def test_ingestion_pause(self):
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
ingestion_config_id = self.get_ingestion_config()
self.start_ingestion(ctd_stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, ctd_stream_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
monitor = DatasetMonitor(dataset_id)
self.addCleanup(monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
self.ingestion_management.pause_data_stream(ctd_stream_id, ingestion_config_id)
monitor.event.clear()
rdt['time'] = np.arange(10,20)
publisher.publish(rdt.to_granule())
self.assertFalse(monitor.event.wait(1))
self.ingestion_management.resume_data_stream(ctd_stream_id, ingestion_config_id)
self.assertTrue(monitor.wait())
granule = self.data_retriever.retrieve(dataset_id)
rdt2 = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_almost_equal(rdt2['time'], np.arange(20))
def test_qc_events(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_qc_pdict()
stream_def_id = self.pubsub_management.create_stream_definition('qc stream def', parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('qc stream', 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()
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)
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.arange(10) * 3
verified = Event()
def verification(event, *args, **kwargs):
self.assertEquals(event.qc_parameter, 'temp_qc')
self.assertEquals(event.temporal_value, 7)
verified.set()
es = EventSubscriber(event_type=OT.ParameterQCEvent, origin=dataset_id, callback=verification, auto_delete=True)
es.start()
self.addCleanup(es.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(verified.wait(10))
def create_test_granules(self, buffer_data=False):
"""
Generate test granules from particles. If buffer data is set to true then
try to buffer data into a granule. If the particle has the new sequence
flag set then a new granule will be generated. This method emulates the
agent_stream_publisher module.
:return: list of granules generated.
"""
base_timestamp = 3583861263.0
connection_index = 0
particles = []
particles.append(
self.get_particle(base_timestamp, 10.5914, 161.06, 4.1870, 2693.0))
particles.append(
self.get_particle(base_timestamp + 1, 10.5915, 161.07, 4.1871,
2693.1))
particles.append(
self.get_particle(base_timestamp + 2, 10.5916, 161.08, 4.1872,
2693.2))
particles.append(
self.get_particle(base_timestamp + 3, 10.5917, 161.09, 4.1873,
2693.3, True))
particles.append(
self.get_particle(base_timestamp + 4, 10.5918, 161.10, 4.1874,
2693.4))
data_groups = []
result_granules = []
data_groups_index = 0
for particle in particles:
# If we need a new connection then start a new group, but only if we have found
# something in the current group
if (particle.get('new_sequence', False) or buffer_data == False) and \
(len(data_groups) > 0 and len(data_groups[data_groups_index]) > 0):
data_groups_index += 1
if len(data_groups) <= data_groups_index:
data_groups.append([])
data_groups[data_groups_index].append(particle)
log.debug("Granules to create: %s", len(data_groups))
for data in data_groups:
connection_id = uuid.uuid4()
connection_index += 1
rdt = RecordDictionaryTool(param_dictionary=self.get_param_dict())
rdt = populate_rdt(rdt, data)
g = rdt.to_granule(data_producer_id='agent_res_id',
connection_id=connection_id.hex,
connection_index=str(connection_index))
result_granules.append(g)
return result_granules
def create_granule(self, stream_name, param_dict_name, particle_list):
pd_id = self.dataset_management.read_parameter_dictionary_by_name(param_dict_name, id_only=True)
stream_def_id = self.pubsub_client.create_stream_definition(name=stream_name, parameter_dictionary_id=pd_id)
stream_def = self.pubsub_client.read_stream_definition(stream_def_id)
rdt = RecordDictionaryTool(stream_definition=stream_def)
