Mailman 3 March 2008 - SciPy-User

fitting a parametric spline from a set of 2D points.
by LB 10 Mar '08

10 Mar '08

Hi, I've got a set of 2D points (~ 200 points) and I would like to fit a parametric spline with a limited number of control points (~ 10). These points can be seen a measures and contain an intrinsic error, so I would like to minimize the distance between theses points and the spline. Is there any tool in scipy which could help me in this task ? Otherwise, does someone know about an external library wich could help ? Thanks, -- LB

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Re: [SciPy-user] Problem with scipy.optimize.leastsq
by Mark Bakker 10 Mar '08

10 Mar '08

I think Dave is right. Whenever I use leastsq, I have a function that gives an array of errors. Leastsq does the squaring and summing for you, Mark I may be on the wrong track here, but what happens if you remove the > squaring > and summing from the chi2gauss - i.e. > > def chi2gauss(ARR): > a,b,x0,y0,s = ARR > noisemap = sqrt(im+RON**2) > return (im - gi(a,b,x0,y0,s,im))/noisemap > > > -Dave > > > > ------------------------------ > > _______________________________________________ > SciPy-user mailing list > SciPy-user(a)scipy.org > http://projects.scipy.org/mailman/listinfo/scipy-user > > > End of SciPy-user Digest, Vol 55, Issue 19 > ****************************************** >

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Re: [SciPy-user] Problem with scipy.optimize.leastsq: Improper input parameters
by Adam Ginsburg 10 Mar '08

10 Mar '08

I'm not sure if my previous e-mail (below) went out earlier, but I've spent a while trying to understand what the problem really meant, and I think it's that I'm trying to use leastsq for something it was not intended for. However, the Levenberg-Marquardt algorithm should be capable of this task. Specifically, I'm trying to minimize a function of 5 variables, while leastsq expects the number of functions m to be larger than the number of variables n. I figured out that my arg() variable(s?) determine the size of m, which seems like a bad idea to me but I can deal with it. My new error, then, is a claim that my 2d array somehow doesn't work (traceback below). Am I wrong in thinking that the function should be able to minimize over a 2D space, or am I just not specifying that properly in my function call? Thanks, and sorry to be flooding the list a little, Adam --------------------------------------------------------------------------- <type 'exceptions.ValueError'> Traceback (most recent call last) <type 'exceptions.ValueError'>: object too deep for desired array Error in sys.excepthook: Traceback (most recent call last): File "/sw/lib/python2.5/site-packages/IPython/iplib.py", line 1714, in excepthook self.showtraceback((etype,value,tb),tb_offset=0) File "/sw/lib/python2.5/site-packages/IPython/iplib.py", line 1514, in showtraceback self.InteractiveTB(etype,value,tb,tb_offset=tb_offset) File "/sw/lib/python2.5/site-packages/IPython/ultraTB.py", line 872, in __call__ self.debugger() File "/sw/lib/python2.5/site-packages/IPython/ultraTB.py", line 729, in debugger while self.tb.tb_next is not None: AttributeError: 'NoneType' object has no attribute 'tb_next' Original exception was: ValueError: object too deep for desired array --------------------------------------------------------------------------- <class 'minpack.error'> Traceback (most recent call last) /Users/adam/classes/probstat/hw7.py in <module>() 52 #a,b,x0,y0,s = fmin(chi2gaussnoglob,x0in,args=[im,RON]) 53 #print "Parameters a: %f b: %f x0: %f y0: %f sigma: %f chi2: %f" % (a,b,x0,y0,s,chi2gaussnoglob([a,b,x0,y0,s],im,RON)) ---> 54 leastsq(chi2gaussnoglob,x0in,args=(im),Dfun=None) 55 #leastsq(chi2gauss,x0in,maxfev=1000) 56 /sw/lib/python2.5/site-packages/scipy/optimize/minpack.py in leastsq(func, x0, args, Dfun, full_output, col_deriv, ftol, xtol, gtol, maxfev, epsfcn, factor, diag, warning) 267 if (maxfev == 0): 268 maxfev = 200*(n+1) --> 269 retval = _minpack._lmdif(func,x0,args,full_output,ftol,xtol,gtol,maxfev,epsfcn,factor,diag) 270 else: 271 if col_deriv: <class 'minpack.error'>: Result from function call is not a proper array of floats. > /sw/lib/python2.5/site-packages/scipy/optimize/minpack.py(269)leastsq() 268 maxfev = 200*(n+1) --> 269 retval = _minpack._lmdif(func,x0,args,full_output,ftol,xtol,gtol,maxfev,epsfcn,factor,diag) 270 else: On Sat, Mar 8, 2008 at 1:39 PM, Adam Ginsburg <adam.ginsburg(a)colorado.edu> wrote: > Hi, I've been trying to get the Levenberg-Marquardt minimization routine > leastsq to work and have received the error "Improper input parameters" when > using my own non-trivial function. > > The function I'm trying to minimize: > > def chi2gauss(ARR): > a,b,x0,y0,s = ARR > noisemap = sqrt(im+RON**2) > mychi2 = (( ( im - gi(a,b,x0,y0,s,im) ) / noisemap )**2).sum() > return mychi2 > > def gi(a,b,x0,y0,s,im): > myx,myy = indices(im.shape) > gi = b + a * exp ( - ( (myx-x0)**2 + (myy-y0)**2)/(2*s**2) ) > return gi > > where in this case im and RON are global variable, though I have also > tested with im and RON specified as input parameters using the args=() input > for leastsq. > > example: > > In [123]: leastsq(chi2gauss,[1,1,1,1,1],full_output=1) > 5 1 1 0.000000 0.000000 0.000000 1200 100.000000Out[123]: > (array([ 1., 1., 1., 1., 1.]), > None, > {'fjac': array([[ 0.], > [ 0.], > [ 0.], > [ 0.], > [ 0.]]), > 'fvec': 24222.5789746, > 'ipvt': array([0, 0, 0, 0, 0]), > 'nfev': 0, > 'qtf': array([ 0., 0., 0., 0., 0.])}, > 'Improper input parameters.', > 0) > > Can anyone help me out? What about my input is improper? > > Thanks, > Adam >

