Update app.py

app.py

@@ -888,8 +888,120 @@ with tab2:
             Strength_var = .01
             scaling_var = 15
         sim_done = 0
+        seed_depth1 = 10
+        Total_Runs = 1000000
+        if Contest_Size <= 1000:
+            strength_grow = .01
+        elif Contest_Size > 1000 and Contest_Size <= 2500:
+            strength_grow = .025
+        elif Contest_Size > 2500 and Contest_Size <= 5000:
+            strength_grow = .05
+        elif Contest_Size > 5000 and Contest_Size <= 20000:
+            strength_grow = .075
+        elif Contest_Size > 20000:
+            strength_grow = .1
+            
+        field_growth = 100 * strength_grow
+
+        Sort_function = 'Median'
+        if Sort_function == 'Median':
+            Sim_function = 'Projection'
+        elif Sort_function == 'Own':
+            Sim_function = 'Own'
+        Sim_Winner_Frame = pd.DataFrame()
+        player_freq = pd.DataFrame()
+        Sim_Winner_Export = pd.DataFrame()
+        qb_freq = pd.DataFrame()
+        rb_freq = pd.DataFrame()
+        wr_freq = pd.DataFrame()
+        te_freq = pd.DataFrame()
+        flex_freq = pd.DataFrame()
+        dst_freq = pd.DataFrame()
             
     with col2:
+        with st.container():
+            if sim_done == 1:
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = static_exposure.Player.values.tolist()
+                if player_split_var2 == 'Specific Players':
+                          Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+                elif player_split_var2 == 'Full Players':
+                          Sim_Winner_Frame = Sim_Winner_Frame
+                          
+                with st.container():
+                    display_winner_dataframe = Sim_Winner_Frame.copy()
+                    st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+                
+                    st.download_button(
+                        label="Export Tables",
+                        data=convert_df_to_csv(Sim_Winner_Export),
+                        file_name='NFL_consim_export.csv',
+                        mime='text/csv',
+                    )
+                
+                with st.container():
+                    freq_container = st.empty()
+                    tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
+                    with tab1:
+                        st.dataframe(player_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=convert_df_to_csv(player_freq),
+                            file_name='player_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab2:
+                        st.dataframe(qb_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=convert_df_to_csv(qb_freq),
+                            file_name='qb_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab3:
+                        st.dataframe(rb_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=convert_df_to_csv(rb_freq),
+                            file_name='rb_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab4:
+                        st.dataframe(wr_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=convert_df_to_csv(wr_freq),
+                            file_name='wr_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab5:
+                        st.dataframe(te_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=convert_df_to_csv(te_freq),
+                            file_name='te_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab6:
+                        st.dataframe(flex_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=convert_df_to_csv(flex_freq),
+                            file_name='flex_freq_export.csv',
+                            mime='text/csv',
+                        )
+                    with tab7:
+                        st.dataframe(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=convert_df_to_csv(dst_freq),
+                            file_name='dst_freq_export.csv',
+                            mime='text/csv',
+                        )
+        
         if st.button("Simulate Contest"):
             with st.container():
                 sim_done = 0
@@ -907,26 +1019,6 @@ with tab2:
                     pass
                 with st.container():
                     st.write('Contest Simulation Starting')
-                    seed_depth1 = 10
-                    Total_Runs = 1000000
-                    if Contest_Size <= 1000:
-                        strength_grow = .01
-                    elif Contest_Size > 1000 and Contest_Size <= 2500:
-                        strength_grow = .025
-                    elif Contest_Size > 2500 and Contest_Size <= 5000:
-                        strength_grow = .05
-                    elif Contest_Size > 5000 and Contest_Size <= 20000:
-                        strength_grow = .075
-                    elif Contest_Size > 20000:
-                        strength_grow = .1
-                        
-                    field_growth = 100 * strength_grow
-            
-                    Sort_function = 'Median'
-                    if Sort_function == 'Median':
-                        Sim_function = 'Projection'
-                    elif Sort_function == 'Own':
-                        Sim_function = 'Own'
                     
                     if slate_var1 == 'User':
                         OwnFrame = proj_dataframe
@@ -1337,85 +1429,4 @@ with tab2:
                                         dst_freq['Team'] = dst_freq['Team'].replace(item_list, team_list)
     
                     dst_freq = dst_freq[['Player', 'Team', 'Position', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
-        with st.container():
-            if sim_done == 1:
-                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'))
-                if player_split_var2 == 'Specific Players':
-                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = player_freq['Player'].unique())
-                elif player_split_var2 == 'Full Players':
-                          find_var2 = static_exposure.Player.values.tolist()
-                if player_split_var2 == 'Specific Players':
-                          Sim_Winner_Frame = Sim_Winner_Frame[np.equal.outer(Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
-                elif player_split_var2 == 'Full Players':
-                          Sim_Winner_Frame = Sim_Winner_Frame
-                          
-                with st.container():
-                    display_winner_dataframe = Sim_Winner_Frame.copy()
-                    st.dataframe(display_winner_dataframe.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-                
-                    st.download_button(
-                        label="Export Tables",
-                        data=convert_df_to_csv(Sim_Winner_Export),
-                        file_name='NFL_consim_export.csv',
-                        mime='text/csv',
-                    )
-                
-                with st.container():
-                    freq_container = st.empty()
-                    tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
-                    with tab1:
-                        st.dataframe(player_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=convert_df_to_csv(player_freq),
-                            file_name='player_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab2:
-                        st.dataframe(qb_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=convert_df_to_csv(qb_freq),
-                            file_name='qb_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab3:
-                        st.dataframe(rb_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=convert_df_to_csv(rb_freq),
-                            file_name='rb_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab4:
-                        st.dataframe(wr_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=convert_df_to_csv(wr_freq),
-                            file_name='wr_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab5:
-                        st.dataframe(te_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=convert_df_to_csv(te_freq),
-                            file_name='te_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab6:
-                        st.dataframe(flex_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=convert_df_to_csv(flex_freq),
-                            file_name='flex_freq_export.csv',
-                            mime='text/csv',
-                        )
-                    with tab7:
-                        st.dataframe(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=convert_df_to_csv(dst_freq),
-                            file_name='dst_freq_export.csv',
-                            mime='text/csv',
-                        )
+