diff --git "a/pitching_summary_graphic_new_fg_api.py" "b/pitching_summary_graphic_new_fg_api.py"
new file mode 100644--- /dev/null
+++ "b/pitching_summary_graphic_new_fg_api.py"
@@ -0,0 +1,2094 @@
+print('Running')
+from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+from configure import base_url
+import shinyswatch
+from matplotlib.pyplot import text
+from shinywidgets import output_widget, render_widget
+
+import time
+import requests
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import figure
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+from scipy import stats
+import matplotlib.lines as mlines
+import matplotlib.transforms as mtransforms
+import numpy as np
+import time
+#import plotly.express as px
+#!pip install chart_studio
+#import chart_studio.tools as tls
+from bs4 import BeautifulSoup
+import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.font_manager as font_manager
+from datetime import datetime
+import pytz
+from matplotlib.ticker import MaxNLocator
+from matplotlib.patches import Ellipse
+import matplotlib.transforms as transforms
+from matplotlib.gridspec import GridSpec
+datetime.now(pytz.timezone('US/Pacific')).strftime('%B %d, %Y')
+# Configure Notebook
+#%matplotlib inline
+plt.style.use('fivethirtyeight')
+sns.set_context("notebook")
+import warnings
+warnings.filterwarnings('ignore')
+# import yfpy
+# from yfpy.query import YahooFantasySportsQuery
+# import yahoo_oauth
+import json
+import urllib
+#import openpyxl
+from sklearn import preprocessing
+from datetime import timedelta
+#import dataframe_image as dfi
+# from google.colab import drive
+def percentile(n):
+    def percentile_(x):
+        return np.percentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+import os
+#import praw
+import matplotlib.pyplot as plt
+import matplotlib.colors
+import matplotlib.colors as mcolors
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#4285f4","#FFFFFF","#F0E442"])
+#import pybaseball
+import math
+import matplotlib.ticker as mtick
+import matplotlib.ticker as ticker
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+import matplotlib.colors as mcolors
+from  matplotlib.ticker import FuncFormatter
+from matplotlib.font_manager import FontProperties
+import matplotlib.patheffects as path_effects
+import pybaseball as pyb
+
+
+def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):
+    """
+    Create a plot of the covariance confidence ellipse of *x* and *y*.
+
+    Parameters
+    ----------
+    x, y : array-like, shape (n, )
+        Input data.
+
+    ax : matplotlib.axes.Axes
+        The axes object to draw the ellipse into.
+
+    n_std : float
+        The number of standard deviations to determine the ellipse's radiuses.
+
+    **kwargs
+        Forwarded to `~matplotlib.patches.Ellipse`
+
+    Returns
+    -------
+    matplotlib.patches.Ellipse
+    """
+    if x.size != y.size:
+        raise ValueError("x and y must be the same size")
+
+    cov = np.cov(x, y)
+    pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])
+    # Using a special case to obtain the eigenvalues of this
+    # two-dimensional dataset.
+    ell_radius_x = np.sqrt(1 + pearson)
+    ell_radius_y = np.sqrt(1 - pearson)
+    ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,
+                      facecolor=facecolor, **kwargs)
+    
+
+    # Calculating the standard deviation of x from
+    # the squareroot of the variance and multiplying
+    # with the given number of standard deviations.
+    scale_x = np.sqrt(cov[0, 0]) * n_std
+    mean_x = np.mean(x)
+    
+
+    # calculating the standard deviation of y ...
+    scale_y = np.sqrt(cov[1, 1]) * n_std
+    mean_y = np.mean(y)
+    
+
+    transf = transforms.Affine2D() \
+        .rotate_deg(45) \
+        .scale(scale_x, scale_y) \
+        .translate(mean_x, mean_y)
+    
+    
+
+    ellipse.set_transform(transf + ax.transData)
+    
+    
+    return ax.add_patch(ellipse)
+
+
+statcast_df_df_pitch = pd.read_csv('statcast_pitch_summary.csv',index_col=[0]).reset_index(drop=True)
+
+
+# statcast_df_df_pitch['whiff_rate'] = statcast_df_df_pitch['whiff']/statcast_df_df_pitch['swings']
+# statcast_df_df_pitch['csw_rate'] = statcast_df_df_pitch['csw']/statcast_df_df_pitch['pitches']
+# statcast_df_df_pitch['chase_percent'] = statcast_df_df_pitch['chase']/statcast_df_df_pitch['out_zone']
+# statcast_df_df_pitch['pitch_percent'] = statcast_df_df_pitch['pitches']/statcast_df_df_pitch['pitches'].sum()
+player_data = requests.get(url=f'https://statsapi.mlb.com/api/v1/sports/{1}/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']]
+
+
+player_list = pd.DataFrame(data={'player_id':id_list,'full_name':fullName_list,'position':position_list,'team_id':team_list})
+
+player_list = player_list.drop_duplicates(subset=['player_id'],keep='last')
+#player_df_all = player_list.merge(right=mlb_teams_df[['team_id','abbreviation']],left_on = 'team_id',right_on='team_id',how='left').drop_duplicates(keep='last')
+player_df_all = player_list[player_list['position'].str.contains('P')]
+pitcher_dicts = player_df_all.set_index('player_id')['full_name'].sort_values().to_dict()
+
+chad_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season=2023&season=2023&month=1000&season1=2023&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching').json()
+
+
+chadwick_df_small = pd.DataFrame(data={
+'key_mlbam':[x['xMLBAMID'] for x in chad_fg['data']],
+'key_fangraphs':[x['playerid'] for x in chad_fg['data']],
+'Name':[x['PlayerName'] for x in chad_fg['data']],
+})    
+
+def server(input,output,session):
+
+    @reactive.Effect
+    #@reactive.event(input.go, ignore_none=False)
+    def _():
+        if input.id() == "":
+            return
+
+        print('this guy')
+        statcast_df = pyb.statcast_pitcher(start_dt='2023-03-30',end_dt='2023-10-02',player_id=int(input.id()))
+        if input.radio_id() != 'a': 
+            statcast_df = statcast_df[statcast_df['stand']==input.radio_id()]
+        #player_df = pd.read_csv('player_df_all.csv',index_col=[0])
+        #player_df = pd.concat([player_df,pd.DataFrame({'player_id':668909,'team_id':114.0,'abbreviation':'CLE'},index=[2000])])
+
+        sport_id=1
+
+
+        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 = [x['parentOrgName']  if 'parentOrgName' in x 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':mlb_teams_parent}).drop_duplicates()
+        ##Create a dataframe of all players in the database
+        #Make an api call to get a dictionary of all players
+        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']]
+
+
+        player_list = pd.DataFrame(data={'player_id':id_list,'full_name':fullName_list,'position':position_list,'team_id':team_list})
+
+        player_list = player_list.drop_duplicates(subset=['player_id'],keep='last')
+        player_df_all = player_list.merge(right=mlb_teams_df[['team_id','abbreviation']],left_on = 'team_id',right_on='team_id',how='left').drop_duplicates(keep='last')
+
+        mlb_teams_df = mlb_teams_df.merge(right=mlb_teams_df[['abbreviation','franchise']],left_on='parent_org',right_on='franchise',how='left').drop_duplicates().reset_index(drop=True)
+        mlb_teams_df = mlb_teams_df[mlb_teams_df.columns[:-1]]
+
+        mlb_teams_df.columns = ['team_id', 'city', 'name', 'franchise', 'abbreviation',
+            'parent_org', 'parent_org_abb']
+
+        statcast_df = statcast_df.merge(right=player_df_all,left_on='batter',right_on='player_id',suffixes=['','_batter'])
+        statcast_df = statcast_df.merge(right=player_df_all,left_on='pitcher',right_on='player_id',suffixes=['','_pitcher'])
+
+        
+        statcast_df['game_opp'] = statcast_df['game_date'].astype(str) + ' vs ' + statcast_df['abbreviation'].astype(str)
+        print(statcast_df['game_opp'])
+
+        opts_dict = pd.concat([pd.DataFrame(data={'game_pk':0,'game_opp':'Season'},index=[0]),
+                               statcast_df[statcast_df.pitcher == int(input.id())].drop_duplicates(subset=['pitcher','game_pk','game_opp'])[['game_pk','game_opp']].sort_values(
+                                by='game_opp')]).set_index('game_pk')['game_opp'].astype(str).to_dict()
+
+
+        ui.update_select(
+            "date_id",
+            label="Select Date",
+            choices=opts_dict,
+        )
+
+    #@output
+    # @render.text
+    # def txt():
+    #     return f'pitcher_id: "{input.pitcher_id()}"'
+
+    @output
+    @render.plot(alt="A histogram")
+    @reactive.event(input.go, ignore_none=False)
+    def plot():
+
+        if input.id() == "":
+            fig = plt.figure(figsize=(12, 12))
+            fig.text(s='Please Select a Pitcher',x=0.5,y=0.5,ha='center')
+            return    
+
+
+        statcast_df = pyb.statcast_pitcher(start_dt='2023-03-30',end_dt='2023-10-02',player_id=int(input.id()))
+        if input.radio_id() != 'a': 
+            statcast_df = statcast_df[statcast_df['stand']==input.radio_id()]
+        #player_df = pd.read_csv('player_df_all.csv',index_col=[0])
+        #player_df = pd.concat([player_df,pd.DataFrame({'player_id':668909,'team_id':114.0,'abbreviation':'CLE'},index=[2000])])
+
+        sport_id=1
+
+
+        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 = [x['parentOrgName']  if 'parentOrgName' in x 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':mlb_teams_parent}).drop_duplicates()
+        ##Create a dataframe of all players in the database
+        #Make an api call to get a dictionary of all players
+        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']]
+
+
+        player_list = pd.DataFrame(data={'player_id':id_list,'full_name':fullName_list,'position':position_list,'team_id':team_list})
+
+        player_list = player_list.drop_duplicates(subset=['player_id'],keep='last')
+        player_df_all = player_list.merge(right=mlb_teams_df[['team_id','abbreviation']],left_on = 'team_id',right_on='team_id',how='left').drop_duplicates(keep='last')
+
+        mlb_teams_df = mlb_teams_df.merge(right=mlb_teams_df[['abbreviation','franchise']],left_on='parent_org',right_on='franchise',how='left').drop_duplicates().reset_index(drop=True)
+        mlb_teams_df = mlb_teams_df[mlb_teams_df.columns[:-1]]
+
+        mlb_teams_df.columns = ['team_id', 'city', 'name', 'franchise', 'abbreviation',
+            'parent_org', 'parent_org_abb']
+
+        statcast_df = statcast_df.merge(right=player_df_all,left_on='batter',right_on='player_id',suffixes=['','_batter'])
+        statcast_df = statcast_df.merge(right=player_df_all,left_on='pitcher',right_on='player_id',suffixes=['','_pitcher'])
+
+
+        end_codes = ['single', 'strikeout', 'walk', 'field_out',
+            'grounded_into_double_play', 'fielders_choice', 'force_out',
+            'double', 'sac_fly', 'field_error', 'home_run', 'triple',
+            'hit_by_pitch', 'sac_bunt', 'double_play', 'intent_walk',
+            'fielders_choice_out', 'strikeout_double_play',
+            'sac_fly_double_play', 'catcher_interf',
+            'other_out','triple_play']
+
+        pa_df_full_na_codes = statcast_df[statcast_df.events.isin(end_codes)]
+        pa_df_full_na_codes['pa'] = 1
+
+        #statcast_df = statcast_df.merge(pa_df_full_na_codes[['pa','play_id']],left_on='play_id',right_on='play_id',how='left')
+
+
+
+        test_df = statcast_df.sort_values(by='full_name_pitcher').drop_duplicates(subset='pitcher').reset_index(drop=True)[['pitcher','full_name_pitcher']]#['pitcher'].to_dict()
+        test_df = test_df.set_index('pitcher')
+        # #test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])
+
+        pitcher_dict = test_df['full_name_pitcher'].to_dict()
+
+        statcast_df['game_opp'] = statcast_df['game_date'].astype(str) + ' vs ' + statcast_df['abbreviation'].astype(str)
+        print(statcast_df['game_opp'])
+
+        date_dict = pd.concat([pd.DataFrame(data={'game_pk':0,'game_opp':'Season'},index=[0]),
+                        statcast_df.drop_duplicates(subset=['pitcher','game_pk','game_opp'])[['game_pk','game_opp']]]).set_index('game_pk').to_dict()
+
+        #chadwick_df_small = pd.read_csv('chadwick_df.csv')
+
+        statcast_df = statcast_df.merge(right=chadwick_df_small[['key_mlbam','key_fangraphs']],left_on = 'pitcher',right_on='key_mlbam',how='left')
+
+        statcast_df['home_away'] = 'h'
+        statcast_df.loc[statcast_df.abbreviation_pitcher == statcast_df.away_team,'home_away'] = 'a'
