Mailman 3 May 2012 - SciPy-Dev

dead link in cookbook
by josef.pktd＠gmail.com May 13, 2012

May 13, 2012

there is a dead link in cookbook to the script http://matplotlib.sf.net/examples/anim.py http://www.scipy.org/Cookbook/Matplotlib/Animations Does anyone know the correct link or is this obsolete? Thanks, Josef clueless

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scipy tutorial
by nicky van foreest May 11, 2012

May 11, 2012

I am still new to github... The end result can also be (without the plots) be checked at. https://github.com/nokfi/scipy/blob/repair-typo/doc/source/tutorial/stats.r….

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documentation and sphinx
by nicky van foreest May 11, 2012

May 11, 2012

Hi, I am happily changing some documentation for scipy stats, and in the process I am assuming that I can type latex commands just as I do in Sphinx. (I also built my home page in sphinx). AFAIK, shpinx makes pngs of the formulas, and I had to change some settings in my config file for sphinx to enable this Can I rely on the shpinx version used for the numpy/scipy doc that formulas are converted to pngs, or do mathematical formulas end up very convoluted? Nicky

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Removing misc functions from module docstring
by Stéfan van der Walt May 10, 2012

May 10, 2012

Hi all, After a bug reported via Debian: http://projects.scipy.org/scipy/ticket/1656 I've made a small doc PR to remove mentions of some misc functions (factorial, comb, lena, etc.) from the scipy module level docstring: https://github.com/scipy/scipy/pull/209/files Please let me know if you feel those function names need to remain in the docstring for some reason. Thanks Stéfan

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STL / LOESS seasonal trend decomposition
by Andreas H. May 10, 2012

May 10, 2012

Hi, googling for a Python implementation of STL (seasonal trend decomposition based on Loess, see http://cs.wellesley.edu/~cs315/Papers/stl%20statistical%20model.pdf) found this: http://projects.scipy.org/scipy/browser/trunk/scipy/sandbox/pyloess/mpyloes… Can anyone tell me something about the status of this code? I'll probably need this for my work anyway, and thought it might be nice to bring it to a level at which it could be included into some package. Where would it belong? scipy.stats? statsmodels? Cheers, Andreas.

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Suggestion: Fast hierarchical clustering
by Daniel Müllner May 9, 2012

May 9, 2012

Dear SciPy developers, I am the author of a package for fast hierarchical clustering: http://cran.r-project.org/web/packages/fastcluster/ (The C++ library has two interfaces for R and Python, hence the source code is published on CRAN.) The package improves the time complexity of the current algorithms in scipy.cluster.hierarchy from O(N^3) to O(N^2). The syntax of the Python interface agrees with the SciPy methods, so that users can quickly switch to the faster algorithms. But really, the best place for the clustering package would be to incorporate it into SciPy so that everyone could use the faster algorithms by default. Here are my questions: (1) Is there sufficient interest to replace scipy.cluster.hierarchy.linkage by faster code so that it makes the effort worthwhile for me? (2) To whom can I submit the suggested changes? Making a SciKit as described on http://www.scipy.org/Developer_Zone does not seem the right approach here since I am not offering a new, independent package but replacement of certain code within the existing module scipy.cluster.hierarchy. (3) Who decides whether the suggested changes are accepted or not? Best, Daniel Here are a few facts that you might want to know: * The core library is in C++ since I use templates a lot. However, I already took care that the R interface compiles on a variety of systems, see: http://cran.r-project.org/web/checks/check_results_fastcluster.html Therefore, I don't expect compilation issues for the Python interface. * The license is currently GPL. I am willing to publish the code under a different license if this is required for SciPy. * The latest version (not published yet) compiles and works under Python 2 and 3, so there are also no issues here.

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scipy stats: doc strings of discrete distributions
by nicky van foreest May 9, 2012

May 9, 2012

Hopefully you don't mind that I try to keep topics separated by sending separated mails. I noticed that the implementation of the doc strings differ for rv_continuous and rv_discrete. rv_continuous implementations look like class expon_gen(....): """ docs... """ def _rvs(...) expon = expon_gen(....) On the other hand, binom looks like class binom_gen(rv_discrete): def _rvs(self, n, p): binom = binom_gen(name='binom',shapes="n, p",extradoc=""" Binomial distribution """ Any objections against moving the extra doc and make the discrete distributions look more like the continuous ones?

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scipy stats, doc string
by nicky van foreest May 8, 2012

May 8, 2012

Hi, As Josef pointed out earlier, the Erlang distribution causes some further problems wrt the documentation too. The automatically generated documentation uses n = 0.9 as a shape parameter, but the documentation says that n should be an int. This is a bit silly of course. There are two ways to repair this IMO. 1) As Josef suggested: port Erlang to the gamma distribution and refer in the erlang doc string to the gamma distribution. I admit that I favor this. 2) Ensure that Erlang only accepts an int as a shape, implement an extra _arg_check (as is currently done), and change the string in distributions.py to automatically set the shape parameter. Specifically: https://github.com/scipy/scipy/blob/master/scipy/stats/distributions.py#L222 Would it be possible to change this line to something like >>> [ %(shapes)s ] = [%(shape_value)f,] * numargs Then it would be possible to pass a distribution dependent shape value. All in all option 1 seems the easiest, and from a user's point of view (I still regard myself a user of scipy.stats) sufficiently clear. Given this, I propose the following docstring for erlang: """An Erlang continuous random variable. %(before_notes)s See Also -------- gamma Notes ----- The Erlang distribution is a special case of the Gamma distribution, with the shape parameter ``a`` an integer. Refer to the ``gamma`` distribution for further examples. """ Note that I also remove the examples. In the gamma docsting, on the other hand, I include some extra info. I'll do a pull request this evening, including this change. Nicky

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ndimage grey morphology tickets
by Jonathan Helmus May 8, 2012

May 8, 2012

All, Trac tickets #1135, #1281 and #1498 point out bugs in the ndimage.grey_dilation and grey_erosion function. I've started a branch that tries to address these issues: https://github.com/jjhelmus/scipy/tree/grey_morphology-fix This branch currently passes the tests in test_ndimage.py and the dilation_test.py file attached to ticket 1135, but I am not certain on two issues that I was hoping someone here might be able to comment on them: 1. Should there be a sanity check on the shape of footprint, structure and size when more than one is provided? 2. Ticket #1281 points out that grey_erosion does not have the parameter checking that grey_dilation has. I added these checks but noticed that to pass tests structure and footprint should not be reversed and the origin negated. Is this correct? If so the commented out lines in the branch should will be deleted. If this would be better discussed in a pull request I'd be happy to make one. http://projects.scipy.org/scipy/ticket/1135 http://projects.scipy.org/scipy/ticket/1281 http://projects.scipy.org/scipy/ticket/1498 Cheers, -Jonathan Helmus

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Announce: scikit-learn v0.11
by Gael Varoquaux May 7, 2012

May 7, 2012

On behalf of Andy Mueller, our release manager, I am happy to announce the 0.11 release of scikit-learn. This release includes some major new features such as randomized sparse models, gradient boosted regression trees, label propagation and many more. The release also has major improvements in the documentation and in stability. Details can be found on the [1]what's new page. We also have a new page with [2]video tutorials on machine learning with scikit-learn and different aspects of the package. Sources and windows binaries are available on sourceforge, through pypi (http://pypi.python.org/pypi/scikit-learn/0.11) or can be installed directly using pip: pip install -U scikit-learn Thanks again to all the contributors who made this release possible. Cheers, Gaël 1. http://scikit-learn.org/stable/whats_new.html 2. http://scikit-learn.org/stable/presentations.html

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