Customization and extension of functionality
DataLad provides numerous commands that cover many use cases. However, there will always be a demand for further customization or extensions of built-in functionality at a particular site, or for an individual user. DataLad addresses this need with a mechanism for extending particular Datalad functionality, such as metadata extractor, or providing entire command suites for a specialized purpose.
As the name suggests, a DataLad extension package is a proper Python package. Consequently, there is a significant amount of boilerplate code involved in the creation of a new Datalad extension. However, this overhead enables a number of useful features for extension developers:
extensions can provide any number of additional commands that can be grouped into labeled command suites, and are automatically exposed via the standard DataLad commandline and Python API
extensions can define entry_points for any number of additional metadata extractors that become automatically available to DataLad
extensions can define entry_points for their test suites, such that the standard datalad test command will automatically run these tests in addition to the tests shipped with Datalad core
extensions can ship additional dataset procedures by installing them into a directory
resources/proceduresunderneath the extension module directory
Using an extension
A DataLad extension is a standard Python package. Beyond installation of the package there is no additional setup required.
Writing your own extensions
A good starting point for implementing a new extension is the “helloworld” demo extension available at https://github.com/datalad/datalad-extension-template. This repository can be cloned and adjusted to suit one’s needs. It includes:
a basic Python package setup
simple demo command implementation
Travis test setup
A more complex extension setup can be seen in the DataLad Neuroimaging extension: https://github.com/datalad/datalad-neuroimaging, including additional metadata extractors, test suite registration, and a sphinx-based documentation setup for a DataLad extension.
As a DataLad extension is a standard Python package, an extension should declare dependencies on an appropriate DataLad version, and possibly other extensions via the standard mechanisms.