import duckdb
import pandas as pd
import os
from typing import Callable


duckdb.default_connection.execute("SET GLOBAL pandas_analyze_sample=100000")

BASE_URL = "https://github.com/nflverse/nflverse-data/releases/download/"


FANTASY_POSITIONS = [
    "QB",
    "RB",
    "WR",
    "TE",
    "FB",
    "K",
]


def get_snap_counts(season_int: int) -> pd.DataFrame:
    df = duckdb.sql(f"SELECT * from snap_counts_snap_counts_{season_int}").df()
    df["fantasy_position"] = df["position"].isin(FANTASY_POSITIONS)
    return df


def get_play_by_play(season_int: int) -> pd.DataFrame:
    df = duckdb.sql(f"SELECT * from pbp_play_by_play_{season_int}").df()
    return df


def get_player_stats(season_int: int) -> pd.DataFrame:
    df = duckdb.sql("SELECT * from player_stats_player_stats").df()
    return df


def get_ftn_charting(season_int: int) -> pd.DataFrame:
    df = duckdb.sql(f"SELECT * from ftn_charting_ftn_charting_{season_int}").df()
    return df


def get_pbp_participation(season_int: int) -> pd.DataFrame:
    df = duckdb.sql(
        f"""
                    SELECT 
                        a.*
                        , b.week
                        , b.down
                        , b.qtr
                        , b.ydstogo
                        , b.play_type
                        , b.pass_length
                        , b.pass_location
                        , 1 as count_col
                    from pbp_participation_pbp_participation_{season_int} a
                    left join pbp_play_by_play_{season_int} b
                    on a.play_id = b.play_id
                    and a.nflverse_game_id = b.game_id
                    where b.week is not null
"""
    ).df()
    return df


def get_nextgen_stats(season_int: int, stat_category: str) -> pd.DataFrame:
    df = duckdb.sql(f"SELECT * from nextgen_stats_ngs_{stat_category} where season = {season_int}").df()
    return df


SEASON = "2023"

NFLVERSE_ASSETS = [
    ("ftn_charting", f"ftn_charting_{SEASON}.parquet"),
    ("espn_data", "qbr_season_level.parquet"),
    ("espn_data", "qbr_week_level.parquet"),
    ("players", "players.parquet"),
    ("pbp_participation", f"pbp_participation_{SEASON}.parquet"),
    ("snap_counts", f"snap_counts_{SEASON}.parquet"),
    ("player_stats", f"player_stats_{SEASON}.parquet"),
    ("player_stats", f"player_stats_def_{SEASON}.parquet"),
    ("player_stats", f"player_stats_kicking_{SEASON}.parquet"),
    ("pfr_advstats", "advstats_season_def.parquet"),
    ("pfr_advstats", "advstats_season_pass.parquet"),
    ("pfr_advstats", "advstats_season_rec.parquet"),
    ("pfr_advstats", "advstats_season_rush.parquet"),
    ("pfr_advstats", f"advstats_week_def_{SEASON}.parquet"),
    ("pfr_advstats", f"advstats_week_pass_{SEASON}.parquet"),
    ("pfr_advstats", f"advstats_week_rec_{SEASON}.parquet"),
    ("pfr_advstats", f"advstats_week_rush_{SEASON}.parquet"),
    ("pbp", f"play_by_play_{SEASON}.parquet"),
    ("nextgen_stats", "ngs_passing.parquet"),
    ("nextgen_stats", "ngs_receiving.parquet"),
    ("nextgen_stats", "ngs_rushing.parquet"),
]


class NflVerseDataAsset:
    def __init__(
        self,
        release_tag: str,
        asset_name: str,
        dataframe_mutation_fxn: Callable[[pd.DataFrame], pd.DataFrame] = lambda x: x,
    ):
        self.release_tag = release_tag
        self.asset_name = asset_name
        self.dataframe_mutation_fxn = dataframe_mutation_fxn
        self.table_name = f"{release_tag}_{asset_name.rsplit('.', 1)[0]}"

    def load_parquet_asset_to_df(self) -> pd.DataFrame:
        location = os.path.join(BASE_URL, self.release_tag, self.asset_name)
        df = pd.read_parquet(location)
        return df

    def register_asset_to_duckdb(self) -> None:
        df = self.load_parquet_asset_to_df()
        df = self.dataframe_mutation_fxn(df)
        duckdb.register(self.table_name, df)


def load_assets():
    for tag, asset in NFLVERSE_ASSETS:
        asset = NflVerseDataAsset(tag, asset)
        asset.register_asset_to_duckdb()


def get_current_tables() -> list[str]:
    current_tables_df = duckdb.sql("SHOW TABLES").df()
    return current_tables_df["name"].tolist()