Refactor position eligibility check in 'exposure_spread' to improve clarity and maintainability, ensuring proper handling of player positions during lineup generation.

global_func/exposure_spread.py

@@ -245,7 +245,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
                     comparable_player_list = comparable_players['player_names'].tolist()
                 except:
                     comparable_player_list = []
-                    
+
                 if comparable_player_list:
                     insert_player = random.choice(comparable_player_list)
                     # Find which column contains the exposure_player
@@ -268,8 +268,7 @@ 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)) &
-                        (projections_df['salary'] <= comp_salary_high + (salary_max - working_frame['salary'][row]))
+                    comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements))
                     ]
                 else:
                     comparable_players = projections_df[
@@ -301,12 +300,12 @@ def exposure_spread(working_frame, exposure_player, exposure_target, ignore_stac
                     for col in working_frame.columns:
                         if row_data[col] in comparable_player_list:
                             if working_frame.iloc[row]['salary'] - projections_df[projections_df['player_names'] == row_data[col]]['salary'].iloc[0] + projections_df[projections_df['player_names'] == exposure_player]['salary'].iloc[0] <= salary_max:
-                                # Get the replacement player's positions
-                                replacement_player_positions = projections_df[projections_df['player_names'] == row_data[col]]['position'].iloc[0].split('/')
-                                exposure_player_positions = projections_df[projections_df['player_names'] == exposure_player]['position'].iloc[0].split('/')
-                                
-                                # Check if the replacement player is eligible for this column
                                 if type_var == 'Classic':
+                                    replacement_player_positions = projections_df[projections_df['player_names'] == row_data[col]]['position'].iloc[0].split('/')
+                                    exposure_player_positions = projections_df[projections_df['player_names'] == exposure_player]['position'].iloc[0].split('/')
+                                    
+                                    # Check if the replacement player is eligible for this column
+                                
                                     if check_position_eligibility(sport_var, col, exposure_player_positions):
                                         working_frame.at[row, col] = exposure_player
                                         change_counter += 1