rdt = populate_rdt(rdt, particle_list)
log.trace("RDT: %s", str(rdt))
g = rdt.to_granule(data_producer_id='fake_agent_id')
return g
def publish_hifi(self,stream_id,stream_route,offset=0):
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def create_granule(self, stream_name, param_dict_name, particle_list):
pd_id = self.dataset_management.read_parameter_dictionary_by_name(param_dict_name, id_only=True)
stream_def_id = self.pubsub_client.create_stream_definition(name=stream_name, parameter_dictionary_id=pd_id)
stream_def = self.pubsub_client.read_stream_definition(stream_def_id)
rdt = RecordDictionaryTool(stream_definition=stream_def)
rdt = populate_rdt(rdt, particle_list)
log.trace("RDT: %s", str(rdt))
g = rdt.to_granule(data_producer_id='fake_agent_id')
return g
def test_retrieve_and_transform(self):
# Stream definition for the CTD data
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
stream_def_id = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
ctd_stream_id, route = self.pubsub_management.create_stream('ctd stream', 'xp1', stream_definition_id=stream_def_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
sal_stream_def_id = self.pubsub_management.create_stream_definition('sal data', parameter_dictionary_id=salinity_pdict_id)
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
#--------------------------------------------------------------------------------
# Again with this ridiculous problem
#--------------------------------------------------------------------------------
self.get_datastore(dataset_id)
self.ingestion_management.persist_data_stream(stream_id=ctd_stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.random.randn(10) * 10 + 30
rdt['conductivity'] = np.random.randn(10) * 2 + 10
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt['time'] = np.arange(10,20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 2)
granule = self.data_retriever.retrieve(dataset_id,
None,
None,
'ion.processes.data.transforms.ctd.ctd_L2_salinity',
'CTDL2SalinityTransformAlgorithm',
kwargs=dict(params=sal_stream_def_id))
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt['salinity']:
self.assertNotEquals(i,0)
def _publish_stream_buffer(self, stream_name):
"""
['quality_flag', 'preferred_timestamp', 'port_timestamp', 'lon', 'raw', 'internal_timestamp', 'time', 'lat', 'driver_timestamp']
['quality_flag', 'preferred_timestamp', 'temp', 'density', 'port_timestamp', 'lon', 'salinity', 'pressure', 'internal_timestamp', 'time', 'lat', 'driver_timestamp', 'conductivit
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "raw",
"values": [{"binary": true, "value": "MzIuMzkxOSw5MS4wOTUxMiwgNzg0Ljg1MywgICA2LjE5OTQsIDE1MDUuMTc5LCAxOSBEZWMgMjAxMiwgMDA6NTI6Mjc=", "value_id": "raw"}]}', 'time': 1355878347.744123}
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "parsed",
"values": [{"value": 32.3919, "value_id": "temp"}, {"value": 91.09512, "value_id": "conductivity"}, {"value": 784.853, "value_id": "pressure"}]}', 'time': 1355878347.744127}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'port_timestamp': [None], 'lon': [None], 'raw': ['-4.9733,16.02390, 539.527, 34.2719, 1506.862, 19 Dec 2012, 01:03:07'],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117]}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'temp': [-4.9733], 'density': [None], 'port_timestamp': [None], 'lon': [None], 'salinity': [None], 'pressure': [539.527],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117], 'conductivity': [16.0239]}
"""
try:
buf_len = len(self._stream_buffers[stream_name])
if buf_len == 0:
return
stream_def = self._stream_defs[stream_name]
if isinstance(stream_def, str):
rdt = RecordDictionaryTool(stream_definition_id=stream_def)
else:
rdt = RecordDictionaryTool(stream_definition=stream_def)
publisher = self._publishers[stream_name]
