Upload 9 files

app.py

@@ -1,857 +1,676 @@
-import polars as pl
-import numpy as np
-import pandas as pd
-import api_scraper
-scrape = api_scraper.MLB_Scrape()
-from functions import df_update
-from functions import pitch_summary_functions
-update = df_update.df_update()
-from stuff_model import feature_engineering as fe
-from stuff_model import stuff_apply
-import requests
-import joblib
-from matplotlib.gridspec import GridSpec
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import seaborn as sns
-from functions.pitch_summary_functions import *
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-colour_palette = ['#FFB000','#648FFF','#785EF0',
-                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
-
-
-year_list = [2017,2018,2019,2020,2021,2022,2023,2024,2025]
-
-
-
-level_dict =  {'1':'MLB',
-               '11':'AAA',
-              # '12':'AA',
-               #'13':'A+',
-               '14':'A',
-               '17':'AFL',
-               '22':'College',
-               '21':'Prospects',
-               '51':'International' }
-
-function_dict={
-    'velocity_kdes':'Velocity Distributions',
-    'break_plot':'Pitch Movement',
-    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
-    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
-    'location_plot_lhb':'Locations vs LHB',
-    'location_plot_rhb':'Locations vs RHB',
-}
-
-
-split_dict =  {'all':'All',
-               'left':'LHH',
-               'right':'RHH'}
-
-split_dict_hand =  {'all':['L','R'],
-               'left':['L'],
-               'right':['R']}
-
-
-type_dict =  {'R':'Regular Season',
-               'S':'Spring',
-               'P':'Playoffs' }
-
-
-
-# List of MLB teams and their corresponding ESPN logo URLs
-mlb_teams = [
-    {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
-    {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
-    {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
-    {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
-    {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
-    {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
-    {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
-    {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
-    {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
-    {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
-    {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
-    {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
-    {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
-    {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
-    {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
-    {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
-    {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
-    {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
-    {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
-    {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
-    {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
-    {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
-    {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
-    {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
-    {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
-    {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
-    {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
-    {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
-    {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
-    {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
-    {"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"}   
-]
-
-
-df_image = pd.DataFrame(mlb_teams)
-image_dict = df_image.set_index('team')['logo_url'].to_dict()
-image_dict_flip = df_image.set_index('logo_url')['team'].to_dict()
-
-
-
-import requests
-
-import os
-CAMPAIGN_ID = os.getenv("CAMPAIGN_ID")
-ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
-BACKUP_PW = os.getenv("BACKUP_PW")
-ADMIN_PW = os.getenv("ADMIN_PW")
-
-url = f"https://www.patreon.com/api/oauth2/v2/campaigns/{CAMPAIGN_ID}/members"
-
-headers = {
-    "Authorization": f"Bearer {ACCESS_TOKEN}"
-}
-
-# Simple parameters, requesting the member's email and currently entitled tiers
-params = {
-    "fields[member]": "full_name,email",  # Request the member's email
-    "include": "currently_entitled_tiers",  # Include the currently entitled tiers
-    "page[size]": 1000  # Fetch up to 1000 patrons per request
-}
-
-response = requests.get(url, headers=headers, params=params)
-
-
-VALID_PASSWORDS = []
-if response.status_code == 200:
-    data = response.json()
-    for patron in data['data']:
-        try:
-            tiers = patron['relationships']['currently_entitled_tiers']['data']
-            if any(tier['id'] == '9078921' for tier in tiers):
-                full_name = patron['attributes']['email']
-                VALID_PASSWORDS.append(full_name)
-        except KeyError:
-            continue
-VALID_PASSWORDS.append(BACKUP_PW)
-VALID_PASSWORDS.append(ADMIN_PW)
-
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-from datetime import datetime
-
-def is_valid_date(date_str):
-    try:
-        datetime.strptime(date_str, "%Y-%m-%d")  # Attempt to parse the date
-        return True
-    except ValueError:
-        return False  # If parsing fails, it's not in the correct format
-
-# Define the login UI
-login_ui = ui.page_fluid(
-    ui.card(
-        ui.h2([
-            "TJStats Pitching Summary App ",
-            ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank")
-        ]),
-        ui.p(
-            "This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", 
-            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/116064432", target="_blank"),
-            "."
-        ),
-        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="50%"),
-        ui.tags.input(
-            type="checkbox",
-            id="authenticated",
-            value=False,
-            disabled=True
-        ),
-        ui.input_action_button("login", "Login", class_="btn-primary"),
-        ui.output_text("login_message"),
-    )
-)
-
-
-# Define the UI layout for the app
-main_ui = ui.page_sidebar(
-    ui.sidebar(
-        # Row for selecting season and level
-        ui.row(
-            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2025)),
-            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
-            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
-        ),
-        # Row for the action button to get player list
-        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
-        # Row for selecting the player
-        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
-        # Row for selecting the date range
-        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
-
-        # Rows for selecting plots and split options
-        ui.row(
-            ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='velocity_kdes')),
-            ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='tj_stuff_roling')),
-            ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='break_plot'))
-        ),
-        ui.row(
-            ui.column(6, ui.input_select('split_id', 'Select Split', split_dict, multiple=False)),
-            ui.column(6, ui.input_numeric('rolling_window', 'Rolling Window (for tjStuff+ Plot)', min=1, value=50))
-        ),
-        ui.row(
-            ui.column(6, ui.input_switch("switch", "Custom Team?", False)),
-            ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False))
-        ),
-        
-        # Row for the action button to generate plot
-        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
-        width="400px"  # Added this parameter to control sidebar width
-    ),
-            
-    # Main content area with tabs (placed directly in page_sidebar)
-    ui.navset_tab(
-        ui.nav_panel("Pitching Summary",
-               ui.output_text("status"),
-               ui.output_plot('plot', width='2100px', height='2100px')
-        ),
-        ui.nav_panel("Game Summary",
-               ui.output_text("status2"),
-               ui.output_plot('game_plot', width='2100px', height='2100px')
-        ),        
-            ui.nav_panel("Table Range",
-                                   ui.output_data_frame("grid")),
-            ui.nav_panel("Table Game",
-                                   ui.output_data_frame("grid_game")),
-        id="tabset"
-    )
-)
-
-# Combined UI with conditional panel
-app_ui = ui.page_fluid(
-    ui.tags.head(
-        ui.tags.script(src="script.js")
-    ),
-
-    ui.panel_conditional(
-        "!input.authenticated",
-        login_ui
-    ),
-    ui.panel_conditional(
-        "input.authenticated",
-        main_ui
-    )
-)
-
-
-def server(input, output, session):
-
-    @reactive.Effect
-    @reactive.event(input.login)
-    def check_password():
-        if input.password() in VALID_PASSWORDS:
-            ui.update_checkbox("authenticated", value=True)
-            ui.update_text("login_message", value="")
-        else:
-            ui.update_text("login_message", value="Invalid password!")
-            ui.update_text("password", value="")
-
-    @output
-    @render.text
-    def login_message():
-        return ""
-    
-    @reactive.calc
-    @reactive.event(input.pitcher_id, input.date_id,input.split_id)
-    def cached_data():
-
-        year_input = int(input.year_input())
-        sport_id = int(input.level_input())
-        player_input = int(input.pitcher_id())
-        
-
-        start_date = str(input.date_id()[0])
-        end_date = str(input.date_id()[1])
-        game_list  = scrape.get_player_games_list(sport_id = sport_id,
-                                                season = year_input, 
-                                                player_id = player_input,
-                                                start_date = start_date,
-                                                end_date = end_date,
-                                                game_type = [input.type_input()])
-        
-        # if input.tabset() == 'Game Summary':
-        #     print(year_input, sport_id, player_input, 'yup')
-        #     print(input.date_id())
-        #     game_list = [input.date_id()]
-            
-
-        data_list = scrape.get_data(game_list_input = game_list[:])
-
-        try:
-            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                                (pl.col("pitcher_id") == player_input)&
-                                (pl.col("is_pitch") == True)&
-                                (pl.col("start_speed") >= 50)&
-                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-    
-                                )))).with_columns(
-                    pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-                ))
-            return df
-
-            
-        except TypeError:
-            print("NONE")
-            return None
-
-    @reactive.calc
-    @reactive.event(input.pitcher_id, input.date_id,input.split_id,input.tabset)
-    def cached_data_daily():
-
-        year_input = int(input.year_input())
-        sport_id = int(input.level_input())
-        player_input = int(input.pitcher_id())
-        
-
-        # start_date = str(input.date_id()[0])
-        # end_date = str(input.date_id()[1])
-        game_list  = [int(input.date_id())]
-        print(game_list)
-        
-        # if input.tabset() == 'Game Summary':
-        #     print(year_input, sport_id, player_input, 'yup')
-        #     print(input.date_id())
-        #     game_list = 
-            
-
-        data_list = scrape.get_data(game_list_input = game_list[:])
-
-        try:
-            df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
-                                (pl.col("pitcher_id") == player_input)&
-                                (pl.col("is_pitch") == True)&
-                                (pl.col("start_speed") >= 50)&
-                                (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
-    
-                                )))).with_columns(
-                    pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
-                ))
-            return df
-
-            
-        except TypeError:
-            print("NONE")
-            return None
-                
-
-    @render.ui
-    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,input.tabset,ignore_none=False)
-    def player_select_ui():
-        # Get the list of pitchers for the selected level and season
-        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
-            (pl.col("position").is_in(['P','TWP']))|
-            (pl.col("player_id").is_in([686846]))
-        
-        ).sort("name")
-        
-        # Create a dictionary of pitcher IDs and names
-        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
-
-
-        
-        
-        # Return a select input for choosing a pitcher
-        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
-
-    @render.ui
-    @reactive.event(input.player_button, input.pitcher_id,input.year_input, input.level_input, input.type_input,input.tabset,ignore_none=False)
-    def date_id():
-        if input.tabset() == 'Pitching Summary' or input.tabset() == 'Table Range':
-            # Create a date range input for selecting the date range within the selected year
-            return ui.input_date_range("date_id", "Select Date Range",
-                                    start=f"{int(input.year_input())}-01-01",
-                                    end=f"{int(input.year_input())}-12-31",
-                                    min=f"{int(input.year_input())}-01-01",
-                                        max=f"{int(input.year_input())}-12-31")
-        
-        if input.tabset() == 'Game Summary' or input.tabset() == 'Table Game':
-            year_input = int(input.year_input())
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-            print('game summary')
-            # start_date = str(input.date_id()[0])
-            # end_date = str(input.date_id()[1])
-
-            game_list = scrape.get_player_games_list(player_id = player_input, 
-                              season = year_input, 
-                              sport_id=sport_id, 
-                              game_type=[input.type_input()],
-                              pitching = True)
-            
-            schedule_df = scrape.get_schedule(year_input=[year_input],
-                    sport_id= [sport_id],
-                    game_type = [input.type_input()])
-            
-            player_schedule_df = schedule_df.filter(pl.col('game_id').is_in(game_list)).to_pandas().sort_values('date')
-
-            player_schedule_df['def'] = player_schedule_df['date'].astype(str) + ' - ' + player_schedule_df['away'] + ' @ ' + player_schedule_df['home'] + ' ' 
-
-
-            game_dict = dict(zip(player_schedule_df['game_id'], player_schedule_df['def']))
-            # print(game_dict)
-
-            return ui.input_select("date_id", "Select Game", game_dict)
-
-    @output
-    @render.text
-    def status():
-        # Only show status when generating
-        if input.generate == 0:
-            return ""
-        return ""   
-
-
-    
-
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def plot():     
-        # Show progress/loading notification
-
-       
-                       
-        with ui.Progress(min=0, max=1) as p:
-            p.set(message="Generating plot", detail="This may take a while...")
-
-
-            p.set(0.3, "Gathering data...")
-            year_input = int(input.year_input())
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-            start_date = str(input.date_id()[0])
-            end_date = str(input.date_id()[1])
-            if not is_valid_date(start_date):
-                    fig = plt.figure(figsize=(26,26))
-                    fig.text(x=0.1,y=0.9,s='Select Date Range and Generate Plot',fontsize=36,ha='left')
-                    return fig         
-            print(year_input, sport_id, player_input, start_date, end_date)
-
-            df = cached_data()
-
-            if df is None:
-                fig = plt.figure(figsize=(26,26))
-                fig.text(x=0.1,y=0.9,s='No Statcast Data For This Pitcher',fontsize=36,ha='left')
-                return fig
-            
-            df = df.clone()
-            
-
-            p.set(0.6, "Creating plot...")
-
-  
-            #plt.rcParams["figure.figsize"] = [10,10]
-            fig = plt.figure(figsize=(26,26))
-            plt.rcParams.update({'figure.autolayout': True})
-            fig.set_facecolor('white')
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            print('this is the one plot')
-
-            gs = gridspec.GridSpec(6, 8,
-                                height_ratios=[6,20,12,36,36,6],
-                                width_ratios=[4,18,18,18,18,18,18,4])
-
-
-            gs.update(hspace=0.2, wspace=0.5)
-
-            # Define the positions of each subplot in the grid
-            ax_headshot = fig.add_subplot(gs[1,1:3])
-            ax_bio = fig.add_subplot(gs[1,3:5])
-            ax_logo = fig.add_subplot(gs[1,5:7])
-
-            ax_season_table = fig.add_subplot(gs[2,1:7])
-
-            ax_plot_1 = fig.add_subplot(gs[3,1:3])
-            ax_plot_2 = fig.add_subplot(gs[3,3:5])
-            ax_plot_3 = fig.add_subplot(gs[3,5:7])
-
-            ax_table = fig.add_subplot(gs[4,1:7])
-
-            ax_footer = fig.add_subplot(gs[-1,1:7])
-            ax_header = fig.add_subplot(gs[0,1:7])
-            ax_left = fig.add_subplot(gs[:,0])
-            ax_right = fig.add_subplot(gs[:,-1])
-
-            # Hide axes for footer, header, left, and right
-            ax_footer.axis('off')
-            ax_header.axis('off')
-            ax_left.axis('off')
-            ax_right.axis('off')
-
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            fig.set_facecolor('white')
-
-            df_teams = scrape.get_teams()
-
-            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
-            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
-
-            if input.switch():
-
-                # Get the logo URL from the image dictionary using the team abbreviation
-                logo_url = input.logo_select()
-        
-                # Send a GET request to the logo URL
-                response = requests.get(logo_url)
-        
-                # Open the image from the response content
-                img = Image.open(BytesIO(response.content))
-        
-                # Display the image on the axis
-                ax_logo.set_xlim(0, 1.3)
-                ax_logo.set_ylim(0, 1)
-                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
-        
-                # Turn off the axis
-                ax_logo.axis('off')
-            
-            else:
-                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
-
-            stat_summary_table(df=df,
-                            ax=ax_season_table,
-                            player_input=player_input,
-                            split=input.split_id(),
-                            sport_id=sport_id,
-                            game_type=[input.type_input()])
-
-            # break_plot(df=df_plot,ax=ax2) 
-            for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
-                if x == 'velocity_kdes':
-                            velocity_kdes(df,
-                            ax=y,
-                            gs=gs,
-                            gs_x=[3,4],
-                            gs_y=[z,z+2],
-                            fig=fig)
-                if x == 'tj_stuff_roling':
-                    tj_stuff_roling(df=df,
-                                window=int(input.rolling_window()),
-                                ax=y)
-                    
-                if x == 'tj_stuff_roling_game':
-                    tj_stuff_roling_game(df=df, 
-                                        window=int(input.rolling_window()),
-                                        ax=y)
-
-                if x == 'break_plot':
-                    break_plot(df = df,ax=y) 
-
-                if x == 'location_plot_lhb':
-                    location_plot(df = df,ax=y,hand='L')
-
-                if x == 'location_plot_rhb':
-                    location_plot(df = df,ax=y,hand='R')
-
-            summary_table(df=df,
-                        ax=ax_table)
-
-            plot_footer(ax_footer)   
-
-            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1) 
-            # Hide axes ticks and labels
-            ax_watermark.set_xticks([])
-            ax_watermark.set_yticks([])
-            ax_watermark.set_frame_on(False)  # Optional: Hide border
-            
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Display the image
-            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.1)
-
-
-            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1) 
-            ax_watermark2.set_xlim(0,1)
-            ax_watermark2.set_ylim(0,1)
-            # Hide axes ticks and labels
-            ax_watermark2.set_xticks([])
-            ax_watermark2.set_yticks([])
-            ax_watermark2.set_frame_on(False)  # Optional: Hide border
-            
-            # Open the image
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Get the original size
-            width, height = img.size
-            # Calculate the new size (50% larger)
-            new_width = int(width * 0.5)
-            new_height = int(height * 0.5)
-            # Resize the image
-            img_resized = img.resize((new_width, new_height))
-            # Display the image
-            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
-
-            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-
-
-
-    
-    @output
-    @render.plot
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def game_plot():     
-        # Show progress/loading notification
-        with ui.Progress(min=0, max=1) as p:
-            print(input.date_id(),'TEST')
-
-            if isinstance(input.date_id(), tuple):
-                fig = plt.figure(figsize=(26,26))
-                fig.text(x=0.1,y=0.9,s='Select Game and Generate Plot',fontsize=36,ha='left')
-                return fig                
-                
-
-            p.set(message="Generating plot", detail="This may take a while...")
-
-            
-            p.set(0.3, "Gathering data...")
