batter_scatter_2.py

from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
import datasets
from datasets import load_dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib
from matplotlib.ticker import MaxNLocator
from matplotlib.gridspec import GridSpec
from scipy.stats import zscore
import math
import matplotlib
from adjustText import adjust_text
import matplotlib.ticker as mtick
from shinywidgets import output_widget, render_widget
import pandas as pd
from configure import base_url
import shinyswatch
import inflect
from matplotlib.pyplot import text


exit_velo_df_codes_summ_batter = pd.read_csv('summary_batter.csv',index_col=[0])
#exit_velo_df_codes_summ = pd.read_csv('summary_pitcher.csv',index_col=[0])

exit_velo_df_codes_summ_non_level = pd.read_csv('summary_batter_level.csv',index_col=[0]).reset_index(drop=True)

exit_velo_df_codes_summ_non_level['levels'] = exit_velo_df_codes_summ_non_level.levels.str.split(', ')

exit_velo_df_codes_summ_non_level = exit_velo_df_codes_summ_non_level.rename(columns={'levels':'level'})



print(exit_velo_df_codes_summ_batter.bb_minus_k_percent)

batter_dict_stat = { 'sweet_spot_percent':{'x_axis':'SweetSpot%','title':'SweetSpot%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'max_launch_speed':{'x_axis':'Max Exit Velocity','title':'Max Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
                'launch_speed_90':{'x_axis':'90th Percentile EV','title':'90th Percentile EV','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
                'launch_speed':{'x_axis':'Exit Velocity','title':'Exit Velocity','flip_p':False,'decimal_format':'string_0','percent_adjust':1},
                'launch_angle':{'x_axis':'Launch Angle','title':'Launch Angle','flip_p':False,'decimal_format':'string_0','percent_adjust':100},
                'avg':{'x_axis':'AVG','title':'AVG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
                'obp':{'x_axis':'OBP','title':'OBP','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
                'slg':{'x_axis':'SLG','title':'SLG','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
                'ops':{'x_axis':'OPS','title':'OPS','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
                'k_percent':{'x_axis':'K%','title':'K%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                'bb_percent':{'x_axis':'BB%','title':'BB%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'bb_over_k_percent':{'x_axis':'BB/K','title':'BB/K','flip_p':False,'decimal_format':'string_1','percent_adjust':100},
                'bb_minus_k_percent':{'x_axis':'BB%-K%','title':'BB%-K%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'csw_percent':{'x_axis':'CSW%','title':'CSW%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                'woba_percent':{'x_axis':'wOBA','title':'wOBA','flip_p':False,'decimal_format':'string_3','percent_adjust':100},
                'hard_hit_percent':{'x_axis':'HardHit%','title':'HardHit%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'barrel_percent':{'x_axis':'Barrel%','title':'Barrel%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'zone_contact_percent':{'x_axis':'Z-Contact%','title':'Z-Contact%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'zone_swing_percent':{'x_axis':'Z-Swing%','title':'Z-Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'zone_percent':{'x_axis':'Zone%','title':'Zone%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'chase_percent':{'x_axis':'O-Swing%','title':'O-Swing%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                'chase_contact':{'x_axis':'O-Contact%','title':'O-Contact%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                'swing_percent':{'x_axis':'Swing%','title':'Swing%','flip_p':False,'decimal_format':'percent_1','percent_adjust':100},
                'whiff_rate':{'x_axis':'Whiff%','title':'Whiff%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                'swstr_rate':{'x_axis':'SwStr%','title':'SwStr%','flip_p':True,'decimal_format':'percent_1','percent_adjust':100},
                }

batter_dict_stat_small = { 'sweet_spot_percent':'SweetSpot%',
                'max_launch_speed':'Max Exit Velocity',
                'launch_speed_90':'90th Percentile EV',
                'launch_speed':'Exit Velocity',
                'launch_angle':'Launch Angle',
                'avg':'AVG',
                'obp':'OBP',
                'slg':'SLG',
                'ops':'OPS',
                'k_percent':'K%',
                'bb_percent':'BB%',
                'bb_over_k_percent':'BB/K',
                'bb_minus_k_percent':'BB%-K%',
                'csw_percent':'CSW%',
                'woba_percent':'wOBA',
                'hard_hit_percent':'HardHit%',
                'barrel_percent':'Barrel%',
                'zone_contact_percent':'Z-Contact%',
                'zone_swing_percent':'Z-Swing%',
                'zone_percent':'Zone%',
                'chase_percent':'O-Swing%',
                'chase_contact':'O-Contact%',
                'swing_percent':'Swing%',
                'whiff_rate':'Whiff%',
                'swstr_rate':'SwStr%',
                }


