app.py

print('Running')
import time
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
import matplotlib.lines as mlines
import matplotlib.transforms as mtransforms
import numpy as np
import time
from urllib.error import HTTPError
#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 matplotlib.ticker import MaxNLocator
from matplotlib.patches import Ellipse
import matplotlib.transforms as transforms
from matplotlib.gridspec import GridSpec
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 dataframe_image as dfi
# from google.colab import drive
def percentile(n):
    def percentile_(x):
        return np.percentile(x, n)
    percentile_.__name__ = 'percentile_%s' % n
    return percentile_

# import os
# import praw
# import matplotlib.pyplot as plt
# import matplotlib.colors
# import matplotlib.colors as mcolors
# cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285f4","#FFFFFF","#F0E442"])
#import pybaseball
import math
# import matplotlib.ticker as mtick
# import matplotlib.ticker as ticker

# colour_palette = ['#FFB000','#648FFF','#785EF0',
                #   '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
# import matplotlib.colors as mcolors
# from  matplotlib.ticker import FuncFormatter
# from matplotlib.font_manager import FontProperties
import matplotlib.ticker as mtick
import numpy as np
# import matplotlib.pyplot as plt
import matplotlib.colors

import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from matplotlib import cm
from datetime import date

def show_values(axs, orient="v", space=-1,stat = [],rank_n=[]):
    def _single(ax):
        if orient == "v":
            i = 0
            for p in ax.patches:

                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height() + (p.get_height()*0.01)
                value = '{:.0f}'.format(p.get_height())
                if isinstance(stat[i], float):
                    value_stat = '{:.2f}'.format(stat[i])
                else:
                    value_stat = stat[i]
                rank = rank_n[i]
                ax.text(_x, -3.8, value_stat, ha="center",fontstyle='italic')    
                ax.text(_x, -4.2, rank, ha="center",fontstyle='italic')  
                i = i+1
        elif orient == "h":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height() - (p.get_height()*0.5)
                value = '{:.0f}'.format(p.get_width())
                ax.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _single(ax)
    else:
        _single(axs)



data_r = requests.get("https://pub-api-ro.fantasysports.yahoo.com/fantasy/v2/league/427.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()

total_list = []

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

    single_list.append(int(x['player']['player_id']))
    single_list.append(int(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']['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)
    total_list.append(single_list)

df_2023 = pd.DataFrame(data=total_list,columns=['player_id','rank_value','full','first','last','average_pick', 'average_cost','display_position','editorial_team_abbr','percent_owned'])

df_2023['pos_new'] = ['D' if "D" in x else 'F' for x in df_2023['display_position']]

player_games = pd.read_csv('Drive/player_games_cards.csv',index_col=[0]).sort_values(by='date').drop_duplicates(subset=['player_id','date'])
summary_2023 = pd.read_csv('Drive/summary_2025.csv',index_col=[0])#.drop_duplicates(subset=['player_id'])

summary_2022 = pd.read_csv('Drive/summary_2024.csv',index_col=[0])
team_games = pd.read_csv('Drive/team_games.csv',index_col=[0])
nhl_logos = pd.read_csv("NHL Logos - NHL Logos.csv")
team_games = team_games.merge(right=nhl_logos[['Team Name','Team']],left_on=['team'],right_on=['Team Name'],how='left')


player_games.player_id = player_games.player_id.astype(int)
summary_2023.player_id = summary_2023.player_id.astype(int)
summary_2022.player_id = summary_2022.player_id.astype(int)

#summary_2022 = summary_2022.drop_duplicates(subset='player_id')

yahoo_to_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')

yahoo_to_nhl_df = yahoo_to_nhl_df.merge(df_2023,left_on='player_id_yahoo',right_on='player_id')
summary_2023 = summary_2023.merge(yahoo_to_nhl_df,left_on='player_id',right_on='nhl_id',suffixes=['','_yahoo'],how='left')

summary_2023 = summary_2023.merge(right=player_games.drop_duplicates(subset=['player_id'],keep='last')[['player_id','Position','Team']],left_on=['player_id'],right_on=['player_id'],how='left')

summary_2023.loc[summary_2023.display_position.isna(),'display_position'] = summary_2023.loc[summary_2023.display_position.isna(),'Position']
summary_2023.display_position = summary_2023.display_position.replace({'L':'LW','R':'RW'})
summary_2023.percent_owned = summary_2023.percent_owned.fillna(0)

player_games['game'] = player_games.groupby(['player_id']).cumcount() + 1 

summary_2023 = summary_2023.drop_duplicates(subset='player_id')
skater_dict = summary_2023.set_index('player_id').sort_values(by='Player')
skater_dict['skater_team'] = skater_dict.Player + ' - ' + skater_dict.Team
skater_dict = skater_dict['skater_team'].to_dict()



from shiny import ui, render, App
import matplotlib.image as mpimg
app_ui = ui.page_fluid(
    #ui.panel_title("Simulate a normal distribution"),

    ui.layout_sidebar(

      ui.panel_sidebar(
        #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
        ui.input_select("id", "Select Skater",skater_dict,width=1,selected=8480069,selectize=True),
        #ui.input_select("id", "Select Skater",skater_dict,width=1),
        ui.input_numeric("last_game_id", "Select Last 'n' Games",value=1,width=1),
        #ui.input_select("ignore_id", "Remove Columns",['Position','Roster%'],multiple=True,selectize=True),
      width=2),
      ui.panel_main(
        ui.output_plot("plot",height = "1350px",width="2400px")
        )
    ),
)




from urllib.request import Request, urlopen
from shiny import App, reactive, ui
from shiny.ui import h2, tags
# importing OpenCV(cv2) module




#print(app_ui)
def server(input, output, session):

    @output
    @render.plot(alt="A histogram")
    def plot():
        # from matplotlib import rc, font_manager
        # rc('text', usetex=True)
        # rc('font',**{'family':'sans-serif','sans-serif':['Century Gothic']})
        #fig.set_facecolor('white')
        sns.set_theme(style="whitegrid", palette="pastel")
        cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])

        try:
            if sum(summary_2023.player_id.isin([int(input.id())]))<1:
                fig, ax = plt.subplots(figsize=(10,16))
                fig.set_facecolor('white')
                fig.text(s=f'Select Player From List',x=0.5,y=0.5,fontsize=28,ha='center')
                return            
        except ValueError:
                fig, ax = plt.subplots(figsize=(10,16))
                fig.set_facecolor('white')
                fig.text(s=f'Select Player From List',x=0.5,y=0.5,fontsize=28,ha='center')
                return   

        player_id = int(input.id())

        last_games = input.last_game_id()



        player_games_one = player_games[player_games['player_id']==player_id].sort_values(by='game',ascending=False)
        
