic import get_rup_array, rupture_dt
from openquake.hazardlib.source.rupture import (
    RuptureProxy, EBRupture, get_ruptures)
from openquake.commonlib import (
    calc, util, logs, readinput, logictree, datastore)
from openquake.risklib.riskinput import str2rsi, rsi2str
from openquake.calculators import base, views
from openquake.calculators.getters import (
    get_rupture_getters, sig_eps_dt, time_dt)
from openquake.calculators.classical import ClassicalCalculator
from openquake.engine import engine

U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
TWO32 = numpy.float64(2 ** 32)

# ######################## GMF calculator ############################ #

[docs]def get_mean_curves(dstore, imt): """ Extract the mean hazard curves from the datastore, as an array of shape (N, L1) """ if 'hcurves-stats' in dstore: # shape (N, S, M, L1) arr = dstore.sel('hcurves-stats', stat='mean', imt=imt) else: # there is only 1 realization arr = dstore.sel('hcurves-rlzs', rlz_id=0, imt=imt) return arr[:, 0, 0, :]
# ########################################################################## #
[docs]def count_ruptures(src): """ Count the number of ruptures on a heavy source """ return {src.source_id: src.count_ruptures()}
[docs]def strip_zeros(gmf_df): # remove the rows with all zero values df = gmf_df[gmf_df.columns[3:]] # strip eid, sid, rlz ok = df.to_numpy().sum(axis=1) > 0 return gmf_df[ok]
[docs]def event_based(proxies, full_lt, oqparam, dstore, monitor): """ Compute GMFs and optionally hazard curves """ alldata = AccumDict(accum=[]) sig_eps = [] times = [] # rup_id, nsites, dt hcurves = {} # key -> poes trt_smr = proxies[0]['trt_smr'] fmon = monitor('filtering ruptures', measuremem=False) cmon = monitor('computing gmfs', measuremem=False) with dstore: trt = full_lt.trts[trt_smr // len(full_lt.sm_rlzs)] srcfilter = SourceFilter( dstore['sitecol'], oqparam.maximum_distance(trt)) rupgeoms = dstore['rupgeoms'] rlzs_by_gsim = full_lt._rlzs_by_gsim(trt_smr) cmaker = ContextMaker(trt, rlzs_by_gsim, oqparam) cmaker.min_mag = getdefault(oqparam.minimum_magnitude, trt) for proxy in proxies: t0 = time.time() with fmon: if proxy['mag'] < cmaker.min_mag: continue sids = srcfilter.close_sids(proxy, trt) if len(sids) == 0: # filtered away continue proxy.geom = rupgeoms[proxy['geom_id']] ebr = proxy.to_ebr(cmaker.trt) # after the geometry is set try: computer = GmfComputer( ebr, srcfilter.sitecol.filtered(sids), cmaker, oqparam.correl_model, oqparam.cross_correl, oqparam._amplifier, oqparam._sec_perils) except FarAwayRupture: continue with cmon: data = computer.compute_all(sig_eps) dt = time.time() - t0 times.append( (, len(computer.ctx.sids), dt)) for key in data: alldata[key].extend(data[key]) for key, val in sorted(alldata.items()): if key in 'eid sid rlz': alldata[key] = U32(alldata[key]) else: alldata[key] = F32(alldata[key]) gmfdata = strip_zeros(pandas.DataFrame(alldata)) if len(gmfdata) and oqparam.hazard_curves_from_gmfs: hc_mon = monitor('building hazard curves', measuremem=False) for (sid, rlz), df in gmfdata.groupby(['sid', 'rlz']): with hc_mon: poes = calc.gmvs_to_poes( df, oqparam.imtls, oqparam.ses_per_logic_tree_path) for m, imt in enumerate(oqparam.imtls): hcurves[rsi2str(rlz, sid, imt)] = poes[m] times = numpy.array([tup + (monitor.task_no,) for tup in times], time_dt) times.sort(order='rup_id') if not oqparam.ground_motion_fields: gmfdata = () return dict(gmfdata=gmfdata, hcurves=hcurves, times=times, sig_eps=numpy.array(sig_eps, sig_eps_dt(oqparam.imtls)))
