Upload 4 files

app.py

@@ -1,155 +1,493 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
-
+import requests
 import pandas as pd
 import seaborn as sns
-import shinyswatch
-
-import shiny.experimental as x
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = x.ui.page_fillable(
-    shinyswatch.theme.minty(),
-    ui.layout_sidebar(
-        ui.panel_sidebar(
-            # Artwork by @allison_horst
-            ui.input_selectize(
-                "xvar",
-                "X variable",
-                numeric_cols,
-                selected="Bill Length (mm)",
-            ),
-            ui.input_selectize(
-                "yvar",
-                "Y variable",
-                numeric_cols,
-                selected="Bill Depth (mm)",
-            ),
-            ui.input_checkbox_group(
-                "species", "Filter by species", species, selected=species
-            ),
-            ui.hr(),
-            ui.input_switch("by_species", "Show species", value=True),
-            ui.input_switch("show_margins", "Show marginal plots", value=True),
-            width=2,
-        ),
-        ui.panel_main(
-            ui.output_ui("value_boxes"),
-            x.ui.output_plot("scatter", fill=True),
-            ui.help_text(
-                "Artwork by ",
-                ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
-                class_="text-end",
-            ),
-        ),
-    ),
-)
-
-
-def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Calc
-    def filtered_df() -> pd.DataFrame:
-        """Returns a Pandas data frame that includes only the desired rows"""
-
-        # This calculation "req"uires that at least one species is selected
-        req(len(input.species()) > 0)
-
-        # Filter the rows so we only include the desired species
-        return df[df["Species"].isin(input.species())]
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import figure
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+from scipy import stats
+import matplotlib.lines as mlines
+import matplotlib.transforms as mtransforms
+import numpy as np
+import plotly.express as px
+#!pip install chart_studio
+# import chart_studio.tools as tls
+from bs4 import BeautifulSoup
+import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.font_manager as font_manager
+from datetime import datetime
+import pytz
+from datetime import date
+datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
+# Configure Notebook
+#%matplotlib inline
+plt.style.use('fivethirtyeight')
+sns.set_context("notebook")
+import warnings
+warnings.filterwarnings('ignore')
+from urllib.request import urlopen
+import json
+from datetime import date, timedelta
+import dataframe_image as dfi
+from os import listdir
+from os.path import isfile, join
+import datetime
+import seaborn as sns
+import os
+import calendar
+from IPython.display import display, HTML
+import matplotlib.image as mpimg
+from skimage import io
+import difflib 
+
+
+from datetime import datetime
+import pytz
+datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
+# Configure Notebook
+#%matplotlib inline
+plt.style.use('fivethirtyeight')
+sns.set_context("notebook")
+import warnings
+warnings.filterwarnings('ignore')
+# import yfpy
+# from yfpy.query import YahooFantasySportsQuery
+# import yahoo_oauth
+import json
+import openpyxl
+from sklearn import preprocessing
+from PIL import Image
+import logging
+import matplotlib.patches as patches
+from matplotlib.patches import Rectangle
+from matplotlib.font_manager import FontProperties
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+
+import requests
+import pickle
+import pandas as pd
+
+# # Loop over the counter and format the API call
+r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-01&endDate=2024-06-01')
+schedule = r.json()
+
+def flatten(t):
+    return [item for sublist in t for item in sublist]
+
+game_id = flatten([[x['gamePk'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_date = flatten([[x['gameDate'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_home = flatten([[x['teams']['home']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+game_away = flatten([[x['teams']['away']['team']['name'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))])
+
+schedule_df = pd.DataFrame(data={'game_id': game_id, 'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away})
+schedule_df.game_date = pd.to_datetime(schedule_df['game_date']).dt.tz_convert(tz='US/Eastern').dt.date
+schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens')
