ev_angle.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
from matplotlib.pyplot import text


### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2023.csv' ])
dataset_train = dataset['train']
exit_velo_df = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)

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


#exit_velo_df = pd.read_csv('exit_velo_df.csv',index_col=[0])

conditions = [
    (exit_velo_df['launch_speed'].isna()),
    (exit_velo_df['launch_speed']*1.5 - exit_velo_df['launch_angle'] >= 117 ) & (exit_velo_df['launch_speed'] + exit_velo_df['launch_angle'] >= 124) & (exit_velo_df['launch_speed'] > 98) & (exit_velo_df['launch_angle'] >= 8) & (exit_velo_df['launch_angle'] <= 50)
]

choices = [False,True]
exit_velo_df['barrel'] = np.select(conditions, choices, default=np.nan)

test_df = exit_velo_df.sort_values(by='batter_name').drop_duplicates(subset='batter_id').reset_index(drop=True)[['batter_id','batter_name']]#['pitcher'].to_dict()
test_df = test_df.set_index('batter_id')

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

batter_dict = test_df['batter_name'].to_dict()

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

angle_ev_list_df = pd.read_csv('angle_ev_list_df.csv')
ev_ranges = list(np.arange(97.5,130,0.1))
angle_ranges = list(range(8,51))
#print


def server(input,output,session):
    @output
    @render.plot(alt="A histogram")
    @reactive.event(input.go, ignore_none=False)
    def plot():
        data_df = exit_velo_df[exit_velo_df.batter_id==int(input.id())]
        #pitch_list = exit_velo_df_small.pitch_type.unique()
        sns.set_theme(style="whitegrid", palette="pastel")
        fig, ax = plt.subplots(1, 1, figsize=(10, 10))
        


        if input.plot_id() == 'dist':
            sns.histplot(x=data_df.launch_angle,y=data_df.launch_speed,cbar=colour_palette,binwidth=(5,2.5),ax=ax,cbar_kws=dict(shrink=.75,label='Count'),binrange=(
            (math.floor((min(data_df.launch_angle.dropna())/5))*5,math.ceil((max(data_df.launch_angle.dropna())/5))*5),(math.floor((min(data_df.launch_speed.dropna())/5))*5,math.ceil((max(data_df.launch_speed.dropna())/5))*5)))
        if input.plot_id() == 'scatter':
            sns.scatterplot(x=data_df.launch_angle,y=data_df.launch_speed,color=colour_palette[1])
        ax.set_xlim(math.floor((min(data_df.launch_angle.dropna())/10))*10,math.ceil((max(data_df.launch_angle.dropna())/10))*10)
        #ticks=np.arange(revels.values.min(),revels.values.max()+1 )
        sns.lineplot(x=angle_ev_list_df.launch_angle,y=angle_ev_list_df.launch_speed,color=colour_palette[0])
        ax.vlines(x=angle_ev_list_df.launch_angle[0],ymin=angle_ev_list_df.launch_speed[0],ymax=ev_ranges[-1],color=colour_palette[0])
        ax.vlines(x=angle_ev_list_df.launch_angle[len(angle_ev_list_df)-1],ymin=angle_ev_list_df.launch_speed[len(angle_ev_list_df)-1],ymax=ev_ranges[-1],color=colour_palette[0])

        groundball = f'{sum(data_df.launch_angle.dropna()<=10)/len(data_df.launch_angle.dropna()):.1%}'
        linedrive = f'{sum((data_df.launch_angle.dropna()<=25) & (data_df.launch_angle.dropna()>10))/len(data_df.launch_angle.dropna()):.1%}'
        flyball = f'{sum((data_df.launch_angle.dropna()<=50) & (data_df.launch_angle.dropna()>25))/len(data_df.launch_angle.dropna()):.1%}'
        popup = f'{sum(data_df.launch_angle.dropna()>50)/len(data_df.launch_angle.dropna()):.1%}'
        percentages_list = [groundball,linedrive,flyball,popup]

        hard_hit_percent = f'{sum(data_df.launch_speed.dropna()>=95)/len(data_df.launch_speed.dropna()):.1%}'

        barrel_percentage = f'{data_df.barrel.dropna().sum()/len(data_df.launch_angle.dropna()):.1%}'

        plt.text(x=27, y=math.ceil((max(data_df.launch_speed.dropna())/5))*5+5-3, s=f'Barrel% {barrel_percentage}',ha='left',bbox=dict(facecolor='white',alpha=0.8, edgecolor=colour_palette[4], pad=5))
        

        sample_dates = np.array([math.floor((min(data_df.launch_angle.dropna())/10))*10,10,25,50])
        sample_text = [f'Groundball ({groundball})',f'Line Drive ({linedrive})',f'Fly Ball ({flyball})',f'Pop-up ({popup})']

        hard_hit_dates = [95]
        hard_hit_text = [f'Hard Hit% ({hard_hit_percent})']



