Upload 34 files

app.py

@@ -1,406 +1,641 @@
-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]
-
-
-
-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()
-
-
-
-# # Define the features to be used for training
-# features_table = ['start_speed',
-#             'spin_rate',
-#             'extension',
-#             'ivb',
-#             'hb',
-#             'x0',
-#             'z0',
-#             'tj_stuff_plus']
-
-from shiny import App, reactive, ui, render
-from shiny.ui import h2, tags
-
-# Define the UI layout for the app
-app_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'))),
-        # 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("Summary Table",
-                                   ui.output_data_frame("grid"))
-    )
-)
-
-
-def server(input, output, session):
-
-    @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])
-        # 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 = (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
-
-    @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']))|
-            (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.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()
-
-            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=[5,20,12,36,36,7],
-                                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)   
-
-            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():
-
-        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,
-            )
-    
-
-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()
+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
+# from functions.PitchPlotFunctions import *
+import functions.PitchPlotFunctions as ppf
+ploter = ppf.PitchPlotFunctions()
+from shiny.plotutils import brushed_points
+from pytabulator import TableOptions, Tabulator, output_tabulator, render_tabulator, theme
+theme.tabulator_site()
+
+colour_palette = ['#FFB000','#648FFF','#785EF0',
+                  '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
+cmap_sum = mcolors.LinearSegmentedColormap.from_list("", ['#648FFF', '#FFFFFF', '#FFB000'])
+
+year_list = [2017,2018,2019,2020,2021,2022,2023,2024]
+
+
+
+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']}
+
+### PITCH COLOURS ###
+
+# Dictionary to map pitch types to their corresponding colors and names
+pitch_colours = {
+    ## Fastballs ##
+    'FF': {'colour': '#FF007D', 'name': '4-Seam Fastball'},
+    'FA': {'colour': '#FF007D', 'name': 'Fastball'},
+    'SI': {'colour': '#98165D', 'name': 'Sinker'},
+    'FC': {'colour': '#BE5FA0', 'name': 'Cutter'},
+
+    ## Offspeed ##
+    'CH': {'colour': '#F79E70', 'name': 'Changeup'},
+    'FS': {'colour': '#FE6100', 'name': 'Splitter'},
+    'SC': {'colour': '#F08223', 'name': 'Screwball'},
+    'FO': {'colour': '#FFB000', 'name': 'Forkball'},
+
+    ## Sliders ##
+    'SL': {'colour': '#67E18D', 'name': 'Slider'},
+    'ST': {'colour': '#1BB999', 'name': 'Sweeper'},
+    'SV': {'colour': '#376748', 'name': 'Slurve'},
+
+    ## Curveballs ##
+    'KC': {'colour': '#311D8B', 'name': 'Knuckle Curve'},
+    'CU': {'colour': '#3025CE', 'name': 'Curveball'},
+    'CS': {'colour': '#274BFC', 'name': 'Slow Curve'},
+    'EP': {'colour': '#648FFF', 'name': 'Eephus'},
+
+    ## Others ##
+    'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
+    'KN': {'colour': '#867A08', 'name': 'Knuckle Ball'},
+    'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
+    'UN': {'colour': '#9C8975', 'name': 'Unknown'},
+}
+
+# Create dictionaries for pitch types and their attributes
+dict_colour = {key: value['colour'] for key, value in pitch_colours.items()}
+dict_pitch = {key: value['name'] for key, value in pitch_colours.items()}
+dict_pitch_desc_type = {value['name']: key for key, value in pitch_colours.items()}
+dict_pitch_desc_type.update({'Four-Seam Fastball':'FF'})
+dict_pitch_desc_type.update({'All':'All'})
+dict_pitch_name = {value['name']: value['colour'] for key, value in pitch_colours.items()}
+dict_pitch_name.update({'Four-Seam Fastball':'#FF007D'})
+dict_pitch_name.update({'4-Seam':'#FF007D'})
+
+
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+
+# Define the UI layout for the app
+app_ui = ui.page_fluid(
+    ui.layout_sidebar(
+        ui.panel_sidebar(
+            # Row for selecting season and level
+            ui.row(
+                ui.column(6, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
+                ui.column(6, ui.input_select('level_input', 'Select Level', level_dict))
+            ),
+            # 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'))),
+
+            ui.row(
+                ui.column(6, ui.input_select('split_id', 'Select Split', split_dict, multiple=False)),
+            ),
+            # Row for the action button to generate plot
+            ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
+            ui.row(ui.input_action_button("generate_table", "Generate Table", class_="btn-warning")),
+            
+        ),
+
+                
+        ui.panel_main(
+            # ui.navset_tab(
+                # Tab for game summary plot
+                # ui.nav(
+                    # "Pitching Summary",
+ui.card(
+    {"style": "width: 870px;"},
+    ui.head_content(
+        ui.tags.script(src="https://cdnjs.cloudflare.com/ajax/libs/d3/7.8.5/d3.min.js"),
+        ui.tags.script("""
+        async function downloadSVG() {
+            const content = document.getElementById('capture-section');
+            
+            // Create a new SVG element
+            const svg = document.createElementNS('http://www.w3.org/2000/svg', 'svg');
