fuellib.gcm.core.GCM

class fuellib.gcm.core.GCM(*, name, property_fns)

Bases: BaseModel

Base class for Core Group Contribution Methods.

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.

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.
name
property_fns

Methods

construct
copy	Returns a copy of the model.
dict
from_orm
get_property	Retrieve the property function for the specified property name.
json
list_property_fns	List the names of all registered property functions.
model_construct	Creates a new instance of the Model class with validated data.
model_copy	!!! abstract "Usage Documentation"
model_dump	!!! abstract "Usage Documentation"
model_dump_json	!!! abstract "Usage Documentation"
model_json_schema	Generates a JSON schema for a model class.
model_parametrized_name	Compute the class name for parametrizations of generic classes.
model_post_init	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild	Try to rebuild the pydantic-core schema for the model.
model_validate	Validate a pydantic model instance.
model_validate_json	!!! abstract "Usage Documentation"
model_validate_strings	Validate the given object with string data against the Pydantic model.
parse_file
parse_obj
parse_raw
predict	Predict the specified property for a fuel's components.
predict_all	Predict all registered property functions for a fuel's components.
register_property	Register a new property implementation for this GCM.
schema
schema_json
update_forward_refs
validate

Parameters:

• name (str)

• property_fns (dict[str, PropertyProtocol])

get_property(property_name)

Retrieve the property function for the specified property name.

Parameters:

property_name (str) – The name of the property function to retrieve.

Returns:

The property function corresponding to the specified property name.

Raises:

ValueError – If the specified property is not implemented by this GCM.

Return type:

PropertyProtocol

list_property_fns()

List the names of all registered property functions.

Returns:

A list of the names of all registered property functions.

Return type:

list[str]

model_config = {'arbitrary_types_allowed': True}

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

name: str

predict(property_name, fuel)

Predict the specified property for a fuel’s components.

Parameters:

• property_name (str) – The name of the property to predict.

• fuel (Fuel) – The fuel object containing the decomposition matrix.

Returns:

The predicted property values for the fuel’s components as a Quantity1D.

Return type:

Quantity[ndarray[tuple[int]]]

predict_all(fuel)

Predict all registered property functions for a fuel’s components.

Parameters:

fuel (Fuel) – The fuel object containing the decomposition matrix.

Returns:

A dictionary mapping property names to their predicted values for the fuel’s

components.

Return type:

dict[str, dict[str, Quantity[ndarray[tuple[int]]]]]

property_fns: dict[str, PropertyProtocol]

register_property(function)

Register a new property implementation for this GCM.

Parameters:

function (PropertyProtocol) – The property function to register.

Returns:

The registered property function.

Return type:

PropertyProtocol