Building API classes

After you have written an extension, you will need a Pythonic way to interact with the data model. To do this, you will need to write some classes that represent the data you defined in your specification extensions.

The hdmf.container module defines two base classes that represent the primitive structures supported by the schema. Data represents datasets and Container represents groups. See the classes in the :py:mod:hdmf.common package for examples.

The register_class function/decorator

When defining a class that represents a data_type (i.e. anything that has a data_type_def) from your extension, you can tell HDMF which data_type it represents using the function register_class. This class can be called on its own, or used as a class decorator. The first argument should be the data_type and the second argument should be the namespace name.

The following example demonstrates how to register a class as the Python class representation of the data_type “MyContainer” from the namespace “my_ns”. The namespace must be loaded prior to the below code using the load_namespaces function.

from hdmf.common import register_class
from hdmf.container import Container

class MyContainer(Container):
    ...

register_class(data_type='MyContainer', namespace='my_ns', container_cls=MyContainer)

Alternatively, you can use register_class as a decorator.

from hdmf.common import register_class
from hdmf.container import Container

@type_map.register_class('MyContainer', 'my_ns')
class MyContainer(Container):
    ...

register_class is used with Data the same way it is used with Container.