Update app.py

app.py

@@ -203,10 +203,80 @@ for x in data_r['fantasy_content']['league']['players']:
         single_list.append(0)
     total_list.append(single_list)
 
+
+def get_all_teams():
+    url = "https://statsapi.web.nhl.com/api/v1/teams"
+    response = requests.get(url)
+    data = response.json()
+
+    # Extract team information from the response
+    teams = []
+
+    for team in data["teams"]:
+        team_id = team["id"]
+        team_name = team["name"]
+        team_abbreviation = team["abbreviation"]
+        team_city = team["locationName"]
+        teams.append({"team_id": team_id, "team_name": team_name, "team_abb": team_abbreviation, "team_city": team_city})
+
+    return teams
+
+
+def get_all_players_teams():
+    url = "https://statsapi.web.nhl.com/api/v1/teams?expand=team.roster"
+    response = requests.get(url)
+    data = response.json()
+
+    # Extract player information from the response
+    players_teams = []
+
+    for team in data["teams"]:
+        team_name = team["id"]
+        #full_name = team["fullName"]
+
+        for player in team["roster"]["roster"]:
+            player_id = player["person"]["id"]
+            full_name = player["person"]["fullName"]
+            players_teams.append({"player_id": player_id,"full_name":full_name,"team_id": team_name})
+
+    return players_teams
+
 df_2023 = pd.DataFrame(data=total_list,columns=['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','projected_auction_value','editorial_team_abbr','percent_owned'])
 
 week_dict = yahoo_weeks.set_index('Number')['Week'].sort_index().to_dict()
 
+
+player_games = pd.read_csv('data/player_games_cards.csv',index_col=[0])
+team_games = pd.read_csv('data/team_games.csv',index_col=[0])
+
+players_df = pd.DataFrame(get_all_players_teams())
+teams_df = pd.DataFrame(get_all_teams())
+team_games = team_games.merge(teams_df,left_on='team',right_on='team_name')
+players_df = players_df.merge(teams_df)
+
+yahoo_to_nhl = pd.read_csv("data/yahoo_to_nhl.csv",index_col=[0], encoding='unicode_escape')
+
+yahoo_df_scrape = pd.DataFrame(total_list,columns=['yahoo_id','idx','full','first','last','average_pick','average_auction_cost','projected_auction_value','position','team','percent_owned'])
+
+yahoo_final_df = yahoo_df_scrape.merge(right=yahoo_to_nhl,left_on='yahoo_id',right_index=True,how='left',suffixes=['','_x'])
+
+player_games_dd = player_games.drop_duplicates(['player_id']).reset_index(drop=True)
+
+player_games = player_games.merge(right=yahoo_to_nhl.reset_index(),left_on='player_id',right_on='nhl_id',how='left')
+
+player_games = player_games.merge(yahoo_final_df,left_on='player_id_yahoo',right_on='yahoo_id',how='left')
+player_games.loc[player_games.position.isna(),'position'] = player_games.loc[player_games.position.isna(),'Position']
+
+
+player_games.position = player_games.position.replace({'L':'LW','R':'RW'})
+player_games.percent_owned = player_games.percent_owned.fillna(0)
+
+
+player_games['S_H_B'] = player_games['Shots'] + player_games['Hits'] + player_games['Shots Blocked']
+
+
+player_games = player_games.merge(right=team_games,left_on=['date','team'],right_on=['date','team_abb'])
+
 from shiny import ui, render, App
 import matplotlib.image as mpimg
 # app_ui = ui.page_fluid(
@@ -239,10 +309,17 @@ app_ui = ui.page_fluid(
             ui.input_switch("a_d_id", "Ascending?"),
             #ui.input_select("date_id", "Select Date",yahoo_weeks['Week'],width=1),
             ui.input_date_range("date_range_id", "Date range input",start = datetime.today().date(), end = datetime.today().date() + timedelta(days=6)),
+            
+            ui.input_date_range("scorer_date_id", "Streamer Date Range",start = max(datetime.today().date()- timedelta(days=21),pd.to_datetime('2023-10-10')), end = datetime.today().date()),
+            ui.input_numeric('min_games','Min. Games (Streamers)',value=3),
             ui.output_table("result"),width=3),
          
