# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2025 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/>.
"""
:mod:`openquake.hazardlib.calc.stochastic` contains
:func:`stochastic_event_set`.
"""
import time
import numpy
import shapely
from openquake.baselib import hdf5
from openquake.baselib.general import AccumDict, random_histogram
from openquake.baselib.performance import Monitor
from openquake.hazardlib.calc.filters import nofilter, SourceFilter
from openquake.hazardlib.source.rupture import (
BaseRupture, EBRupture, rupture_dt)
from openquake.hazardlib.geo.surface.base import to_geom_lons_lats
TWO16 = 2 ** 16 # 65,536
TWO32 = 2 ** 32 # 4,294,967,296
F64 = numpy.float64
U16 = numpy.uint16
U32 = numpy.uint32
U8 = numpy.uint8
I32 = numpy.int32
F32 = numpy.float32
MAX_RUPTURES = 2000
# ######################## rupture calculator ############################ #
# this is really fast
[docs]def get_rup_array(ebruptures, srcfilter=nofilter, model='???', model_geom=None):
"""
Convert a list of EBRuptures into a numpy composite array, by filtering
out the ruptures far away from every site. If a shapely polygon is passed
in model_geom, ruptures outside the polygon are discarded.
"""
if not BaseRupture._code:
BaseRupture.init() # initialize rupture codes
rups = []
geoms = []
for ebrupture in ebruptures:
rup = ebrupture.rupture
geom, lons, lats = to_geom_lons_lats(rup.surface)
hypo = rup.hypocenter.x, rup.hypocenter.y, rup.hypocenter.z
rec = numpy.zeros(1, rupture_dt)[0]
rec['id'] = ebrupture.id
rec['seed'] = ebrupture.seed
rec['minlon'] = minlon = numpy.nanmin(lons) # NaNs are in KiteSurfaces
rec['minlat'] = minlat = numpy.nanmin(lats)
rec['maxlon'] = maxlon = numpy.nanmax(lons)
rec['maxlat'] = maxlat = numpy.nanmax(lats)
rec['mag'] = rup.mag
rec['hypo'] = hypo
rec['model'] = model
# apply magnitude filtering
if srcfilter.integration_distance(rup.mag) == 0:
continue
# apply distance filtering
nsites = 0
if srcfilter.sitecol is not None:
nsites = len(srcfilter.close_sids(rec, rup.tectonic_region_type))
if nsites == 0:
continue
# apply model filtering if any (used in `oq mosaic sample_rups`)
if model_geom and not shapely.contains_xy(
model_geom, hypo[0], hypo[1]):
continue
rate = getattr(rup, 'occurrence_rate', numpy.nan)
tup = (ebrupture.id, ebrupture.seed, ebrupture.source_id,
ebrupture.trt_smr, rup.code, ebrupture.n_occ, rup.mag, rup.rake,
rate, minlon, minlat, maxlon, maxlat, hypo, 0, nsites, 0, model)
rups.append(tup)
# we are storing the geometries as arrays of 32 bit floating points;
# the first element is the number of surfaces, then there are
# 2 * num_surfaces integers describing the first and second
# dimension of each surface, and then the lons, lats and deps of
# the underlying meshes of points; in event_based/case_1 there
# is a point source, i.e. planar surfaces, with shapes = [1, 4]
# and points.reshape(3, 4) containing lons, lats and depths;
# in classical/case_29 there is a non parametric source containing
# 2 KiteSurfaces with shapes=[8, 5, 8, 5] and 240 = 3*2*8*5 coordinates
geoms.append(geom)
if not rups:
return ()
dic = dict(geom=numpy.array(geoms, object))
# NB: PMFs for nonparametric ruptures are not saved since they
# are useless for the GMF computation
arr = numpy.array(rups, rupture_dt)
return hdf5.ArrayWrapper(arr, dic)
[docs]def sample_cluster(group, num_ses, ses_seed):
"""
Yields ruptures generated by a cluster of sources
:param group:
A sequence of sources of the same group
:param num_ses:
Number of stochastic event sets
:param ses_seed:
Global seed for rupture sampling
:yields:
dictionaries with keys rup_array, source_data, eff_ruptures
"""
eb_ruptures = []
seed = group[0].serial(ses_seed)
rng = numpy.random.default_rng(seed)
[trt_smr] = set(src.trt_smr for src in group)
# Set the parameters required to compute the number of occurrences
# of the group of group
samples = getattr(group[0], 'samples', 1)
grp_probability = getattr(group, 'grp_probability', 1.)
