def pre_execute(self):
"""
Inherited from core.EventBasedRiskCalculator.pre_execute.
Enforces no correlation, both on GMFs and assets.
"""
correl_model = models.get_correl_model(self.job)
assert correl_model is None, correl_model
assert not self.rc.asset_correlation, self.rc.asset_correlation
core.EventBasedRiskCalculator.pre_execute(self)
def compute_gmfs_and_curves(job_id, ses_ruptures, sitecol):
"""
:param int job_id:
ID of the currently running job
:param ses_ruptures:
a list of blocks of SESRuptures with homogeneous TrtModel
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:returns:
a dictionary trt_model_id -> (curves_by_gsim, bounding_boxes)
where the list of bounding boxes is empty
"""
job = models.OqJob.objects.get(pk=job_id)
hc = job.get_oqparam()
imts = map(from_string, sorted(hc.intensity_measure_types_and_levels))
result = {} # trt_model_id -> (curves_by_gsim, [])
# NB: by construction each block is a non-empty list with
# ruptures of homogeneous trt_model
trt_model = ses_ruptures[0].rupture.ses_collection.trt_model
rlzs_by_gsim = trt_model.get_rlzs_by_gsim()
gsims = [logictree.GSIM[gsim]() for gsim in rlzs_by_gsim]
calc = GmfCalculator(
sorted(imts), sorted(gsims), trt_model.id,
getattr(hc, 'truncation_level', None), models.get_correl_model(job))
with EnginePerformanceMonitor(
'computing gmfs', job_id, compute_gmfs_and_curves):
for rupture, group in itertools.groupby(
ses_ruptures, operator.attrgetter('rupture')):
r_sites = sitecol if rupture.site_indices is None \
else FilteredSiteCollection(rupture.site_indices, sitecol)
calc.calc_gmfs(
r_sites, rupture, [(r.id, r.seed) for r in group])
if getattr(hc, 'hazard_curves_from_gmfs', None):
with EnginePerformanceMonitor(
'hazard curves from gmfs',
job_id, compute_gmfs_and_curves):
result[trt_model.id] = (calc.to_haz_curves(
sitecol.sids, hc.intensity_measure_types_and_levels,
hc.investigation_time, hc.ses_per_logic_tree_path), [])
else:
result[trt_model.id] = ([], [])
if hc.ground_motion_fields:
with EnginePerformanceMonitor(
'saving gmfs', job_id, compute_gmfs_and_curves):
calc.save_gmfs(rlzs_by_gsim)
return result
def compute_gmfs_and_curves(ses_ruptures, sitecol, rlzs_assoc, monitor):
"""
:param ses_ruptures:
a list of blocks of SESRuptures with homogeneous TrtModel
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param rlzs_assoc:
a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param monitor:
monitor of the currently running job
:returns:
a dictionary trt_model_id -> (curves_by_gsim, bounding_boxes)
where the list of bounding boxes is empty
"""
job = models.OqJob.objects.get(pk=monitor.job_id)
hc = job.get_oqparam()
imts = hc.imtls
result = {} # trt_model_id -> (curves_by_gsim, [])
# NB: by construction each block is a non-empty list with
# ruptures of homogeneous SESCollection
ses_coll = ses_ruptures[0].rupture.ses_collection
trt_model = ses_coll.trt_model
gsims = rlzs_assoc.get_gsims_by_trt_id()[trt_model.id]
calc = GmfCalculator(
sorted(imts), sorted(gsims), ses_coll,
hc.truncation_level, models.get_correl_model(job))
with monitor('computing gmfs', autoflush=True):
for rupture, group in itertools.groupby(
ses_ruptures, operator.attrgetter('rupture')):
r_sites = sitecol if rupture.site_indices is None \
else FilteredSiteCollection(rupture.site_indices, sitecol)
calc.calc_gmfs(
r_sites, rupture, [(r.id, r.seed) for r in group])
if hc.hazard_curves_from_gmfs:
duration = hc.investigation_time * hc.ses_per_logic_tree_path * (
hc.number_of_logic_tree_samples or 1)
with monitor('hazard curves from gmfs', autoflush=True):
result[trt_model.id] = (calc.to_haz_curves(
sitecol.sids, hc.imtls, hc.investigation_time, duration), [])
else:
result[trt_model.id] = ([], [])
if hc.ground_motion_fields:
with monitor('saving gmfs', autoflush=True):
calc.save_gmfs(rlzs_assoc)
return result
def gmfs(job_id, ses_ruptures, sitecol, gmf_id):
"""
:param int job_id: the current job ID
:param ses_ruptures: a set of `SESRupture` instances
:param sitecol: a `SiteCollection` instance
:param int gmf_id: the ID of a `Gmf` instance
"""
job = models.OqJob.objects.get(pk=job_id)
hc = job.hazard_calculation
# distinct is here to make sure that IMTs such as
# SA(0.8) and SA(0.80) are considered the same
imts = distinct(from_string(x) for x in sorted(hc.intensity_measure_types))
gsim = AVAILABLE_GSIMS[hc.gsim]() # instantiate the GSIM class
correlation_model = models.get_correl_model(job)
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], hc.truncation_level,
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)