Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive domain-specific language called DecL, Aggregate enables practitioners and nonprogrammers to work with complex distributions effortlessly. The software verifies the accuracy of its FFT-based numerical approximations by comparing their first three moments to those calculated analytically from the specified frequency and severity. This moment-based validation, combined with carefully chosen default parameters, allows users without in-depth knowledge of the underlying algorithm to be confident in the results. Aggregate supports a wide range of frequency and severity distributions, policy limits and deductibles, and reinsurance structures and has applications in pricing, reserving, risk management, teaching, and research. It is written in Python.

This paper introduces gemact, a Python package for actuarial modeling based on the collective risk model. The library supports applications to risk costing and risk transfer, loss aggregation, and loss reserving. We add new probability distributions to those available in scipy, including the (a, b, 0) and (a, b, 1) discrete distributions, copulas of the Archimedean family, the Gaussian, the Student t and the Fundamental copulas. We provide an implementation of the AEP algorithm for calculating the cumulative distribution function of the sum of dependent, nonnegative random variables, given their dependency structure specified with a copula. The theoretical framework is introduced at the beginning of each section to give the reader with a sufficient understanding of the underlying actuarial models.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.

This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$, $\textsf{R}$, and $\textsf{MATLAB}$. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

Numerical estimators of differential entropy and mutual information can be slow to converge as sample size increases. The offset Kozachenko–Leonenko (KLo) method described here implements an offset version of the Kozachenko–Leonenko estimator that can markedly improve convergence. Its use is illustrated in applications to the comparison of trivariate data from successive scene color images and the comparison of univariate data from stereophonic music tracks. Publicly available code for KLo estimation of both differential entropy and mutual information is provided for R, Python, and MATLAB computing environments at https://github.com/imarinfr/klo.

In recent years, atomic resolution imaging of two-dimensional (2D) materials using scanning transmission electron microscopy (STEM) has become routine. Individual dopant atoms in 2D materials can be located and identified using their contrast in annular dark-field (ADF) STEM. However, in order to understand the effect of these dopant atoms on the host material, there is now the need to locate and quantify them on a larger scale. In this work, we analyze STEM images of MoS2 monolayers that have been ion-implanted with chromium at ultra-low energies. We use functions from the open-source TEMUL Toolkit to create and refine an atomic model of an experimental image based on the positions and intensities of the atomic columns in the image. We then use the refined model to determine the likely composition of each atomic site. Surface contamination stemming from the sample preparation of 2D materials can prevent accurate quantitative identification of individual atoms. We disregard atomic sites from regions of the image with hydrocarbon surface contamination to demonstrate that images acquired using contaminated samples can give significant atom statistics from their clean regions, and can be used to calculate the retention rate of the implanted ions within the host lattice. We find that some of the implanted chromium ions have been successfully integrated into the MoS2 lattice, with 4.1% of molybdenum atoms in the transition metal sublattice replaced with chromium.

The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism.

The exotic internal structure of polar topologies in multiferroic materials offers a rich landscape for materials science research. As the spatial scale of these entities is often subatomic in nature, aberration-corrected transmission electron microscopy (TEM) is the ideal characterization technique. Software to quantify and visualize the slight shifts in atomic placement within unit cells is of paramount importance due to the now routine acquisition of images at such resolution. In the previous ~decade since the commercialization of aberration-corrected TEM, many research groups have written their own code to visualize these polar entities. More recently, open-access Python packages have been developed for the purpose of TEM atomic position quantification. Building on these packages, we introduce the TEMUL Toolkit: a Python package for analysis and visualization of atomic resolution images. Here, we focus specifically on the TopoTEM module of the toolkit where we show an easy to follow, streamlined version of calculating the atomic displacements relative to the surrounding lattice and thus plotting polarization. We hope this toolkit will benefit the rapidly expanding field of topology-based nano-electronic and quantum materials research, and we invite the electron microscopy community to contribute to this open-access project.

The digital twin concept has developed as a method for extracting value from data, and is being developed as a new technique for the design and asset management of high-value engineering systems such as aircraft, energy generating plant, and wind turbines. In terms of implementation, many proprietary digital twin software solutions have been marketed in this domain. In contrast, this paper describes a recently released open-source software framework for digital twins, which provides a browser-based operational platform using Python and Flask. The new platform is intended to maximize connectivity between users and data obtained from the physical twin. This paper describes how this type of digital twin operational platform (DTOP) can be used to connect the physical twin and other Internet-of-Things devices to both users and cloud computing services. The current release of the software—DTOP-Cristallo—uses the example of a three-storey structure as the engineering asset to be managed. Within DTOP-Cristallo, specific engineering software tools have been developed for use in the digital twin, and these are used to demonstrate the concept. At this stage, the framework presented is a prototype. However, the potential for open-source digital twin software using network connectivity is a very large area for future research and development.

The General Structure Analysis System (GSAS-II) package provides materials and crystallographic analysis for all types of diffraction data. It was initially made available with very limited capabilities, but over much of the last decade the features have been expanded, so that GSAS-II is now a comprehensive tool for nearly all types of structural and materials characterization studies. The need to provide materials to teach use of GSAS-II, while the software has been undergoing constant revision and expansion, has required new approaches for documentation. This has included providing tutorials, as each major new capability has been added, and context-sensitive help for each section of the program. Comments in the code are also expanded into a software reference guide. Most recently, video versions of more than half of the tutorials were created and others were provided with animated graphics. All GSAS-II documentation is web-based.