tom = group.temporal_occurrence_model
rate = getattr(tom, 'occurrence_rate', None)
if rate is None: # time dependent sources
tot_num_occ = rng.poisson(grp_probability * samples * num_ses)
else: # poissonian sources with ClusterPoissonTOM
tot_num_occ = rng.poisson(rate * tom.time_span * samples * num_ses)
# Now we process the sources included in the group. Possible cases:
# * The group contains nonparametric sources with mutex ruptures, while
# the sources are indepedent.
# * The group contains mutually exclusive sources. In this case we
# choose the source first and then some ruptures from the source.
if group.rup_interdep == 'mutex' and group.src_interdep == 'indep':
allrups = []
weights = []
rupids = []
for src in group:
rupids.extend(src.offset + numpy.arange(src.num_ruptures))
weights.extend(src.rup_weights)
src_seed = src.serial(ses_seed)
for i, rup in enumerate(src.iter_ruptures()):
rup.src_id = src.id
allrups.append(rup)
# random distribute in bins according to the rup_weights
n_occs = random_histogram(tot_num_occ, weights, seed)
for rup, rupid, n_occ in zip(allrups, rupids, n_occs):
if n_occ:
ebr = EBRupture(rup, rup.src_id, trt_smr, n_occ, rupid)
ebr.seed = ebr.id + ses_seed
eb_ruptures.append(ebr)
elif group.src_interdep == 'mutex' and group.rup_interdep == 'indep':
# random distribute in bins according to the srcs_weights
ws = [src.mutex_weight for src in group]
src_occs = random_histogram(tot_num_occ, ws, seed)
# NB: in event_based/src_mutex num_ses=2000, samples=1
# and there are 10 sources with weights
# 0.368, 0.061, 0.299, 0.049, 0.028, 0.011, 0.011, 0.018, 0.113, 0.042
# => src_occs = [758, 120, 600, 84, 58, 16, 24, 28, 230, 82]
for src, src_occ in zip(group, src_occs):
src_seed = src.serial(ses_seed)
# random distribute in bins equally
n_occs = random_histogram(src_occ, src.num_ruptures, src_seed)
rseeds = src_seed + numpy.arange(src.num_ruptures)
rupids = src.offset + numpy.arange(src.num_ruptures)
for rup, rupid, n_occ, rseed in zip(
src.iter_ruptures(), rupids, n_occs, rseeds):
if n_occ:
ebr = EBRupture(rup, src.id, trt_smr, n_occ, rupid)
ebr.seed = ebr.id + ses_seed
eb_ruptures.append(ebr)
else:
raise NotImplementedError(
f'{group.src_interdep=}, {group.rup_interdep=}')
return eb_ruptures
# NB: there is postfiltering of the ruptures, which is more efficient
[docs]def sample_ruptures(sources, cmaker, sitecol=None, monitor=Monitor()):
"""
:param sources:
a sequence of sources of the same group
:param cmaker:
a ContextMaker instance with ses_per_logic_tree_path, ses_seed
:param sitecol:
SiteCollection instance used for filtering (None for no filtering)
:param monitor:
monitor instance
:yields:
dictionaries with keys rup_array, source_data
"""
model = getattr(cmaker, 'model', '???')
model_geom = getattr(cmaker, 'model_geom', None)
srcfilter = SourceFilter(sitecol, cmaker.maximum_distance)
# AccumDict of arrays with 3 elements nsites, nruptures, calc_time
source_data = AccumDict(accum=[])
# Compute and save stochastic event sets
num_ses = cmaker.ses_per_logic_tree_path
grp_id = sources[0].grp_id
# Compute the number of occurrences of the source group. This is used
# for cluster groups or groups with mutually exclusive sources.
if getattr(sources, 'atomic', False):
t0 = time.time()
eb_ruptures = sample_cluster(sources, num_ses, cmaker.ses_seed)
dt = time.time() - t0
# populate source_data
tot = sum(src.num_ruptures for src in sources)
for src in sources:
source_data['src_id'].append(src.source_id)
source_data['nsites'].append(src.nsites)
source_data['nrups'].append(src.num_ruptures)
source_data['ctimes'].append(dt * src.num_ruptures / tot)
source_data['weight'].append(src.weight)
source_data['taskno'].append(monitor.task_no)
# Yield ruptures
er = sum(src.num_ruptures for src in sources)
dic = dict(
rup_array=get_rup_array(eb_ruptures, srcfilter, model, model_geom),
source_data=source_data, eff_ruptures={grp_id: er})
yield AccumDict(dic)
else:
eb_ruptures = []
eff_ruptures = 0
source_data = AccumDict(accum=[])
for src in sources:
nr = src.num_ruptures
eff_ruptures += nr
if len(eb_ruptures) > MAX_RUPTURES:
# yield partial result to avoid running out of memory
yield AccumDict(dict(
rup_array=get_rup_array(
eb_ruptures, srcfilter, model, model_geom),
source_data={}, eff_ruptures={}))
eb_ruptures.clear()
samples = getattr(src, 'samples', 1)
t0 = time.time()
eb_ruptures.extend(
src.sample_ruptures(samples * num_ses, cmaker.ses_seed))
dt = time.time() - t0
source_data['src_id'].append(src.source_id)
source_data['nsites'].append(src.nsites)
source_data['nrups'].append(nr)
source_data['ctimes'].append(dt)
source_data['weight'].append(src.weight)
source_data['taskno'].append(monitor.task_no)
t0 = time.time()
rup_array = get_rup_array(eb_ruptures, srcfilter, model, model_geom)
dt = time.time() - t0
if len(rup_array):
yield AccumDict(dict(rup_array=rup_array, source_data=source_data,
eff_ruptures={grp_id: eff_ruptures}))
