def __init__(self, job):
super(BaseHazardCalculator, self).__init__(job)
# a dictionary trt_model_id -> num_ruptures
self.num_ruptures = collections.defaultdict(int)
# now a dictionary (trt_model_id, gsim) -> poes
self.acc = general.AccumDict()
self.hc = models.oqparam(self.job.id)
self.mean_hazard_curves = getattr(
self.hc, 'mean_hazard_curves', None)
self.quantile_hazard_curves = getattr(
self.hc, 'quantile_hazard_curves', ())
def list_outputs(job_id, full=True):
"""
List the outputs for a given
:class:`~openquake.engine.db.models.OqJob`.
:param job_id:
ID of a calculation.
:param bool full:
If True produce a full listing, otherwise a short version
"""
outputs = get_outputs(job_id)
if models.oqparam(job_id).calculation_mode == 'scenario':
# ignore SES output
outputs = outputs.filter(output_type='gmf_scenario')
print_outputs_summary(outputs, full)
def list_outputs(job_id, full=True):
"""
List the outputs for a given
:class:`~openquake.engine.db.models.OqJob`.
:param job_id:
ID of a calculation.
:param bool full:
If True produce a full listing, otherwise a short version
"""
outputs = get_outputs(job_id)
if models.oqparam(job_id).calculation_mode == 'scenario':
# ignore SES output
outputs = outputs.filter(output_type='gmf_scenario')
print_outputs_summary(outputs, full)
def gmfs(job_id, ses_ruptures, sitecol, imts, gmf_id):
"""
:param int job_id: the current job ID
:param ses_ruptures: a set of `SESRupture` instances
:param sitecol: a `SiteCollection` instance
:param imts: a list of hazardlib IMT instances
:param int gmf_id: the ID of a `Gmf` instance
"""
hc = models.oqparam(job_id)
gsim = AVAILABLE_GSIMS[hc.gsim]() # instantiate the GSIM class
correlation_model = models.get_correl_model(
models.OqJob.objects.get(pk=job_id))
cache = collections.defaultdict(list) # {site_id, imt -> gmvs}
inserter = writer.CacheInserter(models.GmfData, 1000)
# insert GmfData in blocks of 1000 sites
# NB: ses_ruptures a non-empty list produced by the block_splitter
rupture = ses_ruptures[0].rupture # ProbabilisticRupture instance
with EnginePerformanceMonitor('computing gmfs', job_id, gmfs):
gmf = GmfComputer(rupture, sitecol, imts, [gsim],
getattr(hc, 'truncation_level', None),
correlation_model)
gname = gsim.__class__.__name__
for ses_rup in ses_ruptures:
for (gname, imt), gmvs in gmf.compute(ses_rup.seed):
for site_id, gmv in zip(sitecol.sids, gmvs):
# float may be needed below to convert 1x1 matrices
cache[site_id, imt].append((gmv, ses_rup.id))
with EnginePerformanceMonitor('saving gmfs', job_id, gmfs):
for (site_id, imt_str), data in cache.iteritems():
imt = from_string(imt_str)
gmvs, rup_ids = zip(*data)
inserter.add(
models.GmfData(
gmf_id=gmf_id,
task_no=0,
imt=imt[0],
sa_period=imt[1],
sa_damping=imt[2],
site_id=site_id,
rupture_ids=rup_ids,
gmvs=gmvs))
inserter.flush()
def create_ruptures(self):
oqparam = models.oqparam(self.job.id)
self.imts = map(
from_string, sorted(oqparam.intensity_measure_types_and_levels))
self.rupture = get_rupture(oqparam)
# check filtering
trunc_level = getattr(oqparam, 'truncation_level', None)
maximum_distance = oqparam.maximum_distance
self.sites = filters.filter_sites_by_distance_to_rupture(
self.rupture, maximum_distance, self.site_collection)
if self.sites is None:
raise RuntimeError(
'All sites where filtered out! '
'maximum_distance=%s km' % maximum_distance)
# create ses output
output = models.Output.objects.create(
oq_job=self.job,
display_name='SES Collection',
output_type='ses')
self.ses_coll = models.SESCollection.create(output=output)
# create gmf output
output = models.Output.objects.create(
oq_job=self.job,
display_name="GMF",
output_type="gmf_scenario")
self.gmf = models.Gmf.objects.create(output=output)
with self.monitor('saving ruptures'):
self.tags = ['scenario-%010d' % i for i in xrange(
oqparam.number_of_ground_motion_fields)]
_, self.rupids, self.seeds = create_db_ruptures(
self.rupture, self.ses_coll, self.tags,
self.hc.random_seed)
correlation_model = models.get_correl_model(
models.OqJob.objects.get(pk=self.job.id))
gsim = AVAILABLE_GSIMS[oqparam.gsim]()
self.computer = GmfComputer(
self.rupture, self.site_collection, self.imts, gsim,
trunc_level, correlation_model)
def create_ruptures(self):
oqparam = models.oqparam(self.job.id)
self.imts = map(from_string, oqparam.imtls)
self.rupture = readinput.get_rupture(oqparam)
# check filtering
trunc_level = oqparam.truncation_level
maximum_distance = oqparam.maximum_distance
self.sites = filters.filter_sites_by_distance_to_rupture(
self.rupture, maximum_distance, self.site_collection)
if self.sites is None:
raise RuntimeError(
'All sites where filtered out! '
'maximum_distance=%s km' % maximum_distance)
# create ses output
output = models.Output.objects.create(
oq_job=self.job,
display_name='SES Collection',
output_type='ses')
self.ses_coll = models.SESCollection.create(output=output)
# create gmf output
output = models.Output.objects.create(
oq_job=self.job,
display_name="GMF",
output_type="gmf_scenario")
self.gmf = models.Gmf.objects.create(output=output)
with self.monitor('saving ruptures', autoflush=True):
self.tags = ['scenario-%010d' % i for i in xrange(
oqparam.number_of_ground_motion_fields)]
_, self.rupids, self.seeds = create_db_ruptures(
self.rupture, self.ses_coll, self.tags,
self.oqparam.random_seed)
correlation_model = models.get_correl_model(
models.OqJob.objects.get(pk=self.job.id))
gsim = valid.gsim(oqparam.gsim)
self.computer = GmfComputer(
self.rupture, self.sites, oqparam.imtls, [gsim],
trunc_level, correlation_model)
def create_ruptures(self):
oqparam = models.oqparam(self.job.id)
self.imts = map(from_string, oqparam.imtls)
self.rupture = readinput.get_rupture(oqparam)
# check filtering
trunc_level = oqparam.truncation_level
maximum_distance = oqparam.maximum_distance
self.sites = filters.filter_sites_by_distance_to_rupture(
self.rupture, maximum_distance, self.site_collection)
if self.sites is None:
raise RuntimeError('All sites where filtered out! '
'maximum_distance=%s km' % maximum_distance)
# create ses output
output = models.Output.objects.create(oq_job=self.job,
display_name='SES Collection',
output_type='ses')
self.ses_coll = models.SESCollection.create(output=output)
# create gmf output
output = models.Output.objects.create(oq_job=self.job,
display_name="GMF",
output_type="gmf_scenario")
self.gmf = models.Gmf.objects.create(output=output)
with self.monitor('saving ruptures', autoflush=True):
self.tags = [
'scenario-%010d' % i
for i in xrange(oqparam.number_of_ground_motion_fields)
]
_, self.rupids, self.seeds = create_db_ruptures(
self.rupture, self.ses_coll, self.tags,
self.oqparam.random_seed)
correlation_model = models.get_correl_model(
models.OqJob.objects.get(pk=self.job.id))
gsim = valid.gsim(oqparam.gsim)
self.computer = GmfComputer(self.rupture, self.sites, oqparam.imtls,
[gsim], trunc_level, correlation_model)
def extract(hc_id, a_writer):
hc = models.oqparam(hc_id)
for lt in models.LtRealization.objects.filter(
lt_model__hazard_calculation=hc.oqjob):
for imt in hc.intensity_measure_types:
imt_type, sa_period, _ = from_string(imt)
if imt_type == "PGA":
imt_type_fix = "SA"
sa_period_fix = 0
else:
imt_type_fix = imt_type
sa_period_fix = sa_period
