Mailman 3 October 2007 - NumPy-Discussion

how to select indices from boolean array
by Bernhard Voigt 25 Oct '07

25 Oct '07

Dear list, is nonzero the appropriate function to get the indexes of a boolean array? foo = numpy.random.random(10000) cut = foo > .5 indexes = cut.nonzero()[0] Another question: Why is the the type returned by nonzero a tuple and not an ndarray of the same shape as the input (or self if used on an object)? Thanks! Bernhard

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How to write unit tests in distutils ?
by David Cournapeau 25 Oct '07

25 Oct '07

Hi, I would like to implement some unit tests for the code I am producing for scons support. As the numpy unit test is part of distutils, there is a bootstrap problem, and I am not sure I understand how current unit tests work in distutils: can anyone enlighten me on this one ? cheers, David

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Re: [Numpy-discussion] a simple question about f2py
by Pauli Virtanen 24 Oct '07

24 Oct '07

Lisandro Dalcin <dalcinl(a)gmail.com> kirjoitti: > Im trying to use f2py to wrap some fortran functions wich receive as > argument PETSc objects. The usual way to implement this with > PETSc+fortran is to do something like this > > soubrutine matvec(A,x,y) > Mat A > Vec x > Vec y > end soubrutine > > The 'Mat' and 'Vec' types are actually integers of appropriate kind, > PETSc uses a #define to > define them. I there any way I can teach/hack f2py to translate > Mat/Vec/etc. to a normal fortran type? You can tell f2py to use a different type for the argument as follows: subroutine foo(a) !f2py integer, dimension(10), intent(inout) :: a Mat a end subroutine foo This way, f2py will treat the parameter as a chunk of memory of size 10*sizeof(integer), and disregard any following definitions for it. Using a hack like this, it's also possible to pass derived type object pointers, "type(Typename), pointer", from the Python side to the Fortran side, as opaque handles. -- Pauli Virtanen

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Support universal gfortran compiler on OS X
by Jarrod Millman 24 Oct '07

24 Oct '07

Hello, Could someone confirm whether this ticket can be closed: http://projects.scipy.org/scipy/numpy/ticket/571 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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anyone compiling on IRIX 6.5
by Jarrod Millman 24 Oct '07

24 Oct '07

Hello, I am hoping to close a few of the remaining tickets for the upcoming NumPy 1.0.4 release. Is anyone using NumPy on IRIX? Or have access to IRIX? If so, could you please take a look at this ticket: http://projects.scipy.org/scipy/numpy/ticket/417 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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a simple question about f2py
by Lisandro Dalcin 23 Oct '07

23 Oct '07

Im trying to use f2py to wrap some fortran functions wich receive as argument PETSc objects. The usual way to implement this with PETSc+fortran is to do something like this soubrutine matvec(A,x,y) Mat A Vec x Vec y end soubrutine The 'Mat' and 'Vec' types are actually integers of appropriate kind, PETSc uses a #define to define them. I there any way I can teach/hack f2py to translate Mat/Vec/etc. to a normal fortran type? -- Lisandro Dalcín --------------- Centro Internacional de Métodos Computacionales en Ingeniería (CIMEC) Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) PTLC - Güemes 3450, (3000) Santa Fe, Argentina Tel/Fax: +54-(0)342-451.1594

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ANN: SFE-00.31.06 release
by Robert Cimrman 23 Oct '07

23 Oct '07

I am happy to announce the version 00.31.06 of SFE, featuring acoustic band gaps computation, rigid body motion constraints, new solver classes and reorganization, and regular bug fixes and updates, see http://ui505p06-mbs.ntc.zcu.cz/sfe. SFE is a finite element analysis software written almost entirely in Python. This version is released under BSD license. best wishes, r.

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Bug in piecewise?
by Geoffrey Zhu 19 Oct '07

19 Oct '07

Hi All, If I execute the following code, I find that function f() sometimes is called with an empty array. I am not sure why this is necessary. Is this a bug? def f(x): return x**2 return numpy.piecewise(u, abs(u)<1, [f, 0]) Thanks, Geoffrey

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Sum of the product of three or more arrays
by Geoffrey Zhu 19 Oct '07

19 Oct '07

Hi All, Given three vectors of the same lengths, X, Y, and Z, I am looking for an efficient way to calculate the following: sum(x[i]*y[i]*z[i], for i=1..n ) I am not sure if there is a vectorized way to do this. Thanks, Geoffrey

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Scons and numpy, second milestone: all numpy built with scons.
by David Cournapeau 18 Oct '07

18 Oct '07

Hi, I am happy to announce a second milestone in scons support in numpy. This one is much bigger than the first one, since now I can build the whole numpy using scons (e.g. distutils is not used at all to build non python code). How to use it: ============== just do as before for installing numpy: python setup.py build To use parrallel builds: python setup scons --jobs=4 build Windows build does not work yet, but this should be easy to fix. Do not use it for practical purpose, though: I have not yet tackled the problem of optimization flags, nor the detection of blas/lapack. What has been done: =================== Some of the things which are now possible: - any module can be customized wrt build options (warning flags, etc...). This can be done per object file, also, with a bit some work. - for numpy.core (which took 95 % of my time for this milestone), all the generated code is integrated in the scons dependency graph. I don't know how distutils handled this, but if you change a txt file, or a header, scons will rebuilt the necessary things (the dependencies are found automatically). I also cleanup up some things (done in a separate svn branch). - Numpy configuration is also totally integrated into scons config framework, which makes the thing more understandable, I hope. and of course, everything possible with scons is now available. Some other nice advantages which I did not think about at first: - parallel builds: since scons supports parrallel builds, I thought it would be interesting to support it in distutils. This was trivial to add, and this largely alleviates the cost added by using scons in the first place. For scipy, my guess would be that it could significantly reduce the build time (whereas numpy build time is mostly spent compiling one or two big files). So basically, I believe most of the things planned in http://projects.scipy.org/scipy/numpy/wiki/DistutilsRevamp are now available because they are available in scons, if numpy developers decide to follow the scons route. Before being usable, I need to finish fortran support and blas/lapack/atlas detection: once this is done, numpy should be able to support some platforms not supported yet (intel compiler on windows, sun compiler with performance libraries, etc...), which was the reason I started this work in the first place. cheers, David

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