Accurate collision detection is fundamental to modern game engines and physics simulators, enabling applications in biomechanics, robotic obstacle avoidance, surgical training, and granular material simulation. While polygonal meshes remain the standard, smooth convex shapes, such as ellipses and ellipsoids, offer significant advantages in geometric accuracy, memory efficiency, and computational performance. Although analytical methods exist, they are scattered across disciplines and lack a unified implementation. To address this, we present non-gon a curated library comprised of 14 analytical functions for real-time distance and proximity queries between 2D and 3D smooth convex shapes, implemented in TypeScript and Three.js. In addition to the core analytic functions, the library features curve and surface rendering, along with several interactive web application examples. Finally, we invite all communities working on physics-based engines to contribute to the library, either by integrating its analytic functions into their codebases or by extending the collection of supported contact pairs.
Probit unfolding models (PUMs) are a novel class of scaling models that allow for items with both monotonic and non-monotonic response functions. They have shown great promise in the estimation of preferences from voting data in various deliberative bodies. This paper introduces the R package pumBayes, which enables Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo algorithms that require minimal or no tuning. In addition to functions that carry out the sampling from the posterior distribution of the models, the package also includes various support functions that can be used to pre-process data, select hyperparameters, summarize output, and compute metrics of model fit.
Existing physical therapy literature frequently presents incomplete shortwave diathermy protocols that lack key technical parameters and final dose calculations, severely hindering cross-protocol reproducibility. Although determining the Specific Absorption Rate (SAR) represents the theoretical ideal, it remains clinically unfeasible. To achieve an optimal balance between methodological rigor and clinical efficiency, PhysioDose provides an open-source, offline-ready HTML application to standardize emitted energy reporting. By computing mean power and total energy from machine settings, this zero-dependency software—permanently archived on Zenodo—automates standardized intervention reports. PhysioDose empowers researchers and clinicians to enhance dosimetric documentation, effectively bridging theoretical physics and applied musculoskeletal rehabilitation.
Ireland Nearest Analysis (EPA API + EPSG:29903) is open-source software developed at Quarry Consulting, Ireland, implemented as a QGIS plugin (‘Ireland Nearest Analysis’, module: Nearest_Analysis). It automates nearest-feature spatial analysis between a local project area (SHP) and geospatial datasets provided by the Irish Environmental Protection Agency (EPA) via public web services. The software integrates coordinate reference system (CRS) standardisation, remote data retrieval, spatial filtering, distance computation, and azimuth calculation into a unified workflow to improve efficiency and consistency in environmental GIS analysis. It supports both Web Feature Services (WFS) and ArcGIS REST Feature Services and exports structured results for reporting. The plugin has been applied in real-world environmental assessment workflows in Ireland. The implementation is normalised to the Irish National Grid (EPSG:29903), ensuring compatibility with national regulatory datasets and standard practices. The software is openly available under the MIT License.
Designing online behavioral experiments that require flexible control over trial structure, stimulus presentation, and computational modeling, remains technically challenging. We introduce PyBOLE, a Python-based framework built on Django that enables researchers to develop and deploy fully programmable online experiments. The framework supports controlled presentation of visual and auditory stimuli, response collection, and screen management, while allowing customization without extensive JavaScript development. It also enables integration of external Python libraries, real-time updating of computational models during data collection, and automated recording of both behavioral responses and model states. To illustrate its capabilities, we present an online adaptive psychophysical experiment using the Bayesian QUEST+ procedure. The experiment was executed entirely within the framework, demonstrating stable model convergence and results consistent with a laboratory implementation of the same protocol. PyBOLE is freely available and designed to support experiments ranging from standard behavioral tasks to computationally demanding paradigms, emphasizing flexibility and full control over experimental logic, as a complementary tool for integrating advanced computational models within a Python-based environment.
Human biosignals such as breathing, cardiac rhythms, or skin conductance contain a wealth of information about cognition, emotion, and social connection. Measuring these biosignals is now possible using a range of open-source or commercial sensors. However, the software accompanying each of such sensors stores data in all manner of different file formats. This makes it difficult for researchers across the globe to exchange analysis scripts, which are needed for data reproducibility. Biobabel is an open-source software Python package that reads all the major biosignal file formats and allows programmers to access the data in a unified, straightforward manner. It provides a handy set of tools for inspecting data and performing basic manipulations. Biobabel thus hopes to contribute to a unified, practical foundation allowing researchers interested in biosignals to focus on extracting meaningful insights from these data.
StarPSO is an open source, object-oriented, Python library designed to implement various particle swarm optimization (PSO) algorithms, including: (i) StandardPSO, (ii) BinaryPSO, (iii) IntegerPSO, (iv) QuantumPSO, (v) CategoricalPSO, (vi) BareBonesPSO, and (vii) JackOfAllTradesPSO (for mixed variable type problems). This library addresses the challenge of optimizing diverse problem types through a unified framework. Implemented with performance optimizations using NumPy, Numba, and Joblib, it achieves efficient computation while preserving clean, well-documented, and maintainable code. Provided in a public GitHub repository, StarPSO encourages reuse and collaboration, allowing researchers and practitioners to easily integrate advanced optimization techniques into their own projects and benefit a wide range of applications across different domains.
Time-Aligned Peaks (TAP) is a Python tool for analysing and visualising peak events across multiple time series. TAP pairs a conventional line plot with a stacked peak-presence timeline sharing the same x-axis, making co-occurring peaks immediately visible while preserving alignment. The pipeline ingests tabular data, performs unified timeline alignment with configurable resampling and missing-value policies, detects peaks using a simple slope-change criterion and exports reproducible artefacts: a combined image, a timestamped peak report and a binary peak matrix. TAP supports fast, reliable inspection of multi-series peak dynamics in small-to-medium datasets with single-command reproducibility.
Despite the ubiquity of cluster analysis, not all data consists of multiple natural clusters, and thus clustering is not always pertinent. Testing for clusterability – the degree to which a dataset includes intrinsic cluster structure – can inform whether clustering techniques should be applied. Clusterability evaluation is complicated by the inherently ambiguous nature of clustering and dependence on the clustering requirements of the underlying application. We present the first comprehensive clusterability package, which implements clusterability tests in R. Diverse clusterability tests enable users to select a technique for their specific needs. Detailed installation instructions, examples and R code are included.
Fast-ER is a Python package for GPU-accelerated record linkage and deduplication. These tasks involve computing string similarity metrics for all pairs of values within or between datasets. While these individual calculations are simple, the number of comparisons grows quadratically with dataset size, making record linkage and deduplication prohibitively expensive even for moderately sized datasets. Fast-ER addresses this challenge by harnessing the computational power of CUDA-enabled graphics processing units (GPUs) to accelerate string similarity metrics calculations. Developed in Python, our software library relies heavily on CuPy, an open-source library for array-based numerical computations on GPUs [1]. Fast-ER is readily available on GitHub (https://github.com/jacobmorrier/fast-er) and the Python Package Index (fast-er-link). By enabling fast and efficient linking and deduplication of datasets without consistent identifiers, Fast-ER has wide-ranging applications across fields such as social and health sciences.
Digital inline holography is a well-established experimental technique used to capture three-dimensional (3D) information from a scene onto a two-dimensional image using a single camera. The retrieval of the 3D scene from the recorded hologram is an ill-posed inverse problem. One of the methods to compute the 3D scene from the recorded hologram is via an iterative inversion of the regularized variational formulation of the holographic reconstruction problem. However, such an iterative inversion is complex to implement and could be prohibitively computationally expensive. This has led to a high barrier to entry, resulting in limited adoption. Here, we present an open-source iterative inverse holographic volume reconstruction algorithm with compute unified device architecture (CUDA) accelerated computations that can handle large reconstruction volumes while being computationally efficient. The developed program can get 3D particle tracks from holographic particle data, enabling 3D particle tracking velocimetry and microbial tracking. For ease of use, the program has a graphical user interface (GUI) along with integrated pre-/post-processing utilities.
We present RColSim, a script-based water management model that simulates the operation of reservoir systems of the Columbia River Basin located in the Pacific Northwest. The model simulates the operation of 46 major storage and run-of-the-river dams on the Columbia River, given rule curves and flow targets for system-wide flood control, irrigation, environmental, and hydroelectricity generation. RColSim is written in the R programming language and is publicly available on GitHub and Zenodo. The model can be downloaded, modified, and reused in accordance with the MIT open-source license terms of use. The model provides an open-source simulation tool for researchers, practitioners, and policymakers to investigate the ramifications of various planning and operation scenarios and stressors for Columbia River Basin stakeholders.
Pluvianus is a stand-alone graphical user interface designed for the visual exploration and quality control of calcium imaging results produced by CaImAn’s CNMF and OnACID algorithms. It provides synchronized spatial and temporal views, intuitive visual comparison tools, and interactive component acceptance, while imposing no constraints on the underlying computational workflow or data organization, enhancing integration flexibility. The software was tested on CaImAn demo datasets as well as on in-house measured cortical calcium activity data, demonstrating that the GUI enables verification of CaImAn outputs and manual curation of cell detection. Publicly distributed on GitHub under the MIT license, Pluvianus supports seamless adoption and community-driven development, making it broadly usable for neuroscientists working with calcium imaging data across diverse experimental paradigms.
