Models

class ConfiguredBaseModel

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.

self is explicitly positional-only to allow self as a field name.

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

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_fields: ClassVar[dict[str, FieldInfo]] = {}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class ReftypeOptions(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

ref = 'ref'

reference = 'reference'

object = 'object'

region = 'region'

class QuantityEnum(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

ASTERISK = '*'

QUESTION_MARK = '?'

PLUS_SIGN = '+'

zero_or_many = 'zero_or_many'

one_or_many = 'one_or_many'

zero_or_one = 'zero_or_one'

class FlatDtype(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

float = 'float'

float32 = 'float32'

double = 'double'

float64 = 'float64'

long = 'long'

int64 = 'int64'

int = 'int'

int32 = 'int32'

int16 = 'int16'

short = 'short'

int8 = 'int8'

uint = 'uint'

uint32 = 'uint32'

uint16 = 'uint16'

uint8 = 'uint8'

uint64 = 'uint64'

numeric = 'numeric'

text = 'text'

utf = 'utf'

utf8 = 'utf8'

utf_8 = 'utf-8'

ascii = 'ascii'

bool = 'bool'

isodatetime = 'isodatetime'

class Namespace

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.

self is explicitly positional-only to allow self as a field name.

doc: str

name: str

full_name: str | None

version: str

date: datetime | None

author: List[str] | str

contact: List[str] | str

schema_: List[Schema] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'author': FieldInfo(annotation=Union[List[str], str], required=False, default_factory=list, description='List of strings with the names of the authors of the namespace.'), 'contact': FieldInfo(annotation=Union[List[str], str], required=False, default_factory=list, description='List of strings with the contact information for the authors. Ordering of the contacts should match the ordering of the authors.'), 'date': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, description='Date that a namespace was last modified or released'), 'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'full_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Optional string with extended full name for the namespace.'), 'name': FieldInfo(annotation=str, required=True), 'schema_': FieldInfo(annotation=Union[List[Schema], NoneType], required=False, default_factory=list, alias='schema', alias_priority=2, description='List of the schema to be included in this namespace.'), 'version': FieldInfo(annotation=str, 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.

class Namespaces

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.

self is explicitly positional-only to allow self as a field name.

namespaces: List[Namespace] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'namespaces': FieldInfo(annotation=Union[List[Namespace], NoneType], required=False, default_factory=list)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Schema

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.

self is explicitly positional-only to allow self as a field name.

source: str | None

namespace: str | None

title: str | None

neurodata_types: List[Dataset | Group] | None

doc: str | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'doc': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'namespace': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='describes a named reference to another namespace. In contrast to source, this is a reference by name to a known namespace (i.e., the namespace is resolved during the build and must point to an already existing namespace). This mechanism is used to allow, e.g., extension of a core namespace (here the NWB core namespace) without requiring hard paths to the files describing the core namespace. Either source or namespace must be specified, but not both.'), 'neurodata_types': FieldInfo(annotation=Union[List[Union[Dataset, Group]], NoneType], required=False, default_factory=list, description='an optional list of strings indicating which data types should be included from the given specification source or namespace. The default is null indicating that all data types should be included.'), 'source': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='describes the name of the YAML (or JSON) file with the schema specification. The schema files should be located in the same folder as the namespace file.'), 'title': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='a descriptive title for a file for documentation purposes.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Group

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.

self is explicitly positional-only to allow self as a field name.

neurodata_type_def: str | None

neurodata_type_inc: str | None

name: str | None

default_name: str | None

doc: str

quantity: QuantityEnum | int | None

linkable: bool | None

attributes: List[Attribute] | None

datasets: List[Dataset] | None

groups: List[Group] | None

links: List[Link] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'attributes': FieldInfo(annotation=Union[List[Attribute], NoneType], required=False, default_factory=list), 'datasets': FieldInfo(annotation=Union[List[Dataset], NoneType], required=False, default_factory=list), 'default_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'groups': FieldInfo(annotation=Union[List[Group], NoneType], required=False, default_factory=list), 'linkable': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'links': FieldInfo(annotation=Union[List[Link], NoneType], required=False, default_factory=list), 'name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'neurodata_type_def': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Used alongside neurodata_type_inc to indicate inheritance, naming, and mixins'), 'neurodata_type_inc': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Used alongside neurodata_type_def to indicate inheritance, naming, and mixins'), 'quantity': FieldInfo(annotation=Union[QuantityEnum, int, NoneType], required=False, default=1)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Groups

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.

self is explicitly positional-only to allow self as a field name.

groups: List[Group] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'groups': FieldInfo(annotation=Union[List[Group], NoneType], required=False, default_factory=list)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Link(*, name: str | None = None, doc: str, target_type: str, quantity: QuantityEnum | int | None = 1)

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.

self is explicitly positional-only to allow self as a field name.

