Software
PtyPy
Framework for scientific ptychography including suitable classes for many concepts of ptychography
PyHST2
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.
PyMca
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
PyNX
Python toolkit for accelerated Nano-structures Crystallography and Coherent X-ray Imaging techniques. The software included in this package can be used for: 1. the computing of X-ray scattering using graphical processing units 2. X-ray wavefield propagation (from near to far field) 3. simulation and GPU-accelerated analysis of experiments using the ptychography and coherent diffraction imaging techniques See the full documentation at: http://ftp.esrf.fr/pub/scisoft/PyNX/doc/
XRDUA
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.
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