Last time I tried Octave it was several times slower than Matlab. Why use Octave instead of R, Python, or Julia? There is nothing Octave/Matlab can do that they can't. And anybody needing Matlab surely can afford to pay the licence fee?
Getting approval for licenses is much harder than you'd think. The money is the smallest part.
In the end you just want to run the code once or twice, most of the time octave is going to be enough with zero or minimal changes. Cannot count the amount of times octave saved the day.
We had a few cases where this was the case involving the signal processing toolbox for example and couldn't reproduce the same results. In such cases we just refused to participate in the consortium.
In my old field we were mostly using R. If there's no active use of matlab, it doesn't make any sense to license it for a single collaboration. By the time you need it again, the license would be expired. And this completely ignores the "how do you want to run it", which for us involved a linux-only cluster. It's literally trashing money and time.
Commercially-licensed software is a true pain for research and collaboration, it doesn't matter if it's any good or not.
Commercially-licensed software is a true pain
I'm sorry the fruits of our labor don't come free. Some of us enjoy resort vacations.
Reproducibility is difficult or impossible to assess when there's no source, and it also requires higher barriers to entry (= money). The output of that research will also be tied the commercial license: academics can often get better offers, but you as a user are going to be outpriced.
I'm biased for sure, but I'm a deep believer in open research. The output of what I do/did should be free for everyone.
R being fully open and free is a big part in what it made it so popular in research.
As it sits, they like to get users hooked in college and then they're less likely to ever switch.
Secondly, research institutes have little money. For example: you may think the world has been on HDMI since somewhere around 2010 or so. Think again. End of 2021, my research institute replaced their monitors, which meant i no longer was using VGA out on a daily basis.
(Computers typically are on a 3- or 4-year replacement cycle; monitors, projectors, keyboards, etc. aren't. )
Mathematica is a rare exception as it is generally used in a much more functional programming esque approach than the imperative/OO style common elsewhere. I find pushing data to black box Mathematica functions to be very simple (and they have stuff for practically everything), but some very simple stuff eludes me due to the language model.
So I guess your point stands (especially if it's a large code base).
There's value in running exactly the code which a researcher used in research to evaluate said research.
Oh, and working with Open Source software means I get paid approximately double what an equivalent Microsoft specialist does. Learning Linux has been the biggest payoff of my career.
You're an IT guy for a [insert commodity here] manufacturer. Nothing wrong with that. When the IT itself is what's being sold, you have to protect your product.
It's a big world out there kid.
I use Octave daily, for many numerical tasks (and also python and julia). It has some advantages with respect to Python:
1. The sparse linear solvers in octave are considerably more performant than those available in a default scipy install.
2. No need to import external libraries written in other languages just to multiply two matrices.
3. Translating a formula from the blackboard to octave is really straightforward. This is often not the case for numpy.
As for julia, the base language is even better than octave. However, it is not as useful for one-off scripts due to the slow startup time. You can use octave as a calculator for your shell scripts. For instance, one day I had to warp a collection of images by a collection of affine maps; the affine maps were the product of two matrices stored in individual files. Thus I wrote a three-line shell loop that called octave for each file and did the deed. The loop processed 200 files in five seconds (even without parallelism). In julia this would take a few minutes.
With plot.jl as
$ cat plot.jl
using Plots
x = range(0, 10, length=100)
y = sin.(x)
plot(x, y)
$ time julia plot.jl
4.19s user 0.50s system 106% cpu 4.409 totalI suppose I maybe get the "no need to import external libraries" thing, 'pip3 install numpy' is technically an extra step that wouldn't have been necessary if the standard library contained a decent matrix module.
But why do you care about the "written in other languages" part? That has literally never caused issues for me with Python. Especially something as popular as numpy just works across all platforms I've tried (x86_64 Windows, Linux and macOS, aarch64 Linux and macOS). Do you frequently encounter issues which are caused by the Fortran portion of Numpy's source code?
No practical issues at all. The problem is not the pip install numpy that only happens once, but the import numpy that must appear on every single one of your programs. I interpret it as a permanent reminder that numerical computation is not natively supported in the language, and that we will always be foreigners in there.
The octave interpreter is slower than matlab, but that's because there is a JIT compiler in matlab. octave is still a good tool in my opinion.
Or if you're a gui person, right click on a variable in your workspace and click Rename
In Python and Julia, things tend to be documented more "bottom-up". Which is good for large projects, but tends to get in the way when you're just trying to get a small script to check one result or get one plot. (I don't have as much experience with R, so can't say which direction their docs lean towards.)
And I for example don’t like using matlab - it’s not free (as in freedom), it’s very bulky, GUI is slow and heavy, there’s no way to use it as a console app in linux afaik.
You absolutely can. Many of the Matlab users I know never touch the GUI/IDE. They'll just code in Emacs and run their code from the terminal.
The original generation of MATLAB creators and developers like Cleve tended to come from an academic UNIX/mainframe/HPC type background, and so they were big CL users and lived in vim/emacs. And that carried over to a lot of the programmers working at The MathWorks to this day. For a lot of engineers I worked with who hack on the “guts” of the products (the language parser/interpreter/JIT, math internals, computational geometry and stats libraries, etc), the only time they’d fire up the IDE was at the pre-release bug bash every six months lol!
In my original post, saying the “majority” of TMW ran this way is probably an exaggeration, but a “sizeable minority” would be true. Maybe Ned could run an internal poll and include those results in the blog post :)
* https://www.mathworks.com/matlabcentral/profile/authors/1409...
To learn and use Matlab/Octave requires learning very little programming or the intricacies of programming. To use Python, one has to figure out packages and libraries and object-oriented programming. To even use Julia, which is much better, one has to think about, how can I write my code to avoid the initial JIT delay.
When I am thinking science, I don't want to think about any of those things. I want to think about science and science alone. The intricacies of the programming language should not bother me.
[1] I say this as a 15 year exclusive linux user.
I get your drift but I think your analogy is opposite. I absolutely could not function in windowsland because it's constantly getting in the way of what I want to do whereas Linux just works.
It's true that Python with the use of Jupyter notebooks is getting closer to be a simple to use tool, but you still need to know some programming to use it.
IMO Octave really shines as a REPL you can write short scripts and functions for.
I'd go further to say this is useful for anyone with any level of experience programming!
You could use LD_PRELOAD or LD_LIBRARY_PATH to get Octave to link MKL (or I’m sure you could compile the whole thing for MKL, but LD_PRELOAD let me use the disto version and I’m lazy). You also may have to tell MKL to use GNU threads. I’ve done it, got decent speed ups with a 6 core AVX2 system, so I guess something more recent could be even better.
Anecdotally, I used Octave during my PhD and it was a breath of fresh air (where matlab wasn't, for reasons I cant quite explain myself tbh).
I have also used (and still use) R, Python, and Julia. Octave is still the language I go to first to get things done fast.
The Octave IDE is okish, but the plotting support is really far worse than Matlab. Honestly no open source plotting tool I've found (and I've looked a lot) comes close to Matlab which is why I eventually ended up just paying £125 for it, which I think is a very reasonable price considering it is a perpetual license and very niche software.
I wish there was some CAD software that had such a reasonably priced hobby use option, but SOLIDWORKS is $99 per year, Alibre has a cheapish option but they slightly annoyingly shift a lot of features into the £1000+ versions. Probably the best option at the moment tbf (other than piracy).
God damn Adobe for getting everybody onto the subscription model for professional software.
FreeCAD as a general purpose CAD software is not that bad. I've used it in combination with KiCAD that we use at our company to design PCB and for our needs is more than enough and works fairly well.