Tag Archives: library

Simplifying Julia Package Integration with Extensions

By: Great Lakes Consulting

Re-posted from: https://blog.glcs.io/package-extensions

This post was written by Steven Whitaker.

The Julia programming languageis a high-level languagethat is known, at least in part,for its outstanding composability.Much of Julia’s composabilitystems from its multiple dispatch,which allows functions written in one packageto work with objects from another packagewithout either package needing to depend on or even know about the other.(See another blog post for more details.)

Sometimes, however,it is useful for a packageto be able to extend its functionsto provide additional functionalitywhen given an object of a specific typefrom another package.One way to do sois to add the other package as an explicit dependencyso that its type is availablefor the first package to useto define a specific method for it.

But what if the package can function just finewithout the additional functionality?What if the extra functionalityisn’t integral to what the package doesand only appliesif the userwants to work with objectsof that specific type?In this case,it doesn’t make much senseto make the other package a direct dependency,because then every userpays the price of extra package load timefor functionality that only some users actually want.

The solution is package extensions.A package extension is codethat gets loaded conditionally,depending on what other packagesthe user has explicitly loaded.In other words,when a user loads both the packageand the dependency the extension depends on,the extension gets loaded automatically.This way,users who want to use the packagecan do so without the added dependency,while users who want the extra functionalitycan load the dependency themselves.

In this post,we will learn about some package extensionsthat exist in the Julia package ecosystem.We will also learn how to write a package extensionand how to load the extension.

This post assumes you are familiarwith the structure of a Julia package.If you need to learn more,check out our post on creating Julia packages.

Package Extensions in the Wild

Writing a Package Extension

To create a package extension,one needs to create a modulethat adds method definitionsto functions from one of the packages(either the package being extendedor the package that triggers loading the extension)that dispatch on types from the other package.This module will live in the ext directoryof the package being extended.Additionally,the extended package’s Project.tomlneeds to be updatedto inform the package managerof the existence of the extensionand when to load it.

Let’s look at a concrete example.

Example Package to Extend

This example will build on a custom package called Averages.jlthat we discussed in our blog post on testing Julia packages.The package code is as follows:

module Averagesusing Statistics: meanexport compute_averagecompute_average(x) = (check_real(x); mean(x))function compute_average(a, b...)    check_real(a)    N = length(a)    for (i, x) in enumerate(b)        check_real(x)        check_length(i + 1, x, N)    end    T = float(promote_type(eltype(a), eltype.(b)...))    average = Vector{T}(undef, N)    average .= a    for x in b        average .+= x    end    average ./= length(b) + 1    return a isa Real ? average[1] : averageendfunction check_real(x)    T = eltype(x)    T <: Real || throw(ArgumentError("only real numbers are supported; unsupported type $T"))endfunction check_length(i, x, expected)    N = length(x)    N == expected || throw(DimensionMismatch("the length of input $i does not match the length of the first input: $N != $expected"))endend

Creating the Extension

For this example,we will create an extensionthat implements additional functionality for DataFrames.These are the tasks we need to doto implement the extension:

  1. Create the extensionat Averages/ext/AveragesDataFramesExt.jl.Note that this follows the naming convention for extensions:<PackageName><NameOfPackageThatTriggersExtension>Ext.Inside this file,we create a module called AveragesDataFramesExt(same name as the file)and put the code we want to be includedwhen Averages.jl and DataFrames.jl are loaded together:

    module AveragesDataFramesExtimport Averagesusing Averages: compute_averageusing DataFrames: All, DataFrame, combinefunction Averages.compute_average(df::DataFrame)    @info "Running code in AveragesDataFramesExt!"    df_avg = combine(df, All() .=> compute_average)    return df_avgendend

  2. Add [weakdeps] and [extensions] sectionsto the Project.toml of Averages.jl.(See our previous blog post for the original Project.toml.)In [weakdeps],specify DataFrames.jl and its UUID,and, in [extensions],specify our extension (AveragesDataFramesExt)and its dependency (DataFrames.jl).The UUID of DataFrames.jl can be foundin DataFrames.jl’s Project.toml.

    Here’s the updated Project.toml for Averages.jl:

    name = "Averages"uuid = "1fc6e63b-fe0f-463a-8652-42f2a29b8cc6"version = "0.1.0"[deps]Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"[weakdeps]DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"[extensions]AveragesDataFramesExt = "DataFrames"[extras]Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[targets]test = ["Test"]

    (Note that,just as compatible versions of the [deps] packagescan be specified in a [compat] section,so too can the compatible versions of the [weakdeps] packagesbe specified.)

Using the Extension

First,let’s see what happensif we try this without the extension:

julia> compute_average(DataFrame(a = [1, 2], b = [3.0, 4.0]))ERROR: ArgumentError: only real numbers are supported; unsupported type AnyStacktrace: [1] check_real(x::DataFrame)   @ Averages /path/to/Averages/src/Averages.jl:34 [2] compute_average(x::DataFrame)   @ Averages /path/to/Averages/src/Averages.jl:7 [3] top-level scope   @ REPL[5]:1

So,now let’s see if the extensionallows this function call to work.

To use the extension,install and load Averages.jl and DataFrames.jl(for Averages.jl, use the dev command,i.e., pkg> dev /path/to/Averages)and then call compute_average:

julia> using Averages, DataFramesjulia> compute_average(DataFrame(a = [1, 2], b = [3.0, 4.0]))[ Info: Running code in AveragesDataFramesExt!12 DataFrame Row  a_compute_average  b_compute_average      Float64            Float64   1                1.5                3.5

Nice, it works!And with that,we have an example package extensionthat illustrates how to implement your own.

And remember,a user of Averages.jlwill only incur the cost of loading AveragesDataFramesExtif they load DataFrames.jl.For more details,see the slide annotationsin this screenshot from JuliaCon 2023:

JuliaCon 2023 package extensions talk

(See also the full talk on package extensionsfor even more details.)

Note: Where Should an Extension Live?

By the way,if you’re wondering why we put the extension in Averages.jlinstead of DataFrames.jl,the answer isthat it doesn’t really matterbecause the user experiencewill be the same regardless.If you still want some rules to follow,I’m not aware of any Julia best-practicesin this regard,but here are some rules that make sense to me:

  • If one of the two packages in questiondefines an interface,the extension should go in the packagethat implements the interface.
  • Otherwise,put the extension in the packagethat owns the functionsthat are being extended.In our example,we extended the compute_average function.Since this function is defined in Averages.jl,we put the extension in Averages.jl.
  • An exception to the previous ruleis if getting the new functionality rightrequires a good understandingof the internals of the new data typethat’s being dispatched on,in which case the extensionshould belong in the packagethat defines the type.For example,if compute_average was super complicatedfor some reasonwhen working with DataFrames,it would make sense for those with the needed expertise(i.e., the developers of DataFrames.jl)to own and maintain the extension.

Summary

In this post,we listed some real Julia packagesthat have their own package extensions.We also demonstrated creating our own extensionfor an example packageand showed how to use the extension’s code.

What package extensions have you found useful?Let us know in the comments below!

Additional Links

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Best Practices for Testing Your Julia Packages

By: Great Lakes Consulting

Re-posted from: https://blog.glcs.io/package-testing

This post was written by Steven Whitaker.

The Julia programming languageis a high-level languagethat is known, at least in part,for its excellent package managerand outstanding composability.(See another blog post that illustrates this composability.)

Julia makes it super easyfor anybody to create their own package.Julia’s package manager enables easy development and testing of packages.The ease of package developmentencourages developers to split reusable chunks of codeinto individual packages,further enhancing Julia’s composability.

In our previous post,we discussed how to create and register your own package.However,to encourage people to actually use your package,it helps to have an assurancethat the package works.This is why testing is important.(Plus, you also want to know your package works, right?)

