Source code for openquake.calculators.event_based

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2015-2017 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 time
import os.path
import operator
import logging
import collections
import mock

import numpy

from openquake.baselib import hdf5
from openquake.baselib.python3compat import zip
from openquake.baselib.general import AccumDict, block_splitter, humansize
from openquake.hazardlib.calc.filters import FarAwayRupture
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.stats import compute_pmap_stats
from openquake.hazardlib.geo.surface import PlanarSurface
from openquake.risklib.riskinput import (
    GmfGetter, str2rsi, rsi2str, gmf_data_dt)
from openquake.baselib import parallel
from openquake.commonlib import calc, util
from openquake.calculators import base
from openquake.calculators.classical import ClassicalCalculator, PSHACalculator

U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64
TWO16 = 2 ** 16  # 65,536
TWO32 = 2 ** 32  # 4,294,967,296
TWO48 = 2 ** 48  # 281,474,976,710,656

# ######################## rupture calculator ############################ #

[docs]def get_seq_ids(task_no, num_ids): """ Get an array of sequential indices for the given task. :param task_no: the number of the task :param num_ids: the number of indices to return >>> list(get_seq_ids(1, 3)) [4294967296, 4294967297, 4294967298] """ assert 0 <= task_no < TWO16, task_no assert 0 <= num_ids < TWO32, num_ids start = task_no * TWO32 return numpy.arange(start, start + num_ids, dtype=U64)
[docs]def set_eids(ebruptures, task_no): """ Set event IDs on the given list of ebruptures produced by the given task. :param ebruptures: a non-empty list of ruptures with the same grp_id :param task_no: the number of the task generating the ruptures :returns: the total number of events """ if not ebruptures: return 0 num_events = sum(ebr.multiplicity for ebr in ebruptures) eids = get_seq_ids(task_no, num_events) start = 0 offset = U64(ebruptures[0].grp_id * TWO48) # first 16 bits for grp_id for ebr in ebruptures: m = ebr.multiplicity['eid'] = eids[start: start + m] + offset start += m return num_events
[docs]def compute_ruptures(sources, src_filter, gsims, param, monitor): """ :param sources: List of commonlib.source.Source tuples :param src_filter: a source site filter :param gsims: a list of GSIMs for the current tectonic region model :param param: a dictionary of additional parameters :param monitor: monitor instance :returns: a dictionary src_group_id -> [Rupture instances] """ # NB: by construction each block is a non-empty list with # sources of the same src_group_id grp_id = sources[0].src_group_id eb_ruptures = [] calc_times = [] rup_mon = monitor('filtering ruptures', measuremem=False) # Compute and save stochastic event sets num_ruptures = 0 for src, s_sites in src_filter(sources): t0 = time.time() if s_sites is None: continue num_ruptures += src.num_ruptures num_occ_by_rup = sample_ruptures( src, param['ses_per_logic_tree_path'], param['samples'], param['seed']) # NB: the number of occurrences is very low, << 1, so it is # more efficient to filter only the ruptures that occur, i.e. # to call sample_ruptures *before* the filtering for ebr in _build_eb_ruptures( src, num_occ_by_rup, src_filter.integration_distance, s_sites, param['seed'], rup_mon): eb_ruptures.append(ebr) dt = time.time() - t0 calc_times.append((, dt)) res = AccumDict({grp_id: eb_ruptures}) res.num_events = set_eids(eb_ruptures, getattr(monitor, 'task_no', 0)) res.calc_times = calc_times res.eff_ruptures = {grp_id: num_ruptures} return res
[docs]def sample_ruptures(src, num_ses, num_samples, seed): """ Sample the ruptures contained in the given source. :param src: a hazardlib source object :param num_ses: the number of Stochastic Event Sets to generate :param num_samples: how many samples for the given source :param seed: master seed from the job.ini file :returns: a dictionary of dictionaries rupture -> {ses_id: num_occurrences} """ # the dictionary `num_occ_by_rup` contains a dictionary # ses_id -> num_occurrences for each occurring rupture num_occ_by_rup = collections.defaultdict(AccumDict) # generating ruptures for the given source for rup_no, rup in enumerate(src.iter_ruptures()): rup.seed = src.serial[rup_no] + seed numpy.random.seed(rup.seed) for sampleid in range(num_samples): for ses_idx in range(1, num_ses + 1): num_occurrences = rup.sample_number_of_occurrences() if num_occurrences: num_occ_by_rup[rup] += { (sampleid, ses_idx): num_occurrences} rup.rup_no = rup_no + 1 return num_occ_by_rup
