def make_gmfs(eb_ruptures, sitecol, imts, gsims,
trunc_level, correl_model, monitor=Monitor()):
"""
:param eb_ruptures: a list of EBRuptures
:param sitecol: a SiteCollection instance
:param imts: an ordered list of intensity measure type strings
:param gsims: an order list of GSIM instance
:param trunc_level: truncation level
:param correl_model: correlation model instance
:param monitor: a monitor instance
:returns: a dictionary serial -> GmfaSidsEtags
"""
dic = {} # serial -> GmfaSidsEtags
ctx_mon = monitor('make contexts')
gmf_mon = monitor('compute poes')
sites = sitecol.complete
for ebr in eb_ruptures:
with ctx_mon:
r_sites = site.FilteredSiteCollection(ebr.indices, sites)
computer = calc.gmf.GmfComputer(
ebr.rupture, r_sites, imts, gsims, trunc_level, correl_model)
with gmf_mon:
gmfa = computer.calcgmfs(ebr.multiplicity, ebr.rupture.seed)
dic[ebr.serial] = GmfaSidsEtags(gmfa, r_sites.indices, ebr.etags)
return dic
def gen_ctxs(self, ruptures, sites, src_id, mon=Monitor()):
"""
:param ruptures:
a list of ruptures generated by the same source
:param sites:
a (filtered) SiteCollection
:param src_id:
the ID of the source (for debugging purposes)
:param mon:
a Monitor object
:yields:
fat RuptureContexts
"""
fewsites = len(sites.complete) <= self.max_sites_disagg
for rup in ruptures:
with mon:
try:
ctx, r_sites, dctx = self.make_contexts(
getattr(rup, 'sites', sites), rup)
except FarAwayRupture:
continue
for par in self.REQUIRES_SITES_PARAMETERS:
setattr(ctx, par, r_sites[par])
ctx.sids = r_sites.sids
ctx.src_id = src_id
for par in self.REQUIRES_DISTANCES | {'rrup'}:
setattr(ctx, par, getattr(dctx, par))
if fewsites:
closest = rup.surface.get_closest_points(sites.complete)
ctx.clon = closest.lons[ctx.sids]
ctx.clat = closest.lats[ctx.sids]
yield ctx
def init(cls, poolsize=None, distribute=OQ_DISTRIBUTE):
if distribute == 'processpool' and not hasattr(cls, 'pool'):
cls.pool = multiprocessing.Pool(poolsize, init_workers)
m = Monitor('wakeup')
cls(_wakeup, [(.2, m) for _ in range(cls.pool._processes)])
elif distribute == 'threadpool' and not hasattr(cls, 'pool'):
cls.pool = multiprocessing.dummy.Pool(poolsize)
def collect_bins_data(trt_num,
sources,
site,
cmaker,
imldict,
truncation_level,
n_epsilons,
mon=Monitor()):
sitecol = SiteCollection([site])
# NB: instantiating truncnorm is slow and calls the infamous "doccer"
truncnorm = scipy.stats.truncnorm(-truncation_level, truncation_level)
acc = AccumDict(accum=[])
for source in sources:
try:
trti = trt_num[source.tectonic_region_type]
rupdict = cmaker.disaggregate(sitecol, source.iter_ruptures(),
imldict, truncnorm, n_epsilons, mon)
acc['trti'].extend([trti] * len(rupdict['mags']))
acc += rupdict
except Exception as err:
etype, err, tb = sys.exc_info()
msg = 'An error occurred with source id=%s. Error: %s'
msg %= (source.source_id, err)
raise_(etype, msg, tb)
return acc
def __init__(self, oqparam, calc_id=None):
self.datastore = datastore.DataStore(calc_id)
self._monitor = Monitor('%s.run' % self.__class__.__name__,
measuremem=True)
self.oqparam = oqparam
if 'performance_data' not in self.datastore:
self.datastore.create_dset('performance_data', perf_dt)
def grid_point_sources(sources, ps_grid_spacing, monitor=Monitor()):
"""
:param sources:
a list of sources with the same grp_id (point sources and not)
:param ps_grid_spacing:
value of the point source grid spacing in km; if None, do nothing
:returns:
a dict grp_id -> list of non-point sources and collapsed point sources
"""
grp_id = sources[0].grp_id
for src in sources[1:]:
assert src.grp_id == grp_id, (src.grp_id, grp_id)
if ps_grid_spacing is None:
return {grp_id: sources}
out = [src for src in sources if not hasattr(src, 'location')]
ps = numpy.array([src for src in sources if hasattr(src, 'location')])
if len(ps) < 2: # nothing to collapse
return {grp_id: out + list(ps)}
coords = _coords(ps)
deltax = angular_distance(ps_grid_spacing, lat=coords[:, 1].mean())
deltay = angular_distance(ps_grid_spacing)
grid = groupby_grid(coords[:, 0], coords[:, 1], deltax, deltay)
task_no = getattr(monitor, 'task_no', 0)
for i, idxs in enumerate(grid.values()):
if len(idxs) > 1:
cps = CollapsedPointSource('cps-%d-%d' % (task_no, i), ps[idxs])
cps.id = ps[0].id
cps.grp_id = ps[0].grp_id
cps.trt_smr = ps[0].trt_smr
out.append(cps)
else: # there is a single source
out.append(ps[idxs[0]])
return {grp_id: out}
def __init__(self, oqparam, monitor=Monitor(), calc_id=None):
self.monitor = monitor
self.datastore = datastore.DataStore(calc_id)
self.monitor.calc_id = self.datastore.calc_id
self.monitor.hdf5path = self.datastore.hdf5path
self.datastore.export_dir = oqparam.export_dir
self.oqparam = oqparam
def safely_call(func, args, pickle=False):
"""
Call the given function with the given arguments safely, i.e.
