Mailman 3 February 2015 - NumPy-Discussion

missing FloatingPointError for numpy on cygwin64
by Sebastien Gouezel 02 Feb '15

02 Feb '15

Dear all, I tried to use numpy (version 1.9.1, installed by `pip install numpy`) on cygwin64. I encountered the following weird bug: >>> import numpy >>> with numpy.errstate(all='raise'): ... print 1/float64(0.0) inf I was expecting a FloatingPointError, but it didn't show up. Curiously, with different numerical types (all intxx, or float128), I indeed get the FloatingPointError. Same thing with the most recent git version, or with 1.7.1 provided as a precompiled package by cygwin. This behavior does not happen on cygwin32 (I always get the FloatingPointError there). I wonder if there is something weird with my config, or if this is a genuine reproducible bug. If so, where should I start looking if I want to fix it? (I don't know anything about numpy's code) Sebastien

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Regarding taking up project ideas and GSoC 2015
by Maniteja Nandana 02 Feb '15

02 Feb '15

Hello everyone, I am a third year computer science undergraduate student, from BITS Pilani, India. First of all, I have cross posted to both the mailing lists, since there is a large overlap among contributors of the organisations. Please do mention if there is any objection to do so. I am writing this mail to introduce myself and to try to work on ideas that are hovering around, if possible contribute as much as I can to the community. Apologies in advance for the lengthy mail. I have been in active touch with numpy and scipy since December, though I have been following the mailing lists and discussions for quite some time. There is an ambience which makes me stick to the github page all the time, just so that I keep learning new things and as well as help others if possible. I think I have a working knowledge of git, though not at advanced level, which I learned when working on small patches to numpy and scipy, thanks for the help there. Though not greatly familiar with the structure of modules in the library, I have a basic understanding of the codebase, getting to know things as and when I have been digging at them, during various issues. I have recently looked into the Scipy 1.0 Roadmap, todo wiki and Project Ideas on github wiki. I was interested in the numpy idea for pythonic dtypes and also in the scipy interpolation module, which were close to my academic fields. I would also be eager to learn about the other ideas, but as they are not a part of my curriculum, I would be needing to spend some time to get an idea of them. Also there are various enhancement proposals, mentioned in issues and PRs like the elliptical integrals of third kind, which was brought up recently. It would be great to try to work on such ideas, but need your guidance. I am a pursing a computer science major, and completed courses in linear algebra, differential equations, computer graphics, probability and statistics, machine learning, information retrieval and data mining. I have beginner knowledge in image and signal processing. I was wondering if I could request the concerned people who work in these fields to help beginners like me to look into and learn about the concepts. It would be a really great learning experience to work under you guys, and start as early as possible, irrespective of whether it is a feasible SoC project, but only according to your convenience. I would be more than pleased and happy to be a long term contributor. My github profile is https://github.com/maniteja123. It is just a mix of my modest beginning to open source contribution and my academic projects. Waiting in anticipation for your response. Thanks for spending time to read along my lengthy mail. Cheers, N.Maniteja _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion SciPy-Dev mailing list SciPy-Dev(a)scipy.org http://mail.scipy.org/mailman/listinfo/scipy-dev

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ANN: NumPy 1.9.2 release candidate
by Julian Taylor 01 Feb '15

01 Feb '15

Hi, We have finished the first release candidate of NumPy 1.9.2. The 1.9.2 release will as usual be a bugfix only release to the 1.9.x series. The tarballs and win32 binaries are available on sourceforge: https://sourceforge.net/projects/numpy/files/NumPy/1.9.2rc1/ If no regressions show up the final release is planned next week. The upgrade is recommended for all users of the 1.9.x series. Following issues have been fixed: * gh-5316: fix too large dtype alignment of strings and complex types * gh-5424: fix ma.median when used on ndarrays * gh-5481: Fix astype for structured array fields of different byte order * gh-5155: Fix loadtxt with comments=None and a string None data * gh-4476: Masked array view fails if structured dtype has datetime component * gh-5388: Make RandomState.set_state and RandomState.get_state threadsafe * gh-5390: make seed, randint and shuffle threadsafe * gh-5374: Fixed incorrect assert_array_almost_equal_nulp documentation * gh-5393: Add support for ATLAS > 3.9.33. * gh-5313: PyArray_AsCArray caused segfault for 3d arrays * gh-5492: handle out of memory in rfftf * gh-4181: fix a few bugs in the random.pareto docstring * gh-5359: minor changes to linspace docstring * gh-4723: fix a compile issues on AIX Source tarballs, windows installers and release notes can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.9.2rc1/ Cheers, The NumPy Developer team

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