快手爬票(爬取火车票信息)_ooxfvht1-g-q3jzdtubtmg

今天突发奇想，想搞一个爬虫，再加上PyQt5的GUI实践，就可以打造出来一个快手爬票：也不废话，上代码:

(注：图片文件资源已整合到代码中，用CMD即可运行。)

需要的扩展库:

altgraph==0.17.3
asttokens==2.2.0
attrs==22.1.0
Automat==22.10.0
autopep8==2.0.0
backcall==0.2.0
beautifulsoup4==4.11.1
bs4==0.0.1
certifi==2022.9.24
cffi==1.15.1
charset-normalizer==2.1.1
click==7.1.2
colorama==0.4.6
constantly==15.1.0
cryptography==38.0.4
cssselect==1.2.0
debugpy==1.6.4
decorator==5.1.1
entrypoints==0.4
executing==1.2.0
fake-useragent==1.1.1
filelock==3.8.0
future==0.18.2
hyperlink==21.0.0
idna==3.4
importlib-resources==5.10.1
incremental==22.10.0
ipykernel==6.17.1
ipython==8.7.0
itemadapter==0.7.0
itemloaders==1.0.6
jedi==0.18.2
jmespath==1.0.1
jupyter_client==7.4.7
jupyter_core==5.1.0
lxml==4.9.1
matplotlib-inline==0.1.6
multitasking==0.0.11
mysql==0.0.3
mysql-connector==2.2.9
mysqlclient==2.1.1
nest-asyncio==1.5.6
numpy==1.23.5
packaging==21.3
parsel==1.7.0
parso==0.8.3
pefile==2022.5.30
pickleshare==0.7.5
Pillow==9.3.0
platformdirs==2.5.4
prompt-toolkit==3.0.33
Protego==0.2.1
psutil==5.9.4
pure-eval==0.2.2
py==1.11.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycodestyle==2.10.0
pycparser==2.21
PyDispatcher==2.0.6
pygame==2.1.2
Pygments==2.13.0
pyinstaller==5.6.2
pyinstaller-hooks-contrib==2022.13
PyMySQL==1.0.2
pyOpenSSL==22.1.0
pyparsing==3.0.9
PyQt5==5.15.4
pyqt5-plugins==5.15.4.2.2
PyQt5-Qt5==5.15.2
PyQt5-sip==12.11.0
pyqt5-tools==5.15.4.3.2
PyQt5Designer==5.14.1
python-dateutil==2.8.2
python-dotenv==0.21.0
pywin32==305
pywin32-ctypes==0.2.0
pyzmq==24.0.1
qt5-applications==5.15.2.2.2
qt5-tools==5.15.2.1.2
queuelib==1.6.2
requests==2.28.1
requests-file==1.5.1
retry==0.9.2
Scrapy==2.7.1
service-identity==21.1.0
six==1.16.0
soupsieve==2.3.2.post1
stack-data==0.6.2
tldextract==3.4.0
tomli==2.0.1
tornado==6.2
tqdm==4.64.1
traitlets==5.6.0
Twisted==22.10.0
twisted-iocpsupport==1.0.2
typing_extensions==4.4.0
urllib3==1.26.13
w3lib==2.1.0
wcwidth==0.2.5
wrapcache==1.0.8
zipp==3.11.0
zope.interface==5.5.2

代码:

# -*- coding: utf-8 -*-
 
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
import sys
import time
import re
import requests
import base64
import os
import multiprocessing
 
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPalette, QPixmap, QColor
 
 
work_home_path = os.getcwd()
temp_path = "C:\\Windows\\Temp" 
# result = []  # 保存车次分类后最后的数据
'''5-7 目的地 3  车次 6  出发地 8  出发时间 9  到达时间 10 历时 26 无坐 29 硬座
   24 软座 28 硬卧 33 动卧 23 软卧 21 高级软卧 30 二等座 31 一等座 32 商务座特等座
'''
data = []  # 用于保存整理好的车次信息
type_data = []  # 保存车次分类后最后的数据
 
