Mailman 3 June 2007 - SciPy-Dev

Question about Kaiser Implementation in firwin
by Buehler, Eric (AGRE) 04 Oct '08

04 Oct '08

Hello, I have been using the signal.firwin FIR design tool and I have a question about the implementation. Scipy-0.4.8 Numpy-0.9.6 I am generating a kaiser window with the following parameters: N = 128 Cutoff = ~4e7 width = .3 All of the special kaiser generation parameters in the function follow Oppenheim and Schafer well. Even the sinc function for linear phase is follows exactly. However, the last line of the firwin function is causing me some heartburn. filter_design.py 1538 win = get_window(window,N,fftbins=1) 1539 alpha = N//2 1540 m = numpy.arange(0,N) 1541 h = win*special.sinc(cutoff*(m-alpha)) 1542 return h / sum(h) Line 1542 of filter_design.py, "return h / sum(h)", normalizes the function where it doesn't seem necessary, at least in the kaiser window case. Without the normalization, the kaiser window already returns a value of 1 at the zero frequency point. This normalization scales all of the data, making the window difficult to use in the frequency domain. Can someone point me to the rationale for this line? Looking at the code, this seems to be a pretty recent change (within the last year/year and a half). Thanks, Eric Buehler eric <dot> buehler <at> smiths-aerospace <dot> com ****************************************** The information contained in, or attached to, this e-mail, may contain confidential information and is intended solely for the use of the individual or entity to whom they are addressed and may be subject to legal privilege. If you have received this e-mail in error you should notify the sender immediately by reply e-mail, delete the message from your system and notify your system manager. Please do not copy it for any purpose, or disclose its contents to any other person. The views or opinions presented in this e-mail are solely those of the author and do not necessarily represent those of the company. The recipient should check this e-mail and any attachments for the presence of viruses. The company accepts no liability for any damage caused, directly or indirectly, by any virus transmitted in this email. ******************************************

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Status of sandbox.spline and my other code.
by John Travers 07 Apr '08

07 Apr '08

Hi all, This email is intended to clarify the status of various bits of code I've committed/not committed so I don't leave too much of a mess after me. I'm about to start writing my PhD thesis, so I'll be too busy to work on scipy until September. 1. sandbox.spline stuff The module here was supposed to be a tidy up of scipy.interpolate. All of the dierkx functionality has been moved to f2py wrappers, which I think makes maintenance easier. However, it does not add any new functionality, and in retrospect appears to have been a waste of my time. So I'll leave to you to decide if you want to integrate it into scipy.interpolate or not; though it was originaly planned to be a seperate module to clear up the ambiguity between interpolation and smoothing spline functionality. However I have added 20 unit tests to the code (most of which simply check the wrapper against the pure fortran output, but still useful I think) which could easily be moved over to the current scipy.interpolate module. 2. Radial basis function module in sandbox (rbf) This code has recently had attention from Robert Hetland and is quite improved. I'm not sure if I will ever go into scipy though?? If not, I will put it into a scikit at some point. Related to this, the wiki page has been updated, however, it is at http://www.scipy.org/RadialBasisFunctions but should probably be in the Cookbook. I don't know how to move it over, so some pointers would be helpful. In addition there are four unused attachments I uploaded that could be deleted. 3. Boundary value ODE solver (BVP_SOLVER) I have fairly functional code interfacing to some very good BVP solver code (http://cs.smu.ca/~muir/BVP_SOLVER_Webpage.shtml). The license is good for scipy (as confirmed by the authors), but the code if fortran 95 - therefore cannot apparently go into scipy. I'll make a scikit when I get time (probably September). 4. An interface to the pikaia genetic algorithm routine I have a python interface to pikaia fortran code for genetic algorithm optimisation of real valued functions (http://www.hao.ucar.edu/Public/models/pikaia/pikaia.html). The fortran code itself is robust and highly optimised for numerical work, though it is nowhere near as general as the sandbox.ga module. I think it is useful to go in scipy.optimization alongside the annealing module. If people disagree I'll make it into a scikit. I hope this clears a few things up. Best regards, John Travers