colour_palette = ['#FFB000','#648FFF','#785EF0',
                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']

level_dict = {'MLB':'MLB','AAA':'AAA','AA':'AA','A+':'A+','A':'A','ROK':'ROK'}

batter_test_df = exit_velo_df_codes_summ_batter.sort_values(by='batter').drop_duplicates(subset='batter_id').reset_index(drop=True)[['batter_id','batter']]#['pitcher'].to_dict()
batter_test_df = batter_test_df.set_index('batter_id')


def decimal_format_assign(x):
    if x['decimal_format'] == 'percent_1':
        return mtick.PercentFormatter(1,decimals=1)
    if x['decimal_format'] == 'string_3':
        return mtick.FormatStrFormatter('%.3f')
    if x['decimal_format'] == 'string_0':
        return mtick.FormatStrFormatter('%.0f')
    if x['decimal_format'] == 'string_1':
        return mtick.FormatStrFormatter('%.1f')


#test_df =  test_df[test_df.pitcher == 'Chris Bassitt'].append(test_df[test_df.pitcher != 'Chris Bassitt'])

batter_dict = batter_test_df['batter'].to_dict()

exit_velo_df_codes_summ_batter.position = exit_velo_df_codes_summ_batter.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH'])
exit_velo_df_codes_summ_non_level.position = exit_velo_df_codes_summ_non_level.position.replace(['LF','RF','CF','TWP'],['OF','OF','OF','DH'])

position_list = ['All'] + list(exit_velo_df_codes_summ_batter.position.unique())
team_list = ['All'] + sorted(list(exit_velo_df_codes_summ_batter.parent_org_abb.unique()))



def server(input,output,session):


    @output
    @render.plot(alt="A histogram")
    def plot():
        sns.set_theme(style="whitegrid", palette="pastel")
        print(input.level_id())
        print(input.n())
        print('we made it here',input.team_id(),input.position_id())
        if input.group_level():
            data_df = exit_velo_df_codes_summ_non_level.copy()

            turth_list = []
            #turth_list_2 = []
            for x in range(0,len(data_df.level)):
                turth_list_2 = []
                for y in range(0,len(data_df.level[x])):
                    #print(level_list[x][y])
                    turth_list_2.append(data_df.level[x][y] in input.level_id())
                turth_list.append(turth_list_2)

            final_check_list = [True if True in x else False for x in turth_list]


            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(final_check_list)]
        
        
        else:


            data_df = exit_velo_df_codes_summ_batter.copy()
            data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())&(data_df.level.isin(input.level_id()))]
            print(data_df)
            if 'All' in input.team_id():
                print('nice')#data_df = data_df[(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
            
            else:
                data_df = data_df[(data_df.parent_org_abb.isin(input.team_id()))].reset_index(drop=True)

            if 'All' in input.position_id():
                print('nice')#data_df = data_df[(data_df.level.isin(input.level_id()))&(data_df.pa >= input.n())&(data_df.age <= input.n_age())].reset_index(drop=True)
            
            else:
                data_df = data_df[(data_df.position.isin(input.position_id()))].reset_index(drop=True)
            
            
        #print('we made it here')
        print(data_df)
        data_df = data_df.sort_values(by='level').reset_index(drop=True)
        print(batter_dict_stat[input.stat_x()]['flip_p'])



        x_flip = batter_dict_stat[input.stat_x()]['flip_p']
        y_flip = batter_dict_stat[input.stat_y()]['flip_p']
        cbr_flip = batter_dict_stat[input.stat_z()]['flip_p']

        

        data_df[input.stat_x()+'_percent'] = data_df[input.stat_x()].rank(pct=True,ascending=abs(x_flip-1))

        data_df[input.stat_y()+'_percent'] = data_df[input.stat_y()].rank(pct=True,ascending=abs(y_flip-1))

        data_df[input.stat_z()+'_percent'] = data_df[input.stat_z()].rank(pct=True,ascending=abs(cbr_flip-1))



        fig, ax = plt.subplots(1, 1, figsize=(9, 9))

