Mailman 3 July 2007 - SciPy-User

Finding a point where a function becomes constant
by Mathias Wagner July 17, 2007

July 17, 2007

Hi, I have a numerical function defined f(x) that is zero for x < x_0 and f(x)>0 for x > x_0. I have to find x_0. The problem with conventional root finding is that if the algorithm evaluates the function for any x < x_0 it will return this as a solution. Does anyone know whether there is some suitable algorithm for this problem or do I have to modify an existing method like bisection for my needs? Mathias -- // *************************************************************** // ** Mathias Wagner ** // ** Institut fuer Kernphysik, TU Darmstadt ** // ** Schlossgartenstr. 9, 64289 Darmstadt, Germany ** // ** ** // ** email: mathias.wagner(a)physik.tu-darmstadt.de ** // ** www : http://crunch.ikp.physik.tu-darmstadt.de/~wagner ** // ***************************************************************

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[OT] advice about publishing a scientific software
by massimo sandal July 17, 2007

July 17, 2007

Hi, I am currently developing a (Python-and-Scipy based, of course!) data analysis software (more precisely, a single molecule force spectroscopy data analysis sw). It has been thought to be not the usual quick-and-dirty in house script, but to be an actual application that can be used in general by different labs. In fact, it actually features an easy and working (even if slightly unpolished) plugin API, and most functionality is implemented by mean of independent plugin. Now, I would like to release it as free software (probably LGPL, but still thinking about it) online, however there are two concerns blocking me: - Major one: I would like to publish a peer-reviewed paper about this software. If I want this, do I need *first* to write the paper, get it accepted, *then* to publish the software online? Or, given that functionality is plugin-driven, can I publish the software backbone and later publish a paper that presents the software and some (new, to release) nifty plugin that does some clever kind of data analysis? Or what? What are the usual requirements of journals about this? - Minor one: Where to publish it? I am used to think about SourceForge as THE place for publishing open source software, however a number of people told me about Google Code. Basically what I want, if possible, is no-nonsense SVN access and some integrated mailing list/forum. Google Code seems good at that -it also gives automatically Wiki space, which is a major plus for me. What do you think about? Drawbacks? Licence issues (i.e. something like "since you publish it on Google Code the code is also owned by Google" tiny-letters clause?) Is there some other alternative? Moreover, I'd like to know your opinions about the degree of "polishness" and other requirements for initial publication of a scientific software. As for documentation, I am working on actual, user manual and hacking (mainly but not only focused on plugin writing) manual with tutorials etc.. The sw still lacks an installer but "installation" is braindead (provided that you have all the correct dependencies): put all the files in the same directory and run "python software.py" from where: do you think an installer is absolutely necessary or it is OK to let it go this way, and add the installer later? Thanks a lot, Massimo -- Massimo Sandal University of Bologna Department of Biochemistry "G.Moruzzi" snail mail: Via Irnerio 48, 40126 Bologna, Italy email: massimo.sandal(a)unibo.it tel: +39-051-2094388 fax: +39-051-2094387

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Re: [SciPy-user] (Mac) Close a plot window, crash IPython?
by killian koepsell July 17, 2007

July 17, 2007

hi, i observed a similar problem that persists in matplotlib 0.90.1 (python 2.5, numpy 1.0.1, ipython 0.7.3). the problem occurs when a window is closed and it seems to be specific to the non-interactive mode using the GTK or GTKAgg backend. the following short script runs ok once, but when i try to run it a second time, python hangs: # start script import pylab as P P.ioff() P.figure() P.close() # stop script this is probably a problem of matplotlib and we should continue this thread on the matplotlib email list. cheers, kilian > From: "John Hunter" <jdh2358(a)gmail.com> > > Date: July 13, 2007 1:10:35 PM PDT > > To: "SciPy Users List" < scipy-user(a)scipy.org> > > Subject: Re: [SciPy-user] (Mac) Close a plot window, crash IPython? > > Reply-To: SciPy Users List < scipy-user(a)scipy.org> > > > > On 7/13/07, David Warde-Farley <david.warde.farley(a)utoronto.ca> wrote: > > > >> It does seem as though the *exact* same bug was reported today on > >> matplotlib-devel (what are the odds?), I shall checkout the svn > >> version and see if that fixes it, and make them aware of the other > >> WX- > >> related bug as well. > > > > I hope this does fix your problem, but the bug that was fixed was also > > recently introduced (in a svn commit after the 0.90.1 release) so it > > may not be your problem. But if you can update from svn and see if > > the problem is still there, that would be a great start. See you on > > the matplotlib-devel side :-) > > > > JDH > > _______________________________________________ > > SciPy-user mailing list > > SciPy-user(a)scipy.org > > http://projects.scipy.org/mailman/listinfo/scipy-user > >