by trapping the exceptions. Return a pair (result, exc_type)
where exc_type is None if no exceptions occur, otherwise it
is the exception class and the result is a string containing
error message and traceback.
:param func: the function to call
:param args: the arguments
:param pickle:
if set, the input arguments are unpickled and the return value
is pickled; otherwise they are left unchanged
"""
if pickle:
args = [a.unpickle() for a in args]
ismon = args and isinstance(args[-1], Monitor)
mon = args[-1] if ismon else Monitor()
try:
got = func(*args)
if inspect.isgenerator(got):
got = list(got)
res = got, None, mon
except:
etype, exc, tb = sys.exc_info()
tb_str = ''.join(traceback.format_tb(tb))
res = ('\n%s%s: %s' % (tb_str, etype.__name__, exc), etype, mon)
if pickle:
return Pickled(res)
return res
def collect_bin_data(sources, sitecol, cmaker, iml4,
truncation_level, n_epsilons, monitor=Monitor()):
"""
:param sources: a list of sources
:param sitecol: a SiteCollection instance
:param cmaker: a ContextMaker instance
:param iml4: an ArrayWrapper of intensities of shape (N, R, M, P)
:param truncation_level: the truncation level
:param n_epsilons: the number of epsilons
:param monitor: a Monitor instance
:returns: a dictionary (poe, imt, rlzi) -> probabilities of shape (N, E)
"""
# NB: instantiating truncnorm is slow and calls the infamous "doccer"
truncnorm = scipy.stats.truncnorm(-truncation_level, truncation_level)
epsilons = numpy.linspace(truncnorm.a, truncnorm.b, n_epsilons + 1)
acc = AccumDict(accum=[])
mon = monitor('iter_ruptures', measuremem=False)
for source in sources:
with mon:
ruptures = list(source.iter_ruptures())
try:
acc += cmaker.disaggregate(
sitecol, ruptures, iml4, truncnorm, epsilons, monitor)
except Exception as err:
etype, err, tb = sys.exc_info()
msg = 'An error occurred with source id=%s. Error: %s'
msg %= (source.source_id, err)
raise_(etype, msg, tb)
return pack(acc, 'mags dists lons lats'.split())
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']
[grp_id] = set(src.src_group_id for src in sources)
# 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(rup_array=get_rup_array(eb_ruptures, srcfilter),
calc_times=calc_times,
eff_ruptures={grp_id: 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):
eff_ruptures += 1
t0 = time.time()
if len(eb_ruptures) > MAX_RUPTURES:
# yield partial result to avoid running out of memory
yield AccumDict(rup_array=get_rup_array(eb_ruptures,
srcfilter),
calc_times={}, eff_ruptures={})
eb_ruptures.clear()
samples = getattr(src, 'samples', 1)
n_occ = 0
for rup, n_occ in src.sample_ruptures(samples * num_ses):
ebr = EBRupture(rup, src.id, grp_id, n_occ, samples)
eb_ruptures.append(ebr)
n_occ += ebr.n_occ
dt = time.time() - t0
try:
n_sites = len(_sites)
except (TypeError, ValueError): # for None or a closed dataset
n_sites = 0
calc_times[src.id] += numpy.array([n_occ, n_sites, dt])
rup_array = get_rup_array(eb_ruptures, srcfilter)
yield AccumDict(rup_array=rup_array, calc_times=calc_times,
eff_ruptures={grp_id: eff_ruptures})
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, _ in srcfilter.filter(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, trt_smrlz, n_occ in src.sample_ruptures(
samples * num_ses, param['ses_seed']):
ebr = EBRupture(rup, src.source_id, trt_smrlz, n_occ)
eb_ruptures.append(ebr)
dt = time.time() - t0
calc_times[src.id] += numpy.array([nr, src.nsites, 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}))
def test(self):
mon = Monitor()
iterargs = ((i, mon) for i in range(10))
res = _starmap(double, iterargs, self.host, self.task_in_port,
self.receiver_ports)
num_tasks = next(res)
