# A Very Quick Introduction to Git/Github for Julia Users¶

Julia's package system and Github are very closely intertwined:

• Julia's package management system (METADATA) is a Github repository
• The packages are hosted as Github repositories
• Julia packages are normally referred to with the ending “.jl”
• Repositories register to become part of the central package management by sending a pull request to METADATA.jl
• The packages can be found / investigated at Github.com
• Julia's error messages are hyperlinks to the page in Github

Because of this, it's very useful for everyone using Julia to know a little bit about Git/Github.

## Git Basics¶

• Git is a common Version Control System (VCS)
• A project is a repository (repos)
• After one makes changes to a project, they commit the changes
• Changes are pulled to the main repository hosted online
• Instead of editing the main repository, one edits a branch
• To get the changes of the main branch in yours, you fetch, pull, or rebase
• One asks the owner of the repository to add their changes via a pull request
• Stable versions are cut to releases

## Github Basics¶

• The major online server for git repositories is Github
• Github is a free service
• Anyone can get a Github account
• The code is hosted online, free for everyone to view
• Users can open Issues to ask for features and give bug reports to developers
• Many projects are brought together into organizations (JuliaMath, JuliaDiffEq, JuliaStats, etc.)

An example Github repository for a Julia package is is DifferentialEquations.jl: https://github.com/JuliaDiffEq/DifferentialEquations.jl

## Basic through Advanced / Video Tutorial on Github¶

• For the visually inclined, here is a video tutorial on basic and advanced github workflow for developing and editing Julia Packages, as well as for setting up Continuous Integration (CI).

## Examining a Github Repository¶

Components:

• Top Right: The stars. Click this button to show support for the developer!
• Issues Tab: Go here to file a bug report
• Files: These are the source files in the repository

## Examining A Github Repository¶

The badges on a Github repository show you the current state of the repo. From left to right:

• Gitter Badge: Click this to go to a chatroom and directly chat with the developers
• CI Build Badges: This tell you whether the CI tests pass. Click on them to see what versions of Julia the package works for.
• Coverage: This tells you the percentage of the code that the tests cover. If coverage is low, parts of the package may not work even if the CI tests pass.
• Docs Badges: Click on these to go to the package documentation.

## Github Organizations¶

• A (mostly complete) list of Julia organizations can be found at http://julialang.org/community/
• Organizations manage large domains of packages and ensure that they work well together
• Organizations are informal but have been "unusually effective"
• Packages from the main Julia organizations can be considered official
• Some functionality which used to be in the Base language now exists in organization packages

## Using Julia's Package Manager¶

Julia's package manager functions are mirror the Git functions. Julia's package system is similar to R/Python in that a large number of packages are freely available. You search for them in places like Julia Observer, or from the Julia Package Listing. Let's take a look at the Plots.jl package by Tom Breloff. To add a package, use Pkg.add

In [ ]:
using Pkg
Pkg.update() # You may need to update your local packages first


This will install the package to your local system. However, this will only work for registered packages. To add a non-registered package, go to the Github repository to find the clone URL and use Pkg.clone. For example, to install the ParameterizedFunctions package, we can use:

In [ ]:
Pkg.clone("https://github.com/JuliaDiffEq/ParameterizedFunctions.jl")


### Importing a Package¶

To use a package, you have to import the package. The import statement will import the package without exporting the functions to the namespace. (Note that the first time a package is run, it will precompile a lot of the functionality.) For example:

In [5]:
import Plots
Plots.plot(rand(4,4))

Out[5]:

### Exporting Functionality¶

To instead export the functions (of the developers choosing) to the namespace, we can use the using statement. Since Plots.jl exports the plot command, we can then use it without reference to the package that it came from:

In [6]:
using Plots
plot(rand(4,4))

Out[6]:

What really makes this possible in Julia but not something like Python is that namespace clashes are usually avoided by multiple dispatch. Most packages will define their own types in order to use dispatches, and so when they export the functionality, the methods are only for their own types and thus do not clash with other packages. Therefore it's common in Julia for concise syntax like plot to be part of packages, all without fear of clashing.

Since Julia is currently under lots of development, you may wish to checkout newer versions. By default, Pkg.add is the "latest release", meaning the latest tagged version. However, the main version shown in the Github repository is usually the "master" branch. It's good development practice that the latest release is kept "stable", while the "master" branch is kept "working", and development takes place in another branch (many times labelled "dev"). You can choose which branch your local repository takes from. For example, to checkout the master branch, we can use:

In [ ]:
Pkg.checkout("Plots")


This will usually gives us pretty up to date features (if you are using a "unreleased version of Julia" like building from the source of the Julia nightly, you may need to checkout master in order to get some packages working). However, to go to a specific branch we can give the branch as another argument:

In [ ]:
Pkg.checkout("Plots","dev")


This is not advised if you don't know what you're doing (i.e. talk to the developer or read the pull requests (PR)), but this is common if you talk to a developer and they say "yes, I already implemented that. Checkout the dev branch and use plot(...)).