[Numpy-discussion] Should abs([nan]) be supported?

[Ondřej Čertík]

On Tue, Sep 4, 2012 at 8:38 PM, Travis Oliphant <travis at continuum.io> wrote:
>
> There is an error context that controls how floating point signals are handled.   There is a separate control for underflow, overflow, divide by zero, and invalid.   IIRC, it was decided on this list a while ago to make the default ignore for underflow and warning for  overflow, invalid and divide by zero.
>
> However, an oversight pushed versions of NumPy where all the error handlers where set to "ignore" and this test was probably written then.    I think the test should be changed to check for RuntimeWarning on some of the cases.   This might take a little work as it looks like the code uses generators across multiple tests and would have to be changed to handle expecting warnings.
>
> Alternatively, the error context can be set before the test runs and then restored afterwords:
>
> olderr = np.seterr(invalid='ignore')
> abs(a)
> np.seterr(**olderr)
>
>
> or, using an errstate context ---
>
> with np.errstate(invalid='ignore'):
>       abs(a)

I see --- so abs([nan]) should emit a warning, but in the test we
should suppress it.
I'll work on that.

The only thing that I don't understand is why it only happens on some
platforms and doesn't on some other platforms (apparently). But it's
clear how to fix it now.

Thanks for the information.

Ondrej