具有第二个功能延迟的多处理

Question

我有一个将数据写入文本文件的函数，第二个函数从同一个文本文件中提取数据并显示图表。我想在第一个函数启动后几秒钟启动第二个函数，并一起运行直到两者都完成。通过这种方式，我可以获得实时图表。我在下面编写的代码同时启动了这两个函数，但第二个函数看不到任何文本文件。我需要为第二个函数延迟一些时间，以便给第一个函数创建文本文件的时间。

但是，因为第二个函数（live_graph）不仅需要从文本文件中提取数据，还需要从第一个函数中获取一些参数（例如图表的标题），我不确定这是否是正确的继续方式，似乎不可能从另一个函数中获取“密钥”；我得到“未定义密钥”。也许我还必须将参数写入文本文件？

from multiprocessing import Process
import time

def writing():    
    numentries = 0
    for text in get_all(newlista, "sentence", "text"):

        if text.lower().startswith( key.lower().split(None, 1)[0] ):
            pass
        elif len(text) > 500:
            pass
        elif len(text) < 80:
            pass
        else:
            on_data(text)

            numentries += 1




  def live_graph():
  #pull data from text.txt
   time.sleep(5)


if __name__=='__main__':

 p1 = Process(target = writing)
 p1.start()
 p2 = Process(target = live_graph)
 p2.start()  

Answer 1

您想使用multiprocessing.Queue

例如（来自文档）：

from multiprocessing import Process, Queue

def f(q):
    q.put([42, None, 'hello'])

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=(q,))
    p.start()
    print q.get()    # prints "[42, None, 'hello']"
    p.join()

您可以像这样在代码中使用它：

from multiprocessing import Process, Queue
import time


def writing(q):
    keep_running = True
    numentries = 0
    key = 'something, I assume'
    for text in get_all(newlista, "sentence", "text"):
        # Python lets you do nice comparisons like this,
        # which do what they look like. And make it more
        # obvious that you only want lines of length between
        # 80 and 500 characters
        if 80 < len(text) < 500:
            firstword = key.lower().split(None, 1)[0]
            if text.lower().startswith(firstword):               
                # Note: it is *essential* that in `on_data(text)`
                # after writing to your file you run `file.flush()`
                # or close the file, otherwise the data *may* be
                # buffered and hence, missing, when you go to read
                # the file
                on_data(text)
                q.put(keep_running)

    keep_running = False
    q.put(keep_running)

def live_graph(q):
    keep_running = True
    while keep_running:
        keep_running = q.get()
        # do the graph updates here


if __name__=='__main__':
 q = Queue()
 p1 = Process(target = writing, args=(q,))
 p1.start()
 p2 = Process(target = live_graph, args=(q,))
 p2.start()