Mailman 3 July 2010 - NumPy-Discussion

Ternary plots anywhere?
by "V. Armando Solé" 06 Jul '10

06 Jul '10

Dear all, Perhaps this is a bit off topic for the mailing list, but this is probably the only mailing list that is common to users of all python plotting packages. I am trying to find a python implementation of ternary/triangular plots: http://en.wikipedia.org/wiki/Ternary_plot but I have been unsuccessful. Is there any on-going project around? Thanks for your time. Best regards, Armando

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debian benchmarks
by Sturla Molden 06 Jul '10

06 Jul '10

I was just looking at Debian's benchmark. LuaJIT is now (on median) beating Intel Fortran! Consider that Lua is a dynamic language very similar to Python. I know it's "just a benchmark" but this has to count as insanely impressive. Beating Intel Fortran with a dynamic scripting language... How is that even possible? If this keeps up we'll need a Python to Lua compiler very soon. And LuaJIT 2 is rumoured to be much faster than the current... Looking at median runtimes, here is what I got: gcc 1.10 LuaJIT 1.96 Java 6 -server 2.13 Intel Fortran 2.18 OCaml 3.41 SBCL 3.66 JavaScript V8 7.57 PyPy 31.5 CPython 64.6 Perl 67.2 Ruby 1.9 71.1 This means that LuaJIT can do in less than a day what CPython can do in a month. The only comfort for CPython is that Ruby and Perl did even worse. I wonder how much better CPython would do with NumPy on this benchmark? Sturla

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cython and f2py
by Geoffrey Ely 05 Jul '10

05 Jul '10

Hi All, Sorry if this has been documented or discussed already, but my searches have come up short. Can someone please recommend a way to setup both Cython and Fortran extensions in a single package with numpy.distutils (or something else)? E.g.: from numpy.distutils.core import setup, Extension ext_modules = [ Extension( 'cext', ['cext.pyx'] ), Extension( 'fext', ['fext.f90'] ), ] setup( name = 'test', ext_modules = ext_modules, ) Can numpy.distutils be directed to process *.pyx with Cython rather than Pyrex? Is is kosher to call setup twice in the same script, once for Fortran, and once for Cython using Cython.Distutils.build_ext, or would that do bad things? I guess I could just pre-process the Cython stuff, and have distutils work with the generated C, but I don't like that as much. I am using numpy version 1.4.0, but can update to the development version if that helps. Thanks, Geoff

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ANN: scipy 0.8.0 release candidate 1
by Ralf Gommers 05 Jul '10

05 Jul '10

I'm pleased to announce the availability of the first release candidate of SciPy 0.8.0. Please try it out and report any problems on the scipy-dev mailing list. SciPy is a package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more. This release candidate release comes almost one and a half year after the 0.7.0 release and contains many new features, numerous bug-fixes, improved test coverage, and better documentation. Please note that SciPy 0.8.0rc1 requires Python 2.4-2.6 and NumPy 1.4.1 or greater. For more information, please see the release notes: http://sourceforge.net/projects/scipy/files/scipy/0.8.0rc1/NOTES.txt/view You can download the release from here: https://sourceforge.net/projects/scipy/ Python 2.5/2.6 binaries for Windows and OS X are available, as well as source tarballs for other platforms and the documentation in pdf form. Thank you to everybody who contributed to this release. Enjoy, Ralf

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subtle behavior when subtracting sub-arrays
by Steve Schmerler 05 Jul '10

05 Jul '10

Hi I stumbled upon some numpy behavior which I was not aware of. Say I have an array of shape (2,2,3) and want to subtract the sub-array a[...,0] of shape (2,2) from each a[...,i], i=0,1,2 . ########## ok ########## In [1]: a=arange(2*2*3).reshape(2,2,3) # Copy the array to be subtracted. In [2]: a0=a[...,0].copy() # Trivial approach. That works. In [3]: for k in range(a.shape[-1]): ...: a[...,k] -= a0 ...: ...: # OK In [4]: a Out[4]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) In [5]: a=arange(2*2*3).reshape(2,2,3) # The same, with broadcasting. In [6]: a=a-a[...,0][...,None] # OK In [7]: a Out[7]: array([[[0, 1, 2], [0, 1, 2]], [[0, 1, 2], [0, 1, 2]]]) ########## not ok ########## In [8]: a=arange(2*2*3).reshape(2,2,3) In [9]: a-=a[...,0][...,None] # NOT OK In [10]: a Out[10]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) In [11]: a=arange(2*2*3).reshape(2,2,3) # NOT OK, same as above In [12]: for k in range(a.shape[-1]): ...: a[...,k] -= a[...,0] ...: ...: In [14]: a Out[14]: array([[[ 0, 1, 2], [ 0, 4, 5]], [[ 0, 7, 8], [ 0, 10, 11]]]) To sum up, I find it a bit subtle that a = a - a[...,0][...,None] works as expected, while a -= a[...,0][...,None] does not. I guess the reason is that in the latter case (and the corresponding loop), a[...,0] itself is changed during the loop, while in the former case, numpy makes a copy of a[...,0] ? Is this intended? This is with numpy 1.3.0. best, Steve

