Refactor projections management to use 'portfolio_inc_proj' for editing and optimize lineup filtering by excluding locked players. Cleaned up player pool logic to prevent duplicates.

app.py

@@ -1761,11 +1761,11 @@ if selected_tab == 'Data Load':
 
 
 if selected_tab == 'Projections Management':
-    if 'projections_df' in st.session_state and st.session_state['projections_df'] is not None:
+    if 'portfolio_inc_proj' in st.session_state and st.session_state['portfolio_inc_proj'] is not None:
         st.subheader("Edit Player Projections")
         st.caption("Modify median, ownership, or captain ownership values directly in the table below. Changes will update both the projections and all related mappings.")
         
-        projections_editor_df = st.session_state['projections_df'].copy()
+        projections_editor_df = st.session_state['portfolio_inc_proj'].copy()
         
         if 'origin_portfolio' in st.session_state and 'map_dict' in st.session_state:
             portfolio_df = pd.read_parquet(io.BytesIO(st.session_state['origin_portfolio']))
@@ -2862,9 +2862,7 @@ if selected_tab == 'Manage Portfolio':
                         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':
@@ -2888,11 +2886,11 @@ if selected_tab == 'Manage Portfolio':
                                 modified_rows = exposure_spread(
                                     target_rows_prepared, 
                                     replace_player, 
-                                    0,  # exposure_target = 0 means remove all instances
+                                    0,
                                     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 
@@ -3014,7 +3012,7 @@ if selected_tab == 'Manage Portfolio':
                     optimize_by = st.selectbox("Optimize By", options=['median', 'ownership'], key='optimize_by')
                     lock_teams_optimize = st.multiselect(
                         "Locked Teams", 
-                        options=sorted(list(set(st.session_state['projections_df']['team'].unique()))), 
+                        options=sorted(list(set(st.session_state['portfolio_inc_proj']['team'].unique()))), 
                         default=[],
                         key='lock_teams_optimize'
                     )

global_func/optimize_lineup.py

@@ -72,20 +72,16 @@ def optimize_single_lineup(
     
     # Calculate remaining salary budget
     remaining_salary = salary_max - locked_salary
-
-    # Get all players currently in this row (both locked and unlocked positions)
-    current_players_in_row = set()
-    for col in player_columns:
-        player_name = row[col]
-        if pd.notna(player_name) and player_name:  # Check for valid player names
-            current_players_in_row.add(player_name)
-
-    # Filter player pool: exclude locked teams AND players already in this specific row
+    
+    # Filter player pool: exclude locked teams and already-locked players
     available_players = player_pool[
         (~player_pool['team'].isin(lock_teams)) & 
-        (~player_pool['player_names'].isin(current_players_in_row))  # ✅ Exclude ALL current players
+        (~player_pool['player_names'].isin(locked_player_names))
     ].copy()
 
+    # CRITICAL: Remove duplicate players from available pool
+    available_players = available_players.drop_duplicates(subset=['player_names'], keep='first')
+    
     if available_players.empty:
         return optimized_row