Enhance app.py to differentiate data handling based on 'display_frame_source'. Implement conditional logic for working_frame and export_merge updates in various functionalities, including filtering, trimming, and exposure management, ensuring accurate data processing for both Portfolio and Base sources.

app.py

@@ -913,6 +913,7 @@ with tab1:
 
 with tab2:
     if 'origin_portfolio' in st.session_state and 'projections_df' in st.session_state:
+        st.session_state['display_frame_source'] = 'Portfolio'
         with st.container():
             col1, col2 = st.columns(2)
             with col1:
@@ -1052,8 +1053,12 @@ with tab2:
                                 parsed_frame = parsed_frame[~parsed_frame['Stack'].isin(stack_remove)]
                             else:
                                 parsed_frame = parsed_frame
-                        st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            st.session_state['export_base'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
 
             with st.expander('Micro Filter Options'):
                 with st.form(key='micro_filter_form'):
@@ -1117,9 +1122,12 @@ with tab2:
                         
                         if size_include:
                             parsed_frame = parsed_frame[parsed_frame['Size'].isin(size_include)]
-                            
-                        st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            st.session_state['export_base'] = parsed_frame.sort_values(by='median', ascending=False).reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
 
             with st.expander('Trimming Options'):
                 with st.form(key='trim_form'):
@@ -1151,10 +1159,14 @@ with tab2:
                         st.session_state['settings_base'] = False
                         st.write('initiated')
                         parsed_frame = st.session_state['working_frame'].copy()
-                        
-                        st.session_state['working_frame'] = trim_portfolio(parsed_frame, trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
-                        st.session_state['working_frame'] = st.session_state['working_frame'].sort_values(by='median', ascending=False)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            parsed_frame = trim_portfolio(parsed_frame, trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
+                            st.session_state['working_frame'] = parsed_frame.sort_values(by='median', ascending=False)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            parsed_frame = trim_portfolio(parsed_frame, trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
+                            st.session_state['export_base'] = parsed_frame.copy()
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
             with st.expander('Presets'):
                 st.info("Still heavily in testing here, I'll announce when they are ready for use.")
                 with st.form(key='Small Field Preset'):
@@ -1163,18 +1175,33 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        if preset_choice == 'Small Field (Heavy Own)':
-                            parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
-                        elif preset_choice == 'Large Field (Manage Diversity)':
-                            parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
-                        elif preset_choice == 'Volatility (Heavy Lineup Edge)':
-                            parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
-                        elif preset_choice == 'Hedge Chalk (Manage Leverage)':
-                            parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'], sport_var)
-                        elif preset_choice == 'Reduce Volatility (Manage Own)':
-                            parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
-                        st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            if preset_choice == 'Small Field (Heavy Own)':
+                                parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Large Field (Manage Diversity)':
+                                parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Volatility (Heavy Lineup Edge)':
+                                parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Hedge Chalk (Manage Leverage)':
+                                parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'], sport_var)
+                            elif preset_choice == 'Reduce Volatility (Manage Own)':
+                                parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+
+                            st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            if preset_choice == 'Small Field (Heavy Own)':
+                                parsed_frame = small_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Large Field (Manage Diversity)':
+                                parsed_frame = large_field_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Volatility (Heavy Lineup Edge)':
+                                parsed_frame = volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            elif preset_choice == 'Hedge Chalk (Manage Leverage)':
+                                parsed_frame = hedging_preset(st.session_state['working_frame'], lineup_target, st.session_state['projections_df'], sport_var)
+                            elif preset_choice == 'Reduce Volatility (Manage Own)':
+                                parsed_frame = reduce_volatility_preset(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var)
+                            st.session_state['export_base'] = parsed_frame.copy()
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
             with st.expander('Stratify'):
                 with st.form(key='Stratification'):
                     sorting_choice = st.selectbox("Stat Choice", options=['median', 'Own', 'Weighted Own', 'Geomean', 'Lineup Edge', 'Finish_percentile', 'Diversity'], index=0)
@@ -1182,9 +1209,14 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        parsed_frame = stratification_function(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var, sorting_choice)
-                        st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            parsed_frame = stratification_function(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var, sorting_choice)
+                            st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            parsed_frame = stratification_function(st.session_state['export_base'], lineup_target, excluded_cols, sport_var, sorting_choice)
+                            st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
             with st.expander('Exposure Management'):
                 with st.form(key='Exposures'):
                     exposure_player = st.selectbox("Player", options=sorted(list(player_names)))
@@ -1197,10 +1229,40 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
-                        st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
-                        if type_var == 'Classic':
-                            if sport_var == 'CS2' or sport_var == 'LOL':
+                        if st.session_state['display_frame_source'] == 'Portfolio':
+                            parsed_frame = exposure_spread(st.session_state['working_frame'], exposure_player, exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
+                            st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
+                            if type_var == 'Classic':
+                                if sport_var == 'CS2' or sport_var == 'LOL':
+                                    # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+                                    st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+                                    
+                                    # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+                                    st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+                                    
+                                    # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+                                    st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+
+                                elif sport_var != 'CS2' and sport_var != 'LOL':
+                                    st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
+                                    st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
+                                    st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
+                                    if 'stack_dict' in st.session_state:
+                                        st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].index.map(st.session_state['stack_dict'])
+                                        st.session_state['working_frame']['Size'] = st.session_state['working_frame'].index.map(st.session_state['size_dict'])
+                            elif type_var == 'Showdown':
                                 # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
                                 st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
                                     lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
@@ -1221,42 +1283,75 @@ with tab2:
                                             sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
                                     axis=1
                                 )
-
-                            elif sport_var != 'CS2' and sport_var != 'LOL':
-                                st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
-                                st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
-                                st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
-                                if 'stack_dict' in st.session_state:
-                                    st.session_state['working_frame']['Stack'] = st.session_state['working_frame'].index.map(st.session_state['stack_dict'])
-                                    st.session_state['working_frame']['Size'] = st.session_state['working_frame'].index.map(st.session_state['size_dict'])
-                        elif type_var == 'Showdown':
-                            # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
-                            st.session_state['working_frame']['salary'] = st.session_state['working_frame'].apply(
-                                lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
-                                axis=1
-                            )
+                            # st.session_state['working_frame']['Own'] = st.session_state['working_frame']['Own'].astype('float32')
+                            st.session_state['working_frame']['median'] = st.session_state['working_frame']['median'].astype('float32')
+                            st.session_state['working_frame']['salary'] = st.session_state['working_frame']['salary'].astype('uint16')
                             