self.assertEqual(num_tasks, 10)
self.assertEqual(sum(r[0] for r in res), 90)
def classical(group, src_filter, gsims, param, monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns:
a dictionary {grp_id: pmap} with attributes .grp_ids, .calc_times,
.eff_ruptures
"""
grp_ids = set()
for src in group:
if not src.num_ruptures:
# src.num_ruptures is set when parsing the XML, but not when
# the source is instantiated manually, so it is set here
src.num_ruptures = src.count_ruptures()
grp_ids.update(src.src_group_ids)
maxdist = src_filter.integration_distance
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist, param, monitor)
pmap = AccumDict({
grp_id: ProbabilityMap(len(imtls.array), len(gsims))
for grp_id in grp_ids
})
# AccumDict of arrays with 3 elements weight, nsites, calc_time
pmap.calc_times = AccumDict(accum=numpy.zeros(3, numpy.float32))
pmap.eff_ruptures = AccumDict() # grp_id -> num_ruptures
src_mutex = param.get('src_interdep') == 'mutex'
rup_mutex = param.get('rup_interdep') == 'mutex'
for src, s_sites in src_filter(group): # filter now
t0 = time.time()
try:
poemap = cmaker.poe_map(src, s_sites, imtls, trunclevel,
not rup_mutex)
except Exception as err:
etype, err, tb = sys.exc_info()
msg = '%s (source id=%s)' % (str(err), src.source_id)
raise etype(msg).with_traceback(tb)
if src_mutex: # mutex sources, there is a single group
for sid in poemap:
pcurve = pmap[src.src_group_id].setdefault(sid, 0)
pcurve += poemap[sid] * src.mutex_weight
elif poemap:
for gid in src.src_group_ids:
pmap[gid] |= poemap
pmap.calc_times[src.id] += numpy.array(
[src.weight, len(s_sites),
time.time() - t0])
# storing the number of contributing ruptures too
pmap.eff_ruptures += {
gid: getattr(poemap, 'eff_ruptures', 0)
for gid in src.src_group_ids
}
if src_mutex and param.get('grp_probability'):
pmap[src.src_group_id] *= param['grp_probability']
return pmap
def sample_ruptures(group,
src_filter=filters.source_site_noop_filter,
gsims=(),
param=(),
monitor=Monitor()):
"""
:param group:
a SourceGroup or a sequence of sources of the same group
:param src_filter:
a source site filter (default noop filter)
:param gsims:
a list of GSIMs for the current tectonic region model
:param param:
a dictionary of additional parameters (by default
ses_per_logic_tree_path=1, samples=1, seed=42, filter_distance=1000)
:param monitor:
monitor instance
:returns:
a dictionary with eb_ruptures, num_events, num_ruptures, calc_times
"""
if not param:
param = dict(ses_per_logic_tree_path=1,
samples=1,
seed=42,
filter_distance=1000)
if getattr(group, 'src_interdep', None) == 'mutex':
prob = {src: sw for src, sw in zip(group, group.srcs_weights)}
else:
prob = {src: 1 for src in group}
eb_ruptures = []
calc_times = []
rup_mon = monitor('making contexts', measuremem=False)
# Compute and save stochastic event sets
num_ruptures = 0
eids = numpy.zeros(0)
cmaker = ContextMaker(gsims, src_filter.integration_distance,
param['filter_distance'], monitor)
for src, s_sites in src_filter(group):
t0 = time.time()
num_ruptures += src.num_ruptures
num_occ_by_rup = _sample_ruptures(src, prob[src],
param['ses_per_logic_tree_path'],
param['samples'], param['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, cmaker, s_sites,
param['seed'], rup_mon):
eb_ruptures.append(ebr)
eids = set_eids(eb_ruptures)
src_id = src.source_id.split(':', 1)[0]
dt = time.time() - t0
calc_times.append((src_id, src.nsites, eids, dt))
dic = dict(eb_ruptures=eb_ruptures,
num_events=len(eids),
calc_times=calc_times,
num_ruptures=num_ruptures)
return dic
def run_calc(job_id, oqparam, log_level, log_file, exports,
hazard_calculation_id=None, **kw):
"""
Run a calculation.
