Mailman 3 September 2008 - SciPy-User

vstack / hstack question n dim array without value
by Unknown Sept. 23, 2008

Sept. 23, 2008

Hello I am programming this for the Machine Learning Course. I am studying Computer Science. My Question is can I somehow get rid of the "indices_buffer". Means is there a easy way to initialize n dim arrays without values ? I also came up with filling the array with a colum of zeros and deleting this colum later, but not so elegant in my opinion either. specifically this code: indices_buffer=[] if (indices_buffer==[]): indices_buffer=tmp else: indices_buffer=hstack((indices_buffer,tmp)) -------------------------------------------- data 0.3 0.4 0.4 0.2 0.1 0.3 0.3 0.2 0.4 ... cls 1 1 2 ... def splitcrossval(data,nFold,cls): indices_buffer=[] (rows,columns)=data.shape classes=unique(cls) for i in classes: indices=where(cls==i)[0] tmp=hsplit((permutation(indices)),nFold) if (indices_buffer==[]): indices_buffer=tmp else: indices_buffer=hstack((indices_buffer,tmp)) return indices_buffer

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ANN: Enthought Python Distribution 4.0.300 Beta 2 available
by Travis Vaught Sept. 22, 2008

Sept. 22, 2008

Greetings, We've recently posted the second beta release of the Enthought Python Distribution (EPD) for our upcoming general release of version 4.0.300 with Python 2.5. You may download the beta from here: http://www.enthought.com/products/epdbeta.php Please feel free to test it out and provide feedback on the EPD Trac instance: https://svn.enthought.com/epd You can check out the release notes here: http://www.enthought.com/products/epdbetareleasenotes.php About EPD --------- The Enthought Python Distribution (EPD) is a "kitchen-sink-included" distribution of the Python™ Programming Language, including over 60 additional tools and libraries. The EPD bundle includes NumPy, SciPy, IPython, 2D and 3D visualization, database adapters, and a lot of other tools right out of the box. http://www.enthought.com/products/epd.php It is currently available as a single-click installer for Windows XP (x86), Mac OS X (a universal binary for Intel 10.4 and above) and RedHat EL3 (x86 and amd64). EPD is free for academic use. An annual Subscription and installation support are available for individual commercial use (http://www.enthought.com/products/epddownload.php ). An Enterprise Subscription with support for particular deployment environments is also available for commercial purchase (http://www.enthought.com/products/enterprise.php ). The Beta versions of EPD are available for indefinite free trial. Thanks, Travis

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Talkbox 0.1: a scikits for signal processing processing with a focus on audio
by David Cournapeau Sept. 22, 2008

Sept. 22, 2008

Hi there, A quick email to announce the first release of talkbox, a new scikit of mine. It aims at providing missing features in scipy for signal processing, with a focus on audio (speech and music mostly). The 0.1 is mainly a refactoring of some random code I got lying around on my machine: http://www.ar.media.kyoto-u.ac.jp/members/david/softwares/talkbox/index.html Code available on scikits svn. For this 0.1 release: - code for linear prediction coding (LPC) computation: it solves the Yule Walker equations, both with the straightforward system inversion and Levinson Durbin. A C implementation of Levinson Durbin for real, double autocorrelation is available: it should be reasonably fast (should be at least comparable to matlab lpc implementation I think). - methods for spectral estimation: basic periodogram is implemented. cheers, David

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Proceedings of the SciPy conference.
by Gael Varoquaux Sept. 22, 2008

Sept. 22, 2008

The SciPy conference proceedings are finally available online: http://conference.scipy.org/proceedings/SciPy2008 . I hope you enjoy them. I find it great to have this set of excellent articles talking about works done with, or for, Python in science. For me, it is a reference to remember what was said at the conference. I hope it can also be interesting for people who were not present at the conference. I apologize for being so slow at publishing them. In addition to the round trip between authors and editors taking a while, I have been travelling back home and spent way too much time last week finishing off administrative duties in the US. Gaël

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[ Python(x,y) ] New release : 2.1.0
by Pierre Raybaut Sept. 20, 2008

