can you create a np.complex128 1d array from two np.float 1d arrays without copy?

Question

The Setup :

I have two arrays from shared memory reals and imags :

#/usr/bin/env python2

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

then I make them numpy-arrays, named reals2 and imags2, without any copy :

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

I would like to then make a np.complex128 1D-array data, again without copying the data, but I don't know how to.

The Questions :

Can you make a np.complex128 1D-array data from a pair of float arrays, without copying, yes/no?

If yes, how?

The databuffer for an array, regardless of dtype, is one contiguous block. — hpaulj, Mar 13 '20 at 00:07

Trevor Boyd Smith · Accepted Answer · 2020-03-13T00:40:07.663

Short answer: no. But if you control the sender then there is a solution that does not require copying.

Longer answer:

from my research I do not think there is a way to create a numpy complex array from two separate arrays without copying the data
IMO i think that you can not do this because all the numpy compiled c code assumes interleaved real, imag data

if you control the sender, you can get your data without any copy operations. here's how!

#!/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'

hat tip to: https://stackoverflow.com/a/32877245/52074 as a great starting point.

here's how to do the same processing but with multiple processes being started and getting the data back from each process and verifying the returned data

#!/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'