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app.py

@@ -2,7 +2,7 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-import pitch_summary_functions as psf
+#import pitch_summary_functions as psf
 import requests
 import matplotlib
 from api_scraper import MLB_Scrape
@@ -10,19 +10,20 @@ from shinywidgets import output_widget, render_widget
 import shinyswatch
 
 
-
+season = 2024
+level = 'mlb'
 colour_palette = ['#FFB000','#648FFF','#785EF0',
                   '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
 
 import datasets
 from datasets import load_dataset
 ### Import Datasets
-dataset = load_dataset('nesticot/mlb_data', data_files=['a_pitch_data_2024.csv' ])
+dataset = load_dataset('nesticot/mlb_data', data_files=[f'{level}_pitch_data_{season}.csv' ])
 dataset_train = dataset['train']
 df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys())[0]).reset_index(drop=True).drop_duplicates(subset=['play_id'],keep='last')
 
+# df_2024 = pd.read_csv('C:/Users/thoma/Google Drive/Python/Baseball/season_stats/2024/2024_regular_data.csv',index_col=[0])
 
-# df_2024.loc[(df_2024['pitcher_id']==804636)&(df_2024['pitch_type'].isin(['FF','FC']),'start_speed'] += 3
 # ### Import Datasets
 # import datasets
 # from datasets import load_dataset
@@ -33,7 +34,6 @@ df_2024 = dataset_train.to_pandas().set_index(list(dataset_train.features.keys()
 ### PITCH COLOURS ###
 pitch_colours = {
         'Four-Seam Fastball':'#FF007D',#BC136F
-        'Fastball':'#FF007D',
         'Sinker':'#98165D',#DC267F
         'Cutter':'#BE5FA0',
 
@@ -57,117 +57,48 @@ pitch_colours = {
         'Other':'#9C8975',
         }
 
-spring_teams = df_2024.groupby(['pitcher_id']).tail(1)[['pitcher_id','pitcher_team']].set_index(['pitcher_id'])['pitcher_team'].to_dict()
-
-season_start = '2024-03-20'
-season_end = '2024-09-29'
-season_fg=2024 
-#chad_fg = requests.get(f'https://www.fangraphs.com/api/leaders/major-league/data?age=&pos=all&stats=pit&lg=all&qual=0&season={season_fg}&season={season_fg}&month=1000&season1={season_fg}&ind=0&pageitems=2000000000&pagenum=1&ind=0&rost=0&players=&type=36&postseason=&sortdir=default&sortstat=sp_pitching').json()
-
-
-# chadwick_df_small = pd.DataFrame(data={
-# 'key_mlbam':[x['xMLBAMID'] for x in chad_fg['data']],
-# 'key_fangraphs':[x['playerid'] for x in chad_fg['data']],
-# 'Name':[x['PlayerName'] for x in chad_fg['data']],
-# })    
-
-
-# mlb_fg_dicts = chadwick_df_small.set_index('key_mlbam')['key_fangraphs'].sort_values().to_dict()
-
-
-statcast_pitch_summary = pd.read_csv('statcast_pitch_summary.csv')
-cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
-
-
-df_2024_codes = psf.df_update_code(df_2024)
-
-df_2024_update = psf.df_clean(df_2024_codes)
-import joblib
-model = joblib.load('joblib_model/tjstuff_model_20240318.joblib')
-y_pred_mean = 0.0011434511
-y_pred_std = 0.006554768
+import pitcher_update as pu
+df_2024 = pu.df_update(df_2024)
+df_2024['pitch_count_hand'] = df_2024.groupby(['pitcher_id','batter_hand'])['start_speed'].transform('count')
 
-xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
 
-features = ['start_speed','spin_rate','extension','ivb','hb','x0','z0','fb_max_velo_diff','fb_max_ivb_diff','fb_max_hb_diff']
 
-targets = ['delta_run_exp_mean']
+# DEFINE STRIKE ZONE
+strike_zone = pd.DataFrame({
+'PlateLocSide': [-0.9, -0.9, 0.9, 0.9, -0.9],
+'PlateLocHeight': [1.5, 3.5, 3.5, 1.5, 1.5]
+})
 
+### STRIKE ZONE ###
+def draw_line(axis,alpha_spot=1,catcher_p = True):
 
-df_2024_update['y_pred'] = model.predict(df_2024_update[features])
+    axis.plot(strike_zone['PlateLocSide'], strike_zone['PlateLocHeight'], color='black', linewidth=1.3,zorder=3,alpha=alpha_spot,)
 
-df_2024_update['tj_stuff_plus'] = 100 + 10*((-df_2024_update.y_pred +y_pred_mean) / y_pred_std)
+    # ax.plot([-0.2833333, -0.2833333], [1.6, 3.5], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
+    # ax.plot([0.2833333, 0.2833333], [1.6, 3.5], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
+    # ax.plot([-0.85, 0.85], [2.2, 2.2], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
+    # ax.plot([-0.85, 0.85], [2.9, 2.9], color='black', linestyle='dashed',alpha=alpha_spot,zorder=3)
+    if catcher_p:
+    # Add dashed line
+    # Add home plate
+        axis.plot([-0.708, 0.708], [0.15, 0.15], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([-0.708, -0.708], [0.15, 0.3], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([-0.708, 0], [0.3, 0.5], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([0, 0.708], [0.5, 0.3], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([0.708, 0.708], [0.3, 0.15], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+    else:
+        axis.plot([-0.708, 0.708], [0.4, 0.4], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([-0.708, -0.9], [0.4, -0.1], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([-0.9, 0], [-0.1, -0.35], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([0, 0.9], [-.35, -0.1], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
+        axis.plot([0.9, 0.708], [-0.1,0.4], color='black', linewidth=1,alpha=alpha_spot,zorder=1)
 
-df_2024_update['woba_pred'] = np.nan
-
-df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']  = [sum(x) for x in xwoba_model.predict_proba(df_2024_update.loc[df_2024_update[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
-
-pitcher_dicts = df_2024_update.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict()
+pitcher_dicts = df_2024.set_index('pitcher_id')['pitcher_name'].sort_values().to_dict()
 
 team_logos = pd.read_csv('team_logos.csv')
-
-mlb_stats = MLB_Scrape()
-teams_df = mlb_stats.get_teams()
-team_logo_dict = teams_df.set_index(['team_id'])['parent_org_id'].to_dict()
-
-font_properties = {'family': 'calibi', 'size': 12}
-font_properties_titles = {'family': 'calibi', 'size': 20}
-font_properties_axes = {'family': 'calibi', 'size': 16}
-df_plot = []
-ax2_loc = []
-gs = []
-fig = []
-
-function_dict={
-    'velocity_kde':'Velocity Distributions',
-    'break_plot':'Pitch Movement',
-    'rolling_tj_stuff':'Rolling tjStuff+',
-    'location_lhb':'Locations vs LHB',
-    'location_rhb':'Locations vs RHB',
-}
-
-split_dict =  {'all':'All',
-               'left':'LHB',
-               'right':'RHB'}
-
-split_dict_hand =  {'all':['L','R'],
-               'left':['L'],
-               'right':['R']}
-
-ball_dict =  {'0':'0',
-               '1':'1',
-               '2':'2',
-               '3':'3'}
-
-strike_dict =  {'0':'0',
-               '1':'1',
-               '2':'2'}
-
-# count_dict = {'0_0':'Through 0-0',
-#                  '0_1':'Through 0-1',
-#                  '0_2':'Through 0-2',
-#                  '1_0':'Through 1-0',
-#                  '1_1':'Through 1-1',
-#                  '1_2':'Through 1-2',
-#                  '2_1':'Through 2-1',
-#                  '2_0':'Through 2-0',
-#                  '3_0':'Through 3-0',
-#                  '3_1':'Through 3-1',
-#                  '2_2':'Through 2-2',
-#                  '3_2':'Through 3-2'}
-
-# count_dict_fg = {'0_0':'',
-#                  '0_1':'61',
-#                  '0_2':'62',
-#                  '1_0':'63',
-#                  '1_1':'64',
-#                  '1_2':'65',
-#                  '2_1':'66',
-#                  '2_0':'67',
-#                  '3_0':'68',
-#                  '3_1':'69',
-#                  '2_2':'70',
-#                  '3_2':'71'}
+cmap_sum = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#648FFF','#FFFFFF','#FFB000',])
+cmap_sum2 = matplotlib.colors.LinearSegmentedColormap.from_list("", ['#FFFFFF','#FFB000',])
+        
 
 from urllib.request import Request, urlopen
 from shiny import App, reactive, ui, render
@@ -189,74 +120,39 @@ app_ui = ui.page_fluid(
     shinyswatch.theme.simplex(),
     ui.tags.h4("TJStats"),
     ui.tags.i("Baseball Analytics and Visualizations"),
+    ui.tags.h5("Pitcher Heat Maps"),
     ui.row(
 
     
     ui.layout_sidebar(
  
         ui.panel_sidebar(
-                ui.row(
-                ui.column(6,
-                    ui.input_select('player_id','Select Player',pitcher_dicts,selectize=True,multiple=False)),
-                ui.column(6,    ui.output_ui('test','Select Game'))),
                 
-                ui.row(
-                ui.column(4,
-                    ui.input_select('plot_id_1','Plot Left',function_dict,multiple=False,selected='velocity_kde')),
-                ui.column(4,
-                    ui.input_select('plot_id_2','Plot Middle',function_dict,multiple=False,selected='rolling_tj_stuff')),
-                ui.column(4,
-                    ui.input_select('plot_id_3','Plot Right',function_dict,multiple=False,selected='break_plot'))),
-
-                # ui.input_select('count_id','Count',count_dict,multiple=True,selectize=True,selected='0_0'),
-
-                ui.row(
-                ui.column(6,
-                        ui.input_select('ball_id','Balls',ball_dict,multiple=False,selected='0'),
-                                                ui.input_radio_buttons(
-                                                    "count_id_balls",
-                                                    "Count Filter Balls",
-                                                    {
-                                                        "exact": "Exact Balls",
-                                                        "greater": ">= Balls",
-                                                        "lesser": "<= Balls",
-                                                    },selected='greater')),
-                ui.column(6,                                    
-                        ui.input_select('strike_id','Strikes',strike_dict,multiple=False,selected='0'),
-                        ui.input_radio_buttons(
-                                                    "count_id_strikes",
-                                                    "Count Filter Strikes",
-                                                    {
-                                                        "exact": "Exact Strikes",
-                                                        "greater": ">= Strikes",
-                                                        "lesser": "<= Strikes",
-                                                    },selected='greater'))),
-                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=10))),
 
+                    ui.input_select('player_id','Select Player',pitcher_dicts,selectize=True,multiple=False),
+                    ui.output_ui('game_id_select','Date Range'),
+                                    
+                                    
+                    ui.output_ui('pitch_type_select','Select Pitch Type'),
+                    ui.input_action_button("go", "Generate",class_="btn-primary"),width=2
+                                    
+                
+                ),
 
                 
-                ui.input_action_button("go", "Generate",class_="btn-primary"),
                                
                                        
-                                       width=4)
-                ,
+
+                
+
         ui.panel_main(     
             ui.navset_tab(
             # ui.nav("Raw Data",
             #     ui.output_data_frame("raw_table")),                
             ui.nav("Season Summary",
                    ui.output_plot('plot',
-                                  width='2000px',
-                                  height='2000px')),
-            ui.nav("Game Summary",
-                   ui.output_plot('plot_game',
-                                  width='2000px',
-                                  height='2000px'))
-                ,id="my_tabs"))))))
+                                  width='1600px',
+                                  height='900px')),id="my_tabs"))))))
 
