Update app.py

app.py

@@ -1,29 +1,48 @@
-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
-from scipy import stats
+#from scipy import stats
 import matplotlib.lines as mlines
 import matplotlib.transforms as mtransforms
 import numpy as np
-import time
 #import plotly.express as px
 #!pip install chart_studio
-#import chart_studio.tools as tls
-from bs4 import BeautifulSoup
+# 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
+from datetime import date
+datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
+# Configure Notebook
+#%matplotlib inline
+plt.style.use('fivethirtyeight')
+sns.set_context("notebook")
+import warnings
+warnings.filterwarnings('ignore')
+#from urllib.request import urlopen
+import json
+from datetime import date, timedelta
+#import dataframe_image as dfi
+#from os import listdir
+#from os.path import isfile, join
+import datetime
+import seaborn as sns
+import os
+import calendar
+#from IPython.display import display, HTML
+import matplotlib.image as mpimg
+#from skimage import io
+#import difflib 
+
+
+from datetime import datetime
+import pytz
 datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
 # Configure Notebook
 #%matplotlib inline
@@ -36,50 +55,119 @@ warnings.filterwarnings('ignore')
 # import yahoo_oauth
 import json
 #import openpyxl
-from sklearn import preprocessing
-from datetime import timedelta
-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 sklearn import preprocessing
+from PIL import Image
+import logging
+import matplotlib.patches as patches
+from matplotlib.patches import Rectangle
 from matplotlib.font_manager import FontProperties
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
 
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.colors
+import requests
+#import pickle
+import pandas as pd
+
+# # Loop over the counter and format the API call
+r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-01&endDate=2024-06-01')
+schedule = r.json()
 
-#x,y,c = zip(*np.random.rand(30,3)*4-2)
+def flatten(t):
+    return [item for sublist in t for item in sublist]
 
-#norm=plt.Normalize(-2,2)
-co = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#ffffff","#F0E442"])
+game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_date = flatten([[x['gameDate'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_home = flatten([[x['teams']['home']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_away = flatten([[x['teams']['away']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
 
+schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away})
+schedule_df.game_date = pd.to_datetime(schedule_df['game_date']).dt.tz_convert(tz='US/Eastern').dt.date
+schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
+schedule_df.head()
 
-try: 
-    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()
-    key_check = data_r['fantasy_content']['league']['players']
+team_abv = pd.read_csv('team_abv.csv')
+yahoo_weeks = pd.read_csv('yahoo_weeks.csv')
+#yahoo_weeks['Number'] = yahoo_weeks['Number'].astype(int)
+yahoo_weeks['Start'] = pd.to_datetime(yahoo_weeks['Start'])
+yahoo_weeks['End'] = pd.to_datetime(yahoo_weeks['End'])
+yahoo_weeks.head(5)
+
+def highlight_cols(s):
+    color = '#C2FEE9'
+    return 'background-color: %s' % color
+def highlight_cells(val):
+    color = 'white' if val == ' ' else ''
+    return 'background-color: {}'.format(color)
 
-except KeyError:
-    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=1151;sort=rank_season;search=;out=percent_owned;out=auction_values,ranks;ranks=season;ranks_by_position=season;out=expert_ranks;expert_ranks.rank_type=projected_season_remaining/draft_analysis;cut_types=diamond;slices=last7days?format=json_f").json()
-    print('key_checked')
+import matplotlib.pyplot as plt
+import matplotlib.colors
+cmap_total = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"])
+cmap_off = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
+cmap_back = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"])
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
+
+schedule_df = schedule_df.merge(right=team_abv,left_on='game_away',right_on='team_name',how='inner',suffixes=['','_away']) 
+schedule_df = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='inner',suffixes=['','_home']) 
+schedule_df['away_sym'] = '@'
+schedule_df['home_sym'] = 'vs'
+
+
+#if not os.path.isfile('standings/standings_'+str(date.today())+'.csv'):   
+standings_df_old = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2023_standings.html')[0].append(pd.read_html('https://www.hockey-reference.com/leagues/NHL_2023_standings.html')[1])
+#    standings_df_old.to_csv('standings/standings_'+str(date.today())+'.csv')
+#standings_df_old = pd.read_csv('standings/standings_'+str(date.today())+'.csv',index_col=[0])
+
+standings_df = standings_df_old[standings_df_old['Unnamed: 0'].str[-8:] != 'Division'].sort_values('Unnamed: 0').reset_index(drop=True).rename(columns={'Unnamed: 0':'Team'})#.drop(columns='Unnamed: 0')
+#standings_df = standings_df.replace('St. Louis Blues','St Louis Blues')
+standings_df['GF/GP'] = standings_df['GF'].astype(int)/standings_df['GP'].astype(int)
+standings_df['GA/GP'] = standings_df['GA'].astype(int)/standings_df['GP'].astype(int)
+standings_df['GF_Rank']  = standings_df['GF/GP'].rank(ascending=True,method='first')/10-1.65
+standings_df['GA_Rank'] = standings_df['GA/GP'].rank(ascending=False,method='first')/10-1.65
+standings_df.Team = standings_df.Team.str.strip('*') 
+standings_df = standings_df.merge(right=team_abv,left_on='Team',right_on='team_name')
+
+schedule_stack = pd.DataFrame()
+schedule_stack['date'] = pd.to_datetime(list(schedule_df['game_date'])+list(schedule_df['game_date']))
+schedule_stack['team'] = list(schedule_df['team_name'])+list(schedule_df['team_name_home'])
+schedule_stack['team_abv'] = list(schedule_df['team_abv'])+list(schedule_df['team_abv_home'])
+schedule_stack['symbol'] = list(schedule_df['away_sym'])+list(schedule_df['home_sym'])
+schedule_stack['team_opponent'] =  list(schedule_df['team_name_home'])+list(schedule_df['team_name'])
+schedule_stack['team_abv_home'] = list(schedule_df['team_abv_home'])+list(schedule_df['team_abv'])
+schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y'))
+schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y'))
+
+schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GF_Rank']],left_on='team_abv',right_on='team_abv',how='inner',suffixes=("",'_y'))
+schedule_stack = schedule_stack.merge(right=standings_df[['team_abv','GA_Rank']],left_on='team_abv_home',right_on='team_abv',how='inner',suffixes=("",'_y'))
+ 
+ 
+list_o = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True)
+new_list = [x - y for x, y in zip(list_o['date'][1:], list_o['date'])]
+b2b_list = [0] + [x.days for x in new_list]
+b2b_list = [1 if x==1 else 0 for x in b2b_list]
+test = list(schedule_stack.groupby(by='date').count()['team'])
+offnight = [1 if x<15 else 0 for x in test]
+offnight_df = pd.DataFrame({'date':schedule_stack.sort_values('date').date.unique(),'offnight':offnight}).sort_values('date').reset_index(drop=True)
+schedule_stack = schedule_stack.merge(right=offnight_df,left_on='date',right_on='date',how='right')
+schedule_stack = schedule_stack.sort_values(['team','date'],ascending=[True,True]).reset_index(drop=True)
+schedule_stack['b2b'] = b2b_list
+
+schedule_stack.date = pd.to_datetime(schedule_stack.date)
+
+away_b2b = []
+home_b2b = []
+for i in range(0,len(schedule_stack)):
+    away_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team_opponent[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0])
+    home_b2b.append(schedule_stack[(schedule_stack.date[i]==schedule_stack.date)&(schedule_stack.team[i]==schedule_stack.team)].reset_index(drop=True)['b2b'][0])
+
+schedule_stack['away_b2b'] = away_b2b
+schedule_stack['home_b2b'] = home_b2b
+
+schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(1,' &#128564;')
+schedule_stack['away_b2b'] = schedule_stack['away_b2b'].replace(0,'')
+schedule_stack.head()
+
+FontProperties(fname='/System/Library/Fonts/Apple Color Emoji.ttc')
+
+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 = []
 
@@ -101,266 +189,278 @@ for x in data_r['fantasy_content']['league']['players']:
         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'])
 
-yahoo_df = pd.DataFrame(total_list,columns = ['player_id','rank_value','full','first','last','average_pick','average_auction_cost','display_position','editorial_team_abbr','percent_owned'])
-yahoo_df_2 = yahoo_df.copy()
-
-# Write your code here.
-response = requests.get("https://www.naturalstattrick.com/playerlist.php?fromseason=20232024&thruseason=20232024&stype=2&sit=all&stdoi=oi&rate=n")
-soup = BeautifulSoup(response.text, 'html.parser')
-table_rows = soup.findAll('tr')
-table_rows = table_rows[1:-1]
-table_rows[0].findAll('td')
-
-player_name = []
-player_position = []
-player_team = []
-player_id = []
-
-for i in range(0,len(table_rows)-1):
-    player_name.append(str(table_rows[i].findAll('td')[0].contents[0]))
-    player_position.append(table_rows[i].findAll('td')[1].contents[0])
-    player_team.append(table_rows[i].findAll('td')[2].contents[0])
-    player_id.append(str(table_rows[i].findAll('td')[3].contents[0])[-76:][:7])
-
-player_id_df = pd.DataFrame({'Player':player_name,'Player ID':player_id,'Position':player_position,'Team':player_team})
-#player_id_df.index.name = 'Player Name'
-player_id_df.head()
-
-skater_df = player_id_df[player_id_df['Position'] != 'G']
-goalie_df = player_id_df[player_id_df['Position'] == 'G']
-
-season = 20232024
-seasontype = 2
+week_dict = yahoo_weeks.set_index('Number')['Week'].sort_index().to_dict()
 
-def nat_stat_trick_range_pp_gp(rookie='n',start_date='2022-10-01',end_date=str(pd.to_datetime(datetime.now(pytz.timezone('US/Pacific')).strftime('%Y-%m-%d')).date()),sit='all',gp=1):
-    url = f'https://www.naturalstattrick.com/playerteams.php?fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&stdoi=std&rate=y&team=ALL&pos=S&loc=B&toi=0&gpfilt=gpteam&fd=&td=&tgp='+str(gp)+'&lines=single&draftteam=ALL'
-
-    player_list_all = []
-    response = requests.get(url)
-    soup = BeautifulSoup(response.text, 'html.parser')
-    table_rows = soup.findAll('tr')
-    table_rows = table_rows[1:]
-
-    for j in range(0,len(table_rows)):
-      p_string = [str(x).strip('<td>').strip('</') for x  in list(table_rows[j].findAll('td')) if "<td>" in str(x)]
-      player_list_all.append([p_string[0]]+[str(table_rows[j].findAll('td')[1]).split('>')[2].split('<')[0]]+p_string[1:]+[str(table_rows[j].findAll('td')[1])[98:105].strip('</a></td>')])
-    #table_rows[0].findAll('td')
-
-    if soup != "":
-      columns_list = [str(x).split('>')[1].split('<')[0] for x in soup.findAll('th')]+['player_id']
-      df_url = pd.DataFrame(data=player_list_all,columns=columns_list)
-
-    df_url = df_url.fillna(0)
-    df_url['Shots+Hits+Blocks/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)+df_url['Shots Blocked/60'].astype(float)
-    df_url['Shots+Hits/60'] = df_url['Shots/60'].astype(float)+df_url['Hits/60'].astype(float)
-    #print(url)
-    return df_url
-
-team_abv = pd.read_csv('team_abv.csv')
-team_dict = team_abv.set_index('team_abv').to_dict()
-
-yahoo_nhl_df = pd.read_csv('yahoo_to_nhl.csv', encoding='unicode_escape')
-
-def nat_stat_convert(df):
-    for i in range(0,len(df.columns)):
-        if df.columns[i][-3:]=='/60':
-            if 'ix' not in df.columns[i]:
-                df[df.columns[i]] = np.round(df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60,0)
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
-            else:
-                df[df.columns[i]] = df[df.columns[i]].astype(float)*df['TOI'].astype(float)/60
-                df = df.rename(columns={df.columns[i]: df.columns[i].replace('/60','')})
+from shiny import ui, render, App
+import matplotlib.image as mpimg
+# app_ui = ui.page_fluid(
 
-    df['Faceoffs %'] = df['Faceoffs Won']/(df['Faceoffs Won']+df['Faceoffs Lost'])
+#     # ui.output_plot("plot"),
+#     #ui.h2('MLB Batter Launch Angle vs Exit Velocity'),
+#     ui.layout_sidebar(
+#         ui.panel_sidebar(
+#             ui.input_select("id", "Select Batter",batter_dict),
 
-    return df
+#             ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'})))
+#         ,
 
-from shiny import ui, render, App
-import matplotlib.image as mpimg
+#     ui.panel_main(ui.output_plot("plot",height = "750px",width="1250px")),
+#     #ui.download_button('test','Download'),
+# )
+import shinyswatch
 app_ui = ui.page_fluid(
-    #ui.panel_title("Simulate a normal distribution"),
-
+    #shinyswatch.theme.cosmo(),
     ui.layout_sidebar(
-
+    
+    # Available themes:
+    #  cerulean, cosmo, cyborg, darkly, flatly, journal, litera, lumen, lux,
+    #  materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, slate,
+    #  solar, spacelab, superhero, united, vapor, yeti, zephyr
+        
       ui.panel_sidebar(
-        #ui.input_date_range("date_range_id", "Date range input",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
-        ui.input_select("team_id", "Select Team",team_dict,width=1,size=1,selected='ANA'),
-        ui.input_numeric("n_1", "Last Games x", value=1),
-        ui.input_numeric("n_2", "Last Games y", value=0),
-        ui.input_numeric("n_3", "Last Games z", value=0),
-        ui.input_numeric("top_n", "Show top 'n'", value=10),
-      ),
-
+            ui.input_select("week_id", "Select Week (Set as Season for Custom Date Range)",week_dict,width=1),
+            ui.input_select("sort_id", "Sort Column",['Score','Team','Total','Off-Night','B2B'],width=1),
+            ui.input_switch("a_d_id", "Ascending?"),
+            #ui.input_select("date_id", "Select Date",yahoo_weeks['Week'],width=1),
+            ui.input_date_range("date_range_id", "Date range input",start = datetime.today().date(), end = datetime.today().date() + timedelta(days=6)),
+            ui.output_table("result"),width=3),
+         
+        
         ui.panel_main(ui.tags.h3(""),
                       ui.div({"style": "font-size:2em;"},ui.output_text("txt_title")),
                       #ui.tags.h2("Fantasy Hockey Schedule Summary"),
-                      ui.tags.h5("Created By: @TJStats,     Data: Natural Stat Trick, Yahoo Fantasy"),
+                      ui.tags.h5("Created By: @TJStats,     Data: NHL"),
                       ui.div({"style": "font-size:1.2em;"},ui.output_text("txt")),
-                      ui.output_table("pp_roundup"),
-                      #ui.tags.h5('Legend'),
-                     #ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
-                      #ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
-        )
-    ),
-)
+                      ui.output_table("schedule_result"),
+                      ui.tags.h5('Legend'),
+                      ui.output_table("schedule_result_legend"),
+                      ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
+                      ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
+
+        ))
+        #  ui.row(
+        #         ui.column(
+        #             3,
+        #                 ui.input_date("x", "Date input"),),
+        #         ui.column(
+        #             1,
+        #             ui.input_select("level_id", "Select Level",level_dict,width=1)),
+        #         ui.column(
+        #             3,
+        #             ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1)),
+        #         ui.column(
+        #             2,
+        #             ui.input_numeric("n", "Rolling Window Size", value=50)),
+        #         ),
+        #             ui.output_table("result_batters")),
+
+                # ui.nav(
+                #     "Pitchers",
+
+                #                 ui.row(
+                # ui.column(
+                #     3,
+                #     ui.input_select("id_pitch", "Select Pitcher",pitcher_dict,width=1,selected=675911),
+                #     ),
+                # ui.column(
+                #     1,
+                #     ui.input_select("level_id_pitch", "Select Level",level_dict,width=1)),
+                # ui.column(
+                #     3,
+                #     ui.input_select("stat_id_pitch", "Select Stat",plot_dict_small_pitch,width=1)),
+                # ui.column(
+                #     2,
+                #         ui.input_numeric("n_pitch", "Rolling Window Size", value=50)),
+                # ),
+                #     ui.output_table("result_pitchers")),
+#             )
+#         )
+# )
 
 
 
 
 from urllib.request import Request, urlopen
-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.text
     def txt():
-        return   f'{input.team_id()} Last Games PP Summary'
-      
+        
+        week_set = int(input.week_id())
+        if week_set != 0:
+            if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['Start'].values[0]).year != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['End'].values[0]).year:
+
+                return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d, %Y")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
+            else:
+                if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).month != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).month:
+                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
+                else:
+                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%d, %Y")}'
+        else:
+            if input.date_range_id()[0].year != input.date_range_id()[1].year:
+
+                return f'{input.date_range_id()[0].strftime("%B %d, %Y")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
+            else:
+                if input.date_range_id()[0].month != input.date_range_id()[1].month:
+                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
+                else:
+                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%d, %Y")}'            
+
+
     @output
     @render.text
     def txt_title():
-    
-        return f'Team Last Games PP% Leaders'
+        week_set = int(input.week_id())
+        if week_set != 0:
+            return f'Fantasy Hockey Schedule Summary - Yahoo - Week {input.week_id()}'
+        else:
+            return f'Fantasy Hockey Schedule Summary'
 
+    @output
+    @render.table
+    def result():
+        #print(yahoo_weeks)
+        return yahoo_weeks[['Week','Start','End']]
 
+    @output
+    @render.table
+    def schedule_result():
+        
 
+        week_set = int(input.week_id())
+        print(week_set)
 
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
 
-    @output
-    @render.table
-    def pp_roundup():
 
-        top_n = input.top_n()
-        n_1 = input.n_1()
-        n_2 = input.n_2()
-        n_3 = input.n_3()
+        sort_value='Score'
+        ascend=False
 
-        list_of_columns =  ['Player', 'Team', 'display_position','percent_owned','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
 
-        list_of_columns_name =  ['Player', 'Team', 'Position','Roster%','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI',
-                                 'L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
 
-        if type(n_1) is not int:
-            n_1 = 1
-            
-        if n_2 == 0:
-            list_of_columns.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns.remove(f'L{str(n_2)} PP%')
-            list_of_columns_name.remove(f'L{str(n_2)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_2)} PP%')
 
-        if n_3 == 0:
-            list_of_columns.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns.remove(f'L{str(n_3)} PP%')
-            list_of_columns_name.remove(f'L{str(n_3)} PP TOI')
-            list_of_columns_name.remove(f'L{str(n_3)} PP%')
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
 
-        start_date ='2023-09-01'
-        end_date = '2024-05-01'
 
-        df_pp_1 = nat_stat_trick_range_pp_gp(rookie='n',start_date = start_date,end_date = end_date, sit='pp',gp=n_1)
-        df_pp_2 = nat_stat_trick_range_pp_gp(rookie='n',start_date = start_date,end_date = end_date, sit='pp',gp=n_2)
-        df_pp_3 = nat_stat_trick_range_pp_gp(rookie='n',start_date = start_date,end_date = end_date, sit='pp',gp=n_3)
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],left_on=['Team'],right_index=True,how='left').fillna(0)['offnight']
+        test_df['B2B']= test_df.merge(right=weekly_stack.groupby('team_abv').sum()['b2b'],left_on=['Team'],right_index=True,how='left').fillna(0)['b2b']
 
 
-        df_all_pp_1 = nat_stat_convert(df_pp_1)
-        df_all_pp_2 = nat_stat_convert(df_pp_2)
-        df_all_pp_3 = nat_stat_convert(df_pp_3)
 
-        df_final =  df_all_pp_1.merge( df_all_pp_2,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("","_2"))
-        df_final =  df_final.merge( df_all_pp_3,how='outer',left_on=['player_id'],right_on=['player_id'],suffixes=("_1","_3"))
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
 
 
-        team_report_1 = pd.read_html(f'https://www.naturalstattrick.com/teamtable.php?fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&rate=n&team=all&loc=B&gpf=c&gp='+str(n_1)+'&fd=&td=')[0]
-        team_report_2 = pd.read_html(f'https://www.naturalstattrick.com/teamtable.php?fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&rate=n&team=all&loc=B&gpf=c&gp='+str(n_2)+'&fd=&td=')[0]
-        team_report_3 = pd.read_html(f'https://www.naturalstattrick.com/teamtable.php?fromseason=fromseason={season}&thruseason={season}&stype={seasontype}&sit=pp&score=all&rate=n&team=all&loc=B&gpf=c&gp='+str(n_3)+'&fd=&td=')[0]
-        team_report_1 = team_report_1.merge(team_abv,left_on=['Team'],right_on=['team_name'],how='left')
-        team_report_2 = team_report_2.merge(team_abv,left_on=['Team'],right_on=['team_name'],how='left')
-        team_report_3 = team_report_3.merge(team_abv,left_on=['Team'],right_on=['team_name'],how='left')
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
 
-        test = df_final[['player_id','Player_1','Team_1','Position_1','TOI_1','TOI_2','TOI_3',]]
-        test.columns = ['player_id','Player','Team','Position','TOI_1','TOI_2','TOI_3',]
-        test = test.merge(team_report_1[['TOI','team_abv']],how='left',left_on=['Team'],right_on=['team_abv'], suffixes=('','_1_team'))
-        test = test.merge(team_report_2[['TOI','team_abv']],how='left',left_on=['Team'],right_on=['team_abv'], suffixes=('','_2_team'))
-        test = test.merge(team_report_3[['TOI','team_abv']],how='left',left_on=['Team'],right_on=['team_abv'], suffixes=('','_3_team'))
-        test = test.fillna('0:00')
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
 
-        test['TOI'] = [int(x[:-3])+int(x[-2:])*100/60/100 for x  in test['TOI'] ]
-        test['TOI_1'] = [x if x!='0:00' else 0 for x in test['TOI_1']]
-        test['TOI_2'] = [x if x!='0:00' else 0 for x in test['TOI_2']]
-        test['TOI_3'] = [x if x!='0:00' else 0 for x in test['TOI_3']]
 
-        test['TOI_2_team'] = [int(x[:-3])+int(x[-2:])*100/60/100 for x  in test['TOI_2_team'] ]
-        test['TOI_3_team'] = [int(x[:-3])+int(x[-2:])*100/60/100 for x  in test['TOI_3_team']]
+        team_score = test_df['Total']+test_df['Off-Night']*0.5+test_df['B2B']*-0.2+games_vs_tired*0.3+gf_rank*0.1+ga_rank*0.1
 
-        test['TOI'] = test['TOI'].astype(float)
+        test_df['Score'] = team_score
 
-        test['PP%_1'] = test['TOI_1'].astype(float)/ test['TOI'].astype(float)
-        test['PP%_2'] = test['TOI_2'].astype(float)/ test['TOI_2_team'].astype(float)
-        test['PP%_3'] = test['TOI_3'].astype(float)/ test['TOI_3_team'].astype(float)
-        test = test.fillna(0)
-        test['TOI_1'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_1'].astype(float)]
-        test['TOI_2'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_2'].astype(float)]
-        test['TOI_3'] = ["%d:%02d" % (int(x),(x*60)%60) for x in test['TOI_3'].astype(float)]
-        test = test.drop(['team_abv','team_abv_2_team','team_abv_3_team','TOI','TOI_2_team','TOI_3_team'],axis=1)
-        test.columns = ['player_id','Player','Team','Position','L'+str(n_1)+' PP TOI','L'+str(n_2)+' PP TOI','L'+str(n_3)+' PP TOI','L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%']
 
-        yahoo_df = yahoo_df_2.merge(yahoo_nhl_df,left_on = 'player_id',right_on='player_id_yahoo',suffixes=['','_y'])
-        yahoo_df.nhl_id = yahoo_df.nhl_id.astype(float)
-        test.player_id = test.player_id.astype(float)
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
 
-        test = test.merge(right=yahoo_df,left_on='player_id',right_on='nhl_id',suffixes=['','_y'],how='left')
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
 
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
 
-        print('Column List')
-        print(test.columns)
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
 
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
 
-        print(list_of_columns)
-        test = test[list_of_columns]
-        test = test.rename(columns={'percent_owned':'Roster%'})
-        
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
 
-        top_d_score = test[(test.Team==input.team_id())].sort_values(by=['L'+str(n_1)+' PP%'],ascending=False).reset_index(drop=True)
-        top_d_score = top_d_score.head(min(len(top_d_score),top_n))
+        test_df.style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))
+        test_df_style = test_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
 
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),('border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'75px'},overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False,subset = ((list(test_df.index[:]),test_df.columns[5:]))).set_table_styles(
+            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).hide_index()
+
+        test_df_style = test_df_style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))
 
-        #top_d_score.columns = list_of_columns_name
+        test_df_style = test_df_style.applymap(highlight_cells)
+        test_df_style = test_df_style.background_gradient(cmap=cmap_total,subset = ((list(test_df.index[:-1]),test_df.columns[0])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_total,vmin=0,vmax=np.max(test_df.Total[:len(test_df)-1]),subset = ((list(test_df.index[:-1]),test_df.columns[2])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_off,subset = ((list(test_df.index[:-1]),test_df.columns[3])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_back,subset = ((list(test_df.index[:-1]),test_df.columns[4])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_sum,subset = ((list(test_df.index[-1:]),test_df.columns[L:])),axis=1)
+        test_df_style = test_df_style.set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:-1]),test_df.columns[:]))).set_properties(
+                    **{'min-width':'85px'},subset = ((list(test_df.index[:-1]),test_df.columns[L:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:]),test_df.columns[L:])))
 
-        cols = top_d_score.columns.tolist();
+        test_df_style = test_df_style.format(
+            '{:.0f}',subset=(test_df.index[:-1],test_df.columns[2:L]))
 
-        top_d_score['Deployment'] = "PP2"
-        top_d_score['Deployment'][0:5] = "PP1"
+        test_df_style = test_df_style.format(
+            '{:.1f}',subset=(test_df.index[:-1],test_df.columns[0]))
 
-        # df_style_bang = top_d_score.head(10).style.background_gradient(cmap=co, subset=['L'+str(n_1)+' PP%','L'+str(n_2)+' PP%','L'+str(n_3)+' PP%','Roster%']).hide_index().set_properties(**{'Height': '12px'},**{'text-align': 'center'}).set_table_styles([{
-        #         'selector': 'caption',
-        #         'props': [
-        #             ('color', ''),
-        #             ('fontname', 'Century Gothic'),
-        #             ('font-size', '20px'),
-        #             ('font-style', 'italic'),
-        #             ('font-weight', ''),
-        #             ('text-align', 'centre'),
-        #             ]
 
-        #             },{'selector' :'th', 'props':[('text-align', 'center'),('Height','5px')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '13px'),('fontname', 'Century Gothic')]}]).format(
-        #                 {'L'+str(n_1)+' PP%': '{:.0%}',
-        #                 'L'+str(n_2)+' PP%': '{:.0%}',
-        #                 'L'+str(n_3)+' PP%': '{:.0%}',
-        #                 'Roster%': '{:.0%}',
-        #                 },)
+        print('made it to teh end')
+        return test_df_style
 
-        df_style_bang = top_d_score.head(input.top_n()).style.background_gradient(cmap=co,vmin=0,vmax=1, subset=[x for x in cols if x.endswith('PP%')]).set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+
+        #return exit_velo_df_codes_summ_time_style_set
+
+    # @output
+    # @render.plot(alt="A histogram")
+    # def plot_pitch():
+    #     p
+    @output
+    @render.table
+    def schedule_result_legend():
+
+        off_b2b_df = pd.DataFrame(data={'off':'Off-Night','b2b':'Tired Opp. &#128564;'},index=[0])
+        #off_b2b_df.style.applymap(highlight_cols,subset = ((list(off_b2b_df.index[:-1]),off_b2b_df.columns[0])))
+        off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
             'selector': 'caption',
             'props': [
                 ('color', ''),
@@ -371,24 +471,26 @@ def server(input, output, session):
                 ('text-align', 'centre'),
                 ]
 
-                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),('border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
-                    **{'background-color':'White','index':'White','min-width':'75px'},overwrite=False).set_table_styles(
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
+                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(
             [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
             [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
             [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
-            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).hide_index().format(
-                        {'L'+str(n_1)+' PP%': '{:.0%}',
-                        'L'+str(n_2)+' PP%': '{:.0%}',
-                        'L'+str(n_3)+' PP%': '{:.0%}',
-                        'Roster%': '{:.0%}',
-                        },)
-
-        return df_style_bang
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(
+                **{'background-color':'#C2FEE9'},subset=off_b2b_df.columns[0]).set_properties(
+                **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([
+            {'selector': 'thead', 'props': [('display', 'none')]}
+            ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(
+                    **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])))
+        
+        return off_b2b_df_style
 
 
 
-# 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']]