        print(player_games_one)
        summary_2023_one = summary_2023[summary_2023['player_id']==player_id]
        summary_2022_one = summary_2022[summary_2022['player_id']==player_id]

        last_games = min(last_games,max(summary_2023_one.GP.max()-1,1))

        name = player_games_one.Player.values[0]

        df_last_games = pd.DataFrame(data={'Games':[last_games]})
        df_last_games['TOI'] = player_games_one['TOI'][:last_games].sum()
        df_last_games['TOI/G'] = player_games_one['TOI'][:last_games].sum()/df_last_games.Games
        df_last_games['PP TOI/G'] = player_games_one['TOI_pp'][:last_games].sum()/df_last_games.Games
        df_last_games['Points/60'] = player_games_one['Total Points'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['Shots/60'] = player_games_one['Shots'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['Hits/60'] = player_games_one['Hits'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['Blocks/60'] = player_games_one['Shots Blocked'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['Goals/60'] = player_games_one['Goals'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['ixG/60'] = player_games_one['ixG'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['G-ixG/60'] = df_last_games['Goals/60'] - df_last_games['ixG/60'] 
        df_last_games['iCF/60'] = player_games_one['iCF'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['iSCF/60'] = player_games_one['iSCF'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['iHDCF/60'] = player_games_one['iHDCF'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['GF/60'] = player_games_one['GF'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['xGF/60'] = player_games_one['xGF'][:last_games].sum()/df_last_games.TOI*60
        df_last_games['IPP'] = player_games_one['Total Points'][:last_games].sum()/player_games_one['GF'][:last_games].sum()
        df_last_games['S%'] = player_games_one['Goals'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
        df_last_games['xS%'] = player_games_one['ixG'][:last_games].sum()/player_games_one['Shots'][:last_games].sum()
        df_last_games['1st Assist%'] = player_games_one['First Assists'][:last_games].sum()/player_games_one['Total Assists'][:last_games].sum()
        df_last_games['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:last_games].sum()/(player_games_one['Off.\xa0Zone Starts'][:last_games].sum()+player_games_one['Def.\xa0Zone Starts'][:last_games].sum())
        df_last_games['oiSH%'] = player_games_one['GF'][:last_games].sum()/player_games_one['SF'][:last_games].sum()
        
        df_season = pd.DataFrame(data={'Games':[len(player_games_one)]})
        df_season['TOI'] = player_games_one['TOI'][:len(player_games_one)].sum()
        df_season['TOI/G'] = player_games_one['TOI'][:len(player_games_one)].sum()/df_season.Games
        df_season['PP TOI/G'] = player_games_one['TOI_pp'][:len(player_games_one)].sum()/df_season.Games
        df_season['Points/60'] = player_games_one['Total Points'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['Shots/60'] = player_games_one['Shots'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['Hits/60'] = player_games_one['Hits'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['Blocks/60'] = player_games_one['Shots Blocked'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['Goals/60'] = player_games_one['Goals'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['ixG/60'] = player_games_one['ixG'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['G-ixG/60'] = df_season['Goals/60'] - df_season['ixG/60'] 
        df_season['iCF/60'] = player_games_one['iCF'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['iSCF/60'] = player_games_one['iSCF'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['iHDCF/60'] = player_games_one['iHDCF'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['GF/60'] = player_games_one['GF'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['xGF/60'] = player_games_one['xGF'][:len(player_games_one)].sum()/df_season.TOI*60
        df_season['IPP'] = player_games_one['Total Points'][:len(player_games_one)].sum()/player_games_one['GF'][:len(player_games_one)].sum()
        df_season['S%'] = player_games_one['Goals'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
        df_season['xS%'] = player_games_one['ixG'][:len(player_games_one)].sum()/player_games_one['Shots'][:len(player_games_one)].sum()
        df_season['1st Assist%'] = player_games_one['First Assists'][:len(player_games_one)].sum()/player_games_one['Total Assists'][:len(player_games_one)].sum()
        df_season['Off. Zone Start%'] = player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()/(player_games_one['Off.\xa0Zone Starts'][:len(player_games_one)].sum()+player_games_one['Def.\xa0Zone Starts'][:len(player_games_one)].sum())
        df_season['oiSH%'] = player_games_one['GF'][:len(player_games_one)].sum()/player_games_one['SF'][:len(player_games_one)].sum()
        df_last_games.rename(index={0:'Last '+str(df_last_games.Games[0])+' GP'},inplace=True)
        df_season.rename(index={0:'2023-24 ('+str(df_season.Games[0])+'GP)'},inplace=True)

        # cols_to_use = summary_all.columns.difference(summary_all_on_ice.columns)


        # df_career_total = summary_all.merge(summary_all_on_ice, left_index=True, right_index=True,
        #                  how='outer', suffixes=('', '_y'))

        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)

        # df_career_total = df_career_total.merge(summary_pp, left_index=True, right_index=True,
        #                  how='outer', suffixes=('', '_y'))

        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)

        # df_career_total = df_career_total.merge(summary_pp_on_ice, left_index=True, right_index=True,
        #                  how='outer', suffixes=('', '_y'))

        # df_career_total.drop(df_career_total.filter(regex='_y$').columns, axis=1, inplace=True)
        df_career_total = summary_2022_one.sort_index(ascending=False)
        df_career_total.columns = [c.replace(u"\xa0", u" ") for c in list(df_career_total.columns)]
        df_career = pd.DataFrame(data={'Games':[df_career_total.GP.sum()]})
        df_career['TOI'] = df_career_total['TOI'].sum()
        df_career['TOI/G'] = df_career_total['TOI'].sum()/df_career.Games
        df_career['PP TOI/G'] = df_career_total['TOI_pp'].sum()/df_career.Games
        df_career['Points/60'] = df_career_total['Total_Points'].sum()/df_career_total.TOI.sum()*60
        df_career['Shots/60'] = df_career_total['Shots'].sum()/df_career_total.TOI.sum()*60
        df_career['Hits/60'] = df_career_total['Hits'].sum()/df_career_total.TOI.sum()*60
        df_career['Blocks/60'] = df_career_total['Shots_Blocked'].sum()/df_career_total.TOI.sum()*60
        df_career['Goals/60'] = df_career_total['Goals'].sum()/df_career_total.TOI.sum()*60
        df_career['ixG/60'] = df_career_total['ixG'].sum()/df_career_total.TOI.sum()*60
        df_career['G-ixG/60'] = df_career['Goals/60'] - df_career['ixG/60'] 
        df_career['iCF/60'] = df_career_total['iCF'].sum()/df_career_total.TOI.sum()*60
        df_career['iSCF/60'] = df_career_total['iSCF'].sum()/df_career_total.TOI.sum()*60
        df_career['iHDCF/60'] = df_career_total['iHDCF'].sum()/df_career_total.TOI.sum()*60
        df_career['GF/60'] = df_career_total['GF'].sum()/df_career_total.TOI.sum()*60
        df_career['xGF/60'] = df_career_total['xGF'].sum()/df_career_total.TOI.sum()*60
        df_career['IPP'] = df_career_total['Total_Points'].sum()/df_career_total['GF'].sum()
        df_career['S%'] = df_career_total['Goals'].sum()/df_career_total['Shots'].sum()
        df_career['xS%'] = df_career_total['ixG'].sum()/df_career_total['Shots'].sum()
        df_career['1st Assist%'] = df_career_total['First_Assists'].sum()/df_career_total['Assists'].sum()
        df_career['Off. Zone Start%'] = df_career_total['OZ Start%'].sum()
        df_career['oiSH%'] = df_career_total['GF'].sum()/df_career_total['SF'].sum()
        df_career.rename(index={0:'2023-24 ('+str(df_career.Games[0])+'GP)'},inplace=True)
        df_combined = df_last_games.append([df_season,df_career])
        df_combined_t = round(df_combined,3).transpose()
        df_combined.style.format(formatter={"IPP": "{:.1%}","S%": "{:.1%}","1st Assist%": "{:.1%}","Off. Zone Start%": "{:.1%}","oiSH%": "{:.1%}"}).set_precision(2)
        rows = [idx for idx in df_combined_t.index if '/' not in idx]


