pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small-and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration.

Small-angle X-ray scattering (SAXS) measurements of proteins in solution are becoming increasingly popular with biochemists and structural biologists owing to the presence of dedicated high-throughput beamlines at synchrotron sources. As part of the ESRF Upgrade program a dedicated instrument for performing SAXS from biological macromolecules in solution (BioSAXS) has been installed at the renovated BM29 location. The optics hutch has been equipped with new optical components of which the two principal elements are a fixed-exit double multilayer monochromator and a 1.1 m-long toroidal mirror. These new dedicated optics give improved beam characteristics (compared with the previous set-up on ID14-3) regarding the energy tunability, flux and focusing at the detector plane leading to reduced parasitic scattering and an extended s-range. User experiments on the beamline have been successfully carried out since June 2012. A description of the new BioSAXS beamline and the set-up characteristics are presented together with examples of obtained data.

We present the PyHST2 code which is in service at ESRF for phase-contrast and absorption tomography. This code has been engineered to sustain the high data flow typical of the 3 rd 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. These 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. We give details of the different methods and their implementations while the code is distributed under free license. We provide methods for estimating, in the absence of ground-truth data, the optimal parameters values for a-priori techniques.

2D area detectors like ccd or pixel detectors have become popular in the last 15 years for diffraction experiments (e.g. for waxs, saxs, single crystal and powder diffraction (xrpd)). These detectors have a large sensitive area of millions of pixels with high spatial resolution. The software package pyFAI has been designed to reduce saxs, waxs and xrpd images taken with those detectors into 1D curves (azimuthal integration) usable by other software for in-depth analysis such as Rietveld refinement, or 2D images (a radial transformation named caking). As a library, the aim of pyFAI is to be integrated into other tools like PyMca or edna with a clean pythonic interface. However pyFAI features also command line tools for batch processing, converting data into q-space (q being the momentum transfer) or 2θ-space (θ being the Bragg angle) and a calibration graphical interface for optimizing the geometry of the experiment using the Debye-Scherrer rings of a reference sample. PyFAI shares the geometry definition of spd but can directly import geometries determined by the software fit2d. PyFAI has been designed to work with any kind of detector and geometry (transmission or reflection) and relies on FabIO, a library able to read more than 20 image formats produced by detectors from 12 different manufacturers. During the transformation from cartesian space (x, y) to polar space (2θ, χ), both local and total intensities are conserved in order to obtain accurate quantitative results. Technical details on how this integration is implemented and how it has been ported to native code and parallelized on graphic cards are discussed in this paper.

New calibration tools in the pyFAI suite for processing scattering experiments acquired with area detectors are presented. These include a new graphical user interface for calibrating the detector position in a scattering experiment performed with a fixed large area detector, as well as a library to be used in Jupyter notebooks for calibrating the motion of a detector on a goniometer arm (or any other moving table) to perform diffraction experiments.