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=[],linewidth=2,fontsize=10):
    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())
                value_stat = '{:.2f}'.format(float(stat[i]))
                rank = rank_n[i]
                ax.text(_x, -4.2, value_stat, ha="center",fontstyle='italic',fontsize=10)
                ax.text(_x, -4.7, rank, ha="center",fontstyle='italic',fontsize=10)
                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",fontsize=10)

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

cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285F4","white","#FBBC04"])


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 = df_2023[df_2023['display_position']=='G'].reset_index(drop=True)
df_2023['pos_new'] = 'G'


player_games = pd.read_csv('player_games_goalies.csv',index_col=[0])
summary_2023 = pd.read_csv('summary_2023_goalies.csv',index_col=[0])
summary_2023['pos'] = 'G'

summary_2022 = pd.read_csv('Drive/summary_2022_goalies.csv',index_col=[0])
#team_games = pd.read_csv('Drive/team_games.csv')
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')

# cap_friendly_df = pd.read_csv('cap_friendly.csv')
# cap_friendly_df = cap_friendly_df[cap_friendly_df.POS == "G"]
# cap_friendly_df['POS_new'] = 'G'
# cap_friendly_df['new_length'] = cap_friendly_df['EXP. YEAR'] - 2022
# cap_friendly_df['new_cap'] =  cap_friendly_df['CAP HIT'].astype(str).str[1:]
# cap_friendly_df['new_cap'] =  cap_friendly_df['new_cap'].str.replace(',','').astype(int)/1000000
# cap_friendly_df['TEAM'] =  cap_friendly_df[['TEAM']].replace(['LAK','NJD','SJS','TBL'],['L.A','N.J','S.J','T.B'])
# cap_friendly_df['new_cap'] = round(cap_friendly_df['new_cap'],1)
# cap_friendly_df['player_no_acc'] = cap_friendly_df['PLAYER'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')
# # bios_pd['player_no_acc'] = bios_pd['player'].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')
# cap_friendly_df['sheets'] = "$"+cap_friendly_df['new_cap'].astype(str)+ "M x "+cap_friendly_df['new_length'].astype(str)

# #cap_friendly_df = cap_friendly_df.merge(right=summary_2023_all[['player_no_acc','player_id']],how='left',left_on=['player_no_acc'],right_on=['player_no_acc']).fillna("")
# #cap_friendly_df.to_csv('cap_friendly.csv')

# import difflib
# summary_2023['yahoo_name'] = summary_2023.Player.map(lambda x: (difflib.get_close_matches(x, df_2023.full)[:1] or [None])[0])
# summary_2023['cap_name'] = summary_2023['Player'].apply(lambda x: (difflib.get_close_matches(x, cap_friendly_df['player_no_acc'])[:1] or [None])[0])
yahoo_to_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')
print('made it here')
#summary_2023 = summary_2023.merge(right=cap_friendly_df[['player_no_acc','POS_new','sheets','AGE']],left_on=['cap_name','pos'],right_on=['player_no_acc','POS_new'],how='left')
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=df_2023[['full','pos_new','percent_owned','display_position']],left_on=['cap_name','pos'],right_on=['full','pos_new'],how='left').fillna('')

game_log_all_on_ice = pd.read_csv('player_games_goalies.csv',index_col=[0])
summary_all_on_ice_2023 = pd.read_csv('summary_2023_goalies.csv',index_col=[0])
summary_all_on_ice_2022 = pd.read_csv('Drive/summary_2022_goalies.csv',index_col=[0])

skater_dict = game_log_all_on_ice.sort_values(by='date',ascending=False).drop_duplicates(subset='player_id').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 Goalie",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 = "900px",width="1200px")
        )
    ),
)




