Mailman 3 March 2016 - SciPy-User

Numpy 1.11.0rc2 released.
by Charles R Harris 19 Mar '16

19 Mar '16

Hi All, Numpy 1.11.0rc2 has been released. There have been a few reversions and fixes since 1.11.0rc1, but by and large things have been pretty stable and unless something terrible happens, I will make the final next weekend. Source files and documentation can be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc2/>, while source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be installed from Pypi. Please test and yell loudly if there is a problem. Chuck

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New book: Python Programming and Visualization for Scientists
by Grant W. Petty 14 Mar '16

14 Mar '16

A new book has just been published that may be of interest to some members of this list, especially those relatively new to Python: *Python Programming and Visualization for Scientists*, by Alex DeCaria. Alex has been a regular instructor of short courses in Python at meetings of the American Meteorological Society. This book is designed to be a concise, well-organized tutorial and reference that encompasses both the Python language itself and the most commonly used elements of selected modules, including matplotlib, numpy, time, and others. Additional information can be found on the attached flyer and at the book's website <http://www.sundogpublishing.com/shop/python-programming-and-visualization-f…>. Note that the website currently offers only the spiral-bound version for sale; a conventional 7x10 softcover version will soon be available through other outlets. Sundog Publishing is a very small independent publisher that relies entirely on reviews and word-of-mouth as its primary marketing tools. If you get a copy of Alex's book and think it is useful, either for yourself or for new users of Python, I hope you will help spread the word, including perhaps forwarding the attached flyer and/or above link. Thanks! Grant Petty

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ANN: pandas v0.18.0 Final released
by Jeff Reback 12 Mar '16

12 Mar '16

Hi, This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. This was a release of 3.5 months with 381 commits by 100 authors encompassing 465 issues and 290 pull-requests. *What is it:* *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. *Highlights*: - pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273> - Moving and expanding window functions are now methods on Series and DataFrame similar to .groupby like objects, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - API breaking .resample changes to make it more .groupby like, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - The .to_xarray() function has been added for compatibility with the xarray package <http://xarray.pydata.org/en/stable/> see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> . - The read_sas() function has been enhanced to read sas7bdat files, see here <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - Addition of the .str.extractall() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…>, and API changes to the the .str.extract() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…>, and the .str.cat() method <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html#whatsnew-…> - pd.test() top-level nose test runner is available GH4327 <https://github.com/pydata/pandas/issues/4327> See the Whatsnew <http://pandas.pydata.org/pandas-docs/version/0.18.0/whatsnew.html> for much more information and the full Documentation <http://pandas.pydata.org/pandas-docs/version/0.18.0/> link. *How to get it:* Source tarballs, windows wheels, and macosx wheels are available on PyPI <https://pypi.python.org/pypi/pandas> Installation via conda is: - conda install pandas windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.10 macosx wheels are courtesy of Matthew Brett. *Issues:* Please report any issues on our issue tracker <https://github.com/pydata/pandas/issues>: Jeff *Thanks to all of the contributors* - ARF - Alex Alekseyev - Andrew McPherson - Andrew Rosenfeld - Anthonios Partheniou - Anton I. Sipos - Ben - Ben North - Bran Yang - Chris - Chris Carroux - Christopher C. Aycock - Christopher Scanlin - Cody - Da Wang - Daniel Grady - Dorozhko Anton - Dr-Irv - Erik M. Bray - Evan Wright - Francis T. O'Donovan - Frank Cleary - Gianluca Rossi - Graham Jeffries - Guillaume Horel - Henry Hammond - Isaac Schwabacher - Jean-Mathieu Deschenes - Jeff Reback - Joe Jevnik - John Freeman - John Fremlin - Jonas Hoersch - Joris Van den Bossche - Joris Vankerschaver - Justin Lecher - Justin Lin - Ka Wo Chen - Keming Zhang - Kerby Shedden - Kyle - Marco Farrugia - MasonGallo - MattRijk - Matthew Lurie - Maximilian Roos - Mayank Asthana - Mortada Mehyar - Moussa Taifi - Navreet Gill - Nicolas Bonnotte - Paul Reiners - Philip Gura - Pietro Battiston - RahulHP - Randy Carnevale - Rinoc Johnson - Rishipuri - Sangmin Park - Scott E Lasley - Sereger13 - Shannon Wang - Skipper Seabold - Thierry Moisan - Thomas A Caswell - Toby Dylan Hocking - Tom Augspurger - Travis - Trent Hauck - Tux1 - Varun - Wes McKinney - Will Thompson - Yoav Ram - Yoong Kang Lim - Yoshiki Vázquez Baeza - Young Joong Kim - Younggun Kim - Yuval Langer - alex argunov - behzad nouri - boombard - ian-pantano - chromy - daniel - dgram0 - gfyoung - hack-c - hcontrast - jfoo - kaustuv deolal - llllllllll - ranarag - rockg - scls19fr - seales - sinhrks - srib - surveymedia.ca - tworec

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Using special functions with keyword arguments
by Lutz Maibaum 11 Mar '16

11 Mar '16

I am having difficulties calling the spherical harmonics function with keyword arguments. This works in older versions (for example Numpy 1.8 / SciPy 0.14): >>> from scipy.special import sph_harm >>> sph_harm(m=0,n=0,theta=0,phi=0) array((0.28209479177387814+0j)) In newer versions, such as NumPy 1.10 / SciPy 0.15, I get >>> from scipy.special import sph_harm >>> sph_harm(m=0,n=0,theta=0,phi=0) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: invalid number of arguments Is it still possible to call sph_harm (and presumably other special functions) with keyword arguments? The reason I am trying to do this is that I would like to wrap sph_harm in a functools.partial object that takes only a single argument, and that I can then pass to multiprocessing.Pool.map. Thanks, Lutz

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ANN: pandas v0.18.0rc2 - RELEASE CANDIDATE
by Jeff Reback 09 Mar '16

09 Mar '16

Hi, I'm pleased to announce the availability of the second release candidate of Pandas 0.18.0. Please try this RC and report any issues here: Pandas Issues <https://github.com/pydata/pandas/issues>. Compared to RC1, we have added updated read_sas and fixed float indexing. We will be releasing officially very shortly. THIS IS NOT A PRODUCTION RELEASE This is a major release from 0.17.1 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Highlights include: - pandas >= 0.18.0 will no longer support compatibility with Python version 2.6 GH7718 <https://github.com/pydata/pandas/issues/7718> or version 3.3 GH11273 <https://github.com/pydata/pandas/issues/11273> - Moving and expanding window functions are now methods on Series and DataFrame similar to .groupby like objects, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - Adding support for a RangeIndex as a specialized form of the Int64Index for memory savings, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - API breaking .resample changes to make it more .groupby like, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - Removal of support for positional indexing with floats, which was deprecated since 0.14.0. This will now raise a TypeError, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - The .to_xarray() function has been added for compatibility with the xarray package <http://xarray.pydata.org/en/stable/> see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> . - The read_sas() function has been enhanced to read sas7bdat files, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - Addition of the .str.extractall() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, and API changes to the the .str.extract() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…>, and the .str.cat() method <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#whatsnew-0180…> - pd.test() top-level nose test runner is available GH4327 <https://github.com/pydata/pandas/issues/4327> See the Whatsnew <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html> for much more information. Best way to get this is to install via conda <http://pandas-docs.github.io/pandas-docs-travis/install.html#installing-pan…> from our development channel. Builds for osx-64,linux-64,win-64 for Python 2.7 and Python 3.5 are all available. conda install pandas=v0.18.0rc2 -c pandas Thanks to all who made this release happen. It is a very large release! Jeff

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ANN: release of scikit-image 0.12
by Emmanuelle Gouillart 07 Mar '16

07 Mar '16

Announcement: scikit-image 0.12 =============================== The scikit-image team is very pleased to announce the release of version 0.12 of scikit-image. scikit-image is an image processing toolbox for Python and SciPy, that includes algorithms for segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and more, for 2-D and 3-D (sometimes n-D) images. For more information, examples, and documentation, please visit our website: http://scikit-image.org and our example gallery http://scikit-image.org/docs/dev/auto_examples/ This release includes new features such as image inpainting, seam carving, image comparison metrics, and a parallelization framework based on dask. The release has also improved the support of 3-D images, with 3-D skeletonization, 3-D phantom data and partial support of measure.regionprops for 3-D images. Also note that the handling of background pixels by measure.label, a function labeling connected components, has changed to be consistent with scipy.ndimage.label. For this release, we merged over 200 pull requests from 64 contributors, with bug fixes, cleanups, improved documentation and new features. Release notes are available on http://scikit-image.org/docs/0.12.x/release_notes_and_installation.html#rel… and include a more detailed list of changes, and the complete list of contributors to this release. The release can be downloaded on PyPi https://pypi.python.org/pypi/scikit-image or directly installed using pip pip install --upgrade scikit-image Please let us know any issues you might have on the issue tracker https://github.com/scikit-image/scikit-image/issues Happy image processing! The scikit-image team

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