Difference between revisions of "Summer of Code - Getting Started"

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The following is distilled from the [[Projects]] page for the benefit of potential [https://www.google-melange.com/gsoc/homepage/google/gsoc2015 Google] and [http://sophia.estec.esa.int/socis2015 ESA] Summer of Code (SoC) students. Although students are welcome to attempt any of the projects in that page or any of their own choosing, here we offer some suggestions on what good student projects might be.
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{{Note|GNU Octave is a [https://summerofcode.withgoogle.com/programs/2022/organizations/gnu-octave mentoring organization for GSoC 2022].}}
  
= Steps Toward a Successful Application =
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Since 2011 the GNU Octave project has successfully mentored:
 +
* [[Summer of Code | '''37 participants''' 🙂]]
 +
* [[Summer of Code | '''39 projects''' 📝]]
 +
in [[Summer of Code]] (SoC) programs by [https://summerofcode.withgoogle.com/ Google] and [https://esa.int/ ESA].
  
If you like any of the projects described below, these are the steps you need to follow to apply:
+
Those SoC programs aim to advertise open-source software development and to attract potential new Octave developers.
  
* '''Help Us Get To Know You'''<br>
+
= Steps toward a successful application =
: If you aren't communicating with us before the application is due, your application will not be accepted.
 
:: '''Join the [https://lists.gnu.org/mailman/listinfo/octave-maintainers maintainers mailing list]''' or read the archives and see what topics we discuss and how the developers interact with each other.
 
:: '''Hang out in our [https://webchat.freenode.net/?channels=#octave IRC channel]'''. Ask questions, show us that you are motivated and well-prepared. There will be more applicants than we can effectively mentor, so do ask for feedback on your public application to increase the strength of your proposal!
 
* '''Find Something That Interests You'''
 
: It's '''critical''' that you '''find a project that excites you'''.  You'll be spending most of the summer working on it (we expect you to treat the SoC as a full-time job).
 
: Don't just tell us how interested you are, show us that you're willing and able to '''contribute''' to Octave. You can do that by [https://savannah.gnu.org/bugs/?group=octave fixing a few bugs] or [http://savannah.gnu.org/patch/?group=octave submitting patches] well before the deadline, in addition to regularly interacting with Octave maintainers and users on e-mail and IRC. Our experience shows us that successful SoC students demonstrate their interest early and often.
 
* '''Prepare Your Proposal With Us'''
 
: By working with us to prepare your proposal, you'll be getting to know us and showing us how you approach problems. The best place for this is your Wiki user page and the [https://webchat.freenode.net/?channels=#octave IRC channel].
 
* '''Complete Your Application'''
 
: Fill out our '''''public''''' application template.
 
:: This is best done by '''[[Special:CreateAccount|creating an account at this wiki]]''', and copying the '''[[Template:Student_application_template_public|template]]''' from its page.
 
:: You really only need to copy and answer the '''''public''''' part there, there is no need to showcase everything else to everybody reading your user page!
 
: Fill out our '''''private''''' application template.
 
:: This is best done by copying the '''[[Template:Student_application_template_private|template]]''' from its page and '''adding the required information to your application at Google (melange)''' or at '''ESA'''.<br>
 
:: Only the organization admin and the possible mentors will see this data.  You can still edit it after submitting until the deadline!
 
  
== Things You'll be Expected to Know or Quickly Learn ==
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# 😉💬 '''We want to get to know you (before the deadline).  Communicate with us.'''
 +
#* Join [https://octave.discourse.group/ '''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.
 +
#* '''<span style="color:darkblue;">If you never talked to us, we will likely reject your proposal</span>''', even it looks good 🚮
 +
# 👩‍🔬 '''Get your hands dirty.'''
 +
#* We are curious about your programming skills 🚀
 +
#** Your application will be much stronger if you [https://savannah.gnu.org/bugs/?group=octave fix Octave bugs] or [https://savannah.gnu.org/patch/?group=octave 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.
 +
# 📝💡 '''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 [http://www.catb.org/esr/faqs/smart-questions.html 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 [https://google.github.io/gsocguides/student/writing-a-proposal GSoC]. 📨
  
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, successful applicants 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.
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= How do we judge your application? =
  
* '''The Build System'''
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Depending on the mentors and SoC program there are varieties, but typically the main factors considered would be:
: [http://en.wikipedia.org/wiki/GNU_build_system The GNU build system] is used to build Octave.
 
: While you generally don't need to understand too much unless you actually want to change how Octave is built, you should be able to understand enough to get a general idea of how to build Octave.
 
: If you've ever done a <tt>configure && make && make install</tt> series of commands, you have already used the GNU build system.
 
