Julia Tools and Editors

Co-authored with Arnav Sood

While Jupyter notebooks are a great way to get started with the language, eventually you will want to use more powerful tools.

We’ll discuss a few of them here, such as

  • Text editors like Atom, which come with rich Julia support for debugging, documentation, git integration, plotting and inspecting data, and code execution.
  • The Julia REPL, which has specialized modes for package management, shell commands, and help.

Note that we assume you’ve already completed the getting started and interacting with Julia lectures.

Preliminary Setup

Follow the instructions for setting up Julia on your local computer.


Previously, we discussed basic use of the Julia REPL (“Read-Evaluate-Print Loop”).

Here, we’ll consider some more advanced features.

Shell Mode

Hitting ; brings you into shell mode, which lets you run bash commands (PowerShell on Windows)

In [1]:
; pwd

You can also use Julia variables from shell mode

In [2]:
x = 2
In [3]:
; echo $x

Package Mode

Hitting ] brings you into package mode.

  • ] add Expectations will add a package (here, Expectations.jl).
  • Likewise, ] rm Expectations will remove that package.
  • ] st will show you a snapshot of what you have installed.
  • ] up will (intelligently) upgrade versions of your packages.
  • ] precompile will precompile everything possible.
  • ] build will execute build scripts for all packages.
  • Running ] preview before a command (i.e., ] preview up) will display the changes without executing.

You can get a full list of package mode commands by running

In [4]:
] ?
  Welcome to the Pkg REPL-mode. To return to the julia> prompt, either press
  backspace when the input line is empty or press Ctrl+C.


  pkg> cmd [opts] [args]

  Multiple commands can be given on the same line by interleaving a ; between
  the commands.


  activate: set the primary environment the package manager manipulates

  add: add packages to project

  build: run the build script for packages

  develop: clone the full package repo locally for development

  free: undoes a pin, develop, or stops tracking a repo

  gc: garbage collect packages not used for a significant time

  generate: generate files for a new project

  help: show this message

  instantiate: downloads all the dependencies for the project

  pin: pins the version of packages

  precompile: precompile all the project dependencies

  redo: redo the latest change to the active project

  remove: remove packages from project or manifest

  resolve: resolves to update the manifest from changes in dependencies of
  developed packages

  status: summarize contents of and changes to environment

  test: run tests for packages

  undo: undo the latest change to the active project

  update: update packages in manifest

  registry add: add package registries

  registry remove: remove package registries

  registry status: information about installed registries

  registry update: update package registries

On some operating systems (such as OSX) REPL pasting may not work for package mode, and you will need to access it in the standard way (i.e., hit ] first and then run your commands).

Help Mode

Hitting ? will bring you into help mode.

The key use case is to find docstrings for functions and macros, e.g.

? print

Note that objects must be loaded for Julia to return their documentation, e.g.

? @test

will fail, but

using Test
? @test

will succeed.


As discussed previously, eventually you will want to use a fully fledged text editor.

The most feature-rich one for Julia development is Atom, with the Juno package.

There are several reasons to use a text editor like Atom, including

  • Git integration (more on this in the next lecture).
  • Painless inspection of variables and data.
  • Easily run code blocks, and drop in custom snippets of code.
  • Integration with Julia documentation and plots.

Installation and Configuration

Installing Atom

  1. Download and Install Atom from the Atom website.
  2. (Optional, but recommended): Change default Atom settings

    • Use Ctrl-, to get the Settings pane
    • Choose the Packages tab
    • Type line-ending-selector into the Filter and then click “Settings” for that package

      • Change the default line ending to LF (only necessary on Windows)
    • Choose the Editor tab

      • Turn on Soft Wrap
      • Set the Tab Length default to 4

Installing Juno

  1. Use Ctrl-, to get the Settings pane.
  2. Go to the Install tab.
  3. Type uber-juno into the search box and then click Install on the package that appears.
  4. Wait while Juno installs dependencies.
  5. When it asks you whether or not to use the standard layout, click yes.

At that point, you should see a built-in REPL at the bottom of the screen and be able to start using Julia and Atom.


