Updated with suggestions from JC

app.py

@@ -305,7 +305,7 @@ def nuc_monitor(usr_start_date, usr_end_date, past_date, mongo_db_data):
     results_plants = {plant_name: {date: {"available_capacity": power, "updated_date": pd.to_datetime("1970-01-01", utc=True)} for date in dates_of_interest}
                     for plant_name, power in plants_metadata.items()}
 
-    print(results_sorted)
+    # print(results_sorted)
     for plant, unavailabilities in results_sorted.items():
         # Get the full power of a given plant according to the sorted results
         original_power = plants_metadata[plant]
@@ -398,7 +398,9 @@ def nuc_monitor(usr_start_date, usr_end_date, past_date, mongo_db_data):
     # print(results_plants)
     # Convert datetime key to string to store in mongodb
     output_results = {plant: {str(date): power for date, power in plant_data.items()} for plant, plant_data in output_results.items()}
+    # output_results = pd.DataFrame(output_results)
     # print(output_results)
+
     # -------------------------------------------------
 
     json_data = json.dumps(output_results)
@@ -457,14 +459,14 @@ def run_app():
         # 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")
+        st.write(f"Current View Forecast at {current_date_str} (MW)")
         st.write(df_nucmonitor)  # Display DataFrame
         
-        st.write("Photo Date")
+        st.write(f"Past View Forecast at {past_date_str}")
         st.write(df_photo_date)
 
         # Get info on current forecast Nucmonitor
-        st.title("Total Energy per Day at Current Forecast")
+        st.write(f"Total Energy per Day at Current View Forecast {current_date_str} (MW)")
         
         # Remove the final row 'Total'
         df_nucmonitor_2 = df_nucmonitor.iloc[:-1, :]
@@ -476,7 +478,7 @@ def run_app():
         st.write(df_nucmonitor_2)
 
         # Get info on past date forecast Nucmonitor
-        st.title("Total Energy per Day at Past Date Forecast")
+        st.write(f"Total Energy per Day at Past View Forecast {past_date_str} (MW)")
         
         # Remove the final row 'Total'
         df_photo_date_2 = df_photo_date.iloc[:-1, :]
@@ -551,9 +553,9 @@ def run_app():
             ]
 
             # Display the filtered DataFrames
-            st.write(f"Forecast update {current_date_str}")
+            st.write(f"Forecast at {current_date_str} (MW)")
             st.write(df_nucmonitor_filtered)
-            st.write(f"Forecast update {past_date_str}")
+            st.write(f"Forecast at {past_date_str} (MW)")
             st.write(df_photo_date_filtered)
 
             current_forecast_update = df_nucmonitor_filtered.tolist()
@@ -568,8 +570,8 @@ def run_app():
             # 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,
+                f"Forecast update {current_date_str} (MW)": current_forecast_update,
+                f"Forecast update {past_date_str} (MW)": past_forecast_update,
                 'Delta': delta
             }
             df_display_normal_bool = True
@@ -643,57 +645,36 @@ def run_app():
             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.write("Current forecast (MW)")
         st.line_chart(df_nucmonitor_2)
 
-        st.write("Previous forecast")
+        st.write("Previous forecast (MW)")
         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
+        # Create a new dataframe out of df_nucmonitor_2 call real_avail 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]
+        real_avail = 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)
+        # real_avail = real_avail.reset_index()
+        # print(real_avail)
+        st.write("Observed Historical Availability (MW)")
+        st.line_chart(real_avail)
 
         # 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 = pd.concat([df_nucmonitor_2, df_photo_date_2, real_avail, winter_forecast], axis=1)
+        combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2, real_avail], 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']
+        # combined_df.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}', 'Observed Historical Availability', f'Winter forecast {winter_start}/{winter_end}']
+        combined_df.columns = [f'Forecast {current_date_str} (MW)', f'Forecast {past_date_str} (MW)', 'Observed Historical Availability (MW)']
 
         # print(combined_df)
-        st.write(f"Graph 1. {start_date} to {end_date}")
+        st.write(f"Graph 1. {start_date} to {end_date} (MW)")
         st.line_chart(combined_df)
 
-        # # Set Nucmonitor as a dotted line until the current date
-
-        # 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.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))
-
-        # plt.legend()
-
-        # plt.xticks(rotation=45)
-
-        # 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
-
-
         # Add a download button
         # Create a BytesIO object to hold the Excel data