def test_qc_attachment(self):
instrument_device = InstrumentDevice(name='whatever')
instrument_device_id,_ = self.rrclient.create(instrument_device)
self.addCleanup(self.rrclient.delete, instrument_device_id)
self.client.register_instrument(instrument_device_id)
self.addCleanup(self.client.unregister_instrument, instrument_device_id)
dp = DataProduct(name='instrument output')
dp_id,_ = self.rrclient.create(dp)
self.addCleanup(self.rrclient.delete, dp_id)
parser_id = self.make_grt_parser()
attachment = Attachment(name='qc ref', attachment_type=AttachmentType.REFERENCE,content=global_range_test_document, context=ReferenceAttachmentContext(parser_id=parser_id))
att_id = self.rrclient.create_attachment(dp_id, attachment)
self.addCleanup(self.rrclient.delete_attachment, att_id)
attachment2 = Attachment(name='qc ref2', attachment_type=AttachmentType.REFERENCE, content=global_range_test_document2, context=ReferenceAttachmentContext(parser_id=parser_id))
att2_id = self.rrclient.create_attachment(dp_id, attachment2)
self.addCleanup(self.rrclient.delete_attachment, att2_id)
self.client.assign_data_product(instrument_device_id, dp_id)
self.addCleanup(self.client.unassign_data_product, instrument_device_id, dp_id)
svm = StoredValueManager(self.container)
doc = svm.read_value('grt_CE01ISSM-MF005-01-CTDBPC999_TEMPWAT')
np.testing.assert_array_almost_equal(doc['grt_min_value'], -2.)
def fetch_lookup_values(self):
doc_keys = []
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if '$designator' in context.document_key and 'reference_designator' in self._stream_config:
document_key = document_key.replace(
'$designator',
self._stream_config['reference_designator'])
doc_keys.append(document_key)
lookup_docs = {}
if doc_keys:
svm = StoredValueManager(Container.instance)
doc_list = svm.read_value_mult(doc_keys)
lookup_docs = dict(zip(doc_keys, doc_list))
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if '$designator' in context.document_key and 'reference_designator' in self._stream_config:
document_key = document_key.replace(
'$designator',
self._stream_config['reference_designator'])
doc = lookup_docs[document_key]
if doc is None:
log.debug('Reference Document for %s not found',
document_key)
continue
if context.lookup_value in doc:
self[lv] = [doc[context.lookup_value]
] * self._shp[0] if self._shp else doc[
context.lookup_value]
def fetch_lookup_values(self):
doc_keys = []
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if "$designator" in context.document_key and "reference_designator" in self._stream_config:
document_key = document_key.replace("$designator", self._stream_config["reference_designator"])
doc_keys.append(document_key)
lookup_docs = {}
if doc_keys:
svm = StoredValueManager(Container.instance)
doc_list = svm.read_value_mult(doc_keys)
lookup_docs = dict(zip(doc_keys, doc_list))
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if "$designator" in context.document_key and "reference_designator" in self._stream_config:
document_key = document_key.replace("$designator", self._stream_config["reference_designator"])
doc = lookup_docs[document_key]
if doc is None:
log.debug("Reference Document for %s not found", document_key)
continue
if context.lookup_value in doc:
self[lv] = [doc[context.lookup_value]] * self._shp[0] if self._shp else doc[context.lookup_value]
class StoredValueTransform(TransformStreamListener):
'''
Receives granules from a stream and persists the latest value in the object store
Background:
Platforms publish geospatial information on a separate stream from the
instruments. Various data products require geospatial information about
the instrument to calculate the variables. This component persists the
latest value in a simple data storage container where complex data
containers can access it.
'''
def on_start(self):
TransformStreamListener.on_start(self)
self.document_key = self.CFG.get_safe('process.document_key')
self.stored_value_manager = StoredValueManager(self.container)
def recv_packet(self, msg, route, stream_id):
rdt = RecordDictionaryTool.load_from_granule(msg)
document = {}
for k,v in rdt.iteritems():
value_array = np.atleast_1d(v[:])
if 'f' in value_array.dtype.str:
document[k] = float(value_array[-1])
elif 'i' in value_array.dtype.str:
document[k] = int(value_array[-1])
self.stored_value_manager.stored_value_cas(self.document_key, document)
class StoredValueTransform(TransformStreamListener):
'''
Receives granules from a stream and persists the latest value in the object store
Background:
Platforms publish geospatial information on a separate stream from the
instruments. Various data products require geospatial information about
the instrument to calculate the variables. This component persists the
latest value in a simple data storage container where complex data
containers can access it.
'''
def on_start(self):
TransformStreamListener.on_start(self)
self.document_key = self.CFG.get_safe('process.document_key')
self.stored_value_manager = StoredValueManager(self.container)
def recv_packet(self, msg, route, stream_id):
rdt = RecordDictionaryTool.load_from_granule(msg)
document = {}
for k, v in rdt.iteritems():
value_array = np.atleast_1d(v[:])
if 'f' in value_array.dtype.str:
document[k] = float(value_array[-1])
elif 'i' in value_array.dtype.str:
document[k] = int(value_array[-1])
self.stored_value_manager.stored_value_cas(self.document_key, document)
def parse_document(cls, container, parser, document_path):
svm = StoredValueManager(container)
document = ''
with open(document_path,'r') as f:
document = f.read()
for k,v in parser(document):
svm.stored_value_cas(k,v)
return
def parse_document(cls, container, parser, document_path):
svm = StoredValueManager(container)
document = ''
with open(document_path, 'r') as f:
document = f.read()
for k, v in parser(document):
svm.stored_value_cas(k, v)
return
class TestParsers(IonIntegrationTestCase):
def setUp(self):
self._start_container()
self.svm = StoredValueManager(self.container)
@classmethod
def parse_document(cls, container, parser, document_path):
svm = StoredValueManager(container)
document = ''
with open(document_path,'r') as f:
document = f.read()
for k,v in parser(document):
svm.stored_value_cas(k,v)
return
def test_grt_parser(self):
self.parse_document(self.container, grt_parser, qc_paths['grt'])
ret_doc = self.svm.read_value('grt_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'CONDWAT'))
np.testing.assert_almost_equal(ret_doc['grt_min_value'], 0.)
np.testing.assert_almost_equal(ret_doc['grt_max_value'], 66000.)
def test_spike_parser(self):
self.parse_document(self.container, spike_parser, qc_paths['spike'])
ret_doc = self.svm.read_value('spike_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['acc'], 0.005)
np.testing.assert_almost_equal(ret_doc['spike_n'], 11.)
np.testing.assert_almost_equal(ret_doc['spike_l'], 15.)
self.assertRaises(NotFound, self.svm.read_value,'spike_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'CLOWN'))
def test_stuck_value_parser(self):
self.parse_document(self.container, stuck_value_test_parser, qc_paths['stuck'])
ret_doc = self.svm.read_value('svt_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['svt_resolution'], 1e-9)
np.testing.assert_almost_equal(ret_doc['svt_n'], 1000000)
def test_trend_value_parser(self):
self.parse_document(self.container, trend_parser, qc_paths['trend'])
ret_doc = self.svm.read_value('trend_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['time_interval'], 365.0)
np.testing.assert_almost_equal(ret_doc['polynomial_order'], 1.0)
np.testing.assert_almost_equal(ret_doc['standard_deviation'], 0.0)
def test_lrt_parser(self):
self.parse_document(self.container, lrt_parser, qc_paths['lrt'])
ret_doc = self.svm.read_value('lrt_%s_%s' %('GP02HYPM-SP001-04-CTDPF0999', 'PRACSAL'))
np.testing.assert_array_equal(ret_doc['datlim'][0], np.array([32.289, 32.927]))
self.assertEquals(ret_doc['dims'], ['pressure', 'month'])
def test_global_range_test_parser(self):
svm = StoredValueManager(self.container)
for key,doc in grt_parser(grt_sample_doc):
svm.stored_value_cas(key,doc)
self.addCleanup(svm.delete_stored_value,key)
doc = svm.read_value('grt_sbe37-abc123_TEMPWAT_UHHH')
self.assertEquals(doc['grt_min_value'], 0.)
self.assertEquals(doc['array'],'array 1')
doc = svm.read_value('grt_sbe37-abc123_PRESWAT_flagged')
self.assertEquals(doc['grt_max_value'], 689.47)
def fetch_lookup_values(self):
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
svm = StoredValueManager(Container.instance)
try:
doc = svm.read_value(context.document_key)
except NotFound:
continue
if context.lookup_value in doc:
self[lv] = doc[context.lookup_value]
def test_stuck_value_test(self):
svm = StoredValueManager(self.container)
for key, doc in trend_parser(trend_value_test_sample_doc):
svm.stored_value_cas(key, doc)
self.addCleanup(svm.delete_stored_value, key)
doc = svm.read_value("trend_ssxbt-ssn719_PRESSURE")
self.assertEquals(doc["time_interval"], 1.0)
self.assertEquals(doc["standard_deviation"], 0.0)
doc = svm.read_value("trend_ssxbt-ssn719_COND")
self.assertEquals(doc["time_interval"], 3.0)
self.assertEquals(doc["polynomial_order"], "third")
def test_gradient_test(self):
svm = StoredValueManager(self.container)
for key,doc in gradient_test_parser(gradient_test_sample_doc):
svm.stored_value_cas(key,doc)
self.addCleanup(svm.delete_stored_value,key)
doc = svm.read_value('grad_CHEL-C-32-12_PRESSURE_DENSITY')
self.assertEquals(doc['units_dat'], 'dbar')
self.assertEquals(doc['d_dat_dx'], 2.781)
doc = svm.read_value('grad_CHEW-BA-C-A12_PRESSURE_DENSITY')
self.assertEquals(doc['units_x'], 'kg m-3')
self.assertEquals(doc['tol_dat'], 12.)
def test_stuck_value_test(self):
svm = StoredValueManager(self.container)
for key,doc in stuck_value_test_parser(stuck_value_test_sample_doc):
svm.stored_value_cas(key,doc)
self.addCleanup(svm.delete_stored_value,key)
doc = svm.read_value('svt_ssxbt-ssn719_PRESSURE')
self.assertEquals(doc['svt_resolution'], 1.2)
self.assertEquals(doc['svt_n'], 10.)
doc = svm.read_value('svt_ssxbt-ssn719_COND')
self.assertEquals(doc['svt_resolution'], 0.2)
self.assertEquals(doc['units'], 'S m-1')
def fetch_lookup_values(self):
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if '$designator' in context.document_key and 'reference_designator' in self._stream_config:
document_key = document_key.replace('$designator',self._stream_config['reference_designator'])
svm = StoredValueManager(Container.instance)
try:
doc = svm.read_value(document_key)
except NotFound:
log.debug('Reference Document for %s not found', document_key)
continue
if context.lookup_value in doc:
self[lv] = [doc[context.lookup_value]] * self._shp[0] if self._shp else doc[context.lookup_value]
class TestParsers(IonIntegrationTestCase):
def setUp(self):
self._start_container()
self.svm = StoredValueManager(self.container)
@classmethod
def parse_document(cls, container, parser, document_path):
svm = StoredValueManager(container)
document = ''
with open(document_path,'r') as f:
document = f.read()
for k,v in parser(document):
svm.stored_value_cas(k,v)
return
def test_grt_parser(self):
self.parse_document(self.container, grt_parser, qc_paths['grt'])
ret_doc = self.svm.read_value('grt_%s_%s' % ('GA01SUMO-RI003-03-CTDMOQ999', 'CONDWAT'))
np.testing.assert_almost_equal(ret_doc['grt_min_value'], 0.)
np.testing.assert_almost_equal(ret_doc['grt_max_value'], 66000.)
def test_spike_parser(self):
self.parse_document(self.container, spike_parser, qc_paths['spike'])
ret_doc = self.svm.read_value('spike_%s_%s' % ('GA01SUMO-RI003-03-CTDMOQ999', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['acc'], 0.005)
np.testing.assert_almost_equal(ret_doc['spike_n'], 11.)
np.testing.assert_almost_equal(ret_doc['spike_l'], 15.)
