Mailman 3 May 2009 - SciPy-User

Easy way to make a block diagonal matrix?
by Joseph Smidt 21 May '09

21 May '09

Hi, Is there an easy way to create a block diagonal matrix from existing matrices? For example, lets assume I have three 2x2 matrices a, b and c. Is there something like d = block_diag(a,b,c) which would create a 6x6 block diagonal matrix from a, b and c? If not, is there a straight forward way to accomplish the same thing? Joseph Smidt -- ------------------------------------------------------------------------ Joseph Smidt <josephsmidt(a)gmail.com> Physics and Astronomy 4129 Frederick Reines Hall Irvine, CA 92697-4575 Office: 949-824-3269

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Subclass of TimeSeries
by Robert Ferrell 21 May '09

21 May '09

How do I derive a subclass from TimeSeries? I tried import scikits.timeseries as ts import numpy as np class MyTS(ts.TimeSeries): def __init__(self, label, dataArray, dateArray): ts.TimeSeries.__init__(self, data=dataArray, dates=dateArray) self.label=label return A = np.random.random(10) dts = ts.date_array(start_date = ts.Date('d', '2009-1-1'), length=10, freq='d') myts = MyTS(label='notWork', dataArray=A, dateArray=dts) But that doesn't work. In this case, I got: /Users/Shared/Develop/Sandbox/tsSubClass.py in <module>() 13 A = np.random.random(10) 14 dts = ts.date_array(start_date = ts.Date('d', '2009-1-1'), length=10, freq='d') ---> 15 myts = MyTS(label='notWork', dataArray=A, dateArray=dts) 16 print myts.series 17 <type 'exceptions.TypeError'>: __new__() takes at least 3 non-keyword arguments (1 given) Am I making some silly mistake? Or is this a bit more complicated than I realize? thanks, -robert

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Join us for "Scientific Computing with Python Webinar"
by Travis Oliphant 21 May '09

21 May '09

Hello all Python users: I am pleased to announce the beginning of a free Webinar series that discusses using Python for scientific computing. Enthought will host this free series which will take place once a month for 30-45 minutes. The schedule and length may change based on participation feedback, but for now it is scheduled for the fourth Friday of every month. This free webinar should not be confused with the EPD webinar on the first Friday of each month which is open only to subscribers to the Enthought Python Distribution. I (Travis Oliphant) will be the first speaker at this continuing series. I plan to present a brief (10-15) minute talk on reading binary files with NumPy using memory mapped arrays and structured data- types. This talk will be followed by a demonstration of Chaco for interactive 2-d visualization and Mayavi for interactive 3-d visualization. Both Chaco and Mayavi are open-source tools and part of the Enthought Tool Suite. They can be conveniently installed using the Enthought Python Distribution. Topics for future webinars will be chosen later based on participant feedback. This event will take place on Friday at 3:00pm CDT and will last 30 to 45 minutes depending on questions asked. Space is limited at this event. If you would like to participate, please register by going to https://www1.gotomeeting.com/register/422340144 or by clicking on the appropriate link in the attached announcement. There will be a 10 minute technical help session prior to the on-line meeting which you should plan to use if you have never participated in a GoToWebinar previously. During this time you can test your connection and audio equipment as well as familiarize yourself with the GoTo Meeting software. I am looking forward to interacting with many of you this Friday. Best regards, Travis Oliphant Enthought, Inc. Enthought is the company that sponsored the creation of SciPy and the Enthought Tool Suite. It continues to sponsor the SciPy community by hosting the SciPy mailing list and website and participating in the development of SciPy and NumPy. Enthought creates custom scientific and technical software applications and provides training on using Python for technical computing. Enthought also provides the Enthought Python Distribution. Learn more at http://www.enthought.com Travis Oliphant's bio can be read at http://www.enthought.com/company/executive-team.php > > > > > > Scientific Computing with Python Webinar > > > > > > Each webinar in this continuing series will demonstrate the use of > some aspect of Python to assist with scientific, engineering, and > technical computing. Enthought will host each meeting and select a > specific topic based on feedback from participants > Register for a session now by clicking a date below: > Fri, May 22, 2009 3:00 PM - 3:30 PM CDT > Fri, Jun 19, 2009 1:00 PM - 1:30 PM CDT > Fri, Jul 17, 2009 1:00 PM - 1:30 PM CDT > Once registered you will receive an email confirming your registration > with information you need to join the Webinar. > System Requirements > PC-based attendees > Required: Windows® 2000, XP Home, XP Pro, 2003 Server, Vista > Macintosh®-based attendees > Required: Mac OS® X 10.4 (Tiger®) or newer > >

