Refactor filter options in app.py: reorganize macro and micro filter inputs into expandable sections for improved user experience, and ensure consistent handling of player locking and removal functionalities during portfolio management.

app.py

@@ -881,93 +881,93 @@ with tab2:
                           sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
+        if 'portfolio' in st.session_state:
+            col1, col2 = st.columns([2, 8])
+            with col1:
+                with st.expander('Macro Filter Options'):
+                    with st.form(key='macro_filter_form'):
+                        max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
+                        min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
+                        max_salary = st.number_input("Max acceptable salary?", value=100000, min_value=1000, step=100)
+                        max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
+                        min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
+                        if stack_dict is not None:
+                            stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
+                            stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
+                            stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
+                        
+                        submitted = st.form_submit_button("Submit")
+                with st.expander('Micro Filter Options'):
+                    with st.form(key='micro_filter_form'):
+                        player_names = set()
+                        for col in st.session_state['portfolio'].columns:
+                            if col not in excluded_cols:
+                                player_names.update(st.session_state['portfolio'][col].unique())
+                        player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[])
+                        player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
+                        
+                        submitted = st.form_submit_button("Submit")
+                with st.expander('Trimming Options'):
+                    st.info("Make sure you filter before trimming if you want to filter, trimming before a filter will reset your portfolio")
+                    with st.form(key='trim_form'):
+                        st.write("Sorting and trimming variables:")
+                        perf_var, own_var = st.columns(2)
+                        with perf_var:
+                            performance_type = st.selectbox("Sorting variable", ['median', 'Finish_percentile'], key='sort_var')
+                        with own_var:
+                            own_type = st.selectbox("Trimming variable", ['Own', 'Geomean', 'Weighted Own'], key='trim_var')
 
-        col1, col2 = st.columns([2, 8])
-        with col1:
-            with st.expander('Macro Filter Options'):
-                with st.form(key='macro_filter_form'):
-                    max_dupes = st.number_input("Max acceptable dupes?", value=1000, min_value=1, step=1)
-                    min_salary = st.number_input("Min acceptable salary?", value=1000, min_value=1000, step=100)
-                    max_salary = st.number_input("Max acceptable salary?", value=100000, min_value=1000, step=100)
-                    max_finish_percentile = st.number_input("Max acceptable finish percentile?", value=.50, min_value=0.005, step=.001)
-                    min_lineup_edge = st.number_input("Min acceptable Lineup Edge?", value=-.5, min_value=-1.00, step=.001)
-                    if stack_dict is not None:
-                        stack_toggle = st.selectbox("Include specific stacks?", options=['All Stacks', 'Specific Stacks'], index=0)
-                        stack_selections = st.multiselect("If Specific Stacks, Which to include?", options=sorted(list(set(stack_dict.values()))), default=[])
-                        stack_remove = st.multiselect("If Specific Stacks, Which to remove?", options=sorted(list(set(stack_dict.values()))), default=[])
-                    
-                    submitted = st.form_submit_button("Submit")
-            with st.expander('Micro Filter Options'):
-                with st.form(key='micro_filter_form'):
-                    player_names = set()
-                    for col in st.session_state['portfolio'].columns:
-                        if col not in excluded_cols:
-                            player_names.update(st.session_state['portfolio'][col].unique())
-                    player_lock = st.multiselect("Lock players?", options=sorted(list(player_names)), default=[])
-                    player_remove = st.multiselect("Remove players?", options=sorted(list(player_names)), default=[])
-                    
-                    submitted = st.form_submit_button("Submit")
-            with st.expander('Trimming Options'):
-                st.info("Make sure you filter before trimming if you want to filter, trimming before a filter will reset your portfolio")
-                with st.form(key='trim_form'):
-                    st.write("Sorting and trimming variables:")
-                    perf_var, own_var = st.columns(2)
-                    with perf_var:
-                        performance_type = st.selectbox("Sorting variable", ['median', 'Finish_percentile'], key='sort_var')
-                    with own_var:
-                        own_type = st.selectbox("Trimming variable", ['Own', 'Geomean', 'Weighted Own'], key='trim_var')
-
-                    trim_slack_var = st.number_input("Trim slack (percentile addition to trimming variable ceiling)", value=0.0, min_value=0.0, max_value=1.0, step=0.1, key='trim_slack')
+                        trim_slack_var = st.number_input("Trim slack (percentile addition to trimming variable ceiling)", value=0.0, min_value=0.0, max_value=1.0, step=0.1, key='trim_slack')
 
