Update app

data.py

@@ -0,0 +1,82 @@
+
+import pandas as pd
+import numpy as np
+from gradio_client import Client
+from tqdm.auto import tqdm
+
+import os
+import re
+
+from translate import translate_pa_outcome, translate_pitch_outcome, jp_pitch_to_en_pitch, jp_pitch_to_pitch_code, translate_pitch_outcome, max_pitch_types
+
+# load game data
+game_df = pd.read_csv('game.csv').drop_duplicates()
+assert len(game_df) == len(game_df['game_pk'].unique())
+
+# load pa data
+pa_df = []
+for game_pk in tqdm(game_df['game_pk']):
+  pa_df.append(pd.read_csv(os.path.join('pa', f'{game_pk}.csv'), dtype={'pa_pk': str}))
+pa_df = pd.concat(pa_df, axis='rows')
+
+# load pitch data
+pitch_df = []
+for game_pk in tqdm(game_df['game_pk']):
+  pitch_df.append(pd.read_csv(os.path.join('pitch', f'{game_pk}.csv'), dtype={'pa_pk': str}))
+pitch_df = pd.concat(pitch_df, axis='rows')
+pitch_df
+
+# load player data
+player_df = pd.read_csv('player.csv')
+player_df
+
+# translate pa data
+pa_df['_des'] = pa_df['des'].str.strip()
+pa_df['des'] = pa_df['des'].str.strip()
+pa_df['des_more'] = pa_df['des_more'].str.strip()
+pa_df.loc[pa_df['des'].isna(), 'des'] = pa_df[pa_df['des'].isna()]['des_more']
+pa_df.loc[:, 'des'] = pa_df['des'].apply(lambda item: item.split()[0] if (len(item.split()) > 1 and re.search(r'＋\d+点', item)) else item)
+non_home_plate_outcome = (pa_df['des'].isin(['ボール', '見逃し', '空振り'])) | (pa_df['des'].str.endswith('塁けん制'))
+pa_df.loc[non_home_plate_outcome, 'des'] = pa_df.loc[non_home_plate_outcome, 'des_more']
+pa_df['des'] = pa_df['des'].apply(translate_pa_outcome)
+
+# translate pitch data
+pitch_df = pitch_df[~pitch_df['pitch_name'].isna()]
+pitch_df['jp_pitch_name'] = pitch_df['pitch_name']
+pitch_df['pitch_name'] = pitch_df['jp_pitch_name'].apply(lambda pitch_name: jp_pitch_to_en_pitch[pitch_name])
+pitch_df['pitch_type'] = pitch_df['jp_pitch_name'].apply(lambda pitch_name: jp_pitch_to_pitch_code[pitch_name])
+pitch_df['description'] = pitch_df['description'].apply(lambda item: item.split()[0] if len(item.split()) > 1 else item)
+pitch_df['description'] = pitch_df['description'].apply(translate_pitch_outcome)
+pitch_df['release_speed'] = pitch_df['release_speed'].replace('-', np.nan)
+pitch_df.loc[~pitch_df['release_speed'].isna(), 'release_speed'] = pitch_df.loc[~pitch_df['release_speed'].isna(), 'release_speed'].str.removesuffix('km/h').astype(int)
+pitch_df['plate_x'] = (pitch_df['plate_x'] + 13) - 80
+pitch_df['plate_z'] = 200 - (pitch_df['plate_z'] + 13) - 100
+
+# translate player data
+client = Client("Ramos-Ramos/npb_name_translator")
+en_names = client.predict(
+		jp_names='\n'.join(player_df.name.tolist()),
+		api_name="/predict"
+)
+player_df['jp_name'] = player_df['name']
+player_df['name'] = [name if name != 'nan' else np.nan for name in en_names.splitlines()]
+
+# merge pitch and pa data
+df = pd.merge(pitch_df, pa_df, 'inner', on=['game_pk', 'pa_pk'])
+df = pd.merge(df, player_df.rename(columns={'player_id': 'pitcher'}), 'inner', on='pitcher')
+df['whiff'] = df['description'].isin(['SS', 'K'])
+df['swing'] = ~df['description'].isin(['B', 'BB', 'LS', 'inv_K', 'bunt_K', 'HBP', 'SH', 'SH E', 'SH FC', 'obstruction', 'illegal_pitch', 'defensive_interference'])
+df['csw'] = df['description'].isin(['SS', 'K', 'LS', 'inv_K'])
+df['normal_pitch'] = ~df['description'].isin(['obstruction', 'illegal_pitch', 'defensive_interference']) # guess
+
+whiff_rate = df.groupby(['name', 'pitch_name'])
+whiff_rate = (whiff_rate['whiff'].sum() / whiff_rate['swing'].sum() * 100).round(1).rename('Whiff%').reset_index()
+
+csw_rate = df.groupby(['name', 'pitch_name'])
+csw_rate = (csw_rate['csw'].sum() / csw_rate['normal_pitch'].sum() * 100).round(1).rename('CSW%').reset_index()
+
+pitch_stats = pd.merge(
+    whiff_rate,
+    csw_rate,
+    on=['name', 'pitch_name']
+).set_index(['name', 'pitch_name'])