Sept. 20, 2008

Hi all, As you may already know, Python(x,y) is a free scientific-oriented Python Distribution based on Qt and Eclipse providing a self-consistent scientific development environment. Release 2.1.0 is now available on http://www.pythonxy.com. (Full Edition, Basic Edition, Light Edition, Custom Edition and Update) *Major update* o Python(x,y) installation is now fully customizable: install only what you need! (dependencies are handled by the installer) (http://www.pythonxy.com/plugins.php) o Python(x,y) is now designed for (instead of compatible with) Windows Vista as well as for Windows XP (http://www.pythonxy.com/_tools/img.php?lang=&img=//_images/Interactive%20co…) Version 2.1.0 (09-19-2008) * Added: o Windows installer: fully customizable installation thanks to the new plugin-based installer - now you can install only what you need among all available Python(x,y) plugins (Python, Eclipse and Others) * Updated: o xy 1.0.6 (Installed plugins detection) o IPython 0.9.1 o Parallel Python 1.5.6 * Corrected: o Issue 14: Windows Vista / default Eclipse workspace folder was incorrect if "My Documents" folder has been moved from its default location o Issue 16: PyQt4 / plugins not found in Qt Designer Regards, Pierre Raybaut

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scikits.ann
by Nils Wagner Sept. 20, 2008

