Add stack comparison logic in exposure_spread function to filter out comparable players based on stacking, enhancing the accuracy of player comparisons.

global_func/exposure_spread.py

@@ -11,6 +11,10 @@ import math
 def exposure_spread(working_frame, exposure_player, exposure_target, exposure_stack_bool, projections_df):
     # Find comparable players in the projections
     comparable_players = projections_df[projections_df['player_names'] == exposure_player]
+    if exposure_stack_bool == 'Yes':
+        comparable_stack = comparable_players['Stack'][0]
+    else:
+        comparable_stack = 0
     comparable_players = comparable_players.reset_index(drop=True)
     comp_salary_high = comparable_players['salary'][0]
     comp_salary_low = comparable_players['salary'][0] - 500
@@ -34,6 +38,8 @@ def exposure_spread(working_frame, exposure_player, exposure_target, exposure_st
         (projections_df['median'] <= comp_projection_high) &
         (projections_df['position'].apply(lambda x: has_position_overlap(x, comp_player_position)))
     ]
+    if comparable_stack != 0:
+        comparable_players = comparable_players[comparable_players['Stack'] != comparable_stack]
     
     # Create a list of comparable players
     comparable_player_list = comparable_players['player_names'].tolist()