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PATCH: reference leaks for 'numpy.core._internal' module object
by Lisandro Dalcin 03 Jul '10

03 Jul '10

The simple test below show the issue. import sys import numpy as np from numpy.core import _internal def f(a = np.zeros(4)): a = np.zeros(4) b = memoryview(a) c = np.asarray(b) print sys.getrefcount(_internal) while 1: f() The patch it trivial (I've added a little extra, unrelated NULL check): Index: numpy/core/src/multiarray/buffer.c =================================================================== --- numpy/core/src/multiarray/buffer.c (revision 8468) +++ numpy/core/src/multiarray/buffer.c (working copy) @@ -747,9 +747,13 @@ } str = PyUString_FromStringAndSize(buf, strlen(buf)); free(buf); + if (str == NULL) { + return NULL; + } descr = (PyArray_Descr*)PyObject_CallMethod( _numpy_internal, "_dtype_from_pep3118", "O", str); Py_DECREF(str); + Py_DECREF(_numpy_internal); if (descr == NULL) { PyErr_Format(PyExc_ValueError, "'%s' is not a valid PEP 3118 buffer format string", buf); PS: I think that such implementation should at least handle the very simple one/two character format (eg, 'i', 'f', 'd', 'Zf', 'Zd', etc.) without calling Python code... Of course, complaints without patches should not be taken too seriously ;-) -- Lisandro Dalcin --------------- CIMEC (INTEC/CONICET-UNL) Predio CONICET-Santa Fe Colectora RN 168 Km 472, Paraje El Pozo Tel: +54-342-4511594 (ext 1011) Tel/Fax: +54-342-4511169

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Numpy 1.5 for Python 2.7 and 3.1
by Christoph Gohlke 03 Jul '10

03 Jul '10

Dear NumPy developers, at <http://projects.scipy.org/numpy/ticket/1520> I have posted a patch against numpy svn trunk r8464 that: 1) removes the datetime functionality 2) restores ABI compatibility with numpy 1.4.1 3) enables numpy to build and run on Python 2.7 and 3.1 (at least on Windows) I hope this work can be used to get a numpy 1.5.x branch started. Regarding Python 2.7, which is scheduled to be released within 2 weeks: numpy 1.4.1 does build and work with minor changes (http://projects.scipy.org/numpy/changeset/7926 and followups). However, for Python 2.7, the proposed patch will not be binary compatible with numpy 1.4.1 because it uses PyCapsule instead of PyCObject. Best, Christoph

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? FAIL: test_print.test_complex_types(<type 'numpy.complex64'>, )
by Vincent Davis 03 Jul '10

03 Jul '10

I didn't find these documented anywhere, I have numpy(couple day old snapshot) install on python 2.7 OSX 64bit. Thanks Vincent ====================================================================== FAIL: test_print.test_complex_types(<type 'numpy.complex64'>,) Check formatting of complex types. ---------------------------------------------------------------------- Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/nose/case.py", line 186, in runTest self.test(*self.arg) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/tests/test_print.py", line 61, in check_complex_type err_msg='Failed str formatting for type %s' % tp) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/testing/utils.py", line 313, in assert_equal raise AssertionError(msg) AssertionError: Items are not equal: Failed str formatting for type <type 'numpy.complex64'> ACTUAL: '-1j' DESIRED: '(-0-1j)' >> raise AssertionError("\nItems are not equal: Failed str formatting for type <type 'numpy.complex64'>\n ACTUAL: '-1j'\n DESIRED: '(-0-1j)'") ====================================================================== FAIL: test_print.test_complex_types(<type 'numpy.complex128'>,) Check formatting of complex types. ---------------------------------------------------------------------- Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/nose/case.py", line 186, in runTest self.test(*self.arg) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/tests/test_print.py", line 61, in check_complex_type err_msg='Failed str formatting for type %s' % tp) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/testing/utils.py", line 313, in assert_equal raise AssertionError(msg) AssertionError: Items are not equal: Failed str formatting for type <type 'numpy.complex128'> ACTUAL: '-1j' DESIRED: '(-0-1j)' >> raise AssertionError("\nItems are not equal: Failed str formatting for type <type 'numpy.complex128'>\n ACTUAL: '-1j'\n DESIRED: '(-0-1j)'") ====================================================================== FAIL: test_print.test_complex_types(<type 'numpy.complex256'>,) Check formatting of complex types. ---------------------------------------------------------------------- Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/nose/case.py", line 186, in runTest self.test(*self.arg) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/tests/test_print.py", line 61, in check_complex_type err_msg='Failed str formatting for type %s' % tp) File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/testing/utils.py", line 313, in assert_equal raise AssertionError(msg) AssertionError: Items are not equal: Failed str formatting for type <type 'numpy.complex256'> ACTUAL: '-1j' DESIRED: '(-0-1j)' >> raise AssertionError("\nItems are not equal: Failed str formatting for type <type 'numpy.complex256'>\n ACTUAL: '-1j'\n DESIRED: '(-0-1j)'") ---------------------------------------------------------------------- Ran 2466 tests in 11.029s FAILED (KNOWNFAIL=4, SKIP=1, failures=3) <nose.result.TextTestResult run=2466 errors=0 failures=3>

