import numpy as np
import json
import itertools
from pathlib import Path

rgen = np.random.default_rng(seed=42)


def load_stats():
    db = Path("local_team.db")
    players_list = "prefs_page/src/players.json"
    with open(players_list, "r") as f:
        players = json.load(f)

    preferences = {}

    for line in open(db, "r"):
        date, person, prefs = line.split("\t")
        if not person.strip() or not prefs.strip():
            continue
        preferences[person] = [p.strip() for p in prefs.split(",")]

    for player in players:
        if player not in preferences:
            preferences[player] = []

    return players, preferences


# synthetical data
# rgen = np.random.default_rng(seed=42)

# n_prefs = 8
# preferences = {
#     player: rgen.choice(players, size=n_prefs, replace=False) for player in players
# }


def team_table_json():
    players, preferences = load_stats()
    mean, team0, team1 = apply_brute_force(players, preferences)
    data = {}
    for i, team in enumerate([team0, team1]):
        tablename = f"Team {i+1}"
        data[tablename] = []
        for p in sorted(list(team)):
            prefs = preferences[p]
            matches = sum([pref in team for pref in preferences[p]])
            data[tablename].append([p, matches, len(prefs)])

    with open("prefs_page/src/table.json", "w") as f:
        json.dump(data, f)


def apply_brute_force(players, preferences):
    def evaluate_teams(team0, team1):
        scores = []
        percentages = []
        for team in [team0, team1]:
            for p in team:
                scores.append(sum([pref in team for pref in preferences[p]]))
                if len(preferences[p]) > 0:
                    percentages.append(scores[-1] / len(preferences[p]))
        return np.mean(scores), np.mean(percentages) * 100

    best_score = [(0, [], [])]
    best_percentage = [(0, [], [])]
    for team0 in itertools.combinations(players, 9):
        team1 = {player for player in players if player not in team0}
        score, percentage = evaluate_teams(team0, team1)
        if score > best_score[0][0]:
            best_score = [(score, team0, team1)]
        if score == best_score[0][0] and set(team0) != set(best_score[0][1]):
            best_score.append((score, team0, team1))
        if percentage > best_percentage[0][0]:
            best_percentage = [(percentage, team0, team1)]
        if percentage == best_percentage[0][0] and set(team0) != set(best_score[0][1]):
            best_percentage.append((percentage, team0, team1))

    if __name__ == "__main__":
        for result in best_score:
            print(result[0])
            print(result[1])
            print(result[2])

        for result in best_percentage:
            print(result[0])
            print(result[1])
            print(result[2])

    # team_table(score, team0, team1)
    return best_percentage[0]


if __name__ == "__main__":
    team_table_json()