FmiOutputVariable

class mlfmu.types.fmu_component.FmiOutputVariable(*, name: str, type: ~mlfmu.types.fmu_component.FmiVariableType = FmiVariableType.REAL, description: str | None = None, variability: ~mlfmu.types.fmu_component.FmiVariability | None = None, startValue: float | str | bool | int | None = 0, isArray: bool = False, length: int | None = None, agentOutputIndexes: list[~typing.Annotated[str, ~pydantic.types.StringConstraints(strip_whitespace=True, to_upper=True, to_lower=None, strict=None, min_length=None, max_length=None, pattern=^(\d+|\d+:\d+)$)]] = [], causality: ~mlfmu.types.fmu_component.FmiCausality, variableReferences: list[int])

Bases: OutputVariable

Data class representing an output variable in an FMI component.

__init__(**kwargs: Any) → None

Create an FMI output variable.

Parameters:

• causality (FmiCausality, optional) – Causality of the output variable., by default FmiCausality.OUTPUT

• variable_references (list[int], optional) – List of variable references associated with the output variable., by default []

Methods

__init__(**kwargs)	Create an FMI output variable.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, include, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
causality	The causality of the output variable.
variable_references	The list of variable references associated with the output variable.
agent_output_indexes	Index or range of indices of agent outputs that will be linked to this output signal.
name	Unique name for the port.
type	Data type as defined by FMI standard.
description	Short FMU variable description.
variability	Signal variability as defined by FMI.
start_value	Initial value of the signal at time step 1.
is_array	When dealing with an array signal, it is essential to specify the LENGTH parameter.
length	Defines the number of entries in the signal if the signal is array.

causality: FmiCausality

The causality of the output variable.

model_config: ClassVar[ConfigDict] = {'alias_generator': <function to_camel>, 'populate_by_name': True, 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

variable_references: list[int]

The list of variable references associated with the output variable.