-            year_input = int(input.year_input())
-            sport_id = int(input.level_input())
-            player_input = int(input.pitcher_id())
-
-            # print(input.game_id())
-            # print(year_input, sport_id, player_input)
-
-            # print(year_input, sport_id, player_input, start_date, end_date)
-
-            df = cached_data_daily()
-
-            # start_date = str(df['game_date'][0])
-            # end_date = str(df['game_date'][0])
-            
-            if df is None:
-                fig = plt.figure(figsize=(26,26))
-                fig.text(x=0.1,y=0.9,s='No Statcast Data For This Pitcher',fontsize=36,ha='left')
-                return fig
-            
-            df = df.clone()
-            
-
-            p.set(0.6, "Creating plot...")
-
-  
-            #plt.rcParams["figure.figsize"] = [10,10]
-            fig = plt.figure(figsize=(26,26))
-            plt.rcParams.update({'figure.autolayout': True})
-            fig.set_facecolor('white')
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            print('this is the one plot')
-
-            gs = gridspec.GridSpec(6, 8,
-                                height_ratios=[6,20,12,36,36,6],
-                                width_ratios=[4,18,18,18,18,18,18,4])
-
-
-            gs.update(hspace=0.2, wspace=0.5)
-
-            # Define the positions of each subplot in the grid
-            ax_headshot = fig.add_subplot(gs[1,1:3])
-            ax_bio = fig.add_subplot(gs[1,3:5])
-            ax_logo = fig.add_subplot(gs[1,5:7])
-
-            ax_season_table = fig.add_subplot(gs[2,1:7])
-
-            ax_plot_1 = fig.add_subplot(gs[3,1:3])
-            ax_plot_2 = fig.add_subplot(gs[3,3:5])
-            ax_plot_3 = fig.add_subplot(gs[3,5:7])
-
-            ax_table = fig.add_subplot(gs[4,1:7])
-
-            ax_footer = fig.add_subplot(gs[-1,1:7])
-            ax_header = fig.add_subplot(gs[0,1:7])
-            ax_left = fig.add_subplot(gs[:,0])
-            ax_right = fig.add_subplot(gs[:,-1])
-
-            # Hide axes for footer, header, left, and right
-            ax_footer.axis('off')
-            ax_header.axis('off')
-            ax_left.axis('off')
-            ax_right.axis('off')
-
-            sns.set_theme(style="whitegrid", palette=colour_palette)
-            fig.set_facecolor('white')
-
-            df_teams = scrape.get_teams()
-
-            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
-            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
-
-            if input.switch():
-
-                # Get the logo URL from the image dictionary using the team abbreviation
-                logo_url = input.logo_select()
-        
-                # Send a GET request to the logo URL
-                response = requests.get(logo_url)
-        
-                # Open the image from the response content
-                img = Image.open(BytesIO(response.content))
-        
-                # Display the image on the axis
-                ax_logo.set_xlim(0, 1.3)
-                ax_logo.set_ylim(0, 1)
-                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
-        
-                # Turn off the axis
-                ax_logo.axis('off')
-            
-            else:
-                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
-
-            stat_summary_table(df=df,
-                            ax=ax_season_table,
-                            player_input=player_input,
-                            split=input.split_id(),
-                            sport_id=sport_id,
-                            game_type=[input.type_input()])
-
-            # break_plot(df=df_plot,ax=ax2) 
-            for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
-                if x == 'velocity_kdes':
-                            velocity_kdes(df,
-                            ax=y,
-                            gs=gs,
-                            gs_x=[3,4],
-                            gs_y=[z,z+2],
-                            fig=fig)
-                if x == 'tj_stuff_roling':
-                    tj_stuff_roling(df=df,
-                                window=int(input.rolling_window()),
-                                ax=y)
-                    
-                if x == 'tj_stuff_roling_game':
-                    tj_stuff_roling_game(df=df, 
-                                        window=int(input.rolling_window()),
-                                        ax=y)
-
-                if x == 'break_plot':
-                    break_plot(df = df,ax=y) 
-
-                if x == 'location_plot_lhb':
-                    location_plot(df = df,ax=y,hand='L')
-
-                if x == 'location_plot_rhb':
-                    location_plot(df = df,ax=y,hand='R')
-
-            summary_table(df=df,
-                        ax=ax_table)
-
-            plot_footer(ax_footer)   
-
-            ax_watermark = fig.add_subplot(gs[1:-1,1:-1],zorder=-1) 
-            # Hide axes ticks and labels
-            ax_watermark.set_xticks([])
-            ax_watermark.set_yticks([])
-            ax_watermark.set_frame_on(False)  # Optional: Hide border
-            
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Display the image
-            ax_watermark.imshow(img, extent=[0, 1, 0, 1], origin='upper',zorder=-1, alpha=0.1)
-
-
-            ax_watermark2 = fig.add_subplot(gs[-2:,1:4],zorder=1) 
-            ax_watermark2.set_xlim(0,1)
-            ax_watermark2.set_ylim(0,1)
-            # Hide axes ticks and labels
-            ax_watermark2.set_xticks([])
-            ax_watermark2.set_yticks([])
-            ax_watermark2.set_frame_on(False)  # Optional: Hide border
-            
-            # Open the image
-            img = Image.open('tj stats circle-01_new.jpg')
-            # Get the original size
-            width, height = img.size
-            # Calculate the new size (50% larger)
-            new_width = int(width * 0.5)
-            new_height = int(height * 0.5)
-            # Resize the image
-            img_resized = img.resize((new_width, new_height))
-            # Display the image
-            ax_watermark2.imshow(img, extent=[0.26, 0.46, 0.0,0.2], origin='upper',zorder=-1, alpha=1)
-
-            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
-
-
-
-    @output
-    @render.data_frame
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def grid():
-
-        start_date = str(input.date_id()[0])
-        if not is_valid_date(start_date):
-             return pd.DataFrame({"Message": ["Select range to generate table"]})
-        df = cached_data()
-        df = df.clone()
-        features_table = ['start_speed',
-            'spin_rate',
-            'extension',
-            'ivb',
-            'hb',
-            'x0',
-            'z0']
-        
-        
-        
-        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
-                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
-
-
-
-        return render.DataGrid(
-                df.select(selection).to_pandas().round(1),
-                row_selection_mode='multiple',
-                 height='700px',
-                width='fit-content',
-                filters=True,
-            )
-
-
-    @output
-    @render.data_frame
-    @reactive.event(input.generate_plot, ignore_none=False)   
-    def grid_game():
-        if isinstance(input.date_id(), tuple):
-                return pd.DataFrame({"Message": ["Select game to generate table"]})
-                     
-        df = cached_data_daily()
-        df = df.clone()
-        features_table = ['start_speed',
-            'spin_rate',
-            'extension',
-            'ivb',
-            'hb',
-            'x0',
-            'z0']
-        
-        
-        
-        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
-                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
-
-
-
-        return render.DataGrid(
-                df.select(selection).to_pandas().round(1),
-                row_selection_mode='multiple',
-                 height='700px',
-                width='fit-content',
-                filters=True,
-            )
-
-    
-app = App(app_ui, server)
+import polars as pl
+import numpy as np
+import pandas as pd
+import api_scraper
+scrape = api_scraper.MLB_Scrape()
+from functions import df_update
+from functions import pitch_summary_functions
+update = df_update.df_update()
+import requests
+import joblib
+from matplotlib.gridspec import GridSpec
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import seaborn as sns
+from functions.pitch_summary_functions import *
+from functions.df_update import *
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+from functions.heat_map_functions import *
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+
+
+year_list = [2017,2018,2019,2020,2021,2022,2023,2024,2025]
+
+
+
+level_dict =  {'1':'MLB',
+               '11':'AAA',
+               '12':'AA',
+               '13':'A+',
+               '14':'A',
+               '17':'AFL',
+               '22':'College',
+               '21':'Prospects',
+               '51':'International' }
+
+function_dict={
+    'velocity_kdes':'Velocity Distributions',
+    'break_plot':'Pitch Movement',
+    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
+    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
+    'location_plot_lhb':'Locations vs LHB',
+    'location_plot_rhb':'Locations vs RHB',
+}
+
+
+split_dict =  {'all':'All',
+               'left':'LHH',
+               'right':'RHH'}
+
+split_dict_hand =  {'all':['L','R'],
+               'left':['L'],
+               'right':['R']}
+
+
+type_dict =  {'R':'Regular Season',
+               'S':'Spring',
+               'P':'Playoffs' }
+
+format_dict = {
+    'pitch_percent': '{:.1%}',
+    'pitches': '{:.0f}',
+    'heart_zone_percent': '{:.1%}',
+    'shadow_zone_percent': '{:.1%}',
+    'chase_zone_percent': '{:.1%}',
+    'waste_zone_percent': '{:.1%}',
+    'csw_percent': '{:.1%}',
+    'whiff_rate': '{:.1%}',
+    'zone_whiff_percent': '{:.1%}',
+    'chase_percent': '{:.1%}',
+    'bip': '{:.0f}',
+    'xwoba_percent_contact': '{:.3f}'
+}
+
+format_dict = {
+    'pitch_percent': '{:.1%}',
+    'pitches': '{:.0f}',
+    'heart_zone_percent': '{:.1%}',
+    'shadow_zone_percent': '{:.1%}',
+    'chase_zone_percent': '{:.1%}',
