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@@ -1,13 +1,13 @@
-FROM python:3.9
-
-WORKDIR /code
-
-COPY ./requirements.txt /code/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
-
-COPY . .
-
-EXPOSE 7860
-
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
 CMD ["shiny", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,20 +1,20 @@
----
-title: Pitching Summary
-emoji: 🌍
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
----
-
-This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/). 
-
-
-To get started with a new app do the following:
-
-1) Install Shiny with `pip install shiny`
-2) Create a new app with `shiny create`
-3) Then run the app with `shiny run --reload`
-
-To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).
+---
+title: Pitching Summary
+emoji: 🌍
+colorFrom: yellow
+colorTo: indigo
+sdk: docker
+pinned: false
+license: mit
+---
+
+This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/). 
+
+
+To get started with a new app do the following:
+
+1) Install Shiny with `pip install shiny`
+2) Create a new app with `shiny create`
+3) Then run the app with `shiny run --reload`
+
+To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).

app.py

@@ -1,4 +1,3 @@
-from shiny import App, ui, render, reactive
 import polars as pl
 import numpy as np
 import pandas as pd
@@ -12,477 +11,390 @@ from stuff_model import stuff_apply
 import requests
 import joblib
 from matplotlib.gridspec import GridSpec
-import math
-from pytabulator import TableOptions, Tabulator, output_tabulator, render_tabulator, theme
-theme.tabulator_site()
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import seaborn as sns
+from functions.pitch_summary_functions import *
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
 
 colour_palette = ['#FFB000','#648FFF','#785EF0',
                   '#DC267F','#FE6100','#3D1EB2','#894D80','#16AA02','#B5592B','#A3C1ED']
 
-# df = pl.read_csv("data.csv")
-# df = pl.read_parquet("data_small.parquet")[:]
-# df = pl.read_parquet("data.parquet")[:]
-# print('df')
-season = 2024
 
-df_mlb = pl.read_parquet("data/data_mlb_2024.parquet")[:]
-df_aaa = pl.read_parquet("data/data_aaa_2024.parquet")[:]
-df_a = pl.read_parquet("data/data_a_2024.parquet")[:]
+year_list = [2017,2018,2019,2020,2021,2022,2023,2024]
 
 
 
-def df_final(df:pl.dataframe,year_input:int,sport_id:int):
+level_dict =  {'1':'MLB',
+               '11':'AAA',
+              # '12':'AA',
+               #'13':'A+',
+               '14':'A',
+               '17':'AFL',
+               '22':'College',
+               '21':'Prospects',
+               '51':'International' }
 
-    df_schedule = scrape.get_schedule(year_input=[year_input],sport_id=[sport_id])
-    df = df.join(df_schedule, on='game_id', how='left')
+function_dict={
+    'velocity_kdes':'Velocity Distributions',
+    'break_plot':'Pitch Movement',
+    'tj_stuff_roling':'Rolling tjStuff+ by Pitch',
+    'tj_stuff_roling_game':'Rolling tjStuff+ by Game',
+    'location_plot_lhb':'Locations vs LHB',
+    'location_plot_rhb':'Locations vs RHB',
+}
 
-    df = df.with_columns(
-        pl.when((pl.col('batter_team_id') == pl.col('away_id')))
-        .then(pl.lit('Away'))
-        .when((pl.col('batter_team_id') == pl.col('home_id')))
-        .then(pl.lit('Home'))
-        .otherwise(None)
-        .alias('home_away')
-    )
 
-    df = df.with_columns(
-        pl.when((pl.col('pitcher_team_id') == pl.col('away_id')))
-        .then(pl.lit('Away'))
-        .when((pl.col('pitcher_team_id') == pl.col('home_id')))
-        .then(pl.lit('Home'))
-        .otherwise(None)
-        .alias('home_away_pitcher')
-    )
+split_dict =  {'all':'All',
+               'left':'LHH',
+               'right':'RHH'}
+
+split_dict_hand =  {'all':['L','R'],
+               'left':['L'],
+               'right':['R']}
+
+
+type_dict =  {'R':'Regular Season',
+               'S':'Spring',
+               'P':'Playoffs' }
+
+
+
+# List of MLB teams and their corresponding ESPN logo URLs
+mlb_teams = [
+    {"team": "AZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/ari.png&h=500&w=500"},
+    {"team": "ATH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/oak.png&h=500&w=500"},
+    {"team": "ATL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/atl.png&h=500&w=500"},
+    {"team": "BAL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bal.png&h=500&w=500"},
+    {"team": "BOS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/bos.png&h=500&w=500"},
+    {"team": "CHC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chc.png&h=500&w=500"},
+    {"team": "CWS", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/chw.png&h=500&w=500"},
+    {"team": "CIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cin.png&h=500&w=500"},
+    {"team": "CLE", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/cle.png&h=500&w=500"},
+    {"team": "COL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/col.png&h=500&w=500"},
+    {"team": "DET", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/det.png&h=500&w=500"},
+    {"team": "HOU", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/hou.png&h=500&w=500"},
+    {"team": "KC", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/kc.png&h=500&w=500"},
+    {"team": "LAA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/laa.png&h=500&w=500"},
+    {"team": "LAD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/lad.png&h=500&w=500"},
+    {"team": "MIA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mia.png&h=500&w=500"},
+    {"team": "MIL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/mil.png&h=500&w=500"},
+    {"team": "MIN", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/min.png&h=500&w=500"},
+    {"team": "NYM", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nym.png&h=500&w=500"},
+    {"team": "NYY", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/nyy.png&h=500&w=500"},
+    {"team": "PHI", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/phi.png&h=500&w=500"},
+    {"team": "PIT", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/pit.png&h=500&w=500"},
+    {"team": "SD", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sd.png&h=500&w=500"},
+    {"team": "SF", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sf.png&h=500&w=500"},
+    {"team": "SEA", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/sea.png&h=500&w=500"},
+    {"team": "STL", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/stl.png&h=500&w=500"},
+    {"team": "TB", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tb.png&h=500&w=500"},
+    {"team": "TEX", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tex.png&h=500&w=500"},
+    {"team": "TOR", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/tor.png&h=500&w=500"},
+    {"team": "WSH", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/scoreboard/wsh.png&h=500&w=500"},
+    {"team": "ZZZ", "logo_url": "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png&w=500&h=500"}   
+]
 
