Mailman 3 November 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 20 Feb '08

20 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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Re: [SciPy-dev] Sparse BLAS in scipy
by Anand Patil 17 Jan '08

17 Jan '08

On Nov 28, 2007 4:06 PM, Nathan Bell <wnbell(a)gmail.com> wrote: > On Nov 27, 2007 4:35 PM, Anand Patil <anand.prabhakar.patil(a)gmail.com> > wrote: > > That and the analogous forward multiplication with a triangular matrix > are > > the two things I really need right now, yeah. The backend would be > simple, > > yet painful; upper/lower, transpose and side arguments, in addition to > > accounting for all the possible combinations of matrix formats, and the > > strides when 'B' is dense, would make for a huge number of bug > > opportunities. That's what made me think wrapping a library would be > less > > work than writing even 'two' routines from scratch. > > I guess I don't fully understand what you need here. What I imagined > was two methods for forward/backward solves with lower/upper > triangular matrices in CSR format. > I was thinking the triangular matrices could be any format, and the 'b' matrix/vector could be any format, in which case the multiplicity of functions rivals that of the ordinary multiplications involving sparse matrices. BTW, for such matrices, isn't this is equivalent to Gauss-Seidel[1] > sweep in the appropriate direction. If so, then we might just move > something like [2] to scipy.linsolve. > I can't see any difference... it would be nice to have the L3 version too, though, and in that case b could be in any format. Also, are you sure the sparse LU solvers wouldn't do this for you anyway? > Nope, will have to look more closely at those. > About a year ago I implemented sparsetools from scratch. I looked at > the same packages you listed before and came to the conclusion that > the amount of code needed was rather small and that by writing the > library to fit well with SciPy was worth the effort. > > Ultimately, I think making the backend code amenable to SWIG and > scipy.sparse saved more effort than starting with an existing library. OK, if you've done the research already and come to that conclusion I should just do it! Regardless of the backend (LU, gauss-seidel or from scratch) here's what I'm proposing for the Python interface: B=trimult(A, B, uplo='U', side='L', inplace=True) B=trisolve(A, B, uplo, side, inplace) A is triangular, B is rectangular or a vector. We could trim the uplo and side arguments if there were 'triangular matrix' classes that kept track of whether they're upper or lower, but that would be more trouble than it's worth, no? I'm thinking if inplace=True, B will only be overwritten if it's possible to recycle the memory efficiently, so the only safe usage is to use the return value. How does this sound? Anand

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what the heck is an egg ;-)
by Joe Harrington 01 Dec '07

01 Dec '07

A lot of the add-on software for numpy/scipy is distributed using novel Python install processes (eggs, setup.py) rather than tarballs or the preferred OS-native installers (dpkg, rpm, etc.). I'm sure they are described, perhaps even well, in other places, but, since scipy.org is our portal, I think it would be good to have a few-line description of each method on the Download page and a link to more detailed descriptions elsewhere (or on subsidiary pages). An example of installing a package many will want, like mayavi2, would be great. In particular, many sysadmins (who might be considering a user's request for an install and know nothing about python) get nervous when package managers other than the native one for the OS start mucking around in the system directories, and are hesitant to use something like eggs. Some statements describing what they do and where they put stuff would be good (like, a guarrantee that they only tread in certain directories). How to update and how to completely remove a package would be good. Is there a way to have them check periodically for updates? Of course, a statement near the top of why these methods are used rather than the native OS installers would help a lot. Thanks, --jh--

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documentation generator based on pyparsing
by Robert Cimrman 29 Nov '07

29 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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Re: [SciPy-dev] Scipy-dev Digest, Vol 49, Issue 14
by Anand Patil 29 Nov '07

29 Nov '07

> > Apologies for cross-posting- this one seems to have fallen through the > > cracks on scipy-user, maybe this is a better place to send it. > > > > Is there a set of python-callable sparse BLAS out there yet? I haven't > > found one, and not for lack of Googling. I'm willing to work on > > swigging a library if the need exists, but would like some guidance > > from someone with more experience with these things. > > > > First, it looks like there are three implementations that would be > > good for this purpose: > > > > - NIST sparse BLAS > > - SparseLib++ (superseded by above?) > > - Boost's ublas with sparse template parameters. > > > > Questions: > > - Do I need to do this, or are wrappers already available? > > - If so, which library would be best? I lean toward Boost, just > > because it's so broadly templatized that scripting a wrapper for all > > all the sparse-sparse and sparse-dense versions should be relatively > > easy. > > - What should the calling conventions from Python be like? > > - Any other pointers? (things I should know about numpy.i, for example). > > Have you tried scipy.sparse? > http://projects.scipy.org/scipy/scipy/browser/trunk/scipy/sparse > Of course, and it's great for general sparse matrices but I didn't see anything in there for triangular matrices. I didn't see anything in linsolve either, though quite a bit could no doubt be extracted from SuperLU. Also, I just like working with the BLAS rather than higher-level interfaces when it's code that I'm going to reuse, so as to have more control over when things get overwritten as opposed to copied, etc. Anand

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Sparse BLAS in scipy?
by Anand Patil 28 Nov '07

28 Nov '07

Hi all, Apologies for cross-posting- this one seems to have fallen through the cracks on scipy-user, maybe this is a better place to send it. Is there a set of python-callable sparse BLAS out there yet? I haven't found one, and not for lack of Googling. I'm willing to work on swigging a library if the need exists, but would like some guidance from someone with more experience with these things. First, it looks like there are three implementations that would be good for this purpose: - NIST sparse BLAS - SparseLib++ (superseded by above?) - Boost's ublas with sparse template parameters. Questions: - Do I need to do this, or are wrappers already available? - If so, which library would be best? I lean toward Boost, just because it's so broadly templatized that scripting a wrapper for all all the sparse-sparse and sparse-dense versions should be relatively easy. - What should the calling conventions from Python be like? - Any other pointers? (things I should know about numpy.i, for example). Anand

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Problem about scipy.integrate.odeint
by Couge Chen 27 Nov '07

27 Nov '07

Hi all, I met a weird problem when using scipy.integrate.odeint(). It is followed as: from scipy import * y=integrate.odeint(lambda y,t:sin(y)*y,y0=random.rand(N),t=arange(0,100)) Here, I need to solve a field equation and have to set the large number N. If N>= 73300 or N<=70400, it works well. However, if N is within the range(70400,73300), an error message "MemoryError" shows up. I am very confused about that. My python version is "Python2.4.3", scipy version is "scipy 0.5.0.2033". By the way, my computer's basic configuration is CoreDuo 1.83GHz of CPU, 1GB of RAM, and WinXP. Anyone can help me? Thanks a lot! Couge

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[Fwd: [SciPy-user] install numpy/scipy with Intel Compilers]
by Laurence Viry 27 Nov '07

27 Nov '07

-- Laurence Viry CRIP UJF - Projet MIRAGE ( http://mirage.imag.fr ) Laboratoire de Modélisation et Calcul - IMAG tel: 04 76 63 56 34 fax: 04 76 63 12 63 e-mail: Laurence.Viry(a)imag.fr

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