"""
@author: luojunhui
"""

import asyncio
import aiomysql
from pandas import DataFrame


class TaskMySQLClient(object):
    """
    Async MySQL
    """

    def __init__(self):
        self.mysql_pool = None

    async def init_pool(self):
        """
        初始化连接
        :return:
        """
        self.mysql_pool = await aiomysql.create_pool(
            host="rm-bp1159bu17li9hi94.mysql.rds.aliyuncs.com",
            port=3306,
            user="crawler",
            password="crawler123456@",
            db="piaoquan-crawler",
            charset="utf8mb4",
            connect_timeout=120,
        )
        print("mysql init successfully")

    async def close_pool(self):
        """
        关闭 mysql 连接
        :return:
        """
        self.mysql_pool.close()
        await self.mysql_pool.wait_closed()

    async def async_select(self, sql):
        """
        select method
        :param sql:
        :return:
        """
        async with self.mysql_pool.acquire() as conn:
            async with conn.cursor() as cursor:
                await cursor.execute(sql)
                result = await cursor.fetchall()
                return result

    async def async_insert(self, sql, params):
        """
        insert and update method
        :param params:
        :param sql:
        :return:
        """
        async with self.mysql_pool.acquire() as coon:
            async with coon.cursor() as cursor:
                await cursor.execute(sql, params)
                await coon.commit()


class ArticleDBTools(object):
    """
    长文数据库相关功能
    """

    def __init__(self, mysql_client):
        """
        init mysql
        :param mysql_client:
        """
        self.mysql_client = mysql_client

    async def getSingleAccountArticles(self, account_name):
        """
        获取单个账号的历史文章
        :param gh_id:
        :return:
        appMsgId, title, Type, updateTime, ItemIndex, ContentUrl, show_view_count, show_like_count
        """
        keys = [
            "appMsgId",
            "title",
            "Type",
            "updateTime",
            "ItemIndex",
            "ContentUrl",
            "show_view_count",
            "show_like_count",
        ]
        sql = f"""
            SELECT {", ".join(keys)}
            FROM official_articles
            WHERE accountName = '{account_name}';"""
        result = await self.mysql_client.async_select(sql=sql)
        return DataFrame(result, columns=keys)

    async def getArticleByFilter(
            self,
            account_name,
            index_list=None,
            min_time=None,
            max_time=None,
            msg_type=None,
    ):
        """
        :param account_name:
        :param index_list: index ranges from 1 to 8
        :param min_time: earliest time
        :param max_time: latest time
        :param msg_type: msg_type
        :return:
        """
        if not index_list:
            index_list = [1]
        if not msg_type:
            msg_type = "9"
        if not min_time:
            min_time = 0
        if not max_time:
            # 2099年
            max_time = 4088051123
        articleDataFrame = await self.getSingleAccountArticles(account_name=account_name)
        filterDataFrame = articleDataFrame[
            (articleDataFrame["Type"] == msg_type)
            & (min_time < articleDataFrame["updateTime"])
            & (articleDataFrame["updateTime"] < max_time)
            & (articleDataFrame["ItemIndex"].isin(index_list))
            ]
        return filterDataFrame

    async def get_good_bad_articles(self,
                                    account_name,
                                    method,
                                    rate=0.1,
                                    index_list=None,
                                    min_time=None,
                                    max_time=None,
                                    msg_type=None
                                    ):
        """
        获取质量好和不好的视频
        :return:
        """
        article_data_frame = await self.getArticleByFilter(
            account_name=account_name,
            index_list=index_list,
            min_time=min_time,
            max_time=max_time,
            msg_type=msg_type
        )
        df_rows = len(article_data_frame)
        if df_rows > 0:
            match method:
                case "top":
                    sorted_df = article_data_frame.sort_values(by='show_view_count', reversed=True)
                    topn = max(int(df_rows * rate), 1)
                    top_df = sorted_df.head(topn)
                    tail_df = sorted_df.tail(topn)
                    return top_df, tail_df
                case "avg":
                    avg_view = article_data_frame['show_view_count'].mean()
                    good_df = article_data_frame[(article_data_frame['show_view_count']) > avg_view * (1.0 + rate)]
                    bad_df = article_data_frame[(article_data_frame['show_view_count']) > avg_view * (1.0 - rate)]
                    return good_df, bad_df
        else:
            return None, None