import requests
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
#from scipy import stats
import matplotlib.lines as mlines
import matplotlib.transforms as mtransforms
import numpy as np
#import plotly.express as px
#!pip install chart_studio
# import chart_studio.tools as tls
#from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.font_manager as font_manager
from datetime import datetime
import pytz
from datetime import date
datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
# Configure Notebook
#%matplotlib inline
plt.style.use('fivethirtyeight')
sns.set_context("notebook")
import warnings
warnings.filterwarnings('ignore')
#from urllib.request import urlopen
import json
from datetime import date, timedelta
#import dataframe_image as dfi
#from os import listdir
#from os.path import isfile, join
import datetime
import seaborn as sns
import os
import calendar
#from IPython.display import display, HTML
import matplotlib.image as mpimg
#from skimage import io
#import difflib 


from datetime import datetime
import pytz
datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
# Configure Notebook
#%matplotlib inline
plt.style.use('fivethirtyeight')
sns.set_context("notebook")
import warnings
warnings.filterwarnings('ignore')
# import yfpy
# from yfpy.query import YahooFantasySportsQuery
# import yahoo_oauth
import json
#import openpyxl
#from sklearn import preprocessing
from PIL import Image
import logging
import matplotlib.patches as patches
from matplotlib.patches import Rectangle
from matplotlib.font_manager import FontProperties
from matplotlib.offsetbox import OffsetImage, AnnotationBbox

import requests
#import pickle
import pandas as pd

# # Loop over the counter and format the API call
# r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-01&endDate=2024-06-01')
# schedule = r.json()

# def flatten(t):
#     return [item for sublist in t for item in sublist]

# game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
# game_date = flatten([[x['gameDate'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
# game_home = flatten([[x['teams']['home']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
# game_away = flatten([[x['teams']['away']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])

# schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away})
# schedule_df.game_date = pd.to_datetime(schedule_df['game_date']).dt.tz_convert(tz='US/Eastern').dt.date
# schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
# schedule_df.head()

team_abv = pd.read_csv('team_abv.csv')

schedule = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2025_games.html')[0]
    # schedule.to_csv('schedule/schedule_'+str(date.today())+'.csv')
# schedule = pd.read_csv('2024_schedule_href.csv')
schedule = schedule.replace('St Louis Blues','St. Louis Blues')

schedule_df = schedule.merge(right=team_abv,left_on='Visitor',right_on='team_name',how='inner',suffixes=['','_away']) 
schedule_df = schedule_df.merge(right=team_abv,left_on='Home',right_on='team_name',how='inner',suffixes=['','_home']) 
schedule_df['away_sym'] = '@'
schedule_df['home_sym'] = 'vs'


yahoo_weeks = pd.read_csv('yahoo_weeks.csv')
#yahoo_weeks['Number'] = yahoo_weeks['Number'].astype(int)
yahoo_weeks['Start'] = pd.to_datetime(yahoo_weeks['Start'])
yahoo_weeks['End'] = pd.to_datetime(yahoo_weeks['End'])
yahoo_weeks.head(5)

def highlight_cols(s):
    color = '#C2FEE9'
    return 'background-color: %s' % color
def highlight_cells(val):
    color = 'white' if val == ' ' else ''
    return 'background-color: {}'.format(color)

import matplotlib.pyplot as plt
import matplotlib.colors
cmap_total = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"])
cmap_off = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
cmap_back = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"])
cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])

# schedule_df = schedule_df.merge(right=team_abv,left_on='game_away',right_on='team_name',how='inner',suffixes=['','_away']) 
# schedule_df = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='inner',suffixes=['','_home']) 
# schedule_df['away_sym'] = '@'
# schedule_df['home_sym'] = 'vs'



