test conditionals manager combos

app.py

@@ -2532,6 +2532,158 @@ if selected_tab == 'Manage Portfolio':
                             else:  # Remove
                                 parsed_frame = parsed_frame[~alpha_mask]
                         
+                        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('Conditionals Manager (combos)'):
+                # a set of functions for replacing players in lineups containing specific other players
+                with st.form(key='conditional_combos_form'):
+                    player_names = set()
+                    for col in st.session_state['working_frame'].columns:
+                        if col not in excluded_cols:
+                            player_names.update(st.session_state['working_frame'][col].unique())
+                    
+                    replace_player = st.selectbox("Replace player:", options=sorted(list(player_names)), key='replace_player')
+                    replace_slot = st.selectbox("In slot:", options=['Overall', 'CPT', 'FLEX'], index=0, key='replace_slot')
+                    containing_player = st.selectbox("In Lineups containing:", options=sorted(list(player_names)), key='containing_player')
+                    containing_slot = st.selectbox("In slot:", options=['Overall', 'CPT', 'FLEX'], index=0, key='containing_slot')
+                    
+                    specific_replacements_combo = st.multiselect("Specific replacements?", options=sorted(list(player_names)), default=[], key='specific_replacements_combo')
+                    specific_exclusions_combo = st.multiselect("Specific exclusions?", options=sorted(list(player_names)), default=[], key='specific_exclusions_combo')
+                    
+                    comp_salary_below_combo = st.number_input("Comp Salary Below", value=-5000, min_value=-5000, max_value=0, step=100, key='comp_salary_below_combo')
+                    comp_salary_above_combo = st.number_input("Comp Salary Above", value=5000, min_value=0, max_value=5000, step=100, key='comp_salary_above_combo')
+                    
+                    submitted_col, export_col = st.columns(2)
+                    st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
+                    with submitted_col:
+                        reg_submitted = st.form_submit_button("Portfolio")
+                    with export_col:
+                        exp_submitted = st.form_submit_button("Export")
+                    
+                    if reg_submitted:
+                        st.session_state['settings_base'] = False
+                        parsed_frame = st.session_state['working_frame'].copy()
+                        
+                        # Check if we have both replace and containing players selected
+                        if replace_player and containing_player and replace_player != containing_player:
+                            # Find rows that contain the containing_player in the specified slot
+                            containing_mask = pd.Series([False] * len(parsed_frame), index=parsed_frame.index)
+                            
+                            if type_var == 'Showdown':
+                                if containing_slot == 'Overall':
+                                    containing_mask = parsed_frame.apply(lambda row: containing_player in row.values, axis=1)
+                                elif containing_slot == 'CPT':
+                                    containing_mask = parsed_frame.iloc[:, 0].apply(lambda row: containing_player in row)
+                                elif containing_slot == 'FLEX':
+                                    containing_mask = parsed_frame.iloc[:, 1:].apply(lambda row: containing_player in row.values, axis=1)
+                            else:
+                                containing_mask = parsed_frame.apply(lambda row: containing_player in row.values, axis=1)
+                            
+                            # Filter to only rows containing the target player
+                            target_rows = parsed_frame[containing_mask]
+                            
+                            if not target_rows.empty:
+                                # Prepare DataFrame for exposure_spread to avoid categorical issues
+                                target_rows_prepared = prepare_dataframe_for_exposure_spread(target_rows, st.session_state['player_columns'])
+                                
+                                # Use exposure_spread logic to replace the player in these specific rows
+                                # Set exposure_target to 0 to remove all instances of replace_player
+                                modified_rows = exposure_spread(
+                                    target_rows_prepared, 
+                                    replace_player, 
+                                    0,  # exposure_target = 0 means remove all instances
+                                    comp_salary_below_combo, 
+                                    comp_salary_above_combo, 
+                                    [],  # ignore_stacks
+                                    [],  # remove_teams_exposure
+                                    specific_replacements_combo, 
+                                    specific_exclusions_combo, 
+                                    st.session_state['player_columns'] if replace_slot == 'Overall' else 
+                                    ([st.session_state['player_columns'][0]] if replace_slot == 'CPT' else st.session_state['player_columns'][1:]), 
+                                    st.session_state['portfolio_inc_proj'], 
+                                    sport_var, 
+                                    type_var, 
+                                    salary_max, 
+                                    stacking_sports
+                                )
+                                
+                                # Update the original dataframe with the modified rows
+                                parsed_frame.loc[containing_mask] = modified_rows
+                                
+                                # Use consolidated calculation function
+                                parsed_frame = calculate_lineup_metrics(
+                                    parsed_frame, 
+                                    st.session_state['player_columns'], 
+                                    st.session_state['map_dict'], 
+                                    type_var, 
+                                    sport_var, 
+                                    st.session_state['portfolio_inc_proj']
+                                )
+                        
+                        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()
+                    
+                    elif exp_submitted:
+                        st.session_state['settings_base'] = False
+                        parsed_frame = st.session_state['export_base'].copy()
+                        
+                        # Check if we have both replace and containing players selected
+                        if replace_player and containing_player and replace_player != containing_player:
+                            # Find rows that contain the containing_player in the specified slot
+                            containing_mask = pd.Series([False] * len(parsed_frame), index=parsed_frame.index)
+                            
+                            if type_var == 'Showdown':
+                                if containing_slot == 'Overall':
+                                    containing_mask = parsed_frame.apply(lambda row: containing_player in row.values, axis=1)
+                                elif containing_slot == 'CPT':
+                                    containing_mask = parsed_frame.iloc[:, 0].apply(lambda row: containing_player in row)
+                                elif containing_slot == 'FLEX':
+                                    containing_mask = parsed_frame.iloc[:, 1:].apply(lambda row: containing_player in row.values, axis=1)
+                            else:
+                                containing_mask = parsed_frame.apply(lambda row: containing_player in row.values, axis=1)
+                            
+                            # Filter to only rows containing the target player
+                            target_rows = parsed_frame[containing_mask]
+                            
+                            if not target_rows.empty:
+                                # Prepare DataFrame for exposure_spread to avoid categorical issues
+                                target_rows_prepared = prepare_dataframe_for_exposure_spread(target_rows, st.session_state['player_columns'])
+                                
+                                # Use exposure_spread logic to replace the player in these specific rows
+                                # Set exposure_target to 0 to remove all instances of replace_player
+                                modified_rows = exposure_spread(
+                                    target_rows_prepared, 
+                                    replace_player, 
+                                    0,  # exposure_target = 0 means remove all instances
+                                    comp_salary_below_combo, 
+                                    comp_salary_above_combo, 
+                                    [],  # ignore_stacks
+                                    [],  # remove_teams_exposure
+                                    specific_replacements_combo, 
+                                    specific_exclusions_combo, 
+                                    st.session_state['player_columns'] if replace_slot == 'Overall' else 
+                                    ([st.session_state['player_columns'][0]] if replace_slot == 'CPT' else st.session_state['player_columns'][1:]), 
+                                    st.session_state['portfolio_inc_proj'], 
+                                    sport_var, 
+                                    type_var, 
+                                    salary_max, 
+                                    stacking_sports
+                                )
+                                
+                                # Update the original dataframe with the modified rows
+                                parsed_frame.loc[containing_mask] = modified_rows
+                                
+                                # Use consolidated calculation function for export
+                                parsed_frame = calculate_lineup_metrics(
+                                    parsed_frame, 
+                                    st.session_state['player_columns'], 
+                                    st.session_state['map_dict'], 
+                                    type_var, 
+                                    sport_var, 
+                                    st.session_state['portfolio_inc_proj']
+                                )
+                        
                         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()