 
 
@@ -271,7 +167,7 @@ app_ui = ui.page_fluid(
 def server(input, output, session):
 
     @render.ui
-    def test():
+    def game_id_select():
 
             # @reactive.Effect
             if input.my_tabs() == 'Season Summary':   
@@ -279,19 +175,14 @@ def server(input, output, session):
                 return ui.input_date_range("date_range_id", "Date range input",start = df_2024.game_date.min(),
                                      end = df_2024.game_date.max(),width=2,min=df_2024.game_date.min(),
                                        max=df_2024.game_date.max()),        
-            # @reactive.Effect
-            if input.my_tabs() == 'Game Summary':
-                pitcher_id_select = int(input.player_id())
-                df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)]
-                
 
-                # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
-                df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str)
-                #print(df_plot['game_opp'])
+    @render.ui
+    def pitch_type_select():
+            pitch_dicts = df_2024[(df_2024['pitcher_id']==int(input.player_id()))].set_index('pitch_type')['pitch_description'].sort_values().to_dict()
 
-                date_dict = pd.concat([df_plot.drop_duplicates(subset=['pitcher_id','game_id','game_opp'])[['game_id','game_opp']]]).set_index('game_id').to_dict()
-                return ui.input_select("game_id", "Select Game",date_dict,selectize=True)
-            
+            # @reactive.Effect
+            return ui.input_select('pitch_type','Select Pitch Type',pitch_dicts,selectize=True,multiple=False)
+    
     @output
     @render.plot
     @reactive.event(input.go, ignore_none=False)
@@ -307,559 +198,576 @@ def server(input, output, session):
             ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center')
             ax.grid('off')
             return                
-        
-        pitcher_id_select = int(input.player_id())
 
 
-        df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)]
+        pitcher_input = int(input.player_id())
+        pitch_input = input.pitch_type()
+
+        df_plot_full = df_2024[(df_2024['pitcher_id']==pitcher_input)]
+        df_plot_full['h_s_b'] = df_plot_full.groupby(['batter_hand','strikes', 'balls']).transform('count')['pitcher_id']
+        df_plot_full['h_s_b_pitch'] = df_plot_full.groupby(['batter_hand','strikes', 'balls','pitch_type']).transform('count')['pitcher_id']
+        df_plot_full['h_s_b_pitch_percent'] = df_plot_full['h_s_b_pitch']/df_plot_full['h_s_b']
+
+
+        df_plot = df_plot_full[(df_plot_full['pitch_type']==pitch_input)]
         df_plot = df_plot[(pd.to_datetime(df_plot['game_date']).dt.date>=input.date_range_id()[0])&
                                             (pd.to_datetime(df_plot['game_date']).dt.date<=input.date_range_id()[1])]
         
-        df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])]
-
-        if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0:
-            ball_title = ''
-            strike_title = ''
-        else: 
-            if input.count_id_balls()=='exact':
-                df_plot = df_plot[df_plot['balls']==int(input.ball_id())]
-                ball_title = str(f'{(input.ball_id())} Ball Count; ')                                                   
-            elif input.count_id_balls()=='greater':
-                df_plot = df_plot[df_plot['balls']>=int(input.ball_id())]
-                ball_title = str(f'At Least {(input.ball_id())} Ball Count; ')                         
-            elif input.count_id_balls()=='lesser':  
-                df_plot = df_plot[df_plot['balls']<=int(input.ball_id())]
-                ball_title = str(f'At Most {(input.ball_id())} Ball Count; ')                                        
-
-            if input.count_id_strikes()=='exact':
-                df_plot = df_plot[df_plot['strikes']==int(input.strike_id())]     
-                strike_title = str(f'{(input.strike_id())} Strike Count; ')                           
-            elif input.count_id_strikes()=='greater':
-                df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())]     
-                strike_title = str(f'At Least {(input.strike_id())} Strike Count; ')            
-            elif input.count_id_strikes()=='lesser':
-                df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())]  
-                strike_title = str(f'At Most {(input.strike_id())} Strike Count; ')                                        
-                                      
-
-
-        if input.split_id() == 'all':
-            split_title = ''
-
-        elif input.split_id() == 'left':
-            split_title = 'vs. LHH'
-            
-        elif input.split_id() == 'right':
-            split_title = 'vs. RHH'
-
-
-        if len(df_plot)<1:
-            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
-            ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center')
-            ax.grid('off')
-            return
 
-        df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1
-        df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1
-        #df_plot = df_plot.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left')
-        df_plot = df_plot.sort_values(by=['pitch_description'])
-        df_plot = df_plot.sort_values(by=['start_time'])
+        print("THIS IS HERE")
+        print(df_plot)
+        pivot_table_l = df_plot[df_plot['batter_hand'].isin(['L'])].groupby(['batter_hand','strikes', 'balls'])[['h_s_b_pitch_percent']].mean().reset_index().pivot('strikes','balls','h_s_b_pitch_percent')#.fillna(0).style.background_gradient(cmap=cmap_sum2, axis=None).format("{:.0%}")
+        # Create a new index and columns range
+        new_index = range(3)
+        new_columns = range(4)
 
-        grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description'])
-        grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand'])
+        # Reindex the pivot table
+        pivot_table_l = pivot_table_l.reindex(index=new_index, columns=new_columns)
 
+        # Fill any missing values with 0
+        pivot_table_l = pivot_table_l.fillna(0)
 
+        pivot_table_l = df_plot[df_plot['batter_hand']=='L'].groupby(['batter_hand','strikes', 'balls'])[['h_s_b_pitch_percent']].mean().reset_index().pivot('strikes','balls','h_s_b_pitch_percent')#.fillna(0).style.background_gradient(cmap=cmap_sum2, axis=None).format("{:.0%}")
+        # Create a new index and columns range
+        new_index = range(3)
+        new_columns = range(4)
 
-        from matplotlib.gridspec import GridSpec
-        plt.rcParams['font.family'] = 'Calibri'
-        df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum")
-        label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()
+        # Reindex the pivot table
+        pivot_table_l = pivot_table_l.reindex(index=new_index, columns=new_columns)
 
-        #plt.rcParams["figure.figsize"] = [10,10]
-        fig = plt.figure(figsize=(20, 20))
-        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(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5])
-        gs = GridSpec(5, 5,  height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1])
-        #### NO FG
-        ####gs = GridSpec(5, 5,  height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1])
-        #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4])
+        # Fill any missing values with 0
+        pivot_table_l = pivot_table_l.fillna(0)
 
-        gs.update(hspace=0.2, wspace=0.3)
+        pivot_table_r = df_plot[df_plot['batter_hand']=='R'].groupby(['batter_hand','strikes', 'balls'])[['h_s_b_pitch_percent']].mean().reset_index().pivot('strikes','balls','h_s_b_pitch_percent')#.fillna(0).style.background_gradient(cmap=cmap_sum2, axis=None).format("{:.0%}")
+        # Create a new index and columns range
+        new_index = range(3)
+        new_columns = range(4)
 
-        # Add subplots to the grid
-        ax0 = fig.add_subplot(gs[0, :]) 
-        ax1_table = fig.add_subplot(gs[1, :])
-        ax2_left = fig.add_subplot(gs[2, 1]) 
-        ax2_middle = fig.add_subplot(gs[2, 2]) 
-        ax2_right = fig.add_subplot(gs[2, 3])
-        ax3 = fig.add_subplot(gs[-2, :])
-        #axfooter = fig.add_subplot(gs[-1, :])
+        # Reindex the pivot table
+        pivot_table_r = pivot_table_r.reindex(index=new_index, columns=new_columns)
 
-        ax1_table.axis('off')
+        # Fill any missing values with 0
+        pivot_table_r = pivot_table_r.fillna(0)
+        
+        
 
-        sns.set_theme(style="whitegrid", palette=colour_palette)
-        fig.set_facecolor('white')
+        # Assuming you have a DataFrame called 'df_plot_full' with columns 'pitch_type', 'strikes', and 'balls'
+
+        # Filter the dataset to include only slider pitches
+        # slider_pitches = df_plot_full[df_plot_full['pitch_type'] == 'SL']
+
+        # Group the filtered dataset by strike and ball counts
+        # grouped_counts = slider_pitches.groupby(['pitcher_hand','strikes', 'balls']).size().reset_index(name='total_pitches')
 
-        font_properties = {'family': 'calibi', 'size': 12}
-        font_properties_titles = {'family': 'calibi', 'size': 20}
-        font_properties_axes = {'family': 'calibi', 'size': 16}
+        # Calculate the proportion of slider pitches for each strike and ball count
+        # grouped_counts['proportion'] = grouped_counts['total_pitches'] / grouped_counts['total_pitches'].sum()
 
-        # ## FANGRAPHS TABLE ###
-        # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select,
-        #                     split=input.split_id(),
-        #                     start_date=input.date_range_id()[0],
-        #                     end_date=input.date_range_id()[1])
+        # Print the resulting DataFrame
+        df_summ = df_plot.groupby(['batter_hand']).agg(
+                pitch_count = ('pitch_count_hand','max'),
+                pa = ('pa','sum'),
+                ab = ('ab','sum'),
+                obp_pa = ('obp','sum'),
+                hits = ('hits','sum'),
+                on_base = ('on_base','sum'),
+                k = ('k','sum'),
+                bb = ('bb','sum'),
+                bb_minus_k = ('bb_minus_k','sum'),
+                csw = ('csw','sum'),
+                bip = ('bip','sum'),
+                bip_div = ('bip_div','sum'),
+                tb = ('tb','sum'),
+                woba = ('woba','sum'),
+                woba_contact = ('woba_contact','sum'),
+                xwoba = ('woba_pred','sum'),
+                xwoba_contact = ('woba_pred_contact','sum'),       
+                woba_codes = ('woba_codes','sum'),
+                hard_hit = ('hard_hit','sum'),
+                barrel = ('barrel','sum'),
+                sweet_spot = ('sweet_spot','sum'),
+                max_launch_speed = ('launch_speed','max'),
+                launch_speed = ('launch_speed','mean'),
+                launch_angle = ('launch_angle','mean'),
+                pitches = ('is_pitch','sum'),
+                swings =  ('swings','sum'),
+                in_zone = ('in_zone','sum'),
+                out_zone = ('out_zone','sum'),
+                whiffs = ('whiffs','sum'),
+                zone_swing = ('zone_swing','sum'),
+                zone_contact = ('zone_contact','sum'),
+                ozone_swing = ('ozone_swing','sum'),
+                ozone_contact = ('ozone_contact','sum'),
+                ground_ball = ('trajectory_ground_ball','sum'),
+                line_drive = ('trajectory_line_drive','sum'),
+                fly_ball =('trajectory_fly_ball','sum'),
+                pop_up = ('trajectory_popup','sum'),
+                attack_zone = ('attack_zone','count'),
+                heart = ('heart','sum'),
+                shadow = ('shadow','sum'),
+                chase = ('chase','sum'),
+                waste = ('waste','sum'),
+                heart_swing = ('heart_swing','sum'),
+                shadow_swing = ('shadow_swing','sum'),
+                chase_swing = ('chase_swing','sum'),
+                waste_swing = ('waste_swing','sum'),
+                heart_whiff = ('heart_whiff','sum'),
+                shadow_whiff = ('shadow_whiff','sum'),
+                chase_whiff = ('chase_whiff','sum'),
+                waste_whiff = ('waste_whiff','sum'),
+                ).reset_index()       
 
