{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Computes finite difference numerical derivatives and solves partial differential equations on arrays of any dimension, with support for arbitrary accuracy orders, boundary conditions, and compact (implicit) schemes.","skillfed_tags":["numerical-methods","pde-solver","scientific-computing"],"use_cases":["Compute spatial derivatives on gridded data for physics simulations or numerical analysis.","Solve time-dependent PDEs with Dirichlet or Neumann boundary conditions.","Generate finite difference coefficient tables for custom numerical schemes.","Build matrix representations of differential operators for linear algebra workflows.","Apply compact finite differences to achieve high accuracy with narrow stencils on periodic or non-periodic grids."],"what_it_does":"findiff is a numerical differentiation library that applies finite difference stencils to numpy arrays to compute derivatives and solve PDEs. It lets you define derivative operators symbolically (e.g., d\u00b2/dx\u00b2) and apply them to data, with control over accuracy order, boundary handling, and grid periodicity. The package supports standard finite differences and newer compact (implicit) schemes that couple derivative values at neighboring points for spectral-like accuracy from small stencils.\n\nYou specify a grid spacing and derivative order, then apply the operator to your data. It handles multidimensional arrays, arbitrary linear combinations of derivatives with variable coefficients, and can return matrix representations of differential operators. Recent versions added periodic boundary conditions and compact schemes with automatic stencil selection, making it suitable for both simple derivative calculations and complex PDE solving workflows.","worth_installing":"Yes. findiff is actively maintained, has no known vulnerabilities, installs with low friction, and solves a clear problem in numerical computing. It is well-suited for anyone doing finite difference calculations or PDE solving in Python. The recent additions of compact schemes and periodic boundary conditions make it competitive for modern scientific computing tasks."},"id":"findiff","links":{"html":"https://skillfed.io/packages/findiff","md":"https://skillfed.io/packages/findiff.md","pypi":"https://pypi.org/project/findiff/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-19","license_spdx":null,"license_treatment":"permissive","name":"findiff","python_support":"supports_current","summary":"A Python package for finite difference derivatives in any number of dimensions."},"popularity":{"monthly_downloads":84039,"position":14033,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.13.1"}