verde 1.7.0

About

Verde is a Python library for processing spatial data (bathymetry, geophysics surveys, etc) and interpolating it on regular grids (i.e., gridding).

Our core interpolation methods are inspired by machine-learning. As such, Verde implements an interface that is similar to the popular scikit-learn library. We also provide other analysis methods that are often used in combination with gridding, like trend removal, blocked/windowed operations, cross-validation, and more!

Project goals

• Provide a machine-learning inspired interface for gridding spatial data

• Integration with the Scipy stack: numpy, pandas, scikit-learn, and xarray

• Include common processing and data preparation tasks, like blocked means and 2D trends

• Support for gridding scalar and vector data (like wind speed or GPS velocities)

• Support for both Cartesian and geographic coordinates

The first release of Verde was focused on meeting most of these initial goals and establishing the look and feel of the library. Later releases will focus on expanding the range of gridders available, optimizing the code, and improving algorithms so that larger-than-memory datasets can also be supported.

Contacting us

Find out more about how to reach us at fatiando.org/contact

Citing Verde

This is research software made by scientists (see AUTHORS.md). Citations help us justify the effort that goes into building and maintaining this project. If you used Verde for your research, please consider citing us.

See our CITATION.rst file to find out more.

Contributing

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.