        df_combined_t = df_combined_t.astype(float).fillna(0)
        # df_combined_t_style = df_combined_t.style.set_table_styles([{
        #     'selector': 'caption',
        #     'props': [
        #         ('color', ''),
        #         ('fontname', 'Century Gothic'),
        #         ('font-size', '12px'),
        #         ('font-style', 'italic'),
        #         ('font-weight', ''),
        #         ('text-align', 'centre'),
        #         ]

        #         },{'selector' :'th', 'props':[('text-align', 'center'),('Height','8px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '10px')]}],overwrite=False).set_properties(
        #             **{'background-color':'White','index':'White','min-width':'60px'},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', '18px')]}],overwrite=False).set_properties(
        #     **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).apply(

        #     lambda x: ["background: #FEEFC1" if (i < 1 and v > x.iloc[1]*1.05)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FEEFC1" if (i == 1 and v > x.iloc[2]*1.05)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #D0E1FD" if (i < 1 and v < x.iloc[1]*0.95)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #D0E1FD" if (i == 1 and v < x.iloc[2]*0.95)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FDDE82" if (i < 1 and v > x.iloc[1]*1.1)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FDDE82" if (i == 1 and v > x.iloc[2]*1.1)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #A1C2FA" if (i < 1 and v < x.iloc[1]*0.9)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #A1C2FA" if (i == 1 and v < x.iloc[2]*0.9)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FCCD43" if (i < 1 and v > x.iloc[1]*1.15)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FCCD43" if (i == 1 and v > x.iloc[2]*1.15)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #72A4F7" if (i < 1 and v < x.iloc[1]*0.85)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #72A4F7" if (i == 1 and v < x.iloc[2]*0.85)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FBBC04" if (i < 1 and v > x.iloc[1]*1.2)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #FBBC04" if (i == 1 and v > x.iloc[2]*1.2)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["color: #ffffff" if (i < 1 and v < x.iloc[1]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["color: #ffffff" if (i == 1 and v < x.iloc[2]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #4285F4" if (i == 0 and v < x.iloc[1]*0.8)  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #4285F4" if (i == 1 and v < x.iloc[2]*0.8) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #ffffff" if (i == 0 and v == x.iloc[1])  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["background: #ffffff" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["color: #000000" if (i < 1 and v == x.iloc[1])  else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).apply(
        #     lambda x: ["color: #000000" if (i == 1 and v == x.iloc[2]) else "" for i,v in enumerate(x)], axis = 1,subset = (list(df_combined_t.index[2:]), list(df_combined_t.columns))).format(
        #         "{:.1%}",subset=(rows[2:],df_combined_t.columns)).format(
        #         '{:.0f}',subset=(rows[0],df_combined_t.columns)).set_precision(2).set_properties(
        #             **{'index':'White','min-width':'60px'},overwrite=False)
                
                
                
        #         #.set_table_styles([index_names, headers])
        
        # df_combined_t_style = df_combined_t_style.format(
        #     {df_combined_t_style.columns[0]: '{:,.1%}'.format,
        #      df_combined_t_style.columns[1]: '{:,.1%}'.format,
        #      df_combined_t_style.columns[2]: '{:,.1%}'.format},subset=(rows[2:],df_combined_t.columns)
        # )  
        # df_combined_t_style = df_combined_t_style.format(
        #         '{:.0f}',subset=(rows[0],df_combined_t.columns))


        # df_combined_t_style


            

        # dfi.export(df_combined_t_style, 'players/'+name+'_'+str(last_games)+'.png',fontsize=9,dpi=600,table_conversion='chrome')


        percent_owned = summary_2023_one['percent_owned'].reset_index(drop=True)[0]
        yahoo_position = summary_2023_one['display_position'].reset_index(drop=True)[0]



        # image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        # logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
        # fig, ax = plt.subplots(figsize=(10,16))
        # fig.set_facecolor('white')

        # # img = mpimg.imread('players/'+name+'_'+str(last_games)+'.png')
        # # ax.imshow(img)
        # # ax.axis('off')
        # # fig.tight_layout()

        # ax.axis('off')
        # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
        # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
        # ax.imshow(im)
        # ax.axis('off')
        # fig.tight_layout()

        # fig.text(x=0.5,y=-0.05,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=16,fontname='Century Gothic')
        # # fig.text(x=0.05,y=-0.1,s='Created By: @TJStats',horizontalalignment='left',fontsize=24,fontname='Century Gothic')
        # # fig.text(x=0.95,y=-0.1,s='Data: Natural Stat Trick',horizontalalignment='right',fontsize=24,fontname='Century Gothic')
        # fig.text(x=0.5,y=1.13,s='NHL Player Summary',horizontalalignment='center',fontsize=52, fontweight='bold')
        # fig.text(x=0.5,y=1.08,s='2023-24 Season',horizontalalignment='center',fontsize=36,fontname='Century Gothic', fontstyle='italic')
        # fig.text(x=0.12,y=1.03,s='Player',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        # fig.text(x=0.12,y=1.0,s='Team',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        # fig.text(x=0.12,y=0.97,s='Position',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        # #fig.text(x=0.12,y=0.94,s='Age',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        # # fig.text(x=0.12,y=0.91,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        # #fig.text(x=0.12,y=0.88,s='Roster%',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')