        #data_df['bb_over_obp'] = data_df['bb']/data_df['k']
        
        #data_df[input.stat_z()]= data_df[input.stat_z()].fillna(-100000)
        
        
        if cbr_flip:
            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[0],colour_palette[3],colour_palette[1]])
            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())

        else:
            cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],colour_palette[3],colour_palette[0]])
            norm = plt.Normalize(data_df[input.stat_z()].min(), data_df[input.stat_z()].max())

        sm = plt.cm.ScalarMappable(cmap=cmap_hue, norm=norm)
        print('we made it here')

        # sns.regplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[6],ax=ax,scatter=False,
        #             line_kws=dict(alpha=0.3,linewidth=2,zorder=1))
        # scatter_plot = sns.scatterplot(x = stat_x, y = stat_y, data=data_df, color = colour_palette[0],ax=ax,hue=stat_z,palette=cmap_hue)



        # r, p = sp.stats.pearsonr(data_df[input.stat_x()], data_df[input.stat_y()])
        # ax = plt.gca()
        # # ax.text(.25, 0.3, 'r={:.2f}, p={:.2g}'.format(r, p),
        # #         transform=ax.transAxes, fontsize=12)

        # ax.annotate('R²={:.2f}'.format(r, p), ( math.ceil(data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_x()]['percent_adjust']*(1-batter_dict_stat[input.stat_x()]['flip_p']), 
        #                                       math.floor(data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']/5)*5/batter_dict_stat[input.stat_y()]['percent_adjust']*(1-batter_dict_stat[input.stat_y()]['flip_p'])), 
        #                                         fontsize=18,fontname='Century Gothic',ha='right')

        if input.group_level():
            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3')
            #ax.get_legend().remove()
            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue)
        else:
            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = '#b3b3b3',style='level')    
            #ax.get_legend().remove()
            scatter = sns.scatterplot(x = input.stat_x(), y = input.stat_y(), data=data_df, color = colour_palette[0],ax=ax,hue=input.stat_z(),palette=cmap_hue,style='level')
        sns.set_theme(style="whitegrid", palette="pastel")

        fig.set_facecolor('#F0F0F0')
        ax.set_facecolor('white')

        print('we made it here')
        # for i in range(0,len(pitch_group_unique)):
        #     data_df = elly_zone_df[elly_zone_df.pitch_group==pitch_group_unique[i]]
        #     len_df.append(len(data_df))
        #     sns.lineplot(x=range(1,len(data_df)+1),y=data_df.swings.rolling(window=rolling_window_input).sum()/data_df.pitches.rolling(window=rolling_window_input).sum(),color=colour_palette[i],linewidth=3,ax=ax,
        #                  label=f'{pitch_group_unique[i]} (Season Average {float(data_df.swings.sum()/data_df.pitches.sum()):.1%})',zorder=i+10)
        #     ax.hlines(xmin=0,xmax=len(elly_zone_df),y=data_df.swings.sum()/data_df.pitches.sum(),color=colour_palette[i],linewidth=3,linestyle='-.',alpha=0.4,zorder=i)

        ts=[]
        print(input.player_id())

        print(len(data_df))
        if input.names():
            for i in range(len(data_df)):
                if (data_df[input.stat_x()+'_percent'].values[i] < input.n_percent_bot_x()  or data_df[input.stat_x()+'_percent'].values[i] > 1 - input.n_percent_top_x() ) \
                or (data_df[input.stat_y()+'_percent'].values[i] < input.n_percent_bot_y()  or data_df[input.stat_y()+'_percent'].values[i] > 1 -input.n_percent_top_y()) \
                or (data_df[input.stat_z()+'_percent'].values[i] < input.n_percent_bot_z()  or data_df[input.stat_z()+'_percent'].values[i] > 1 -input.n_percent_top_z() )\
                or (str(data_df.batter_id[i]) in (input.player_id())):
                    # print(data_df.batter[i])
                    # ax.annotate(data_df.batter[i], xy=((data_df[input.stat_x()][i])+0.025/batter_dict_stat[input.stat_x()]['percent_adjust'], data_df[input.stat_y()][i]+0.01/batter_dict_stat[input.stat_x()]['percent_adjust']), xytext=(-20,20), 
                    # textcoords='offset points', ha='center', va='bottom',fontsize=7,
                    # bbox=dict(boxstyle='round,pad=0', fc=colour_palette[6], alpha=0.0),
                    # arrowprops=dict(arrowstyle='->', connectionstyle="angle,angleA=-90,angleB=-10,rad=2", 
                    #                 color=colour_palette[8]))
                    