+            const bbox = content.getBoundingClientRect();
+            
+            // Set SVG attributes
+            svg.setAttribute('width', bbox.width);
+            svg.setAttribute('height', bbox.height);
+            svg.setAttribute('viewBox', `0 0 ${bbox.width} ${bbox.height}`);
+            
+            // Create foreignObject to contain HTML content
+            const foreignObject = document.createElementNS('http://www.w3.org/2000/svg', 'foreignObject');
+            foreignObject.setAttribute('width', '100%');
+            foreignObject.setAttribute('height', '100%');
+            foreignObject.setAttribute('x', '0');
+            foreignObject.setAttribute('y', '0');
+            
+            // Clone the content and its styles
+            const clonedContent = content.cloneNode(true);
+            
+            // Add necessary style context
+            const style = document.createElement('style');
+            Array.from(document.styleSheets).forEach(sheet => {
+                try {
+                    Array.from(sheet.cssRules).forEach(rule => {
+                        style.innerHTML += rule.cssText + '\\n';
+                    });
+                } catch (e) {
+                    console.warn('Could not access stylesheet rules');
+                }
+            });
+            
+            // Create a wrapper div to hold styles and content
+            const wrapper = document.createElement('div');
+            wrapper.appendChild(style);
+            wrapper.appendChild(clonedContent);
+            
+            foreignObject.appendChild(wrapper);
+            svg.appendChild(foreignObject);
+            
+            // Convert to SVG string with XML declaration and DTD
+            const svgString = new XMLSerializer().serializeToString(svg);
+            const svgBlob = new Blob([
+                '<?xml version="1.0" standalone="no"?>\\n',
+                '<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">\\n',
+                svgString
+            ], {type: 'image/svg+xml;charset=utf-8'});
+            
+            // Create and trigger download
+            const url = URL.createObjectURL(svgBlob);
+            const link = document.createElement('a');
+            link.href = url;
+            link.download = 'plot_and_table.svg';
+            document.body.appendChild(link);
+            link.click();
+            document.body.removeChild(link);
+            URL.revokeObjectURL(url);
+        }
+        
+        $(document).on('click', '#capture_btn', function() {
+            downloadSVG();
+        });
+        """)
+    ),
+    ui.output_text("status"),
+    ui.div(
+        {
+            "id": "capture-section",
+            "style": "background-color: white; padding: 0; margin-left: 20px; margin-right: 20px; margin-top: 20px; margin-bottom: 20px;"  # Added margin-right
+        },
+        # Plot section with relative positioning for brush
+        ui.div(
+            {"style": "position: relative;"},
+            ui.output_ui("plot_ui")
+        ),
+        # Table section
+        ui.div(
+            {"style": "margin-top: 20px;"},
+            ui.row(ui.tags.b("Pitches in Selection"), ui.output_table("in_brush")),
+            
+
+        ),
+        ui.div({"style": "height: 20px;"}) 
+    ),
+    ui.input_action_button("capture_btn", "Save as SVG", class_="btn-primary"),
+)
+            #     ),
+            # )
+        )
+    )
+)
+
+
+def server(input, output, session):
+
+    @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])
+        # 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)
+
+        data_list = scrape.get_data(game_list_input = game_list[:])
+        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('batter_hand').is_in(split_dict_hand[input.split_id()]))
+
+                            )))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            ))
+        
+        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
+
+    @render.ui
+    @reactive.event(input.player_button, input.level_input,input.year_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())).filter(
+            pl.col("position").is_in(['P'])).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, 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())}-03-31",
+                                   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 ""   
+
+    @render.ui
+    @reactive.event(input.generate_plot)   
+    def plot_ui():
+        brush_opts_kwargs = {}
+        brush_opts_kwargs["direction"] = 'xy'
+        brush_opts_kwargs["delay"] = 60  # Optional: adds a small delay for better performance
+        brush_opts_kwargs["delay_type"] = "throttle"
+        
+    
+        return ui.output_plot('plot', 
+                            width='800px', 
+                            height='800px',
+                            brush=ui.brush_opts(**brush_opts_kwargs))
+
+    @render.table
+    @reactive.event(input.plot_brush, input.generate_table)  # Note: changed to match the brush ID
+    def in_brush():
+        # if input.plot_brush() is None:  # Note: changed to match the brush ID
+        #     return None
+        brushed_df = pl.DataFrame(brushed_points(
+            cached_data().to_pandas(),
+            input.plot_brush(),
+            xvar="hb",
+            yvar="ivb",
+            all_rows=False
+        ))
+
+    
+        brushed_df_final = (((brushed_df.group_by(['pitcher_id', 'pitch_description'])
+                .agg([
+                    pl.col('is_pitch').drop_nans().count().alias('pitches'),
