Add stratification sample range slider to app.py and update stratification_function to accept threshold parameters for improved player selection control.

app.py

@@ -1312,6 +1312,7 @@ with tab2:
                 with st.form(key='Stratification'):
                     sorting_choice = st.selectbox("Stat Choice", options=['median', 'Own', 'Weighted Own', 'Geomean', 'Lineup Edge', 'Finish_percentile', 'Diversity'], index=0)
                     lineup_target = st.number_input("Lineups to produce", value=150, min_value=1, step=1)
+                    strat_sample = st.slider("Sample range", value=0.0, min_value=0.0, max_value=1.0, step=0.05)
                     submitted_col, export_col = st.columns(2)
                     st.info("Portfolio Button applies to your overall Portfolio, Export button applies to your Custom Export")
                     with submitted_col:
@@ -1320,12 +1321,12 @@ with tab2:
                         exp_submitted = st.form_submit_button("Export")
                     if reg_submitted:
                         st.session_state['settings_base'] = False
-                        parsed_frame = stratification_function(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var, sorting_choice)
+                        parsed_frame = stratification_function(st.session_state['working_frame'], lineup_target, excluded_cols, sport_var, sorting_choice, strat_sample[0], strat_sample[1])
                         st.session_state['working_frame'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['working_frame'].copy()
                     elif exp_submitted:
                         st.session_state['settings_base'] = False
-                        parsed_frame = stratification_function(st.session_state['export_base'], lineup_target, excluded_cols, sport_var, sorting_choice)
+                        parsed_frame = stratification_function(st.session_state['export_base'], lineup_target, excluded_cols, sport_var, sorting_choice, strat_sample[0], strat_sample[1])
                         st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
                         st.session_state['export_merge'] = st.session_state['export_base'].copy()
             with st.expander('Exposure Management'):

global_func/stratification_function.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str, sorting_choice: str):
+def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude_cols: list, sport: str, sorting_choice: str, low_threshold: float, high_threshold: float):
     excluded_cols = ['salary', 'median', 'Own', 'Finish_percentile', 'Dupes', 'Stack', 'Size', 'Win%', 'Lineup Edge', 'Weighted Own', 'Geomean', 'Diversity']
     player_columns = [col for col in portfolio.columns if col not in excluded_cols]
     
@@ -12,8 +12,8 @@ def stratification_function(portfolio: pd.DataFrame, lineup_target: int, exclude
         concat_portfolio = concat_portfolio.sort_values(by=sorting_choice, ascending=False).reset_index(drop=True)
 
     # Calculate target similarity scores for linear progression
-    similarity_floor = concat_portfolio[sorting_choice].min()
-    similarity_ceiling = concat_portfolio[sorting_choice].max()
+    similarity_floor = concat_portfolio[sorting_choice].min() + (concat_portfolio[sorting_choice].min() * low_threshold)
+    similarity_ceiling = concat_portfolio[sorting_choice].max() - (concat_portfolio[sorting_choice].max() * high_threshold)
     
     # Create evenly spaced target similarity scores
     target_similarities = np.linspace(similarity_floor, similarity_ceiling, lineup_target)