[docs]def compute_avg_gmf(gmf_df, weights, min_iml): """ :param gmf_df: a DataFrame with colums eid, sid, rlz, gmv... :param weights: E weights associated to the realizations :param min_iml: array of M minimum intensities :returns: a dictionary site_id -> array of shape (2, M) """ dic = {} E = len(weights) M = len(min_iml) for sid, df in gmf_df.groupby(gmf_df.index): eid = df.pop('eid') if len(df) < E: gmvs = numpy.ones((E, M), F32) * min_iml gmvs[eid.to_numpy()] = df.to_numpy() else: gmvs = df.to_numpy() dic[sid] = geom_avg_std(gmvs, weights) return dic
[docs]@base.calculators.add('event_based', 'scenario', 'ucerf_hazard') class EventBasedCalculator(base.HazardCalculator): """ Event based PSHA calculator generating the ground motion fields and the hazard curves from the ruptures, depending on the configuration parameters. """ core_task = event_based is_stochastic = True accept_precalc = ['event_based', 'ebrisk', 'event_based_risk']
[docs] def init(self): if self.oqparam.cross_correl.__class__.__name__ == 'GodaAtkinson2009': logging.warning( 'The truncation_level param is ignored with GodaAtkinson2009') if hasattr(self, 'csm'): self.check_floating_spinning() if hasattr(self.oqparam, 'maximum_distance'): self.srcfilter = self.src_filter() else: self.srcfilter = nofilter if not self.datastore.parent: self.datastore.create_dset('ruptures', rupture_dt) self.datastore.create_dset('rupgeoms', hdf5.vfloat32)
[docs] def acc0(self): """ Initial accumulator, a dictionary rlz -> ProbabilityMap """ self.L = self.oqparam.imtls.size return {r: ProbabilityMap(self.L) for r in range(self.R)}
[docs] def build_events_from_sources(self): """ Prefilter the composite source model and store the source_info """ oq = self.oqparam gsims_by_trt = self.csm.full_lt.get_gsims_by_trt() sources = self.csm.get_sources() # weighting the heavy sources nrups = parallel.Starmap( count_ruptures, [(src,) for src in sources if src.code in b'AMC'], progress=logging.debug ).reduce() for src in sources: try: src.num_ruptures = nrups[src.source_id] except KeyError: src.num_ruptures = src.count_ruptures() src.weight = src.num_ruptures maxweight = sum(sg.weight for sg in self.csm.src_groups) / ( self.oqparam.concurrent_tasks or 1) eff_ruptures = AccumDict(accum=0) # grp_id => potential ruptures source_data = AccumDict(accum=[]) allargs = [] if self.oqparam.is_ucerf(): # manage the filtering in a special way for sg in self.csm.src_groups: for src in sg: src.src_filter = self.srcfilter srcfilter = nofilter # otherwise it would be ultra-slow else: srcfilter = self.srcfilter'Building ruptures') for sg in self.csm.src_groups: if not sg.sources: continue'Sending %s', sg) cmaker = ContextMaker(sg.trt, gsims_by_trt[sg.trt], oq) for src_group in sg.split(maxweight): allargs.append((src_group, cmaker, srcfilter.sitecol)) smap = parallel.Starmap( sample_ruptures, allargs, h5=self.datastore.hdf5) mon = self.monitor('saving ruptures') self.nruptures = 0 # estimated classical ruptures within maxdist for dic in smap: # NB: dic should be a dictionary, but when the calculation dies # for an OOM it can become None, thus giving a very confusing error if dic is None: raise MemoryError('You ran out of memory!') rup_array = dic['rup_array'] if len(rup_array) == 0: continue if dic['source_data']: source_data += dic['source_data'] if dic['eff_ruptures']: eff_ruptures += dic['eff_ruptures'] with mon: n = len(rup_array) rup_array['id'] = numpy.arange( self.nruptures, self.nruptures + n) self.nruptures += n hdf5.extend(self.datastore['ruptures'], rup_array) hdf5.extend(self.datastore['rupgeoms'], rup_array.geom) if len(self.datastore['ruptures']) == 0: raise RuntimeError('No ruptures were generated, perhaps the ' 'investigation time is too short') # don't change the order of the 3 things below! self.store_source_info(source_data) self.store_rlz_info(eff_ruptures) imp = calc.RuptureImporter(self.datastore) with self.monitor('saving ruptures and events'): imp.import_rups_events( self.datastore.getitem('ruptures')[()], get_rupture_getters)
