http://permalink.gmane.org/gmane.comp.python.scientific.user hourly 1 1901-01-01T00:00+00:00 http://gmane.org/img/gmane-25t.png http://gmane.org http://permalink.gmane.org/gmane.comp.python.scientific.user/18453 Well, if you can just use optimize.fmax and optimize.fmin together. if F is the function you want to minmax: def Fminimum(x): y0 = comeUpWithSomeStartingPoint() return optimize.fmin(F, y0, args = (x,)) Xminmax = optimize.fmax(Fminimum, x0) I am sure there are much better algorithms for this, though. On Dec 1, 5:34 am, Neal Becker <ndbeck...< at >gmail.com> wrote: John Salvatier 2008-12-02T02:11:38 http://permalink.gmane.org/gmane.comp.python.scientific.user/18452 Thank you all for your help. I think that writing a set of subroutines that creates derived data types from arguments passed to it and vice versa is probably my best bet, but I may try Huard's solution. On Dec 1, 9:58 am, "David Huard" <david.hu...< at >gmail.com> wrote: John Sal 2008-12-02T01:56:20 http://permalink.gmane.org/gmane.comp.python.scientific.user/18451 This looks like a spaces in path problem: your problematic path mixes short windows names and long windows names and is not in quotes ("...") c:\docume~1\wd15\locals~1\temp\wd15\python25_intermediate\compiler_08edc7e348e1c33f63a33ab500aef08e\Release\program files\pythonxy\python\lib\site-packages\scipy\weave\scxx\weave_imp.o I tried the same example on my Windows XP, where neither python nor scipy paths ( which is not in Python directory) have spaces and it compiles without errors. As a relatively quick fix, I would move scipy on a path without spaces, just move directory and link to new parent directory in easy-install.path. A quick look at my compilation log, shows that only spaces in the scipy.weave path are relevant. The spaces in the python shouldn't be a problem, because your link and include directories, e.g. -L"C:\Program Files\pythonxy\python\libs" seem all to be correctly quoted for the Windows command shell. I usually avoid any paths with spaces, because getting the quoting always right is a josef.pktd< at >gmail.com 2008-12-02T00:23:22 http://permalink.gmane.org/gmane.comp.python.scientific.user/18450 I have a similar issue when using pythonxy (2.1.4) (python version 2.5.2) and windows. The following import scipy print 'scipy.__version__',scipy.__version__ print 'scipy.__path__',scipy.__path__ from scipy import weave weave.inline('printf("hello world");', verbose=2) returns scipy.__version__ 0.6.0 scipy.__path__ ['C:\\Program Files\\pythonxy\\python\\lib\\site-packages\\scipy'] <weave: compiling> kw {'extra_link_args': [], 'define_macros': [], 'libraries': [], 'sources': ['C:\\Program Files\\pythonxy\\python\\lib\\site-packages\\scipy\\weave\\scxx\\weave_imp.cpp'], 'extra_compile_args': [], 'library_dirs': [], 'include_dirs': ['C:\\Program Files\\pythonxy\\python\\lib\\site-packages\\scipy\\weave', 'C:\\Program Files\\pythonxy\\python\\lib\\site-packages\\scipy\\weave\\scxx']} running build_ext running build_src building extension "sc_5c84b188757e017720cf8a0a3b0555304" sources customize Mingw32CCompiler customize Mingw32CCompiler using build_ext Daniel Wheeler 2008-12-01T23:53:36 http://permalink.gmane.org/gmane.comp.python.scientific.user/18449 Robert, Thx a lot for reporting, I'll take a better look ASAP. On Dec 1, 2008, at 3:21 PM, Robert Ferrell wrote: Pierre GM 2008-12-01T22:57:19 http://permalink.gmane.org/gmane.comp.python.scientific.user/18448 On Dec 1, 2008, at 11:54 AM, Pierre GM wrote: In [741]: matplotlib.__version__ Out[741]: '0.98.3' In [742]: ts.__version__ Out[742]: '0.67.0.dev-r1570' The error seems benign enough that I can ignore it. -robert Robert Ferrell 2008-12-01T20:21:52 http://permalink.gmane.org/gmane.comp.python.scientific.user/18447 Thought this might be of interest to the scipy crowd... Like PyTables it lets you store array data in a hierarchical format, and perform slicing and partial I/O, but it has a simpler, NumPy-oriented interface and also provides access to the majority of the HDF5 C API. However, it doesn't have the database-style indexing and query support of tables. ===================================== Announcing HDF5 for Python (h5py) 1.0 ===================================== What is h5py? ------------- HDF5 for Python (h5py) is a general-purpose Python interface to the Hierarchical Data Format library, version 5. HDF5 is a versatile, mature scientific software library designed for the fast, flexible storage of enormous amounts of