The LSST Science Pipelines

The LSST Science Pipelines enable optical and near-infrared astronomy in the big data era. We are building the Science Pipelines for the Large Synoptic Survey Telescope (LSST), but our command line task and Python API can be extended for any optical or near-infrared dataset.

The latest release is 14.0: learn what’s new.

Getting started

If you’re new to the LSST Science Pipelines, these tutorials will get you up and running with step-by-step data processing tutorials.

• Data processing tutorial series: Part 1 Data repositories · Part 2 Single frame processing · Part 3 Image and catalog display · Part 4 Image coaddition · Part 5 Source measurement · Part 6 Multi-band catalog analysis.

Join us on the LSST Community forum to get help and share ideas.

Installation

Recommended installation path:

• Installing with newinstall.sh
• Setting up installed LSST Science Pipelines
• Top-level packages to install

Alternative distributions and installation methods:

• Running with Docker
• Installing from source with lsstsw
• CernVM FS (contributed by CC-IN2P3)

Related topics:

• Configuring Git LFS for data packages
• Building a package with the installed Science Pipelines stack

To install the LSST Simulation software, such as MAF, please follow the LSST Simulations documentation.

Python modules

• lsst.pipe.base
• lsst.verify

Packages

• pipe_base
• verify
• verify_metrics

Release details

• Release Notes
• Known Issues
• Characterization Metric Reports

More info

• Join us on the LSST Community forum, community.lsst.org.
• Fork our code on GitHub at https://github.com/lsst.
• Report issues in JIRA.
• API documentation is currently published with Doxygen.
• DM Developer guidance is at https://developer.lsst.io.
• Learn more about LSST Data Management by visiting http://lsst.org/about/dm.
• Contribute to our documentation. This guide is on GitHub at lsst/pipelines_lsst_io.