Mailman 3 September 2006 - NumPy-Discussion

return type diffences of indexed arrays with Intel C++ compiler (Python 2.5)
by Lars Bittrich 29 Sep '06

29 Sep '06

Hi all, recently I tried to compile numpy/scipy with Python 2.5 and Intel C++ compiler 9.1. At first everything was fine, but the scipy test produced a few errors. The reason was a little difference: latest svn(1.0.dev3233) with Intel C compiler (Python 2.5): ----------------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:x[i] Out[4]:1.0 latest svn(1.0.dev3233) with GCC 3.3 (Python 2.3): -------------------------------------------------- In [1]:from numpy import ones, zeros, integer In [2]: In [2]:x = ones(1) In [3]:i = zeros(1, integer) In [4]:print x[i] Out[4]:array([1]) The Intel version gives me a scalar whereas the gcc version an array. Maybe Python 2.5 is causing this problem but my first guess was the compiler. A typical error message from scipy.test(10): ====================================================================== ERROR: check_chebyc (scipy.special.tests.test_basic.test_chebyc) ---------------------------------------------------------------------- Traceback (most recent call last): File "[...]/lib/python2.5/site-packages/scipy/special/tests/test_basic.py", line 667, in check_chebyc C1 = chebyc(1) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 461, in chebyc p = orthopoly1d(x,w,hn,kn,wfunc=lambda x: 1.0/sqrt(1-x*x/4.0),limits=(-2,2),monic=monic) File "[...]/lib/python2.5/site-packages/scipy/special/orthogonal.py", line 78, in __init__ equiv_weights = [weights[k] / wfunc(roots[k]) for k in range(len(roots))] TypeError: object of type 'numpy.complex128' has no len() Does anyone know how to resolve the problem? Best regards, Lars

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storage order and concatenate
by David Cournapeau 29 Sep '06

29 Sep '06

Hi, I noticed a behaviour which I found counter-intuitive at least when using concatenate. I have a function which takes a numpy array of rank 2 as input, let's say foo(in): a = N.randn((10, 2)) foo(a) To test a ctype implementation of foo against the python version, my test has something like X1 = N.linspace(-2, 2, 10)[:, N.newaxis] X2 = N.linspace(-1, 3, 10)[:, N.newaxis] a = N.concatenate(([X1, X2]), 1) which has Fortran storage (column major order), where as creating a as a = N.zeros((10, 2)) a[:,0] = N.linspace(-2, 2, 10) a[:,1] = N.linspace(-1, 3, 10) has C storage (row major order). What are the rules concerning storage with numpy ? I thought it was always C, except if stated explicitly. I can obviously understand why concatenate gives a Fortran order from an implementation point of view, but this looks kind of strange to me, David

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32/64-bit machines, integer arrays and python ints
by Bill Spotz 29 Sep '06

29 Sep '06

I am wrapping code using swig and extending it to use numpy. One class method I wrap (let's call it myElements()) returns an array of ints, and I convert it to a numpy array with PyArray_SimpleNew(1,n,'i'); I obtain the data pointer, fill in the values and return it as the method return argument. In python, it is common to want to loop over this array and treat its elements as integers: for row in map.myElements(): matrix.setElements(row, [row-1,row,row+1], [-1.0,2.0,-1.0]) On a 32-bit machine, this has worked fine, but on a 64-bit machine, I get a type error: TypeError: in method 'setElements', argument 2 of type 'int' because row is a <type 'int32scalar'>. It would be nice if I could get the integer conversion to work automatically under the covers, but I'm not exactly sure how to make that work. ** Bill Spotz ** ** Sandia National Laboratories Voice: (505)845-0170 ** ** P.O. Box 5800 Fax: (505)284-5451 ** ** Albuquerque, NM 87185-0370 Email: wfspotz(a)sandia.gov **

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return value of negative power
by Stefan van der Walt 28 Sep '06

28 Sep '06

Hi all, Currently, the power function returns '0' for negative powers of integers: In [1]: N.power(3,-2) Out[1]: 0 (or, more confusingly) In [1]: N.power(a,b) Out[1]: 0 which is almost certainly not the answer you want. Two possible solutions may be to upcast the input to float before calculation, or to return nan. This would be consistent with a function like sqrt: In [10]: N.sqrt(3) Out[10]: 1.7320508075688772 In [11]: N.sqrt(-3) Out[11]: nan Does anyone have an opinion on whether the change is necessary, and if so, on which one would work best? Regards Stéfan

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how to compile numpy with visual-studio-2003?
by mg 28 Sep '06

28 Sep '06

Hello, I just download the newly Python-2.5 and Numpy-1.0rc1 and all work fine on Linux-x86 32 and 64bit platforms. Now, I try to compile the both distributions on WindowsXP with VisualStudio2003. No problem to compile Python-2.5, but i have some troubles with Numpy-1.0rc1 and I didn't find any help in the provided setup.py. So, does someone can tell me how to do it? Thanks, Mathieu.

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lexsort segfault sorting strings
by eric 28 Sep '06

28 Sep '06

I've been using the new record arrays and lexsort from numpy quite a lot lately. Very cool stuff. Using the nightly egg for numpy from here (I believe it is up to date...): http://code.enthought.com/enstaller/eggs/numpy-nightly-py2.4-win32.egg I get segfaults when using lexsort on character arrays. A lot of my columns in record arrays are string based, so sorting the arrays based on these columns would be really handy. Here is an example that crashes for me. <http://code.enthought.com/enstaller/eggs/numpy-nightly-py2.4-win32.egg>C:\wrk\mt\trunk\src\lib\mt\statement\tests>python Python 2.4.3 - Enthought Edition 1.0.0 (#69, Aug 2 2006, 12:09:59) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> from numpy import lexsort >>> lst = [1,2,3] >>> lexsort((lst,)) array([0, 1, 2]) >>> lst = ['abc','cde','fgh'] >>> lexsort((lst,)) <seg-fault> Do others see this? I've opened a ticket at: http://projects.scipy.org/scipy/numpy/ticket/298 thanks, eric

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compiling ATLAS for numpy
by Emanuele Olivetti 28 Sep '06

28 Sep '06

Hi, I'm installing numpy on a 2 cpus intel pentium 4 Linux box. I'm installing BLAS and LAPACK from sources too and I need to tune compiler flags. Here is the question: which are the proper flags for compiling LAPACK? Inside lapack.tgz make.inc.LINUX says: OPTS = -funroll-all-loops -fno-f2c -O3 instead on scipy.org[0] it's suggested: OPTS = -O2 I assume that using -O3 and unrolling loops should have definitely better performance that just -O2 but I'm wondering if the speedy options can be a problem for current numpy release (numpy-1.0rc1). Is it safe to use '-funroll-all-loops -fno-f2c -O3'? Thanks in advance for answers, Emanuele [0]: http://scipy.org/Installing_SciPy P.S.: my GCC is v3.4.5

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streetcar
by Neddie Cunningham 28 Sep '06

28 Sep '06

Okay, so not quite as much as those people doing shows every night of the week, but it's still been really busy here. And where is their remembrance? The city becomes awash with media-obsessed types, and it's difficult not to get caught up in the hysteria. Certainly, yesterday I felt really quite fragile, and didn't get much done, except for a quick visit to the supermarket for supplies during the afternoon. I was blown away by some truely amazing singing, and some rather fine acting too. Some of their number are modestly decent musicians, and can actually hold a tune. To help track that proces. She's also quite a creative person, who likes using self-made drawing to illustrate her musings. : Drake Gold Resources Inc. I've been watching a bit of TV, reading a bit, and surfing the net for the rest of time. Even with the usual summer lull, there's still been a steady flow of decent jobs over the last few weeks, which indicates a fairly bouyant employment market. But the others obviously carried on without me, and were quite drunk by the time I met up with them again a few hours later. Luckily, I had arrived a good ten minutes early, and had a great spot right at the front. It felt a bit strange to have the bar open for normal business while filming was in progress, but also interesting for us to be in the midst of all this action. But the web-based review sites don't have that limitation, and so there seems little excuse for not publishing the promised reviews. Since then, I've perked up a bit; that's despite having received another job rejection letter. But even without an amorous advance in your direction, you'll leave with a big grin on your face. But even without an amorous advance in your direction, you'll leave with a big grin on your face. And so, by the time we were thrown out at closing time, I was pissed again. Sure, it'll be great to have a rest. : Drake Gold Resources Inc. She's also quite a creative person, who likes using self-made drawing to illustrate her musings. It's an obsessive practice that I think a lot of amateur Fringe performers get dragged into. In all, I've sung in a total of four different concerts and five different church services in the last month. This departing friend had organised his final farewell drinks in one of Edinburgh's most famous public houses. And that's certainly dented my confidence somewhat.

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Release candidate 2.0 will come out mid-week next week
by Travis Oliphant 28 Sep '06

28 Sep '06

Hi all, I'd like to release numpy 1.0rc2 on about October 5 of next week. Then, the release of numpy 1.0 official should happen on Monday, October 17. Please try and get all fixes and improvements in before then. Backward-incompatible changes are not acceptable at this point (unless they are minor or actually bug-fixes). I think NumPy has been cooking long enough. Any remaining problems can be fixed with maintenance releases. When 1.0 comes out, we will make a 1.0 release branch where bug-fixes should go as well as on the main trunk (I'd love for a way to do that automatically). There are lots of projects that need to start converting to NumPy 1.0 if we are going to finally have a "merged" community. The actual release of NumPy 1.0 will indicate that we are now committing to stability. Thanks to all that have been contributing so much to the project. -Travis

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Question answered incorrectly at NumPy Tutorial
by Travis Oliphant 28 Sep '06

28 Sep '06

During my NumPy Tutorial at the SciPy conference last month, somebody asked the question about the memory requirements of index arrays that I gave the wrong impression about. Here is the context and the correct response that should alleviate concerns about large cross-product index arrays. I was noting how copy-based (advanced) indexing using index arrays works in multiple-dimensions by creating an array of the same-shape of the input index arrays constructed by selecting the elements indicated by respective elements of the index arrays. If a is 2-d, then a[[10,12,14],[13, 15, 17]] returns a 1-d array with elements [a[10,13], a[12,15], a[14,17]]. This is *not* the cross-product that some would expect. The cross-product can be generated using the ix_ function a[ix_([10,12,14], [13,15,17])] is equivalent to a[[[10,10,10],[12,12,12],[14,14,14]], [[13,15,17],[13,15,17],[13,15,17]]] which will return [[a[10,13] a[10,15], a[10,17]], [a[12,13] a[12,15], a[12,17]], [a[14,13] a[14,15], a[14,17]]] The concern mentioned at the conference was that the cross-product would generate large intermediate index arrays for large input arrays to ix_. At the time, I think I validated the concern. However, the concern is unfounded. This is because the cross product function does not actually create a large intermediate array, but uses the broad-casting implementation of indexing to generate the 2-d indexing array "on-the-fly" (much like ogrid and other tools in NumPy). Notice: ix_([10,12,14], [13,15,17]) (array([[10], [12], [14]]), array([[13, 15, 17]])) The first indexing array is 3x1, while the second is 1x3. The result array will be 3x3, but the 2-d indexing array is never actually stored. Just to set my mind at ease about possible mis-information I spread during the tutorial, and give a little tutorial on advanced indexing. Best, -Travis

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