# -*- coding:utf-8 -*-
import requests
import json
import re
import pandas as pd
import time
from pyecharts import options as opts
from pyecharts.charts import Bar, Page,Pie,Line,Timeline,Grid,Map
from pyecharts.commons.utils import JsCode


def sleeptime(hour, min, sec):  # 时间转换
    return hour * 3600 + min * 60 + sec


def Get_HTML():
    headers = {'User-Agent': 'Mozilla/5.0'}
    url = 'https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
    try:
        r = requests.get(url, headers=headers)
        r.raise_for_status()
        r.encoding = r.apparent_encoding
        re = r.text[7:-2]
        response = json.loads(re)
        return response
    except:
        print("error!")

def China_data_Total():
    url='http://m.look.360.cn/subject/400?sign=360_6aa05217'
    headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:99.0) Gecko/20100101 Firefox/99.0'}
    html = requests.get(url, headers=headers).text
    pattern=re.compile('"qcmsint1":"0"},"feiyanTrend":(.*),"local":',re.S)
    item=re.findall(pattern,html)
    response=json.loads(item[0])
    data = {
        "截止日期":response['total']['modifyTime'],
        "现存确诊":response['total']['currentConfirmed'],
        "境外输入":response['total']['overseasInput'],
        "现存无症状":response['total']['asymptom'],
        "累计确诊":response['total']['diagnosed'],
        "累计治愈":response['total']['cured'],
        "累计死亡":response['total']['died'],
        "现存_较昨日":response['newDiff']['currentConfirmed'],
        "境外输入_较昨日":response['newDiff']['overseasInput'],
        "无症状_较昨日":response['newDiff']['asymptom'],
        "累计_较昨日":response['newDiff']['diagnosed'],
        "治愈_较昨日":response['newDiff']['cured'],
        "死亡_较昨日":response['newDiff']['died']
    }
    return data

def China_data(response):  # 国内数据
    C_name, Ljqz, Cured, Died, Xcqz, Xzqz = [], [], [], [], [], []
    for i in range(34):
        c_name = response['data'][i]['city']  # 城市名称
        ljqz = response['data'][i]['diagnosed']  # 累计确诊
        cured = response['data'][i]['cured']  # 治愈
        died = response['data'][i]['died']  # 死亡
        xcqz = response['data'][i]['currentConfirmed']  # 现存确诊
        xzqz = response['data'][i]['diffDiagnosed']  # 新增确诊
        C_name.append(c_name)
        Ljqz.append(ljqz)
        Cured.append(cured)
        Died.append(died)
        Xcqz.append(xcqz)
        Xzqz.append(xzqz)
    data = {
        "地区": C_name,
        "新增确诊": Xzqz,
        "现存确诊": Xcqz,
        "累计确诊": Ljqz,
        "治愈人数": Cured,
        "死亡人数": Died
    }
    return data


def Abroad_data(response):  # 国外数据
    C_name, Ljqz, Cured, Died, Xcqz, Xzqz = [], [], [], [], [], []
    for i in range(196):
        c_name = response['country'][i]['provinceName']  # 城市名称
        ljqz = response['country'][i]['diagnosed']  # 累计确诊
        cured = response['country'][i]['cured']  # 治愈
        died = response['country'][i]['died']  # 死亡
        xzqz = response['country'][i]['diffDiagnosed']  # 新增确诊
        C_name.append(c_name)
        Ljqz.append(ljqz)
        Cured.append(cured)
        Died.append(died)
        Xzqz.append(xzqz)
    data = {
        "地区": C_name,
        "新增确诊": Xzqz,
        "累计确诊": Ljqz,
        "治愈人数": Cured,
        "死亡人数": Died
    }
    return data


def View(data_1, data_2):  # 可视化
    # 柱状图
    area_color_js = (
        "new echarts.graphic.LinearGradient(0, 0, 0, 1, "
        "[{offset: 0, color: '#7bbfea'}, {offset: 1, color: '#3fbbff0d'}], false)"
    )

    t_1 = Timeline()
    t_1.add_schema(play_interval=1000,
                  label_opts=opts.series_options.LabelOpts( color='#cfe2f3'))

