A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files' metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated.

The ESRF tomography software is undergoing a major re-write, targeted at unifying the tomography user experience, data acquisition, data format, and processing tools. To cope with the inherent large complexity of handling data coming from multiple beamlines, techniques and facilities, we are developing an open source software called NXtomomill. It is named after the NXtomo application class of the NeXus data format, and it will offer advanced data conversion, manipulation and reduction functions. This includes azimuthal integration for X-ray Diffraction CT, and elemental fitting for X-ray Fluorescence CT. This work will have a remarkable impact on resource management, software robustness, and data storage. With the addition of the new functionality of the ESRF data portal, it will also unlock unimagined opportunities with respect to the automation of artificial intelligence techniques on large and heterogeneous collections of datasets.

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