Mailman 3 November 2005 - NumPy-Discussion

Final release of Numeric is actually 24.1
by Travis Oliphant 07 Nov '05

07 Nov '05

I made a final release of Numeric (24.1). This is "really" the last release of Numeric ;-) I did it because of David Cooke's excellent array_protocol enhancement of __array_struct__. I wanted to make sure the final Numeric fully supported the array protocol including the "faster version." I also tracked down a bug today in scipy core that was inherited from Numeric. A part of me wanted to not fix the bug in Numeric so that people who need a stable platform will move to scipy core :-) But the better part of me won, and I fixed the problem and made a new Numeric release. I do hope people are encouraged to move toward scipy core, however. It is stabilizing quite rapidly. All of scipy now builds with the new scipy core, and all of (full) scipy's 1300 tests pass for both 32-bit and 64-bit systems. I will be making a release of scipy_core (probably called version 0.6 this weekend). It is still missing finished records.py, ma.py, and chararray.py modules (being worked on). When these are available I will make release of scipy core version 1.0 Best regards, -Travis

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Numeric 24.1 tar ball and CVS were not consistent
by Travis Oliphant 07 Nov '05

07 Nov '05

I just updated the CVS of Numeric with changes that went into the Numeric 24.1 release but were (unfortunately) not committed to CVS. These changes are likely responsible for the test failures that people have experienced when testing scipy with CVS versions of Numeric. Sorry about --- too many things to think about apparently... -Travis

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bus error in check_kurtosis
by Rob Managan 07 Nov '05

07 Nov '05

With todays svn versions (newcore 1440, newscipy 1423) scipy.test(level=1,verbosity=2) dies with a bus error on check_kurtosis check_basic (scipy.stats.stats.test_stats.test_mean) ... ok check_ravel (scipy.stats.stats.test_stats.test_mean) ... ok check_basic (scipy.stats.stats.test_stats.test_median) ... ok check_basic (scipy.stats.stats.test_stats.test_mode) ... ok check_kurtosis (scipy.stats.stats.test_stats.test_moments)Bus error This is on Mac OSX 10.3.9, python 2.4.1, gcc 3.3. Is this a known problem? -- *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- Rob Managan email managan at llnl.gov LLNL phone: 925-423-0903 P.O. Box 808, L-095 FAX: 925-422-3389 Livermore, CA 94551-0808

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numpy 24.x on 64-bit Athlon
by Darren Dale 06 Nov '05

06 Nov '05

I just built Numeric 24.0 and 24.1 against ATLAS on a 64 bit athlon system. I get the following error for both packages. python test.py ........E.................................... ====================================================================== ERROR: Test the diagonal function. ---------------------------------------------------------------------- Traceback (most recent call last): File "test.py", line 584, in testDiagonal assert_eq(Numeric.diagonal(c,1), [[2,7,4], [2,7,4]]) File "test.py", line 28, in assert_eq assert eq(a,b) File "test.py", line 23, in eq raise ValueError("sequences have different shapes:\na%s=%r\nb%s=%r" % ValueError: sequences have different shapes: a(4, 2)=array([[5, 1], [6, 2], [7, 3], [8, 4]]) b(2, 3)=[[2, 7, 4], [2, 7, 4]] I also get the following error when I run newscipy's test(5,10): bench_random (scipy.fftpack.basic.test_basic.test_fft) Fast Fourier Transform ================================================= | real input | complex input ------------------------------------------------- size | scipy | Numeric | scipy | Numeric ------------------------------------------------- 100 | 0.10Segmentation fault Has anyone else had a problem building Numeric-24.x on a 64-bit AMD system? Thanks, Darren

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question about dotblas and ACML
by Darren Dale 05 Nov '05

05 Nov '05

I just got a new Athlon 64 bit system. I'm in the process of building Numeric-24.1 against the AMD Core Math Library. Does anyone know if it is possible to enable the use_dotblas option in customize.py with ACML? Thanks, Darren

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Yet another problem in CVS numarray
by Francesc Altet 04 Nov '05

