Mailman 3 February 2007 - NumPy-Discussion

Requests for NumPy Ports?
by Travis Oliphant 03 Feb '07

03 Feb '07

I'm trying to help out the conversion to NumPy by offering patches to various third-party packages that have used Numeric in the past. Does anybody here have requests for which packages they would like to see converted to use NumPy? Ultimately, we are at the mercy of the package-owners to complete the conversion by applying the patches. But, I think it helps when there are patches already available for trying out. The packages I've worked on so far are here: http://www.scipy.org/Porting_to_NumPy Thanks, -Travis

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Complex arange
by Russel Howe 02 Feb '07

02 Feb '07

Should this work? Python 2.4.3 (#1, Dec 27 2006, 21:18:13) [GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as N >>> N.__version__ '1.0.2.dev3531' >>> N.arange(1j, 5j) array([], dtype=complex128) >>> N.arange(1j, 5j, 1j) array([], dtype=complex128) Currently, the real direction is determined to be of zero length (multiarraymodule.c _calc_length), and the length of the array is the minimal length. I can understand the first one not working (default step is 1, it takes 0 of those to cover this range), but the second seems like a bug.

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large memory address space on Mac OS X (intel)
by Louis Wicker 02 Feb '07

02 Feb '07

Dear list: I cannot seem to figure how to create arrays > 2 GB on a Mac Pro (using Intel chip and Tiger, 4.8). I have hand compiled both Python 2.5 and numpy 1.0.1, and cannot make arrays bigger than 2 GB. I also run out of space if I try and 3-6 several arrays of 1000 mb or so (the mem-alloc failure does not seem consistent, depends on whether I am creating them with a "numpy.ones()" call, or creating them on the fly by doing math with the other arrays "e.g., c = 4.3*a + 3.1*b"). Is this a numpy issue, or a Python 2.5 issue for the Mac? I have tried this on the SGI Altix, and this works fine. If there is a compile flag to turn on 64 bit support in the Mac compile, I would be glad to find out about it. Or do I have to wait for Leopard? Thanks. Lou Wicker ------------------------------------------------------------------------ ---- | Dr. Louis J. Wicker | NSSL/WRDD | National Weather Center | 120 David L. Boren Boulevard, Norman, OK 73072-7323 | | E-mail: Louis.Wicker(a)noaa.gov | HTTP: www.nssl.noaa.gov/~lwicker | Phone: (405) 325-6340 | Fax: (405) 325-6780 | | "Programming is not just creating strings of instructions | for a computer to execute. It's also 'literary' in that you | are trying to communicate a program structure to | other humans reading the code." - Paul Rubin | |"Real efficiency comes from elegant solutions, not optimized programs. | Optimization is always just a few correctness-preserving transformations | away." - Jonathan Sobel ------------------------------------------------------------------------ ---- | | "The contents of this message are mine personally and | do not reflect any position of the Government or NOAA." | ------------------------------------------------------------------------ ----

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Release 0.6.1 of pyaudio, renamed pyaudiolab
by David Cournapeau 02 Feb '07

02 Feb '07

Hi There, A few months ago, I posted the first release of pyaudio, a python module to give numpy/scipy environment audio file IO capabilities (ala matlab wavread and co). I recently took time to update it significantly, and as several people showed interest in pyaudio recently, I thought this may interest some people here. The main improvements since last public annoucement: - renamed to pyaudiolab to avoid nameclash with other package pyaudio. - ability to seek into audio files and sync files for flushing IO buffers - matlab-like API: wavread and wavwrite - improvements of the API: more similar to numpy conventions, ability to retrieve most meta-data of a file. - some bug fixing (one bug was quite severe, leading to data corruption on ubuntu with python2.5, due to some weirdness in ubuntu python2.5 packaging) - a real documentation If some people manage to use it on something else than linux, I would happy to hear (particularly Mac OS X, which I cannot test myself). Cheers, David ====== pyaudiolab ====== * WHAT FOR ?: The Goal of pyaudiolab is to give to a numpy/scipy environment some basic audio IO facilities (ala sound, wavread, wavwrite of matlab). With pyaudiolab, you should be able to read and write most common audio files from and to numpy arrays. The underlying IO operations are done using libsndfile from Erik Castro Lopo (http://www.mega-nerd.com/libsndfile/), so he is the one who did the hard work. As libsndfile has support for a vast number of audio files (including wav, aiff, but also htk, ircam, and flac, an open source lossless codec), pyaudiolab enables the import from and export to a fairly large number of file formats. * WHERE TO GET ?: http://www.ar.media.kyoto-u.ac.jp/members/david/softwares/pyaudiolab/index.… bzr archive: http://www.ar.media.kyoto-u.ac.jp/members/david/archives/pyaudiolab/pyaudio… # HOW TO USE ?: http://www.ar.media.kyoto-u.ac.jp/members/david/softwares/pyaudiolab/index.…

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New may_share_memory function
by Travis Oliphant 02 Feb '07