 
-    print('schedule')
-
-    df_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df))
-    print('stuff')
-    df_up = update.update(df)
-    print('update')
-    df_total = df_up.join(df_stuff[['play_id','tj_stuff_plus']], on='play_id', how='left')
-    print('total')
-    return df_total
-
-
-df_mlb_total = df_final(df=df_mlb,year_input=season,sport_id=1)
-df_aaa_total = df_final(df=df_aaa,year_input=season,sport_id=11)
-df_a_total = df_final(df=df_a.drop_nulls(subset=['start_speed']),year_input=season,sport_id=14)
-
-rounding_dict = {
-    'pa': 0,
-    'bip': 0,
-    'hits': 0,
-    'k': 0,
-    'bb': 0,
-    'max_launch_speed': 1,
-    'launch_speed_90': 1,
-    'launch_speed': 1,
-    'pitches': 0,
-    'tj_stuff_plus_avg': 0,
-    'avg': 3,
-    'obp': 3,
-    'slg': 3,
-    'ops': 3,
-    'k_percent': 3,
-    'bb_percent': 3,
-    'k_minus_bb_percent': 3,
-    'sweet_spot_percent': 3,
-    'woba_percent': 3,
-    'xwoba_percent': 3,
-    'woba_percent_contact': 3,
-    'xwoba_percent_contact': 3,
-    'hard_hit_percent': 3,
-    'barrel_percent': 3,
-    'zone_contact_percent': 3,
-    'zone_swing_percent': 3,
-    'zone_percent': 3,
-    'chase_percent': 3,
-    'chase_contact': 3,
-    'swing_percent': 3,
-    'whiff_rate': 3,
-    'swstr_rate': 3,
-    'ground_ball_percent': 3,
-    'line_drive_percent': 3,
-    'fly_ball_percent': 3,
-    'pop_up_percent': 3,
-    'heart_zone_swing_percent': 3,
-    'shadow_zone_swing_percent': 3,
-    'chase_zone_swing_percent': 3,
-    'waste_zone_swing_percent': 3,
-    'heart_zone_whiff_percent': 3,
-    'shadow_zone_whiff_percent': 3,
-    'chase_zone_whiff_percent': 3,
-    'waste_zone_whiff_percent': 3,
-    'start_speed_avg': 1,
-    'vb_avg': 1,
-    'ivb_avg': 1,
-    'hb_avg': 1,
-    'z0_avg': 1,
-    'x0_avg': 1,
-    'vaa_avg': 1,
-    'haa_avg': 1,
-    'spin_rate_avg': 0,
-    'extension_avg': 1   
-}
+df_image = pd.DataFrame(mlb_teams)
+image_dict = df_image.set_index('team')['logo_url'].to_dict()
+image_dict_flip = df_image.set_index('logo_url')['team'].to_dict()
 
-columns = [
-    { "title": "PA", "field": "pa", "width": 150},
-    { "title": "BBE", "field": "bip", "width": 150 },
-    { "title": "H", "field": "hits", "width": 150 },
-    { "title": "K", "field": "k", "width": 150 },
-    { "title": "BB", "field": "bb", "width": 150 },
-    { "title": "Max EV", "field": "max_launch_speed", "width": 150 },
-    { "title": "90th% EV", "field": "launch_speed_90", "width": 150 },
-    { "title": "EV", "field": "launch_speed", "width": 150 },
-    { "title": "Pitches", "field": "pitches", "width": 150 },
-    { "title": "AVG", "field": "avg", "width": 150 },
-    { "title": "OBP", "field": "obp", "width": 150 },
-    { "title": "SLG", "field": "slg", "width": 150 },
-    { "title": "OPS", "field": "ops", "width": 150 },
-    { "title": "K%", "field": "k_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "BB%", "field": "bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "K-BB%", "field": "k_minus_bb_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "SwSpot%", "field": "sweet_spot_percent", "width": 150,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "wOBA", "field": "woba_percent", "width": 150 },
-    { "title": "xwOBA", "field": "xwoba_percent", "width": 150 },
-    { "title": "wOBACON", "field": "woba_percent_contact", "width": 150 },
-    { "title": "xwOBACON", "field": "xwoba_percent_contact", "width": 150 },
-    { "title": "HardHit%", "field": "hard_hit_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Barrel%", "field": "barrel_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Z-Contact%", "field": "zone_contact_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Z-Swing%", "field": "zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Zone%", "field": "zone_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "O-Swing%", "field": "chase_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "O-Contact%", "field": "chase_contact", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Swing%", "field": "swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Whiff%", "field": "whiff_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "SwStr%", "field": "swstr_rate", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "GB%", "field": "ground_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "LD%", "field": "line_drive_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "FB%", "field": "fly_ball_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "PU%", "field": "pop_up_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Heart Swing%", "field": "heart_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Shadow Swing%", "field": "shadow_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Chase Swing%", "field": "chase_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Waste Swing%", "field": "waste_zone_swing_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Heart Whiff%", "field": "heart_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Shadow Whiff%", "field": "shadow_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Chase Whiff%", "field": "chase_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "Waste Whiff%", "field": "waste_zone_whiff_percent", "width": 150 ,"formatter": "money", "formatterParams":{"decimal":".","thousand":".","symbol":"%","symbolAfter":"%","negativeSign":True,"precision":1}},
-    { "title": "tjStuff+", "field": "tj_stuff_plus_avg", "width": 150 },
-    { "title": "Velocity", "field": "start_speed_avg", "width": 150 },
-    { "title": "Extension", "field": "extension_avg", "width": 150 },
-    { "title": "VB", "field": "vb_avg", "width": 150 },
-    { "title": "iVB", "field": "ivb_avg", "width": 150 },
-    { "title": "HB", "field": "hb_avg", "width": 150 },
-    { "title": "vRel", "field": "z0_avg", "width": 150 },
-    { "title": "hRel", "field": "x0_avg", "width": 150 },
-    { "title": "VAA", "field": "vaa_avg", "width": 150 },
-    { "title": "HAA", "field": "haa_avg", "width": 150 },
-    { "title": "Spin Rate", "field": "spin_rate_avg", "width": 150 },
-    { "title": "Extension", "field": "extension_avg", "width": 150 },
 
