def test_completed_failure(self):
self.job.status = 'failed'
self.job.save()
try:
tasks.get_running_job(self.job.id)
except tasks.JobCompletedError as exc:
self.assertEqual(exc.message, self.job.id)
else:
self.fail("JobCompletedError wasn't raised")
def test_completed_failure(self):
self.job.status = 'failed'
self.job.save()
try:
tasks.get_running_job(self.job.id)
except tasks.JobCompletedError as exc:
self.assertEqual(exc.message, self.job.id)
else:
self.fail("JobCompletedError wasn't raised")
def compute_mean_curves(job_id, sites, realizations):
"""Compute the mean hazard curve for each site given."""
# We don't actually need the JobContext returned by this function
# (yet) but this does check if the calculation is still in progress.
utils_tasks.get_running_job(job_id)
HAZARD_LOG.info("Computing MEAN curves for %s sites (job_id %s)"
% (len(sites), job_id))
return general.compute_mean_hazard_curves(job_id, sites, realizations)
def compute_mean_curves(job_id, sites, realizations):
"""Compute the mean hazard curve for each site given."""
# We don't actually need the JobContext returned by this function
# (yet) but this does check if the calculation is still in progress.
utils_tasks.get_running_job(job_id)
HAZARD_LOG.info("Computing MEAN curves for %s sites (job_id %s)" %
(len(sites), job_id))
return general.compute_mean_hazard_curves(job_id, sites, realizations)
def compute_mgm_intensity(job_id, block_id, site_id):
"""Compute mean ground intensity for a specific site."""
# We don't actually need the JobContext returned by this function
# (yet) but this does check if the calculation is still in progress.
utils_tasks.get_running_job(job_id)
kvs_client = kvs.get_client()
mgm_key = kvs.tokens.mgm_key(job_id, block_id, site_id)
mgm = kvs_client.get(mgm_key)
return json.JSONDecoder().decode(mgm)
def compute_mgm_intensity(job_id, block_id, site_id):
"""Compute mean ground intensity for a specific site."""
# We don't actually need the JobContext returned by this function
# (yet) but this does check if the calculation is still in progress.
utils_tasks.get_running_job(job_id)
kvs_client = kvs.get_client()
mgm_key = kvs.tokens.mgm_key(job_id, block_id, site_id)
mgm = kvs_client.get(mgm_key)
return json.JSONDecoder().decode(mgm)
def compute_disagg_matrix_task(job_id, site, realization, poe, result_dir):
""" Compute a complete 5D Disaggregation matrix. This task leans heavily
on the DisaggregationCalculator (in the OpenQuake Java lib) to handle this
computation.
:param job_id: id of the calculation record in the KVS
:type job_id: `str`
:param site: a single site of interest
:type site: :class:`openquake.shapes.Site` instance`
:param int realization: logic tree sample iteration number
:param poe: Probability of Exceedence
:type poe: `float`
:param result_dir: location for the Java code to write the matrix in an
HDF5 file (in a distributed environment, this should be the path of a
mounted NFS)
:returns: 2-tuple of (ground_motion_value, path_to_h5_matrix_file)
"""
job_ctxt = get_running_job(job_id)
log_msg = (
"Computing full disaggregation matrix for job_id=%s, site=%s, "
"realization=%s, PoE=%s. Matrix results will be serialized to `%s`.")
log_msg %= (job_ctxt.job_id, site, realization, poe, result_dir)
LOG.info(log_msg)
return compute_disagg_matrix(job_ctxt, site, poe, result_dir)
def compute_disagg_matrix_task(job_id, realization, poe, result_dir, site):
""" Compute a complete 5D Disaggregation matrix. This task leans heavily
on the DisaggregationCalculator (in the OpenQuake Java lib) to handle this
computation.
:param job_id: id of the calculation record in the KVS
:type job_id: `str`
:param int realization: logic tree sample iteration number
:param poe: Probability of Exceedence
:type poe: `float`
:param result_dir: location for the Java code to write the matrix in an
HDF5 file (in a distributed environment, this should be the path of a
mounted NFS)
:param site: a single site of interest
:type site: :class:`openquake.shapes.Site` instance`
:returns: 2-tuple of (ground_motion_value, path_to_h5_matrix_file)
"""
job_ctxt = get_running_job(job_id)
log_msg = (
"Computing full disaggregation matrix for job_id=%s, site=%s, "
"realization=%s, PoE=%s. Matrix results will be serialized to `%s`.")
log_msg %= (job_ctxt.job_id, site, realization, poe, result_dir)
LOG.info(log_msg)
return compute_disagg_matrix(job_ctxt, site, poe, result_dir)
def compute_uhs_task(job_id, realization, site):
"""Compute Uniform Hazard Spectra for a given site of interest and 1 or
more Probability of Exceedance values. The bulk of the computation will
be done by utilizing the `UHSCalculator` class in the Java code.
UHS results will be written directly to the database.
:param int job_id:
ID of the job record in the DB/KVS.
:param realization:
Logic tree sample number (from 1 to N, where N is the
NUMBER_OF_LOGIC_TREE_SAMPLES param defined in the job config.
:param site:
The site of interest (a :class:`openquake.shapes.Site` object).
"""
job_ctxt = utils_tasks.get_running_job(job_id)
log_msg = (
"Computing UHS for job_id=%s, site=%s, realization=%s."
" UHS results will be serialized to the database.")
log_msg %= (job_ctxt.job_id, site, realization)
LOG.info(log_msg)
uhs_results = compute_uhs(job_ctxt, site)
write_uhs_spectrum_data(job_ctxt, realization, site, uhs_results)
def test_get_running_job(self):
self.job.status = 'pending'
self.job.save()
# No 'JobCompletedError' should be raised.
job_ctxt = tasks.get_running_job(self.job.id)
self.assertEqual(self.params, job_ctxt.params)
self.assertTrue(model_equals(
self.job_profile, job_ctxt.oq_job_profile,
ignore=('_owner_cache',)))
self.assertTrue(model_equals(
self.job, job_ctxt.oq_job,
ignore=('_owner_cache',)))
def test_get_running_job(self):
self.job.status = 'pending'
self.job.save()
# No 'JobCompletedError' should be raised.
job_ctxt = tasks.get_running_job(self.job.id)
self.assertEqual(self.params, job_ctxt.params)
self.assertTrue(
model_equals(self.job_profile,
job_ctxt.oq_job_profile,
ignore=('_owner_cache', )))
self.assertTrue(
model_equals(self.job, job_ctxt.oq_job, ignore=('_owner_cache', )))