headers = {"User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/106.0.0.0 Safari/537.36 Edg/106.0.1370.34',
           'Cookie':('_uab_collina=166540513502597168978317; JSESSIONID=49400086441FF78D34FCD98E03E6413C; BIGipServerotn=250610186.38945.0000; BIGipServerpool_passport=26660097'
                     '0.50215.0000; RAIL_EXPIRATION=1665740164850; RAIL_DEVICEID=Rd-p1RfV4HO3-335FgWGIFjWjkxmkLQqffje8akvp4ayg6lZ6KFXGw1W-oJp_VQ4ScPgPg_OgsOQH6uMRmNC4vbrvyl'
                     'Jy0WB2pnYbXZ9CCDwjcbtY7_aI6_DouxHqUMImqhMCx42U_eIsSU0-s0qhKTc81li2ckH; guidesStatus=off; highContrastMode=defaltMode; cursorStatus=off; fo=vb7ixu8llfc0pzmxVU0hEmk_tOBZFa8BpTvcu29wDM17XXg-vNFlfQFZi7pqLezVw8NPrQ5BKUFrsVbgZs487IESG7aRK9RT2nVl7bRf24KWgp2Z3tPSexYGassPycrMH8RxBd4WbQ90b0R3XJuWBdBXhCbyDsGO6MyiUcUPhhGUeOZP6yCJuA6rDp8; route=9036359bb8a8a461c164a04f8f50b252; _jc_save_fromStation=%u5317%u4EAC%2CBJP; _jc_save_toStation=%u4E0A%u6D77%2CSHH; _jc_save_toDate=2022-10-10; _jc_save_wfdc_flag=dc; _jc_save_fromDate=2022-10-11')}
 
 
 
def query(date, from_station, to_station):
    data.clear()  # 清空数据
    type_data.clear()  # 清空车次分类保存的数据
    # 查询请求地址
    url = ('https://kyfw.12306.cn/otn/leftTicket/query?leftTicketDTO.train_date='
           '{}&leftTicketDTO.from_station={}&leftTicketDTO.to_station={}&purpose_codes=ADULT').format(
        date, from_station, to_station)
    # 发送查询请求
    response = requests.get(url, headers=headers)
    # # 将json数据转换为字典类型，通过键值对取数据
    result = response.json()
    result = result['data']['result']
    # 判断车站文件是否存在
    if isStations() == True:
        stations = eval(read())  # 读取所有车站并转换为dic类型
        if len(result) != 0:  # 判断返回数据是否为空
            for i in result:
                # # 分割数据并添加到列表中
                tmp_list = i.split('|')
                # 因为查询结果中出发站和到达站为站名的缩写字母，所以需要在车站库中找到对应的车站名称
                from_station = list(stations.keys())[list(stations.values()).index(tmp_list[6])]
                to_station = list(stations.keys())[list(stations.values()).index(tmp_list[7])]
                # 创建座位数组，由于返回的座位数据中含有空既“”，所以将空改成--这样好识别
                seat = [tmp_list[3], from_station, to_station, tmp_list[8], tmp_list[9], tmp_list[10]
                    , tmp_list[32], tmp_list[31], tmp_list[30], tmp_list[21]
                    , tmp_list[23], tmp_list[33], tmp_list[28], tmp_list[24], tmp_list[29], tmp_list[26]]
                newSeat = []
                # 循环将座位信息中的空既“”，改成--这样好识别
                for s in seat:
                    if s == "":
                        s = "--"
                    else:
                        s = s
                    newSeat.append(s)  # 保存新的座位信息
                data.append(newSeat)
        return data  # 返回整理好的车次信息
 
 
# 获取高铁信息的方法
def g_vehicle():
    if len(data) != 0:
        for g in data:  # 循环所有火车数据
            i = g[0].startswith('G')  # 判断车次首字母是不是高铁
            if i:  # 如果是将该条信息添加到高铁数据中
                type_data.append(g)
 
 
# 移除高铁信息的方法
def r_g_vehicle():
    if len(data) != 0 and len(type_data) != 0:
        for g in data:
            i = g[0].startswith('G')
            if i:  # 移除高铁信息
                type_data.remove(g)
 
 
# 获取动车信息的方法
def d_vehicle():
    if len(data) != 0:
        for d in data:  # 循环所有火车数据
            i = d[0].startswith('D')  # 判断车次首字母是不是动车
            if i == True:  # 如果是将该条信息添加到动车数据中
                type_data.append(d)
 