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ZVODE, a Double Precision Complex ODE Solver
by Nils Wagner 19 Feb '08

19 Feb '08

This might be of interest. Nils From: Alan Hindmarsh <alanh(a)llnl.gov> Date: Wed, 6 Dec 2006 18:20:32 -0500 Subject: ZVODE, a Double Precision Complex ODE Solver Of the many solvers freely available for ODE initial value problems, there seem to be very few, if any, for the case of double precision complex dependent variables, even though there have been occasional user calls for this functionality. (Of course a complex system can be treated as a real system of twice the size, but direct treatment as a complex system is preferable.) I have provided such a solver: ZVODE. ZVODE is a straightforward modification of the Fortran ODE solver DVODE, for double precision real systems, making it part of a trio, SVODE/DVODE/ZVODE. ZVODE is available in two locations: (1) Netlib, in /ode (see file zvode.f), and (2) the LLNL CASC software site, www.llnl.gov/CASC/software.html (see VODE). The latter site also includes a demonstration program, zvdemo. Alan Hindmarsh Center for Applied Scientific Computing Lawrence Livermore National Laboratory

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equivalent to online Octave calculator?
by dmitrey 02 Jul '07

02 Jul '07

Hi all, has numpy/scipy project something equivalent to online Octave calculator? http://www.online-utility.org/math/math_calculator.jsp If no, I guess it would be very useful for checking numpy/scipy bugs - are those related to user's old scipy/numpy version or they are due to build options and/or lack of some libraries (atlas, blas, lapack etc; or due to their obsolete versions installed). Also, I think it would be very useful and convenient if there will be radiobutton provided which version to use: either latest release or nightly build. (or maybe some scipy releases + some numpy releases, including nightly builds). Also, scikits might be connected in future to the frame; and some limits could be implemented (for example, no more than 60 sec cputime per day from single IP) So, what are your suggestions? Regards, Dmitrey.

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GSoC weekly report
by dmitrey 30 Jun '07

30 Jun '07

Hi all, see http://openopt.blogspot.com/ for my reports. Briefly: 1. QP class created (the only one connected qp solver is cvxopt_qp, I wrote cvxopt_mosek qp binding but i didn't tested the one because of problems with mosek binary libs) see from scikits.openopt import QP help(QP) for more details. 2. General constrained NLP solver "lincher" (LINearisation solver from CHERkassy town) had been written. However, it requires QP solver, and cvxopt QP is GPL-licensed. In future I guess I can add native OO QP solver (+bonus - handling of QC- Quadratic Constraints, but positive-defined only, elseware the problem could be NP-hard), it will be able of handling problems with nVars up to 1000, including ill-conditioned ones and other difficulties. However, it requires consulting with some people from my department, and they are very busy for now with their own work. Also, lincher requires line-search solver, currently I use fminbound from scipy, but I intend to remove the dependence later. see from scikits.openopt import NLP help(NLP) for more details. or see the notes at http://openopt.blogspot.com/ Regards, D.

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Status of sandbox.pysparse
by Nils Wagner 29 Jun '07

29 Jun '07

Hi all, Is pysparse still maintained ? I am mainly interested in the Jacobi-Davidson (JDSYM) eigensolver . I have enabled the sandbox package. However it fails if I try to import the package. Python 2.4.1 (#1, May 25 2007, 18:41:31) [GCC 4.0.2 20050901 (prerelease) (SUSE Linux)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from scipy.sandbox import pysparse Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib64/python2.4/site-packages/scipy/sandbox/pysparse/__init__.py", line 4, in ? from spmatrix import * ImportError: No module named spmatrix Nils

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Scipy weave error with mingw-3.4.5
by Christopher Galvan 29 Jun '07