-]
 
-stat_titles = dict(zip([col["field"] for col in columns],[col["title"] for col in columns]))
-
-stat_selection = [key for key in stat_titles.keys()]
-
-agg_titles = {'batter_id':'Batter ID',
-    'batter_name':'Batter Name',
-    'batter_team':'Batter Team',
-    'batter_hand':'Batter Hand',
-    'pitcher_id':'Pitcher ID',
-    'pitcher_name':'Pitcher Name',
-    'pitcher_team':'Pitcher Team',
-    'pitcher_hand':'Pitcher Hand',
-    'pitch_type':'Pitch Type',
-    'pitch_group':'Pitch Group',
-    'home_away_batter':'Home/Away Batter',
-    'home_away_pitcher':'Home/Away Pitcher',
-    'is_swing':'Is Swing?',
-    'is_bip':'Is BIP?',
-    'in_zone_final':'In Zone?',
-    'attack_zone_final':'Attack Zone'}
-
-
-columns_group = [
-    { "title": "Batter ID", "field": "batter_id", "width": 150, "headerFilter":"input","frozen":True,},
-    { "title": "Batter Name", "field": "batter_name", "width": 200,"frozen":True, "headerFilter":"input"  },
-    { "title": "Batter Team", "field": "batter_team", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Batter Hand", "field": "batter_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitcher ID", "field": "pitcher_id", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitcher Name", "field": "pitcher_name", "width": 200,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitcher Team", "field": "pitcher_team", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitcher Hand", "field": "pitcher_hand", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitch Type", "field": "pitch_type", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Pitch Group", "field": "pitch_group", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Home/Away Batter", "field": "home_away_batter", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Home/Away Pitcher", "field": "home_away_pitcher", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Is Swing?", "field": "is_swing", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Is BIP?", "field": "is_bip", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "In Zone?", "field": "in_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  },
-    { "title": "Attack Zone", "field": "attack_zone_final", "width": 150,"frozen":True, "headerFilter":"input"  } 
-    ]
+# # Define the features to be used for training
+# features_table = ['start_speed',
+#             'spin_rate',
+#             'extension',
+#             'ivb',
+#             'hb',
+#             'x0',
+#             'z0',
+#             'tj_stuff_plus']
 
