_thread模块是python 中多线程操作的一种模块方式,主要的原理是派生出多线程,然后给线程加锁,当线程结束的 时候取消锁,然后执行主程序

thread 模块和锁对象的说明
start_new_thred(function,args,kwargs=none) 派生一个新的线程,使用给定的argvs,和可选的kwargs 来执行function
allocate_lock() 分配locktype 锁对象
exit() 给线程退去指令
   
 locktype   锁对象的方法
qcquire(wait=none) 尝试获取锁对象
Locked() 如果获取了锁对象则返回true ,否则false
release() 释放锁
   

程序的代码

 #!/usr/bin/python
from time import sleep,ctime
import _thread
loops=[4,2] #定义任务的时间长短
def loop(nloop,nsec,lock): # nloop 任务的名称 nesc 任务执行的时间 lock 锁
print ('loop',nloop,'start at:', ctime()) #输出任务的开始的时间
sleep(nsec) # 任务的执行时间
print('loop',nloop, 'done at:',ctime())#输出任务的结束时间
lock.release() #释放任务的锁
def main():
print ('starting at:',ctime()) #开始执行任务的当前时间
locks=[] # 定义一个空的锁列表
nloops=range(len(loops)) #主要作用是为了下面循环区分具体的任务 for i in nloops:
lock=_thread.allocate_lock() #给任务加上锁
lock.acquire()#获取锁对象
locks.append(lock) #把具体的锁对象加到锁列表里面去
for i in nloops:#循环时间长短
_thread.start_new_thread(loop,(i, loops[i],locks[i])) #派生出两个新的线程 并传递给循环,其中loops[i]传递给 nesc ,locks[i] 传递给 lock
for i in nloops:# 循环时间长短
while locks[i].locked():#判断派生的线程有没有锁,,如果有暂停主线程,直到所有的锁都释放了才会执行主线程
pass
print ('all doneat:', ctime()) if __name__ == '__main__': #执行函数
main()
任务执行的结果

关于给任务加锁的说明  (第一个for 循环)

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" 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任务执行的结果