29 Jun '07

Hello, I was trying to run an example I found of compiling C code into a python module. I have attached the .py file I ran. The resulting error was given when I was using MinGW-3.4.5, but the process ran fine on MinGW-3.2.3. Is there some kind of compatibility issue that I am missing? Any help would be greatly appreciated. C:\eric_class\exercises\exercise8>python exercise8.py <weave: compiling> c:\docume~1\chrisg~1\locals~1\temp\Chris Galvan\python25_intermediate\compiler_8 94ad5ed761bb51736c6d2b7872dc212\Release\python25\lib\site-packages\scipy-0.5.3.d ev2400-py2.5-win32.egg\scipy\weave\scxx\weave_imp.o:weave_imp.cpp:(.text+0xac5): undefined reference to `std::string::_Rep::_S_empty_rep_storage' c:\docume~1\chrisg~1\locals~1\temp\Chris Galvan\python25_intermediate\compiler_8 94ad5ed761bb51736c6d2b7872dc212\Release\python25\lib\site-packages\scipy-0.5.3.d ev2400-py2.5-win32.egg\scipy\weave\scxx\weave_imp.o:weave_imp.cpp:(.text+0xb94): undefined reference to `std::string::_Rep::_S_empty_rep_storage' c:\docume~1\chrisg~1\locals~1\temp\Chris Galvan\python25_intermediate\compiler_8 94ad5ed761bb51736c6d2b7872dc212\Release\python25\lib\site-packages\scipy-0.5.3.d ev2400-py2.5-win32.egg\scipy\weave\scxx\weave_imp.o:weave_imp.cpp:(.text+0xbdc): undefined reference to `__gnu_cxx::__exchange_and_add(int volatile*, int)' c:\docume~1\chrisg~1\locals~1\temp\Chris Galvan\python25_intermediate\compiler_8 94ad5ed761bb51736c6d2b7872dc212\Release\python25\lib\site-packages\scipy-0.5.3.d ev2400-py2.5-win32.egg\scipy\weave\scxx\weave_imp.o:weave_imp.cpp:(.text+0xc17): undefined reference to `__gnu_cxx::__exchange_and_add(int volatile*, int)' collect2: ld returned 1 exit status Traceback (most recent call last): File "exercise8.py", line 21, in <module> weave.blitz("result = a+b*(c-d)") File "c:\python25\lib\site-packages\scipy-0.5.3.dev2400-py2.5-win32.egg\scipy\ weave\blitz_tools.py", line 63, in blitz **kw) File "c:\python25\lib\site-packages\scipy-0.5.3.dev2400-py2.5-win32.egg\scipy\ weave\inline_tools.py", line 447, in compile_function verbose=verbose, **kw) File "c:\python25\lib\site-packages\scipy-0.5.3.dev2400-py2.5-win32.egg\scipy\ weave\ext_tools.py", line 365, in compile verbose = verbose, **kw) File "c:\python25\lib\site-packages\scipy-0.5.3.dev2400-py2.5-win32.egg\scipy\ weave\build_tools.py", line 269, in build_extension setup(name = module_name, ext_modules = [ext],verbose=verb) File "c:\python25\lib\site-packages\numpy-1.0.2.dev3484-py2.5-win32.egg\numpy\ distutils\core.py", line 174, in setup return old_setup(**new_attr) File "C:\Python25\lib\distutils\core.py", line 168, in setup raise SystemExit, "error: " + str(msg) distutils.errors.CompileError: error: Command "g++ -mno-cygwin -shared "c:\docum e~1\chrisg~1\locals~1\temp\Chris Galvan\python25_intermediate\compiler_894ad5ed7 61bb51736c6d2b7872dc212\Release\docume~1\chrisg~1\locals~1\temp\chris galvan\pyt hon25_compiled\sc_421d46af877a66479b224b3cb3ed5b051.o" "c:\docume~1\chrisg~1\loc als~1\temp\Chris Galvan\python25_intermediate\compiler_894ad5ed761bb51736c6d2b78 72dc212\Release\python25\lib\site-packages\scipy-0.5.3.dev2400-py2.5-win32.egg\s cipy\weave\scxx\weave_imp.o" -Lc:\python25\libs -Lc:\python25\PCBuild -lpython25 -lmsvcr71 -o "c:\docume~1\chrisg~1\locals~1\temp\Chris Galvan\python25_compiled \sc_421d46af877a66479b224b3cb3ed5b051.pyd"" failed with exit status 1 -- Chris Galvan import time from numpy import arange, empty, float64 from scipy import weave N = 1000000 a = arange(N,dtype=float64) b = arange(N,dtype=float64) c = arange(N,dtype=float64) d = arange(N,dtype=float64) result = empty(N,dtype=float64) t1= time.clock() result = a + b*(c-d) t2=time.clock() numpy_time = t2-t1 t1= time.clock() weave.blitz("result = a+b*(c-d)") t2=time.clock() blitz_time = t2-t1 print N, numpy_time, blitz_time, numpy_time/blitz_time code = """ for(int i=0;i<Na[0];i++) { result[i] = a[i]+b[i]*(c[i]-d[i]); } """ t1= time.clock() weave.inline(code,['a','b','c','d','result'], compiler='gcc') t2=time.clock() inline_time = t2-t1 print N, numpy_time, blitz_time, inline_time print numpy_time/blitz_time, numpy_time/inline_time

