PyFastNER is the Python implementation of FastNER. It uses hash function to process multiple rules at the same time. Similar to FastNER, PyFastNER supports token-based rules and character-based rules.

This package provides a Python implementation of IAMsystem algorithm, a fast dictionary-based approach for semantic annotation, a.k.a entity linking.

PyRuSH is the python implementation of RuSH, which is originally developed using Java. RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy.

seqeval is a Python framework for sequence labeling evaluation. seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the NLP and IR communities.

FxT is a fast tracing engine that can be used either in user land, in kernel land, or both. It can record developer-specified events in compact "traces", with minimal run-time overhead.

EZTrace is a tool that aims at generating automatically execution trace from high performance computing (HPC) programs. It generates execution trace files that can be interpreted by visualization tools such as ViTE.

An open-source benchmark and tracking library for C++ projects, designed to provide deep insights into function performance with minimal overhead. CTRACK is a powerful tool that can be seamlessly integrated into both development and production environments. It allows developers to effortlessly monitor applications and identify bottlenecks, requiring minimal setup and maintenance.

MojitO/S is an Open Source System, Energy and Network Monitoring Tools at the O/S level. MojitO/S runs on GNU/Linux.

This package provides a Python implementation of a statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of "Asymptotic formulae for likelihood-based tests of new physics". The aim is also to support modern computational graph libraries such as PyTorch and TensorFlow in order to make use of features such as autodifferentiation and GPU acceleration.

Stand-alone application and Python tools for interactive and/or batch processing analysis of X-Ray Fluorescence Spectra.

This package provides a Pythonic Jupyter-friendly Python API for the HepMC3 library.

pyhepmc has been optimised for safety, usability, and efficiency by a human expert, something that an automatic tool cannot provide. It brings these unique features:

• Python idioms are supported where appropriate.

• Simple IO with pyhepmc.open.

• An alternative Numpy API whih accelerates event processing.

• The public API is fully documented with Python docstrings.

• Objects are inspectable in Jupyter notebooks.

• Events render as graphs in Jupyter notebooks.

This package provides a library for statistical inference aiming to cover the needs High Energy Physics.

hdf5plugin provides HDF5 compression filters (namely: Blosc, Blosc2, BitShuffle, BZip2, FciDecomp, LZ4, Sperr, SZ, SZ3, Zfp, ZStd) and makes them usable from h5py.

A KOkkos based colLIsion OPerator (KoLiOp) for Gysela that computes the evolution of the distribution function due to collisions.

This package provides a small and thin Python interface to read Les Houches Event (LHE) files.

PyFAI is an azimuthal integration library that tries to be fast (as fast as C and even more using OpenCL and GPU). It is based on histogramming of the 2theta/Q positions of each (center of) pixel weighted by the intensity of each pixel, but parallel version uses a SparseMatrix-DenseVector multiplication. Neighboring output bins get also a contribution of pixels next to the border thanks to pixel splitting. Finally pyFAI provides also tools to calibrate the experimental setup using Debye-Scherrer rings of a reference compound.

DCAP (dCache access protocol) client library: DCAP is the native random access I/O protocol for files within dCache. In addition to the usual data transfer mechanisms, it supports all necessary file metadata and name space manipulation operations.

ROOT is a data analysis framework developed by CERN for tasks such as data storage, processing, and visualization. It provides tools for histograms, statistical tests, fitting, simulations, and machine learning. It can handle large datasets and uses a specialized file format.

CLHEP is a set of HEP-specific foundation and utility classes such as random generators, physics vectors, geometry and linear algebra. CLHEP is structured in a set of packages independent of any external package.

The HepMC package is an object oriented C++ event record for High Energy Physics Monte Carlo generators and simulation.

CLHEP is a set of HEP-specific foundation and utility classes such as random generators, physics vectors, geometry and linear algebra. CLHEP is structured in a set of packages independent of any external package.

pythonocc provides 3D modeling and dataexchange features. It is intended for CAD/PDM/PLM/BIM development. It is based on the OpenCascade Technology modeling kernel.

pythonocc provides the following features:

• Full access from Python to almost all of the thousand OpenCascade C++ classes. Classes and methods/functions share the same names, and, as possible as it can be, the same signature;

• 3D visualization from the most famous Python Gui (tkinter, pyQt5 and 6, PySide2 and 6, wxPython);

• 3D visualization in a web browser using threejs or x3dom frameworks;

• 3D visualization and work within a jupyter notebook;

• Data exchange using most famous formats IGES/STEP/STL/PLY/OBJ/GLTF;

• Utility Python classes/methods for Topology operations, inertia computations, and more.

VDT is a library of mathematical functions, implemented in double and single precision. The implementation is fast and with the aid of modern compilers (e.g. gcc 4.7) vectorisable. VDT exploits also Pade polynomials.