    for i in range(len(data_1['地区'])):
        c_1 = (
            Bar(init_opts=opts.InitOpts(width="300px",height='300px'))
                .add_xaxis(['新增确诊','现存确诊','累计确诊','治愈人数','死亡人数'],)
                .add_yaxis("",[data_1['新增确诊'][i],data_1['现存确诊'][i],data_1['累计确诊'][i],data_1['治愈人数'][i],data_1['死亡人数'][i]],
                          color='#44cef6',)
                .reversal_axis()
                .set_global_opts(
                title_opts=opts.TitleOpts(" (地区: {} )".format(data_1['地区'][i]),title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
                xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white')),#调整x轴字体颜色
                yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white'))
            )
        )
        t_1.add(c_1, "{}".format(data_1['地区'][i]))
    # 柱状图
    t_2 = Timeline()
    t_2.add_schema(play_interval=1000,
                   label_opts=opts.series_options.LabelOpts(color='#cfe2f3'))
    for i in range(len(data_2['地区'])):
        c_2 = (
            Bar()
                .add_xaxis(['新增确诊', '累计确诊', '治愈人数', '死亡人数'], )
                .add_yaxis("", [data_2['新增确诊'][i], data_2['累计确诊'][i], data_2['治愈人数'][i],data_2['死亡人数'][i]],
                           label_opts=opts.LabelOpts(position="right"),
                           color='#44cef6', )
                .reversal_axis()
                .set_global_opts(
                title_opts=opts.TitleOpts(" (国家: {} )".format(data_2['地区'][i]),pos_right='0',title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
                xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white',)),  # 调整x轴字体颜色
                yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white'),position='right')
            )
        )
        t_2.add(c_2, "{}".format(data_2['地区'][i]))
    # 饼图
    # 转换数据为类型，#转换后数据为['美国', 1010774]
    # data_pair = [list(z) for z in zip(x_data, y_data)]  # 转换后数据为['美国', 1010774]
    c_3 = (
        Line()
        .add_xaxis(data_2['地区'])
        .add_yaxis(
            series_name="新增确诊",
            y_axis=data_2['新增确诊'],
            label_opts=opts.LabelOpts(is_show=False,),
        )
        .add_yaxis(
            series_name="累计确诊",
            y_axis=data_2['累计确诊'],
            label_opts=opts.LabelOpts(is_show=False,),
        )
        .add_yaxis(
            series_name="治愈人数",
            y_axis=data_2['治愈人数'],
            label_opts=opts.LabelOpts(is_show=False,),
        )
            .add_yaxis(
            series_name="死亡人数",
            y_axis=data_2['死亡人数'],
            label_opts=opts.LabelOpts(is_show=False,),
        )

        .set_global_opts(
            title_opts=opts.TitleOpts(title="国外疫情数据",pos_right='0',title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
            datazoom_opts=opts.DataZoomOpts(),
            tooltip_opts=opts.TooltipOpts(trigger="axis"),
             xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(color='white', )),  # 调整x轴字体颜色
            yaxis_opts=opts.AxisOpts(
                position='right',
                axistick_opts=opts.AxisTickOpts(is_show=True,),
                splitline_opts=opts.SplitLineOpts(is_show=True),
                axislabel_opts=opts.LabelOpts(color='white')
            ),
        )
    )
    c_5 = (      #地图
        Map()
            .add("累计确诊", [['河南', 2840], ['香港', 308175], ['吉林', 37712] , ['台湾', 27410], ['上海', 11366], ['浙江', 2819], ['福建', 2905],['山东', 2646], ['黑龙江', 2505], ['广东', 6681], ['四川', 1948], ['辽宁', 1604], ['河北', 1981], ['江苏', 2083], ['广西', 1518], ['北京', 1821], ['海南', 243], ['山西', 350], ['陕西', 3247], ['天津', 1798], ['云南', 2079], ['安徽', 1054], ['江西', 1063], ['湖南', 1335], ['青海', 43], ['内蒙古', 1687],['重庆',2423], ['贵州', 178], ['湖北', 68392], ['甘肃', 681], ['新疆', 999], ['宁夏', 122], ['澳门', 82], ['西藏', 1]], "china")
            .set_global_opts(
            title_opts=opts.TitleOpts(title="国内现存确诊",title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),visualmap_opts=opts.VisualMapOpts(max_=10000))

    )

    data = China_data_Total()
    c_6 = (
        Pie(init_opts=opts.InitOpts())
            .set_global_opts(
            title_opts=opts.TitleOpts(
                title="现存确诊:" + str(data['现存确诊']) + " " * 5 + "境外输入:" + str(data['境外输入']) + " " * 5 + "现存无症状:" + str(
                    data['现存无症状']), title_textstyle_opts=opts.TextStyleOpts(font_size=15, color="#ffb3a7"), ))
    )
    c_7 = (
        Pie(init_opts=opts.InitOpts())
            .set_global_opts(
            title_opts=opts.TitleOpts(
                title="累计确诊:" + str(data['累计确诊']) + " " * 5 + "累计治愈:" + str(data['累计治愈']) + " " * 5 + "累计死亡:" + str(
                    data['累计死亡']), title_textstyle_opts=opts.TextStyleOpts(font_size=15, color="#ffb3a7"), ))
    )
    c_8 = (
        Pie(init_opts=opts.InitOpts())
            .set_global_opts(
            title_opts=opts.TitleOpts(
                title="较昨日:" + str(data['现存_较昨日']) + " " * 25 + "较昨日:" + str(data['境外输入_较昨日']) + " " * 25 + "较昨日:" + str(
                    data['无症状_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef"), ))
    )
    c_9 = (
        Pie(init_opts=opts.InitOpts())
            .set_global_opts(
            title_opts=opts.TitleOpts(
                title="较昨日:" + str(data['累计_较昨日']) + " " * 25 + "较昨日:" + str(data['治愈_较昨日']) + " " * 25 + "较昨日:" + str(
                    data['死亡_较昨日']), title_textstyle_opts=opts.TextStyleOpts(font_size=10, color="#e9e7ef"), ))
    )
    c_10 = (  # 地图
        Map()
            .add("死亡人数",[['河南', 22], ['香港', 8827], ['吉林', 5], ['台湾', 854], ['上海', 7], ['浙江', 1], ['福建', 1], ['山东', 7], ['广东', 8], ['黑龙江', 13], ['四川', 3], ['江苏', 0], ['河北', 7], ['辽宁', 2], ['广西', 2], ['北京', 9], ['海南', 6], ['山西', 0], ['陕西', 3], ['云南', 2], ['安徽', 6], ['湖南', 4], ['江西', 1], ['天津', 3], ['青海', 0], ['内蒙古', 1], ['重庆', 6], ['贵州', 2], ['湖北', 4512], ['甘肃', 2], ['新疆', 3], ['宁夏', 0], ['澳门', 0], ['西藏', 0]], "china")
            .set_global_opts(
            title_opts=opts.TitleOpts(title="国内各地死亡人数", title_textstyle_opts=opts.TextStyleOpts(color="#cfe2f3")),
            visualmap_opts=opts.VisualMapOpts(max_=10000))

    )

    tu_1 = (
        Line(init_opts=opts.InitOpts(width="1500px",
                                     height="850px",
                                     bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
            .add_xaxis([None])
            .add_yaxis("", [None])
            .set_global_opts(
            title_opts=opts.TitleOpts(),
            yaxis_opts=opts.AxisOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(is_show=False))
    )

    tu_1.add_js_funcs(
        """
        var img = new Image(); img.src = './images/bg_body.jpg';

        """
    )
    tu_2 = (
        Line(init_opts=opts.InitOpts(width="600px",
                                     height="700px",
                                     bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
            .add_xaxis([None])
            .add_yaxis("", [None])
            .set_global_opts(
            title_opts=opts.TitleOpts(
                                      pos_left='center',
                                      title_textstyle_opts=opts.TextStyleOpts(font_size=25, color='#51c2d5'),
                                      pos_top='5%'),
            yaxis_opts=opts.AxisOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(is_show=False))
    )
    tu_2.add_js_funcs(
        """
        var img = new Image(); img.src = './images/data_icon.png';

        """
    )
    tu_3 = (
        Line(init_opts=opts.InitOpts(width="1500px",
                                     height="200px",
                                     bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}))
            .add_xaxis([None])
            .add_yaxis("", [None])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="全球疫情信息 截至"+data['截止日期'],
                pos_left='center',
                title_textstyle_opts=opts.TextStyleOpts(font_size=21, color='#51c2d5'),
                pos_top='5%'),
            yaxis_opts=opts.AxisOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(is_show=False))
    )
    tu_3.add_js_funcs(
        """
        var img = new Image(); img.src = './images/bg_title.png';

        """
    )

    # page = Page(layout=Page.DraggablePageLayout, page_title="模拟")
    # page.add(tu_1,t_1, t_2, c_3,c_5,c_6,c_7,c_8,c_9,tu_2,tu_3,c_10)
    # page.render('全球疫情信息.html')

    # 固定样式
    Page.save_resize_html(source="全球疫情信息.html",  # 上面的HTML文件名称
                          cfg_file="chart_config.json",  # 保存的json配置文件
                          dest="new_全球疫情信息.html"  # 新HTML文件名称，可以空缺，默认resize_render.html
                          )

def main():
    second = sleeptime(5, 0, 0)  # 设置时间
    data_1 = China_data(Get_HTML())  # 国内数据
    data_2 = Abroad_data(Get_HTML())  # 国外数据
    data_11 = pd.DataFrame(data_1)
    data_22 = pd.DataFrame(data_2)
    while True:
        data_11.to_csv('./国内疫情数据.csv')
        data_22.to_csv('./国外疫情数据.csv')
        View(data_1, data_2)
        time.sleep(second)


# main()

data_1 = China_data(Get_HTML())  # 国内数据
data_2 = Abroad_data(Get_HTML())  # 国外数据
data_11 = pd.DataFrame(data_1)
data_22 = pd.DataFrame(data_2)
# data_11
# data_22
View(data_1,data_2)
# print(data_1)

from pyecharts.commons.utils import JsCode
value=[7.0,11.0,50.0,112.0]
color_function = """
        function (params) {
            if (params.value == 7.0) 
                return '#66CCCC';
            else if (params.value ==11.0) 
                return '#CCCCFF';
                else if (params.value ==50.0) 
                    return '#FF6666';
            else return '#CCFFCC';
        }
        """
c_3 = (
    Bar(init_opts=opts.InitOpts(width="800px",height="400px",))
    .add_xaxis(['睡觉','说话','离开','学习'],)
    .add_yaxis("",value,itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="国内疫情数据"),
        legend_opts=opts.LegendOpts(pos_left="20%"),
        datazoom_opts=opts.DataZoomOpts(type_="inside"),
        )
    )

from pyecharts.charts import Bar
from pyecharts import options as opts
from snapshot_phantomjs import snapshot
# 导入输出图片工具
from pyecharts.render import make_snapshot
# 使用snapshot-selenium 渲染图片
from snapshot_selenium import snapshot

# 创建一个柱状图Bar实例

bar = (
    Bar()
    # 添加X轴数据
    .add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
    # 添加Y轴数据,系列的名称
    .add_yaxis("商家A", [5, 20, 36, 10, 75, 90])
    .add_yaxis("商家B", [8, 15, 60, 20, 25, 30])
    # 添加标题
    .set_global_opts(title_opts=opts.TitleOpts(title="主标题: 双十一销量", subtitle="副标题:服饰类"))
)

# 输出保存为图片
make_snapshot(snapshot, bar.render("./图片.html"), "./003_Options配置项_自定义样式_保存图片.jpeg")
# make_snapshot(snapshot, bar.render(), "./图片.png")

import requests
import json
from lxml import etree
import pandas as pd
import time
from pyecharts import options as opts
from pyecharts.charts import Bar

def sleeptime(hour, min, sec):   #时间转换
    return hour * 3600 + min * 60 + sec

def Get_HTML(url):
    headers={'User-Agent':'Mozilla/5.0'}
    try:
        r=requests.get(url,headers=headers)
        r.raise_for_status()
        r.encoding=r.apparent_encoding
        return r.text
    except:
        print("error!")
def Parse_page():
    C_name,Ljqz,Cured,Died,Xcqz,Xzqz=[],[],[],[],[],[]
    url='https://m.look.360.cn/events/feiyan?sv=&version=&market=&device=2&net=4&stype=&scene=&sub_scene=&refer_scene=&refer_subscene=&f=jsonp&location=true&sort=2&_=1649252949072&callback=jsonp2'
    re=Get_HTML(url)
    re=re[7:-2]
    response=json.loads(re)
    for i in range(34):
        c_name=response['data'][i]['data']['provinceName']  #城市名称
        ljqz=response['data'][i]['diagnosed']               #累计确诊
        cured=response['data'][i]['cured']           #治愈
        died=response['data'][i]['died']             #死亡
        xcqz=response['data'][i]['currentConfirmed']   #现存确诊
        xzqz=response['data'][i]['diffDiagnosed']      #新增确诊
        C_name.append(c_name)
        Ljqz.append(ljqz)
        Cured.append(cured)
        Died.append(died)
        Xcqz.append(xcqz)
        Xzqz.append(xzqz)
#     columns=['新增确诊','现存确诊','累计确诊','治愈人数','死亡人数']
#     index=C_name
    data={
        "地区":C_name,
        "新增确诊":Xzqz,
        "现存确诊":Xcqz,
        "累计确诊":Ljqz,
        "治愈人数":Cured,
        "死亡人数":Died
    }
#     print(data)
    return data
def View():
    data=Parse_page()
    c = (
        Bar()
        .add_xaxis(data['地区'])
        .add_yaxis("新增确诊",data['新增确诊'], stack="stack1")
        .add_yaxis("现存确诊",data['现存确诊'] , stack="stack1")
        .add_yaxis("累计确诊",data['累计确诊'] , stack="stack1")
        .add_yaxis("治愈人数",data['治愈人数'] , stack="stack1")
        .add_yaxis("死亡人数",data['死亡人数'] , stack="stack1")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="国内疫情数据数据（全部）"),
            datazoom_opts=opts.DataZoomOpts(type_="inside"),
            )
        .render("国内疫情数据数据.html")
    )
def main():
    data=Parse_page()
    data=pd.DataFrame(data)
    second = sleeptime(24,0,0)
    while True:
#         print(data)
        data.to_csv('./国内疫情数据.csv')
        View()
        time.sleep(second)
# main()

data=Parse_page()
data=pd.DataFrame(data)
data

import requests
def BaiDu_fanyi(url):
    kw = input('请输入待翻译的英文/中文单词：')
    data={'kw':kw}
    headers = {
    'User_Agent':'Mozilla/5.0',
    'content-length': str(len(data)),
    'content-type': 'application/x-www-form-urlencoded; charset=UTF-8',
    'referer': 'https://fanyi.baidu.com/',
    'x-requested-with': 'XMLHttpRequest'
    }
    response=requests.post(url,headers=headers,data=data)
#     print(response.json())
# 请输入待翻译的英文/中文单词：word
# {'errno': 0, 'data': [{'k': 'word', 'v': 'n. 单词; 话语; 诺言; 消息 vt. 措辞，用词; 用言语表达 vi. 讲话'}, 
# {'k': 'Word', 'v': '微软公司生产的文字处理软件。; [人名] 沃德'}, {'k': 'words', 'v': 'n. 字( word的名词复数 );
# （说的）话; 诺言; 口令'}, {'k': 'Words', 'v': '[医]言语'}, {'k': 'WORDS', 'v': 'abbr. Western Operational Research Discussion Soci'}]}
    result=''
    for i in response.json()['data']:
        result+=i['v']+'\n'
    print("{}的翻译结果为：".format(kw))
    print(result)
url='https://fanyi.baidu.com/sug'
BaiDu_fanyi(url)

打开摄像头

import cv2
import numpy as np

def video_demo():
    capture = cv2.VideoCapture(0)#0为电脑内置摄像头
    while(True):
        ret, frame = capture.read()#摄像头读取,ret为是否成功打开摄像头,true,false。 frame为视频的每一帧图像
        frame = cv2.flip(frame, 1)#摄像头是和人对立的，将图像左右调换回来正常显示。
        cv2.imshow("video", frame)
        c = cv2.waitKey(50)
        if c == 27:
            break
video_demo()
cv2.destroyAllWindows()