Added error handling for: Calls not including winter months; Calls with less than 5 months

app.py

@@ -533,6 +533,8 @@ def run_app():
     start_date = st.date_input("Start Date")
     end_date = st.date_input("End Date")
     past_date = st.date_input("Cutoff Date")
+    # winter_date = st.date_input("Winter Cutoff Date")
+
     current_date = datetime.datetime.now()
 
     with st.form("nucmonitor_form"):
@@ -545,6 +547,7 @@ def run_app():
         st.write("Data received from Flask:")
         df_nucmonitor = get_nucmonitor_data(start_date, end_date, current_date)
         df_photo_date = get_nucmonitor_data(start_date, end_date, past_date)
+        # df_winter_date = get_nucmonitor_data(start_date, end_date, winter_date)
         current_date_str = str(current_date.strftime('%Y-%m-%d'))
         past_date_str = str(past_date.strftime('%Y-%m-%d'))
         st.write("Nucmonitor")
@@ -577,81 +580,185 @@ 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)
+# --------------------------------- AVERAGE EXPECTED AVAILABILITY M-1 M M+1 M+2 PIPELINE --------------------------------- #
+
+        # Create a Table that displays the forecast of each dataframe total for two months before date and two months after
+        # 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')
+        one_month_before = (current_date - pd.DateOffset(months=1)).strftime('%Y-%m')
+        one_month_after = (current_date + pd.DateOffset(months=1)).strftime('%Y-%m')
+        two_months_after = (current_date + pd.DateOffset(months=2)).strftime('%Y-%m')
+
+        # Assuming df is the DataFrame containing the date index and the 'Total' column
+
+        # # Convert the index to datetime if it's not already
+        # df_nucmonitor_2.index = pd.to_datetime(df_nucmonitor_2.index)
+        # df_photo_date_2.index = pd.to_datetime(df_photo_date_2.index)
+
+        # # Calculate monthly averages with date in yyyy-mm format
+        # monthly_average_nucmonitor = df_nucmonitor_2.resample('M').mean()
+        # monthly_average_photo_date = df_photo_date_2.resample('M').mean()
+
+        # Convert the index to datetime if it's not already
+        df_nucmonitor_2.index = pd.to_datetime(df_nucmonitor_2.index)
+        df_photo_date_2.index = pd.to_datetime(df_photo_date_2.index)
+
+        # Calculate monthly averages with date in yyyy-mm format
+        monthly_average_nucmonitor = df_nucmonitor_2.resample('M').mean()
+        monthly_average_nucmonitor.index = monthly_average_nucmonitor.index.strftime('%Y-%m')
+
+        monthly_average_photo_date = df_photo_date_2.resample('M').mean()
+        monthly_average_photo_date.index = monthly_average_photo_date.index.strftime('%Y-%m')
+
+
+        print(monthly_average_nucmonitor)
+        print(monthly_average_nucmonitor.index)
+        print(len(monthly_average_nucmonitor.index) < 5)
+        if (len(monthly_average_nucmonitor.index) < 5) or (two_months_before not in monthly_average_nucmonitor.index or two_months_after not in monthly_average_nucmonitor.index):
+            df_display_normal_bool = False
+
+        else:
+            print(two_months_before, one_month_before, current_date.strftime('%Y-%m'), one_month_after, two_months_after)
+            # Filter DataFrames based on date ranges
+            df_nucmonitor_filtered = monthly_average_nucmonitor[
+                (monthly_average_nucmonitor.index == two_months_before) |
+                (monthly_average_nucmonitor.index == one_month_before) |
+                (monthly_average_nucmonitor.index == current_date.strftime('%Y-%m')) |
+                (monthly_average_nucmonitor.index == one_month_after) |
+                (monthly_average_nucmonitor.index == two_months_after)
+            ]
+
+            df_photo_date_filtered = monthly_average_photo_date[
+                (monthly_average_photo_date.index == two_months_before) |
+                (monthly_average_photo_date.index == one_month_before) |
+                (monthly_average_photo_date.index == current_date.strftime('%Y-%m')) |
+                (monthly_average_photo_date.index == one_month_after) |
+                (monthly_average_photo_date.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)]
+
+            print('Dates:', [two_months_before, one_month_before, current_date.strftime('%Y-%m'), one_month_after, two_months_after])
+            print(f"Forecast update {current_date_str}", current_forecast_update)
+            print(f"Forecast update {past_date_str}", past_forecast_update,)
+            print('Delta', delta)
+
+            # Create a DataFrame for display
+            data_avg_expected_normal = {
+                'Dates': [two_months_before, one_month_before, current_date.strftime('%Y-%m'), 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_normal_bool = True
+
+# --------------------------------- AVERAGE EXPECTED AVAILABILITY M-1 M M+1 M+2 PIPELINE --------------------------------- #
+
+# --------------------------------- AVERAGE EXPECTED AVAILABILITY WINTER PIPELINE --------------------------------- #
+        # Create a Table that displays the forecast of each dataframe for the Winter months (Nov, Dec, Jan, Feb, Mar)
+
+        # Create a table that gets the forecast for winter. This involves creating a new dataframe with
+        # only the winter months with the total of each day, and another dataframe with the average of each month. Each month
+        # included will only be 20xx-11, 12, and 20xx+1-01, 02, 03
         
