The goal of Pangeo is to make it quicker and easier to go from raw data to scientific insights. We want to spend less time writing boilerplate code and be able to perform calculations quickly and interactively on very large datasets. Pangeo is based around the python programming language and the scientific python software ecosystem. Pangeo is aimed at users who already have basic familiarity with using scientific python and are looking for ways to make their analysis workflow more efficient, scalable, and pleasant. If you are completely new to scientific python, you should first familiarize yourself with the basics before moving forward with this guide.
Pangeo is not a specific software package. A Pangeo environment is made of up of many different open-source software packages. Each of these packages has its own repository, documentation, and development team. You can learn more about these packages by following the links below. This loose collection of packages is often referred to as the Pangeo stack.
For more information about how these packages relate to each other, consult the Technical Architecture page.
Pangeo Gallery showcases real-world science applications of the Pangeo stack. The use cases are formatted as Jupyter Notebooks, a self-describing computational document including text, code, and figures. A great way to get started is to find a use case that is similar to the analysis you want to do and modify it to suit your needs.
A great way for novices to learn how to use Pangeo is to start from one of these use cases and modify it for your needs.
The best way to try out Pangeo is to explore one of the interactive Binders in Pangeo Gallery. If you’re interested in working in a cloud based environment, you can sign up for Pangeo Cloud. If none of these meets your needs, you can create your own deployment on HPC or cloud by following the Deployment Setup Guides.
Pangeo is a work in progress. We need engagement from scientists like you to realize the goals of the project. Currently, the best way to provide your feedback and interact with the Pangeo community is via the Pangeo GitHub issue tracker. We use this a forum to discuss all details of the project. Please drop in and share what you are working on, including any challenges your have encountered along the way.
If you are using Pangeo for your research, we would love to include an example in the Pangeo Gallery. It’s easy to add a new gallery! Feel free to raise an issue on the Pangeo Discourse Forum to propose your use case.
Datasets are central to Pangeo. Our goal is to bring the computing to the data, rather than the other way around. If you have data you would like to make accessible to Pangeo, your best bet is to place it on a large shared HPC cluster or in cloud storage. You can then add your dataset to catalog.pangeo.io. via the GitHub repo https://github.com/pangeo-data/pangeo-datastore.
The success of the Pangeo project depends on the sustainable development of the underlying Packages. A great way to contribute to Pangeo is to contribute to those packages. Some ways to do this include:
helping to improve the documentation of these packages
raising issues on the package github repositories to report bugs or identify missing features
fixing bugs or implementing new features yourself