:param job_id:
ID of the current job
:param oqparam:
:class:`openquake.commonlib.oqvalidation.OqParam` instance
:param str log_level:
The desired logging level. Valid choices are 'debug', 'info',
'progress', 'warn', 'error', and 'critical'.
:param str log_file:
Complete path (including file name) to file where logs will be written.
If `None`, logging will just be printed to standard output.
:param exports:
A comma-separated string of export types.
"""
monitor = Monitor('total runtime', measuremem=True)
with logs.handle(job_id, log_level, log_file): # run the job
if USE_CELERY and os.environ.get('OQ_DISTRIBUTE') == 'celery':
set_concurrent_tasks_default()
calc = base.calculators(oqparam, monitor, calc_id=job_id)
monitor.hdf5path = calc.datastore.hdf5path
calc.from_engine = True
tb = 'None\n'
try:
logs.dbcmd('set_status', job_id, 'executing')
_do_run_calc(calc, exports, hazard_calculation_id, **kw)
duration = monitor.duration
expose_outputs(calc.datastore)
monitor.flush()
records = views.performance_view(calc.datastore)
logs.dbcmd('save_performance', job_id, records)
calc.datastore.close()
logs.LOG.info('Calculation %d finished correctly in %d seconds',
job_id, duration)
logs.dbcmd('finish', job_id, 'complete')
except:
tb = traceback.format_exc()
try:
logs.LOG.critical(tb)
logs.dbcmd('finish', job_id, 'failed')
except: # an OperationalError may always happen
sys.stderr.write(tb)
raise
finally:
# if there was an error in the calculation, this part may fail;
# in such a situation, we simply log the cleanup error without
# taking further action, so that the real error can propagate
try:
if USE_CELERY:
celery_cleanup(TERMINATE, parallel.Starmap.task_ids)
except:
# log the finalization error only if there is no real error
if tb == 'None\n':
logs.LOG.error('finalizing', exc_info=True)
return calc
def info(calculators, gsims, views, exports, report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
logging.basicConfig(level=logging.INFO)
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
assert node['xmlns'].endswith('nrml/0.5'), node['xmlns']
print(source_model_info(node[0]))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
def __init__(self, oqparam, calc_id):
self.datastore = datastore.DataStore(calc_id)
init_performance(self.datastore.hdf5)
self._monitor = Monitor('%s.run' % self.__class__.__name__,
measuremem=True,
h5=self.datastore)
# NB: using h5=self.datastore.hdf5 would mean losing the performance
# info about Calculator.run since the file will be closed later on
self.oqparam = oqparam
def classical(group, src_filter, gsims, param, monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns:
a dictionary with keys pmap, calc_times, rup_data, extra
"""
if not hasattr(src_filter, 'sitecol'): # do not filter
src_filter = SourceFilter(src_filter, {})
# Get the parameters assigned to the group
src_mutex = getattr(group, 'src_interdep', None) == 'mutex'
cluster = getattr(group, 'cluster', None)
trts = set()
maxradius = 0
for src in group:
if not src.num_ruptures:
# src.num_ruptures may not be set, so it is set here
src.num_ruptures = src.count_ruptures()
# set the proper TOM in case of a cluster
if cluster:
src.temporal_occurrence_model = FatedTOM(time_span=1)
trts.add(src.tectonic_region_type)
if hasattr(src, 'radius'): # for prefiltered point sources
maxradius = max(maxradius, src.radius)
param['maximum_distance'] = src_filter.integration_distance
[trt] = trts # there must be a single tectonic region type
cmaker = ContextMaker(trt, gsims, param, monitor)
try:
cmaker.tom = group.temporal_occurrence_model
except AttributeError: # got a list of sources, not a group
time_span = param.get('investigation_time') # None for nonparametric
cmaker.tom = PoissonTOM(time_span) if time_span else None
