Update app.py

app.py

@@ -245,9 +245,9 @@ def create_random_portfolio(Total_Sample_Size, raw_baselines, field_growth):
                 if max_var <= 10:
                     ranges_dict['g_range'] = round(max_var)
                 elif max_var > 10 and max_var <= 16:
-                    ranges_dict['dst_range'] = round(max_var)
+                    ranges_dict['dst_range'] = round(max_var / 1.5)
                 elif max_var > 16:
-                    ranges_dict['dst_range'] = round(max_var)
+                    ranges_dict['dst_range'] = round(max_var / 2)
                 
                 # Generate random portfolios
                 rng = np.random.default_rng()
@@ -287,6 +287,8 @@ def get_correlated_dk_portfolio_for_sim(Total_Sample_Size, sharp_split, field_gr
     
     RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict = create_random_portfolio(sizesplit, raw_baselines, field_growth)
     
+    st.table(RandomPortfolio.head(50))
+    
     RandomPortfolio['C1'] = pd.Series(list(RandomPortfolio['C1'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
     RandomPortfolio['C2'] = pd.Series(list(RandomPortfolio['C2'].map(full_pos_player_dict['pos_dicts'][0])), dtype="string[pyarrow]")
     RandomPortfolio['W1'] = pd.Series(list(RandomPortfolio['W1'].map(full_pos_player_dict['pos_dicts'][1])), dtype="string[pyarrow]")
@@ -1115,6 +1117,7 @@ with tab2:
                     ds_raw = ds_raw.sort_values(by=['Own', 'Value'], ascending=False)
                     
                     gs_raw = Overall_Proj[Overall_Proj.Position == 'G']
+                    gs_raw = gs_raw[gs_raw['Median'] > 0]
                     gs_raw.dropna(subset=['Median']).reset_index(drop=True)
                     gs_raw = gs_raw.reset_index(drop=True)
                     gs_raw = gs_raw.sort_values(by=['Own', 'Median'], ascending=False)