[Numpy-discussion] request for SWIG numpy.i users
Thu Jun 20 07:26:03 CDT 2013
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.
On Tue, Jun 18, 2013 at 6:36 AM, Egor Zindy <email@example.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).
> I also made a blog post to explain the new typemaps I would like included:
> Any comments appreciated.
> Kind regards,
> On 9 June 2013 09:20, Egor Zindy <firstname.lastname@example.org> 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
> > 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
> >> It worked fine, although I use only a fraction of the capabilities that
> >> includes.
> > Same here, but overall, it should be quit easy to choose the data type
> > 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
> > when I have a sec)
> > ... and choose the number of dimensions you need (1/2/3/4). I can't
> > 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,
> > 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 <email@example.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
> >> In file included from
> >> from
> >> from
> >> from simple_wrap.cpp:3062:
> >> 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
> >> 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 <firstname.lastname@example.org>
> >> wrote:
> >>> Hi,
> >>> If you're using or are very familiar with SWIG and the numpy.i
> >>> to it, please help to test and/or review
> >>> https://github.com/numpy/numpy/pull/3148. It's a fairly major update
> >>> 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
> >>> the regular reviewers of numpy PRs are familiar with numpy.i,
> therefore help
> >>> would be much appreciated.
> >>> Thanks,
> >>> Ralf
> >>> _______________________________________________
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