python-multithreading

I need to print a message every x seconds, at the same time , I need to listen to the user's input. If 'q' is pressed, it should kill the program. For example some message . . # after specified interval . some message q # program should end The current problem I face now is that raw_input is blocking which stops the my function from repeating the message. How do I get input reading and my function to run in parallel? EDIT: It turns out that raw_input was not blocking. I misunderstood how multi-threading works. I'll leave this here in case any one stumbles upon it. You can use threading to

I'm having an issue with threading in python, the issue seems to be that when I call a thread subsequently calling raw_input() blocks the thread. Here is the minimal example import threading import time class tread_test(threading.Thread): def __init__(self): self.running = True threading.Thread.__init__(self) # def run(self): self.foo() # def foo(self): print "Spam, Spam, Spam" self.task = threading.Timer(0.5, self.foo) self.task.start() # def stop(self): self.running = False self.task.cancel() # # if __name__ == "__main__": a = tread_test() print "Starting now" a.start() raw_input() a.stop()

I wrote a asynchorous client using python asyncore, and met some problems. I have solved this with the help of this: Asyncore client in thread makes the whole program crash when sending data immediately But now I meet some other problem. My client program: import asyncore, threading, socket class Client(threading.Thread, asyncore.dispatcher): def __init__(self, host, port): threading.Thread.__init__(self) self.daemon = True self._thread_sockets = dict() asyncore.dispatcher.__init__(self, map=self._thread_sockets) self.host = host self.port = port self.output_buffer = [] self.start() def send

I was working on multitasking a basic 2-D DLA simulation. Diffusion Limited Aggregation (DLA) is when you have particles performing a random walk and aggregate when they touch the current aggregate. In the simulation, I have 10.000 particles walking to a random direction at each step. I use a pool of worker and a queue to feed them. I feed them with a list of particles and the worker perform the method .updatePositionAndggregate() on each particle. If I have one worker, I feed it with a list of 10.000 particles, if I have two workers, i feed them with a list of 5.000 particles each, if I have