vals = []
for x in xrange(buf_len):
vals.append(self._stream_buffers[stream_name].pop())
rdt = populate_rdt(rdt, vals)
log.info('Outgoing granule: %s',
['%s: %s' % (k, v) for k, v in rdt.iteritems()])
g = rdt.to_granule(data_producer_id=self._agent.resource_id,
connection_id=self._connection_ID.hex,
connection_index=str(
self._connection_index[stream_name]))
publisher.publish(g)
log.info(
'Instrument agent %s published data granule on stream %s.',
self._agent._proc_name, stream_name)
log.info('Connection id: %s, connection index: %i.',
self._connection_ID.hex,
self._connection_index[stream_name])
self._connection_index[stream_name] += 1
except:
log.exception(
'Instrument agent %s could not publish data on stream %s.',
self._agent._proc_name, stream_name)
def publish_loop(self):
t_i = 0
while not self.finished.is_set():
rdt = RecordDictionaryTool(stream_definition_id=self.stream_def._id)
rdt['time'] = numpy.arange(10) + t_i*10
rdt['temp'] = numpy.random.random_sample(10)*(30-0)+0
self.publish(rdt.to_granule())
gevent.sleep(self.interval)
t_i += 1
def test_retrieve_and_transform(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(ctd_stream_id, dataset_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
"ctd_parsed_param_dict", id_only=True
)
sal_stream_def_id = self.pubsub_management.create_stream_definition(
"sal data", parameter_dictionary_id=salinity_pdict_id
)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt["time"] = np.arange(10)
rdt["temp"] = np.random.randn(10) * 10 + 30
rdt["conductivity"] = np.random.randn(10) * 2 + 10
rdt["pressure"] = np.random.randn(10) * 1 + 12
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt["time"] = np.arange(10, 20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 20)
granule = self.data_retriever.retrieve(
dataset_id,
None,
None,
"ion.processes.data.transforms.ctd.ctd_L2_salinity",
"CTDL2SalinityTransformAlgorithm",
kwargs=dict(params=sal_stream_def_id),
)
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt["salinity"]:
self.assertNotEquals(i, 0)
self.streams.append(ctd_stream_id)
self.stop_ingestion(ctd_stream_id)
def publish_loop(self):
#@todo - add lots of comments in here
while not self.finished.is_set():
length = 10
#Explicitly make these numpy arrays...
c = numpy.array(
[random.uniform(0.0, 75.0) for i in xrange(length)])
t = numpy.array(
[random.uniform(-1.7, 21.0) for i in xrange(length)])
p = numpy.array(
[random.lognormvariate(1, 2) for i in xrange(length)])
lat = numpy.array(
[random.uniform(-90.0, 90.0) for i in xrange(length)])
lon = numpy.array(
[random.uniform(0.0, 360.0) for i in xrange(length)])
tvar = numpy.array(
[self.last_time + i for i in xrange(1, length + 1)])
self.last_time = max(tvar)
parameter_dictionary = self._create_parameter()
rdt = RecordDictionaryTool(param_dictionary=parameter_dictionary)
# This is an example of using groups it is not a normative statement about how to use groups
rdt['temp'] = t
rdt['conductivity'] = c
rdt['pressure'] = p
#add a value sequence of raw bytes - not sure the type below is correct?
with open('/dev/urandom', 'r') as rand:
rdt['raw_fixed'] = numpy.array(
[rand.read(32) for i in xrange(length)], dtype='a32')
#add a value sequence of raw bytes - not sure the type below is correct?
with open('/dev/urandom', 'r') as rand:
rdt['raw_blob'] = numpy.array(
[rand.read(random.randint(1, 40)) for i in xrange(length)],
dtype=object)
rdt['time'] = tvar
rdt['lat'] = lat
rdt['lon'] = lon
g = rdt.to_granule()
log.info('Sending %d values!' % length)
if isinstance(g, Granule):
self.publish(g)
gevent.sleep(self.interval)
def _publish_stream_buffer(self, stream_name):
"""
['quality_flag', 'preferred_timestamp', 'port_timestamp', 'lon', 'raw', 'internal_timestamp', 'time', 'lat', 'driver_timestamp']