         
-        ui.panel_main(ui.tags.h3(""),
+        ui.panel_main(
+            
+            ui.navset_tab(
+                    ui.nav("Schedule",
+                      ui.tags.h3(""),
                       ui.div({"style": "font-size:2em;"},ui.output_text("txt_title")),
                       #ui.tags.h2("Fantasy Hockey Schedule Summary"),
                       ui.tags.h5("Created By: @TJStats,     Data: NHL"),
@@ -251,9 +328,39 @@ app_ui = ui.page_fluid(
                       ui.tags.h5('Legend'),
                       ui.output_table("schedule_result_legend"),
                       ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
-                      ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
-
-        ))
+                      ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') ),
+
+                    ui.nav("Scorers Streamers",
+                        ui.tags.h3(""),
+                        ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title_streamers")),
+                        ui.tags.h5("Created By: @TJStats,     Data: NHL, Natural Stat Trick, Yahoo Fantasy"),
+                        ui.div({"style": "font-size:2em;"},'Forwards Scoring Streamer Targets (<50% Rostered)'),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_f")),
+                        ui.output_table("scorer_streamers_f"),
+                        ui.tags.h3(""),
+                        ui.div({"style": "font-size:2em;"},'Defence Scoring Streamer Targets (<50% Rostered)'),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_d")),
+                        ui.output_table("scorer_streamers_d"),                           
+                           
+                           ),
+
+                    ui.nav("Bangers Streamers",
+                        ui.tags.h3(""),
+                        ui.div({"style": "font-size:2.7em;"},ui.output_text("txt_title_streamers_bang")),
+                        ui.tags.h5("Created By: @TJStats,     Data: NHL, Natural Stat Trick, Yahoo Fantasy"),
+                        ui.div({"style": "font-size:2em;"},'Forwards Bangers Streamer Targets (<50% Rostered)'),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_f_bang")),
+                        ui.output_table("banger_streamers_f"),
+                        ui.tags.h3(""),
+                        ui.div({"style": "font-size:2em;"},'Defence Bangers Streamer Targets (<50% Rostered)'),
+                        ui.div({"style": "font-size:1em;"},ui.output_text("txt_title_streamers_dates_d_bang")),
+                        ui.output_table("banger_streamers_d"),                           
+                           
+                           )
+                    
+                           
+
+        ))))
         #  ui.row(
         #         ui.column(
         #             3,
@@ -337,6 +444,56 @@ def server(input, output, session):
             return f'Fantasy Hockey Schedule Summary - Yahoo - Week {input.week_id()}'
         else:
             return f'Fantasy Hockey Schedule Summary'
+        
+
+
+    @output
+    @render.text
+    def txt_title_streamers():
+        week_set = int(input.week_id())
+        if week_set != 0:
+            return f'Fantasy Hockey Scorers Streamers - Week {input.week_id()}'
+        else:
+            return f'Fantasy Hockey Scorers Streamers'
+
+    @output
+    @render.text
+    def txt_title_streamers_bang():
+        week_set = int(input.week_id())
+        if week_set != 0:
+            return f'Fantasy Hockey Scorers Streamers - Week {input.week_id()}'
+        else:
+            return f'Fantasy Hockey Scorers Streamers'
+        
+
+    @output
+    @render.text
+    def txt_title_streamers_dates_f():
+        return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'
+
+    @output
+    @render.text
+    def txt_title_streamers_dates_d():
+        return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'
+    
+
+    def txt_title_streamers_bang():
+        week_set = int(input.week_id())
+        if week_set != 0:
+            return f'Fantasy Hockey Scorers Streamers - Week {input.week_id()}'
+        else:
+            return f'Fantasy Hockey Scorers Streamers'
+        
+
+    @output
+    @render.text
+    def txt_title_streamers_dates_f_bang():
+        return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'
+
+    @output
+    @render.text
+    def txt_title_streamers_dates_d_bang():
+        return f'2023-24 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'
 
