Summer of Code - Getting Started
Since 2011 the GNU Octave project has successfully mentored:
in Summer of Code (SoC) programs by Google and ESA.
Those SoC programs aim to advertise open-source software development and to attract potential new Octave developers.
Steps toward a successful application
- 😉💬 We want to get to know you (before the deadline). Communicate with us.
- Join Octave Discourse or IRC. Using a nickname is fine.
- Show us that you're motivated to work on Octave 💻. There is no need to present an overwhelming CV 🏆; evidence of involvement with Octave is more important.
- If you never talked to us, we will likely reject your proposal, even it looks good 🚮
- 👩🔬 Get your hands dirty.
- We are curious about your programming skills 🚀
- Your application will be much stronger if you fix Octave bugs or submit patches before or during the application period.
- You can take a look at the short projects for some simple bugs to start with.
- Use Octave!
- If you come across something that does not work the way you like ➡️ try to fix that 🔧
- Or if you find a missing function ➡️ try to implement it.
- We are curious about your programming skills 🚀
- 📝💡 Tell us what you are going to do.
- Do not write just to say what project you're interested in. Be specific about what you are going to do, include links 🔗, show us you know what you are talking about 💡, and ask many smart questions 🤓
- Remember, we are volunteer developers and not your boss 🙂
- 📔 Prepare your proposal with us.
- Try to show us as early as possible a draft of your proposal 📑
- If we see your proposal for the first time after the application deadline, it might easily contain some paragraphs not fully clear to us. Ongoing interaction will give us more confidence that you are capable of working on your project 🙂👍
- Then submit the proposal following the applicable rules, e.g. for GSoC. 📨
How do we judge your application?
Depending on the mentors and SoC program there are varieties, but typically the main factors considered would be:
- You have demonstrated interest in Octave and an ability to make substantial modifications to Octave
- The most important thing is that you've contributed some interesting code samples to judge your skills. It's OK during the application period to ask for help on how to format these code samples, which normally are Mercurial patches.
- You showed understanding of your topic
- Your proposal should make it clear that you're reasonably well versed in the subject area and won't need all summer just to read up on it.
- Well thought out, adequately detailed, realistic project plan
- "I'm good at this, so trust me" isn't enough. In your proposal, you should describe which algorithms you'll use and how you'll integrate with existing Octave code. You should also prepare a project timeline and goals for the midterm and final evaluations.
What you should know about Octave
GNU Octave is mostly written in C++ and its own scripting language that is mostly compatible with Matlab. There are bits and pieces of Fortran, Perl, C, awk, and Unix shell scripts here and there. In addition to being familiar with C++ and Octave's scripting language, you as successful applicant will be familiar with or able to quickly learn about Octave's infrastructure. You can't spend the whole summer learning how to build Octave or prepare a changeset and still successfully complete your project 😇
You should know:
- How to build Octave from its source code using the GNU build system.
- Read in this wiki: Developer FAQ, Building
- Tools to know: gcc, make
- How to submit patches (changesets).
- Read in this wiki: Contribution guidelines, Mercurial
- Tools to know: Mercurial (hg), git
Suggested projects
The following suggested projects are distilled from the Projects page for the benefit of potential SoC participants. You can also look at our completed past projects for more inspiration.
ode15{i,s} : Matlab Compatible DAE solvers
An initial implementation of Matlab compatible Differential Algebraic Equations (DAE) solvers, ode15i and ode15s, based on SUNDIALS,
was done by Francesco Faccio during GSoC 2016. The code is maintained in the main Octave repository and consists mainly of the following three files: libinterp/dldfcn/__ode15__.cc, scripts/ode/ode15i.m and scripts/ode/ode15s.m.
The decic function for selecting consistent initial conditions for ode15i can be made more Matlab compatible by using another algorithm. Another useful extension is to make ode15{i,s} work with datatypes other than double and to improve interpolation at intermediate time steps.
- Project size [?] and Difficulty
- ~350 hours (medium)
- Required skills
- Octave, C/C++; familiarity with numerical methods for DAEs
- Potential mentors
- Francesco Faccio, Carlo de Falco, Marco Caliari, Jacopo Corno, Sebastian Schöps
Symbolic package
The Symbolic package provides symbolic computing and other computer algebra system tools. The main component of Symbolic is a pure m-file class "@sym" which uses the Python package SymPy to do (most of) the actual computations. The package aims to expose much of the functionality of SymPy while also providing a high level of compatibility with the Matlab Symbolic Math Toolbox. The Symbolic package requires communication between Octave and Python. In 2016 a GSoC project successfully re-implemented this communication using the new Pythonic package. In 2022, a GSoC project revitalized and improved the maintainability of the package.
Possible ideas for improving the Symbolic package:
- exposing more functionality of SymPy with less glue in between. For example, we could allow OO-style method calls such as
f.diff(x)as well asdiff(f, x). - Fix the storage of non-expressions by working with upstream SymPy: currently we rely on deprecated functionality in SymPy.
- Fix classdef-related issues in Octave itself.
Improve TIFF image support
Tag Image File Format (TIFF) is the de facto standard for scientific images. Octave uses the GraphicsMagic (GM) C++ library to handle TIFF and many others image formats. However, GM still has several limitations:
- GM has build option
quantumwhich defines the bitdepth to use when reading an image:- Building GM with high quantum means that images of smaller bitdepth will take a lot more memory when reading.
- Building GM with low quantum will make it impossible to read images of higher bitdepth. It also means that the image needs to always be rescaled to the correct range.
- GM supports unsigned integers only, thus incorrectly reading files such as TIFF with floating-point data.
- GM hides details of the image such as whether the image file is indexed. This makes it hard to access the real data stored on file.
This project aims to implement better TIFF image support using libtiff, while leaving GM handle all other image formats. After writing a classdef interface to libtiff, improve the Octave functions imread, imwrite, and imfinfo to make use of it.
- Project size [?] and Difficulty
- ~175 hours (medium)
- Required skills
- Octave, C/C++
- Potential mentors
PolarAxes and Plotting Improvements
Octave currently provides supports for polar axes by using a Cartesian 2-D axes and adding a significant number of properties and callback listeners to get things to work. What is needed is the implementation of a dedicated "polaraxes" object in C++. This will require creating a new fundamental graphics object type, and programming in C++/OpenGL to render the object. When "polaraxes" exists as an object type, then m-files will be written to access them, including polaraxes.m, polarplot.m, rticks.m, rticklabels.m, thetaticks, thetaticklabels.m, rlim.m, thetalim.m. This relates to bug #49804.
- Project size [?] and Difficulty
- ~350 hours (medium)
- Required skills
- Octave, C/C++; optional experience with OpenGL programming
- Potential mentors
Table datatype
In 2013, Matlab introduced a new table datatype to conveniently organize and access data in tabular form. This datatype has not been introduced to Octave yet (see bug #44571). However, there are two initial implementation approaches https://github.com/apjanke/octave-tablicious and https://github.com/gnu-octave/table.
Based upon the existing approaches, the goal of this project is to define an initial subset of table functions, which involve sorting, splitting, merging, and file I/O and implement it within the given time frame.
- Project size [?] and Difficulty
- ~350 hours (hard)
- Required skills
- Octave, C/C++
- Potential mentors
YAML encoding/decoding
YAML, is a very common human readable and structured data format. Unfortunately, GNU Octave (and Matlab) still lacks of builtin support of that omnipresent data format. Having YAML support, Octave can easily read and write config files, which often use YAML or JSON. The latter JSON format has been successfully implemented for Octave during GSoC 2020.
The goal of this project is to repeat the GSoC 2020 success story with Rapid YAML or another fast C/C++ library.
The first step is research about existing Octave/Matlab and C/C++ implementations, for example:
- https://code.google.com/archive/p/yamlmatlab/ (uses Java)
- http://vision.is.tohoku.ac.jp/~kyamagu/ja/software/yaml/ (uses Java)
Then evaluate (and to cherry pick from) existing implementations above, compare strength and weaknesses. After this, an Octave package containing en- and decoding functions (for example yamlencode and yamldecode) shall be created. This involves proper documentation of the work and unit tests to ensure the correctness of the implementation.
Finally, the package is considered to be merged into core Octave, probably after the GSoC project. However, it can be used immediately from Octave as package and is backwards-compatible with older Octave versions.
- Project size [?] and Difficulty
- ~175 hours (easy)
- Required skills
- Octave, C/C++
- Potential mentors
TISEAN package
The TISEAN package provides an Octave interface to TISEAN is a suite of code for nonlinear time series analysis. In 2015, another GSoC project started with the work to create interfaces to many TISEAN functions, but there is still work left to do. There are missing functions to do computations on spike trains, to simulate autoregresive models, to create specialized plots, etc. These are of importance for many scientific disciplines involving statistical computations and signal processing.
- Project size [?] and Difficulty
- ~350 hours (medium)
- Required skills
- Octave, C/C++; FORTRAN API knowledge
- Potential mentors
Better tab completion
Links: https://savannah.gnu.org/bugs/index.php?62492 and https://savannah.gnu.org/bugs/?53384
Description: currently pressing Tab at the Octave command prompt attempts autocompletion of all identifiers in scope (variables, functions, classdefs etc) as well as files and directories in the path. It is not context sensitive.
Project: Improve tab completion. For example,
- Typing
- load x
and then pressing tab should ideally give all loadable files and directories starting with x, not unrelated results like variables or functions.
- Typing
- cd
and tab should only give directories.
- Any file and directory names that are included in the results should include those with spaces and special characters including those that would be interpreted as operators by Octave.
- Typing commands like
- axis
or
- format
and pressing tab should give only those options relevant to that command. E.g. format can be followed by short / long / loose / compact etc but not by a file or variable. Similarly axis can be followed by ij / xy / tight / equal / actual limits etc but not by files or directories. And so on for other commands. This should be made possible for both preexisting commands and for yet-to-be-written commands without any rewriting of existing function code or documentation.
To get more examples, see how bash completion works. You can type git or hg and then tab and it will give the list of available commands. If you type "sort --r" and then tab, it gives the list of options to sort starting with "--r", etc.
Graphics rendering back to front sorting
Several incompatibilities have been identified in how Octave plots transparent objects in 3D, causing certain transparent objects to hide opaque objects behind them even though they're not supposed to. The vast majority of them were isolated to one problem: if the objects to be drawn are rendered such that the one farthest away from the viewer is rendered first and nearer objects are rendered on top of that, then transparency would be automatically achieved, but this needs very careful coding to stay performant and to avoid rendering objects that will be overwritten fully by others. See [1] for a summary.
This project can be a GSoC for a student who understands 3D graphics rendering.
Project sizes
Since GSoC 2022 there exist two project sizes[1][2]:
- ~175 hours (~12 weeks, Jun 13 - Sept 12)
- ~350 hours (~22 weeks, Jun 13 - Nov 21)