datalad drop

Synopsis

datalad drop [-h] [-d DATASET] [-r] [--recursion-limit LEVELS] [--nocheck] [--if-dirty {fail,save-before,ignore}] [PATH [PATH ...]]

Description

Drop file content from datasets

This command takes any number of paths of files and/or directories. If a common (super)dataset is given explicitly, the given paths are interpreted relative to this dataset.

Recursion into subdatasets needs to be explicitly enabled, while recursion in subdirectories within a dataset is always done automatically. An optional recursion limit is applied relative to each given input path.

By default, the availability of at least one remote copy is verified, before file content is dropped. As these checks could lead to slow operation (network latencies, etc), they can be disabled.

Examples:

Drop all file content in a dataset:

~/some/dataset$ datalad drop

Drop all file content in a dataset and all its subdatasets:

~/some/dataset$ datalad drop --recursive

Options

PATH

path/name of the component to be dropped. Constraints: value must be a string [Default: None]

-h, –help, –help-np

show this help message. –help-np forcefully disables the use of a pager for displaying the help message

-d DATASET, –dataset DATASET

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. Constraints: Value must be a Dataset or a valid identifier of a Dataset (e.g. a path) [Default: None]

-r, –recursive

if set, recurse into potential subdataset. [Default: False]

–recursion-limit LEVELS

limit recursion into subdataset to the given number of levels. Constraints: value must be convertible to type ‘int’ [Default: None]

–nocheck

whether to perform checks to assure the configured minimum number (remote) source for data. Give this option to skip checks. [Default: True]

–if-dirty {fail,save-before,ignore}

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’]

Authors

datalad is developed by The DataLad Team and Contributors <team@datalad.org>.