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Contributing to PyQtGraph

Contributions to pyqtgraph are welcome! Be kind and respectful! See our Code of Conduct for details.

Please use the following guidelines when preparing changes:

Development Environment Creation

First thing to do is fork the repository, and clone your own fork to your local computer.

git clone https://github.com/<username>/pyqtgraph.git
cd pyqtgraph

While there is nothing preventing users from using conda environments, as a general principle, we recommend using the venv module for creating an otherwise empty virtual environment. Furthermore, at this time, WSL is not supported (it can likely be made to work, but you're on your own if you go down this route).

python3.9 -m venv .venv
source .venv/bin/activate
# on windows this would be .venv/Scripts/activate
python -m pip install --upgrade wheel setuptools pip
python -m pip install numpy scipy pyside6 -e .

Before making changes to the code-base, create a different branch with a name that should be unique (this makes it easier for maintainers to examine the proposed changes locally).

git switch -c my-new-feature

When you're ready to submit the pull request, do so via the github, and the target of the pull request should be the master branch in the pyqtgraph repo.

Pull requests should include only a focused and related set of changes. Mixed features and unrelated changes may be rejected.

For major changes, it is recommended to discuss your plans on the mailing list or in a github issue/discussion before putting in too much effort.

PyQtGraph has adopted NEP-29 which governs the timeline for phasing out support for numpy and python versions.

Documentation

  • Writing proper documentation and unit tests is highly encouraged. PyQtGraph uses pytest for testing.
  • Documentation is generated with sphinx, and usage of numpy-docstyle is encouraged (many places in the library do not use this docstring style at present, it's a gradual process to migrate).
  • The docs built for this PR can be previewed by clicking on the "Details" link for the read-the-docs entry in the checks section of the PR conversation page.

Templates

PyQtGraph makes use of .ui files where are compiled using uic. These files are identified by ending with _generic.py and have a .ui file next to them in the same directory. In past versions of PyQtGraph, there was a file for each binding. These are generated using tools/rebuildUi.py. Upon completion, the compiled files need to be modified such that they do not inherit from PyQt6 but from pyqtgraph's Qt abstraction layer.

Style guidelines

Formatting Rules Suggestions

  • PyQtGraph prefers PEP8 for most style issues, but this is not enforced rigorously as long as the code is clean and readable.
  • Variable and Function/Methods that are intended to be part of the public API should be camelCase.
  • "Private" methods/variables should have a leading underscore (_) before the name.

Pre-Commit

PyQtGraph developers are highly encouraged to (but not required) to use pre-commit. pre-commit does a number of checks when attempting to commit the code to being committed, such as ensuring no large files are accidentally added, address mixed-line-endings types and so on. Check the pre-commit documentation on how to setup.

Testing

Basic Setup

  • tox
  • pytest
  • pytest-cov
  • pytest-xdist
  • Optional: pytest-xvfb (used on linux with headless displays)

To run the test suite, after installing the above dependencies run

python -m pytest pyqtgraph/examples tests

Tox

As PyQtGraph supports a wide array of Qt-bindings, and python versions, we make use of tox to test against as many supported configurations as feasible. With tox installed, simply run tox and it will run through all the configurations. This should be done if there is uncertainty regarding changes working on specific combinations of PyQt bindings and/or python versions.

Continuous Integration

For our Continuous Integration, we utilize Github Actions. Tested configurations are visible on README.

Benchmarks

( Still under development ) To ensure this library is performant, we use Air Speed Velocity (asv) to run benchmarks. For developing on core functions and classes, be aware of any impact your changes have on their speed. To configure and run asv:

pip install asv
python setup.py asv_config
asv run

( TODO publish results )