# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2012-2020 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 sys
import time
import numpy
from openquake.baselib import hdf5
from openquake.baselib.general import AccumDict
from openquake.baselib.performance import Monitor
from openquake.baselib.python3compat import raise_
from openquake.hazardlib.calc.filters import nofilter
from openquake.hazardlib.source.rupture import BaseRupture, EBRupture
from openquake.hazardlib.geo.mesh import surface_to_array
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
# this is used in acceptance/stochastic_test.py, not in the engine
[docs]def stochastic_event_set(sources, source_site_filter=nofilter, **kwargs):
"""
Generates a 'Stochastic Event Set' (that is a collection of earthquake
ruptures) representing a possible *realization* of the seismicity as
described by a source model.
The calculator loops over sources. For each source, it loops over ruptures.
For each rupture, the number of occurrence is randomly sampled by
calling
:meth:`openquake.hazardlib.source.rupture.BaseProbabilisticRupture.sample_number_of_occurrences`
.. note::
This calculator is using random numbers. In order to reproduce the
same results numpy random numbers generator needs to be seeded, see
http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.seed.html
:param sources:
An iterator of seismic sources objects (instances of subclasses
of :class:`~openquake.hazardlib.source.base.BaseSeismicSource`).
:param source_site_filter:
The source filter to use (default noop filter)
:returns:
Generator of :class:`~openquake.hazardlib.source.rupture.Rupture`
objects that are contained in an event set. Some ruptures can be
missing from it, others can appear one or more times in a row.
"""
shift_hypo = kwargs['shift_hypo'] if 'shift_hypo' in kwargs else False
for source, s_sites in source_site_filter(sources):
try:
for rupture in source.iter_ruptures(shift_hypo=shift_hypo):
[n_occ] = rupture.sample_number_of_occurrences()
for _ in range(n_occ):
yield rupture
except Exception as err:
etype, err, tb = sys.exc_info()
msg = 'An error occurred with source id=%s. Error: %s'
msg %= (source.source_id, str(err))
raise_(etype, msg, tb)
# ######################## rupture calculator ############################ #
rupture_dt = numpy.dtype([
('id', U32), ('serial', U32), ('source_id', '<S16'), ('grp_id', U16),
('code', U8), ('n_occ', U32), ('mag', F32), ('rake', F32),
('occurrence_rate', F32),
('minlon', F32), ('minlat', F32), ('maxlon', F32), ('maxlat', F32),
('hypo', (F32, 3)), ('geom_id', U32), ('s1', U16), ('s2', U16),
('e0', U32), ('e1', U32)])
# this is really fast
[docs]def get_rup_array(ebruptures, srcfilter=nofilter):
"""
Convert a list of EBRuptures into a numpy composite array, by filtering
out the ruptures far away from every site
"""
if not BaseRupture._code:
BaseRupture.init() # initialize rupture codes
rups = []
geoms = []
nbytes = 0
for ebrupture in ebruptures:
rup = ebrupture.rupture
mesh = surface_to_array(rup.surface)
sy, sz = mesh.shape[1:] # sanity checks; sx == 3
assert sy < TWO16, 'Too many multisurfaces: %d' % sy
assert sz < TWO16, 'The rupture mesh spacing is too small'
hypo = rup.hypocenter.x, rup.hypocenter.y, rup.hypocenter.z
points = mesh.reshape(3, -1).T # shape (n, 3)
rec = numpy.zeros(1, rupture_dt)[0]
rec['serial'] = rup.rup_id
rec['minlon'] = minlon = points[:, 0].min()
rec['minlat'] = minlat = points[:, 1].min()
rec['maxlon'] = maxlon = points[:, 0].max()
rec['maxlat'] = maxlat = points[:, 1].max()
rec['mag'] = rup.mag
rec['hypo'] = hypo
if srcfilter.integration_distance and len(
srcfilter.close_sids(rec, rup.tectonic_region_type)) == 0:
continue
rate = getattr(rup, 'occurrence_rate', numpy.nan)
tup = (0, ebrupture.rup_id, ebrupture.source_id, ebrupture.grp_id,
rup.code, ebrupture.n_occ, rup.mag, rup.rake, rate,
minlon, minlat, maxlon, maxlat, hypo, 0, sy, sz, 0, 0)
rups.append(tup)
geoms.append(points.flatten())
nbytes += rupture_dt.itemsize + mesh.nbytes
if not rups:
return ()
dic = dict(geom=numpy.array(geoms, object), nbytes=nbytes)
# NB: PMFs for nonparametric ruptures are not saved since they
# are useless for the GMF computation
return hdf5.ArrayWrapper(numpy.array(rups, rupture_dt), dic)
[docs]def sample_cluster(sources, srcfilter, num_ses, param):
"""
Yields ruptures generated by a cluster of sources.