: '''You must demonstrate that you are able to build the development version of Octave from sources before the application deadline.''' You will be able to find instructions how to it on this wiki, and the manual. Linux is arguably the easiest system to work on.
 
:* [[Building_for_Linux_systems]]
 
:* [[Building]]
 
:* [https://www.gnu.org/software/octave/doc/interpreter/Building-the-Development-Sources.html Octave Manual on Building the Development Sources]
 
:* [https://www.gnu.org/software/octave/doc/interpreter/Installation.html Octave Manual on Installing Octave]
 
* '''The Version Control System'''
 
: We use [http://mercurial.selenic.com/ Mercurial] (abbreviated hg).
 
: Mercurial is the [http://en.wikipedia.org/wiki/Distributed_Version_Control_System distributed version control system] (DVCS) we use for managing our source code. You should have some basic understanding of how a DVCS works, but hg is pretty easy to pick up, especially if you already know a VCS like git or svn.
 
* '''The Procedure for Contributing Changesets'''
 
: You will be expected to follow the same procedures as other contributors and core developers.
 
: You will be helping current and future Octave developers by using our standard style for changes, commit messages, and so on.  You should also read the same [https://www.gnu.org/software/octave/doc/interpreter/Contributing-Guidelines.html contribution] [http://hg.savannah.gnu.org/hgweb/octave/file/tip/etc/HACKING guidelines] we have for everyone.
 
: [[Hg_instructions_for_mentors#Mercurial_Tips_for_SoC_students | This page]] describes the procedures students are expected to use to publicly display their progress in a public mercurial repo during their work.
 
* '''The Maintainers Mailing List'''
 
: We primarily use [https://lists.gnu.org/mailman/listinfo/octave-maintainers mailing lists] for communication among developers.
 
: The mailing list is used most often for discussions about non-trivial changes to Octave, or for setting the direction of development.
 
: You should follow basic mailing list etiquette. For us, this mostly means "do not [https://en.wikipedia.org/wiki/Posting_style#Top-posting top post]".
 
* '''The IRC Channel'''
 
: We also have [http://webchat.freenode.net?channels=octave the #octave IRC channel in Freenode].
 
: You should be familiar with the IRC channel.  It's very helpful for new contributors (you) to get immediate feedback on ideas and code.
 
: Unless your primary mentor has a strong preference for some other method of communication, the IRC channel will likely be your primary means of communicating with your mentor and Octave developers.
 
* '''The Octave Forge Project'''
 
: [http://octave.sf.net Octave-Forge] is a collection of contributed packages that enhance the capabilities of core Octave. They are somewhat analogous to Matlab's toolboxes.
 
* '''Related Skills'''
 
: In addition, you probably should know '''some''' mathematics, engineering, experimental science, or something of the sort.
 
: If so, you probably have already been exposed to the kinds of problems that Octave is used for.
 
  
== Criteria by which applications are judged ==
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* '''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.
  
These might vary somewhat depending on the mentors and coordinators for a particular Summer of Code, but typically the main factors considered would be:
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* '''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.
  
* '''Applicant has demonstrated an ability to make substantial modifications to Octave'''
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* '''Well thought out, adequately detailed, realistic project plan'''
: The most important thing is that you've contributed some interesting code samples to judge you by. It's OK during the application period to ask for help on how to format these code samples, which normally are Mercurial patches.
+
*: "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.
  
* '''Applicant shows understanding of topic'''
+
= What you should know about Octave =
: Your application 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.
 
  
* '''Applicant shows understanding of and interest in Octave development'''
+
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 😇
: The best evidence for this is previous contributions and interactions.
 
  
* '''Well thought out, adequately detailed, realistic project plan'''
+
You should know:
: "I'm good at this, so trust me" isn't enough. You should describe which algorithms you'll use and how you'll integrate with existing Octave code. You should also prepare a full timeline and goals for the midterm and final evaluations.
+
# How to build Octave from its source code using [http://en.wikipedia.org/wiki/GNU_build_system the GNU build system].
 +
#* Read in this wiki: [[Developer FAQ]], [[Building]]
 +
#* Tools to know: [https://en.wikipedia.org/wiki/GNU_Compiler_Collection gcc], [https://en.wikipedia.org/wiki/Make_(software) make]
 +
# How to submit patches (changesets).
 +
#* Read in this wiki: [[Contribution guidelines]], [[Mercurial]]
 +
#* Tools to know: [https://en.wikipedia.org/wiki/Mercurial Mercurial (hg)], [https://en.wikipedia.org/wiki/Git git]
  
 
= Suggested projects =
 
= Suggested projects =
  
The following projects are broadly grouped by category and probable skills required to tackle each. Remember to check [[Projects]] for more ideas if none of these suit you, and your own ideas are always welcome.
+
The following suggested projects are distilled from the [[Projects]] page for the benefit of potential SoC participants. You can also look at our [[Summer of Code|completed past projects]] for more inspiration.
 
 
{{Note|these are suggested projects but you are welcome to propose your own projects provided you find an Octave mentor}}
 
  
== Numerical ==
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{{Note|Do you use Octave at your working place or university? Do you have some numerical project in mind?  You are always welcome to '''propose your own projects'''.  If you are passionate about your project, it will be easy to find an Octave developer to mentor and guide you.}}
  
These projects involve implementing certain mathematical functions in Octave.
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== openlibm ==
  
=== Matlab Compatible DAE solver ===
+
Over the years Octave faced many issues (see [[openlibm | the openlibm page in this wiki]] for examples) about different [https://en.wikipedia.org/wiki/C_mathematical_functions#libm C mathematical functions library] (in short: "libm") implementations on various systems.  To overcome similar issues, developers of the [https://en.wikipedia.org/wiki/Julia_(programming_language) Julia Programming Language] started the [https://openlibm.org/ openlibm] project "to have a good libm [ ...] that work[s] consistently across compilers and operating systems, and in 32-bit and 64-bit environments".  openlibm is supported by major Linux distributions (e.g. [https://packages.ubuntu.com/focal/libopenlibm-dev Debian/Ubuntu], [https://src.fedoraproject.org/rpms/openlibm RHEL/Fedora],[https://software.opensuse.org/package/openlibm SLES/openSUSE], ...) and the [https://hg.octave.org/mxe-octave/rev/480f60641fc2 MS Windows MXE package] was added as well.
  
The goal is to implement a Matlab compatible adaptive BDF solver for Differential Algebraic Equations (DAEs).
+
This project consists of learning about the usage of [https://en.wikipedia.org/wiki/GNU_Autotools GNU Autotools] in Octave and ways to detect openlibm. As the next step the Octave code base has to be reviewed under the guidance of a mentor and relevant code changes should be performed.  Finally, relevant code changes in the [[Tests | Octave test suite]] are performed and tested on various Linux, MS Windows, and macOS machines with the help of the Octave community.
The interface would need to be compatible with ode15s while for the backend the
 
[https://computation.llnl.gov/casc/sundials/main.html SUNDIALS] library would be used, which has both a C and a MEX interface.
 
This function should eventually be included in Octave core together with the other [http://hg.savannah.gnu.org/hgweb/octave/file/tip/scripts/ode/ ODE solvers] that will be released with version 4.2, but could be intially developed as an addition to the [https://sourceforge.net/p/octave/odepkg/ci/default/tree/ odepkg] package.
 
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~175 hours (easy)
 
* '''Required skills'''
 
* '''Required skills'''
: C++; C; familiarity with numerical methods for DAEs; Basic knowledge of makefiles and/or autotools.
+
: Octave, C/C++, Autotools
* '''Difficulty'''
 
: Medium.
 
 
* '''Potential mentors'''
 
* '''Potential mentors'''
: Carlo de Falco, Marco Caliari, Jacopo Corno, Sebastian Schöps
+
: [https://octave.discourse.group/u/cdf Carlo de Falco], [https://octave.discourse.group/u/siko1056 Kai]
 +
 
 +
== ode15{i,s} : Matlab Compatible DAE solvers ==
  
=== Improve logm, sqrtm, funm ===
+
An initial implementation of Matlab compatible Differential Algebraic Equations (DAE) solvers, {{manual|ode15i}} and {{manual|ode15s}}, based on [https://computing.llnl.gov/projects/sundials SUNDIALS],
 +
was done by [https://gsoc2016ode15s.blogspot.com/ Francesco Faccio during GSoC 2016].  The code is maintained in the main Octave repository and consists mainly of the following three files: [https://hg.savannah.gnu.org/hgweb/octave/file/tip/libinterp/dldfcn/__ode15__.cc {{path|libinterp/dldfcn/__ode15__.cc}}], [https://hg.savannah.gnu.org/hgweb/octave/file/tip/scripts/ode/ode15i.m {{path|scripts/ode/ode15i.m}}] and [https://hg.savannah.gnu.org/hgweb/octave/file/tip/scripts/ode/ode15s.m {{path|scripts/ode/ode15s.m}}].
  
The goal here is to implement some missing Matlab functions related to matrix functions like the [http://en.wikipedia.org/wiki/Matrix_exponential matrix exponential]. There is [http://octave.1599824.n4.nabble.com/matrix-functions-td3137935.html a general discussion] of the problem. A good starting point for available algorithms and open-source implementations is Higham and Deadman's  [http://eprints.ma.man.ac.uk/2102/01/covered/MIMS_ep2014_8.pdf "A Catalogue of Software for Matrix Functions"].
+
The {{manual|decic}} function for selecting consistent initial conditions for ode15i can be made more Matlab compatible by using [http://dx.doi.org/10.1515/JNMA.2002.291 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''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~350 hours (medium)
 
* '''Required skills'''
 
* '''Required skills'''
: Read and Write both C++ and Octave code, find and read research papers, research experience in numerical analysis, familiarity with analysis of algorithms.
+
: Octave, C/C++; familiarity with numerical methods for DAEs
* '''Difficulty'''
 
: Difficult.
 
 
* '''Potential mentors'''
 
* '''Potential mentors'''
: Jordi Gutiérrez Hermoso
+
: Francesco Faccio, [https://octave.discourse.group/u/cdf Carlo de Falco], [https://octave.discourse.group/u/marco_caliari Marco Caliari], Jacopo Corno, [https://octave.discourse.group/u/schoeps Sebastian Schöps]
  
=== Generalised eigenvalue problem ===
+
== Symbolic package ==
  
[http://www.mathworks.com/help/techdoc/ref/eig.html Certain calling forms] of the <tt>eig</tt> function are currently missing, including preliminary balancing; computing left eigenvectors as a third output; and choosing among generalized eigenvalue algorithms. See also [http://octave.1599824.n4.nabble.com/General-eigenvalue-problem-proposal-td4651990.html this discussion].  
+
The [[Symbolic package]] provides symbolic computing and other [https://en.wikipedia.org/wiki/Computer_algebra_system computer algebra system] tools. The main component of Symbolic is a pure m-file class "@sym" which uses the Python package [https://www.sympy.org 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 another GSoC project successfully re-implemented this communication using the new [[Pythonic|Pythonic package]].
  
* '''Required skills'''
+
This project proposes to take this work further while also improving the long-term viability of the Symbolic package.  Some goals include:
: C++; familiarity with numerical linear algebra and LAPACK.
+
* the possibility of using Pythonic directly rather than as one possible communication layer.  For example, we might make "@sym" a subclass of "@pyobject".  We also could stop using the "pycall_sympy__" interface and use Pythonic directly from methods.  Note: there are open questions about how to do this during a transition time when we still support other IPC mechanisms.
* '''Difficulty'''
+
* exposing more functionality of SymPy with ''less glue'' in between.  For example, we could allow OO-style method calls such as <code>f.diff(x)</code> as well as <code>diff(f, x)</code>.
: Medium.
+
* Improvements to the Pythonic package and its long-term maintenance.
* '''Potential mentor'''
+
* fixing up Symbolic to work with the latest releases of SymPy and Octave.  The project has lagged for a few years and needs some efforts to port to recent and upcoming changes in SymPy code.
: Nir Krakauer
+
* making Symbolic easier to maintain.  The project currently has a low ''bus factor'': improving the CI, making regular releases easier, improving other aspects of maintenance and making the project more welcoming to newcomers.
  
=== TISEAN package ===
+
Working on this project involves and interesting and challenging mix of m-file code, Python code, and in the case of Pythonic, perhaps some lower-level C code.
  
[http://www.mpipks-dresden.mpg.de/~tisean/Tisean_3.0.1/index.html TISEAN] is a suite of code for nonlinear time series analysis. It has have been [http://wiki.octave.org/TISEAN_package partially re-implemented] as libre software. The objective is to integrate TISEAN as an octave package as it was done for the Control package.
+
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
[[TISEAN_package | Lot has been completed]] but [[TISEAN_package:Procedure | there is still work left to do]].
+
: ~350 hours (medium)
 
+
* '''Required skills'''
There missing functions to do computations on spike trains, to simulate autoregresive models, to create specialized plots, etc. Do check [[TISEAN_package:Procedure#Table_of_functions|the progress of the project]] to see if you are interested.
+
: Octave, C/C++, Python; object-oriented programming (OOP) in Octave
+
* '''Potential mentors'''
* [http://octave.sourceforge.net/tisean/overview.html Package help at source forge.]  
+
: [https://octave.discourse.group/u/cbm Colin B. Macdonald], [https://octave.discourse.group/u/mtmiller Mike Miller], Abhinav Tripathi
* [https://sourceforge.net/p/octave/tisean/ci/default/tree/ Package repository at source forge.]  
 
  
* '''Required skills'''
+
== Improve TIFF image support ==
: m-file scripting, C, C++, and FORTRAN API knowledge.
 