        # fig.text(x=0.25,y=1.03,s=name,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # fig.text(x=0.25,y=1.0,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # fig.text(x=0.25,y=0.97,s=yahoo_position,horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # #fig.text(x=0.25,y=0.94,s=str(int(summary_2023_one.reset_index().AGE[0])),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # # fig.text(x=0.25,y=0.91,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # #fig.text(x=0.25,y=0.88,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=22,fontname='Century Gothic')


        # im = plt.imread(image)
        # newax = fig.add_axes([0.5,0.8,0.22,0.22], anchor='NW', zorder=1)
        # newax.imshow(im)

        # imr = plt.imread(logo)
        # newerax = fig.add_axes([0.75,0.8,0.22,0.22], anchor='NW', zorder=1)
        # newerax.imshow(imr)

        # newax.axis('off')
        # newerax.axis('off')

        # plt.savefig('players/'+name+"_"+str(last_games)+' int.png',bbox_inches="tight")
        # plt.close()

        #test.loc[name == test.Player].reset_index().TOI[0]/1.5
        min_time = min(math.floor(summary_2023.loc[player_id == summary_2023.player_id].reset_index().TOI[0]/100)*100,summary_2023.GP.max()*10)
        # min_time = min(math.floor(test.loc[name == test.Player].reset_index().TOI[0]/100)*100,test.GP.max()*10)
        #summary_2023 = summary_2023.drop_duplicates(subset=['player_id'])
        test_filter = summary_2023[(summary_2023.TOI >= min_time)&(summary_2023.pos == summary_2023.loc[player_id == summary_2023.player_id].reset_index().pos[0])]
        test_filter['Goals/GP'] = test_filter['Goals']/test_filter['GP']
        test_filter['Total Assists/GP'] = test_filter['Assists']/test_filter['GP']
        test_filter['Total Points/GP'] = test_filter['Total_Points']/test_filter['GP']
        test_filter['PP Points/GP'] = test_filter['Total_Points_pp']/test_filter['GP']
        test_filter['Shots/GP'] = test_filter['Shots']/test_filter['GP']
        test_filter['Hits/GP'] = test_filter['Hits']/test_filter['GP']
        test_filter['Shots Blocked/GP'] = test_filter['Shots_Blocked']/test_filter['GP']
        test_filter['ixG/60'] = test_filter['ixG']/test_filter['TOI']*60
        test_filter['Goals/60'] = test_filter['Goals']/test_filter['TOI']*60
        test_filter['G-xG/60'] = test_filter['G-ixG']/test_filter['TOI']*60
        test_filter['iCF/60'] = test_filter['iCF']/test_filter['TOI']*60
        test_filter['iSCF/60'] = test_filter['iSCF']/test_filter['TOI']*60
        test_filter['First Assists/60'] = test_filter['First_Assists']/test_filter['TOI']*60
        test_filter['Total Points/60'] = test_filter['Total_Points']/test_filter['TOI']*60


        test_filter['G-xG/60'] = test_filter['Goals/60'] - test_filter['ixG/60']
        test_filter['Goals Z-Score'] = (test_filter['Goals/GP']-test_filter['Goals/GP'].mean())/test_filter['Goals/GP'].std()
        test_filter['Assists Z-Score'] = (test_filter['Total Assists/GP']-test_filter['Total Assists/GP'].mean())/test_filter['Total Assists/GP'].std()
        test_filter['Points Z-Score'] = (test_filter['Total Points/GP']-test_filter['Total Points/GP'].mean())/test_filter['Total Points/GP'].std()
        test_filter['PP Points Z-Score'] = (test_filter['PP Points/GP']-test_filter['PP Points/GP'].mean())/test_filter['PP Points/GP'].std()
        test_filter['Shots Z-Score'] = (test_filter['Shots/GP']-test_filter['Shots/GP'].mean())/test_filter['Shots/GP'].std()
        test_filter['Hits Z-Score'] = (test_filter['Hits/GP']-test_filter['Hits/GP'].mean())/test_filter['Hits/GP'].std()
        test_filter['Blocks Z-Score'] = (test_filter['Shots Blocked/GP']-test_filter['Shots Blocked/GP'].mean())/test_filter['Shots Blocked/GP'].std()


        test_filter['ixG Z-Score'] = (test_filter['ixG/60']-test_filter['ixG/60'].mean())/test_filter['ixG/60'].std()
        test_filter['G Z-Score'] = (test_filter['Goals/60']-test_filter['Goals/60'].mean())/test_filter['Goals/60'].std()
        test_filter['G-xG Z-Score'] = (test_filter['G-xG/60']-test_filter['G-xG/60'].mean())/test_filter['G-xG/60'].std()
        test_filter['iCF Z-Score'] = (test_filter['iCF/60']-test_filter['iCF/60'].mean())/test_filter['iCF/60'].std()
        test_filter['iSCF Z-Score'] = (test_filter['iSCF/60']-test_filter['iSCF/60'].mean())/test_filter['iSCF/60'].std()
        test_filter['First Assists Z-Score'] = (test_filter['First Assists/60']-test_filter['First Assists/60'].mean())/test_filter['First Assists/60'].std()
        test_filter['P Z-Score'] = (test_filter['Total Points/60']-test_filter['Total Points/60'].mean())/test_filter['Total Points/60'].std()
        values_1 = test_filter.loc[player_id == test_filter.player_id][['Goals/GP','Total Assists/GP','Total Points/GP','PP Points/GP','Shots/GP','Hits/GP','Shots Blocked/GP']].reset_index(drop=True).loc[0]
        values_2 = test_filter.loc[player_id == test_filter.player_id][['ixG/60','Goals/60','G-xG/60','iCF/60','iSCF/60','First Assists/60','Total Points/60']].reset_index(drop=True).loc[0]
        categories = [i[:-11]+'/GP' for i in test_filter.columns[-14:-7]]

        #categories = [*categories]

        player = list(np.around(test_filter.loc[player_id == test_filter.player_id][test_filter.columns[-14:-7]].values.flatten().tolist()))

