MockDataset¶

Bases: BaseModel

The in-memory dataset type used by MockStorageClass.

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.
`storage_class`

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, ...])
`make_derived`(**kwargs)	Return a new MockDataset that represents applying some storage class operation to this one.
`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]] = {'converted_from': FieldInfo(annotation=Union[MockDataset, NoneType], required=False), 'data_id': FieldInfo(annotation=dict[str, Union[int, str, NoneType]], required=True), 'dataset_id': FieldInfo(annotation=Union[UUID, NoneType], required=True), 'dataset_type': FieldInfo(annotation=SerializedDatasetType, required=True), 'output_connection_name': FieldInfo(annotation=Union[str, NoneType], required=False), 'parameters': FieldInfo(annotation=Union[dict[str, str], NoneType], required=False), 'parent': FieldInfo(annotation=Union[MockDataset, NoneType], required=False), 'quantum': FieldInfo(annotation=Union[MockDatasetQuantum, NoneType], required=False), 'run': 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.

storage_class¶

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¶

make_derived(**kwargs: Any) → MockDataset¶

Return a new MockDataset that represents applying some storage class operation to this one.

Parameters:

**kwargsAny: Keyword arguments are fields of MockDataset or SerializedDatasetType to override in the result.

Returns:

derivedMockDataset: The newly-mocked dataset.

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¶

Navigation

MockDataset¶