.. _coding-guide: ************ Coding guide ************ .. _pull-request-checklist: Pull request checklist ====================== This checklist should be consulted when creating pull requests to make sure they are complete before merging. These are not intended to be rigidly followed---it's just an attempt to list in one place all of the items that are necessary for a good pull request. Of course, some items will not always apply. Branch selection ---------------- * In general, simple bugfixes that are unlikely to introduce new bugs of their own should be merged onto the maintenance branch. New features, or anything that changes the API, should be made against master. The rules are fuzzy here -- when in doubt, try to get some consensus. * Once changes are merged into the maintenance branch, they should be merged into master. Style ----- * Formatting should follow `PEP8 `_. Exceptions to these rules are acceptable if it makes the code objectively more readable. - You should consider installing/enabling automatic PEP8 checking in your editor. Part of the test suite is checking PEP8 compliance, things go smoother if the code is mostly PEP8 compliant to begin with. * No tabs (only spaces). No trailing whitespace. - Configuring your editor to remove these things upon saving will save a lot of trouble. * Import the following modules using the standard scipy conventions:: import numpy as np import numpy.ma as ma import matplotlib as mpl from matplotlib import pyplot as plt import matplotlib.cbook as cbook import matplotlib.collections as mcol import matplotlib.patches as mpatches * See below for additional points about :ref:`keyword-argument-processing`, if code in your pull request does that. * Adding a new pyplot function involves generating code. See :ref:`new-pyplot-function` for more information. Documentation ------------- * Every new feature should be documented. If it's a new module, don't forget to add a new rst file to the API docs. * Docstrings should be in `numpydoc format `_. Don't be thrown off by the fact that many of the existing docstrings are not in that format; we are working to standardize on `numpydoc`. Docstrings should look like (at a minimum):: def foo(bar, baz=None): """ This is a prose description of foo and all the great things it does. Parameters ---------- bar : (type of bar) A description of bar baz : (type of baz), optional A description of baz Returns ------- foobar : (type of foobar) A description of foobar foobaz : (type of foobaz) A description of foobaz """ # some very clever code return foobar, foobaz * Each high-level plotting function should have a simple example in the `Example` section of the docstring. This should be as simple as possible to demonstrate the method. More complex examples should go in the `examples` tree. * Build the docs and make sure all formatting warnings are addressed. * See :ref:`documenting-matplotlib` for our documentation style guide. * If your changes are non-trivial, please make an entry in the :file:`CHANGELOG`. * If your change is a major new feature, add an entry to :file:`doc/users/whats_new.rst`. * If you change the API in a backward-incompatible way, please document it in :file:`doc/api/api_changes.rst`. Testing ------- Using the test framework is discussed in detail in the section :ref:`testing`. * If the PR is a bugfix, add a test that fails prior to the change and passes with the change. Include any relevant issue numbers in the docstring of the test. * If this is a new feature, add a test that exercises as much of the new feature as possible. (The `--with-coverage` option may be useful here). * Make sure the Travis tests are passing before merging. - The Travis tests automatically test on all of the Python versions matplotlib supports whenever a pull request is created or updated. The `tox` support in matplotlib may be useful for testing locally. Installation ------------ * If you have added new files or directories, or reorganized existing ones, make sure the new files included in the match patterns in :file:`MANIFEST.in`, and/or in `package_data` in `setup.py`. C/C++ extensions ---------------- * Extensions may be written in C or C++. * Code style should conform to PEP7 (understanding that PEP7 doesn't address C++, but most of its admonitions still apply). * Interfacing with Python may be done either with the raw Python/C API or Cython. Use of PyCXX is discouraged for new code. * Python/C interface code should be kept separate from the core C/C++ code. The interface code should be named `FOO_wrap.cpp`. * Header file documentation (aka docstrings) should be in Numpydoc format. We don't plan on using automated tools for these docstrings, and the Numpydoc format is well understood in the scientific Python community. Style guide =========== .. _keyword-argument-processing: Keyword argument processing --------------------------- Matplotlib makes extensive use of ``**kwargs`` for pass-through customizations from one function to another. A typical example is in :func:`matplotlib.pylab.text`. The definition of the pylab text function is a simple pass-through to :meth:`matplotlib.axes.Axes.text`:: # in pylab.py def text(*args, **kwargs): ret = gca().text(*args, **kwargs) draw_if_interactive() return ret :meth:`~matplotlib.axes.Axes.text` in simplified form looks like this, i.e., it just passes all ``args`` and ``kwargs`` on to :meth:`matplotlib.text.Text.