import sqlite3
import pandas as pd
import csv

pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)

db_path = 'database/L2/L2_Main.sqlite'

def verify():
    conn = sqlite3.connect(db_path)
    
    print("--- Counts ---")
    tables = [
        'dim_players',
        'dim_maps',
        'fact_matches',
        'fact_match_players',
        'fact_match_players_t',
        'fact_match_players_ct',
        'fact_rounds',
        'fact_round_events',
        'fact_round_player_economy'
    ]
    for t in tables:
        count = conn.execute(f"SELECT COUNT(*) FROM {t}").fetchone()[0]
        print(f"{t}: {count}")

    print("\n--- Data Source Distribution ---")
    dist = pd.read_sql("SELECT data_source_type, COUNT(*) as cnt FROM fact_matches GROUP BY data_source_type", conn)
    print(dist)
    
    print("\n--- Sample Round Events (Leetify vs Classic) ---")
    # Fetch one event from a leetify match
    leetify_match = conn.execute("SELECT match_id FROM fact_matches WHERE data_source_type='leetify' LIMIT 1").fetchone()
    if leetify_match:
        mid = leetify_match[0]
        print(f"Leetify Match: {mid}")
        df = pd.read_sql(f"SELECT * FROM fact_round_events WHERE match_id='{mid}' AND event_type='kill' LIMIT 1", conn)
        print(df[['event_type', 'attacker_steam_id', 'trade_killer_steam_id', 'attacker_pos_x', 'score_change_attacker']])
    
    # Fetch one event from a classic match
    classic_match = conn.execute("SELECT match_id FROM fact_matches WHERE data_source_type='classic' LIMIT 1").fetchone()
    if classic_match:
        mid = classic_match[0]
        print(f"Classic Match: {mid}")
        df = pd.read_sql(f"SELECT * FROM fact_round_events WHERE match_id='{mid}' AND event_type='kill' LIMIT 1", conn)
        print(df[['event_type', 'attacker_steam_id', 'trade_killer_steam_id', 'attacker_pos_x', 'score_change_attacker']])

    print("\n--- Sample Player Stats (New Fields) ---")
    df_players = pd.read_sql("SELECT steam_id_64, rating, rating3, elo_change, rank_score, flash_duration, jump_count FROM fact_match_players LIMIT 5", conn)
    print(df_players)

    print("\n--- Integrity Checks ---")
    missing_players = conn.execute("""
        SELECT COUNT(*) FROM fact_match_players f
        LEFT JOIN dim_players d ON f.steam_id_64 = d.steam_id_64
        WHERE d.steam_id_64 IS NULL
    """).fetchone()[0]
    print(f"fact_match_players missing dim_players: {missing_players}")

    missing_round_matches = conn.execute("""
        SELECT COUNT(*) FROM fact_rounds r
        LEFT JOIN fact_matches m ON r.match_id = m.match_id
        WHERE m.match_id IS NULL
    """).fetchone()[0]
    print(f"fact_rounds missing fact_matches: {missing_round_matches}")

    missing_event_rounds = conn.execute("""
        SELECT COUNT(*) FROM fact_round_events e
        LEFT JOIN fact_rounds r ON e.match_id = r.match_id AND e.round_num = r.round_num
        WHERE r.match_id IS NULL
    """).fetchone()[0]
    print(f"fact_round_events missing fact_rounds: {missing_event_rounds}")

    side_zero_t = conn.execute("""
        SELECT COUNT(*) FROM fact_match_players_t
        WHERE COALESCE(kills,0)=0 AND COALESCE(deaths,0)=0 AND COALESCE(assists,0)=0
    """).fetchone()[0]
    side_zero_ct = conn.execute("""
        SELECT COUNT(*) FROM fact_match_players_ct
        WHERE COALESCE(kills,0)=0 AND COALESCE(deaths,0)=0 AND COALESCE(assists,0)=0
    """).fetchone()[0]
    print(f"fact_match_players_t zero K/D/A: {side_zero_t}")
    print(f"fact_match_players_ct zero K/D/A: {side_zero_ct}")

