Julia is a programming language.
Its origin dates back to 2009, when Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman decide to set their hands to a new languages that is both high-level and fast. In 2012, a website is created with a blog post to explain the mission of the language, and it is the moment that the language made its debut.
At the time the author is writing this note (May 2023), the stable release number of Julia is 1.9.0, and its syntax is now considered stable.
According to its officiel website, Julia boasts the following advantages: fast, dynamic, reproducible, composable, general, open source. What these features mean to me is that I don't need to pay MATLAB to enjoy a similar syntax, and it is reasonably fast yet concise that I don't need to resort to C/C++ to pursue performance. To me, the more I code with the language, the better I realize how much these merits benefits the scientific computing.
The officiel website includes a great abundance of resources, ranging from the download link of the compiler, the official documentation (with detailed yet comprehensible explanations) to learning materials. I closely follow one of the texts, and if I found something unfamiliar, I will look it up in the documentation or simply google it.
Here I include some of the useful resources:
If you are familiar with other programming languages, see https://docs.julialang.org/en/v1.9.0/manual/noteworthy-differences/ for a comparison and https://cheatsheets.quantecon.org/ for a cheat sheet if you are familiar with MATLAB or python.
A quick cheatsheet for Julia syntax and its abilities is at https://juliadocs.github.io/Julia-Cheat-Sheet/ and a similar cheatsheet for the Plots package is at https://github.com/sswatson/cheatsheets/blob/master/plotsjl-cheatsheet.pdf
Go to Julia's website https://julialang.org and download and install the latest stable release of Julia.
For Windows users, download the installer and click on the installer to install, or download the portable version and extract files.
For Linux or Mac users, download the corresponding package and extract/install it.
Run Julia REPL [1] by executing bin/julia!
Pluto.jl is an lightweight interactive notebook for Julia. It is helpful for the users to conduct experiments, and it comes in handy when you want to export your computation as html to create a live demo.
To install Pluto [2], type ] to enter package manager Pkg in Julia:
julia> ]Enter add Pluto to add the package:
(@v1.9) pkg> add PlutoTo exit package manager, press Ctrl+C or Backspace
(@v1.9) pkg> ^CTo use Pluto, we need to first import it
julia> import Pluto
julia> Pluto.run()It will automatically open a webpage of Pluto notebook. If not, you may copy the link printed on screen (something like http://localhost:1234/?secret=xxxxxxxx) to the browser.
| [1] | See for example here for an explanation. |
| [2] | Check also here if you face any difficulties. |