Mailman 3 November 2009 - SciPy-User

sinc interpolation
by David Trem Nov. 25, 2009

Nov. 25, 2009

Hello, Is sinc interpolation available in Scipy ? I've just ask this question to Travis Oliphant during the entought webinar that had just ended but unfortunately I was not able to ear the reply due to poor sound quality just at that moment :-( Hope someone could give me his or a reply to this question. Thanks, David

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problem with optimize.curve_fit
by Luca Delucchi Nov. 25, 2009

Nov. 25, 2009

Hi everybody i try to use optimize.curve_fit but i have a error gis@srvcavit:~/meteo_python$ python prova_optimize.py Traceback (most recent call last): File "prova_optimize.py", line 23, in <module> popt, pcov = curve_fit(func,x,old_y,2) File "/usr/lib/python2.5/site-packages/scipy/optimize/minpack.py", line 423, in curve_fit raise RuntimeError, "Optimal parameters not found: " + mesg RuntimeError: Optimal parameters not found: Both actual and predicted relative reductions in the sum of squares are at most 0.000000 and the relative error between two consecutive iterates is at most 0.000000 i see that is a bug [0], i try to modify the script with the solution proposed by kael but nothing change, here [1] you can find the script that i use, how can i solve my problem? thanks Luca [0] http://projects.scipy.org/scipy/ticket/984 [1] http://pastebin.com/m3c721c6f

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gamma ppf weirdness
by Art Poon Nov. 25, 2009

Nov. 25, 2009

Hello, I'm trying to hammer out some quick simulation code and need to calculate a bunch of inverse CDF values from the gamma distribution. SciPy seems like a great resource for this. However, I've encountered some strangeness that is probably my own fault: >>> g = stats.gamma(1,0,1) >>> g <scipy.stats.distributions.rv_frozen object at 0x1b9b2b0> >>> g.ppf(0.1) 0.10536051565782635 >>> g.ppf(0.25) 0.0 >>> g.ppf(0.2500001) 0.28768220578512327 >>> g.ppf(0.2499999) 0.28768193911845646 >>> g.ppf(0.25) 0.0 I'm just dying to know where I've gone wrong here! In the meantime, I'm coding up a function to compute the inverse CDF from MATLAB code. I'm using Snow Leopard with Python 2.5.4 (bypassing default system Python 2.6.1), numpy-1.3.0 and scipy-0.7.1, both compiled from source. Thanks! - Art.

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ANN: SfePy 2009.4
by Robert Cimrman Nov. 25, 2009

Nov. 25, 2009

I am pleased to announce release 2009.4 of SfePy. Description ----------- SfePy (simple finite elements in Python) is a software, distributed under the BSD license, for solving systems of coupled partial differential equations by the finite element method. The code is based on NumPy and SciPy packages. Mailing lists, issue tracking, git repository: http://sfepy.org Home page: http://sfepy.kme.zcu.cz New documentation site: http://docs.sfepy.org/doc Many thanks to Logan Sorenson for the new documentation contents, and Vladimir Lukes for setting up the server. Highlights of this release -------------------------- - unified handling of user-defined functions (for defining subdomains, heterogeneous material properties, boundary conditions etc.) - greatly improved postprocessing and visualization capabilities, namely: - support for file sequences (evolutionary simulations) - animations (using ffmpeg) - automatic scalar bars - sfepy_gui.py: Mayavi2-based GUI to launch simulations Major improvements ------------------ Apart from many bug-fixes, let us mention: - quasistatic time stepping - graphical logging: - dynamic adding of data groups (new axes) to Log and ProcessPlotter - linear algebra: - reversed Cuthill-McKee permutation algorithm, graph in-place permutation - setting of parameter variables by a user-defined function - new tests and terms For more information on this release, see http://sfepy.googlecode.com/svn/web/releases/2009.4_RELEASE_NOTES.txt (full release notes, rather long). Best regards, Robert Cimrman

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Mailing list?
by Simon Friedberger Nov. 23, 2009

Nov. 23, 2009

Hi everybody, I sent a message about the K-Means algorithm a couple of days ago but it seems like it never made it on the list. Are new members moderated or something? Best Simon

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Modified Bessel functions of the first kind
by Matthieu Brucher Nov. 23, 2009

Nov. 23, 2009

Hi, I need the zero-order modified Bessel function of the first kind. I've seen the scipy.special.besselpoly, but I don't know if it really is what I'm looking for... Does someone know? Matthieu -- Information System Engineer, Ph.D. Website: http://matthieu-brucher.developpez.com/ Blogs: http://matt.eifelle.com and http://blog.developpez.com/?blog=92 LinkedIn: http://www.linkedin.com/in/matthieubrucher

