##### games.,py #####

# Import modules
from shiny import *
import shinyswatch
#import plotly.express as px
from shinywidgets import output_widget, render_widget
import pandas as pd
from configure import base_url
import math
import datetime
import datasets
from datasets import load_dataset
import numpy as np
import matplotlib
from matplotlib.ticker import MaxNLocator
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde

### Import Datasets
dataset = load_dataset('nesticot/mlb_data', data_files=['mlb_pitch_data_2023.csv',
                                                        'mlb_pitch_data_2022.csv'])
dataset_train = dataset['train']
df_2023 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True)
# Paths to data
### Normalize Hit Locations
df_2023['hit_x'] = df_2023['hit_x'] - 126#df_2023['hit_x'].median()
df_2023['hit_y'] = -df_2023['hit_y']+204.5#df_2023['hit_y'].quantile(0.9999)

df_2023['hit_x_og'] = df_2023['hit_x']
df_2023.loc[df_2023['batter_hand'] == 'R','hit_x'] = -1*df_2023.loc[df_2023['batter_hand'] == 'R','hit_x']

### Calculate Horizontal Launch Angles
df_2023['h_la'] = np.arctan(df_2023['hit_x'] / df_2023['hit_y'])*180/np.pi
conditions_ss = [
    (df_2023['h_la']<-16+5/6),
    (df_2023['h_la']<16+5/6)&(df_2023['h_la']>=-16+5/6),
    (df_2023['h_la']>=16+5/6)
]

choices_ss = ['Oppo','Straight','Pull']
df_2023['traj'] = np.select(conditions_ss, choices_ss, default=np.nan)
df_2023['bip'] = [1 if x > 0  else np.nan for x in df_2023['launch_speed']]

conditions_woba = [
        (df_2023['event_type']=='walk'),
        (df_2023['event_type']=='hit_by_pitch'),
        (df_2023['event_type']=='single'),
        (df_2023['event_type']=='double'),
        (df_2023['event_type']=='triple'),
        (df_2023['event_type']=='home_run'),
    ]

choices_woba = [0.698,
                    0.728,
                    0.887,
                    1.253,
                    1.583,
                    2.027]

df_2023['woba'] = np.select(conditions_woba, choices_woba, default=0)



df_2023_bip = df_2023[~df_2023['bip'].isnull()].dropna(subset=['h_la','launch_angle'])
df_2023_bip['h_la'] = df_2023_bip['h_la'].round(0)


df_2023_bip['season'] = df_2023_bip['game_date'].str[0:4].astype(int)

df_2023_bip = df_2023_bip[df_2023_bip['season'] == 2023]
df_2022_bip = df_2023_bip[df_2023_bip['season'] == 2022]

batter_dict = df_2023_bip.sort_values('batter_name').set_index('batter_id')['batter_name'].to_dict()





def server(input,output,session):
    @output
    @render.plot(alt="plot")
    @reactive.event(input.go, ignore_none=False)
    def plot():

        batter_id_select = int(input.batter_id())
        df_batter_2023 = df_2023_bip.loc[(df_2023_bip['batter_id'] == batter_id_select)&(df_2023_bip['season']==2023)]
        df_batter_2022 = df_2023_bip.loc[(df_2023_bip['batter_id'] == batter_id_select)&(df_2023_bip['season']==2022)]

        df_non_batter_2023 = df_2023_bip.loc[(df_2023_bip['batter_id'] != batter_id_select)&(df_2023_bip['season']==2023)]
        df_non_batter_2022 = df_2023_bip.loc[(df_2023_bip['batter_id'] != batter_id_select)&(df_2023_bip['season']==2022)]

        traj_df = df_batter_2023.groupby(['traj'])['launch_speed'].count() / len(df_batter_2023)
        trajectory_df = df_batter_2023.groupby(['trajectory'])['launch_speed'].count() / len(df_batter_2023)#.loc['Oppo']




        colour_palette = ['#FFB000','#648FFF','#785EF0',
                        '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
        
        fig = plt.figure(figsize=(10, 10))



        # Create a 2x2 grid of subplots using GridSpec
        gs = GridSpec(3, 3, width_ratios=[0.1,0.8,0.1], height_ratios=[0.1,0.8,0.1])

        # ax00 = fig.add_subplot(gs[0, 0]) 
        ax01 = fig.add_subplot(gs[0, :])  # Subplot at the top-right position
        # ax02 = fig.add_subplot(gs[0, 2])
        # Subplot spanning the entire bottom row
        ax10 = fig.add_subplot(gs[1, 0]) 
        ax11 = fig.add_subplot(gs[1, 1])  # Subplot at the top-right position
        ax12 = fig.add_subplot(gs[1, 2])
        # ax20 = fig.add_subplot(gs[2, 0]) 
        ax21 = fig.add_subplot(gs[2, :])  # Subplot at the top-right position
        # ax22 = fig.add_subplot(gs[2, 2])

        initial_position = ax12.get_position()

