QuantumReport¶
- class lsst.pipe.base.quantum_reports.QuantumReport(dataId: DataId, taskLabel: str, status: ExecutionStatus = ExecutionStatus.SUCCESS, exitCode: int | None = None, exceptionInfo: ExceptionInfo | None = None, quantumId: uuid.UUID | None = None)¶
Bases:
BaseModelTask execution report for a single Quantum.
- Parameters:
- dataId
DataId Quantum data ID.
- taskLabel
str Label for task executing this Quantum.
- status
ExecutionStatus Status of this quantum execution.
- exitCode
intorNone, optional Exit code for sub-process executing this Quantum.
Nonefor in-process execution. Negative if process was killed by a signal.- exceptionInfo
ExceptionInfoorNone, optional Exception information if an exception was raised.
- quantumId
uuid.UUID, optional Unique identifier for the quantum.
- dataId
Attributes Summary
Configuration for the model, should be a dictionary conforming to [
ConfigDict][pydantic.config.ConfigDict].Get extra fields set during validation.
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.dict(*[, include, exclude, by_alias, ...])from_exception(exception, dataId, taskLabel, *)Construct report instance from an exception and other pieces of data.
from_exit_code(exitCode, dataId, taskLabel)Construct report instance from an exit code and other pieces of data.
from_orm(obj)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__andmodel_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
Noneifconfig.extrais not set to"allow".
- model_fields = {'dataId': FieldInfo(annotation=dict[str, Union[int, str]], required=True), 'exceptionInfo': FieldInfo(annotation=Union[ExceptionInfo, NoneType], required=False, default=None), 'exitCode': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'quantumId': FieldInfo(annotation=Union[UUID, NoneType], required=False, default=None), 'status': FieldInfo(annotation=ExecutionStatus, required=False, default=<ExecutionStatus.SUCCESS: 'success'>), 'taskLabel': FieldInfo(annotation=Union[str, NoneType], 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
- 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_exception(exception: Exception, dataId: DataCoordinate | Mapping[str, Any], taskLabel: str, *, exitCode: int | None = None, quantumId: UUID | None = None) QuantumReport¶
Construct report instance from an exception and other pieces of data.
- Parameters:
- classmethod from_exit_code(exitCode: int, dataId: DataCoordinate | Mapping[str, Any], taskLabel: str, quantumId: UUID | None = None) QuantumReport¶
Construct report instance from an exit code and other pieces of data.
- 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_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
Modelwith 2 type variables and a concrete modelModel[str, int], the value(str, int)would be passed toparams.
- Returns:
String representing the new class where
paramsare passed toclsas 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__andmodel_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 toTrue. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults toNone.- Returns:
Returns
Noneif the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrueif rebuilding was successful, otherwiseFalse.
- 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_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) Self¶