[Numpy-discussion] Should abs([nan]) be supported?
Wed Sep 5 00:06:43 CDT 2012
The framework for catching errors relies on hardware flags getting set and our C code making the right calls to detect those flags.
This has usually worked correctly in the past --- but it is an area where changes in compilers or platforms could create problems.
We should test to be sure that the correct warnings are issued, I would think. Perhaps using a catch_warnings context would be helpful (from http://docs.python.org/library/warnings.html)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
# Trigger a warning.
# Verify some things
assert len(w) == 1
assert issubclass(w[-1].category, DeprecationWarning)
assert "deprecated" in str(w[-1].message)
On Sep 4, 2012, at 10:49 PM, Ondřej Čertík wrote:
> On Tue, Sep 4, 2012 at 8:38 PM, Travis Oliphant <firstname.lastname@example.org> 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')
>> or, using an errstate context ---
>> with np.errstate(invalid='ignore'):
> 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.
> NumPy-Discussion mailing list
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