     @output
     @render.table
@@ -506,7 +663,545 @@ def server(input, output, session):
         
         return off_b2b_df_style
 
+    @output
+    @render.table
+    def scorer_streamers_f():
+        
+
+        week_set = int(input.week_id())
+        print(week_set)
+
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
+
+
+        sort_value='Score'
+        ascend=False
+
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
+
+
+
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
+
+
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score']
+        test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b']
+
+
+
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
+
+
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
+
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
+
+        print('test_df')
+        print(test_df)
+        #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        test_df['Score'] = team_score
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight']
+        
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
+
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
+
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
+
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
+
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
+
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
+
+
+
+        
+        start_date = input.scorer_date_id()[0]
+        end_date = input.scorer_date_id()[1]
+        gp_min = input.min_games()
+
+        df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))]
+
+        team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.8]['Team'].values
+        if(len(team_top)) < 5:
+            team_top = test_df.Team.values[:5]
+
+        df_dated_score_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg(
+            GP = ('GP','sum'),
+            TOI = ('TOI','sum'),
+            Goals = ('Goals','sum'),
+            ixG = ('ixG','sum'),
+            Assists = ('Total Assists','sum'),
+            Points = ('Total Points','sum'),
+            Shots = ('Shots','sum'),
+            PP_Points = ('Total Points_pp','sum'),
+            PP_toi = ('TOI_pp','sum'),
+            team_pp = ('pp_toi','sum'),
+        )#.reset_index()
+
+        df_dated_score_group = df_dated_score_group[(df_dated_score_group.GP >= gp_min)]
+        df_dated_score_group[df_dated_score_group.columns[1:]] = df_dated_score_group[df_dated_score_group.columns[1:]].divide(df_dated_score_group.GP,axis=0)
+        df_dated_score_group['PP_percent'] = df_dated_score_group.PP_toi / df_dated_score_group.team_pp
+        df_dated_score_group = df_dated_score_group.reset_index()
+        df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= .50 )]
+        df_dated_score_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_score_group['TOI'].astype(float)]
+        df_dated_score_group_table = df_dated_score_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI',
+            'Goals', 'ixG', 'Assists', 'Points', 'Shots', 'PP_Points', 'PP_percent']].sort_values(['Points','PP_percent','Goals','Shots'],ascending=False)
+
+        df_dated_score_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP',
+            'Goals/GP', 'ixG/GP','Assists/GP', 'Points/GP', 'Shots/GP', 'PPP/GP', 'PP%']
+
+        return df_dated_score_group_table[df_dated_score_group_table.Position != 'D'].head(15).style.background_gradient(cmap=cmap_off, subset=['Points/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'})\
+        .set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format(
+                    {'Roster%': '{:.0%}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}',
+                    'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}'},).set_properties(
+                        **{'border': '1px black solid !important'}).set_properties(
+                    **{'min-width':'175px'},subset = ((df_dated_score_group_table.columns[0])),overwrite=False).set_properties(
+                    **{'min-width':'50px'},subset = ((df_dated_score_group_table.columns[1:])),overwrite=False)
+    
+
+    @output
+    @render.table
+    def scorer_streamers_d():
+        
+
+        week_set = int(input.week_id())
+        print(week_set)
+
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
+
+
+        sort_value='Score'
+        ascend=False
+
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
+
+
+
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
+
+
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score']
+        test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b']
+
+
+
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
+
+
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
+
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
+
+        print('test_df')
+        print(test_df)
+        #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        test_df['Score'] = team_score
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight']
+        
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
+
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
+
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
+
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
+
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
+
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
+
+
+
+        
+        start_date = input.scorer_date_id()[0]
+        end_date = input.scorer_date_id()[1]
+        gp_min = input.min_games()
+
+        df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))]
+
+        team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.8]['Team'].values
+        if(len(team_top)) < 5:
+            team_top = test_df.Team.values[:5]
+
+        df_dated_score_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg(
+            GP = ('GP','sum'),
+            TOI = ('TOI','sum'),
+            Goals = ('Goals','sum'),
+            ixG = ('ixG','sum'),
+            Assists = ('Total Assists','sum'),
+            Points = ('Total Points','sum'),
+            Shots = ('Shots','sum'),
+            PP_Points = ('Total Points_pp','sum'),
+            PP_toi = ('TOI_pp','sum'),
+            team_pp = ('pp_toi','sum'),
+        )#.reset_index()
+
+        df_dated_score_group = df_dated_score_group[(df_dated_score_group.GP >= gp_min)]
+        df_dated_score_group[df_dated_score_group.columns[1:]] = df_dated_score_group[df_dated_score_group.columns[1:]].divide(df_dated_score_group.GP,axis=0)
+        df_dated_score_group['PP_percent'] = df_dated_score_group.PP_toi / df_dated_score_group.team_pp
+        df_dated_score_group = df_dated_score_group.reset_index()
+        df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= .50 )]
+        df_dated_score_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_score_group['TOI'].astype(float)]
+        df_dated_score_group_table = df_dated_score_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI',
+            'Goals', 'ixG', 'Assists', 'Points', 'Shots', 'PP_Points', 'PP_percent']].sort_values(['Points','PP_percent','Goals','Shots'],ascending=False)
+
+        df_dated_score_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP',
+            'Goals/GP', 'ixG/GP','Assists/GP', 'Points/GP', 'Shots/GP', 'PPP/GP', 'PP%']
+
+        return df_dated_score_group_table[df_dated_score_group_table.Position == 'D'].head(15).style.background_gradient(cmap=cmap_off, subset=['Points/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'})\
+        .set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format(
+                    {'Roster%': '{:.0%}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}',
+                    'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}'},).set_properties(
+                        **{'border': '1px black solid !important'}).set_properties(
+                    **{'min-width':'175px'},subset = ((df_dated_score_group_table.columns[0])),overwrite=False).set_properties(
+                    **{'min-width':'50px'},subset = ((df_dated_score_group_table.columns[1:])),overwrite=False)
+
+    @output
+    @render.table
+    def banger_streamers_f():
+        
+
+        week_set = int(input.week_id())
+        print(week_set)
+
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
+
+
+        sort_value='Score'
+        ascend=False
+
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
+
+
+
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
+
+
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score']
+        test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b']
+
+
+
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
+
+
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
+
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
+
+        print('test_df')
+        print(test_df)
+        #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        test_df['Score'] = team_score
 
