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* [[Processing Blog]] describing how we solved typical problems.  
 
* [[Processing Blog]] describing how we solved typical problems.  
 
* [[Processing L498 thermolysin]] details instructions to reproduce the results published in the forthcoming ''cctbx.xfel'' paper.
 
* [[Processing L498 thermolysin]] details instructions to reproduce the results published in the forthcoming ''cctbx.xfel'' paper.
 +
* [[2017 Tutorials]]. Berkeley Lab Serial Crystallography Workshop, 16-17 Feb 2017.
 
* [[2014 Tutorials | 2014 Workshop Tutorial: ''cctbx.xfel'' ]]. Note, due to recent changes at LCLS, many of these tutorials are out of date for data collected late 2014 and onwards.  For more up to date information, see above [[tutorials]].  For data collected before late 2014, these workshop tutorials are useful.
 
* [[2014 Tutorials | 2014 Workshop Tutorial: ''cctbx.xfel'' ]]. Note, due to recent changes at LCLS, many of these tutorials are out of date for data collected late 2014 and onwards.  For more up to date information, see above [[tutorials]].  For data collected before late 2014, these workshop tutorials are useful.
 
* [[2014_workshop | Powerpoint presentations]]
 
* [[2014_workshop | Powerpoint presentations]]

Revision as of 15:06, 8 February 2017

Open-source tools for free-electron laser data processing

cctbx.xfel is a suite of software tools designed to process diffraction data from serial femtosecond crystallography (SFX) measurements at an X-ray free-electron laser (XFEL). Built on the Computational Crystallographic Toolbox (cctbx), the same toolbox on which PHENIX, LABELIT, and post-refinement and merging program, PRIME are built, it enables the user to solve difficult problems relating to processing XFEL data. The programs and modules provided by cctbx.xfel can reduce a large set of still diffraction images recorded at Stanford’s Linac Coherent Light Source (LCLS) to a single MTZ file containing merged reflection intensities suitable for structure solution.

cctbx.xfel resources

The tutorials on this wiki provide detailed instructions for indexing and integrating still diffraction images extracted from the raw data streams recorded at the LCLS, including pre-processing steps such as dark pedestal generation and refinement of the detector geometry of the Cornell–SLAC pixel array detectors (CSPAD) in use at the CXI and XPP end stations. The tutorials also cover tools to efficiently leverage the LCLS computing cluster to process the thousands to millions of diffraction images that can be recorded in a short time.

Other related information:

This project is under active development. For any assistance, please contact the authors.