+schedule_df.head()
+
+team_abv = pd.read_csv('team_abv.csv')
+yahoo_weeks = pd.read_csv('yahoo_weeks.csv')
+#yahoo_weeks['Number'] = yahoo_weeks['Number'].astype(int)
+yahoo_weeks['Start'] = pd.to_datetime(yahoo_weeks['Start'])
+yahoo_weeks['End'] = pd.to_datetime(yahoo_weeks['End'])
+yahoo_weeks.head(5)
+
+def highlight_cols(s):
+    color = '#C2FEE9'
+    return 'background-color: %s' % color
+def highlight_cells(val):
+    color = 'white' if val == ' ' else ''
+    return 'background-color: {}'.format(color)
+
+import matplotlib.pyplot as plt
+import matplotlib.colors
+cmap_total = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#56B4E9","#FFFFFF","#F0E442"])
+cmap_off = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
+cmap_back = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#56B4E9"])
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF","#F0E442"])
+
+schedule_df = schedule_df.merge(right=team_abv,left_on='game_away',right_on='team_name',how='inner',suffixes=['','_away']) 
+schedule_df = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='inner',suffixes=['','_home']) 
+schedule_df['away_sym'] = '@'
+schedule_df['home_sym'] = 'vs'
+
+
+if not os.path.isfile('standings/standings_'+str(date.today())+'.csv'):   
+    standings_df_old = pd.read_html('https://www.hockey-reference.com/leagues/NHL_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 = []
+
+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'])
+
+week_dict = yahoo_weeks.set_index('Number')['Week'].sort_index().to_dict()
+
+from shiny import ui, render, App
+import matplotlib.image as mpimg
+# app_ui = ui.page_fluid(
+
+#     # ui.output_plot("plot"),
+#     #ui.h2('MLB Batter Launch Angle vs Exit Velocity'),
+#     ui.layout_sidebar(
+#         ui.panel_sidebar(
+#             ui.input_select("id", "Select Batter",batter_dict),
+
+#             ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'})))
+#         ,
+
+#     ui.panel_main(ui.output_plot("plot",height = "750px",width="1250px")),
+#     #ui.download_button('test','Download'),
+# )
+app_ui = ui.page_fluid(ui.layout_sidebar(
+    # Available themes:
+    #  cerulean, cosmo, cyborg, darkly, flatly, journal, litera, lumen, lux,
+    #  materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, slate,
+    #  solar, spacelab, superhero, united, vapor, yeti, zephyr
+        
+      ui.panel_sidebar(
+            ui.input_select("week_id", "Select Week (Set as Season for Custom Date Range)",week_dict,width=1),
+            ui.input_select("sort_id", "Sort Column",['Score','Team','Total','Off-Night','B2B'],width=1),
+            ui.input_switch("a_d_id", "Ascending?"),
+            #ui.input_select("date_id", "Select Date",yahoo_weeks['Week'],width=1),
+            ui.input_date_range("date_range_id", "Date range input",start = datetime.today().date(), end = datetime.today().date() + timedelta(days=6)),
+            ui.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: NHL"),
+                      ui.div({"style": "font-size:1.2em;"},ui.output_text("txt")),
+                      ui.output_table("schedule_result"),
+                      ui.tags.h5('Legend'),
+                      ui.output_table("schedule_result_legend"),
+                      ui.tags.h6('An Off Night is defined as a day in which less than half the teams in the NHL are playing'),
+                      ui.tags.h6('The scores are determined by using games played, off-nights, B2B, and strength of opponents') )
+
+        ))
+        #  ui.row(
+        #         ui.column(
+        #             3,
+        #                 ui.input_date("x", "Date input"),),
+        #         ui.column(
+        #             1,
+        #             ui.input_select("level_id", "Select Level",level_dict,width=1)),
+        #         ui.column(
+        #             3,
+        #             ui.input_select("stat_id", "Select Stat",plot_dict_small,width=1)),
+        #         ui.column(
+        #             2,
+        #             ui.input_numeric("n", "Rolling Window Size", value=50)),
+        #         ),
+        #             ui.output_table("result_batters")),
+
+                # ui.nav(
+                #     "Pitchers",
+
+                #                 ui.row(
+                # ui.column(
+                #     3,
+                #     ui.input_select("id_pitch", "Select Pitcher",pitcher_dict,width=1,selected=675911),
+                #     ),
+                # ui.column(
+                #     1,
