spb/tools-spboucher-ai

1{2  "slug": "scipy",3  "name": "SciPy",4  "aliases": [],5  "vendor": { "name": "SciPy developers (NumFOCUS)", "slug": "scipy", "website": "https://scipy.org" },6  "homepage_url": "https://scipy.org/",7  "docs_url": "https://docs.scipy.org/doc/scipy/",8  "repo_url": "https://github.com/scipy/scipy",9  "license_url": "https://github.com/scipy/scipy/blob/main/LICENSE.txt",10  "product_type": "library",11  "product_status": "active",12  "summary": "Core open-source Python library for scientific computing on NumPy arrays: optimization, integration, interpolation, linear algebra, sparse matrices, statistics, signal processing",13  "description": "SciPy is the foundational scientific-computing library of the Python ecosystem, built on NumPy arrays and released under the BSD 3-Clause license. Its subpackages cover optimization and root finding (scipy.optimize), numerical integration and ODE solvers (integrate), interpolation, dense and sparse linear algebra (linalg, sparse), statistics with distributions and hypothesis tests (stats), signal and image processing (signal, ndimage), fast Fourier transforms (fft), spatial algorithms and distances (spatial) and special functions (special). Much of the heavy lifting is implemented in C, C++, Fortran and Cython.\n\nIn econometrics and finance SciPy is rarely the user-facing layer; it sits underneath statsmodels, scikit-learn, arch and linearmodels and is called directly for optimizers, distributions and sparse solvers. The library is maintained by a volunteer community under NumFOCUS fiscal sponsorship with regular releases (1.18.0 at the time of writing) and runs wherever Python does, including Apple silicon. It targets single-machine, in-memory computation; GPU execution and distributed arrays require other projects.",14  "capabilities": ["Optimization, root finding and curve fitting", "Numerical integration and ODE/BVP solvers", "Dense and sparse linear algebra", "Probability distributions and statistical tests", "Signal processing, filtering and FFT", "Interpolation and spatial algorithms", "Special functions"],15  "workflows": ["Maximum-likelihood estimation with scipy.optimize", "Numerical integration in structural models", "Sparse linear systems for large fixed-effects problems"],16  "advantages": ["BSD-licensed and ubiquitous", "Stable, well-documented API", "Underpins the scientific Python stack"],17  "limitations": ["CPU and single-node only", "Lower-level than statsmodels for regression work", "Some legacy APIs coexist with newer ones"],18  "prerequisites": ["Python and NumPy"],19  "audiences": ["Scientists and engineers using Python", "Quantitative researchers building custom estimators"],20  "alternatives": ["numpy", "statsmodels", "matlab", "julia", "sympy"],21  "comparison_notes": "NumPy provides the array type SciPy builds on; statsmodels adds regression and time-series models on top of SciPy. MATLAB and Julia offer comparable numerical toolkits in other languages; SymPy handles symbolic rather than numeric mathematics.",22  "categories": [{ "slug": "math-stats-tools", "primary": true }],23  "tags": ["python", "open-source", "numerical-computing", "statistics"],24  "use_cases": ["estimate-causal-effects", "analyze-intraday-data"],25  "operating_systems": ["macos", "linux", "windows"],26  "execution_modes": ["local"],27  "interfaces": ["library"],28  "deployment_models": ["embedded"],29  "integrations": ["NumPy", "statsmodels", "scikit-learn", "pandas", "Matplotlib"],30  "data_formats": ["NumPy arrays", "MATLAB .mat (scipy.io)", "WAV", "Matrix Market"],31  "languages": ["Python", "C", "C++", "Fortran", "Cython"],32  "source_model": "open-source",33  "license_spdx": "BSD-3-Clause",34  "pricing_model": ["free", "open-source"],35  "self_hostable": true,36  "sources": [37    { "url": "https://scipy.org/", "type": "official_site", "title": "SciPy", "supports": ["name", "summary"], "checked_at": "2026-11-02" },38    { "url": "https://docs.scipy.org/doc/scipy/", "type": "official_docs", "title": "SciPy documentation", "supports": ["capabilities", "description"], "checked_at": "2026-11-02", "note": "Shows version 1.18.0" },39    { "url": "https://github.com/scipy/scipy/blob/main/LICENSE.txt", "type": "license_page", "title": "scipy/scipy LICENSE.txt", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }40  ],41  "confidence": "high",42  "discovery_method": "editorial-research",43  "depth": "standard",44  "editorial_status": "reviewed",45  "visibility": "public",46  "featured": false,47  "author": "batch-E"48}49