Contributor’s Guide#

Welcome to the Contributor’s Guide for UXarray!

Welcome to the team! If you are reading this document, we hope that you are already or soon-to-be a UXarray contributor, please keep reading!


If you haven’t done so yet, you may want to give a quick read through our README as it provides a lot of significant information about UXarray.

1. Overview#

UXarray is a community-owned, open-development effort that is a result of the collaboration between NSF’s Project Raijin and DOE’s SEATS Project. Even though the UXarray team has initiated and been expanding this package, outside contributions are welcome and vital for its long-term sustainability. Therefore, we invite other community members to become part of this collaboration at any level of contribution.

1.1. Many Ways to Contribute#

There are many different ways to contribute to UXarray. Anyone can, for example,

  • Write or revise documentation (including this document)

  • Implement data analysis operators for unstructured grids from scratch or from their workflows

  • Develop example notebooks that demonstrate how a particular function is used

  • Answer a support question

  • Request a feature or report a bug

All of these activities are signicant contributions to the on-going development and maintenance of UXarray.

1.2. About This Guide#

The UXarray content is hosted on GitHub, and through this document, we aim to ease the community’s experience with contributing to this project. However, this guide might still be missing case-specific details; please do not hesitate to reach out to us to consult any such cases.


Much of the information in this guide has been co-opted from the GeoCAT project and Project Pythia.

1.3. Project-specific Resources#

Some important UXarray resources are as follows:

2. Configuring GitHub & Git, and Setting Up Python Environment#

In this section, we will detail what is needed to be done before starting to contribute UXarray.

2.1. Getting Started with GitHub and Git#

Contributing to UXarray requires using GitHub, and contributing to a GitHub repository follows almost the same process by any open source Python project maintained on GitHub. However, it can still seem complex and somewhat varied from one project to another. As such, we will refer the reader to comprehensive resources for basic learning and detailed information about GitHub (such as the Getting Started with Github guide).


To emphasize the details of how UXarray uses GitHub and how to contribute to it, we will provide significant details in the 3. How UXarray Uses Git/GitHub section.

Git is an open source, command line tool for collaborative software version control, while GitHub is an online, web-accessible service that greatly simplifies using the powerful, yet often complex, Git. Just like GitHub, we believe that the basics of Git is out of scope of this guide, so we will refer the reader to Git-specific guides for that purpose (e.g. GitHub’s Set up Git guide and Git’s homepage).


Git has lots and lots of commands, each with lots and lots of options. Even if we can cover some of them throughout this guide, your best friend for figuring out to do things with Git may be Google, and in particular StackOverflow.

Configure your environment to authenticate with GitHub from Git. This is a complicated process, so we suggest that you refer to the details in the Authenticating with GitHub from Git guide to complete this step.


The basic steps for GitHub/Git configuration, which can be learned from the linked guides here, need to be performed before contributing to UXarray!

2.2. Python Environment Setup#

Before starting any Python or documentation development, you’ll need to create a Python environment along with a package manager. When we use the term “Python environment” here, we are referring to the Python programming language. Because there are so many Python packages available, maintaining interoperability between them is a huge challenge. To overcome some of these difficulties, we strongly recommend the use of Anaconda or Miniconda as a package manager to manage your Python ecosystem. These package managers allow you to create a separate, custom Python environment for each specific Python set of tools. Yes, this unfortunately results in multiple copies of Python on your system, but it greatly reduces breaking toolchains whenever a change is made to your Python environment (and is more reliable than any other solution we’ve encountered). Also, the use of Anaconda/Miniconda is standard practice for working with Python in general, not simply for using UXarray.

To configure your Python environment:

1. Install either Anaconda or Miniconda.


We recommend Miniconda as it does not unnecessarily install many packages that will not be needed for UXarray development.

2. Make sure your conda is up to date by running this command from the terminal:

$ conda update conda


Don’t type the $ character. This simply indicates the command line prompt.

At this point, you will have a current version of conda available on your system. Before using your Python environment to work on UXarray development, you’ll need to perform additional steps that are going to be described in the next section.

3. How UXarray Uses Git/GitHub#

UXarray uses the GitHub Flow model for its workflow. UXarray also uses an automated formatter, which is described in 3.4. Install and Setup Pre-commit Hooks in detail, on all commits in local development environment in order to ensure code formatting. This changes the normal workflow slightly, so in order to avoid any confusions, follow these steps:

3.1. Select An Issue to Work on#

Virtually any work should be addressing an issue. There are a few options to select an issue to work on:

1. First, check the existing UXarray issues. These issues might have been created from either the:

2. If you are going to work on one of those issues above, self-assign that issue before you start working.

3. Or else, if you are going to work on something else, create an issue and assign yourself. The title, description, and label of an issue could be very helpful for the team to triage the issue and move forward with the work needed.

  • Title needs to reflect the purpose of the issue. Some examples:

    • Start with “Add …” if it is a new functionality needed

    • Start with “Error …”, “Exception …”, or “Bug …” if that is a bug

  • Use issue labels accordingly, e.g. “bug”, “support”, “enhancement”, “documentation”, etc.

  • Clearly describe the issue (e.g. what the work should be, which parts of UXarray it would need to change, if known, etc.)


The work that addresses an issue will, most of the time, be eventually turned into a “pull request” (or maybe multiple in some cases) that lets you tell others about changes you’re aiming to make on the project. Once a pull request (PR) is opened, it will require others’ thorough review and discussion before your changes can take place in the project. Hence, a rule of thumb about pull requests should be that they should target logically connected, as atomic as possible, work.


If you need any clarification/discussion about any requirements, or if you think the implementation of that issue will require significant changes to the code, design, documentation, etc. that issue itself is the right place to manage such discussions with the other UXarray-ers. Don’t hesitate to ask ad-hoc meetings, etc.

Do not forget, early requirements analysis, specifications, and design discussions can avoid redundat code review and modifications later!

3.2. Fork or Locally Clone The UXarray Repository#

Let us first make a decision of whether to fork or locally clone:

3.2.1. Should You Fork?#

Forking a repository as described below in this section creates a copy of the repository under your own account or any GitHub organization of which you are a member on GitHub. Forking can be advisable for cases such as:

  • You want to safely make changes to the forked repository contents without changing the actual UXarray repository. This is because any changes you make to your fork will only be seen by you until you are ready to share them with others, and hopefully “merge” your changes into UXarray.

  • You are not a regular contributor to the UXarray repository; thus, from time to time, you would sync your fork to UXarray and make one or more contributions.

  • You are planning to initiate a new project by altering the forked repo significantly from UXarray, but this case is out of this guide’s scope.

You can refer to the Atlassian’s Forking workflow tutorial to further learn about forking.

If you decide on forking the UXarray repository, here is how to do that:

Forking the UXarray repository

Refer to GitHub’s Forking a repository guide and apply instructions regarding the UXarray repository.


The above GitHub guide about forking a repository will also walk you through cloning your forked repository into your local work environment. Please do not forget to follow those instructions as well. Or, refer to GitHub’s Cloning a repository guide, and apply those instructions for the UXarray fork that is listed under your GitHub account or organization.

After these steps, you will have two copies of the forked UXarray repo, one remote and one local.

3.2.2. Should You Locally Clone Instead?#

In contrast to above cases that might be better suitable for fork, a regular UXarray contributor who is comfortable with working on their local clone of the actual UXarray repository and making their changes immediately viewable by the other UXarray-ers (i.e. after pushing their commits) can choose to locally clone the UXarray repository.

Locally cloning the UXarray repository

Refer to GitHub’s Cloning a repository guide and apply those instructions for the actual UXarray repository.


Regardless of whether you fork or clone, there will be a local directory created in the name “uxarray” (unless you specified a different name at the step with the git clone command). You can type the following command in the terminal/shell to go into your local UXarray repository:

$ cd uxarray

3.3. Configure UXarray Conda Environment#

In your local UXarray directory, creating a UXarray conda environment is needed for development purposes. Use the following commands for this:

$ conda env create --file ci/environment.yml
$ conda activate uxarray_build

THe above commands will use the environment.yml conda environment definition file that is hosted under the ci folder and create a conda environment with the name uxarray_build. Once you activate that environment with the help of the second command, you will be able to develop UXarray codes in your local configuration.

3.4. Install and Setup Pre-commit Hooks#

Pre-commit hooks are useful for identifying simple issues with the code format before submission to code review (i.e. in your local before commits are processed). You specify a list of hooks you want (this list has already been specified for UXarray in the .pre-commit-config.yaml file, so there is no action needed), and install pre-commit as described below. Pre-commit manages the installation and execution of the hooks specified.

Hooks are run on every commit to automatically point out issues in code formatting such as the number of characters in each line, missing semicolons, trailing whitespace, debug statements, etc. By pointing these issues out before code review, this allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks. Refer to the Pre-commit documentation for further learning.


We also use pre-commit GitHub Actions workflow to make sure every code contribution to our UXarray’s GitHub repository (i.e. pull request) aligns with our code standards. Therefore, the code changes that are not being checked through local pre-commit hooks will eventually be tested against our workflow in the GitHub server as described in GitHub Actions checks. If there are any issues with the code format, it will lead the pull request to fail the pre-commit checks.

