#!/usr/bin/python
# -*- coding=utf-8 -*-
# @Create Time:		2024-01-19 13:13:17
# @Last Modified time: 2024-02-21 13:30:33
from threading import Thread
import pandas as pd

class UserVisitAnalyzeSpace(object):
    """用户访问分析
    接收用户创建的分析任务
    时间范围：起始时间~结束时间
    在接收用户创建的任务请求之后，会将任务信息插入MD的task表中，任务参数以JSON格式封装
    执行submit，将taskid作为参数传递给submit
    """
    def __init__(self, user: str):
        self.user = user
        self.pool_size = 10
        self.start_point = 0
        self.end_point = 0
        # 一级缓存
        self.cache_item = dict()
        # 二级缓存数据
        self.df_queue = pd.DataFrame(columns=['time','user','method','hasform','domain','一级标题','二级标题','三级标题','四级标题','text', 'stay'])

    def __repr__(self):
        return f'<User: {self.user} DF: {self.df_queue}>'

    def push(self, item: dict):
        # 消息进队列
        self.df_queue.loc[self.end_point] = item
        self.end_point = (self.end_point + 1) % self.pool_size

    def pop(self):
        # 消息出队列
        item = self.df_queue.loc[self.start_point]
        self.start_point = (self.start_point + 1) % self.pool_size
        return item

    def send(self, item: dict):
        # 当前缓存无数据
        if not self.cache_item:
            self.cache_item = item
        else:
            # 判断缓存内数据和新数据哪个更新
            if item['time'] > self.cache_item['time']:
                # 当前数据较新,计算页面持续时间
                delay = (item['time'] - self.cache_item['time']).seconds
                if (delay < self.cache_item['stay'] + 2) and (item['三级标题'] == self.cache_item['三级标题']):
                    self.cache_item['stay'] += delay
                else:
                    self.push(self.cache_item)
                    self.cache_item = item
                    t = Thread(target=func, args=(self.pop(),))
                    t.start()
            else:
                # 老数据较新，数据发送产生异常，数据产生积压
                pass


        # self.df = pd.concat([self.df, pd.DataFrame([item])])
        # if self.df.shape[0] >= self.pool_size:
        #     # 新建临时表
        #     dt = self.df[0:1]
        #     index = 0
        #     for i in range(1, self.pool_size):
        #         d1, d2 = self.pooling(dt.iloc[index], self.df.iloc[i])
        #         if isinstance(d2, pd.DataFrame):
        #             dt.iloc[index] = d1
        #             index += 1
        #             dt.iloc[index] = d2
        #             session = DM8Session()
        #             result = session.execute("""INSERT INTO FRAGMENT("Time", "User_Id", "Dom", "Title1", "Title2", "Title3", "Title4", "Behavior", "Stay") VALUES(?,?,?,?,?,?,?,?)""",
        #                     args=(d1['time'], d1['user'], d1['domain'], d1['一级标题'], d1['二级标题'], d1['三级标题'], d1.get('四级标题'), d1['text'], d1['stay']))
        #         else:
        #             dt.iloc[index] = d1
        #     self.df = pd.concat([dt, self.df[self.pool_size:]])

    def getActionByDateRange(self):
        """获取指定日期范围内的用户访问行为数据
        """
        pass

    def getSession2Action(self) -> DataFrame:
        """获取sessionid到访问行为数据的映射的
        """
        pass

    def aggregateBySession(self):
        """对行为数据按session粒度进行聚合,并将用户信息join后聚合
        """
        pass

    def filterSsessionAndAggrStat(self):
        """过滤session数据，并进行计数器值累加
        """
        pass

    def getSession2detail(self):
        """获取通过筛选条件的session的访问明细数据
        """
        pass

    def getTopKCategory(self):
        """获取topK热门分类
        第一步：获取符合条件的访问过的所有品类
        第二步：计算各品类的点击的次数
        第三步：自定义二次排序key
        第四步：将数据映射成<CategorySortKey,info>格式的RDD，然后进行二次排序（降序）
        第五步：取出top10热门品类，并写入DM
        """
        pass

    def getClickCategory2Count(self) -> DataFrame:
        """获取各分类点击次数
        1、过滤出点击行为数据，点击数据只占总数据的一小部分，所以过滤后数据可能不均匀
        """
        pass

    def getTopKSession(self):
        """获取每个分类top10活跃用户
        1、将topK热门分类的id，生成DF
        2、计算topK分类被各用户访问的次数
        3、分组取TopK算法实现，获取每个分类的topK活跃用户
        4、获取topK活跃用户的明细数据，并写入DM
        """
        pass

    # --- 池化 --- #
    def pooling(self, line1: dict, line2: dict):
        if line1['time'] > line2['time']:
            deltat = (line1['time'] - line2['time']).seconds
            if (deltat < 2) and (line1['三级标题'] == line2['三级标题']):
                line2['stay'] += deltat
                return line2, None
            else:
                return line2, line1
        else:
            deltat = (line2['time'] - line1['time']).seconds
            if (deltat < 2) and (line1['三级标题'] == line2['三级标题']):
                line1['stay'] += deltat
                return line1, None
            else:
                return line1, line2