WorkflowConfig

Top-level configuration model for a simulation workflow.

Defines the root directory, simulations to run, builder logic, and aggregation strategies. Can be saved/loaded from YAML.

def my_function(x, y):
    result = x + y
    return result

Bases: BaseModel

Top-level configuration model for a simulation workflow.

This class defines how simulations are structured, executed, and aggregated. It is typically serialized/deserialized to YAML for reproducible workflows.

Attributes:

Name	Type	Description
root_folder	PathType	Root directory where simulation results will be saved. default: 'results'
keep_hfss_solutions	bool	If True the HFSS solution are kept (allowing for field plotting) for every iteration. Should be False as keeping all solution takes a lot of memory. default: False
pyaedt_file_parameters	PyaedtFileParameters	Configuration for how the .aedt file is opened and managed during simulation. See PyaedtFileParameters for full control over versioning, licensing, and graphical behavior.
simulations	dict[str, SUPPORTED_ANALYSIS]	Mapping of simulation names to simulation configuration objects. Each value must be one of the supported analysis types: EigenmodeAnalysis QuantumEPR These are selected using a type field discriminator, as defined in SUPPORTED_ANALYSIS.
builder	SUPPORTED_BUILDERS | None	Optional object used to modify the HFSS model before simulation. Supported builder types: DesignVariableBuilder FunctionBuilder ModuleBuilder The builder must define a type field used for runtime selection.
builder_sweep	list[DictSweep]	parameter sweep applied to the builder phase. each DictSweep instance allows for iteration over dict values. For example: `DictSweep(constants={'a':1}, parameters={'b': [1,2], 'c':[3,4]}, strategy='product') --> {'a': 1, 'b': 1, 'c': 3} --> {'a': 1, 'b': 1, 'c': 4} --> {'a': 1, 'b': 2, 'c': 3} --> {'a': 1, 'b': 2, 'c': 4} `
aggregation_dict	dict[str, list[str]]	Optional aggregation rules for result post-processing. Each key maps to a list of strings which should be all simulation identifiers. This dict is converted to Aggregator which than go for each key and aggregate its list of identifiers (e.g., flattening, validation, merging by UID). See pycaddy.aggregator.Aggregator for behavior.

load_from_yaml classmethod

load_from_yaml(path: str | Path) -> WorkflowConfig

Load a workflow configuration from a YAML file.

Parameters:

Name	Type	Description	Default
path	str | Path	Source file path.	required

Returns:

Name	Type	Description
WorkflowConfig	WorkflowConfig	Parsed configuration object.

Source code in src/quansys/workflow/config.py

@classmethod
def load_from_yaml(cls, path: str | Path) -> WorkflowConfig:
    """
    Load a workflow configuration from a YAML file.

    Args:
        path: Source file path.

    Returns:
        WorkflowConfig: Parsed configuration object.
    """
    return parse_yaml_file_as(cls, path)

save_to_yaml

save_to_yaml(path: str | Path) -> None

Save this configuration to a YAML file.

Parameters:

Name	Type	Description	Default
path	str | Path	Target file path.	required

Source code in src/quansys/workflow/config.py

def save_to_yaml(self, path: str | Path) -> None:
    """
    Save this configuration to a YAML file.

    Args:
        path: Target file path.
    """
    to_yaml_file(path, self, map_indent=4)