# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2016 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 time
import os.path
import operator
import logging
import functools
import collections
import numpy
from openquake.baselib.python3compat import encode
from openquake.baselib.general import AccumDict, split_in_blocks
from openquake.hazardlib.calc.filters import \
filter_sites_by_distance_to_rupture
from openquake.hazardlib.calc.hazard_curve import ProbabilityMap
from openquake.hazardlib.probability_map import PmapStats
from openquake.hazardlib import geo
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.commonlib import parallel, calc
from openquake.commonlib.util import max_rel_diff_index, Rupture
from openquake.risklib.riskinput import GmfGetter, str2rsi, rsi2str
from openquake.calculators import base
from openquake.calculators.classical import ClassicalCalculator, PSHACalculator
# ######################## rupture calculator ############################ #
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
event_dt = numpy.dtype([('eid', U32), ('ses', U32), ('occ', U32),
('sample', U32)])
stored_event_dt = numpy.dtype([
('rupserial', U32), ('ses', U32), ('occ', U32),
('sample', U32), ('grp_id', U16), ('source_id', 'S30')])
[docs]def get_geom(surface, is_from_fault_source, is_multi_surface):
"""
The following fields can be interpreted different ways,
depending on the value of `is_from_fault_source`. If
`is_from_fault_source` is True, each of these fields should
contain a 2D numpy array (all of the same shape). Each triple
of (lon, lat, depth) for a given index represents the node of
a rectangular mesh. If `is_from_fault_source` is False, each
of these fields should contain a sequence (tuple, list, or
numpy array, for example) of 4 values. In order, the triples
of (lon, lat, depth) represent top left, top right, bottom
left, and bottom right corners of the the rupture's planar
surface. Update: There is now a third case. If the rupture
originated from a characteristic fault source with a
multi-planar-surface geometry, `lons`, `lats`, and `depths`
will contain one or more sets of 4 points, similar to how
planar surface geometry is stored (see above).
:param rupture: an instance of :class:`openquake.hazardlib.source.rupture.BaseProbabilisticRupture`
:param is_from_fault_source: a boolean
:param is_multi_surface: a boolean
"""
if is_from_fault_source:
# for simple and complex fault sources,
# rupture surface geometry is represented by a mesh
surf_mesh = surface.get_mesh()
lons = surf_mesh.lons
lats = surf_mesh.lats
depths = surf_mesh.depths
else:
if is_multi_surface:
# `list` of
# openquake.hazardlib.geo.surface.planar.PlanarSurface
# objects:
surfaces = surface.surfaces
# lons, lats, and depths are arrays with len == 4*N,
# where N is the number of surfaces in the
# multisurface for each `corner_*`, the ordering is:
# - top left
# - top right
# - bottom left
# - bottom right
lons = numpy.concatenate([x.corner_lons for x in surfaces])
lats = numpy.concatenate([x.corner_lats for x in surfaces])
depths = numpy.concatenate([x.corner_depths for x in surfaces])
else:
# For area or point source,
# rupture geometry is represented by a planar surface,
# defined by 3D corner points
lons = numpy.zeros((4))
lats = numpy.zeros((4))
depths = numpy.zeros((4))
# NOTE: It is important to maintain the order of these
# corner points. TODO: check the ordering
for i, corner in enumerate((surface.top_left,
surface.top_right,
surface.bottom_left,
surface.bottom_right)):
lons[i] = corner.longitude
lats[i] = corner.latitude
depths[i] = corner.depth
return lons, lats, depths
[docs]class EBRupture(object):
"""
An event based rupture. It is a wrapper over a hazardlib rupture
object, containing an array of site indices affected by the rupture,
as well as the tags of the corresponding seismic events.
"""
def __init__(self, rupture, indices, events, source_id, grp_id, serial):
self.rupture = rupture
self.indices = indices
self.events = events
self.source_id = source_id
self.grp_id = grp_id
self.serial = serial
self.weight = len(indices) * len(events)
@property
def etags(self):
"""
An array of tags for the underlying seismic events
"""
tags = []
for (eid, ses, occ, sampleid) in self.events:
tag = 'trt=%02d~ses=%04d~src=%s~rup=%d-%02d' % (
self.grp_id, ses, self.source_id, self.serial, occ)
if sampleid > 0:
tag += '~sample=%d' % sampleid
tags.append(encode(tag))
return numpy.array(tags)
@property
def eids(self):
"""
An array with the underlying event IDs
"""
return self.events['eid']
@property
def multiplicity(self):
"""
How many times the underlying rupture occurs.
