Custom Operations

Custom Monte Carlo transforms can run directly from any callable. Bundle-safe custom transforms must use NumpyCustomFunc or the custom_operation decorator so the function source and accepted globals can travel with the .sdsge archive.

`NumpyCustomFunc`

class NumpyCustomFunc()

Opt-in wrapper that validates and snapshots a numerical Python function.

NumpyCustomFunc(func: Callable[..., Any] | NumpyCustomFunc) -> NumpyCustomFunc

Properties:

Name	Type	Description
name	`str`	Original function name.
source	`str`	Author-side source text for receiver audit.
captured_globals	`Mapping[str, Any]`	Snapshot of accepted globals referenced by the function.
safe_namespace_version	`int`	Version of the safe-namespace contract used at wrap time.

Methods:

NumpyCustomFunc.from_source(source: str) -> NumpyCustomFunc  # @classmethod
NumpyCustomFunc.__call__(*args: Any, **kwargs: Any) -> Any

from_source validates source text directly. This is used for code typed into a UI/editor where inspect.getsource(...) cannot recover a real file or notebook cell.

`custom_operation`

custom_operation(func: Callable[..., Any]) -> NumpyCustomFunc

Decorator form of NumpyCustomFunc. The decorated name becomes a callable NumpyCustomFunc and can be passed to transform_step(...).

Validation Contract

Allowed	Description
One top-level `def`	Lambdas, nested functions, methods, partials, builtins, and C-extension callables are rejected.
Numeric safe namespace	`numpy` as `np`, selected standard modules, selected builtins, and captured immutable/numpy globals.
Explicit source	Source must be recoverable or supplied through `from_source(...)`.

Rejected	Reason
Imports, `global`, `nonlocal`, async, yield, nested `def`, classes	These make the shipped function harder to audit and reproduce.
Closure captures	Promote values to accepted globals or pass them as kwargs.
Unsupported globals	The wrapper snapshots only accepted numeric/scalar/container helpers.

Not a security sandbox

NumpyCustomFunc is a reproducibility and audit contract, not a sandbox. Loading a bundle with custom operations should be treated like running Python code from the bundle author.