Based on the guide from: https://github.com/Unidata/MetPy

Interested in helping build pyproj? Have code from your research that you believe others will find useful? Have a few minutes to tackle an issue? In this guide we will get you setup and integrated into contributing to pyproj!

First off, thank you for considering contributing to pyproj. pyproj is community-driven project. It's people like you that make pyproj useful and successful. There are many ways to contribute, from writing tutorials or examples, improvements to the documentation, submitting bug reports and feature requests, or even writing code which can be incorporated into pyproj for everyone to use.

Following these guidelines helps to communicate that you respect the time of the developers managing and developing this open source project. In return, they should reciprocate that respect in addressing your issue, assessing changes, and helping you finalize your pull requests.

So, please take a few minutes to read through this guide and get setup for success with your pyproj contributions. We're glad you're here!

• Tackle any issues you wish! We have a special label for issues that beginners might want to try. Have a look at our current beginner issues. Also have a look at if the issue is already assigned to someone - this helps us make sure that work is not duplicated if the issue is already being worked on by someone else.

• Contribute code you already have. It does not need to be perfect! We will help you clean things up, test it, etc.

• Make a tutorial or example of how to do something.

• Improve documentation of a feature you found troublesome.

• File a new issue if you run into problems!

The goal is to maintain a diverse community that's pleasant for everyone. Please be considerate and respectful of others by following our code of conduct.

Other items:

• Each pull request should consist of a logical collection of changes. You can include multiple bug fixes in a single pull request, but they should be related. For unrelated changes, please submit multiple pull requests.
• Do not commit changes to files that are irrelevant to your feature or bugfix (eg: .gitignore).
• Be willing to accept criticism and work on improving your code; we don't want to break other users' code, so care must be taken not to introduce bugs.
• Be aware that the pull request review process is not immediate, and is generally proportional to the size of the pull request.

The easiest way to get involved is to report issues you encounter when using pyproj or by requesting something you think is missing.

• Head over to the issues page.
• Search to see if your issue already exists or has even been solved previously.
• If you indeed have a new issue or request, click the "New Issue" button.
• Fill in as much of the issue template as is relevant. Please be as specific as possible. Include the version of the code you were using, as well as what operating system you are running. If possible, include complete, minimal example code that reproduces the problem.

We recommend using the conda package manager for your Python environments. Our recommended setup for contributing is:

• Install miniconda on your system.
• Install git on your system if it is not already there (install XCode command line tools on a Mac or git bash on Windows)
• Login to your GitHub account and make a fork of the pyproj repository by clicking the "Fork" button.
• Clone your fork of the pyproj repository (in terminal on Mac/Linux or git shell/ GUI on Windows) in the location you'd like to keep it. We are partial to creating a git_repos directory in our home folder. git clone https://github.com/your-user-name/pyproj.git
• Navigate to that folder in the terminal or in Anaconda Prompt if you're on Windows. cd pyproj
• Connect your repository to the upstream (main project). git remote add upstream https://github.com/pyproj4/pyproj.git
• Create the development environment by running conda create -n devel -c conda-forge cython proj numpy shapely xarray pandas.
  • If the minimum PROJ version is not yet available, you can build PROJ from source using:

    export PROJ_DIR=$PWD/pyproj/proj_dir
    mkdir $PROJ_DIR
    bash ci/proj-compile.sh git

• Activate our new development environment conda activate devel on Mac/Linux or activate devel on Windows.
• Install development requirements python -m pip install -r requirements-dev.txt
• Make an editable install of pyproj by running python -m pip install -e .
• Setup pre-commit hooks pre-commit install

Now you're all set! You have an environment called devel that you can work in. You'll need to make sure to activate that environment next time you want to use it after closing the terminal or your system. If you want to get back to the root environment, just run source deactivate (just deactivate on Windows).

The changes to the pyproj source (and documentation) should be made via GitHub pull requests against main, even for those with administration rights. While it's tempting to make changes directly to main and push them up, it is better to make a pull request so that others can give feedback. If nothing else, this gives a chance for the automated tests to run on the PR. This can eliminate "brown paper bag" moments with buggy commits on the main branch.

During the Pull Request process, before the final merge, it's a good idea to rebase the branch and squash together smaller commits. It's not necessary to flatten the entire branch, but it can be nice to eliminate small fixes and get the merge down to logically arranged commits. This can also be used to hide sins from history--this is the only chance, since once it hits main, it's there forever!

Working on your first Pull Request? You can learn how from this free video series How to Contribute to an Open Source Project on GitHub, Aaron Meurer's tutorial on the git workflow, or the guide “How to Contribute to Open Source".

Commit the changes you made. Chris Beams has written a guide on how to write good commit messages.

Push to your fork and submit a pull request.

