Abstract¶
Generating Voronoi diagrams and interpolating / smoothing with spline tensions are among a list of new features in Stripy 2.0
We’ve been busy creating the next major release of Stripy. To refresh your memory, Stripy is a Python tool for triangulating scattered points either in Cartesian coordinates or on the sphere. It wraps a bunch of Fortran codes in a neat, object-oriented Python interface that can be used for many geographical applications.
What’s new?¶
Spline tension - a lot of data transformations in Stripy are underpinned by cubic splines (e.g. interpolation, derivatives, smoothing). In v2.0 you can now add spline tension which avoids overshoot / undershoot artefacts. The most visible improvements are in accuracy of derivatives at points along the boundary and extrapolation of data beyond the boundary of a mesh.
Difference between derivatives evaluated at the poles with spline tension vs. without. Looks like a beautiful flower. (Graphic generated with LavaVu).
Voronoi diagram - the Voronoi diagram is the dual of a Delaunay triangulation. For every triangle in the mesh, there is a voronoi point which lies at an equal radius from each node. The diagram is constructed by connecting up the voronoi points from each neighbouring triangle.
Voronoi diagram on the sphere. Graphic generated with Matplotlib and Cartopy.
Other** notable new features** include:
a new equispaced elliptical mesh in Cartesian coordinates
central area node weights for any mesh
efficient evaluation of second derivatives in Cartesian coordinates
better documentation, LGPLv3 license, and other small bug fixes
You can install the latest release of Stripy with pip
pip install stripyor Conda:
conda install -c underworldcode stripyMake Stripy better!¶
We welcome contributions to the code. If you want to add something you think is missing in Stripy, submit a pull request and if it looks good we’ll merge your changes. Check out our contribution guidelines for more details.