functions/PitchPlotFunctions.py

import polars as pl
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
import requests
from io import BytesIO
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.ticker import FuncFormatter
import matplotlib.transforms as transforms
from matplotlib.patches import Ellipse
import matplotlib.gridspec as gridspec
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
from matplotlib.figure import Figure
import streamlit as st
import api_scraper

# Initialize the scraper
scraper = api_scraper.MLB_Scrape()

class PitchPlotFunctions:
    # Define the pitch_colours method
    def pitch_colours(self):
        # Dictionary of pitch types and their corresponding colors and names
        pitch_colours = {
            'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
            'FA': {'colour': '#FF007D', 'name': 'Fastball'},
            'SI': {'colour': '#98165D', 'name': 'Sinker'},
            'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},
            'CH': {'colour': '#F79E70', 'name': 'Changeup'},
            'FS': {'colour': '#FE6100', 'name': 'Splitter'},
            'SC': {'colour': '#F08223', 'name': 'Screwball'},
            'FO': {'colour': '#FFB000', 'name': 'Forkball'},
            'SL': {'colour': '#67E18D', 'name': 'Slider'},
            'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
            'SV': {'colour': '#376748', 'name': 'Slurve'},
            'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
            'CU': {'colour': '#3025CE', 'name': 'Curveball'},
            'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
            'EP': {'colour': '#648FFF', 'name': 'Eephus'},
            'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
            'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
            'UN': {'colour': '#9C8975', 'name': 'Unknown'},
        }

        # Create dictionaries mapping pitch types to their colors and names
        dict_colour = dict(zip(pitch_colours.keys(), [pitch_colours[key]['colour'] for key in pitch_colours]))
        dict_pitch = dict(zip(pitch_colours.keys(), [pitch_colours[key]['name'] for key in pitch_colours]))

        return dict_colour, dict_pitch

    # Define the sns_custom_theme method
    def sns_custom_theme(self):
        # Custom theme for seaborn plots
        custom_theme = {
            "axes.facecolor": "white",
            "axes.edgecolor": ".8",
            "axes.grid": True,
            "axes.axisbelow": True,
            "axes.labelcolor": ".15",
            "figure.facecolor": "#f9f9f9",
            "grid.color": ".8",
            "grid.linestyle": "-",
            "text.color": ".15",
            "xtick.color": ".15",
            "ytick.color": ".15",
            "xtick.direction": "out",
            "ytick.direction": "out",
            "lines.solid_capstyle": "round",
            "patch.edgecolor": "w",
            "patch.force_edgecolor": True,
            "image.cmap": "rocket",
            "font.family": ["sans-serif"],
            "font.sans-serif": ["Arial", "DejaVu Sans", "Liberation Sans", "Bitstream Vera Sans", "sans-serif"],
            "xtick.bottom": False,
            "xtick.top": False,
            "ytick.left": False,
            "ytick.right": False,
            "axes.spines.left": True,
            "axes.spines.bottom": True,
            "axes.spines.right": True,
            "axes.spines.top": True
        }

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

        return custom_theme, colour_palette

    # Define the sport_id_dict method
    def sport_id_dict(self):
        # Dictionary mapping sport IDs to their names
        dict = {1: 'MLB', 11: 'AAA'}
        return dict

    # Define the team_logos method
    def team_logos(self):
        # 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"}
        ]

        # Create a DataFrame from the list of dictionaries
        df_image = pl.DataFrame(mlb_teams)
        # Set the index to 'team' and convert 'logo_url' to a dictionary
        image_dict = df_image.select(['team', 'logo_url']).to_dict(as_series=False)['logo_url']

        # Convert to the desired dictionary format
        image_dict = {row['team']: row['logo_url'] for row in df_image.select(['team', 'logo_url']).to_dicts()}

        return image_dict

    # Function to get an image from a URL and display it on the given axis
    def player_headshot(self, pitcher_id: str, ax: plt.Axes, sport_id: int):
        """
        Fetches and displays the player's headshot image on the given axis.

        Parameters:
        pitcher_id (str): The ID of the pitcher.
        ax (plt.Axes): The matplotlib axis to display the image on.
        sport_id (int): The sport ID to determine the URL format.
        """
        # Construct the URL for the player's headshot image
        if sport_id == 1:
            url = f'https://img.mlbstatic.com/mlb-photos/image/'\
                  f'upload/d_people:generic:headshot:67:current.png'\
                  f'/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
        response = requests.get(url)
        # Open the image from the response content
        img = Image.open(BytesIO(response.content))
        # Display the image on the axis
        ax.set_xlim(0, 2)
        ax.set_ylim(0, 1)
        ax.imshow(img, extent=[0.0, 1, 0, 1], origin='upper')
        # Turn off the axis
        ax.axis('off')

    # Function to display player bio information on the given axis
    def player_bio(self, pitcher_id: str, ax: plt.Axes, start_date: str, end_date: str, batter_hand: list):
        """
        Fetches and displays the player's bio information on the given axis.

        Parameters:
        pitcher_id (str): The ID of the pitcher.
        ax (plt.Axes): The matplotlib axis to display the bio information on.
        start_date (str): The start date for the bio information.
        end_date (str): The end date for the bio information.
        batter_hand (list): The list of batter hands (e.g., ['R'] or ['L']).
        """
        # Construct the URL to fetch player data
        url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
        # Send a GET request to the URL and parse the JSON response
        data = requests.get(url).json()
        # Extract player information from the JSON data
        player_name = data['people'][0]['fullName']
        pitcher_hand = data['people'][0]['pitchHand']['code']
        age = data['people'][0]['currentAge']
        height = data['people'][0]['height']
        weight = data['people'][0]['weight']
        # Display the player's name, handedness, age, height, and weight on the axis
        ax.text(0.5, 1, f'{player_name}', va='top', ha='center', fontsize=20)
        ax.text(0.5, 0.65, f'{pitcher_hand}HP, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=12)
        # Determine the batter hand text
        if batter_hand == ['R']:
            batter_hand_text = ', vs RHH'
        elif batter_hand == ['L']:
            batter_hand_text = ', vs LHH'
        else:
            batter_hand_text = ''
        ax.text(0.5, 0.40, f'{start_date} to {end_date}{batter_hand_text}', va='top', ha='center', fontsize=12, fontstyle='italic')
        # Turn off the axis
        ax.axis('off')

    # Function to display the team logo on the given axis
    def plot_logo(self, pitcher_id: str, ax: plt.Axes):
        """
        Fetches and displays the team logo on the given axis.

        Parameters:
        pitcher_id (str): The ID of the pitcher.
        ax (plt.Axes): The matplotlib axis to display the logo on.
        """
        # Construct the URL to fetch player data
        url = f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}&hydrate=currentTeam"
        # Send a GET request to the URL and parse the JSON response
        data = requests.get(url).json()
        # Construct the URL to fetch team data
        try:
            url_team = 'https://statsapi.mlb.com/' + data['people'][0]['currentTeam']['link']
            # Send a GET request to the team URL and parse the JSON response
            data_team = requests.get(url_team).json()
            # Get the logo URL from the image dictionary using the team abbreviation
            
            if data_team['teams'][0]['sport']['id'] == 1:
                team_abb = data_team['teams'][0]['abbreviation']
                logo_url = self.team_logos()[team_abb]
            else:
                team_abb = data_team['teams'][0]['parentOrgId']
                logo_url = self.team_logos()[dict(scraper.get_teams().select(['team_id', 'parent_org_abbreviation']).iter_rows())[team_abb]]
        except KeyError:
            logo_url = "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png?w=500&h=500&transparent=true"
        # 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.set_xlim(0, 2)
        ax.set_ylim(0, 1)
        ax.imshow(img, extent=[1, 2, 0, 1], origin='upper')
        # Turn off the axis
        ax.axis('off')

