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)