+                #     ui.input_select("level_id_pitch", "Select Level",level_dict,width=1)),
+                # ui.column(
+                #     3,
+                #     ui.input_select("stat_id_pitch", "Select Stat",plot_dict_small_pitch,width=1)),
+                # ui.column(
+                #     2,
+                #         ui.input_numeric("n_pitch", "Rolling Window Size", value=50)),
+                # ),
+                #     ui.output_table("result_pitchers")),
+#             )
+#         )
+# )
+
+
+
+
+from urllib.request import Request, urlopen
+# importing OpenCV(cv2) module
+
+
+
+
+def server(input, output, session):
+
+    @output
+    @render.text
+    def txt():
+        
+        week_set = int(input.week_id())
+        if week_set != 0:
+            if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['Start'].values[0]).year != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]['End'].values[0]).year:
+
+                return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d, %Y")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
+            else:
+                if pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).month != pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).month:
+                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%B %d, %Y")}'
+                else:
+                    return f'{pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["Start"].values[0]).strftime("%B %d")} to {pd.to_datetime(yahoo_weeks[yahoo_weeks.Number == week_set]["End"].values[0]).strftime("%d, %Y")}'
+        else:
+            if input.date_range_id()[0].year != input.date_range_id()[1].year:
+
+                return f'{input.date_range_id()[0].strftime("%B %d, %Y")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
+            else:
+                if input.date_range_id()[0].month != input.date_range_id()[1].month:
+                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%B %d, %Y")}'
+                else:
+                    return f'{input.date_range_id()[0].strftime("%B %d")} to {input.date_range_id()[1].strftime("%d, %Y")}'            
+
+
+    @output
+    @render.text
+    def txt_title():
+        week_set = int(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.plot
-    def scatter():
-        """Generates a plot for Shiny to display to the user"""
-
-        # The plotting function to use depends on whether margins are desired
-        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
-        plotfunc(
-            data=filtered_df(),
-            x=input.xvar(),
-            y=input.yvar(),
-            palette=palette,
-            hue="Species" if input.by_species() else None,
-            hue_order=species,
-            legend=False,
-        )
+    @render.table
+    def result():
+        #print(yahoo_weeks)
+        return yahoo_weeks
 
     @output
-    @render.ui
-    def value_boxes():
-        df = filtered_df()
-
-        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
-            return x.ui.value_box(
-                title,
-                count,
-                {"class_": "pt-1 pb-0"},
-                showcase=x.ui.as_fill_item(
-                    ui.tags.img(
-                        {"style": "object-fit:contain;"},
-                        src=showcase_img,
-                    )
-                ),
-                theme_color=None,
-                style=f"background-color: {bgcol};",
-            )
-
-        if not input.by_species():
-            return penguin_value_box(
-                "Penguins",
-                len(df.index),
-                bg_palette["default"],
-                # Artwork by @allison_horst
-                showcase_img="penguins.png",
-            )
-
-        value_boxes = [
-            penguin_value_box(
-                name,
-                len(df[df["Species"] == name]),
-                bg_palette[name],
-                # Artwork by @allison_horst
-                showcase_img=f"{name}.png",
-            )
-            for name in species
-            # Only include boxes for _selected_ species
-            if name in input.species()
-        ]
-
-        return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
-
-
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
-
-palette: Dict[str, Tuple[float, float, float]] = {
-    "Adelie": colors[0],
-    "Chinstrap": colors[1],
-    "Gentoo": colors[2],
-    "default": sns.color_palette()[0],  # type: ignore
-}
-
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
