PredictedQuantumDatasetsModel

class lsst.pipe.base.quantum_graph.PredictedQuantumDatasetsModel(*, quantum_id: ~uuid.UUID, task_label: str, data_coordinate: list[int | str] = <factory>, inputs: dict[str, list[lsst.pipe.base.quantum_graph._predicted.PredictedDatasetModel]] = <factory>, outputs: dict[str, list[lsst.pipe.base.quantum_graph._predicted.PredictedDatasetModel]] = <factory>, datastore_records: dict[str, lsst.daf.butler.datastore.record_data.SerializedDatastoreRecordData] = <factory>)

Bases: BaseModel

Data model for a description of a single predicted quantum that includes its inputs and outputs.

Attributes Summary

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.

Methods Summary

construct([_fields_set])
copy(*args, **kwargs)	See pydantic.BaseModel.copy.
deserialize_datastore_records()	Deserialize the mapping of datastore records.
dict(*[, include, exclude, by_alias, ...])
from_execution_quantum(task_node, quantum, ...)	Construct from an lsst.daf.butler.Quantum instance.
from_old_quantum_graph_init(task_init_node, ...)	Construct from the init-input and init-output dataset types of a task in an old QuantumGraph instance.
from_orm(obj)
iter_dataset_ids()	Return an iterator over the UUIDs of all datasets referenced by this quantum.
iter_input_dataset_ids()	Return an iterator over the UUIDs of all datasets consumed by this quantum.
iter_output_dataset_ids()	Return an iterator over the UUIDs of all datasets produced by this quantum.
json(*[, include, exclude, by_alias, ...])
model_construct(*args, **kwargs)	See pydantic.BaseModel.model_construct.
model_copy(*args, **kwargs)	See pydantic.BaseModel.model_copy.
model_dump(*args, **kwargs)	See pydantic.BaseModel.model_dump.
model_dump_json(*args, **kwargs)	See pydantic.BaseModel.model_dump_json.
model_json_schema(*args, **kwargs)	See pydantic.BaseModel.model_json_schema.
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(*args, **kwargs)	See pydantic.BaseModel.model_validate.
model_validate_json(*args, **kwargs)	See pydantic.BaseModel.model_validate_json.
model_validate_strings(*args, **kwargs)	See pydantic.BaseModel.model_validate_strings.
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 Documentation

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

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

model_extra

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields = {'data_coordinate': FieldInfo(annotation=list[Union[int, str]], required=False, default_factory=list), 'datastore_records': FieldInfo(annotation=dict[str, SerializedDatastoreRecordData], required=False, default_factory=dict), 'inputs': FieldInfo(annotation=dict[str, list[PredictedDatasetModel]], required=False, default_factory=dict), 'outputs': FieldInfo(annotation=dict[str, list[PredictedDatasetModel]], required=False, default_factory=dict), 'quantum_id': FieldInfo(annotation=UUID, required=True), 'task_label': FieldInfo(annotation=str, required=True)}

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

Methods Documentation

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

copy(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.copy.

deserialize_datastore_records() → dict[str, lsst.daf.butler.datastore.record_data.DatastoreRecordData]

Deserialize the mapping of datastore records.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

classmethod from_execution_quantum(task_node: TaskNode, quantum: Quantum, quantum_id: UUID) → PredictedQuantumDatasetsModel

Construct from an lsst.daf.butler.Quantum instance.

Parameters:

task_nodepipeline_graph.TaskNode

Task node from the pipeline graph.

quantumlsst.daf.butler.quantum

Quantum object.

quantum_iduuid.UUID

ID for this quantum.

Returns:

modelPredictedFullQuantumModel

Model for this quantum.

classmethod from_old_quantum_graph_init(task_init_node: TaskInitNode, old_quantum_graph: QuantumGraph) → PredictedQuantumDatasetsModel

Construct from the init-input and init-output dataset types of a task in an old QuantumGraph instance.

Parameters:

task_init_nodepipeline_graph.TaskNode

Task init node from the pipeline graph.

old_quantum_graphQuantumGraph

Quantum graph.

Returns:

modelPredictedFullQuantumModel

Model for this “init” quantum.

classmethod from_orm(obj: Any) → Self

iter_dataset_ids() → Iterator[UUID]

Return an iterator over the UUIDs of all datasets referenced by this quantum.

Returns:

iterIterator [ uuid.UUID ]

Iterator over dataset IDs.

iter_input_dataset_ids() → Iterator[UUID]

Return an iterator over the UUIDs of all datasets consumed by this quantum.

Returns:

iterIterator [ uuid.UUID ]

Iterator over dataset IDs.

iter_output_dataset_ids() → Iterator[UUID]

Return an iterator over the UUIDs of all datasets produced by this quantum.

Returns:

iterIterator [ uuid.UUID ]

Iterator over dataset IDs.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

classmethod model_construct(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_construct.

model_copy(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_copy.

model_dump(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_dump.

model_dump_json(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_dump_json.

classmethod model_json_schema(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_json_schema.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Args:

params: Tuple of types of the class. Given a generic class

Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args:

force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_validate.

classmethod model_validate_json(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_validate_json.

classmethod model_validate_strings(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.model_validate_strings.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod parse_obj(obj: Any) → Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

classmethod update_forward_refs(**localns: Any) → None

classmethod validate(value: Any) → Self