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Contributing Guidelines

🎉 First off, thank you for considering contributing to our project! 🎉

This is a community-driven project, so it's people like you that make it useful and successful. These are some of the many ways to contribute:

  • 🐛 Submitting bug reports and feature requests
  • 📝 Writing tutorials or examples
  • 🔍 Fixing typos and improving the documentation
  • 💡 Writing code for everyone to use
  • 🧑‍🤝‍🧑 Community engagement and outreach

If you get stuck at any point you can create an issue on GitHub or contact us at one of the other channels mentioned below.

For more information on contributing to open source projects, GitHub's own guide is a great starting point if you are new to version control. Also, checkout the Zen of Scientific Software Maintenance for some guiding principles on how to create high quality scientific software contributions.

Ground Rules

The goal is to maintain a diverse community that's pleasant for everyone. Please be considerate and respectful of others. Everyone must abide by our Code of Conduct and we encourage all to read it carefully.

Contents

What Can I Do?

  • Tackle any issue that you wish! Some issues are labeled as "good first issues" to indicate that they are beginner friendly, meaning that they don't require extensive knowledge of the project.
  • Make a tutorial or gallery example of how to do something.
  • Provide feedback about how we can improve the project or about your particular use case.
  • Contribute code you already have. It doesn't need to be perfect! We will help you clean things up, test it, etc.

How Can I Talk to You?

Discussion often happens in the issues and pull requests. In addition, there is a Discourse forum for the project where you can ask questions.

Reporting a Bug

Find the Issues tab on the top of the GitHub repository and click New Issue. You'll be prompted to choose between different types of issue, like bug reports and feature requests. Choose the one that best matches your need. The Issue will be populated with one of our templates. Please try to fillout the template with as much detail as you can. Remember: the more information we have, the easier it will be for us to solve your problem.

Editing the Documentation

If you're browsing the documentation and notice a typo or something that could be improved, please consider letting us know by creating an issue or submitting a fix (even better 🌟).

You can submit fixes to the documentation pages completely online without having to download and install anything:

  • On each documentation page, there should be an "Improve This Page" link at the very top.
  • Click on that link to open the respective source file (usually an .rst file in the doc/ folder or a .py file in the examples/ folder) on GitHub for editing online (you'll need a GitHub account).
  • Make your desired changes.
  • When you're done, scroll to the bottom of the page.
  • Fill out the two fields under "Commit changes": the first is a short title describing your fixes; the second is a more detailed description of the changes. Try to be as detailed as possible and describe why you changed something.
  • Choose "Create a new branch for this commit and start a pull request" and click on the "Propose changes" button to open a pull request.
  • The pull request will run the GMT automated tests and make a preview deployment. You can see how your change looks in the PyGMT documentation by clicking the "View deployment" button after the Vercel bot has finished (usually 5-10 minutes after the pull request was created).
  • We'll review your pull request, recommend changes if necessary, and then merge them in if everything is OK.
  • Done 🎉🍺

Alternatively, you can make the changes offline to the files in the doc folder or the example scripts. See Contributing Code for instructions.

Gallery plots

The gallery and tutorials are managed by sphinx-gallery. The source files for the example gallery are .py scripts in examples/gallery/ that generate one or more figures. They are executed automatically by sphinx-gallery when the documentation is built. The output is gathered and assembled into the gallery.

You can add a new plot by placing a new .py file in one of the folders inside the examples/gallery folder of the repository. See the other examples to get an idea for the format.

General guidelines for making a good gallery plot:

  • Examples should highlight a single feature/command. Good: how to add a label to a colorbar. Bad: how to add a label to the colorbar and use two different CPTs and use subplots.
  • Try to make the example as simple as possible. Good: use only commands that are required to show the feature you want to highlight. Bad: use advanced/complex Python features to make the code smaller.
  • Use a sample dataset from pygmt.datasets if you need to plot data. If a suitable dataset isn't available, open an issue requesting one and we'll work together to add it.
  • Add comments to explain things are aren't obvious from reading the code. Good: Use a Mercator projection and make the plot 15 centimeters wide. Bad: Draw coastlines and plot the data.
  • Describe the feature that you're showcasing and link to other relevant parts of the documentation.
  • SI units should be used in the example code for gallery plots.