+from shiny import App, reactive, ui, render
+from shiny.ui import h2, tags
 
+# Define the UI layout for the app
 app_ui = ui.page_sidebar(
     ui.sidebar(
-        ui.input_selectize(
-            "level_input",
-            "Select Level:",
-            choices=['MLB','AAA','A'],
-            multiple=False,
-            selected=['MLB']
+        # Row for selecting season and level
+        ui.row(
+            ui.column(4, ui.input_select('year_input', 'Select Season', year_list, selected=2024)),
+            ui.column(4, ui.input_select('level_input', 'Select Level', level_dict)),
+            ui.column(4, ui.input_select('type_input', 'Select Type', type_dict,selected='R'))
         ),
-        ui.input_selectize(
-            "list_input",
-            "Select Aggregation:",
-            choices=agg_titles,
-            multiple=True,
-            selected=['batter_id', 'batter_name']
+        # Row for the action button to get player list
+        ui.row(ui.input_action_button("player_button", "Get Player List", class_="btn-primary")),
+        # Row for selecting the player
+        ui.row(ui.column(12, ui.output_ui('player_select_ui', 'Select Player'))),
+        # Row for selecting the date range
+        ui.row(ui.column(12, ui.output_ui('date_id', 'Select Date'))),
+
+        # Rows for selecting plots and split options
+        ui.row(
+            ui.column(4, ui.input_select('plot_id_1', 'Plot Left', function_dict, multiple=False, selected='velocity_kdes')),
+            ui.column(4, ui.input_select('plot_id_2', 'Plot Middle', function_dict, multiple=False, selected='tj_stuff_roling')),
+            ui.column(4, ui.input_select('plot_id_3', 'Plot Right', function_dict, multiple=False, selected='break_plot'))
         ),
-        ui.input_selectize(
-            "list_stats",
-            "Select Stats:",
-            choices=stat_titles,
-            multiple=True,
-            selected=['pa']
+        ui.row(
+            ui.column(6, ui.input_select('split_id', 'Select Split', split_dict, multiple=False)),
+            ui.column(6, ui.input_numeric('rolling_window', 'Rolling Window (for tjStuff+ Plot)', min=1, value=50))
         ),
-        ui.input_date_range(
-            "date_id", 
-            "Select Date Range",
-            start=f'{season}-01-01',
-            end=f'{season}-12-01',
-            min=f'{season}-01-01',
-            max=f'{season}-12-01',
+        ui.row(
+            ui.column(6, ui.input_switch("switch", "Custom Team?", False)),
+            ui.column(6, ui.input_select('logo_select', 'Select Custom Logo', image_dict_flip, multiple=False))
         ),
-        ui.hr(),
-        ui.h4("Filters"),
-        ui.div(
-            {"id": "filter-container"},
-            ui.div(
-                {"class": "filter-row", "id": "filter_row_1"},  # Add id for deletion
-                ui.row(
-                    ui.column(5,  # Adjusted column widths to make room for delete button
-                        ui.input_select(
-                            "filter_column_1",
-                            "Metric",
-                            choices={}
-                        )
-                    ),
-                    ui.column(3,
-                        ui.input_select(
-                            "filter_operator_1",
-                            "Operator",
-                            choices=[">=", "<="]
-                        ),
-                    ),
-                    ui.column(3,
-                        ui.input_numeric(
-                            "filter_value_1",
-                            "Value",
-                            value=0
-                        )
-                    ),
-                    ui.column(1,
-                        ui.markdown("&nbsp;"),
-                 
-
-                        ui.input_action_button(
-                            f"delete_filter_1",
-                            "",
-                            class_="btn-danger btn-sm",
-                            style="padding: 3px 6px;",
-                            icon='✖'
-                        
-                    )
-                    )
-                )
-            )
-        ),
-        ui.input_action_button(
-            "add_filter",
-            "Add Filter",
-            class_="btn-secondary"
-        ),
-        ui.br(),
-        ui.br(),
-        ui.input_action_button(
-            "generate_table",
-            "Generate Table",
-            class_="btn-primary"
-        ),
-        width="400px"
+        
+        # Row for the action button to generate plot
+        ui.row(ui.input_action_button("generate_plot", "Generate Plot", class_="btn-primary")),
+        width="400px"  # Added this parameter to control sidebar width
     ),
+            
+    # Main content area with tabs (placed directly in page_sidebar)
     ui.navset_tab(
-        ui.nav_panel("Leaderboard",
-            ui.card(
-                #ui.card_header("Leaderboard"),
-                output_tabulator("tabulator")
-            )
+        ui.nav_panel("Pitching Summary",
+               ui.output_text("status"),
+               ui.output_plot('plot', width='2100px', height='2100px')
         ),
-
+            ui.nav_panel("Summary Table",
+                                   ui.output_data_frame("grid"))
     )
 )
 
+
 def server(input, output, session):
-    # Store the number of active filters
-    filter_count = reactive.value(1)
-    # Store active filter IDs
-    active_filters = reactive.value([1])
-
-    @reactive.effect
-    @reactive.event(input.list_stats)
-    def _():
-        stat_choices = {k: k for k in input.list_stats()}
-        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
-        ui.update_select("filter_column_1", choices=filtered_stat_choices)
-
-    @reactive.effect
-    @reactive.event(input.add_filter)
-    def _():
-        current_count = filter_count.get()
-        new_count = current_count + 1
-        
-        stat_choices = {k: k for k in input.list_stats()}
-        filtered_stat_choices = {key: stat_titles[key] for key in stat_choices}   
-        
-        ui.insert_ui(
-            selector="#filter-container",
-            where="beforeEnd",
-            ui=ui.div(
-                {"class": "filter-row", "id": f"filter_row_{new_count}"},
-                ui.row(
-                    ui.column(5,
-                        ui.input_select(
-                            f"filter_column_{new_count}",
-                            "Metric",
-                            choices=filtered_stat_choices
-                        ),
-                    ),
-                    ui.column(3,
-                        ui.input_select(
-                            f"filter_operator_{new_count}",
-                            "Operator",
-                            choices=[">=", "<="]
-                        ),
-                    ),
-                    ui.column(3,
-                        ui.input_numeric(
-                            f"filter_value_{new_count}",
-                            "Value",
-                            value=0
-                        )
-                    ),
-                    ui.column(1,
-                        ui.markdown("&nbsp;"),
-                 
-
-                        ui.input_action_button(
-                            f"delete_filter_{new_count}",
-                            "",
-                            class_="btn-danger btn-sm",
-                            style="padding: 3px 6px;",
-                            icon='✖'
-                        
-                    )
-                    )
-                )
-            )
-        )
-        filter_count.set(new_count)
-        current_filters = active_filters.get()
-        current_filters.append(new_count)
-        active_filters.set(current_filters)
-
-    @reactive.effect
-    def _():
-        # Monitor all possible delete buttons
-        for i in range(1, filter_count.get() + 1):
-            try:
-                if getattr(input, f"delete_filter_{i}")() > 0:
-                    # Remove the filter row
-                    ui.remove_ui(f"#filter_row_{i}")
-                    # Update active filters
-                    current_filters = active_filters.get()
-                    if i in current_filters:
-                        current_filters.remove(i)
-                        active_filters.set(current_filters)
-            except:
-                continue
 
