"""
@author: luojunhui
cal each account && position reading rate
"""
from tqdm import tqdm
from datetime import datetime, timezone, timedelta
from pandas import DataFrame

from applications import DeNetMysql, PQMySQL, longArticlesMySQL

STATISTICS_PERIOD = 31 * 24 * 60 * 60


def filter_outlier_data(group, key='show_view_count'):
    """

    :param group:
    :param key:
    :return:
    """
    mean = group[key].mean()
    std = group[key].std()
    # 过滤二倍标准差的数据
    filtered_group = group[(group[key] > mean - 2 * std) & (group[key] < mean + 2 * std)]
    # 过滤均值倍数大于5的数据
    new_mean = filtered_group[key].mean()
    filtered_group = filtered_group[filtered_group[key] < new_mean * 5]
    return filtered_group


def timestamp_to_str(timestamp) -> str:
    """
    :param timestamp:
    """
    dt_object = datetime.utcfromtimestamp(timestamp).replace(tzinfo=timezone.utc).astimezone()
    date_string = dt_object.strftime('%Y-%m-%d')
    return date_string


def str_to_timestamp(date_string) -> int:
    """
    :param date_string:
    :return:
    """
    date_obj = datetime.strptime(date_string, '%Y-%m-%d')

    # 使用timestamp()方法将datetime对象转换为时间戳
    timestamp = date_obj.timestamp()
    return timestamp


def get_account_fans_by_dt(db_client) -> dict:
    """
    获取每个账号发粉丝，通过日期来区分
    :return:
    """
    sql = f"""
        SELECT 
            t1.date_str, 
            t1.fans_count, 
            t2.gh_id
        FROM datastat_wx t1
        JOIN publish_account t2 ON t1.account_id = t2.id
        WHERE 
            t2.channel = 5 
        AND t2.status = 1 
        AND t1.date_str >= '2024-07-01' 
        ORDER BY t1.date_str;
    """
    result = db_client.select(sql)
    D = {}
    for line in result:
        dt = line[0]
        fans = line[1]
        gh_id = line[2]
        if D.get(gh_id):
            D[gh_id][dt] = fans
        else:
            D[gh_id] = {dt: fans}
    return D


def get_publishing_accounts(db_client) -> list[dict]:
    """
    获取每日正在发布的账号
    :return:
    """
    sql = f"""
    SELECT DISTINCT
        t3.`name`,
        t3.gh_id,
        t3.follower_count,
        t6.account_source_name,
        t6.mode_type,
        t6.account_type,
        t6.`status`
    FROM
        publish_plan t1
        JOIN publish_plan_account t2 ON t1.id = t2.plan_id
        JOIN publish_account t3 ON t2.account_id = t3.id
        LEFT JOIN publish_account_wx_type t4 on t3.id = t4.account_id
        LEFT JOIN wx_statistics_group_source_account t5 on t3.id = t5.account_id
        LEFT JOIN wx_statistics_group_source t6 on t5.group_source_name = t6.account_source_name
    WHERE
        t1.plan_status = 1
        AND t3.channel = 5
        AND t3.follower_count > 0
        GROUP BY t3.id;
    """
    account_list = db_client.select(sql)
    result_list = [
        {
            "account_name": i[0],
            "gh_id": i[1]
        } for i in account_list
    ]
    return result_list


def get_account_articles_detail(db_client, gh_id_tuple) -> list[dict]:
    """
    get articles details
    :return:
    """
    sql = f"""
            SELECT 
                ghId, accountName, updateTime, ItemIndex, show_view_count 
            FROM 
                official_articles_v2
            WHERE 
                ghId IN {gh_id_tuple} and Type = '9';
            """
    result = db_client.select(sql)
    response_list = [
        {
            "ghId": i[0],
            "accountName": i[1],
            "updateTime": i[2],
            "ItemIndex": i[3],
            "show_view_count": i[4]
        }
        for i in result
    ]
    return response_list


