Efficient way to create a complex array from two real arrays

Dear NumPy developers,

This may be more like a question. I have two real arrays (a and b), and I would like create a complex array (c) which takes the two real arrays as its real and imaginary parts respectively.

The simplest one would be c = a + b * 1.0j However, since my data size is quite large, such code is not very efficient.

We can also do the following, c = np.empty(data_shape, dtype=np.complex128) c.real = a c.imag = b

I am wondering is there a better way to do that (e.g. using buffer or something)?

Thank you very much! Zhihao

[eric-wieser]

Your approach using empty is as optimal as you can get, short of avoiding the intermediate a and b arrays completely.

[zhcui]

The elements are contiguous and interleaved: c[0].real, c[0].imag, c[1].real, c[1].imag, not c[0].real, c[1].real, … c[n-1].real, c[0].imag, c[1].imag, …

I see, I guess this is the reason why we can view the (n, 2) double array to a complex array.