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Elastic multi-component interpolation of ground displacement

Project description

Elastic multi-component interpolation of ground displacement

Documentation | Documentation (dev version) | Contact | Part of the Fatiando a Terra project

Latest version on PyPI TravisCI build status Test coverage status Compatible Python versions. Digital Object Identifier

Disclaimer

🚨 This package is in early stages of design and implementation. 🚨

We welcome any feedback and ideas! Let us know by submitting issues on Github or send us a message on our Slack chatroom.

About

Erizo is a Python package for interpolation (gridding) of multi-component ground displacement measurements (from GPS/GNSS or InSAR, for example). It uses an elastic Green’s functions approach for interpolation based on [Verde](https://www.fatiando.org/verde).

Project goals

  • 2- and 3-component velocity/displacement interpolation.
  • Use simple elastic Green’s functions.
  • Include cross-validated versions of all interpolators.
  • Provide an interface similar to scikit-learn for machine learning style interpolation.

Contacting Us

Contributing

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Contributing Guidelines

Please read our Contributing Guide to see how you can help and give feedback.

Imposter syndrome disclaimer

We want your help. No, really.

There may be a little voice inside your head that is telling you that you’re not ready to be an open source contributor; that your skills aren’t nearly good enough to contribute. What could you possibly offer?

We assure you that the little voice in your head is wrong.

Being a contributor doesn’t just mean writing code. Equality important contributions include: writing or proof-reading documentation, suggesting or implementing tests, or even giving feedback about the project (including giving feedback about the contribution process). If you’re coming to the project with fresh eyes, you might see the errors and assumptions that seasoned contributors have glossed over. If you can write any code at all, you can contribute code to open source. We are constantly trying out new skills, making mistakes, and learning from those mistakes. That’s how we all improve and we are happy to help others learn.

This disclaimer was adapted from the MetPy project.

License

This is free software: you can redistribute it and/or modify it under the terms of the BSD 3-clause License. A copy of this license is provided in LICENSE.txt.

Documentation for other versions

Project details


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