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
ifconfig.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 themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_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 thevalues
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 ofGenerateJsonSchema
with your desired modificationsmode (
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 classModel
with 2 type variables and a concrete modelModel[str, int]
, the value(str, int)
would be passed toparams
.- Return type:
str
- 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.
- 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 toFalse
.raise_errors (
bool
, default:True
) – Whether to raise errors, defaults toTrue
._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 toNone
.
- Return type:
bool
|None
- 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, *, 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
orexclude
, 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 toTrue
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 whichto_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 ofNone
.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 ofNone
.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__
andmodel_construct
. This is useful if you want to do some validation that requires the entire model to be initialized.- Return type:
None