Dataset¶

Adapter for NWB datasets to linkml Classes

class DatasetMap¶

abstract classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

abstract classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapScalar¶

Datasets that are just a single value should just be a scalar value, not an array with size 1

Replaces the built class with a slot.

Examples

NWB Schema

datasets:
- name: MyScalar
  doc: A scalar
  dtype: int32
  quantity: '?'

LinkML

attributes:
- name: MyScalar
  description: A scalar
  multivalued: false
  range: int32
  required: false

classmethod check(cls: Dataset) → bool¶

Attr	Value
`neurodata_type_inc`	`None`
`attributes`	`None`
`dims`	`None`
`shape`	`None`
`name`	`str`

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapScalarAttributes¶

A scalar with attributes gets an additional slot “value” that contains the actual scalar value of this field

classmethod check(cls: Dataset) → bool¶

Attr	Value
`neurodata_type_inc`	`None`
`attributes`	Truthy
`dims`	`None`
`shape`	`None`
`name`	`str`

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapListlike¶

Datasets that refer to other datasets (that handle their own arrays)

classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapArraylike¶

Datasets without any additional attributes don’t create their own subclass, they’re just an array :).

Replace the base class with the array class, and make a slot that refers to it.

classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapArrayLikeAttributes¶

The most general case - treat everything that isn’t handled by one of the special cases as an array!

Specifically, we make an Arraylike class such that:

Each slot within a subclass indicates a possible dimension.
Only dimensions that are present in all the dimension specifiers in the original schema are required.
Shape requirements are indicated using max/min cardinalities on the slot.
The arraylike object should be stored in the array slot on the containing class (since there are already properties named data)

NEEDS_NAME = True¶

classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class Map1DVector¶

VectorData is subclassed with a name but without dims or attributes, treat this as a normal 1D array slot that replaces any class that would be built for this

classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

class MapNVectors¶

An unnamed container that indicates an arbitrary quantity of some other neurodata type.

Most commonly: VectorData is subclassed without a name and with a ‘*’ quantity to indicate arbitrary columns.

classmethod check(cls: Dataset) → bool¶: Check if this map applies to the given item to read

classmethod apply(cls: Dataset, res: BuildResult | None = None, name: str | None = None) → BuildResult¶: Actually apply the map!

pydantic model DatasetAdapter¶

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:

cls (nwb_schema_language.datamodel.nwb_schema_pydantic.Dataset)

field cls: Dataset [Required]¶

build() → BuildResult¶

Make this abstract so it can’t be instantiated directly.

Subclasses call build_base() to get the basics true of both groups and datasets

make_arraylike(cls: Dataset, name: str | None = None) → ClassDefinition¶

is_1d(cls: Dataset) → bool¶

has_attrs(cls: Dataset) → bool¶