[FrancescAlted]

Supersedes #735.

Menudet is a little language for portable computation on arrays and tables, with Python-like syntax and native execution through miniexpr. Validated kernels can be saved and loaded without reconstructing Python functions, while preserving defined numeric semantics and logical computation groups. Menudet 1.0 remains a draft specification.

Main changes

• Add standalone, validated kernel artifacts, @blosc2.menudet authoring with annotated or inferred dtypes, and native execution without Python reconstruction or execution fallback.
• Define NumPy-style numerical semantics, including wrapping array integers, checked weak-scalar conversions, operand-driven floating-point types, and interpreted complex arithmetic. Support control flow, fixed-width strings, logical coordinates and ordered block reductions.
• Add native expression and staged graph execution, parallel resident maps/reductions, and bounded readers for direct computation over compressed operands.
• Remove NumExpr as a runtime dependency. Automatically interpret single-return expressions and prefer JIT for multi-statement programs, while honoring explicit execution settings. Unsupported optional JIT falls back before execution; execution failures propagate without replaying numerical work.
• Improve NumPy-output evaluation with explicit output buffers, layout/casting handling, reusable evaluators and operand-free preparation caches. Reduce cold overhead through shared syntax analysis, prepared-plan reuse, fewer Menudet artifact imports and native uniform-cast reuse.
• Execute supported DSL-backed LazyUDFs and array constructors through Menudet. Preserve logical reduction groups through persistence, partial reads and output rechunking.
• Integrate portable persistence across LazyUDFs, frames, MessagePack and object stores. Restore validated numerical LazyExpr recipes under safe loading; retain explicit deserialize="full" for legacy Python-specific recipes and reject unsupported saves before destination writes.
• Add CTable computed/generated transformers with explicit independent-row semantics, and regression coverage for pandas-free Arrow dictionary imports.
• Expose explicit math-library and floating-point policies, update the pinned native dependency, and expand native/WASM qualification, documentation, examples and benchmarks.
• Retain independent math certification with 284 samples across 40 functions, float32 and float64; these are finite-corpus checks, not universal accuracy bounds.

Performance and validation

For x + (y - 1) over 100K float64 NumPy operands on macOS arm64, with 12 configured threads and fresh NumPy outputs, preparation-cold medians from 151 samples were:

Engine	Median
numexpr (comparison only)	210 µs
LazyExpr-NumPy	168 µs
evaluate	186 µs
Menudet	177 µs

Preparation/compiler caches were cleared before each cold sample; inputs were prebuilt and CPU/OS caches were not flushed. Measurements used a rebuilt native extension containing the pinned uniform-cast optimization. Compressed-output LazyExpr is excluded from this comparison.

• Full local Python suite after merging current main: 11,796 passed, 87 skipped, including a subprocess that blocks pandas imports and the unsigned dictionary overflow regression.
• Native uniform-cast parity tests and 15 additional native graph/portable checks passed.
• Ruff, formatting and applicable commit hooks passed. Heavy/network tests and release-wheel qualification were not rerun for these final changes.