Developer Guide#

The Panel library is a project which provides a wide range of data interfaces and an extensible set of plotting backends, which means the development and testing process involves a wide set of libraries.

This guide describes how to install and configure the development environment either simplified for first time contributors or fully as done by core developers.

If you have any problems with the steps here, please reach out in the dev channel on Discord or on Discourse.

Preliminaries#

Basic understanding of how to contribute to Open Source#

If this is your first open source contribution, please study one or more of the below resources.

Git#

The Panel source code is stored in a Git source control repository. The first step to working on Panel is to install Git on to your system. There are different ways to do this depending on whether, you are using Windows, OSX, or Linux.

To install Git on any platform, refer to the Installing Git section of the Pro Git Book.

In order to contribute to Panel you will also need Github account and knowledge of the fork and pull request workflow.

Pip#

First time contributors can get quickly up to speed using pip instead of conda.

Conda#

Developing all aspects of Panel requires a wide range of packages that are not easily and quickly available using pip. To make this more manageable, core developers rely heavily on the conda package manager for the free Anaconda Python distribution. However, conda can also install non-Python package dependencies, which helps streamline Panel development greatly. It is strongly recommended that any experienced or regular contributor use conda.

To install Conda on any platform, see the Download conda section of the conda documentation_.

Cloning the Project#

The source code for the Panel project is hosted on GitHub.

Non-core developer#

To clone the source repository

git clone https://github.com/<Your UserName Here>/panel.git

Core developer#

Core developers can work directly with the Panel repository. To clone run

git clone https://github.com/holoviz/panel.git

Installing the Project#

The instructions for cloning above created a panel directory at your file system location. This panel directory is referred to as the source checkout for the remainder of this document. For the remainder of this document we will assume your current working directory is the source checkout directory.

Fetch tags from upstream#

Make sure to fetch tags from upstream repository before installing

git remote add upstream https://github.com/holoviz/panel.git
git fetch --tags upstream
git push --tags

Basic Install with pip#

We recommend this install to first time contributors that

  • want to make a simple, quick contribution to notebooks, docs or the Python code

  • can use pip to create and manage virtual environments.

Create a new virtual environment and activate it.

Run

pip install -e . jupyterlab pre-commit
panel bundle --all
pre-commit install

We install the pre-commit package above to avoid pushing and reviewing code with obvious issues.

You can start Jupyter Lab by running

jupyter lab

If you start seeing ImportError due to missing packages, you can install them manually using pip or consider if its time to switch to a full install with conda.

Full Install with conda#

This is the full install used by the core developers.

Create a development environment#

Since Panel interfaces with a large range of different libraries the full test suite requires a wide range of dependencies. To make it easier to install and run different parts of the test suite across different platforms Panel uses a library called pyctdev to make things more consistent and general. To start with cd into the panel directory and set up conda using the following commands:

cd panel
conda install -c pyviz "pyctdev>0.5.0"

Once pyctdev is available and you are in the cloned panel repository you can set up an environment with:

doit env_create -c pyviz/label/dev -c conda-forge --name=panel_dev --python=3.9

Specify the desired Python version, currently Panel officially supports Python 3.9 or later. Once the environment has been created you can activate it with:

conda activate panel_dev

Install Panel in editable mode#

To perform an editable install of Panel, including all the dependencies required to run the full unit test suite, run the following:

doit develop_install -c pyviz/label/dev -c conda-forge -c bokeh -o build -o tests -o recommended

The above command installs Panel’s dependencies using conda, then performs a pip editable install of Panel. If it fails, nodejs>=14.0.0 may be missing from your environment, fix it with conda install -c conda-forge nodejs then rerun above command.

If you also want to run the UI tests you’ll need to install pytest-playwright with conda:

conda install pytest-playwright -c microsoft -c conda-forge

or with pip (if you prefer it, or if there’s no conda package found for your platform):

pip install pytest-playwright

then run:

playwright install chromium

Enable the Jupyter extension#

If you are running UI tests or intend to use the Panel Preview feature in Jupyter you must enable the server extension. To enable the classic notebook server extension:

jupyter serverextension enable panel.io.jupyter_server_extension --sys-prefix

For Jupyter Server:

jupyter server extension enable panel.io.jupyter_server_extension --sys-prefix

Setting up pre-commit#

Panel uses pre-commit to automatically apply linting to Panel code. If you intend to contribute to Panel we recommend you enable it with:

pre-commit install

This will ensure that every time you make a commit linting will automatically be applied.

Developing custom models#

Panel ships with a number of custom Bokeh models, which have both Python and Javascript components. When developing Panel these custom models have to be compiled. This happens automatically with SETUPTOOLS_ENABLE_FEATURES=legacy-editable pip install -e . or python setup.py develop, however when running actively developing you can rebuild the extension with panel build panel. The build command is just an alias for bokeh build; see the Bokeh developer guide for more information about developing bokeh models.

Just like any other Javascript (or Typescript) library Panel defines a package.json and package-lock.json files. When adding, updating or removing a dependency in the package.json file ensure you commit the changes to the package-lock.json after running npm install.

Bundling resources#

Panel bundles external resources required for custom models and templates into the panel/dist directory. The bundled resources have to be collected whenever they change, so rerun SETUPTOOLS_ENABLE_FEATURES=legacy-editable pip install -e . or python setup.py develop whenever you change one of the following:

  • A new model is added with a __javascript_raw__ declaration or an existing model is updated

  • A new template with a _resources declaration is added or an existing template is updated

  • A CSS file in one of template directories (panel/template/*/) is added or modified

Next Steps#

You will likely want to check out the

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