Mailman 3 April 2012 - SciPy-User

upgrading scipy on windows seven 64 bits
by servant mathieu April 28, 2012

April 28, 2012

Hi there, The Python (x,y) distribution (version 2.7) is already installed on my computer (running natively windows seven 64 bits). At the moment, the scipy version is '0.9'. I'm interested in using the scipy.optimize.minimize function ( http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize…), but I can't manage to find it. Did you change the name of this general-purpose optimization function? Cheers, Mat

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please add to planet
by Tim Michelsen April 28, 2012

April 28, 2012

Please add the following feeds to planet.scipy.org http://pythonxynews.blogspot.de/ http://spyder-ide.blogspot.de/ Thanks.

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(no subject)
by Dinesh Vadhia April 28, 2012

April 28, 2012

http://lutedeniz.com/orndkty/62076.html

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ANN: statsmodels 0.4.0
by josef.pktd＠gmail.com April 27, 2012

April 27, 2012

We are pleased to announce the release of statsmodels 0.4.0. The big changes in this release are that most models can now be used with Pandas dataframes, and that we dropped the scikits namespace. Importing scikits.statsmodels is still possible but will be removed in the future. Pandas is now a required dependency. For more changes including some breaks in backwards compatibility see below. Josef and Skipper What it is ========== Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Documentation for the 0.4 version is currently at http://statsmodels.sourceforge.net/devel/ Main Changes and Additions in 0.4.0 ----------------------------------- * Added pandas dependency. * Cython source is built automatically if cython and compiler are present * Support use of dates in timeseries models * Improved plots - Violin plots - Bean Plots - QQ Plots * Added lowess function * Support for pandas Series and DataFrame objects. Results instances return pandas objects if the models are fit using pandas objects. * Full Python 3 compatibility * Fix bugs in genfromdta. Convert Stata .dta format to structured array preserving all types. Conversion is much faster now. * Improved documentation * Models and results are pickleable via save/load, optionally saving the model data. * Kernel Density Estimation now uses Cython and is considerably faster. * Diagnostics for outlier and influence statistics in OLS * Added El Nino Sea Surface Temperatures dataset * Numerous bug fixes * Internal code refactoring * Improved documentation including examples as part of HTML * ... *Changes that break backwards compatibility* * Deprecated scikits namespace. The recommended import is now:: import statsmodels.api as sm * model.predict methods signature is now (params, exog, ...) where before it assumed that the model had been fit and omitted the params argument. (this removed circularity between models and results instances) * For consistency with other multi-equation models, the parameters of MNLogit are now transposed. * tools.tools.ECDF -> distributions.ECDF * tools.tools.monotone_fn_inverter -> distributions.monotone_fn_inverter * tools.tools.StepFunction -> distributions.StepFunction Main Features ============= * linear regression models: Generalized least squares (including weighted least squares and least squares with autoregressive errors), ordinary least squares. * glm: Generalized linear models with support for all of the one-parameter exponential family distributions. * discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators * rlm: Robust linear models with support for several M-estimators. * tsa: models for time series analysis - univariate time series analysis: AR, ARIMA - vector autoregressive models, VAR and structural VAR - descriptive statistics and process models for time series analysis * nonparametric : (Univariate) kernel density estimators * datasets: Datasets to be distributed and used for examples and in testing. * stats: a wide range of statistical tests - diagnostics and specification tests - goodness-of-fit and normality tests - functions for multiple testing - various additional statistical tests * iolib - Tools for reading Stata .dta files into numpy arrays. - printing table output to ascii, latex, and html * miscellaneous models * sandbox: statsmodels contains a sandbox folder with code in various stages of developement and testing which is not considered "production ready". This covers among others Mixed (repeated measures) Models, GARCH models, general method of moments (GMM) estimators, kernel regression, various extensions to scipy.stats.distributions, panel data models, generalized additive models and information theoretic measures. Where to get it =============== The master branch on GitHub is the most up to date code https://www.github.com/statsmodels/statsmodels Source download of release tags are available on GitHub https://github.com/statsmodels/statsmodels/tags Binaries and source distributions are available from PyPi http://pypi.python.org/pypi/statsmodels/

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Re: [SciPy-User] Testing scipy: a curiosity (Ralf Gommers)
by Sergio Rojas April 27, 2012

