def test_05_get_stream_ids_of_owner(self):
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
by_id = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd")
self.assertIsInstance(by_id, list)
self.assertEqual(by_id[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test")
self.assertIsInstance(by_name_id, list)
self.assertEqual(by_name_id[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
start_time)
self.assertIsInstance(by_name_id_start_time, list)
self.assertEqual(by_name_id_start_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time_end_time = Metadata(
self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
start_time, end_time)
self.assertIsInstance(by_name_id_start_time_end_time, list)
self.assertEqual(by_name_id_start_time_end_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
def store_stream(self, datastream: DataStream):
"""
:param datastream:
"""
ownerID = datastream.owner
name = datastream.name
data_descriptor = datastream.data_descriptor
execution_context = datastream.execution_context
annotations = datastream.annotations
stream_type = datastream.datastream_type
data = datastream.data
if data:
if isinstance(data, list):
total_dp = len(data) - 1
new_start_time = data[0].start_time
new_end_time = data[total_dp].start_time
else:
new_start_time = data.start_time
new_end_time = data.start_time
result = Metadata(self.CC_obj).is_id_created(
ownerID, name, execution_context)
stream_identifier = result["id"]
Metadata(self.CC_obj).store_stream_info(
stream_identifier, ownerID, name, data_descriptor,
execution_context, annotations, stream_type, new_start_time,
new_end_time, result["status"])
dataframe = self.map_datapoint_to_dataframe(
stream_identifier, data)
self.store_data(dataframe, self.datapointTable)
def test_01_setup_data(self):
data_descriptor = {}
execution_context = json.loads(
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
)
annotations = {}
stream_type = "datastream"
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
result = Metadata(self.CC).is_id_created(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
execution_context)
if result["status"] == "new":
stream_identifier = "6db98dfb-d6e8-4b27-8d55-95b20fa0f754"
else:
stream_identifier = result["id"]
self.assertEqual(stream_identifier,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
Metadata(self.CC).store_stream_info(
stream_identifier, "06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", data_descriptor, execution_context, annotations,
stream_type, start_time, end_time, result["status"])
def test_03_append_annotations(self):
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {}, {}, {}, "datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {}, {"some": "none"}, {},
"datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {"a": "b"}, {}, {}, "datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-diagnostic_diff", {}, {}, {}, "datastream1")
annotations_unchanged = Metadata(self.CC).append_annotations(
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test", {},
json.loads(
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
), {}, "datastream")
self.assertEqual(annotations_unchanged, "unchanged")
def store_stream(self, datastream: DataStream, type):
"""
:param datastream:
:param type: support types are formatted json object or CC Datastream objects
"""
if (type == "json"):
datastream = self.json_to_datastream(datastream)
elif (type != "json" and type != "datastream"):
raise ValueError(type + " is not supported data type")
ownerID = datastream.owner
name = datastream.name
data_descriptor = datastream.data_descriptor
execution_context = datastream.execution_context
annotations = datastream.annotations
stream_type = datastream.datastream_type
data = datastream.data
if data:
if isinstance(data, list):
total_dp = len(data) - 1
if not datastream._start_time:
new_start_time = data[0].start_time
else:
new_start_time = datastream._start_time
if not datastream._end_time:
new_end_time = data[total_dp].start_time
else:
new_end_time = datastream._end_time
else:
if not datastream._start_time:
new_start_time = data.start_time
else:
new_start_time = datastream._start_time
if not datastream._end_time:
new_end_time = data.start_time
else:
new_end_time = datastream._end_time
stream_identifier = datastream.identifier
result = Metadata(self.CC_obj).is_id_created(stream_identifier)
