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‎.github/PULL_REQUEST_TEMPLATE.md‎

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@@ -14,13 +14,15 @@ Additionally, please summarize the changes in the title, for example "Raise Valu
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non-numeric input to set_xlim" and avoid non-descriptive titles such as "Addresses
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issue #8576".
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If possible, please provide a minimum self-contained example. If you have used
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generative AI as an aid in preparing this PR, see
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If possible, please provide a minimum self-contained example.
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-->
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## AI Disclosure
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<!-- If you used AI in writing this PR, please briefly describe how.
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Read our policy at
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https://matplotlib.org/devdocs/devel/contribute.html#restrictions-on-generative-ai-usage
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-->
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## PR checklist
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<!-- Please mark any checkboxes that do not apply to this PR as [N/A].-->
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‎.github/workflows/codeql-analysis.yml‎

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persist-credentials: false
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- name: Initialize CodeQL
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uses: github/codeql-action/init@9e907b5e64f6b83e7804b09294d44122997950d6 # v4.32.3
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uses: github/codeql-action/init@89a39a4e59826350b863aa6b6252a07ad50cf83e # v4.32.4
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with:
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languages: ${{ matrix.language }}
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pip install --user -v .
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- name: Perform CodeQL Analysis
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uses: github/codeql-action/analyze@9e907b5e64f6b83e7804b09294d44122997950d6 # v4.32.3
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uses: github/codeql-action/analyze@89a39a4e59826350b863aa6b6252a07ad50cf83e # v4.32.4

‎.github/workflows/good-first-issue.yml‎

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### Good first issue - notes for new contributors
2020
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This issue is suited to new contributors because it does not require
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understanding of the Matplotlib internals. This is a low priority task
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understanding of the Matplotlib internals. This is a non-urgent task
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intended for human contributors to learn how to contribute; therefore please
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do not try to automate a solution using AI. To get started, please see our
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[contributing guide](https://matplotlib.org/stable/devel/index).

‎.github/workflows/stale-tidy.yml‎

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if: github.repository == 'matplotlib/matplotlib'
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runs-on: ubuntu-latest
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steps:
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- uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
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- uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v10.2.0
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with:
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repo-token: ${{ secrets.GITHUB_TOKEN }}
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operations-per-run: 300

‎.github/workflows/stale.yml‎

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if: github.repository == 'matplotlib/matplotlib'
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runs-on: ubuntu-latest
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steps:
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- uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
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- uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v10.2.0
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with:
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repo-token: ${{ secrets.GITHUB_TOKEN }}
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operations-per-run: 20

‎doc/api/next_api_changes/behavior/31021-AYS.rst‎

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Rendering of images now more accurate
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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There have been several fixes to improve the accuracy of how images are
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resampled and placed during rendering. Some inaccuracies were up to a pixel off
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in the output. The most apparent improvement is that the alignment of data
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pixels with tick marks and grid lines is now reliable. Nearly all image output
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has changed, but often only at a subtle level that is not obvious qualitatively.

‎doc/devel/api_changes.rst‎

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__ https://emptysqua.re/blog/api-evolution-the-right-way/#adding-parameters
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Add new rcParams
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^^^^^^^^^^^^^^^^
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When adding a new rcParam, the following files must be updated:
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1. :file:`lib/matplotlib/rcsetup.py` - Add a validator entry to the
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``_validators`` dict and a corresponding ``_Param`` entry with default value
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and description.
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2. :file:`lib/matplotlib/mpl-data/matplotlibrc` - Add a commented-out entry
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showing the default value.
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3. :file:`lib/matplotlib/typing.py` - Add the key to the ``RcKeyType`` Literal
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so that it is recognized as a valid rcParam key.
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Add or change colormaps, color sequences, and styles
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----------------------------------------------------
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Visual changes are considered an API break. Therefore, we generally do not modify