April 27, 2012

On Fri, Apr 20, 2012 at 6:53 PM, Sergio Rojas <sergio_r(a)mail.com> wrote: > > Hello all, > > > Running the scipy tests in sequence: > > scipy.test() > then, after the previous test finished without errors > or failures I run: > scipy.test('full') > some tests fails (see below). > > Exiting and starting a new python session and > executing only the test suite: > > scipy.test('full') > > No failures or errors are reported. The test ends with: > ... > ---------------------------------------------------------------------- > Ran 5832 tests in 423.039s > OK (KNOWNFAIL=14, SKIP=42) > <nose.result.TextTestResult run=5832 errors=0 failures=0> > > What could be the problem?. Can we trust these tests results? > Python's default behavior is to raise warnings from the same place in the code only once. So when you re-run the tests it doesn't raise those warnings. The tests check that a warning is raised. Hence the failures. Ralf Thanks for replying, Ralf. A natural questions then raises Ralf, Can one obtain fictitious python's error messages or failures of a computation because of such python's default behavior? How can we toggle that to a most reasonable behavior? or how can we associate an error or failure (what hint can we have from the system) to such behavior? Sergio

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SciPy 2012 - The Eleventh Annual Conference on Scientific Computing with Python
by Warren Weckesser April 26, 2012

April 26, 2012

SciPy 2012, the eleventh annual Conference on Scientific Computing with Python, will be held July 16–21, 2012, in Austin, Texas. At this conference, novel scientific applications and libraries related to data acquisition, analysis, dissemination and visualization using Python are presented. Attended by leading figures from both academia and industry, it is an excellent opportunity to experience the cutting edge of scientific software development. The conference is preceded by two days of tutorials, during which community experts provide training on several scientific Python packages. Following the main conference will be two days of coding sprints. We invite you to give a talk or present a poster at SciPy 2012. The list of topics that are appropriate for the conference includes (but is not limited to): - new Python libraries for science and engineering; - applications of Python in solving scientific or computational problems; - high performance, parallel and GPU computing with Python; - use of Python in science education. Specialized Tracks Two specialized tracks run in parallel to the main conference: - High Performance Computing with Python Whether your algorithm is distributed, threaded, memory intensive or latency bound, Python is making headway into the problem. We are looking for performance driven designs and applications in Python. Candidates include the use of Python within a parallel application, new architectures, and ways of making traditional applications execute more efficiently. - Visualization They say a picture is worth a thousand words--we’re interested in both! Python provides numerous visualization tools that allow scientists to show off their work, and we want to know about any new tools and techniques out there. Come show off your latest graphics, whether it’s an old library with a slick new feature, a new library out to challenge the status quo, or simply a beautiful result. Domain-specific Mini-symposia Mini-symposia on the following topics are also being organized: - Computational bioinformatics - Meteorology and climatology - Astronomy and astrophysics - Geophysics Talks, papers and posters We invite you to take part by submitting a talk or poster abstract. Instructions are on the conference website: <http://conference.scipy.org/scipy2012/papers.php> http://conference.scipy.org/scipy2012/talks.php<http://conference.scipy.org/scipy2012/papers.php> <http://conference.scipy.org/scipy2012/papers.php> Selected talks are included as papers in the peer-reviewed conference proceedings, to be published online. Tutorials Tutorials will be given July 16–17. We invite instructors to submit proposals for half-day tutorials on topics relevant to scientific computing with Python. See http://conference.scipy.org/scipy2012/tutorials.php<http://conference.scipy.org/scipy2011/tutorials.php> <http://conference.scipy.org/scipy2011/tutorials.php> for information about submitting a tutorial proposal. To encourage tutorials of the highest quality, the instructor (or team of instructors) is given a $1,000 stipend for each half day tutorial. Student/Community Scholarships We anticipate providing funding for students and for active members of the SciPy community who otherwise might not be able to attend the conference. See http://conference.scipy.org/scipy2012/student.php<http://conference.scipy.org/scipy2011/student.php> <http://conference.scipy.org/scipy2011/student.php> for scholarship application guidelines. Be a Sponsor The SciPy conference could not run without the generous support of the institutions and corporations who share our enthusiasm for Python as a tool for science. Please consider sponsoring SciPy 2012. For more information, see http://conference.scipy.org/scipy2012/sponsor/index.php Important dates: Monday, April 30: Talk abstracts and tutorial proposals due. Monday, May 7: Accepted tutorials announced. Monday, May 13: Accepted talks announced. Monday, June 18: Early registration ends. (Price increases after this date.) Sunday, July 8: Online registration ends. Monday-Tuesday, July 16 - 17: Tutorials Wednesday-Thursday, July 18 - July 19: Conference Friday-Saturday, July 20 - July 21: Sprints We look forward to seeing you all in Austin this year! The SciPy 2012 Team http://conference.scipy.org/scipy2012/organizers.php

