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设置：

我有两个来自共享内存的数组reals和imags：

#/usr/bin/env python2

reals = multiprocessing.RawArray('d', 10000000)
imags = multiprocessing.RawArray('d', 10000000)

然后我将它们numpy设为 -arrays，命名为reals2and imags2，没有任何副本：

import numpy as np

reals2 = np.frombuffer(reals)
imags2 = np.frombuffer(imags)

# check if the objects did a copy
assert reals2.flags['OWNDATA'] is False
assert imags2.flags['OWNDATA'] is False

然后我想制作一个np.complex1281D-array data，再次不复制数据，但我不知道该怎么做。

问题：

你能从一对浮点数组中制作一个np.complex128一维数组，而不复制，是/否？data

如果是，如何？

4

1 回答 1

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简短的回答：没有。但是，如果您控制发件人，那么有一个不需要复制的解决方案。

更长的答案：

  • 根据我的研究，我认为没有一种方法可以在numpy不复制数据的情况下从两个单独的数组创建一个复杂的数组
  • IMO我认为你不能这样做，因为所有numpy编译的c代码都假设交错的真实图像数据

如果您控制发件人，则无需任何复制操作即可获取您的数据。就是这样！

#!/usr/bin/env python2
import multiprocessing
import numpy as np

# parent process creates some data that needs to be shared with the child processes
data = np.random.randn(10) + 1.0j * np.random.randn(10)
assert data.dtype == np.complex128
# copy the data from the parent process to shared memory
shared_data = multiprocessing.RawArray('d', 2 * data.size)
shared_data[0::2] = data.real
shared_data[1::2] = data.imag
# simulate the child process getting only the shared_data
data2 = np.frombuffer(shared_data)
assert data2.flags['OWNDATA'] is False
assert data2.dtype == np.float64
assert data2.size == 2 * data.size
# convert reals to complex
data3 = data2.view(np.complex128)
assert data3.flags['OWNDATA'] is False
assert data3.dtype == np.complex128
assert data3.size == data.size
assert np.all(data3 == data)
# done - if no AssertionError then success
print 'success'

帽子提示：https ://stackoverflow.com/a/32877245/52074作为一个很好的起点。

这是如何进行相同的处理，但启动多个进程并从每个进程获取数据并验证返回的数据

#!/usr/bin/env python2
import multiprocessing
import os
# third-party
import numpy as np

# constants
# =========
N_POINTS = 3
N_THREADS = 4

# functions
# =========
def func(index, shared_data, results_dict):
    # simulate the child process getting only the shared_data
    data2 = np.frombuffer(shared_data)
    assert data2.flags['OWNDATA'] is False
    assert data2.dtype == np.float64
    # convert reals to complex
    data3 = data2.view(np.complex128)
    assert data3.flags['OWNDATA'] is False
    assert data3.dtype == np.complex128
    print '[child.pid=%s,type=%s]: %s'%(os.getpid(), type(shared_data), data3)
    # return the results in a SLOW but relatively easy way
    results_dict[os.getpid()] = np.copy(data3) * index

# the script
# ==========
if __name__ == '__main__':
    # parent process creates some data that needs to be shared with the child processes
    data = np.random.randn(N_POINTS) + 1.0j * np.random.randn(N_POINTS)
    assert data.dtype == np.complex128

    # copy the data from the parent process to shared memory
    shared_data = multiprocessing.RawArray('d', 2 * data.size)
    shared_data[0::2] = data.real
    shared_data[1::2] = data.imag
    print '[parent]: ', type(shared_data), data

    # do multiprocessing
    manager = multiprocessing.Manager()
    results_dict = manager.dict()
    processes = []
    for index in xrange(N_THREADS):
        process = multiprocessing.Process(target=func, args=(index, shared_data, results_dict))
        processes.append(process)
    for process in processes:
        process.start()
    for process in processes:
        process.join()

    # get the results back from the processes
    results = [results_dict[process.pid] for process in processes]
    # verify the values from the processes
    for index in xrange(N_THREADS):
        result = results[index]
        assert np.all(result == data * index)
    del processes

    # done
    print 'success'
于 2020-03-12T22:35:22.250 回答