        #player = [*player, player[0]]


        label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(player))

        players_stats_all_on_filter = test_filter.copy()
        players_stats_all_on_filter['GF/60'] = (players_stats_all_on_filter['GF'])/players_stats_all_on_filter['TOI']*60
        players_stats_all_on_filter['xGF/60'] = (players_stats_all_on_filter['xGF'])/players_stats_all_on_filter['TOI']*60
        players_stats_all_on_filter['CF/60'] = (players_stats_all_on_filter['CF'])/players_stats_all_on_filter['TOI']*60
        # players_stats_all_on_filter['oiSH%'] = (players_stats_all_on_filter['On-Ice SH%']-players_stats_all_on_filter['On-Ice SH%'].mean())/players_stats_all_on_filter['On-Ice SH%'].std()
        #players_stats_all_on_filter['OZ Start%'] = (players_stats_all_on_filter['CF']-players_stats_all_on_filter['CF'])/players_stats_all_on_filter['CF']
        players_stats_all_on_filter['GF Z-Score'] = (players_stats_all_on_filter['GF/60']-players_stats_all_on_filter['GF/60'].mean())/players_stats_all_on_filter['GF/60'].std()
        players_stats_all_on_filter['xGF Z-Score'] = (players_stats_all_on_filter['xGF/60']-players_stats_all_on_filter['xGF/60'].mean())/players_stats_all_on_filter['xGF/60'].std()
        players_stats_all_on_filter['CF Z-Score'] = (players_stats_all_on_filter['CF/60']-players_stats_all_on_filter['CF/60'].mean())/players_stats_all_on_filter['CF/60'].std()
        players_stats_all_on_filter['oiSH% Z-Score'] = (players_stats_all_on_filter['oiSH']-players_stats_all_on_filter['oiSH'].mean())/players_stats_all_on_filter['oiSH'].std()
        players_stats_all_on_filter['OZ Start% Z-Score'] = (players_stats_all_on_filter['OZ Start%']-players_stats_all_on_filter['OZ Start%'].mean())/players_stats_all_on_filter['OZ Start%'].std()
        categories_on = [i[:-8]+'/60' for i in players_stats_all_on_filter.columns[-5:]]
        categories_on = ['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']
        player_on = list(np.around(players_stats_all_on_filter.loc[player_id == test_filter.player_id][players_stats_all_on_filter.columns[-5:]].values.flatten().tolist(),2))
        categories_1 = [i[:-8]+'/GP' for i in test_filter.columns[-14:-7]]
        categories_2 = [i[:-8]+'/60' for i in test_filter.columns[-7:]]
        categories = categories_1+categories_2
        categories[12] = '1A/60'
        player = list(np.around(test_filter.loc[player_id == test_filter.player_id][test_filter.columns[-14:]].values.flatten().tolist(),2))

        # color_scheme = []
        # for i in player:
        #     if i <= -2:
        #         color_scheme.append('#4285F4')
        #     if i > -2 and i <= -1:
        #         color_scheme.append('#A1C2FA')
        #     if i > -1 and i <= 0:
        #         color_scheme.append('#D0E1FD')
        #     if i > 0 and i <= 1:
        #         color_scheme.append('#FEEFC1')
        #     if i > 1 and i <= 2:
        #         color_scheme.append('#FDDE82')
        #     if i > 2:
        #         color_scheme.append('#FBBC04')
            

        goals_above_expected = test_filter.loc[player_id == test_filter.player_id]['Goals'] - test_filter.loc[player_id == test_filter.player_id]['ixG']
        goals_above_expected = test_filter.loc[player_id == test_filter.player_id]['Goals'] - test_filter.loc[player_id == test_filter.player_id]['ixG']

        # color_scheme_on= []
        # for i in player_on:
        #     if i <= -2:
        #         color_scheme_on.append('#4285F4')
        #     if i > -2 and i <= -1:
        #         color_scheme_on.append('#A1C2FA')
        #     if i > -1 and i <= 0:
        #         color_scheme_on.append('#D0E1FD')
        #     if i > 0 and i <= 1:
        #         color_scheme_on.append('#FEEFC1')
        #     if i > 1 and i <= 2:
        #         color_scheme_on.append('#FDDE82')
        #     if i > 2:
        #         color_scheme_on.append('#FBBC04')

        fig = plt.figure(figsize=(24, 13.5),dpi=300)
        fig.set_facecolor('white')

        cmap_new = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white",'#FBBC04'])
        colormap_new = plt.get_cmap(cmap_new)
        norm_new = Normalize(vmin=-3, vmax=3)

        color_scheme = [colormap_new(norm_new(x)) for x in player]
        color_scheme_on = [colormap_new(norm_new(x)) for x in player_on]
            
        players_stats_all_on_filter = players_stats_all_on_filter.rename(columns={'On-Ice SH%':'oiSH%','Off.\xa0Zone Start %':'OZ Start%'})
        values_on = players_stats_all_on_filter.loc[player_id == test_filter.player_id][['GF/60', 'xGF/60', 'CF/60', 'oiSH', 'OZ Start%']].reset_index(drop=True).loc[0]
        position_player = summary_2023_one['pos'].reset_index().pos[0]
        rank_1 = list(test_filter[values_1.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0])
        rank_2 = list(test_filter[values_2.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0])
        rank_3 = list(players_stats_all_on_filter[values_on.index].rank(method='min',ascending=False).loc[player_id == test_filter.player_id].reset_index(drop=True).astype(int).iloc[0][categories_on])
        #rank_3 = rank_3 + list(players_stats_all_on_filter[values_on.index].rank(method='first',ascending=True).loc[player_id == test.player_id].reset_index(drop=False).astype(int).iloc[0][categories_on[3:5]])

        values_on = [float(x) for x in values_on]

        player_games_one = player_games_one.merge(right=team_games[['Team','pp_toi','date']],left_on=['Team','date'],right_on=['Team','date'],how='left').fillna(0)
        y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi'])[last_games:]
        player_games_one = player_games_one.sort_values(by='date').reset_index(drop=True)

        # fig, ([ax1,ax2],[ax3,ax4])= plt.subplots(nrows=2, ncols=2,figsize=(16,10))



        colormap = plt.get_cmap(cmap)

        value = 1
        # Normalize the value
        norm = Normalize(vmin=0.8, vmax=1.2)
        normalized_value = norm(value)

        col_3_colour = ['white']*len(df_combined_t)
        col_2_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[1]].values) / (df_combined_t[df_combined_t.columns[2]].values)))]
        col_1_colour = [colormap(norm(x)) for x in list(((df_combined_t[df_combined_t.columns[0]].values) / (df_combined_t[df_combined_t.columns[1]].values)))]
        colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour]).T.values

        if df_combined_t[df_combined_t.columns[2]].values[0] == 0:
            
            for i in range(len(colour_df)):
                colour_df[[i],[1]] = 'white'
        
        colour_df[[0],[0]] = 'white'
        colour_df[[1],[0]] = 'white'
        colour_df[[0],[1]] = 'white'
        colour_df[[1],[1]] = 'white'


        if df_combined_t.values[[10],[0]] < 0 and df_combined_t.values[[10],[1]] < 0:
            cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
            norm = Normalize(vmin=0.8, vmax=1.2)
            colour_df[[10],[0]] = tuple(cmap_flip(norm(df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))

        elif df_combined_t.values[[10],[0]] < 0 and df_combined_t.values[[10],[1]] > 0:
            #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
            norm = Normalize(vmin=-1, vmax=0)
            colour_df[[10],[0]] = tuple(cmap_y(norm(df_combined_t.values[[10],[0]] - df_combined_t.values[[10],[1]])))

