diff --git "a/app_all.py" "b/app_all.py" new file mode 100644--- /dev/null +++ "b/app_all.py" @@ -0,0 +1,3474 @@ +import pandas as pd +import numpy as np +import requests +import math +import matplotlib.pyplot as plt +import seaborn as sns +import matplotlib.patches as patches +import matplotlib.colors as mcolors +import matplotlib +import inflect +infl = inflect.engine() +from matplotlib.offsetbox import (OffsetImage, AnnotationBbox) +from matplotlib.colors import Normalize +from matplotlib.ticker import FuncFormatter +import matplotlib.ticker as mtick +from matplotlib.colors import Normalize +import urllib +import urllib.request +import urllib.error +from urllib.error import HTTPError +import time +from shinywidgets import output_widget, render_widget +import shinyswatch +from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui + +column_list = ['woba_percent', + 'woba_percent_contact', + 'barrel_percent', + 'sweet_spot_percent', + 'hard_hit_percent', + 'launch_speed', + 'launch_speed_90', + 'max_launch_speed', + 'k_percent', + 'bb_percent', + 'swing_percent', + 'whiff_rate', + 'zone_swing_percent', + 'zone_contact_percent', + 'chase_percent', + 'chase_contact'] +column_list_pitch = ['pitches','bip','woba_percent_contact','whiff_rate','chase_percent'] + +import joblib + + +loaded_model = joblib.load('C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/joblib_model/barrel_model.joblib') +in_zone_model = joblib.load('C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/joblib_model/in_zone_model_knn_20240410.joblib') + +stat_plot_dict = {'woba_percent':{'name':'wOBA','format':'.3f','flip':False}, + 'woba_percent_contact':{'name':'wOBACON','format':'.3f','flip':False}, + 'barrel_percent':{'name':'Barrel%','format':'.1%','flip':False}, + 'max_launch_speed':{'name':'Max EV','format':'.1f','flip':False}, + 'launch_speed_90':{'name':'90th% EV','format':'.1f','flip':False}, + 'launch_speed':{'name':'Avg EV','format':'.1f','flip':False}, + 'sweet_spot_percent':{'name':'SwSpot%','format':'.1%','flip':False}, + 'hard_hit_percent':{'name':'HardHit%','format':'.1%','flip':False}, + 'k_percent':{'name':'K%','format':'.1%','flip':True}, + 'bb_percent':{'name':'BB%','format':'.1%','flip':False}, + 'zone_contact_percent':{'name':'Z-Contact%','format':'.1%','flip':False}, + 'zone_swing_percent':{'name':'Z-Swing%','format':'.1%','flip':False}, + 'zone_percent':{'name':'Zone%','format':'.1%','flip':False}, + 'chase_percent':{'name':'O-Swing%','format':'.1%','flip':True}, + 'chase_contact':{'name':'O-Contact%','format':'.1%','flip':False}, + 'swing_percent':{'name':'Swing%','format':'.1%','flip':False}, + 'whiff_rate':{'name':'Whiff%','format':'.1%','flip':True}, + 'bip':{'name':'Balls in Play','format':'.0f','flip':False}, + 'pitches':{'name':'Pitches','format':'.0f','flip':False},} + +stat_plot_dict_rolling = {'woba_percent':{'name':'wOBA','format':'.3f','flip':False,'y':'woba','div':'woba_codes','y_min':0.2,'y_max':0.6,'x_label':'wOBA PA','form':'3f'}, + 'k_percent':{'name':'K%','format':'.1%','flip':True,'y':'k','div':'pa','y_min':0.0,'y_max':0.4,'x_label':'PA','form':'1%'}, + 'bb_percent':{'name':'BB%','format':'.1%','flip':False,'y':'bb','div':'pa','y_min':0.0,'y_max':0.3,'x_label':'PA','form':'1%'}, + 'zone_contact_percent':{'name':'Z-Contact%','format':'.1%','flip':False,'y':'zone_contact','div':'zone_swing','y_min':0.6,'y_max':1.0,'x_label':'In-Zone Swings','form':'1%'}, + 'zone_swing_percent':{'name':'Z-Swing%','format':'.1%','flip':False,'y':'zone_swing','div':'in_zone','y_min':0.5,'y_max':1.0,'x_label':'In-Zone Pitches','form':'1%'}, + 'zone_percent':{'name':'Zone%','format':'.1%','flip':False,'y':'in_zone','div':'pitches','y_min':0.3,'y_max':0.7,'x_label':'Pitches','form':'1%'}, + 'chase_percent':{'name':'O-Swing%','format':'.1%','flip':True,'y':'ozone_swing','div':'out_zone','y_min':0.1,'y_max':0.4,'x_label':'Out-of-Zone Pitches','form':'1%'}, + 'chase_contact':{'name':'O-Contact%','format':'.1%','flip':False,'y':'ozone_contact','div':'ozone_swing','y_min':0.4,'y_max':0.8,'x_label':'Out-of-Zone Swings','form':'1%'}, + 'swing_percent':{'name':'Swing%','format':'.1%','flip':False,'y':'swings','div':'pitches','y_min':0.3,'y_max':0.7,'x_label':'Pitches','form':'1%'}, + 'whiff_rate':{'name':'Whiff%','format':'.1%','flip':True,'y':'whiffs','div':'swings','y_min':0.0,'y_max':0.5,'x_label':'Swings','form':'1%'},} + +cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#0C7BDC","#FFFFFF","#FFB000"]) +cmap_sum_r = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFB000","#FFFFFF","#0C7BDC",]) +cmap_sum.set_bad(color='#C7C7C7', alpha=1.0) +cmap_sum_r.set_bad(color='#C7C7C7', alpha=1.0) + +from batting_update import df_update,df_update_summ_avg,df_update_summ,df_summ_batter_pitch_up,df_summ_changes,df_summ_filter_out + +def percentile(n): + def percentile_(x): + return np.nanpercentile(x, n) + percentile_.__name__ = 'percentile_%s' % n + return percentile_ + +print('Reading A') + +df_a = pd.read_csv("C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/milb/lo_a/2024_regular_data.csv",index_col=[0]) +print('Reading A+') +df_ha = pd.read_csv("C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/milb/hi_a/2024_regular_data.csv",index_col=[0]) +print('Reading AA') +df_aa = pd.read_csv("C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/milb/aa/2024_regular_data.csv",index_col=[0]) +print('Reading AAA') +df_aaa = pd.read_csv("C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/milb/aaa/2024_regular_data.csv",index_col=[0]) +print('Reading MLB') +df_mlb = pd.read_csv("C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/2024_regular_data.csv",index_col=[0]) +print('Reading LIDOM') +#df_dom = pd.read_csv('dom_win_league.csv',index_col=[0]) +# df_dom_update = pd.read_csv('dom_win_league_update.csv',index_col=[0]) + +print('Reading A') +df_a_update = df_update(df_a) +print('Reading A+') +df_ha_update = df_update(df_ha) +print('Reading AA') +df_aa_update = df_update(df_aa) +print('Reading AAA') +df_aaa_update = df_update(df_aaa) +print('Reading MLB') +df_mlb_update = df_update(df_mlb) + + +df_a_update['batter_name'] = df_a_update['batter_name'].str.strip(' ') +df_ha_update['batter_name'] = df_ha_update['batter_name'].str.strip(' ') +df_aa_update['batter_name'] = df_aa_update['batter_name'].str.strip(' ') +df_aaa_update['batter_name'] = df_aaa_update['batter_name'].str.strip(' ') +df_mlb_update['batter_name'] = df_mlb_update['batter_name'].str.strip(' ') +# df_dom_update['batter_name'] = df_dom_update['batter_name'].str.strip(' ') + +df_a_update['bip'] = df_a_update['bip'].replace({'0':False,'False':False,'True':True}) +df_ha_update['bip'] = df_ha_update['bip'].replace({'0':False,'False':False,'True':True}) +df_aa_update['bip'] = df_aa_update['bip'].replace({'0':False,'False':False,'True':True}) +df_aaa_update['bip'] = df_aaa_update['bip'].replace({'0':False,'False':False,'True':True}) +df_mlb_update['bip'] = df_mlb_update['bip'].replace({'0':False,'False':False,'True':True}) +# df_dom_update['bip'] = df_dom_update['in_play'] +# df_dom_update['bip'] = df_dom_update['bip'].replace({'0':False,'False':False,'True':True}) + + +# conditions_woba = [ +# (df_dom_update['event_type']=='walk'), +# (df_dom_update['event_type']=='hit_by_pitch'), +# (df_dom_update['event_type']=='single'), +# (df_dom_update['event_type']=='double'), +# (df_dom_update['event_type']=='triple'), +# (df_dom_update['event_type']=='home_run'), +# ] + +choices_woba = [0.696, + 0.726, + 0.883, + 1.244, + 1.569, + 2.004] + +# df_dom_update['woba'] = np.select(conditions_woba, choices_woba, default=np.nan) + + +woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch', + 'double', 'sac_fly', 'force_out', 'home_run', + 'grounded_into_double_play', 'fielders_choice', 'field_error', + 'triple', 'sac_bunt', 'double_play', + 'fielders_choice_out', 'strikeout_double_play', + 'sac_fly_double_play', 'other_out'] + + + +# + + +# conditions_woba_code = [ +# (df_dom_update['event_type'].isin(woba_codes)) +# ] + +# choices_woba_code = [1] + +# df_dom_update['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan) + + +# df_dom_update['woba_contact'] = [df_dom_update['woba'].values[x] if df_dom_update['bip'].values[x] == 1 else np.nan for x in range(len(df_dom_update['woba_codes']))] + + + +df_a_update['bip_div'] = ~df_a_update.launch_speed.isna() +df_ha_update['bip_div'] = ~df_ha_update.launch_speed.isna() +df_aa_update['bip_div'] = ~df_aa_update.launch_speed.isna() +df_aaa_update['bip_div'] = ~df_aaa_update.launch_speed.isna() +df_mlb_update['bip_div'] = ~df_mlb_update.launch_speed.isna() +# df_dom_update['bip_div'] = ~df_dom_update.launch_speed.isna() +df_a_update['average'] = 'average' +df_ha_update['average'] = 'average' +df_aa_update['average'] = 'average' +df_aaa_update['average'] = 'average' +df_mlb_update['average'] = 'average' +#df_dom_update['average'] = 'average' + +#df_u['is_pitch'] + +df_summ_a_update = df_summ_changes(df_update_summ(df_a_update)).set_index(['batter_id','batter_name']) +df_summ_ha_update = df_summ_changes(df_update_summ(df_ha_update)).set_index(['batter_id','batter_name']) +df_summ_aa_update = df_summ_changes(df_update_summ(df_aa_update)).set_index(['batter_id','batter_name']) +df_summ_aaa_update = df_summ_changes(df_update_summ(df_aaa_update)).set_index(['batter_id','batter_name']) +df_summ_mlb_update = df_summ_changes(df_update_summ(df_mlb_update)).set_index(['batter_id','batter_name']) +# df_summ_dom_update = df_summ_changes(df_update_summ(df_dom_update)).set_index(['batter_id','batter_name']) + +df_summ_avg_a_update = df_summ_changes(df_update_summ_avg(df_a_update)).set_index(['average']) +df_summ_avg_ha_update = df_summ_changes(df_update_summ_avg(df_ha_update)).set_index(['average']) +df_summ_avg_aa_update = df_summ_changes(df_update_summ_avg(df_aa_update)).set_index(['average']) +df_summ_avg_aaa_update = df_summ_changes(df_update_summ_avg(df_aaa_update)).set_index(['average']) +df_summ_avg_mlb_update = df_summ_changes(df_update_summ_avg(df_mlb_update)).set_index(['average']) +# df_summ_avg_dom_update = df_summ_changes(df_update_summ_avg(df_dom_update)).set_index(['average']) + +stat_roll_dict = dict(zip(stat_plot_dict_rolling.keys(), + [stat_plot_dict_rolling[x]['name'] for x in stat_plot_dict_rolling])) + +mlb_player_dict = df_mlb_update.drop_duplicates( + 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] +aaa_player_dict = df_aaa_update.drop_duplicates( + 