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scipy.optimize.fmin_cg
by Jason Rennie 20 May '09

20 May '09

Hello, I'm planning to use this function to optimize a least squares objective. I noticed that the "norm" argument defaults to "inf" or max norm. Does this mean that (by default) the search is done in max-norm space rather than L2/Euclidean norm space? Should I be worried about this setting? Thanks, Jason -- Jason Rennie Research Scientist, ITA Software http://www.itasoftware.com/

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Sage 3.4 and scipy speed
by Scott Askey 20 May '09

20 May '09

Is running the scipy .6 and python 2.5 under sage in anyway inferior (beyond the version number) to the packages that are in my Linux distro? I have to use Centos 5 at work. V/R Scott

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numpy 64 bit build on mac os x
by Roger Herikstad 20 May '09

20 May '09

Hi all, I'm trying to build numpy from svn (rev 6997) on Mac OS X 10.5.7. I've managed to build a 4-way universal of Python 2.6.2. When I try using that to build numpy, I get a bunch of warnings from ld saying certain files are not of the required architecture. I traced one of these files, _sortmodule.c, and it seems that, when building the extensions, the required arch flags are not transmitted properly, causing the files to built using only the active architecture. Here are the lines in the build log I think are relevant: 925 building 'numpy.core._sort' extension 926 compiling C sources 927 C compiler: gcc -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/usr/local/include -I/usr/local/include 928 929 compile options: '-Inumpy/core/include -Ibuild/src.macosx-10.5-universal-2.6/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/src/multiarray -Inumpy/core/s rc/umath -Inumpy/core/include -I/Library/Frameworks/Python64.framework/Versions/2.6/include/python2.6 -Ibuild/src.macosx-10.5-universal-2.6/numpy/core/src/multiarray -Ibuild/src.macosx-10.5-universal-2.6/numpy/core/src/umath -c' 930 gcc: build/src.macosx-10.5-universal-2.6/numpy/core/src/_sortmodule.c 931 gcc -arch i386 -arch ppc -arch ppc64 -arch x86_64 -isysroot / -L/usr/local/lib -bundle -undefined dynamic_lookup -L/usr/local/lib -I/usr/local/include -I/usr/loc al/include build/temp.macosx-10.5-universal-2.6/build/src.macosx-10.5-universal-2.6/numpy/core/src/_sortmodule.o -Lbuild/temp.macosx-10.5-universal-2.6 -o build/ lib.macosx-10.5-universal-2.6/numpy/core/_sort.so 932 ld warning: in build/temp.macosx-10.5-universal-2.6/build/src.macosx-10.5-universal-2.6/numpy/core/src/_sortmodule.o, file is not of required architecture 933 ld warning: in build/temp.macosx-10.5-universal-2.6/build/src.macosx-10.5-universal-2.6/numpy/core/src/_sortmodule.o, file is not of required architecture 934 ld warning: in build/temp.macosx-10.5-universal-2.6/build/src.macosx-10.5-universal-2.6/numpy/core/src/_sortmodule.o, file is not of required architecture The compile options do not contain the arch flags, and I'm not sure how I should go about forcing the build process to use them. Any suggestions anyone? Is there something obvious I'm missing here? Thanks! ~ Roger

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Sundials
by Nils Wagner 19 May '09

19 May '09

FWIW, a new release of Sundials is available. See below for details. Nils From: Radu Serban <radu(a)xulu.com> Date: Thu, 14 May 2009 15:07:36 -0400 Subject: Sundials 2.4.0 release Announcing the release of Sundials version 2.4.0. The suite includes the following five solvers: - CVODE (v. 2.6.0), for integration of ODE initial value problems; - CVODES (v. 2.6.0), for integration and sensitivity analysis of ODE IVP; - IDA (v. 2.6.0), for integration of DAE initial value problems; - IDAS (v. 1.0.0), for integration and sensitivity analysis of DAE IVP; - KINSOL (v. 2.6.0), for nonlinear algebraic systems. The Sundials solvers provide robust time integrators (with optional sensitivity analysis capabilities) and nonlinear solvers that can easily be incorporated into existing simulation codes. The solvers are independent of the data representation and can be used both on serial and parallel computers. They are written in ANSI C, with CVODE, IDA, and KINSOL also providing a Fortran interface. In addition, sundialsTB provides a Matlab interface to CVODES, IDAS, and KINSOL. Sundials is freely available, under a BSD license, at http://www.llnl.gov/casc/sundials For the Sundials team, Radu Serban

