Mailman 3 September 2019 - NumPy-Discussion

NumPy Community Meeting Wednesday, Sep. 11
by Sebastian Berg 10 Sep '19

10 Sep '19

Hi all, There will be a NumPy Community meeting Wednesday September 11 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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[JOB] Principal Software Engineer position at IHME
by Chun-Wei Yuan 10 Sep '19

10 Sep '19

*The Institute for Health Metrics and Evaluation (IHME) *has an outstanding opportunity for a full-time *Principal Software Engineer *on our Forecasting/Future Health Scenarios (FHS) team*.* The development arm of the team is responsible for the design and implementation of software to support this effort, and the Principal Software Engineer will lead the development work and supervise engineers on that team. IHME’s aim within the FHS portfolio is to create an analytic engine that can model the impact of a wide array of determinants on the trajectory of health outcomes and risks in different countries, projected 25 years into the future, that will allow decision-makers to assess the impact of their potential actions analytically. A recent publication can be found here: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31694-5/… If you join IHME, you’ll be joining a team of mission-oriented people who are committed to creating a welcoming and diverse workforce that respects and appreciates differences, and embraces collaboration. *Further Information: *See IHME’s website: www.healthdata.org *To Apply and see the whole job description: *Please apply at uw.edu/jobs <https://uwhires.admin.washington.edu/eng/candidates/default.cfm?szCategory=…> and search for req 171527 Please direct your questions to Megan at mkmason(a)uw.edu

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shipping sdists without generated C sources from Cython code
by Ralf Gommers 08 Sep '19

08 Sep '19

Hi all, There are several open issues about people not being able to compile the latest release with Python 3.8 betas due to our release containing generated C code with a too old version of Cython. This happened for Python 3.7 as well. With the Python packaging system having improved that build dependencies are no longer insane, I think we should stop shipping the generated C sources. We've discussed this a couple of times before on GitHub, but I've now opened a PR for this (https://github.com/numpy/numpy/pull/14453) so I thought it would be good to mention here in case anyone sees an issue with doing this. Cheers, Ralf

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NumPy 1.17.2 released.
by Charles R Harris 07 Sep '19

07 Sep '19

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.17.2 has been released. This release contains fixes for bugs reported against NumPy 1.17.1 along with some documentation improvements. The most important fix is for lexsort when the keys are of type (u)int8 or (u)int16. If you are currently using 1.17 you should upgrade. The Python versions supported in this release are 3.5-3.7, Python 3.8b4 should work with the released source packages, but there are no future guarantees. Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid wrong results on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those problems. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.17.2/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.17.2>. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - CakeWithSteak + - Charles Harris - Dan Allan - Hameer Abbasi - Lars Grueter - Matti Picus - Sebastian Berg *Pull requests merged* A total of 8 pull requests were merged for this release. - #14418: BUG: Fix aradixsort indirect indexing. - #14420: DOC: Fix a minor typo in dispatch documentation. - #14421: BUG: test, fix regression in converting to ctypes - #14430: BUG: Do not show Override module in private error classes. - #14432: BUG: Fixed maximum relative error reporting in assert_allclose. - #14433: BUG: Fix uint-overflow if padding with linear_ramp and negative... - #14436: BUG: Update 1.17.x with 1.18.0-dev pocketfft.py. - #14446: REL: Prepare for NumPy 1.17.2 release. Cheers, Charles Harris

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Proposal to accept NEP #29: Recommend Python and Numpy version support as a community policy standard
by Thomas Caswell 06 Sep '19

06 Sep '19

https://numpy.org/neps/nep-0029-deprecation_policy.html The outstanding concern in https://github.com/numpy/numpy/pull/14086 was that some projects want to continue to support additional versions of Python and numpy outside of the minimum support windows. The language has been changed to specify that these are _minimum_ support windows and that projects _should_ not _will_ drop support as they can. There is one trivial wording change PR open ( https://github.com/numpy/numpy/pull/14444). If there are no substantive objections within 7 days from this email, then the NEP will be accepted; see NEP 0 for more details. Tom -- Thomas Caswell tcaswell(a)gmail.com

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1.17.2 release.
by Charles R Harris 06 Sep '19

06 Sep '19

Hi All, I'm planning to make a 1.17.2 release Friday or Saturday in order to fix some newly reported regressions. If there is anything that you think absolutely needs to be in that release, please yell. Chuck

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Integer array indexing (numpy.take) as function composition
by Jim Pivarski 05 Sep '19

05 Sep '19

Hi, I'm a long-time user of Numpy; I had a question and I didn't know where else to ask. (It's not a bug—otherwise I would have posted it at https://github.com/numpy/numpy/issues). Has anyone noticed that indexing an array with integer arrays (i.e. numpy.take) is a function composition? For example, suppose you have any two non-negative functions of integers: def f(x): return x**2 - 5*x + 10 def g(y): return max(0, 2*y - 10) + 3 and you sample them as arrays, as well as their composition g(f(•)): F = numpy.array([f(i) for i in range(10)]) # F is f at 10 elements G = numpy.array([g(i) for i in range(100)]) # G is g at enough elements to include max(f) GoF = numpy.array([g(f(i)) for i in range(10)]) # GoF is g∘f at 10 elements Indexing G by F (G[F]) returns the same result as the sampled composition ( GoF): print("G\u2218F =", G[F]) # integer indexing print("g\u2218f =", GoF) # array of the composed functions G∘F = [13 5 3 3 5 13 25 41 61 85] g∘f = [13 5 3 3 5 13 25 41 61 85] This isn't a proof, but I think it's easy to see that it would be true for any non-negative functions (negative index handling spoils this property). It might sound like a purely academic point, but I've noticed that I've been able to optimize and simplify some code by taking advantage of the associative property of function composition, repeatedly applying numpy.take on arrays of integers before applying the fully composed index to my data. As an example of an optimization, if I have to do the same thing to N data arrays, it helps to prepare a single integer index and apply it to the N data arrays instead of modifying all N data arrays in multiple steps. As an example of a simplification, if I need to modify arrays in recursion, it's easier to reason about the recursion if only the terminal case applies an index to data, with the non-terminal steps applying indexes to indexes. This is such a basic property that I bet it has a name, and there's probably some literature on it, like what you could find if you were interested in monads in Haskell. But I haven't been able to find the right search strings—what would you call this property? Is there a literature on it and its uses? Thanks! -- Jim

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Re: [Numpy-discussion] NEP 32: Remove the financial functions from NumPy
by Martin.Gfeller＠swisscom.com 04 Sep '19

04 Sep '19

Dear all As a user of Numpy in finance, I'm absolutely in favour of removing these functions. They're too domain-specific, not flexible and general enough for widespread use, and probably not easy to maintain. Best regards Martin

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NumPy Community Meeting Wednesday, Sep. 4
by Sebastian Berg 03 Sep '19

03 Sep '19

Hi all, There will be a NumPy Community meeting Wednesday September 4 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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Allowing Dependabot access to the numpy repo
by Matti Picus 01 Sep '19

01 Sep '19

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