Contributor’s guide

Thank you for your interest in contributing to chemometrics. Your help is greatly appreciated. The source code is hosted at You can obtain the latest changes to chemometrics by cloning the repository.


If you have any questions, would like to request features, discovered a bug or have a feature request, please feel free to reach out to me or open an issue on Github.

General points

Some general points to consider when working on chemometrics:

  • chemometrics extends the capabilities of scikit-learn for chemometric applications. It should be easy to include chemometrics objects into existing scikit-learn workflows (e.g. pipelines).

  • Build on existing functionality of scikit-learn, numpy and scipy (e.g. consider subclassing the existing scikit-learn classes, if they provide the core functionality you would like to implement).

  • Code coverage with unittests should be >=90%, ideally 100% coverage.

  • Your code should come with documentation of similar or better quality than the rest of chemometrics.

Focus areas

Some topics which need improvement:

  • Implement readers for commercial spectroscopic and other chemical data formats.

  • Integrate existing 3rd party packages into chemometrics. Generally, there are some very useful small packages already available for chemometrics in Python. However, those packages are not curated under a common framework which reduces reusability and makes it more difficult for users to use those methods. Integrating the functionality into chemometrics simplifies the usage.

  • Add example sections in docstrings.


Code pushed to chemometrics will be released under GPLv3. If you are pushing to chemometrics, you agree to the provided code under this license.