+        print('home_away')
+        print(statcast_df.home_away)
+        # stuff_plus_season_df = pd.read_csv('stuff_df_melt.csv',index_col=[0])
+        # loc_plus_season_df = pd.read_csv('loc_df_melt.csv',index_col=[0])
+        # pitching_plus_season_df = pd.read_csv('pitching_df_melt.csv',index_col=[0])
+
+        # stuff_plus_full_df = pd.read_csv('stuff_plus_full.csv',index_col=[0])
+        # loc_plus_full_df = pd.read_csv('loc_plus_full.csv',index_col=[0])
+        # pitching_plus_full_df = pd.read_csv('pitching_plus_full.csv',index_col=[0])
+
+
+
+
+        types_in = ['hit_into_play', 'ball', 'swinging_strike', 'foul', 'blocked_ball',
+            'called_strike', 'foul_tip', 'swinging_strike_blocked',
+            'hit_by_pitch', 'foul_bunt', 'pitchout', 'missed_bunt',
+            'bunt_foul_tip']
+
+        whiffs_in = ['swinging_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        swing_in = ['foul_bunt','foul','hit_into_play','swinging_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        csw_in = ['swinging_strike', 'called_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        conditions_pitch = [
+            (statcast_df['description'].isin(types_in)),
+        ]
+        choices_pitch = [True]
+        statcast_df['pitch'] = np.select(conditions_pitch, choices_pitch,default=np.nan)
+
+
+        conditions_swings = [
+            (statcast_df['description'].isin(swing_in)),
+        ]
+        choices_swings = [True]
+        statcast_df['swing'] = np.select(conditions_swings, choices_swings, default=np.nan)
+
+
+        conditions_whiff = [
+            (statcast_df['description'].isin(whiffs_in)),
+        ]
+        choices_whiff = [True]
+        statcast_df['whiff'] = np.select(conditions_whiff, choices_whiff, default=np.nan)
+
+
+        conditions_csw = [
+            (statcast_df['description'].isin(csw_in)),
+        ]
+        choices_csw = [True]
+        statcast_df['csw'] = np.select(conditions_csw, choices_csw, default=np.nan)
+
+
+        # conditions_out = [
+        #     (statcast_df['zone']>9),
+        # ]
+        # choices_out = [True]
+        # statcast_df['outside'] = np.select(conditions_out, choices_out, default=np.nan)
+
+        statcast_df['in_zone'] = statcast_df.zone < 10
+        statcast_df['out_zone'] = statcast_df.zone >= 10
+
+        conditions_chase = [
+            ((statcast_df['description'].isin(swing_in))&(statcast_df.out_zone)),
+        ]
+        choices_chase = [True]
+        statcast_df['chase'] = np.select(conditions_chase, choices_chase, default=np.nan)
+
+        statcast_df  = statcast_df[statcast_df.pitch==1].reset_index(drop=True)
+        statcast_df.loc[(statcast_df.swing==1)&(statcast_df.whiff!=1),'whiff'] = 0
+        statcast_df.loc[(statcast_df.pitch==1)&(statcast_df.csw!=1),'csw'] = 0
+        statcast_df['cs'] = 0
+        statcast_df.loc[(statcast_df.csw==1)&(statcast_df.whiff!=1),'cs'] = 1
+
+
+        bip_in = ['field_out', 'double', 'single',
+            'sac_fly', 'home_run', 'grounded_into_double_play', 'triple',
+            'force_out', 'field_error', 'double_play', 
+            'fielders_choice_out', 'sac_bunt', 'fielders_choice',
+            'sac_fly_double_play', 'other_out']
+
+        strikeout_in = ['strikeout','strikeout_double_play']
+
+        walk_in = ['walk']
+
+
+        conditions_bip = [
+            (statcast_df['events'].isin(bip_in)),
+        ]
+        choices_bip = [True]
+        statcast_df['bip'] = np.select(conditions_bip, choices_bip, default=np.nan)
+
+
+        conditions_k = [
+            (statcast_df['events'].isin(strikeout_in)),
+        ]
+        choices_k = [True]
+        statcast_df['k'] = np.select(conditions_k, choices_k, default=np.nan)
+
+
+        conditions_bb = [
+            (statcast_df['events'].isin(walk_in)),
+        ]
+        choices_bb = [True]
+        statcast_df['bb'] = np.select(conditions_bb, choices_bb, default=np.nan)
+
+        statcast_df.game_date = pd.to_datetime(statcast_df.game_date).dt.date
+
+
+
+
+
+
+
+
+
+        # pitches_all_df = statcast_df[statcast_df['pitch']==1].groupby('pitch').agg(
+        #     pitches = ('pitch','sum'), 
+        #     swings = ('swing','sum'), 
+        #     whiff = ('whiff','sum'), 
+        #     csw = ('csw','sum'),
+        #     chase = ('chase','sum'), 
+        #     out_zone = ('out_zone','sum'), 
+        #     pitch_velocity = ('release_speed','mean'), 
+        #     spin_rate = ('release_spin_rate','mean'), 
+        #     exit_velocity = ('launch_speed','mean'), 
+        #     pitch_velocity_std = ('release_speed','std'), 
+        #     spin_rate_std = ('release_spin_rate','std'), 
+        #     exit_velocity_std = ('launch_speed','std'), 
+
+        #     pfx_x = ('pfx_x','mean'), 
+        #     pfx_z = ('pfx_z','mean'), 
+        #     extension = ('release_extension','mean'), 
+        #     release_x = ('release_pos_x','mean'), 
+        #     release_z = ('release_pos_z','mean'),
+        #     zone_percent = ('in_zone','mean') ,
+        #     xwOBA = ('estimated_woba_using_speedangle','mean')
+
+        #     #pitch_velocity = ('pitch_velocity','mean'),
+        #     # pitch_velocity =  ('launch_speed',percentile(95)),
+        #     # launch_speed = ('launch_speed','mean'),
+        #     # launch_angle = ('launch_angle','mean'),
+        # ).sort_values(by='pitches',ascending=False).reset_index()
+
+        # pitches_all_df['pitch_name'] = 'All'
+
+        #statcast_df_df_pitch = pd.concat([statcast_df_df_pitch,pitches_all_df]).reset_index(drop=True)
+
+
+        statcast_df = statcast_df.merge(statcast_df_df_pitch[['pitch_name','whiff_rate','csw_rate','xwOBA']],left_on='pitch_name',right_on='pitch_name')
+        statcast_df = statcast_df.rename(columns={'whiff_rate':'whiff_rate_league','csw_rate':'csw_rate_league'})
+
+        statcast_df['whiff_rate_diff'] = statcast_df.whiff - statcast_df.whiff_rate_league
+        statcast_df['csw_rate_diff'] = statcast_df.csw - statcast_df.csw_rate_league
+        statcast_df['xwobacon_diff'] = statcast_df.estimated_woba_using_speedangle - statcast_df.xwOBA
+
+        statcast_df['whiff_rate_diff_100'] = (statcast_df.whiff/statcast_df.whiff_rate_league)*100
+        statcast_df['csw_rate_diff_100']  = (statcast_df.csw/statcast_df.csw_rate_league)*100
+        statcast_df['xwobacon_diff_100'] = (statcast_df.estimated_woba_using_speedangle/statcast_df.xwOBA)*100
+
+        print('all df')
+        print(statcast_df_df_pitch)
+
+        pitch_colours = {
+        '4-Seam Fastball':'#648FFF',
+        'Slider':'#785EF0',
+        'Sinker':'#49A71E',
+        'Changeup':'#FE6100',
+        'Cutter':'#FFB000',
+        'Curveball':'#D9E54B',
+        'Sweeper':'#904039',
+        'Split-Finger':'#79B3FC',
+        'Knuckle Curve':'#450C37',
+        'Slurve':'#BEABD8',
+        'Other':'#9C8975',
+        'Forkball':'#F98A6C',
+        'Eephus':'#5CD0D2',
+        'Screwball':'#D64012',
+        'Slow Curve':'#601CF9',
+        'Pitch Out':'#6F2F5C',
+        'Knuckleball':'#534B26'}
+
+        home_away_dict = {
+        'a':'Away',
+        'h':'Home '}
+
+
+        dict_plots = {
+        'pitch_heat':{'title':'Pitch Distribution','note':'Pitches'},
+        'whiff_rate':{'stat':'whiff','decimal_format':'percent_1','title':'Whiff%','plus':'whiff_rate_diff_100','note':'Swings'},
+        'csw_rate':{'stat':'csw','decimal_format':'percent_1','title':'CSW%','plus':'csw_rate_diff_100','note':'Pitches'},
+        'xwOBA':{'stat':'estimated_woba_using_speedangle','decimal_format':'string_3','title':'xwOBACON','plus':'xwobacon_diff_100','title':'xwOBACON','note':'Balls In Play'}
+        }
+
+        dict_plots_name = {
+        'pitch_heat':'Pitch Locations',
+        'whiff_rate':'Whiff%',
+        'csw_rate':'CSW%',
+        'xwOBA':'xwOBACON',
+        }
+
+        #stand_list = ['L','R']
+        cbar_dict = {
+        'stat':[0,1],
+        }
+
+        # def decimal_format_assign(x):
+        #     if dict_plots[stat_pick]['decimal_format'] == 'percent_1':
+        #         return mtick.PercentFormatter(1,decimals=1)
+        #     if dict_plots[stat_pick]['decimal_format'] == 'string_3':
+        #         return mtick.FormatStrFormatter('%.3f')
+        #     if dict_plotsp[stat_pick]['decimal_format'] == 'string_0':
+        #         return mtick.FormatStrFormatter('%.0f')
+        #     if dict_plots[stat_pick]['decimal_format'] == 'string_1':
+        #         return mtick.FormatStrFormatter('%.1f')
+
+        # headers = {'User-agent': 'your bot 0.1'}
+        headers = {'User-agent': 'your bot 0.1'}
+
+        fangraphs_table = 7
+
+
+        input_id = input.id()
+        input_date_range_id = input.date_range_id()
+
+        input_date_id = input.date_id()
+        print('EURY DF')
+        print(statcast_df.head())
+        #print(int(input.id()))
+        #print(statcast_df.pitcher.astype(int))
+        print(int(input.id())==statcast_df.pitcher.astype(int).values[0])
+        eury_df = statcast_df[(statcast_df.pitcher.astype(int) == int(input.id()))].sort_values(by=['game_date','game_pk','at_bat_number','pitch_number'])
+        #print(input.id())
+        #print(input_date_range_id == '0')
+        print('EURY DF LENGTH')
+        print(len(eury_df))
+        #print(str(input_date_id[0]))
+
+        #print('this is the one')
+        #print(len(eury_df))
+        if input_date_id == '0':
+            if input_date_range_id[0] == statcast_df.game_date.min() and input_date_range_id[1] == statcast_df.game_date.max():
+                data_df = eury_df.copy()
+                data_df = data_df.reset_index(drop=True)
+                data_df = data_df.dropna(subset=['pitch_name'])
+            else:
+                data_df = eury_df[(eury_df.game_date >= input_date_range_id[0]) & (eury_df.game_date <= input_date_range_id[1])].reset_index(drop=True)
+                data_df = data_df.reset_index(drop=True)
+                data_df = data_df.dropna(subset=['pitch_name'])
+
+        else:
+            
+            data_df = eury_df[eury_df.game_pk == int(input_date_id)].reset_index(drop=True)
+            data_df = data_df.dropna(subset=['pitch_name'])
+            
+        print(data_df)
+        print(len(data_df))
+        print('Data DF LENGTH')
+        print(len(data_df))
+        if len(data_df) < 1:
+            fig, ax = plt.subplots(1, 1, figsize=(16, 16))
+            ax.text(x=0.5,y=0.5,s='Plot Is Generating',fontsize=32,ha='center')
+            ###return #
+
+        # if input.radio_id() != 'a':
+        #     data_df = data_df[data_df.stand ==  input.radio_id()]
+
+        # if input.home_id() != 'all':
+        #     data_df = data_df[data_df.home_away ==  input.home_id()]
+        start_date = pd.to_datetime(data_df['game_date'].values[0]).strftime('%m/%d/%Y')
+        end_date = pd.to_datetime(data_df['game_date'].values[-1]).strftime('%m/%d/%Y')  
+
+        start_dt = pd.to_datetime(data_df['game_date'].values[0]).strftime('%Y-%m-%d')
+        end_dt  = pd.to_datetime(data_df['game_date'].values[-1]).strftime('%Y-%m-%d')     
+        #data_df = data_df.reset_index(drop=True)
+        data_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season=2023&season=2023&month=1000&season1=2023&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching&startdate={start_dt}&enddate={end_dt}').json()
+
+        stuff_df = pd.DataFrame(data={
+        'player_id':[x['xMLBAMID'] for x in data_fg['data']],
+        'fg_id':[x['playerid'] for x in data_fg['data']],
+        'Name':[x['PlayerName'] for x in data_fg['data']],
+        'CH':[x['sp_s_CH'] for x in data_fg['data']],
+        'CU':[x['sp_s_CU'] for x in data_fg['data']],
+        'FF':[x['sp_s_FF'] for x in data_fg['data']],
+        'SI':[x['sp_s_SI'] for x in data_fg['data']],
+        'SL':[x['sp_s_SL'] for x in data_fg['data']],
+        'KC':[x['sp_s_KC'] for x in data_fg['data']],
+        'FC':[x['sp_s_FC'] for x in data_fg['data']],
+        'FS':[x['sp_s_FS'] for x in data_fg['data']],
+        'FO':[x['sp_s_FO'] for x in data_fg['data']],
+        'ST':[x['sp_s_SL'] for x in data_fg['data']],
+        'SV':[x['sp_s_CU'] for x in data_fg['data']],
+        'Stuff+':[x['sp_stuff'] for x in data_fg['data']],
+        'Location+':[x['sp_location'] for x in data_fg['data']],
+        'Pitching+':[x['sp_pitching'] for x in data_fg['data']]
