Advanced Visualization Cookbook

This Project Pythia Cookbook covers advanced visualization techniques building upon and combining various Python packages.

Motivation

The possibilities of data visualization in Python are almost endless. Already using matplotlib the workhorse behind many visualization packages, the user has a lot of customization options available to them. cartopy, metpy, seaborn, geocat-viz, and datashader are all also great packages that can offer unique additions to your Python visualization toolbox.

This Cookbook will house various visualization workflow examples that use different visualization packages, highlight the differences in functionality between the packages, any noteable syntax distinctions, and demonstrate combining tools to achieve a specific outcome.

Authors

Julia Kent, Anissa Zacharias, Orhan Eroglu, Philip Chmielowiec, John Clyne

Contributors

Structure

This cookbook is broken up into a few sections - a “Basics of Geoscience Visualization” intro that compares different visualization packages and plot elements, and then example workflows of advanced visualization applications that are further subdivided.

Basics of Geoscience Visualization

Here we introduce the basics of geoscience visualization, the elements of a plot, different types of plots, and some unique considerations when dealing with model and measured data. Here we also share a comparison of different visualization packages available to the Scientific Python programmer.

Specialty Plots

There are some plot types that are unique to atmospheric science such as Taylor Diagrams or Skew-T plots. Here we will use metpy and geocat-viz to demonstrate these specialty plots.

Visualization of Structured Grids

In this section we will demonstrate how to visualize data that is on a structured grid. Namely, we will look at Spaghetti Hurricane plots. Here we will have workflows that utilize packages such as cartopy and geocat-viz.

Animation

Animated plots are great tools for science communication and outreach. We will demonstrate how to make your plots come to life. In this book, we use “animated plots” to refer to stable animations, such as the creation of gifs or videos.

Interactivity

Dynamically rendering, animating, panning & zooming over a plot can be great to increase data fidelity. We will showcase how to use Holoviz technologies with Bokeh backend to create interactive plots, utilizing an unstructured grid data in the Model for Prediction Across Scales (MPAS) format.

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 com

Clone the https://github.com/ProjectPythia/advanced-viz-cookbook repository:

 git clone https://github.com/ProjectPythia/advanced-viz-cookbook.git

Move into the advanced-viz-cookbook directory
```
cd advanced-viz-cookbook
```

Create and activate your conda environment from the environment.yml file

conda env create -f environment.yml
conda activate advanced-viz-cookbook

Move into the notebooks directory and start up Jupyterlab
```
cd notebooks/
jupyter lab
```