ruptures = sorted(
[r.id for r in models.SESRupture.objects.filter(
rupture__ses_collection__lt_realization=lt)])
for site in hc.hazardsite_set.all().order_by('id'):
gmvs = []
gmvs_data = dict()
for ses_coll in models.SESCollection.objects.filter(
lt_realization=lt).order_by('id'):
for ses in ses_coll:
for gmf in models.GmfData.objects.filter(
ses_id=ses.ordinal,
site=site,
imt=imt_type, sa_period=sa_period):
gmvs_data.update(
dict(zip(gmf.rupture_ids, gmf.gmvs)))
gmvs.extend([gmvs_data.get(r, 0.0) for r in ruptures])
a_writer.writerow([lt.id, site.location.x, site.location.y,
imt_type_fix, sa_period_fix] + gmvs)
def compute_disagg(sitecol, sources, trt_model_id,
trt_num, curves_dict, bin_edges, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param list sources:
list of hazardlib source objects
:param lt_model:
an instance of :class:`openquake.engine.db.models.LtSourceModel`
:param dict trt_num:
a dictionary Tectonic Region Type -> incremental number
:param curves_dict:
a dictionary with the hazard curves for sites, realizations and IMTs
:param bin_egdes:
a dictionary (lt_model_id, site_id) -> edges
:param monitor:
monitor of the currently running job
:returns:
a dictionary of probability arrays, with composite key
(site.id, rlz.id, poe, imt, iml, trt_names).
"""
hc = models.oqparam(monitor.job_id)
trt_model = models.TrtModel.objects.get(pk=trt_model_id)
gsims = trt_model.get_gsim_instances()
lt_model_id = trt_model.lt_model.id
rlzs = trt_model.get_rlzs_by_gsim()
trt_names = tuple(trt_model.lt_model.get_tectonic_region_types())
result = {} # site.id, rlz.id, poe, imt, iml, trt_names -> array
collecting_mon = monitor('collecting bins')
arranging_mon = monitor('arranging bins')
for site in sitecol:
# edges as wanted by disagg._arrange_data_in_bins
try:
edges = bin_edges[lt_model_id, site.id]
except KeyError:
# bin_edges for a given site are missing if the site is far away
continue
# generate source, rupture, sites once per site
source_ruptures = list(
gen_ruptures_for_site(site, sources, hc.maximum_distance, monitor))
if not source_ruptures:
continue
logs.LOG.info('Collecting bins from %d ruptures close to %s',
sum(len(rupts) for src, rupts in source_ruptures),
site.location)
with collecting_mon:
bdata = _collect_bins_data(
trt_num, source_ruptures, site, curves_dict[site.id],
trt_model_id, gsims, hc.imtls,
hc.poes_disagg, hc.truncation_level,
hc.num_epsilon_bins, monitor)
if not bdata.pnes: # no contributions for this site
continue
for poe in hc.poes_disagg:
for imt in hc.imtls:
for gsim in gsims:
for rlz in rlzs[gsim.__class__.__name__]:
# extract the probabilities of non-exceedance for the
# given realization, disaggregation PoE, and IMT
iml_pne_pairs = [pne[rlz.id, poe, imt]
for pne in bdata.pnes]
iml = iml_pne_pairs[0][0]
probs = numpy.array(
[p for (i, p) in iml_pne_pairs], float)
# bins in a format handy for hazardlib
bins = [bdata.mags, bdata.dists,
bdata.lons, bdata.lats,
bdata.trts, None, probs]
# call disagg._arrange_data_in_bins
with arranging_mon:
key = (site.id, rlz.id, poe, imt, iml, trt_names)
matrix = disagg._arrange_data_in_bins(
bins, edges + (trt_names,))
result[key] = numpy.array(
[fn(matrix) for fn in disagg.pmf_map.values()])
collecting_mon.flush()
arranging_mon.flush()
return result
def compute_ruptures(sources, sitecol, info, monitor):
"""
Celery task for the stochastic event set calculator.
Samples logic trees and calls the stochastic event set calculator.
Once stochastic event sets are calculated, results will be saved to the
database. See :class:`openquake.engine.db.models.SESCollection`.