def _build_eb_ruptures( src, num_occ_by_rup, idist, s_sites, random_seed, rup_mon): """ Filter the ruptures stored in the dictionary num_occ_by_rup and yield pairs (rupture, <list of associated EBRuptures>) """ for rup in sorted(num_occ_by_rup, key=operator.attrgetter('rup_no')): with rup_mon: try: r_sites, dists = idist.get_closest(s_sites, rup) except FarAwayRupture: # ignore ruptures which are far away del num_occ_by_rup[rup] # save memory continue # creating EBRuptures serial = rup.seed - random_seed + 1 events = [] for (sampleid, ses_idx), num_occ in sorted( num_occ_by_rup[rup].items()): for _ in range(num_occ): # NB: the 0 below is a placeholder; the right eid will be # set a bit later, in set_eids events.append((0, ses_idx, sampleid)) if events: yield calc.EBRupture( rup, r_sites.indices, numpy.array(events, calc.event_dt), src.src_group_id, serial) def _count(ruptures): if isinstance(ruptures, int): # passed the number of ruptures return ruptures return sum(ebr.multiplicity for ebr in ruptures)
[docs]def get_events(ebruptures): """ Extract an array of dtype stored_event_dt from a list of EBRuptures """ events = [] year = 0 # to be set later for ebr in ebruptures: for event in rec = (event['eid'], ebr.serial, year, event['ses'], event['sample']) events.append(rec) return numpy.array(events, calc.stored_event_dt)
[docs]class EventBasedRuptureCalculator(PSHACalculator): """ Event based PSHA calculator generating the ruptures only """ core_task = compute_ruptures is_stochastic = True
[docs] def init(self): """ Set the random seed passed to the SourceManager and the minimum_intensity dictionary. """ oq = self.oqparam self.min_iml = calc.fix_minimum_intensity( oq.minimum_intensity, oq.imtls) self.rupser = calc.RuptureSerializer(self.datastore) self.csm_info = self.datastore['csm_info']
[docs] def zerodict(self): """ Initial accumulator, a dictionary (grp_id, gsim) -> curves """ zd = AccumDict() zd.calc_times = [] zd.eff_ruptures = AccumDict() self.grp_trt = self.csm_info.grp_trt() return zd
[docs] def agg_dicts(self, acc, ruptures_by_grp_id): """ Accumulate dictionaries of ruptures and populate the `events` dataset in the datastore. :param acc: accumulator dictionary :param ruptures_by_grp_id: a nested dictionary grp_id -> ruptures """ if hasattr(ruptures_by_grp_id, 'calc_times'): acc.calc_times.extend(ruptures_by_grp_id.calc_times) if hasattr(ruptures_by_grp_id, 'eff_ruptures'): acc.eff_ruptures += ruptures_by_grp_id.eff_ruptures acc += ruptures_by_grp_id self.save_ruptures(ruptures_by_grp_id) return acc
[docs] def save_ruptures(self, ruptures_by_grp_id): """Extend the 'events' dataset with the given ruptures""" with self.monitor('saving ruptures', autoflush=True): for grp_id, ebrs in ruptures_by_grp_id.items(): if len(ebrs): events = get_events(ebrs) dset = self.datastore.extend( 'events/grp-%02d' % grp_id, events) if self.oqparam.save_ruptures: initial_eidx = len(dset) - len(events), initial_eidx)
[docs] def post_execute(self, result): """ Save the SES collection """ self.rupser.close() num_events = sum(_count(ruptures) for ruptures in result.values()) if num_events == 0: raise RuntimeError( 'No seismic events! Perhaps the investigation time is too ' 'small or the maximum_distance is too small')'Setting %d event years', num_events) with self.monitor('setting event years', measuremem=True, autoflush=True): inv_time = int(self.oqparam.investigation_time) numpy.random.seed(self.oqparam.ses_seed) for sm in sorted(self.datastore['events']): set_random_years(self.datastore, 'events/' + sm, inv_time) h5 = self.datastore.hdf5 if 'ruptures' in h5: self.datastore.set_nbytes('ruptures') if 'events' in h5: self.datastore.set_attrs('events', num_events=num_events) self.datastore.set_nbytes('events')
[docs]def set_random_years(dstore, events_sm, investigation_time): """ Sort the `events` array and attach year labels sensitive to the SES ordinal and the investigation time. """ events = dstore[events_sm].value eids = numpy.sort(events['eid']) years = numpy.random.choice(investigation_time, len(events)) + 1 year_of = dict(zip(eids, years)) for event in events: idx = event['ses'] - 1 # starts from 0 event['year'] = idx * investigation_time + year_of[event['eid']] dstore[events_sm] = events
# ######################## GMF calculator ############################ #
[docs]def compute_gmfs_and_curves(getter, oq, monitor): """ :param getter: a GmfGetter instance :param oq: an OqParam instance :param monitor: a Monitor instance :returns: a dictionary with keys gmfcoll and hcurves """ with monitor('making contexts', measuremem=True): getter.init() grp_id = getter.grp_id hcurves = {} # key -> poes gmfcoll = {} # grp_id, rlz -> gmfa if oq.hazard_curves_from_gmfs: hc_mon = monitor('building hazard curves', measuremem=False) duration = oq.investigation_time * oq.ses_per_logic_tree_path for gsim in getter.rlzs_by_gsim: with monitor('building hazard', measuremem=True): gmfcoll[grp_id, gsim] = data = numpy.fromiter( getter.gen_gmv(gsim), gmf_data_dt) hazard = getter.get_hazard(gsim, data) for r, rlz in enumerate(getter.rlzs_by_gsim[gsim]): hazardr = hazard[r] for sid in getter.sids: for imti, imt in enumerate(getter.imts): array = hazardr[sid, imti] if len(array) == 0: # no data continue with hc_mon: poes = calc._gmvs_to_haz_curve( array['gmv'], oq.imtls[imt], oq.investigation_time, duration) hcurves[rsi2str(rlz.ordinal, sid, imt)] = poes else: # fast lane for gsim in getter.rlzs_by_gsim: with monitor('building hazard', measuremem=True): gmfcoll[grp_id, gsim] = numpy.fromiter( getter.gen_gmv(gsim), gmf_data_dt) return dict(gmfcoll=gmfcoll if oq.ground_motion_fields else None, hcurves=hcurves, gmdata=getter.gmdata)
[docs]def get_ruptures_by_grp(dstore): """ Extracts the dictionary `ruptures_by_grp` from the given calculator """ n = 0 for grp in dstore['ruptures']: n += len(dstore['ruptures/' + grp])'Reading %d ruptures from the datastore', n) # disable check on PlaceSurface to support UCERF ruptures with mock.patch( 'openquake.hazardlib.geo.surface.PlanarSurface.' 'IMPERFECT_RECTANGLE_TOLERANCE', numpy.inf): ruptures_by_grp = AccumDict(accum=[]) for grp in dstore['ruptures']: grp_id = int(grp[4:]) # strip 'grp-' ruptures_by_grp[grp_id] = list(calc.get_ruptures(dstore, grp_id)) return ruptures_by_grp
[docs]def save_gmdata(calc, n_rlzs): """ Save a composite array `gmdata` in the datastore. :param calc: a calculator with a dictionary .gmdata {rlz: data} :param n_rlzs: the total number of realizations """ n_sites = len(calc.sitecol) dtlist = ([(imt, F32) for imt in calc.oqparam.imtls] + [('events', U32), ('nbytes', U32)]) array = numpy.zeros(n_rlzs, dtlist) for rlzi in sorted(calc.gmdata): data = calc.gmdata[rlzi] # (imts, events, nbytes) events = data[-2] nbytes = data[-1] gmv = data[:-2] / events / n_sites array[rlzi] = tuple(gmv) + (events, nbytes) calc.datastore['gmdata'] = array'Generated %s of GMFs', humansize(array['nbytes'].sum()))
[docs]class EventBasedCalculator(ClassicalCalculator): """ Event based PSHA calculator generating the ground motion fields and the hazard curves from the ruptures, depending on the configuration parameters. """ pre_calculator = 'event_based_rupture' core_task = compute_gmfs_and_curves is_stochastic = True
[docs] def combine_pmaps_and_save_gmfs(self, acc, res): """ Combine the hazard curves (if any) and save the gmfs (if any) sequentially; notice that the gmfs may come from different tasks in any order. :param acc: an accumulator for the hazard curves :param res: a dictionary rlzi, imt -> [gmf_array, curves_by_imt] :returns: a new accumulator """ sav_mon = self.monitor('saving gmfs') agg_mon = self.monitor('aggregating hcurves') self.gmdata += res['gmdata'] if res['gmfcoll'] is not None: with sav_mon: for (grp_id, gsim), array in res['gmfcoll'].items(): if len(array): key = 'gmf_data/grp-%02d/%s' % (grp_id, gsim) hdf5.extend3(self.datastore.hdf5path, key, array) slicedic = self.oqparam.imtls.slicedic with agg_mon: for key, poes in res['hcurves'].items(): rlzi, sid, imt = str2rsi(key) array = acc[rlzi].setdefault(sid, 0).array[slicedic[imt], 0] array[:] = 1. - (1. - array) * (1. - poes) sav_mon.flush() agg_mon.flush() self.datastore.flush() if 'ruptures' in res: vars(EventBasedRuptureCalculator)['save_ruptures']( self, res['ruptures']) return acc