Sept. 20, 2008

Hi all, I cannot find a component "ann" in the trac http://scipy.org/scipy/scikits/ Where can I report a bug ? python setup.py install running install Checking .pth file support in /usr/local/lib64/python2.5/site-packages /usr/bin/python -E -c pass TEST PASSED: /usr/local/lib64/python2.5/site-packages appears to support .pth files running bdist_egg running egg_info running build_src building extension "scikits.ann._ANN" sources creating build creating build/src.linux-x86_64-2.5 creating build/src.linux-x86_64-2.5/scikits creating build/src.linux-x86_64-2.5/scikits/ann swig++: scikits/ann/ANN.i swig -python -c++ -Iscikits/ann -I/usr/local/include -o build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp -outdir build/src.linux-x86_64-2.5/scikits/ann scikits/ann/ANN.i building data_files sources writing requirements to scikits.ann.egg-info/requires.txt writing scikits.ann.egg-info/PKG-INFO writing namespace_packages to scikits.ann.egg-info/namespace_packages.txt writing top-level names to scikits.ann.egg-info/top_level.txt writing dependency_links to scikits.ann.egg-info/dependency_links.txt writing manifest file 'scikits.ann.egg-info/SOURCES.txt' installing library code to build/bdist.linux-x86_64/egg running install_lib running build_py creating build/lib.linux-x86_64-2.5 creating build/lib.linux-x86_64-2.5/scikits copying ./scikits/__init__.py -> build/lib.linux-x86_64-2.5/scikits creating build/lib.linux-x86_64-2.5/scikits/ann copying scikits/ann/info.py -> build/lib.linux-x86_64-2.5/scikits/ann copying scikits/ann/__init__.py -> build/lib.linux-x86_64-2.5/scikits/ann copying scikits/ann/setup.py -> build/lib.linux-x86_64-2.5/scikits/ann copying scikits/ann/version.py -> build/lib.linux-x86_64-2.5/scikits/ann copying build/src.linux-x86_64-2.5/scikits/ann/ANN.py -> build/lib.linux-x86_64-2.5/scikits/ann running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext customize UnixCCompiler customize UnixCCompiler using build_ext building 'scikits.ann._ANN' extension compiling C++ sources C compiler: g++ -pthread -fno-strict-aliasing -DNDEBUG -O2 -fmessage-length=0 -Wall -D_FORTIFY_SOURCE=2 -g -fPIC creating build/temp.linux-x86_64-2.5 creating build/temp.linux-x86_64-2.5/build creating build/temp.linux-x86_64-2.5/build/src.linux-x86_64-2.5 creating build/temp.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scikits creating build/temp.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scikits/ann compile options: '-Iscikits/ann -I/usr/local/include -I/usr/local/lib64/python2.5/site-packages/numpy/core/include -I/usr/include/python2.5 -c' g++: build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp:2563:25: error: ANN/ANN.h: No such file or directory In file included from build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp:2564: scikits/ann/kdtree.h:22:21: error: ANN/ANN.h: No such file or directory scikits/ann/kdtree.h:101:3: warning: no newline at end of file scikits/ann/kdtree.h:27: error: ANNpointArray does not name a type scikits/ann/kdtree.h:28: error: ISO C++ forbids declaration of ANNkd_tree with no type scikits/ann/kdtree.h:28: error: expected ; before * token scikits/ann/kdtree.h:43: error: ANN_KD_SUGGEST was not declared in this scope scikits/ann/kdtree.h: In constructor _kdtree::_kdtree(double*, int, int, int, int): scikits/ann/kdtree.h:44: error: pts was not declared in this scope scikits/ann/kdtree.h:44: error: annAllocPts was not declared in this scope scikits/ann/kdtree.h:53: error: tree was not declared in this scope scikits/ann/kdtree.h:53: error: expected type-specifier before ANNkd_tree scikits/ann/kdtree.h:53: error: expected `;' before ANNkd_tree scikits/ann/kdtree.h: In destructor _kdtree::~_kdtree(): scikits/ann/kdtree.h:57: error: pts was not declared in this scope scikits/ann/kdtree.h:57: error: annDeallocPts was not declared in this scope scikits/ann/kdtree.h:58: error: tree was not declared in this scope scikits/ann/kdtree.h: In member function void _kdtree::_knn2(double*, int, int, int, int, int*, int, int, double*, double) const: scikits/ann/kdtree.h:71: error: tree was not declared in this scope scikits/ann/kdtree.h: In member function const char* _kdtree::stringRep(bool) const: scikits/ann/kdtree.h:98: error: tree was not declared in this scope scikits/ann/kdtree.h:98: error: ANNtrue was not declared in this scope scikits/ann/kdtree.h:98: error: ANNfalse was not declared in this scope build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp:2563:25: error: ANN/ANN.h: No such file or directory In file included from build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp:2564: scikits/ann/kdtree.h:22:21: error: ANN/ANN.h: No such file or directory scikits/ann/kdtree.h:101:3: warning: no newline at end of file scikits/ann/kdtree.h:27: error: ANNpointArray does not name a type scikits/ann/kdtree.h:28: error: ISO C++ forbids declaration of ANNkd_tree with no type scikits/ann/kdtree.h:28: error: expected ; before * token scikits/ann/kdtree.h:43: error: ANN_KD_SUGGEST was not declared in this scope scikits/ann/kdtree.h: In constructor _kdtree::_kdtree(double*, int, int, int, int): scikits/ann/kdtree.h:44: error: pts was not declared in this scope scikits/ann/kdtree.h:44: error: annAllocPts was not declared in this scope scikits/ann/kdtree.h:53: error: tree was not declared in this scope scikits/ann/kdtree.h:53: error: expected type-specifier before ANNkd_tree scikits/ann/kdtree.h:53: error: expected `;' before ANNkd_tree scikits/ann/kdtree.h: In destructor _kdtree::~_kdtree(): scikits/ann/kdtree.h:57: error: pts was not declared in this scope scikits/ann/kdtree.h:57: error: annDeallocPts was not declared in this scope scikits/ann/kdtree.h:58: error: tree was not declared in this scope scikits/ann/kdtree.h: In member function void _kdtree::_knn2(double*, int, int, int, int, int*, int, int, double*, double) const: scikits/ann/kdtree.h:71: error: tree was not declared in this scope scikits/ann/kdtree.h: In member function const char* _kdtree::stringRep(bool) const: scikits/ann/kdtree.h:98: error: tree was not declared in this scope scikits/ann/kdtree.h:98: error: ANNtrue was not declared in this scope scikits/ann/kdtree.h:98: error: ANNfalse was not declared in this scope error: Command "g++ -pthread -fno-strict-aliasing -DNDEBUG -O2 -fmessage-length=0 -Wall -D_FORTIFY_SOURCE=2 -g -fPIC -Iscikits/ann -I/usr/local/include -I/usr/local/lib64/python2.5/site-packages/numpy/core/include -I/usr/include/python2.5 -c build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.cpp -o build/temp.linux-x86_64-2.5/build/src.linux-x86_64-2.5/scikits/ann/ANN_wrap.o" failed with exit status 1 Nils

