doc/developer/new_contributor_faq.rst
.. _contributing_faq:
New Contributor FAQ
A collection of frequently-asked questions by newcomers to open-source development and first-time contributors to NetworkX.
To contribute to NetworkX, you will need three things:
Steps 1 & 2 are covered extensively in :ref:Development Workflow <dev_workflow>.
There is no generic answer for step 3. There are many ways that NetworkX can
be improved, from adding new algorithms, improving existing algorithms,
improving the test suite (e.g. increasing test coverage), and improving the
documentation.
The "best" way to find a place to start is to follow your own personal
interests!
That said, a few places to check for ideas on where to get started:
The issue tracker <https://github.com/networkx/networkx/issues>_ lists
known bugs and feature requests.Algorithms discussion_ includes a listing of algorithms that users
would like to have but that are not yet included in NetworkX... _Algorithms discussion: https://github.com/networkx/networkx/discussions/categories/algorithms
NetworkX doesn't typically assign issues to contributors. If you find an issue or feature request on the issue tracker that you'd like to work on, you should first check the issue thread to see if there are any linked pull requests. If not, then feel free to open a new PR to address the issue - no need to ask for permission - and don't forget to reference the issue number in the PR comments so that others know you are now working on it!
The example gallery is great place to contribute, particularly if you have an
interesting application or visualization that uses NetworkX.
The gallery is generated using :doc:sphinx-gallery <sphinx-gallery:index>
from Python scripts stored in the examples/ directory.
For instance, let's say I'd like to contribute an example of visualizing a
complete graph <networkx.generators.classic.complete_graph> using a
circular layout <networkx.drawing.layout.circular_layout>.
Assuming you have already followed the procedure for
:ref:setting up a development environment <dev_workflow>, start by
creating a new branch:
.. code-block:: bash
git checkout -b complete-graph-circular-layout-example
.. note:: It's generally a good idea to give your branch a descriptive name so that it's easy to remember what you are working on.
Now you can begin work on your example. Sticking with the circular layout idea,
you might create a file in examples/drawing called plot_circular_layout.py
with the following contents::
import networkx as nx import matplotlib.pyplot as plt
G = nx.complete_graph(10) # A complete graph with 10 nodes nx.draw_networkx(G, pos=nx.circular_layout(G))
.. note:: It may not be clear where exactly an example belongs. Our circular
layout example is very simple, so perhaps it belongs in examples/basic.
It would also make sense for it to be in examples/drawing since it deals
with visualization. Don't worry if you're not sure: questions like this will
be resolved during the review process.
At this point, your contribution is ready to be reviewed. You can make the
changes on your complete-graph-circular-layout-example branch visible to
other NetworkX developers by
creating a pull request__.
__ PR_
.. seealso:: The :ref:developer guide <dev_workflow> has more details on
creating pull requests.
Assuming you have followed the instructions for
:ref:setting up the development workflow <dev_workflow>, there are several
ways of determining where the in the source code a particular function or
class is defined.
For example, let's say you are interested in making a change to the
~networkx.drawing.layout.kamada_kawai_layout function, so you need to know
where it is defined. In an IPython terminal, you can use ? --- the source file is
listed in the File: field:
.. code-block:: ipython3
In [1]: import networkx as nx In [2]: nx.kamada_kawai_layout?
.. code-block:: text
Signature: <clipped for brevity> Docstring: <clipped for brevity> File: ~/networkx/networkx/drawing/layout.py Type: function
Command line utilities like grep or git grep are also very useful.
For example, from the NetworkX source directory:
.. code-block:: bash
$ grep -r "def kamada_kawai_layout" . ./networkx/drawing/layout.py:def kamada_kawai_layout(
There is no official policy setting explicit inclusion criteria for new
algorithms in NetworkX. New algorithms are more likely to be included if they
have been published and are cited by others. More important than number of
citations is how well proposed additions fit the project :ref:mission_and_values.
Testing is also an important factor in determining whether algorithms should be included. Proposals that include thorough tests which illustrate expected behavior are much easier to review, and therefore likely to progress more rapidly.
.. note:: Thorough does not mean exhaustive. The quality of unit tests is much more important than quantity. Thorough tests should address questions like: