PredictedThinGraphModel¶
- class lsst.pipe.base.quantum_graph.PredictedThinGraphModel(*, quanta: dict[str, list[lsst.pipe.base.quantum_graph._predicted.PredictedThinQuantumModel]] = <factory>, edges: list[tuple[int, int]] = <factory>)¶
Bases:
BaseModel
Data model for the predicted quantum graph component that maps each task label to the data IDs and internal integer IDs of its quanta.
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_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
(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
ifconfig.extra
is not set to"allow"
.
- model_fields = {'edges': FieldInfo(annotation=list[tuple[int, int]], required=False, default_factory=list), 'quanta': FieldInfo(annotation=dict[str, list[PredictedThinQuantumModel]], required=False, default_factory=dict)}¶
- 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] ¶
- 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
.
- 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 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(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
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: 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_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) Self ¶