# openpiv

Skill for measuring fluid velocity fields from particle image pairs. Handles window cross-correlation, vector validation, vorticity and turbulence calculation — built for experimental flow visualization analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 943 installs · ★ 54652
- Author: K-Dense-AI
- License: BSD-3-Clause · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/openpiv/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/openpiv

## What it does

OpenPIV measures fluid motion from displaced particle images. The skill takes two sequential photographs, divides them into windows, finds displacement through cross-correlation, discards noisy vectors, and scales results to physical units. Output is a velocity field (x, y, u, v) plus flags marking outliers.

Core functions come from openpiv 0.26.1: `extended_search_area_piv` for correlation, `validation.sig2noise_val` for filtering, `filters.replace_outliers` for outlier interpolation, `windef.simple_multipass` for multi-pass processing. Physical scaling uses `scaling.uniform`. Needs Python 3.10+ and `uv pip install openpiv` — numpy, scipy, scikit-image, matplotlib arrive as dependencies. No network access after install.

Note: signal-to-noise thresholds for `peak2mean` and `peak2peak` methods are on different scales — a threshold tuned for one will fail on the other. Examples use `backend="scipy"`, tested against it.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/openpiv/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/openpiv/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/openpiv/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/openpiv/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install K-Dense-AI/scientific-agent-skills openpiv
```

2. **Manually: put the files in place**
- File: `.github/skills/openpiv/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/openpiv
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/openpiv/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/openpiv/SKILL.md

## When to use it
- extract velocity field from a pair of PIV images
- filter spurious vectors and replace outliers with interpolated values
- convert pixel displacements to physical units with scaling factor
- compute vorticity, strain rate and turbulence statistics from velocity vectors
- tune interrogation window size and overlap for resolution–correlation trade-off

## Example prompts
- Analyze these two PIV photos as a pair: cross-correlate with 32×32 window, 12-pixel overlap, scale 96.52 pixels per mm
- Load images from `frame_a.bmp` and `frame_b.bmp`, run multi-pass analysis with windows 64, 32, 16 pixels, signal-to-noise threshold 1.05
- Build an intensity mask for both frames, then treat the region outside the mask as excluded physical space

## Source text · SKILL.md · BSD-3-Clause

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 49e43fe8c23d8770.

BEGIN THIRD-PARTY FILE 49e43fe8c23d8770
````text
---
name: openpiv
description: Performs 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.
license: BSD-3-Clause
compatibility: Requires Python 3.10+ with openpiv installed (uv pip install openpiv). numpy, scipy, scikit-image, and matplotlib arrive as dependencies. No network access needed after install.
allowed-tools: Read Write Edit Bash
metadata:
  version: "1.3"
  skill-author: OpenPIV Team
  tested-against: "openpiv 0.26.1"
  last-reviewed: "2026-10-01"
---

# 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.

Targets **openpiv 0.26.1**. Synthetic checks ran on Python 3.13, NumPy 2.5.3, SciPy 1.18.1,
scikit-image 0.26.0, and Matplotlib 3.11.2. Rust was not installed; use `backend="scipy"` for the
tested path. See [review evidence](references/advanced_algorithms.md#review-sources-and-verification).

## 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==0.26.1"
```

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")

# Always returns (u, v, s2n); s2n is all NaN if sig2noise_method=None.
u, v, s2n = pyprocess.extended_search_area_piv(
    frame_a.astype(np.float32),
    frame_b.astype(np.float32),
    window_size=32,
    overlap=12,
    dt=0.02,
    search_area_size=38,
    correlation_method="linear",   # circular also supports extended search
    normalized_correlation=True,
    backend="scipy",
    sig2noise_method="peak2peak",
)
````
END THIRD-PARTY FILE 49e43fe8c23d8770

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