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[ANN] numscons 0.5.1: building scipy
by David Cournapeau 08 Mar '08

08 Mar '08

Hi, Mumscons 0.5.1 is available through pypi (eggs and tarballs). This is the first version which can build the whole scipy source tree. To build scipy with numscons, you should first get the code in the branch: svn co http://svn.scipy.org/svn/scipy/branches/build_with_scons And then build it like numpy: python setupscons.py install Technically speaking, you can build scipy with numscons above a numpy build the standard way, but that's not a good idea (because of potential libraries and compilers mismatches between distutils and numscons). See http://projects.scipy.org/scipy/numpy/wiki/NumScons for more details. The only tested platform for now are: - linux + gcc; other compilers on linux should work as well. - solaris + sunstudio with sunperf. On both those platforms, only a few tests do not pass. I don't expect windows or mac OS X to work yet, but I can not test those platforms ATM. I am releasing the current state of numscons because I won't have much time to work on numscons the next few weeks unfortunately. PLEASE DO NOT USE IT FOR PRODUCTION USE ! There are still some serious issues: - I painfully discovered that at least g77 is extremely sensitive to different orders of linker flags (can cause crashes). I don't have any problem anymore on my workstation (Ubuntu 32 bits, atlas + gcc/g77), but this needs more testing. - there are some race conditions with f2py which I do not fully understand yet, and which prevents parallel build to work (so do not use the scons command --jobs option) - optimization flags of proprietary compilers: they are a PITA. They often break IEEE conformance in quite a hard way, and this causes crashes or wrong results (for example, the -fast option of sun compilers breaks the argsort function of numpy). So again, this is really just a release for people to test things if they want, but nothing else. cheers, David

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Re: [SciPy-user] empty()
by Dinesh B Vadhia 07 Mar '08

07 Mar '08

Thanks! What I need to do is 1) create a very large sparse matrix A (with integer 1's and A is very sparse), 2) pickle the matrix and 3) unpickle it when needed in various modules. Performance of unpickling (ie. step 3) is important as the matrix is very large. Hence, in step 1, I didn't really want to initialize the matrix A with zeroes() because it is adding unnecessary 'weight' to the pickling/unpickling and hence performance. Appreciate your thoughts. Cheers! Dinesh ............................................................... Message: 4 Date: Thu, 6 Mar 2008 20:05:50 +0100 From: "Anne Archibald" <peridot.faceted(a)gmail.com> Subject: Re: [SciPy-user] empty() To: "SciPy Users List" <scipy-user(a)scipy.org> Message-ID: <ce557a360803061105o2cd1cdb7s82e7f777c732bafd(a)mail.gmail.com> Content-Type: text/plain; charset=UTF-8 On 06/03/2008, Dinesh B Vadhia <dineshbvadhia(a)hotmail.com> wrote: > I have to ask but in > > A= scipy.asmatrix(scipy.empty((I, J), int)) > > does the empty() function really 'empty' out the array or does it just > allocate space for the array leaving spurious content in the array? > > I want to make sure before starting to use empty() with wild abandon! empty() creates a fresh new array but does not touch the contents. Thus they can be anything; for freshly-allocated very large arrays they may be all zeros on some systems, but otherwise they usually contain malloc bookkeeping information or old data. Thus the array is packed with spurious content. It is probably a good idea to either use A = numpy.zeros(<whatever>) or, if you want to be sure to notice if you fail to set some element of the array, A = numpy.zeros(<whatever>)/0.0 (this latter ensures that the array is filled with "Not a Number", a special floating-point value that indicates invalid data). Only if you find your program has no bugs and is too slow is empty() a good idea. It's usually also worth trying to construct the array directly with the correct values - for example using arange() or eye() - rather than filling in values after the fact. I should also point out that both empy() and asmatrix() are *numpy* functions, not scipy. That they are also available in scipy is a historical quirk; scipy simply reexports some of the numpy functions in its own namespace (complicating the process of finding the real scipy functions). It's a better idea to access these functions through the numpy namespace. Anne