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():
        def show_values(axs, orient="v", space=-1,stat = [],rank_n=[],linewidth=2,fontsize=10):
            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())
                        value_stat = '{:.2f}'.format(float(stat[i]))
                        rank = rank_n[i]
                        ax.text(_x, -4.2, value_stat, ha="center",fontstyle='italic',fontsize=10)
                        ax.text(_x, -4.7, rank, ha="center",fontstyle='italic',fontsize=10)
                        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",fontsize=10)

            if isinstance(axs, np.ndarray):
                for idx, ax in np.ndenumerate(axs):
                    _single(ax)
            else:
                _single(axs)
        # from matplotlib import rc, font_manager
        # rc('text', usetex=True)
        # rc('font',**{'family':'sans-serif','sans-serif':['Century Gothic']})


        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())
        print('made it here')
        last_games = input.last_game_id()
        name = summary_2023.loc[summary_2023.player_id == player_id].Player.values[0]
        #last_games = int(input.last_game_id())
        today = str(datetime.now().date())
        

        game_log_all_on_ice_small = game_log_all_on_ice[game_log_all_on_ice.player_id==player_id].reset_index(drop=True)
        summary_all_on_ice_2023_small = summary_all_on_ice_2023[summary_all_on_ice_2023.player_id==player_id].reset_index(drop=True)
        summary_all_on_ice_2022_small = summary_all_on_ice_2022[summary_all_on_ice_2022.player_id==player_id].reset_index(drop=True)

        df_last_games = pd.DataFrame(data={'Games':[last_games]})

        df_last_games['TOI'] = game_log_all_on_ice_small['TOI'][:last_games].sum()
        df_last_games['TOI/G'] = game_log_all_on_ice_small['TOI'][:last_games].sum()/df_last_games.Games
        df_last_games['PK TOI/G'] = game_log_all_on_ice_small['TOI_pk'][:last_games].sum()/df_last_games.Games
        df_last_games['All SV%'] = 1 - game_log_all_on_ice_small['Goals Against'][:last_games].sum()/game_log_all_on_ice_small['Shots Against'][:last_games].sum()
        df_last_games['5v5 SV%'] = 1 - game_log_all_on_ice_small['Goals Against_5v5'][:last_games].sum()/game_log_all_on_ice_small['Shots Against_5v5'][:last_games].sum()
        df_last_games['PK SV%'] = 1 - game_log_all_on_ice_small['Goals Against_pk'][:last_games].sum()/game_log_all_on_ice_small['Shots Against_pk'][:last_games].sum()
        df_last_games['LDSV%'] = 1 - game_log_all_on_ice_small['LD Goals Against'][:last_games].sum()/game_log_all_on_ice_small['LD Shots Against'][:last_games].sum()
        df_last_games['MDSV%'] = 1 - game_log_all_on_ice_small['MD Goals Against'][:last_games].sum()/game_log_all_on_ice_small['MD Shots Against'][:last_games].sum()
        df_last_games['HDSV%'] = 1 - game_log_all_on_ice_small['HD Goals Against'][:last_games].sum()/game_log_all_on_ice_small['HD Shots Against'][:last_games].sum()
        df_last_games['GSAx/60'] = (game_log_all_on_ice_small['xG Against'][:last_games].sum() - game_log_all_on_ice_small['Goals Against'][:last_games].sum())/df_last_games.TOI*60
        df_last_games['xGA/60'] = (game_log_all_on_ice_small['xG Against'][:last_games].sum() )/df_last_games.TOI*60
        df_last_games['GA/60'] = (game_log_all_on_ice_small['Goals Against'][:last_games].sum())/df_last_games.TOI*60