[docs] def agg_dicts(self, acc, result): """ :param acc: accumulator dictionary :param result: an AccumDict with events, ruptures, gmfs and hcurves """ sav_mon = self.monitor('saving gmfs') agg_mon = self.monitor('aggregating hcurves') primary = self.oqparam.get_primary_imtls() sec_imts = self.oqparam.get_sec_imts() with sav_mon: df = result.pop('gmfdata') if len(df): dset = self.datastore['gmf_data/sid'] times = result.pop('times') [task_no] = numpy.unique(times['task_no']) rupids = list(times['rup_id']) self.datastore['gmf_data/time_by_rup'][rupids] = times hdf5.extend(dset, df.sid.to_numpy()) hdf5.extend(self.datastore['gmf_data/eid'], df.eid.to_numpy()) for m in range(len(primary)): hdf5.extend(self.datastore[f'gmf_data/gmv_{m}'], df[f'gmv_{m}']) for sec_imt in sec_imts: hdf5.extend(self.datastore[f'gmf_data/{sec_imt}'], df[sec_imt]) sig_eps = result.pop('sig_eps') hdf5.extend(self.datastore['gmf_data/sigma_epsilon'], sig_eps) self.offset += len(df) if self.offset >= TWO32: raise RuntimeError( 'The gmf_data table has more than %d rows' % TWO32) imtls = self.oqparam.imtls with agg_mon: for key, poes in result.get('hcurves', {}).items(): r, sid, imt = str2rsi(key) array = acc[r].setdefault(sid, 0).array[imtls(imt), 0] array[:] = 1. - (1. - array) * (1. - poes) self.datastore.flush() return acc
def _read_scenario_ruptures(self): oq = self.oqparam gsim_lt = readinput.get_gsim_lt(self.oqparam) G = gsim_lt.get_num_paths() if oq.calculation_mode.startswith('scenario'): ngmfs = oq.number_of_ground_motion_fields if oq.inputs['rupture_model'].endswith('.xml'): # check the number of branchsets bsets = len(gsim_lt._ltnode) if bsets > 1: raise InvalidFile( '%s for a scenario calculation must contain a single ' 'branchset, found %d!' % (oq.inputs['job_ini'], bsets)) [(trt, rlzs_by_gsim)] = gsim_lt.get_rlzs_by_gsim_trt().items() self.cmaker = ContextMaker(trt, rlzs_by_gsim, oq) rup = readinput.get_rupture(oq) if self.N > oq.max_sites_disagg: # many sites, split rupture ebrs = [EBRupture(copyobj(rup, rup_id=rup.rup_id + i), 'NA', 0, G, e0=i * G, scenario=True) for i in range(ngmfs)] else: # keep a single rupture with a big occupation number ebrs = [EBRupture(rup, 'NA', 0, G * ngmfs, rup.rup_id, scenario=True)] srcfilter = SourceFilter(self.sitecol, oq.maximum_distance(trt)) aw = get_rup_array(ebrs, srcfilter) if len(aw) == 0: raise RuntimeError( 'The rupture is too far from the sites! Please check the ' 'maximum_distance and the position of the rupture') elif oq.inputs['rupture_model'].endswith('.csv'): aw = get_ruptures(oq.inputs['rupture_model']) if len(gsim_lt.values) == 1: # fix for scenario_damage/case_12 aw['trt_smr'] = 0 # a single TRT if oq.calculation_mode.startswith('scenario'): # rescale n_occ by ngmfs and nrlzs aw['n_occ'] *= ngmfs * gsim_lt.get_num_paths() else: raise InvalidFile("Something wrong in %s" % oq.inputs['job_ini']) rup_array = aw.array hdf5.extend(self.datastore['rupgeoms'], aw.geom) if len(rup_array) == 0: raise RuntimeError( 'There are no sites within the maximum_distance' ' of %s km from the rupture' % oq.maximum_distance( rup.tectonic_region_type)(rup.mag)) fake = logictree.FullLogicTree.fake(gsim_lt) self.realizations = fake.get_realizations() self.datastore['full_lt'] = fake self.store_rlz_info({}) # store weights self.save_params() imp = calc.RuptureImporter(self.datastore) imp.import_rups_events(rup_array, get_rupture_getters)
