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| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from matplotlib.pyplot import figure | |
| from matplotlib.offsetbox import OffsetImage, AnnotationBbox | |
| from scipy import stats | |
| import pickle | |
| import json | |
| from datetime import timedelta | |
| from urllib.request import urlopen | |
| from datetime import date | |
| from datetime import datetime | |
| import pytz | |
| import json | |
| from matplotlib.ticker import MaxNLocator | |
| import matplotlib.font_manager as font_manager | |
| import numpy as np | |
| from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui | |
| # team_games_df = pd.read_csv('data/team_games_all.csv',index_col=[0]) | |
| # player_games_df = pd.read_csv('data/player_games_cards.csv',index_col=[0]).sort_values(by='date').reset_index(drop=True) | |
| team_abv_nst = pd.read_csv('data/team_abv_nst.csv') | |
| #player_games_df = player_games_df.loc[:, ~player_games_df.columns.str.contains('^Unnamed')] | |
| #team_abv = pd.read_csv('team_abv.csv') | |
| #team_games_df = team_games_df.merge(right=team_abv,left_on='team',right_on='team_name',how='left').drop(columns='team_name') | |
| team_abv = pd.read_csv('data/team_abv.csv') | |
| import pickle | |
| from datetime import timedelta | |
| # # Loop over the counter and format the API call | |
| # r = requests.get('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2022-10-01&endDate=2023-06-01') | |
| # schedule = r.json() | |
| # schedule = json.loads(urlopen('https://statsapi.web.nhl.com/api/v1/schedule?startDate=2023-10-07&endDate=2024-04-19').read()) | |
| # 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_type = flatten([[x['gameType'] for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))]) | |
| # game_date = flatten([[(pd.to_datetime(x['gameDate']) - timedelta(hours=8)) for x in schedule['dates'][y]['games']] for y in range(0,len(schedule['dates']))]) | |
| # game_final = flatten([[x['status']['detailedState'] 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_type':game_type,'game_date' : game_date, 'game_home' : game_home, 'game_away' : game_away,'status' : game_final}) | |
| # schedule_df = schedule_df[schedule_df.game_type == 'R'].reset_index(drop=True) | |
| # schedule_df = schedule_df[schedule_df.status != 'Postponed'] | |
| # schedule_df = schedule_df.replace('Montréal Canadiens','Montreal Canadiens') | |
| schedule = pd.read_csv('2024_schedule_href.csv') | |
| #schedule = pd.read_html('https://www.hockey-reference.com/leagues/NHL_2024_games.html')[0] | |
| #schedule.to_csv('schedule/schedule_'+str(date.today())+'.csv') | |
| #schedule = pd.read_csv('schedule/schedule_'+str(date.today())+'.csv') | |
| schedule = schedule.replace('St Louis Blues','St. Louis Blues') | |
| schedule_df = schedule.merge(right=team_abv,left_on='Visitor',right_on='team_name',how='inner',suffixes=['','_away']) | |
| schedule_df = schedule_df.merge(right=team_abv,left_on='Home',right_on='team_name',how='inner',suffixes=['','_home']) | |
| schedule_df = schedule_df.rename(columns={'Visitor':'game_away','Home':'game_home','Date':'game_date'}) | |
| schedule_df_merge = schedule_df.merge(right=team_abv,left_on='game_home',right_on='team_name',how='left') | |
