Numba Patterns

What problem does it solve?

This Skill codifies rules and best practices to ensure Numba JIT-compiled functions in src/argus/analysis are pure numeric, deterministic, and meet strict performance budgets so developers avoid hidden compilation costs and non-JIT-safe patterns that break benchmarks.

Core Features & Use Cases

• JIT purity enforcement: Defines which operations are allowed inside @njit/@jit(nopython=True) functions and which Python objects, I/O, or libraries are forbidden.
• Test warmup guidance: Requires calling warmup_jit() in test setup to separate compilation time from runtime benchmarks.
• Performance targets & patterns: Specifies targets (e.g., ZigZag 1M <50ms post-warmup) and a wrapper pattern where lightweight Python wrappers convert raw JIT arrays into higher-level objects.
• Use Case: Validate that pivot-finding and harmonic pattern scans meet microsecond/millisecond budgets in CI benchmarks without including compilation overhead.

Quick Start

Run the Numba Patterns guidance to check that all @njit functions in src/argus/analysis are pure-math, that tests call warmup_jit() before timing, and that wrapper functions convert raw arrays into Python objects.