Openpiv

---
name: openpiv
description: 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: '1.1'
  category: physical-sciences
  maintainer: Kalaris Labs
  tested-against: openpiv 0.25.4
  contributor: OpenPIV Team
---

# 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) whenever sig2noise_method is not None.
u, v, s2n = pyprocess.extended_search_area_piv(
    frame_a.astype(np.int32),
    frame_b.astype(np.int32),
    window_size=32,
    overlap=12,
    dt=0.02,
    search_area_size=38,
    correlation_method="linear",   # required for search_area_size > window_size
    sig2noise_method="peak2peak",
)

x, y = pyprocess.get_coordinates(
    image_size=frame_a.shape,
    search_area_size=38,
    overlap=12,
)

# flags is a boolean array: True marks a spurious vector.
flags = validation.sig2noise_val(s2n, threshold=1.05)
u, v = filters.replace_outliers(u, v, flags, method="localmean", max_iter=3, kernel_size=2)

# Scale to physical units, then flip to image coordinates for plotting.
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
x, y, u, v = tools.transform_coordinates(x, y, u, v)

tools.save("vectors.txt", x, y, u, v, flags)
```

Or use the bundled CLI, which wraps exactly that pipeline:

```bash
python skills/openpiv/scripts/runner.py \
    --image frame_a.bmp --image frame_b.bmp --output_dir results --verbose
```

## 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 (`frame_a`, `frame_b`) 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

**`window_size`** — 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 `window_size`). Higher
overlap raises vector density and cost, but adjacent vectors become correlated rather than
independent.

**`search_area_size`** — the window searched in the second frame. Must be ≥ `window_size`; a few
pixels larger accommodates larger displacements. Pair an extended search area with
`correlation_method="linear"` — the default `"circular"` relies on FFT wrap-around and aliases large
displacements into small ones. See `references/advanced_algorithms.md`.

Rules of thumb: keep the largest displacement under about a quarter of `window_size`, and aim for
5–10 particles per window.

### Signal-to-Noise Ratio

`s2n` measures how distinct the correlation peak is. `sig2noise_method` controls how it is computed —
`"peak2mean"` (the function default) or `"peak2peak"`. **The two are on different scales**, so a
threshold tuned for one is meaningless for the other. Typical `peak2peak` thresholds are 1.05–1.3.

```python
flags = validation.sig2noise_val(s2n, threshold=1.05)
# flags is bool: True == spurious. `~flags` selects the good vectors.
```

## Common Operations

### Dynamic Masking

Masking lives in `openpiv.preprocess`, **not** in an `openpiv.masking` module. It returns an
`(image, mask)` tuple and expects a float image.

```python
from openpiv import preprocess

# method="edges" for dark, sharp-edged objects; "intensity" for high-contrast objects.
frame_a_masked, mask_a = preprocess.dynamic_masking(
    frame_a.astype(np.float64), method="intensity", filter_size=7, threshold=0.005
)
frame_b_masked, mask_b = preprocess.dynamic_masking(
    frame_b.astype(np.float64), method="intensity", filter_size=7, threshold=0.005
)
```

Feed the **returned image** into the correlation step — it already has the masked region zeroed. Do
not multiply the original frame by `mask`: masking is already applied, and for `method="edges"` the
mask comes back as `uint8` 0/255 rather than boolean, so multiplying rescales the image by 255.

### Multi-Pass Processing

Multi-pass (window deformation) lives in `openpiv.windef`, driven by a `PIVSettings` dataclass.
`pyprocess` has no multi-pass entry point.

```python
import numpy as np
from openpiv import scaling, windef

settings = windef.PIVSettings()
settings.windowsizes = (64, 32, 16)   # one entry per pass, decreasing (this is also the default)
settings.overlap = (32, 16, 8)        # same length as windowsizes
settings.num_iterations = 3           # number of passes to actually run
settings.sig2noise_threshold = 1.05

x, y, u, v, flags = windef.simple_multipass(
    frame_a.astype(np.int32), frame_b.astype(np.int32), settings
)

# Output is in PIXELS PER FRAME -- convert yourself. scaling.uniform only divides
# by scaling_factor, so apply dt separately.
dt = 0.02
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
u, v = u / dt, v / dt
```

`simple_multipass` already validates, replaces outliers, fills remaining NaNs with zeros, and calls
`transform_coordinates` — do not repeat those steps.

**Units trap:** `PIVSettings` has `dt` and `scaling_factor` fields, but `windef` never uses either —
`first_pass` calls `extended_search_area_piv` without `dt`, so the whole multi-pass chain works in
pixels per frame. Setting `settings.dt = 0.02` changes nothing about the returned values. Convert
after the fact, as above.

For control over individual passes, `windef.first_pass` and `windef.multipass_img_deform` are the
lower-level building blocks.

## Validation and Post-Processing

### Validation Methods

Every validator returns a boolean array where **True marks a spurious vector**.

