你可以从两个 np.float 一维数组创建一个 np.complex128 一维数组而不复制吗？

Posted 2023-02-16

【中文标题】你可以从两个 np.float 一维数组创建一个 np.complex128 一维数组而不复制吗？

【英文标题】：can you create a np.complex128 1d array from two np.float 1d arrays without copy?

【发布时间】：2020-03-12 22:35:22

【问题描述】：

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)

然后我将它们设为numpy-arrays，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

然后我想创建一个np.complex128 1D-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创建一个np.complex128一维数组data，而不需要复制，是/否？

如果是，怎么做？

【问题讨论】：

数组的数据缓冲区，无论 dtype 是什么，都是一个连续的块。

【参考方案1】：

简短的回答：没有。但是如果你控制了发件人，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，我认为没有办法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 IMO 我认为你不能这样做，因为所有 numpy 编译的 c 代码都假定交错的真实图像数据

如果您控制发送者，您无需任何复制操作即可获取您的数据。方法如下！

#!/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://***.com/a/32877245/52074 作为一个很好的起点。

这里是如何执行相同的处理，但启动多个进程并从每个进程取回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'