if cluster:
cmaker.tom = FatedTOM(time_span=1)
pmap, rup_data, calc_times = PmapMaker(cmaker, src_filter, group).make()
extra = {}
extra['task_no'] = getattr(monitor, 'task_no', 0)
extra['trt'] = trt
extra['source_id'] = src.source_id
extra['grp_id'] = src.grp_id
extra['maxradius'] = maxradius
group_probability = getattr(group, 'grp_probability', None)
if src_mutex and group_probability:
pmap *= group_probability
if cluster:
tom = getattr(group, 'temporal_occurrence_model')
pmap = _cluster(param['imtls'], tom, gsims, pmap)
return dict(pmap=pmap,
calc_times=calc_times,
rup_data=rup_data,
extra=extra)
def classical(group, src_filter, gsims, param, monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns:
a dictionary {grp_id: pmap} with attributes .grp_ids, .calc_times,
.eff_ruptures
"""
if getattr(group, 'src_interdep', None) == 'mutex':
mutex_weight = {
src.source_id: weight
for src, weight in zip(group.sources, group.srcs_weights)
}
else:
mutex_weight = None
grp_ids = set()
for src in group:
grp_ids.update(src.src_group_ids)
maxdist = src_filter.integration_distance
imtls = param['imtls']
trunclevel = param.get('truncation_level')
cmaker = ContextMaker(gsims, maxdist, param['filter_distance'], monitor)
pmap = AccumDict({
grp_id: ProbabilityMap(len(imtls.array), len(gsims))
for grp_id in grp_ids
})
# AccumDict of arrays with 4 elements weight, nsites, calc_time, split
pmap.calc_times = AccumDict(accum=numpy.zeros(4))
pmap.eff_ruptures = AccumDict() # grp_id -> num_ruptures
for src, s_sites in src_filter(group): # filter now
t0 = time.time()
indep = group.rup_interdep == 'indep' if mutex_weight else True
poemap = cmaker.poe_map(src, s_sites, imtls, trunclevel, indep)
if mutex_weight: # mutex sources
weight = mutex_weight[src.source_id]
for sid in poemap:
pcurve = pmap[group.id].setdefault(sid, 0)
pcurve += poemap[sid] * weight
elif poemap:
for grp_id in src.src_group_ids:
pmap[grp_id] |= poemap
src_id = src.source_id.split(':', 1)[0]
pmap.calc_times[src_id] += numpy.array(
[src.weight, len(s_sites),
time.time() - t0, 1])
# storing the number of contributing ruptures too
pmap.eff_ruptures += {
grp_id: getattr(poemap, 'eff_ruptures', 0)
for grp_id in src.src_group_ids
}
if mutex_weight and group.grp_probability is not None:
pmap[group.id] *= group.grp_probability
return pmap
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, calc_times
"""
srcfilter = SourceFilter(sitecol, cmaker.maximum_distance)
# 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 = 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) and
getattr(sources, 'cluster', False)):
eb_ruptures, calc_times = sample_cluster(
sources, srcfilter, num_ses, vars(cmaker))
# Yield ruptures
er = sum(src.num_ruptures for src, _ in srcfilter.filter(sources))
yield AccumDict(dict(rup_array=get_rup_array(eb_ruptures, srcfilter),
calc_times=calc_times, eff_ruptures={grp_id: er}))
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, _ in srcfilter.filter(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, trt_smr, n_occ in src.sample_ruptures(
samples * num_ses, cmaker.ses_seed):
ebr = EBRupture(rup, src.source_id, trt_smr, n_occ)
eb_ruptures.append(ebr)
dt = time.time() - t0
calc_times[src.id] += numpy.array([nr, src.nsites, dt])
rup_array = get_rup_array(eb_ruptures, srcfilter)
yield AccumDict(dict(rup_array=rup_array, calc_times=calc_times,
eff_ruptures={grp_id: eff_ruptures}))
def pmap_from_grp(sources,
source_site_filter,
imtls,
gsims,
truncation_level=None,
bbs=(),
monitor=Monitor()):
"""
Compute the hazard curves for a set of sources belonging to the same
tectonic region type for all the GSIMs associated to that TRT.
The arguments are the same as in :func:`calc_hazard_curves`, except
for ``gsims``, which is a list of GSIM instances.
