Mailman 3 November 2007 - NumPy-Discussion

scipy binary for macosx tiger on ppc
by Giorgio F. Gilestro 30 Nov '07

30 Nov '07

Hi guys, does anyone of you happen to have sitting somewhere a DMG of a recent version of SciPy compiled for MacOSX 10.4? The SciPy webpage does not carry official releases and it is sending me to the Scipy Superpack by Chris Fonnesbeck but that superpack seems to be for intel cpu only. I didn't think making SciPy working in mac would have been such a pain in the bum. Linux and Win worked without problem but mac is driving me crazy (yes, compiling is a problem too.) Thanks! -- giorgio(a)gilestro.tk http://www.cafelamarck.it

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documentation generator based on pyparsing
by Robert Cimrman 30 Nov '07

30 Nov '07

Hi, At http://scipy.org/Generate_Documentation you can find a very small documentation generator for NumPy/SciPy modules based on pyparsing package (by Paul McGuire). I am not sure if this belongs to where I put it, so feel free to (re)move the page as needed. I hope it might be interesting for you. r.

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display numpy array as image
by Zachary Pincus 29 Nov '07

29 Nov '07

Hello all, I'm curious if people have experience with / preferences for how to display a numpy array onscreen as an image. Pyglet looks relatively easy -- you can feed an image buffer object with a string or a ctypes pointer. I presume getting a string from an array is plenty fast, but the ctypes pointer option is intriguing as it allows for dealing with simple strided arrays (the image objects allow for an arbitrary number of bytes between rows). Is it possible to get a ctypes pointer to the beginning of the array buffer from numpy without too much ugliness? wxPython looks pretty easy too, as there are facilities for getting pixels from a buffer. Does anyone have any experience with these? Are there ways of allowing a numpy array and a wxPython image to point to the same memory? Anyhow, these are specific questions, but I'd also appreciate any general thoughts about good approaches for getting pixels from numpy arrays onscreen. Thanks, Zach

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How to remove loops over inner()
by Hans-Andreas Engel 29 Nov '07

29 Nov '07

Dear all: After using numpy for several weeks, I am very happy about it and deeply impressed about the performance improvements it brings in my python code. Now I have stumbled upon a problem, where I cannot use numpy to eliminate all my loops in python. Currently the return value of inner(a, b) is defined as inner(a, b)[I, J] = sum_k a[I, k] * b[J, k], for some super indices I and J. Somewhat more general is the tensordot() function that allows to specify over which axes K is summed over. However, if I understand numpy correctly, the following more general version is currently missing: inner(a, b, keep_axis=0)[H, I, J] = sum_k a[H, I, k] * b[H, J, k]. Here H would be an additional super index (specified via the keep_axis keyword), on which no outer product is taken, i.e., the same index is used for a[] and b[]. This more general definition would allow elimination of an extra level of loops. For example, I wish to calculate the following a = rand(200, 5, 2) b = rand(200, 4, 2) r = empty(a.shape[:-1] + b.shape[1:-1]) for h in range(a.shape[0]): r[h] = inner(a[h], b[h]) How could I eliminate the loop? It would be great if there would be the mentioned generalized version of the inner() [or tensordot()] function, since it would eliminate this loop and make my code much faster. What are your opinions? Would such a feature be desirable (or is it already implemented)? Thank you, Best, Hansres

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Building NumPy on Mac OS X without Apple GCC
by Joshua Lippai 29 Nov '07

29 Nov '07

I updated my GCC to a more recent version a day ago, since Apple's Xcode Tools only provide GCC 4.0 and the current release of GNU's GCC is 4.2. I successfully achieved this, but now I run into a problem when trying to build NumPy: gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option "-arch" cc1: error: unrecognized command line option "-arch" cc1: error: unrecognized command line option "-Wno-long-double" gcc: unrecognized option '-no-cpp-precomp' cc1: error: unrecognized command line option "-arch" cc1: error: unrecognized command line option "-arch" cc1: error: unrecognized command line option "-Wno-long-double" Upon investigation into the matter, I found out that these options (no-cpp-precomp and Wno-long-double) are only valid in Apple's GCC and not the regular GNU release. Yet it seems NumPy automatically assumes Apple's GCC is being used when it realizes the target is OS X. Is there a way around this, or at least some way to specify Apple's GCC? NumPy is the only package I've tried building so far that has a problem with this.