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Timeseries Cannot specify output type twice
by Lionel Roubeyrie Sept. 19, 2008

Sept. 19, 2008

Hi all, trying to use the last SVN version of the TimeSeries module, I run directly into problem on a simple test like this one : #################################### >> import numpy as np >>> import scikits.timeseries as ts >>> data = np.random.uniform(-100,100,600) >>> today = ts.now('B') >>> series = ts.time_series(data, dtype=np.float_, freq='B', start_date=today-600) >>> series Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib/python2.5/site-packages/scikits/timeseries/tseries.py", line 715, in __repr__ return desc_short % {'data': str(self._series), File "/usr/lib/python2.5/site-packages/scikits/timeseries/tseries.py", line 485, in _get_series return self.view(MaskedArray) File "/usr/lib/python2.5/site-packages/scikits/timeseries/tseries.py", line 471, in view output = MaskedArray.view(self, dtype=dtype, type=type) ValueError: Cannot specify output type twice. >>> np.__version__ '1.1.1' >>> ts.__version__ '0.67.0.dev-r1404' #################################### Is it a know problem or this problem comes from my installation? Thanks for your help -- Lionel Roubeyrie - lroubeyrie(a)limair.asso.fr Chargé d'études et de maintenance LIMAIR - la Surveillance de l'Air en Limousin http://www.limair.asso.fr

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How to get a ppf for scipy.stats.beta
by Michael Sept. 19, 2008

Sept. 19, 2008

Hi list, i am trying to use the percent point function to sample from the cdf for a beta distribution beta.ppf() returns an array instead of x; also you cannot create a ppf using the same idiom as beta.cdf(x,a,b,size=n) How do i go about getting the ppf for a beta dist? thanks in advance - much hairpulling on this <grr> Michael from scipy.stats import norm from scipy.stats import beta from scipy import linspace print norm.cdf(1.2) # 0.884930329778 print norm.ppf(0.884930329778) # 1.2 a=2 b=7 n=10e3 x=linspace(0,1,n) cdf=beta.cdf(x,a,b,size=n) print cdf[2371] # 0.6 B=beta(x,a,b,size=n) print B.cdf(2371) print B.ppf(0.6) # <-- should be 2371?

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delays and odeint
by Brian Blais Sept. 19, 2008

Sept. 19, 2008

Hello, I am trying to do some dynamics systems modeling, specifically modeling population dynamics. One piece of the model involves a delay, where you have something like: dy=x-delay(x,10) where a population, y, has an amount x added to it and an amount of x, from 10 time steps before, exiting. In this way the population, y, had members adding to it that last in population y for 10 time steps and then exit. Is there a good way of representing this in such a way that I can still use odeint to solve the resulting diff- eqs? I could write an Euler-method myself, and represent the delay as a list which I push from one end and pop from the other, but that seems a little hacky. odeint uses an adaptive step-size, so that solution wouldn't work. I wasn't sure if there already existed a tool for helping with this sort of thing. thanks, Brian Blais -- Brian Blais bblais(a)bryant.edu http://web.bryant.edu/~bblais

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Width of the gaussian in stats.kde.gaussian_kde ?
by torii＠gmx.com Sept. 19, 2008

Sept. 19, 2008

Dear scipy users, I used the kernel-density estimate to make some 2D density plots (stats.kde.gaussian_kde) and I was very happy with the result. But when I do the same exercise over a much larger area, I completely lost the details I had with my previous analysis... If I understand correctly, this is related to the adaptation of the elementary gaussian to the scale of my dataset which now includes large areas with almost no data. Question: is there a way to control the width of the gaussian in stats.kde.gaussian_kde? or should I switch to another technique? Note: I am both a newbie in python and stats... Anthony

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