self.assertRaises(NotFound, self.svm.read_value,'spike_%s_%s' % ('GA01SUMO-RI003-03-CTDMOQ999', 'DENSITY'))
def test_stuck_value_parser(self):
self.parse_document(self.container, stuck_value_test_parser, qc_paths['stuck'])
ret_doc = self.svm.read_value('svt_%s_%s' % ('GA01SUMO-RI003-03-CTDMOQ999', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['svt_resolution'], 0.0001)
np.testing.assert_almost_equal(ret_doc['svt_n'], 20.)
def test_trend_value_parser(self):
self.parse_document(self.container, trend_parser, qc_paths['trend'])
ret_doc = self.svm.read_value('trend_%s_%s' % ('RS01SBVM-LJ01A-05-HPIESA101', 'IESPRES'))
np.testing.assert_almost_equal(ret_doc['time_interval'], 90.)
np.testing.assert_almost_equal(ret_doc['polynomial_order'], 1.0)
np.testing.assert_almost_equal(ret_doc['standard_deviation'], 5.0)
def on_start(self): #pragma no cover
#--------------------------------------------------------------------------------
# Explicit on_start
#--------------------------------------------------------------------------------
# Skip TransformStreamListener and go to StreamProcess to avoid the subscriber being created
# We want explicit management of the thread and subscriber object for ingestion
TransformStreamProcess.on_start(self)
self.queue_name = self.CFG.get_safe('process.queue_name',self.id)
self.subscriber = StreamSubscriber(process=self, exchange_name=self.queue_name, callback=self.receive_callback)
self.thread_lock = RLock()
#--------------------------------------------------------------------------------
# Normal on_start after this point
#--------------------------------------------------------------------------------
BaseIngestionWorker.on_start(self)
self._rpc_server = self.container.proc_manager._create_listening_endpoint(from_name=self.id, process=self)
self.add_endpoint(self._rpc_server)
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.input_product = self.CFG.get_safe('process.input_product','')
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent, callback=self._add_lookups, auto_delete=True)
self.add_endpoint(self.lookup_monitor)
self.connection_id = ''
self.connection_index = None
self.start_listener()
def on_start(self):
TransformDataProcess.on_start(self)
self.pubsub_management = PubsubManagementServiceProcessClient(
process=self)
self.stored_values = StoredValueManager(self.container)
self.input_data_product_ids = self.CFG.get_safe(
'process.input_products', [])
self.output_data_product_ids = self.CFG.get_safe(
'process.output_products', [])
self.lookup_docs = self.CFG.get_safe('process.lookup_docs', [])
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(
event_type=OT.ExternalReferencesUpdatedEvent,
callback=self._add_lookups,
auto_delete=True)
self.lookup_monitor.start()
def test_rdt_lookup(self):
rdt = self.create_lookup_rdt()
self.assertTrue('offset_a' in rdt.lookup_values())
self.assertFalse('offset_b' in rdt.lookup_values())
rdt['time'] = [0]
rdt['temp'] = [10.0]
rdt['offset_a'] = [2.0]
self.assertEquals(rdt['offset_b'], None)
self.assertEquals(rdt.lookup_values(), ['offset_a'])
np.testing.assert_array_almost_equal(rdt['calibrated'], np.array([12.0]))
svm = StoredValueManager(self.container)
svm.stored_value_cas('coefficient_document', {'offset_b':2.0})
rdt.fetch_lookup_values()
np.testing.assert_array_equal(rdt['offset_b'], np.array([2.0]))
np.testing.assert_array_equal(rdt['calibrated_b'], np.array([14.0]))
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = self.ph.create_simple_qc_pdict()
self.stream_def_id = self.pubsub_management.create_stream_definition('global range', parameter_dictionary_id=pdict_id, stream_configuration={'reference_designator':'QCTEST'})
self.addCleanup(self.pubsub_management.delete_stream_definition, self.stream_def_id)
self.rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
self.svm = StoredValueManager(self.container)
def on_start(self):
TransformDataProcess.on_start(self)
self.pubsub_management = PubsubManagementServiceProcessClient(process=self)
self.stored_values = StoredValueManager(self.container)
self.input_data_product_ids = self.CFG.get_safe('process.input_products', [])
self.output_data_product_ids = self.CFG.get_safe('process.output_products', [])
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent,callback=self._add_lookups, auto_delete=True)
self.lookup_monitor.start()
def fetch_lookup_values(self):
for lv in self._lookup_values():
context = self.context(lv)
if context.document_key:
document_key = context.document_key
if '$designator' in context.document_key and 'reference_designator' in self._stream_config:
document_key = document_key.replace(
'$designator',
self._stream_config['reference_designator'])
svm = StoredValueManager(Container.instance)
try:
doc = svm.read_value(document_key)
except NotFound:
log.debug('Reference Document for %s not found',
document_key)
continue
if context.lookup_value in doc:
self[lv] = [doc[context.lookup_value]
] * self._shp[0] if self._shp else doc[
context.lookup_value]
def test_global_range_lookup(self):
reference_designator = "CE01ISSM-MF005-01-CTDBPC999"
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_simple_qc_pdict()
svm = StoredValueManager(self.container)
doc_key = 'grt_%s_TEMPWAT' % reference_designator
svm.stored_value_cas(doc_key, {'grt_min_value':-2, 'grt_max_value':40})
stream_def_id = self.pubsub_management.create_stream_definition('qc parsed', parameter_dictionary_id=pdict_id, stream_configuration={'reference_designator':reference_designator})
self.addCleanup(self.pubsub_management.delete_stream_definition,stream_def_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [20]
rdt.fetch_lookup_values()
min_field = [i for i in rdt.fields if 'grt_min_value' in i][0]
max_field = [i for i in rdt.fields if 'grt_max_value' in i][0]
np.testing.assert_array_almost_equal(rdt[min_field], [-2.])
np.testing.assert_array_almost_equal(rdt[max_field], [40.])
np.testing.assert_array_almost_equal(rdt['tempwat_glblrng_qc'],[1])
def populate_qc_tables(self):
svm = StoredValueManager(self.container)
svm.stored_value_cas('grt_QCTEST_TEMPWAT', {
'grt_min_value': -2.,
'grt_max_value': 40.
})
svm.stored_value_cas('svt_QCTEST_TEMPWAT', {
'svt_resolution': 0.001,
'svt_n': 4
})
svm.stored_value_cas('spike_QCTEST_TEMPWAT', {
'acc': 0.1,
'spike_n': 5,
'spike_l': 5
})
def on_start(self): #pragma no cover
super(ScienceGranuleIngestionWorker,self).on_start()
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.input_product = self.CFG.get_safe('process.input_product','')
self.qc_enabled = self.CFG.get_safe('process.qc_enabled', True)
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent, callback=self._add_lookups, auto_delete=True)
self.lookup_monitor.start()
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
self.connection_id = ''
self.connection_index = None
def test_basic_stored_docs(self):
doc = {'key':'value'}
doc_key = 'doc_id'
svm = StoredValueManager(self.container)
doc_id, rev = svm.stored_value_cas(doc_key, doc)
self.addCleanup(svm.delete_stored_value, doc_key)
doc = svm.read_value(doc_key)
self.assertTrue('key' in doc and doc['key']=='value')
svm.stored_value_cas(doc_key,{'key2':'value2'})
doc = svm.read_value(doc_key)
self.assertTrue('key' in doc and doc['key']=='value')
self.assertTrue('key2' in doc and doc['key2']=='value2')
def on_start(self): #pragma no cover
#--------------------------------------------------------------------------------
# Explicit on_start
#--------------------------------------------------------------------------------
# Skip TransformStreamListener and go to StreamProcess to avoid the subscriber being created
# We want explicit management of the thread and subscriber object for ingestion
TransformStreamProcess.on_start(self)
self.queue_name = self.CFG.get_safe('process.queue_name',self.id)
self.subscriber = StreamSubscriber(process=self, exchange_name=self.queue_name, callback=self.receive_callback)
self.thread_lock = RLock()
#--------------------------------------------------------------------------------
# Normal on_start after this point
#--------------------------------------------------------------------------------
BaseIngestionWorker.on_start(self)
self._rpc_server = self.container.proc_manager._create_listening_endpoint(from_name=self.id, process=self)
self.add_endpoint(self._rpc_server)
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.input_product = self.CFG.get_safe('process.input_product','')
self.qc_enabled = self.CFG.get_safe('process.qc_enabled', True)
self.ignore_gaps = self.CFG.get_safe('service.ingestion.ignore_gaps', True)
if not self.ignore_gaps:
log.warning("Gap handling is not supported in release 2")
self.ignore_gaps = True
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent, callback=self._add_lookups, auto_delete=True)
self.add_endpoint(self.lookup_monitor)
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
self.connection_id = ''
self.connection_index = None
self.start_listener()
def test_rdt_lookup(self):
rdt = self.create_lookup_rdt()
self.assertTrue('offset_a' in rdt.lookup_values())
self.assertFalse('offset_b' in rdt.lookup_values())
rdt['time'] = [0]
rdt['temp'] = [10.0]
rdt['offset_a'] = [2.0]
self.assertEquals(rdt['offset_b'], None)
self.assertEquals(rdt.lookup_values(), ['offset_a'])
np.testing.assert_array_almost_equal(rdt['calibrated'], np.array([12.0]))
svm = StoredValueManager(self.container)
svm.stored_value_cas('coefficient_document', {'offset_b':2.0})
svm.stored_value_cas("GA03FLMA-RI001-13-CTDMOG999_OFFSETC", {'offset_c':3.0})
rdt.fetch_lookup_values()
np.testing.assert_array_equal(rdt['offset_b'], np.array([2.0]))
np.testing.assert_array_equal(rdt['calibrated_b'], np.array([14.0]))
np.testing.assert_array_equal(rdt['offset_c'], np.array([3.0]))
class TestQCFunctions(DMTestCase):
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = self.ph.create_simple_qc_pdict()
self.stream_def_id = self.pubsub_management.create_stream_definition('global range', parameter_dictionary_id=pdict_id, stream_configuration={'reference_designator':'QCTEST'})
self.addCleanup(self.pubsub_management.delete_stream_definition, self.stream_def_id)
self.rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
self.svm = StoredValueManager(self.container)
def test_global_range_test(self):
self.svm.stored_value_cas('grt_QCTEST_TEMPWAT', {'grt_min_value':10., 'grt_max_value':20.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [9, 10, 16, 17, 18, 19, 20, 25]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_glblrng_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
def test_spike_test(self): # I know how redundant this sounds
self.svm.stored_value_cas('spike_QCTEST_TEMPWAT', {'acc':0.1, 'spike_n':5., 'spike_l':5.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [-1, 3, 40, -1, 1, -6, -6, 1]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_spketst_qc'], [1, 1, 0, 1, 1, 1, 1, 1])
def test_stuck_value_test(self):
self.svm.stored_value_cas('svt_QCTEST_TEMPWAT', {'svt_resolution':0.001, 'svt_n': 4.})
self.rdt['time'] = np.arange(10)
self.rdt['temp'] = [4.83, 1.40, 3.33, 3.33, 3.33, 3.33, 4.09, 2.97, 2.85, 3.67]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_stuckvl_qc'], [1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def setUp(self):
self._start_container()
self.svm = StoredValueManager(self.container)
class TestParsers(IonIntegrationTestCase):
def setUp(self):
self._start_container()
self.svm = StoredValueManager(self.container)
@classmethod
def parse_document(cls, container, parser, document_path):
svm = StoredValueManager(container)
document = ''
with open(document_path, 'r') as f:
document = f.read()
for k, v in parser(document):
svm.stored_value_cas(k, v)
return
def test_grt_parser(self):
self.parse_document(self.container, grt_parser, qc_paths['grt'])
ret_doc = self.svm.read_value(
'grt_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'CONDWAT'))
np.testing.assert_almost_equal(ret_doc['grt_min_value'], 0.)
np.testing.assert_almost_equal(ret_doc['grt_max_value'], 66000.)
def test_spike_parser(self):
self.parse_document(self.container, spike_parser, qc_paths['spike'])
ret_doc = self.svm.read_value(
'spike_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['acc'], 0.005)
np.testing.assert_almost_equal(ret_doc['spike_n'], 11.)
np.testing.assert_almost_equal(ret_doc['spike_l'], 15.)