+    'waste_zone_percent': '{:.1%}',
+    'csw_percent': '{:.1%}',
+    'whiff_rate': '{:.1%}',
+    'zone_whiff_percent': '{:.1%}',
+    'chase_percent': '{:.1%}',
+    'bip': '{:.0f}',
+    'xwoba_percent_contact': '{:.3f}'
+}
+label_translation_dict = {
+    'pitch_percent': 'Pitch%',
+    'pitches': 'Pitches',
+    'heart_zone_percent': 'Heart%',
+    'shadow_zone_percent': 'Shado%',
+    'chase_zone_percent': 'Chas%',
+    'waste_zone_percent': 'Waste%',
+    'csw_percent': 'CSW%',
+    'whiff_rate': 'Whiff%',
+    'zone_whiff_percent': 'Z-Whiff%',
+    'chase_percent': 'O-Swing%',
+    'bip': 'BBE',
+    'xwoba_percent_contact': 'xwOBACON'
+}
+
+cmap_sum22 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFB000',])
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
+cmap_sum2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000','#FE6100'])
+cmap_sum_r = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFB000','#FFFFFF','#648FFF',])
+
+
+import requests
+
+import os
+CAMPAIGN_ID = os.getenv("CAMPAIGN_ID")
+ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
+BACKUP_PW = os.getenv("BACKUP_PW")
+ADMIN_PW = os.getenv("ADMIN_PW")
+
+url = f"https://www.patreon.com/api/oauth2/v2/campaigns/{CAMPAIGN_ID}/members"
+
+headers = {
+    "Authorization": f"Bearer {ACCESS_TOKEN}"
+}
+
+# Simple parameters, requesting the member's email and currently entitled tiers
+params = {
+    "fields[member]": "full_name,email",  # Request the member's email
+    "include": "currently_entitled_tiers",  # Include the currently entitled tiers
+    "page[size]": 1000  # Fetch up to 1000 patrons per request
+}
+
+response = requests.get(url, headers=headers, params=params)
+
+
+VALID_PASSWORDS = []
+if response.status_code == 200:
+    data = response.json()
+    for patron in data['data']:
+        try:
+            tiers = patron['relationships']['currently_entitled_tiers']['data']
+            if any(tier['id'] == '9078921' for tier in tiers):
+                full_name = patron['attributes']['email']
+                VALID_PASSWORDS.append(full_name)
+        except KeyError:
+            continue
+VALID_PASSWORDS.append(BACKUP_PW)
+VALID_PASSWORDS.append(ADMIN_PW)
+# VALID_PASSWORDS.append('')
+
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+
+# Define the login UI
+login_ui = ui.page_fluid(
+    ui.card(
+        ui.h2([
+            "TJStats Pitching Heat Maps App ",
+            ui.tags.a("(@TJStats)", href="https://twitter.com/TJStats", target="_blank")
+        ]),
+        ui.p(
+            "This App is available to Superstar Patrons. Please enter your Patreon email address in the box below. If you're having trouble, please refer to the ", 
+            ui.tags.a("Patreon post", href="https://www.patreon.com/posts/117909954", target="_blank"),
+            "."
+        ),
+        ui.input_password("password", "Enter Patreon Email (or Password from Link):", width="25%"),
+        ui.tags.input(
+            type="checkbox",
+            id="authenticated",
+            value=False,
+            disabled=True
+        ),
+        ui.input_action_button("login", "Login", class_="btn-primary"),
+        ui.output_text("login_message"),
+    )
+)
+
+
+main_ui = ui.page_sidebar(
+    ui.sidebar(
+        # Row for selecting season and level
+        ui.row(
+            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
+            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
+            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
+        ),
+        # Row for the action button to get player list
+        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
+        # Row for selecting the player
+        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
+
+
+        ui.row(ui.input_action_button("get_pitches", "Get Pitch Types", class_="btn-secondary")),
+
+        
+        # Rows for selecting plots and split options
+        ui.row(ui.column(12, ui.output_ui('pitch_type_ui', 'Select Pitch Type'))),
+        ui.row(ui.column(12, ui.input_select('plot_type', 'Select Plot', ['Pitch%','Whiff%','xwOBACON']))),
+        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
+        
+        # Row for the action button to generate plot
+        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
+        width="400px"  # Added this parameter to control sidebar width
+    ),
+    
+    # Main content (former panel_main content)
+    ui.navset_tab(
+        # Tab for game summary plot
+        ui.nav("Pitching Summary",
+               ui.output_text("status"),
+               ui.output_plot('plot', width='1440px', height=f'{900/1600*1440}px')
+        ),
+    )
+)
+
+
+# Combined UI with conditional panel
+app_ui = ui.page_fluid(
+    ui.tags.head(
+        ui.tags.script(src="script.js")
+    ),
+
+    ui.panel_conditional(
+        "!input.authenticated",
+        login_ui
+    ),
+    ui.panel_conditional(
+        "input.authenticated",
+        main_ui
+    )
+)
+
+
+def server(input, output, session):
+
+    @reactive.Effect
+    @reactive.event(input.login)
+    def check_password():
+        if input.password() in VALID_PASSWORDS:
+            ui.update_checkbox("authenticated", value=True)
+            ui.update_text("login_message", value="")
+        else:
+            ui.update_text("login_message", value="Invalid password!")
+            ui.update_text("password", value="")
+
+    @output
+    @render.text
+    def login_message():
+        return ""
+
+    # Instead of using @reactive.calc with @reactive.event
+    cached_data_value = reactive.value(None)  # Initialize with None
+
+    @reactive.calc
+    @reactive.event(input.date_id,input.pitcher_id)
+    def cached_data():
+
+        if not hasattr(input, 'pitcher_id') or input.pitcher_id() is None or not hasattr(input, 'date_id') or input.date_id() is None:
+            return  # Exit early if required inputs aren't ready
+        year_input = int(input.year_input())
+        sport_id = int(input.level_input())
+        player_input = int(input.pitcher_id())
+        start_date = str(input.date_id()[0])
+        end_date = str(input.date_id()[1])
+        # Simulate an expensive data operation
+        game_list  = scrape.get_player_games_list(sport_id = sport_id,
+                                                season = year_input, 
+                                                player_id = player_input,
+                                                start_date = start_date,
+                                                end_date = end_date,
+                                                game_type = [input.type_input()])
+
+        data_list = scrape.get_data(game_list_input = game_list[:])
+        df = (update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("pitcher_id") == player_input)&
+                            (pl.col("is_pitch") == True)
+                            
+
+                            ))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            )
+        return df
+
+
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def player_select_ui():
+        # Get the list of pitchers for the selected level and season
+        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
+            pl.col("position").is_in(['P','TWP'])).sort("name")
+        
+        # Create a dictionary of pitcher IDs and names
+        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
+        
+        # Return a select input for choosing a pitcher
+        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
+
+    is_loading = reactive.value(False)
+    data_result = reactive.value(None)
+
+    @reactive.effect
+    @reactive.event(input.get_pitches)
+    def load_data():
+        is_loading.set(True)
+        data_result.set(None)  # Clear any previous data
+        try:
+            # This will fetch the data
+            result = cached_data()
+            data_result.set(result)
+        except Exception as e:
+            # Handle any errors
+            print(f"Error loading data: {e}")
+        finally:
+            is_loading.set(False)
+
+    @output
+    @render.ui
+    def pitch_type_ui():
+        # Make sure to add dependencies on both values
+        input.get_pitches()
+        loading = is_loading()
+        data = data_result()
+        
+        # If loading, show spinner
+        if loading:
+            return ui.div(
+                ui.span("Loading pitch types... ", class_="me-2"),
+                ui.tags.div(class_="spinner-border spinner-border-sm text-primary"),
+                style="padding: 10px; background-color: #f8f9fa; border-radius: 5px;"
+            )
+        
+        # If data is loaded, show dropdown
+        elif data is not None:
+            df = data
+            df = df.clone() if hasattr(df, 'clone') else df.copy()
+            pitch_dict = dict(zip(df['pitch_type'], df['pitch_description']))
+            return ui.input_select(
+                "pitch_type_input", 
+                "Select Pitch Type", 
+                pitch_dict, 
+                selectize=True
+            )
+        
+        # Initial state or after reset
+        else:
+            return ui.div(
+                ui.p("Click 'Get Pitch Types' to load the dropdown.", class_="text-muted"),
+                style="text-align: center; padding: 10px;"
+            )  # Empty div with instructions
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def date_id():
+        # Create a date range input for selecting the date range within the selected year
+        return ui.input_date_range("date_id", "Select Date Range",
+                                   start=f"{int(input.year_input())}-01-01",
+                                   end=f"{int(input.year_input())}-12-31",
+                                   min=f"{int(input.year_input())}-01-01",
+                                    max=f"{int(input.year_input())}-12-31")
+    
+
+
+    @output
+    @render.text
+    def status():
+        # Only show status when generating
+        if input.generate == 0:
+            return ""
+        return ""   
+    
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def plot():     
+        # Show progress/loading notification
+        with ui.Progress(min=0, max=1) as p:
+            p.set(message="Generating plot", detail="This may take a while...")