Sometimes, Juno will fail to find the Julia executable (say, if it’s installed somewhere nonstandard, or you have multiple).

To do this

  1. Ctrl-, to get Settings pane, and select the Packages tab.
  2. Type in julia-client and choose Settings.
  3. Find the Julia Path, and fill it in with the location of the Julia binary.
  • To find the binary, you could run Sys.BINDIR in the REPL, then add in an additional /julia to the end of the screen.
  • e.g. C:\Users\YOURUSERNAME\AppData\Local\Julia-1.0.1\bin\julia.exe on Windows as /Applications/Julia-1.0.app/Contents/Resources/julia/bin/julia on OSX.

See the setup instructions for Juno if you have further issues.

If you upgrade Atom and it breaks Juno, run the following in a terminal.

apm uninstall ink julia-client
apm install ink julia-client

If you aren’t able to install apm in your PATH, you can do the above by running the following in PowerShell:


Then navigating to a folder like C:\Users\USERNAME\AppData\Local\atom\bin (which will contain the apm tool), and running:

./apm uninstall ink julia-client
./apm install ink julia-client

Upgrading Julia

To get a new release working with Jupyter, run (in the new version’s REPL)

] add IJulia
] build IJulia

This will install (and build) the IJulia kernel.

To get it working with Atom, open the command palette and type “Julia Client: Settings.”

Then, in the box labelled “Julia Path,” enter the path to yor Julia executabe.

You can find the folder by running Sys.BINDIR in a new REPL, and then add the /julia at the end to give the exact path.

For example:

Standard Layout

If you follow the instructions, you should see something like this when you open a new file.

If you don’t, simply go to the command palette and type “Julia standard layout”

The bottom pane is a standard REPL, which supports the different modes above.

The “workspace” pane is a snapshot of currently-defined objects.

For example, if we define an object in the REPL

In [5]:
x = 2

Our workspace should read

The ans variable simply captures the result of the last computation.

The Documentation pane simply lets us query Julia documentation

The Plots pane captures Julia plots output (the code is as follows)

using Plots
gr(fmt = :png);
data = rand(10, 10)
h = heatmap(data)

Note: The plots feature is not perfectly reliable across all plotting backends, see the Basic Usage page.

Other Features

  • Shift + Enter will evaluate a highlighted selection or line (as above).
  • The run symbol in the left sidebar (or Ctrl+Shift+Enter) will run the whole file.

See basic usage for an exploration of features, and the FAQ for more advanced steps.

Package Environments

Julia’s package manager lets you set up Python-style “virtualenvs,” or subsets of packages that draw from an underlying pool of assets on the machine.

This way, you can work with (and specify) the dependencies (i.e., required packages) for one project without worrying about impacts on other projects.

  • An environment is a set of packages specified by a Project.toml (and optionally, a Manifest.toml).
  • A registry is a git repository corresponding to a list of (typically) registered packages, from which Julia can pull (for more on git repositories, see version control).
  • A depot is a directory, like ~/.julia, which contains assets (compile caches, registries, package source directories, etc.).

Essentially, an environment is a dependency tree for a project, or a “frame of mind” for Julia’s package manager.