self.assertRaises(
NotFound, self.svm.read_value,
'spike_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'CLOWN'))
def test_stuck_value_parser(self):
self.parse_document(self.container, stuck_value_test_parser,
qc_paths['stuck'])
ret_doc = self.svm.read_value(
'svt_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['svt_resolution'], 1e-9)
np.testing.assert_almost_equal(ret_doc['svt_n'], 1000000)
def test_trend_value_parser(self):
self.parse_document(self.container, trend_parser, qc_paths['trend'])
ret_doc = self.svm.read_value(
'trend_%s_%s' % ('CE04OSBP-LJ01C-06-CTDBPO108', 'PRACSAL'))
np.testing.assert_almost_equal(ret_doc['time_interval'], 365.0)
np.testing.assert_almost_equal(ret_doc['polynomial_order'], 1.0)
np.testing.assert_almost_equal(ret_doc['standard_deviation'], 0.0)
def test_lrt_parser(self):
self.parse_document(self.container, lrt_parser, qc_paths['lrt'])
ret_doc = self.svm.read_value(
'lrt_%s_%s' % ('GP02HYPM-SP001-04-CTDPF0999', 'PRACSAL'))
np.testing.assert_array_equal(ret_doc['datlim'][0],
np.array([32.289, 32.927]))
self.assertEquals(ret_doc['dims'], ['pressure', 'month'])
def setUp(self):
self._start_container()
self.svm = StoredValueManager(self.container)
def test_lookup_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsubcli.create_stream_definition(
'lookup', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsubcli.delete_stream_definition, stream_def_id)
data_product = DataProduct(name='lookup data product')
tdom, sdom = time_series_domain()
data_product.temporal_domain = tdom.dump()
data_product.spatial_domain = sdom.dump()
data_product_id = self.dpsc_cli.create_data_product(
data_product, stream_definition_id=stream_def_id)
self.addCleanup(self.dpsc_cli.delete_data_product, data_product_id)
data_producer = DataProducer(name='producer')
data_producer.producer_context = DataProcessProducerContext()
data_producer.producer_context.configuration['qc_keys'] = [
'offset_document'
]
data_producer_id, _ = self.rrclient.create(data_producer)
self.addCleanup(self.rrclient.delete, data_producer_id)
assoc, _ = self.rrclient.create_association(
subject=data_product_id,
object=data_producer_id,
predicate=PRED.hasDataProducer)
self.addCleanup(self.rrclient.delete_association, assoc)
document_keys = self.damsclient.list_qc_references(data_product_id)
self.assertEquals(document_keys, ['offset_document'])
svm = StoredValueManager(self.container)
svm.stored_value_cas('offset_document', {'offset_a': 2.0})
self.dpsc_cli.activate_data_product_persistence(data_product_id)
dataset_ids, _ = self.rrclient.find_objects(subject=data_product_id,
predicate=PRED.hasDataset,
id_only=True)
dataset_id = dataset_ids[0]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [20.]
granule = rdt.to_granule()
stream_ids, _ = self.rrclient.find_objects(subject=data_product_id,
predicate=PRED.hasStream,
id_only=True)
stream_id = stream_ids[0]
route = self.pubsubcli.read_stream_route(stream_id=stream_id)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['temp'], rdt2['temp'])
np.testing.assert_array_almost_equal(rdt2['calibrated'],
np.array([22.0]))
svm.stored_value_cas('updated_document', {'offset_a': 3.0})
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ep = EventPublisher(event_type=OT.ExternalReferencesUpdatedEvent)
ep.publish_event(origin=data_product_id,
reference_keys=['updated_document'])
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [1]
rdt['temp'] = [20.]
granule = rdt.to_granule()
gevent.sleep(2) # Yield so that the event goes through
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt2['temp'], np.array([20., 20.]))
np.testing.assert_array_almost_equal(rdt2['calibrated'],
np.array([22.0, 23.0]))
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
self.pdict_id = self.ph.create_simple_qc_pdict()
self.svm = StoredValueManager(self.container)
class ScienceGranuleIngestionWorker(TransformStreamListener,
BaseIngestionWorker):
CACHE_LIMIT = CFG.get_safe('container.ingestion_cache', 5)
def __init__(self, *args, **kwargs):
TransformStreamListener.__init__(self, *args, **kwargs)
BaseIngestionWorker.__init__(self, *args, **kwargs)
#--------------------------------------------------------------------------------
# Ingestion Cache
# - Datasets
# - Coverage instances
#--------------------------------------------------------------------------------
self._datasets = collections.OrderedDict()
self._coverages = collections.OrderedDict()
self._bad_coverages = {}
self.time_stats = Accumulator(format='%3f')
# unique ID to identify this worker in log msgs
self._id = uuid.uuid1()
def on_start(self): #pragma no cover
#--------------------------------------------------------------------------------
# Explicit on_start
#--------------------------------------------------------------------------------
# Skip TransformStreamListener and go to StreamProcess to avoid the subscriber being created
# We want explicit management of the thread and subscriber object for ingestion
TransformStreamProcess.on_start(self)
self.queue_name = self.CFG.get_safe('process.queue_name', self.id)
self.subscriber = StreamSubscriber(process=self,
exchange_name=self.queue_name,
callback=self.receive_callback)
self.thread_lock = RLock()
#--------------------------------------------------------------------------------
# Normal on_start after this point
#--------------------------------------------------------------------------------
BaseIngestionWorker.on_start(self)
self._rpc_server = self.container.proc_manager._create_listening_endpoint(
from_name=self.id, process=self)
self.add_endpoint(self._rpc_server)
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs', [])
self.input_product = self.CFG.get_safe('process.input_product', '')
self.qc_enabled = self.CFG.get_safe('process.qc_enabled', True)
self.ignore_gaps = self.CFG.get_safe('service.ingestion.ignore_gaps',
False)
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(
event_type=OT.ExternalReferencesUpdatedEvent,
callback=self._add_lookups,
auto_delete=True)
self.add_endpoint(self.lookup_monitor)
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
self.connection_id = ''
self.connection_index = None
self.start_listener()
def on_quit(self): #pragma no cover
self.event_publisher.close()
self.qc_publisher.close()
if self.subscriber_thread:
self.stop_listener()
for stream, coverage in self._coverages.iteritems():
try:
coverage.close(timeout=5)
except:
log.exception('Problems closing the coverage')
self._coverages.clear()
TransformStreamListener.on_quit(self)
BaseIngestionWorker.on_quit(self)
def start_listener(self):
# We use a lock here to prevent possible race conditions from starting multiple listeners and coverage clobbering
with self.thread_lock:
self.subscriber_thread = self._process.thread_manager.spawn(
self.subscriber.listen, thread_name='%s-subscriber' % self.id)
def stop_listener(self):
# Avoid race conditions with coverage operations (Don't start a listener at the same time as closing one)
with self.thread_lock:
self.subscriber.close()
self.subscriber_thread.join(timeout=10)
for stream, coverage in self._coverages.iteritems():
try:
coverage.close(timeout=5)
except:
log.exception('Problems closing the coverage')
self._coverages.clear()
self.subscriber_thread = None
def pause(self):
if self.subscriber_thread is not None:
self.stop_listener()
def resume(self):
if self.subscriber_thread is None:
self.start_listener()
def _add_lookups(self, event, *args, **kwargs):
if event.origin == self.input_product:
if isinstance(event.reference_keys, list):
self.new_lookups.put(event.reference_keys)
def _new_dataset(self, stream_id):
'''
Adds a new dataset to the internal cache of the ingestion worker
'''
rr_client = ResourceRegistryServiceClient()
datasets, _ = rr_client.find_subjects(subject_type=RT.Dataset,
predicate=PRED.hasStream,
object=stream_id,
id_only=True)
if datasets:
return datasets[0]
return None
def get_dataset(self, stream_id):
'''
Memoization (LRU) of _new_dataset
'''
try:
result = self._datasets.pop(stream_id)
except KeyError:
result = self._new_dataset(stream_id)
if result is None:
return None
if len(self._datasets) >= self.CACHE_LIMIT:
self._datasets.popitem(0)
self._datasets[stream_id] = result
return result
def get_coverage(self, stream_id):
'''
Memoization (LRU) of _get_coverage
'''
try:
result = self._coverages.pop(stream_id)
except KeyError:
dataset_id = self.get_dataset(stream_id)
if dataset_id is None:
return None
result = DatasetManagementService._get_simplex_coverage(dataset_id,
mode='a')
if result is None:
return None
if len(self._coverages) >= self.CACHE_LIMIT:
k, coverage = self._coverages.popitem(0)
coverage.close(timeout=5)
self._coverages[stream_id] = result
return result
def gap_coverage(self, stream_id):
try:
old_cov = self._coverages.pop(stream_id)
dataset_id = self.get_dataset(stream_id)
sdom, tdom = time_series_domain()
new_cov = DatasetManagementService._create_simplex_coverage(
dataset_id, old_cov.parameter_dictionary, sdom, tdom,
old_cov._persistence_layer.inline_data_writes)
old_cov.close()
result = new_cov
except KeyError:
result = self.get_coverage(stream_id)
self._coverages[stream_id] = result
return result
def dataset_changed(self, dataset_id, extents, window):
self.event_publisher.publish_event(origin=dataset_id,
author=self.id,
extents=extents,
window=window)
def evaluate_qc(self, rdt, dataset_id):
if self.qc_enabled:
for field in rdt.fields:
if not (field.endswith('glblrng_qc')
or field.endswith('loclrng_qc')):
continue
try:
values = rdt[field]
if values is not None:
if not all(values):
topology = np.where(values == 0)
timestamps = rdt[rdt.temporal_parameter][
topology[0]]
self.flag_qc_parameter(dataset_id, field,
timestamps.tolist(), {})
except:
continue
def flag_qc_parameter(self, dataset_id, parameter, temporal_values,
configuration):
data_product_ids, _ = self.container.resource_registry.find_subjects(
object=dataset_id,
predicate=PRED.hasDataset,
subject_type=RT.DataProduct,
id_only=True)
for data_product_id in data_product_ids:
description = 'Automated Quality Control Alerted on %s' % parameter
self.qc_publisher.publish_event(origin=data_product_id,
qc_parameter=parameter,
temporal_values=temporal_values,
configuration=configuration,
description=description)
def update_connection_index(self, connection_id, connection_index):
self.connection_id = connection_id
try:
connection_index = int(connection_index)
self.connection_index = connection_index
except ValueError:
pass
def has_gap(self, connection_id, connection_index):
if connection_id:
if not self.connection_id:
self.update_connection_index(connection_id, connection_index)
return False
else:
if connection_id != self.connection_id:
return True
if connection_index:
if self.connection_index is None:
self.update_connection_index(connection_id, connection_index)
return False
try:
connection_index = int(connection_index)
if connection_index != self.connection_index + 1:
return True
except ValueError:
pass
return False
def splice_coverage(self, dataset_id, coverage):
log.info('Splicing new coverage')
DatasetManagementService._splice_coverage(dataset_id, coverage)
@handle_stream_exception()
def recv_packet(self, msg, stream_route, stream_id):
''' receive packet for ingestion '''
log.trace('received granule for stream %s', stream_id)
if msg == {}:
log.error('Received empty message from stream: %s', stream_id)
return
# Message validation
if not isinstance(msg, Granule):
log.error('Ingestion received a message that is not a granule: %s',
msg)
return
rdt = RecordDictionaryTool.load_from_granule(msg)
if rdt is None:
log.error('Invalid granule (no RDT) for stream %s', stream_id)
return
if not len(rdt):
log.debug('Empty granule for stream %s', stream_id)
return
self.persist_or_timeout(stream_id, rdt)
def persist_or_timeout(self, stream_id, rdt):
""" retry writing coverage multiple times and eventually time out """
done = False
timeout = 2
start = time.time()
while not done:
try:
self.add_granule(stream_id, rdt)
done = True
except:
log.exception('An issue with coverage, retrying after a bit')
if (time.time() -
start) > MAX_RETRY_TIME: # After an hour just give up
dataset_id = self.get_dataset(stream_id)
log.error(
"We're giving up, the coverage needs to be inspected %s",
DatasetManagementService._get_coverage_path(
dataset_id))
raise
if stream_id in self._coverages:
log.info('Popping coverage for stream %s', stream_id)
