[Numpy-discussion] ANN: Numpy 1.8.0 beta 1 release

[David Cournapeau]

On Wed, Sep 4, 2013 at 1:51 AM, Chris Barker - NOAA Federal <
chris.barker at noaa.gov> wrote:

> FWIW,
>
> You all may know this already, but a "long" is 64 bit on most 64 bit
> platforms, but 32 bit on Windows.
>

The correct solution in this context is to use (s)size_t, intptr_t and
ptrdiff_t depending on the use case, not hardcoding the bitwidth.

David

>
> Can we start using stdint.h and int32_t and friends?
>
> -CHB
>
>
> On Sep 3, 2013, at 5:18 PM, Charles R Harris <charlesr.harris at gmail.com>
> wrote:
>
>
>
>
> On Tue, Sep 3, 2013 at 6:09 PM, Christoph Gohlke <cgohlke at uci.edu> wrote:
>
>> On 9/3/2013 4:45 PM, Charles R Harris wrote:
>> >
>> >
>> >
>> > On Tue, Sep 3, 2013 at 5:40 PM, Christoph Gohlke <cgohlke at uci.edu
>> > <mailto:cgohlke at uci.edu>> wrote:
>> >
>> >     On 9/3/2013 2:51 PM, Charles R Harris wrote:
>> >     >
>> >     >
>> >     >
>> >     > On Tue, Sep 3, 2013 at 3:23 PM, Christoph Gohlke <cgohlke at uci.edu<mailto:
>> cgohlke at uci.edu>
>> >     > <mailto:cgohlke at uci.edu <mailto:cgohlke at uci.edu>>> wrote:
>> >     >
>> >     >     On 9/1/2013 9:54 AM, Charles R Harris wrote:
>> >     >
>> >     >         Hi all,
>> >     >
>> >     >         I'm happy to announce the first beta release of Numpy
>> 1.8.0.
>> >     >         Please try
>> >     >         this beta and report any issues on the numpy-dev mailing
>> list.
>> >     >
>> >     >         Source tarballs and release notes can be found at
>> >     >https://sourceforge.net/__projects/numpy/files/NumPy/1.__8.0b1/
>> >     >         <
>> https://sourceforge.net/projects/numpy/files/NumPy/1.8.0b1/>.
>> >     >         The Windows
>> >     >         and OS X installers will follow when the infrastructure
>> issues
>> >     >         are dealt
>> >     >         with.
>> >     >
>> >     >         Chuck
>> >     >
>> >     >
>> >     >     Hello,
>> >     >
>> >     >     I tried numpy-1.8.0.dev-86a6e6c with msvc9 and MKL 11.1 on
>> >     >     win-amd64-py2.7. It builds OK but there are 23 test errors
>> and 6
>> >     >     failures (attached).
>> >     >
>> >     >     Some 3rd party packages (e.g. scipy, numexpr, pytables,
>> bottleneck,
>> >     >     pandas and matplotlib) that were built against numpy-MKL 1.7
>> fail
>> >     >     tests when used with numpy-MKL 1.8. Other packages test OK
>> (e.g.
>> >     >     skimage, sklearn, statsmodels, mahotas, pygame). See
>> >     >     <
>> http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/
>> >     <
>> http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7-__numpy-1.8.0.dev-86a6e6c/
>> >
>> >     >     <
>> http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7-numpy-1.8.0.dev-86a6e6c/
>> >>
>> >     >     compared to
>> >     >     <
>> http://www.lfd.uci.edu/~__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/
>> >     <
>> http://www.lfd.uci.edu/%7E__gohlke/pythonlibs/tests/__20130902-win-amd64-py2.7/
>> >
>> >     >     <
>> http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20130902-win-amd64-py2.7/
>> >>.
>> >     >
>> >     >
>> >     >     I have not looked in more detail or at other Python versions
>> yet.
>> >     >
>> >     >
>> >     > Thanks Christoph,
>> >     >
>> >     > Looks like some work to do. I wonder what is different between
>> windows
>> >     > and linux here?
>> >     >
>> >     > Chuck
>> >     >
>> >
>> >     Looks like the fundamental PyArray_PyIntAsIntp function is broken
>> on 64
>> >     bit Windows. 64 bit PyLong values are intermediately stored in a 32
>> bit
>> >     C long variable. But maybe I am missing something...
>> >     <
>> https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L729
>> >
>> >     <
>> https://github.com/numpy/numpy/blob/maintenance/1.8.x/numpy/core/src/multiarray/conversion_utils.c#L767
>> >
>> >
>> > My, that does look suspicious. That function is new in 1.8 I believe.
>> > Looks like it needs fixing whatever else it fixes.
>> >
>> > Chuck
>> >
>>
>> In fact, using a npy_longlong instead of npy_long fixes all numpy test
>> errors and failures. But it probably foils the recent optimizations.
>>
>
> Great! I think the function is not used for numeric things so I'm not sure
> what optimizations could be affected. I'll put up a PR and backport it.
>
> Chuck
>
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