[Numpy-discussion] Portable macro to get NAN, INF, positive and negative zero
Thu Mar 12 15:21:38 CDT 2009
David Cournapeau wrote:
> On Thu, Mar 12, 2009 at 10:19 PM, Bruce Southey <firstname.lastname@example.org> wrote:
>> David Cournapeau wrote:
>>> For the record, I have just added the following functionalities to
>>> numpy, which may simplify some C code:
>>> - NPY_NAN/NPY_INFINITY/NPY_PZERO/NPY_NZERO: macros to get nan, inf,
>>> positive and negative zeros. Rationale: some code use NAN, _get_nan,
>>> etc... NAN is a GNU C extension, INFINITY is not available on many C
>>> compilers. The NPY_ macros are defined from the IEEE754 format, and as
>>> such should be very fast (the values should be inlined).
>>> - we can now use inline safely in numpy C code: it is defined to
>>> something recognized by the compiler or nothing if inline is not
>>> supported. It is NOT defined publicly to avoid namespace pollution.
>>> - NPY_INLINE is a macro which can be used publicly, and has the same
>>> usage as inline.
>>> Numpy-discussion mailing list
>> I am curious how this relates to Zach's comment in the thread on
>> 'Infinity Definitions':
> It does not directly - but I implemented those macro after Pauli,
> Charles (Harris) and me worked on improving formatting; those macro
> replace several ad-hoc solutions through the numpy code base.
> Concerning formatting, there is much more consistency with python 2.6
> (because python itself bypasses the C runtime and does the parsing
> itself), and we followed them. With numpy 1.3, you should almost never
> see anything else than nan/inf on any platform. There are still some
> cases where it fails, and some cases we can't do anything about (print
> '%s' % a, print a, print '%f' % a all go through different codepath,
> and we can't control at least one of them, I don't remember which
>> If this is no longer a concern then we should be able to remove those
>> duplicate definitions and use of uppercase.
> Yes, we should also fix the pretty print options, so that arrays and
> not just scalar arrays print nicely:
> a = np.array([np.nan, 1, 2])
> print a -> NaN, ...
> print a -> nan
> But this is much easier, as the code is in python.
> Numpy-discussion mailing list
I have created ticket 1051 for this change with hopefully patches that
address this. The patches remove these duplicate definitions and
uppercase names but these other usages should be depreciated (but I do
not know how). After the changes, all tests pass only Linux system for
Python 2.4, 2.5 and 2.6.
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