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight']
+        
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
+
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
+
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
+
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
+
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
+
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
+
+
+
+        
+        start_date = input.scorer_date_id()[0]
+        end_date = input.scorer_date_id()[1]
+        gp_min = input.min_games()
+
+        df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))]
+
+        team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.8]['Team'].values
+        if(len(team_top)) < 5:
+            team_top = test_df.Team.values[:5]
+
+        df_dated_bangers_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg(
+            GP = ('GP','sum'),
+            TOI = ('TOI','sum'),
+            Goals = ('Goals','sum'),
+            Assists = ('Total Assists','sum'),  
+            Points = ('Total Points','sum'),    
+            Shots = ('Shots','sum'),
+            Hits = ('Total Points','sum'),
+            Blocks = ('Shots Blocked','sum'),
+            S_H_B = ('S_H_B','sum'),
+        )#.reset_index()
+
+        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.GP >= gp_min)]
+        df_dated_bangers_group[df_dated_bangers_group.columns[1:]] = df_dated_bangers_group[df_dated_bangers_group.columns[1:]].divide(df_dated_bangers_group.GP,axis=0)
+        #df_dated_bangers_group['PP_percent'] = df_dated_bangers_group.PP_toi / df_dated_bangers_group.team_pp
+        df_dated_bangers_group = df_dated_bangers_group.reset_index()
+        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= .50 )]
+        df_dated_bangers_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_bangers_group['TOI'].astype(float)]
+        df_dated_bangers_group_table = df_dated_bangers_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI',
+            'Goals', 'Assists', 'Points', 'Shots', 'Hits', 'Blocks','S_H_B']].sort_values(['S_H_B','Shots','Hits','Blocks','Points'],ascending=False)
+            
+        df_dated_bangers_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP',
+            'Goals/GP', 'Assists/GP', 'Points/GP', 'Shots/GP', 'Hits/GP', 'Blocks/GP','S+H+B/GP']
+
+        #df_dated_bangers_group_table.head(15)
+
+        return df_dated_bangers_group_table[df_dated_bangers_group_table.Position != 'D'].head(15).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'})\
+        .set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format(
+                    {'Roster%': '{:.0%}','Goals/GP': '{:.2f}','Assists/GP': '{:.2f}','Points/GP': '{:.2f}','PIM/GP': '{:.2f}','Shots/GP': '{:.2f}','Hits/GP': '{:.2f}','Blocks/GP': '{:.2f}','S+H+B/GP': '{:.2f}'},).set_properties(
+                **{'border': '1px black solid !important'}).set_properties(
+                    **{'min-width':'175px'},subset = ((df_dated_bangers_group_table.columns[0])),overwrite=False).set_properties(
+                    **{'min-width':'50px'},subset = ((df_dated_bangers_group_table.columns[1:])),overwrite=False)
+        
+
+    @output
+    @render.table
+    def banger_streamers_d():
+        
+
+        week_set = int(input.week_id())
+        print(week_set)
+
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
+
+
+        sort_value='Score'
+        ascend=False
+
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
+
+
+
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
+
+
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight_score'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight_score']
+        test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b']
+
+
+
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
+
+
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
+
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
+
+        print('test_df')
+        print(test_df)
+        #team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        team_score = test_df['Total']+test_df['Off-Night']*0.03+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
+        test_df['Score'] = team_score
+
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight']
+        
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
+
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
+
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
+
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
+
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
+
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
+
+
+
+        
+        start_date = input.scorer_date_id()[0]
+        end_date = input.scorer_date_id()[1]
+        gp_min = input.min_games()
+
+        df_dated = player_games[(pd.to_datetime(player_games.date) >= pd.to_datetime(start_date))&(pd.to_datetime(player_games.date) <= pd.to_datetime(end_date))]
+
+        team_top = test_df[test_df.Score!=''][np.array(test_df[test_df.Score!=''].Score) >= np.array(test_df[test_df.Score!='']['Score']).max()*0.8]['Team'].values
+        if(len(team_top)) < 5:
+            team_top = test_df.Team.values[:5]
+
+        df_dated_bangers_group = df_dated.groupby(['player_id','Player','Team','position','percent_owned']).agg(
+            GP = ('GP','sum'),
+            TOI = ('TOI','sum'),
+            Goals = ('Goals','sum'),
+            Assists = ('Total Assists','sum'),  
+            Points = ('Total Points','sum'),    
+            Shots = ('Shots','sum'),
+            Hits = ('Total Points','sum'),
+            Blocks = ('Shots Blocked','sum'),
+            S_H_B = ('S_H_B','sum'),
+        )#.reset_index()
+
+        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.GP >= gp_min)]
+        df_dated_bangers_group[df_dated_bangers_group.columns[1:]] = df_dated_bangers_group[df_dated_bangers_group.columns[1:]].divide(df_dated_bangers_group.GP,axis=0)
+        #df_dated_bangers_group['PP_percent'] = df_dated_bangers_group.PP_toi / df_dated_bangers_group.team_pp
+        df_dated_bangers_group = df_dated_bangers_group.reset_index()
+        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= .50 )]
+        df_dated_bangers_group['TOI'] = ["%d:%02d" % (int(x),(x*60)%60) for x in df_dated_bangers_group['TOI'].astype(float)]
+        df_dated_bangers_group_table = df_dated_bangers_group[['Player','Team' ,'position', 'percent_owned', 'GP', 'TOI',
+            'Goals', 'Assists', 'Points', 'Shots', 'Hits', 'Blocks','S_H_B']].sort_values(['S_H_B','Shots','Hits','Blocks','Points'],ascending=False)
+            
+        df_dated_bangers_group_table.columns = ['Player','Team' ,'Position', 'Roster%', 'GP', 'TOI/GP',
+            'Goals/GP', 'Assists/GP', 'Points/GP', 'Shots/GP', 'Hits/GP', 'Blocks/GP','S+H+B/GP']
+
+        #df_dated_bangers_group_table.head(15)
+
+        return df_dated_bangers_group_table[df_dated_bangers_group_table.Position == 'D'].head(15).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'})\
+        .set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px'),('border', '1px black solid !important') ]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('border', '1px black solid !important')]}]).format(
+                    {'Roster%': '{:.0%}','Goals/GP': '{:.2f}','Assists/GP': '{:.2f}','Points/GP': '{:.2f}','PIM/GP': '{:.2f}','Shots/GP': '{:.2f}','Hits/GP': '{:.2f}','Blocks/GP': '{:.2f}','S+H+B/GP': '{:.2f}'},).set_properties(
+                **{'border': '1px black solid !important'}).set_properties(
+                    **{'min-width':'175px'},subset = ((df_dated_bangers_group_table.columns[0])),overwrite=False).set_properties(
+                    **{'min-width':'50px'},subset = ((df_dated_bangers_group_table.columns[1:])),overwrite=False)
+
+
+
+
+
+