| schedule_df_merge = schedule_df_merge.merge(right=team_abv,left_on='game_away',right_on='team_name',how='left') | |
| schedule_df_merge = schedule_df_merge.drop(columns={'team_name_x','team_name_y'}) | |
| schedule_df_merge = schedule_df_merge.rename(columns={'team_abv_x' : 'team_abv_home','team_abv_y' : 'team_abv_away'}) | |
| schedule_df_merge = schedule_df_merge.loc[:,~schedule_df_merge.columns.duplicated()].copy() | |
| #schedule_df_merge.game_date = pd.to_datetime(schedule_df_merge['game_date']).dt.tz_convert(tz='US/Eastern').dt.date | |
| # schedule_df_merge = schedule_df_merge.set_index(pd.DatetimeIndex(schedule_df_merge.game_date).strftime('%Y-%m-%d')) | |
| schedule_df_merge.index = pd.to_datetime(schedule_df_merge.game_date) | |
| schedule_df_merge = schedule_df_merge.drop(columns='game_date') | |
| #schedule_df_merge.index = schedule_df_merge.index.tz_convert('US/Pacific') | |
| schedule_df_merge.index = schedule_df_merge.index.date | |
| schedule_df_merge = schedule_df_merge.sort_index() | |
| schedule_df_merge = schedule_df_merge[schedule_df_merge.index <= date(2024,5,1)] | |
| schedule_df_merge_final = schedule_df_merge[schedule_df_merge.index<date.today()] | |
| schedule_ccount_df = pd.DataFrame(data={'date':list(schedule_df_merge_final.index)*2,'team':list(schedule_df_merge_final.team_abv_away)+list(schedule_df_merge_final.team_abv_home)}).sort_values(by='date').reset_index(drop=True) | |
| schedule_ccount_df['team_game'] = schedule_ccount_df.groupby('team').cumcount()+1 | |
| schedule_ccount_df.date = pd.to_datetime(schedule_ccount_df.date) | |
| today = pd.to_datetime(datetime.now(pytz.timezone('US/Pacific')).strftime('%Y-%m-%d')) | |
| team_schdule = schedule_df_merge[(schedule_df_merge['team_abv_home']=='EDM')|(schedule_df_merge['team_abv_away']=='EDM')] | |
| team_schdule_live = team_schdule[team_schdule.index <= today] | |
| team_schdule_live.head() | |
| team_games_df = pd.read_csv('data/team_games_all.csv',index_col=[0]) | |
| player_games_df = pd.read_csv('data/player_games_cards.csv',index_col=[0]).sort_values(by='date').reset_index(drop=True) | |
| team_abv_df = pd.read_csv('data/team_abv.csv') | |
| player_games_df = player_games_df.loc[:, ~player_games_df.columns.str.contains('^Unnamed')] | |
| team_games_df = team_games_df.merge(right=team_abv_df,left_on='team',right_on='team_name',how='left').drop(columns='team_name') | |
| player_games_df = player_games_df.drop_duplicates(subset=['player_id','date'],keep='last').reset_index(drop=True) | |
| player_games_df.date = pd.to_datetime(player_games_df.date) | |
| team_games_df['date'] = pd.to_datetime(team_games_df['date']).dt.date | |
| team_games_df = team_games_df[team_games_df['date']<date.today()] | |
| #schedule_df_merge_final = schedule_df_merge[schedule_df_merge['status']=='Final'] | |
| schedule_ccount_df = pd.DataFrame(data={'date':list(schedule_df_merge_final.index)*2,'team':list(schedule_df_merge_final.team_abv_away)+list(schedule_df_merge_final.team_abv_home)}).sort_values(by='date').reset_index(drop=True) | |
| schedule_ccount_df['team_game'] = schedule_ccount_df.groupby('team').cumcount()+1 | |
| schedule_ccount_df.date = pd.to_datetime(schedule_ccount_df.date) | |
| team_games_df['team_game'] = team_games_df.groupby('team').cumcount()+1 | |
