class TestDMEnd2End(IonIntegrationTestCase):
def setUp(self): # Love the non pep-8 convention
self._start_container()
self.container.start_rel_from_url('res/deploy/r2deploy.yml')
self.process_dispatcher = ProcessDispatcherServiceClient()
self.pubsub_management = PubsubManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self.ingestion_management = IngestionManagementServiceClient()
self.data_retriever = DataRetrieverServiceClient()
self.event = Event()
self.exchange_space_name = 'test_granules'
self.exchange_point_name = 'science_data'
self.i = 0
self.cci = 0
#--------------------------------------------------------------------------------
# Helper/Utility methods
#--------------------------------------------------------------------------------
def create_dataset(self, parameter_dict_id=''):
'''
Creates a time-series dataset
'''
if not parameter_dict_id:
parameter_dict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict', id_only=True)
dataset = Dataset('test_dataset_%i'%self.i)
dataset_id = self.dataset_management.create_dataset(dataset, parameter_dictionary_id=parameter_dict_id)
self.addCleanup(self.dataset_management.delete_dataset, dataset_id)
return dataset_id
def get_datastore(self, dataset_id):
'''
Gets an instance of the datastore
This method is primarily used to defeat a bug where integration tests in multiple containers may sometimes
delete a CouchDB datastore and the other containers are unaware of the new state of the datastore.
'''
dataset = self.dataset_management.read_dataset(dataset_id)
datastore_name = dataset.datastore_name
datastore = self.container.datastore_manager.get_datastore(datastore_name, DataStore.DS_PROFILE.SCIDATA)
return datastore
def get_ingestion_config(self):
'''
Grab the ingestion configuration from the resource registry
'''
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration,id_only=True)
return ingest_configs[0]
def launch_producer(self, stream_id=''):
'''
Launch the producer
'''
pid = self.container.spawn_process('better_data_producer', 'ion.processes.data.example_data_producer', 'BetterDataProducer', {'process':{'stream_id':stream_id}})
self.addCleanup(self.container.terminate_process, pid)
def make_simple_dataset(self):
'''
Makes a stream, a stream definition and a dataset, the essentials for most of these tests
'''
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 %i' % self.i, parameter_dictionary_id=pdict_id)
self.addCleanup(self.pubsub_management.delete_stream_definition, stream_def_id)
stream_id, route = self.pubsub_management.create_stream('ctd stream %i' % self.i, 'xp1', stream_definition_id=stream_def_id)
self.addCleanup(self.pubsub_management.delete_stream, stream_id)
dataset_id = self.create_dataset(pdict_id)
# self.get_datastore(dataset_id)
self.i += 1
return stream_id, route, stream_def_id, 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 publish_fake_data(self,stream_id, route):
'''
Make four granules
'''
for i in xrange(4):
self.publish_hifi(stream_id,route,i)
def start_ingestion(self, stream_id, dataset_id):
'''
Starts ingestion/persistence for a given dataset
'''
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
def stop_ingestion(self, stream_id):
ingest_config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id)
def validate_granule_subscription(self, msg, route, stream_id):
'''
Validation for granule format
'''
if msg == {}:
return
rdt = RecordDictionaryTool.load_from_granule(msg)
log.info('%s', rdt.pretty_print())
self.assertIsInstance(msg,Granule,'Message is improperly formatted. (%s)' % type(msg))
self.event.set()
def wait_until_we_have_enough_granules(self, dataset_id='',data_size=40):
'''
Loops until there is a sufficient amount of data in the dataset
'''
done = False
with gevent.Timeout(40):
while not done:
extents = self.dataset_management.dataset_extents(dataset_id, 'time')
granule = self.data_retriever.retrieve_last_data_points(dataset_id, 1)
rdt = RecordDictionaryTool.load_from_granule(granule)
if rdt['time'] and rdt['time'][0] != rdt._pdict.get_context('time').fill_value and extents >= data_size:
done = True
else:
gevent.sleep(0.2)
#--------------------------------------------------------------------------------
# Test Methods
#--------------------------------------------------------------------------------
def test_dm_end_2_end(self):
#--------------------------------------------------------------------------------
# Set up a stream and have a mock instrument (producer) send data
#--------------------------------------------------------------------------------
self.event.clear()
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_definition = self.pubsub_management.create_stream_definition('ctd data', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('producer', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
#--------------------------------------------------------------------------------
# Start persisting the data on the stream
# - Get the ingestion configuration from the resource registry
# - Create the dataset
# - call persist_data_stream to setup the subscription for the ingestion workers
# on the stream that you specify which causes the data to be persisted
#--------------------------------------------------------------------------------
