Mailman 3 December 2017 - NumPy-Discussion

Applying logical operations along an axis of a boolean array?
by hanno_lists＠gmx.net Dec. 18, 2017

Dec. 18, 2017

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masked_values behaviour
by Jesper Larsen Dec. 18, 2017

Dec. 18, 2017

Hi numpy people, I was just wondering whether this behaviour is intended: >>> import numpy as np >>> np.ma.masked_values(np.array([-32768.0]), np.int16(-32768)) masked_array(data = [-32768.], mask = False, fill_value = -32768.0) So the resulting masked array is not masked. On the other hand it is masked in the three cases below: >>> np.ma.masked_values(np.array([-32767.0]), np.int16(-32767)) masked_array(data = [--], mask = [ True], fill_value = -32767.0) >>> np.ma.masked_values(np.array([-32768.0]), -32768.0) masked_array(data = [--], mask = [ True], fill_value = -32768.0) >>> np.ma.masked_values(np.array([-32768.0]), -32768) masked_array(data = [--], mask = [ True], fill_value = -32768.0) Best regards, Jesper

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NumFOCUS End-of-Year Fundraising Drive (NumPy fiscal sponsor)
by Gina Helfrich Dec. 15, 2017

Dec. 15, 2017

Hi folks, NumFOCUS, the nonprofit that provides fiscal sponsorship for NumPy, kicks off our end of year fundraising drive officially on Monday and would really appreciate your support! Andy Terrel, NumFOCUS Board President, has generously put up the first $5,000 donation as a matching challenge to the community. For every dollar the community donates, Andy will donate a dollar, up to $5k. Please spread the word and use this link to donate: https://www.flipcause.com/secure/cause_pdetails/Mjc2NTg= Thanks so much for your support of NumPy and NumFOCUS! Cheers, Gina -- Dr. Gina Helfrich Communications Director, NumFOCUS <https://www.numfocus.org?utm_source=email&utm_medium=Gina_signature> gina(a)numfocus.org 512-222-5449 <(512)%20222-5449>

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The meaning of threshold in numpy.set_printoptions()?
by Peng Yu Dec. 15, 2017

Dec. 15, 2017

Hi, The help says. threshold : int, optional Total number of array elements which trigger summarization rather than full repr (default 1000). But the following code shows that 7 triggers summarization instead of 5. Does anybody know how to interpret the meaning of `threshold`? Thanks. In [1]: import numpy as np ...: np.set_printoptions(threshold=5) ...: print(np.arange(6)) ...: print(np.arange(7)) ...: [0 1 2 3 4 5] [0 1 2 ..., 4 5 6] -- Regards, Peng

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NumPy 1.14.0rc1 release
by Charles R Harris Dec. 14, 2017

Dec. 14, 2017

Hi All, On behalf of the NumPy team, I am pleased to announce NumPy 1.14.0rc1. Numpy 1.14.0rc1 is the result of seven months of work and contains a large number of bug fixes and new features, along with several changes with potential compatibility issues. The major change that users will notice are the stylistic changes in the way numpy arrays and scalars are printed, a change that will affect doctests. See the release notes for details on how to preserve the old style printing when needed. A major decision affecting future development concerns the schedule for dropping Python 2.7 support in the runup to 2020. The decision has been made to support 2.7 for all releases made in 2018, with the last release being designated a long term release with support for bug fixes extending through the end of 2019. Starting from January, 2019 support for 2.7 will be dropped in all new releases. More details can be found in the relevant NEP <https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-prop…> . This release supports Python 2.7 and 3.4 - 3.6. Wheels for the pre-release are available on PyPI. Source tarballs, zipfiles, release notes, and the changelog are available on github <https://github.com/numpy/numpy/releases/tag/v1.14.0rc1>. *Highlights* - The ``np.einsum`` function uses BLAS when possible - ``genfromtxt``, ``loadtxt``, ``fromregex`` and ``savetxt`` can now handle files with arbitrary Python supported encoding. - Major improvements to printing of NumPy arrays and scalars. *New functions* - ``parametrize``: decorator added to numpy.testing - ``chebinterpolate``: Interpolate function at Chebyshev points. - ``format_float_positional`` and ``format_float_scientific`` : format floating-point scalars unambiguously with control of rounding and padding. - ``PyArray_ResolveWritebackIfCopy`` and ``PyArray_SetWritebackIfCopyBase``, new C-API functions useful in achieving PyPy compatibity. *Contributors* A total of 101 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexey Brodkin + - Allan Haldane - Andras Deak + - Andrew Lawson + - Antoine Pitrou - Bernhard M. Wiedemann + - Bob Eldering + - Brandon Carter - CJ Carey - Charles Harris - Chris Lamb - Christoph Boeddeker + - Christoph Gohlke - Daniel Hrisca + - Daniel Smith - Danny Hermes - David Freese - David Hagen - David Linke + - David Schaefer + - Dillon Niederhut + - Egor Panfilov + - Emilien Kofman - Eric Wieser - Erik Bray + - Erik Quaeghebeur + - Garry Polley + - Gunjan + - Henke Adolfsson + - Hidehiro NAGAOKA + - Hong Xu + - Iryna Shcherbina + - Jaime Fernandez - James Bourbeau + - Jamie Townsend + - Jarrod Millman - Jean Helie + - Jeroen Demeyer + - John Goetz + - John Kirkham - John Zwinck - Jonathan Helmus - Joseph Fox-Rabinovitz - Joseph Paul Cohen + - Joshua Leahy + - Julian Taylor - Jörg Döpfert + - Keno Goertz + - Kevin Sheppard + - Kexuan Sun + - Konrad Kapp + - Kristofor Maynard + - Licht Takeuchi + - Loïc Estève - Lukas Mericle + - Marten van Kerkwijk - Matheus Portela + - Matthew Brett - Matti Picus - Michael Lamparski + - Michael Odintsov + - Michael Schnaitter + - Michael Seifert - Mike Nolta - Nathaniel J. Smith - Nelle Varoquaux + - Nicholas Del Grosso + - Nico Schlömer + - Oleg Zabluda + - Oleksandr Pavlyk - Pauli Virtanen - Pim de Haan + - Ralf Gommers - Robert T. McGibbon + - Roland Kaufmann - Sebastian Berg - Serhiy Storchaka + - Shitian Ni + - Spencer Hill + - Srinivas Reddy Thatiparthy + - Stefan Winkler + - Stephan Hoyer - Steven Maude + - SuperBo + - Thomas Köppe + - Toon Verstraelen - Vedant Misra + - Warren Weckesser - Wirawan Purwanto + - Yang Li + - Ziyan + - chaoyu3 + - hemildesai + - k_kapp(a)yahoo.com + - nickdg + - orbit-stabilizer + - schnaitterm + - solarjoe - wufangjie + - xoviat + - Élie Gouzien + Enjoy, Chuck

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[REL] Matplotlib 2.1.1
by Thomas Caswell Dec. 11, 2017

Dec. 11, 2017

Folks, Happy to announce Matplotlib 2.1.1 This is primarily a bug fix release, see https://github.com/matplotlib/matplotlib/releases/tag/v2.1.1 for details. The next planned release is a 2.2 feature release in January/February 2018. Thank you to everyone who worked on this release! Tom

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Numpy 1.14.x has been branch, master is open for 1.15 development.
by Charles R Harris Dec. 8, 2017

Dec. 8, 2017

Hi All, Just a heads up that NumPy 1.14.x has been branched. Chuck

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NumPy 1.14 branch
by Charles R Harris Dec. 7, 2017

Dec. 7, 2017

Hi All, I plan to branch NumPy 1.14.x tomorrow, 08-12-1017. If anyone feels that there is a current PR that needs to be in, please comment. Chuck

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NEP process update
by Jarrod Millman Dec. 7, 2017

Dec. 7, 2017

Hi all, Since we expect to be writing some NEPs in the near future, Nathaniel and I were looking at how they're organized, and realized that the process is a bit underspecified and it's a hard to tell the status of things. So I'm thinking of putting together some better tools and documentation, and wanted to get some quick feedback before I runoff in the wrong direction. The goal is not to add a ton of new process, but to better document the current process. I would also like to add a little additional structure to make it easier to understand the process for new contributors and to make the status of NEPS easier to understand. After reviewing the existing system, looking at how other projects do this, and discussing this with Nathaniel, I tentatively plan to work on the following: 1. Standardize the NEP metadata (e.g., whether they're draft/accepted/implemented) 2. Write a NEP to explain the purpose and process (think PEP 1) 3. Create a NEP template (think PEP 12) 4. Add metadata to old NEPs 5. Automate updating the NEP website and autogenerating the index based on the NEP metadata. Nathaniel and I have already started discussing some of these items and would love to get some feedback soon. Assuming that sounds good, my tentative next steps are: - I'll draft a purpose and process NEP based on PEP 1 and a few other projects. - I'll also create a draft NEP template. - I'll move the NEPs into their own repo (something like numpy/neps), and set up an automated system (RTD or Github pages) to render and publish them with some useful index. Does this seem like a reasonable way to start? Best regards, Jarrod

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Type annotations for NumPy
by Stephan Hoyer Dec. 5, 2017

Dec. 5, 2017

There's been growing interest in supporting PEP-484 style type annotations in NumPy: https://github.com/numpy/numpy/issues/7370 This would allow NumPy users to add type-annotations to their code that uses NumPy, which they could check with mypy, pycharm or pytype. For example: def f(x: np.ndarray) -> np.ndarray: """Identity function on a NumPy array.""" return x Eventually, we could include data types and potentially array shapes as part of the type. This gets quite a bit more complicated, and to do in a really satisfying way would require new features in Python's typing system. To help guide discussion, I wrote a doc describing use-cases and needs for typing array shapes in more detail: https://docs.google.com/document/d/1vpMse4c6DrWH5rq2tQSx3qwP_m_0lyn-Ij4WHqQ… Nathaniel Smith and I recently met with group in San Francisco interested in this topic, including several mypy/typeshed developers (Jelle Zijlstra and Ethan Smith). We discussed and came up with a plan for moving forward: 1. Release basic type stubs for numpy.ndarray without dtypes or shapes, as separate "numpy_stubs" package on PyPI per PEP 561. This will let us iterate rapidly on (experimental) type annotations without coupling to NumPy's release cycle. 2. Add support for dtypes in ndarray type-annotations. This might be as simple as writing np.ndarray[np.float64], but will need a decision about appropriate syntax for shape typing to ensure that this is forwards compatible with typing shapes. Note: this will likely require minor changes to NumPy itself, e.g., to add __class_getitem__ per PEP 560. 3. Add support for shapes in ndarray type-annotations, and define a broader standard for typing array shapes. This will require collaboration with type-checker developers on the required typing features (for details, see my doc above). Eventually, this may entail writing a PEP. I'm writing to gauge support for this general plan, and specifically to get support for step 1. Cheers, Stephan

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