Extract velocity fields from PIV image pairs using cross-correlation analysis.
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill openpiv
Please help me install this Agent Skill. Skill: openpiv Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/openpiv Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill openpiv
Measuring fluid velocity from experimental Particle Image Velocimetry image pairs requires cross-correlation, vector validation, outlier replacement, and unit scaling, which is error-prone to implement from scratch and easy to misconfigure across OpenPIV versions.
Use the openpiv skill to analyze my PIV image pair frame_a.bmp and frame_b.bmp and generate the velocity vector field with vorticity statistics.
High-intent search queries and answers about installing and using this skill.
Use openpiv's pyprocess.extended_search_area_piv to cross-correlate interrogation windows between two frames, then validate vectors with sig2noise_val and replace outliers with filters.replace_outliers. The bundled runner.py script wraps this full pipeline as a CLI.
A 32x32 pixel window suits most cases, with overlap at 50-75% of window size. Keep the largest displacement under about a quarter of the window size and aim for 5-10 particles per interrogation window.
Yes, openpiv.windef.simple_multipass runs multi-pass analysis with decreasing window sizes driven by a PIVSettings dataclass. Note that its output is in pixels per frame, so you must apply dt and scaling factor conversion yourself afterward.
Thresholds must match the units of your velocity output. extended_search_area_piv divides by dt, so px/frame thresholds like (-30, 30) reject everything when applied to px/s output; scale thresholds by 1/dt or validate before scaling.
Use linear correlation whenever search_area_size exceeds window_size, since circular correlation relies on FFT wrap-around and aliases large displacements into small ones. Circular is faster but only safe for small displacements.
Yes, the skill's PIVAnalyzer class computes vorticity, strain rate, and single-frame statistics from a saved params.npz, inferring physical grid spacing and axis orientation from the coordinates. True Reynolds decomposition requires an ensemble of image pairs averaged over time.
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