Optionally (specified in the job configuration using the
`ground_motion_fields` parameter), GMFs can be computed from each rupture
in each stochastic event set. GMFs are also saved to the database.
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param info:
a :class:`openquake.commonlib.source.CompositionInfo` instance
:param monitor:
monitor of the currently running job.
:returns:
a dictionary trt_model_id -> tot_ruptures
"""
# NB: all realizations in gsims correspond to the same source model
trt_model_id = sources[0].trt_model_id
trt_model = models.TrtModel.objects.get(pk=trt_model_id)
ses_coll = {sc.ordinal: sc for sc in models.SESCollection.objects.filter(
trt_model=trt_model)}
hc = models.oqparam(monitor.job_id)
tot_ruptures = 0
filter_sites_mon = monitor('filtering sites', measuremem=False)
generate_ruptures_mon = monitor('generating ruptures', measuremem=False)
filter_ruptures_mon = monitor('filtering ruptures', measuremem=False)
save_ruptures_mon = monitor('saving ruptures', measuremem=False)
# Compute and save stochastic event sets
for src in sources:
t0 = time.time()
with filter_sites_mon: # filtering sources
s_sites = src.filter_sites_by_distance_to_source(
hc.maximum_distance, sitecol)
if s_sites is None:
continue
with generate_ruptures_mon:
num_occ_by_rup = sample_ruptures(
src, hc.ses_per_logic_tree_path, info)
with filter_ruptures_mon:
pairs = list(
build_ses_ruptures(
src, num_occ_by_rup, s_sites, hc.maximum_distance, sitecol
))
# saving ses_ruptures
with save_ruptures_mon:
for rup, rups in pairs:
for col_idx in set(r.col_idx for r in rups):
prob_rup = models.ProbabilisticRupture.create(
rup, ses_coll[col_idx], rups[0].indices)
for r in rups:
if r.col_idx == col_idx:
models.SESRupture.objects.create(
rupture=prob_rup, ses_id=r.ses_idx,
tag=r.tag, seed=r.seed)
if num_occ_by_rup:
num_ruptures = len(num_occ_by_rup)
occ_ruptures = sum(num for rup in num_occ_by_rup
for num in num_occ_by_rup[rup].itervalues())
tot_ruptures += occ_ruptures
else:
num_ruptures = 0
occ_ruptures = 0
# save SourceInfo
source_inserter.add(
models.SourceInfo(trt_model_id=trt_model_id,
source_id=src.source_id,
source_class=src.__class__.__name__,
num_sites=len(s_sites),
num_ruptures=num_ruptures,
occ_ruptures=occ_ruptures,
uniq_ruptures=num_ruptures,
calc_time=time.time() - t0))
filter_sites_mon.flush()
generate_ruptures_mon.flush()
filter_ruptures_mon.flush()
save_ruptures_mon.flush()
source_inserter.flush()
return {trt_model_id: tot_ruptures}
def compute_disagg(sitecol, sources, trt_model_id, trt_num, curves_dict,
bin_edges, monitor):
# see https://bugs.launchpad.net/oq-engine/+bug/1279247 for an explanation
# of the algorithm used
"""
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param list sources:
list of hazardlib source objects
:param lt_model:
an instance of :class:`openquake.engine.db.models.LtSourceModel`
:param dict trt_num:
a dictionary Tectonic Region Type -> incremental number
:param curves_dict:
a dictionary with the hazard curves for sites, realizations and IMTs
:param bin_egdes:
a dictionary (lt_model_id, site_id) -> edges
:param monitor:
monitor of the currently running job
:returns:
a dictionary of probability arrays, with composite key
(site.id, rlz.id, poe, imt, iml, trt_names).