 
-        # psf.fangraphs_table(data=data_pull,
-        #                     stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'],
-        #                     ax=ax1_table)
+        df_summ['avg'] = [df_summ.hits[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['obp'] = [df_summ.on_base[x]/df_summ.obp_pa[x] if df_summ.obp_pa[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['slg'] =  [df_summ.tb[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        start_date = str(pd.to_datetime(input.date_range_id()[0]).strftime('%m/%d/%Y'))
-        end_date = str(pd.to_datetime(input.date_range_id()[1]).strftime('%m/%d/%Y'))
+        df_summ['ops'] = df_summ['obp']+df_summ['slg']
 
+        df_summ['k_percent'] = [df_summ.k[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['bb_percent'] =[df_summ.bb[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['bb_minus_k_percent'] =[(df_summ.bb_minus_k[x])/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json()
+        df_summ['bb_over_k_percent'] =[df_summ.bb[x]/df_summ.k[x] if df_summ.k[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
-        pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
-        pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0])
-        pitcher_stats_call_df['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced']
-        pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced']
-        pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent']
-        pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','era','whip','k_percent','bb_percent','k_bb_percent']]
 
-        pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
-        pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
-        pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 
 
-        table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center',
-                        bbox=[0.04, 0.2, 0.92, 0.8])
 
-        min_font_size = 20
-        table_fg.set_fontsize(min_font_size)
+        df_summ['csw_percent'] =[df_summ.csw[x]/df_summ.pitches[x] if df_summ.pitches[x] != 0 else np.nan for x in range(len(df_summ))]
 
 
-        new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ERA}$','$\\bf{WHIP}$','$\\bf{K\%}$','$\\bf{BB\%}$','$\\bf{K-BB\%}$']
-        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
-        for i, col_name in enumerate(new_column_names):
-            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
+        df_summ['sweet_spot_percent'] = [df_summ.sweet_spot[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        ax1_table.axis('off')
+        df_summ['woba_percent'] = [df_summ.woba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['woba_percent_contact'] = [df_summ.woba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+        #df_summ['hard_hit_percent'] = [df_summ.sweet_spot[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['hard_hit_percent'] = [df_summ.hard_hit[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
 
 
+        df_summ['barrel_percent'] = [df_summ.barrel[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax2_left,ax2_middle,ax2_right],[1,2,3]):
-            if x == 'velocity_kde':
-                psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig)
-            if x == 'rolling_tj_stuff':
-                psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y)
-            if x == 'break_plot':
-                psf.break_plot(df=df_plot,ax=y) 
-            if x == 'location_lhb':
-                psf.location_plot(df=df_plot,ax=y,hand='L')
-            if x == 'location_rhb':
-                psf.location_plot(df=df_plot,ax=y,hand='R')
+        df_summ['zone_contact_percent'] = [df_summ.zone_contact[x]/df_summ.zone_swing[x] if df_summ.zone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        pitches_list = df_plot['pitch_description'].unique()
-        colour_pitches = [pitch_colours[x] for x in pitches_list]
-        
-    #     handles, labels = ax2_right.get_legend_handles_labels()
+        df_summ['zone_swing_percent'] = [df_summ.zone_swing[x]/df_summ.in_zone[x] if df_summ.in_zone[x] != 0 else np.nan for x in range(len(df_summ))]
 
-    #   # Manually create handles and labels for each pitch-color pair
-        handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
-        labels = pitches_list  
-        
-                
+        df_summ['zone_percent'] = [df_summ.in_zone[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
 
+        df_summ['chase_percent'] = [df_summ.ozone_swing[x]/(df_summ.pitches[x] - df_summ.in_zone[x]) if (df_summ.pitches[x]- df_summ.in_zone[x]) != 0 else np.nan for x in range(len(df_summ))]
 
-        ### FANGRAPHS TABLE ###
-        psf.table_summary(df=df_plot.copy(),
-                        pitcher_id=pitcher_id_select,
-                        ax=ax3,
-                        df_group=grouped_ivb.copy(),
-                        df_group_all=grouped_ivb_all.copy(),
-                        statcast_pitch_summary=statcast_pitch_summary.copy())
+        df_summ['chase_contact'] = [df_summ.ozone_contact[x]/df_summ.ozone_swing[x] if df_summ.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
 
+        df_summ['swing_percent'] = [df_summ.swings[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
 
-        # ############ FOOTER ################
-        # #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
-        # axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
-        # axfooter.text(x=1-0.05,y=1,s='Data: MLB, Fangraphs',ha='right',fontname='Calibri',fontsize=24,va='top')
+        df_summ['whiff_rate'] = [df_summ.whiffs[x]/df_summ.swings[x] if df_summ.swings[x] != 0 else np.nan for x in range(len(df_summ))]
 
+        df_summ['swstr_rate'] = [df_summ.whiffs[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
 
-        # axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
-        #             ha='center',va='center',fontname='Calibri',fontsize=16)
-        # axfooter.axis('off')
-        # #fig.tight_layout()
+        df_summ['ground_ball_percent'] = [df_summ.ground_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        
+        df_summ['line_drive_percent'] = [df_summ.line_drive[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        # Get value counts of the column and sort in descending order
-        sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False)
+        df_summ['fly_ball_percent'] = [df_summ.fly_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        # Get the list of items ordered from most to least frequent
-        items_in_order = sorted_value_counts.index.tolist()
-        # Create a dictionary to map names to colors
-        name_to_color = dict(zip(labels, handles))
+        df_summ['pop_up_percent'] = [df_summ.pop_up[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]  
 
-        # Order the colors based on the correct order of names
-        ordered_colors = [name_to_color[name] for name in items_in_order]
 
 
-        ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5,
-                fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20})
+        df_summ['heart_zone_percent'] = [df_summ.heart[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
 
+        df_summ['shadow_zone_percent'] = [df_summ.shadow[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        ################## Title ##########
-        title_spot = f'{df_plot.pitcher_name.values[0]}'
+        df_summ['chase_zone_percent'] = [df_summ.chase[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
 
+        df_summ['waste_zone_percent'] = [df_summ.waste[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top')
-        ax0.text(x=0.5,y=0.35,s='A Season Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic')
 
-        player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
-        #ax0.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
-        ax0.axis('off')
-        ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top')
+        df_summ['heart_zone_swing_percent'] = [df_summ.heart_swing[x]/df_summ.heart[x] if df_summ.heart[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
-        # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')
-        # ax0.axis('off')
+        df_summ['shadow_zone_swing_percent'] = [df_summ.shadow_swing[x]/df_summ.shadow[x] if df_summ.shadow[x] != 0 else np.nan for x in range(len(df_summ))]    
 
+        df_summ['chase_zone_swing_percent'] = [df_summ.chase_swing[x]/df_summ.chase[x] if df_summ.chase[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        ax0.text(x=0.5,y=0.15,s=f'{input.date_range_id()[0]} to {input.date_range_id()[1]}',fontname='Calibri',ha='center',fontsize=30,va='top',fontstyle='italic')
-        
-        ax0.text(x=0.5,y=0.0,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
-        ax0.axis('off')
+        df_summ['waste_zone_swing_percent'] = [df_summ.waste_swing[x]/df_summ.waste[x] if df_summ.waste[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)
-        import urllib
-        import urllib.request
-        import urllib.error
-        from urllib.error import HTTPError
+        df_summ['heart_zone_whiff_percent'] = [df_summ.heart_whiff[x]/df_summ.heart_swing[x] if df_summ.heart_swing[x] != 0 else np.nan for x in range(len(df_summ))]
 
-        try: 
-            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png'
-            test_mage = plt.imread(url)
-        except urllib.error.HTTPError as err:
-            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png'                
-            test_mage = plt.imread(url)    
-        imagebox = OffsetImage(test_mage, zoom = 0.5)
-        ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False)
-        ax0.add_artist(ab)
+        df_summ['shadow_zone_whiff_percent'] = [df_summ.shadow_whiff[x]/df_summ.shadow_swing[x] if df_summ.shadow_swing[x] != 0 else np.nan for x in range(len(df_summ))]    
 
-        #player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
+        df_summ['chase_zone_whiff_percent'] = [df_summ.chase_whiff[x]/df_summ.chase_swing[x] if df_summ.chase_swing[x] != 0 else np.nan for x in range(len(df_summ))]    
 
+        df_summ['waste_zone_whiff_percent'] = [df_summ.waste_whiff[x]/df_summ.waste_swing[x] if df_summ.waste_swing[x] != 0 else np.nan for x in range(len(df_summ))]    
+        df_summ['xwoba_percent'] = [df_summ.xwoba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+        df_summ['xwoba_percent_contact'] = [df_summ.xwoba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
 
+        df_summ['pitch_percent'] = [df_summ.pitches[x]/df_summ.pitch_count[x] if df_summ.pitch_count[x] != 0 else np.nan for x in range(len(df_summ))]
 
+        table_left = df_summ[df_summ['batter_hand']=='L'][['pitch_percent',
+                                                        'pitches',
+                                            'heart_zone_percent',
+                                            'shadow_zone_percent',
+                                            'chase_zone_percent',
+                                            'waste_zone_percent',
+                                            'csw_percent',
+                                            'whiff_rate',
+                                            'chase_percent',
+                                            'bip',
+                                            'xwoba_percent_contact'
+                                            ]]
 
-        if 'currentTeam' in player_bio['people'][0]:
-            try:
-                url = team_logos[team_logos['id'] == team_logo_dict[player_bio['people'][0]['currentTeam']['id']]]['imageLink'].values[0]
+        ### GET COLOURS##
+        import matplotlib.colors
+        import matplotlib.colors as mcolors
+        def get_color(value,normalize):
+            color = cmap_sum(normalize(value))
+            return mcolors.to_hex(color)
 
-                im = plt.imread(url)
-                # response = requests.get(url)
-                # im = Image.open(BytesIO(response.content))
-                # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0])
-                # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
-                imagebox = OffsetImage(im, zoom = 0.4)
-                ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False)
-                ax0.add_artist(ab)
-            except IndexError:
-                print()
-            
+        normalize = mcolors.Normalize(vmin=table_left['pitch_percent']*0.5, 
+                                    vmax=table_left['pitch_percent']*1.5) # Define the range of values
 
-        ############ FOOTER ################
-        #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
-        axfooter = fig.add_subplot(gs[-1, :])
-        axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
-        axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top')
 
 
-        axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
-                    ha='center',va='center',fontname='Calibri',fontsize=16)
-        axfooter.axis('off')
-        #fig.tight_layout()
+        df_colour_left = pd.DataFrame(data=[[get_color(x,normalize) for x in pivot_table_l.loc[0]],
+                    [get_color(x,normalize) for x in pivot_table_l.loc[1]],
+                    [get_color(x,normalize) for x in pivot_table_l.loc[2]]],)
 
