Update app.py

app.py

@@ -47,7 +47,58 @@ freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs):
+def sim_contest(Sim_size, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port):
+    SimVar = 1
+    Sim_Winners = []
+    fp_array = FinalPortfolio.values
+    
+    if insert_port == 1:
+        up_array = CleanPortfolio.values
+    
+    # Pre-vectorize functions
+    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    
+    if insert_port == 1:
+        vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
+        vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
+    
+    st.write('Simulating contest on frames')
+    
+    while SimVar <= Sim_size:
+        if insert_port == 1:
+            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
+        elif insert_port == 0:
+            fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+            
+        sample_arrays1 = np.c_[
+            fp_random, 
+            np.sum(np.random.normal(
+                loc=vec_projection_map(fp_random[:, :-5]),
+                scale=vec_stdev_map(fp_random[:, :-5])),
+            axis=1)
+        ]
+
+        if insert_port == 1:
+            sample_arrays2 = np.c_[
+                up_array, 
+                np.sum(np.random.normal(
+                    loc=vec_up_projection_map(up_array[:, :-5]),
+                    scale=vec_up_stdev_map(up_array[:, :-5])),
+                axis=1)
+            ]
+            sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
+        else:
+            sample_arrays = sample_arrays1
+
+        final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners
+
+def run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs, field_growth):
     RunsVar = 1
     seed_depth_def = seed_depth1
     Strength_var_def = Strength_var
@@ -925,59 +976,9 @@ with tab2:
                     'team_check_map':dict(zip(cleaport_players.Player,nerf_frame.Team))
                     }
                 
-                st.write('Seed frame creation')
-                FinalPortfolio, maps_dict = run_seed_frame(seed_depth1, Strength_var, strength_grow, Teams_used, Total_Runs)
-                
-                Sim_size = linenum_var1
-                SimVar = 1
-                Sim_Winners = []
-                fp_array = FinalPortfolio.values
-                
-                if insert_port == 1:
-                    up_array = CleanPortfolio.values
-                
-                # Pre-vectorize functions
-                vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
-                vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
-                
-                if insert_port == 1:
-                    vec_up_projection_map = np.vectorize(up_dict['Projection_map'].__getitem__)
-                    vec_up_stdev_map = np.vectorize(up_dict['STDev_map'].__getitem__)
-                
-                st.write('Simulating contest on frames')
-                
-                while SimVar <= Sim_size:
-                    if insert_port == 1:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size-len(CleanPortfolio))]
-                    elif insert_port == 0:
-                        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
-                        
-                    sample_arrays1 = np.c_[
-                        fp_random, 
-                        np.sum(np.random.normal(
-                            loc=vec_projection_map(fp_random[:, :-5]),
-                            scale=vec_stdev_map(fp_random[:, :-5])),
-                        axis=1)
-                    ]
-            
-                    if insert_port == 1:
-                        sample_arrays2 = np.c_[
-                            up_array, 
-                            np.sum(np.random.normal(
-                                loc=vec_up_projection_map(up_array[:, :-5]),
-                                scale=vec_up_stdev_map(up_array[:, :-5])),
-                            axis=1)
-                        ]
-                        sample_arrays = np.vstack((sample_arrays1, sample_arrays2))
-                    else:
-                        sample_arrays = sample_arrays1
-            
-                    final_array = sample_arrays[sample_arrays[:, 9].argsort()[::-1]]
-                    best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
-                    Sim_Winners.append(best_lineup)
-                    SimVar += 1
+                FinalPortfolio, maps_dict = run_seed_frame(5, Strength_var, strength_grow, Teams_used, 1000000, field_growth)
                     
