def get_data(timeframe):
"""
Name in documentation: 'get_data'
Load the klassified data.
:raises DBExeption: If the Database is not aviable.
:return: The klassified data
"""
try:
#print(timeframe)
classified_data = reader.read_data('db_klassifizierte_daten' ,measurement = 'classified',
start_utc= str(convert_time(timeframe[0])),
end_utc= str(convert_time(timeframe[1])))
classified_data = classified_data.astype('float64')
#print("Get_Data Ausgabe:")
#print(classified_data)
#print("________________________________")
#print("Get_Data Ausgabe nur True-gesetzte:")
#print(classified_data.loc[classified_data.abtauzyklus == True]) #only True cycle
#print("________________________________")
except exe.DBException:
raise exe.DBException("Database is not available. Get_data() failed", 901)
return classified_data
def test_do_not_resolve_register(self):
expected_query = "select from test_measurement where register = 'freshAirIntake'"
when2(rm.read_query, "test", expected_query).thenReturn("success!")
output = rm.read_data("test",
measurement="test_measurement",
register="freshAirIntake"
)
self.assertEqual(output, "success!")
def test_only_end_set(self):
expected_query = "select from test_measurement where register = '201' AND time < 1ms"
when2(rm.read_query, "test", expected_query).thenReturn("success!")
output = rm.read_data("test",
measurement="test_measurement",
register="freshAirIntake",
resolve_register="True",
end_utc=1
)
self.assertEqual(output, "success!")
def test_all_values_in_args(self):
expected_query = "select from test_measurement where register = '201' AND time > 1ms AND time < 2ms"
when2(rm.read_query, "test", expected_query).thenReturn("success!")
output = rm.read_data("test",
measurement="test_measurement",
register="freshAirIntake",
resolve_register="True",
start_utc=1,
end_utc=2
)
self.assertEqual(output, "success!")
def calculate_prediction(config):
logger.info("Starting prediction calculation...")
cfg_validator.validate_config(config)
database_options = config["database_options"]["prediction"]
datasource_forecast_dbname = database_options.get(
"datasource_forecast_dbname")
datasource_forecast_measurement = database_options.get(
"datasource_forecast_measurement")
datasource_forecast_register = database_options.get(
"datasource_forecast_register")
datasink_prediction_dbname = database_options.get(
"datasink_prediction_dbname")
datasink_prediction_measurement = database_options.get(
"datasink_prediction_measurement")
selected_value = config.get("selected_value")
all_prediction_units = config.get("prediction_options").get(selected_value)
known_data_sources = db_read.read_data(
datasource_forecast_dbname,
measurement=datasource_forecast_measurement)
known_data_sources = known_data_sources.astype('float64')
known_data_sources = known_data_sources.rename(
columns={datasource_forecast_register: 'outdoor'})
time_start = known_data_sources.first_valid_index()
time_end = known_data_sources.last_valid_index()
logger.info("Fetched relevant data...")
all_prediction_models = model_persistor.load()
while all_prediction_units:
logger.info("Known data sources: " +
', '.join(known_data_sources.columns.values))
for prediction_unit in all_prediction_units:
independent_data = prediction_unit.get("independent")
if set(independent_data).issubset(
set(known_data_sources.columns.values)):
logger.info("Predicting " +
', '.join(prediction_unit.get("dependent")))
apply_model(prediction_unit, known_data_sources,
all_prediction_models)
all_prediction_units.remove(prediction_unit)
logger.info("Prediction finished. Sending data to database")
db_write.write_dataframe(datasink_prediction_dbname, known_data_sources,
datasink_prediction_measurement)
classify_prediction(time_start, time_end, config)