"""
return len(self.events)
[docs] def export(self, mesh):
"""
Yield :class:`openquake.commonlib.util.Rupture` objects, with all the
attributes set, suitable for export in XML format.
"""
rupture = self.rupture
for etag in self.etags:
new = Rupture(etag, self.indices)
new.mesh = mesh[self.indices]
new.etag = etag
new.rupture = new
new.is_from_fault_source = iffs = isinstance(
rupture.surface, (geo.ComplexFaultSurface,
geo.SimpleFaultSurface))
new.is_multi_surface = ims = isinstance(
rupture.surface, geo.MultiSurface)
new.lons, new.lats, new.depths = get_geom(
rupture.surface, iffs, ims)
new.surface = rupture.surface
new.strike = rupture.surface.get_strike()
new.dip = rupture.surface.get_dip()
new.rake = rupture.rake
new.hypocenter = rupture.hypocenter
new.tectonic_region_type = rupture.tectonic_region_type
new.magnitude = new.mag = rupture.mag
new.top_left_corner = None if iffs or ims else (
new.lons[0], new.lats[0], new.depths[0])
new.top_right_corner = None if iffs or ims else (
new.lons[1], new.lats[1], new.depths[1])
new.bottom_left_corner = None if iffs or ims else (
new.lons[2], new.lats[2], new.depths[2])
new.bottom_right_corner = None if iffs or ims else (
new.lons[3], new.lats[3], new.depths[3])
yield new
def __lt__(self, other):
return self.serial < other.serial
def __repr__(self):
return '<%s #%d, grp_id=%d>' % (self.__class__.__name__,
self.serial, self.grp_id)
[docs]def compute_ruptures(sources, sitecol, gsims, monitor):
"""
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param gsims:
a list of GSIMs for the current tectonic region model
: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
src_group_id = sources[0].src_group_id
trt = sources[0].tectonic_region_type
max_dist = monitor.maximum_distance[trt]
cmaker = ContextMaker(gsims)
params = sorted(cmaker.REQUIRES_RUPTURE_PARAMETERS)
rup_data_dt = numpy.dtype(
[('rupserial', U32), ('multiplicity', U16),
('numsites', U32), ('occurrence_rate', F64)] + [
(param, F64) for param in params])
eb_ruptures = []
rup_data = []
calc_times = []
rup_mon = monitor('filtering ruptures', measuremem=False)
num_samples = monitor.samples
num_events = 0
# Compute and save stochastic event sets
for src in sources:
t0 = time.time()
s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
if s_sites is None:
continue
rupture_filter = functools.partial(
filter_sites_by_distance_to_rupture,
integration_distance=max_dist, sites=s_sites)
num_occ_by_rup = sample_ruptures(
src, monitor.ses_per_logic_tree_path, num_samples,
monitor.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, rupture_filter, monitor.seed, rup_mon):
nsites = len(ebr.indices)
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
rc = cmaker.make_rupture_context(ebr.rupture)
ruptparams = tuple(getattr(rc, param) for param in params)
rup_data.append((ebr.serial, ebr.multiplicity, nsites, rate) +
ruptparams)
eb_ruptures.append(ebr)
num_events += ebr.multiplicity
dt = time.time() - t0
calc_times.append((src.id, dt))
res = AccumDict({src_group_id: eb_ruptures})
res.num_events = num_events
res.calc_times = calc_times
res.rup_data = numpy.array(rup_data, rup_data_dt)
res.trt = trt
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
[docs]def build_eb_ruptures(
src, num_occ_by_rup, rupture_filter, random_seed, rup_mon):
"""
Filter the ruptures stored in the dictionary num_occ_by_rup and
yield pairs (rupture, <list of associated EBRuptures>)
"""
eid = 0
for rup in sorted(num_occ_by_rup, key=operator.attrgetter('rup_no')):
with rup_mon:
r_sites = rupture_filter(rup)
if r_sites is None:
# 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 occ_no in range(1, num_occ + 1):
# NB: the eid below is a placeholder; the right eid will be
# set later, in EventBasedRuptureCalculator.post_execute
events.append((eid, ses_idx, occ_no, sampleid))
eid += 1
if events:
yield EBRupture(rup, r_sites.indices,
numpy.array(events, event_dt),
src.source_id, src.src_group_id, serial)
@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.random_seed = oq.random_seed
self.rlzs_assoc = self.datastore['csm_info'].get_rlzs_assoc()
self.min_iml = calc.fix_minimum_intensity(
oq.minimum_intensity, oq.imtls)
self.rup_data = {}
[docs] def count_eff_ruptures(self, ruptures_by_grp_id, src_group):
"""
Returns the number of ruptures sampled in the given src_group.
