[Numpy-discussion] request for SWIG numpy.i users

Egor Zindy ezindy@gmail....
Thu Jun 20 14:58:17 CDT 2013


Thanks Tom,

> It helps to see your motivation and some
> examples.  I'll keep it in mind if I ever have a list of large arrays to
> process for which creating a numpy array first is not desirable.

by documenting numpy.i typemaps, I'm hoping to get a good feel for
whichever use they are best suited for. Doing it in the open means I
get valuable feedback in return.

Cheers,
Egor

On 20 June 2013 13:26, Tom Krauss <thomas.p.krauss@gmail.com> wrote:
> Hi Egor - I just read through your blog post, thanks for describing those
> new list-of-array type maps.  It helps to see your motivation and some
> examples.  I'll keep it in mind if I ever have a list of large arrays to
> process for which creating a numpy array first is not desirable.
> - Tom
>
>
> On Tue, Jun 18, 2013 at 6:36 AM, Egor Zindy <ezindy@gmail.com> wrote:
>>
>> Dear all,
>>
>> after some code clean-up / testing and a few additions, I've now sent
>> a pull request to numpy:master (#3451).
>> https://github.com/numpy/numpy/pull/3451
>>
>> I also made a blog post to explain the new typemaps I would like included:
>>
>> http://egorzindy.blogspot.co.uk/2013/06/new-numpyi-typemaps-for-working-with.html
>>
>> Any comments appreciated.
>>
>> Kind regards,
>> Egor
>>
>>
>> On 9 June 2013 09:20, Egor Zindy <ezindy@gmail.com> wrote:
>> > Thanks Tom,
>> >
>> > before we ship it, I'd love to have some feedback on the new
>> > ARGOUT_VIEWM
>> > type.
>> >
>> > I used to create my managed arrays using
>> >
>> >     PyObject* cap = PyCObject_FromVoidPtr((void*)(*$1), free);
>> >
>> > but since this function is deprecated, and because of Bill's background
>> > work
>> > to bring numpy.i up to date, I now use capsules for this:
>> >
>> >     PyObject* cap = PyCapsule_New((void*)(*$1), SWIGPY_CAPSULE_NAME,
>> > free_cap);
>> >
>> > ... I'll admit it took longer than expected to get this right.
>> >
>> > Would you mind testing my latest numpy.i changes hosted on github?
>> > https://github.com/zindy/numpy/tree/numpy-swig/doc/swig
>> >
>> > It's great that you are testing on a mac, I don't have one to test on
>> > yet.
>> >
>> >
>> >> It worked fine, although I use only a fraction of the capabilities that
>> >> it
>> >> includes.
>> >
>> > Same here, but overall, it should be quit easy to choose the data type
>> > you
>> > need. Narrow down it down to a type between IN_ARRAY / INPLACE_ /
>> > ARGOUT_ /
>> > ARGOUT_VIEW/VIEWM
>> > http://wiki.scipy.org/Cookbook/SWIG_NumPy_examples
>> > http://wiki.scipy.org/Cookbook/SWIG_Memory_Deallocation (I'll update
>> > these
>> > when I have a sec)
>> >
>> > ... and choose the number of dimensions you need (1/2/3/4). I can't
>> > comment
>> > on the Fortran arrays data types though as I don't use them.
>> >
>> > Also I've introduced a few of my more esoteric data types in this week,
>> > but
>> > I have no idea how popular they will be. If you ever need to speed-up:
>> >
>> > a = numpy.ones((1024,1024),numpy.uint8)
>> > la = [a]*100
>> > b = numpy.mean(numpy.array(la,float),axis=0).astype(numpy.uint8)
>> >
>> > I have just the right type for that :)
>> > DATA_TYPE** IN_ARRAY3, DIM_TYPE DIM1, DIM_TYPE DIM2, DIM_TYPE DIM3
>> >
>> > Kind regards,
>> > Egor
>> >
>> >
>> > On 9 June 2013 03:33, Tom Krauss <thomas.p.krauss@gmail.com> wrote:
>> >>
>> >> Hi folks,
>> >>
>> >> I just downloaded Bill's numpy.i at commit 4dcb0679, and tried it out a
>> >> bit on some of my personal projects.  It worked fine, although I use
>> >> only a
>> >> fraction of the capabilities that it includes.
>> >>
>> >>  And, it made the warning go away!
>> >>
>> >> I used to get this warning
>> >>
>> >> g++ -g -fPIC -c simple_wrap.cpp -I/usr/include/python2.7
>> >>
>> >> -I/Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include
>> >> In file included from
>> >>
>> >> /Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/ndarraytypes.h:1722,
>> >>                  from
>> >>
>> >> /Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/ndarrayobject.h:17,
>> >>                  from
>> >>
>> >> /Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/arrayobject.h:15,
>> >>                  from simple_wrap.cpp:3062:
>> >>
>> >>
>> >> /Users/tkrauss/projects/dev_env/lib/python2.7/site-packages/numpy-1.8.0.dev_f2f0ac0_20120725-py2.7-macosx-10.8-x86_64.egg/numpy/core/include/numpy/npy_deprecated_api.h:11:2:
>> >> warning: #warning "Using deprecated NumPy API, disable it by #defining
>> >> NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION"
>> >>
>> >> but not with this version.
>> >>
>> >> You can see which version of numpy I am using there, and that I am on
>> >> Mac
>> >> OS X 10.8. (10.8.4 specifically)  Python 2.7.2
>> >>
>> >> I'd say SHIP IT!
>> >>
>> >> Nice work, thanks for all your work on numpy and numpy.i.
>> >>
>> >> - Tom Krauss
>> >>
>> >>
>> >>
>> >> On Tue, Jun 4, 2013 at 3:13 PM, Ralf Gommers <ralf.gommers@gmail.com>
>> >> wrote:
>> >>>
>> >>> Hi,
>> >>>
>> >>> If you're using or are very familiar with SWIG and the numpy.i
>> >>> interface
>> >>> to it, please help to test and/or review
>> >>> https://github.com/numpy/numpy/pull/3148. It's a fairly major update
>> >>> to
>> >>> numpy.i by Bill Spotz, containing the following:
>> >>>   - support for 4D arrays and memory managed output arguments
>> >>>   - rework for the deprecated API's in numpy 1.6 and 1.7
>> >>>   - a bug fix in a 3D typemap
>> >>>   - documentation improvements
>> >>>
>> >>> It would be good to have this merged before branching 1.8.x. Not many
>> >>> of
>> >>> the regular reviewers of numpy PRs are familiar with numpy.i,
>> >>> therefore help
>> >>> would be much appreciated.
>> >>>
>> >>> Thanks,
>> >>> Ralf
>> >>>
>> >>>
>> >>> _______________________________________________
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>> >>>
>> >>
>> >>
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