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] +aa_player_dict = df_aa_update.drop_duplicates( + 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] +ha_player_dict = df_ha_update.drop_duplicates( + 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] +a_player_dict = df_a_update.drop_duplicates( + 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] +# dom_player_dict = df_summ_dom_update.reset_index().drop_duplicates( +# 'batter_id')[['batter_id','batter_name']].sort_values(by='batter_name').set_index('batter_id').to_dict()['batter_name'] + + + + +def server(input: Inputs, output: Outputs, session: Session): + @render.ui + def test(): + # @reactive.Effect + if input.my_tabs() == 'MLB': + print(mlb_player_dict) + return ui.input_select("player_id", "Select Batter",mlb_player_dict,selectize=True) + if input.my_tabs() == 'AAA': + return ui.input_select("player_id", "Select Batter",aaa_player_dict,selectize=True) + if input.my_tabs() == 'AA': + return ui.input_select("player_id", "Select Batter",aa_player_dict,selectize=True) + if input.my_tabs() == 'High-A': + return ui.input_select("player_id", "Select Batter",ha_player_dict,selectize=True) + if input.my_tabs() == 'A': + return ui.input_select("player_id", "Select Batter",a_player_dict,selectize=True) + # if input.my_tabs() == 'LIDOM': + # return ui.input_select("player_id", "Select Batter",dom_player_dict,selectize=True) + + + + @output + @render.plot(alt="MLB Plot") + @reactive.event(input.go, ignore_none=False) + def mlb_plot(): + ### Iniput data for the level + #time.sleep(2) + df_update = df_mlb_update.copy() + df_summ_update = df_summ_mlb_update.copy() + df_summ_avg_update = df_summ_avg_mlb_update.copy() + + if len(input.player_id()) < 1: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + batter_select = int(input.player_id()) + sport_id_input = 1 + df_roll = df_update[df_update['batter_id']==batter_select] + if len(df_roll) == 0: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + + df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # Normalize the value within the range [0, 1] + normalized_value = (value - vmin) / (vmax - vmin) + + # Get the colormap + cmap = plt.get_cmap(cmap_name) + + # Map the normalized value to a color in the colormap + color = cmap(normalized_value) + + # Convert the color from RGBA to hexadecimal format + hex_color = mcolors.rgb2hex(color) + + return hex_color + + def get_players(sport_id=1): + player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + #Select relevant data that will help distinguish players from one another + fullName_list = [x['fullName'] for x in player_data['people']] + id_list = [x['id'] for x in player_data['people']] + position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + team_list = [x['currentTeam']['id']for x in player_data['people']] + age_list = [x['currentAge']for x in player_data['people']] + + player_df = pd.DataFrame(data={'player_id':id_list, + 'name':fullName_list, + 'position':position_list, + 'team':team_list, + 'age':age_list}) + return player_df + + def get_teams(): + teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + #Select only teams that are at the MLB level + # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + #Create a dataframe of all the teams + mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + 'city':mlb_teams_franchise, + 'name':mlb_teams_name, + 'franchise':mlb_teams_franchise, + 'abbreviation':mlb_teams_abb, + 'parent_org_id':mlb_teams_parent_id, + 'parent_org':mlb_teams_parent, + 'league_id':mlb_teams_league_id, + 'league_name':mlb_teams_league_name + + }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + 'city': 'Major League Baseball', + 'name': 'Major League Baseball', + 'franchise': 'Free Agent', + 'abbreviation': 'MLB', + 'parent_org_id': 11, + 'parent_org': 'Major League Baseball', + 'league_id': 1.0, + 'league_name': 'Major League Baseball', + 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + return mlb_teams_df + + def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + ax=ax, + color="#FFB000", + zorder=10) + + + + # ["#0C7BDC","#FFFFFF","#FFB000"]) + ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#0C7BDC",linestyles='-.') + ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#FFB000",linestyles='--') + #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + ax.grid(True,alpha=0.2) + + + if stat_plot_dict_rolling[stat]['form'] == '3f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1%': + ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + return plot + + dict_level = {1:'MLB', + 11:'MiLB AAA', + 12:'MiLB AA', + 13:'MiLB High-A', + 14:'MiLB A'} + + def plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_update = df_summ_update, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ): + + #player_df = get_players(sport_id=sport_id_input) + mlb_teams = get_teams() + team_logos = pd.read_csv('team_logos.csv') + if sport_id_input == 1: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + else: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + fig = plt.figure(figsize=(10, 10))#,dpi=600) + plt.rcParams.update({'figure.autolayout': True}) + fig.set_facecolor('white') + sns.set_theme(style="whitegrid", palette="pastel") + from matplotlib.gridspec import GridSpec + gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + #gs.update(hspace=0, wspace=0) + + # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # ax1 = plt.subplot(4,1,1) + # ax2 = plt.subplot(2,2,2) + # ax3 = plt.subplot(2,2,3) + # ax4 = plt.subplot(4,1,4) + #ax2 = plt.subplot(3,3,2) + + # Add subplots to the grid + ax = fig.add_subplot(gs[0, :]) + #ax1 = fig.add_subplot(gs[2, 0]) + # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # fig, ax = plt.subplots(1,1,figsize=(10,12)) + ax.axis('off') + + width = 0.08 + height = width*2.45 + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player['sweet_spot_percent'] = np.nan + df_summ_player['barrel_percent'] = np.nan + df_summ_player['hard_hit_percent'] = np.nan + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player_pct['sweet_spot_percent'] = np.nan + df_summ_player_pct['barrel_percent'] = np.nan + df_summ_player_pct['hard_hit_percent'] = np.nan + # x = 0.1 + # y = 0.9 + for cat in range(len(column_list)): + + # if cat < len(column_list)/2: + x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # else: + # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + #print( x_adjust, y_adjust) + if sum(df_summ_player[column_list[cat]].isna()) < 1: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 16, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + else: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + + ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + x = x_adjust, + y = y_adjust-0.14, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 12, + ha='center', + va='center') + + ax.text(s = f"{player_bio['people'][0]['fullName']}", + + x = 0.5, + y = 0.95, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 28, + ha='center', + va='center') + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + f"B/T: {player_bio['people'][0]['batSide']['code']}/" + f"{player_bio['people'][0]['pitchHand']['code']} " + f"{player_bio['people'][0]['height']}/" + f"{player_bio['people'][0]['weight']}", + + x = 0.5, + y = 0.785, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + + f"DOB: {player_bio['people'][0]['birthDate']} " + f"Age: {player_bio['people'][0]['currentAge']}", + x = 0.5, + y = 0.72, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + if sport_id_input == 1: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + else: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content), cmap='viridis') + # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.35) + ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + ax.add_artist(ab) + + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + else: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + x = 0.5, + y = 0.62, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 20, + ha='center', + va='center') + + df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + df_plot = df_plot[df_plot['pitches'] > 0] + + df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + value = 1 + + # df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_plot['pitches'].to_dict()) + # df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + # Normalize the value + colormap = plt.get_cmap(cmap_sum) + colormap_r = plt.get_cmap(cmap_sum_r) + norm = Normalize(vmin=0, vmax=1) + + col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + + + # col_5_colour = [colormap_r(norm(x)) for x in list((df_plot['chase_percent']))] + # col_4_colour = [colormap_r(norm(x)) for x in list((df_plot['whiff_rate']))] + # col_3_colour = [colormap(norm(x)) for x in list((df_plot['woba_percent_contact']))] + # col_2_colour = ['white']*len(df_plot) + # col_1_colour = ['white']*len(df_plot) + # colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + ax_table = fig.add_subplot(gs[2, 1:-1]) + ax_table.axis('off') + table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + loc='center',cellColours=colour_df) + ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + w, h = table[0,1].get_width(), table[0,1].get_height() + table.add_cell(0, -1, w,h, text='Pitch Type') + min_font_size = 12 + # Set table properties + table.auto_set_font_size(False) + table.set_fontsize(min_font_size) + #table.set_fontname('arial') + table.scale(1, len(df_plot)*0.3) + + + for n_col in range(0,len(df_plot.columns)): + #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + format_col = df_plot[df_plot.columns[n_col]].astype(str) + n_c = 0 + for cell in table.get_celld().values(): + # print([cell.get_text().get_text()],format_col.astype(str).values) + if cell.get_text().get_text() in format_col.astype(str).values: + + + #print(cell.get_text().get_text() in format_col.astype(str).values) + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + n_c = n_c + 1 + + stat_1 = input.stat_1() + window_width_1 = input.window_1() + stat_2 = input.stat_2() + window_width_2 = input.window_2() + stat_3 = input.stat_3() + window_width_3 = input.window_3() + + + inset_ax = ax = fig.add_subplot(gs[3, 1]) + rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 2]) + rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 3]) + rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + ax_bot = ax = fig.add_subplot(gs[4, :]) + + ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + ax_bot.axis('off') + + + ax_cbar = fig.add_subplot(gs[1,1:-1]) + + cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + cmap=cmap_sum) + #ax_cbar.axis('off') + ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + ax_cbar.set_xticks([]) + ax_cbar.set_yticks([]) + ax_cbar.set_xticklabels([]) + ax_cbar.set_yticklabels([]) + + # Display only the outline of the axis + for spine in ax_cbar.spines.values(): + spine.set_visible(True) # Show only the outline + spine.set_color('black') # Set the color to black + + # fig.set_facecolor('#ffffff') + + return fig.tight_layout() + + + + return plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ) + @output + @render.plot(alt="AAA Plot") + @reactive.event(input.go, ignore_none=False) + def aaa_plot(): + ### Iniput data for the level + #time.sleep(2) + df_update = df_aaa_update.copy() + df_summ_update = df_summ_aaa_update.copy() + df_summ_avg_update = df_summ_avg_aaa_update.copy() + + if len(input.player_id()) < 1: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + batter_select = int(input.player_id()) + sport_id_input = 11 + df_roll = df_update[df_update['batter_id']==batter_select] + if len(df_roll) == 0: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + #df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.dropna() + def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # Normalize the value within the range [0, 1] + normalized_value = (value - vmin) / (vmax - vmin) + + # Get the colormap + cmap = plt.get_cmap(cmap_name) + + # Map the normalized value to a color in the colormap + color = cmap(normalized_value) + + # Convert the color from RGBA to hexadecimal format + hex_color = mcolors.rgb2hex(color) + + return hex_color + + def get_players(sport_id=1): + player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + #Select relevant data that will help distinguish players from one another + fullName_list = [x['fullName'] for x in player_data['people']] + id_list = [x['id'] for x in player_data['people']] + position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + team_list = [x['currentTeam']['id']for x in player_data['people']] + age_list = [x['currentAge']for x in player_data['people']] + + player_df = pd.DataFrame(data={'player_id':id_list, + 'name':fullName_list, + 'position':position_list, + 'team':team_list, + 'age':age_list}) + return player_df + + def get_teams(): + teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + #Select only teams that are at the MLB level + # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + #Create a dataframe of all the teams + mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + 'city':mlb_teams_franchise, + 'name':mlb_teams_name, + 'franchise':mlb_teams_franchise, + 'abbreviation':mlb_teams_abb, + 'parent_org_id':mlb_teams_parent_id, + 'parent_org':mlb_teams_parent, + 'league_id':mlb_teams_league_id, + 'league_name':mlb_teams_league_name + + }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + 'city': 'Major League Baseball', + 'name': 'Major League Baseball', + 'franchise': 'Free Agent', + 'abbreviation': 'MLB', + 'parent_org_id': 11, + 'parent_org': 'Major League Baseball', + 'league_id': 1.0, + 'league_name': 'Major League Baseball', + 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + return mlb_teams_df + + def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + ax=ax, + color="#FFB000", + zorder=10) + + + + # ["#0C7BDC","#FFFFFF","#FFB000"]) + ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#0C7BDC",linestyles='-.') + ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#FFB000",linestyles='--') + #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + ax.grid(True,alpha=0.2) + + + if stat_plot_dict_rolling[stat]['form'] == '3f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1%': + ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + return plot + + dict_level = {1:'MLB', + 11:'MiLB AAA', + 12:'MiLB AA', + 13:'MiLB High-A', + 14:'MiLB A'} + + def plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_update = df_summ_update, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ): + + #player_df = get_players(sport_id=sport_id_input) + mlb_teams = get_teams() + team_logos = pd.read_csv('team_logos.csv') + if sport_id_input == 1: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + else: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + fig = plt.figure(figsize=(10, 10))#,dpi=600) + plt.rcParams.update({'figure.autolayout': True}) + fig.set_facecolor('white') + sns.set_theme(style="whitegrid", palette="pastel") + from matplotlib.gridspec import GridSpec + gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + #gs.update(hspace=0, wspace=0) + + # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # ax1 = plt.subplot(4,1,1) + # ax2 = plt.subplot(2,2,2) + # ax3 = plt.subplot(2,2,3) + # ax4 = plt.subplot(4,1,4) + #ax2 = plt.subplot(3,3,2) + + # Add subplots to the grid + ax = fig.add_subplot(gs[0, :]) + #ax1 = fig.add_subplot(gs[2, 0]) + # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # fig, ax = plt.subplots(1,1,figsize=(10,12)) + ax.axis('off') + + width = 0.08 + height = width*2.45 + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player['sweet_spot_percent'] = np.nan + df_summ_player['barrel_percent'] = np.nan + df_summ_player['hard_hit_percent'] = np.nan + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player_pct['sweet_spot_percent'] = np.nan + df_summ_player_pct['barrel_percent'] = np.nan + df_summ_player_pct['hard_hit_percent'] = np.nan + # x = 0.1 + # y = 0.9 + for cat in range(len(column_list)): + + # if cat < len(column_list)/2: + x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # else: + # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + #print( x_adjust, y_adjust) + if sum(df_summ_player[column_list[cat]].isna()) < 1: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 16, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + else: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + + ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + x = x_adjust, + y = y_adjust-0.14, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 12, + ha='center', + va='center') + + ax.text(s = f"{player_bio['people'][0]['fullName']}", + + x = 0.5, + y = 0.95, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 28, + ha='center', + va='center') + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + f"B/T: {player_bio['people'][0]['batSide']['code']}/" + f"{player_bio['people'][0]['pitchHand']['code']} " + f"{player_bio['people'][0]['height']}/" + f"{player_bio['people'][0]['weight']}", + + x = 0.5, + y = 0.785, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + + f"DOB: {player_bio['people'][0]['birthDate']} " + f"Age: {player_bio['people'][0]['currentAge']}", + x = 0.5, + y = 0.72, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + if sport_id_input == 1: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + else: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content), cmap='viridis') + # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.35) + ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + ax.add_artist(ab) + + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + else: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + x = 0.5, + y = 0.62, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 20, + ha='center', + va='center') + + df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False) + df_plot = df_plot[df_plot['pitches'] > 0] + + df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + value = 1 + # Normalize the value + colormap = plt.get_cmap(cmap_sum) + colormap_r = plt.get_cmap(cmap_sum_r) + norm = Normalize(vmin=0, vmax=1) + + + + col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + ax_table = fig.add_subplot(gs[2, 1:-1]) + ax_table.axis('off') + print(colour_df) + print(df_plot) + table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + loc='center',cellColours=colour_df) + ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + w, h = table[0,1].get_width(), table[0,1].get_height() + table.add_cell(0, -1, w,h, text='Pitch Type') + min_font_size = 12 + # Set table properties + table.auto_set_font_size(False) + table.set_fontsize(min_font_size) + #table.set_fontname('arial') + table.scale(1, len(df_plot)*0.3) + + + for n_col in range(0,len(df_plot.columns)): + #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + format_col = df_plot[df_plot.columns[n_col]].astype(str) + n_c = 0 + for cell in table.get_celld().values(): + # print([cell.get_text().get_text()],format_col.astype(str).values) + if cell.get_text().get_text() in format_col.astype(str).values: + + + #print(cell.get_text().get_text() in format_col.astype(str).values) + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + n_c = n_c + 1 + + stat_1 = input.stat_1() + window_width_1 = input.window_1() + stat_2 = input.stat_2() + window_width_2 = input.window_2() + stat_3 = input.stat_3() + window_width_3 = input.window_3() + + + inset_ax = ax = fig.add_subplot(gs[3, 1]) + rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 2]) + rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 3]) + rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + ax_bot = ax = fig.add_subplot(gs[4, :]) + + ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + ax_bot.axis('off') + + + ax_cbar = fig.add_subplot(gs[1,1:-1]) + + cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + cmap=cmap_sum) + #ax_cbar.axis('off') + ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + ax_cbar.set_xticks([]) + ax_cbar.set_yticks([]) + ax_cbar.set_xticklabels([]) + ax_cbar.set_yticklabels([]) + + # Display only the outline of the axis + for spine in ax_cbar.spines.values(): + spine.set_visible(True) # Show only the outline + spine.set_color('black') # Set the color to black + + # fig.set_facecolor('#ffffff') + + return fig.tight_layout() + + + + return plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ) + @output + @render.plot(alt="AA Plot") + @reactive.event(input.go, ignore_none=False) + def aa_plot(): + ### Iniput data for the level + #time.sleep(2) + df_update = df_aa_update.copy() + df_summ_update = df_summ_aa_update.copy() + df_summ_avg_update = df_summ_avg_aa_update.copy() + + if len(input.player_id()) < 1: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + batter_select = int(input.player_id()) + sport_id_input = 12 + df_roll = df_update[df_update['batter_id']==batter_select] + if