Example #1
0
def run_parrallel_iterations(ps, landscape, config):
    iteration = 0

    if config.num_iterations > 1:
        means = [np.zeros(landscape.shape) for _ in xrange(ps.max_time_steps)]

    #run_iteration(ps, landscape)

    #Perform the iterations using a process pool for concurrency
    pool = mp.Pool(num_processors)
    print "running...", num_iterations, "iterations for", ps.max_time_steps, "timesteps on", num_processors, "processors"
    results = [
        pool.apply_async(run_iteration, args=[ps, landscape])
        for _ in xrange(num_iterations)
    ]
    pool.close()

    #Process iterations as they complete
    while len(results) > 0:
        completed = [i for i, r in enumerate(results) if r.ready()]
        for i in reversed(
                completed
        ):  #reversed so that indices aren't invalidated as we pop
            print "processing iteration " + str(iteration + 1)
            #Get the result from the list and save the iteration to file
            Ns = results.pop(i).get(None)
            config.ext = "/iteration " + str(iteration + 1)
            process_iteration(Ns, ps, landscape, config)
            iteration += 1

            if config.num_iterations > 1:
                #Add the population for each time step in the iteration to the total
                for t, N in enumerate(Ns):
                    means[t] += N

        time.sleep(15)

    pool.close()
    pool.join()
    #run_iteration(ps, landscape)

    if config.num_iterations > 1:
        for N in means:
            N /= config.num_iterations
        config.ext = "/means"
        io.process_iteration(means, ps, landscape, config)
Example #2
0
def run_parrallel_iterations(ps, landscape, config):
	iteration = 0

	if config.num_iterations > 1:
		means = [np.zeros(landscape.shape) for _ in xrange(ps.max_time_steps)]

	#run_iteration(ps, landscape)

	#Perform the iterations using a process pool for concurrency
	pool = mp.Pool(num_processors)
	print "running...", num_iterations, "iterations for", ps.max_time_steps,"timesteps on",num_processors,"processors"
	results = [pool.apply_async(run_iteration, args=[ps, landscape]) for _ in xrange(num_iterations)]
	pool.close()
	
	#Process iterations as they complete
	while len(results) > 0:
		completed = [i for i, r in enumerate(results) if r.ready()]
		for i in reversed(completed): #reversed so that indices aren't invalidated as we pop
			print "processing iteration " + str(iteration + 1)
			#Get the result from the list and save the iteration to file
			Ns = results.pop(i).get(None)
			config.ext = "/iteration " + str(iteration + 1)
			process_iteration(Ns, ps, landscape, config)
			iteration += 1
			
			if config.num_iterations > 1:
				#Add the population for each time step in the iteration to the total
				for t, N in enumerate(Ns):
					means[t] += N
		
		time.sleep(15)
	
	pool.close()
	pool.join()
	#run_iteration(ps, landscape)

	if config.num_iterations > 1:
		for N in means:
			N /= config.num_iterations
		config.ext = "/means"
		io.process_iteration(means, ps, landscape, config)
Example #3
0
def process_iteration(Ns, ps, landscape, config):
    io.process_iteration(Ns, ps, landscape, config)
Example #4
0
def process_iteration(Ns, ps, landscape, config):
	io.process_iteration(Ns, ps, landscape, config)