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cluster.hierarchy.fcluster: Choosing value for threshold t?
by Pundurs Mark (Nokia-LC/Chicago) April 25, 2012

April 25, 2012

I have ~8000 observations of 1-D data (not a standard use case for cluster.hierarchy, I suspect). I send the output of linkage to fcluster; if I choose a t of 1.5, the output array has only one unique value, and if I choose t=0.5, the output array has as many unique values as does the input data. I can see from dendrogram that there are intermediate levels of clustering; apart from trial and error, how do I find a t that returns such a clustering? (Does the vertical axis of the dendrogram have anything to do with fcluster's t argument? In the dendrogram, clusters split at integer values of the vertical component.) Mark Pundurs Data Analyst - Traffic Location & Commerce Chicago The information contained in this communication may be CONFIDENTIAL and is intended only for the use of the recipient(s) named above. If you are not the intended recipient, you are hereby notified that any dissemination, distribution, or copying of this communication, or any of its contents, is strictly prohibited. If you have received this communication in error, please notify the sender and delete/destroy the original message and any copy of it from your computer or paper files.

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Job Oportunities in Brazil
by Flavio Coelho April 25, 2012

April 25, 2012

First of all I'd like to apologize for the cross-post, but I'd like to bring to the attention of these Important Python scientific communities, a couple of opportunities for young PhDs in our institution, located at Rio de Janeiro, Brasil. Getulio Vargas Foundation is one of the top Research and Educational Institutions in Brasil. We are the largest Think-Tank of Latin America, and rank among the top 30 in the world[1]. Though we are not an University we have both undergraduate and graduate programs, which are very highly ranked. - Statistician: http://emap.fgv.br/files/edital-estatistica-en.pdf - Mathematician: http://emap.fgv.br/files/edital-matematica-aplicada-en.pdf These two jobs are to work at the Applied Mathematics School, where I work. I'd be delighted to have more academic Pythonistas around me, so please take your time to look through the job descriptions, and if you feel up to the challenge, please apply! cheers, [1] http://www.postwesternworld.com/2012/01/26/fgv-ranks-among-worlds-top-thirt… -- Flávio Codeço Coelho ================ +55(21) 3799-5567 Professor Escola de Matemática Aplicada Fundação Getúlio Vargas Rio de Janeiro - RJ Brasil

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OT: global optimization, hybrid global local search
by josef.pktd＠gmail.com April 24, 2012

April 24, 2012

I'm looking at nonlinear regression, function estimation again. I'm interested in combining global search with local optimizers, which I think should be much faster in many of our problems. Anyone with ideas, experience, code? I just searched and browsed a few papers, but it's not my specialization. "In particular, the problems which are very hard to be solved by gradient descent-based methods, or the ones which have computationally expensive objective functions are very good candidates to be solved by the proposed methods." *) *) Hybrid optimization with improved tabu search http://www.sciencedirect.com/science/article/pii/S1568494610001511 Genetic and Nelder–Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions http://www.sciencedirect.com/science/article/pii/S0377221702004010 A hybrid method combining continuous tabu search and Nelder–Mead simplex algorithms for the global optimization of multiminima functions http://www.sciencedirect.com/science/article/pii/S0377221703006301 Hybridizing exact methods and metaheuristics: A taxonomy http://www.sciencedirect.com/science/article/pii/S0377221708003597 mostly integer programming Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization http://www.tandfonline.com/doi/abs/10.1080/1055678021000030084 Josef (Why does scipy not have any good global optimizers?)

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[ANN] Optimization with categorical variables, disjunctive (and other logical) constraints
by Dmitrey April 24, 2012

April 24, 2012

hi all, free solver interalg for global nonlinear optimization with specifiable accuracy now can handle categorical variables, disjunctive (and other logical) constraints, thus making it available to solve GDP, possibly in multiobjective form. There are ~ 2 months till next OpenOpt release, but I guess someone may find it useful for his purposes right now. See here for more details. Regards, D.

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