    ### PITCH ELLIPSE ###
    def confidence_ellipse( self,
                            x:np.array,
                            y:np.array, 
                            ax:plt.Axes, 
                            n_std:float=3.0,
                            facecolor:str='none',
                            **kwargs):
        """
        Create a plot of the covariance confidence ellipse of *x* and *y*.
        Parameters
        ----------
        x, y : array-like, shape (n, )
            Input data.
        ax : matplotlib.axes.Axes
            The axes object to draw the ellipse into.
        n_std : float
            The number of standard deviations to determine the ellipse's radiuses.
        **kwargs
            Forwarded to `~matplotlib.patches.Ellipse`
        Returns
        -------
        matplotlib.patches.Ellipse
        """
        
        if x.shape != y.shape:
            raise ValueError("x and y must be the same size")
        try:
            cov = np.cov(x, y)
            pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])
            # Using a special case to obtain the eigenvalues of this
            # two-dimensional dataset.
            ell_radius_x = np.sqrt(1 + pearson)
            ell_radius_y = np.sqrt(1 - pearson)
            ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,
                            facecolor=facecolor,linewidth=2,linestyle='--', **kwargs)
            

            # Calculating the standard deviation of x from
            # the squareroot of the variance and multiplying
            # with the given number of standard deviations.
            scale_x = np.sqrt(cov[0, 0]) * n_std
            mean_x = x.mean()
            

            # calculating the standard deviation of y ...
            scale_y = np.sqrt(cov[1, 1]) * n_std
            mean_y = y.mean()
            

            transf = transforms.Affine2D() \
                .rotate_deg(45) \
                .scale(scale_x, scale_y) \
                .translate(mean_x, mean_y)
            
            

            ellipse.set_transform(transf + ax.transData)
        except ValueError:
            return    
            
        return ax.add_patch(ellipse)

    
    def break_plot_big(self, df: pl.DataFrame, ax: plt.Axes, sport_id: int):
        """
        Plots a big break plot for the given DataFrame on the provided axis.

        Parameters:
        df (pl.DataFrame): The DataFrame containing pitch data.
        ax (plt.Axes): The matplotlib axis to plot on.
        sport_id (int): The sport ID to determine the plot title.
        """
        # Set font properties for different elements of the plot
        font_properties = {'size': 10}
        font_properties_titles = {'size': 16}
        font_properties_axes = {'size': 14}
        
        # Get unique pitch types sorted by 'prop' and 'pitch_type'
        label_labels = df.sort(by=['prop', 'pitch_type'], descending=[False, True])['pitch_type'].unique()
        j = 0
        dict_colour, dict_pitch = self.pitch_colours()
        custom_theme, colour_palette = self.sns_custom_theme()
        
        # Loop through each pitch type and plot confidence ellipses
        for label in label_labels:
            subset = df.filter(pl.col('pitch_type') == label)
            if len(subset) > 4:
                try:
                    if df['pitcher_hand'][0] == 'R':
                        self.confidence_ellipse(subset['hb']* 1, subset['ivb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
                    if df['pitcher_hand'][0] == 'L':
                        self.confidence_ellipse(subset['hb'] * 1, subset['ivb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
                except ValueError:
                    return
                j += 1
            else:
                j += 1
        
        # Plot scatter plot of pitch data
        if df['pitcher_hand'][0] == 'R':
            sns.scatterplot(ax=ax, x=df['hb'] * 1, y=df['ivb'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=35)
        if df['pitcher_hand'][0] == 'L':
            sns.scatterplot(ax=ax, x=df['hb'] * 1, y=df['ivb'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=35)
        
        # Set plot limits and labels
        ax.set_xlim((-25, 25))
        ax.set_ylim((-25, 25))
        ax.hlines(y=0, xmin=-50, xmax=50, color=colour_palette[8], alpha=0.5, linestyles='--', zorder=1)
        ax.vlines(x=0, ymin=-50, ymax=50, color=colour_palette[8], alpha=0.5, linestyles='--', zorder=1)
        ax.set_xlabel('Horizontal Break (in)', fontdict=font_properties_axes)
        ax.set_ylabel('Induced Vertical Break (in)', fontdict=font_properties_axes)
        ax.set_title(f"{self.sport_id_dict()[sport_id]} - Short Form Pitch Movement Plot", fontdict=font_properties_titles)
        
        # Remove legend and set tick labels
        ax.get_legend().remove()
        ax.set_xticklabels(ax.get_xticks(), fontdict=font_properties)
        ax.set_yticklabels(ax.get_yticks(), fontdict=font_properties)
        
        # Add text annotations based on pitcher hand
        if df['pitcher_hand'][0] == 'R':
            ax.text(-24.5, -24.5, s='← Glove Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=13, zorder=3)
            ax.text(24.5, -24.5, s='Arm Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=13, zorder=3)
        if df['pitcher_hand'][0] == 'L':
            ax.invert_xaxis()
            ax.text(24.5, -24.5, s='← Arm Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=13, zorder=3)
            ax.text(-24.5, -24.5, s='Glove Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=13, zorder=3)
        
        # Set aspect ratio and format tick labels
        ax.set_aspect('equal', adjustable='box')
        ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))

    ### BREAK PLOT ###
    def break_plot_big_long(self, df: pl.DataFrame, ax: plt.Axes, sport_id: int):
        """
        Plots a long break plot for the given DataFrame on the provided axis.

