If you are a first-time contributor:
Go to https://github.com/networkx/networkx and click the "fork" button to create your own copy of the project.
Clone the project to your local computer:
git clone [email protected]:your-username/networkx.git
Navigate to the folder networkx and add the upstream repository:
git remote add upstream [email protected]:networkx/networkx.git
Now, you have remote repositories named:
upstream, which refers to thenetworkxrepositoryorigin, which refers to your personal fork
Next, you need to set up your build environment. Here are instructions for two popular environment managers:
venv(pip based)# Create a virtualenv named ``networkx-dev`` that lives in the directory of # the same name python -m venv networkx-dev # Activate it source networkx-dev/bin/activate # Install main development and runtime dependencies of networkx pip install -r <(cat requirements/{default,developer,doc,optional,test}.txt) # # (Optional) Install pygraphviz, pydot, and gdal packages # These packages require that you have your system properly configured # and what that involves differs on various systems. # pip install -r requirements/extra.txt # # Build and install networkx from source pip install -e . # Test your installation PYTHONPATH=. pytest networkxconda(Anaconda or Miniconda)# Create a conda environment named ``networkx-dev`` conda create --name networkx-dev # Activate it conda activate networkx-dev # Install main development and runtime dependencies of networkx conda install -c conda-forge `for i in requirements/{default,developer,doc,optional,test}.txt; do echo -n " --file $i "; done` # # (Optional) Install pygraphviz, pydot, and gdal packages # These packages require that you have your system properly configured # and what that involves differs on various systems. # pip install -r requirements/extra.txt # # Install networkx from source pip install -e . --no-deps # Test your installation PYTHONPATH=. pytest networkx
Finally, we recommend you use a pre-commit hook, which runs black when you type
git commit:pre-commit install
Develop your contribution:
Pull the latest changes from upstream:
git checkout master git pull upstream master
Create a branch for the feature you want to work on. Since the branch name will appear in the merge message, use a sensible name such as 'bugfix-for-issue-1480':
git checkout -b bugfix-for-issue-1480
Commit locally as you progress (
git addandgit commit)
Test your contribution:
Run the test suite locally (see Testing for details):
PYTHONPATH=. pytest networkx
Running the tests locally before submitting a pull request helps catch problems early and reduces the load on the continuous integration system.
Submit your contribution:
Push your changes back to your fork on GitHub:
git push origin bugfix-for-issue-1480
Go to GitHub. The new branch will show up with a green Pull Request button---click it.
If you want, post on the mailing list to explain your changes or to ask for review.
For a more detailed discussion, read these :doc:`detailed documents
<gitwash/index>` on how to use Git with networkx
(https://networkx.org/documentation/latest/developer/gitwash/index.html).
Review process:
- Reviewers (the other developers and interested community members) will write inline and/or general comments on your Pull Request (PR) to help you improve its implementation, documentation, and style. Every single developer working on the project has their code reviewed, and we've come to see it as friendly conversation from which we all learn and the overall code quality benefits. Therefore, please don't let the review discourage you from contributing: its only aim is to improve the quality of project, not to criticize (we are, after all, very grateful for the time you're donating!).
- To update your pull request, make your changes on your local repository and commit. As soon as those changes are pushed up (to the same branch as before) the pull request will update automatically.
- Travis-CI, a continuous integration service, is triggered after each Pull Request update to build the code and run unit tests of your branch. The Travis tests must pass before your PR can be merged. If Travis fails, you can find out why by clicking on the "failed" icon (red cross) and inspecting the build and test log.
- AppVeyor, is another continuous integration service that we use. You will also need to make sure that the AppVeyor tests pass.
Note
If the PR closes an issue, make sure that GitHub knows to automatically close the issue when the PR is merged. For example, if the PR closes issue number 1480, you could use the phrase "Fixes #1480" in the PR description or commit message.
Document changes
If your change introduces any API modifications, please update
doc/release/release_dev.rst.If your change introduces a deprecation, add a reminder to
doc/developer/deprecations.rstfor the team to remove the deprecated functionality in the future.Note
To reviewers: make sure the merge message has a brief description of the change(s) and if the PR closes an issue add, for example, "Closes #123" where 123 is the issue number.
