def _reject_all(self):
while self._msg_buffer:
msg = self._msg_buffer.pop()
try:
msg.reject()
except:
log.critical('Failed to reject message')
def _ack_all(self):
while self._msg_buffer:
msg = self._msg_buffer.pop()
try:
msg.ack()
except:
log.critical('Failed to ack message')
def _reject_all(self):
while self._msg_buffer:
msg = self._msg_buffer.pop()
try:
msg.reject()
except:
log.critical('Failed to reject message')
def _ack_all(self):
while self._msg_buffer:
msg = self._msg_buffer.pop()
try:
msg.ack()
except:
log.critical('Failed to ack message')
def get_coverage_doctor(self, dataset_id, data_product_id=None):
# Get the associated objects required for rebuilding
dset_obj = self.get_dataset_object(dataset_id)
if data_product_id is None:
# Go find the first data product associated with dataset_id
data_product_id, _ = self.resource_registry.find_subjects(object=dataset_id, predicate=PRED.hasDataset, id_only=True)
data_product_id = data_product_id[0] if len(data_product_id) > 0 else None
if data_product_id is None:
raise ValueError('Cannot find any Data Products associated with dataset_id \'{0}\''.format(dataset_id))
dprod_obj = self.get_data_product_object(data_product_id)
# Get the path to the editible coverage - also ensures ingestion is paused
cpth = None
try:
with self.get_editable_coverage(dataset_id) as cov:
cpth = cov.persistence_dir
except IOError, ex:
fs = 'Unable to open reference coverage: \''
accessibility_errors = [
'unable to create file (File accessability: Unable to open file)',
'unable to open file (File accessibilty: Unable to open file)',
'unable to open file (File accessability: Unable to open file)']
if fs in ex.message: # The view coverage couldn't load it's underlying reference coverage
cpth = ex.message[len(fs):-1]
for err in accessibility_errors:
if err in ex.message:
cpth = self.get_coverage_path(dataset_id)
self.pause_ingestion(self.get_stream_id(dataset_id))
break
else:
log.critical("Unmatched error: %s", ex.message)
raise
def run_qc(self, data_product, reference_designator, parameter, qc_mapping):
'''
Determines which algorithm the parameter should run, then evaluates the QC
'''
# We key off of the OOI Short Name
# DATAPRD_ALGRTHM_QC
dp_ident, alg, qc = parameter.ooi_short_name.split('_')
if dp_ident not in qc_mapping:
return # No input!
input_name = qc_mapping[dp_ident]
try:
doc = self.container.object_store.read_doc(reference_designator)
except NotFound:
return # NO QC lookups found
if dp_ident not in doc:
log.critical("Data product %s not in doc", dp_ident)
return # No data product of this listing in the RD's entry
# Lookup table has the rows for the QC inputs
lookup_table = doc[dp_ident]
# An instance of the coverage is loaded if we need to run an algorithm
dataset_id = self.get_dataset(data_product)
coverage = self.get_coverage(dataset_id)
if not coverage.num_timesteps: # No data = no qc
coverage.close()
return
try:
# Get the lookup table info then run
if alg.lower() == 'glblrng':
row = self.recent_row(lookup_table['global_range'])
min_value = row['min_value']
max_value = row['max_value']
self.process_glblrng(coverage, parameter, input_name, min_value, max_value)
elif alg.lower() == 'stuckvl':
row = self.recent_row(lookup_table['stuck_value'])
resolution = row['resolution']
N = row['consecutive_values']
self.process_stuck_value(coverage, parameter,input_name, resolution, N)
elif alg.lower() == 'trndtst':
row = self.recent_row(lookup_table['trend_test'])
ord_n = row['polynomial_order']
nstd = row['standard_deviation']
self.process_trend_test(coverage, parameter, input_name, ord_n, nstd)
elif alg.lower() == 'spketst':
row = self.recent_row(lookup_table['spike_test'])
acc = row['accuracy']
N = row['range_multiplier']
L = row['window_length']
self.process_spike_test(coverage, parameter, input_name, acc, N, L)
elif alg.lower() == "gradtst":
row = self.recent_row(lookup_table["gradient_test"])
ddatdx = row["ddatdx"]
mindx = row["mindx"]
startdat = row["startdat"]
if isinstance(startdat, basestring) and not startdat:
startdat = np.nan
if isinstance(mindx, basestring) and not mindx:
mindx = np.nan
toldat = row["toldat"]
self.process_gradient_test(coverage, parameter, input_name, ddatdx, mindx, startdat, toldat)
elif alg.lower() == 'loclrng':
pass
except KeyError: # No lookup table
self.set_error(coverage, parameter)
finally:
coverage.close()
def run_qc(self, data_product, reference_designator, parameter, qc_mapping):
'''
Determines which algorithm the parameter should run, then evaluates the QC
'''
# We key off of the OOI Short Name
# DATAPRD_ALGRTHM_QC
log.error("Running QC %s (%s)", data_product.name, parameter.name)
dp_ident, alg, qc = parameter.ooi_short_name.split('_')
log.error("Identifier: %s", dp_ident)
log.error("Test: %s", alg)
if dp_ident not in qc_mapping:
return # No input!
