DataLad NEXT extension
This DataLad extension can be thought of as a staging area for additional functionality, or for improved performance and user experience. Unlike other topical or more experimental extensions, the focus here is on functionality with broad applicability. This extension is a suitable dependency for other software packages that intend to build on this improved set of functionality.
Installation and usage
Install from PyPi or Github like any other Python package:
# create and enter a new virtual environment (optional)
$ virtualenv --python=python3 ~/env/dl-next
$ . ~/env/dl-next/bin/activate
# install from PyPi
$ python -m pip install datalad-next
Once installed, additional commands provided by this extension are immediately available. However, in order to fully benefit from all improvements, the extension has to be enabled for auto-loading by executing:
git config --global --add datalad.extensions.load next
Doing so will enable the extension to also alter the behavior the core DataLad package and its commands.
Functionality provided by DataLad NEXT
The following table of contents offers entry points to the main components provided by this extension. The project README offers a more detailed summary in a different format.
Developing with DataLad NEXT
This extension package moves fast in comparison to the DataLad core package. Nevertheless, attention is paid to API stability, adequate semantic versioning, and informative changelogs.
Besides the DataLad commands shipped with this extension package, a number of Python utilities are provided that facilitate the implementation of workflows and additional functionality. An overview is available in the reference manual.
Public vs internal Python API
Anything that can be imported directly from any of the top-level sub-packages in datalad_next is considered to be part of the public API. Changes to this API determine the versioning, and development is done with the aim to keep this API as stable as possible. This includes signatures and return value behavior.
As an example:
from datalad_next.runners import iter_git_subproc
imports a part of the public API, but:
from datalad_next.runners.git import iter_git_subproc
Use of the internal API
Developers can obviously use parts of the non-public API. However, this should only be done with the understanding that these components may change from one release to another, with no guarantee of transition periods, deprecation warnings, etc.
Developers are advised to never reuse any components with names starting with _ (underscore). Their use should be limited to their individual sub-package.