        df_season = pd.DataFrame(data={'Games':[summary_all_on_ice_2023_small.GP[0]]})
        df_season['TOI'] = summary_all_on_ice_2023_small['TOI'].sum()
        df_season['TOI/G'] = summary_all_on_ice_2023_small['TOI'].sum()/df_season.Games
        df_season['PK TOI/G'] = summary_all_on_ice_2023_small['TOI_pk'].sum()/df_season.Games
        df_season['All SV%'] = 1 - summary_all_on_ice_2023_small['GA'].sum()/summary_all_on_ice_2023_small['SA'].sum()
        df_season['5v5 SV%'] = 1 - summary_all_on_ice_2023_small['GA_5v5'].sum()/summary_all_on_ice_2023_small['SA_5v5'].sum()
        df_season['PK SV%'] = 1 - summary_all_on_ice_2023_small['GA_pk'].sum()/summary_all_on_ice_2023_small['SA_pk'].sum()
        df_season['LDSV%'] = 1 - summary_all_on_ice_2023_small['LDGA'].sum()/summary_all_on_ice_2023_small['LDSA'].sum()
        df_season['MDSV%'] = 1 - summary_all_on_ice_2023_small['MDGA'].sum()/summary_all_on_ice_2023_small['MDSA'].sum()
        df_season['HDSV%'] = 1 - summary_all_on_ice_2023_small['HDGA'].sum()/summary_all_on_ice_2023_small['HDSA'].sum()
        df_season['GSAx/60'] = (summary_all_on_ice_2023_small['xGA'].sum() - summary_all_on_ice_2023_small['GA'].sum())/summary_all_on_ice_2023_small['TOI'].sum()*60
        df_season['xGA/60'] = (summary_all_on_ice_2023_small['xGA'].sum())/summary_all_on_ice_2023_small['TOI'].sum()*60
        df_season['GA/60'] = (summary_all_on_ice_2023_small['GA'].sum())/summary_all_on_ice_2023_small['TOI'].sum()*60

        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)

        df_career_total = pd.DataFrame(data={'Games':[summary_all_on_ice_2022_small.GP[0]]})
        df_career_total['TOI'] = summary_all_on_ice_2022_small['TOI'].sum()
        df_career_total['TOI/G'] = summary_all_on_ice_2022_small['TOI'].sum()/df_career_total.Games
        df_career_total['PK TOI/G'] = summary_all_on_ice_2022_small['TOI_pk'].sum()/df_career_total.Games
        df_career_total['All SV%'] = 1 - summary_all_on_ice_2022_small['GA'].sum()/summary_all_on_ice_2022_small['SA'].sum()
        df_career_total['5v5 SV%'] = 1 - summary_all_on_ice_2022_small['GA_5v5'].sum()/summary_all_on_ice_2022_small['SA_5v5'].sum()
        df_career_total['PK SV%'] = 1 - summary_all_on_ice_2022_small['GA_pk'].sum()/summary_all_on_ice_2022_small['SA_pk'].sum()
        df_career_total['LDSV%'] = 1 - summary_all_on_ice_2022_small['LDGA'].sum()/summary_all_on_ice_2022_small['LDSA'].sum()
        df_career_total['MDSV%'] = 1 - summary_all_on_ice_2022_small['MDGA'].sum()/summary_all_on_ice_2022_small['MDSA'].sum()
        df_career_total['HDSV%'] = 1 - summary_all_on_ice_2022_small['HDGA'].sum()/summary_all_on_ice_2022_small['HDSA'].sum()
        df_career_total['GSAx/60'] = (summary_all_on_ice_2022_small['xGA'].sum() - summary_all_on_ice_2022_small['GA'].sum())/summary_all_on_ice_2022_small['TOI'].sum()*60
        df_career_total['xGA/60'] = (summary_all_on_ice_2022_small['xGA'].sum())/summary_all_on_ice_2022_small['TOI'].sum()*60
        df_career_total['GA/60'] = (summary_all_on_ice_2022_small['xGA'].sum())/summary_all_on_ice_2022_small['TOI'].sum()*60

        df_career_total.rename(index={0:'2022-23 ('+str(df_career_total.Games[0])+'GP)'},inplace=True)

        df_career_total.rename(index={0:'2022-23 ('+str(df_career_total.Games[0])+'GP)'},inplace=True)
        df_combined = df_last_games.append([df_season,df_career_total])
        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.fillna(0)


        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

        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:
            if 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(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))

        if df_combined_t.values[[10],[1]] < 0:
            if 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(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))

        #age = summary_2023[summary_2023.player_id==player_id].reset_index(drop=True)['AGE'][0]
        percent_owned = summary_2023[summary_2023.player_id==player_id].reset_index(drop=True).fillna(0)['percent_owned'][0]
        yahoo_position = summary_2023[summary_2023.player_id==player_id].reset_index(drop=True).fillna('G')['display_position'][0]


