def sub_listen(msg, headers):
assertions(isinstance(msg, StreamGranuleContainer),
'replayed message is not a granule.')
hdf_string = msg.identifiables[data_stream_id].values
sha1 = hashlib.sha1(hdf_string).hexdigest().upper()
assertions(sha1 == msg.identifiables[encoding_id].sha1,
'Checksum failed.')
assertions(
msg.identifiables[element_count_id].value == 1,
'record replay count is incorrect %d.' %
msg.identifiables[element_count_id].value)
output_file = FileSystem.mktemp()
output_file.write(msg.identifiables[data_stream_id].values)
output_file_path = output_file.name
output_file.close()
output_vectors = acquire_data([output_file_path], fields, 2).next()
for field in fields:
comparison = (input_vectors[field]['values'] ==
output_vectors[field]['values'])
assertions(
comparison.all(), 'vector mismatch: %s vs %s' %
(input_vectors[field]['values'],
output_vectors[field]['values']))
FileSystem.unlink(output_file_path)
ar.set(True)
def _get_hdf_from_string(self, hdf_string):
'''
@param hdf_string binary string consisting of an HDF5 file.
@return temporary file (full path) where the string was written to.
@note client's responsible to unlink when finished.
'''
f = FileSystem.mktemp()
f.write(hdf_string)
retval = f.name
f.close()
return retval
def _get_hdf_from_string(self, hdf_string):
'''
@param hdf_string binary string consisting of an HDF5 file.
@return temporary file (full path) where the string was written to.
@note client's responsible to unlink when finished.
'''
f = FileSystem.mktemp()
f.write(hdf_string)
retval = f.name
f.close()
return retval
def sub_listen(msg, headers):
assertions(isinstance(msg,StreamGranuleContainer),'replayed message is not a granule.')
hdf_string = msg.identifiables[data_stream_id].values
sha1 = hashlib.sha1(hdf_string).hexdigest().upper()
assertions(sha1 == msg.identifiables[encoding_id].sha1,'Checksum failed.')
assertions(msg.identifiables[element_count_id].value==1, 'record replay count is incorrect %d.' % msg.identifiables[element_count_id].value)
output_file = FileSystem.mktemp()
output_file.write(msg.identifiables[data_stream_id].values)
output_file_path = output_file.name
output_file.close()
output_vectors = acquire_data([output_file_path],fields,2).next()
for field in fields:
comparison = (input_vectors[field]['values']==output_vectors[field]['values'])
assertions(comparison.all(), 'vector mismatch: %s vs %s' %
(input_vectors[field]['values'],output_vectors[field]['values']))
FileSystem.unlink(output_file_path)
ar.set(True)
def __init__(self, hdf_string):
"""
@param hdf_string
"""
#try:
assert isinstance(hdf_string, basestring), 'The input for instantiating the HDFDecoder object is not a string'
#except AssertionError as err:
# raise HDFDecoderException(err.message)
#self.filename = FileSystem.get_url(fs=FS.TEMP, filename=hashlib.sha1(hdf_string).hexdigest(), ext='_decoder.hdf5')
f = FileSystem.mktemp(ext='.hdf5')
# save an hdf string to disk - in /tmp to so we can open it as an hdf file and read data from it
f.write(hdf_string)
f.close()
self._list_of_datasets = []
self.filename = f.name
def test_replay_integration(self):
'''
test_replay_integration
'''
import numpy as np
# Keep the import it's used in the vector comparison below even though pycharm says its unused.
cc = self.container
XP = self.XP
assertions = self.assertTrue
### Every thing below here can be run as a script:
log.debug('Got it')
pubsub_management_service = PubsubManagementServiceClient(node=cc.node)
ingestion_management_service = IngestionManagementServiceClient(node=cc.node)
dataset_management_service = DatasetManagementServiceClient(node=cc.node)
data_retriever_service = DataRetrieverServiceClient(node=cc.node)
datastore_name = 'dm_test_replay_integration'
producer = Publisher(name=(XP,'stream producer'))
ingestion_configuration_id = ingestion_management_service.create_ingestion_configuration(
exchange_point_id=XP,
couch_storage=CouchStorage(datastore_name=datastore_name,datastore_profile='SCIDATA'),
hdf_storage=HdfStorage(),
number_of_workers=1
)
ingestion_management_service.activate_ingestion_configuration(
ingestion_configuration_id=ingestion_configuration_id
)
definition = SBE37_CDM_stream_definition()
data_stream_id = definition.data_stream_id
encoding_id = definition.identifiables[data_stream_id].encoding_id
element_count_id = definition.identifiables[data_stream_id].element_count_id
stream_def_id = pubsub_management_service.create_stream_definition(
container=definition
)
stream_id = pubsub_management_service.create_stream(
stream_definition_id=stream_def_id
)
dataset_id = dataset_management_service.create_dataset(
stream_id=stream_id,
datastore_name=datastore_name,
view_name='datasets/dataset_by_id'
)
ingestion_management_service.create_dataset_configuration(
dataset_id=dataset_id,
archive_data=True,
archive_metadata=True,
ingestion_configuration_id = ingestion_configuration_id
)
definition.stream_resource_id = stream_id
packet = _create_packet(definition)
input_file = FileSystem.mktemp()
input_file.write(packet.identifiables[data_stream_id].values)
input_file_path = input_file.name
input_file.close()
fields=[
'conductivity',
'height',
'latitude',
'longitude',
'pressure',
'temperature',
'time'
]
input_vectors = acquire_data([input_file_path],fields , 2).next()
producer.publish(msg=packet, to_name=(XP,'%s.data' % stream_id))
replay_id, replay_stream_id = data_retriever_service.define_replay(dataset_id)
ar = gevent.event.AsyncResult()
def sub_listen(msg, headers):
assertions(isinstance(msg,StreamGranuleContainer),'replayed message is not a granule.')
