This paper marks the first release of Verde, a Python library for processing and gridding spatial data. Verde is the first part of a large-scale refactoring of the Fatiando a Terra project into separate packages.
The peer-review at JOSS is open and can be found at openjournals/joss-reviews#957.
Figure: Example of using
verde.BlockMean to calculate weighted means in spatial
blocks assuming different uncertainty models. Full source code to produce this image is
available in the Verde example
Verde is a Python library for gridding spatial data using different Green's functions.
It differs from the radial basis functions in
scipy.interpolate by providing an API
inspired by scikit-learn. The Verde API should be familiar to scikit-learn users but
is tweaked to work with spatial data, which has Cartesian or geographic coordinates and
multiple data components instead of an
X feature matrix and
y label vector. The
library also includes more specialized Green's functions, utilities for trend estimation
and data decimation (which are often required prior to gridding), and more. Some of
these interpolation and data processing methods already exist in the Generic Mapping
Tools (GMT), a command-line program popular in the Earth Sciences. However, there are no
model selection tools in GMT and it can be difficult to separate parts of the processing
that are done internally by its modules. Verde is designed to be modular, easily
extended, and integrated into the scientific Python ecosystem. It can be used to
implement new interpolation methods by subclassing the
requiring only the implementation of the new Green's function. For example, it is
currently being used to develop a method for interpolation of 3-component GPS data.
Uieda, L. (2018). Verde: Processing and gridding spatial data using Green's functions. Journal of Open Source Software, 3(30), 957. doi:10.21105/joss.00957