| player_games_df = player_games_df.merge(right=schedule_ccount_df,left_on=['Team','date'],right_on=['team','date'],how='left') | |
| player_games_df['player_game'] = player_games_df.groupby('player_id').cumcount()+1 | |
| date_range_list = pd.date_range(start=player_games_df.date.min()+timedelta(days=6),end=player_games_df.date.max()) | |
| team_abv_nst_dict = {'All':''} | team_abv_nst.set_index('team_abv')['team_name'].to_dict() | |
| position_dict = {'All':'','F':'Forwards','D':'Defense'} | |
| player_games_df.player_id = player_games_df.player_id.astype(int) | |
| player_games_df = player_games_df.rename(columns={'Total Points_pp':'PP Points'}) | |
| stat_input_list = ['TOI', 'Goals', 'Total Assists', | |
| 'First Assists', 'Total Points', 'PP Points','Shots', 'Hits', | |
| 'Shots Blocked'] | |
| df_cum_stat_total = player_games_df.groupby(['player_id','Player','Position']).agg( | |
| GP = ('GP','count'), | |
| Total_Points = ('Total Points','sum') | |
| ).reset_index() | |
| df_all_sort = df_cum_stat_total.copy() | |
| stat_pick = 'Total_Points' | |
| count=11 | |
| not_position = '' | |
| team = '' | |
| df_all_sort = df_all_sort[(df_all_sort['Position']!=not_position)] | |
| df_all_sort[stat_pick+' per game'] = df_all_sort[stat_pick]/df_all_sort['GP'] | |
| df_all_sort[stat_pick+' Rank'] = df_all_sort[stat_pick].rank(ascending=False,method='min') | |
| df_all_sort = df_all_sort[df_all_sort[stat_pick+' Rank']<=count] | |
| df_all_sort[stat_pick+' per game Rank'] = df_all_sort[stat_pick+' per game'].rank(ascending=False,method='min') | |
| # #df_all_sort.sort_values(by=[stat_pick,stat_pick+' per game','Total Points'],ascending = (False, False,False)) | |
| df_all_sort_list = df_all_sort[df_all_sort[stat_pick+' Rank']<max(df_all_sort[stat_pick+' Rank'])].sort_values(by=[stat_pick,stat_pick+' per game','Total_Points'],ascending = (False, False,False)) | |
| # # df_all_sort = df_all_sort.sort_values(by=[stat_pick,stat_pick+' per game','Total Points'],ascending = (False, False,False))[(df_all_sort['Position']!=not_position)&(df_all_sort['Team']!=team)].head(count)['Player'] | |
| temp_df = df_all_sort[df_all_sort[stat_pick+' Rank']==max(df_all_sort[stat_pick+' Rank'])]#[stat_pick+' per game Rank'].rank().sort_values(ascending=True).reset_index(drop=True)[count-len(df_all_sort_list)-1] | |
| temp_df['temp'] = temp_df[stat_pick+' per game Rank'].rank()#.sort_values(ascending=True)#.reset_index(drop=True) | |
| temp_df = temp_df.sort_values(by='temp',ascending=True)#.reset_index(drop=True) | |
| temp = temp_df[temp_df['temp']<=(count-len(df_all_sort_list))] | |
| players_list = list(pd.concat([df_all_sort_list,temp]).reset_index(drop=True)['player_id']) | |
| rookie_df = pd.read_csv('data/player_rookies.csv',index_col=[0]) | |
| rookie_list = rookie_df.player_id.values | |
| skater_dict = df_cum_stat_total.sort_values(by=['Total_Points','GP'],ascending=[False,True]).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['Player'].to_dict() | |
| # players_list = list(df_all_sort['Player']) | |
| print(players_list) | |
| from shiny import ui, render, App | |
| from shiny import App, reactive, ui | |
| from shiny.ui import h2, tags | |
| import matplotlib.image as mpimg | |
| # app_ui = ui.page_fluid( | |
| # # ui.output_plot("plot"), | |
| # #ui.h2('MLB Batter Launch Angle vs Exit Velocity'), | |
