Setting up Matplotlib for development#

To set up Matplotlib for development follow these steps:

Fork the Matplotlib repository#

Matplotlib is hosted at matplotlib/matplotlib.git. If you plan on solving issues or submit pull requests to the main Matplotlib repository, you should first fork this repository by visiting matplotlib/matplotlib.git and clicking on the Fork button on the top right of the page (see the GitHub documentation for more details.)

Retrieve the latest version of the code#

Now that your fork of the repository lives under your GitHub username, you can retrieve the most recent version of the source code with one of the following commands (replace <your-username> with your GitHub username):

git clone<your-username>/matplotlib.git
git clone [email protected]:<your-username>/matplotlib.git

This requires you to setup an SSH key in advance, but saves you from typing your password at every connection.

This will place the sources in a directory matplotlib below your current working directory and set the remote name origin to point to your fork. Change into this directory before continuing:

cd matplotlib

Now set the remote name upstream to point to the Matplotlib main repository:

git remote add upstream
git remote add upstream [email protected]:matplotlib/matplotlib.git

You can now use upstream to retrieve the most current snapshot of the source code, as described in Development workflow.

Additional git and GitHub resources

Create a dedicated environment#

You should set up a dedicated environment to decouple your Matplotlib development from other Python and Matplotlib installations on your system.

The simplest way to do this is to use either Python's virtual environment venv or conda.

Create a new venv environment with

python -m venv <file folder location>

and activate it with one of the following

source <file folder location>/bin/activate  # Linux/macOS
<file folder location>\Scripts\activate.bat  # Windows cmd.exe
<file folder location>\Scripts\Activate.ps1  # Windows PowerShell

On some systems, you may need to type python3 instead of python. For a discussion of the technical reasons, see PEP-394.

Create a new conda environment with

conda env create -f environment.yml

You can use mamba instead of conda in the above command if you have mamba installed.

Activate the environment using

conda activate mpl-dev

Remember to activate the environment whenever you start working on Matplotlib.

Install Dependencies#

Most Python dependencies will be installed when setting up the environment but non-Python dependencies like C++ compilers, LaTeX, and other system applications must be installed separately. For a full list, see Dependencies.

Install Matplotlib in editable mode#

Install Matplotlib in editable mode from the matplotlib directory using the command

python -m pip install -ve .

The 'editable/develop mode', builds everything and places links in your Python environment so that Python will be able to import Matplotlib from your development source directory. This allows you to import your modified version of Matplotlib without re-installing after every change. Note that this is only true for *.py files. If you change the C-extension source (which might also happen if you change branches) you will have to re-run python -m pip install -ve .

Verify the Installation#

Run the following command to make sure you have correctly installed Matplotlib in editable mode. The command should be run when the virtual environment is activated

python -c "import matplotlib; print(matplotlib.__file__)"

This command should return : <matplotlib_local_repo>\lib\matplotlib\

We encourage you to run tests and build docs to verify that the code installed correctly and that the docs build cleanly, so that when you make code or document related changes you are aware of the existing issues beforehand.

Install pre-commit hooks#

pre-commit hooks save time in the review process by identifying issues with the code before a pull request is formally opened. Most hooks can also aide in fixing the errors, and the checks should have corresponding development workflow and pull request guidelines. Hooks are configured in .pre-commit-config.yaml and include checks for spelling and formatting, flake 8 conformity, accidentally committed files, import order, and incorrect branching.

Install pre-commit hooks

python -m pip install pre-commit
pre-commit install

Hooks are run automatically after the git commit stage of the editing workflow. When a hook has found and fixed an error in a file, that file must be staged and committed again.

Hooks can also be run manually. All the hooks can be run, in order as listed in .pre-commit-config.yaml, against the full codebase with

pre-commit run --all-files

To run a particular hook manually, run pre-commit run with the hook id

pre-commit run <hook id> --all-files