data. store data, organized by name in a tree-like fashion. You can create datasets (arrays on disk) hundreds of gigabytes in size, and perform random-access I/O on desired sections. Datasets are organized in a filesystem-like hierarchy using containers called "groups", and accesed using Andrew Collette 2008-12-01T20:09:56 http://permalink.gmane.org/gmane.comp.python.scientific.user/18446 On Dec 1, 2008, at 1:44 PM, Robert Ferrell wrote: I'll let other users answer that. I never used that frequency myself. Looks familiar... What version of matplotlib and scikits.timeseries are you using? No. I never took the time to find out what I can't get the x axis position under the cursor either, but the two issues are unrelated: the error you see comes from an update of matplotlib that hasn't been ported yet to scikits.timeseries. Pierre GM 2008-12-01T18:54:29 http://permalink.gmane.org/gmane.comp.python.scientific.user/18445 On Nov 28, 2008, at 12:03 PM, Pierre GM wrote: It does work quite well. When I plot I have a few holes in the data (at holidays), but that's about the only issue I haven't resolved. How do you (or other users) use the Business frequency? Also, I get this error when I use tsplot: --------------------------------------------------------------------------- <type 'exceptions.AttributeError'> Traceback (most recent call last) /Users/Shared/Develop/Financial/<ipython console> in <module>() /Library/Frameworks/Python.framework/Versions/2.5.2001/lib/python2.5/ site-packages/scikits/timeseries/lib/plotlib.py in tsplot(self, *args, **kwargs) 1021 # when adding a right axis (using add_yaxis), for some reason the 1022 # x axis limits don't get properly set. This gets around the problem -> 1023 if self.get_xlim().tolist() == [0., 1.]: 1024 # if xlim still at default values, autoscale the axis 1025 self.autoscale_view() <type 'e Robert Ferrell 2008-12-01T18:44:52 http://permalink.gmane.org/gmane.comp.python.scientific.user/18444 _______________________________________________ SciPy-user mailing list SciPy-user< at >scipy.org http://projects.scipy.org/mailman/listinfo/scipy-user David Huard 2008-12-01T17:58:59 http://permalink.gmane.org/gmane.comp.python.scientific.user/18443 Any suggestions on techniques for minimax optimization? Neal Becker 2008-12-01T13:34:52 http://permalink.gmane.org/gmane.comp.python.scientific.user/18442 brent doesn't do constrained optimisation, while fminbound does. Consider: In [1]: import scipy.optimize as opt In [2]: f = lambda x: x*x In [3]: opt.brent(f, brack=(3, 4)) Out[3]: 0.0 In [4]: opt.fminbound(f, x1=3, x2=4) Out[4]: 3.00000596086 Hope that helps. Neil Muller 2008-12-01T12:29:11 http://permalink.gmane.org/gmane.comp.python.scientific.user/18441 Based on the documentation, I'm a bit unclear on how fminbound and brent, as optimization algorithms, differ. Could someone clarify this for me? -gideon Gideon Simpson 2008-12-01T00:01:58 http://permalink.gmane.org/gmane.comp.python.scientific.user/18440 A feasible way to achieve this would be to write a Fortran wrapper around your routine(x) that decomposes your derived data type to standard types and exposes these in the interface. Than you can compose the derived data type again in the wrapper and pass it to the original routine. :: module geom type Point real :: x, y end type Point type Circle type (Point) :: Center real :: Radius end type Circle end module geom subroutine test(c) use geom type (Circle) :: c print*, c%Radius print*, c%Center%X print*, c%Center%Y end subroutine test subroutine w_test(x, y, r) use geom real :: x, y, z type (Circle) :: C c%Radius = r c%Center%X = x c%Center%Y = y call test(c) end subroutine w_test Wrapping w_test should be trivial using f2py Regards Berthold Berthold Höllmann 2008-11-30T21:38:14 http://permalink.gmane.org/gmane.comp.python.scientific.user/18439 Note that this release includes binaries for Linux/Windows/MacOSX. The embedding interface is now more robust and works under all platforms, including Windows. Veusz 1.2.1 ----------- Velvet Ember Under Sky Zenith ----------------------------- http://home.gna.org/veusz/ Veusz is Copyright (C) 2003-2008 Jeremy Sanders <jeremy< at >jeremysanders.net> Licenced under the GPL (version 2 or greater). Veusz is a scientific plotting package. It is written in Python, using PyQt4 for display and user-interfaces, and numpy for handling the numeric data. Veusz is designed to produce publication-ready Postscript/PDF output. The user interface aims to be