With the growing data volume and acquisition rates of SAXS/GISAXS beamlines, users require software that can manage and reduce terabytes of data with real-time feedback. These large data sets may be multimodal or multispectral (RSoXS/TReXS), and may fill a large parameter space (temperature, time, composition, etc.). Xi-CAM allows users to slice, process, and visualize these large data sets to expose further detailed information.
XAS and related techniques (i.e. NEXAFS, XANES) have grown similarly to SAXS beamlines with the addition of high throughput, burst, and scanning modes. Xi-CAM aims to provide an interface for managing, slicing, and visualizing these large sets of spectra. This interface is designed to be clean and usable with uniquely interactive rendering.
The tomography plugin exposes all of the functionality (filters, normalization, artifact correction, etc.) available in reconstruction packages such as TomoPy. This interface allows users to visualize the images resulting from each of the steps in a reconstruction workflow. More advanced users may produce a python script corresponding to the pipeline they have built and optimize this pipeline based on the real-time visualization feedback. Workflow scripts can be modified by advanced users to add additional custom functions or leverage use from software packages that are not included in the interface. Additionally, users are able to build a workflow/batch process which may be executed locally or remotely, with data that is either local or remote
This plugin provides an interface to design model systems for simulation in HipGISAXS, a robust forward GISAXS simulator using the Distorted Wave Born Approximation. A visual representation of the model is displayed as it is designed, giving visual confirmation and strengthening the connection between real space and reflection mode reciprocal space for novice users. Simulated data can be viewed along with experimental data, allowing quick comparison and tuning of model parameters. More complex models with higher computational cost may be executed using remote compute resources, leveraging HipGISAXS massively parallel algorithms