chaning for to while for lineup add

global_func/exposure_spread.py

@@ -231,7 +231,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
         lineups_to_remove = (player_exposure * len(working_frame))
     else:
         lineups_to_remove = ((player_exposure - exposure_target) * len(working_frame)) * 1.01
-        lineups_to_add = ((exposure_target - player_exposure) * (len(working_frame) - (player_exposure * len(working_frame)))) * 1.10
+        lineups_to_add = ((exposure_target - player_exposure) * len(working_frame)) * 1.10
         
     # isolate the rows that contain the player
     player_rows = working_frame[player_mask]
@@ -360,71 +360,71 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
             if not random_row_indices_insert:
                 break
     else:
-        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))
+        while change_counter < math.ceil(lineups_to_add) and random_row_indices_replace:
+            row = random_row_indices_replace.pop(0)
+            if specific_replacements != []:
+                comparable_players = projections_df[(projections_df['player_names'].isin(specific_replacements))
+                ]
+            else:
+                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:
-                    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 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(
-                            lambda row: not any(team in list(row) for team in remove_teams), axis=1
+                    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 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]
+                        comparable_players = comparable_players[remove_mask]
+            
+            if remove_teams is not None:
+                remove_mask = comparable_players.apply(
+                        lambda row: not any(team in list(row) for team in remove_teams), axis=1
+                    )
+                comparable_players = comparable_players[remove_mask]
 
-                comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
+            comparable_players = comparable_players[comparable_players['player_names'] != exposure_player]
 
-                # Create a list of comparable players
-                comparable_player_list = comparable_players['player_names'].tolist()
-                
-                if exposure_player in working_frame.iloc[row].values:
-                    comparable_player_list = []
+            # Create a list of comparable players
+            comparable_player_list = comparable_players['player_names'].tolist()
+            
+            if exposure_player in working_frame.iloc[row].values:
+                comparable_player_list = []
 
-                if comparable_player_list:
-                    # Find which column contains the exposure_player
-                    if specific_columns != []:
-                        row_data = working_frame.iloc[row][specific_columns]
-                        working_columns = specific_columns
-                    else:
-                        row_data = working_frame.iloc[row]
-                        working_columns = working_frame.columns
-    
-                    for col in working_columns:
-                        if row_data[col] in comparable_player_list:
-                            current_lineup_salary = working_frame.iloc[row]['salary']
-                            current_player = row_data[col]
-                            
-                            # Check salary eligibility using helper function
-                            if check_salary_eligibility(current_lineup_salary, current_player, exposure_player, col, projections_df, type_var, salary_max):
-                                if type_var == 'Classic':
-                                    # For Classic types, also check position eligibility
-                                    exposure_player_positions = projections_df[projections_df['player_names'] == exposure_player]['position'].iloc[0].split('/')
-                                    if check_position_eligibility(sport_var, col, exposure_player_positions):
-                                        working_frame.at[row, col] = exposure_player
-                                        change_counter += 1
-                                        break
-                                else:
-                                    # For non-Classic types, salary check is sufficient (position eligibility handled elsewhere)
+            if comparable_player_list:
+                # Find which column contains the exposure_player
+                if specific_columns != []:
+                    row_data = working_frame.iloc[row][specific_columns]
+                    working_columns = specific_columns
+                else:
+                    row_data = working_frame.iloc[row]
+                    working_columns = working_frame.columns
+
+                for col in working_columns:
+                    if row_data[col] in comparable_player_list:
+                        current_lineup_salary = working_frame.iloc[row]['salary']
+                        current_player = row_data[col]
+                        
+                        # Check salary eligibility using helper function
+                        if check_salary_eligibility(current_lineup_salary, current_player, exposure_player, col, projections_df, type_var, salary_max):
+                            if type_var == 'Classic':
+                                # For Classic types, also check position eligibility
+                                exposure_player_positions = projections_df[projections_df['player_names'] == exposure_player]['position'].iloc[0].split('/')
+                                if check_position_eligibility(sport_var, col, exposure_player_positions):
                                     working_frame.at[row, col] = exposure_player
                                     change_counter += 1
                                     break
+                            else:
+                                # For non-Classic types, salary check is sufficient (position eligibility handled elsewhere)
+                                working_frame.at[row, col] = exposure_player
+                                change_counter += 1
+                                break
     return working_frame