def cal_account_read_rate(gh_id_tuple) -> DataFrame:
    """
    计算账号位置的阅读率
    :return:
    """
    pq_db = PQMySQL()
    de_db = DeNetMysql()
    response = []
    fans_dict_each_day = get_account_fans_by_dt(db_client=de_db)
    account_article_detail = get_account_articles_detail(
        db_client=pq_db,
        gh_id_tuple=gh_id_tuple
    )
    for line in account_article_detail:
        gh_id = line['ghId']
        dt = timestamp_to_str(line['updateTime'])
        fans = fans_dict_each_day.get(gh_id, {}).get(dt, 0)
        line['fans'] = fans
        if fans:
            line['readRate'] = line['show_view_count'] / fans if fans else 0
            response.append(line)
    return DataFrame(response,
                     columns=['ghId', 'accountName', 'updateTime', 'ItemIndex', 'show_view_count', 'readRate'])


def cal_avg_account_read_rate(df, gh_id, index, dt) -> tuple:
    """
    计算账号的阅读率均值
    :return:
    """
    max_time = str_to_timestamp(dt)
    min_time = max_time - STATISTICS_PERIOD

    filterDataFrame = df[
        (df["ghId"] == gh_id)
        & (min_time <= df["updateTime"])
        & (df["updateTime"] <= max_time)
        & (df['ItemIndex'] == index)
        ]
    finalDF = filter_outlier_data(filterDataFrame)
    return (
        finalDF['readRate'].mean(),
        finalDF['updateTime'].max(),
        finalDF['updateTime'].min(),
        len(finalDF)
    )


def update_single_day(dt, account_list, article_df, lam):
    """
    更新单天数据
    :param article_df:
    :param lam:
    :param account_list:
    :param dt:
    :return:
    """
    index_list = [1, 2, 3, 4, 5, 6, 7, 8]
    for account in tqdm(account_list):
        for index in index_list:
            avg_rate, max_time, min_time, a_count = cal_avg_account_read_rate(article_df, account['gh_id'], index, dt)
            # print(account['account_name'], "\t", index, "\t", avg_rate, "\t", max_time, "\t", min_time, "\t", a_count,
            #       "\t", account['gh_id'])
            try:
                if avg_rate == 0:
                    continue
                insert_sql = f"""
                    INSERT INTO long_articles_read_rate_dev
                    (account_name, gh_id, position, read_rate_avg, remark, articles_count, earliest_publish_time, latest_publish_time, dt_version, is_delete)
                    values
                    (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s);
                """
                lam.update(
                    sql=insert_sql,
                    params=(
                        account['account_name'],
                        account['gh_id'],
                        index,
                        avg_rate,
                        "从 {} 开始往前计算 31  天".format(dt),
                        a_count,
                        timestamp_to_str(min_time),
                        timestamp_to_str(max_time),
                        dt.replace("-", ""),
                        0
                    )
                )
            except Exception as e:
                print(e)


def main() -> None:
    """
    main function
    :return:
    """
    lam = longArticlesMySQL()
    de = DeNetMysql()
    # dt = '2024-10-22'
    account_list = get_publishing_accounts(db_client=de)
    # 获取这些账号所有的文章
    df = cal_account_read_rate(tuple([i['gh_id'] for i in account_list]))
    start_dt = start_date = datetime(2024, 8, 1)
    end_date = datetime(2024, 10, 22)
    # 计算日期差
    delta = end_date - start_date
    # 生成日期字符串列表
    date_strings = []
    for i in range(delta.days + 1):
        date_strings.append((start_date + timedelta(days=i)).strftime('%Y-%m-%d'))

    # 打印结果
    date_str = '2024-09-11'
    date_strings = [date_str,]
    for date_str in tqdm(date_strings):
        update_single_day(date_str, account_list, df, lam)


if __name__ == '__main__':
    main()