Mailman 3 October 2008 - NumPy-Discussion

PyArray_New bug?
by Ravi 04 Oct '08

04 Oct '08

Hi, PyArray_New seems to return a fortran contiguous array regardless of the requested array type. I am using numpy version 1.1.1. PyObject *obj = PyArray_New( &PyArray_Type, 2, dims, /*whatever*/, NULL, NULL, 0, NPY_CARRAY, NULL ); PyObject *obj = PyArray_New( &PyArray_Type, 2, dims, /*whatever*/, NULL, NULL, 0, NPY_FARRAY, NULL ); Both the above return a array who PyArray_ISFORTRAN( obj ) succeeds. I can verify this by checking bits 0 and 1 (LSB is bit 0) of PyArray_FLAGS. Regards, Ravi

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Warnings for Travis
by Charles R Harris 04 Oct '08

04 Oct '08

Hi Travis, The following turned up on the 64-bit windows buildbot. I don't know what triggers them as they don't normally show, but they look legit. numpy\core\src\ufuncobject.c(1700) : warning C4244: '=' : conversion from 'npy_intp' to 'int', possible loss of data numpy\core\src\ufuncobject.c(1701) : warning C4244: '=' : conversion from 'npy_intp' to 'int', possible loss of data numpy\core\src\ufuncobject.c(2422) : warning C4244: '=' : conversion from 'npy_intp' to 'int', possible loss of data They involve some of the parameters in the loop object. If this object is not exposed I would suggest making them npy_intp. That said, I don't think casting to int is dangerous in this case. Chuck

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fast matrix vector operations
by Gideon Simpson 04 Oct '08

04 Oct '08

Suppose I have a toeplitz matrix, A. There is a well known algorithm for computing the matrix vector product Ax, in NlogN operations. An exact reference escapes me, but it may be in Golub & van Loan's book. My question is, how could I best take advantage of this algorithm within numpy/scipy? I could code it python. However, since python is a high level language, it's not clear to me that I'd see an execution time benefit over numpy.dot(A,x). Alternatively, I could write it in a compiled language and build python bindings to it. Thoughts? -gideon

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converting nan to an integer
by joep 04 Oct '08

04 Oct '08

Why does converting nan to an integer not throw an exception (as with inf), instead numpy silently replaces nan by zero? >>> inti = np.array([0,1,2]) >>> inti.dtype dtype('int32') >>> inti[1] = np.inf Traceback (most recent call last): File "<pyshell#34>", line 1, in ? inti[1] = np.inf OverflowError: cannot convert float infinity to long >>> inti[1] = np.nan >>> inti array([0, 0, 2]) This is based on an example in numpy.stats.distribution.py Josef

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Re: [Numpy-discussion] Co-existing Numeric and numpy?
by Ralf W. Grosse-Kunstleve 03 Oct '08

03 Oct '08

Thank you very much for the information! There are weird but important reasons why we are stuck with Numeric 24.2 for the foreseeable future. It is extremely valuable that you provide a smooth upgrade path. Ralf ----- Original Message ---- From: Christopher Barker <Chris.Barker(a)noaa.gov> To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> Sent: Friday, October 3, 2008 12:12:48 PM Subject: Re: [Numpy-discussion] Co-existing Numeric and numpy? Ralf W. Grosse-Kunstleve wrote: > We have some older 3rd party packages that require Numeric (24.2), but like to also use newer > 3rd party packages that require a recent numpy. Can Numeric and numpy co-exist in the same > process? -- I'm mainly worried about clashes at the C API level. nope, there are no problems. In fact, converting between the two arrays types with asarray() works very efficiently. I sure wish everyone would make the transition, though. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

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Co-existing Numeric and numpy?
by Ralf W. Grosse-Kunstleve 03 Oct '08

03 Oct '08

Hi, We have some older 3rd party packages that require Numeric (24.2), but like to also use newer 3rd party packages that require a recent numpy. Can Numeric and numpy co-exist in the same process? -- I'm mainly worried about clashes at the C API level. Thanks! Ralf

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Problème pour construire les tests Numpy-Swig
by Michel Dupront 03 Oct '08

03 Oct '08

Bonjour, Je viens d'installer Numpy. Je suis intéressé par l'utilisation de swig. Lorsque je tente de construire les tests j'ai cette erreur: """ swig -c++ -python Array.i :9: Error: Macro '%typecheck' expects 1 argument :36: Error: Macro '%typecheck' expects 1 argument :64: Error: Macro '%typecheck' expects 1 argument :92: Error: Macro '%typecheck' expects 1 argument :119: Error: Macro '%typecheck' expects 1 argument :148: Error: Macro '%typecheck' expects 1 argument :177: Error: Macro '%typecheck' expects 1 argument :206: Error: Macro '%typecheck' expects 1 argument :235: Error: Macro '%typecheck' expects 1 argument ....... """ Il semble que ca provienne des directives %numpy_typemaps a la fin du fichier numpy.i: """ /* Concrete instances of the %numpy_typemaps() macro: Each invocation * below applies all of the typemaps above to the specified data type. */ %numpy_typemaps(signed char , NPY_BYTE , int) %numpy_typemaps(unsigned char , NPY_UBYTE , int)*/ %numpy_typemaps(short , NPY_SHORT , int) /*%numpy_typemaps(unsigned short , NPY_USHORT , int) %numpy_typemaps(int , NPY_INT , int) %numpy_typemaps(unsigned int , NPY_UINT , int) %numpy_typemaps(long , NPY_LONG , int) %numpy_typemaps(unsigned long , NPY_ULONG , int) %numpy_typemaps(long long , NPY_LONGLONG , int) %numpy_typemaps(unsigned long long, NPY_ULONGLONG, int) %numpy_typemaps(float , NPY_FLOAT , int) %numpy_typemaps(double , NPY_DOUBLE , int) """ Est ce que quelqu'un a rencontré ce problème ? Merci de bien vouloir m'aider. Amicalement Michel _________________________________________________________________ Email envoyé avec Windows Live Hotmail. Dites adieux aux spam et virus, passez à Hotmail ! C'est gratuit ! http://www.windowslive.fr/hotmail/default.asp

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Array shape
by Kelly Vincent 03 Oct '08

03 Oct '08

I'm using Numpy to do some basic array manipulation, and I'm getting some unexpected behavior from shape. Specifically, I have some 3x3 and 2x2 matrices, and shape gives me (5, 3) and (3, 2) for their respective sizes. I was expecting (3, 3) and (2, 2), for number of rows, number of columns. I'm assuming I must either be misunderstanding what shape gives you or doing something wrong. Can anybody give me any advice? I'm using Python 2.5 and Numpy 1.1.0. Thanks, Kelly

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Generating random samples without repeats
by Paul Moore 03 Oct '08

03 Oct '08

I want to generate a series of random samples, to do simulations based on them. Essentially, I want to be able to produce a SAMPLESIZE * N matrix, where each row of N values consists of either 1. Integers between 1 and M (simulating M rolls of an N-sided die), or 2. A sample of N numbers between 1 and M without repeats (simulating deals of N cards from an M-card deck). Example (1) is easy, numpy.random.random_integers(1, M, (SAMPLESIZE, N)) But I can't find an obvious equivalent for (2). Am I missing something glaringly obvious? I'm using numpy - is there maybe something in scipy I should be looking at? Also, in evaluating samples, I'm likely to want to calculate combinatorial functions, such as the list of all pairs of items from a sample (imagine looking at how many pairs add up to 15 in a cribbage hand). Clearly, I can write a normal Python function which does this for one row, and use apply_along_axis - but that's *slow*. I'm looking for a function that, given an N*M array and a sample size S, gives a C(N,S)*S*M array of all the combinations, which runs at array-processing speeds (preferably without having to code it in C myself!!) Is there anywhere with this type of function available? This type of combinatorial simulation seems to me to be a fairly good fit for numpy's capabilities, and yet I can't seem to find things that seem relevant. Is it simly not something that people use numpy for? Or am I looking in the wrong places in the documentation? Thanks for any help, Paul.

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1.2.0rc2 tagged! --PLEASE TEST--
by Jarrod Millman 03 Oct '08

03 Oct '08

Hello, The 1.2.0rc2 is now available: http://svn.scipy.org/svn/numpy/tags/1.2.0rc2 The source tarball is here: https://cirl.berkeley.edu/numpy/numpy-1.2.0rc2.tar.gz Here is the universal Mac binary: https://cirl.berkeley.edu/numpy/numpy-1.2.0rc2-py2.5-macosx10.5.dmg Here are the Window's binaries: http://www.ar.media.kyoto-u.ac.jp/members/david/archives/numpy/numpy-1.2.0r… http://www.ar.media.kyoto-u.ac.jp/members/david/archives/numpy/numpy-1.2.0r… Here are the release notes: http://scipy.org/scipy/numpy/milestone/1.2.0 Please test this release ASAP and let us know if there are any problems. If there are no show stoppers, this will become the 1.2.0 release. Thanks, -- Jarrod Millman Computational Infrastructure for Research Labs 10 Giannini Hall, UC Berkeley phone: 510.643.4014 http://cirl.berkeley.edu/

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