+                    pl.col('start_speed').drop_nans().mean().round(1).alias('start_speed'),
+                    pl.col('vb').drop_nans().mean().round(1).alias('vb'),
+                    pl.col('ivb').drop_nans().mean().round(1).alias('ivb'),
+                    pl.col('hb').drop_nans().mean().round(1).alias('hb'),
+                    pl.col('spin_rate').drop_nans().mean().round(0).alias('spin_rate'),
+                    pl.col('x0').drop_nans().mean().round(1).alias('x0'),
+                    pl.col('z0').drop_nans().mean().round(1).alias('z0'),
+                    pl.col('tj_stuff_plus').drop_nans().mean().round(0).alias('tj_stuff_plus'),
+                ])
+                .with_columns(
+                    (pl.col('pitches') / pl.col('pitches').sum().over('pitcher_id'))
+                    # .round(1)
+                    # .map_elements(lambda x: f"{x}%", return_dtype=pl.Utf8)  # Properly append "%"
+                    .alias('proportion')
+                )
+                )).sort('proportion', descending=True).
+                select(["pitch_description", "pitches", "proportion", "start_speed", "ivb", "hb",
+                                                                "spin_rate", "x0", "z0",'tj_stuff_plus'])
+                .with_columns(
+            pl.when(pl.col("pitch_description") == "Four-Seam Fastball")
+            .then(pl.lit("4-Seam"))
+            .otherwise(pl.col("pitch_description"))
+            .alias("pitch_description")
+        )
+                .rename({
+                    'pitch_description': 'Pitch Type',
+                    'pitches': 'Pitches',
+                    'proportion': 'Prop',
+                    'start_speed': 'Velo',
+                    'ivb': 'iVB',
+                    'hb': 'HB',
+                    'spin_rate': 'Spin',
+                    'x0': 'hRel',
+                    'z0': 'vRel',
+                    'tj_stuff_plus': 'tjStuff+'
+                }))
+        
+        # brushed_df_final = brushed_df_final
+        
+        # print(brushed_df_final)
+       
+        def change_font(val):
+            if val == "Cutter":
+                return "color: red; font-weight: bold;"
+            else:
+                ''
+                return "font-weight: bold;"
+        df_brush_style = (brushed_df_final.to_pandas().style.set_precision(1)
+                          
+                          .set_properties(**{'border': '3 px'},overwrite=False).set_table_styles([{
+            'selector': 'caption',
+            'props': [
+                ('color', ''),
+                ('fontname', 'Century Gothic'),
+                ('font-size', '16px'),
+                ('font-style', 'italic'),
+                ('font-weight', ''),
+                ('text-align', 'centre'),
+                ]
+
+                },{'selector' :'th', 'props':[('font-size', '16px'),('text-align', 'center'),('Height','px'),('color','black'),('border', '1px black solid !important')]},{'selector' :'td', 'props':[('text-align', 'center'),('font-size', '16px'),('color','black')]}],overwrite=False)
+                .set_properties(**{'background-color':'White','index':'White','min-width':'72px'},overwrite=False)
+                .set_table_styles([{'selector': 'th:first-child', 'props': [('background-color', 'white')]}],overwrite=False)
+                .set_table_styles([{'selector': 'tr:first-child', 'props': [('background-color', 'white')]}],overwrite=False)
+                .set_table_styles([{'selector': 'tr', 'props': [('line-height', '20px')]}],overwrite=False)
+                .set_properties(**{'Height': '8px'},**{'text-align': 'center'},overwrite=False)
+                .hide_index()
+                .set_properties(**{'border': '1px black solid !important'})
+                .format('{:.0%}',subset=(brushed_df_final.columns[2]))
+                .format('{:.0f}',subset=(brushed_df_final.columns[6]))
+                .format('{:.0f}',subset=(brushed_df_final.columns[-1]))
+                .set_properties(subset=brushed_df_final.columns, **{'height': '30px'})
+                .set_table_styles([{'selector': 'thead th', 'props': [('height', '30px')]}], overwrite=False)
+                # .set_table_styles([{'selector': 'table', 'props': [('width', '100px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(1)', 'props': [('min-width', '125px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(2)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(3)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(4)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(5)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(6)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(7)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .set_table_styles([{'selector': 'thead th:nth-child(8)', 'props': [('min-width', '40px')]}], overwrite=False)
+                .background_gradient(cmap=cmap_sum,subset = (brushed_df_final.columns[-1]),vmin=80,vmax=120)
+                .applymap(lambda x: f'background-color: {dict_pitch_name.get(x, "")}', subset=['Pitch Type'])
+                
+                                                
+    )
+
+        return df_brush_style
+
+        # return Tabulator(
+        #     brushed_df.to_pandas(),
+        #     table_options=TableOptions(
+        #         height=800,
+        #         resizable_column_fit=True,
+        #     )
+        # )
+        # return brushed_points(
+        #     ((brushed_df.group_by(['pitcher_id', 'pitch_description'])
+        #             .agg([
+        #                 pl.col('is_pitch').drop_nans().count().alias('pitches'),
