Added Graphs and tables

app.py

@@ -12,6 +12,9 @@ import json
 from calendar import monthrange
 import pymongo
 from mongoengine import StringField, ListField, DateTimeField, DictField
+import matplotlib.pyplot as plt
+from matplotlib.dates import MonthLocator
+
 
 def mongo_unavs_call(user_input_start_date, user_input_end_date, user_input_past_date):
     print("Starting mongo_unavs_call")
@@ -541,19 +544,13 @@ 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)
-
+        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(df_nucmonitor)  # Display DataFrame
         
         st.write("Photo Date")
         st.write(df_photo_date)
-        
-        # # Create a line chart using Streamlit
-        # st.title("Power Plant Data Visualization")
-        # df1 = df.iloc[:-1, :-1]
-        # # Create a line chart using Streamlit
-        # st.line_chart(df1)
-        # st.write(df1)
 
         # Get info on current forecast Nucmonitor
         st.title("Total Energy per Day at Current Forecast")
@@ -563,9 +560,9 @@ def run_app():
         # Get the last column
         df_nucmonitor_2 = df_nucmonitor_2.iloc[:, -1]
         
-        st.write(df_nucmonitor_2)
+        print(df_nucmonitor_2)
 
-        st.line_chart(df_nucmonitor_2)
+        st.write(df_nucmonitor_2)
 
         # Get info on past date forecast Nucmonitor
         st.title("Total Energy per Day at Past Date Forecast")
@@ -575,12 +572,98 @@ def run_app():
         # Get the last column
         df_photo_date_2 = df_photo_date_2.iloc[:, -1]
         
+        print(df_photo_date_2)
+
         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)
 
+        # Combine dataframes
+        combined_df = pd.concat([df_nucmonitor_2, df_photo_date_2], axis=1)
+        combined_df.columns = [f'Forecast {current_date_str}', f'Forecast {past_date_str}']
+
+        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
+
+        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)
+
+
         # Add a download button
         # Create a BytesIO object to hold the Excel data
+
         excel_buffer = io.BytesIO()
 
     
@@ -594,7 +677,7 @@ def run_app():
 
 
         # Save the DataFrame to the BytesIO object as an Excel file
-        df_nucmonitor.to_excel(excel_buffer, index=True)
+        df_nucmonitor_2.to_excel(excel_buffer, index=True)
         # Set the cursor position to the beginning of the BytesIO object
         excel_buffer.seek(0)
 
@@ -606,5 +689,6 @@ def run_app():
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
         )
 
+
 if __name__ == '__main__':
     run_app()

requirements.txt

@@ -1,3 +1,4 @@
 mongoengine==0.26.0
 pymongo==4.3.3
-openpyxl
+openpyxl
+matplotlib