+        })
+
+        loc_df = pd.DataFrame(data={
+        'player_id':[x['xMLBAMID'] for x in data_fg['data']],
+        'fg_id':[x['playerid'] for x in data_fg['data']],
+        'Name':[x['PlayerName'] for x in data_fg['data']],
+        'CH':[x['sp_l_CH'] for x in data_fg['data']],
+        'CU':[x['sp_l_CU'] for x in data_fg['data']],
+        'FF':[x['sp_l_FF'] for x in data_fg['data']],
+        'SI':[x['sp_l_SI'] for x in data_fg['data']],
+        'SL':[x['sp_l_SL'] for x in data_fg['data']],
+        'KC':[x['sp_l_KC'] for x in data_fg['data']],
+        'FC':[x['sp_l_FC'] for x in data_fg['data']],
+        'FS':[x['sp_l_FS'] for x in data_fg['data']],
+        'FO':[x['sp_l_FO'] for x in data_fg['data']],
+        'ST':[x['sp_l_SL'] for x in data_fg['data']],
+        'SV':[x['sp_l_CU'] for x in data_fg['data']],
+        'Stuff+':[x['sp_stuff'] for x in data_fg['data']],
+        'Location+':[x['sp_location'] for x in data_fg['data']],
+        'Pitching+':[x['sp_pitching'] for x in data_fg['data']]
+        })
+
+        stuff_plus_full_df_cut = stuff_df.melt(id_vars=['fg_id','Name']).dropna().sort_values(by='Name').reset_index(drop=True)
+        stuff_plus_full_df_cut.fg_id = stuff_plus_full_df_cut.fg_id.astype(int)
+
+        loc_plus_full_df_cut = loc_df.melt(id_vars=['fg_id','Name']).dropna().sort_values(by='Name').reset_index(drop=True)
+        loc_plus_full_df_cut.fg_id = loc_plus_full_df_cut.fg_id.astype(int)
+        data_df = data_df.merge(right=stuff_plus_full_df_cut,left_on=['key_fangraphs','pitch_type'],right_on=['fg_id','variable'],how='left')
+        data_df = data_df.merge(right=loc_plus_full_df_cut,left_on=['key_fangraphs','pitch_type'],right_on=['fg_id','variable'],how='left',suffixes=['','_loc'])
+        #data_df = data_df.merge(right=pitching_plus_full_df_cut,left_on=['key_fangraphs','pitch_type'],right_on=['fg_id','variable'],how='left',suffixes=['','_pitching'])
+        print('Data DF LENGTH')
+        print(len(data_df))
+
+        data_df['value'] = data_df['value'].astype(float)
+        data_df['value_loc'] = data_df['value_loc'].astype(float)
+        data_df['value_pitching'] = 0
+
+
+
+        print('this is the one2')
+
+        data_df['prop'] = data_df.groupby("pitch_name")["pitch"].transform("sum")
+
+
+        data_df = data_df.sort_values(by=['prop','value','pitch_name'],ascending=[False,False,True])
+        sitCodes=''
+        if input.radio_id() == 'R':
+            sitCodes=',sitCodes=[vr]'
+        if input.radio_id() == 'L':
+            sitCodes=',sitCodes=[vl]'
+
+        if input_date_id == '0':
+            if input_date_range_id[0] == statcast_df.game_date.min() and input_date_range_id[1] == statcast_df.game_date.max():
+                if input.radio_id() != 'a':
+
+                    season_sum = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&appContext=majorLeague&"
+                                    f"hydrate=currentTeam,"
+                                    f"stats(group=[pitching],type=[yearByYear]{sitCodes})").json()
+                    print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&appContext=majorLeague&"
+                                    f"hydrate=currentTeam,"
+                                    f"stats(group=[pitching],type=[yearByYear]{sitCodes})")
+                else:
+                    season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&appContext=majorLeague&"
+                                "hydrate=currentTeam,awards,"
+                                "stats(group=[pitching],type=[yearByYear])").json()    
+                    print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&appContext=majorLeague&"
+                                    f"hydrate=currentTeam,"
+                                    f"stats(group=[pitching],type=[yearByYear]{sitCodes})")
+                p_ip = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['inningsPitched'] if 'inningsPitched' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_hits = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['whip'] if 'whip' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_er = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['era'] if 'era' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_pa = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['battersFaced'] if 'battersFaced'  in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_k = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['strikeOuts'] if 'strikeOuts' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_bb = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['baseOnBalls'] if 'baseOnBalls' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+
+                summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+
+                summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']
+            
+
+            else:
+                print('we are in this area')
+                print(data_df['key_fangraphs'].values[0])
+                print(pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d'))
+                print(pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d'))
+                start_date = pd.to_datetime(input_date_range_id[0]).strftime('%m/%d/%Y')
+                end_date = pd.to_datetime(input_date_range_id[1]).strftime('%m/%d/%Y')
+                print(str((int(data_df.pitcher.reset_index(drop=True)[0]))),start_date,end_date)
+                if input.radio_id() == 'R':
+                    print('we are in this area right')
+                    # url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})"
+                    # season_sum = requests.get(url).json()
+
+                    # #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})").json()
+                    # print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+ 
+                # Define the API endpoint
+                    url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
+
+                    # Define the payload as a dictionary
+                    payload_standard = {
+                        "strPlayerId": str(data_df['key_fangraphs'].values[0]),
+                        "strSplitArr": [6],
+                        "strGroup": "season",
+                        "strPosition": "P",
+                        "strType": "1",
+                        "strStartDate": str(pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d')),
+                        "strEndDate": str(pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d')),
+                        "strSplitTeams": False,
+                        "dctFilters": [],
+                        "strStatType": "player",
+                        "strAutoPt": False,
+                        "arrPlayerId": [],
+                        "strSplitArrPitch": [],
+                        "arrWxTemperature": None,
+                        "arrWxPressure": None,
+                        "arrWxAirDensity": None,
+                        "arrWxElevation": None,
+                        "arrWxWindSpeed": None
+                    }
+
+                    payload = {
+                        "strPlayerId": str(data_df['key_fangraphs'].values[0]),
+                        "strSplitArr": [6],
+                        "strGroup": "season",
+                        "strPosition": "P",
+                        "strType": "2",
+                        "strStartDate": str(pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d')),
+                        "strEndDate": str(pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d')),
+                        "strSplitTeams": False,
+                        "dctFilters": [],
+                        "strStatType": "player",
+                        "strAutoPt": False,
+                        "arrPlayerId": [],
+                        "strSplitArrPitch": [],
+                        "arrWxTemperature": None,
+                        "arrWxPressure": None,
+                        "arrWxAirDensity": None,
+                        "arrWxElevation": None,
+                        "arrWxWindSpeed": None
+                    }
+                    json_payload = json.dumps(payload)
+                    json_payload_standard = json.dumps(payload_standard)
+                    headers = {'Content-Type': 'application/json'}
+
+                    response = requests.post(url, data=json_payload, headers=headers)
+                    response_standard = requests.post(url, data=json_payload_standard, headers=headers)
+                    # Check if the request was successful
+                    # if response.status_code == 200:
+                    #     # Print the response content
+                    #     print(response.json())
+                    # else:
+                    #     print("Request failed with status code:", response.status_code)
+
+                    data_pull = response.json()['data'][0]
+                    data_pull_standard = response_standard.json()['data'][0]
+
+
+                    data_pull.update(data_pull_standard)
+                    print(data_pull)
+                    p_ip = data_pull['IP'] if 'IP' in data_pull else '---'
+
+                    p_hits = round(data_pull['WHIP'],2) if 'WHIP' in data_pull else '---'
+                    p_er = round(data_pull['ERA'],2) if 'ERA' in data_pull else '---'
+                    p_pa = int(data_pull['TBF']) if 'TBF' in data_pull else '---'
+                    p_k = data_pull['K%'] if 'K%' in data_pull else '---'
+                    p_bb = data_pull['BB%'] if 'BB' in data_pull else '---'      
+ 
+
+                    summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+                    #summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                    #summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']                             
+                    summary_df_pitch['hits'] = summary_df_pitch['hits'].apply(lambda x: '{:.2f}'.format(x))                  
+                    summary_df_pitch['er'] = summary_df_pitch['er'].apply(lambda x: '{:.2f}'.format(x)) 
+                elif input.radio_id() == 'L':
+                    # url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})"
+                    # season_sum = requests.get(url).json()
+
+                    # #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})").json()
+                    # print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+ 
+                # Define the API endpoint
+                    url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
+
+                    # Define the payload as a dictionary
+                    payload_standard = {
+                        "strPlayerId": str(int(data_df['key_fangraphs'].values[0])),
+                        "strSplitArr": [5],
+                        "strGroup": "season",
+                        "strPosition": "P",
+                        "strType": "1",
+                        "strStartDate": pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d'),
+                        "strEndDate": pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d'),
+                        "strSplitTeams": False,
+                        "dctFilters": [],
+                        "strStatType": "player",
+                        "strAutoPt": False,
+                        "arrPlayerId": [],
+                        "strSplitArrPitch": [],
+                        "arrWxTemperature": None,
+                        "arrWxPressure": None,
+                        "arrWxAirDensity": None,
+                        "arrWxElevation": None,
+                        "arrWxWindSpeed": None
+                    }
+
+                    payload = {
+                        "strPlayerId": str(int(data_df['key_fangraphs'].values[0])),
+                        "strSplitArr": [5],
+                        "strGroup": "season",
+                        "strPosition": "P",
+                        "strType": "2",
+                        "strStartDate": pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d'),
+                        "strEndDate": pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d'),
+                        "strSplitTeams": False,
+                        "dctFilters": [],
+                        "strStatType": "player",
+                        "strAutoPt": False,
+                        "arrPlayerId": [],
+                        "strSplitArrPitch": [],
+                        "arrWxTemperature": None,
+                        "arrWxPressure": None,
+                        "arrWxAirDensity": None,
+                        "arrWxElevation": None,
+                        "arrWxWindSpeed": None
+                    }
+                    json_payload = json.dumps(payload)
+                    json_payload_standard = json.dumps(payload_standard)
+                    headers = {'Content-Type': 'application/json'}
+
+                    response = requests.post(url, data=json_payload, headers=headers)
+                    response_standard = requests.post(url, data=json_payload_standard, headers=headers)
+                    # Check if the request was successful
+                    if response.status_code == 200:
+                        # Print the response content
+                        print(response.json())
+                    else:
+                        print("Request failed with status code:", response.status_code)
+
+                    data_pull = response.json()['data'][0]
+                    data_pull_standard = response_standard.json()['data'][0]
+
+
+                    data_pull.update(data_pull_standard)
+
+                    p_ip = data_pull['IP'] if 'IP' in data_pull else '---'
+
+                    p_hits = round(data_pull['WHIP'],2) if 'WHIP' in data_pull else '---'
+                    p_er = round(data_pull['ERA'],2) if 'ERA' in data_pull else '---'
+                    p_pa = int(data_pull['TBF']) if 'TBF' in data_pull else '---'
+                    p_k = data_pull['K%'] if 'K%' in data_pull else '---'
+                    p_bb = data_pull['BB%'] if 'BB' in data_pull else '---'      
+
+                    summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+                    #summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                    #summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']     
+                    # summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                    # summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']       
+                    # Round the values in column 'A' to two decimal places
+                    # Convert the rounded values back to strings with two decimal places
+                    summary_df_pitch['hits'] = summary_df_pitch['hits'].apply(lambda x: '{:.2f}'.format(x))                  
+                    summary_df_pitch['er'] = summary_df_pitch['er'].apply(lambda x: '{:.2f}'.format(x)) 
+
+                else:
+                    url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[byDateRange],startDate={start_date},endDate={end_date})"
+                    season_sum = requests.get(url).json()
+
+                    #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[byDateRange],startDate={start_date},endDate={end_date})").json()
+                    print(url)                  
+                #print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+                #season_sum = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[hitting],type=[byDateRange],startDate={start_date},endDate={end_date},season=2023)').json()
+                #print(season_sum)
+                #test_json['people'][0]['stats'][0]['splits'][0]['stat']
+
+                    p_ip = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['inningsPitched'] if 'inningsPitched' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                    p_hits = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['whip'] if 'whip' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                    p_er = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['era'] if 'era' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                    p_pa = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['battersFaced'] if 'battersFaced'  in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                    p_k = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['strikeOuts'] if 'strikeOuts' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                    p_bb = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['baseOnBalls'] if 'baseOnBalls' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+
+                    summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+
+                    summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                    summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']
+
+            
+
+        else:
+            print('we are in this area')
+            print(data_df['key_fangraphs'].values[0])
+            print(pd.to_datetime(data_df['game_date'].values[0]).strftime('%Y-%m-%d'))
+            print(pd.to_datetime(data_df['game_date'].values[-1]).strftime('%Y-%m-%d'))
+            start_date = pd.to_datetime(data_df['game_date'].values[0]).strftime('%m/%d/%Y')
+            end_date = pd.to_datetime(data_df['game_date'].values[-1]).strftime('%m/%d/%Y')
+            print(str((int(data_df.pitcher.reset_index(drop=True)[0]))),start_date,end_date)
+            if input.radio_id() == 'R':
+                print('we are in this area right')
+                # url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})"
+                # season_sum = requests.get(url).json()
+
+                # #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})").json()
+                # print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+
+            # Define the API endpoint
+                url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
+
+                # Define the payload as a dictionary
+                payload_standard = {
+                    "strPlayerId": str(data_df['key_fangraphs'].values[0]),
+                    "strSplitArr": [6],
+                    "strGroup": "season",
+                    "strPosition": "P",
+                    "strType": "1",
+                    "strStartDate": str(pd.to_datetime(data_df['game_date'].values[0]).strftime('%Y-%m-%d')),
+                    "strEndDate": str(pd.to_datetime(data_df['game_date'].values[-1]).strftime('%Y-%m-%d')),
+                    "strSplitTeams": False,
+                    "dctFilters": [],
+                    "strStatType": "player",
+                    "strAutoPt": False,
+                    "arrPlayerId": [],
+                    "strSplitArrPitch": [],
+                    "arrWxTemperature": None,
+                    "arrWxPressure": None,
+                    "arrWxAirDensity": None,
+                    "arrWxElevation": None,
+                    "arrWxWindSpeed": None
+                }
+
+                payload = {
+                    "strPlayerId": str(data_df['key_fangraphs'].values[0]),
+                    "strSplitArr": [6],
+                    "strGroup": "season",
+                    "strPosition": "P",
+                    "strType": "2",
+                    "strStartDate": str(pd.to_datetime(data_df['game_date'].values[0]).strftime('%Y-%m-%d')),
+                    "strEndDate": str(pd.to_datetime(data_df['game_date'].values[-1]).strftime('%Y-%m-%d')),
+                    "strSplitTeams": False,
+                    "dctFilters": [],
+                    "strStatType": "player",
+                    "strAutoPt": False,
+                    "arrPlayerId": [],
+                    "strSplitArrPitch": [],
+                    "arrWxTemperature": None,
+                    "arrWxPressure": None,
+                    "arrWxAirDensity": None,
+                    "arrWxElevation": None,
+                    "arrWxWindSpeed": None
+                }
+                json_payload = json.dumps(payload)
+                json_payload_standard = json.dumps(payload_standard)
+                headers = {'Content-Type': 'application/json'}
+
+                response = requests.post(url, data=json_payload, headers=headers)
+                response_standard = requests.post(url, data=json_payload_standard, headers=headers)
+                # Check if the request was successful
+                # if response.status_code == 200:
+                #     # Print the response content
+                #     print(response.json())
+                # else:
+                #     print("Request failed with status code:", response.status_code)
+
+                data_pull = response.json()['data'][0]
+                data_pull_standard = response_standard.json()['data'][0]
+
+
+                data_pull.update(data_pull_standard)
+                print(data_pull)
+                p_ip = data_pull['IP'] if 'IP' in data_pull else '---'
+
+                p_hits = int(data_pull['H']) if 'H' in data_pull else '---'
+                p_er = int(data_pull['ER']) if 'ER' in data_pull else '---'
+                p_pa = int(data_pull['TBF']) if 'TBF' in data_pull else '---'
+                p_k = int(data_pull['SO']) if 'SO' in data_pull else '---'
+                p_bb = int(data_pull['BB']) if 'BB' in data_pull else '---'  
+
+
+                summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+                #summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                #summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']                             
+
+            elif input.radio_id() == 'L':
+                # url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})"
+                # season_sum = requests.get(url).json()
+
+                # #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})").json()
+                # print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+
+            # Define the API endpoint
+                url = "https://www.fangraphs.com/api/leaders/splits/splits-leaders"
+
+                # Define the payload as a dictionary
+                payload_standard = {
+                    "strPlayerId": str(int(data_df['key_fangraphs'].values[0])),
+                    "strSplitArr": [5],
+                    "strGroup": "season",
+                    "strPosition": "P",
+                    "strType": "1",
+                    "strStartDate": pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d'),
+                    "strEndDate": pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d'),
+                    "strSplitTeams": False,
+                    "dctFilters": [],
+                    "strStatType": "player",
+                    "strAutoPt": False,
+                    "arrPlayerId": [],
+                    "strSplitArrPitch": [],
+                    "arrWxTemperature": None,
+                    "arrWxPressure": None,
+                    "arrWxAirDensity": None,
+                    "arrWxElevation": None,
+                    "arrWxWindSpeed": None
+                }
+
+                payload = {
+                    "strPlayerId": str(int(data_df['key_fangraphs'].values[0])),
+                    "strSplitArr": [5],
+                    "strGroup": "season",
+                    "strPosition": "P",
+                    "strType": "2",
+                    "strStartDate": pd.to_datetime(input_date_range_id[0]).strftime('%Y-%m-%d'),
+                    "strEndDate": pd.to_datetime(input_date_range_id[1]).strftime('%Y-%m-%d'),
+                    "strSplitTeams": False,
+                    "dctFilters": [],
+                    "strStatType": "player",
+                    "strAutoPt": False,
+                    "arrPlayerId": [],
+                    "strSplitArrPitch": [],
+                    "arrWxTemperature": None,
+                    "arrWxPressure": None,
+                    "arrWxAirDensity": None,
+                    "arrWxElevation": None,
+                    "arrWxWindSpeed": None
+                }
+                json_payload = json.dumps(payload)
+                json_payload_standard = json.dumps(payload_standard)
+                headers = {'Content-Type': 'application/json'}
+
+                response = requests.post(url, data=json_payload, headers=headers)
+                response_standard = requests.post(url, data=json_payload_standard, headers=headers)
+                data_pull = response.json()['data'][0]
+                data_pull_standard = response_standard.json()['data'][0]
+
+
+                data_pull.update(data_pull_standard)
+                print(data_pull)