ingest_config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingest_config_id, dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
#--------------------------------------------------------------------------------
# Now the granules are ingesting and persisted
#--------------------------------------------------------------------------------
self.launch_producer(stream_id)
self.wait_until_we_have_enough_granules(dataset_id,40)
#--------------------------------------------------------------------------------
# Now get the data in one chunk using an RPC Call to start_retreive
#--------------------------------------------------------------------------------
replay_data = self.data_retriever.retrieve(dataset_id)
self.assertIsInstance(replay_data, Granule)
rdt = RecordDictionaryTool.load_from_granule(replay_data)
self.assertTrue((rdt['time'][:10] == np.arange(10)).all(),'%s' % rdt['time'][:])
self.assertTrue((rdt['binary'][:10] == np.array(['hi']*10, dtype='object')).all())
#--------------------------------------------------------------------------------
# Now to try the streamed approach
#--------------------------------------------------------------------------------
replay_stream_id, replay_route = self.pubsub_management.create_stream('replay_out', exchange_point=self.exchange_point_name, stream_definition_id=stream_definition)
self.replay_id, process_id = self.data_retriever.define_replay(dataset_id=dataset_id, stream_id=replay_stream_id)
log.info('Process ID: %s', process_id)
replay_client = ReplayClient(process_id)
#--------------------------------------------------------------------------------
# Create the listening endpoint for the the retriever to talk to
#--------------------------------------------------------------------------------
sub_id = self.pubsub_management.create_subscription(self.exchange_space_name,stream_ids=[replay_stream_id])
self.addCleanup(self.pubsub_management.delete_subscription, sub_id)
self.pubsub_management.activate_subscription(sub_id)
self.addCleanup(self.pubsub_management.deactivate_subscription, sub_id)
subscriber = StandaloneStreamSubscriber(self.exchange_space_name, self.validate_granule_subscription)
subscriber.start()
self.addCleanup(subscriber.stop)
self.data_retriever.start_replay_agent(self.replay_id)
self.assertTrue(replay_client.await_agent_ready(5), 'The process never launched')
replay_client.start_replay()
self.assertTrue(self.event.wait(10))
self.data_retriever.cancel_replay_agent(self.replay_id)
#--------------------------------------------------------------------------------
# Test the slicing capabilities
#--------------------------------------------------------------------------------
granule = self.data_retriever.retrieve(dataset_id=dataset_id, query={'tdoa':slice(0,5)})
rdt = RecordDictionaryTool.load_from_granule(granule)
b = rdt['time'] == np.arange(5)
self.assertTrue(b.all() if not isinstance(b,bool) else b)
def test_coverage_transform(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_parsed()
stream_def_id = self.pubsub_management.create_stream_definition('ctd parsed', 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)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=ingestion_config_id, dataset_id=dataset_id)
self.addCleanup(self.ingestion_management.unpersist_data_stream, stream_id, ingestion_config_id)
publisher = StandaloneStreamPublisher(stream_id, route)
rdt = ph.get_rdt(stream_def_id)
ph.fill_parsed_rdt(rdt)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
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'], rdt['time'])
np.testing.assert_array_almost_equal(rdt_out['temp'], rdt['temp'])
np.testing.assert_allclose(rdt_out['conductivity_L1'], np.array([42.914]))
np.testing.assert_allclose(rdt_out['temp_L1'], np.array([20.]))
np.testing.assert_allclose(rdt_out['pressure_L1'], np.array([3.068]))
np.testing.assert_allclose(rdt_out['density'], np.array([1021.7144739593881], dtype='float32'))
np.testing.assert_allclose(rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
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_last_granule(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)
self.publish_hifi(stream_id,route, 0)
self.publish_hifi(stream_id,route, 1)
self.wait_until_we_have_enough_granules(dataset_id,20) # I just need two
success = False
def verifier():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id, 10)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(10) + 10
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verifier)
self.assertTrue(success)
success = False
def verify_points():
replay_granule = self.data_retriever.retrieve_last_data_points(dataset_id,5)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(15,20)
if not isinstance(comp,bool):
return comp.all()
return False
success = poll(verify_points)
self.assertTrue(success)
def test_replay_with_parameters(self):
#--------------------------------------------------------------------------------
# Create the configurations and the dataset
#--------------------------------------------------------------------------------
# Get a precompiled parameter dictionary with basic ctd fields
pdict_id = self.dataset_management.read_parameter_dictionary_by_name('ctd_parsed_param_dict',id_only=True)
context_ids = self.dataset_management.read_parameter_contexts(pdict_id, id_only=True)