        # logging.getLogger('matplotlib.font_manager').disabled = True
        nhl_logos = pd.read_csv("NHL Logos - NHL Logos.csv")


        # image = image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        # logo = nhl_logos[nhl_logos.Team==game_log_all_on_ice_small.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([0,0.08,1,0.85], 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=18,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=0.91,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=game_log_all_on_ice_small['Team'].reset_index(drop=True)[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(age),horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # #fig.text(x=0.25,y=0.91,s=cap_friendly_df.loc[cap_friendly_df['player_no_acc']==name].reset_index()['sheets'][0],horizontalalignment='left',fontsize=22,fontname='Century Gothic')
        # fig.text(x=0.25,y=0.91,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 = summary_2023.copy()
        #test.loc[name == test.Player].reset_index().TOI[0]/1.5
        min_time = min(math.floor(test.loc[player_id == test.player_id].reset_index().TOI[0]/100)*100,test.GP.max()*30,math.ceil(test.loc[player_id == test.player_id].reset_index().TOI[0]/200)*100)
        # min_time = min(math.floor(test.loc[name == test.Player].reset_index().TOI[0]/100)*100,test.GP.max()*10)
        test_filter = test[(test.TOI >= min_time)]#.reset_index(drop=True)

        test_filter['GSAx'] = test_filter['xGA'] - test_filter['GA']
        test_filter['GSAx/60'] = (test_filter['GSAx'])/(test_filter['TOI'])*60
        test_filter['GAA'] = (test_filter['GA'])/(test_filter['TOI'])*60
        test_filter['xG Against/60'] = (test_filter['xGA'])/(test_filter['TOI'])*60

        test_filter['SV% (All)'] = (test_filter['SV'])/(test_filter['SA'])
        test_filter['SV% (5v5)'] = (test_filter['SV_5v5'])/(test_filter['SA_5v5'])
        test_filter['SV% (PK)'] = (test_filter['SV_pk'])/(test_filter['SA_pk'])

        test_filter['LDSV%'] = (test_filter['LDSV'])/(test_filter['LDSA'])
        test_filter['MDSV%'] = (test_filter['MDSV'])/(test_filter['MDSA'])
        test_filter['HDSV%'] = (test_filter['HDSV'])/(test_filter['HDSA'])

        test_filter['GSAx/60 Z-Score'] = (test_filter['GSAx/60']-test_filter['GSAx/60'].mean())/test_filter['GSAx/60'].std()
        test_filter['GA/60 Z-Score'] = -(test_filter['GAA']-test_filter['GAA'].mean())/test_filter['GAA'].std()
        test_filter['xGA/60 Z-Score'] = -(test_filter['xG Against/60']-test_filter['xG Against/60'].mean())/test_filter['xG Against/60'].std()

        test_filter['SV% (All) Z-Score'] = (test_filter['SV% (All)']-test_filter['SV% (All)'].mean())/test_filter['SV% (All)'].std()
        test_filter['SV% (5v5) Z-Score'] = (test_filter['SV% (5v5)']-test_filter['SV% (5v5)'].mean())/test_filter['SV% (5v5)'].std()
        test_filter['SV% (PK) Z-Score'] = (test_filter['SV% (PK)']-test_filter['SV% (PK)'].mean())/test_filter['SV% (PK)'].std()

        test_filter['LDSV% Z-Score'] = (test_filter['LDSV%']-test_filter['LDSV%'].mean())/test_filter['LDSV%'].std()
        test_filter['MDSV% Z-Score'] = (test_filter['MDSV%']-test_filter['MDSV%'].mean())/test_filter['MDSV%'].std()
        test_filter['HDSV% Z-Score'] = (test_filter['HDSV%']-test_filter['HDSV%'].mean())/test_filter['HDSV%'].std()






        values_1 = test_filter.loc[name == test_filter.Player][['GSAx/60','GAA','xG Against/60']].reset_index(drop=True).loc[0]
        values_2 = test_filter.loc[name == test_filter.Player][['SV% (All)','SV% (5v5)','SV% (PK)']].reset_index(drop=True).loc[0]
        values_3 = test_filter.loc[name == test_filter.Player][['LDSV%','MDSV%','HDSV%']].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[name == test_filter.Player][test_filter.columns[-9:]].values.flatten().tolist(),2))

