data_sources = []
start_device_id = 10000
for (data_source_name, metric_name) in [child1, child2]:
sim_parameters['start_entity_id'] = start_device_id
entity = EntityType(
data_source_name, db, Column(metric_name, Float()),
bif.EntityDataGenerator(parameters=sim_parameters,
data_item='is_generated'), **{
'_timestamp': 'evt_timestamp',
'_db_schema': db_schema
})
dim = '%s_dim' % data_source_name
entity.drop_tables([dim])
entity.make_dimension(
dim, # build name automatically
Column('work_area', String(50)))
entity.exec_local_pipeline()
data_sources.append(entity)
start_device_id += 10000
'''
This is what the generated data looks like: Time series data for temperature:
DEVICEID EVT_TIMESTAMP DEVICETYPE LOGICALINTERFACE_ID EVENTTYPE FORMAT UPDATED_UTC TEMPERATURE
10001 2019-08-22-21.18.10.884128 temp_data ep 21.602888736326957
10017 2019-08-22-21.23.10.884128 temp_data ye 23.517696994037884
derived data items. The EntityDataGenerator doesn't have an incoming entity data to
work with, so it builds its own. It loads this data directly into the entity's time
series input data where is can be read by transform functions.
To test the execution of kpi calculations defined for the entity type locally
use 'test_local_pipeline'.
'''
entity.exec_local_pipeline()
'''
So far we have only looked at the ability to create numerical and categorical time
series data. You can also automatically generate dimensional attributes.
'''
entity.make_dimension('sim_test_dimension', Column('manufacturer', String(50)), )
entity.register(raise_error=True)
entity.generate_data(days=0.5, drop_existing=True, **sim_parameters)
'''
Look at the data that was loaded into sim_test_dimension.
You should see something like this.
device_id manufacturer
73004 GHI Industries
73000 GHI Industries
73003 Rentech
73002 GHI Industries
entity = EntityType(
entity_name, db, Column('TURBINE_ID', String(50)),
Column('TEMPERATURE', Float()), Column('PRESSURE', Float()),
Column('STEP', Float()), Column('PRESS_X', Float()),
Column('PRESS_Y', Float()), Column('TEMP_X', Float()),
Column('TEMP_Y', Float()),
Issue455HTTPPreload(
request='GET',
url="https://turbine-simulator.mybluemix.net/v1/api/reading",
output_item='http_preload_done'), **{
'_timestamp': 'evt_timestamp',
'_db_schema': db_schema
})
entity.make_dimension(entity_dimension_upper, Column('CLIENT', String(50)),
Column('ORGANIZATION', String(50)),
Column('FUNCTION', String(50)), **{'schema': db_schema})
entity_dimension = entity.get_attributes_dict()['_dimension_table_name']
'''
When creating an EntityType object you will need to specify the name of the entity, the database
object that will contain entity data
After creating an EntityType you will need to register it so that it visible in the Add Data to Entity Function UI.
To also register the functions and constants associated with the entity type, specify
'publish_kpis' = True.
'''
entity.register(raise_error=False)
# When creating a custom preload function you can register it by uncommenting the following lines
# You must unregister_functions if you change the method signature or required inputs.
db.unregister_functions(['Issue455HTTPPreload'])