ANKAphase processes X-ray inline phase-contrast radiographs by reconstructing the projected thickness of the object(s) imaged. The tool uses a single-distance non-iterative phase-retrieval algorithm described in a paper by D. Paganin et al. J. Microsc. vol. 206 (2002). It has an easy-to-use graphical user interface and can be run either as a standalone application or as a plugin to ImageJ. It works on powerful clusters but also on your office laptop.
FabIO is a Python library for reading and handling data from 2-D X-ray detectors. FabIO provides a function for reading any image and returning a FabioImage object which contains both metadata (header information) and raw data. All FabioImage objects offer additional methods to extract information about the image and open other detector images from the same data series.
FIT2D is a general purpose 1 and 2 dimensional data analysis program. It is used for both interactive and "batch" data processing, and is used for different purposes. Calibration and correction of detector distortions is one of the main uses of FIT2D. Difficult data analysis problems may be tackled using fitting of user specified models. To enable model fitting to be performed on a wide variety of input data, many other more basic data analysis operations are also available. A wide variety of performant graphical display methods are available.
The iFit library (pronounce [eye-fit]) is a set of methods to load, analyse, plot, fit and optimize models, and export results. iFit is based on Matlab, but can also be launched without Matlab license (stand-alone version).Matlab It does not currently include advanced graphical user interfaces (GUI), and rather focuses on doing the math right. Any text file can be imported straight away, and a set of binary files are supported. Any data dimensionality can be handled, including event based data sets (even though not all methods do work for these). Any model can be assembled for fitting data sets. Last, a number of routines are dedicated to the analyses of S(q,w) and S(alpha,beta). More advanced features include the full automation to compute phonon dispersions in materials, using DFT codes such as ABINIT, ELK, VASP, QuantumEspresso, GPAW and more (Models/sqw_phonons). The software can also compute the neutron TAS resolution function (4D) and fits to experimental data with full resolution convolution (ResLibCal). An interface for McStas and McXtrace is also available to automate and optimize instrument simulations.
Nabu is a tomography processing software being developed at ESRF by the ADA Unit. It is part of the new ESRF tomography software suite. The European Synchrotron has several tomography beamlines. Each of them use dedicated software, which over the years led a variety of different tools spread over the beamlines with poor maintainability. This is summarized in ESRF current situation for tomography software. Nabu is an effort to unify tomography software in a new toolkit with the following requirements: Library of tomography processing, with “applications” built on top of it, usable by both non-experts and power-users High performance processing (parallelization with Cuda/OpenCL, computations distribution, memory re-use) Support of multiple techniques, not only absorption and phase contrast Extensively documented Focus on maintainability with a bus factor greater than one Compatible with ESRF legacy software, progressively replacing it Nabu does not aim at being the new universal tomography reconstruction software. Well-established software like Astra, tomopy, Savu and UFO have an extensive set of features. Nabu foremost focuses on ESRF needs, while being designed so that it can be re-used in other projects.
pyFAI is an azimuthal integration library that tries to be fast (as fast as C and even more using OpenCL and GPU). It is based on histogramming of the 2theta/Q positions of each (center of) pixel weighted by the intensity of each pixel, but parallel version uses a SparseMatrix-DenseVector multiplication
Hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities. PyHST2 (formerly known as PyHST) has been engineered to sustain the high data flow typical of the third generation synchrotron facilities (10 terabytes per experiment) by adopting a distributed and pipelined architecture. The code implements, beside a default filtered backprojection reconstruction, iterative reconstruction techniques with a-priori knowledge. The latter are used to improve the reconstruction quality or in order to reduce the required data volume and reach a given quality goal. The implemented a-priori knowledge techniques are based on the total variation penalisation and a new recently found convex functional which is based on overlapping patches.
X-ray Fluorescence Toolkit (visualization and analysis of energy-dispersive X-ray fluorescence data). . The program allows both interactive and batch processing of large data sets and is particularly well suited for X-ray imaging. Its implementation of a complete description of the M shell is particularly helpful for analysis of data collected at low energies. It features, among many other things, the fundamental parameters method
Matlab based graphical user interfaces for the online processing and analysis of Small Angle X-ray Scattering data. In particular: -online treatment and fitting of SAXS data -averaging, background subtraction, normalization of ASCII data, etc. -processing of 2D SAXS images -averaging, subtraction, etc. of EDF images
XOP (X-ray Oriented Programs) is a widget-based driver program used as a common front-end interface for modelling of x-ray sources characteristics of optical devices (mirror, filters, crystals, multilayers, etc.); multipurpose data visualizations and analyses
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.
- ← Previous
- Next →