import datetime
import mimetypes
from collections import defaultdict
import time
from urllib.parse import quote

import pandas as pd
import numpy as np
from fastapi import APIRouter, Depends, Request
from motor.motor_asyncio import AsyncIOMotorDatabase
from pandas import DataFrame
from starlette.responses import StreamingResponse

import crud, schemas
from common import *

from api import deps
from db import get_database
from db.ckdb import get_ck_db, CKDrive
from db.redisdb import get_redis_pool, RedisDrive

from models.behavior_analysis import BehaviorAnalysis
from models.user_analysis import UserAnalysis
from models.x_analysis import XAnalysis
from utils import DfToStream

router = APIRouter()


@router.post("/ltv_model_sql")
async def ltv_model_sql(
        request: Request,
        game: str,
        analysis: XAnalysis = Depends(XAnalysis),
        current_user: schemas.UserDB = Depends(deps.get_current_user)
) -> schemas.Msg:
    """ ltv模型sql """
    await analysis.init(data_where=current_user.data_where)
    data = analysis.ltv_model_sql()
    return schemas.Msg(code=0, msg='ok', data=[data])


@router.post("/ltv_model")
async def ltv_model_sql(
        request: Request,
        game: str,
        analysis: XAnalysis = Depends(XAnalysis),
        ckdb: CKDrive = Depends(get_ck_db),
        current_user: schemas.UserDB = Depends(deps.get_current_user)
) -> schemas.Msg:
    """ ltv模型sql """
    await analysis.init(data_where=current_user.data_where)

    res = analysis.ltv_model_sql()
    sql = res['sql']
    quota = res['quota']  #字段名
    ltv_n = res['ltv_n']
    df = await ckdb.query_dataframe(sql)
    if df.empty:
        return schemas.Msg(code=-9, msg='查无数据')
    df.fillna(0, inplace=True)   #修改原对象，以0填补空缺值

    # for d in set(res['date_range']) - set(df['date']):  # 时间的差集运算 最后为空
    #     df.loc[len(df)] = 0
    #     df.loc[len(df) - 1, 'date'] = d
    #     days = (pd.Timestamp.now().date() - d).days  # 时间差
    #     # if days + 2 >= ltv_len:
    #     #     continue
    #     df.iloc[len(df) - 1, days + 3:] = '-'
    # df.sort_values('date', inplace=True)  # 根据date进行倒叙排序

    for d in set(res['date_range']) - set(df['date']):
        #在有效日期最后一行补充行数据（值都为0），补充的行数为两个集合的差集长度
        df.loc[len(df)] = '-'
        #在date此列补充多行数据（值为两个集合差集的子元素）
        df.loc[len(df) - 1, 'date'] = d
        # days = (d-pd.Timestamp.now().date()).days
        # # if days + 2 >= ltv_len:
        # #     continue
        # if days>0:
        #     df.iloc[len(df) - 1, 1:] = '-'

    df.sort_values('date', inplace=True)
    df.rename(columns={'date': '注册日期'}, inplace=True)   #True为将结果返回赋值给原变量，修改原对象，columns为列名
    cat = '角色数'
    if quota == '#distinct_id':  #如果字段名=字段名
        cat = '设备数'
    df.rename(columns={'cnt1': cat}, inplace=True)   #原数据基础上修改df里面列名为cnt1为设备数
    df1 = df[['注册日期', cat, *[f'LTV{i}' for i in ltv_n]]]   #1, 2, 3, 4, 5, 6, 7, 8, 9, ~~到360
    df2 = df[['注册日期', cat, *[f'sumpay_{i}' for i in ltv_n]]]
    df2.replace('-', 0, inplace=True)  #True改变原数据，前面是需要替换的值，后面是替换后的值。 在原数据把下划线替换成0
    #修改下面代码
    #求相差天数
    str_time = str(res['date_range'][0])
    split_time = str_time.split('-')
    now_time = time.strftime("%Y-%m-%d", time.localtime())
    split_now_time = now_time.split('-')
    today = datetime.datetime(int(split_time[0]), int(split_time[1]), int(split_time[2]))
    now_day = datetime.datetime(int(split_now_time[0]), int(split_now_time[1]), int(split_now_time[2]))
    newday = (now_day - today).days + 1
    #计算方法运算每个LTV的均值
    _listData = {}
    for i in ltv_n:
        if i <=newday:
            avgLtv=(df2[[f'sumpay_{i}']][0:newday+1-i].sum()/df2[cat][0:newday+1-i].sum()).round(2)

            new_avgLtv=str(avgLtv).split('\n')[0].split(' ')
            new_avgLtv01=new_avgLtv[len(new_avgLtv)-1]
            if new_avgLtv01 == 'NaN':
                _listData[f'sumpay_{i}'] = '-'
            else:
                _listData[f'sumpay_{i}'] = new_avgLtv01
        else:
            _listData[f'sumpay_{i}']='-'
    avgLtvlist = pd.Series(_listData)

    _listname=[]
    #计算总累计LTV最后一个值
    for k, v in _listData.items():
        if v !=0:
            _listname.append(k)
    max_nmu=max(_listname)
    max_num=(df2[[max_nmu]].sum()/df2[cat].sum()).round(2)
    max_number=str(max_num[0])
    df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avgLtvlist]

    # avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
    #df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
    df1.insert(2, '累计LTV', 0)
    last_ltv = []
    for items in df1.values:
        for item in items[::-1]:
            if item != '-':
                last_ltv.append(item)
                break
    #修改累计LTV中最后一个值
    last_ltv[-1]=max_number


    df1['累计LTV'] = last_ltv


    #把列中累计LTV等于0的值改成'-'
    df1.loc[df1['累计LTV']==0, '累计LTV'] = '-'
    #剔除行，列的累计LTV=='-'的剔除出去
    df3 = df1.drop(df1[(df1.累计LTV=='-')].index)

    days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
    df1.iloc[len(df1) - 1, days + 4:] = '-'

    data = {
        #'title': df1.columns.tolist(),
        #'rows': df1.values.tolist(),
        'title': df3.columns.tolist(),
        'rows': df3.values.tolist(),

        'start_date': res['start_date'],
        'end_date': res['end_date']
    }

    return schemas.Msg(code=0, msg='ok', data=data)


@router.post("/ltv_model_export")
async def ltv_model_export(request: Request,
                           game: str,
                           ckdb: CKDrive = Depends(get_ck_db),
                           analysis: XAnalysis = Depends(XAnalysis),
                           current_user: schemas.UserDB = Depends(deps.get_current_user)
                           ):
    """ ltv分析 数据导出"""
    await analysis.init(data_where=current_user.data_where)
    data = analysis.ltv_model_sql()
    file_name = quote(f'lvt.xlsx')
    mime = mimetypes.guess_type(file_name)[0]

    sql = data['sql']
    df = await ckdb.query_dataframe(sql)
    if df.empty:
        return schemas.Msg(code=-9, msg='查无数据')
    df_to_stream = DfToStream((df, 'ltv'))
    with df_to_stream as d:
        export = d.to_stream()
    return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})