ProvenanceDatasetModel

class lsst.pipe.base.quantum_graph.ProvenanceDatasetModel(*, dataset_id: ~uuid.UUID, dataset_type_name: str, data_coordinate: list[int | str] = <factory>, run: str, produced: bool, producer: ~uuid.UUID | None = None, consumers: list[~uuid.UUID] = <factory>)

Bases: PredictedDatasetModel

Data model for the datasets in a provenance quantum graph file.

Attributes Summary

model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
node_id	Alias for the dataset ID.

Methods Summary

copy(*args, **kwargs)	See pydantic.BaseModel.copy.
from_predicted(predicted[, producer, consumers])	Construct from a predicted dataset model.
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_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.

Attributes Documentation

model_config: ClassVar[ConfigDict] = {}

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

node_id

Alias for the dataset ID.

Methods Documentation

copy(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.copy.

classmethod from_predicted(predicted: PredictedDatasetModel, producer: UUID | None = None, consumers: Iterable[UUID] = ()) → ProvenanceDatasetModel

Construct from a predicted dataset model.

Parameters

predictedPredictedDatasetModel

Information about the dataset from the predicted graph.

produceruuid.UUID or None, optional

ID of the quantum that was predicted to produce this dataset.

consumersIterable [uuid.UUID], optional

IDs of the quanta that were predicted to consume this dataset.

Returns

provenanceProvenanceDatasetModel

Provenance dataset model.

Notes

This initializes produced to True when producer is None and False otherwise, on the assumption that it will be updated later.

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_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.