QuantumReport¶
- class lsst.ctrl.mpexec.QuantumReport(dataId: DataId, taskLabel: str, status: ExecutionStatus = ExecutionStatus.SUCCESS, exitCode: int | None = None, exceptionInfo: ExceptionInfo | None = None)¶
Bases:
BaseModel
Task 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
int
orNone
, optional Exit code for sub-process executing this Quantum.
None
for in-process execution. Negative if process was killed by a signal.- exceptionInfo
ExceptionInfo
orNone
, optional Exception information if an exception was raised.
- dataId
Attributes Summary
Get the computed fields of this model instance.
Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].Get extra fields set during validation.
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo
][pydantic.fields.FieldInfo].Returns the set of fields that have been explicitly set on this model instance.
Methods Summary
construct
([_fields_set])copy
(*[, include, exclude, update, deep])Returns a copy of the model.
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
([_fields_set])Creates a new instance of the
Model
class with validated data.model_copy
(*[, update, deep])Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
model_dump
(*[, mode, include, exclude, ...])Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
model_dump_json
(*[, indent, include, ...])Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
model_json_schema
([by_alias, ref_template, ...])Generates a JSON schema for a model class.
model_parametrized_name
(params)Compute the class name for parametrizations of generic classes.
model_post_init
(_BaseModel__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
(obj, *[, strict, ...])Validate a pydantic model instance.
model_validate_json
(json_data, *[, strict, ...])Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
model_validate_strings
(obj, *[, strict, context])Validate the given object contains 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¶
Get the computed fields of this model instance.
- Returns:
A dictionary of computed field names and their corresponding
ComputedFieldInfo
objects.
- 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
ifconfig.extra
is not set to"allow"
.
- model_fields: ClassVar[dict[str, FieldInfo]] = {'dataId': FieldInfo(annotation=dict[str, Union[int, str, NoneType]], required=True), 'exceptionInfo': FieldInfo(annotation=Union[ExceptionInfo, NoneType], required=False), 'exitCode': FieldInfo(annotation=Union[int, NoneType], required=False), 'status': FieldInfo(annotation=ExecutionStatus, required=False, default=<ExecutionStatus.SUCCESS: 'success'>), 'taskLabel': FieldInfo(annotation=Union[str, NoneType], required=True)}¶
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo
][pydantic.fields.FieldInfo].This replaces
Model.__fields__
from Pydantic V1.
- 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
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Model ¶
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, use:`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `
- Args:
- include: Optional set or mapping
specifying which fields to include in the copied model.
- exclude: Optional set or mapping
specifying which fields to exclude in the copied model.
- update: Optional dictionary of field-value pairs to override field values
in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep copied.
- Returns:
A copy of the model with included, excluded and updated fields as specified.
- dict(*, include: IncEx = None, exclude: IncEx = 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) QuantumReport ¶
Construct report instance from an exception and other pieces of data.
- 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.
- classmethod from_orm(obj: Any) Model ¶
- json(*, include: IncEx = None, exclude: IncEx = 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(_fields_set: set[str] | None = None, **values: Any) Model ¶
Creates a new instance of the
Model
class with validated data.Creates a new model setting
__dict__
and__pydantic_fields_set__
from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as ifConfig.extra = 'allow'
was set since it adds all passed values- Args:
_fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.
- Returns:
A new instance of the
Model
class with validated data.
- model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) Model ¶
Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
Returns a copy of the model.
- Args:
- 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.
- model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = 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] ¶
Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- Args:
- mode: The mode in which
to_python
should run. If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.
include: A list of fields to include in the output. exclude: A list of fields to exclude from the output. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value from the output. exclude_none: Whether to exclude fields that have a value of
None
from the output. round_trip: Whether to enable serialization and deserialization round-trip support. warnings: Whether to log warnings when invalid fields are encountered.- mode: The mode in which
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = 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 ¶
Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic’s
to_json
method.- Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. Can take either a string or set of strings. exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that have the default value. exclude_none: Whether to exclude fields that have a value of
None
. round_trip: Whether to use serialization/deserialization between JSON and class instance. warnings: Whether to show any warnings that occurred during serialization.- Returns:
A JSON string representation of the model.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') dict[str, Any] ¶
Generates a JSON schema for a model class.
- Args:
by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of
GenerateJsonSchema
with your desired modificationsmode: The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- 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 modelModel[str, int]
, the value(str, int)
would be passed toparams
.
- Returns:
String representing the new class where
params
are passed tocls
as type variables.- Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
- model_post_init(_BaseModel__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: dict[str, Any] | 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
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) Model ¶
Validate a pydantic model instance.
- Args:
obj: The object to validate. strict: Whether to raise an exception on invalid fields. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.
- 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: dict[str, Any] | None = None) Model ¶
Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#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.
- Returns:
The validated Pydantic model.
- Raises:
ValueError: If
json_data
is not a JSON string.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) Model ¶
Validate the given object contains string data against the Pydantic model.
- Args:
obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
- 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) Model ¶
- classmethod parse_obj(obj: Any) Model ¶
- classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) Model ¶
- classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) str ¶
- classmethod validate(value: Any) Model ¶