Now that you've made your awesome contribution, it's time to tell the world how to use it. Writing documentation strings is really important to make sure others use your functionality properly. Didn't write new functions? That's fine, but be sure that the documentation for the code you touched is still in great shape. It is not uncommon to find some strange wording or clarification that you can take care of while you are here. If you added a new function make sure that it gets marked as included if appropriate in the GEMPAK conversion table.

You can write examples in the documentation if they are simple concepts to demonstrate. If your feature is more complex, consider adding to the examples or tutorials for pyproj.

You can build the documentation locally to see how your changes will look.

• Install docs requirements: make install-docs
• Build the docs: make docs
• Or, to build and open in a browser: make docs-browser

Unit tests are the lifeblood of the project, as it ensures that we can continue to add and change the code and stay confident that things have not broken. Running the tests requires pytest, which is easily available through conda or pip. It was also installed if you made our default devel environment.

Running the tests can be done by running python -m pytest. Make sure you install the test requirements before running the tests python -m pip install -r requirements-test.txt.

Running the whole test suite isn't that slow, but can be a burden if you're working on just one module or a specific test. It is easy to run tests on a single directory:

python -m pytest pyproj/calc

A specific test can be run as:

python -m pytest -k test_my_test_func_name

Tests should ideally hit all of the lines of code added or changed. We have automated services that can help track down lines of code that are missed by tests. Watching the coverage has even helped find sections of dead code that could be removed!

Let's say we are adding a simple function to add two numbers and return the result as a float or as a string. (This would be a silly function, but go with us here for demonstration purposes.)

def add_as_float_or_string(a, b, as_string=False):
    res = a + b
    if as_string:
       return string(res)
    return res

I can see two easy tests here: one for the results as a float and one for the results as a string. If I had added this to the calc module, I'd add those two tests in tests/test_calc.py.

def test_add_as_float_or_string_defaults():
    res = add_as_float_or_string(3, 4)
    assert(res, 7)


def test_add_as_float_or_string_string_return():
    res = add_as_float_or_string(3, 4, as_string=True)
    assert(res, '7')

There are plenty of more advanced testing concepts, like dealing with floating point comparisons, parameterizing tests, testing that exceptions are raised, and more. Have a look at the existing tests to get an idea of some of the common patterns.

pyproj uses the Python code style outlined in PEP8 and black.

We enforce this style as code is added to keep everything clean and uniform. To this end, part of the automated testing for pyproj checks style. To check style locally within the source directory you can use the flake8 and black tools. Running it from the root of the source directory is as easy as running pre-commit run --all in the base of the repository.

You can also just submit your PR and the kind robots will comment on all style violations as well. It can be a pain to make sure you have the right number of spaces around things, imports in order, and all of the other nits that the bots will find. It is very important though as this consistent style helps us keep pyproj readable, maintainable, and uniform.

You've make your changes, documented them, added some tests, and submitted a pull request. What now?

First, our army of never sleeping robots will begin a series of automated checks. The test suite, documentation, style, and more will be checked on various versions of Python with current and legacy packages. Travis CI will run testing on Linux and Mac, Appveyor will run tests on Windows. Other services will kick in and check if there is a drop in code coverage or any style variations that should be corrected. If you see a red mark by a service, something failed and clicking the "Details" link will give you more information. We're happy to help if you are stuck.

The robots can be difficult to satisfy, but they are there to help everyone write better code. In some cases, there will be exceptions to their suggestions, but these are rare. If you make changes to your code and push again, the tests will automatically run again.

At this point you're waiting on us. You should expect to hear at least a comment within a couple of days. We may suggest some changes or improvements or alternatives.

Some things that will increase the chance that your pull request is accepted quickly:

• Write tests.
• Follow PEP8 for style. (The flake8 utility can help with this.)
• Use black
• Write a good commit message and consider using commit conventions.

Pull requests will automatically have tests run by Travis. This includes running both the unit tests as well as the flake8 code linter.

Once we're all happy with the pull request, it's time for it to get merged in. Only the maintainers can merge pull requests and you should never merge a pull request you have commits on as it circumvents the code review. If this is your first or second pull request, we'll likely help by rebasing and cleaning up the commit history for you. As your development skills increase, we'll help you learn how to do this.

We do our best to recognize contributions, but we may miss some. If we did, please let us know. Also, if you would prefer not to be recognized on the README, please let us know.

If you're stuck somewhere or are interested in being a part of the community in other ways, feel free to contact us:

• pyproj's Gitter Channel
• "pyproj" tag on GIS Stack Exchange

There are a ton of great resources out there on contributing to open source and on the importance of writing tested and maintainable software.

• GitHub's Contributing to Open Source Guide
• Zen of Scientific Software Maintenance