len(df_roll) == 0: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + #df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.dropna() + def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # Normalize the value within the range [0, 1] + normalized_value = (value - vmin) / (vmax - vmin) + + # Get the colormap + cmap = plt.get_cmap(cmap_name) + + # Map the normalized value to a color in the colormap + color = cmap(normalized_value) + + # Convert the color from RGBA to hexadecimal format + hex_color = mcolors.rgb2hex(color) + + return hex_color + + def get_players(sport_id=1): + player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + #Select relevant data that will help distinguish players from one another + fullName_list = [x['fullName'] for x in player_data['people']] + id_list = [x['id'] for x in player_data['people']] + position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + team_list = [x['currentTeam']['id']for x in player_data['people']] + age_list = [x['currentAge']for x in player_data['people']] + + player_df = pd.DataFrame(data={'player_id':id_list, + 'name':fullName_list, + 'position':position_list, + 'team':team_list, + 'age':age_list}) + return player_df + + def get_teams(): + teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + #Select only teams that are at the MLB level + # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + #Create a dataframe of all the teams + mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + 'city':mlb_teams_franchise, + 'name':mlb_teams_name, + 'franchise':mlb_teams_franchise, + 'abbreviation':mlb_teams_abb, + 'parent_org_id':mlb_teams_parent_id, + 'parent_org':mlb_teams_parent, + 'league_id':mlb_teams_league_id, + 'league_name':mlb_teams_league_name + + }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + 'city': 'Major League Baseball', + 'name': 'Major League Baseball', + 'franchise': 'Free Agent', + 'abbreviation': 'MLB', + 'parent_org_id': 11, + 'parent_org': 'Major League Baseball', + 'league_id': 1.0, + 'league_name': 'Major League Baseball', + 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + return mlb_teams_df + + def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + ax=ax, + color="#FFB000", + zorder=10) + + + + # ["#0C7BDC","#FFFFFF","#FFB000"]) + ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#0C7BDC",linestyles='-.') + ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#FFB000",linestyles='--') + #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + ax.grid(True,alpha=0.2) + + + if stat_plot_dict_rolling[stat]['form'] == '3f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1%': + ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + return plot + + dict_level = {1:'MLB', + 11:'MiLB AAA', + 12:'MiLB AA', + 13:'MiLB High-A', + 14:'MiLB A'} + + def plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_update = df_summ_update, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ): + + #player_df = get_players(sport_id=sport_id_input) + mlb_teams = get_teams() + team_logos = pd.read_csv('team_logos.csv') + if sport_id_input == 1: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + else: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + fig = plt.figure(figsize=(10, 10))#,dpi=600) + plt.rcParams.update({'figure.autolayout': True}) + fig.set_facecolor('white') + sns.set_theme(style="whitegrid", palette="pastel") + from matplotlib.gridspec import GridSpec + gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + #gs.update(hspace=0, wspace=0) + + # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # ax1 = plt.subplot(4,1,1) + # ax2 = plt.subplot(2,2,2) + # ax3 = plt.subplot(2,2,3) + # ax4 = plt.subplot(4,1,4) + #ax2 = plt.subplot(3,3,2) + + # Add subplots to the grid + ax = fig.add_subplot(gs[0, :]) + #ax1 = fig.add_subplot(gs[2, 0]) + # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # fig, ax = plt.subplots(1,1,figsize=(10,12)) + ax.axis('off') + + width = 0.08 + height = width*2.45 + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player['sweet_spot_percent'] = np.nan + df_summ_player['barrel_percent'] = np.nan + df_summ_player['hard_hit_percent'] = np.nan + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player_pct['sweet_spot_percent'] = np.nan + df_summ_player_pct['barrel_percent'] = np.nan + df_summ_player_pct['hard_hit_percent'] = np.nan + # x = 0.1 + # y = 0.9 + for cat in range(len(column_list)): + + # if cat < len(column_list)/2: + x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # else: + # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + #print( x_adjust, y_adjust) + if sum(df_summ_player[column_list[cat]].isna()) < 1: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 16, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + else: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + + ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + x = x_adjust, + y = y_adjust-0.14, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 12, + ha='center', + va='center') + + ax.text(s = f"{player_bio['people'][0]['fullName']}", + + x = 0.5, + y = 0.95, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 28, + ha='center', + va='center') + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + f"B/T: {player_bio['people'][0]['batSide']['code']}/" + f"{player_bio['people'][0]['pitchHand']['code']} " + f"{player_bio['people'][0]['height']}/" + f"{player_bio['people'][0]['weight']}", + + x = 0.5, + y = 0.785, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + + f"DOB: {player_bio['people'][0]['birthDate']} " + f"Age: {player_bio['people'][0]['currentAge']}", + x = 0.5, + y = 0.72, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + if sport_id_input == 1: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + else: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content), cmap='viridis') + # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.35) + ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + ax.add_artist(ab) + + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + else: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + x = 0.5, + y = 0.62, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 20, + ha='center', + va='center') + + df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + df_plot = df_plot[df_plot['pitches'] > 0] + + df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + value = 1 + # Normalize the value + colormap = plt.get_cmap(cmap_sum) + colormap_r = plt.get_cmap(cmap_sum_r) + norm = Normalize(vmin=0, vmax=1) + + + + col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + ax_table = fig.add_subplot(gs[2, 1:-1]) + ax_table.axis('off') + print(colour_df) + print(df_plot) + table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + loc='center',cellColours=colour_df) + ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + w, h = table[0,1].get_width(), table[0,1].get_height() + table.add_cell(0, -1, w,h, text='Pitch Type') + min_font_size = 12 + # Set table properties + table.auto_set_font_size(False) + table.set_fontsize(min_font_size) + #table.set_fontname('arial') + table.scale(1, len(df_plot)*0.3) + + + for n_col in range(0,len(df_plot.columns)): + #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + format_col = df_plot[df_plot.columns[n_col]].astype(str) + n_c = 0 + for cell in table.get_celld().values(): + # print([cell.get_text().get_text()],format_col.astype(str).values) + if cell.get_text().get_text() in format_col.astype(str).values: + + + #print(cell.get_text().get_text() in format_col.astype(str).values) + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + n_c = n_c + 1 + + stat_1 = input.stat_1() + window_width_1 = input.window_1() + stat_2 = input.stat_2() + window_width_2 = input.window_2() + stat_3 = input.stat_3() + window_width_3 = input.window_3() + + + inset_ax = ax = fig.add_subplot(gs[3, 1]) + rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 2]) + rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 3]) + rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + ax_bot = ax = fig.add_subplot(gs[4, :]) + + ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + ax_bot.axis('off') + + + ax_cbar = fig.add_subplot(gs[1,1:-1]) + + cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + cmap=cmap_sum) + #ax_cbar.axis('off') + ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + ax_cbar.set_xticks([]) + ax_cbar.set_yticks([]) + ax_cbar.set_xticklabels([]) + ax_cbar.set_yticklabels([]) + + # Display only the outline of the axis + for spine in ax_cbar.spines.values(): + spine.set_visible(True) # Show only the outline + spine.set_color('black') # Set the color to black + + # fig.set_facecolor('#ffffff') + + return fig.tight_layout() + + + + return plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ) + @output + @render.plot(alt="High-A Plot") + @reactive.event(input.go, ignore_none=False) + def ha_plot(): + ### Iniput data for the level + #time.sleep(2) + df_update = df_ha_update.copy() + df_summ_update = df_summ_ha_update.copy() + df_summ_avg_update = df_summ_avg_a_update.copy() + if len(input.player_id()) < 1: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + batter_select = int(input.player_id()) + sport_id_input = 13 + df_roll = df_update[df_update['batter_id']==batter_select] + if len(df_roll) == 0: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + #df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.dropna() + def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # Normalize the value within the range [0, 1] + normalized_value = (value - vmin) / (vmax - vmin) + + # Get the colormap + cmap = plt.get_cmap(cmap_name) + + # Map the normalized value to a color in the colormap + color = cmap(normalized_value) + + # Convert the color from RGBA to hexadecimal format + hex_color = mcolors.rgb2hex(color) + + return hex_color + + def get_players(sport_id=1): + player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + #Select relevant data that will help distinguish players from one another + fullName_list = [x['fullName'] for x in player_data['people']] + id_list = [x['id'] for x in player_data['people']] + position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + team_list = [x['currentTeam']['id']for x in player_data['people']] + age_list = [x['currentAge']for x in player_data['people']] + + player_df = pd.DataFrame(data={'player_id':id_list, + 'name':fullName_list, + 'position':position_list, + 'team':team_list, + 