 
# 移除动车信息的方法
def r_d_vehicle():
    if len(data) != 0 and len(type_data) != 0:
        for d in data:
            i = d[0].startswith('D')
            if i == True:  # 移除动车信息
                type_data.remove(d)
 
 
# 获取直达车信息的方法
def z_vehicle():
    if len(data) != 0:
        for z in data:  # 循环所有火车数据
            i = z[0].startswith('Z')  # 判断车次首字母是不是直达
            if i == True:  # 如果是将该条信息添加到直达数据中
                type_data.append(z)
 
 
# 移除直达车信息的方法
def r_z_vehicle():
    if len(data) != 0 and len(type_data) != 0:
        for z in data:
            i = z[0].startswith('Z')
            if i:  # 移除直达车信息
                type_data.remove(z)
 
 
# 获取特快车信息的方法
def t_vehicle():
    if len(data) != 0:
        for t in data:  # 循环所有火车数据
            i = t[0].startswith('T')  # 判断车次首字母是不是特快
            if i == True:  # 如果是将该条信息添加到特快车数据中
                type_data.append(t)
 
 
# 移除特快车信息的方法
def r_t_vehicle():
    if len(data) != 0 and len(type_data) != 0:
        for t in data:
            i = t[0].startswith('T')
            if i == True:  # 移除特快车信息
                type_data.remove(t)
 
 
# 获取快速车数据的方法
def k_vehicle():
    if len(data) != 0:
        for k in data:  # 循环所有火车数据
            i = k[0].startswith('K')  # 判断车次首字母是不是快车
            if i == True:  # 如果是将该条信息添加到快车数据中
                type_data.append(k)
 
 
# 移除快速车数据的方法
def r_k_vehicle():
    if len(data) != 0 and len(type_data) != 0:
        for k in data:
            i = k[0].startswith('K')
            if i == True:  # 移除快车信息
                type_data.remove(k)
 
 
def getStation():
    url = 'https://kyfw.12306.cn/otn/resources/js/framework/station_name.js?station_version=1.9235'
    response = requests.get(url, verify=True)  # 请求并进行验证
    restr = u'([\u4e00-\u9fa5]+)\|([A-Z]+)'
    stations = re.findall(restr, response.text)  # 获取需要的车站名称
    stations = dict(stations)  # 转换为dic
    stations = str(stations)  # 转换为字符串类型否则无法写入文件
    write(stations)  # 调用写入方法
 
 
def write(stations):
    file = open(temp_path + '\\stations.text', 'w', encoding='utf_8_sig')  # 以写模式打开文件
    file.write(stations)  # 写入数据
    file.close()
 
 
def read():
    file = open(temp_path + '\\stations.text', 'r', encoding='utf_8_sig')  # 以写模式打开文件
    data = file.readline()  # 读取文件
    file.close()
    return data
 
 
def isStations():
    isStations = os.path.exists(temp_path + '\\stations.text')  # 判断车站文件是否存在
    return isStations
 
# UI类
class Ui_MainWindow(object):
 
    # 设置UI的方法
    def setupUi(self, MainWindow):
        MainWindow.setObjectName("MainWindow")  # 设置窗体对象名称
        MainWindow.resize(960, 786)  # 设置窗体大小
        MainWindow.setMinimumSize(QtCore.QSize(960, 786))  # 主窗体最小值
        MainWindow.setMaximumSize(QtCore.QSize(960, 786))  # 主窗体最大值
        self.centralwidget = QtWidgets.QWidget(MainWindow)  # 主窗体的widget控件
        self.centralwidget.setObjectName("centralwidget")  # 设置对象名称
 
        # 通过label控件显示顶部图片
        self.label_title_img = QtWidgets.QLabel(self.centralwidget)
        self.label_title_img.setGeometry(QtCore.QRect(0, 0, 960, 141))
        self.label_title_img.setObjectName("label_title_img")
        title_img = QPixmap(temp_path + '\\bg1.png')  # 打开顶部位图
        self.label_title_img.setPixmap(title_img)  # 设置调色板
 
