Mailman 3 May 2012 - SciPy-Dev

scipy.stats documentation
by nicky van foreest May 7, 2012

May 7, 2012

Hi, I am still struggling to understand some of the scipy stats package, and ran into some obscure points. 1) What is actually the shape parameter? Let me include some references to show my confusion here. In expon it does not seem to exist: https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L27… Then, in Erlang it is called 'n'. I suppose this would mean the number of stages. So in Erlang, why then is the scale parameter corresponding to the shape? BTW: should the scale in the erlang dist dosctring not be explained? Then, from the gamma dist I learn the following: https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L33… So that would mean that in the expon dist the shape is set to 1. Then, here: http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.erlang.html it states that the shape parameter should be an int, but in the examples section it is set to 0.9, i.e., the documentation states this: >>> from scipy.stats import erlang >>> numargs = erlang.numargs >>> [ n ] = [0.9,] * numargs >>> rv = erlang(n) from which I infer that the shape is set to 0.9. All in all, I don't quite know what to expect with regard to the use and purpose of shape. Is the shape parameter explained somewhere explicitly? if not, wouldn't the stats tutorial be the best place? Who is the author of this doc? How can I help change it? 2) Would it be a good idea to make the use of the loc and scale parameter explicit in the doc strings of the distributions? I recall that, as a first time user, I had no clue what they meant, and that it took some struggling and searching to figure out what they came down to. Besides, the doc strings are not allways complete. For instance, this is the string for the epx distribution: The probability density function for `expon` is:: expon.pdf(x) = exp(-x) for ``x >= 0``. The scale parameter is equal to ``scale = 1.0 / lambda``. So, what is lambda here? Is it: pdf(x) = lambda * exp(-x lambda), or is it pdf(x) = exp(-x/lambda)/lamda? After some experimentation I found out, but the documentation is not explicit enough in my opinion. Suppose we would restate it like this: cdf(x) = 1. - exp( -(x-loc)/scale). Then I think it would be clear immediately, and also interpretation-free. Likewise for other distributions. 3) I am really willing to help improve stats and the documentation at points more consistent, but I don't quite know where to start. In the process I raise all these points. Is this list the best place, or should I send my comments to Josef (?)? Nicky

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scipy.sparse.csgraph merged
by Ralf Gommers May 7, 2012

May 7, 2012

Hi, I finally merged https://github.com/scipy/scipy/pull/119, the sparse graph algorithms module written by Jake Vanderplas. This will certainly by one of the highlights of the 0.11 release (which shouldn't be too far off). Thanks again to Jake for all the work he put in. Ralf

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scipy stats: argcheck
by nicky van foreest May 7, 2012

May 7, 2012

Shouldn't the distribution gamma_gen be equiped with an argcheck function like so: def _argcheck(self, a: return (a > 0) compare the line https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L34… Likewise for https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L26… ---- One more question about the workflow. I notice that while I am trying to understand the stats module I encounter lots of minor points/unclarities and so on, like the above. I have the feeling the easiest way to deal with such points is as follows 1: send a mail to this list. Discuss this until it is resolved. 2: if it is a minor point, I implement it and make a pull request. 3: If it turns out to be a major point, file a report in scipy.trac. If Josef (or other maintainers) prefer otherwise, please let me know.

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ANN Scikit-learn 0.11-beta
by Gael Varoquaux May 6, 2012

May 6, 2012

On behalf of our release manager, Andreas Mueller, and all the scikit-learn contributors, I am happy to announce the 0.11 beta. We are doing a quick beta and will hopefuly be releasing the final version tomorrow. The purpose of this beta is to get feedback on any release-critical bugs such as build issues. You can download the zip files of the beta on: https://github.com/scikit-learn/scikit-learn/zipball/0.11-beta You can also retrieve the latest code on https://github.com/scikit-learn/scikit-learn/zipball/master or using 'git clone git@github.com:scikit-learn/scikit-learn.git' Any feedback is more than welcome, Cheers, Gaël

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ANN: PyNE v0.1
by Anthony Scopatz May 5, 2012

May 5, 2012

Hello All, I am pleased to announce the first release of PyNE, or <goog_179606944> Python for Nuclear Engineering <http://pyne.github.com/>. While this is a domain specific package, inside of the nuclear industry it is similar is scope to SciPy. I am announcing here to hopefully gain some traction with interested parties who may be monitoring SciPy. Moreover, there are some parts which may hold a more general interest (dealing with wrapping C++) if anyone is interested in breaking these out. Please feel free to contact me with questions, comments, complaints, or contributions. The release notes are posted below. Be Well Anthony ====================== PyNE 0.1 Release Notes ====================== PyNE 0.1 is the first release of Python for Nuclear Engineering project after an initial last year of effort. PyNE is a free and open source (BSD licensed) project which is meant to compliment other project, such as NumPy and SciPy, as a necessary package in the computational nuclear engineer's toolkit. It is meant to play nicely with existing, industry standard nuclear engineering tools. The goal of PyNE is to be both fast and useful. As such, this is only the begging! Release highlights: - Support for many I/O routines. - Nuclear data interface. - C/C++ library which may be linked against independent of Python. - Cython wrappers for C++ standard library containers. Please visit our website for more information: http://pyne.github.com/ PyNE requires Python 2.7, NumPy 1.5+, PyTables 2.1+. New features ============ Nuclide Naming in ``pyne.nucname`` ---------------------------------- This module may be used to convert between various nuclide naming schemes. Currently the following naming conventions are supported: zzaaam, human readable names, MCNP, Serpent, NIST, and CINDER. This module is implemented in C. Basic Nuclear Data via ``pyne.data`` ------------------------------------ This aims to provide quick access to very high fidelity nuclear data. Usually values are taken from the nuc_data.h5 library which is generated with the new ``nuc_data_make`` utility at install. Current data includes atomic masses, decay data, neutron scattering lengths, and simple cross section data. 63-group neutron cross sections, photon cross sections, and fission product yields are also added when CINDER is available. This module is implemented in C. Material Class in ``pyne.material`` ----------------------------------- Materials are the primary container for radionuclides throughout PyNE. They map nuclides to mass weights, though they contain methods for converting to/from atom fractions as well. In many ways they take inspiration from numpy arrays and python dictionaries. Materials are implemented in C++ and support both text and HDF5 I/O. CCCC Formats in ``pyne.cccc`` ----------------------------- The CCCC module contains a number of classes for reading various cross section, flux, geometry, and data files with specifications given by the Committee for Computer Code Coordination. The following types of files can be read using classes from this module: ISOTXS, DLAYXS, BRKOXS, RTFLUX, ATFLUX, RZFLUX, MATXS, and SPECTR. ACE Cross Sections in ``pyne.ace`` ---------------------------------- This module is for reading ACE-format cross sections. ACE stands for "A Compact ENDF" format and originated from work on MCNP. It is used in a number of other Monte Carlo particle transport codes. Cross Section Interface via ``pyne.xs`` --------------------------------------- This is a top-level interface for computing (and caching) multigroup neutron cross sections. These cross sections will be computed from a variety of available data sources (stored in nuc_data.h5). In order of preference: 1. CINDER 63-group cross sections, 2. A two-point fast/thermal interpolation (using 'simple_xs' data from KAERI), 3. or physical models implemented in this sub-package. In the future, this package will support generating multigroup cross sections from user-specified pointwise data sources (such as ENDF or ACE files). ORIGEN 2.2 Support in ``pyne.origen22`` --------------------------------------- This provides an interface for reading, writing, and merging certain ORIGEN 2.2 input and output files. Specifically, tapes 4, 5, 6, and 9 are supported. Serpent Support in ``pyne.serpent`` ----------------------------------- Serpent is a continuous energy Monte Carlo reactor physics code. Pyne contains support for reading in Serpent's three types of output files: res, dep, and det. These are all in Matlab's ``*.m`` format and are read in as Python dictionaries of numpy arrays and Materials. They may be optionally written out to corresponding ``*.py`` files and imported later. C++ Standard Library Converters in ``pyne.stlconverters`` --------------------------------------------------------- This module contains wrapper classes for commonly used containers in the C++ standard library. This module is largely used by PyNE under the covers, in Cython and elsewhere. However, these classes are of more general interest so feel free to use them in your own code as well. Currently implemented are SetInt, SetStr, MapStrInt, MapIntStr, MapIntDouble, and MapIntComplex. Nuclear Data Generation in ``pyne.dbgen`` ----------------------------------------- Pyne provides an easy-to-use, repeatable aggregation utility for nuclear data. This command line utility is called ``nuc_data_make`` builds and installs an HDF5 file named ``nuc_data.h5`` to the current PyNE install. Nuclear data is gathered from a variety of sources, including the web and the data files for other programs installed on your system (such as MCNP). Authors ======= This release contains code written by the following people (in alphabetical order): * Christopher Dembia * Robert Flanagan * Paul Romano * Anthony Scopatz * Paul Wilson Additionally, we would like to thank the following people for their inspiration, guidance, and testing: * Katy Huff * Seth Johnson * Joshua Peterson * Rachel Slaybaugh * Nick Touran * Morgan White

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spam on ticket
by josef.pktd＠gmail.com May 4, 2012

May 4, 2012

The problem with a responsive trac is that it's easier to get spam http://projects.scipy.org/scipy/ticket/162 and two more so far What's the procedure to remove them? Thanks, Josef

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LAPACK problems with *gges wrappers
by Skipper Seabold May 3, 2012

May 3, 2012

Hi, Can someone take a look at the comments on this pull request [1]? I'm unable to replicate the failures and it's unclear to me that there are problems with the python code and the fortran wrappers. Thanks, Skipper [1] https://github.com/jseabold/scipy/commit/da19fbffa5a745fe441745987302abac20…

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google releases ceres solver
by Neal Becker May 3, 2012

May 3, 2012

http://google-opensource.blogspot.com/2012/05/introducing-ceres-solver- nonlinear.html

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scipy sprint @ EuroSciPy '12?
by Ralf Gommers May 1, 2012

May 1, 2012

Hi all, Would people be interested in having a scipy sprint at EuroSciPy this year? Last year we tried to do a last minute mini-sprint and ended up hunting for wifi access for half the time, so it would be good to organize things better this time around. I'm thinking a one-day sprint on Wed Aug 22nd would be good. If there's interest, I'm happy to organize (contact the conference organizers for a room, create a wiki page, etc.). Ralf

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Independent T-tests with unequal variances
by Junkshops May 1, 2012

May 1, 2012

Hello all, I hope, as an utter newb poking my nose into this list, that I'm not giving the author of Miss Manner's Book of Netiquette the vapours. This is a follow up to Deniz Turgut's recent email: http://article.gmane.org/gmane.comp.python.scientific.devel/16291/ "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." I was just caught by this problem and heartily agree with Deniz's views. However, I didn't see any explicit plans to add Welch's test to scipy/stats/stats.py, and I needed an implementation of the test and so implemented it. A diff against scipy 0.10.1 is attached if anyone might find it useful. Cheers, Gavin

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