+        #                 pl.col('start_speed').drop_nans().mean().round(1).alias('start_speed'),
+        #                 pl.col('vb').drop_nans().mean().round(1).alias('vb'),
+        #                 pl.col('ivb').drop_nans().mean().round(1).alias('ivb'),
+        #                 pl.col('hb').drop_nans().mean().round(1).alias('hb'),
+        #                 pl.col('spin_rate').drop_nans().mean().round(0).alias('spin_rate'),
+        #                 pl.col('x0').drop_nans().mean().round(1).alias('x0'),
+        #                 pl.col('z0').drop_nans().mean().round(1).alias('z0'),
+        #                 pl.col('tj_stuff_plus').drop_nans().mean().round(0).alias('tj_stuff_plus'),
+        #             ])
+        #             .with_columns(
+        #                 (pl.col('pitches') / pl.col('pitches').sum().over('pitcher_id') * 100)
+        #                 .round(1)
+        #                 .map_elements(lambda x: f"{x}%", return_dtype=pl.Utf8)  # Properly append "%"
+        #                 .alias('proportion')
+        #             )
+        #             )).sort('proportion', descending=True).
+        #             select(["pitch_description", "pitches", "proportion", "start_speed", "ivb", "hb",
+        #                                                            "spin_rate", "x0", "z0",'tj_stuff_plus'])
+        #             .rename({
+        #                 'pitch_description': 'Pitch Type',
+        #                 'pitches': 'Pitches',
+        #                 'proportion': 'Proportion',
+        #                 'start_speed': 'Velocity',
+        #                 'ivb': 'iVB',
+        #                 'hb': 'HB',
+        #                 'spin_rate': 'Spin Rate',
+        #                 'x0': 'hRel',
+        #                 'z0': 'vRel',
+        #                 'tj_stuff_plus': 'tjStuff+'
+        #             }).to_pandas(),
+        #             input.plot_brush(),  # Note: changed to match the brush ID
+        #             xvar="HB",  # Replace "x" with your actual x-axis column name
+        #             yvar="iVB",  # Replace "y" with your actual y-axis column name
+        #             all_rows=False
+        # )
+       
+        
+
+        # return brushed_points(
+        #     ((cached_data().group_by(['pitcher_id', 'pitch_description'])
+        #             .agg([
+        #                 pl.col('is_pitch').drop_nans().count().alias('pitches'),
+        #                 pl.col('start_speed').drop_nans().mean().round(1).alias('start_speed'),
+        #                 pl.col('vb').drop_nans().mean().round(1).alias('vb'),
+        #                 pl.col('ivb').drop_nans().mean().round(1).alias('ivb'),
+        #                 pl.col('hb').drop_nans().mean().round(1).alias('hb'),
+        #                 pl.col('spin_rate').drop_nans().mean().round(0).alias('spin_rate'),
+        #                 pl.col('x0').drop_nans().mean().round(1).alias('x0'),
+        #                 pl.col('z0').drop_nans().mean().round(1).alias('z0'),
+        #                 pl.col('tj_stuff_plus').drop_nans().mean().round(0).alias('tj_stuff_plus'),
+        #             ])
+        #             .with_columns(
+        #                 (pl.col('pitches') / pl.col('pitches').sum().over('pitcher_id') * 100)
+        #                 .round(1)
+        #                 .map_elements(lambda x: f"{x}%", return_dtype=pl.Utf8)  # Properly append "%"
+        #                 .alias('proportion')
+        #             )
+        #             )).sort('proportion', descending=True).
+        #             select(["pitch_description", "pitches", "proportion", "start_speed", "ivb", "hb",
+        #                                                            "spin_rate", "x0", "z0",'tj_stuff_plus'])
+        #             .rename({
+        #                 'pitch_description': 'Pitch Type',
+        #                 'pitches': 'Pitches',
+        #                 'proportion': 'Prop',
+        #                 'start_speed': 'Velocity',
+        #                 'ivb': 'iVB',
+        #                 'hb': 'HB',
+        #                 'spin_rate': 'Spin Rate',
+        #                 'x0': 'hRel',
+        #                 'z0': 'vRel',
+        #                 'tj_stuff_plus': 'tjStuff+'
+        #             }).to_pandas(),
+        #             input.plot_brush(),  # Note: changed to match the brush ID
+        #             xvar="HB",  # Replace "x" with your actual x-axis column name
+        #             yvar="iVB",  # Replace "y" with your actual y-axis column name
+        #             all_rows=False
+        # )
+    # @output
+    @render.plot
+    @reactive.event(input.generate_plot)   
+    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()
+
+            p.set(0.6, "Creating plot...")
+
+
+            ploter.final_plot(
+                df=df,
+                pitcher_id=player_input,
+                plot_picker='short_form_movement',#plot_picker,
+                sport_id=sport_id)
+
+  
+            # #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=[5,20,12,36,36,7],
+            #                     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)
+            # 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)
+
+            # # 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)   
+
+            # fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
+
+            # fig.savefig('test.svg')
+            
+            
+
 app = App(app_ui, server)