-    @output
-    @render_tabulator
-    @reactive.event(input.generate_table, ignore_none=False) 
-    def tabulator():
-        columns_c = columns.copy()
-        selection_list = list(input.list_input())
+    @reactive.calc
+    @reactive.event(input.pitcher_id, input.date_id,input.split_id)
+    def cached_data():
+
+        year_input = int(input.year_input())
+        sport_id = int(input.level_input())
+        player_input = int(input.pitcher_id())
         start_date = str(input.date_id()[0])
         end_date = str(input.date_id()[1])
+        # Simulate an expensive data operation
+        game_list  = scrape.get_player_games_list(sport_id = sport_id,
+                                                season = year_input, 
+                                                player_id = player_input,
+                                                start_date = start_date,
+                                                end_date = end_date,
+                                                game_type = [input.type_input()])
+
+        data_list = scrape.get_data(game_list_input = game_list[:])
+        df = (stuff_apply.stuff_apply(fe.feature_engineering(update.update(scrape.get_data_df(data_list = data_list).filter(
+                            (pl.col("pitcher_id") == player_input)&
+                            (pl.col("is_pitch") == True)&
+                            (pl.col("start_speed") >= 50)&
+                            (pl.col('batter_hand').is_in(split_dict_hand[input.split_id()]))
+
+                            )))).with_columns(
+                pl.col('pitch_type').count().over('pitch_type').alias('pitch_count')
+            ))
+        return df
+
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def player_select_ui():
+        # Get the list of pitchers for the selected level and season
+        df_pitcher_info = scrape.get_players(sport_id=int(input.level_input()), season=int(input.year_input()), game_type = [input.type_input()]).filter(
+            pl.col("position").is_in(['P','TWP'])).sort("name")
+        
+        # Create a dictionary of pitcher IDs and names
+        pitcher_dict = dict(zip(df_pitcher_info['player_id'], df_pitcher_info['name']))
         
+        # Return a select input for choosing a pitcher
+        return ui.input_select("pitcher_id", "Select Pitcher", pitcher_dict, selectize=True)
+
+    @render.ui
+    @reactive.event(input.player_button, input.year_input, input.level_input, input.type_input,ignore_none=False)
+    def date_id():
+        # Create a date range input for selecting the date range within the selected year
+        return ui.input_date_range("date_id", "Select Date Range",
+                                   start=f"{int(input.year_input())}-01-01",
+                                   end=f"{int(input.year_input())}-12-31",
+                                   min=f"{int(input.year_input())}-01-01",
+                                    max=f"{int(input.year_input())}-12-31")
+    @output
+    @render.text
+    def status():
+        # Only show status when generating
+        if input.generate == 0:
+            return ""
+        return ""   
+    
+    @output
+    @render.plot
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def plot():     
+        # Show progress/loading notification
+        with ui.Progress(min=0, max=1) as p:
+            p.set(message="Generating plot", detail="This may take a while...")
+
+            
+            p.set(0.3, "Gathering data...")
+            year_input = int(input.year_input())
+            sport_id = int(input.level_input())
+            player_input = int(input.pitcher_id())
+            start_date = str(input.date_id()[0])
+            end_date = str(input.date_id()[1])
+
+            print(year_input, sport_id, player_input, start_date, end_date)
 
-        if input.level_input() == "MLB":
-            df_agg = update.update_summary_select(df=df_mlb_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
-                                    selection=selection_list)
+            df = cached_data()
+            df = df.clone()
 
-        elif input.level_input() == "AAA":
-            df_agg = update.update_summary_select(df=df_aaa_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
-                                    selection=selection_list)
+            p.set(0.6, "Creating plot...")
+
+  
+            #plt.rcParams["figure.figsize"] = [10,10]
+            fig = plt.figure(figsize=(26,26))
+            plt.rcParams.update({'figure.autolayout': True})
+            fig.set_facecolor('white')
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            print('this is the one plot')
+
+            gs = gridspec.GridSpec(6, 8,
+                                height_ratios=[5,20,12,36,36,7],
+                                width_ratios=[4,18,18,18,18,18,18,4])
+
+
+            gs.update(hspace=0.2, wspace=0.5)
+
+            # Define the positions of each subplot in the grid
+            ax_headshot = fig.add_subplot(gs[1,1:3])
+            ax_bio = fig.add_subplot(gs[1,3:5])
+            ax_logo = fig.add_subplot(gs[1,5:7])
+
+            ax_season_table = fig.add_subplot(gs[2,1:7])
+
+            ax_plot_1 = fig.add_subplot(gs[3,1:3])
+            ax_plot_2 = fig.add_subplot(gs[3,3:5])
+            ax_plot_3 = fig.add_subplot(gs[3,5:7])
+
+            ax_table = fig.add_subplot(gs[4,1:7])
+
+            ax_footer = fig.add_subplot(gs[-1,1:7])
+            ax_header = fig.add_subplot(gs[0,1:7])
+            ax_left = fig.add_subplot(gs[:,0])
+            ax_right = fig.add_subplot(gs[:,-1])
+
+            # Hide axes for footer, header, left, and right
+            ax_footer.axis('off')
+            ax_header.axis('off')
+            ax_left.axis('off')
+            ax_right.axis('off')
+
+            sns.set_theme(style="whitegrid", palette=colour_palette)
+            fig.set_facecolor('white')
+
+            df_teams = scrape.get_teams()
+
+            player_headshot(player_input=player_input, ax=ax_headshot,sport_id=sport_id,season=year_input)
+            player_bio(pitcher_id=player_input, ax=ax_bio,sport_id=sport_id,year_input=year_input)
+
+            if input.switch():
+
+                # Get the logo URL from the image dictionary using the team abbreviation
+                logo_url = input.logo_select()
         
-        elif input.level_input() == "A":
-            df_agg = update.update_summary_select(df=df_a_total.filter((pl.col('game_date')>=start_date)&(pl.col('game_date')<=end_date)),
-                                    selection=selection_list)
-                
+                # Send a GET request to the logo URL
+                response = requests.get(logo_url)
         