+
+            
+            p.set(0.3, "Gathering data...")
+            year_input = int(input.year_input())
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+            start_date = str(input.date_id()[0])
+            end_date = str(input.date_id()[1])
+            
+
+            print(year_input, sport_id, player_input, start_date, end_date)
+
+            df = cached_data()
+            df = df.clone()
+
+            pitch_input = input.pitch_type_input()
+
+            df_plot = pitch_heat_map(pitch_input, df)
+            pivot_table_l = pitch_prop(df=df_plot, hand = 'L')
+            pivot_table_r = pitch_prop(df=df_plot, hand = 'R')
+
+
+            table_left = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'L'), selection=['pitcher_hand'])
+            table_left = table_left.with_columns(
+                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'L'))).alias('pitch_percent')
+            )
+
+            table_right = df_update().update_summary_select(df=df_plot.filter(pl.col('batter_hand') == 'R'), selection=['pitcher_hand'])
+            table_right = table_right.with_columns(
+                (pl.col('pitches')/len(df.filter(pl.col('batter_hand') == 'R'))).alias('pitch_percent')
+            )
+            try:
+                normalize = mcolors.Normalize(vmin=table_left['pitch_percent']*0.5, 
+                                            vmax=table_left['pitch_percent']*1.5) # Define the range of values
+    
+    
+                df_colour_left = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_l[0]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[1]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_l[2]]])
+                df_colour_left[0] = '#ffffff'
+            except ValueError:
+                normalize = mcolors.Normalize(vmin=0, 
+                                            vmax=1) # Define the range of values
+                df_colour_left = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
+                    
+            try: 
+                normalize = mcolors.Normalize(vmin=table_right['pitch_percent']*0.5, 
+                                            vmax=table_right['pitch_percent']*1.5) # Define the range of values
+    
+    
+                df_colour_right = pd.DataFrame(data=[[get_color(x,normalize,cmap_sum2) for x in pivot_table_r[0]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[1]],
+                            [get_color(x,normalize,cmap_sum2) for x in pivot_table_r[2]]])
+                df_colour_right[0] = '#ffffff'
+                
+            except ValueError:
+                normalize = mcolors.Normalize(vmin=0, 
+                                            vmax=1) # Define the range of values
+                df_colour_right = pd.DataFrame(data=[['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff'],
+                            ['#ffffff','#ffffff','#ffffff','#ffffff']])
+
+            table_left = table_left.select(
+                'pitch_percent',
+                'pitches',
+                'heart_zone_percent',
+                'shadow_zone_percent',
+                'chase_zone_percent',
+                'waste_zone_percent',
+                'csw_percent',
+                'whiff_rate',
+                'zone_whiff_percent',
+                'chase_percent',
+                'bip',
+                'xwoba_percent_contact').to_pandas().T
+
+            table_right = table_right.select(
+                'pitch_percent',
+                'pitches',
+                'heart_zone_percent',
+                'shadow_zone_percent',
+                'chase_zone_percent',
+                'waste_zone_percent',
+                'csw_percent',
+                'whiff_rate',
+                'zone_whiff_percent',
+                'chase_percent',
+                'bip',
+                'xwoba_percent_contact').to_pandas().T
+
+            table_right = table_right.replace({'nan%':'—'})
+            table_right = table_right.replace({'nan':'—'})
+
+
+
+
+
+            p.set(0.6, "Creating plot...")
+
+            import matplotlib.pyplot as plt
+            fig = plt.figure(figsize=(16, 9))
+            fig.set_facecolor('white')
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            gs = GridSpec(3, 5,  height_ratios=[2,9,1],width_ratios=[1,9,1,9,1])
+            gs.update(hspace=0.2, wspace=0.3)
+
+            # Add subplots to the grid
+            ax_header = fig.add_subplot(gs[0, :]) 
+            ax_left = fig.add_subplot(gs[1, 1]) 
+            ax_right = fig.add_subplot(gs[1, 3]) 
+
+            axfooter = fig.add_subplot(gs[-1, :])
+
+
+            if input.plot_type() == 'Pitch%':
+                heat_map_plot(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum2,
+                                hand='L')
+
+                heat_map_plot(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum2,
+                                hand='R')
+
+
+            if input.plot_type() == 'Whiff%':
+                heat_map_plot_hex_whiff(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum,
+                                hand='L')
+
+                heat_map_plot_hex_whiff(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum,
+                                hand='R')
+
+            if input.plot_type() == 'xwOBACON':
+                heat_map_plot_hex_damage(df=df_plot, 
+                                ax=ax_left,
+                                cmap=cmap_sum,
+                                hand='L')
+
+                heat_map_plot_hex_damage(df=df_plot, 
+                                ax=ax_right,
+                                cmap=cmap_sum,
+                                hand='R')
+
+
+            # Load the image
+            img = mpimg.imread('images/left.png')
+            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
+            ab = AnnotationBbox(imagebox, (1.25, -0.5), box_alignment=(0, 0), frameon=False)
+            ax_left.add_artist(ab)
+
+
+            # Load the image
+            img = mpimg.imread('images/right.png')
+            imagebox = OffsetImage(img, zoom=0.58)  # adjust zoom as needed
+            # Create an AnnotationBbox
+            ab = AnnotationBbox(imagebox, (-1.25, -0.5), box_alignment=(1, 0), frameon=False)
+
+            ax_right.add_artist(ab)
+
+
+            table_plot(ax=ax_left,
+                        table=table_left,
+                            hand='L')
+
+            table_plot_pivot(ax=ax_left,
+                        pivot_table=pivot_table_l,
+                        df_colour=df_colour_left)
+
+
+            table_plot(ax=ax_right,
+                        table=table_right,
+                            hand='R')
+
+            table_plot_pivot(ax=ax_right,
+                        pivot_table=pivot_table_r,
+                        df_colour=df_colour_right)
+
+
+            from matplotlib.cm import ScalarMappable
+            from matplotlib.colors import Normalize
+            # Create a ScalarMappable with the same colormap and normalization
+            if input.plot_type() == 'Pitch%':
+                sm = ScalarMappable(cmap=cmap_sum2, norm=Normalize(vmin=0, vmax=1))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['Least', 'Most'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+            if input.plot_type() == 'Whiff%':
+                sm = ScalarMappable(cmap=cmap_sum, norm=Normalize(vmin=0.15, vmax=0.35))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['15%', '35%'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+
+            if input.plot_type() == 'xwOBACON':
+                sm = ScalarMappable(cmap=cmap_sum_r, norm=Normalize(vmin=0.25, vmax=0.5))
+
+                cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+                cbar.set_ticks([])
+
+                cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+                cbar.ax.set_xticklabels(['.000', '.500'])
+                cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+                labels = cbar.ax.get_xticklabels()
+
+                labels[0].set_horizontalalignment('left')
+                labels[-1].set_horizontalalignment('right')
+                labels = cbar.ax.get_xticklabels()
+
+
+                cbar.ax.set_xticklabels(labels)
+                cbar.ax.tick_params(length=0)
+
+
+            axfooter.text(x=0.02,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=14,va='top')
+            axfooter.text(x=1-0.02,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=14,va='top')
+
+            axfooter.axis('off')
+
+            # Display the image on the axis
+            ax_header.set_xlim(-12,12)
+            ax_header.set_ylim(0, 2)
+
+
+            if input.plot_type() == 'Pitch%':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Pitch Frequency",ha='center',fontsize=24,va='top')
+            if input.plot_type() == 'Whiff%':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} Whiff%",ha='center',fontsize=24,va='top')
+            if input.plot_type() == 'xwOBACON':
+                ax_header.text(x=0,y=2,s=f"{df_plot['pitcher_name'][0]} - {df_plot['pitcher_hand'][0]}HP\n{df_plot['pitch_description'][0]} xwOBACON",ha='center',fontsize=24,va='top')
+
+            ax_header.text(x=0,y=0.7,s=f"{year_input} {level_dict[str(sport_id)]} Season",ha='center',fontsize=16,va='top')            
+            ax_header.text(x=0,y=0.3,s=f"{df_plot['game_date'][0]} to {df_plot['game_date'][-1]}",ha='center',fontsize=16,va='top',fontstyle='italic')
+
+            ax_header.axis('off')
+
+
+            import urllib
+            import urllib.request
+            import urllib.error
+            from urllib.error import HTTPError
+
+
+            plot_header(pitcher_id=player_input,
+                    ax=ax_header, 
+                    df_team=scrape.get_teams(), 
+                    df_players=scrape.get_players(sport_id,year_input),