#if not os.path.isfile('standings/standings_'+str(date.today())+'.csv'):   
standings_df_old = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2025_standings.html')[0].append(pd.read_html('https://www.hockey-reference.com/leagues/NHL_2025_standings.html')[1])
# standings_df_old = pd.read_csv('2024_standings_href.csv')
#    standings_df_old.to_csv('standings/standings_'+str(date.today())+'.csv')
#standings_df_old = pd.read_csv('standings/standings_'+str(date.today())+'.csv',index_col=[0])

standings_df = standings_df_old[standings_df_old['Unnamed: 0'].str[-8:] != 'Division'].sort_values('Unnamed: 0').reset_index(drop=True).rename(columns={'Unnamed: 0':'Team'})#.drop(columns='Unnamed: 0')
#standings_df = standings_df.replace('St. Louis Blues','St Louis Blues')
standings_df['GF/GP'] = standings_df['GF'].astype(int)/standings_df['GP'].astype(int)
standings_df['GA/GP'] = standings_df['GA'].astype(int)/standings_df['GP'].astype(int)
standings_df['GF_Rank']  = standings_df['GF/GP'].rank(ascending=True,method='first')/10-1.65
standings_df['GA_Rank'] = standings_df['GA/GP'].rank(ascending=False,method='first')/10-1.65
standings_df.Team = standings_df.Team.str.strip('*') 
standings_df = standings_df.merge(right=team_abv,left_on='Team',right_on='team_name')

schedule_stack = pd.DataFrame()
# schedule_stack['date'] = pd.to_datetime(list(schedule_df['game_date'])+list(schedule_df['game_date']))
schedule_stack['date'] = pd.to_datetime(list(schedule_df['Date'])+list(schedule_df['Date']))
schedule_stack['team'] = list(schedule_df['team_name'])+list(schedule_df['team_name_home'])
schedule_stack['team_abv'] = list(schedule_df['team_abv'])+list(schedule_df['team_abv_home'])
schedule_stack['symbol'] = list(schedule_df['away_sym'])+list(schedule_df['home_sym'])
schedule_stack['team_opponent'] =  list(schedule_df['team_name_home'])+list(schedule_df['team_name'])
schedule_stack['team_abv_home'] = list(schedule_df['team_abv_home'])+list(schedule_df['team_abv'])
schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y'))
schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y'))

schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y'))
schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y'))
 
 
list_o = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True)
new_list = [x - y for x, y in zip(list_o['date'][1:], list_o['date'])]
b2b_list = [0] + [x.days for x in new_list]
b2b_list = [1 if x==1 else 0 for x in b2b_list]
test = list(schedule_stack.groupby(by='date').count()['team'])
offnight = [1 if x<15 else 0 for x in test]
offnight_df = pd.DataFrame({'date':schedule_stack.sort_values('date').date.unique(),'offnight':offnight}).sort_values('date').reset_index(drop=True)
schedule_stack = schedule_stack.merge(right=offnight_df,left_on='date',right_on='date',how='right')
schedule_stack = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True)
schedule_stack['b2b'] = b2b_list

schedule_stack.date = pd.to_datetime(schedule_stack.date)

away_b2b = []
home_b2b = []
for i in range(0,len(schedule_stack)):
    away_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team_opponent[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0])
    home_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0])

schedule_stack['away_b2b'] = away_b2b
schedule_stack['home_b2b'] = home_b2b

schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(1,' &#128564;')
schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(0,'')

schedule_stack['offnight_score'] = schedule_stack.groupby('date').team.transform('count')
print('we made it',schedule_stack.offnight_score.max())
schedule_stack.offnight_score = schedule_stack.offnight_score - 16
#schedule_stack.loc[schedule_stack['offnight_score'] > 0,'offnight_score'] = 0
schedule_stack.offnight_score = -1*schedule_stack.offnight_score
#print(schedule_stack.offnight_score.max())
schedule_stack.head()