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Questions about scipy.optimize.fmin_cobyla
by fdu.xiaojf＠gmail.com July 17, 2007

July 17, 2007

Hi all, I'm trying to minimize a function f(X) = f(x1,x2,...,xn) using cobyla. In my minimization, all variables should be larger than 0, so I define constraint functions like this: cons = [] for i in range(n): # n is the number of variables c =lambda x: x[i] - 1e-10 cons.append(c) And then I use fmin_cobyla(func=f, x0=[0.1]*n, cons=cons) to minimize the function. There are two problems: 1) In the constrain functions, I expected the value of i to be bounded to the specific constrain function, but it doesn't work as I expected. Here is a demonstration: In [14]: a = [] In [15]: for i in range(4): b = lambda : i**2 a.append(b) ....: ....: In [18]: for f in a: print f() ....: ....: 9 9 9 9 What I want is that every function in list a should print different values. The value of a[0](), a[1](), a[2](), a[3]() should be 0, 1, 4, 9. How to achieve this? 2) In function f(X), there are item of log(xi). Although I have constrained the value of xi in the constrain functions, xi still may be equal or less than 0 during minimization, then "math domain error" occurred. What should I do ? My method is when "math domain error" occurred, catch it and set the return value of f to a very large number. Should this work or not? Thanks a lot! Xiao Jianfeng

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Numerical Recipes robust fit implementation
by Angus McMorland July 17, 2007

July 17, 2007

Hi all, I'm in need of a robust linear-fit algorithm- Numerical Recipes (in C) medfit routine will do nicely. I've had a quick look and don't see any similar routines in scipy. Is there one I'm missing? If not, I'd lke to implement the NR routine for use with my numpy-arrayed data. What are my options? Obviously I could translate the routine into python, but I realise a potentially easier alternative is to interface the existing NR C code. Having never done that before, can someone give me some pointers/tips on how to start - there's quite a range of linking tools listed on http://www.scipy.org/Topical_Software. Thanks, Angus. -- AJC McMorland, PhD Student Physiology, University of Auckland

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ANN: SciPy 2007 Conference Updates
by Travis Vaught July 15, 2007

July 15, 2007

Greetings, We're excited to have *Ivan Krstić*, the director of security architecture for the One Laptop Per Child project as our Keynote Speaker this year. The planning for the SciPy 2007 Conference is moving along. Please see below for some important updates. Schedule Available ------------------ The full schedule of talks has been posted here: http://www.scipy.org/SciPy2007/ConferenceSchedule Early Registration Extended --------------------------- If you haven't yet registered for the conference, the early registration deadline has been extended to Wednesday, July 18th, 2007. For more information on the conference see: http://www.scipy.org/SciPy2007 Student Sponsorship ------------------- Enthought, Inc. (http://www.enthought.com) is sponsoring the registration fees for up to 5 college or graduate students to attend the conference. To apply, please send a short description of what you are studying and why you'd like to attend to info(a)enthought.com. Please include telephone contact information. BOFs & Sprints -------------- If you're planning to attend and are interested in selecting BOF or Sprint Session topics, please weigh in at: BOFs: http://www.scipy.org/SciPy2007/BoFs Sprints: http://www.scipy.org/SciPy2007/Sprints We're looking forward to a great conference this year! Best, Travis

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(Mac) Close a plot window, crash IPython?
by David Warde-Farley July 13, 2007

July 13, 2007

Hi folks, I've recently acquired myself a shiny new Mac and have vowed not to install Matlab on it, so I've been investigating going to SciPy entirely for my scientific computing needs. I installed MacPython 2.5 and the SciPy Superpack listed on the SciPy website. My problem is this: if I start IPython with ipython -pylab, and say, plot(arange(0,9),arange(0,9)**2) then close the plot window, and repeat the same command, IPython crashes completely. Is this normal behaviour? The obvious solution is "don't close plot windows" but that seems rather unsatisfactory. Any ideas, hints, fixes, workarounds? Thanks, David

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[ANN] PyTables and PyTables Pro 2.0 released
by Francesc Altet July 13, 2007