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Re: [SciPy-User] timeseries forwardfill
by Christopher Clarke Nov. 23, 2009

Nov. 23, 2009

Hi Something seems to have gone wrong with my initial reply!! Anyway, I often encounter the "initial values" use case when I am creating business day time series out of RDBMS tables using a subset of the observations in the table. For example i have a SQL query fragment like WHERE symbol='SFC' and dateix BETWEEN '2009-01-01' AND '2009-09-01' Now suppose that 2009-01-02 and 2009-01-05 are missing (trading is sparse on many of the exchanges i'm dealing with) i am supposed to forward_fill using the last traded value for SFC which may or may not be 2008-12-31. Hence i have a query that find the last traded values and i use these as the "initial values". Anyway here is by forward_fill wrapper. Its not very efficient as i'm copying and forward_fill is copying etc but.. I'm actually starting to have reservation about the usefulness forward_fill on 2d as opposed to the individual the individual series arrays as i am finding that i've often got to do loads of transformations and checking on the individuals arrays before i can combine them into a single ma array for filling anyway def forward_fill2(marr,maxgap=None,init_vals=None): """ init_vals a list with the same no. of elements as marr.shape[1] """ arr=ma.array(marr,copy=True if arr.ndim == 1: if init_vals: if arr.mask.any() and arr.mask[0]: if init_vals: arr[0] = init_vals[0] return forward_fill(arr,maxgap) else: n = arr.shape[1] if init_vals: mask=ma.getmask(arr) if len(init_vals) != n: raise ValueError, 'Initial Values sequence does no match number of columns' for c in range(len(init_vals)): if arr.mask.any() and mask[0,c]: if init_vals[c]: arr[0,c]=init_vals[c] arr = ma.hsplit(arr, n)[0] return ma.column_stack([forward_fill(np.squeeze(a),maxgap) for a in arr]) On Thu, Nov 19, 2009 at 5:07 AM, Chris Clarke <cclarke(a)chrisdev.com> wrote: > Hi > The "initial value" > > On Nov 18, 2009, at 9:17 PM, Pierre GM wrote: > > > On Nov 18, 2009, at 5:18 PM, Chris Clarke wrote: > > Sorry for the later reply. Yes forward_fill is still there and it > > works!!! > > > Good > > But it seemed to have some more capability (initial values, 2d arrays) > > when it was in the sandbox?? > > I may be wrong and mixing up with some other library. > > > That does sound familiar, but i don't think it was part of > scikits.timeseries... > A patch for 2D would be welcome, I'm not quite sure what you mean by > initial value, though > > _______________________________________________ > SciPy-User mailing list > SciPy-User(a)scipy.org > http://mail.scipy.org/mailman/listinfo/scipy-user > > >

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Incoherent results with signal.impulse
by Cédrick FAURY Nov. 22, 2009

Nov. 22, 2009

Hello, I have scipy 0.7.1, python 2.6, and when I do : n = scipy.array([1]) d = scipy.array([0.01, 0.2, 1.0]) T, yout = scipy.signal.impulse((n,d)) it gives incoherent results for yout. And that doesn't occurs with [1.0, 2.0, 1.0] denominator. Is it a bug ? I'm doing something wrong ? Is anybody knows a solution ? Thanks by advance Cédrick FAURY

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Fitting a curve on a log-normal distributed data
by Gökhan Sever Nov. 21, 2009

Nov. 21, 2009

Hello, I have a data which represents aerosol size distribution in between 0.1 to 3.0 micrometer ranges. I would like extrapolate the lower size down to 10 nm. The data in this context is log-normally distributed. Therefore I am looking a way to fit a log-normal curve onto my data. Could you please give me some pointers to solve this problem? Thank you. -- Gökhan

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finite element packages
by Young, Karl Nov. 21, 2009

Nov. 21, 2009

I'm trying to model a flexible flywheel (hence my question about Wierstrass elliptic functions a couple of weeks ago - thanks again for the helpful replies). I'm now trying to consider realistic models with elastic materials that go beyond my abilities to model analytically and figured I need to look at finite element models. I haven't used finite element packages and was wondering if anyone on the list had any recommendations, preferably scipythonic but I'm just curious generally about what people would consider using for a problem like this (i.e. a rotating flexible rope type problem). Thanks for any thoughts, -- Karl

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