nbproject.dev.MetaContainer¶

class nbproject.dev.MetaContainer(**data)¶

Bases: BaseModel

The metadata stored in the notebook file.

Attributes¶

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}¶

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property model_extra: dict[str, Any] | None¶

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]] = {'id': FieldInfo(annotation=str, required=True), 'parent': FieldInfo(annotation=Union[str, List[str], NoneType], required=False, default=None), 'pypackage': FieldInfo(annotation=Union[Mapping[str, str], NoneType], required=False, default=None), 'time_init': FieldInfo(annotation=str, required=True), 'user_handle': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'user_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'user_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'version': FieldInfo(annotation=str, required=False, default='1')}¶

property model_fields_set: set[str]¶

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.

Class methods¶

classmethod construct(_fields_set=None, **values)¶

Return type:: Self

classmethod from_orm(obj)¶

Return type:: Self

classmethod model_construct(_fields_set=None, **values)¶

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.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (set[str] | None, default: None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')¶

Generates a JSON schema for a model class.

Parameters:

by_alias (bool, default: True) – Whether to use attribute aliases or not.
ref_template (str, default: '#/$defs/{model}') – The reference template.
schema_generator (type[GenerateJsonSchema], default: <class 'pydantic.json_schema.GenerateJsonSchema'>) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization'], default: 'validation') – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)¶

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – 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.
Return type:: str
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.

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=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.

Parameters:

force (bool, default: False) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool, default: True) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int, default: 2) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Mapping[str, Any] | None, default: None) – The types namespace, defaults to None.

Return type:

bool | 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, *, strict=None, from_attributes=None, context=None)¶

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (bool | None, default: None) – Whether to enforce types strictly.
from_attributes (bool | None, default: None) – Whether to extract data from object attributes.
context (Any | None, default: None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (bool | None, default: None) – Whether to enforce types strictly.
context (Any | None, default: None) – Extra variables to pass to the validator.

Return type:

Self

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, *, strict=None, context=None)¶

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

Parameters:

obj (Any) – The object containing string data to validate.
strict (bool | None, default: None) – Whether to enforce types strictly.
context (Any | None, default: None) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)¶

Return type:: Self

classmethod parse_obj(obj)¶

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)¶

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')¶

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)¶

Return type:: str

classmethod update_forward_refs(**localns)¶

Return type:: None

classmethod validate(value)¶

Return type:: Self

Methods¶

copy(*, include=None, exclude=None, update=None, deep=False)¶

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include (default: None) – Optional set or mapping specifying which fields to include in the copied model.
exclude (default: None) – Optional set or mapping specifying which fields to exclude in the copied model.
update (default: None) – Optional dictionary of field-value pairs to override field values in the copied model.
deep (default: False) – 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=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)¶

Return type:: Dict[str, Any]

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)¶

Return type:: str

model_copy(*, update=None, deep=False)¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Mapping[str, Any] | None, default: None) – 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 (bool, default: False) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Literal['json', 'python'] | str, default: 'python') – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
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, default: None) – A set of fields to include in the output.
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, default: None) – A set of fields to exclude from the output.
context (Any | None, default: None) – Additional context to pass to the serializer.
by_alias (bool, default: False) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool, default: False) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool, default: False) – Whether to exclude fields that are set to their default value.
exclude_none (bool, default: False) – Whether to exclude fields that have a value of None.
round_trip (bool, default: False) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error'], default: True) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool, default: False) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)¶

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (int | None, default: None) – Indentation to use in the JSON output. If None is passed, the output will be compact.
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, default: None) – Field(s) to include in the JSON output.
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, default: None) – Field(s) to exclude from the JSON output.
context (Any | None, default: None) – Additional context to pass to the serializer.
by_alias (bool, default: False) – Whether to serialize using field aliases.
exclude_unset (bool, default: False) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool, default: False) – Whether to exclude fields that are set to their default value.
exclude_none (bool, default: False) – Whether to exclude fields that have a value of None.
round_trip (bool, default: False) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (bool | Literal['none', 'warn', 'error'], default: True) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool, default: False) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

model_post_init(_BaseModel__context)¶

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.

Return type:: None