demo.py

@@ -3,10 +3,13 @@ import gradio as gr
 import pandas as pd
 
 from math import ceil
+import os
 
-from gradio_function import player_df, jp_pitch_to_en_pitch, get_data
-from translate import max_pitch_types
+from data import player_df
+from gradio_function import get_data
+from translate import jp_pitch_to_en_pitch, max_pitch_types
 
+os.makedirs('files', exist_ok=True)
 
 css = '''
 .pitch-usage {height: 256px}
@@ -32,6 +35,7 @@ with gr.Blocks(css=css) as demo:
   ''')
   player = gr.Dropdown(choices=sorted(player_df['name'].dropna().tolist()), label='Player')
   player_info = gr.Markdown()
+  download_file = gr.DownloadButton(label='Download player data')
   with gr.Row():
     with gr.Column():
       gr.Markdown('## Placeholder')
@@ -87,9 +91,9 @@ with gr.Blocks(css=css) as demo:
     '''
     )
 
-  player.input(get_data, inputs=player, outputs=[player_info, usage, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, pitch_velo_summary])
+  player.input(get_data, inputs=player, outputs=[player_info, download_file, usage, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, pitch_velo_summary])
 
 demo.launch(
-    share=True
-    # debug=True
+    share=True,
+    debug=True
 )

