Update app.py

app.py

@@ -88,6 +88,9 @@ app_ui = ui.page_fluid(
             ),
             ui.nav("Daily Pitches",
                 ui.row(
+                    ui.column(1,
+                        ui.download_button("download_daily", "Download Data", class_="btn-sm mb-3")
+                    ),
                     ui.column(2,
                         ui.div(
                             {"class": "input-group"},
@@ -242,6 +245,103 @@ def server(input, output, session):
 
         return df_merge
 
+
+    @reactive.Calc
+    def ts_data_daily():
+
+
+        df_spring = spring_data().unique(subset=['play_id'])
+
+        # df_year_old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb,df_aaa,df_a,df_afl])))
+        # df_year_2old = stuff_apply.stuff_apply(fe.feature_engineering(pl.concat([df_mlb_2023])))
+        df_spring_stuff = stuff_apply.stuff_apply(fe.feature_engineering(df_spring))
+
+
+
+        import polars as pl
+
+        # Compute total pitches for each pitcher
+        df_pitcher_totals = df_spring_stuff.group_by(["pitcher_id",'game_id','game_date']).agg(
+            pl.col("start_speed").count().alias("pitcher_total")
+        )
+
+        df_pitcher_totals_hands = (
+            df_spring_stuff
+            .group_by(["pitcher_id", "batter_hand",'game_id','game_date'])
+            .agg(pl.col("start_speed").count().alias("pitcher_total"))
+            .pivot(
+                values="pitcher_total",
+                index=["pitcher_id",'game_id'],
+                columns="batter_hand",
+                aggregate_function="sum"
+            )
+            .rename({"L": "pitcher_total_left", "R": "pitcher_total_right"})
+            .fill_null(0)  # Fill missing values with 0 if some pitchers don't face both hands
+        )
+                
+        
+        df_spring_group = df_spring_stuff.group_by(['pitcher_id', 'pitcher_name', 'pitch_type','game_id','game_date']).agg([
+            pl.col('start_speed').count().alias('count'),
+            pl.col('start_speed').mean().alias('start_speed'),
+            pl.col('start_speed').max().alias('max_start_speed'),
+            pl.col('ivb').mean().alias('ivb'),
+            pl.col('hb').mean().alias('hb'),
+            pl.col('release_pos_z').mean().alias('release_pos_z'),
+            pl.col('release_pos_x').mean().alias('release_pos_x'),
+            pl.col('extension').mean().alias('extension'),
+            pl.col('tj_stuff_plus').mean().alias('tj_stuff_plus'),
+            (pl.col("batter_hand").eq("R").sum()).alias("rhh_count"),  # Corrected: Counts RHH (batter_hand == "R")
+            (pl.col("batter_hand").eq("L").sum()).alias("lhh_count")   # Corrected: Counts LHH (batter_hand == "L")
+        ])
+
+        # Join total pitches per pitcher to the grouped DataFrame on pitcher_id
+        df_spring_group = df_spring_group.join(df_pitcher_totals, on=["pitcher_id",'game_id'], how="left")
+        df_spring_group = df_spring_group.join(df_pitcher_totals_hands, on=["pitcher_id",'game_id'], how="left")
+
+        # Now calculate the pitch percent for each pitcher/pitch_type combination
+        df_spring_group = df_spring_group.with_columns(
+            (pl.col("count") / pl.col("pitcher_total")).alias("pitch_percent")
+        )
+
+        # Optionally, if you want the percentage of left/right-handed batters within the group:
+        df_spring_group = df_spring_group.with_columns([
+            (pl.col("rhh_count") / pl.col("pitcher_total_right")).alias("rhh_percent"),
+            (pl.col("lhh_count") / pl.col("pitcher_total_left")).alias("lhh_percent")
+        ])
+
+        df_merge = df_spring_group.join(df_year_old_group,on=['pitcher_id','pitch_type'],how='left',suffix='_old')
+
+
+        df_merge = df_merge.with_columns(
+            pl.col('pitcher_id').is_in(df_year_old_group['pitcher_id']).alias('exists_in_old')
+        )
+
+        df_merge = df_merge.with_columns(
+            pl.when(pl.col('start_speed_old').is_null() & pl.col('exists_in_old'))
+            .then(pl.lit(True))
+            .otherwise(pl.lit(None))
+            .alias("new_pitch")
+        )
+
+        df_merge = df_merge.select([
+            'pitcher_id',
+            'pitcher_name',
+            'pitch_type',
+            'count',
+            'pitch_percent',
+            'rhh_percent',
+            'lhh_percent',
+            'start_speed',
+            'max_start_speed',
+            'ivb',
+            'hb',
+            'release_pos_z',
+            'release_pos_x',
+            'extension',
+            'tj_stuff_plus',
+        ])
+
+        return df_merge    
     # @reactive.Calc
     # def ts_data():
 
@@ -401,6 +501,9 @@ def server(input, output, session):
     def download_all():
         yield ts_data().write_csv()
 
+    @session.download(filename="data_daily.csv")
+    def download_daily():
+        yield ts_data_daily().write_csv()
 
     @session.download(filename="data_tjstuff.csv")
     def download_tjsumm():