        Parameters:
        df (pl.DataFrame): The DataFrame containing pitch data.
        ax (plt.Axes): The matplotlib axis to plot on.
        sport_id (int): The sport ID to determine the plot title.
        """
        # Set font properties for different elements of the plot
        font_properties = {'size': 20}
        font_properties_titles = {'size': 32}
        font_properties_axes = {'size': 24}
        
        # Get unique pitch types sorted by 'prop' and 'pitch_type'
        label_labels = df.sort(by=['prop', 'pitch_type'], descending=[False, True])['pitch_type'].unique()
        dict_colour, dict_pitch = self.pitch_colours()
        custom_theme, colour_palette = self.sns_custom_theme()
        j = 0
        
        # Loop through each pitch type and plot confidence ellipses
        for label in label_labels:
            subset = df.filter(pl.col('pitch_type') == label)
            print(label)
            if len(subset) > 4:
                try:
                    if df['pitcher_hand'][0] == 'R':
                        self.confidence_ellipse(subset['hb'], subset['vb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
                    if df['pitcher_hand'][0] == 'L':
                        self.confidence_ellipse(subset['hb'] * -1, subset['vb'], ax=ax, edgecolor=dict_colour[label], n_std=2, facecolor=dict_colour[label], alpha=0.2)
                except ValueError:
                    return
                j += 1
            else:
                j += 1
        
        # Plot scatter plot of pitch data
        if df['pitcher_hand'][0] == 'R':
            sns.scatterplot(ax=ax, x=df['hb'] * 1, y=df['vb'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=50)
        if df['pitcher_hand'][0] == 'L':
            sns.scatterplot(ax=ax, x=df['hb'] * -1, y=df['vb'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=50)
        
        # Set plot limits and labels
        ax.set_xlim((-40, 40))
        ax.set_ylim((-80, 0))
        ax.axhline(y=0, color=colour_palette[8], alpha=0.5, linestyle='--', zorder=1)
        ax.axvline(x=0, color=colour_palette[8], alpha=0.5, linestyle='--', zorder=1)
        ax.set_xlabel('Horizontal Break (in)', fontdict=font_properties_axes)
        ax.set_ylabel('Vertical Break (in)', fontdict=font_properties_axes)
        ax.set_title(f"{self.sport_id_dict()[sport_id]} - Long Form Pitch Movement Plot", fontdict=font_properties_titles)
        
        # Remove legend and set tick labels
        ax.get_legend().remove()
        ax.set_xticklabels(ax.get_xticks(), fontdict=font_properties)
        ax.set_yticklabels(ax.get_yticks(), fontdict=font_properties)
        
        # Add text annotations based on pitcher hand
        if df['pitcher_hand'][0] == 'R':
            ax.text(-39.5, -79.5, s='← Glove Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
            ax.text(39.5, -79.5, s='Arm Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
        if df['pitcher_hand'][0] == 'L':
            ax.invert_xaxis()
            ax.text(39.5, -79.5, s='← Arm Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
            ax.text(-39.5, -79.5, s='Glove Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
        
        # Set aspect ratio and format tick labels
        ax.set_aspect('equal', adjustable='box')
        ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))

    ### BREAK PLOT ###
    def release_point_plot(self, df: pl.DataFrame, ax: plt.Axes, sport_id: int):
        """
        Plots the release points for the given DataFrame on the provided axis.

