VaryingParameter

class optimas.core.VaryingParameter(name, lower_bound, upper_bound, is_fidelity=False, fidelity_target_value=None, default_value=None, dtype=<class 'float'>)

Defines an input parameter to be varied during optimization.

Parameters:

namestr

The name of the parameter.

lower_bound, upper_boundfloat

Lower and upper bounds of the range in which the parameter can vary.

is_fidelitybool, optional

Indicates whether the parameter is a fidelity. Only needed for multifidelity optimization.

fidelity_target_valuefloat, optional

The target value of the fidelity. Only needed for multifidelity optimization.

default_valuefloat, optional

Default value of the parameter when it is not being varied. Only needed for some generators.

dtypedata-type

The data type of the parameter. Any object that can be converted to a numpy dtype.

Methods

construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
fix_value(value)	Fix the value of the parameter.
free_value()	Free the value of the parameter.
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, ensure_ascii, ...])	!!! 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, /)	This function is meant to behave like a BaseModel method to initialize private attributes.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	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)
update_range(lower_bound, upper_bound)	Update range of the parameter.
validate(value)

Attributes

is_fixed	Get whether the parameter is fixed to a certain value.
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.
lower_bound
upper_bound
is_fidelity
fidelity_target_value
default_value
name
dtype