'age':age_list}) + return player_df + + def get_teams(): + teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + #Select only teams that are at the MLB level + # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + #Create a dataframe of all the teams + mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + 'city':mlb_teams_franchise, + 'name':mlb_teams_name, + 'franchise':mlb_teams_franchise, + 'abbreviation':mlb_teams_abb, + 'parent_org_id':mlb_teams_parent_id, + 'parent_org':mlb_teams_parent, + 'league_id':mlb_teams_league_id, + 'league_name':mlb_teams_league_name + + }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + 'city': 'Major League Baseball', + 'name': 'Major League Baseball', + 'franchise': 'Free Agent', + 'abbreviation': 'MLB', + 'parent_org_id': 11, + 'parent_org': 'Major League Baseball', + 'league_id': 1.0, + 'league_name': 'Major League Baseball', + 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + return mlb_teams_df + + def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + ax=ax, + color="#FFB000", + zorder=10) + + + + # ["#0C7BDC","#FFFFFF","#FFB000"]) + ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#0C7BDC",linestyles='-.') + ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#FFB000",linestyles='--') + #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + ax.grid(True,alpha=0.2) + + + if stat_plot_dict_rolling[stat]['form'] == '3f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1%': + ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + return plot + + dict_level = {1:'MLB', + 11:'MiLB AAA', + 12:'MiLB AA', + 13:'MiLB High-A', + 14:'MiLB A'} + + def plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_update = df_summ_update, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ): + + #player_df = get_players(sport_id=sport_id_input) + mlb_teams = get_teams() + team_logos = pd.read_csv('team_logos.csv') + if sport_id_input == 1: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + else: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + fig = plt.figure(figsize=(10, 10))#,dpi=600) + plt.rcParams.update({'figure.autolayout': True}) + fig.set_facecolor('white') + sns.set_theme(style="whitegrid", palette="pastel") + from matplotlib.gridspec import GridSpec + gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + #gs.update(hspace=0, wspace=0) + + # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # ax1 = plt.subplot(4,1,1) + # ax2 = plt.subplot(2,2,2) + # ax3 = plt.subplot(2,2,3) + # ax4 = plt.subplot(4,1,4) + #ax2 = plt.subplot(3,3,2) + + # Add subplots to the grid + ax = fig.add_subplot(gs[0, :]) + #ax1 = fig.add_subplot(gs[2, 0]) + # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # fig, ax = plt.subplots(1,1,figsize=(10,12)) + ax.axis('off') + + width = 0.08 + height = width*2.45 + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player['sweet_spot_percent'] = np.nan + df_summ_player['barrel_percent'] = np.nan + df_summ_player['hard_hit_percent'] = np.nan + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player_pct['sweet_spot_percent'] = np.nan + df_summ_player_pct['barrel_percent'] = np.nan + df_summ_player_pct['hard_hit_percent'] = np.nan + # x = 0.1 + # y = 0.9 + for cat in range(len(column_list)): + + # if cat < len(column_list)/2: + x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # else: + # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + #print( x_adjust, y_adjust) + if sum(df_summ_player[column_list[cat]].isna()) < 1: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 16, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + else: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + + ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + x = x_adjust, + y = y_adjust-0.14, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 12, + ha='center', + va='center') + + ax.text(s = f"{player_bio['people'][0]['fullName']}", + + x = 0.5, + y = 0.95, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 28, + ha='center', + va='center') + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + f"B/T: {player_bio['people'][0]['batSide']['code']}/" + f"{player_bio['people'][0]['pitchHand']['code']} " + f"{player_bio['people'][0]['height']}/" + f"{player_bio['people'][0]['weight']}", + + x = 0.5, + y = 0.785, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + + f"DOB: {player_bio['people'][0]['birthDate']} " + f"Age: {player_bio['people'][0]['currentAge']}", + x = 0.5, + y = 0.72, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + if sport_id_input == 1: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + else: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content), cmap='viridis') + # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.35) + ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + ax.add_artist(ab) + + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + else: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + x = 0.5, + y = 0.62, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 20, + ha='center', + va='center') + + df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + df_plot = df_plot[df_plot['pitches'] > 0] + + df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + value = 1 + # Normalize the value + colormap = plt.get_cmap(cmap_sum) + colormap_r = plt.get_cmap(cmap_sum_r) + norm = Normalize(vmin=0, vmax=1) + + + + col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + ax_table = fig.add_subplot(gs[2, 1:-1]) + ax_table.axis('off') + print(colour_df) + print(df_plot) + table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + loc='center',cellColours=colour_df) + ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + w, h = table[0,1].get_width(), table[0,1].get_height() + table.add_cell(0, -1, w,h, text='Pitch Type') + min_font_size = 12 + # Set table properties + table.auto_set_font_size(False) + table.set_fontsize(min_font_size) + #table.set_fontname('arial') + table.scale(1, len(df_plot)*0.3) + + + for n_col in range(0,len(df_plot.columns)): + #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + format_col = df_plot[df_plot.columns[n_col]].astype(str) + n_c = 0 + for cell in table.get_celld().values(): + # print([cell.get_text().get_text()],format_col.astype(str).values) + if cell.get_text().get_text() in format_col.astype(str).values: + + + #print(cell.get_text().get_text() in format_col.astype(str).values) + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + n_c = n_c + 1 + + stat_1 = input.stat_1() + window_width_1 = input.window_1() + stat_2 = input.stat_2() + window_width_2 = input.window_2() + stat_3 = input.stat_3() + window_width_3 = input.window_3() + + + inset_ax = ax = fig.add_subplot(gs[3, 1]) + rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 2]) + rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 3]) + rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + ax_bot = ax = fig.add_subplot(gs[4, :]) + + ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + ax_bot.axis('off') + + + ax_cbar = fig.add_subplot(gs[1,1:-1]) + + cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + cmap=cmap_sum) + #ax_cbar.axis('off') + ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + ax_cbar.set_xticks([]) + ax_cbar.set_yticks([]) + ax_cbar.set_xticklabels([]) + ax_cbar.set_yticklabels([]) + + # Display only the outline of the axis + for spine in ax_cbar.spines.values(): + spine.set_visible(True) # Show only the outline + spine.set_color('black') # Set the color to black + + # fig.set_facecolor('#ffffff') + + return fig.tight_layout() + + + + return plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ) + @output + @render.plot(alt="A Plot") + @reactive.event(input.go, ignore_none=False) + def a_plot(): + ### Iniput data for the level + #time.sleep(2) + df_update = df_a_update.copy() + df_summ_update = df_summ_a_update.copy() + df_summ_avg_update = df_summ_avg_a_update.copy() + if len(input.player_id()) < 1: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + + batter_select = int(input.player_id()) + sport_id_input = 14 + df_roll = df_update[df_update['batter_id']==batter_select] + if len(df_roll) == 0: + fig, ax = plt.subplots(1,1,figsize=(10,10)) + ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + ax.axis('off') + return fig + + df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + #df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.dropna() + def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # Normalize the value within the range [0, 1] + normalized_value = (value - vmin) / (vmax - vmin) + + # Get the colormap + cmap = plt.get_cmap(cmap_name) + + # Map the normalized value to a color in the colormap + color = cmap(normalized_value) + + # Convert the color from RGBA to hexadecimal format + hex_color = mcolors.rgb2hex(color) + + return hex_color + + def get_players(sport_id=1): + player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + #Select relevant data that will help distinguish players from one another + fullName_list = [x['fullName'] for x in player_data['people']] + id_list = [x['id'] for x in player_data['people']] + position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + team_list = [x['currentTeam']['id']for x in player_data['people']] + age_list = [x['currentAge']for x in player_data['people']] + + player_df = pd.DataFrame(data={'player_id':id_list, + 'name':fullName_list, + 'position':position_list, + 'team':team_list, + 'age':age_list}) + return player_df + + def get_teams(): + teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + #Select only teams that are at the MLB level + # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + #Create a dataframe of all the teams + mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + 'city':mlb_teams_franchise, + 'name':mlb_teams_name, + 'franchise':mlb_teams_franchise, + 'abbreviation':mlb_teams_abb, + 'parent_org_id':mlb_teams_parent_id, + 'parent_org':mlb_teams_parent, + 'league_id':mlb_teams_league_id, + 'league_name':mlb_teams_league_name + + }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + 'city': 'Major League Baseball', + 'name': 'Major League Baseball', + 'franchise': 'Free Agent', + 'abbreviation': 'MLB', + 'parent_org_id': 11, + 'parent_org': 'Major League Baseball', + 'league_id': 1.0, + 'league_name': 'Major League Baseball', + 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + return mlb_teams_df + + def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + ax=ax, + color="#FFB000", + zorder=10) + + + + # ["#0C7BDC","#FFFFFF","#FFB000"]) + ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#0C7BDC",linestyles='-.') + ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + xmin=window_width, + xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + color="#FFB000",linestyles='--') + #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + ax.grid(True,alpha=0.2) + + + if stat_plot_dict_rolling[stat]['form'] == '3f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1f': + ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + elif stat_plot_dict_rolling[stat]['form'] == '1%': + ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + return plot + + dict_level = {1:'MLB', + 11:'MiLB AAA', + 12:'MiLB AA', + 13:'MiLB High-A', + 14:'MiLB A'} + + def plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_update = df_summ_update, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ): + + #player_df = get_players(sport_id=sport_id_input) + mlb_teams = get_teams() + team_logos = pd.read_csv('team_logos.csv') + if sport_id_input == 1: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + else: + player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + fig = plt.figure(figsize=(10, 10))#,dpi=600) + plt.rcParams.update({'figure.autolayout': True}) + fig.set_facecolor('white') + sns.set_theme(style="whitegrid", palette="pastel") + from matplotlib.gridspec import GridSpec + gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + #gs.update(hspace=0, wspace=0) + + # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # ax1 = plt.subplot(4,1,1) + # ax2 = plt.subplot(2,2,2) + # ax3 = plt.subplot(2,2,3) + # ax4 = plt.subplot(4,1,4) + #ax2 = plt.subplot(3,3,2) + + # Add subplots to the grid + ax = fig.add_subplot(gs[0, :]) + #ax1 = fig.add_subplot(gs[2, 0]) + # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # fig, ax = plt.subplots(1,1,figsize=(10,12)) + ax.axis('off') + + width = 0.08 + height = width*2.45 + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player['sweet_spot_percent'] = np.nan + df_summ_player['barrel_percent'] = np.nan + df_summ_player['hard_hit_percent'] = np.nan + if df_summ_player['launch_speed'].isna().values[0]: + df_summ_player_pct['sweet_spot_percent'] = np.nan + df_summ_player_pct['barrel_percent'] = np.nan + df_summ_player_pct['hard_hit_percent'] = np.nan + # x = 0.1 + # y = 0.9 + for cat in range(len(column_list)): + + # if cat < len(column_list)/2: + x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # else: + # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + #print( x_adjust, y_adjust) + if sum(df_summ_player[column_list[cat]].isna()) < 1: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 16, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + else: + print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + x = x_adjust, + y = y_adjust, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + if stat_plot_dict[column_list[cat]]['flip']: + + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + ax.add_patch(bbox) + + + else: + bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + ax.add_patch(bbox) + + ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + x = x_adjust, + y = y_adjust-0.14, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 12, + ha='center', + va='center') + + ax.text(s = f"{player_bio['people'][0]['fullName']}", + + x = 0.5, + y = 0.95, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 28, + ha='center', + va='center') + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + x = 0.5, + y = 0.85, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + f"B/T: {player_bio['people'][0]['batSide']['code']}/" + f"{player_bio['people'][0]['pitchHand']['code']} " + f"{player_bio['people'][0]['height']}/" + f"{player_bio['people'][0]['weight']}", + + x = 0.5, + y = 0.785, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + + ax.text(s = + + f"DOB: {player_bio['people'][0]['birthDate']} " + f"Age: {player_bio['people'][0]['currentAge']}", + x = 0.5, + y = 0.72, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 14, + ha='center', + va='center') + if sport_id_input == 1: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + else: + try: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + test_mage = plt.imread(url) + except urllib.error.HTTPError as err: + url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content), cmap='viridis') + # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.35) + ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + ax.add_artist(ab) + + if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + else: + url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # im = plt.imread(url) + # response = requests.get(url) + # im = Image.open(BytesIO(response.content)) + #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + imagebox = OffsetImage(im, zoom = 0.25) + ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + ax.add_artist(ab) + + ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + x = 0.5, + y = 0.62, + color='black', + #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + fontsize = 20, + ha='center', + va='center') + + df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + df_plot = df_plot[df_plot['pitches'] > 0] + + df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + value = 1 + # Normalize the value + colormap = plt.get_cmap(cmap_sum) + colormap_r = plt.get_cmap(cmap_sum_r) + norm = Normalize(vmin=0, vmax=1) + + + + col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + ax_table = fig.add_subplot(gs[2, 1:-1]) + ax_table.axis('off') + print(colour_df) + print(df_plot) + table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + loc='center',cellColours=colour_df) + ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + w, h = table[0,1].get_width(), table[0,1].get_height() + table.add_cell(0, -1, w,h, text='Pitch Type') + min_font_size = 12 + # Set table properties + table.auto_set_font_size(False) + table.set_fontsize(min_font_size) + #table.set_fontname('arial') + table.scale(1, len(df_plot)*0.3) + + + for n_col in range(0,len(df_plot.columns)): + #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + format_col = df_plot[df_plot.columns[n_col]].astype(str) + n_c = 0 + for cell in table.get_celld().values(): + # print([cell.get_text().get_text()],format_col.astype(str).values) + if cell.get_text().get_text() in format_col.astype(str).values: + + + #print(cell.get_text().get_text() in format_col.astype(str).values) + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + n_c = n_c + 1 + + stat_1 = input.stat_1() + window_width_1 = input.window_1() + stat_2 = input.stat_2() + window_width_2 = input.window_2() + stat_3 = input.stat_3() + window_width_3 = input.window_3() + + + inset_ax = ax = fig.add_subplot(gs[3, 1]) + rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 2]) + rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + inset_ax = ax = fig.add_subplot(gs[3, 3]) + rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + ax_bot = ax = fig.add_subplot(gs[4, :]) + + ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + ax_bot.axis('off') + + + ax_cbar = fig.add_subplot(gs[1,1:-1]) + + cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + cmap=cmap_sum) + #ax_cbar.axis('off') + ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + ax_cbar.set_xticks([]) + ax_cbar.set_yticks([]) + ax_cbar.set_xticklabels([]) + ax_cbar.set_yticklabels([]) + + # Display only the outline of the axis + for spine in ax_cbar.spines.values(): + spine.set_visible(True) # Show only the outline + spine.set_color('black') # Set the color to black + + # fig.set_facecolor('#ffffff') + + return fig.tight_layout() + + + + return plot_card(sport_id_input=sport_id_input, + batter_select=batter_select, + df_roll=df_roll, + df_summ_player=df_summ_player, + df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + ) + + # @render.plot(alt="LIDOM Plot") + # def dom_plot(): + # ### Iniput data for the level + # #time.sleep(2) + # df_update = df_dom_update.copy() + # df_summ_update = df_summ_dom_update.copy() + # df_summ_avg_update = df_summ_avg_dom_update.copy() + # if len(input.player_id()) < 1: + # fig, ax = plt.subplots(1,1,figsize=(10,10)) + # ax.text(s='Please Select a Batter',x=0.5,y=0.5, ha='center') + # ax.axis('off') + # return fig + + + # batter_select = int(input.player_id()) + # sport_id_input = 17 + # df_roll = df_update[df_update['batter_id']==batter_select] + # if len(df_roll) == 0: + # fig, ax = plt.subplots(1,1,figsize=(10,10)) + # ax.text(s='Card is Generating',x=0.5,y=0.5, ha='center') + # ax.axis('off') + # return fig + + # df_summ_filter = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[0] + # df_summ_filter_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[1] + # df_summ_player = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[2] + # df_summ_player_pct = df_summ_filter_out(df_summ=df_summ_update,batter_select = batter_select)[3] + + # df_summ_batter_pitch = df_summ_batter_pitch_up(df= df_update).set_index(['batter_id','batter_name','pitch_category']) + + + # df_summ_batter_pitch_pct = df_summ_batter_pitch.loc[df_summ_filter.index.get_level_values(0)] + # df_summ_batter_pitch_pct = df_summ_batter_pitch_pct[df_summ_batter_pitch_pct['pitches']>0] + # df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct.groupby(level='pitch_category').apply(lambda x: x.rank(pct=True)).xs(batter_select,level=0) + + # df_summ_batter_pitch_pct_rank['pitch_count'] = df_summ_batter_pitch_pct_rank.index.get_level_values(1).map(df_summ_batter_pitch.xs(batter_select,level=0).reset_index().set_index('pitch_category')['pitches'].to_dict()) + # df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.sort_values('pitch_count',ascending=False) + # #df_summ_batter_pitch_pct_rank = df_summ_batter_pitch_pct_rank.dropna() + # def get_color(value, vmin, vmax, cmap_name=cmap_sum): + # # Normalize the value within the range [0, 1] + # normalized_value = (value - vmin) / (vmax - vmin) + + # # Get the colormap + # cmap = plt.get_cmap(cmap_name) + + # # Map the normalized value to a color in the colormap + # color = cmap(normalized_value) + + # # Convert the color from RGBA to hexadecimal format + # hex_color = mcolors.rgb2hex(color) + + # return hex_color + + # def get_players(sport_id=1): + # player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{sport_id}/players').json() + + # #Select relevant data that will help distinguish players from