I am trying to implement a MATLAB code which is iterating and accessing elements using vectorization of array. Code snippet is below:
z=z(1:2:end)+j*z(2:2:end);
where "z" is an array containing I/Q stream value i.e alternating i & q like "iqiqiqiq...". I am trying to implement it using numpy array but with no success.
Note: Also looking for suggestions regarding any other way to implement the above logic which is even faster than copying MATLAB approach using numpy array and python-3.x.
If z is a numpy array of 64 bit floating points values, and the data in z is contiguous (e.g. you didn't form z by slicing it from a bigger array), you can create a complex view of z with no copying:
In [56]: z
Out[56]: array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
In [57]: z.view(np.complex128)
Out[57]: array([ 0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j, 8.+9.j])
For completeness, here is the more direct (and less efficient) translation of your Matlab code. It produces a copy:
In [63]: z[::2] + z[1::2]*1j
Out[63]: array([ 0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j, 8.+9.j])
A more verbose but more efficient method to create a copy is:
In [73]: w = np.empty(len(z)//2, dtype=np.complex128)
In [74]: w.real = z[::2]
In [75]: w.imag = z[1::2]
In [76]: w
Out[76]: array([ 0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j, 8.+9.j])
(I used len(z)//2 for the size of w. This assumes z is a one-dimensional array.)