-                            # Calculate median (CPT uses cpt_proj_map, others use proj_map)
-                            st.session_state['working_frame']['median'] = st.session_state['working_frame'].apply(
-                                lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
-                                axis=1
-                            )
+                            print(st.session_state['working_frame'].head(10))
+                            st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
+                            st.session_state['export_merge'] = st.session_state['working_frame'].copy()
+                        else:
+                            parsed_frame = exposure_spread(st.session_state['export_base'], exposure_player, exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max)
+                            st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
+                            if type_var == 'Classic':
+                                if sport_var == 'CS2' or sport_var == 'LOL':
+                                    # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+                                    st.session_state['export_base']['salary'] = st.session_state['export_base'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+                                    
+                                    # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+                                    st.session_state['export_base']['median'] = st.session_state['export_base'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+                                    
+                                    # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+                                    st.session_state['export_base']['Own'] = st.session_state['export_base'].apply(
+                                        lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                                                sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
+                                        axis=1
+                                    )
+
+                                elif sport_var != 'CS2' and sport_var != 'LOL':
+                                    st.session_state['export_base']['salary'] = st.session_state['export_base'].apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
+                                    st.session_state['export_base']['median'] = st.session_state['export_base'].apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
+                                    st.session_state['export_base']['Own'] = st.session_state['export_base'].apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
+                                    if 'stack_dict' in st.session_state:
+                                        st.session_state['export_base']['Stack'] = st.session_state['export_base'].index.map(st.session_state['stack_dict'])
+                                        st.session_state['export_base']['Size'] = st.session_state['export_base'].index.map(st.session_state['size_dict'])
+                            elif type_var == 'Showdown':
+                                # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+                                st.session_state['export_base']['salary'] = st.session_state['export_base'].apply(
+                                    lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                                    axis=1
+                                )
+                                
+                                # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+                                st.session_state['export_base']['median'] = st.session_state['export_base'].apply(
+                                    lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                                    axis=1
+                                )
+                                
+                                # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+                                st.session_state['export_base']['Own'] = st.session_state['export_base'].apply(
+                                    lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
+                                    axis=1
+                                )
+                            # st.session_state['export_base']['Own'] = st.session_state['export_base']['Own'].astype('float32')
+                            st.session_state['export_base']['median'] = st.session_state['export_base']['median'].astype('float32')
+                            st.session_state['export_base']['salary'] = st.session_state['export_base']['salary'].astype('uint16')
                             
-                            # Calculate ownership (CPT uses cpt_own_map, others use own_map)
-                            st.session_state['working_frame']['Own'] = st.session_state['working_frame'].apply(
-                                lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
-                                axis=1
-                            )
-                        # st.session_state['working_frame']['Own'] = st.session_state['working_frame']['Own'].astype('float32')
-                        st.session_state['working_frame']['median'] = st.session_state['working_frame']['median'].astype('float32')
-                        st.session_state['working_frame']['salary'] = st.session_state['working_frame']['salary'].astype('uint16')
+                            print(st.session_state['export_base'].head(10))
+                            st.session_state['export_base'] = predict_dupes(st.session_state['export_base'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
+                            st.session_state['export_merge'] = st.session_state['export_base'].copy()
                         
-                        print(st.session_state['working_frame'].head(10))
-                        st.session_state['working_frame'] = predict_dupes(st.session_state['working_frame'], st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var)
-                        st.session_state['export_merge'] = st.session_state['working_frame'].copy()
         with st.container():
             if 'export_base' not in st.session_state:
                     st.session_state['export_base'] = pd.DataFrame(columns=st.session_state['working_frame'].columns)