-                    st.write("Sorting threshold range:")
-                    min_sort, max_sort = st.columns(2)
-                    with min_sort:
-                        performance_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_sort')
-                    with max_sort:
-                        performance_threshold_high = st.number_input("Max", value=st.session_state['portfolio'][performance_type].max(), min_value=0.0, step=1.0, key='max_sort')
-                    
-                    st.write("Trimming threshold range:")
-                    min_trim, max_trim = st.columns(2)
-                    with min_trim:
-                        own_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_trim')
-                    with max_trim:
-                        own_threshold_high = st.number_input("Max", value=st.session_state['portfolio'][own_type].max(), min_value=0.0, step=1.0, key='max_trim')
-                    
-                    submitted = st.form_submit_button("Trim")
-                    if submitted:
-                        st.write('initiated')
-                        st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
-                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
-                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
-                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
-                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
-                        st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
-                        if stack_dict is not None:
-                            if stack_toggle == 'All Stacks':
-                                st.session_state['portfolio'] = st.session_state['portfolio']
-                                st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
-                            else:
-                                st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
-                                st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
-                        if player_remove:
-                            # Create mask for lineups that contain any of the removed players
-                            player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-                            remove_mask = st.session_state['portfolio'][player_columns].apply(
-                                lambda row: not any(player in list(row) for player in player_remove), axis=1
-                            )
-                            st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
+                        st.write("Sorting threshold range:")
+                        min_sort, max_sort = st.columns(2)
+                        with min_sort:
+                            performance_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_sort')
+                        with max_sort:
+                            performance_threshold_high = st.number_input("Max", value=st.session_state['portfolio'][performance_type].max(), min_value=0.0, step=1.0, key='max_sort')
                         
-                        if player_lock:
-                            # Create mask for lineups that contain all locked players
-                            player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
-                            
-                            lock_mask = st.session_state['portfolio'][player_columns].apply(
-                                lambda row: all(player in list(row) for player in player_lock), axis=1
-                            )
-                            st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
+                        st.write("Trimming threshold range:")
+                        min_trim, max_trim = st.columns(2)
+                        with min_trim:
+                            own_threshold_low = st.number_input("Min", value=0.0, min_value=0.0, step=1.0, key='min_trim')
+                        with max_trim:
+                            own_threshold_high = st.number_input("Max", value=st.session_state['portfolio'][own_type].max(), min_value=0.0, step=1.0, key='max_trim')
                         
-                        st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
-                        st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
+                        submitted = st.form_submit_button("Trim")
+                        if submitted:
+                            st.write('initiated')
+                            st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
+                            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Dupes'] <= max_dupes]
+                            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] >= min_salary]
+                            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['salary'] <= max_salary]
+                            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Finish_percentile'] <= max_finish_percentile]
+                            st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Lineup Edge'] >= min_lineup_edge]
+                            if stack_dict is not None:
+                                if stack_toggle == 'All Stacks':
+                                    st.session_state['portfolio'] = st.session_state['portfolio']
+                                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+                                else:
+                                    st.session_state['portfolio'] = st.session_state['portfolio'][st.session_state['portfolio']['Stack'].isin(stack_selections)]
+                                    st.session_state['portfolio'] = st.session_state['portfolio'][~st.session_state['portfolio']['Stack'].isin(stack_remove)]
+                            if player_remove:
+                                # Create mask for lineups that contain any of the removed players
+                                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                                remove_mask = st.session_state['portfolio'][player_columns].apply(
+                                    lambda row: not any(player in list(row) for player in player_remove), axis=1
+                                )
+                                st.session_state['portfolio'] = st.session_state['portfolio'][remove_mask]
+                            
+                            if player_lock:
+                                # Create mask for lineups that contain all locked players
+                                player_columns = [col for col in st.session_state['portfolio'].columns if col not in excluded_cols]
+                                
+                                lock_mask = st.session_state['portfolio'][player_columns].apply(
+                                    lambda row: all(player in list(row) for player in player_lock), axis=1
+                                )
+                                st.session_state['portfolio'] = st.session_state['portfolio'][lock_mask]
+                            
+                            st.session_state['portfolio'] = trim_portfolio(st.session_state['portfolio'], trim_slack_var, performance_type, own_type, performance_threshold_high, performance_threshold_low, own_threshold_high, own_threshold_low)
+                            st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
 
         with col2:
             st.write('initiated')