# Add a field that supports binary data input.
bin_context = ParameterContext('binary', param_type=ArrayType())
context_ids.append(self.dataset_management.create_parameter_context('binary', bin_context.dump()))
# Add another field that supports dictionary elements.
rec_context = ParameterContext('records', param_type=RecordType())
context_ids.append(self.dataset_management.create_parameter_context('records', rec_context.dump()))
pdict_id = self.dataset_management.create_parameter_dictionary('replay_pdict', parameter_context_ids=context_ids, temporal_context='time')
stream_def_id = self.pubsub_management.create_stream_definition('replay_stream', parameter_dictionary_id=pdict_id)
stream_id, route = self.pubsub_management.create_stream('replay_with_params', exchange_point=self.exchange_point_name, stream_definition_id=stream_def_id)
config_id = self.get_ingestion_config()
dataset_id = self.create_dataset(pdict_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id, ingestion_configuration_id=config_id, dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
self.publish_fake_data(stream_id, route)
self.assertTrue(dataset_monitor.wait())
query = {
'start_time': 0 - 2208988800,
'end_time': 19 - 2208988800,
'stride_time' : 2,
'parameters': ['time','temp']
}
retrieved_data = self.data_retriever.retrieve(dataset_id=dataset_id,query=query)
rdt = RecordDictionaryTool.load_from_granule(retrieved_data)
np.testing.assert_array_equal(rdt['time'], np.arange(0,20,2))
self.assertEquals(set(rdt.iterkeys()), set(['time','temp']))
extents = self.dataset_management.dataset_extents(dataset_id=dataset_id, parameters=['time','temp'])
self.assertTrue(extents['time']>=20)
self.assertTrue(extents['temp']>=20)
def test_repersist_data(self):
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
self.publish_hifi(stream_id,route,0)
self.publish_hifi(stream_id,route,1)
self.wait_until_we_have_enough_granules(dataset_id,20)
config_id = self.get_ingestion_config()
self.ingestion_management.unpersist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id)
self.ingestion_management.persist_data_stream(stream_id=stream_id,ingestion_configuration_id=config_id,dataset_id=dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
self.publish_hifi(stream_id,route,2)
self.publish_hifi(stream_id,route,3)
self.wait_until_we_have_enough_granules(dataset_id,40)
success = False
with gevent.timeout.Timeout(5):
while not success:
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(replay_granule)
comp = rdt['time'] == np.arange(0,40)
if not isinstance(comp,bool):
success = comp.all()
gevent.sleep(1)
self.assertTrue(success)
@unittest.skip('deprecated')
def test_correct_time(self):
# There are 2208988800 seconds between Jan 1 1900 and Jan 1 1970, i.e.
# the conversion factor between unix and NTP time
unix_now = np.floor(time.time())
ntp_now = unix_now + 2208988800
unix_ago = unix_now - 20
ntp_ago = unix_ago + 2208988800
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
coverage = DatasetManagementService._get_simplex_coverage(dataset_id, mode='a')
coverage.insert_timesteps(20)
coverage.set_parameter_values('time', np.arange(ntp_ago,ntp_now))
temporal_bounds = self.dataset_management.dataset_temporal_bounds(dataset_id)
self.assertTrue( np.abs(temporal_bounds[0] - unix_ago) < 2)
self.assertTrue( np.abs(temporal_bounds[1] - unix_now) < 2)
@attr('LOCOINT')
@unittest.skipIf(os.getenv('CEI_LAUNCH_TEST', False), 'Host requires file-system access to coverage files, CEI mode does not support.')
