adjusting comp_salary_high out of equiation for Exposure Spread

global_func/exposure_spread.py

@@ -177,7 +177,7 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
     
     comparable_players = comparable_players.reset_index(drop=True)
-    comp_salary_high = comparable_players['salary'][0]
+    comp_salary_high_base = comparable_players['salary'][0]
     if type_var == 'Showdown':
         comp_salary_low = comparable_players['salary'][0] + comp_salary_below
         comp_salary_high = comparable_players['salary'][0] + comp_salary_above
@@ -260,12 +260,12 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
             
             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]))
+                    (projections_df['salary'] <= comp_salary_high_base + (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['salary'] <= comp_salary_high_base + (salary_max - working_frame['salary'][row])) &
                     (projections_df['median'] >= comp_projection_low) &
                     (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
                 ]
@@ -369,12 +369,12 @@ def exposure_spread(working_frame, exposure_player, exposure_target, comp_salary
                 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]))
+                        (projections_df['salary'] <= comp_salary_high_base + (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['salary'] <= comp_salary_high_base + (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: