On Tue, Sep 4, 2012 at 8:38 PM, Travis Oliphant <[email protected]> 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 _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