def test_out_of_band_retrieve(self):
# Setup the environemnt
stream_id, route, stream_def_id, dataset_id = self.make_simple_dataset()
self.start_ingestion(stream_id, dataset_id)
# Fill the dataset
self.publish_fake_data(stream_id, route)
self.wait_until_we_have_enough_granules(dataset_id,40)
# Retrieve the data
granule = DataRetrieverService.retrieve_oob(dataset_id)
rdt = RecordDictionaryTool.load_from_granule(granule)
self.assertTrue((rdt['time'] == np.arange(40)).all())
def publish_and_wait(self, dataset_id, granule):
stream_ids, _ = self.resource_registry.find_objects(dataset_id, PRED.hasStream,id_only=True)
stream_id=stream_ids[0]
route = self.pubsub_management.read_stream_route(stream_id)
publisher = StandaloneStreamPublisher(stream_id,route)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(granule)
self.assertTrue(dataset_monitor.wait())
def test_sparse_values(self):
ph = ParameterHelper(self.dataset_management, self.addCleanup)
pdict_id = ph.create_sparse()
stream_def_id = self.pubsub_management.create_stream_definition('sparse', 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)
dataset_id = self.create_dataset(pdict_id)
self.start_ingestion(stream_id,dataset_id)
self.addCleanup(self.stop_ingestion, stream_id)
# Publish initial granule
# the first one has the sparse value set inside it, sets lat to 45 and lon to -71
ntp_now = time.time() + 2208988800
rdt = ph.get_rdt(stream_def_id)
rdt['time'] = [ntp_now]
rdt['internal_timestamp'] = [ntp_now]
rdt['temp'] = [300000]
rdt['preferred_timestamp'] = ['driver_timestamp']
rdt['port_timestamp'] = [ntp_now]
rdt['quality_flag'] = ['']
rdt['lat'] = [45]
rdt['conductivity'] = [4341400]
rdt['driver_timestamp'] = [ntp_now]
rdt['lon'] = [-71]
rdt['pressure'] = [256.8]
publisher = StandaloneStreamPublisher(stream_id, route)
dataset_monitor = DatasetMonitor(dataset_id)
self.addCleanup(dataset_monitor.stop)
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
# Check the values and make sure they're correct
np.testing.assert_allclose(rdt_out['time'], rdt['time'])
np.testing.assert_allclose(rdt_out['temp'], rdt['temp'])
np.testing.assert_allclose(rdt_out['lat'], np.array([45]))
np.testing.assert_allclose(rdt_out['lon'], np.array([-71]))
np.testing.assert_allclose(rdt_out['conductivity_L1'], np.array([42.914]))
np.testing.assert_allclose(rdt_out['temp_L1'], np.array([20.]))
np.testing.assert_allclose(rdt_out['pressure_L1'], np.array([3.068]))
np.testing.assert_allclose(rdt_out['density'], np.array([1021.7144739593881], dtype='float32'))
np.testing.assert_allclose(rdt_out['salinity'], np.array([30.935132729668283], dtype='float32'))
# We're going to change the lat/lon
rdt = ph.get_rdt(stream_def_id)
rdt['time'] = time.time() + 2208988800
rdt['lat'] = [46]
rdt['lon'] = [-73]
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_allclose(rdt_out['time'], rdt['time'])
for i in xrange(9):
ntp_now = time.time() + 2208988800
rdt['time'] = [ntp_now]
rdt['internal_timestamp'] = [ntp_now]
rdt['temp'] = [300000]
rdt['preferred_timestamp'] = ['driver_timestamp']
rdt['port_timestamp'] = [ntp_now]
rdt['quality_flag'] = [None]
rdt['conductivity'] = [4341400]
rdt['driver_timestamp'] = [ntp_now]
rdt['pressure'] = [256.8]
publisher.publish(rdt.to_granule())
self.assertTrue(dataset_monitor.wait())
dataset_monitor.reset()
replay_granule = self.data_retriever.retrieve(dataset_id)
rdt_out = RecordDictionaryTool.load_from_granule(replay_granule)
np.testing.assert_allclose(rdt_out['pressure'], np.array([256.8] * 10))
np.testing.assert_allclose(rdt_out['lat'], np.array([45] + [46] * 9))
np.testing.assert_allclose(rdt_out['lon'], np.array([-71] + [-73] * 9))
class DirectCoverageAccess(object):
def __init__(self):
self.ingestion_management = IngestionManagementServiceClient()
self.resource_registry = ResourceRegistryServiceClient()
self.data_product_management = DataProductManagementServiceClient()
self.dataset_management = DatasetManagementServiceClient()
self._paused_streams = []
self._w_covs = {}
self._ro_covs = {}
self._context_managed = False
def __enter__(self):
self._context_managed = True
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.clean_up()
def clean_up(self, ro_covs=False, w_covs=False, streams=False):
if not ro_covs and not w_covs and not streams:
ro_covs = w_covs = streams = True
if ro_covs:
# Close any open read-only coverages
for dsid, c in self._ro_covs.iteritems():