-        df_agg = df_agg.select(selection_list + list(input.list_stats()))#.sort('pa', descending=True)
+                # Open the image from the response content
+                img = Image.open(BytesIO(response.content))
         
-        # Apply filters - only for active filters
-        for i in active_filters.get():
-            try:
-                col_name = getattr(input, f"filter_column_{i}")()
-                if col_name:  # Only apply filter if column is selected
-                    operator = getattr(input, f"filter_operator_{i}")()
-                    if col_name in [col["field"] for col in columns_c if col.get("formatter") == "money"]:
-                        value =  getattr(input, f"filter_value_{i}")()/100
-                    else:
-                        value = getattr(input, f"filter_value_{i}")()
-                
-                    if operator == ">=":
-                        df_agg = df_agg.filter(pl.col(col_name) >= value)
-                    elif operator == "<=":
-                        df_agg = df_agg.filter(pl.col(col_name) <= value)
-            except:
-                continue
+                # Display the image on the axis
+                ax_logo.set_xlim(0, 1.3)
+                ax_logo.set_ylim(0, 1)
+                ax_logo.imshow(img, extent=[0.3, 1.3, 0, 1], origin='upper')
         
-        for col in df_agg.columns[len(selection_list):]:
-            if col in rounding_dict:
-                df_agg = df_agg.with_columns(pl.col(col).round(rounding_dict[col]))
+                # Turn off the axis
+                ax_logo.axis('off')
+            
+            else:
+                plot_logo(pitcher_id=player_input, ax=ax_logo, df_team=df_teams,df_players=scrape.get_players(sport_id,year_input))
+
+            stat_summary_table(df=df,
+                            ax=ax_season_table,
+                            player_input=player_input,
+                            split=input.split_id(),
+                            sport_id=sport_id,
+                            game_type=[input.type_input()])
+
+            # break_plot(df=df_plot,ax=ax2) 
+            for x,y,z in zip([input.plot_id_1(),input.plot_id_2(),input.plot_id_3()],[ax_plot_1,ax_plot_2,ax_plot_3],[1,3,5]):
+                if x == 'velocity_kdes':
+                            velocity_kdes(df,
+                            ax=y,
+                            gs=gs,
+                            gs_x=[3,4],
+                            gs_y=[z,z+2],
+                            fig=fig)
+                if x == 'tj_stuff_roling':
+                    tj_stuff_roling(df=df,
+                                window=int(input.rolling_window()),
+                                ax=y)
+                    
+                if x == 'tj_stuff_roling_game':
+                    tj_stuff_roling_game(df=df, 
+                                        window=int(input.rolling_window()),
+                                        ax=y)
+
+                if x == 'break_plot':
+                    break_plot(df = df,ax=y) 
+
+                if x == 'location_plot_lhb':
+                    location_plot(df = df,ax=y,hand='L')
+
+                if x == 'location_plot_rhb':
+                    location_plot(df = df,ax=y,hand='R')
+
+            summary_table(df=df,
+                        ax=ax_table)
+
+            plot_footer(ax_footer)   
+
+            fig.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
 
-        for column in columns_c:
-            if column.get("formatter") == "money" and column.get("field") in df_agg.columns:
-                df_agg = df_agg.with_columns(pl.col(column.get("field"))*100)
 
-        col_group = []
-        for column in columns_group:
-            if column.get("field") in df_agg.columns:
-                col_group.append(column)
+    @output
+    @render.data_frame
+    @reactive.event(input.generate_plot, ignore_none=False)   
+    def grid():
+
+        df = cached_data()
+        df = df.clone()
+        features_table = ['start_speed',
+            'spin_rate',
+            'extension',
+            'ivb',
+            'hb',
+            'x0',
+            'z0']
+        
+        
+        
+        selection = ['game_id','pitcher_id','pitcher_name','batter_id','batter_name','pitcher_hand',
+                      'batter_hand','balls','strikes','play_code','event_type','pitch_type','vaa','haa']+features_table+['tj_stuff_plus']
 
-        col_group_stats = []
-        for column in columns_c:
-            if column.get("field") in df_agg.columns:
-                col_group_stats.append(column)
 
-        columns_c = col_group + col_group_stats
-        
-        return Tabulator(
-            df_agg.to_pandas(),
-            table_options=TableOptions(
-                height=800,
-                columns=columns_c,
+
+        return render.DataGrid(
+                df.select(selection).to_pandas().round(1),
+                row_selection_mode='multiple',
+                 height='700px',
+                width='fit-content',
+                filters=True,
             )
-        )
+    
 
 app = App(app_ui, server)
+
+            
+
+app = App(app_ui, server)

functions/df_update.py

@@ -138,28 +138,23 @@ class df_update:
 
         ])
 