If GitHub indicates that the branch of your Pull Request can no longer be merged automatically, merge the master branch into yours:
git fetch upstream master git merge upstream/master
If any conflicts occur, they need to be fixed before continuing. See which files are in conflict using:
git status
Which displays a message like:
Unmerged paths: (use "git add <file>..." to mark resolution) both modified: file_with_conflict.txt
Inside the conflicted file, you'll find sections like these:
<<<<<<< HEAD The way the text looks in your branch ======= The way the text looks in the master branch >>>>>>> master
Choose one version of the text that should be kept, and delete the rest:
The way the text looks in your branch
Now, add the fixed file:
git add file_with_conflict.txt
Once you've fixed all merge conflicts, do:
git commit
Note
Advanced Git users are encouraged to rebase instead of merge, but we squash and merge most PRs either way.
All code should have tests.
All code should be documented, to the same standard as NumPy and SciPy.
All changes are reviewed. Ask on the mailing list if you get no response to your pull request.
Default dependencies are listed in
requirements/default.txtand extra (i.e., optional) dependencies are listed inrequirements/extra.txt. We don't often add new default and extra dependencies. If you are considering adding code that has a dependency, you should first consider adding a gallery example. Typically, new proposed dependencies would first be added as extra dependencies. Extra dependencies should be easy to install on all platforms and widely-used. New default dependencies should be easy to install on all platforms, widely-used in the community, and have demonstrated potential for wide-spread use in NetworkX.Use the following import conventions:
import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.pyplot as plt import pandas as pd import networkx as nx
After importing sp` for
scipy:import scipy as sp
use the following imports:
import scipy.linalg # call as sp.linalg import scipy.sparse # call as sp.sparse import scipy.sparse.linalg # call as sp.sparse.linalg import scipy.stats # call as sp.stats import scipy.optimize # call as sp.optimize
For example, many libraries have a
linalgsubpackage:nx.linalg,np.linalg,sp.linalg,sp.sparse.linalg. The above import pattern makes the origin of any particular instance oflinalgexplicit.Use the decorator
not_implemented_forinnetworkx/utils/decorators.pyto designate that a function doesn't accept 'directed', 'undirected', 'multigraph' or 'graph'. The first argument of the decorated function should be the graph object to be checked.@nx.not_implemented_for('directed', 'multigraph') def function_not_for_MultiDiGraph(G, others): # function not for graphs that are directed *and* multigraph pass @nx.not_implemented_for('directed') @nx.not_implemented_for('multigraph') def function_only_for_Graph(G, others): # function not for directed graphs *or* for multigraphs pass
networkx has an extensive test suite that ensures correct
execution on your system. The test suite has to pass before a pull
request can be merged, and tests should be added to cover any
modifications to the code base.
We make use of the pytest
testing framework, with tests located in the various
networkx/submodule/tests folders.
To run all tests:
$ PYTHONPATH=. pytest networkx
Or the tests for a specific submodule:
$ PYTHONPATH=. pytest networkx/readwrite
Or tests from a specific file:
$ PYTHONPATH=. pytest networkx/readwrite/tests/test_yaml.py
Or a single test within that file:
$ PYTHONPATH=. pytest networkx/readwrite/tests/test_yaml.py::TestYaml::testUndirected
Use --doctest-modules to run doctests.
For example, run all tests and all doctests using:
$ PYTHONPATH=. pytest --doctest-modules networkx
Tests for a module should ideally cover all code in that module, i.e., statement coverage should be at 100%.
To measure the test coverage, run:
$ PYTHONPATH=. pytest --cov=networkx networkx
This will print a report with one line for each file in networkx, detailing the test coverage:
Name Stmts Miss Branch BrPart Cover ---------------------------------------------------------------------------------- networkx/__init__.py 33 2 2 1 91% networkx/algorithms/__init__.py 114 0 0 0 100% networkx/algorithms/approximation/__init__.py 12 0 0 0 100% networkx/algorithms/approximation/clique.py 42 1 18 1 97% ...
Please report bugs on GitHub.