July 13, 2007

======================================== Announcing PyTables & PyTables Pro 2.0 ======================================== 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. After more than one year of continuous development and about five months of alpha, beta and release candidates, we are very happy to announce that the PyTables and PyTables Pro 2.0 are here. We are pretty confident that the 2.0 versions are ready to be used in production scenarios, bringing higher performance, better portability (specially in 64-bit environments) and more stability than the 1.x series. You can download a source package of the PyTables 2.0 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 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 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 2.0 version. You can find an HTML version of this document at http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x Introducing PyTables Pro 2.0 ============================ The main difference between PyTables Pro and regular PyTables is that the Pro version includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than 1 tenth of a second. Wearing more than 15000 tests and having passed the complete test suite in the most common platforms (Windows, Mac OS X, Linux 32-bit and Linux 64-bit), we are pretty confident that PyTables Pro 2.0 is ready to be used in production scenarios, bringing maximum stability and top performance to those users who need it. For more info about PyTables Pro, see: http://www.carabos.com/products/pytables-pro For the operational details and benchmarks see the OPSI white paper: http://www.carabos.com/docs/OPSI-indexes.pdf Coinciding with the publication of PyTables Pro we are introducing an innovative liberation process that will allow to ultimate release the PyTables Pro 2.x series as open source. You may want to know that, by buying a PyTables Pro license, you are contributing to this process. For details, see: http://www.carabos.com/liberation New features of PyTables 2.0 series =================================== - A complete refactoring of many, many modules in PyTables. With this, the different parts of the code are much better integrated and code redundancy is kept under a minimum. A lot of new optimizations have been included as well, making working with it a smoother experience than ever before. - NumPy is finally at the core! That means that PyTables no longer needs numarray in order to operate, although it continues to be supported (as well as Numeric). This also means that you should be able to run PyTables in scenarios combining Python 2.5 and 64-bit platforms (these are a source of problems with numarray/Numeric because they don't support this combination as of this writing). - Most of the operations in PyTables have experimented noticeable speed-ups (sometimes up to 2x, like in regular Python table selections). This is a consequence of both using NumPy internally and a considerable effort in terms of refactorization and optimization of the new code. - Combined conditions are finally supported for in-kernel selections. So, now it is possible to perform complex selections like:: result = [ row['var3'] for row in table.where('(var2 < 20) | (var1 == "sas")') ] or:: complex_cond = '((%s <= col5) & (col2 <= %s)) ' \ '| (sqrt(col1 + 3.1*col2 + col3*col4) > 3)' result = [ row['var3'] for row in table.where(complex_cond % (inf, sup)) ] and run them at full C-speed (or perhaps more, due to the cache-tuned computing kernel of Numexpr, which has been integrated into PyTables). - Now, it is possible to get fields of the ``Row`` iterator by specifying their position, or even ranges of positions (extended slicing is supported). For example, you can do:: result = [ row[4] for row in table # fetch field #4 if row[1] < 20 ] result = [ row[:] for row in table # fetch all fields if row['var2'] < 20 ] result = [ row[1::2] for row in # fetch odd fields table.iterrows(2, 3000, 3) ] in addition to the classical:: result = [row['var3'] for row in table.where('var2 < 20')] - ``Row`` has received a new method called ``fetch_all_fields()`` in order to easily retrieve all the fields of a row in situations like:: [row.fetch_all_fields() for row in table.where('column1 < 0.3')] The difference between ``row[:]`` and ``row.fetch_all_fields()`` is that the former will return all the fields as a tuple, while the latter will return the fields in a NumPy void type and should be faster. Choose whatever fits better to your needs. - Now, all data that is read from disk is converted, if necessary, to the native byteorder of the hosting machine (before, this only happened with ``Table`` objects). This should help to accelerate applications that have to do computations with data generated in platforms with a byteorder different than the user machine. - The modification of values in ``*Array`` objects (through __setitem__) now doesn't make a copy of the value in the case that the shape of the value passed is the same as the slice to be overwritten. This results in considerable memory savings when you are modifying disk objects with big array values. - All leaf constructors (except for ``Array``) have received a new ``chunkshape`` argument that lets the user explicitly select the chunksizes for the underlying HDF5 datasets (only for advanced users). - All leaf constructors have received a new parameter called ``byteorder`` that lets the user specify the byteorder of their data *on disk*. This effectively allows to create datasets in other byteorders than the native platform. - Native HDF5 datasets with ``H5T_ARRAY`` datatypes are fully supported for reading now. - The test suites for the different packages are installed now, so you don't need a copy of the PyTables sources to run the tests. Besides, you can run the test suite from the Python console by using:: >>> tables.tests() Resources ========= Go to the PyTables web site for more details: http://www.pytables.org/ Go to the PyTables Pro web page for more details: http://www.carabos.com/products/pytables-pro 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 -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

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scipy fblas.so functions not found
by Charlie Yanaitis July 13, 2007

July 13, 2007

I'm hoping somebody could offer some advice. I've been stymied for a while trying to build/install a good working version of scipy-0.5.2 on a Saturn Cluster that's running Red Hat RHEL4-U4. I'm using gcc version 3.4.6 and python-2.5. I've tried to build the Atlas variation, but was unsuccessful, so I back-tracked and reverted to building and installing numpy and then scipy. Going this route, scipy at least built and installed OK, but now, the fblas.so library is missing functions and a user reported to me that they got the following error: ImportError: /usr/local/lib/python2.5/site-packages/scipy/linalg/fblas.so: undefined symbol: srotmg_ If anybody can offer some advice on a fix or work around for this, I'd appreciate it! Thanks in advance! Charlie Yanaitis

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Re: [SciPy-user] BLAS, LAPACK, ATLAS libraries.
by fie.pye＠atlas.cz July 12, 2007

July 12, 2007

>--------------------------------------------------------- >Od: David Cournapeau >Přijato: 3.7.2007 4:16:32 >Předmět: Re: [SciPy-user] BLAS, LAPACK, ATLAS libraries. > >fie.pye(a)atlas.cz wrote: > >> Hello. > >> > >> I would like to install NumPy and SciPy modules. I compiled and install BLAS, LAPACK and ATLAS libraries. After installation and importing NumPy I obtained message: > >> > >>>>> import numpy > >> Traceback (most recent call last): > >> File "", line 1, in > >> File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/__init__.py", line 43, in > >> import linalg > >> File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/__init__.py", line 4, in > >> from linalg import * > >> File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/linalg.py", line 25, in > >> from numpy.linalg import lapack_lite > >> ImportError: liblapack.so: cannot open shared object file: No such file or directory > >> It seams to me that BLAS and LAPACK installation is OK. > >Well, it may not be :) If you do (once you exported LD_LIBRARY_PATH): > > > >ldd > >/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/lapack_lite.so > > > >What do you get ? The most useful information, actually, is to give us > >the log of python setup.py config &> log when building numpy and scipy. > >> > >> # LAPACK installation Make.inc > >> PLAT = _LINUX > >> FORTRAN = gfortran > >> OPTS = -funroll-all-loops -O3 -fpic > >> DRVOPTS = $(OPTS) > >> NOOPT = > >Here you forgot NOOPT = -fpic. Then, how did you compile atlas ? You > >should try first without atlas at all, I think: compile blas, lapack, > >and check that python setup.py config picks up the libraries. > > > >As a note, there are source rpms available for blas/lapack/atlas/scipy: > >the binary rpms do not support CENTOS, but I think building the rpm on > >CENTOS should not be too difficult. Everything is taken care of in the > >rpms: options, compatible fortran ABI, full pic version of liblapack to > >build complete LAPACK based on ATLAS, etc... > > > >http://software.opensuse.org/download/home:/ashigabou/ (pick up source > >rpm for FC 6 or 7, this should be the more similar to centos). > > > >If this does not work, I would like to hear about it, > > > >David > >_______________________________________________ > >SciPy-user mailing list > >SciPy-user(a)scipy.org > >http://projects.scipy.org/mailman/listinfo/scipy-user > > > Hi David. Thank you for your response. Based on your notes I recompiled BLAS and LAPACK libraries and build and installed python2.5.1 and numpy again. Unfortunately the error appears again >>> import numpy Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/__init__.py", line 43, in <module> import linalg File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/__init__.py", line 4, in <module> from linalg import * File "/usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/linalg.py", line 25, in <module> from numpy.linalg import lapack_lite ImportError: /usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/lapack_lite.so: undefined symbol: _gfortran_concat_string Installation information: # ldd /usr/local/python/python2.5.1/lib/python2.5/site-packages/numpy/linalg/lapack_lite.so libg2c.so.0 => /usr/lib64/libg2c.so.0 (0x00002aaaaae52000) libm.so.6 => /lib64/libm.so.6 (0x00002aaaab073000) libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00002aaaab2f6000) libc.so.6 => /lib64/libc.so.6 (0x00002aaaab504000) /lib64/ld-linux-x86-64.so.2 (0x0000555555554000) # /usr/local/lib64/blas -rw-r--r-- 1 root root 612242 Jul 10 23:25 libblas.a -rwxr-xr-x 1 root root 358087 Jul 10 23:25 libblas.so lrwxrwxrwx 1 root root 9 Jul 10 23:27 libfblas.a -> libblas.a # /usr/local/lib64/lapack lrwxrwxrwx 1 root root 11 Jul 10 23:36 libflapack.a -> liblapack.a -rw-r--r-- 1 root root 10938922 Jul 10 23:34 liblapack.a -rwxr-xr-x 1 root root 6948895 Jul 10 23:38 liblapack.so for python2.5.1 configuration see attached file: python_config.log.gz for numpy building and installation see attached files: numpy_build.log.gz, numpy_install.log.gz fie pye

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