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAikAAAD1CAIAAAAakvI0AAAUUUlEQVR4nO3dT4tc15nH8fsy5mUIYcng9xEQmNiTjTdZBIwxiiHBYBTFkD8mi8QbbTwYgvFAFsGZWYRckoWTEUY2wWOMosnCGy0MavoNZBZSl6rvOed3nue59z51q/r7wZiu0+c+5091nV9XdZd6+Py/Xhx+8dPPnjz59tv777x74+0vvnn06NGXX375b+989S/pl7/6zWD2y1/9pijw8L2XXnrv4aWmT14bhtc+abV88trzgvtXPnzvpV37Rd/9tkl3AMCBDefnn//0F8Orf83PHgDAFTWcn5+f/d+D2PMe13856wEAbN9wfn5+dnb2xJ89AADEkD0AgGzx19wAAIh5+rzn41dvD8Obw/DGdz/4huwBAKxr/zW3bx8/fvwN2QMAWBnZAwDIRvYAALKRPQCAbGQPACAb2QMAyKayx/7PtQEA4ED2AACykT0AgGybzZ5xHMdxPOwcYsaatUcxXhKrb2l3De3dH9E/vMllf1cRMa6oU21f6SsE2DSyZ3G7aU8+WGNF9tTxHqzVm61279DeOsaj3Jsc1WuNi2rdFHvVCir7uMDpmJM9Sz1atvaoW29dh8oeb+cFs6fbM3DWt9rD2eO9X8L7E54wcGrInpI46Wae9WSP5VPd50mt9upTjdZVZA9wSN3sGS8TjZNPVVuMdURl/dnqp6oCRYyV9/tXW6qbEBtFXFhdnbGsHiVQrdW52q7vF/sorTux7Cy+rixaeyJGX2Rc4Ijp7NGPJf3QEgeB8Uzsnv7iHNF0/6VOgdj8XRNw3UGxdXVXESvimpLl/vWWmtT0fglZ5mbJnvC4wHFzZc+E+Gx5POnOxvZWzTkHR3L2VG9uNnvEEsIH9Jz5tC6cmWE5+x/oD5ws12tuk2tbB8p48UpCt7P+lOuh25qkGLQ6TzEfr2PPHu/R6SoVmM8i0yiFCwb2h+wBnnH9roHroZWZPXNOVbLHUnlmwZnZI8ZdJMAC64rtD9kDPLPIz3uqHwSyZ85Dd9zTGqg6ohilHG7m8W3JHtco3Q3R8/GWzckePXN7ZuidNA4Rvta4ZNe4wOnoZo8+06vHxHjxOsb+B6JIWad6ibgZOBPLeep1tRp1/eqiujftA7UmGbgLqkXEKuwzbF3SqiA2v6yg61eH6C7NuyjLuN3+xv0ETsRm/10DF+Ohdhomx+XB5gEAYZXs+e9b1z74w3Flz+D/fvx4XZ2VAjhZZfbc+/Vw7ce3jy57AABHY5o9X7x9/Z0f/eHYXnMDAByTy9nz6ds/eeFH94/v5z0AgGOynz2f/u7Gy386yt81AAAck1323P/9zRv3PvzLcf6eGwDgmJyfn5+dPbj78+Hm7+8//t93Xnh9GH4wDN8f9rPngL9Pxa9ylco3l6yxS4H6rml03+zirdYqYikl+oc3uezvKiLGFXWq7St9hQCzPM2ez/6x//6eP97+8bXJe0vzJ3bAoTdutyeTD9bYLmNB751VRoVu9w7trWM8yr3JUb3WuKjWTbFXraCyjwvkqb239N53Lv+O9QG/ahcZeqn5b7/OobLH23nB7On2DJz1rfZw9njvl/D+hCcMZCuz54+/vT55bynZk1PH9dShWofssXxKPCnR11afarSuInsApcieD24V7+9pPcB2dPvuY+/Z2hq61bM1tJ5ntcVepzuf7iQnn7Ut91n/aktr/wOjiAurqzOW1aMEqrU6V9v1/WIfpXUnlp2r94tda0/E6IuMC6zocvZ8+PLrL98rfs/N+CWu25c9U4zzaRWpHna7m/aHumU+3d0Lq1a2nOOxhXhvxoYYFv0eJfD1s1SpSU3LEN65WR6Y4XGBdT3Lnr++Orw5DG9894Pa71gvkj3iciGWPfbPlsec7myZjK4fq9Mqrlu2nz1iCeEDes58WhfOzLCc/Q/0Bw7G8u9Ybz97BvmySetgGi9ekTCO6D0Nq/W9dfQQumXj2eM9Ol2lAvNZZBqlcMHA/pA9OBonkz3iKj3/NbJH1HfVsY9Sbdly9ljSIi17xLiLBFhgXbH9IXtwNNKyp9VHi32H2J1edWLdEe1LEPVFnWpnyyitlupArlH0obx4pOVkj565PTP0ThqHCF9rXLJrXCDPo0fnDx+eff31k6+++vbvf3/84ME3f/vboz//uf93S13t48UrD67TpDVEoHNrnuUHekT7Esr63Tqx/Wm1iJuuVYhxXVtXLSJW4drn6iWtCmLzywq6fnWI7tK8i7KM2+1v3E8gic6epfB1f0Qmx+XB5gHghCVkD992HZfWt88AsJic5z0AADxH9gAAspE9AIBsZA8AIBvZAwDIZsmeQ/2+E79qVVW++WONXQrUd02j+2YUb7VWEUsp0T+8yWV/VxExrqhTbV/pKwSYxfje0vyJ7QblYTNR7szof+uud6xuN+/BWr3ZavcO7a1jPMq9yVG91rio1k2xV62gso8L5Nnm857wY95S7YTrHCp7vJ0XzJ5uz8BZ32oPZ4/3fgnvz7IPHGBFZM926lS/b3XVIXssnxJPSvS11acaravIHkAJZ8+4R7fvPrafrYGH0HiZaJx8qtpir9OdT3eSk89aiu/6V1ta+x8YRVxYXZ2xrB4lUK3Vudqu7xf7KK07sexcvV/sWnsiRl9kXGBFsexpPQZEu+tM8R5qur+evzhQwmerqO+qYx9l12I5x2ML8d6MDTH4M1gMbayj769YqUlNyxDeuVkemOFxgXWlZY+4vDSzv/2z5TGnO+tpWOrH6rSK65btZ49YQviAnjOf1oUzMyxn/wP9gYM5jewZ5MsmrYNpvHhFwjiW9zSs1vfW0UPolo1nj/fodJUKzGeRaZTCBQP7Q/bgaJxM9rgu329ZI3tEfVcd+yjVli1njyUt0rJHjLtIgAXWFdsfsgdHY7N/t9TV3/iQq34QyB57dpb1RZ1qZ8sorZbqQK5R9KG8eKTlZI+euT0z9E4ahwhfa1yya1wgj86e8bL9C13t48UrD96z1dhfzLNVatcy+UAUCUypdYm9UdefbPJ+i7jpWoUY17V11SJiFa59rl7SqiA2v6yg61eH6C7NuyjLuN3+xv0Eklie98zH1/0RmRyXB5sHgBOWkD1823VcWt8+A8Bicp73AADwHNkDAMhG9gAAspE9AIBsZA8AIFvsvaVp+FWrUvnmjzV2KVDfNY3um1G81VpFLKVE//Amz1yX6C+KVNtX+goBZjG+tzR/YuGhT/6Rtlvd5IM1Fu49KGNlyxXZR68O7a1jPMpdC5wE2Jz5VEu1sq1V6rQfFDg+J/m8Z+MPs6WmJw6apbgKHiR7uj2NAWZpP3j2dOuEJwxkI3vyiZNu5llP9lg+Ve5zLHta99d48TTUWMc+LtmD0xHOnnGPbt997D1bW0O3elan1J1Pd/4x3eLVg8M1aOvoaa03MIq4sLo6Y1k9SqBaq3O1PXynV69q9WltTmCXujXFDIfGlyJwYJv9d6xdnefMx9Lfq1p8ftluncXXpS9cZLu6q4gVcU1J31+xUousa5BzszwwLUsDDiAte8TlwnrZ42oPqx43q2ZP9eZms0cswVWt2jm8z+G7zJITc9YVyB7dHzgYsmfSPmGcZ3VK48UrHnq2c4bQLRvPHu/R6SoVmM8i06h+CcUKBvaH7MHRIHtEe9h+HbLHUnlmwZnZI8ZdJMAC64rtD9mDo3F6P+8RY62Xka357E/GMtzMzbGsyzWKPpQXj7Sc7NEzt2eG3sk52WOZnnHJYhrAIW3275aKoXX/yUBlEePNmQ/U6mTKz3Ybdf3JJhuXaR+oNclJnfEy++TFKuwzbF3SqiA2v6yg61eHqF6y1P505y9W0R0XyMPfLcXEeDkJDjYPACeMv1uKCdd36AAQwb9jDQDIRvYAALKRPQCAbGQPACAb2QMAyLblv6HAr1pVlW/+WGOXAvVd0+i+GcVbrVXEUkr0d81Hj+talBhX1Km2ezcTyLDlv1s6+cB+4Wk/0sqdGf1v3fWO1e3mPVirN1vt3qG9dYxHebjO4Px6FuOKvWoFlX1cIM82n/cEzqA5/ZMtNT1x0CzFVfAg2dPtGTjrW+2HyjD7/sx84AB5yJ584oSaedaTPZZPlfscy4yl6tj7kz04Hdv8u6WBh9B4mX0+3fnHdItXDw7XoK2jp7XewCjiwurqjGX1KIFqrc7V9vCdXr3KUmfmRrX2pPUFMLlpWRqQbZv/jrX3sTpnPpb+XtXi88t26yy+LtcZGltXdxWxIq4p6fvLXqpVZ85GueqUHSxLAw5gm3+/JyF7FplnV/UYXTV7qjc3mz1iCeEDes58WhfOjNWc/Q/0Bw6G7Jm0T+hx9ZTGi1c89GznDKFbNp493qPTVSown/nTaNWJFQzsD9mDo/H5+fnH/zEMt4fhzWF4YxheH4YfDMP3r13Z7NEDGe3XIXsslWcWnJk9Ytw5i5qz/7H9IXtwNF58a/j3/9ncz3tETd1Zj7V4Rnbnsz8Zy3Azj2/Lulyj6EN58UjLyR49cxE83fvLcq3367lV1rhkMSXgkL53f6N/t9Tbf3/Q/QvL+RhvznygVidTfrbbqOtPNtm4TPtArUlO6oyX2ScvVmGfYeuSVgWx+WUFXd9YR/QXM4yNK1ZhGRrI8tYw/PDFu//46JXnr7ndep+/W3qFTZLgYPMAcMLOz8/Pzh7c/fmrv336vOeLt6//+h5/t/Qqa337DACLScgeAAAue2sYfjgMt8keAEAWnvcAALKRPQCAdLzmBgBINud5j3j/xLKT3KbyTRhrLDxQ3zWN7ptCvNVaRSylRH/XfPS4rkWJcUWdart3M4FTFsse/WiMPcCO7pG5m+3kgzUWYizo3f/yUNbt3qG9dYxHebjO0F6jd