# Vitalyvorobyev Ringgrid Tests Synthetic Fixtures

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- Skill: `tomevault-io/vitalyvorobyev-ringgrid-tests-synthetic-fixtures` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/vitalyvorobyev-ringgrid-tests-synthetic-fixtures`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/vitalyvorobyev-ringgrid-tests-synthetic-fixtures/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/vitalyvorobyev-ringgrid-tests-synthetic-fixtures

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name: tests-synthetic-fixtures
description: Use this to create deterministic test images/signals with known subpixel ground truth (edges, stripes, circles). Helps lock correctness before optimizing.
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# Synthetic fixtures (deterministic)

## Principles

* Deterministic by default (no RNG), or seeded RNG only.
* Tests should check:

  * geometry error (px)
  * stability under small noise/blur
  * edge cases (odd sizes, borders, ROI truncation)

## Useful fixtures

* **Half-plane step edge** with known line equation (slanted edge):

  * `I(p)=A` if dot(n, p) < t else `B`
  * Optional blur along `n` (binomial/gaussian-ish) to make it realistic
* **Stripe (laser)** as difference of two steps (bright-on-dark):

  * known left/right subpixel positions → known center + width
* **Circle / ring boundary** (later):

  * implicit distance function `d = sqrt((x-cx)^2+(y-cy)^2)-r`
  * intensity from sign(d) and controlled blur

## Expected-value checks

* Keep early tests loose (~0.1–0.2 px).
* Add tighter tests later (aim 0.05 px) on higher-fidelity fixtures.

## Regression strategy

* When a bug appears, capture it as a small synthetic fixture.

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