        elif df_combined_t.values[[10],[0]] > 0 and df_combined_t.values[[10],[1]] < 0:
            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])
            norm = Normalize(vmin=0, vmax=1)
            colour_df[[10],[0]] = tuple(cmap_y(norm(df_combined_t.values[[10],[0]] - df_combined_t.values[[10],[1]])))


        if df_combined_t.values[[10],[1]] < 0 and df_combined_t.values[[10],[2]] < 0:
            cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
            norm = Normalize(vmin=0.8, vmax=1.2)
            colour_df[[10],[1]] = tuple(cmap_flip(norm(df_combined_t.values[[10],[1]] / df_combined_t.values[[10],[2]])))

        elif df_combined_t.values[[10],[1]] < 0 and df_combined_t.values[[10],[2]] > 0:
            #cmap_flip = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FBBC04","white","#4285F4"])
            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#4285F4"])
            norm = Normalize(vmin=-1, vmax=0)
            colour_df[[10],[1]] = tuple(cmap_y(norm(df_combined_t.values[[10],[1]] - df_combined_t.values[[10],[2]])))

        elif df_combined_t.values[[10],[1]] > 0 and df_combined_t.values[[10],[2]] < 0:
            cmap_y = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#FBBC04"])
            norm = Normalize(vmin=0, vmax=1)
            colour_df[[10],[1]] = tuple(cmap_y(norm(df_combined_t.values[[10],[1]] - df_combined_t.values[[10],[2]])))

        
        ax1 = plt.subplot(1,3,1)
        ax2 = plt.subplot(3,3,2)
        ax3 = plt.subplot(3,3,5)
        ax4 = plt.subplot(3,3,8)
        ax5 = plt.subplot(3,3,3)
        ax6 = plt.subplot(3,3,6)
        ax7 = plt.subplot(3,3,9)
        #axbot = plt.subplot(3,1,1)
        axes = [ax1, ax2, ax3, ax4, ax5,ax6,ax7]
        # ax[0][0].axis('off')
        # im = plt.imread('players/'+name+'_'+str(last_games)+' int.png')
        # ax[0][0] = fig.add_axes([0,0,1,1], anchor='W', zorder=1)
        # ax[0][0].imshow(im)

        
        # try:
        #     #image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        #     image = "https://assets.nhle.com/mugs/nhl/20232024/PIT/"+str(player_id)+".png"
        #     im = plt.imread(image)

        # except HTTPError as err:
        #     if err.code == 403:
        #             image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/skater.png"
        #             im = plt.imread(image)
        #     else:
        #         raise
            
        #logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]
        #im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
        #ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
        #ax.imshow(im)
        #index_player = player_id
        try:
            image = requests.get(f"https://api-web.nhle.com/v1/player/{player_id}/landing/").json()['headshot']
            #image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(index_player)+".png"
            im = plt.imread(image)

        except HTTPError as err:
            if err.code == 403:
                    image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/skater.png"
                    image = "https://assets.nhle.com/mugs/nhl/default-skater.png"
                    im = plt.imread(image)
            else:
                raise
        try:
            logo = nhl_logos[nhl_logos.Team==list(player_games_one['Team'])[0]].reset_index().URL[0]

        except KeyError:
            
                logo = 'https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/nhl.png&w=1000&h=1000'


        #im = plt.imread(image)
        im = OffsetImage(im, zoom=.33)
        ab = AnnotationBbox(im, (0.69, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
        ax1.add_artist(ab)
        #axim = fig.add_axes([0.25,0.76,0.12,0.12], anchor='NW', zorder=1)
        #axim.imshow(im)
        #axim.axis('off')

        imr = plt.imread(logo)
        imr = OffsetImage(imr, zoom=.1)
        ab = AnnotationBbox(imr, (0.45, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
        ax1.add_artist(ab)
        # axim = fig.add_axes([0.18,0.76,0.75,0.075], anchor='NW', zorder=1)
        # axim.imshow(imr)
        # axim.axis('off')

        sub_value  = 0.16
        ax1.text(x=0.5,y=1.13-sub_value,s='NHL Player Summary',horizontalalignment='center',fontsize=36, fontweight='bold')
        ax1.text(x=0.5,y=1.08-sub_value,s='2024-25 Season',horizontalalignment='center',fontsize=28,fontname='Century Gothic', fontstyle='italic')
        ax1.text(x=0.05,y=1.04-sub_value,s='Player',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
        ax1.text(x=0.05,y=1.005-sub_value,s='Team',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
        ax1.text(x=0.05,y=0.97-sub_value,s='Position',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
        #ax1.text(x=0.1,y=0.94-sub_value,s='Age',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')
        #ax1.text(x=0.12,y=0.91-sub_value,s='Cap Hit',horizontalalignment='center',fontsize=22,fontname='Century Gothic', fontweight='bold')
        ax1.text(x=0.05,y=0.935-sub_value,s='Roster%',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontweight='bold')

        ax1.text(x=0.175,y=1.04-sub_value,s=name,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
        ax1.text(x=0.175,y=1.005-sub_value,s=list(player_games_one['Team'])[0],horizontalalignment='left',fontsize=18,fontname='Century Gothic')
        ax1.text(x=0.175,y=0.97-sub_value,s=yahoo_position,horizontalalignment='left',fontsize=18,fontname='Century Gothic')
        #ax1.text(x=0.25,y=0.94-sub_value,s=str(summary_2023_one.reset_index().AGE[0]),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        #ax1.text(x=0.25,y=0.91-sub_value,s=summary_2023_one.loc[summary_2023_one['player_id']==player_id].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        ax1.text(x=0.175,y=0.935-sub_value,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=18,fontname='Century Gothic')




        ax1.axis("off")





        table = ax1.table(cellText=df_combined_t.values, colLabels=df_combined_t.columns,rowLabels=df_combined_t.index
                        , cellLoc='center',rowLoc='center', bbox=[0.15, 0.05, 0.8, 0.7],cellColours=colour_df)
        #table.auto_set_font_size(True)
        table.set_fontsize(20)
        table.scale(1, 1.5)  



        format_col = df_combined_t[df_combined_t.columns[0]]
        n_c = 0
        for cell in table.get_celld().values():
            if n_c < 1*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
            elif n_c < 16*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
            else:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
            n_c = n_c + 1 


        format_col = df_combined_t[df_combined_t.columns[1]]
        n_c = 0
        for cell in table.get_celld().values():
            if n_c < 1*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
            elif n_c < 16*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
            else:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
            n_c = n_c + 1 

        format_col = df_combined_t[df_combined_t.columns[2]]
        n_c = 0
        for cell in table.get_celld().values():
            if n_c < 1*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
            elif n_c < 16*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.2f}'.format(float(cell.get_text().get_text())))
            else:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
            n_c = n_c + 1 

        #ax1.text('')