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How can I constrain linear_least_squares to integer solutions?
by Mark Schmucker 28 Nov '07

28 Nov '07

Hi, I have successfully used LinearAlgebra.linear_least_squares to estimate solutions to continuous functions. The coefficients returned in that case are of course floating-point values. Now I have a problem where some of the terms are continuous but some must be constrained to integer multiples. As a simple example, y = c1 * x^2 + c0 * x where c1 is floating-point in the usual way, but c0 can only be integer-valued. (The actual problem has many more terms.) Can anyone tell me how to constrain some of the coefficients to integer values? Thank you for any insight. Mark

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Converting char array to float
by Sameer DCosta 28 Nov '07

28 Nov '07

I'm trying to convert a character array to a floating point array. I'm using one of the recent svn builds. It is surprising that astype does not do the job. However if I first convert the char array to an array and then use astype everything works fine. Is this a bug? import numpy as N print N.__version__ # Output = '1.0.5.dev4426' a = N.char.array(['123.45', '234.56']) b = N.array(a).astype(N.float) # This works. print b b = a.astype(N.float) # This does not work and raises an exception ValueError: Can only create a chararray from string data. Thanks. Sameer

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Appending a numpy array to existing text file
by Andy Cheesman 28 Nov '07

28 Nov '07

Hi people, Just a quick question, how do I append a numpy array to an existing ascii output file? I've looked at the numpy.savetxt function and it performs all the formating functions I desire for my output file but it doesn't let me specify how I save my array type Is there a clever work around for this or does anyone have any suggestions? Many Thanks for your help Andy

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how do I speed up this?
by Giorgio F. Gilestro 27 Nov '07

27 Nov '07

Hello everyone, ma and new_ma are bi-dimensional array with shape (a1, a2) on which I am performing the following iteration: for fd in range(a1): new_ma[fd] = [( ma[fd][i-5:i+5].sum() == 0 )*1 for i in range (a2)] Is there any faster more elegant way to do that with numpy? Thanks a lot! Giorgio -- giorgio(a)gilestro.tk http://www.cafelamarck.it

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ANN: PyTables & PyTables Pro 2.0.2 are out!
by Ivan Vilata i Balaguer 27 Nov '07

27 Nov '07

Hi everyone, We at Carabos are happy to announce the simultaneous release of the new 2.0.2 versions of both PyTables and PyTables Pro. They are mainly bugfix releases, and users of previous versions are encouraged to upgrade. And now the official announce: ============================================ Announcing PyTables and PyTables Pro 2.0.2 ============================================ PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. PyTables Pro adds OPSI, a powerful indexing engine for executing very fast queries in large tables. In this version, some bugs have been fixed, being the most important a problem when moving or renaming a group. Some small improvements have been added as well. Besides, a *critical* bug has been fixed in the Pro version (the problem arose when doing repeated queries using the same index). Because of this, an upgrade is strongly recommended. In case you want to know more in detail what has changed in this version, have a look at ``RELEASE_NOTES.txt``. Find the HTML version for this document at: http://www.pytables.org/moin/ReleaseNotes/Release_2.0.2 You can download a source package of the version 2.0.2 with generated PDF and HTML docs and binaries for Windows from http://www.pytables.org/download/stable/ For an on-line version of the manual, visit: http://www.pytables.org/docs/manual-2.0.2 Migration Notes for PyTables 1.x users ====================================== If you are a user of PyTables 1.x, probably it is worth for you to look at ``MIGRATING_TO_2.x.txt`` file where you will find directions on how to migrate your existing PyTables 1.x apps to the 2.x versions. You can find an HTML version of this document at http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x Resources ========= Go to the PyTables web site for more details: http://www.pytables.org About the HDF5 library: http://hdfgroup.org/HDF5/ About NumPy: http://numpy.scipy.org/ To know more about the company behind the development of PyTables, see: http://www.carabos.com/ Acknowledgments =============== Thanks to many users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Many thanks also to SourceForge who have helped to make and distribute this package! And last, but not least thanks a lot to the HDF5 and NumPy (and numarray!) makers. Without them, PyTables simply would not exist. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team :: Ivan Vilata i Balaguer >qo< http://www.carabos.com/ Cárabos Coop. V. V V Enjoy Data ""

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