app_streamlit.py

@@ -0,0 +1,253 @@
+import seaborn as sns
+import streamlit as st
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+import PitchPlotFunctions as ppf
+import requests
+import polars as pl
+from datetime import date
+import api_scraper
+
+
+
+# Display the app title and description
+st.markdown("""
+## MLB & AAA Pitch Plots App
+
+##### By: Thomas Nestico ([@TJStats](https://x.com/TJStats))
+##### Code: [GitHub Repo](https://github.com/tnestico/streamlit_pitch_plots)
+##### Data: [MLB](https://baseballsavant.mlb.com/)
+
+#### About
+This Streamlit app retrieves MLB and AAA Pitching Data for a selected pitcher from the MLB Stats API and is accessed using my [MLB Stats API Scraper](https://github.com/tnestico/mlb_scraper).
+
+The app outputs the pitcher's data into both a plot and table to illustrate and summarize the data. 
+It can also display data for games currently in progress.
+
+*More information about the data and plots is shown at the bottom of this page.*
+
+"""
+)
+
+# Initialize the plotter object from PitchPlotFunctions
+ploter = ppf.PitchPlotFunctions()
+# Initialize the scraper object
+scraper = api_scraper.MLB_Scrape()
+
+# Dictionary mapping league names to sport IDs
+sport_id_dict = {'MLB': 1, 'AAA': 11}
+
+# Create two columns for league and pitcher selection
+st.write("#### Plot")
+col_1, col_2 = st.columns(2)
+with col_1:
+    # Select league
+    selected_league = st.selectbox('##### Select League', list(sport_id_dict.keys()))
+    selected_sport_id = sport_id_dict[selected_league]
+
+with col_2:
+    # Get player data and filter for pitchers
+    df_player = scraper.get_players(sport_id=selected_sport_id)
+    df_player = df_player.filter(pl.col('position').str.contains('P'))
+    df_player = df_player.with_columns(
+        (pl.concat_str(["name", "player_id"], separator=" - ").alias("pitcher_name_id"))
+    )
+    
+    # Select specific columns and convert to dictionary
+    pitcher_name_id_dict = dict(df_player.select(['pitcher_name_id', 'player_id']).iter_rows())
+    
+    # Initialize session state for previous selection
+    if 'prev_pitcher_id' not in st.session_state:
+        st.session_state.prev_pitcher_id = None
+    
+    # Display a selectbox for pitcher selection
+    selected_pitcher = st.selectbox("##### Select Pitcher", list(pitcher_name_id_dict.keys()))
+    pitcher_id = pitcher_name_id_dict[selected_pitcher]
+
+# Clear cache if selection changes
+if pitcher_id != st.session_state.prev_pitcher_id:
+    st.cache_data.clear()
+    st.session_state.prev_pitcher_id = pitcher_id
+    st.session_state.cache_cleared = False
+    st.write('Cache cleared!')
+
+# Initialize session state for cache status
+if 'cache_cleared' not in st.session_state:
+    st.session_state.cache_cleared = False
+
+# Dictionary for batter hand selection
+batter_hand_picker = {
+    'All': ['L', 'R'],
+    'LHH': ['L'],
+    'RHH': ['R']
+}
+
+# Define date range for the season
+min_date = date(2024, 3, 20)
+max_date = date(2024, 11, 30)
+
+# Create columns for input widgets
+st.write("##### Filters")
+col1, col2, col3 = st.columns(3)
+with col1:
+    # Selectbox for batter handedness
+    batter_hand_select = st.selectbox('Batter Handedness:', list(batter_hand_picker.keys()))
+    batter_hand = batter_hand_picker[batter_hand_select]
+with col2:
+    # Date input for start date
+    start_date = st.date_input('Start Date:', 
+                  value=min_date, 
+                  min_value=min_date, 
+                  max_value=max_date, 
+                  format="YYYY-MM-DD")
+with col3:
+    # Date input for end date
+    end_date = st.date_input('End Date:', 
+                  value="default_value_today", 
+                  min_value=min_date, 
+                  max_value=max_date, 
+                  format="YYYY-MM-DD")
+
+# Dictionary for plot type selection
+plot_picker_dict = {
+    'Short Form Movement': 'short_form_movement',
+    'Long Form Movement': 'long_form_movement',
+    'Release Points': 'release_point'
+}
+
+# Selectbox for plot type
+plot_picker_select = st.selectbox('Select Plot Type:', list(plot_picker_dict.keys()))
+plot_picker = plot_picker_dict[plot_picker_select]
+
+# Extract season from start date
+season = str(start_date)[0:4]
+
+# Get list of games for the selected player and date range
+player_games = scraper.get_player_games_list(player_id=pitcher_id, season=season,
+                                             start_date=str(start_date), end_date=str(end_date),
+                                             sport_id=selected_sport_id,
+                                             game_type = ['R','P'])
+
+# Function to fetch data and cache it
+@st.cache_data
+def fetch_data():
+    data = scraper.get_data(game_list_input=player_games)
+    df = scraper.get_data_df(data_list=data)
+    return df
+
+# Fetch data and manage cache status
+if not st.session_state.cache_cleared:
+    df_original = fetch_data()
+    st.session_state.cache_cleared = True
+else:
+    df_original = fetch_data()
+
+# Button to generate plot
+if st.button('Generate Plot'):
+    try:
+        # Convert dataframe to polars and filter based on inputs
+        df = ploter.df_to_polars(df_original=df_original,
+                                 pitcher_id=pitcher_id,
+                                 start_date=str(start_date),