[docs] def execute(self): oq = self.oqparam dstore = self.datastore if oq.ground_motion_fields and oq.min_iml.sum() == 0: logging.warning('The GMFs are not filtered: ' 'you may want to set a minimum_intensity') else:'minimum_intensity=%s', oq.minimum_intensity) self.offset = 0 if oq.hazard_calculation_id: # from ruptures dstore.parent = elif hasattr(self, 'csm'): # from sources self.build_events_from_sources() if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False): return {} elif 'rupture_model' not in oq.inputs: logging.warning( 'There is no rupture_model, the calculator will just ' 'import data without performing any calculation') fake = logictree.FullLogicTree.fake() dstore['full_lt'] = fake # needed to expose the outputs dstore['weights'] = [1.] return {} else: # scenario self._read_scenario_ruptures() if (oq.ground_motion_fields is False and oq.hazard_curves_from_gmfs is False): return {} if oq.ground_motion_fields: imts = oq.get_primary_imtls() nrups = len(dstore['ruptures']) base.create_gmf_data(dstore, imts, oq.get_sec_imts()) dstore.create_dset('gmf_data/sigma_epsilon', sig_eps_dt(oq.imtls)) dstore.create_dset('gmf_data/time_by_rup', time_dt, (nrups,), fillvalue=None) # event_based in parallel nr = len(dstore['ruptures'])'Reading {:_d} ruptures'.format(nr)) scenario = 'scenario' in oq.calculation_mode proxies = [RuptureProxy(rec, scenario) for rec in dstore['ruptures'][:]] full_lt = self.datastore['full_lt'] dstore.swmr_on() # must come before the Starmap smap = parallel.Starmap.apply_split( self.core_task.__func__, (proxies, full_lt, oq, self.datastore), key=operator.itemgetter('trt_smr'), weight=operator.itemgetter('n_occ'), h5=dstore.hdf5, concurrent_tasks=oq.concurrent_tasks or 1, duration=oq.time_per_task, outs_per_task=oq.outs_per_task) acc = smap.reduce(self.agg_dicts, self.acc0()) if 'gmf_data' not in dstore: return acc if oq.ground_motion_fields: with self.monitor('saving avg_gmf', measuremem=True): self.save_avg_gmf() return acc
[docs] def save_avg_gmf(self): """ Compute and save avg_gmf, unless there are too many GMFs """ size = self.datastore.getsize('gmf_data') maxsize = self.oqparam.gmf_max_gb * 1024 ** 3'Stored {humansize(size)} of GMFs') if size > maxsize: logging.warning( f'There are more than {humansize(maxsize)} of GMFs,' ' not computing avg_gmf') return numpy.unique(self.datastore['gmf_data/eid'][:]) rlzs = self.datastore['events']['rlz_id'] self.weights = self.datastore['weights'][:][rlzs] gmf_df = self.datastore.read_df('gmf_data', 'sid') for sec_imt in self.oqparam.get_sec_imts(): # ignore secondary perils del gmf_df[sec_imt] rel_events = gmf_df.eid.unique() e = len(rel_events) if e == 0: raise RuntimeError( 'No GMFs were generated, perhaps they were ' 'all below the minimum_intensity threshold') elif e < len(self.datastore['events']): self.datastore['relevant_events'] = rel_events'Stored {:_d} relevant event IDs'.format(e)) # really compute and store the avg_gmf M = len(self.oqparam.min_iml) avg_gmf = numpy.zeros((2, self.N, M), F32) for sid, avgstd in compute_avg_gmf( gmf_df, self.weights, self.oqparam.min_iml).items(): avg_gmf[:, sid] = avgstd self.datastore['avg_gmf'] = avg_gmf return rel_events