Tutorials

The tutorials (the User Guide in the docs) are also built by sphinx-gallery from the .py files in the examples/tutorials folder of the repository. To add a new tutorial:

  • Include a .py file in the examples/tutorials folder on the base of the repository.
  • Write the tutorial in "notebook" style with code mixed with paragraphs explaining what is being done. See the other tutorials for the format.
  • Include the tutorial in the table of contents of the documentation (side bar). Do this by adding a line to the User Guide toc directive in doc/index.rst. Notice that the file included is the .rst generated by sphinx-gallery.
  • Choose the most representative figure as the thumbnail figure by adding a comment line # sphinx_gallery_thumbnail_number = <fig_number> to any place (usually at the top) in the tutorial. The fig_number starts from 1.

Guidelines for a good tutorial:

  • Each tutorial should focus on a particular set of tasks that a user might want to accomplish: plotting grids, interpolation, configuring the frame, projections, etc.
  • The tutorial code should be as simple as possible. Avoid using advanced/complex Python features or abbreviations.
  • Explain the options and features in as much detail as possible. The gallery has concise examples while the tutorials are detailed and full of text.
  • SI units should be used in the example code for tutorial plots.

Note that the Figure.show() function needs to be called for a plot to be inserted into the documentation.

Example code standards

When editing documentation, use the following standards to demonstrate the example code:

  1. Python arguments, such as import statements, Boolean expressions, and function arguments should be wrapped as code by using `` on both sides of the code. Examples: ``import pygmt`` results in import pygmt, ``True`` results in True, ``style="v"`` results in style="v".
  2. Literal GMT arguments should be bold by wrapping the arguments with ** (two asterisks) on both sides. The argument description should be in italicized with * (single asterisk) on both sides. Examples: **+l**\ *label* results in +llabel, **05m** results in 05m.
  3. Optional arguments are wrapped with [ ] (square brackets).
  4. Arguments that are mutually exclusive are separated with a | (bar) to denote "or".
  5. Default arguments for parameters and configuration settings are wrapped with [ ] (square brackers) with the prefix "Default is". Example: [Default is p].

Contributing Code

Is this your first contribution? Please take a look at these resources to learn about git and pull requests (don't hesitate to ask questions):

General guidelines

We follow the git pull request workflow to make changes to our codebase. Every change made goes through a pull request, even our own, so that our continuous integration services have a change to check that the code is up to standards and passes all our tests. This way, the master branch is always stable.

General guidelines for pull requests (PRs):

  • Open an issue first describing what you want to do. If there is already an issue that matches your PR, leave a comment there instead to let us know what you plan to do.
  • Each pull request should consist of a small and logical collection of changes.
  • Larger changes should be broken down into smaller components and integrated separately.
  • Bug fixes should be submitted in separate PRs.
  • Use underscores for all Python (*.py) files as per PEP8, not hyphens. Directory names should also use underscores instead of hyphens.
  • Describe what your PR changes and why this is a good thing. Be as specific as you can. The PR description is how we keep track of the changes made to the project over time.
  • Do not commit changes to files that are irrelevant to your feature or bugfix (eg: .gitignore, IDE project files, etc).
  • Write descriptive commit messages. Chris Beams has written a guide on how to write good commit messages.
  • 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.

Setting up your environment

We highly recommend using Anaconda and the conda package manager to install and manage your Python packages. It will make your life a lot easier!

The repository includes a conda environment file environment.yml with the specification for all development requirements to build and test the project. Once you have forked and cloned the repository to your local machine, you can use this file to create an isolated environment on which you can work. Run the following on the base of the repository:

conda env create

Before building and testing the project, you have to activate the environment:

conda activate pygmt

You'll need to do this every time you start a new terminal.

See the environment.yml file for the list of dependencies and the environment name.

We have a Makefile that provides commands for installing, running the tests and coverage analysis, running linters, etc. If you don't want to use make, open the Makefile and copy the commands you want to run.

To install the current source code into your testing environment, run:

make install

This installs your project in editable mode, meaning that changes made to the source code will be available when you import the package (even if you're on a different directory).

Code style

We use some tools:

to format the code so we don't have to think about it. Black and blackdoc loosely follows the PEP8 guide but with a few differences. Regardless, you won't have to worry about formatting the code yourself. Before committing, run it to automatically format your code:

make format

For consistency, we also use UNIX-style line endings (\n) and file permission 644 (-rw-r--r--) throughout the whole project. Don't worry if you forget to do it. Our continuous integration systems will warn us and you can make a new commit with the formatted code. Even better, you can just write /format in the first line of any comment in a Pull Request to lint the code automatically.

