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Dedent docs in contributing.rst bullet/numbered lists.
A lot of bullet/numbered lists in contributing.rst are overindented, causing them to be displayed with too much indentation in the rendered rst. Compare e.g. https://matplotlib.org/devdocs/devel/documenting_mpl.html#keyword-arguments (correct indentation of "1. single point configuration etc.") and https://matplotlib.org/devdocs/devel/contributing.html#submitting-a-bug-report (extra indentation of "1. A short, top-level summary etc.")
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‎doc/devel/contributing.rst

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@@ -19,26 +19,26 @@ welcome to post feature requests or pull requests.
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If you are reporting a bug, please do your best to include the following:
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1. A short, top-level summary of the bug. In most cases, this should be 1-2
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sentences.
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1. A short, top-level summary of the bug. In most cases, this should be 1-2
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sentences.
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2. A short, self-contained code snippet to reproduce the bug, ideally allowing
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a simple copy and paste to reproduce. Please do your best to reduce the code
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snippet to the minimum required.
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2. A short, self-contained code snippet to reproduce the bug, ideally allowing
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a simple copy and paste to reproduce. Please do your best to reduce the code
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snippet to the minimum required.
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3. The actual outcome of the code snippet
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3. The actual outcome of the code snippet.
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4. The expected outcome of the code snippet
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4. The expected outcome of the code snippet.
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5. The Matplotlib version, Python version and platform that you are using. You
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can grab the version with the following commands::
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5. The Matplotlib version, Python version and platform that you are using. You
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can grab the version with the following commands::
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>>> import matplotlib
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>>> matplotlib.__version__
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'1.5.3'
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>>> import platform
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>>> platform.python_version()
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'2.7.12'
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>>> import matplotlib
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>>> matplotlib.__version__
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'1.5.3'
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>>> import platform
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>>> platform.python_version()
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'2.7.12'
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We have preloaded the issue creation page with a Markdown template that you can
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use to organize this information.
@@ -175,39 +175,36 @@ documented in the :ref:`development-workflow` section.
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A brief overview is:
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1. `Create an account <https://github.com/join>`_ on
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GitHub if you do not already have one.
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1. `Create an account <https://github.com/join>`_ on GitHub if you do not
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already have one.
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2. Fork the `project repository
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<https://github.com/matplotlib/matplotlib>`__: click on the 'Fork' button
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near the top of the page. This creates a copy of the code under your
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account on the GitHub server.
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2. Fork the `project repository <https://github.com/matplotlib/matplotlib>`_:
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click on the 'Fork' button near the top of the page. This creates a copy of
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the code under your account on the GitHub server.
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3. Clone this copy to your local disk::
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3. Clone this copy to your local disk::
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$ git clone https://github.com/YourLogin/matplotlib.git
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$ git clone https://github.com/YourLogin/matplotlib.git
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4. Create a branch to hold your changes::
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4. Create a branch to hold your changes::
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$ git checkout -b my-feature origin/master
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$ git checkout -b my-feature origin/master
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and start making changes. Never work in the ``master`` branch!
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and start making changes. Never work in the ``master`` branch!
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5. Work on this copy, on your computer, using Git to do the version
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control. When you're done editing e.g., ``lib/matplotlib/collections.py``,
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do::
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5. Work on this copy, on your computer, using Git to do the version control.
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When you're done editing e.g., ``lib/matplotlib/collections.py``, do::
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$ git add lib/matplotlib/collections.py
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$ git commit
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$ git add lib/matplotlib/collections.py
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$ git commit
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to record your changes in Git, then push them to GitHub with::
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to record your changes in Git, then push them to GitHub with::
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$ git push -u origin my-feature
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$ git push -u origin my-feature
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Finally, go to the web page of your fork of the Matplotlib repo,
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and click 'Pull request' to send your changes to the maintainers for review.
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You may want to consider sending an email to the mailing list for more
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visibility.