  • We can see the default (v1.1) environment as such
In [6]:
] st
Status `~/repos/lecture-source-jl/_build/website/jupyter/executed/Project.toml`
  [2169fc97] AlgebraicMultigrid v0.2.2
  [28f2ccd6] ApproxFun v0.11.14
  [7d9fca2a] Arpack v0.4.0
  [aae01518] BandedMatrices v0.15.15
  [6e4b80f9] BenchmarkTools v0.5.0
  [a134a8b2] BlackBoxOptim v0.5.0
  [ffab5731] BlockBandedMatrices v0.8.11
  [324d7699] CategoricalArrays v0.8.3
  [34da2185] Compat v2.2.1
  [a93c6f00] DataFrames v0.21.8
  [1313f7d8] DataFramesMeta v0.6.0
  [39dd38d3] Dierckx v0.5.1
  [c894b116] DiffEqJump v6.9.3
  [9fdde737] DiffEqOperators v4.12.0
  [31c24e10] Distributions v0.21.12
  [2fe49d83] Expectations v1.1.1
  [a1e7a1ef] Expokit v0.2.0
  [d4d017d3] ExponentialUtilities v1.8.0
  [442a2c76] FastGaussQuadrature v0.4.4
  [1a297f60] FillArrays v0.8.14
  [9d5cd8c9] FixedEffectModels v1.1.0
  [c8885935] FixedEffects v1.0.0
  [587475ba] Flux v0.11.1
  [f6369f11] ForwardDiff v0.10.14
  [38e38edf] GLM v1.3.11
  [28b8d3ca] GR v0.52.0
  [40713840] IncompleteLU v0.2.0
  [43edad99] InstantiateFromURL v0.5.0
  [a98d9a8b] Interpolations v0.12.10
  [b6b21f68] Ipopt v0.6.5
  [42fd0dbc] IterativeSolvers v0.8.4
  [4076af6c] JuMP v0.21.3
  [5ab0869b] KernelDensity v0.5.1
  [ba0b0d4f] Krylov v0.6.0
  [0b1a1467] KrylovKit v0.5.2
  [b964fa9f] LaTeXStrings v1.2.0
  [5078a376] LazyArrays v0.16.16
  [0fc2ff8b] LeastSquaresOptim v0.8.1
  [093fc24a] LightGraphs v1.3.3
  [7a12625a] LinearMaps v2.7.0
  [5c8ed15e] LinearOperators v1.2.0
  [961ee093] ModelingToolkit v3.1.1
  [76087f3c] NLopt v0.6.0
  [2774e3e8] NLsolve v4.4.1
  [429524aa] Optim v0.20.1
  [1dea7af3] OrdinaryDiffEq v5.42.3
  [d96e819e] Parameters v0.12.1
  [14b8a8f1] PkgTemplates v0.7.13
  [91a5bcdd] Plots v1.6.8
  [f27b6e38] Polynomials v1.1.13
  [af69fa37] Preconditioners v0.3.0
  [92933f4c] ProgressMeter v1.4.0
  [1fd47b50] QuadGK v2.4.1
  [fcd29c91] QuantEcon v0.16.2
  [1a8c2f83] Query v1.0.0
  [ce6b1742] RDatasets v0.7.3
  [d519eb52] RegressionTables v0.5.0
  [295af30f] Revise v3.1.9
  [f2b01f46] Roots v1.0.7
  [47a9eef4] SparseDiffTools v1.10.2
  [684fba80] SparsityDetection v0.3.3
  [90137ffa] StaticArrays v0.12.5
  [2913bbd2] StatsBase v0.32.2
  [3eaba693] StatsModels v0.6.15
  [f3b207a7] StatsPlots v0.14.17
  [789caeaf] StochasticDiffEq v6.20.0
  [a759f4b9] TimerOutputs v0.5.5 #master (https://github.com/KristofferC/TimerOutputs.jl)
  [112f6efa] VegaLite v2.3.0
  [e88e6eb3] Zygote v0.5.1
  [37e2e46d] LinearAlgebra 
  [9a3f8284] Random 
  [2f01184e] SparseArrays 
  [10745b16] Statistics 
  [8dfed614] Test 
  • We can also create and activate a new environment
In [7]:
] generate ExampleEnvironment
 Generating  project ExampleEnvironment:
  • And go to it
In [8]:
; cd ExampleEnvironment
  • To activate the directory, simply
In [9]:
] activate .
 Activating environment at `~/repos/lecture-source-jl/_build/website/jupyter/executed/more_julia/ExampleEnvironment/Project.toml`

where “.” stands in for the “present working directory”.