+                p_ip = data_pull['IP'] if 'IP' in data_pull else '---'
+
+                p_hits = int(data_pull['H']) if 'H' in data_pull else '---'
+                p_er = int(data_pull['ER']) if 'ER' in data_pull else '---'
+                p_pa = int(data_pull['TBF']) if 'TBF' in data_pull else '---'
+                p_k = int(data_pull['SO']) if 'SO' in data_pull else '---'
+                p_bb = int(data_pull['BB']) if 'BB' in data_pull else '---'    
+
+                summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+            
+            else:
+                url = f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[byDateRange],startDate={start_date},endDate={end_date})"
+                season_sum = requests.get(url).json()
+
+                #season_sum = requests.get(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[byDateRange],startDate={start_date},endDate={end_date})").json()
+                print(url)                  
+                #print(f"https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[pitching],type=[statSplits]{sitCodes},startDate={start_date},endDate={end_date})")
+                #season_sum = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={str((int(data_df.pitcher.reset_index(drop=True)[0])))}&hydrate=stats(group=[hitting],type=[byDateRange],startDate={start_date},endDate={end_date},season=2023)').json()
+                #print(season_sum)
+                #test_json['people'][0]['stats'][0]['splits'][0]['stat']
+
+                p_ip = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['inningsPitched'] if 'inningsPitched' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_hits = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['hits'] if 'hits' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_er = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['earnedRuns'] if 'earnedRuns' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_pa = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['battersFaced'] if 'battersFaced'  in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_k = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['strikeOuts'] if 'strikeOuts' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+                p_bb = season_sum['people'][0]['stats'][0]['splits'][-1]['stat']['baseOnBalls'] if 'baseOnBalls' in season_sum['people'][0]['stats'][0]['splits'][-1]['stat'] else '---'
+
+                # summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb,'pitches':p_pa}, index=[0])
+
+                # summary_df_pitch['k'] = summary_df_pitch['k']/summary_df_pitch['pitches']
+                # summary_df_pitch['bb'] = summary_df_pitch['bb']/summary_df_pitch['pitches']
+
+                summary_df_pitch = pd.DataFrame(data={'ip':p_ip,'hits':p_hits,'er':p_er,'k':p_k,'bb':p_bb}, index=[0])
+
+        types_in = ['hit_into_play', 'ball', 'swinging_strike', 'foul', 'blocked_ball',
+            'called_strike', 'foul_tip', 'swinging_strike_blocked',
+            'hit_by_pitch', 'foul_bunt', 'pitchout', 'missed_bunt',
+            'bunt_foul_tip']
+
+        whiffs_in = ['swinging_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        swing_in = ['foul_bunt','foul','hit_into_play','swinging_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        csw_in = ['swinging_strike', 'called_strike', 'foul_tip', 'swinging_strike_blocked','missed_bunt','bunt_foul_tip']
+
+        conditions_pitch = [
+            (data_df['description'].isin(types_in)),
+        ]
+        choices_pitch = [True]
+        data_df['pitch'] = np.select(conditions_pitch, choices_pitch, default=np.nan)
+
+
+        conditions_swings = [
+            (data_df['description'].isin(swing_in)),
+        ]
+        choices_swings = [True]
+        data_df['swing'] = np.select(conditions_swings, choices_swings, default=np.nan)
+
+
+        conditions_whiff = [
+            (data_df['description'].isin(whiffs_in)),
+        ]
+        choices_whiff = [True]
+        data_df['whiff'] = np.select(conditions_whiff, choices_whiff, default=np.nan)
+
+
+        conditions_csw = [
+            (data_df['description'].isin(csw_in)),
+        ]
+        choices_csw = [True]
+        data_df['csw'] = np.select(conditions_csw, choices_csw, default=np.nan)
+
+
+        bip_in = ['field_out', 'double', 'single',
+            'sac_fly', 'home_run', 'grounded_into_double_play', 'triple',
+            'force_out', 'field_error', 'double_play', 
+            'fielders_choice_out', 'sac_bunt', 'fielders_choice',
+            'sac_fly_double_play', 'other_out']
+
+        strikeout_in = ['strikeout','strikeout_double_play']
+
+        walk_in = ['walk']
+
+
+        conditions_bip = [
+            (data_df['events'].isin(bip_in)),
+        ]
+        choices_bip = [True]
+        data_df['bip'] = np.select(conditions_bip, choices_bip, default=np.nan)
+
+
+        conditions_k = [
+            (data_df['events'].isin(strikeout_in)),
+        ]
+        choices_k = [True]
+        data_df['k'] = np.select(conditions_k, choices_k, default=np.nan)
+
+
+        conditions_bb = [
+            (data_df['events'].isin(walk_in)),
+        ]
+        choices_bb = [True]
+        data_df['bb'] = np.select(conditions_bb, choices_bb, default=np.nan)
+
+        data_df.game_date = pd.to_datetime(data_df.game_date).dt.date
+
+        data_df['in_zone'] = data_df['zone'] < 10
+        data_df['out_zone'] = data_df['zone'] >= 10
+
+        print('OUT OF THE ZONE')
+        print(data_df['chase'].sum())
+        print(data_df['out_zone'].sum())
+
+        conditions_chase = [
+            ((data_df['description'].isin(swing_in))&(data_df.out_zone)),
+        ]
+
+        choices_chase = [True]
+        data_df['chase'] = np.select(conditions_chase, choices_chase, default=np.nan)
+
+
+        pitch_df_pitch = data_df[data_df['pitch']==1].groupby(['pitch_name']).agg(
+            pitches = ('pitch','sum'), 
+            swings = ('swing','sum'), 
+            whiff = ('whiff','sum'), 
+            csw = ('csw','sum'), 
+            out_zone = ('out_zone','sum'), 
+            chase = ('chase','sum'), 
+            pitch_velocity = ('release_speed','mean'), 
+            spin_rate = ('release_spin_rate','mean'), 
+            exit_velocity = ('launch_speed','mean'), 
+            pfx_x = ('pfx_x','mean'), 
+            pfx_z = ('pfx_z','mean'), 
+            extension = ('release_extension','mean'), 
+            release_x = ('release_pos_x','mean'), 
+            release_z = ('release_pos_z','mean'),
+            zone_percent = ('in_zone','mean') ,
+            xwOBA = ('estimated_woba_using_speedangle','mean') ,
+
+
+
+            stuff_plus = ('value','mean'), 
+            loc_plus = ('value_loc','mean'), 
+            pitching_plus = ('value_pitching','mean'), 
+
+            #pitch_velocity = ('pitch_velocity','mean'),
+            # pitch_velocity =  ('launch_speed',percentile(95)),
+            # launch_speed = ('launch_speed','mean'),
+            # launch_angle = ('launch_angle','mean'),
+        ).sort_values(by='pitches',ascending=False).reset_index()
+        print('plot df')
+        print(pitch_df_pitch)
+        stuff_plus_all_day_df = stuff_plus_full_df_cut.copy()
+
+        data_df['spin_axis_pitch'] = [(x + 180) for x in data_df.spin_axis]
+        (((data_df.groupby('pitch_name').mean()[['spin_axis_pitch']] %360 % 30 / 30 /100 *60).round(2) *10).round(0)//1.5/4 )
+        clock_time = ((data_df.groupby('pitch_name').mean()['spin_axis_pitch']) %360 // 30 )+  (((data_df.groupby('pitch_name').mean()['spin_axis_pitch'] %360 % 30 / 30 /100 *60).round(2) *10).round(0)//1.5/4 )
+        print('Clocks')
+        print(clock_time)
+        clock_time = (clock_time.astype(int) + clock_time%1*60/100).round(2).astype(str).str.replace('.',':').str.replace(':0',':00').str.replace(':3',':30').to_frame()
+        #print()
+        pitch_df_pitch = pitch_df_pitch.merge(right=clock_time,left_on='pitch_name',right_index=True)
+        #print(pitch_df_pitch['clock_time'])
+        # if len(stuff_plus_all_day_df) < 1: 
+        #     stuff_plus_all_day_df = pd.DataFrame(columns=['fg_id', 'Name', 'Team', 'IP', 'variable', 'value'])
+        #     # loc_plus_full_df_cut = pd.DataFrame(columns=['fg_id', 'Name', 'Team', 'IP', 'variable', 'value'])
+        #     # pitching_plus_full_df_cut = pd.DataFrame(columns=['fg_id', 'Name', 'Team', 'IP', 'variable', 'value'])
+
+
+        # if input_date_id != '0':
+        #     stuff_plus_all_day_df = stuff_plus_full_df[(stuff_plus_full_df.fg_id == data_df.reset_index(drop=True).key_fangraphs[0]) & 
+        #            (stuff_plus_full_df.date == str(data_df.reset_index(drop=True).game_date[0]))]
+            
+        # else:
+        #     if input_date_range_id[0] == statcast_df.game_date.min() and input_date_range_id[1] == statcast_df.game_date.max():
+        #         stuff_plus_all_day_df = stuff_plus_season_df[(stuff_plus_season_df.fg_id == data_df.reset_index(drop=True).key_fangraphs[0])]
+        #     else:
+        #         stuff_plus_all_day_df = stuff_plus_full_df_cut[(stuff_plus_full_df_cut.fg_id == data_df.reset_index(drop=True).key_fangraphs[0])]
+
+        pitch_df_pitch_all = data_df[data_df['pitch']==1].groupby(['pitcher']).agg(
+            pitches = ('pitch','sum'), 
+            swings = ('swing','sum'), 
+            whiff = ('whiff','sum'), 
+            csw = ('csw','sum'), 
+            out_zone = ('out_zone','sum'), 
+            chase = ('chase','sum'), 
+            pitch_velocity = ('release_speed','mean'), 
+            spin_rate = ('release_spin_rate','mean'), 
+            exit_velocity = ('launch_speed','mean'), 
+            pfx_x = ('pfx_x','mean'), 
+            pfx_z = ('pfx_z','mean'), 
+            extension = ('release_extension','mean'), 
+            release_x = ('release_pos_x','mean'), 
+            release_z = ('release_pos_z','mean'),
+            zone_percent = ('in_zone','mean') ,
+            xwOBA = ('estimated_woba_using_speedangle','mean') ,
+
+
+
+            stuff_plus = ('value','mean'), 
+            loc_plus = ('value_loc','mean'), 
+            pitching_plus = ('value_pitching','mean'), 
+            ).sort_values(by='pitches',ascending=False).reset_index()
+        #print('stff df')
+        #print(stuff_plus_all_day_df)
+        print('Pitch Sum')
+        print(pitch_df_pitch_all)
+
+        if len(stuff_plus_all_day_df) > 0:
+            stuff_plus_all_day_df = stuff_plus_all_day_df[stuff_plus_all_day_df.fg_id == data_df.key_fangraphs[0]]
+
+        else:
+            stuff_plus_all_day_df = pd.DataFrame(columns=['fg_id', 'Name', 'Team', 'IP', 'variable', 'value'])
+
+        pitch_df_pitch_all['pitch_name'] = 'All'
+
+        if len(stuff_plus_all_day_df) > 0:
+            pitch_df_pitch_all['stuff_plus'] = int(stuff_plus_all_day_df[stuff_plus_all_day_df.variable == 'Stuff+'].reset_index(drop=True)['value'][0])
+            pitch_df_pitch_all['loc_plus'] = int(stuff_plus_all_day_df[stuff_plus_all_day_df.variable == 'Location+'].reset_index(drop=True)['value'][0])
+            pitch_df_pitch_all['pitching_plus'] = int(stuff_plus_all_day_df[stuff_plus_all_day_df.variable == 'Pitching+'].reset_index(drop=True)['value'][0])
+
+        else:
+            pitch_df_pitch_all['stuff_plus'] = np.nan
+            pitch_df_pitch_all['loc_plus'] = np.nan
+            pitch_df_pitch_all['pitching_plus'] = np.nan
+
+        
+        print('Pitch Sum')
+        print(pitch_df_pitch_all)
+
+        if input_date_id != '0':
+            summary_df_pitch['pitcher'] = data_df.full_name_pitcher.unique()[0]
+            
+            summary_df_pitch['pitches'] = pitch_df_pitch.pitches.sum()
+            summary_df_pitch['pitches'] = summary_df_pitch['pitches'].astype(int)
+            summary_df_pitch= summary_df_pitch[['pitcher', 'pitches','ip', 'hits', 'er', 'k', 'bb']]
+            #summary_df_pitch_new.columns = ['Pitcher', 'Pitches','IP', 'Hits', 'ER', 'K', 'BB']
+        else:
+            summary_df_pitch['pitcher'] = data_df.full_name_pitcher.unique()[0]    
+            summary_df_pitch = summary_df_pitch[['pitcher', 'pitches','ip', 'hits', 'er', 'k', 'bb']]
+
+
+        pitch_df_pitch['whiff_rate'] = pitch_df_pitch['whiff']/pitch_df_pitch['swings']
+        pitch_df_pitch['csw_rate'] = pitch_df_pitch['csw']/pitch_df_pitch['pitches']
+        pitch_df_pitch['chase_percent'] = pitch_df_pitch['chase']/pitch_df_pitch['out_zone']
