import pandas as pd
import re
import os

# 数据预处理
import util

with open('data/岗位信息.txt', 'rb') as file:
    job_list = []
    while True:
        line = file.readline()
        if not line:
            break
        line = eval(line.decode('utf-8'))
        try:
            line['位置'] = re.split('-', line['位置'])[1]
            danwei = re.findall('[\u4e00-\u9fa5]+', line['薪资'])
            xinzi = re.findall('\d+.*\d', line['薪资'])[0].split('-')
            if not xinzi[1]:
                xinzi[1] = xinzi[0]
            if danwei[0][0] == '万' and danwei[1] == '月':
                line['薪资'] = round((float(xinzi[0]) + float(xinzi[1])) / 2, 2)
            elif danwei[0][0] == '万' and danwei[1] == '年':
                line['薪资'] = round((float(xinzi[0]) + float(xinzi[1])) / 2 / 12, 2)
            elif danwei[0] == '千' and danwei[1] == '月':
                line['薪资'] = round((float(xinzi[0]) + float(xinzi[1])) / 2 / 10, 2)
            elif danwei[0] == '元' and danwei[1:] == '小时':
                line['薪资'] = round((float(xinzi[0]) + float(xinzi[1])) * 8 * 22 / 2 / 100, 2)
        except:
            continue
        job_list.append(line)
job_list_DF = pd.DataFrame(job_list)
xingzhengqu = [item for item in set(job_list_DF.get(key='位置')) if item]

# 广州各区岗位分布
from pyecharts import options as opts
from pyecharts.charts import Pie


def getAreaWeight():
    result = job_list_DF.groupby('位置').size().reset_index(name='count')
    areaName = list(result.位置.values)
    areaWeight = list(map(int, result['count'].values))
    areaName_tmp = []
    for index, item in enumerate(areaName):
        areaName_tmp.append(item + str(round(areaWeight[index] / sum(areaWeight) * 100, 2)) + '%')
    return (areaName_tmp, areaWeight)


pie = Pie(init_opts=opts.InitOpts(width='800px', height='800px'))
data = getAreaWeight()
pie.add("", [list(z) for z in zip(data[0], data[1])])
pie.set_global_opts(title_opts=opts.TitleOpts(title='广州各区岗位分布'))
image_dir="images/job"
if os.path.exists(image_dir):
    util.clearDir(image_dir)
else:
   os.makedirs(image_dir)
pie.render('images/job/广州各区岗位分布.html')

# 求广州单月薪资
from pyecharts.charts import TreeMap


def getAvgPrice(xingzhengqu):
    totalPrice = 0
    total = 0
    for item in job_list:
        if item['位置'] == xingzhengqu:
            total = total + 1
            totalPrice = totalPrice + item['薪资']
    return totalPrice / total if total > 0 else 0


# 获取每个区 单月薪资
def getTotalAvgPrice():
    totalAvgPriceList = []
    totalAvgPriceDirList = []
    for index, item in enumerate(xingzhengqu):
        avg_price = getAvgPrice(item)
        totalAvgPriceList.append(round(avg_price, 2))
        totalAvgPriceDirList.append(
            {'value': round(avg_price, 2), 'name': item + " ￥" + str(round(avg_price, 2)) + ' 万'})
    return totalAvgPriceDirList


data = getTotalAvgPrice()
treemap = TreeMap(init_opts=opts.InitOpts(width='1200px', height='1400px'))
treemap.add('广州各区每月薪资:万/月', data, label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=13))
treemap.render('images/job/广州各区每月薪资.html')

# 获取每个区 单日薪资
from pyecharts.charts import Bar

totalAvgPriceList = []
for index, item in enumerate(xingzhengqu):
    avg_price = getAvgPrice(item)
    totalAvgPriceList.append(round(avg_price * 10000 / 30, 2))
attr, value = (xingzhengqu, totalAvgPriceList)
bar = Bar(init_opts=opts.InitOpts(width='1200px', height='1400px'))
bar.add_xaxis(attr)
bar.add_yaxis("广州", value)
bar.set_global_opts(title_opts=opts.TitleOpts(title='广州各区单日薪资:元/日'),
                    xaxis_opts=opts.AxisOpts(axislabel_opts={"rotate": "270"}))
bar.render('images/job/广州各区单日薪资.html')

# 获取岗位数据
from pyecharts.charts import WordCloud


def getRooms():
    results = job_list_DF.groupby('岗位').size().reset_index(name='count')
    room_list = list(results.岗位.values)
    weight_list = list(map(int, results['count'].values))
    return (room_list, weight_list)


attr, value = getRooms()
wordcloud = WordCloud(init_opts=opts.InitOpts(width='900px', height='400px'))
wordcloud.add('', zip(attr, value), word_size_range=[2, 100])
wordcloud.render('images/job/广州岗位数据.html')

# 获取各个区的岗位数量比重
from pyecharts.charts import Pie


def getAreaWeight():
    result = job_list_DF.groupby('位置').size().reset_index(name='count')
    areaName = list(result.位置.values)
    areaWeight = list(map(int, result['count'].values))
    areaName_tmp = []
    for index, item in enumerate(areaName):
        areaName_tmp.append(item + str(round(areaWeight[index] / sum(areaWeight) * 100, 2)) + '%')
    return (areaName_tmp, areaWeight)


pie = Pie(init_opts=opts.InitOpts(width='1200px', height='1200px'))
data = getAreaWeight()
pie.add("", [list(z) for z in zip(data[0], data[1])])
pie.set_global_opts(title_opts=opts.TitleOpts(title='广州各区岗位数量分布'))
pie.render('images/job/广州各区岗位数量分布.html')