+                    sport_id=sport_id,)
+
+
+
+
+
+
+            fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
+
+
+    
+
+app = App(app_ui, server)
+
+            

functions/df_update.py

@@ -323,6 +323,7 @@ class df_update:
             (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
             (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
             (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
+            (1 - (pl.col('zone_contact') / pl.col('zone_swing'))).alias('zone_whiff_percent'),
             (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
             (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
             (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),
@@ -442,6 +443,7 @@ class df_update:
             (pl.col('hard_hit') / pl.col('bip_div')).alias('hard_hit_percent'),
             (pl.col('barrel') / pl.col('bip_div')).alias('barrel_percent'),
             (pl.col('zone_contact') / pl.col('zone_swing')).alias('zone_contact_percent'),
+            (1 - (pl.col('zone_contact') / pl.col('zone_swing'))).alias('zone_whiff_percent'),
             (pl.col('zone_swing') / pl.col('in_zone')).alias('zone_swing_percent'),
             (pl.col('in_zone') / pl.col('pitches')).alias('zone_percent'),
             (pl.col('ozone_swing') / (pl.col('pitches') - pl.col('in_zone'))).alias('chase_percent'),

functions/heat_map_functions.py

@@ -0,0 +1,582 @@
+import pandas as pd
+import numpy as np
+import json
+from  matplotlib.ticker import FuncFormatter
+from matplotlib.ticker import MaxNLocator
+import math
+from matplotlib.patches import Ellipse
+import matplotlib.transforms as transforms
+import matplotlib.colors
+import matplotlib.colors as mcolors
+import seaborn as sns
+import matplotlib.pyplot as plt
+import requests
+import polars as pl
+from PIL import Image
+import requests
+from io import BytesIO
+from matplotlib.offsetbox import OffsetImage, AnnotationBbox
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import PIL
+from matplotlib.transforms import Bbox
+import matplotlib.image as mpimg
+from scipy.stats import gaussian_kde
+from statsmodels.nonparametric.kernel_regression import KernelReg
+
+
+format_dict = {
+    'pitch_percent': '{:.1%}',
+    'pitches': '{:.0f}',
+    'heart_zone_percent': '{:.1%}',
+    'shadow_zone_percent': '{:.1%}',
+    'chase_zone_percent': '{:.1%}',
+    'waste_zone_percent': '{:.1%}',
+    'csw_percent': '{:.1%}',
+    'whiff_rate': '{:.1%}',
+    'zone_whiff_percent': '{:.1%}',
+    'chase_percent': '{:.1%}',
+    'bip': '{:.0f}',
+    'xwoba_percent_contact': '{:.3f}'
+}
+label_translation_dict = {
+    'pitch_percent': 'Pitch%',
+    'pitches': 'Pitches',
+    'heart_zone_percent': 'Heart%',
+    'shadow_zone_percent': 'Shadow%',
+    'chase_zone_percent': 'Chase%',
+    'waste_zone_percent': 'Waste%',
+    'csw_percent': 'CSW%',
+    'whiff_rate': 'Whiff%',
+    'zone_whiff_percent': 'Z-Whiff%',
+    'chase_percent': 'O-Swing%',
+    'bip': 'BBE',
+    'xwoba_percent_contact': 'xwOBACON'
+}
+
+def pitch_heat_map(pitch_input, df):
+
+    df = df.with_columns([
+        pl.col('pitcher_id').count().over(['batter_hand', 'strikes', 'balls']).alias('h_s_b'),
+        pl.col('pitcher_id').count().over(['batter_hand', 'strikes', 'balls', 'pitch_type']).alias('h_s_b_pitch')
+    ])
+
+    df = df.with_columns([
+        (pl.col('h_s_b_pitch') / pl.col('h_s_b')).alias('h_s_b_pitch_percent')
+    ])
+
+    df_plot = df.filter(pl.col('pitch_type') == pitch_input)
+
+    return df_plot
+
+def pitch_prop(df: pl.DataFrame, hand: str = 'R') -> pd.DataFrame:
+    df_plot_pd = df.to_pandas()
+    pivot_table = (df_plot_pd[df_plot_pd['batter_hand'].isin([hand])]
+                    .groupby(['batter_hand','strikes', 'balls'])[['h_s_b_pitch_percent']]
+                    .mean()
+                    .reset_index()
+                    .pivot(index='strikes',columns='balls',values='h_s_b_pitch_percent'))
+    # Create a new index and columns range
+    new_index = range(3)
+    new_columns = range(4)
+
+    # Reindex the pivot table
+    pivot_table = pivot_table.reindex(index=new_index, columns=new_columns)
+
+    # Fill any missing values with 0
+    pivot_table = pivot_table.fillna(0)
+    df_hand = pl.DataFrame(pivot_table.reset_index())
+    return df_hand
+
+
+
+# DEFINE STRIKE ZONE
+strike_zone = pd.DataFrame({
+'PlateLocSide': [-0.9, -0.9, 0.9, 0.9, -0.9],
+'PlateLocHeight': [1.5, 3.5, 3.5, 1.5, 1.5]
+})
+
+### STRIKE ZONE ###
+def draw_line(axis, alpha_spot=1, catcher_p=True):
+    # Ensure strike_zone columns are NumPy arrays
+    plate_side = strike_zone['PlateLocSide'].to_numpy()
+    plate_height = strike_zone['PlateLocHeight'].to_numpy()
+
+    # Plot the strike zone
+    axis.plot(plate_side, plate_height, color='black', linewidth=1.3, zorder=3, alpha=alpha_spot)
+
+    if catcher_p:
+        # Add dashed lines and home plate for catcher perspective
+        axis.plot([-0.708, 0.708], [0.15, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.708], [0.15, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, 0], [0.3, 0.5], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.708], [0.5, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.708, 0.708], [0.3, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+    else:
+        # Add dashed lines and home plate for other perspective
+        axis.plot([-0.708, 0.708], [0.4, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.9], [0.4, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.9, 0], [-0.1, -0.35], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.9], [-0.35, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.9, 0.708], [-0.1, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+
+def heat_map_plot(df:pl.DataFrame, 
+                  ax:plt.Axes,
+                  cmap:matplotlib.colors.LinearSegmentedColormap,
+                  hand:str):
+    if df.filter(pl.col('batter_hand')==hand).shape[0] > 3:
+        sns.kdeplot(data=df.filter(pl.col('batter_hand')==hand),
+                        x='px',
+                            y='pz',
+                            cmap=cmap,
+                            shade=True,
+                            ax=ax,
+                            thresh=0.3,
+                            bw_adjust=1)
+    elif df.filter(pl.col('batter_hand')==hand).shape[0] > 0:
+        sns.scatterplot(data=df.filter(pl.col('batter_hand')==hand),
+                    x='px',
+                    y='pz',
+                    cmap=cmap,
+                    ax=ax)    
+        
+
+    draw_line(ax,alpha_spot=1,catcher_p = False)
+
+    
+    ax.axis('off')
+    ax.axis('square')
+    ax.set_xlim(-2.75,2.75)
+    ax.set_ylim(-0.5,5)
+
+def format_as_percentage(val):
+        return f'{val * 100:.0f}%'
+
+
+def table_plot(ax:plt.Axes,
+               table:pl.DataFrame,
+               hand='R'):
+
+    # Create a transformation that converts from data coordinates to axes coordinates
+    trans = ax.transData + ax.transAxes.inverted()
+
+    if hand == 'R':
+        bbox_data = Bbox.from_bounds(1.7, -0.5, 2.5, 5)
+
+    else:
+        bbox_data = Bbox.from_bounds(-4.2, -0.5, 2.5, 5) # replace width and height with the desired values
+    
+
+
+    bbox_axes = trans.transform_bbox(bbox_data)
+
+    if hand == 'R':
+        ax.text(s='Against RHH',x=2.95,y=4.65,fontsize=18,fontweight='bold',ha='center')
+    else:
+        ax.text(s='Against LHH',x=-2.95,y=4.65,fontsize=18,fontweight='bold',ha='center')
+    
+
+    table = table.apply(lambda x: format_dict[x.name].format(x[0]) if x[0] != '—' else '—', axis=1)
+    table.index = [label_translation_dict[x] for x in table.index]
+    
+
+
+    table_plot = ax.table(cellText=table.reset_index().values, 
+                loc='right',
+                cellLoc='center',
+                colWidths=[0.52,0.3],
+                bbox=bbox_axes.bounds,zorder=100)
+
+
+    min_font_size = 14
+    # Set table properties
+    table_plot.auto_set_font_size(False)
+    #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+    table_plot.set_fontsize(min_font_size)
+    #table_left_plot.scale(1,3)
+    # Calculate the bbox in axes coordinates
+    bbox_data = Bbox.from_bounds(-1.25, 5, 2.5, 1)  # replace width and height with the desired values
+    bbox_axes = trans.transform_bbox(bbox_data)
+
+
+
+def table_plot_pivot(ax:plt.Axes,
+               pivot_table:pl.DataFrame,
+               df_colour:pd.DataFrame):
+
+  
+    trans = ax.transData + ax.transAxes.inverted()
+    bbox_data = Bbox.from_bounds(-0.75, 5, 2.5, 1)  # replace width and height with the desired values
+    bbox_axes = trans.transform_bbox(bbox_data)
+
+    table_plot_pivot = ax.table(cellText=[[format_as_percentage(val) for val in row] for row in pivot_table.select(pivot_table.columns[-4:]).to_numpy()],
+                                    colLabels =pivot_table.columns[-4:],
+                                    rowLabels =['  0          ','  1         ','  2          '],
+                                    loc='center',
+                                    cellLoc='center',
+                                    colWidths=[0.3,0.3,0.30,0.3],
+                                    bbox=bbox_axes.bounds,zorder=100,
+                                    cellColours = df_colour[df_colour.columns[-4:]].values)
+
+
+    min_font_size = 11
+    # Set table properties
+    table_plot_pivot.auto_set_font_size(False)
+    #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+    table_plot_pivot.set_fontsize(min_font_size)
+
+
+    ax.text(x=-2.0, y=5.08, s='Strikes', rotation=90,fontweight='bold')
+    ax.text(x=0, y=6.05, s='Balls',fontweight='bold',ha='center')
+
+
+def plot_header(pitcher_id: str, ax: plt.Axes, df_team: pl.DataFrame, df_players: pl.DataFrame,sport_id:int):
+    """
+    Display the team logo for the given pitcher on the specified axis.