Metadata(self.CC_obj).store_stream_info(stream_identifier, ownerID, name,
data_descriptor, execution_context,
annotations,
stream_type, new_start_time, new_end_time, result["status"])
self.add_to_cassandra(stream_identifier, data)
def get_participant_streams(self, participant_id: uuid) -> dict:
"""
:param participant_id:
:return:
"""
return Metadata(self).get_participant_streams(participant_id)
def get_all_participants(self, study_name: str) -> dict:
"""
:param study_name:
:return:
"""
return Metadata(self).get_all_participants(study_name)
def __init__(self,
CC_obj: CerebralCortex,
export_dir_path: str,
owner_ids: List = None,
owner_user_names: List = None,
owner_name_regex: str = None,
start_time: str = None,
end_time: str = None):
"""
:param CC_obj:
:param export_dir_path:
:param owner_ids: owner_user_name and owner_name_regex must be None if using owner_id
:param owner_user_names: owner_id and owner_name_regex must be None if using owner_user_name
:param owner_name_regex: owner_id and owner_user_name must be None if using owner_name_reges
:param start_time:
:param end_time:
"""
self.streamData = Data(CC_obj)
self.export_dir_path = export_dir_path
self.metadata = Metadata(CC_obj)
self.owner_ids = owner_ids
self.owner_user_names = owner_user_names
self.owner_name_regex = str(owner_name_regex)
self.start_time = start_time
self.end_time = end_time
def login_user(self, user_name: str, password: str) -> bool:
"""
:param user_name:
:param password:
:return:
"""
return Metadata(self).login_user(user_name, password)
def get_stream_start_end_time(self, stream_id: uuid) -> dict:
"""
:param stream_id:
:param time_type: acceptable parameters are start_time OR end_time
:return:
"""
return Metadata(self).get_stream_start_end_time(stream_id)
def store_or_update_Kafka_offset(self, topic: str, topic_partition: str, offset_start: str, offset_until: str):
"""
:param topic:
:param topic_partition:
:param offset_start:
:param offset_until:
"""
Metadata(self).store_or_update_Kafka_offset(topic, topic_partition, offset_start, offset_until)
def is_auth_token_valid(self, token_owner: str, auth_token: str, auth_token_expiry_time: datetime) -> bool:
"""
:param token_owner:
:param auth_token:
:param auth_token_expiry_time:
:return:
"""
return Metadata(self).is_auth_token_valid(token_owner, auth_token, auth_token_expiry_time)
def get_stream_ids_of_owner(self, owner_id: uuid, stream_name: str = None, start_time: datetime = None,
end_time: datetime = None) -> List:
"""
Returns a list of all stream IDs owned by an owner
:param owner_id:
:param stream_name:
:param start_time:
:param end_time:
:return:
"""
return Metadata(self).get_stream_ids_of_owner(owner_id, stream_name, start_time, end_time)
def get_stream_ids_by_name(self, stream_name: str, owner_id: uuid = None, start_time: datetime = None,
end_time: datetime = None) -> str:
"""
It returns a list of all the stream ids that match the name provided in the argument
:param stream_name:
:param owner_id:
:param start_time:
:param end_time:
:return:
"""
return Metadata(self).get_stream_ids_by_name(stream_name, owner_id, start_time, end_time)
def update_auth_token(self, username: str, auth_token: str, auth_token_issued_time: datetime,
auth_token_expiry_time: datetime)->str:
"""
:param username:
:param auth_token:
:param auth_token_issued_time:
:param auth_token_expiry_time:
:return: uuid of the current user
"""
user_uuid = Metadata(self).update_auth_token(username, auth_token, auth_token_issued_time, auth_token_expiry_time)
return user_uuid
def filter_stream(self, data_stream_id: uuid, annotation_stream_name: uuid, annotation: str,
start_time: datetime = None, end_time: datetime = None) -> List[DataPoint]:
"""
This method maps derived annotation stream to a data stream and returns a List of mapped Datapoints
:param data_stream_id:
:param annotation_stream_name:
:param annotation:
:param start_time:
:param end_time:
:return:
"""
annotation_stream_id = Metadata(self).get_annotation_id(data_stream_id, annotation_stream_name)
return Data(self).get_annotation_stream(data_stream_id, annotation_stream_id, annotation, start_time, end_time)
def update_auth_token(self, user_name: str, auth_token: str,
auth_token_issued_time: datetime,
auth_token_expiry_time: datetime):
"""
:param user_name:
:param auth_token:
:param auth_token_issued_time:
:param auth_token_expiry_time:
:return:
"""
return Metadata(self).update_auth_token(user_name, auth_token,
