Overview

Here we give a high-level overview of geovista.

We briefly discuss its goals and motivation, and introduce you to the rich, powerful ecosystem that underpins its evolving capability.

Motivation

geovista is a pure Python package that offers performant, publication quality 3-D cartographic rendering of Earth Science data.

Its goal is to lower the bar for scientists, such that they can easily analyze, visualize, and explore their rectilinear, curvilinear and unstructured geospatial data interactively in 3-D, whilst also supporting traditional 2-D static plots, cartographic projections and coordinate transformations.

Our intention is for geovista to be a cartographic gateway into the powerful world of PyVista, and all that it offers. Opening the door for scientists to rediscover their data through a responsive visualization toolchain that leverages the power of the GPU.

Simply put,

“GeoVista is to PyVista”, as “Cartopy is to Matplotlib”

That’s our aspiration.

Akin to Cartopy, we intend geovista to remain as flexible and open-ended as possible to the Scientific Python community. This means that geovista will remain agnostic to packages that specialize in preparing your spatial data for visualization, such as geopandas, iris and xarray.

Ecosystem

geovista is built on the shoulders of giants, namely PyVista and the Visualization Toolkit (VTK), thus allowing it to easily leverage the power of the GPU.

Fig. 1: Ecosystem of geovista

As shown above, geovista is built upon a performant, established, and feature rich VTK toolchain, which is supported and maintained by Kitware and the open-source community.

This toolchain is enabled through PyVista, which exposes the powerful visualization backend of VTK through an intuitive Pythonic interface.

PyVista in a Nutshell

Fig. 2: PyVista Examples

PyVista is a powerful open-source 3-D visualization library built on VTK, providing:

• A Pythonic, high-level, and streamlined API to VTK

• Mesh data structures

• Batteries-included access to a rich suite of mesh filtering algorithms

• GPU accelerated visualization

• Scales up to large and complex geometries

• Interoperability with ParaView through VTK (see Fig. 1:)

• Static and interactive plot integration within Jupyter ecosystem

Development Status

It’s still early days for geovista, but we’re already excited about the potential and promise that it might offer to the Earth Science community.

Our primary focus at the moment is to provision a stable API and core set of features … we’re working hard on that, but we’re not there yet!

Thanks for your patience 🙏