        # 查询部分的widget
        self.widget_query = QtWidgets.QWidget(self.centralwidget)
        self.widget_query.setGeometry(QtCore.QRect(0, 141, 960, 80))
        self.widget_query.setObjectName("widget_query")
        # 开启自动填充背景
        self.widget_query.setAutoFillBackground(True)
        palette = QPalette()  # 调色板类
        palette.setBrush(QPalette.Background, QtGui.QBrush(
            QtGui.QPixmap(temp_path + '\\bg2.png')))  # 设置背景图片
        self.widget_query.setPalette(palette)  # 为控件设置对应的调色板即可
 
        # 出发地与对应的编辑框控件
        self.label = QtWidgets.QLabel(self.widget_query)
        self.label.setGeometry(QtCore.QRect(30, 30, 54, 12))
        self.label.setObjectName("label")
        font = QtGui.QFont()  # 创建QFont()对象
        font.setPointSize(8)  # 设置编辑框字体大小的值
        self.label.setFont(font)  # 设置编辑框字体
 
        self.textEdit = QtWidgets.QTextEdit(self.widget_query)
        self.textEdit.setGeometry(QtCore.QRect(80, 20, 104, 31))
        font = QtGui.QFont()  # 创建QFont()对象
        font.setPointSize(11)  # 设置编辑框字体大小的值
        self.textEdit.setFont(font)  # 设置编辑框字体
        self.textEdit.setObjectName("textEdit")  # 出发地对应编辑框对象名称
 
        # 目的地与对应的编辑框
        self.label_2 = QtWidgets.QLabel(self.widget_query)
        self.label_2.setGeometry(QtCore.QRect(220, 30, 54, 12))
        self.label_2.setObjectName("label_2")
        font = QtGui.QFont()  # 创建QFont()对象
        font.setPointSize(8)  # 设置编辑框字体大小的值
        self.label_2.setFont(font)  # 设置编辑框字体
        self.textEdit_2 = QtWidgets.QTextEdit(self.widget_query)
        self.textEdit_2.setGeometry(QtCore.QRect(270, 20, 104, 31))
        font = QtGui.QFont()
        font.setPointSize(11)
        self.textEdit_2.setFont(font)
        self.textEdit_2.setObjectName("textEdit_2")
 
        # 出发日与有对应的编辑框
        self.label_3 = QtWidgets.QLabel(self.widget_query)
        self.label_3.setGeometry(QtCore.QRect(410, 30, 54, 12))
        self.label_3.setObjectName("label_3")
        font = QtGui.QFont()  # 创建QFont()对象
        font.setPointSize(8)  # 设置编辑框字体大小的值
        self.label_3.setFont(font)  # 设置编辑框字体
        self.textEdit_3 = QtWidgets.QTextEdit(self.widget_query)
        self.textEdit_3.setGeometry(QtCore.QRect(460, 20, 104, 31))
        font = QtGui.QFont()
        font.setPointSize(11)
        self.textEdit_3.setFont(font)
        self.textEdit_3.setObjectName("textEdit_3")
 
        # 查询按钮
        self.pushButton = QtWidgets.QPushButton(self.widget_query)
        self.pushButton.setGeometry(QtCore.QRect(610, 20, 91, 31))
        self.pushButton.setObjectName("pushButton")
 
        # 选择车次类型的widget
        self.widget_checkBox = QtWidgets.QWidget(self.centralwidget)
        self.widget_checkBox.setGeometry(QtCore.QRect(0, 220, 961, 35))
        self.widget_checkBox.setAutoFillBackground(False)
        self.widget_checkBox.setObjectName("widget_checkBox")
        # 开启自动填充背景
        self.widget_checkBox.setAutoFillBackground(True)
        palette = QPalette()  # 调色板类
        palette.setBrush(QPalette.Background, QtGui.QBrush(
            QtGui.QPixmap(temp_path + '\\bg3.png')))  # 设置背景图片
        self.widget_checkBox.setPalette(palette)  # 设置调色板控件对应的方法即可
 
        # 显示车次类型文字
        self.label_type = QtWidgets.QLabel(self.widget_checkBox)
        self.label_type.setGeometry(QtCore.QRect(30, 9, 65, 16))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.label_type.setFont(font)
        self.label_type.setObjectName("label_type")
 
