}
# ActivityDuration is configured to process both types of activity
fn_activity = bif.ActivityDuration(
table_name=activity_name,
activity_codes=['maintenance', 'break'],
activity_duration=['maintenance_duration', 'break_duration'])
# ActivityDuration is configured to eliminate overlaps across both types of
# activity. Only one type of activity of any type can take place at any
# point in time.
fn_activity.remove_gaps = 'across_all' # configure ActivityDuration to
entity.drop_tables(recreate=True)
entity = EntityType(
entity_name, db, Column('temp', Float()), Column('pressure', Float()),
Column('company_code', String(50)), Column('category_code', String(5)),
bif.EntityDataGenerator(parameters=sim_parameters,
data_item='is_generated'),
bif.ShiftCalendar(shift_definition=shift_dict,
period_start_date='shift_start_date',
period_end_date='shift_end_date',
shift_day='shift_day',
shift_id='shift_id'),
bif.SCDLookup(table_name=scd_name, output_item='crew'), fn_activity, **{
'_timestamp': 'evt_timestamp',
'_db_schema': db_schema
})
# create entity types and execute the calc pipeline to get some test data
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