Cartopy is a Python package which builds on Proj.4 to define coordinate reference systems for the transformation and visualisation of geospatial data. It has a simple matplotlib interface for publication quality visualisation. This talk will outline some of cartopy's functionality and demonstrate some practical applications within the realm of scientific presentation of geospatial data.
The practice of representing geospatial data upon a flat surface is known as cartography, and the topological implications of projecting fundamentally 3D data onto a 2 dimensional surface has been the challenge of map-makers since time immemorial. Geospatial visualisation software is often implemented without consideration for the 3rd dimension and this commonly results in problems around the dateline or at the poles. For small areas these problems are often not apparent and mostly surmountable, but at a global scale, such as when visualising output from GCMs (General circulation models), the underlying representation must be addressed head-on in order to visualise the data "impact free".
Cartopy is a Python package which builds on top of Proj.4 to define coordinate reference systems for the transformation and visualisation of geospatial data. As well as the fundamental transformations there is also a matplotlib interface allowing easy generation of maps with the same publication quiality expected of matplotlib. Cartopy employs several techniques to handle geospatial data correctly, including true spherical interpolation for raster data, and Shapely geometry interpolate-and-cut transformations for geospatial vector data.
This talk will outline some of the capabilities of cartopy, and continue onto its practical application within the realm of scientific presentation of geospatial data.