FontProperties(fname='/System/Library/Fonts/Apple Color Emoji.ttc')

try: 
    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/453.l.public;out=settings/players;position=ALL;start=0;count=3000;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
    key_check = data_r['fantasy_content']['league']['players']

except KeyError:
    data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/453.l.public;out=settings/players;position=ALL;start=0;count=400;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
    print('key_checked')

total_list = []

for x in data_r['fantasy_content']['league']['players']:
    single_list = []

    single_list.append(int(x['player']['player_id']))
    single_list.append(x['player']['player_ranks'][0]['player_rank']['rank_value'])
    single_list.append(x['player']['name']['full'])
    single_list.append(x['player']['name']['first'])
    single_list.append(x['player']['name']['last'])
    single_list.append(x['player']['draft_analysis']['average_pick'])
    single_list.append(x['player']['average_auction_cost'])
    single_list.append(x['player']['projected_auction_value'])
    single_list.append(x['player']['display_position'])
    single_list.append(x['player']['editorial_team_abbr'])
    if 'value' in x['player']['percent_owned']:
        single_list.append(x['player']['percent_owned']['value']/100)
    else:
        single_list.append(0)

    if 'status' in x['player']:
        single_list.append(x['player']['status'])
    else:
        single_list.append(True)
    
    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','status'])


# df_2023 = pd.read_csv('df_2023_small.csv',index_col=[0])
df_2023.percent_owned = df_2023.percent_owned.astype(float)
#df_2023.columns = ['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','projected_auction_value','editorial_team_abbr','percent_owned','status']
df_2023.status = df_2023.status.astype(str)
df_2023 = df_2023[df_2023.status.isin(['True','DTD'])]

df_2023.percent_owned = df_2023.percent_owned.astype(float)
print('Jack Hughes',df_2023[df_2023.full == 'Jack Hughes'])

yahoo_weeks['Number'] = yahoo_weeks['Number'].astype(float).astype(int)
#yahoo_weeks['Week'] = yahoo_weeks['Week'].astype(float).astype(int)
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]).sort_values(by='date').drop_duplicates(subset=['player_id','date'])
team_games = pd.read_csv('data/team_games.csv',index_col=[0])


team_last = player_games.drop_duplicates(subset=['player_id'],keep='last')[['player_id','Team']].set_index('player_id')['Team'].to_dict()

player_games['Team'] = player_games['player_id'].map(team_last)
# players_df = pd.DataFrame(get_all_players_teams())

# teams_df = pd.DataFrame(get_all_teams())
# teams_df.team_name = teams_df.team_name.replace({'Montréal Canadiens':'Montreal Canadiens'})
# teams_df.team_abb  = teams_df.team_name.map(team_abv.set_index('team_name').team_abv.to_dict())

teams_df = pd.read_csv('teams.csv',index_col=[0])
team_games = team_games.replace('St. Louis Blues','St Louis Blues')
team_games = team_games.replace('St Louis Blues','St. Louis Blues')
# team_games = team_games.replace('Montréal Canadiens','Montreal Canadiens')
# team_games = team_games.replace('Montreal Canadiens','Montréal Canadiens')
team_games = team_games.merge(teams_df,left_on='team',right_on='team_name')
team_games.team_abv = team_games.team.map(team_abv.set_index('team_name').team_abv.to_dict())

# players_df = players_df.merge(teams_df)


team_games.team_abb = team_games.team_abv.replace({'LAK':'L.A','SJS':'S.J','NJD':'N.J','TBL':'T.B'})
yahoo_to_nhl = pd.read_csv("data/yahoo_to_nhl.csv",index_col=[0], encoding='unicode_escape')

#yahoo_df_scrape = pd.read_csv('df_2023_small.csv',index_col=[0])


# yahoo_df_scrape = pd.read_csv('df_2023_small.csv',index_col=[0])
yahoo_df_scrape = df_2023.copy()
yahoo_df_scrape.percent_owned = yahoo_df_scrape.percent_owned.astype(float)
yahoo_df_scrape.columns = ['yahoo_id','idx','full','first','last','average_pick','average_auction_cost','projected_auction_value','position','team','percent_owned','status']
yahoo_df_scrape.status = yahoo_df_scrape.status.astype(str)
yahoo_df_scrape = yahoo_df_scrape[yahoo_df_scrape.status.isin(['True','DTD'])]
#percent_roster_dict = yahoo_df_scrape.
#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'])
#yahoo_final_df = yahoo_final_df.dropna(subset=['status']) 
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'})
print('Jack Hughes',player_games[player_games.Player == 'Jack Hughes'].percent_owned)
player_games.percent_owned = player_games.percent_owned.fillna(0)
player_games = player_games.dropna(subset=['status'])

