Refactor reassess_finish_percentile function in reassess_edge.py to remove unnecessary parameters, streamlining the calculation of finish_percentile. Update references in reassess_edge function to align with the new function signature, enhancing code clarity and maintainability.

global_func/reassess_edge.py

@@ -44,7 +44,7 @@ def calculate_weighted_ownership_single_row(row_ownerships):
     # Convert back to percentage form
     return weighted * 10000
 
-def reassess_finish_percentile(row: pd.Series, own_columns: list) -> float:
+def reassess_finish_percentile(row: pd.Series) -> float:
     own_diff = float(row['own_diff'])
     median_diff = float(row['median_diff'])
     finish_percentile = row['Finish_percentile'] + (own_diff / 2) - (median_diff / 100)
@@ -112,7 +112,7 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
 
     for lineups in change_mask.index:
         refactored_df.loc[lineups, 'Dupes'] = reassess_dupes(refactored_df.loc[lineups, :], salary_max)
-        refactored_df.loc[lineups, 'Finish_percentile'] = reassess_finish_percentile(refactored_df.loc[lineups, :], own_columns, Contest_Size)
+        refactored_df.loc[lineups, 'Finish_percentile'] = reassess_finish_percentile(refactored_df.loc[lineups, :])
         refactored_df.loc[lineups, 'Win%'] = refactored_df.loc[lineups, 'Win%']
         refactored_df.loc[lineups, 'Edge'] = reassess_lineup_edge(refactored_df.loc[lineups, :], Contest_Size)
         refactored_df.loc[lineups, 'Weighted Own'] = calculate_weighted_ownership_single_row(refactored_df.loc[lineups, own_columns])