[docs] def gen_args(self, ruptures_by_grp): """ :param ruptures_by_grp: a dictionary of EBRupture objects :yields: the arguments for compute_gmfs_and_curves """ oq = self.oqparam monitor = self.monitor(self.core_task.__name__) imts = list(oq.imtls) min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts) correl_model = oq.get_correl_model() for grp_id in ruptures_by_grp: ruptures = ruptures_by_grp[grp_id] if not ruptures: continue rlzs_by_gsim = self.rlzs_assoc.get_rlzs_by_gsim(grp_id) for block in block_splitter(ruptures, oq.ruptures_per_block): samples = self.rlzs_assoc.samples[grp_id] getter = GmfGetter(grp_id, rlzs_by_gsim, block, self.sitecol, imts, min_iml, oq.truncation_level, correl_model, samples) yield getter, oq, monitor
[docs] def execute(self): """ Run in parallel `core_task(sources, sitecol, monitor)`, by parallelizing on the ruptures according to their weight and tectonic region type. """ oq = self.oqparam if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields: return if self.oqparam.ground_motion_fields: calc.check_overflow(self) with self.monitor('reading ruptures', autoflush=True): ruptures_by_grp = (self.precalc.result if self.precalc else get_ruptures_by_grp(self.datastore.parent)) self.csm_info = self.datastore['csm_info'] self.sm_id = {tuple(sm.path): sm.ordinal for sm in self.csm_info.source_models} L = len(oq.imtls.array) rlzs = self.rlzs_assoc.realizations res = parallel.Starmap( self.core_task.__func__, self.gen_args(ruptures_by_grp) ).submit_all() self.gmdata = {} acc = res.reduce(self.combine_pmaps_and_save_gmfs, { rlz.ordinal: ProbabilityMap(L, 1) for rlz in rlzs}) save_gmdata(self, len(rlzs)) return acc
[docs] def save_gmf_bytes(self): """Save the attribute nbytes in the gmf_data datasets""" ds = self.datastore for sm_id in ds['gmf_data']: for rlzno in ds['gmf_data/' + sm_id]: ds.set_nbytes('gmf_data/%s/%s' % (sm_id, rlzno)) ds['gmf_data'].attrs['num_sites'] = len(self.sitecol.complete) ds['gmf_data'].attrs['num_imts'] = len(self.oqparam.imtls) ds.set_nbytes('gmf_data')
[docs] def post_execute(self, result): """ :param result: a dictionary (src_group_id, gsim) -> haz_curves or an empty dictionary if hazard_curves_from_gmfs is false """ oq = self.oqparam if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields: return elif oq.hazard_curves_from_gmfs: rlzs = self.rlzs_assoc.realizations # save individual curves for i in sorted(result): key = 'hcurves/rlz-%03d' % i if result[i]: self.datastore[key] = result[i] else: self.datastore[key] = ProbabilityMap(oq.imtls.array.size)'Zero curves for %s', key) # compute and save statistics; this is done in process # we don't need to parallelize, since event based calculations # involves a "small" number of sites (<= 65,536) weights = [rlz.weight for rlz in rlzs] hstats = self.oqparam.hazard_stats() if len(hstats) and len(rlzs) > 1: for kind, stat in hstats: pmap = compute_pmap_stats(result.values(), [stat], weights) self.datastore['hcurves/' + kind] = pmap if 'gmf_data' in self.datastore: self.save_gmf_bytes() 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 # one could also set oq.number_of_logic_tree_samples = 0 = ClassicalCalculator(oq, self.monitor('classical')) # TODO: perhaps it is possible to avoid reprocessing the source # model, however usually this is quite fast and do not dominate # the computation cl_mean_curves = get_mean_curves( eb_mean_curves = get_mean_curves(self.datastore) for imt in eb_mean_curves.dtype.names: rdiff, index = util.max_rel_diff_index( cl_mean_curves[imt], eb_mean_curves[imt]) logging.warn('Relative difference with the classical ' 'mean curves for IMT=%s: %d%% at site index %d', imt, rdiff * 100, index)
[docs]def get_mean_curves(dstore): """ Extract the mean hazard curves from the datastore, as a composite array of length nsites. """ imtls = dstore['oqparam'].imtls nsites = len(dstore['sitecol']) hcurves = dstore['hcurves'] if 'mean' in hcurves: mean = dstore['hcurves/mean'] elif len(hcurves) == 1: # there is a single realization mean = dstore['hcurves/rlz-0000'] return mean.convert(imtls, nsites)