In this post,we will learn about some of the toolsJulia provides for testing packages.We will also learn how to use GitHub Actionsto run package testsagainst commits and/or pull requeststo check whether code changes break package functionality.

This post assumes you are comfortable navigating the Julia REPL.If you need a refresher,check out our post on the Julia REPL.

Example Package

We will use a custom package called Averages.jlto illustrate how to implement testing in Julia.

The Project.toml looks like:

name = "Averages"uuid = "1fc6e63b-fe0f-463a-8652-42f2a29b8cc6"version = "0.1.0"[deps]Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"[extras]Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[targets]test = ["Test"]

Note that this Project.toml has two more sections besides [deps]:

  • [extras] is used to indicate additional packagesthat are not direct dependencies of the package.In this example,Test is not used in Averages.jl itself;Test is used only when running tests.
  • [targets] is used to specify what packages are used where.In this example,test = ["Test"] indicates that the Test package should be usedwhen testing Averages.jl.

The actual package code in src/Averages.jl looks like:

module Averagesusing Statisticsexport compute_averagecompute_average(x) = (check_real(x); mean(x))function compute_average(a, b...)    check_real(a)    N = length(a)    for (i, x) in enumerate(b)        check_real(x)        check_length(i + 1, x, N)    end    T = float(promote_type(eltype(a), eltype.(b)...))    average = Vector{T}(undef, N)    average .= a    for x in b        average .+= x    end    average ./= length(b) + 1    return a isa Real ? average[1] : averageendfunction check_real(x)    T = eltype(x)    T <: Real || throw(ArgumentError("only real numbers are supported; unsupported type $T"))endfunction check_length(i, x, expected)    N = length(x)    N == expected || throw(DimensionMismatch("the length of input $i does not match the length of the first input: $N != $expected"))endend

Adding Tests

Tests for a package live in test/runtests.jl.(The file name is important!)Inside this file there are two main testing utilities that are used:@testset and @test.Additionally,@test_throws can also be useful for testing.The Test standard library package provides all of these macros.

  • @testset is used to organize tests into cohesive blocks.
  • @test is used to actually test package functionality.
  • @test_throws is used to ensure the package throws the errors it should.

Here is how test/runtests.jl might look for Averages.jl:

using Averagesusing Test@testset "Averages.jl" begin    a = [1, 2, 3]    b = [4.0, 5.0, 6.0]    c = (BigInt(7), 8f0, Int32(9))    d = 10    e = 11.0    bad = ["hi", "hello", "hey"]    @testset "`compute_average(x)`" begin        @test compute_average(a) == 2        @test compute_average(a) isa Float64        @test compute_average(c) == 8        @test compute_average(c) isa BigFloat        @test compute_average(d) == 10    end    @testset "`compute_average(a, b...)`" begin        @test compute_average(a, a) == a        @test compute_average(a, b) == [2.5, 3.5, 4.5]        @test compute_average(a, b, c) == b        @test compute_average(a, b, c) isa Vector{Float64}        @test compute_average(b, b, b) == b        @test compute_average(d, e) == 10.5    end    @testset "Error Handling" begin        @test_throws ArgumentError compute_average(im)        @test_throws ArgumentError compute_average(a, bad)        @test_throws ArgumentError compute_average(bad, c)        @test_throws DimensionMismatch compute_average(a, b[1:2])        @test_throws DimensionMismatch compute_average(a[1:2], b)    endend

Now let’s look more closely at the macros used:

  • @testset can be given a labelto help organize the reporting Julia doesat the end of testing.Besides that,@testset wraps around a set of tests(including other @testsets).
  • @test is given an expressionthat evaluates to a boolean.If the boolean is true, the test passes;otherwise it fails.
  • @test_throws takes two inputs:an error type and then an expression.The test passes if the expressionthrows an error of the given type.

Testing Against Other Packages

In some cases,you might want to ensure your packageis compatible with a type defined in another package.For our example,let’s test against StaticArrays.jl.Our package does not depend on StaticArrays.jl,so we need to add it as a test-only dependencyby editing the [extras] and [targets] sectionsin the Project.toml:

[extras]StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"[targets]test = ["StaticArrays", "Test"]

(Note that I grabbed the UUID for StaticArrays.jlfrom its Project.toml on GitHub.)

Then we can add some teststo make sure compute_average is generic enoughto work with StaticArrays:

using Averagesusing Testusing StaticArrays@testset "Averages.jl" begin        @testset "StaticArrays.jl" begin        s = SA[12, 13, 14]        @test compute_average(s) == 13        @test compute_average(s, s) == [12, 13, 14]        @test compute_average(a, b, s) == [17/3, 20/3, 23/3]        @test compute_average(s, a, c) == [20/3, 23/3, 26/3]    endend

Running Tests Locally

Now Averages.jl is ready for testing.To run package tests on your own computer,start Julia, activate the package environment,and then run test from the package prompt:

(@v1.X) pkg> activate /path/to/Averages(Averages) pkg> test

The first thing test doesis set up a temporary package environment for testingthat includes the packages defined in the test targetin the Project.toml.Then it runs the tests and displays the result:

     Testing Running tests...Test Summary: | Pass  Total  TimeAverages.jl   |   20     20  0.7s     Testing Averages tests passed

If a test fails,the result looks like this:

     Testing Running tests...`compute_average(a, b...)`: Test Failed at /path/to/Averages/test/runtests.jl:27  Expression: compute_average(a, b) == [2.0, 3.5, 4.5]   Evaluated: [2.5, 3.5, 4.5] == [2.0, 3.5, 4.5]Stacktrace: [1] macro expansion   @ /path/to/julia-1.X.Y/share/julia/stdlib/v1.X/Test/src/Test.jl:672 [inlined] [2] macro expansion   @ /path/to/Averages/test/runtests.jl:27 [inlined] [3] macro expansion   @ /path/to/julia-1.X.Y/share/julia/stdlib/v1.X/Test/src/Test.jl:1577 [inlined] [4] macro expansion   @ /path/to/Averages/test/runtests.jl:26 [inlined] [5] macro expansion   @ /path/to/julia-1.X.Y/share/julia/stdlib/v1.X/Test/src/Test.jl:1577 [inlined] [6] top-level scope   @ /path/to/Averages/test/runtests.jl:7Test Summary:                | Pass  Fail  Total  TimeAverages.jl                  |   19     1     20  0.9s  `compute_average(x)`       |    5            5  0.1s  `compute_average(a, b...)` |    5     1      6  0.6s  Error Handling             |    5            5  0.0s  StaticArrays.jl            |    4            4  0.2sERROR: LoadError: Some tests did not pass: 19 passed, 1 failed, 0 errored, 0 broken.in expression starting at /path/to/Averages/test/runtests.jl:5ERROR: Package Averages errored during testing

Some things to note:

  • When all tests in a test set pass,the test summary does not report the individual resultsof nested test sets.When a test fails,results of nested test sets are reported individuallyto report more precisely where the failure occurred.
  • When a test fails,the file and line number of the failing test are reported,along with the expression that failed.This information is displayedfor all failures that occur.
  • The test summary reports how many tests passed and how many failedin each test set,in addition to how long each test set took.
  • Tests in a test set continue to run after a test fails.To have a test set stop on failure,use the failfast option: 
    @testset failfast = true "Averages.jl" begin
    (This option is available only in Julia 1.9 and later.)

Now, when developing Averages.jl,we can run the tests locallyto ensure we don’t break any functionality!

Running Tests with GitHub Actions

Besides running tests locally,one can use GitHub Actions to run testson one of GitHub’s servers.One advantageis that it enables automated testingon various machines/operating systemsand across various Julia versions.Automating tests in this way is an essential part of continuous integration (CI)(so much so that the phrase “running CI”is equivalent to “running tests via GitHub Actions”,even though CI technically involves more than just testing).

