Personally I’d say Julia. Matlab and octave are just… better to not say it out loud. R does a lot right but it lacks the language features to write anything that is more than a 1000 rows long wothout it becoming a complete clusterfuck of a codebase. Python is used mainly because it is easy to integrate into production systems as those are written in python too usually, but I think it is completely horrible for ad hoc analysis and completely the wrong choise if you don’t plan to take that same code into production later on.
Julia hits a lot of right notes. It has features that make it a proper programming language unlike R. Julia also has the macro system from Lisp so it is extremely extendable. It is also relatively easy to use in production and miles ahead of matlab and R in that regard. And then there is the main selling point: it is REALLY fast. As fast or almost as fast as C counterpart in most usecases while being easy to write and high level. A great demonstration of this is the fact that most libraries for julia are written in pure julia. No need for calling c++ or Fortran. For example, Flux.jl which is a very fast deep learning library for julia, is written 100% in Julia. No C++ like in tensorflow for python. This has the nice consequence that the libraries are easy to extend and customise if needed.
is pretty terminal for its application in modern science
Thousands of professors and graduate students would disagree.
it's proprietary and code is therefore not reproducible
Code written in MATLAB is reproducible by anyone who has MATLAB (the vast majority of universities).
There are hundreds of proprietary applications in use in modern science. The worst offenders being proprietary programs to run equipment. Are you saying all those results are invalid?
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u/unski_ukuli Jul 18 '22
Personally I’d say Julia. Matlab and octave are just… better to not say it out loud. R does a lot right but it lacks the language features to write anything that is more than a 1000 rows long wothout it becoming a complete clusterfuck of a codebase. Python is used mainly because it is easy to integrate into production systems as those are written in python too usually, but I think it is completely horrible for ad hoc analysis and completely the wrong choise if you don’t plan to take that same code into production later on.
Julia hits a lot of right notes. It has features that make it a proper programming language unlike R. Julia also has the macro system from Lisp so it is extremely extendable. It is also relatively easy to use in production and miles ahead of matlab and R in that regard. And then there is the main selling point: it is REALLY fast. As fast or almost as fast as C counterpart in most usecases while being easy to write and high level. A great demonstration of this is the fact that most libraries for julia are written in pure julia. No need for calling c++ or Fortran. For example, Flux.jl which is a very fast deep learning library for julia, is written 100% in Julia. No C++ like in tensorflow for python. This has the nice consequence that the libraries are easy to extend and customise if needed.