Xi-cam: a versatile interface for data visualization and analysis

Abstract

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Comparative morphology of cheliceral muscles using high‐resolution X‐ray microcomputed‐tomography in palpimanoid spiders (Araneae, Palpimanoidea)

“…X‐ray energies of 33.5 kV were used, and images were collected with a LuAG:Ce scintillator and either a 10X or 5X objective lens and a PCO.edge CMOS detector, yielding a reconstructed 3D voxel size of 0.65 × 0.65 × 0.65 μm or 1.3 × 1.3 × 1.3 μm, respectively. Image stacks (TIFF format) were produced using Xi‐CAM (Pandolfi et al, ), with the gridrec algorithm as implemented in TomoPy (Gürsoy, De Carlo, Xiao, & Jacobsen, ). Using Avizo software, 3D‐reconstructions were created from the resulting image stacks that could be rotated and digitally sliced.…”

Synchrotron Big Data Science

“…This is not an isolated attempt and several solutions at different large facilities have been developed in order to address the Big Data demands of the tomography community, and thus deal with these new data deluge challenges. Among other prominent examples, Python‐based TomoPy framework at APS has been a very successful data‐intensive approach, which has also been adopted at ALS and equipped with a modern interface to allow a further increase in productivity for tomography users. Other examples are: the open‐source Python‐based Savu framework at Diamond Light Source which reconstructs both full‐field tomography data and multimodal, mapping tomography data, and allows concurrent processing of multiple datasets; the SPOT framework at ALS, equipped with a powerful web portal that allows users to quickly visualize their data even on lightweight terminals, which are then transparently linked to supercomputer facilities for further data processing; the PyHST2 at European Synchrotron Radiation Facility (ESRF), a hybrid distributed code for high‐speed tomographic reconstruction capable of handling Big Data at the latest generation synchrotrons with a data rate of ≈10 terabytes per experiment; the UFO framework at Karlsruhe Institute of Technology (KIT), a powerful tomography reconstruction pipeline implemented to take full advantage of the modern GPU clusters …”

“…The CAMERA project team pursues applied mathematical research, designs new algorithms, develops prototype codes, packages, and graphical user interfaces (GUIs). The project releases software, with the corresponding documentation, in order to meet the needs of users of the large DOE research facilities, and one focus is targeted at implementing massively parallel modeling and simulation for DOE's synchrotron light sources by interfacing these models with supercomputer facilities nation‐wide . A last example is the “Discovery Engines for Big Data” project at the Advanced Photon Source (APS) within Argonne National Laboratory (ANL) .…”

SAS-cam: a program for automatic processing and analysis of small-angle scattering data

“…Considering the large quantities of data being collected with advanced instruments that support high repetition rates, efficient usage of synchrotron beamlines becomes a critical issue to the synchrotron user community (Pandolfi et al, 2018). Several state-of-the-art software packages have been developed to analyse biological SAXS data, such as ATSAS (Franke et al, 2017), SASTBX and BioXTAS RAW (Nielsen et al, 2009).…”