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'],how='left')
player_games = player_games.fillna(0)

player_games = player_games.drop_duplicates(subset=['player_id','date'])
#player_games = player_games.replace([np.inf, -np.inf], 0, inplace=True)

from shiny import ui, render, App
import matplotlib.image as mpimg
# app_ui = ui.page_fluid(

#     # ui.output_plot("plot"),
#     #ui.h2('MLB Batter Launch Angle vs Exit Velocity'),
#     ui.layout_sidebar(
#         ui.panel_sidebar(
#             ui.input_select("id", "Select Batter",batter_dict),

#             ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'})))
#         ,

#     ui.panel_main(ui.output_plot("plot",height = "750px",width="1250px")),
#     #ui.download_button('test','Download'),
# )
#import shinyswatch
app_ui = ui.page_fluid(
    #shinyswatch.theme.cosmo(),
    ui.layout_sidebar(
    
    # Available themes:
    #  cerulean, cosmo, cyborg, darkly, flatly, journal, litera, lumen, lux,
    #  materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, slate,
    #  solar, spacelab, superhero, united, vapor, yeti, zephyr
        
      ui.panel_sidebar(
            ui.input_select("week_id", "Select Week (Set as Season for Custom Date Range)",week_dict,width=1),
            ui.input_select("sort_id", "Sort Column",['Score','Team','Total','Off-Night','B2B'],width=1),
            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 Stats 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.input_numeric('max_head','Number of Rows (Streamers)',value=15),
            ui.input_numeric('roster_p','Max Roster Percent% (Streamers)',value=50),
            ui.output_table("result"),width=3),
         
        
        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"),
                      ui.div({"style": "font-size:1.2em;"},ui.output_text("txt")),
                      ui.output_table("schedule_result"),
                      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.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;"},ui.output_text("txt_title_streamers_roster_f")),
                        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;"},ui.output_text("txt_title_streamers_roster_d")),
                        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;"},ui.output_text("txt_title_streamers_roster_f_bang")),
                        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;"},ui.output_text("txt_title_streamers_roster_d_bang")),
                        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,
        #                 ui.input_date("x", "Date input"),),
        #         ui.column(
        #             1,
        #             ui.input_select("level_id", "Select Level",level_dict,width=1)),
        #         ui.column(
        #             3,
        #             ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1)),
        #         ui.column(
        #             2,
        #             ui.input_numeric("n", "Rolling Window Size", value=50)),
        #         ),
        #             ui.output_table("result_batters")),

                # ui.nav(
                #     "Pitchers",

                #                 ui.row(
                # ui.column(
                #     3,
                #     ui.input_select("id_pitch", "Select Pitcher",pitcher_dict,width=1,selected=675911),
                #     ),
                # ui.column(
                #     1,
                #     ui.input_select("level_id_pitch", "Select Level",level_dict,width=1)),
                # ui.column(
                #     3,
                #     ui.input_select("stat_id_pitch", "Select Stat",plot_dict_small_pitch,width=1)),
                # ui.column(
                #     2,
                #         ui.input_numeric("n_pitch", "Rolling Window Size", value=50)),
                # ),
                #     ui.output_table("result_pitchers")),
#             )
#         )
# )




from urllib.request import Request, urlopen
# importing OpenCV(cv2) module




def server(input, output, session):

    @output
    @render.text
    def txt():
        
        week_set = int(float(input.week_id()))
        if week_set != 0:
            if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['Start'].values[0]).year != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['End'].values[0]).year:

                return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d, %Y")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
            else:
                if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).month != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).month:
                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
                else:
                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%d, %Y")}'
        else:
            if input.date_range_id()[0].year != input.date_range_id()[1].year:

                return f'{input.date_range_id()[0].strftime("%B %d, %Y")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
            else:
                if input.date_range_id()[0].month != input.date_range_id()[1].month:
                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
                else:
                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%d, %Y")}'            


