Mailman 3 April 2012 - SciPy-Dev

My Intro
by Patrick "Kai" Baker April 21, 2012

April 21, 2012

I have never worked on an open source project before so I'm not exactly sure what I'm doing. But I'm real excited about doing this. I have an advanced degree in mathematics and an emphasis on mathematical physics. I love numerical analysis and coding so I'm real excited about joining the SciPy team. At some point, once I get a handle on things, I will like to contribute to the documentation as well. I guess I will hang out, figure out what's going on, read the archives, and check out the source code. Real excited about this! Regards, Kai

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PR #196: simplification of optimization wrappers
by Denis Laxalde April 19, 2012

April 19, 2012

Hi, In pull request #196, I am proposing to simplify the signature of optimization wrappers (minimize, minimize_scalar, etc.) to get something like: x, info = minimize(fun, x0, [jac, constraints], options) This boils down to eliminating the full_output and retall parameters from respective function. Besides, the info dictionnary would always be returned. Comments welcome - https://github.com/scipy/scipy/pull/196 Thanks, -- Denis

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PR #195: optimize.root - interface to root finding algorithms
by Denis Laxalde April 18, 2012

April 18, 2012

Hi, In pull request #195, I am proposing the addition of a unified interface `optimize.root` for root finding algorithms for multivariate functions, similar to `optimize.minimize` and `optimize.minimize_scalar` (already merged). Available solvers are: - hybr: MINPACK's hybrid Powell algorithm (fsolve) - lm: MINPACK's Levenberg-Marquardt algorithm (leastsq) - and solvers from optimize.nonlin (broyden1, krylov, etc.) See <https://github.com/scipy/scipy/pull/195>. Comments welcome. Thanks, Denis.

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testbench for some non-derivative optimizers in NLopt and scipy.optimize
by denis April 17, 2012

April 17, 2012

Folks, fwiw, https://github.com/denis-bz/opt has testbenches for some of the non-derivative optimizers in NLopt and scipy.optimize. >From opt-py.Readme -- These few test functions are *very* noisy, sensitive to random seed: fmin 0.4 neval 369 LN_BOBYQA fmin 3 neval 580 LN_BOBYQA fmin 0.06 neval 423 LN_BOBYQA fmin 12 neval 385 LN_BOBYQA fmin 2 neval 325 LN_BOBYQA ... ==> we need test functions. Comments, any birds of a feather ? cheers -- denis

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ANN: SpikeSort 0.12
by Bartosz Telenczuk April 16, 2012

April 16, 2012

We are pleased to announce the first official release of the SpikeSort, a new spike sorting library based on dynamic and interactive language (Python). SpikeSort 0.12 is available for download at http://spikesort.org SpikeSort aims to be both flexible and user-friendly. It achieves that by defining a set of components that can be mix-and-matched to fit specific needs. These components are based on standard scientific libraries, so they can be easily re-used in custom applications. Main features: * user-friendly and customisable, * interactive command-line interface in Python, * visualization widgets, * k-means and gaussian-mixture-models (GMM) clustering algorithms and manual cluster cutting, * support for multi-channel data (for example, from tetrodes), * support for binary datasets and HDF5 files (support for other formats planned in future version). SpikeSort includes >60 tests making it one of the best-tested spike sorting software on the market. The distribution also contains full documentation, sample scripts and sample data, so that you can start playing with it almost immediately. SpikeSort is offered free of charge under a liberal Open Source license (two-clause BSD license) allowing for non-commercial and commercial use. The project was partially supported by Deutsche Forschungsgemeischaft (SFB 618 ``Theoretische Biologie'', project B4). The sample data is available by courtesy of Stuart Baker and Wellcome Trust who provided funding for data collection. Yours, Bartosz Telenczuk Dmytro Bielievtsov

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Trac unlock?
by Ralf Gommers April 15, 2012

April 15, 2012

Hi, SciPy Trac now seems permanently locked. Can someone who knows how please kick it so it works again after a few tries (or even on the first try)? Thanks, Ralf

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Could anyone compile win32 Py-2.7.2 module for SciPy-0.11dev as is?
by Евгений Черкашин April 12, 2012

April 12, 2012

Hi! I need some last features of signal processing developed in SciPy-0.11dev for merging in my project for Windows users, but I am not able to compile it myself for Win32 and Python-2.7.2. Can Anyone help me and send me a compiled module. It not necessary to the module to be stable one. Thank You and Best regards, Evgeny

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wrapped FITPACK's `sphere.f`
by Andreas H. April 11, 2012

April 11, 2012

Hi, I just wrapped FITPACK's `sphere.f` for scipy.interpolate, to make interpolation/smoothing in spherical coordinates possible. Here's the pull request: https://github.com/scipy/scipy/pull/192 Sorry for not discussing this here earlier, I just now found out about the "Contributing to SciPy" guide. Cheers, Andreas.

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stats.ttest_ind doc
by Deniz Turgut April 6, 2012

April 6, 2012

docs [1] say that ttest_ind: Calculates the T-test for the means of TWO INDEPENDENT samples of scores. This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. This function performs t-test for two independent samples with *identical variances*. There is also a form of t-test for independent samples with different variances, also known as Welch's t-test [2]. I think it is better to include the 'identical variance' assumption in the doc to avoid confusion. PS: I could do the edit. I registered but looks like I need edit rights. And apparently this is the place to ask for it. My username: avaris [1] http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.h… [2] http://en.wikipedia.org/wiki/Welch's_t_test

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cephes_smirnov never returns on mips/sparc/...
by Yaroslav Halchenko April 1, 2012

April 1, 2012

I have reported this issue some time ago on Debian http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=653948 and then forgot about it until now that I ran into it again. With recent scipy (including git master 0fbfdbc) scipy.stats.ksone.fit seems to stall (never return) on big-endian boxes and return (1.0, nan, nan) on x86. It seems that it is working correctly with scipy 0.7.2 (as it is now in Debian stable). So -- is that anything known? snippet to replicate: import numpy as np import scipy.stats as ss d = np.array([-0.18879233, 0.15734249, 0.18695107, 0.27908787, -0.248649, -0.2171497 , 0.12233512, 0.15126419, 0.03119282, 0.4365294 , 0.08930393, -0.23509903, 0.28231224, -0.09974875, -0.25196048, 0.11102028, 0.1427649 , 0.10176452, 0.18754054, 0.25826724, 0.05988819, 0.0531668 , 0.21906056, 0.32106729, 0.2117662 , 0.10886442, 0.09375789, 0.24583286, -0.22968366, -0.07842391, -0.31195432, -0.21271196, 0.1114243 , -0.13293002, 0.01331725, -0.04330977, -0.09485776, -0.28434547, 0.22245721, -0.18518199, -0.10943985, -0.35243174, 0.06897665, -0.03553363, -0.0701746 , -0.06037974, 0.37670779, -0.21684405]) print "Fitting now" print ss.ksone.fit(d) -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic

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