Note: this post was originally written by David Ketcheson.
I primarily develop code in Python and Fortran, but I also use MATLAB for certain things. For instance, I haven’t found a Python-friendly nonlinear optimization package that measures up to the capabilities of MATLAB’s optimization toolbox (fmincon). So my RK-opt package for optimising Runge-Kutta methods is written all in MATLAB.
The trouble is that working in Python has spoiled me for other languages. Python has the excellent Sphinx package for writing beautiful documentation. Python has the nosetests harness for easily writing and running tests. And Python has a simple syntax for including optional function arguments with default values.
MATLAB doesn’t support any of these things so elegantly*.
*This was true one year ago, when I started writing this. But it seems things have improved – see below.
In any case, all is not lost – I have found reasonable approximations in the MATLAB ecosystem, and in some cases I’ve adapted the Python tools to work with MATLAB.
Documenting MATLAB projects using Sphinx
In principle, Sphinx can be used to write documentation for packages written in any language. However, its autodoc functionality, which automatically extracts Python docstrings, doesn’t work with MATLAB. For RK-Opt, I hacked together a simple workaround in this 74-line Python file. It goes through a given directory, extracts the MATLAB docstring for each function, and compiles them into an .rst file for Sphinx processing. You can see an example of the results here.
Update: as I’m writing this, I’ve discovered a new MATLAB extension for Sphinx’s autodoc. I will have to try it out sometime; please let me know in the comments if you’ve used it.
Automated testing in MATLAB
I’ve become convinced that writing at least one or two tests is worthwhile for even small, experimental packages. In Python, it’s simple to include test in the docs and run them with doctest, or write test suites and run them with nosetest. For MATLAB, I would have recommended the third-party xunit framework. But it seems that this year Mathworks finally added this functionality to MATLAB. Even so, you might want to use xunit because it’s possible to run doctests with it but not with MATLAB’s new built-in framework. Also, you can get XML output from xunit, which a number of other tools can analyze (for instance, to tell you about code coverage). For an example of how to use xunit, see RK-Opt.
Again, I’d be interested to hear from you in the comments if you’ve used MATLAB’s new built-in test harness.
Optional arguments with default values
MATLAB does allow the user to specify only some subset of the input arguments to a function – as long as the omitted ones all come after the included ones. I used to take advantage of this, with this kind of code inside the function:
if nargin<5 rmax=50; end if nargin<4 eps=1.e-10; end
This is a reasonable solution in very small functions, but it breaks if you want to add new arguments that don’t come at the end, and if you want to specify the very last value then you have to specify them all. A better general solution is the inputParser object. It’s much less natural than Python’s syntax, but the result for the user is the same: arbitrary subsets of the optional arguments can be specified; default values will be used for the rest. As a bonus, you can check the types of the inputs. Here’s an example of usage.
If you know of better ways to do any of these things, please let me know in the comments!
Of course, it’s entirely possible to develop large, well-documented, well-tested, user-friendly packages in MATLAB – Chebfun is one example. It’s just that this is the exception and not the rule in the MATLAB community. Hopefully better integration with testing and documentation tools will improve this situation.