auth_token_issued_time,
auth_token_expiry_time)
def test_02_get_stream_info(self):
stream_info = Metadata(
self.CC).get_stream_info("6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
self.assertEqual(stream_info[0]["identifier"],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
self.assertEqual(stream_info[0]["owner"],
"06634264-56bc-4c92-abd7-377dbbad79dd")
self.assertEqual(stream_info[0]["name"], "data-store-test")
self.assertEqual(stream_info[0]["data_descriptor"], "{}")
self.assertEqual(
stream_info[0]["execution_context"],
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
)
self.assertEqual(stream_info[0]["annotations"], "{}")
self.assertEqual(stream_info[0]["type"], "datastream")
def map_datapoint_and_metadata_to_datastream(self, stream_id: int,
data: list) -> DataStream:
"""
This method will map the datapoint and metadata to datastream object
:param stream_id:
:param data: list
:return: datastream object
"""
# query datastream(mysql) for metadata
datastream_info = Metadata(self.CC_obj).get_stream_info(stream_id)
ownerID = datastream_info[0]["owner"]
name = datastream_info[0]["name"]
data_descriptor = json.loads(datastream_info[0]["data_descriptor"])
execution_context = json.loads(datastream_info[0]["execution_context"])
annotations = json.loads(datastream_info[0]["annotations"])
stream_type = datastream_info[0]["type"]
start_time = datastream_info[0]["start_time"]
end_time = datastream_info[0]["end_time"]
return DataStream(stream_id, ownerID, name, data_descriptor,
execution_context, annotations, stream_type,
start_time, end_time, data)
def store_datastream(self, datastream):
datastream_identifier = datastream.get_identifier()
study_ids = datastream.getStudyIDs(
) # TO-DO, only add study-ids if they exist
user_id = datastream.userObj.getID()
processing_module_id = datastream.processingModuleObj.getID()
datastream_type = datastream.get_datastream_type()
metadata = datastream.getMetadata().getMetadata()
source_ids = datastream.get_source_ids()
data = datastream.data
# if datastream_identifier is empty then create a new datastream_identifier in MySQL database and return the newly added datastream_identifier
lastAddedRecordID = Metadata(self.configuration).storeDatastrem(
datastream_identifier, study_ids, user_id, processing_module_id,
source_ids, datastream_type, metadata)
if datastream_identifier == "":
datastream_identifier = lastAddedRecordID
dataframe = self.map_datapoint_to_dataframe(datastream_identifier,
data)
self.save_datapoint(dataframe)
def store_data_to_influxdb(self, datastream: DataStream):
"""
:param datastream:
"""
st = datetime.now()
client = InfluxDBClient(host=self.influxdbIP, port=self.influxdbPort, username=self.influxdbUser, password=self.influxdbPassword, database=self.influxdbDatabase)
datapoints = datastream.data
stream_identifier = datastream.identifier
stream_owner_id = datastream.owner
stream_owner_name = Metadata(self.CC_obj).owner_id_to_name(stream_owner_id)
stream_name = datastream.name
if datastream.data_descriptor:
total_dd_columns = len(datastream.data_descriptor)
data_descriptor = datastream.data_descriptor
else:
data_descriptor = []
total_dd_columns = 0
influx_data = []
for datapoint in datapoints:
object = {}
object['measurement'] = stream_name
object['tags'] = {'stream_id':stream_identifier, 'owner_id': stream_owner_id, 'owner_name': stream_owner_name}
object['time'] = datapoint.start_time
values = datapoint.sample
if isinstance(values, tuple):
values = list(values)
else:
try:
values = [float(values)]
except:
try:
values = list(map(float, values.split(',')))
except:
values = values
try:
object['fields'] = {}
if isinstance(values, list):
for i, sample_val in enumerate(values):
if len(values)==total_dd_columns:
dd = data_descriptor[i]
if "NAME" in dd:
object['fields'][dd["NAME"]] = sample_val
else:
object['fields']['value_'+str(i)] = sample_val
else:
object['fields']['value_'+str(i)] = sample_val
else:
dd = data_descriptor[0]
if not values:
values = "NULL"
try:
values = float(values)
except:
values = values
if "NAME" in dd:
object['fields'][dd["NAME"]] = values
else:
object['fields']['value_0'] = values
except:
try:
values = json.dumps(values)
object['fields']['value_0'] = values
except:
cc_log("Datapoint sample values conversion: "+str(values),"WARNING")
object['fields']['value_0'] = str(values)
influx_data.append(object)
et = datetime