self._coverages.pop(stream_id)
gevent.sleep(timeout)
if timeout > (60 * 5):
timeout = 60 * 5
else:
timeout *= 2
def expand_coverage(self, coverage, elements, stream_id):
try:
coverage.insert_timesteps(elements, oob=False)
except IOError as e:
log.error("Couldn't insert time steps for coverage: %s",
coverage.persistence_dir,
exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def get_stored_values(self, lookup_value):
if not self.new_lookups.empty():
new_values = self.new_lookups.get()
self.lookup_docs = new_values + self.lookup_docs
lookup_value_document_keys = self.lookup_docs
for key in lookup_value_document_keys:
try:
document = self.stored_value_manager.read_value(key)
if lookup_value in document:
return document[lookup_value]
except NotFound:
log.warning('Specified lookup document does not exist')
return None
def fill_lookup_values(self, rdt):
rdt.fetch_lookup_values()
for field in rdt.lookup_values():
value = self.get_stored_values(rdt.context(field).lookup_value)
if value:
rdt[field] = value
def insert_sparse_values(self, coverage, rdt, stream_id):
self.fill_lookup_values(rdt)
for field in rdt.fields:
if rdt[field] is None:
continue
if not isinstance(
rdt.context(field).param_type, SparseConstantType):
# We only set sparse values before insert
continue
value = rdt[field]
try:
coverage.set_parameter_values(param_name=field, value=value)
except ValueError as e:
if "'lower_bound' cannot be >= 'upper_bound'" in e.message:
continue
else:
raise
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir,
exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def insert_values(self, coverage, rdt, stream_id):
elements = len(rdt)
start_index = coverage.num_timesteps - elements
for k, v in rdt.iteritems():
if isinstance(v, SparseConstantValue):
continue
slice_ = slice(start_index, None)
try:
coverage.set_parameter_values(param_name=k,
tdoa=slice_,
value=v)
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir,
exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
if 'ingestion_timestamp' in coverage.list_parameters():
t_now = time.time()
ntp_time = TimeUtils.ts_to_units(
coverage.get_parameter_context('ingestion_timestamp').uom,
t_now)
coverage.set_parameter_values(param_name='ingestion_timestamp',
tdoa=slice_,
value=ntp_time)
def add_granule(self, stream_id, rdt):
''' Appends the granule's data to the coverage and persists it. '''
debugging = log.isEnabledFor(DEBUG)
timer = Timer() if debugging else None
if stream_id in self._bad_coverages:
log.info(
'Message attempting to be inserted into bad coverage: %s',
DatasetManagementService._get_coverage_path(
self.get_dataset(stream_id)))
#--------------------------------------------------------------------------------
# Gap Analysis
#--------------------------------------------------------------------------------
if not self.ignore_gaps:
gap_found = self.has_gap(rdt.connection_id, rdt.connection_index)
if gap_found:
log.error(
'Gap Found! New connection: (%s,%s)\tOld Connection: (%s,%s)',
rdt.connection_id, rdt.connection_index,
self.connection_id, self.connection_index)
self.gap_coverage(stream_id)
#--------------------------------------------------------------------------------
# Coverage determiniation and appending
#--------------------------------------------------------------------------------
dataset_id = self.get_dataset(stream_id)
if not dataset_id:
log.error('No dataset could be determined on this stream: %s',
stream_id)
return
try:
coverage = self.get_coverage(stream_id)
except IOError as e:
log.error(
"Couldn't open coverage: %s",
DatasetManagementService._get_coverage_path(
self.get_dataset(stream_id)))
raise CorruptionError(e.message)
if debugging:
path = DatasetManagementService._get_coverage_path(dataset_id)
log.debug(
'%s: add_granule stream %s dataset %s coverage %r file %s',
self._id, stream_id, dataset_id, coverage, path)
if not coverage:
log.error(
'Could not persist coverage from granule, coverage is None')
return
#--------------------------------------------------------------------------------
# Actual persistence
#--------------------------------------------------------------------------------
elements = len(rdt)
if rdt[rdt.temporal_parameter] is None:
elements = 0
self.insert_sparse_values(coverage, rdt, stream_id)
if debugging:
timer.complete_step('checks') # lightweight ops, should be zero
self.expand_coverage(coverage, elements, stream_id)
if debugging:
timer.complete_step('insert')
self.insert_values(coverage, rdt, stream_id)
if debugging:
timer.complete_step('keys')
DatasetManagementService._save_coverage(coverage)
if debugging:
timer.complete_step('save')
start_index = coverage.num_timesteps - elements
self.dataset_changed(dataset_id, coverage.num_timesteps,
(start_index, start_index + elements))
if not self.ignore_gaps and gap_found:
self.splice_coverage(dataset_id, coverage)
self.evaluate_qc(rdt, dataset_id)
if debugging:
timer.complete_step('notify')
self._add_timing_stats(timer)
self.update_connection_index(rdt.connection_id, rdt.connection_index)
def _add_timing_stats(self, timer):
""" add stats from latest coverage operation to Accumulator and periodically log results """
self.time_stats.add(timer)
if self.time_stats.get_count() % REPORT_FREQUENCY > 0:
return
if log.isEnabledFor(TRACE):
# report per step
for step in 'checks', 'insert', 'keys', 'save', 'notify':
log.debug('%s step %s times: %s', self._id, step,
self.time_stats.to_string(step))
# report totals
log.debug('%s total times: %s', self._id, self.time_stats)
class TestQCFunctions(DMTestCase):
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
self.pdict_id = self.ph.create_simple_qc_pdict()
self.svm = StoredValueManager(self.container)
def new_rdt(self,ref='QCTEST'):
self.stream_def_id = self.create_stream_definition(uuid4().hex, parameter_dictionary_id=self.pdict_id, stream_configuration={'reference_designator':'QCTEST'})
self.rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
def test_qc_functions(self):
self.check_global_range()
self.check_spike()
self.check_stuck_value()
self.check_trend()
self.check_gradient()
self.check_localrange()
self.check_propagate()
def check_global_range(self):
log.info('check_global_range')
self.new_rdt()
self.svm.stored_value_cas('grt_QCTEST_TEMPWAT', {'grt_min_value':10., 'grt_max_value':20.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [9, 10, 16, 17, 18, 19, 20, 25]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_glblrng_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
def check_spike(self):
log.info('check_spike')
self.new_rdt()
self.svm.stored_value_cas('spike_QCTEST_TEMPWAT', {'acc':0.1, 'spike_n':5., 'spike_l':5.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [-1, 3, 40, -1, 1, -6, -6, 1]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_spketst_qc'], [1, 1, 0, 1, 1, 1, 1, 1])
def check_stuck_value(self):
log.info('check_stuck_value')
self.new_rdt()
self.svm.stored_value_cas('svt_QCTEST_TEMPWAT', {'svt_resolution':0.001, 'svt_n': 4.})
self.rdt['time'] = np.arange(10)
self.rdt['temp'] = [4.83, 1.40, 3.33, 3.33, 3.33, 3.33, 4.09, 2.97, 2.85, 3.67]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_stuckvl_qc'], [1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def check_trend(self):
log.info('check_trend')
self.new_rdt()
self.svm.stored_value_cas('trend_QCTEST_TEMPWAT', {'time_interval':0, 'polynomial_order': 1, 'standard_deviation': 3})
self.rdt['time'] = np.arange(10)
self.rdt['temp'] = [0.8147, 0.9058, 0.1270, 0.9134, 0.6324, 0.0975, 0.2785, 0.5469, 0.9575, 0.9649]
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_trndtst_qc'], [1] * 10)
def check_propagate(self):
log.info('check_propagate')
self.new_rdt()
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [9, 10, 16, 17, 18, 19, 20, 25]
self.rdt['tempwat_glblrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_spketst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_stuckvl_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_gradtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_trndtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_loclrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_glblrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_spketst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_stuckvl_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_gradtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_trndtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_loclrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
np.testing.assert_array_equal(self.rdt['cmbnflg_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
def check_gradient(self):
log.info('check_gradient')
self.new_rdt()
self.svm.stored_value_cas('grad_QCTEST_TEMPWAT_time', {'d_dat_dx': 50, 'min_dx': 0, 'start_dat': 0, 'tol_dat': 5})
self.rdt['time'] = np.arange(5)
self.rdt['temp'] = [3, 5, 98, 99, 4]
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_gradtst_qc'], [1, 1, 0, 0, 1])
def check_localrange(self):
log.info('check_localrange')
self.new_rdt()
t = np.array([3580144703.7555027, 3580144704.7555027, 3580144705.7555027, 3580144706.7555027, 3580144707.7555027, 3580144708.7555027, 3580144709.7555027, 3580144710.7555027, 3580144711.7555027, 3580144712.7555027])
pressure = np.random.rand(10) * 2 + 33.0
t_v = ntp_to_month(t)
dat = t_v + pressure + np.arange(16,26)
def lim1(p,m):
return p+m+10
def lim2(p,m):
return p+m+20
pressure_grid, month_grid = np.meshgrid(np.arange(0,150,10), np.arange(11))
points = np.column_stack([pressure_grid.flatten(), month_grid.flatten()])
datlim_0 = lim1(points[:,0], points[:,1])
datlim_1 = lim2(points[:,0], points[:,1])
datlim = np.column_stack([datlim_0, datlim_1])
datlimz = points
self.svm.stored_value_cas('lrt_QCTEST_TEMPWAT', {'datlim':datlim.tolist(), 'datlimz':datlimz.tolist(), 'dims':['pressure', 'month']})
self.rdt['time'] = t
self.rdt['temp'] = dat
self.rdt['pressure'] = pressure
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_loclrng_qc'], [1 ,1 ,1 ,1 ,1 ,0 ,0 ,0 ,0 ,0])
def test_lookup_values_ingest_replay(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsub_management.create_stream_definition(
'lookups', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition,
stream_def_id)
stream_id, route = self.pubsub_management.create_stream(
'example',
exchange_point=self.exchange_point_name,
stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
config = DotDict()
config.process.lookup_docs = ['test1', 'test2']
self.ingestion_management.persist_data_stream(
stream_id=stream_id,
ingestion_configuration_id=ingestion_config_id,
dataset_id=dataset_id,
config=config)
self.addCleanup(self.ingestion_management.unpersist_data_stream,
stream_id, ingestion_config_id)
stored_value_manager = StoredValueManager(self.container)
stored_value_manager.stored_value_cas('test1', {
'offset_a': 10.0,
'offset_b': 13.1
})
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(20))
np.testing.assert_array_almost_equal(rdt_out['temp'],
np.array([20.] * 20))
np.testing.assert_array_almost_equal(rdt_out['calibrated'],
np.array([30.] * 20))
np.testing.assert_array_equal(
rdt_out['offset_b'],
np.array([rdt_out.fill_value('offset_b')] * 20))
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20, 40)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor.event.clear()
stored_value_manager.stored_value_cas('test1', {'offset_a': 20.0})
stored_value_manager.stored_value_cas('coefficient_document',
{'offset_b': 10.0})
gevent.sleep(2)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(40))
np.testing.assert_array_almost_equal(rdt_out['temp'],
np.array([20.] * 20 + [20.] * 20))
np.testing.assert_array_equal(rdt_out['offset_b'],
np.array([10.] * 40))
np.testing.assert_array_almost_equal(rdt_out['calibrated'],
np.array([30.] * 20 + [40.] * 20))
np.testing.assert_array_almost_equal(rdt_out['calibrated_b'],
np.array([40.] * 20 + [50.] * 20))
class TransformPrime(TransformDataProcess):
binding=['output']
'''
Transforms which have an incoming stream and an outgoing stream.
Parameters:
process.stream_id Outgoing stream identifier.
process.exchange_point Route's exchange point.
process.routing_key Route's routing key.
process.queue_name Name of the queue to listen on.
process.routes streams,actor for each route {(stream_input_id, stream_output_id):actor}
Either the stream_id or both the exchange_point and routing_key need to be provided.