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gutsy amd64
by osman 07 Mar '08

07 Mar '08

Hi, I have ubunto 7.10 64 bit on AMD64 machine. The usual apt-get install will not install both scipy and libumfpack. One removes the other. Is there a fix to this? I have also downloaded the latest svn but it does not biuld. Errors like: build/src.linux-x86_64-2.4/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.c:6196: error: expected expression before ‘)’ token build/src.linux-x86_64-2.4/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.c:6196: error: too few arguments to function ‘SWIG_Python_NewPointerObj’ error: Command "gcc -pthread -fno-strict-aliasing -DNDEBUG -g -O2 -Wall -Wstrict-prototypes -fPIC -DSCIPY_UMFPACK_H -DSCIPY_AMD_H -DNO_ATLAS_INFO=2 -I/usr/local/include -I/usr/include -I/usr/lib/python2.4/site-packages/numpy/core/include -I/usr/include/python2.4 -c build/src.linux-x86_64-2.4/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.c -o build/temp.linux-x86_64-2.4/build/src.linux-x86_64-2.4/scipy/sparse/linalg/dsolve/umfpack/_umfpack_wrap.o" failed with exit status 1 stargate:/home/osman/SCIPY/scipy-bash-> my swig is : stargate:/home/osman/SCIPY/scipy-bash-> swig -version SWIG Version 1.3.31 Compiled with g++ [x86_64-unknown-linux-gnu] Please see http://www.swig.org for reporting bugs and further information stargate:/home/osman/SCIPY/scipy-bash-> any help is much appreciated. TIA -osman

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empty()
by Dinesh B Vadhia 06 Mar '08

06 Mar '08

I have to ask but in > A= scipy.asmatrix(scipy.empty((I, J), int)) does the empty() function really 'empty' out the array or does it just allocate space for the array leaving spurious content in the array? I want to make sure before starting to use empty() with wild abandon! Cheers

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help about sparse matrix
by jose luis Lopez Martinez 06 Mar '08

06 Mar '08

i am working in my thesis of master and i will work with sparse matrix and solve equations of lineal algebra of A . x = B the size of matrix is 128^4(268,435,456 rows)x 128^4(268,435,456 colums). the matrix have diagonald dominancies that is a sufficient condition for convergence of Jacobi iteration , gauss seidel and xor(relaxion method), but is slow for converge. it exist a command in scipy that solve sparse matrix using at the Gauss-Seidel iteration method o krylov iterative methods? thanks

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Re: [SciPy-user] unexpected MLP.solve() output: bug? [scikits-openopt]
by dmitrey 06 Mar '08

06 Mar '08

Ok, I have committed the changes. However, first of all you should see r.isFeasible==True and/or r.istop>=0. If r.isFeasible=true, r.ff was always a single number (lake it was before), if it's false - r.ff can be anything - inf, nan, float or ndarray ect. So I have made some changes and committed to svn (and updated tar.gz.file in OO install page), try now (I had no much time, so maybe this bug still remains somewhere and/or a new one have appeared) and inform if any other bugs will be encountered. Also, for more safety you could use by yourself f_opt = asfarray(r.ff).flatten()[0] Regards, D. > Dear Dmitrey, > > It happens sometimes that the object returned by NLP.solve() has > the .ff field of numpy.ndarray type instead of a float64 type. The > array has just one element, which is the current minimum of the > function, as expected. > > Example: > ---- > p=NLP(....) > r = p.solve('ralg') > print type(r.ff) > ---- > The output is usually "float64" and sometimes "numpy.ndarray". > > Since this is pretty unexpected and caused me some troubles (easily > solved) could you change to one of the two behaviors (or motivate)? > > Details: > openopt 0.15 > numpy 1.04 > scipy 0.5.2 > ubuntu linux x86_64 > > In any case, many thanks for you optimization library. > > Emanuele

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unexpected MLP.solve() output: bug? [scikits-openopt]
by Emanuele Olivetti 06 Mar '08

06 Mar '08

Dear Dmitrey, It happens sometimes that the object returned by NLP.solve() has the .ff field of numpy.ndarray type instead of a float64 type. The array has just one element, which is the current minimum of the function, as expected. Example: ---- p=NLP(....) r = p.solve('ralg') print type(r.ff) ---- The output is usually "float64" and sometimes "numpy.ndarray". Since this is pretty unexpected and caused me some troubles (easily solved) could you change to one of the two behaviors (or motivate)? Details: openopt 0.15 numpy 1.04 scipy 0.5.2 ubuntu linux x86_64 In any case, many thanks for you optimization library. Emanuele

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