A. G. Buckley, L. D. Corpe, Matthew J. Filipovich et al. · SciPost Physics Codebases · 2025
Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.
Cited by 2Open access
Fabio Bellacanzone, Simona Bordoni, Theodore Shepherd · Zenodo (CERN European Organization for Nuclear Research) · 2025
ENSO-ERA5 Climate Analysis Codebase This release accompanies the manuscript: "South Tropical Atlantic and South Atlantic Convergence Zone: Opportunities for Long-Lead ENSO Diversity Prediction" (Bellacanzone, Bordoni & Shepherd, 2025) Features Processing pipeline (scripts 1-10): seasonal preprocessing of ERA5 data Analysis suite (scripts 12-20): PCMCI+ causal discovery, regression diagnostics, composites, and predictive modeling MCA/REOF optimization modules for precursor identification Minimal reproducibility data (~400 KB time series) Full documentation: DATA.md, CONTRIBUTING.md, environment setup
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Stefano Carignano · SciPost Physics Codebases · 2026
Transverse contraction methods are extremely promising tools for the efficient contraction of tensor networks associated with the time evolution of quantum many-body systems, allowing in some cases to circumvent the entanglement barrier that would normally prevent the study of quantum dynamics with classical resources. We present here the ITransverse.jl package, written in Julia and based on ITensors.jl, containing several of these high-level algorithms, including novel prescriptions for efficient truncations of temporal matrix product states.
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Robin Schäfer, David J. Luitz · SciPost Physics Codebases · 2025
The complexity of quantum many-body problems scales exponentially with the size of the system, rendering any finite-size scaling analysis a formidable challenge. This is particularly true for methods based on the full representation of the wave function, where one simply accepts the enormous Hilbert space dimensions and performs linear algebra operations, e.g., for finding the ground state of the Hamiltonian. If the system satisfies an underlying symmetry where an operator with a degenerate spectrum commutes with the Hamiltonian, it can be block-diagonalized, thus reducing the complexity at the expense of additional bookkeeping. At the most basic level required for Krylov space techniques (like the Lanczos algorithm), it is necessary to implement a matrix-vector product of a block of the Hamiltonian with arbitrary block-wavefunctions, potentially without holding the Hamiltonian block in memory. An efficient implementation of this operation requires the calculation of the position of an arbitrary basis vector in the canonical ordering of the basis of the block. We present here an elegant and powerful, multi-dimensional approach to this problem for the U(1) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false" form="prefix">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy="false" form="postfix">)</mml:mo></mml:mrow></mml:math> symmetry appearing in problems with particle number conservation. Our divide-and-conquer algorithm uses multiple subsystems and hence generalizes previous approaches to make them scalable. In addition to the theoretical presentation of our algorithm, we provide DanceQ, a flexible and modern – header only – C++20 implementation to manipulate, enumerate, and map to its index any basis state in a given particle number sector as open source software under https://DanceQ.gitlab.io/danceq.
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Benjamin Campillo Aveleira, A. Gehrmann–De Ridder, T. Gehrmann et al. · SciPost Physics Codebases · 2025
We present a major update of the publicly available EERAD3 package to calculate perturbative corrections in the strong coupling in hadronic Higgs and Z <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Z</mml:mi> </mml:math> -boson decays. We describe the theoretical framework underlying the numerical implementation and provide a guide to the usage of the program.
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Radost Waszkiewicz, Maciej Bartczak, Kamil Kolasa et al. · SciPost Physics Codebases · 2023
In the last decade, Python-powered physics simulations ecosystem has been growing steadily, allowing greater interoperability, and becoming an important tool in numerical exploration of physical phenomena, particularly in soft matter systems. Driven by the need for fast and precise numerical integration in colloidal dynamics, here we formulate the problem of Brownian Dynamics (BD) in a mathematically consistent formalism of the Itō calculus, and develop a Python package to assist numerical computations. We show that, thanks to the automatic differentiation packages, the classical truncated Taylor-Itō integrators can be implemented without the burden of computing the derivatives of the coefficient functions beforehand. Furthermore, we show how to circumvent the difficulties of BD simulations such as calculations of the divergence of the mobility tensor in the diffusion equation and discontinuous trajectories encountered when working with dynamics on S^2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>S</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> and SO(3) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>S</mml:mi><mml:mi>O</mml:mi><mml:mrow><mml:mo stretchy="true" form="prefix">(</mml:mo><mml:mn>3</mml:mn><mml:mo stretchy="true" form="postfix">)</mml:mo></mml:mrow></mml:mrow></mml:math> . The resulting Python package, Pychastic, is capable of performing BD simulations including hydrodynamic interactions at speeds comparable to dedicated implementations in lower-level programming languages, but with a much simpler end-user interface.
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Atis Yosprakob · SciPost Physics Codebases · 2023
We present GrassmannTN, a Python package for the computation of the Grassmann tensor network. The package is built to assist in the numerical computation without the need to input the fermionic sign factor manually. It prioritizes coding readability by designing every tensor manipulating function around the tensor subscripts. The computation of the Grassmann tensor renormalization group and Grassmann isometries using GrassmannTN are given as the use case examples.
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João Victor V. Cassiano, A. L. Araújo, Paulo E. Faria et al. · SciPost Physics Codebases · 2024
The k\cdot p <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>k</mml:mi><mml:mo>⋅</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:math> method, combined with group theory, is an efficient approach to obtain the low energy effective Hamiltonians of crystalline materials. Although the Hamiltonian coefficients are written as matrix elements of the generalized momentum operator \pi= p+ p_{SOC} <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>π</mml:mi><mml:mo>=</mml:mo><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mrow><mml:mi>S</mml:mi><mml:mi>O</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> (including spin-orbit coupling corrections), their numerical values must be determined from outside sources, such as experiments or ab initio methods. Here, we develop a code to explicitly calculate the Kane (linear in crystal momentum) and Luttinger (quadratic in crystal momentum) parameters of k\cdot p <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>k</mml:mi><mml:mo>⋅</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:math> effective Hamiltonians directly from ab initio wavefunctions provided by Quantum ESPRESSO. Additionally, the code analyzes the symmetry transformations of the wavefunctions to optimize the final Hamiltonian. This is an optional step in the code, where it numerically finds the unitary transformation U <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>U</mml:mi></mml:math> that rotates the basis towards an optimal symmetry-adapted representation informed by the user. Throughout the paper, we present the methodology in detail and illustrate the capabilities of the code applying it to a selection of relevant materials. Particularly, we show a “hands-on” example of how to run the code for graphene (with and without spin-orbit coupling). The code is open source and available at https://gitlab.com/dft2kp/dft2kp.
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G. H. Stark, Camila Aristimuno Ots, Mike Hance · SciPost Physics Codebases · 2024
Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MADGRAPH, PYTHIA8, and DELPHES into workflows steered by TOML configuration files, and bundled into the mapyde Python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.
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Goddard, Joshua · Figshare · 2018
The purpose of this project is to improve the codebase of Signbank, a web-application written in Django that provides information relating to sign languages. The codebase is suffering from a lack of tests, from being out of date (with respect to the latest versions of Python and Django), from lacking documentation, and from containing components that should be independent from one another but are not.By the end of this project, the Signbank codebase was improved by writing tests for it, by making components that should be independent from one another independent from one another, by writing documentation, and by upgrading the codebase to the latest versions of Django and Python, versions 1.10 and 3.5 respectively. The project did fall short in some areas, (as is to be expected from a large software project). Some advice is given to future developers on how they can tie up the loose ends left on this project.
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Fabio Bellacanzone, Simona Bordoni, Theodore Shepherd · Zenodo (CERN European Organization for Nuclear Research) · 2025
ENSO-ERA5 Climate Analysis Codebase This release accompanies the manuscript: "South Tropical Atlantic and South Atlantic Convergence Zone: Opportunities for Long-Lead ENSO Diversity Prediction" (Bellacanzone, Bordoni & Shepherd, 2025) Features Processing pipeline (scripts 1-10): seasonal preprocessing of ERA5 data Analysis suite (scripts 12-20): PCMCI+ causal discovery, regression diagnostics, composites, and predictive modeling MCA/REOF optimization modules for precursor identification Minimal reproducibility data (~400 KB time series) Full documentation: DATA.md, CONTRIBUTING.md, environment setup
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Daniel Isamon · Zenodo (CERN European Organization for Nuclear Research) · 2025
A secure, modular JavaScript/TypeScript solution for retrieving, displaying, and exporting payment billing address and card details with AES-256-GCM encryption and PBKDF2 key derivation.
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Daniel Otto · Zenodo (CERN European Organization for Nuclear Research) · 2024
No description provided.
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Huang, Dingyun, Cole, Jacqueline · Zenodo (CERN European Organization for Nuclear Research) · 2025
Scripts used in the work "Cost-Efficient Domain-Adaptive Pretraining of Language Models for Optoelectronics Applications". Not production ready, citation info needs to be confirmed.
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Ludvig af Klinteberg · Zenodo (CERN European Organization for Nuclear Research) · 2023
Singularity Swap Quadrature for the trapezoidal rule
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Jeff Guo · Figshare · 2025
Saturn codebase archive. It can also be found here: https://github.com/schwallergroup/saturn/tree/b0163fbd4790be2fbb8e617357c6baa07e42912d
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Watkins, Jerry; Tezaur, Irina Kalashnikova · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020
Abstract not provided.
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Li, Bryan, Cowen-Rivers, Alexander, Kozakowski, Piotr et al. · Zenodo (CERN European Organization for Nuclear Research) · 2019
Vast reinforcement learning (RL) research groups, such as DeepMind and OpenAI, have their internal (private) reinforcement learning codebases, which enable quick prototyping and comparing of ideas to many SOTA methods. We argue the five fundamental properties of a sophisticated research codebase are; modularity, reproducibility, many RL algorithms pre-implemented, speed and ease of running on different hardware/ integration with visualization packages. Currently, there does not exist any RL codebase, to the author's knowledge, which contains all the five properties, particularly with TensorBoard logging and abstracting away cloud hardware such as TPU's from the user. The codebase aims to help distil the best research practices into the community as well as ease the entry access and accelerate the pace of the field. More detailed documentation can be found here.
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Gincheva, Andrina, Royé, Dominic, Torres Vázquez, Miguel Ángel et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024
We present the R code used to Development and Validation Codebase analyses detailed in our paper "ONFIRE Dataset: Monthly Gridded Burned Area data". For more information, please consult the following source: Gincheva, A., & Turco, M. (2023). ONFIRE dataset: Monthly Gridded Burned Area data (Version 1.0) [Data set]. Zenodo. Available at https://doi.org/10.5281/zenodo.8289245. Authors: Andrina Gincheva, University of Murcia (andrina@um.es) Marco Turco, University of Murcia (marco.turco@um.es) General instructions This project is designed to be executed with QGIS and with R codes. Execute script files in the order they are listed. Please refer to the 'Readme-Instructions_to_reproduce_the_study' before proceeding. Data sources -Fire in Australia: New South Wales: https://datasets.seed.nsw.gov.au/dataset/fire-history-wildfires-and-prescribed-burns-1e8b6 Queensland: https://qldspatial.information.qld.gov.au/catalogue/custom/ Northern Territory: https://firenorth.org.au/nafi3/downloads/firescars/ South Australia: https://data.sa.gov.au/data/dataset/fire-history Tasmania: http://listdata.thelist.tas.gov.au/opendata/ Victoria: https://discover.data.vic.gov.au/dataset/ Western Australia: https://catalogue.data.wa.gov.au/dataset/dbca-fire-history - Australian regions: https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files#downloads-for-gda2020-digital-boundary-files - EFFIS data: contact https://effis.jrc.ec.europa.eu/apps/data.request.form/ - European regions (NUTS3): http://ec.europa.eu/eurostat/web/nuts/history - NFDB dataset for Canada: https://cwfis.cfs.nrcan.gc.ca/downloads/nfdb/fire_pnt/current_version/NFDB_point_large_fires.zip - NBAC dataset for Canada: https://cwfis.cfs.nrcan.gc.ca/downloads/nbac/nbac_1986_to_2020_20210810.zip - CONAF dataset for Chile: https://www.conaf.cl/incendios-forestales/incendios-forestales-en-chile/estadisticas-historicas/ (download the document "Estadísticas - Daño (Superficie Afectada) por Incendios Forestales según Mes 1985 - 2022") - Chile regions shapefile: https://www.bcn.cl/obtienearchivo?id=repositorio/10221/10398/2/Regiones.zip - FPA-FOD dataset for the U.S.: https://www.fs.usda.gov/rds/archive/catalog/RDS-2013-0009.6 - MTBS dataset for the U.S.: https://mtbs.gov/direct-download - 1ºx1º grid: https://github.com/SantanderMetGroup/ATLAS/raw/main/reference-grids/land_sea_mask_1degree.nc4 Notes regarding reproducibility: Script files starting with "1_" in their name are for data preprocessing. Most of these script files will NOT run because we do not include the raw data because files are simply too large to conveniently share. We suggest you run scripts starting with "2_" which directly reproduces the results in the paper. Please refer to the 'Readme-Instructions_to_reproduce_the_study' before proceeding. If you have any questions or wish to express any comment to the authors, please contact Dr. Marco Turco or/and Andrina Gincheva at the email indicated above. A.G. thanks to the Ministerio de Ciencia, Innovación y Universidades of Spain for Ph.D. contract FPU19/06536. M.T. acknowledges funding by the Spanish Ministry of Science, Innovation, and Universities through the Ramón y Cajal Grant Reference RYC2019-027115-I and through the project ONFIRE, grant PID2021-123193OB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe".
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Deknop, Céline · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2023
This thesis concerns advanced code differencing techniques in the context of automated large-scale refactoring for legacy software systems. Commonly, differencing compares two artefacts (code, but not only), and is based on the largest common subsequence algorithm developed by Douglas McIlroy and James Hunt in 1976. This algorithm considers both artefacts as blobs of text and outputs the shortest sequence of modifications (adds and deletes) that is necessary to go from one version to the next. Flat textual comparison like this does not reflect developer goals and obscures the intent of the changes due to the excess of low-level information displayed, which can lead to frustrating and tedious experiences. We explored ways to enhance the experience of differencing users by creating new differencing techniques that take advantage of modern techniques and knowledge of the nature of the artefacts they are analysing. This thesis was done in collaboration with a company, Raincode Labs. They are compiler experts and offer various services in the realm of legacy software and automated refactoring. One of those services, which is the use case of this thesis, is automated refactoring of generated COBOL code. While this process is well-known to experts within the company, they have found that, since the process is fairly intricate and behaves as a black box, clients have a hard time trusting the newly refactored code. This causes communication issue and experts have reported that they wished for a tool allowing them to better transmit their expertise to their clients. Combining the new differencing technique and the need present at Raincode Labs to better explain their process, everal tools and techniques were created during this thesis, including a log-based behavioural differencing algorithm, a COBOL semi-parser, and a trace equivalence comparator. These tools are accompanied by visualisation modules, facilitating better communication with clients and reinforcing trust in automated refactoring processes. The contributions of this work have the potential to benefit both Raincode Labs and the wider software engineering community, providing solutions to challenges in legacy software maintenance and refactoring.
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