np.clip with complex input is untested and has odd behavior

This came up in https://github.com/pytorch/pytorch/issues/33568. This seems odd:

>>> np.clip([3 + 4.j], -1, 2)                                                                                       
array([2.+0.j])
>>> np.clip([3 + 4.j], -1+1.j, 2+12.j)  # imaginary component goes up                                                                   
array([2.+12.j])
>>> np.clip([1 + 4.j], -1+1.j, 2+12.j)  # imaginary component doesn't go up                                                                             
array([1.+4.j])

The only test for complex input is that it doesn’t segfault.

Reasonable behavior could be one of:

• Clip real and imaginary parts separately
• Clip absolute value, not changing the phase There may be other options. As long as it’s documented, I’m not sure it matters much which choice is made.

I don’t think this is a very important issue, but it would be nice to at least have the desired behavior documented here.

[seberg]

@mruberry, I agreed, in any case I am not even sure anyone had even a use-case for this, which would go a long way to motivate me to put in anything. As one argument to make it a bit less bad, if you ensure that min.real <= max.real and min.imag <= max.imag as opposed to only min <= max, than things are fine (although we currently do not actually ensure this in NumPy).

[anirudh2290]

just reading through this issue, i think that given the lexicographic comparison in numpy, this behavior actually makes sense. if we are deprecating lexicographic comparison though, clip, max and min should also follow. I am assuming that this was the consensus reached here, but I wasn’t very sure from the comments and the linked PRs.