['quality_flag', 'preferred_timestamp', 'temp', 'density', 'port_timestamp', 'lon', 'salinity', 'pressure', 'internal_timestamp', 'time', 'lat', 'driver_timestamp', 'conductivit
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "raw",
"values": [{"binary": true, "value": "MzIuMzkxOSw5MS4wOTUxMiwgNzg0Ljg1MywgICA2LjE5OTQsIDE1MDUuMTc5LCAxOSBEZWMgMjAxMiwgMDA6NTI6Mjc=", "value_id": "raw"}]}', 'time': 1355878347.744123}
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "parsed",
"values": [{"value": 32.3919, "value_id": "temp"}, {"value": 91.09512, "value_id": "conductivity"}, {"value": 784.853, "value_id": "pressure"}]}', 'time': 1355878347.744127}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'port_timestamp': [None], 'lon': [None], 'raw': ['-4.9733,16.02390, 539.527, 34.2719, 1506.862, 19 Dec 2012, 01:03:07'],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117]}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'temp': [-4.9733], 'density': [None], 'port_timestamp': [None], 'lon': [None], 'salinity': [None], 'pressure': [539.527],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117], 'conductivity': [16.0239]}
"""
try:
buf_len = len(self._stream_buffers[stream_name])
if buf_len == 0:
return
stream_def = self._stream_defs[stream_name]
if isinstance(stream_def, str):
rdt = RecordDictionaryTool(stream_definition_id=stream_def)
else:
rdt = RecordDictionaryTool(stream_definition=stream_def)
publisher = self._publishers[stream_name]
vals = []
for x in xrange(buf_len):
vals.append(self._stream_buffers[stream_name].pop())
rdt = populate_rdt(rdt, vals)
#log.info('Outgoing granule: %s',
#['%s: %s'%(k,v) for k,v in rdt.iteritems()])
#log.info('Outgoing granule preferred timestamp: %s' % rdt['preferred_timestamp'][0])
#log.info('Outgoing granule destined for stream: %s', stream_name)
g = rdt.to_granule(data_producer_id=self._agent.resource_id, connection_id=self._connection_ID.hex,
connection_index=str(self._connection_index[stream_name]))
publisher.publish(g)
#log.info('Instrument agent %s published data granule on stream %s.',
#self._agent._proc_name, stream_name)
#log.info('Connection id: %s, connection index: %i.',
#self._connection_ID.hex, self._connection_index[stream_name])
self._connection_index[stream_name] += 1
except:
log.exception('Instrument agent %s could not publish data on stream %s.',
self._agent._proc_name, stream_name)
def test_ingestion_gap_analysis(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
connection1 = uuid4().hex
connection2 = uuid4().hex
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [0]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index='0'))
rdt['time'] = [1]
rdt['temp'] = [1]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index=1))
rdt['time'] = [2]
rdt['temp'] = [2]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection1,connection_index='3')) # Gap, missed message
rdt['time'] = [3]
rdt['temp'] = [3]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index='3')) # Gap, new connection
rdt['time'] = [4]
rdt['temp'] = [4]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index='4'))
rdt['time'] = [5]
rdt['temp'] = [5]
self.publish_and_wait(dataset_id, rdt.to_granule(connection_id=connection2,connection_index=5))
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['time'], np.arange(6))
np.testing.assert_array_equal(rdt['temp'], np.arange(6))
return dataset_id
def publish_hifi(self,stream_id,stream_route,offset=0):
'''
Publish deterministic data
'''
pub = StandaloneStreamPublisher(stream_id, stream_route)
stream_def = self.pubsub_management.read_stream_definition(stream_id=stream_id)
stream_def_id = stream_def._id
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10) + (offset * 10)
rdt['temp'] = np.arange(10) + (offset * 10)
pub.publish(rdt.to_granule())
def test_retrieve_and_transform(self):
