Contributing to DAQuiri

We would gladly appreciate contributions from users to improve DAQuiri and the documentation. We also welcome reports of any issues with software or the clarity of the documentation, and ideas that might advance the core aims of the project: to let scientists write free data acquisition software (quickly!).

If you want to contribute to the code, get in touch (see the authors page) and check out some of the features on our backlog. You’ll need a few tools in order to work on DAQuiri:

  1. git
  2. Python >= 3.7 (Anaconda/Miniconda is best because some dependencies are otherwise difficult to install).

Installing a development copy of DAQuiri

Clone (or fork) the repository::
git clone https://github.com/chstan/daquiri.git cd daquiri

Install an environment you can work with using Anaconda or Miniconda (preferably). This process is well documented elsewhere, and there’s nothing unusual in the process for us.

Once in your environment, install DAQuiri and its requirements locally with

# conda activate {my environment}

pip install -e .

# ...much time passes...
# at this point you should be able to run DAQuiri and the examples.

python -m daquiri.examples.scanning_experiment

Working on a new feature

After 1.0.0 (before which there will likely be a lot of code churn), development will roughly follow A Successful Git Branching Model, albeit with the more standard branch names develop -> master and master -> release.

Please contribute new features on feature branches and issue pull/merge requests in order to make changes.

If you make a large change such as adding a new feature, please contribute or recruit a willing volunteer to make sure the adjustment is reflected in the documentation and tests.