hdmf_common_base#

pydantic model ConfiguredBaseModel#

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field hdf5_path: str | None = None#

The absolute path that this object is stored in an NWB file

pydantic model LinkML_Meta#

Bases: BaseModel

Extra LinkML Metadata stored as a class attribute

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Fields:
field tree_root: bool = False#
pydantic model Data#

Bases: ConfiguredBaseModel

An abstract data type for a dataset.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: str [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model Container#

Bases: ConfiguredBaseModel

An abstract data type for a group storing collections of data and metadata. Base type for all data and metadata containers.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field name: str [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#
pydantic model SimpleMultiContainer#

Bases: Container

A simple Container for holding onto multiple containers.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field children: Dict[str, Container] | None [Optional]#
field name: str [Required]#
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)#