reactor_class

fusdb.reactor_class

Reactor class for loading and solving fusion reactor design specifications.

This module provides the Reactor class which loads reactor specifications from YAML files, manages variables, filters applicable relations, and orchestrates the solving process.

Reactor dataclass

Container for fusion reactor specifications, variables, and solving logic.

A Reactor represents a fusion reactor design with its parameters (geometry, plasma properties, etc.) and the physics relations that govern it. The class can load a configuration (currently from YAML), filter applicable relations based on tags, and solve for unknown variables.

Attributes:

Name	Type	Description
path	Path | None	Path to the source reactor file.
id	str | None	Unique reactor identifier (e.g., "ARC_2015", "DEMO_2022").
name	str | None	Reactor name.
organization	str | None	Organization (industrial or academic) developing the reactor.
country	str | None	Country where reactor is being developed/built.
year	int | None	Design year.
doi	str | None	DOI reference to published design.
notes	str | None	Additional notes.
tags	list[str]	Classification tags (e.g., "tokamak", "hmode",...). Used for relations filtering.
solving_order	list[str]	Ordered domains/relations to solve (e.g., ["geometry", "fusion_power"]).
solver_mode	str	How to handle computed vs input values: - "overwrite": Replace input values with computed (default) - "check": Only check consistency, don't modify
verbose	bool	Enable detailed solver logging.
relations	list[Relation]	Filtered list of applicable relations for this reactor.
variables_dict	dict[str, Variable]	Dictionary of Variable objects keyed by name.

__post_init__

__post_init__() -> None

Initialize per-instance logger with context.

from_yaml classmethod

from_yaml(path_like: str | Path) -> 'Reactor'

Create a Reactor object from a YAML file.

Parses the YAML file containing reactor metadata, tags, variables, and solver settings. Automatically filters relations based on tags and creates a fully initialized Reactor object ready for solving.

Parameters:

Name	Type	Description	Default
path_like	str | Path	Path to reactor.yaml file or directory containing it.	required

Returns:

Type	Description
'Reactor'	Reactor object with variables and relations loaded.

Example

reactor = Reactor.from_yaml('reactors/ARC_2015/reactor.yaml') reactor.solve()

solve

solve(mode: str | None = None, *, verbose: bool | None = None, enforce_constraint_tags: Iterable[str] | None = None, enforce_constraint_names: Iterable[str] | None = None) -> None

Solve for unknown reactor variables using physics relations.

Executes the iterative solver to compute unknown variables from known ones. If solving_order is specified, solves each domain in sequence.

Parameters:

Name	Type	Description	Default
mode	str | None	Override solver mode ("overwrite" or "check"). If None, uses self.solver_mode.	None
verbose	bool | None	Override verbosity setting. If None, uses self.verbose.	None
enforce_constraint_tags	Iterable[str] | None	Relation tags whose soft constraints should be enforced.	None
enforce_constraint_names	Iterable[str] | None	Relation names/outputs or variable names whose soft constraints should be enforced.	None

Example: >>> reactor = Reactor.from_yaml('reactors/ARC_2015/reactor.yaml') >>> reactor.solve(verbose=True) # Show detailed solving progress

diagnose

diagnose() -> dict[str, object]

Run comprehensive diagnostics on reactor consistency.

Checks all applicable relations to see which are violated and identifies likely culprit variables causing inconsistencies. Useful for debugging reactor specifications.

Returns:

Type	Description
dict[str, object]	Dictionary containing:
dict[str, object]	"violated_relations": List of relation names that are violated
dict[str, object]	"likely_culprits": Variables most likely causing violations
dict[str, object]	"variable_issues": Variables with missing or problematic values

Example

reactor = Reactor.from_yaml('reactors/ARC_2015/reactor.yaml') diag = reactor.diagnose() print(f"Violated: {len(diag['violated_relations'])} relations")

popcon

popcon(scan_axes: dict[str, Sequence[float]], *, outputs: Iterable[str] | None = None, constraints: Iterable[str] | None = None, constraint_tags: Iterable[str] | None = ('constraint',), exclude_constraints: Iterable[str] | None = None, where: dict[str, tuple[float | None, float | None]] | None = None, chunk_size: int | None = None) -> dict[str, object]

Evaluate a POPCON-style scan over one or more axes.

This method always uses the dense matrix path of RelationSystem.evaluate.

Parameters:

Name	Type	Description	Default
scan_axes	dict[str, Sequence[float]]	Mapping of variable name -> 1D sequence of scan values.	required
outputs	Iterable[str] | None	Optional output variable names to return.	None
constraints	Iterable[str] | None	Explicit constraint relation names/outputs.	None
constraint_tags	Iterable[str] | None	Relation tags used to auto-select constraints.	('constraint',)
exclude_constraints	Iterable[str] | None	Relation names/outputs to exclude.	None
where	dict[str, tuple[float | None, float | None]] | None	Optional thresholds {name: (min, max)}.	None
chunk_size	int | None	Optional row-chunk size passed to RelationSystem.evaluate.	None

Returns:

Type	Description
dict[str, object]	Dict with axes, outputs, margins, allowed mask, and diagnostics.

plot_popcon

plot_popcon(result: dict[str, object], *, x: str, y: str, fill: str, contours: list[str] | None = None, contour_levels: dict[str, list[float]] | None = None, contour_counts: dict[str, int] | None = None, constraint_contours: bool = True, slice: dict[str, int | float] | None = None, reduce: dict[str, str] | None = None, best: dict[str, str] | None = None, ax=None)

Plot POPCON results with masked fill and contour overlays.

__repr__

__repr__() -> str

Return a string representation of the reactor for display.

Shows the reactor name, ID, and key metadata in a readable format.

plot_cross_sections

plot_cross_sections(*, ax=None, label: str | None = None)

Plot plasma cross-section using 95% flux surface geometry.