        #sample_dates = mdates.date2num(sample_dates)
        plt.hlines(y=hard_hit_dates,xmin=math.floor((min(data_df.launch_angle.dropna())/10))*10, xmax=math.ceil((max(data_df.launch_angle.dropna())/10))*10, color = colour_palette[4],linestyles='--')
        plt.vlines(x=sample_dates, ymin=0, ymax=130, color = colour_palette[3],linestyles='--')


        # ax.vlines(x=10,ymin=0,ymax=ev_ranges[-1],color=colour_palette[3],linestyles='--')
        # ax.vlines(x=25,ymin=0,ymax=ev_ranges[-1],color=colour_palette[3],linestyles='--')
        # ax.vlines(x=50,ymin=0,ymax=ev_ranges[-1],color=colour_palette[3],linestyles='--')



        for i, x in enumerate(hard_hit_dates):
            text(math.ceil((max(data_df.launch_angle.dropna())/10))*10-2.5, x+1.25,hard_hit_text[i], rotation=0, ha='right',
                bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[4], pad=5))


        for i, x in enumerate(sample_dates):
            text(x+0.75, (math.floor((min(data_df.launch_speed.dropna())/5))*5)+1,sample_text[i], rotation=90, verticalalignment='bottom',
                bbox=dict(facecolor='white',alpha=0.5, edgecolor=colour_palette[3], pad=5))
        #ax.vlines(x=math.floor((min(data_df.launch_angle.dropna())/10))*10+1,ymin=0,ymax=ev_ranges[-1],color=colour_palette[3],linestyles='--')

        ax.set_xlim((math.floor((min(data_df.launch_angle.dropna())/10))*10,math.ceil((max(data_df.launch_angle.dropna())/10))*10))
        ax.set_ylim((math.floor((min(data_df.launch_speed.dropna())/5))*5,math.ceil((max(data_df.launch_speed.dropna())/5))*5+5))
        # ax.set_xlim(-90,90)
        # ax.set_ylim(0,125)
        ax.set_title(f'MLB - {data_df.batter_name.unique()[0]} Launch Angle vs EV Plot', fontsize=18,fontname='Century Gothic',)
        #vals = ax.get_yticks()
        ax.set_xlabel('Launch Angle', fontsize=16,fontname='Century Gothic')
        ax.set_ylabel('Exit Velocity', fontsize=16,fontname='Century Gothic')
        ax.fill_between(angle_ev_list_df.launch_angle, 130, angle_ev_list_df.launch_speed, interpolate=True, color=colour_palette[3],alpha=0.1,label='Barrel')
        #fig.colorbar(plot_dist, ax=ax)
        #fig.colorbar(plot_dist)
        #fig.axes[0].invert_yaxis()
        ax.legend(fontsize='16',loc='upper left')
        fig.text(x=0.03,y=0.02,s='By: @TJStats')
        fig.text(x=1-0.03,y=0.02,s='Data: MLB',ha='right')

        # fig.text(x=0.25,y=0.02,s='Data: MLB',ha='right')
        # fig.text(x=0.25,y=0.02,s='Data: MLB',ha='right')
        # fig.text(x=0.25,y=0.02,s='Data: MLB',ha='right')
        #cbar = plt.colorbar()
        #fig.subplots_adjust(wspace=.02, hspace=.02)
        #ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
        fig.set_facecolor('white')
        fig.tight_layout()
        #matplotlib.rcParams["figure.dpi"] = 300

            # ax.set_xlim(input.n(),exit_velo_df_small.pitch.max())
            #ax.axis('off')
        
        #fig.set_facecolor('white')
        #fig.tight_layout()
        #ax.hist(exit_velo_df[exit_velo_df.pitcher_id==int(input.id())]['pitch_velocity'],input.n(),density=True)
        #plt.show()
        #return g 

# This is a shiny.App object. It must be named `app`.

# fig, ax = plt.subplots()
#print(input.pitcher_id())
# print(input)
# plt.hist(x=exit_velo_df[exit_velo_df.pitcher_id==input.x()]['pitch_velocity'])
# plt.show()


ev_angle = 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 & Damage",
                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 Batter",batter_dict,width=1),
        ui.input_select("plot_id", "Select Plot",{'scatter':'Scatter Plot','dist':'Distribution Plot'},width=1),
        
                ui.input_action_button("go", "Generate",class_="btn-primary",
                                       )),
      

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