InputVariable

class mlfmu.types.fmu_component.InputVariable(*, 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, agentInputIndexes: 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+)$)]] = [])

Bases: Variable

Represents an input variable for an FMU component.

Examples

An example of agent_input_indexes can be [“10”, “10:20”, “30”].

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
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.
agent_input_indexes	Index or range of indices of ONNX (agent) inputs to which this FMU signal shall be linked to.
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.

agent_input_indexes: list[Annotated[str, StringConstraints(strip_whitespace=True, to_upper=True, pattern='^(\\d+|\\d+:\\d+)$')]]

Index or range of indices of ONNX (agent) inputs to which this FMU signal shall be linked to. Note: The FMU signal and the ONNX (agent) inputs need to have the same length. Defaults to an empty list.

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].