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


        label_loc = np.linspace(start=0, stop=2 * np.pi, num=len(player))
        # 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 = [i[:-8] for i in test_filter.columns[-9:]]
        #categories[0] = 'GSAx/60'
        player = list(np.around(test_filter.loc[name == test_filter.Player][test_filter.columns[-9:]].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')








        rank_1 = list(test_filter[values_1.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0][[categories[0]]])
        rank_1 = rank_1 + list(test_filter[values_1.index].rank(method='min',ascending=True).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0][['GAA','xG Against/60']])
        rank_2 = list(test_filter[values_2.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0])
        rank_3 = list(test_filter[values_3.index].rank(method='min',ascending=False).loc[name == test_filter.Player].reset_index(drop=True).astype(int).iloc[0])
        import scipy as sp
        import scipy.interpolate
        game_log_all_on_ice_small['GSAx'] = game_log_all_on_ice_small['xG Against'] - game_log_all_on_ice_small['Goals Against']
        #game_log_all_on_ice_small['game'] = game_log_all_on_ice_small.groupby('player_id').cumcount()
        game_log_all_on_ice_small = game_log_all_on_ice_small.sort_values(by=['game'])
        new_length = game_log_all_on_ice_small.game.max()*100
        new_x = np.linspace(game_log_all_on_ice_small.game.min(), game_log_all_on_ice_small.game.max(), new_length)
        new_y = sp.interpolate.interp1d(game_log_all_on_ice_small.game,game_log_all_on_ice_small['GSAx'].cumsum(),kind='linear')(new_x)
        new_df = pd.DataFrame(columns=['Game','GSAx'])
        new_df['Game'] = new_x
        new_df['GSAx'] = new_y
        # 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

        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:
            if 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(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))

        if df_combined_t.values[[10],[1]] < 0:
            if 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(colormap(norm(-df_combined_t.values[[10],[0]] / df_combined_t.values[[10],[1]])))


        sns.set_theme(style="whitegrid", palette="pastel")
        fig = plt.figure(figsize=(12, 9),dpi=300)
        fig.set_facecolor('white')

        ax1 = plt.subplot(1,2,1)
        ax2 = plt.subplot(3,2,2)
        ax3 = plt.subplot(3,2,4)
        ax4 = plt.subplot(3,2,6)
        axes = [ax1, ax2, ax3, ax4]
        # 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)


        # ax[0][0].axis('off')
        # ax[1][0].axis('off')


        # ax[0][0].axis('off')
        # ax[1][0].axis('off')
        # image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        # logo = nhl_logos[nhl_logos.Team==game_log_all_on_ice_small.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)


        # im = plt.imread(image)
        # ax1 = fig.add_axes([0.25,0.76,0.12,0.12], anchor='NW', zorder=1)
        # ax1.imshow(im)
        # ax1.axis('off')

        # imr = plt.imread(logo)
        # ax1 = fig.add_axes([0.18,0.76,0.75,0.075], anchor='NW', zorder=1)
        # ax1.imshow(imr)
        # ax1.axis('off')
        
        # image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        #f"https://assets.nhle.com/mugs/nhl/20232024/BUF/8482221.png"
        #image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"

        # 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:
        #         print('Error')
        
        #image = "https://cms.nhl.bamgrid.com/images/headshots/current/168x168/"+str(player_id)+".png"
        logo = nhl_logos[nhl_logos.Team==game_log_all_on_ice_small.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()