        Parameters:
        df (pl.DataFrame): The DataFrame containing pitch data.
        ax (plt.Axes): The matplotlib axis to plot on.
        sport_id (int): The sport ID to determine the plot title.
        """
        # Set font properties for different elements of the plot
        font_properties = {'size': 20}
        font_properties_titles = {'size': 32}
        font_properties_axes = {'size': 24}
        dict_colour, dict_pitch = self.pitch_colours()
        custom_theme, colour_palette = self.sns_custom_theme()
        
        # Plot scatter plot of release points based on pitcher hand
        if df['pitcher_hand'][0] == 'R':
            sns.scatterplot(ax=ax, x=df['x0'] * -1, y=df['z0'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=50)
        if df['pitcher_hand'][0] == 'L':
            sns.scatterplot(ax=ax, x=df['x0'] * 1, y=df['z0'] * 1, hue=df['pitch_type'], palette=dict_colour, ec='black', alpha=1, zorder=2, s=50)
        
        # Add patches to the plot
        ax.add_patch(plt.Circle((0, 10 / 12 - 18), radius=18, edgecolor='black', facecolor='#a63b17'))
        ax.add_patch(plt.Rectangle((-0.5, 9 / 12), 1, 1 / 6, edgecolor='black', facecolor='white'))
        
        # Set plot limits and labels
        ax.set_xlim((-4, 4))
        ax.set_ylim((0, 8))
        ax.axhline(y=0, color=colour_palette[8], alpha=0.5, linestyle='--', zorder=1)
        ax.axvline(x=0, color=colour_palette[8], alpha=0.5, linestyle='--', zorder=1)
        ax.set_ylabel('Vertical Release (ft)', fontdict=font_properties_axes)
        ax.set_xlabel('Horizontal Release (ft)', fontdict=font_properties_axes)
        ax.set_title(f"{self.sport_id_dict()[sport_id]} - Release Points Catcher Perspective", fontdict=font_properties_titles)
        
        # Remove legend and set tick labels
        ax.get_legend().remove()
        ax.set_xticklabels(ax.get_xticks(), fontdict=font_properties)
        ax.set_yticklabels(ax.get_yticks(), fontdict=font_properties)
        
        # Add text annotations based on pitcher hand
        if df['pitcher_hand'][0] == 'L':
            ax.text(-3.95, 0.05, s='← Glove Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
            ax.text(3.95, 0.05, s='Arm Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
        if df['pitcher_hand'][0] == 'R':
            ax.invert_xaxis()
            ax.text(3.95, 0.05, s='← Arm Side', fontstyle='italic', ha='left', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
            ax.text(-3.95, 0.05, s='Glove Side →', fontstyle='italic', ha='right', va='bottom', bbox=dict(facecolor='white', edgecolor='black'), fontsize=16, zorder=3)
        
        # Set aspect ratio and format tick labels
        ax.set_aspect('equal', adjustable='box')
        ax.xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))
        ax.yaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))

    def df_to_polars(self, df_original: pl.DataFrame, pitcher_id: str, start_date: str, end_date: str, batter_hand: list):
        """
        Filters and processes the original DataFrame to a Polars DataFrame.

        Parameters:
        df_original (pl.DataFrame): The original DataFrame containing pitch data.
        pitcher_id (str): The ID of the pitcher.
        start_date (str): The start date for filtering the data.
        end_date (str): The end date for filtering the data.
        batter_hand (list): The list of batter hands (e.g., ['R'] or ['L']).

        Returns:
        pl.DataFrame: The filtered and processed Polars DataFrame.
        """
        df = df_original.clone()
        df = df.filter((pl.col('pitcher_id') == pitcher_id) & 
                       (pl.col('is_pitch')) & (pl.col('pitch_type').is_not_null()) &
                       (pl.col('pitch_type') != 'NaN') &
                       (pl.col('game_date') >= start_date) &
                       (pl.col('game_date') <= end_date) &
                       (pl.col('batter_hand').is_in(batter_hand)))
        df = df.with_columns(
            prop_percent=(pl.col('is_pitch') / pl.col('is_pitch').sum()).over("pitch_type"),
            prop=pl.col('is_pitch').sum().over("pitch_type")
        )
        return df
    
    def final_plot(self, df: pl.DataFrame, pitcher_id: str, plot_picker: str, sport_id: int):
        """
        Creates a final plot with player headshot, bio, logo, and pitch movement plots.