04 Nov '05

Hi, I've detected another problem in CVS numarray when converting non-contiguous Numeric objects. With numarray 1.3.3 the next works: >>> Numeric.__version__ '23.8' >>> numarray.__version__ '1.3.3' >>> num=Numeric.array([1,2,3,4],'b') >>> num2=num[::2] >>> num2.iscontiguous() 0 >>> na=numarray.array(num2) >>> na array([1, 3]) but, with CVS version of numarray: >>> numarray.__version__ '1.4.2' >>> Numeric.__version__ '24.1' >>> na=numarray.array([1,2,3,4],'b') >>> num=Numeric.array([1,2,3,4],'b') >>> num2=num[::2] >>> num2.iscontiguous() 0 >>> na=numarray.array(num2) Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line 376, in array a = a.astype(type) File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line 863, in astype return self.copy() File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line 923, in copy c = _gen.NDArray.copy(self) File "/usr/lib/python2.4/site-packages/numarray/generic.py", line 724, in copy arr._itemsize) numarray.libnumarray.error: copy1bytes: access beyond buffer. offset=2 buffersize=2 Cheers, -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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How to install SciPy Core
by Shu Li 04 Nov '05

04 Nov '05

Hi, I am a little confused about how to install SciPy Core. I am new to SciPy so I think starting with the new core is a good idea. When I installed the core package first and then scipy, in the process of installing scipy, it showed it also installed a lot of stuff to the core directory which made me worry that part of the new core is overwritten by old core. And when I installed scipy first, then some words on the web saying some "__init__.py" file will "break the scipy" certainly worried me because I don't know what the break here means and why people are not fixing it if it is avoidable. Also even when new core(0.4.2 beta) is installed the scipy still complained not seeing "Numeric", which added to the confusion. Could anybody offer some explanation? Thanks a lot! -- best, Sam (AKA Shu Li)

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Options for wrapping C and C++ code for use with Numeric
by Chris Barker 04 Nov '05

04 Nov '05

Hi all, I suddenly seem to have the need for working with a bunch of different existing C and C++ code, so I'm looking for a way to make it easier. I love NumPy, so I really want to use NumPy arrays My needs fall into three categories: 1) Writing custom extension code from scratch: In the past, I've done this by just using the NumPy API, but it seems that I shouldn't have to do all that book keeping myself. 2) Wrapping existing libraries: At the moment, I'd like to wrap Richard Shewchuk's triangle: http://www.cs.cmu.edu/~quake/triangle.html Which is straight C, as well as the Proj4 map projections library, also C, and there may be others. 3) Wrapping in house code, C & C++, that is under development, but I want to be able to use it from Python and C++, and also perhaps to write tests for it in Python. The options I'm looking at are: Pyrex: This seems like perhaps the easiest way to write extension code, but it doesn't do any automatic wrapping. Boost::Python: This looks like a very nice way to write extension modules, and it even appears to already include support for NumPy arrays (which ones? is that support being maintained, and will it support SciPy.base?) SWIG: The has the major advantage of automatically wrapping code. I see this as a particular strength for wrapping our in house code that is under development. In theory, once I've written a bunch of type maps, i can just re-run it whenever the code base changes. For each of these, I'd love to hear what people's experiences have been, and it would be great if anyone can point me to samples that are small but non-trivial. Other options I should consider would be great too. One more question: Should I use NumPy arrays to pass data back and forth between Python and C, or should I use the new array object/protocol instead? If so, has anyone developed examples, SWIG type maps, etc for this? Thanks for any feedback, -Chris -- Christopher Barker, Ph.D. Oceanographer NOAA/OR&R/HAZMAT (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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Re: Adams to you
by Chrissy Lanctot 03 Nov '05

03 Nov '05

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Options for wrapping C and C++ code for use with Numeric
by RayS 03 Nov '05

03 Nov '05

Have you looked at ctypes code generator? http://starship.python.net/crew/theller/ctypes/codegen.html "The generator converts declarations in C header files into executable Python code: enums, structs, unions, function declarations, com interfaces, and preprocessor definitions." It has progressed well in the last several months, in my experience. I did some tests a while back that found it compared well with weave etc. http://sourceforge.net/mailarchive/forum.php?thread_id=7636431&forum_id=246… Ray Schumacher

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