{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"Provides a JSON-based format and evaluator for correction factors\u2014functions that map scalar inputs to scalar outputs\u2014commonly used in high-energy physics analysis, with C++ and Python bindings.","skillfed_tags":["physics-analysis","hep-tools","json-schema"],"use_cases":["Store and evaluate physics calibration factors (e.g., lepton efficiency corrections) in a standardized JSON format shared across a collaboration.","Apply multi-dimensional binned corrections to particle physics data during analysis, with formula-based outputs for complex transformations.","Migrate correction data between schema versions using forward-porting tools to maintain compatibility across analysis code.","Validate correction objects during construction using pydantic type checking before serializing to JSON.","Compose simple correction nodes (binning, formulas, categorical maps) into complex correction graphs for detector response modeling."],"what_it_does":"Correctionlib is a library for defining, storing, and evaluating correction factors\u2014mathematical functions that adjust or transform scalar inputs to produce scalar outputs. It is widely used in high-energy physics to encode calibration factors, efficiency corrections, and other data-driven adjustments in a standardized, versioned JSON format. The library provides both a C++ evaluator and Python bindings, allowing corrections to be defined once and reused across analysis pipelines.\n\nThe package supports multiple function types: multi-dimensional binned lookups, binned lookups with formula-based outputs, categorical maps, input transforms, and compositions of these primitives. Formulas are parsed using a formal grammar and support a subset of ROOT's TFormula syntax. Corrections are validated against JSON schemas, and the library maintains backward compatibility across minor versions while allowing forward-incompatible additions (such as new node types in v2.9.0). It depends on numpy, pydantic, rich, and packaging.","worth_installing":"Yes. Correctionlib is actively maintained, has no known vulnerabilities, and is production-stable (Development Status 5). It is purpose-built for high-energy physics workflows and widely adopted in that domain. Install it if you work with HEP data or need a standardized, versioned correction-factor framework; the medium install friction is typical for compiled scientific packages and poses no practical barrier."},"id":"correctionlib","links":{"html":"https://skillfed.io/packages/correctionlib","md":"https://skillfed.io/packages/correctionlib.md","pypi":"https://pypi.org/project/correctionlib/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-16","license_spdx":null,"license_treatment":"permissive","name":"correctionlib","python_support":"supports_current","summary":"A generic correction library"},"popularity":{"monthly_downloads":786529,"position":5064,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.9.0"}