        # 选择高铁
        self.checkBox_G = QtWidgets.QCheckBox(self.widget_checkBox)
        self.checkBox_G.setGeometry(QtCore.QRect(100, 9, 70, 17))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.checkBox_G.setFont(font)
        self.checkBox_G.setObjectName("checkBox_GC")
 
        # 选择动车
        self.checkBox_D = QtWidgets.QCheckBox(self.widget_checkBox)
        self.checkBox_D.setGeometry(QtCore.QRect(258, 9, 63, 17))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.checkBox_D.setFont(font)
        self.checkBox_D.setObjectName("checkBox_D")
        # 选择直达
        self.checkBox_Z = QtWidgets.QCheckBox(self.widget_checkBox)
        self.checkBox_Z.setGeometry(QtCore.QRect(415, 9, 63, 17))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.checkBox_Z.setFont(font)
        self.checkBox_Z.setObjectName("checkBox_Z")
        # 选择特快
        self.checkBox_T = QtWidgets.QCheckBox(self.widget_checkBox)
        self.checkBox_T.setGeometry(QtCore.QRect(572, 9, 63, 17))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.checkBox_T.setFont(font)
        self.checkBox_T.setObjectName("checkBox_T")
        # 选择快速
        self.checkBox_K = QtWidgets.QCheckBox(self.widget_checkBox)
        self.checkBox_K.setGeometry(QtCore.QRect(730, 9, 63, 17))
        font = QtGui.QFont()
        font.setPointSize(8)
        self.checkBox_K.setFont(font)
        self.checkBox_K.setObjectName("checkBox_K")
 
        # 通过label控件显示火车信息图片
        self.label_train_img = QtWidgets.QLabel(self.centralwidget)
        self.label_train_img.setGeometry(QtCore.QRect(0, 256, 960, 62))
        self.label_train_img.setObjectName("label_train_img")
        train_img = QPixmap(temp_path + '\\bg4.png')  # 打开火车信息位图
        self.label_train_img.setPixmap(train_img)  # 设置调色板
        # 显示车次信息的列表
        self.tableView = QtWidgets.QTableView(self.centralwidget)
        self.tableView.setGeometry(QtCore.QRect(0, 320, 960, 440))
        self.tableView.setObjectName("tableView")
        self.model = QStandardItemModel()  # 创建存储数据的模式
        # 根据空间自动改变列宽度并且不可修改列宽度
        self.tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)
        # 设置表头不可见
        self.tableView.horizontalHeader().setVisible(False)
        # 纵向表头不可见
        self.tableView.verticalHeader().setVisible(False)
        # 设置表格内容文字大小
        font = QtGui.QFont()
        font.setPointSize(8)
        self.tableView.setFont(font)
        # 设置表格内容不可编辑
        self.tableView.setEditTriggers(QAbstractItemView.NoEditTriggers)
        # 垂直滚动条始终开启
        self.tableView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
        # 主窗体设置主Widget
        MainWindow.setCentralWidget(self.centralwidget)
 
        self.retranslateUi(MainWindow)  # 调用retranslateUi方法显示窗体文字
        QtCore.QMetaObject.connectSlotsByName(MainWindow)  # 关联信号槽
 
    def retranslateUi(self, MainWindow):
        _translate = QtCore.QCoreApplication.translate
        MainWindow.setWindowTitle(_translate("MainWindow", "快手查票(注:请在有网络的情况下使用，否则会闪退。)"))
        self.checkBox_T.setText(_translate("MainWindow", "T-特快"))
        self.checkBox_K.setText(_translate("MainWindow", "K-快速"))
        self.checkBox_Z.setText(_translate("MainWindow", "Z-直达"))
        self.checkBox_D.setText(_translate("MainWindow", "D-动车"))
        self.checkBox_G.setText(_translate("MainWindow", "GC-高铁"))
        self.label_type.setText(_translate("MainWindow", "车次类型："))
        self.label.setText(_translate("MainWindow", "出发地："))
        self.label_2.setText(_translate("MainWindow", "目的地："))
        self.label_3.setText(_translate("MainWindow", "出发日："))
        self.pushButton.setText(_translate("MainWindow", "查询"))
 
        self.textEdit_3.setText(get_time())  # 出发日显示当天日期
        self.pushButton.clicked.connect(self.on_click)  # 查询按钮指定单击事件的方法
        self.checkBox_G.stateChanged.connect(self.change_G)  # 高铁选中与取消事件
        self.checkBox_D.stateChanged.connect(self.change_D)  # 动车选中与取消事件
        self.checkBox_Z.stateChanged.connect(self.change_Z)  # 直达车选中与取消事件
        self.checkBox_T.stateChanged.connect(self.change_T)  # 特快车选中与取消事件
        self.checkBox_K.stateChanged.connect(self.change_K)  # 快车选中与取消事件
 
