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.core.datasets.ref.SerializedDatasetRef, list[int]]], inputs: Mapping[str, list[tuple[lsst.daf.butler.core.datasets.ref.SerializedDatasetRef, list[int]]]], outputs: Mapping[str, list[tuple[lsst.daf.butler.core.datasets.ref.SerializedDatasetRef, list[int]]]], dimensionRecords: dict[int, lsst.daf.butler.core.dimensions._records.SerializedDimensionRecord] | None = None, datastoreRecords: dict[str, lsst.daf.butler.core.datastoreRecordData.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_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.core.datasets.ref.SerializedDatasetRef, list[int]]], required=True), 'inputs': ModelField(name='inputs', type=Mapping[str, list[tuple[lsst.daf.butler.core.datasets.ref.SerializedDatasetRef, list[int]]]], required=True), 'outputs': ModelField(name='outputs', type=Mapping[str, list[tuple[lsst.daf.butler.core.datasets.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

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.

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_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¶

Navigation

SerializedQuantum¶