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 to the documentation 💡Writing code for everyone to use
If you get stuck at any point you can create an issue on GitHub (look for the Issues tab in the repository) 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.
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.
- What Can I Do?
- How Can I Talk to You?
- Reporting a Bug
- Editing the Documentation
- Contributing Code
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 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 Gitter chatroom 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
.rstfile in the
docfolder) 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.
- Click on the "Commit changes" button to open a pull request (see below).
- We'll review your changes and then merge them in if everything is OK.
Alternatively, you can make the changes offline to the files in the
doc folder or the
example scripts. See Contributing Code for instructions.
The gallery and tutorials are managed by
(currently, we need the development version from the Github master branch).
The source files for the example gallery are
.py scripts in
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
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.datasetsif 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 6 inches wide. Bad: Draw coastlines and plot the data.
- Describe the feature that you're showcasing and link to other relevant parts of the documentation.
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
.pyfile in the
examples/tutorialsfolder 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
doc/index.rst. Notice that the file included is the
.rstgenerated by sphinx-gallery.
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.
Note that the
Figure.plot function needs to be called for a plot to be inserted into
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):
- How to Contribute to Open Source.
- Aaron Meurer's tutorial on the git workflow
- How to Contribute to an Open Source Project on GitHub
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.
- 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
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 clone the repository to your local machine, you 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:
source activate ENVIRONMENT_NAME
You'll need to do this every time you start a new terminal.
environment.yml file for the list of dependencies and the
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
To install the current source code into your testing environment, run:
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).
We use Black to format the code so we don't have to think about it. Black 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:
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.
make check # Runs flake8 and black (in check mode) make lint # Runs pylint, which is a bit slower
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
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.
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.
Run the tests and calculate test coverage using:
The coverage report will let you know which lines of code are touched by the tests. 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.
We use the pytest-mpl plug-in to test plot
Every time the tests are run,
pytest-mpl compares the generated plots with known
correct ones stored in
If your test created a
pygmt.Figure object, you can test it by adding a decorator and
@pytest.mark.mpl_image_compare def test_my_plotting_case(): "Test that my plotting function works" fig = Figure() fig.psbasemap(region=[0, 360, -90, 90], projection='W7i', frame=True, portrait=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:
py.test --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
Visually inspect the one corresponding to your test function.
If it's correct, copy it (and only it) to
When you run
make test the next time, your test should be executed and
Don't forget to commit the baseline image as well.
NOTE: We currently need the latest version of sphinx-gallery from the Github master branch. Please install it with
pip install --upgrade git+https://github.com/sphinx-gallery/sphinx-gallery.gitbefore building the docs.
Most documentation sources are in the
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/index.html in your browser to view the pages.
The API reference is manually assembled in
The autodoc sphinx extension will automatically create pages for each
function/class/module listed there.
You can reference classes, functions, and modules from anywhere (including docstrings)
Sphinx will create a link to the automatically generated page for that
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.
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 TravisCI. 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.