+        pitch_df_pitch['pitch_percent'] = pitch_df_pitch['pitches']/pitch_df_pitch['pitches'].sum()
+
+        pitch_df_pitch_all['whiff_rate'] = pitch_df_pitch_all['whiff']/pitch_df_pitch_all['swings']
+        pitch_df_pitch_all['csw_rate'] = pitch_df_pitch_all['csw']/pitch_df_pitch_all['pitches']
+        pitch_df_pitch_all['chase_percent'] = pitch_df_pitch_all['chase']/pitch_df_pitch_all['out_zone']
+        pitch_df_pitch_all['pitch_percent'] = pitch_df_pitch_all['pitches']/pitch_df_pitch_all['pitches'].sum()
+
+        pitch_df_pitch_all['spin_axis_pitch'] = '—'
+
+        pitch_df_pitch = pd.concat([pitch_df_pitch,pitch_df_pitch_all]).reset_index(drop=True)
+
+        #fig, ax = plt.subplots(3, 2, figsize=(9, 9))
+        label_labels = data_df.sort_values(by=['prop','value','pitch_name'],ascending=[False,False,True]).pitch_name.unique()
+
+
+
+
+        #plt.rcParams["figure.figsize"] = [10,10]
+        fig = plt.figure(figsize=(15, 15))
+        plt.rcParams.update({'figure.autolayout': True})
+        fig.set_facecolor('white')
+        sns.set_theme(style="whitegrid", palette="pastel")
+
+
+
+        print('this is the one plot')
+        # gs = GridSpec(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5])
+        gs = GridSpec(5, 1, width_ratios=[1], height_ratios=[1.5,1,5.5,2.5,0.5])
+        #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4])
+        gs.update(hspace=0.1, wspace=0.2)
+        #gs.update(hspace=0.1/(len(label_labels)/4), wspace=0.2)
+        # 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
+        ax0 = fig.add_subplot(gs[0, :]) 
+        #ax1 = fig.add_subplot(gs[2, 0]) 
+        ax2 = fig.add_subplot(gs[2, :])  # Subplot at the top-right position
+        ax3 = fig.add_subplot(gs[-2, :])
+        ax4 = fig.add_subplot(gs[1, :])  # Subplot spanning the entire bottom row
+        axfooter = fig.add_subplot(gs[-1, :])  # Subplot spanning the entire bottom row
+
+
+        # a = {}
+        # k = 0
+        # while k < len(label_labels):
+        #     # dynamically create key
+        #     key = f'{k}_plot'  # Subplot at the top-left position
+        #     # calculate value
+        #     value = fig.add_subplot(gs[1+k, 0])  # Subplot at the top-left position
+        #     a[key] = value 
+        #     k += 1
+        # ax1 = fig.add_subplot(gs[1, 0])  # Subplot at the top-left position
+
+        # ax3.yaxis.set_visible(False)
+        # ax4.yaxis.set_visible(False)
+        # Customize subplots
+
+
+        ax3.tick_params(left = False, right = False , labelleft = False ,
+                        labelbottom = False, bottom = False)
+
+
+        ax4.tick_params(left = False, right = False , labelleft = False ,
+                        labelbottom = False, bottom = False)
+        ax3.axis('off')
+        ax4.axis('off')
+        ax0.axis('off')
+        # Calculate and set the position of the subplot
+
+        ax3.set_anchor('C')
+        ax4.set_anchor('C')
+
+
+        sns.set_theme(style="whitegrid", palette="pastel")
+        fig.set_facecolor('white')
+        # ax2.set_facecolor('white')
+
+        ## Legend Plot
+        # sns.scatterplot(ax=ax4,x=data_df.plate_x,y=data_df.plate_z,hue=data_df.pitch_name,palette=colour_palette[:len(data_df.pitch_name.unique())],s=1)
+
+
+        # ax4.legend(loc='center',bbox_to_anchor=(0, -0.1, 1, 0.1),
+        #           ncol=len(data_df['pitch_name'].unique()), fancybox=True, fontsize=16,facecolor='white',handleheight=2, framealpha=1.0)
+
+        # # Show the plot
+
+        # ax4.axis('off')
+
+
+        ## Pitch Plot
+        # label_labels = data_df['pitch_name'].unique()
+        # j = 0
+        # for label in label_labels:
+        #     subset = data_df[data_df['pitch_name'] == label]
+        #     confidence_ellipse(subset['plate_x'], subset['plate_z'], ax=ax1,edgecolor= colour_palette[j],n_std=1,facecolor= colour_palette[j],alpha=0.2)
+        #     j=j+1
+
+        font_properties = {'family': 'century gothic', 'size': 16}
+
+        # n = 0
+
+        # ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
+        # # ax1.set_xlim(1,max(data_df['pitch_count']))
+
+        # j = 0
+        # for label in label_labels:
+            
+        #     subset = data_df[data_df['pitch_name'] == label]
+            
+            
+        #     if len(subset) >= 1:
+        #         print('test',label, len(subset),colour_palette[j])
+        #         confidence_ellipse(subset['release_speed'], subset['release_spin_rate'], ax=ax1,edgecolor= pitch_colours[label],n_std=2,facecolor = pitch_colours[label],alpha=0.2)
+        #         j=j+1
+        #     else:
+        #         j=j+1  
+
+        # #sns.kdeplot(data=data_df[data_df.pitch_name == label_labels[n]].release_speed,ax=a[x],color=colour_palette[n],fill=True)
+        # #sns.lineplot(data=data_df[data_df.pitch_name==x],x='pitch_count',y='release_speed',color=colour_palette[n],ax=ax1,zorder=1)
+        # sns.scatterplot(data=data_df,x='release_speed',y='release_spin_rate',hue='pitch_name',palette=pitch_colours,ax=ax1,marker='o',size=50,ec='black',zorder=100,alpha=1)
+
+
+        # # ax1.hlines(y=data_df[data_df.pitch_name == label_labels[n]].release_speed.mean(),xmin=-1,xmax=max(data_df['pitch_count']),color=colour_palette[n],linestyles='--',linewidth=1)
+        # # ax1.hlines(y=statcast_df[statcast_df.pitch_name == label_labels[n]].release_speed.mean(),xmin=-1,xmax=max(data_df['pitch_count']),color=colour_palette[n],linestyles='dotted',linewidth=1)
+
+
+        # # ax1.text(1.5,statcast_df[statcast_df.pitch_name == label_labels[n]].release_speed.mean(),'League Average', rotation=0, verticalalignment='bottom',ha='left',
+        # #                 bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[n], pad=1),fontsize=4)
+
+
+        # n = n+1
+            
+        # ax1.set_xticklabels(ax1.get_xticks(), fontdict=font_properties)
+        # ax1.set_yticklabels(ax1.get_yticks(), fontdict=font_properties)
+
+        # #a[x].set_ylim(0,1)
+        # ax1.set_xlabel('Velocity (mph)', fontdict=font_properties)
+        # ax1.set_ylabel('Spin Rate (rpm)', fontdict=font_properties)
+
+
+        # ax1.set_title('Spin Rate vs Velocity',fontdict={'family': 'century gothic', 'size': 12})
+        # a[x].set_yticks([])
+        # a[x].vlines(x=data_df[data_df.pitch_name == label_labels[n]].release_speed.mean(),ymin=0,ymax=1,color=colour_palette[n],linestyles='--')
+        # a[x].vlines(x=statcast_df[statcast_df.pitch_name == label_labels[n]].release_speed.mean(),ymin=0,ymax=1,color=colour_palette[n],linestyles='dotted')
+
+
+
+            
+
+
+        # sns.scatterplot(ax=ax1,x=data_df.release_pos_x,y=data_df.release_pos_z,hue=data_df.pitch_name,palette=colour_palette[:len(data_df.pitch_name.unique())],s=50,ec='black',alpha=0.7)
+
+        # ax1.set_xlim(-3.5,3.5)
+        # ax1.set_ylim(0,7)
+
+
+
+
+        # ax1.hlines(y=statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95),xmin=-17/12/2,xmax=17/12/2,color=colour_palette[8],alpha=0.5,linestyles='-')
+        # ax1.hlines(y=statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),xmin=-17/12/2,xmax=17/12/2,color=colour_palette[8],alpha=0.5,linestyles='-')
+        # ax1.hlines(y=(-statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05)+
+        #               statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95))/3+statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),xmin=-17/12/2,xmax=17/12/2,color=colour_palette[8],alpha=0.5,linestyles='dotted')
+        # ax1.hlines(y=(-statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05)+
+        #               statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95))/3*2+statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),xmin=-17/12/2,xmax=17/12/2,color=colour_palette[8],alpha=0.5,linestyles='dotted')
+
+        # ax1.vlines(x=-17/12/2,ymin=statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),ymax=statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95),color=colour_palette[8],alpha=0.5,linestyles='-')
+        # ax1.vlines(x=17/12/2,ymin=statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),ymax=statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95),color=colour_palette[8],alpha=0.5,linestyles='-')
+
+        # ax1.vlines(x=(-17/12/2)+17/12/3,ymin=statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),ymax=statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95),color=colour_palette[8],alpha=0.5,linestyles='dotted')
+        # ax1.vlines(x=(-17/12/2)+17/12*2/3,ymin=statcast_df[statcast_df.zone.isin([7,8,9])].plate_z.quantile(0.05),ymax=statcast_df[statcast_df.zone.isin([1,2,3])].plate_z.quantile(0.95),color=colour_palette[8],alpha=0.5,linestyles='dotted')
+
+
+
+
+
+        # ax1.set_xlabel("Catcher's Presepctive (ft)", fontsize=10,fontname='Century Gothic')
+        # ax1.set_ylabel('Vertical Distance From Plate (ft)', fontsize=10,fontname='Century Gothic')
+
+
+
+        ## Break Plot
+
+        j = 0
+        for label in label_labels:
+            
+            subset = data_df[data_df['pitch_name'] == label]
+            print(label)
+            if len(subset) > 1:
+                if data_df['p_throws'].values[0] == 'R':
+                    subset['pfx_x'] = subset['pfx_x']*-12
+                if data_df['p_throws'].values[0] == 'L':
+                    subset['pfx_x'] = subset['pfx_x']*12
+                subset['pfx_z'] = subset['pfx_z']*12
+                confidence_ellipse(subset['pfx_x'], subset['pfx_z'], ax=ax2,edgecolor =  pitch_colours[label],n_std=2,facecolor=  pitch_colours[label],alpha=0.2)
+                j=j+1
+            else:
+                j=j+1  
+
+        #data_df = data_df.sort_values(by='prop',ascending=False)
+                
+        if data_df['p_throws'].values[0] == 'R':
+            sns.scatterplot(ax=ax2,x=data_df.pfx_x*-12,y=data_df.pfx_z*12,hue=data_df.pitch_name,palette=pitch_colours,ec='black',alpha=0.7)
+        if data_df['p_throws'].values[0] == 'L':
+            sns.scatterplot(ax=ax2,x=data_df.pfx_x*12,y=data_df.pfx_z*12,hue=data_df.pitch_name,palette=pitch_colours,ec='black',alpha=0.7)
+        # ax2.set_xlim(min(-25,-abs(math.floor((data_df['pfx_x'].min()*12-0.01)/5)*5),
+        #                     -abs(math.floor((data_df['pfx_z'].min()*12-0.01)/5)*5),
+        #                     -abs(math.ceil((data_df['pfx_x'].max()*12+0.01)/5)*5),
+        #                     -abs(math.ceil((data_df['pfx_z'].max()*12+0.01)/5)*5)),
+        #             max(25,abs(math.floor((data_df['pfx_x'].min()*12-0.01)/5)*5),
+        #                     abs(math.floor((data_df['pfx_z'].min()*12-0.01)/5)*5),
+        #                     abs(math.ceil((data_df['pfx_x'].max()*12+0.01)/5)*5),
+        #                     abs(math.ceil((data_df['pfx_z'].max()*12+0.01)/5)*5)))
+
+        # ax2.set_ylim(min(-25,-abs(math.floor((data_df['pfx_x'].min()*12-0.01)/5)*5),
+        #                     -abs(math.floor((data_df['pfx_z'].min()*12-0.01)/5)*5),
+        #                     -abs(math.ceil((data_df['pfx_x'].max()*12+0.01)/5)*5),
+        #                     -abs(math.ceil((data_df['pfx_z'].max()*12+0.01)/5)*5)),
+        #             max(25,abs(math.floor((data_df['pfx_x'].min()*12-0.01)/5)*5),
+        #                     abs(math.floor((data_df['pfx_z'].min()*12-0.01)/5)*5),
+        #                     abs(math.ceil((data_df['pfx_x'].max()*12+0.01)/5)*5),
+        #                     abs(math.ceil((data_df['pfx_z'].max()*12+0.01)/5)*5)))
+        ax2.set_xlim((-25,25))
+        ax2.set_ylim((-25,25))
+
+
+        ax2.set_title('Pitch Breaks',fontdict={'family': 'century gothic', 'size': 20})
+        # ax2.set_xlim(math.floor((data_df['pfx_x'].min()*12-0.01)/5)*5,math.ceil((data_df['pfx_x'].max()*12+0.01)/5)*5)
+        # ax2.set_ylim(math.floor((data_df['pfx_z'].min()*12-0.01)/5)*5,math.ceil((data_df['pfx_z'].max()*12+0.01)/5)*5)
+
+
+
+
+        ax2.hlines(y=0,xmin=-50,xmax=50,color=colour_palette[8],alpha=0.5,linestyles='--')
+        ax2.vlines(x=0,ymin=-50,ymax=50,color=colour_palette[8],alpha=0.5,linestyles='--')
+        ax2.set_xlabel('Horizontal Break (in)', fontsize=14,fontname='Century Gothic')
+        ax2.set_ylabel('Induced Vertical Break (in)', fontsize=14,fontname='Century Gothic')
+
+        ## Table Plot
+        ## Table Plot
+
+
+
+        print('this is the one')
