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Comment by pmart123 | Hacker News Reader

As someone who used Matlab and Mathematica in college, I'm not sure I'd want to have a closed source solution really take off again at this point. While they provided great products and documentation, they also made it much more difficult to share code/visualizations due to restrictive and expensive licensing. Ultimately, I think the ability to share information easily should help spur scientific advancement.

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As a single piece of anecdata, this is exactly what prevented me from using the free licenses I had through my university to learn these programs. After my experiences with Stata, seeing the non-affiliated prices and having to do my work in a computer lab instead of on my own laptop, I didn't want to be tied to software that I might not have access to in the future, and that I couldn't share with other people.

The one time I made a handsome demo in Mathematica, I realized that there was no straightforward way to share it, so I gave up and redid it in R.

I'm grateful I never spent time learning those programs. I still miss Mathematica now and then, but free software is the only way to go for me nowadays.

Dumping SolidWorks for the less elegant but much cheaper Fusion360 for the same reason. You can’t build a community at corporate license prices.

Mathematica/Python/MATLAB user. Mathematica and MATLAB do both have great documentation and intuitive examples shipped with their product, however, document your own code using their system is surprisingly hard (compared to Sphinx). Jupyter learnt the idea of the notebook from Mathematica (and some other notebook based math software) but the killer point is that it extends itself to more and more general purpose programming language with Python, R, Julia and even C/C++ kernels. MATLAB still popular in the field of control engineering, communication partly because of its SIMULINK simulation environment. I have been looking for a Python alternative but the closest I have found so far is Modelica. In my workflow, Mathematica is used to derive the mathematical formula, Matlab for simulation and finally software is written and distributed as Python package.

My workflow is very similar. But I am often forced to use matlab for simulation because the vendors I work with only deliver matlab libraries. They choose matlab because many engineers know how to use it and have licenses for it but also it gives mechanisms to protect IP which is a big deal to many firms.

https://www.mathworks.com/help/matlab/matlab_prog/protect-yo...

I assume this situation also holds true for those using LabView to build their virtual instruments. IP wasn't really an issue for us since we have decided to open-source our project to broaden the knowledge of the public: https://github.com/Maritime-Robotics-Student-Society/sailing...

As a note, in Python, there is pyconcrete for code encryption. https://github.com/Falldog/pyconcrete

You might want to try sympy or sage as a mathematica replacement.

You could try the SciPy Simulink look-a-like https://www.scilab.org/scilab/gallery/xcos

SciLab != SciPy

Same curious, it has Python extension but not really close to it.

I share your sentiment, but in truth, Mathematica's symbolic algebra capabilities are above and beyond what everything else offers by a wide margin.

For now

For last 20 years at the least, and the gap widens if anything

Sure, Mathematica has had a long head start. I don't see the gap widening at all, though. For me, e.g. SymPy (and more specialized algebra systems for e.g. quantum mechanics I've built on top of it) now match and sometimes surpass what I could do with Mathematica. More importantly though, Mathematica's closed nature makes it quite hard to integrate with other systems and workflows (as others have pointed out in this thread). The benefits of the open scientific ecosystem around Jupyter far outweighs the few areas were Mathematica still leads in functionality. This is a subjective assessment, of course.

Sympy is great (especially the ability to automatically codegen numerical expressions from symbolic ones), but I've found it to be orders of magnitude slower than Mathematica for some relatively simple problems. I also find the sympy documentation to be somewhat sparse and hard to navigate.

A number of years ago I knew a postdoc who bought a student copy of Mathematica at the university bookstore so he could use it as part of a short-term collaboration without dropping a non-trivial chunk of his grants on a full license. Shortly after he installed it, the department chair got a personal email from Stephen Wolfram asking what someone who wasn't a graduate student was doing with a student license.

This supported the upward trend of interest in open source and especially ipython around the labs.

That’s pretty horrifying.

That's all nice but you go to real-world space/robotics/drones etc. shops and they all use Matlab or similar for their scientific computation and validation and treat Jupyter stack as a toy. Similar to telling a photographer to use Linux because there is GIMP and that should be enough for them.

But Matlab has much less restrictive terms when it comes to code, right?

And then there's LabView, used widely for lab and process automation, which really has no open-source or free alternative.

You can really do photography on Linux nowadays though. Darktable and rawtherapee are shaping up nicely.

I am a pro photographer and still have to use Lightroom for proper color adjustments. I even had competitions with Darktable pros to see who can get a better picture and unfortunately Lightroom was outclassing them (and I hate Adobe's forced subscriptions). It still needs a lot of work to be cutting edge.

Can you share some specific weaknesses Darktable has? (I've never used either.)

Yeah, I work at a startup where we specialize in signal processing + ML. We have a robust set of infrastructure in MATLAB (use our own ML libraries rather than MATLAB's libs) and try as we might, we haven't been able to switch altogether to Python (despite a number of us being fans of Python).

MATLAB has a TON of things that you don't get with Python + Numpy + Scipy. Complicated plots/graphics with interactivity are a pain point in Python compared to MATLAB. Similarly, the debugging capabilities in MATLAB are truly magical compared to a pretty terrible experience on the Python side of things. Even though we deploy software in Python, we are much faster prototyping in MATLAB and deploying finalized algorithms/software to Python than trying to do everything in Python from the get go. MATLAB's JIT is also pretty great and while Numba is pretty great, it still requires more work and can be brittle at times.

Have you tried bokeh for interactive visualizations? https://bokeh.pydata.org/en/latest/

I think a better analogy would be how large swathes of the population use Windows for everyday computation and treat Linux as a toy, whereas individuals with more domain knowledge take Linux seriously and use it in production in a variety of environments (e.g., high frequency trading, web hosts, commerce sites, etc). Engineers may prefer MATLAB as a "serious" development environment, but most software developers find it to be seriously lacking. Most general purpose programming languages are far more powerful than MATLAB, but simply lack the toolboxes/convenience. Both of these points are nothing new and have been observed since the creation of MATLAB (indeed, the latter is the reason why MATLAB was created, since MATLAB is basically supposed to be a pretty face for FORTRAN).

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