core_nwb_base

class ConfiguredBaseModel(*, hdf5_path: str | None = None, object_id: str | None = None)

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.

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

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

hdf5_path: str | None

object_id: str | None

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 LinkMLMeta(root: RootModelRootType = PydanticUndefined)

Bases: RootModel

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.

root: Dict[str, Any]

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_fields: ClassVar[dict[str, FieldInfo]] = {'root': FieldInfo(annotation=Dict[str, Any], required=False, default={})}

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 NWBData(*, hdf5_path: str | None = None, object_id: str | None = None, name: str)

Bases: Data

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.

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

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 Image(*, hdf5_path: str | None = None, object_id: str | None = None, name: str, resolution: ~numpy.float32 | None = None, description: str | None = None, array: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* x, * y], ~numpy.number] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* x, * y, 3 r_g_b], ~numpy.number] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* x, * y, 4 r_g_b_a], ~numpy.number] | None = None)

Bases: NWBData

An abstract data type for an image. Shape can be 2-D (x, y), or 3-D where the third dimension can have three or four elements, e.g. (x, y, (r, g, b)) or (x, y, (r, g, b, a)).

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

resolution: np.float32 | None

description: str | None

array: NDArray[Shape['* x, * y'], np.number] | NDArray[Shape['* x, * y, 3 r_g_b'], np.number] | NDArray[Shape['* x, * y, 4 r_g_b_a'], np.number] | None

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'array': FieldInfo(annotation=Union[NDArray, NDArray, NDArray, NoneType], required=False, default=None), 'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Description of the image.'), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object'), 'resolution': FieldInfo(annotation=Union[float32, NoneType], required=False, default=None, description='Pixel resolution of the image, in pixels per centimeter.')}

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 NWBContainer(*, hdf5_path: str | None = None, object_id: str | None = None, name: str)

Bases: Container

An abstract data type for a generic container 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.

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

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 NWBDataInterface(*, hdf5_path: str | None = None, object_id: str | None = None, name: str)

Bases: NWBContainer

An abstract data type for a generic container storing collections of data, as opposed to metadata.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 TimeSeries(*, hdf5_path: str | None = None, object_id: str | None = None, name: str, description: str | None = None, comments: str | None = None, data: ~nwb_linkml.models.pydantic.core.v2_2_1.core_nwb_base.TimeSeriesData, starting_time: ~nwb_linkml.models.pydantic.core.v2_2_1.core_nwb_base.TimeSeriesStartingTime | None = None, timestamps: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times], ~numpy.float64] | None = None, control: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times], ~numpy.uint8] | None = None, control_description: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_control_values], ~numpy.bytes_] | None = None, sync: ~nwb_linkml.models.pydantic.core.v2_2_1.core_nwb_base.TimeSeriesSync | None = None)

Bases: NWBDataInterface

General purpose time series.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

description: str | None

comments: str | None

data: TimeSeriesData

starting_time: TimeSeriesStartingTime | None

timestamps: NDArray[Shape['* num_times'], np.float64] | None

control: NDArray[Shape['* num_times'], np.uint8] | None

control_description: NDArray[Shape['* num_control_values'], str] | None

sync: TimeSeriesSync | None

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'comments': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Human-readable comments about the TimeSeries. This second descriptive field can be used to store additional information, or descriptive information if the primary description field is populated with a computer-readable string.'), 'control': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='Numerical labels that apply to each time point in data for the purpose of querying and slicing data by these values. If present, the length of this array should be the same size as the first dimension of data.', json_schema_extra={'linkml_meta': {'array': {'dimensions': [{'alias': 'num_times'}]}}}), 'control_description': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.', json_schema_extra={'linkml_meta': {'array': {'dimensions': [{'alias': 'num_control_values'}]}}}), 'data': FieldInfo(annotation=TimeSeriesData, required=True, description='Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.'), 'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Description of the time series.'), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object'), 'starting_time': FieldInfo(annotation=Union[TimeSeriesStartingTime, NoneType], required=False, default=None, description='Timestamp of the first sample in seconds. When timestamps are uniformly spaced, the timestamp of the first sample can be specified and all subsequent ones calculated from the sampling rate attribute.'), 'sync': FieldInfo(annotation=Union[TimeSeriesSync, NoneType], required=False, default=None, description="Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of 'sync' are mostly for archival purposes."), 'timestamps': FieldInfo(annotation=Union[NDArray, NoneType], required=False, default=None, description='Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.', json_schema_extra={'linkml_meta': {'array': {'dimensions': [{'alias': 'num_times'}]}}})}

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 TimeSeriesData(*, hdf5_path: str | None = None, object_id: str | None = None, name: ~typing.Literal['data'] = 'data', conversion: ~numpy.float32 | None = None, resolution: ~numpy.float32 | None = None, unit: str | None = None, array: ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times, * num_dim2], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times, * num_dim2, * num_dim3], ~typing.Any] | ~nptyping.base_meta_classes.NDArray[~nptyping.base_meta_classes.Shape[* num_times, * num_dim2, * num_dim3, * num_dim4], ~typing.Any] | None = None)