We also use flake8 and pylint to check the quality of the code and quickly catch common errors. The Makefile contains rules for running both checks:

make check   # Runs black, blackdoc, docformatter, flake8 and isort (in check mode)
make lint    # Runs pylint, which is a bit slower

Docstrings

All docstrings should follow the numpy style guide. All functions/classes/methods should have docstrings with a full description of all arguments and return values.

While the maximum line length for code is automatically set by Black, docstrings must be formatted manually. To play nicely with Jupyter and IPython, keep docstrings limited to 79 characters per line.

Testing your code

Automated testing helps ensure that our code is as free of bugs as it can be. It also lets us know immediately if a change we make breaks any other part of the code.

All of our test code and data are stored in the tests subpackage. We use the pytest framework to run the test suite.

Please write tests for your code so that we can be sure that it won't break any of the existing functionality. Tests also help us be confident that we won't break your code in the future.

When writing tests, don't test everything that the GMT function already tests, such as the every unique combination arguments. An exception to this would be the most popular modules, such as plot and basemap. The highest priority for tests should be the Python-specific code, such as numpy, pandas, and xarray objects and the virtualfile mechanism.

If you're new to testing, see existing test files for examples of things to do. Don't let the tests keep you from submitting your contribution! If you're not sure how to do this or are having trouble, submit your pull request anyway. We will help you create the tests and sort out any kind of problem during code review.

Pull the baseline images, run the tests, and calculate test coverage using:

dvc status  # should report any files 'not_in_cache'
dvc pull  # pull down files from DVC remote cache (fetch + checkout)
make test

The coverage report will let you know which lines of code are touched by the tests. If all the tests pass, you can view the coverage reports by opening htmlcov/index.html in your browser. Strive to get 100% coverage for the lines you changed. It's OK if you can't or don't know how to test something. Leave a comment in the PR and we'll help you out.

You can also run tests in just one test script using:

pytest pygmt/tests/NAME_OF_TEST_FILE.py

or run tests which contain names that match a specific keyword expression:

pytest -k KEYWORD pygmt/tests

Testing plots

Writing an image-based test is only slightly more difficult than a simple test. The main consideration is that you must specify the "baseline" or reference image, and compare it with a "generated" or test image. This is handled using the decorator functions @pytest.mark.mpl_image_compare and @check_figures_equal whose usage are further described below.

Using mpl_image_compare

This is the preferred way to test plots whenever possible.

This method uses the pytest-mpl plug-in to test plot generating code. Every time the tests are run, pytest-mpl compares the generated plots with known correct ones stored in pygmt/tests/baseline. If your test created a pygmt.Figure object, you can test it by adding a decorator and returning the pygmt.Figure object:

@pytest.mark.mpl_image_compare
def test_my_plotting_case():
    "Test that my plotting function works"
    fig = Figure()
    fig.basemap(region=[0, 360, -90, 90], projection='W7i', frame=True)
    return fig

Your test function must return the pygmt.Figure object and you can only test one figure per function.

Before you can run your test, you'll need to generate a baseline (a correct version) of your plot. Run the following from the repository root:

pytest --mpl-generate-path=baseline pygmt/tests/NAME_OF_TEST_FILE.py

This will create a baseline folder with all the plots generated in your test file. Visually inspect the one corresponding to your test function. If it's correct, copy it (and only it) to pygmt/tests/baseline. When you run make test the next time, your test should be executed and passing.

Don't forget to commit the baseline image as well! The images should be pushed up into a remote repository using dvc (instead of git) as will be explained in the next section.

Using data version control (dvc) to manage test images

As the baseline images are quite large blob files that can change often (e.g. with new GMT versions), it is not ideal to store them in git (which is meant for tracking plain text files). Instead, we will use dvc which is like git but for data. What dvc does is to store the hash (md5sum) of a file. For example, given an image file like test_logo.png, dvc will generate a test_logo.png.dvc plain text file containing the hash of the image. This test_logo.png.dvc file can be stored as usual on GitHub, while the test_logo.png file can be stored separately on our dvc remote at https://dagshub.com/GenericMappingTools/pygmt.