-
         df = df.with_columns([
             pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('woba_codes'),
             pl.when(df['event_type'].is_in(woba_codes)).then(1).otherwise(None).alias('xwoba_codes'),
             pl.when((pl.col('tb') >= 0)).then(df['woba']).otherwise(None).alias('woba_contact'),
             pl.when(pl.col('px').is_null()).then(pl.col('px_predict')).otherwise(pl.col('px')).alias('px'),
             pl.when(pl.col('pz').is_null()).then(pl.col('pz_predict')).otherwise(pl.col('pz')).alias('pz'),
-            pl.when(pl.col('in_zone').is_null()).then(pl.col('in_zone_predict')).otherwise(pl.col('in_zone')).alias('in_zone_final'),
-
-        ])
-
-        df = df.with_columns([
+            pl.when(pl.col('in_zone').is_null()).then(pl.col('in_zone_predict')).otherwise(pl.col('in_zone')).alias('in_zone'),
             pl.when(df['launch_speed'].is_null()).then(None).otherwise(df['barrel']).alias('barrel'),
             pl.lit('average').alias('average'),
-            pl.when(pl.col('in_zone_final') == False).then(True).otherwise(False).alias('out_zone'),
-            pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'),
-            pl.when((pl.col('in_zone_final') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'),
-            pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'),
-            pl.when((pl.col('in_zone_final') == False) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('ozone_contact'),
+                pl.when(pl.col('in_zone') == False).then(True).otherwise(False).alias('out_zone'),
+            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('zone_swing'),
+            pl.when((pl.col('in_zone') == True) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('zone_contact'),
+            pl.when((pl.col('in_zone') == False) & (pl.col('swings') == 1)).then(True).otherwise(False).alias('ozone_swing'),
+            pl.when((pl.col('in_zone') == False) & (pl.col('swings') == 1) & (pl.col('whiffs') == 0)).then(True).otherwise(False).alias('ozone_contact'),
             pl.when(pl.col('event_type').str.contains('strikeout')).then(True).otherwise(False).alias('k'),
             pl.when(pl.col('event_type').is_in(['walk', 'intent_walk'])).then(True).otherwise(False).alias('bb'),
-            pl.when(pl.col('attack_zone').is_null()).then(pl.col('attack_zone_predict')).otherwise(pl.col('attack_zone')).alias('attack_zone_final'),
+            pl.when(pl.col('attack_zone').is_null()).then(pl.col('attack_zone_predict')).otherwise(pl.col('attack_zone')).alias('attack_zone'),
             
 
         ])
@@ -168,18 +163,18 @@ class df_update:
             (df['k'].cast(pl.Float32) - df['bb'].cast(pl.Float32)).alias('k_minus_bb'),
             (df['bb'].cast(pl.Float32) - df['k'].cast(pl.Float32)).alias('bb_minus_k'),
             (df['launch_speed'] > 0).alias('bip_div'),
-            (df['attack_zone_final'] == 0).alias('heart'),
-            (df['attack_zone_final'] == 1).alias('shadow'),
-            (df['attack_zone_final'] == 2).alias('chase'),
-            (df['attack_zone_final'] == 3).alias('waste'),
-            ((df['attack_zone_final'] == 0) & (df['swings'] == 1)).alias('heart_swing'),
-            ((df['attack_zone_final'] == 1) & (df['swings'] == 1)).alias('shadow_swing'),
-            ((df['attack_zone_final'] == 2) & (df['swings'] == 1)).alias('chase_swing'),
-            ((df['attack_zone_final'] == 3) & (df['swings'] == 1)).alias('waste_swing'),
-            ((df['attack_zone_final'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'),
-            ((df['attack_zone_final'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'),
-            ((df['attack_zone_final'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'),
-            ((df['attack_zone_final'] == 3) & (df['whiffs'] == 1)).alias('waste_whiff')
+            (df['attack_zone'] == 0).alias('heart'),
+            (df['attack_zone'] == 1).alias('shadow'),
+            (df['attack_zone'] == 2).alias('chase'),
+            (df['attack_zone'] == 3).alias('waste'),
+            ((df['attack_zone'] == 0) & (df['swings'] == 1)).alias('heart_swing'),
+            ((df['attack_zone'] == 1) & (df['swings'] == 1)).alias('shadow_swing'),
+            ((df['attack_zone'] == 2) & (df['swings'] == 1)).alias('chase_swing'),
+            ((df['attack_zone'] == 3) & (df['swings'] == 1)).alias('waste_swing'),
+            ((df['attack_zone'] == 0) & (df['whiffs'] == 1)).alias('heart_whiff'),
+            ((df['attack_zone'] == 1) & (df['whiffs'] == 1)).alias('shadow_whiff'),
+            ((df['attack_zone'] == 2) & (df['whiffs'] == 1)).alias('chase_whiff'),
+            ((df['attack_zone'] == 3) & (df['whiffs'] == 1)).alias('waste_whiff')
         ])
 
 
@@ -234,84 +229,7 @@ class df_update:
         # Check if 'trajectory_null' column exists and drop it
         if 'trajectory_null' in df.columns:
             df = df.drop('trajectory_null')
-
-
-        pitch_cat = {'FA': None,
-                            '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': None,
-                            'IN': None,
-                            'PO': None,
-                            'AB': None,
-                            'AS': None,
-                            'NP': None}
-        df = df.with_columns(
-            df["pitch_type"].map_elements(lambda x: pitch_cat.get(x, x)).alias("pitch_group")
-        )
-
-        df = df.with_columns([
             
-            (-(pl.col('vy0')**2 - (2 * pl.col('ay') * (pl.col('y0') - 17/12)))**0.5).alias('vy_f'),
-            ])
-
-        df = df.with_columns([
-            ((pl.col('vy_f') - pl.col('vy0')) / pl.col('ay')).alias('t'),
-            ])
-
-        df = df.with_columns([
-            (pl.col('vz0') + (pl.col('az') * pl.col('t'))).alias('vz_f'),
-            (pl.col('vx0') + (pl.col('ax') * pl.col('t'))).alias('vx_f')
-            ])
-
-        df = df.with_columns([
-                (-np.arctan(pl.col('vz_f') / pl.col('vy_f')) * (180 / np.pi)).alias('vaa'),
-                (-np.arctan(pl.col('vx_f') / pl.col('vy_f')) * (180 / np.pi)).alias('haa')
-            ])
-
-        # Mirror horizontal break for left-handed pitchers
-        df = df.with_columns(
-            pl.when(pl.col('pitcher_hand') == 'L')
-            .then(-pl.col('ax'))
-            .otherwise(pl.col('ax'))
-            .alias('ax')
-        )
-
-        # Mirror horizontal break for left-handed pitchers
-        df = df.with_columns(
-            pl.when(pl.col('pitcher_hand') == 'L')
-            .then(-pl.col('hb'))
-            .otherwise(pl.col('hb'))
-            .alias('hb')
-        )
-
-        # Mirror horizontal release point for left-handed pitchers
-        df = df.with_columns(
-            pl.when(pl.col('pitcher_hand') == 'L')
-            .then(pl.col('x0'))
-            .otherwise(-pl.col('x0'))
-            .alias('x0')
-        )
-
-        df = df.with_columns([
-            pl.when(df['swings'].is_null()).then(None).otherwise(df['swings']).alias('is_swing'),
-            pl.when(df['bip'].is_null()).then(None).otherwise(df['bip']).alias('is_bip')])
-
-
         return df
 
