使用python多处理动态创建共享数组列表

我想使用python的多处理模块在不同的子进程之间共享几个numpy数组。我希望阵列可以单独锁定，并且我希望在运行时动态确定阵列的数量。这可能吗

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在本文中，J.F.Sebastian提出了一种在多处理时在共享内存中使用python的numpy数组的好方法。阵列是可锁定的，这就是我想要的。我想做一些非常类似的事情，除了使用数量可变的共享数组。数组的数量将在运行时确定。他的示例代码非常清晰，几乎完全符合我的要求，但我不清楚如何声明一个可变数量的数组，而不给每个数组一个硬编码名称，如

shared\u arr\u 1

，

shared\u arr\u 2

，等等。做这件事的正确方法是什么？

事实证明这比我想象的要容易！在J.F.塞巴斯蒂安的鼓励下，以下是我的答案：

import time
import ctypes
import logging
import Queue
import multiprocessing as mp
import numpy as np

info = mp.get_logger().info

def main():
    logger = mp.log_to_stderr()
    logger.setLevel(logging.INFO)

    data_pipeline = Image_Data_Pipeline(
        num_data_buffers=5,
        buffer_shape=(60, 256, 512))
    start = time.clock()
    data_pipeline.load_buffers(data_pipeline.num_data_buffers)
    end = time.clock()
    data_pipeline.close()
    print "Elapsed time:", end-start


class Image_Data_Pipeline:
    def __init__(self, num_data_buffers, buffer_shape):
        """
        Allocate a bunch of 16-bit buffers for image data
        """
        self.num_data_buffers = num_data_buffers
        self.buffer_shape = buffer_shape
        pix_per_buf = np.prod(buffer_shape)
        self.data_buffers = [mp.Array(ctypes.c_uint16, pix_per_buf)
                             for b in range(num_data_buffers)]
        self.idle_data_buffers = range(num_data_buffers)

        """
        Launch the child processes that make up the pipeline
        """
        self.camera = Data_Pipeline_Process(
            target=child_process, name='Camera',
            data_buffers=self.data_buffers, buffer_shape=buffer_shape)
        self.display_prep = Data_Pipeline_Process(
            target=child_process, name='Display Prep',
            data_buffers=self.data_buffers, buffer_shape=buffer_shape,
            input_queue=self.camera.output_queue)
        self.file_saving = Data_Pipeline_Process(
            target=child_process, name='File Saving',
            data_buffers=self.data_buffers, buffer_shape=buffer_shape,
            input_queue=self.display_prep.output_queue)
        return None

    def load_buffers(self, N, timeout=0):
        """
        Feed the pipe!
        """
        for i in range(N):
            self.camera.input_queue.put(self.idle_data_buffers.pop())

        """
        Wait for the buffers to idle. Here would be a fine place to
        feed them back to the pipeline, too.
        """
        while True:
            try:
                self.idle_data_buffers.append(
                    self.file_saving.output_queue.get_nowait())
                info("Buffer %i idle"%(self.idle_data_buffers[-1]))
            except Queue.Empty:
                time.sleep(0.01)
            if len(self.idle_data_buffers) >= self.num_data_buffers:
                break
        return None

    def close(self):
        self.camera.input_queue.put(None)
        self.display_prep.input_queue.put(None)
        self.file_saving.input_queue.put(None)
        self.camera.child.join()
        self.display_prep.child.join()
        self.file_saving.child.join()


class Data_Pipeline_Process:
    def __init__(
        self,
        target,
        name,
        data_buffers,
        buffer_shape,
        input_queue=None,
        output_queue=None,
        ):
        if input_queue is None:
            self.input_queue = mp.Queue()
        else:
            self.input_queue = input_queue

        if output_queue is None:
            self.output_queue = mp.Queue()
        else:
            self.output_queue = output_queue

        self.command_pipe = mp.Pipe() #For later, we'll send instrument commands

        self.child = mp.Process(
            target=target,
            args=(name, data_buffers, buffer_shape,
                  self.input_queue, self.output_queue, self.command_pipe),
            name=name)
        self.child.start()
        return None

