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.
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```bash uv pip install openpiv # Pin it when the analysis needs to be reproducible -- this is the version every # snippet below was checked against. uv pip install "openpiv==0.25.4" ``` Run PIV analysis on an image pair: ```python import numpy as np from openpiv import tools, pyprocess, validation, filters, scaling frame_a = tools.imread("image_a.bmp") frame_b = tools.imread("image_b.bmp") # Cross-correlate. Returns (u, v, s2n) whene
- Overview
- When to use
- Quick Start
- Core Concepts
- PIV Fundamentals
- Interrogation Window Parameters
- Signal-to-Noise Ratio
- Common Operations
- Dynamic Masking
- Multi-Pass Processing
- Validation and Post-Processing
- Validation Methods
- Outlier Replacement
- Smoothing
uv pip install openpiv Pin it when the analysis needs to be reproducible -- this is the version every snippet below was checked against. uv pip install "openpiv==0.25.4" python skills/openpiv/scripts/runner.py \ Basic run Tuned parameters with dynamic masking python skills/openpiv/scripts/run_example.py --output_dir /tmp/openpiv-demo
What does the openpiv skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.