[docs] def post_execute(self, result): oq = self.oqparam if (not result or not oq.ground_motion_fields and not oq.hazard_curves_from_gmfs): return N = len(self.sitecol.complete) M = len(oq.imtls) # 0 in scenario L = oq.imtls.size L1 = L // (M or 1) # check seed dependency unless the number of GMFs is huge if 'gmf_data' in self.datastore and self.datastore.getsize( 'gmf_data/gmv_0') < 4E9:'Checking stored GMFs') msg = views.view('extreme_gmvs', self.datastore) logging.warning(msg) if oq.hazard_curves_from_gmfs: rlzs = self.datastore['full_lt'].get_realizations() # compute and save statistics; this is done in process and can # be very slow if there are thousands of realizations weights = [rlz.weight for rlz in rlzs] # NB: in the future we may want to save to individual hazard # curves if oq.individual_rlzs is set; for the moment we # save the statistical curves only hstats = oq.hazard_stats() S = len(hstats) pmaps = list(result.values()) R = len(weights) if len(pmaps) != R: # this should never happen, unless I break the # logic tree reduction mechanism during refactoring raise AssertionError('Expected %d pmaps, got %d' % (len(weights), len(pmaps))) if oq.individual_rlzs:'Saving individual hazard curves') self.datastore.create_dset('hcurves-rlzs', F32, (N, R, M, L1)) self.datastore.set_shape_descr( 'hcurves-rlzs', site_id=N, rlz_id=R, imt=list(oq.imtls), lvl=numpy.arange(L1)) if oq.poes: P = len(oq.poes) M = len(oq.imtls) ds = self.datastore.create_dset( 'hmaps-rlzs', F32, (N, R, M, P)) self.datastore.set_shape_descr( 'hmaps-rlzs', site_id=N, rlz_id=R, imt=list(oq.imtls), poe=oq.poes) for r, pmap in enumerate(pmaps): arr = numpy.zeros((N, M, L1), F32) for sid in pmap: arr[sid] = pmap[sid].array.reshape(M, L1) self.datastore['hcurves-rlzs'][:, r] = arr if oq.poes: hmap = calc.make_hmap(pmap, oq.imtls, oq.poes) for sid in hmap: ds[sid, r] = hmap[sid].array if S:'Computing statistical hazard curves') self.datastore.create_dset('hcurves-stats', F32, (N, S, M, L1)) self.datastore.set_shape_descr( 'hcurves-stats', site_id=N, stat=list(hstats), imt=list(oq.imtls), lvl=numpy.arange(L1)) if oq.poes: P = len(oq.poes) M = len(oq.imtls) ds = self.datastore.create_dset( 'hmaps-stats', F32, (N, S, M, P)) self.datastore.set_shape_descr( 'hmaps-stats', site_id=N, stat=list(hstats), imt=list(oq.imtls), poes=oq.poes) for s, stat in enumerate(hstats): pmap = compute_pmap_stats( pmaps, [hstats[stat]], weights, oq.imtls) arr = numpy.zeros((N, M, L1), F32) for sid in pmap: arr[sid] = pmap[sid].array.reshape(M, L1) self.datastore['hcurves-stats'][:, s] = arr if oq.poes: hmap = calc.make_hmap(pmap, oq.imtls, oq.poes) for sid in hmap: ds[sid, s] = hmap[sid].array if self.datastore.parent:'r') if oq.compare_with_classical: # compute classical curves export_dir = os.path.join(oq.export_dir, 'cl') if not os.path.exists(export_dir): os.makedirs(export_dir) oq.export_dir = export_dir oq.calculation_mode = 'classical' with logs.init('job', vars(oq)) as log: = ClassicalCalculator(oq, log.calc_id) # TODO: perhaps it is possible to avoid reprocessing the source # model, however usually this is quite fast and do not dominate # the computation engine.expose_outputs( for imt in oq.imtls: cl_mean_curves = get_mean_curves(self.datastore, imt) eb_mean_curves = get_mean_curves(self.datastore, imt) self.rdiff, index = util.max_rel_diff_index( cl_mean_curves, eb_mean_curves) logging.warning( 'Relative difference with the classical ' 'mean curves: %d%% at site index %d, imt=%s', self.rdiff * 100, index, imt)