Enhance player selection logic in 'exposure_spread' by adding salary constraints for specific replacements, improving accuracy in lineup generation and incorporating error handling for existing players.

global_func/exposure_spread.py

@@ -212,7 +212,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
         for row in random_row_indices_insert:
             if change_counter < math.ceil(lineups_to_remove):
                 if specific_replacements != []:
-                    comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements))
+                    comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements)) &
+                        (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row]))
                     ]
                 else:
                     comparable_players = projections_df[
@@ -236,7 +237,10 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
                 
                 # Filter out players that are already present in this row
                 existing_players = set(current_row_data.values)
-                comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+                try:
+                    comparable_players = comparable_players[~comparable_players['player_names'].isin(existing_players)]
+                except:
+                    pass
 
                 # Create a list of comparable players
                 comparable_player_list = comparable_players['player_names'].tolist()
@@ -262,7 +266,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
         for row in random_row_indices_replace:
             if change_counter < math.ceil(lineups_to_add):
                 if specific_replacements != []:
-                    comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements))
+                    comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements)) &
+                        (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row]))
                     ]
                 else:
                     comparable_players = projections_df[