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OT: request help building pymex win64
by Robin 03 Jul '10

03 Jul '10

Hi, Sorry for the offtopic post but I wondered if any Windows experts who are familiar with topics like linking python on windows and visual studio runtimes etc. might be able to help. I'm on a bit of a mission to get pymex built for 64 bit windows. Pymex ( http://github.com/kw/pymex ) is a matlab package that embeds the Python interpreter in a mex file and provides a very elegant interface for manipulating python objects from matlab, as well as converting between data times when necessary. It builds easily on mac, linux and win32 with mingw, but I really need it also for 64 bit windows. (It works very well with numpy as well so not completely OT). I have looked at trying to get a 64bit mingw working to build mex files, but that seemed quite difficult, so instead I am trying to build with VS 2008 Express Edition + Windows 7 SDK (for 64 bit support). As far as I can tell this is installed OK as I can build the example mex64 files OK. I have made some modifications to pymex to get it to build under vs 2008 ( http://github.com/robince/pymex/tree/win64 ). And I can get it to build and link (I believe using the implicit dll method of linking against C:\Python26\libs\python26.lib of the amd64 python.org python) without errors, but when I run it seems to segfaults whenever a pointer is passed between the mex side and python26.dll. I asked this stackoverflow question which has some more details (build log) http://stackoverflow.com/questions/3167134/trying-to-embed-python-into-matl… Anyway I'm completely in the dark but wondered if some of the experts on here would be able to spot something (perhaps to do with incompatible C runtimes - I am not sure what runtime Python is built with but I thought it was VS 2008). Cheers Robin

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Re: [Numpy-discussion] [Matplotlib-users] Vectorization
by Benjamin Root 02 Jul '10

02 Jul '10

I am moving this over to numpy-discussion maillist... I don't have a firm answer for you, but I did notice one issue in your code. You call arange(len(dx) - 1) for your loops, but you probably really need arange(1, len(dx) - 1) because you are accessing elements both after *and* before the current index. An index of -1 is actually valid because that means the last element of the array, and may not be what you intended. Ben Root On Fri, Jul 2, 2010 at 1:15 PM, Nicolas Bigaouette <nbigaouette(a)gmail.com>wrote: > Hi all, > > I don't really know where to ask, so here it is. > > I was able to vectorize the normalization calculation in quantum mechanics: > <phi|phi>. Basically it's a volume integral of a scalar field. Using: > >> norm = 0.0 >> for i in numpy.arange(len(dx)-1): >> for j in numpy.arange(len(dy)-1): >> for k in numpy.arange(len(dz)-1): >> norm += psi[k,j,i]**2 * dx[i] * dy[j] * dz[k] >> > if dead slow. I replaced that with: > >> norm = (psi**2 * >> dx*dy[:,numpy.newaxis]*dz[:,numpy.newaxis,numpy.newaxis]).sum() >> > which is almost instantanious. > > I want to do the same for the calculation of the kinetic energy: > <phi|p^2|phi>/2m. There is a laplacian in the volume integral which > complicates things: > >> K = 0.0 >> for i in numpy.arange(len(dx)-1): >> for j in numpy.arange(len(dy)-1): >> for k in numpy.arange(len(dz)-1): >> K += -0.5 * m * phi[k,j,i] * ( >> (phi[k,j,i-1] - 2.0*phi[k,j,i] + phi[k,j,i+1]) / >> dx[i]**2 >> + (phi[k,j-1,i] - 2.0*phi[k,j,i] + phi[k,j+1,i]) / >> dy[j]**2 >> + (phi[k-1,j,i] - 2.0*phi[k,j,i] + phi[k+1,j,i]) / >> dz[k]**2 >> ) >> > > My question is, how would I vectorize such loops? I don't know how I would > manage the "numpy.newaxis" code-foo with neighbours dependency... Any idea? > > Thanx! > > > ------------------------------------------------------------------------------ > This SF.net email is sponsored by Sprint > What will you do first with EVO, the first 4G phone? > Visit sprint.com/first -- http://p.sf.net/sfu/sprint-com-first > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > >

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NumPy-Discussion July 2010