+                                 end_date=str(end_date),
+                                 batter_hand=batter_hand)
+        print(df)
+        if len(df) == 0:
+            st.write('Please select different parameters.')
+        else:
+            # Generate the final plot
+            ploter.final_plot(
+                df=df,
+                pitcher_id=pitcher_id,
+                plot_picker=plot_picker,
+                sport_id=selected_sport_id)
+            
+            # Use a container to control the width of the AgGrid display
+            with st.container():
+                # Group the data by pitch type
+                grouped_df = (
+                    df.group_by(['pitcher_id', 'pitch_description'])
+                    .agg([
+                        pl.col('is_pitch').drop_nans().count().alias('pitches'),
+                        pl.col('start_speed').drop_nans().mean().round(1).alias('start_speed'),
+                        pl.col('vb').drop_nans().mean().round(1).alias('vb'),
+                        pl.col('ivb').drop_nans().mean().round(1).alias('ivb'),
+                        pl.col('hb').drop_nans().mean().round(1).alias('hb'),
+                        pl.col('spin_rate').drop_nans().mean().round(0).alias('spin_rate'),
+                        pl.col('x0').drop_nans().mean().round(1).alias('x0'),
+                        pl.col('z0').drop_nans().mean().round(1).alias('z0'),
+                    ])
+                    .with_columns(
+                        (pl.col('pitches') / pl.col('pitches').sum().over('pitcher_id') * 100).round(3).alias('proportion')
+                    )).sort('proportion', descending=True).select(["pitch_description", "pitches", "proportion", "start_speed", "vb", "ivb", "hb",
+                                                                   "spin_rate", "x0", "z0"])
+
+                st.write("#### Pitching Data")
+                column_config_dict = {
+                    'pitcher_id': 'Pitcher ID',
+                    'pitch_description': 'Pitch Type',
+                    'pitches': 'Pitches',
+                    'start_speed': 'Velocity',
+                    'vb': 'VB',
+                    'ivb': 'iVB',
+                    'hb': 'HB',
+                    'spin_rate': 'Spin Rate',
+                    'proportion': st.column_config.NumberColumn("Pitch%", format="%.1f%%"),
+                    'x0': 'hRel',
+                    'z0': 'vRel',
+                }
+
+                st.markdown(f"""##### {selected_pitcher.split('-')[0]} {selected_league} Pitch Data""")
+                st.dataframe(grouped_df,
+                             hide_index=True,
+                             column_config=column_config_dict,
+                             width=1500)
+
+                # Configure the AgGrid options
+                # gb = GridOptionsBuilder.from_dataframe(grouped_df)
+                # # Set display names for columns
+                # for col, display_name in zip(grouped_df.columns, grouped_df.columns):
+                #     gb.configure_column(col, headerName=display_name)
+
+                # grid_options = gb.build()
+
+                # # Display the dataframe using AgGrid
+                # grid_response = AgGrid(
+                #     grouped_df,
+                #     gridOptions=grid_options,
+                #     height=300,
+                #     allow_unsafe_jscode=True,
+                # )
+
+    except IndexError:
+        st.write('Please select different parameters.')
+
+# Display column and plot descriptions
+st.markdown("""
+#### Column Descriptions
+
+- **`Pitch Type`**: Describes the type of pitch thrown (e.g., 4-Seam Fastball, Curveball, Slider).
+- **`Pitches`**: The total number of pitches thrown by the pitcher.
+- **`Pitch%`**: Proportion of pitch thrown.
+- **`Velocity`**: The initial velocity of the pitch as it leaves the pitcher's hand, measured in miles per hour (mph).
+- **`VB`**: Vertical Break (VB), representing the amount movement of a pitch due to spin and gravity, measured in inches (in).
+- **`iVB`**: Induced Vertical Break (iVB), representing the amount movement of a pitch strictly due to the spin imparted on the ball, measured in inches (in).
+- **`HB`**: Horizontal Break (HB), indicating the amount of horizontal movement of a pitch, measured in inches (in).
+- **`Spin Rate`**: The rate of spin of the pitch as it is released, measured in revolutions per minute (rpm).
+- **`hRel`**: The horizontal release point of the pitch, measured in feet from the center of the pitcher's mound (ft).
+- **`vRel`**: The vertical release point of the pitch, measured in feet above the ground (ft).
+
+#### Plot Descriptions
+
+- **`Short Form Movement`**: Illustrates the movement of the pitch due to spin, where (0,0) indicates a pitch with perfect gyro-spin (e.g. Like a Football).
+- **`Long Form Movement`**: Illustrates the movement of the pitch due to spin and gravity.
+- **`Release Points`**: Illustrates a pitchers release points from the catcher's perspective.
+
+#### Acknowledgements
+
+Big thanks to [Michael Rosen](https://twitter.com/bymichaelrosen) and [Jeremy Maschino](https://twitter.com/pitchprofiler) for inspiration for this project
+
+Check Out Michael's [Pitch Plotting App](https://pitchplotgenerator.streamlit.app/)
+
+Check Out Jeremy's Website [Pitch Profiler](http://www.mlbpitchprofiler.com/)
+"""
+)

