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MATLAB, an acronym for Matrix Laboratory, is a high-level programming language, development & simulation environment, modelling, and data visualisation platform all rolled into one. Thanks to its gamut of potent features and versatility, the language has become incredibly popular and a major player in the scientific & technical domains.
MATLAB has become an integral part of signal processing, communications engineering, control & instrumentation, ASIC designing, AI, data science, & several other technical domains. Developed by Cleve Moler, MATLAB was developed using FORTRAN's source code and two of its numerical computing libraries, LINPACK and EISPACK., as the foundation for the first draft. The first incarnation of the programming language was quite portable but had only 80 built-in functions and support for only a single data type, the matrix. Eventually, the FORTRAN source code had to be modified to accommodate the addition of new programs. The initial versions of the application were run on time-sharing systems powered by mainframe computers at the University of New Mexico. Besides his ambition of creating a powerful numerical analysis system, Moler wanted a language/environment that gave his university students easy access to the powerful capabilities of FORTRAN, LINPACK, and EISPACK without having to learn the languages from scratch.
The language is quite different from any other conventional programming language out there. The focus on linear algebra and numerical computing can make MATLAB programming seem alien to even the most practiced programmer. Things become even more challenging when you need to develop codes for complex problem scenarios such as AI, image processing, data analysis, etc.
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➢ Always use structures instead of globals.
➢ Pass structures to and form functions. This ensures modularity in functions and keeps everything clear & organised.
➢ Use strings and SWITCH as proxies for enumerated types. They are readable, easier to use, and much faster.
➢ Add underscores for long variable names.
➢ Use comments as often as possible. This boosts readability and makes understanding & debugging easier.
➢ Use capital letters for declaring global variables.
➢ Keep functions small and follow the ‘Single Responsibility Principle” when designing them.
➢ Keep nested functions within the same file.
➢ There’s no real need to match function names with file names.
➢ Include large-space indents and keep lines short.
➢ Remember to stick to just one sort of capitalisation.
➢ Use structures of arrays instead of arrays of structures; they are much more efficient & faster.
➢ Preallocate data to increase performance & speed.
➢ Use commands like FIND, PROD, SUM, CUMSUM, NaN, REPMAT, RESHAPE, ONES, and ZEROS to make your codes more efficient & vectorised.
➢ Make sure to use TRY, CATCH, and END quite generously. Error catching is essential, as you may already know.
➢ Also, make generous use of the MATLAB debugger.
➢ Check the number and type of arguments in every function of your code.
➢ Talk with your professors and/or do your research into the practices of clean coding. Understand them & try to implement them.
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