datalad.api.uninstall(path=None, dataset=None, recursive=False, check=True, if_dirty='save-before')

Uninstall subdatasets

This command can be used to uninstall any number of installed subdatasets. This command will error if individual files or non-dataset directories are given as input (use the drop or remove command depending on the desired goal), nor will it uninstall top-level datasets (i.e. datasets that are not a subdataset in another dataset; use the remove command for this purpose).

By default, the availability of at least one remote copy for each currently available file in any dataset is verified. As these checks could lead to slow operation (network latencies, etc), they can be disabled.

Any number of paths to process can be given as input. Recursion into subdatasets needs to be explicitly enabled, while recursion into subdirectories within a dataset is done automatically. An optional recursion limit is applied relative to each given input path.


Uninstall a subdataset (undo installation):

> uninstall(path='path/to/subds')

Uninstall a subdataset and all potential subdatasets:

> uninstall(path='path/to/subds', recursive=True)

Skip checks that ensure a minimal number of (remote) sources:

> uninstall(path='path/to/subds', check=False)
  • path (sequence of str or None, optional) – path/name of the component to be uninstalled. [Default: None]
  • dataset (Dataset or None, optional) – specify the dataset to perform the operation on. If no dataset is given, an attempt is made to identify a dataset based on the path given. [Default: None]
  • recursive (bool, optional) – if set, recurse into potential subdataset. [Default: False]
  • check (bool, optional) – whether to perform checks to assure the configured minimum number (remote) source for data. [Default: True]
  • if_dirty – desired behavior if a dataset with unsaved changes is discovered: ‘fail’ will trigger an error and further processing is aborted; ‘save-before’ will save all changes prior any further action; ‘ignore’ let’s datalad proceed as if the dataset would not have unsaved changes. [Default: ‘save-before’]
  • on_failure ({'ignore', 'continue', 'stop'}, optional) – behavior to perform on failure: ‘ignore’ any failure is reported, but does not cause an exception; ‘continue’ if any failure occurs an exception will be raised at the end, but processing other actions will continue for as long as possible; ‘stop’: processing will stop on first failure and an exception is raised. A failure is any result with status ‘impossible’ or ‘error’. Raised exception is an IncompleteResultsError that carries the result dictionaries of the failures in its failed attribute. [Default: ‘continue’]
  • result_filter (callable or None, optional) – if given, each to-be-returned status dictionary is passed to this callable, and is only returned if the callable’s return value does not evaluate to False or a ValueError exception is raised. If the given callable supports **kwargs it will additionally be passed the keyword arguments of the original API call. [Default: None]
  • result_renderer ({'default', 'json', 'json_pp', 'tailored'} or None, optional) – format of return value rendering on stdout. [Default: None]
  • result_xfm ({'datasets', 'successdatasets-or-none', 'paths', 'relpaths', 'metadata'} or callable or None, optional) – if given, each to-be-returned result status dictionary is passed to this callable, and its return value becomes the result instead. This is different from result_filter, as it can perform arbitrary transformation of the result value. This is mostly useful for top- level command invocations that need to provide the results in a particular format. Instead of a callable, a label for a pre-crafted result transformation can be given. [Default: None]
  • return_type ({'generator', 'list', 'item-or-list'}, optional) – return value behavior switch. If ‘item-or-list’ a single value is returned instead of a one-item return value list, or a list in case of multiple return values. None is return in case of an empty list. [Default: ‘list’]