        # ax1.axis('off')
        # img = mpimg.imread('players/'+name+'_'+str(last_games)+' int.png')

        # im = plt.imread('players/'+name+'_'+str(last_games)+'.png')
        # ax1 = fig.add_axes([0.03,0.,1,1], anchor='SW')
        # ax1.imshow(im)

        # ax1.imshow(img)
        # ax1.axis('off')
        # fig.tight_layout()



        categories[0:7] = ['G/GP','A/GP','P/GP','PPP/GP','S/GP','Hits/GP','Blk/GP']
        _ = sns.barplot(data=test_filter[test_filter.columns[56:63]],x=categories[0:7],y=player[0:7],palette=color_scheme[0:7],edgecolor='black',ax=ax2)

        ax2.set_title(f"Individual Per Game Z-Score (vs {position_player}, min. {min_time:.0f} TOI)",fontsize=14,fontname='Century Gothic')


        #plt.rcParams['xtick.color']='#333F4B'
        #ax[0][1].set_prop_cycle(ytick.color='#333F4B')


        ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
        #plt.ylabel('Z-Score', fontsize=15,fontname='Century Gothic')

        #plt.xlabel('Percentile', fontsize=12, color = '#000000',fontname='Century Gothic')
        #plt.tick_params(axis='x', which='both', labelsize=10,bottom=False,top=False)
        ax2.set_ylim([-3, 3])
        plt.style.use('classic')
        show_values(ax2,stat = values_1,rank_n=rank_1)
        ax2.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)

        ax2.set_axisbelow(True)
        #ax[1].hlines(y=0,xmin=0,xmax=len(player),color='black')


        #plt.savefig('players/'+name+"_"+str(last_games)+'_Z.png')#,bbox_inches="tight")
        # ax[1][0] = sns.barplot(data=test,x=categories,y=player,palette=color_scheme,edgecolor='black')
        # show_values(ax2,stat = values)
        # data_input = df_shots[(df_shots.shooterName==name) & (df_shots.event!='MISS')]
        # sns.kdeplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"],fill=True,thresh=0.4,cmap=cmap,ax=ax3)
        # #sns.scatterplot(data=data_input, x=data_input["yCordAdjusted"]*-1, y=data_input["xCordAdjusted"], alpha=1,zorder=25,cmap='Blues',s=400,hue='shotType',palette="Set2")
        # rink = NHLRink(rotation=270)
        # #x, y = rink.convert_xy(x, y)
        # rink.draw(ax=ax3,display_range='dzone')
        # ax3.set_title(name+" Shooting Heat Map",fontsize=16,fontname='Century Gothic')
        #plt.suptitle('NHL Shot Locations ', fontsize=32, y = 0.91)
        #plt.title('All Shots', fontsize=16, y=1.02)
        sns.barplot(x=categories[7:14],y=player[7:14],palette=color_scheme[7:14],edgecolor='black',ax=ax3)
        ax3.set_title(f"Individual Rate Z-Score (vs {position_player}, min. {min_time:.0f} TOI)",fontsize=14,fontname='Century Gothic')
        ax3.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax3.set_ylim([-3, 3])
        ax3.set_axisbelow(True)
        ax3.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
        #plt.rcParams['xtick.color']='#333F4B'
        #ax[0][1].set_prop_cycle(ytick.color='#333F4B')


        sns.barplot(x=categories_on,y=player_on,palette=color_scheme_on,edgecolor='black',ax=ax4)
        ax4.set_ylim([-3, 3])
        ax4.set_title(f"On-Ice All Situations Rate Z-Score (vs {position_player}, min. {min_time:.0f} TOI)",fontsize=14,fontname='Century Gothic')
        ax4.set_axisbelow(True)
        ax4.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
        ax4.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)

        ax2.set_ylabel('Z-Score', fontsize=15,fontname='Century Gothic')
        show_values(ax3,stat = values_2,rank_n=rank_2)
        values_on[3] = str(round(values_on[3]*100,1))+'%'
        values_on[4] = str(round(values_on[4]*100,1))+'%'
        show_values(ax4,stat = values_on,rank_n=rank_3)


        ax2.text(-0.5, -3.8, '2024-25', ha="right",fontstyle='italic',zorder=1) 
        ax3.text(-0.5, -3.8, '2024-25', ha="right",fontstyle='italic') 
        ax4.text(-0.5, -3.8, '2024-25', ha="right",fontstyle='italic') 


        ax2.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 
        ax3.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 
        ax4.text(-0.5, -4.2, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',zorder=100) 


        ax2.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)  
        ax3.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)
        ax4.tick_params(axis='x', which='both', labelsize=12,bottom=False,top=False)

        if summary_2023_one.GP.values[0] < 2:
            line_text_value = 1
        else:
            line_text_value = last_games+0.5

        if position_player == 'F':
            ax5.hlines(y=18,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="1st",y=18,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
            ax5.hlines(y=16,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="2nd",y=16,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
            ax5.hlines(y=14,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="3rd",y=14,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
            ax5.hlines(y=12,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="4th",y=12,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))

        if position_player == 'D':
            ax5.hlines(y=23,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="1st",y=23,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
            ax5.hlines(y=20,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="2nd",y=20,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))
            ax5.hlines(y=17,xmin=0, xmax=100, color='black',linewidth=1,linestyles='--',alpha=0.5)
            ax5.text(s="3rd",y=17,x=line_text_value, color='black',fontsize=8,bbox=dict(facecolor='white', alpha=0.5, pad=1.0))


        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games,min_periods=last_games).mean()['TOI'],ax=ax5,color='#FFB000',linewidth = 2,label='TOI/GP',legend=False)




        ax5.xaxis.set_major_locator(MaxNLocator(integer=True))
        ax5.set_xlim([last_games, np.max(player_games_one.game)])
        axn = ax5.twinx()
        #sns.lineplot(x=game_log_pp.Game,y=game_log_pp.rolling(last_games).mean()['PP_TOI'],ax=axn,color='#DCAD23',linewidth = 2,label='PP TOI/GP',legend=False)
        #ax5.set_ylim([min(math.floor(min(player_games_one.TOI)/5)*5,10), math.ceil(max((player_games_one.rolling(last_games).mean().fillna(0)['TOI']))/5)*5])
        ax5.set_ylim([10,30])
        ax5.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax5.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax5.set_title(str(last_games)+" Game Rolling Average - TOI and PP%",fontsize=14,fontname='Century Gothic')
        ax5.set(xlabel='Game', ylabel='TOI/GP')
        axn.set(xlabel='Game', ylabel='PP%')
        axn.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
        axn.set_ylim([0, 1])


        sns.lineplot(x=player_games_one.game,y=(player_games_one.rolling(last_games).sum()['TOI_pp']/player_games_one.rolling(last_games).sum()['pp_toi']),ax=axn,color='#648FFF',linewidth = 2,label='PP%',legend=False)
        #
        handles, labels = [ax5.get_legend_handles_labels()[0]+axn.get_legend_handles_labels()[0]]+[ax5.get_legend_handles_labels()[1]+axn.get_legend_handles_labels()[1]]
        ax5.legend(handles, labels, fontsize=10,ncol=2,loc=9)