"""
hc = models.oqparam(monitor.job_id)
trt_model = models.TrtModel.objects.get(pk=trt_model_id)
gsims = trt_model.get_gsim_instances()
lt_model_id = trt_model.lt_model.id
rlzs = trt_model.get_rlzs_by_gsim()
trt_names = tuple(trt_model.lt_model.get_tectonic_region_types())
result = {} # site.id, rlz.id, poe, imt, iml, trt_names -> array
collecting_mon = monitor('collecting bins')
arranging_mon = monitor('arranging bins')
for site in sitecol:
# edges as wanted by disagg._arrange_data_in_bins
try:
edges = bin_edges[lt_model_id, site.id]
except KeyError:
# bin_edges for a given site are missing if the site is far away
continue
# generate source, rupture, sites once per site
source_ruptures = list(
gen_ruptures_for_site(site, sources, hc.maximum_distance, monitor))
if not source_ruptures:
continue
logs.LOG.info('Collecting bins from %d ruptures close to %s',
sum(len(rupts) for src, rupts in source_ruptures),
site.location)
with collecting_mon:
bdata = _collect_bins_data(trt_num, source_ruptures, site,
curves_dict[site.id], trt_model_id,
gsims, hc.imtls, hc.poes_disagg,
hc.truncation_level,
hc.num_epsilon_bins, monitor)
if not bdata.pnes: # no contributions for this site
continue
for poe in hc.poes_disagg:
for imt in hc.imtls:
for gsim in gsims:
for rlz in rlzs[gsim.__class__.__name__]:
# extract the probabilities of non-exceedance for the
# given realization, disaggregation PoE, and IMT
iml_pne_pairs = [
pne[rlz.id, poe, imt] for pne in bdata.pnes
]
iml = iml_pne_pairs[0][0]
probs = numpy.array([p for (i, p) in iml_pne_pairs],
float)
# bins in a format handy for hazardlib
bins = [
bdata.mags, bdata.dists, bdata.lons, bdata.lats,
bdata.trts, None, probs
]
# call disagg._arrange_data_in_bins
with arranging_mon:
key = (site.id, rlz.id, poe, imt, iml, trt_names)
matrix = disagg._arrange_data_in_bins(
bins, edges + (trt_names, ))
result[key] = numpy.array(
[fn(matrix) for fn in disagg.pmf_map.values()])
collecting_mon.flush()
arranging_mon.flush()
return result
def compute_hazard_curves(sources, sitecol, gsims_by_trt_id, monitor):
"""
This task computes R2 * I hazard curves (each one is a
numpy array of S * L floats) from the given source_ruptures
pairs.
:param sources:
a block of source objects
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param gsims_by_trt_id:
a dictionary trt_id -> gsim instances
:param monitor:
monitor of the currently running job
:returns:
a dictionary trt_model_id -> (curves_by_gsim, bounding_boxes)
"""
hc = models.oqparam(monitor.job_id)
trt_model_id = sources[0].trt_model_id
total_sites = len(sitecol)
sitemesh = sitecol.mesh
sorted_imts = sorted(hc.imtls)
sorted_imls = [hc.imtls[imt]
for imt in sorted_imts]
sorted_imts = map(from_string, sorted_imts)
gsims = gsims_by_trt_id[trt_model_id]
curves = [[numpy.ones([total_sites, len(ls)]) for ls in sorted_imls]
for gsim in gsims]
bbs = []
mon = monitor('getting ruptures', measuremem=False)
make_ctxt_mon = monitor('making contexts', measuremem=False)
calc_poes_mon = monitor('computing poes', measuremem=False)
num_sites = 0
# NB: rows are namedtuples with fields (source, rupture, rupture_sites)
for source, rows in itertools.groupby(
gen_ruptures(sources, sitecol, hc.maximum_distance, mon),
key=operator.attrgetter('source')):
t0 = time.time()
num_ruptures = 0
for _source, rupture, r_sites in rows:
num_sites = max(num_sites, len(r_sites))
num_ruptures += 1
if hc.poes_disagg: # doing disaggregation
jb_dists = rupture.surface.get_joyner_boore_distance(sitemesh)
closest_points = rupture.surface.get_closest_points(sitemesh)
for bb, dist, point in itertools.izip(
bbs, jb_dists, closest_points):
if dist < hc.maximum_distance:
# ruptures too far away are ignored
bb.update([dist], [point.longitude], [point.latitude])
# compute probabilities for all realizations
for gsim, curv in itertools.izip(gsims, curves):
for i, pnes in enumerate(_calc_pnes(
gsim, r_sites, rupture, sorted_imts, sorted_imls,
hc.truncation_level, make_ctxt_mon, calc_poes_mon)):
curv[i] *= pnes
inserter.add(
models.SourceInfo(trt_model_id=trt_model_id,
source_id=source.source_id,
source_class=source.__class__.__name__,
num_sites=num_sites,
num_ruptures=num_ruptures,
calc_time=time.time() - t0))
make_ctxt_mon.flush()
calc_poes_mon.flush()
inserter.flush()
# the 0 here is a shortcut for filtered sources giving no contribution;
# this is essential for performance, we want to avoid returning
# big arrays of zeros (MS)
curves_by_gsim = [
(gsim.__class__.__name__,
[0 if general.all_equal(c, 1) else 1. - c for c in curv])
for gsim, curv in zip(gsims, curves)]
return {trt_model_id: (curves_by_gsim, bbs)}
def compute_ruptures(job_id, sources, sitecol):
"""
Celery task for the stochastic event set calculator.