-        # # 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']
+        # Define date ranges for winter months
+        # winter_start_date = current_date.replace(month=11, day=1)
+        # winter_end_date = (current_date.replace(year=current_date.year+1, month=3, day=31))
+        winter_start = f"{current_date.year}-11"
+        winter_end = f"{current_date.year+1}-03"
+        winter_start_str = str(winter_start)
+        winter_end_str = str(winter_end)
+        print("winter_start_str", winter_start)
+        print("winter_end_str", winter_end)
+        print("monthly_average_nucmonitor.index", monthly_average_nucmonitor.index)
+        print(monthly_average_nucmonitor.index == winter_start)
+        print(monthly_average_nucmonitor.index == winter_end)
+        if monthly_average_nucmonitor.index.any() != winter_start or monthly_average_nucmonitor.index.any() != winter_end:
+            df_display_winter_bool = False
+
+        else:
+            # Filter DataFrames based on winter date range
+            df_nucmonitor_winter = monthly_average_nucmonitor[(monthly_average_nucmonitor.index >= winter_start_str) & (monthly_average_nucmonitor.index <= winter_end_str)]
+
+            df_photo_date_winter = monthly_average_photo_date[(monthly_average_photo_date.index >= winter_start_str) & (monthly_average_photo_date.index <= winter_end_str)]
+
+            # Display the forecast DataFrames for winter
+            st.title("Forecast for Winter Months")
+            st.write(f"Forecast for {current_date.year}-{current_date.year+1} (Nov, Dec, Jan, Feb, Mar)")
+            st.write("Nucmonitor Forecast:")
+            st.write(df_nucmonitor_winter)
+            st.write("Photo Date Forecast:")
+            st.write(df_photo_date_winter)
+            
+            current_winter_forecast_update = df_nucmonitor_winter.tolist()
+            past_winter_forecast_update = df_photo_date_winter.tolist()
+            winter_delta = [current - past for current, past in zip(current_winter_forecast_update, past_winter_forecast_update)]
+            print("current_winter_forecast_update:", current_winter_forecast_update)
+            print("past_winter_forecast_update:", past_winter_forecast_update)
+
+            # Create a DataFrame for display
+            data_avg_expected_winter = {
+                'Dates': [f'Nov-{current_date.year}', f'Dec-{current_date.year}', f'Jan-{current_date.year+1}', f'Feb-{current_date.year+1}', f'Mar-{current_date.year+1}'],
+                f"Forecast update {current_date_str}": current_winter_forecast_update,
+                f"Forecast update {past_date_str}": past_winter_forecast_update,
+                'Delta': winter_delta
+            }
+            print(data_avg_expected_winter)
+            df_display_winter_bool = True
+
+# --------------------------------- AVERAGE EXPECTED AVAILABILITY WINTER PIPELINE --------------------------------- #
 
-        # print(combined_df)
-        # st.write(f"Graph 1. {start_date} to {end_date}")
-        # st.line_chart(combined_df)
+# --------------------------------- VISUALIZE --------------------------------- #
+        if df_display_normal_bool:
+            df_display_normal = pd.DataFrame(data_avg_expected_normal)
+            # 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_normal)
+        
+        if df_display_winter_bool:
+            df_display_winter = pd.DataFrame(data_avg_expected_winter)
+            st.write(f"Table 2. Average expected availability on the French nuclear fleet (MW) - Winter {winter_start}/{winter_end}")
+            st.table(df_display_winter)
+
+        # 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]
+
+        # Winter forecast still not the correct one, this is just a placeholder
+        # winter_forecast = df_nucmonitor_2.loc[(df_nucmonitor_2.index >= winter_start_date) & (df_nucmonitor_2.index <= winter_end_date)]
+        
+        # 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, winter_forecast], axis=1)
+        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', f'Winter forecast {winter_start}/{winter_end}']
+        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)
 
         # # Set Nucmonitor as a dotted line until the current date