#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@Author  :   liuyuqi
@Contact :   liuyuqi.gov@msn.cn
@Time    :   2019/08/27 15:30:32
@Version :   1.0
@License :   (C)Copyright 2019
@Desc    :   微博热搜爬虫，分析
'''
## 调用要使用的包
from pyecharts.charts import Page, WordCloud, TreeMap
from pyecharts.charts import Calendar, Pie
from pyecharts.globals import SymbolType
from pyecharts import GraphicShapeOpts
from pyecharts import options as opts
import json
import random
import requests
import time
import pandas as pd
import os
import jieba
from scipy.misc import imread  # 这是一个处理图像的函数
from wordcloud import WordCloud, ImageColorGenerator
import matplotlib.pyplot as plt
import datetime
from collections import Counter

os.chdir('D:/爬虫/微博热搜')

## 获得日期
def getBetweenDay(begin_date, end_date):
    date_list = []
    begin_date = datetime.datetime.strptime(begin_date, "%Y/%m/%d")
    end_date = datetime.datetime.strptime(end_date, "%Y/%m/%d")
    while begin_date <= end_date:
        date_str = begin_date.strftime("%Y/%m/%d")
        date_list.append(date_str)
        begin_date += datetime.timedelta(days=1)
    return date_list

## 分词
def get_words_list(df):
    df['words_list'] = []
    word_generator = jieba.cut_for_search(df['title'])
    for word in word_generator:
        df['words_list'].append(word)
    return df

## explode
def dataframe_explode(dataframe, fieldname):
    temp_fieldname = fieldname + '_made_tuple_'
    dataframe[temp_fieldname] = dataframe[fieldname].apply(tuple)
    list_of_dataframes = []
    for values in dataframe[temp_fieldname].unique().tolist():
        list_of_dataframes.append(pd.DataFrame({
            temp_fieldname: [values] * len(values),
            fieldname: list(values),
        }))
    dataframe = dataframe[list(set(dataframe.columns) - set([fieldname]))].merge(
        pd.concat(list_of_dataframes), how='left', on=temp_fieldname)
    del dataframe[temp_fieldname]
    return dataframe

## 绘制词云
def draw_word_cloud(word):
    word_title = resou_word[resou_word['title'].str.contains(word)]
    word_title = word_title.groupby(
        ['title'], as_index=False).agg({'searchCount': ['max']})
    word_title.columns = ['title', 'count']
    data = [(word_title['title'][i], word_title['count'][i]/1000000)
            for i in range(word_title.shape[0])]
    wc = (WordCloud(init_opts=opts.InitOpts(theme=ThemeType.ROMA))
          .add("", data, word_size_range=[20, 50], shape='pentagon')
          .set_global_opts(title_opts=opts.TitleOpts(title=''))
          .render('{}词云.html'.format(word))
          )


## 设置headers和cookie，数据爬取
header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win32; x32; rv:54.0) Gecko/20100101 Firefox/54.0',
          'Connection': 'keep-alive'}
cookies = 'v=3; iuuid=1A6E888B4A4B29B16FBA1299108DBE9CDCB327A9713C232B36E4DB4FF222CF03; webp=true; ci=1%2C%E5%8C%97%E4%BA%AC; __guid=26581345.3954606544145667000.1530879049181.8303; _lxsdk_cuid=1646f808301c8-0a4e19f5421593-5d4e211f-100200-1646f808302c8; _lxsdk=1A6E888B4A4B29B16FBA1299108DBE9CDCB327A9713C232B36E4DB4FF222CF03; monitor_count=1; _lxsdk_s=16472ee89ec-de2-f91-ed0%7C%7C5; __mta=189118996.1530879050545.1530936763555.1530937843742.18'
cookie = {}
for line in cookies.split(';'):
    name, value = cookies.strip().split('=', 1)
    cookie[name] = value

resou = pd.DataFrame(columns=['date', 'title', 'searchCount', 'rank'])
resou_date = getBetweenDay('2019/01/01', '2019/07/12')
for i in resou_date:
    print(i)
    url = 'https://www.enlightent.cn/research/top/getWeiboHotSearchDayAggs.do?date={}'.format(
        str(i))
    html = requests.get(url=url, cookies=cookie, headers=header).content
    data = json.loads(html.decode('utf-8'))
    for j in range(100):
        resou = resou.append({'date': i, 'title': data[j]['keyword'],
                              'searchCount': data[j]['searchCount'], 'rank': j+1}, ignore_index=True)


## 按天统计
resou = resou.apply(get_words_list, axis=1)
resou.to_excel('热搜数据.xlsx')
resou_dt = resou.groupby('date', as_index=False).agg({'searchCount': ['mean']})
resou_dt.columns = ['date', 'avg_count']


## 绘制日历图
data = [
    [resou_dt['date'][i], resou_dt['avg_count'][i]]
    for i in range(resou_dt.shape[0])
]


calendar = (
    Calendar(init_opts=opts.InitOpts(width='1800px', height='1500px'))
    .add("", data, calendar_opts=opts.CalendarOpts(range_=['2019-01-01', '2019-07-12']))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="2019每日热搜平均指数", pos_left='15%'),
        visualmap_opts=opts.VisualMapOpts(
            max_=3600000,
            min_=0,
            orient="horizontal",
            is_piecewise=False,
            pos_top="230px",
            pos_left="100px",
            pos_right="10px"
        )
    )
    .render('日期热力图.html')
)


## 词频统计
resou_word = dataframe_explode(resou, 'words_list')
resou_word_stat = resou_word.groupby(
    ['words_list'], as_index=False).agg({'title': ['count']})
resou_word_stat.columns = ['word', 'num']
resou_word_stat.to_excel('词频统计.xlsx')

func = pd.read_excel('名词.xlsx')
love = pd.read_excel('婚恋.xlsx')
person = pd.read_excel('人物.xlsx')
resou_word_func = pd.merge(resou_word_stat, func, how='inner', on='word')
resou_word_love = pd.merge(resou_word_stat, love, how='inner', on='word')
resou_word_person = pd.merge(resou_word_stat, person, how='inner', on='word')


resou_word_func = resou_word_func.sort_values(
    'count', ascending=False).reset_index()
words = [(resou_word_func['word'][i], resou_word_func['num'][i])
         for i in range(resou_word_func.shape[0])]
pie = (
    Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
    .add("", words)
    .set_global_opts(title_opts=opts.TitleOpts(title="微博常用词出现次数", pos_left='center'),
                     legend_opts=opts.LegendOpts(is_show=False))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}", font_size=16),)
    .render('热搜词饼图.html')
)


resou_word_love = resou_word_love.sort_values(
    'count', ascending=False).reset_index()
words = [(resou_word_love['word'][i], resou_word_love['num'][i])
         for i in range(resou_word_love.shape[0])]
pie = (
    Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK))
    .add("", words)
    .set_global_opts(title_opts=opts.TitleOpts(title="微博婚恋类词语出现次数", pos_left='center'),
                     legend_opts=opts.LegendOpts(is_show=False))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}", font_size=16),)
    .render('婚恋类热搜词饼图.html')
)


resou_word_person = resou_word_person.sort_values(
    'num', ascending=False).reset_index()
words = [{'"value": {}, "name": {}"'.format(str(resou_word_person['num'][i]),
                                            resou_word_person['name'][i])} for i in range(resou_word_person.shape[0])]

tree = (
    TreeMap(init_opts=opts.InitOpts(theme=ThemeType.ESSOS))
    .add("", words, pos_left=0, pos_right=0, pos_top=50, pos_bottom=50)
    .set_global_opts(title_opts=opts.TitleOpts(title="热搜明星出现次数排名", pos_left='center'),
                     legend_opts=opts.LegendOpts(is_show=False))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}\n\n {c}", font_size=17,
                                               color='black', position='inside', font_weight='bolder'))
    .render('排序.html')
)