        # #im = plt.imread(image)
        # im = OffsetImage(im, zoom=.50)
        # ab = AnnotationBbox(im, (0.73, 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=.065)
        ab = AnnotationBbox(imr, (0.5, 0.765), xycoords='axes fraction', box_alignment=(0.0,0.0),bboxprops={'edgecolor':'white'})
        ax1.add_artist(ab)

        sub_value  = 0.16
        ax1.text(x=0.5,y=1.13-sub_value,s='NHL Player Summary',horizontalalignment='center',fontsize=28, fontweight='bold',ha='center')
        ax1.text(x=0.5,y=1.08-sub_value,s='2023-24 Season',horizontalalignment='center',fontsize=18,fontname='Century Gothic', fontstyle='italic',ha='center')
        ax1.text(x=0.125,y=1.04-sub_value,s='Player',horizontalalignment='center',fontsize=14,fontname='Century Gothic', fontweight='bold')
        ax1.text(x=0.125,y=1.005-sub_value,s='Team',horizontalalignment='center',fontsize=14,fontname='Century Gothic', fontweight='bold')
        ax1.text(x=0.125,y=0.97-sub_value,s='Position',horizontalalignment='center',fontsize=14,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=0.125,y=0.935-sub_value,s='Roster%',horizontalalignment='center',fontsize=14,fontname='Century Gothic', fontweight='bold')

        ax1.text(x=0.25,y=1.04-sub_value,s=name,horizontalalignment='left',fontsize=14,fontname='Century Gothic')
        ax1.text(x=0.25,y=1.005-sub_value,s=list(game_log_all_on_ice_small['Team'].reset_index(drop=True))[0],horizontalalignment='left',fontsize=14,fontname='Century Gothic')
        ax1.text(x=0.25,y=0.97-sub_value,s=yahoo_position,horizontalalignment='left',fontsize=14,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=0.25,y=0.935-sub_value,s=str(int(percent_owned*100))+'%',horizontalalignment='left',fontsize=14,fontname='Century Gothic')

        ax1.text(x=0.55,y=0.07,s='Note: Last Games compares to 2023-24. 2023-24 compares to 2022-23.',horizontalalignment='center',fontsize=9,fontname='Century Gothic',va='bottom')


        ax1.axis("off")
        ax1.set_facecolor('white')


        # [colormap(norm(0.8)),colormap(norm(1)),colormap(norm(1.2))]
        #table_scale = ax1.table(cellText=['-20%','±0%','+20%'],cellLoc='center',rowLoc='center', bbox=[0.20, 0.1, 0.8, 0.05],cellColours=["#4285F4","white",'#FBBC04'])

        table_scale = ax1.table(cellText=[['-20%','±0%','+20%']],rowLabels=['   Scale   '],
                        cellLoc='center',rowLoc='center', bbox=[0.20, 0.03, 0.8, 0.03],cellColours=[[colormap(norm(0.8)),colormap(norm(1)),colormap(norm(1.2))]])
        table_scale.set_fontsize(10)
        table_scale.scale(1, 0.5)


        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:
            print('1')
            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:
            print('2')
            #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:
            print('3')
            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]])))


        table = ax1.table(cellText=df_combined_t.values, colLabels=df_combined_t.columns,rowLabels=df_combined_t.index
                        , cellLoc='center',rowLoc='center', bbox=[0.20, 0.1, 0.8, 0.65],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 < 4*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())))
            elif n_c < 10*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.3f}'.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('{:,.2f}'.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 < 4*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())))
            elif n_c < 10*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.3f}'.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('{:,.2f}'.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 < 4*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())))
            elif n_c < 10*3:
                if cell.get_text().get_text() in format_col.astype(str).values:
                    cell.get_text().set_text('{:,.3f}'.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('{:,.2f}'.format(float(cell.get_text().get_text())))
            n_c = n_c + 1



        #ax1.text('')
        #fig.tight_layout()
        #categories[0:7] = ['G/GP','A/GP','P/GP','PPP/GP','S/GP','Hits/GP','Blk/GP']
            
        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]

        sns.barplot(data=test_filter,x=categories[0:3],y=player[0:3],palette=color_scheme[0:3],edgecolor='black',ax=ax2)

        ax2.set_title("Goals Against Rates Z-Score (min. "+str(min_time)+' 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,fontsize=10)
        ax2.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax2.tick_params(axis='x', which='major', labelsize=6)
        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[3:9],y=player[3:9],palette=color_scheme[3:9],edgecolor='black',ax=ax3)
        ax3.set_title("Save% Z-Score (min. "+str(min_time)+' 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')


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

        def show_values(axs, orient="v", space=-1,stat = [],rank_n=[],fontsize=10):
            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())
                        value_stat = '{:.3f}'.format(float(stat[i]))
                        rank = rank_n[i]
                        ax.text(_x, -4.2, value_stat, ha="center",fontstyle='italic',fontsize=fontsize)
                        ax.text(_x, -4.7, rank, ha="center",fontstyle='italic',fontsize=fontsize)
                        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",fontsize=fontsize)

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



        show_values(ax3,stat = pd.concat([values_2,values_3]),rank_n=rank_2+rank_3,fontsize=10)
        # show_values(ax4,stat = values_3,rank_n=rank_3)


        ax2.text(-0.5, -4.2, '2023-24', ha="right",fontstyle='italic',zorder=1,fontsize=10)
        ax3.text(-0.5, -4.2, '2023-24', ha="right",fontstyle='italic',fontsize=10)
        # ax4.text(-0.5, -4.2, '2022-23', ha="right",fontstyle='italic')


        ax2.text(-0.5, -4.7, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',fontsize=10)
        ax3.text(-0.5, -4.7, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic',fontsize=10)
        # ax4.text(-0.5, -4.7, 'Rank (of '+str(len(test_filter))+")", ha="right",fontstyle='italic')


        # 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)


        # sns.lineplot(x=game_log_all.Game,y=game_log_all.rolling(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(game_log_all.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([math.floor(min(game_log_all.TOI)/5)*5, math.ceil(max((game_log_all.rolling(last_games).mean().fillna(0)['TOI']))/5)*5])
        # 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=game_log_pp.Game,y=game_log_pp.rolling(last_games).sum()['PP_TOI']/game_log_pp.rolling(last_games).sum()['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=game_log_all.Game,y=game_log_all.rolling(last_games).mean()['Total Points'],ax=ax6,color='#FFB000',label='Points',linewidth = 2)
        # sns.lineplot(x=game_log_pp.Game,y=game_log_pp.rolling(last_games).mean()['Total Points'],ax=ax6,color='#648FFF',label='PP Points',linewidth = 2)
        # sns.lineplot(x=game_log_all.Game,y=game_log_all.rolling(last_games).mean()['Goals'],ax=ax6,color='#DC267F',label='Goals',linewidth = 2)
        # sns.lineplot(x=game_log_all.Game,y=game_log_all.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(game_log_all.Game)])
        # ax6.set_ylim([0, round(max((game_log_all.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,fontname='Century Gothic')
        # ax6.set(xlabel='Game', ylabel='Value Per Game')
        positive_df = new_df.copy()
        positive_df[positive_df.GSAx < 0] = np.NaN

        negative_df = new_df.copy()
        negative_df[negative_df.GSAx > 0] = np.NaN

        # positive_df = positive_df.sort_values(by='Game')
        # negative_df = negative_df.sort_values(by='Game')

        #sns.lineplot(x=game_log_all_on_ice_small.Game,y=game_log_all_on_ice_small.rolling(last_games).mean()['GSAx'],ax=ax4,color='#FFB000',linewidth = 2,label='Rolling GSAx')
        if not positive_df.dropna().empty and negative_df.dropna().empty:
            sns.lineplot(x=positive_df.Game,y=positive_df.GSAx,ax=ax4,color='#FFB000',linewidth = 1,label='Cumulative GSAx',alpha=1,linestyle='-')
        #threshold_plot(ax4, new_x,new_y, 0, '#648FFF', '#FFB000',linewidth=2)
        elif positive_df.dropna().empty and not negative_df.dropna().empty:
            sns.lineplot(x=negative_df.Game,y=negative_df.GSAx,ax=ax4,color='#648FFF',linewidth = 1,alpha=1,linestyle='-',label='Cumulative GSAx')
            print('this')
        else:
            sns.lineplot(x=positive_df.Game,y=positive_df.GSAx,ax=ax4,color='#FFB000',linewidth = 1,label='Cumulative GSAx',alpha=1,linestyle='-')
            sns.lineplot(x=negative_df.Game,y=negative_df.GSAx,ax=ax4,color='#648FFF',linewidth = 1,alpha=1,linestyle='-')

