Savu is a Python package to assist with the processing and reconstruction of parallel-beam tomography data. The project originated in the Data Analysis Group at the Diamond Light Source (UK synchrotron) to address the growing, and increasingly complex, needs of the tomography community. Designed to allow greater flexibility in tomography data processing, Savu is capable of processing N-dimensional full-field tomography and mapping tomography data, along with concurrent processing of multiple datasets such as those collected as part of a multi-modal setup. Savu process lists, tailored to a specific experiment and passed to the framework at runtime along with the data, detail the processing steps that are required. A Savu process list is created using the Savu configurator tool, which stacks together plugins chosen from a repository. Each plugin performs a specific independent task, such as correction, filtering, reconstruction. For a list of available plugins see plugin API. Savu is currently in use across the tomography beamlines at Diamond to reconstruct both full-field tomography data and multi-modal, mapping tomography data.
simex_platform is a python library to facilitate setup, execution, and analysis of simulations of experiments at advanced laser light sources. As an example, consider a molecule radiated by highly brilliant, ultrashort x-ray pulses such as delivered by an X-Ray Free Electron Laser (X-FEL). The simulation platform allows to combine tools and codes for the simulation of each step of the experiment: Generation of radiation in the photon source, propagation through optics and waveguides to the interaction point, photon-matter interaction, scattering of the radiation into the far field and detection of the latter. The platform provides slots and interfaces for the various simulation steps.
SYRMEP Tomo Project (STP) has been developed for the users of the SYRMEP beamline of the Elettra synchrotron facility (http://www.elettra.eu) to perform the digital image processing required by parallel beam propagation-based phase contrast CT experiments. The underlying idea is to let users perform post-beamtime optimization, fine tuning and/or additional tests with common hardware at their home institution. The software has been also developed for teaching and educational purposes. SYRMEP Tomo Project is available only for Windows 64-bit machines.
XMI-MSIM is an open source tool designed for predicting the spectral response of energy-dispersive X-ray fluorescence spectrometers using Monte Carlo simulations. It comes with a fully functional graphical user interface in order to make it as user friendly as possible. Considerable effort has been taken to ensure easy installation on all major platforms. A manuscript has been published in Spectrochimica Acta Part B that covers the algorithms that power XMI-MSIM. Please include a reference to this publication in your own work if you decide to use XMI-MSIM for academic purposes. A second manuscript was published that covers our XMI-MSIM based quantification plug-in for PyMca. XMI-MSIM is released under the terms of the GPLv3.
software package developed by the Antwerp X-ray Imaging/Instrumentation Laboratory (AXiL) at the University of Antwerp. Its main purpose is to automate the processing of two dimensional x-ray diffraction images from scanning micro-XRPD or micro-XRPD tomography. It accepts images from flat area detectors and allows correction, calibration and modeling (Rietveld, Pawley, Pattern Decomposition). The primary goal is to visualize crystalline phase distributions in projection (2D scanning) or in a virtual cross section (tomography) of the object under investigation. Apart from the amount of material, structural properties and their changes within the object can be calculated and visualized as well.
XRMC is a Monte Carlo program for accurate simulation of X-ray imaging and spectroscopy experiments in heterogeneous samples. The use of the Monte Carlo method makes the code suitable for the detailed simulation of complex experiments on generic samples. Variance reduction techniques are used for reducing considerably the computation time compared to general purpose Monte Carlo programs. The program is written in C++ and has been tested on Linux, Mac OS X and MS Windows platforms. XRMC is released under the terms of the GPLv3.