1yxV62Hhn1c4Aoo31t6uOc9yQ/LpYYTB81SXAUPkj3dnoGzvtV+qAyz7094A4GrwvLvWLecavZ4n7iQPZae1V0yDqS7LVXH3p/sAebqZs94obx2kewZL6u277e0plQtYulsORO7S6i2tOYfGEVcWF2dsaweJVDN9SVhvxN1NdcXg2W23XG7daodLEsDrgqdPdXDSLTo9m7P7mO7PHbtdezz96pWds3TO8SyR2rrqsBBOfNLQt9f9lKtOnM2ylVHpyPxA1hfc6seQwfJHlf7fkty9lRvbjZ7xBLCB/Sc+bQunBmrOfsf6A9cOcbX3IbNPO9ptU/s9/HO3+vYs8d7dLpKBeYzfxqtOrGCgf0he4COo3vNzdJerU/2WCrPLDgze8S4cxY1Z/9j+0P2AB3G7BnHce3s2R9i8UzS8y8vn3l8W+bpGkUfyotHWk726JmL4OneX5ZrA8FTLWtcspgScBW5XnObHOU7u2qtdmG/c2uIsqe42TqYqlOyN1rm75qna6DWJCd1xsvskxersM9Q3AXiErEuyzxddUR/McPYuGIVlqGBEzfn/T1Y1SQJDjYPAFgc2bNZ3mceAHA0yB4AQDayBwCQLZY9+lWg6g9g9TRaPxPWV4Ur6095JxOePABcUfb39+zzZo/3EvGjjvJXj8RvFulxu6sz/rSf7AEAH8vvWO8+mHPcW7JH1OxWE0GyKzj5oDpWK2+6QTUZzr4WALhy7NmzzxIkgzmu7EHS6iP6d6NFd9hfiy7YXRQA4BnXz3t0ipTt9ucKuoMYXWdbtbKl//78y/9X59ldFADgmfV+16B1LpdntDF7AnQWThp1dHVjrGwpCwIAhkFmT3kQG89WV5YEssc1jWp+6GDoLtk1W0sHALhaYj/v0XYHdDckut1EqFTHFY1lKd1ffKwHLVdXvRAAri5X9hifBIioqH4sju8qe0SVnxpradcaV+SWniepAwCKN3sml5fJVHbTFbplLf0txcu5dbOq9f9q/259AMAziz/vKS9pfUo0drOnRRTfZYO4sJxt+aluurSqAQCeWfB5T6ux+pxAFzGe2pZu3iA0Xtvqbw9FALi6jNkjnjSUNe2NrU/plHLF4WT+xlEsA036i3HLykZr9weAw7D/3dIh+rxnaBzE3f6tnq1IqM5zEiSuCOmGnCV9q5MxInsAnKbwa27imYR4eiFO6rFtcm03w/Q8LUOInqJ/q5E8AICJ7318dvbkyZM7Ny8arl+/du1269/UETfLxuq34YsczToSDqU1h4NPDAC25enzns/u3rx55/7T5z33bg1DI3sAAFjAxWtuH73yvO3W+/zdUgDAeio/77l3i+c9AIAVPc2euzdv3rm/lz3feZ/sAQCs5cV3Pz87O7t7c7+N19wAAGuK/f0eAADiyB4AQDayBwCQjewBAGQjewAA2cgeAEA2sgcAkI3sAQBkI3sAANnIHgBANrIHAJCN7AEAZCN7AADZyB4AQDayBwCQjewBAGQjewAA2cgeAEA2sgcAkI3sAQBkI3sAANnIHgBANrIHAJCN7AEAZCN7AADZyB4AQDayBwCQjewBAGQjewAA2cgeAEA2sgcAkO0iez6787NheGN44T//QvYAANa1/7zn09/dePlPPO8BAKzsefZ8fefGvQ95zQ0AsLp3//k0ez565Wd3PuXnPQCABPyuAQAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAbGQPACAb2QMAyEb2AACykT0AgGxkDwAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAbGQPACAb2QMAyEb2AACykT0AgGxkDwAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAbGQPACAb2QMAyEb2AACykT0AgGxkDwAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAbGQPACAb2QMAyEb2AACykT0AgGxkDwAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAbGQPACAb2QMAyEb2AACykT0AgGxkDwAgG9kDAMhG9gAAspE9AIBsZA8AIBvZAwDIRvYAALKRPQCAZP8P1Q6UMTJKK5kAAAAASUVORK5CYII=" alt="" />

关于程序的思路流程:

导入时间模块

导入 _thread  模块

定义一个任务时间长短列表

执行mian 函数

输出开始执行的时间

定义空的锁列表

第一个循环主要是获取锁对象,给任务加上锁 把具体的锁对象加到锁列表里面去

第二个循环主要的作用是派生出两个新的线程。执行,loop函数,传递参数给 loop 函数

判断线程有没有锁。如果有暂定主线程,没有的话  执行下面代码结束输入

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