gradio_function.py

@@ -1,95 +1,14 @@
 
-import pandas as pd
-import numpy as np
-from tqdm.auto import tqdm
 import plotly.express as px
 import plotly.graph_objects as go
 import plotly.colors as pc
 from scipy.stats import gaussian_kde
 import numpy as np
+import pandas as pd
 import gradio as gr
-from gradio_client import Client
-
-from scipy.stats import gaussian_kde
-import numpy as np
-
-import os
-import re
-
-from translate import translate_pa_outcome, translate_pitch_outcome, jp_pitch_to_en_pitch, jp_pitch_to_pitch_code, translate_pitch_outcome, max_pitch_types
-
-# load game data
-game_df = pd.read_csv('game.csv').drop_duplicates()
-assert len(game_df) == len(game_df['game_pk'].unique())
-
-# load pa data
-pa_df = []
-for game_pk in tqdm(game_df['game_pk']):
-  pa_df.append(pd.read_csv(os.path.join('pa', f'{game_pk}.csv'), dtype={'pa_pk': str}))
-pa_df = pd.concat(pa_df, axis='rows')
-
-# load pitch data
-pitch_df = []
-for game_pk in tqdm(game_df['game_pk']):
-  pitch_df.append(pd.read_csv(os.path.join('pitch', f'{game_pk}.csv'), dtype={'pa_pk': str}))
-pitch_df = pd.concat(pitch_df, axis='rows')
-pitch_df
-
-# load player data
-player_df = pd.read_csv('player.csv')
-player_df
-
-# translate pa data
-pa_df['_des'] = pa_df['des'].str.strip()
-pa_df['des'] = pa_df['des'].str.strip()
-pa_df['des_more'] = pa_df['des_more'].str.strip()
-pa_df.loc[pa_df['des'].isna(), 'des'] = pa_df[pa_df['des'].isna()]['des_more']
-pa_df.loc[:, 'des'] = pa_df['des'].apply(lambda item: item.split()[0] if (len(item.split()) > 1 and re.search(r'＋\d+点', item)) else item)
-non_home_plate_outcome = (pa_df['des'].isin(['ボール', '見逃し', '空振り'])) | (pa_df['des'].str.endswith('塁けん制'))
-pa_df.loc[non_home_plate_outcome, 'des'] = pa_df.loc[non_home_plate_outcome, 'des_more']
-pa_df['des'] = pa_df['des'].apply(translate_pa_outcome)
-
-# translate pitch data
-pitch_df = pitch_df[~pitch_df['pitch_name'].isna()]
-pitch_df['jp_pitch_name'] = pitch_df['pitch_name']
-pitch_df['pitch_name'] = pitch_df['jp_pitch_name'].apply(lambda pitch_name: jp_pitch_to_en_pitch[pitch_name])
-pitch_df['pitch_type'] = pitch_df['jp_pitch_name'].apply(lambda pitch_name: jp_pitch_to_pitch_code[pitch_name])
-pitch_df['description'] = pitch_df['description'].apply(lambda item: item.split()[0] if len(item.split()) > 1 else item)
-pitch_df['description'] = pitch_df['description'].apply(translate_pitch_outcome)
-pitch_df['release_speed'] = pitch_df['release_speed'].replace('-', np.nan)
-pitch_df.loc[~pitch_df['release_speed'].isna(), 'release_speed'] = pitch_df.loc[~pitch_df['release_speed'].isna(), 'release_speed'].str.removesuffix('km/h').astype(int)
-pitch_df['plate_x'] = (pitch_df['plate_x'] + 13) - 80
-pitch_df['plate_z'] = 200 - (pitch_df['plate_z'] + 13) - 100
-
-# translate player data
-client = Client("Ramos-Ramos/npb_name_translator")
-en_names = client.predict(
-		jp_names='\n'.join(player_df.name.tolist()),
-		api_name="/predict"
-)
-player_df['jp_name'] = player_df['name']
-player_df['name'] = [name if name != 'nan' else np.nan for name in en_names.splitlines()]
-
-# merge pitch and pa data
-df = pd.merge(pitch_df, pa_df, 'inner', on=['game_pk', 'pa_pk'])
-df = pd.merge(df, player_df.rename(columns={'player_id': 'pitcher'}), 'inner', on='pitcher')
-df['whiff'] = df['description'].isin(['SS', 'K'])
-df['swing'] = ~df['description'].isin(['B', 'BB', 'LS', 'inv_K', 'bunt_K', 'HBP', 'SH', 'SH E', 'SH FC', 'obstruction', 'illegal_pitch', 'defensive_interference'])
-df['csw'] = df['description'].isin(['SS', 'K', 'LS', 'inv_K'])
-df['normal_pitch'] = ~df['description'].isin(['obstruction', 'illegal_pitch', 'defensive_interference']) # guess
-
-whiff_rate = df.groupby(['name', 'pitch_name'])
-whiff_rate = (whiff_rate['whiff'].sum() / whiff_rate['swing'].sum() * 100).round(1).rename('Whiff%').reset_index()
-
-csw_rate = df.groupby(['name', 'pitch_name'])
-csw_rate = (csw_rate['csw'].sum() / csw_rate['normal_pitch'].sum() * 100).round(1).rename('CSW%').reset_index()
-
-pitch_stats = pd.merge(
-    whiff_rate,
-    csw_rate,
-    on=['name', 'pitch_name']
-).set_index(['name', 'pitch_name'])
 
+from translate import max_pitch_types
+from data import df, pitch_stats
 
 # GRADIO FUNCTIONS
 
@@ -343,6 +262,7 @@ def get_data(player):
   player_name = f'# {player}'
 
   _df = df.set_index('name').loc[player]
+  _df.to_csv(f'files/npb.csv', index=False)
   _df_by_pitch_name = _df.set_index('pitch_name')
 
   usage_fig = px.pie(_df['pitch_name'], names='pitch_name')
@@ -387,4 +307,4 @@ def get_data(player):
 
   pitch_velo_summary = plot_all_pitch_velo(player=player, player_df=_df_by_pitch_name, pitch_counts=pitch_counts.sort_values(ascending=True))
 
-  return player_name, usage_fig, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, pitch_velo_summary
+  return player_name, 'files/npb.csv', usage_fig, *pitch_groups, *pitch_names, *pitch_infos, *pitch_velos, *pitch_maps, pitch_velo_summary