‎doc/devel/contribute.rst‎

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:open:
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:icon: person-fill
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100% yes! There are so many ways to contribute to our community. Take a look
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at the following sections to learn more.
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There are a few typical new contributor profiles:
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Here are a few typical new contributor profiles. If you
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don't fit into one of them, come talk to us on `Discourse
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<https://discourse.matplotlib.org/>`_ about how you might contribute.
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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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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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Understanding the entire codebase is a long-term project, and nobody
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expects you to do this right away. Start by building your experience in
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using Matplotlib: make complicated visualizations, use niche features,
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dive deep into one part of the library. This will help you build the
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context to then look at the existing issues and pull requests. You can
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then reach out to us at the :ref:`new contributor <new_contributors>`
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meeting or Discourse channel (incubator) to discuss what you would like
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to work on.
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.. _contribute_code:
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- If you use generative AI tools as an aid in developing code or documentation
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changes, ensure that you fully understand the proposed changes and can
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explain why they are the correct approach.
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- If you use AI for translation or grammar correction, please keep the wording as close
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as possible to the original wording. It is much easier to come to a mutual
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understanding when we are working off a direct translation of your words/thoughts
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rather than a text an LLM may have embellished with possibly spurious information.
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Make sure you have added value based on your personal competency to your
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contributions. Just taking some input, feeding it to an AI and posting the
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Good first issues
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-----------------
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While any contributions are welcome, we have marked some issues as
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particularly suited for new contributors by the label `good first issue
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<https://github.com/matplotlib/matplotlib/labels/good%20first%20issue>`_. These
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are well documented issues, that do not require a deep understanding of the
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internals of Matplotlib. The issues may additionally be tagged with a
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difficulty. ``Difficulty: Easy`` is suited for people with little Python
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experience. ``Difficulty: Medium`` and ``Difficulty: Hard`` require more
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programming experience. This could be for a variety of reasons, among them,
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though not necessarily all at the same time:
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- The issue is in areas of the code base which have more interdependencies,
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or legacy code.
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- It has less clearly defined tasks, which require some independent
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exploration, making suggestions, or follow-up discussions to clarify a good
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path to resolve the issue.
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- It involves Python features such as decorators and context managers, which
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have subtleties due to our implementation decisions.
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We have marked some issues as `good first issue
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<https://github.com/matplotlib/matplotlib/labels/good%20first%20issue>`_ because we
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think they are a good entry point into the process of contributing to Matplotlib. These
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issues are well documented, do not require a deep understanding of the internals of
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Matplotlib, and do not need urgent resolution. Good first issues are intended to onboard
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newcomers with a genuine interest in improving Matplotlib, in the hopes that they will
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continue to participate in our development community; therefore, pull requests that are
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:ref:`AI generated <generative_ai>` will be closed.
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.. _first_contribution:
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& `"Good first Issue" <https://github.com/matplotlib/matplotlib/labels/good%20first%20issue>`_ (optional).
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3. Click on an issue you would like to work on, and check to see if the issue has a pull request opened to resolve it.
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* A good way to judge if you chose a suitable issue is by asking yourself, "Can I independently submit a PR in 1-2 weeks?"
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* A good way to judge if you chose a suitable issue is by asking yourself, "Can I
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independently submit a PR in a reasonable time frame?" This should (at most)
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be a few days for an easy issue. If it takes longer, let us know why.
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* If you are new to open source, we strongly recommend that you do not choose a
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complicated issue for your first contribution - please ask in the incubator
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channel if you need help gauging the complexity of the work.
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4. Check existing pull requests (e.g., :ghpull:`28476`) and filter by the issue number to make sure the issue is not in progress:
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* If the issue has a pull request (is in progress), tag the user working on the issue, and ask to collaborate (optional).
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PR is an outline, unlikely to work, or stalled, and the original author is
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unresponsive, feel free to open a new PR referencing the old one.
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Difficulty
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----------
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Issues may additionally be tagged with a difficulty. ``Difficulty: Easy`` is suitable
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for people with beginner scientific Python experience, i.e. fluency with Python syntax
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and some experience using libraries like Numpy, Pandas, or Xarray. ``Difficulty: Medium``
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and ``Difficulty: Hard`` require more programming experience. This could be for a variety
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of reasons, for example:
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- requires understanding intermediate to advanced Python features, such as decorators,
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context managers, or meta-programming
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- is in areas of the code base which have more interdependencies or is legacy code.
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- involves complex or significant changes to algorithms or architecture.
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Generally, the difficulty level is correlated with how much conceptual (and contextual)
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understanding of Matplotlib is required to resolve it.
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.. _how-to-pull-request:
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Start a pull request

‎doc/devel/testing.rst‎

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``--capture=no`` or ``-s`` Do not capture stdout
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============================= ===========
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Some tests may use a large amount of memory (>0.5GiB); to enable those tests, set the
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environment variable ``MPL_TEST_EXPENSIVE``.
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To run a single test from the command line, you can provide a file path, optionally
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followed by the function separated by two colons, e.g., (tests do not need to be
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@image_comparison(baseline_images=['line_dashes'], remove_text=True,
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extensions=['png'], style='mpl20')
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@image_comparison(baseline_images=['line_dashes.png'], remove_text=True,
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style='mpl20')
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def test_line_dashes():
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fig, ax = plt.subplots()
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ax.plot(range(10), linestyle=(0, (3, 3)), lw=5)
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the tests, they should now pass.
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If you wish to compare multiple file formats, then omit the extension from the
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baseline image name and optionally pass the *extensions* argument::
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@image_comparison(baseline_images=['line_dashes'], remove_text=True,
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extensions=['png', 'svg'], style='mpl20')
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def test_line_dashes():
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fig, ax = plt.subplots()
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ax.plot(range(10), linestyle=(0, (3, 3)), lw=5)
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It is preferred that new tests use ``style='mpl20'`` as this leads to smaller
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figures and reflects the newer look of default Matplotlib plots. Also, if the
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‎doc/release/next_whats_new/README.rst‎

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=============================================
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.. whats-new-guide-start
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Each new feature (e.g. function, parameter, config value, behavior, ...) must
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be described through a "What's new" entry.
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Each new feature (e.g. function, parameter, rcParam, config value, behavior,
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...) must be described through a "What's new" entry.
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Each entry is written into a separate file in the
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:file:`doc/release/next_whats_new/` directory. They are sorted and merged into

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