        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points'],ax=ax6,color='#FFB000',label='Points',linewidth = 2)
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Points_pp'],ax=ax6,color='#648FFF',label='PP Points',linewidth = 2)
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Goals'],ax=ax6,color='#DC267F',label='Goals',linewidth = 2)
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Total Assists'],ax=ax6,color='#FE6100',label='Assists',linewidth = 2)
        ax6.legend(fontsize=10,ncol=4,loc=9)
        ax6.xaxis.set_major_locator(MaxNLocator(integer=True))
        ax6.set_xlim([last_games, np.max(player_games_one.game)])
        ax6.set_ylim([0, round(max((player_games_one.rolling(last_games).mean().fillna(0)['Total Points'])+1)*2)/2])
        ax6.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax6.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax6.set_title(str(last_games)+" Game Rolling Average - Points",fontsize=14)
        ax6.set(xlabel='Game', ylabel='Value Per Game')


        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots'],ax=ax7,color='#FFB000',linewidth = 2,label='Shots')
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iCF'],ax=ax7,color='#648FFF',linewidth = 2,label='iCF')
        # sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['iSCF'],ax=ax7,color='#DC267F',linewidth = 2,label='iSCF')
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Hits'],ax=ax7,color='#DC267F',linewidth = 2,label='Hits')
        sns.lineplot(x=player_games_one.game,y=player_games_one.rolling(last_games).mean()['Shots Blocked'],ax=ax7,color="#FE6100",linewidth = 2,label='Blocks')

        ax7.legend(fontsize=10,ncol=5,loc=9)
        ax7.xaxis.set_major_locator(MaxNLocator(integer=True))
        ax7.set_xlim([last_games, np.max(player_games_one.game)])
        ax7.set_ylim([0, max(round(max((player_games_one.rolling(last_games).mean().fillna(0)['iCF'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Hits'])+1)*2)/2+1,round(max((player_games_one.rolling(last_games).mean().fillna(0)['Shots Blocked'])+1)*2)/2+1)])
        ax7.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax7.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax7.set_title(str(last_games)+" Game Rolling Average - Shots and Bangers",fontsize=14,fontname='Century Gothic')
        ax7.set(xlabel='Game', ylabel='Value Per Game')
        ax2.hlines(y=0,xmin=-0.5, xmax=len(ax2.patches)-0.5, color='black',linewidth=1)
        ax3.hlines(y=0,xmin=-0.5, xmax=len(ax3.patches)-0.5, color='black',linewidth=1)
        ax4.hlines(y=0,xmin=-0.5, xmax=len(ax4.patches)-0.5, color='black',linewidth=1)

        ax2.set_xlim([-0.5, len(ax2.patches)-0.5])
        ax3.set_xlim([-0.5, len(ax3.patches)-0.5])
        ax4.set_xlim([-0.5, len(ax4.patches)-0.5])

        #plt.tight_layout(pad=5, w_pad=2, h_pad=2)

        #abot = fig.add_axes([0.075,0.025,0.9,0.025], anchor='NW', zorder=1)

        print('here')

        ax1.text(x=0.0,y=-0.025,s='Created By: @TJStats',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
        ax1.text(x=0.0,y=-0.05,s='Data: Natural Stat Trick, CapFriendly, Yahoo Fantasy',horizontalalignment='left',fontsize=14)#,fontname='Century Gothic')
        ax1.text(x=0.725,y=-0.025,s=f'Generated: {str(date.today())}',horizontalalignment='right',fontsize=14)#,fontname='Century Gothic')

        from matplotlib.font_manager import FontProperties
        font_properties_label = FontProperties(family='century gothic', size=16)
        ax5.set_xlabel(xlabel=ax5.get_xlabel(),fontproperties=font_properties_label) 
        ax6.set_xlabel(xlabel=ax6.get_xlabel(),fontproperties=font_properties_label) 
        ax7.set_xlabel(xlabel=ax7.get_xlabel(),fontproperties=font_properties_label) 
        ax5.set_ylabel(ylabel=ax5.get_ylabel(),fontproperties=font_properties_label) 
        ax6.set_ylabel(ylabel=ax6.get_ylabel(),fontproperties=font_properties_label) 
        ax7.set_ylabel(ylabel=ax7.get_ylabel(),fontproperties=font_properties_label) 

        font_properties_title = FontProperties(family='century gothic', size=16)
        ax2.set_title(label=ax2.get_title(),fontproperties=font_properties_title) 
        ax3.set_title(label=ax3.get_title(),fontproperties=font_properties_title) 
        ax4.set_title(label=ax4.get_title(),fontproperties=font_properties_title) 
        ax5.set_title(label=ax5.get_title(),fontproperties=font_properties_title) 
        ax6.set_title(label=ax6.get_title(),fontproperties=font_properties_title) 
        ax7.set_title(label=ax7.get_title(),fontproperties=font_properties_title) 

        ax1.text(x=0.43,y=0.025,s='Note: Last Games compares to 2024-25. 2024-25 compares to 2024-25.',horizontalalignment='center',fontsize=12,fontname='Century Gothic')
        # font_properties_ticks = FontProperties(family='century gothic', size=12)
        # ax5.set_xticklabels(ax5.get_xticks(),fontproperties=font_properties_label) 

        #plt.xticks(fontname = 'century gothic')
        for key, cell in table.get_celld().items():
            cell.set_linewidth(0)


        #abot.axis('off')
        #ax1.set_zorder(2)
        fig.tight_layout()

        # for i in range(0,len(df_combined_t.index)):
        #     ax1.text(s=df_combined_t.index[i],x=10,y=700+(10*i),fontsize=10,bbox=dict(facecolor='red', alpha=1))


        # #plt.figure(dpi=300)
        # if not os.path.exists('player_cards/skaters/'+name): 
        #     os.makedirs('player_cards/skaters/'+name)

        # dir = 'players/'
        # for f in os.listdir(dir):
        #     os.remove(os.path.join(dir, f))


        #plt.savefig('player_cards/skaters/'+name+'/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")

        #matplotlib.rcParams["figure.dpi"] = 600

        #plt.savefig('players/'+name+"_"+str(last_games)+'_Znew.png',dpi=600,bbox_inches="tight")



# test = test.fillna(0)
#test['PP TOI'] = ["%d:%02d" % (int(x),(x*60)%60) if x>0 else '0:00' for x in test['PP TOI']]



app = App(app_ui, server)
#time.sleep(60)