```python
# Signal-to-noise
flags = validation.sig2noise_val(s2n, threshold=1.05)

# Global range -- takes (min, max) TUPLES, positionally or as u_thresholds/v_thresholds.
flags = validation.global_val(u, v, (-300, 300), (-300, 300))

# Local median -- u_threshold and v_threshold are REQUIRED; size is the neighbourhood half-width.
flags = validation.local_median_val(u, v, u_threshold=30.0, v_threshold=30.0, size=1)

# Combine with boolean OR (not np.maximum -- these are bool arrays).
flags = (
    validation.sig2noise_val(s2n, threshold=1.05)
    | validation.global_val(u, v, (-300, 300), (-300, 300))
    | validation.local_median_val(u, v, u_threshold=30.0, v_threshold=30.0)
)
```

**Set these thresholds in the units of `u` and `v`, not in pixels per frame.**
`extended_search_area_piv` divides by `dt`, so with `dt=0.02` a 3 px/frame displacement arrives as
150 px/s. The thresholds above suit that case; the `(-30, 30)` figure that PIV literature and
`PIVSettings.min_max_u_disp` use is a px/frame limit, and applying it to px/s output rejects the
entire field. Either validate before scaling, or scale the thresholds by `1/dt` too.

### Outlier Replacement

```python
u, v = filters.replace_outliers(
    u, v, flags, method="localmean", max_iter=3, tol=1e-3, kernel_size=2
)
```

`method` accepts `"localmean"`, `"disk"`, or `"distance"` — and only those three. An unrecognized
name is not rejected; it falls through to an all-zero kernel and silently returns a useless field.
Note that replacement *fills* the flagged
positions with interpolated values — if you then overwrite them with NaN, the replacement was
wasted. Choose one or the other:

```python
# Keep flagged vectors out of the analysis entirely, instead of interpolating them.
u = np.where(flags, np.nan, u)
v = np.where(flags, np.nan, v)
```

### Smoothing

Smoothing is `openpiv.smoothn.smoothn`; there is no `openpiv.smooth` module. It returns a tuple
whose first element is the smoothed field, and it does not accept NaN input.

```python
from openpiv.smoothn import smoothn

u_smooth, *_ = smoothn(np.nan_to_num(u), s=0.5)  # s: larger == smoother
v_smooth, *_ = smoothn(np.nan_to_num(v), s=0.5)
u_smooth = np.asarray(u_smooth)
```

## Visualization

### Vector Field Plotting

`display_vector_field` reads a saved vectors file and calls `plt.show()` internally, so select a
non-interactive backend for batch runs.

```python
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from openpiv import tools

fig, ax = plt.subplots(figsize=(8, 8))
tools.display_vector_field(
    "vectors.txt",
    ax=ax,
    scaling_factor=96.52,   # same factor used in scaling.uniform, to map back onto the image
    scale=50,
    width=0.0035,
    on_img=True,
    image_name="frame_a.bmp",
)
fig.savefig("vector_field.png", dpi=150, bbox_inches="tight")
plt.close(fig)
```

### Custom Visualization

```python
import numpy as np
import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

mag = np.sqrt(u**2 + v**2)
for ax, field, title, cmap in [
    (axes[0], mag, "Velocity Magnitude", "viridis"),
    (axes[1], u, "U Velocity", "RdBu_r"),
    (axes[2], v, "V Velocity", "RdBu_r"),
]:
    im = ax.imshow(field, cmap=cmap)
    ax.set_title(title)
    plt.colorbar(im, ax=ax)

fig.tight_layout()
fig.savefig("velocity_components.png")
plt.close(fig)
```

## Analysis Functions

`scripts/analyze.py` bundles these against a `params.npz` written by `runner.py`. It infers the
physical grid spacing from the saved coordinates, so the derivatives come out per unit length:

```python
import sys
sys.path.insert(0, "skills/openpiv/scripts")
from analyze import PIVAnalyzer

piv = PIVAnalyzer("results/params.npz")
vorticity = piv.compute_vorticity()          # dv/dx - du/dy
exx, eyy, exy = piv.compute_strain()
stats = piv.compute_statistics()             # u_mean, v_mean, rms_u, rms_v, tke
piv.plot_vector_field(save_path="quiver.png")
```

The standalone forms, if you would rather compute them inline:

### Vorticity

```python
def compute_vorticity(u, v, dx=1.0, dy=None):
    """Out-of-plane vorticity dv/dx - du/dy. Pass the physical grid spacing, not 1.0."""
    dy = dx if dy is None else dy
    return np.gradient(v, dx, axis=1) - np.gradient(u, dy, axis=0)
```

The grid spacing is `(window_size - overlap) / scaling_factor` in physical units, so leaving `dx=1.0`
yields vorticity per grid cell, not per unit length.

**Sign convention:** `runner.py` ends with `transform_coordinates`, which relabels the grid into a
right-handed y-up frame but leaves the rows in image order, so the saved `y` *decreases* as the row
index grows. The standalone forms above assume the opposite, so on a `params.npz` field they return
`-du/dy` and flip the sign of the vorticity and the shear strain — negate the `axis=0` derivatives, or
use `PIVAnalyzer`, which reads the orientation off the saved coordinates.

### Strain Rate