    print("\n--- Full vs T/CT Comparison ---")
    cols = [
        'kills', 'deaths', 'assists', 'headshot_count', 'adr', 'rating', 'rating2',
        'rating3', 'rws', 'mvp_count', 'flash_duration', 'jump_count', 'is_win'
    ]
    df_full = pd.read_sql(
        "SELECT match_id, steam_id_64, " + ",".join(cols) + " FROM fact_match_players",
        conn
    )
    df_t = pd.read_sql(
        "SELECT match_id, steam_id_64, " + ",".join(cols) + " FROM fact_match_players_t",
        conn
    ).rename(columns={c: f"{c}_t" for c in cols})
    df_ct = pd.read_sql(
        "SELECT match_id, steam_id_64, " + ",".join(cols) + " FROM fact_match_players_ct",
        conn
    ).rename(columns={c: f"{c}_ct" for c in cols})

    df = df_full.merge(df_t, on=['match_id', 'steam_id_64'], how='left')
    df = df.merge(df_ct, on=['match_id', 'steam_id_64'], how='left')

    def is_empty(s):
        return s.isna() | (s == 0)

    for c in cols:
        empty_count = is_empty(df[c]).sum()
        print(f"{c} empty: {empty_count}")

    additive = ['kills', 'deaths', 'assists', 'headshot_count', 'mvp_count', 'flash_duration', 'jump_count']
    for c in additive:
        t_sum = df[f"{c}_t"].fillna(0) + df[f"{c}_ct"].fillna(0)
        tol = 0.01 if c == 'flash_duration' else 0
        diff = (df[c].fillna(0) - t_sum).abs() > tol
        print(f"{c} full != t+ct: {diff.sum()}")

    non_additive = ['adr', 'rating', 'rating2', 'rating3', 'rws', 'is_win']
    for c in non_additive:
        side_nonempty = (~is_empty(df[f"{c}_t"])) | (~is_empty(df[f"{c}_ct"]))
        full_empty_side_nonempty = is_empty(df[c]) & side_nonempty
        full_nonempty_side_empty = (~is_empty(df[c])) & (~side_nonempty)
        print(f"{c} full empty but side has: {full_empty_side_nonempty.sum()}")
        print(f"{c} full has but side empty: {full_nonempty_side_empty.sum()}")

    print("\n--- Rating Detail ---")
    rating_cols = ['rating', 'rating2', 'rating3']
    for c in rating_cols:
        full_null = df[c].isna().sum()
        full_zero = (df[c] == 0).sum()
        full_nonzero = ((~df[c].isna()) & (df[c] != 0)).sum()
        side_t_nonzero = ((~df[f"{c}_t"].isna()) & (df[f"{c}_t"] != 0)).sum()
        side_ct_nonzero = ((~df[f"{c}_ct"].isna()) & (df[f"{c}_ct"] != 0)).sum()
        side_any_nonzero = ((~df[f"{c}_t"].isna()) & (df[f"{c}_t"] != 0)) | ((~df[f"{c}_ct"].isna()) & (df[f"{c}_ct"] != 0))
        full_nonzero_side_zero = ((~df[c].isna()) & (df[c] != 0) & (~side_any_nonzero)).sum()
        full_zero_side_nonzero = (((df[c].isna()) | (df[c] == 0)) & side_any_nonzero).sum()
        print(f"{c} full null: {full_null} full zero: {full_zero} full nonzero: {full_nonzero}")
        print(f"{c} side t nonzero: {side_t_nonzero} side ct nonzero: {side_ct_nonzero}")
        print(f"{c} full nonzero but side all zero: {full_nonzero_side_zero}")
        print(f"{c} full zero but side has: {full_zero_side_nonzero}")

