MDANSE (Molecular Dynamics Analysis for Neutron Scattering Experiments) is a python application designed for computing properties that can be directly compared with neutron scattering experiments such as the coherent and incoherent intermediate scattering functions and their Fourier transforms, the elastic incoherent structure factor, the static coherent structure factor or the radial distribution function. Moreover, it can also compute quantities such as the mean-square displacement, the velocity autocorrelation function as well as its Fourier Transform (the so-called vibrational density of states) enlarging the scope of the program to a broader range of physico-chemical properties. Most of MDANSE calculations can be applied to the whole system or to arbitrary subsets that can be defined in the graphical interface while less common selections can be specified via the command-line interface. MDANSE is written in Python and currently works on Linux/debian, MacOS and Windows.
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.
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