functions/PitchPlotFunctions.py

@@ -0,0 +1,629 @@
+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)
+
+
+
+
+    
+

functions/pitch_summary_functions.py

@@ -50,7 +50,6 @@ pitch_colours = {
 
     ## Others ##
     'KN': {'colour': '#867A08', 'name': 'Knuckleball'},
-    'KN': {'colour': '#867A08', 'name': 'Knuckle Ball'},
     'PO': {'colour': '#472C30', 'name': 'Pitch Out'},
     'UN': {'colour': '#9C8975', 'name': 'Unknown'},
 }
@@ -625,7 +624,7 @@ def summary_table(df: pl.DataFrame, ax: plt.Axes):
         # Apply color to specific columns based on normalized values
         columns_to_color = [(3, 'release_speed', 0.95, 1.05), (11, 'release_extension', 0.9, 1.1), (13, None, 80, 120), 
                             (14, None, 30, 70), (15, 'in_zone_rate', 0.7, 1.3), (16, 'chase_rate', 0.7, 1.3), 
-                            (17, 'whiff_rate', 0.7, 1.3), (18, 'xwobacon', 0.7, 1.3)]
+                            (17, 'whiff_rate', 0.7, 1.3), (18, 'xwoba', 0.7, 1.3)]
 
         for col, stat, vmin_factor, vmax_factor in columns_to_color:
             cell_value = table.get_celld()[(i + 1, col)].get_text().get_text()
@@ -765,7 +764,7 @@ def player_bio(pitcher_id: str, ax: plt.Axes, sport_id: int, year_input: int):
 
     # 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=56)
-    ax.text(0.5, 0.7, f'{pitcher_hand}HP, Age: {age}, {height}/{weight}', va='top', ha='center', fontsize=30)
+    ax.text(0.5, 0.7, f'{pitcher_hand}HP, Age:{age}, {height}/{weight}', va='top', ha='center', fontsize=30)
     ax.text(0.5, 0.45, f'Season Pitching Summary', va='top', ha='center', fontsize=40)
 
     # Make API call to retrieve sports information
@@ -867,7 +866,7 @@ def plot_logo(pitcher_id: str, ax: plt.Axes, df_team: pl.DataFrame, df_players:
 
         # Turn off the axis
         ax.axis('off')
-    except (KeyError,IndexError) as e:
+    except KeyError:
         ax.axis('off')
         return
 
@@ -1056,8 +1055,7 @@ def stat_summary_table(df: pl.DataFrame,
                         player_input: int, 
                         sport_id: int, 
                         ax: plt.Axes,
-                        split: str = 'All',
-                        game_type: list = ['R']):
+                        split: str = 'All'):
     """
     Create a summary table of player statistics.
 
