Source code for openquake.calculators.classical

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2017 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 <>.

from __future__ import division
import math
import logging
import operator
import numpy

from openquake.baselib import parallel
from openquake.baselib.python3compat import encode
from openquake.baselib.general import AccumDict
from openquake.hazardlib.calc.hazard_curve import classical, ProbabilityMap
from openquake.hazardlib.stats import compute_pmap_stats
from openquake.hazardlib import source
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.calculators import getters
from openquake.calculators import base

U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
weight = operator.attrgetter('weight')

source_data_dt = numpy.dtype(
    [('taskno', U16), ('nsites', U32), ('nruptures', U32), ('weight', F32)])

[docs]def get_src_ids(sources): """ :returns: a string with the source IDs of the given sources, stripping the extension after the colon, if any """ src_ids = [] for src in sources: long_src_id = src.source_id try: src_id, ext = long_src_id.rsplit(':', 1) except ValueError: src_id = long_src_id src_ids.append(src_id) return ' '.join(set(src_ids))
[docs]def saving_sources_by_task(iterargs, dstore): """ Yield the iterargs again by populating 'task_info/source_data' """ source_ids = [] data = [] for i, args in enumerate(iterargs, 1): source_ids.append(get_src_ids(args[0])) for src in args[0]: # collect source data data.append((i, src.nsites, src.num_ruptures, src.weight)) yield args dstore['task_info/task_sources'] = encode(source_ids) dstore.extend('task_info/source_data', numpy.array(data, source_data_dt))
[docs]@base.calculators.add('psha') class PSHACalculator(base.HazardCalculator): """ Classical PSHA calculator """ core_task = classical
[docs] def agg_dicts(self, acc, pmap_by_grp): """ Aggregate dictionaries of hazard curves by updating the accumulator. :param acc: accumulator dictionary :param pmap_by_grp: dictionary grp_id -> ProbabilityMap """ with self.monitor('aggregate curves', autoflush=True): acc.eff_ruptures += pmap_by_grp.eff_ruptures for grp_id in pmap_by_grp: if pmap_by_grp[grp_id]: acc[grp_id] |= pmap_by_grp[grp_id] self.nsites.append(len(pmap_by_grp[grp_id])) for srcid, (srcweight, nsites, calc_time, split) in \ pmap_by_grp.calc_times.items(): info = self.csm.infos[srcid] info.calc_time += calc_time info.num_sites += nsites info.num_split += split return acc
[docs] def zerodict(self): """ Initial accumulator, a dict grp_id -> ProbabilityMap(L, G) """ csm_info = zd = AccumDict() num_levels = len(self.oqparam.imtls.array) for grp in self.csm.src_groups: num_gsims = len(csm_info.gsim_lt.get_gsims(grp.trt)) zd[] = ProbabilityMap(num_levels, num_gsims) zd.calc_times = [] zd.eff_ruptures = AccumDict() # grp_id -> eff_ruptures return zd
[docs] def execute(self): """ Run in parallel `core_task(sources, sitecol, monitor)`, by parallelizing on the sources according to their weight and tectonic region type. """ try: self.csm except AttributeError: raise RuntimeError('No CompositeSourceModel, did you forget to ' 'run the hazard or the --hc option?') with self.monitor('managing sources', autoflush=True): allargs = self.gen_args(self.monitor('classical')) iterargs = saving_sources_by_task(allargs, self.datastore) if isinstance(allargs, list): # there is a trick here: if the arguments are known # (a list, not an iterator), keep them as a list # then the Starmap will understand the case of a single # argument tuple and it will run in core the task iterargs = list(iterargs) ires = parallel.Starmap( self.core_task.__func__, iterargs).submit_all() self.nsites = [] acc = ires.reduce(self.agg_dicts, self.zerodict()) if not self.nsites: raise RuntimeError('All sources were filtered out!')'Effective sites per task: %d', numpy.mean(self.nsites)) with self.monitor('store source_info', autoflush=True): self.store_source_info(self.csm.infos, acc) return acc
[docs] def gen_args(self, monitor): """ Used in the case of large source model logic trees. :param monitor: a :class:`openquake.baselib.performance.Monitor` :yields: (sources, sites, gsims, monitor) tuples """ oq = self.oqparam opt = self.oqparam.optimize_same_id_sources num_tiles = math.ceil(len(self.sitecol) / oq.sites_per_tile) tasks_per_tile = math.ceil(oq.concurrent_tasks / math.sqrt(num_tiles)) if num_tiles > 1: tiles = self.sitecol.split_in_tiles(num_tiles) else: tiles = [self.sitecol] param = dict(truncation_level=oq.truncation_level, imtls=oq.imtls) minweight = source.MINWEIGHT * math.sqrt(len(self.sitecol)) totweight = 0 for tile_i, tile in enumerate(tiles, 1): num_tasks = 0 num_sources = 0 with self.monitor('prefiltering'):'Prefiltering tile %d of %d', tile_i, len(tiles)) src_filter = SourceFilter(tile, oq.maximum_distance) csm = self.csm.filter(src_filter) totweight += csm.weight if tile_i == 1: # set it only on the first tile maxweight = csm.get_maxweight(tasks_per_tile, minweight) if maxweight == minweight:'Using minweight=%d', minweight) else:'Using maxweight=%d', maxweight) if csm.has_dupl_sources and not opt: logging.warn('Found %d duplicated sources, use oq info', csm.has_dupl_sources) for sg in csm.src_groups: if sg.src_interdep == 'mutex': gsims = yield sg, csm.src_filter, gsims, param, monitor num_tasks += 1 num_sources += len(sg.sources) # NB: csm.get_sources_by_trt discards the mutex sources for trt, sources in csm.get_sources_by_trt(opt).items(): gsims = for block in csm.split_in_blocks(maxweight, sources): yield block, src_filter, gsims, param, monitor num_tasks += 1 num_sources += len(block)'Sent %d sources in %d tasks', num_sources, num_tasks) = totweight source.split_map.clear()
