LongArticlesJob/cal_account_read_rate_avg_daily.py

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

from applications import DeNetMysql, PQMySQL, longArticlesMySQL, bot

STATISTICS_PERIOD = 31 * 24 * 60 * 60


def float_to_percentage(value, decimals=3) -> str:
    """
    把小数转化为百分数
    :param value:
    :param decimals:
    :return:
    """
    percentage_value = round(value * 100, decimals)
    return "{}%".format(percentage_value)


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()
    # print("阅读均值", new_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 int(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)
        ]
    # print("位置", index)
    finalDF = filter_outlier_data(filterDataFrame)
    # finalDF = finalDF.sort_values(by=['updateTime'], ascending=False)
    # if index == 1:
    #     for i in finalDF.values.tolist():
    #         print(datetime.fromtimestamp(i[2]).strftime('%Y-%m-%d'), i)
    return (
        finalDF['readRate'].mean(),
        finalDF['updateTime'].max(),
        finalDF['updateTime'].min(),
        len(finalDF)
    )


def check_each_position(db_client, gh_id, index, dt, avg_rate) -> dict:
    """
    检验某个具体账号的具体文章的阅读率均值和前段日子的比较
    :param avg_rate: 当天计算出的阅读率均值
    :param db_client: 数据库连接
    :param gh_id: 账号 id
    :param index: 账号  index
    :param dt:
    :return:
    """
    dt = int(dt.replace("-", ""))
    select_sql = f"""
        SELECT account_name, read_rate_avg
        FROM long_articles_read_rate
        WHERE gh_id = '{gh_id}' and position = {index} and dt_version < {dt}
        ORDER BY dt_version DESC limit 1;
    """
    result = db_client.select(select_sql)
    if result:
        account_name = result[0][0]
        previous_read_rate_avg = result[0][1]
        relative_value = (avg_rate - previous_read_rate_avg) / previous_read_rate_avg
        if -0.05 <= relative_value <= 0.05:
            return {}
        else:
            response = {
                "账号名称": account_name,
                "位置": index,
                "当天阅读率均值": float_to_percentage(avg_rate),
                "前一天阅读率均值": float_to_percentage(previous_read_rate_avg),
                "相对变化率": float_to_percentage(relative_value)
            }
            return response


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]
    error_list = []
    insert_error_list = []
    for account in tqdm(account_list):
        for index in index_list:
            avg_rate, max_time, min_time, articles_count = cal_avg_account_read_rate(article_df, account['gh_id'], index, dt)
            if articles_count > 0:
                if index in {1, 2}:
                    error_obj = check_each_position(
                        db_client=lam,
                        gh_id=account['gh_id'],
                        index=index,
                        dt=dt,
                        avg_rate=avg_rate
                    )
                    if error_obj:
                        error_list.append(error_obj)
                # continue
                try:
                    if avg_rate == 0:
                        continue
                    insert_sql = f"""
                        INSERT INTO long_articles_read_rate
                        (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),
                            articles_count,
                            timestamp_to_str(min_time),
                            timestamp_to_str(max_time),
                            dt.replace("-", ""),
                            0
                        )
                    )
                except Exception as e:
                    insert_error_list.append(e)

    if insert_error_list:
        bot(
            title="更新阅读率均值，存在sql 插入失败",
            detail=insert_error_list
        )

    if error_list:
        bot(
            title="更新阅读率均值，头次出现异常值通知",
            detail={
                "时间": dt,
                "异常列表": error_list
            }
        )

    if not error_list and not insert_error_list:
        bot(
            title="阅读率均值表，更新成功",
            detail={
                "日期": dt
            }
        )


def main() -> None:
    """
    main function
    :return:
    """
    parser = ArgumentParser()
    parser.add_argument("--run-date",
                        help="Run only once for date in format of %Y-%m-%d. \
                                    If no specified, run as daily jobs.")
    args = parser.parse_args()
    if args.run_date:
        dt = args.run_date
    else:
        dt = datetime.today().strftime('%Y-%m-%d')

    lam = longArticlesMySQL()
    de = DeNetMysql()
    account_list = get_publishing_accounts(db_client=de)
    df = cal_account_read_rate(tuple([i['gh_id'] for i in account_list]))

    update_single_day(dt, account_list, df, lam)

    # 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()