Python Process.Daemon方法代码示例

# 需要导入模块: from multiprocessing import Process [as 别名]
# 或者: from multiprocessing.Process import Daemon [as 别名]
def assign_blocks(centroids, data, processes):
'''
Inputs: list of centroids
        numpy array of blocks
        number of prcesses (integer)
Outputs: Calls graph function which plots every block

This function assigns each block to one of the centroids.
It's almost identical to using pool.map in linear_search_pool.py,
but since pool.map turned out to slow down the algorithm,
after consulting with Professor Wachs, we manually splitted up
the data into chunks and input each split chunk to each processor
which turned out to be approximately 'processes' times faster 
'''
    Districts = dc.create_districts(centroids)
    data = rm_centroids_from_data(centroids, data)

    q = Queue()

    colors_dict = get_colors(Districts)
   
    # used for stopping conditon with EPSILON
    unassigned_blocks = data.shape[0]

    while data.shape[0] != 0:
        data_splitted = split_data(data, processes)
        priority_district = dc.return_low_pop(Districts)

        for subdata in data_splitted:
            p = Process(target=find_nearest_block, args=(subdata, priority_district.centroid, q))
            p.Daemon = True
            p.start()

        for subdata in data_splitted:
            p.join()

        blocks = []
        while(not q.empty()):
            blocks.append(q.get())
        # [1:] part gets rid of the distance 
        nearest_block = list(min(blocks)[1:])

        plt.scatter(nearest_block[2], nearest_block[1], color=colors_dict[priority_district.id])

        priority_district.add_block(nearest_block, Districts) #should i get rid of distance before 
        idx = np.where(data[:,0] == nearest_block[0])
        data = np.delete(data, idx, 0)

        if (unassigned_blocks - EPSILON) == data.shape[0]:
           break

    graph(Districts, data)

# 需要导入模块: from multiprocessing import Process [as 别名]
# 或者: from multiprocessing.Process import Daemon [as 别名]
def searching_all(filename, number):
	Grid, data, dim, lat, lon = build_grid(filename, number)
	
	Districts = dc.create_districts(CENTROID_L)
	unassigned_blocks = data.shape[0]

	q = Queue()
	processes = 5
	colors_dict = get_colors(Districts)
	
	while unassigned_blocks != 0:
		tol = 1
		priority_district = dc.return_low_pop(Districts)

		subset = searching_neighborhood(priority_district, tol, Grid, dim, lat, lon)
		print(subset.shape)

		split_subset = np.array_split(subset, processes)

		for subdata in split_subset:
			p = Process(target=find_nearest_block, args=(subdata, priority_district.centroid, q))
			p.Daemon = True
			p.start()

		for subdata in split_subset:
			p.join()

		while q.empty():
			tol += 1
			print("changed tolerance.")
			subset = searching_neighborhood(priority_district, tol, Grid, dim, lat, lon)

		blocks = []
		while(not q.empty()):
			blocks.append(q.get())
		nearest_block = list(min(blocks)[1:])

		priority_district.add_block(nearest_block[:-2], Districts)

		Grid[int(nearest_block[-2])][int(nearest_block[-1])].remove(nearest_block[:-2])
		plt.scatter(nearest_block[2], nearest_block[1], color=colors_dict[priority_district.id])
		unassigned_blocks -= 1

	graph(Districts, data)

# 需要导入模块: from multiprocessing import Process [as 别名]
# 或者: from multiprocessing.Process import Daemon [as 别名]
from time import sleep
#q是任务队列
#NUM是并发线程总数
#JOBS是有多少任务
q = JoinableQueue()
NUM = 12
JOBS = 100
#具体的处理函数，负责处理单个任务
def do_somthing_using(arguments):
    i=0
    while i < 100000:
        i += 1
    print(arguments)
#这个是工作进程，负责不断从队列取数据并处理
def working():
    while True:
        arguments = q.get()
        do_somthing_using(arguments)
#       sleep(1)
        q.task_done()
#fork NUM个线程等待队列
for i in range(NUM):
    t = Process(target=working)
    t.Daemon=True
    t.start()
#把JOBS排入队列
for i in range(JOBS):
    q.put(i)
#等待所有JOBS完成
q.join()

# 需要导入模块: from multiprocessing import Process [as 别名]
# 或者: from multiprocessing.Process import Daemon [as 别名]
 def go(scores, s, i):
     t = Process(target=worker, args=(scores, s, i))
     t.Daemon = True
     t.start()