您能否从两个没有复制的np.float 1d数组中创建一个np.complex128 1d数组？

Question

安装程序：

我有来自共享内存reals和imags的两个数组：

#/usr/bin/env python2

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

然后我将它们制成numpy数组，分别命名为reals2和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

我想再制作一个np.complex128一维数组data，但又不复制数据，但我不知道该怎么做。

问题：

您能否从一对浮点数组中制作一个np.complex128 1D数组data，而不复制，是/否？

如果是，如何？

Answer 1

简短回答：否。但是，如果您控制发件人，那么有一种解决方案不需要复制。

更长的答案：

• 从我的研究中，我认为没有一种方法可以从两个单独的数组创建numpy复杂数组而不复制数据
• IMO，我认为您无法执行此操作，因为所有numpy编译的c代码都假定交错的真实imag数据

如果您控制发送者，则无需任何复制操作就可以获取数据。这是怎么回事！

#!/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'

提示：https://stackoverflow.com/a/32877245/52074是一个很好的起点。

这里是执行相同处理，但要启动多个进程并从每个进程获取数据并验证返回数据的方法

#!/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'