previous version with only nucmonitor

app.py

@@ -577,99 +577,99 @@ def run_app():
 
         st.write(df_photo_date_2)
 
-        # Create a Table that displays the forecast of each dataframe total for two months before date and two months after
-        # Create a Table that displays the forecast of each dataframe for the Winter months (Nov, Dec, Jan, Feb, Mar)
-
-        # Filter dates for two months before and after the current date
-        # Define date ranges
-        two_months_before = (current_date - pd.DateOffset(months=2)).strftime('%Y-%m-%d')
-        one_month_before = (current_date - pd.DateOffset(months=1)).strftime('%Y-%m-%d')
-        one_month_after = (current_date + pd.DateOffset(months=1)).strftime('%Y-%m-%d')
-        two_months_after = (current_date + pd.DateOffset(months=2)).strftime('%Y-%m-%d')
-
-        # Filter DataFrames based on date ranges
-        df_nucmonitor_filtered = df_nucmonitor_2[
-            (df_nucmonitor_2.index == two_months_before) |
-            (df_nucmonitor_2.index == one_month_before) |
-            (df_nucmonitor_2.index == current_date_str) |
-            (df_nucmonitor_2.index == one_month_after) |
-            (df_nucmonitor_2.index == two_months_after)
-        ]
-
-        df_photo_date_filtered = df_photo_date_2[
-            (df_photo_date_2.index == two_months_before) |
-            (df_photo_date_2.index == one_month_before) |
-            (df_photo_date_2.index == current_date_str) |
-            (df_photo_date_2.index == one_month_after) |
-            (df_photo_date_2.index == two_months_after)
-        ]
-
-        # Display the filtered DataFrames
-        st.write(f"Forecast update {current_date_str}")
-        st.write(df_nucmonitor_filtered)
-        st.write(f"Forecast update {past_date_str}")
-        st.write(df_photo_date_filtered)
-        current_forecast_update = df_nucmonitor_filtered.tolist()
-        past_forecast_update = df_photo_date_filtered.tolist()
-        delta = [current - past for current, past in zip(current_forecast_update, past_forecast_update)]
-
-        # Create a DataFrame for display
-        data = {
-            'Dates': [two_months_before, one_month_before, current_date_str, one_month_after, two_months_after],
-            f"Forecast update {current_date_str}": current_forecast_update,
-            f"Forecast update {past_date_str}": past_forecast_update,
-            'Delta': delta
-        }
-
-        df_display = pd.DataFrame(data)
-
-        # Display the DataFrame as a horizontal table
-        st.write("Table 1. Average expected availability on the French nuclear fleet (MW) - M-1, M, M+1, M+2, M+3")
-        st.table(df_display)
-
-
-        # Line charts of the forecasts (need to combine them so they appear in the same chart)
-        st.write("Current forecast")
-        st.line_chart(df_nucmonitor_2)
-
-        st.write("Previous forecast")
-        st.line_chart(df_photo_date_2)
-        # Create a new dataframe out of df_nucmonitor_2 call real_forecast that contains df_nucmonitor_2 up until current_date
-
-        # Slice the DataFrame to include data up until current_date
-        real_forecast = df_nucmonitor_2.loc[df_nucmonitor_2.index <= current_date_str]
-
-        # Optionally, if you want to reset the index
-        # real_forecast = real_forecast.reset_index()
-        print(real_forecast)
-        st.write("Real forecast")
-        st.line_chart(real_forecast)
+        # # Create a Table that displays the forecast of each dataframe total for two months before date and two months after
+        # # Create a Table that displays the forecast of each dataframe for the Winter months (Nov, Dec, Jan, Feb, Mar)
+
+        # # Filter dates for two months before and after the current date
+        # # Define date ranges
+        # two_months_before = (current_date - pd.DateOffset(months=2)).strftime('%Y-%m-%d')
+        # one_month_before = (current_date - pd.DateOffset(months=1)).strftime('%Y-%m-%d')
+        # one_month_after = (current_date + pd.DateOffset(months=1)).strftime('%Y-%m-%d')
+        # two_months_after = (current_date + pd.DateOffset(months=2)).strftime('%Y-%m-%d')
+
+        # # Filter DataFrames based on date ranges
+        # df_nucmonitor_filtered = df_nucmonitor_2[
+        #     (df_nucmonitor_2.index == two_months_before) |
+        #     (df_nucmonitor_2.index == one_month_before) |
+        #     (df_nucmonitor_2.index == current_date_str) |
+        #     (df_nucmonitor_2.index == one_month_after) |
+        #     (df_nucmonitor_2.index == two_months_after)
+        # ]
+
+        # df_photo_date_filtered = df_photo_date_2[
+        #     (df_photo_date_2.index == two_months_before) |
+        #     (df_photo_date_2.index == one_month_before) |
+        #     (df_photo_date_2.index == current_date_str) |
+        #     (df_photo_date_2.index == one_month_after) |
+        #     (df_photo_date_2.index == two_months_after)
+        # ]
+
+        # # Display the filtered DataFrames
+        # st.write(f"Forecast update {current_date_str}")
+        # st.write(df_nucmonitor_filtered)
+        # st.write(f"Forecast update {past_date_str}")
+        # st.write(df_photo_date_filtered)
+        # current_forecast_update = df_nucmonitor_filtered.tolist()
+        # past_forecast_update = df_photo_date_filtered.tolist()
+        # delta = [current - past for current, past in zip(current_forecast_update, past_forecast_update)]
+
+        # # Create a DataFrame for display
+        # data = {
+        #     'Dates': [two_months_before, one_month_before, current_date_str, one_month_after, two_months_after],
+        #     f"Forecast update {current_date_str}": current_forecast_update,
+        #     f"Forecast update {past_date_str}": past_forecast_update,
+        #     'Delta': delta
+        # }
+
+        # df_display = pd.DataFrame(data)
+
+        # # Display the DataFrame as a horizontal table
+        # st.write("Table 1. Average expected availability on the French nuclear fleet (MW) - M-1, M, M+1, M+2, M+3")
+        # st.table(df_display)
+
+
+        # # Line charts of the forecasts (need to combine them so they appear in the same chart)
+        # st.write("Current forecast")
+        # st.line_chart(df_nucmonitor_2)
+
+        # st.write("Previous forecast")
+        # st.line_chart(df_photo_date_2)
+        # # Create a new dataframe out of df_nucmonitor_2 call real_forecast that contains df_nucmonitor_2 up until current_date
+
+        # # Slice the DataFrame to include data up until current_date
+        # real_forecast = df_nucmonitor_2.loc[df_nucmonitor_2.index <= current_date_str]
+
+        # # Optionally, if you want to reset the index
+        # # real_forecast = real_forecast.reset_index()
+        # print(real_forecast)
+        # st.write("Real forecast")
+        # st.line_chart(real_forecast)
         
