Mailman 3 February 2010 - SciPy-User

Re: [SciPy-User] noisy interpolators, smoothers and fitters
by Jonathan Stickel 25 Feb '10

25 Feb '10

On 2/25/10 scipy-user-request(a)scipy.org wrote: > Date: Thu, 25 Feb 2010 06:48:29 -0800 (PST) > From: denis <denis-bz-gg(a)t-online.de> > Subject: Re: [SciPy-User] noisy interpolators, smoothers and fitters > > On Feb 24, 4:11?pm, josef.p...(a)gmail.com wrote: >> > see also offer by Jonathan Stickel which looks useful with >> > penalization on derivatives (if my quick reading is correct). > > make a scikit ? > I like to see doc *first* > but that's just my preference, grew up on paper doc. > Trying to follow along, and this is the first clear suggestion for what to do with my code. I was not aware of scikits when I first made my inquiry. That seems like a good place for now, and I will look into how to submit through that interface. Denis: in my previous post, I included a link to a publication on the method. Email me offlist if you want me to send you a copy, or if you want to see the docstrings for my code. Jonathan

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Re: [SciPy-User] preconditioned conjugate gradient
by Dominique Orban 25 Feb '10

25 Feb '10

On Thu, Feb 25, 2010 at 5:38 PM, <scipy-user-request(a)scipy.org> wrote: > > ---------- Forwarded message ---------- > From: Jake VanderPlas <jakevdp(a)gmail.com> > To: scipy-user(a)scipy.org > Date: Tue, 23 Feb 2010 11:14:53 -0800 > Subject: [SciPy-User] preconditioned conjugate gradient > Hello, > I'm looking for a method to solve a sparse linear equation A*x=b, > where A is a NxN symmetric scipy.sparse.LinearOperator object, and b > is a 1D numpy vector. The obvious choice would be something like > scipy.sparse.linalg.cg. The problem is, the condition number of A is > very large - on order of 10^26. From a search through relevant > literature, I know that matlab's preconditioned conjugate gradient > (pcg) routine works well for the type of problem I'm dealing with. Is > there any similar routine in scipy? > I've looked at scipy.sparse.linalg.eigen.lobpcg, which seems to be > along the lines of what I need. I could use this to find the inverse, > but that would involve computing an NxN dense matrix of eigenvectors, > which will cause memory problems in my case. Any help would be > appreciated! > -Jake > Note that your matrix A must also be (positive or negative) definite, or the conjugate gradient method may break down. If A is indefinite, you will probably be safer with MINRES, CRAIG or SYMMLQ. As another reader pointed out, you still need to supply a good preconditioner. Often, the application suggests the kind of preconditioner that works well. -- Dominique

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putmask and NaNs
by kpal 25 Feb '10

25 Feb '10

Hi, I observe a strange thing. scipy.putmask seems to treat NaN as zero in the following piece of code: mydata = scipy.array(((1,2,3,4,5),(1,1,-1,1,-1))).T scipy.putmask(mydata[:,0], mydata[:,1]<0, [scipy.NaN]) At this moment I expect some of the values in mydata to be replaced by NaNs, but they get replaced by zeros. Is this behaviour normal? Or is it a bug? Or is there another way to put NaNs according to a mask? Python 2.5, scipy 0.7.0 Thanks,

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Re: [SciPy-User] monic option of orthogonal polynomials
by Jonathan Stickel 25 Feb '10

25 Feb '10

Yep, that must be it. Thanks! Jonathan On 2/24/10 scipy-user-request(a)scipy.org wrote: > Date: Wed, 24 Feb 2010 22:25:20 +0100 > From: nicky van foreest <vanforeest(a)gmail.com> > Subject: Re: [SciPy-User] monic option of orthogonal polynomials > To: SciPy Users List <scipy-user(a)scipy.org> > > Hi, > > The coefficient of a highest order term is 1 for a monic polynomial. > Might it be this? > > bye > > Nicky > > On 24 February 2010 22:20, Jonathan Stickel <jjstickel(a)vcn.com> wrote: >> > What does the "monic" option do when calling an orthogonal polynomial >> > function, e.g. special.legendre? ?It does not seem to be documented, or >> > at least I have not found it. >> > >> > Thanks, >> > Jonathan >> >

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noisy interpolators, smoothers and fitters
by josef.pktd＠gmail.com 25 Feb '10

25 Feb '10

Is there a need for expanding this in scipy.interpolate? see also offer by Jonathan Stickel which looks useful with penalization on derivatives (if my quick reading is correct). Josef

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preconditioned conjugate gradient
by Jake VanderPlas 25 Feb '10

25 Feb '10

Hello, I'm looking for a method to solve a sparse linear equation A*x=b, where A is a NxN symmetric scipy.sparse.LinearOperator object, and b is a 1D numpy vector. The obvious choice would be something like scipy.sparse.linalg.cg. The problem is, the condition number of A is very large - on order of 10^26. From a search through relevant literature, I know that matlab's preconditioned conjugate gradient (pcg) routine works well for the type of problem I'm dealing with. Is there any similar routine in scipy? I've looked at scipy.sparse.linalg.eigen.lobpcg, which seems to be along the lines of what I need. I could use this to find the inverse, but that would involve computing an NxN dense matrix of eigenvectors, which will cause memory problems in my case. Any help would be appreciated! -Jake

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Parallel Differential Evolution
by Debabrata Midya 25 Feb '10