        Parameters:
        df (pl.DataFrame): The DataFrame containing pitch data.
        pitcher_id (str): The ID of the pitcher.
        plot_picker (str): The type of plot to create ('short_form_movement', 'long_form_movement', 'release_point').
        sport_id (int): The sport ID to determine the plot title.
        """
        # Set the theme for seaborn plots
        sns.set_theme(style="whitegrid", rc=self.sns_custom_theme()[0])
        
        # Create a figure and a gridspec with 6 rows and 5 columns
        fig = plt.figure(figsize=(9, 9))
        gs = gridspec.GridSpec(6, 5, figure=fig, height_ratios=[0.00000000005, 5, 30, 7, 2, 0.00000000005], width_ratios=[1, 10, 10, 10, 1])
        gs.update(hspace=0.1, wspace=0.1)
        # Create subplots for player headshot, bio, and logo
        ax_headshot = fig.add_subplot(gs[1, 1])
        ax_bio = fig.add_subplot(gs[1, 2])
        ax_logo = fig.add_subplot(gs[1, 3])

        # Get the start and end dates and unique batter hands from the DataFrame
        start_date = df['game_date'].min()
        end_date = df['game_date'].max()
        batter_hand = list(df['batter_hand'].unique())

        # Plot player headshot, bio, and logo
        self.player_headshot(pitcher_id=pitcher_id, ax=ax_headshot, sport_id=sport_id)
        self.player_bio(pitcher_id=pitcher_id, ax=ax_bio, start_date=start_date, end_date=end_date, batter_hand=batter_hand)
        self.plot_logo(pitcher_id=pitcher_id, ax=ax_logo)

        # Create subplot for the main plot
        ax_main_plot = fig.add_subplot(gs[2, :])

        # Create subplot for the legend
        ax_legend = fig.add_subplot(gs[3, :])
        

        # Create subplot for the footer
        ax_footer = fig.add_subplot(gs[-2, :])

        # Plot the selected pitch movement plot
        if plot_picker == 'short_form_movement':
            self.break_plot_big(df, ax_main_plot, sport_id=sport_id)
        elif plot_picker == 'long_form_movement':
            self.break_plot_big_long(df, ax_main_plot, sport_id=sport_id)
        elif plot_picker == 'release_point':
            self.release_point_plot(df, ax_main_plot, sport_id=sport_id)

        # Sort the DataFrame and get unique pitch types
        items_in_order = list(df.sort(by=['prop', 'pitch_type'], descending=[True, True])['pitch_type'].unique(maintain_order=True))

        # Get pitch colors and names
        dict_colour, dict_pitch = self.pitch_colours()
        ordered_colors = [dict_colour[x] for x in items_in_order]
        items_in_order = [dict_pitch[x] for x in items_in_order]

        # Create custom legend handles with circles
        legend_handles = [mlines.Line2D([], [], color=color, marker='o', linestyle='None', markersize=5, label=label) for color, label in zip(ordered_colors, items_in_order)]

        # Add legend to ax_legend
        if len(items_in_order) <= 5: 
            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=5, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
        else:
            ax_legend.legend(handles=legend_handles, bbox_to_anchor=(0.1, 0, 0.8, 0.7), ncol=5, fancybox=True, loc='center', fontsize=10, framealpha=1.0, markerscale=2, prop={'size': 10})
                    
        # Add footer text
        ax_footer.text(x=0.075, y=0, s='By: Thomas Nestico\n      @TJStats', fontname='Calibri', ha='left', fontsize=12, va='bottom')
        ax_footer.text(x=1-0.075, y=0, s='Data: MLB', ha='right', fontname='Calibri', fontsize=12, va='bottom')
        

        # Create subplots for the borders
        
        ax_top_border = fig.add_subplot(gs[0, :])
        ax_left_border = fig.add_subplot(gs[:, 0])
        ax_right_border = fig.add_subplot(gs[:, -1])
        ax_bottom_border = fig.add_subplot(gs[-1, :])

        # Turn off the axes for the border subplots
        ax_top_border.axis('off')
        ax_left_border.axis('off')
        ax_right_border.axis('off')
        ax_bottom_border.axis('off')
        ax_footer.axis('off')
        ax_legend.axis('off')

        # Adjust layout and show the figure
        # fig.tight_layout()
        fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
        # st.pyplot(fig)