     # Assuming df is your Polars DataFrame
@@ -462,7 +380,6 @@ class df_update:
             pl.col('k').sum().alias('k'),
             pl.col('bb').sum().alias('bb'),
             pl.col('bb_minus_k').sum().alias('bb_minus_k'),
-            pl.col('k_minus_bb').sum().alias('k_minus_bb'),
             pl.col('csw').sum().alias('csw'),
             pl.col('bip').sum().alias('bip'),
             pl.col('bip_div').sum().alias('bip_div'),
@@ -506,17 +423,7 @@ class df_update:
             pl.col('shadow_whiff').sum().alias('shadow_whiff'),
             pl.col('chase_whiff').sum().alias('chase_whiff'),
             pl.col('waste_whiff').sum().alias('waste_whiff'),
-            pl.col('tj_stuff_plus').sum().alias('tj_stuff_plus'),
-            pl.col('start_speed').sum(),
-            pl.col('vb').sum(),
-            pl.col('ivb').sum(),
-            pl.col('hb').sum(),
-            pl.col('x0').sum(),
-            pl.col('z0').sum(),
-            pl.col('vaa').sum(),
-            pl.col('haa').sum(),
-            pl.col('spin_rate').sum(),
-            pl.col('extension').sum(),
+            pl.col('tj_stuff_plus').sum().alias('tj_stuff_plus')
         ])
 
         # Add calculated columns to the summary DataFrame
@@ -528,7 +435,6 @@ class df_update:
             (pl.col('k') / pl.col('pa')).alias('k_percent'),
             (pl.col('bb') / pl.col('pa')).alias('bb_percent'),
             (pl.col('bb_minus_k') / pl.col('pa')).alias('bb_minus_k_percent'),
-            (pl.col('k_minus_bb') / pl.col('pa')).alias('k_minus_bb_percent'),
             (pl.col('bb') / pl.col('k')).alias('bb_over_k_percent'),
             (pl.col('csw') / pl.col('pitches')).alias('csw_percent'),
             (pl.col('sweet_spot') / pl.col('bip_div')).alias('sweet_spot_percent'),
@@ -563,16 +469,6 @@ class df_update:
             (pl.col('xwoba') / pl.col('xwoba_codes')).alias('xwoba_percent'),
             (pl.col('xwoba_contact') / pl.col('bip')).alias('xwoba_percent_contact'),
             (pl.col('tj_stuff_plus') / pl.col('pitches')).alias('tj_stuff_plus_avg'),
-            (pl.col('start_speed')/ pl.col('pitches')).alias('start_speed_avg'),
-            (pl.col('vb')/ pl.col('pitches')).alias('vb_avg'),
-            (pl.col('ivb')/ pl.col('pitches')).alias('ivb_avg'),
-            (pl.col('hb')/ pl.col('pitches')).alias('hb_avg'),
-            (pl.col('x0')/ pl.col('pitches')).alias('x0_avg'),
-            (pl.col('z0')/ pl.col('pitches')).alias('z0_avg'),
-            (pl.col('vaa')/ pl.col('pitches')).alias('vaa_avg'),
-            (pl.col('haa')/ pl.col('pitches')).alias('haa_avg'),
-            (pl.col('spin_rate')/ pl.col('pitches')).alias('spin_rate_avg'),
-            (pl.col('extension')/ pl.col('pitches')).alias('extension_avg'),
             
         ])
 

joblib_model/barrel_model.joblib

@@ -1,3 +1,3 @@
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-size 11447
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joblib_model/in_zone.joblib

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joblib_model/in_zone_model_knn_20240410.joblib

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joblib_model/linear_reg_model_x.joblib

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-size 579
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joblib_model/model_attack_zone.joblib

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-size 50570139
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joblib_model/no_swing.joblib

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-size 279409
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joblib_model/swing.joblib

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requirements.txt

@@ -1,16 +1,15 @@
-joblib==1.3.2
-lightgbm
-matplotlib==3.5.1
-numpy==1.23.5
-pandas==1.5.2
-polars==1.12.0
-Requests==2.31.0
-scipy==1.11.1
-seaborn==0.11.1
-scikit-learn==1.0.1
-shiny==0.6.1
-Jinja2==3.1.4
-tqdm==4.62.3
-pyarrow
-tabulator
-
+joblib==1.3.2
+lightgbm
+matplotlib==3.5.1
+numpy==1.23.5
+pandas==1.5.2
+polars==1.12.0
+Requests==2.31.0
+scipy==1.11.1
+seaborn==0.11.1
+scikit-learn==1.0.1
+shiny==0.6.1
+Jinja2==3.1.4
+tqdm==4.62.3
+pyarrow
+

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