-                st.write('Contest simulation complete')
+                Sim_Winners = sim_contest(5000, FinalPortfolio, CleanPortfolio, maps_dict, up_dict, insert_port)
                 # Initial setup
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=FinalPortfolio.columns.tolist() + ['Fantasy'])
                 Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['Projection'] + Sim_Winner_Frame['Fantasy']) / 2
@@ -1011,7 +1012,7 @@ with tab2:
                 st.session_state.player_freq['Position'] = st.session_state.player_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.player_freq['Salary'] = st.session_state.player_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.player_freq['Proj Own'] = st.session_state.player_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(Sim_size)
+                st.session_state.player_freq['Exposure'] = st.session_state.player_freq['Freq']/(5000)
                 st.session_state.player_freq['Edge'] = st.session_state.player_freq['Exposure'] - st.session_state.player_freq['Proj Own']
                 st.session_state.player_freq['Team'] = st.session_state.player_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1023,7 +1024,7 @@ with tab2:
                 st.session_state.qb_freq['Position'] = st.session_state.qb_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.qb_freq['Salary'] = st.session_state.qb_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.qb_freq['Proj Own'] = st.session_state.qb_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(Sim_size)
+                st.session_state.qb_freq['Exposure'] = st.session_state.qb_freq['Freq']/(5000)
                 st.session_state.qb_freq['Edge'] = st.session_state.qb_freq['Exposure'] - st.session_state.qb_freq['Proj Own']
                 st.session_state.qb_freq['Team'] = st.session_state.qb_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1035,7 +1036,7 @@ with tab2:
                 st.session_state.rb_freq['Position'] = st.session_state.rb_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.rb_freq['Salary'] = st.session_state.rb_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.rb_freq['Proj Own'] = st.session_state.rb_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/Sim_size
+                st.session_state.rb_freq['Exposure'] = st.session_state.rb_freq['Freq']/5000
                 st.session_state.rb_freq['Edge'] = st.session_state.rb_freq['Exposure'] - st.session_state.rb_freq['Proj Own']
                 st.session_state.rb_freq['Team'] = st.session_state.rb_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1047,7 +1048,7 @@ with tab2:
                 st.session_state.wr_freq['Position'] = st.session_state.wr_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.wr_freq['Salary'] = st.session_state.wr_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.wr_freq['Proj Own'] = st.session_state.wr_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/Sim_size
+                st.session_state.wr_freq['Exposure'] = st.session_state.wr_freq['Freq']/5000
                 st.session_state.wr_freq['Edge'] = st.session_state.wr_freq['Exposure'] - st.session_state.wr_freq['Proj Own']
                 st.session_state.wr_freq['Team'] = st.session_state.wr_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1059,7 +1060,7 @@ with tab2:
                 st.session_state.flex_freq['Position'] = st.session_state.flex_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.flex_freq['Salary'] = st.session_state.flex_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.flex_freq['Proj Own'] = st.session_state.flex_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/Sim_size
+                st.session_state.flex_freq['Exposure'] = st.session_state.flex_freq['Freq']/5000
                 st.session_state.flex_freq['Edge'] = st.session_state.flex_freq['Exposure'] - st.session_state.flex_freq['Proj Own']
                 st.session_state.flex_freq['Team'] = st.session_state.flex_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1071,7 +1072,7 @@ with tab2:
                 st.session_state.dst_freq['Position'] = st.session_state.dst_freq['Player'].map(maps_dict['Pos_map'])
                 st.session_state.dst_freq['Salary'] = st.session_state.dst_freq['Player'].map(maps_dict['Salary_map'])
                 st.session_state.dst_freq['Proj Own'] = st.session_state.dst_freq['Player'].map(maps_dict['Own_map']) / 100
-                st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/Sim_size
+                st.session_state.dst_freq['Exposure'] = st.session_state.dst_freq['Freq']/5000
                 st.session_state.dst_freq['Edge'] = st.session_state.dst_freq['Exposure'] - st.session_state.dst_freq['Proj Own']
                 st.session_state.dst_freq['Team'] = st.session_state.dst_freq['Player'].map(maps_dict['Team_map'])
                 for checkVar in range(len(team_list)):
@@ -1100,8 +1101,7 @@ with tab2:
                     )
             
             with st.container():
-                freq_container = st.empty()
-                tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'FLEX Exposures', 'S_FLEX Exposures'])
+                tab1, tab2, tab3, tab4, tab5 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'FLEX Exposures'])
                 with tab1:
                     if 'player_freq' in st.session_state:
                         st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
@@ -1147,14 +1147,6 @@ with tab2:
                             file_name='flex_freq_export.csv',
                             mime='text/csv',
                         )
-                with tab6:
-                    if 'dst_freq' in st.session_state:
-                        st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                        st.download_button(
-                            label="Export Exposures",
-                            data=st.session_state.dst_freq.to_csv().encode('utf-8'),
-                            file_name='dst_freq_export.csv',
-                            mime='text/csv',
-                        )
 
-gc.collect()
+
+gc.collect()