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

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'