Context Class

EnOptContext is the validated, frozen container that holds every setting needed to execute a single optimization run. It is typically built from a plain dict (EnOptContext.model_validate(CONFIG)).

For a narrative overview of all fields — including broadcasting rules, index-based sharing of plugin instances, defaults, and worked examples — see the Configuration user-manual page.

ropt.context

The ropt.context module provides the context class used by optimization workflows.

EnOptContext

Bases: BaseModel

The primary context object for a single optimization run.

EnOptContext holds all information needed to run an ensemble-based optimization: variables, objectives, constraints, realizations, gradient settings, samplers, filters, and the optimizer/backend. It is constructed from plain Python dicts or config objects and validated on creation.

See the Configuration guide for an in-depth description of broadcasting rules, index-based sharing of plugin instances, the names attribute, and how dicts are resolved into plugin instances.

Warning

EnOptContext objects are immutable after construction. Do not attempt to serialize and round-trip them (e.g., to/from JSON): numpy arrays and plugin instances cannot survive a round-trip faithfully. Persist the raw input dicts instead.

Attributes:

Name	Type	Description
variables	VariablesConfig	Variable settings.
objectives	ObjectiveFunctionsConfig	Objective function settings.
linear_constraints	LinearConstraintsConfig | None	Optional linear constraint settings.
nonlinear_constraints	NonlinearConstraintsConfig | None	Optional nonlinear constraint settings.
realizations	RealizationsConfig	Ensemble realization settings.
optimizer	OptimizerConfig	Optimizer settings.
backend	BackendInstance	Backend plugin instance used for function evaluations.
gradient	GradientConfig	Gradient estimation settings.
realization_filters	tuple[RealizationFilterInstance, ...]	Tuple of realization filter plugin instances.
function_estimators	tuple[FunctionEstimatorInstance, ...]	Tuple of function estimator plugin instances.
samplers	tuple[SamplerInstance, ...]	Tuple of sampler plugin instances.
variable_transforms	tuple[VariableTransformInstance, ...]	Tuple of variable transform plugin instances.
objective_transforms	tuple[ObjectiveTransformInstance, ...]	Tuple of objective transform plugin instances.
nonlinear_constraint_transforms	tuple[NonlinearConstraintTransformInstance, ...]	Tuple of nonlinear constraint transform plugin instances.
names	dict[str, tuple[str | int, ...]]	Optional mapping of axis names to label sequences.

lock

lock() -> None

Lock the object to prevent sharing and re-use.

Raises:

Type	Description
RuntimeError	If the object is already locked.