-        fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
 
+        table_left['pitch_percent'] = table_left['pitch_percent'].map('{:.1%}'.format)
+        table_left['pitches'] = table_left['pitches'].astype(int).astype(str)
+        # table_left['pa'] = table_left['pa'].astype(int).astype(str)
+        # table_left['k_percent'] = table_left['k_percent'].map('{:.1%}'.format)
+        # table_left['bb_percent'] = table_left['bb_percent'].map('{:.1%}'.format)
+        table_left['heart_zone_percent'] = table_left['heart_zone_percent'].map('{:.1%}'.format)
+        table_left['shadow_zone_percent'] = table_left['shadow_zone_percent'].map('{:.1%}'.format)
+        table_left['chase_zone_percent'] = table_left['chase_zone_percent'].map('{:.1%}'.format)
+        table_left['waste_zone_percent'] = table_left['waste_zone_percent'].map('{:.1%}'.format)
+        table_left['csw_percent'] = table_left['csw_percent'].map('{:.1%}'.format)
+        table_left['whiff_rate'] = table_left['whiff_rate'].map('{:.1%}'.format)
+        table_left['chase_percent'] = table_left['chase_percent'].map('{:.1%}'.format)
+        table_left['bip'] = table_left['bip'].astype(int).astype(str)
+        table_left['xwoba_percent_contact'] = table_left['xwoba_percent_contact'].map('{:.3f}'.format)
+        table_left.columns = ['Usage%','Pitches','Heart%','Shadow%','Chase%','Waste%','CSW%','Whiff%','O-Swing%','BBE','xwOBACON']
 
-    @output
-    @render.plot
-    @reactive.event(input.go, ignore_none=False)
-    def plot_game():
-        #fig, ax = plt.subplots(3, 2, figsize=(9, 9))
 
-        font_properties = {'family': 'calibi', 'size': 12}
-        font_properties_titles = {'family': 'calibi', 'size': 20}
-        font_properties_axes = {'family': 'calibi', 'size': 16}
-                
-        if len((input.player_id()))<1:
-            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
-            ax.text(x=0.5,y=0.5,s='Please Select\nA Player',fontsize=150,ha='center')
-            ax.grid('off')
-            return        
-        
-        pitcher_id_select = int(input.player_id())
-
-
-
-
-        df_plot = df_2024_update[(df_2024_update['pitcher_id']==pitcher_id_select)&(df_2024_update['game_id']==int(input.game_id()))]
-        df_plot = df_plot[df_plot['batter_hand'].isin(split_dict_hand[input.split_id()])]
-
-        if input.count_id_balls()=='greater' and input.count_id_strikes()=='greater' and int(input.ball_id())==0 and int(input.strike_id())==0:
-            ball_title = ''
-            strike_title = ''
-        else: 
-            if input.count_id_balls()=='exact':
-                df_plot = df_plot[df_plot['balls']==int(input.ball_id())]
-                ball_title = str(f'{(input.ball_id())} Ball Count; ')                                                   
-            elif input.count_id_balls()=='greater':
-                df_plot = df_plot[df_plot['balls']>=int(input.ball_id())]
-                ball_title = str(f'At Least {(input.ball_id())} Ball Count; ')                         
-            elif input.count_id_balls()=='lesser':  
-                df_plot = df_plot[df_plot['balls']<=int(input.ball_id())]
-                ball_title = str(f'At Most {(input.ball_id())} Ball Count; ')                                        
-
-            if input.count_id_strikes()=='exact':
-                df_plot = df_plot[df_plot['strikes']==int(input.strike_id())]     
-                strike_title = str(f'{(input.strike_id())} Strike Count; ')                           
-            elif input.count_id_strikes()=='greater':
-                df_plot = df_plot[df_plot['strikes']>=int(input.strike_id())]     
-                strike_title = str(f'At Least {(input.strike_id())} Strike Count; ')            
-            elif input.count_id_strikes()=='lesser':
-                df_plot = df_plot[df_plot['strikes']<=int(input.strike_id())]  
-                strike_title = str(f'At Most {(input.strike_id())} Strike Count; ')                                        
-                                      
+        table_left = table_left.replace({'nan%':'—'})
+        table_left = table_left.replace({'nan':'—'})
+        table_left = table_left.T
 
-                                    
 
+        table_right = df_summ[df_summ['batter_hand']=='R'][['pitch_percent',
+                                                        'pitches',
+                                            'heart_zone_percent',
+                                            'shadow_zone_percent',
+                                            'chase_zone_percent',
+                                            'waste_zone_percent',
+                                            'csw_percent',
+                                            'whiff_rate',
+                                            'chase_percent',
+                                            'bip',
+                                            'xwoba_percent_contact'
+                                            ]]
 
-        if input.split_id() == 'all':
-            split_title = ''
+        normalize = mcolors.Normalize(vmin=table_right['pitch_percent']*0.5, 
+                                    vmax=table_right['pitch_percent']*1.5) # Define the range of values
 
-        elif input.split_id() == 'left':
-            split_title = 'vs. LHH'
-            
-        elif input.split_id() == 'right':
-            split_title = 'vs. RHH'
 
-        if len(df_plot)<1:
-            fig, ax = plt.subplots(1, 1, figsize=(9, 9))
-            ax.text(x=0.5,y=0.5,s='Please Select\nOther Parameters',fontsize=150,ha='center')
-            ax.grid('off')
-            return
+        df_colour_right = pd.DataFrame(data=[[get_color(x,normalize) for x in pivot_table_r.loc[0]],
+                    [get_color(x,normalize) for x in pivot_table_r.loc[1]],
+                    [get_color(x,normalize) for x in pivot_table_r.loc[2]]],)
 
 
-        df_plot['pitch_type_count'] = df_plot.groupby(['pitcher_id'])['pitch_type'].cumcount()+1
-        df_plot['pitch_type_count_each'] = df_plot.groupby(['pitch_type'])['pitch_type'].cumcount()+1
-        #df_plot = df_plot.merge(df_2024_update[['tj_stuff_plus','play_id']],left_on=['play_id'],right_on=['play_id'],how='left')
-        df_plot = df_plot.sort_values(by=['pitch_description'])
-        df_plot = df_plot.sort_values(by=['start_time'])
 
-        # ax0.text(x=0.5,y=0.30,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
-        df_plot['game_opp'] = df_plot['game_date'].astype(str) + ' vs ' + df_plot['batter_team'].astype(str)
-        #print(df_plot['game_opp'])
+        table_right['pitch_percent'] = table_right['pitch_percent'].map('{:.1%}'.format)
+        table_right['pitches'] = table_right['pitches'].astype(int).astype(str)
+        # table_right['pa'] = table_right['pa'].astype(int).astype(str)
+        # table_right['k_percent'] = table_right['k_percent'].map('{:.1%}'.format)
+        # table_right['bb_percent'] = table_right['bb_percent'].map('{:.1%}'.format)
+        table_right['heart_zone_percent'] = table_right['heart_zone_percent'].map('{:.1%}'.format)
+        table_right['shadow_zone_percent'] = table_right['shadow_zone_percent'].map('{:.1%}'.format)
+        table_right['chase_zone_percent'] = table_right['chase_zone_percent'].map('{:.1%}'.format)
+        table_right['waste_zone_percent'] = table_right['waste_zone_percent'].map('{:.1%}'.format)
+        table_right['csw_percent'] = table_right['csw_percent'].map('{:.1%}'.format)
+        table_right['whiff_rate'] = table_right['whiff_rate'].map('{:.1%}'.format)
+        table_right['chase_percent'] = table_right['chase_percent'].map('{:.1%}'.format)
+        table_right['bip'] = table_right['bip'].astype(int).astype(str)
+        table_right['xwoba_percent_contact'] = table_right['xwoba_percent_contact'].map('{:.3f}'.format)
+        table_right.columns = ['Usage%','Pitches','Heart%','Shadow%','Chase%','Waste%','CSW%','Whiff%','O-Swing%','BBE','xwOBACON']
+
+
+        table_right = table_right.replace({'nan%':'—'})
+        table_right = table_right.replace({'nan':'—'})
+        table_right = table_right.T
+
+
+        import matplotlib.pyplot as plt
+        import seaborn as sns
+        import matplotlib.gridspec as gridspec
+        from matplotlib.gridspec import GridSpec
 
-        #date_dict = pd.concat([df_plot.drop_duplicates(subset=['pitcher_id','game_id','game_opp'])[['game_id','game_opp']]]).set_index('game_id').to_dict()
+        # Assuming you have a list of pitch locations called 'pitch_locations'
+        # where each location is a tuple of (x, y) coordinates
 
-        grouped_ivb = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand','pitch_type','pitch_description'])
-        grouped_ivb_all = psf.group_ivb_update(df=df_plot,agg_list=['pitcher_id','pitcher_name','pitcher_hand'])
+        fig = plt.figure(figsize=(16, 9))
+        fig.set_facecolor('white')
+        sns.set_theme(style="whitegrid", palette=colour_palette)
+        gs = GridSpec(3, 5,  height_ratios=[2,9,1],width_ratios=[2,9,0.5,9,2])
+        gs.update(hspace=0.2, wspace=0.2)
 
+        # Add subplots to the grid
+        axheader = fig.add_subplot(gs[0, :]) 
+        ax_left = fig.add_subplot(gs[1, 1]) 
+        ax_right = fig.add_subplot(gs[1, 3]) 
 
+        axfooter = fig.add_subplot(gs[-1, :])
 
-        from matplotlib.gridspec import GridSpec
-        plt.rcParams['font.family'] = 'Calibri'
-        df_plot['prop'] = df_plot.groupby("pitch_type")["is_pitch"].transform("sum")
-        label_labels = df_plot.sort_values(by=['prop','pitch_type'],ascending=[False,True]).pitch_description.unique()
 
-        #plt.rcParams["figure.figsize"] = [10,10]
-        fig = plt.figure(figsize=(20, 20))
-        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(7, 2, width_ratios=[1,1], height_ratios=[1.5,1,1,1,1,1,2.5])
-        gs = GridSpec(5, 5,  height_ratios=[150,75,225,325,50],width_ratios=[1,100,100,100,1])
-        #### NO FG
-        ####gs = GridSpec(5, 5,  height_ratios=[225,0,225,325,50],width_ratios=[1,100,100,100,1])
-        #gs = GridSpec(4, 1, width_ratios=[1], height_ratios=[1,0.75,7-len(label_labels)/4,1+len(label_labels)/4])
 
-        gs.update(hspace=0.2, wspace=0.3)
+        if df_plot[df_plot['batter_hand']=='L'].shape[0] > 3:
+            sns.kdeplot(data=df_plot[df_plot['batter_hand']=='L'],
+                        x='px',
+                        y='pz',
+                        cmap=cmap_sum,
+                        shade=True,
+                        ax=ax_left,
+                        thresh=0.3,
+                        bw_adjust=0.5)
+        else:
+            sns.scatterplot(data=df_plot[df_plot['batter_hand']=='L'],
+                        x='px',
+                        y='pz',
+                        cmap=cmap_sum,
+                        ax=ax_left,
+                        s=125)    
 
-        # Add subplots to the grid
-        ax0 = fig.add_subplot(gs[0, :]) 
-        ax1_table = fig.add_subplot(gs[1, :])
-        ax2_left = fig.add_subplot(gs[2, 1]) 
-        ax2_middle = fig.add_subplot(gs[2, 2]) 
-        ax2_right = fig.add_subplot(gs[2, 3])
-        ax3 = fig.add_subplot(gs[-2, :])
-        # axfooter = fig.add_subplot(gs[-1, :])
+        if df_plot[df_plot['batter_hand']=='R'].shape[0] > 3:
+            sns.kdeplot(data=df_plot[df_plot['batter_hand']=='R'],
+                        x='px',
+                        y='pz',
+                        cmap=cmap_sum,
+                        shade=True,
+                        ax=ax_right,
+                        thresh=0.3,
+                        bw_adjust=0.5)
+        else:
+                sns.scatterplot(data=df_plot[df_plot['batter_hand']=='R'],
+                        x='px',
+                        y='pz',
+                        cmap=cmap_sum,
+                        ax=ax_right,
+                        s=125)
 