| # ui.layout_sidebar( | |
| # ui.panel_sidebar( | |
| # ui.input_select("id", "Select Batter",batter_dict), | |
| # ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'}))) | |
| # , | |
| # ui.panel_main(ui.output_plot("plot",height = "750px",width="1250px")), | |
| # #ui.download_button('test','Download'), | |
| # ) | |
| #import shinyswatch | |
| app_ui = ui.page_fluid( | |
| #shinyswatch.theme.cosmo(), | |
| ui.layout_sidebar( | |
| # Available themes: | |
| # cerulean, cosmo, cyborg, darkly, flatly, journal, litera, lumen, lux, | |
| # materia, minty, morph, pulse, quartz, sandstone, simplex, sketchy, slate, | |
| # solar, spacelab, superhero, united, vapor, yeti, zephyr | |
| ui.panel_sidebar( | |
| ui.input_select("id", "Select Skater (max. 10 Skaters)",skater_dict,width=1,selected=list(players_list[0:10]),selectize=True,multiple=True), | |
| ui.input_select("stat", "Stat Input",stat_input_list,width=1,size=1,selectize=False,selected='Total Points'), | |
| ui.input_select("team_select", "Team",team_abv_nst_dict,width=1,size=1,selectize=False,selected='All'), | |
| ui.input_select("position_select", "Position",position_dict,width=1,size=1,selectize=False,selected='All'), | |
| ui.input_date("date", "Date input",value = datetime.today().date() - timedelta(days=1),min='2023-10-10', max=datetime.today().date() - timedelta(days=1)), | |
| ui.input_switch("rookie_switch", "Rookies Only"), | |
| ui.input_action_button("go", "Generate",class_="btn-primary"), | |
| ui.output_table("result"), | |
| width=3), | |
| ui.panel_main( | |
| ui.navset_tab( | |
| ui.nav("Chart Races", | |
| ui.panel_main( | |
| ui.output_plot("plot",height = "1200px",width="1200px")), | |
| ) | |
| )))) | |
| # 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): | |
| def _(): | |
| team_select_list = [input.team_select()] | |
| position_select_list = [input.position_select()] | |
| if team_select_list[0] == 'All': | |
| team_select_list = team_abv_nst.team_abv.unique() | |
| if position_select_list[0] == 'All': | |
| position_select_list = player_games_df.Position.unique() | |
| elif position_select_list[0] == 'F': | |
| position_select_list = player_games_df[player_games_df.Position != 'D'].Position.unique() | |
| else: | |
| position_select_list = ['D'] | |
| print(team_select_list) | |
| if input.rookie_switch(): | |
| df_cum_stat_total = player_games_df[(player_games_df.date <= pd.to_datetime(input.date())) | |
| &(player_games_df.player_id.isin(rookie_list)) | |
| &(player_games_df.Team.isin(team_select_list)) | |
| &(player_games_df.Position.isin(position_select_list))].groupby(['player_id','Player','Position']).agg( | |
| GP = ('GP','count'), | |
| Total_Points = (f'{input.stat()}','sum') | |
| ).reset_index() | |
| else: | |
| df_cum_stat_total = player_games_df[(player_games_df.date <= pd.to_datetime(input.date())) | |
| &(player_games_df.Team.isin(team_select_list)) | |
| &(player_games_df.Position.isin(position_select_list))].groupby(['player_id','Player','Position']).agg( | |
| GP = ('GP','count'), | |
| Total_Points = (f'{input.stat()}','sum') | |
| ).reset_index() | |
| df_all_sort = df_cum_stat_total.copy() | |
| stat_pick = 'Total_Points' | |
| count=6 | |
| not_position = '' | |
| team = '' | |