# Make a simple dataset and start ingestion, pretty standard stuff.
ctd_stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(ctd_stream_id, dataset_id)
# Stream definition for the salinity data
salinity_pdict_id = self.dataset_management.read_parameter_dictionary_by_name(
'ctd_parsed_param_dict', id_only=True)
sal_stream_def_id = self.pubsub_management.create_stream_definition(
'sal data', parameter_dictionary_id=salinity_pdict_id)
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = np.arange(10)
rdt['temp'] = np.random.randn(10) * 10 + 30
rdt['conductivity'] = np.random.randn(10) * 2 + 10
rdt['pressure'] = np.random.randn(10) * 1 + 12
publisher = StandaloneStreamPublisher(ctd_stream_id, route)
publisher.publish(rdt.to_granule())
rdt['time'] = np.arange(10, 20)
publisher.publish(rdt.to_granule())
self.wait_until_we_have_enough_granules(dataset_id, 20)
granule = self.data_retriever.retrieve(
dataset_id,
None,
None,
'ion.processes.data.transforms.ctd.ctd_L2_salinity',
'CTDL2SalinityTransformAlgorithm',
kwargs=dict(params=sal_stream_def_id))
rdt = RecordDictionaryTool.load_from_granule(granule)
for i in rdt['salinity']:
self.assertNotEquals(i, 0)
self.streams.append(ctd_stream_id)
self.stop_ingestion(ctd_stream_id)
def publish_loop(self):
t_i = 0
while not self.finished.is_set():
rdt = RecordDictionaryTool(stream_definition_id=self.stream_def._id)
rdt['time'] = numpy.arange(10) + t_i*10
rdt['temp'] = numpy.random.random(10) * 10
rdt['lat'] = numpy.array([0] * 10)
rdt['lon'] = numpy.array([0] * 10)
rdt['conductivity'] = numpy.random.random(10) * 10
rdt['binary'] = numpy.array(['hi'] * 10, dtype='object')
self.publish(rdt.to_granule())
gevent.sleep(self.interval)
t_i += 1
def publish_loop(self):
#@todo - add lots of comments in here
while not self.finished.is_set():
length = 10
#Explicitly make these numpy arrays...
c = numpy.array([random.uniform(0.0,75.0) for i in xrange(length)])
t = numpy.array([random.uniform(-1.7, 21.0) for i in xrange(length)])
p = numpy.array([random.lognormvariate(1,2) for i in xrange(length)])
lat = numpy.array([random.uniform(-90.0, 90.0) for i in xrange(length)])
lon = numpy.array([random.uniform(0.0, 360.0) for i in xrange(length)])
tvar = numpy.array([self.last_time + i for i in xrange(1,length+1)])
self.last_time = max(tvar)
parameter_dictionary = self._create_parameter()
rdt = RecordDictionaryTool(param_dictionary=parameter_dictionary)
# This is an example of using groups it is not a normative statement about how to use groups
rdt['temp'] = t
rdt['conductivity'] = c
rdt['pressure'] = p
#add a value sequence of raw bytes - not sure the type below is correct?
with open('/dev/urandom','r') as rand:
rdt['raw_fixed'] = numpy.array([rand.read(32) for i in xrange(length)], dtype='a32')
#add a value sequence of raw bytes - not sure the type below is correct?
with open('/dev/urandom','r') as rand:
rdt['raw_blob'] = numpy.array([rand.read(random.randint(1,40)) for i in xrange(length)], dtype=object)
rdt['time'] = tvar
rdt['lat'] = lat
rdt['lon'] = lon
g = rdt.to_granule()
log.info('Sending %d values!' % length)
if isinstance(g,Granule):
self.publish(g)
gevent.sleep(self.interval)
def create_test_granules(self, buffer_data=False):
"""
Generate test granules from particles. If buffer data is set to true then
try to buffer data into a granule. If the particle has the new sequence
flag set then a new granule will be generated. This method emulates the
agent_stream_publisher module.
:return: list of granules generated.