'''
def on_start(self):
TransformDataProcess.on_start(self)
self.pubsub_management = PubsubManagementServiceProcessClient(process=self)
self.stored_values = StoredValueManager(self.container)
self.input_data_product_ids = self.CFG.get_safe('process.input_products', [])
self.output_data_product_ids = self.CFG.get_safe('process.output_products', [])
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent,callback=self._add_lookups, auto_delete=True)
self.lookup_monitor.start()
def on_quit(self):
self.lookup_monitor.stop()
TransformDataProcess.on_quit(self)
def _add_lookups(self, event, *args, **kwargs):
if event.origin in self.input_data_product_ids + self.output_data_product_ids:
if isinstance(event.reference_keys, list):
self.new_lookups.put(event.reference_keys)
@memoize_lru(100)
def read_stream_def(self,stream_id):
return self.pubsub_management.read_stream_definition(stream_id=stream_id)
def recv_packet(self, msg, stream_route, stream_id):
process_routes = self.CFG.get_safe('process.routes', {})
for stream_in_id,routes in process_routes.iteritems():
if stream_id == stream_in_id:
for stream_out_id, actor in routes.iteritems():
if actor is None:
rdt_out = self._execute_transform(msg, (stream_in_id, stream_out_id))
self.publish(rdt_out.to_granule(), stream_out_id)
else:
outgoing = self._execute_actor(msg, actor, (stream_in_id, stream_out_id))
self.publish(outgoing, stream_out_id)
def publish(self, msg, stream_out_id):
publisher = getattr(self, stream_out_id)
publisher.publish(msg)
def _load_actor(self, actor):
'''
Returns callable execute method if it exists, otherwise it raises a BadRequest
'''
try:
module = __import__(actor['module'], fromlist=[''])
except ImportError:
log.exception('Actor could not be loaded')
raise
try:
cls = getattr(module, actor['class'])
except AttributeError:
log.exception('Module %s does not have class %s', repr(module), actor['class'])
raise
try:
execute = getattr(cls,'execute')
except AttributeError:
log.exception('Actor class does not contain execute method')
raise
return execute
def _execute_actor(self, msg, actor, streams):
stream_in_id,stream_out_id = streams
stream_def_out = self.read_stream_def(stream_out_id)
params = self.CFG.get_safe('process.params', {})
config = self.CFG.get_safe('process')
#do the stuff with the actor
params['stream_def'] = stream_def_out._id
executor = self._load_actor(actor)
try:
rdt_out = executor(msg, None, config, params, None)
except:
log.exception('Error running actor for %s', self.id)
raise
return rdt_out
def _merge_pdicts(self, pdict1, pdict2):
incoming_pdict = ParameterDictionary.load(pdict1)
outgoing_pdict = ParameterDictionary.load(pdict2)
merged_pdict = ParameterDictionary()
for k,v in incoming_pdict.iteritems():
ordinal, v = v
if k not in merged_pdict:
merged_pdict.add_context(v)
for k,v in outgoing_pdict.iteritems():
ordinal, v = v
if k not in merged_pdict:
merged_pdict.add_context(v)
return merged_pdict
def _merge_rdt(self, stream_def_in, stream_def_out):
incoming_pdict_dump = stream_def_in.parameter_dictionary
outgoing_pdict_dump = stream_def_out.parameter_dictionary
merged_pdict = self._merge_pdicts(incoming_pdict_dump, outgoing_pdict_dump)
rdt_temp = RecordDictionaryTool(param_dictionary=merged_pdict)
return rdt_temp
def _get_lookup_value(self, lookup_value):
if not self.new_lookups.empty():
new_values = self.new_lookups.get()
self.lookup_docs = new_values + self.lookup_docs
lookup_value_document_keys = self.lookup_docs
for key in lookup_value_document_keys:
try:
document = self.stored_values.read_value(key)
if lookup_value in document:
return document[lookup_value]
except NotFound:
log.warning('Specified lookup document does not exist')
return None
def _execute_transform(self, msg, streams):
stream_in_id,stream_out_id = streams
stream_def_in = self.read_stream_def(stream_in_id)
stream_def_out = self.read_stream_def(stream_out_id)
rdt_temp = self._merge_rdt(stream_def_in, stream_def_out)
rdt_in = RecordDictionaryTool.load_from_granule(msg)
for field in rdt_temp.fields:
if not isinstance(rdt_temp._pdict.get_context(field).param_type, ParameterFunctionType):
try:
rdt_temp[field] = rdt_in[field]
except KeyError:
pass
rdt_temp.fetch_lookup_values()
for lookup_field in rdt_temp.lookup_values():
s = lookup_field
stored_value = self._get_lookup_value(rdt_temp.context(s).lookup_value)
if stored_value is not None:
rdt_temp[s] = stored_value
for field in rdt_temp.fields:
if isinstance(rdt_temp._pdict.get_context(field).param_type, ParameterFunctionType):
rdt_temp[field] = rdt_temp[field]
rdt_out = RecordDictionaryTool(stream_definition_id=stream_def_out._id)
for field in rdt_out.fields:
rdt_out[field] = rdt_temp[field]
return rdt_out
class ScienceGranuleIngestionWorker(TransformStreamListener):
CACHE_LIMIT=CFG.get_safe('container.ingestion_cache',5)
def __init__(self, *args,**kwargs):
super(ScienceGranuleIngestionWorker, self).__init__(*args, **kwargs)
#--------------------------------------------------------------------------------
# Ingestion Cache
# - Datasets
# - Coverage instances
#--------------------------------------------------------------------------------
self._datasets = collections.OrderedDict()
self._coverages = collections.OrderedDict()
self._bad_coverages = {}
self.time_stats = Accumulator(format='%3f')
# unique ID to identify this worker in log msgs
self._id = uuid.uuid1()
def on_start(self): #pragma no cover
super(ScienceGranuleIngestionWorker,self).on_start()
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.input_product = self.CFG.get_safe('process.input_product','')
self.qc_enabled = self.CFG.get_safe('process.qc_enabled', True)
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent, callback=self._add_lookups, auto_delete=True)
self.lookup_monitor.start()
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
self.connection_id = ''
self.connection_index = None
def on_quit(self): #pragma no cover
super(ScienceGranuleIngestionWorker, self).on_quit()
for stream, coverage in self._coverages.iteritems():
try:
coverage.close(timeout=5)
except:
log.exception('Problems closing the coverage')
def _add_lookups(self, event, *args, **kwargs):
if event.origin == self.input_product:
if isinstance(event.reference_keys, list):
self.new_lookups.put(event.reference_keys)
def _new_dataset(self, stream_id):
'''
Adds a new dataset to the internal cache of the ingestion worker
'''
rr_client = ResourceRegistryServiceClient()
datasets, _ = rr_client.find_subjects(subject_type=RT.Dataset,predicate=PRED.hasStream,object=stream_id,id_only=True)
if datasets:
return datasets[0]
return None
def get_dataset(self,stream_id):
'''
Memoization (LRU) of _new_dataset
'''
try:
result = self._datasets.pop(stream_id)
except KeyError:
result = self._new_dataset(stream_id)
if result is None:
return None
if len(self._datasets) >= self.CACHE_LIMIT:
self._datasets.popitem(0)
self._datasets[stream_id] = result
return result
def get_coverage(self, stream_id):
'''
Memoization (LRU) of _get_coverage
'''
try:
result = self._coverages.pop(stream_id)
except KeyError:
dataset_id = self.get_dataset(stream_id)
if dataset_id is None:
return None
result = DatasetManagementService._get_simplex_coverage(dataset_id, mode='a')
if result is None:
return None
if len(self._coverages) >= self.CACHE_LIMIT:
k, coverage = self._coverages.popitem(0)
coverage.close(timeout=5)
self._coverages[stream_id] = result
return result
def gap_coverage(self,stream_id):
try:
old_cov = self._coverages.pop(stream_id)
dataset_id = self.get_dataset(stream_id)
sdom, tdom = time_series_domain()
new_cov = DatasetManagementService._create_simplex_coverage(dataset_id, old_cov.parameter_dictionary, sdom, tdom, old_cov._persistence_layer.inline_data_writes)
old_cov.close()
result = new_cov
except KeyError:
result = self.get_coverage(stream_id)
self._coverages[stream_id] = result
return result
def dataset_changed(self, dataset_id, extents, window):
self.event_publisher.publish_event(origin=dataset_id, author=self.id, extents=extents, window=window)
def evaluate_qc(self, rdt, dataset_id):
if self.qc_enabled:
for field in rdt.fields:
if not field.endswith('_qc'):
continue
try:
values = rdt[field]
if values is not None:
if not all(values):
topology = np.nonzero(values)
first_occurrence = topology[0][0]
ts = rdt[rdt.temporal_parameter][first_occurrence]
self.flag_qc_parameter(dataset_id, field, ts, {})
except:
continue
def flag_qc_parameter(self, dataset_id, parameter, temporal_value, configuration):
self.qc_publisher.publish_event(origin=dataset_id, qc_parameter=parameter, temporal_value=temporal_value, configuration=configuration)
def update_connection_index(self, connection_id, connection_index):
self.connection_id = connection_id
try:
connection_index = int(connection_index)
self.connection_index = connection_index
except ValueError:
pass
def has_gap(self, connection_id, connection_index):
if connection_id:
if not self.connection_id:
self.update_connection_index(connection_id, connection_index)
return False
else:
if connection_id != self.connection_id:
return True
if connection_index:
if self.connection_index is None:
self.update_connection_index(connection_id, connection_index)
return False
try:
connection_index = int(connection_index)
if connection_index != self.connection_index+1:
return True
except ValueError:
pass
return False
def splice_coverage(self, dataset_id, coverage):
log.info('Splicing new coverage')
DatasetManagementService._splice_coverage(dataset_id, coverage)
@handle_stream_exception()
def recv_packet(self, msg, stream_route, stream_id):
''' receive packet for ingestion '''
log.trace('received granule for stream %s', stream_id)
if msg == {}:
log.error('Received empty message from stream: %s', stream_id)
return
# Message validation
if not isinstance(msg, Granule):
log.error('Ingestion received a message that is not a granule: %s', msg)
return
rdt = RecordDictionaryTool.load_from_granule(msg)
if rdt is None:
log.error('Invalid granule (no RDT) for stream %s', stream_id)
return
if not len(rdt):
log.debug('Empty granule for stream %s', stream_id)
return
self.persist_or_timeout(stream_id, rdt)
def persist_or_timeout(self, stream_id, rdt):
""" retry writing coverage multiple times and eventually time out """
done = False
timeout = 2
start = time.time()
while not done:
try:
self.add_granule(stream_id, rdt)
done = True
except:
log.exception('An issue with coverage, retrying after a bit')
if (time.time() - start) > MAX_RETRY_TIME: # After an hour just give up
dataset_id = self.get_dataset(stream_id)
log.error("We're giving up, the coverage needs to be inspected %s", DatasetManagementService._get_coverage_path(dataset_id))
raise
if stream_id in self._coverages:
log.info('Popping coverage for stream %s', stream_id)
self._coverages.pop(stream_id)
gevent.sleep(timeout)
if timeout > (60 * 5):
timeout = 60 * 5
else:
timeout *= 2
def expand_coverage(self, coverage, elements, stream_id):
try:
coverage.insert_timesteps(elements, oob=False)
except IOError as e:
log.error("Couldn't insert time steps for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def get_stored_values(self, lookup_value):
if not self.new_lookups.empty():
new_values = self.new_lookups.get()
self.lookup_docs = new_values + self.lookup_docs
lookup_value_document_keys = self.lookup_docs
for key in lookup_value_document_keys:
try:
document = self.stored_value_manager.read_value(key)
if lookup_value in document:
return document[lookup_value]
except NotFound:
