Unstructured Grid Visualization Cookbook

nightly-build Binder DOI

This Cookbook is a comprehensive showcase of workflows & techniques for visualizing Unstructured Grids using UXarray.


High-level, scalable visualizations of native unstructured grids is a much-needed ability in the Scientific Python Ecosystem. To achieve this, the process needs to:

  • Not regrid the unstructured grids into structured grids

  • Use provided information such as connectivity variables that come with the grid

  • Limit the amount of pre-processing needed to prepare the data for Python visualization tools

UXarray enables such visualization methods that operate directly on unstructured grid data, providing Xarray-styled functionality to better read in and use unstructured grid datasets that follow standard conventions. UXarray supports a variety of unstructured grid formats including UGRID, MPAS, SCRIP, and Exodus, and is extendable for other formats.

This cookbook covers an introduction to unstructured grids and UXarray, provides an overview of the visualization methods and libraries, and showcases several UXarray visualization functions.


This cookbook is split up into a few chapters that provide a detailed overview of how to use UXarray to work with and visualize unstructured grid datasets:

1. Introduction to UXarray & Unstructured Grids

Here we cover what unstructured grids are and how they are different than structured grids as well as whay UXarray could play a significant role in unstructured grid visualization.

2. Methods & Libraries for Unstructured Grid Visualization

In this chapter, we briefly introduce plotting libraries and their specific technologies as well as rendering techniques that could be used for unstructured grid plotting and are used as part of UXarray.

3. UXarray Visualization

Several visualization cases and examples that can be realized using UXarray are provided in this chapter; grid topology plots, polygons, points, to name a few. Also in this section, the usage of UXarray plotting API and a discussion of visualization at scale are also provided.

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing Shift+Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

  1. Clone the https://github.com/ProjectPythia/unstructured-grid-viz-cookbook repository:

     git clone https://github.com/ProjectPythia/unstructured-grid-viz-cookbook.git
  2. Move into the unstructured-grid-viz-cookbook directory

    cd unstructured-grid-viz-cookbook
  3. Create and activate your conda environment from the environment.yml file

    conda env create -f environment.yml
    conda activate unstructured-grid-viz-cookbook-dev
  4. Move into the notebooks directory and start up Jupyterlab

    cd notebooks/
    jupyter lab