Contributions to the instrument repository and the main code base are highly encouraged. This section outlines the basic work-flow for new contributors.
Using the development version
New features are added to the development version of PyMeasure, hosted on GitHub. We use Git version control to track and manage changes to the source code. On Windows, we recommend using GitHub Desktop. Make sure you have an appropriate version of Git (or GitHub Desktop) installed and that you have a GitHub account.
In order to add your feature, you need to first fork PyMeasure. This will create a copy of the repository under your GitHub account.
The instructions below assume that you have set up Anaconda, as described in the Quick Start guide and describe the terminal commands necessary. If you are using GitHub Desktop, take a look through their documentation to understand the corresponding steps.
Clone your fork of PyMeasure
your-github-username/pymeasure. In the following terminal commands replace your desired path and GitHub username.
cd /path/for/code git clone https://github.com/your-github-username/pymeasure.git
If you had already installed PyMeasure using
pip, make sure to uninstall it before continuing.
pip uninstall pymeasure
Install PyMeasure in the editable mode.
cd /path/for/code/pymeasure pip install -e .
This will allow you to edit the files of PyMeasure and see the changes reflected. Make sure to reset your notebook kernel or Python console when doing so. Now you have your own copy of the development version of PyMeasure installed!
Working on a new feature
We use branches in Git to allow multiple features to be worked on simultaneously, without causing conflicts. The master branch contains the stable development version. Instead of working on the master branch, you will create your own branch off the master and merge it back into the master when you are finished.
Create a new branch for your feature before editing the code. For example, if you want to add the new instrument “Extreme 5000” you will make a new branch “dev/extreme-5000”.
git branch dev/extreme-5000
You can also make a new branch on GitHub. If you do so, you will have to fetch these changes before the branch will show up on your local computer.
Once you have created the branch, change your current branch to match the new one.
git checkout dev/extreme-5000
Now you are ready to write your new feature and make changes to the code. To ensure consistency, please follow the coding standards for PyMeasure. Use
git status to check on the files that have been changed. As you go, commit your changes and push them to your fork.
git add file-that-changed.py git commit -m "A short description about what changed" git push
Making a pull request
While you are working, it is helpful to start a pull request (PR) targeting the
master branch of
pymeasure/pymeasure. This will allow you to discuss your feature with other contributors. We encourage you to start this pull request already after your first commit.
You may mark a pull request as a draft, if it is in an early state.
There is some automation in place to run the unit tests and check some coding standards. Annotations in the “Files changed” tab indicate problems for you to correct (e.g. linting or docstring warnings).
Your pull-request will be reviewed by the PyMeasure maintainers. Frequently there is some iteration and discussion based on that feedback until a pull request can be merged. This will happen either in the conversation tab or in inline code comments.
Be aware that due to maintainer manpower limitations it might take a long time until PRs get reviewed and/or merged. In general, review effort scales badly with PR size. Therefore, smaller PRs are much preferred. Try to limit your contribution to one “aspect”, e.g. one instrument (or a few if closely related), one bug fix, or one feature contribution.
If you placed your contribution in a separate branch as suggested above, you can easily use your contribution in the meantime – just check out your feature branch instead of master.
Unit tests are run each time a new commit is made to a branch. The purpose is to catch changes that break the current functionality, by testing each feature unit. PyMeasure relies on pytest to preform these tests, which are run on TravisCI and Appveyor for Linux/macOS and Windows respectively.
Running the unit tests while you develop is highly encouraged. This will ensure that you have a working contribution when you create a pull request.
If your feature can be tested, unit tests are required. This will ensure that your features keep working as new features are added.
Now you are familiar with all the pieces of the PyMeasure development work-flow. We look forward to seeing your pull-request!