Hi everyone, I have encountered an initially rather confusing problem in a piece of code that attempted to symmetrize a matrix: `h += h.T` The problem of course appears due to `h.T` being a view of `h`, and some elements being overwritten during the __iadd__ call. What made it nasty is that it appeared in code that was already covered by tests, but due to the way __iadd__ works internally, the result only became wrong for matrix sizes > 90. Even worse, it was actually `h += h.T.conj()`, but h was real so conj() was also returning a view. I imagine this was already discussed before (also Charles Harris in https://github.com/numpy/numpy/issues/6119 indicated the issue is known), but I would like to still reopen the question since I was unable to find previous iterations of this discussion. First of all does anyone know of a simple way to test for this issue? Specifically I'm concerned about all in-place array modifications that rely on the data being modified. I considered monkey-patching just to figure out where the problem appears, but numpy.ndarray isn't written in Python so I'm not sure it can be monkey-patched. Secondly, is there no way to automatically check for this behavior in numpy itself? I'm not sure how reliable it is, but would adding np.may_share_memory be a reasonably reliable and fast check? This behavior very much c-like, and in my case it was only discovered by accident in production code that's been out for a couple of years. Best, Anton