#print('InfluxDB - Yielding:', stream_owner_id, len(influx_data), stream_identifier)
try:
client.write_points(influx_data)
et2 = datetime.now()
#print("Influx Time BreakDown: Processing: ", et-st, " Inserting: ",et2-et, " Size: ",len(influx_data))
except:
cc_log()
class TestDataStoreEngine(unittest.TestCase):
testConfigFile = os.path.join(os.path.dirname(__file__),
'res/test_configuration.yml')
CC = CerebralCortex(testConfigFile,
master="local[*]",
name="Cerebral Cortex DataStoreEngine Tests",
time_zone="US/Central",
load_spark=True)
configuration = CC.configuration
meta_obj = Metadata(CC)
def test_01_setup_data(self):
data_descriptor = {}
execution_context = json.loads(
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
)
annotations = {}
stream_type = "datastream"
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
result = Metadata(self.CC).is_id_created(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
execution_context)
if result["status"] == "new":
stream_identifier = "6db98dfb-d6e8-4b27-8d55-95b20fa0f754"
else:
stream_identifier = result["id"]
self.assertEqual(stream_identifier,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
Metadata(self.CC).store_stream_info(
stream_identifier, "06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", data_descriptor, execution_context, annotations,
stream_type, start_time, end_time, result["status"])
def test_02_get_stream_info(self):
stream_info = Metadata(
self.CC).get_stream_info("6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
self.assertEqual(stream_info[0]["identifier"],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
self.assertEqual(stream_info[0]["owner"],
"06634264-56bc-4c92-abd7-377dbbad79dd")
self.assertEqual(stream_info[0]["name"], "data-store-test")
self.assertEqual(stream_info[0]["data_descriptor"], "{}")
self.assertEqual(
stream_info[0]["execution_context"],
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
)
self.assertEqual(stream_info[0]["annotations"], "{}")
self.assertEqual(stream_info[0]["type"], "datastream")
def test_03_append_annotations(self):
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {}, {}, {}, "datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {}, {"some": "none"}, {},
"datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-store-test", {"a": "b"}, {}, {}, "datastream1")
self.assertRaises(Exception,
Metadata(self.CC).append_annotations,
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd",
"data-diagnostic_diff", {}, {}, {}, "datastream1")
annotations_unchanged = Metadata(self.CC).append_annotations(
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754",
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test", {},
json.loads(
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
), {}, "datastream")
self.assertEqual(annotations_unchanged, "unchanged")
def test_04_get_stream_ids_by_name(self):
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
by_name = Metadata(self.CC).get_stream_ids_by_name("data-store-test")
self.assertIsInstance(by_name, list)
self.assertEqual(by_name[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id = Metadata(self.CC).get_stream_ids_by_name(
"data-store-test", "06634264-56bc-4c92-abd7-377dbbad79dd")
self.assertIsInstance(by_name_id, list)
self.assertEqual(by_name_id[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time = Metadata(self.CC).get_stream_ids_by_name(
"data-store-test", "06634264-56bc-4c92-abd7-377dbbad79dd",
start_time)
self.assertIsInstance(by_name_id_start_time, list)
self.assertEqual(by_name_id_start_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time_end_time = Metadata(
self.CC).get_stream_ids_by_name(
"data-store-test", "06634264-56bc-4c92-abd7-377dbbad79dd",
start_time, end_time)
self.assertIsInstance(by_name_id_start_time_end_time, list)
self.assertEqual(by_name_id_start_time_end_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
def test_05_get_stream_ids_of_owner(self):
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
by_id = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd")
self.assertIsInstance(by_id, list)
self.assertEqual(by_id[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test")
self.assertIsInstance(by_name_id, list)
self.assertEqual(by_name_id[0], "6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time = Metadata(self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
start_time)