  • Let’s make some changes to this
In [10]:
] add Expectations Parameters
   Updating registry at `~/.julia/registries/General`

   Updating git-repo `https://github.com/JuliaRegistries/General.git`

  Resolving package versions...
   Updating `~/repos/lecture-source-jl/_build/website/jupyter/executed/more_julia/ExampleEnvironment/Project.toml`
  [2fe49d83] + Expectations v1.6.0
  [d96e819e] + Parameters v0.12.1
   Updating `~/repos/lecture-source-jl/_build/website/jupyter/executed/more_julia/ExampleEnvironment/Manifest.toml`
  [56f22d72] + Artifacts v1.3.0
  [34da2185] + Compat v3.23.0
  [e66e0078] + CompilerSupportLibraries_jll v0.3.4+0
  [9a962f9c] + DataAPI v1.4.0
  [864edb3b] + DataStructures v0.18.8
  [31c24e10] + Distributions v0.23.12
  [2fe49d83] + Expectations v1.6.0
  [442a2c76] + FastGaussQuadrature v0.4.4
  [1a297f60] + FillArrays v0.9.7
  [692b3bcd] + JLLWrappers v1.1.3
  [e1d29d7a] + Missings v0.4.4
  [efe28fd5] + OpenSpecFun_jll v0.5.3+4
  [bac558e1] + OrderedCollections v1.3.2
  [90014a1f] + PDMats v0.10.1
  [d96e819e] + Parameters v0.12.1
  [1fd47b50] + QuadGK v2.4.1
  [79098fc4] + Rmath v0.6.1
  [f50d1b31] + Rmath_jll v0.2.2+1
  [a2af1166] + SortingAlgorithms v0.3.1
  [276daf66] + SpecialFunctions v0.10.3
  [90137ffa] + StaticArrays v0.12.5
  [2913bbd2] + StatsBase v0.33.2
  [4c63d2b9] + StatsFuns v0.9.6
  [3a884ed6] + UnPack v1.0.2
  [2a0f44e3] + Base64 
  [ade2ca70] + Dates 
  [8bb1440f] + DelimitedFiles 
  [8ba89e20] + Distributed 
  [b77e0a4c] + InteractiveUtils 
  [76f85450] + LibGit2 
  [8f399da3] + Libdl 
  [37e2e46d] + LinearAlgebra 
  [56ddb016] + Logging 
  [d6f4376e] + Markdown 
  [a63ad114] + Mmap 
  [44cfe95a] + Pkg 
  [de0858da] + Printf 
  [3fa0cd96] + REPL 
  [9a3f8284] + Random 
  [ea8e919c] + SHA 
  [9e88b42a] + Serialization 
  [1a1011a3] + SharedArrays 
  [6462fe0b] + Sockets 
  [2f01184e] + SparseArrays 
  [10745b16] + Statistics 
  [4607b0f0] + SuiteSparse 
  [8dfed614] + Test 
  [cf7118a7] + UUIDs 
  [4ec0a83e] + Unicode 

Note the lack of commas

  • To see the changes, simply open the ExampleEnvironment directory in an editor like Atom.

The Project TOML should look something like this

name = "ExampleEnvironment"
uuid = "14d3e79e-e2e5-11e8-28b9-19823016c34c"
authors = ["QuantEcon User <quanteconuser@gmail.com>"]
version = "0.1.0"

Expectations = "2fe49d83-0758-5602-8f54-1f90ad0d522b"
Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"

We can also

In [11]:
] precompile
Precompiling project...
┌ Info: Precompiling ExampleEnvironment [cdbd9e4e-950c-4af8-8ab4-08d18c28f6f7]
└ @ Base loading.jl:1260

Note The TOML files are independent of the actual assets (which live in ~/.julia/packages, ~/.julia/dev, and ~/.julia/compiled).

You can think of the TOML as specifying demands for resources, which are supplied by the ~/.julia user depot.

  • To return to the default Julia environment, simply
In [12]:
] activate
 Activating environment at `~/repos/lecture-source-jl/_build/website/jupyter/executed/Project.toml`

without any arguments.

  • Lastly, let’s clean up
In [13]:
; cd ..
In [14]:
; rm -rf ExampleEnvironment


With this knowledge, we can explain the operation of the setup block

In [15]:
using InstantiateFromURL
# optionally add arguments to force installation: instantiate = true, precompile = true
github_project("QuantEcon/quantecon-notebooks-julia", version = "0.8.0")

What this github_project function does is activate (and if necessary, download, instantiate and precompile) a particular Julia environment.