    @output
    @render.text
    def txt_title():
        week_set = int(float(input.week_id()))
        if week_set != 0:
            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(float(input.week_id()))
        if week_set != 0:
            return f'Fantasy Hockey Scorers Targets - Week {input.week_id()}'
        else:
            return f'Fantasy Hockey Scorers Targets'

    @output
    @render.text
    def txt_title_streamers_bang():
        week_set = int(input.week_id())
        if week_set != 0:
            return f'Fantasy Hockey Bangers Targets - Week {input.week_id()}'
        else:
            return f'Fantasy Hockey Bangers Targets'

    @output
    @render.text
    def txt_title_streamers_dates_f():
        return f'2024-25 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'
    
    @output
    @render.text
    def txt_title_streamers_roster_f():
        return f'Forwards Scorers Targets (<{input.roster_p()/100:.0%} Rostered)'
    
    @output
    @render.text
    def txt_title_streamers_roster_d():
        return f'Defense Scorers Targets (<{input.roster_p()/100:.0%} Rostered)'
    
    @output
    @render.text
    def txt_title_streamers_roster_f_bang():
        return f'Forwards Bangers Targets (<{input.roster_p()/100:.0%} Rostered)'


    @output
    @render.text
    def txt_title_streamers_roster_d_bang():
        return f'Defense Bangers Targets (<{input.roster_p()/100:.0%} Rostered)'

    @output
    @render.text
    def txt_title_streamers_dates_d():
        return f'2024-25 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'2024-25 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'2024-25 Season - {input.scorer_date_id()[0]} to {input.scorer_date_id()[1]} (min. {input.min_games()} GP)'

    @output
    @render.table
    def result():
        #print(yahoo_weeks)
        return yahoo_weeks[['Week','Start','End']]

    @output
    @render.table
    def schedule_result():
        

        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).zfill(2)+'-'+'{: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:]]

        test_df.style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))
        test_df_style = test_df.style.set_properties(**{'border': '3 px'},overwrite=False).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','px'),('color','black'),('border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
                    **{'background-color':'White','index':'White','min-width':'75px'},overwrite=False).set_properties(
                    **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False,subset = ((list(test_df.index[:]),test_df.columns[5:]))).set_table_styles(
            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).hide_index()

        test_df_style = test_df_style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))

        test_df_style = test_df_style.applymap(highlight_cells)
        test_df_style = test_df_style.background_gradient(cmap=cmap_total,subset = ((list(test_df.index[:-1]),test_df.columns[0])))
        test_df_style = test_df_style.background_gradient(cmap=cmap_total,vmin=0,vmax=np.max(test_df.Total[:len(test_df)-1]),subset = ((list(test_df.index[:-1]),test_df.columns[2])))
        test_df_style = test_df_style.background_gradient(cmap=cmap_off,subset = ((list(test_df.index[:-1]),test_df.columns[3])))
        test_df_style = test_df_style.background_gradient(cmap=cmap_back,subset = ((list(test_df.index[:-1]),test_df.columns[4])))
        test_df_style = test_df_style.background_gradient(cmap=cmap_sum,subset = ((list(test_df.index[-1:]),test_df.columns[L:])),axis=1)
        test_df_style = test_df_style.set_properties(
                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:-1]),test_df.columns[:]))).set_properties(
                    **{'min-width':'85px'},subset = ((list(test_df.index[:-1]),test_df.columns[L:])),overwrite=False).set_properties(**{
                                'color': 'black'},overwrite=False).set_properties(
                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:]),test_df.columns[L:])))

        test_df_style = test_df_style.format(
            '{:.0f}',subset=(test_df.index[:-1],test_df.columns[2:L]))

        test_df_style = test_df_style.format(
            '{:.1f}',subset=(test_df.index[:-1],test_df.columns[0]))