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Finally, go to the web page of your fork of the Matplotlib repo, and click
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'Pull request' to send your changes to the maintainers for review. You may
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want to consider sending an email to the mailing list for more visibility.
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.. seealso::
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@@ -221,62 +218,60 @@ Contributing pull requests
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It is recommended to check that your contribution complies with the following
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rules before submitting a pull request:
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* If your pull request addresses an issue, please use the title to describe
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the issue and mention the issue number in the pull request description
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to ensure a link is created to the original issue.
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* If your pull request addresses an issue, please use the title to describe the
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issue and mention the issue number in the pull request description to ensure
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that a link is created to the original issue.
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* All public methods should have informative docstrings with sample
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usage when appropriate. Use the
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`numpy docstring standard <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_
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* All public methods should have informative docstrings with sample usage when
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appropriate. Use the `numpy docstring standard
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<https://numpydoc.readthedocs.io/en/latest/format.html>`_.
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* Formatting should follow `PEP8 recommendation
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<https://www.python.org/dev/peps/pep-0008/>`_. You should consider
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installing/enabling automatic PEP8 checking in your editor. Part of the
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test suite is checking PEP8 compliance, things go smoother if the code is
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mostly PEP8 compliant to begin with.
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* Formatting should follow the recommendations of `PEP8
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<https://www.python.org/dev/peps/pep-0008/>`__. You should consider
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installing/enabling automatic PEP8 checking in your editor. Part of the test
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suite is checking PEP8 compliance, things go smoother if the code is mostly
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PEP8 compliant to begin with.
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* Each high-level plotting function should have a simple example in
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the ``Example`` section of the docstring. This should be as simple as
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possible to demonstrate the method. More complex examples should go
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in the ``examples`` tree.
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* Each high-level plotting function should have a simple example in the
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``Example`` section of the docstring. This should be as simple as possible
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to demonstrate the method. More complex examples should go in the
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``examples`` tree.
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* Changes (both new features and bugfixes) should be tested. See
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:ref:`testing` for more details.
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* Changes (both new features and bugfixes) should be tested. See :ref:`testing`
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for more details.
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* Import the following modules using the standard scipy conventions::
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* Import the following modules using the standard scipy conventions::
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import numpy as np
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import numpy.ma as ma
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
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import matplotlib.patches as mpatches
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import numpy as np
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import numpy.ma as ma
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import matplotlib.cbook as cbook
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import matplotlib.patches as mpatches
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* If your change is a major new feature, add an entry to the ``What's new``
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section by adding a new file in ``doc/users/whats_new`` (see
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:file:`doc/users/whats_new/README` for more information).
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* If your change is a major new feature, add an entry to the ``What's new``
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section by adding a new file in ``doc/users/next_whats_new`` (see
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:file:`doc/users/next_whats_new/README.rst` for more information).
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* If you change the API in a backward-incompatible way, please
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document it in `doc/api/api_changes`, by adding a new file describing your
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changes (see :file:`doc/api/api_changes/README` for more information)
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* If you change the API in a backward-incompatible way, please document it in
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`doc/api/api_changes`, by adding a new file describing your changes (see
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:file:`doc/api/api_changes/README.rst` for more information)
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* See below for additional points about
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:ref:`keyword-argument-processing`, if code in your pull request
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does that.
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* See below for additional points about :ref:`keyword-argument-processing`, if
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applicable for your pull request.
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In addition, you can check for common programming errors with the following
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tools:
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* Code with a good unittest coverage (at least 70%, better 100%), check
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with::
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* Code with a good unittest coverage (at least 70%, better 100%), check with::
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python -mpip install coverage
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python tests.py --with-coverage
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python -mpip install coverage
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python tests.py --with-coverage
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* No pyflakes warnings, check with::
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* No pyflakes warnings, check with::
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python -mpip install pyflakes
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pyflakes path/to/module.py
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python -mpip install pyflakes
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pyflakes path/to/module.py
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.. note::
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