        # Change the size of the axis
        # new_width = 0.06  # Set your desired width
        # new_height = 0.4  # Set your desired height
        # new_position = [initial_position.x0-0.01, initial_position.y0+0.065, new_width, new_height]
        # ax12.set_position(new_position)

        cmap_hue = matplotlib.colors.LinearSegmentedColormap.from_list("", [colour_palette[1],'#ffffff',colour_palette[0]])
        # Generate two sets of two-dimensional data
        # data1 = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 1]], 1000)
        # data2 = np.random.multivariate_normal([3, 3], [[1, -0.5], [-0.5, 1]], 1000)
        bat_hand = df_batter_2023.groupby('batter_hand')['launch_speed'].count().sort_values(ascending=False).index[0]

        bat_hand_value = 1

        if bat_hand == 'R':
            bat_hand_value = -1

        kde1_df = df_batter_2023[['h_la','launch_angle']]
        kde1_df['h_la'] = kde1_df['h_la'] * bat_hand_value
        kde2_df = df_non_batter_2023[['h_la','launch_angle']].sample(n=50000, random_state=42)
        kde2_df['h_la'] = kde2_df['h_la'] * bat_hand_value


        # Calculate 2D KDE for each dataset
        kde1 = gaussian_kde(kde1_df.values.T)
        kde2 = gaussian_kde(kde2_df.values.T)

        # Generate a grid of points for evaluation
        x, y = np.meshgrid(np.arange(-45, 46,1 ), np.arange(-30, 61,1 ))
        positions = np.vstack([x.ravel(), y.ravel()])

        # Evaluate the KDEs on the grid
        kde1_values = np.reshape(kde1(positions).T, x.shape)
        kde2_values = np.reshape(kde2(positions).T, x.shape)

        # Subtract one KDE from the other
        result_kde_values = kde1_values - kde2_values

        # Normalize the array to the range [0, 1]
        # result_kde_values = (result_kde_values - np.min(result_kde_values)) / (np.max(result_kde_values) - np.min(result_kde_values))
        result_kde_values = (result_kde_values - np.mean(result_kde_values)) / (np.std(result_kde_values))

        result_kde_values = np.clip(result_kde_values, -3, 3)
        # # Plot the original KDEs
        # plt.contourf(x, y, kde1_values, cmap='Blues', alpha=0.5, levels=20)
        # plt.contourf(x, y, kde2_values, cmap='Reds', alpha=0.5, levels=20)

        # Plot the subtracted KDE
        # Set the number of levels and midrange value
        # Set the number of levels and midrange value
        num_levels = 14
        midrange_value = 0

        # Create a filled contour plot with specified levels
        levels = np.linspace(-3, 3, num_levels)

        batter_plot = ax11.contourf(x, y, result_kde_values, cmap=cmap_hue, levels=levels, vmin=-3, vmax=3)


        ax11.hlines(y=10,xmin=45,xmax=-45,color=colour_palette[3],linewidth=1)
        ax11.hlines(y=25,xmin=45,xmax=-45,color=colour_palette[3],linewidth=1)
        ax11.hlines(y=50,xmin=45,xmax=-45,color=colour_palette[3],linewidth=1)

        ax11.vlines(x=-15,ymin=-30,ymax=60,color=colour_palette[3],linewidth=1)
        ax11.vlines(x=15,ymin=-30,ymax=60,color=colour_palette[3],linewidth=1)
        #ax11.axis('square')
        #ax11.axis('off')
        #ax.hlines(y=10,xmin=-45,xmax=-45)
        # Add labels and legend
        #plt.xlabel('X-axis')
        #plt.ylabel('Y-axis')
        #ax.plot('equal')
        #plt.gca().set_aspect('equal')

        #Choose a mappable (can be any plot or image)
        ax12.set_ylim(0,1)
        cbar = plt.colorbar(batter_plot, cax=ax12, orientation='vertical',shrink=1)
        cbar.set_ticks([])
        # Set the colorbar to have 13 levels
        cbar_locator = MaxNLocator(nbins=13)
        cbar.locator = cbar_locator
        cbar.update_ticks()
        #cbar.set_clim(vmin=-3, vmax=)
        # Set ticks and tick labels
        # cbar.set_ticks(np.linspace(-3, 3, 13))
        # cbar.set_ticklabels(np.linspace(0, 3, 13))
        cbar.set_ticks([])




        ax10.text(s=f"Pop Up\n({trajectory_df.loc['popup']:.1%})",
                x=1,
                y=0.95,va='center',ha='right',fontsize=16)
        # Choose a mappable (can be any plot or image)
        ax10.text(s=f"Fly Ball\n({trajectory_df.loc['fly_ball']:.1%})",
                x=1,
                y=0.75,va='center',ha='right',fontsize=16)

        ax10.text(s=f"Line\nDrive\n({trajectory_df.loc['line_drive']:.1%})",
                x=1,
                y=0.53,va='center',ha='right',fontsize=16)