[docs] def post_execute(self, pmap_by_grp_id): """ Collect the hazard curves by realization and export them. :param pmap_by_grp_id: a dictionary grp_id -> hazard curves """ grp_trt ="trt") grp_name ="name") with self.monitor('saving probability maps', autoflush=True): for grp_id, pmap in pmap_by_grp_id.items(): if pmap: # pmap can be missing if the group is filtered away fix_ones(pmap) # avoid saving PoEs == 1 key = 'poes/grp-%02d' % grp_id self.datastore[key] = pmap self.datastore.set_attrs(key, trt=grp_trt[grp_id], name=str(grp_name[grp_id])) if 'poes' in self.datastore: self.datastore.set_nbytes('poes')
[docs]def fix_ones(pmap): """ Physically, an extremely small intensity measure level can have an extremely large probability of exceedence, however that probability cannot be exactly 1 unless the level is exactly 0. Numerically, the PoE can be 1 and this give issues when calculating the damage (there is a log(0) in :class:`openquake.risklib.scientific.annual_frequency_of_exceedence`). Here we solve the issue by replacing the unphysical probabilities 1 with .9999999999999999 (the float64 closest to 1). """ for sid in pmap: array = pmap[sid].array array[array == 1.] = .9999999999999999
[docs]def build_hcurves_and_stats(pgetter, hstats, monitor): """ :param pgetter: an :class:`openquake.commonlib.getters.PmapGetter` :param hstats: a list of pairs (statname, statfunc) :param monitor: instance of Monitor :returns: a dictionary kind -> ProbabilityMap The "kind" is a string of the form 'rlz-XXX' or 'mean' of 'quantile-XXX' used to specify the kind of output. """ with monitor('combine pmaps'): pgetter.init() # if not already initialized try: pmaps = pgetter.get_pmaps(pgetter.sids) except IndexError: # no data return {} if sum(len(pmap) for pmap in pmaps) == 0: # no data return {} pmap_by_kind = {} for kind, stat in hstats: with monitor('compute ' + kind): pmap = compute_pmap_stats(pmaps, [stat], pgetter.weights) pmap_by_kind[kind] = pmap return pmap_by_kind
[docs]@base.calculators.add('classical') class ClassicalCalculator(PSHACalculator): """ Classical PSHA calculator """ pre_calculator = 'psha' core_task = build_hcurves_and_stats
[docs] def execute(self): """ Build statistical hazard curves from the stored PoEs """ if 'poes' not in self.datastore: # for short report return oq = self.oqparam num_rlzs = self.datastore['csm_info'].get_num_rlzs() if num_rlzs == 1: # no stats to compute return {} elif not oq.hazard_stats(): if oq.hazard_maps or oq.uniform_hazard_spectra: logging.warn('mean_hazard_curves was false in the job.ini, ' 'so no outputs were generated.\nYou can compute ' 'the statistics without repeating the calculation' ' with the --hc option') return {} # initialize datasets N = len(self.sitecol) L = len(oq.imtls.array) attrs = dict( __pyclass__='openquake.hazardlib.probability_map.ProbabilityMap', sids=numpy.arange(N, dtype=numpy.uint32)) nbytes = N * L * 4 # bytes per realization (32 bit floats) totbytes = 0 if num_rlzs > 1: for name, stat in oq.hazard_stats(): self.datastore.create_dset( 'hcurves/' + name, F32, (N, L, 1), attrs=attrs) totbytes += nbytes if 'hcurves' in self.datastore: self.datastore.set_attrs('hcurves', nbytes=totbytes) self.datastore.flush() with self.monitor('sending pmaps', autoflush=True, measuremem=True): ires = parallel.Starmap( self.core_task.__func__, self.gen_args() ).submit_all() nbytes = ires.reduce(self.save_hcurves) return nbytes
[docs] def gen_args(self): """ :yields: pgetter, hstats, monitor """ monitor = self.monitor('build_hcurves_and_stats') hstats = self.oqparam.hazard_stats() parent = self.can_read_parent() if parent is None: parent = self.datastore for t in self.sitecol.split_in_tiles(self.oqparam.concurrent_tasks): pgetter = getters.PmapGetter(parent, t.sids, self.rlzs_assoc) if parent is self.datastore: # read now, not in the workers'Reading PoEs on %d sites', len(t)) pgetter.init() yield pgetter, hstats, monitor
[docs] def save_hcurves(self, acc, pmap_by_kind): """ Works by side effect by saving hcurves and statistics on the datastore; the accumulator stores the number of bytes saved. :param acc: dictionary kind -> nbytes :param pmap_by_kind: a dictionary of ProbabilityMaps """ with self.monitor('saving statistical hcurves', autoflush=True): for kind in pmap_by_kind: pmap = pmap_by_kind[kind] if pmap: key = 'hcurves/' + kind dset = self.datastore.getitem(key) for sid in pmap: dset[sid] = pmap[sid].array # in the datastore we save 4 byte floats, thus we # divide the memory consumption by 2: pmap.nbytes / 2 acc += {kind: pmap.nbytes // 2} self.datastore.flush() return acc
[docs] def post_execute(self, acc): """Save the number of bytes per each dataset""" for kind, nbytes in acc.items(): self.datastore.getitem('hcurves/' + kind).attrs['nbytes'] = nbytes