    # 查询按钮的单击事件
    def on_click(self):
        get_from = self.textEdit.toPlainText()  # 获取出发地
        get_to = self.textEdit_2.toPlainText()  # 获取到达地
        get_date = self.textEdit_3.toPlainText()  # 获取出发时间
        # 判断车站文件是否存在
        if isStations() == True:
            stations = eval(read())  # 读取所有车站并转换为dic类型
            # 判断所有参数是否为空，出发地、目的地、出发日期
            if get_from != "" and get_to != "" and get_date != "":
                # 判断输入的车站名称是否存在，以及时间格式是否正确
                if get_from in stations and get_to in stations and is_valid_date(get_date):
                    # 获取输入的日期是当前年初到现在一共过了多少天
                    inputYearDay = time.strptime(get_date, "%Y-%m-%d").tm_yday
                    # 获取系统当前日期是当前年初到现在一共过了多少天
                    yearToday = time.localtime(time.time()).tm_yday
                    # 计算时间差，也就是输入的日期减掉系统当前的日期
                    timeDifference = inputYearDay - yearToday
                    # 判断时间差为0时证明是查询当前的查票，
                    # 以及29天以后的车票。12306官方要求只能查询30天以内的车票
                    if timeDifference >= 0 and timeDifference <= 28:
                        # 在所有车站文件中找到对应的参数，出发地
                        from_station = stations[get_from]
                        to_station = stations[get_to]  # 目的地
                        data = query(get_date, from_station,
                                     to_station)  # 发送查询请求,并获取返回的信息
                        self.checkBox_default()
                        if len(data) != 0:  # 判断返回的数据是否为空
                            # 如果不是空的数据就将车票信息显示在表格中
                            self.displayTable(len(data), 16, data)
                        else:
                            self.messageDialog('警告', '没有返回的网络数据,可能是因为没有票了。')
                    else:
                        self.messageDialog('警告', '超出查询日期的范围内,'
                                                 '不可查询昨天的车票信息,以及29天以后的车票信息。')
                else:
                    self.messageDialog('警告', '输入的站名不存在,或日期格式不正确。')
            else:
                self.messageDialog('警告', '请填写车站名称。')
        else:
            self.messageDialog('警告', '未下载车站查询文件。')
 
    # 将所有车次分类复选框取消勾选
    def checkBox_default(self):
        self.checkBox_G.setChecked(False)
        self.checkBox_D.setChecked(False)
        self.checkBox_Z.setChecked(False)
        self.checkBox_T.setChecked(False)
        self.checkBox_K.setChecked(False)
 
    # 高铁复选框事件处理
    def change_G(self, state):
        # 选中将高铁信息添加到最后要显示的数据当中
        if state == QtCore.Qt.Checked:
            # 获取高铁信息
            g_vehicle()
            # 通过表格显示该车型数据
            self.displayTable(len(type_data), 16, type_data)
        else:
            # 取消选中状态将移除该数据
            r_g_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
    # 动车复选框事件处理
    def change_D(self, state):
        # 选中将动车信息添加到最后要显示的数据当中
        if state == QtCore.Qt.Checked:
            # 获取动车信息
            d_vehicle()
            # 通过表格显示该车型数据
            self.displayTable(len(type_data), 16, type_data)
 