hdf_string = msg.identifiables[data_stream_id].values
sha1 = hashlib.sha1(hdf_string).hexdigest().upper()
assertions(sha1 == msg.identifiables[encoding_id].sha1,'Checksum failed.')
assertions(msg.identifiables[element_count_id].value==1, 'record replay count is incorrect %d.' % msg.identifiables[element_count_id].value)
output_file = FileSystem.mktemp()
output_file.write(msg.identifiables[data_stream_id].values)
output_file_path = output_file.name
output_file.close()
output_vectors = acquire_data([output_file_path],fields,2).next()
for field in fields:
comparison = (input_vectors[field]['values']==output_vectors[field]['values'])
assertions(comparison.all(), 'vector mismatch: %s vs %s' %
(input_vectors[field]['values'],output_vectors[field]['values']))
FileSystem.unlink(output_file_path)
ar.set(True)
subscriber = Subscriber(name=(XP,'replay listener'),callback=sub_listen)
g = gevent.Greenlet(subscriber.listen, binding='%s.data' % replay_stream_id)
g.start()
data_retriever_service.start_replay(replay_id)
ar.get(timeout=10)
FileSystem.unlink(input_file_path)
def test_replay_integration(self):
'''
test_replay_integration
'''
import numpy as np
# Keep the import it's used in the vector comparison below even though pycharm says its unused.
cc = self.container
XP = self.XP
assertions = self.assertTrue
### Every thing below here can be run as a script:
log.debug('Got it')
pubsub_management_service = PubsubManagementServiceClient(node=cc.node)
ingestion_management_service = IngestionManagementServiceClient(
node=cc.node)
dataset_management_service = DatasetManagementServiceClient(
node=cc.node)
data_retriever_service = DataRetrieverServiceClient(node=cc.node)
datastore_name = 'dm_test_replay_integration'
producer = Publisher(name=(XP, 'stream producer'))
ingestion_configuration_id = ingestion_management_service.create_ingestion_configuration(
exchange_point_id=XP,
couch_storage=CouchStorage(datastore_name=datastore_name,
datastore_profile='SCIDATA'),
hdf_storage=HdfStorage(),
number_of_workers=1)
ingestion_management_service.activate_ingestion_configuration(
ingestion_configuration_id=ingestion_configuration_id)
definition = SBE37_CDM_stream_definition()
data_stream_id = definition.data_stream_id
encoding_id = definition.identifiables[data_stream_id].encoding_id
element_count_id = definition.identifiables[
data_stream_id].element_count_id
stream_def_id = pubsub_management_service.create_stream_definition(
container=definition)
stream_id = pubsub_management_service.create_stream(
stream_definition_id=stream_def_id)
dataset_id = dataset_management_service.create_dataset(
stream_id=stream_id,
datastore_name=datastore_name,
view_name='datasets/dataset_by_id')
ingestion_management_service.create_dataset_configuration(
dataset_id=dataset_id,
archive_data=True,
archive_metadata=True,
ingestion_configuration_id=ingestion_configuration_id)
definition.stream_resource_id = stream_id
packet = _create_packet(definition)
input_file = FileSystem.mktemp()
input_file.write(packet.identifiables[data_stream_id].values)
input_file_path = input_file.name
input_file.close()
fields = [
'conductivity', 'height', 'latitude', 'longitude', 'pressure',
'temperature', 'time'
]
input_vectors = acquire_data([input_file_path], fields, 2).next()
producer.publish(msg=packet, to_name=(XP, '%s.data' % stream_id))
replay_id, replay_stream_id = data_retriever_service.define_replay(
dataset_id)
ar = gevent.event.AsyncResult()
def sub_listen(msg, headers):
assertions(isinstance(msg, StreamGranuleContainer),
'replayed message is not a granule.')
hdf_string = msg.identifiables[data_stream_id].values
sha1 = hashlib.sha1(hdf_string).hexdigest().upper()
assertions(sha1 == msg.identifiables[encoding_id].sha1,
'Checksum failed.')
assertions(
msg.identifiables[element_count_id].value == 1,
'record replay count is incorrect %d.' %
msg.identifiables[element_count_id].value)
output_file = FileSystem.mktemp()
output_file.write(msg.identifiables[data_stream_id].values)
output_file_path = output_file.name
output_file.close()
output_vectors = acquire_data([output_file_path], fields, 2).next()
for field in fields:
comparison = (input_vectors[field]['values'] ==
output_vectors[field]['values'])
assertions(
comparison.all(), 'vector mismatch: %s vs %s' %
(input_vectors[field]['values'],
output_vectors[field]['values']))
FileSystem.unlink(output_file_path)
ar.set(True)
subscriber = Subscriber(name=(XP, 'replay listener'),
callback=sub_listen)
g = gevent.Greenlet(subscriber.listen,
binding='%s.data' % replay_stream_id)
g.start()
data_retriever_service.start_replay(replay_id)
ar.get(timeout=10)
FileSystem.unlink(input_file_path)