        ax10.text(s=f"Ground\nBall\n({trajectory_df.loc['ground_ball']:.1%})",
                x=1,
                y=0.23,va='center',ha='right',fontsize=16)
        #ax12.axis(True)
        # Set equal aspect ratio for the contour plot

        if bat_hand == 'R':


            ax21.text(s=f"Pull\n({traj_df.loc['Pull']:.1%})",
                    x=0.2+1/16*0.8,
                    y=1,va='top',ha='center',fontsize=16)

            ax21.text(s=f"Straight\n({traj_df.loc['Straight']:.1%})",
                    x=0.5,
                    y=1,va='top',ha='center',fontsize=16)

            ax21.text(s=f"Oppo\n({traj_df.loc['Oppo']:.1%})",
                    x=0.8-1/16*0.8,
                    y=1,va='top',ha='center',fontsize=16)

        else:

            ax21.text(s=f"Pull\n({traj_df.loc['Pull']:.1%})",
                    x=0.8-1/16*0.8,
                    y=1,va='top',ha='center',fontsize=16)

            ax21.text(s=f"Straight\n({traj_df.loc['Straight']:.1%})",
                    x=0.5,
                    y=1,va='top',ha='center',fontsize=16)

            ax21.text(s=f"Oppo\n({traj_df.loc['Oppo']:.1%})",
                    x=0.2+1/16*0.8,
                    y=1,va='top',ha='center',fontsize=16)
            
        # Define the initial position of the axis

        # Customize colorbar properties
        # cbar = fig.colorbar(orientation='vertical', pad=0.1,ax=ax12)
        #cbar.set_label('Difference', rotation=270, labelpad=15)
        # Show the plot
        # ax21.text(0.0, 0., "By: Thomas Nestico\n      @TJStats",ha='left', va='bottom',fontsize=12)
        # ax21.text(1, 0., "Data: MLB",ha='right', va='bottom',fontsize=12)
        # ax21.text(0.5, 0., "Inspired by @blandalytics",ha='center', va='bottom',fontsize=12)

        # ax00.axis('off')
        ax01.axis('off')
        # ax02.axis('off')
        ax10.axis('off')
        #ax11.axis('off')
        #ax12.axis('off')
        # ax20.axis('off')
        ax21.axis('off')
        # ax22.axis('off')

        ax21.text(0.0, 0., "By: Thomas Nestico\n      @TJStats",ha='left', va='bottom',fontsize=12)
        ax21.text(0.98, 0., "Data: MLB",ha='right', va='bottom',fontsize=12)
        ax21.text(0.5, 0., "Inspired by @blandalytics",ha='center', va='bottom',fontsize=12)


        ax11.set_xticks([])
        ax11.set_yticks([])

        # ax12.text(s='Same',x=np.mean([x for x in ax12.get_xlim()]),y=np.median([x for x in ax12.get_ylim()]),
        #           va='center',ha='center',fontsize=12)

        # ax12.text(s='More\nOften',x=0.5,y=0.74,
        #         va='top',ha='center',fontsize=12)

        ax12.text(s='+3σ',x=0.5,y=3-1/14*3,
                va='center',ha='center',fontsize=12)

        ax12.text(s='+2σ',x=0.5,y=2-1/14*2,
                va='center',ha='center',fontsize=12)

        ax12.text(s='+1σ',x=0.5,y=1-1/14*1,
                va='center',ha='center',fontsize=12)


        ax12.text(s='±0σ',x=0.5,y=0,
                va='center',ha='center',fontsize=12)

        ax12.text(s='-1σ',x=0.5,y=-1-1/14*-1,
                va='center',ha='center',fontsize=12)

        ax12.text(s='-2σ',x=0.5,y=-2-1/14*-2,
                va='center',ha='center',fontsize=12)

        ax12.text(s='-3σ',x=0.5,y=-3-1/14*-3,
                va='center',ha='center',fontsize=12)

        # # ax12.text(s='Less\nOften',x=0.5,y=0.26,
        # #         va='bottom',ha='center',fontsize=12)

        ax01.text(s=f"{df_batter_2023['batter_name'].values[0]}'s 2023 Batted Ball Tendencies",
                x=0.5,
                y=0.8,va='top',ha='center',fontsize=20)

        ax01.text(s=f"(Compared to rest of MLB)",
                x=0.5,
                y=0.3,va='top',ha='center',fontsize=16)
        
        #plt.show()

spray = 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("batter_id",
                                "Select Batter",
                                 batter_dict,
                                 width=1,
                                 size=1,
                                 selectize=True),
                ui.input_action_button("go", "Generate",class_="btn-primary",
                                       )),

   ui.panel_main(     
        ui.navset_tab(

            ui.nav("2023 vs MLB",
                   ui.output_plot('plot',
                                  width='1000px',
                                  height='1000px')),
        ))
    )),)),server)