PySCIPOpt is a Python interface to the SCIP optimization solver, providing a comprehensive modeling environment for linear, mixed-integer, nonlinear, and constraint programming. The library wraps SCIP's C API through Cython, allowing users to formulate optimization problems using Python objects (variables, constraints, expressions) and to solve them with SCIP's state-of-the-art algorithms. The core architecture revolves around the `Model` class, which exposes all solver functionality, and a suite of plugin base classes (branching rules, heuristics, separators, constraint handlers, etc.) that enable users to customize the solution process. The expression system (`Expr`, `GenExpr`, `MatrixExpr`) supports algebraic formulation with operator overloading, while the recipes subpackage provides higher-level modeling helpers for common tasks such as piecewise linear constraints, infeasibility diagnosis, and convergence tracking. The repository is actively maintained and closely tracks SCIP releases, but also explicitly seeks new maintainers to sustain long-term development.
Perception is a Python library for perceptual image and video hashing, developed by Thorn. It provides a unified interface for multiple hashing algorithms (e.g., pHash, dHash, PDQ, TMK), approximate nearest neighbor search via FAISS with graph-based clustering, and a benchmarking framework for evaluating hasher robustness under various transformations. The library is designed for production use cases like near-duplicate detection, content matching, and CSAM detection via integration with Thorn's Safer service. The codebase demonstrates strong engineering practices: clean abstractions, Cython acceleration, optional dependency management, and extensive test coverage. While primarily an application/library, it includes research-oriented benchmarking pipelines and evaluation datasets, making it a hybrid of production tooling and research infrastructure.
PyEMD is a pure Python library for computing the Earth Mover's Distance (also known as Wasserstein distance) between histograms. As of version 2.0, it serves as a thin wrapper around the POT (Python Optimal Transport) library, providing three main functions: `emd()` for computing the EMD value, `emd_with_flow()` for getting both the EMD value and the flow matrix, and `emd_samples()` for computing EMD directly from sample arrays. The library implements optimizations from the original C++ implementation, including pre-flow cancellation of mass within same bins for zero-cost transport. It follows modern Python packaging practices with pyproject.toml, uses setuptools_scm for version management, and maintains comprehensive test coverage.