-        ax1_table.axis('off')
+        draw_line(ax_left,alpha_spot=1,catcher_p = False)
+        draw_line(ax_right,alpha_spot=1,catcher_p = False)
 
-        sns.set_theme(style="whitegrid", palette=colour_palette)
-        fig.set_facecolor('white')
+        ax_left.axis('off')
+        ax_right.axis('off')
 
-        font_properties = {'family': 'calibi', 'size': 12}
-        font_properties_titles = {'family': 'calibi', 'size': 20}
-        font_properties_axes = {'family': 'calibi', 'size': 16}
+        ax_left.axis('square')
+        ax_right.axis('square')
 
-        print(df_2024_update['game_date'].values[0])
-        # ## FANGRAPHS TABLE ###
-        # data_pull = psf.fangraphs_scrape(pitcher_id=pitcher_id_select,
-        #                     split=input.split_id(),
-        #                     start_date=df_plot['game_date'].values[0],
-        #                     end_date=df_plot['game_date'].values[0])
+        ax_left.set_xlim(-2.75,2.75)
+        ax_right.set_xlim(-2.75,2.75)
 
-        start_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y'))
-        end_date = str(pd.to_datetime(df_plot['game_date'].values[0]).strftime('%m/%d/%Y'))
+        ax_left.set_ylim(-0.5,5)
+        ax_right.set_ylim(-0.5,5)
 
 
-        pitcher_stats_call = requests.get(f'https://statsapi.mlb.com/api/v1/people/{pitcher_id_select}?appContext=minorLeague&hydrate=stats(group=[pitching],type=[byDateRange],sportId=14,startDate={start_date},endDate={end_date})').json()
-        
-        pitcher_stats_call_header = [x for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
-        pitcher_stats_call_values = [pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat'][x] for x in pitcher_stats_call['people'][0]['stats'][0]['splits'][0]['stat']]
-        pitcher_stats_call_df = pd.DataFrame(data=dict(zip(pitcher_stats_call_header,pitcher_stats_call_values)),index=[0])
-        # pitcher_stats_call_df['k_percent'] = pitcher_stats_call_df['strikeOuts']/pitcher_stats_call_df['battersFaced']
-        # pitcher_stats_call_df['bb_percent'] = pitcher_stats_call_df['baseOnBalls']/pitcher_stats_call_df['battersFaced']
-        # pitcher_stats_call_df['k_bb_percent'] = pitcher_stats_call_df['k_percent']-pitcher_stats_call_df['bb_percent']
-        pitcher_stats_call_df_small = pitcher_stats_call_df[['inningsPitched','battersFaced','earnedRuns','hits','strikeOuts','baseOnBalls','hitByPitch','homeRuns']]
-        pitcher_stats_call_df_small['whiffs'] = int(df_plot['is_whiff'].sum())
-        # pitcher_stats_call_df_small['k_percent'] = pitcher_stats_call_df_small['k_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
-        # pitcher_stats_call_df_small['bb_percent'] = pitcher_stats_call_df_small['bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x))  
-        # pitcher_stats_call_df_small['k_bb_percent'] = pitcher_stats_call_df_small['k_bb_percent'].astype(float).apply(lambda x: '{:.1%}'.format(x)) 
-
-        table_fg = ax1_table.table(cellText=pitcher_stats_call_df_small.values, colLabels=pitcher_stats_call_df_small.columns, cellLoc='center',
-                        bbox=[0.04, 0.2, 0.92, 0.8])
-
-        min_font_size = 20
-        table_fg.set_fontsize(min_font_size)
-
-
-        new_column_names = ['$\\bf{IP}$','$\\bf{PA}$','$\\bf{ER}$','$\\bf{H}$','$\\bf{K}$','$\\bf{BB}$','$\\bf{HBP}$','$\\bf{HR}$','$\\bf{Whiffs}$']
-        # #new_column_names = ['Pitch Name', 'Pitch%', 'Velocity', 'Spin Rate','Exit Velocity', 'Whiff%', 'CSW%']
-        for i, col_name in enumerate(new_column_names):
-            table_fg.get_celld()[(0, i)].get_text().set_text(col_name)
-
-        ax1_table.axis('off')
-
-
-        # psf.fangraphs_table(data=data_pull,
-        #                     stats=['IP','WHIP','ERA','FIP','TBF','K%','BB%','K-BB%'],
-        #                     ax=ax1_table)
-
-
-        # psf.velocity_kdes(df=df_plot,
-        #                 ax=ax2_loc,
-        #                 gs=gs,
-        #                 fig=fig)
-
-        # # psf.tj_stuff_roling(df = df_plot,
-        # #                     window = 5,
-        # #                     ax=ax2_velo)
-        # psf.location_plot(df=df_plot,ax=ax2_velo,hand='L')
-
-        # psf.location_plot(df=df_plot,ax=ax2_loc,hand='R')
-        # # # ## Break Plot
-        # psf.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()],[ax2_left,ax2_middle,ax2_right],[1,2,3]):
-            if x == 'velocity_kde':
-                psf.velocity_kdes(df=df_plot,ax=y,gs=gs,gs_list=z,fig=fig)
-            if x == 'rolling_tj_stuff':
-                psf.tj_stuff_roling(df = df_plot,window = int(input.rolling_window()),ax=y)
-            if x == 'break_plot':
-                psf.break_plot(df=df_plot,ax=y) 
-            if x == 'location_lhb':
-                psf.location_plot(df=df_plot,ax=y,hand='L')
-            if x == 'location_rhb':
-                psf.location_plot(df=df_plot,ax=y,hand='R')
-
-        pitches_list = df_plot['pitch_description'].unique()
-        colour_pitches = [pitch_colours[x] for x in pitches_list]
-        
-    #     handles, labels = ax2_right.get_legend_handles_labels()
+        import matplotlib.pyplot as plt
 
-    #   # Manually create handles and labels for each pitch-color pair
-        handles = [plt.scatter([], [], color=color, marker='o', s=100) for color in colour_pitches]
-        labels = pitches_list  
+        import matplotlib.image as mpimg
+        from matplotlib.offsetbox import OffsetImage, AnnotationBbox
 
+        # Load the image
+        img = mpimg.imread('left.png')
+        imagebox = OffsetImage(img, zoom=0.7)  # adjust zoom as needed
+        ab = AnnotationBbox(imagebox, (1.25, -0.5), box_alignment=(0, 0), frameon=False)
+        ax_left.add_artist(ab)
 
-        ### FANGRAPHS TABLE ###
-        psf.table_summary(df=df_plot.copy(),
-                        pitcher_id=pitcher_id_select,
-                        ax=ax3,
-                        df_group=grouped_ivb.copy(),
-                        df_group_all=grouped_ivb_all.copy(),
-                        statcast_pitch_summary=statcast_pitch_summary.copy())
 
+        # Load the image
+        img = mpimg.imread('right.png')
+        imagebox = OffsetImage(img, zoom=0.7)  # adjust zoom as needed
+        # Create an AnnotationBbox
+        ab = AnnotationBbox(imagebox, (-1.25, -0.5), box_alignment=(1, 0), frameon=False)
 
+        ax_right.add_artist(ab)
 
 
+        from matplotlib.transforms import Bbox
+        # Create a transformation that converts from data coordinates to axes coordinates
+        trans = ax_left.transData + ax_left.transAxes.inverted()
+
+        # Calculate the bbox in axes coordinates
+        bbox_data = Bbox.from_bounds(-4.2, -0.5, 2.5, 5)  # replace width and height with the desired values
+        bbox_axes = trans.transform_bbox(bbox_data)
+
+
+        table_left_plot = ax_left.table(cellText=table_left.reset_index().values, 
+                    loc='right',
+                    cellLoc='center',
+                    colWidths=[0.52,0.3],
+                    bbox=bbox_axes.bounds,zorder=100)
+
+
+        min_font_size = 14
+        # Set table properties
+        table_left_plot.auto_set_font_size(False)
+        #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table_left_plot.set_fontsize(min_font_size)
+        #table_left_plot.scale(1,3)
+        # Calculate the bbox in axes coordinates
+        bbox_data = Bbox.from_bounds(-0.75, 5, 2.5, 1)  # replace width and height with the desired values
+        bbox_axes = trans.transform_bbox(bbox_data)
+
+        def format_as_percentage(val):
+            return f'{val * 100:.0f}%'
+
+        table_left_plot_pivot = ax_left.table(cellText=[[format_as_percentage(val) for val in row] for row in pivot_table_l.values],
+                                        colLabels =pivot_table_l.columns,
+                                        rowLabels =['  0          ','  1         ','  2          '],
+                    loc='center',
+                    cellLoc='center',
+                    colWidths=[0.3,0.3,0.30,0.3],
+                    bbox=bbox_axes.bounds,zorder=100,cellColours =df_colour_left.values)
+
+
+        min_font_size = 11
+        # Set table properties
+        table_left_plot_pivot.auto_set_font_size(False)
+        #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table_left_plot_pivot.set_fontsize(min_font_size)
 
-        # Get value counts of the column and sort in descending order
-        sorted_value_counts = df_plot['pitch_description'].value_counts().sort_values(ascending=False)
 
-        # Get the list of items ordered from most to least frequent
-        items_in_order = sorted_value_counts.index.tolist()
-        # Create a dictionary to map names to colors
-        name_to_color = dict(zip(labels, handles))
 
-        # Order the colors based on the correct order of names
-        ordered_colors = [name_to_color[name] for name in items_in_order]
 
+        # Create a transformation that converts from data coordinates to axes coordinates
+        trans = ax_right.transData + ax_right.transAxes.inverted()
 
-        ax3.legend(ordered_colors, items_in_order, bbox_to_anchor=(0.1, 0.81, 0.8, 0.2), ncol=5,
-                fancybox=True,loc='lower center',fontsize=20,framealpha=1.0, markerscale=2,prop={'family': 'calibi', 'size': 20})
+        # Calculate the bbox in axes coordinates
+        bbox_data = Bbox.from_bounds(1.7, -0.5, 2.5, 5)  # replace width and height with the desired values
+        bbox_axes = trans.transform_bbox(bbox_data)
 
 
-        ################## Title ##########
-        title_spot = f'{df_plot.pitcher_name.values[0]}'
+        table_right_plot = ax_right.table(cellText=table_right.reset_index().values, 
+                    loc='right',
+                    cellLoc='center',
+                    colWidths=[0.52,0.3],
+                    bbox=bbox_axes.bounds,zorder=100)
 
 
-        ax0.text(x=0.5,y=0.8,s=title_spot,fontname='Calibri',ha='center',fontsize=56,va='top')
-        ax0.text(x=0.5,y=0.35,s='A Game Pitching Summary',fontname='Calibri',ha='center',fontsize=40,va='top',fontstyle='italic')
 
+        min_font_size = 14
+        # Set table properties
+        table_right_plot.auto_set_font_size(False)
+        #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table_right_plot.set_fontsize(min_font_size)
+        table_right_plot.scale(0.5,3)
 
