Update app.py

app.py

@@ -174,51 +174,39 @@ with tab1:
         if st.button("Load Data", key='load_data'):
             if site_var1 == 'Draftkings':
                 if 'working_seed' in st.session_state:
-                    column_indices = [12, 13]
-                    filter_mask = np.logical_or(
-                        np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
-                        np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
-                    )
-                    st.session_state.working_seed = st.session_state.working_seed[filter_mask]
+                    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)
                     if 'data_export_display' in st.session_state:
+                        time.sleep(1)
                         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)
                 else:
                     st.session_state.working_seed = DK_seed.copy()
-                    column_indices = [12, 13]
-                    filter_mask = np.logical_or(
-                        np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
-                        np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
-                    )
-                    st.session_state.working_seed = st.session_state.working_seed[filter_mask]
+                    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)
                     if 'data_export_display' in st.session_state:
+                        time.sleep(1)
                         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':
                 if 'working_seed' in st.session_state:
-                    column_indices = [11, 12]
-                    filter_mask = np.logical_or(
-                        np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
-                        np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
-                    )
-                    st.session_state.working_seed = st.session_state.working_seed[filter_mask]
+                    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 'data_export_display' in st.session_state:
+                        time.sleep(1)
                         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)
                 else:
                     st.session_state.working_seed = FD_seed.copy()
-                    column_indices = [11, 12]
-                    filter_mask = np.logical_or(
-                        np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
-                        np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
-                    )
-                    st.session_state.working_seed = st.session_state.working_seed[filter_mask]
+                    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 'data_export_display' in st.session_state:
+                        time.sleep(1)
                         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)
                 
@@ -255,15 +243,15 @@ with tab2:
             raw_baselines = fd_raw
             column_names = fd_columns
             
-        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Massive'))
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
             Contest_Size = 1000
         elif contest_var1 == 'Medium':
             Contest_Size = 5000
         elif contest_var1 == 'Large':
             Contest_Size = 10000
-        elif contest_var1 == 'Massive':
-            Contest_Size = 100000
+        elif contest_var1 == 'Custom':
+            Contest_Size = st.number_input("Insert contest size", value=None, placeholder="Type a number under 10,000...")
         strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Average', 'Not Very'))
         if strength_var1 == 'Not Very':
             sharp_split = 500000