Writing Reusable Code in Julia - Carlos Paniagua

Use case

  • You are working on a research project that requires computation. 💻👩‍💻

  • You write your code; you test it (👍) and it just works! 🚀

  • You decide your code is general enough that it could be useful for solving related problems. 🤓

  • You add some documentation and host your code in some public repository and tell other people about it. 🎤

  • Other people realize your code could be useful to solve their problems! 😎

  • They take your code, try to use it and... it doesn't work 😱

  • If they're lucky, the code "might work" but the results are not the expected ones 😱😱

This is a very common problem in the scientific research community. Here we present an introduction about best practices to building reusable code in the Julia ecosystem.

Our definition of reusable code

Reusable code is code that is easy to distribute and use (correctly and consistently).

Note: If you'd like to follow along with the rest of the discussion, you'll need Git, a text editor or IDE (VS Code recommended with the Julia extension) and Julia (naturally).

To write reusable Julia code we need to understand how to manage dependencies in Julia. Dependency management facilitates code maintainability, promotes collaboration by simplifying environment setup for other developers, and mitigates security risks by allowing for timely updates and vulnerability monitoring.

Note: Read more about the advantages of dependency management here.

Managing Dependencies

A dependency (dep) is code other people (even ourselves) wrote and we are using in a project.

Let's add some deps to our base Julia installation using Julia's Package Manager.

  1. Add a few deps with the add command

    $ julia
    julia> # get to pkg mode with `]`
    (@v1.9) pkg> add Example StaticArrays # add the `Example` and `StaticArrays` packages
  2. Look at the deps in your base Julia with the status or st command

    (@v1.9) pkg> status
    Status `C:\Users\MyUserName\.julia\environments\v1.9\Project.toml`
    [7876af07] Example v0.5.3
    [90137ffa] StaticArrays v1.6.3
  3. Remove a dep with rm

    (@v1.9) pkg> rm StaticArrays
  4. Seeing is believing: look at your deps again

    (@v1.9) pkg> st # and be lazy
    Status `C:\Users\MyUserName\.julia\environments\v1.9\Project.toml`
    [7876af07] Example v0.5.3

Make a package!

A package is code with structure that leverages available tooling for efficient distribution and use. We could add this structure to our code manually but there is tooling for this already! 🛠️

Some options:

  • Use Pkg.generate for a bare-bones package structure
  • Use PkgTemplates.jl for more features (Readme, License, Documentation, test suite, CI and more)
  • Clone some base repo and customize to your needs (Example.jl, MyAwesomePackage.jl)

For the rest of the discussion we'll use generate from the Julia package manager.

  1. Navigate to where you want to your project/package.

  2. From Julia's package manager issue generate MyPackage

     (@v1.9) pkg> generate MyPackage

    You will see a new folder MyPackage was created.

    $ cd MyPackage
    $ tree
    ├── Project.toml
    └── src
        └── MyPackage.jl
  3. Let's add some deps and code to our package by editing src/MyPackage.jl

    module MyPackage
    using Example: domath # added this dep
    fact() = println("FYI, 2 + 5 = $(domath(2))") # added this new feature
    export fact
  4. Let's test our new features! 🤞

    (@v1.9) pkg> activate .
      Activating project at `C:\Users\MyUserName\Documents\Reusable-code-Julia\MyPackage`
    julia> using MyPackage
    [ Info: Precompiling MyPackage [934d1629-71ee-47e4-906a-ddb3ea0dd61f]
    ERROR: LoadError: ArgumentError: Package MyPackage does not have Example in its dependencies:
    - You may have a partially installed environment. Try `Pkg.instantiate()`
      to ensure all packages in the environment are installed.
    - Or, if you have MyPackage checked out for development and have
      added Example as a dependency but haven't updated your primary
      environment's manifest file, try `Pkg.resolve()`.
    - Otherwise you may need to report an issue with MyPackage
    [More error feedback here]

    Although we added the Example package to our base Julia environment, we are under a new independent environment MyPackage that does not know about Example.

