Refactor threshold logic in distribute_preset function to improve clarity and accuracy in filtering. Introduced a lower threshold variable for better readability and consistency in handling 'Finish_percentile' across all finishing ranges.

global_func/distribute_preset.py

@@ -7,18 +7,19 @@ def distribute_preset(portfolio: pd.DataFrame, lineup_target: int, exclude_cols:
 
         for finishing_range in range(1, 20):
             finish_threshold = (finishing_range / 100)
+            lower_threshold = ((finish_threshold - 1) / 100)
             rows_to_drop = []
             working_portfolio = portfolio.copy()
             if finishing_range == 1:
                 working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold]
             elif finishing_range == 20:
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= (finish_threshold - 1)]
+                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= lower_threshold]
             else:
                 working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] <= finish_threshold]
-                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= (finish_threshold - 1)]
+                working_portfolio = working_portfolio[working_portfolio['Finish_percentile'] >= lower_threshold]
             working_portfolio = working_portfolio.sort_values(by='median', ascending = False)
             working_portfolio = working_portfolio.reset_index(drop=True)
-            print(working_portfolio.head(10))
+            print(working_portfolio[['Finish_percentile', 'Weighted Own']].head(10))
             curr_own_type_max = working_portfolio.loc[0, 'Weighted Own'] + (slack_var / 20 * working_portfolio.loc[0, 'Weighted Own'])
 
             for i in range(1, len(working_portfolio)):