"""
base_timestamp = 3583861263.0
connection_index = 0
particles = []
particles.append(self.get_particle(base_timestamp, 10.5914, 161.06, 4.1870, 2693.0))
particles.append(self.get_particle(base_timestamp+1, 10.5915, 161.07, 4.1871, 2693.1))
particles.append(self.get_particle(base_timestamp+2, 10.5916, 161.08, 4.1872, 2693.2))
particles.append(self.get_particle(base_timestamp+3, 10.5917, 161.09, 4.1873, 2693.3, True))
particles.append(self.get_particle(base_timestamp+4, 10.5918, 161.10, 4.1874, 2693.4))
data_groups = []
result_granules = []
data_groups_index = 0
for particle in particles:
# If we need a new connection then start a new group, but only if we have found
# something in the current group
if (particle.get('new_sequence', False) or buffer_data == False) and \
(len(data_groups) > 0 and len(data_groups[data_groups_index]) > 0):
data_groups_index += 1
if len(data_groups) <= data_groups_index:
data_groups.append([])
data_groups[data_groups_index].append(particle)
log.debug("Granules to create: %s", len(data_groups))
for data in data_groups:
connection_id = uuid.uuid4()
connection_index += 1
rdt = RecordDictionaryTool(param_dictionary=self.get_param_dict())
rdt = populate_rdt(rdt, data)
g = rdt.to_granule(data_producer_id='agent_res_id', connection_id=connection_id.hex,
connection_index=str(connection_index))
result_granules.append(g)
return result_granules
def test_ingestion_gap_analysis(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset(
)
self.start_ingestion(stream_id, dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
connection1 = uuid4().hex
connection2 = uuid4().hex
rdt = RecordDictionaryTool(stream_definition_id=stream_def_id)
rdt['time'] = [0]
rdt['temp'] = [0]
self.publish_and_wait(
dataset_id,
rdt.to_granule(connection_id=connection1, connection_index='0'))
rdt['time'] = [1]
rdt['temp'] = [1]
self.publish_and_wait(
dataset_id,
rdt.to_granule(connection_id=connection1, connection_index='1'))
rdt['time'] = [2]
rdt['temp'] = [2]
self.publish_and_wait(dataset_id,
rdt.to_granule(
connection_id=connection1,
connection_index='3')) # Gap, missed message
rdt['time'] = [3]
rdt['temp'] = [3]
self.publish_and_wait(dataset_id,
rdt.to_granule(
connection_id=connection2,
connection_index='3')) # Gap, new connection
rdt['time'] = [4]
rdt['temp'] = [4]
self.publish_and_wait(
dataset_id,
rdt.to_granule(connection_id=connection2, connection_index='4'))
rdt['time'] = [5]
rdt['temp'] = [5]
self.publish_and_wait(
dataset_id,
rdt.to_granule(connection_id=connection2, connection_index='5'))
granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
np.testing.assert_array_equal(rdt['time'], np.arange(6))
np.testing.assert_array_equal(rdt['temp'], np.arange(6))
return dataset_id
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))
def _publish_stream_buffer(self, stream_name):
"""
['quality_flag', 'preferred_timestamp', 'port_timestamp', 'lon', 'raw', 'internal_timestamp', 'time', 'lat', 'driver_timestamp']
['quality_flag', 'preferred_timestamp', 'temp', 'density', 'port_timestamp', 'lon', 'salinity', 'pressure', 'internal_timestamp', 'time', 'lat', 'driver_timestamp', 'conductivit
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "raw",
"values": [{"binary": true, "value": "MzIuMzkxOSw5MS4wOTUxMiwgNzg0Ljg1MywgICA2LjE5OTQsIDE1MDUuMTc5LCAxOSBEZWMgMjAxMiwgMDA6NTI6Mjc=", "value_id": "raw"}]}', 'time': 1355878347.744123}
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "parsed",
"values": [{"value": 32.3919, "value_id": "temp"}, {"value": 91.09512, "value_id": "conductivity"}, {"value": 784.853, "value_id": "pressure"}]}', 'time': 1355878347.744127}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'port_timestamp': [None], 'lon': [None], 'raw': ['-4.9733,16.02390, 539.527, 34.2719, 1506.862, 19 Dec 2012, 01:03:07'],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117]}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'temp': [-4.9733], 'density': [None], 'port_timestamp': [None], 'lon': [None], 'salinity': [None], 'pressure': [539.527],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117], 'conductivity': [16.0239]}
"""
try:
### Flush the agent state to the object store. This was added for the dataset agent publishers who store
### their driver state in the object store. We had talked about about flushing the state after publiction
### by grabbing current state here, doing out work, and then saving this state. However, flush_state
### doesn't accept parameters. It seems more complex than simply flushing here. There is a slight downside
### if publishing fails then the state will be slightly out of sync.
if self._flush_on_publish:
log.debug("ASP Flush Agent State")
self._agent._flush_state()
buf_len = len(self._stream_buffers[stream_name])
if buf_len == 0:
return
stream_def = self._stream_defs[stream_name]
if isinstance(stream_def, str):
rdt = RecordDictionaryTool(stream_definition_id=stream_def)
else:
rdt = RecordDictionaryTool(stream_definition=stream_def)
publisher = self._publishers[stream_name]
vals = []
for x in xrange(buf_len):
vals.append(self._stream_buffers[stream_name].pop())
rdt = populate_rdt(rdt, vals)
#log.info('Outgoing granule: %s',
#['%s: %s'%(k,v) for k,v in rdt.iteritems()])
#log.info('Outgoing granule preferred timestamp: %s' % rdt['preferred_timestamp'][0])
#log.info('Outgoing granule destined for stream: %s', stream_name)
g = rdt.to_granule(data_producer_id=self._agent.resource_id, connection_id=self._connection_ID.hex,
connection_index=str(self._connection_index[stream_name]))
publisher.publish(g)
#log.info('Instrument agent %s published data granule on stream %s.',
#self._agent._proc_name, stream_name)
#log.info('Connection id: %s, connection index: %i.',
#self._connection_ID.hex, self._connection_index[stream_name])
self._connection_index[stream_name] += 1
except:
log.exception('Instrument agent %s could not publish data on stream %s.',
self._agent._proc_name, stream_name)
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))
def _publish_stream_buffer(self, stream_name):
"""
['quality_flag', 'preferred_timestamp', 'port_timestamp', 'lon', 'raw', 'internal_timestamp', 'time', 'lat', 'driver_timestamp']
['quality_flag', 'preferred_timestamp', 'temp', 'density', 'port_timestamp', 'lon', 'salinity', 'pressure', 'internal_timestamp', 'time', 'lat', 'driver_timestamp', 'conductivit
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "raw",
"values": [{"binary": true, "value": "MzIuMzkxOSw5MS4wOTUxMiwgNzg0Ljg1MywgICA2LjE5OTQsIDE1MDUuMTc5LCAxOSBEZWMgMjAxMiwgMDA6NTI6Mjc=", "value_id": "raw"}]}', 'time': 1355878347.744123}
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "parsed",
"values": [{"value": 32.3919, "value_id": "temp"}, {"value": 91.09512, "value_id": "conductivity"}, {"value": 784.853, "value_id": "pressure"}]}', 'time': 1355878347.744127}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'port_timestamp': [None], 'lon': [None], 'raw': ['-4.9733,16.02390, 539.527, 34.2719, 1506.862, 19 Dec 2012, 01:03:07'],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117]}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'temp': [-4.9733], 'density': [None], 'port_timestamp': [None], 'lon': [None], 'salinity': [None], 'pressure': [539.527],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117], 'conductivity': [16.0239]}
"""
try:
buf_len = len(self._stream_buffers[stream_name])
if buf_len == 0:
return
stream_def = self._stream_defs[stream_name]
rdt = RecordDictionaryTool(stream_definition_id=stream_def)
publisher = self._publishers[stream_name]
vals = []
for x in range(buf_len):
vals.append(self._stream_buffers[stream_name].pop())
data_arrays = {}
for x in rdt.fields:
data_arrays[x] = [None for y in range(buf_len)]
for i in range(buf_len):
tomato = vals[i]
for (tk, tv) in tomato.iteritems():
if tk == 'values':
for tval_dict in tv:
tval_id = tval_dict['value_id']
if tval_id in rdt:
tval_val = tval_dict['value']
if tval_dict.get('binary', None):
tval_val = base64.b64decode(tval_val)
data_arrays[tval_id][i] = tval_val
elif tk in rdt:
data_arrays[tk][i] = tv
if tk == 'driver_timestamp':
data_arrays['time'][i] = tv
for (k,v) in data_arrays.iteritems():
