Update app.py

app.py

@@ -1,45 +1,431 @@
-### Thomas Nestico
-### @TJStats
-#Import modules
-from starlette.applications import Starlette
-from starlette.routing import Mount
-from starlette.staticfiles import StaticFiles
-from shiny import App, ui
+##### 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 pages
-from home import home
+### 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)
 
-from spray_new import spray
-from decision_value import decision_value
-from damage import damage
-from batter_scatter import batter_scatter
-#from ev_angle import ev_angle
-from rolling_batter import rolling_batter
-from statcast_compare import statcast_compare
+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']
 
-from rolling_pitcher import rolling_pitcher
-from pitching_summary_graphic_new_fg_api import pitching_summary_graphic_new
-from pitcher_scatter import pitcher_scatter
+### 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'),
+    ]
 
-# Create app
-routes = [
-    Mount('/home', app=home),
+choices_woba = [0.698,
+                    0.728,
+                    0.887,
+                    1.253,
+                    1.583,
+                    2.027]
 
-    Mount('/spray',app=spray),
-    Mount('/decision_value',app=decision_value),
-    Mount('/damage_model',app=damage),
-    Mount('/batter_scatter',app=batter_scatter),
-    #Mount('/ev_angle',app=ev_angle),
-    Mount('/rolling_batter',app=rolling_batter),
-    Mount('/statcast_compare',app=statcast_compare),
+df_2023['woba'] = np.select(conditions_woba, choices_woba, default=0)
 
-    Mount('/rolling_pitcher',app=rolling_pitcher),
-    Mount('/pitching_summary_graphic_new',app=pitching_summary_graphic_new),
-    Mount('/pitcher_scatter',app=pitcher_scatter),
-]
 
-#Run App
-app = Starlette(routes=routes)
+
+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()
+
+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.a(
+                "MLB/MiLB Cards",
+                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)