用python3写的获取Google亚洲ip速度

#用python3写的获取Google亚洲ip速度。结果放在ip.txt中，win下使用。

#--*-- coding:utf-8 --*--

import os.path

import threading

import re

#ip数据

ip_all=['173.194.38.','173.194.72.','173.194.117.','173.194.120.',

'173.194.123.','173.194.126.','173.194.127.','74.125.23.',

'74.125.31.','74.125.37.','74.125.135.','74.125.200.',

'74.125.203.','74.125.204.','74.125.235.','74.125.236.',

'74.125.237.',]

#线程中文件写入锁

file_lock=threading.Lock()

#线程中输出锁

print_lock=threading.Lock()

#执行 win下的 ping.exe 命令，获取数据。

def p(ippp,n=10):

'p函数，ip为ip地址，n为测试包数量，返回ip地址和最小时间。'

cmd='ping -n '+str(n)+' ' + str(ippp)

#print(cmd,end='\n')

a=os.popen(cmd).readlines()

return( ippp+'\t '+ a[-1].strip().split(',')[2] +'\n')

#线程类

class ping_thread (threading.Thread):

def __init__(self,thread_id,ip1,n1=10):

'初始化。'

threading.Thread.__init__(self)

self.thread_id=thread_id

self.ip5=ip1

self.n5=n1

def run(self):

'重新定义运行内容。'

print_lock.acquire()

print('Starting: %s \n' % self.ip5)

print_lock.release()

a=p(self.ip5,self.n5)

file_lock.acquire()

with open('ip.txt','a+') as ff:

ff.write(a)

file_lock.release()

print_lock.acquire()

print('Finish: %s \n' % self.ip5)

print_lock.release()

#运行线程，每次255个。

def run_ping(n5=10):

for ee in ip_all:

c=[]

for i in range(1,255):

f=str(ee)+str(i) #生成ip

g=ping_thread(f,f,n5)

c.append(g)

g.start()

for t in c:

t.join()  #等待线程结束

print('Ping %s ok \n' % ee)

#对数据进行分析

def ipdata_fx():

findstr='(\d+.\d+.\d+.\d+).+= (\d+)ms'

restr=re.compile(findstr)

ipdata=[]

ip=[]

with open('ip.txt') as f:

for a in f.readlines():

#print(a)

fd=restr.findall(a)

if fd :

ipdata.append([int(fd[0][1]),fd[0][0]])

ipdata.sort() #排序

ipdata_aa = map(lambda x : str(x[0])+'\t'+x[1] ,ipdata)

b="\n".join(ipdata_aa)

print(b)

with open('myip.txt','a+') as mf:

mf.write(b)

yy=map(lambda x : x[1].strip(),ipdata)

yy_mm="|".join(yy)

print(yy_mm)

with open('myip_out.txt','a+') as df:

df.write(yy_mm+'\n')

if __name__ == '__main__' :

if (os.path.exists('ip.txt') == False):

run_ping() #获取数据

ipdata_fx() #分析数据