self.assertIsInstance(by_name_id_start_time, list)
self.assertEqual(by_name_id_start_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
by_name_id_start_time_end_time = Metadata(
self.CC).get_stream_ids_of_owner(
"06634264-56bc-4c92-abd7-377dbbad79dd", "data-store-test",
start_time, end_time)
self.assertIsInstance(by_name_id_start_time_end_time, list)
self.assertEqual(by_name_id_start_time_end_time[0],
"6db98dfb-d6e8-4b27-8d55-95b20fa0f754")
def test_06_store_stream(self):
identifier = "6db98dfb-d6e8-4b27-8d55-95b20fa0f754"
owner = "06634264-56bc-4c92-abd7-377dbbad79dd"
name = "data-store-test"
data_descriptor = {}
execution_context = json.loads(
'{"execution_context": {"algorithm": {"method": "cerebralcortex.data_processor.data_diagnostic.BatteryDataMarker"}}}'
)
annotations = {}
datapoints = []
stream_type = "datastream"
start_time = datetime.datetime(2017, 4, 24, 0, 0, 1)
end_time = datetime.datetime(2017, 4, 24, 0, 0, 2)
localtz = timezone('US/Central')
start_time = localtz.localize(start_time)
end_time = localtz.localize(end_time)
sample = {'Foo3': 123}
dp1 = DataPoint(start_time=start_time,
end_time=end_time,
sample=sample)
datapoints.append(dp1)
ds = DataStream(identifier, owner, name, data_descriptor,
execution_context, annotations, stream_type,
start_time, end_time, datapoints)
self.CC.save_datastream(ds)
stream = self.CC.get_datastream(identifier, data_type=DataSet.COMPLETE)
self.assertEqual(stream._identifier, identifier)
self.assertEqual(stream._owner, owner)
self.assertEqual(stream._name, name)
self.assertEqual(stream._data_descriptor, data_descriptor)
self.assertEqual(stream._execution_context, execution_context)
self.assertEqual(stream._annotations, annotations)
self.assertEqual(stream._datastream_type, stream_type)
self.assertEqual(stream.data[0].start_time, start_time)
self.assertEqual(stream.data[0].end_time, end_time)
self.assertEqual(stream.data[0].sample, sample)
def test_07_stream_filter(self):
identifier_anno = "6db98dfb-d6e8-4b27-8d55-95b20fa0f750"
identifier_data = "6db98dfb-d6e8-4b27-8d55-95b20fa0f751"
owner_id = "06634264-56bc-4c92-abd7-377dbbad79dd"
name_anno = "data-store-test-annotation"
name_data = "data-store-test-data"
data_descriptor = {}
execution_context_anno = json.loads(
'{"execution_context": {"algorithm": {"method": "test.data_store.annotation.filter"}}}'
)
execution_context_data = json.loads(
'{"execution_context": {"algorithm": {"method": "test.data_store.data.filter"}}}'
)
annotations_data = json.loads(
'[{"name": "test-case","identifier": "6db98dfb-d6e8-4b27-8d55-95b20fa0f750"}]'
)
annotations_anno = {}
datapoints_anno = []
datapoints_data = []
result_data = Metadata(self.CC).is_id_created(owner_id, name_data,
execution_context_data)
if result_data["status"] != "new":
identifier_data = result_data["id"]
Metadata(self.CC).store_stream_info(
identifier_anno, owner_id, name_anno, data_descriptor,
execution_context_anno, annotations_anno, "annotations",
datetime.datetime(2017, 4, 24, 0, 0, 1),
datetime.datetime(2017, 4, 24, 0, 0, 5), result_data["status"])
result_anno = Metadata(self.CC).is_id_created(owner_id, name_data,
execution_context_data)
if result_anno["status"] != "new":
identifier_anno = result_anno["id"]
Metadata(self.CC).store_stream_info(
identifier_data, owner_id, name_data, data_descriptor,
execution_context_data, annotations_data, "datastream",
datetime.datetime(2017, 4, 24, 0, 0, 1),
datetime.datetime(2017, 4, 24, 0, 0, 5), result_anno["status"])
for i in range(0, 5):
if (i % 2 == 0):
sample_anno = 'good'
else:
sample_anno = 'bad'
sample_data = i, i + 2, i + 3
start_time_anno = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_anno = datetime.datetime(2017, 4, 24, 0, 0, (5 + i))
start_time_data = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_data = datetime.datetime(2017, 4, 24, 0, 0, (3 + i))
localtz = timezone('US/Central')
start_time_anno = localtz.localize(start_time_anno)
end_time_anno = localtz.localize(end_time_anno)
start_time_data = localtz.localize(start_time_data)
end_time_data = localtz.localize(end_time_data)
datapoints_anno.append(
DataPoint(start_time=start_time_anno,
end_time=end_time_anno,
sample=sample_anno))
datapoints_data.append(
DataPoint(start_time=start_time_data,
end_time=end_time_data,
sample=sample_data))
ds_anno = DataStream(uuid.UUID(identifier_anno), owner_id, name_anno,
data_descriptor, execution_context_anno,