rdt[k] = numpy.array(v)
log.info('Outgoing granule: %s',
['%s: %s'%(k,v) for k,v in rdt.iteritems()])
g = rdt.to_granule(data_producer_id=self._agent.resource_id)
g.connection_id = self._connection_ID.hex
g.connection_index = self._connection_index[stream_name]
publisher.publish(g)
log.info('Instrument agent %s published data granule on stream %s.',
self._agent._proc_name, stream_name)
log.info('Connection id: %s, connection index: %i.',
self._connection_ID.hex, self._connection_index[stream_name])
except:
log.exception('Instrument agent %s could not publish data on stream %s.',
self._agent._proc_name, stream_name)
else:
self._connection_index[stream_name] += 1
def _publish_stream_buffer(self, stream_name):
"""
['quality_flag', 'preferred_timestamp', 'port_timestamp', 'lon', 'raw', 'internal_timestamp', 'time', 'lat', 'driver_timestamp']
['quality_flag', 'preferred_timestamp', 'temp', 'density', 'port_timestamp', 'lon', 'salinity', 'pressure', 'internal_timestamp', 'time', 'lat', 'driver_timestamp', 'conductivit
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "raw",
"values": [{"binary": true, "value": "MzIuMzkxOSw5MS4wOTUxMiwgNzg0Ljg1MywgICA2LjE5OTQsIDE1MDUuMTc5LCAxOSBEZWMgMjAxMiwgMDA6NTI6Mjc=", "value_id": "raw"}]}', 'time': 1355878347.744123}
{"driver_timestamp": 3564867147.743795, "pkt_format_id": "JSON_Data", "pkt_version": 1, "preferred_timestamp": "driver_timestamp", "quality_flag": "ok", "stream_name": "parsed",
"values": [{"value": 32.3919, "value_id": "temp"}, {"value": 91.09512, "value_id": "conductivity"}, {"value": 784.853, "value_id": "pressure"}]}', 'time': 1355878347.744127}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'port_timestamp': [None], 'lon': [None], 'raw': ['-4.9733,16.02390, 539.527, 34.2719, 1506.862, 19 Dec 2012, 01:03:07'],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117]}
{'quality_flag': [u'ok'], 'preferred_timestamp': [u'driver_timestamp'], 'temp': [-4.9733], 'density': [None], 'port_timestamp': [None], 'lon': [None], 'salinity': [None], 'pressure': [539.527],
'internal_timestamp': [None], 'time': [3564867788.0627117], 'lat': [None], 'driver_timestamp': [3564867788.0627117], 'conductivity': [16.0239]}
"""
try:
### Flush the agent state to the object store. This was added for the dataset agent publishers who store
### their driver state in the object store. We had talked about about flushing the state after publiction
### by grabbing current state here, doing out work, and then saving this state. However, flush_state
### doesn't accept parameters. It seems more complex than simply flushing here. There is a slight downside
### if publishing fails then the state will be slightly out of sync.
if self._flush_on_publish:
log.debug("ASP Flush Agent State")
self._agent._flush_state()
buf_len = len(self._stream_buffers[stream_name])
if buf_len == 0:
return
stream_def = self._stream_defs[stream_name]
if isinstance(stream_def, str):
rdt = RecordDictionaryTool(stream_definition_id=stream_def)
else:
rdt = RecordDictionaryTool(stream_definition=stream_def)
publisher = self._publishers[stream_name]
vals = []
for x in xrange(buf_len):
vals.append(self._stream_buffers[stream_name].pop())
rdt = populate_rdt(rdt, vals)
#log.info('Outgoing granule: %s',
#['%s: %s'%(k,v) for k,v in rdt.iteritems()])
#log.info('Outgoing granule preferred timestamp: %s' % rdt['preferred_timestamp'][0])
#log.info('Outgoing granule destined for stream: %s', stream_name)
g = rdt.to_granule(data_producer_id=self._agent.resource_id, connection_id=self._connection_ID.hex,
connection_index=str(self._connection_index[stream_name]))
publisher.publish(g)
#log.info('Instrument agent %s published data granule on stream %s.',
#self._agent._proc_name, stream_name)
#log.info('Connection id: %s, connection index: %i.',
#self._connection_ID.hex, self._connection_index[stream_name])
self._connection_index[stream_name] += 1
except:
log.exception('Instrument agent %s could not publish data on stream %s.',
self._agent._proc_name, stream_name)