:param ruptures_by_grp_id: a dictionary with key grp_id
:param src_group: a SourceGroup instance
"""
nr = sum(
len(ruptures) for grp_id, ruptures in ruptures_by_grp_id.items()
if src_group.id == grp_id)
return nr
[docs] def zerodict(self):
"""
Initial accumulator, a dictionary (grp_id, gsim) -> curves
"""
zd = AccumDict()
zd.calc_times = []
zd.eff_ruptures = AccumDict()
self.eid = collections.Counter() # sm_id -> event_id
self.sm_by_grp = self.csm.info.get_sm_by_grp()
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 given ruptures"""
with self.monitor('saving ruptures', autoflush=True):
for grp_id, ebrs in ruptures_by_grp_id.items():
events = []
i = 0
sm_id = self.sm_by_grp[grp_id]
for ebr in ebrs:
for event in ebr.events:
event['eid'] = self.eid[sm_id]
rec = (ebr.serial,
event['ses'],
event['occ'],
event['sample'],
ebr.grp_id,
ebr.source_id)
events.append(rec)
self.eid[sm_id] += 1
i += 1
if self.oqparam.save_ruptures:
self.datastore['sescollection/%s' % ebr.serial] = ebr
if events:
ev = 'events/sm-%04d' % sm_id
self.datastore.extend(
ev, numpy.array(events, stored_event_dt))
# save rup_data
if hasattr(ruptures_by_grp_id, 'rup_data'):
trt = ruptures_by_grp_id.trt
self.rup_data[trt] = self.datastore.extend(
'rup_data/' + trt, ruptures_by_grp_id.rup_data)
[docs] def post_execute(self, result):
"""
Save the SES collection
"""
nr = sum(len(result[grp_id]) for grp_id in result)
logging.info('Saved %d ruptures, %d events',
nr, sum(self.eid.values()))
if 'sescollection' in self.datastore:
self.datastore.set_nbytes('sescollection')
self.datastore.set_nbytes('events')
for dset in self.rup_data.values():
if len(dset):
numsites = dset['numsites']
multiplicity = dset['multiplicity']
spr = numpy.average(numsites, weights=multiplicity)
mul = numpy.average(multiplicity, weights=numsites)
self.datastore.set_attrs(dset.name, sites_per_rupture=spr,
multiplicity=mul)
if self.rup_data:
self.datastore.set_nbytes('rup_data')
# ######################## GMF calculator ############################ #
gmv_dt = numpy.dtype([('sid', U32), ('eid', U32), ('imti', U8), ('gmv', F32)])
[docs]def compute_gmfs_and_curves(getter, rlzs, monitor):
"""
:param eb_ruptures:
a list of blocks of EBRuptures of the same SESCollection
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param imts:
a list of intensity measure type strings
:param rlzs_by_gsim:
a dictionary gsim -> associated realizations
:param monitor:
a Monitor instance
:returns:
a dictionary with keys gmfcoll and hcurves
"""
oq = monitor.oqparam
haz = {sid: {} for sid in getter.sids}
gmfcoll = {} # rlz -> gmfa
for rlz in rlzs:
gmfcoll[rlz] = []
for sid, gmvdict in zip(getter.sids, getter(rlz)):
if gmvdict:
for imti, imt in enumerate(getter.imts):
if oq.hazard_curves_from_gmfs:
haz[sid][imt, rlz] = gmvdict[imt]
for rec in gmvdict[imt]:
gmfcoll[rlz].append(
(sid, rec['eid'], imti, rec['gmv']))
for rlz in gmfcoll:
gmfcoll[rlz] = numpy.array(gmfcoll[rlz], gmv_dt)
result = dict(gmfcoll=gmfcoll if oq.ground_motion_fields else None,
hcurves={})
if oq.hazard_curves_from_gmfs:
with monitor('building hazard curves', measuremem=False):
duration = oq.investigation_time * oq.ses_per_logic_tree_path
