A4 portrait - PET: Python Entre Todos - Python Argentina

Context managers 12
print "Starting to work"
time.sleep(2)
print "Finished working"
def main():
print "Starting the main program"
thread = processing.Process(target=trabajador)
print "Launching thread"
thread.start()
print "Thread has been launched"
# isAlive() is False when the thread ends.
while thread.isAlive():
# Here you would have the code to make the app
# look "alive", a progress bar, or maybe just
# keep on working as usual.
print "The thread is still running"
# Wait a little bit, or until the thread ends,
# whatever's shorter.
thread.join(.3)
print "Program ended"
# Important: the modules should not execute code
# when they are imported
if __name__ == '__main__':
main()
Yes, the only change is import multiprocessing instead of import threading and
Process instead of Thread. Now the worker function runs in a separate Python
interpreter. Since they are separate processes, this will use as many cores as
processes you have, so it may be much faster on a modern computer.
I mentioned deadlocks earlier. You may believe that with a little care, if you place
locks around variables you can avoid them. Well, no. Let’s see two functions f1 and
f2 which use two variables x and y protected by locks lockx and locky.
# -*- coding: utf-8 -*-
import threading
import time
x = 4
y = 6
lock_x = threading.Lock()
lock_y = threading.Lock()
def f1():
lock_x.acquire()
time.sleep(2)
lock_y.acquire()
time.sleep(2)
lock_x.release()
lock_y.release()
def f2():
lock_y.acquire()
time.sleep(2)
lock_x.acquire()
time.sleep(2)
lock_y.release()
lock_x.release()
def main():
PET: English Translation (Issue 1, August 2010) — http://revista.python.org.ar