one another + # fullName_list = [x['fullName'] for x in player_data['people']] + # id_list = [x['id'] for x in player_data['people']] + # position_list = [x['primaryPosition']['abbreviation'] for x in player_data['people']] + # team_list = [x['currentTeam']['id']for x in player_data['people']] + # age_list = [x['currentAge']for x in player_data['people']] + + # player_df = pd.DataFrame(data={'player_id':id_list, + # 'name':fullName_list, + # 'position':position_list, + # 'team':team_list, + # 'age':age_list}) + # return player_df + + # def get_teams(): + # teams = requests.get(url='https://statsapi.mlb.com/api/v1/teams/').json() + # #Select only teams that are at the MLB level + # # mlb_teams_city = [x['franchiseName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # # mlb_teams_name = [x['teamName'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # # mlb_teams_franchise = [x['name'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # # mlb_teams_id = [x['id'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + # # mlb_teams_abb = [x['abbreviation'] for x in teams['teams'] if x['sport']['name'] == 'Major League Baseball'] + + # mlb_teams_city = [x['franchiseName'] if 'franchiseName' in x else None for x in teams['teams']] + # mlb_teams_name = [x['teamName'] if 'franchiseName' in x else None for x in teams['teams']] + # mlb_teams_franchise = [x['name'] if 'franchiseName' in x else None for x in teams['teams']] + # mlb_teams_id = [x['id'] if 'franchiseName' in x else None for x in teams['teams']] + # mlb_teams_abb = [x['abbreviation'] if 'franchiseName' in x else None for x in teams['teams']] + # mlb_teams_parent_id = [x['parentOrgId'] if 'parentOrgId' in x else None for x in teams['teams']] + # mlb_teams_parent = [x['parentOrgName'] if 'parentOrgName' in x else None for x in teams['teams']] + # mlb_teams_league_id = [x['league']['id'] if 'id' in x['league'] else None for x in teams['teams']] + # mlb_teams_league_name = [x['league']['name'] if 'name' in x['league'] else None for x in teams['teams']] + + + + # #Create a dataframe of all the teams + # mlb_teams_df = pd.DataFrame(data={'team_id':mlb_teams_id, + # 'city':mlb_teams_franchise, + # 'name':mlb_teams_name, + # 'franchise':mlb_teams_franchise, + # 'abbreviation':mlb_teams_abb, + # 'parent_org_id':mlb_teams_parent_id, + # 'parent_org':mlb_teams_parent, + # 'league_id':mlb_teams_league_id, + # 'league_name':mlb_teams_league_name + + # }).drop_duplicates().dropna(subset=['team_id']).reset_index(drop=True).sort_values('team_id') + + # mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'parent_org_id'] = mlb_teams_df.loc[mlb_teams_df['parent_org_id'].isnull(),'team_id'] + # mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'parent_org'] = mlb_teams_df.loc[mlb_teams_df['parent_org'].isnull(),'franchise'] + + + # mlb_teams_df['parent_org_abbreviation'] = mlb_teams_df['parent_org_id'].map(mlb_teams_df.set_index('team_id')['abbreviation'].to_dict()) + + # mlb_teams_df = pd.concat([mlb_teams_df, pd.DataFrame({'team_id': 11, + # 'city': 'Major League Baseball', + # 'name': 'Major League Baseball', + # 'franchise': 'Free Agent', + # 'abbreviation': 'MLB', + # 'parent_org_id': 11, + # 'parent_org': 'Major League Baseball', + # 'league_id': 1.0, + # 'league_name': 'Major League Baseball', + # 'parent_org_abbreviation': 'MLB'},index=[0])]).reset_index(drop=True) + + # #mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'parent_org'] = mlb_teams_df.loc[mlb_teams_df.franchise.isin(mlb_teams_df.parent_org.unique()),'franchise'] + + # return mlb_teams_df + + # def rolling_plot(stat='k_percent',window_width=100,ax=0,df_r=df_roll,df_r_summ_avg=pd.DataFrame(),stat_plot_dict_rolling=stat_plot_dict_rolling): + # plot = sns.lineplot(x=range(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]>0])+1), + # y=df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1].fillna(0).rolling(window=window_width)[stat_plot_dict_rolling[stat]['y']].sum().dropna()/window_width, + # ax=ax, + # color="#FFB000", + # zorder=10) + + + # print(df_r_summ_avg) + # # ["#0C7BDC","#FFFFFF","#FFB000"]) + # ax.set_xlim(window_width,len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1])) + # ax.set_xlabel(stat_plot_dict_rolling[stat]['x_label'],fontsize=8) + # ax.set_ylabel(stat_plot_dict_rolling[stat]['name'],fontsize=8) + + # ax.hlines(df_r_summ_avg[stat_plot_dict_rolling[stat]['y']]/df_r_summ_avg[stat_plot_dict_rolling[stat]['div']], + # xmin=window_width, + # xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + # color="#0C7BDC",linestyles='-.') + # ax.hlines(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna()), + # xmin=window_width, + # xmax=len(df_r[df_r[stat_plot_dict_rolling[stat]['div']]==1]), + # color="#FFB000",linestyles='--') + # #print(sum(df_r[stat_plot_dict_rolling[stat]['y']].dropna())/sum(df_r[stat_plot_dict_rolling[stat]['div']].dropna())) + # ax.tick_params(axis='x', labelsize=8) # Set x-axis ticks size + # ax.tick_params(axis='y', labelsize=8) # Set y-axis ticks size + # ax.set_title(f"{window_width} {stat_plot_dict_rolling[stat]['x_label']} Rolling {stat_plot_dict_rolling[stat]['name']}",fontsize=8) + # ax.set_ylim(stat_plot_dict_rolling[stat]['y_min'],stat_plot_dict_rolling[stat]['y_max']) + # ax.grid(True,alpha=0.2) + + + # if stat_plot_dict_rolling[stat]['form'] == '3f': + # ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.3f}')) + + # elif stat_plot_dict_rolling[stat]['form'] == '1f': + # ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:.1f}')) + + # elif stat_plot_dict_rolling[stat]['form'] == '1%': + # ax.yaxis.set_major_formatter(mtick.PercentFormatter(1)) + + # return plot + + # dict_level = {1:'MLB', + # 11:'MiLB AAA', + # 12:'MiLB AA', + # 13:'MiLB High-A', + # 14:'MiLB A', + # 17:'Dominican Winter League'} + + # def plot_card(sport_id_input=sport_id_input, + # batter_select=batter_select, + # df_roll=df_roll, + # df_summ_player=df_summ_player, + # df_summ_update = df_summ_update, + # df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + # ): + + # #player_df = get_players(sport_id=sport_id_input) + # mlb_teams = get_teams() + # team_logos = pd.read_csv('team_logos.csv') + # if sport_id_input == 1: + # player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=majorLeague&hydrate=currentTeam').json() + # else: + # player_bio = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={batter_select}&appContext=minorLeague&hydrate=currentTeam').json() + + # fig = plt.figure(figsize=(10, 10))#,dpi=600) + # plt.rcParams.update({'figure.autolayout': True}) + # fig.set_facecolor('white') + # sns.set_theme(style="whitegrid", palette="pastel") + # from matplotlib.gridspec import GridSpec + # gs = GridSpec(5, 5, width_ratios=[0.2,1,1,1,0.2], height_ratios=[0.6,0.05,0.15,.30,0.025]) + # #gs.update(hspace=0, wspace=0) + + # # gs.update(left=0.1,right=0.9,top=0.97,bottom=0.03,wspace=0.3,hspace=0.09) + + # # ax1 = plt.subplot(4,1,1) + # # ax2 = plt.subplot(2,2,2) + # # ax3 = plt.subplot(2,2,3) + # # ax4 = plt.subplot(4,1,4) + # #ax2 = plt.subplot(3,3,2) + + # # Add subplots to the grid + # ax = fig.add_subplot(gs[0, :]) + # #ax1 = fig.add_subplot(gs[2, 0]) + # # ax2 = fig.add_subplot(gs[2, :]) # Subplot at the top-right position + # # fig, ax = plt.subplots(1,1,figsize=(10,12)) + # ax.axis('off') + + # width = 0.08 + # height = width*2.45 + # if df_summ_player['launch_speed'].isna().values[0]: + # df_summ_player['sweet_spot_percent'] = np.nan + # df_summ_player['barrel_percent'] = np.nan + # df_summ_player['hard_hit_percent'] = np.nan + # if df_summ_player['launch_speed'].isna().values[0]: + # df_summ_player_pct['sweet_spot_percent'] = np.nan + # df_summ_player_pct['barrel_percent'] = np.nan + # df_summ_player_pct['hard_hit_percent'] = np.nan + # # x = 0.1 + # # y = 0.9 + # for cat in range(len(column_list)): + + # # if cat < len(column_list)/2: + # x_adjust, y_adjust =(0.85/7*8)*cat/8+0.075 - (0.85/7*8)*math.floor((cat)/8), 0.45-math.floor((cat)/8)/3.2 + + # # else: + # # x_adjust, y_adjust = (cat-len(column_list)/2)*(1.7/(math.ceil((len(column_list)-1))))+0.1, 0.5 + # #print( x_adjust, y_adjust) + # if sum(df_summ_player[column_list[cat]].isna()) < 1: + # print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + # ax.text(s = f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}'.format().strip(), + + # x = x_adjust, + # y = y_adjust, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 16, + # ha='center', + # va='center') + + # if stat_plot_dict[column_list[cat]]['flip']: + + # bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + # facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + # ax.add_patch(bbox) + + + # else: + # bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + # facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + # ax.add_patch(bbox) + # else: + # print(f'{df_summ_player[column_list[cat]].values[0]:{stat_plot_dict[column_list[cat]]["format"]}}') + # ax.text(s = f'{df_summ_player[column_list[cat]].fillna("N/A").values[0]}', + + # x = x_adjust, + # y = y_adjust, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 14, + # ha='center', + # va='center') + + # if stat_plot_dict[column_list[cat]]['flip']: + + # bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + # facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum_r)) + # ax.add_patch(bbox) + + + # else: + # bbox = patches.Rectangle((x_adjust- width/2,y_adjust- height/2), width, height, linewidth=1,edgecolor='black', + # facecolor = get_color(df_summ_player_pct[column_list[cat]].values[0],0,1,cmap_name=cmap_sum)) + # ax.add_patch(bbox) + + # ax.text(s = stat_plot_dict[column_list[cat]]['name'], + + # x = x_adjust, + # y = y_adjust-0.14, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 12, + # ha='center', + # va='center') + + # ax.text(s = f"{player_bio['people'][0]['fullName']}", + + # x = 0.5, + # y = 0.95, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 28, + # ha='center', + # va='center') + # if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + + # ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {mlb_teams[mlb_teams['team_id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['franchise'].values[0]}", + + # x = 0.5, + # y = 0.85, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 14, + # ha='center', + # va='center') + + # else: ax.text(s = f"{player_bio['people'][0]['primaryPosition']['abbreviation']}, {player_bio['people'][0]['currentTeam']['name']}", + + # x = 0.5, + # y = 0.85, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 14, + # ha='center', + # va='center') + + # ax.text(s = + # f"B/T: {player_bio['people'][0]['batSide']['code']}/" + # f"{player_bio['people'][0]['pitchHand']['code']} " + # f"{player_bio['people'][0]['height']}/" + # f"{player_bio['people'][0]['weight']}", + + # x = 0.5, + # y = 0.785, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 14, + # ha='center', + # va='center') + + # ax.text(s = + + # f"DOB: {player_bio['people'][0]['birthDate']} " + # f"Age: {player_bio['people'][0]['currentAge']}", + # x = 0.5, + # y = 0.72, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 14, + # ha='center', + # va='center') + # if sport_id_input == 1: + # try: + # url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{batter_select}/headshot/67/current.png' + # test_mage = plt.imread(url) + # except urllib.error.HTTPError as err: + # url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + + # else: + # try: + # url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{batter_select}/headshot/milb/current.png' + # test_mage = plt.imread(url) + # except urllib.error.HTTPError as err: + # url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png' + # im = plt.imread(url) + # # response = requests.get(url) + # # im = Image.open(BytesIO(response.content), cmap='viridis') + # # im = plt.imread(np.array(PIL.Image.open(urllib.request.urlopen(url)))) + + # # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + # imagebox = OffsetImage(im, zoom = 0.35) + # ab = AnnotationBbox(imagebox, (0.125, 0.8), frameon = False) + # ax.add_artist(ab) + + # if 'parentOrgId' in player_bio['people'][0]['currentTeam']: + # url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0] + + # im = plt.imread(url) + # # response = requests.get(url) + # # im = Image.open(BytesIO(response.content)) + # # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + # # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + # imagebox = OffsetImage(im, zoom = 0.25) + # ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + # ax.add_artist(ab) + + # else: + # url = team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0] + # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['id']]['imageLink'].values[0]) + + # # im = plt.imread(url) + # # response = requests.get(url) + # # im = Image.open(BytesIO(response.content)) + # #im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0]) + + # # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1) + # imagebox = OffsetImage(im, zoom = 0.25) + # ab = AnnotationBbox(imagebox, (0.875, 0.8), frameon = False) + # ax.add_artist(ab) + + # ax.text(s = f'2023 {dict_level[sport_id_input]} Metrics', + + # x = 0.5, + # y = 0.62, + # color='black', + # #bbox=dict(facecolor='none', edgecolor='black', pad=10.0), + # fontsize = 20, + # ha='center', + # va='center') + + # df_plot = df_summ_batter_pitch[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + # df_plot = df_plot[df_plot['pitches'] > 0] + + # df_plot_pct = df_summ_batter_pitch_pct[column_list_pitch].xs([batter_select,df_summ_update.xs(batter_select,level=0).index[0]]).sort_values('pitches',ascending=False)#.dropna() + + # value = 1 + # # Normalize the value + # colormap = plt.get_cmap(cmap_sum) + # colormap_r = plt.get_cmap(cmap_sum_r) + # norm = Normalize(vmin=0, vmax=1) + + + + # col_5_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['chase_percent']))] + # col_4_colour = [colormap_r(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['whiff_rate']))] + # col_3_colour = [colormap(norm(x)) for x in list((df_summ_batter_pitch_pct_rank['woba_percent_contact']))] + # col_2_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + # col_1_colour = ['white']*len(df_summ_batter_pitch_pct_rank) + # colour_df = pd.DataFrame(data=[col_1_colour,col_2_colour,col_3_colour,col_4_colour,col_5_colour]).T.values + + # ax_table = fig.add_subplot(gs[2, 1:-1]) + # ax_table.axis('off') + # print(colour_df) + # print(df_plot) + # table = ax_table.table(cellText=df_plot.values, colLabels=[stat_plot_dict[x]['name'] for x in df_plot.columns],rowLabels=df_plot.index, cellLoc='center', + # bbox=[0.12, 0.0, 0.88, 1],colWidths=[0.03]+[0.03]*(len(df_plot.columns)), + # loc='center',cellColours=colour_df) + # ax_table.text(x=0.5,y=1.1,s='Metrics By Pitch Type',ha='center',fontdict={ 'size': 12},fontname='arial') + + # w, h = table[0,1].get_width(), table[0,1].get_height() + # table.add_cell(0, -1, w,h, text='Pitch Type') + # min_font_size = 12 + # # Set table properties + # table.auto_set_font_size(False) + # table.set_fontsize(min_font_size) + # #table.set_fontname('arial') + # table.scale(1, len(df_plot)*0.3) + + + # for n_col in range(0,len(df_plot.columns)): + # #print(df_plot.columns[n_col],f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}") + # format_col = df_plot[df_plot.columns[n_col]].astype(str) + # n_c = 0 + # for cell in table.get_celld().values(): + # # print([cell.get_text().get_text()],format_col.astype(str).values) + # if cell.get_text().get_text() in format_col.astype(str).values: + + + # #print(cell.get_text().get_text() in format_col.astype(str).values) + # cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + # elif cell.get_text().get_text()[:-2] in format_col.astype(str).values: + # cell.get_text().set_text(f"{{:{stat_plot_dict[df_plot.columns[n_col]]['format']}}}".format(float(cell.get_text().get_text()))) + # n_c = n_c + 1 + + # stat_1 = input.stat_1() + # window_width_1 = input.window_1() + # stat_2 = input.stat_2() + # window_width_2 = input.window_2() + # stat_3 = input.stat_3() + # window_width_3 = input.window_3() + + + # inset_ax = ax = fig.add_subplot(gs[3, 1]) + # rolling_plot(stat=stat_1,window_width=window_width_1,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + # inset_ax = ax = fig.add_subplot(gs[3, 2]) + # rolling_plot(stat=stat_2,window_width=window_width_2,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + # inset_ax = ax = fig.add_subplot(gs[3, 3]) + # rolling_plot(stat=stat_3,window_width=window_width_3,ax=inset_ax,df_r=df_roll,df_r_summ_avg=df_summ_avg_update) + + # ax_bot = ax = fig.add_subplot(gs[4, :]) + + # ax_bot.text(x=0.05,y=-0.5,s='By: @TJStats',ha='left',fontdict={ 'size': 14},fontname='arial') + # ax_bot.text(x=1-0.05,y=-0.5,s='Data: MLB',ha='right',fontdict={ 'size': 14},fontname='arial') + # ax_bot.axis('off') + + + # ax_cbar = fig.add_subplot(gs[1,1:-1]) + + # cb = matplotlib.colorbar.ColorbarBase(ax_cbar, orientation='horizontal', + # cmap=cmap_sum) + # #ax_cbar.axis('off') + # ax_cbar.text(x=0.5,y=1.2,s='Colour Scale - Percentiles',ha='center',fontdict={ 'size': 12},fontname='arial') + # ax_cbar.text(s='0%',x=0.01,y=0.5,va='center',ha='left') + # ax_cbar.text(s='100%',x=0.99,y=0.5,va='center',ha='right') + # # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # # ax_cbar.text(s='50%',x=0.5,y=0.5,va='center',ha='center') + # ax_cbar.set_xticks([]) + # ax_cbar.set_yticks([]) + # ax_cbar.set_xticklabels([]) + # ax_cbar.set_yticklabels([]) + + # # Display only the outline of the axis + # for spine in ax_cbar.spines.values(): + # spine.set_visible(True) # Show only the outline + # spine.set_color('black') # Set the color to black + + # # fig.set_facecolor('#ffffff') + + # return fig.tight_layout() + + + + # return plot_card(sport_id_input=sport_id_input, + # batter_select=batter_select, + # df_roll=df_roll, + # df_summ_player=df_summ_player, + # df_summ_batter_pitch_pct=df_summ_batter_pitch_pct, + # ) + + +from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui + + + +app = App(ui.page_fluid( +# ui.tags.base(href=base_url), + ui.tags.div( + {"style": "width:90%;margin: 0 auto;max-width: 1600px;"}, + ui.tags.style( + """ + h4 { + margin-top: 1em;font-size:35px; + } + h2{ + font-size:25px; + } + """ + ), + shinyswatch.theme.simplex(), + ui.tags.h4("TJStats"), + ui.tags.i("Baseball Analytics and Visualizations"), + ui.markdown("""Support me on Patreon for Access to 2024 Apps1"""), + + ui.navset_tab( + ui.nav_control( + ui.a( + "Home", + href="https://nesticot-tjstats-site.hf.space/home/" + ), + ), + ui.nav_menu( + "Batter Charts", + ui.nav_control( + ui.a( + "Batting Rolling", + href="https://nesticot-tjstats-site-rolling-batter.hf.space/" + ), + ui.a( + "Spray", + href="https://nesticot-tjstats-site-spray.hf.space/" + ), + ui.a( + "Decision Value", + href="https://nesticot-tjstats-site-decision-value.hf.space/" + ), + ui.a( + "Damage Model", + href="https://nesticot-tjstats-site-damage.hf.space/" + ), + ui.a( + "Batter Scatter", + href="https://nesticot-tjstats-site-batter-scatter.hf.space/" + ), + ui.a( + "EV vs LA Plot", + href="https://nesticot-tjstats-site-ev-angle.hf.space/" + ), + ui.a( + "Statcast Compare", + href="https://nesticot-tjstats-site-statcast-compare.hf.space/" + ), + ui.a( + "MLB/MiLB Cards", + href="https://nesticot-tjstats-site-mlb-cards.hf.space/" + ) + ), + ), + ui.nav_menu( + "Pitcher Charts", + ui.nav_control( + ui.a( + "Pitcher Rolling", + href="https://nesticot-tjstats-site-rolling-pitcher.hf.space/" + ), + ui.a( + "Pitcher Summary", + href="https://nesticot-tjstats-site-pitching-summary-graphic-new.hf.space/" + ), + ui.a( + "Pitcher Scatter", + href="https://nesticot-tjstats-site-pitcher-scatter.hf.space" + ) + ), + )), ui.row( + ui.layout_sidebar( + + ui.panel_sidebar(ui.output_ui('test',"Select Batter"), + ui.input_select('stat_1',"Select Rolling Stat 1",stat_roll_dict,selectize=True), + ui.input_numeric('window_1',"Select Rolling Window 1",value=100), + ui.input_select('stat_2',"Select Rolling Stat 2",stat_roll_dict,selected='k_percent',selectize=True), + ui.input_numeric('window_2',"Select Rolling Stat 2",value=100), + ui.input_select('stat_3',"Select Rolling Stat 3",stat_roll_dict,selected='bb_percent',selectize=True), + ui.input_numeric('window_3',"Select Rolling Stat 3",value=100), + ui.input_action_button("go", "Generate",class_="btn-primary"),width=2), + + ui.page_navbar( + + ui.nav_panel("MLB", + ui.output_plot('mlb_plot',width='1000px',height='1000px')), + ui.nav_panel("AAA", + ui.output_plot('aaa_plot',width='1000px',height='1000px')), + ui.nav_panel("AA", + ui.output_plot('aa_plot',width='1000px',height='1000px')), + ui.nav_panel("High-A", + ui.output_plot('ha_plot',width='1000px',height='1000px')), + ui.nav_panel("A", + ui.output_plot('a_plot',width='1000px',height='1000px')), + id="my_tabs", + ))),)),server) + + + +# app = App(app_ui, server)