+    Parameters
+    ----------
+    pitcher_id : str
+        The ID of the pitcher.
+    ax : plt.Axes
+        The axis to display the logo on.
+    df_team : pl.DataFrame
+        The DataFrame containing team data.
+    df_players : pl.DataFrame
+        The DataFrame containing player data.
+    """
+    # List of MLB teams and their corresponding ESPN logo URLs
+    mlb_teams = [
+        {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
+        {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
+        {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
+        {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
+        {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
+        {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
+        {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
+        {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
+        {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
+        {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
+        {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
+        {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
+        {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
+        {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
+        {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
+        {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
+        {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
+        {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
+        {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
+        {"team": "OAK", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
+        {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
+        {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
+        {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
+        {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
+        {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
+        {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
+        {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
+        {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
+        {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
+        {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
+        {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
+    ]
+
+    try:
+        # Construct the URL for the player's headshot image based on sport ID
+        if int(sport_id) == 1:
+            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_640,q_auto:best/v1/people/{pitcher_id}/headshot/silo/current.png'
+        else:
+            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_640/v1/people/{pitcher_id}/headshot/milb/current.png'
+
+        # Send a GET request to the URL and open the image from the response content
+        response = requests.get(url)
+        img = Image.open(BytesIO(response.content))
+
+        # Display the image on the axis
+        ax.imshow(img, extent=[-11.5, -9.5, 0, 2] if sport_id == 1 else [-11.5+2/6, -9.5-2/6, 0, 2], origin='upper')
+    except PIL.UnidentifiedImageError:
+        ax.axis('off')
+        
+
+
+    try:
+        # Create a DataFrame from the list of dictionaries
+        df_image = pd.DataFrame(mlb_teams)
+        image_dict = df_image.set_index('team')['logo_url'].to_dict()
+
+        # Get the team ID for the given pitcher
+        team_id = df_players.filter(pl.col('player_id') == pitcher_id)['team'][0]
+
+        # Construct the URL to fetch team data
+        url_team = f'https://statsapi.mlb.com/api/v1/teams/{team_id}'
+
+        # Send a GET request to the team URL and parse the JSON response
+        data_team = requests.get(url_team).json()
+
+        # Extract the team abbreviation
+        if data_team['teams'][0]['id'] in df_team['parent_org_id']:
+            team_abb = df_team.filter(pl.col('team_id') == data_team['teams'][0]['id'])['parent_org_abbreviation'][0]
+        else:
+            team_abb = df_team.filter(pl.col('parent_org_id') == data_team['teams'][0]['parentOrgId'])['parent_org_abbreviation'][0]
+
+        # Get the logo URL from the image dictionary using the team abbreviation
+        logo_url = image_dict[team_abb]
+
+        # Send a GET request to the logo URL
+        response = requests.get(logo_url)
+
+        # Open the image from the response content
+        img = Image.open(BytesIO(response.content))
+
+
+        ax.imshow(img, extent=[9.5, 11.5, 0, 2], origin='upper')
+
+        # Turn off the axis
+        # ax.axis('off')
+
+
+    except (KeyError,IndexError) as e:
+        ax.axis('off')
+        return
+    
+
+
+
+# DEFINE STRIKE ZONE
+strike_zone = pd.DataFrame({
+'PlateLocSide': [-0.9, -0.9, 0.9, 0.9, -0.9],
+'PlateLocHeight': [1.5, 3.5, 3.5, 1.5, 1.5]
+})
+
+### STRIKE ZONE ###
+def draw_line(axis, alpha_spot=1, catcher_p=True):
+    # Ensure strike_zone columns are NumPy arrays
+    plate_side = strike_zone['PlateLocSide'].to_numpy()
+    plate_height = strike_zone['PlateLocHeight'].to_numpy()
+
+    # Plot the strike zone
+    axis.plot(plate_side, plate_height, color='black', linewidth=1.3, zorder=3, alpha=alpha_spot)
+
+    if catcher_p:
+        # Add dashed lines and home plate for catcher perspective
+        axis.plot([-0.708, 0.708], [0.15, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.708], [0.15, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, 0], [0.3, 0.5], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.708], [0.5, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.708, 0.708], [0.3, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+    else:
+        # Add dashed lines and home plate for other perspective
+        axis.plot([-0.708, 0.708], [0.4, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.9], [0.4, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.9, 0], [-0.1, -0.35], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.9], [-0.35, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.9, 0.708], [-0.1, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+
+from matplotlib.patches import Rectangle
+# Function to draw the strike zone and home plate
+# Import necessary libraries
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.patches import Rectangle
+from matplotlib import gridspec
+import numpy as np
+import pandas as pd
+from statsmodels.nonparametric.kernel_regression import KernelReg
+
+
+# DEFINE STRIKE ZONE
+strike_zone = pd.DataFrame({
+'PlateLocSide': [-0.9, -0.9, 0.9, 0.9, -0.9],
+'PlateLocHeight': [1.5, 3.5, 3.5, 1.5, 1.5]
+})
+
+### STRIKE ZONE ###
+def draw_line(axis, alpha_spot=1, catcher_p=True):
+    # Ensure strike_zone columns are NumPy arrays
+    plate_side = strike_zone['PlateLocSide'].to_numpy()
+    plate_height = strike_zone['PlateLocHeight'].to_numpy()
+
+    # Plot the strike zone
+    axis.plot(plate_side, plate_height, color='black', linewidth=1.3, zorder=3, alpha=alpha_spot)
+
+    if catcher_p:
+        # Add dashed lines and home plate for catcher perspective
+        axis.plot([-0.708, 0.708], [0.15, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.708], [0.15, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, 0], [0.3, 0.5], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.708], [0.5, 0.3], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.708, 0.708], [0.3, 0.15], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+    else:
+        # Add dashed lines and home plate for other perspective
+        axis.plot([-0.708, 0.708], [0.4, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.708, -0.9], [0.4, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([-0.9, 0], [-0.1, -0.35], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0, 0.9], [-0.35, -0.1], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+        axis.plot([0.9, 0.708], [-0.1, 0.4], color='black', linewidth=1, alpha=alpha_spot, zorder=1)