    (MyPackage) pkg> st
    Project MyPackage v0.1.0
    Status `C:\Users\MyUserName\Documents\DSCoV-Reusable-Julia\MyPackage\Project.toml` (empty project)
    (MyPackage) pkg> 

    In the error message above Julia is suggesting that we have an undocumented/uninstalled/broken dependency. Here we just need to add it to our project. Let's do it!

    (MyPackage) pkg> add Example
       Resolving package versions...
        Updating `C:\Users\MyUserName\Documents\Reusable-code-Julia\MyPackage\Project.toml`
      [7876af07] + Example v0.5.3
        Updating `C:\Users\MyUserName\Documents\Reusable-code-Julia\MyPackage\Manifest.toml`
      [7876af07] + Example v0.5.3
    Precompiling project...
      1 dependency successfully precompiled in 1 seconds. 1 already precompiled.
    (MyPackage) pkg> st # again seeing is believing
    Project MyPackage v0.1.0
    Status `C:\Users\MyUserName\Documents\Reusable-code-Julia\MyPackage\Project.toml`
    [7876af07] Example v0.5.3

    Now that the Example dep is documented and compiled let's try testing our package again!

    julia> using MyPackage
    julia> fact()
    FYI, 2 + 5 = 7

Success! Our feature works and we are ready (sort of) to share our package with the world!

Before sharing your code with the world, especially larger code bases, it's a good idea to have our code conform to a standard style. There is tooling for this available and there are many associated benefits:

  1. Consistency: It ensures that your code has a consistent style, which makes it easier to read and understand.

  2. Saves Time: You don't have to spend time manually formatting your code or arguing about coding styles in code reviews (when working with other people).

  3. Prevents Bugs: Some formatters can catch and fix minor errors, such as missing semicolons, unused variables, etc.

  4. Integration with IDEs: Most modern IDEs support automatic formatting on save, which makes it effortless to keep your code well-formatted.

  5. Focus on Logic: With automatic formatting, you can focus on the logic of your code, rather than its appearance.

Here is a suggested configuration for VS Code with the Julia extension.

  1. Add a .JuliaFormatter.toml file with the following

    style = "blue"

    Read about the blue style here.

  2. Add a .gitignore file


The .gitignore file will prevent the configuration file just created from getting pushed to our remote repository. Here we have added the .JuliaFormatter.toml configuration file we created earlier.

Publish your package!

Use tooling within VS Code (Source Control panel) for the following or Git to place your project/package in a remote repository.

Make the project a git repository, commit changes, and push your project to a remote repo to share with the world!

$ git init
$ git add .
$ git commit -m 'My commit message'
$ git remote add origin https://github.com/MyUserName/MyRepo.git
$ git push origin main

Now others can use your package!

(@v1.9) pkg> add https://github.com/MyUserName/MyPackage.git
     Cloning git-repo `https://github.com/MyUserName/MyPackage.git`
    Updating git-repo `https://github.com/MyUserName/MyPackage.git`
   Resolving package versions...
    Updating `C:\Users\MyUserName\.julia\environments\v1.9\Project.toml`
  [e06b4af7] + MyPackage v0.1.0 `https://github.com/MyUserName/MyPackage.git#main`
    Updating `C:\Users\MyUserName\.julia\environments\v1.9\Manifest.toml`
  [e06b4af7] + MyPackage v0.1.0 `https://github.com/MyUserName/MyPackage.git#main`
Precompiling project...
  1 dependency successfully precompiled in 5 seconds. 231 already precompiled. 1 skipped during auto due to previous errors.

julia> using MyPackage

julia> fact()
FYI, 2 + 5 = 7

By following these simple practices, our code becomes more reliable, user-friendly, and easier to maintain, ensuring smoother workflows for other members of the scientific community. 🎉

Carlos Paniagua

Senior Data Scientist

Carlos is a data scientist at the CCV. Previously a Mathematics tenure-track faculty member at universities in the US and the Dominican Republic, Carlos likes using his skills working with data for wider social impact.