SerializedQuantum

class lsst.daf.butler.SerializedQuantum(*, taskName: str | None = None, dataId: SerializedDataCoordinate | None = None, datasetTypeMapping: Mapping[str, SerializedDatasetType], initInputs: Mapping[str, tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]], inputs: Mapping[str, list[tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]]], outputs: Mapping[str, list[tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]]], dimensionRecords: dict[int, lsst.daf.butler.dimensions._records.SerializedDimensionRecord] | None = None, datastoreRecords: dict[str, lsst.daf.butler.datastore.record_data.SerializedDatastoreRecordData] | None = None)

Bases: _BaseModelCompat

Simplified model of a Quantum suitable for serialization.

Attributes Summary

model_fields

Methods Summary

construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
direct(*, taskName, dataId, ...)	Construct a SerializedQuantum directly without validators.
from_orm(obj)
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
model_construct([_fields_set])
model_copy(*[, update, deep])
model_dump(*[, mode, include, exclude, ...])
model_dump_json(*[, indent, include, ...])
model_rebuild(*[, force, raise_errors, ...])
model_validate(obj, *[, strict, ...])
model_validate_json(json_data, *[, strict, ...])
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)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes Documentation

model_fields = {'dataId': ModelField(name='dataId', type=Optional[SerializedDataCoordinate], required=False, default=None), 'datasetTypeMapping': ModelField(name='datasetTypeMapping', type=Mapping[str, SerializedDatasetType], required=True), 'datastoreRecords': ModelField(name='datastoreRecords', type=Optional[Mapping[str, SerializedDatastoreRecordData]], required=False, default=None), 'dimensionRecords': ModelField(name='dimensionRecords', type=Optional[Mapping[int, SerializedDimensionRecord]], required=False, default=None), 'initInputs': ModelField(name='initInputs', type=Mapping[str, tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]], required=True), 'inputs': ModelField(name='inputs', type=Mapping[str, list[tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]]], required=True), 'outputs': ModelField(name='outputs', type=Mapping[str, list[tuple[lsst.daf.butler._dataset_ref.SerializedDatasetRef, list[int]]]], required=True), 'taskName': ModelField(name='taskName', type=Optional[str], required=False, default=None)}

Methods Documentation

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = 'allow' was set since it adds all passed values

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns:

new model instance

dict(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod direct(*, taskName: str | None, dataId: dict | None, datasetTypeMapping: Mapping[str, dict], initInputs: Mapping[str, tuple[dict, list[int]]], inputs: Mapping[str, list[tuple[dict, list[int]]]], outputs: Mapping[str, list[tuple[dict, list[int]]]], dimensionRecords: dict[int, dict] | None, datastoreRecords: dict[str, dict] | None) → SerializedQuantum

Construct a SerializedQuantum directly without validators.

Parameters:

taskNamestr or None

The name of the task.

dataIddict or None

The dataId of the quantum.

datasetTypeMappingMapping [str, dict]

Dataset type definitions.

initInputsMapping

The quantum init inputs.

inputsMapping

The quantum inputs.

outputsMapping

The quantum outputs.

dimensionRecordsdict [int, dict] or None

The dimension records.

datastoreRecordsdict [str, dict] or None

The datastore records.

Returns:

quantumSerializedQuantum

Serializable model of the quantum.

Notes

This differs from the pydantic “construct” method in that the arguments are explicitly what the model requires, and it will recurse through members, constructing them from their corresponding direct methods.

This method should only be called when the inputs are trusted.

classmethod from_orm(obj: Any) → Model

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Self

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, exclude: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]

model_dump_json(*, indent: int | None = None, include: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, exclude: set[int] | set[str] | dict[int, Any] | dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Self

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Self

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

classmethod parse_obj(obj: Any) → Model

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

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

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

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model