Update portfolio management to include player names from projections; fix lineup optimization constraint to use correct player column count.

app.py

@@ -1748,7 +1748,7 @@ if selected_tab == 'Data Load':
             st.session_state['origin_portfolio'] = buffer.getvalue()
 
             portfolio_inc_proj = pd.DataFrame()
-            portfolio_inc_proj['player_names'] = get_portfolio_names(st.session_state['portfolio'])
+            portfolio_inc_proj['player_names'] = get_portfolio_names(st.session_state['portfolio']) + st.session_state['projections_df']['player_names'].tolist()
             portfolio_inc_proj['position'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['pos_map'].get(x, 'FLEX'))
             portfolio_inc_proj['team'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['team_map'].get(x, 'Unknown'))
             portfolio_inc_proj['salary'] = portfolio_inc_proj['player_names'].map(lambda x: st.session_state['map_dict']['salary_map'].get(x, 0))
@@ -2926,7 +2926,7 @@ if selected_tab == 'Manage Portfolio':
 
             with st.expander('Exposure Management'):
                 with st.form(key='Exposures'):
-                    exposure_player = st.selectbox("Player", options=sorted(list(set(get_portfolio_names(st.session_state['working_frame'])))), key='exposure_player')
+                    exposure_player = st.selectbox("Player", options=sorted(list(set(get_portfolio_names(st.session_state['working_frame']) + st.session_state['projections_df']['player_names'].tolist()))), key='exposure_player')
                     exposure_target = st.number_input("Target Exposure", value=.50, min_value=0.0, max_value=1.0, step=0.01)
                     comp_salary_below = st.number_input("Comp Salary Below", value=-5000, min_value=-5000, max_value=0, step=100)
                     comp_salary_above = st.number_input("Comp Salary Above", value=5000, min_value=0, max_value=5000, step=100)
@@ -2935,8 +2935,8 @@ if selected_tab == 'Manage Portfolio':
                     else:
                         ignore_stacks = []
                     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(get_portfolio_names(st.session_state['working_frame'])))), default=[])
-                    specific_exclusions = st.multiselect("Specific exclusions?", options=sorted(list(set(get_portfolio_names(st.session_state['working_frame'])))), default=[])
+                    specific_replacements = st.multiselect("Specific Replacements?", options=sorted(list(set(get_portfolio_names(st.session_state['working_frame']) + st.session_state['projections_df']['player_names'].tolist()))), default=[])
+                    specific_exclusions = st.multiselect("Specific exclusions?", options=sorted(list(set(get_portfolio_names(st.session_state['working_frame']) + st.session_state['projections_df']['player_names'].tolist()))), default=[])
                     specific_columns = st.multiselect("Specific Positions?", options=sorted(list(st.session_state['player_columns'])), default=[])
                     submitted_col, export_col = st.columns(2)
                     st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
@@ -2946,7 +2946,6 @@ if selected_tab == 'Manage Portfolio':
                         exp_submitted = st.form_submit_button("Export")
                     if reg_submitted:
                         st.session_state['settings_base'] = False
-                        # Prepare DataFrame for exposure_spread to avoid categorical issues
                         working_frame_prepared = prepare_dataframe_for_exposure_spread(st.session_state['working_frame'], st.session_state['player_columns'])
                         parsed_frame = exposure_spread(working_frame_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_exclusions, specific_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
                         
@@ -2985,7 +2984,6 @@ if selected_tab == 'Manage Portfolio':
                             st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
-                        # Prepare DataFrame for exposure_spread to avoid categorical issues
                         export_base_prepared = prepare_dataframe_for_exposure_spread(st.session_state['export_base'], st.session_state['player_columns'])
                         parsed_frame = exposure_spread(export_base_prepared, st.session_state['exposure_player'], exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams_exposure, specific_replacements, specific_exclusions, specific_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
                         

global_func/optimize_lineup.py

@@ -92,6 +92,7 @@ def optimize_single_lineup(
     player_list = available_players.to_dict('records')
     num_players = len(player_list)
     num_open_cols = len(open_columns)
+    num_player_columns = len(player_columns)
     
     # x[i][j] = 1 if player i is assigned to open column j
     x = {}
@@ -105,7 +106,7 @@ def optimize_single_lineup(
     
     # Constraint 2: Each player can only be used once across all open columns
     for i in range(num_players):
-        solver.Add(sum(x[i, j] for j in range(num_open_cols)) <= 1)
+        solver.Add(sum(x[i, j] for j in range(num_player_columns)) <= 1)
     
     # Constraint 3: Position eligibility
     for i, player in enumerate(player_list):