Contributor’s guide

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

Note

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.

Licensing

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