        print('made it to teh end')
        return test_df_style


        #return exit_velo_df_codes_summ_time_style_set

    # @output
    # @render.plot(alt="A histogram")
    # def plot_pitch():
    #     p
    @output
    @render.table
    def schedule_result_legend():

        off_b2b_df = pd.DataFrame(data={'off':'Off-Night','b2b':'Tired Opp. &#128564;'},index=[0])
        #off_b2b_df.style.applymap(highlight_cols,subset = ((list(off_b2b_df.index[:-1]),off_b2b_df.columns[0])))
        off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).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','px'),('color','black'),(
                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
                    **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(
            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(
                **{'background-color':'#C2FEE9'},subset=off_b2b_df.columns[0]).set_properties(
                **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([
            {'selector': 'thead', 'props': [('display', 'none')]}
            ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(
                    **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{
                                'color': 'black'},overwrite=False).set_properties(
                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])))
        
        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()*0.75]['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.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team'])
        df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= input.roster_p()/100 )]
        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','Off-Night']].sort_values(['Points','Off-Night','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%','Off-Night']
        
        

        return df_dated_score_group_table[df_dated_score_group_table.Position != 'D'].head(input.max_head()).style.background_gradient(
            cmap=cmap_off, subset=['Points/GP']).background_gradient(cmap=cmap_total, subset=['Roster%']).background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_score_group_table['Off-Night'].max()).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%}','GP': '{:.0f}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}',
                    'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}','Off-Night': '{:.0f}'},).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()*0.75]['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.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team'])
        df_dated_score_group = df_dated_score_group[(df_dated_score_group.Team.isin(team_top))&(df_dated_score_group.percent_owned <= input.roster_p()/100 )]
        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','Off-Night']].sort_values(['Points','Off-Night','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%','Off-Night']

        return df_dated_score_group_table[df_dated_score_group_table.Position == 'D'].head(input.max_head()) \
        .style.background_gradient(cmap=cmap_off, subset=['Points/GP']) \
        .background_gradient(cmap=cmap_total, subset=['Roster%']) \
        .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_score_group_table['Off-Night'].max()) \
        .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%}','GP': '{:.0f}','Goals/GP': '{:.2f}','ixG/GP': '{:.2f}','Assists/GP': '{:.2f}',
                    'Points/GP': '{:.2f}','Shots/GP': '{:.2f}','PPP/GP': '{:.2f}','PP%': '{:.0%}','Off-Night': '{:.0f}'},).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()*0.75]['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 = ('Hits','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.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team'])
        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= input.roster_p()/100 )]
        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','Off-Night']].sort_values(['S_H_B','Off-Night','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','Off-Night']

        #df_dated_bangers_group_table.head(15)

        return df_dated_bangers_group_table[df_dated_bangers_group_table.Position != 'D'] \
            .head(input.max_head()).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP'])\
            .background_gradient(cmap=cmap_total, subset=['Roster%'])\
            .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_bangers_group_table['Off-Night'].max())\
            .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%}','GP': '{:.0f}','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}','Off-Night': '{:.0f}'},).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()*0.75]['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 = ('Hits','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.merge(test_df[['Team','Off-Night']],left_on=['Team'],right_on=['Team'])
        df_dated_bangers_group = df_dated_bangers_group[(df_dated_bangers_group.Team.isin(team_top))&(df_dated_bangers_group.percent_owned <= input.roster_p()/100 )]
        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','Off-Night']].sort_values(['S_H_B','Off-Night','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','Off-Night']

        #df_dated_bangers_group_table.head(15)

        return df_dated_bangers_group_table[df_dated_bangers_group_table.Position == 'D']\
            .head(input.max_head()).style.background_gradient(cmap=cmap_off, subset=['S+H+B/GP'])\
            .background_gradient(cmap=cmap_total, subset=['Roster%'])\
            .background_gradient(cmap=cmap_off, subset=['Off-Night'],vmin=0,vmax=df_dated_bangers_group_table['Off-Night'].max())\
            .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%}','GP': '{:.0f}','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}','Off-Night': '{:.0f}'},).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)


        




app = App(app_ui, server)