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Instruments / Technical Area	Publication Details	Published
I13-2-Diamond Manchester Imaging	CrystalGrowthTracker: A Python package to analyse crystal face advancement rates from time lapse synchrotron radiography Joanna Leng , Jonathan H. Pickering , Sven L. M. Schroeder , Gunjan Das Journal Of Open Source Software 7 DOI: 10.21105/joss.04333 Diamond Proposal Number(s): [20984] Open Access Show Abstract ▼ Abstract: X-ray synchrotron radiation allows the investigation of many physical processes in unprecedented ways. One application, important to the fine chemicals industry, is characterizing the early stages of crystallization. To aid chemical engineers working in this area we developed the CrystalGrowthTracker package. It allows crystals to be found in videos and their growth rates measured, see Figure 1. We hope that this work can provide the basis for fully automated and 3D systems.	Nov 2022
	Volume segmantics: a Python package for semantic segmentation of volumetric data using pre-trained PyTorch deep learning models Oliver N. F. King , Dimitrios Bellos , Mark Basham Journal Of Open Source Software 7 DOI: 10.21105/joss.04691 Open Access Show Abstract ▼ Abstract: Segmentation of 3-dimensional (3D, volumetric) images is a widely used technique that allows interpretation and quantification of experimental data collected using a number of techniques (for example, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Electron Tomography (ET)). Although the idea of semantic segmentation is a relatively simple one, giving each pixel a label that defines what it represents (e.g cranial bone versus brain tissue); due to the subjective and laborious nature of the manual labelling task coupled with the huge size of the data (multi-GB files containing billions of pixels) this process is often a bottleneck in imaging workflows. In recent years, deep learning has brought models capable of fast and accurate interpretation of image data into the toolbox available to scientists. These models are often trained on large image datasets that have been annotated at great expense. In many cases however, scientists working on novel samples and using new imaging techniques do not yet have access to large stores of annotated data. To overcome this issue, simple software tools that allow the scientific community to create segmentation models using relatively small amounts of training data are required. Volume Segmantics is a Python package that provides a command line interface (CLI) as well as an Application Programming Interface (API) for training 2-dimensional (2D) PyTorch (Paszke et al., 2019) deep learning models on small amounts of annotated 3D image data. The package also enables applying these models to new (often much larger) 3D datasets to speed up the process of semantic segmentation.	Oct 2022
	islatu: A Python package for the reduction ofreflectometry data Richard Brearton , Andrew Mccluskey , Tim Snow Journal Of Open Source Software 7 DOI: 10.21105/joss.04397 Open Access Show Abstract ▼ Abstract: The interaction between light and matter provides a sensitive probe of the electronic structure of materials, on length scales determined by the difference between the incident and outgoing wavevector of the light. Reflectometry techniques involve scattering off the surface of a material, placing a detector at a point such that any light reaching the detector must scatter through a vector approximately parallel to the material’s surface normal. Typically, for various experiment-specific reasons, the raw data recorded by a detector will not be proportional to the quantity of interest: the modulus squared of the scattering matrix element ⟨𝑘⃗′ \|𝑉 \|̂𝑘⟩⃗. This is particularly true when the length of the scattering vector \|𝑄\| = \| ⃗ 𝑘 −⃗ 𝑘⃗′ \| is small, as is the case in reflectivity experiments. Then, in addition to corrections that must be applied in any scattering experiment, the finite size of the sample will affect the intensity of the reflected beam, and it is often necessary to also correct for manual changes to the beam’s attenuation. For the above reasons, all reflectivity experiments need at least some form of data reduction, with the exact requirements being experiment specific and often numerous. islatu provides a simple, performant and rigorously tested library and command-line interface for carrying out these correction steps, which aims to substantially simplify the process of converting instrument data to a reflectivity curve. This curve can then be analysed using one of the many widely available reflectivity fitting tools, such as (Björck & Andersson, 2007), (A. R. Nelson & Prescott, 2019) and (A. Nelson, 2006).	Sep 2022
	FitBenchmarking: an open source Python package comparing data fitting software Anders Markvardsen , Tyrone Rees , Michael Wathen , Andrew Lister , Patrick Odagiu , Atijit Anuchitanukul , Tom Farmer , Anthony Lim , Federico Montesino , Tim Snow , Andrew Mccluskey Journal Of Open Source Software 6 DOI: 10.21105/joss.03127 Open Access Show Abstract ▼ Abstract: Fitting a mathematical model to data is a fundamental task across all scientific disciplines. FitBenchmarking has been designed to help: Scientists, who want to know the best algorithm for fitting their data to a given model using specific hardware; Scientific software developers, who want to identify the best fitting algorithms and implementations. This allows them to recommend a default solver, to see if it is worth adding a new minimizer, and to test their implementation; Mathematicians and numerical software developers, who want to understand the types of problems on which current algorithms do not perform well, and to have a route to expose newly developed methods to users. Representatives of each of these communities have got together to build FitBenchmarking. We hope this tool will help foster fruitful interactions and collaborations across the disciplines.	Jun 2021
	Surfaxe: Systematic surface calculations Katarina Brlec , Daniel Davies , David O. Scanlon Journal Of Open Source Software 6 DOI: 10.21105/joss.03171 Open Access Show Abstract ▼ Abstract: Surface science is key to understanding the properties of a wide range of materials for energy applications, from catalysts to solar cells to battery components. Computational modelling based on quantum mechanics is often used to calculate surface properties of materials, which in turn determine their stability and performance. The maturity of these “first-principles” methods, coupled with the huge amount of computational power accessible today, means they can now be used predictively in high-throughput screening workflows to suggest new materials for specific applications before they are synthesised. The surfaxe package provides a framework for such screening workflows, automating each stage of the process.	May 2021
	Galore: Broadening and weighting for simulation of photoelectron spectroscopy Adam J. Jackson , Alex M. Ganose , Anna Regoutz , Russell G. Egdell , David O. Scanlon Journal Of Open Source Software 3 DOI: 10.21105/joss.00773 Open Access Show Abstract ▼ Abstract: Galore simplifies and automates the process of simulating photoelectron spectra from ab initio calculations. This replaces the tedious process of extracting and interpolating crosssectional weights from reference data and generates tabulated data or publication-ready plots as needed. The broadening tools may also be used to obtain realistic simulated spectra from a theoretical set of discrete lines (e.g. infrared or Raman spectroscopy).	Jun 2018

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