                    #if data_df['batter'][i] != 'Jo Adell':
                    # ax.annotate(data_df.batter[i], (data_df[input.stat_x()][i]-len(data_df.batter[i])*0.00025, data_df[input.stat_y()][i]+0.001),fontsize=8)
                    ts.append(ax.text(data_df[input.stat_x()][i], data_df[input.stat_y()][i], data_df.batter[i],fontsize=8))



        ax.hlines(xmin=(math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
                    xmax= (math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
                    y=data_df[input.stat_y()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)

        print('we made it here')

        ax.vlines(ymin=(math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
                    ymax= (math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust']+0.01)/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
                    x=data_df[input.stat_x()].mean(),color='gray',linewidth=3,linestyle='dotted',alpha=0.4)

        print(data_df[input.stat_x()].min())
        print(batter_dict_stat[input.stat_x()]['percent_adjust'])
        print((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust']-0.01)/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'])


        ax.set_xlim((math.floor((data_df[input.stat_x()].min()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'],
                    (math.ceil((data_df[input.stat_x()].max()*batter_dict_stat[input.stat_x()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_x()]['percent_adjust'])


        ax.set_ylim((math.floor((data_df[input.stat_y()].min()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'],
                    (math.ceil((data_df[input.stat_y()].max()*batter_dict_stat[input.stat_y()]['percent_adjust'])/5))*5/batter_dict_stat[input.stat_y()]['percent_adjust'])
       


        title_level = str([x .strip("\'")for x in input.level_id()]).strip('[').strip(']').replace("'",'')

        if title_level == 'AAA, AA, A+, A':
            title_level='MiLB'
        #title_level = input.level_id()[0]
        if input.n_age() >= 50:
            title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA)'
        
        else:
            title_spot = f'{title_level} Batter {batter_dict_stat[input.stat_y()]["title"]} vs {batter_dict_stat[input.stat_x()]["title"]} (min. {input.n()} PA, Max Age {input.n_age()})'

        ax.set_title(title_spot, fontsize=24/(len(title_spot)*0.03),fontname='Century Gothic')
        # #vals = ax.get_yticks()
        ax.set_xlabel(batter_dict_stat[input.stat_x()]['x_axis'], fontsize=16,fontname='Century Gothic')
        ax.set_ylabel(batter_dict_stat[input.stat_y()]['x_axis'], fontsize=16,fontname='Century Gothic')
        

        if input.group_level():
            ax.get_legend().remove()

        if not input.group_level():
            if len(input.level_id()) > 1:
                h,l = scatter.get_legend_handles_labels()
                l[-(len(input.level_id())+1)] = 'Level'
                ax.legend(h[-(len(input.level_id())+1):],l[-(len(input.level_id())+1):], borderaxespad=0.1,loc=0)

            else:
                ax.get_legend().remove()

        #plt.show(g)
        # ax.figure.colorbar(sm, ax=ax)
        
        cbar  = ax.figure.colorbar(sm, ax=ax,format=decimal_format_assign(x=batter_dict_stat[input.stat_z()]),orientation='vertical',aspect=30)
        cbar.set_label(batter_dict_stat[input.stat_z()]['x_axis'])
        #fig.axes[0].invert_yaxis()
        print('we made it here5')
        fig.subplots_adjust(wspace=.02, hspace=.02)
        # ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
        #ax.set_yticks([0,0.1,0.2,0.3,0.4,0.5])
        # fig.colorbar(plot_dist, ax=ax)
        # fig.colorbar(plot_dist)

        if batter_dict_stat[input.stat_x()]['flip_p']:
            fig.axes[0].invert_xaxis()

        if batter_dict_stat[input.stat_y()]['flip_p']:
            fig.axes[0].invert_yaxis()


        # ax.xaxis.set_major_formatter(mtick.PercentFormatter(1,decimals=0))
        # ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))

        
        