To enable testing via GitHub Actions,we just need to add an appropriate .yml filein the .github/workflows directory of our package.As mentioned in our previous post,PkgTemplates.jl can automatically generatethe necessary .yml file.This is the default CI workflow generated by PkgTemplates.jl:

name: CIon:  push:    branches:      - main    tags: ['*']  pull_request:  workflow_dispatch:concurrency:  # Skip intermediate builds: always.  # Cancel intermediate builds: only if it is a pull request build.  group: ${{ github.workflow }}-${{ github.ref }}  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}jobs:  test:    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}    runs-on: ${{ matrix.os }}    timeout-minutes: 60    permissions: # needed to allow julia-actions/cache to proactively delete old caches that it has created      actions: write      contents: read    strategy:      fail-fast: false      matrix:        version:          - '1.10'          - '1.6'          - 'pre'        os:          - ubuntu-latest        arch:          - x64    steps:      - uses: actions/checkout@v4      - uses: julia-actions/setup-julia@v2        with:          version: ${{ matrix.version }}          arch: ${{ matrix.arch }}      - uses: julia-actions/cache@v2      - uses: julia-actions/julia-buildpkg@v1      - uses: julia-actions/julia-runtest@v1

For most users,the most relevant fields to customizeare version and os(under jobs: test: strategy: matrix).Under os,specify the operating systems to run tests on(e.g., ubuntu-latest, windows-latest, macOS-latest).Under version,specify the versions of Julia to use when testing:

  • '1.X' means run on Julia 1.X.Y,where Y is the largest patchof Julia 1.X that has been released.For example,'1.9' means run on Julia 1.9.4.
  • '1' means run on the latest stable version of Julia.
  • 'pre' means run on the latest pre-release version of Julia.
  • 'lts' means run on Julia’s long-term support (LTS) version.

Usually,it makes sense just to test '1' and 'pre'to ensure compatibility with the currentand upcoming Julia versions.

One can also fine-tune the version and os fields,as well as other fields,when generating a packagewith PkgTemplates.jl.For example,to generate the .yml fileto run tests only on Windowswith Julia 1.8 and the latest pre-release version of Julia:

using PkgTemplatesgha = GitHubActions(; linux = false, windows = true, extra_versions = ["1.8", "pre"])t = Template(; dir = ".", plugins = [gha])t("MyPackage")

Note that the .yml file generatedwill also include testing on Julia 1.6.The Template constructor has a keyword argument juliathat sets the minimum version of Juliayou want your package to support,and this version is included in testing.As of this writing,by default the minimum version is Julia 1.6.

See the PkgTemplates.jl docsabout Template and GitHubActionsfor more detailson customizing the .yml file.See also the GitHub Actions docs,and in particular the workflow syntax docs,for more details on what makes up the .yml file.(Be warned, these docs are quite lengthyand probably aren’t practically usefulfor most people to get a CI workflow up and running.For a more approachable overview of the .yml file,consider looking at this tutorial for building and testing Python.)

Once we push .github/workflows/CI.yml to GitHub,whenever branch main is pushed to,or a pull request (PR) is opened or pushed to,our package’s tests will run.This is the essence of CI:continuously making sure changes we make to our codeintegrate well with the code base(i.e., don’t break anything).By running tests against PRs,we can be sure changes madedon’t break existing functionality.

One neat thing about GitHub Actionsis that GitHub provides a status badge/iconthat you can display in your package’s README.This badge lets people know

  1. that your package is regularly tested, and
  2. whether the current state of your package passes those tests.

In other words,this badge is a good wayto boost confidence that your package is suitable for use.You can add this badge to your package’s READMEby adding something like the following markdown:

[![CI](https://github.com/username/Averages.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/username/Averages.jl/actions/workflows/CI.yml)

And it will display as follows:

GitHub CI badge

Summary

In this post,we learned how to add teststo our own Julia package.We also learned how to enable CI with GitHub Actionsto run our tests against code changesto ensure our package remains in working order.

How difficult was it for you to set up CI for the first time?Do you have any tips for beginners?Let us know in the comments below!

