Source code for openquake.calculators.event_based

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2017 GEM Foundation
#
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# (at your option) any later version.
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import time
import os.path
import operator
import itertools
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.risklib.riskinput import GmfGetter, str2rsi, rsi2str, indices_dt
from openquake.baselib import parallel
from openquake.commonlib import calc, util, readinput
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 set_eids(ebruptures): """ Set event IDs on the given list of ebruptures. :param ebruptures: a non-empty list of ruptures with the same grp_id :returns: the total number of events set """ if not ebruptures: return 0 num_events = sum(ebr.multiplicity for ebr in ebruptures) for ebr in ebruptures: assert ebr.multiplicity < TWO32, ebr.multiplicity eids = U64(TWO32 * ebr.serial) + numpy.arange( ebr.multiplicity, dtype=U64) ebr.events['eid'] = eids 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((src.id, dt)) res = AccumDict({grp_id: eb_ruptures}) res.num_events = set_eids(eb_ruptures) 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): 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 ebr.events: rec = (event['eid'], ebr.serial, ebr.grp_id, year, event['ses'], event['sample']) events.append(rec) return numpy.array(events, readinput.stored_event_dt)
@base.calculators.add('event_based_rupture')
[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 events from the given ruptures; also, save the ruptures if the flag `save_ruptures` is on. :param ruptures_by_grp_id: a dictionary grp_id -> list of EBRuptures """ 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', events) if self.oqparam.save_ruptures: self.rupser.save(ebrs, eidx=len(dset)-len(events))
[docs] def post_execute(self, result): """ Save the SES collection """ self.rupser.close() num_events = sum(_count(ruptures) for ruptures in result.values()) num_ruptures = sum(len(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') logging.info('Setting %d event years on %d ruptures', num_events, num_ruptures) with self.monitor('setting event years', measuremem=True, autoflush=True): numpy.random.seed(self.oqparam.ses_seed) set_random_years(self.datastore, int(self.oqparam.investigation_time))
[docs]def set_random_years(dstore, investigation_time): """ Sort the `events` array and attach year labels sensitive to the SES ordinal and the investigation time. """ events = dstore['events'].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'] = 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() hcurves = {} # key -> poes if oq.hazard_curves_from_gmfs: hc_mon = monitor('building hazard curves', measuremem=False) duration = oq.investigation_time * oq.ses_per_logic_tree_path with monitor('building hazard', measuremem=True): gmfdata = numpy.fromiter(getter.gen_gmv(), getter.gmf_data_dt) hazard = getter.get_hazard(data=gmfdata) for sid, hazardr in zip(getter.sids, hazard): for rlzi, array in hazardr.items(): if len(array) == 0: # no data continue with hc_mon: gmvs = array['gmv'] for imti, imt in enumerate(getter.imtls): poes = calc._gmvs_to_haz_curve( gmvs[:, imti], oq.imtls[imt], oq.investigation_time, duration) hcurves[rsi2str(rlzi, sid, imt)] = poes else: # fast lane with monitor('building hazard', measuremem=True): gmfdata = numpy.fromiter(getter.gen_gmv(), getter.gmf_data_dt) indices = [] if oq.ground_motion_fields: gmfdata.sort(order=('sid', 'rlzi', 'eid')) start = stop = 0 for sid, rows in itertools.groupby(gmfdata['sid']): for row in rows: stop += 1 indices.append((sid, start, stop)) start = stop else: gmfdata = None return dict(gmfdata=gmfdata, hcurves=hcurves, gmdata=getter.gmdata, taskno=monitor.task_no, indices=numpy.array(indices, (U32, 3)))
[docs]def get_ruptures_by_grp(dstore): """ Extracts the dictionary `ruptures_by_grp` from the given calculator """ events = dstore['events'] n = 0 for grp in dstore['ruptures']: n += len(dstore['ruptures/' + grp]) logging.info('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 = list(calc.get_ruptures(dstore, events, grp_id)) ruptures_by_grp[grp_id] = ruptures 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 logging.info('Generated %s of GMFs', humansize(array['nbytes'].sum()))
[docs]def update_nbytes(dstore, key, array): nbytes = dstore.get_attr(key, 'nbytes', 0) dstore.set_attrs(key, nbytes=nbytes + array.nbytes)
@base.calculators.add('event_based')
[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'] data = res['gmfdata'] if data is not None: with sav_mon: hdf5.extend3(self.datastore.hdf5path, 'gmf_data/data', data) # it is important to save the number of bytes while the # computation is going, to see the progress update_nbytes(self.datastore, 'gmf_data/data', data) for sid, start, stop in res['indices']: self.indices[sid].append( (start + self.offset, stop + self.offset)) self.offset += len(data) 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() try: csm_info = self.csm.info except AttributeError: # no csm csm_info = self.datastore['csm_info'] samples_by_grp = csm_info.get_samples_by_grp() for grp_id in ruptures_by_grp: ruptures = ruptures_by_grp[grp_id] if not ruptures: continue rlzs_by_gsim = self.rlzs_assoc.rlzs_by_gsim[grp_id] for block in block_splitter(ruptures, oq.ruptures_per_block): samples = samples_by_grp[grp_id] getter = GmfGetter(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) R = len(self.datastore['realizations']) allargs = list(self.gen_args(ruptures_by_grp)) res = parallel.Starmap(self.core_task.__func__, allargs).submit_all() self.gmdata = {} self.offset = 0 self.indices = collections.defaultdict(list) # sid -> indices acc = res.reduce(self.combine_pmaps_and_save_gmfs, { r: ProbabilityMap(L) for r in range(R)}) save_gmdata(self, R) if self.indices: logging.info('Saving gmf_data/indices') with self.monitor('saving gmf_data/indices', measuremem=True, autoflush=True): self.datastore.save_vlen( 'gmf_data/indices', [numpy.array(self.indices[sid], indices_dt) for sid in self.sitecol.complete.sids]) 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']: ds.set_nbytes('gmf_data/' + sm_id) 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.datastore['realizations'].value # 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) logging.info('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 self.cl = 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 self.cl.run(close=False) cl_mean_curves = get_mean_curves(self.cl.datastore) 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)