annotations_data, "annotations", start_time_anno,
end_time_anno, datapoints_anno)
ds_data = DataStream(uuid.UUID(identifier_data), owner_id, name_data,
data_descriptor, execution_context_data,
annotations_anno, "datastream", start_time_anno,
end_time_anno, datapoints_data)
self.CC.save_datastream(ds_anno)
self.CC.save_datastream(ds_data)
filted_stream = self.CC.filter_stream(identifier_data, "test-case",
"good")
self.assertEqual(len(filted_stream), 5)
for i in range(0, 5):
sample_data = [i, i + 2, i + 3]
start_time_data = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_data = datetime.datetime(2017, 4, 24, 0, 0, (3 + i))
start_time_data = localtz.localize(start_time_data)
end_time_data = localtz.localize(end_time_data)
self.assertEqual(filted_stream[i].start_time, start_time_data)
self.assertEqual(filted_stream[i].end_time, end_time_data)
self.assertEqual(filted_stream[i].sample, sample_data)
def test_07_stream_filter(self):
identifier_anno = "6db98dfb-d6e8-4b27-8d55-95b20fa0f750"
identifier_data = "6db98dfb-d6e8-4b27-8d55-95b20fa0f751"
owner_id = "06634264-56bc-4c92-abd7-377dbbad79dd"
name_anno = "data-store-test-annotation"
name_data = "data-store-test-data"
data_descriptor = {}
execution_context_anno = json.loads(
'{"execution_context": {"algorithm": {"method": "test.data_store.annotation.filter"}}}'
)
execution_context_data = json.loads(
'{"execution_context": {"algorithm": {"method": "test.data_store.data.filter"}}}'
)
annotations_data = json.loads(
'[{"name": "test-case","identifier": "6db98dfb-d6e8-4b27-8d55-95b20fa0f750"}]'
)
annotations_anno = {}
datapoints_anno = []
datapoints_data = []
result_data = Metadata(self.CC).is_id_created(owner_id, name_data,
execution_context_data)
if result_data["status"] != "new":
identifier_data = result_data["id"]
Metadata(self.CC).store_stream_info(
identifier_anno, owner_id, name_anno, data_descriptor,
execution_context_anno, annotations_anno, "annotations",
datetime.datetime(2017, 4, 24, 0, 0, 1),
datetime.datetime(2017, 4, 24, 0, 0, 5), result_data["status"])
result_anno = Metadata(self.CC).is_id_created(owner_id, name_data,
execution_context_data)
if result_anno["status"] != "new":
identifier_anno = result_anno["id"]
Metadata(self.CC).store_stream_info(
identifier_data, owner_id, name_data, data_descriptor,
execution_context_data, annotations_data, "datastream",
datetime.datetime(2017, 4, 24, 0, 0, 1),
datetime.datetime(2017, 4, 24, 0, 0, 5), result_anno["status"])
for i in range(0, 5):
if (i % 2 == 0):
sample_anno = 'good'
else:
sample_anno = 'bad'
sample_data = i, i + 2, i + 3
start_time_anno = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_anno = datetime.datetime(2017, 4, 24, 0, 0, (5 + i))
start_time_data = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_data = datetime.datetime(2017, 4, 24, 0, 0, (3 + i))
localtz = timezone('US/Central')
start_time_anno = localtz.localize(start_time_anno)
end_time_anno = localtz.localize(end_time_anno)
start_time_data = localtz.localize(start_time_data)
end_time_data = localtz.localize(end_time_data)
datapoints_anno.append(
DataPoint(start_time=start_time_anno,
end_time=end_time_anno,
sample=sample_anno))
datapoints_data.append(
DataPoint(start_time=start_time_data,
end_time=end_time_data,
sample=sample_data))
ds_anno = DataStream(uuid.UUID(identifier_anno), owner_id, name_anno,
data_descriptor, execution_context_anno,
annotations_data, "annotations", start_time_anno,
end_time_anno, datapoints_anno)
ds_data = DataStream(uuid.UUID(identifier_data), owner_id, name_data,
data_descriptor, execution_context_data,
annotations_anno, "datastream", start_time_anno,
end_time_anno, datapoints_data)
self.CC.save_datastream(ds_anno)
self.CC.save_datastream(ds_data)
filted_stream = self.CC.filter_stream(identifier_data, "test-case",
"good")
self.assertEqual(len(filted_stream), 5)
for i in range(0, 5):
sample_data = [i, i + 2, i + 3]
start_time_data = datetime.datetime(2017, 4, 24, 0, 0, i)
end_time_data = datetime.datetime(2017, 4, 24, 0, 0, (3 + i))
start_time_data = localtz.localize(start_time_data)
end_time_data = localtz.localize(end_time_data)
self.assertEqual(filted_stream[i].start_time, start_time_data)
self.assertEqual(filted_stream[i].end_time, end_time_data)
self.assertEqual(filted_stream[i].sample, sample_data)
def get_kafka_offsets(self, topic: str) -> dict:
"""
:param topic:
:return:
"""
return Metadata(self).get_kafka_offsets(topic)