Rename reassess_edge function to reassess_lineup_edge in reassess_edge.py for clarity, and update references accordingly to improve code readability and maintainability.

global_func/reassess_edge.py

@@ -59,7 +59,7 @@ def calculate_weighted_ownership_wrapper(row_ownerships):
 def reassess_dupes(row: pd.Series, salary_max: int) -> float:
     return math.ceil(row['Dupes'] + ((row['salary_diff'] / 100) + ((salary_max + (salary_max - row['salary'])) / 100)) * (1 - (row['own_diff'] / 100)))
 
-def reassess_edge(row: pd.Series, Contest_Size: int) -> float:
+def reassess_lineup_edge(row: pd.Series, Contest_Size: int) -> float:
     row['Lineup Edge'] = row['Win%'] * ((.5 - row['Finish_percentile']) * (Contest_Size / 2.5))
     row['Lineup Edge'] = row.apply(lambda row: row['Lineup Edge'] / (row['Dupes'] + 1) if row['Dupes'] > 0 else row['Lineup Edge'], axis=1)
     
@@ -84,7 +84,7 @@ def reassess_edge(refactored_frame: pd.DataFrame, original_frame: pd.DataFrame,
         refactored_df.loc[lineups, 'Dupes'] = reassess_dupes(refactored_df.loc[lineups, :], salary_max)
         refactored_df.loc[lineups, 'Finish_percentile'] = refactored_df.loc[lineups, 'Finish_percentile']
         refactored_df.loc[lineups, 'Win%'] = refactored_df.loc[lineups, 'Win%']
-        refactored_df.loc[lineups, 'Edge'] = reassess_edge(refactored_df.loc[lineups, :], Contest_Size)
+        refactored_df.loc[lineups, 'Edge'] = reassess_lineup_edge(refactored_df.loc[lineups, :], Contest_Size)
         refactored_df.loc[lineups, 'Weighted Own'] = refactored_df[own_columns].apply(calculate_weighted_ownership_wrapper, axis=1)
         refactored_df.loc[lineups, 'Geomean'] = np.power((refactored_df.loc[lineups, own_columns] * 100).product(axis=1), 1 / len(own_columns))