Numerical experiments, Tips, Tricks and Gotchas
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments [1], [2]. Julia intends to combine the best from Matlab, R and Python into one language that is supposed to be consistent, well designed and most importantly fast [3].
A nice review by Evan Miller: [4]. Another review by John White: [5]. A review with some code samples by Chris Wellons: [6].
Julia, Matlab, and C comparison and benchmark by Justin Domke [7].
Numeric matrix manipulation in MATLAB, Python NumPy, R, and Julia by Sebastian Raschka [8].
Searchable listing of all registered packages for the Julia programming language [9].
Aannotated tutorial links: Learning Julia [10].
Tutorials, overviews: Julia Documentation [2], A Beginner's Look at Julia [11], Learn Julia in Y Minutes [12], Programming in Julia [13].
Step-by-step setup, including Jupyter with IJulia [14]. This is from Part 1: Programming in Julia [13] of their course in Quantitative Economics [15], [16].
Install the current release from the download page [17].
After Julia installation, onpen Julia terminal and install the following useful packages:
julia> Pkg.update() julia> Pkg.add("DataFrames") julia> Pkg.add("PyPlot") julia> Pkg.add("IJulia") julia> exit()Jutyter notebook can be started by this command from a system terminal:
$ ipython notebook --profile=juliaAlternatively it can be started in Julia [18]:
julia> using IJulia julia> notebook()
© Nikolai Shokhirev, 2012-2024
email: nikolai(dot)shokhirev(at)gmail(dot)com