Adding exclusions from exposure management, fixing some database query stuff

app.py

@@ -2545,6 +2545,7 @@ if selected_tab == 'Manage Portfolio':
                         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_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")
@@ -2556,7 +2557,7 @@ if selected_tab == 'Manage Portfolio':
                         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_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
+                        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)
                         
                         # Use consolidated calculation function
                         parsed_frame = calculate_lineup_metrics(
@@ -2593,7 +2594,7 @@ if selected_tab == 'Manage Portfolio':
                         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_columns, st.session_state['portfolio_inc_proj'], sport_var, type_var, salary_max, stacking_sports)
+                        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)
                         
                         # Use consolidated calculation function for export
                         parsed_frame = calculate_lineup_metrics(

database_queries.py

@@ -93,7 +93,7 @@ def init_nfl_baselines(type_var: str, site_var: str, slate_var: str):
         fd_sd_roo_raw = fd_sd_roo_raw.drop(columns=['player_id', 'slate'])
 
         dk_sd_roo_raw = dk_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
-        fd_sd_roo_raw = fd_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain_ownership'})
+        fd_sd_roo_raw = fd_sd_roo_raw.rename(columns={'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'Median': 'median', 'Own': 'ownership', 'CPT_Own': 'captain ownership'})
 
         dk_roo_raw = None
         fd_roo_raw = None

global_func/exposure_spread.py

@@ -173,7 +173,9 @@ def check_salary_eligibility(current_lineup_salary, current_player, new_player,
     salary_diff = calculate_salary_difference(current_player, new_player, column_name, projections_df, type_var)
     return current_lineup_salary + salary_diff <= salary_max
 
-def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams, specific_replacements, specific_columns, projections_df, sport_var, type_var, salary_max, stacking_sports):
+def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary_below, comp_salary_above, ignore_stacks, remove_teams, specific_replacements, specific_exclusions, specific_columns, projections_df, sport_var, type_var, salary_max, stacking_sports):
+    if specific_exclusions != []:
+        projections_df = projections_df[~projections_df['player_names'].isin(specific_exclusions)]
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)