-    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
-    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
-
-
-app = App(
-    app_ui,
-    server,
-    static_assets=str(www_dir),
-)
+    @render.table
+    def schedule_result():
+        
+
+        week_set = int(input.week_id())
+        print(week_set)
+
+        if week_set == 0:
+            start_point = input.date_range_id()[0]
+            end_point = input.date_range_id()[1]
+        else:   
+            start_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['Start'][0]
+            end_point = yahoo_weeks[yahoo_weeks.Number==week_set].reset_index(drop=True)['End'][0]
+
+
+        sort_value='Score'
+        ascend=False
+
+        weekly_stack = schedule_stack[(schedule_stack['date'].dt.date>=start_point)&(schedule_stack['date'].dt.date<=end_point)]
+        date_list = pd.date_range(start_point,end_point,freq='d')
+        test_list = [[]] * len(date_list)
+
+
+
+        for i in range(0,len(date_list)):
+            test_list[i]  = team_abv.merge(right=weekly_stack[weekly_stack['date']==date_list[i]],left_on='team_abv',right_on='team_abv',how='left')
+            test_list[i] = test_list[i].fillna("")
+            test_list[i]['new_text'] = test_list[i]['symbol'] + ' '+ test_list[i]['team_abv_home'] + test_list[i]['away_b2b']
+
+
+        test_df = pd.DataFrame()
+        test_df['Team'] = list(team_abv['team_abv'])
+        test_df['Total'] = test_df.merge(right=weekly_stack.groupby('team_abv')['team_abv'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['team_abv']
+        test_df['Off-Night'] = test_df.merge(right=weekly_stack.groupby('team_abv').sum()['offnight'],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']
+
+
+
+        gf_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GF_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GF_Rank'])
+        ga_rank = np.array(test_df.merge(right=weekly_stack.groupby('team_abv').mean()['GA_Rank'],left_on=['Team'],right_index=True,how='left').fillna(0)['GA_Rank'])
+
+
+        #games_vs_tired = np.array([float(i)*0.4 for i in list(weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()))])
+
+        games_vs_tired = 0.4*np.array(test_df.merge(right=weekly_stack.groupby('team_abv')['away_b2b'].apply(lambda x: x[x != ''].count()),left_on=['Team'],right_index=True,how='left').fillna(0)['away_b2b'])
+
+
+        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_df['Score'] = team_score
+
+
+        cols = test_df.columns.tolist();
+        L = len(cols) 
+        test_df = test_df[cols[4:]+cols[0:4]]
+        #return test_df#[cols[4:]+cols[0:4]]
+
+        test_df = test_df.sort_values(by=[sort_value,'Score'],ascending = ascend)
+
+        for i in range(0,len(date_list)):
+            test_df[calendar.day_name[date_list[i].weekday()]+'<br>'+str(date_list[i].month)+'-'+'{:02d}'.format(date_list[i].day)] = test_list[i]['new_text']
+
+        row = ['']*L
+        for x in  test_df[test_df.columns[L:]]:
+            row.append(int(sum(test_df[x]!=" ")/2))
+
+        test_df = test_df.sort_values(by=input.sort_id(),ascending=input.a_d_id())
+
+        test_df.loc[32] = row
+        #test_df_html = HTML( test_df.to_html().replace("\\n","<br>") ) 
+        offnight_list = [True if x <8 else False for x in test_df.iloc[-1][L:]]
+
+        test_df.style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))
+        test_df_style = test_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),('border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'75px'},overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'100px'},overwrite=False,subset = ((list(test_df.index[:]),test_df.columns[5:]))).set_table_styles(
+            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).hide_index()
+
+        test_df_style = test_df_style.applymap(highlight_cols,subset = ((list(test_df.index[:-1]),test_df.columns[L:][offnight_list])))
+
+        test_df_style = test_df_style.applymap(highlight_cells)
+        test_df_style = test_df_style.background_gradient(cmap=cmap_total,subset = ((list(test_df.index[:-1]),test_df.columns[0])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_total,vmin=0,vmax=np.max(test_df.Total[:len(test_df)-1]),subset = ((list(test_df.index[:-1]),test_df.columns[2])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_off,subset = ((list(test_df.index[:-1]),test_df.columns[3])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_back,subset = ((list(test_df.index[:-1]),test_df.columns[4])))