| df_all_sort = df_all_sort[(df_all_sort['Position']!=not_position)] | |
| df_all_sort[stat_pick+' per game'] = df_all_sort[stat_pick]/df_all_sort['GP'] | |
| df_all_sort[stat_pick+' Rank'] = df_all_sort[stat_pick].rank(ascending=False,method='min') | |
| df_all_sort = df_all_sort[df_all_sort[stat_pick+' Rank']<=count] | |
| df_all_sort[stat_pick+' per game Rank'] = df_all_sort[stat_pick+' per game'].rank(ascending=False,method='min') | |
| # #df_all_sort.sort_values(by=[stat_pick,stat_pick+' per game','Total Points'],ascending = (False, False,False)) | |
| df_all_sort_list = df_all_sort[df_all_sort[stat_pick+' Rank']<max(df_all_sort[stat_pick+' Rank'])].sort_values(by=[stat_pick,stat_pick+' per game','Total_Points'],ascending = (False, False,False)) | |
| # # df_all_sort = df_all_sort.sort_values(by=[stat_pick,stat_pick+' per game','Total Points'],ascending = (False, False,False))[(df_all_sort['Position']!=not_position)&(df_all_sort['Team']!=team)].head(count)['Player'] | |
| temp_df = df_all_sort[df_all_sort[stat_pick+' Rank']==max(df_all_sort[stat_pick+' Rank'])]#[stat_pick+' per game Rank'].rank().sort_values(ascending=True).reset_index(drop=True)[count-len(df_all_sort_list)-1] | |
| temp_df['temp'] = temp_df[stat_pick+' per game Rank'].rank()#.sort_values(ascending=True)#.reset_index(drop=True) | |
| temp_df = temp_df.sort_values(by='temp',ascending=True)#.reset_index(drop=True) | |
| temp = temp_df[temp_df['temp']<=(count-len(df_all_sort_list))] | |
| players_list_new = list(pd.concat([df_all_sort_list,temp]).reset_index(drop=True)['player_id']) | |
| skater_dict = df_cum_stat_total.sort_values(by=['Total_Points','GP'],ascending=[False,True]).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['Player'].to_dict() | |
| # players_list = list(df_all_sort['Player']) | |
| ui.update_select( | |
| "id", | |
| label="Select Skater (max. 10 Skaters)", | |
| choices=skater_dict, | |
| selected=list(players_list_new[0:10])) | |
| def result(): | |
| if input.rookie_switch(): | |
| return player_games_df[(player_games_df.date <= pd.to_datetime(input.date()))&(player_games_df.player_id.isin(rookie_list))].groupby(['player_id','Player','Position']).agg( | |
| GP = ('GP','count'), | |
| Stat = (f'{input.stat()}','sum') | |
| ).reset_index().sort_values(by=['Stat','GP'],ascending=[False,True]).reset_index(drop=True) | |
| else: | |
| return player_games_df[player_games_df.date <= pd.to_datetime(input.date())].groupby(['player_id','Player','Position']).agg( | |
| GP = ('GP','count'), | |
| Stat = (f'{input.stat()}','sum') | |
| ).reset_index().sort_values(by=['Stat','GP'],ascending=[False,True]).reset_index(drop=True) | |
| def plot(): | |
| team_select_list = [input.team_select()] | |
| position_select_list = [input.position_select()] | |
| if team_select_list[0] == 'All': | |
| team_select_title = 'NHL ' | |
| else: | |
| team_select_title = f'{team_abv_nst_dict[team_select_list[0]]} ' | |
| if position_select_list[0] == 'All': | |
| position_select_title = '' | |
| elif position_select_list[0] == 'F': | |
| position_select_title = 'Forwards ' | |
| else: | |
| position_select_title = 'Defense ' | |
| rookie = '' | |
| if input.rookie_switch(): | |
| rookie = 'Rookie ' | |
| i = 0 | |
| #rookie = '' | |
| current_season = '2023' | |
| start_season = '2024' | |