Bases: ConfiguredBaseModel

Data values. Data can be in 1-D, 2-D, 3-D, or 4-D. The first dimension should always represent time. This can also be used to store binary data (e.g., image frames). This can also be a link to data stored in an external file.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base'})

name: Literal['data']

conversion: np.float32 | None

resolution: np.float32 | None

unit: str | None

array: NDArray[Shape['* num_times'], Any] | NDArray[Shape['* num_times, * num_dim2'], Any] | NDArray[Shape['* num_times, * num_dim2, * num_dim3'], Any] | NDArray[Shape['* num_times, * num_dim2, * num_dim3, * num_dim4'], Any] | None

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'array': FieldInfo(annotation=Union[NDArray, NDArray, NDArray, NDArray, NoneType], required=False, default=None), 'conversion': FieldInfo(annotation=Union[float32, NoneType], required=False, default=None, description="Scalar to multiply each element in data to convert it to the specified 'unit'. If the data are stored in acquisition system units or other units that require a conversion to be interpretable, multiply the data by 'conversion' to convert the data to the specified 'unit'. e.g. if the data acquisition system stores values in this object as signed 16-bit integers (int16 range -32,768 to 32,767) that correspond to a 5V range (-2.5V to 2.5V), and the data acquisition system gain is 8000X, then the 'conversion' multiplier to get from raw data acquisition values to recorded volts is 2.5/32768/8000 = 9.5367e-9."), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=Literal['data'], required=False, default='data', json_schema_extra={'linkml_meta': {'equals_string': 'data', 'ifabsent': 'string(data)'}}), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object'), 'resolution': FieldInfo(annotation=Union[float32, NoneType], required=False, default=None, description='Smallest meaningful difference between values in data, stored in the specified by unit, e.g., the change in value of the least significant bit, or a larger number if signal noise is known to be present. If unknown, use -1.0.'), 'unit': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description="Base unit of measurement for working with the data. Actual stored values are not necessarily stored in these units. To access the data in these units, multiply 'data' by 'conversion'.")}

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 TimeSeriesStartingTime(*, hdf5_path: str | None = None, object_id: str | None = None, name: Literal['starting_time'] = 'starting_time', rate: float32 | None = None, unit: str | None = None, value: float64)

Bases: ConfiguredBaseModel

Timestamp of the first sample in seconds. When timestamps are uniformly spaced, the timestamp of the first sample can be specified and all subsequent ones calculated from the sampling rate 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.

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

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base'})

name: Literal['starting_time']

rate: np.float32 | None

unit: str | None

value: np.float64

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=Literal['starting_time'], required=False, default='starting_time', json_schema_extra={'linkml_meta': {'equals_string': 'starting_time', 'ifabsent': 'string(starting_time)'}}), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object'), 'rate': FieldInfo(annotation=Union[float32, NoneType], required=False, default=None, description='Sampling rate, in Hz.'), 'unit': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description="Unit of measurement for time, which is fixed to 'seconds'."), 'value': FieldInfo(annotation=float64, 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 TimeSeriesSync(*, hdf5_path: str | None = None, object_id: str | None = None, name: Literal['sync'] = 'sync')

Bases: ConfiguredBaseModel

Lab-specific time and sync information as provided directly from hardware devices and that is necessary for aligning all acquired time information to a common timebase. The timestamp array stores time in the common timebase. This group will usually only be populated in TimeSeries that are stored external to the NWB file, in files storing raw data. Once timestamp data is calculated, the contents of ‘sync’ are mostly for archival purposes.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base'})

name: Literal['sync']

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=Literal['sync'], required=False, default='sync', json_schema_extra={'linkml_meta': {'equals_string': 'sync', 'ifabsent': 'string(sync)'}}), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 ProcessingModule(*, hdf5_path: str | None = None, object_id: str | None = None, name: str, children: List[DynamicTable | NWBDataInterface] | None = None)

Bases: NWBContainer

A collection of processed data.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

children: List[DynamicTable | NWBDataInterface] | None

name: str

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'children': FieldInfo(annotation=Union[List[Union[DynamicTable, NWBDataInterface]], NoneType], required=False, default=None, json_schema_extra={'linkml_meta': {'any_of': [{'range': 'NWBDataInterface'}, {'range': 'DynamicTable'}]}}), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'name': FieldInfo(annotation=str, required=True), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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 Images(*, hdf5_path: str | None = None, object_id: str | None = None, name: str = 'Images', description: str | None = None, image: List[Image])

Bases: NWBDataInterface

A collection of images.

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.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'from_schema': 'core.nwb.base', 'tree_root': True})

name: str

description: str | None

image: List[Image]

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

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Description of this collection of images.'), 'hdf5_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='The absolute path that this object is stored in an NWB file'), 'image': FieldInfo(annotation=List[Image], required=True, description='Images stored in this collection.'), 'name': FieldInfo(annotation=str, required=False, default='Images', json_schema_extra={'linkml_meta': {'ifabsent': 'string(Images)'}}), 'object_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Unique UUID for each object')}

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

This replaces Model.__fields__ from Pydantic V1.