+
+
+    
+def heat_map_plot_hex_whiff(df:pl.DataFrame, 
+                  ax:plt.Axes,
+                  cmap:matplotlib.colors.LinearSegmentedColormap,
+                  hand:str):
+    
+    # Generate a grid of x and z coordinates for the strike zone area
+
+    heatmap_df = df.filter((pl.col('batter_hand')==hand)&((pl.col('is_swing')))).to_pandas() # Load your data here
+    heatmap_df['is_whiff'] = heatmap_df['is_whiff'].fillna(0)
+
+    bin_size = max(0.1, min(0.1, 1 / np.sqrt(len(heatmap_df))))
+
+    zone_df = pd.DataFrame(columns=['px', 'pz'])
+    for x in np.arange(-2.75, 2.85,bin_size):
+        for y in np.arange(-0.5, 5.6,bin_size):
+            zone_df.loc[len(zone_df)] = [round(x,1), round(y,1)]
+
+
+
+    heatmap_df.loc[heatmap_df['px'].notna(),'kde_x'] = np.clip(heatmap_df.loc[heatmap_df['px'].notna(),'px'].astype('float').mul(10).astype('int').div(10),
+                                                -2.75,
+                                                2.75)
+    heatmap_df.loc[heatmap_df['pz'].notna(),'kde_z'] = np.clip(heatmap_df.loc[heatmap_df['pz'].notna(),'pz'].astype('float').mul(10).astype('int').div(10),
+                                                    -0.5,
+                                                    5)
+
+
+    # Dynamically determine bandwidth for KDE
+    # bandwidth = np.clip(heatmap_df.shape[0] / 2000, 0.2, 0.2)
+    bandwidth = np.clip(1 / np.sqrt(len(df)), 0.3, 0.5)
+
+    # Kernel Regression for smoothing the metric values
+    v_center = 0.25
+    kde_df = pd.merge(zone_df,
+                        heatmap_df
+                        .dropna(subset=['is_whiff', 'px', 'pz'])
+                        [['kde_x', 'kde_z', 'is_whiff']],
+                        how='left',
+                        left_on=['px', 'pz'],
+                        right_on=['kde_x', 'kde_z']).fillna({'is_whiff': v_center})
+
+
+
+    kernel_regression = KernelReg(endog=kde_df['is_whiff'], 
+                                    exog=[kde_df['px'], kde_df['pz']], 
+                                    bw=[bandwidth,bandwidth],
+                                    var_type='cc')
+
+    kde_df['kernel_stat'] = kernel_regression.fit([kde_df['px'], kde_df['pz']])[0]
+    kde_df = kde_df.pivot_table(columns='px', index='pz', values='kernel_stat', aggfunc='mean')
+    kde_df = kde_df.round(3)
+
+    # Set up a gridspec layout for heatmap and colorbar
+
+    from matplotlib.colors import LinearSegmentedColormap
+
+    # Define the custom color palette
+    kde_min = '#648FFF'  # Blue
+    kde_mid = '#ffffff'  # White
+    kde_max = '#FFB000'  # Orange
+    # Create a custom colormap
+    # kde_palette = LinearSegmentedColormap.from_list("kde_palette", [kde_min, kde_mid, kde_max])
+    # kde_palette = (sns.color_palette(f'blend:{kde_min},{kde_mid}', n_colors=101)[:-1] +
+    #                sns.color_palette(f'blend:{kde_mid},{kde_max}', n_colors=101)[:-1])
+    # kde_palette = (sns.color_palette(f'blend:{kde_min},{kde_mid}', n_colors=101)[:-1] +
+    #                sns.color_palette(f'blend:{kde_mid},{kde_max}', n_colors=101)[:-1])
+
+    # # Generate the heatmap
+    # heatmap = sns.heatmap(data=kde_df, cmap=kde_palette, center=v_center, vmin=0.15, vmax=0.35, cbar=False, ax=ax)
+
+    ax.imshow(kde_df.values, extent=[-2.25, 2.25, -0.5, 5], origin='lower', cmap=cmap, vmin=0.15, vmax=0.35,
+              interpolation='bilinear')# Customize axes
+    ax.axis('square')
+    ax.set(xlabel=None, ylabel=None)
+    ax.set_xlim(-2.75, 2.75)
+    ax.set_ylim(-0.5, 5)
+    # ax.set_xticks([])
+    # ax.set_yticks([])
+    # ax.invert_yaxis()
+    # ax.grid(False)
+    ax.axis('off')
+
+    draw_line(ax,alpha_spot=1,catcher_p = False)
+
+
+def heat_map_plot_hex_damage(df:pl.DataFrame, 
+                  ax:plt.Axes,
+                  cmap:matplotlib.colors.LinearSegmentedColormap,
+                  hand:str):
+    
+    heatmap_df = df.filter((pl.col('batter_hand')==hand)&((pl.col('launch_speed')>0))).to_pandas() # Load your data here
+    heatmap_df['woba_pred_contact'] = heatmap_df['woba_pred_contact'].fillna(0)
+    bin_size = max(0.2, min(0.3, 1 / np.sqrt(len(heatmap_df))))
+
+    # Generate a grid of x and z coordinates for the strike zone area
+    zone_df = pd.DataFrame(columns=['px', 'pz'])
+    for x in np.arange(-2.75, 2.95,bin_size):
+        for y in np.arange(-0.5, 5.7,bin_size):
+            zone_df.loc[len(zone_df)] = [round(x,1), round(y,1)]
+
+
+    heatmap_df.loc[heatmap_df['px'].notna(),'kde_x'] = np.clip(heatmap_df.loc[heatmap_df['px'].notna(),'px'].astype('float').mul(10).astype('int').div(10),
+                                                -2.75,
+                                                2.75)
+    heatmap_df.loc[heatmap_df['pz'].notna(),'kde_z'] = np.clip(heatmap_df.loc[heatmap_df['pz'].notna(),'pz'].astype('float').mul(10).astype('int').div(10),
+                                                    -0.5,
+                                                    5)
+
+
+
+    # Dynamically determine bandwidth for KDE
+    # bandwidth = np.clip(heatmap_df.shape[0] / 2000, 0.2, 0.2)
+    bandwidth = np.clip(1 / np.sqrt(len(df)), 0.3, 0.5)
+
+    # Kernel Regression for smoothing the metric values
+    v_center = 0.375
+    kde_df = pd.merge(zone_df,
+                        heatmap_df
+                        .dropna(subset=['woba_pred_contact', 'px', 'pz'])
+                        [['kde_x', 'kde_z', 'woba_pred_contact']],
+                        how='left',
+                        left_on=['px', 'pz'],
+                        right_on=['kde_x', 'kde_z']).fillna({'woba_pred_contact': v_center})
+
+
+
+    kernel_regression = KernelReg(endog=kde_df['woba_pred_contact'], 
+                                    exog=[kde_df['px'], kde_df['pz']], 
+                                    bw=[bandwidth,bandwidth],
+                                    var_type='cc')
+
+    kde_df['kernel_stat'] = kernel_regression.fit([kde_df['px'], kde_df['pz']])[0]
+    kde_df = kde_df.pivot_table(columns='px', index='pz', values='kernel_stat', aggfunc='mean')
+    kde_df = kde_df.round(3)
+
+    # Set up a gridspec layout for heatmap and colorbar
+
+    from matplotlib.colors import LinearSegmentedColormap
+
+    # Define the custom color palette
+    kde_min = '#648FFF'  # Blue
+    kde_mid = '#ffffff'  # White
+    kde_max = '#FFB000'  # Orange
+    # Create a custom colormap
+    # kde_palette = LinearSegmentedColormap.from_list("kde_palette", [kde_min, kde_mid, kde_max])
+    # kde_palette = (sns.color_palette(f'blend:{kde_min},{kde_mid}', n_colors=101)[:-1] +
+    #                sns.color_palette(f'blend:{kde_mid},{kde_max}', n_colors=101)[:-1])
+    # kde_palette = (sns.color_palette(f'blend:{kde_min},{kde_mid}', n_colors=101)[:-1] +
+    #                sns.color_palette(f'blend:{kde_mid},{kde_max}', n_colors=101)[:-1])
+
+    # # Generate the heatmap
+    # heatmap = sns.heatmap(data=kde_df, cmap=kde_palette, center=v_center, vmin=0.15, vmax=0.35, cbar=False, ax=ax)
+
+    ax.imshow(kde_df.values, extent=[-2.25, 2.25, -0.5, 5], origin='lower', cmap=cmap, vmin=0.25, vmax=0.5,
+              interpolation='bilinear')# Customize axes
+    ax.axis('square')
+    ax.set(xlabel=None, ylabel=None)
+    ax.set_xlim(-2.75, 2.75)
+    ax.set_ylim(-0.5, 5)
+    # ax.set_xticks([])
+    # ax.set_yticks([])
+    # ax.invert_yaxis()
+    # ax.grid(False)
+    ax.axis('off')
+
+    draw_line(ax,alpha_spot=1,catcher_p = False)

functions/statcast_2024_grouped.csv

@@ -1,19 +1,19 @@
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