To pull or sync files from the dvc remote to your local repository, use the commands below. Note how dvc commands are very similar to git.

dvc status  # should report any files 'not_in_cache'
dvc pull  # pull down files from DVC remote cache (fetch + checkout)

Once the sync/download is complete, you should notice two things. There will be images stored in the pygmt/tests/baseline folder (e.g. test_logo.png) and these images are technically reflinks/symlinks/copies of the files under the .dvc/cache folder. You can now run the image comparison test suite as per usual.

pytest pygmt/tests/test_logo.py  # run only one test
make test  # run the entire test suite

To push or sync changes from your local repository up to the dvc remote at DAGsHub, you will first need to set up authentication using the commands below. This only needs to be done once, i.e. the first time you contribute a test image to the PyGMT project.

dvc remote modify upstream --local auth basic
dvc remote modify upstream --local user "$DAGSHUB_USER"
dvc remote modify upstream --local password "$DAGSHUB_PASS"

The configuration will be stored inside your .dvc/config.local file. Note that the $DAGSHUB_PASS token can be generated at https://dagshub.com/user/settings/tokens after creating a DAGsHub account (can be linked to your GitHub account). Once you have an account set up, please ask one of the PyGMT maintainers to add you as a collaborator at https://dagshub.com/GenericMappingTools/pygmt/settings/collaboration before proceeding with the next steps.

The entire workflow for generating or modifying baseline test images can be summarized as follows:

# Sync with both git and dvc remotes
git pull
dvc pull

# Generate new baseline images
pytest --mpl-generate-path=baseline pygmt/tests/test_logo.py
mv baseline/*.png pygmt/tests/baseline/

# Generate hash for baseline image and stage the *.dvc file in git
git rm -r --cached 'pygmt/tests/baseline/test_logo.png'  # only run if migrating existing image from git to dvc
dvc status  # check which files need to be added to dvc
dvc add pygmt/tests/baseline/test_logo.png
git add pygmt/tests/baseline/test_logo.png.dvc

# Commit changes and push to both the git and dvc remotes
git commit -m "Add test_logo.png into DVC"
git push
dvc push

Using check_figures_equal

This approach draws the same figure using two different methods (the reference method and the tested method), and checks that both of them are the same. It takes two pygmt.Figure objects ('fig_ref' and 'fig_test'), generates a png image, and checks for the Root Mean Square (RMS) error between the two. Here's an example:

@check_figures_equal()
def test_my_plotting_case():
  "Test that my plotting function works"
  fig_ref, fig_test = Figure(), Figure()
  fig_ref.grdimage("@earth_relief_01d_g", projection="W120/15c", cmap="geo")
  fig_test.grdimage(grid, projection="W120/15c", cmap="geo")
  return fig_ref, fig_test

Documentation

Building the documentation

Most documentation sources are in the doc folder. We use sphinx to build the web pages from these sources. To build the HTML files:

cd doc
make all

This will build the HTML files in doc/_build/html. Open doc/_build/html/index.html in your browser to view the pages.

Cross-referencing with Sphinx

The API reference is manually assembled in doc/api/index.rst. The autodoc sphinx extension will automatically create pages for each function/class/module listed there.

You can reference functions, classes, methods, and modules from anywhere (including docstrings) using:

  • :func:`package.module.function`
  • :class:`package.module.class`
  • :meth:`package.module.method`
  • :mod:`package.module`

An example would be to use :meth:`pygmt.Figure.grdview` to link to https://www.pygmt.org/latest/api/generated/pygmt.Figure.grdview.html. PyGMT documentation that is not a class, method, or module can be linked with :doc:`Any Link Text </path/to/the/file>`. For example, :doc:`Install instructions </install>` links to https://www.pygmt.org/latest/install.html.

Linking to the GMT documentation and GMT configuration parameters can be done using:

  • :gmt-docs:`page_name.html`
  • :gmt-term:`GMT_PARAMETER`

An example would be using :gmt-docs:`makecpt.html` to link to https://docs.generic-mapping-tools.org/latest/makecpt.html. For GMT configuration parameters, an example is :gmt-term:`COLOR_FOREGROUND` to link to https://docs.generic-mapping-tools.org/latest/gmt.conf.html#term-COLOR_FOREGROUND.

Sphinx will create a link to the automatically generated page for that function/class/module.

All docstrings should follow the numpy style guide. All functions/classes/methods should have docstrings with a full description of all arguments and return values.

Code Review

After you've submitted a pull request, 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 a good and detailed description of what the PR does.
  • Write tests for the code you wrote/modified.
  • Readable code is better than clever code (even with comments).
  • Write documentation for your code (docstrings) and leave comments explaining the reason behind non-obvious things.
  • Include an example of new features in the gallery or tutorials.
  • Follow the PEP8 style guide for code and the numpy guide for documentation.

Pull requests will automatically have tests run by GitHub Actions. This includes running both the unit tests as well as code linters. GitHub will show the status of these checks on the pull request. Try to get them all passing (green). If you have any trouble, leave a comment in the PR or get in touch.