Hello all, especially those who recently joined and are interested in our
Google Season of Docs projects.
It is great to hear from you!
For now, all the information you need about the program is in the official
GSoD website (https://developers.google.com/season-of-docs), and in our
If you are interested, I would advise working on the following:
- Familiarizing yourself with the NumPy docs and the workflow for building
- Reading NEP 44 to have an idea of the current vision and focus for the
- Working on your own proposal, so we can analyze it later.
You have until June 8 to do this, and if you need help or have questions
about any of the above, please get in touch so we can help. Make sure you
get in touch with the mentors directly by sending your message to
I would also recomment that you attend our Documentation Team meeting
taking place next Monday, May 18, at 3PM UTC (more details in a separate
message to follow shortly).
I was going through the project titled "External tutorial content curation
and adaptation" with mentors as Melissa Mendonca Weber, Ralf Gommers. I
propose we can do the following -
1. Gather all the info about external tutorials in the form of videos,
blogs, etc. any content that can help a user understand numpy would be
2. Categorise all the content into different types, i.e. Video, 3rd party
documentation, blogs, etc.
3. Obtain the required permissions to source the content.
After this I am not sure as to how to present it to the larger community,
we can either go about it as, I was thinking of something like this
Add a separate section in the wiki and add all the links along with their
descriptions and what it's about
Any feedback would be appreciated on this.
I am pursuing electronics and communication. I have knowledge of python,
html, css, js and react.js, and I have just started contributing to open
I am Hmrishav Bandyopadhyay, an Undergrad at Jadavpur University, India.
One of the application pre-requisites for applying to GSOD under numpy
require me to have technical writing experience. Being a student, I have
not had any professional technical writing job or internship per-se but I
have been writing tutorials and such in Towards Data Science(Medium) for
some time now, most of which have been curated by the website. Does this
make me eligible for applying to numpy? Any help or pointers would be
Link to one such blog for reference --
I am interested in contributing towards NumPy's documentation , but I'm
still a bit confused on where to start.
Request your guidance on the same .Hoping to hear from you soon.
I am very excited to introduce myself to the NumPy community!
I am Themistoklis Spanoudis, I come from Greece and I have recently
finished my 5-year (Integrated Masters) Degree in Mechanical Engineering at
Aristotle University of Thessaloniki.
I am very happy to see that NumPy has been selected for Google Season of
Docs 2020, because I love scientific computing and Python and I would
really like to contribute in one of your GSoD projects. Specifically, what
caught my attention is “Creating high-level documentation, such as
Tutorials and How-Tos, covering topics that are missing from the official
documentation”. I have a pretty clear idea on where I would like to make
this project go, but first let me share a few things about my background.
My first introduction to scientific computing came during the first year of
my studies through a semester course on this topic, based on MATLAB. During
that semester I had a lot of course assignments that eventually got me
hooked with scientific computing. My journey in this area continued with me
getting to know NumPy and Python and choosing a plethora of courses during
the following years of my studies that included computational assignments.
During those years I built programs that range from Structural Finite
Element Analysis, Fatigue Analysis and Lagrangian Dynamics to Statistical
Quality Control, Operations Research and Supply Chain Optimization.
Moreover, last year I completed my Master Thesis during a 6-month full-time
position at Airbus Helicopters in Germany, during which I had the chance to
work on flight trajectory optimization and data-driven flight dynamics
modelling. This involved a lot of scientific programming building
state-space models and defining optimization problems as well as tasks
related to working with a large amount of data such as, cleaning,
filtering, transforming to extract training examples and utilizing them in
Additionally, last summer I participated in Google Summer of Code 2019 with
AerospaceResearch, which is an international space community helping
realise space exploration. My project involved the development of a
software module to be integrated within a research project tackling
electric propulsion system optimization for small satellites. The module is
responsible for the visualization of genetic algorithm data in order to
extract insights about the evolution process that can be used both to
improve the algorithm and as heuristics by human designers. This project
involved working with evolution data to automatically create static plots
as well as animations that are completely configurable through a
user-readable XML input file. My work along with other advancements in the
research project was published at the 36th International Electric
On a similar note, last fall I participated in Google Season of Docs 2019
with OpenSCAD, which is a scripting software for creating solid 3D CAD
models. My project involved the creation of a tutorial focused on new
OpenSCAD users. My mentors introduced me to the great presentation “What
nobody tells you about documentation” by Daniele Procida at PyCon Australia
2017, which was a major influence for my work. Closely following the
guidelines of the presentation for the “tutorial” type of documentation and
reviewing existing material and references, I developed a hands-on,
follow-along tutorial designed to get new users started with creating their
own models as soon as possible, while gradually introducing more advanced
features and building their confidence by following a steady progression
and a consistent style.
So that was about my background and experience, let me now say a few words
about my plans for this year.
For Google Season of Docs 2020 I would like to work with the NumPy
community to create a more advanced, application-based tutorial that will
serve as the next step to the previous year’s project “NumPy: the absolute
basics for beginners”. Having gone through most of the currently available
documentation under https://numpy.org/devdocs/index.html as well the
external linked educational material, I believe this project would be a
great addition to the existing documents. It would help new users
understand how NumPy can be used in practice to solve real problems, get
them familiar with more advanced features not referenced in the basic
tutorial and get them ready to work on their own projects.
This tutorial would include step-by-step explanations providing a lot of
context to the users as well as follow-along exercises/challenges. The
topics presented on this tutorial can be focused around scientific
simulation, optimization and data science. In my opinion data science is a
stronger candidate for this purpose, since the same techniques and
methodologies are directly applicable to a wider audience across different
fields, compared to scientific simulation which is more coupled to domain
knowledge and could potentially repel users lacking the relevant
background. The exact presented cases of course is something to be
discussed and adapted to fit the educative flow and the covered material.
In order to avoid this introductory message getting too long and to give
you a practical idea of what I mean by step-by-step, follow-along
explanations as well as by providing context and explaining the thought
process behind doing things a certain way, I would like to link to my work
from Google Summer of Code 2019 and Google Season of Docs 2019.
Google Summer of Code 2019
-Official project listing on Google’s website:
-Final report: https://aerospaceresearch.net/?p=1812
-Blog posts presenting the functionality of the developed module through
example use cases (highly recommended if you are into genetic algorithms):
---Blog post 1: https://aerospaceresearch.net/?p=1542
---Blog post 2: https://aerospaceresearch.net/?p=1571
---Blog post 3: https://aerospaceresearch.net/?p=1785
Google Season of Docs
-Official project listing on Google’s website:
-Root page of developed tutorial:
-Alternative linking to the individual tutorial chapters through OpenSCAD’s
I would also like to add here my 100+ page Master Thesis as an additional
example of my technical writing work but unfortunately it is currently set
confidential by Airbus.
I think this is a good overall introduction to my background, my work and
my intentions regarding working with NumPy to get the discussion going
about the specific project that I would like to complete during this year’s
Google Season of Docs program. I would highly appreciate your input,
feedback, thoughts and comments.
I am looking forward to hearing from you!
On behalf of the community and contributors, I’m pleased to announce PyData/Sparse v0.10.0.
PyData/Sparse is a package that provides sparse arrays mimicking NumPy API for the PyData ecosystem.
This was a mainly bug-fix focused release with a few features and performance improvements. Most notably, it fixes deprecation warnings that arise when pairing it with Numba v0.49, which will turn into errors with Numba v0.50. The full change-log can be found at https://sparse.pydata.org/en/latest/changelog.html
This version increases the minimum required Numba version to 0.49. We recommend users who can upgrade to Numba 0.49 or later to upgrade to this release. For users who cannot update to this version, we recommend staying with 0.9.x until you can.
I'm Qiyu8 <https://github.com/Qiyu8>, Currently, Numpy recommend a command
line method to run test case as demonstrated in Testing Guidelines
is heavy and inconvenient, It's also a little painful if test cases can not
debug in a visualize way, I happened to find an easier way to running and
debugging numpy test case as below:
*1.Download VS Code **here <https://code.visualstudio.com/Download>.*
*2.Open numpy source folder. File -> Open Folder.*
*3.Install the Python extension from your extensions:*
*4.Configuring Tests: Pytest*
The VS Code Python extension supports unit tests as well as *pytest*.
Here's how to enable the framework:
Open *Command Palette (*ctrl +shift +P) and start typing ‘python: configure
tests.’ It will display a list of available python linters. You can add any
of the settings to your user settings.json file (opened with the *File* >
*Preferences* > *Settings)*
Edit the settings.json file like this:
Click "Discover Tests"
Then you can running and debugging any test case that you want.
The fallback of this method is that you have to rebuild numpy before
execute testcase if you changed numpy's source code.