Samples logic trees and calls the stochastic event set calculator.
Once stochastic event sets are calculated, results will be saved to the
database. See :class:`openquake.engine.db.models.SESCollection`.
Optionally (specified in the job configuration using the
`ground_motion_fields` parameter), GMFs can be computed from each rupture
in each stochastic event set. GMFs are also saved to the database.
:param int job_id:
ID of the currently running job.
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:returns:
a dictionary trt_model_id -> tot_ruptures
"""
# NB: all realizations in gsims correspond to the same source model
trt_model_id = sources[0].trt_model_id
trt_model = models.TrtModel.objects.get(pk=trt_model_id)
ses_coll = models.SESCollection.objects.get(trt_model=trt_model)
hc = models.oqparam(job_id)
all_ses = range(1, hc.ses_per_logic_tree_path + 1)
tot_ruptures = 0
filter_sites_mon = LightMonitor(
'filtering sites', job_id, compute_ruptures)
generate_ruptures_mon = LightMonitor(
'generating ruptures', job_id, compute_ruptures)
filter_ruptures_mon = LightMonitor(
'filtering ruptures', job_id, compute_ruptures)
save_ruptures_mon = LightMonitor(
'saving ruptures', job_id, compute_ruptures)
# Compute and save stochastic event sets
rnd = random.Random()
for src in sources:
t0 = time.time()
rnd.seed(src.seed)
with filter_sites_mon: # filtering sources
s_sites = src.filter_sites_by_distance_to_source(
hc.maximum_distance, sitecol)
if s_sites is None:
continue
# the dictionary `ses_num_occ` contains [(ses, num_occurrences)]
# for each occurring rupture for each ses in the ses collection
ses_num_occ = collections.defaultdict(list)
with generate_ruptures_mon: # generating ruptures for the given source
for rup_no, rup in enumerate(src.iter_ruptures(), 1):
rup.rup_no = rup_no
for ses_idx in all_ses:
numpy.random.seed(rnd.randint(0, models.MAX_SINT_32))
num_occurrences = rup.sample_number_of_occurrences()
if num_occurrences:
ses_num_occ[rup].append((ses_idx, num_occurrences))
# 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_number_of_occurrences() *before* the filtering
for rup in sorted(ses_num_occ, key=operator.attrgetter('rup_no')):
with filter_ruptures_mon: # filtering ruptures
r_sites = filters.filter_sites_by_distance_to_rupture(
rup, hc.maximum_distance, s_sites
) if hc.maximum_distance else s_sites
if r_sites is None:
# ignore ruptures which are far away
del ses_num_occ[rup] # save memory
continue
# saving ses_ruptures
with save_ruptures_mon:
# using a django transaction make the saving faster
with transaction.commit_on_success(using='job_init'):
indices = r_sites.indices if len(r_sites) < len(sitecol) \
else None # None means that nothing was filtered
prob_rup = models.ProbabilisticRupture.create(
rup, ses_coll, indices)
for ses_idx, num_occurrences in ses_num_occ[rup]:
for occ_no in range(1, num_occurrences + 1):
rup_seed = rnd.randint(0, models.MAX_SINT_32)
models.SESRupture.create(
prob_rup, ses_idx, src.source_id,
rup.rup_no, occ_no, rup_seed)
if ses_num_occ:
num_ruptures = len(ses_num_occ)
occ_ruptures = sum(num for rup in ses_num_occ
for ses, num in ses_num_occ[rup])
tot_ruptures += occ_ruptures
else:
num_ruptures = rup_no
occ_ruptures = 0
# save SourceInfo
source_inserter.add(
models.SourceInfo(trt_model_id=trt_model_id,
source_id=src.source_id,
source_class=src.__class__.__name__,
num_sites=len(s_sites),
num_ruptures=rup_no,
occ_ruptures=occ_ruptures,
uniq_ruptures=num_ruptures,
calc_time=time.time() - t0))
filter_sites_mon.flush()
generate_ruptures_mon.flush()
filter_ruptures_mon.flush()
save_ruptures_mon.flush()
source_inserter.flush()
return {trt_model_id: tot_ruptures}
def compute_hazard_curves(sources, sitecol, info, monitor):
"""
This task computes R2 * I hazard curves (each one is a
numpy array of S * L floats) from the given source_ruptures
pairs.