:param sources:
A sequence of sources of the same group
:param num_ses:
Number of stochastic event sets
:param param:
a dictionary of additional parameters including
ses_per_logic_tree_path
:yields:
dictionaries with keys rup_array, calc_times, eff_ruptures
"""
eb_ruptures = []
numpy.random.seed(sources[0].serial)
[grp_id] = set(src.grp_id for src in sources)
# AccumDict of arrays with 3 elements nsites, nruptures, calc_time
calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
# Set the parameters required to compute the number of occurrences
# of the group of sources
# assert param['oqparam'].number_of_logic_tree_samples > 0
samples = getattr(sources[0], 'samples', 1)
tom = getattr(sources, 'temporal_occurrence_model')
rate = tom.occurrence_rate
time_span = tom.time_span
# Note that using a single time interval corresponding to the product
# of the investigation time and the number of realisations as we do
# here is admitted only in the case of a time-independent model
grp_num_occ = numpy.random.poisson(rate * time_span * samples *
num_ses)
# Now we process the sources included in the group. Possible cases:
# * The group is a cluster. In this case we choose one rupture per each
# source; uncertainty in the ruptures can be handled in this case
# using mutually exclusive ruptures (note that this is admitted
# only for nons-parametric sources).
# * The group contains mutually exclusive sources. In this case we
# choose one source and then one rupture from this source.
rup_counter = {}
rup_data = {}
for rlz_num in range(grp_num_occ):
if sources.cluster:
for src, _sites in srcfilter(sources):
# Track calculation time
t0 = time.time()
rup = src.get_one_rupture()
# The problem here is that we do not know a-priori the
# number of occurrences of a given rupture.
if src.id not in rup_counter:
rup_counter[src.id] = {}
rup_data[src.id] = {}
if rup.idx not in rup_counter[src.id]:
rup_counter[src.id][rup.idx] = 1
rup_data[src.id][rup.idx] = [rup, src.id, grp_id]
else:
rup_counter[src.id][rup.idx] += 1
# Store info
dt = time.time() - t0
calc_times[src.source_id] += numpy.array(
[len(rup_data[src.id]), len(_sites), dt])
elif param['src_interdep'] == 'mutex':
raise NotImplementedError('src_interdep == mutex')
# Create event based ruptures
for src_key in rup_data:
for rup_key in rup_data[src_key]:
rup, source_id, grp_id = rup_data[src_key][rup_key]
cnt = rup_counter[src_key][rup_key]
ebr = EBRupture(rup, source_id, grp_id, cnt, samples)
eb_ruptures.append(ebr)
return eb_ruptures, calc_times
# NB: there is postfiltering of the ruptures, which is more efficient
[docs]def sample_ruptures(sources, srcfilter, param, monitor=Monitor()):
"""
:param sources:
a sequence of sources of the same group
:param srcfilter:
SourceFilter instance used also for bounding box post filtering
:param param:
a dictionary of additional parameters including
ses_per_logic_tree_path
:param monitor:
monitor instance
:yields:
dictionaries with keys rup_array, calc_times
"""
# AccumDict of arrays with 3 elements num_ruptures, num_sites, calc_time
calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
# Compute and save stochastic event sets
num_ses = param['ses_per_logic_tree_path']
trt = sources[0].tectonic_region_type
# 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) and
getattr(sources, 'cluster', False)):
eb_ruptures, calc_times = sample_cluster(
sources, srcfilter, num_ses, param)
# Yield ruptures
yield AccumDict(dict(rup_array=get_rup_array(eb_ruptures, srcfilter),
calc_times=calc_times,
eff_ruptures={trt: len(eb_ruptures)}))
else:
eb_ruptures = []
eff_ruptures = 0
# AccumDict of arrays with 2 elements weight, calc_time
calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
for src, _sites in srcfilter(sources):
nr = src.num_ruptures
eff_ruptures += nr
t0 = time.time()
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),
calc_times={}, eff_ruptures={}))
eb_ruptures.clear()
samples = getattr(src, 'samples', 1)
for rup, grp_id, n_occ in src.sample_ruptures(samples * num_ses):
ebr = EBRupture(rup, src.source_id, grp_id, n_occ, samples)
eb_ruptures.append(ebr)
dt = time.time() - t0
try:
n_sites = len(_sites)
except (TypeError, ValueError): # for None or a closed dataset
n_sites = 0
calc_times[src.source_id] += numpy.array([nr, n_sites, dt])
rup_array = get_rup_array(eb_ruptures, srcfilter)
yield AccumDict(dict(rup_array=rup_array, calc_times=calc_times,
eff_ruptures={trt: eff_ruptures}))