import polars as pl
import numpy as np
import requests

def calculate_arm_angles(df: pl.DataFrame,pitcher_id:int) -> pl.DataFrame:
    df_arm_angle = pl.read_csv('stuff_model/pitcher_arm_angles_2024.csv')
    #pitcher_id = 489446
    df_filter = df.filter(pl.col("pitcher_id") == pitcher_id).drop_nulls(subset=["release_pos_x", "release_pos_z"])
    # data = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}').json()

    if pitcher_id not in df_arm_angle["pitcher"]:

        data = requests.get(f'https://statsapi.mlb.com/api/v1/people?personIds={pitcher_id}').json()
        height_in = data['people'][0]['height']
        height = int(height_in.split("'")[0]) * 12 + int(height_in.split("'")[1].split('"')[0])
        df_filter = (df_filter.with_columns(
                (pl.col("release_pos_x") * 12).alias("release_pos_x"),
                (pl.col("release_pos_z") * 12).alias("release_pos_z"),
                (pl.lit(height * 0.70)).alias("shoulder_pos"),
            )
            .with_columns(
                (pl.col("release_pos_z") - pl.col("shoulder_pos")).alias("Opp"),
                pl.col("release_pos_x").abs().alias("Adj"),
            )
            .with_columns(
                pl.struct(["Opp", "Adj"]).map_elements(lambda x: np.arctan2(x["Opp"], x["Adj"])).alias("arm_angle_rad")
            ))

        df_filter = (df_filter.with_columns(    
            
                pl.col("arm_angle_rad").degrees().alias("arm_angle")
            
            #.drop(["Opp", "arm_angle_rad"])
            ))
            
    else:
        shoulder_x = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["relative_shoulder_x"][0]
        shoulder_z = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["shoulder_z"][0]
        rel_x = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["relative_release_ball_x"][0]
        rel_z = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["release_ball_z"][0]


        ball_angle = df_arm_angle.filter(pl.col("pitcher") == pitcher_id)["ball_angle"][0]

        hyp = np.sqrt((rel_x - shoulder_x)**2 + (rel_z - shoulder_z)**2)

        print(shoulder_x, shoulder_z)

        df_filter = (df_filter.with_columns(

            )
            .with_columns(
                (pl.col("release_pos_z") - shoulder_z).alias("Opp"),
                (pl.lit(hyp)).alias("Hyp"),
            )
            .with_columns(
                pl.struct(["Opp","Hyp"]).map_elements(lambda x: np.arcsin(x["Opp"] / x["Hyp"])).alias("arm_angle_rad")
            )
            .with_columns(
                pl.col("arm_angle_rad").degrees().alias("arm_angle")
            )
            #.drop(["Opp", "arm_angle_rad"])
            )
    
        df_filter = df_filter.with_columns(
                ((pl.col("arm_angle") * 0.5) + (ball_angle * 0.5)).alias("arm_angle")
            )
    


    
    return df_filter