Python 是否可以从两个np.float 1d数组创建一个np.complex128 1d数组而不复制？ 设置：

我有两个来自共享内存

reals

和

imags

的数组：

#/usr/bin/env python2

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

然后我制作了

numpy

-数组，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

然后我想制作一个

np.complex128

1D数组

data

，同样不复制数据，但我不知道如何操作

问题是： 您能否从一对浮点数组中生成一个

np.complex128

1D数组

data

，而无需复制，是/否

如果是，如何进行？

简短回答：否。但是如果您控制发件人，则有一种解决方案不需要复制。 详细回答：

• 根据我的研究，我认为没有一种方法可以在不复制数据的情况下从两个单独的数组创建

  numpy

  复杂数组
• 我认为您不能这样做，因为所有编译的c代码都假定交错的实imag数据

如果控制发件人，则无需任何复制操作即可获取数据。这就是为什么！ 帽子提示：作为一个伟大的起点

这里介绍了如何进行相同的处理，但要启动多个进程，从每个进程获取数据并验证返回的数据

---

无论数据类型如何，数组的数据缓冲区都是一个连续块。

#!/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'

#!/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'