for sid, haz_by_imt_rlz in haz.items():
for imt, rlz in haz_by_imt_rlz:
gmvs = haz_by_imt_rlz[imt, rlz]['gmv']
poes = calc._gmvs_to_haz_curve(
gmvs, oq.imtls[imt], oq.investigation_time, duration)
key = rsi2str(rlz.ordinal, sid, imt)
result['hcurves'][key] = poes
return result
@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')
if res['gmfcoll'] is not None:
with sav_mon:
for rlz, array in res['gmfcoll'].items():
if len(array):
key = 'gmf_data/%04d' % rlz.ordinal
self.datastore.extend(key, array)
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, ebruptures):
"""
:param ebruptures: a list of EBRupture objects to be split
:yields: the arguments for compute_gmfs_and_curves
"""
oq = self.oqparam
monitor = self.monitor(self.core_task.__name__)
monitor.oqparam = oq
imts = list(oq.imtls)
min_iml = calc.fix_minimum_intensity(oq.minimum_intensity, imts)
grp_trt = {sg.id: sg.trt for sm in self.csm.info.source_models
for sg in sm.src_groups}
rlzs_by_grp = self.rlzs_assoc.get_rlzs_by_grp_id()
correl_model = oq.get_correl_model()
for block in split_in_blocks(
ebruptures, oq.concurrent_tasks or 1,
key=operator.attrgetter('grp_id')):
grp_id = block[0].grp_id
trt = grp_trt[grp_id]
gsims = [dic[trt] for dic in self.rlzs_assoc.gsim_by_trt]
samples = self.rlzs_assoc.samples[grp_id]
getter = GmfGetter(gsims, block, self.sitecol,
imts, min_iml, oq.truncation_level,
correl_model, samples)
yield getter, rlzs_by_grp[grp_id], 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
self.sesruptures = []
if self.precalc: # the ruptures are already in memory
for grp_id, sesruptures in self.precalc.result.items():
for sr in sesruptures:
self.sesruptures.append(sr)
else: # read the ruptures from the datastore
for serial in self.datastore['sescollection']:
sr = self.datastore['sescollection/' + serial]
self.sesruptures.append(sr)
self.sesruptures.sort(key=operator.attrgetter('serial'))
if self.oqparam.ground_motion_fields:
calc.check_overflow(self)
L = len(oq.imtls.array)
res = parallel.starmap(
self.core_task.__func__, self.gen_args(self.sesruptures)
).submit_all()
acc = functools.reduce(self.combine_pmaps_and_save_gmfs, res, {
rlz.ordinal: ProbabilityMap(L, 1)
for rlz in self.rlzs_assoc.realizations})
self.save_data_transfer(res)
return acc
[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.rlzs_assoc.realizations
# save individual curves
if self.oqparam.individual_curves:
for i in sorted(result):
key = 'hcurves/rlz-%03d' % i
if result[i]:
self.datastore[key] = result[i]
else:
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 = (None if self.oqparam.number_of_logic_tree_samples
else [rlz.weight for rlz in rlzs])
pstats = PmapStats(self.oqparam.quantile_hazard_curves, weights)
for kind, stat in pstats.compute(
self.sitecol.sids, list(result.values())):
if kind == 'mean' and not self.oqparam.mean_hazard_curves:
continue
self.datastore['hcurves/' + kind] = stat
if ('gmf_data' in self.datastore and 'nbytes' not
in self.datastore['gmf_data'].attrs):
self.datastore.set_nbytes('gmf_data')
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)
# 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 = 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)