        #ax4.legend(fontsize=10,ncol=5,loc='upper left')
        
        ax4.xaxis.set_major_locator(MaxNLocator(integer=True))
        ax4.set_xlim([1, np.max(game_log_all_on_ice_small.game)])
        ax4.set_ylim([min(round(min((game_log_all_on_ice_small['GSAx'].cumsum()))*1)/1-1,-1), max(round(max((game_log_all_on_ice_small['GSAx'].cumsum()))*1)/1+2,3)])
        ax4.grid(axis = 'x',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax4.grid(axis = 'y',linestyle = '-', linewidth = 0.5,alpha=0.3)
        ax4.set_title("Cumulative Goals Saved Above Expected",fontsize=14,fontname='Century Gothic')
        ax4.set(xlabel='Game', ylabel='GSAx')
        ax4.yaxis.set_major_locator(MaxNLocator(integer=True))
        ax4.get_legend().remove()


        fig.text(x=0.05,y=0.045,s='Created By: @TJStats',horizontalalignment='left',fontsize=10)#,fontname='Century Gothic')
        fig.text(x=0.05,y=0.02,s='Data: Natural Stat Trick, CapFriendly, Yahoo Fantasy',horizontalalignment='left',fontsize=10)#,fontname='Century Gothic')
        fig.text(x=0.5,y=0.02,s=str(date.today()),horizontalalignment='center',fontsize=8)#,fontname='Century Gothic')

        # #ax1.set_zorder(2)
        ax4.stackplot(new_df.Game[new_df.GSAx>=0],new_df.GSAx[new_df.GSAx>=0], alpha=0.2,color='#FFB000')
        ax4.stackplot(new_df.Game[new_df.GSAx<0],new_df.GSAx[new_df.GSAx<0], alpha=0.2,color='#648FFF')

        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=1,xmax=max(game_log_all_on_ice_small['game']),color='black',linewidth=0.75)

        #ax4.stackplot(game_log_all_on_ice_small["Game"], game_log_all_on_ice_small['GSAx'].cumsum(), color=['r' if v < 0 else '#FFB000' for v in game_log_all_on_ice_small['GSAx']],alpha=0.3)
        ax2.tick_params(axis='both', which='major', labelsize=10,bottom=False,top=False)
        ax3.tick_params(axis='both', which='major', labelsize=10,bottom=False,top=False)
        ax4.tick_params(axis='both', which='major', labelsize=10,bottom=False,top=False)
        # im_rain = plt.imread('https://upload.wikimedia.org/wikipedia/commons/thumb/3/31/Rainbow-gradient-fully-saturated.svg/1024px-Rainbow-gradient-fully-saturated.svg.png')
        # ax1 = fig.add_axes([0,0,1,1], anchor='C')
        # ax1.imshow(im_rain,alpha=0.3)
        # ax1.set_aspect('auto')
        # ax1.axis('off')
        for key, cell in table.get_celld().items():
                    cell.set_linewidth(0)

        for key, cell in table_scale.get_celld().items():
                    cell.set_linewidth(0)

        ax2.set_xlim([-0.5, 2.5])
        ax3.set_xlim([-0.5, 5.5])

        plt.tight_layout(pad=0.3, w_pad=1, h_pad=0.5)
        # if not os.path.exists('player_cards/goalies/'+name):
        #     os.makedirs('player_cards/goalies/'+name)

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

        # plt.savefig('player_cards/goalies/'+name+'/'+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)