log.warning('Specified lookup document does not exist')
return None
def fill_lookup_values(self, rdt):
rdt.fetch_lookup_values()
for field in rdt.lookup_values():
value = self.get_stored_values(rdt.context(field).lookup_value)
if value:
rdt[field] = value
def insert_sparse_values(self, coverage, rdt, stream_id):
self.fill_lookup_values(rdt)
for field in rdt._lookup_values():
if rdt[field] is None:
continue
if not isinstance(rdt.context(field).param_type, SparseConstantType):
# We only set sparse values before insert
continue
value = rdt[field]
try:
coverage.set_parameter_values(param_name=field, value=value)
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def insert_values(self, coverage, rdt, stream_id):
elements = len(rdt)
start_index = coverage.num_timesteps - elements
for k,v in rdt.iteritems():
if isinstance(v, SparseConstantValue):
continue
slice_ = slice(start_index, None)
try:
coverage.set_parameter_values(param_name=k, tdoa=slice_, value=v)
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
if 'ingestion_timestamp' in coverage.list_parameters():
t_now = time.time()
ntp_time = TimeUtils.ts_to_units(coverage.get_parameter_context('ingestion_timestamp').uom, t_now)
coverage.set_parameter_values(param_name='ingestion_timestamp', tdoa=slice_, value=ntp_time)
def add_granule(self,stream_id, rdt):
''' Appends the granule's data to the coverage and persists it. '''
debugging = log.isEnabledFor(DEBUG)
timer = Timer() if debugging else None
if stream_id in self._bad_coverages:
log.info('Message attempting to be inserted into bad coverage: %s',
DatasetManagementService._get_coverage_path(self.get_dataset(stream_id)))
#--------------------------------------------------------------------------------
# Gap Analysis
#--------------------------------------------------------------------------------
gap_found = self.has_gap(rdt.connection_id, rdt.connection_index)
if gap_found:
log.error('Gap Found! New connection: (%s,%s)\tOld Connection: (%s,%s)', rdt.connection_id, rdt.connection_index, self.connection_id, self.connection_index)
self.gap_coverage(stream_id)
#--------------------------------------------------------------------------------
# Coverage determiniation and appending
#--------------------------------------------------------------------------------
dataset_id = self.get_dataset(stream_id)
if not dataset_id:
log.error('No dataset could be determined on this stream: %s', stream_id)
return
try:
coverage = self.get_coverage(stream_id)
except IOError as e:
log.error("Couldn't open coverage: %s",
DatasetManagementService._get_coverage_path(self.get_dataset(stream_id)))
raise CorruptionError(e.message)
if debugging:
path = DatasetManagementService._get_coverage_path(dataset_id)
log.debug('%s: add_granule stream %s dataset %s coverage %r file %s',
self._id, stream_id, dataset_id, coverage, path)
if not coverage:
log.error('Could not persist coverage from granule, coverage is None')
return
#--------------------------------------------------------------------------------
# Actual persistence
#--------------------------------------------------------------------------------
elements = len(rdt)
self.insert_sparse_values(coverage,rdt,stream_id)
if debugging:
timer.complete_step('checks') # lightweight ops, should be zero
self.expand_coverage(coverage, elements, stream_id)
if debugging:
timer.complete_step('insert')
self.insert_values(coverage, rdt, stream_id)
if debugging:
timer.complete_step('keys')
DatasetManagementService._save_coverage(coverage)
if debugging:
timer.complete_step('save')
start_index = coverage.num_timesteps - elements
self.dataset_changed(dataset_id,coverage.num_timesteps,(start_index,start_index+elements))
if gap_found:
self.splice_coverage(dataset_id, coverage)
self.evaluate_qc(rdt, dataset_id)
if debugging:
timer.complete_step('notify')
self._add_timing_stats(timer)
self.update_connection_index(rdt.connection_id, rdt.connection_index)
def _add_timing_stats(self, timer):
""" add stats from latest coverage operation to Accumulator and periodically log results """
self.time_stats.add(timer)
if self.time_stats.get_count() % REPORT_FREQUENCY>0:
return
if log.isEnabledFor(TRACE):
# report per step
for step in 'checks', 'insert', 'keys', 'save', 'notify':
log.debug('%s step %s times: %s', self._id, step, self.time_stats.to_string(step))
# report totals
log.debug('%s total times: %s', self._id, self.time_stats)
def populate_qc_tables(self):
svm = StoredValueManager(self.container)
svm.stored_value_cas('grt_QCTEST_TEMPWAT', {'grt_min_value':-2., 'grt_max_value':40.})
svm.stored_value_cas('svt_QCTEST_TEMPWAT', {'svt_resolution':0.001, 'svt_n': 4})
svm.stored_value_cas('spike_QCTEST_TEMPWAT', {'acc': 0.1, 'spike_n':5, 'spike_l':5})
class TransformPrime(TransformDataProcess):
binding = ['output']
'''
Transforms which have an incoming stream and an outgoing stream.
Parameters:
process.stream_id Outgoing stream identifier.
process.exchange_point Route's exchange point.
process.routing_key Route's routing key.
process.queue_name Name of the queue to listen on.
process.routes streams,actor for each route {(stream_input_id, stream_output_id):actor}
Either the stream_id or both the exchange_point and routing_key need to be provided.
'''
def on_start(self):
TransformDataProcess.on_start(self)
self.pubsub_management = PubsubManagementServiceProcessClient(
process=self)
self.stored_values = StoredValueManager(self.container)
self.input_data_product_ids = self.CFG.get_safe(
'process.input_products', [])
self.output_data_product_ids = self.CFG.get_safe(
'process.output_products', [])
self.lookup_docs = self.CFG.get_safe('process.lookup_docs', [])
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(
event_type=OT.ExternalReferencesUpdatedEvent,
callback=self._add_lookups,
auto_delete=True)
self.lookup_monitor.start()
def on_quit(self):
self.lookup_monitor.stop()
TransformDataProcess.on_quit(self)
def _add_lookups(self, event, *args, **kwargs):
if event.origin in self.input_data_product_ids + self.output_data_product_ids:
if isinstance(event.reference_keys, list):
self.new_lookups.put(event.reference_keys)
@memoize_lru(100)
def read_stream_def(self, stream_id):
return self.pubsub_management.read_stream_definition(
stream_id=stream_id)
def recv_packet(self, msg, stream_route, stream_id):
process_routes = self.CFG.get_safe('process.routes', {})
for stream_in_id, routes in process_routes.iteritems():
if stream_id == stream_in_id:
for stream_out_id, actor in routes.iteritems():
if actor is None:
rdt_out = self._execute_transform(
msg, (stream_in_id, stream_out_id))
self.publish(rdt_out.to_granule(), stream_out_id)
else:
outgoing = self._execute_actor(
msg, actor, (stream_in_id, stream_out_id))
self.publish(outgoing, stream_out_id)
def publish(self, msg, stream_out_id):
publisher = getattr(self, stream_out_id)
publisher.publish(msg)
def _load_actor(self, actor):
'''
Returns callable execute method if it exists, otherwise it raises a BadRequest
'''
try:
module = __import__(actor['module'], fromlist=[''])
except ImportError:
log.exception('Actor could not be loaded')
raise
try:
cls = getattr(module, actor['class'])
except AttributeError:
log.exception('Module %s does not have class %s', repr(module),
actor['class'])
raise
try:
execute = getattr(cls, 'execute')
except AttributeError:
log.exception('Actor class does not contain execute method')
raise
return execute
def _execute_actor(self, msg, actor, streams):
stream_in_id, stream_out_id = streams
stream_def_out = self.read_stream_def(stream_out_id)
params = self.CFG.get_safe('process.params', {})
config = self.CFG.get_safe('process')
#do the stuff with the actor
params['stream_def'] = stream_def_out._id
executor = self._load_actor(actor)
try:
rdt_out = executor(msg, None, config, params, None)
except:
log.exception('Error running actor for %s', self.id)
raise
return rdt_out
def _merge_pdicts(self, pdict1, pdict2):
incoming_pdict = ParameterDictionary.load(pdict1)
outgoing_pdict = ParameterDictionary.load(pdict2)
merged_pdict = ParameterDictionary()
for k, v in incoming_pdict.iteritems():
ordinal, v = v
if k not in merged_pdict:
merged_pdict.add_context(v)
for k, v in outgoing_pdict.iteritems():
ordinal, v = v
if k not in merged_pdict:
merged_pdict.add_context(v)
return merged_pdict
def _merge_rdt(self, stream_def_in, stream_def_out):
incoming_pdict_dump = stream_def_in.parameter_dictionary
outgoing_pdict_dump = stream_def_out.parameter_dictionary
merged_pdict = self._merge_pdicts(incoming_pdict_dump,
outgoing_pdict_dump)
rdt_temp = RecordDictionaryTool(param_dictionary=merged_pdict)
return rdt_temp
def _get_lookup_value(self, lookup_value):
if not self.new_lookups.empty():
new_values = self.new_lookups.get()
self.lookup_docs = new_values + self.lookup_docs
lookup_value_document_keys = self.lookup_docs
for key in lookup_value_document_keys:
try:
document = self.stored_values.read_value(key)
if lookup_value in document:
return document[lookup_value]
except NotFound:
log.warning('Specified lookup document does not exist')
return None
def _execute_transform(self, msg, streams):
stream_in_id, stream_out_id = streams
stream_def_in = self.read_stream_def(stream_in_id)
stream_def_out = self.read_stream_def(stream_out_id)
rdt_temp = self._merge_rdt(stream_def_in, stream_def_out)
rdt_in = RecordDictionaryTool.load_from_granule(msg)
for field in rdt_temp.fields:
if not isinstance(
rdt_temp._pdict.get_context(field).param_type,
ParameterFunctionType):
try:
rdt_temp[field] = rdt_in[field]
except KeyError:
pass
rdt_temp.fetch_lookup_values()
for lookup_field in rdt_temp.lookup_values():
s = lookup_field
stored_value = self._get_lookup_value(
rdt_temp.context(s).lookup_value)
if stored_value is not None:
rdt_temp[s] = stored_value
for field in rdt_temp.fields:
if isinstance(
rdt_temp._pdict.get_context(field).param_type,
ParameterFunctionType):
rdt_temp[field] = rdt_temp[field]
rdt_out = RecordDictionaryTool(stream_definition_id=stream_def_out._id)
for field in rdt_out.fields:
rdt_out[field] = rdt_temp[field]
return rdt_out
def on_start(self):
TransformStreamListener.on_start(self)
self.document_key = self.CFG.get_safe('process.document_key')
self.stored_value_manager = StoredValueManager(self.container)
def on_start(self):
TransformStreamListener.on_start(self)
self.document_key = self.CFG.get_safe('process.document_key')
self.stored_value_manager = StoredValueManager(self.container)
class TestQCFunctions(DMTestCase):
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
self.pdict_id = self.ph.create_simple_qc_pdict()
self.svm = StoredValueManager(self.container)
def new_rdt(self,ref='QCTEST'):
self.stream_def_id = self.create_stream_definition(uuid4().hex, parameter_dictionary_id=self.pdict_id, stream_configuration={'reference_designator':'QCTEST'})
self.rdt = RecordDictionaryTool(stream_definition_id=self.stream_def_id)
def test_qc_functions(self):
self.check_global_range()
self.check_spike()
self.check_stuck_value()
self.check_trend()
self.check_gradient()
self.check_localrange()
self.check_propagate()
def check_global_range(self):
log.info('check_global_range')
self.new_rdt()
self.svm.stored_value_cas('grt_QCTEST_TEMPWAT', {'grt_min_value':10., 'grt_max_value':20.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [9, 10, 16, 17, 18, 19, 20, 25]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_glblrng_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
def check_spike(self):