+        df_plot = pitch_df_pitch[['pitch_name','pitches','pitch_percent','pitch_velocity','pfx_z','pfx_x',
+                                'extension','release_z','stuff_plus','loc_plus','whiff_rate','chase_percent','zone_percent','xwOBA','spin_axis_pitch']]
+
+        df_plot['pitches'] = [int(x) if not math.isnan(x) else np.nan for x in df_plot['pitches']]
+        df_plot['pitch_percent'] = df_plot['pitch_percent'].round(3)
+        df_plot['pitch_velocity'] = df_plot['pitch_velocity'].round(1)
+        df_plot['pfx_z'] = (df_plot['pfx_z']*12).round(1)
+        df_plot['pfx_x'] = (df_plot['pfx_x']*12).round(1)
+        df_plot['extension'] = df_plot['extension'].round(1)
+        df_plot['release_z'] = df_plot['release_z'].round(1)
+
+
+
+        
+        df_plot['stuff_plus'] = [int(x) if not math.isnan(x) else np.nan for x in df_plot['stuff_plus']]
+        df_plot['loc_plus'] = [int(x) if not math.isnan(x) else np.nan for x in df_plot['loc_plus']]
+        # df_plot['pitching_plus'] = [int(x) if not math.isnan(x) else np.nan for x in df_plot['pitching_plus']]
+        df_plot['whiff_rate'] = [round(x,3) if not math.isnan(x) else '—' for x in df_plot['whiff_rate']]
+        df_plot['chase_percent'] = [round(x,3) if not math.isnan(x) else '—' for x in df_plot['chase_percent']]
+        df_plot['zone_percent'] = [round(x,3) if not math.isnan(x) else '—' for x in df_plot['zone_percent']]
+        df_plot['xwOBA'] = [round(x,3) if not math.isnan(x) else '—' for x in df_plot['xwOBA']]
+        #df_plot['spin_axis_pitch'] = [x if not np.nan else '—' for x in df_plot['spin_axis_pitch']]
+
+
+
+        [['pitch_name','pitch_percent','pitch_velocity','pfx_z','pfx_x',
+                                'extension','release_z','stuff_plus','loc_plus','whiff_rate','zone_percent','xwOBA']]
+
+        plt.rcParams['font.family'] = 'Century Gothic'
+        table = ax3.table(cellText=df_plot.values, colLabels=df_plot.columns, cellLoc='center',
+                        colWidths=[0.08,0.04,0.04,.04,0.03, 0.03, 0.04,.06, 0.04,.06, 0.04,.04,0.04, 0.06,0.06], bbox=[0.025, 0, 0.95, 0.8])
+
+        min_font_size = 11
+        # Set table properties
+        table.auto_set_font_size(False)
+        table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table.scale(1, 0.5)
+
+        # Customize cell colors
+        #table.get_celld()[(0, 0)].set_facecolor('#56B4E9')  # Header cell color
+
+        def get_color(value):
+            color = cmap_sum(normalize(value))
+            return mcolors.to_hex(color)
+
+        up_percent = 1.5
+        down_percent = 0.5
+        print(df_plot)
+        label_labels_plot = df_plot.pitch_name.unique()
+
+        for i in range(len(df_plot)):
+
+            if table.get_celld()[(i+1, 0)].get_text().get_text() != 'All':
+                #print(float(table.get_celld()[(i+1, 3)].get_text().get_text()))
+                #print(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))
+                #colour_of_pitch
+                table.get_celld()[(i+1, 0)].set_facecolor(pitch_colours[table.get_celld()[(i+1, 0)].get_text().get_text()])  # Header cell color
+                if table.get_celld()[(i+1, 0)].get_text().get_text() in ['Curveball','Split-Finger','Slurve','Forkball']:
+                    table.get_celld()[(i+1, 0)].set_text_props(color='#000000',fontweight='bold')
+                else:
+                    table.get_celld()[(i+1, 0)].set_text_props(color='#ffffff',fontweight='bold')
+                #table.get_celld()[(i+1, 0)].set_path_effects([path_effects.withStroke(linewidth=2, foreground='black')])  # Header cell color
+
+            print(label_labels_plot[i])
+            select_df = statcast_df_df_pitch[statcast_df_df_pitch.pitch_name == label_labels_plot[i]]
+            print(f'test: {select_df.pitch_velocity_std.mean()}')
+
+            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
+
+            print(select_df.pitch_velocity.mean()-select_df.pitch_velocity_std.mean(),select_df.pitch_velocity.mean()+select_df.pitch_velocity_std.mean(),)
+            #print(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))
+            normalize = mcolors.Normalize(vmin=select_df.pitch_velocity.mean()-select_df.pitch_velocity_std.mean(), 
+                                        vmax=select_df.pitch_velocity.mean()+select_df.pitch_velocity_std.mean()) # Define the range of values
+            
+            if table.get_celld()[(i+1, 3)].get_text().get_text() != '—':
+                #print(float(table.get_celld()[(i+1, 3)].get_text().get_text()))
+                print(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))
+                table.get_celld()[(i+1, 3)].set_facecolor(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))  # Header cell color
+            #Header cell color
+
+            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
+            normalize = mcolors.Normalize(vmin=70,vmax=130)
+            table.get_celld()[(i+1, 8)].set_facecolor(get_color(float(table.get_celld()[(i+1, 8)].get_text().get_text())))
+
+            table.get_celld()[(i+1, 9)].set_facecolor(get_color(float(table.get_celld()[(i+1, 9)].get_text().get_text())))
+            #table.get_celld()[(i+1, 4)].set_facecolor(get_color(float(table.get_celld()[(i+1, 4)].get_text().get_text())))  # Header cell color
+            
+            
+            normalize = mcolors.Normalize(vmin=select_df.whiff_rate.mean()*down_percent, vmax=select_df.whiff_rate.mean()*up_percent)
+            if table.get_celld()[(i+1, 10)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 10)].set_facecolor(get_color(float(table.get_celld()[(i+1, 10)].get_text().get_text())))  # Header cell color
+
+            normalize = mcolors.Normalize(vmin=select_df.chase_percent.mean()*down_percent, vmax=select_df.chase_percent.mean()*up_percent)
+            if table.get_celld()[(i+1, 11)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 11)].set_facecolor(get_color(float(table.get_celld()[(i+1, 11)].get_text().get_text())))  # Header cell color
+
+            normalize = mcolors.Normalize(vmin=select_df.zone_percent.mean()*down_percent, vmax=select_df.zone_percent.mean()*up_percent)
+            if table.get_celld()[(i+1, 12)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 12)].set_facecolor(get_color(float(table.get_celld()[(i+1, 12)].get_text().get_text())))  # Header cell color
+
+            normalize = mcolors.Normalize(vmin=select_df.xwOBA.mean()*down_percent, vmax=select_df.xwOBA.mean()*up_percent)
+            if table.get_celld()[(i+1, 13)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 13)].set_facecolor(get_color(float(table.get_celld()[(i+1, 13)].get_text().get_text())))  # Header cell color
+            
+            # normalize = mcolors.Normalize(vmin=select_df[df_plot.columns[2]].mean()*0.9, vmax=select_df[df_plot.columns[2]].mean()*1.1)
+            # if table.get_celld()[(i+1, 3)].get_text().get_text() != '—':
+            #     table.get_celld()[(i+1, 3)].set_facecolor(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))  # Header cell color
+
+            # normalize = mcolors.Normalize(vmin=select_df[df_plot.columns[3]].mean()*down_percent, vmax=select_df[df_plot.columns[3]].mean()*down_percent)
+            # if table.get_celld()[(i+1, 3)].get_text().get_text() != '—':
+            #     table.get_celld()[(i+1, 3)].set_facecolor(get_color(float(table.get_celld()[(i+1, 3)].get_text().get_text())))  # Header cell color
+            
+            # normalize = mcolors.Normalize(vmin=select_df[df_plot.columns[4]].mean()*down_percent, vmax=select_df[df_plot.columns[4]].mean()*down_percent)
+            # if table.get_celld()[(i+1, 4)].get_text().get_text() != '—':
+            #     table.get_celld()[(i+1, 4)].set_facecolor(get_color(float(table.get_celld()[(i+1, 4)].get_text().get_text())))  # Header cell color
+            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',]) 
+            normalize = mcolors.Normalize(vmin=select_df[df_plot.columns[6]].mean()*0.9, vmax=select_df[df_plot.columns[6]].mean()*1.1)
+            if table.get_celld()[(i+1, 6)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 6)].set_facecolor(get_color(float(table.get_celld()[(i+1, 6)].get_text().get_text())))  # Header cell color
+            
+            # normalize = mcolors.Normalize(vmin=select_df[df_plot.columns[6]].mean()*down_percent, vmax=[df_plot.columns[6]].mean()*down_percent)
+            # if table.get_celld()[(i+1, 6)].get_text().get_text() != '—':
+            #     table.get_celld()[(i+1, 6)].set_facecolor(get_color(float(table.get_celld()[(i+1, 6)].get_text().get_text())))  # Header cell color
+
+
+            normalize = mcolors.Normalize(vmin=select_df.xwOBA.mean()*down_percent, vmax=select_df.xwOBA.mean()*up_percent)
+            cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFB000','#FFFFFF','#648FFF',])
+            if table.get_celld()[(i+1, 13)].get_text().get_text() != '—':
+                table.get_celld()[(i+1, 13)].set_facecolor(get_color(float(table.get_celld()[(i+1, 13)].get_text().get_text())))  # Header cell color
+
+            # normalize = mcolors.Normalize(vmin=select_df.csw_rate.mean()*down_percent, vmax=select_df.csw_rate.mean()*up_percent)  # Define the range of values
+            # table.get_celld()[(i+1, 6)].set_facecolor(get_color(float(table.get_celld()[(i+1, 6)].get_text().get_text())))  # Header cell color
+
+            normalize = mcolors.Normalize(vmin=select_df.pitch_velocity.mean()-select_df.pitch_velocity_std.mean(), 
+                                        vmax=select_df.pitch_velocity.mean()+select_df.pitch_velocity_std.mean()) # Define the range of values
+            #Header cell color
+
+
+
+        # [['pitch_name','pitch_percent','pitch_velocity','pfx_z','pfx_x',
+        #                           'extension','release_z','stuff_plus','loc_plus','whiff_rate','zone_percent','xwOBA']]
+
+
+        new_column_names = ['$\\bf{Pitch\ Name}$',
+        '$\\bf{Count}$',
+        '$\\bf{Pitch\%}$',
+        '$\\bf{Velo}$',
+        '$\\bf{iVB}$',
+        '$\\bf{HB}$',
+        '$\\bf{Ext.}$',
+        '$\\bf{Rel.\ Height}$',
+
+
+
+
+
+        '$\\bf{Stuff+}$',
+        '$\\bf{Location+}$',
+
+        '$\\bf{Whiff\%}$',
+        '$\\bf{Chase\%}$',
+        '$\\bf{Zone\%}$',
+        '$\\bf{xwOBACON}$',
+        '$\\bf{Spin\ Axis}$']
+
+
+
+        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
+        for i, col_name in enumerate(new_column_names):
+            table.get_celld()[(0, i)].get_text().set_text(col_name)
+
+
+
+        pitch_col = df_plot['pitch_percent']
+        for cell in table.get_celld().values():
+            if cell.get_text().get_text() in pitch_col.astype(str).values:
+                cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+        pitch_col = df_plot['whiff_rate']
+        for cell in table.get_celld().values():
+            if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+        
+        pitch_col = df_plot['chase_percent']
+        for cell in table.get_celld().values():
+            if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+        pitch_col = df_plot['zone_percent']
+        for cell in table.get_celld().values():
+            if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+        pitch_col = df_plot['xwOBA']
+        for cell in table.get_celld().values():
+            
+            if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                print('xwOBA')
+                cell.get_text().set_text('{:,.3f}'.format(float(cell.get_text().get_text())))
+
+
+
+        float_list = ['pitch_velocity','pfx_z','pfx_x',
+                                 'extension','release_z']
+        for fl in float_list:
+            pitch_col = df_plot[fl]
+            for cell in table.get_celld().values():
+                if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                    cell.get_text().set_text('{:,.1f}'.format(float(cell.get_text().get_text())))
+
+        int_list = ['stuff_plus','loc_plus',]
+        for fl in int_list:
+            pitch_col = df_plot[fl]
+            for cell in table.get_celld().values():
+                if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                    cell.get_text().set_text('{:,.0f}'.format(float(cell.get_text().get_text())))
+
+        # pitch_col = df_plot['csw_rate']