3.4.1. Pre-commit Setup#

If you configured your Conda environment via the instructions in 3.3. Configure UXarray Conda Environment, you will have the pre-commit package already installed in your environment (Otherwise, you will need to run conda install -c conda-forge pre_commit to get it installed into your Conda environment for UXarray development). The only thing you will have to do additionally in order to set up pre-commit is to run the following command in your terminal in the UXarray root directory:

$ pre-commit install

At this step, you are good to go with your pre-commit hooks to check your future commits. If, at any time, you’d like to run pre-commit hooks on your all files, you can run the following command:

$ pre-commit run --all-files

3.5. Use Feature Branches#

In your local clone, make a new branch off of the main branch (this is the way to go most of the time, but there might be specific cases where, for example, a branch is needed to be created off of another feature branch). Naming this branch, whenever applicable, like the following is not required but may be helpful for tracking purposes: “issue_XXX” where XXX is the number of the issue or something that is representative of the changes you’re making, e.g. “do-this-work”, “add-that-function”, etc.

Here are example commands that assume, you are checking out the main branch first, pulling from the remote server to have everything in your local up-to-date, and creating a new branch off of main:

$ git checkout main
$ git pull
$ git checkout -b <new_branch>

Once you create the new branch, you are good to go with your local changes in the UXarray directory!

3.6. Local Changes, Commits, and Pushes#

The local development process can very basically be itemized as follows:

  1. Make changes to your local copy of the UXarray repository


    Please refer to 3.7.3. Common Elements of Pull Requests to make sure your local changes have all of the elements they should cover.

  2. Add your local changes to the “staged” changes for them to be included in the commit.

    • You can see any uncommitted changes you’ve made to your local copy of the repository by running the following command from anywhere (any directory) within the directory where you ran git checkout:

      $ git status
    • So, add changed file(s) into the staged changes to be included in a single commit:

      $ git add PATH/TO/NEW/FILE

      where PATH/TO/NEW/FILE is the path name of the newly created file.

  3. Now, commit the staged change(s):

    $ git commit -m "Descriptive comment about what this commit does"
    • Limiting the commit to file(s) changed for an atomic task would be very much helpful in cases you need to review and maybe revert commits.

    • Do not forget that pre-commit hooks will check your code changes at this point. If some of those hooks fail, you will need to add those files again and attempt to make the same commit again as the hooks will have changed those file(s) to make them comply with the code formatting rules.

    • A good practice is to run:

      $ git status

      after your commit to verify everything looks as expected.

  4. Push your commit(s) into the remote repository:

    $ git push


Remember that we are still not proposing to merge our work into the UXarray GitHub repository, which will be described in the next subsection, 3.7. Pull Requests.

3.7. Pull Requests#

Pull requests are GitHub’s way for developers to propose and collaborate on changes to a repository. If you are interested in learning the foundations about pull requests, please refer to GitHub’s Pull requests documentation.

Once you have completed making changes to your local copy of the UXarray repository, pushed them all, and are ready to have your changes merged into the repository on GitHub, you need to submit a PR asking the UXarray maintainers to consider your merge request.

The merge will occur between your personal branch that should have all of your commits from local in the GitHub repository (either in your fork or in the actual UXarray repo) and the main branch, if not other, in the UXarray’s GitHub repository. There might be some exceptions to this generic case, which can always be learned through reading or discussed with the maintainers and community.


While pull requests are supposed to be based on changes that are ready to be tested, reviewed, and eventually be merged to repositories; there is an exception to this: Draft Pull Requests. We encourage Draft PRs if you want to just start a conversation about your code that isn’t in any state to be judged, and get feedback as well as some guidance from others. Please read this GitHub blog about draft PRs.

Please refer to Github’s Creating a pull request guide for the instructions. We are especially avoiding to detail such instructions here in this guide as there are many references to GitHub’s graphical user interface, which might be changed in the future.


Below are significant things about pull requests that can be very helpful throughout the entire contribution process (i.e. review and merge) process when performed:

3.7.2. Review Your Changes#

Before you finalize opening the actual PR, it is a good practice to review the changes that you’ve made. You should be able to review all of the changes that will go into this PR just before you press the Create pull request button.

If there are any changes you want to make in the PR, you can delay creating the PR and push new commits, revert existing changes, etc. You can then create the PR.

3.7.3. Common Elements of Pull Requests#

Despite the fact that pull requests can differ regarding their purposes (e.g. correction of a single typo in only one file could make a PR), most of the PRs may consist of code changes that should, most of the time, include some other elements with it. Having all of such elements addressed in a PR could make the review and merge process a lot easier. Assuming such code changes, these are what might accompany them: Unit tests#

Virtually all new UXarray code needs to include unit tests of the functionality implemented. The UXarray project makes use of diverse technologies for unit testing as follows:

  • All the unit tests of every single Python module (i.e. .py file) should be implemented as a separate test script under the \test folder of the repository’s root directory.