log.info('check_spike')
self.new_rdt()
self.svm.stored_value_cas('spike_QCTEST_TEMPWAT', {'acc':0.1, 'spike_n':5., 'spike_l':5.})
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [-1, 3, 40, -1, 1, -6, -6, 1]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_spketst_qc'], [1, 1, 0, 1, 1, 1, 1, 1])
def check_stuck_value(self):
log.info('check_stuck_value')
self.new_rdt()
self.svm.stored_value_cas('svt_QCTEST_TEMPWAT', {'svt_resolution':0.001, 'svt_n': 4.})
self.rdt['time'] = np.arange(10)
self.rdt['temp'] = [4.83, 1.40, 3.33, 3.33, 3.33, 3.33, 4.09, 2.97, 2.85, 3.67]
self.rdt.fetch_lookup_values()
np.testing.assert_array_almost_equal(self.rdt['tempwat_stuckvl_qc'], [1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def check_trend(self):
log.info('check_trend')
self.new_rdt()
self.svm.stored_value_cas('trend_QCTEST_TEMPWAT', {'time_interval':0, 'polynomial_order': 1, 'standard_deviation': 3})
self.rdt['time'] = np.arange(10)
self.rdt['temp'] = [0.8147, 0.9058, 0.1270, 0.9134, 0.6324, 0.0975, 0.2785, 0.5469, 0.9575, 0.9649]
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_trndtst_qc'], [1] * 10)
def check_propagate(self):
log.info('check_propagate')
self.new_rdt()
self.rdt['time'] = np.arange(8)
self.rdt['temp'] = [9, 10, 16, 17, 18, 19, 20, 25]
self.rdt['tempwat_glblrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_spketst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_stuckvl_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_gradtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_trndtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['tempwat_loclrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_glblrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_spketst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_stuckvl_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_gradtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_trndtst_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
self.rdt['preswat_loclrng_qc'] = [0, 1, 1, 1, 1, 1, 1, 0]
np.testing.assert_array_equal(self.rdt['cmbnflg_qc'], [0, 1, 1, 1, 1, 1, 1, 0])
def check_gradient(self):
log.info('check_gradient')
self.new_rdt()
self.svm.stored_value_cas('grad_QCTEST_TEMPWAT_time', {'d_dat_dx': 50, 'min_dx': 0, 'start_dat': 0, 'tol_dat': 5})
self.rdt['time'] = np.arange(5)
self.rdt['temp'] = [3, 5, 98, 99, 4]
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_gradtst_qc'], [1, 1, 0, 0, 1])
def check_localrange(self):
log.info('check_localrange')
self.new_rdt()
t = np.array([3580144703.7555027, 3580144704.7555027, 3580144705.7555027, 3580144706.7555027, 3580144707.7555027, 3580144708.7555027, 3580144709.7555027, 3580144710.7555027, 3580144711.7555027, 3580144712.7555027])
pressure = np.random.rand(10) * 2 + 33.0
t_v = ntp_to_month(t)
dat = t_v + pressure + np.arange(16,26)
def lim1(p,m):
return p+m+10
def lim2(p,m):
return p+m+20
pressure_grid, month_grid = np.meshgrid(np.arange(0,150,10), np.arange(11))
points = np.column_stack([pressure_grid.flatten(), month_grid.flatten()])
datlim_0 = lim1(points[:,0], points[:,1])
datlim_1 = lim2(points[:,0], points[:,1])
datlim = np.column_stack([datlim_0, datlim_1])
datlimz = points
self.svm.stored_value_cas('lrt_QCTEST_TEMPWAT', {'datlim':datlim.tolist(), 'datlimz':datlimz.tolist(), 'dims':['pressure', 'month']})
self.rdt['time'] = t
self.rdt['temp'] = dat
self.rdt['pressure'] = pressure
self.rdt.fetch_lookup_values()
np.testing.assert_array_equal(self.rdt['tempwat_loclrng_qc'], [1 ,1 ,1 ,1 ,1 ,0 ,0 ,0 ,0 ,0])
def test_lookup_values_ingest_replay(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsub_management.create_stream_definition('lookups', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('example', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
ingestion_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
config = DotDict()
config.process.lookup_docs = ['test1', 'test2']
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id, config=config)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
stored_value_manager = StoredValueManager(self.container)
stored_value_manager.stored_value_cas('test1',{'offset_a':10.0, 'offset_b':13.1})
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(20))
np.testing.assert_array_almost_equal(rdt_out['temp'], np.array([20.] * 20))
np.testing.assert_array_almost_equal(rdt_out['calibrated'], np.array([30.]*20))
np.testing.assert_array_equal(rdt_out['offset_b'], np.array([rdt_out.fill_value('offset_b')] * 20))
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(20,40)
rdt['temp'] = [20.0] * 20
granule = rdt.to_granule()
dataset_monitor.event.clear()
stored_value_manager.stored_value_cas('test1',{'offset_a':20.0})
stored_value_manager.stored_value_cas('coefficient_document',{'offset_b':10.0})
gevent.sleep(2)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(30))
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_array_almost_equal(rdt_out['time'], np.arange(40))
np.testing.assert_array_almost_equal(rdt_out['temp'], np.array([20.] * 20 + [20.] * 20))
np.testing.assert_array_equal(rdt_out['offset_b'], np.array([10.] * 40))
np.testing.assert_array_almost_equal(rdt_out['calibrated'], np.array([30.]*20 + [40.]*20))
np.testing.assert_array_almost_equal(rdt_out['calibrated_b'], np.array([40.] * 20 + [50.] * 20))
class ScienceGranuleIngestionWorker(TransformStreamListener, BaseIngestionWorker):
CACHE_LIMIT=CFG.get_safe('container.ingestion_cache',5)
def __init__(self, *args,**kwargs):
TransformStreamListener.__init__(self, *args, **kwargs)
BaseIngestionWorker.__init__(self, *args, **kwargs)
#--------------------------------------------------------------------------------
# Ingestion Cache
# - Datasets
# - Coverage instances
#--------------------------------------------------------------------------------
self._datasets = collections.OrderedDict()
self._coverages = collections.OrderedDict()
self._bad_coverages = {}
self.time_stats = Accumulator(format='%3f')
# unique ID to identify this worker in log msgs
self._id = uuid.uuid1()
def on_start(self): #pragma no cover
#--------------------------------------------------------------------------------
# Explicit on_start
#--------------------------------------------------------------------------------
# Skip TransformStreamListener and go to StreamProcess to avoid the subscriber being created
# We want explicit management of the thread and subscriber object for ingestion
TransformStreamProcess.on_start(self)
self.queue_name = self.CFG.get_safe('process.queue_name',self.id)
self.subscriber = StreamSubscriber(process=self, exchange_name=self.queue_name, callback=self.receive_callback)
self.thread_lock = RLock()
#--------------------------------------------------------------------------------
# Normal on_start after this point
#--------------------------------------------------------------------------------
BaseIngestionWorker.on_start(self)
self._rpc_server = self.container.proc_manager._create_listening_endpoint(from_name=self.id, process=self)
self.add_endpoint(self._rpc_server)
self.event_publisher = EventPublisher(OT.DatasetModified)
self.stored_value_manager = StoredValueManager(self.container)
self.lookup_docs = self.CFG.get_safe('process.lookup_docs',[])
self.input_product = self.CFG.get_safe('process.input_product','')
self.qc_enabled = self.CFG.get_safe('process.qc_enabled', True)
self.ignore_gaps = self.CFG.get_safe('service.ingestion.ignore_gaps', True)
if not self.ignore_gaps:
log.warning("Gap handling is not supported in release 2")
self.ignore_gaps = True
self.new_lookups = Queue()
self.lookup_monitor = EventSubscriber(event_type=OT.ExternalReferencesUpdatedEvent, callback=self._add_lookups, auto_delete=True)
self.add_endpoint(self.lookup_monitor)
self.qc_publisher = EventPublisher(event_type=OT.ParameterQCEvent)
self.connection_id = ''
self.connection_index = None
self.start_listener()
def on_quit(self): #pragma no cover
self.event_publisher.close()
self.qc_publisher.close()
if self.subscriber_thread:
self.stop_listener()
for stream, coverage in self._coverages.iteritems():
try:
coverage.close(timeout=5)
except:
log.exception('Problems closing the coverage')
self._coverages.clear()
TransformStreamListener.on_quit(self)
BaseIngestionWorker.on_quit(self)
def start_listener(self):
# We use a lock here to prevent possible race conditions from starting multiple listeners and coverage clobbering
with self.thread_lock:
self.subscriber_thread = self._process.thread_manager.spawn(self.subscriber.listen, thread_name='%s-subscriber' % self.id)
def stop_listener(self):
# Avoid race conditions with coverage operations (Don't start a listener at the same time as closing one)
with self.thread_lock:
self.subscriber.close()
self.subscriber_thread.join(timeout=10)
for stream, coverage in self._coverages.iteritems():
try:
coverage.close(timeout=5)
except:
log.exception('Problems closing the coverage')
self._coverages.clear()
self.subscriber_thread = None
def pause(self):
if self.subscriber_thread is not None:
self.stop_listener()
def resume(self):
if self.subscriber_thread is None:
self.start_listener()
def _add_lookups(self, event, *args, **kwargs):
if event.origin == self.input_product:
if isinstance(event.reference_keys, list):
self.new_lookups.put(event.reference_keys)
def _new_dataset(self, stream_id):
'''
Adds a new dataset to the internal cache of the ingestion worker
'''
rr_client = self.container.resource_registry
datasets, _ = rr_client.find_subjects(subject_type=RT.Dataset,predicate=PRED.hasStream,object=stream_id,id_only=True)
if datasets:
return datasets[0]
return None
def _get_data_products(self, dataset_id):
rr_client = self.container.resource_registry
data_products, _ = rr_client.find_subjects(object=dataset_id, predicate=PRED.hasDataset, subject_type=RT.DataProduct, id_only=False)
return data_products
def initialize_metadata(self, dataset_id, rdt):
'''
Initializes a metadata document in the object store. The document
contains information about the bounds and extents of the dataset as
well other metadata to improve performance.
'''
object_store = self.container.object_store
key = dataset_id
bounds = {}
extents = {}
last_values = {}
rough_size = 0
for k,v in rdt.iteritems():
v = v[:].flatten()
if v.dtype.char not in ('S', 'O', 'U', 'V'):
bounds[k] = (np.min(v), np.max(v))
last_values[k] = v[-1]
extents[k] = len(rdt)
rough_size += len(rdt) * 4
doc = {'bounds':bounds, 'extents':extents, 'last_values':last_values, 'size': rough_size}
doc = numpy_walk(doc)
object_store.create_doc(doc, object_id=key)
return
def update_metadata(self, dataset_id, rdt):
'''
Updates the metada document with the latest information available
'''
self.update_data_product_metadata(dataset_id, rdt)
# Grab the document
object_store = self.container.object_store
key = dataset_id
try:
doc = object_store.read_doc(key)
except NotFound:
return self.initialize_metadata(dataset_id, rdt)
# These are the fields we're interested in
bounds = doc['bounds']
extents = doc['extents']
last_values = doc['last_values']
rough_size = doc['size']
for k,v in rdt.iteritems():
if k not in bounds:
continue
v = v[:].flatten() # Get the numpy representation (dense array).
if v.dtype.char not in ('S', 'O', 'U', 'V'):
l_min = np.min(v)
l_max = np.max(v)
o_min, o_max = bounds[k]
bounds[k] = (min(l_min, o_min), max(l_max, o_max))
last_values[k] = v[-1]
# Update the bounds
# Increase the extents
extents[k] = extents[k] + len(rdt)
