from collections import Counter

import pandas as pd
from sqlalchemy import select

from data.database import Base, dal, project_variables
import util.drawing as drawutil


def load_dataframe(table_name: str, use_rule_change_date: bool = True) -> pd.DataFrame:
    """connect to the database and load game data into a pandas dataframe"""

    dal.connect()
    session = dal.Session()

    game_dict = drawutil.check_table_name(table_name=table_name)
    if game_dict:
        start_date = game_dict['rule_date'] if use_rule_change_date else None
        target_table = Base.metadata.tables.get(game_dict['db_table_name'])
        if start_date is not None:
            sql_statement = select(target_table).where(target_table.columns.draw_date >= start_date)
        else:
            sql_statement = select(target_table)

        return pd.read_sql(sql_statement, session.bind)
    else:
        raise ValueError('An invalid table name was provided')


def get_most_common_number(
        data_frame: pd.DataFrame,
        columns: list | None = None,
        top: int = 1
    ) -> list[int]:
    if columns is None:
        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
    flat_numbers = data_frame[columns].values.flatten()
    counts = Counter(flat_numbers)
    return [num for num, _ in counts.most_common(top)]


def get_least_common_number(
        data_frame: pd.DataFrame,
        columns: list | None = None,
        bottom: int = 1
) -> list[int]:
    if columns is None:
        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
    flat_numbers = data_frame[columns].values.flatten()
    counts = Counter(flat_numbers)
    return list(reversed([num for num, _ in counts.most_common()[-bottom:]]))