QgraphSummary

class lsst.pipe.base.QgraphSummary(*, graphID: BuildId, cmdLine: str | None = None, creationUTC: str | None = None, inputCollection: list[str] | None = None, outputCollection: str | None = None, outputRun: str | None = None, qgraphTaskSummaries: dict[str, lsst.pipe.base.graph.graphSummary.QgraphTaskSummary] = {})

Bases: BaseModel

Report for the QuantumGraph creation or reading.

Attributes Summary

model_computed_fields

Get the computed fields of this model instance.

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

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

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_orm(obj)

json(*[, include, exclude, by_alias, ...])

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

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(_BaseModel__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, ...])

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 if config.extra is not set to "allow".

model_fields: ClassVar[dict[str, FieldInfo]] = {'cmdLine': FieldInfo(annotation=Union[str, NoneType], required=False), 'creationUTC': FieldInfo(annotation=Union[str, NoneType], required=False), 'graphID': FieldInfo(annotation=NewType, required=True), 'inputCollection': FieldInfo(annotation=Union[list[str], NoneType], required=False), 'outputCollection': FieldInfo(annotation=Union[str, NoneType], required=False), 'outputRun': FieldInfo(annotation=Union[str, NoneType], required=False), 'qgraphTaskSummaries': FieldInfo(annotation=dict[str, QgraphTaskSummary], required=False, default={})}

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

classmethod construct(_fields_set: set[str] | None = None, **values: Any) Model
copy(*args: Any, **kwargs: Any) Any

See pydantic.BaseModel.copy.

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_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 if Config.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(*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(_BaseModel__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: 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 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: 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(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) Model