:returns: a ProbabilityMap instance
"""
if isinstance(sources, SourceGroup):
group = sources
sources = group.sources
trt = sources[0].tectonic_region_type
else: # list of sources
trt = sources[0].tectonic_region_type
group = SourceGroup(trt, sources, 'src_group', 'indep', 'indep')
try:
maxdist = source_site_filter.integration_distance[trt]
except:
maxdist = source_site_filter.integration_distance
if hasattr(gsims, 'keys'): # dictionary trt -> gsim
gsims = [gsims[trt]]
with GroundShakingIntensityModel.forbid_instantiation():
imtls = DictArray(imtls)
cmaker = ContextMaker(gsims, maxdist)
ctx_mon = monitor('making contexts', measuremem=False)
pne_mon = monitor('computing poes', measuremem=False)
disagg_mon = monitor('get closest points', measuremem=False)
src_indep = group.src_interdep == 'indep'
pmap = ProbabilityMap(len(imtls.array), len(gsims))
pmap.calc_times = [] # pairs (src_id, delta_t)
pmap.grp_id = sources[0].src_group_id
for src, s_sites in source_site_filter(sources):
t0 = time.time()
poemap = poe_map(src, s_sites, imtls, cmaker, truncation_level,
bbs, group.rup_interdep == 'indep', ctx_mon,
pne_mon, disagg_mon)
if src_indep: # usual composition of probabilities
pmap |= poemap
else: # mutually exclusive probabilities
weight = float(group.srcs_weights[src.source_id])
for sid in poemap:
pmap[sid] += poemap[sid] * weight
pmap.calc_times.append(
(src.source_id, len(s_sites), time.time() - t0))
# storing the number of contributing ruptures too
pmap.eff_ruptures = {pmap.grp_id: pne_mon.counts}
return pmap
def run_calc(job_id,
oqparam,
log_level='info',
log_file=None,
exports='',
hazard_calculation_id=None):
"""
Run a calculation.
:param job_id:
ID of the current job
:param oqparam:
:class:`openquake.commonlib.oqvalidation.OqParam` instance
:param str log_level:
The desired logging level. Valid choices are 'debug', 'info',
'progress', 'warn', 'error', and 'critical'.
:param str log_file:
Complete path (including file name) to file where logs will be written.
If `None`, logging will just be printed to standard output.
:param exports:
A comma-separated string of export types.
"""
if USE_CELERY:
set_concurrent_tasks_default()
monitor = Monitor('total runtime', measuremem=True)
with logs.handle(job_id, log_level, log_file): # run the job
calc = base.calculators(oqparam, monitor, calc_id=job_id)
tb = 'None\n'
try:
_do_run_calc(calc, exports, hazard_calculation_id)
logs.dbcmd('finish', job_id, 'complete')
logs.dbcmd('expose_outputs', job_id)
logs.LOG.info('Calculation %d finished correctly', job_id)
except:
tb = traceback.format_exc()
try:
logs.LOG.critical(tb)
logs.dbcmd('finish', job_id, 'failed')
except: # an OperationalError may always happen
sys.stderr.write(tb)
raise
finally:
# if there was an error in the calculation, this part may fail;
# in such a situation, we simply log the cleanup error without
# taking further action, so that the real error can propagate
try:
if USE_CELERY:
celery_cleanup(TERMINATE, parallel.TaskManager.task_ids)
except:
# log the finalization error only if there is no real error
if tb == 'None\n':
logs.LOG.error('finalizing', exc_info=True)
return calc
def disaggregate(self,
sitecol,
ruptures,
iml4,
truncnorm,
epsilons,
monitor=Monitor()):
"""
Disaggregate (separate) PoE of `imldict` in different contributions
each coming from `n_epsilons` distribution bins.
