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/procedures underneath 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.