+        # for cell in table.get_celld().values():
+        #     if cell.get_text().get_text() in pitch_col.astype(str).values:
+        #         cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+
+        # for (row, col), cell in table.get_celld().items():
+        #     if (row == len(df_plot)):
+        #         cell.set_text_props(fontproperties=FontProperties(weight='bold',style='italic'),fontsize=(min(12,max(12/((len(label_labels)/4)),8))))
+
+        # # new_column_names = ['$\\bf{'+str(x)+'}$' for x in list(df_plot.loc[len(df_plot)-1])]
+
+        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
+        # # for i in len(df_plot.columns):
+        # #     table.get_celld()[(len(df_plot), i)].get_text().set_fontweight('bold')
+
+
+
+
+
+
+        table2 = ax4.table(cellText=summary_df_pitch.values, colLabels=summary_df_pitch.columns, cellLoc='center',
+                        colWidths=[0.1,0.05,.05,.05, 0.05, 0.05,.05,.05], bbox=[0.05, 0.4, 0.90, .80])
+
+
+        # table2 = ax4.table(cellText=summary_df_pitch.values, colLabels=summary_df_pitch.columns, cellLoc='center',
+        #                 colWidths=[0.1,0.05,.05,.05, 0.05, 0.05,.05,.05], bbox=[0.00, 0.4, 0.955, min(.8,0.8/(len(df_plot)/4))])
+
+
+
+
+        # Set table properties
+        table2.auto_set_font_size(False)
+        min_font_size = 11
+        table2.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table2.scale(1, 1)
+
+        if input_date_id == '0':
+            pitch_col = summary_df_pitch['k']
+            for cell in table2.get_celld().values():
+                if cell.get_text().get_text() in pitch_col.astype(str).values and cell.get_text().get_text() != '—':
+                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+            pitch_col = summary_df_pitch['bb']
+            for cell in table2.get_celld().values():
+                if cell.get_text().get_text() in pitch_col.astype(str).values:
+                    cell.get_text().set_text('{:,.1%}'.format(float(cell.get_text().get_text())))
+
+            new_column_names = ['$\\bf{Pitcher}$',
+            '$\\bf{PA}$',
+            '$\\bf{IP}$',
+            '$\\bf{WHIP}$',
+            '$\\bf{ERA}$',
+            '$\\bf{K\%}$',
+            '$\\bf{BB\%}$']
+
+        else:
+            new_column_names = ['$\\bf{Pitcher}$',
+            '$\\bf{Pitches}$',
+            '$\\bf{IP}$',
+            '$\\bf{Hits}$',
+            '$\\bf{ER}$',
+            '$\\bf{K}$',
+            '$\\bf{BB}$']
+
+
+
+
+        for i, col_name in enumerate(new_column_names):
+            table2.get_celld()[(0, i)].get_text().set_text(col_name)
+
+
+
+
+
+        for (row, col), cell in table.get_celld().items():
+            if (row == len(df_plot)):
+                cell.set_text_props(fontproperties=FontProperties(weight='bold',style='italic'),fontsize=(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10))))
+
+
+
+        # # table = ax3.table(cellText=pitch_df_pitch[['pitch_name','pitch_percent','spin_rate','exit_velocity','whiff_rate','csw_rate']].values, colLabels=pitch_df_pitch[['pitch_name','pitch_percent','spin_rate','exit_velocity','whiff_rate','csw_rate']].columns, loc='center')
+
+        # # Set the table properties
+        # table.auto_set_font_size(False)
+        # table.set_fontsize(12)
+        # table.scale(1.2, 1.2)
+
+
+        #ax1.get_legend().remove()
+
+        ax2.get_legend().remove()
+
+
+        # ax1.set_xticklabels(ax1.get_xticks(), fontdict=font_properties)
+        ax2.set_xticklabels(ax2.get_xticks(), fontdict=font_properties)
+
+        # ax1.set_yticklabels(ax1.get_yticks(), fontdict=font_properties)
+        ax2.set_yticklabels(ax2.get_yticks(), fontdict=font_properties)
+
+
+        # ax1.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+        # ax2.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+
+        # ax1.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+        # ax2.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))
+
+        # ax1.set_facecolor('white')
+        # ax2.set_facecolor('white')
+
+        
+        # ax1.xaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f'))
+        # ax2.yaxis.set_major_formatter(decimal_format_assign(x=pitcher_dict_stat[input.stat_y()]))
+
+        # ax1.xaxis.set_major_formatter(decimal_format_assign(x=pitcher_dict_stat[input.stat_x()]))
+        # ax2.yaxis.set_major_formatter(decimal_format_assign(x=pitcher_dict_stat[input.stat_y()]))
+        # ax1.legend(loc='upper center', bbox_to_anchor=(1, 1.05),
+        #           ncol=len(label_labels), fancybox=True, shadow=True)
+        handles, labels = ax2.get_legend_handles_labels()
+        ax2.legend(handles, labels, bbox_to_anchor=(0.77, 0.50, 1, 0.1), ncol=1,fancybox=True,loc='center',fontsize=16,framealpha=1.0, markerscale=2)
+
+
+
+        title_spot = f'{summary_df_pitch.pitcher[0]} Pitching Summary'
+        if input_date_id != '0':
+            if sum(data_df.home_team == data_df.abbreviation_pitcher.reset_index(drop=True)[0]) > 0:
+                line2 = f"{mlb_teams_df[mlb_teams_df.team_id == data_df.team_id_pitcher.reset_index(drop=True)[0]].reset_index(drop=True)['franchise'][0]} vs {mlb_teams_df[mlb_teams_df.team_id == data_df.team_id.reset_index(drop=True)[0]].reset_index(drop=True)['franchise'][0]}"
+
+            if sum(data_df.away_team == data_df.abbreviation_pitcher.reset_index(drop=True)[0]) > 0:
+                line2 = f"{mlb_teams_df[mlb_teams_df.team_id == data_df.team_id_pitcher.reset_index(drop=True)[0]].reset_index(drop=True)['franchise'][0]} @ {mlb_teams_df[mlb_teams_df.team_id == data_df.team_id.reset_index(drop=True)[0]].reset_index(drop=True)['franchise'][0]}"
+ 
+            
+            if input.radio_id() != 'a':
+                line2 = f"{line2} - vs {input.radio_id()}HB"
+
+            # if input.home_id() != 'all':
+            #     line2 = f"{line2} , {home_away_dict[input.home_id()]}"
+            
+            ax0.text(x=0.5,y=0.25,s=line2,fontname='Century Gothic',ha='center',fontstyle='italic',fontsize=20,va='bottom')
+
+        else:
+            if input_date_range_id[0] <= datetime.strptime('2023-03-30', '%Y-%m-%d').date() and input_date_range_id[1] >= datetime.strptime('2023-10-02', '%Y-%m-%d').date():
+
+                line2 = f'2023 Season'
+            else:
+                line2 = f'{str(input_date_range_id[0])} to {str(input_date_range_id[1])}'
+
+            if input.radio_id() != 'a':
+                line2 = f"{line2} - vs {input.radio_id()}HB"
+
+            # if input.home_id() != 'all':
+            #     line2 = f"{line2} , {home_away_dict[input.home_id()]}"
+
+            ax0.text(x=0.5,y=0.25,s=line2,fontname='Century Gothic',ha='center',fontstyle='italic',fontsize=20,va='bottom')
+
+        ax0.text(x=0.5,y=0.9,s=title_spot,fontname='Century Gothic',ha='center',fontsize=36,va='top')
+
+        if input_date_id != '0':    
+            ax0.text(x=0.5,y=0.1,s=data_df.game_date[0],fontname='Century Gothic',ha='center',fontstyle='italic',fontsize=16,va='bottom')
+
+        #ax1.set_aspect('equal', adjustable='box')
+        if data_df['p_throws'].values[0] == 'R':
+            ax2.text(-24.5,-24.5,s='← Glove Side',fontstyle='italic',ha='left',va='bottom',
+                    bbox=dict(facecolor='white', edgecolor='black'))
+            ax2.text(24.5,-24.5,s='Arm Side →',fontstyle='italic',ha='right',va='bottom',
+                     bbox=dict(facecolor='white', edgecolor='black'))
+            #ax2.invert_xaxis()
+        if data_df['p_throws'].values[0] == 'L':
+            ax2.invert_xaxis()    
+            ax2.text(24.5,-24.5,s='← Arm Side',fontstyle='italic',ha='left',va='bottom',
+                    bbox=dict(facecolor='white', edgecolor='black'))
+            ax2.text(-24.5,-24.5,s='Glove Side →',fontstyle='italic',ha='right',va='bottom',
+                     bbox=dict(facecolor='white', edgecolor='black'))
+        ax2.set_aspect('equal', adjustable='box')
+        #ax1.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        ax2.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        ax2.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
+        #ax1.set_xticklabels(range(1,data_df.pitch_count.max()+1))
+        #fig.text(x=0.05,y=0.03,s='By: @TJStats',fontname='Century Gothic',ha='left',fontsize=16)
+        #fig.text(x=1-0.05,y=0.03,s='Data: MLB, Eno Sarris',ha='right',fontname='Century Gothic',fontsize=16)
+
+
+        #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Century Gothic',fontsize=10)
+        axfooter.text(x=0.05,y=0.6,s='By: @TJStats',fontname='Century Gothic',ha='left',fontsize=16)
+        axfooter.text(x=1-0.05,y=0.6,s='Data: MLB, Eno Sarris, Fangraphs',ha='right',fontname='Century Gothic',fontsize=16)
+
+
+        axfooter.text(x=0.5,y=1,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Century Gothic',fontsize=10,va='bottom')
+        axfooter.axis('off')
+        #fig.tight_layout()
+
+        #fig.set_size_inches(10, 10) 
+        #fig.subplots_adjust(left=0.03, right=0.97, bottom=0.05, top=0.95)
+        #matplotlib.rcParams["figure.dpi"] = 600
+        #plt.axis('scaled')
+        fig.subplots_adjust(left=0.02, right=0.98, top=0.97, bottom=0.03)
+
+
+
+pitching_summary_graphic_new = 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("""<a href='https://www.patreon.com/tj_stats'>Support me on Patreon for Access to 2024 Apps</a><sup>1</sup>"""),
+    ui.navset_tab(
+        ui.nav_control(
+             ui.a(
+                "Home",
+                href="home/"
+            ),
+        ),
+        ui.nav_menu(
+            "Batter Charts",
+            ui.nav_control(
+            ui.a(
+                "Batting Rolling",
+                href="rolling_batter/"
+            ),
+            ui.a(
+                "Spray",
+                href="spray/"
+            ),
+            ui.a(
+                "Decision Value",
+                href="decision_value/"
+            ),
+            ui.a(
+                "Damage Model",
+                href="damage_model/"
+            ),
+            ui.a(
+                "Batter Scatter",
+                href="batter_scatter/"
+            ),
+            ui.a(
+                "EV vs LA Plot",
+                href="ev_angle/"
+            ),
+            ui.a(
+                "Statcast Compare",
+                href="statcast_compare/"
+            )
+        ),
+        ),
+        ui.nav_menu(
+            "Pitcher Charts",
+            ui.nav_control(
+             ui.a(
+                "Pitcher Rolling",
+                href="rolling_pitcher/"
+            ),
+             ui.a(
+                "Pitcher Summary",
+                href="pitching_summary_graphic_new/"
+            ),
+             ui.a(
+                "Pitcher Scatter",
+                href="pitcher_scatter/"
+            )
+        ),
+        )),ui.row(
+        ui.layout_sidebar(
+
+        ui.panel_sidebar(
+                ui.input_select("id", "Select Pitcher",pitcher_dicts,width=1,selectize=True),
+                ui.input_select("date_id", "Select Date",{0:'Season'},width=1),
+                ui.input_date_range("date_range_id", "Date range input (Set 'Select Date' to 'Season')",start = '2023-03-30', end = '2023-10-02'),
+                ui.input_radio_buttons("radio_id", "Handedness", {"a": "All", "R": "Right","L": "Left"}),
+                ui.input_action_button("go", "Generate",class_="btn-primary"),width=2)
+            ,
+            # ui.panel_sidebar(
+            #     ui.input_select("id", "Select Pitcher",pitcher_dict,width=1),
+            #     ui.input_select("date_id", "Select Date",date_dict,width=1),
+            #     ui.input_date_range("date_range_id", "Date range input (Set 'Select Date' to 'Season')",start = statcast_df.game_date.min(), end = statcast_df.game_date.max()),
+            #     ui.input_radio_buttons("radio_id", "Handedness", {"a": "All", "R": "Right","L": "Left"}),
+            #     ui.input_radio_buttons("home_id", "Setting", {"all": "All", "h": "Home","a": "Away"}),
+            #     ui.input_radio_buttons("heat_id", "Heat Map Plot (On 2nd Tab)", dict_plots_name                                            
+            # ),width=2
+            # ),
+
+            ui.panel_main(
+                ui.output_plot("plot",height = "1400px",width="1400px")
+            ),
+    )),)),server)
\ No newline at end of file