# Advanced Visualization Cookbook [](https://binder.projectpythia.org/v2/gh/ProjectPythia/advanced-viz-cookbook.git/main?labpath=notebooks) [](https://zenodo.org/badge/latestdoi/671205314) 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](@jukent), [Anissa Zacharias](@anissa111), [Orhan Eroglu](@erogluorhan), [Philip Chmielowiec](@philipc2), [John Clyne](@clyne) ### Contributors ## Structure This cookbook is broken up into a few sections - a "Review of Visualization Foundations" intro that compares different visualization packages and plot elements, and then example workflows of advanced visualization applications that are further subdivided. ### Review of Visualization Foundations 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. We also share a comparison of different visualization packages available in the scientific Python ecosystem. ### Specialty Plots There are some plot types that are unique to atmospheric science such as Taylor Diagrams and Skew-T plots. Here we use [`metpy`](https://unidata.github.io/MetPy/latest/index.html) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/) to demonstrate these specialty plots. We also demonstrate the use of spaghetti plots for hurricane applications using packages such as [`cartopy`](https://scitools.org.uk/cartopy/docs/latest/) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/). ### 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. ## Running the Notebooks You can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine. ### Running on Binder The simplest way to interact with a Jupyter Notebook is through [Binder](https://binder.projectpythia.org/), which enables the execution of a [Jupyter Book](https://jupyterbook.org) 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 {kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with a live Jupyter notebook are described in [Getting Started with Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html). ### Running on Your Own Machine If you are interested in running this material locally on your com 1. Clone the `https://github.com/ProjectPythia/advanced-viz-cookbook` repository: ```bash git clone https://github.com/ProjectPythia/advanced-viz-cookbook.git ``` 1. Move into the `advanced-viz-cookbook` directory ```bash cd advanced-viz-cookbook ``` 1. Create and activate your conda environment from the `environment.yml` file ```bash conda env create -f environment.yml conda activate advanced-viz-cookbook ``` 1. Move into the `notebooks` directory and start up Jupyterlab ```bash cd notebooks/ jupyter lab ```