Update app.py

app.py

@@ -313,7 +313,7 @@ elif section == 'Forecasts':
     operation_forecast_res.columns = [col.replace('_entsoe.', '_').replace('Naive.1D', 'DailyNaiveSeasonal') for col in operation_forecast_res.columns]
     Historical_and_Load=add_feature(operation_forecast_load, historical_forecast)
     Historical_and_operational=add_feature(operation_forecast_res, Historical_and_Load)
-    #print(Historical_and_operational.filter(like='Forecast_elia', axis=1))
+
     best_forecast = Historical_and_operational.filter(like='Forecast_elia', axis=1)
     df_combined = Historical_and_operational.join(Data_BE, how='inner')
     last_week_best_forecast = best_forecast.loc[best_forecast.index >= (best_forecast.index[-24] - pd.Timedelta(days=8))]
@@ -505,7 +505,6 @@ elif section == 'Forecasts':
                 model_mae = calculate_mae(aligned_actual_values, aligned_model_predictions)
                 # Calculate MAE for the entsoe forecast
                 entsoe_forecast = real_values_df[f'{category_prefix}_forecast_entsoe'].loc[common_indices]
-                #print(entsoe_forecast.index)
                 entsoe_mae = calculate_mae(aligned_actual_values, entsoe_forecast)
                 # Calculate MAE ratio
                 mae_ratio = model_mae / entsoe_mae
@@ -559,13 +558,10 @@ elif section == 'Forecasts':
             hours_with_data = []
             mae_ratios = []
 
-            #print(f"Processing {model_key}...")  # Debugging print
-
             for hour in hours:
                 file_name = f'Predictions_{hour}h.csv'
                 df = df_dict.get(file_name, None)
                 if df is None:
-                    #print(f"No data for hour {hour}. Skipping...")
                     continue
 
                 if isinstance(df.index, pd.DatetimeIndex):
@@ -596,7 +592,6 @@ elif section == 'Forecasts':
                 hours_with_data.append(hour)
 
             if mae_ratios:
-                print(f"Adding {model_key} to the plot with {len(mae_ratios)} points.")  # Debugging print
                 fig.add_trace(go.Scatterpolar(
                     r=mae_ratios + [mae_ratios[0]],  # Ensure closure of the polar plot
                     theta=[h * 15 for h in hours_with_data] + [0],  # Ensure closure at 0 degrees
@@ -623,36 +618,6 @@ elif section == 'Forecasts':
 
 
     if country_code == "BE":
-        #-------------------------------------------------
-        #st.header('EDS Forecasts by Hour')
-
-        #solar_fig = plot_category(forecast_dict, 'Solar', 'Solar Predictions')
-        #st.plotly_chart(solar_fig)
-
-        #wind_offshore_fig = plot_category(forecast_dict, 'Wind_offshore', 'Wind Offshore Predictions')
-        #st.plotly_chart(wind_offshore_fig)
-
-        #wind_onshore_fig = plot_category(forecast_dict, 'Wind_onshore', 'Wind Onshore Predictions')
-        #st.plotly_chart(wind_onshore_fig)
-
-        #load_fig = plot_category(forecast_dict, 'Load', 'Load Predictions')
-        #st.plotly_chart(load_fig)
-
-        #-------------------------------------------------
-        #st.header('MAE Ratio Comparison by Forecast Hour')
-        #st.write("This graph shows the relative Mean Absolute Error (rMAE) of different forecasting models "
-                #"compared to the ENTSO-E forecast, by the hour at which the forecast was made. "
-                #"The rMAE is calculated as the ratio of the model's MAE to the ENTSO-E forecast's MAE.")
-        #mae_comparison_fig = plot_mae_comparison(forecast_dict, 'Solar', 'rMAE Ratio Comparison for Solar', real_values_df=Data_BE)
-        #st.plotly_chart(mae_comparison_fig)
-        # Similarly for Wind_onshore, Wind_offshore, and Load
-        #mae_comparison_fig_wind_onshore = plot_mae_comparison(forecast_dict, 'Wind_onshore', 'MAE Ratio Comparison for Wind Onshore', real_values_df=Data_BE)
-        #st.plotly_chart(mae_comparison_fig_wind_onshore)
-        #mae_comparison_fig_wind_offshore = plot_mae_comparison(forecast_dict, 'Wind_offshore', 'MAE Ratio Comparison for Wind Offshore', real_values_df=Data_BE)
-        #st.plotly_chart(mae_comparison_fig_wind_offshore)
-        #mae_comparison_fig_load = plot_mae_comparison(forecast_dict, 'Load', 'MAE Ratio Comparison for Load', real_values_df=Data_BE)
-        #st.plotly_chart(mae_comparison_fig_load)
-        #-------------------------------------------------
 
         st.header('MAE Ratio Comparison by Forecast Hour')
         st.write("These clock-plots shows the relative Mean Absolute Error (rMAE) of different forecasting models compared to the ENTSO-E forecast, by the hour at which the forecast was made. "
@@ -793,7 +758,6 @@ elif section == 'Forecasts':
 
         # Convert the dictionaries to DataFrames and sort by rMAE
         df_wind_onshore = pd.DataFrame.from_dict(results_wind_onshore, orient='index').sort_values(by='rMAE')
-        print(df_wind_onshore)
         df_wind_offshore = pd.DataFrame.from_dict(results_wind_offshore, orient='index').sort_values(by='rMAE')
         df_load = pd.DataFrame.from_dict(results_load, orient='index').sort_values(by='rMAE')
         df_solar = pd.DataFrame.from_dict(results_solar, orient='index').sort_values(by='rMAE')
@@ -941,4 +905,5 @@ elif section == 'Insights':
                 fig.update_layout(title=f'{weather_col} vs {actual_col}')
                 st.plotly_chart(fig)
 
+