+        test_df_style = test_df_style.background_gradient(cmap=cmap_sum,subset = ((list(test_df.index[-1:]),test_df.columns[L:])),axis=1)
+        test_df_style = test_df_style.set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:-1]),test_df.columns[:]))).set_properties(
+                    **{'min-width':'85px'},subset = ((list(test_df.index[:-1]),test_df.columns[L:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(test_df.index[:]),test_df.columns[L:])))
+
+        test_df_style = test_df_style.format(
+            '{:.0f}',subset=(test_df.index[:-1],test_df.columns[2:L]))
+
+        test_df_style = test_df_style.format(
+            '{:.1f}',subset=(test_df.index[:-1],test_df.columns[0]))
+
+
+        print('made it to teh end')
+        return test_df_style
+
+
+        #return exit_velo_df_codes_summ_time_style_set
+
+    # @output
+    # @render.plot(alt="A histogram")
+    # def plot_pitch():
+    #     p
+    @output
+    @render.table
+    def schedule_result_legend():
+
+        off_b2b_df = pd.DataFrame(data={'off':'Off-Night','b2b':'Tired Opp. &#128564;'},index=[0])
+        #off_b2b_df.style.applymap(highlight_cols,subset = ((list(off_b2b_df.index[:-1]),off_b2b_df.columns[0])))
+        off_b2b_df_style = off_b2b_df.style.set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '20px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('text-align', 'center'),('Height','px'),('color','black'),(
+                    'border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '18px'),('color','black')]}],overwrite=False).set_properties(
+                    **{'background-color':'White','index':'White','min-width':'150px'},overwrite=False).set_table_styles(
+            [{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False).set_table_styles(
+            [{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False).set_properties(
+            **{'Height': '8px'},**{'text-align': 'center'},overwrite=False).set_properties(
+                **{'background-color':'#C2FEE9'},subset=off_b2b_df.columns[0]).set_properties(
+                **{'color':'black'},subset=off_b2b_df.columns[:]).hide_index().set_table_styles([
+            {'selector': 'thead', 'props': [('display', 'none')]}
+            ]).set_properties(**{'border': '3 px','color':'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:]))).set_properties(
+                    **{'min-width':'130'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])),overwrite=False).set_properties(**{
+                                'color': 'black'},overwrite=False).set_properties(
+                    **{'border': '1px black solid !important'},subset = ((list(off_b2b_df.index[:]),off_b2b_df.columns[:])))
+        
+        return off_b2b_df_style
+
+
+
+
+
+
+app = App(app_ui, server)

team_abv.csv

@@ -0,0 +1,33 @@
+team_abv,team_name
+ANA,Anaheim Ducks
+ARI,Arizona Coyotes
+BOS,Boston Bruins
+BUF,Buffalo Sabres
+CAR,Carolina Hurricanes
+CBJ,Columbus Blue Jackets
+CGY,Calgary Flames
+CHI,Chicago Blackhawks
+COL,Colorado Avalanche
+DAL,Dallas Stars
+DET,Detroit Red Wings
+EDM,Edmonton Oilers
+FLA,Florida Panthers
+L.A,Los Angeles Kings
+MIN,Minnesota Wild
+MTL,Montreal Canadiens
+N.J,New Jersey Devils
+NSH,Nashville Predators
+NYI,New York Islanders
+NYR,New York Rangers
+OTT,Ottawa Senators
+PHI,Philadelphia Flyers
+PIT,Pittsburgh Penguins
+S.J,San Jose Sharks
+SEA,Seattle Kraken
+STL,St. Louis Blues
+T.B,Tampa Bay Lightning
+TOR,Toronto Maple Leafs
+VAN,Vancouver Canucks
+VGK,Vegas Golden Knights
+WPG,Winnipeg Jets
+WSH,Washington Capitals

yahoo_weeks.csv

@@ -0,0 +1,28 @@
+Week,Number,Start,End
+Week 1,1,2023-10-10,2023-10-15
+Week 2,2,2023-10-16,2023-10-22
+Week 3,3,2023-10-23,2023-10-29
+Week 4,4,2023-10-30,2023-11-05
+Week 5,5,2023-11-06,2023-11-12
+Week 6,6,2023-11-13,2023-11-19
+Week 7,7,2023-11-20,2023-11-26
+Week 8,8,2023-11-27,2023-12-03
+Week 9,9,2023-12-04,2023-12-10
+Week 10,10,2023-12-11,2023-12-17
+Week 11,11,2023-12-18,2023-12-24
+Week 12,12,2023-12-25,2023-12-31
+Week 13,13,2024-01-01,2024-01-07
+Week 14,14,2024-01-08,2024-01-14
+Week 15,15,2024-01-15,2024-01-21
+Week 16,16,2024-01-22,2024-01-28
+Week 17,17,2024-01-29,2024-02-11
+Week 18,18,2024-02-12,2024-02-18
+Week 19,19,2024-02-19,2024-02-25
+Week 20,20,2024-02-26,2024-03-03
+Week 21,21,2024-03-04,2024-03-10
+Week 22,22,2024-03-11,2024-03-17
+Week 23,23,2024-03-18,2024-03-24
+Week 24,24,2024-03-25,2024-03-31
+Week 25,25,2024-04-01,2024-04-07
+Week 26,26,2024-04-08,2024-04-18
+Season,0,2023-10-10,2024-04-18