-        # Combine dataframes
-        combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2, real_forecast], axis=1)
-        combined_df.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}', 'Real Forecast']
+        # # Combine dataframes
+        # combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2, real_forecast], axis=1)
+        # combined_df.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}', 'Real Forecast']
 
-        print(combined_df)
-        st.write(f"Graph 1. {start_date} to {end_date}")
-        st.line_chart(combined_df)
+        # print(combined_df)
+        # st.write(f"Graph 1. {start_date} to {end_date}")
+        # st.line_chart(combined_df)
 
-        # Set Nucmonitor as a dotted line until the current date
+        # # Set Nucmonitor as a dotted line until the current date
 
-        fig, ax = plt.subplots(figsize=(10, 6))
+        # fig, ax = plt.subplots(figsize=(10, 6))
 
-        plt.plot(combined_df.index, combined_df[f'Forecast {current_date_str}'], 'r--', label=f'Forecast {current_date_str}')
-        plt.plot(combined_df.index, combined_df[f'Forecast {past_date_str}'], 'b-', label=f'Forecast {past_date_str}')
+        # plt.plot(combined_df.index, combined_df[f'Forecast {current_date_str}'], 'r--', label=f'Forecast {current_date_str}')
+        # plt.plot(combined_df.index, combined_df[f'Forecast {past_date_str}'], 'b-', label=f'Forecast {past_date_str}')
 
-        plt.axvline(current_date_str, color='k', linestyle='--', linewidth=1, label='Current Date')
+        # plt.axvline(current_date_str, color='k', linestyle='--', linewidth=1, label='Current Date')
 
-        # Set the x-axis to show only the first day of every month
-        ax.xaxis.set_major_locator(MonthLocator(bymonthday=1))
+        # # Set the x-axis to show only the first day of every month
+        # ax.xaxis.set_major_locator(MonthLocator(bymonthday=1))
 
-        plt.legend()
+        # plt.legend()
 
-        plt.xticks(rotation=45)
+        # plt.xticks(rotation=45)
 
-        st.pyplot(fig)
+        # st.pyplot(fig)
 
         # For Historical Winter Availability, can just get the max and min of each month, store as list in a column, and try to graph that