>>> np.arange(0, 10, 2)
array([0, 2, 4, 6, 8])

# as above, cast to complex
>>> np.arange(0 + 0j, 10 + 0j, 2 + 0j)
array([], dtype=complex128)  #what?

# bizarre input needed to give expected output
>>> np.arange(0 + 0j, 10 + 10j, 2 + 0j)
# array([ 0.+0.j,  2.+0.j,  4.+0.j,  6.+0.j,  8.+0.j])

For some reason, it deliberately computes len as (c14792d)

c_len = (start - stop) / step
len = min(ceil(c_len.real), ceil(c_len.imag))

which for real-only values, gives 0.

I think a better approach would be to use one of

1. ceil(abs(c_len)) - has semantics "use the circle centered at start and passing through end" as the end point

2. ceil(c_len.real) - has semantics "use the projection of end onto the line start + k*step as the endpoint"

3. above with assert c_len.imag == 0 - requires that end lies on that line, but prone to rounding error

4. Project step onto the line between start and end, and use that instead

I like the look of 2 and 4