| # player_lookup_list = ['Connor McDavid','David Pastrnak','Nathan MacKinnon'] | |
| type(input.id()) | |
| print(input.id()) | |
| player_lookup_list = list(input.id())[0:10] | |
| stat = input.stat() | |
| sns.set_theme(style="whitegrid", palette="pastel") | |
| #print(type([input.date())) | |
| date_range_list = [pd.to_datetime(input.date())] | |
| for k in range(len(date_range_list)): | |
| print(date_range_list[k]) | |
| stat = input.stat() | |
| team_schedule_url_merge = [] | |
| max_games_player = [] | |
| max_games_team = [] | |
| max_stat = [] | |
| per_game = False | |
| for i in range(0,len(player_lookup_list)): | |
| team_schedule_url_merge.append(player_games_df[(player_games_df.player_id == int(player_lookup_list[i]))&(date_range_list[k] >= player_games_df.date)].reset_index(drop=True)) | |
| #print('touble',i, player_lookup_list[i],len(player_games_df[(player_games_df.player_id == player_lookup_list[i])])) | |
| team_schedule_url_merge[i].index = team_schedule_url_merge[i].team_game | |
| team_schedule_url_merge[i] = team_schedule_url_merge[i].reindex(np.arange(team_schedule_url_merge[i].team_game.min(), team_schedule_url_merge[i].team_game.max() + 1)).reset_index(drop=True) | |
| #team_schedule_url_merge[0]['team_game'] = team_schedule_url_merge[0]['index'] | |
| #team_schedule_url_merge[0]['player_game'] = | |
| #schedule_ccount_df[schedule_ccount_df['team'].isin(team_schedule_url_merge[0].Team.unique())].merge(right=team_schedule_url_merge[0],left_on=['date','team'],right_on=['date','Team'],how='left') | |
| team_schedule_url_merge[i]['stat'] = team_schedule_url_merge[i][stat].cumsum() | |
| #team_schedule_url_merge[i]['stat'] = team_schedule_url_merge[i][stat_pick] | |
| team_schedule_url_merge[i] = team_schedule_url_merge[i].append(team_schedule_url_merge[i]).sort_index() | |
| team_schedule_url_merge[i] = team_schedule_url_merge[i].append(team_schedule_url_merge[i].iloc[0]).sort_index().reset_index(drop=True) | |
| team_schedule_url_merge[i]['team_game'][0] = 0 | |
| team_schedule_url_merge[i]['player_game'][0] = 0 | |
| team_schedule_url_merge[i]['stat'][0] = 0 | |
| for j in range(1,len(team_schedule_url_merge[i]),2): | |
| team_schedule_url_merge[i]['player_game'][j] = team_schedule_url_merge[i]['player_game'][j]-1 | |
| team_schedule_url_merge[i]['team_game'][j] = team_schedule_url_merge[i]['team_game'][j]-1 | |
| team_schedule_url_merge[i]['stat'][j] = team_schedule_url_merge[i]['stat'][j] - team_schedule_url_merge[i][stat][j] | |
| if len(team_schedule_url_merge[i]) >3: | |
| if pd.isna(team_schedule_url_merge[i].iloc[3]['player_game']) and pd.isna(team_schedule_url_merge[i].iloc[1]['player_game']) == True: | |
| team_schedule_url_merge[i]['player_game'][2] = np.nan | |
| team_schedule_url_merge[i]['stat'][2] = np.nan | |
| if len(team_schedule_url_merge[i]) >3: | |
| if pd.isna(team_schedule_url_merge[i].iloc[len(team_schedule_url_merge[i])-1]['player_game']) == True: | |
| team_schedule_url_merge[i]['stat'][len(team_schedule_url_merge[i])-1] = np.nanmax(team_schedule_url_merge[i]['stat']) | |
| if not (team_schedule_url_merge[i]['team_game'].values[1] == team_schedule_url_merge[i]['player_game'].values[0]): | |
| team_schedule_url_merge[i].loc[0,'team_game'] = np.nan | |
