Refactor player comparison logic in 'exposure_spread' function to streamline conditions based on 'type_var', ensuring accurate filtering for Showdown scenarios while maintaining existing functionality.

global_func/exposure_spread.py

@@ -301,18 +301,23 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
                     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 type_var == 'Showdown':
+                        comparable_players = projections_df[
+                            (projections_df['salary'] >= comp_salary_low) &
+                            (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row]))
+                        ]
+                    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']:
-                                remove_mask = comparable_players.apply(
-                                    lambda player_row: not any(team in list(player_row) for team in [working_frame.iloc[row]['Stack']]), axis=1
-                                )
-                                comparable_players = comparable_players[remove_mask]
+                        if working_frame.iloc[row]['Size'] == 5 and comp_team != working_frame.iloc[row]['Stack']:
+                            remove_mask = comparable_players.apply(
+                                lambda player_row: not any(team in list(player_row) for team in [working_frame.iloc[row]['Stack']]), axis=1
+                            )
+                            comparable_players = comparable_players[remove_mask]
                 
                 if remove_teams is not None:
                     remove_mask = comparable_players.apply(
@@ -324,6 +329,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
 
                 # Create a list of comparable players
                 comparable_player_list = comparable_players['player_names'].tolist()
+                print(comp_salary_low)
+                print(comp_salary_high)
                 print(comparable_player_list)
                 print("^^^^ comparable player list")
                 if comparable_player_list: