Related papers: HIPSTER -- A python package for particle physics a…
The Hofstadter model successfully describes the behavior of non-interacting quantum particles hopping on a lattice coupled to a gauge field, and hence is ubiquitous in many fields of research, including condensed matter, optical, and atomic…
This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN compression algorithms implementations, with tools, tutorials…
HOTTBOX is a Python library for exploratory analysis and visualisation of multi-dimensional arrays of data, also known as tensors. The library includes methods ranging from standard multi-way operations and data manipulation through to…
This paper presents the HiPart package, an open-source native python library that provides efficient and interpret-able implementations of divisive hierarchical clustering algorithms. HiPart supports interactive visualizations for the…
PySEMTools is a Python-based library for post-processing simulation data produced with high-order hexahedral elements in the context of the spectral element method in computational fluid dynamics. It aims to minimize intermediate steps…
We present an application, EasyScan_HEP, for connecting programs to scan the parameter space of High Energy Physics (HEP) models using various sampling algorithms. We develop EasyScan_HEP according to the principle of flexibility and…
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within…
An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to…
pynucastro is a python library that provides visualization and analyze techniques to classify, construct, and evaluate nuclear reaction rates and networks. It provides tools that allow users to determine the importance of each rate in the…
In high-energy physics~(HEP) experiments, visualization software plays a pivotal role in detector design, offline software development, and event data analysis. The visualization tools integrate detailed detector geometry with complex event…
High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a…
HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since March 2007 and available open source since August 2008.…
High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of…
The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without…
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities. HyperStream overcomes the limitations of other…
Instance Space Analysis is a methodology to evaluate algorithm performance across diverse problem fields. Through visualisation and exploratory data analysis techniques, Instance Space Analysis offers objective, data-driven insights into…
The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on…
In high-energy physics (HEP), both the exclusion and discovery of new theories depend not only on the acquisition of high-quality experimental data but also on the rigorous application of statistical methods. These methods provide…
Scikit-HEP is a community-driven and community-oriented project with the goal of providing an ecosystem for particle physics data analysis in Python. Scikit-HEP is a toolset of approximately twenty packages and a few "affiliated" packages.…
The analysis of experimental results with Python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many users. The qspec Python…