input_name = qc_mapping[dp_ident]
try:
doc = self.container.object_store.read_doc(reference_designator)
except NotFound:
return # NO QC lookups found
if dp_ident not in doc[reference_designator]:
log.critical("Data product %s not in doc", dp_ident)
return # No data product of this listing in the RD's entry
# Lookup table has the rows for the QC inputs
lookup_table = doc[reference_designator][dp_ident]
log.error("lookup table found")
# An instance of the coverage is loaded if we need to run an algorithm
dataset_id = self.get_dataset(data_product)
coverage = self.get_coverage(dataset_id)
if not coverage.num_timesteps: # No data = no qc
coverage.close()
return
try:
# Get the lookup table info then run
if alg.lower() == 'glblrng':
row = self.recent_row(lookup_table['global_range'])
min_value = row['min_value']
max_value = row['max_value']
self.process_glblrng(coverage, parameter, input_name, min_value, max_value)
elif alg.lower() == 'stuckvl':
log.error("Running Stuck Value")
row = self.recent_row(lookup_table['stuck_value'])
resolution = row['resolution']
N = row['consecutive_values']
self.process_stuck_value(coverage, parameter,input_name, resolution, N)
elif alg.lower() == 'trndtst':
log.error("Running Trend Test")
row = self.recent_row(lookup_table['trend_test'])
ord_n = row['polynomial_order']
nstd = row['standard_deviation']
self.process_trend_test(coverage, parameter, input_name, ord_n, nstd)
elif alg.lower() == 'spketst':
log.error("Runnign Spike Test")
row = self.recent_row(lookup_table['spike_test'])
acc = row['accuracy']
N = row['range_multiplier']
L = row['window_length']
self.process_spike_test(coverage, parameter, input_name, acc, N, L)
finally:
coverage.close()
def run_qc(self, data_product, reference_designator, parameter, qc_mapping,
parameters):
'''
Determines which algorithm the parameter should run, then evaluates the QC
data_product - Data Product Resource
reference_designator - reference designator string
parameter - parameter context resource
qc_mapping - a dictionary of { data_product_name : parameter_name }
'''
# We key off of the OOI Short Name
# DATAPRD_ALGRTHM_QC
dp_ident, alg, qc = parameter.ooi_short_name.split('_')
if dp_ident not in qc_mapping:
return # No input!
input_name = self.calibrated_candidates(data_product, parameter,
qc_mapping, parameters)
try:
doc = self.container.object_store.read_doc(reference_designator)
except NotFound:
return # NO QC lookups found
if dp_ident not in doc:
log.critical("Data product %s not in doc", dp_ident)
return # No data product of this listing in the RD's entry
# Lookup table has the rows for the QC inputs
lookup_table = doc[dp_ident]
# An instance of the coverage is loaded if we need to run an algorithm
dataset_id = self.get_dataset(data_product)
coverage = self.get_coverage(dataset_id)
if not coverage.num_timesteps: # No data = no qc
coverage.close()
return
try:
# Get the lookup table info then run
if alg.lower() == 'glblrng':
row = self.recent_row(lookup_table['global_range'])
min_value = row['min_value']
max_value = row['max_value']
self.process_glblrng(coverage, parameter, input_name,
min_value, max_value)
elif alg.lower() == 'stuckvl':
row = self.recent_row(lookup_table['stuck_value'])
resolution = row['resolution']
N = row['consecutive_values']
self.process_stuck_value(coverage, parameter, input_name,
resolution, N)
elif alg.lower() == 'trndtst':
row = self.recent_row(lookup_table['trend_test'])
ord_n = row['polynomial_order']
nstd = row['standard_deviation']
self.process_trend_test(coverage, parameter, input_name, ord_n,
nstd)
elif alg.lower() == 'spketst':
row = self.recent_row(lookup_table['spike_test'])
acc = row['accuracy']
N = row['range_multiplier']
L = row['window_length']
self.process_spike_test(coverage, parameter, input_name, acc,
N, L)
elif alg.lower() == "gradtst":
row = self.recent_row(lookup_table["gradient_test"])
ddatdx = row["ddatdx"]
mindx = row["mindx"]
startdat = row["startdat"]
if isinstance(startdat, basestring) and not startdat:
startdat = np.nan
if isinstance(mindx, basestring) and not mindx:
mindx = np.nan
toldat = row["toldat"]
self.process_gradient_test(coverage, parameter, input_name,
ddatdx, mindx, startdat, toldat)
elif alg.lower() == 'loclrng':
row = self.recent_row(lookup_table["local_range"])
table = row['table']
dims = []
datlimz = []
for key in table.iterkeys():
# Skip the datlims
if 'datlim' in key:
continue
dims.append(key)
datlimz.append(table[key])
datlimz = np.column_stack(datlimz)
datlim = np.column_stack([table['datlim1'], table['datlim2']])
self.process_local_range_test(coverage, parameter, input_name,
datlim, datlimz, dims)
except KeyError: # No lookup table
self.set_error(coverage, parameter)
finally:
coverage.close()