-        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
-        #ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')
-        #ax0.text(x=0.5,y=0.25,s=f'2024 Spring Training',fontname='Calibri',ha='center',fontsize=30,va='top')
-        # ax0.text(x=0.5,y=0.25,s=f'{season_fg} MLB Season',fontname='Calibri',ha='center',fontsize=30,va='top')
+        # Calculate the bbox in axes coordinates
+        # Create a transformation that converts from data coordinates to axes coordinates
+        trans = ax_right.transData + ax_right.transAxes.inverted()
+        bbox_data = Bbox.from_bounds(-0.75, 5, 2.5, 1)  # replace width and height with the desired values
+        bbox_axes = trans.transform_bbox(bbox_data)
 
-        ax0.text(x=0.5,y=0.15,s= df_plot['game_opp'].values[0],fontname='Calibri',ha='center',fontstyle='italic',fontsize=30,va='top')
-        
-        ax0.text(x=0.5,y=0.00,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
-        ax0.axis('off')
+        table_right_plot_pivot = ax_right.table(cellText=[[format_as_percentage(val) for val in row] for row in pivot_table_r.values],
+                                        colLabels =pivot_table_r.columns,
+                                        rowLabels =['  0          ','  1         ','  2          '],
+                    loc='center',
+                    cellLoc='center',
+                    colWidths=[0.3,0.3,0.30,0.3],
+                    bbox=bbox_axes.bounds,zorder=100,cellColours =df_colour_right.values)
+
+
+        min_font_size = 11
+        # Set table properties
+        table_right_plot_pivot.auto_set_font_size(False)
+        #table.set_fontsize(min(min_font_size,max(min_font_size/((len(label_labels)/4)),10)))
+        table_right_plot_pivot.set_fontsize(min_font_size)
+
+        from matplotlib.cm import ScalarMappable
+        from matplotlib.colors import Normalize
+        # Create a ScalarMappable with the same colormap and normalization
+        sm = ScalarMappable(cmap=cmap_sum, norm=Normalize(vmin=0, vmax=1))
+
+        #from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+#######################
+        # Create a new Subplot object for the colorbar
+        # Create a new Axes object for the colorbar at the bottom middle of the figure
+        cbar = fig.colorbar(sm, ax=axfooter, orientation='horizontal',aspect=100)
+        # cbar.ax.set_aspect(20)
+                
+        # cbar = plt.colorbar(batter_plot, cax=ax12, orientation='vertical',shrink=1, cmap=cmap_hue)
+
+        # cbar = plt.colorbar(batter_plot, cax=ax12, orientation='vertical',shrink=1)
+        cbar.set_ticks([])
+        # # Create an inset axes for the colorbar
+        # cax = inset_axes(axfooter,
+        #                 width="50%",  # width = 50% of parent_bbox width
+        #                 height="100%",  # height : 5%
+        #                 loc='center')
+
+        # # Add the colorbar to the inset axes
+        # cbar = fig.colorbar(sm, cax=cax, orientation='horizontal')
+        # # Set the labels on the low and high ends of the colorbar
+        # # Set the xticks to only include the low and high ends of the colorbar
+        cbar.set_ticks([sm.norm.vmin, sm.norm.vmax])
+
+        # # Set the labels on the low and high ends of the colorbar
+        cbar.ax.set_xticklabels(['Least', 'Most'])
+        # # Place the xticks on top of the colorbar
+        cbar.ax.tick_params(labeltop=True, labelbottom=False, labelsize=14)
+
+        # # Get the labels
+        labels = cbar.ax.get_xticklabels()
+
+        # # Set the alignment of the labels
+        labels[0].set_horizontalalignment('left')
+        labels[-1].set_horizontalalignment('right')
+        # # Get the labels
+        labels = cbar.ax.get_xticklabels()
+
+        # # Set the font size of the labels
+        # for label in labels:
+        #     label.set_fontsize(16)
+
+        # # Set the labels
+        cbar.ax.set_xticklabels(labels)
+        # # Remove the tick lines on the colorbar
+        cbar.ax.tick_params(length=0)
+
+
+
+        axfooter.text(x=0.02,y=0.5,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=18,va='top')
+        axfooter.text(x=1-0.02,y=0.5,s='Data: MLB',ha='right',fontname='Calibri',fontsize=18,va='top')
+
+        axfooter.axis('off')
+
+
+        axheader.text(x=0.5,y=1.2,s=f"{df_plot['pitcher_name'].values[0]} - {df_plot['pitcher_hand'].values[0]}HP\n{season} {df_plot['pitch_description'].values[0]} Pitch Frequency",ha='center',fontsize=24,va='top')
+        axheader.axis('off')
 
 
-        from matplotlib.offsetbox import (OffsetImage, AnnotationBbox)
         import urllib
         import urllib.request
         import urllib.error
         from urllib.error import HTTPError
 
         try: 
-            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto/w_180/v1/people/{pitcher_id_select}/headshot/milb/current.png'
+            url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/{df_plot["pitcher_id"].values[0]}/headshot/67/current.png'
             test_mage = plt.imread(url)
         except urllib.error.HTTPError as err:
             url = f'https://img.mlbstatic.com/mlb-photos/image/upload/d_people:generic:headshot:67:current.png/w_213,q_auto:best/v1/people/1/headshot/67/current.png'                
-            test_mage = plt.imread(url)
-        imagebox = OffsetImage(test_mage, zoom = 0.5)
-        ab = AnnotationBbox(imagebox, (0.125, 0.4), frameon = False)
-        ax0.add_artist(ab)
+        imagebox = OffsetImage(test_mage, zoom = 0.4)
+        ab = AnnotationBbox(imagebox, (0.075, 0.4), frameon = False)
+        axheader.add_artist(ab)
 
-        #player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
+        player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={df_plot['pitcher_id'].values[0]}&hydrate=currentTeam").json()
 
-        player_bio = requests.get(url=f"https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id_select}&hydrate=currentTeam").json()
-        #ax0.text(x=0.5,y=0.05,s=f'{ball_title}{strike_title}{split_title}',fontname='Calibri',ha='center',fontsize=20,va='top')
-        ax0.axis('off')
-        ax0.text(x=0.5,y=0.5,s=f"{ player_bio['people'][0]['pitchHand']['code']}HP, Age: {player_bio['people'][0]['currentAge']}, {player_bio['people'][0]['height']}/{player_bio['people'][0]['weight']}",fontname='Calibri',ha='center',fontsize=24,va='top')
 
+        team_logos = pd.read_csv('team_logos.csv')
+
+
+        mlb_stats = MLB_Scrape()
+        teams_df = mlb_stats.get_teams()
+        team_logo_dict = teams_df.set_index(['team_id'])['parent_org_id'].to_dict()
 
 
         if 'currentTeam' in player_bio['people'][0]:
@@ -871,25 +779,24 @@ def server(input, output, session):
                 # im = Image.open(BytesIO(response.content))
                 # im = plt.imread(team_logos[team_logos['id'] == player_bio['people'][0]['currentTeam']['parentOrgId']]['imageLink'].values[0])
                 # ax = fig.add_axes([0,0,1,0.85], anchor='C', zorder=1)
-                imagebox = OffsetImage(im, zoom = 0.4)
-                ab = AnnotationBbox(imagebox, (0.875, 0.40), frameon = False)
-                ax0.add_artist(ab)
+                imagebox = OffsetImage(im, zoom = 0.3)
+                ab = AnnotationBbox(imagebox, (0.925, 0.40), frameon = False)
+                axheader.add_artist(ab)
             except IndexError:
                 print()
-            
-        ############ FOOTER ################
-        #fig.text(x=0.5,y=0.05,s='Note: Colour Coding Compares to League Average By Pitch',ha='center',fontname='Calibri',fontsize=10)
-        axfooter = fig.add_subplot(gs[-1, :])
-        axfooter.text(x=0.05,y=1,s='By: Thomas Nestico\n      @TJStats',fontname='Calibri',ha='left',fontsize=24,va='top')
-        axfooter.text(x=1-0.05,y=1,s='Data: MLB',ha='right',fontname='Calibri',fontsize=24,va='top')
 
 
-        axfooter.text(x=0.5,y=0.8,s='Colour Coding Compares to League Average By Pitch\ntjStuff+ calculates the Expected Run Value (xRV) of a pitch regardless of type\ntjStuff+ is normally distributed, where 100 is the mean and Standard Deviation is 10',
-                    ha='center',va='center',fontname='Calibri',fontsize=16)
-        axfooter.axis('off')
-        #fig.tight_layout()
+        ax_left.text(s='Against LHH',x=-2.95,y=4.65,fontsize=18,fontweight='bold',ha='center')
+        ax_right.text(s='Against RHH',x=2.95,y=4.65,fontsize=18,fontweight='bold',ha='center')
+        # Center the labels
 
+        ax_left.text(x=-1.72, y=5.08, s='Strikes', rotation=90,fontweight='bold')
+        ax_right.text(x=-1.72, y=5.08, s='Strikes', rotation=90,fontweight='bold')
 
-        fig.subplots_adjust(left=0.03, right=0.97, top=0.97, bottom=0.03)
+        ax_left.text(x=0, y=6.1, s='Balls',ha='center',fontweight='bold')
+        ax_right.text(x=0, y=6.1, s='Balls',ha='center',fontweight='bold')
+        #cbar.ax.set_xticklabels(cbar.ax.get_xticklabels(), ha='center')
+        fig.subplots_adjust(left=0.01, right=0.99, top=0.95, bottom=0.05)
+        return
 
 app = App(app_ui, server)