name: str | None

doc: str

target_type: str

quantity: QuantityEnum | int | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'quantity': FieldInfo(annotation=Union[QuantityEnum, int, NoneType], required=False, default=1), 'target_type': FieldInfo(annotation=str, required=True, description='Describes the neurodata_type of the target that the reference points to')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Datasets

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.

self is explicitly positional-only to allow self as a field name.

datasets: List[Dataset] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'datasets': FieldInfo(annotation=Union[List[Dataset], NoneType], required=False, default_factory=list)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class ReferenceDtype(*, target_type: str, reftype: ReftypeOptions | None = None)

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.

self is explicitly positional-only to allow self as a field name.

target_type: str

reftype: ReftypeOptions | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'reftype': FieldInfo(annotation=Union[ReftypeOptions, NoneType], required=False, default=None, description='describes the kind of reference'), 'target_type': FieldInfo(annotation=str, required=True, description='Describes the neurodata_type of the target that the reference points to')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class CompoundDtype(*, name: str, doc: str, dtype: FlatDtype | ReferenceDtype)

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.

self is explicitly positional-only to allow self as a field name.

name: str

doc: str

dtype: FlatDtype | ReferenceDtype

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'dtype': FieldInfo(annotation=Union[FlatDtype, ReferenceDtype], required=True), 'name': FieldInfo(annotation=str, 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.

class DtypeMixin(*, dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None = None)

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.

self is explicitly positional-only to allow self as a field name.

dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'dtype': FieldInfo(annotation=Union[List[CompoundDtype], FlatDtype, ReferenceDtype, NoneType], required=False, default_factory=list)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Attribute(*, dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None = None, name: str, dims: List[Any | str] | None = None, shape: List[Any | int | str] | None = None, value: Any | None = None, default_value: Any | None = None, doc: str, required: bool | None = True)

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.

self is explicitly positional-only to allow self as a field name.

name: str

dims: List[Any | str] | None

shape: List[Any | int | str] | None

value: Any | None

default_value: Any | None

doc: str

required: bool | None

dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'default_value': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, description='Optional default value for variable-valued attributes.'), 'dims': FieldInfo(annotation=Union[List[Union[Any, str]], NoneType], required=False, default_factory=list), 'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'dtype': FieldInfo(annotation=Union[List[CompoundDtype], FlatDtype, ReferenceDtype, NoneType], required=False, default_factory=list), 'name': FieldInfo(annotation=str, required=True), 'required': FieldInfo(annotation=Union[bool, NoneType], required=False, default=True, description='Optional boolean key describing whether the attribute is required. Default value is True.'), 'shape': FieldInfo(annotation=Union[List[Union[Any, int, str]], NoneType], required=False, default_factory=list), 'value': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, description='Optional constant, fixed value for the attribute.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class Dataset(*, dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None = None, neurodata_type_def: str | None = None, neurodata_type_inc: str | None = None, name: str | None = None, default_name: str | None = None, dims: List[Any | str] | None = None, shape: List[Any | int | str] | None = None, value: Any | None = None, default_value: Any | None = None, doc: str, quantity: QuantityEnum | int | None = 1, linkable: bool | None = None, attributes: List[Attribute] | None = None)

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.

self is explicitly positional-only to allow self as a field name.

neurodata_type_def: str | None

neurodata_type_inc: str | None

name: str | None

default_name: str | None

dims: List[Any | str] | None

shape: List[Any | int | str] | None

value: Any | None

default_value: Any | None

doc: str

quantity: QuantityEnum | int | None

linkable: bool | None

attributes: List[Attribute] | None

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

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_fields: ClassVar[dict[str, FieldInfo]] = {'attributes': FieldInfo(annotation=Union[List[Attribute], NoneType], required=False, default_factory=list), 'default_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'default_value': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, description='Optional default value for variable-valued attributes.'), 'dims': FieldInfo(annotation=Union[List[Union[Any, str]], NoneType], required=False, default_factory=list), 'doc': FieldInfo(annotation=str, required=True, description='Description of corresponding object.'), 'dtype': FieldInfo(annotation=Union[List[CompoundDtype], FlatDtype, ReferenceDtype, NoneType], required=False, default_factory=list), 'linkable': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'neurodata_type_def': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Used alongside neurodata_type_inc to indicate inheritance, naming, and mixins'), 'neurodata_type_inc': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Used alongside neurodata_type_def to indicate inheritance, naming, and mixins'), 'quantity': FieldInfo(annotation=Union[QuantityEnum, int, NoneType], required=False, default=1), 'shape': FieldInfo(annotation=Union[List[Union[Any, int, str]], NoneType], required=False, default_factory=list), 'value': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, description='Optional constant, fixed value for the attribute.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

dtype: List[CompoundDtype] | FlatDtype | ReferenceDtype | None