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Development setup

To set up Matplotlib for development follow these steps:

Fork the Matplotlib repository

Matplotlib is hosted at https://github.com/matplotlib/matplotlib.git. If you plan on solving issues or submitting pull requests to the main Matplotlib repository, you should first fork this repository by clicking the :octicon:`repo-forked` Fork button near the top of the project repository page.

This creates a copy of the code under your account on the GitHub server. See the GitHub documentation for more details.

Set up development environment

You can either work locally on your machine, or online in GitHub Codespaces, a cloud-based in-browser development environment.

local:If you are making extensive or frequent contributions to Matplotlib then it is probably worth taking the time to set up on your local machine: As well as having the convenience of your local familiar tools, you will not need to worry about Codespace's monthly usage limits.
codespaces:If you are making a one-off, relatively simple, change then working in GitHub Codespaces can be a good option because most of the setting up is done for you and you can skip the next few sections.

If you want to use Codespaces, skip to :ref:`development-codespaces`, otherwise, continue with the next section.

Create local environment

Get most recent 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):

.. tab-set::

   .. tab-item:: https

      .. code-block:: bash

         git clone https://github.com/<your-username>/matplotlib.git

   .. tab-item:: ssh

      .. code-block:: bash

         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.

      .. _SSH key: https://docs.github.com/en/authentication/connecting-to-github-with-ssh

This will place the sources in a directory :file:`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:

.. tab-set::

   .. tab-item:: https

      .. code-block:: bash

         git remote add upstream https://github.com/matplotlib/matplotlib.git

   .. tab-item:: ssh

      .. code-block:: bash

         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 :ref:`development-workflow`.

.. dropdown:: Additional ``git`` and ``GitHub`` resources
   :color: info
   :open:

   For more information on ``git`` and ``GitHub``, see:

   * `Git documentation <https://git-scm.com/doc>`_
   * `GitHub-Contributing to a Project
     <https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project>`_
   * `GitHub Skills <https://skills.github.com/>`_
   * :external+scipy:ref:`using-git`
   * :external+scipy:ref:`git-resources`
   * `Installing git <https://git-scm.com/book/en/v2/Getting-Started-Installing-Git>`_
   * `Managing remote repositories
     <https://docs.github.com/en/get-started/getting-started-with-git/managing-remote-repositories>`_
   * https://tacaswell.github.io/think-like-git.html
   * https://tom.preston-werner.com/2009/05/19/the-git-parable.html
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.

We recommend using one of the following options for a dedicated development environment because these options are configured to install the Python dependencies as part of their setup.

.. tab-set::

   .. tab-item:: venv environment

      Create a new `venv`_ environment with ::

        python -m venv <file folder location>

      and activate it with one of the following :

      .. tab-set::

         .. tab-item:: Linux and macOS

            .. code-block:: bash

               source <file folder location>/bin/activate  # Linux/macOS

         .. tab-item:: Windows cmd.exe

            .. code-block:: bat

               <file folder location>\Scripts\activate.bat

         .. tab-item:: Windows PowerShell

            .. code-block:: ps1con

               <file folder location>\Scripts\Activate.ps1

      On some systems, you may need to type ``python3`` instead of ``python``.
      For a discussion of the technical reasons, see `PEP-394 <https://peps.python.org/pep-0394>`_.

      Install the Python dependencies with ::

        pip install -U pip  # You may skip this step if pip 25.1 is already available.
        pip install --group dev

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

   .. tab-item:: conda environment

      Create a new `conda`_ environment and install the Python dependencies with ::

        conda env create -f environment.yml

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

      .. _mamba: https://mamba.readthedocs.io/en/latest/

      Activate the environment using ::

        conda activate mpl-dev

      Remember to activate the environment whenever you start working on Matplotlib!
Install external dependencies

Python dependencies were installed as part of :ref:`setting up the environment <dev-environment>`. Additionally, the following non-Python dependencies must also be installed locally:

.. rst-class:: checklist

For a full list of dependencies, see :ref:`dependencies`. External dependencies do not need to be installed when working in codespaces.

Create GitHub Codespace :octicon:`codespaces`

GitHub Codespaces is a cloud-based in-browser development environment that comes with the appropriate setup to contribute to Matplotlib.

  1. Open codespaces on your fork by clicking on the green :octicon:`code` Code button on the GitHub web interface and selecting the Codespaces tab.

  2. Next, click on "Open codespaces on <your branch name>". You will be able to change branches later, so you can select the default main branch.

  3. After the codespace is created, you will be taken to a new browser tab where you can use the terminal to activate a pre-defined conda environment called mpl-dev:

    conda activate mpl-dev

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

If you need to open a GUI window with Matplotlib output on Codespaces, our configuration includes a light-weight Fluxbox-based desktop. You can use it by connecting to this desktop via your web browser. To do this:

  1. Press F1 or Ctrl/Cmd+Shift+P and select Ports: Focus on Ports View in the VSCode session to bring it into focus. Open the ports view in your tool, select the noVNC port, and click the Globe icon.
  2. In the browser that appears, click the Connect button and enter the desktop password (vscode by default).

Check the GitHub instructions for more details on connecting to the desktop.

If you also built the documentation pages, you can view them using Codespaces. Use the "Extensions" icon in the activity bar to install the "Live Server" extension. Locate the doc/build/html folder in the Explorer, right click the file you want to open and select "Open with Live Server."

Install Matplotlib in editable mode

Install Matplotlib in editable mode from the :file:`matplotlib` directory using the command

python -m pip install --verbose --no-build-isolation --group dev --editable .

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 having to re-install after changing a .py or compiled extension file.

When working on a branch that does not have Meson enabled, meaning it does not have :ghpull:`26621` in its history (log), you will have to reinstall from source each time you change any compiled extension code.

If the installation is not working, please consult the :ref:`troubleshooting guide <troubleshooting-faq>`. If the guide does not offer a solution, please reach out via discourse or :ref:`open an issue <submitting-a-bug-report>`.

Build options

If you are working heavily with files that need to be compiled, you may want to inspect the compilation log. This can be enabled by setting the environment variable :envvar:`MESONPY_EDITABLE_VERBOSE` or by setting the editable-verbose config during installation

python -m pip install --no-build-isolation --config-settings=editable-verbose=true --editable .

For more information on installation and other configuration options, see the Meson Python :external+meson-python:ref:`editable installs guide <how-to-guides-editable-installs>`.

For a list of the other environment variables you can set before install, see :ref:`environment-variables`.

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\__init__.py

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

prek 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 :ref:`development workflow <development-workflow>` and :ref:`pull request <pr-guidelines>` 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 prek
prek install

Hooks are run automatically after the git commit stage of the :ref:`editing workflow<edit-flow>`. 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

prek run --all-files

To run a particular hook manually, run prek run with the hook id

prek run <hook id> --all-files

Please note that the mypy pre-commit hook cannot check the :ref:`type-hints` for new functions; instead the stubs for new functions are checked using the stubtest :ref:`CI check <automated-tests>` and can be checked locally using tox -e stubtest.