Update app.py

app.py

@@ -171,41 +171,42 @@ with tab1:
                     stack_var2 = [4, 3, 2, 1, 0]
             
     with col2:
-        if site_var1 == 'Draftkings':
-            
-            st.session_state.working_seed = DK_seed.copy()
-            st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], team_var2)]
-            st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 13], stack_var2)]
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
-            
-            st.session_state.data_export_freq = pd.DataFrame(calculate_DK_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
-            st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
-            
-        elif site_var1 == 'Fanduel':
-            
-            st.session_state.working_seed = FD_seed.copy()
-            st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
-            st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
-            st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+        if st.button("Load Data", key='load_data')
+            if site_var1 == 'Draftkings':
+                
+                st.session_state.working_seed = DK_seed.copy()
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 13], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+                st.session_state.data_export_freq = pd.DataFrame(calculate_DK_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
+                st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
+                
+            elif site_var1 == 'Fanduel':
+                
+                st.session_state.working_seed = FD_seed.copy()
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 11], team_var2)]
+                st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 12], stack_var2)]
+                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+                st.session_state.data_export_freq = pd.DataFrame(calculate_FD_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
+                st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
+                
+            with st.container():
+                if 'data_export_display' in st.session_state:
+                    st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
+            with st.container():
+                if 'data_export_freq' in st.session_state:
+                    st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
             
-            st.session_state.data_export_freq = pd.DataFrame(calculate_FD_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
-            st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
-            
-        with st.container():
-            if 'data_export_display' in st.session_state:
-                st.dataframe(st.session_state.data_export_display.style.format(precision=2), height=500, use_container_width=True)
-        with st.container():
-            if 'data_export_freq' in st.session_state:
-                st.dataframe(st.session_state.data_export_freq.style.format(percentages_format, precision=2), height=500, use_container_width=True)
-        
-        if st.button("Prepare data export", key='data_export'):
-            data_export = st.session_state.working_seed.copy()
-            st.download_button(
-                label="Export optimals set",
-                data=convert_df(data_export),
-                file_name='MLB_optimals_export.csv',
-                mime='text/csv',
-            )
+            if st.button("Prepare data export", key='data_export'):
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='MLB_optimals_export.csv',
+                    mime='text/csv',
+                )
             
 with tab2:
     col1, col2 = st.columns([1, 7])
@@ -241,41 +242,86 @@ with tab2:
 
     
     with col2:
-        maps_dict = {
-                'Floor_map':dict(zip(raw_baselines.Player,raw_baselines.Floor)),
-                'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
-                'Ceiling_map':dict(zip(raw_baselines.Player,raw_baselines.Ceiling)),
-                'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
-                'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
-                'Own_map':dict(zip(raw_baselines.Player,raw_baselines.Own)),
-                'Small_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Small Field Own%'])),
-                'Large_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Large Field Own%'])),
-                'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
-                'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
-                'team_check_map':dict(zip(raw_baselines.Player,raw_baselines.Team))
-                }
-        Sim_Winners = sim_contest(500, st.session_state.working_seed)
-        
-        st.table(Sim_Winners.head(10))
-                    
-        # # 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
-        # Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
-        # Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
-        
-        # # Type Casting
-        # type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
-        # Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
-        
-        # del FinalPortfolio, insert_port, type_cast_dict
-        
-        # # Sorting
-        # st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-        # st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
-        
-        # # Data Copying
-        # st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
-        
-        # # Data Copying
-        # st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+        if st.button("Run Contest Sim"):
+            if 'working_seed' in st.session_state:
+                maps_dict = {
+                        'Floor_map':dict(zip(raw_baselines.Player,raw_baselines.Floor)),
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Ceiling_map':dict(zip(raw_baselines.Player,raw_baselines.Ceiling)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own%'])),
+                        'Small_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Small Field Own%'])),
+                        'Large_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Large Field Own%'])),
+                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                        'team_check_map':dict(zip(raw_baselines.Player,raw_baselines.Team))
+                        }
+                Sim_Winners = sim_contest(500, st.session_state.working_seed)
+                
+                st.table(Sim_Winners.head(10))
+                            
+                # # 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
+                # Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
+                # Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # # Type Casting
+                # type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                # Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # del FinalPortfolio, insert_port, type_cast_dict
+                
+                # # Sorting
+                # st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                # st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+                # # Data Copying
+                # st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # # Data Copying
+                # st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    st.session_state.working_seed = DK_seed.copy()
+                elif sim_site_var1 == 'Fanduel':
+                    st.session_state.working_seed = FD_seed.copy()
+                maps_dict = {
+                        'Floor_map':dict(zip(raw_baselines.Player,raw_baselines.Floor)),
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Ceiling_map':dict(zip(raw_baselines.Player,raw_baselines.Ceiling)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own%'])),
+                        'Small_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Small Field Own%'])),
+                        'Large_Own_map':dict(zip(raw_baselines.Player,raw_baselines['Large Field Own%'])),
+                        'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
+                        'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
+                        'team_check_map':dict(zip(raw_baselines.Player,raw_baselines.Team))
+                        }
+                Sim_Winners = sim_contest(500, st.session_state.working_seed)
+                
+                st.table(Sim_Winners.head(10))
+                            
+                # # 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
+                # Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['Projection'].astype(str) + Sim_Winner_Frame['Salary'].astype(str) + Sim_Winner_Frame['Own'].astype(str)
+                # Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # # Type Casting
+                # type_cast_dict = {'Salary': int, 'Projection': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32}
+                # Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # del FinalPortfolio, insert_port, type_cast_dict
+                
+                # # Sorting
+                # st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                # st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+                # # Data Copying
+                # st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # # Data Copying
+                # st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()