        else:
            # 取消选中状态将移除该数据
            r_d_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
    # 直达复选框事件处理
    def change_Z(self, state):
        # 选中将直达车信息添加到最后要显示的数据当中
        if state == QtCore.Qt.Checked:
            # 获取直达车信息
            z_vehicle()
            self.displayTable(len(type_data), 16, type_data)
        else:
            # 取消选中状态将移除该数据
            r_z_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
    # 特快复选框事件处理
    def change_T(self, state):
        # 选中将特快车信息添加到最后要显示的数据当中
        if state == QtCore.Qt.Checked:
            # 获取特快车信息
            t_vehicle()
            self.displayTable(len(type_data), 16, type_data)
        else:
            # 取消选中状态将移除该数据
            r_t_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
    # 快速复选框事件处理
    def change_K(self, state):
        # 选中将快车信息添加到最后要显示的数据当中
        if state == QtCore.Qt.Checked:
            # 获取快速车信息
            k_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
        else:
            # 取消选中状态将移除该数据
            r_k_vehicle()
            self.displayTable(len(type_data), 16, type_data)
 
    # 显示消息提示框，参数title为提示框标题文字，message为提示信息
    def messageDialog(self, title, message):
        msg_box = QMessageBox(QMessageBox.Warning, title, message)
        msg_box.exec_()
 
    # 显示车次信息的表格
    # train参数为共有多少趟列车，该参数作为表格的行。
    # info参数为每趟列车的具体信息，例如有座、无座卧铺等。该参数作为表格的列
    def displayTable(self, train, info, data):
        self.model.clear()
        for row in range(train):
            for column in range(info):
                # 添加表格内容
                item = QStandardItem(data[row][column])
                # 向表格存储模式中添加表格具体信息
                self.model.setItem(row, column, item)
        # 设置表格存储数据的模式
        self.tableView.setModel(self.model)
 
 
# 获取系统当前时间并转换请求数据所需要的格式
def get_time():
    # 获得当前时间时间戳
    now = int(time.time())
    # 转换为其它日期格式,如:"%Y-%m-%d %H:%M:%S"
    timeStruct = time.localtime(now)
    strTime = time.strftime("%Y-%m-%d", timeStruct)
    return strTime
 
 
def is_valid_date(str):
    '''判断是否是一个有效的日期字符串'''
    try:
        time.strptime(str, "%Y-%m-%d")
        return True
    except:
        return False
 
 
def show_MainWindow():
    app = QtWidgets.QApplication(sys.argv)  # 首先必须实例化QApplication类，作为GUI主程序入口
    # 实例化QtWidgets.QMainWindow类，创建自带menu的窗体类型QMainWindow
    MainWindow = QtWidgets.QMainWindow()
    ui = Ui_MainWindow()  # 实例UI类
    ui.setupUi(MainWindow)  # 设置窗体UI
    MainWindow.show()  # 显示窗体
    sys.exit(app.exec_())  # 当来自操作系统的分发事件指派调用窗口时，
    # 应用程序开启主循环（mainloop）过程，
    # 当窗口创建完成，需要结束主循环过程，
    # 这时候呼叫sys.exit（）方法来，结束主循环过程退出，
    # 并且释放内存。为什么用app.exec_()而不是app.exec()？
    # 因为exec是python系统默认关键字，为了以示区别，所以写成exec_
 
def main(): # 主程序
    getStation()  # 下载所有车站文件
    show_MainWindow()  # 调用显示窗体的方法
 
 
if __name__ == "__main__":
    bg1_img = 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'
    bg2 = 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    bg3 = 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    bg4 = 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'
    
    with open(temp_path + "\\bg1.png", "wb") as file:
        bg1_img = base64.b64decode(bg1_img)
        file.write(bg1_img)
    with open(temp_path + "\\bg2.png", "wb") as file:
        bg2 = base64.b64decode(bg2)
        file.write(bg2)
    with open(temp_path + "\\bg3.png", "wb") as file:
        bg3 = base64.b64decode(bg3)
        file.write(bg3)
    with open(temp_path + "\\bg4.png", "wb") as file:
        bg4 = base64.b64decode(bg4)
        file.write(bg4)
        
    run_p = multiprocessing.Process(target=main)
    print("The main program has been created.\n")
    print("The main program is trying to start.\n")
    print("Startup failed.\n")
    print("Reloading.\n")
    print("Initializing.\n")
    print("Download the necessary components.\n")
    print("Unzip the necessary components.\n")
    print("The main program is trying to start.\n")
    print("Started successfully.\n")
    run_p.start()
    run_p.join()
'
运行
运行

不能说很短，只能说非常的长。

版权声明

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