| max_games_player.append(np.around(np.nanmax(team_schedule_url_merge[i]['player_game']))) | |
| max_games_team.append(np.around(np.nanmax(team_schedule_url_merge[i]['team_game']))) | |
| max_stat.append((np.around(np.nanmax(team_schedule_url_merge[i]['stat'])))) | |
| fig, ax = plt.subplots(figsize=(15,15)) | |
| cgfont = {'fontname':'Century Gothic'} | |
| font = font_manager.FontProperties(family='Century Gothic', | |
| style='normal', size=14) | |
| ax.axhline(0,color='black',linestyle ="--",linewidth=2,alpha=1.0,label='Missed Games') | |
| ax.axhline(0,color='black',linestyle ="-",linewidth=2,alpha=1.0) | |
| if 'Total' in stat: | |
| stat = stat.replace('Total ',"") | |
| colour_scheme = ['#648FFF','#785EF0','#DC267F','#FE6100','#FFB000','#FAEF3B','#861318','#2ED3BC','#341BBF','#B37E2C'] | |
| for i in range(len(team_schedule_url_merge)): | |
| sns.lineplot(team_schedule_url_merge[i].reset_index()['team_game'],team_schedule_url_merge[i].reset_index()['stat'],linewidth=3-i*.2,color=colour_scheme[i]) | |
| plt.plot(team_schedule_url_merge[i]['team_game'],team_schedule_url_merge[i]['stat'],color=ax.lines[i*2+2].get_color(),label=str(i+1)+'. '+team_schedule_url_merge[i]['Player'][0]+', '+str(int(max_stat[i]))+' '+stat+' in '+str(int(max(team_schedule_url_merge[i]['player_game'])))+' Games',linewidth=6) | |
| ax.lines[i*2+2].set_linestyle("--") | |
| fig.set_facecolor('#ffffff') | |
| ax.set(xlim=(0,max([team_schedule_url_merge[x].team_game.max() for x in range(len(team_schedule_url_merge))]))) | |
| ax.set(ylim=(0,max([team_schedule_url_merge[x].stat.max() for x in range(len(team_schedule_url_merge))]))) | |
| ax.legend_.remove() | |
| if per_game == False: | |
| fig.suptitle(f'{rookie}{team_select_title}{position_select_title}{stat} Race',y=.98,fontsize=32,color='black',**cgfont) | |
| ax.set_ylabel(stat,fontsize=20,color='black',**cgfont) | |
| # else: | |
| # fig.suptitle(stat+' Per Game, All Situations',y=.99,fontsize=48,color='black',**cgfont) | |
| # ax.set_ylabel(stat+"/GP",fontsize=20,color='black',**cgfont) | |
| ax.set_title(str(current_season)[0:4]+'-'+str(start_season)[-4:]+' Season',y=1.01,fontsize=18,color='black',**cgfont,x=0,ha='left') | |
| ax.set_xlabel('Team Game',fontsize=20,color='black',**cgfont) | |
| ax.tick_params(axis="x", labelsize=24,colors='black') | |
| ax.set_facecolor('#ffffff') | |
| ax.xaxis.set_major_locator(MaxNLocator(integer=True)) | |
| ax.tick_params(axis="y", labelsize=24,colors='black') | |
| ax.yaxis.set_major_locator(MaxNLocator(integer=True)) | |
| fig.text(x=0.025,y=0.01,s="Created By: @TJStats",color='black', fontsize=20, horizontalalignment='left',**cgfont) | |
| fig.text(x=0.975,y=0.01,s="Data: Natural Stat Trick",color='black', fontsize=20, horizontalalignment='right',**cgfont) | |
| fig.text(x=.975,y=0.92,s='Date: '+input.date().strftime('%B %d, %Y'),color='black', fontsize=18, horizontalalignment='right',**cgfont) | |
| ax.legend(prop=font,bbox_to_anchor=(0.01, 0.99),loc='upper left',framealpha=1,frameon=True) | |
| plt.tight_layout() | |
| #fig.savefig('gif_race/'+stat+rookie+str(date_range_list[k].date())+'.png', facecolor=fig.get_facecolor(), edgecolor='none',bbox_inches='tight',dpi=100) | |
| #plt.close() | |
| #fig.legend(prop=font,loc='best',framealpha=1,frameon=True) | |
| app = App(app_ui, server) | |