pitcher_update.py

@@ -0,0 +1,562 @@
+import pandas as pd
+import numpy as np
+import joblib
+import math
+import pickle
+
+loaded_model = joblib.load('joblib_model/barrel_model.joblib')
+in_zone_model = joblib.load('joblib_model/in_zone_model_knn_20240410.joblib')
+attack_zone_model = joblib.load('joblib_model/model_attack_zone.joblib')
+xwoba_model = joblib.load('joblib_model/xwoba_model.joblib')
+px_model = joblib.load('joblib_model/linear_reg_model_x.joblib')
+pz_model = joblib.load('joblib_model/linear_reg_model_z.joblib')
+
+
+def percentile(n):
+    def percentile_(x):
+        return np.nanpercentile(x, n)
+    percentile_.__name__ = 'percentile_%s' % n
+    return percentile_
+
+
+def df_update(df=pd.DataFrame()):
+    df.loc[df['sz_top']==0,'sz_top'] = np.nan
+    df.loc[df['sz_bot']==0,'sz_bot'] = np.nan
+
+
+    df['in_zone'] = [x < 10 if x > 0 else np.nan for x in df['zone']]
+    if len(df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px']) > 0:
+        df.loc[(~df['x'].isnull())&(df['px'].isnull()),'px'] = px_model.predict(df.loc[(~df['x'].isnull())&(df['px'].isnull())][['x']])
+        df.loc[(~df['y'].isnull())&(df['pz'].isnull()),'pz'] = px_model.predict(df.loc[(~df['y'].isnull())&(df['pz'].isnull())][['y']]) + 3.2
+
+    
+        # df['in_zone'] =  [x < 10 if x > 0 else np.nan for x in df['zone']]
+    if len(df.loc[(~df['px'].isna())&
+                  (df['in_zone'].isna())&
+                  (~df['sz_top'].isna())]) > 0:
+            print('We found missing data')
+            df.loc[(~df['px'].isna())&
+                            (df['in_zone'].isna())&
+                            (~df['sz_top'].isna())&
+                            (~df['pz'].isna())&
+                            (~df['sz_bot'].isna())
+                            ,'in_zone'] = in_zone_model.predict(df.loc[(~df['px'].isna())&
+                                                                (df['in_zone'].isna())&
+                                                                (~df['sz_top'].isna())&
+                                                                (~df['pz'].isna())&
+                                                                (~df['sz_bot'].isna())][['px','pz','sz_top','sz_bot']].values)
+    hit_codes = ['single',
+        'double','home_run', 'triple']
+
+    ab_codes = ['single', 'strikeout', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'field_error', 'home_run', 'triple',
+        'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'other_out','triple_play']
+
+
+    obp_true_codes = ['single', 'walk',
+        'double','home_run', 'triple',
+        'hit_by_pitch', 'intent_walk']
+
+    obp_codes = ['single', 'strikeout', 'walk', 'field_out',
+        'grounded_into_double_play', 'fielders_choice', 'force_out',
+        'double', 'sac_fly', 'field_error', 'home_run', 'triple',
+        'hit_by_pitch', 'double_play', 'intent_walk',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play',
+        'other_out','triple_play']
+
+
+    contact_codes = ['In play, no out',
+            'Foul', 'In play, out(s)',
+        'In play, run(s)',
+        'Foul Bunt']
+
+
+
+    conditions_hit = [df.event_type.isin(hit_codes)]
+    choices_hit = [True]
+    df['hits'] = np.select(conditions_hit, choices_hit, default=False)
+
+    conditions_ab = [df.event_type.isin(ab_codes)]
+    choices_ab = [True]
+    df['ab'] = np.select(conditions_ab, choices_ab, default=False)
+
+    conditions_obp_true = [df.event_type.isin(obp_true_codes)]
+    choices_obp_true = [True]
+    df['on_base'] = np.select(conditions_obp_true, choices_obp_true, default=False)
+
+    conditions_obp = [df.event_type.isin(obp_codes)]
+    choices_obp = [True]
+    df['obp'] = np.select(conditions_obp, choices_obp, default=False)
+
+    bip_codes = ['In play, no out', 'In play, run(s)','In play, out(s)']
+    
+    conditions_bip = [df.play_description.isin(bip_codes)]
+    choices_bip = [True]
+    df['bip'] = np.select(conditions_bip, choices_bip, default=False)
+
+    conditions = [
+        (df['launch_speed'].isna()),
+        (df['launch_speed']*1.5 - df['launch_angle'] >= 117 ) & (df['launch_speed'] + df['launch_angle'] >= 124) & (df['launch_speed'] > 98) & (df['launch_angle'] >= 8) & (df['launch_angle'] <= 50)
+    ]
+    df['bip_div'] = ~df.launch_speed.isna()
+    choices = [False,True]
+    df['barrel'] = np.select(conditions, choices, default=np.nan)
+    df['barrel'] = loaded_model.predict(df[['launch_speed','launch_angle']].fillna(0).values)
+    conditions_ss = [
+        (df['launch_angle'].isna()),
+        (df['launch_angle'] >= 8 ) * (df['launch_angle'] <= 32 )
+    ]
+
+    choices_ss = [False,True]
+    df['sweet_spot'] = np.select(conditions_ss, choices_ss, default=np.nan)
+
+    conditions_hh = [
+        (df['launch_speed'].isna()),
+        (df['launch_speed'] >= 94.5 )
+    ]
+
+    choices_hh = [False,True]
+    df['hard_hit'] = np.select(conditions_hh, choices_hh, default=np.nan)
+
+
+    conditions_tb = [
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_tb = [1,2,3,4]
+
+    df['tb'] = np.select(conditions_tb, choices_tb, default=np.nan)
+
+    conditions_woba = [
+        (df['event_type'].isin(['strikeout', 'field_out', 'sac_fly', 'force_out',
+                                'grounded_into_double_play', 'fielders_choice', 'field_error',
+                                'sac_bunt', 'double_play', 'fielders_choice_out', 'strikeout_double_play',
+                                 'sac_fly_double_play', 'other_out'])),
+        (df['event_type']=='walk'),
+        (df['event_type']=='hit_by_pitch'),
+        (df['event_type']=='single'),
+        (df['event_type']=='double'),
+        (df['event_type']=='triple'),
+        (df['event_type']=='home_run'),
+    ]
+
+    choices_woba =    [0,
+                    0.696,
+                    0.726,
+                    0.883,
+                    1.244,
+                    1.569,
+                    2.004]
+
+    df['woba'] = np.select(conditions_woba, choices_woba, default=np.nan)
+
+
+    woba_codes = ['strikeout', 'field_out', 'single', 'walk', 'hit_by_pitch',
+        'double', 'sac_fly', 'force_out', 'home_run',
+        'grounded_into_double_play', 'fielders_choice', 'field_error',
+        'triple', 'sac_bunt', 'double_play',
+        'fielders_choice_out', 'strikeout_double_play',
+        'sac_fly_double_play', 'other_out']
+
+
+    
+    
+    
+
+    conditions_woba_code = [
+        (df['event_type'].isin(woba_codes))
+    ]
+
+    choices_woba_code = [1]
+
+    df['woba_codes'] = np.select(conditions_woba_code, choices_woba_code, default=np.nan)
+
+
+    df['woba_contact'] = [df['woba'].values[x] if df['bip'].values[x] == 1 else np.nan for x in range(len(df['woba_codes']))]
+
+    #df['in_zone'] =  [x < 10 if type(x) == int else np.nan for x in df['zone']]
+
+    # df['in_zone_2'] = in_zone_model.predict(df[['x','y','sz_bot','sz_top']].fillna(0).values)
+    # df['in_zone_3'] = df['in_zone_2'] < 10
+    # df.loc[df['in_zone'].isna(),'in_zone'] = df.loc[df['in_zone'].isna(),'in_zone_3'].fillna(0)
+
+
+    df['whiffs'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')) else 0 for x in df.play_code]
+    df['csw'] = [1 if ((x == 'S')|(x == 'W')|(x =='T')|(x == 'C')) else 0 for x in df.play_code]
+    df['swings'] = [1 if x == True else 0 for x in df.is_swing]
+
+
+    df['out_zone'] = df.in_zone == False
+    df['zone_swing'] = (df.in_zone == True)&(df.swings == 1)
+    df['zone_contact'] = (df.in_zone == True)&(df.swings == 1)&(df.whiffs == 0)
+    df['ozone_swing'] = (df.in_zone==False)&(df.swings == 1)
+    df['ozone_contact'] = (df.in_zone==False)&(df.swings == 1)&(df.whiffs == 0)
+
+    df['k'] = df.event_type.isin(list(filter(None, [x if 'strikeout' in x else '' for x in df.event_type.dropna().unique()])))
+    df['bb'] = df.event_type.isin(['walk','intent_walk'])
+
+    df['k_minus_bb'] = df['k'].astype(np.float32)-df['bb'].astype(np.float32)
+    df['bb_minus_k'] = df['bb'].astype(np.float32)-df['k'].astype(np.float32)
+
+    df['pa']  = [1 if isinstance(x, str) else 0 for x in df.event_type]
+    df['pitches']  = [1 if x else 0 for x in df.is_pitch]
+
+
+    df.loc[df['launch_speed'].isna(),'barrel'] = np.nan
+
+
+    pitch_cat = {'FA':'Fastball',
+    'FF':'Fastball',
+    'FT':'Fastball',
+    'FC':'Fastball',
+    'FS':'Off-Speed',
+    'FO':'Off-Speed',
+    'SI':'Fastball',
+    'ST':'Breaking',
+    'SL':'Breaking',
+    'CU':'Breaking',
+    'KC':'Breaking',
+    'SC':'Off-Speed',
+    'GY':'Off-Speed',
+    'SV':'Breaking',
+    'CS':'Breaking',
+    'CH':'Off-Speed',
+    'KN':'Off-Speed',
+    'EP':'Breaking',
+    'UN':np.nan,
+    'IN':np.nan,
+    'PO':np.nan,
+    'AB':np.nan,
+    'AS':np.nan,
+    'NP':np.nan}
+    #df['pitch_type'] = df['pitch_type'].map(pitch_cat).fillna('Unknown')
+    df['average'] = 'average'
+
+    df.loc[df['trajectory'] == 'bunt_popup','trajectory'] = 'popup'
+    df.loc[df['trajectory'] == 'bunt_grounder','trajectory'] = 'ground_ball'
+    df.loc[df['trajectory'] == '','trajectory'] = np.nan
+    df.loc[df['trajectory'] == 'bunt_line_drive','trajectory'] = 'line_drive'
+    df[['trajectory_fly_ball','trajectory_ground_ball','trajectory_line_drive','trajectory_popup']] = pd.get_dummies(df['trajectory'], prefix='trajectory')
+
+    df['attack_zone'] = np.nan
+
+
+
+    df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0,'attack_zone'] = attack_zone_model.predict(df.loc[df[['px','pz','sz_top','sz_bot']].isnull().sum(axis=1)==0][['px','pz','sz_top','sz_bot']])
+
+
+
+    df['heart'] = df['attack_zone'] == 0
+    df['shadow'] = df['attack_zone'] == 1
+    df['chase'] = df['attack_zone'] == 2
+    df['waste'] = df['attack_zone'] == 3
+
+    df['heart_swing'] = (df['attack_zone'] == 0)&(df['swings']==1)
+    df['shadow_swing'] = (df['attack_zone'] == 1)&(df['swings']==1)
+    df['chase_swing'] = (df['attack_zone'] == 2)&(df['swings']==1)
+    df['waste_swing'] = (df['attack_zone'] == 3)&(df['swings']==1)
+
+    df['heart_whiff'] = (df['attack_zone'] == 0)&(df['whiffs']==1)
+    df['shadow_whiff'] = (df['attack_zone'] == 1)&(df['whiffs']==1)
+    df['chase_whiff'] = (df['attack_zone'] == 2)&(df['whiffs']==1)
+    df['waste_whiff'] = (df['attack_zone'] == 3)&(df['whiffs']==1)  
+
+    df['woba_pred'] = np.nan
+    df['woba_pred_contact'] = np.nan
+
+    if len(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']) > 0:
+        
+
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+
+        ## Assign a value of 0.696 to every walk in the dataset
+        df.loc[df['event_type'].isin(['walk']),'woba_pred'] = 0.696
+
+        ## Assign a value of 0.726 to every hit by pitch in the dataset
+        df.loc[df['event_type'].isin(['hit_by_pitch']),'woba_pred'] = 0.726
+
+        ## Assign a value of 0 to every Strikeout in the dataset 
+        df.loc[df['event_type'].isin(['strikeout','strikeout_double_play']),'woba_pred'] = 0
+
+        
+        df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0,'woba_pred_contact']  = [sum(x) for x in xwoba_model.predict_proba(df.loc[df[['launch_angle','launch_speed']].isnull().sum(axis=1)==0][['launch_angle','launch_speed']]) * ([0, 0.883,1.244,1.569,2.004])]
+    
+
+    return df
+
+def df_update_summ(df=pd.DataFrame()):
+    df_summ = df.groupby(['pitcher_id','pitcher_name']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_contact','sum'),
+        xwoba = ('woba_pred','sum'),
+        xwoba_contact = ('woba_pred_contact','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+    return df_summ
+
+def df_update_summ_avg(df=pd.DataFrame()):
+    df_summ_avg = df.groupby(['average']).agg(
+       
+        ).reset_index()
+    return df_summ_avg
+