Additional Links

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How to Create a Julia Package from Scratch

By: Great Lakes Consulting

Re-posted from: https://blog.glcs.io/package-creation

This post was written by Steven Whitaker.

The Julia programming languageis a high-level languagethat is known, at least in part,for its excellent package managerand outstanding composability.(See another blog post that illustrates this composability.)

Julia makes it super easyfor anybody to create their own package.Julia’s package manager enables easy development and testing of packages.The ease of package developmentencourages developers to split reusable chunks of codeinto individual packages,further enhancing Julia’s composability.

In this post,we will learn what comprises a Julia package.We will also discuss toolsthat automate the creation of packages.Finally,we will talk about the basics of package developmentand walk through how to publish (register) a packagefor others to use.

This post assumes you are comfortable navigating the Julia REPL.If you need a refresher,check out our post on the Julia REPL.

Components of a Package

Packages are easy enough to use:just install them with add PkgName in the package promptand then run using PkgName in the julia prompt.But what actually goes into a package?

Packages must follow a specific directory structureand include certain informationto be recognized as a package by Julia.

Suppose we are creating a package called PracticePackage.jl.First, we create a directory called PracticePackage.This directory is the package root.Within the root directory we need a file called Project.tomland another directory called src.

The Project.toml requires the following information:

name = "PracticePackage"uuid = "11111111-2222-3333-aaaa-bbbbbbbbbbbb"authors = ["Your Name <youremail@email.com>"]version = "0.1.0"
  • uuid stands for universally unique identifier,and can be generated in Julia withusing UUIDs; uuid4().The purpose of a UUID is to allow different packages of the same name to coexist.
  • version should be set to whatever version is appropriate for your package,typically "0.1.0" or "1.0.0" for an initial release.The versioning of Julia packages follows SemVer.
  • The Project.toml will also include informationabout package dependencies,but more on that later.

The src directory requires one Julia filenamed PracticePackage.jlthat defines a module named PracticePackage:

module PracticePackage# Package code goes here.end

So, the directory structure of the packagelooks like the following:

PracticePackage Project.toml src     PracticePackage.jl

And that’s all there is to a package!(Well, at least minimally.)

Some Technicalities

Feel free to skip this section,but if you are curious about some technicalitiesfor what comprises a valid package,read on.

  • The Project.toml only needs the name and uuid fieldsfor Julia to recognize the package.Without the version field,Julia treats the version as v0.0.0.
    • However, the version and authors fields are neededto register the package.
  • The name of the package root directory doesn’t matter,meaning it doesn’t have to match the package name.However, the name field in Project.tomldoes have to match the name of the moduledefined in src/PracticePackage.jl,and the file name of src/PracticePackage.jl also has to match.
    • For example,we could change the name of the packageby setting name = "Oops" in Project.toml,renaming src/PracticePackage.jl to src/Oops.jl,and defining module Oops in that file.We would not have to rename the package root directoryfrom PracticePackage to Oops(though that would be a good idea to avoid confusion).

Automatically Generating Packages

The basic structure of a package is pretty simple,so there ought to be a way to automate it, right?(I mean, who wants to manually generate a UUID?)Good news: package creation can be automated!

Package generate Command

Julia comes with a generate package command built-in.First, change directoriesto where the package root directory should live,then run generate in the Julia package prompt:

pkg> generate PracticePackage

This command creates the package root directory PracticePackageand the Project.toml and src/PracticePackage.jl files.Some notes:

  • The Project.toml is pre-filled with the correct fields and values,including an automatically generated UUID.When I ran generate on my computer,it also pre-filled the authors fieldwith my name and email from my ~/.gitconfig file.
  • src/PracticePackage.jl is pre-filledwith a definition for the module PracticePackage.It also defines a function greet in the module,but typically you will replace that with your own code.

PkgTemplates.jl

The generate command works fine,but it’s barebones.For example,if you are planning on hosting your package on GitHub,you might want to include a GitHub Actionfor continuous integration (CI),so it would be niceto automate the creation of the appropriate .yml file.This is where PkgTemplates.jl comes in.