__init__`:: # in axes.py def text(self, x, y, s, fontdict=None, withdash=False, **kwargs): t = Text(x=x, y=y, text=s, **kwargs) and :meth:`~matplotlib.text.Text.__init__` (again with liberties for illustration) just passes them on to the :meth:`matplotlib.artist.Artist.update` method:: # in text.py def __init__(self, x=0, y=0, text='', **kwargs): Artist.__init__(self) self.update(kwargs) ``update`` does the work looking for methods named like ``set_property`` if ``property`` is a keyword argument. I.e., no one looks at the keywords, they just get passed through the API to the artist constructor which looks for suitably named methods and calls them with the value. As a general rule, the use of ``**kwargs`` should be reserved for pass-through keyword arguments, as in the example above. If all the keyword args are to be used in the function, and not passed on, use the key/value keyword args in the function definition rather than the ``**kwargs`` idiom. In some cases, you may want to consume some keys in the local function, and let others pass through. You can ``pop`` the ones to be used locally and pass on the rest. For example, in :meth:`~matplotlib.axes.Axes.plot`, ``scalex`` and ``scaley`` are local arguments and the rest are passed on as :meth:`~matplotlib.lines.Line2D` keyword arguments:: # in axes.py def plot(self, *args, **kwargs): scalex = kwargs.pop('scalex', True) scaley = kwargs.pop('scaley', True) if not self._hold: self.cla() lines = [] for line in self._get_lines(*args, **kwargs): self.add_line(line) lines.append(line) Note: there is a use case when ``kwargs`` are meant to be used locally in the function (not passed on), but you still need the ``**kwargs`` idiom. That is when you want to use ``*args`` to allow variable numbers of non-keyword args. In this case, python will not allow you to use named keyword args after the ``*args`` usage, so you will be forced to use ``**kwargs``. An example is :meth:`matplotlib.contour.ContourLabeler.clabel`:: # in contour.py def clabel(self, *args, **kwargs): fontsize = kwargs.get('fontsize', None) inline = kwargs.get('inline', 1) self.fmt = kwargs.get('fmt', '%1.3f') colors = kwargs.get('colors', None) if len(args) == 0: levels = self.levels indices = range(len(self.levels)) elif len(args) == 1: ...etc... Hints ===== This section describes how to add certain kinds of new features to matplotlib. .. _custom_backend: Developing a new backend ------------------------ If you are working on a custom backend, the *backend* setting in :file:`matplotlibrc` (:ref:`customizing-matplotlib`) supports an external backend via the ``module`` directive. if :file:`my_backend.py` is a matplotlib backend in your :envvar:`PYTHONPATH`, you can set use it on one of several ways * in matplotlibrc:: backend : module://my_backend * with the use directive is your script:: import matplotlib matplotlib.use('module://my_backend') * from the command shell with the -d flag:: > python simple_plot.py -d module://my_backend .. _sample-data: Writing examples ---------------- We have hundreds of examples in subdirectories of :file:`matplotlib/examples`, and these are automatically generated when the website is built to show up both in the `examples <../examples/index.html>`_ and `gallery <../gallery.html>`_ sections of the website. Any sample data that the example uses should be kept small and distributed with matplotlib in the `lib/matplotlib/mpl-data/sample_data/` directory. Then in your example code you can load it into a file handle with:: import matplotlib.cbook as cbook fh = cbook.get_sample_data('mydata.dat') .. _new-pyplot-function: Writing a new pyplot function ----------------------------- A large portion of the pyplot interface is automatically generated by the `boilerplate.py` script (in the root of the source tree). To add or remove a plotting method from pyplot, edit the appropriate list in `boilerplate.py` and then run the script which will update the content in `lib/matplotlib/pyplot.py`. Both the changes in `boilerplate.py` and `lib/matplotlib/pyplot.py` should be checked into the repository. Note: boilerplate.py looks for changes in the installed version of matplotlib and not the source tree. If you expect the pyplot.py file to show your new changes, but they are missing, this might be the cause. Install your new files by running `python setup.py build` and `python setup.py install` followed by `python boilerplate.py`. The new pyplot.py file should now have the latest changes.