Source code for openquake.calculators.ucerf_classical

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2019 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# 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 <http://www.gnu.org/licenses/>.
import os
import logging
import operator
import numpy as np
from openquake.baselib import parallel, general
from openquake.hazardlib.calc.hazard_curve import classical
from openquake.commonlib.source_model_factory import random_filtered_sources
from openquake.calculators import base
from openquake.calculators.classical import (
    ClassicalCalculator, classical_split_filter)


[docs]@base.calculators.add('ucerf_classical') class UcerfClassicalCalculator(ClassicalCalculator): """ UCERF classical calculator. """ accept_precalc = ['ucerf_classical']
[docs] def pre_execute(self): super().pre_execute() self.csm_info = self.csm.info for sm in self.csm.source_models: # one branch at the time [grp] = sm.src_groups for src in grp: grp.tot_ruptures += src.num_ruptures
[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. """ monitor = self.monitor(self.core_task.__name__) oq = self.oqparam acc = self.acc0() self.nsites = [] # used in agg_dicts param = dict(imtls=oq.imtls, truncation_level=oq.truncation_level, filter_distance=oq.filter_distance, maxweight=1E10, task_duration=1000) self.calc_times = general.AccumDict(accum=np.zeros(3, np.float32)) [gsims] = sorted(self.csm.info.gsim_lt.values.values()) sample = .001 if os.environ.get('OQ_SAMPLE_SOURCES') else None for sm in self.csm.source_models: # one branch at the time [grp] = sm.src_groups [src] = grp srcs = list(src) if sample: srcs = random_filtered_sources(srcs, self.src_filter, 1) acc = parallel.Starmap.apply( classical_split_filter, (srcs, self.src_filter, gsims, param, monitor), weight=operator.attrgetter('weight'), concurrent_tasks=oq.concurrent_tasks, hdf5path=self.datastore.filename ).reduce(self.agg_dicts, acc) ucerf = grp.sources[0].orig logging.info('Getting background sources from %s', ucerf.source_id) srcs = ucerf.get_background_sources(self.src_filter, sample) acc = parallel.Starmap.apply( classical, (srcs, self.src_filter, gsims, param, monitor), weight=operator.attrgetter('weight'), concurrent_tasks=oq.concurrent_tasks, hdf5path=self.datastore.filename ).reduce(self.agg_dicts, acc) self.store_rlz_info(acc.eff_ruptures) self.store_source_info(self.calc_times) return acc # {grp_id: pmap}