:param sitecol: a SiteCollection
:param ruptures: an iterator over ruptures with the same TRT
:param iml4: a 4d array of IMLs of shape (N, R, M, P)
:param truncnorm: an instance of scipy.stats.truncnorm
:param epsilons: the epsilon bins
:param monitor: a Monitor instance
:returns: an AccumDict
"""
sitemesh = sitecol.mesh
acc = AccumDict(accum=[])
ctx_mon = monitor('make_contexts', measuremem=False)
pne_mon = monitor('disaggregate_pne', measuremem=False)
for rupture in ruptures:
with ctx_mon:
sctx, rctx, orig_dctx = self.make_contexts(sitecol,
rupture,
filter=False)
if (self.maximum_distance
and orig_dctx.rjb.min() > self.maximum_distance(
rupture.tectonic_region_type, rupture.mag)):
continue # rupture away from all sites
cache = {}
for r, gsim in self.gsim_by_rlzi.items():
dctx = orig_dctx.roundup(gsim.minimum_distance)
for m, imt in enumerate(iml4.imts):
for p, poe in enumerate(iml4.poes_disagg):
iml = tuple(iml4.array[:, r, m, p])
try:
pne = cache[gsim, imt, iml]
except KeyError:
with pne_mon:
pne = gsim.disaggregate_pne(
rupture, sctx, rctx, dctx, imt, iml,
truncnorm, epsilons)
cache[gsim, imt, iml] = pne
acc[poe, str(imt), r].append(pne)
closest_points = rupture.surface.get_closest_points(sitemesh)
acc['mags'].append(rupture.mag)
acc['dists'].append(dctx.rjb)
acc['lons'].append(closest_points.lons)
acc['lats'].append(closest_points.lats)
return acc
def get_hazard(self, rlzs_assoc, monitor=Monitor()):
"""
:param rlzs_assoc:
:class:`openquake.commonlib.source.RlzsAssoc` instance
:param monitor:
a :class:`openquake.baselib.performance.Monitor` instance
:returns:
list of hazard dictionaries imt -> rlz -> haz per each site
"""
return [{
self.imt: rlzs_assoc.combine(hazard)
} for hazard in self.hazard_by_site]
def main(dirname):
dname = pathlib.Path(dirname)
with hdf5new() as hdf5: # create a new datastore
monitor = Monitor('count', hdf5) # create a new monitor
iterargs = ((open(dname/fname, encoding='utf-8').read(),)
for fname in os.listdir(dname)
if fname.endswith('.rst')) # read the docs
c = collections.Counter() # intially empty counter
for counter in Starmap(count, iterargs, monitor):
c += counter
print(c) # total counts
print('Performance info stored in', hdf5)
def __init__(self, trt, gsims, param=None, monitor=Monitor()):
param = param or {} # empty in the gmpe-smtk
self.af = param.get('af', None)
self.max_sites_disagg = param.get('max_sites_disagg', 10)
self.collapse_level = param.get('collapse_level', False)
self.trt = trt
self.gsims = gsims
self.single_site_opt = numpy.array(
[hasattr(gsim, 'get_mean_std1') for gsim in gsims])
self.maximum_distance = (param.get('maximum_distance')
or MagDepDistance({}))
self.investigation_time = param.get('investigation_time')
self.trunclevel = param.get('truncation_level')
self.num_epsilon_bins = param.get('num_epsilon_bins', 1)
self.grp_id = param.get('grp_id', 0)
self.effect = param.get('effect')
self.task_no = getattr(monitor, 'task_no', 0)
for req in self.REQUIRES:
reqset = set()
for gsim in gsims:
reqset.update(getattr(gsim, 'REQUIRES_' + req))
setattr(self, 'REQUIRES_' + req, reqset)
# self.pointsource_distance is a dict mag -> dist, possibly empty
psd = param.get('pointsource_distance')
if hasattr(psd, 'ddic'):
self.pointsource_distance = psd.ddic.get(trt, {})
else:
self.pointsource_distance = {}
if 'imtls' in param:
self.imtls = param['imtls']
elif 'hazard_imtls' in param:
self.imtls = DictArray(param['hazard_imtls'])
else:
self.imtls = {}
self.imts = [imt_module.from_string(imt) for imt in self.imtls]
self.reqv = param.get('reqv')
if self.reqv is not None:
self.REQUIRES_DISTANCES.add('repi')
self.mon = monitor
self.ctx_mon = monitor('make_contexts', measuremem=False)
self.loglevels = DictArray(self.imtls) if self.imtls else {}
self.shift_hypo = param.get('shift_hypo')
with warnings.catch_warnings():
# avoid RuntimeWarning: divide by zero encountered in log
warnings.simplefilter("ignore")
for imt, imls in self.imtls.items():
if imt != 'MMI':
self.loglevels[imt] = numpy.log(imls)
# instantiate monitors
self.gmf_mon = monitor('computing mean_std', measuremem=False)
self.poe_mon = monitor('get_poes', measuremem=False)
def disaggregate(self,
sitecol,
ruptures,
iml4,
truncnorm,
epsilons,
monitor=Monitor()):
"""
Disaggregate (separate) PoE of `imldict` in different contributions
each coming from `n_epsilons` distribution bins.
