core_nwb_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.

self is explicitly positional-only 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.

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

Fields:
field tree_root: bool = False
pydantic model NWBData

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.

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 TimeSeriesReferenceVectorData

Bases: VectorData

Column storing references to a TimeSeries (rows). For each TimeSeries this VectorData column stores the start_index and count to indicate the range in time to be selected as well as an object reference to the TimeSeries.

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* dim0'], Any] | NDArray[Shape['* dim0, * dim1'], Any] | NDArray[Shape['* dim0, * dim1, * dim2'], Any] | NDArray[Shape['* dim0, * dim1, * dim2, * dim3'], Any] | None = None
field description: str | None = None

Description of what these vectors represent.

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

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: NDArray[Shape['* x, * y'], Number] | NDArray[Shape['* x, * y, 3 r_g_b'], Number] | NDArray[Shape['* x, * y, 3 r_g_b, 4 r_g_b_a'], Number] | None = None
field description: str | None = None

Description of the image.

field name: str [Required]
field resolution: float | None = None

Pixel resolution of the image, in pixels per centimeter.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)
pydantic model ImageReferences

Bases: NWBData

Ordered dataset of references to Image objects.

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: List[Image] | Image | None = None
field name: str [Required]
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)
pydantic model NWBContainer

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.

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 NWBDataInterface

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.

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 TimeSeries

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field comments: str | None = None

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.

field control: List[int] | None [Optional]

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.

field control_description: List[str] | None [Optional]

Description of each control value. Must be present if control is present. If present, control_description[0] should describe time points where control == 0.

field data: TimeSeriesData [Required]

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.

field description: str | None = None

Description of the time series.

field name: str [Required]
field starting_time: TimeSeriesStartingTime | None = None

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.

field sync: TimeSeriesSync | None = None

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.

field timestamps: List[float] | None [Optional]

Timestamps for samples stored in data, in seconds, relative to the common experiment master-clock stored in NWBFile.timestamps_reference_time.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)
pydantic model TimeSeriesData

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field 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 = None
field continuity: str | None = None

Optionally describe the continuity of the data. Can be “continuous”, “instantaneous”, or “step”. For example, a voltage trace would be “continuous”, because samples are recorded from a continuous process. An array of lick times would be “instantaneous”, because the data represents distinct moments in time. Times of image presentations would be “step” because the picture remains the same until the next timepoint. This field is optional, but is useful in providing information about the underlying data. It may inform the way this data is interpreted, the way it is visualized, and what analysis methods are applicable.

field conversion: float | None = None

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.

field name: Literal['data'] = 'data'
field offset: float | None = None

Scalar to add to the data after scaling by ‘conversion’ to finalize its coercion to the specified ‘unit’. Two common examples of this include (a) data stored in an unsigned type that requires a shift after scaling to re-center the data, and (b) specialized recording devices that naturally cause a scalar offset with respect to the true units.

field resolution: float | None = None

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.

field unit: str | None = None

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’ and add ‘offset’.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)
pydantic model TimeSeriesStartingTime

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.

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: Literal['starting_time'] = 'starting_time'
field rate: float | None = None

Sampling rate, in Hz.

field unit: str | None = None

Unit of measurement for time, which is fixed to ‘seconds’.

field value: float [Required]
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)
pydantic model TimeSeriesSync

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.

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: Literal['sync'] = 'sync'
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)
pydantic model ProcessingModule

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.

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, DynamicTable | NWBDataInterface] | None [Optional]
field name: str [Required]
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)
pydantic model Images

Bases: NWBDataInterface

A collection of images with an optional way to specify the order of the images using the “order_of_images” dataset. An order must be specified if the images are referenced by index, e.g., from an IndexSeries.

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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field description: str | None = None

Description of this collection of images.

field image: List[Image] [Optional]

Images stored in this collection.

field name: str [Required]
field order_of_images: ImagesOrderOfImages | None = None

Ordered dataset of references to Image objects stored in the parent group. Each Image object in the Images group should be stored once and only once, so the dataset should have the same length as the number of images.

linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=True), frozen=True)
pydantic model ImagesOrderOfImages

Bases: ImageReferences

Ordered dataset of references to Image objects stored in the parent group. Each Image object in the Images group should be stored once and only once, so the dataset should have the same length as the number 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.

Config:

  • validate_assignment: bool = True

  • validate_default: bool = True

  • extra: str = forbid

  • arbitrary_types_allowed: bool = True

  • use_enum_values: bool = True

Fields:
field array: List[Image] | Image | None = None
field name: Literal['order_of_images'] = 'order_of_images'
linkml_meta: ClassVar[LinkML_Meta] = FieldInfo(annotation=NoneType, required=False, default=LinkML_Meta(tree_root=False), frozen=True)