Add specific replacements feature in 'Manage Portfolio' to enhance player selection options and improve exposure calculations.

app.py

@@ -1517,6 +1517,7 @@ if selected_tab == 'Manage Portfolio':
                     else:
                         exposure_stack_bool = 'No'
                     remove_teams_exposure = st.multiselect("Removed/Locked teams?", options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), default=[])
+                    specific_replacements = st.multiselect("Specific Replacements?", options=sorted(list(set(st.session_state['projections_df']['player_names'].unique()))), default=[])
                     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:
@@ -1526,7 +1527,7 @@ if selected_tab == 'Manage Portfolio':
                     if reg_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['working_frame'].copy()
-                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(st.session_state['working_frame'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, specific_replacements, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':
@@ -1619,7 +1620,7 @@ if selected_tab == 'Manage Portfolio':
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
                         prior_frame = st.session_state['export_base'].copy()
-                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
+                        parsed_frame = exposure_spread(st.session_state['export_base'], st.session_state['exposure_player'], exposure_target, exposure_stack_bool, remove_teams_exposure, specific_replacements, st.session_state['projections_df'], sport_var, type_var, salary_max, stacking_sports)
                         
                         if type_var == 'Classic':
                             if sport_var == 'CS2' or sport_var == 'LOL':

global_func/exposure_spread.py

@@ -148,7 +148,7 @@ def check_position_eligibility(sport, column_name, player_positions):
         # Default fallback - assume exact position match
         return column_name in player_positions
 
-def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, remove_teams, projections_df, sport_var, type_var, salary_max, stacking_sports):
+def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, remove_teams, specific_replacements, projections_df, sport_var, type_var, salary_max, stacking_sports):
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)
@@ -215,12 +215,15 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     if lineups_to_remove > 0:
         for row in random_row_indices_insert:
             if change_counter < math.ceil(lineups_to_remove):
-                comparable_players = projections_df[
-                    (projections_df['salary'] >= comp_salary_low) &
-                    (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
-                    (projections_df['median'] >= comp_projection_low) &
-                    (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
-                ]
+                if specific_replacements != []:
+                    comparable_players = projections_df[projections_df['player_names'].isin(specific_replacements)]
+                else:
+                    comparable_players = projections_df[
+                        (projections_df['salary'] >= comp_salary_low) &
+                        (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
+                        (projections_df['median'] >= comp_projection_low) &
+                        (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
+                    ]
 
                 if exposure_target == 0:
                     comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
@@ -261,11 +264,14 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
     else:
         for row in random_row_indices_replace:
             if change_counter < math.ceil(lineups_to_add):
-                comparable_players = projections_df[
-                    (projections_df['salary'] >= comp_salary_low) &
-                    (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
-                    (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
-                ]
+                if specific_replacements != []:
+                    comparable_players = projections_df[projections_df['player_names'].isin(specific_replacements)]
+                else:
+                    comparable_players = projections_df[
+                        (projections_df['salary'] >= comp_salary_low) &
+                        (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row])) &
+                        (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
+                    ]
                 if sport_var in stacking_sports:
                     if sport_var in stacking_sports:
                         if working_frame.iloc[row]['Size'] == 5 and comp_team != working_frame.iloc[row]['Stack']: