Hello everyone!
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
various models.
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
Propulsion Conference.
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 u
