def get_sim_data():
sim = SimRes('demo_results.mat')
dfDymola = sim.to_pandas([
'combiTimeTable.y[2]', 'combiTimeTable.y[3]',
'boiler_system.temperature_sensor_2.T', 'combiTimeTable.y[4]'
])
#getting list of column names: names contain the unit
df_col = list(dfDymola.columns)
##extract unit from column name
df_col_split = []
for x in df_col:
df_col_split = df_col_split + [x.split()[0]]
#rename columns without units
for i in range(len(df_col)):
dfDymola = dfDymola.rename(index=str,
columns={df_col[i]: df_col_split[i]})
# print(dfDymola)
dfDymola.index = pd.to_numeric(dfDymola.index)
dfDymola["time_diff"] = dfDymola.index
# dfDymola["time_diff"]=pd.to_numeric(dfDymola["time_diff"])
dfDymola["time_diff"] = (dfDymola["time_diff"].shift(-1) -
dfDymola["time_diff"])
# dfDymola=dfDymola[~dfDymola.index.duplicated(keep='last')]
## dfDymola.drop_duplicates(keep='last', inplace=True)
# max_range = max(dfDymola.index) + 1
#
#
# dfDymola = dfDymola.reindex(index=range(1, int(max_range)), method='ffill', fill_value=0)
dfDymola = reindex_int(dfDymola)
return dfDymola
def export(self, output_vars=None):
sim = SimRes(self.mat_file)
if self.csv_file is not None:
self.csv_file = os.path.splitext(self.mat_file)[0] + '.csv'
else:
self.csv_file = self.csv_file
if output_vars is not None:
self.output_vars = output_vars
data = sim.to_pandas(self.output_vars)
else:
data = sim.to_pandas()
data = data.loc[~data.index.duplicated(keep='last')]
data.drop(data.head(1).index, inplace=True)
data.to_csv(self.csv_file, encoding='utf-8')
def check_data(filename_model, filename_results, filename_database, budo_label):
sim = SimRes(filename_results)
variable_name_sim = get_complete_variable_name(filename_model, budo_label)
dfDymola=sim.to_pandas([variable_name_sim])
dfDymola=preprocess_sim_data(dfDymola)
dfDymola=reindex_int(dfDymola)
"read out of monitoring database"
"(in real a specialized timeseries database)"
disk_engine = create_engine('sqlite:///'+filename_database)
df = pd.read_sql_query('SELECT * FROM timeseries', disk_engine)
df = preprocess_monitoring_data(df)
(df, dfDymola)=check_length(df, dfDymola)
predicted_values = dfDymola[variable_name_sim]
measured_values = df[budo_label]
(iae_, rmse_, nrmse_)=calculate_values(measured_values, predicted_values)
print("IAE between real and simulated data is: "+str(iae_))
print("RMSE between real and simulated data is "+str(rmse_))
print("NRMSE between real and simulated data is "+str(nrmse_))
