Tom.schoonjans


Tom.Schoonjans
Bio
Real name Tom Schoonjans
Website https://tschoonj.github.io/
Location Oxford, United Kingdom
Institute Diamond Light Source
Member since 20/09/17 09:10:30
Profile views 1

Software

Participates in 7 items of software

ATSAS

ATSAS is a program suite for small-angle scattering data analysis from biological macromolecules. It includes multiplatform data manipulation and displays tools, programs for automated data processing and calculation of overall parameters, usage of high- and low-resolution models from other structural methods, algorithms to build three-dimensional models from weakly interacting oligomeric systems and complexes, and enhanced tools to analyse data from mixtures and flexible systems.

DAWN

DAWN, the Data Analysis WorkbeNch, is an Eclipse based application for scientific data analysis. It comes with a range of tools for visualization (1D, 2D and 3D), code development environments (for Python, Jython and Eclipse plug-ins) as well as processing workflows with visual algorithms for analyzing scientific datasets. It is primarily developed at Diamond Light Source, but external contributions are most welcome! DAWN is distributed freely and is released under the Eclipse Public License.

Demeter

Demeter is a comprehensive system for processing and analyzing X-ray Absorption Spectroscopy data. It contains several packages such as Athena, Artemis and Hephaestus, which are widely used in the XAFS community.

Savu

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.

XMI-MSIM

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.

xraylib

Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available. xraylib provides access to some of the most respected databases of physical data in the field of X-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C. Furthermore, the xraylib package contains bindings to several popular programming languages: Fortran 2003, Perl, Python, Java, IDL, Lua, Ruby, PHP and .NET, as well as a command-line utility which can be used as a pocket-calculator. Although not officially supported, xraylib has been reported to be useable from within Matlab and LabView. The source code is known to compile and run on the following platforms: Linux, Mac OS X, Solaris, FreeBSD and Windows. It is very likely that xraylib will also work on other platforms: we would be grateful if you would report your successes in this regard. Please note that not all of the bindings are currently working on all platforms. A paper was published concerning xraylib by A. Brunetti, M. Sanchez del Rio, B. Golosio, A. Simionovici and A. Somogyi, “A library for X-ray matter interaction cross sections for X-ray fluorescence applications”, Spectrochimica Acta B 59 (2004) 1725-1731. This paper was recently superseded by a new manuscript, covering all features of xraylib upto version 2.15.0, written by T. Schoonjans, A. Brunetti, B. Golosio, M. Sanchez del Rio, V. A. Solé, C. Ferrero and L. Vincze, named "The xraylib library for X-ray—matter interactions. Recent developments". You are kindly requested to include this paper in the reference list of your published work when you would decide to use xraylib for scientific purposes.

XRMC

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.