PkgTemplates.jl is a normal Julia package,so install it as usual and run using PkgTemplates.Then we can create our PracticePackage.jl:

t = Template(; dir = ".")t("PracticePackage")

Running this code creates the packagewith the following directory structure:

PracticePackage .git    .github    dependabot.yml    workflows        CI.yml        CompatHelper.yml        TagBot.yml .gitignore LICENSE Manifest.toml Project.toml README.md src    PracticePackage.jl test     runtests.jl

As you can see,PkgTemplates.jl automatically generates a lot of filesthat aid in following package development best practices,like adding CI and tests.

Note that many optionscan be supplied to Templateto customize what files are generated.See the PkgTemplates.jl docs for all the options.

Checklist of settings

Basic Package Development

Once your package is set up,the next step is to actually add code.Add the functions, types, constants, etc.that your package needsdirectly in the PracticePackage module in src/PracticePackage.jl,or add additional files in the src directoryand include them in the module.(See a previous blog post for more information about modules,though note that using modules directly works slightly differentlythan using packages.)

To add dependencies for your package to use,you will need to activate your project’s package environmentand then add packages.For example,if you want your package to use the DataFrames.jl package,start Julia and navigate to your package root directory.Then, activate the package environment and add the package:

(@v1.X) pkg> activate .(PracticePackage) pkg> add DataFrames

After this,you will be able to include using DataFramesin your package codeto enable the functionality provided by DataFrames.jl.

Adding packages after activating the package environmentedits the package’s Project.toml file.It adds a [deps] sectionthat lists the added packages and their UUIDs.In the example above,adding DataFrames.jladds the following lines to the Project.toml file:

[deps]DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

(And (PracticePackage) pkg> rm DataFrames would remove the DataFrames = ... line,so it is best not to edit the [deps] section manually.)

Finally,to try out your package,activate your package environment (as above)and then load your package as usual:

julia> using PracticePackage # No need to `add PracticePackage` first.

Note that by default Julia will have to be restartedto reload any changes you make to your package code.If you want to avoid restarting Juliawhenever you make changes,check out Revise.jl.

Publishing/Registering a Package

Once your package is in working order,it is natural to want to publish the packagefor others to use.

A package can be publishedby registering it in a package registry,which basically is a map that tells the Julia package managerwhere to find a packageso it can be downloaded.

Treasure map

The General registry is the largest registryas well as the default registry used by Julia;most, if not all, of the most popular open-source packages(DataFrames.jl, Plots.jl, StaticArrays.jl, ModelingToolkit.jl, etc.)exist in General.Once a package is registered in General,it can be installed with pkg> add PracticePackage.

(Note that if registering a package is not desired for some reason,a package can be added via URL, e.g.,pkg> add https://github.com/username/PracticePackage.jl,assuming the package is in a public git repository.However,the package manager has limited abilityto manage packages added in this way;in particular,managing package versions must be done manually.)

The most common wayto register a package in Generalis to use Registrator.jl as a GitHub App.See the README for detailed instructions,but the process basically boils down to:

  1. Write/test package code.
  2. Update the version field in the Project.toml(e.g., to "0.1.0" or "1.0.0" for the first registered version).
  3. Add a comment with @JuliaRegistrator registerto the latest commit that should be includedin the registered version of the package.

Note that there are additional steps for preparing a package for publishingthat we did not discuss in this post(such as specifying compatible versionsof Julia and package dependencies).Refer to the General registry’s documentation and links therein for details.

Summary

In this post,we discussed creating Julia packages.We learned what comprises a package,how to automate package creation,and how to register a package in Julia’s General registry.

What package development tips do you have?Let us know in the comments below!

Additional Links

Cover image background provided by www.proflowers.com athttps://www.flickr.com/photos/127365614@N08/16011252136.

Treasure map image source: https://openclipart.org/detail/299283/x-marks-the-spot

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