[Numpy-discussion] printoption to allow hexified floats?
Thu Dec 2 11:41:25 CST 2010
On Thu, Dec 2, 2010 at 11:17 AM, Ken Basye <firstname.lastname@example.org> wrote:
> Thanks for the replies.
> Robert is right; many numerical operations, particularly complex ones,
> generate different values across platforms, and we deal with these by
> storing the values from some platform as a reference and using
> allclose(), which requires extra work. But many basic operations
> generate the same underlying values on IEEE 754-compliant platforms but
> don't always format floats consistently (see
> http://bugs.python.org/issue1580 for a lengthy discussion on this). My
> impression is that Python 2.7 does a better job here, but at this point
> a lot of differences also crop up between 2.6 (or less) and 2.7 due to
> the changed formatting built into 2.7, and these are the result of
> formatting differences; the numbers themselves are identical (in our
> experience so far, at any rate). This is a current pain-point which an
> exact representation would alleviate.
> In response to David, we haven't implemented a separate print; we rely
> on the Numpy repr/str for ndarrays and the printoptions that allow some
> control over float formatting. I'm basically proposing to add a bit
> more control there. And thanks for the info on supported versions of
Another approach to consider is to save the numerical data in a
platform-independent standard file format (maybe like netcdf?). While this
isn't a fool-proof approach because the calculations themselves may
introduce differences that are platform dependent, this at least puts strong
controls on one aspect of the overall problem.
One caveat that does come across my mind is if the save/load process for the
file might have some platform-dependent differences based on the
compression/decompression schemes. For example, the GRIB file format does a
compression where the mean value and the differences from those means are
stored. Calculations like these might introduce some slight differences on
Just food for thought,
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