Skip to content

Latest commit

 

History

History
184 lines (149 loc) · 8.27 KB

CONTRIBUTING.md

File metadata and controls

184 lines (149 loc) · 8.27 KB
Raw

Feedback and Contribution

We welcome any input, feedback, bug reports, and contributions via Altair's GitHub Repository. In particular, we welcome companion efforts from other visualization libraries to render the Vega-Lite specifications output by Altair. We see this portion of the effort as much bigger than Altair itself: the Vega and Vega-Lite specifications are perhaps the best existing candidates for a principled lingua franca of data visualization.

We are also seeking contributions of additional Jupyter notebook-based examples in our separate GitHub repository: https://github.com/altair-viz/altair_notebooks.

How To Contribute Code to Altair

Setting Up Your Environment

Fork the Altair repository on GitHub and then clone the fork to you local machine. For more details on forking see the GitHub Documentation.

$ git clone https://github.com/YOUR-USERNAME/altair.git

To keep your fork up to date with changes in this repo, you can use the fetch upstream button on GitHub.

Now you can install the latest version of Altair locally using pip. The -e flag indicates that your local changes will be reflected every time you open a new Python interpreter (instead of having to reinstall the package each time).

$ cd altair/ 
$ python -m pip install -e '.[dev]'

'[dev]' indicates that pip should also install the development requirements which you can find in requirements_dev.txt

Creating a Branch

Once your local environment is up-to-date, you can create a new git branch which will contain your contribution (always create a new branch instead of making changes to the master branch):

$ git switch -c <branch-name>

With this branch checked-out, make the desired changes to the package.

Testing your Changes

Before suggesting your contributing your changing to the main Altair repository, it is recommended that you run the Altair test suite, which includes a number of tests to validate the correctness of your code:

$ make test

This also runs the black code formatter and flake8 linter.

Study the output of any failed tests and try to fix the issues before proceeding to the next section.

Creating a Pull Request

When you are happy with your changes, you can commit them to your branch by running

$ git add <modified-file>
$ git commit -m "Some descriptive message about your change"
$ git push origin <branch-name>

You will then need to submit a pull request (PR) on GitHub asking to merge your example branch into the main Altair repository. For details on creating a PR see GitHub documentation Creating a pull request. You can add more details about your example in the PR such as motivation for the example or why you thought it would be a good addition. You will get feed back in the PR discussion if anything needs to be changed. To make changes continue to push commits made in your local example branch to origin and they will be automatically shown in the PR.

Hopefully your PR will be answered in a timely manner and your contribution will help others in the future.

Documentation

Altair documentation is written in reStructuredText and compiled into html pages using Sphinx. Contributing to the documentation requires some extra dependencies and we have some conventions and plugins that are used to help navigate the docs and generate great Altair visualizations.

Note that the Altair website is only updated when a new version is released so your contribution might not show up for a while.

Adding Examples

We are always interested in new examples contributed from the community. These could be everything from simple one-panel scatter and line plots, to more complicated layered or stacked plots, to more advanced interactive features. Before submitting a new example check the Altair Example Gallery to make sure that your idea has not already been implemented.

Once you have an example you would like to add there are a few guide lines to follow. Every example should:

  • be saved as a stand alone script in the altair/examples/ directory.
  • have a descriptive docstring, which will eventually be extracted for the documentation website.
  • contain a category tag.
  • define a chart variable with the main chart object (This will be used both in the unit tests to confirm that the example executes properly, and also eventually used to display the visualization on the documentation website).
  • not make any external calls to download data within the script (i.e. don't use urllib). You can define your data directly within the example file, generate your data using pandas and numpy, or you can use data available in the vega_datasets package.

The easiest way to get started would be to adapt examples from the Vega-Lite example gallery which are missing in the Altair gallery. Or you can feel free to be creative and build your own visualizations.

Often it is convenient to draft an example outside of the main repository, such as Google Colab, to avoid difficulties when working with git. Once you have an example you would like to add, follow the same contribution procedure outlined above.

Some additional notes:

  • all examples should be in their own file in the altair/examples directory, and the format and style of new contributions should generally match that of existing examples.
  • The file docstring will be rendered into HTML via reStructuredText, so use that format for any hyperlinks or text styling. In particular, be sure you include a title in the docstring underlined with ---, and be sure that the size of the underline exactly matches the size of the title text.
  • If your example fits into a chart type but involves significant configuration it should be in the Case Studies category.
  • For consistency all data used for a visualization should be assigned to the variable source. Then source is passed to the alt.Chart object. See other examples for guidance.
  • Example code should not require downloading external datasets. We suggest using the vega_datasets package if possible. If you are using the vega_datasets package there are multiple ways to refer to a data source. If the dataset you would like to use is included in local installation (vega_datasets.local_data.list_datasets()) then the data can be referenced directly, such as source = data.iris(). If the data is not included then it should be referenced by URL, such as source = data.movies.url. This is to ensure that Altair's automated test suite does not depend on availability of external HTTP resources.
  • If VlConvert does not support PNG export of the chart (e.g. in the case of emoji), then add the name of the example to the SVG_EXAMPLES set in tests/examples_arguments_syntax/__init__.py and tests/examples_methods_syntax/__init__.py

Building the Documentation Locally

In addition to the development dependencies mentioned further above, you will also need to install the dependencies for the documentation listed in docs/requirements.txt. Note that geopandas might require you to first install some other dependencies, see their installation page for details.

pip install -r doc/requirements.txt

Once you have all the dependencies, you can build the documentation using various commands defined in the Makefile. From the doc folder, you can use make help to see all of the available commands which control the type of documentation you want to generate.

Usually, you will want to run make html which will generate the documentation in a sub folder _build/html. You can then view the documentation by running:

cd _build/html
python -m http.server

and then opening http://localhost:8000 in your browser.