Version 12.0 of the LSST Science Pipelines (aka W16/X16) is now available.
If you have previously consumed an official release of the LSST Stack, please take note of these major distribution changes from previous releases:
New Documentation at pipelines.lsst.io
Our release documentation and installation instructions are no longer on the corporate wiki; you can find them at http://pipelines.lsst.io/.
You are welcome and encouraged to contribute improvements to the repo on GitHub: https://github.com/lsst/pipelines_docs/.
Major Feature Developments
With a new release schedule, version 12.0 saw prolonged development. This release features implementations of algorithms originally developed in the Hyper-SuprimeCam pipeline, in addition to many other improvements. Even Astropy views of LSST catalog objects are available.
Full details can be found in the extensive release notes.
New: Install with Anaconda
In addition to the customary source installation, this release features two binary installation methods:
- CernVM FS (contributed by Fabio Hernandez)
If you are unsure which one to use, we recommend you try the conda binaries. This is the first conda binary release though and it is always possible we did not anticipate all the problems “in the wild;” if you experience difficulties, please report them so that we can improve our testing (and help you!).
New Release Schedule
The timing of the formal releases (which mark the end of each DM planning cycle) has precessed from Winter/Summer to Spring/Fall. This has been done to avoid conflict between DM cycle planning activities and the LSST All-Hands Meetings in August and February. In the future, formal releases will be in June and December. In order to precess the cycles to the new schedule, an Extra 3-month cycle (aka X16) was added, so 12.0 is a “bumper” release containing approximately 9 months of activity.
Join Us on the Community Forum
If you have questions or comments or comments about the LSST Science Pipelines, visit our new community forum for advice: https://community.lsst.org/.