  • The pytest testing framework is used as runner for the tests. If you configured your Conda environment via the instructions in 3.3. Configure UXarray Conda Environment, you will have the pytest package already installed in your environment (Otherwise, you will need to run conda install -c conda-forge pytest to get it installed into your Conda environment for UXarray development).

  • Test scripts themselves are not intended to use pytest through implementation.

Instead, pytest should be used only for running test scripts as follows:

$ pytest test/<test_script_name>.py

, or:

$ python -m pytest test/<test_script_name>.py

, the latter of which will also add the current directory to sys.path.

Not using pytest for implementation allows the unit tests to be also run by using (a number of benefits/conveniences coming from using pytest can be seen here though):

$ python -m unittest test/<test_script_name>.py

Also, all of the test scripts can be run at once with the following command:

$ pytest test
  • Python’s unit testing framework, unittest is used for implementation of the test scripts.

  • Reference results (i.e. expected output or ground truth for not all but the most cases)

should not be magic values (i.e. they need to be justified and/or documented).

  • Recommended, but not mandatory, implementation approach is as follows:

    • Common data structures, variables and functions, as well as expected outputs, which could be used by multiple test methods throughout the test script, are defined either under a base test class or in the very beginning of the test script for being used by multiple unit test cases.

    • Any group of testing functions dedicated to testing a particular phenomenon (e.g. a specific edge case, data structure, etc.) is implemented by a class, which inherits TestCase from Python’s unittest and likely the base test class implemented for the purpose mentioned above.

    • Assertions are used for testing various cases such as array comparison.

    • Please see previously implemented test cases for reference of the recommended testing approach


Our test suite that includes all the unit tests is executed automatically for PRs with the help of GitHub Actions workflows to ensure new code passes tests. Hence, please check GitHub Actions checks to make sure your PR tests are all passing before asking others to review your work. Docstrings#

All Python functions must contain a Google Style Python docstring (i.e. triple quoted comment blocks). These docstrings are accessed from the Python interpreter whenever the user types:


They are also automatically converted into web-accessible documentation available from the UXarray documentation.

The docstrings must contain:

  1. A brief description of the functionality provided. What does this function do?

  2. If available, references to the algorithm or implementation employed

  3. A complete description of arguments and return values in Google format

    • Please refer to the existing functions that already have this

  4. One or more very short usage examples that demonstrates how to invoke the function, and possibly what to expect it to return

    • No need for these examples to actually be executable

    • If a usage example is longer than a handful of lines, a more complete example may be created instead, referring to Usage Examples. Documentation#

As we mentioned a few times throughout this guide, UXarray has a static documentation index page that is being generated automatically from the repository’s code structure. However, there needs to be some manual additions to the proper documentation index file(s) for the automation to work.

The index files docs/user_api/index.rst and docs/internal_api/index.rst (paths relative from the root directory) are used for UXarray documentation to allow the User API and Internal API, respectively, to be automatically generated.

That being said, the code changes, which might be a new function implementation or some modifications to existing ones, must be added to the appropriate index.rst file so that its documentation page is automatically generated. Usage examples#


This may not be required for every PR and can be handled as separate PRs when needed. However, it would be a great practice to provide usage examples in the same PR, especially for demonstrating the use of complex UXarray functions.

The UXarray documentation houses examples/<example-name>.ipynb files (paths relative from the root directory) to provide Usage Examples to be automatically generated. If you prefer to provide usage examples for the work you have put together, please be sure to put your notebook(s) under this same directory.

3.7.4. After You Open The Pull Request#

Here are a few more tips that could be helpful throughout getting your PR merged: Addressing reviews#

Now that you have opened a PR that has the necessary elements with it and got reviewers assigned, you will hopefully begin to receive reviews soon from them. Please make sure to address those review comments, change requests, etc to get your PR ready to be merged.


When you addressed all of a reviewer’s comments/requests, do not forget to re-request their next review! GitHub Actions checks#

UXarray employs a number of GitHub Actions workflows (please refer to the GitHub Actions guide for detailed information) to make sure our PRs, branches, etc. pass certain test scenarios such as the pre-commit hooks, code test suite, and documentation generation. The pre-commit hooks workflow ensures the code being proposed to be merged is complying with code standards. The test suite workflows ensure that the changes are passing for those tests described in Unit tests for a matrix of various platforms and Python versions. The documentation generation workflow ensures the changes proposed are still allowing our documentation to be generated without any issues.


We require PRs to pass all of these checks before getting merged in order to always ensure our main branch stability.

These checks can be extremely helpful for contributors to be sure about they are changing things in correct directions and their PRs are ready to be reviewed and merged. For example, the docs/ check can show whether the UXarray documentation is able to be generated (i.e. pass/fail) and if it passes, it also shows, just by clicking to its Details, the corresponding documentation being generated for the PR.