```python
def compute_strain(u, v, dx=1.0, dy=None):
    """Return (exx, eyy, exy) of the 2D strain-rate tensor."""
    dy = dx if dy is None else dy
    du_dx = np.gradient(u, dx, axis=1)
    du_dy = np.gradient(u, dy, axis=0)
    dv_dx = np.gradient(v, dx, axis=1)
    dv_dy = np.gradient(v, dy, axis=0)
    return du_dx, dv_dy, 0.5 * (du_dy + dv_dx)
```

### Turbulence Statistics

```python
def compute_statistics(u, v):
    """Single-frame spatial statistics. NOT Reynolds decomposition."""
    u_prime = u - np.nanmean(u)
    v_prime = v - np.nanmean(v)
    rms_u, rms_v = np.nanstd(u_prime), np.nanstd(v_prime)
    return {
        "u_mean": np.nanmean(u),
        "v_mean": np.nanmean(v),
        "rms_u": rms_u,
        "rms_v": rms_v,
        "tke": 0.5 * (rms_u**2 + rms_v**2),
    }
```

**Caveat:** subtracting the *spatial* mean of one frame measures spatial variance, which equals
turbulent intensity only for a homogeneous field. Genuine Reynolds decomposition needs an ensemble of
image pairs: average over the time axis, then subtract that mean field from each realization.

## CLI Usage

```bash
# Basic run
python skills/openpiv/scripts/runner.py \
    --image img1.bmp --image img2.bmp --output_dir results --verbose

# Tuned parameters with dynamic masking
python skills/openpiv/scripts/runner.py \
    --image frame_a.bmp \
    --image frame_b.bmp \
    --output_dir results \
    --window_size 32 \
    --overlap 12 \
    --search_area 38 \
    --dt 0.02 \
    --scaling 96.52 \
    --threshold 1.05 \
    --mask dynamic \
    --mask_method intensity \
    --verbose
```

### CLI Options

| Option | Default | Description |
|--------|---------|-------------|
| `--image` | required | Image file; specify exactly twice for the pair |
| `--output_dir` | `results` | Output directory (created if absent) |
| `--window_size` | 32 | Interrogation window size (px) |
| `--overlap` | 12 | Window overlap (px) |
| `--search_area` | 38 | Search area size (px), must be ≥ `--window_size` |
| `--dt` | 0.02 | Time between frames (s) |
| `--scaling` | 96.52 | Scaling factor, pixels per physical unit (e.g. px/mm) |
| `--threshold` | 1.05 | `peak2peak` signal-to-noise threshold |
| `--mask` | `none` | `none` or `dynamic` (`openpiv.preprocess.dynamic_masking`) |
| `--mask_method` | `intensity` | `edges` or `intensity`, used only with `--mask dynamic` |
| `--drop_invalid` | off | NaN out flagged vectors instead of keeping interpolated values |
| `--verbose` | off | Print progress messages |

Verify an install end to end against OpenPIV's own bundled image pair:

```bash
python skills/openpiv/scripts/run_example.py --output_dir /tmp/openpiv-demo
```

## Output Files

- **vectors.txt** — tab-delimited, `%.4e` formatted, with a `# x y u v flags mask` comment header
- **params.npz** — NumPy archive with `x`, `y`, `u`, `v`, `flags` arrays
- **vector_field.png** — vector field drawn over the first frame

```text
# x	y	u	v	flags	mask
2.1757e-01	3.5226e+00	-6.2220e-02	-2.7081e+00	0.0000e+00	0.0000e+00
4.8695e-01	3.5226e+00	-3.1587e-01	-2.9800e+00	0.0000e+00	0.0000e+00
```

`flags` is written as a float, `0` for a valid vector and `1` for a flagged one.

## Best Practices

### Parameter Selection

1. **Window size** — 32×32 suits most cases. 64/128 for better correlation at coarser resolution;
   16/24 for finer resolution at the cost of noise.
2. **Overlap** — 50–75% of window size.
3. **Threshold** — raise it to reject more vectors; always re-tune after switching
   `sig2noise_method`.
4. **Scaling factor** — calibrate against a known reference such as a calibration grid, and keep the
   units straight (`96.52` in OpenPIV's `test1` tutorial data is px/mm).

### Image Quality

- Particles visible and evenly distributed, 5–10 per interrogation window
- No saturated or overexposed regions
- Minimal background noise; consider background subtraction across a run

### Processing Tips

1. Start from the defaults, then tune against the vector field you get.
2. Inspect the `s2n` distribution — a low median means poor correlation, not a bad threshold.
3. Visualize early; obvious problems (uniform vectors, edge artifacts) show up immediately.
4. Use multi-pass (`windef`) for flows with large velocity gradients or displacements.
5. Mask reflections and solid boundaries rather than letting them generate vectors.

## Resources

### references/

- `advanced_algorithms.md` — correlation and subpixel methods, multi-pass window deformation,
  `PIVSettings` fields, 3D and phase-separation modules

Load the reference when detailed algorithm or settings information is needed.

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