c.close()
if w_covs:
# Close any open write coverages
for sid, c in self._w_covs.iteritems():
c.close()
if streams:
# Resume any paused ingestion workers
for s in self._paused_streams:
self.resume_ingestion(s)
def get_ingestion_config(self):
'''
Grab the ingestion configuration from the resource registry
'''
# The ingestion configuration should have been created by the bootstrap service
# which is configured through r2deploy.yml
ingest_configs, _ = self.resource_registry.find_resources(restype=RT.IngestionConfiguration,id_only=True)
return ingest_configs[0]
def get_coverage_path(self, dataset_id):
pth = DatasetManagementService._get_coverage_path(dataset_id)
if not os.path.exists(pth):
raise ValueError('Coverage with id \'{0}\' does not exist!'.format(dataset_id))
return pth
def pause_ingestion(self, stream_id):
if not self._context_managed:
warn_user('Warning: Pausing ingestion when not using a context manager is potentially unsafe - '
'be sure to resume ingestion for all streams by calling self.clean_up(streams=True)')
if stream_id not in self._paused_streams:
self.ingestion_management.pause_data_stream(stream_id, self.get_ingestion_config())
self._paused_streams.append(stream_id)
def resume_ingestion(self, stream_id):
if stream_id in self._paused_streams:
self.ingestion_management.resume_data_stream(stream_id, self.get_ingestion_config())
self._paused_streams.remove(stream_id)
def get_stream_id(self, dataset_id):
sid, _ = self.resource_registry.find_objects(dataset_id, predicate=PRED.hasStream, id_only=True)
return sid[0] if len(sid) > 0 else None
def get_dataset_object(self, dataset_id):
return self.dataset_management.read_dataset(dataset_id=dataset_id)
def get_data_product_object(self, data_product_id):
return self.data_product_management.read_data_product(data_product_id=data_product_id)
def get_read_only_coverage(self, dataset_id):
if not self._context_managed:
warn_user('Warning: Coverages will remain open until they are closed or go out of scope - '
'be sure to close coverage instances when you are finished working with them or call self.clean_up(ro_covs=True)')
# Check if we already have the coverage
if dataset_id in self._ro_covs:
cov = self._ro_covs[dataset_id]
# If it's not closed, return it
if not cov.closed:
return cov
# Otherwise, remove it from self._ro_covs and carry on
del self._ro_covs[dataset_id]
self._ro_covs[dataset_id] = DatasetManagementService._get_coverage(dataset_id, mode='r')
return self._ro_covs[dataset_id]
def get_editable_coverage(self, dataset_id):
sid = self.get_stream_id(dataset_id)
# Check if we already have the coverage
if sid in self._paused_streams:
cov = self._w_covs[sid]
# If it's not closed, return it
if not cov.closed:
return cov
# Otherwise, remove it from self._ro_covs and carry on
del self._w_covs[sid]
self.pause_ingestion(sid)
if not self._context_managed:
warn_user('Warning: Coverages will remain open until they are closed or go out of scope - '
'be sure to close coverage instances when you are finished working with them or call self.clean_up(w_covs=True)')
try:
self._w_covs[sid] = DatasetManagementService._get_simplex_coverage(dataset_id, mode='w')
return self._w_covs[sid]
except:
self.resume_ingestion(sid)
raise
@classmethod
def get_parser(cls, data_file_path, config_path=None):
return SimpleDelimitedParser.get_parser(data_file_path, config_path=config_path)
def manual_upload(self, dataset_id, data_file_path, config_path=None):
# First, ensure we can get a parser and parse the data file
parser = self.get_parser(data_file_path, config_path)
dat = parser.parse()
# Get the coverage
with self.get_editable_coverage(dataset_id) as cov:
# Find the indices for the times in the data file
try:
time_dat = dat[cov.temporal_parameter_name]
except ValueError, ve:
if ve.message == 'field named %s not found.' % cov.temporal_parameter_name:
raise ValueError('Temporal parameter name {0} not in upload data'.format(cov.temporal_parameter_name))
else:
raise
cov_times = cov.get_time_values()
tinds = [utils.find_nearest_index(cov_times, ti) for ti in time_dat]
sl = (tinds,)
cparams = cov.list_parameters()
for n in dat.dtype.names:
if n != cov.temporal_parameter_name:
if n in cparams:
cov.set_parameter_values(n, dat[n], sl)
else:
warn_user('Skipping column \'%s\': matching parameter not found in coverage!' % n)