@@ -1074,18 +1072,6 @@ def stat_summary_table(df: pl.DataFrame,
     split : str, optional
         The split type (default is 'All').
     """
-
-    type_dict =  {'R':'Regular Season',
-                   'S':'Spring',
-                   'P':'Playoffs' }
-
-    split_title = {
-        'all':'',
-        'right':' vs RHH',
-        'left':' vs LHH'
-    }
-
-    
     # Format start and end dates
     start_date_format = str(pd.to_datetime(df['game_date'][0]).strftime('%m/%d/%Y'))
     end_date_format = str(pd.to_datetime(df['game_date'][-1]).strftime('%m/%d/%Y'))
@@ -1093,14 +1079,11 @@ def stat_summary_table(df: pl.DataFrame,
     # Determine app context based on sport ID
     appContext = 'majorLeague' if sport_id == 1 else 'minorLeague'
 
-    game_type_str = ','.join([str(x) for x in game_type])
-
     # Fetch player stats from MLB API
     pitcher_stats_call = requests.get(
-        f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])'
+        f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format})'
     ).json()
-    print('HERE')
-    print(f'https://statsapi.mlb.com/api/v1/people/{player_input}?appContext={appContext}&hydrate=stats(group=[pitching],type=[byDateRange],sportId={sport_id},startDate={start_date_format},endDate={end_date_format},gameType=[{game_type_str}])')
+
     # Extract stats and create DataFrame
     pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
     pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][-1]['stat']]
@@ -1120,11 +1103,11 @@ def stat_summary_table(df: pl.DataFrame,
     if df['game_id'][0] == df['game_id'][-1]:
         pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'earnedRuns', 'hits', 'strikeOuts', 'baseOnBalls', 'hitByPitch', 'homeRuns', 'strikePercentage', 'whiffs'])
         new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{ER}$', '$\\bf{H}$', '$\\bf{K}$', '$\\bf{BB}$', '$\\bf{HBP}$', '$\\bf{HR}$', '$\\bf{Strike\%}$', '$\\bf{Whiffs}$']
-        title = f'{df["game_date"][0]} vs {df["batter_team"][0]} ({type_dict[game_type[0]]}){split_title[split]}'
-    elif sport_id != 1 or game_type[0] in ['S','P']:
+        title = f'{df["game_date"][0]} vs {df["batter_team"][0]}'
+    elif sport_id != 1:
         pitcher_stats_call_df_small = pitcher_stats_call_df.select(['inningsPitched', 'battersFaced', 'whip', 'era', 'fip', 'k_percent', 'bb_percent', 'k_bb_percent', 'strikePercentage'])
         new_column_names = ['$\\bf{IP}$', '$\\bf{PA}$', '$\\bf{WHIP}$', '$\\bf{ERA}$', '$\\bf{FIP}$', '$\\bf{K\%}$', '$\\bf{BB\%}$', '$\\bf{K-BB\%}$', '$\\bf{Strike\%}$']
-        title = f'{df["game_date"][0]} to {df["game_date"][-1]} ({type_dict[game_type[0]]}{split_title[split]})'
+        title = f'{df["game_date"][0]} to {df["game_date"][-1]}'
     else:
         fangraphs_table(df=df, ax=ax, player_input=player_input, season=int(df['game_date'][0][0:4]), split=split)
         return

functions/statcast_2024_grouped.csv

@@ -1,19 +1,19 @@
-pitch_type,pitch,release_speed,pfx_z,pfx_x,release_spin_rate,release_pos_x,release_pos_z,release_extension,delta_run_exp,swing,whiff,in_zone,out_zone,chase,xwoba,xwobacon,pitch_usage,whiff_rate,in_zone_rate,chase_rate,delta_run_exp_per_100,all
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+pitch_type,pitch,release_speed,pfx_z,pfx_x,release_spin_rate,release_pos_x,release_pos_z,release_extension,delta_run_exp,swing,whiff,in_zone,out_zone,chase,xwoba,pitch_usage,whiff_rate,in_zone_rate,chase_rate,delta_run_exp_per_100,all
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