Mailman 3 July 2019 - NumPy-Discussion

Creating a sine wave with exponential decay
by Ram Rachum July 26, 2019

July 26, 2019

Hi everyone! Total Numpy newbie here. I'd like to create an array with a million numbers, that has a sine wave with exponential decay on the amplitude. In other words, I want the value of each cell n to be sin(n) * 2 ** (-n * factor). What would be the most efficient way to do that? Someone suggested I do something like this: y = np.sin(x) * np.exp(newfactor * x) But this would create 2 arrays, wouldn't it? Isn't that wasteful? Does Numpy provide an efficient way of doing that without creating a redundant array? Thanks for your help, Ram Rachum.

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Season of Docs applications
by Ralf Gommers July 25, 2019

July 25, 2019

Hi GSoD applicants, To start with, I want to thank all of you who applied! There's a lot of interest in helping improve NumPy's documentation and web presence, which is awesome to see. During the application phase, we have interacted with some of you already, and I will reach out to others over the next few days. I'm just writing this email to say thank you and to confirm that yes we did receive your application. Due to the volume of applications, it will be difficult to reach out personally to all of you (I'll do my best though!), hence this email. Really looking forward to what will happen next! Cheers, Ralf

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The FREE Demystifying Data Science Conference is happening again! July 30-31!!!
by Michael Elnajami July 24, 2019

July 24, 2019

Metis <http://thisismetis.com/> is hosting our 3rd annual *FREE *Demystifying Data Science Conference <https://www.thisismetis.com/demystifying-data-science> July 30-31 from 10am - 5pm ET! There will be 16 interactive data science talks and 6 workshops led by industry-leading speakers. Experience interactivity via real-time chat with world-wide audience, polling, and social sharing. Plus, get access to recordings of every presentation and workshop. I know many of you NumPy users also are data scientists, so please check this out! This is a great opportunity to learn and connect with like minded people! Day 1 - July 30 will be for aspiring data scientists. Day 2 - July 31 will be for business leaders and practitioners Registration page: https://www.thisismetis.com/demystifying-data-science

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NumPy Community Meeting Wednesday, July 24
by Sebastian Berg July 23, 2019

July 23, 2019

Hi all, There will be a NumPy Community meeting Wednesday July 24 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 Best wishes Sebastian

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creating a .github repo
by Ralf Gommers July 20, 2019

July 20, 2019

Hi all, For the GitHub "community health" files like CoC, CONTRIBUTING, FUNDING, SECURITY, etc., there's now the option to put them in a .github repository so they show up for all other repos in the organization. See https://help.github.com/en/articles/creating-a-default-community-health-fil… I propose to do this in the next couple of days. If anyone sees an issue with this, please reply. Cheers, Ralf

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converting a C bytes array to two dimensional numpy array
by Omry Levy July 20, 2019

July 20, 2019

Hi, I have a question, regarding conversion of C (unsigned char *) buffer to a two dimensional numpy array this is what i am doing: 1) I get a C network buffer of unsigned char * let's call it the source buffer the size of the source buffer is: W * H * 2 bytes 2) I am using PyByteArray_FromStringAndSize() to convert the source buffer (a C unsigned char *) to python bytes array. a = PyByteArray_FromStringAndSize(source buffer, W * H * 2) 3) i am using numpy.frombuffer to convert the python bytes array to a 1 dimensional numpy array of size W *H *2 bytes b = numpy.frombuffer(a, dtype = np.uint8) 4) i am creating a 2 dimensional numpy array from (3) when each element in that array is made of 2 bytes from the python bytes array c = b.view(np.uint16).reshape((H, W)) Is there a way to optimize this some how ? Can you suggest a faster and better solution ? Thanks,

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Helping with website improvements
by Dashamir Hoxha July 18, 2019

July 18, 2019

Hi, With respect to this call for contributions: https://github.com/numpy/numpy/pull/13988/files I would like to help with improving the website of numpy (and maybe scipy as well). I have also applied for the Google Season of Docs 2019, and if accepted, I will be starting by the beginning of August. The improvements that I would like to make include: - Making the website responsive (so that it looks nice on small screens as well). There are responsive themes for sphinx and I may use one of them. - Improving the main page (or the landing page) so that it looks a bit more modern and attractive. - Reorganizing the structure of the information on the website, so that people from different backgrounds (students, professionals, etc.) can find more easily the relevant information that they are looking for. Including references to the external tutorials or courses about NumPy/SciPy. Other tentative improvements may be these: - Reorganize the docs so that each major release has its own version of docs. Major releases are those that may introduce new features, or in general, changes in the API (minor releases are the maintenance releases, which fix bugs, or make small changes, for example to improve the efficiency, but do not change the API). For example major releases may be v1.15, v1.16, v1.17 (however I am not sure about this). - Reorganize the docs so that the core API functionality is shown more prominently than the rest, and so that functions that may be deprecated in the future can be marked so (in order to discourage users from using them) and alternative solutions are suggested instead of them, etc. - Allow the users to add comments for each function or package. These may be usage examples for the benefit of other users, or pitfall alerts, or even bug reports. Reporting bugs on GitHub is better of course, but this may be a bit easier for the users. Since these are not incremental changes, if may be better if I work on a fork of the website repository, until they are finished and the new website is ready. Regards, Dashamir Hoxha

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numpy.org website redesign proposal
by Ralf Gommers July 17, 2019

July 17, 2019

Hi all, I just opened https://github.com/numpy/numpy/pull/14032, which contains a NEP for a redesign of the NumPy website (just the top-level site, not the docs). The part that most warrants discussion is probably the translation part. Additions to the NEP from anyone who has useful experiences with managing translations like that would be quite welcome. There's a couple of motivations for writing this as a NEP: 1. Ensure that we're all on the same page before doing quite a bit of work 2. Empowering people who have offered help with the website to go ahead and make changes. We got a few offers of help from people who are new to the project, which is awesome. I want to make sure they feel comfortable making some decisions. 3. Pushing forward with the website redesign now prepares the ground for GSoD, where we have tech writers and people with marketing skills that I hope will generate much better content than we have now. Integrating that content is much easier in a well-designed site than into the current Sphinx site. I also will start inviting our new volunteers to the NumPy web team, so they have the permissions needed to work on the website repo. Cheers, Ralf

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Stricter numpydoc validation
by Marc Garcia July 16, 2019

July 16, 2019

In pandas we've been, for more than a year now, enforcing a stricter numpydoc standard. And we've got a script to help with it, which validates things like capitalization and punctuation of paragraphs, the documented parameters (they must match the ones in the signature, have both a type and a description...), PEP-8 of the standards, and many more things, so all our docstrings are consistent. I saw that there is an issue with a discussion on having a more strict standard for numpydoc, I added a comment there on whether would make sense to move the pandas standard and validation code to numpydoc: https://github.com/numpy/numpydoc/issues/213#issuecomment-511760580 I think it's worth opening the discussion here too. Is there interest in the rest of the community on having a stricter standard, and move the pandas validation (with the required updates) to numpydoc? Of course we can discuss also the exact standard, but probably worth finding out first if a stricter numpydoc standard would make sense for everyone. You can find the documentation of our standard at: https://dev.pandas.io/development/contributing_docstring.html And the script that we use to validate, as well as the exact list of errors we detect in: https://github.com/pandas-dev/pandas/blob/master/scripts/validate_docstring… Cheers!

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Using a pyproject.toml file
by Matti Picus July 16, 2019

July 16, 2019

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