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‎doc/devel/contribute.rst

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@@ -30,55 +30,56 @@ existing issue and pull request discussions, and following the conversations
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during pull request reviews to get context. Or you can deep-dive into a subset
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of the code-base to understand what is going on.
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Do I really have something to contribute to Matplotlib?
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-------------------------------------------------------
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100% yes. There are so many ways to contribute to our community. Take a look at
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the next sections to learn more. There are a few typical new contributor
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profiles:
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* **You are a Matplotlib user, and you see a bug, a potential improvement, or
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something that annoys you, and you can fix it.**
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You can search our issue tracker for an existing issue that describes your problem or
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open a new issue to inform us of the problem you observed and discuss the best approach
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to fix it. If your contributions would not be captured on GitHub (social media,
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communication, educational content), you can also reach out to us on gitter_,
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`Discourse <https://discourse.matplotlib.org/>`__ or attend any of our `community
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meetings <https://scientific-python.org/calendars>`__.
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* **You are not a regular Matplotlib user but a domain expert: you know about
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visualization, 3D plotting, design, technical writing, statistics, or some
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other field where Matplotlib could be improved.**
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Awesome -- you have a focus on a specific application and domain and can
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start there. In this case, maintainers can help you figure out the best
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implementation; open an issue or pull request with a starting point, and we'll
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be happy to discuss technical approaches.
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If you prefer, you can use the `GitHub functionality for "draft" pull requests
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<https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request#converting-a-pull-request-to-a-draft>`__
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and request early feedback on whatever you are working on, but you should be
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aware that maintainers may not review your contribution unless it has the
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"Ready to review" state on GitHub.
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* **You are new to Matplotlib, both as a user and contributor, and want to start
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contributing but have yet to develop a particular interest.**
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Having some previous experience or relationship with the library can be very
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helpful when making open-source contributions. It helps you understand why
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things are the way they are and how they *should* be. Having first-hand
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experience and context is valuable both for what you can bring to the
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conversation (and given the breadth of Matplotlib's usage, there is a good
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chance it is a unique context in any given conversation) and make it easier to
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understand where other people are coming from.
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Understanding the entire codebase is a long-term project, and nobody expects
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you to do this right away. If you are determined to get started with
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Matplotlib and want to learn, going through the basic functionality,
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choosing something to focus on (3d, testing, documentation, animations, etc.)
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and gaining context on this area by reading the issues and pull requests
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touching these subjects is a reasonable approach.
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.. dropdown:: Do I really have something to contribute to Matplotlib?
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:open:
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:icon: person-add
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100% yes. There are so many ways to contribute to our community. Take a look at
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the next sections to learn more. There are a few typical new contributor
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profiles:
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* **You are a Matplotlib user, and you see a bug, a potential improvement, or
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something that annoys you, and you can fix it.**
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44+
You can search our issue tracker for an existing issue that describes your problem or
45+
open a new issue to inform us of the problem you observed and discuss the best approach
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to fix it. If your contributions would not be captured on GitHub (social media,
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communication, educational content), you can also reach out to us on gitter_,
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`Discourse <https://discourse.matplotlib.org/>`__ or attend any of our `community
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meetings <https://scientific-python.org/calendars>`__.
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* **You are not a regular Matplotlib user but a domain expert: you know about
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visualization, 3D plotting, design, technical writing, statistics, or some
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other field where Matplotlib could be improved.**
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Awesome -- you have a focus on a specific application and domain and can
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start there. In this case, maintainers can help you figure out the best
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implementation; open an issue or pull request with a starting point, and we'll
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be happy to discuss technical approaches.
59+
60+
If you prefer, you can use the `GitHub functionality for "draft" pull requests
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<https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request#converting-a-pull-request-to-a-draft>`__
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and request early feedback on whatever you are working on, but you should be
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aware that maintainers may not review your contribution unless it has the
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"Ready to review" state on GitHub.
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* **You are new to Matplotlib, both as a user and contributor, and want to start
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contributing but have yet to develop a particular interest.**
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Having some previous experience or relationship with the library can be very
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helpful when making open-source contributions. It helps you understand why
71+
things are the way they are and how they *should* be. Having first-hand
72+
experience and context is valuable both for what you can bring to the
73+
conversation (and given the breadth of Matplotlib's usage, there is a good
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chance it is a unique context in any given conversation) and make it easier to
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understand where other people are coming from.
76+
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Understanding the entire codebase is a long-term project, and nobody expects
78+
you to do this right away. If you are determined to get started with
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Matplotlib and want to learn, going through the basic functionality,
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choosing something to focus on (3d, testing, documentation, animations, etc.)
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and gaining context on this area by reading the issues and pull requests
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touching these subjects is a reasonable approach.
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.. _contribute_code:
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GitHub Codespaces workflows
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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* If you need to open a GUI window with Matplotlib output on Codespaces, our
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configuration includes a `light-weight Fluxbox-based desktop
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<https://github.com/devcontainers/features/tree/main/src/desktop-lite>`_.
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You can use it by connecting to this desktop via your web browser. To do this:
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If you need to open a GUI window with Matplotlib output on Codespaces, our
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configuration includes a `light-weight Fluxbox-based desktop
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<https://github.com/devcontainers/features/tree/main/src/desktop-lite>`_.
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You can use it by connecting to this desktop via your web browser. To do this:
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#. Press ``F1`` or ``Ctrl/Cmd+Shift+P`` and select
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``Ports: Focus on Ports View`` in the VSCode session to bring it into
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#. In the browser that appears, click the Connect button and enter the desktop
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password (``vscode`` by default).
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Check the `GitHub instructions
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<https://github.com/devcontainers/features/tree/main/src/desktop-lite#connecting-to-the-desktop>`_
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for more details on connecting to the desktop.
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Check the `GitHub instructions
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<https://github.com/devcontainers/features/tree/main/src/desktop-lite#connecting-to-the-desktop>`_
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for more details on connecting to the desktop.
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* If you also built the documentation pages, you can view them using Codespaces.
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Use the "Extensions" icon in the activity bar to install the "Live Server"
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extension. Locate the ``doc/build/html`` folder in the Explorer, right click
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the file you want to open and select "Open with Live Server."
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View documentation
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""""""""""""""""""
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If you also built the documentation pages, you can view them using Codespaces.
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Use the "Extensions" icon in the activity bar to install the "Live Server"
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extension. Locate the ``doc/build/html`` folder in the Explorer, right click
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the file you want to open and select "Open with Live Server."
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Open a pull request on Matplotlib

‎doc/devel/index.rst

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@@ -114,7 +114,7 @@ For code, documentation, or triage, please follow the corresponding
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:link-type: ref
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:shadow: none
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:octicon:`people;1em;sd-text-info` Community
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:octicon:`globe;1em;sd-text-info` Community
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