Agent skill · research science · k-dense-ai
openpiv
Particle Image Velocimetry (PIV) analysis with OpenPIV. Use when extracting velocity fields from PIV image pairs, analyzing fluid dynamics or flow visualization experiments, cross-correlating interrogation windows, validating and replacing spurious PIV vectors, or computing vorticity, strain rate, and turbulence statistics from measured velocity fields.
Why this skill is useful
Provides specific commands and workflows for analyzing fluid dynamics using OpenPIV that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, openpiv, scikit-image, scipy installed locally. About 9k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
OpenPIV Overview OpenPIV (Open Particle Image Velocimetry) analyzes fluid flow from PIV image pairs. It covers preprocessing, cross-correlation, vector validation, outlier replacement, smoothing, and scaling to physical units. Everything below is verified against openpiv 0.25.4. The API moves between releases — check inspect.signature() before trusting a snippet against a different version. When to use Use this skill when working with experimental PIV or flow-visualization image pairs: measuring 2D velocity fields, tuning interrogation-window parameters, validating vectors, or deriving vorticity, strain rate, and turbulence statistics. For simulating flow rather than measuring it, use a CFD skill instead. Quick Start Install OpenPIV: Run PIV analysis on an image pair: Or use the bundled CLI, which wraps exactly that pipeline: Core Concepts PIV Fundamentals Particle Image Velocimetry is an optical method for measuring fluid velocity by tracking illuminated tracer particles between two images. Process flow: 1. Capture an image pair (framea, frameb) separated by a known time dt. 2. Divide the images into interrogation windows. 3. Cross-correlate matching windows to find peak displacement. 4. Validate vectors (signal-to-noise, global range, local median). 5. Replace spurious vectors with interpolated values. 6. Scale pixel displacements to physical units. Interrogation Window Parameters windowsize — correlation window in pixels (typically 16–128). Larger windows give better correlation but coarser spatial resolution. overlap — pixels shared between adjacent windows (typically 50–75% of windowsize). Higher overlap raises vector density and cost, but adjacent vectors become correlated rather than independent. searchareasize — the window searched in the second frame. Must be ≥ windowsize; a few pixels larger accommodates larger displacements. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/openpiv