:param sitecol: a SiteCollection
:param ruptures: an iterator over ruptures with the same TRT
:param iml4: a 4d array of IMLs of shape (N, R, M, P)
:param truncnorm: an instance of scipy.stats.truncnorm
:param epsilons: the epsilon bins
:param monitor: a Monitor instance
:returns:
an AccumDict with keys (poe, imt, rlzi) and mags, dists, lons, lats
"""
acc = AccumDict(accum=[])
ctx_mon = monitor('disagg_contexts', measuremem=False)
pne_mon = monitor('disaggregate_pne', measuremem=False)
clo_mon = monitor('get_closest', measuremem=False)
for rupture in ruptures:
with ctx_mon:
orig_dctx = DistancesContext(
(param, get_distances(rupture, sitecol, param))
for param in self.REQUIRES_DISTANCES)
self.add_rup_params(rupture)
with clo_mon: # this is faster than computing orig_dctx
closest_points = rupture.surface.get_closest_points(sitecol)
cache = {}
for r, gsim in self.gsim_by_rlzi.items():
dctx = orig_dctx.roundup(gsim.minimum_distance)
for m, imt in enumerate(iml4.imts):
for p, poe in enumerate(iml4.poes_disagg):
iml = tuple(iml4.array[:, r, m, p])
try:
pne = cache[gsim, imt, iml]
except KeyError:
with pne_mon:
pne = gsim.disaggregate_pne(
rupture, sitecol, dctx, imt, iml,
truncnorm, epsilons)
cache[gsim, imt, iml] = pne
acc[poe, str(imt), r].append(pne)
acc['mags'].append(rupture.mag)
acc['dists'].append(getattr(dctx, self.filter_distance))
acc['lons'].append(closest_points.lons)
acc['lats'].append(closest_points.lats)
return acc
def get_hazard(self, rlzs_assoc, monitor=Monitor()):
"""
:param rlzs_assoc:
:class:`openquake.commonlib.source.RlzsAssoc` instance
:param monitor:
a :class:`openquake.baselib.performance.Monitor` instance
:returns:
lists of N hazard dictionaries imt -> rlz -> Gmvs
"""
gmfcoll = create(GmfCollector, self.ses_ruptures, self.sitecol,
self.imts, rlzs_assoc, self.trunc_level,
self.correl_model, self.min_iml, monitor)
return gmfcoll
def disaggregate(cmaker,
sitecol,
rupdata,
iml2,
truncnorm,
epsilons,
monitor=Monitor()):
"""
Disaggregate (separate) PoE in different contributions.
:param cmaker: a ContextMaker instance
:param sitecol: a SiteCollection with N=1 site
:param ruptures: an iterator over ruptures with the same TRT
:param iml2: a 2D array of IMLs of shape (M, P)
:param truncnorm: an instance of scipy.stats.truncnorm
:param epsilons: the epsilon bins
:param monitor: a Monitor instance
:returns:
an AccumDict with keys (poe, imt, rlzi) and mags, dists, lons, lats
"""
assert len(sitecol) == 1, sitecol
acc = AccumDict(accum=[], mags=[], dists=[], lons=[], lats=[])
try:
gsim = cmaker.gsim_by_rlzi[iml2.rlzi]
except KeyError:
return acc
pne_mon = monitor('disaggregate_pne', measuremem=False)
[sid] = sitecol.sids
acc['mags'] = rupdata['mag']
acc['lons'] = rupdata['lon'][:, sid]
acc['lats'] = rupdata['lat'][:, sid]
acc['dists'] = dists = rupdata[cmaker.filter_distance][:, sid]
if gsim.minimum_distance:
dists[dists < gsim.minimum_distance] = gsim.minimum_distance
# compute epsilon bin contributions only once
eps_bands = truncnorm.cdf(epsilons[1:]) - truncnorm.cdf(epsilons[:-1])
for rec in rupdata:
rctx = contexts.RuptureContext(rec)
dctx = contexts.DistancesContext(
(param, rec[param][[sid]])
for param in cmaker.REQUIRES_DISTANCES).roundup(
gsim.minimum_distance)
for m, imt in enumerate(iml2.imts):
for p, poe in enumerate(iml2.poes_disagg):
iml = iml2[m, p]
with pne_mon:
pne = disaggregate_pne(gsim, rctx, sitecol, dctx, imt, iml,
truncnorm, epsilons, eps_bands)
acc[poe, str(imt), iml2.rlzi].append(pne)
return acc
def get_calc(self, testfile, job_ini, **kw):
"""
Return the outputs of the calculation as a dictionary
"""
self.testdir = os.path.dirname(testfile) if os.path.isfile(testfile) \
else testfile
inis = [os.path.join(self.testdir, ini) for ini in job_ini.split(',')]
params = readinput.get_params(inis)
params.update(kw)
oq = oqvalidation.OqParam(**params)
oq.validate()
# change this when debugging the test
monitor = Monitor(self.testdir)
return base.calculators(oq, monitor)
