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This release provides a cloud-ready Docker image for Python–R geospatial and earth-science workflows, designed for use in shared JupyterHub and similar multi-user environments.
py-rocket-geospatial-2 integrates modern Python and R geospatial stacks with tools commonly used across oceanography, climate science, hydrology, and remote sensing. The image follows design patterns from the Pangeo ecosystem and emphasizes scalable, array-based analysis of large spatiotemporal datasets.
What this image provides
- Python geospatial stack optimized for large datasets, distributed computing, and cloud-native object storage
- R + RStudio with geospatial packages installed using Rocker Project scripts
- JupyterLab with both Python and R kernel support
- Desktop environment (VNC) for GUI-based tools such as QGIS and Panoply
- VS Code OSS configured for scientific notebooks and Quarto
- Publishing toolchain including Quarto, JupyterBook, MyST, Pandoc, and TeX Live
- Helper scripts (
pyrocket_scripts,rocker_scripts) to support customization and extension
Intended use
This image is intended to lower barriers for reproducible, cloud-ready analysis of large earth-system datasets, including workflows that authenticate and access data from NASA Earthdata, NOAA and other major earth-observation archives.
Licensing & attribution
The resulting container image is released under the Apache-2.0 License.
Rocker installation scripts retain their original GPL-2.0-or-later licensing.
Attribution is appreciated when this container image is used, adapted, or redistributed.
What's Changed
- Install AWS tools
awsv2 --install