Speaker
Dr
Nicola Wadeson
(Diamond Light Source UK)
Description
Savu is an open-source, portable, tomography reconstruction and processing
pipeline developed at the Diamond Light Source as a post-processing tool for tomography data collected with a parallel beam geometry. Written in object-oriented python, it runs in parallel using mpi-based cluster computing or serially on a PC. The parallel
HDF5 backend handles big data, and the design allows processing of multi-modal, n-dimensional data. The actual processing is performed by plugins, which are abstracted from the rest of the framework that handles the movement of the data and controls the plugins.
Each plugin performs an independent processing step (such as filtering or reconstruction).
From a developer perspective, both existing (python packages, C/C++ code)
and new functionality is easy to integrate, as plugins are stand-alone and only need to provide the framework with information detailing the amount and type of data (e.g. projection or sinogram), that it would like to receive. From
a user perspective, a simple process list is required, which specifies the order of plugins that should be applied to their data. Process lists are provided to the user, ensuring they do not require any knowledge of tomography data processing, and these lists
can be tailored specifically to their data. More advanced users and beamline staff can experiment with different process lists containing a series of plugins chosen from the plugin repository.
Primary author
Dr
Nicola Wadeson
(Diamond Light Source UK)
Co-author
Dr
Mark Basham
(Diamond Light Source)
