previous version

app.py

@@ -577,55 +577,31 @@ def run_app():
 
         st.write(df_photo_date_2)
 
-# --------------------------------- 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
+        # 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')
-        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()
+        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')
 
-        # 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(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_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 = 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)
+        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
@@ -633,82 +609,24 @@ def run_app():
         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],
+        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
         }
-# --------------------------------- 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
-        
-        # 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}-3"
-        winter_start_str = str(winter_start)
-        winter_end_str = str(winter_end)
-        print("winter_start_str", winter_start_str)
-        print("winter_end_str", winter_end_str)
-        print(monthly_average_nucmonitor.index)
-        # 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("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}'])
-        print(f"Forecast update {current_date_str}:", current_winter_forecast_update)
-        print(f"Forecast update {past_date_str}:", past_winter_forecast_update)
-        print('Delta:', winter_delta)
-        # 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
-        }
-
-# --------------------------------- AVERAGE EXPECTED AVAILABILITY WINTER PIPELINE --------------------------------- #
-
-# --------------------------------- VISUALIZE --------------------------------- #
-
-        df_display_normal = pd.DataFrame(data_avg_expected_normal)
-        df_display_winter = pd.DataFrame(data_avg_expected_winter)
+        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_normal)
+        st.table(df_display)
 
-        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")
@@ -721,17 +639,15 @@ def run_app():
         # 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.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}', 'Real Forecast', f'Winter forecast {winter_start}/{winter_end}']
+        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}")