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Re: [SciPy-dev] [SciPy-user] [scikits] Updated generic optimizer (and egg download link)
by Alan G Isaac 29 Jun '07

29 Jun '07

On Fri, 29 Jun 2007, Matthieu Brucher apparently wrote: > You're right, it's not an official scikit. I don't think > that someone in charge said something about it when > I asked if it should be a scikit or not, only Michael > McNeil Forbes. You were exposing much less of the project back then, if I recall. Anyway, I certainly did not realize how much stuff you had already coded. At this point, it should definitely be a SciKit, IMO. If there are no objections, you should add it to the repository. Make sure to use the project structure discussed here: https://projects.scipy.org/scipy/scikits/ If you do not yet have SVN commit rights, you will have to ask for them. I think these requests often go to this list, but it would be nice to clarify if there is an established procedure for requesting this. Cheers, Alan Isaac

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I can't find howto get exp(x) or a^x in NumPy_for_Matlab_Users page
by dmitrey 28 Jun '07

28 Jun '07

howto find exp(x)? I can't find it in NumPy_for_Matlab_Users page http://www.scipy.org/NumPy_for_Matlab_Users Thx, D.

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[pymachine] moving code outside the sandbox into scikits ?
by David Cournapeau 28 Jun '07

28 Jun '07

Hi, Following a discussion with my mentor (Jarrod Millman) and Fernando Perez, we decided to move most of the code useful for pymachine from the scipy.sandbox to scikits. Before doing so, we would like to gather other's opinion. Why ? As part of my SoC project, I was planning to move some of the existing scipy.sandbox code out of the sandbox (e.g., scipy.sandbox.pyem -> scikits.machinelearning.em). Now we are proposing to move that code into a scikits package instead because: 1. this would give all the machine learning related code a unified home under one common namespace 2. some of the code relies on ctypes, which is not allowed in scipy 3. some of the code uses matplotlib, which is not allowed in scipy 4. the code hasn't been as widely used as other parts of scipy 5. this would allow the code to be developed and released on its own timeschedule independent of scipy What ? The concerned packages are: - scipy.sandbox.pyem - scipy.sandbox.svm - and possibly others (e.g., scipy.sandbox.ga, scipy.sandbox.ann) And (just to be clear) scipy.cluster would remain where it is. Potential problems? The obvious consequence is that you will now need to install the new scikits' package to use the code currently available in the scipy.sandbox. As you need to install sandboxed code from the source anyway, this shouldn't be too big of a problem. Also the namespace changes, but that would be true if the code was moved out of the sandbox as originally planned. Better name for pymachine ? We have been calling the project pymachine. But we would rather use a more descriptive name for the scikit package (and one that doesn't contain 'py'). Here are some ideas: - scikits.learning - scikits.learn - scikits.machinelearning - scikits.mlearn What do you think of these names? Does anyone have better name in mind? If anyone has concerns with the above plan, please speak up! Cheers, D. Cournapeau, J. Millman, B. Hawthorne, and F. Perez

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