simple, consistent and powerful. Veusz provides a GUI, command line, embedding and scripting interface (based on Python) to its plotting facilities. It also allows for manipulation and editing of datasets. Change in 1.2.1: * Fix crash when adding a key without any key text defined. Changes in 1.2: * Boxes, ellipses, lines, arrows and image files can now be added to Jeremy Sanders 2008-11-30T13:14:55 http://permalink.gmane.org/gmane.comp.python.scientific.user/18438 2008/11/30 David Cournapeau <david< at >ar.media.kyoto-u.ac.jp>: I've read an article (I don't remember where though, possibly CiSE) that stated that it's really not an easy task, as each Fortran compiler can do as it pleases it. So depending on the compiler and the Fortran standard, it can be possible, or not. So as there are no guaranties, you should write a function that transforms the Fortran structure in several pieces that are then passed to the C function. Matthieu Matthieu Brucher 2008-11-30T12:21:20 http://permalink.gmane.org/gmane.comp.python.scientific.user/18437 It is possible as long as you can pass the structure from fortran to C. I don't know anything about Fortran derived data types, but if it is a non trivial object (more than a set of fundamental types), I am afraid it will be difficult. Does F90 supports POD data ? Otherwise, you will need a scheme for marshalling your data from Fortran to C (to match exactly how the structure would look like in C at the binary level). David David Cournapeau 2008-11-30T08:49:09 http://permalink.gmane.org/gmane.comp.python.scientific.user/18436 _______________________________________________ SciPy-user mailing list SciPy-user< at >scipy.org http://projects.scipy.org/mailman/listinfo/scipy-user John Salvatier 2008-11-30T07:45:51 http://permalink.gmane.org/gmane.comp.python.scientific.user/18435 attachment new_kstest.py Josef ''' this contains new kstest, test function for test suite and functions to compare with R, including Monte Carlo ------------------------------------------------------------------------------------------------------------- #simple example comparison to R (0.15865525393145707, 0.97727773578380395) # comparing with R array([ 2.77555756e-17, 2.56370438e-02]) (0.44435602715924361, 0.038850142705171065) # comparing with R array([ 0.00000000e+00, 2.61838240e-09]) (0.12464329735846891, 0.040989164077641749) (0.0072115233216311081, 0.98531158590396395) (0.12464329735846891, 0.08944488871182088) array([ 0.00000000e+00, 1.11022302e-16]) array([ 0., 0.]) array([ 8.67361738e-18, 1.66533454e-15]) raise attribute error instead of silent replace: with old version (0.058051726828984629, 0.49080925022181865) (0.058051726828984629, 0.49080925022181865) ''' import numpy as np import numpy.testing as npt from scipy import stats, s josef.pktd< at >gmail.com 2008-11-29T21:48:20 http://permalink.gmane.org/gmane.comp.python.scientific.user/18434 Since the old scipy.stats.kstest wasn't correct, I spent quite some time fixing and testing it. Now, I know more about the Kolmogorov-Smirnov test, than I wanted to. The kstest now resembles the one in R and in matlab, giving the option for two-sided or one-sided tests. The names of the keyword options are a mixture of matlab and R, which I liked best. Since the exact distribution of the two-sided test is not available in scipy, I use an approximation, that seems to work very well. In several Monte Carlo studies against R, I get very close results, especially for small p-values. (For those interested, for small p-values, I use ksone.sf(D,n)*2; for large p-values or large n, I use the asymptotic distribution kstwobign) example signature and options: kstest(x,testdistfn.name, alternative = 'unequal', mode='approx')) kstest(x,testdistfn.name, alternative = 'unequal', mode='asymp')) kstest(x,testdistfn.name, alternative = 'larger')) kstest(x,testdistfn.name, alternative = 'smal josef.pktd< at >gmail.com 2008-11-29T21:46:14 http://permalink.gmane.org/gmane.comp.python.scientific.user/18433 You have an explanation of the structure on the TRAC : http://scipy.org/scipy/scikits/wiki/Optimization There is a link to a tutorial, and the list of stuff that still needs to be implemented (and tested, of course). I didn't find the time to do that for the moment. My job takes a lot of my time, and I do not use the framework for it. Matthieu Matthieu Brucher 2008-11-29T12:20:11 Search the mailing list at Gmane query http://search.gmane.org/?group=$group=gmane.comp.python.scientific.user