def child_process(
    name,
    data_buffers,
    buffer_shape,
    input_queue,
    output_queue,
    command_pipe):
    if name == 'Display Prep':
        display_buffer = np.empty(buffer_shape, dtype=np.uint16)
    while True:
        try:
            process_me = input_queue.get_nowait()
        except Queue.Empty:
            time.sleep(0.01)
            continue
        if process_me is None:
            break #We're done
        else:
            info("start buffer %i"%(process_me))
            with data_buffers[process_me].get_lock():
                a = np.frombuffer(data_buffers[process_me].get_obj(),
                                  dtype=np.uint16)
                if name == 'Camera':
                    """
                    Fill the buffer with data (eventually, from the
                    camera, dummy data for now)
                    """
                    a.fill(1)
                elif name == 'Display Prep':
                    """
                    Process the 16-bit image into a display-ready
                    8-bit image. Fow now, just copy the data to a
                    similar buffer.
                    """
                    display_buffer[:] = a.reshape(buffer_shape)
                elif name == 'File Saving':
                    """
                    Save the data to disk.
                    """
                    a.tofile('out.raw')
            info("end buffer %i"%(process_me))
            output_queue.put(process_me)
    return None

if __name__ == '__main__':
    main()

背景：这是数据采集管道的骨架。我希望以非常高的速率获取数据，处理数据以在屏幕上显示，并将其保存到磁盘。我从不希望显示速率或磁盘速率限制采集，这就是为什么我认为在单个处理循环中使用单独的子进程是合适的

以下是虚拟程序的典型输出：

C:\code\instrument_control>c:\Python27\python.exe test.py
[INFO/MainProcess] allocating a new mmap of length 15728640
[INFO/MainProcess] allocating a new mmap of length 15728640
[INFO/MainProcess] allocating a new mmap of length 15728640
[INFO/MainProcess] allocating a new mmap of length 15728640
[INFO/MainProcess] allocating a new mmap of length 15728640
[[INFO/Camera] child process calling self.run()
INFO/Display Prep] child process calling self.run()
[INFO/Camera] start buffer 4
[INFO/File Saving] child process calling self.run()
[INFO/Camera] end buffer 4
[INFO/Camera] start buffer 3
[INFO/Camera] end buffer 3
[INFO/Camera] start buffer 2
[INFO/Display Prep] start buffer 4
[INFO/Camera] end buffer 2
[INFO/Camera] start buffer 1
[INFO/Camera] end buffer 1
[INFO/Camera] start buffer 0
[INFO/Camera] end buffer 0
[INFO/Display Prep] end buffer 4
[INFO/Display Prep] start buffer 3
[INFO/File Saving] start buffer 4
[INFO/Display Prep] end buffer 3
[INFO/Display Prep] start buffer 2
[INFO/File Saving] end buffer 4
[INFO/File Saving] start buffer 3
[INFO/MainProcess] Buffer 4 idle
[INFO/Display Prep] end buffer 2
[INFO/Display Prep] start buffer 1
[INFO/File Saving] end buffer 3
[INFO/File Saving] start buffer 2
[INFO/MainProcess] Buffer 3 idle
[INFO/Display Prep] end buffer 1
[INFO/Display Prep] start buffer 0
[INFO/File Saving] end buffer 2
[INFO/File Saving] start buffer 1
[[INFO/MainProcess] Buffer 2 idle
INFO/Display Prep] end buffer 0
[INFO/File Saving] end buffer 1
[INFO/File Saving] start buffer 0
[INFO/MainProcess] Buffer 1 idle
[INFO/File Saving] end buffer 0
[INFO/MainProcess] Buffer 0 idle
[INFO/Camera] process shutting down
[INFO/Camera] process exiting with exitcode 0
[INFO/Display Prep] process shutting down
[INFO/File Saving] process shutting down
[INFO/Display Prep] process exiting with exitcode 0
[INFO/File Saving] process exiting with exitcode 0
Elapsed time: 0.263240348548
[INFO/MainProcess] process shutting down

C:\code\instrument_control>

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它似乎实现了我想要的功能：数据被处理以显示并保存到磁盘，而不会影响采集速率。

您是否尝试过共享阵列的普通Python列表？它是如何失败的？另外，也许您需要一个多维numpy数组；然后在1d numpy数组上调用

.reformate（）

。不，我没有！显然，我不明白共享内存是如何工作的。我想我应该像处理数组一样将列表声明为全局数组？关于多维数组，我怀疑我不能一次锁定一个切片；您可以使用共享数组的元组。我已经提到多维数组是解决标题中描述的问题的有效选项。。