Refactor app.py to streamline data handling by removing 'display_frame_source' checks. Update working_frame and export_merge assignments to ensure consistent data processing across functionalities, including filtering, trimming, and exposure management, enhancing overall code clarity and maintainability.

app.py

@@ -913,7 +913,6 @@ 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:
@@ -1034,9 +1033,13 @@ with tab2:
                         stack_remove_toggle = st.selectbox("Remove specific stacks?", options=['No', 'Yes'], index=0)
                         stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(st.session_state['stack_dict'].values()))), default=[])
                     
-                    submitted = st.form_submit_button("Submit")
+                    submitted_col, export_col = st.columns(2)
+                    with submitted_col:
+                        reg_submitted = st.form_submit_button("regSubmit")
+                    with export_col:
+                        exp_submitted = st.form_submit_button("expSubmit")
                     
-                    if submitted:
+                    if reg_submitted:
                         st.session_state['settings_base'] = False
                         parsed_frame = st.session_state['working_frame'].copy()
                         parsed_frame = parsed_frame[parsed_frame['Dupes'] <= max_dupes]
@@ -1053,12 +1056,27 @@ with tab2:
                                 parsed_frame = parsed_frame[~parsed_frame['Stack'].isin(stack_remove)]
                             else:
                                 parsed_frame = parsed_frame
-                        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()
+                        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 exp_submitted:
+                        st.session_state['settings_base'] = False
+                        parsed_frame = st.session_state['working_frame'].copy()
+                        parsed_frame = parsed_frame[parsed_frame['Dupes'] <= max_dupes]
+                        parsed_frame = parsed_frame[parsed_frame['salary'] >= min_salary]
+                        parsed_frame = parsed_frame[parsed_frame['salary'] <= max_salary]
+                        parsed_frame = parsed_frame[parsed_frame['Finish_percentile'] <= max_finish_percentile]
+                        parsed_frame = parsed_frame[parsed_frame['Lineup Edge'] >= min_lineup_edge]
+                        if 'Stack' in parsed_frame.columns:
+                            if stack_include_toggle == 'All Stacks':
+                                parsed_frame = parsed_frame
+                            else:
+                                parsed_frame = parsed_frame[parsed_frame['Stack'].isin(stack_selections)]
+                            if stack_remove_toggle == 'Yes':
+                                parsed_frame = parsed_frame[~parsed_frame['Stack'].isin(stack_remove)]
+                            else:
+                                parsed_frame = parsed_frame
+                        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'):
@@ -1122,12 +1140,8 @@ with tab2:
                         
                         if size_include:
                             parsed_frame = parsed_frame[parsed_frame['Size'].isin(size_include)]
-                        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()
+                        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()
 
             with st.expander('Trimming Options'):
                 with st.form(key='trim_form'):
@@ -1159,14 +1173,9 @@ with tab2:
                         st.session_state['settings_base'] = False
                         st.write('initiated')
                         parsed_frame = 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()
+                        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()
             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'):
@@ -1175,33 +1184,19 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        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()
+                        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()
             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)
@@ -1209,14 +1204,9 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        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()
+                        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()
             with st.expander('Exposure Management'):
                 with st.form(key='Exposures'):
                     exposure_player = st.selectbox("Player", options=sorted(list(player_names)))
@@ -1229,40 +1219,10 @@ with tab2:
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         st.session_state['settings_base'] = False
-                        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':
+                        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) + 
@@ -1283,74 +1243,42 @@ with tab2:
                                             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')
+
+                            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
+                            )
                             
-                            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 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['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()
+                            # 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['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: