Update app.py

app.py

@@ -92,17 +92,16 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R
     while RunsVar <= seed_depth_def:
         if RunsVar <= 3:
             FieldStrength = Strength_var_def
-            RandomPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio = RandomPortfolio
-            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
+            FinalPortfolio, maps_dict = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
+            FinalPortfolio2, maps_dict2 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
             FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio2], axis=0)
             maps_dict.update(maps_dict2)
             del FinalPortfolio2
             del maps_dict2
         elif RunsVar > 3 and RunsVar <= 4:
             FieldStrength += (strength_grow_def + ((30 - len(Teams_used_def)) * .001))
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
+            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
+            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
             FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
             FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
             FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
@@ -114,8 +113,8 @@ def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_R
             del maps_dict4
         elif RunsVar > 4:
             FieldStrength = 1
-            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1)
-            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1)
+            FinalPortfolio3, maps_dict3 = get_correlated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
+            FinalPortfolio4, maps_dict4 = get_uncorrelated_portfolio_for_sim(Total_Runs_def * .1, sharp_split)
             FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio3], axis=0)
             FinalPortfolio = pd.concat([FinalPortfolio, FinalPortfolio4], axis=0)
             FinalPortfolio = FinalPortfolio.drop_duplicates(subset = ['Projection', 'Own'],keep = 'last').reset_index(drop = True)
@@ -225,13 +224,14 @@ def create_random_portfolio(Total_Sample_Size, raw_baselines):
             RandomPortfolio = pd.DataFrame(np.hstack(all_choices), columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
             RandomPortfolio['User/Field'] = 0
             
+            del rng
             del total_elements
             del all_choices
             del O_merge
           
             return RandomPortfolio, maps_dict, ranges_dict, full_pos_player_dict
 
-def get_correlated_portfolio_for_sim(Total_Sample_Size):
+def get_correlated_portfolio_for_sim(Total_Sample_Size, sharp_split):
     
     sizesplit = round(Total_Sample_Size * sharp_split)
     
@@ -345,11 +345,13 @@ def get_correlated_portfolio_for_sim(Total_Sample_Size):
     
     RandomPortfolio = RandomPortfolioDF.sort_values(by=Sim_function, ascending=False)
     
+    del RandomPortfolioDF
+    
     RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']]
     
     return RandomPortfolio, maps_dict
           
-def get_uncorrelated_portfolio_for_sim(Total_Sample_Size):
+def get_uncorrelated_portfolio_for_sim(Total_Sample_Size, sharp_split):
     
     sizesplit = round(Total_Sample_Size * (1-sharp_split))
 
@@ -462,6 +464,8 @@ def get_uncorrelated_portfolio_for_sim(Total_Sample_Size):
     
     RandomPortfolio = RandomPortfolio[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own']]
     
+    del RandomPortfolioDF
+    
     return RandomPortfolio, maps_dict
 
 
@@ -847,18 +851,6 @@ with tab2:
     with col2:
         with st.container():
             if st.button("Simulate Contest"):
-                try:
-                    del dst_freq
-                    del flex_freq
-                    del te_freq
-                    del wr_freq
-                    del rb_freq
-                    del qb_freq
-                    del player_freq
-                    del Sim_Winner_Export
-                    del Sim_Winner_Frame
-                except:
-                    pass
                 with st.container():
                     st.write('Contest Simulation Starting')
                     for key in st.session_state.keys():
@@ -885,7 +877,7 @@ with tab2:
                         Sim_function = 'Own'
                     
                     if slate_var1 == 'User':
-                        OwnFrame = proj_dataframe
+                        OwnFrame = proj_dataframe.copy()
                         if contest_var1 == 'Small':
                             OwnFrame['Own%'] = np.where((OwnFrame['Position'] == 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (10 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] == 'QB', 'Own'].mean(), OwnFrame['Own'])
                             OwnFrame['Own%'] = np.where((OwnFrame['Position'] != 'QB') & (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean() >= 0), OwnFrame['Own'] * (5 * (OwnFrame['Own'] - OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean())/100) + OwnFrame.loc[OwnFrame['Position'] != 'QB', 'Own'].mean(), OwnFrame['Own%'])
@@ -927,7 +919,6 @@ with tab2:
                             OwnFrame['Own'] = OwnFrame['Own%'] * (900 / OwnFrame['Own%'].sum())
                         Overall_Proj = OwnFrame[['Player', 'Team', 'Position', 'Median', 'Own', 'Salary']]
                         
-                        del raw_baselines
                         del initial_proj
                         del drop_frame
                         del OwnFrame
@@ -954,7 +945,7 @@ with tab2:
                     Teams_used['team_item'] = Teams_used['index'] + 1
                     Teams_used = Teams_used.drop(columns=['index'])
                     Teams_used_dictraw = Teams_used.drop(columns=['team_item'])
-                    Teams_used_dict = Teams_used_dictraw.to_dict()
+                    # Teams_used_dict = Teams_used_dictraw.to_dict()
                     
                     del Teams_used_dictraw
             
@@ -1034,6 +1025,8 @@ with tab2:
                             CleanPortfolio['User/Field'] = CleanPortfolio['index'] + 1
                             CleanPortfolio.drop(columns=['index'], inplace=True)
                             
+                            del positions
+                            
                             CleanPortfolio.replace('', np.nan, inplace=True)
                             CleanPortfolio.dropna(subset=['QB'], inplace=True)
                             
@@ -1162,8 +1155,15 @@ with tab2:
                         best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
                         Sim_Winners.append(best_lineup)
                         SimVar += 1
-    
-                            
+                    
+                    del SimVar
+                    del ref_dict, up_dict
+                    del linenum_var1, UserPortfolio
+                    try:
+                        del up_array
+                    except:
+                        pass
+                    del CleanPortfolio
                     del vec_projection_map
                     del vec_stdev_map
                     del sample_arrays
@@ -1202,8 +1202,7 @@ with tab2:
                     
                     for col in columns_to_replace:
                         st.session_state.Sim_Winner_Export[col].replace(replace_dict, inplace=True)
-    
-                    
+     
                     player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:9].values, return_counts=True)),
                                                 columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                     player_freq['Freq'] = player_freq['Freq'].astype(int)
@@ -1308,8 +1307,12 @@ with tab2:
     
                     st.session_state.dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
                     del dst_freq
+                    
+                    del Sim_size
                     del maps_dict
-                
+                    del team_list
+                    del item_list
+                    
         with st.container():
             simulate_container = st.empty()
             if 'player_freq' in st.session_state: