Summer of Code - Getting Started

The following is distilled from the Projects page for the benefit of potential Google and 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.

Steps Toward a Successful Application

Help Us Get To Know You

• If you aren't communicating with us before the application is due, your application will not be accepted.

  • Join the 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 IRC channel. Ask questions, answer questions from users, 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!

• Do not wait for us to tell you what to do

  You should be doing something that interests you, and should not need us to tell you what to do. Similarly, you shouldn't ask us what to do either.

  • When you email the list and mentors, do not write it to say on what project you're interested. Be specific about your questions and clear on the email subject. For example, do not write an email with the subject "GSoC student interested in the ND images projects". Such email is likely be ignored. Instead, show you are already working on the topic, and email "Problem implementing morphological operators with bitpacked ND images".
  • It is good to ask advice on how to solve something you can't but you must show some work done. Remember, we are mentors and not your boss. Read How to ask questions the smart way:

    Prepare your question. Think it through. Hasty-sounding questions get hasty answers, or none at all. The more you do to demonstrate that having put thought and effort into solving your problem before seeking help, the more likely you are to actually get help.

  • It can be difficult at the beginning to think on something to do. This is nature of free and open source software development. You will need to break the mental barrier that prevents you from thinking on what can be done. Once you do that, you will have no lack of ideas for what to do next.
  • Use Octave. Eventually you will come accross somethings that does not work the way you like. Fix that. Or you will come accross a missing function. Implement it. It may be a difficult problem (they usually are) but while solving that problem you may find other missing functions (). Implemenent and contribute those to Octave.
  • Take a look at the Short projects for something that may be simple to start with.

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 fixing a few bugs or submitting patches well before the deadline, in addition to regularly interacting with Octave maintainers and users on the mailing list 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 IRC channel.

Complete Your Application

• Fill out our public application template.

  • This is best done by creating an account at this wiki, and copying the 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 from its page and adding the required information to your application at Google (melange) or at ESA.
    
  • 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 On Your Own

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.

• The 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 configure && make && make install 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
  • Octave Manual on Installing Octave

• The Version Control System

  We use Mercurial (abbreviated hg).

  Mercurial is the 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 contribution guidelines we have for everyone.

  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 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 top post".

• The IRC Channel

  We also have 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

  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

These might vary somewhat depending on the mentors and coordinators for a particular Summer of Code, but typically the main factors considered would be:

• Applicant has demonstrated an ability to make substantial modifications to Octave

  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.

• Applicant shows understanding of topic

  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

  The best evidence for this is previous contributions and interactions.

• Well thought out, adequately detailed, realistic project plan

  "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.

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.

these are suggested projects but you are welcome to propose your own projects provided you find an Octave mentor

Summary table

Title	Mentor	co-Mentors	Class	New?	Difficulty	Last active
Make specfuns special again	Marco Caliari	Colin Macdonald	Numerical	Yes	Medium	GSoC 2017
ode15{i,s} : Matlab Compatible DAE solvers	Carlo de Falco	Francesco Faccio, Marco Caliari, Jacopo Corno, Sebastian Schöps	Numerical	No	Medium	GSoC 2016
Improve logm, sqrtm, funm	Jordi Gutiérrez Hermoso	Marco Caliari, Mudit Sharma	Numerical	No	Hard	Independent devs 2016
Improve iterative methods for sparse linear systems	Marco Caliari	Carlo de Falco	Numerical	No	Hard	SOCIS 2016
Neural Networks package: Convolutional Neural Networks	Francesco Faccio	Ankit	Octave Forge	Yes	Hard	Never
EPA hydrology software suite	KaKiLa	?	Octave Forge	Yes	Medium	Never
FullSWOF overland flow simulator	KaKiLa	?	Octave Forge	Yes	Medium	Never
TISEAN: Nonlinear Time Series Analysis	KaKiLa	?	Octave Forge	No	Medium	GSoC 2015
Octave Package management	Sebastian Schöps	KaKiLa, Carnë Draug, Carlo de Falco	Infrastructure	Yes	Medium	Never
Symbolic package	Colin B. Macdonald	Mike Miller, Abhinav Tripathi	Octave Forge	Octsympy	Medium	GSoC 2016
Interval package	Oliver Heimlich	Kai T. Ohlhus	Octave Forge, Numerical	Yes	Medium	Never
Pytave project	Mike Miller	Colin B. Macdonald, Abhinav Tripathi	Infrastructure	No	Medium	some in GSoC 2016
Jupyter integration	Mike Miller	Colin B. Macdonald	Infrastructure	Yes	Medium	Never
Chebfun in Octave	Colin B. Macdonald	KaKiLa, needs core-Octave mentor/comentor	Infrastructure, Numerical	Yes	Hard	Never
Octave code sharing	Kai T. Ohlhus	?	Infrastructure	Yes	Medium	Never

Numerical

These projects involve implementing certain mathematical functions, primarily in core Octave.

Make specfuns special again

Traditionally, problem solving environments like Octave provide simple interfaces to numerical linear algebra, special function evaluation, root finding, and other tools. Special functions (such as Bessel functions, exponential integrals, LambertW, etc) are expected by users to "just work". But many of Octave's special functions could be improved to improve their numerical accuracy. Generally a user might expect these to be accurate to full 15 digits. Software testing is important to Octave; this project would improve the tests of many special functions, in particular by comparing the output with slow-but-accurate symbolic computations.

State: some bugs include #48307 (sinc), #47738 (expint), #47800 (gammainc), #48036 (gammaincinv) #48316 (besselj) TODO: add others? The unmaintained specfun pkg had some poor implementations (e.g., divergence for large x, see [1].). See also the Symbolic functions in `@double`: these probably should have native double implementations.

• Required skills

Octave m-file programming, some familiarity with Approximation Theory (a branch of mathematics).

• Difficulty

Medium (mathematics needed, but on the other hand, perhaps little or no C++).

• Potential mentors

Marco Caliari, Colin Macdonald, others?

How to get started: pick a special function, see if it has tests: contribute a patch that adds more tests, e.g., comparing its values to symbolic computations or other highly accurate solutions

ode15{i,s} : Matlab Compatible DAE solvers

An initial implementation of a Matlab compatible ode15{i,s} solver, based on SUNDIALS, was done by Francesco Faccio during GSOC 2016. The blog describing the work is here. The resulting code has been pushed into the main Octave repository in the development branch and consists mainly of the following three files __ode15__.cc, ode15i.m and ode15s.m. The list of outsanding tracker tickets concerning this implementation can be found here

Possible useful improvements that could be done in a new project include:

• Implement a better funtion for selecting consistent initial conditions compatible with Matlab's decic.m. The algorithm to use is described here

• make ode15{i,s} with datatypes other than double

• improve interpolation at intermediate time steps.

• general code profiling and optimization

Other tasks, not strictly connected to ode15{i,s} but closely related that could be added to a possible project plan would be improving documentation and tests in odepkg and removing overlaps with the documentation in core Octave.

• Required skills

C++; C; familiarity with numerical methods for DAEs; Basic knowledge of makefiles and/or autotools.

• Difficulty

Medium.

• Potential mentors

Francesco Faccio, Carlo de Falco, Marco Caliari, Jacopo Corno, Sebastian Schöps

Improve logm, sqrtm, funm

The goal here is to implement some missing Matlab functions related to matrix functions like the matrix exponential. There is a general discussion of the problem. A good starting point for available algorithms and open-source implementations is Higham and Deadman's "A Catalogue of Software for Matrix Functions".

• 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.

• Difficulty

Difficult.

• Potential mentors

Jordi Gutiérrez Hermoso

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, bicgstab, cgs, gmres, and qmr (general matrices). The description of some of them (pcr, qmr) 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, starting from the project SOCIS2016, whose latest patch, still to be included, is here.

In Matlab, some additional methods are available: minres and symmlq (symmetric matrices), bicgstabl (general matrices), lsqr (least 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]

• Required skills

numerical linear algebra, m-file programming.

• Difficulty

Maybe hard the mathematical part, medium the programming part.

• Mentor

Chebfun in Octave

Chebfun is a mathematics and software project for "numerical computing with functions". Basically it approximates functions to machine precision accuracy (10^-15) using piecewise Chebyshev polynomial interpolants. Operations on those functions (arithmetic, derivatives, root-finding, etc) are then overloaded and return new interpolating polynomials, which are themselves proxies for the actual solution.

Chebfun makes extensive use of classdef classes, and is one of the largest Free Software projects to do so. Unfortunately it currently only works in Matlab. This project seeks to (1) improve Octave's classdef support and (2) tweak Chebfun to work under Octave, for example, removing undocumented classdef features. The final goal is to have at least basic Chebfun features working on Octave. An additional goal would be making "pkg install chebfun.zip" work in Octave.

This project is important for both technical reasons (to improve Octave's classdef support) and ethical reasons (to allow Chebfun to run without proprietary software).

• Required skills

Octave m-file programming, C++, some familiarity with Approximation Theory (a branch of mathematics).

• Difficulty

Medium to Hard (probably requires a deep dive into how Octave supports OO).

• Potential mentors

Colin B. Macdonald, KaKiLa, Mike Miller (?), Carnë Draug (?), someone from Chebfun team (?).

How to get started: learn about Chebfun, browse Octave's bug list for classdef-related bugs, play with other classdef projects (Pytave, https://github.com/cbm755/octsympy/issues/545)

Adding functionality to Forge packages

Neural Networks package: Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have recently become the state-of-the-art for image recognition and are widely used for solving classification and regression problems and for image generation. The goal of the project is to implement a Matlab compatible CNN toolbox using Google's library TensorFlow, which has a Python and C++ interface. As execution environment for the training function, the user will be able to choose between single/multiple CPUs and GPUs.

• Required skills

C, C++, Python, m-file scripting, familiarity with Machine Learning algorithms, basic knowledge of Makefiles, experience with parallel computing and distributed systems.

• Difficulty

Difficult.

• Mentors

Francesco Faccio, Ankit

EPA hydrology software suite

Create native interfaces to the EPA software suites.

Starting points

• epanet-octave.
• Open Water Analytics

SWMM

• • Official page
  • Check work done in MatSWMM article

EPANET

• • Official page

Required skills

m-file scripting, C, C++, API knowledge, file I/O, classdef (optional).

Difficulty

easy/medium

Mentor

KaKiLa

FullSWOF overland flow simulator

Create scripting tools for (optional: native interfaces).

Starting points

• The FullSWOF Project.
• FullSWOF: A software for overland flow simulation
• Initial work on Bitbucket

Required skills

m-file scripting, C, C++, API knowledge, file I/O, classdef (optional).

Difficulty

easy/medium

Mentor

KaKiLa

TISEAN package

TISEAN is a suite of code for nonlinear time series analysis. It has been partially re-implemented as libre software. The objective is to integrate TISEAN as an Octave Forge package, as was done for the Control package. A lot has been completed 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. Do check the progress of the project to see if you are interested.

• Package help at source forge.
• Package repository at source forge.

• Required skills

m-file scripting, C, C++, and FORTRAN API knowledge.

• Difficulty

easy/medium

• Mentor

KaKiLa

Symbolic package

Octave's 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 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. The Symbolic package requires communication between Octave and Python. Recently, a GSoC2016 project successfully re-implemented this communication using the new Pytave tool.

This project proposes to go further: instead of using Pytave only for the communication layer, we'll use it throughout the Symbolic project. For example, we might make "@sym" a subclass of "@pyobject". We also could stop using the "python_cmd" interface and use Pytave directly from methods. The main goal was already mentioned: to expose the *full functionality* of SymPy. For example, we would allow OO-style method calls such as "f.diff(x)" instead of "diff(f, x)".

• Required skills

OO-programming with m-files, Python, and possibly C/C++ for improving Pytave (if needed).

• Difficulty

easy/medium

• Mentors and/or other team members

Colin B. Macdonald, Mike Miller, Abhinav Tripathi

Interval package

The 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 list of functions, basically any missing numeric Octave function could be implemented as an interval extension in the package. Potential projects:

• 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
• 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, …)
• Integrate functions from VERSOFT [2] in the package (some work has already been done and current progress is tracked in Interval_package#VERSOFT). This basically involves conversion of the documentation into Texinfo format, use Octave coding guidelines [3] and to make sure that any called functions are available in the interval package. VERSOFT is originally based on INTLAB, a proprietary Octave package. Some functions may be missing. Also, the interval package doesn't support complex numbers, so it might not be possible to migrate some functions.
• List more interesting use cases of interval arithmetic in the package's manual [4]

• Required skills

m-file scripting, basic knowledge of computer arithmetics (especially floating-point computations), interval analysis (depending on the functions to implement).

• Difficulty

Medium.

• Mentor and co-mentor

Oliver Heimlich, Kai T. Ohlhus

Infrastructure

Jupyter Integration

Jupyter Notebook is a web-based worksheet interface for computing. There is a Octave kernel for Jupyter. This project seeks to improve that kernel to make Octave a first-class experience within the Jupyter Notebook.

• Mentors

Colin B. Macdonald, Mike Miller, others?

Using Python within Octave

Pytave allows one to call Python functions and interact with Python objects from within Octave .m file code and from the Octave command line interface. Ideally, Pytave will not be a separate project, but rather a core feature of Octave. This project aims to improve Pytave with the goal of merging the code into the core Octave code base.

Based on a previous summer project related to Pytave, this work will consist of fast-paced collaborative software development based on tackling the pytave issue list. You would also be expected to participate in software design decisions and discussion, as well as improve documentation, doctests and unit tests. As an example of the sorts of decision decisions being made, note that Octave indexes from 1 whereas Python typically indexes from 0; in which cases is it appropriate to make this transparent to the user?

• Mentors

Mike Miller, Colin B. Macdonald, Abhinav Tripathi, others?

Octave Package management

Octave management of installed packages is performed by a single function, 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:

• install from URLs
• install and update from repositories (hg and git)
• automatic handling of dependencies
• easily load, update or check specific package versions
• management of tests and demos in C++ sources of packages
• more flexibility on dependencies, e.g., dependent on specific Octave build options or being dependent in one of multiple packages
• support for multiple version packages
• support for multiple Octave installs
• support for system-wide and user installed packages

The main objective of this project is to make pkg more user friendly and to make it a tool to foster third party participation in Octave. pkg needs to be more flexible and intelligent when dealing with packages, different verisons and different sources, as well as options on how to build and install the package. There are also advance features of pkg that are useful for testing packages. However, the current pkg also performs some maintenance functions which it probably should not. Instead a package for developers should be created with such tools.

To do this enhacenment effectively, a refactoring of the current pkg code will be needed. This job was started once, but due to diverging and growing specifications, it stalled. In this project we will focus on the most needed features, keeping the requirements to a minimum.

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 (base directory spec) and Windows seems to have its own standards. See bugs #36477 and #40444 for more details.

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 image::morphology). A new pkg would think all this things now, or allow their implementation at a later time. Read the unfinished plan for more details.

• Minimum requirements

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

KaKiLa, Carnë Draug, Carlo de Falco, Sebastian Schöps

Command line suggestion feature

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 #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 #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

Octave code sharing

Recently, on the OctConf_2017 a talk about uploading published Octave code to a MediaWiki (like this one) as an easy way to share code was given. With the talk, a repository containing example code for the MediaWiki upload is given. This might continue the idea of an unfinished Agora code sharing website, but one is not restricted to fully stay with the proposed approach. Another, but more ambitious idea is for example Scipy Central - a website for Scipy code sharing. Their Code is released under a BSD license.

• Minimum requirements

Ability to read and write Octave code, some familiarity with C++ (libcurl), web development (GET/POST/FORM data, cookies), and MediaWiki.

• Difficulty

Medium.

• Mentor

Kai T. Ohlhus

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, imwrite and 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 imclose and imopen was better implemented by supporting imerode and 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 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.

• 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.

• Difficulty

Difficult.

• Potential mentor

Carnë Draug

Improve Octave's image IO

There are a lot of image formats. To handle this, Octave uses GraphicsMagic (GM), a library capable of handling a lot of them in a single C++ interface. However, GraphicsMagick still has its limitations. The most important are:

• 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, 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.
• 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:

• 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

• Required skills

Knowledge of C++ and C since most libraries are written in those languages.

• Difficulty

Medium.

• Potential mentor

Carnë Draug