* '''Difficulty'''
 
: easy/medium
 
* '''Mentor'''
 
: [[User:KaKiLa]]
 
  
=== Symbolic package ===
+
[https://en.wikipedia.org/wiki/TIFF Tag Image File Format (TIFF)] is the de facto standard for scientific images.  Octave uses the [http://www.graphicsmagick.org/ GraphicsMagic] (GM) C++ library to handle [http://www.graphicsmagick.org/formats.html TIFF and many others image formats]. However, GM still has several limitations:
  
Octave's [https://github.com/cbm755/octsympy Symbolic package] handles symbolic computing and other CAS tools.  The main component of Symbolic is a pure m-file class "@sym" which uses the Python package [https://www.sympy.org SymPy] to do (most of) the actual computations.  The package aims to expose the full functionality of SymPy while also providing a high-level of compatibility with the Matlab Symbolic Math Toolbox.  Currently, communication between Octave and Python is handled with a pipe (see "help popen2") and parsing textHowever, this is fragile when things go wrong: for example, catching exceptions from Python is a bit ad hoc.
+
* GM has build option {{codeline|quantum}} which 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 indexedThis makes it hard to access the real data stored on file.
  
The main aim of this proposed project is to implement (or even better co-opt an existing) C++ oct-file interface that interacts with Python as a library, and e.g., deals gracefully with exceptionsThis could either supplement the existing IPC or replace it altogether.
+
This project aims to implement better TIFF image support using [https://en.wikipedia.org/wiki/Libtiff libtiff], while leaving GM handle all other image formatsAfter writing a [https://octave.org/doc/v6.1.0/classdef-Classes.html classdef] interface to libtiff, improve the Octave functions {{manual|imread}}, {{manual|imwrite}}, and {{manual|imfinfo}} to make use of it.
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~175 hours (medium)
 
* '''Required skills'''
 
* '''Required skills'''
: m-file scripting, C (for Python internals), C++ (for Octave internals), and Python
+
: Octave, C/C++
* '''Difficulty'''
+
* '''Potential mentors'''
: easy/medium
+
: [https://octave.discourse.group/u/carandraug Carnë Draug]
* '''Mentor'''
 
: Colin B. Macdonald
 
  
=== Interval package ===
+
== PolarAxes and Plotting Improvements ==
  
The [[Interval_package|interval package]] provides several arithmetic functions with accurate and guaranteed error bounds. Its development started in the end of 2014 and there is some fundamental functionality left to be implemented. See the [http://octave.sourceforge.net/interval/overview.html list of functions], basically any missing numeric Octave function could be implemented as an interval extension in the package. Potential projects:
+
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 {{bug|49804}}.
* Implement missing algorithms (as m-files)—difficulty and whether knowledge in interval analysis is required depends on the particular function. Of course, you may use papers which present such algorithms.
 
* Improve existing algorithms (support more options for plotting, support more options for optimizers, increase accuracy, …)
 
* Make the package support N-dimensional arrays, this requires less knowledge of interval arithmetic but can be a rather exhaustive job since it affects most function files in the package
 
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~350 hours (medium)
 
* '''Required skills'''
 
* '''Required skills'''
: m-file scripting, basic knowledge of computer arithmetics (especially floating-point computations), interval analysis (depending on the functions to implement).
+
: Octave, C/C++; optional experience with OpenGL programming
* '''Difficulty'''
+
* '''Potential mentors'''
: Medium.
+
: [https://octave.discourse.group/u/rik Rik]
* '''Mentor'''
 
: [[User:oheim|Oliver Heimlich]]
 
 
 
=== Improve iterative methods for sparse linear systems ===
 
  
GNU Octave currently has the following Krylov subspace methods for sparse linear systems: pcg (spd matrices) and pcr (Hermitian matrices), bicg,
+
== Table datatype ==
bicgstab, cgs, gmres, and qmr (general matrices). Their descriptions and their error messages are not aligned. Moreover, they have similar blocks of code (input check for instance) which can be written once and for all in common functions. The first step in this project could be a revision
 
and a synchronization of the codes.
 
  
In Matlab, some additional methods are available: minres and symmlq (symmetric matrices), tfqmr and bicgstabl (generale matrices), lsqr (least
+
In 2013, Matlab introduced a [https://www.mathworks.com/help/matlab/tables.html new table datatype] to conveniently organize and access data in tabular form.  This datatype has not been introduced to Octave yet (see bug {{bug|44571}}).  However, there are two initial implementation approaches https://github.com/apjanke/octave-tablicious and https://github.com/gnu-octave/table.
squares). The second step in this project could be the implementation of some of these missing functions.
 