    df_rating_src = pd.read_sql(
        "SELECT f.rating, f.rating2, f.rating3, m.data_source_type FROM fact_match_players f JOIN fact_matches m ON f.match_id = m.match_id",
        conn
    )
    for c in rating_cols:
        grp = df_rating_src.groupby('data_source_type')[c].apply(lambda s: (s != 0).sum()).reset_index(name='nonzero')
        print(f"{c} nonzero by source")
        print(grp)

    print("\n--- Schema Coverage (fight_any) ---")
    schema_path = 'database/original_json_schema/schema_flat.csv'
    paths = []
    with open(schema_path, 'r', encoding='utf-8') as f:
        reader = csv.reader(f)
        _ = next(reader, None)
        for row in reader:
            if len(row) >= 2:
                paths.append(row[1])
    fight_keys = set()
    for p in paths:
        if 'data.group_N[].fight_any.' in p:
            key = p.split('fight_any.')[1].split('.')[0]
            fight_keys.add(key)
    l2_cols = set(pd.read_sql("PRAGMA table_info(fact_match_players)", conn)['name'].tolist())
    alias = {
        'kills': 'kill',
        'deaths': 'death',
        'assists': 'assist',
        'headshot_count': 'headshot',
        'mvp_count': 'is_mvp',
        'flash_duration': 'flash_enemy_time',
        'jump_count': 'jump_total',
        'awp_kills': 'awp_kill'
    }
    covered = set()
    for c in l2_cols:
        if c in fight_keys:
            covered.add(c)
        elif c in alias and alias[c] in fight_keys:
            covered.add(alias[c])
    missing_keys = sorted(list(fight_keys - covered))
    print(f"fight_any keys: {len(fight_keys)}")
    print(f"covered by L2 columns: {len(covered)}")
    print(f"uncovered fight_any keys: {len(missing_keys)}")
    if missing_keys:
        print(missing_keys)

    print("\n--- Coverage Zero Rate (fight_any -> fact_match_players) ---")
    fight_cols = [k for k in fight_keys if k in l2_cols or k in alias.values()]
    col_map = {}
    for k in fight_cols:
        if k in l2_cols:
            col_map[k] = k
        else:
            for l2k, src in alias.items():
                if src == k:
                    col_map[k] = l2k
                    break
    select_cols = ["steam_id_64"] + list(set(col_map.values()))
    df_fight = pd.read_sql(
        "SELECT " + ",".join(select_cols) + " FROM fact_match_players",
        conn
    )
    total_rows = len(df_fight)
    stats = []
    for fight_key, col in sorted(col_map.items()):
        s = df_fight[col]
        zeros = (s == 0).sum()
        nulls = s.isna().sum()
        nonzero = total_rows - zeros - nulls
        stats.append({
            "fight_key": fight_key,
            "column": col,
            "nonzero": nonzero,
            "zero": zeros,
            "null": nulls,
            "zero_rate": 0 if total_rows == 0 else round(zeros / total_rows, 4)
        })
    df_stats = pd.DataFrame(stats).sort_values(["zero_rate", "nonzero"], ascending=[False, True])
    print(df_stats.head(30))
    print("\n-- zero_rate top (most zeros) --")
    print(df_stats.head(10))
    print("\n-- zero_rate bottom (most nonzero) --")
    print(df_stats.tail(10))

    print("\n--- Schema Coverage (leetify economy) ---")
    econ_keys = [
        'data.leetify_data.round_stat[].bron_equipment.',
        'data.leetify_data.round_stat[].player_t_score.',
        'data.leetify_data.round_stat[].player_ct_score.',
        'data.leetify_data.round_stat[].player_bron_crash.'
    ]
    for k in econ_keys:
        count = sum(1 for p in paths if k in p)
        print(f"{k} paths: {count}")

    conn.close()

if __name__ == "__main__":
    verify()