While a common workflow for data analysis exists in the nanopore research field, limitations remain. Notably, the fragmentation of methods and tools hinders interoperability and limits broader collaboration. Poriscope addresses these challenges by providing a unifying, modular framework for the analysis of single-molecule time series data acquired with nanopore sensors. It implements a plugin-based architecture that enables extensibility and interoperability while allowing users to customize each analytical stage independently from the overall framework. Any existing analysis methodology can be implemented as a self-contained plugin that integrates seamlessly into the overall pipeline via the Poriscope application programming interface (API). Poriscope is available both as a graphical desktop application and as a scripting module that can be imported into custom analysis scripts as needed. For more information, visit: https://tcossalab.github.io/poriscope/.
MOLTSA (a portmanteau of molten salt) is an R-based, browser-accessible software platform for molten salt thermodynamics that integrates data access, visualization, and model evaluation to support thermochemical assessments. It renders experimental literature data alongside modeled phase equilibria and enthalpies of mixing, with extensible support for additional properties. Core modules enable FAIR-aligned data contribution, 3D visualization of ternary and pseudo-ternary phase equilibria, retrieval of optimizer input files, computation of the ionic size-difference parameter δ12, fitting of Maier–Kelley heat-capacity functions, and processing of raw differential scanning calorimetry (DSC) data with calibration and uncertainty estimation. By unifying these capabilities in a single interactive environment, MOLTSA streamlines thermochemical database development, improves transparency and reproducibility, and reduces the time from experiment to validated thermodynamic models.
Event-based vision has recently emerged as a powerful paradigm for capturing visual information with high temporal precision and minimal redundancy. Unlike traditional frame-based cameras, event cameras asynchronously detect per-pixel brightness changes with microsecond resolution. This bio-inspired sensing approach offers substantial advantages for dynamic scenes, low-latency perception, and energy-efficient computation, making it highly relevant to applications in robotics, vision, and neuromorphic computing. A fundamental tool in analyzing visual and temporal signals is the Fast Fourier Transform (FFT), which efficiently computes the spectral representation and forms the foundation of modern signal processing, communication, and control systems. However, extending Fourier analysis to asynchronous, event-driven data poses unique computational and theoretical challenges. This paper presents an open-source implementation of the event-based Fourier Transform (eFFT), a novel algorithm for efficiently computing the exact 2D discrete Fourier transform of the spatial information in asynchronous events generated by an event camera. The proposed eFFT-C++ translates the theoretical method into a modular and optimized C++17 library, implemented as a header-only package with Eigen3 dependencies. It supports both event-by-event and packet-based processing modes, reusing intermediate computations to minimize overhead while allowing the current 2D spectrum to be queried after each update. Benchmarks against FFTW3 demonstrate exactness and efficiency, with per-event update times in the order of microsecond for common frame sizes. The library enables reproducible research, provides validated code to accompany the original eFFT publication, and offers a foundation for extending event-based frequency analysis to robotics, computer vision, and neuromorphic computing in applications where rapidly updating spectra is useful, such as denoising and filtering, pattern analysis, or tracking.
The Lidar Radar Open Software Environment (LROSE) is a suite of high-performance C++ applications for format conversion, quality control, visualization, and analysis of weather radar and lidar data, developed over 30 years at the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) and Colorado State University (CSU). Distributed under a Berkeley Software Distribution (BSD) license on GitHub, LROSE supports real-time and archival scientific analysis workflows. Future distribution via the conda-forge Python channel is planned.
Timeline-kun is a desktop Graphical User Interface (GUI) application for planning, executing, and documenting time-managed protocols in human-subject research. Schedules are defined in a simple CSV format editable in spreadsheet software, a text editor, or within the application, then visualized as a timeline and exported to Scalable Vector Graphics (SVG). The schedule can be executed as a timer that supports rehearsal or restart from an arbitrary stage. During execution, Timeline-kun plays WAV alarms and records logs in Brain Imaging Data Structure (BIDS) events.tsv format. It can also optionally control GoPro cameras over Bluetooth Low Energy (BLE). The software is implemented in Python and distributed as both a package and a standalone Windows executable.
Exploratory data analysis (EDA) relies on visualization to reveal patterns, structures, and relationships before formal modeling. As modern datasets increase in size, dimension, and heterogeneity, exposing the association structure becomes challenging. Matrix visualization (MV) methods (heatmaps) address this by arranging samples and variables in a matrix layout, with values encoded as colors to highlight structural patterns. We propose the GAPR package that implements generalized association plots (GAP), a framework for rearranging heatmap layouts through seriation and flipping mechanisms to visualize association structures in data matrices. Written in R with optimized C++ backends, GAPR provides efficient MV for EDA for statisticians and data scientists. Its flexibility and efficiency make it suitable for diverse applications, and it is available on CRAN and GitHub for reproducible research.