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Trivial quadratic equation question
by Douglas Macdonald 19 May '09

19 May '09

Hi, Does anyone know if scipy already has a direct quadratic (ax^2 + bx +c =0) equation solver? Thank you in advance. Kind regards, Douglas

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scipy.signal.firwin
by Jan Rauberg 19 May '09

19 May '09

I'm missing the functionality of firwin like in matlab/octave fir1, so that I can give a 'low', 'high' and 'stop' option. I don't know how to create a FIR window based high pass filter. Or is there something planned for the future? Thank you Jan

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SciPy 2009 Call for Papers
by Jarrod Millman 18 May '09

18 May '09

========================== SciPy 2009 Call for Papers ========================== SciPy 2009, the 8th Python in Science conference, will be held from August 18-23, 2009 at Caltech in Pasadena, CA, USA. Each year SciPy attracts leading figures in research and scientific software development with Python from a wide range of scientific and engineering disciplines. The focus of the conference is both on scientific libraries and tools developed with Python and on scientific or engineering achievements using Python. We welcome contributions from the industry as well as the academic world. Indeed, industrial research and development as well academic research face the challenge of mastering IT tools for exploration, modeling and analysis. We look forward to hearing your recent breakthroughs using Python! Submission of Papers ==================== The program features tutorials, contributed papers, lightning talks, and bird-of-a-feather sessions. We are soliciting talks and accompanying papers (either formal academic or magazine-style articles) that discuss topics which center around scientific computing using Python. These include applications, teaching, future development directions, and research. A collection of peer-reviewed articles will be published as part of the proceedings. Proposals for talks are submitted as extended abstracts. There are two categories of talks: Paper presentations These talks are 35 minutes in duration (including questions). A one page abstract of no less than 500 words (excluding figures and references) should give an outline of the final paper. Proceeding papers are due two weeks after the conference, and may be in a formal academic style, or in a more relaxed magazine-style format. Rapid presentations These talks are 10 minutes in duration. An abstract of between 300 and 700 words should describe the topic and motivate its relevance to scientific computing. In addition, there will be an open session for lightning talks during which any attendee willing to do so is invited to do a couple-of-minutes-long presentation. If you wish to present a talk at the conference, please create an account on the website (http://conference.scipy.org). You may then submit an abstract by logging in, clicking on your profile and following the "Submit an abstract" link. Submission Guidelines --------------------- * Submissions should be uploaded via the online form. * Submissions whose main purpose is to promote a commercial product or service will be refused. * All accepted proposals must be presented at the SciPy conference by at least one author. * Authors of an accepted proposal can provide a final paper for publication in the conference proceedings. Final papers are limited to 7 pages, including diagrams, figures, references, and appendices. The papers will be reviewed to help ensure the high-quality of the proceedings. For further information, please visit the conference homepage: http://conference.scipy.org. Important Dates =============== * Friday, June 26: Abstracts Due * Saturday, July 4: Announce accepted talks, post schedule * Friday, July 10: Early Registration ends * Tuesday-Wednesday, August 18-19: Tutorials * Thursday-Friday, August 20-21: Conference * Saturday-Sunday, August 22-23: Sprints * Friday, September 4: Papers for proceedings due Tutorials ========= Two days of tutorials to the scientific Python tools will precede the conference. There will be two tracks: one for introduction of the basic tools to beginners and one for more advanced tools. Tutorials will be announced later. Birds of a Feather Sessions =========================== If you wish to organize a birds-of-a-feather session to discuss some specific area of scientific development with Python, please contact the organizing committee. Executive Committee =================== * Jarrod Millman, UC Berkeley, USA (Conference Chair) * Gaël Varoquaux, INRIA Saclay, France (Program Co-Chair) * Stéfan van der Walt, University of Stellenbosch, South Africa (Program Co-Chair) * Fernando Pérez, UC Berkeley, USA (Tutorial Chair)

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