Do you want to learn about Matlab How To Open Source Code but don’t know how to start? I have spent some time learning about it and so can help you with your questions. Just take your time and read my guide. Don’t skip around to different sections. Read everything in the order it is given.
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It’s been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack, an ecosystem of Python-based math, science, and engineering software. NumPy is licensed under the BSD license, and packages are available for Linux, Windows, and Mac OS X.
Scilab is another open source option for numerical computing that runs across all the major platforms: Windows, Mac, and Linux included. Scilab is perhaps the best known alternative outside of Octave, and (like Octave) it is very similar to MATLAB in its implementation, although exact compatibility is not a goal of the project’s developers.
SageMath is another open source mathematics software system that might be a good option for those seeking a MATLAB alternative. It’s built on top of a variety of well-known Python-based scientific computing libraries, and its own language is syntactically similar to Python. It has many features including a command-line interface, browser-based notebooks, tools for embedding formulas in other documents, and of course, many mathematical libraries.
Julia is a dynamically typed programming language featuring Lisp-style macros, built-in primitives for parallel computing, and functions designed for matrix manipulation, data visualization, and much more. It’s designed to feel like a scripting language rather than a C-style programming-language and even has an interactive mode (REPL), and can be embedded into other languages through its embedding API.
Users of Julia have many reasons for loving its syntax and capabilities, but some of the popular examples include its broadcasting feature, which lets you apply a function to one or more arrays without a writing a complex loop, its simple array functions that let you rotate and reshape arrays, matrix transforms, autodiff, native Unicode support, integrated unit testing, easy paralellisation, and all-round simpler syntax with no loss of functionality (and improved code efficiency.)
Julia has an active community around its development and its use, so it’s also been tailored for domain-specific purposes, including image processing (JuliaImages), biology (BioJulia), quantum physics (QuantumBFS), nonlinear dynamics (JuliaDynamics), economics (QuantEcon), astronomy (JuliaAstro) and more.
GNU Octave may be the best-known alternative to MATLAB. In active development for almost three decades, Octave runs on Linux, Windows, and Mac—and is packaged for most major distributions. If you’re looking for a project that is as close to the actual MATLAB language as possible, Octave may be a good fit for you; it strives for exact compatibility, so many of your projects developed for MATLAB may run in Octave with no modification necessary.
Octave has many different choices available for a front-end interaction outside of the default that now ships with version 4; some resemble MATLAB’s interface more than others. Octave’s Wikipedia page lists several options.
Open-source code is available to the public. You can find tons of open source codes on the internet, mostly on the Matlab Central File Exchange. The authors have made them available free for anyone to use. Matlab has evolved into a powerful tool that allows user-supplied tools and functions.