datalad.api.install

datalad.api.install(path=None, source=None, dataset=None, get_data=False, description=None, recursive=False, recursion_limit=None, save=True, reckless=False, jobs=None)

Install a dataset from a (remote) source.

This command creates a local sibling of an existing dataset from a (remote) location identified via a URL or path. Optional recursion into potential subdatasets, and download of all referenced data is supported. The new dataset can be optionally registered in an existing superdataset by identifying it via the dataset argument (the new dataset’s path needs to be located within the superdataset for that).

It is recommended to provide a brief description to label the dataset’s nature and location, e.g. “Michael’s music on black laptop”. This helps humans to identify data locations in distributed scenarios. By default an identifier comprised of user and machine name, plus path will be generated.

When only partial dataset content shall be obtained, it is recommended to use this command without the get-data flag, followed by a get() operation to obtain the desired data.

Note

Power-user info: This command uses git clone, and git annex init to prepare the dataset. Registering to a superdataset is performed via a git submodule add operation in the discovered superdataset.

Parameters:
  • path – path/name of the installation target. If no path is provided a destination path will be derived from a source URL similar to git clone. [Default: None]
  • source (str or None, optional) – URL or local path of the installation source. [Default: None]
  • dataset (Dataset or None, optional) – specify the dataset to perform the install operation on. If no dataset is given, an attempt is made to identify the dataset in a parent directory of the current working directory and/or the path given. [Default: None]
  • get_data (bool, optional) – if given, obtain all data content too. [Default: False]
  • description (str or None, optional) – short description to use for a dataset location. Its primary purpose is to help humans to identify a dataset copy (e.g., “mike’s dataset on lab server”). Note that when a dataset is published, this information becomes available on the remote side. [Default: None]
  • recursive (bool, optional) – if set, recurse into potential subdataset. [Default: False]
  • recursion_limit (int or None, optional) – limit recursion into subdataset to the given number of levels. [Default: None]
  • save (bool, optional) – by default all modifications to a dataset are immediately saved. Given this option will disable this behavior. [Default: True]
  • reckless (bool, optional) – Set up the dataset to be able to obtain content in the cheapest/fastest possible way, even if this poses a potential risk the data integrity (e.g. hardlink files from a local clone of the dataset). Use with care, and limit to “read-only” use cases. With this flag the installed dataset will be marked as untrusted. [Default: False]
  • jobs (int or None or {'auto'}, optional) – how many parallel jobs (where possible) to use. [Default: None]
  • 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 ({'paths', 'relpaths', 'datasets', 'successdatasets-or-none'} 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’]
  • run_after – Like run_before, but plugins are executed after the main command has finished. [Default: None]
  • run_before – DataLad plugin to run before the command. PLUGINSPEC is a list comprised of a plugin name plus optional 2-tuples of key-value pairs with arguments for the plugin call (see plugin command documentation for details). PLUGINSPECs must be wrapped in list where each item configures one plugin call. Plugins are called in the order defined by this list. For running plugins that require a dataset argument it is important to provide the respective dataset as the dataset argument of the main command, if it is not in the list of plugin arguments. [Default: None]