+def df_summ_changes(df_summ=pd.DataFrame()):
+    df_summ['avg'] = [df_summ.hits[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['obp'] = [df_summ.on_base[x]/df_summ.obp_pa[x] if df_summ.obp_pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['slg'] =  [df_summ.tb[x]/df_summ.ab[x] if df_summ.ab[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ops'] = df_summ['obp']+df_summ['slg']
+
+    df_summ['k_percent'] = [df_summ.k[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_percent'] =[df_summ.bb[x]/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['bb_minus_k_percent'] =[(df_summ.bb_minus_k[x])/df_summ.pa[x] if df_summ.pa[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['bb_over_k_percent'] =[df_summ.bb[x]/df_summ.k[x] if df_summ.k[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+
+
+    df_summ['csw_percent'] =[df_summ.csw[x]/df_summ.pitches[x] if df_summ.pitches[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['sweet_spot_percent'] = [df_summ.sweet_spot[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['woba_percent'] = [df_summ.woba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['woba_percent_contact'] = [df_summ.woba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    #df_summ['hard_hit_percent'] = [df_summ.sweet_spot[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['hard_hit_percent'] = [df_summ.hard_hit[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+
+    df_summ['barrel_percent'] = [df_summ.barrel[x]/df_summ.bip_div[x] if df_summ.bip_div[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_contact_percent'] = [df_summ.zone_contact[x]/df_summ.zone_swing[x] if df_summ.zone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_swing_percent'] = [df_summ.zone_swing[x]/df_summ.in_zone[x] if df_summ.in_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['zone_percent'] = [df_summ.in_zone[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_percent'] = [df_summ.ozone_swing[x]/(df_summ.pitches[x] - df_summ.in_zone[x]) if (df_summ.pitches[x]- df_summ.in_zone[x]) != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['chase_contact'] = [df_summ.ozone_contact[x]/df_summ.ozone_swing[x] if df_summ.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swing_percent'] = [df_summ.swings[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['whiff_rate'] = [df_summ.whiffs[x]/df_summ.swings[x] if df_summ.swings[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['swstr_rate'] = [df_summ.whiffs[x]/df_summ.pitches[x] if df_summ.pitches[x] > 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['ground_ball_percent'] = [df_summ.ground_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['line_drive_percent'] = [df_summ.line_drive[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['fly_ball_percent'] = [df_summ.fly_ball[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['pop_up_percent'] = [df_summ.pop_up[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]  
+
+
+    
+    df_summ['heart_zone_percent'] = [df_summ.heart[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_percent'] = [df_summ.shadow[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_percent'] = [df_summ.chase[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_percent'] = [df_summ.waste[x]/df_summ.attack_zone[x] if df_summ.attack_zone[x] != 0 else np.nan for x in range(len(df_summ))]    
+    
+    
+    df_summ['heart_zone_swing_percent'] = [df_summ.heart_swing[x]/df_summ.heart[x] if df_summ.heart[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ['shadow_zone_swing_percent'] = [df_summ.shadow_swing[x]/df_summ.shadow[x] if df_summ.shadow[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['chase_zone_swing_percent'] = [df_summ.chase_swing[x]/df_summ.chase[x] if df_summ.chase[x] != 0 else np.nan for x in range(len(df_summ))]    
+
+    df_summ['waste_zone_swing_percent'] = [df_summ.waste_swing[x]/df_summ.waste[x] if df_summ.waste[x] != 0 else np.nan for x in range(len(df_summ))]    
+    
+
+
+
+    df_summ['xwoba_percent'] = [df_summ.xwoba[x]/df_summ.woba_codes[x] if df_summ.woba_codes[x] != 0 else np.nan for x in range(len(df_summ))]
+    df_summ['xwoba_percent_contact'] = [df_summ.xwoba_contact[x]/df_summ.bip[x] if df_summ.bip[x] != 0 else np.nan for x in range(len(df_summ))]
+
+    df_summ = df_summ.dropna(subset=['bip'])
+    return df_summ
+
+def df_summ_filter_out(df_summ=pd.DataFrame(),batter_select = 0):
+    df_summ_filter = df_summ[df_summ['pa'] >= min(math.floor(df_summ.xs(batter_select,level=0)['pa']/10)*10,500)]
+    df_summ_filter_pct = df_summ_filter.rank(pct=True,ascending=True)
+    df_summ_player = df_summ.xs(batter_select,level=0)
+    df_summ_player_pct = df_summ_filter_pct.xs(batter_select,level=0)
+    return df_summ_filter,df_summ_filter_pct,df_summ_player,df_summ_player_pct
+
+def df_summ_batter_pitch_up(df=pd.DataFrame()):
+    df_summ_batter_pitch = df.dropna(subset=['pitch_type']).groupby(['pitcher_id','pitcher_name','pitch_type']).agg(
+        pa = ('pa','sum'),
+        ab = ('ab','sum'),
+        obp_pa = ('obp','sum'),
+        hits = ('hits','sum'),
+        on_base = ('on_base','sum'),
+        k = ('k','sum'),
+        bb = ('bb','sum'),
+        bb_minus_k = ('bb_minus_k','sum'),
+        csw = ('csw','sum'),
+        bip = ('bip','sum'),
+        bip_div = ('bip_div','sum'),
+        tb = ('tb','sum'),
+        woba = ('woba','sum'),
+        woba_contact = ('woba_pred_contact','sum'),
+        xwoba = ('woba_pred','sum'),
+        xwoba_contact = ('woba_pred','sum'),       
+        woba_codes = ('woba_codes','sum'),
+        hard_hit = ('hard_hit','sum'),
+        barrel = ('barrel','sum'),
+        sweet_spot = ('sweet_spot','sum'),
+        max_launch_speed = ('launch_speed','max'),
+        launch_speed_90 =  ('launch_speed',percentile(90)),
+        launch_speed = ('launch_speed','mean'),
+        launch_angle = ('launch_angle','mean'),
+        pitches = ('is_pitch','sum'),
+        swings =  ('swings','sum'),
+        in_zone = ('in_zone','sum'),
+        out_zone = ('out_zone','sum'),
+        whiffs = ('whiffs','sum'),
+        zone_swing = ('zone_swing','sum'),
+        zone_contact = ('zone_contact','sum'),
+        ozone_swing = ('ozone_swing','sum'),
+        ozone_contact = ('ozone_contact','sum'),
+        ground_ball = ('trajectory_ground_ball','sum'),
+        line_drive = ('trajectory_line_drive','sum'),
+        fly_ball =('trajectory_fly_ball','sum'),
+        pop_up = ('trajectory_popup','sum'),
+        attack_zone = ('attack_zone','count'),
+        heart = ('heart','sum'),
+        shadow = ('shadow','sum'),
+        chase = ('chase','sum'),
+        waste = ('waste','sum'),
+        heart_swing = ('heart_swing','sum'),
+        shadow_swing = ('shadow_swing','sum'),
+        chase_swing = ('chase_swing','sum'),
+        waste_swing = ('waste_swing','sum'),
+        ).reset_index()
+
+    #return df_summ_batter_pitch
+    df_summ_batter_pitch['avg'] = [df_summ_batter_pitch.hits[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['obp'] = [df_summ_batter_pitch.on_base[x]/df_summ_batter_pitch.obp_pa[x] if df_summ_batter_pitch.obp_pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['slg'] =  [df_summ_batter_pitch.tb[x]/df_summ_batter_pitch.ab[x] if df_summ_batter_pitch.ab[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['ops'] = df_summ_batter_pitch['obp']+df_summ_batter_pitch['slg']
+
+    df_summ_batter_pitch['k_percent'] = [df_summ_batter_pitch.k[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['bb_minus_k_percent'] =[(df_summ_batter_pitch.bb_minus_k[x])/df_summ_batter_pitch.pa[x] if df_summ_batter_pitch.pa[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['bb_over_k_percent'] =[df_summ_batter_pitch.bb[x]/df_summ_batter_pitch.k[x] if df_summ_batter_pitch.k[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['csw_percent'] =[df_summ_batter_pitch.csw[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['sweet_spot_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['woba_percent'] = [df_summ_batter_pitch.woba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['woba_percent_contact'] = [df_summ_batter_pitch.woba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    #df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.sweet_spot[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['hard_hit_percent'] = [df_summ_batter_pitch.hard_hit[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+    df_summ_batter_pitch['barrel_percent'] = [df_summ_batter_pitch.barrel[x]/df_summ_batter_pitch.bip_div[x] if df_summ_batter_pitch.bip_div[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_contact_percent'] = [df_summ_batter_pitch.zone_contact[x]/df_summ_batter_pitch.zone_swing[x] if df_summ_batter_pitch.zone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_swing_percent'] = [df_summ_batter_pitch.zone_swing[x]/df_summ_batter_pitch.in_zone[x] if df_summ_batter_pitch.in_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['zone_percent'] = [df_summ_batter_pitch.in_zone[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_percent'] = [df_summ_batter_pitch.ozone_swing[x]/(df_summ_batter_pitch.pitches[x] - df_summ_batter_pitch.in_zone[x]) if (df_summ_batter_pitch.pitches[x]- df_summ_batter_pitch.in_zone[x]) != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['chase_contact'] = [df_summ_batter_pitch.ozone_contact[x]/df_summ_batter_pitch.ozone_swing[x] if df_summ_batter_pitch.ozone_swing[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swing_percent'] = [df_summ_batter_pitch.swings[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['whiff_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.swings[x] if df_summ_batter_pitch.swings[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['swstr_rate'] = [df_summ_batter_pitch.whiffs[x]/df_summ_batter_pitch.pitches[x] if df_summ_batter_pitch.pitches[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['heart_zone_percent'] = [df_summ_batter_pitch.heart[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_percent'] = [df_summ_batter_pitch.shadow[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_percent'] = [df_summ_batter_pitch.chase[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_percent'] = [df_summ_batter_pitch.waste[x]/df_summ_batter_pitch.attack_zone[x] if df_summ_batter_pitch.attack_zone[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+    
+    df_summ_batter_pitch['heart_zone_swing_percent'] = [df_summ_batter_pitch.heart_swing[x]/df_summ_batter_pitch.heart[x] if df_summ_batter_pitch.heart[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+    df_summ_batter_pitch['shadow_zone_swing_percent'] = [df_summ_batter_pitch.shadow_swing[x]/df_summ_batter_pitch.shadow[x] if df_summ_batter_pitch.shadow[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['chase_zone_swing_percent'] = [df_summ_batter_pitch.chase_swing[x]/df_summ_batter_pitch.chase[x] if df_summ_batter_pitch.chase[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+
+    df_summ_batter_pitch['waste_zone_swing_percent'] = [df_summ_batter_pitch.waste_swing[x]/df_summ_batter_pitch.waste[x] if df_summ_batter_pitch.waste[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]    
+    
+
+
+
+    df_summ_batter_pitch['xwoba_percent'] = [df_summ_batter_pitch.xwoba[x]/df_summ_batter_pitch.woba_codes[x] if df_summ_batter_pitch.woba_codes[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+    df_summ_batter_pitch['xwoba_percent_contact'] = [df_summ_batter_pitch.xwoba_contact[x]/df_summ_batter_pitch.bip[x] if df_summ_batter_pitch.bip[x] != 0 else np.nan for x in range(len(df_summ_batter_pitch))]
+
+
+
+
+    df_summ_batter_pitch['bip'] = df_summ_batter_pitch['bip'].fillna(0)
+
+    return df_summ_batter_pitch