:param sources:
a block of source objects
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param info:
a :class:`openquake.commonlib.source.CompositionInfo` instance
:param monitor:
monitor of the currently running job
:returns:
a dictionary trt_model_id -> (curves_by_gsim, bounding_boxes)
"""
hc = models.oqparam(monitor.job_id)
trt_model_id = sources[0].trt_model_id
total_sites = len(sitecol)
sitemesh = sitecol.mesh
sorted_imts = sorted(hc.imtls)
sorted_imls = [hc.imtls[imt] for imt in sorted_imts]
sorted_imts = map(from_string, sorted_imts)
trt_model = models.TrtModel.objects.get(pk=trt_model_id)
gsims = trt_model.get_gsim_instances()
curves = [[numpy.ones([total_sites, len(ls)]) for ls in sorted_imls]
for gsim in gsims]
if hc.poes_disagg: # doing disaggregation
lt_model_id = trt_model.lt_model.id
bbs = [BoundingBox(lt_model_id, site_id) for site_id in sitecol.sids]
else:
bbs = []
mon = monitor('getting ruptures', measuremem=False)
make_ctxt_mon = monitor('making contexts', measuremem=False)
calc_poes_mon = monitor('computing poes', measuremem=False)
num_sites = 0
# NB: rows are namedtuples with fields (source, rupture, rupture_sites)
for source, rows in itertools.groupby(gen_ruptures(sources, sitecol,
hc.maximum_distance,
mon),
key=operator.attrgetter('source')):
t0 = time.time()
num_ruptures = 0
for _source, rupture, r_sites in rows:
num_sites = max(num_sites, len(r_sites))
num_ruptures += 1
if hc.poes_disagg: # doing disaggregation
jb_dists = rupture.surface.get_joyner_boore_distance(sitemesh)
closest_points = rupture.surface.get_closest_points(sitemesh)
for bb, dist, point in itertools.izip(bbs, jb_dists,
closest_points):
if dist < hc.maximum_distance:
# ruptures too far away are ignored
bb.update([dist], [point.longitude], [point.latitude])
# compute probabilities for all realizations
for gsim, curv in itertools.izip(gsims, curves):
for i, pnes in enumerate(
_calc_pnes(gsim, r_sites, rupture, sorted_imts,
sorted_imls, hc.truncation_level,
make_ctxt_mon, calc_poes_mon)):
curv[i] *= pnes
inserter.add(
models.SourceInfo(trt_model_id=trt_model_id,
source_id=source.source_id,
source_class=source.__class__.__name__,
num_sites=num_sites,
num_ruptures=num_ruptures,
calc_time=time.time() - t0))
make_ctxt_mon.flush()
calc_poes_mon.flush()
inserter.flush()
# the 0 here is a shortcut for filtered sources giving no contribution;
# this is essential for performance, we want to avoid returning
# big arrays of zeros (MS)
curves_by_gsim = [
(gsim.__class__.__name__,
[0 if general.all_equal(c, 1) else 1. - c for c in curv])
for gsim, curv in zip(gsims, curves)
]
return {trt_model_id: (curves_by_gsim, bbs)}