  
The reference book is available [www-users.cs.umn.edu/~saad/IterMethBook_2ndEd.pdf here]
+
Based upon the existing approaches, the goal of this project is to define an initial subset of [https://www.mathworks.com/help/matlab/tables.htmlMatlab's table functions], which involve sorting, splitting, merging, and file I/O and implement it within the given time frame.
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~350 hours (hard)
 
* '''Required skills'''
 
* '''Required skills'''
: Not yet listed.
+
: Octave, C/C++
* '''Difficulty'''
+
* '''Potential mentors'''
: Not yet listed.
+
: [https://octave.discourse.group/u/siko1056 Kai] [https://octave.discourse.group/u/Abdallah_Elshamy Abdallah]
* '''Mentor'''
 
: Marco Caliari, Carlo de Falco
 
 
 
== Infrastructure ==
 
 
 
=== Octave Package management ===
 
 
 
Octave management of installed packages is performed by a single function, {{codeline|pkg}}, which does pretty much everything. This function has a few limitations which are hard to implement with the current codebase, and will most likely require a full rewrite.
 
  
The planned improvements are:
+
== YAML encoding/decoding ==
  
* support for multiple Octave installs
+
[https://en.wikipedia.org/wiki/YAML 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 [[Summer of Code#GSoC_2020 | successfully implemented for Octave during GSoC 2020]].
* support for multiple version packages
 
* support for system-wide and user installed packages
 
* automatic handling of dependencies
 
* more flexibility on dependencies, e.g., dependent on specific Octave build options or being dependent in one of multiple packages
 
* management of tests and demos in C++ sources of packages
 
* think ahead for multiple
 
* easily load or check specific package versions
 
  
The current {{codeline|pkg}} also performs some functions which probably should not. Instead a package for developers should be created with such tools.
+
The goal of this project is to repeat the GSoC 2020 success story with [https://github.com/biojppm/rapidyaml Rapid YAML] or another fast C/C++ library.
  
Many of these problems have been solved in other languages. Familiarity with how other languages handle this problem will be useful to come up with elegant solutions. In some cases, there are standards to follow. For example, there are specifications published by freedesktop.org about where files should go ([http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html base directory spec]) and Windows seems to have its own standards. See bugs {{bug|36477}} and {{bug|40444}} for more details.
+
The first step is research about existing Octave/Matlab and C/C++ implementations, for example:
  
In addition, package names may start to collide very easily. One horrible way to workaround this by is choosing increasingly complex package names that give no hint on the package purpose. A much better is option is providing an Authority category like Perl 6 does. Nested packages is also an easy way to provide packages for specialized subjects (think {{codeline|image::morphology}}). A new {{codeline|pkg}} would think all this things now, or allow their implementation at a later time. Read the [[OEP:pkg|unfinished plan]] for more details.
+
* https://code.google.com/archive/p/yamlmatlab/ (uses Java)
 +
* http://vision.is.tohoku.ac.jp/~kyamagu/ja/software/yaml/ (uses Java)
  
* '''Minimum requirements'''
+
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 <code>yamlencode</code> and <code>yamldecode</code>) shall be created.  This involves proper documentation of the work and unit tests to ensure the correctness of the implementation.
: Ability to read and write Octave code, experience with Octave packages, and understanding of the basics of autotools. The most important skill is software design.
 
* '''Difficulty'''
 
: Easy to Medium.
 
* '''Mentor'''
 
: Carnë Draug
 
  
=== Command line suggestion feature ===
+
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.
 
 
Currently Octave has no mechanism for suggesting corrections to typographic errors on the command line. An autocomplete/suggestion function is provided (using the double-TAB shortcut), but recent discussions have indicated a desire for a more proactive measure to catch user error.  Potential applicants are referred to bug {{bug|46881}} regarding the usage of grey vs. gray.
 
 
 
Suggested improvements are:
 
* provide one or more suggested corrections to the user when a command line entry produces an error.
 
* recognition and suggested correction for apparent syntax errors
 
* function suggestion(s) when a 'close' match is found (close remains to be defined)
 
* multiple suggestions if more than one option seems likely, along with a user-friendly method of selecting the appropriate choice.
 
* user selectable option to disable and/or customize the suggestion behavior
 
* correct operation, or graceful degradation, whether Octave is run in GUI or command-line mode.
 
 
 
As mentioned in the bug {{bug|46881}} discussion, this project has little-to-no relation to m-code compatibility. As such, emulation of the behavior of other software is not required, nor even necessarily desired. Octave is free to implement as simple or complex a solution to this feature request as is necessary to provide the best experience to the user. There may be tools, features, or code from other license-compatible projects that can be of use here, and the applicant would be encouraged to identify and leverage such resources as appropriate.
 
 
 
* '''Minimum requirements'''
 
: TBD
 
* '''Difficulty'''
 
: Easy to Medium.
 
* '''Mentor'''
 
: Undetermined
 
 
 
== Image Analysis ==
 
 
 
=== Improvements to N-dimensional image processing ===
 
 
 
The image package has partial functionality for N-dimensional images. These images exist for example in medical imaging where slices from scans are assembled to form anatomical 3D images. If taken over time and at different laser wavelengths or light filters, they can also result in 5D images. Albeit less common, images with even more dimensions also exist. However, their existence is irrelevant since most of the image processing operations are mathematical operations which are independent of the number of dimensions.
 
 
 
As part of GSoC 2013, the core functions for image IO, {{codeline|imwrite}} and {{codeline|imread}}, were extended to better support this type of images. Likewise, many functions in the image package, mostly morphology operators, were expanded to deal with this type of image. Since then, many other functions have been improved, sometimes completely rewritten, to abstract from the number of dimensions. In a certain way, supporting ND images is also related to choosing good algorithms since such large images tend to be quite large.
 
 
 
This project will continue on the previous work, and be mentored by the previous GSoC student and current image package maintainer. Planning the project requires selection of functions lacking ND support and identifying their dependencies. For example, supporting {{codeline|imclose}} and {{codeline|imopen}} was better implemented by supporting {{codeline|imerode}} and {{codeline|imdilate}} which then propagated ND support to all of its dependencies. These dependencies need to be discovered first since often they are not being used yet, and may even be missing function. This project can also be about implementing functions that have [http://wiki.octave.org/Image_package#Missing_functions not yet been implemented]. Also note that while some functions in the image package will accept ND images as input, they are actually not correctly implemented and will give incorrect results.
 
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~175 hours (easy)
 
* '''Required skills'''
 
* '''Required skills'''
: m-file scripting, and a fair amount of C++ since a lot of image analysis cannot be vectorized. Familiarity with common CS algorithms and willingness to read literature describing new algorithms will be useful.
+
: Octave, C/C++
* '''Difficulty'''
+
* '''Potential mentors'''
: Difficult.
+
: [https://octave.discourse.group/u/siko1056 Kai], [https://octave.discourse.group/u/Abdallah_Elshamy Abdallah]
* '''Potential mentor'''
 
: Carnë Draug
 
 
 
=== Improve Octave's image IO ===
 
 
 
There are a lot of image formats. To handle this, Octave uses [http://www.graphicsmagick.org/ GraphicsMagic] (GM), a library capable of handling [http://www.graphicsmagick.org/formats.html a lot of them] in a single C++ interface. However, GraphicsMagick still has its limitations. The most important are:
 
  
* GM has build option {{codeline|quantum}} which 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, but building it too low 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.
+
== TISEAN package ==
* GM supports unsigned integers only thus incorrectly reading files such as TIFF with floating point data
 
* GM hides away 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 would implement better image IO for scientific file formats while leaving GM handle the others. Since TIFF is the de facto standard for scientific images, this should be done first. Among the targets for the project are:
+
The [[TISEAN package]] provides an Octave interface to [https://www.pks.mpg.de/~tisean/Tisean_3.0.1/index.html 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 [[TISEAN_package:Procedure | 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.
 
 
* implement the Tiff class which is a wrap around libtiff, using classdef. To avoid creating too many private __oct functions, this project could also create a C++ interface to declare new Octave classdef functions.
 
* improve imread, imwrite, and imfinfo for tiff files using the newly created Tiff class
 
* port the bioformats into Octave and prepare a package for it
 
* investigate other image IO libraries
 
* clean up and finish the dicom package to include into Octave core
 
* prepare a matlab compatible implementation of the FITS package for inclusion in Octave core
 
  
 +
* '''Project size''' [[#Project sizes | [?]]] and '''Difficulty'''
 +
: ~350 hours (medium)
 
* '''Required skills'''
 
* '''Required skills'''
: Knowledge of C++ and C since most libraries are written in those languages.
+
: Octave, C/C++; FORTRAN API knowledge
* '''Difficulty'''
+
* '''Potential mentors'''
: Medium.
+
: [https://octave.discourse.group/u/kakila KaKiLa]
* '''Potential mentor'''
 
: Carnë Draug
 
  
 +
= Project sizes =
  
== Missing Core Matlab functions ==
+
Since GSoC 2022 there exist two project sizes<ref>https://groups.google.com/g/google-summer-of-code-announce/c/_ekorpcglB8</ref><ref>https://google.github.io/gsocguides/mentor/defining-a-project-ideas-list</ref>:
 +
* '''~175 hours''' (~12 weeks, Jun 13 - Sept 12)
 +
* '''~350 hours''' (~22 weeks, Jun 13 - Nov 21)
  
=== Implement boolean operations on polygons ===
+
= Footnotes =
  
The goal is to implement a Matlab-compatible set of boolean operations and supporting function for acting on polygons. These include the standard set of potential operations such as union/OR, intersection/AND, difference/subtraction, and exclusiveor/XOR. There are a variety of existing polygon libraries that implement much of the functionality and thus this would be incorporating the library into GNU Octave. The libraries with acceptable licenses are [http://www.angusj.com/delphi/clipper.php ClipperLib], [http://www.boost.org/doc/libs/1_60_0/libs/polygon/doc/index.htm Boost::Polygon], [https://github.com/boostorg/geometry Boost::Geometry], or [http://boolean.klaasholwerda.nl/bool.html kbool]. This would include implementing the following functions: polybool, ispolycw, poly2ccw, poly2cw, poly2fv, polyjoin, and polysplit. A partial implementation with ClipperLib and GPC can be found [https://sites.google.com/site/ulfgri/numerical/polybool here].
+
<references />
 
 
* '''Required skills'''
 
: Knowledge of C++; C; familiarity with boolean logic; polygons, windings, and geometry
 
* '''Difficulty'''
 
: Easy to Medium.
 
* '''Potential mentor'''
 
: John Swensen
 
  
 +
= See also =
  
 +
* https://summerofcode.withgoogle.com/
 +
* [https://google.github.io/gsocguides/student/ GSoC Student Guide]
 +
* [https://google.github.io/gsocguides/mentor/ GSoC Mentor Guide]
 +
* [https://developers.google.com/open-source/gsoc/timeline GSoC Timeline]
  
<noinclude>
 
 
[[Category:Summer of Code]]
 
[[Category:Summer of Code]]
 
[[Category:Project Ideas]]
 
[[Category:Project Ideas]]
</noinclude>
 

Latest revision as of 14:09, 15 April 2022

Info icon.svg

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[edit]

  1. 😉💬 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 🚮
  2. 👩‍🔬 Get your hands dirty.
    • We are curious about your programming skills 🚀
    • 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.
  3. 📝💡 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 🙂
  4. 📔 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?[edit]

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[edit]

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:

  1. How to build Octave from its source code using the GNU build system.
  2. How to submit patches (changesets).

Suggested projects[edit]

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.

Info icon.svg
Do you use Octave at your working place or university? Do you have some numerical project in mind? You are always welcome to propose your own projects. If you are passionate about your project, it will be easy to find an Octave developer to mentor and guide you.

openlibm[edit]

Over the years Octave faced many issues (see the openlibm page in this wiki for examples) about different C mathematical functions library (in short: "libm") implementations on various systems. To overcome similar issues, developers of the Julia Programming Language started the openlibm project "to have a good libm [ ...] that work[s] consistently across compilers and operating systems, and in 32-bit and 64-bit environments". openlibm is supported by major Linux distributions (e.g. Debian/Ubuntu, RHEL/Fedora,SLES/openSUSE, ...) and the MS Windows MXE package was added as well.

This project consists of learning about the usage of GNU Autotools in Octave and ways to detect openlibm. As the next step the Octave code base has to be reviewed under the guidance of a mentor and relevant code changes should be performed. Finally, relevant code changes in the Octave test suite are performed and tested on various Linux, MS Windows, and macOS machines with the help of the Octave community.

  • Project size [?] and Difficulty
~175 hours (easy)
  • Required skills
Octave, C/C++, Autotools
  • Potential mentors
Carlo de Falco, Kai

ode15{i,s} : Matlab Compatible DAE solvers[edit]

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[edit]

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 another GSoC project successfully re-implemented this communication using the new Pythonic package.

This project proposes to take this work further while also improving the long-term viability of the Symbolic package. Some goals include:

  • the possibility of using Pythonic directly rather than as one possible communication layer. For example, we might make "@sym" a subclass of "@pyobject". We also could stop using the "pycall_sympy__" interface and use Pythonic directly from methods. Note: there are open questions about how to do this during a transition time when we still support other IPC mechanisms.
  • 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 as diff(f, x).
  • Improvements to the Pythonic package and its long-term maintenance.
  • fixing up Symbolic to work with the latest releases of SymPy and Octave. The project has lagged for a few years and needs some efforts to port to recent and upcoming changes in SymPy code.
  • making Symbolic easier to maintain. The project currently has a low bus factor: improving the CI, making regular releases easier, improving other aspects of maintenance and making the project more welcoming to newcomers.

Working on this project involves and interesting and challenging mix of m-file code, Python code, and in the case of Pythonic, perhaps some lower-level C code.

  • Project size [?] and Difficulty
~350 hours (medium)
  • Required skills
Octave, C/C++, Python; object-oriented programming (OOP) in Octave
  • Potential mentors
Colin B. Macdonald, Mike Miller, Abhinav Tripathi

Improve TIFF image support[edit]

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 quantum which 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
Carnë Draug

PolarAxes and Plotting Improvements[edit]

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
Rik

Table datatype[edit]

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
Kai Abdallah

YAML encoding/decoding[edit]

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:

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
Kai, Abdallah

TISEAN package[edit]

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
KaKiLa

Project sizes[edit]

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)

Footnotes[edit]

See also[edit]