add city data

read_data.py

@@ -4,9 +4,9 @@ import csv
 
 pv_yield_file_name = 'read_data/Serbia.csv'
 # factory_demand_file_name = 'factory_power1.xlsx'
-factory_demand_file_name = 'factory_power1.csv'
-electricity_price_data = 'electricity_price_data.csv'
-electricity_price_data_sell = 'electricity_price_data_sell.csv'
+factory_demand_file_name = 'read_data/factory_power1.csv'
+electricity_price_data = 'read_data/electricity_price_data.csv'
+electricity_price_data_sell = 'read_data/electricity_price_data_sell.csv'
 
 pv_df = pd.read_csv(pv_yield_file_name, index_col='Time', usecols=['Time', 'PV yield[kW/kWp]'])
 pv_df.index = pd.to_datetime(pv_df.index)

read_data/convert_data.py

@@ -55,15 +55,19 @@ def process(file_name):
     sums = df.groupby('group_id')[energy_row_name].sum()
     sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')
 
+    pv_energy_column_name = 'PV yield[kW/kWp]'
+    sums_df = sums_df.rename(columns={'Energy': pv_energy_column_name})
+
     time_frame = generate_min_df(15)
     sums_df = pd.concat([time_frame, sums_df], axis=1)
-    sums_df.set_index('Time', inplace=True)
-    max_value = sums_df['Energy'].max()
-    sums_df['Energy'] = sums_df['Energy'] / 390.
-    sums_df['Energy'] = sums_df['Energy'].round(4)
-    sums_df['Energy'].replace(0.0, -0.0)
+    # sums_df.set_index('Time', inplace=True)
+    # max_value = sums_df[pv_energy_column_name].max()
+    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name] / 390.
+    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name].round(4)
+    sums_df[pv_energy_column_name].replace(0.0, -0.0)
 
-    save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])
+    sums_df.to_csv(f'{city}.csv')
+    # save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])
 
 if __name__ == '__main__':
     city_list = ['Riyahd', 'Cambodge', 'Berlin', 'Serbia']