QuantumReport

class lsst.ctrl.mpexec.QuantumReport(dataId: DataId, taskLabel: str, status: ExecutionStatus = ExecutionStatus.SUCCESS, exitCode: int | None = None, exceptionInfo: ExceptionInfo | None = None)

Bases: QuantumReport

Deprecated since version v30: The QuantumReport class has moved to lsst.pipe.base.quantum_reports. This forwarding shim will be removed after v30.

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.
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__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
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 = {'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), '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

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

copy(*args: Any, **kwargs: Any) → Any

See pydantic.BaseModel.copy.

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) → QuantumReport

Construct report instance from an exception and other pieces of data.

Parameters:

exceptionException

Exception caught from processing quantum.

dataIdDataId

Data ID of quantum.

taskLabelstr

Label of task.

exitCodeint, optional

Exit code for the process, used when it is known that the process will exit with that exit code.

classmethod from_exit_code(exitCode: int, dataId: DataCoordinate | Mapping[str, Any], taskLabel: str) → QuantumReport

Construct report instance from an exit code and other pieces of data.

Parameters:

exitCodeint

The exit code of the subprocess.

dataIdDataId

The quantum Data ID.

taskLabelstr

The task label.

classmethod from_orm(obj: Any) → Self

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(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Args:

obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

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

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Args:

json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Args:

obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

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