# How about the last value?
rough_size += len(rdt) * 4
doc['size'] = rough_size
# Sanitize it
doc = numpy_walk(doc)
object_store.update_doc(doc)
def update_data_product_metadata(self, dataset_id, rdt):
data_products = self._get_data_products(dataset_id)
for data_product in data_products:
self.update_time(data_product, rdt[rdt.temporal_parameter][:])
self.update_geo(data_product, rdt)
self.container.resource_registry.update(data_product)
def update_time(self, data_product, t):
#TODO: Account for non NTP-based timestamps
t_min = np.min(t)
t_min -= 2208988800
t_max = np.max(t)
t_max -= 2208988800
if not data_product.nominal_datetime.start_datetime:
data_product.nominal_datetime.start_datetime = t_min
data_product.nominal_datetime.end_datetime = t_max
def update_geo(self, data_product, rdt):
lat = None
lon = None
for p in rdt:
if rdt._rd[p] is None:
continue
# TODO: Not an all encompassing list of acceptable names for lat and lon
if p.lower() in ('lat', 'latitude', 'y_axis'):
lat = np.asscalar(rdt[p][-1])
elif p.lower() in ('lon', 'longitude', 'x_axis'):
lon = np.asscalar(rdt[p][-1])
if lat and lon:
break
if lat and lon:
data_product.geospatial_bounds.geospatial_latitude_limit_north = lat
data_product.geospatial_bounds.geospatial_latitude_limit_south = lat
data_product.geospatial_bounds.geospatial_longitude_limit_east = lon
data_product.geospatial_bounds.geospatial_longitude_limit_west = lon
def get_dataset(self,stream_id):
'''
Memoization (LRU) of _new_dataset
'''
try:
result = self._datasets.pop(stream_id)
except KeyError:
result = self._new_dataset(stream_id)
if result is None:
return None
if len(self._datasets) >= self.CACHE_LIMIT:
self._datasets.popitem(0)
self._datasets[stream_id] = result
return result
def get_coverage(self, stream_id):
'''
Memoization (LRU) of _get_coverage
'''
try:
result = self._coverages.pop(stream_id)
except KeyError:
dataset_id = self.get_dataset(stream_id)
if dataset_id is None:
return None
result = DatasetManagementService._get_simplex_coverage(dataset_id, mode='a')
if result is None:
return None
if len(self._coverages) >= self.CACHE_LIMIT:
k, coverage = self._coverages.popitem(0)
coverage.close(timeout=5)
self._coverages[stream_id] = result
return result
def gap_coverage(self,stream_id):
try:
old_cov = self._coverages.pop(stream_id)
dataset_id = self.get_dataset(stream_id)
sdom, tdom = time_series_domain()
new_cov = DatasetManagementService._create_simplex_coverage(dataset_id, old_cov.parameter_dictionary, sdom, tdom, old_cov._persistence_layer.inline_data_writes)
old_cov.close()
result = new_cov
except KeyError:
result = self.get_coverage(stream_id)
self._coverages[stream_id] = result
return result
def dataset_changed(self, dataset_id, extents, window):
self.event_publisher.publish_event(origin=dataset_id, author=self.id, extents=extents, window=window)
def evaluate_qc(self, rdt, dataset_id):
if self.qc_enabled:
for field in rdt.fields:
if not (field.endswith('glblrng_qc') or field.endswith('loclrng_qc')):
continue
try:
values = rdt[field]
if values is not None:
if not all(values):
topology = np.where(values==0)
timestamps = rdt[rdt.temporal_parameter][topology[0]]
self.flag_qc_parameter(dataset_id, field, timestamps.tolist(), {})
except:
continue
def flag_qc_parameter(self, dataset_id, parameter, temporal_values, configuration):
data_product_ids, _ = self.container.resource_registry.find_subjects(object=dataset_id, predicate=PRED.hasDataset, subject_type=RT.DataProduct, id_only=True)
for data_product_id in data_product_ids:
description = 'Automated Quality Control Alerted on %s' % parameter
self.qc_publisher.publish_event(origin=data_product_id, qc_parameter=parameter, temporal_values=temporal_values, configuration=configuration, description=description)
def update_connection_index(self, connection_id, connection_index):
self.connection_id = connection_id
try:
connection_index = int(connection_index)
self.connection_index = connection_index
except ValueError:
pass
def has_gap(self, connection_id, connection_index):
if connection_id:
if not self.connection_id:
self.update_connection_index(connection_id, connection_index)
return False
else:
if connection_id != self.connection_id:
return True
if connection_index:
if self.connection_index is None:
self.update_connection_index(connection_id, connection_index)
return False
try:
connection_index = int(connection_index)
if connection_index != self.connection_index+1:
return True
except ValueError:
pass
return False
def splice_coverage(self, dataset_id, coverage):
log.info('Splicing new coverage')
DatasetManagementService._splice_coverage(dataset_id, coverage)
@handle_stream_exception()
def recv_packet(self, msg, stream_route, stream_id):
''' receive packet for ingestion '''
log.trace('received granule for stream %s', stream_id)
if msg == {}:
log.error('Received empty message from stream: %s', stream_id)
return
# Message validation
if not isinstance(msg, Granule):
log.error('Ingestion received a message that is not a granule: %s', msg)
return
rdt = RecordDictionaryTool.load_from_granule(msg)
if rdt is None:
log.error('Invalid granule (no RDT) for stream %s', stream_id)
return
if not len(rdt):
log.debug('Empty granule for stream %s', stream_id)
return
self.persist_or_timeout(stream_id, rdt)
def persist_or_timeout(self, stream_id, rdt):
""" retry writing coverage multiple times and eventually time out """
done = False
timeout = 2
start = time.time()
while not done:
try:
self.add_granule(stream_id, rdt)
done = True
except:
log.exception('An issue with coverage, retrying after a bit')
if (time.time() - start) > MAX_RETRY_TIME: # After an hour just give up
dataset_id = self.get_dataset(stream_id)
log.error("We're giving up, the coverage needs to be inspected %s", DatasetManagementService._get_coverage_path(dataset_id))
raise
if stream_id in self._coverages:
log.info('Popping coverage for stream %s', stream_id)
self._coverages.pop(stream_id)
gevent.sleep(timeout)
if timeout > (60 * 5):
timeout = 60 * 5
else:
timeout *= 2
def expand_coverage(self, coverage, elements, stream_id):
try:
coverage.insert_timesteps(elements, oob=False)
except IOError as e:
log.error("Couldn't insert time steps for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def get_stored_values(self, lookup_value):
if not self.new_lookups.empty():
new_values = self.new_lookups.get()
self.lookup_docs = new_values + self.lookup_docs
lookup_value_document_keys = self.lookup_docs
for key in lookup_value_document_keys:
try:
document = self.stored_value_manager.read_value(key)
if lookup_value in document:
return document[lookup_value]
except NotFound:
log.warning('Specified lookup document does not exist')
return None
def fill_lookup_values(self, rdt):
rdt.fetch_lookup_values()
for field in rdt.lookup_values():
value = self.get_stored_values(rdt.context(field).lookup_value)
if value:
rdt[field] = value
def insert_sparse_values(self, coverage, rdt, stream_id):
self.fill_lookup_values(rdt)
for field in rdt.fields:
if rdt._rd[field] is None:
continue
if not isinstance(rdt.context(field).param_type, SparseConstantType):
# We only set sparse values before insert
continue
value = rdt[field]
try:
coverage.set_parameter_values(param_name=field, value=value)
except ValueError as e:
if "'lower_bound' cannot be >= 'upper_bound'" in e.message:
continue
else:
raise
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
def insert_values(self, coverage, rdt, stream_id):
elements = len(rdt)
start_index = coverage.num_timesteps - elements
for k,v in rdt.iteritems():
if isinstance(v, SparseConstantValue):
continue
slice_ = slice(start_index, None)
try:
coverage.set_parameter_values(param_name=k, tdoa=slice_, value=v)
except IOError as e:
log.error("Couldn't insert values for coverage: %s",
coverage.persistence_dir, exc_info=True)
try:
coverage.close()
finally:
self._bad_coverages[stream_id] = 1
raise CorruptionError(e.message)
except IndexError as e:
log.error("Value set: %s", v[:])
data_products, _ = self.container.resource_registry.find_subjects(object=stream_id, predicate=PRED.hasStream, subject_type=RT.DataProduct)
for data_product in data_products:
log.exception("Index exception with %s, trying to insert %s into coverage with shape %s",
data_product.name,
k,
v.shape)
if 'ingestion_timestamp' in coverage.list_parameters():
t_now = time.time()
ntp_time = TimeUtils.ts_to_units(coverage.get_parameter_context('ingestion_timestamp').uom, t_now)
coverage.set_parameter_values(param_name='ingestion_timestamp', tdoa=slice_, value=ntp_time)
def add_granule(self,stream_id, rdt):
''' Appends the granule's data to the coverage and persists it. '''
debugging = log.isEnabledFor(DEBUG)
timer = Timer() if debugging else None
if stream_id in self._bad_coverages:
log.info('Message attempting to be inserted into bad coverage: %s',
DatasetManagementService._get_coverage_path(self.get_dataset(stream_id)))
#--------------------------------------------------------------------------------
# Gap Analysis
#--------------------------------------------------------------------------------
if not self.ignore_gaps:
gap_found = self.has_gap(rdt.connection_id, rdt.connection_index)
if gap_found:
log.warning('Gap Found! New connection: (%s,%s)\tOld Connection: (%s,%s)', rdt.connection_id, rdt.connection_index, self.connection_id, self.connection_index)
self.gap_coverage(stream_id)
#--------------------------------------------------------------------------------
# Coverage determiniation and appending
#--------------------------------------------------------------------------------
dataset_id = self.get_dataset(stream_id)
if not dataset_id:
log.error('No dataset could be determined on this stream: %s', stream_id)
return
try:
coverage = self.get_coverage(stream_id)
except IOError as e:
log.error("Couldn't open coverage: %s",
DatasetManagementService._get_coverage_path(self.get_dataset(stream_id)))
raise CorruptionError(e.message)
if debugging:
path = DatasetManagementService._get_coverage_path(dataset_id)
log.debug('%s: add_granule stream %s dataset %s coverage %r file %s',
self._id, stream_id, dataset_id, coverage, path)
if not coverage:
log.error('Could not persist coverage from granule, coverage is None')
return
#--------------------------------------------------------------------------------
# Actual persistence
#--------------------------------------------------------------------------------
elements = len(rdt)
if rdt[rdt.temporal_parameter] is None:
elements = 0
self.insert_sparse_values(coverage,rdt,stream_id)
if debugging:
timer.complete_step('checks') # lightweight ops, should be zero
self.expand_coverage(coverage, elements, stream_id)
if debugging:
timer.complete_step('insert')
self.insert_values(coverage, rdt, stream_id)
if debugging:
timer.complete_step('keys')
DatasetManagementService._save_coverage(coverage)
if debugging:
timer.complete_step('save')
start_index = coverage.num_timesteps - elements
if not self.ignore_gaps and gap_found:
self.splice_coverage(dataset_id, coverage)
self.evaluate_qc(rdt, dataset_id)
if debugging:
timer.complete_step('notify')
self._add_timing_stats(timer)
self.update_connection_index(rdt.connection_id, rdt.connection_index)
self.update_metadata(dataset_id, rdt)
self.dataset_changed(dataset_id,coverage.num_timesteps,(start_index,start_index+elements))
def _add_timing_stats(self, timer):
""" add stats from latest coverage operation to Accumulator and periodically log results """
self.time_stats.add(timer)
if self.time_stats.get_count() % REPORT_FREQUENCY>0:
return
if log.isEnabledFor(TRACE):
# report per step
for step in 'checks', 'insert', 'keys', 'save', 'notify':
log.debug('%s step %s times: %s', self._id, step, self.time_stats.to_string(step))
# report totals
log.debug('%s total times: %s', self._id, self.time_stats)
def test_lookup_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_lookups()
stream_def_id = self.pubsubcli.create_stream_definition('lookup', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsubcli.delete_stream_definition, stream_def_id)
data_product = DataProduct(name='lookup data product')
tdom, sdom = time_series_domain()
data_product.temporal_domain = tdom.dump()
data_product.spatial_domain = sdom.dump()
data_product_id = self.dpsc_cli.create_data_product(data_product, stream_definition_id=stream_def_id)
self.addCleanup(self.dpsc_cli.delete_data_product, data_product_id)
data_producer = DataProducer(name='producer')
data_producer.producer_context = DataProcessProducerContext()
data_producer.producer_context.configuration['qc_keys'] = ['offset_document']
data_producer_id, _ = self.rrclient.create(data_producer)
self.addCleanup(self.rrclient.delete, data_producer_id)
assoc,_ = self.rrclient.create_association(subject=data_product_id, object=data_producer_id, predicate=PRED.hasDataProducer)
self.addCleanup(self.rrclient.delete_association, assoc)
document_keys = self.damsclient.list_qc_references(data_product_id)
self.assertEquals(document_keys, ['offset_document'])
svm = StoredValueManager(self.container)
svm.stored_value_cas('offset_document', {'offset_a':2.0})
self.dpsc_cli.activate_data_product_persistence(data_product_id)
dataset_ids, _ = self.rrclient.find_objects(subject=data_product_id, predicate=PRED.hasDataset, id_only=True)
dataset_id = dataset_ids[0]
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [20.]
granule = rdt.to_granule()
stream_ids, _ = self.rrclient.find_objects(subject=data_product_id, predicate=PRED.hasStream, id_only=True)
stream_id = stream_ids[0]
route = self.pubsubcli.read_stream_route(stream_id=stream_id)
publisher = StandaloneStreamPublisher(stream_id, route)
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['temp'], rdt2['temp'])
np.testing.assert_array_almost_equal(rdt2['calibrated'], np.array([22.0]))
svm.stored_value_cas('updated_document', {'offset_a':3.0})
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
ep = EventPublisher(event_type=OT.ExternalReferencesUpdatedEvent)
ep.publish_event(origin=data_product_id, reference_keys=['updated_document'])
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [1]
rdt['temp'] = [20.]
granule = rdt.to_granule()
gevent.sleep(2) # Yield so that the event goes through
publisher.publish(granule)
self.assertTrue(dataset_monitor.event.wait(10))
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt2['temp'],np.array([20.,20.]))
np.testing.assert_array_almost_equal(rdt2['calibrated'], np.array([22.0,23.0]))
def setUp(self):
DMTestCase.setUp(self)
self.ph = ParameterHelper(self.dataset_management, self.addCleanup)
self.pdict_id = self.ph.create_simple_qc_pdict()
self.svm = StoredValueManager(self.container)