        

        print('we made it here6')

        ax.xaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_x()]))
        ax.yaxis.set_major_formatter(decimal_format_assign(x=batter_dict_stat[input.stat_y()]))


        print('we made it here7')
        # ax.text(0.5, 0.5, '/u/tomstoms', transform=ax.transAxes,
        #         fontsize=60, color='gray', alpha=0.075,
        #         ha='center', va='center', rotation=45)

        print(ts)
        if len(ts) > 0:
            adjust_text(ts,
                        arrowprops=dict(arrowstyle="-", color=colour_palette[4], lw=1),ax=ax)

        #ax.legend(fontsize='16')
        fig.text(x=0.03,y=0.02,s='By: @TJStats',fontname='Century Gothic')
        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right',fontname='Century Gothic')
        fig.tight_layout()
        #matplotlib.rcParams["figure.dpi"] = 600
        #plt.show()


rolling_pitcher = 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.navset_tab(
        ui.nav_control(
             ui.a(
                "Home",
                href="home/"
            ),
        ),
        ui.nav_menu(
            "Batter Charts",
            ui.nav_control(
             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.nav_menu(
            "Goalie Charts",
            ui.nav_control(
             ui.a(
                "GSAx Timeline",
                href="gsax-timeline/"
            ),
             ui.a(
                "GSAx Leaderboard",
                href="gsax-leaderboard/"
            ),
             ui.a(
                "GSAx Comparison",
                href="gsax-comparison/"
            )
        ),
        ),ui.nav_menu(
            "Team Charts",
            ui.nav_control(
             ui.a(
                "Team xG Rates",
                href="team-xg-rates/"
            ),
        ),
        ),ui.nav_control(
             ui.a(
                "Games",
                href="games/"
            ),
        ),ui.nav_control(
             ui.a(
                "About",
                href="about/"
            ),
        ),ui.nav_control(
             ui.a(
                "Articles",
                href="articles/"
            ),
        )),ui.row(
    ui.layout_sidebar(
        
   

      ui.panel_sidebar(
        #ui.input_select("id", "Select Batter",batter_dict,selected=675911,width=1,size=1),
        ui.row(
            ui.column(4,ui.input_select("level_id", "Select Level",level_dict,width=1,size=1,multiple=True,selected='MLB',selectize=True),),
            ui.column(4,ui.input_select("team_id", "Select Team",team_list,width=1,size=1,multiple=True,selected='All',selectize=True),),
            ui.column(4,ui.input_select("position_id", "Select Position",position_list,width=1,size=1,selected='All',multiple=True,selectize=True))),
        ui.row(
            ui.column(6,ui.input_numeric("n", "Minimum PA", value=100)),
            ui.column(6,ui.input_numeric("n_age", "Maximum Age", value=50))),
        ui.row(
            ui.column(4,ui.input_select("stat_x", "X-Axis",batter_dict_stat_small,selected='k_percent',width=1,size=1)),
            ui.column(4,ui.input_select("stat_y", "Y-Axis",batter_dict_stat_small,selected='bb_percent',width=1,size=1)),
            ui.column(4,ui.input_select("stat_z", "Colour-Bar Axis",batter_dict_stat_small,selected='bb_over_k_percent',width=1,size=1))),
       
        ui.row(
            ui.column(6,ui.input_numeric("n_percent_top_x", "Top 'n' Percentile X-Labels", value=0.01)),
            ui.column(6,ui.input_numeric("n_percent_bot_x", "Bottom 'n' Percentile X-Labels", value=0.01))),
        ui.row(
            ui.column(6,ui.input_numeric("n_percent_top_y", "Top 'n' Percentile Y-Labels", value=0.01)),
            ui.column(6,ui.input_numeric("n_percent_bot_y", "Bottom 'n' Percentile Y-Labels", value=0.01))),
        ui.row(
            ui.column(6,ui.input_numeric("n_percent_top_z", "Top 'n' Percentile Z-Labels", value=0.01)),
            ui.column(6,ui.input_numeric("n_percent_bot_z", "Bottom 'n' Percentile Z-Labels", value=0.01))),

        ui.input_select("player_id", "Label Player",batter_dict,width=1,size=1,multiple=True,selectize=True),
        ui.row(
            ui.input_switch("names", "Toggle Names"),
            ui.input_switch("group_level", "Group Levels")),
     ),

      ui.panel_main(
        ui.output_plot("plot",height = "1000px",width="1000px")
      ,
    ),
    )),)),server)