hdmf_common_table#

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 VectorData#

Bases: Data

An n-dimensional dataset representing a column of a DynamicTable. If used without an accompanying VectorIndex, first dimension is along the rows of the DynamicTable and each step along the first dimension is a cell of the larger table. VectorData can also be used to represent a ragged array if paired with a VectorIndex. This allows for storing arrays of varying length in a single cell of the DynamicTable by indexing into this VectorData. The first vector is at VectorData[0:VectorIndex[0]]. The second vector is at VectorData[VectorIndex[0]:VectorIndex[1]], and so on.

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 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 VectorIndex#

Bases: VectorData

Used with VectorData to encode a ragged array. An array of indices into the first dimension of the target VectorData, and forming a map between the rows of a DynamicTable and the indices of the VectorData. The name of the VectorIndex is expected to be the name of the target VectorData object followed by “_index”.

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 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]#
field target: VectorData | None = None#

Reference to the target dataset that this index applies to.

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

Bases: Data

A list of unique identifiers for values within a dataset, e.g. rows of a DynamicTable.

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 array: NDArray[Shape['* num_elements'], Int] | 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 DynamicTableRegion#

Bases: VectorData

DynamicTableRegion provides a link from one table to an index or region of another. The table attribute is a link to another DynamicTable, indicating which table is referenced, and the data is int(s) indicating the row(s) (0-indexed) of the target array. DynamicTableRegion`s can be used to associate rows with repeated meta-data without data duplication. They can also be used to create hierarchical relationships between multiple `DynamicTable`s. `DynamicTableRegion objects may be paired with a VectorIndex object to create ragged references, so a single cell of a DynamicTable can reference many rows of another DynamicTable.

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 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 this table region points to.

field name: str [Required]#
field table: DynamicTable | None = None#

Reference to the DynamicTable object that this region applies to.

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

Bases: Container

A group containing multiple datasets that are aligned on the first dimension (Currently, this requirement if left up to APIs to check and enforce). These datasets represent different columns in the table. Apart from a column that contains unique identifiers for each row, there are no other required datasets. Users are free to add any number of custom VectorData objects (columns) here. DynamicTable also supports ragged array columns, where each element can be of a different size. To add a ragged array column, use a VectorIndex type to index the corresponding VectorData type. See documentation for VectorData and VectorIndex for more details. Unlike a compound data type, which is analogous to storing an array-of-structs, a DynamicTable can be thought of as a struct-of-arrays. This provides an alternative structure to choose from when optimizing storage for anticipated access patterns. Additionally, this type provides a way of creating a table without having to define a compound type up front. Although this convenience may be attractive, users should think carefully about how data will be accessed. DynamicTable is more appropriate for column-centric access, whereas a dataset with a compound type would be more appropriate for row-centric access. Finally, data size should also be taken into account. For small tables, performance loss may be an acceptable trade-off for the flexibility of a DynamicTable.

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 colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

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

Bases: DynamicTable

DynamicTable container that supports storing a collection of sub-tables. Each sub-table is a DynamicTable itself that is aligned with the main table by row index. I.e., all DynamicTables stored in this group MUST have the same number of rows. This type effectively defines a 2-level table in which the main data is stored in the main table implemented by this type and additional columns of the table are grouped into categories, with each category being represented by a separate DynamicTable stored within the 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.

__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, DynamicTable] | None [Optional]#
field colnames: str | None = None#

The names of the columns in this table. This should be used to specify an order to the columns.

field description: str | None = None#

Description of what is in this dynamic table.

field id: List[int] [Optional]#

Array of unique identifiers for the rows of this dynamic table.

field name: str [Required]#
field vector_data: List[VectorData] | None [Optional]#

Vector columns, including index columns, of this dynamic table.

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