SlideRule
Process Earth science datasets in the cloud through REST API calls to SlideRule web services.
- Latest Version:
v4.3.1, http://slideruleearth.io/web/rtd/release_notes/release_notes.html
- GitHub:
- Web:
- PyPi:
- Conda:
- Node.js:
SlideRule is a web service for on-demand science data processing, which provides researchers and other Earth science data systems low-latency access to customized data products using processing parameters supplied at the time of the request. SlideRule runs in AWS us-west-2 and has access to ICESat-2, GEDI, Landsat, ArcticDEM, REMA, and other datasets stored in S3 (see Assets for a full list).
“Using SlideRule” typically means running a Python script you’ve developed to analyze Earth science data, and in that script calling functions in the sliderule Python package to make processing requests to SlideRule web services to perform some of the data intensive parts of your analysis. Most of the documentation and examples we provide are focused on this use-case. We do provide other means of interacting with SlideRule (most notably the current demo and future web client), but those features are still under development and documentation for them is sparse.
Where To Begin
SlideRule Demo
Try out an interactive widgets demo.
Examples
Jump right in and learn from examples.
Getting Started
Walkthrough what SlideRule can do.
Contacting Us
SlideRule is openly developed on GitHub at https://github.com/ICESat2-SlideRule. We welcome all feedback and contributions! For more details on the different ways to reach out to us, see our Contact Us page.
Project Information
The SlideRule project is funded by NASA’s ICESat-2 program and is led by the University of Washington in collaboration with NASA Goddard Space Flight Center. The first public release of SlideRule occurred in April 2021. Since then we’ve continued to add new services, new algorithms, and new datasets, while also making improvements to our processing architecture. Looking to the future, we hope to make SlideRule an indispensable component in the analysis of a broad array of Earth Science datasets that help us better understand the planet we call home.