25 Feb '10

Hi SciPy-Users, Thanks in advance. I am Deb, Statistician at NSW Department of Services, Technology and Administration, Sydney, Australia. I am new to this user group as well as to Python and Parallel Python. I am interested to use Differential Evolution (DE) using Parallel Python on Windows XP. I have some experience in DE using GNU GCC compiler. Is there any software for it? If yes, anyone can assist me what are the softwares I need to install and their locations please. Once again, thank you very much for the time you have given. I am looking forward for your reply. Regards, Deb

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[Fwd: New physicsworld.com webinar – Why software fails, and how to fix it]
by James Turner 25 Feb '10

25 Feb '10

Hi everyone (long time no post), I thought some of you might be interested in this Web seminar on scientific software... As far as I can tell it is open to non-members. Cheers, James. -------- Original Message -------- Subject: New physicsworld.com webinar – Why software fails, and how to fix it Date: Thu, 25 Feb 2010 02:22:11 +0000 From: <removed to avoid spam> We are pleased to announce the latest physicsworld.com webinar sponsored by Tessella. A Crack in the Code: Why software fails in scientific research, and how to fix it Date: Thursday 25 March 2010 Time: 3 p.m. GMT (4 p.m. Central Europe, 11 a.m. East Coast US) Where: online - registration is FREE at http://physicsworld.com/cws/go/webinar9 Why you should attend: Millions of lines of code have been developed to support scientific research. Although an increasingly important part of almost all research projects, most of this software is barely fit for purpose compared with equivalent systems in the commercial world. The code is hard to understand and maintain, lacking documentation and version control, and is continually "re-invented" as the software developers move on to new projects. This webinar will investigate how this situation has come about, why it is important to the future of research, and what can be done about it. Reserve your free place now - follow this link http://physicsworld.com/cws/go/webinar9 Kind regards Angela Gage Marketing executive http://physicsworld.com *Data Protection* The Institute of Physics (and other companies in its group, including IOP Publishing Limited) may like to send you further notifications like this. If you would prefer not to receive these, then please reply to this e-mail with the word "unsubscribe" in the subject line. We will never rent or sell your e-mail address to any third parties.

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Scipy dblquad Calculation
by Scott Barlow 25 Feb '10

25 Feb '10

Hello all. I'm writing some code and need to compute a double integral. I'm trying to calculate this using dblquad, but I'm having an issue with the result. I get a result and no error messages, but the result is not as expected. I've check the code over and over and I see no mistakes, so I'm asking for suggestions here (or reasons for the difference). I realize that the difference could be from (quadrature) estimations, but I don't think that is it. Python version: 2.6.4 SciPy version: 0.7.0 NumPy version: 1.3.0 Please see the code below. This describes the equation for the bending rigidity of an elliptical composite laminate of 16 plies under bending. Note that "a" and "b" would be the length in inches of the major and minor axes, and I have them both set to 2.0 here, thus this is actually a circle. The bottom equation (Final_Result_2) is the reduced equation for a circular tube (no integration required). Thus when a=b, the result between the two equations should be the same. When run, I get the following: 21560941.3345 (Final_Result) 22852651.614 (Final_Result_2) As mentioned before, I've checked the equations numerous times for typos, but I haven't found any. I'm quite sure the equation is input correctly. I've also tried using arctan2 instead of arctan, but I get the same result. Here is the python code: --------------------------------------------------------------------------- ------------- import scipy import numpy from scipy.integrate import dblquad Q11 = 20009576.0 Q22 = 1397653.8 Qbar11 = [Q11, Q11, Q11, Q11, Q22, Q22, Q22, Q22, Q22, Q22, Q22, Q22, Q11, Q11, Q11, Q11]; a = 2.0; b = 2.0; thickness = 0.005; Final_Result = 0.0; for i in range(1, 17): Temp_Result = dblquad(lambda theta, t: Qbar11[i-1] * scipy.sin(theta)**2.0 * ( 1.0*((a**2.0 * b**2.0)/((a**2.0 - b**2.0)*scipy.sin(theta)**2.0 + b**2.0)) + t**2.0 + 2.0*((a**2.0 * b**2.0)/((a**2.0 - b**2.0)*scipy.sin(theta)**2.0 + b**2.0))**(0.5)* t*scipy.sin(theta + scipy.arctan((a**2.0/b**2.0)*(1.0/scipy.tan(theta)))) )**(3.0/2.0), thickness*(i-1), thickness*(i), lambda x: 0.0, lambda x: 2.0*scipy.pi, epsabs = 1.4899999999999999e-12, epsrel = 1.4899999999999999e-12); Final_Result += Temp_Result[0]; print Final_Result; ri = a; Final_Result_2 = 0.0; for i in range(1, 17): Final_Result_2 += (scipy.pi/4.0) * Qbar11[i-1] * ((ri + thickness*(i))**4.0 - (ri + thickness*(i-1))**4.0); print Final_Result_2; --------------------------------------------------------------------------- ------------- Thanks for any help, Scott

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Bala Chandar wants to stay in touch on LinkedIn
by Bala Chandar 25 Feb '10

25 Feb '10

LinkedIn ------------ I'd like to add you to my professional network on LinkedIn. - Bala Chandar Confirm that you know Bala Chandar https://www.linkedin.com/e/isd/1101386560/jWSr8Hwq/EML-invg_56/ ------ (c) 2010, LinkedIn Corporation

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