02 Feb '07

In SVN there is a new function may_share_memory(a,b) which will return True if the memory foot-print of the two arrays over-lap. >>> may_share_memory(a, flipud(a)) True This is based on another utility function byte_bounds that returns the byte-boundaries of any object exporting the Python side of the array interface. Perhaps these utilities will help (I know they can be used to make the who function a bit more intelligent about how many bytes are being used). -Travis

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array copy-to-self and views
by Zachary Pincus 01 Feb '07

01 Feb '07

Hello folks, I recently was trying to write code to modify an array in-place (so as not to invalidate any references to that array) via the standard python idiom for lists, e.g.: a[:] = numpy.flipud(a) Now, flipud returns a view on 'a', so assigning that to 'a[:]' provides pretty strange results as the buffer that is being read (the view) is simultaneously modified. Here is an example: In [2]: a = numpy.arange(10).reshape((5,2)) In [3]: a Out[3]: array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) In [4]: numpy.flipud(a) Out[4]: array([[8, 9], [6, 7], [4, 5], [2, 3], [0, 1]]) In [5]: a[:] = numpy.flipud(a) In [6]: a Out[6]: array([[8, 9], [6, 7], [4, 5], [6, 7], [8, 9]]) A question, then: Does this represent a bug? Or perhaps there is a better idiom for modifying an array in-place than 'a[:] = ...'? Or is incumbent on the user to ensure that any time an array is directly modified, that the modifying array is not a view of the original array? Thanks for any thoughts, Zach Pincus Program in Biomedical Informatics and Department of Biochemistry Stanford University School of Medicine

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Cutting 1.0.2 release
by Travis Oliphant 01 Feb '07

01 Feb '07

I think it's time for the 1.0.2 release of NumPy. What outstanding issues need to be resolved before we do it? Hopefully, we can do it by the end of the week. -Travis

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problem with installation of numpy: undefined symbols
by Eric Emsellem 01 Feb '07

01 Feb '07

Hi, after trying to solve an installation problem with scipy, I had to reinstall everything from scratch, and so I now turned back to numpy the installation of which does not work for me (which may in fact explain the pb I had with scipy). To be clear on what I do: - I install blas first, and create a libfblas.a which I copy under /usr/local/lib (using a g77 -fno-second-underscore -O2 -c *.f, ar r libfblas.a *.o, ranlib libfblas.a) - I then define BLAS environment variable accordingly (/usr/local/lib/libfblas.a) - I compile lapack-3.1.0, using "make lapacklib" - I then use a precompiled ATLAS Linux version P4SSE2 (I tried compiling it but it does have the same result) - I copy all ATLAS .a and .h in /usr/local/lib/atlas, and define the ATLAS variable accordingly - I then merge the *.o to have an extended liblapack.a by doing the usual: cd /usr/local/lib/atlas cp liblapack.a liblapack.a_ATLAS mkdir tmp cd tmp ar x ../liblapack.a_ATLAS cp /soft/python/tar/Science_Packages/lapack-3.1.0/lapack_LINUX.a ../liblapack.a ar r ../liblapack.a *.o - I finally get the svn version of numpy, and do the "python setup.py install" (no site.cfg ! but environment variables defined as mentioned above) - I go into ipython, and try: import numpy and I get : exceptions.ImportError Traceback (most recent call last) ................ ---> 40 import linalg ................ ImportError: /usr/local/lib/python2.4/site-packages/numpy/linalg/lapack_lite.so: undefined symbol: ATL_cGetNB I have tried many ways to avoid this problem but does not manage to solve it. Any help is welcome there. (and sorry for those who already saw my numerous posts on scipy forum: I am trying to get to the heart of the pb...) thanks in advance! Eric

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Re: [Numpy-discussion] [SciPy-user] What it the best way to install on a Mac?
by Fernando Perez 01 Feb '07

01 Feb '07

On 1/31/07, Sanjiv Das <srdas(a)scu.edu> wrote: > OK - will give that a shot as well. Its a good suggestion! > cheers Great. And it looks like the bulk of that work has already been done for you, as I found out today thanks to the good and trusty Google Alerts service: http://mohinish.blogspot.com/2007/01/python-for-matlab-users-on-mac.html Feel free to open up a page on the ipython wiki: http://ipython.scipy.org/moin Just make a login for yourself and type away! Cheers, f

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usage of __del__ in python classes
by Sebastian Haase 01 Feb '07

01 Feb '07

Hi, Before I start I want to admit that I don't understand much about this. I just saw that the memmap class defines __del__ and that I had problems in the past when I added a 'def __del__' to a class of mine. So here is a quote, I would like to know if this is "standard knowledge" on this list or not. # I found the info originally here: http://arctrix.com/nas/python/gc/ # Circular references which are garbage are detected when the optional cycle detector is enabled (it's on by default), but can only be cleaned up if there are no Python-level __del__() methods involved. Refer to the documentation for the 'gc' module for more information about how __del__() methods are handled by the cycle detector, particularly the description of the garbage value. Notice: [warning] Due to the precarious circumstances under which __del__() methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to sys.stderr instead. Also, when __del__() is invoked in response to a module being deleted (e.g., when execution of the program is done), other globals referenced by the __del__() method may already have been deleted. For this reason, __del__() methods should do the absolute minimum needed to maintain external invariants. Cheers, Sebastian Haase

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